diff --git a/.gitattributes b/.gitattributes index 5b3c15bb..3ac08bbe 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,3 +1,4 @@ /web/assets/** linguist-generated /web/** linguist-vendored comfy_api_nodes/apis/__init__.py linguist-generated +models/loras/*.safetensors filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 4e8cea71..6815f167 100644 --- a/.gitignore +++ b/.gitignore @@ -3,10 +3,19 @@ __pycache__/ /output/ /input/ !/input/example.png -/models/ +/models/* +!/models/loras/ +/models/loras/* +!/models/loras/Anime_CRABDM.safetensors +!/models/loras/CharacterDesign-FluxV2.safetensors +!/models/loras/FluxMythSharpL1nes.safetensors +!/models/loras/ILLUSTRATION (FLUX) - V3.1.safetensors +!/models/loras/Mezzotint_Artstyle_for_Flux_-_by_Ethanar.safetensors +!/models/loras/oil-paint.safetensors +!/models/loras/Phandigrams_III.safetensors /temp/ -/custom_nodes/ -!custom_nodes/example_node.py.example +/custom_nodes/**/.* +!/custom_nodes/**/.gitignore extra_model_paths.yaml /.vs .vscode/ diff --git a/custom_nodes/ComfyMath/.gitignore b/custom_nodes/ComfyMath/.gitignore new file mode 100644 index 00000000..eeb8a6ec --- /dev/null +++ b/custom_nodes/ComfyMath/.gitignore @@ -0,0 +1 @@ +**/__pycache__ diff --git a/custom_nodes/ComfyMath/LICENSE b/custom_nodes/ComfyMath/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/custom_nodes/ComfyMath/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/ComfyMath/README.md b/custom_nodes/ComfyMath/README.md new file mode 100644 index 00000000..54ec13db --- /dev/null +++ b/custom_nodes/ComfyMath/README.md @@ -0,0 +1,19 @@ +# ComfyMath + +Provides Math Nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) + +## Features + +Provides nodes for: +* Boolean Logic +* Integer Arithmetic +* Floating Point Arithmetic and Functions +* Vec2, Vec3, and Vec4 Arithmetic and Functions + +## Installation + +From the `custom_nodes` directory in your ComfyUI installation, run: + +```sh +git clone https://github.com/evanspearman/ComfyMath.git +``` diff --git a/custom_nodes/ComfyMath/__init__.py b/custom_nodes/ComfyMath/__init__.py new file mode 100644 index 00000000..5d668256 --- /dev/null +++ b/custom_nodes/ComfyMath/__init__.py @@ -0,0 +1,29 @@ +from .src.comfymath.convert import NODE_CLASS_MAPPINGS as convert_NCM +from .src.comfymath.bool import NODE_CLASS_MAPPINGS as bool_NCM +from .src.comfymath.int import NODE_CLASS_MAPPINGS as int_NCM +from .src.comfymath.float import NODE_CLASS_MAPPINGS as float_NCM +from .src.comfymath.number import NODE_CLASS_MAPPINGS as number_NCM +from .src.comfymath.vec import NODE_CLASS_MAPPINGS as vec_NCM +from .src.comfymath.control import NODE_CLASS_MAPPINGS as control_NCM +from .src.comfymath.graphics import NODE_CLASS_MAPPINGS as graphics_NCM + + +NODE_CLASS_MAPPINGS = { + **convert_NCM, + **bool_NCM, + **int_NCM, + **float_NCM, + **number_NCM, + **vec_NCM, + **control_NCM, + **graphics_NCM, +} + + +def remove_cm_prefix(node_mapping: str) -> str: + if node_mapping.startswith("CM_"): + return node_mapping[3:] + return node_mapping + + +NODE_DISPLAY_NAME_MAPPINGS = {key: remove_cm_prefix(key) for key in NODE_CLASS_MAPPINGS} diff --git a/custom_nodes/ComfyMath/pyproject.toml b/custom_nodes/ComfyMath/pyproject.toml new file mode 100644 index 00000000..25764444 --- /dev/null +++ b/custom_nodes/ComfyMath/pyproject.toml @@ -0,0 +1,19 @@ +[tool.poetry] +name = "comfymath" +version = "0.1.0" +description = "Math nodes for ComfyUI" +authors = ["Evan Spearman "] +license = { text = "Apache License 2.0" } +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.10" +numpy = "^1.25.1" + +[tool.poetry.group.dev.dependencies] +mypy = "^1.4.1" +black = "^23.7.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/custom_nodes/ComfyMath/requirements.txt b/custom_nodes/ComfyMath/requirements.txt new file mode 100644 index 00000000..24ce15ab --- /dev/null +++ b/custom_nodes/ComfyMath/requirements.txt @@ -0,0 +1 @@ +numpy diff --git a/custom_nodes/ComfyMath/src/comfymath/__init__.py b/custom_nodes/ComfyMath/src/comfymath/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyMath/src/comfymath/bool.py b/custom_nodes/ComfyMath/src/comfymath/bool.py new file mode 100644 index 00000000..ca58d5e8 --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/bool.py @@ -0,0 +1,59 @@ +from typing import Any, Callable, Mapping + +DEFAULT_BOOL = ("BOOLEAN", {"default": False}) + + +BOOL_UNARY_OPERATIONS: Mapping[str, Callable[[bool], bool]] = { + "Not": lambda a: not a, +} + +BOOL_BINARY_OPERATIONS: Mapping[str, Callable[[bool, bool], bool]] = { + "Nor": lambda a, b: not (a or b), + "Xor": lambda a, b: a ^ b, + "Nand": lambda a, b: not (a and b), + "And": lambda a, b: a and b, + "Xnor": lambda a, b: not (a ^ b), + "Or": lambda a, b: a or b, + "Eq": lambda a, b: a == b, + "Neq": lambda a, b: a != b, +} + + +class BoolUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": {"op": (list(BOOL_UNARY_OPERATIONS.keys()),), "a": DEFAULT_BOOL} + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "op" + CATEGORY = "math/bool" + + def op(self, op: str, a: bool) -> tuple[bool]: + return (BOOL_UNARY_OPERATIONS[op](a),) + + +class BoolBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(BOOL_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_BOOL, + "b": DEFAULT_BOOL, + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "op" + CATEGORY = "math/bool" + + def op(self, op: str, a: bool, b: bool) -> tuple[bool]: + return (BOOL_BINARY_OPERATIONS[op](a, b),) + + +NODE_CLASS_MAPPINGS = { + "CM_BoolUnaryOperation": BoolUnaryOperation, + "CM_BoolBinaryOperation": BoolBinaryOperation, +} diff --git a/custom_nodes/ComfyMath/src/comfymath/control.py b/custom_nodes/ComfyMath/src/comfymath/control.py new file mode 100644 index 00000000..7927db0b --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/control.py @@ -0,0 +1,3 @@ +from typing import Any, Mapping + +NODE_CLASS_MAPPINGS: Mapping[str, Any] = {} diff --git a/custom_nodes/ComfyMath/src/comfymath/convert.py b/custom_nodes/ComfyMath/src/comfymath/convert.py new file mode 100644 index 00000000..045caaf0 --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/convert.py @@ -0,0 +1,273 @@ +from typing import Any, Mapping + +from .vec import VEC2_ZERO, VEC3_ZERO, VEC4_ZERO +from .types import Number, Vec2, Vec3, Vec4 + + +class BoolToInt: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("BOOLEAN", {"default": False})}} + + RETURN_TYPES = ("INT",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: bool) -> tuple[int]: + return (int(a),) + + +class IntToBool: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("INT", {"default": 0})}} + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: int) -> tuple[bool]: + return (a != 0,) + + +class FloatToInt: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("FLOAT", {"default": 0.0, "round": False})}} + + RETURN_TYPES = ("INT",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: float) -> tuple[int]: + return (int(a),) + + +class IntToFloat: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("INT", {"default": 0})}} + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: int) -> tuple[float]: + return (float(a),) + + +class IntToNumber: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("INT", {"default": 0})}} + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: int) -> tuple[Number]: + return (a,) + + +class NumberToInt: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("NUMBER", {"default": 0.0})}} + + RETURN_TYPES = ("INT",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: Number) -> tuple[int]: + return (int(a),) + + +class FloatToNumber: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("FLOAT", {"default": 0.0, "round": False})}} + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: float) -> tuple[Number]: + return (a,) + + +class NumberToFloat: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("NUMBER", {"default": 0.0})}} + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: Number) -> tuple[float]: + return (float(a),) + + +class ComposeVec2: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "x": ("FLOAT", {"default": 0.0, "round": False}), + "y": ("FLOAT", {"default": 0.0, "round": False}), + } + } + + RETURN_TYPES = ("VEC2",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, x: float, y: float) -> tuple[Vec2]: + return ((x, y),) + + +class FillVec2: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "a": ("FLOAT", {"default": 0.0, "round": False}), + } + } + + RETURN_TYPES = ("VEC2",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: float) -> tuple[Vec2]: + return ((a, a),) + + +class BreakoutVec2: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("VEC2", {"default": VEC2_ZERO})}} + + RETURN_TYPES = ("FLOAT", "FLOAT") + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: Vec2) -> tuple[float, float]: + return (a[0], a[1]) + + +class ComposeVec3: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "x": ("FLOAT", {"default": 0.0}), + "y": ("FLOAT", {"default": 0.0}), + "z": ("FLOAT", {"default": 0.0}), + } + } + + RETURN_TYPES = ("VEC3",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, x: float, y: float, z: float) -> tuple[Vec3]: + return ((x, y, z),) + + +class FillVec3: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "a": ("FLOAT", {"default": 0.0}), + } + } + + RETURN_TYPES = ("VEC3",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: float) -> tuple[Vec3]: + return ((a, a, a),) + + +class BreakoutVec3: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("VEC3", {"default": VEC3_ZERO})}} + + RETURN_TYPES = ("FLOAT", "FLOAT", "FLOAT") + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: Vec3) -> tuple[float, float, float]: + return (a[0], a[1], a[2]) + + +class ComposeVec4: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "x": ("FLOAT", {"default": 0.0}), + "y": ("FLOAT", {"default": 0.0}), + "z": ("FLOAT", {"default": 0.0}), + "w": ("FLOAT", {"default": 0.0}), + } + } + + RETURN_TYPES = ("VEC4",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, x: float, y: float, z: float, w: float) -> tuple[Vec4]: + return ((x, y, z, w),) + + +class FillVec4: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "a": ("FLOAT", {"default": 0.0}), + } + } + + RETURN_TYPES = ("VEC4",) + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: float) -> tuple[Vec4]: + return ((a, a, a, a),) + + +class BreakoutVec4: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"a": ("VEC4", {"default": VEC4_ZERO})}} + + RETURN_TYPES = ("FLOAT", "FLOAT", "FLOAT", "FLOAT") + FUNCTION = "op" + CATEGORY = "math/conversion" + + def op(self, a: Vec4) -> tuple[float, float, float, float]: + return (a[0], a[1], a[2], a[3]) + + +NODE_CLASS_MAPPINGS = { + "CM_BoolToInt": BoolToInt, + "CM_IntToBool": IntToBool, + "CM_FloatToInt": FloatToInt, + "CM_IntToFloat": IntToFloat, + "CM_IntToNumber": IntToNumber, + "CM_NumberToInt": NumberToInt, + "CM_FloatToNumber": FloatToNumber, + "CM_NumberToFloat": NumberToFloat, + "CM_ComposeVec2": ComposeVec2, + "CM_ComposeVec3": ComposeVec3, + "CM_ComposeVec4": ComposeVec4, + "CM_BreakoutVec2": BreakoutVec2, + "CM_BreakoutVec3": BreakoutVec3, + "CM_BreakoutVec4": BreakoutVec4, +} diff --git a/custom_nodes/ComfyMath/src/comfymath/float.py b/custom_nodes/ComfyMath/src/comfymath/float.py new file mode 100644 index 00000000..07839248 --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/float.py @@ -0,0 +1,159 @@ +import math + +from typing import Any, Callable, Mapping + +DEFAULT_FLOAT = ("FLOAT", {"default": 0.0, "step": 0.001, "round": False}) + +FLOAT_UNARY_OPERATIONS: Mapping[str, Callable[[float], float]] = { + "Neg": lambda a: -a, + "Inc": lambda a: a + 1, + "Dec": lambda a: a - 1, + "Abs": lambda a: abs(a), + "Sqr": lambda a: a * a, + "Cube": lambda a: a * a * a, + "Sqrt": lambda a: math.sqrt(a), + "Exp": lambda a: math.exp(a), + "Ln": lambda a: math.log(a), + "Log10": lambda a: math.log10(a), + "Log2": lambda a: math.log2(a), + "Sin": lambda a: math.sin(a), + "Cos": lambda a: math.cos(a), + "Tan": lambda a: math.tan(a), + "Asin": lambda a: math.asin(a), + "Acos": lambda a: math.acos(a), + "Atan": lambda a: math.atan(a), + "Sinh": lambda a: math.sinh(a), + "Cosh": lambda a: math.cosh(a), + "Tanh": lambda a: math.tanh(a), + "Asinh": lambda a: math.asinh(a), + "Acosh": lambda a: math.acosh(a), + "Atanh": lambda a: math.atanh(a), + "Round": lambda a: round(a), + "Floor": lambda a: math.floor(a), + "Ceil": lambda a: math.ceil(a), + "Trunc": lambda a: math.trunc(a), + "Erf": lambda a: math.erf(a), + "Erfc": lambda a: math.erfc(a), + "Gamma": lambda a: math.gamma(a), + "Radians": lambda a: math.radians(a), + "Degrees": lambda a: math.degrees(a), +} + +FLOAT_UNARY_CONDITIONS: Mapping[str, Callable[[float], bool]] = { + "IsZero": lambda a: a == 0.0, + "IsPositive": lambda a: a > 0.0, + "IsNegative": lambda a: a < 0.0, + "IsNonZero": lambda a: a != 0.0, + "IsPositiveInfinity": lambda a: math.isinf(a) and a > 0.0, + "IsNegativeInfinity": lambda a: math.isinf(a) and a < 0.0, + "IsNaN": lambda a: math.isnan(a), + "IsFinite": lambda a: math.isfinite(a), + "IsInfinite": lambda a: math.isinf(a), + "IsEven": lambda a: a % 2 == 0.0, + "IsOdd": lambda a: a % 2 != 0.0, +} + +FLOAT_BINARY_OPERATIONS: Mapping[str, Callable[[float, float], float]] = { + "Add": lambda a, b: a + b, + "Sub": lambda a, b: a - b, + "Mul": lambda a, b: a * b, + "Div": lambda a, b: a / b, + "Mod": lambda a, b: a % b, + "Pow": lambda a, b: a**b, + "FloorDiv": lambda a, b: a // b, + "Max": lambda a, b: max(a, b), + "Min": lambda a, b: min(a, b), + "Log": lambda a, b: math.log(a, b), + "Atan2": lambda a, b: math.atan2(a, b), +} + +FLOAT_BINARY_CONDITIONS: Mapping[str, Callable[[float, float], bool]] = { + "Eq": lambda a, b: a == b, + "Neq": lambda a, b: a != b, + "Gt": lambda a, b: a > b, + "Gte": lambda a, b: a >= b, + "Lt": lambda a, b: a < b, + "Lte": lambda a, b: a <= b, +} + + +class FloatUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_UNARY_OPERATIONS.keys()),), + "a": DEFAULT_FLOAT, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/float" + + def op(self, op: str, a: float) -> tuple[float]: + return (FLOAT_UNARY_OPERATIONS[op](a),) + + +class FloatUnaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_UNARY_CONDITIONS.keys()),), + "a": DEFAULT_FLOAT, + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "op" + CATEGORY = "math/float" + + def op(self, op: str, a: float) -> tuple[bool]: + return (FLOAT_UNARY_CONDITIONS[op](a),) + + +class FloatBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_FLOAT, + "b": DEFAULT_FLOAT, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/float" + + def op(self, op: str, a: float, b: float) -> tuple[float]: + return (FLOAT_BINARY_OPERATIONS[op](a, b),) + + +class FloatBinaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_BINARY_CONDITIONS.keys()),), + "a": DEFAULT_FLOAT, + "b": DEFAULT_FLOAT, + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "op" + CATEGORY = "math/float" + + def op(self, op: str, a: float, b: float) -> tuple[bool]: + return (FLOAT_BINARY_CONDITIONS[op](a, b),) + + +NODE_CLASS_MAPPINGS = { + "CM_FloatUnaryOperation": FloatUnaryOperation, + "CM_FloatUnaryCondition": FloatUnaryCondition, + "CM_FloatBinaryOperation": FloatBinaryOperation, + "CM_FloatBinaryCondition": FloatBinaryCondition, +} diff --git a/custom_nodes/ComfyMath/src/comfymath/graphics.py b/custom_nodes/ComfyMath/src/comfymath/graphics.py new file mode 100644 index 00000000..f90b87f8 --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/graphics.py @@ -0,0 +1,153 @@ +from abc import ABC, abstractmethod +from typing import Any, Mapping, Sequence, Tuple + + +SDXL_SUPPORTED_RESOLUTIONS = [ + (1024, 1024, 1.0), + (1152, 896, 1.2857142857142858), + (896, 1152, 0.7777777777777778), + (1216, 832, 1.4615384615384615), + (832, 1216, 0.6842105263157895), + (1344, 768, 1.75), + (768, 1344, 0.5714285714285714), + (1536, 640, 2.4), + (640, 1536, 0.4166666666666667), +] + +SDXL_EXTENDED_RESOLUTIONS = [ + (512, 2048, 0.25), + (512, 1984, 0.26), + (512, 1920, 0.27), + (512, 1856, 0.28), + (576, 1792, 0.32), + (576, 1728, 0.33), + (576, 1664, 0.35), + (640, 1600, 0.4), + (640, 1536, 0.42), + (704, 1472, 0.48), + (704, 1408, 0.5), + (704, 1344, 0.52), + (768, 1344, 0.57), + (768, 1280, 0.6), + (832, 1216, 0.68), + (832, 1152, 0.72), + (896, 1152, 0.78), + (896, 1088, 0.82), + (960, 1088, 0.88), + (960, 1024, 0.94), + (1024, 1024, 1.0), + (1024, 960, 1.8), + (1088, 960, 1.14), + (1088, 896, 1.22), + (1152, 896, 1.30), + (1152, 832, 1.39), + (1216, 832, 1.47), + (1280, 768, 1.68), + (1344, 768, 1.76), + (1408, 704, 2.0), + (1472, 704, 2.10), + (1536, 640, 2.4), + (1600, 640, 2.5), + (1664, 576, 2.90), + (1728, 576, 3.0), + (1792, 576, 3.12), + (1856, 512, 3.63), + (1920, 512, 3.76), + (1984, 512, 3.89), + (2048, 512, 4.0), +] + + +class Resolution(ABC): + @classmethod + @abstractmethod + def resolutions(cls) -> Sequence[Tuple[int, int, float]]: ... + + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "resolution": ([f"{res[0]}x{res[1]}" for res in cls.resolutions()],) + } + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("width", "height") + FUNCTION = "op" + CATEGORY = "math/graphics" + + def op(self, resolution: str) -> tuple[int, int]: + width, height = resolution.split("x") + return (int(width), int(height)) + + +class NearestResolution(ABC): + @classmethod + @abstractmethod + def resolutions(cls) -> Sequence[Tuple[int, int, float]]: ... + + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return {"required": {"image": ("IMAGE",)}} + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("width", "height") + FUNCTION = "op" + CATEGORY = "math/graphics" + + def op(self, image) -> tuple[int, int]: + image_width = image.size()[2] + image_height = image.size()[1] + print(f"Input image resolution: {image_width}x{image_height}") + image_ratio = image_width / image_height + differences = [ + (abs(image_ratio - resolution[2]), resolution) + for resolution in self.resolutions() + ] + smallest = None + for difference in differences: + if smallest is None: + smallest = difference + else: + if difference[0] < smallest[0]: + smallest = difference + if smallest is not None: + width = smallest[1][0] + height = smallest[1][1] + else: + width = 1024 + height = 1024 + print(f"Selected resolution: {width}x{height}") + return (width, height) + + +class SDXLResolution(Resolution): + @classmethod + def resolutions(cls): + return SDXL_SUPPORTED_RESOLUTIONS + + +class SDXLExtendedResolution(Resolution): + @classmethod + def resolutions(cls): + return SDXL_EXTENDED_RESOLUTIONS + + +class NearestSDXLResolution(NearestResolution): + @classmethod + def resolutions(cls): + return SDXL_SUPPORTED_RESOLUTIONS + + +class NearestSDXLExtendedResolution(NearestResolution): + @classmethod + def resolutions(cls): + return SDXL_EXTENDED_RESOLUTIONS + + +NODE_CLASS_MAPPINGS = { + "CM_SDXLResolution": SDXLResolution, + "CM_NearestSDXLResolution": NearestSDXLResolution, + "CM_SDXLExtendedResolution": SDXLExtendedResolution, + "CM_NearestSDXLExtendedResolution": NearestSDXLExtendedResolution, +} diff --git a/custom_nodes/ComfyMath/src/comfymath/int.py b/custom_nodes/ComfyMath/src/comfymath/int.py new file mode 100644 index 00000000..576fc6aa --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/int.py @@ -0,0 +1,129 @@ +import math + +from typing import Any, Callable, Mapping + +DEFAULT_INT = ("INT", {"default": 0}) + +INT_UNARY_OPERATIONS: Mapping[str, Callable[[int], int]] = { + "Abs": lambda a: abs(a), + "Neg": lambda a: -a, + "Inc": lambda a: a + 1, + "Dec": lambda a: a - 1, + "Sqr": lambda a: a * a, + "Cube": lambda a: a * a * a, + "Not": lambda a: ~a, + "Factorial": lambda a: math.factorial(a), +} + +INT_UNARY_CONDITIONS: Mapping[str, Callable[[int], bool]] = { + "IsZero": lambda a: a == 0, + "IsNonZero": lambda a: a != 0, + "IsPositive": lambda a: a > 0, + "IsNegative": lambda a: a < 0, + "IsEven": lambda a: a % 2 == 0, + "IsOdd": lambda a: a % 2 == 1, +} + +INT_BINARY_OPERATIONS: Mapping[str, Callable[[int, int], int]] = { + "Add": lambda a, b: a + b, + "Sub": lambda a, b: a - b, + "Mul": lambda a, b: a * b, + "Div": lambda a, b: a // b, + "Mod": lambda a, b: a % b, + "Pow": lambda a, b: a**b, + "And": lambda a, b: a & b, + "Nand": lambda a, b: ~a & b, + "Or": lambda a, b: a | b, + "Nor": lambda a, b: ~a & b, + "Xor": lambda a, b: a ^ b, + "Xnor": lambda a, b: ~a ^ b, + "Shl": lambda a, b: a << b, + "Shr": lambda a, b: a >> b, + "Max": lambda a, b: max(a, b), + "Min": lambda a, b: min(a, b), +} + +INT_BINARY_CONDITIONS: Mapping[str, Callable[[int, int], bool]] = { + "Eq": lambda a, b: a == b, + "Neq": lambda a, b: a != b, + "Gt": lambda a, b: a > b, + "Lt": lambda a, b: a < b, + "Geq": lambda a, b: a >= b, + "Leq": lambda a, b: a <= b, +} + + +class IntUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": {"op": (list(INT_UNARY_OPERATIONS.keys()),), "a": DEFAULT_INT} + } + + RETURN_TYPES = ("INT",) + FUNCTION = "op" + CATEGORY = "math/int" + + def op(self, op: str, a: int) -> tuple[int]: + return (INT_UNARY_OPERATIONS[op](a),) + + +class IntUnaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": {"op": (list(INT_UNARY_CONDITIONS.keys()),), "a": DEFAULT_INT} + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/int" + + def op(self, op: str, a: int) -> tuple[bool]: + return (INT_UNARY_CONDITIONS[op](a),) + + +class IntBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(INT_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_INT, + "b": DEFAULT_INT, + } + } + + RETURN_TYPES = ("INT",) + FUNCTION = "op" + CATEGORY = "math/int" + + def op(self, op: str, a: int, b: int) -> tuple[int]: + return (INT_BINARY_OPERATIONS[op](a, b),) + + +class IntBinaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(INT_BINARY_CONDITIONS.keys()),), + "a": DEFAULT_INT, + "b": DEFAULT_INT, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/int" + + def op(self, op: str, a: int, b: int) -> tuple[bool]: + return (INT_BINARY_CONDITIONS[op](a, b),) + + +NODE_CLASS_MAPPINGS = { + "CM_IntUnaryOperation": IntUnaryOperation, + "CM_IntUnaryCondition": IntUnaryCondition, + "CM_IntBinaryOperation": IntBinaryOperation, + "CM_IntBinaryCondition": IntBinaryCondition, +} diff --git a/custom_nodes/ComfyMath/src/comfymath/number.py b/custom_nodes/ComfyMath/src/comfymath/number.py new file mode 100644 index 00000000..d2d3868e --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/number.py @@ -0,0 +1,94 @@ +from dataclasses import dataclass +from typing import Any, Callable, Mapping + +from .float import ( + FLOAT_UNARY_OPERATIONS, + FLOAT_UNARY_CONDITIONS, + FLOAT_BINARY_OPERATIONS, + FLOAT_BINARY_CONDITIONS, +) +from .types import Number + +DEFAULT_NUMBER = ("NUMBER", {"default": 0.0}) + + +class NumberUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_UNARY_OPERATIONS.keys()),), + "a": DEFAULT_NUMBER, + } + } + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "op" + CATEGORY = "math/number" + + def op(self, op: str, a: Number) -> tuple[float]: + return (FLOAT_UNARY_OPERATIONS[op](float(a)),) + + +class NumberUnaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_UNARY_CONDITIONS.keys()),), + "a": DEFAULT_NUMBER, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/Number" + + def op(self, op: str, a: Number) -> tuple[bool]: + return (FLOAT_UNARY_CONDITIONS[op](float(a)),) + + +class NumberBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_NUMBER, + "b": DEFAULT_NUMBER, + } + } + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "op" + CATEGORY = "math/number" + + def op(self, op: str, a: Number, b: Number) -> tuple[float]: + return (FLOAT_BINARY_OPERATIONS[op](float(a), float(b)),) + + +class NumberBinaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(FLOAT_BINARY_CONDITIONS.keys()),), + "a": DEFAULT_NUMBER, + "b": DEFAULT_NUMBER, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/float" + + def op(self, op: str, a: Number, b: Number) -> tuple[bool]: + return (FLOAT_BINARY_CONDITIONS[op](float(a), float(b)),) + + +NODE_CLASS_MAPPINGS = { + "CM_NumberUnaryOperation": NumberUnaryOperation, + "CM_NumberUnaryCondition": NumberUnaryCondition, + "CM_NumberBinaryOperation": NumberBinaryOperation, + "CM_NumberBinaryCondition": NumberBinaryCondition, +} diff --git a/custom_nodes/ComfyMath/src/comfymath/py.typed b/custom_nodes/ComfyMath/src/comfymath/py.typed new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyMath/src/comfymath/types.py b/custom_nodes/ComfyMath/src/comfymath/types.py new file mode 100644 index 00000000..7ef12183 --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/types.py @@ -0,0 +1,16 @@ +import sys + +if sys.version_info[1] < 10: + from typing import Tuple, Union + + Number = Union[int, float] + Vec2 = Tuple[float, float] + Vec3 = Tuple[float, float, float] + Vec4 = Tuple[float, float, float, float] +else: + from typing import TypeAlias + + Number: TypeAlias = int | float + Vec2: TypeAlias = tuple[float, float] + Vec3: TypeAlias = tuple[float, float, float] + Vec4: TypeAlias = tuple[float, float, float, float] diff --git a/custom_nodes/ComfyMath/src/comfymath/vec.py b/custom_nodes/ComfyMath/src/comfymath/vec.py new file mode 100644 index 00000000..0b7180fd --- /dev/null +++ b/custom_nodes/ComfyMath/src/comfymath/vec.py @@ -0,0 +1,500 @@ +import numpy + +from typing import Any, Callable, Mapping + +from .types import Vec2, Vec3, Vec4 + +VEC2_ZERO = (0.0, 0.0) +DEFAULT_VEC2 = ("VEC2", {"default": VEC2_ZERO}) + +VEC3_ZERO = (0.0, 0.0, 0.0) +DEFAULT_VEC3 = ("VEC3", {"default": VEC3_ZERO}) + +VEC4_ZERO = (0.0, 0.0, 0.0, 0.0) +DEFAULT_VEC4 = ("VEC4", {"default": VEC4_ZERO}) + +VEC_UNARY_OPERATIONS: Mapping[str, Callable[[numpy.ndarray], numpy.ndarray]] = { + "Neg": lambda a: -a, + "Normalize": lambda a: a / numpy.linalg.norm(a), +} + +VEC_TO_SCALAR_UNARY_OPERATION: Mapping[str, Callable[[numpy.ndarray], float]] = { + "Norm": lambda a: numpy.linalg.norm(a).astype(float), +} + +VEC_UNARY_CONDITIONS: Mapping[str, Callable[[numpy.ndarray], bool]] = { + "IsZero": lambda a: not numpy.any(a).astype(bool), + "IsNotZero": lambda a: numpy.any(a).astype(bool), + "IsNormalized": lambda a: numpy.allclose(a, a / numpy.linalg.norm(a)), + "IsNotNormalized": lambda a: not numpy.allclose(a, a / numpy.linalg.norm(a)), +} + +VEC_BINARY_OPERATIONS: Mapping[ + str, Callable[[numpy.ndarray, numpy.ndarray], numpy.ndarray] +] = { + "Add": lambda a, b: a + b, + "Sub": lambda a, b: a - b, + "Cross": lambda a, b: numpy.cross(a, b), +} + +VEC_TO_SCALAR_BINARY_OPERATION: Mapping[ + str, Callable[[numpy.ndarray, numpy.ndarray], float] +] = { + "Dot": lambda a, b: numpy.dot(a, b), + "Distance": lambda a, b: numpy.linalg.norm(a - b).astype(float), +} + +VEC_BINARY_CONDITIONS: Mapping[str, Callable[[numpy.ndarray, numpy.ndarray], bool]] = { + "Eq": lambda a, b: numpy.allclose(a, b), + "Neq": lambda a, b: not numpy.allclose(a, b), +} + +VEC_SCALAR_OPERATION: Mapping[str, Callable[[numpy.ndarray, float], numpy.ndarray]] = { + "Mul": lambda a, b: a * b, + "Div": lambda a, b: a / b, +} + + +def _vec2_from_numpy(a: numpy.ndarray) -> Vec2: + return ( + float(a[0]), + float(a[1]), + ) + + +def _vec3_from_numpy(a: numpy.ndarray) -> Vec3: + return ( + float(a[0]), + float(a[1]), + float(a[2]), + ) + + +def _vec4_from_numpy(a: numpy.ndarray) -> Vec4: + return ( + float(a[0]), + float(a[1]), + float(a[2]), + float(a[3]), + ) + + +class Vec2UnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_UNARY_OPERATIONS.keys()),), + "a": DEFAULT_VEC2, + } + } + + RETURN_TYPES = ("VEC2",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2) -> tuple[Vec2]: + return (_vec2_from_numpy(VEC_UNARY_OPERATIONS[op](numpy.array(a))),) + + +class Vec2ToScalarUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_TO_SCALAR_UNARY_OPERATION.keys()),), + "a": DEFAULT_VEC2, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2) -> tuple[float]: + return (VEC_TO_SCALAR_UNARY_OPERATION[op](numpy.array(a)),) + + +class Vec2UnaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_UNARY_CONDITIONS.keys()),), + "a": DEFAULT_VEC2, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2) -> tuple[bool]: + return (VEC_UNARY_CONDITIONS[op](numpy.array(a)),) + + +class Vec2BinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_VEC2, + "b": DEFAULT_VEC2, + } + } + + RETURN_TYPES = ("VEC2",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2, b: Vec2) -> tuple[Vec2]: + return ( + _vec2_from_numpy(VEC_BINARY_OPERATIONS[op](numpy.array(a), numpy.array(b))), + ) + + +class Vec2ToScalarBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_TO_SCALAR_BINARY_OPERATION.keys()),), + "a": DEFAULT_VEC2, + "b": DEFAULT_VEC2, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2, b: Vec2) -> tuple[float]: + return (VEC_TO_SCALAR_BINARY_OPERATION[op](numpy.array(a), numpy.array(b)),) + + +class Vec2BinaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_BINARY_CONDITIONS.keys()),), + "a": DEFAULT_VEC2, + "b": DEFAULT_VEC2, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2, b: Vec2) -> tuple[bool]: + return (VEC_BINARY_CONDITIONS[op](numpy.array(a), numpy.array(b)),) + + +class Vec2ScalarOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_SCALAR_OPERATION.keys()),), + "a": DEFAULT_VEC2, + "b": ("FLOAT",), + } + } + + RETURN_TYPES = ("VEC2",) + FUNCTION = "op" + CATEGORY = "math/vec2" + + def op(self, op: str, a: Vec2, b: float) -> tuple[Vec2]: + return (_vec2_from_numpy(VEC_SCALAR_OPERATION[op](numpy.array(a), b)),) + + +class Vec3UnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_UNARY_OPERATIONS.keys()),), + "a": DEFAULT_VEC3, + } + } + + RETURN_TYPES = ("VEC3",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3) -> tuple[Vec3]: + return (_vec3_from_numpy(VEC_UNARY_OPERATIONS[op](numpy.array(a))),) + + +class Vec3ToScalarUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_TO_SCALAR_UNARY_OPERATION.keys()),), + "a": DEFAULT_VEC3, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3) -> tuple[float]: + return (VEC_TO_SCALAR_UNARY_OPERATION[op](numpy.array(a)),) + + +class Vec3UnaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_UNARY_CONDITIONS.keys()),), + "a": DEFAULT_VEC3, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3) -> tuple[bool]: + return (VEC_UNARY_CONDITIONS[op](numpy.array(a)),) + + +class Vec3BinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_VEC3, + "b": DEFAULT_VEC3, + } + } + + RETURN_TYPES = ("VEC3",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3, b: Vec3) -> tuple[Vec3]: + return ( + _vec3_from_numpy(VEC_BINARY_OPERATIONS[op](numpy.array(a), numpy.array(b))), + ) + + +class Vec3ToScalarBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_TO_SCALAR_BINARY_OPERATION.keys()),), + "a": DEFAULT_VEC3, + "b": DEFAULT_VEC3, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3, b: Vec3) -> tuple[float]: + return (VEC_TO_SCALAR_BINARY_OPERATION[op](numpy.array(a), numpy.array(b)),) + + +class Vec3BinaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_BINARY_CONDITIONS.keys()),), + "a": DEFAULT_VEC3, + "b": DEFAULT_VEC3, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3, b: Vec3) -> tuple[bool]: + return (VEC_BINARY_CONDITIONS[op](numpy.array(a), numpy.array(b)),) + + +class Vec3ScalarOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_SCALAR_OPERATION.keys()),), + "a": DEFAULT_VEC3, + "b": ("FLOAT",), + } + } + + RETURN_TYPES = ("VEC3",) + FUNCTION = "op" + CATEGORY = "math/vec3" + + def op(self, op: str, a: Vec3, b: float) -> tuple[Vec3]: + return (_vec3_from_numpy(VEC_SCALAR_OPERATION[op](numpy.array(a), b)),) + + +class Vec4UnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_UNARY_OPERATIONS.keys()),), + "a": DEFAULT_VEC4, + } + } + + RETURN_TYPES = ("VEC4",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4) -> tuple[Vec4]: + return (_vec4_from_numpy(VEC_UNARY_OPERATIONS[op](numpy.array(a))),) + + +class Vec4ToScalarUnaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_TO_SCALAR_UNARY_OPERATION.keys()),), + "a": DEFAULT_VEC4, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4) -> tuple[float]: + return (VEC_TO_SCALAR_UNARY_OPERATION[op](numpy.array(a)),) + + +class Vec4UnaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_UNARY_CONDITIONS.keys()),), + "a": DEFAULT_VEC4, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4) -> tuple[bool]: + return (VEC_UNARY_CONDITIONS[op](numpy.array(a)),) + + +class Vec4BinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_BINARY_OPERATIONS.keys()),), + "a": DEFAULT_VEC4, + "b": DEFAULT_VEC4, + } + } + + RETURN_TYPES = ("VEC4",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4, b: Vec4) -> tuple[Vec4]: + return ( + _vec4_from_numpy(VEC_BINARY_OPERATIONS[op](numpy.array(a), numpy.array(b))), + ) + + +class Vec4ToScalarBinaryOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_TO_SCALAR_BINARY_OPERATION.keys()),), + "a": DEFAULT_VEC4, + "b": DEFAULT_VEC4, + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4, b: Vec4) -> tuple[float]: + return (VEC_TO_SCALAR_BINARY_OPERATION[op](numpy.array(a), numpy.array(b)),) + + +class Vec4BinaryCondition: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_BINARY_CONDITIONS.keys()),), + "a": DEFAULT_VEC4, + "b": DEFAULT_VEC4, + } + } + + RETURN_TYPES = ("BOOL",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4, b: Vec4) -> tuple[bool]: + return (VEC_BINARY_CONDITIONS[op](numpy.array(a), numpy.array(b)),) + + +class Vec4ScalarOperation: + @classmethod + def INPUT_TYPES(cls) -> Mapping[str, Any]: + return { + "required": { + "op": (list(VEC_SCALAR_OPERATION.keys()),), + "a": DEFAULT_VEC4, + "b": ("FLOAT",), + } + } + + RETURN_TYPES = ("VEC4",) + FUNCTION = "op" + CATEGORY = "math/vec4" + + def op(self, op: str, a: Vec4, b: float) -> tuple[Vec4]: + return (_vec4_from_numpy(VEC_SCALAR_OPERATION[op](numpy.array(a), b)),) + + +NODE_CLASS_MAPPINGS = { + "CM_Vec2UnaryOperation": Vec2UnaryOperation, + "CM_Vec2UnaryCondition": Vec2UnaryCondition, + "CM_Vec2ToScalarUnaryOperation": Vec2ToScalarUnaryOperation, + "CM_Vec2BinaryOperation": Vec2BinaryOperation, + "CM_Vec2BinaryCondition": Vec2BinaryCondition, + "CM_Vec2ToScalarBinaryOperation": Vec2ToScalarBinaryOperation, + "CM_Vec2ScalarOperation": Vec2ScalarOperation, + "CM_Vec3UnaryOperation": Vec3UnaryOperation, + "CM_Vec3UnaryCondition": Vec3UnaryCondition, + "CM_Vec3ToScalarUnaryOperation": Vec3ToScalarUnaryOperation, + "CM_Vec3BinaryOperation": Vec3BinaryOperation, + "CM_Vec3BinaryCondition": Vec3BinaryCondition, + "CM_Vec3ToScalarBinaryOperation": Vec3ToScalarBinaryOperation, + "CM_Vec3ScalarOperation": Vec3ScalarOperation, + "CM_Vec4UnaryOperation": Vec4UnaryOperation, + "CM_Vec4UnaryCondition": Vec4UnaryCondition, + "CM_Vec4ToScalarUnaryOperation": Vec4ToScalarUnaryOperation, + "CM_Vec4BinaryOperation": Vec4BinaryOperation, + "CM_Vec4BinaryCondition": Vec4BinaryCondition, + "CM_Vec4ToScalarBinaryOperation": Vec4ToScalarBinaryOperation, + "CM_Vec4ScalarOperation": Vec4ScalarOperation, +} diff --git a/custom_nodes/ComfyUI-Crystools/.gitignore b/custom_nodes/ComfyUI-Crystools/.gitignore new file mode 100644 index 00000000..fd5a6a26 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/.gitignore @@ -0,0 +1,156 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ +*.pyc + +.idea +/node_modules +/node.zip diff --git a/custom_nodes/ComfyUI-Crystools/LICENSE b/custom_nodes/ComfyUI-Crystools/LICENSE new file mode 100644 index 00000000..e4217d06 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Crystian + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/ComfyUI-Crystools/README.md b/custom_nodes/ComfyUI-Crystools/README.md new file mode 100644 index 00000000..77c92027 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/README.md @@ -0,0 +1,672 @@ +# comfyui-crystools [![Donate](https://img.shields.io/badge/Donate-PayPal-blue.svg)](https://paypal.me/crystian77) Open in Colab + +**_πŸͺ› A powerful set of tools for your belt when you work with ComfyUI πŸͺ›_** + +With this suit, you can see the resources monitor, progress bar & time elapsed, metadata and compare between two images, compare between two JSONs, show any value to console/display, pipes, and more! +This provides better nodes to load/save images, previews, etc, and see "hidden" data without loading a new workflow. + +![Show metadata](./docs/jake.gif) + +# Table of contents +- [General](#general) +- [Metadata](#metadata) +- [Debugger](#debugger) +- [Image](#image) +- [Pipe](#pipe) +- [Utils](#utils) +- [Primitives](#primitives) +- [List](#list) +- [Switch](#switch) +- Others: [About](#about), [To do](#to-do), [Changelog](#changelog), [Installation](#installation), [Use](#use) + +--- + +## General + +### Resources monitor + +**πŸŽ‰Finally, you can see the resources used by ComfyUI (CPU, GPU, RAM, VRAM, GPU Temp and space) on the menu in real-time!** + +Horizontal: +![Monitors](./docs/monitor1.webp) + +Vertical: +![Monitors](./docs/monitor3.webp) + +Now you can identify the bottlenecks in your workflow and know when it's time to restart the server, unload models or even close some tabs! + +You can configure the refresh rate which resources to show: + +![Monitors](./docs/monitor-settings.png) + +> **Notes:** +> - The GPU data is only available when you use CUDA (only NVIDIA cards, sorry AMD users). +> - This extension needs ComfyUI 1915 (or higher). +> - The cost of the monitor is low (0.1 to 0.5% of utilization), you can disable it from settings (`Refres rate` to `0`). +> - Data comes from these libraries: +> - [psutil](https://pypi.org/project/psutil/) +> - [torch](https://pytorch.org/) +> - [pynvml](https://pypi.org/project/pynvml/) (official NVIDIA library) + + +### Progress bar + +You can see the progress of your workflow with a progress bar on the menu! + +![Progress bar](./docs/progress-bar.png) + +https://github.com/crystian/comfyui-crystools/assets/3886806/35cc1257-2199-4b85-936e-2e31d892959c + +Additionally, it shows the time elapsed at the end of the workflow, and you can `click` on it to see the **current working node.** + +> **Notes:** +> - If you don't want to see it, you can turn it off from settings (`Show progress bar in menu`) + + +## Metadata + +### Node: Metadata extractor + +This node is used to extract the metadata from the image and handle it as a JSON source for other nodes. +You can see **all information**, even metadata from other sources (like Photoshop, see sample). + +The input comes from the [load image with metadata](#node-load-image-with-metadata) or [preview from image](#node-preview-from-image) nodes (and others in the future). + +![Metadata extractor](./docs/metadata-extractor.png) + +**Sample:** [metadata-extractor.json](./samples/metadata-extractor.json) + +>
+> Other metadata sample (photoshop) +> +> With metadata from Photoshop +![Metadata extractor](./docs/metadata-extractor-photoshop.png) +>
+ +>
+> Parameters +> +> - input: +> - metadata_raw: The metadata raw from the image or preview node +> - Output: +> - prompt: The prompt used to produce the image. +> - workflow: The workflow used to produce the image (all information about nodes, values, etc). +> - file info: The file info of the image/metadata (resolution, size, etc) is human readable. +> - raw to JSON: The entire metadata is raw but formatted/readable. +> - raw to property: The entire metadata is raw in "properties" format. +> - raw to csv: The entire metadata is raw in "csv" format. +>
+ +
+ +### Node: Metadata comparator + +This node is so useful for comparing two metadata and seeing the differences (**the main reason why I created this extension!**) + +You can compare 3 inputs: "Prompt", "Workflow" and "Fileinfo" + +There are three potential "outputs": `values_changed`, `dictionary_item_added`, and `dictionary_item_removed` (in this order of priority). + +![Metadata extractor](./docs/metadata-comparator.png) + +**Sample:** [metadata-comparator.json](./samples/metadata-comparator.json) + +**Notes:** +- I use [DeepDiff](https://pypi.org/project/deepdiff) for that. For more info check the link. +- If you want to compare two JSONs, you can use the [JSON comparator](#node-JSON-comparator) node. + + +>
+> Parameters +> +> - options: +> - what: What to compare, you can choose between "Prompt", "Workflow" and "Fileinfo" +> - input: +> - metadata_raw_old: The metadata raw to start comparing +> - metadata_raw_new: The metadata raw to compare +> - Output: +> - diff: This is the same output you can see in the display of the node; you can use it on other nodes. +>
+ +
+ +--- + +## Debugger + +### Node: Show Metadata + +With this node, you will be able to see the JSON produced from your entire prompt and workflow so that you can know all the values (and more) of your prompt quickly without opening the file (PNG or JSON). + +![Show metadata](./docs/debugger-show-metadata.png) + +**Sample:** [debugger-metadata.json](./samples/debugger-metadata.json) + +>
+> Parameters +> +> - Options: +> - Active: Enable/disable the node +> - Parsed: Show the parsed JSON or plain text +> - What: Show the prompt or workflow (prompt are values to produce the image, and workflow is the entire workflow of ComfyUI) +>
+ +
+ +### Node: Show any + +You can see on the console or display any text or data from the nodes. Connect it to what you want to inspect, and you will see it. + +![Show any](./docs/debugger-show-any.png) + +**Sample:** [debugger-any.json](./samples/debugger-any.json) + +>
+> Parameters +> +> - Input: +> - any_value: Any value to show, which can be a string, number, etc. +> - Options: +> - Console: Enable/disable write to console +> - Display: Enable/disable write on this node +> - Prefix: Prefix to console +>
+ +
+ +### Node: Show any to JSON + +It is the same as the previous one, but it formatted the value to JSON (only display). + +![Show any](./docs/debugger-show-json.png) + +**Sample:** [debugger-json.json](./samples/debugger-json.json) + +>
+> Parameters +> +> - Input: +> - any_value: Any value to try to convert to JSON +> - Output: +> - string: The same string is shown on the display +>
+ +
+ +--- + +## Image + +### Node: Load image with metadata +This node is the same as the default one, but it adds three features: Prompt, Metadata, and supports **subfolders** of the "input" folder. + +![Load image with metadata](./docs/image-load.png) + +**Sample:** [image-load.json](./samples/image-load.json) + +>
+> Parameters +> +> - Input: +> - image: Read the images from the input folder (and subfolder) (you can drop the image here or even paste an image from the clipboard) +> - Output: +> - Image/Mask: The same as the default node +> - Prompt: The prompt used to produce the image (not the workflow) +> - Metadata RAW: The metadata raw of the image (full workflow) as string +>
+ +**Note:** The subfolders support inspired on: [comfyui-imagesubfolders](https://github.com/catscandrive/comfyui-imagesubfolders) + +
+ +### Node: Save image with extra metadata +This node is the same as the default one, but it adds two features: Save the workflow in the png or not, and you can add any piece of metadata (as JSON). + +This saves custom data on the image, so you can share it with others, and they can see the workflow and metadata (see [preview from metadata](#node-preview-from-metadata)), even your custom data. + +It can be any type of information that supports text and JSON. + +![Save image with extra metadata](./docs/image-save.png) + +**Sample:** [image-save.json](./samples/image-save.json) + +>
+> Parameters +> +> - options: +> - with_workflow: If you want to save into the image workflow (special to share the workflow with others) +> - Input: +> - image: The image to save (same as the default node) +> - Output: +> - Metadata RAW: The metadata raw of the image (full workflow) as string +>
+ +**Note:** The data is saved as special "exif" (as ComfyUI does) in the png file; you can read it with [Load image with metadata](#node-load-image-with-metadata). + +> **Important:** +> - If you want to save your workflow with a particular name and your data as creator, you need to use the [ComfyUI-Crystools-save](https://github.com/crystian/ComfyUI-Crystools-save) extension; try it! +![Crystools-save](./docs/crystools-save.png) + + +
+ +### Node: Preview from image + +This node is used to preview the image with the **current prompt** and additional features. + +![Preview from image](./docs/image-preview.png) + +**Feature:** It supports cache (shows as "CACHED") (not permanent yet!), so you can disconnect the node and still see the image and data, so you can use it to compare with others! + +![Preview from image](./docs/image-preview-diff.png) + +As you can see the seed, steps, and cfg were changed + +**Sample:** [image-preview-image.json](./samples/image-preview-image.json) + +>
+> Parameters +> +> - Input: +> - image: Any kind of image link +> - Output: +> - Metadata RAW: The metadata raw of the image and full workflow. +> - You can use it to **compare with others** (see [metadata comparator](#node-metadata-comparator)) +> - The file info like filename, resolution, datetime and size with **the current prompt, not the original one!** (see important note) +>
+ +> **Important:** +> - If you want to read the metadata of the image, you need to use the [load image with metadata](#node-load-image-with-metadata) and use the output "metadata RAW" not the image link. +> - To do a preview, it is necessary to save it first on the temporal folder, and the data shown is from the temporal image, **not the original one** even **the prompt!** + +
+ +### Node: Preview from metadata + +This node is used to preview the image from the metadata and shows additional data (all around this one). +It supports the same features as [preview from image](#node-preview-from-image) (cache, metadata raw, etc.). But the important difference is you see **real data from the image** (not the temporal one or the current prompt). + +![Preview from metadata](./docs/image-preview-metadata.png) + +**Sample:** [image-preview-metadata.json](./samples/image-preview-metadata.json) + +
+ +### Node: Show resolution + +This node is used to show the resolution of an image. + +> Can be used with any image link. + +![Show resolution](./docs/image-resolution.png) + +**Sample:** [image-resolution.json](./samples/image-resolution.json) + +>
+> Parameters +> +> - Input: +> - image: Any kind of image link +> - Output: +> - Width: The width of the image +> - Height: The height of the image +>
+ +
+ +--- + +## Pipe + +### Nodes: Pipe to/edit any, Pipe from any + +This powerful set of nodes is used to better organize your pipes. + +The "Pipe to/edit any" node is used to encapsulate multiple links into a single one. It includes support for editing and easily adding the modified content back to the same pipe number. + +The "Pipe from any" node is used to extract the content of a pipe. + +Typical example: + +![Pipes](./docs/pipe-0.png) + +With pipes: + +![Pipes](./docs/pipe-1.png) + +**Sample:** [pipe-1.json](./samples/pipe-1.json) + +Editing pipes: + +![Pipes](./docs/pipe-2.png) + +**Sample:** [pipe-2.json](./samples/pipe-2.json) + +>
+> Parameters +> +> - Input: +> - CPipeAny: This is the type of this pipe you can use to edit (see the sample) +> - any_*: 6 possible inputs to use +> - Output: +> - CPipeAny: You can continue the pipe with this output; you can use it to bifurcate the pipe (see the sample) +>
+ +>**Important:** +> - Please note that it supports "any," meaning it does not validate (not yet!) the correspondence of input nodes with the output ones. When creating the link, it is recommended to link consciously number by number. +> - "RecursionError" It's crucial to note that the flow of links **must be in the same direction**, and they cannot be mixed with other flows that use the result of this one. Otherwise, this may lead to recursion and block the server (you need to restart it!) + + +>
+> Bad example with "RecursionError: maximum recursion depth exceeded" +> +> If you see something like this on your console, you need to check your pipes. That is bad sample of pipes, you can't mix the flows. +![Pipes](./docs/pipe-3.png) +>
+ +
+ +--- + +## Utils + +Some useful nodes to use in your workflow. + +### Node: JSON comparator + +This node is so useful to compare two JSONs and see the differences. + +![JSON comparator](./docs/utils-json-comparator.png) + +**Sample:** [utils-json-comparator.json](./samples/utils-json-comparator.json) + + +>
+> Parameters +> +> - input: +> - json_old: The first JSON to start compare +> - json_new: The JSON to compare +> - Output: +> - diff: A new JSON with the differences +>
+ + +**Notes:** +As you can see, it is the same as the [metadata comparator](#node-metadata-comparator) but with JSONs. +The other is intentionally simple to compare two images metadata; this is more generic. +The main difference is that you can compare any JSON, not only metadata. + +
+ +### Node: Stats system + +This node is used to show the system stats (RAM, VRAM, and Space). +It **should** connect as a pipe. + +![JSON comparator](./docs/utils-stats.png) + +**Sample:** [utils-stats.json](./samples/utils-stats.json) + +>
+> Parameters +> +> - input: +> - latent: The latent to use to measure the stats +> - Output: +> - latent: Return the same latent to continue the pipe +>
+ +**Notes:** The original is in [WAS](https://github.com/WASasquatch/was-node-suite-comfyui), I only show it on the display. + +
+ +--- + +## Primitives + +### Nodes: Primitive boolean, Primitive integer, Primitive float, Primitive string, Primitive string multiline + +A set of nodes with primitive values to use in your prompts. + +![Primitives](./docs/primitives.png) + +
+ +--- + +## List +A set of nodes with a list of values (any or strings/texts) for any proposal (news nodes to use it coming soon!). + +> **Important:** You can use other nodes like "Show any" to see the values of the list + +### Node: List of strings + +**Feature:** You can concatenate them. + +![Lists](./docs/list-string.png) + +**Sample:** [list-strings.json](./samples/list-strings.json) + +>
+> Parameters +> +> - Input: +> - string_*: 8 possible inputs to use +> - delimiter: Use to concatenate the values on the output +> - Output: +> - concatenated: A string with all values concatenated +> - list_string: The list of strings (only with values) +>
+ +
+ +### Node: List of any + +You can concatenate any value (it will try to convert it to a string and show the value), so it is util to see several values at the same time. + +![Lists](./docs/list-any.png) + +**Sample:** [list-any.json](./samples/list-any.json) + +>
+> Parameters +> +> - Input: +> - any_*: 8 possible inputs to use +> - Output: +> - list_any: The list of any elements (only with values) +>
+ +
+ +--- + +## Switch +A set of nodes to switch between flows. + +All switches are boolean; you can switch between flows by simply changing the value of the switch. +You have predefined switches (string, latent, image, conditioning) but you can use "Switch any" for any value/type. + +![Switches](./docs/switches.png) + +**Sample:** [switch.json](./samples/switch.json) + +
+ +--- + +## About + +**Notes from the author:** +- This is my first project in python Β―\\_(ツ)_/Β― (PR are welcome!) +- I'm a software engineer but in other languages (web technologies) +- My Instagram is: https://www.instagram.com/crystian.ia I'll publish my works on it, so consider following me for news! :) +- I'm not a native English speaker, so sorry for my English :P + +--- + +## To do +- [ ] Several unit tests +- [ ] Add permanent cache for preview/metadata image (to survive to F5! or restart the server) + +--- + +## Changelog + +### Crystools + +### 1.27.0 (17/08/2025) +- revert the lower case on name, cannot change on registry Β―\_(ツ)_/Β― +- zluda check removed, it is not necessary anymore + +### 1.25.3 (27/07/2025) +- change the name to lower case + +### 1.25.1 (02/06/2025) +- fix issues with switches on settings menu +- node: "Switch from any" added +- load image with metadata: filtered (exclude hidden folders an typically metadata files) +- other fixes + +### 1.24.0 (02/06/2025) +- PRs by community merged +- Improved VRAM usage/readout +- HDD error handling +- Lazy switches + +### 1.23.0 (02/06/2025) +- Jetson support added by @johnnynunez +- some ui fixes + +### 1.20.0 (21/10/2024) +- BETA of JSON file reader and extractor, to allow you to read your own JSON files and extract the values to use in your workflow + +### 1.19.0 (06/10/2024) +- HORIZONTAL UI! New version is ready! πŸŽ‰ + +### 1.18.0 (21/09/2024) +- HORIZONTAL UI! πŸŽ‰ +- Configurable size of monitors on settings menu + +### 1.17.0 (21/09/2024) +- Settings menu reorganized +- Preparing for horizontal UI +- Update from ComfyUI (typescript and new features) + +### 1.16.0 (31/07/2024) +- Rollback of AMD support by manager does not support other repository parameter (https://test.pypi.org/simple by pyrsmi) + +### 1.15.0 (21/07/2024) +- AMD Branch merged to the main branch, should work for AMD users on **Linux** + +### 1.14.0 (15/07/2024) +- Tried to use AMD info, but it breaks installation on windows, so I removed it Β―\_(ツ)_/Β― +- AMD Branch added, if you use AMD and Linux, you can try it (not tested for me) + +### 1.13.0 (01/07/2024) +- Integrate with new ecosystem of ComfyUI +- Webp support added on load image with metadata node + +### 1.12.0 (27/03/2024) +- GPU Temperature added + +### 1.10.0 (17/01/2024) +- Multi-gpu added + +### 1.9.2 (15/01/2024) +- Big refactor on hardwareInfo and monitor.ts, gpu was separated on another file, preparing for multi-gpu support + +### 1.8.0 (14/01/2024) - internal +- HDD monitor selector on settings + +### 1.7.0 (11/01/2024) - internal +- Typescript added! + +### 1.6.0 (11/01/2024) +- Fix issue [#7](https://github.com/crystian/comfyui-crystools/issues/7) to the thread deadlock on concurrency + +### 1.5.0 (10/01/2024) +- Improvements on the resources monitor and how handle the threads +- Some fixes + +### 1.3.0 (08/01/2024) +- Added in general Resources monitor (CPU, GPU, RAM, VRAM, and space) +- Added this icon to identify this set of tools: πŸͺ› + +### 1.2.0 (05/01/2024) +- progress bar added + +### 1.1.0 (29/12/2023) +- Node added: "Save image with extra metadata" +- Support to **read** Jpeg metadata added (not save) + +### 1.0.0 (26/12/2023) +- First release + + +### Crystools-save - DEPRECATED (01/06/2025) + +### 1.1.0 (07/01/2024) +- Labeling updated according to the new version of Crystools (this project) + +### 1.0.0 (29/12/2023) +- Created another extension to save the info about the author on workflow: [ComfyUI-Crystools-save](https://github.com/crystian/ComfyUI-Crystools-save) + +--- + +## Installation + +### Install from GitHub +1. Install [ComfyUi](https://github.com/comfyanonymous/ComfyUI). +2. Clone this repo into `custom_nodes`: + ``` + cd ComfyUI/custom_nodes + git clone https://github.com/crystian/comfyui-crystools.git + cd comfyui-crystools + pip install -r requirements.txt + ``` +3. Start up ComfyUI. + +#### For AMD users +If you are an AMD user with Linux, you can try the AMD branch: + +**ATTENTION:** Don't install with the manager, you need to install manually: + + ``` + cd ComfyUI/custom_nodes + git clone -b AMD https://github.com/crystian/comfyui-crystools.git + cd comfyui-crystools + pip install -r requirements.txt + ``` + +### Install from manager + +Search for `crystools` in the [manager](https://github.com/ltdrdata/ComfyUI-Manager.git) and install it. + +### Using on Google Colab + +You can use it on Google Colab, but you need to install it manually: + +[Google Colab](https://colab.research.google.com/drive/1xiTiPmZkcIqNOsLQPO1UNCdJZqgK3U5k?usp=sharing) + +* Run the first cell to install ComfyUI and launch the server +* After it finishes, use the link to open the a new tab, looking a line like this: +``` +This is the URL to access ComfyUI: https://identifying-complications-fw-some.trycloudflare.com +``` + +--- + +## Use + +You can use it as any other node, just using the menu in the category `crystools` or double clicking on the canvas (I recommended using the "oo" to fast filter), all nodes were post fixing with `[Crystools]`. + +![Menu](./docs/menu.png) +![shortcut](./docs/shortcut.png) + +If for some reason you need to see the logs, you can define the environment variable `CRYSTOOLS_LOGLEVEL` and set the [value](https://docs.python.org/es/3/howto/logging.html). + +--- + +Made with ❀️ by Crystian. diff --git a/custom_nodes/ComfyUI-Crystools/__init__.py b/custom_nodes/ComfyUI-Crystools/__init__.py new file mode 100644 index 00000000..e2eaf6c0 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/__init__.py @@ -0,0 +1,108 @@ +""" +@author: Crystian +@title: Crystools +@nickname: Crystools +@version: 1.27.4 +@project: "https://github.com/crystian/comfyui-crystools", +@description: Plugins for multiples uses, mainly for debugging, you need them! IG: https://www.instagram.com/crystian.ia +""" + +from .core import version, logger +logger.info(f'Crystools version: {version}') + +from .nodes._names import CLASSES +from .nodes.primitive import CBoolean, CText, CTextML, CInteger, CFloat +from .nodes.switch import CSwitchBooleanAny, CSwitchBooleanLatent, CSwitchBooleanConditioning, CSwitchBooleanImage, \ + CSwitchBooleanString, CSwitchBooleanMask, CSwitchFromAny +from .nodes.debugger import CConsoleAny, CConsoleAnyToJson +from .nodes.image import CImagePreviewFromImage, CImageLoadWithMetadata, CImageGetResolution, CImagePreviewFromMetadata, \ + CImageSaveWithExtraMetadata +from .nodes.list import CListAny, CListString +from .nodes.pipe import CPipeToAny, CPipeFromAny +from .nodes.utils import CUtilsCompareJsons, CUtilsStatSystem +from .nodes.metadata import CMetadataExtractor, CMetadataCompare +from .nodes.parameters import CJsonFile, CJsonExtractor +from .server import * +from .general import * + +NODE_CLASS_MAPPINGS = { + CLASSES.CBOOLEAN_NAME.value: CBoolean, + CLASSES.CTEXT_NAME.value: CText, + CLASSES.CTEXTML_NAME.value: CTextML, + CLASSES.CINTEGER_NAME.value: CInteger, + CLASSES.CFLOAT_NAME.value: CFloat, + + CLASSES.CDEBUGGER_CONSOLE_ANY_NAME.value: CConsoleAny, + CLASSES.CDEBUGGER_CONSOLE_ANY_TO_JSON_NAME.value: CConsoleAnyToJson, + + CLASSES.CLIST_ANY_NAME.value: CListAny, + CLASSES.CLIST_STRING_NAME.value: CListString, + + CLASSES.CSWITCH_FROM_ANY_NAME.value: CSwitchFromAny, + CLASSES.CSWITCH_ANY_NAME.value: CSwitchBooleanAny, + CLASSES.CSWITCH_LATENT_NAME.value: CSwitchBooleanLatent, + CLASSES.CSWITCH_CONDITIONING_NAME.value: CSwitchBooleanConditioning, + CLASSES.CSWITCH_IMAGE_NAME.value: CSwitchBooleanImage, + CLASSES.CSWITCH_MASK_NAME.value: CSwitchBooleanMask, + CLASSES.CSWITCH_STRING_NAME.value: CSwitchBooleanString, + + CLASSES.CPIPE_TO_ANY_NAME.value: CPipeToAny, + CLASSES.CPIPE_FROM_ANY_NAME.value: CPipeFromAny, + + CLASSES.CIMAGE_LOAD_METADATA_NAME.value: CImageLoadWithMetadata, + CLASSES.CIMAGE_GET_RESOLUTION_NAME.value: CImageGetResolution, + CLASSES.CIMAGE_PREVIEW_IMAGE_NAME.value: CImagePreviewFromImage, + CLASSES.CIMAGE_PREVIEW_METADATA_NAME.value: CImagePreviewFromMetadata, + CLASSES.CIMAGE_SAVE_METADATA_NAME.value: CImageSaveWithExtraMetadata, + + CLASSES.CMETADATA_EXTRACTOR_NAME.value: CMetadataExtractor, + CLASSES.CMETADATA_COMPARATOR_NAME.value: CMetadataCompare, + CLASSES.CUTILS_JSON_COMPARATOR_NAME.value: CUtilsCompareJsons, + CLASSES.CUTILS_STAT_SYSTEM_NAME.value: CUtilsStatSystem, + CLASSES.CJSONFILE_NAME.value: CJsonFile, + CLASSES.CJSONEXTRACTOR_NAME.value: CJsonExtractor, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + CLASSES.CBOOLEAN_NAME.value: CLASSES.CBOOLEAN_DESC.value, + CLASSES.CTEXT_NAME.value: CLASSES.CTEXT_DESC.value, + CLASSES.CTEXTML_NAME.value: CLASSES.CTEXTML_DESC.value, + CLASSES.CINTEGER_NAME.value: CLASSES.CINTEGER_DESC.value, + CLASSES.CFLOAT_NAME.value: CLASSES.CFLOAT_DESC.value, + + CLASSES.CDEBUGGER_CONSOLE_ANY_NAME.value: CLASSES.CDEBUGGER_ANY_DESC.value, + CLASSES.CDEBUGGER_CONSOLE_ANY_TO_JSON_NAME.value: CLASSES.CDEBUGGER_CONSOLE_ANY_TO_JSON_DESC.value, + + CLASSES.CLIST_ANY_NAME.value: CLASSES.CLIST_ANY_DESC.value, + CLASSES.CLIST_STRING_NAME.value: CLASSES.CLIST_STRING_DESC.value, + + CLASSES.CSWITCH_FROM_ANY_NAME.value: CLASSES.CSWITCH_FROM_ANY_DESC.value, + CLASSES.CSWITCH_ANY_NAME.value: CLASSES.CSWITCH_ANY_DESC.value, + CLASSES.CSWITCH_LATENT_NAME.value: CLASSES.CSWITCH_LATENT_DESC.value, + CLASSES.CSWITCH_CONDITIONING_NAME.value: CLASSES.CSWITCH_CONDITIONING_DESC.value, + CLASSES.CSWITCH_IMAGE_NAME.value: CLASSES.CSWITCH_IMAGE_DESC.value, + CLASSES.CSWITCH_MASK_NAME.value: CLASSES.CSWITCH_MASK_DESC.value, + CLASSES.CSWITCH_STRING_NAME.value: CLASSES.CSWITCH_STRING_DESC.value, + + CLASSES.CPIPE_TO_ANY_NAME.value: CLASSES.CPIPE_TO_ANY_DESC.value, + CLASSES.CPIPE_FROM_ANY_NAME.value: CLASSES.CPIPE_FROM_ANY_DESC.value, + + CLASSES.CIMAGE_LOAD_METADATA_NAME.value: CLASSES.CIMAGE_LOAD_METADATA_DESC.value, + CLASSES.CIMAGE_GET_RESOLUTION_NAME.value: CLASSES.CIMAGE_GET_RESOLUTION_DESC.value, + CLASSES.CIMAGE_PREVIEW_IMAGE_NAME.value: CLASSES.CIMAGE_PREVIEW_IMAGE_DESC.value, + CLASSES.CIMAGE_PREVIEW_METADATA_NAME.value: CLASSES.CIMAGE_PREVIEW_METADATA_DESC.value, + CLASSES.CIMAGE_SAVE_METADATA_NAME.value: CLASSES.CIMAGE_SAVE_METADATA_DESC.value, + + CLASSES.CMETADATA_EXTRACTOR_NAME.value: CLASSES.CMETADATA_EXTRACTOR_DESC.value, + CLASSES.CMETADATA_COMPARATOR_NAME.value: CLASSES.CMETADATA_COMPARATOR_DESC.value, + + CLASSES.CUTILS_JSON_COMPARATOR_NAME.value: CLASSES.CUTILS_JSON_COMPARATOR_DESC.value, + CLASSES.CUTILS_STAT_SYSTEM_NAME.value: CLASSES.CUTILS_STAT_SYSTEM_DESC.value, + + CLASSES.CJSONFILE_NAME.value: CLASSES.CJSONFILE_DESC.value, + CLASSES.CJSONEXTRACTOR_NAME.value: CLASSES.CJSONEXTRACTOR_DESC.value, +} + + +WEB_DIRECTORY = "./web" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] diff --git a/custom_nodes/ComfyUI-Crystools/core/__init__.py b/custom_nodes/ComfyUI-Crystools/core/__init__.py new file mode 100644 index 00000000..932d3182 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/__init__.py @@ -0,0 +1,6 @@ +from .logger import * +from .keys import * +from .types import * +from .config import * +from .common import * +from .version import * diff --git a/custom_nodes/ComfyUI-Crystools/core/common.py b/custom_nodes/ComfyUI-Crystools/core/common.py new file mode 100644 index 00000000..7bebf2da --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/common.py @@ -0,0 +1,119 @@ +import os +import json +import torch +from deepdiff import DeepDiff +from ..core import CONFIG, logger + + +# just a helper function to set the widget values (or clear them) +def setWidgetValues(value=None, unique_id=None, extra_pnginfo=None) -> None: + if unique_id and extra_pnginfo: + workflow = extra_pnginfo["workflow"] + node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id), None) + + if node: + node["widgets_values"] = value + + return None + + +# find difference between two jsons +def findJsonStrDiff(json1, json2): + msgError = "Could not compare jsons" + returnJson = {"error": msgError} + try: + # TODO review this + # dict1 = json.loads(json1) + # dict2 = json.loads(json2) + + returnJson = findJsonsDiff(json1, json2) + + returnJson = json.dumps(returnJson, indent=CONFIG["indent"]) + except Exception as e: + logger.warn(f"{msgError}: {e}") + + return returnJson + + +def findJsonsDiff(json1, json2): + msgError = "Could not compare jsons" + returnJson = {"error": msgError} + + try: + diff = DeepDiff(json1, json2, ignore_order=True, verbose_level=2) + + returnJson = {k: v for k, v in diff.items() if + k in ('dictionary_item_added', 'dictionary_item_removed', 'values_changed')} + + # just for print "values_changed" at first + returnJson = dict(reversed(returnJson.items())) + + except Exception as e: + logger.warn(f"{msgError}: {e}") + + return returnJson + + +# powered by: +# https://github.com/WASasquatch/was-node-suite-comfyui/blob/main/WAS_Node_Suite.py +# class: WAS_Samples_Passthrough_Stat_System +def get_system_stats(): + import psutil + + # RAM + ram = psutil.virtual_memory() + ram_used = ram.used / (1024 ** 3) + ram_total = ram.total / (1024 ** 3) + ram_stats = f"Used RAM: {ram_used:.2f} GB / Total RAM: {ram_total:.2f} GB" + + # VRAM (with PyTorch) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + vram_used = torch.cuda.memory_allocated(device) / (1024 ** 3) + vram_total = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3) + vram_stats = f"Used VRAM: {vram_used:.2f} GB / Total VRAM: {vram_total:.2f} GB" + + # Hard Drive Space + hard_drive = psutil.disk_usage("/") + used_space = hard_drive.used / (1024 ** 3) + total_space = hard_drive.total / (1024 ** 3) + hard_drive_stats = f"Used Space: {used_space:.2f} GB / Total Space: {total_space:.2f} GB" + + return [ram_stats, vram_stats, hard_drive_stats] + + +# return x and y resolution of an image (torch tensor) +def getResolutionByTensor(image=None) -> dict: + res = {"x": 0, "y": 0} + + if image is not None: + img = image.movedim(-1, 1) + + res["x"] = img.shape[3] + res["y"] = img.shape[2] + + return res + + +# by https://stackoverflow.com/questions/6080477/how-to-get-the-size-of-tar-gz-in-mb-file-in-python +def get_size(path): + size = os.path.getsize(path) + if size < 1024: + return f"{size} bytes" + elif size < pow(1024, 2): + return f"{round(size / 1024, 2)} KB" + elif size < pow(1024, 3): + return f"{round(size / (pow(1024, 2)), 2)} MB" + elif size < pow(1024, 4): + return f"{round(size / (pow(1024, 3)), 2)} GB" + + +def get_nested_value(data, dotted_key, default=None): + keys = dotted_key.split('.') + for key in keys: + if isinstance(data, str): + data = json.loads(data) + if isinstance(data, dict) and key in data: + data = data[key] + else: + return default + return data diff --git a/custom_nodes/ComfyUI-Crystools/core/config.py b/custom_nodes/ComfyUI-Crystools/core/config.py new file mode 100644 index 00000000..80c811e6 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/config.py @@ -0,0 +1,7 @@ +import os +import logging + +CONFIG = { + "loglevel": int(os.environ.get("CRYSTOOLS_LOGLEVEL", logging.INFO)), + "indent": int(os.environ.get("CRYSTOOLS_INDENT", 2)) +} diff --git a/custom_nodes/ComfyUI-Crystools/core/keys.py b/custom_nodes/ComfyUI-Crystools/core/keys.py new file mode 100644 index 00000000..f56814da --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/keys.py @@ -0,0 +1,29 @@ +from enum import Enum + + +class TEXTS(Enum): + CUSTOM_NODE_NAME = "Crystools" + LOGGER_PREFIX = "Crystools" + CONCAT = "concatenated" + INACTIVE_MSG = "inactive" + INVALID_METADATA_MSG = "Invalid metadata raw" + FILE_NOT_FOUND = "File not found!" + + +class CATEGORY(Enum): + TESTING = "_for_testing" + MAIN = "crystools πŸͺ›" + PRIMITIVE = "/Primitive" + DEBUGGER = "/Debugger" + LIST = "/List" + SWITCH = "/Switch" + PIPE = "/Pipe" + IMAGE = "/Image" + UTILS = "/Utils" + METADATA = "/Metadata" + + +# remember, all keys should be in lowercase! +class KEYS(Enum): + LIST = "list_string" + PREFIX = "prefix" diff --git a/custom_nodes/ComfyUI-Crystools/core/logger.py b/custom_nodes/ComfyUI-Crystools/core/logger.py new file mode 100644 index 00000000..4c21af5c --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/logger.py @@ -0,0 +1,39 @@ +# by https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/blob/main/control/logger.py +import sys +import copy +import logging +from .keys import TEXTS +from .config import CONFIG + + +class ColoredFormatter(logging.Formatter): + COLORS = { + "DEBUG": "\033[0;36m", # CYAN + "INFO": "\033[0;32m", # GREEN + "WARNING": "\033[0;33m", # YELLOW + "ERROR": "\033[0;31m", # RED + "CRITICAL": "\033[0;37;41m", # WHITE ON RED + "RESET": "\033[0m", # RESET COLOR + } + + def format(self, record): + colored_record = copy.copy(record) + levelname = colored_record.levelname + seq = self.COLORS.get(levelname, self.COLORS["RESET"]) + colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}" + return super().format(colored_record) + + +# Create a new logger +logger = logging.getLogger(TEXTS.LOGGER_PREFIX.value) +logger.propagate = False + +# Add handler if we don't have one. +if not logger.handlers: + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter(ColoredFormatter("[%(name)s %(levelname)s] %(message)s")) + logger.addHandler(handler) + +# Configure logger +loglevel = CONFIG["loglevel"] +logger.setLevel(loglevel) diff --git a/custom_nodes/ComfyUI-Crystools/core/types.py b/custom_nodes/ComfyUI-Crystools/core/types.py new file mode 100644 index 00000000..3b7c5423 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/types.py @@ -0,0 +1,36 @@ +import sys + +FLOAT = ("FLOAT", {"default": 1, + "min": -sys.float_info.max, + "max": sys.float_info.max, + "step": 0.01}) + +BOOLEAN = ("BOOLEAN", {"default": True}) +BOOLEAN_FALSE = ("BOOLEAN", {"default": False}) + +INT = ("INT", {"default": 1, + "min": -sys.maxsize, + "max": sys.maxsize, + "step": 1}) + +STRING = ("STRING", {"default": ""}) + +STRING_ML = ("STRING", {"multiline": True, "default": ""}) + +STRING_WIDGET = ("STRING", {"forceInput": True}) + +JSON_WIDGET = ("JSON", {"forceInput": True}) + +METADATA_RAW = ("METADATA_RAW", {"forceInput": True}) + +class AnyType(str): + """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" + + def __eq__(self, _) -> bool: + return True + + def __ne__(self, __value: object) -> bool: + return False + + +any = AnyType("*") diff --git a/custom_nodes/ComfyUI-Crystools/core/version.py b/custom_nodes/ComfyUI-Crystools/core/version.py new file mode 100644 index 00000000..9d799718 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/core/version.py @@ -0,0 +1 @@ +version = "1.27.4" diff --git a/custom_nodes/ComfyUI-Crystools/docs/crystools-save.png b/custom_nodes/ComfyUI-Crystools/docs/crystools-save.png new file mode 100644 index 00000000..d730c65e Binary files /dev/null and b/custom_nodes/ComfyUI-Crystools/docs/crystools-save.png differ diff --git a/custom_nodes/ComfyUI-Crystools/docs/debugger-show-any.png b/custom_nodes/ComfyUI-Crystools/docs/debugger-show-any.png new file mode 100644 index 00000000..da304cc7 Binary files /dev/null and b/custom_nodes/ComfyUI-Crystools/docs/debugger-show-any.png differ diff --git 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00000000..4801eac1 Binary files /dev/null and b/custom_nodes/ComfyUI-Crystools/docs/utils-stats.png differ diff --git a/custom_nodes/ComfyUI-Crystools/general/__init__.py b/custom_nodes/ComfyUI-Crystools/general/__init__.py new file mode 100644 index 00000000..304c5f2a --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/general/__init__.py @@ -0,0 +1,3 @@ +from .monitor import * +from .hdd import * +from .gpu import * diff --git a/custom_nodes/ComfyUI-Crystools/general/gpu.py b/custom_nodes/ComfyUI-Crystools/general/gpu.py new file mode 100644 index 00000000..64f1ea7f --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/general/gpu.py @@ -0,0 +1,300 @@ +import torch +import comfy.model_management +from ..core import logger +import os +import platform + +def is_jetson() -> bool: + """ + Determines if the Python environment is running on a Jetson device by checking the device model + information or the platform release. + """ + PROC_DEVICE_MODEL = '' + try: + with open('/proc/device-tree/model', 'r') as f: + PROC_DEVICE_MODEL = f.read().strip() + logger.info(f"Device model: {PROC_DEVICE_MODEL}") + return "NVIDIA" in PROC_DEVICE_MODEL + except Exception as e: + # logger.warning(f"JETSON: Could not read /proc/device-tree/model: {e} (If you're not using Jetson, ignore this warning)") + # If /proc/device-tree/model is not available, check platform.release() + platform_release = platform.release() + logger.info(f"Platform release: {platform_release}") + if 'tegra' in platform_release.lower(): + logger.info("Detected 'tegra' in platform release. Assuming Jetson device.") + return True + else: + logger.info("JETSON: Not detected.") + return False + +IS_JETSON = is_jetson() + +class CGPUInfo: + """ + This class is responsible for getting information from GPU (ONLY). + """ + cuda = False + pynvmlLoaded = False + jtopLoaded = False + cudaAvailable = False + torchDevice = 'cpu' + cudaDevice = 'cpu' + cudaDevicesFound = 0 + switchGPU = True + switchVRAM = True + switchTemperature = True + gpus = [] + gpusUtilization = [] + gpusVRAM = [] + gpusTemperature = [] + + def __init__(self): + if IS_JETSON: + # Try to import jtop for Jetson devices + try: + from jtop import jtop + self.jtopInstance = jtop() + self.jtopInstance.start() + self.jtopLoaded = True + logger.info('jtop initialized on Jetson device.') + except ImportError as e: + logger.error('jtop is not installed. ' + str(e)) + except Exception as e: + logger.error('Could not initialize jtop. ' + str(e)) + else: + # Try to import pynvml for non-Jetson devices + try: + import pynvml + self.pynvml = pynvml + self.pynvml.nvmlInit() + self.pynvmlLoaded = True + logger.info('pynvml (NVIDIA) initialized.') + except ImportError as e: + logger.error('pynvml is not installed. ' + str(e)) + except Exception as e: + logger.error('Could not init pynvml (NVIDIA). ' + str(e)) + + self.anygpuLoaded = self.pynvmlLoaded or self.jtopLoaded + + try: + self.torchDevice = comfy.model_management.get_torch_device_name(comfy.model_management.get_torch_device()) + except Exception as e: + logger.error('Could not pick default device. ' + str(e)) + + if self.pynvmlLoaded and not self.jtopLoaded and not self.deviceGetCount(): + logger.warning('No GPU detected, disabling GPU monitoring.') + self.anygpuLoaded = False + self.pynvmlLoaded = False + self.jtopLoaded = False + + if self.anygpuLoaded: + if self.deviceGetCount() > 0: + self.cudaDevicesFound = self.deviceGetCount() + + logger.info(f"GPU/s:") + + for deviceIndex in range(self.cudaDevicesFound): + deviceHandle = self.deviceGetHandleByIndex(deviceIndex) + + gpuName = self.deviceGetName(deviceHandle, deviceIndex) + + logger.info(f"{deviceIndex}) {gpuName}") + + self.gpus.append({ + 'index': deviceIndex, + 'name': gpuName, + }) + + # Same index as gpus, with default values + self.gpusUtilization.append(True) + self.gpusVRAM.append(True) + self.gpusTemperature.append(True) + + self.cuda = True + logger.info(self.systemGetDriverVersion()) + else: + logger.warning('No GPU with CUDA detected.') + else: + logger.warning('No GPU monitoring libraries available.') + + self.cudaDevice = 'cpu' if self.torchDevice == 'cpu' else 'cuda' + self.cudaAvailable = torch.cuda.is_available() + + if self.cuda and self.cudaAvailable and self.torchDevice == 'cpu': + logger.warning('CUDA is available, but torch is using CPU.') + + def getInfo(self): + logger.debug('Getting GPUs info...') + return self.gpus + + def getStatus(self): + gpuUtilization = -1 + gpuTemperature = -1 + vramUsed = -1 + vramTotal = -1 + vramPercent = -1 + + gpuType = '' + gpus = [] + + if self.cudaDevice == 'cpu': + gpuType = 'cpu' + gpus.append({ + 'gpu_utilization': -1, + 'gpu_temperature': -1, + 'vram_total': -1, + 'vram_used': -1, + 'vram_used_percent': -1, + }) + else: + gpuType = self.cudaDevice + + if self.anygpuLoaded and self.cuda and self.cudaAvailable: + for deviceIndex in range(self.cudaDevicesFound): + deviceHandle = self.deviceGetHandleByIndex(deviceIndex) + + gpuUtilization = -1 + vramPercent = -1 + vramUsed = -1 + vramTotal = -1 + gpuTemperature = -1 + + # GPU Utilization + if self.switchGPU and self.gpusUtilization[deviceIndex]: + try: + gpuUtilization = self.deviceGetUtilizationRates(deviceHandle) + except Exception as e: + logger.error('Could not get GPU utilization. ' + str(e)) + logger.error('Monitor of GPU is turning off.') + self.switchGPU = False + + if self.switchVRAM and self.gpusVRAM[deviceIndex]: + try: + memory = self.deviceGetMemoryInfo(deviceHandle) + vramUsed = memory['used'] + vramTotal = memory['total'] + + # Check if vramTotal is not zero or None + if vramTotal and vramTotal != 0: + vramPercent = vramUsed / vramTotal * 100 + except Exception as e: + logger.error('Could not get GPU memory info. ' + str(e)) + self.switchVRAM = False + + # Temperature + if self.switchTemperature and self.gpusTemperature[deviceIndex]: + try: + gpuTemperature = self.deviceGetTemperature(deviceHandle) + except Exception as e: + logger.error('Could not get GPU temperature. Turning off this feature. ' + str(e)) + self.switchTemperature = False + + gpus.append({ + 'gpu_utilization': gpuUtilization, + 'gpu_temperature': gpuTemperature, + 'vram_total': vramTotal, + 'vram_used': vramUsed, + 'vram_used_percent': vramPercent, + }) + + return { + 'device_type': gpuType, + 'gpus': gpus, + } + + def deviceGetCount(self): + if self.pynvmlLoaded: + return self.pynvml.nvmlDeviceGetCount() + elif self.jtopLoaded: + # For Jetson devices, we assume there's one GPU + return 1 + else: + return 0 + + def deviceGetHandleByIndex(self, index): + if self.pynvmlLoaded: + return self.pynvml.nvmlDeviceGetHandleByIndex(index) + elif self.jtopLoaded: + return index # On Jetson, index acts as handle + else: + return 0 + + def deviceGetName(self, deviceHandle, deviceIndex): + if self.pynvmlLoaded: + gpuName = 'Unknown GPU' + + try: + gpuName = self.pynvml.nvmlDeviceGetName(deviceHandle) + try: + gpuName = gpuName.decode('utf-8', errors='ignore') + except AttributeError: + pass + + except UnicodeDecodeError as e: + gpuName = 'Unknown GPU (decoding error)' + logger.error(f"UnicodeDecodeError: {e}") + + return gpuName + elif self.jtopLoaded: + # Access the GPU name from self.jtopInstance.gpu + try: + gpu_info = self.jtopInstance.gpu + gpu_name = next(iter(gpu_info.keys())) + return gpu_name + except Exception as e: + logger.error('Could not get GPU name. ' + str(e)) + return 'Unknown GPU' + else: + return '' + + def systemGetDriverVersion(self): + if self.pynvmlLoaded: + return f'NVIDIA Driver: {self.pynvml.nvmlSystemGetDriverVersion()}' + elif self.jtopLoaded: + # No direct method to get driver version from jtop + return 'NVIDIA Driver: unknown' + else: + return 'Driver unknown' + + def deviceGetUtilizationRates(self, deviceHandle): + if self.pynvmlLoaded: + return self.pynvml.nvmlDeviceGetUtilizationRates(deviceHandle).gpu + elif self.jtopLoaded: + # GPU utilization from jtop stats + try: + gpu_util = self.jtopInstance.stats.get('GPU', -1) + return gpu_util + except Exception as e: + logger.error('Could not get GPU utilization. ' + str(e)) + return -1 + else: + return 0 + + def deviceGetMemoryInfo(self, deviceHandle): + if self.pynvmlLoaded: + mem = self.pynvml.nvmlDeviceGetMemoryInfo(deviceHandle) + return {'total': mem.total, 'used': mem.used} + elif self.jtopLoaded: + mem_data = self.jtopInstance.memory['RAM'] + total = mem_data['tot'] + used = mem_data['used'] + return {'total': total, 'used': used} + else: + return {'total': 1, 'used': 1} + + def deviceGetTemperature(self, deviceHandle): + if self.pynvmlLoaded: + return self.pynvml.nvmlDeviceGetTemperature(deviceHandle, self.pynvml.NVML_TEMPERATURE_GPU) + elif self.jtopLoaded: + try: + temperature = self.jtopInstance.stats.get('Temp gpu', -1) + return temperature + except Exception as e: + logger.error('Could not get GPU temperature. ' + str(e)) + return -1 + else: + return 0 + + def close(self): + if self.jtopLoaded and self.jtopInstance is not None: + self.jtopInstance.close() diff --git a/custom_nodes/ComfyUI-Crystools/general/hardware.py b/custom_nodes/ComfyUI-Crystools/general/hardware.py new file mode 100644 index 00000000..f8adbb97 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/general/hardware.py @@ -0,0 +1,132 @@ +import platform +import re +import cpuinfo +from cpuinfo import DataSource +import psutil +from .gpu import CGPUInfo +from .hdd import getDrivesInfo + +from ..core import logger + + +class CHardwareInfo: + """ + This is only class to get information from hardware. + Specially for share it to other software. + """ + switchCPU = False + switchHDD = False + switchRAM = False + whichHDD = '/' # breaks linux + + @property + def switchGPU(self): + return self.GPUInfo.switchGPU + @switchGPU.setter + def switchGPU(self, value): + self.GPUInfo.switchGPU = value + + @property + def switchVRAM(self): + return self.GPUInfo.switchVRAM + @switchVRAM.setter + def switchVRAM(self, value): + self.GPUInfo.switchVRAM = value + + def __init__(self, switchCPU=False, switchGPU=False, switchHDD=False, switchRAM=False, switchVRAM=False): + self.switchCPU = switchCPU + self.switchHDD = switchHDD + self.switchRAM = switchRAM + + self.print_sys_info() + + self.GPUInfo = CGPUInfo() + self.switchGPU = switchGPU + self.switchVRAM = switchVRAM + + def print_sys_info(self): + brand = None + if DataSource.is_windows: # Windows + brand = DataSource.winreg_processor_brand().strip() + elif DataSource.has_proc_cpuinfo(): # Linux + return_code, output = DataSource.cat_proc_cpuinfo() + if return_code == 0 and output is not None: + for line in output.splitlines(): + r = re.search(r'model name\s*:\s*(.+)', line) + if r: + brand = r.group(1) + break + elif DataSource.has_sysctl(): # macOS + return_code, output = DataSource.sysctl_machdep_cpu_hw_cpufrequency() + if return_code == 0 and output is not None: + for line in output.splitlines(): + r = re.search(r'machdep\.cpu\.brand_string\s*:\s*(.+)', line) + if r: + brand = r.group(1) + break + + # fallback to use cpuinfo.get_cpu_info() + if not brand: + brand = cpuinfo.get_cpu_info().get('brand_raw', "Unknown") + + arch_string_raw = 'Arch unknown' + + try: + arch_string_raw = DataSource.arch_string_raw + except: + pass + + specName = 'CPU: ' + brand + specArch = 'Arch: ' + arch_string_raw + specOs = 'OS: ' + str(platform.system()) + ' ' + str(platform.release()) + logger.info(f"{specName} - {specArch} - {specOs}") + + def getHDDsInfo(self): + return getDrivesInfo() + + def getGPUInfo(self): + return self.GPUInfo.getInfo() + + def getStatus(self): + cpu = -1 + ramTotal = -1 + ramUsed = -1 + ramUsedPercent = -1 + hddTotal = -1 + hddUsed = -1 + hddUsedPercent = -1 + + if self.switchCPU: + cpu = psutil.cpu_percent() + + if self.switchRAM: + ram = psutil.virtual_memory() + ramTotal = ram.total + ramUsed = ram.used + ramUsedPercent = ram.percent + + if self.switchHDD: + try: + hdd = psutil.disk_usage(self.whichHDD) + hddTotal = hdd.total + hddUsed = hdd.used + hddUsedPercent = hdd.percent + except Exception as e: + logger.error(f"Error getting disk usage for {self.whichHDD}: {e}") + hddTotal = -1 + hddUsed = -1 + hddUsedPercent = -1 + + getStatus = self.GPUInfo.getStatus() + + return { + 'cpu_utilization': cpu, + 'ram_total': ramTotal, + 'ram_used': ramUsed, + 'ram_used_percent': ramUsedPercent, + 'hdd_total': hddTotal, + 'hdd_used': hddUsed, + 'hdd_used_percent': hddUsedPercent, + 'device_type': getStatus['device_type'], + 'gpus': getStatus['gpus'], + } diff --git a/custom_nodes/ComfyUI-Crystools/general/hdd.py b/custom_nodes/ComfyUI-Crystools/general/hdd.py new file mode 100644 index 00000000..9b6273f5 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/general/hdd.py @@ -0,0 +1,10 @@ +import psutil +from ..core import logger + +def getDrivesInfo(): + hdds = [] + logger.debug('Getting HDDs info...') + for partition in psutil.disk_partitions(): + hdds.append(partition.mountpoint) + + return hdds diff --git a/custom_nodes/ComfyUI-Crystools/general/monitor.py b/custom_nodes/ComfyUI-Crystools/general/monitor.py new file mode 100644 index 00000000..6bbedc67 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/general/monitor.py @@ -0,0 +1,67 @@ +import asyncio +import server +import time +import threading +from .hardware import CHardwareInfo + +from ..core import logger + +lock = threading.Lock() + + +class CMonitor: + monitorThread = None + threadController = threading.Event() + rate = 0 + hardwareInfo = None + + def __init__(self, rate=5, switchCPU=False, switchGPU=False, switchHDD=False, switchRAM=False, switchVRAM=False): + self.rate = rate + self.hardwareInfo = CHardwareInfo(switchCPU, switchGPU, switchHDD, switchRAM, switchVRAM) + + self.startMonitor() + + async def send_message(self, data) -> None: + # I'm not sure if it is ok, but works Β―\_(ツ)_/Β― + # I tried to use async with send_json, but eventually that don't send the message + server.PromptServer.instance.send_sync('crystools.monitor', data) + + def startMonitorLoop(self): + # logger.debug('Starting monitor loop...') + asyncio.run(self.MonitorLoop()) + + async def MonitorLoop(self): + while self.rate > 0 and not self.threadController.is_set(): + data = self.hardwareInfo.getStatus() + # logger.debug('data to send' + str(data)) + await self.send_message(data) + await asyncio.sleep(self.rate) + + def startMonitor(self): + if self.monitorThread is not None: + self.stopMonitor() + logger.debug('Restarting monitor...') + else: + if self.rate == 0: + logger.debug('Monitor rate is 0, not starting monitor.') + return None + + logger.debug('Starting monitor...') + + self.threadController.clear() + + if self.monitorThread is None or not self.monitorThread.is_alive(): + lock.acquire() + self.monitorThread = threading.Thread(target=self.startMonitorLoop) + lock.release() + self.monitorThread.daemon = True + self.monitorThread.start() + + + def stopMonitor(self): + logger.debug('Stopping monitor...') + self.threadController.set() + + +cmonitor = CMonitor(1, True, True, True, True, True) + diff --git a/custom_nodes/ComfyUI-Crystools/nodes/__init__.py b/custom_nodes/ComfyUI-Crystools/nodes/__init__.py new file mode 100644 index 00000000..39dff2cc --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/__init__.py @@ -0,0 +1 @@ +# intentionally left blank diff --git a/custom_nodes/ComfyUI-Crystools/nodes/_names.py b/custom_nodes/ComfyUI-Crystools/nodes/_names.py new file mode 100644 index 00000000..55f57146 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/_names.py @@ -0,0 +1,80 @@ +from enum import Enum + +prefix = 'πŸͺ› ' + +# IMPORTANT DON'T CHANGE THE 'NAME' AND 'TYPE' OF THE ENUMS, IT WILL BREAK THE COMPATIBILITY! +# remember: NAME is for search, DESC is for contextual +class CLASSES(Enum): + CBOOLEAN_NAME = 'Primitive boolean [Crystools]' + CBOOLEAN_DESC = prefix + 'Primitive boolean' + CTEXT_NAME = 'Primitive string [Crystools]' + CTEXT_DESC = prefix + 'Primitive string' + CTEXTML_NAME = 'Primitive string multiline [Crystools]' + CTEXTML_DESC = prefix + 'Primitive string multiline' + CINTEGER_NAME = 'Primitive integer [Crystools]' + CINTEGER_DESC = prefix + 'Primitive integer' + CFLOAT_NAME = 'Primitive float [Crystools]' + CFLOAT_DESC = prefix + 'Primitive float' + + CDEBUGGER_CONSOLE_ANY_NAME = 'Show any [Crystools]' + CDEBUGGER_ANY_DESC = prefix + 'Show any value to console/display' + CDEBUGGER_CONSOLE_ANY_TO_JSON_NAME = 'Show any to JSON [Crystools]' + CDEBUGGER_CONSOLE_ANY_TO_JSON_DESC = prefix + 'Show any to JSON' + + CLIST_ANY_TYPE = 'ListAny' + CLIST_ANY_NAME = 'List of any [Crystools]' + CLIST_ANY_DESC = prefix + 'List of any' + CLIST_STRING_TYPE = 'ListString' + CLIST_STRING_NAME = 'List of strings [Crystools]' + CLIST_STRING_DESC = prefix + 'List of strings' + + CSWITCH_FROM_ANY_NAME = 'Switch from any [Crystools]' + CSWITCH_FROM_ANY_DESC = prefix + 'Switch from any' + CSWITCH_ANY_NAME = 'Switch any [Crystools]' + CSWITCH_ANY_DESC = prefix + 'Switch any' + CSWITCH_STRING_NAME = 'Switch string [Crystools]' + CSWITCH_STRING_DESC = prefix + 'Switch string' + CSWITCH_CONDITIONING_NAME = 'Switch conditioning [Crystools]' + CSWITCH_CONDITIONING_DESC = prefix + 'Switch conditioning' + CSWITCH_IMAGE_NAME = 'Switch image [Crystools]' + CSWITCH_IMAGE_DESC = prefix + 'Switch image' + CSWITCH_MASK_NAME = 'Switch mask [Crystools]' + CSWITCH_MASK_DESC = prefix + 'Switch mask' + CSWITCH_LATENT_NAME = 'Switch latent [Crystools]' + CSWITCH_LATENT_DESC = prefix + 'Switch latent' + + CPIPE_ANY_TYPE = 'CPipeAny' + CPIPE_TO_ANY_NAME = 'Pipe to/edit any [Crystools]' + CPIPE_TO_ANY_DESC = prefix + 'Pipe to/edit any' + CPIPE_FROM_ANY_NAME = 'Pipe from any [Crystools]' + CPIPE_FROM_ANY_DESC = prefix + 'Pipe from any' + + CIMAGE_LOAD_METADATA_NAME = 'Load image with metadata [Crystools]' + CIMAGE_LOAD_METADATA_DESC = prefix + 'Load image with metadata' + CIMAGE_GET_RESOLUTION_NAME = 'Get resolution [Crystools]' + CIMAGE_GET_RESOLUTION_DESC = prefix + 'Get resolution' + CIMAGE_PREVIEW_IMAGE_NAME = 'Preview from image [Crystools]' + CIMAGE_PREVIEW_IMAGE_DESC = prefix + 'Preview from image' + CIMAGE_PREVIEW_METADATA_NAME = 'Preview from metadata [Crystools]' + CIMAGE_PREVIEW_METADATA_DESC = prefix + 'Preview from metadata' + CIMAGE_SAVE_METADATA_NAME = 'Save image with extra metadata [Crystools]' + CIMAGE_SAVE_METADATA_DESC = prefix + 'Save image with extra metadata' + + CMETADATA_EXTRACTOR_NAME = 'Metadata extractor [Crystools]' + CMETADATA_EXTRACTOR_DESC = prefix + 'Metadata extractor' + CMETADATA_COMPARATOR_NAME = 'Metadata comparator [Crystools]' + CMETADATA_COMPARATOR_DESC = prefix + 'Metadata comparator' + + CUTILS_JSON_COMPARATOR_NAME = 'JSON comparator [Crystools]' + CUTILS_JSON_COMPARATOR_DESC = prefix + 'JSON comparator' + CUTILS_STAT_SYSTEM_NAME = 'Stats system [Crystools]' + CUTILS_STAT_SYSTEM_DESC = prefix + 'Stats system (powered by WAS)' + + # CPARAMETERS_NAME = 'External parameter from JSON file [Crystools]' + # CPARAMETERS_DESC = prefix + 'External parameters from JSON file' + + CJSONFILE_NAME = 'Read JSON file [Crystools]' + CJSONFILE_DESC = prefix + 'Read JSON file (BETA)' + + CJSONEXTRACTOR_NAME = 'JSON extractor [Crystools]' + CJSONEXTRACTOR_DESC = prefix + 'JSON extractor (BETA)' diff --git a/custom_nodes/ComfyUI-Crystools/nodes/debugger.py b/custom_nodes/ComfyUI-Crystools/nodes/debugger.py new file mode 100644 index 00000000..6b0cd3b3 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/debugger.py @@ -0,0 +1,123 @@ +import json +from ..core import CONFIG, CATEGORY, BOOLEAN, BOOLEAN_FALSE, KEYS, TEXTS, STRING, logger, any + +class CConsoleAny: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "any_value": (any,), + "console": BOOLEAN_FALSE, + "display": BOOLEAN, + KEYS.PREFIX.value: STRING, + }, + "hidden": { + # "unique_id": "UNIQUE_ID", + # "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.DEBUGGER.value + INPUT_IS_LIST = True + + RETURN_TYPES = () + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, any_value=None, console=False, display=True, prefix=None): + console = console[0] + display = display[0] + prefix = prefix[0] + text = "" + textToDisplay = TEXTS.INACTIVE_MSG.value + + if any_value is not None: + try: + if type(any_value) == list: + for item in any_value: + try: + text += str(item) + except Exception as e: + text += "source exists, but could not be serialized.\n" + logger.warn(e) + else: + logger.warn("any_value is not a list") + + except Exception: + try: + text = json.dumps(any_value)[1:-1] + except Exception: + text = 'source exists, but could not be serialized.' + + logger.debug(f"Show any to console is running...") + + if console: + if prefix is not None and prefix != "": + print(f"{prefix}: {text}") + else: + print(text) + + if display: + textToDisplay = text + + value = [console, display, prefix, textToDisplay] + # setWidgetValues(value, unique_id, extra_pnginfo) + + return {"ui": {"text": value}} + + +class CConsoleAnyToJson: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "any_value": (any,), + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.DEBUGGER.value + INPUT_IS_LIST = True + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, any_value=None): + text = TEXTS.INACTIVE_MSG.value + + if any_value is not None and isinstance(any_value, list): + item = any_value[0] + + if isinstance(item, dict): + try: + text = json.dumps(item, indent=CONFIG["indent"]) + except Exception as e: + text = "The input is a dict, but could not be serialized.\n" + logger.warn(e) + + elif isinstance(item, list): + try: + text = json.dumps(item, indent=CONFIG["indent"]) + except Exception as e: + text = "The input is a list, but could not be serialized.\n" + logger.warn(e) + + else: + text = str(item) + + logger.debug(f"Show any-json to console is running...") + + return {"ui": {"text": [text]}, "result": (text,)} diff --git a/custom_nodes/ComfyUI-Crystools/nodes/image.py b/custom_nodes/ComfyUI-Crystools/nodes/image.py new file mode 100644 index 00000000..10caabc0 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/image.py @@ -0,0 +1,517 @@ +import fnmatch +import os +import random +import sys +import json +import piexif +import hashlib +from datetime import datetime +import torch +import numpy as np +from pathlib import Path +from PIL import Image, ImageOps +from PIL.ExifTags import TAGS, GPSTAGS, IFD +from PIL.PngImagePlugin import PngImageFile +from PIL.JpegImagePlugin import JpegImageFile +from nodes import PreviewImage, SaveImage +import folder_paths + +from ..core import CATEGORY, CONFIG, BOOLEAN, METADATA_RAW,TEXTS, setWidgetValues, logger, getResolutionByTensor, get_size + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + + +class CImagePreviewFromImage(PreviewImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + self.data_cached = None + self.data_cached_text = None + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + # if it is required, in next node does not receive any value even the cache! + }, + "optional": { + "image": ("IMAGE",), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.IMAGE.value + RETURN_TYPES = ("METADATA_RAW",) + RETURN_NAMES = ("Metadata RAW",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, image=None, prompt=None, extra_pnginfo=None): + text = "" + title = "" + data = { + "result": [''], + "ui": { + "text": [''], + "images": [], + } + } + + if image is not None: + saved = self.save_images(image, "crystools/i", prompt, extra_pnginfo) + image = saved["ui"]["images"][0] + image_path = Path(self.output_dir).joinpath(image["subfolder"], image["filename"]) + + img, promptFromImage, metadata = buildMetadata(image_path) + + images = [image] + result = metadata + + data["result"] = [result] + data["ui"]["images"] = images + + title = "Source: Image link \n" + text += buildPreviewText(metadata) + text += f"Current prompt (NO FROM IMAGE!):\n" + text += json.dumps(promptFromImage, indent=CONFIG["indent"]) + + self.data_cached_text = text + self.data_cached = data + + elif image is None and self.data_cached is not None: + title = "Source: Image link - CACHED\n" + data = self.data_cached + text = self.data_cached_text + + else: + logger.debug("Source: Empty on CImagePreviewFromImage") + text = "Source: Empty" + + data['ui']['text'] = [title + text] + return data + + +class CImagePreviewFromMetadata(PreviewImage): + def __init__(self): + self.data_cached = None + self.data_cached_text = None + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + # if it is required, in next node does not receive any value even the cache! + }, + "optional": { + "metadata_raw": METADATA_RAW, + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.IMAGE.value + RETURN_TYPES = ("METADATA_RAW",) + RETURN_NAMES = ("Metadata RAW",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, metadata_raw=None): + text = "" + title = "" + data = { + "result": [''], + "ui": { + "text": [''], + "images": [], + } + } + + if metadata_raw is not None and metadata_raw != '': + promptFromImage = {} + if "prompt" in metadata_raw: + promptFromImage = metadata_raw["prompt"] + + title = "Source: Metadata RAW\n" + text += buildPreviewText(metadata_raw) + text += f"Prompt from image:\n" + text += json.dumps(promptFromImage, indent=CONFIG["indent"]) + + images = self.resolveImage(metadata_raw["fileinfo"]["filename"]) + result = metadata_raw + + data["result"] = [result] + data["ui"]["images"] = images + + self.data_cached_text = text + self.data_cached = data + + elif metadata_raw is None and self.data_cached is not None: + title = "Source: Metadata RAW - CACHED\n" + data = self.data_cached + text = self.data_cached_text + + else: + logger.debug("Source: Empty on CImagePreviewFromMetadata") + text = "Source: Empty" + + data["ui"]["text"] = [title + text] + return data + + def resolveImage(self, filename=None): + images = [] + + if filename is not None: + image_input_folder = os.path.normpath(folder_paths.get_input_directory()) + image_input_folder_abs = Path(image_input_folder).resolve() + + image_path = os.path.normpath(filename) + image_path_abs = Path(image_path).resolve() + + if Path(image_path_abs).is_file() is False: + raise Exception(TEXTS.FILE_NOT_FOUND.value) + + try: + # get common path, should be input/output/temp folder + common = os.path.commonpath([image_input_folder_abs, image_path_abs]) + + if common != image_input_folder: + raise Exception("Path invalid (should be in the input folder)") + + relative = os.path.normpath(os.path.relpath(image_path_abs, image_input_folder_abs)) + + images.append({ + "filename": Path(relative).name, + "subfolder": os.path.dirname(relative), + "type": "input" + }) + + except Exception as e: + logger.warn(e) + + return images + + +class CImageGetResolution: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.IMAGE.value + RETURN_TYPES = ("INT", "INT",) + RETURN_NAMES = ("width", "height",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, image, extra_pnginfo=None, unique_id=None): + res = getResolutionByTensor(image) + text = [f"{res['x']}x{res['y']}"] + setWidgetValues(text, unique_id, extra_pnginfo) + logger.debug(f"Resolution: {text}") + return {"ui": {"text": text}, "result": (res["x"], res["y"])} + + +# subfolders based on: https://github.com/catscandrive/comfyui-imagesubfolders +class CImageLoadWithMetadata: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + input_dir = folder_paths.get_input_directory() + + exclude_files = {"Thumbs.db", "*.DS_Store", "desktop.ini", "*.lock" } + exclude_folders = {"clipspace", ".*"} + + file_list = [] + + for root, dirs, files in os.walk(input_dir, followlinks=True): + # Exclude specific folders + dirs[:] = [d for d in dirs if not any(fnmatch.fnmatch(d, exclude) for exclude in exclude_folders)] + files = [f for f in files if not any(fnmatch.fnmatch(f, exclude) for exclude in exclude_files)] + + + for file in files: + relpath = os.path.relpath(os.path.join(root, file), start=input_dir) + # fix for windows + relpath = relpath.replace("\\", "/") + file_list.append(relpath) + + return { + "required": { + "image": (sorted(file_list), {"image_upload": True}) + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.IMAGE.value + RETURN_TYPES = ("IMAGE", "MASK", "JSON", "METADATA_RAW") + RETURN_NAMES = ("image", "mask", "prompt", "Metadata RAW") + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, image): + image_path = folder_paths.get_annotated_filepath(image) + + imgF = Image.open(image_path) + img, prompt, metadata = buildMetadata(image_path) + if imgF.format == 'WEBP': + # Use piexif to extract EXIF data from WebP image + try: + exif_data = piexif.load(image_path) + prompt, metadata = self.process_exif_data(exif_data) + except ValueError: + prompt = {} + + img = ImageOps.exif_transpose(img) + image = img.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in img.getbands(): + mask = np.array(img.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + return image, mask.unsqueeze(0), prompt, metadata + + def process_exif_data(self, exif_data): + metadata = {} + # ζ£€ζŸ₯ '0th' ι”δΈ‹ηš„ 271 ε€ΌοΌŒζε– Prompt 俑息 + if '0th' in exif_data and 271 in exif_data['0th']: + prompt_data = exif_data['0th'][271].decode('utf-8') + # η§»ι™€ε―θƒ½ηš„ε‰ηΌ€ 'Prompt:' + prompt_data = prompt_data.replace('Prompt:', '', 1) + # ε‡θΎ prompt_data ζ˜―δΈ€δΈͺε­—η¬¦δΈ²οΌŒε°θ―•ε°†ε…Άθ½¬ζ’δΈΊ JSON 对豑 + try: + metadata['prompt'] = json.loads(prompt_data) + except json.JSONDecodeError: + metadata['prompt'] = prompt_data + + # ζ£€ζŸ₯ '0th' ι”δΈ‹ηš„ 270 ε€ΌοΌŒζε– Workflow 俑息 + if '0th' in exif_data and 270 in exif_data['0th']: + workflow_data = exif_data['0th'][270].decode('utf-8') + # η§»ι™€ε―θƒ½ηš„ε‰ηΌ€ 'Workflow:' + workflow_data = workflow_data.replace('Workflow:', '', 1) + try: + # ε°θ―•ε°†ε­—θŠ‚ε­—η¬¦δΈ²θ½¬ζ’δΈΊ JSON 对豑 + metadata['workflow'] = json.loads(workflow_data) + except json.JSONDecodeError: + # ε¦‚ζžœθ½¬ζ’ε€±θ΄₯οΌŒεˆ™ε°†εŽŸε§‹ε­—η¬¦δΈ²ε­˜ε‚¨εœ¨ metadata δΈ­ + metadata['workflow'] = workflow_data + + metadata.update(exif_data) + return metadata + + @classmethod + def IS_CHANGED(cls, image): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(cls, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + + +class CImageSaveWithExtraMetadata(SaveImage): + def __init__(self): + super().__init__() + self.data_cached = None + self.data_cached_text = None + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + # if it is required, in next node does not receive any value even the cache! + "image": ("IMAGE",), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "with_workflow": BOOLEAN, + }, + "optional": { + "metadata_extra": ("STRING", {"multiline": True, "default": json.dumps({ + "Title": "Image generated by Crystian", + "Description": "More info: https:\/\/www.instagram.com\/crystian.ia", + "Author": "crystian.ia", + "Software": "ComfyUI", + "Category": "StableDiffusion", + "Rating": 5, + "UserComment": "", + "Keywords": [ + "" + ], + "Copyrights": "", + }, indent=CONFIG["indent"]).replace("\\/", "/"), + }), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.IMAGE.value + RETURN_TYPES = ("METADATA_RAW",) + RETURN_NAMES = ("Metadata RAW",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, image=None, filename_prefix="ComfyUI", with_workflow=True, metadata_extra=None, prompt=None, extra_pnginfo=None): + data = { + "result": [''], + "ui": { + "text": [''], + "images": [], + } + } + if image is not None: + if with_workflow is True: + extra_pnginfo_new = extra_pnginfo.copy() + prompt = prompt.copy() + else: + extra_pnginfo_new = None + prompt = None + + if metadata_extra is not None and metadata_extra != 'undefined': + try: + # metadata_extra = json.loads(f"{{{metadata_extra}}}") // a fix? + metadata_extra = json.loads(metadata_extra) + except Exception as e: + logger.error(f"Error parsing metadata_extra (it will send as string), error: {e}") + metadata_extra = {"extra": str(metadata_extra)} + + if isinstance(metadata_extra, dict): + for k, v in metadata_extra.items(): + if extra_pnginfo_new is None: + extra_pnginfo_new = {} + + extra_pnginfo_new[k] = v + + saved = super().save_images(image, filename_prefix, prompt, extra_pnginfo_new) + + image = saved["ui"]["images"][0] + image_path = Path(self.output_dir).joinpath(image["subfolder"], image["filename"]) + img, promptFromImage, metadata = buildMetadata(image_path) + + images = [image] + result = metadata + + data["result"] = [result] + data["ui"]["images"] = images + + else: + logger.debug("Source: Empty on CImageSaveWithExtraMetadata") + + return data + + + +def buildMetadata(image_path): + if Path(image_path).is_file() is False: + raise Exception(TEXTS.FILE_NOT_FOUND.value) + + img = Image.open(image_path) + + metadata = {} + prompt = {} + + metadata["fileinfo"] = { + "filename": Path(image_path).as_posix(), + "resolution": f"{img.width}x{img.height}", + "date": str(datetime.fromtimestamp(os.path.getmtime(image_path))), + "size": str(get_size(image_path)), + } + + # only for png files + if isinstance(img, PngImageFile): + metadataFromImg = img.info + + # for all metadataFromImg convert to string (but not for workflow and prompt!) + for k, v in metadataFromImg.items(): + # from ComfyUI + if k == "workflow": + try: + metadata["workflow"] = json.loads(metadataFromImg["workflow"]) + except Exception as e: + logger.warn(f"Error parsing metadataFromImg 'workflow': {e}") + + # from ComfyUI + elif k == "prompt": + try: + metadata["prompt"] = json.loads(metadataFromImg["prompt"]) + + # extract prompt to use on metadataFromImg + prompt = metadata["prompt"] + except Exception as e: + logger.warn(f"Error parsing metadataFromImg 'prompt': {e}") + + else: + try: + # for all possible metadataFromImg by user + metadata[str(k)] = json.loads(v) + except Exception as e: + logger.debug(f"Error parsing {k} as json, trying as string: {e}") + try: + metadata[str(k)] = str(v) + except Exception as e: + logger.debug(f"Error parsing {k} it will be skipped: {e}") + + if isinstance(img, JpegImageFile): + exif = img.getexif() + + for k, v in exif.items(): + tag = TAGS.get(k, k) + if v is not None: + metadata[str(tag)] = str(v) + + for ifd_id in IFD: + try: + if ifd_id == IFD.GPSInfo: + resolve = GPSTAGS + else: + resolve = TAGS + + ifd = exif.get_ifd(ifd_id) + ifd_name = str(ifd_id.name) + metadata[ifd_name] = {} + + for k, v in ifd.items(): + tag = resolve.get(k, k) + metadata[ifd_name][str(tag)] = str(v) + + except KeyError: + pass + + + return img, prompt, metadata + + +def buildPreviewText(metadata): + text = f"File: {metadata['fileinfo']['filename']}\n" + text += f"Resolution: {metadata['fileinfo']['resolution']}\n" + text += f"Date: {metadata['fileinfo']['date']}\n" + text += f"Size: {metadata['fileinfo']['size']}\n" + return text diff --git a/custom_nodes/ComfyUI-Crystools/nodes/list.py b/custom_nodes/ComfyUI-Crystools/nodes/list.py new file mode 100644 index 00000000..9f3b4328 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/list.py @@ -0,0 +1,149 @@ +from ..core import STRING, TEXTS, KEYS, CATEGORY, any, logger +from ._names import CLASSES + + +class CListAny: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "any_1": (any,), + "any_2": (any,), + "any_3": (any,), + "any_4": (any,), + "any_5": (any,), + "any_6": (any,), + "any_7": (any,), + "any_8": (any,), + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.LIST.value + RETURN_TYPES = (any,), + RETURN_NAMES = ("any_list",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "execute" + + def execute(self, + any_1=None, + any_2=None, + any_3=None, + any_4=None, + any_5=None, + any_6=None, + any_7=None, + any_8=None): + + list_any = [] + + if any_1 is not None: + try: + list_any.append(any_1) + except Exception as e: + logger.warn(e) + if any_2 is not None: + try: + list_any.append(any_2) + except Exception as e: + logger.warn(e) + if any_3 is not None: + try: + list_any.append(any_3) + except Exception as e: + logger.warn(e) + if any_4 is not None: + try: + list_any.append(any_4) + except Exception as e: + logger.warn(e) + if any_5 is not None: + try: + list_any.append(any_5) + except Exception as e: + logger.warn(e) + if any_6 is not None: + try: + list_any.append(any_6) + except Exception as e: + logger.warn(e) + if any_7 is not None: + try: + list_any.append(any_7) + except Exception as e: + logger.warn(e) + if any_8 is not None: + try: + list_any.append(any_8) + except Exception as e: + logger.warn(e) + + # yes, double brackets are needed because of the OUTPUT_IS_LIST... Β―\_(ツ)_/Β― + return [[list_any]] + + +class CListString: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "string_1": STRING, + "string_2": STRING, + "string_3": STRING, + "string_4": STRING, + "string_5": STRING, + "string_6": STRING, + "string_7": STRING, + "string_8": STRING, + "delimiter": ("STRING", {"default": " "}), + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.LIST.value + RETURN_TYPES = ("STRING", CLASSES.CLIST_STRING_TYPE.value,) + RETURN_NAMES = (TEXTS.CONCAT.value, KEYS.LIST.value) + OUTPUT_IS_LIST = (False, True, ) + + FUNCTION = "execute" + + def execute(self, + string_1=None, + string_2=None, + string_3=None, + string_4=None, + string_5=None, + string_6=None, + string_7=None, + string_8=None, + delimiter=""): + + list_str = [] + + if string_1 is not None and string_1 != "": + list_str.append(string_1) + if string_2 is not None and string_2 != "": + list_str.append(string_2) + if string_3 is not None and string_3 != "": + list_str.append(string_3) + if string_4 is not None and string_4 != "": + list_str.append(string_4) + if string_5 is not None and string_5 != "": + list_str.append(string_5) + if string_6 is not None and string_6 != "": + list_str.append(string_6) + if string_7 is not None and string_7 != "": + list_str.append(string_7) + if string_8 is not None and string_8 != "": + list_str.append(string_8) + + return delimiter.join(list_str), [list_str] diff --git a/custom_nodes/ComfyUI-Crystools/nodes/metadata.py b/custom_nodes/ComfyUI-Crystools/nodes/metadata.py new file mode 100644 index 00000000..211a347b --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/metadata.py @@ -0,0 +1,164 @@ +import json +import re +from ..core import CATEGORY, CONFIG, METADATA_RAW, TEXTS, findJsonsDiff, logger + + +class CMetadataExtractor: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "metadata_raw": METADATA_RAW, + }, + "optional": { + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.METADATA.value + RETURN_TYPES = ("JSON", "JSON", "JSON", "JSON", "STRING", "STRING") + RETURN_NAMES = ("prompt", "workflow", "file info", "raw to JSON", "raw to property", "raw to csv") + # OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, metadata_raw=None): + prompt = {} + workflow = {} + fileinfo = {} + text = "" + csv = "" + + if metadata_raw is not None and isinstance(metadata_raw, dict): + try: + for key, value in metadata_raw.items(): + + if isinstance(value, dict): + # yes, double json.dumps is needed for jsons + value = json.dumps(json.dumps(value)) + else: + value = json.dumps(value) + + text += f"\"{key}\"={value}\n" + # remove spaces + # value = re.sub(' +', ' ', value) + value = re.sub('\n', ' ', value) + csv += f'"{key}"\t{value}\n' + + if csv != "": + csv = '"key"\t"value"\n' + csv + + except Exception as e: + logger.warn(e) + + try: + if "prompt" in metadata_raw: + prompt = metadata_raw["prompt"] + else: + raise Exception("Prompt not found in metadata_raw") + except Exception as e: + logger.warn(e) + + try: + if "workflow" in metadata_raw: + workflow = metadata_raw["workflow"] + else: + raise Exception("Workflow not found in metadata_raw") + except Exception as e: + logger.warn(e) + + try: + if "fileinfo" in metadata_raw: + fileinfo = metadata_raw["fileinfo"] + else: + raise Exception("Fileinfo not found in metadata_raw") + except Exception as e: + logger.warn(e) + + elif metadata_raw is None: + logger.debug("metadata_raw is None") + else: + logger.warn(TEXTS.INVALID_METADATA_MSG.value) + + return (json.dumps(prompt, indent=CONFIG["indent"]), + json.dumps(workflow, indent=CONFIG["indent"]), + json.dumps(fileinfo, indent=CONFIG["indent"]), + json.dumps(metadata_raw, indent=CONFIG["indent"]), + text, csv) + + +class CMetadataCompare: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "metadata_raw_old": METADATA_RAW, + "metadata_raw_new": METADATA_RAW, + "what": (["Prompt", "Workflow", "Fileinfo"],), + }, + "optional": { + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.METADATA.value + RETURN_TYPES = ("JSON",) + RETURN_NAMES = ("diff",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, what, metadata_raw_old=None, metadata_raw_new=None): + prompt_old = {} + workflow_old = {} + fileinfo_old = {} + prompt_new = {} + workflow_new = {} + fileinfo_new = {} + diff = "" + + if type(metadata_raw_old) == dict and type(metadata_raw_new) == dict: + + if "prompt" in metadata_raw_old: + prompt_old = metadata_raw_old["prompt"] + else: + logger.warn("Prompt not found in metadata_raw_old") + + if "workflow" in metadata_raw_old: + workflow_old = metadata_raw_old["workflow"] + else: + logger.warn("Workflow not found in metadata_raw_old") + + if "fileinfo" in metadata_raw_old: + fileinfo_old = metadata_raw_old["fileinfo"] + else: + logger.warn("Fileinfo not found in metadata_raw_old") + + if "prompt" in metadata_raw_new: + prompt_new = metadata_raw_new["prompt"] + else: + logger.warn("Prompt not found in metadata_raw_new") + + if "workflow" in metadata_raw_new: + workflow_new = metadata_raw_new["workflow"] + else: + logger.warn("Workflow not found in metadata_raw_new") + + if "fileinfo" in metadata_raw_new: + fileinfo_new = metadata_raw_new["fileinfo"] + else: + logger.warn("Fileinfo not found in metadata_raw_new") + + if what == "Prompt": + diff = findJsonsDiff(prompt_old, prompt_new) + elif what == "Workflow": + diff = findJsonsDiff(workflow_old, workflow_new) + else: + diff = findJsonsDiff(fileinfo_old, fileinfo_new) + + diff = json.dumps(diff, indent=CONFIG["indent"]) + + else: + invalid_msg = TEXTS.INVALID_METADATA_MSG.value + logger.warn(invalid_msg) + diff = invalid_msg + + return {"ui": {"text": [diff]}, "result": (diff,)} diff --git a/custom_nodes/ComfyUI-Crystools/nodes/parameters.py b/custom_nodes/ComfyUI-Crystools/nodes/parameters.py new file mode 100644 index 00000000..2bda3914 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/parameters.py @@ -0,0 +1,170 @@ +import json +from ..core import CONFIG, any, JSON_WIDGET, CATEGORY, STRING, INT, FLOAT, BOOLEAN, logger, get_nested_value + +# class CParameter: +# def __init__(self): +# pass +# +# @classmethod +# def INPUT_TYPES(cls): +# return { +# "required": { +# }, +# "optional": { +# "path_to_json": STRING, +# "key": STRING, +# "default": STRING, +# }, +# } +# +# CATEGORY = CATEGORY.MAIN.value + CATEGORY.UTILS.value +# INPUT_IS_LIST = False +# +# RETURN_TYPES = (any,) +# RETURN_NAMES = ("any",) +# +# FUNCTION = "execute" +# +# def execute(self, path_to_json=None, key=True, default=None): +# text = default +# value = text +# +# if path_to_json is not None and path_to_json != "": +# logger.debug(f"External parameter from: '{path_to_json}'") +# try: +# with open(path_to_json, 'r') as file: +# data = json.load(file) +# logger.debug(f"File found, data: '{data}'") +# +# result = get_value(data, key, default) +# text = result["text"] +# value = result["value"] +# +# except Exception as e: +# logger.error(e) +# text = f"Error reading file: {e}\nReturning default value: '{default}'" +# value = default +# +# return {"ui": {"text": [text]}, "result": [value]} + +class CJsonFile: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "path_to_json": STRING, + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.UTILS.value + INPUT_IS_LIST = False + + RETURN_TYPES = ("JSON",) + RETURN_NAMES = ("json",) + + FUNCTION = "execute" + + def IS_CHANGED(path_to_json=None): + return True + + def execute(self, path_to_json=None): + text = "" + data = {} + + if path_to_json is not None and path_to_json != "": + logger.debug(f"Open json file: '{path_to_json}'") + try: + with open(path_to_json, 'r') as file: + data = json.load(file) + text = json.dumps(data, indent=CONFIG["indent"]) + logger.debug(f"File found, data: '{str(data)}'") + + except Exception as e: + logger.error(e) + text = f"Error reading file: {e}" + + return {"ui": {"text": [text]}, "result": [data]} + +class CJsonExtractor: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "json": JSON_WIDGET, + }, + "optional": { + "key": STRING, + "default": STRING, + }, + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.UTILS.value + INPUT_IS_LIST = False + + RETURN_TYPES = (any, "STRING", "INT", "FLOAT", "BOOLEAN") + RETURN_NAMES = ("any", "string", "int", "float", "boolean") + + # OUTPUT_IS_LIST = (False,) + + FUNCTION = "execute" + + def execute(cls, json=None, key=True, default=None): + result = get_value(json, key, default) + + result["any"] = result["value"] + try: + result["string"] = str(result["value"]) + except Exception as e: + result["string"] = result["value"] + + try: + result["int"] = int(result["value"]) + except Exception as e: + result["int"] = result["value"] + + try: + result["float"] = float(result["value"]) + except Exception as e: + result["float"] = result["value"] + + try: + result["boolean"] = result["value"].lower() == "true" + except Exception as e: + result["boolean"] = result["value"] + + return { + "ui": {"text": [result["text"]]}, + "result": [ + result["any"], + result["string"], + result["int"], + result["float"], + result["boolean"] + ] + } + +def get_value(data, key, default=None): + text = "" + val = "" + + if key is not None and key != "": + val = get_nested_value(data, key, default) + if default != val: + text = f"Key found, return value: '{val}'" + else: + text = f"Key no found, return default value: '{val}'" + else: + text = f"Key is empty, return default value: '{val}'" + + return { + "text": text, + "value": val + } diff --git a/custom_nodes/ComfyUI-Crystools/nodes/pipe.py b/custom_nodes/ComfyUI-Crystools/nodes/pipe.py new file mode 100644 index 00000000..22bf4135 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/pipe.py @@ -0,0 +1,74 @@ +from ..core import CATEGORY, any +from ._names import CLASSES + + +class CPipeToAny: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": { + CLASSES.CPIPE_ANY_TYPE.value: (CLASSES.CPIPE_ANY_TYPE.value,), + "any_1": (any,), + "any_2": (any,), + "any_3": (any,), + "any_4": (any,), + "any_5": (any,), + "any_6": (any,), + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PIPE.value + RETURN_TYPES = (CLASSES.CPIPE_ANY_TYPE.value,) + + FUNCTION = "execute" + + def execute(self, CPipeAny=None, any_1=None, any_2=None, any_3=None, any_4=None, any_5=None, any_6=None): + any_1_original = None + any_2_original = None + any_3_original = None + any_4_original = None + any_5_original = None + any_6_original = None + + if CPipeAny != None: + any_1_original, any_2_original, any_3_original, any_4_original, any_5_original, any_6_original = CPipeAny + + CAnyPipeMod = [] + + CAnyPipeMod.append(any_1 if any_1 is not None else any_1_original) + CAnyPipeMod.append(any_2 if any_2 is not None else any_2_original) + CAnyPipeMod.append(any_3 if any_3 is not None else any_3_original) + CAnyPipeMod.append(any_4 if any_4 is not None else any_4_original) + CAnyPipeMod.append(any_5 if any_5 is not None else any_5_original) + CAnyPipeMod.append(any_6 if any_6 is not None else any_6_original) + + return (CAnyPipeMod,) + + +class CPipeFromAny: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + CLASSES.CPIPE_ANY_TYPE.value: (CLASSES.CPIPE_ANY_TYPE.value,), + }, + "optional": { + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PIPE.value + RETURN_TYPES = (CLASSES.CPIPE_ANY_TYPE.value, any, any, any, any, any, any,) + RETURN_NAMES = (CLASSES.CPIPE_ANY_TYPE.value, "any_1", "any_2", "any_3", "any_4", "any_5", "any_6",) + + FUNCTION = "execute" + + def execute(self, CPipeAny=None, ): + any_1, any_2, any_3, any_4, any_5, any_6 = CPipeAny + return CPipeAny, any_1, any_2, any_3, any_4, any_5, any_6 diff --git a/custom_nodes/ComfyUI-Crystools/nodes/primitive.py b/custom_nodes/ComfyUI-Crystools/nodes/primitive.py new file mode 100644 index 00000000..ae4d4d68 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/primitive.py @@ -0,0 +1,111 @@ +from ..core import BOOLEAN, CATEGORY, STRING, INT, FLOAT, STRING_ML + + +class CBoolean: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + + FUNCTION = "execute" + + def execute(self, boolean=True): + return (boolean,) + + +class CText: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string": STRING, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + + FUNCTION = "execute" + + def execute(self, string=""): + return (string,) + + +class CTextML: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string": STRING_ML, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + + FUNCTION = "execute" + + def execute(self, string=""): + return (string,) + + +class CInteger: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int": INT, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("int",) + + FUNCTION = "execute" + + def execute(self, int=True): + return (int,) + + +class CFloat: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "float": FLOAT, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.PRIMITIVE.value + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("float",) + + FUNCTION = "execute" + + def execute(self, float=True): + return (float,) diff --git a/custom_nodes/ComfyUI-Crystools/nodes/switch.py b/custom_nodes/ComfyUI-Crystools/nodes/switch.py new file mode 100644 index 00000000..72d78292 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/switch.py @@ -0,0 +1,225 @@ +from ..core import BOOLEAN, STRING, CATEGORY, any, logger + + +class CSwitchFromAny: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any": (any, ), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = (any, any,) + RETURN_NAMES = ("on_true", "on_false",) + + FUNCTION = "execute" + + def execute(self, any,boolean=True): + logger.debug("Any switch: " + str(boolean)) + + if boolean: + return any, None + else: + return None, any + +class CSwitchBooleanAny: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "on_true": (any, {"lazy": True}), + "on_false": (any, {"lazy": True}), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = (any,) + + FUNCTION = "execute" + + def check_lazy_status(self, on_true=None, on_false=None, boolean=True): + needed = "on_true" if boolean else "on_false" + return [needed] + + def execute(self, on_true, on_false, boolean=True): + logger.debug("Any switch: " + str(boolean)) + + if boolean: + return (on_true,) + else: + return (on_false,) + + +class CSwitchBooleanString: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "on_true": ("STRING", {"default": "", "lazy": True}), + "on_false": ("STRING", {"default": "", "lazy": True}), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + + FUNCTION = "execute" + + def check_lazy_status(self, on_true=None, on_false=None, boolean=True): + needed = "on_true" if boolean else "on_false" + return [needed] + + def execute(self, on_true, on_false, boolean=True): + logger.debug("String switch: " + str(boolean)) + + if boolean: + return (on_true,) + else: + return (on_false,) + + +class CSwitchBooleanConditioning: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "on_true": ("CONDITIONING", {"lazy": True}), + "on_false": ("CONDITIONING", {"lazy": True}), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("conditioning",) + + FUNCTION = "execute" + + def check_lazy_status(self, on_true=None, on_false=None, boolean=True): + needed = "on_true" if boolean else "on_false" + return [needed] + + def execute(self, on_true, on_false, boolean=True): + logger.debug("Conditioning switch: " + str(boolean)) + + if boolean: + return (on_true,) + else: + return (on_false,) + + +class CSwitchBooleanImage: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "on_true": ("IMAGE", {"lazy": True}), + "on_false": ("IMAGE", {"lazy": True}), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + + FUNCTION = "execute" + + def check_lazy_status(self, on_true=None, on_false=None, boolean=True): + needed = "on_true" if boolean else "on_false" + return [needed] + + def execute(self, on_true, on_false, boolean=True): + logger.debug("Image switch: " + str(boolean)) + + if boolean: + return (on_true,) + else: + return (on_false,) + + +class CSwitchBooleanLatent: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "on_true": ("LATENT", {"lazy": True}), + "on_false": ("LATENT", {"lazy": True}), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent",) + + FUNCTION = "execute" + + def check_lazy_status(self, on_true=None, on_false=None, boolean=True): + needed = "on_true" if boolean else "on_false" + return [needed] + + def execute(self, on_true, on_false, boolean=True): + logger.debug("Latent switch: " + str(boolean)) + + if boolean: + return (on_true,) + else: + return (on_false,) + + +class CSwitchBooleanMask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "on_true": ("MASK", {"lazy": True}), + "on_false": ("MASK", {"lazy": True}), + "boolean": BOOLEAN, + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("mask",) + + FUNCTION = "execute" + + def check_lazy_status(self, on_true=None, on_false=None, boolean=True): + needed = "on_true" if boolean else "on_false" + return [needed] + + def execute(self, on_true, on_false, boolean=True): + logger.debug("Mask switch: " + str(boolean)) + + if boolean: + return (on_true,) + else: + return (on_false,) diff --git a/custom_nodes/ComfyUI-Crystools/nodes/utils.py b/custom_nodes/ComfyUI-Crystools/nodes/utils.py new file mode 100644 index 00000000..051896af --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/nodes/utils.py @@ -0,0 +1,55 @@ +from ..core import CATEGORY, JSON_WIDGET, findJsonStrDiff, get_system_stats, logger + + +class CUtilsCompareJsons: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "json_old": JSON_WIDGET, + "json_new": JSON_WIDGET, + }, + "optional": { + + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.UTILS.value + RETURN_TYPES = ("JSON",) + RETURN_NAMES = ("json_compared",) + OUTPUT_NODE = True + + FUNCTION = "execute" + + def execute(self, json_old, json_new): + json = findJsonStrDiff(json_old, json_new) + return (str(json),) + + +# Credits to: https://github.com/WASasquatch/was-node-suite-comfyui for the following node! +class CUtilsStatSystem: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "latent": ("LATENT",), + } + } + + CATEGORY = CATEGORY.MAIN.value + CATEGORY.UTILS.value + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent",) + + FUNCTION = "execute" + + def execute(self, latent): + log = "Samples Passthrough:\n" + for stat in get_system_stats(): + log += stat + "\n" + + logger.debug(log) + + return {"ui": {"text": [log]}, "result": (latent,)} diff --git a/custom_nodes/ComfyUI-Crystools/package-lock.json b/custom_nodes/ComfyUI-Crystools/package-lock.json new file mode 100644 index 00000000..e64be97b --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/package-lock.json @@ -0,0 +1,2761 @@ +{ + "name": "crystools-typescript", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "crystools-typescript", + "version": "1.0.0", + "devDependencies": { + "@typescript-eslint/eslint-plugin": "6.18.1", + "@typescript-eslint/parser": "6.18.1", + "eslint": "8.56.0", + "eslint-plugin-import": "2.29.1", + "typescript": "5.3.3" + } + 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b/custom_nodes/ComfyUI-Crystools/package.json @@ -0,0 +1,20 @@ +{ + "name": "crystools-typescript", + "private": true, + "version": "1.0.0", + "type": "module", + "scripts": { + "start": "tsc --watch", + "tsc": "tsc", + "lint": "eslint web --ext ts --report-unused-disable-directives --max-warnings 0", + "lint-fix": "eslint web --ext ts --fix", + "validate": "npm run tsc && npm run lint" + }, + "devDependencies": { + "@typescript-eslint/eslint-plugin": "6.18.1", + "@typescript-eslint/parser": "6.18.1", + "eslint": "8.56.0", + "eslint-plugin-import": "2.29.1", + "typescript": "5.3.3" + } +} diff --git a/custom_nodes/ComfyUI-Crystools/pyproject.toml b/custom_nodes/ComfyUI-Crystools/pyproject.toml new file mode 100644 index 00000000..0f83ee1b --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "ComfyUI-Crystools" +description = "With this suit, you can see the resources monitor, progress bar & time elapsed, metadata and compare between two images, compare between two JSONs, show any value to console/display, pipes, and more!\nThis provides better nodes to load/save images, previews, etc, and see \"hidden\" data without loading a new workflow." +version = "1.27.4" +license = { file = "LICENSE" } +dependencies = ["deepdiff", "torch", "numpy", "Pillow", "pynvml", "py-cpuinfo"] + +classifiers = [ + "Operating System :: OS Independent", + "Environment :: GPU :: NVIDIA CUDA", +] + +[project.urls] +Repository = "https://github.com/crystian/ComfyUI-Crystools" +Documentation = "https://github.com/crystian/ComfyUI-Crystools/blob/main/README.md" +"Bug Tracker" = "https://github.com/crystian/ComfyUI-Crystools/issues" + +[tool.comfy] +PublisherId = "crystian" +DisplayName = "ComfyUI-Crystools" +Icon = "https://raw.githubusercontent.com/crystian/ComfyUI-Crystools/main/docs/screwdriver.png" diff --git a/custom_nodes/ComfyUI-Crystools/requirements.txt b/custom_nodes/ComfyUI-Crystools/requirements.txt new file mode 100644 index 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{ + "name": "positive", + "type": "CONDITIONING", + "link": 4 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 6 + }, + { + "name": "latent_image", + "type": "LATENT", + "link": 2 + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 10 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "KSampler" + }, + "widgets_values": [ + 156680208700286, + "fixed", + 20, + 8, + "euler", + "normal", + 1 + ] + }, + { + "id": 10, + "type": "Stats system [Crystools]", + "pos": [ + 1275, + 175 + ], + "size": [ + 350, + 125 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": "latent", + "type": "LATENT", + "link": 10 + } + ], + "outputs": [ + { + "name": "latent", + "type": "LATENT", + "links": [ + 11 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "Stats system [Crystools]" + } + }, + { + "id": 9, + "type": "SaveImage", + "pos": [ + 1925, + 300 + ], + "size": [ + 200, + 275 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 9 + } + ], + "properties": {}, + "widgets_values": [ + "ComfyUI" + ] + } + ], + "links": [ + [ + 1, + 4, + 0, + 3, + 0, + "MODEL" + ], + [ + 2, + 5, + 0, + 3, + 3, + "LATENT" + ], + [ + 3, + 4, + 1, + 6, + 0, + "CLIP" + ], + [ + 4, + 6, + 0, + 3, + 1, + "CONDITIONING" + ], + [ + 5, + 4, + 1, + 7, + 0, + "CLIP" + ], + [ + 6, + 7, + 0, + 3, + 2, + "CONDITIONING" + ], + [ + 8, + 4, + 2, + 8, + 1, + "VAE" + ], + [ + 9, + 8, + 0, + 9, + 0, + "IMAGE" + ], + [ + 10, + 3, + 0, + 10, + 0, + "LATENT" + ], + [ + 11, + 10, + 0, + 8, + 0, + "LATENT" + ] + ], + "groups": [], + "config": {}, + "extra": {}, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Crystools/server/__init__.py b/custom_nodes/ComfyUI-Crystools/server/__init__.py new file mode 100644 index 00000000..b320e351 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/server/__init__.py @@ -0,0 +1 @@ +from .monitor import * \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Crystools/server/monitor.py b/custom_nodes/ComfyUI-Crystools/server/monitor.py new file mode 100644 index 00000000..5e0accd6 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/server/monitor.py @@ -0,0 +1,125 @@ +from server import PromptServer +from aiohttp import web +from ..core import logger +from ..general import cmonitor + +@PromptServer.instance.routes.patch("/crystools/monitor") +async def newSettings(request): + try: + settings = await request.json() + # print(settings) + + if 'rate' in settings is not None: + rate = settings['rate'] + if type(rate) is not int and type(rate) is not float: + raise Exception('Rate must be an number.') + + if cmonitor.rate == 0 and rate > 0: + cmonitor.rate = rate + cmonitor.startMonitor() + else: + cmonitor.rate = rate + + + if 'switchCPU' in settings is not None: + switchCPU = settings['switchCPU'] + if type(switchCPU) is not bool: + raise Exception('switchCPU must be an boolean.') + + cmonitor.hardwareInfo.switchCPU = switchCPU + + if 'switchHDD' in settings is not None: + switchHDD = settings['switchHDD'] + if type(switchHDD) is not bool: + raise Exception('switchHDD must be an boolean.') + + cmonitor.hardwareInfo.switchHDD = switchHDD + + if 'switchRAM' in settings is not None: + switchRAM = settings['switchRAM'] + if type(switchRAM) is not bool: + raise Exception('switchRAM must be an boolean.') + + cmonitor.hardwareInfo.switchRAM = switchRAM + + if 'whichHDD' in settings is not None: + whichHDD = settings['whichHDD'] + if type(whichHDD) is not str: + raise Exception('whichHDD must be an string.') + + cmonitor.hardwareInfo.whichHDD = whichHDD + + + return web.Response(status=200) + except Exception as e: + logger.error(e) + return web.Response(status=400, text=str(e)) + + +@PromptServer.instance.routes.post("/crystools/monitor/switch") +async def monitorSwitch(request): + try: + switch = await request.json() + + if 'monitor' in switch is not None: + monitor = switch['monitor'] + if type(monitor) is not bool: + raise Exception('monitor must be an boolean.') + + if monitor: + cmonitor.startMonitor() + else: + cmonitor.stopMonitor() + + return web.Response(status=200) + except Exception as e: + logger.error(e) + return web.Response(status=400, text=str(e)) + + +@PromptServer.instance.routes.get("/crystools/monitor/HDD") +def getHDDs(request): + try: + return web.json_response(cmonitor.hardwareInfo.getHDDsInfo()) + except Exception as e: + logger.error(e) + return web.Response(status=400, text=str(e)) + + +@PromptServer.instance.routes.get("/crystools/monitor/GPU") +def getGPUs(request): + try: + gpuInfo = cmonitor.hardwareInfo.getGPUInfo() + return web.json_response(gpuInfo) + except Exception as e: + logger.error(e) + return web.Response(status=400, text=str(e)) + + +@PromptServer.instance.routes.patch("/crystools/monitor/GPU/{index}") +async def getGPUs(request): + try: + index = request.match_info["index"] + settings = await request.json() + if 'utilization' in settings is not None: + if type(settings['utilization']) is not bool: + raise Exception('utilization must be an boolean.') + + cmonitor.hardwareInfo.GPUInfo.gpusUtilization[int(index)] = settings['utilization'] + + if 'vram' in settings is not None: + if type(settings['vram']) is not bool: + raise Exception('vram must be an boolean.') + + cmonitor.hardwareInfo.GPUInfo.gpusVRAM[int(index)] = settings['vram'] + + if 'temperature' in settings is not None: + if type(settings['temperature']) is not bool: + raise Exception('temperature must be an boolean.') + + cmonitor.hardwareInfo.GPUInfo.gpusTemperature[int(index)] = settings['temperature'] + + return web.Response(status=200) + except Exception as e: + logger.error(e) + return web.Response(status=400, text=str(e)) diff --git a/custom_nodes/ComfyUI-Crystools/tsconfig.json b/custom_nodes/ComfyUI-Crystools/tsconfig.json new file mode 100644 index 00000000..15e34776 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/tsconfig.json @@ -0,0 +1,67 @@ +{ + "compilerOptions": { + "target": "es2020", + "module": "ESNext", + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "typeRoots": [ + // "./web/ts/typings", + ], +// "rootDir": "../", + "baseUrl": "../../web", +// "baseUrl": "./", + "paths": { +// "/scripts/*": ["../../web/scripts/*"], // works + }, + "rootDirs": [ + "../../web", +// "web" + ], + "preserveSymlinks": true, + "checkJs": false, + "allowJs": true, + // if set this, it makes folders in the output folder... + // "outDir": "web/dist", + "declaration": true, + // "declarationDir": "./web/dist/types", + + "strict": true, + "alwaysStrict": true, + "noImplicitAny": true, + "strictNullChecks": true, + "strictFunctionTypes": true, + "strictBindCallApply": true, + "noImplicitThis": true, + "noImplicitReturns": true, + "noImplicitOverride": true, + "noUnusedLocals": true, + "noUnusedParameters": false, + "strictPropertyInitialization": false, + "exactOptionalPropertyTypes": false, + + "noResolve": false, + "moduleResolution": "Bundler", + "resolveJsonModule": true, + "skipLibCheck": true, + "useUnknownInCatchVariables": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedIndexedAccess": true, + "noPropertyAccessFromIndexSignature": true, + "allowUnusedLabels": true, + "allowSyntheticDefaultImports": false, + "useDefineForClassFields": true, + "allowImportingTsExtensions": false, + "isolatedModules": true, + "removeComments": true, + "noEmit": false + }, + "include": [ +// "./custom_nodes/comfyui-crystools/web/*.ts", +// "../../../web/*.ts", +// "../../web/*.ts", + "web/*.ts", +// "web/types.d.ts" + ], + "exclude": [ + "node_modules" + ] +} diff --git a/custom_nodes/ComfyUI-Crystools/version b/custom_nodes/ComfyUI-Crystools/version new file mode 100644 index 00000000..d6201580 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/version @@ -0,0 +1 @@ +1.27.4 diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/index.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/index.d.ts new file mode 100644 index 00000000..82395511 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/index.d.ts @@ -0,0 +1,5 @@ +export type * from './scripts.js'; +export * from './scripts.js'; +export type { ComfyApp } from './typings/comfy.js'; +export * from './liteGraph.js'; +export type * from './liteGraph.js'; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/index.js b/custom_nodes/ComfyUI-Crystools/web/comfy/index.js new file mode 100644 index 00000000..e364fb02 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/index.js @@ -0,0 +1,2 @@ +export * from './scripts.js'; +export * from './liteGraph.js'; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/index.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/index.ts new file mode 100644 index 00000000..82395511 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/index.ts @@ -0,0 +1,5 @@ +export type * from './scripts.js'; +export * from './scripts.js'; +export type { ComfyApp } from './typings/comfy.js'; +export * from './liteGraph.js'; +export type * from './liteGraph.js'; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.d.ts new file mode 100644 index 00000000..52ecb7ef --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.d.ts @@ -0,0 +1,19 @@ +export type * from './liteGraph.types.js'; +import type { IWidget as IWidgetOld, LGraphNode as TypeGraphNode, TypeLiteGraph } from './liteGraph.types.js'; +declare const LGraphNode: typeof TypeGraphNode; +export interface IWidget extends IWidgetOld { + onRemove?: () => void; + serializeValue?: () => Promise; +} +export declare class TLGraphNode extends LGraphNode { + static category: string; + static shape: number; + static color: string; + static bgcolor: string; + static collapsable: boolean; + isVirtualNode?: boolean; + widgets_values?: any[]; + name?: string; + prototype: TLGraphNode; +} +export declare const LiteGraph: TypeLiteGraph; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.js b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.js new file mode 100644 index 00000000..38d7b924 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.js @@ -0,0 +1,30 @@ +export class TLGraphNode extends LGraphNode { + constructor() { + super(...arguments); + Object.defineProperty(this, "isVirtualNode", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "widgets_values", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "name", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "prototype", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + } +} +export const LiteGraph = window.LiteGraph; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.ts new file mode 100644 index 00000000..e2d4689f --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.ts @@ -0,0 +1,32 @@ +// / +// A LOTS OF PATCHES FOR LITEGRAPH TYPES Β―\_(ツ)_/Β― +export type * from './liteGraph.types.js'; + +import type { IWidget as IWidgetOld, LGraphNode as TypeGraphNode, TypeLiteGraph } from './liteGraph.types.js'; + +declare const LGraphNode: typeof TypeGraphNode; // just for get the type + +export interface IWidget extends IWidgetOld { + onRemove?: () => void; + serializeValue?: () => Promise; +} + +export class TLGraphNode extends LGraphNode { + // on discovery... + static category: string; + static shape: number; + static color: string; + static bgcolor: string; + static collapsable: boolean; + + // widgets?: IWidget[]; + isVirtualNode?: boolean; + // override onResize?: (size: [number, number]) => void; + widgets_values?: any[]; + name?: string; + + prototype: TLGraphNode; // yes itself +} + +// from globals +export const LiteGraph: TypeLiteGraph = (window as any).LiteGraph; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.types.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.types.d.ts new file mode 100644 index 00000000..0281a915 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/liteGraph.types.d.ts @@ -0,0 +1,19 @@ +// A LOTS OF PATCHES FOR LITEGRAPH TYPES Β―\_(ツ)_/Β― +import { LGraph } from './typings/litegraph.js'; + +export type * from './typings/litegraph.js'; + +export declare type TypeLiteGraph = typeof LiteGraph & { + graph: LGraph; +}; + +// export declare type ComfyApi = typeof api; +export declare type ComfyNode = any; + +// I prefer not use global, but if I change of opinion: +// / +// import { LGraphNode as TLGraphNode, LiteGraph as TLiteGraph } from '/types/litegraph'; +// export declare const LGraphNode: typeof TLGraphNode; +// declare global { +// const LGraphNode: LGraphNode; +// } diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.d.ts new file mode 100644 index 00000000..25a4a60d --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.d.ts @@ -0,0 +1,5 @@ +export { ComfyWidgets } from '../../../scripts/widgets.js'; +export { app } from '../../../scripts/app.js'; +export { api } from '../../../scripts/api.js'; +export * as utils from '../../../scripts/utils.js'; +export { ComfyButtonGroup } from '../../../scripts/ui/components/buttonGroup.js'; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.js b/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.js new file mode 100644 index 00000000..25a4a60d --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.js @@ -0,0 +1,5 @@ +export { ComfyWidgets } from '../../../scripts/widgets.js'; +export { app } from '../../../scripts/app.js'; +export { api } from '../../../scripts/api.js'; +export * as utils from '../../../scripts/utils.js'; +export { ComfyButtonGroup } from '../../../scripts/ui/components/buttonGroup.js'; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.ts new file mode 100644 index 00000000..6d18b5ae --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/scripts.ts @@ -0,0 +1,10 @@ +// @ts-expect-error I could not find a way to make this work +export { ComfyWidgets } from '../../../scripts/widgets.js'; +// @ts-expect-error I could not find a way to make this work +export { app } from '../../../scripts/app.js'; +// @ts-expect-error I could not find a way to make this work +export { api } from '../../../scripts/api.js'; +// @ts-expect-error I could not find a way to make this work +export * as utils from '../../../scripts/utils.js'; +// @ts-expect-error I could not find a way to make this work +export { ComfyButtonGroup } from '../../../scripts/ui/components/buttonGroup.js'; diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/typings/README.md b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/README.md new file mode 100644 index 00000000..58fde3e0 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/README.md @@ -0,0 +1,5 @@ +COPIED FROM rgthree + +The typings in node_modules or in ComfyUI's web/ directory were not that well covered. These typings are hacked together with some of the inconsistencies I found. + +To be honest, I have no idea why I needed a bizarre workaround for litegraph's types. Usually the '/// <reference>' comment should have picked up the types, but it wasn't having it. Β―\_(ツ)_/Β― diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/typings/comfy.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/comfy.d.ts new file mode 100644 index 00000000..bbecb80c --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/comfy.d.ts @@ -0,0 +1,227 @@ +import type { LGraphGroup as TLGraphGroup, LGraphNode as TLGraphNode, IWidget, SerializedLGraphNode, LGraph as TLGraph, LGraphCanvas as TLGraphCanvas, LiteGraph as TLiteGraph } from "./litegraph.js"; +import type {Constructor, SerializedGraph} from './index.js'; + +declare global { + const LiteGraph: typeof TLiteGraph; + const LGraph: typeof TLGraph; + const LGraphNode: typeof TLGraphNode; + const LGraphCanvas: typeof TLGraphCanvas; + const LGraphGroup: typeof TLGraphGroup; +} + +// @rgthree: Types on ComfyApp as needed. +export interface ComfyApp { + extensions: ComfyExtension[]; + async queuePrompt(number?: number, batchCount = 1): Promise; + graph: TLGraph; + canvas: TLGraphCanvas; + clean() : void; + registerExtension(extension: ComfyExtension): void; + getPreviewFormatParam(): string; + getRandParam(): string; + loadApiJson(apiData: {}, fileName: string): void; + async graphToPrompt(graph?: TLGraph, clean?: boolean): Promise; + // workflow: ComfyWorkflowInstance ??? + async loadGraphData(graphData: {}, clean?: boolean, restore_view?: boolean, workflow?: any|null): Promise + ui: { + settings: { + addSetting(config: {id: string, name: string, type: () => HTMLElement}) : void; + } + } + // Just marking as any for now. + menu?: any; +} + +export interface ComfyWidget extends IWidget { + // https://github.com/comfyanonymous/ComfyUI/issues/2193 Changes from SerializedLGraphNode to + // LGraphNode... + serializeValue(nodeType: TLGraphNode, index: number): Promise; + afterQueued(): void; + inputEl?: HTMLTextAreaElement; + width: number; +} + +export interface ComfyGraphNode extends TLGraphNode { + getExtraMenuOptions: (node: TLGraphNode, options: ContextMenuItem[]) => void; + onExecuted(message: any): void; +} + +export interface ComfyNode extends TLGraphNode { + comfyClass: string; +} + +// @rgthree +export interface ComfyNodeConstructor extends Constructor { + static title: string; + static type?: string; + static comfyClass: string; +} + +export type NodeMode = 0|1|2|3|4|undefined; + + +export interface ComfyExtension { + /** + * The name of the extension + */ + name: string; + /** + * Allows any initialisation, e.g. loading resources. Called after the canvas is created but before nodes are added + * @param app The ComfyUI app instance + */ + init?(app: ComfyApp): Promise; + /** + * Allows any additonal setup, called after the application is fully set up and running + * @param app The ComfyUI app instance + */ + setup?(app: ComfyApp): Promise; + /** + * Called before nodes are registered with the graph + * @param defs The collection of node definitions, add custom ones or edit existing ones + * @param app The ComfyUI app instance + */ + addCustomNodeDefs?(defs: Record, app: ComfyApp): Promise; + /** + * Allows the extension to add custom widgets + * @param app The ComfyUI app instance + * @returns An array of {[widget name]: widget data} + */ + getCustomWidgets?( + app: ComfyApp + ): Promise< + Record { widget?: IWidget; minWidth?: number; minHeight?: number }> + >; + /** + * Allows the extension to add additional handling to the node before it is registered with LGraph + * @rgthree changed nodeType from `typeof LGraphNode` to `ComfyNodeConstructor` + * @param nodeType The node class (not an instance) + * @param nodeData The original node object info config object + * @param app The ComfyUI app instance + */ + beforeRegisterNodeDef?(nodeType: ComfyNodeConstructor, nodeData: ComfyObjectInfo, app: ComfyApp): Promise; + /** + * Allows the extension to register additional nodes with LGraph after standard nodes are added + * @param app The ComfyUI app instance + */ + // @rgthree - add void for non async + registerCustomNodes?(app: ComfyApp): void|Promise; + /** + * Allows the extension to modify a node that has been reloaded onto the graph. + * If you break something in the backend and want to patch workflows in the frontend + * This is the place to do this + * @param node The node that has been loaded + * @param app The ComfyUI app instance + */ + loadedGraphNode?(node: TLGraphNode, app: ComfyApp); + /** + * Allows the extension to run code after the constructor of the node + * @param node The node that has been created + * @param app The ComfyUI app instance + */ + nodeCreated?(node: TLGraphNode, app: ComfyApp); +} + +export type ComfyObjectInfo = { + name: string; + display_name?: string; + description?: string; + category: string; + input?: { + required?: Record; + optional?: Record; + hidden?: Record; + }; + output?: string[]; + output_name: string[]; + // @rgthree + output_node?: boolean; +}; + +export type ComfyObjectInfoConfig = [string | any[]] | [string | any[], any]; + +// @rgthree +type ComfyApiInputLink = [ + /** The id string of the connected node. */ + string, + /** The output index. */ + number, +] + +// @rgthree +export type ComfyApiFormatNode = { + "inputs": { + [input_name: string]: string|number|boolean|ComfyApiInputLink, + }, + "class_type": string, + "_meta": { + "title": string, + } +} + +// @rgthree +export type ComfyApiFormat = { + [node_id: string]: ComfyApiFormatNode +} + +// @rgthree +export type ComfyApiPrompt = { + workflow: SerializedGraph, + output: ComfyApiFormat, +} + +// @rgthree +export type ComfyApiEventDetailStatus = { + exec_info: { + queue_remaining: number; + }; +}; + +// @rgthree +export type ComfyApiEventDetailExecutionStart = { + prompt_id: string; +}; + +// @rgthree +export type ComfyApiEventDetailExecuting = null | string; + +// @rgthree +export type ComfyApiEventDetailProgress = { + node: string; + prompt_id: string; + max: number; + value: number; +}; + +// @rgthree +export type ComfyApiEventDetailExecuted = { + node: string; + prompt_id: string; + output: any; +}; + +// @rgthree +export type ComfyApiEventDetailCached = { + nodes: string[]; + prompt_id: string; +}; + +// @rgthree +export type ComfyApiEventDetailExecuted = { + prompt_id: string; + node: string; + output: any; +}; + +// @rgthree +export type ComfyApiEventDetailError = { + prompt_id: string; + exception_type: string; + exception_message: string; + node_id: string; + node_type: string; + node_id: string; + traceback: string; + executed: any[]; + current_inputs: {[key: string]: (number[]|string[])}; + current_outputs: {[key: string]: (number[]|string[])}; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/typings/index.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/index.d.ts new file mode 100644 index 00000000..a574726e --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/index.d.ts @@ -0,0 +1,55 @@ +import { LGraph } from "./litegraph.js"; + +export type Constructor = new(...args: any[]) => T; + +export type SerializedLink = [ + number, // this.id, + number, // this.origin_id, + number, // this.origin_slot, + number, // this.target_id, + number, // this.target_slot, + string, // this.type +]; + +export interface SerializedNodeInput { + name: string; + type: string; + link: number; +} +export interface SerializedNodeOutput { + name: string; + type: string; + link: number; + slot_index: number; + links: number[]; +} +export interface SerializedNode { + id: number; + inputs: SerializedNodeInput[]; + outputs: SerializedNodeOutput[]; + mode: number; + order: number; + pos: [number, number]; + properties: any; + size: [number, number]; + type: string; + widgets_values: Array; +} + +export interface SerializedGraph { + config: any; + extra: any; + groups: any; + last_link_id: number; + last_node_id: number; + links: SerializedLink[]; + nodes: SerializedNode[]; +} + +export interface BadLinksData { + hasBadLinks: boolean; + fixed: boolean; + graph: T; + patched: number; + deleted: number; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/comfy/typings/litegraph.d.ts b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/litegraph.d.ts new file mode 100644 index 00000000..730103ef --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/comfy/typings/litegraph.d.ts @@ -0,0 +1,1750 @@ +// Type definitions for litegraph.js 0.7.0 +// Project: litegraph.js +// Definitions by: NateScarlet + +export type Vector2 = [number, number]; +export type Vector4 = [number, number, number, number]; +export type widgetTypes = + | "number" + | "slider" + | "combo" + | "text" + | "toggle" + | "button"; +export type SlotShape = + | typeof LiteGraph.BOX_SHAPE + | typeof LiteGraph.CIRCLE_SHAPE + | typeof LiteGraph.ARROW_SHAPE + | typeof LiteGraph.SQUARE_SHAPE + | number; // For custom shapes + +/** https://github.com/jagenjo/litegraph.js/tree/master/guides#node-slots */ +export interface INodeSlot { + name: string; + type: string | -1; + label?: string; + dir?: + | typeof LiteGraph.UP + | typeof LiteGraph.RIGHT + | typeof LiteGraph.DOWN + | typeof LiteGraph.LEFT; + color_on?: string; + color_off?: string; + shape?: SlotShape; + locked?: boolean; + nameLocked?: boolean; + pos?: Vector2; + // @rgthree + hidden?: boolean; + // @rgthree + disabled?: boolean; + // @rgthree - Found this checked in getSlotMenuOptions default. + removable?: boolean; + // @rgthree - A status we put on some nodes so we can draw things around it. + rgthree_status?: 'WARN' | 'ERROR'; +} + +export interface INodeInputSlot extends INodeSlot { + link: LLink["id"] | null; + // @rgthree - add comfy widget info + widget?: { + name: string; + } +} + +export interface INodeOutputSlot extends INodeSlot { + links: LLink["id"][] | null; +} + +export type WidgetCallback = ( + this: T, + value: T["value"], + graphCanvas: LGraphCanvas, + node: LGraphNode, + pos: Vector2, + event?: MouseEvent +) => void; + +// @rgthree +export type WidgetComboCallback = ( + this: T, + value: T["value"][0], + graphCanvas: LGraphCanvas, + node: LGraphNode, + pos: Vector2, + event?: MouseEvent +) => void; + +// @rgthree +export type IWidgetOptions = { + y?: number; // ? + property?: string; + serialize?: boolean; // ComfyUI in app.js + forceInput?: boolean; // ComfyUI in app.js + defaultInput?: boolean; // ComfyUI in app.js +} + +// @rgthree +export type IWidgetToggleOptions = IWidgetOptions & { + on?: string; + off?: string; +} + +// @rgthree +export type IWidgetNumberOptions = IWidgetOptions & { + precision?: number; + max?: number; + min?: number; +} + +// @rgthree +export type IWidgetSliderOptions = IWidgetNumberOptions & { + slider_color?: string; + marker_color?: string; +} + + +// @rgthree +export type IWidgetComboOptions = IWidgetOptions & { + values?: string[] | ((widget: IComboWidget, node: LGraphNode) => string[]); +} + +export interface IWidget { + name: string | null; + // @rgthree + label?: string | null; + value: TValue; + options?: TOptions; + // @rgthree - extend to string for custom + type?: widgetTypes | string; + y?: number; + property?: string; + last_y?: number; + clicked?: boolean; + marker?: boolean; + disabled?: boolean; + callback?: WidgetCallback; + // crystools + onRemove?: () => void; + /** Called by `LGraphCanvas.drawNodeWidgets` */ + draw?( + ctx: CanvasRenderingContext2D, + node: LGraphNode, + width: number, + posY: number, + height: number + ): void; + /** + * Called by `LGraphCanvas.processNodeWidgets` + * https://github.com/jagenjo/litegraph.js/issues/76 + */ + mouse?( + event: MouseEvent, + pos: Vector2, + node: LGraphNode + ): boolean; + /** Called by `LGraphNode.computeSize` */ + computeSize?(width: number): [number, number]; + // @rgthree - make optional, since it is in the code. + serializeValue?(serializedNode: SerializedLGraphNode, widgetIndex: number): TValue; + // @rgthree - Checked in LGraphCanvas.prototype.processNodeWidgets, and figured I'd use it too. + width?: number; +} +export interface IButtonWidget extends IWidget { + type: "button"; +} +// @rgthree: adding options +export interface IToggleWidget extends IWidget { + type: "toggle"; +} +// @rgthree: adding options +export interface ISliderWidget extends IWidget { + type: "slider"; +} +// @rgthree: adding options +export interface INumberWidget extends IWidget { + type: "number"; +} +// @rgthree: adding options +export interface IComboWidget extends IWidget { + value: T[0]; + type: "combo"; + callback?: WidgetComboCallback; +} + +export interface ITextWidget extends IWidget { + type: "text"; +} + +export interface IContextMenuItem { + // @rgthree - Make optional because, I guess it is? + content?: string; + value?: any; + callback?: ContextMenuEventListener; + /** Used as innerHTML for extra child element */ + title?: string; + disabled?: boolean; + has_submenu?: boolean; + submenu?: { + options: ContextMenuItem[]; + } & IContextMenuOptions; + className?: string; + // @rgthree - Added for menu_auto_nest + rgthree_originalValue?: IContextMenuItem; + // @rgthree - this was missing and passed through for getSlotMenuOptions default. + slot?: {input?: INodeInputSlot, output?: INodeOutputSlot}; +} +export interface IContextMenuOptions { + callback?: ContextMenuEventListener; + ignore_item_callbacks?: Boolean; + event?: MouseEvent | CustomEvent | AdjustedMouseEvent; + parentMenu?: ContextMenu|null; + autoopen?: boolean; + title?: string; + extra?: any; + // @rgthree + scale?: number; + // @rgthree + left?: number; + // @rgthree + top?: number; + // @rgthree + className?: string; + // @rgthree - Added for menu_auto_nest + rgthree_originalCallback?: ContextMenuEventListener; + // @rgthree - No idea since it's not documented, but we'll use it to pass data, like rgthree_doNotNest + extra?: any +} + +export type ContextMenuItem = IContextMenuItem | null; +export type ContextMenuEventListener = ( + value: ContextMenuItem, + options: IContextMenuOptions, + event: MouseEvent, + parentMenu: ContextMenu | undefined, + node: LGraphNode +) => boolean | void; + +export const LiteGraph: { + VERSION: number; + + CANVAS_GRID_SIZE: number; + + NODE_TITLE_HEIGHT: number; + NODE_TITLE_TEXT_Y: number; + NODE_SLOT_HEIGHT: number; + NODE_WIDGET_HEIGHT: number; + NODE_WIDTH: number; + NODE_MIN_WIDTH: number; + NODE_COLLAPSED_RADIUS: number; + NODE_COLLAPSED_WIDTH: number; + NODE_TITLE_COLOR: string; + NODE_TEXT_SIZE: number; + NODE_TEXT_COLOR: string; + NODE_SUBTEXT_SIZE: number; + NODE_DEFAULT_COLOR: string; + NODE_DEFAULT_BGCOLOR: string; + NODE_DEFAULT_BOXCOLOR: string; + NODE_DEFAULT_SHAPE: string; + // @rgthree - didn't exist. + NODE_BOX_OUTLINE_COLOR: string; + DEFAULT_SHADOW_COLOR: string; + DEFAULT_GROUP_FONT: number; + // @rgthree - Seems to have been missing. + NODE_BOX_OUTLINE_COLOR: string; + + WIDGET_BGCOLOR: string; + WIDGET_OUTLINE_COLOR: string; + WIDGET_TEXT_COLOR: string; + WIDGET_SECONDARY_TEXT_COLOR: string; + + LINK_COLOR: string; + EVENT_LINK_COLOR: string; + CONNECTING_LINK_COLOR: string; + + MAX_NUMBER_OF_NODES: number; //avoid infinite loops + DEFAULT_POSITION: Vector2; //default node position + VALID_SHAPES: ["default", "box", "round", "card"]; //,"circle" + + //shapes are used for nodes but also for slots + BOX_SHAPE: 1; + ROUND_SHAPE: 2; + CIRCLE_SHAPE: 3; + CARD_SHAPE: 4; + ARROW_SHAPE: 5; + GRID_SHAPE: 6; + + //enums + INPUT: 1; + OUTPUT: 2; + + EVENT: -1; //for outputs + ACTION: -1; //for inputs + + ALWAYS: 0; + ON_EVENT: 1; + NEVER: 2; + ON_TRIGGER: 3; + + UP: 1; + DOWN: 2; + LEFT: 3; + RIGHT: 4; + CENTER: 5; + + STRAIGHT_LINK: 0; + LINEAR_LINK: 1; + SPLINE_LINK: 2; + + NORMAL_TITLE: 0; + NO_TITLE: 1; + TRANSPARENT_TITLE: 2; + AUTOHIDE_TITLE: 3; + + node_images_path: string; + + // @rgthree. These just weren't there. Note, LiteGraph initializes these as an array, but + // ComfyUI overrides these to a string-keye'd object... ??? + slot_types_default_out: {[key: string]: string[]}; + slot_types_default_in: {[key: string]: string[]}; + + debug: boolean; + catch_exceptions: boolean; + throw_errors: boolean; + /** if set to true some nodes like Formula would be allowed to evaluate code that comes from unsafe sources (like node configuration), which could lead to exploits */ + allow_scripts: boolean; + /** node types by string */ + registered_node_types: Record; + /** used for dropping files in the canvas */ + node_types_by_file_extension: Record; + /** node types by class name */ + Nodes: Record; + + /** used to add extra features to the search box */ + searchbox_extras: Record< + string, + { + data: { outputs: string[][]; title: string }; + desc: string; + type: string; + } + >; + + //@rgthree + isValidConnection(type: string|string[], type: string|string[]):boolean; + overlapBounding(a: Vector4, b: Vector4) : boolean; + + createNode(type: string): T; + /** Register a node class so it can be listed when the user wants to create a new one */ + registerNodeType(type: string, base: { new (title?: string): T }): void; + /** removes a node type from the system */ + unregisterNodeType(type: string): void; + /** Removes all previously registered node's types. */ + clearRegisteredTypes(): void; + /** + * Create a new node type by passing a function, it wraps it with a proper class and generates inputs according to the parameters of the function. + * Useful to wrap simple methods that do not require properties, and that only process some input to generate an output. + * @param name node name with namespace (p.e.: 'math/sum') + * @param func + * @param param_types an array containing the type of every parameter, otherwise parameters will accept any type + * @param return_type string with the return type, otherwise it will be generic + * @param properties properties to be configurable + */ + wrapFunctionAsNode( + name: string, + func: (...args: any[]) => any, + param_types?: string[], + return_type?: string, + properties?: object + ): void; + + /** + * Adds this method to all node types, existing and to be created + * (You can add it to LGraphNode.prototype but then existing node types wont have it) + */ + addNodeMethod(name: string, func: (...args: any[]) => any): void; + + /** + * Create a node of a given type with a name. The node is not attached to any graph yet. + * @param type full name of the node class. p.e. "math/sin" + * @param name a name to distinguish from other nodes + * @param options to set options + */ + createNode( + type: string, + title: string, + options: object + ): T; + + /** + * Returns a registered node type with a given name + * @param type full name of the node class. p.e. "math/sin" + */ + getNodeType(type: string): LGraphNodeConstructor; + + /** + * Returns a list of node types matching one category + * @method getNodeTypesInCategory + * @param {String} category category name + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the node classes + */ + getNodeTypesInCategory( + category: string, + filter: string + ): LGraphNodeConstructor[]; + + /** + * Returns a list with all the node type categories + * @method getNodeTypesCategories + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the names of the categories + */ + getNodeTypesCategories(filter: string): string[]; + + /** debug purposes: reloads all the js scripts that matches a wildcard */ + reloadNodes(folder_wildcard: string): void; + + getTime(): number; + LLink: typeof LLink; + LGraph: typeof LGraph; + DragAndScale: typeof DragAndScale; + compareObjects(a: object, b: object): boolean; + distance(a: Vector2, b: Vector2): number; + colorToString(c: string): string; + isInsideRectangle( + x: number, + y: number, + left: number, + top: number, + width: number, + height: number + ): boolean; + growBounding(bounding: Vector4, x: number, y: number): Vector4; + isInsideBounding(p: Vector2, bb: Vector4): boolean; + hex2num(hex: string): [number, number, number]; + num2hex(triplet: [number, number, number]): string; + ContextMenu: typeof ContextMenu; + extendClass(target: A, origin: B): A & B; + getParameterNames(func: string): string[]; + // @rgthree + closeAllContextMenus(ref_window?: Window): void; +}; + +export type serializedLGraph< + TNode = ReturnType, + // https://github.com/jagenjo/litegraph.js/issues/74 + TLink = [number, number, number, number, number, string], + TGroup = ReturnType +> = { + last_node_id: LGraph["last_node_id"]; + last_link_id: LGraph["last_link_id"]; + nodes: TNode[]; + links: TLink[]; + groups: TGroup[]; + config: LGraph["config"]; + version: typeof LiteGraph.VERSION; +}; + +export declare class LGraph { + static supported_types: string[]; + static STATUS_STOPPED: 1; + static STATUS_RUNNING: 2; + + constructor(o?: object); + + filter: string; + catch_errors: boolean; + /** custom data */ + config: object; + elapsed_time: number; + fixedtime: number; + fixedtime_lapse: number; + globaltime: number; + inputs: any; + iteration: number; + last_link_id: number; + last_node_id: number; + last_update_time: number; + links: Record; + list_of_graphcanvas: LGraphCanvas[]; + outputs: any; + runningtime: number; + starttime: number; + status: typeof LGraph.STATUS_RUNNING | typeof LGraph.STATUS_STOPPED; + + // @rgthree, remove private; it's not really private b/c it's javascript. + _nodes: LGraphNode[]; + // @rgthree, remove private; it's not really private b/c it's javascript. + _groups: LGraphGroup[]; + private _nodes_by_id: Record; + /** nodes that are executable sorted in execution order */ + private _nodes_executable: + | (LGraphNode & { onExecute: NonNullable }[]) + | null; + /** nodes that contain onExecute */ + private _nodes_in_order: LGraphNode[]; + private _version: number; + + getSupportedTypes(): string[]; + /** Removes all nodes from this graph */ + clear(): void; + /** Attach Canvas to this graph */ + attachCanvas(graphCanvas: LGraphCanvas): void; + /** Detach Canvas to this graph */ + detachCanvas(graphCanvas: LGraphCanvas): void; + /** + * Starts running this graph every interval milliseconds. + * @param interval amount of milliseconds between executions, if 0 then it renders to the monitor refresh rate + */ + start(interval?: number): void; + /** Stops the execution loop of the graph */ + stop(): void; + /** + * Run N steps (cycles) of the graph + * @param num number of steps to run, default is 1 + */ + runStep(num?: number, do_not_catch_errors?: boolean): void; + /** + * Updates the graph execution order according to relevance of the nodes (nodes with only outputs have more relevance than + * nodes with only inputs. + */ + updateExecutionOrder(): void; + /** This is more internal, it computes the executable nodes in order and returns it */ + computeExecutionOrder(only_onExecute: boolean, set_level: any): T; + /** + * Returns all the nodes that could affect this one (ancestors) by crawling all the inputs recursively. + * It doesn't include the node itself + * @return an array with all the LGraphNodes that affect this node, in order of execution + */ + getAncestors(node: LGraphNode): LGraphNode[]; + /** + * Positions every node in a more readable manner + */ + arrange(margin?: number,layout?: string): void; + /** + * Returns the amount of time the graph has been running in milliseconds + * @return number of milliseconds the graph has been running + */ + getTime(): number; + + /** + * Returns the amount of time accumulated using the fixedtime_lapse var. This is used in context where the time increments should be constant + * @return number of milliseconds the graph has been running + */ + getFixedTime(): number; + + /** + * Returns the amount of time it took to compute the latest iteration. Take into account that this number could be not correct + * if the nodes are using graphical actions + * @return number of milliseconds it took the last cycle + */ + getElapsedTime(): number; + /** + * Sends an event to all the nodes, useful to trigger stuff + * @param eventName the name of the event (function to be called) + * @param params parameters in array format + */ + sendEventToAllNodes(eventName: string, params: any[], mode?: any): void; + + sendActionToCanvas(action: any, params: any[]): void; + /** + * Adds a new node instance to this graph + * @param node the instance of the node + */ + add(node: LGraphNode, skip_compute_order?: boolean): void; + /** + * Called when a new node is added + * @param node the instance of the node + */ + onNodeAdded(node: LGraphNode): void; + /** Removes a node from the graph */ + remove(node: LGraphNode): void; + /** + * Returns a node by its id. + * @rgthree - make id options/nullable. + */ + getNodeById(id?: number|null): LGraphNode | undefined; + /** + * Returns a list of nodes that matches a class + * @param classObject the class itself (not an string) + * @return a list with all the nodes of this type + */ + findNodesByClass( + classObject: LGraphNodeConstructor + ): T[]; + /** + * Returns a list of nodes that matches a type + * @param type the name of the node type + * @return a list with all the nodes of this type + */ + findNodesByType(type: string): T[]; + /** + * Returns the first node that matches a name in its title + * @param title the name of the node to search + * @return the node or null + */ + findNodeByTitle(title: string): T | null; + /** + * Returns a list of nodes that matches a name + * @param title the name of the node to search + * @return a list with all the nodes with this name + */ + findNodesByTitle(title: string): T[]; + /** + * Returns the top-most node in this position of the canvas + * @param x the x coordinate in canvas space + * @param y the y coordinate in canvas space + * @param nodes_list a list with all the nodes to search from, by default is all the nodes in the graph + * @return the node at this position or null + */ + getNodeOnPos( + x: number, + y: number, + node_list?: LGraphNode[], + margin?: number + ): T | null; + /** + * Returns the top-most group in that position + * @param x the x coordinate in canvas space + * @param y the y coordinate in canvas space + * @return the group or null + */ + getGroupOnPos(x: number, y: number): LGraphGroup | null; + + onAction(action: any, param: any): void; + trigger(action: any, param: any): void; + /** Tell this graph it has a global graph input of this type */ + addInput(name: string, type: string, value?: any): void; + /** Assign a data to the global graph input */ + setInputData(name: string, data: any): void; + /** Returns the current value of a global graph input */ + getInputData(name: string): T; + /** Changes the name of a global graph input */ + renameInput(old_name: string, name: string): false | undefined; + /** Changes the type of a global graph input */ + changeInputType(name: string, type: string): false | undefined; + /** Removes a global graph input */ + removeInput(name: string): boolean; + /** Creates a global graph output */ + addOutput(name: string, type: string, value: any): void; + /** Assign a data to the global output */ + setOutputData(name: string, value: string): void; + /** Returns the current value of a global graph output */ + getOutputData(name: string): T; + + /** Renames a global graph output */ + renameOutput(old_name: string, name: string): false | undefined; + /** Changes the type of a global graph output */ + changeOutputType(name: string, type: string): false | undefined; + /** Removes a global graph output */ + removeOutput(name: string): boolean; + triggerInput(name: string, value: any): void; + setCallback(name: string, func: (...args: any[]) => any): void; + beforeChange(info?: LGraphNode): void; + afterChange(info?: LGraphNode): void; + connectionChange(node: LGraphNode): void; + /** returns if the graph is in live mode */ + isLive(): boolean; + /** clears the triggered slot animation in all links (stop visual animation) */ + clearTriggeredSlots(): void; + /* Called when something visually changed (not the graph!) */ + change(): void; + setDirtyCanvas(fg: boolean, bg: boolean): void; + /** Destroys a link */ + removeLink(link_id: number): void; + /** Creates a Object containing all the info about this graph, it can be serialized */ + serialize(): T; + /** + * Configure a graph from a JSON string + * @param data configure a graph from a JSON string + * @returns if there was any error parsing + */ + configure(data: object, keep_old?: boolean): boolean | undefined; + load(url: string): void; +} + +export type SerializedLLink = [number, string, number, number, number, number]; +export declare class LLink { + id: number; + type: string; + origin_id: number; + origin_slot: number; + target_id: number; + target_slot: number; + constructor( + id: number, + type: string, + origin_id: number, + origin_slot: number, + target_id: number, + target_slot: number + ); + configure(o: LLink | SerializedLLink): void; + serialize(): SerializedLLink; + // @rgthree + color?: string; +} + +export type SerializedLGraphNode = { + id: T["id"]; + type: T["type"]; + pos: T["pos"]; + size: T["size"]; + flags: T["flags"]; + mode: T["mode"]; + inputs: T["inputs"]; + outputs: T["outputs"]; + title: T["title"]; + properties: T["properties"]; + widgets_values?: IWidget["value"][]; +}; + +/** https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#lgraphnode */ +export declare class LGraphNode { + + // @rgthree added + findInputSlotByType(type: string, returnObj?: boolean, preferFreeSlot?: boolean, doNotUseOccupied?: boolean): number + findOutputSlotByType(type: string, returnObj?: boolean, preferFreeSlot?: boolean, doNotUseOccupied?: boolean): number + onShowCustomPanelInfo(panel: HTMLElement): void; + onDblClick?(event: AdjustedMouseEvent, pos: Vector2, canvas: LGraphCanvas): void; + inResizeCorner(x: number, y:number) : boolean; + onWidgetChanged?(widgetName: string, widgetValue: any, oldWidgetValue: any, widget: IWidget): void; + + // end @rgthree added + + static title_color?: string; + static title: string; + static type: null | string; + static widgets_up: boolean; + constructor(title?: string); + + title: string; + // @rgthree - made undefined since ComfyNode does it (even through LiteGraph does not..) + type?: null | string; + size: Vector2; + graph: null | LGraph; + graph_version: number; + pos: Vector2; + is_selected: boolean; + mouseOver: boolean; + // @rgthree - missing. + block_delete: boolean; + + id: number; + + widgets: IWidget[]; + //inputs available: array of inputs + inputs: INodeInputSlot[]; + outputs: INodeOutputSlot[]; + connections: any[]; + + // @rgthree + _collapsed_width?: number; + + //local data + properties: Record; + properties_info: any[]; + + flags: Partial<{ + collapsed: boolean + // @rgthree + allow_interaction: boolean; + // @rgthree + pinned: boolean; + }>; + + color: string; + bgcolor: string; + boxcolor: string; + shape: + | typeof LiteGraph.BOX_SHAPE + | typeof LiteGraph.ROUND_SHAPE + | typeof LiteGraph.CIRCLE_SHAPE + | typeof LiteGraph.CARD_SHAPE + | typeof LiteGraph.ARROW_SHAPE; + + serialize_widgets: boolean; + skip_list: boolean; + + /** Used in `LGraphCanvas.onMenuNodeMode` */ + mode?: + | typeof LiteGraph.ON_EVENT + | typeof LiteGraph.ON_TRIGGER + | typeof LiteGraph.NEVER + | typeof LiteGraph.ALWAYS + | 4; // Comfy App "Bypass" + + /** If set to true widgets do not start after the slots */ + widgets_up: boolean; + /** widgets start at y distance from the top of the node */ + widgets_start_y: number; + /** if you render outside the node, it will be clipped */ + clip_area: boolean; + /** if set to false it wont be resizable with the mouse */ + resizable: boolean; + /** slots are distributed horizontally */ + horizontal: boolean; + /** if true, the node will show the bgcolor as 'red' */ + has_errors?: boolean; + + // @rgthree + setSize(size: Vector2): void; + onResize?(size: Vector2): void; + onInputClick(slot: number, event: MouseEvent): void; + onOutputClick(slot: number, event: MouseEvent): void; + getConnectionPos(isInput: boolean, slotNumber: number, out: Vector2): Vector2; + + /** configure a node from an object containing the serialized info */ + configure(info: SerializedLGraphNode): void; + /** serialize the content */ + serialize(): SerializedLGraphNode; + /** Creates a clone of this node */ + clone(): this; + /** serialize and stringify */ + toString(): string; + /** get the title string */ + getTitle(): string; + /** sets the value of a property */ + setProperty(name: string, value: any): void; + /** sets the output data */ + setOutputData(slot: number, data: any): void; + /** sets the output data */ + setOutputDataType(slot: number, type: string): void; + /** + * Retrieves the input data (data traveling through the connection) from one slot + * @param slot + * @param force_update if set to true it will force the connected node of this slot to output data into this link + * @return data or if it is not connected returns undefined + */ + getInputData(slot: number, force_update?: boolean): T; + /** + * Retrieves the input data type (in case this supports multiple input types) + * @param slot + * @return datatype in string format + */ + getInputDataType(slot: number): string; + /** + * Retrieves the input data from one slot using its name instead of slot number + * @param slot_name + * @param force_update if set to true it will force the connected node of this slot to output data into this link + * @return data or if it is not connected returns null + */ + getInputDataByName(slot_name: string, force_update?: boolean): T; + /** tells you if there is a connection in one input slot */ + isInputConnected(slot: number): boolean; + /** tells you info about an input connection (which node, type, etc) */ + getInputInfo( + slot: number + ): { link: number; name: string; type: string | 0 } | null; + /** returns the node connected in the input slot */ + getInputNode(slot: number): LGraphNode | null; + /** returns the value of an input with this name, otherwise checks if there is a property with that name */ + getInputOrProperty(name: string): T; + /** tells you the last output data that went in that slot */ + getOutputData(slot: number): T | null; + /** tells you info about an output connection (which node, type, etc) */ + getOutputInfo( + slot: number + ): { name: string; type: string; links: number[] } | null; + /** tells you if there is a connection in one output slot */ + isOutputConnected(slot: number): boolean; + /** tells you if there is any connection in the output slots */ + isAnyOutputConnected(): boolean; + /** retrieves all the nodes connected to this output slot */ + getOutputNodes(slot: number): LGraphNode[]; + /** Triggers an event in this node, this will trigger any output with the same name */ + trigger(action: string, param: any): void; + /** + * Triggers an slot event in this node + * @param slot the index of the output slot + * @param param + * @param link_id in case you want to trigger and specific output link in a slot + */ + triggerSlot(slot: number, param: any, link_id?: number): void; + /** + * clears the trigger slot animation + * @param slot the index of the output slot + * @param link_id in case you want to trigger and specific output link in a slot + */ + clearTriggeredSlot(slot: number, link_id?: number): void; + /** + * add a new property to this node + * @param name + * @param default_value + * @param type string defining the output type ("vec3","number",...) + * @param extra_info this can be used to have special properties of the property (like values, etc) + */ + addProperty( + name: string, + default_value: any, + type: string, + extra_info?: object + ): T; + /** + * add a new output slot to use in this node + * @param name + * @param type string defining the output type ("vec3","number",...) + * @param extra_info this can be used to have special properties of an output (label, special color, position, etc) + */ + addOutput( + name: string, + type: string | -1, + extra_info?: Partial + ): INodeOutputSlot; + /** + * add a new output slot to use in this node + * @param array of triplets like [[name,type,extra_info],[...]] + */ + addOutputs( + array: [string, string | -1, Partial | undefined][] + ): void; + /** remove an existing output slot */ + removeOutput(slot: number): void; + /** + * add a new input slot to use in this node + * @param name + * @param type string defining the input type ("vec3","number",...), it its a generic one use 0 + * @param extra_info this can be used to have special properties of an input (label, color, position, etc) + */ + addInput( + name: string, + type: string | -1, + extra_info?: Partial + ): INodeInputSlot; + /** + * add several new input slots in this node + * @param array of triplets like [[name,type,extra_info],[...]] + */ + addInputs( + array: [string, string | -1, Partial | undefined][] + ): void; + /** remove an existing input slot */ + removeInput(slot: number): void; + /** + * add an special connection to this node (used for special kinds of graphs) + * @param name + * @param type string defining the input type ("vec3","number",...) + * @param pos position of the connection inside the node + * @param direction if is input or output + */ + addConnection( + name: string, + type: string, + pos: Vector2, + direction: string + ): { + name: string; + type: string; + pos: Vector2; + direction: string; + links: null; + }; + setValue(v: any): void; + /** computes the size of a node according to its inputs and output slots */ + computeSize(out?: Vector2): [number, number]; + /** + * https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#node-widgets + * @return created widget + */ + addWidget( + type: T["type"], + name: string, + value: T["value"], + // @rgthree + callback?: T["callback"] | string,//WidgetCallback | string, + options?: T["options"] + ): T; + + addCustomWidget(customWidget: T): T; + + /** + * returns the bounding of the object, used for rendering purposes + * @return [x, y, width, height] + */ + getBounding(): Vector4; + /** checks if a point is inside the shape of a node */ + isPointInside( + x: number, + y: number, + margin?: number, + skipTitle?: boolean + ): boolean; + /** checks if a point is inside a node slot, and returns info about which slot */ + getSlotInPosition( + x: number, + y: number + ): { + input?: INodeInputSlot; + output?: INodeOutputSlot; + slot: number; + link_pos: Vector2; + }; + /** + * returns the input slot with a given name (used for dynamic slots), -1 if not found + * @param name the name of the slot + * @return the slot (-1 if not found) + */ + findInputSlot(name: string): number; + /** + * returns the output slot with a given name (used for dynamic slots), -1 if not found + * @param name the name of the slot + * @return the slot (-1 if not found) + */ + findOutputSlot(name: string): number; + /** + * connect this node output to the input of another node + * @param slot (could be the number of the slot or the string with the name of the slot) + * @param targetNode the target node + * @param targetSlot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger) + * @return {Object} the link_info is created, otherwise null + */ + connect( + slot: number | string, + targetNode: LGraphNode, + targetSlot: number | string + ): T | null; + + connectByTypeOutput( + slot: number | string, + sourceNode: LGraphNode, + sourceSlotType: string, + optsIn: string + ): T | null; + + connectByType( + slot: number | string, + sourceNode: LGraphNode, + sourceSlotType: string, + optsIn: string + ): T | null; + + + /** + * disconnect one output to an specific node + * @param slot (could be the number of the slot or the string with the name of the slot) + * @param target_node the target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected] + * @return if it was disconnected successfully + */ + disconnectOutput(slot: number | string, targetNode?: LGraphNode): boolean; + /** + * disconnect one input + * @param slot (could be the number of the slot or the string with the name of the slot) + * @return if it was disconnected successfully + */ + disconnectInput(slot: number | string): boolean; + /** + * returns the center of a connection point in canvas coords + * @param is_input true if if a input slot, false if it is an output + * @param slot (could be the number of the slot or the string with the name of the slot) + * @param out a place to store the output, to free garbage + * @return the position + **/ + getConnectionPos( + is_input: boolean, + slot: number | string, + out?: Vector2 + ): Vector2; + /** Force align to grid */ + alignToGrid(): void; + /** Console output */ + trace(msg: string): void; + /** Forces to redraw or the main canvas (LGraphNode) or the bg canvas (links) */ + setDirtyCanvas(fg: boolean, bg: boolean): void; + loadImage(url: string): void; + /** Allows to get onMouseMove and onMouseUp events even if the mouse is out of focus */ + captureInput(v: any): void; + /** Collapse the node to make it smaller on the canvas */ + collapse(force: boolean): void; + /** Forces the node to do not move or realign on Z */ + pin(v?: boolean): void; + localToScreen(x: number, y: number, graphCanvas: LGraphCanvas): Vector2; + + // https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#custom-node-appearance + onDrawBackground?( + ctx: CanvasRenderingContext2D, + // @rgthree fixed + canvas: LGraphCanvas + ): void; + onDrawForeground?( + ctx: CanvasRenderingContext2D, + // @rgthree fixed + canvas: LGraphCanvas + ): void; + + // https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#custom-node-behaviour + onMouseDown?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseMove?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseUp?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseEnter?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseLeave?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onKey?(event: KeyboardEvent, pos: Vector2, graphCanvas: LGraphCanvas): void; + // @rgthree + onKeyDown?(event: KeyboardEvent): void; + // @rgthree + onKeyUp?(event: KeyboardEvent): void; + + onExecuted(message: any): void; + /** Called by `LGraphNode.createNode` */ + onNodeCreated?(): void; + /** Called by `LGraphCanvas.selectNodes` */ + onSelected?(): void; + /** Called by `LGraphCanvas.deselectNode` */ + onDeselected?(): void; + /** Called by `LGraph.runStep` `LGraphNode.getInputData` */ + onExecute?(): void; + /** Called by `LGraph.serialize` */ + onSerialize?(o: SerializedLGraphNode): void; + /** Called by `LGraph.configure` */ + onConfigure?(o: SerializedLGraphNode): void; + /** + * when added to graph (warning: this is called BEFORE the node is configured when loading) + * Called by `LGraph.add` + */ + onAdded?(graph: LGraph): void; + /** + * when removed from graph + * Called by `LGraph.remove` `LGraph.clear` + */ + onRemoved?(): void; + /** + * if returns false the incoming connection will be canceled + * Called by `LGraph.connect` + * @param inputIndex target input slot number + * @param outputType type of output slot + * @param outputSlot output slot object + * @param outputNode node containing the output + * @param outputIndex index of output slot + */ + onConnectInput?( + inputIndex: number, + outputType: INodeOutputSlot["type"], + outputSlot: INodeOutputSlot, + outputNode: LGraphNode, + outputIndex: number + ): boolean; + /** + * if returns false the incoming connection will be canceled + * Called by `LGraph.connect` + * @param outputIndex target output slot number + * @param inputType type of input slot + * @param inputSlot input slot object + * @param inputNode node containing the input + * @param inputIndex index of input slot + */ + onConnectOutput?( + outputIndex: number, + inputType: INodeInputSlot["type"], + inputSlot: INodeInputSlot, + inputNode: LGraphNode, + inputIndex: number + ): boolean; + + /** + * Called just before connection (or disconnect - if input is linked). + * A convenient place to switch to another input, or create new one. + * This allow for ability to automatically add slots if needed + * @param inputIndex + * @return selected input slot index, can differ from parameter value + */ + onBeforeConnectInput?( + inputIndex: number + ): number; + + /** a connection changed (new one or removed) (LiteGraph.INPUT or LiteGraph.OUTPUT, slot, true if connected, link_info, input_info or output_info ) */ + onConnectionsChange( + type: number, + slotIndex: number, + isConnected: boolean, + link: LLink, + // @rgthree - Make it INodeSlot instead of union + ioSlot: INodeSlot + ): void; + + /** + * if returns false, will abort the `LGraphNode.setProperty` + * Called when a property is changed + * @param property + * @param value + * @param prevValue + */ + onPropertyChanged?(property: string, value: any, prevValue: any): void | boolean; + + /** Called by `LGraphCanvas.processContextMenu` */ + getMenuOptions?(graphCanvas: LGraphCanvas): ContextMenuItem[]; + // @rgthree. This is fixed because the INodeSlot is wrong and, also, null can be returned to not trigger a menu. + // getSlotMenuOptions?(slot: INodeSlot): ContextMenuItem[]; + getSlotMenuOptions(slot: {input?: INodeInputSlot, output?: INodeOutputSlot}): ContextMenuItem[] | null; + + getExtraMenuOptions?(canvas: LGraphCanvas, options: ContextMenuItem[]): void; + + // @rgthree - Called in LiteGraph.core when the properties panel is constructed. + onShowCustomPanelInfo(panel: HTMLElement): void; +} + +export type LGraphNodeConstructor = { + new (): T; + + // @rgthree + title_mode?: + typeof LiteGraph.NORMAL_TITLE | + typeof LiteGraph.TRANSPARENT_TITLE | + typeof LiteGraph.AUTOHIDE_TITLE | + typeof LiteGraph.NO_TITLE; + title: string; + category: string; + type: string; + comfyClass?: string; +}; + +export type SerializedLGraphGroup = { + title: LGraphGroup["title"]; + bounding: LGraphGroup["_bounding"]; + color: LGraphGroup["color"]; + font: LGraphGroup["font"]; +}; +export declare class LGraphGroup { + title: string; + // @rgthree - mark unprivate + _bounding: Vector4; + // @rgthree - updated to make optional because it seems to be so. + color?: string|null; + font: string; + // @rgthree + _nodes: LGraphNode[]; + // @rgthree + _pos: Vector2; + // @rgthree + _size: Vector2; + // @rgthree + graph: LGraph; + // @rgthree - apparently it is available? + size: Vector2; + // @rgthree - apparently it is available? + pos: Vector2; + + + configure(o: SerializedLGraphGroup): void; + serialize(): SerializedLGraphGroup; + move(deltaX: number, deltaY: number, ignoreNodes?: boolean): void; + recomputeInsideNodes(): void; + isPointInside: LGraphNode["isPointInside"]; + setDirtyCanvas: LGraphNode["setDirtyCanvas"]; +} + +export declare class DragAndScale { + constructor(element?: HTMLElement, skipEvents?: boolean); + offset: [number, number]; + scale: number; + max_scale: number; + min_scale: number; + onredraw: Function | null; + enabled: boolean; + last_mouse: Vector2; + element: HTMLElement | null; + visible_area: Vector4; + bindEvents(element: HTMLElement): void; + computeVisibleArea(): void; + onMouse(e: MouseEvent): void; + toCanvasContext(ctx: CanvasRenderingContext2D): void; + convertOffsetToCanvas(pos: Vector2): Vector2; + convertCanvasToOffset(pos: Vector2): Vector2; + mouseDrag(x: number, y: number): void; + changeScale(value: number, zooming_center?: Vector2): void; + changeDeltaScale(value: number, zooming_center?: Vector2): void; + reset(): void; +} + +// @rgthree. +interface CanvasDivDialog extends HTMLDivElement { + close: () => void; + modified: () => void; + is_modified: boolean; +} + +/** + * This class is in charge of rendering one graph inside a canvas. And provides all the interaction required. + * Valid callbacks are: onNodeSelected, onNodeDeselected, onShowNodePanel, onNodeDblClicked + * + * @param canvas the canvas where you want to render (it accepts a selector in string format or the canvas element itself) + * @param graph + * @param options { skip_rendering, autoresize } + */ +export declare class LGraphCanvas { + static node_colors: Record< + string, + { + color: string; + bgcolor: string; + groupcolor: string; + } + >; + static link_type_colors: Record; + static gradients: object; + static search_limit: number; + + static getFileExtension(url: string): string; + static decodeHTML(str: string): string; + + static onMenuCollapseAll(): void; + static onMenuNodeEdit(): void; + static onShowPropertyEditor( + item: any, + options: any, + e: any, + menu: any, + node: any + ): void; + /** Create menu for `Add Group` */ + static onGroupAdd: ContextMenuEventListener; + /** Create menu for `Add Node` */ + static onMenuAdd: ContextMenuEventListener; + static showMenuNodeOptionalInputs: ContextMenuEventListener; + static showMenuNodeOptionalOutputs: ContextMenuEventListener; + static onShowMenuNodeProperties: ContextMenuEventListener; + static onResizeNode: ContextMenuEventListener; + static onMenuNodeCollapse: ContextMenuEventListener; + static onMenuNodePin: ContextMenuEventListener; + static onMenuNodeMode: ContextMenuEventListener; + static onMenuNodeColors: ContextMenuEventListener; + static onMenuNodeShapes: ContextMenuEventListener; + static onMenuNodeRemove: ContextMenuEventListener; + static onMenuNodeClone: ContextMenuEventListener; + + // @rgthree + static onShowPropertyEditor: ContextMenuEventListener; + + constructor( + canvas: HTMLCanvasElement | string, + graph?: LGraph, + options?: { + skip_render?: boolean; + autoresize?: boolean; + } + ); + + // @rgthree. This was "HTMLCanvasElement" but that is just wrong... it's LGraphCanvas + static active_canvas: LGraphCanvas; + + // @rgthree + pointer_is_down: boolean; + + allow_dragcanvas: boolean; + allow_dragnodes: boolean; + /** allow to control widgets, buttons, collapse, etc */ + allow_interaction: boolean; + /** allows to change a connection with having to redo it again */ + allow_reconnect_links: boolean; + /** allow selecting multi nodes without pressing extra keys */ + multi_select: boolean; + /** No effect */ + allow_searchbox: boolean; + always_render_background: boolean; + autoresize?: boolean; + background_image: string; + bgcanvas: HTMLCanvasElement; + bgctx: CanvasRenderingContext2D; + canvas: HTMLCanvasElement; + canvas_mouse: Vector2; + // @rgthree - Looks like this is to replace canvas_mouse. + graph_mouse: Vector2; + clear_background: boolean; + // connecting_node: LGraphNode | null; + // // @rgthree - for overriding. + // _connecting_node: LGraphNode | null; + // @rgthree + // connecting_input: INodeInputSlot | null; + // // @rgthree + // connecting_output: INodeOutputSlot | null; + // // @rgthree + // connecting_slot: number; + // // @rgthree + // connecting_pos: Vector2 | null; + // @rgthree - for some reason, the new comfyUI update renamed connecting_node to connecting_links (maybe) + connecting_links: { + node: LGraphNode, input?: INodeInputSlot, output?: INodeOutputSlot, pos: Vector2, slot: number + }[] | null; + _connecting_links: this['connecting_links'] | null; + + connections_width: number; + ctx: CanvasRenderingContext2D; + current_node: LGraphNode | null; + default_connection_color: { + input_off: string; + input_on: string; + output_off: string; + output_on: string; + }; + default_link_color: string; + dirty_area: Vector4 | null; + dirty_bgcanvas?: boolean; + dirty_canvas?: boolean; + drag_mode: boolean; + dragging_canvas: boolean; + dragging_rectangle: Vector4 | null; + // @rgthree; mark undefined. + // It doesn't look like this should ever be undefined.. but something changed in Comfy and folks + // reported https://github.com/rgthree/rgthree-comfy/issues/71. I couldn't reproduce, but we can + // handle it. + ds?: DragAndScale; + /** used for transition */ + editor_alpha: number; + filter: any; + fps: number; + frame: number; + graph: LGraph; + highlighted_links: Record; + highquality_render: boolean; + inner_text_font: string; + is_rendering: boolean; + last_draw_time: number; + last_mouse: Vector2; + /** + * Possible duplicated with `last_mouse` + * https://github.com/jagenjo/litegraph.js/issues/70 + */ + last_mouse_position: Vector2; + /** Timestamp of last mouse click, defaults to 0 */ + last_mouseclick: number; + links_render_mode: + | typeof LiteGraph.STRAIGHT_LINK + | typeof LiteGraph.LINEAR_LINK + | typeof LiteGraph.SPLINE_LINK; + live_mode: boolean; + node_capturing_input: LGraphNode | null; + node_dragged: LGraphNode | null; + node_in_panel: LGraphNode | null; + node_over: LGraphNode | null; + node_title_color: string; + node_widget: [LGraphNode, IWidget] | null; + /** Called by `LGraphCanvas.drawBackCanvas` */ + onDrawBackground: + | ((ctx: CanvasRenderingContext2D, visibleArea: Vector4) => void) + | null; + /** Called by `LGraphCanvas.drawFrontCanvas` */ + onDrawForeground: + | ((ctx: CanvasRenderingContext2D, visibleArea: Vector4) => void) + | null; + onDrawOverlay: ((ctx: CanvasRenderingContext2D) => void) | null; + /** Called by `LGraphCanvas.processMouseDown` */ + onMouse: ((event: MouseEvent) => boolean) | null; + /** Called by `LGraphCanvas.drawFrontCanvas` and `LGraphCanvas.drawLinkTooltip` */ + onDrawLinkTooltip: ((ctx: CanvasRenderingContext2D, link: LLink, _this: this) => void) | null; + /** Called by `LGraphCanvas.selectNodes` */ + onNodeMoved: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.processNodeSelected` */ + onNodeSelected: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.deselectNode` */ + onNodeDeselected: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.processNodeDblClicked` */ + onShowNodePanel: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.processNodeDblClicked` */ + onNodeDblClicked: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.selectNodes` */ + onSelectionChange: ((nodes: Record) => void) | null; + /** Called by `LGraphCanvas.showSearchBox` */ + onSearchBox: + | (( + helper: Element, + value: string, + graphCanvas: LGraphCanvas + ) => string[]) + | null; + onSearchBoxSelection: + | ((name: string, event: MouseEvent, graphCanvas: LGraphCanvas) => void) + | null; + pause_rendering: boolean; + render_canvas_border: boolean; + render_collapsed_slots: boolean; + render_connection_arrows: boolean; + render_connections_border: boolean; + render_connections_shadows: boolean; + render_curved_connections: boolean; + render_execution_order: boolean; + render_only_selected: boolean; + render_shadows: boolean; + render_title_colored: boolean; + round_radius: number; + selected_group: null | LGraphGroup; + selected_group_resizing: boolean; + selected_nodes: Record; + show_info: boolean; + title_text_font: string; + /** set to true to render title bar with gradients */ + use_gradients: boolean; + visible_area: DragAndScale["visible_area"]; + visible_links: LLink[]; + visible_nodes: LGraphNode[]; + zoom_modify_alpha: boolean; + + /** clears all the data inside */ + clear(): void; + /** assigns a graph, you can reassign graphs to the same canvas */ + setGraph(graph: LGraph, skipClear?: boolean): void; + /** opens a graph contained inside a node in the current graph */ + openSubgraph(graph: LGraph): void; + /** closes a subgraph contained inside a node */ + closeSubgraph(): void; + /** assigns a canvas */ + setCanvas(canvas: HTMLCanvasElement, skipEvents?: boolean): void; + /** binds mouse, keyboard, touch and drag events to the canvas */ + bindEvents(): void; + /** unbinds mouse events from the canvas */ + unbindEvents(): void; + + /** + * this function allows to render the canvas using WebGL instead of Canvas2D + * this is useful if you plant to render 3D objects inside your nodes, it uses litegl.js for webgl and canvas2DtoWebGL to emulate the Canvas2D calls in webGL + **/ + enableWebGL(): void; + + /** + * marks as dirty the canvas, this way it will be rendered again + * @param fg if the foreground canvas is dirty (the one containing the nodes) + * @param bg if the background canvas is dirty (the one containing the wires) + */ + setDirty(fg: boolean, bg: boolean): void; + + /** + * Used to attach the canvas in a popup + * @return the window where the canvas is attached (the DOM root node) + */ + getCanvasWindow(): Window; + /** starts rendering the content of the canvas when needed */ + startRendering(): void; + /** stops rendering the content of the canvas (to save resources) */ + stopRendering(): void; + + processMouseDown(e: MouseEvent): boolean | undefined; + processMouseMove(e: MouseEvent): boolean | undefined; + processMouseUp(e: MouseEvent): boolean | undefined; + processMouseWheel(e: MouseEvent): boolean | undefined; + + /** returns true if a position (in graph space) is on top of a node little corner box */ + isOverNodeBox(node: LGraphNode, canvasX: number, canvasY: number): boolean; + /** returns true if a position (in graph space) is on top of a node input slot */ + isOverNodeInput( + node: LGraphNode, + canvasX: number, + canvasY: number, + slotPos: Vector2 + ): boolean; + + /** process a key event */ + processKey(e: KeyboardEvent): boolean | undefined; + + // @rgthree - added param + copyToClipboard(nodes: LGraphNode[]|{[key:number]:LGraphNode}): void; + pasteFromClipboard(): void; + processDrop(e: DragEvent): void; + checkDropItem(e: DragEvent): void; + processNodeDblClicked(n: LGraphNode): void; + processNodeSelected(n: LGraphNode, e: MouseEvent): void; + processNodeDeselected(node: LGraphNode): void; + + /** selects a given node (or adds it to the current selection) */ + selectNode(node: LGraphNode, add?: boolean): void; + /** selects several nodes (or adds them to the current selection) */ + selectNodes(nodes?: LGraphNode[], add?: boolean): void; + /** removes a node from the current selection */ + deselectNode(node: LGraphNode): void; + /** removes all nodes from the current selection */ + deselectAllNodes(): void; + /** deletes all nodes in the current selection from the graph */ + deleteSelectedNodes(): void; + + /** centers the camera on a given node */ + // @rgthree - narrow parameter + centerOnNode(node: {pos: Vector2, size: Vector2}): void; + /** changes the zoom level of the graph (default is 1), you can pass also a place used to pivot the zoom */ + setZoom(value: number, center: Vector2): void; + /** brings a node to front (above all other nodes) */ + bringToFront(node: LGraphNode): void; + /** sends a node to the back (below all other nodes) */ + sendToBack(node: LGraphNode): void; + /** checks which nodes are visible (inside the camera area) */ + computeVisibleNodes(nodes: LGraphNode[]): LGraphNode[]; + /** renders the whole canvas content, by rendering in two separated canvas, one containing the background grid and the connections, and one containing the nodes) */ + draw(forceFG?: boolean, forceBG?: boolean): void; + /** draws the front canvas (the one containing all the nodes) */ + drawFrontCanvas(): void; + /** draws some useful stats in the corner of the canvas */ + renderInfo(ctx: CanvasRenderingContext2D, x: number, y: number): void; + /** draws the back canvas (the one containing the background and the connections) */ + drawBackCanvas(): void; + /** draws the given node inside the canvas */ + drawNode(node: LGraphNode, ctx: CanvasRenderingContext2D): void; + /** draws graphic for node's slot */ + drawSlotGraphic(ctx: CanvasRenderingContext2D, pos: number[], shape: SlotShape, horizontal: boolean): void; + /** draws the shape of the given node in the canvas */ + drawNodeShape( + node: LGraphNode, + ctx: CanvasRenderingContext2D, + size: [number, number], + fgColor: string, + bgColor: string, + selected: boolean, + mouseOver: boolean + ): void; + /** draws every connection visible in the canvas */ + drawConnections(ctx: CanvasRenderingContext2D): void; + /** + * draws a link between two points + * @param a start pos + * @param b end pos + * @param link the link object with all the link info + * @param skipBorder ignore the shadow of the link + * @param flow show flow animation (for events) + * @param color the color for the link + * @param startDir the direction enum + * @param endDir the direction enum + * @param numSublines number of sublines (useful to represent vec3 or rgb) + **/ + renderLink( + a: Vector2, + b: Vector2, + link: object, + skipBorder: boolean, + flow: boolean, + color?: string, + startDir?: number, + endDir?: number, + numSublines?: number + ): void; + + computeConnectionPoint( + a: Vector2, + b: Vector2, + t: number, + startDir?: number, + endDir?: number + ): void; + + drawExecutionOrder(ctx: CanvasRenderingContext2D): void; + /** draws the widgets stored inside a node */ + drawNodeWidgets( + node: LGraphNode, + posY: number, + ctx: CanvasRenderingContext2D, + activeWidget: object + ): void; + /** process an event on widgets */ + processNodeWidgets( + node: LGraphNode, + pos: Vector2, + event: Event, + activeWidget: object + ): void; + /** draws every group area in the background */ + drawGroups(canvas: any, ctx: CanvasRenderingContext2D): void; + adjustNodesSize(): void; + /** resizes the canvas to a given size, if no size is passed, then it tries to fill the parentNode */ + resize(width?: number, height?: number): void; + /** + * switches to live mode (node shapes are not rendered, only the content) + * this feature was designed when graphs where meant to create user interfaces + **/ + switchLiveMode(transition?: boolean): void; + onNodeSelectionChange(): void; + touchHandler(event: TouchEvent): void; + + showLinkMenu(link: LLink, e: any): false; + prompt( + title: string, + value: any, + callback: Function, + event: any + ): HTMLDivElement; + showSearchBox(event?: MouseEvent): void; + showEditPropertyValue(node: LGraphNode, property: any, options: any): void; + createDialog( + html: string, + options?: { position?: Vector2; event?: MouseEvent } + // @rgthree - Fix return type from void (added above) + ): CanvasDivDialog; + + + + convertOffsetToCanvas: DragAndScale["convertOffsetToCanvas"]; + convertCanvasToOffset: DragAndScale["convertCanvasToOffset"]; + /** converts event coordinates from canvas2D to graph coordinates */ + // @rgthree - change MouseEvent to less restrictive {clientX: number, clientY: number} that + // implementation uses. + convertEventToCanvasOffset(e: {clientX: number, clientY: number}): Vector2; + /** adds some useful properties to a mouse event, like the position in graph coordinates */ + adjustMouseEvent(e: MouseEvent): void; + + getCanvasMenuOptions(): ContextMenuItem[]; + getNodeMenuOptions(node: LGraphNode): ContextMenuItem[]; + getGroupMenuOptions(): ContextMenuItem[]; + /** Called by `getCanvasMenuOptions`, replace default options */ + getMenuOptions?(): ContextMenuItem[]; + /** Called by `getCanvasMenuOptions`, append to default options */ + getExtraMenuOptions?(): ContextMenuItem[]; + /** Called when mouse right click */ + processContextMenu(node: LGraphNode, event: Event): void; + + // @rgthree - Adding this for ComfyUI, since they add this in their own overload in app.js + selected_group_moving?: boolean; + + // @rgthree + showShowNodePanel(node: LGraphNode): void; +} + +// @rgthree - The adjusted pointer event after calling adjustMouseEvent +export interface AdjustedMouseEvent extends PointerEvent { + deltaX: number; + deltaY: number; + canvasX: number; + canvasY: number; +} + +declare class ContextMenu { + static trigger( + element: HTMLElement, + event_name: string, + params: any, + origin: any + ): void; + static isCursorOverElement(event: MouseEvent, element: HTMLElement): void; + static closeAllContextMenus(window: Window): void; + constructor(values: ContextMenuItem[]|string[], options?: IContextMenuOptions, window?: Window); + options: IContextMenuOptions; + parentMenu?: ContextMenu; + lock: boolean; + current_submenu?: ContextMenu; + addItem( + name: string, + value: ContextMenuItem, + options?: IContextMenuOptions + ): void; + close(e?: MouseEvent, ignore_parent_menu?: boolean): void; + getTopMenu(): void; + getFirstEvent(): void; +} + +declare global { + interface Math { + clamp(v: number, min: number, max: number): number; + } +} diff --git a/custom_nodes/ComfyUI-Crystools/web/common.d.ts b/custom_nodes/ComfyUI-Crystools/web/common.d.ts new file mode 100644 index 00000000..6c4101ac --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/common.d.ts @@ -0,0 +1,4 @@ +import type { TLGraphNode } from './comfy/index.js'; +import { ComfyApp } from './comfy/index.js'; +export declare const commonPrefix = "\uD83E\uDE9B"; +export declare function displayContext(nodeType: TLGraphNode, appFromArg: ComfyApp, index?: number, serialize_widgets?: boolean, isVirtualNode?: boolean): void; diff --git a/custom_nodes/ComfyUI-Crystools/web/common.js b/custom_nodes/ComfyUI-Crystools/web/common.js new file mode 100644 index 00000000..1cceb83d --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/common.js @@ -0,0 +1,50 @@ +import { ComfyWidgets } from './comfy/index.js'; +export const commonPrefix = 'πŸͺ›'; +export function displayContext(nodeType, appFromArg, index = 0, serialize_widgets = false, isVirtualNode = false) { + function populate(text) { + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === 'text'); + if (pos !== -1) { + for (let i = pos; i < this.widgets.length; i++) { + this.widgets[i]?.onRemove?.(); + } + this.widgets.length = pos; + } + } + this.serialize_widgets = serialize_widgets; + this.isVirtualNode = isVirtualNode; + const widget = ComfyWidgets.STRING(this, 'text', [ + 'STRING', { multiline: true }, + ], appFromArg).widget; + widget.inputEl.readOnly = true; + widget.inputEl.style.opacity = 0.6; + if (Array.isArray(text) && index !== undefined && text[index] !== undefined) { + text = text[index]; + } + widget.value = text || ''; + widget.serializeValue = async () => { }; + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + appFromArg.graph.setDirtyCanvas(true, false); + }); + } + const onExecutedOriginal = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecutedOriginal?.apply(this, arguments); + populate.call(this, message.text); + }; + const onConfigureOriginal = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + onConfigureOriginal?.apply(this, arguments); + if (this.widgets_values?.length) { + populate.call(this, this.widgets_values); + } + }; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/common.ts b/custom_nodes/ComfyUI-Crystools/web/common.ts new file mode 100644 index 00000000..a221b25c --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/common.ts @@ -0,0 +1,92 @@ +import type { TLGraphNode } from './comfy/index.js'; +import { ComfyWidgets, ComfyApp } from './comfy/index.js'; + +export const commonPrefix = 'πŸͺ›'; + +export function displayContext( + nodeType: TLGraphNode, + appFromArg: ComfyApp, + index = 0, serialize_widgets = false, isVirtualNode = false, +): void { + function populate(this: TLGraphNode, text: string | string[]): void { + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === 'text'); + if (pos !== -1) { + for (let i = pos; i < this.widgets.length; i++) { + + this.widgets[i]?.onRemove?.(); + } + this.widgets.length = pos; + } + } + // If you want to do not save properties in the node (be careful with F5) + // BUG on isVirtualNode, with "true", it ignores OUTPUT_NODE on py file! + this.serialize_widgets = serialize_widgets; + this.isVirtualNode = isVirtualNode; + + const widget = ComfyWidgets.STRING(this, 'text', [ + 'STRING', { multiline: true }, + ], appFromArg).widget; + widget.inputEl.readOnly = true; + widget.inputEl.style.opacity = 0.6; + if (Array.isArray(text) && index !== undefined && text[index] !== undefined) { + // @ts-ignore + text = text[index]; + } + widget.value = text || ''; + // eslint-disable-next-line @typescript-eslint/no-empty-function + widget.serializeValue = async(): Promise => {}; // just for no serialized to itself! + + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + appFromArg.graph.setDirtyCanvas(true, false); + }); + } + + // When the node is executed we will be sent the input text, display this in the widget + + // @ts-ignore + // eslint-disable-next-line @typescript-eslint/unbound-method + const onExecutedOriginal = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function(message: { text: string }): void { + // @ts-ignore + onExecutedOriginal?.apply(this, arguments); + populate.call(this, message.text); + }; + + // eslint-disable-next-line @typescript-eslint/unbound-method + const onConfigureOriginal = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function(): void { + // @ts-ignore + onConfigureOriginal?.apply(this, arguments); + if (this.widgets_values?.length) { + populate.call(this, this.widgets_values); + } + }; +} + + +// propagate the output value to the dependents nodes, it does not work with some nodes Β―\_(ツ)_/Β― +// const propagateOutputToDependentsNodes = function(output: serializedLGraph, value: string) { +// if (output.links?.length) { +// for (const l of output.links) { +// const link_info = app.graph.links[l]; +// const outNode = app.graph.getNodeById(link_info.target_id); +// const outIn = outNode?.inputs?.[link_info.target_slot]; +// if (outIn.widget) { +// const widget = outNode.widgets.find((w: IWidget) => w.name === outIn.widget.name); +// if (!widget) { +// continue; +// } +// widget.value = value; +// } +// } +// } +// }; diff --git a/custom_nodes/ComfyUI-Crystools/web/debugger.d.ts b/custom_nodes/ComfyUI-Crystools/web/debugger.d.ts new file mode 100644 index 00000000..cb0ff5c3 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/debugger.d.ts @@ -0,0 +1 @@ +export {}; diff --git a/custom_nodes/ComfyUI-Crystools/web/debugger.js b/custom_nodes/ComfyUI-Crystools/web/debugger.js new file mode 100644 index 00000000..395edd28 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/debugger.js @@ -0,0 +1,77 @@ +import { app, api, ComfyWidgets, LiteGraph, TLGraphNode } from './comfy/index.js'; +import { commonPrefix, displayContext } from './common.js'; +app.registerExtension({ + name: 'Crystools.Debugger.ConsoleAny', + beforeRegisterNodeDef(nodeType, nodeData, appFromArg) { + if (nodeData.name === 'Show any [Crystools]') { + displayContext(nodeType, appFromArg, 3); + } + }, +}); +app.registerExtension({ + name: 'Crystools.Debugger.Metadata', + registerCustomNodes() { + class MetadataNode extends TLGraphNode { + constructor() { + super(`${commonPrefix} Show Metadata `); + Object.defineProperty(this, "fillMetadataWidget", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + return app.graphToPrompt() + .then((workflow) => { + let result = 'inactive'; + if (this.widgets?.length !== 4) { + console.error('Something is wrong with the widgets, should be 4!'); + return 'error'; + } + const output = this.widgets[0]; + const active = this.widgets[1]?.value; + const parsed = this.widgets[2]?.value; + let what = this.widgets[3]?.value.toLowerCase(); + if (active) { + what = what === 'prompt' ? 'output' : what; + result = workflow[what]; + if (parsed) { + result = JSON.stringify(result, null, 2); + } + else { + result = JSON.stringify(result); + } + } + if (output) { + output.value = result; + } + else { + console.error('Something is wrong with the widgets, output is undefined!'); + return 'error'; + } + return result; + }); + } + }); + this.serialize_widgets = false; + this.isVirtualNode = true; + const widget = ComfyWidgets.STRING(this, '', [ + '', { default: '', multiline: true }, + ], app).widget; + widget.inputEl.readOnly = true; + ComfyWidgets.BOOLEAN(this, 'Active', [ + '', { default: true }, + ]); + ComfyWidgets.BOOLEAN(this, 'Parsed', [ + '', { default: true }, + ]); + ComfyWidgets.COMBO(this, 'What', [ + ['Prompt', 'Workflow'], { default: 'Prompt' }, + ]); + api.addEventListener('executed', this.fillMetadataWidget, false); + } + } + LiteGraph.registerNodeType('Show Metadata [Crystools]', MetadataNode); + MetadataNode.category = `crystools ${commonPrefix}/Debugger`; + MetadataNode.shape = LiteGraph.BOX_SHAPE; + MetadataNode.title = `${commonPrefix} Show Metadata`; + }, +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/debugger.ts b/custom_nodes/ComfyUI-Crystools/web/debugger.ts new file mode 100644 index 00000000..b37f8a2c --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/debugger.ts @@ -0,0 +1,86 @@ +import type { ComfyNode } from './comfy/index.js'; +import { app, api, ComfyWidgets, LiteGraph, TLGraphNode, ComfyApp } from './comfy/index.js'; +import { commonPrefix, displayContext } from './common.js'; + +// "Show any" Node +app.registerExtension({ + name: 'Crystools.Debugger.ConsoleAny', + beforeRegisterNodeDef(nodeType: ComfyNode, nodeData: TLGraphNode, appFromArg: ComfyApp) { + if (nodeData.name === 'Show any [Crystools]') { + // 3 is the index of the text field in the node + displayContext(nodeType, appFromArg, 3); + } + }, +}); + +app.registerExtension({ + name: 'Crystools.Debugger.Metadata', + registerCustomNodes() { + class MetadataNode extends TLGraphNode { + constructor() { + super(`${commonPrefix} Show Metadata `); + this.serialize_widgets = false; + this.isVirtualNode = true; + + const widget = ComfyWidgets.STRING(this, '', [ + '', {default: '', multiline: true}, + ], app).widget; + widget.inputEl.readOnly = true; + ComfyWidgets.BOOLEAN(this, 'Active', [ + '', {default: true}, + ]); + ComfyWidgets.BOOLEAN(this, 'Parsed', [ + '', {default: true}, + ]); + ComfyWidgets.COMBO(this, 'What', [ + ['Prompt', 'Workflow'], {default: 'Prompt'}, + ]); + + // It runs at finish on each prompt queue + api.addEventListener('executed', this.fillMetadataWidget, false); + } + + fillMetadataWidget = (): Promise => { + return app.graphToPrompt() + .then((workflow: any): string => { + let result = 'inactive'; + if (this.widgets?.length !== 4) { + console.error('Something is wrong with the widgets, should be 4!'); + return 'error'; + } + const output = this.widgets[0]; + const active = this.widgets[1]?.value; + const parsed = this.widgets[2]?.value; + let what = this.widgets[3]?.value.toLowerCase(); + + if (active) { + what = what === 'prompt' ? 'output' : what; // little fix for better understanding + // @ts-ignore + result = workflow[what]; + if (parsed) { + result = JSON.stringify(result, null, 2); + } else { + result = JSON.stringify(result); + } + } + if (output) { + output.value = result; + } else { + console.error('Something is wrong with the widgets, output is undefined!'); + return 'error'; + } + return result; + }); + }; + } + + // I'm not sure for what they're using prototype and lots of black magic, don't change the order! + LiteGraph.registerNodeType('Show Metadata [Crystools]', MetadataNode); + MetadataNode.category = `crystools ${commonPrefix}/Debugger`; + MetadataNode.shape = LiteGraph.BOX_SHAPE; + MetadataNode.title = `${commonPrefix} Show Metadata`; + // MetadataNode.collapsable = false; + // MetadataNode.color = '#FF2222'; + // MetadataNode.bgcolor = '#000000'; + }, +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/extensions.d.ts b/custom_nodes/ComfyUI-Crystools/web/extensions.d.ts new file mode 100644 index 00000000..cb0ff5c3 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/extensions.d.ts @@ -0,0 +1 @@ +export {}; diff --git a/custom_nodes/ComfyUI-Crystools/web/extensions.js b/custom_nodes/ComfyUI-Crystools/web/extensions.js new file mode 100644 index 00000000..0c78b493 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/extensions.js @@ -0,0 +1,32 @@ +import { app } from './comfy/index.js'; +import { displayContext } from './common.js'; +const crystoolsExtensionsSerialized = { + 'Read JSON file [Crystools]': 'Crystools.Utils.ReadJsonFile', + 'JSON extractor [Crystools]': 'Crystools.Utils.JsonExtractor', +}; +const crystoolsExtensions = { + 'Get resolution [Crystools]': 'Crystools.Image.GetResolution', + 'Preview from image [Crystools]': 'Crystools.Image.PreviewFromImage', + 'Preview from metadata [Crystools]': 'Crystools.Image.PreviewFromMetadata', + 'Metadata comparator [Crystools]': 'Crystools.Metadata.MetadataComparator', + 'Stats system [Crystools]': 'Crystools.Utils.StatsSystem', + 'Show any to JSON [Crystools]': 'Crystools.Debugger.ConsoleAnyToJson', +}; +Object.keys(crystoolsExtensionsSerialized).forEach(prop => { + crystoolsExtensions[prop] = crystoolsExtensionsSerialized[prop]; +}); +Object.keys(crystoolsExtensions).forEach(key => { + app.registerExtension({ + name: crystoolsExtensions[key], + beforeRegisterNodeDef(nodeType, nodeData, appFromArg) { + if (nodeData.name === key) { + if (nodeData.name in crystoolsExtensionsSerialized) { + displayContext(nodeType, appFromArg, 0, true); + } + else { + displayContext(nodeType, appFromArg, 0); + } + } + }, + }); +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/extensions.ts b/custom_nodes/ComfyUI-Crystools/web/extensions.ts new file mode 100644 index 00000000..533c92d3 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/extensions.ts @@ -0,0 +1,38 @@ +import { app, TLGraphNode, ComfyApp } from './comfy/index.js'; +import type { ComfyNode } from './comfy/index.js'; +import { displayContext } from './common.js'; + +const crystoolsExtensionsSerialized: Record = { + // 'External parameter from JSON file [Crystools]': 'Crystools.Utils.ExternalParameterFromJson', + 'Read JSON file [Crystools]': 'Crystools.Utils.ReadJsonFile', + 'JSON extractor [Crystools]': 'Crystools.Utils.JsonExtractor', +}; + +const crystoolsExtensions: Record = { + 'Get resolution [Crystools]': 'Crystools.Image.GetResolution', + 'Preview from image [Crystools]': 'Crystools.Image.PreviewFromImage', + 'Preview from metadata [Crystools]': 'Crystools.Image.PreviewFromMetadata', + 'Metadata comparator [Crystools]': 'Crystools.Metadata.MetadataComparator', + 'Stats system [Crystools]': 'Crystools.Utils.StatsSystem', + 'Show any to JSON [Crystools]': 'Crystools.Debugger.ConsoleAnyToJson', +}; + +Object.keys(crystoolsExtensionsSerialized).forEach(prop => { + // @ts-ignore + crystoolsExtensions[prop] = crystoolsExtensionsSerialized[prop]; +}); + +Object.keys(crystoolsExtensions).forEach(key => { + app.registerExtension({ + name: crystoolsExtensions[key], + beforeRegisterNodeDef(nodeType: ComfyNode, nodeData: TLGraphNode, appFromArg: ComfyApp) { + if (nodeData.name === key) { + if (nodeData.name in crystoolsExtensionsSerialized) { + displayContext(nodeType, appFromArg, 0, true); // serialize_widgets = true + } else { + displayContext(nodeType, appFromArg, 0); + } + } + }, + }); +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/monitor.css b/custom_nodes/ComfyUI-Crystools/web/monitor.css new file mode 100644 index 00000000..90f84b7d --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitor.css @@ -0,0 +1,188 @@ +/* Vertical UI */ +.comfy-menu, .side-bar-panel { + #crystools-monitors-root { + /*background-color: red;*/ + box-shadow: 3px 3px 10px rgba(0, 0, 0, 0.3); + background-color: var(--comfy-menu-bg); + border-radius: 6px; + border: 1px solid var(--border-color); + display: flex; + width: 100%; + flex-direction: column; + margin: 7px; + padding: 5px 0; + cursor: crosshair; + + /* MONITORS */ + + .crystools-monitor { + margin: 1px 10px; + height: 12px; + display: flex; + align-items: center; + + .crystools-text { + width: 40px; + font-size: 10px; + text-align: right; + margin-right: 3px; + + &:hover { + font-weight: 600; + color: var(--input-text); + } + } + + .crystools-label { + &:hover { + font-weight: 700; + color: var(--input-text); + } + } + } + + .crystools-content { + flex-grow: 1; + position: relative; + background-color: var(--content-hover-bg) + } + + .crystools-slider { + position: absolute; + height: 100%; + width: 0; + box-shadow: inset 2px 2px 10px rgba(0, 0, 0, 0.2); + } + + .crystools-label { + position: relative; + color: var(--input-text); + font-size: 10px; + } + } + + /* PROGRESS BAR */ + + #crystools-progressBar-root { + cursor: pointer; + margin: 0 12px 4px; + height: 18px; + position: relative; + border-radius: 3px; + width: 100%; + background-color: var(--comfy-input-bg); + + .crystools-slider { + position: absolute; + height: 100%; + width: 50%; + transition: width 0.2s; + background-color: green; + box-shadow: inset 2px 2px 10px rgba(0, 0, 0, 0.3); + } + + .crystools-label { + position: absolute; + margin: auto 0; + width: 100%; + color: var(--input-text); + font-size: 14px; + } + } +} + +/* side-bar-panel */ +.side-bar-panel { + #crystools-monitors-root { + width: 230px; + margin-left: auto; + margin-right: auto; + } +} + +.comfyui-queue-button { + /*width: unset !important;*/ +} + +/* Horizontal UI */ +#crystools-monitors-root { + /*background-color: red;*/ + /*display: none;*/ + flex-direction: row; + /*flex-grow: 5;*/ + justify-content: flex-end; + /*flex: auto;*/ + flex-shrink: 1; + /*min-width: max-content;*/ + /*height: 100%;*/ + /* max-width: 40vw;*/ + /* min-width: 10vw;*/ + /*gap: 5px;*/ + /*margin: 0 auto;*/ + /*align-items: center;*/ + /*align-self: center;*/ + /*width: min-content;*/ + /*width: 100%;*/ + cursor: crosshair; + /*margin: 4px 0;*/ + display: flex; /* by progress bar base */ + flex-wrap: wrap; + /*flex-direction: row;*/ + /*justify-content: flex-end;*/ + /*align-items: center;*/ + gap: 5px; + align-self: center; + + .crystools-monitor { + background-color: var(--comfy-input-bg); + position: relative; + align-items: center; + flex-direction: row; + /*width: 60px;*/ + /*height: 100%;*/ + + .crystools-text { + font-size: 10px; + text-align: right; + margin-left: 3px; + position: absolute; + font-weight: 100; + bottom: 2px; + z-index: 10; + + /*&:hover {*/ + /* font-weight: 600;*/ + /* color: var(--input-text);*/ + /*}*/ + } + + /*.crystools-label {*/ + /* &:hover {*/ + /* font-weight: 800;*/ + /* color: var(--input-text);*/ + /* }*/ + /*}*/ + } + + .crystools-content { + position: relative; + } + + .crystools-slider { + position: absolute; + height: 100%; + width: 0; + box-shadow: inset 2px 2px 10px rgba(0, 0, 0, 0.2); + } + + .crystools-label { + position: relative; + width: 100%; + color: var(--input-text); + font-weight: 500; + font-size: 11px; + right: 2px; + top: 2px; + text-align: right; + } +} diff --git a/custom_nodes/ComfyUI-Crystools/web/monitor.d.ts b/custom_nodes/ComfyUI-Crystools/web/monitor.d.ts new file mode 100644 index 00000000..cb0ff5c3 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitor.d.ts @@ -0,0 +1 @@ +export {}; diff --git a/custom_nodes/ComfyUI-Crystools/web/monitor.js b/custom_nodes/ComfyUI-Crystools/web/monitor.js new file mode 100644 index 00000000..b1fa1ed8 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitor.js @@ -0,0 +1,656 @@ +import { app, api, ComfyButtonGroup } from './comfy/index.js'; +import { commonPrefix } from './common.js'; +import { MonitorUI } from './monitorUI.js'; +import { Colors } from './styles.js'; +import { convertNumberToPascalCase } from './utils.js'; +import { ComfyKeyMenuDisplayOption, MenuDisplayOptions } from './progressBarUIBase.js'; +class CrystoolsMonitor { + constructor() { + Object.defineProperty(this, "idExtensionName", { + enumerable: true, + configurable: true, + writable: true, + value: 'Crystools.monitor' + }); + Object.defineProperty(this, "menuPrefix", { + enumerable: true, + configurable: true, + writable: true, + value: commonPrefix + }); + Object.defineProperty(this, "menuDisplayOption", { + enumerable: true, + configurable: true, + writable: true, + value: MenuDisplayOptions.Disabled + }); + Object.defineProperty(this, "crystoolsButtonGroup", { + enumerable: true, + configurable: true, + writable: true, + value: null + }); + Object.defineProperty(this, "settingsRate", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "settingsMonitorHeight", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "settingsMonitorWidth", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "monitorCPUElement", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "monitorRAMElement", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "monitorHDDElement", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "settingsHDD", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "monitorGPUSettings", { + enumerable: true, + configurable: true, + writable: true, + value: [] + }); + Object.defineProperty(this, "monitorVRAMSettings", { + enumerable: true, + configurable: true, + writable: true, + value: [] + }); + Object.defineProperty(this, "monitorTemperatureSettings", { + enumerable: true, + configurable: true, + writable: true, + value: [] + }); + Object.defineProperty(this, "monitorUI", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "monitorWidthId", { + enumerable: true, + configurable: true, + writable: true, + value: 'Crystools.MonitorWidth' + }); + Object.defineProperty(this, "monitorWidth", { + enumerable: true, + configurable: true, + writable: true, + value: 60 + }); + Object.defineProperty(this, "monitorHeightId", { + enumerable: true, + configurable: true, + writable: true, + value: 'Crystools.MonitorHeight' + }); + Object.defineProperty(this, "monitorHeight", { + enumerable: true, + configurable: true, + writable: true, + value: 30 + }); + Object.defineProperty(this, "createSettingsRate", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.settingsRate = { + id: 'Crystools.RefreshRate', + name: 'Refresh per second', + category: ['Crystools', this.menuPrefix + ' Configuration', 'refresh'], + tooltip: 'This is the time (in seconds) between each update of the monitors, 0 means no refresh', + type: 'slider', + attrs: { + min: 0, + max: 2, + step: .25, + }, + defaultValue: .5, + onChange: async (value) => { + let valueNumber; + try { + valueNumber = parseFloat(value); + if (isNaN(valueNumber)) { + throw new Error('invalid value'); + } + } + catch (error) { + console.error(error); + return; + } + try { + await this.updateServer({ rate: valueNumber }); + } + catch (error) { + console.error(error); + return; + } + const data = { + cpu_utilization: 0, + device: 'cpu', + gpus: [ + { + gpu_utilization: 0, + gpu_temperature: 0, + vram_total: 0, + vram_used: 0, + vram_used_percent: 0, + }, + ], + hdd_total: 0, + hdd_used: 0, + hdd_used_percent: 0, + ram_total: 0, + ram_used: 0, + ram_used_percent: 0, + }; + if (valueNumber === 0) { + this.monitorUI.updateDisplay(data); + } + this.monitorUI?.updateAllAnimationDuration(valueNumber); + }, + }; + } + }); + Object.defineProperty(this, "createSettingsMonitorWidth", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.settingsMonitorWidth = { + id: this.monitorWidthId, + name: 'Pixel Width', + category: ['Crystools', this.menuPrefix + ' Configuration', 'width'], + tooltip: 'The width of the monitor in pixels on the UI (only on top/bottom UI)', + type: 'slider', + attrs: { + min: 60, + max: 100, + step: 1, + }, + defaultValue: this.monitorWidth, + onChange: (value) => { + let valueNumber; + try { + valueNumber = parseInt(value); + if (isNaN(valueNumber)) { + throw new Error('invalid value'); + } + } + catch (error) { + console.error(error); + return; + } + const h = app.extensionManager.setting.get(this.monitorHeightId); + this.monitorUI?.updateMonitorSize(valueNumber, h); + }, + }; + } + }); + Object.defineProperty(this, "createSettingsMonitorHeight", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.settingsMonitorHeight = { + id: this.monitorHeightId, + name: 'Pixel Height', + category: ['Crystools', this.menuPrefix + ' Configuration', 'height'], + tooltip: 'The height of the monitor in pixels on the UI (only on top/bottom UI)', + type: 'slider', + attrs: { + min: 16, + max: 50, + step: 1, + }, + defaultValue: this.monitorHeight, + onChange: async (value) => { + let valueNumber; + try { + valueNumber = parseInt(value); + if (isNaN(valueNumber)) { + throw new Error('invalid value'); + } + } + catch (error) { + console.error(error); + return; + } + const w = await app.extensionManager.setting.get(this.monitorWidthId); + this.monitorUI?.updateMonitorSize(w, valueNumber); + }, + }; + } + }); + Object.defineProperty(this, "createSettingsCPU", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.monitorCPUElement = { + id: 'Crystools.ShowCpu', + name: 'CPU Usage', + category: ['Crystools', this.menuPrefix + ' Hardware', 'Cpu'], + type: 'boolean', + label: 'CPU', + symbol: '%', + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.CPU, + onChange: async (value) => { + await this.updateServer({ switchCPU: value }); + this.updateWidget(this.monitorCPUElement); + }, + }; + } + }); + Object.defineProperty(this, "createSettingsRAM", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.monitorRAMElement = { + id: 'Crystools.ShowRam', + name: 'RAM Used', + category: ['Crystools', this.menuPrefix + ' Hardware', 'Ram'], + type: 'boolean', + label: 'RAM', + symbol: '%', + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.RAM, + onChange: async (value) => { + await this.updateServer({ switchRAM: value }); + this.updateWidget(this.monitorRAMElement); + }, + }; + } + }); + Object.defineProperty(this, "createSettingsGPUUsage", { + enumerable: true, + configurable: true, + writable: true, + value: (name, index, moreThanOneGPU) => { + if (name === undefined || index === undefined) { + console.warn('getGPUsFromServer: name or index undefined', name, index); + return; + } + let label = 'GPU '; + label += moreThanOneGPU ? index : ''; + const monitorGPUNElement = { + id: 'Crystools.ShowGpuUsage' + convertNumberToPascalCase(index), + name: ' Usage', + category: ['Crystools', `${this.menuPrefix} Show GPU [${index}] ${name}`, 'Usage'], + type: 'boolean', + label, + symbol: '%', + monitorTitle: `${index}: ${name}`, + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.GPU, + onChange: async (value) => { + await this.updateServerGPU(index, { utilization: value }); + this.updateWidget(monitorGPUNElement); + }, + }; + this.monitorGPUSettings[index] = monitorGPUNElement; + app.ui.settings.addSetting(this.monitorGPUSettings[index]); + this.monitorUI.createDOMGPUMonitor(this.monitorGPUSettings[index]); + } + }); + Object.defineProperty(this, "createSettingsGPUVRAM", { + enumerable: true, + configurable: true, + writable: true, + value: (name, index, moreThanOneGPU) => { + if (name === undefined || index === undefined) { + console.warn('getGPUsFromServer: name or index undefined', name, index); + return; + } + let label = 'VRAM '; + label += moreThanOneGPU ? index : ''; + const monitorVRAMNElement = { + id: 'Crystools.ShowGpuVram' + convertNumberToPascalCase(index), + name: 'VRAM', + category: ['Crystools', `${this.menuPrefix} Show GPU [${index}] ${name}`, 'VRAM'], + type: 'boolean', + label: label, + symbol: '%', + monitorTitle: `${index}: ${name}`, + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.VRAM, + onChange: async (value) => { + await this.updateServerGPU(index, { vram: value }); + this.updateWidget(monitorVRAMNElement); + }, + }; + this.monitorVRAMSettings[index] = monitorVRAMNElement; + app.ui.settings.addSetting(this.monitorVRAMSettings[index]); + this.monitorUI.createDOMGPUMonitor(this.monitorVRAMSettings[index]); + } + }); + Object.defineProperty(this, "createSettingsGPUTemp", { + enumerable: true, + configurable: true, + writable: true, + value: (name, index, moreThanOneGPU) => { + if (name === undefined || index === undefined) { + console.warn('getGPUsFromServer: name or index undefined', name, index); + return; + } + let label = 'Temp '; + label += moreThanOneGPU ? index : ''; + const monitorTemperatureNElement = { + id: 'Crystools.ShowGpuTemperature' + convertNumberToPascalCase(index), + name: 'Temperature', + category: ['Crystools', `${this.menuPrefix} Show GPU [${index}] ${name}`, 'Temperature'], + type: 'boolean', + label: label, + symbol: 'Β°', + monitorTitle: `${index}: ${name}`, + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.TEMP_START, + cssColorFinal: Colors.TEMP_END, + onChange: async (value) => { + await this.updateServerGPU(index, { temperature: value }); + this.updateWidget(monitorTemperatureNElement); + }, + }; + this.monitorTemperatureSettings[index] = monitorTemperatureNElement; + app.ui.settings.addSetting(this.monitorTemperatureSettings[index]); + this.monitorUI.createDOMGPUMonitor(this.monitorTemperatureSettings[index]); + } + }); + Object.defineProperty(this, "createSettingsHDD", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.monitorHDDElement = { + id: 'Crystools.ShowHdd', + name: 'Show HDD Used', + category: ['Crystools', this.menuPrefix + ' Show Hard Disk', 'Show'], + type: 'boolean', + label: 'HDD', + symbol: '%', + defaultValue: false, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.DISK, + onChange: async (value) => { + await this.updateServer({ switchHDD: value }); + this.updateWidget(this.monitorHDDElement); + }, + }; + this.settingsHDD = { + id: 'Crystools.WhichHdd', + name: 'Partition to show', + category: ['Crystools', this.menuPrefix + ' Show Hard Disk', 'Which'], + type: 'combo', + defaultValue: '/', + options: [], + onChange: async (value) => { + await this.updateServer({ whichHDD: value }); + }, + }; + } + }); + Object.defineProperty(this, "createSettings", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + app.ui.settings.addSetting(this.settingsRate); + app.ui.settings.addSetting(this.settingsMonitorHeight); + app.ui.settings.addSetting(this.settingsMonitorWidth); + app.ui.settings.addSetting(this.monitorRAMElement); + app.ui.settings.addSetting(this.monitorCPUElement); + void this.getHDDsFromServer().then((data) => { + this.settingsHDD.options = data; + app.ui.settings.addSetting(this.settingsHDD); + }); + app.ui.settings.addSetting(this.monitorHDDElement); + void this.getGPUsFromServer().then((gpus) => { + let moreThanOneGPU = false; + if (gpus.length > 1) { + moreThanOneGPU = true; + } + gpus?.forEach(({ name, index }) => { + this.createSettingsGPUTemp(name, index, moreThanOneGPU); + this.createSettingsGPUVRAM(name, index, moreThanOneGPU); + this.createSettingsGPUUsage(name, index, moreThanOneGPU); + }); + this.finishedLoad(); + }); + } + }); + Object.defineProperty(this, "finishedLoad", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.monitorUI.orderMonitors(); + this.updateAllWidget(); + this.moveMonitor(this.menuDisplayOption); + const w = app.extensionManager.setting.get(this.monitorWidthId); + const h = app.extensionManager.setting.get(this.monitorHeightId); + this.monitorUI.updateMonitorSize(w, h); + } + }); + Object.defineProperty(this, "updateDisplay", { + enumerable: true, + configurable: true, + writable: true, + value: (value) => { + if (value !== this.menuDisplayOption) { + this.menuDisplayOption = value; + this.moveMonitor(this.menuDisplayOption); + } + } + }); + Object.defineProperty(this, "moveMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: (menuPosition) => { + let parentElement; + switch (menuPosition) { + case MenuDisplayOptions.Disabled: + parentElement = document.getElementById('queue-button'); + if (parentElement && this.monitorUI.rootElement) { + parentElement.insertAdjacentElement('afterend', this.crystoolsButtonGroup.element); + } + else { + console.error('Crystools: parentElement to move monitors not found!', parentElement); + } + break; + case MenuDisplayOptions.Top: + case MenuDisplayOptions.Bottom: + app.menu?.settingsGroup.element.before(this.crystoolsButtonGroup.element); + } + } + }); + Object.defineProperty(this, "updateAllWidget", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.updateWidget(this.monitorCPUElement); + this.updateWidget(this.monitorRAMElement); + this.updateWidget(this.monitorHDDElement); + this.monitorGPUSettings.forEach((monitorSettings) => { + monitorSettings && this.updateWidget(monitorSettings); + }); + this.monitorVRAMSettings.forEach((monitorSettings) => { + monitorSettings && this.updateWidget(monitorSettings); + }); + this.monitorTemperatureSettings.forEach((monitorSettings) => { + monitorSettings && this.updateWidget(monitorSettings); + }); + } + }); + Object.defineProperty(this, "updateWidget", { + enumerable: true, + configurable: true, + writable: true, + value: (monitorSettings) => { + if (this.monitorUI) { + const value = app.extensionManager.setting.get(monitorSettings.id); + this.monitorUI.showMonitor(monitorSettings, value); + } + } + }); + Object.defineProperty(this, "updateServer", { + enumerable: true, + configurable: true, + writable: true, + value: async (data) => { + const resp = await api.fetchApi('/crystools/monitor', { + method: 'PATCH', + body: JSON.stringify(data), + cache: 'no-store', + }); + if (resp.status === 200) { + return await resp.text(); + } + throw new Error(resp.statusText); + } + }); + Object.defineProperty(this, "updateServerGPU", { + enumerable: true, + configurable: true, + writable: true, + value: async (index, data) => { + const resp = await api.fetchApi(`/crystools/monitor/GPU/${index}`, { + method: 'PATCH', + body: JSON.stringify(data), + cache: 'no-store', + }); + if (resp.status === 200) { + return await resp.text(); + } + throw new Error(resp.statusText); + } + }); + Object.defineProperty(this, "getHDDsFromServer", { + enumerable: true, + configurable: true, + writable: true, + value: async () => { + return this.getDataFromServer('HDD'); + } + }); + Object.defineProperty(this, "getGPUsFromServer", { + enumerable: true, + configurable: true, + writable: true, + value: async () => { + return this.getDataFromServer('GPU'); + } + }); + Object.defineProperty(this, "getDataFromServer", { + enumerable: true, + configurable: true, + writable: true, + value: async (what) => { + const resp = await api.fetchApi(`/crystools/monitor/${what}`, { + method: 'GET', + cache: 'no-store', + }); + if (resp.status === 200) { + return await resp.json(); + } + throw new Error(resp.statusText); + } + }); + Object.defineProperty(this, "setup", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + if (this.monitorUI) { + return; + } + this.createSettingsRate(); + this.createSettingsMonitorHeight(); + this.createSettingsMonitorWidth(); + this.createSettingsCPU(); + this.createSettingsRAM(); + this.createSettingsHDD(); + this.createSettings(); + const currentRate = parseFloat(app.extensionManager.setting.get(this.settingsRate.id)); + this.menuDisplayOption = app.extensionManager.setting.get(ComfyKeyMenuDisplayOption); + app.ui.settings.addEventListener(`${ComfyKeyMenuDisplayOption}.change`, (e) => { + this.updateDisplay(e.detail.value); + }); + this.crystoolsButtonGroup = new ComfyButtonGroup(); + app.menu?.settingsGroup.element.before(this.crystoolsButtonGroup.element); + this.monitorUI = new MonitorUI(this.crystoolsButtonGroup.element, this.monitorCPUElement, this.monitorRAMElement, this.monitorHDDElement, this.monitorGPUSettings, this.monitorVRAMSettings, this.monitorTemperatureSettings, currentRate); + this.updateDisplay(this.menuDisplayOption); + this.registerListeners(); + } + }); + Object.defineProperty(this, "registerListeners", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + api.addEventListener('crystools.monitor', (event) => { + if (event?.detail === undefined) { + return; + } + this.monitorUI.updateDisplay(event.detail); + }, false); + } + }); + } +} +const crystoolsMonitor = new CrystoolsMonitor(); +app.registerExtension({ + name: crystoolsMonitor.idExtensionName, + setup: crystoolsMonitor.setup, +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/monitor.ts b/custom_nodes/ComfyUI-Crystools/web/monitor.ts new file mode 100644 index 00000000..a97a56f7 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitor.ts @@ -0,0 +1,588 @@ +import { app, api, ComfyButtonGroup } from './comfy/index.js'; +import { commonPrefix } from './common.js'; +import { MonitorUI } from './monitorUI.js'; +import { Colors } from './styles.js'; +import { convertNumberToPascalCase } from './utils.js'; +import { ComfyKeyMenuDisplayOption, MenuDisplayOptions } from './progressBarUIBase.js'; + +// enum MonitorPosition { +// 'Top' = 'Top', +// 'Sidebar' = 'Sidebar', +// 'Floating' = 'Floating', +// } + +class CrystoolsMonitor { + readonly idExtensionName = 'Crystools.monitor'; + private readonly menuPrefix = commonPrefix; + private menuDisplayOption: MenuDisplayOptions = MenuDisplayOptions.Disabled; + private crystoolsButtonGroup: ComfyButtonGroup = null; + + // private settingsMonitorPosition: TMonitorSettings; + private settingsRate: TMonitorSettings; + private settingsMonitorHeight: TMonitorSettings; + private settingsMonitorWidth: TMonitorSettings; + private monitorCPUElement: TMonitorSettings; + private monitorRAMElement: TMonitorSettings; + private monitorHDDElement: TMonitorSettings; + private settingsHDD: TMonitorSettings; + private monitorGPUSettings: TMonitorSettings[] = []; + private monitorVRAMSettings: TMonitorSettings[] = []; + private monitorTemperatureSettings: TMonitorSettings[] = []; + + private monitorUI: MonitorUI; + + // private readonly monitorPositionId = 'Crystools.MonitorPosition'; + private readonly monitorWidthId = 'Crystools.MonitorWidth'; + private readonly monitorWidth = 60; + private readonly monitorHeightId = 'Crystools.MonitorHeight'; + private readonly monitorHeight = 30; + + // NO POSIBLE TO IMPLEMENT INSIDE THE PANEL + // createSettingsMonitorPosition = (): void => { + // const position = app.extensionManager.setting.get(this.monitorPositionId); + // console.log('position', position); + // this.settingsMonitorPosition = { + // id: this.monitorPositionId, + // name: 'Position (floating not implemented yet)', + // category: ['Crystools', this.menuPrefix + ' Configuration', 'position'], + // tooltip: 'Only for new UI', + // experimental: true, + // // data: [], + // type: 'combo', + // options: [ + // MonitorPoistion.Top, + // MonitorPoistion.Sidebar, + // MonitorPoistion.Floating + // ], + // + // defaultValue: MonitorPoistion.Sidebar, + // // @ts-ignore + // onChange: (_value: string): void => { + // // if (this.monitorUI) { + // // console.log('onChange', _value); + // // this.moveMonitor(this.menuDisplayOption); + // // } + // }, + // }; + // }; + + createSettingsRate = (): void => { + this.settingsRate = { + id: 'Crystools.RefreshRate', + name: 'Refresh per second', + category: ['Crystools', this.menuPrefix + ' Configuration', 'refresh'], + tooltip: 'This is the time (in seconds) between each update of the monitors, 0 means no refresh', + type: 'slider', + attrs: { + min: 0, + max: 2, + step: .25, + }, + defaultValue: .5, + + // @ts-ignore + onChange: async(value: string): Promise => { + let valueNumber: number; + + try { + valueNumber = parseFloat(value); + if (isNaN(valueNumber)) { + throw new Error('invalid value'); + } + } catch (error) { + console.error(error); + return; + } + try { + await this.updateServer({rate: valueNumber}); + } catch (error) { + console.error(error); + return; + } + + const data = { + cpu_utilization: 0, + device: 'cpu', + + gpus: [ + { + gpu_utilization: 0, + gpu_temperature: 0, + vram_total: 0, + vram_used: 0, + vram_used_percent: 0, + }, + ], + hdd_total: 0, + hdd_used: 0, + hdd_used_percent: 0, + ram_total: 0, + ram_used: 0, + ram_used_percent: 0, + }; + if (valueNumber === 0) { + this.monitorUI.updateDisplay(data); + } + + this.monitorUI?.updateAllAnimationDuration(valueNumber); + }, + }; + }; + + createSettingsMonitorWidth = (): void => { + this.settingsMonitorWidth = { + id: this.monitorWidthId, + name: 'Pixel Width', + category: ['Crystools', this.menuPrefix + ' Configuration', 'width'], + tooltip: 'The width of the monitor in pixels on the UI (only on top/bottom UI)', + type: 'slider', + attrs: { + min: 60, + max: 100, + step: 1, + }, + defaultValue: this.monitorWidth, + // @ts-ignore + onChange: (value: string): void => { + let valueNumber: number; + + try { + valueNumber = parseInt(value); + if (isNaN(valueNumber)) { + throw new Error('invalid value'); + } + } catch (error) { + console.error(error); + return; + } + + const h = app.extensionManager.setting.get(this.monitorHeightId); + this.monitorUI?.updateMonitorSize(valueNumber, h); + }, + }; + }; + + createSettingsMonitorHeight = (): void => { + this.settingsMonitorHeight = { + id: this.monitorHeightId, + name: 'Pixel Height', + category: ['Crystools', this.menuPrefix + ' Configuration', 'height'], + tooltip: 'The height of the monitor in pixels on the UI (only on top/bottom UI)', + type: 'slider', + attrs: { + min: 16, + max: 50, + step: 1, + }, + defaultValue: this.monitorHeight, + // @ts-ignore + onChange: async(value: string): void => { + let valueNumber: number; + + try { + valueNumber = parseInt(value); + if (isNaN(valueNumber)) { + throw new Error('invalid value'); + } + } catch (error) { + console.error(error); + return; + } + + const w = await app.extensionManager.setting.get(this.monitorWidthId); + this.monitorUI?.updateMonitorSize(w, valueNumber); + }, + }; + }; + + createSettingsCPU = (): void => { + // CPU Variables + this.monitorCPUElement = { + id: 'Crystools.ShowCpu', + name: 'CPU Usage', + category: ['Crystools', this.menuPrefix + ' Hardware', 'Cpu'], + type: 'boolean', + label: 'CPU', + symbol: '%', + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.CPU, + // @ts-ignore + onChange: async(value: boolean): Promise => { + await this.updateServer({switchCPU: value}); + this.updateWidget(this.monitorCPUElement); + }, + }; + }; + + createSettingsRAM = (): void => { + // RAM Variables + this.monitorRAMElement = { + id: 'Crystools.ShowRam', + name: 'RAM Used', + category: ['Crystools', this.menuPrefix + ' Hardware', 'Ram'], + type: 'boolean', + label: 'RAM', + symbol: '%', + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.RAM, + // @ts-ignore + onChange: async(value: boolean): Promise => { + await this.updateServer({switchRAM: value}); + this.updateWidget(this.monitorRAMElement); + }, + }; + }; + + createSettingsGPUUsage = (name: string, index: number, moreThanOneGPU: boolean): void => { + if (name === undefined || index === undefined) { + console.warn('getGPUsFromServer: name or index undefined', name, index); + return; + } + + let label = 'GPU '; + label += moreThanOneGPU ? index : ''; + + const monitorGPUNElement: TMonitorSettings = { + id: 'Crystools.ShowGpuUsage' + convertNumberToPascalCase(index), + name: ' Usage', + category: ['Crystools', `${this.menuPrefix} Show GPU [${index}] ${name}`, 'Usage'], + type: 'boolean', + label, + symbol: '%', + monitorTitle: `${index}: ${name}`, + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.GPU, + // @ts-ignore + onChange: async(value: boolean): Promise => { + await this.updateServerGPU(index, {utilization: value}); + this.updateWidget(monitorGPUNElement); + }, + }; + + this.monitorGPUSettings[index] = monitorGPUNElement; + app.ui.settings.addSetting(this.monitorGPUSettings[index]); + this.monitorUI.createDOMGPUMonitor(this.monitorGPUSettings[index]); + }; + + createSettingsGPUVRAM = (name: string, index: number, moreThanOneGPU: boolean): void => { + if (name === undefined || index === undefined) { + console.warn('getGPUsFromServer: name or index undefined', name, index); + return; + } + + let label = 'VRAM '; + label += moreThanOneGPU ? index : ''; + + // GPU VRAM Variables + const monitorVRAMNElement: TMonitorSettings = { + id: 'Crystools.ShowGpuVram' + convertNumberToPascalCase(index), + name: 'VRAM', + category: ['Crystools', `${this.menuPrefix} Show GPU [${index}] ${name}`, 'VRAM'], + type: 'boolean', + label: label, + symbol: '%', + monitorTitle: `${index}: ${name}`, + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.VRAM, + // @ts-ignore + onChange: async(value: boolean): Promise => { + await this.updateServerGPU(index, {vram: value}); + this.updateWidget(monitorVRAMNElement); + }, + }; + + this.monitorVRAMSettings[index] = monitorVRAMNElement; + app.ui.settings.addSetting(this.monitorVRAMSettings[index]); + this.monitorUI.createDOMGPUMonitor(this.monitorVRAMSettings[index]); + }; + + createSettingsGPUTemp = (name: string, index: number, moreThanOneGPU: boolean): void => { + if (name === undefined || index === undefined) { + console.warn('getGPUsFromServer: name or index undefined', name, index); + return; + } + + let label = 'Temp '; + label += moreThanOneGPU ? index : ''; + + // GPU Temperature Variables + const monitorTemperatureNElement: TMonitorSettings = { + id: 'Crystools.ShowGpuTemperature' + convertNumberToPascalCase(index), + name: 'Temperature', + category: ['Crystools', `${this.menuPrefix} Show GPU [${index}] ${name}`, 'Temperature'], + type: 'boolean', + label: label, + symbol: 'Β°', + monitorTitle: `${index}: ${name}`, + defaultValue: true, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.TEMP_START, + cssColorFinal: Colors.TEMP_END, + // @ts-ignore + onChange: async(value: boolean): Promise => { + await this.updateServerGPU(index, {temperature: value}); + this.updateWidget(monitorTemperatureNElement); + }, + }; + + this.monitorTemperatureSettings[index] = monitorTemperatureNElement; + app.ui.settings.addSetting(this.monitorTemperatureSettings[index]); + this.monitorUI.createDOMGPUMonitor(this.monitorTemperatureSettings[index]); + }; + + createSettingsHDD = (): void => { + // HDD Variables + this.monitorHDDElement = { + id: 'Crystools.ShowHdd', + name: 'Show HDD Used', + category: ['Crystools', this.menuPrefix + ' Show Hard Disk', 'Show'], + type: 'boolean', + label: 'HDD', + symbol: '%', + // tooltip: 'See Partition to show (HDD)', + defaultValue: false, + htmlMonitorRef: undefined, + htmlMonitorSliderRef: undefined, + htmlMonitorLabelRef: undefined, + cssColor: Colors.DISK, + // @ts-ignore + onChange: async(value: boolean): Promise => { + await this.updateServer({switchHDD: value}); + this.updateWidget(this.monitorHDDElement); + }, + }; + + this.settingsHDD = { + id: 'Crystools.WhichHdd', + name: 'Partition to show', + category: ['Crystools', this.menuPrefix + ' Show Hard Disk', 'Which'], + type: 'combo', + defaultValue: '/', + options: [], + // @ts-ignore + onChange: async(value: string): Promise => { + await this.updateServer({whichHDD: value}); + }, + }; + }; + + createSettings = (): void => { + app.ui.settings.addSetting(this.settingsRate); + app.ui.settings.addSetting(this.settingsMonitorHeight); + app.ui.settings.addSetting(this.settingsMonitorWidth); + // app.ui.settings.addSetting(this.settingsMonitorPosition); + app.ui.settings.addSetting(this.monitorRAMElement); + app.ui.settings.addSetting(this.monitorCPUElement); + + void this.getHDDsFromServer().then((data: string[]): void => { + // @ts-ignore + this.settingsHDD.options = data; + app.ui.settings.addSetting(this.settingsHDD); + }); + app.ui.settings.addSetting(this.monitorHDDElement); + + void this.getGPUsFromServer().then((gpus: TGpuName[]): void => { + let moreThanOneGPU = false; + if (gpus.length > 1) { + moreThanOneGPU = true; + } + + gpus?.forEach(({name, index}) => { + this.createSettingsGPUTemp(name, index, moreThanOneGPU); + this.createSettingsGPUVRAM(name, index, moreThanOneGPU); + this.createSettingsGPUUsage(name, index, moreThanOneGPU); + }); + + this.finishedLoad(); + }); + }; + + finishedLoad = (): void => { + this.monitorUI.orderMonitors(); + this.updateAllWidget(); + this.moveMonitor(this.menuDisplayOption); + + const w = app.extensionManager.setting.get(this.monitorWidthId); + const h = app.extensionManager.setting.get(this.monitorHeightId); + this.monitorUI.updateMonitorSize(w, h); + }; + + updateDisplay = (value: MenuDisplayOptions): void => { + if (value !== this.menuDisplayOption) { + this.menuDisplayOption = value; + this.moveMonitor(this.menuDisplayOption); + } + }; + + moveMonitor = (menuPosition: MenuDisplayOptions): void => { + // console.log('moveMonitor', menuPosition); + // setTimeout(() => { + let parentElement: Element | null | undefined; + + switch (menuPosition) { + case MenuDisplayOptions.Disabled: + parentElement = document.getElementById('queue-button'); + if (parentElement && this.monitorUI.rootElement) { + parentElement.insertAdjacentElement('afterend', this.crystoolsButtonGroup.element); + } else { + console.error('Crystools: parentElement to move monitors not found!', parentElement); + } + break; + + case MenuDisplayOptions.Top: + case MenuDisplayOptions.Bottom: + // const position = app.extensionManager.setting.get(this.monitorPositionId); + // if(position === MonitorPosition.Top) { + app.menu?.settingsGroup.element.before(this.crystoolsButtonGroup.element); + // } else { + // parentElement = document.getElementsByClassName('comfy-vue-side-bar-header')[0]; + // if(parentElement){ + // parentElement.insertBefore(this.crystoolsButtonGroup.element, parentElement.firstChild); + // } else { + // console.error('Crystools: parentElement to move monitors not found! back to top'); + // app.ui.settings.setSettingValue(this.monitorPositionId, MonitorPoistion.Top); + // } + // } + } + // }, 100); + }; + + updateAllWidget = (): void => { + this.updateWidget(this.monitorCPUElement); + this.updateWidget(this.monitorRAMElement); + this.updateWidget(this.monitorHDDElement); + + this.monitorGPUSettings.forEach((monitorSettings) => { + monitorSettings && this.updateWidget(monitorSettings); + }); + this.monitorVRAMSettings.forEach((monitorSettings) => { + monitorSettings && this.updateWidget(monitorSettings); + }); + this.monitorTemperatureSettings.forEach((monitorSettings) => { + monitorSettings && this.updateWidget(monitorSettings); + }); + }; + + /** + * for the settings menu + * @param monitorSettings + */ + updateWidget = (monitorSettings: TMonitorSettings): void => { + if (this.monitorUI) { + const value = app.extensionManager.setting.get(monitorSettings.id); + this.monitorUI.showMonitor(monitorSettings, value); + } + }; + + updateServer = async(data: TStatsSettings): Promise => { + const resp = await api.fetchApi('/crystools/monitor', { + method: 'PATCH', + body: JSON.stringify(data), + cache: 'no-store', + }); + if (resp.status === 200) { + return await resp.text(); + } + throw new Error(resp.statusText); + }; + + updateServerGPU = async(index: number, data: TGpuSettings): Promise => { + const resp = await api.fetchApi(`/crystools/monitor/GPU/${index}`, { + method: 'PATCH', + body: JSON.stringify(data), + cache: 'no-store', + }); + if (resp.status === 200) { + return await resp.text(); + } + throw new Error(resp.statusText); + }; + + getHDDsFromServer = async(): Promise => { + return this.getDataFromServer('HDD'); + }; + + getGPUsFromServer = async(): Promise => { + return this.getDataFromServer('GPU'); + }; + + getDataFromServer = async (what: string): Promise => { + const resp = await api.fetchApi(`/crystools/monitor/${what}`, { + method: 'GET', + cache: 'no-store', + }); + if (resp.status === 200) { + return await resp.json(); + } + throw new Error(resp.statusText); + }; + + setup = (): void => { + if (this.monitorUI) { + return; + } + // this.createSettingsMonitorPosition(); + this.createSettingsRate(); + this.createSettingsMonitorHeight(); + this.createSettingsMonitorWidth(); + this.createSettingsCPU(); + this.createSettingsRAM(); + this.createSettingsHDD(); + this.createSettings(); + + const currentRate = + parseFloat(app.extensionManager.setting.get(this.settingsRate.id)); + + this.menuDisplayOption = app.extensionManager.setting.get(ComfyKeyMenuDisplayOption); + app.ui.settings.addEventListener(`${ComfyKeyMenuDisplayOption}.change`, (e: any) => { + this.updateDisplay(e.detail.value); + }, + ); + + this.crystoolsButtonGroup = new ComfyButtonGroup(); + app.menu?.settingsGroup.element.before(this.crystoolsButtonGroup.element); + + this.monitorUI = new MonitorUI( + this.crystoolsButtonGroup.element, + this.monitorCPUElement, + this.monitorRAMElement, + this.monitorHDDElement, + this.monitorGPUSettings, + this.monitorVRAMSettings, + this.monitorTemperatureSettings, + currentRate, + ); + + this.updateDisplay(this.menuDisplayOption); + this.registerListeners(); + }; + + registerListeners = (): void => { + api.addEventListener('crystools.monitor', (event: CustomEvent) => { + if (event?.detail === undefined) { + return; + } + this.monitorUI.updateDisplay(event.detail); + }, false); + }; +} + +const crystoolsMonitor = new CrystoolsMonitor(); +app.registerExtension({ + name: crystoolsMonitor.idExtensionName, + setup: crystoolsMonitor.setup, +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/monitorUI.d.ts b/custom_nodes/ComfyUI-Crystools/web/monitorUI.d.ts new file mode 100644 index 00000000..526c4366 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitorUI.d.ts @@ -0,0 +1,26 @@ +import { ProgressBarUIBase } from './progressBarUIBase.js'; +export declare class MonitorUI extends ProgressBarUIBase { + rootElement: HTMLElement; + private monitorCPUElement; + private monitorRAMElement; + private monitorHDDElement; + private monitorGPUSettings; + private monitorVRAMSettings; + private monitorTemperatureSettings; + private currentRate; + lastMonitor: number; + styleSheet: HTMLStyleElement; + maxVRAMUsed: Record; + constructor(rootElement: HTMLElement, monitorCPUElement: TMonitorSettings, monitorRAMElement: TMonitorSettings, monitorHDDElement: TMonitorSettings, monitorGPUSettings: TMonitorSettings[], monitorVRAMSettings: TMonitorSettings[], monitorTemperatureSettings: TMonitorSettings[], currentRate: number); + createDOM: () => void; + createDOMGPUMonitor: (monitorSettings?: TMonitorSettings) => void; + orderMonitors: () => void; + updateDisplay: (data: TStatsData) => void; + updateMonitor: (monitorSettings: TMonitorSettings, percent: number, used?: number, total?: number) => void; + updateAllAnimationDuration: (value: number) => void; + updatedAnimationDuration: (monitorSettings: TMonitorSettings, value: number) => void; + createMonitor: (monitorSettings?: TMonitorSettings) => HTMLDivElement; + updateMonitorSize: (width: number, height: number) => void; + showMonitor: (monitorSettings: TMonitorSettings, value: boolean) => void; + resetMaxVRAM: () => void; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/monitorUI.js b/custom_nodes/ComfyUI-Crystools/web/monitorUI.js new file mode 100644 index 00000000..1f2c22df --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitorUI.js @@ -0,0 +1,303 @@ +import { ProgressBarUIBase } from './progressBarUIBase.js'; +import { createStyleSheet, formatBytes } from './utils.js'; +export class MonitorUI extends ProgressBarUIBase { + constructor(rootElement, monitorCPUElement, monitorRAMElement, monitorHDDElement, monitorGPUSettings, monitorVRAMSettings, monitorTemperatureSettings, currentRate) { + super('crystools-monitors-root', rootElement); + Object.defineProperty(this, "rootElement", { + enumerable: true, + configurable: true, + writable: true, + value: rootElement + }); + Object.defineProperty(this, "monitorCPUElement", { + enumerable: true, + configurable: true, + writable: true, + value: monitorCPUElement + }); + Object.defineProperty(this, "monitorRAMElement", { + enumerable: true, + configurable: true, + writable: true, + value: monitorRAMElement + }); + Object.defineProperty(this, "monitorHDDElement", { + enumerable: true, + configurable: true, + writable: true, + value: monitorHDDElement + }); + Object.defineProperty(this, "monitorGPUSettings", { + enumerable: true, + configurable: true, + writable: true, + value: monitorGPUSettings + }); + Object.defineProperty(this, "monitorVRAMSettings", { + enumerable: true, + configurable: true, + writable: true, + value: monitorVRAMSettings + }); + Object.defineProperty(this, "monitorTemperatureSettings", { + enumerable: true, + configurable: true, + writable: true, + value: monitorTemperatureSettings + }); + Object.defineProperty(this, "currentRate", { + enumerable: true, + configurable: true, + writable: true, + value: currentRate + }); + Object.defineProperty(this, "lastMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: 1 + }); + Object.defineProperty(this, "styleSheet", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "maxVRAMUsed", { + enumerable: true, + configurable: true, + writable: true, + value: {} + }); + Object.defineProperty(this, "createDOM", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + if (!this.rootElement) { + throw Error('Crystools: MonitorUI - Container not found'); + } + this.rootElement.appendChild(this.createMonitor(this.monitorCPUElement)); + this.rootElement.appendChild(this.createMonitor(this.monitorRAMElement)); + this.rootElement.appendChild(this.createMonitor(this.monitorHDDElement)); + this.updateAllAnimationDuration(this.currentRate); + } + }); + Object.defineProperty(this, "createDOMGPUMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: (monitorSettings) => { + if (!(monitorSettings && this.rootElement)) { + return; + } + this.rootElement.appendChild(this.createMonitor(monitorSettings)); + this.updateAllAnimationDuration(this.currentRate); + } + }); + Object.defineProperty(this, "orderMonitors", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + try { + this.monitorCPUElement.htmlMonitorRef.style.order = '' + this.lastMonitor++; + this.monitorRAMElement.htmlMonitorRef.style.order = '' + this.lastMonitor++; + this.monitorGPUSettings.forEach((_monitorSettings, index) => { + this.monitorGPUSettings[index].htmlMonitorRef.style.order = '' + this.lastMonitor++; + this.monitorVRAMSettings[index].htmlMonitorRef.style.order = '' + this.lastMonitor++; + this.monitorTemperatureSettings[index].htmlMonitorRef.style.order = '' + this.lastMonitor++; + }); + this.monitorHDDElement.htmlMonitorRef.style.order = '' + this.lastMonitor++; + } + catch (error) { + console.error('orderMonitors', error); + } + } + }); + Object.defineProperty(this, "updateDisplay", { + enumerable: true, + configurable: true, + writable: true, + value: (data) => { + this.updateMonitor(this.monitorCPUElement, data.cpu_utilization); + this.updateMonitor(this.monitorRAMElement, data.ram_used_percent, data.ram_used, data.ram_total); + this.updateMonitor(this.monitorHDDElement, data.hdd_used_percent, data.hdd_used, data.hdd_total); + if (data.gpus === undefined || data.gpus.length === 0) { + console.warn('UpdateAllMonitors: no GPU data'); + return; + } + this.monitorGPUSettings.forEach((monitorSettings, index) => { + if (data.gpus[index]) { + const gpu = data.gpus[index]; + if (gpu === undefined) { + return; + } + this.updateMonitor(monitorSettings, gpu.gpu_utilization); + } + else { + } + }); + this.monitorVRAMSettings.forEach((monitorSettings, index) => { + if (data.gpus[index]) { + const gpu = data.gpus[index]; + if (gpu === undefined) { + return; + } + this.updateMonitor(monitorSettings, gpu.vram_used_percent, gpu.vram_used, gpu.vram_total); + } + else { + } + }); + this.monitorTemperatureSettings.forEach((monitorSettings, index) => { + if (data.gpus[index]) { + const gpu = data.gpus[index]; + if (gpu === undefined) { + return; + } + this.updateMonitor(monitorSettings, gpu.gpu_temperature); + if (monitorSettings.cssColorFinal && monitorSettings.htmlMonitorSliderRef) { + monitorSettings.htmlMonitorSliderRef.style.backgroundColor = + `color-mix(in srgb, ${monitorSettings.cssColorFinal} ${gpu.gpu_temperature}%, ${monitorSettings.cssColor})`; + } + } + else { + } + }); + } + }); + Object.defineProperty(this, "updateMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: (monitorSettings, percent, used, total) => { + if (!(monitorSettings.htmlMonitorSliderRef && monitorSettings.htmlMonitorLabelRef)) { + return; + } + if (percent < 0) { + return; + } + const prefix = monitorSettings.monitorTitle ? monitorSettings.monitorTitle + ' - ' : ''; + let title = `${Math.floor(percent)}${monitorSettings.symbol}`; + let postfix = ''; + if (used !== undefined && total !== undefined) { + const gpuIndex = parseInt(monitorSettings.monitorTitle?.split(':')[0] || '0'); + if (!this.maxVRAMUsed[gpuIndex] || this.maxVRAMUsed[gpuIndex] > total) { + this.maxVRAMUsed[gpuIndex] = 0; + } + if (used > this.maxVRAMUsed[gpuIndex]) { + this.maxVRAMUsed[gpuIndex] = used; + } + postfix = ` - ${formatBytes(used)} / ${formatBytes(total)}`; + postfix += ` Max: ${formatBytes(this.maxVRAMUsed[gpuIndex])}`; + } + title = `${prefix}${title}${postfix}`; + if (monitorSettings.htmlMonitorRef) { + monitorSettings.htmlMonitorRef.title = title; + } + monitorSettings.htmlMonitorLabelRef.innerHTML = `${Math.floor(percent)}${monitorSettings.symbol}`; + monitorSettings.htmlMonitorSliderRef.style.width = `${Math.floor(percent)}%`; + } + }); + Object.defineProperty(this, "updateAllAnimationDuration", { + enumerable: true, + configurable: true, + writable: true, + value: (value) => { + this.updatedAnimationDuration(this.monitorCPUElement, value); + this.updatedAnimationDuration(this.monitorRAMElement, value); + this.updatedAnimationDuration(this.monitorHDDElement, value); + this.monitorGPUSettings.forEach((monitorSettings) => { + monitorSettings && this.updatedAnimationDuration(monitorSettings, value); + }); + this.monitorVRAMSettings.forEach((monitorSettings) => { + monitorSettings && this.updatedAnimationDuration(monitorSettings, value); + }); + this.monitorTemperatureSettings.forEach((monitorSettings) => { + monitorSettings && this.updatedAnimationDuration(monitorSettings, value); + }); + } + }); + Object.defineProperty(this, "updatedAnimationDuration", { + enumerable: true, + configurable: true, + writable: true, + value: (monitorSettings, value) => { + const slider = monitorSettings.htmlMonitorSliderRef; + if (!slider) { + return; + } + slider.style.transition = `width ${value.toFixed(1)}s`; + } + }); + Object.defineProperty(this, "createMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: (monitorSettings) => { + if (!monitorSettings) { + return document.createElement('div'); + } + const htmlMain = document.createElement('div'); + htmlMain.classList.add(monitorSettings.id); + htmlMain.classList.add('crystools-monitor'); + monitorSettings.htmlMonitorRef = htmlMain; + if (monitorSettings.title) { + htmlMain.title = monitorSettings.title; + } + const htmlMonitorText = document.createElement('div'); + htmlMonitorText.classList.add('crystools-text'); + htmlMonitorText.innerHTML = monitorSettings.label; + htmlMain.append(htmlMonitorText); + const htmlMonitorContent = document.createElement('div'); + htmlMonitorContent.classList.add('crystools-content'); + htmlMain.append(htmlMonitorContent); + const htmlMonitorSlider = document.createElement('div'); + htmlMonitorSlider.classList.add('crystools-slider'); + if (monitorSettings.cssColorFinal) { + htmlMonitorSlider.style.backgroundColor = + `color-mix(in srgb, ${monitorSettings.cssColorFinal} 0%, ${monitorSettings.cssColor})`; + } + else { + htmlMonitorSlider.style.backgroundColor = monitorSettings.cssColor; + } + monitorSettings.htmlMonitorSliderRef = htmlMonitorSlider; + htmlMonitorContent.append(htmlMonitorSlider); + const htmlMonitorLabel = document.createElement('div'); + htmlMonitorLabel.classList.add('crystools-label'); + monitorSettings.htmlMonitorLabelRef = htmlMonitorLabel; + htmlMonitorContent.append(htmlMonitorLabel); + htmlMonitorLabel.innerHTML = '0%'; + return monitorSettings.htmlMonitorRef; + } + }); + Object.defineProperty(this, "updateMonitorSize", { + enumerable: true, + configurable: true, + writable: true, + value: (width, height) => { + this.styleSheet.innerText = `#crystools-monitors-root .crystools-monitor .crystools-content {height: ${height}px; width: ${width}px;}`; + } + }); + Object.defineProperty(this, "showMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: (monitorSettings, value) => { + if (monitorSettings.htmlMonitorRef) { + monitorSettings.htmlMonitorRef.style.display = value ? 'flex' : 'none'; + } + } + }); + Object.defineProperty(this, "resetMaxVRAM", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.maxVRAMUsed = {}; + } + }); + this.createDOM(); + this.styleSheet = createStyleSheet('crystools-monitors-size'); + } +} diff --git a/custom_nodes/ComfyUI-Crystools/web/monitorUI.ts b/custom_nodes/ComfyUI-Crystools/web/monitorUI.ts new file mode 100644 index 00000000..1482bd4b --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/monitorUI.ts @@ -0,0 +1,250 @@ +import { ProgressBarUIBase } from './progressBarUIBase.js'; +import { createStyleSheet, formatBytes } from './utils.js'; + +export class MonitorUI extends ProgressBarUIBase { + lastMonitor = 1; // just for order on monitors section + styleSheet: HTMLStyleElement; + maxVRAMUsed: Record = {}; // Add this to track max VRAM per GPU + + constructor( + public override rootElement: HTMLElement, + private monitorCPUElement: TMonitorSettings, + private monitorRAMElement: TMonitorSettings, + private monitorHDDElement: TMonitorSettings, + private monitorGPUSettings: TMonitorSettings[], + private monitorVRAMSettings: TMonitorSettings[], + private monitorTemperatureSettings: TMonitorSettings[], + private currentRate: number, + ) { + super('crystools-monitors-root', rootElement); + this.createDOM(); + + this.styleSheet = createStyleSheet('crystools-monitors-size'); + } + + createDOM = (): void => { + if (!this.rootElement) { + throw Error('Crystools: MonitorUI - Container not found'); + } + + // this.container.style.order = '2'; + this.rootElement.appendChild(this.createMonitor(this.monitorCPUElement)); + this.rootElement.appendChild(this.createMonitor(this.monitorRAMElement)); + this.rootElement.appendChild(this.createMonitor(this.monitorHDDElement)); + this.updateAllAnimationDuration(this.currentRate); + }; + + createDOMGPUMonitor = (monitorSettings?: TMonitorSettings): void => { + if (!(monitorSettings && this.rootElement)) { + return; + } + + this.rootElement.appendChild(this.createMonitor(monitorSettings)); + this.updateAllAnimationDuration(this.currentRate); + }; + + orderMonitors = (): void => { + try { + // @ts-ignore + this.monitorCPUElement.htmlMonitorRef.style.order = '' + this.lastMonitor++; + // @ts-ignore + this.monitorRAMElement.htmlMonitorRef.style.order = '' + this.lastMonitor++; + // @ts-ignore + this.monitorGPUSettings.forEach((_monitorSettings, index) => { + // @ts-ignore + this.monitorGPUSettings[index].htmlMonitorRef.style.order = '' + this.lastMonitor++; + // @ts-ignore + this.monitorVRAMSettings[index].htmlMonitorRef.style.order = '' + this.lastMonitor++; + // @ts-ignore + this.monitorTemperatureSettings[index].htmlMonitorRef.style.order = '' + this.lastMonitor++; + }); + // @ts-ignore + this.monitorHDDElement.htmlMonitorRef.style.order = '' + this.lastMonitor++; + } catch (error) { + console.error('orderMonitors', error); + } + }; + + updateDisplay = (data: TStatsData): void => { + this.updateMonitor(this.monitorCPUElement, data.cpu_utilization); + this.updateMonitor(this.monitorRAMElement, data.ram_used_percent, data.ram_used, data.ram_total); + this.updateMonitor(this.monitorHDDElement, data.hdd_used_percent, data.hdd_used, data.hdd_total); + + if (data.gpus === undefined || data.gpus.length === 0) { + console.warn('UpdateAllMonitors: no GPU data'); + return; + } + + this.monitorGPUSettings.forEach((monitorSettings, index) => { + if (data.gpus[index]) { + const gpu = data.gpus[index]; + if (gpu === undefined) { + // console.error('UpdateAllMonitors: no GPU data for index', index); + return; + } + + this.updateMonitor(monitorSettings, gpu.gpu_utilization); + } else { + // console.error('UpdateAllMonitors: no GPU data for index', index); + } + }); + + this.monitorVRAMSettings.forEach((monitorSettings, index) => { + if (data.gpus[index]) { + const gpu = data.gpus[index]; + if (gpu === undefined) { + // console.error('UpdateAllMonitors: no GPU VRAM data for index', index); + return; + } + + this.updateMonitor(monitorSettings, gpu.vram_used_percent, gpu.vram_used, gpu.vram_total); + } else { + // console.error('UpdateAllMonitors: no GPU VRAM data for index', index); + } + }); + + this.monitorTemperatureSettings.forEach((monitorSettings, index) => { + if (data.gpus[index]) { + const gpu = data.gpus[index]; + if (gpu === undefined) { + // console.error('UpdateAllMonitors: no GPU VRAM data for index', index); + return; + } + + this.updateMonitor(monitorSettings, gpu.gpu_temperature); + if (monitorSettings.cssColorFinal && monitorSettings.htmlMonitorSliderRef) { + monitorSettings.htmlMonitorSliderRef.style.backgroundColor = + `color-mix(in srgb, ${monitorSettings.cssColorFinal} ${gpu.gpu_temperature}%, ${monitorSettings.cssColor})`; + } + } else { + // console.error('UpdateAllMonitors: no GPU VRAM data for index', index); + } + }); + }; + + // eslint-disable-next-line complexity + updateMonitor = (monitorSettings: TMonitorSettings, percent: number, used?: number, total?: number): void => { + if (!(monitorSettings.htmlMonitorSliderRef && monitorSettings.htmlMonitorLabelRef)) { + return; + } + + if (percent < 0) { + return; + } + + const prefix = monitorSettings.monitorTitle ? monitorSettings.monitorTitle + ' - ' : ''; + let title = `${Math.floor(percent)}${monitorSettings.symbol}`; + let postfix = ''; + + // Add max VRAM tracking for VRAM monitors + if (used !== undefined && total !== undefined) { + // Extract GPU index from monitorTitle (assuming format "X: GPU Name") + const gpuIndex = parseInt(monitorSettings.monitorTitle?.split(':')[0] || '0'); + + // Initialize max VRAM if not set or glitch + if (!this.maxVRAMUsed[gpuIndex] || this.maxVRAMUsed[gpuIndex]! > total) { + this.maxVRAMUsed[gpuIndex] = 0; + } + + // Update max VRAM if current usage is higher + if ( used > this.maxVRAMUsed[gpuIndex]!) { + this.maxVRAMUsed[gpuIndex] = used; + } + + postfix = ` - ${formatBytes(used)} / ${formatBytes(total)}`; + // Add max VRAM to tooltip + postfix += ` Max: ${formatBytes(this.maxVRAMUsed[gpuIndex]!)}`; + } + + title = `${prefix}${title}${postfix}`; + + if (monitorSettings.htmlMonitorRef) { + monitorSettings.htmlMonitorRef.title = title; + } + monitorSettings.htmlMonitorLabelRef.innerHTML = `${Math.floor(percent)}${monitorSettings.symbol}`; + monitorSettings.htmlMonitorSliderRef.style.width = `${Math.floor(percent)}%`; + }; + + updateAllAnimationDuration = (value: number): void => { + this.updatedAnimationDuration(this.monitorCPUElement, value); + this.updatedAnimationDuration(this.monitorRAMElement, value); + this.updatedAnimationDuration(this.monitorHDDElement, value); + this.monitorGPUSettings.forEach((monitorSettings) => { + monitorSettings && this.updatedAnimationDuration(monitorSettings, value); + }); + this.monitorVRAMSettings.forEach((monitorSettings) => { + monitorSettings && this.updatedAnimationDuration(monitorSettings, value); + }); + this.monitorTemperatureSettings.forEach((monitorSettings) => { + monitorSettings && this.updatedAnimationDuration(monitorSettings, value); + }); + }; + + updatedAnimationDuration = (monitorSettings: TMonitorSettings, value: number): void => { + const slider = monitorSettings.htmlMonitorSliderRef; + if (!slider) { + return; + } + + slider.style.transition = `width ${value.toFixed(1)}s`; + }; + + createMonitor = (monitorSettings?: TMonitorSettings): HTMLDivElement => { + if (!monitorSettings) { + // just for typescript + return document.createElement('div'); + } + + const htmlMain = document.createElement('div'); + htmlMain.classList.add(monitorSettings.id); + htmlMain.classList.add('crystools-monitor'); + + monitorSettings.htmlMonitorRef = htmlMain; + + if (monitorSettings.title) { + htmlMain.title = monitorSettings.title; + } + + const htmlMonitorText = document.createElement('div'); + htmlMonitorText.classList.add('crystools-text'); + htmlMonitorText.innerHTML = monitorSettings.label; + htmlMain.append(htmlMonitorText); + + const htmlMonitorContent = document.createElement('div'); + htmlMonitorContent.classList.add('crystools-content'); + htmlMain.append(htmlMonitorContent); + + const htmlMonitorSlider = document.createElement('div'); + htmlMonitorSlider.classList.add('crystools-slider'); + if (monitorSettings.cssColorFinal) { + htmlMonitorSlider.style.backgroundColor = + `color-mix(in srgb, ${monitorSettings.cssColorFinal} 0%, ${monitorSettings.cssColor})`; + } else { + htmlMonitorSlider.style.backgroundColor = monitorSettings.cssColor; + } + monitorSettings.htmlMonitorSliderRef = htmlMonitorSlider; + htmlMonitorContent.append(htmlMonitorSlider); + + const htmlMonitorLabel = document.createElement('div'); + htmlMonitorLabel.classList.add('crystools-label'); + monitorSettings.htmlMonitorLabelRef = htmlMonitorLabel; + htmlMonitorContent.append(htmlMonitorLabel); + htmlMonitorLabel.innerHTML = '0%'; + return monitorSettings.htmlMonitorRef; + }; + + updateMonitorSize = (width: number, height: number): void => { + // eslint-disable-next-line max-len + this.styleSheet.innerText = `#crystools-monitors-root .crystools-monitor .crystools-content {height: ${height}px; width: ${width}px;}`; + }; + + showMonitor = (monitorSettings: TMonitorSettings, value: boolean): void => { + if (monitorSettings.htmlMonitorRef) { + monitorSettings.htmlMonitorRef.style.display = value ? 'flex' : 'none'; + } + }; + + resetMaxVRAM = (): void => { + this.maxVRAMUsed = {}; + }; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBar.d.ts b/custom_nodes/ComfyUI-Crystools/web/progressBar.d.ts new file mode 100644 index 00000000..cb0ff5c3 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBar.d.ts @@ -0,0 +1 @@ +export {}; diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBar.js b/custom_nodes/ComfyUI-Crystools/web/progressBar.js new file mode 100644 index 00000000..178c7ec8 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBar.js @@ -0,0 +1,187 @@ +import { app, api } from './comfy/index.js'; +import { commonPrefix } from './common.js'; +import { ProgressBarUI } from './progressBarUI.js'; +import { ComfyKeyMenuDisplayOption, EStatus, MenuDisplayOptions } from './progressBarUIBase.js'; +class CrystoolsProgressBar { + constructor() { + Object.defineProperty(this, "idExtensionName", { + enumerable: true, + configurable: true, + writable: true, + value: 'Crystools.progressBar' + }); + Object.defineProperty(this, "idShowProgressBar", { + enumerable: true, + configurable: true, + writable: true, + value: 'Crystools.ProgressBar' + }); + Object.defineProperty(this, "defaultShowStatus", { + enumerable: true, + configurable: true, + writable: true, + value: true + }); + Object.defineProperty(this, "menuPrefix", { + enumerable: true, + configurable: true, + writable: true, + value: commonPrefix + }); + Object.defineProperty(this, "menuDisplayOption", { + enumerable: true, + configurable: true, + writable: true, + value: MenuDisplayOptions.Disabled + }); + Object.defineProperty(this, "currentStatus", { + enumerable: true, + configurable: true, + writable: true, + value: EStatus.executed + }); + Object.defineProperty(this, "currentProgress", { + enumerable: true, + configurable: true, + writable: true, + value: 0 + }); + Object.defineProperty(this, "currentNode", { + enumerable: true, + configurable: true, + writable: true, + value: undefined + }); + Object.defineProperty(this, "timeStart", { + enumerable: true, + configurable: true, + writable: true, + value: 0 + }); + Object.defineProperty(this, "progressBarUI", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "createSettings", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + app.ui.settings.addSetting({ + id: this.idShowProgressBar, + name: 'Show progress bar', + category: ['Crystools', this.menuPrefix + ' Progress Bar', 'Show'], + tooltip: 'This apply only on "Disabled" (old) menu', + type: 'boolean', + defaultValue: this.defaultShowStatus, + onChange: this.progressBarUI.showProgressBar, + }); + } + }); + Object.defineProperty(this, "updateDisplay", { + enumerable: true, + configurable: true, + writable: true, + value: (menuDisplayOption) => { + if (menuDisplayOption !== this.menuDisplayOption) { + this.menuDisplayOption = menuDisplayOption; + this.progressBarUI.showSection(this.menuDisplayOption === MenuDisplayOptions.Disabled); + } + if (this.menuDisplayOption === MenuDisplayOptions.Disabled && this.progressBarUI.showProgressBarFlag) { + this.progressBarUI.updateDisplay(this.currentStatus, this.timeStart, this.currentProgress); + } + } + }); + Object.defineProperty(this, "setup", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + if (this.progressBarUI) { + this.progressBarUI + .showProgressBar(app.extensionManager.setting.get(this.idShowProgressBar)); + return; + } + this.menuDisplayOption = app.extensionManager.setting.get(ComfyKeyMenuDisplayOption); + app.ui.settings.addEventListener(`${ComfyKeyMenuDisplayOption}.change`, (e) => { + this.updateDisplay(e.detail.value); + }); + const progressBarElement = document.createElement('div'); + progressBarElement.classList.add('crystools-monitors-container'); + this.progressBarUI = new ProgressBarUI(progressBarElement, (this.menuDisplayOption === MenuDisplayOptions.Disabled), this.centerNode); + const parentElement = document.getElementById('queue-button'); + if (parentElement) { + parentElement.insertAdjacentElement('afterend', progressBarElement); + } + else { + console.error('Crystools: parentElement to move monitors not found!', parentElement); + } + this.createSettings(); + this.updateDisplay(this.menuDisplayOption); + this.registerListeners(); + } + }); + Object.defineProperty(this, "registerListeners", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + api.addEventListener('status', ({ detail }) => { + this.currentStatus = this.currentStatus === EStatus.execution_error ? EStatus.execution_error : EStatus.executed; + const queueRemaining = detail?.exec_info.queue_remaining; + if (queueRemaining) { + this.currentStatus = EStatus.executing; + } + this.updateDisplay(this.menuDisplayOption); + }, false); + api.addEventListener('progress', ({ detail }) => { + const { value, max, node } = detail; + const progress = Math.floor((value / max) * 100); + if (!isNaN(progress) && progress >= 0 && progress <= 100) { + this.currentProgress = progress; + this.currentNode = node; + } + this.updateDisplay(this.menuDisplayOption); + }, false); + api.addEventListener('executed', ({ detail }) => { + if (detail?.node) { + this.currentNode = detail.node; + } + this.updateDisplay(this.menuDisplayOption); + }, false); + api.addEventListener('execution_start', ({ _detail }) => { + this.currentStatus = EStatus.executing; + this.timeStart = Date.now(); + this.updateDisplay(this.menuDisplayOption); + }, false); + api.addEventListener('execution_error', ({ _detail }) => { + this.currentStatus = EStatus.execution_error; + this.updateDisplay(this.menuDisplayOption); + }, false); + } + }); + Object.defineProperty(this, "centerNode", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + const id = this.currentNode; + if (!id) { + return; + } + const node = app.graph.getNodeById(id); + if (!node) { + return; + } + app.canvas.centerOnNode(node); + } + }); + } +} +const crystoolsProgressBar = new CrystoolsProgressBar(); +app.registerExtension({ + name: crystoolsProgressBar.idExtensionName, + setup: crystoolsProgressBar.setup, +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBar.ts b/custom_nodes/ComfyUI-Crystools/web/progressBar.ts new file mode 100644 index 00000000..cadf8d35 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBar.ts @@ -0,0 +1,140 @@ +import { app, api } from './comfy/index.js'; +import { commonPrefix } from './common.js'; +import { ProgressBarUI } from './progressBarUI.js'; +import { ComfyKeyMenuDisplayOption, EStatus, MenuDisplayOptions } from './progressBarUIBase.js'; + +class CrystoolsProgressBar { + idExtensionName = 'Crystools.progressBar'; + idShowProgressBar = 'Crystools.ProgressBar'; + defaultShowStatus = true; + menuPrefix = commonPrefix; + menuDisplayOption: MenuDisplayOptions = MenuDisplayOptions.Disabled; + + currentStatus = EStatus.executed; + currentProgress = 0; + currentNode?: number = undefined; + timeStart = 0; + + progressBarUI: ProgressBarUI; + + // not on setup because this affect the order on settings, I prefer to options at first + createSettings = (): void => { + app.ui.settings.addSetting({ + id: this.idShowProgressBar, + name: 'Show progress bar', + category: ['Crystools', this.menuPrefix + ' Progress Bar', 'Show'], + tooltip: 'This apply only on "Disabled" (old) menu', + type: 'boolean', + defaultValue: this.defaultShowStatus, + onChange: this.progressBarUI.showProgressBar, + }); + }; + + updateDisplay = (menuDisplayOption: MenuDisplayOptions): void => { + if (menuDisplayOption !== this.menuDisplayOption) { + this.menuDisplayOption = menuDisplayOption; + this.progressBarUI.showSection(this.menuDisplayOption === MenuDisplayOptions.Disabled); + } + if (this.menuDisplayOption === MenuDisplayOptions.Disabled && this.progressBarUI.showProgressBarFlag) { + this.progressBarUI.updateDisplay(this.currentStatus, this.timeStart, this.currentProgress); + } + }; + + // automatically called by ComfyUI + setup = (): void => { + if (this.progressBarUI) { + this.progressBarUI + .showProgressBar(app.extensionManager.setting.get(this.idShowProgressBar)); + return; + } + + this.menuDisplayOption = app.extensionManager.setting.get(ComfyKeyMenuDisplayOption); + app.ui.settings.addEventListener(`${ComfyKeyMenuDisplayOption}.change`, (e: any) => { + this.updateDisplay(e.detail.value); + }, + ); + + const progressBarElement = document.createElement('div'); + progressBarElement.classList.add('crystools-monitors-container'); + + this.progressBarUI = new ProgressBarUI( + progressBarElement, + (this.menuDisplayOption === MenuDisplayOptions.Disabled), + this.centerNode, + ); + + const parentElement = document.getElementById('queue-button'); + if (parentElement) { + parentElement.insertAdjacentElement('afterend', progressBarElement); + } else { + console.error('Crystools: parentElement to move monitors not found!', parentElement); + } + + this.createSettings(); + this.updateDisplay(this.menuDisplayOption); + this.registerListeners(); + }; + + registerListeners = (): void => { + api.addEventListener('status', ({detail}: any) => { + this.currentStatus = this.currentStatus === EStatus.execution_error ? EStatus.execution_error : EStatus.executed; + const queueRemaining = detail?.exec_info.queue_remaining; + + if (queueRemaining) { + this.currentStatus = EStatus.executing; + } + this.updateDisplay(this.menuDisplayOption); + }, false); + + api.addEventListener('progress', ({detail}: any) => { + const {value, max, node} = detail; + const progress = Math.floor((value / max) * 100); + + if (!isNaN(progress) && progress >= 0 && progress <= 100) { + this.currentProgress = progress; + this.currentNode = node; + } + + this.updateDisplay(this.menuDisplayOption); + }, false); + + api.addEventListener('executed', ({detail}: any) => { + if (detail?.node) { + this.currentNode = detail.node; + } + + this.updateDisplay(this.menuDisplayOption); + }, false); + + api.addEventListener('execution_start', ({_detail}: any) => { + this.currentStatus = EStatus.executing; + this.timeStart = Date.now(); + + this.updateDisplay(this.menuDisplayOption); + }, false); + + api.addEventListener('execution_error', ({_detail}: any) => { + this.currentStatus = EStatus.execution_error; + + this.updateDisplay(this.menuDisplayOption); + }, false); + }; + + centerNode = (): void => { + const id = this.currentNode; + if (!id) { + return; + } + const node = app.graph.getNodeById(id); + if (!node) { + return; + } + app.canvas.centerOnNode(node); + }; +} + +const crystoolsProgressBar = new CrystoolsProgressBar(); +app.registerExtension({ + name: crystoolsProgressBar.idExtensionName, + setup: crystoolsProgressBar.setup, +}); diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBarUI.d.ts b/custom_nodes/ComfyUI-Crystools/web/progressBarUI.d.ts new file mode 100644 index 00000000..e1bcf119 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBarUI.d.ts @@ -0,0 +1,18 @@ +import { EStatus, ProgressBarUIBase } from './progressBarUIBase.js'; +export declare class ProgressBarUI extends ProgressBarUIBase { + rootElement: HTMLElement; + showSectionFlag: boolean; + private centerNode; + htmlProgressSliderRef: HTMLDivElement; + htmlProgressLabelRef: HTMLDivElement; + currentStatus: EStatus; + timeStart: number; + currentProgress: number; + showProgressBarFlag: boolean; + constructor(rootElement: HTMLElement, showSectionFlag: boolean, centerNode: () => void); + createDOM: () => void; + updateDisplay: (currentStatus: EStatus, timeStart: number, currentProgress: number) => void; + showSection: (value: boolean) => void; + showProgressBar: (value: boolean) => void; + private displaySection; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBarUI.js b/custom_nodes/ComfyUI-Crystools/web/progressBarUI.js new file mode 100644 index 00000000..ed1d4fb9 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBarUI.js @@ -0,0 +1,142 @@ +import { EStatus, ProgressBarUIBase } from './progressBarUIBase.js'; +export class ProgressBarUI extends ProgressBarUIBase { + constructor(rootElement, showSectionFlag, centerNode) { + super('crystools-progressBar-root', rootElement); + Object.defineProperty(this, "rootElement", { + enumerable: true, + configurable: true, + writable: true, + value: rootElement + }); + Object.defineProperty(this, "showSectionFlag", { + enumerable: true, + configurable: true, + writable: true, + value: showSectionFlag + }); + Object.defineProperty(this, "centerNode", { + enumerable: true, + configurable: true, + writable: true, + value: centerNode + }); + Object.defineProperty(this, "htmlProgressSliderRef", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "htmlProgressLabelRef", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "currentStatus", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "timeStart", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "currentProgress", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "showProgressBarFlag", { + enumerable: true, + configurable: true, + writable: true, + value: void 0 + }); + Object.defineProperty(this, "createDOM", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.rootElement.setAttribute('title', 'click to see the current working node'); + this.rootElement.addEventListener('click', this.centerNode); + const progressBar = document.createElement('div'); + progressBar.classList.add('crystools-progress-bar'); + this.rootElement.append(progressBar); + const progressSlider = document.createElement('div'); + this.htmlProgressSliderRef = progressSlider; + progressSlider.classList.add('crystools-slider'); + progressBar.append(this.htmlProgressSliderRef); + const progressLabel = document.createElement('div'); + progressLabel.classList.add('crystools-label'); + progressLabel.innerHTML = '0%'; + this.htmlProgressLabelRef = progressLabel; + progressBar.append(this.htmlProgressLabelRef); + } + }); + Object.defineProperty(this, "updateDisplay", { + enumerable: true, + configurable: true, + writable: true, + value: (currentStatus, timeStart, currentProgress) => { + if (!(this.showSectionFlag && this.showProgressBarFlag)) { + return; + } + if (!(this.htmlProgressLabelRef && this.htmlProgressSliderRef)) { + console.error('htmlProgressLabelRef or htmlProgressSliderRef is undefined'); + return; + } + this.currentStatus = currentStatus; + this.timeStart = timeStart; + this.currentProgress = currentProgress; + if (currentStatus === EStatus.executed) { + this.htmlProgressLabelRef.innerHTML = 'cached'; + const timeElapsed = Date.now() - timeStart; + if (timeStart > 0 && timeElapsed > 0) { + this.htmlProgressLabelRef.innerHTML = new Date(timeElapsed).toISOString().substr(11, 8); + } + this.htmlProgressSliderRef.style.width = '0'; + } + else if (currentStatus === EStatus.execution_error) { + this.htmlProgressLabelRef.innerHTML = 'ERROR'; + this.htmlProgressSliderRef.style.backgroundColor = 'var(--error-text)'; + } + else if (currentStatus === EStatus.executing) { + this.htmlProgressLabelRef.innerHTML = `${currentProgress}%`; + this.htmlProgressSliderRef.style.width = this.htmlProgressLabelRef.innerHTML; + this.htmlProgressSliderRef.style.backgroundColor = 'green'; + } + } + }); + Object.defineProperty(this, "showSection", { + enumerable: true, + configurable: true, + writable: true, + value: (value) => { + this.showSectionFlag = value; + this.displaySection(); + } + }); + Object.defineProperty(this, "showProgressBar", { + enumerable: true, + configurable: true, + writable: true, + value: (value) => { + this.showProgressBarFlag = value; + this.displaySection(); + } + }); + Object.defineProperty(this, "displaySection", { + enumerable: true, + configurable: true, + writable: true, + value: () => { + this.rootElement.style.display = (this.showSectionFlag && this.showProgressBarFlag) ? 'block' : 'none'; + } + }); + this.createDOM(); + } +} diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBarUI.ts b/custom_nodes/ComfyUI-Crystools/web/progressBarUI.ts new file mode 100644 index 00000000..218d53b6 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBarUI.ts @@ -0,0 +1,95 @@ +import { EStatus, ProgressBarUIBase } from './progressBarUIBase.js'; + +export class ProgressBarUI extends ProgressBarUIBase { + htmlProgressSliderRef: HTMLDivElement; + htmlProgressLabelRef: HTMLDivElement; + currentStatus: EStatus; + timeStart: number; + currentProgress: number; + showProgressBarFlag: boolean; + + constructor( + public override rootElement: HTMLElement, + public showSectionFlag: boolean, + private centerNode: () => void, + ) { + super('crystools-progressBar-root', rootElement); + this.createDOM(); + } + + createDOM = (): void => { + this.rootElement.setAttribute('title', 'click to see the current working node'); + this.rootElement.addEventListener('click', this.centerNode); + + const progressBar = document.createElement('div'); + progressBar.classList.add('crystools-progress-bar'); + this.rootElement.append(progressBar); + + const progressSlider = document.createElement('div'); + this.htmlProgressSliderRef = progressSlider; + progressSlider.classList.add('crystools-slider'); + progressBar.append(this.htmlProgressSliderRef); + + const progressLabel = document.createElement('div'); + progressLabel.classList.add('crystools-label'); + progressLabel.innerHTML = '0%'; + this.htmlProgressLabelRef = progressLabel; + progressBar.append(this.htmlProgressLabelRef); + }; + + // eslint-disable-next-line complexity + updateDisplay = (currentStatus: EStatus, timeStart: number, currentProgress: number): void => { + if (!(this.showSectionFlag && this.showProgressBarFlag)) { + return; + } + + if (!(this.htmlProgressLabelRef && this.htmlProgressSliderRef)) { + console.error('htmlProgressLabelRef or htmlProgressSliderRef is undefined'); + return; + } + + // console.log('only if showSection and progressBar', timeStart, currentProgress); + + this.currentStatus = currentStatus; + this.timeStart = timeStart; + this.currentProgress = currentProgress; + + if (currentStatus === EStatus.executed) { + // finished + this.htmlProgressLabelRef.innerHTML = 'cached'; + + const timeElapsed = Date.now() - timeStart; + if (timeStart > 0 && timeElapsed > 0) { + this.htmlProgressLabelRef.innerHTML = new Date(timeElapsed).toISOString().substr(11, 8); + } + this.htmlProgressSliderRef.style.width = '0'; + + } else if (currentStatus === EStatus.execution_error) { + // an error occurred + this.htmlProgressLabelRef.innerHTML = 'ERROR'; + this.htmlProgressSliderRef.style.backgroundColor = 'var(--error-text)'; + + } else if (currentStatus === EStatus.executing) { + // on going + this.htmlProgressLabelRef.innerHTML = `${currentProgress}%`; + this.htmlProgressSliderRef.style.width = this.htmlProgressLabelRef.innerHTML; + this.htmlProgressSliderRef.style.backgroundColor = 'green'; // by reset the color + } + + }; + + public showSection = (value: boolean): void => { + this.showSectionFlag = value; + this.displaySection(); + }; + + // remember it can't have more parameters because it is used on settings automatically + public showProgressBar = (value: boolean): void => { + this.showProgressBarFlag = value; + this.displaySection(); + }; + + private displaySection = (): void => { + this.rootElement.style.display = (this.showSectionFlag && this.showProgressBarFlag) ? 'block' : 'none'; + }; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.d.ts b/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.d.ts new file mode 100644 index 00000000..dace4020 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.d.ts @@ -0,0 +1,18 @@ +export declare enum EStatus { + executing = "Executing", + executed = "Executed", + execution_error = "Execution error" +} +export declare const ComfyKeyMenuDisplayOption = "Comfy.UseNewMenu"; +export declare enum MenuDisplayOptions { + 'Disabled' = "Disabled", + 'Top' = "Top", + 'Bottom' = "Bottom" +} +export declare abstract class ProgressBarUIBase { + rootId: string; + rootElement: HTMLElement | null | undefined; + protected htmlClassMonitor: string; + protected constructor(rootId: string, rootElement: HTMLElement | null | undefined); + abstract createDOM(): void; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.js b/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.js new file mode 100644 index 00000000..c9fb1745 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.js @@ -0,0 +1,42 @@ +export var EStatus; +(function (EStatus) { + EStatus["executing"] = "Executing"; + EStatus["executed"] = "Executed"; + EStatus["execution_error"] = "Execution error"; +})(EStatus || (EStatus = {})); +export const ComfyKeyMenuDisplayOption = 'Comfy.UseNewMenu'; +export var MenuDisplayOptions; +(function (MenuDisplayOptions) { + MenuDisplayOptions["Disabled"] = "Disabled"; + MenuDisplayOptions["Top"] = "Top"; + MenuDisplayOptions["Bottom"] = "Bottom"; +})(MenuDisplayOptions || (MenuDisplayOptions = {})); +export class ProgressBarUIBase { + constructor(rootId, rootElement) { + Object.defineProperty(this, "rootId", { + enumerable: true, + configurable: true, + writable: true, + value: rootId + }); + Object.defineProperty(this, "rootElement", { + enumerable: true, + configurable: true, + writable: true, + value: rootElement + }); + Object.defineProperty(this, "htmlClassMonitor", { + enumerable: true, + configurable: true, + writable: true, + value: 'crystools-monitors-container' + }); + if (this.rootElement && this.rootElement.children.length === 0) { + this.rootElement.setAttribute('id', this.rootId); + this.rootElement.classList.add(this.htmlClassMonitor); + this.rootElement.classList.add(this.constructor.name); + } + else { + } + } +} diff --git a/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.ts b/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.ts new file mode 100644 index 00000000..9b9486bd --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/progressBarUIBase.ts @@ -0,0 +1,32 @@ +export enum EStatus { + executing = 'Executing', + executed = 'Executed', + execution_error = 'Execution error', +} + +export const ComfyKeyMenuDisplayOption = 'Comfy.UseNewMenu'; +export enum MenuDisplayOptions { + 'Disabled' = 'Disabled', + 'Top' = 'Top', + 'Bottom' = 'Bottom', +} + +export abstract class ProgressBarUIBase { + protected htmlClassMonitor = 'crystools-monitors-container'; + + protected constructor( + public rootId: string, + public rootElement: HTMLElement | null | undefined, + ) { + // IMPORTANT duplicate on crystools-save + if (this.rootElement && this.rootElement.children.length === 0) { + this.rootElement.setAttribute('id', this.rootId); + this.rootElement.classList.add(this.htmlClassMonitor); + this.rootElement.classList.add(this.constructor.name); + } else { + // it was created before + } + } + + abstract createDOM(): void; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/styles.d.ts b/custom_nodes/ComfyUI-Crystools/web/styles.d.ts new file mode 100644 index 00000000..4d8a4db1 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/styles.d.ts @@ -0,0 +1,12 @@ +export declare enum Styles { + 'BARS' = "BARS" +} +export declare enum Colors { + 'CPU' = "#0AA015", + 'RAM' = "#07630D", + 'DISK' = "#730F92", + 'GPU' = "#0C86F4", + 'VRAM' = "#176EC7", + 'TEMP_START' = "#00ff00", + 'TEMP_END' = "#ff0000" +} diff --git a/custom_nodes/ComfyUI-Crystools/web/styles.js b/custom_nodes/ComfyUI-Crystools/web/styles.js new file mode 100644 index 00000000..843268d4 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/styles.js @@ -0,0 +1,16 @@ +import { utils } from './comfy/index.js'; +utils.addStylesheet('extensions/ComfyUI-Crystools/monitor.css'); +export var Styles; +(function (Styles) { + Styles["BARS"] = "BARS"; +})(Styles || (Styles = {})); +export var Colors; +(function (Colors) { + Colors["CPU"] = "#0AA015"; + Colors["RAM"] = "#07630D"; + Colors["DISK"] = "#730F92"; + Colors["GPU"] = "#0C86F4"; + Colors["VRAM"] = "#176EC7"; + Colors["TEMP_START"] = "#00ff00"; + Colors["TEMP_END"] = "#ff0000"; +})(Colors || (Colors = {})); diff --git a/custom_nodes/ComfyUI-Crystools/web/styles.ts b/custom_nodes/ComfyUI-Crystools/web/styles.ts new file mode 100644 index 00000000..3426fb95 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/styles.ts @@ -0,0 +1,17 @@ +import { utils } from './comfy/index.js'; + +utils.addStylesheet('extensions/ComfyUI-Crystools/monitor.css'); + +export enum Styles { + 'BARS' = 'BARS' +} + +export enum Colors { + 'CPU' = '#0AA015', + 'RAM' = '#07630D', + 'DISK' = '#730F92', + 'GPU' = '#0C86F4', + 'VRAM' = '#176EC7', + 'TEMP_START' = '#00ff00', + 'TEMP_END' = '#ff0000', +} diff --git a/custom_nodes/ComfyUI-Crystools/web/types.d.ts b/custom_nodes/ComfyUI-Crystools/web/types.d.ts new file mode 100644 index 00000000..3480acf0 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/types.d.ts @@ -0,0 +1,61 @@ + +type TGpuStatData = { + gpu_utilization: number, + gpu_temperature: number, + vram_total: number, + vram_used: number, + vram_used_percent: number, +} + +type TGpuSettings = { + utilization?: boolean, + vram?: boolean, + temperature?: boolean, +} + +type TGpuName = { + name: string, + index: number, +} + +type TStatsData = { + cpu_utilization: number, + device: string, + gpus: TGpuStatData[], + hdd_total: number, + hdd_used: number, + hdd_used_percent: number, + ram_total: number, + ram_used: number, + ram_used_percent: number, +} + +type TStatsSettings = { + rate?: number, + switchCPU?: boolean, + switchGPU?: boolean, + switchHDD?: boolean, + switchRAM?: boolean, + switchVRAM?: boolean, + whichHDD?: string, +} + +type TMonitorSettings = { + id: string, + name: string, + category: string[], // for settings location + label: string, // on monitor + symbol: string, // on monitor + monitorTitle?: string, // on monitor + title?: string, // on monitor + tooltip?: string, // on settings + type: 'boolean' | 'number' | 'string' | 'slider' | 'combo', + defaultValue: boolean | number | string, + data?: any, + onChange: (value: boolean | number | string) => Promise, + htmlMonitorRef?: HTMLDivElement, + htmlMonitorSliderRef?: HTMLDivElement, + htmlMonitorLabelRef?: HTMLDivElement, + cssColor: string, + cssColorFinal?: string, +} diff --git a/custom_nodes/ComfyUI-Crystools/web/utils.d.ts b/custom_nodes/ComfyUI-Crystools/web/utils.d.ts new file mode 100644 index 00000000..31bb65ac --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/utils.d.ts @@ -0,0 +1,5 @@ +export declare function numberToWords(num: number): string; +export declare function toPascalCase(strings: string[]): string; +export declare function convertNumberToPascalCase(num: number): string; +export declare function formatBytes(bytes: number): string; +export declare function createStyleSheet(id: string): HTMLStyleElement; diff --git a/custom_nodes/ComfyUI-Crystools/web/utils.js b/custom_nodes/ComfyUI-Crystools/web/utils.js new file mode 100644 index 00000000..54362dd1 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/utils.js @@ -0,0 +1,56 @@ +export function numberToWords(num) { + if (num === 0) + return 'zero'; + const belowTwenty = [ + '', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', + 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen' + ]; + const tens = ['', '', 'twenty', 'thirty', 'forty', 'fifty', 'sixty', 'seventy', 'eighty', 'ninety']; + const thousands = ['', 'thousand', 'million', 'billion']; + function helper(n) { + if (n === 0) + return ''; + else if (n < 20) + return belowTwenty[n] + ' '; + else if (n < 100) + return tens[Math.floor(n / 10)] + ' ' + helper(n % 10); + return belowTwenty[Math.floor(n / 100)] + ' hundred ' + helper(n % 100); + } + let word = ''; + let i = 0; + while (num > 0) { + if (num % 1000 !== 0) { + word = helper(num % 1000) + thousands[i] + ' ' + word; + } + num = Math.floor(num / 1000); + i++; + } + return word.trim(); +} +export function toPascalCase(strings) { + if (!Array.isArray(strings)) + return ''; + function capitalize(word) { + return word.charAt(0).toUpperCase() + word.slice(1).toLowerCase(); + } + return strings.map(capitalize).join(''); +} +export function convertNumberToPascalCase(num) { + return toPascalCase(numberToWords(num).split(' ')); +} +export function formatBytes(bytes) { + if (bytes === 0) + return '0 Bytes'; + const sizes = ['Bytes', 'KB', 'MB', 'GB', 'TB']; + const i = Math.floor(Math.log(bytes) / Math.log(1024)); + const formattedSize = (bytes / Math.pow(1024, i)).toFixed(2); + return `${formattedSize} ${sizes[i]}`; +} +export function createStyleSheet(id) { + const style = document.createElement('style'); + style.setAttribute('id', id); + style.setAttribute('rel', 'stylesheet'); + style.setAttribute('type', 'text/css'); + document.head.appendChild(style); + return style; +} diff --git a/custom_nodes/ComfyUI-Crystools/web/utils.ts b/custom_nodes/ComfyUI-Crystools/web/utils.ts new file mode 100644 index 00000000..f6930ca4 --- /dev/null +++ b/custom_nodes/ComfyUI-Crystools/web/utils.ts @@ -0,0 +1,64 @@ +export function numberToWords(num: number): string { + if (num === 0) return 'zero'; + + const belowTwenty = [ + '', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', + 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen' + ]; + const tens = ['', '', 'twenty', 'thirty', 'forty', 'fifty', 'sixty', 'seventy', 'eighty', 'ninety']; + const thousands = ['', 'thousand', 'million', 'billion']; + + function helper(n: number): string { + if (n === 0) return ''; + else if (n < 20) return belowTwenty[n] + ' '; + else if (n < 100) return tens[Math.floor(n / 10)] + ' ' + helper(n % 10); + return belowTwenty[Math.floor(n / 100)] + ' hundred ' + helper(n % 100); + } + + let word = ''; + let i = 0; + + while (num > 0) { + if (num % 1000 !== 0) { + word = helper(num % 1000) + thousands[i] + ' ' + word; + } + num = Math.floor(num / 1000); + i++; + } + + return word.trim(); +} + +export function toPascalCase(strings: string[]): string { + if (!Array.isArray(strings)) return ''; + + function capitalize(word: string): string { + return word.charAt(0).toUpperCase() + word.slice(1).toLowerCase(); + } + + return strings.map(capitalize).join(''); +} + + +export function convertNumberToPascalCase(num: number): string { + return toPascalCase(numberToWords(num).split(' ')); +} + +export function formatBytes(bytes: number): string { + if (bytes === 0) return '0 Bytes'; + + const sizes = ['Bytes', 'KB', 'MB', 'GB', 'TB']; + const i = Math.floor(Math.log(bytes) / Math.log(1024)); + const formattedSize = (bytes / Math.pow(1024, i)).toFixed(2); + + return `${formattedSize} ${sizes[i]}`; +} + +export function createStyleSheet(id: string): HTMLStyleElement { + const style = document.createElement('style'); + style.setAttribute('id', id); + style.setAttribute('rel', 'stylesheet'); + style.setAttribute('type', 'text/css'); + document.head.appendChild(style); + return style; +} diff --git a/custom_nodes/ComfyUI-Easy-Use/.gitignore b/custom_nodes/ComfyUI-Easy-Use/.gitignore new file mode 100644 index 00000000..3bbb1109 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/.gitignore @@ -0,0 +1,20 @@ +__pycache__ +.DS_Store +*.cache +*.ini +*.bak +wildcards/** +styles/** +workflow/** +autocomplete/** +web_beta/** +web_version/dev/** +docs/** +.vscode/ +.idea/ +mmb-preset.custom.txt +config.yaml +node.tar.gz + +.cursorrules +tools/ComfyUI-Easy-Use.json \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/LICENSE b/custom_nodes/ComfyUI-Easy-Use/LICENSE new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. diff --git a/custom_nodes/ComfyUI-Easy-Use/README.ZH_CN.md b/custom_nodes/ComfyUI-Easy-Use/README.ZH_CN.md new file mode 100644 index 00000000..3efe4216 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/README.ZH_CN.md @@ -0,0 +1,573 @@ +![comfyui-easy-use](https://github.com/user-attachments/assets/9b7a5e44-f5e2-4c27-aed2-d0e6b50c46bb) + + + +**ComfyUI-Easy-Use** ζ˜―δΈ€δΈͺεŒ–ηΉδΈΊη€ηš„θŠ‚η‚Ήζ•΄εˆεŒ…, 在 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) ηš„εŸΊη‘€δΈŠθΏ›θ‘Œε»Άε±•οΌŒεΉΆι’ˆε―ΉδΊ†θ―Έε€šδΈ»ζ΅ηš„θŠ‚η‚ΉεŒ…εšδΊ†ζ•΄εˆδΈŽδΌ˜εŒ–οΌŒδ»₯θΎΎεˆ°ζ›΄εΏ«ζ›΄ζ–ΉδΎΏδ½Ώη”¨ComfyUIηš„η›ηš„οΌŒεœ¨δΏθ―θ‡ͺη”±εΊ¦ηš„εŒζ—ΆθΏ˜εŽŸδΊ†ζœ¬ε±žδΊŽStable Diffusionηš„ζžθ‡΄η•…εΏ«ε‡Ίε›Ύδ½“ιͺŒγ€‚ + +## πŸ‘¨πŸ»β€πŸŽ¨ 特色介绍 + +- 沿用了 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) ηš„ζ€θ·―οΌŒε€§ε€§ε‡ε°‘δΊ†ζŠ˜θ…Ύε·₯δ½œζ΅ηš„ζ—Άι—΄ζˆζœ¬γ€‚ +- UIη•Œι’ηΎŽεŒ–οΌŒι¦–ζ¬‘ε‰θ£…ηš„η”¨ζˆ·οΌŒε¦‚ιœ€δ½Ώη”¨UI主钘，请在 Settings -> Color Palette δΈ­θ‡ͺθ‘Œεˆ‡ζ’δΈ»ι’˜εΉΆ**εˆ·ζ–°ι‘΅ι’**即可 +- ε’žεŠ δΊ†ι’„ι‡‡ζ ·ε‚ζ•°ι…η½ηš„θŠ‚η‚ΉοΌŒε―δΈŽι‡‡ζ ·θŠ‚η‚Ήεˆ†η¦»οΌŒζ›΄ζ–ΉδΎΏι’„θ§ˆγ€‚ +- ζ”―ζŒι€šι…η¬¦δΈŽLoraηš„ζη€Ίθ―θŠ‚η‚ΉοΌŒε¦‚ιœ€δ½Ώη”¨Lora Block Weightη”¨ζ³•οΌŒιœ€ε…ˆδΏθ―θ‡ͺεšδΉ‰θŠ‚η‚ΉεŒ…δΈ­ε‰θ£…δΊ† [ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) +- ε―ε€šι€‰ηš„ι£Žζ ΌεŒ–ζη€Ίθ―ι€‰ζ‹©ε™¨οΌŒι»˜θ€ζ˜―Fooocusηš„ζ ·εΌjson，可θ‡ͺεšδΉ‰jsonζ”Ύεœ¨stylesεΊ•δΈ‹οΌŒsamplesζ–‡δ»Άε€Ήι‡Œε―ζ”Ύι’„θ§ˆε›Ύ(εη§°ε’Œname一致,ε›Ύη‰‡ζ–‡δ»Άεε¦‚ζœ‰η©Ίζ Όιœ€θ½¬δΈΊδΈ‹εˆ’ηΊΏ'_') +- εŠ θ½½ε™¨ε―εΌ€ε―A1111ζη€Ίθ―ι£Žζ Όζ¨‘εΌοΌŒε―ι‡ηŽ°δΈŽwebuiη”ŸζˆθΏ‘δΉŽη›ΈεŒηš„ε›Ύεƒ +- 可使用`easy latentNoisy`ζˆ–`easy preSamplingNoiseIn`θŠ‚η‚ΉεžηŽ°ε―Ήζ½œη©Ίι—΄ηš„ε™ͺ声注ε…₯ +- η€εŒ– SD1.x、SD2.x、SDXL、SVD、Zero123等桁程 +- η€εŒ– Stable Cascade [瀺例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#1-13-stable-cascade) +- η€εŒ– Layer Diffuse [瀺例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-3-layerdiffusion) +- η€εŒ– InstantID [瀺例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-2-instantid), ιœ€ε…ˆδΏθ―θ‡ͺεšδΉ‰θŠ‚η‚ΉεŒ…δΈ­ε‰θ£…δΊ† [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) +- η€εŒ– IPAdapter, ιœ€ε…ˆδΏθ―θ‡ͺεšδΉ‰θŠ‚η‚ΉεŒ…δΈ­ε‰θ£…ζœ€ζ–°η‰ˆv2ηš„ [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) +- 扩展 XYplot ηš„ε―η”¨ζ€§ +- ζ•΄εˆδΊ†Fooocus InpaintεŠŸθƒ½ +- ζ•΄εˆδΊ†εΈΈη”¨ηš„ι€»θΎ‘θ‘η—γ€θ½¬ζ’η±»εž‹γ€ε±•η€Ίζ‰€ζœ‰η±»εž‹η­‰ +- ζ”―ζŒθŠ‚η‚ΉδΈŠcheckpoint、loraζ¨‘εž‹ε­η›ε½•εˆ†η±»εŠι’„θ§ˆε›Ύ (请在θΎη½δΈ­εΌ€ε―δΈŠδΈ‹ζ–‡θœε•ε΅Œε₯—子η›ε½•) +- ζ”―ζŒBriaAIηš„RMBG-1.4ζ¨‘εž‹ηš„θƒŒζ™―εŽ»ι™€θŠ‚η‚ΉοΌŒ[ζŠ€ζœ―ε‚θ€ƒ](https://huggingface.co/briaai/RMBG-1.4) +- ζ”―ζŒ εΌΊεˆΆζΈ…η†comfyUIζ¨‘εž‹ζ˜Ύε­˜ε η”¨ +- ζ”―ζŒStable Diffusion 3 倚账号APIθŠ‚η‚Ή +- ζ”―ζŒIC-Lightηš„εΊ”η”¨ [瀺例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-5-ic-light) | [δ»£η ζ•΄εˆζ₯源](https://github.com/huchenlei/ComfyUI-IC-Light) | [ζŠ€ζœ―ε‚θ€ƒ](https://github.com/lllyasviel/IC-Light) +- 中文提瀺词θ‡ͺεŠ¨θ―†εˆ«οΌŒδ½Ώη”¨[opus-mt-zh-enζ¨‘εž‹](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) +- ζ”―ζŒ sd3 ζ¨‘εž‹ +- ζ”―ζŒ kolors ζ¨‘εž‹ +- ζ”―ζŒ flux ζ¨‘εž‹ +- ζ”―ζŒ ζƒ°ζ€§ζ‘δ»Άεˆ€ζ–­οΌˆifElseοΌ‰ε’Œ forεΎͺ环 + +## πŸ‘¨πŸ»β€πŸ”§ ε‰θ£… + +1. ε°†ε­˜ε‚¨εΊ“ε…‹ιš†εˆ° **custom_nodes** η›ε½•εΉΆε‰θ£…依衖 +```shell +#1. gitδΈ‹θ½½ +git clone https://github.com/yolain/ComfyUI-Easy-Use +#2. ε‰θ£…依衖 +εŒε‡»install.batε‰θ£…依衖 +``` + +## πŸ“œ ζ›΄ζ–°ζ—₯εΏ— + +**v1.3.6** + +- 恒倍 `easy showAnything` ε―ΉδΊŽεˆ—θ‘¨η±»εž‹ηš„ζ”―ζŒοΌˆδ½†δΈ€δΊ›ζƒ…ε†΅δΈ‹ε±•η€ΊεΊžε€§ζ•°ζζ—Άδ»δΌšε―Όθ‡΄ComfyUIε΄©ζΊƒοΌ‰ +- δΏε€θ‡ͺεšδΉ‰ε°ιƒ¨δ»Άδ»₯ζ”―ζŒε­ε›Ύε’Œ Nodes 2.0 #942 +- 添加 `easy multiAngle` θŠ‚η‚Ή +- ε°† `prompt.py` 转捒为 V3 Schema +- δΏε€ `easy humanSegmentation` ι”™θ―― +- 添加 `easy stringJoinLines`、`easy stringToIntList`、`easy simpleMath` +- δΏε€ `easy ifElse` ε’Œ `easy anythingIndexSwitch` εœ¨ζŸδΊ›ηŽ―ε’ƒδΈ‹ε€±θ΄₯ηš„ι—ι’˜ + +**v1.3.5** + +- δΏε€`isNone` +- ε°†`preview_rescale`添加到`easy imageChooser` +- δΏε€ε°ιƒ¨δ»Άιšθ—#910 +- ε°† max ε‚ζ•°ζ·»εŠ εˆ° `wildcardsPromptMatrix` 偏移量 #909 +- δΏε€ε­ε›ΎθŠ‚η‚ΉδΈŠηš„ζ ‡ι’˜ζ‘†ζ ·εΌ +- 在 `easypromptLine` 上添加 `remove_empty_lines` + +**v1.3.4** + +- δΏε€ `easy seedList` ζœ€ε€§ε€Ό #879 +- δΈΊxyplot添加controlnet input #877 +- δΈΊ `easy indexAnything` ζ”―ζŒ `反向紒引` + +**v1.3.3** + +- εˆ ι™€CSS类名称`gird-cols-1` #859 +- δΏε€ι”εšη§ε­εœ¨ `easy promptAwait` δΈ­δΈθ΅·δ½œη”¨ +- ι‡ε‘½εθŠ‚η‚Ήε›Ύ +- δΏε€`easy ImageChooser`θΎ“ε‡Ίι”™θ――η±»εž‹ #845 + +**v1.3.2** + +- ζ”Ήι€  `easy imageChooser` θŠ‚η‚Ήδ»₯ε…ΌεΉ frontend>=v1.24.2, θ§£ε†³ζ–Ήζ‘ˆε‚θ€ƒθ‡ͺ [Comfyui_LG_Tools](https://github.com/LAOGOU-666/Comfyui_LG_Tools) +- ζ”Ήι€  `easy stylesSelector` θŠ‚η‚Ή, 你可在 [other styles files](https://github.com/yolain/EasyUse-Styles-Templates) δΈ‹θ½½εˆ° `styles` ζ–‡δ»Άε€ΉδΈ‹ +- ζ”Ήι€  `easy humanSegmentation` θŠ‚η‚Ή +- δΏε€ `easy makeImageForICLora` θŠ‚η‚Ή. +- 添加 `easy joycaption3API` θŠ‚η‚Ή +- 添加 `easy promptAwait` θŠ‚η‚Ή + +**v1.3.1** + +- 重写 drawNodeWidget δΏε€η»„θŠ‚η‚Ήι’„θ§ˆηš„ι—ι’˜. +- ζ›΄ζ–°δΊ†δΈ€δΊ› XYPlot ηš„εŠŸθƒ½ by [mekinney](https://github.com/mekinney) +- 添加 `easy seedList` θŠ‚η‚Ή (εƒε―ΉεΎͺηŽ―θŠ‚η‚Ήζœ‰η”¨) + +**v1.3.0** + +- ε°†εΎͺηŽ―θŠ‚η‚ΉθΎη½δΈΊζœ€ε€§θΎ“ε…₯ε’ŒθΎ“ε‡Ίζ•°ι‡δΈΊ20 +- 添加 `uniform width` ζ–ΉεΌεˆ° `easy makeImageForICLora` +- 咞加 `wildcardsPromptMatrix` ι€šι…η¬¦ζη€Ίθ―ηŸ©ι˜΅οΌŒη”± [Rosmeowtis](https://github.com/Rosmeowtis) θ΄‘ηŒ + +**v1.2.9** + +- δΏε€ Imagechooser δΌšε―Όθ‡΄ε·₯δ½œζ΅ε€„η†ε–ζΆˆ +- δΏε€ brushnet tensor(640) ι”™θ―― +- δΏε€v1.6.0ε‰η«―δΉ‹εŽζ— ζ³•ιšθ—ε°ιƒ¨δ»Άηš„bug +- δΏε€ε›Ύεƒι€‰ζ‹©ε™¨ζ— ζ³•ι€‰ζ‹©ε›Ύεƒ +- δΏε€ContextMenu MonkeyδΏθ‘₯δ»₯影响θ‡ͺεšδΉ‰θ„šζœ¬οΌˆPYSSSSοΌ‰θŠ‚η‚Ή + +**v1.2.8** + +- δΏε€δΊ†δΈ€δΊ›BUG (😹) +- ε’žεŠ δΊ†ε€šθ―­θ¨€η›ε½• + +**v1.2.7** + +- δΌ˜εŒ–η‘η†θŠ‚η‚Ήη»„ζ˜Ύη€Ί +- 在 `easy imageRemBg` 上添加 `ben2` +- 添加 joyCaption2 APIη‰ˆθŠ‚η‚ΉοΌˆ https://github.com/siliconflow/BizyAir οΌ‰ +- δ½Ώη”¨δΈ€η§ζ–°ηš„ζ–ΉεΌεœ¨ loader δΈ­ζ˜Ύη€Ίζ¨‘εž‹ηΌ©η•₯ε›ΎοΌˆζ”―ζŒ diffusion_models、lors、checkpointsοΌ‰ + +**v1.2.6** + +- δΏε€δΊ†εœ¨ηΌΊε°‘θ‡ͺεšδΉ‰θŠ‚η‚Ήζ—ΆηΌΊε°‘ β€œηΊ’θ‰²ζ‘†ζ‘†β€ ζ ·εΌηš„ι—ι’˜γ€‚ +- εœ¨δΈ€δΊ›η€ε•ηš„εŠ θ½½ε™¨δΈ­οΌŒε°† `clip_skip` ηš„ι»˜θ€ε€Όδ»Ž `-1` θ°ƒζ•΄δΈΊ `-2`。 +- δΏε€ε› θΎη½θŠ‚η‚ΉδΈ­ηΌΊε°‘η›ΈθΏžζŽ₯ηš„θ‡ͺεšδΉ‰θŠ‚η‚Ήθ€Œε―Όθ‡΄εΌ„δΉ±η”»εΈƒηš„ι—ι’˜ +- δΏε€ 'easy imageChooser' 不能εΎͺηŽ―δ½Ώη”¨ηš„ι—ι’˜γ€‚ + +**v1.2.5** + +- 在 `easy preSamplingCustom` ε’Œ `easy preSamplingAdvanced` 上咞加 `enable (GPU=A1111)` ε™ͺζ³’η”Ÿζˆζ¨‘εΌι€‰ζ‹©ι‘Ή +- 咞加 `easy makeImageForICLora` +- 在 `easy ipadapterApply` 添加 `REGULAR - FLUX and SD3.5 only (high strength)` ι’„η½ι‘Ήδ»₯ζ”―ζŒ InstantX Flux ipadapter +- δΏε€brushnet ζ— ζ³•εœ¨ `--fast` 樑式下使用 +- ζ”―ζŒbriaai RMBG-2.0 +- ζ”―ζŒmochiζ¨‘εž‹ +- εžηŽ°εœ¨εΎͺηŽ―δΈ»δ½“δΈ­ι‡ε€δ½Ώη”¨η»ˆη«―θŠ‚η‚ΉθΎ“ε‡ΊοΌˆδΎ‹ε¦‚ι’„θ§ˆε›Ύεƒε’Œζ˜Ύη€Ίδ»»δ½•ε†…εΉη­‰θΎ“ε‡ΊθŠ‚η‚Ή...οΌ‰ + +**v1.2.4** + +- 咞加 `easy imageSplitTiles` and `easy imageTilesFromBatch` - ε›Ύεƒεˆ†ε— +- ζ”―ζŒ `model_override`,`vae_override`,`clip_override` 可δ»₯在 `easy fullLoader` 中单独输ε…₯ +- 咞加 `easy saveImageLazy` +- 咞加 `easy loadImageForLoop` +- 咞加 `easy isFileExist` +- 咞加 `easy saveText` + +**v1.2.3** + +- `easy showAnything` ε’Œ `easy cleanGPUUsed` ε’žεŠ θΎ“ε‡Ίζ’ζ§½ +- ζ·»εŠ ζ–°ηš„δΊΊδ½“εˆ†ε‰²εœ¨ `easy humanSegmentation` θŠ‚η‚ΉδΈŠ - 代码从 [ComfyUI_Human_Parts](https://github.com/metal3d/ComfyUI_Human_Parts) ζ•΄εˆ +- ε½“δ½ εœ¨ `easy preSamplingCustom` θŠ‚η‚ΉδΈŠι€‰ζ‹©basicGuider，CFG>0 δΈ”ε½“ε‰ζ¨‘εž‹δΈΊFluxζ—ΆοΌŒε°†δ½Ώη”¨FluxGuidance +- 咞加 `easy loraStackApply` and `easy controlnetStackApply` + +**v1.2.2** + +- 咞加 `easy batchAny` +- 咞加 `easy anythingIndexSwitch` +- 咞加 `easy forLoopStart` ε’Œ `easy forLoopEnd` +- 咞加 `easy ifElse` +- 咞加 v2 η‰ˆζœ¬ζ–°ε‰η«―δ»£η  +- 咞加 `easy fluxLoader` +- 咞加 `controlnetApply` η›Έε…³θŠ‚η‚Ήε―Ήsd3ε’ŒhunyuanDiTηš„ζ”―ζŒ +- δΏε€ 当使用fooocus inpaintεŽοΌŒε†δ½Ώη”¨Loraζ¨‘εž‹ζ— ζ³•η”Ÿζ•ˆηš„ι—ι’˜ + +**v1.2.1** + +- 咞加 `easy ipadapterApplyFaceIDKolors` +- `easy ipadapterApply` ε’Œ `easy ipadapterApplyADV` 咞加 **PLUS (kolors genernal)** ε’Œ **FACEID PLUS KOLORS** ι’„η½ι‘Ή +- `easy imageRemBg` 咞加 **inspyrenet** 选鑹 +- 咞加 `easy controlnetLoader++` +- εŽ»ι™€ `easy positive` `easy negative` η­‰promptθŠ‚η‚Ήηš„θ‡ͺεŠ¨ε°†δΈ­ζ–‡ηΏ»θ―‘εŠŸθƒ½οΌŒθ‡ͺεŠ¨ηΏ»θ―‘δ»…εœ¨ `easy a1111Loader` η­‰δΈζ”―ζŒδΈ­ζ–‡TEηš„εŠ θ½½ε™¨δΈ­η”Ÿζ•ˆ +- 咞加 `easy kolorsLoader` - ε―η΅εŠ θ½½ε™¨οΌŒε‚θ€ƒδΊ† [MinusZoneAI](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ) ε’Œ [kijai](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper) ηš„δ»£η γ€‚ + +**v1.2.0** + +- 咞加 `easy pulIDApply` ε’Œ `easy pulIDApplyADV` +- 咞加 `easy hunyuanDiTLoader` ε’Œ `easy pixArtLoader` +- ε½“ζ–°θœε•ηš„δ½η½εœ¨δΈŠζˆ–θ€…δΈ‹ζ—Άε’žεŠ δΈŠ crystools ηš„ζ˜Ύη€ΊοΌŒζŽ¨θεΌ€δΈ€δΈͺε°±ε₯½οΌˆε¦‚ζžœεŽη»­crystoolsζœ‰ζ›΄ζ–°UIι€‚ι…ζˆ‘ε―θƒ½δΌšεˆ ι™€ζŽ‰οΌ‰ +- 咞加 **easy sliderControl** - ζ»‘ε—ζŽ§εˆΆθŠ‚η‚ΉοΌŒε½“ε‰ε―η”¨δΊŽζŽ§εˆΆipadapterMSηš„ε‚ζ•° (εŒε‡»ζ»‘ε—ε―ι‡η½δΈΊι»˜θ€ε€Ό) +- 咞加 **layer_weights** ε±žζ€§εœ¨ `easy ipadapterApplyADV` θŠ‚η‚Ή + +**v1.1.9** + +- 咞加 ζ–°ηš„θ°ƒεΊ¦ε™¨ **gitsScheduler** +- 咞加 `easy imageBatchToImageList` ε’Œ `easy imageListToImageBatch` (δΏε€Impactη‰ˆηš„δΈ€η‚Ήε°ι—ι’˜) +- ι€’ε½’ζ¨‘εž‹ε­η›ε½•ε΅Œε₯— +- ζ”―ζŒ sd3 ζ¨‘εž‹ +- 咞加 `easy applyInpaint` - ε±€ιƒ¨ι‡η»˜ε…¨ζ¨‘εΌθŠ‚η‚Ή (η›Έζ―”δΈŽδΉ‹ε‰ηš„kSamplerInpatingθŠ‚η‚Ήι€»θΎ‘δΌšζ›΄εˆη†δΊ›) + +**v1.1.8** + +- ε’žεŠ δΈ­ζ–‡ζη€Ίθ―θ‡ͺεŠ¨ηΏ»θ―‘οΌŒδ½Ώη”¨[opus-mt-zh-enζ¨‘εž‹](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en), 默θ€ε·²ε―Ήwildcard、loraζ­£εˆ™ε€„η†, ε…Άδ»–ιœ€θ¦δΏη•™ηš„δΈ­ζ–‡οΌŒε―δ½Ώη”¨`@δ½ ηš„ζη€Ίθ―@`εŒ…θ£Ή (θ‹₯依衖ε‰θ£…εŒζˆεŽζŠ₯ι”™, 请重启)οΌŒζ΅‹η—ε€§ηΊ¦δΌšε 0.3GB显存 +- 咞加 `easy controlnetStack` - controlnetε † +- 咞加 `easy applyBrushNet` - [瀺例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4brushnet_1.1.8.json) +- 咞加 `easy applyPowerPaint` - [瀺例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4powerpaint_outpaint_1.1.8.json) + +**v1.1.7** + +- δΏε€ δΈ€δΊ›ζ¨‘εž‹(如controlnetζ¨‘εž‹η­‰)ζœͺζˆεŠŸε†™ε…₯ηΌ“ε­˜οΌŒε―Όθ‡΄δΏζ”Ήε‰η½θŠ‚η‚ΉζŸε‚ζ•°οΌˆε¦‚ζη€Ίθ―οΌ‰ιœ€θ¦δΊŒζ¬‘θ½½ε…₯ζ¨‘εž‹ηš„ι—ι’˜ +- 咞加 `easy prompt` - δΈ»δ½“ε’Œε…‰ε½±ι’„η½ι‘ΉοΌŒεŽζœŸε―θƒ½δΌšθ°ƒζ•΄ +- 咞加 `easy icLightApply` - ι‡η»˜ε…‰ε½±, 从[ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light)δΌ˜εŒ– +- 咞加 `easy imageSplitGrid` - ε›Ύεƒη½‘ζ Όζ‹†εˆ† +- `easy kSamplerInpainting` ηš„ **additional** ε±žζ€§ε’žεŠ ε·εΌ‚ζ‰©ζ•£ε’Œbrushnet等相关选鑹 +- 咞加 brushnetζ¨‘εž‹εŠ θ½½ηš„ζ”―ζŒ - [ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet) +- 咞加 `easy applyFooocusInpaint` - Fooocusε†…θ‘₯θŠ‚η‚Ή ζ›Ώδ»£εŽŸζœ‰ηš„ FooocusInpaintLoader +- 移陀 `easy fooocusInpaintLoader` - εΉζ˜“bugοΌŒδΈε†δ½Ώη”¨ +- δΏζ”Ή easy kSamplerη­‰ι‡‡ζ ·ε™¨δΈ­εΉΆθ”ηš„model 不再替捒输出中pipeι‡Œηš„model + +**v1.1.6** + +- 咞加ζ­₯调齐整适配 - εœ¨ζ‰€ζœ‰ηš„ι’„ι‡‡ζ ·ε’Œε…¨ι‡‡ζ ·ε™¨θŠ‚η‚ΉδΈ­ηš„ 调度器(schedulder) ε’žεŠ δΊ† **alignYourSteps** 选鑹 +- `easy kSampler` ε’Œ `easy fullkSampler` ηš„ **image_output** 咞加 **Preview&Choose**选鑹 +- 咞加 `easy styleAlignedBatchAlign` - 风格对齐 [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy) +- 咞加 `easy ckptNames` +- 咞加 `easy controlnetNames` +- 咞加 `easy imagesSplitimage` - ζ‰Ήζ¬‘ε›Ύεƒζ‹†εˆ†ε•εΌ  +- 咞加 `easy imageCount` - 图像数量 +- 咞加 `easy textSwitch` - ζ–‡ε­—εˆ‡ζ’ + +
+v1.1.5 + +- 重写 `easy cleanGPUUsed` - ε―εΌΊεˆΆζΈ…η†comfyUIηš„ζ¨‘εž‹ζ˜Ύε­˜ε η”¨ +- 咞加 `easy humanSegmentation` - ε€šη±»εˆ†ε‰²γ€δΊΊεƒεˆ†ε‰² +- 咞加 `easy imageColorMatch` +- 咞加 `easy ipadapterApplyRegional` +- 咞加 `easy ipadapterApplyFromParams` +- 咞加 `easy imageInterrogator` - ε›ΎεƒεζŽ¨ +- 咞加 `easy stableDiffusion3API` - η€ζ˜“ηš„Stable Diffusion 3 倚账号APIθŠ‚η‚Ή +
+ +
+v1.1.4 + +- 咞加 `easy imageChooser` - 从[cg-image-picker](https://github.com/chrisgoringe/cg-image-picker)η€εŒ–ηš„ε›Ύη‰‡ι€‰ζ‹©ε™¨ +- 咞加 `easy preSamplingCustom` - θ‡ͺεšδΉ‰ι’„ι‡‡ζ ·οΌŒε―ζ”―ζŒcosXL-edit +- 咞加 `easy ipadapterStyleComposition` +- 咞加 在Loaders上右ι”θœε•ε―ζŸ₯ηœ‹ checkpoints、lora 俑息 +- δΏε€ `easy preSamplingNoiseIn`、`easy latentNoisy`、`east Unsampler` δ»₯ε…ΌεΉComfyUI Revision>=2098 [0542088e] δ»₯δΈŠη‰ˆζœ¬ +- δΏε€ FooocusInpaintδΏζ”ΉModelPatcherθ‘η—ζƒι‡εΌ•ε‘ηš„ι—ι’˜οΌŒη†εΊ”εœ¨η”ŸζˆmodelεŽι‡η½ModelPatcher为默θ€ε€Ό +
+ +
+v1.1.3 + +- `easy ipadapterApply` 咞加 **COMPOSITION** ι’„η½ι‘Ή +- 咞加 ε―Ή[ResAdapter](https://huggingface.co/jiaxiangc/res-adapter) loraζ¨‘εž‹ ηš„εŠ θ½½ζ”―ζŒ +- 咞加 `easy promptLine` +- 咞加 `easy promptReplace` +- 咞加 `easy promptConcat` +- `easy wildcards` 咞加 **multiline_mode**ε±žζ€§ +- 咞加 ε½“θŠ‚η‚Ήιœ€θ¦δΈ‹θ½½ζ¨‘εž‹ζ—ΆοΌŒθ‹₯huggingface连ζŽ₯θΆ…ζ—ΆοΌŒδΌšεˆ‡ζ’θ‡³ι•œεƒεœ°ε€δΈ‹θ½½ζ¨‘εž‹ +
+ +
+v1.1.2 + +- 改写 EasyUse η›Έε…³θŠ‚η‚Ήηš„ιƒ¨εˆ†ζ’ζ§½ζŽ¨θθŠ‚η‚Ή +- 咞加 **ε―η”¨δΈŠδΈ‹ζ–‡θœε•θ‡ͺ动塌ε₯—子η›ε½•** θΎη½ι‘ΉοΌŒι»˜θ€δΈΊε―η”¨ηŠΆζ€οΌŒε―εˆ†η±»ε­η›ε½•εŠcheckpoints、lorasι’„θ§ˆε›Ύ +- 咞加 `easy sv3dLoader` +- 咞加 `easy dynamiCrafterLoader` +- 咞加 `easy ipadapterApply` +- 咞加 `easy ipadapterApplyADV` +- 咞加 `easy ipadapterApplyEncoder` +- 咞加 `easy ipadapterApplyEmbeds` +- 咞加 `easy preMaskDetailerFix` +- `easy kSamplerInpainting` 咞加 **additional** ε±žζ€§οΌŒε―θΎη½ζˆ Differential Diffusion ζˆ– Only InpaintModelConditioning +- δΏε€ `easy stylesSelector` 当ζœͺι€‰ζ‹©ζ ·εΌζ—ΆοΌŒεŽŸζœ‰ζη€Ίθ―ε‘η”ŸδΊ†ε˜εŒ– +- δΏε€ `easy pipeEdit` 提瀺词输ε…₯loraζ—ΆζŠ₯ι”™ +- δΏε€ layerDiffuse xyplotη›Έε…³bug +
+ +
+v1.1.1 + +- δΏε€ι¦–欑添加含seedηš„θŠ‚η‚ΉδΈ”ε½“ε‰ζ¨‘εΌδΈΊcontrol_before_generateζ—ΆοΌŒseedδΈΊ0ηš„ι—ι’˜ +- `easy preSamplingAdvanced` 咞加 **return_with_leftover_noise** +- δΏε€ `easy stylesSelector` 当选择θ‡ͺεšδΉ‰ζ ·εΌζ–‡δ»Άζ—ΆθΏθ‘Œι˜Ÿεˆ—ζŠ₯ι”™ +- `easy preSamplingLayerDiffusion` 咞加 mask 可选传ε…₯参数 +- ε°†ζ‰€ζœ‰ **seed_num** θ°ƒζ•΄ε›ž **seed** +- δΏθ‘₯ε˜ζ–ΉBUG: 当control_modeδΈΊbefore εœ¨ι¦–ζ¬‘εŠ θ½½ι‘΅ι’ζ—ΆζœͺδΏζ”ΉθŠ‚η‚ΉδΈ­widget名称为 control_before_generate +- εŽ»ι™€εΌΊεˆΆ**control_before_generate**θΎεš +- 咞加 `easy imageRemBg` - 默θ€δΈΊBriaAIηš„RMBG-1.4ζ¨‘εž‹, η§»ι™€θƒŒζ™―ζ•ˆζžœζ›΄εŠ οΌŒι€ŸεΊ¦ζ›΄εΏ« +
+ +
+v1.1.0 + +- 咞加 `easy imageSplitList` - ζ‹†εˆ†ζ― N 张图像 +- 咞加 `easy preSamplingDiffusionADDTL` - 可配η½ε‰ζ™―γ€θƒŒζ™―γ€blendedηš„additional_promptη­‰ +- 咞加 `easy preSamplingNoiseIn` ε―ζ›Ώδ»£ιœ€θ¦ε‰η½ηš„`easy latentNoisy`θŠ‚η‚Ή εžηŽ°ζ•ˆζžœζ›΄ε₯½ηš„ε™ͺ声注ε…₯ +- `easy pipeEdit` 咞加 村仢拼ζŽ₯ζ¨‘εΌι€‰ζ‹©οΌŒε―ι€‰ζ‹©ζ›Ώζ’γ€εˆεΉΆγ€θ”η»“γ€εΉ³ε‡γ€θΎη½ζ‘δ»Άζ—Άι—΄ +- 咞加 `easy pipeEdit` - 可编辑Pipeηš„θŠ‚η‚ΉοΌˆεŒ…ε«ε―ι‡ζ–°θΎ“ε…₯提瀺词） +- 咞加 `easy preSamplingLayerDiffusion` 与 `easy kSamplerLayerDiffusion` （连ζŽ₯ `easy kSampler` δΉŸθƒ½ι€šοΌ‰ +- 咞加 在 εŠ θ½½ε™¨γ€ι’„ι‡‡ζ ·γ€ι‡‡ζ ·ε™¨γ€Controlnetη­‰θŠ‚η‚ΉδΈŠε³ι”ε―εΏ«ι€Ÿζ›Ώζ’εŒη±»εž‹θŠ‚η‚Ήηš„δΎΏζ·θœε• +- 咞加 `easy instantIDApplyADV` 可连ε…₯ positive 与 negative +- δΏε€ `easy wildcards` 读取loraζœͺ呫写εŒζ•΄θ·―εΎ„ζ—Άζœͺθ‡ͺεŠ¨ζ£€η΄’ε―Όθ‡΄εŠ θ½½loraε€±θ΄₯ηš„ι—ι’˜ +- δΏε€ `easy instantIDApply` mask ζœͺδΌ ε…₯ζ­£η‘ε€Ό +- δΏε€ 在 非a1111ζη€Ίθ―ι£Žζ ΌδΈ‹ BREAK δΈη”Ÿζ•ˆηš„ι—ι’˜ +
+ +
+v1.0.9 + +- δΏε€ζœͺε‰θ£… ComfyUI-Impack-Pack ε’Œ ComfyUI_InstantID ζ—ΆζŠ₯ι”™ +- δΏε€ `easy pipeIn` - pipeθΎδΈΊε―不必选 +- 咞加 `easy instantIDApply` - ιœ€θ¦ε…ˆε‰θ£… [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID), ε·₯δ½œζ΅ε‚θ€ƒ[η€ΊδΎ‹](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-2-instantid) +- δΏε€ `easy detailerFix` ζœͺζ·»εŠ εˆ°δΏε­˜ε›Ύη‰‡ζ ΌεΌεŒ–ζ‰©ε±•εε―η”¨θŠ‚η‚Ήεˆ—θ‘¨ +- δΏε€ `easy XYInputs: PromptSR` εœ¨ζ›Ώζ’θ΄Ÿι’ζη€Ίθ―ζ—ΆζŠ₯ι”™ +
+ +
+v1.0.8 + +- `easy cascadeLoader` stage_c 与 stage_b ζ”―ζŒcheckpointζ¨‘εž‹ (ιœ€θ¦δΈ‹θ½½[checkpoints](https://huggingface.co/stabilityai/stable-cascade/tree/main/comfyui_checkpoints)) +- `easy styleSelector` ζœη΄’ζ‘†δΏζ”ΉδΈΊδΈεŒΊεˆ†ε€§ε°ε†™εŒΉι… +- `easy fullLoader` 咞加 **positive**、**negative**、**latent** θΎ“ε‡Ίι‘Ή +- δΏε€ SDXLClipModel 在 ComfyUI δΏθ’η‰ˆζœ¬ε· 2016[c2cb8e88] 及δ»₯δΈŠηš„ζŠ₯ι”™οΌˆεˆ€ζ–­δΊ†η‰ˆζœ¬ε·ε―ε…ΌεΉθ€η‰ˆζœ¬οΌ‰ +- δΏε€ `easy detailerFix` ζ‰Ήζ¬‘ε€§ε°ε€§δΊŽ1ζ—Άη”Ÿζˆε‡Ίι”™ +- δΏε€`easy preSampling`η­‰ latentδΌ ε…₯εŽζ— ζ³•ζ Ήζζ‰Ήζ¬‘η΄’εΌ•η”Ÿζˆηš„ι—ι’˜ +- δΏε€ `easy svdLoader` ζŠ₯ι”™ +- δΌ˜εŒ–δ»£η οΌŒε‡ε°‘δΊ†θ―Έε€šε†—δ½™οΌŒζε‡θΏθ‘Œι€ŸεΊ¦ +- εŽ»ι™€δΈ­ζ–‡ηΏ»θ―‘ε―Ήη…§ζ–‡ζœ¬ + +οΌˆηΏ»θ―‘ε―Ήη…§ε·²η”± [AIGODLIKE-COMFYUI-TRANSLATION](https://github.com/AIGODLIKE/AIGODLIKE-ComfyUI-Translation) η»ŸδΈ€η»΄ζŠ€ε•¦οΌ +ι¦–ζ¬‘δΈ‹θ½½ζˆ–θ€…η‰ˆζœ¬θΎƒζ—©ηš„ζœ‹ε‹θ―·ζ›΄ζ–° AIGODLIKE-COMFYUI-TRANSLATION ε’Œζœ¬θŠ‚η‚ΉεŒ…θ‡³ζœ€ζ–°η‰ˆζœ¬γ€‚οΌ‰ +
+ +
+v1.0.7 + +- 咞加 `easy cascadeLoader` - stable cascade εŠ θ½½ε™¨ +- 咞加 `easy preSamplingCascade` - stabled cascade stage_c 钄采样参数 +- 咞加 `easy fullCascadeKSampler` - stable cascade stage_c εŒζ•΄η‰ˆι‡‡ζ ·ε™¨ +- 咞加 `easy cascadeKSampler` - stable cascade stage-c ksampler simple +
+ +
+v1.0.6 + +- 咞加 `easy XYInputs: Checkpoint` +- 咞加 `easy XYInputs: Lora` +- `easy seed` ε’žεŠ ε›Ίεšη§ε­ε€Όζ—Άε―ζ‰‹εŠ¨εˆ‡ζ’ιšζœΊη§ +- δΏε€ `easy fullLoader`η­‰εŠ θ½½ε™¨εˆ‡ζ’loraζ—Άθ‡ͺεŠ¨θ°ƒζ•΄θŠ‚η‚Ήε€§ε°ηš„ι—ι’˜ +- εŽ»ι™€εŽŸζœ‰ttnηš„ε›Ύη‰‡δΏε­˜ι€»θΎ‘εΉΆι€‚ι…ComfyUI默θ€ηš„ε›Ύη‰‡δΏε­˜ζ ΌεΌεŒ–ζ‰©ε±• +
+ +
+v1.0.5 + +- 咞加 `easy isSDXL` +- `easy svdLoader` 咞加提瀺词控刢, ε―ι…εˆopen_clipζ¨‘εž‹θΏ›θ‘Œδ½Ώη”¨ +- `easy wildcards` 咞加 **populated_text** ε―θΎ“ε‡Ίι€šι…ε‘«ε……εŽζ–‡ζœ¬ +
+ +
+v1.0.4 + +- 咞加 `easy showLoaderSettingsNames` ε―ζ˜Ύη€ΊδΈŽθΎ“ε‡ΊεŠ θ½½ε™¨ιƒ¨δ»ΆδΈ­ηš„ ζ¨‘εž‹δΈŽVAE名称 +- 咞加 `easy promptList` - ζη€Ίθ―εˆ—θ‘¨ +- 咞加 `easy fooocusInpaintLoader` - Fooocusε†…θ‘₯θŠ‚η‚ΉοΌˆδ»…ζ”―ζŒXLζ¨‘εž‹ηš„ζ΅η¨‹οΌ‰ +- 咞加 **Logic** ι€»θΎ‘η±»θŠ‚η‚Ή - εŒ…ε«η±»εž‹γ€θ‘η—γ€εˆ€ζ–­ε’Œθ½¬ζ’η±»εž‹η­‰ +- 咞加 `easy imageSave` - εΈ¦ζ—₯ζœŸθ½¬ζ’ε’Œε½ι«˜ζ ΌεΌεŒ–ηš„ε›ΎεƒδΏε­˜θŠ‚η‚Ή +- 咞加 `easy joinImageBatch` - εˆεΉΆε›Ύεƒζ‰Ήζ¬‘ +- `easy showAnything` ε’žεŠ ζ”―ζŒθ½¬ζ’ε…Άδ»–η±»εž‹οΌˆε¦‚οΌštensorη±»εž‹ηš„ζ‘δ»Άγ€ε›Ύεƒη­‰οΌ‰ +- `easy kSamplerInpainting` 咞加 **patch** δΌ ε…₯ε€ΌοΌŒι…εˆFooocusε†…θ‘₯θŠ‚η‚Ήδ½Ώη”¨ +- `easy imageSave` 咞加 **only_preivew** + +- δΏε€ xyplot在pillow>9.5δΈ­ζŠ₯ι”™ +- δΏε€ `easy wildcards` εœ¨δ½Ώη”¨PSζ‰©ε±•ζ’δ»ΆθΏθ‘Œζ—ΆζŠ₯ι”™ +- δΏε€ `easy latentCompositeMaskedWithCond` +- δΏε€ `easy XYInputs: ControlNet` ζŠ₯ι”™ +- δΏε€ `easy loraStack` **toggle** δΈΊ disabled ζ—ΆζŠ₯ι”™ + +- δΏζ”Ήι¦–欑ε‰θ£…θŠ‚η‚ΉεŒ…δΈε†θ‡ͺεŠ¨ζ›Ώζ’δΈ»ι’˜οΌŒιœ€ζ‰‹εŠ¨θ°ƒζ•΄εΉΆεˆ·ζ–°ι‘΅ι’ +
+ +
+v1.0.3 + +- 咞加 `easy stylesSelector` ι£Žζ ΌεŒ–ζη€Ίθ―ι€‰ζ‹©ε™¨ +- ε’žεŠ ι˜Ÿεˆ—θΏ›εΊ¦ζ‘θΎη½ι‘ΉοΌŒι»˜θ€δΈΊζœͺε―η”¨ηŠΆζ€ +- `easy controlnetLoader` ε’Œ `easy controlnetLoaderADV` ε’žεŠ ε‚ζ•° **scale_soft_weights** + + +- δΏε€ `easy XYInputs: Sampler/Scheduler` ζŠ₯ι”™ +- δΏε€ ε³δΎ§θœε• η‚Ήε‡»ζŒ‰ι’ζ—Άθ€ζ˜―θ·‘δ½ηš„ι—ι’˜ +- δΏε€ styles θ·―εΎ„εœ¨ε…Άδ»–ηŽ―ε’ƒζŠ₯ι”™ +- δΏε€ `easy comfyLoader` 读取错误 +- δΏε€ xyPlot 在连ζŽ₯ zero123 ζ—ΆζŠ₯ι”™ +- δΏε€εŠ θ½½ε™¨δΈ­ζη€Ίθ―δΈΊη»„δ»Άζ—ΆζŠ₯ι”™ +- δΏε€ `easy getNode` ε’Œ `easy setNode` εŠ θ½½ζ—Άζ ‡ι’˜ζœͺζ›΄ζ”Ή +- δΏε€ζ‰€ζœ‰ι‡‡ζ ·ε™¨δΈ­ε­˜ε‚¨ε›Ύη‰‡δ½Ώη”¨ε­η›ε½•ε‰ηΌ€δΈη”Ÿζ•ˆηš„ι—ι’˜ + + +- θ°ƒζ•΄UI主钘 +
+ +
+v1.0.2 + +- 咞加 **autocomplete** ζ–‡δ»Άε€ΉοΌŒε¦‚ζžœζ‚¨ε‰θ£…δΊ† [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts), ε°†εœ¨ε―εŠ¨ζ—ΆεˆεΉΆθ―₯ζ–‡δ»Άε€ΉδΈ‹ηš„ζ‰€ζœ‰txtζ–‡δ»ΆεΉΆθ¦†η›–εˆ°pyssssεŒ…ι‡Œηš„autocomplete.txt文仢。 +- 咞加 `easy XYPlotAdvanced` ε’Œ `easy XYInputs` η­‰η›Έε…³θŠ‚η‚Ή +- 咞加 **Alt+1到9** 快捷ι”οΌŒε―εΏ«ι€Ÿη²˜θ΄΄ Node templates ηš„θŠ‚η‚Ήι’„θΎ οΌˆε―ΉεΊ” 1到9 鑺序） + +- δΏε€ `easy imageInsetCrop` ζ΅‹ι‡ε€ΌδΈΊη™Ύεˆ†ζ―”ζ—Άζ­₯θΏ›δΈΊ1 +- δΏε€ 开启 `a1111_prompt_style` ζ—ΆXYε›Ύθ‘¨ζ— ζ³•δ½Ώη”¨ηš„ι—ι’˜ +- 右ι”θœε•δΈ­ε’žεŠ δΊ†δΈ€δΈͺ `πŸ“œGroups Map(EasyUse)` + +- δΏε€εœ¨Comfyζ–°η‰ˆζœ¬δΈ­UI加载倱θ΄₯ +- δΏε€ `easy pipeToBasicPipe` ζŠ₯ι”™ +- δΏζ”Ή `easy fullLoader` ε’Œ `easy a1111Loader` δΈ­ηš„ **a1111_prompt_style** 默θ€ε€ΌδΈΊ False +- `easy XYInputs ModelMergeBlocks` ζ”―ζŒcsvζ–‡δ»Άε―Όε…₯ζ•°ε€Ό + +- 替捒了XYε›Ύη”Ÿζˆζ—Άηš„ε­—δ½“ζ–‡δ»Ά + +- 移陀 `easy imageRemBg` +- η§»ι™€εŒ…δΈ­ηš„δ»‹η»ε›Ύε’Œε·₯δ½œζ΅ζ–‡δ»ΆοΌŒε‡ε°‘εŒ…δ½“η§― + +
+ +
+v1.0.1 + +- ζ–°ε’ž `easy seed` - η€ζ˜“ιšζœΊη§ +- `easy preDetailerFix` ζ–°ε’žδΊ† `optional_image` δΌ ε…₯ε›Ύεƒε―ι€‰οΌŒε¦‚ζœͺ传默θ€ε–ε€ΌδΈΊpipeι‡Œηš„ε›Ύεƒ +- ζ–°ε’ž `easy kSamplerInpainting` η”¨δΊŽε†…θ‘₯ζ½œη©Ίι—΄ηš„ι‡‡ζ ·ε™¨ +- ζ–°ε’ž `easy pipeToBasicPipe` η”¨δΊŽθ½¬ζ’εˆ°Impactηš„ζŸδΊ›θŠ‚η‚ΉδΈŠ + +- δΏε€ `easy comfyLoader` ζŠ₯ι”™ +- δΏε€ζ‰€ζœ‰εŒ…ε«θΎ“ε‡Ίε›Ύη‰‡ε°Ίε―Έηš„θŠ‚η‚Ήε–ε€Όζ–ΉεΌζ— ζ³•ζ‰Ήε€„η†ηš„ι—ι’˜ +- δΏε€ `width` ε’Œ `height` ζ— ζ³•εœ¨ `easy svdLoader` θ‡ͺεšδΉ‰ηš„ζŠ₯ι”™ι—ι’˜ +- δΏε€ζ‰€ζœ‰ι‡‡ζ ·ε™¨ι’„θ§ˆε›Ύη‰‡ηš„εœ°ε€ι“ΎζŽ₯ (θ§£ε†³εœ¨ MACOS η³»η»ŸδΈ­ε›Ύη‰‡ζ— ζ³•εœ¨ι‡‡ζ ·ε™¨δΈ­ι’„θ§ˆηš„ι—ι’˜οΌ‰ +- δΏε€ `vae_name` 在 `easy fullLoader` ε’Œ `easy a1111Loader` ε’Œ `easy comfyLoader` 中选择但ζœͺζ›Ώζ’εŽŸε§‹vaeι—ι’˜ +- δΏε€ `easy fullkSampler` 陀pipeε€–ε…Άδ»–θΎ“ε‡Ίε€Όηš„ζŠ₯ι”™ +- δΏε€ `easy hiresFix` θΎ“ε…₯连ζŽ₯pipeε’Œimage、vaeεŒζ—Άε­˜εœ¨ζ—ΆζŠ₯ι”™ +- δΏε€ `easy fullLoader` δΈ­ `model_override` 连ζŽ₯后ζœͺζ‰§θ‘Œ +- δΏε€ ε› ζ–°ε’ž`easy seed` 导致actionι”™θ―― +- δΏε€ `easy xyplot` ηš„ε­—δ½“ζ–‡δ»Άθ·―εΎ„θ―»ε–ι”™θ―― +- δΏε€ convert 到 `easy seed` ιšζœΊη§ζ— ζ³•ε›Ίεšηš„ι—ι’˜ +- δΏε€ `easy pipeIn` ε€ΌδΌ ε…₯ηš„ζŠ₯ι”™ι—ι’˜ +- δΏε€ `easy zero123Loader` ε’Œ `easy svdLoader` θ―»ε–ζ¨‘εž‹ζ—Άε°†ζ¨‘εž‹εŠ ε…₯εˆ°ηΌ“ε­˜δΈ­ +- δΏε€ `easy kSampler` `easy kSamplerTiled` `easy detailerFix` ηš„ `image_output` 默θ€ε€ΌδΈΊ Preview +- `easy fullLoader` ε’Œ `easy a1111Loader` ζ–°ε’žδΊ† `a1111_prompt_style` 参数可δ»₯ι‡ηŽ°ε’Œwebuiη”Ÿζˆη›ΈεŒηš„ε›ΎεƒοΌŒε½“ε‰ζ‚¨ιœ€θ¦ε‰θ£… [ComfyUI_smZNodes](https://github.com/shiimizu/ComfyUI_smZNodes) ζ‰θƒ½δ½Ώη”¨ζ­€εŠŸθƒ½ +
+ +
+v1.0.0 + +- ζ–°ε’ž`easy positive` - η€ζ˜“ζ­£ι’ζη€Ίθ―ζ–‡ζœ¬ +- ζ–°ε’ž`easy negative` - η€ζ˜“θ΄Ÿι’ζη€Ίθ―ζ–‡ζœ¬ +- ζ–°ε’ž`easy wildcards` - ζ”―ζŒι€šι…η¬¦ε’ŒLoraι€‰ζ‹©ηš„ζη€Ίθ―ζ–‡ζœ¬ +- ζ–°ε’ž`easy portraitMaster` - θ‚–εƒε€§εΈˆv2.2 +- ζ–°ε’ž`easy loraStack` - Loraε † +- ζ–°ε’ž`easy fullLoader` - εŒζ•΄η‰ˆηš„εŠ θ½½ε™¨ +- ζ–°ε’ž`easy zero123Loader` - η€ζ˜“zero123εŠ θ½½ε™¨ +- ζ–°ε’ž`easy svdLoader` - η€ζ˜“svdεŠ θ½½ε™¨ +- ζ–°ε’ž`easy fullkSampler` - εŒζ•΄η‰ˆηš„ι‡‡ζ ·ε™¨οΌˆζ— εˆ†η¦»οΌ‰ +- ζ–°ε’ž`easy hiresFix` - ζ”―ζŒPipeηš„ι«˜ζΈ…δΏε€ +- ζ–°ε’ž`easy predetailerFix` `easy DetailerFix` - ζ”―ζŒPipeηš„η»†θŠ‚δΏε€ +- ζ–°ε’ž`easy ultralyticsDetectorPipe` `easy samLoaderPipe` - ζ£€ζ΅‹εŠ θ½½ε™¨οΌˆη»†θŠ‚δΏε€ηš„θΎ“ε…₯ι‘ΉοΌ‰ +- ζ–°ε’ž`easy pipein` `easy pipeout` - Pipeηš„θΎ“ε…₯δΈŽθΎ“ε‡Ί +- ζ–°ε’ž`easy xyPlot` - η€ζ˜“ηš„xyplot (εŽη»­δΌšζ›΄ζ–°ζ›΄ε€šε―ζŽ§ε‚ζ•°) +- ζ–°ε’ž`easy imageRemoveBG` - ε›ΎεƒεŽ»ι™€θƒŒζ™― +- ζ–°ε’ž`easy imagePixelPerfect` - 图像εŒηΎŽεƒη΄  +- ζ–°ε’ž`easy poseEditor` - ε§ΏεŠΏηΌ–θΎ‘ε™¨ +- ζ–°ε’žUIδΈ»ι’˜οΌˆι»‘ζ›œηŸ³οΌ‰- 默θ€θ‡ͺ动加载UI, 也可在θΎη½δΈ­θ‡ͺθ‘Œζ›΄ζ›Ώ + +- δΏε€ `easy globalSeed` δΈη”Ÿζ•ˆι—ι’˜ +- δΏε€ζ‰€ζœ‰ηš„`seed_num` ε›  [cg-use-everywhere](https://github.com/chrisgoringe/cg-use-everywhere) εžζ—Άζ›΄ζ–°ε›Ύθ‘¨ε―Όθ‡΄ε€Όι”™δΉ±ηš„ι—ι’˜ +- δΏε€`easy imageSize` `easy imageSizeBySide` `easy imageSizeByLongerSide` ε―δ½œδΈΊη»ˆθŠ‚η‚Ή +- δΏε€ `seed_num` (ιšζœΊη§ε­ε€Ό) εœ¨εŽ†ε²θ°ε½•δΈ­θ―»ε–ζ— ζ³•δΈ€θ‡΄ηš„Bug +
+ + +
+v0.5 + +- ζ–°ε’ž `easy controlnetLoaderADV` θŠ‚η‚Ή +- ζ–°ε’ž `easy imageSizeBySide` θŠ‚η‚ΉοΌŒε―ι€‰θΎ“ε‡ΊδΈΊι•ΏθΎΉζˆ–ηŸ­θΎΉ +- ζ–°ε’ž `easy LLLiteLoader` θŠ‚η‚ΉοΌŒε¦‚ζžœζ‚¨ι’„ε…ˆε‰θ£…θΏ‡ kohya-ss/ControlNet-LLLite-ComfyUI εŒ…οΌŒθ―·ε°† models ι‡Œηš„ζ¨‘εž‹ζ–‡δ»Άη§»εŠ¨θ‡³ ComfyUI\models\controlnet\ (即comfy默θ€ηš„controlnetθ·―εΎ„ι‡ŒοΌŒθ―·ε‹ΏδΏζ”Ήζ¨‘εž‹ηš„ζ–‡δ»ΆεοΌŒδΈη„ΆδΌšθ―»ε–δΈεˆ°)。 +- ζ–°ε’ž `easy imageSize` ε’Œ `easy imageSizeByLongerSize` θΎ“ε‡Ίηš„ε°Ίε―Έζ˜Ύη€Ίγ€‚ +- ζ–°ε’ž `easy showSpentTime` θŠ‚η‚Ήη”¨δΊŽε±•η€Ίε›Ύη‰‡ζŽ¨η†θŠ±θ΄Ήζ—Άι—΄δΈŽVAEθ§£η θŠ±θ΄Ήζ—Άι—΄γ€‚ +- `easy controlnetLoaderADV` ε’Œ `easy controlnetLoader` ζ–°ε’ž `control_net` 可选传ε…₯参数 +- `easy preSampling` ε’Œ `easy preSamplingAdvanced` ζ–°ε’ž `image_to_latent` 可选传ε…₯参数 +- `easy a1111Loader` ε’Œ `easy comfyLoader` ζ–°ε’ž `batch_size` δΌ ε…₯参数 + +- δΏζ”Ή `easy controlnetLoader` 到 loader εˆ†η±»εΊ•δΈ‹γ€‚ +
+ +## ζ•΄εˆε‚θ€ƒεˆ°ηš„η›Έε…³θŠ‚η‚ΉεŒ… + +声明: ιžεΈΈε°Šι‡θΏ™δΊ›εŽŸδ½œθ€…δ»¬ηš„δ»˜ε‡ΊοΌŒεΌ€ζΊδΈζ˜“οΌŒζˆ‘δ»…δ»…εͺζ˜―εšδΊ†δΈ€δΊ›ζ•΄εˆδΈŽδΌ˜εŒ–γ€‚ + +| θŠ‚η‚Ήε (搜紒名) | η›Έε…³ηš„εΊ“ | εΊ“η›Έε…³ηš„θŠ‚η‚Ή | +|:-------------------------------|:----------------------------------------------------------------------------|:------------------------| +| easy setNode | [ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.SetNode | +| easy getNode | [ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.GetNode | +| easy bookmark | [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) | Bookmark πŸ”– | +| easy portraitMarker | [comfyui-portrait-master](https://github.com/florestefano1975/comfyui-portrait-master) | Portrait Master | +| easy LLLiteLoader | [ControlNet-LLLite-ComfyUI](https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI) | LLLiteLoader | +| easy globalSeed | [ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) | Global Seed (Inspire) | +| easy preSamplingDynamicCFG | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | +| dynamicThresholdingFull | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | +| easy imageInsetCrop | [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) | ImageInsetCrop | +| easy poseEditor | [ComfyUI_Custom_Nodes_AlekPet](https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet) | poseNode | +| easy if | [ComfyUI-Logic](https://github.com/theUpsider/ComfyUI-Logic) | IfExecute | +| easy preSamplingLayerDiffusion | [ComfyUI-layerdiffusion](https://github.com/huchenlei/ComfyUI-layerdiffusion) | LayeredDiffusionApplyη­‰ | +| easy dynamiCrafterLoader | [ComfyUI-layerdiffusion](https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter) | Apply Dynamicrafter | +| easy imageChooser | [cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) | Preview Chooser | +| easy styleAlignedBatchAlign | [style_aligned_comfy](https://github.com/chrisgoringe/cg-image-picker) | styleAlignedBatchAlign | +| easy icLightApply | [ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light) | ICLightApplyη­‰ | +| easy kolorsLoader | [ComfyUI-Kolors-MZ](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ) | kolorsLoader | + +## Credits + +[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - εŠŸθƒ½εΌΊε€§δΈ”ζ¨‘ε—εŒ–ηš„Stable Diffusion GUI + +[ComfyUI-ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) - ComfyUIη‘理器 + +[tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) - η‘ι“θŠ‚η‚ΉοΌˆθŠ‚η‚ΉζŸοΌ‰θ©η”¨ζˆ·ε‡ε°‘δΊ†δΈεΏ…θ¦ηš„θΏžζŽ₯ + +[ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) - diffus3ηš„θŽ·ε–δΈŽθΎη½η‚Ήθ©η”¨ζˆ·ε―δ»₯εˆ†η¦»ε·₯δ½œζ΅ζž„ζˆ + +[ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack) - εΈΈθ§„ζ•΄εˆεŒ…1 + +[ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) - εΈΈθ§„ζ•΄εˆεŒ…2 + +[ComfyUI-Logic](https://github.com/theUpsider/ComfyUI-Logic) - ComfyUIι€»θΎ‘θΏη— + +[ComfyUI-ResAdapter](https://github.com/jiaxiangc/ComfyUI-ResAdapter) - θ©ζ¨‘εž‹η”ŸζˆδΈε—θ­η»ƒεˆ†θΎ¨ηŽ‡ι™εˆΆ + +[ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) - 风格迁移 + +[ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) - 人脸迁移 + +[ComfyUI_PuLID](https://github.com/cubiq/PuLID_ComfyUI) - 人脸迁移 + +[ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) - pyssss ε°θ›‡πŸθ„šζœ¬ + +[cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) - 图片选择器 + +[ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet) - BrushNet ε†…θ‘₯θŠ‚η‚Ή + +[ComfyUI_ExtraModels](https://github.com/city96/ComfyUI_ExtraModels) - DiTζžΆζž„η›Έε…³θŠ‚η‚ΉοΌˆPixart、混元DiTη­‰οΌ‰ + +## ε…θ΄£ε£°ζ˜Ž + +ζœ¬εΌ€ζΊι‘Ήη›εŠε…Άε†…εΉζŒ‰ β€œεŽŸζ · β€ζδΎ›οΌŒδΈδ½œδ»»δ½•ζ˜Žη€Ίζˆ–ζš—η€Ίηš„δΏθ―οΌŒεŒ…ζ‹¬δ½†δΈι™δΊŽι€‚ι”€ζ€§γ€η‰Ήεšη”¨ι€”ι€‚η”¨ζ€§ε’ŒιžδΎ΅ζƒδΏθ―γ€‚εœ¨δ»»δ½•ζƒ…ε†΅δΈ‹οΌŒδ½œθ€…ζˆ–ε…Άδ»–η‰ˆζƒζ‰€ζœ‰θ€…ε‡δΈε―Ήε› ζœ¬θ½―δ»Άζˆ–ζœ¬θ½―δ»Άηš„δ½Ώη”¨ζˆ–ε…Άδ»–δΊ€ζ˜“θ€ŒδΊ§η”Ÿγ€εΌ•θ΅·ζˆ–δΈŽδΉ‹η›Έε…³ηš„δ»»δ½•η΄’θ΅”γ€ζŸε³ζˆ–ε…Άδ»–θ΄£δ»»ζ‰Ώζ‹…θ΄£δ»»οΌŒζ— θΊζ˜―εˆεŒθ―‰θΌγ€δΎ΅ζƒθ―‰θΌθΏ˜ζ˜―ε…Άδ»–θ―‰θΌγ€‚ + +η”¨ζˆ·εΊ”θ‡ͺ葌负责η‘δΏεœ¨δ½Ώη”¨ζœ¬θ½―δ»Άζˆ–ε‘εΈƒη”±ζœ¬θ½―δ»Άη”Ÿζˆηš„ε†…εΉζ—ΆοΌŒι΅εˆζ‰€εœ¨εΈζ³•η‘θΎ–εŒΊηš„ζ‰€ζœ‰ι€‚η”¨ζ³•εΎ‹ε’Œζ³•θ§„γ€‚δ½œθ€…ε’Œη‰ˆζƒζ‰€ζœ‰θ€…δΈε―Ήη”¨ζˆ·εœ¨ε…Άε„θ‡ͺζ‰€εœ¨εœ°θΏεζ³•εΎ‹ζˆ–ζ³•θ§„ηš„θ‘ŒδΈΊθ΄Ÿθ΄£γ€‚ + +## β˜•οΈ ζŠ•ε–‚ + +**Comfyui-Easy-Use** ζ˜―δΈ€δΈͺ GPL θΈε―ηš„εΌ€ζΊι‘Ήη›γ€‚δΈΊδΊ†ι‘Ήη›ε–εΎ—ζ›΄ε₯½γ€ε―ζŒη»­ηš„ε‘ε±•οΌŒζˆ‘εΈŒζœ›θƒ½ε€ŸθŽ·εΎ—ζ›΄ε€šηš„ζ”―ζŒγ€‚ ε¦‚ζžœζˆ‘ηš„θ‡ͺεšδΉ‰θŠ‚η‚ΉδΈΊζ‚¨ηš„δΈ€ε€©ε’žζ·»δΊ†δ»·ε€ΌοΌŒθ―·θ€ƒθ™‘ε–ζ―ε’–ε•‘ζ₯θΏ›δΈ€ζ­₯θ‘₯充能量！ πŸ’–ζ„Ÿθ°’ζ‚¨ηš„ζ”―ζŒοΌŒζ―δΈ€ζ―ε’–ε•‘ιƒ½ζ˜―ζˆ‘εˆ›δ½œηš„εŠ¨εŠ›οΌ + +- [BiliBiliε……η”΅](https://space.bilibili.com/1840885116) +- [Wechat/Alipay](https://github.com/user-attachments/assets/803469bd-ed6a-4fab-932d-50e5088a2d03) + +ζ„Ÿθ°’ζ‚¨ηš„ζεŠ©οΌŒζˆ‘ε°†η”¨θΏ™δΊ›θ΄Ήη”¨ζ₯η§Ÿη”¨ GPU ζˆ–θ΄­δΉ°ε…Άδ»– GPT 服劑，δ»₯δΎΏζ›΄ε₯½εœ°θ°ƒθ―•ε’ŒεŒε–„ ComfyUI-Easy-Use εŠŸθƒ½ + +## πŸŒŸε€§ε―Œε€§θ΄΅ηš„δΊΊε„Ώ + +ζˆ‘ε―Ήι‚£δΊ›ζ…·ζ…¨ηš„θ΅δΊˆδΈ€ι’—ζ˜Ÿηš„δΊΊθ‘¨η€Ίζ„Ÿθ°’γ€‚ιžεΈΈζ„Ÿθ°’ζ‚¨ηš„ζ”―ζŒοΌ + +[![Stargazers repo roster for @yolain/ComfyUI-Easy-Use](https://reporoster.com/stars/yolain/ComfyUI-Easy-Use)](https://github.com/yolain/ComfyUI-Easy-Use/stargazers) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/README.md b/custom_nodes/ComfyUI-Easy-Use/README.md new file mode 100644 index 00000000..46854bb8 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/README.md @@ -0,0 +1,555 @@ +![comfyui-easy-use](https://github.com/user-attachments/assets/9b7a5e44-f5e2-4c27-aed2-d0e6b50c46bb) + + + +**ComfyUI-Easy-Use** is an efficiency custom nodes integration package, which is extended on the basis of [TinyTerraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes). It has been integrated and optimized for many popular awesome custom nodes to achieve the purpose of faster and more convenient use of ComfyUI. While ensuring the degree of freedom, it restores the ultimate smooth image production experience that belongs to Stable Diffusion. + +## πŸ‘¨πŸ»β€πŸŽ¨ Introduce + +- Inspire by [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes), which greatly reduces the time cost of tossing workflows。 +- UI interface beautification, the first time you install the user, if you need to use the UI theme, please switch the theme in Settings -> Color Palette and refresh page. +- Added a node for pre-sampling parameter configuration, which can be separated from the sampling node for easier previewing +- Wildcards and lora's are supported, for Lora Block Weight usage, ensure that the custom node package has the [ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) +- Multi-selectable styled cue word selector, default is Fooocus style json, custom json can be placed under styles, samples folder can be placed in the preview image (name and name consistent, image file name such as spaces need to be converted to underscores '_') +- The loader enables the A1111 prompt mode, which reproduces nearly identical images to those generated by webui. +- Noise injection into the latent space can be achieved using the `easy latentNoisy` or `easy preSamplingNoiseIn` node +- Simplified processes for SD1.x, SD2.x, SDXL, SVD, Zero123, etc. [Example](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#StableDiffusion) +- Simplified Stable Cascade [Example](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#StableCascade) +- Simplified Layer Diffuse [Example](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#LayerDiffusion)，The first time you use it you may need to run `pip install -r requirements.txt` to install the required dependencies. +- Simplified InstantID [Example](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#InstantID), You need to make sure that the custom node package has the [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) +- Extending the usability of XYplot +- Fooocus Inpaint integration +- Integration of common logical calculations, conversion of types, display of all types, etc. +- Background removal nodes for the RMBG-1.4 model supporting BriaAI, [BriaAI Guide](https://huggingface.co/briaai/RMBG-1.4) +- Forcibly cleared the memory usage of the comfy UI model are supported +- Stable Diffusion 3 multi-account API nodes are supported +- Support SD3's model +- Support Kolorsβ€˜s model +- Support Flux's model +- Support lazy if else and for loops + +## πŸ‘¨πŸ»β€πŸ”§ Installation +Clone the repo into the **custom_nodes** directory and install the requirements: +```shell +#1. Clone the repo +git clone https://github.com/yolain/ComfyUI-Easy-Use +#2. Install the requirements +Double-click install.bat to install the required dependencies +``` + +## πŸ“œ Changelog + +**v1.3.6** + +- Restored `easy showAnything` support for list types (but displaying large data in some cases may still cause ComfyUI to crash) +- Fix custom widgets to support subgraph and Nodes 2.0 #942 +- Add `easy multiAngle` node +- Convert `prompt.py` to V3 Schema +- Fix `easy humanSegmentation` error +- Add `easy stringJoinLines`，`easy stringToIntList`, `easy simpleMath` +- Fix `easy ifElse` and `easy anythingIndexSwitch` fails in certain environments + +**v1.3.5** + +- Fix `isNone` +- Add `preview_rescale` to `easy imageChooser` +- Fix widget hidden #910 +- Add max parameter to `wildcardsPromptMatrix` offset #909 +- Fix title box style on subgraph node +- Add `remove_empty_lines` on `easy promptLine` + +**v1.3.4** + +- Fix `easy seedList` max_num #879 +- Add controlnet input to xyplot #877 +- Support `nagative indexing` for `easy indexAnything` + +**v1.3.3** + +- Removed the definition of the CSS class name gird-cols-1 #859 +- Fix lock seed not working in `easy promptAwait` +- Rename the nodes map +- Fix `easy imageChooser` output error type #845 + +**v1.3.2** + +- Revamp `easy imageChooser` node to adapt frontend>=v1.24.2, solution referenced from [Comfyui_LG_Tools](https://github.com/LAOGOU-666/Comfyui_LG_Tools) +- Revamp `easy stylesSelector` node, and you can download [other styles files](https://github.com/yolain/EasyUse-Styles-Templates) to the `styles` folder +- Revamp `easy humanSegmentation` node +- Fix `easy makeImageForICLora` node issue, that occurred when the heights of two images were the same during image stitching on. +- Add `easy joycaption3API` node +- Add `easy promptAwait` node + +**v1.3.1** + +- Rewrite drawNodeWidget and fix the GroupNode preview issue. +- Updated some features of XYPlot by [mekinney](https://github.com/mekinney) +- Add `easy seedList` node (It's useful for in loops) + +**v1.3.0** + +- Set loop nodes maximum number of inputs and outputs to 20 +- Add `uniform width` method to `easy makeImageForICLora` +- Add `wildcardsPromptMatrix` Node by [Rosmeowtis](https://github.com/Rosmeowtis) + +**v1.2.9** + +- Fix ImageChooser causes workflow processing to cancel +- Fix brushnet tensor(640) error +- Fix widgets not hidden after v1.6.0 frontend +- Fix image chooser can not select images +- Fix contextMenu monkey patching to affect custom scripts (pysssss) nodes + +**v1.2.8** + +- Added the multi-language catalog +- Fix CLIP vision model download URLs for IPAdapter and DynamiCrafter +- Improve error handling for model downloads with clearer error messages and better handling of download failures + +**v1.2.7** + +- Optimize display of the node maps +- Added `ben2` on `easy imageRemBg` +- Using a new way to display the models thumbnails in the loaders (supported diffusion_models、lors、checkpoints) + +**v1.2.6** + +- Fix missing the "Red Rect" styles when you are missing custom nodes. +- Adjust the default value of `clip_skip` from `-1` to `-2` in some easy loaders. +- Fix the issue due to set nodes missing custom nodes which their connected, causing canvas to be messed up. +- Fix the `easy imageChooser` can not using in a loop. + +**v1.2.5** + +- Added `enable (GPU=A1111)` noise mode on `easy preSamplingCustom` and `easy preSamplingAdvanced` +- Added `easy makeImageForICLora` +- Added `REGULAR - FLUX and SD3.5 only (high strength)` preset for InstantX Flux ipadapter on `easy ipadapterApply` +- Fix brushnet can not be used with startup arg `--fast` mode +- Support briaai RMBG-2.0 +- Support mochi +- Implement reuse of end nodes output in the loop body (e.g: previewImage and showAnything and sth.) + +**v1.2.4** + +- Added `easy imageSplitTiles` and `easy imageTilesFromBatch` +- Support `model_override`,`vae_override`,`clip_override` can be input separately to `easy fullLoader` +- Added `easy saveImageLazy` +- Added `easy loadImageForLoop` +- Added `easy isFileExist` +- Added `easy saveText` + +**v1.2.3** + +- `easy showAnything` and `easy cleanGPUUsed` added slot of output +- Added human parts segmentation to `easy humanSegmentation` - Code based on [ComfyUI_Human_Parts](https://github.com/metal3d/ComfyUI_Human_Parts) +- Using FluxGuidance when you are using a flux model and choose basicGuider and set the cfg>0 on `easy preSamplingCustom` +- Added `easy loraStackApply` and `easy controlnetStackApply` - Apply loraStack and controlnetStack + +**v1.2.2** + +- Added `easy batchAny` +- Added `easy anythingIndexSwitch` +- Added `easy forLoopStart` and `easy forLoopEnd` +- Added `easy ifElse` +- Added v2 web frond-end code +- Added `easy fluxLoader` +- Added support for `controlnetApply` Related nodes with SD3 and hunyuanDiT +- Fixed after using `easy applyFooocusInpaint`, all lora models become unusable + +**v1.2.1** + +- Added `easy ipadapterApplyFaceIDKolors` +- Added **inspyrenet** to `easy imageRemBg` +- Added `easy controlnetLoader++` +- Added **PLUS (kolors genernal)** and **FACEID PLUS KOLORS** preset to `easy ipadapterApply` and `easy ipadapterApplyADV` (Supported kolors ipadapterοΌ‰ +- Added `easy kolorsLoader` - Code based on [MinusZoneAI](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ)'s and [kijai](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper)'s repo, thanks for their contribution. + +**v1.2.0** + +- Added `easy pulIDApply` and `easy pulIDApplyADV` +- Added `easy huanyuanDiTLoader` and `easy pixArtLoader` +- Added **easy sliderControl** - Slider control node, which can currently be used to control the parameters of ipadapterMS (double-click the slider to reset to default) +- Added **layer_weights** in `easy ipadapterApplyADV` + +**v1.1.9** + +- Added **gitsScheduler** +- Added `easy imageBatchToImageList` and `easy imageListToImageBatch` +- Recursive subcategories nested for models +- Support for Stable Diffusion 3 model +- Added `easy applyInpaint` - All inpainting mode in this node + +**v1.1.8** + +- Added `easy controlnetStack` +- Added `easy applyBrushNet` - [Workflow Example](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4brushnet_1.1.8.json) +- Added `easy applyPowerPaint` - [Workflow Example](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4powerpaint_outpaint_1.1.8.json) + +**v1.1.7** + +- Added `easy prompt` - Subject and light presets, maybe adjusted later +- Added `easy icLightApply` - Light and shadow migration, Code based on [ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light) +- Added `easy imageSplitGrid` +- `easy kSamplerInpainting` added options such as different diffusion and brushnet in **additional** widget +- Support for brushnet model loading - [ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet) +- Added `easy applyFooocusInpaint` - Replace FooocusInpaintLoader +- Removed `easy fooocusInpaintLoader` + +**v1.1.6** + +- Added **alignYourSteps** to **schedulder** widget in all `easy preSampling` and `easy fullkSampler` +- Added **Preview&Choose** to **image_output** widget in `easy kSampler` & `easy fullkSampler` +- Added `easy styleAlignedBatchAlign` - Credit of [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy) +- Added `easy ckptNames` +- Added `easy controlnetNames` +- Added `easy imagesSplitimage` - Batch images split into single images +- Added `easy imageCount` - Get Image Count +- Added `easy textSwitch` - Text Switch + +
+v1.1.5 + +- Rewrite `easy cleanGPUUsed` - the memory usage of the comfyUI can to be cleared +- Added `easy humanSegmentation` - Human Part Segmentation +- Added `easy imageColorMatch` +- Added `easy ipadapterApplyRegional` +- Added `easy ipadapterApplyFromParams` +- Added `easy imageInterrogator` - Image To Prompt +- Added `easy stableDiffusion3API` - Easy Stable Diffusion 3 Multiple accounts API Node +
+ +
+v1.1.4 + +- Added `easy preSamplingCustom` - Custom-PreSampling, can be supported cosXL-edit +- Added `easy ipadapterStyleComposition` +- Added the right-click menu to view checkpoints and lora information in all Loaders +- Fixed `easy preSamplingNoiseIn`、`easy latentNoisy`、`east Unsampler` compatible with ComfyUI Revision>=2098 [0542088e] or later +
+ +
+v1.1.3 + +- `easy ipadapterApply` Added **COMPOSITION** preset +- Supported [ResAdapter](https://huggingface.co/jiaxiangc/res-adapter) when load ResAdapter lora +- Added `easy promptLine` +- Added `easy promptReplace` +- Added `easy promptConcat` +- `easy wildcards` Added **multiline_mode** +
+ +
+v1.1.2 + +- Optimized some of the recommended nodes for slots related to EasyUse +- Added **Enable ContextMenu Auto Nest Subdirectories** The setting item is enabled by default, and it can be classified into subdirectories, checkpoints and loras previews +- Added `easy sv3dLoader` +- Added `easy dynamiCrafterLoader` +- Added `easy ipadapterApply` +- Added `easy ipadapterApplyADV` +- Added `easy ipadapterApplyEncoder` +- Added `easy ipadapterApplyEmbeds` +- Added `easy preMaskDetailerFix` +- Fixed `easy stylesSelector` is change the prompt when not select the style +- Fixed `easy pipeEdit` error when add lora to prompt +- Fixed layerDiffuse xyplot bug +- `easy kSamplerInpainting` add *additional* widget，you can choose 'Differential Diffusion' or 'Only InpaintModelConditioning' +
+ +
+v1.1.1 + +- The issue that the seed is 0 when a node with a seed control is added and **control before generate** is fixed for the first time run queue prompt. +- `easy preSamplingAdvanced` Added **return_with_leftover_noise** +- Fixed `easy stylesSelector` error when choose the custom file +- `easy preSamplingLayerDiffusion` Added optional input parameter for mask +- Renamed all nodes widget name named seed_num to seed +- Remove forced **control_before_generate** settings。 If you want to use control_before_generate, change widget_value_control_mode to before in system settings +- Added `easy imageRemBg` - The default is BriaAI's RMBG-1.4 model, which removes the background effect more and faster +
+ +
+v1.1.0 + +- Added `easy imageSplitList` - to split every N images +- Added `easy preSamplingDiffusionADDTL` - It can modify foreground、background or blended additional prompt +- Added `easy preSamplingNoiseIn` It can replace the `easy latentNoisy` node that needs to be fronted to achieve better noise injection +- `easy pipeEdit` Added conditioning splicing mode selection, you can choose to replace, concat, combine, average, and set timestep range +- Added `easy pipeEdit` - nodes that can edit pipes (including re-enterable prompts) +- Added `easy preSamplingLayerDiffusion` and `easy kSamplerLayerDiffusion` +- Added a convenient menu to right-click on nodes such as Loader, Presampler, Sampler, Controlnet, etc. to quickly replace nodes of the same type +- Added `easy instantIDApplyADV` can link positive and negative +- Fixed layerDiffusion error when batch size greater than 1 +- Fixed `easy wildcards` When LoRa is not filled in completely, LoRa is not automatically retrieved, resulting in failure to load LoRa +- Fixed the issue that 'BREAK' non-initiation when didn't use a1111 prompt style +- Fixed `easy instantIDApply` mask not input right +
+ +
+v1.0.9 + +- Fixed the error when ComfyUI-Impack-Pack and ComfyUI_InstantID were not installed +- Fixed `easy pipeIn` +- Added `easy instantIDApply` - you need installed [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) fisrt, Workflow[Example](https://github.com/yolain/ComfyUI-Easy-Use/blob/main/README.en.md#InstantID) +- Fixed `easy detailerFix` not added to the list of nodes available for saving images formatting extensions +- Fixed `easy XYInputs: PromptSR` errors are reported when replacing negative prompts +
+ +
+v1.0.8 + +- `easy cascadeLoader` stage_c and stage_b support the checkpoint model (Download [checkpoints](https://huggingface.co/stabilityai/stable-cascade/tree/main/comfyui_checkpoints) models) +- `easy styleSelector` The search box is modified to be case-insensitive +- `easy fullLoader` **positive**、**negative**、**latent** added to the output items +- Fixed the issue that 'easy preSampling' and other similar node, latent could not be generated based on the batch index after passing in +- Fixed `easy svdLoader` error when the positive or negative is empty +- Fixed the error of SDXLClipModel in ComfyUI revision 2016[c2cb8e88] and above (the revision number was judged to be compatible with the old revision) +- Fixed `easy detailerFix` generation error when batch size is greater than 1 +- Optimize the code, reduce a lot of redundant code and improve the running speed +
+ +
+v1.0.7 + +- Added `easy cascadeLoader` - stable cascade Loader +- Added `easy preSamplingCascade` - stable cascade preSampling Settings +- Added `easy fullCascadeKSampler` - stable cascade stage-c ksampler full +- Added `easy cascadeKSampler` - stable cascade stage-c ksampler simple +- +- Optimize the image to image[Example](https://github.com/yolain/ComfyUI-Easy-Use/blob/main/README.en.md#image-to-image) +
+ +
+v1.0.6 + +- Added `easy XYInputs: Checkpoint` +- Added `easy XYInputs: Lora` +- `easy seed` can manually switch the random seed when increasing the fixed seed value +- Fixed `easy fullLoader` and all loaders to automatically adjust the node size when switching LoRa +- Removed the original ttn image saving logic and adapted to the default image saving format extension of ComfyUI +
+ +
+v1.0.5 + +- Added `easy isSDXL` +- Added prompt word control on `easy svdLoader`, which can be used with open_clip model +- Added **populated_text** on `easy wildcards`, wildcard populated text can be output +
+ +
+v1.0.4 + +- `easy showAnything` added support for converting other types (e.g., tensor conditions, images, etc.) +- Added `easy showLoaderSettingsNames` can display the model and VAE name in the output loader assembly +- Added `easy promptList` +- Added `easy fooocusInpaintLoader` （only the process of SDXLModel is supportedοΌ‰ +- Added **Logic** nodes +- Added `easy imageSave` - Image saving node with date conversion and aspect and height formatting +- Added `easy joinImageBatch` +- `easy kSamplerInpainting` Added the **patch** input value to be used with the FooocusInpaintLoader node + +- Fixed xyplot error when with Pillow>9.5 +- Fixed `easy wildcards` An error is reported when running with the PS extension +- Fixed `easy XYInputs: ControlNet` Error +- Fixed `easy loraStack` error when **toggle** is disabled + + +- Changing the first-time install node package no longer automatically replaces the theme, you need to manually adjust and refresh the page +- `easy imageSave` added **only_preivew** +- Adjust the `easy latentCompositeMaskedWithCond` node +
+ +
+v1.0.3 + +- Added `easy stylesSelector` +- Added **scale_soft_weights** in `easy controlnetLoader` and `easy controlnetLoaderADV` +- Added the queue progress bar setting item, which is not enabled by default + + +- Fixed `easy XYInputs: Sampler/Scheduler` Error +- Fixed the right menu has a problem when clicking the button +- Fixed `easy comfyLoader` error +- Fixed xyPlot error when connecting to zero123 +- Fixed the error message in the loader when the prompt word was component +- Fixed `easy getNode` and `easy setNode` the title does not change when loading +- Fixed all samplers using subdirectories to store images + + +- Adjust the UI theme, divided into two sets of styles: the official default background and the dark black background, which can be switched in the color palette in the settings +- Modify the styles path to be compatible with other environments +
+ +
+v1.0.2 + +- Added `easy XYPlotAdvanced` and some nodes about `easy XYInputs` +- Added **Alt+1-Alt+9** Shortcut keys to quickly paste node presets for Node templates (corresponding to 1~9 sequences) +- Added a `πŸ“œGroups Map(EasyUse)` to the context menu. +- An `autocomplete` folder has been added, If you have [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) installed, the txt files in that folder will be merged and overwritten to the autocomplete .txt file of the pyssss package at startup. + + +- Fixed XYPlot is not working when `a1111_prompt_style` is True +- Fixed UI loading failure in the new version of ComfyUI +- `easy XYInputs ModelMergeBlocks` Values can be imported from CSV files +- Fixed `easy pipeToBasicPipe` Bug + +- Removed `easy imageRemBg` +- Remove the introductory diagram and workflow files from the package to reduce the package size +- Replaced the font file used in the generation of XY diagrams +
+ +
+v1.0.1 + +- Fixed `easy comfyLoader` error +- Fixed All nodes that contain the value of the image size +- Added `easy kSamplerInpainting` +- Added `easy pipeToBasicPipe` +- Fixed `width` and `height` can not customize in `easy svdLoader` +- Fixed all preview image path (Previously, it was not possible to preview the image on the Mac system) +- Fixed `vae_name` is not working in `easy fullLoader` and `easy a1111Loader` and `easy comfyLoader` +- Fixed `easy fullkSampler` outputs error +- Fixed `model_override` is not working in `easy fullLoader` +- Fixed `easy hiresFix` error +- Fixed `easy xyplot` font file path error +- Fixed seed that cannot be fixed when you convert `seed_num` to `easy seed` +- Fixed `easy pipeIn` inputs bug +- `easy preDetailerFix` have added a new parameter `optional_image` +- Fixed `easy zero123Loader` and `easy svdLoader` model into cache. +- Added `easy seed` +- Fixed `image_output` default value is "Preview" +- `easy fullLoader` and `easy a1111Loader` have added a new parameter `a1111_prompt_style`,that can reproduce the same image generated from stable-diffusion-webui on comfyui, but you need to install [ComfyUI_smZNodes](https://github.com/shiimizu/ComfyUI_smZNodes) to use this feature in the current version +
+ +
+v1.0.0 + +- Added `easy positive` - simple positive prompt text +- Added `easy negative` - simple negative prompt text +- Added `easy wildcards` - support for wildcards and hint text selected by Lora +- Added `easy portraitMaster` - PortraitMaster v2.2 +- Added `easy loraStack` - Lora stack +- Added `easy fullLoader` - full version of the loader +- Added `easy zero123Loader` - simple zero123 loader +- Added `easy svdLoader` - easy svd loader +- Added `easy fullkSampler` - full version of the sampler (no separation) +- Added `easy hiresFix` - support for HD repair of Pipe +- Added `easy predetailerFix` and `easy DetailerFix` - support for Pipe detail fixing +- Added `easy ultralyticsDetectorPipe` and `easy samLoaderPipe` - Detect loader (detail fixed input) +- Added `easy pipein` `easy pipeout` - Pipe input and output +- Added `easy xyPlot` - simple xyplot (more controllable parameters will be updated in the future) +- Added `easy imageRemoveBG` - image to remove background +- Added `easy imagePixelPerfect` - image pixel perfect +- Added `easy poseEditor` - Pose editor +- New UI Theme (Obsidian) - Auto-load UI by default, which can also be changed in the settings + +- Fixed `easy globalSeed` is not working +- Fixed an issue where all `seed_num` values were out of order due to [cg-use-everywhere](https://github.com/chrisgoringe/cg-use-everywhere) updating the chart in real time +- Fixed `easy imageSize`, `easy imageSizeBySide`, `easy imageSizeByLongerSide` as end nodes +- Fixed the bug that `seed_num` (random seed value) could not be read consistently in history +
+ +
+Updated at 12/14/2023 + +- `easy a1111Loader` and `easy comfyLoader` added `batch_size` of required input parameters +- Added the `easy controlnetLoaderADV` node +- `easy controlnetLoaderADV` and `easy controlnetLoader` added `control_net ` of optional input parameters +- `easy preSampling` and `easy preSamplingAdvanced` added `image_to_latent` optional input parameters +- Added the `easy imageSizeBySide` node, which can be output as a long side or a short side +
+ +
+Updated at 12/13/2023 + +- Added the `easy LLLiteLoader` node, if you have pre-installed the kohya-ss/ControlNet-LLLite-ComfyUI package, please move the model files in the models to `ComfyUI\models\controlnet\` (i.e. in the default controlnet path of comfy, please do not change the file name of the model, otherwise it will not be read). +- Modify `easy controlnetLoader` to the bottom of the loader category. +- Added size display for `easy imageSize` and `easy imageSizeByLongerSize` outputs. +
+ +
+Updated at 12/11/2023 +- Added the `showSpentTime` node to display the time spent on image diffusion and the time spent on VAE decoding images +
+ +## The relevant node package involved + +Disclaimer: Opened source was not easy. I have a lot of respect for the contributions of these original authors. I just did some integration and optimization. + +| Nodes Name(Search Name) | Related libraries | Library-related node | +|:-------------------------------|:----------------------------------------------------------------------------|:-------------------------| +| easy setNode | [ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.SetNode | +| easy getNode | [ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.GetNode | +| easy bookmark | [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) | Bookmark πŸ”– | +| easy portraitMarker | [comfyui-portrait-master](https://github.com/florestefano1975/comfyui-portrait-master) | Portrait Master | +| easy LLLiteLoader | [ControlNet-LLLite-ComfyUI](https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI) | LLLiteLoader | +| easy globalSeed | [ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) | Global Seed (Inspire) | +| easy preSamplingDynamicCFG | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | +| dynamicThresholdingFull | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | +| easy imageInsetCrop | [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) | ImageInsetCrop | +| easy poseEditor | [ComfyUI_Custom_Nodes_AlekPet](https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet) | poseNode | +| easy preSamplingLayerDiffusion | [ComfyUI-layerdiffusion](https://github.com/huchenlei/ComfyUI-layerdiffusion) | LayeredDiffusionApply... | +| easy dynamiCrafterLoader | [ComfyUI-layerdiffusion](https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter) | Apply Dynamicrafter | +| easy imageChooser | [cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) | Preview Chooser | +| easy styleAlignedBatchAlign | [style_aligned_comfy](https://github.com/chrisgoringe/cg-image-picker) | styleAlignedBatchAlign | +| easy kolorsLoader | [ComfyUI-Kolors-MZ](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ) | kolorsLoader | + + +## Credits + +[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - Powerful and modular Stable Diffusion GUI + +[ComfyUI-ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) - ComfyUI Manager + +[tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) - Pipe nodes (node bundles) allow users to reduce unnecessary connections + +[ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) - Diffus3 gets and sets points that allow the user to detach the composition of the workflow + +[ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack) - General modpack 1 + +[ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) - General Modpack 2 + +[ComfyUI-ResAdapter](https://github.com/jiaxiangc/ComfyUI-ResAdapter) - Make model generation independent of training resolution + +[ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) - Style migration + +[ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) - Face migration + +[ComfyUI_PuLID](https://github.com/cubiq/PuLID_ComfyUI) - Face migration + +[ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) - pyssss🐍 + +[cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) - Image Preview Chooser + +[ComfyUI_ExtraModels](https://github.com/city96/ComfyUI_ExtraModels) - DiT custom nodes + +## Disclaimer + +This software is provided β€œas is,” without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. + +Users are solely responsible for ensuring that their use of this software complies with all applicable laws and regulations in the jurisdiction where they use the software or publish content generated by it. The authors and copyright holders are not responsible for any violations of laws or regulations by users in their respective locations. + +## β˜•οΈ Donation + +**Comfyui-Easy-Use** is an GPL-licensed open source project. In order to achieve better and sustainable development of the project, i expect to gain more backers.
+If my custom nodes has added value to your day, consider indulging in a coffee to fuel it further!
+πŸ’–You can support me in any of the following ways: + +- [BiliBili](https://space.bilibili.com/1840885116) +- [Wechat / Alipay](https://github.com/user-attachments/assets/803469bd-ed6a-4fab-932d-50e5088a2d03) + +## 🌟Stargazers + +My gratitude extends to the generous souls who bestow a star. Your support is much appreciated! + +[![Stargazers repo roster for @yolain/ComfyUI-Easy-Use](https://reporoster.com/stars/yolain/ComfyUI-Easy-Use)](https://github.com/yolain/ComfyUI-Easy-Use/stargazers) diff --git a/custom_nodes/ComfyUI-Easy-Use/__init__.py b/custom_nodes/ComfyUI-Easy-Use/__init__.py new file mode 100644 index 00000000..08223335 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/__init__.py @@ -0,0 +1,95 @@ +__version__ = "1.3.7" + +import yaml +import json +import os +import folder_paths +import importlib + +cwd_path = os.path.dirname(os.path.realpath(__file__)) +comfy_path = folder_paths.base_path + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +importlib.import_module('.py.routes', __name__) +importlib.import_module('.py.server', __name__) +nodes_list = ["util", "seed", "prompt", "loaders", "adapter", "inpaint", "preSampling", "samplers", "fix", "pipe", "xyplot", "image", "logic", "api", "deprecated"] +for module_name in nodes_list: + imported_module = importlib.import_module(".py.nodes.{}".format(module_name), __name__) + NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS} + NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS} + +#Wildcards +from .py.libs.wildcards import read_wildcard_dict +wildcards_path = os.path.join(os.path.dirname(__file__), "wildcards") +if not os.path.exists(wildcards_path): + os.mkdir(wildcards_path) + +# Add custom wildcards example +example_path = os.path.join(wildcards_path, "example.txt") +if not os.path.exists(example_path): + with open(example_path, 'w') as f: + text = "blue\nred\nyellow\ngreen\nbrown\npink\npurple\norange\nblack\nwhite" + f.write(text) +read_wildcard_dict(wildcards_path) + +#Styles +styles_path = os.path.join(os.path.dirname(__file__), "styles") +samples_path = os.path.join(os.path.dirname(__file__), "styles", "samples") +if os.path.exists(styles_path): + if not os.path.exists(samples_path): + os.mkdir(samples_path) +else: + os.mkdir(styles_path) + os.mkdir(samples_path) + +# Add custom styles example +example_path = os.path.join(styles_path, "your_styles.json.example") +if not os.path.exists(example_path): + import json + data = [ + { + "name": "Example Style", + "name_cn": "瀺例样式", + "prompt": "(masterpiece), (best quality), (ultra-detailed), {prompt} ", + "negative_prompt": "text, watermark, logo" + }, + ] + # Write to file + with open(example_path, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=4, ensure_ascii=False) + + +web_default_version = 'v2' +# web directory +config_path = os.path.join(cwd_path, "config.yaml") +if os.path.isfile(config_path): + with open(config_path, 'r') as f: + data = yaml.load(f, Loader=yaml.FullLoader) + if data and "WEB_VERSION" in data: + directory = f"web_version/{data['WEB_VERSION']}" + with open(config_path, 'w') as f: + yaml.dump(data, f) + elif web_default_version != 'v1': + if not data: + data = {'WEB_VERSION': web_default_version} + elif 'WEB_VERSION' not in data: + data = {**data, 'WEB_VERSION': web_default_version} + with open(config_path, 'w') as f: + yaml.dump(data, f) + directory = f"web_version/{web_default_version}" + else: + directory = f"web_version/v1" + if not os.path.exists(os.path.join(cwd_path, directory)): + print(f"web root {data['WEB_VERSION']} not found, using default") + directory = f"web_version/{web_default_version}" + WEB_DIRECTORY = directory +else: + directory = f"web_version/{web_default_version}" + WEB_DIRECTORY = directory + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"] + +print(f'\033[34m[ComfyUI-Easy-Use] server: \033[0mv{__version__} \033[92mLoaded\033[0m') +print(f'\033[34m[ComfyUI-Easy-Use] web root: \033[0m{os.path.join(cwd_path, directory)} \033[92mLoaded\033[0m') diff --git a/custom_nodes/ComfyUI-Easy-Use/install.bat b/custom_nodes/ComfyUI-Easy-Use/install.bat new file mode 100644 index 00000000..1fbe1c15 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/install.bat @@ -0,0 +1,26 @@ +@echo off + +set "requirements_txt=%~dp0\requirements.txt" +set "requirements_repair_txt=%~dp0\repair_dependency_list.txt" +set "python_exec=..\..\..\python_embeded\python.exe" +set "aki_python_exec=..\..\python\python.exe" + +echo Installing EasyUse Requirements... + +if exist "%python_exec%" ( + echo Installing with ComfyUI Portable + "%python_exec%" -s -m pip install -r "%requirements_txt%" +)^ +else if exist "%aki_python_exec%" ( + echo Installing with ComfyUI Aki + "%aki_python_exec%" -s -m pip install -r "%requirements_txt%" + for /f "delims=" %%i in (%requirements_repair_txt%) do ( + %aki_python_exec% -s -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple "%%i" + ) +)^ +else ( + echo Installing with system Python + pip install -r "%requirements_txt%" +) + +pause \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/install.sh b/custom_nodes/ComfyUI-Easy-Use/install.sh new file mode 100644 index 00000000..8e7788d2 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/install.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +requirements_txt="$(dirname "$0")/requirements.txt" +requirements_repair_txt="$(dirname "$0")/repair_dependency_list.txt" +python_exec="../../../python_embeded/python.exe" +aki_python_exec="../../python/python.exe" + +echo "Installing EasyUse Requirements..." + +if [ -f "$python_exec" ]; then + echo "Installing with ComfyUI Portable" + "$python_exec" -s -m pip install -r "$requirements_txt" +elif [ -f "$aki_python_exec" ]; then + echo "Installing with ComfyUI Aki" + "$aki_python_exec" -s -m pip install -r "$requirements_txt" + while IFS= read -r line; do + "$aki_python_exec" -s -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple "$line" + done < "$requirements_repair_txt" +else + echo "Installing with system Python" + pip install -r "$requirements_txt" +fi + +read -p "Press any key to continue..." \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/en/main.json b/custom_nodes/ComfyUI-Easy-Use/locales/en/main.json new file mode 100644 index 00000000..f5b4fb11 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/en/main.json @@ -0,0 +1,31 @@ +{ + "settingsCategories": { + "Hotkeys": "Hotkeys", + "Nodes": "Nodes", + "NodesMap": "NodesMap", + "StylesSelector": "StylesSelector" + }, + "nodeCategories": { + "Util": "Util", + "Seed": "Seed", + "Prompt": "Prompt", + "Loaders": "Loaders", + "Adapter": "Adapter", + "Inpaint": "Inpaint", + "PreSampling": "PreSampling", + "Sampler": "Sampler", + "Fix": "Fix", + "Pipe": "Pipe", + "XY Inputs": "XY Inputs", + "Image": "Image", + "Segmentation": "Segmentation", + "\uD83D\uDEAB Deprecated": "\uD83D\uDEAB Deprecated", + "Type": "Type", + "Math": "Math", + "Switch": "Switch", + "Index Switch": "Index Switch", + "While Loop": "While Loop", + "For Loop": "For Loop", + "LoadImage": "Load Image" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/en/nodeDefs.json b/custom_nodes/ComfyUI-Easy-Use/locales/en/nodeDefs.json new file mode 100644 index 00000000..601dc61b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/en/nodeDefs.json @@ -0,0 +1,6877 @@ +{ + "easy showLoaderSettingsNames": { + "display_name": "Show Loader Settings Names", + "inputs": { + "pipe": { + "name": "pipe" + } + }, + "outputs": { + "0": { + "name": "ckpt_name" + }, + "1": { + "name": "vae_name" + }, + "2": { + "name": "lora_name" + } + } + }, + "easy sliderControl": { + "display_name": "Easy Slider Control", + "inputs": { + "mode": { + "name": "mode" + }, + "model_type": { + "name": "model_type" + } + }, + "outputs": { + "0": { + "name": "layer_weights" + } + } + }, + "easy ckptNames": { + "display_name": "Ckpt Names", + "inputs": { + "ckpt_name": { + "name": "ckpt_name" + } + }, + "outputs": { + "0": { + "name": "ckpt_name" + } + } + }, + "easy controlnetNames": { + "display_name": "ControlNet Names", + "inputs": { + "controlnet_name": { + "name": "controlnet_name" + } + }, + "outputs": { + "0": { + "name": "controlnet_name" + } + } + }, + "easy seed": { + "display_name": "EasySeed", + "inputs": { + "seed": { + "name": "seed" + } + }, + "outputs": { + "0": { + "name": "seed" + } + } + }, + "easy globalSeed": { + "display_name": "EasyGlobalSeed", + "inputs": { + "value": { + "name": "value" + }, + "mode": { + "name": "mode" + }, + "action": { + "name": "action" + }, + "last_seed": { + "name": "last_seed" + } + }, + "outputs": {} + }, + "easy positive": { + "display_name": "Positive", + "inputs": { + "positive": { + "name": "positive" + } + }, + "outputs": { + "0": { + "name": "positive" + } + } + }, + "easy negative": { + "display_name": "Negative", + "inputs": { + "negative": { + "name": "negative" + } + }, + "outputs": { + "0": { + "name": "negative" + } + } + }, + "easy wildcards": { + "display_name": "Wildcards", + "inputs": { + "text": { + "name": "text" + }, + "Select to add LoRA": { + "name": "Select to add LoRA" + }, + "Select to add Wildcard": { + "name": "Select to add Wildcard" + }, + "seed": { + "name": "seed" + }, + "multiline_mode": { + "name": "multiline_mode" + } + }, + "outputs": { + "0": { + "name": "text" + }, + "1": { + "name": "populated_text" + } + } + }, + "easy wildcardsMatrix": { + "display_name": "Wildcards Matrix", + "inputs": { + "Select to add LoRA": { + "name": "Select to add LoRA" + }, + "Select to add Wildcard": { + "name": "Select to add Wildcard" + }, + "offset": { + "name": "Offset in All Probilities" + }, + "output_limit": { + "name": "Output Limit", + "tooltip": "Output n fill wildcards, -1 is output all possibilities (force offset to zero), the default value is 1" + } + }, + "outputs": { + "0": { + "name": "Replaced Prompt" + }, + "1": { + "name": "Total Count of the Probilities" + }, + "2": { + "name": "Factors" + } + } + }, + "easy prompt": { + "display_name": "Prompt", + "inputs": { + "text": { + "name": "text" + }, + "prefix": { + "name": "prefix" + }, + "subject": { + "name": "subject" + }, + "action": { + "name": "action" + }, + "clothes": { + "name": "clothes" + }, + "environment": { + "name": "environment" + }, + "background": { + "name": "background" + }, + "nsfw": { + "name": "nsfw" + } + }, + "outputs": { + "0": { + "name": "prompt" + } + } + }, + "easy promptList": { + "display_name": "PromptList", + "inputs": { + "prompt_1": { + "name": "prompt_1" + }, + "prompt_2": { + "name": "prompt_2" + }, + "prompt_3": { + "name": "prompt_3" + }, + "prompt_4": { + "name": "prompt_4" + }, + "prompt_5": { + "name": "prompt_5" + }, + "optional_prompt_list": { + "name": "optional_prompt_list" + } + }, + "outputs": { + "0": { + "name": "prompt_list" + }, + "1": { + "name": "prompt_strings" + } + } + }, + "easy promptLine": { + "display_name": "PromptLine", + "inputs": { + "prompt": { + "name": "prompt" + }, + "start_index": { + "name": "start_index" + }, + "max_rows": { + "name": "max_rows" + }, + "remove_empty_lines":{ + "name": "remove_empty_lines" + } + }, + "outputs": { + "0": { + "name": "STRING" + }, + "1": { + "name": "COMBO" + } + } + }, + "easy promptConcat": { + "display_name": "PromptConcat", + "inputs": { + "prompt1": { + "name": "prompt1" + }, + "prompt2": { + "name": "prompt2" + }, + "separator": { + "name": "separator" + } + }, + "outputs": { + "0": { + "name": "prompt" + } + } + }, + "easy promptReplace": { + "display_name": "PromptReplace", + "inputs": { + "prompt": { + "name": "prompt" + }, + "find1": { + "name": "find1" + }, + "replace1": { + "name": "replace1" + }, + "find2": { + "name": "find2" + }, + "replace2": { + "name": "replace2" + }, + "find3": { + "name": "find3" + }, + "replace3": { + "name": "replace3" + } + }, + "outputs": { + "0": { + "name": "prompt" + } + } + }, + "easy stylesSelector": { + "display_name": "Styles Selector", + "inputs": { + "styles": { + "name": "styles" + }, + "positive": { + "name": "positive" + }, + "negative": { + "name": "negative" + } + }, + "outputs": { + "0": { + "name": "positive" + }, + "1": { + "name": "negative" + } + } + }, + "easy portraitMaster": { + "display_name": "Portrait Master", + "inputs": { + "shot": { + "name": "shot" + }, + "shot_weight": { + "name": "shot_weight" + }, + "gender": { + "name": "gender" + }, + "age": { + "name": "age" + }, + "nationality_1": { + "name": "nationality_1" + }, + "nationality_2": { + "name": "nationality_2" + }, + "nationality_mix": { + "name": "nationality_mix" + }, + "body_type": { + "name": "body_type" + }, + "body_type_weight": { + "name": "body_type_weight" + }, + "model_pose": { + "name": "model_pose" + }, + "eyes_color": { + "name": "eyes_color" + }, + "facial_expression": { + "name": "facial_expression" + }, + "facial_expression_weight": { + "name": "facial_expression_weight" + }, + "face_shape": { + "name": "face_shape" + }, + "face_shape_weight": { + "name": "face_shape_weight" + }, + "facial_asymmetry": { + "name": "facial_asymmetry" + }, + "hair_style": { + "name": "hair_style" + }, + "hair_color": { + "name": "hair_color" + }, + "disheveled": { + "name": "disheveled" + }, + "beard": { + "name": "beard" + }, + "skin_details": { + "name": "skin_details" + }, + "skin_pores": { + "name": "skin_pores" + }, + "dimples": { + "name": "dimples" + }, + "freckles": { + "name": "freckles" + }, + "moles": { + "name": "moles" + }, + "skin_imperfections": { + "name": "skin_imperfections" + }, + "skin_acne": { + "name": "skin_acne" + }, + "tanned_skin": { + "name": "tanned_skin" + }, + "eyes_details": { + "name": "eyes_details" + }, + "iris_details": { + "name": "iris_details" + }, + "circular_iris": { + "name": "circular_iris" + }, + "circular_pupil": { + "name": "circular_pupil" + }, + "light_type": { + "name": "light_type" + }, + "light_direction": { + "name": "light_direction" + }, + "light_weight": { + "name": "light_weight" + }, + "photorealism_improvement": { + "name": "photorealism_improvement" + }, + "prompt_start": { + "name": "prompt_start" + }, + "prompt_additional": { + "name": "prompt_additional" + }, + "prompt_end": { + "name": "prompt_end" + }, + "negative_prompt": { + "name": "negative_prompt" + } + }, + "outputs": { + "0": { + "name": "positive" + }, + "1": { + "name": "negative" + } + } + }, + "easy multiAngle":{ + "display_name": "Multi Angle Prompt", + "inputs": { + }, + "outputs": { + "0": { + "name": "prompt" + }, + "1":{ + "name": "params" + } + } + }, + "easy fullLoader": { + "display_name": "EasyLoader (Full)", + "inputs": { + "ckpt_name": { + "name": "ckpt_name" + }, + "config_name": { + "name": "config_name" + }, + "vae_name": { + "name": "vae_name" + }, + "clip_skip": { + "name": "clip_skip" + }, + "lora_name": { + "name": "lora_name" + }, + "lora_model_strength": { + "name": "lora_model_strength" + }, + "lora_clip_strength": { + "name": "lora_clip_strength" + }, + "resolution": { + "name": "resolution" + }, + "empty_latent_width": { + "name": "empty_latent_width" + }, + 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"end_at_step" + }, + "source": { + "name": "source" + }, + "seed": { + "name": "seed" + }, + "pipe": { + "name": "pipe" + }, + "optional_model": { + "name": "optional_model" + }, + "optional_latent": { + "name": "optional_latent" + } + }, + "outputs": { + "0": { + "name": "pipe" + }, + "1": { + "name": "latent" + }, + "2": { + "name": "sigma" + } + } + }, + "easy latentCompositeMaskedWithCond": { + "display_name": "LatentCompositeMaskedWithCond (🚫Deprecated)", + "inputs": { + "pipe": { + "name": "pipe" + }, + "text_combine": { + "name": "text_combine" + }, + "source_latent": { + "name": "source_latent" + }, + "source_mask": { + "name": "source_mask" + }, + "destination_mask": { + "name": "destination_mask" + }, + "text_combine_mode": { + "name": "text_combine_mode" + }, + "replace_text": { + "name": "replace_text" + } + }, + "outputs": { + "0": { + "name": "pipe" + }, + "1": { + "name": "latent" + }, + "2": { + "name": "conditioning" + } + } + }, + "easy injectNoiseToLatent": { + "display_name": "InjectNoiseToLatent (🚫Deprecated)", + "inputs": { + "strength": { + "name": "strength" + }, + "normalize": { + "name": "normalize" + }, + "average": { + "name": "average" + }, + "pipe_to_noise": { + "name": "pipe_to_noise" + }, + "image_to_latent": { + "name": "image_to_latent" + }, + "latent": { + "name": "latent" + }, + "noise": { + "name": "noise" + }, + "mask": { + "name": "mask" + }, + "mix_randn_amount": { + "name": "mix_randn_amount" + }, + "seed": { + "name": "seed" + } + }, + "outputs": {} + }, + "easy stableDiffusion3API": { + "display_name": "StableDiffusion3API (🚫Deprecated)", + "inputs": { + "positive": { + "name": "positive" + }, + "negative": { + "name": "negative" + }, + "model": { + "name": "model" + }, + "aspect_ratio": { + "name": "aspect_ratio" + }, + "seed": { + "name": "seed" + }, + "denoise": { + "name": "denoise" + }, + "optional_image": { + "name": "optional_image" + } + }, + "outputs": { + "0": { + "name": "image" + } + } + }, + "easy saveImageLazy": { + "display_name": "SaveImageLazy (🚫Deprecated)", + "inputs": { + "images": { + "name": "images" + }, + "filename_prefix": { + "name": "filename_prefix" + }, + "save_metadata": { + "name": "save_metadata" + } + }, + "outputs": { + "0": { + "name": "images" + } + } + }, + "easy saveTextLazy": { + "display_name": "SaveTextLazy (🚫Deprecated)", + "inputs": { + "text": { + "name": "text" + }, + "output_file_path": { + "name": "output_file_path" + }, + "file_name": { + "name": "file_name" + }, + "file_extension": { + "name": "file_extension" + }, + "overwrite": { + "name": "overwrite" + }, + "image": { + "name": "image" + } + }, + "outputs": { + "0": { + "name": "text" + }, + "1": { + "name": "image" + } + } + }, + "easy showAnythingLazy": { + "display_name": "ShowAnythingLazy (🚫Deprecated)", + "inputs": { + "anything": { + "name": "anything" + } + }, + "outputs": { + "0": { + "name": "output" + } + } + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/en/settings.json b/custom_nodes/ComfyUI-Easy-Use/locales/en/settings.json new file mode 100644 index 00000000..7789d42f --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/en/settings.json @@ -0,0 +1,67 @@ +{ + "EasyUse_Hotkeys_AddGroup": { + "name": "Enable Shift+g to add the selected nodes to a group", + "tooltip": "From v1.2.39, you can use Ctrl+g instead" + }, + "EasyUse_Hotkeys_cleanVRAMUsed": { + "name": "Enable Shift+r to unload model and node cache" + }, + "EasyUse_Hotkeys_toggleNodesMap": { + "name": "Enable Shift+m to toggle nodes map" + }, + "EasyUse_Hotkeys_AlignSelectedNodes": { + "name": "Enable Shift+Up/Down/Left/Right and Shift+Ctrl+Alt+Left/Right to align selected nodes", + "tooltip": "Shift+Up/Down/Left/Right can align selected nodes, Shift+Ctrl+Alt+Left/Right can distribute nodes horizontally/vertically" + }, + "EasyUse_Hotkeys_NormalizeSelectedNodes": { + "name": "Enable Shift+Ctrl+Left/Right to normalize selected nodes", + "tooltip": "Enable Shift+Ctrl+Left to normalize width and Shift+Ctrl+Right to normalize height" + }, + "EasyUse_Hotkeys_NodesTemplate": { + "name": "Enable Alt+1~9 to paste node templates into the workflow" + }, + "EasyUse_Hotkeys_JumpNearestNodes": { + "name": "Enable Up/Down/Left/Right to jump to the nearest node" + }, + "EasyUse_ContextMenu_SubDirectories": { + "name": "Enable automatic nesting of subdirectories in the context menu" + }, + "EasyUse_ContextMenu_ModelsThumbnails": { + "name": "Enable model preview thumbnails" + }, + "EasyUse_ContextMenu_NodesSort": { + "name": "Enable A~Z sorting of new nodes in the context menu" + }, + "EasyUse_ContextMenu_QuickOptions": { + "name": "Use three quick buttons in the context menu", + "options": { + "At the forefront": "At the forefront", + "At the end": "At the end", + "Disable": "Disable" + } + }, + "EasyUse_Nodes_Runtime": { + "name": "Enable node runtime display" + }, + "EasyUse_Nodes_ChainGetSet": { + "name": "Enable chaining of get and set points with the parent node" + }, + "EasyUse_NodesMap_Sorting": { + "name": "Manage nodes group sorting mode", + "tooltip": "Automatically sort by default. If set to manual, groups can be drag and dropped and the order will be saved.", + "options": { + "Auto sorting": "Auto sorting", + "Manual drag&drop sorting": "Manual drag&drop sorting" + } + }, + "EasyUse_NodesMap_DisplayNodeID": { + "name": "Enable node ID display" + }, + "EasyUse_NodesMap_DisplayGroupOnly": { + "name": "Show groups only" + }, + "EasyUse_NodesMap_Enable": { + "name": "Enable Group Map", + "tooltip": "You need to refresh the page to update successfully" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/fr/main.json b/custom_nodes/ComfyUI-Easy-Use/locales/fr/main.json new file mode 100644 index 00000000..2c6d1d5b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/fr/main.json @@ -0,0 +1,30 @@ +{ + "settingsCategories": { + "Hotkeys": "Raccourcis", + "Nodes": "NΕ“uds", + "NodesMap": "Carte des nΕ“uds" + }, + "nodeCategories": { + "Util": "Utilitaire", + "Seed": "Graine", + "Prompt": "Prompt", + "Loaders": "Chargeurs", + "Adapter": "Adaptateur", + "Inpaint": "Retouche", + "PreSampling": "PrΓ©-Γ©chantillonnage", + "Sampler": "Γ‰chantillonneur", + "Fix": "Correction", + "Pipe": "Pipeline", + "XY Inputs": "EntrΓ©es XY", + "Image": "Image", + "Segmentation": "Segmentation", + "\uD83D\uDEAB Deprecated": "\uD83D\uDEAB ObsolΓ¨te", + "Type": "Type", + "Math": "MathΓ©matiques", + "Switch": "Interrupteur", + "Index Switch": "Interrupteur d'index", + "While Loop": "Boucle While", + "For Loop": "Boucle For", + "LoadImage": "Charger l'image" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/fr/settings.json b/custom_nodes/ComfyUI-Easy-Use/locales/fr/settings.json new file mode 100644 index 00000000..07f14dc7 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/fr/settings.json @@ -0,0 +1,67 @@ +{ + "EasyUse_Hotkeys_AddGroup": { + "name": "Activer Shift+g pour ajouter les nΕ“uds sΓ©lectionnΓ©s Γ  un groupe", + "tooltip": "Depuis la v1.2.39, vous pouvez utiliser Ctrl+g Γ  la place" + }, + "EasyUse_Hotkeys_cleanVRAMUsed": { + "name": "Activer Shift+r pour dΓ©charger le cache du modΓ¨le et des nΕ“uds" + }, + "EasyUse_Hotkeys_toggleNodesMap": { + "name": "Activer Shift+m pour basculer la carte des nΕ“uds" + }, + "EasyUse_Hotkeys_AlignSelectedNodes": { + "name": "Activer Shift+Up/Down/Left/Right et Shift+Ctrl+Alt+Left/Right pour aligner les nΕ“uds sΓ©lectionnΓ©s", + "tooltip": "Shift+Up/Down/Left/Right peut aligner les nΕ“uds sΓ©lectionnΓ©s, Shift+Ctrl+Alt+Left/Right peut les rΓ©partir horizontalement/verticalement" + }, + "EasyUse_Hotkeys_NormalizeSelectedNodes": { + "name": "Activer Shift+Ctrl+Left/Right pour normaliser les nΕ“uds sΓ©lectionnΓ©s", + "tooltip": "Activer Shift+Ctrl+Left pour normaliser la largeur et Shift+Ctrl+Right pour normaliser la hauteur" + }, + "EasyUse_Hotkeys_NodesTemplate": { + "name": "Activer Alt+1~9 pour coller les modΓ¨les de nΕ“uds dans le workflow" + }, + "EasyUse_Hotkeys_JumpNearestNodes": { + "name": "Activer Up/Down/Left/Right pour passer au nΕ“ud le plus proche" + }, + "EasyUse_ContextMenu_SubDirectories": { + "name": "Activer l'imbrication automatique des sous-rΓ©pertoires dans le menu contextuel" + }, + "EasyUse_ContextMenu_ModelsThumbnails": { + "name": "Activer les vignettes d'aperΓ§u du modΓ¨le" + }, + "EasyUse_ContextMenu_NodesSort": { + "name": "Activer le tri A~Z des nouveaux nΕ“uds dans le menu contextuel" + }, + "EasyUse_ContextMenu_QuickOptions": { + "name": "Utiliser trois boutons rapides dans le menu contextuel", + "options": { + "At the forefront": "Γ€ l'avant-plan", + "At the end": "Γ€ la fin", + "Disable": "DΓ©sactiver" + } + }, + "EasyUse_Nodes_Runtime": { + "name": "Activer l'affichage du temps d'exΓ©cution des nΕ“uds" + }, + "EasyUse_Nodes_ChainGetSet": { + "name": "Activer le chaΓnage des points get et set avec le nΕ“ud parent" + }, + "EasyUse_NodesMap_Sorting": { + "name": "GΓ©rer le mode de tri des groupes de nΕ“uds", + "tooltip": "Tri automatique par dΓ©faut. Si dΓ©fini sur manuel, les groupes peuvent Γͺtre glissΓ©s-dΓ©posΓ©s et l'ordre sera sauvegardΓ©.", + "options": { + "Auto sorting": "Tri automatique", + "Manual drag&drop sorting": "Tri manuel par glisser-dΓ©poser" + } + }, + "EasyUse_NodesMap_DisplayNodeID": { + "name": "Activer l'affichage de l'ID du nΕ“ud" + }, + "EasyUse_NodesMap_DisplayGroupOnly": { + "name": "Afficher uniquement les groupes" + }, + "EasyUse_NodesMap_Enable": { + "name": "Activer la carte des groupes", + "tooltip": "Vous devez actualiser la page pour mettre Γ  jour" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/ja/main.json b/custom_nodes/ComfyUI-Easy-Use/locales/ja/main.json new file mode 100644 index 00000000..266f241b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/ja/main.json @@ -0,0 +1,30 @@ +{ + "settingsCategories": { + "Hotkeys": "γ‚·γƒ§γƒΌγƒˆγ‚«γƒƒγƒˆγ‚­γƒΌ", + "Nodes": "γƒŽγƒΌγƒ‰", + "NodesMap": "γƒŽγƒΌγƒ‰γƒžγƒƒγƒ—" + }, + "nodeCategories": { + "Util": "ユーティγƒͺティ", + "Seed": "シード", + "Prompt": "γƒ—γƒ­γƒ³γƒ—γƒˆ", + "Loaders": "ローダー", + "Adapter": "をダプター", + "Inpaint": "γ‚€γƒ³γƒšγ‚€γƒ³γƒˆ", + "PreSampling": "γƒ—γƒͺァンプγƒͺング", + "Sampler": "ァンプラー", + "Fix": "フィックス", + "Pipe": "パむプ", + "XY Inputs": "XYε…₯εŠ›", + "Image": "画像", + "Segmentation": "セグパンテーション", + "\uD83D\uDEAB Deprecated": "🚫 非推ε₯¨", + "Type": "γ‚Ώγ‚€γƒ—", + "Math": "ζ•°ε­¦", + "Switch": "スむッチ", + "Index Switch": "むンデックススむッチ", + "While Loop": "Whileループ", + "For Loop": "Forループ", + "LoadImage": "画像θͺ­γΏθΎΌγΏ" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/ja/settings.json b/custom_nodes/ComfyUI-Easy-Use/locales/ja/settings.json new file mode 100644 index 00000000..7935c65f --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/ja/settings.json @@ -0,0 +1,67 @@ +{ + "EasyUse_Hotkeys_AddGroup": { + "name": "Shift+gγ‚’δ½Ώη”¨γ—γ¦ιΈζŠžγ—γŸγƒŽγƒΌγƒ‰γ‚’γ‚°γƒ«γƒΌγƒ—γ«θΏ½εŠ γ™γ‚‹", + "tooltip": "v1.2.39δ»₯降、Ctrl+gγŒδ½Ώη”¨γ§γγΎγ™" + }, + "EasyUse_Hotkeys_cleanVRAMUsed": { + "name": "Shift+rγ‚’δ½Ώη”¨γ—γ¦γƒ’γƒ‡γƒ«γŠγ‚ˆγ³γƒŽγƒΌγƒ‰γ‚­γƒ£γƒƒγ‚·γƒ₯ををンロードする" + }, + "EasyUse_Hotkeys_toggleNodesMap": { + "name": "Shift+mγ‚’δ½Ώη”¨γ—γ¦γƒŽγƒΌγƒ‰γƒžγƒƒγƒ—γ‚’θ‘¨η€Ί/ιžθ‘¨η€Ίγ«γ—γΎγ™" + }, + "EasyUse_Hotkeys_AlignSelectedNodes": { + "name": "Shift+上/δΈ‹/ε·¦/ε³γŠγ‚ˆγ³Shift+Ctrl+Alt+ε·¦/ε³γ‚’δ½Ώη”¨γ—γ¦ιΈζŠžγ—γŸγƒŽγƒΌγƒ‰γ‚’ζ•΄εˆ—γ™γ‚‹", + "tooltip": "Shift+上/δΈ‹/ε·¦/ε³γ§ιΈζŠžγ—γŸγƒŽγƒΌγƒ‰γ‚’ζ•΄εˆ—γ—γ€Shift+Ctrl+Alt+ε·¦/右で水平方向/εž‚η›΄ζ–Ήε‘γ«εˆ†εΈƒγ•γ›γ‚‹" + }, + "EasyUse_Hotkeys_NormalizeSelectedNodes": { + "name": "Shift+Ctrl+ε·¦/ε³γ‚’δ½Ώη”¨γ—γ¦ιΈζŠžγ—γŸγƒŽγƒΌγƒ‰γγ‚΅γ‚€γ‚Ίγ‚’ζ­£θ¦εŒ–γ™γ‚‹", + "tooltip": "Shift+Ctrl+左で幅を、Shift+Ctrl+ε³γ§ι«˜γ•γ‚’ζ­£θ¦εŒ–γ™γ‚‹" + }, + "EasyUse_Hotkeys_NodesTemplate": { + "name": "Alt+1~9γ‚’δ½Ώη”¨γ—γ¦γƒ―γƒΌγ‚―γƒ•γƒ­γƒΌγ«γƒŽγƒΌγƒ‰γƒ†γƒ³γƒ—γƒ¬γƒΌγƒˆγ‚’θ²Όγ‚Šδ»˜γ‘γ‚‹" + }, + "EasyUse_Hotkeys_JumpNearestNodes": { + "name": "上/δΈ‹/ε·¦/ε³γ‚’δ½Ώη”¨γ—γ¦ζœ€γ‚‚θΏ‘γ„γƒŽγƒΌγƒ‰γ«γ‚Έγƒ£γƒ³γƒ—γ™γ‚‹" + }, + "EasyUse_ContextMenu_SubDirectories": { + "name": "γ‚³γƒ³γƒ†γ‚­γ‚Ήγƒˆγƒ‘γƒ‹γƒ₯γƒΌγ§γ‚΅γƒ–γƒ‡γ‚£γƒ¬γ‚―γƒˆγƒͺγ‚’θ‡ͺε‹•γ§γƒγ‚Ήγƒˆγ™γ‚‹" + }, + "EasyUse_ContextMenu_ModelsThumbnails": { + "name": "ヒデルプレビγƒ₯γƒΌγ‚΅γƒ γƒγ‚€γƒ«γ‚’ζœ‰εŠΉγ«γ™γ‚‹" + }, + "EasyUse_ContextMenu_NodesSort": { + "name": "γ‚³γƒ³γƒ†γ‚­γ‚Ήγƒˆγƒ‘γƒ‹γƒ₯γƒΌγ§ζ–°θ¦γƒŽγƒΌγƒ‰γ‚’A~Zι †γ«δΈ¦γΉζ›Ώγˆγ‚‹" + }, + "EasyUse_ContextMenu_QuickOptions": { + "name": "γ‚³γƒ³γƒ†γ‚­γ‚Ήγƒˆγƒ‘γƒ‹γƒ₯ーで3぀γγ‚―γ‚€γƒƒγ‚―γƒœγ‚Ώγƒ³γ‚’δ½Ώη”¨γ™γ‚‹", + "options": { + "At the forefront": "ζœ€ε‰ι’γ«", + "At the end": "ζœ€εΎŒγ«", + "Disable": "η„‘εŠΉ" + } + }, + "EasyUse_Nodes_Runtime": { + "name": "γƒŽγƒΌγƒ‰γεŸθ‘Œζ™‚ι–“θ‘¨η€Ίγ‚’ζœ‰εŠΉγ«γ™γ‚‹" + }, + "EasyUse_Nodes_ChainGetSet": { + "name": "θ¦ͺγƒŽγƒΌγƒ‰γ¨ε–εΎ—/θ¨­εšγƒγ‚€γƒ³γƒˆγ‚’ι€£η΅γ™γ‚‹γ“γ¨γ‚’ζœ‰εŠΉγ«γ™γ‚‹" + }, + "EasyUse_NodesMap_Sorting": { + "name": "γƒŽγƒΌγƒ‰γ‚°γƒ«γƒΌγƒ—γδΈ¦γΉζ›Ώγˆγƒ’ードをη‘理する", + "tooltip": "γƒ‡γƒ•γ‚©γƒ«γƒˆγ§θ‡ͺε‹•ηš„γ«δΈ¦γΉζ›ΏγˆγΎγ™γ€‚γƒžγƒ‹γƒ₯をルに設εšγ—γŸε ΄εˆγ€γ‚°γƒ«γƒΌγƒ—γ‚’γƒ‰γƒ©γƒƒγ‚°γ‚’γƒ³γƒ‰γƒ‰γƒ­γƒƒγƒ—γ§δΈ¦γΉζ›Ώγˆγ€ι †εΊγŒδΏε­˜γ•γ‚ŒγΎγ™γ€‚", + "options": { + "Auto sorting": "θ‡ͺε‹•δΈ¦γΉζ›Ώγˆ", + "Manual drag&drop sorting": "ζ‰‹ε‹•γƒ‰γƒ©γƒƒγ‚°γ‚’γƒ³γƒ‰γƒ‰γƒ­γƒƒγƒ—γ«γ‚ˆγ‚‹δΈ¦γΉζ›Ώγˆ" + } + }, + "EasyUse_NodesMap_DisplayNodeID": { + "name": "γƒŽγƒΌγƒ‰IDγθ‘¨η€Ίγ‚’ζœ‰εŠΉγ«γ™γ‚‹" + }, + "EasyUse_NodesMap_DisplayGroupOnly": { + "name": "グループγγΏθ‘¨η€Ίγ™γ‚‹" + }, + "EasyUse_NodesMap_Enable": { + "name": "γ‚°γƒ«γƒΌγƒ—γƒžγƒƒγƒ—γ‚’ζœ‰εŠΉγ«γ™γ‚‹", + "tooltip": "γƒšγƒΌγ‚Έγ‚’ζ›΄ζ–°γ™γ‚‹εΏ…θ¦γŒγ‚γ‚ŠγΎγ™" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/ko/main.json b/custom_nodes/ComfyUI-Easy-Use/locales/ko/main.json new file mode 100644 index 00000000..f6363bc1 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/ko/main.json @@ -0,0 +1,30 @@ +{ + "settingsCategories": { + "Hotkeys": "단좕킀", + "Nodes": "λ…Έλ“œ", + "NodesMap": "λ…Έλ“œ 맡" + }, + "nodeCategories": { + "Util": "μœ ν‹Έ", + "Seed": "μ‹œλ“œ", + "Prompt": "ν”„λ‘¬ν”„νŠΈ", + "Loaders": "λ‘œλ”", + "Adapter": "μ–΄λŒ‘ν„°", + "Inpaint": "인페인트", + "PreSampling": "사전 μƒ˜ν”Œλ§", + "Sampler": "μƒ˜ν”ŒλŸ¬", + "Fix": "ν”½μŠ€", + "Pipe": "νŒŒμ΄ν”„", + "XY Inputs": "XY μž…λ ₯", + "Image": "이미지", + "Segmentation": "λΆ„ν• ", + "\uD83D\uDEAB Deprecated": "\uD83D\uDEAB μ‚¬μš© 쀑단", + "Type": "μœ ν˜•", + "Math": "μˆ˜ν•™", + "Switch": "μŠ€μœ„μΉ˜", + "Index Switch": "인덱슀 μŠ€μœ„μΉ˜", + "While Loop": "while 루프", + "For Loop": "for 루프", + "LoadImage": "이미지 λ‘œλ“œ" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/ko/settings.json b/custom_nodes/ComfyUI-Easy-Use/locales/ko/settings.json new file mode 100644 index 00000000..ee3be3fa --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/ko/settings.json @@ -0,0 +1,67 @@ +{ + "EasyUse_Hotkeys_AddGroup": { + "name": "Shift+g λ₯Ό μ‚¬μš©ν•˜μ—¬ μ„ νƒλœ λ…Έλ“œλ₯Ό 그룹에 μΆ”κ°€ν•©λ‹ˆλ‹€", + "tooltip": "v1.2.39λΆ€ν„°λŠ” Ctrl+g λ₯Ό μ‚¬μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€" + }, + "EasyUse_Hotkeys_cleanVRAMUsed": { + "name": "Shift+r λ₯Ό μ‚¬μš©ν•˜μ—¬ λͺ¨λΈ 및 λ…Έλ“œ μΊμ‹œλ₯Ό μ–Έλ‘œλ“œν•©λ‹ˆλ‹€" + }, + "EasyUse_Hotkeys_toggleNodesMap": { + "name": "Shift+m λ₯Ό μ‚¬μš©ν•˜μ—¬ λ…Έλ“œ 맡을 μ „ν™˜ν•©λ‹ˆλ‹€" + }, + "EasyUse_Hotkeys_AlignSelectedNodes": { + "name": "Shift+Up/Down/Left/Right 와 Shift+Ctrl+Alt+Left/Right λ₯Ό μ‚¬μš©ν•˜μ—¬ μ„ νƒλœ λ…Έλ“œλ₯Ό μ •λ ¬ν•©λ‹ˆλ‹€", + "tooltip": "Shift+Up/Down/Left/Right λŠ” μ„ νƒλœ λ…Έλ“œλ₯Ό μ •λ ¬ν•˜λ©°, Shift+Ctrl+Alt+Left/Right λŠ” λ…Έλ“œλ₯Ό μˆ˜ν‰/수직으둜 λΆ„λ°°ν•©λ‹ˆλ‹€" + }, + "EasyUse_Hotkeys_NormalizeSelectedNodes": { + "name": "Shift+Ctrl+Left/Right λ₯Ό μ‚¬μš©ν•˜μ—¬ μ„ νƒλœ λ…Έλ“œλ₯Ό μ •κ·œν™”ν•©λ‹ˆλ‹€", + "tooltip": "Shift+Ctrl+Left λŠ” λ„ˆλΉ„λ₯Ό, Shift+Ctrl+Right λŠ” 높이λ₯Ό μ •κ·œν™”ν•©λ‹ˆλ‹€" + }, + "EasyUse_Hotkeys_NodesTemplate": { + "name": "Alt+1~9 λ₯Ό μ‚¬μš©ν•˜μ—¬ μ›Œν¬ν”Œλ‘œμš°μ— λ…Έλ“œ ν…œν”Œλ¦Ώμ„ λΆ™μ—¬λ„£μŠ΅λ‹ˆλ‹€" + }, + "EasyUse_Hotkeys_JumpNearestNodes": { + "name": "Up/Down/Left/Right λ₯Ό μ‚¬μš©ν•˜μ—¬ κ°€μž₯ κ°€κΉŒμš΄ λ…Έλ“œλ‘œ μ΄λ™ν•©λ‹ˆλ‹€" + }, + "EasyUse_ContextMenu_SubDirectories": { + "name": "μ»¨ν…μŠ€νŠΈ λ©”λ‰΄μ—μ„œ μžλ™μœΌλ‘œ ν•˜μœ„ 디렉토리λ₯Ό μ€‘μ²©ν•©λ‹ˆλ‹€" + }, + "EasyUse_ContextMenu_ModelsThumbnails": { + "name": "λͺ¨λΈ 미리보기 썸넀일을 ν™œμ„±ν™”ν•©λ‹ˆλ‹€" + }, + "EasyUse_ContextMenu_NodesSort": { + "name": "μ»¨ν…μŠ€νŠΈ λ©”λ‰΄μ—μ„œ μƒˆλ‘œμš΄ λ…Έλ“œλ₯Ό A~Z 순으둜 μ •λ ¬ν•©λ‹ˆλ‹€" + }, + "EasyUse_ContextMenu_QuickOptions": { + "name": "μ»¨ν…μŠ€νŠΈ 메뉴에 3개의 λΉ λ₯Έ μ˜΅μ…˜ λ²„νŠΌμ„ μ‚¬μš©ν•©λ‹ˆλ‹€", + "options": { + "At the forefront": "μ•žμͺ½μ—", + "At the end": "λ’€μͺ½μ—", + "Disable": "λΉ„ν™œμ„±ν™”" + } + }, + "EasyUse_Nodes_Runtime": { + "name": "λ…Έλ“œ μ‹€ν–‰ μ‹œκ°„ ν‘œμ‹œλ₯Ό ν™œμ„±ν™”ν•©λ‹ˆλ‹€" + }, + "EasyUse_Nodes_ChainGetSet": { + "name": "λΆ€λͺ¨ λ…Έλ“œμ™€ μ—°κ²°λœ get/ set 포인트 체이닝을 ν™œμ„±ν™”ν•©λ‹ˆλ‹€" + }, + "EasyUse_NodesMap_Sorting": { + "name": "λ…Έλ“œ κ·Έλ£Ή μ •λ ¬ λͺ¨λ“œλ₯Ό κ΄€λ¦¬ν•©λ‹ˆλ‹€", + "tooltip": "기본값은 μžλ™ μ •λ ¬μž…λ‹ˆλ‹€. μˆ˜λ™μœΌλ‘œ μ„€μ •ν•˜λ©΄ 그룹을 λ“œλž˜κ·Έ μ•€ λ“œλ‘­ν•  수 있으며 μˆœμ„œκ°€ μ €μž₯λ©λ‹ˆλ‹€.", + "options": { + "Auto sorting": "μžλ™ μ •λ ¬", + "Manual drag&drop sorting": "μˆ˜λ™ λ“œλž˜κ·Έ μ•€ λ“œλ‘­ μ •λ ¬" + } + }, + "EasyUse_NodesMap_DisplayNodeID": { + "name": "λ…Έλ“œ ID ν‘œμ‹œλ₯Ό ν™œμ„±ν™”ν•©λ‹ˆλ‹€" + }, + "EasyUse_NodesMap_DisplayGroupOnly": { + "name": "그룹만 ν‘œμ‹œν•©λ‹ˆλ‹€" + }, + "EasyUse_NodesMap_Enable": { + "name": "κ·Έλ£Ή 맡을 ν™œμ„±ν™”ν•©λ‹ˆλ‹€", + "tooltip": "μ—…λ°μ΄νŠΈλ₯Ό μœ„ν•΄ νŽ˜μ΄μ§€λ₯Ό μƒˆλ‘œκ³ μΉ¨ν•΄μ•Ό ν•©λ‹ˆλ‹€" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/ru/main.json b/custom_nodes/ComfyUI-Easy-Use/locales/ru/main.json new file mode 100644 index 00000000..cc0db6d1 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/ru/main.json @@ -0,0 +1,30 @@ +{ + "settingsCategories": { + "Hotkeys": "ГорячиС клавиши", + "Nodes": "Π£Π·Π»Ρ‹", + "NodesMap": "ΠšΠ°Ρ€Ρ‚Π° ΡƒΠ·Π»ΠΎΠ²" + }, + "nodeCategories": { + "Util": "Π£Ρ‚ΠΈΠ»ΠΈΡ‚Ρ‹", + "Seed": "Π‘ΠΈΠ΄", + "Prompt": "Подсказка", + "Loaders": "Π—Π°Π³Ρ€ΡƒΠ·Ρ‡ΠΈΠΊΠΈ", + "Adapter": "АдаптСр", + "Inpaint": "Π Π΅Ρ‚ΡƒΡˆΡŒ", + "PreSampling": "ΠŸΡ€Π΅Π΄Π²Ρ‹Π±ΠΎΡ€ΠΊΠ°", + "Sampler": "БэмплСр", + "Fix": "Π˜ΡΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠ΅", + "Pipe": "ΠšΠΎΠ½Π²Π΅ΠΉΠ΅Ρ€", + "XY Inputs": "Π’Π²ΠΎΠ΄ XY", + "Image": "Π˜Π·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅", + "Segmentation": "БСгмСнтация", + "\uD83D\uDEAB Deprecated": "\uD83D\uDEAB УстарСло", + "Type": "Π’ΠΈΠΏ", + "Math": "ΠœΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠ°", + "Switch": "ΠŸΠ΅Ρ€Π΅ΠΊΠ»ΡŽΡ‡Π°Ρ‚Π΅Π»ΡŒ", + "Index Switch": "ΠŸΠ΅Ρ€Π΅ΠΊΠ»ΡŽΡ‡Π°Ρ‚Π΅Π»ΡŒ индСксов", + "While Loop": "Π¦ΠΈΠΊΠ» while", + "For Loop": "Π¦ΠΈΠΊΠ» for", + "LoadImage": "Π—Π°Π³Ρ€ΡƒΠ·ΠΊΠ° изобраТСния" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/ru/settings.json b/custom_nodes/ComfyUI-Easy-Use/locales/ru/settings.json new file mode 100644 index 00000000..fb9f5c16 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/ru/settings.json @@ -0,0 +1,67 @@ +{ + "EasyUse_Hotkeys_AddGroup": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Shift+g для добавлСния Π²Ρ‹Π΄Π΅Π»Π΅Π½Π½Ρ‹Ρ… ΡƒΠ·Π»ΠΎΠ² Π² Π³Ρ€ΡƒΠΏΠΏΡƒ", + "tooltip": "Начиная с вСрсии v1.2.39, ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ Ctrl+g" + }, + "EasyUse_Hotkeys_cleanVRAMUsed": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Shift+r для Π²Ρ‹Π³Ρ€ΡƒΠ·ΠΊΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈ кэша ΡƒΠ·Π»ΠΎΠ²" + }, + "EasyUse_Hotkeys_toggleNodesMap": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Shift+m для ΠΏΠ΅Ρ€Π΅ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΡ ΠΊΠ°Ρ€Ρ‚Ρ‹ ΡƒΠ·Π»ΠΎΠ²" + }, + "EasyUse_Hotkeys_AlignSelectedNodes": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Shift+Π‘Ρ‚Ρ€Π΅Π»ΠΊΠΈ для выравнивания Π²Ρ‹Π΄Π΅Π»Π΅Π½Π½Ρ‹Ρ… ΡƒΠ·Π»ΠΎΠ² ΠΈ Shift+Ctrl+Alt+Π‘Ρ‚Ρ€Π΅Π»ΠΊΠΈ для распрСдСлСния ΡƒΠ·Π»ΠΎΠ² ΠΏΠΎ Π³ΠΎΡ€ΠΈΠ·ΠΎΠ½Ρ‚Π°Π»ΠΈ/Π²Π΅Ρ€Ρ‚ΠΈΠΊΠ°Π»ΠΈ", + "tooltip": "Shift+Π‘Ρ‚Ρ€Π΅Π»ΠΊΠΈ Π²Ρ‹Ρ€Π°Π²Π½ΠΈΠ²Π°ΡŽΡ‚ Π²Ρ‹Π΄Π΅Π»Π΅Π½Π½Ρ‹Π΅ ΡƒΠ·Π»Ρ‹, Shift+Ctrl+Alt+Π‘Ρ‚Ρ€Π΅Π»ΠΊΠΈ Ρ€Π°ΡΠΏΡ€Π΅Π΄Π΅Π»ΡΡŽΡ‚ ΡƒΠ·Π»Ρ‹ ΠΏΠΎ Π³ΠΎΡ€ΠΈΠ·ΠΎΠ½Ρ‚Π°Π»ΠΈ/Π²Π΅Ρ€Ρ‚ΠΈΠΊΠ°Π»ΠΈ" + }, + "EasyUse_Hotkeys_NormalizeSelectedNodes": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Shift+Ctrl+Π‘Ρ‚Ρ€Π΅Π»ΠΊΠΈ для Π½ΠΎΡ€ΠΌΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ Π²Ρ‹Π΄Π΅Π»Π΅Π½Π½Ρ‹Ρ… ΡƒΠ·Π»ΠΎΠ²", + "tooltip": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Shift+Ctrl+Π›Π΅Π²ΠΎ для Π½ΠΎΡ€ΠΌΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ ΡˆΠΈΡ€ΠΈΠ½Ρ‹ ΠΈ Shift+Ctrl+ΠŸΡ€Π°Π²ΠΎ для Π½ΠΎΡ€ΠΌΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ высоты" + }, + "EasyUse_Hotkeys_NodesTemplate": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Alt+1~9 для вставки шаблонов ΡƒΠ·Π»ΠΎΠ² Π² Ρ€Π°Π±ΠΎΡ‡ΠΈΠΉ процСсс" + }, + "EasyUse_Hotkeys_JumpNearestNodes": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ Π‘Ρ‚Ρ€Π΅Π»ΠΊΠΈ для ΠΏΠ΅Ρ€Π΅Ρ…ΠΎΠ΄Π° ΠΊ Π±Π»ΠΈΠΆΠ°ΠΉΡˆΠ΅ΠΌΡƒ ΡƒΠ·Π»Ρƒ" + }, + "EasyUse_ContextMenu_SubDirectories": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ автоматичСскоС Π²Π»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠ΄ΠΊΠ°Ρ‚Π°Π»ΠΎΠ³ΠΎΠ² Π² контСкстном мСню" + }, + "EasyUse_ContextMenu_ModelsThumbnails": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ ΠΏΡ€Π΅Π²ΡŒΡŽ ΠΌΠΈΠ½ΠΈΠ°Ρ‚ΡŽΡ€ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ" + }, + "EasyUse_ContextMenu_NodesSort": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ A~Z сортировку Π½ΠΎΠ²Ρ‹Ρ… ΡƒΠ·Π»ΠΎΠ² Π² контСкстном мСню" + }, + "EasyUse_ContextMenu_QuickOptions": { + "name": "Π˜ΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ Ρ‚Ρ€ΠΈ быстрых ΠΊΠ½ΠΎΠΏΠΊΠΈ Π² контСкстном мСню", + "options": { + "At the forefront": "Π’ Π½Π°Ρ‡Π°Π»Π΅", + "At the end": "Π’ ΠΊΠΎΠ½Ρ†Π΅", + "Disable": "ΠžΡ‚ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΎ" + } + }, + "EasyUse_Nodes_Runtime": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ ΠΎΡ‚ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ выполнСния ΡƒΠ·Π»ΠΎΠ²" + }, + "EasyUse_Nodes_ChainGetSet": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ связываниС Ρ‚ΠΎΡ‡Π΅ΠΊ получСния ΠΈ установки с Ρ€ΠΎΠ΄ΠΈΡ‚Π΅Π»ΡŒΡΠΊΠΈΠΌ ΡƒΠ·Π»ΠΎΠΌ" + }, + "EasyUse_NodesMap_Sorting": { + "name": "Π£ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠ΅ Ρ€Π΅ΠΆΠΈΠΌΠΎΠΌ сортировки Π³Ρ€ΡƒΠΏΠΏ ΡƒΠ·Π»ΠΎΠ²", + "tooltip": "По ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ автоматичСская сортировка. ΠŸΡ€ΠΈ Ρ€ΡƒΡ‡Π½ΠΎΠΌ Ρ€Π΅ΠΆΠΈΠΌΠ΅ Π³Ρ€ΡƒΠΏΠΏΡ‹ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Ρ‰Π°Ρ‚ΡŒ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ пСрСтаскивания, ΠΈ порядок Π±ΡƒΠ΄Π΅Ρ‚ сохранён.", + "options": { + "Auto sorting": "АвтоматичСская сортировка", + "Manual drag&drop sorting": "Ручная сортировка пСрСтаскиваниСм" + } + }, + "EasyUse_NodesMap_DisplayNodeID": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ ΠΎΡ‚ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ ID ΡƒΠ·Π»ΠΎΠ²" + }, + "EasyUse_NodesMap_DisplayGroupOnly": { + "name": "ΠŸΠΎΠΊΠ°Π·Ρ‹Π²Π°Ρ‚ΡŒ Ρ‚ΠΎΠ»ΡŒΠΊΠΎ Π³Ρ€ΡƒΠΏΠΏΡ‹" + }, + "EasyUse_NodesMap_Enable": { + "name": "Π’ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ ΠΊΠ°Ρ€Ρ‚Ρƒ Π³Ρ€ΡƒΠΏΠΏ", + "tooltip": "НСобходимо ΠΎΠ±Π½ΠΎΠ²ΠΈΡ‚ΡŒ страницу для ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎΠ³ΠΎ обновлСния" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/zh/main.json b/custom_nodes/ComfyUI-Easy-Use/locales/zh/main.json new file mode 100644 index 00000000..9adc24e2 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/zh/main.json @@ -0,0 +1,33 @@ +{ + "settingsCategories": { + "Hotkeys": "快捷ι”", + "Nodes": "θŠ‚η‚Ήη›Έε…³", + "NodesMap": "η‘η†θŠ‚η‚Ήη»„", + "StylesSelector": "样式选择器", + "MultiAngle": "ζ‘„ε½±ζœΊε€šθ§’εΊ¦ζη€Ίθ―" + }, + "nodeCategories": { + "Util": "ε·₯ε…·", + "Seed": "随机种", + "Prompt": "提瀺词", + "Loaders": "ζ¨‘εž‹εŠ θ½½ε™¨", + "Adapter": "ζ¨‘εž‹ι€‚ι…ε™¨", + "Inpaint": "ε†…θ‘₯ι‡η»˜", + "PreSampling": "钄采样参数", + "Sampler": "采样器", + "Fix": "δΏε€η›Έε…³", + "Pipe": "θŠ‚η‚ΉζŸ", + "XY Inputs": "XY图葨输ε…₯ι‘Ή", + "Image": "图像", + "Segmentation": "εˆ†ε‰²", + "Logic": "ι€»θΎ‘", + "\uD83D\uDEAB Deprecated": "\uD83D\uDEAB 已弃用", + "Type": "η±»εž‹", + "Math": "ζ•°ε­¦θ‘η—", + "Switch": "εΌ€ε…³", + "Index Switch": "η΄’εΌ•εΌ€ε…³", + "While Loop": "WhileεΎͺ环", + "For Loop": "ForεΎͺ环", + "LoadImage": "εŠ θ½½ε›Ύεƒ" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/zh/nodeDefs.json b/custom_nodes/ComfyUI-Easy-Use/locales/zh/nodeDefs.json new file mode 100644 index 00000000..138801cb --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/zh/nodeDefs.json @@ -0,0 +1,6752 @@ +{ + "easy bookmark": { + "display_name": "δΉ¦η­Ύ πŸ”–", + "inputs": { + "shortcut_key": { + "name": "快捷ι”" + }, + "zoom": { + "name": "ηΌ©ζ”Ύζ―”δΎ‹" + } + } + }, + "easy setNode": { + "display_name": "θΎη½η‚Ή", + "inputs": { + "Constant": { + "name": "ε˜ι‡ε" + } + } + }, + "easy getNode": { + "display_name": "θŽ·ε–η‚Ή", + "inputs": { + "Constant": { + "name": "ε˜ι‡ε" + } + } + }, + "easy positive": { + "display_name": "正青提瀺词", + "inputs": {}, + "outputs": { + "0": { + "name": "正青提瀺词" + } + } + }, + "easy negative": { + "display_name": "负青提瀺词", + "inputs": {}, + "outputs": { + "0": { + "name": "负青提瀺词" + } + } + }, + "easy wildcards": { + "display_name": "ι€šι…η¬¦ζη€Ίθ―", + "inputs": { + "Select to add LoRA": { + "name": "ι€‰ζ‹©ζ·»εŠ Lora" + }, + "Select to add Wildcard": { + "name": "ι€‰ζ‹©ζ·»εŠ ι€šι…η¬¦" + }, + "seed_num": { + "name": "随机种" + }, + "seed": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "multiline_mode": { + "name": "倚葌樑式" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + }, + "1": { + "name": "ι€šι…ε‘«ε……θ―" + } + } + }, + "easy wildcardsMatrix": { + "display_name": "ι€šι…η¬¦ζη€Ίθ―ηŸ©ι˜΅", + "inputs": { + "Select to add LoRA": { + "name": "ι€‰ζ‹©ζ·»εŠ Lora" + }, + "Select to add Wildcard": { + "name": "ι€‰ζ‹©ζ·»εŠ ι€šι…η¬¦" + }, + "offset": { + "name": "偏移量" + }, + "output_limit": { + "name": "θΎ“ε‡ΊδΈͺζ•°ι™εˆΆ", + "tooltip": "θΎ“ε‡ΊnδΈͺε‘«ε……εŽι€šι…η¬¦, -1δΈΊθΎ“ε‡Ίζ‰€ζœ‰ε―θƒ½ζ€§οΌˆεη§»ε€Όε€±ζ•ˆοΌ‰οΌŒι»˜θ€ε€ΌδΈΊ1" + } + }, + "outputs": { + "0": { + "name": "ι€šι…ε‘«ε……θ―" + }, + "1": { + "name": "可能性总数" + }, + "2": { + "name": "ε―θƒ½ζ€§ζ€»ζ•°οΌˆζ―ι€šι…η¬¦οΌ‰" + } + } + }, + "easy prompt": { + "display_name": "提瀺词", + "inputs": { + "prefix": { + "name": "前缀" + }, + "subject": { + "name": "主体" + }, + "action": { + "name": "动作" + }, + "clothes": { + "name": "董服" + }, + "environment": { + "name": "ηŽ―ε’ƒ" + }, + "background": { + "name": "θƒŒζ™―" + }, + "nsfw": { + "name": "δΈΊηˆ±ιΌ“ζŽŒ" + } + } + }, + "easy promptList": { + "display_name": "ζη€Ίθ―εˆ—θ‘¨", + "inputs": { + "optional_prompt_list": { + "name": "εˆ—θ‘¨οΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "εˆ—θ‘¨" + } + } + }, + "easy promptLine": { + "display_name": "提瀺词葌", + "inputs": { + "prompt": { + "name": "提瀺词" + }, + "start_index": { + "name": "衷始紒引" + }, + "max_rows": { + "name": "ζœ€ε€§θ‘Œζ•°" + }, + "remove_empty_lines":{ + "name": "εŽ»ι™€η©Ίθ‘Œ" + } + }, + "outputs": { + "0": { + "name": "字符串" + }, + "1": { + "name": "COMBO" + } + } + }, + "easy promptAwait": { + "display_name": "提瀺词等待", + "inputs": { + "now": { + "name": "当前" + }, + "prev": { + "name": "δΈŠδΈ€ζ­₯" + }, + "prompt": { + "name": "提瀺词", + "placeholder": "θΎ“ε…₯ζη€Ίθ―ζˆ–δ½Ώη”¨θ―­ιŸ³θΎ“ε…₯转文字" + } + }, + "outputs": { + "0": { + "name": "θΎ“ε‡Ί" + }, + "1": { + "name": "提瀺词" + }, + "2": { + "name": "继续" + }, + "3": { + "name": "随机种" + } + } + }, + "easy promptConcat": { + "display_name": "提瀺词联结", + "inputs": { + "prompt1": { + "name": "提瀺词1" + }, + "prompt2": { + "name": "提瀺词2" + }, + "separator": { + "name": "ι—΄ιš”η¬¦" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + } + } + }, + "easy promptReplace": { + "display_name": "提瀺词替捒", + "inputs": { + "prompt": { + "name": "提瀺词" + }, + "find1": { + "name": "搜紒_1" + }, + "replace1": { + "name": "替捒_1" + }, + "find2": { + "name": "搜紒_2" + }, + "replace2": { + "name": "替捒_2" + }, + "find3": { + "name": "搜紒_3" + }, + "replace3": { + "name": "替捒_3" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + } + } + }, + "easy stylesSelector": { + "display_name": "ι£Žζ Όζη€Ίθ―ι€‰ζ‹©ε™¨", + "inputs": { + "positive": { + "name": "ζ­£ι’ζη€Ίθ―οΌˆε―ι€‰οΌ‰" + }, + "negative": { + "name": "θ΄Ÿι’ζη€Ίθ―οΌˆε―ι€‰οΌ‰" + }, + "styles": { + "name": "ι£Žζ Όη±»εž‹" + } + }, + "outputs": { + "0": { + "name": "正青提瀺词" + }, + "1": { + "name": "负青提瀺词" + } + } + }, + "easy portraitMaster": { + "display_name": "θ‚–εƒε€§εΈˆ", + "inputs": { + "shot": { + "name": "ι•œε€΄" + }, + "shot_weight": { + "name": "ι•œε€΄ζ―”ι‡" + }, + "gender": { + "name": "ζ€§εˆ«" + }, + "age": { + "name": "εΉ΄ιΎ„" + }, + "nationality_1": { + "name": "国籍1" + }, + "nationality_2": { + "name": "国籍2" + }, + "nationality_mix": { + "name": "ε›½η±θžεˆζ―”ι‡" + }, + "body_type": { + "name": "δ½“εž‹" + }, + "body_type_weight": { + "name": "δ½“εž‹ζ―”ι‡" + }, + "model_pose": { + "name": "ζ¨‘η‰Ήε§ΏεŠΏ (Pose)" + }, + "eyes_color": { + "name": "ηœΌη›ι’œθ‰²" + }, + "facial_expression": { + "name": "青部葨情" + }, + "facial_expression_weight": { + "name": "青部葨情比重" + }, + "face_shape": { + "name": "θ„Έεž‹" + }, + "face_shape_weight": { + "name": "θ„Έεž‹ζ―”ι‡" + }, + "facial_asymmetry": { + "name": "青部对称比重" + }, + "hair_style": { + "name": "ε‘εž‹" + }, + "hair_color": { + "name": "发色" + }, + "disheveled": { + "name": "倴发蓬松程度" + }, + "beard": { + "name": "胑子" + }, + "skin_details": { + "name": "ηšθ‚€η»†θŠ‚" + }, + "skin_pores": { + "name": "ηšθ‚€ζ―›ε­”" + }, + "dimples": { + "name": "ι…’ηͺ" + }, + "wrinkles": { + "name": "皱纹" + }, + "freckles": { + "name": "雀斑" + }, + "moles": { + "name": "η—£" + }, + "skin_imperfections": { + "name": "ηšθ‚€η‘•η–΅" + }, + "skin_acne": { + "name": "ηšθ‚€η—€η–" + }, + "tanned_skin": { + "name": "ζ™’ι»‘ηš„ηšθ‚€" + }, + "eyes_details": { + "name": "ηœΌη›η»†θŠ‚" + }, + "iris_details": { + "name": "θ™Ήθ†œη»†θŠ‚" + }, + "circular_iris": { + "name": "εœ†ε½’θ™Ήθ†œ" + }, + "circular_pupil": { + "name": "εœ†ε½’ηž³ε­”" + }, + "light_type": { + "name": "ε…‰ζΊη±»εž‹" + }, + "light_direction": { + "name": "光源方向" + }, + "light_weight": { + "name": "光源比重" + }, + "photorealism_improvement": { + "name": "η…§η‰‡ηœŸεžεΊ¦" + } + }, + "outputs": { + "0": { + "name": "正青提瀺词" + }, + "1": { + "name": "负青提瀺词" + } + } + }, + "easy multiAngle":{ + "display_name": "ε€šθ§†θ§’ζη€Ίθ―", + "inputs": { + }, + "outputs": { + "0": { + "name": "提瀺词" + }, + "1":{ + "name": "参数" + } + } + }, + "easy fullLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨ (εŒζ•΄η‰ˆ)", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †οΌˆε―ι€‰οΌ‰" + }, + "optional_controlnet_stack": { + "name": "ControlNetε †οΌˆε―ι€‰οΌ‰" + }, + "model_override": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "vae_override": { + "name": "VAEοΌˆε―ι€‰οΌ‰" + }, + "clip_override": { + "name": "CLIPοΌˆε―ι€‰οΌ‰" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "config_name": { + "name": "配η½εη§°" + }, + "vae_name": { + "name": "VAE名称" + }, + "clip_skip": { + "name": "CLIPεœζ­’ε±‚" + }, + "lora_name": { + "name": "LoRA名称" + }, + "lora_model_strength": { + "name": "ζ¨‘εž‹εΌΊεΊ¦" + }, + "lora_clip_strength": { + "name": "CLIPεΌΊεΊ¦" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "batch_size": { + "name": "批欑倧小" + }, + "positive": { + "name": "正青提瀺词" + }, + "positive_token_normalization": { + "name": "正青Tokenθ§„ζ ΌεŒ–" + }, + "positive_weight_interpretation": { + "name": "正青权重插值方式" + }, + "negative": { + "name": "负青提瀺词" + }, + "negative_token_normalization": { + "name": "负青Tokenθ§„ζ ΌεŒ–" + }, + "negative_weight_interpretation": { + "name": "θ΄Ÿι’ζƒι‡ζ’ε€Όζ–ΉεΌ" + }, + "a1111_prompt_style": { + "name": "A1111提瀺词风格" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + }, + "3": { + "name": "CLIP" + }, + "4": { + "name": "正青村仢" + }, + "5": { + "name": "负青村仢" + }, + "6": { + "name": "LATENT" + } + } + }, + "easy a1111Loader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆA1111οΌ‰", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †οΌˆε―ι€‰οΌ‰" + }, + "optional_controlnet_stack": { + "name": "ControlNetε †οΌˆε―ι€‰οΌ‰" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "clip_skip": { + "name": "CLIPεœζ­’ε±‚" + }, + "lora_name": { + "name": "LoRAζ¨‘εž‹" + }, + "lora_model_strength": { + "name": "ζ¨‘εž‹εΌΊεΊ¦" + }, + "lora_clip_strength": { + "name": "CLIPεΌΊεΊ¦" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "batch_size": { + "name": "批欑倧小" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "a1111_prompt_style": { + "name": "A1111提瀺词风格" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + } + } + }, + "easy comfyLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆComfyοΌ‰", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †οΌˆε―ι€‰οΌ‰" + }, + "optional_controlnet_stack": { + "name": "ControlNetε †οΌˆε―ι€‰οΌ‰" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "clip_skip": { + "name": "CLIPεœζ­’ε±‚" + }, + "lora_name": { + "name": "LoRAζ¨‘εž‹" + }, + "lora_model_strength": { + "name": "ζ¨‘εž‹εΌΊεΊ¦" + }, + "lora_clip_strength": { + "name": "CLIPεΌΊεΊ¦" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "batch_size": { + "name": "批欑倧小" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy hunyuanDiTLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆHunYuanοΌ‰", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †(可选)" + }, + "optional_controlnet_stack": { + "name": "ControlNetε †(可选)" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "lora_name": { + "name": "LoRAζ¨‘εž‹" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "batch_size": { + "name": "批欑倧小" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + } + } + }, + "easy cascadeLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆCascadeοΌ‰", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †οΌˆε―ι€‰οΌ‰" + }, + "clip_name": { + "name": "CLIPζ¨‘εž‹" + }, + "lora_name": { + "name": "LoRA名称" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "compression": { + "name": "εŽ‹ηΌ©η­‰ηΊ§" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "batch_size": { + "name": "批欑倧小" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "a1111_prompt_style": { + "name": "A1111提瀺词风格" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹_C" + }, + "2": { + "name": "Latent_C" + } + } + }, + "easy kolorsLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆKolorsοΌ‰", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †(可选)" + }, + "optional_controlnet_stack": { + "name": "ControlNetε †(可选)" + }, + "unet_name": { + "name": "UNET名称" + }, + "vae_name": { + "name": "VAE" + }, + "chatglm3_name": { + "name": "ChatGLM3名称" + }, + "lora_name": { + "name": "LoRAζ¨‘εž‹" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "batch_size": { + "name": "批欑倧小" + }, + "auto_clean_gpu": { + "name": "θ‡ͺεŠ¨ζΈ…η†GPU" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + } + } + }, + "easy fluxLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆFluxοΌ‰", + "inputs": { + "model_override": { + "name": "ζ¨‘εž‹(可选)" + }, + "clip_override": { + "name": "CLIP(可选)" + }, + "vae_override": { + "name": "VAE(可选）" + }, + "optional_lora_stack": { + "name": "LoRAε †(可选)" + }, + "optional_controlnet_stack": { + "name": "ControlNetε †(可选)" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "lora_name": { + "name": "LoRAζ¨‘εž‹" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "positive": { + "name": "正青提瀺词" + }, + "batch_size": { + "name": "批欑倧小" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + } + } + }, + "easy pixArtLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆPixArtοΌ‰", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †(可选)" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "model_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "t5_type": { + "name": "T5η±»εž‹" + }, + "clip_name": { + "name": "CLIPζ¨‘εž‹" + }, + "padding": { + "name": "εˆ†ε—" + }, + "lora_name": { + "name": "LoRAζ¨‘εž‹" + }, + "ratio": { + "name": "ratio" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "batch_size": { + "name": "批欑倧小" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + } + } + }, + "easy mochiLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆMochiοΌ‰", + "inputs": { + "model_override": { + "name": "ζ¨‘εž‹(可选)" + }, + "clip_override": { + "name": "CLIP(可选)" + }, + "vae_override": { + "name": "VAE(可选）" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "length": { + "name": "视钑长度" + }, + "batch_size": { + "name": "批欑倧小" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy zero123Loader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆZero123οΌ‰", + "inputs": { + "init_image": { + "name": "图像" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "batch_size": { + "name": "批欑倧小" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "elevation": { + "name": "δΏ―δ»°θ§’" + }, + "azimuth": { + "name": "方位角" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy dynamiCrafterLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆDynamiCrafterοΌ‰", + "inputs": { + "init_image": { + "name": "图像" + }, + "optional_vae": { + "name": "VAE(可选)" + }, + "model_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "clip_skip": { + "name": "CLIPεœζ­’ε±‚" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "use_interpolate": { + "name": "使用插值" + }, + "fps": { + "name": "εΈ§ηŽ‡" + }, + "frames": { + "name": "εΈ§ζ•°" + }, + "scale_latents": { + "name": "ηΌ©ζ”ΎLatent" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "VAE" + } + } + }, + "easy svdLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆSVDοΌ‰", + "inputs": { + "init_image": { + "name": "图像" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "clip_name": { + "name": "CLIPζ¨‘εž‹" + }, + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "empty_latent_height": { + "name": "高度" + }, + "video_frames": { + "name": "εΈ§ζ•°" + }, + "motion_bucket_id": { + "name": "εŠ¨ζ€ζ‘ΆID" + }, + "negative": { + "name": "负青提瀺词" + }, + "fps": { + "name": "εΈ§ηŽ‡" + }, + "augmentation_level": { + "name": "咞强" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy sv3dLoader": { + "display_name": "η€ζ˜“εŠ θ½½ε™¨οΌˆSV3DοΌ‰", + "inputs": { + "init_image": { + "name": "图像" + }, + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "vae_name": { + "name": "VAE" + }, + "empty_latent_height": { + "name": "高度" + }, + "empty_latent_width": { + "name": "ε½εΊ¦" + }, + "batch_size": { + "name": "批欑倧小" + }, + "interp_easing": { + "name": "ζ’ε€ΌζΈε˜" + }, + "easing_mode": { + "name": "渐变樑式" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "插值ζ—₯εΏ—" + } + } + }, + "easy loraStack": { + "display_name": "η€ζ˜“Loraε †", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †οΌˆε―ι€‰οΌ‰" + }, + "toggle": { + "name": "εΌ€ε…³" + }, + "mode": { + "name": "樑式" + }, + "num_loras": { + "name": "LoRA数量" + }, + "lora_1_name": { + "name": "LoRA_1_名称" + }, + "lora_1_strength": { + "name": "LoRA_1_权重" + }, + "lora_2_name": { + "name": "LoRA_2_名称" + }, + "lora_2_strength": { + "name": "LoRA_2_权重" + }, + "lora_3_name": { + "name": "LoRA_3_名称" + }, + "lora_3_strength": { + "name": "LoRA_3_权重" + }, + "lora_4_name": { + "name": "LoRA_4_名称" + }, + "lora_4_strength": { + "name": "LoRA_4_权重" + }, + "lora_5_name": { + "name": "LoRA_5_名称" + }, + "lora_5_strength": { + "name": "LoRA_5_权重" + }, + "lora_6_name": { + "name": "LoRA_6_名称" + }, + "lora_6_strength": { + "name": "LoRA_6_权重" + }, + "lora_7_name": { + "name": "LoRA_7_名称" + }, + "lora_7_strength": { + "name": "LoRA_7_权重" + }, + "lora_8_name": { + "name": "LoRA_8_名称" + }, + "lora_8_strength": { + "name": "LoRA_8_权重" + }, + "lora_9_name": { + "name": "LoRA_9_名称" + }, + "lora_9_strength": { + "name": "LoRA_9_权重" + }, + "lora_10_name": { + "name": "LoRA_10_名称" + }, + "lora_10_strength": { + "name": "LoRA_10_权重" + } + }, + "outputs": { + "0": { + "name": "LoRAε †" + } + } + }, + "easy loraSwitcher": { + "display_name": "η€ζ˜“Loraεˆ‡ζ’ε™¨", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †οΌˆε―ι€‰οΌ‰" + }, + "toggle": { + "name": "εΌ€ε…³" + }, + "select": { + "name": "选择鑹" + }, + "num_loras": { + "name": "LoRA数量" + } + }, + "outputs": { + "0": { + "name": "LoRAε †" + }, + "1": { + "name": "LoRA名称" + } + } + }, + "easy controlnetStack": { + "display_name": "η€ζ˜“ ControlNet ε †", + "inputs": { + "optional_controlnet_stack": { + "name": "ControlNetε †(可选)" + }, + "image_1": { + "name": "图像_1" + }, + "image_2": { + "name": "图像_2" + }, + "image_3": { + "name": "图像_3" + }, + "image_4": { + "name": "图像_4" + }, + "image_5": { + "name": "图像_5" + }, + "image_6": { + "name": "图像_6" + }, + "image_7": { + "name": "图像_7" + }, + "image_8": { + "name": "图像_8" + }, + "image_9": { + "name": "图像_9" + }, + "image_10": { + "name": "图像_10" + }, + "toggle": { + "name": "εΌ€ε…³" + }, + "mode": { + "name": "樑式" + }, + "num_controlnet": { + "name": "ControlNet数量" + }, + "controlnet_1": { + "name": "ControlNet_1_名称" + }, + "controlnet_1_strength": { + "name": "ControlNet_1_权重" + }, + "start_percent_1": { + "name": "开始位η½_1" + }, + "end_percent_1": { + "name": "η»“ζŸδ½η½_1" + }, + "scale_soft_weight_1": { + "name": "ControlNet_1_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_2": { + "name": "ControlNet_2_名称" + }, + "controlnet_2_strength": { + "name": "ControlNet_2_权重" + }, + "start_percent_2": { + "name": "开始位η½_2" + }, + "end_percent_2": { + "name": "η»“ζŸδ½η½_2" + }, + "scale_soft_weight_2": { + "name": "ControlNet_2_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_3": { + "name": "ControlNet_3_名称" + }, + "controlnet_3_strength": { + "name": "ControlNet_3_权重" + }, + "start_percent_3": { + "name": "开始位η½_3" + }, + "end_percent_3": { + "name": "η»“ζŸδ½η½_3" + }, + "scale_soft_weight_3": { + "name": "ControlNet_3_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_4": { + "name": "ControlNet_4_名称" + }, + "controlnet_4_strength": { + "name": "ControlNet_4_权重" + }, + "start_percent_4": { + "name": "开始位η½_4" + }, + "end_percent_4": { + "name": "η»“ζŸδ½η½_4" + }, + "scale_soft_weight_4": { + "name": "ControlNet_4_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_5": { + "name": "ControlNet_5_名称" + }, + "controlnet_5_strength": { + "name": "ControlNet_5_权重" + }, + "start_percent_5": { + "name": "开始位η½_5" + }, + "end_percent_5": { + "name": "η»“ζŸδ½η½_5" + }, + "scale_soft_weight_5": { + "name": "ControlNet_5_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_6": { + "name": "ControlNet_6_名称" + }, + "controlnet_6_strength": { + "name": "ControlNet_6_权重" + }, + "start_percent_6": { + "name": "开始位η½_6" + }, + "end_percent_6": { + "name": "η»“ζŸδ½η½_6" + }, + "scale_soft_weight_6": { + "name": "ControlNet_6_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_7": { + "name": "ControlNet_7_名称" + }, + "controlnet_7_strength": { + "name": "ControlNet_7_权重" + }, + "start_percent_7": { + "name": "开始位η½_7" + }, + "end_percent_7": { + "name": "η»“ζŸδ½η½_7" + }, + "scale_soft_weight_7": { + "name": "ControlNet_7_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_8": { + "name": "ControlNet_8_名称" + }, + "controlnet_8_strength": { + "name": "ControlNet_8_权重" + }, + "start_percent_8": { + "name": "开始位η½_8" + }, + "end_percent_8": { + "name": "η»“ζŸδ½η½_8" + }, + "scale_soft_weight_8": { + "name": "ControlNet_8_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_9": { + "name": "ControlNet_9_名称" + }, + "controlnet_9_strength": { + "name": "ControlNet_9_权重" + }, + "start_percent_9": { + "name": "开始位η½_9" + }, + "end_percent_9": { + "name": "η»“ζŸδ½η½_9" + }, + "scale_soft_weight_9": { + "name": "ControlNet_9_ζŸ”ε’ŒηΌ©ζ”Ύ" + }, + "controlnet_10": { + "name": "ControlNet_10_名称" + }, + "controlnet_10_strength": { + "name": "ControlNet_10_权重" + }, + "start_percent_10": { + "name": "开始位η½_10" + }, + "end_percent_10": { + "name": "η»“ζŸδ½η½_10" + }, + "scale_soft_weight_10": { + "name": "ControlNet_10_ζŸ”ε’ŒηΌ©ζ”Ύ" + } + }, + "outputs": { + "0": { + "name": "ControlNetε †" + } + } + }, + "easy controlnetLoader": { + "display_name": "η€ζ˜“Controlnet", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "control_net": { + "name": "ControlNet (可选)" + }, + "image": { + "name": "图像" + }, + "control_net_name": { + "name": "ControlNet" + }, + "strength": { + "name": "εΌΊεΊ¦" + }, + "scale_soft_weights": { + "name": "ηΌ©ζ”ΎζŸ”ε’Œζƒι‡" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "正青村仢" + }, + "2": { + "name": "负青村仢" + } + } + }, + "easy controlnetLoaderADV": { + "display_name": "η€ζ˜“ControlnetοΌˆι«˜ηΊ§οΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "control_net": { + "name": "ControlNet (可选)" + }, + "image": { + "name": "图像" + }, + "control_net_name": { + "name": "ControlNet" + }, + "strength": { + "name": "εΌΊεΊ¦" + }, + "start_percent": { + "name": "开始既间" + }, + "end_percent": { + "name": "η»“ζŸζ—Άι—΄" + }, + "scale_soft_weights": { + "name": "ηΌ©ζ”ΎζŸ”ε’Œζƒι‡" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "正青村仢" + }, + "2": { + "name": "负青村仢" + } + } + }, + "easy controlnetLoader++": { + "display_name": "η€ζ˜“ControlNet++", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "control_net": { + "name": "ControlNet" + }, + "control_net_name": { + "name": "ControlNet" + }, + "strength": { + "name": "εΌΊεΊ¦" + }, + "start_percent": { + "name": "开始既间" + }, + "end_percent": { + "name": "η»“ζŸζ—Άι—΄" + }, + "scale_soft_weights": { + "name": "ηΌ©ζ”ΎζŸ”ε’Œζƒι‡" + }, + "union_type": { + "name": "Unionη±»εž‹" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "正青村仢" + }, + "2": { + "name": "负青村仢" + } + } + }, + "easy LLLiteLoader": { + "display_name": "η€ζ˜“LLLite", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "cond_image": { + "name": "图像" + }, + "model_name": { + "name": "LLLiteζ¨‘εž‹" + }, + "strength": { + "name": "εΌΊεΊ¦" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "start_percent": { + "name": "开始既间" + }, + "end_percent": { + "name": "η»“ζŸζ—Άι—΄" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy anythingIndexSwitch": { + "display_name": "ηΌ–ε·εˆ‡ζ’", + "inputs": { + "value0": { + "name": "ε€Ό0" + }, + "value1": { + "name": "ε€Ό1" + }, + "index": { + "name": "编号" + } + }, + "outputs": { + "0": { + "name": "ε€Ό" + } + } + }, + "easy imageIndexSwitch": { + "display_name": "ε›ΎεƒηΌ–ε·εˆ‡ζ’", + "inputs": { + "image0": { + "name": "图像0" + }, + "image1": { + "name": "图像1" + }, + "index": { + "name": "编号" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy textIndexSwitch": { + "display_name": "ζ–‡ζœ¬ηΌ–ε·εˆ‡ζ’", + "inputs": { + "text0": { + "name": "ζ–‡ζœ¬0" + }, + "text1": { + "name": "ζ–‡ζœ¬1" + }, + "index": { + "name": "编号" + } + }, + "outputs": { + "0": { + "name": "ζ–‡ζœ¬" + } + } + }, + "easy conditioningIndexSwitch": { + "display_name": "ζ‘δ»ΆηΌ–ε·εˆ‡ζ’", + "inputs": { + "cond0": { + "name": "村仢0" + }, + "cond1": { + "name": "村仢1" + }, + "index": { + "name": "编号" + } + }, + "outputs": { + "0": { + "name": "村仢" + } + } + }, + "easy mathString": { + "display_name": "字符串比较", + "inputs": { + "a": { + "name": "a" + }, + "b": { + "name": "b" + }, + "operation": { + "name": "ζ“δ½œ" + }, + "case_sensitive": { + "name": "εŒΊεˆ†ε€§ε°ε†™" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”" + } + } + }, + "easy mathInt": { + "display_name": "整数运η—", + "inputs": { + "a": { + "name": "a" + }, + "b": { + "name": "b" + }, + "operation": { + "name": "ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "ζ•΄ζ•°" + } + } + }, + "easy mathFloat": { + "display_name": "ζ΅η‚Ήζ―”θΎƒ", + "inputs": { + "a": { + "name": "a" + }, + "b": { + "name": "b" + }, + "operation": { + "name": "ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”" + } + } + }, + "easy latentNoisy": { + "display_name": "ε™ͺζ³’LATENT（SigmaδΉ˜η§―οΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "optional_model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "optional_latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + }, + "sample_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "start_at_step": { + "name": "开始ζ­₯ζ•°" + }, + "end_at_step": { + "name": "η»“ζŸζ­₯ζ•°" + }, + "source": { + "name": "η”ŸζˆθΎε€‡" + }, + "seed": { + "name": "随机种" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "LATENT" + }, + "2": { + "name": "伽马值" + } + } + }, + "easy latentCompositeMaskedWithCond": { + "display_name": "Latentιη½©ε€εˆ (带村仢)", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "text_combine": { + "name": "ζ–‡ε­—ζ‹ΌζŽ₯" + }, + "source_latent": { + "name": "LATENTοΌˆζΊοΌ‰" + }, + "source_mask": { + "name": "ιη½©οΌˆζΊοΌ‰" + }, + "destination_mask": { + "name": "ιη½©οΌˆη›ζ ‡οΌ‰" + }, + "text_combine_mode": { + "name": "ζ–‡ε­—ζ‹ΌζŽ₯樑式" + }, + "replace_text": { + "name": "替捒文字" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "LATENT" + }, + "2": { + "name": "村仢" + } + } + }, + "easy injectNoiseToLatent": { + "display_name": "插ε…₯ε™ͺ泒到Latent", + "inputs": { + "pipe_to_noise": { + "name": "ε™ͺζ³’θŠ‚η‚ΉζŸ" + }, + "image_to_latent": { + "name": "图像转Latent" + }, + "latent": { + "name": "LATENT" + }, + "noise": { + "name": "ε™ͺζ³’" + }, + "mask": { + "name": "ιη½©" + }, + "strength": { + "name": "εΌΊεΊ¦" + }, + "normalize": { + "name": "θ§„ζ ΌεŒ–" + }, + "average": { + "name": "平均" + }, + "mix_randn_amount": { + "name": "ζ··εˆιšζœΊζ•°" + } + }, + "outputs": { + "0": { + "name": "LATENT" + } + } + }, + "easy seed": { + "display_name": "随机种", + "inputs": { + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "随机种" + } + } + }, + "easy seedList": { + "display_name": "ιšζœΊη§εˆ—θ‘¨", + "description": "ε―η”¨δΊŽforεΎͺηŽ―ηš„ιšζœΊζ•°η§ε­εˆ—θ‘¨οΌŒι€šθΏ‡δΈŽeasy forLoopStartθŠ‚η‚Ήηš„η΄’εΌ•δΈŽeasy indexAnyθŠ‚η‚Ήη›ΈθΏžζŽ₯可εžηŽ°εœ¨εΎͺηŽ―δΈ­δ½Ώη”¨δΈεŒη§ε­ε€ΌθΏ›θ‘Œι‡‡ζ ·", + "inputs": { + "min_num": { + "name": "ζœ€ε°ε€Ό" + }, + "max_num": { + "name": "ζœ€ε€§ε€Ό" + }, + "method": { + "name": "η”Ÿζˆζ–ΉεΌ" + }, + "total": { + "name": "总量" + }, + "seed": { + "name": "εˆ—θ‘¨εΊε·" + } + }, + "outputs": { + "0": { + "name": "随机种" + }, + "1": { + "name": "总量" + } + } + }, + "easy globalSeed": { + "display_name": "ε…¨ε±€ιšζœΊη§", + "inputs": { + "value": { + "name": "随机种" + }, + "mode": { + "name": "η”Ÿζˆζ—ΆεΊ" + }, + "action": { + "name": "θΏθ‘Œζ“δ½œ" + }, + "last_seed": { + "name": "上欑随机种" + } + } + }, + "easy humanSegmentation": { + "display_name": "人体Segmentation", + "inputs": { + "image": { + "name": "图像" + }, + "method": { + "name": "方法" + }, + "confidence": { + "name": "η½δΏ‘εΊ¦" + }, + "crop_multi": { + "name": "裁ε‰ͺδΉ˜ζ•°" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + }, + "2": { + "name": "BBox" + } + } + }, + "easy preSampling": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆεŸΊη‘€οΌ‰", + "inputs": { + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed": { + "name": "随机种" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image_to_latent": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy preSamplingAdvanced": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆι«˜ηΊ§οΌ‰", + "inputs": { + "steps": { + "name": "ζ­₯ζ•°" + }, + "start_at_step": { + "name": "开始ζ­₯ζ•°" + }, + "end_at_step": { + "name": "η»“ζŸζ­₯ζ•°" + }, + "add_noise": { + "name": "咞加ε™ͺζ³’" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed": { + "name": "随机种" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "return_with_leftover_noise": { + "name": "θΏ”ε›žε™ͺζ³’" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image_to_latent": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy preSamplingNoiseIn": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆζ’ε…₯ε™ͺζ³’οΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "optional_latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + }, + "optional_noise_seed": { + "name": "ιšζœΊη§οΌˆε―ι€‰οΌ‰" + }, + "factor": { + "name": "η³»ζ•°" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed": { + "name": "随机种" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy preSamplingCustom": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆθ‡ͺεšδΉ‰οΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image_to_latent": { + "name": "图像转Latent" + }, + "latent": { + "name": "LATENT" + }, + "optional_sampler": { + "name": "采样器(可选)" + }, + "optional_sigmas": { + "name": "Sigmas(可选)" + }, + "guider": { + "name": "εΌ•ε―Ό" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "flip_sigmas": { + "name": "翻转Sigmas" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "add_noise": { + "name": "添加ε™ͺζ³’" + }, + "seed": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy preSamplingSdTurbo": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆSDTurboοΌ‰", + "inputs": { + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed": { + "name": "随机种" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image_to_latent": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy preSamplingCascade": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆCascadeοΌ‰", + "inputs": { + "encode_vae_name": { + "name": "编码VAE" + }, + "decode_vae_name": { + "name": "解码VAE" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed": { + "name": "随机种" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model_c": { + "name": "ζ¨‘εž‹_CοΌˆε―ι€‰οΌ‰" + }, + "image_to_latent_c": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent_c": { + "name": "LatentCοΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹_B" + }, + "2": { + "name": "Latent_B" + } + } + }, + "easy preSamplingLayerDiffusion": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆLayerDiffusionοΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "blended_image": { + "name": "ζ··εˆε›Ύεƒ" + }, + "mask": { + "name": "ιη½©" + }, + "method": { + "name": "方法" + }, + "weight": { + "name": "权重" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy preSamplingLayerDiffusionADDTL": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆLayerDiffusionε‰ζ™―θƒŒζ™―οΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "optional_fg_cond": { + "name": "ε‰ζ™―ζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "optional_bg_cond": { + "name": "θƒŒζ™―ζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "optional_blended_cond": { + "name": "ζ··εˆζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "optional_fg_promt": { + "name": "前景提瀺词" + }, + "optional_bg_promt": { + "name": "θƒŒζ™―ζη€Ίθ―" + }, + "optional_blended_promt": { + "name": "混合提瀺词" + }, + "foreground_promt": { + "name": "前景提瀺词" + }, + "background_promt": { + "name": "θƒŒζ™―ζη€Ίθ―" + }, + "blended_promt": { + "name": "混合提瀺词" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "dynamicThresholdingFull": { + "display_name": "εŠ¨ζ€CFG", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "mimic_scale": { + "name": "ζ¨‘ζ‹ŸCFG" + }, + "threshold_percentile": { + "name": "ι˜ˆε€Όη™Ύεˆ†ζ―”" + }, + "mimic_mode": { + "name": "ζ¨‘ζ‹Ÿζ¨‘εΌ" + }, + "mimic_scale_min": { + "name": "ζ¨‘ζ‹ŸCFGζœ€ε°ε€Ό" + }, + "cfg_mode": { + "name": "CFG樑式" + }, + "cfg_scale_min": { + "name": "CFGζœ€ε°ε€Ό" + }, + "sched_val": { + "name": "θ°ƒεΊ¦ε˜ι‡" + }, + "separate_feature_channels": { + "name": "εˆ†η¦»εŠŸθƒ½ι€šι“" + }, + "scaling_startpoint": { + "name": "缩放衷始点" + }, + "variability_measure": { + "name": "ε˜εŒ–ι‡ζ΅‹ι‡" + }, + "interpolate_phi": { + "name": "插值phi" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy preSamplingDynamicCFG": { + "display_name": "ι’„ι‡‡ζ ·ε‚ζ•°οΌˆεŠ¨ζ€CFGοΌ‰", + "inputs": { + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "cfg_mode": { + "name": "CFG樑式" + }, + "cfg_scale_min": { + "name": "CFGζœ€ε°ε€Ό" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image_to_latent": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy fullkSampler": { + "display_name": "η€ζ˜“kι‡‡ζ ·ε™¨οΌˆεŒζ•΄η‰ˆοΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "positive": { + "name": "ζ­£ι’ζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "negative": { + "name": "θ΄Ÿι’ζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "image_to_latent": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "start_at_step": { + "name": "开始ζ­₯ζ•°" + }, + "end_at_step": { + "name": "η»“ζŸζ­₯ζ•°" + }, + "add_noise": { + "name": "添加ε™ͺζ³’" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "image_output": { + "name": "图像输出" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + }, + "2": { + "name": "ζ¨‘εž‹" + }, + "3": { + "name": "正青村仢" + }, + "4": { + "name": "负青村仢" + }, + "5": { + "name": "LATENT" + }, + "6": { + "name": "VAE" + }, + "7": { + "name": "CLIP" + }, + "8": { + "name": "seed" + } + } + }, + "easy kSampler": { + "display_name": "η€ζ˜“K采样器", + "inputs": { + "image_output": { + "name": "图像输出" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + } + } + }, + "easy kSamplerTiled": { + "display_name": "η€ζ˜“kι‡‡ζ ·ε™¨οΌˆεˆ†ε—θ§£η οΌ‰", + "inputs": { + "image_output": { + "name": "图像输出" + }, + "tile_size": { + "name": "εˆ†ε—ε€§ε°" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + } + } + }, + "easy kSamplerCustom": { + "display_name": "η€ζ˜“kι‡‡ζ ·ε™¨οΌˆθ‡ͺεšδΉ‰οΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "θΎ“ε‡Ί" + }, + "2": { + "name": "降ε™ͺθΎ“ε‡Ί" + }, + "3": { + "name": "图像" + } + } + }, + "easy kSamplerLayerDiffusion": { + "display_name": "η€ζ˜“kι‡‡ζ ·ε™¨οΌˆLayerDiffusionοΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ι€ζ˜Žε›Ύεƒ" + }, + "2": { + "name": "εŽŸε›Ύεƒ" + }, + "3": { + "name": "Alpha" + } + } + }, + "easy kSamplerInpainting": { + "display_name": "η€ζ˜“K采样器 (ε†…θ‘₯)", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "mask": { + "name": "ιη½©οΌˆε―选）" + }, + "grow_mask_by": { + "name": "ιη½©ζ‰©ε±•" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + } + } + }, + "easy kSamplerDownscaleUnet": { + "display_name": "η€ζ˜“K采样器 (ζ”ΆηΌ©Unet)", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "downscale_mode": { + "name": "攢缩樑式" + }, + "block_number": { + "name": "块编号" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + } + } + }, + "easy kSamplerSDTurbo": { + "display_name": "η€ζ˜“kι‡‡ζ ·ε™¨οΌˆSDTurboοΌ‰", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + } + } + }, + "easy fullCascadeKSampler": { + "display_name": "η€ζ˜“级联k采样器 (εŒζ•΄η‰ˆ)", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model_c": { + "name": "ζ¨‘εž‹CοΌˆε―ι€‰οΌ‰" + }, + "image_to_latent_c": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "latent_c": { + "name": "LatentCοΌˆε―ι€‰οΌ‰" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "seed_num": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹B" + }, + "2": { + "name": "LatentB" + } + } + }, + "easy cascadeKSampler": { + "display_name": "η€ζ˜“级联k采样器", + "inputs": { + "image_output": { + "name": "图像输出" + }, + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model_c": { + "name": "ζ¨‘εž‹CοΌˆε―ι€‰οΌ‰" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + } + } + }, + "easy unSampler": { + "display_name": "逆(不)采样器", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "optional_model": { + "name": "ζ¨‘εž‹οΌˆε―ι€‰οΌ‰" + }, + "optional_positive": { + "name": "ζ­£ι’ζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "optional_negative": { + "name": "θ΄Ÿι’ζ‘δ»ΆοΌˆε―ι€‰οΌ‰" + }, + "optional_latent": { + "name": "LATENTοΌˆε―ι€‰οΌ‰" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "end_at_step": { + "name": "η»“ζŸζ­₯ζ•°" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "normalize": { + "name": "θ§„ζ ΌεŒ–" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "LATENT" + } + } + }, + "easy pipeIn": { + "display_name": "θŠ‚η‚ΉζŸθΎ“ε…₯", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹" + }, + "positive": { + "name": "正青村仢" + }, + "negative": { + "name": "负青村仢" + }, + "image_to_latent": { + "name": "图像转Latent" + }, + "latent": { + "name": "LATENT" + }, + "image": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy pipeOut": { + "display_name": "θŠ‚η‚ΉζŸθΎ“ε‡Ί", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "正青村仢" + }, + "3": { + "name": "负青村仢" + }, + "4": { + "name": "图像转Latent" + }, + "5": { + "name": "LATENT" + }, + "6": { + "name": "图像" + } + } + }, + "easy pipeToBasicPipe": { + "display_name": "θŠ‚η‚ΉζŸθ½¬ζ’εŸΊη‘€θŠ‚η‚ΉζŸ", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + } + }, + "outputs": { + "0": { + "name": "εŸΊη‘€θŠ‚η‚ΉζŸ" + } + } + }, + "easy pipeBatchIndex": { + "display_name": "θŠ‚η‚ΉζŸζ‰Ήζ¬‘η΄’εΌ•", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "batch_index": { + "name": "批欑紒引" + }, + "length": { + "name": "ι•ΏεΊ¦" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy pipeEdit": { + "display_name": "θŠ‚η‚ΉζŸηΌ–θΎ‘", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹" + }, + "positive": { + "name": "正青村仢" + }, + "pos": { + "name": "正青村仢" + }, + "negative": { + "name": "负青村仢" + }, + "neg": { + "name": "负青村仢" + }, + "VAE": { + "name": "VAE" + }, + "CLIP": { + "name": "CLIP" + }, + "image_to_latent": { + "name": "图像转Latent" + }, + "latent": { + "name": "LATENT" + }, + "image": { + "name": "图像" + }, + "clip_skip": { + "name": "CLIPεœζ­’ε±‚" + }, + "optional_positive": { + "name": "正青提瀺词(可选)" + }, + "optional_negative": { + "name": "负青提瀺词(可选)" + }, + "positive_token_normalization": { + "name": "正青Tokenθ§„ζ ΌεŒ–" + }, + "positive_weight_interpretation": { + "name": "正青权重插值方式" + }, + "negative_token_normalization": { + "name": "负青Tokenθ§„ζ ΌεŒ–" + }, + "negative_weight_interpretation": { + "name": "θ΄Ÿι’ζƒι‡ζ’ε€Όζ–ΉεΌ" + }, + "a1111_prompt_style": { + "name": "A1111提瀺词风格" + }, + "conditioning_mode": { + "name": "村仢樑式" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "正青村仢" + }, + "3": { + "name": "正青村仢" + }, + "4": { + "name": "负青村仢" + }, + "5": { + "name": "负青村仢" + }, + "6": { + "name": "VAE" + }, + "7": { + "name": "CLIP" + }, + "8": { + "name": "图像转Latent" + }, + "9": { + "name": "LATENT" + }, + "10": { + "name": "图像" + } + } + }, + "easy pipeEditPrompt": { + "display_name": "θŠ‚η‚ΉζŸηΌ–θΎ‘ζη€Ίθ―", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy XYPlot": { + "display_name": "η€ζ˜“XY图葨", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "grid_spacing": { + "name": "ι—΄ιš”" + }, + "output_individuals": { + "name": "单独输出" + }, + "flip_xy": { + "name": "XY互捒" + }, + "x_axis": { + "name": "X轴" + }, + "y_axis": { + "name": "Y轴" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy XYPlotAdvanced": { + "display_name": "η€ζ˜“XY图葨 (高级)", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "grid_spacing": { + "name": "ι—΄ιš”" + }, + "output_individuals": { + "name": "单独输出" + }, + "flip_xy": { + "name": "XY互捒" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy XYInputs: Seeds++ Batch": { + "display_name": "XYθΎ“ε…₯: 随机种δΈͺζ•°", + "inputs": { + "batch_count": { + "name": "δΈͺζ•°" + } + } + }, + "easy XYInputs: Steps": { + "display_name": "XYθΎ“ε…₯: ζ­₯ζ•°", + "inputs": { + "selection_count": { + "name": "选择数量" + }, + "target_parameter": { + "name": "η›ζ ‡ε‚ζ•°" + }, + "batch_count": { + "name": "δΈͺζ•°" + }, + "first_step": { + "name": "衷始ζ­₯ζ•°" + }, + "last_step": { + "name": "ζœ€η»ˆζ­₯ζ•°" + }, + "first_start_step": { + "name": "衷始开始ζ­₯ζ•°" + }, + "last_start_step": { + "name": "ζœ€η»ˆεΌ€ε§‹ζ­₯ζ•°" + }, + "first_end_step": { + "name": "θ΅·ε§‹η»“ζŸζ­₯ζ•°" + }, + "last_end_step": { + "name": "ζœ€η»ˆη»“ζŸζ­₯ζ•°" + }, + "first_refine_step": { + "name": "θ΅·ε§‹δΌ˜εŒ–ζ­₯ζ•°" + }, + "last_refine_step": { + "name": "ζœ€η»ˆδΌ˜εŒ–ζ­₯ζ•°" + } + } + }, + "easy XYInputs: CFG Scale": { + "display_name": "XYθΎ“ε…₯: CFG", + "inputs": { + "batch_count": { + "name": "δΈͺζ•°" + }, + "first_cfg": { + "name": "εˆε§‹CFG" + }, + "end_cfg": { + "name": "ζœ€η»ˆCFG" + }, + "last_cfg": { + "name": "ζœ€η»ˆCFG" + } + } + }, + "easy XYInputs: FluxGuidance": { + "display_name": "XYθΎ“ε…₯: FluxεΌ•ε―Ό", + "inputs": { + "batch_count": { + "name": "δΈͺζ•°" + }, + "first_guidance": { + "name": "εˆε§‹εΌ•ε―Ό" + }, + "end_guidance": { + "name": "ζœ€η»ˆεΌ•ε―Ό" + }, + "last_guidance": { + "name": "ζœ€η»ˆεΌ•ε―Ό" + } + } + }, + "easy XYInputs: Denoise": { + "display_name": "XYθΎ“ε…₯: 降ε™ͺ", + "inputs": { + "select_count": { + "name": "选择数量" + }, + "first_denoise": { + "name": "εˆε§‹ι™ε™ͺ" + }, + "end_denoise": { + "name": "ζœ€η»ˆι™ε™ͺ" + }, + "batch_count": { + "name": "δΈͺζ•°" + }, + "last_denoise": { + "name": "ζœ€η»ˆι™ε™ͺ" + } + } + }, + "easy XYInputs: ControlNet": { + "display_name": "XYθΎ“ε…₯: ControlNet", + "inputs": { + "control_net": { + "name": "ControlNet" + }, + "image": { + "name": "图像" + }, + "cnet_stack": { + "name": "ControlNetε †" + }, + "control_net_name": { + "name": "ControlNet" + }, + "target_parameter": { + "name": "η›ζ ‡ε‚ζ•°" + }, + "batch_count": { + "name": "δΈͺζ•°" + }, + "first_strength": { + "name": "εˆε§‹εΌΊεΊ¦" + }, + "last_strength": { + "name": "ζœ€η»ˆεΌΊεΊ¦" + }, + "first_start_percent": { + "name": "εˆε§‹εΌ€ε§‹εΌ•ε―Όζ—Άι—΄" + }, + "last_start_percent": { + "name": "ζœ€η»ˆεΌ€ε§‹εΌ•ε―Όζ—Άι—΄" + }, + "first_end_percent": { + "name": "εˆε§‹η»“ζŸεΌ•ε―Όζ—Άι—΄" + }, + "last_end_percent": { + "name": "ζœ€η»ˆη»“ζŸεΌ•ε―Όζ—Άι—΄" + }, + "strength": { + "name": "εΌΊεΊ¦" + }, + "start_percent": { + "name": "开始引导既间" + }, + "end_percent": { + "name": "η»“ζŸεΌ•ε―Όζ—Άι—΄" + } + } + }, + "easy XYInputs: ModelMergeBlocks": { + "display_name": "XYθΎ“ε…₯: ζ¨‘εž‹θžεˆ", + "inputs": { + "ckpt_name_1": { + "name": "ζ¨‘εž‹_1" + }, + "ckpt_name_2": { + "name": "ζ¨‘εž‹_2" + }, + "vae_use": { + "name": "VAE使用" + }, + "preset": { + "name": "ι’„η½ε‚ζ•°" + }, + "choose .csv file to input values": { + "name": "选择.csvζ–‡δ»Άθ¦†η›–εˆ°ζ•°ε€ΌδΈ­" + }, + "choose .csv file into values": { + "name": "选择.csvζ–‡δ»Άθ¦†η›–εˆ°ζ•°ε€ΌδΈ­" + } + } + }, + "easy XYInputs: Checkpoint": { + "display_name": "XYθΎ“ε…₯: ζ¨‘εž‹", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †(可选)" + }, + "input_mode": { + "name": "θΎ“ε…₯樑式" + }, + "ckpt_count": { + "name": "ζ¨‘εž‹ζ•°ι‡" + }, + "ckpt_name_1": { + "name": "ζ¨‘εž‹εη§°1" + }, + "ckpt_name_2": { + "name": "ζ¨‘εž‹εη§°2" + }, + "ckpt_name_3": { + "name": "ζ¨‘εž‹εη§°3" + }, + "ckpt_name_4": { + "name": "ζ¨‘εž‹εη§°4" + }, + "ckpt_name_5": { + "name": "ζ¨‘εž‹εη§°5" + }, + "ckpt_name_6": { + "name": "ζ¨‘εž‹εη§°6" + }, + "ckpt_name_7": { + "name": "ζ¨‘εž‹εη§°7" + }, + "ckpt_name_8": { + "name": "ζ¨‘εž‹εη§°8" + }, + "ckpt_name_9": { + "name": "ζ¨‘εž‹εη§°9" + }, + "ckpt_name_10": { + "name": "ζ¨‘εž‹εη§°10" + }, + "clip_skip_1": { + "name": "CLIPεœζ­’ε±‚1" + }, + "clip_skip_2": { + "name": "CLIPεœζ­’ε±‚2" + }, + "clip_skip_3": { + "name": "CLIPεœζ­’ε±‚3" + }, + "clip_skip_4": { + "name": "CLIPεœζ­’ε±‚4" + }, + "clip_skip_5": { + "name": "CLIPεœζ­’ε±‚5" + }, + "clip_skip_6": { + "name": "CLIPεœζ­’ε±‚6" + }, + "clip_skip_7": { + "name": "CLIPεœζ­’ε±‚7" + }, + "clip_skip_8": { + "name": "CLIPεœζ­’ε±‚8" + }, + "clip_skip_9": { + "name": "CLIPεœζ­’ε±‚9" + }, + "clip_skip_10": { + "name": "CLIPεœζ­’ε±‚10" + }, + "vae_name_1": { + "name": "VAE名称1" + }, + "vae_name_2": { + "name": "VAE名称2" + }, + "vae_name_3": { + "name": "VAE名称3" + }, + "vae_name_4": { + "name": "VAE名称4" + }, + "vae_name_5": { + "name": "VAE名称5" + }, + "vae_name_6": { + "name": "VAE名称6" + }, + "vae_name_7": { + "name": "VAE名称7" + }, + "vae_name_8": { + "name": "VAE名称8" + }, + "vae_name_9": { + "name": "VAE名称9" + }, + "vae_name_10": { + "name": "VAE名称10" + } + } + }, + "easy XYInputs: Lora": { + "display_name": "XYθΎ“ε…₯: Lora", + "inputs": { + "optional_lora_stack": { + "name": "LoRAε †(可选)" + }, + "input_mode": { + "name": "θΎ“ε…₯樑式" + }, + "lora_count": { + "name": "LoRA数量" + }, + "model_strength": { + "name": "ζ¨‘εž‹εΌΊεΊ¦" + }, + "clip_strength": { + "name": "CLIPεΌΊεΊ¦" + }, + "lora_name_1": { + "name": "LoRA名称1" + }, + "lora_name_2": { + "name": "LoRA名称2" + }, + "lora_name_3": { + "name": "LoRA名称3" + }, + "lora_name_4": { + "name": "LoRA名称4" + }, + "lora_name_5": { + "name": "LoRA名称5" + }, + "lora_name_6": { + "name": "LoRA名称6" + }, + "lora_name_7": { + "name": "LoRA名称7" + }, + "lora_name_8": { + "name": "LoRA名称8" + }, + "lora_name_9": { + "name": "LoRA名称9" + }, + "lora_name_10": { + "name": "LoRA名称10" + }, + "model_str_1": { + "name": "ζ¨‘εž‹εΌΊεΊ¦1" + }, + "model_str_2": { + "name": "ζ¨‘εž‹εΌΊεΊ¦2" + }, + "model_str_3": { + "name": "ζ¨‘εž‹εΌΊεΊ¦3" + }, + "model_str_4": { + "name": "ζ¨‘εž‹εΌΊεΊ¦4" + }, + "model_str_5": { + "name": "ζ¨‘εž‹εΌΊεΊ¦5" + }, + "model_str_6": { + "name": "ζ¨‘εž‹εΌΊεΊ¦6" + }, + "model_str_7": { + "name": "ζ¨‘εž‹εΌΊεΊ¦7" + }, + "model_str_8": { + "name": "ζ¨‘εž‹εΌΊεΊ¦8" + }, + "model_str_9": { + "name": "ζ¨‘εž‹εΌΊεΊ¦9" + }, + "model_str_10": { + "name": "ζ¨‘εž‹εΌΊεΊ¦10" + }, + "clip_str_1": { + "name": "CLIPεΌΊεΊ¦1" + }, + "clip_str_2": { + "name": "CLIPεΌΊεΊ¦2" + }, + "clip_str_3": { + "name": "CLIPεΌΊεΊ¦3" + }, + "clip_str_4": { + "name": "CLIPεΌΊεΊ¦4" + }, + "clip_str_5": { + "name": "CLIPεΌΊεΊ¦5" + }, + "clip_str_6": { + "name": "CLIPεΌΊεΊ¦6" + }, + "clip_str_7": { + "name": "CLIPεΌΊεΊ¦7" + }, + "clip_str_8": { + "name": "CLIPεΌΊεΊ¦8" + }, + "clip_str_9": { + "name": "CLIPεΌΊεΊ¦9" + }, + "clip_str_10": { + "name": "CLIPεΌΊεΊ¦10" + } + } + }, + "easy XYInputs: PositiveCond": { + "display_name": "XYθΎ“ε…₯: 正青村仢x4", + "inputs": { + "positive_1": { + "name": "正青村仢_1" + }, + "positive_2": { + "name": "正青村仢_2" + }, + "positive_3": { + "name": "正青村仢_3" + }, + "positive_4": { + "name": "正青村仢_4" + } + } + }, + "easy XYInputs: NegativeCond": { + "display_name": "XYθΎ“ε…₯: 负青村仢x4", + "inputs": { + "positive_1": { + "name": "负青村仢_1" + }, + "positive_2": { + "name": "负青村仢_2" + }, + "positive_3": { + "name": "负青村仢_3" + }, + "positive_4": { + "name": "负青村仢_4" + } + } + }, + "easy XYInputs: PositiveCondList": { + "display_name": "XYθΎ“ε…₯: ζ­£ι’ζ‘δ»Άεˆ—θ‘¨", + "inputs": { + "positive": { + "name": "ζ­£ι’ζ‘δ»Άεˆ—θ‘¨" + } + } + }, + "easy XYInputs: NegativeCondList": { + "display_name": "XYθΎ“ε…₯: θ΄Ÿι’ζ‘δ»Άεˆ—θ‘¨", + "inputs": { + "negative": { + "name": "θ΄Ÿι’ζ‘δ»Άεˆ—θ‘¨" + } + } + }, + "easy XYInputs: PromptSR": { + "display_name": "XYθΎ“ε…₯: 替捒提瀺词", + "inputs": { + "target_prompt": { + "name": "η›ζ ‡ζη€Ίθ―" + }, + "search_txt": { + "name": "ζ£€η΄’" + }, + "replace_count": { + "name": "替捒数量" + }, + "replace_all_text": { + "name": "ζ›Ώζ’ζ‰€ζœ‰ζ–‡ε­—" + }, + "replace_1": { + "name": "替捒_1" + }, + "replace_2": { + "name": "替捒_2" + }, + "replace_3": { + "name": "替捒_3" + }, + "replace_4": { + "name": "替捒_4" + }, + "replace_5": { + "name": "替捒_5" + }, + "replace_6": { + "name": "替捒_6" + }, + "replace_7": { + "name": "替捒_7" + }, + "replace_8": { + "name": "替捒_8" + }, + "replace_9": { + "name": "替捒_9" + }, + "replace_10": { + "name": "替捒_10" + }, + "replace_11": { + "name": "替捒_11" + }, + "replace_12": { + "name": "替捒_12" + }, + "replace_13": { + "name": "替捒_13" + }, + "replace_14": { + "name": "替捒_14" + }, + "replace_15": { + "name": "替捒_15" + }, + "replace_16": { + "name": "替捒_16" + }, + "replace_17": { + "name": "替捒_17" + }, + "replace_18": { + "name": "替捒_18" + }, + "replace_19": { + "name": "替捒_19" + }, + "replace_20": { + "name": "替捒_20" + }, + "replace_21": { + "name": "替捒_21" + }, + "replace_22": { + "name": "替捒_22" + }, + "replace_23": { + "name": "替捒_23" + }, + "replace_24": { + "name": "替捒_24" + }, + "replace_25": { + "name": "替捒_25" + }, + "replace_26": { + "name": "替捒_26" + }, + "replace_27": { + "name": "替捒_27" + }, + "replace_28": { + "name": "替捒_28" + }, + "replace_29": { + "name": "替捒_29" + }, + "replace_30": { + "name": "替捒_30" + }, + "replace_31": { + "name": "替捒_31" + }, + "replace_32": { + "name": "替捒_32" + }, + "replace_33": { + "name": "替捒_33" + }, + "replace_34": { + "name": "替捒_34" + }, + "replace_35": { + "name": "替捒_35" + }, + "replace_36": { + "name": "替捒_36" + }, + "replace_37": { + "name": "替捒_37" + }, + "replace_38": { + "name": "替捒_38" + }, + "replace_39": { + "name": "替捒_39" + }, + "replace_40": { + "name": "替捒_40" + }, + "replace_41": { + "name": "替捒_41" + }, + "replace_42": { + "name": "替捒_42" + }, + "replace_43": { + "name": "替捒_43" + }, + "replace_44": { + "name": "替捒_44" + }, + "replace_45": { + "name": "替捒_45" + }, + "replace_46": { + "name": "替捒_46" + }, + "replace_47": { + "name": "替捒_47" + }, + "replace_48": { + "name": "替捒_48" + }, + "replace_49": { + "name": "替捒_49" + } + } + }, + "easy XYInputs: Sampler/Scheduler": { + "display_name": "XYθΎ“ε…₯: 采样调度器", + "inputs": { + "target_parameter": { + "name": "η›ζ ‡ε‚ζ•°" + }, + "sampler": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "sampler & scheduler": { + "name": "ι‡‡ζ ·ε’Œθ°ƒεΊ¦" + }, + "input_count": { + "name": "θΎ“ε…₯数量" + }, + "sampler_1": { + "name": "采样器_1" + }, + "sampler_2": { + "name": "采样器_2" + }, + "sampler_3": { + "name": "采样器_3" + }, + "sampler_4": { + "name": "采样器_4" + }, + "sampler_5": { + "name": "采样器_5" + }, + "sampler_6": { + "name": "采样器_6" + }, + "sampler_7": { + "name": "采样器_7" + }, + "sampler_8": { + "name": "采样器_8" + }, + "sampler_9": { + "name": "采样器_9" + }, + "sampler_10": { + "name": "采样器_10" + }, + "sampler_11": { + "name": "采样器_11" + }, + "sampler_12": { + "name": "采样器_12" + }, + "sampler_13": { + "name": "采样器_13" + }, + "sampler_14": { + "name": "采样器_14" + }, + "sampler_15": { + "name": "采样器_15" + }, + "sampler_16": { + "name": "采样器_16" + }, + "sampler_17": { + "name": "采样器_17" + }, + "sampler_18": { + "name": "采样器_18" + }, + "sampler_19": { + "name": "采样器_19" + }, + "sampler_20": { + "name": "采样器_20" + }, + "sampler_21": { + "name": "采样器_21" + }, + "sampler_22": { + "name": "采样器_22" + }, + "sampler_23": { + "name": "采样器_23" + }, + "sampler_24": { + "name": "采样器_24" + }, + "sampler_25": { + "name": "采样器_25" + }, + "sampler_26": { + "name": "采样器_26" + }, + "sampler_27": { + "name": "采样器_27" + }, + "sampler_28": { + "name": "采样器_28" + }, + "sampler_29": { + "name": "采样器_29" + }, + "sampler_30": { + "name": "采样器_30" + }, + "sampler_31": { + "name": "采样器_31" + }, + "sampler_32": { + "name": "采样器_32" + }, + "sampler_33": { + "name": "采样器_33" + }, + "sampler_34": { + "name": "采样器_34" + }, + "sampler_35": { + "name": "采样器_35" + }, + "sampler_36": { + "name": "采样器_36" + }, + "sampler_37": { + "name": "采样器_37" + }, + "sampler_38": { + "name": "采样器_38" + }, + "sampler_39": { + "name": "采样器_39" + }, + "sampler_40": { + "name": "采样器_40" + }, + "sampler_41": { + "name": "采样器_41" + }, + "sampler_42": { + "name": "采样器_42" + }, + "sampler_43": { + "name": "采样器_43" + }, + "sampler_44": { + "name": "采样器_44" + }, + "sampler_45": { + "name": "采样器_45" + }, + "sampler_46": { + "name": "采样器_46" + }, + "sampler_47": { + "name": "采样器_47" + }, + "sampler_48": { + "name": "采样器_48" + }, + "sampler_49": { + "name": "采样器_49" + }, + "sampler_50": { + "name": "采样器_50" + }, + "scheduler_1": { + "name": "调度器_1" + }, + "scheduler_2": { + "name": "调度器_2" + }, + "scheduler_3": { + "name": "调度器_3" + }, + "scheduler_4": { + "name": "调度器_4" + }, + "scheduler_5": { + "name": "调度器_5" + }, + "scheduler_6": { + "name": "调度器_6" + }, + "scheduler_7": { + "name": "调度器_7" + }, + "scheduler_8": { + "name": "调度器_8" + }, + "scheduler_9": { + "name": "调度器_9" + }, + "scheduler_10": { + "name": "调度器_10" + }, + "scheduler_11": { + "name": "调度器_11" + }, + "scheduler_12": { + "name": "调度器_12" + }, + "scheduler_13": { + "name": "调度器_13" + }, + "scheduler_14": { + "name": "调度器_14" + }, + "scheduler_15": { + "name": "调度器_15" + }, + "scheduler_16": { + "name": "调度器_16" + }, + "scheduler_17": { + "name": "调度器_17" + }, + "scheduler_18": { + "name": "调度器_18" + }, + "scheduler_19": { + "name": "调度器_19" + }, + "scheduler_20": { + "name": "调度器_20" + }, + "scheduler_21": { + "name": "调度器_21" + }, + "scheduler_22": { + "name": "调度器_22" + }, + "scheduler_23": { + "name": "调度器_23" + }, + "scheduler_24": { + "name": "调度器_24" + }, + "scheduler_25": { + "name": "调度器_25" + }, + "scheduler_26": { + "name": "调度器_26" + }, + "scheduler_27": { + "name": "调度器_27" + }, + "scheduler_28": { + "name": "调度器_28" + }, + "scheduler_29": { + "name": "调度器_29" + }, + "scheduler_30": { + "name": "调度器_30" + }, + "scheduler_31": { + "name": "调度器_31" + }, + "scheduler_32": { + "name": "调度器_32" + }, + "scheduler_33": { + "name": "调度器_33" + }, + "scheduler_34": { + "name": "调度器_34" + }, + "scheduler_35": { + "name": "调度器_35" + }, + "scheduler_36": { + "name": "调度器_36" + }, + "scheduler_37": { + "name": "调度器_37" + }, + "scheduler_38": { + "name": "调度器_38" + }, + "scheduler_39": { + "name": "调度器_39" + }, + "scheduler_40": { + "name": "调度器_40" + }, + "scheduler_41": { + "name": "调度器_41" + }, + "scheduler_42": { + "name": "调度器_42" + }, + "scheduler_43": { + "name": "调度器_43" + }, + "scheduler_44": { + "name": "调度器_44" + }, + "scheduler_45": { + "name": "调度器_45" + }, + "scheduler_46": { + "name": "调度器_46" + }, + "scheduler_47": { + "name": "调度器_47" + }, + "scheduler_48": { + "name": "调度器_48" + }, + "scheduler_49": { + "name": "调度器_49" + }, + "scheduler_50": { + "name": "调度器_50" + } + } + }, + "easy hiresFix": { + "display_name": "ι«˜ζΈ…δΏε€", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "model_name": { + "name": "ζ¨‘εž‹εη§°" + }, + "rescale_after_model": { + "name": "重缩放" + }, + "rescale_method": { + "name": "缩放方法" + }, + "rescale": { + "name": "重缩放尺寸" + }, + "percent": { + "name": "η™Ύεˆ†ζ―”" + }, + "width": { + "name": "ε½" + }, + "height": { + "name": "高" + }, + "crop": { + "name": "θ£εˆ‡" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + }, + "2": { + "name": "LATENT" + } + } + }, + "easy ultralyticsDetectorPipe": { + "display_name": "ζ£€ζ΅‹εŠ θ½½ε™¨οΌˆη»†θŠ‚δΏε€θŠ‚η‚ΉζŸοΌ‰", + "inputs": { + "model_name": { + "name": "ζ¨‘εž‹" + }, + "bbox_threshold": { + "name": "BBoxι˜ˆε€Ό" + }, + "bbox_dilation": { + "name": "BBox膨胀" + }, + "bbox_crop_factor": { + "name": "BBox裁ε‰ͺη³»ζ•°" + }, + "open_segm": { + "name": "开启Segmζ£€ζ΅‹" + } + }, + "outputs": { + "0": { + "name": "BBoxθŠ‚η‚ΉζŸ" + } + } + }, + "easy samLoaderPipe": { + "display_name": "Samζ¨‘εž‹εŠ θ½½ε™¨οΌˆη»†θŠ‚δΏε€θŠ‚η‚ΉζŸοΌ‰", + "inputs": { + "model_name": { + "name": "ζ¨‘εž‹" + }, + "device_mode": { + "name": "θΎε€‡ζ¨‘式" + }, + "sam_detection_hint": { + "name": "SAM检桋提瀺" + }, + "center-1": { + "name": "δΈ­εΏƒ" + }, + "horizontal-2": { + "name": "ζ°΄εΉ³" + }, + "vertical-2": { + "name": "η«–η›΄" + }, + "rect-4": { + "name": "ζ–œθ§’" + }, + "diamond-4": { + "name": "菱归" + }, + "mask-area": { + "name": "ιη½©εŒΊεŸŸ" + }, + "mask-points": { + "name": "ιη½©η‚Ή" + }, + "mask-point-bbox": { + "name": "ιη½©η‚ΉBBox" + }, + "sam_dilation": { + "name": "SAM膨胀" + }, + "sam_threshold": { + "name": "SAMι˜ˆε€Ό" + }, + "sam_bbox_expansion": { + "name": "SAMBBox扩展" + }, + "sam_mask_hint_threshold": { + "name": "SAMιη½©ζ£€ζ΅‹ι˜ˆε€Ό" + }, + "sam_mask_hint_use_negative": { + "name": "SAM负青ιη½©ζη€Ί" + } + }, + "outputs": { + "0": { + "name": "SAMζ¨‘εž‹θŠ‚η‚ΉζŸ" + } + } + }, + "easy preMaskDetailerFix": { + "display_name": "ι’„ιη½©η»†θŠ‚δΏε€", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "mask": { + "name": "ιη½©" + }, + "optional_image": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "guide_size": { + "name": "引导倧小" + }, + "guide_size_for": { + "name": "εΌ•ε―Όη›ζ ‡" + }, + "max_size": { + "name": "ζœ€ε€§ε°Ίε―Έ" + }, + "mask_mode": { + "name": "ιη½©ζ¨‘式" + }, + "seed": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "feather": { + "name": "ηΎ½εŒ–" + }, + "crop_factor": { + "name": "裁ε‰ͺη³»ζ•°" + }, + "drop_size": { + "name": "ζœ€ε°ε°Ίε―Έ" + }, + "refiner_ratio": { + "name": "Refinerζ―”ηŽ‡" + }, + "batch_size": { + "name": "批欑倧小" + }, + "cycle": { + "name": "εΎͺ环" + }, + "inpaint_model": { + "name": "ε†…θ‘₯ζ¨‘εž‹" + }, + "noise_mask_feather": { + "name": "ε™ͺζ³’ιη½©ηΎ½εŒ–" + } + } + }, + "easy preDetailerFix": { + "display_name": "ι’„η»†θŠ‚δΏε€", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "model": { + "name": "ζ¨‘εž‹" + }, + "CLIP": { + "name": "CLIP" + }, + "VAE": { + "name": "VAE" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "bbox_segm_pipe": { + "name": "BBox&SegmθŠ‚η‚ΉζŸ" + }, + "sam_pipe": { + "name": "SAMζ¨‘εž‹θŠ‚η‚ΉζŸ" + }, + "optional_image": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "guide_size": { + "name": "引导倧小" + }, + "max_size": { + "name": "ζœ€ε€§ε°Ίε―Έ" + }, + "guide_size_for": { + "name": "εΌ•ε―Όη›ζ ‡" + }, + "bbox": { + "name": "BBox" + }, + "crop_region": { + "name": "裁ε‰ͺ区域" + }, + "seed": { + "name": "随机种" + }, + "control_after_generate": { + "name": "θΏθ‘ŒεŽζ“δ½œ" + }, + "fixed": { + "name": "ε›Ίεš" + }, + "increment": { + "name": "咞加" + }, + "decrement": { + "name": "减少" + }, + "randomize": { + "name": "随机" + }, + "steps": { + "name": "ζ­₯ζ•°" + }, + "cfg": { + "name": "CFG" + }, + "sampler_name": { + "name": "采样器" + }, + "scheduler": { + "name": "调度器" + }, + "denoise": { + "name": "降ε™ͺ" + }, + "feather": { + "name": "ηΎ½εŒ–" + }, + "noise_mask": { + "name": "δ»…η”Ÿζˆιη½©" + }, + "force_inpaint": { + "name": "εΌΊεˆΆι‡η»˜" + }, + "drop_size": { + "name": "ζœ€ε°ε°Ίε―Έ" + }, + "wildcard": { + "name": "ι€šι…η¬¦" + }, + "cycle": { + "name": "εΎͺ环" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + }, + "2": { + "name": "η»†εŒ–ε›Ύεƒ" + }, + "3": { + "name": "η»†εŒ–ιƒ¨εˆ†" + }, + "4": { + "name": "ιη½©" + }, + "5": { + "name": "η»†εŒ–θŠ‚η‚ΉζŸ" + }, + "6": { + "name": "ControlNet图像" + } + } + }, + "easy detailerFix": { + "display_name": "η»†θŠ‚δΏε€", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "model": { + "name": "ζ¨‘εž‹" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "图像" + }, + "2": { + "name": "η»†εŒ–ε›Ύεƒ" + }, + "3": { + "name": "η»†εŒ–ιƒ¨εˆ†" + } + } + }, + "easy imageRemoveBG": { + "display_name": "ε›ΎεƒεŽ»ι™€θƒŒζ™―", + "inputs": { + "image": { + "name": "图像" + }, + "image_output": { + "name": "图像输出" + }, + "save_prefix": { + "name": "δΏε­˜ε‰ηΌ€" + }, + "add_background": { + "name": "ζ·»εŠ θƒŒζ™―" + }, + "refine_foreground": { + "name": "δΌ˜εŒ–ε‰ζ™―" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + } + } + }, + "easy fooocusInpaintLoader": { + "display_name": "foocusε†…θ‘₯εŠ θ½½ε™¨", + "inputs": { + "head": { + "name": "head" + }, + "patch": { + "name": "patch" + } + }, + "outputs": { + "0": { + "name": "patch" + } + } + }, + "easy loraPromptApply":{ + "display_name": "应用提瀺词LoRA", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "clip": { + "name": "CLIP" + }, + "positive":{ + "name": "正青提瀺词" + }, + "negative":{ + "name": "负青提瀺词" + } + }, + "outputs":{ + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "CLIP" + }, + "2": { + "name": "正青提瀺词" + }, + "3": { + "name": "负青提瀺词" + } + } + }, + "easy loraStackApply": { + "display_name": "应用LoRAε †", + "inputs": { + "lora_stack": { + "name": "LoRA堆可选" + }, + "model": { + "name": "ζ¨‘εž‹" + }, + "optional_clip": { + "name": "CLIP(可选)" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "CLIP" + } + } + }, + "easy controlnetStackApply": { + "display_name": "应用ControlNetε †", + "inputs": { + "controlnet_stack": { + "name": "ControlNetε †" + }, + "pipe": { + "name": "pipe" + } + }, + "outputs": { + "0": { + "name": "pipe" + } + } + }, + "easy ipadapterApply": { + "display_name": "应用IPAdapter", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image": { + "name": "图像" + }, + "attn_mask": { + "name": "关注层ιη½©" + }, + "optional_ipadapter": { + "name": "IPAdapter(可选)" + }, + "preset": { + "name": 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"embeds_scaling": { + "name": "塌ε…₯η»„ηΌ©ζ”Ύ" + }, + "cache_mode": { + "name": "ηΌ“ε­˜ζ¨‘εΌ" + }, + "use_tiled": { + "name": "εˆ†ε—" + }, + "use_batch": { + "name": "εˆ†ζ‰Ή" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "εˆ†ε—ε›Ύεƒ" + }, + "2": { + "name": "ιη½©" + }, + "3": { + "name": "IPAdapter" + } + } + }, + "easy ipadapterApplyFaceIDKolors": { + "display_name": "应用IPAdapter(FaceIDKolors)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image": { + "name": "图像" + }, + "image_negative": { + "name": "θ΄Ÿι’ε›Ύεƒ" + }, + "attn_mask": { + "name": "关注层ιη½©" + }, + "clip_vision": { + "name": "CLIP视觉" + }, + "optional_ipadapter": { + "name": "IPAdapter(可选)" + }, + "preset": { + "name": "ι’„η½ε‚ζ•°" + }, + "provider": { + "name": "θΎε€‡" + }, + "weight": { + "name": "权重" + }, + "weight_faceidv2": { + "name": "FaceIDV2权重" + }, + "weight_kolors": { + "name": "Kolors权重" + }, + "weight_type": { + "name": "ζƒι‡η±»εž‹" + }, + "combine_embeds": { + "name": "合幢塌ε…₯η»„" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + }, + "embeds_scaling": { + "name": "塌ε…₯η»„ηΌ©ζ”Ύ" + }, + "cache_mode": { + "name": "ηΌ“ε­˜ζ¨‘εΌ" + }, + "use_batch": { + "name": "εˆ†ζ‰Ή" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "εˆ†ε—ε›Ύεƒ" + }, + "2": { + "name": "ιη½©" + }, + "3": { + "name": "IPAdapter" + } + } + }, + "easy ipadapterApplyEncoder": { + "display_name": "应用IPAdapter(编码)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image1": { + "name": "图像_1" + }, + "image2": { + "name": "图像_2" + }, + "image3": { + "name": "图像_3" + }, + "mask1": { + "name": "ιη½©_1" + }, + "mask2": { + "name": "ιη½©_2" + }, + "mask3": { + "name": "ιη½©_3" + }, + "optional_ipadapter": { + "name": "IPAdapter(可选)" + }, + "pos_embeds": { + "name": "正青塌ε…₯η»„" + }, + "neg_embeds": { + "name": "负青塌ε…₯η»„" + }, + "preset": { + "name": "ι’„η½ε‚ζ•°" + }, + "num_embeds": { + "name": "塌ε…₯组数量" + }, + "weight1": { + "name": "权重_1" + }, + "weight2": { + "name": "权重_2" + }, + "combine_method": { + "name": "εˆεΉΆζ–ΉεΌ" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "IPAdapter" + }, + "2": { + "name": "正青塌ε…₯η»„" + }, + "3": { + "name": "负青塌ε…₯η»„" + } + } + }, + "easy ipadapterApplyEmbeds": { + "display_name": "应用IPAdapter(塌ε…₯η»„)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "ipadapter": { + "name": "IPAdapter" + }, + "pos_embed": { + "name": "正青塌ε…₯η»„" + }, + "neg_embed": { + "name": "负青塌ε…₯η»„" + }, + "attn_mask": { + "name": "关注层ιη½©" + }, + "weight": { + "name": "权重" + }, + "weight_type": { + "name": "ζƒι‡η±»εž‹" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + }, + "embeds_scaling": { + "name": "塌ε…₯η»„ηΌ©ζ”Ύ" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "IPAdapter" + } + } + }, + "easy ipadapterStyleComposition": { + "display_name": "应用IPAdapter(风格合成)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image_style": { + "name": "ι£Žζ Όε›Ύεƒ" + }, + "image_composition": { + "name": "εˆζˆε›Ύεƒ" + }, + "image_negative": { + "name": "θ΄Ÿι’ε›Ύεƒ" + }, + "attn_mask": { + "name": "关注层ιη½©" + }, + "clip_vision": { + "name": "CLIP视觉" + }, + "optional_ipadapter": { + "name": "IPAdapter(可选)" + }, + "preset": { + "name": "ι’„η½ε‚ζ•°" + }, + "weight_style": { + "name": "ι£Žζ Όζƒι‡" + }, + "weight_type": { + "name": "ζƒι‡η±»εž‹" + }, + "weight_composition": { + "name": "εˆζˆζƒι‡" + }, + "expand_style": { + "name": "ζ‰©ε±•ι£Žζ Ό" + }, + "combine_embeds": { + "name": "合幢塌ε…₯η»„" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + }, + "embeds_scaling": { + "name": "塌ε…₯η»„ηΌ©ζ”Ύ" + }, + "cache_mode": { + "name": "ηΌ“ε­˜ζ¨‘εΌ" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "IPAdapter" + } + } + }, + "easy ipadapterApplyRegional": { + "display_name": "应用IPAdapter(区域)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image": { + "name": "图像" + }, + "mask": { + "name": "ιη½©" + }, + "optional_ipadapter_params": { + "name": "IPAdapter参数组(可选)" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "image_weight": { + "name": "图像权重" + }, + "prompt_weight": { + "name": "提瀺词权重" + }, + "weight_type": { + "name": "ζƒι‡η±»εž‹" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "IPAdapter参数组" + }, + "2": { + "name": "正青村仢" + }, + "3": { + "name": "负青村仢" + } + } + }, + "easy ipadapterApplyFromParams": { + "display_name": "应用IPAdapter(参数组)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "ipadapter_params": { + "name": "IPAdapter参数组" + }, + "optional_ipadapter": { + "name": "IPAdapter" + }, + "image_negative": { + "name": "θ΄Ÿι’ε›Ύεƒ" + }, + "preset": { + "name": "ι’„η½ε‚ζ•°" + }, + "combine_embeds": { + "name": "合幢塌ε…₯η»„" + }, + "embeds_scaling": { + "name": "塌ε…₯η»„ηΌ©ζ”Ύ" + }, + "cache_mode": { + "name": "ηΌ“ε­˜ζ¨‘εΌ" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + }, + "1": { + "name": "IPAdapter" + } + } + }, + "easy instantIDApply": { + "display_name": "应用InstantID", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "image_kps": { + "name": "图像_kps" + }, + "mask": { + "name": "ιη½©" + }, + "control_net": { + "name": "ControlNet" + }, + "instantid_file": { + "name": "InstantID" + }, + "insightface": { + "name": "InsightFace" + }, + "control_net_name": { + "name": "ControlNet" + }, + "cn_strength": { + "name": "ControlnNet权重" + }, + "cn_soft_weights": { + "name": "ControlNetζŸ”ε’Œζƒι‡" + }, + "weight": { + "name": "权重" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + }, + "noise": { + "name": "ε™ͺζ³’" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + }, + "1": { + "name": "ζ¨‘εž‹" + }, + "2": { + "name": "正青村仢" 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"display_name": "应用PuLID", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image": { + "name": "图像" + }, + "attn_mask": { + "name": "关注层ιη½©" + }, + "pulid_file": { + "name": "PuLID" + }, + "insightface": { + "name": "InsightFace" + }, + "method": { + "name": "方法" + }, + "weight": { + "name": "权重" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy pulIDApplyADV": { + "display_name": "应用PuLID(高级)", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "image": { + "name": "图像" + }, + "attn_mask": { + "name": "关注层ιη½©" + }, + "pulid_file": { + "name": "PuLID" + }, + "insightface": { + "name": "InsightFace" + }, + "method": { + "name": "方法" + }, + "weight": { + "name": "权重" + }, + "projection": { + "name": "ι’„ζ΅‹" + }, + "fidelity": { + "name": "η²Ύη‘εΊ¦" + }, + "noise": { + "name": "ε™ͺζ³’" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy styleAlignedBatchAlign": { + "display_name": "风格对齐", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "share_norm": { + "name": "share_norm" + }, + "share_attn": { + "name": "share_attn" + }, + "scale": { + "name": "scale" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy icLightApply": { + "display_name": "应用ICLight", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "latent": { + "name": "LATENT" + }, + "mode": { + "name": "樑式" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy stableDiffusion3API": { + "display_name": "StableDiffusion 3 API", + "inputs": { + "optional_image": { + "name": "ε›ΎεƒοΌˆε―ι€‰οΌ‰" + }, + "positive": { + "name": "正青提瀺词" + }, + "negative": { + "name": "负青提瀺词" + }, + "model": { + "name": "ζ¨‘εž‹" + }, + "aspect_ratio": { + "name": "ε½ι«˜ζ―”" + }, + "seed": { + "name": "随机种" + }, + "control_before_generate": { + "name": "θΏθ‘Œε‰ζ“δ½œ" + }, 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"easy imageSizeBySide": { + "display_name": "ε›Ύεƒε°Ίε―ΈοΌˆθΎΉοΌ‰", + "inputs": { + "image": { + "name": "图像" + }, + "side": { + "name": "选择边" + } + }, + "outputs": { + "0": { + "name": "εˆ†θΎ¨ηŽ‡" + } + } + }, + "easy imageSizeByLongerSide": { + "display_name": "ε›Ύεƒε°Ίε―ΈοΌˆι•ΏθΎΉοΌ‰", + "inputs": { + "image": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "εˆ†θΎ¨ηŽ‡" + } + } + }, + "easy imageSizeShow": { + "display_name": "ε›Ύεƒε°Ίε―Έζ˜Ύη€Ί", + "inputs": { + "resize_mode": { + "name": "拉伸樑式" + }, + "Just Resize": { + "name": "仅拉伸" + }, + "Crop and Resize": { + "name": "裁ε‰ͺ幢拉伸" + }, + "Resize and Fill": { + "name": "拉伸幢呫充" + }, + "image": { + "name": "图像" + } + } + }, + "easy imageScaleDown": { + "display_name": "图像缩小", + "inputs": { + "images": { + "name": "图像" + }, + "width": { + "name": "ε½εΊ¦" + }, + "height": { + "name": "高度" + }, + "crop": { + "name": "θ£εˆ‡" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageScaleDownBy": { + "display_name": "ε›ΎεƒηΌ©ε°οΌˆζŒ‰ζ―”δΎ‹οΌ‰", + "inputs": { + "images": { + "name": "图像" + }, + "scale_by": { + "name": "缩小比例" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageScaleDownToSize": { + "display_name": "ε›ΎεƒηΌ©ε°οΌˆζŒ‰θΎΉοΌ‰", + "inputs": { + "images": { + "name": "图像" + }, + "size": { + "name": "ε°Ίε―Έ" + }, + "mode": { + "name": "樑式" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageScaleToNormPixels": { + "display_name": "ε›ΎεƒηΌ©ζ”Ύεˆ°ζ ‡ε‡†εƒη΄ ", + "inputs": { + "image": { + "name": "图像" + }, + "upscale_method": { + "name": "缩放方式" + }, + "scale_by": { + "name": "ηΌ©ζ”Ύζ―”δΎ‹" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageRatio": { + "display_name": "ε›Ύεƒζ―”ηŽ‡", + "inputs": { + "image": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "ε½εΊ¦ζ―”ηŽ‡ζ•΄ζ•°" + }, + "1": { + "name": "ι«˜εΊ¦ζ―”ηŽ‡ζ•΄ζ•°" + }, + "2": { + "name": "ε½εΊ¦ζ―”ηŽ‡ζ΅η‚Ή" + }, + "3": { + "name": "ι«˜εΊ¦ζ―”ηŽ‡ζ΅η‚Ή" + } + } + }, + "easy imageToMask": { + "display_name": "ε›Ύη‰‡εˆ°ιη½©", + "inputs": { + "image": { + "name": "图像" + }, + "channel": { + "name": "ι€šι“" + } + }, + "outputs": { + "0": { + "name": "ιη½©" + } + } + }, + "easy imageConcat": { + "display_name": "图像联结", + "inputs": { + "image1": { + "name": "图像_1" + }, + "image2": { + "name": "图像_2" + }, + "direction": { + "name": "方向" + }, + "match_image_size": { + "name": "εŒΉι…ε›Ύεƒε°Ίε―Έ" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageListToImageBatch": { + "display_name": "ε›Ύεƒεˆ—θ‘¨εˆ°ε›Ύεƒζ‰Ήζ¬‘", + "inputs": { + "images": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageBatchToImageList": { + "display_name": "ε›Ύεƒζ‰Ήζ¬‘εˆ°ε›Ύεƒεˆ—θ‘¨", + "inputs": { + "image": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageSplitList": { + "display_name": "ε›Ύεƒζ‹†εˆ†", + "inputs": { + "images": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageSplitGrid": { + "display_name": "ε›Ύεƒζ‹†εˆ†η½‘ζ Ό", + "inputs": { + "images": { + "name": "图像" + }, + "row": { + "name": "葌" + }, + "column": { + "name": "εˆ—" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imagesSplitImage": { + "display_name": "ε›Ύεƒζ‹†εˆ†ε›Ύεƒ", + "inputs": { + "images": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "图像_1" + }, + "1": { + "name": "图像_2" + }, + "2": { + "name": "图像_3" + }, + "3": { + "name": "图像_4" + }, + "4": { + "name": "图像_5" + } + } + }, + "easy imageSplitTiles": { + "display_name": "ε›Ύεƒεˆ†ε—", + "inputs": { + "image": { + "name": "图像" + }, + "overlap_ratio": { + "name": "重叠比例" + }, + "overlap_offset": { + "name": "重叠偏移" + }, + "tiles_rows": { + "name": "εˆ†ε—θ‘Œζ•°" + }, + "tiles_columns": { + "name": "εˆ†ε—εˆ—ζ•°" + }, + "norm": { + "name": "ζ ‡ε‡†εŒ–" + } + }, + "outputs": { + "0": { + "name": "εˆ†ε—ε›Ύεƒ" + }, + "1": { + "name": "ιη½©" + }, + "2": { + "overlap": "重叠BBox" + }, + "3": { + "name": "εˆ†ε—ζ€»ζ•°" + } + } + }, + "easy imageTilesFromBatch": { + "display_name": "δ»Žε›Ύεƒζ‰Ήζ¬‘θŽ·ε–εˆ†ε—", + "inputs": { + "tiles": { + "name": "εˆ†ε—" + }, + "masks": { + "name": "ιη½©" + }, + "overlap": { + "name": "重叠位η½" + }, + "index": { + "name": "η΄’εΌ•" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + } + } + }, + "easy imagecropFromMask": { + "display_name": "图像ιη½©θ£ε‰ͺ", + "inputs": { + "image": { + "name": "图像" + }, + "mask": { + "name": "ιη½©" + }, + "image_crop_multi": { + "name": "图像裁ε‰ͺδΉ˜ζ•°" + }, + "mask_crop_multi": { + "name": "ιη½©θ£ε‰ͺδΉ˜ζ•°" + }, + "bbox_smooth_alpha": { + "name": "BBoxεΉ³ζ»‘Alpha" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + }, + "2": { + "name": "BBox" + } + } + }, + "easy imageCropFromMask": { + "display_name": "ιη½©θ£ε‰ͺ图像", + "inputs": { + "image": { + "name": "图像" + }, + "mask": { + "name": "ιη½©" + }, + "image_crop_multi": { + "name": "图像裁ε‰ͺδΉ˜ζ•°" + }, + "mask_crop_multi": { + "name": "ιη½©θ£ε‰ͺδΉ˜ζ•°" + }, + "bbox_smooth_alpha": { + "name": "BBoxεΉ³ζ»‘Alpha" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + }, + "2": { + "name": "BBox" + } + } + }, + "easy imageUncropFromBBOX": { + "display_name": "图像BBoxUncrop", + "inputs": { + "original_image": { + "name": "εŽŸε›Ύεƒ" + }, + "crop_image": { + "name": "裁ε‰ͺ图像" + }, + "bbox": { + "name": "BBox" + }, + "optional_mask": { + "name": "ιη½©(可选)" + }, + "border_blending": { + "name": "θΎΉζ‘†θžεˆ" + }, + "use_square_mask": { + "name": "ζ–Ήε½’ιη½©" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imagePixelPerfect": { + "display_name": "图像εŒηΎŽεƒη΄ ", + "inputs": { + "resize_mode": { + "name": "拉伸樑式" + }, + "Just Resize": { + "name": "仅拉伸" + }, + "Crop and Resize": { + "name": "裁ε‰ͺ幢拉伸" + }, + "Resize and Fill": { + "name": "拉伸幢呫充" + }, + "image": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "εˆ†θΎ¨ηŽ‡οΌˆζ•΄ζ•°οΌ‰" + } + } + }, + "easy imageSave": { + "display_name": "ε›ΎεƒδΏε­˜", + "inputs": { + "images": { + "name": "图像" + }, + "filename_prefix": { + "name": "文仢名前缀" + }, + "only_preview": { + "name": "δ»…ι’„θ§ˆ" + } + } + }, + "easy imageRemBg": { + "display_name": "ε›ΎεƒθƒŒζ™―η§»ι™€", + "inputs": { + "images": { + "name": "图像" + }, + "rem_mode": { + "name": "移陀樑式" + }, + "image_output": { + "name": "图像输出" + }, + "add_background": { + "name": "ζ·»εŠ θƒŒζ™―" + }, + "refine_foreground": { + "name": "δΌ˜εŒ–ε‰ζ™―" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + } + } + }, + "easy imageChooser": { + "display_name": "图像选择器", + "inputs": { + "images": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageColorMatch": { + "display_name": "ε›Ύεƒι’œθ‰²εŒΉι…", + "inputs": { + "image_ref": { + "name": "参考图像" + }, + "image_target": { + "name": "η›ζ ‡ε›Ύεƒ" + }, + "method": { + "name": "方法" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageDetailTransfer": { + "display_name": "ε›Ύεƒη»†θŠ‚θΏη§»", + "inputs": { + "target": { + "name": "η›ζ ‡ε›Ύεƒ" + }, + "source": { + "name": "源图像" + }, + "mask": { + "name": "ιη½©" + }, + "mode": { + "name": "樑式" + }, + "blur_sigma": { + "name": "樑糊" + }, + "blend_factor": { + "name": "混合" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy imageInterrogator": { + "display_name": "ε›ΎεƒεζŽ¨", + "inputs": { + "image": { + "name": "图像" + }, + "mode": { + "name": "樑式" + }, + "use_lowvram": { + "name": "低显存" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + } + } + }, + "easy imageToBase64": { + "display_name": "ε›Ύεƒεˆ°Base64", + "inputs": { + "image": { + "name": "图像" + } + }, + "outputs": { + "0": { + "name": "字符串" + } + } + }, + "easy joinImageBatch": { + "display_name": "εˆεΉΆε›Ύεƒζ‰Ήζ¬‘", + "inputs": { + "images": { + "name": "图像" + }, + "mode": { + "name": "合幢樑式" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy removeLocalImage": { + "display_name": "η§»ι™€ζœ¬εœ°ε›Ύεƒ", + "inputs": { + "file_name": { + "name": "文仢名" + } + } + }, + "easy makeImageForICLora": { + "display_name": "刢作ICLora图像", + "inputs": { + "image_1": { + "name": "图像1" + }, + "image_2": { + "name": "图像2" + }, + "mask_1": { + "name": "ιη½©1" + }, + "mask_2": { + "name": "ιη½©2" + }, + "direction": { + "name": "方向" + }, + "pixels": { + "name": "ι™εˆΆεƒη΄ " + }, + "method": { + "name": "ι™εˆΆζ–ΉεΌ" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + }, + "2": { + "name": "δΈŠδΈ‹ζ–‡ιη½©" + }, + "3": { + "name": "ε½" + }, + "4": { + "name": "高" + } + } + }, + "easy poseEditor": { + "display_name": "ε§ΏεŠΏηΌ–θΎ‘ε™¨", + "inputs": { + "image": { + "name": "图像" + }, + "Add pose": { + "name": "添加姿势" + }, + "Reset pose": { + "name": "重η½ε§ΏεŠΏ" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy loadImageBase64": { + "display_name": "εŠ θ½½ε›ΎεƒοΌˆBase64οΌ‰", + "inputs": { + "base64_data": { + "name": "Bae64ζ•°ζ" + }, + "image_output": { + "name": "图像输出" + } + }, + "outputs": { + "0": { + "name": "图像" + }, + "1": { + "name": "ιη½©" + } + } + }, + "easy applyFooocusInpaint": { + "display_name": "应用Fooocusε±€ιƒ¨ι‡η»˜", + "inputs": { + "model": { + "name": "ζ¨‘εž‹" + }, + "latent": { + "name": "LATENT" + }, + "head": { + "name": "head" + }, + "patch": { + "name": "patch" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹" + } + } + }, + "easy applyBrushNet": { + "display_name": "应用BrushNet", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "mask": { + "name": "ιη½©" + }, + "brushnet": { + "name": "BrushNet" + }, + "dtype": { + "name": "ε‰ͺζžη±»εž‹" + }, + "scale": { + "name": "ηΌ©ζ”Ύ" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy applyPowerPaint": { + "display_name": "应用PowerPaint", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "mask": { + "name": "ιη½©" + }, + "powerpaint_model": { + "name": "PowerPaintζ¨‘εž‹" + }, + "powerpaint_clip": { + "name": "PowerPaintCLIP" + }, + "dtype": { + "name": "ε‰ͺζžη±»εž‹" + }, + "fitting": { + "name": "适应" + }, + "function": { + "name": "樑式" + }, + "scale": { + "name": "ηΌ©ζ”Ύ" + }, + "start_at": { + "name": "开始位η½" + }, + "end_at": { + "name": "η»“ζŸδ½η½" + }, + "save_memory": { + "name": "save_memory" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy applyInpaint": { + "display_name": "应用Inpaint", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + }, + "image": { + "name": "图像" + }, + "mask": { + "name": "ιη½©" + }, + "inpaint_mode": { + "name": "樑式" + }, + "encode": { + "name": "编码" + }, + "grow_mask_by": { + "name": "ιη½©ζ‰©ε±•" + } + }, + "outputs": { + "0": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy showSpentTime": { + "display_name": "ζ˜Ύη€ΊζŽ¨η†ζ—Άι—΄", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + } + } + }, + "easy showLoaderSettingsNames": { + "display_name": "ζ˜Ύη€ΊεŠ θ½½ε™¨ε‚ζ•°εη§°", + "inputs": { + "pipe": { + "name": "θŠ‚η‚ΉζŸ" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹εη§°" + }, + "1": { + "name": "VAE名称" + }, + "2": { + "name": "LORA名称" + } + } + }, + "easy sliderControl": { + "display_name": "滑村青板", + "inputs": { + "mode": { + "name": "樑式" + }, + "model_type": { + "name": "ζ¨‘εž‹η±»εž‹" + } + }, + "outputs": { + "0": { + "name": "层权重" + } + } + }, + "easy ckptNames": { + "display_name": "ckptεη§°εˆ—θ‘¨", + "inputs": { + "ckpt_name": { + "name": "ζ¨‘εž‹εη§°" + } + }, + "outputs": { + "0": { + "name": "ζ¨‘εž‹εη§°" + } + } + }, + "easy controlnetNames": { + "display_name": "ControlNetεη§°εˆ—θ‘¨", + "inputs": { + "controlnet_name": { + "name": "ControlNet名称" + } + }, + "outputs": { + "0": { + "name": "ControlNet名称" + } + } + }, + "easy string": { + "display_name": "字符串", + "inputs": { + "value": { + "name": "ε€Ό" + } + }, + "outputs": { + "0": { + "name": "字符串" + } + } + }, + "easy int": { + "display_name": "ζ•΄ζ•°", + "inputs": { + "value": { + "name": "ε€Ό" + } + }, + "outputs": { + "0": { + "name": "ζ•΄ζ•°" + } + } + }, + "easy rangeInt": { + "display_name": "ζ•΄ζ•°οΌˆθŒƒε›΄οΌ‰", + "inputs": { + "range_mode": { + "name": "θŒƒε›΄ζ¨‘εΌ" + }, + "start": { + "name": "开始" + }, + "stop": { + "name": "终歒" + }, + "step": { + "name": "ζ­₯θΏ›" + }, + "end_mode": { + "name": "η»“ζŸζ¨‘εΌ" + }, + "num_steps": { + "name": "ζ­₯进数量" + } + }, + "outputs": { + "0": { + "name": "θŒƒε›΄" + }, + "1": { + "name": "θŒƒε›΄ε€§ε°" + } + } + }, + "easy float": { + "display_name": "ζ΅η‚Ήζ•°", + "inputs": { + "value": { + "name": "ε€Ό" + } + }, + "outputs": { + "0": { + "name": "ζ΅η‚Ή" + } + } + }, + "easy rangeFloat": { + "display_name": "ζ΅η‚Ήζ•°οΌˆθŒƒε›΄οΌ‰", + "inputs": { + "range_mode": { + "name": "θŒƒε›΄ζ¨‘εΌ" + }, + "start": { + "name": "开始" + }, + "stop": { + "name": "终歒" + }, + "step": { + "name": "ζ­₯θΏ›" + }, + "end_mode": { + "name": "η»“ζŸζ¨‘εΌ" + }, + "num_steps": { + "name": "ζ­₯进数量" + } + }, + "outputs": { + "0": { + "name": "θŒƒε›΄" + }, + "1": { + "name": "θŒƒε›΄ε€§ε°" + } + } + }, + "easy whileLoopStart": { + "display_name": "WhileεΎͺ环-开始", + "inputs": { + "condition": { + "name": "开始εΎͺ环" + } + }, + "outputs": { + "0": { + "name": "开始" + } + } + }, + "easy whileLoopEnd": { + "display_name": "WhileεΎͺ环-η»“ζŸ", + "inputs": { + "flow": { + "name": "η»“ζŸ" + }, + "condition": { + "name": "继续εΎͺ环" + } + }, + "outputs": { + } + }, + "easy forLoopStart": { + "display_name": "ForεΎͺ环-开始", + "inputs": { + "total": { + "name": "总量" + } + }, + "outputs": { + "0": { + "name": "开始" + }, + "1": { + "name": "η΄’εΌ•" + } + } + }, + "easy forLoopEnd": { + "display_name": "ForεΎͺ环-η»“ζŸ", + "inputs": { + "flow": { + "name": "η»“ζŸ" + } + }, + "outputs": { + "0": { + "name": "ε€Ό1" + } + } + }, + "easy ab": { + "display_name": "A ζˆ– B", + "inputs": { + "in":{ + "name": "θΎ“ε…₯" + } + } + }, + "easy blocker": { + "display_name": "ι˜»ε‘žε™¨", + "inputs": { + "in":{ + "name": "θΎ“ε…₯" + }, + "continue": { + "name": "是否继续" + } + }, + "outputs": { + "0": { + "name": "θΎ“ε‡Ί" + } + } + }, + "easy ifElse": { + "display_name": "ζ˜―ε¦εˆ€ζ–­", + "inputs": { + "on_true": { + "name": "ηœŸζ΅η¨‹" + }, + "on_false": { + "name": "假桁程" + }, + "boolean": { + "name": "是否为真" + } + } + }, + "easy isNone": { + "display_name": "是否为空", + "inputs": { + "value": { + "any": "任何" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”ε€Ό" + } + } + }, + "easy isFileExist": { + "display_name": "ζ–‡δ»Άζ˜―ε¦ε­˜εœ¨", + "inputs": { + "file_path": { + "name": "ζ–‡δ»Άθ·―εΎ„" + }, + "file_name": { + "name": "文仢名" + }, + "file_extension": { + "name": "文仢扩展名" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”ε€Ό" + } + } + }, + "easy isMaskEmpty": { + "display_name": "ιη½©ζ˜―否为空", + "inputs": { + "mask": { + "name": "ιη½©" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”ε€Ό" + } + } + }, + "easy outputToList": { + "display_name": "θΎ“ε‡Ίεˆ°εˆ—θ‘¨", + "inputs": { + "tuple": { + "name": "ε…ƒη»„" + } + }, + "outputs": { + "0": { + "name": "εˆ—θ‘¨" + } + } + }, + "easy pixels": { + "display_name": "ζ ‡ε‡†εŒ–εƒη΄ ", + "inputs": { + "resolution": { + "name": "εˆ†θΎ¨ηŽ‡" + }, + "width": { + "name": "ε½εΊ¦" + }, + "height": { + "name": "高度" + }, + "scale": { + "name": "ηΌ©ζ”Ύη³»ζ•°" + }, + "flip_w/h": { + "name": "翻转ε½ι«˜" + } + }, + "outputs": { + "0": { + "name": "ε½εΊ¦ζ ‡ε‡†εŒ–" + }, + "1": { + "name": "ι«˜εΊ¦ζ ‡ε‡†εŒ–" + }, + "2": { + "name": "ε½εΊ¦" + }, + "3": { + "name": "高度" + }, + "4": { + "name": "放倧系数" + } + } + }, + "easy boolean": { + "display_name": "εΈƒε°”", + "inputs": { + "value": { + "name": "ε€Ό" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”" + } + } + }, + "easy compare": { + "display_name": "ζ―”θΎƒ", + "inputs": { + "a": { + "name": "A" + }, + "b": { + "name": "B" + }, + "comparison": { + "name": "比较方式" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”" + } + } + }, + "easy imageSwitch": { + "display_name": "ε›Ύεƒεˆ‡ζ’", + "inputs": { + "image_a": { + "name": "图像_A" + }, + "image_b": { + "name": "图像_B" + }, + "boolean": { + "name": "εΈƒε°”" + } + }, + "outputs": { + "0": { + "name": "图像" + } + } + }, + "easy textSwitch": { + "display_name": "ζ–‡ζœ¬εˆ‡ζ’", + "inputs": { + "text1": { + "name": "ζ–‡ζœ¬_1" + }, + "text2": { + "name": "ζ–‡ζœ¬_2" + }, + "input": { + "name": "θΎ“ε…₯" + } + }, + "outputs": { + "0": { + "name": "字符串" + } + } + }, + "easy anythingInversedSwitch": { + "display_name": "δ»»δ½•εθ½¬εˆ‡ζ’", + "inputs": { + "in": { + "name": "θΎ“ε…₯" + }, + "index": { + "name": "η΄’εΌ•" + } + } + }, + "easy isSDXL": { + "display_name": "εˆ€ζ–­SDXL", + "inputs": { + "optional_pipe": { + "name": "θŠ‚η‚ΉζŸ(可选)" + }, + "optional_clip": { + "name": "CLIP(可选)" + } + }, + "outputs": { + "0": { + "name": "εΈƒε°”" + } + } + }, + "easy xyAny": { + "display_name": "任意XYθΎ“ε…₯", + "inputs": { + "X": { + "name": "X" + }, + "Y": { + "name": "Y" + }, + "direction": { + "name": "方向" + } + }, + "outputs": { + "0": { + "name": "X" + }, + "1": { + "name": "Y" + } + } + }, + "easy lengthAnything": { + "display_name": "任何长度", + "inputs": { + "any": { + "name": "θΎ“ε…₯任何" + } + }, + "outputs": { + "0": { + "name": "ι•ΏεΊ¦" + } + } + }, + "easy indexAnything": { + "display_name": "任何紒引", + "inputs": { + "any": { + "name": "θΎ“ε…₯任何" + }, + "index": { + "name": "η΄’εΌ•" + } + }, + "outputs": { + "0": { + "name": "θΎ“ε‡Ί" + } + } + }, + "easy batchAnything": { + "display_name": "δ»»δ½•ζ‰Ήζ¬‘η»„εˆ", + "inputs": { + "any_1": { + "name": "任何1" + }, + "any_2": { + "name": "任何2" + } + }, + "outputs": { + "0": { + "name": "批欑" + } + } + }, + "easy convertAnything": { + "display_name": "转捒任何", + "inputs": { + "anything": { + "name": "θΎ“ε…₯任何" + }, + "output_type": { + "name": "θΎ“ε‡Ίη±»εž‹" + } + } + }, + "easy showAnything": { + "display_name": "展瀺任何", + "inputs": { + "anything": { + "name": "θΎ“ε…₯任何" + } + }, + "outputs": { + "0": { + "name": "θΎ“ε‡Ί" + } + } + }, + "easy showTensorShape": { + "display_name": "显瀺Tensor归犢", + "inputs": { + "tensor": { + "name": "Tensor" + } + } + }, + "easy clearCacheKey": { + "display_name": "ζΈ…ι™€ηΌ“ε­˜ι”", + "inputs": { + "anything": { + "name": "θΎ“ε…₯任何" + }, + "cache_key": { + "name": "ηΌ“ε­˜ι”" + } + } + }, + "easy clearCacheAll": { + "display_name": "ζΈ…ι™€ε…¨ιƒ¨ηΌ“ε­˜", + "inputs": { + "anything": { + "name": "θΎ“ε…₯任何" + } + } + }, + "easy cleanGpuUsed": { + "display_name": "ζΈ…η†ζ˜Ύε­˜ε η”¨", + "inputs": { + "anything": { + "name": "θΎ“ε…₯任何" + } + } + }, + "easy saveText": { + "display_name": "δΏε­˜ζ–‡ζœ¬", + "inputs": { + "image": { + "name": "图像(可选)" + }, + "text": { + "name": "ζ–‡ζœ¬" + }, + "output_file_path": { + "name": "θΎ“ε‡Ίζ–‡δ»Άθ·―εΎ„" + }, + "filename": { + "name": "文仢名" + }, + "overwrite": { + "name": "ζ˜―ε¦θ¦†η›–" + } + }, + "outputs": { + "0": { + "name": "ζ–‡ζœ¬" + }, + "1": { + "name": "图像" + } + } + }, + "easy sleep": { + "display_name": "ε»ΆθΏŸζ‰§θ‘Œ", + "inputs": { + "any": { + "name": "θΎ“ε…₯任何" + }, + "delay": { + "name": "η§’ζ•°" + } + }, + "outputs": { + "0": { + "name": "θΎ“ε‡Ί" + } + } + }, + "easy fluxPromptGenAPI": { + "display_name": "ζη€Ίθ―ζ‰©ε†™οΌˆFLUXAIοΌ‰", + "inputs": { + "cookie_override": { + "name": "Cookie可选" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + } + } + }, + "easy joyCaption2API": { + "display_name": "JoyCaption2οΌˆη‘…εŸΊζ΅εŠ¨οΌ‰", + "inputs": { + "image": { + "name": "图像" + }, + "do_sample": { + "name": "ζ‰§θ‘Œι‡‡ζ ·" + }, + "temperature": { + "name": "ζΈ©εΊ¦" + }, + "max_tokens": { + "name": "ζœ€ε€§θ―ε…ƒζ•°" + }, + "caption_type": { + "name": "ζη€Ίθ―η±»εž‹" + }, + "caption_length": { + "name": "提瀺词长度" + }, + "name_input": { + "name": "名称输ε…₯" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + } + } + }, + "easy joyCaption3API": { + "display_name": "JoyCaption3οΌˆη‘…εŸΊζ΅εŠ¨οΌ‰", + "inputs": { + "image": { + "name": "图像" + }, + "do_sample": { + "name": "ζ‰§θ‘Œι‡‡ζ ·" + }, + "temperature": { + "name": "ζΈ©εΊ¦" + }, + "max_tokens": { + "name": "ζœ€ε€§θ―ε…ƒζ•°" + }, + "caption_type": { + "name": "ζη€Ίθ―η±»εž‹" + }, + "caption_length": { + "name": "提瀺词长度" + }, + "name_input": { + "name": "名称输ε…₯" + } + }, + "outputs": { + "0": { + "name": "提瀺词" + } + } + }, + "easy if": { + "display_name": "Ifεˆ€ζ–­", + "inputs": { + "any": { + "name": "θΎ“ε…₯任何" + } + } + } +} diff --git a/custom_nodes/ComfyUI-Easy-Use/locales/zh/settings.json b/custom_nodes/ComfyUI-Easy-Use/locales/zh/settings.json new file mode 100644 index 00000000..013cd1af --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/locales/zh/settings.json @@ -0,0 +1,86 @@ +{ + "EasyUse_Hotkeys_AddGroup": { + "name": "启用 Shift+g ι”ε°†ι€‰δΈ­ηš„θŠ‚η‚Ήζ·»εŠ δΈ€δΈͺη»„", + "tooltip": "从v1.2.39εΌ€ε§‹οΌŒε―δ»₯使用Ctrl+g代替" + }, + "EasyUse_Hotkeys_cleanVRAMUsed": { + "name": "启用 Shift+r ι”εΈθ½½ζ¨‘εž‹ε’ŒθŠ‚η‚ΉηΌ“ε­˜" + }, + "EasyUse_Hotkeys_toggleNodesMap": { + "name": "启用 Shift+m ι”ζ˜Ύιšη‘η†θŠ‚η‚Ήη»„" + }, + "EasyUse_Hotkeys_AlignSelectedNodes": { + "name": "启用 Shift+上/δΈ‹/ε·¦/右 ε’Œ Shift+Ctrl+Alt+ε·¦/右 ι”ε―Ήι½ι€‰δΈ­ηš„θŠ‚η‚Ή", + "tooltip": "Shift+上/δΈ‹/ε·¦/右 可δ»₯ε―Ήι½ι€‰δΈ­ηš„θŠ‚η‚Ή, Shift+Ctrl+Alt+ε·¦/右 可δ»₯ζ°΄εΉ³/εž‚η›΄εˆ†εΈƒθŠ‚η‚Ή" + }, + "EasyUse_Hotkeys_NormalizeSelectedNodes": { + "name": "启用 Shift+Ctrl+ε·¦/右 ι”θ§„θŒƒεŒ–ι€‰δΈ­ηš„θŠ‚η‚Ή", + "tooltip": "启用 Shift+Ctrl+ε·¦ ι”θ§„θŒƒεŒ–ε½εΊ¦ε’Œ Shift+Ctrl+右 ι”θ§„θŒƒεŒ–ι«˜εΊ¦" + }, + "EasyUse_Hotkeys_NodesTemplate": { + "name": "启用 Alt+1~9 δ»ŽθŠ‚η‚Ήζ¨‘ζΏη²˜θ΄΄εˆ°ε·₯作桁中" + }, + "EasyUse_Hotkeys_JumpNearestNodes": { + "name": "启用 上/δΈ‹/ε·¦/右 ι”θ·³θ½¬εˆ°ζœ€θΏ‘ηš„ε‰εŽθŠ‚η‚Ή" + }, + "EasyUse_ContextMenu_SubDirectories": { + "name": "ε―η”¨δΈŠδΈ‹ζ–‡θœε•θ‡ͺ动塌ε₯—子η›ε½•" + }, + "EasyUse_ContextMenu_ModelsThumbnails": { + "name": "ε―εŠ¨ζ¨‘εž‹ι’„θ§ˆε›Ύζ˜Ύη€Ί" + }, + "EasyUse_ContextMenu_NodesSort": { + "name": "启用右ι”θœε•δΈ­ζ–°ε»ΊθŠ‚η‚ΉA~ZζŽ’εΊ" + }, + "EasyUse_ContextMenu_QuickOptions": { + "name": "在右ι”θœε•δΈ­δ½Ώη”¨δΈ‰δΈͺεΏ«ζ·ζŒ‰ι’", + "options": { + "At the forefront": "εœ¨ζœ€ε‰ι’", + "At the end": "εœ¨ζœ€εŽι’", + "Disable": "禁用" + } + }, + "EasyUse_Nodes_Runtime": { + "name": "ε―εŠ¨θŠ‚η‚ΉθΏθ‘Œζ—Άι—΄ζ˜Ύη€Ί" + }, + "EasyUse_Nodes_ChainGetSet": { + "name": "ε―η”¨ε°†θŽ·ε–η‚Ήε’ŒθΎη½η‚ΉδΈŽηˆΆθŠ‚η‚Ήι“Ύεœ¨δΈ€θ΅·" + }, + "EasyUse_NodesMap_Sorting": { + "name": "η‘η†θŠ‚η‚Ήη»„ζŽ’εΊζ¨‘εΌ", + "tooltip": "默θ€θ‡ͺεŠ¨ζŽ’εΊοΌŒε¦‚ζžœθΎη½δΈΊζ‰‹εŠ¨οΌŒη»„可δ»₯ζ‹–ζ”ΎεΉΆδΏε­˜ζŽ’εΊη»“ζžœγ€‚", + "options": { + "Auto sorting": "θ‡ͺεŠ¨ζŽ’εΊ", + "Manual drag&drop sorting": "ζ‰‹εŠ¨ζ‹–ζ‹½ζŽ’εΊ" + } + }, + "EasyUse_NodesMap_DisplayNodeID": { + "name": "ε―η”¨θŠ‚η‚ΉID显瀺" + }, + "EasyUse_NodesMap_DisplayGroupOnly": { + "name": "δ»…ζ˜Ύη€Ίη»„" + }, + "EasyUse_NodesMap_Enable": { + "name": "启用η‘η†θŠ‚η‚Ήη»„", + "tooltip": "ζ‚¨ιœ€θ¦εˆ·ζ–°ι‘΅ι’δ»₯ζˆεŠŸζ›΄ζ–°" + }, + "EasyUse_StylesSelector_DisplayType": { + "name": "ζ ·εΌι€‰ζ‹©ε™¨ζ˜Ύη€Ίη±»εž‹", + "tooltip": "ζ ·εΌι€‰ζ‹©ε™¨ζ˜Ύη€Ίη±»εž‹οΌŒε¦‚ζžœθΎη½δΈΊβ€œη½‘ζ Όβ€οΌŒεˆ™ζ˜Ύη€ΊδΈΊη½‘ζ ΌοΌŒε¦‚ζžœθΎη½δΈΊβ€œεˆ—θ‘¨β€οΌŒεˆ™ζ˜Ύη€ΊδΈΊεˆ—θ‘¨", + "options": { + "Grid": "网格", + "List": "εˆ—θ‘¨" + } + }, + "EasyUse_MultiAngle_InvertRotate": { + "name": "启用反转旋转樑式", + "tooltip": "εœ¨ε€šθ§’εΊ¦θŠ‚η‚ΉδΈ­ε―η”¨εθ½¬ζ—‹θ½¬ζ¨‘εΌοΌŒδ½Ώζ—‹θ½¬ζ–Ήε‘δΈŽε€§ε€šζ•°3D软仢一致" + }, + "EasyUse_MultiAngle_HollowMode": { + "name": "ε―η”¨ε€šθ§’εΊ¦ι•‚η©Ίε±•η€Ίζ¨‘εΌ", + "tooltip": "εœ¨ε€šθ§’εΊ¦θŠ‚η‚ΉδΈ­ε―η”¨ι•‚η©Ίε±•η€Ίζ¨‘εΌοΌŒε―δ»₯ζ›΄η›΄θ§‚εœ°ζŸ₯ηœ‹η›ΈζœΊθ§’εΊ¦" + }, + "EasyUse_MultiAngle_AddAnglePrompt": { + "name": "ε―η”¨ζ·»εŠ ε€šθ§’εΊ¦ζη€Ίθ―" + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/prestartup_script.py b/custom_nodes/ComfyUI-Easy-Use/prestartup_script.py new file mode 100644 index 00000000..07194214 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/prestartup_script.py @@ -0,0 +1,34 @@ +import folder_paths +import os +def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions): + for full_folder_path in full_folder_paths: + folder_paths.add_model_folder_path(folder_name, full_folder_path) + if folder_name in folder_paths.folder_names_and_paths: + current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name] + updated_extensions = current_extensions | extensions + folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions) + else: + folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions) + +image_suffixs = set([".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff", ".svg", ".ico", ".apng", ".tif", ".hdr", ".exr"]) + +model_path = folder_paths.models_dir +add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("ultralytics_segm", [os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("ultralytics", [os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("mmdets_bbox", [os.path.join(model_path, "mmdets", "bbox")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("mmdets_segm", [os.path.join(model_path, "mmdets", "segm")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("mmdets", [os.path.join(model_path, "mmdets")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("sams", [os.path.join(model_path, "sams")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("onnx", [os.path.join(model_path, "onnx")], {'.onnx'}) +add_folder_path_and_extensions("instantid", [os.path.join(model_path, "instantid")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("pulid", [os.path.join(model_path, "pulid")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("layer_model", [os.path.join(model_path, "layer_model")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("rembg", [os.path.join(model_path, "rembg")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("ipadapter", [os.path.join(model_path, "ipadapter")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("dynamicrafter_models", [os.path.join(model_path, "dynamicrafter_models")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("mediapipe", [os.path.join(model_path, "mediapipe")], set(['.tflite','.pth'])) +add_folder_path_and_extensions("inpaint", [os.path.join(model_path, "inpaint")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("prompt_generator", [os.path.join(model_path, "prompt_generator")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("t5", [os.path.join(model_path, "t5")], folder_paths.supported_pt_extensions) +add_folder_path_and_extensions("llm", [os.path.join(model_path, "LLM")], folder_paths.supported_pt_extensions) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/__init__.py new file mode 100644 index 00000000..4c43e998 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/__init__.py @@ -0,0 +1,6 @@ +from .libs.loader import easyLoader +from .libs.sampler import easySampler + +sampler = easySampler() +easyCache = easyLoader() + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/config.py b/custom_nodes/ComfyUI-Easy-Use/py/config.py new file mode 100644 index 00000000..dee0a4bf --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/config.py @@ -0,0 +1,407 @@ +import os +import folder_paths +from pathlib import Path + +BASE_RESOLUTIONS = [ + ("width", "height"), + (512, 512), + (512, 768), + (576, 1024), + (768, 512), + (768, 768), + (768, 1024), + (768, 1280), + (768, 1344), + (768, 1536), + (816, 1920), + (832, 1152), + (832, 1216), + (896, 1152), + (896, 1088), + (1024, 1024), + (1024, 576), + (1024, 768), + (1080, 1920), + (1440, 2560), + (1088, 896), + (1216, 832), + (1152, 832), + (1152, 896), + (1280, 768), + (1344, 768), + (1536, 640), + (1536, 768), + (1920, 816), + (1920, 1080), + (2560, 1440), +] +MAX_SEED_NUM = 1125899906842624 + + +RESOURCES_DIR = os.path.join(Path(__file__).parent.parent, "resources") + +# inpaint +INPAINT_DIR = os.path.join(folder_paths.models_dir, "inpaint") +FOOOCUS_STYLES_DIR = os.path.join(Path(__file__).parent.parent, "styles") +FOOOCUS_STYLES_SAMPLES = 'https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/' +FOOOCUS_INPAINT_HEAD = { + "fooocus_inpaint_head": { + "model_url": "https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/fooocus_inpaint_head.pth" + } +} +FOOOCUS_INPAINT_PATCH = { + "inpaint_v26 (1.32GB)": { + "model_url": "https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch" + }, + "inpaint_v25 (2.58GB)": { + "model_url": "https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v25.fooocus.patch" + }, + "inpaint (1.32GB)": { + "model_url": "https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch" + }, +} +BRUSHNET_MODELS = { + "random_mask": { + "sd1": { + "model_url": "https://huggingface.co/Kijai/BrushNet-fp16/resolve/main/brushnet_random_mask_fp16.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/yolain/brushnet/resolve/main/brushnet_random_mask_sdxl.safetensors" + } + }, + "segmentation_mask": { + "sd1": { + "model_url": "https://huggingface.co/Kijai/BrushNet-fp16/resolve/main/brushnet_segmentation_mask_fp16.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/yolain/brushnet/resolve/main/brushnet_segmentation_mask_sdxl.safetensors" + } + } +} +POWERPAINT_MODELS = { + "base_fp16": { + "model_url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/text_encoder/model.fp16.safetensors" + }, + "v2.1": { + "model_url": "https://huggingface.co/JunhaoZhuang/PowerPaint-v2-1/resolve/main/PowerPaint_Brushnet/diffusion_pytorch_model.safetensors", + "clip_url": "https://huggingface.co/JunhaoZhuang/PowerPaint-v2-1/resolve/main/PowerPaint_Brushnet/pytorch_model.bin", + } +} + +# layerDiffuse +LAYER_DIFFUSION_DIR = os.path.join(folder_paths.models_dir, "layer_model") +LAYER_DIFFUSION_VAE = { + "encode": { + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/vae_transparent_encoder.safetensors" + } + }, + "decode": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_vae_transparent_decoder.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/vae_transparent_decoder.safetensors" + } + } +} +LAYER_DIFFUSION = { + "Attention Injection": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_transparent_attn.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_attn.safetensors" + }, + }, + "Conv Injection": { + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_conv.safetensors" + }, + "sd1": { + "model_url": None + } + }, + "Everything": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_joint.safetensors" + }, + "sdxl": { + "model_url": None + } + }, + "Foreground": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_fg2bg.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_fg2ble.safetensors" + } + }, + "Foreground to Background": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_fg2bg.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_fgble2bg.safetensors" + } + }, + "Background": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_bg2fg.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_bg2ble.safetensors" + } + }, + "Background to Foreground": { + "sd1": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_bg2fg.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_bgble2fg.safetensors" + } + }, +} + +# IC Light +IC_LIGHT_MODELS = { + "Foreground": { + "sd1": { + "model_url": "https://huggingface.co/huchenlei/IC-Light-ldm/resolve/main/iclight_sd15_fc_unet_ldm.safetensors" + }, + "sdxl": { + "model_url": None + } + }, + "Foreground&Background": { + "sd1": { + "model_url": "https://huggingface.co/huchenlei/IC-Light-ldm/resolve/main/iclight_sd15_fbc_unet_ldm.safetensors" + }, + "sdxl": { + "model_url": None + } + } +} + + +# REMBG +REMBG_DIR = os.path.join(folder_paths.models_dir, "rembg") +REMBG_MODELS = { + "RMBG-1.4": { + "model_url": "https://huggingface.co/briaai/RMBG-1.4/resolve/main/model.pth" + }, + "RMBG-2.0": { + "model_url": "briaai/RMBG-2.0" + }, + "BEN2": { + "model_url": "https://huggingface.co/PramaLLC/BEN2/resolve/main/BEN2_Base.pth" + } +} + +#ipadapter +IPADAPTER_DIR = os.path.join(folder_paths.models_dir, "ipadapter") +IPADAPTER_MODELS = { + "LIGHT - SD1.5 only (low strength)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light_v11.bin" + }, + "sdxl": { + "model_url": "" + } + }, + "STANDARD (medium strength)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors" + } + }, + "VIT-G (medium strength)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors" + } + }, + "PLUS (high strength)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors" + } + }, + "PLUS (kolors genernal)": { + "sd1": { + "model_url": "" + }, + "sdxl": { + "model_url":"https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus/resolve/main/ip_adapter_plus_general.bin" + } + }, + "REGULAR - FLUX and SD3.5 only (high strength)": { + "flux": { + "model_url": "https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter/resolve/main/ip-adapter.bin", + "model_file_name": "ip-adapter_flux_1_dev.bin", + }, + "sd3": { + "model_url": "https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter/resolve/main/ip-adapter.bin", + "model_file_name": "ip-adapter_sd35.bin", + }, + }, + "PLUS FACE (portraits)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors" + } + }, + "FULL FACE - SD1.5 only (portraits stronger)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors" + }, + "sdxl": { + "model_url": "" + } + }, + "FACEID": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin", + "lora_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl.bin", + "lora_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl_lora.safetensors" + } + }, + "FACEID PLUS - SD1.5 only": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15.bin", + "lora_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15_lora.safetensors" + }, + "sdxl": { + "model_url": "", + "lora_url": "" + } + }, + "FACEID PLUS V2": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15.bin", + "lora_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl.bin", + "lora_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl_lora.safetensors" + } + }, + "FACEID PLUS KOLORS":{ + "sd1":{ + + }, + "sdxl":{ + "model_url":"https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus/resolve/main/ipa-faceid-plus.bin" + } + }, + "FACEID PORTRAIT (style transfer)": { + "sd1": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait-v11_sd15.bin", + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl.bin", + } + }, + "FACEID PORTRAIT UNNORM - SDXL only (strong)": { + "sd1": { + "model_url":"" + }, + "sdxl": { + "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl_unnorm.bin", + } + }, + "COMPOSITION": { + "sd1": { + "model_url": "https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sd15.safetensors" + }, + "sdxl": { + "model_url": "https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sdxl.safetensors" + } + } +} +IPADAPTER_CLIPVISION_MODELS = { + "clip-vit-large-patch14-336":{ + "model_url": "https://huggingface.co/openai/clip-vit-large-patch14-336/resolve/main/pytorch_model.bin" + }, + "clip-vit-h-14-laion2B-s32B-b79K":{ + "model_url": "https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_model.safetensors" + }, + "sigclip_vision_patch14_384":{ + "model_url": "https://huggingface.co/Comfy-Org/sigclip_vision_384/resolve/main/sigclip_vision_patch14_384.safetensors" + } +} + +# dynamiCrafter +DYNAMICRAFTER_DIR = os.path.join(folder_paths.models_dir, "dynamicrafter_models") +DYNAMICRAFTER_MODELS = { + "dynamicrafter_unet_512 (2.98GB)": { + "model_url": "https://huggingface.co/ExponentialML/DynamiCrafterUNet/resolve/main/dynamicrafter_unet_512.safetensors", + "vae_url": "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors", + "clip_url": "https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/text_encoder/model.safetensors", + "clip_vision_url": "https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_model.safetensors", + }, + "dynamicrafter_unet_512_interp (2.98GB)": { + "model_url": "https://huggingface.co/ExponentialML/DynamiCrafterUNet/resolve/main/dynamicrafter_unet_512_interp.safetensors" + }, + "dynamicrafter_unet_1024 (2.98GB)": { + "model_url": "https://huggingface.co/ExponentialML/DynamiCrafterUNet/resolve/main/dynamicrafter_unet_1024.safetensors" + }, + "dynamicrafter_unet_256 (2.98GB)": { + "model_url": "https://huggingface.co/ExponentialML/DynamiCrafterUNet/resolve/main/dynamicrafter_unet_256.safetensors" + }, +} + +#humanParsing +HUMANPARSING_MODELS = { + "parsing_lip": { + "model_url": "https://huggingface.co/levihsu/OOTDiffusion/resolve/main/checkpoints/humanparsing/parsing_lip.onnx", + }, + "human-parts":{ + "model_url":"https://huggingface.co/Metal3d/deeplabv3p-resnet50-human/resolve/main/deeplabv3p-resnet50-human.onnx", + }, + "segformer_b3_clothes":{ + "model_name": "sayeed99/segformer_b3_clothes", + }, + "segformer_b3_fashion":{ + "model_name": "sayeed99/segformer-b3-fashion", + }, + "face_parsing":{ + "model_name": "jonathandinu/face-parsing" + } +} + +#mediapipe +MEDIAPIPE_DIR = os.path.join(folder_paths.models_dir, "mediapipe") +MEDIAPIPE_MODELS = { + "selfie_multiclass_256x256": { + "model_url": "https://huggingface.co/yolain/selfie_multiclass_256x256/resolve/main/selfie_multiclass_256x256.tflite" + } +} + + +#prompt template +PROMPT_TEMPLATE = { + "prefix": ["Detailed photo of", "Amateur photo of", "Flicker 2008 photo of", "Fantastic artwork of", + "Vintage photograph of", "Unreal 5 render of", "Surrealist painting of", + "Professional advertising design of"], + "subject": ["a man", "a woman", "a young man", "a young woman", "a handsome man", "a beautiful woman", "a monster", "a toy", "a product", "a buddha", "a dog", "a cat"], + "action": ["looking at viewer", "looking away", "looking up", "looking down", "looking back", "open mouth", "half-closed mouth", "closed mouth", "open eyes", "half-closed eyes", "closed eyes", "wink", "standing", "sitting", "lying", "walking", "running", "adjusting hair", "waving", "hand on hip", "crossed arms", "smile", "sad", "angry", "sleepy", "tired", "expressionless"], + "clothes": ["underwear", "clothed", "casual", "dress", "swimsuit", "uniform", "bikini", "one-piece swimsuit", "shirt", "blouse", "sweater", "hoodie", "jeans", "pants", "shorts", "skirt", "vest", "coat", "trenchoat", "jacket", "short dress", "long dress", "off-shoulder", "backless", "hairbow", "hair ribbon", "hair tie", "hairband", "cap", "beanie", "bucket hat", "sun hat", "straw hat", "rice hat", "witch hat", "crown", "chain necklace", "tooth necklace", "choker", "pendant", "bracelet", "watch", "ring", "earring", "anklet", "belt", "scarf", "gloves", "mittens", "socks", "stockings", "tights", "leggings", "boots", "sneakers", "heels", "sandals", "flip-flops", "slippers", "loafers", "mules", "oxfords", "brogues", "derbies", "monk shoes", "chelsea boots", "combat boots", "riding boots", "rain boots", "wedge heels", "platform heels", "stilettos", "block heels", "kitten heels", "moccasins", "espadrilles", "pumps", "flats", "ballet flats", "mary janes", "slingbacks", "peep-toe", "mule sandals", "gladiator sandals", "thong sandals", "slide sandals", "espadrille sandals", "wedge sandals", "platform sandals", "ankle boots", "knee-high boots", "over-the-knee boots", "thigh-high boots", "wellington boots", "chukka boots", "desert boots", "chelsea boots", "hiking boots", "work boots", "snow boots", "rain boots", "riding boots", "cowboy boots", "combat boots", "biker boots", "duck boots", "military boots", "western boots", "ankle strap heels", "block heels", "chunky heels", "cone heels", "kitten heels", "platform heels", "pumps", "slingback heels", "stiletto heels", "wedge heels", "mules", "slingbacks", "slides", "thong sandals", "gladiator sandals", "espadrilles", "wedge sandals", "platform sandals", "ankle boots", "knee-high boots", "over-the-knee boots", "thigh-high boots", "wellington boots", "chukka boots", "desert boots", "chelsea boots", "hiking boots", "work boots", "snow boots", "rain boots", "riding boots", "cowboy boots", "combat boots", "biker boots", "duck boots", "military boots", "western boots", "ankle strap heels", "block heels" ], + "environment": ["sunshine from window", "neon night, city", "sunset over sea", "golden time", "sci-fi RGB glowing, cyberpunk", "natural lighting", "warm atmosphere, at home, bedroom", "magic lit", "evil, gothic, in a cave", "light and shadow", "shadow from window", "soft studio lighting", "home atmosphere, cozy bedroom illumination", "neon, Wong Kar-wai, warm", "moonlight through curtains", "stormy sky lighting", "underwater glow, deep sea", "foggy forest at dawn", "golden hour in a meadow", "rainbow reflections, neon", "cozy candlelight", "apocalyptic, smoky atmosphere", "red glow, emergency lights", "mystical glow, enchanted forest", "campfire light", "harsh, industrial lighting", "sunrise in the mountains", "evening glow in the desert", "moonlight in a dark alley", "golden glow at a fairground", "midnight in the forest", "purple and pink hues at twilight", "foggy morning, muted light", "candle-lit room, rustic vibe", "fluorescent office lighting", "lightning flash in storm", "night, cozy warm light from fireplace", "ethereal glow, magical forest", "dusky evening on a beach", "afternoon light filtering through trees", "blue neon light, urban street", "red and blue police lights in rain", "aurora borealis glow, arctic landscape", "sunrise through foggy mountains", "golden hour on a city skyline", "mysterious twilight, heavy mist", "early morning rays, forest clearing", "colorful lantern light at festival", "soft glow through stained glass", "harsh spotlight in dark room", "mellow evening glow on a lake", "crystal reflections in a cave", "vibrant autumn lighting in a forest", "gentle snowfall at dusk", "hazy light of a winter morning", "soft, diffused foggy glow", "underwater luminescence", "rain-soaked reflections in city lights", "golden sunlight streaming through trees", "fireflies lighting up a summer night", "glowing embers from a forge", "dim candlelight in a gothic castle", "midnight sky with bright starlight", "warm sunset in a rural village", "flickering light in a haunted house", "desert sunset with mirage-like glow", "golden beams piercing through storm clouds"], + "background": ["cars and people", "a cozy bed and a lamp", "a forest clearing with mist", "a bustling marketplace", "a quiet beach at dusk", "an old, cobblestone street", "a futuristic cityscape", "a tranquil lake with mountains", "a mysterious cave entrance", "bookshelves and plants in the background", "an ancient temple in ruins", "tall skyscrapers and neon signs", "a starry sky over a desert", "a bustling cafΓ©", "rolling hills and farmland", "a modern living room with a fireplace", "an abandoned warehouse", "a picturesque mountain range", "a starry night sky", "the interior of a futuristic spaceship", "the cluttered workshop of an inventor", "the glowing embers of a bonfire", "a misty lake surrounded by trees", "an ornate palace hall", "a busy street market", "a vast desert landscape", "a peaceful library corner", "bustling train station", "a mystical, enchanted forest", "an underwater reef with colorful fish", "a quiet rural village", "a sandy beach with palm trees", "a vibrant coral reef, teeming with life", "snow-capped mountains in distance", "a stormy ocean, waves crashing", "a rustic barn in open fields", "a futuristic lab with glowing screens", "a dark, abandoned castle", "the ruins of an ancient civilization", "a bustling urban street in rain", "an elegant grand ballroom", "a sprawling field of wildflowers", "a dense jungle with sunlight filtering through", "a dimly lit, vintage bar", "an ice cave with sparkling crystals", "a serene riverbank at sunset", "a narrow alley with graffiti walls", "a peaceful zen garden with koi pond", "a high-tech control room", "a quiet mountain village at dawn", "a lighthouse on a rocky coast", "a rainy street with flickering lights", "a frozen lake with ice formations", "an abandoned theme park", "a small fishing village on a pier", "rolling sand dunes in a desert", "a dense forest with towering redwoods", "a snowy cabin in the mountains", "a mystical cave with bioluminescent plants", "a castle courtyard under moonlight", "a bustling open-air night market", "an old train station with steam", "a tranquil waterfall surrounded by trees", "a vineyard in the countryside", "a quaint medieval village", "a bustling harbor with boats", "a high-tech futuristic mall", "a lush tropical rainforest"], + "nsfw": ["nude", "breast", "small breast", "middle breast", "large breast", "nipples", "clothes lift", "pussy juice trail", "pussy juice puddle", "small testicles", "medium testicles", "large testicles", "disembodied penis", "cum on body", "cum inside", "cum outside", "fingering", "handjob", "fellatio", "licking penis", "paizuri", "doggystyle", "cowgirl", "reversed cowgirl", "piledriver", "suspended congress", "full nelson",], +} + +NEW_SCHEDULERS = ['align_your_steps', 'gits'] diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/add_resources.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/add_resources.py new file mode 100644 index 00000000..8d04ca3a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/add_resources.py @@ -0,0 +1,113 @@ +import urllib.parse +from os import PathLike +from aiohttp import web +from aiohttp.web_urldispatcher import AbstractRoute, UrlDispatcher +from server import PromptServer +from pathlib import Path + +# ζ–‡δ»Άι™εˆΆε€§ε°οΌˆMBοΌ‰ +max_size = 50 +def suffix_limiter(self: web.StaticResource, request: web.Request): + suffixes = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff", ".svg", ".ico", ".apng", ".tif", ".hdr", ".exr"} + rel_url = request.match_info["filename"] + try: + filename = Path(rel_url) + if filename.anchor: + raise web.HTTPForbidden() + filepath = self._directory.joinpath(filename).resolve() + if filepath.exists() and filepath.suffix.lower() not in suffixes: + raise web.HTTPForbidden(reason="File type is not allowed") + finally: + pass + +def filesize_limiter(self: web.StaticResource, request: web.Request): + rel_url = request.match_info["filename"] + try: + filename = Path(rel_url) + filepath = self._directory.joinpath(filename).resolve() + if filepath.exists() and filepath.stat().st_size > max_size * 1024 * 1024: + raise web.HTTPForbidden(reason="File size is too large") + finally: + pass +class LimitResource(web.StaticResource): + limiters = [] + + def push_limiter(self, limiter): + self.limiters.append(limiter) + + async def _handle(self, request: web.Request) -> web.StreamResponse: + try: + for limiter in self.limiters: + limiter(self, request) + except (ValueError, FileNotFoundError) as error: + raise web.HTTPNotFound() from error + + return await super()._handle(request) + + def __repr__(self) -> str: + name = "'" + self.name + "'" if self.name is not None else "" + return f' {self._directory!r}>' + +class LimitRouter(web.StaticDef): + def __repr__(self) -> str: + info = [] + for name, value in sorted(self.kwargs.items()): + info.append(f", {name}={value!r}") + return f' {self.path}{"".join(info)}>' + + def register(self, router: UrlDispatcher) -> list[AbstractRoute]: + # resource = router.add_static(self.prefix, self.path, **self.kwargs) + def add_static( + self: UrlDispatcher, + prefix: str, + path: PathLike, + *, + name=None, + expect_handler=None, + chunk_size: int = 256 * 1024, + show_index: bool = False, + follow_symlinks: bool = False, + append_version: bool = False, + ) -> web.AbstractResource: + assert prefix.startswith("/") + if prefix.endswith("/"): + prefix = prefix[:-1] + resource = LimitResource( + prefix, + path, + name=name, + expect_handler=expect_handler, + chunk_size=chunk_size, + show_index=show_index, + follow_symlinks=follow_symlinks, + append_version=append_version, + ) + resource.push_limiter(suffix_limiter) + resource.push_limiter(filesize_limiter) + self.register_resource(resource) + return resource + resource = add_static(router, self.prefix, self.path, **self.kwargs) + routes = resource.get_info().get("routes", {}) + return list(routes.values()) + +def path_to_url(path): + if not path: + return path + path = path.replace("\\", "/") + if not path.startswith("/"): + path = "/" + path + while path.startswith("//"): + path = path[1:] + path = path.replace("//", "/") + return path + +def add_static_resource(prefix, path,limit=False): + app = PromptServer.instance.app + prefix = path_to_url(prefix) + prefix = urllib.parse.quote(prefix) + prefix = path_to_url(prefix) + if limit: + route = LimitRouter(prefix, path, {"follow_symlinks": True}) + else: + route = web.static(prefix, path, follow_symlinks=True) + app.add_routes([route]) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/adv_encode.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/adv_encode.py new file mode 100644 index 00000000..0ffda3ba --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/adv_encode.py @@ -0,0 +1,427 @@ +import torch +import numpy as np +import re +import itertools + +from comfy import model_management +from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG +try: + from comfy.text_encoders.sd3_clip import SD3ClipModel, T5XXLModel +except ImportError: + from comfy.sd3_clip import SD3ClipModel, T5XXLModel + +from nodes import NODE_CLASS_MAPPINGS, ConditioningConcat, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine + +def _grouper(n, iterable): + it = iter(iterable) + while True: + chunk = list(itertools.islice(it, n)) + if not chunk: + return + yield chunk + + +def _norm_mag(w, n): + d = w - 1 + return 1 + np.sign(d) * np.sqrt(np.abs(d) ** 2 / n) + # return np.sign(w) * np.sqrt(np.abs(w)**2 / n) + + +def divide_length(word_ids, weights): + sums = dict(zip(*np.unique(word_ids, return_counts=True))) + sums[0] = 1 + weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0 + for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] + return weights + + +def shift_mean_weight(word_ids, weights): + delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x, y) if id != 0]) + weights = [[w if id == 0 else w + delta + for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] + return weights + + +def scale_to_norm(weights, word_ids, w_max): + top = np.max(weights) + w_max = min(top, w_max) + weights = [[w_max if id == 0 else (w / top) * w_max + for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] + return weights + + +def from_zero(weights, base_emb): + weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device) + weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape) + return base_emb * weight_tensor + + +def mask_word_id(tokens, word_ids, target_id, mask_token): + new_tokens = [[mask_token if wid == target_id else t + for t, wid in zip(x, y)] for x, y in zip(tokens, word_ids)] + mask = np.array(word_ids) == target_id + return (new_tokens, mask) + + +def batched_clip_encode(tokens, length, encode_func, num_chunks): + embs = [] + for e in _grouper(32, tokens): + enc, pooled = encode_func(e) + enc = enc.reshape((len(e), length, -1)) + embs.append(enc) + embs = torch.cat(embs) + embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1)) + return embs + + +def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266): + pooled_base = base_emb[0, length - 1:length, :] + wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True) + weight_dict = dict((id, w) + for id, w in zip(wids, np.array(weights).reshape(-1)[inds]) + if w != 1.0) + + if len(weight_dict) == 0: + return torch.zeros_like(base_emb), base_emb[0, length - 1:length, :] + + weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device) + weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape) + + # m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0) + # TODO: find most suitable masking token here + m_token = (m_token, 1.0) + + ws = [] + masked_tokens = [] + masks = [] + + # create prompts + for id, w in weight_dict.items(): + masked, m = mask_word_id(tokens, word_ids, id, m_token) + masked_tokens.extend(masked) + + m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device) + m = m.reshape(1, -1, 1).expand(base_emb.shape) + masks.append(m) + + ws.append(w) + + # batch process prompts + embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens)) + masks = torch.cat(masks) + + embs = (base_emb.expand(embs.shape) - embs) + pooled = embs[0, length - 1:length, :] + + embs *= masks + embs = embs.sum(axis=0, keepdim=True) + + pooled_start = pooled_base.expand(len(ws), -1) + ws = torch.tensor(ws).reshape(-1, 1).expand(pooled_start.shape) + pooled = (pooled - pooled_start) * (ws - 1) + pooled = pooled.mean(axis=0, keepdim=True) + + return ((weight_tensor - 1) * embs), pooled_base + pooled + + +def mask_inds(tokens, inds, mask_token): + clip_len = len(tokens[0]) + inds_set = set(inds) + new_tokens = [[mask_token if i * clip_len + j in inds_set else t + for j, t in enumerate(x)] for i, x in enumerate(tokens)] + return new_tokens + + +def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266): + w, w_inv = np.unique(weights, return_inverse=True) + + if np.sum(w < 1) == 0: + return base_emb, tokens, base_emb[0, length - 1:length, :] + # m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0) + # using the comma token as a masking token seems to work better than aos tokens for SD 1.x + m_token = (m_token, 1.0) + + masked_tokens = [] + + masked_current = tokens + for i in range(len(w)): + if w[i] >= 1: + continue + masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token) + masked_tokens.extend(masked_current) + + embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens)) + embs = torch.cat([base_emb, embs]) + w = w[w <= 1.0] + w_mix = np.diff([0] + w.tolist()) + w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1, 1, 1)) + + weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True) + return weighted_emb, masked_current, weighted_emb[0, length - 1:length, :] + + +def scale_emb_to_mag(base_emb, weighted_emb): + norm_base = torch.linalg.norm(base_emb) + norm_weighted = torch.linalg.norm(weighted_emb) + embeddings_final = (norm_base / norm_weighted) * weighted_emb + return embeddings_final + + +def recover_dist(base_emb, weighted_emb): + fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean()) + embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean()) + return embeddings_final + + +def A1111_renorm(base_emb, weighted_emb): + embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb + return embeddings_final + + +def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266, + length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False): + tokens = [[t for t, _, _ in x] for x in tokenized] + weights = [[w for _, w, _ in x] for x in tokenized] + word_ids = [[wid for _, _, wid in x] for x in tokenized] + + # weight normalization + # ==================== + + # distribute down/up weights over word lengths + if token_normalization.startswith("length"): + weights = divide_length(word_ids, weights) + + # make mean of word tokens 1 + if token_normalization.endswith("mean"): + weights = shift_mean_weight(word_ids, weights) + + # weight interpretation + # ===================== + pooled = None + + if weight_interpretation == "comfy": + weighted_tokens = [[(t, w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)] + weighted_emb, pooled_base = encode_func(weighted_tokens) + pooled = pooled_base + else: + unweighted_tokens = [[(t, 1.0) for t, _, _ in x] for x in tokenized] + base_emb, pooled_base = encode_func(unweighted_tokens) + + if weight_interpretation == "A1111": + weighted_emb = from_zero(weights, base_emb) + weighted_emb = A1111_renorm(base_emb, weighted_emb) + pooled = pooled_base + + if weight_interpretation == "compel": + pos_tokens = [[(t, w) if w >= 1.0 else (t, 1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)] + weighted_emb, _ = encode_func(pos_tokens) + weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func) + + if weight_interpretation == "comfy++": + weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) + weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights] + # unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down] + embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) + weighted_emb += embs + + if weight_interpretation == "down_weight": + weights = scale_to_norm(weights, word_ids, w_max) + weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) + + if return_pooled: + if apply_to_pooled: + return weighted_emb, pooled + else: + return weighted_emb, pooled_base + return weighted_emb, None + + +def encode_token_weights_g(model, token_weight_pairs): + return model.clip_g.encode_token_weights(token_weight_pairs) + + +def encode_token_weights_l(model, token_weight_pairs): + l_out, pooled = model.clip_l.encode_token_weights(token_weight_pairs) + return l_out, pooled + +def encode_token_weights_t5(model, token_weight_pairs): + return model.t5xxl.encode_token_weights(token_weight_pairs) + + +def encode_token_weights(model, token_weight_pairs, encode_func): + if model.layer_idx is not None: + # 2016 [c2cb8e88] 及δ»₯δΈŠη‰ˆζœ¬εŽ»ι™€δΊ†sdxl clipηš„clip_layer方法 + # if compare_revision(2016): + model.cond_stage_model.set_clip_options({'layer': model.layer_idx}) + # else: + # model.cond_stage_model.clip_layer(model.layer_idx) + + model_management.load_model_gpu(model.patcher) + return encode_func(model.cond_stage_model, token_weight_pairs) + +def prepareXL(embs_l, embs_g, pooled, clip_balance): + l_w = 1 - max(0, clip_balance - .5) * 2 + g_w = 1 - max(0, .5 - clip_balance) * 2 + if embs_l is not None: + return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled + else: + return embs_g, pooled + +def prepareSD3(out, pooled, clip_balance): + lg_w = 1 - max(0, clip_balance - .5) * 2 + t5_w = 1 - max(0, .5 - clip_balance) * 2 + if out.shape[0] > 1: + return torch.cat([out[0] * lg_w, out[1] * t5_w], dim=-1), pooled + else: + return out, pooled + +def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, + apply_to_pooled=True, width=1024, height=1024, crop_w=0, crop_h=0, target_width=1024, target_height=1024, a1111_prompt_style=False, steps=1): + + # Use clip text encode by smzNodes like same as a1111, when if you need installed the smzNodes + if a1111_prompt_style: + if "smZ CLIPTextEncode" in NODE_CLASS_MAPPINGS: + cls = NODE_CLASS_MAPPINGS['smZ CLIPTextEncode'] + embeddings_final, = cls().encode(clip, text, weight_interpretation, True, True, False, False, 6, 1024, 1024, 0, 0, 1024, 1024, '', '', steps) + return embeddings_final + else: + raise Exception(f"[smzNodes Not Found] you need to install 'ComfyUI-smzNodes'") + + time_start = 0 + time_end = 1 + match = re.search(r'TIMESTEP.*$', text) + if match: + timestep = match.group() + timestep = timestep.split(' ') + timestep = timestep[0] + text = text.replace(timestep, '') + value = timestep.split(':') + if len(value) >= 3: + time_start = float(value[1]) + time_end = float(value[2]) + elif len(value) == 2: + time_start = float(value[1]) + time_end = 1 + elif len(value) == 1: + time_start = 0.1 + time_end = 1 + + pass3 = [x.strip() for x in text.split("BREAK")] + pass3 = [x for x in pass3 if x != ''] + + if len(pass3) == 0: + pass3 = [''] + + # pass3_str = [f'[{x}]' for x in pass3] + # print(f"CLIP: {str.join(' + ', pass3_str)}") + + conditioning = None + + for text in pass3: + tokenized = clip.tokenize(text, return_word_ids=True) + if SD3ClipModel and isinstance(clip.cond_stage_model, SD3ClipModel): + lg_out = None + pooled = None + out = None + + if len(tokenized['l']) > 0 or len(tokenized['g']) > 0: + if clip.cond_stage_model.clip_l is not None: + lg_out, l_pooled = advanced_encode_from_tokens(tokenized['l'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_l), + w_max=w_max, return_pooled=True,) + else: + l_pooled = torch.zeros((1, 768), device=model_management.intermediate_device()) + + if clip.cond_stage_model.clip_g is not None: + g_out, g_pooled = advanced_encode_from_tokens(tokenized['g'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_g), + w_max=w_max, return_pooled=True) + if lg_out is not None: + lg_out = torch.cat([lg_out, g_out], dim=-1) + else: + lg_out = torch.nn.functional.pad(g_out, (768, 0)) + else: + g_out = None + g_pooled = torch.zeros((1, 1280), device=model_management.intermediate_device()) + + if lg_out is not None: + lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) + out = lg_out + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + + # t5xxl + if 't5xxl' in tokenized: + t5_out, t5_pooled = advanced_encode_from_tokens(tokenized['t5xxl'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_t5), + w_max=w_max, return_pooled=True) + if lg_out is not None: + out = torch.cat([lg_out, t5_out], dim=-2) + else: + out = t5_out + + if out is None: + out = torch.zeros((1, 77, 4096), device=model_management.intermediate_device()) + + if pooled is None: + pooled = torch.zeros((1, 768 + 1280), device=model_management.intermediate_device()) + + embeddings_final, pooled = prepareSD3(out, pooled, clip_balance) + cond = [[embeddings_final, {"pooled_output": pooled}]] + + elif isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)): + embs_l = None + embs_g = None + pooled = None + if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel): + embs_l, _ = advanced_encode_from_tokens(tokenized['l'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_l), + w_max=w_max, + return_pooled=False) + if 'g' in tokenized: + embs_g, pooled = advanced_encode_from_tokens(tokenized['g'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, + encode_token_weights_g), + w_max=w_max, + return_pooled=True, + apply_to_pooled=apply_to_pooled) + + embeddings_final, pooled = prepareXL(embs_l, embs_g, pooled, clip_balance) + + cond = [[embeddings_final, {"pooled_output": pooled}]] + # cond = [[embeddings_final, + # {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, + # "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]] + else: + embeddings_final, pooled = advanced_encode_from_tokens(tokenized['l'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_l), + w_max=w_max,return_pooled=True,) + cond = [[embeddings_final, {"pooled_output": pooled}]] + + if conditioning is not None: + conditioning = ConditioningConcat().concat(conditioning, cond)[0] + else: + conditioning = cond + + # setTimeStepRange + if time_start > 0 or time_end < 1: + conditioning_2, = ConditioningSetTimestepRange().set_range(conditioning, 0, time_start) + conditioning_1, = ConditioningZeroOut().zero_out(conditioning) + conditioning_1, = ConditioningSetTimestepRange().set_range(conditioning_1, time_start, time_end) + conditioning, = ConditioningCombine().combine(conditioning_1, conditioning_2) + + return conditioning + + + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/api/bizyair.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/api/bizyair.py new file mode 100644 index 00000000..b7b45815 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/api/bizyair.py @@ -0,0 +1,372 @@ +import yaml +import pathlib +import base64 +import io +import json +import os +import pickle +import zlib +import urllib.parse +import urllib.request +import urllib.error +from enum import Enum +from functools import singledispatch +from typing import Any, List, Union + +import numpy as np +import torch +from PIL import Image + +root_path = pathlib.Path(__file__).parent.parent.parent.parent +config_path = os.path.join(root_path, 'config.yaml') + +class BizyAIRAPI: + def __init__(self): + self.base_url = 'https://bizyair-api.siliconflow.cn/x/v1' + self.api_key = None + + + def getAPIKey(self): + if self.api_key is None: + if os.path.isfile(config_path): + with open(config_path, 'r') as f: + data = yaml.load(f, Loader=yaml.FullLoader) + if 'BIZYAIR_API_KEY' not in data: + raise Exception("Please add BIZYAIR_API_KEY to config.yaml") + self.api_key = data['BIZYAIR_API_KEY'] + else: + raise Exception("Please add config.yaml to root path") + return self.api_key + + def send_post_request(self, url, payload, headers): + try: + data = json.dumps(payload).encode("utf-8") + req = urllib.request.Request(url, data=data, headers=headers, method="POST") + with urllib.request.urlopen(req) as response: + response_data = response.read().decode("utf-8") + return response_data + except urllib.error.URLError as e: + if "Unauthorized" in str(e): + raise Exception( + "Key is invalid, please refer to https://cloud.siliconflow.cn to get the API key.\n" + "If you have the key, please click the 'BizyAir Key' button at the bottom right to set the key." + ) + else: + raise Exception( + f"Failed to connect to the server: {e}, if you have no key, " + ) + + # joycaption + def joyCaption(self, payload, image, apikey_override=None, API_URL='/supernode/joycaption2'): + if apikey_override is not None: + api_key = apikey_override + else: + api_key = self.getAPIKey() + url = f"{self.base_url}{API_URL}" + print('Sending request to:', url) + auth = f"Bearer {api_key}" + headers = { + "accept": "application/json", + "content-type": "application/json", + "authorization": auth, + } + input_image = encode_data(image, disable_image_marker=True) + payload["image"] = input_image + + ret: str = self.send_post_request(url=url, payload=payload, headers=headers) + ret = json.loads(ret) + + try: + if "result" in ret: + ret = json.loads(ret["result"]) + except Exception as e: + raise Exception(f"Unexpected response: {ret} {e=}") + + if ret["type"] == "error": + raise Exception(ret["message"]) + + msg = ret["data"] + if msg["type"] not in ("comfyair", "bizyair",): + raise Exception(f"Unexpected response type: {msg}") + + caption = msg["data"] + + return caption + +bizyairAPI = BizyAIRAPI() + + + +BIZYAIR_DEBUG = True +# Marker to identify base64-encoded tensors +TENSOR_MARKER = "TENSOR:" +IMAGE_MARKER = "IMAGE:" + + +class TaskStatus(Enum): + PENDING = "pending" + PROCESSING = "processing" + COMPLETED = "completed" + + +def convert_image_to_rgb(image: Image.Image) -> Image.Image: + if image.mode != "RGB": + return image.convert("RGB") + return image + + +def encode_image_to_base64( + image: Image.Image, format: str = "png", quality: int = 100, lossless=False +) -> str: + image = convert_image_to_rgb(image) + with io.BytesIO() as output: + image.save(output, format=format, quality=quality, lossless=lossless) + output.seek(0) + img_bytes = output.getvalue() + if BIZYAIR_DEBUG: + print(f"encode_image_to_base64: {format_bytes(len(img_bytes))}") + return base64.b64encode(img_bytes).decode("utf-8") + + +def decode_base64_to_np(img_data: str, format: str = "png") -> np.ndarray: + img_bytes = base64.b64decode(img_data) + if BIZYAIR_DEBUG: + print(f"decode_base64_to_np: {format_bytes(len(img_bytes))}") + with io.BytesIO(img_bytes) as input_buffer: + img = Image.open(input_buffer) + # https://github.com/comfyanonymous/ComfyUI/blob/a178e25912b01abf436eba1cfaab316ba02d272d/nodes.py#L1511 + img = img.convert("RGB") + return np.array(img) + + +def decode_base64_to_image(img_data: str) -> Image.Image: + img_bytes = base64.b64decode(img_data) + with io.BytesIO(img_bytes) as input_buffer: + img = Image.open(input_buffer) + if BIZYAIR_DEBUG: + format_info = img.format.upper() if img.format else "Unknown" + print(f"decode image format: {format_info}") + return img + + +def format_bytes(num_bytes: int) -> str: + """ + Converts a number of bytes to a human-readable string with units (B, KB, or MB). + + :param num_bytes: The number of bytes to convert. + :return: A string representing the number of bytes in a human-readable format. + """ + if num_bytes < 1024: + return f"{num_bytes} B" + elif num_bytes < 1024 * 1024: + return f"{num_bytes / 1024:.2f} KB" + else: + return f"{num_bytes / (1024 * 1024):.2f} MB" + + +def _legacy_encode_comfy_image(image: torch.Tensor, image_format="png") -> str: + input_image = image.cpu().detach().numpy() + i = 255.0 * input_image[0] + input_image = np.clip(i, 0, 255).astype(np.uint8) + base64ed_image = encode_image_to_base64( + Image.fromarray(input_image), format=image_format + ) + return base64ed_image + + +def _legacy_decode_comfy_image( + img_data: Union[List, str], image_format="png" +) -> torch.tensor: + if isinstance(img_data, List): + decoded_imgs = [decode_comfy_image(x, old_version=True) for x in img_data] + + combined_imgs = torch.cat(decoded_imgs, dim=0) + return combined_imgs + + out = decode_base64_to_np(img_data, format=image_format) + out = np.array(out).astype(np.float32) / 255.0 + output = torch.from_numpy(out)[None,] + return output + + +def _new_encode_comfy_image(images: torch.Tensor, image_format="WEBP", **kwargs) -> str: + """https://docs.comfy.org/essentials/custom_node_snippets#save-an-image-batch + Encode a batch of images to base64 strings. + + Args: + images (torch.Tensor): A batch of images. + image_format (str, optional): The format of the images. Defaults to "WEBP". + + Returns: + str: A JSON string containing the base64-encoded images. + """ + results = {} + for batch_number, image in enumerate(images): + i = 255.0 * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + base64ed_image = encode_image_to_base64(img, format=image_format, **kwargs) + results[batch_number] = base64ed_image + + return json.dumps(results) + + +def _new_decode_comfy_image(img_datas: str, image_format="WEBP") -> torch.tensor: + """ + Decode a batch of base64-encoded images. + + Args: + img_datas (str): A JSON string containing the base64-encoded images. + image_format (str, optional): The format of the images. Defaults to "WEBP". + + Returns: + torch.Tensor: A tensor containing the decoded images. + """ + img_datas = json.loads(img_datas) + + decoded_imgs = [] + for img_data in img_datas.values(): + decoded_image = decode_base64_to_np(img_data, format=image_format) + decoded_image = np.array(decoded_image).astype(np.float32) / 255.0 + decoded_imgs.append(torch.from_numpy(decoded_image)[None,]) + + return torch.cat(decoded_imgs, dim=0) + + +def encode_comfy_image( + image: torch.Tensor, image_format="WEBP", old_version=False, lossless=False +) -> str: + if old_version: + return _legacy_encode_comfy_image(image, image_format) + return _new_encode_comfy_image(image, image_format, lossless=lossless) + + +def decode_comfy_image( + img_data: Union[List, str], image_format="WEBP", old_version=False +) -> torch.tensor: + if old_version: + return _legacy_decode_comfy_image(img_data, image_format) + return _new_decode_comfy_image(img_data, image_format) + + +def tensor_to_base64(tensor: torch.Tensor, compress=True) -> str: + tensor_np = tensor.cpu().detach().numpy() + + tensor_bytes = pickle.dumps(tensor_np) + if compress: + tensor_bytes = zlib.compress(tensor_bytes) + + tensor_b64 = base64.b64encode(tensor_bytes).decode("utf-8") + return tensor_b64 + + +def base64_to_tensor(tensor_b64: str, compress=True) -> torch.Tensor: + tensor_bytes = base64.b64decode(tensor_b64) + + if compress: + tensor_bytes = zlib.decompress(tensor_bytes) + + tensor_np = pickle.loads(tensor_bytes) + + tensor = torch.from_numpy(tensor_np) + return tensor + + +@singledispatch +def decode_data(input, old_version=False): + raise NotImplementedError(f"Unsupported type: {type(input)}") + + +@decode_data.register(int) +@decode_data.register(float) +@decode_data.register(bool) +@decode_data.register(type(None)) +def _(input, **kwargs): + return input + + +@decode_data.register(dict) +def _(input, **kwargs): + return {k: decode_data(v, **kwargs) for k, v in input.items()} + + +@decode_data.register(list) +def _(input, **kwargs): + return [decode_data(x, **kwargs) for x in input] + + +@decode_data.register(str) +def _(input: str, **kwargs): + if input.startswith(TENSOR_MARKER): + tensor_b64 = input[len(TENSOR_MARKER) :] + return base64_to_tensor(tensor_b64) + elif input.startswith(IMAGE_MARKER): + tensor_b64 = input[len(IMAGE_MARKER) :] + old_version = kwargs.get("old_version", False) + return decode_comfy_image(tensor_b64, old_version=old_version) + return input + + +@singledispatch +def encode_data(output, disable_image_marker=False, old_version=False): + raise NotImplementedError(f"Unsupported type: {type(output)}") + + +@encode_data.register(dict) +def _(output, **kwargs): + return {k: encode_data(v, **kwargs) for k, v in output.items()} + + +@encode_data.register(list) +def _(output, **kwargs): + return [encode_data(x, **kwargs) for x in output] + + +def is_image_tensor(tensor) -> bool: + """https://docs.comfy.org/essentials/custom_node_datatypes#image + + Check if the given tensor is in the format of an IMAGE (shape [B, H, W, C] where C=3). + + `Args`: + tensor (torch.Tensor): The tensor to check. + + `Returns`: + bool: True if the tensor is in the IMAGE format, False otherwise. + """ + try: + if not isinstance(tensor, torch.Tensor): + return False + + if len(tensor.shape) != 4: + return False + + B, H, W, C = tensor.shape + if C != 3: + return False + + return True + except: + return False + + +@encode_data.register(torch.Tensor) +def _(output, **kwargs): + if is_image_tensor(output) and not kwargs.get("disable_image_marker", False): + old_version = kwargs.get("old_version", False) + lossless = kwargs.get("lossless", True) + return IMAGE_MARKER + encode_comfy_image( + output, image_format="WEBP", old_version=old_version, lossless=lossless + ) + return TENSOR_MARKER + tensor_to_base64(output) + + +@encode_data.register(int) +@encode_data.register(float) +@encode_data.register(bool) +@encode_data.register(type(None)) +def _(output, **kwargs): + return output + + +@encode_data.register(str) +def _(output, **kwargs): + return output diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/api/fluxai.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/api/fluxai.py new file mode 100644 index 00000000..eaaa4d48 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/api/fluxai.py @@ -0,0 +1,51 @@ +import json +import os +import yaml +import requests +import pathlib +from aiohttp import web + +root_path = pathlib.Path(__file__).parent.parent.parent.parent +config_path = os.path.join(root_path,'config.yaml') +class FluxAIAPI: + def __init__(self): + self.api_url = "https://fluxaiimagegenerator.com/api" + self.origin = "https://fluxaiimagegenerator.com" + self.user_agent = None + self.cookie = None + + def promptGenerate(self, text, cookies=None): + cookie = self.cookie if cookies is None else cookies + if cookie is None: + if os.path.isfile(config_path): + with open(config_path, 'r') as f: + data = yaml.load(f, Loader=yaml.FullLoader) + if 'FLUXAI_COOKIE' not in data: + raise Exception("Please add FLUXAI_COOKIE to config.yaml") + if "FLUXAI_USER_AGENT" in data: + self.user_agent = data["FLUXAI_USER_AGENT"] + self.cookie = cookie = data['FLUXAI_COOKIE'] + + headers = { + "Cookie": cookie, + "Referer": "https://fluxaiimagegenerator.com/flux-prompt-generator", + "Origin": self.origin, + "Content-Type": "application/json", + } + if self.user_agent is not None: + headers['User-Agent'] = self.user_agent + + url = self.api_url + '/prompt' + json = { + "prompt": text + } + + response = requests.post(url, json=json, headers=headers) + res = response.json() + if "error" in res: + return res['error'] + elif "data" in res and "prompt" in res['data']: + return res['data']['prompt'] + +fluxaiAPI = FluxAIAPI() + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/api/stability.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/api/stability.py new file mode 100644 index 00000000..3b40db2e --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/api/stability.py @@ -0,0 +1,200 @@ +import json +import os +import yaml +import requests +import pathlib +from aiohttp import web +from server import PromptServer +from ..image import tensor2pil, pil2tensor, image2base64, pil2byte +from ..log import log_node_error + + +root_path = pathlib.Path(__file__).parent.parent.parent.parent +config_path = os.path.join(root_path,'config.yaml') +default_key = [{'name':'Default', 'key':''}] + + +class StabilityAPI: + def __init__(self): + self.api_url = "https://api.stability.ai" + self.api_keys = None + self.api_current = 0 + self.user_info = {} + + def getErrors(self, code): + errors = { + 400: "Bad Request", + 403: "ApiKey Forbidden", + 413: "Your request was larger than 10MiB.", + 429: "You have made more than 150 requests in 10 seconds.", + 500: "Internal Server Error", + } + return errors.get(code, "Unknown Error") + + def getAPIKeys(self): + if os.path.isfile(config_path): + with open(config_path, 'r') as f: + data = yaml.load(f, Loader=yaml.FullLoader) + if not data: + data = {'STABILITY_API_KEY': default_key, 'STABILITY_API_DEFAULT':0} + with open(config_path, 'w') as f: + yaml.dump(data, f) + if 'STABILITY_API_KEY' not in data: + data['STABILITY_API_KEY'] = default_key + data['STABILITY_API_DEFAULT'] = 0 + with open(config_path, 'w') as f: + yaml.dump(data, f) + api_keys = data['STABILITY_API_KEY'] + self.api_current = data['STABILITY_API_DEFAULT'] + self.api_keys = api_keys + return api_keys + else: + # create a yaml file + with open(config_path, 'w') as f: + data = {'STABILITY_API_KEY': default_key, 'STABILITY_API_DEFAULT':0} + yaml.dump(data, f) + return data['STABILITY_API_KEY'] + pass + + def setAPIKeys(self, api_keys): + if len(api_keys) > 0: + self.api_keys = api_keys + # load and save the yaml file + with open(config_path, 'r') as f: + data = yaml.load(f, Loader=yaml.FullLoader) + data['STABILITY_API_KEY'] = api_keys + with open(config_path, 'w') as f: + yaml.dump(data, f) + return True + + def setAPIDefault(self, current): + if current is not None: + self.api_current = current + # load and save the yaml file + with open(config_path, 'r') as f: + data = yaml.load(f, Loader=yaml.FullLoader) + data['STABILITY_API_DEFAULT'] = current + with open(config_path, 'w') as f: + yaml.dump(data, f) + return True + + def generate_sd3_image(self, prompt, negative_prompt, aspect_ratio, model, seed, mode='text-to-image', image=None, strength=1, output_format='png', node_name='easy stableDiffusion3API'): + url = f"{self.api_url}/v2beta/stable-image/generate/sd3" + api_key = self.api_keys[self.api_current]['key'] + files = None + data = { + "prompt": prompt, + "mode": mode, + "model": model, + "seed": seed, + "output_format": output_format, + } + if model == 'sd3': + data['negative_prompt'] = negative_prompt + + if mode == 'text-to-image': + files = {"none": ''} + data['aspect_ratio'] = aspect_ratio + elif mode == 'image-to-image': + pil_image = tensor2pil(image) + image_byte = pil2byte(pil_image) + files = {"image": ("output.png", image_byte, 'image/png')} + data['strength'] = strength + + response = requests.post(url, + headers={"authorization": f"{api_key}", "accept": "application/json"}, + files=files, + data=data, + ) + if response.status_code == 200: + PromptServer.instance.send_sync('stable-diffusion-api-generate-succeed',{"model":model}) + json_data = response.json() + image_base64 = json_data['image'] + image_data = image2base64(image_base64) + output_t = pil2tensor(image_data) + return output_t + else: + if 'application/json' in response.headers['Content-Type']: + error_info = response.json() + log_node_error(node_name, error_info.get('name', 'No name provided')) + log_node_error(node_name, error_info.get('errors', ['No details provided'])) + error_status_text = self.getErrors(response.status_code) + PromptServer.instance.send_sync('easyuse-toast',{"type": "error", "content": error_status_text}) + raise Exception(f"Failed to generate image: {error_status_text}") + + # get user account + async def getUserAccount(self, cache=True): + url = f"{self.api_url}/v1/user/account" + api_key = self.api_keys[self.api_current]['key'] + name = self.api_keys[self.api_current]['name'] + if cache and name in self.user_info: + return self.user_info[name] + else: + response = requests.get(url, headers={"Authorization": f"Bearer {api_key}"}) + if response.status_code == 200: + user_info = response.json() + self.user_info[name] = user_info + return user_info + else: + PromptServer.instance.send_sync('easyuse-toast',{'type': 'error', 'content': self.getErrors(response.status_code)}) + return None + + # get user balance + async def getUserBalance(self): + url = f"{self.api_url}/v1/user/balance" + api_key = self.api_keys[self.api_current]['key'] + response = requests.get(url, headers={ + "Authorization": f"Bearer {api_key}" + }) + if response.status_code == 200: + return response.json() + else: + PromptServer.instance.send_sync('easyuse-toast', {'type': 'error', 'content': self.getErrors(response.status_code)}) + return None + +stableAPI = StabilityAPI() + +@PromptServer.instance.routes.get("/easyuse/stability/api_keys") +async def get_stability_api_keys(request): + stableAPI.getAPIKeys() + return web.json_response({"keys": stableAPI.api_keys, "current": stableAPI.api_current}) + +@PromptServer.instance.routes.post("/easyuse/stability/set_api_keys") +async def set_stability_api_keys(request): + post = await request.post() + api_keys = post.get("api_keys") + current = post.get('current') + if api_keys is not None: + api_keys = json.loads(api_keys) + stableAPI.setAPIKeys(api_keys) + if current is not None: + print(current) + stableAPI.setAPIDefault(int(current)) + account = await stableAPI.getUserAccount() + balance = await stableAPI.getUserBalance() + return web.json_response({'account': account, 'balance': balance}) + else: + return web.json_response({'status': 'ok'}) + else: + return web.Response(status=400) + +@PromptServer.instance.routes.post("/easyuse/stability/set_apikey_default") +async def set_stability_api_default(request): + post = await request.post() + current = post.get("current") + if current is not None and current < len(stableAPI.api_keys): + stableAPI.api_current = current + return web.json_response({'status': 'ok'}) + else: + return web.Response(status=400) + +@PromptServer.instance.routes.get("/easyuse/stability/user_info") +async def get_account_info(request): + account = await stableAPI.getUserAccount() + balance = await stableAPI.getUserBalance() + return web.json_response({'account': account, 'balance': balance}) + +@PromptServer.instance.routes.get("/easyuse/stability/balance") +async def get_balance_info(request): + balance = await stableAPI.getUserBalance() + return web.json_response({'balance': balance}) diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/cache.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/cache.py new file mode 100644 index 00000000..271ec3e6 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/cache.py @@ -0,0 +1,86 @@ +import itertools +from typing import Optional + +class TaggedCache: + def __init__(self, tag_settings: Optional[dict]=None): + self._tag_settings = tag_settings or {} # tag cache size + self._data = {} + + def __getitem__(self, key): + for tag_data in self._data.values(): + if key in tag_data: + return tag_data[key] + raise KeyError(f'Key `{key}` does not exist') + + def __setitem__(self, key, value: tuple): + # value: (tag: str, (islist: bool, data: *)) + + # if key already exists, pop old value + for tag_data in self._data.values(): + if key in tag_data: + tag_data.pop(key, None) + break + + tag = value[0] + if tag not in self._data: + + try: + from cachetools import LRUCache + + default_size = 20 + if 'ckpt' in tag: + default_size = 5 + elif tag in ['latent', 'image']: + default_size = 100 + + self._data[tag] = LRUCache(maxsize=self._tag_settings.get(tag, default_size)) + + except (ImportError, ModuleNotFoundError): + # TODO: implement a simple lru dict + self._data[tag] = {} + self._data[tag][key] = value + + def __delitem__(self, key): + for tag_data in self._data.values(): + if key in tag_data: + del tag_data[key] + return + raise KeyError(f'Key `{key}` does not exist') + + def __contains__(self, key): + return any(key in tag_data for tag_data in self._data.values()) + + def items(self): + yield from itertools.chain(*map(lambda x :x.items(), self._data.values())) + + def get(self, key, default=None): + """D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.""" + for tag_data in self._data.values(): + if key in tag_data: + return tag_data[key] + return default + + def clear(self): + # clear all cache + self._data = {} + +cache_settings = {} +cache = TaggedCache(cache_settings) +cache_count = {} + +def update_cache(k, tag, v): + cache[k] = (tag, v) + cnt = cache_count.get(k) + if cnt is None: + cnt = 0 + cache_count[k] = cnt + else: + cache_count[k] += 1 +def remove_cache(key): + global cache + if key == '*': + cache = TaggedCache(cache_settings) + elif key in cache: + del cache[key] + else: + print(f"invalid {key}") \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/chooser.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/chooser.py new file mode 100644 index 00000000..139a0645 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/chooser.py @@ -0,0 +1,153 @@ +from threading import Event + +import torch + +from server import PromptServer +from aiohttp import web +from comfy import model_management as mm +from comfy_execution.graph import ExecutionBlocker +import time + +class ChooserCancelled(Exception): + pass + +def get_chooser_cache(): + """θŽ·ε–ι€‰ζ‹©ε™¨ηΌ“ε­˜""" + if not hasattr(PromptServer.instance, '_easyuse_chooser_node'): + PromptServer.instance._easyuse_chooser_node = {} + return PromptServer.instance._easyuse_chooser_node + +def cleanup_session_data(node_id): + """ζΈ…η†δΌšθ―ζ•°ζ""" + node_data = get_chooser_cache() + if node_id in node_data: + session_keys = ["event", "selected", "images", "total_count", "cancelled"] + for key in session_keys: + if key in node_data[node_id]: + del node_data[node_id][key] + +def wait_for_chooser(id, images, mode, period=0.1): + try: + node_data = get_chooser_cache() + images = [images[i:i + 1, ...] for i in range(images.shape[0])] + if mode == "Keep Last Selection": + if id in node_data and "last_selection" in node_data[id]: + last_selection = node_data[id]["last_selection"] + if last_selection and len(last_selection) > 0: + valid_indices = [idx for idx in last_selection if 0 <= idx < len(images)] + if valid_indices: + try: + PromptServer.instance.send_sync("easyuse-image-keep-selection", { + "id": id, + "selected": valid_indices + }) + except Exception as e: + pass + cleanup_session_data(id) + indices_str = ','.join(str(i) for i in valid_indices) + images = [images[idx] for idx in valid_indices] + images = torch.cat(images, dim=0) + return {"result": (images,)} + + if id in node_data: + del node_data[id] + + event = Event() + node_data[id] = { + "event": event, + "images": images, + "selected": None, + "total_count": len(images), + "cancelled": False, + } + + while id in node_data: + node_info = node_data[id] + if node_info.get("cancelled", False): + cleanup_session_data(id) + raise ChooserCancelled("Manual selection cancelled") + + if "selected" in node_info and node_info["selected"] is not None: + break + + time.sleep(period) + + if id in node_data: + node_info = node_data[id] + selected_indices = node_info.get("selected") + + if selected_indices is not None and len(selected_indices) > 0: + valid_indices = [idx for idx in selected_indices if 0 <= idx < len(images)] + if valid_indices: + selected_images = [images[idx] for idx in valid_indices] + + if id not in node_data: + node_data[id] = {} + node_data[id]["last_selection"] = valid_indices + cleanup_session_data(id) + selected_images = torch.cat(selected_images, dim=0) + return {"result": (selected_images,)} + else: + cleanup_session_data(id) + return {"result": (images[0] if len(images) > 0 else ExecutionBlocker(None),)} + else: + cleanup_session_data(id) + return { + "result": (images[0] if len(images) > 0 else ExecutionBlocker(None),)} + else: + return {"result": (images[0] if len(images) > 0 else ExecutionBlocker(None),)} + + except ChooserCancelled: + raise mm.InterruptProcessingException() + except Exception as e: + node_data = get_chooser_cache() + if id in node_data: + cleanup_session_data(id) + if 'image_list' in locals() and len(images) > 0: + return {"result": (images[0])} + else: + return {"result": (ExecutionBlocker(None),)} + + +@PromptServer.instance.routes.post('/easyuse/image_chooser_message') +async def handle_image_selection(request): + try: + data = await request.json() + node_id = data.get("node_id") + selected = data.get("selected", []) + action = data.get("action") + + node_data = get_chooser_cache() + + if node_id not in node_data: + return web.json_response({"code": -1, "error": "Node data does not exist"}) + + try: + node_info = node_data[node_id] + + if "total_count" not in node_info: + return web.json_response({"code": -1, "error": "The node has been processed"}) + + if action == "cancel": + node_info["cancelled"] = True + node_info["selected"] = [] + elif action == "select" and isinstance(selected, list): + valid_indices = [idx for idx in selected if isinstance(idx, int) and 0 <= idx < node_info["total_count"]] + if valid_indices: + node_info["selected"] = valid_indices + node_info["cancelled"] = False + else: + return web.json_response({"code": -1, "error": "Invalid Selection Index"}) + else: + return web.json_response({"code": -1, "error": "Invalid operation"}) + + node_info["event"].set() + return web.json_response({"code": 1}) + + except Exception as e: + if node_id in node_data and "event" in node_data[node_id]: + node_data[node_id]["event"].set() + return web.json_response({"code": -1, "message": "Processing Failed"}) + + except Exception as e: + return web.json_response({"code": -1, "message": "Request Failed"}) diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/colorfix.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/colorfix.py new file mode 100644 index 00000000..7792e7c1 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/colorfix.py @@ -0,0 +1,115 @@ +import torch +from PIL import Image +from torch import Tensor +from torch.nn import functional as F + +from torchvision.transforms import ToTensor, ToPILImage + +def adain_color_fix(target: Image, source: Image): + # Convert images to tensors + to_tensor = ToTensor() + target_tensor = to_tensor(target).unsqueeze(0) + source_tensor = to_tensor(source).unsqueeze(0) + + # Apply adaptive instance normalization + result_tensor = adaptive_instance_normalization(target_tensor, source_tensor) + + # Convert tensor back to image + to_image = ToPILImage() + result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0)) + + return result_image + +def wavelet_color_fix(target: Image, source: Image): + source = source.resize(target.size, resample=Image.Resampling.LANCZOS) + + # Convert images to tensors + to_tensor = ToTensor() + target_tensor = to_tensor(target).unsqueeze(0) + source_tensor = to_tensor(source).unsqueeze(0) + + # Apply wavelet reconstruction + result_tensor = wavelet_reconstruction(target_tensor, source_tensor) + + # Convert tensor back to image + to_image = ToPILImage() + result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0)) + + return result_image + +def calc_mean_std(feat: Tensor, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, 'The input feature should be 4D tensor.' + b, c = size[:2] + feat_var = feat.view(b, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(b, c, 1, 1) + feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) + return feat_mean, feat_std + +def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor): + """Adaptive instance normalization. + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + +def wavelet_blur(image: Tensor, radius: int): + """ + Apply wavelet blur to the input tensor. + """ + # input shape: (1, 3, H, W) + # convolution kernel + kernel_vals = [ + [0.0625, 0.125, 0.0625], + [0.125, 0.25, 0.125], + [0.0625, 0.125, 0.0625], + ] + kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device) + # add channel dimensions to the kernel to make it a 4D tensor + kernel = kernel[None, None] + # repeat the kernel across all input channels + kernel = kernel.repeat(3, 1, 1, 1) + image = F.pad(image, (radius, radius, radius, radius), mode='replicate') + # apply convolution + output = F.conv2d(image, kernel, groups=3, dilation=radius) + return output + +def wavelet_decomposition(image: Tensor, levels=5): + """ + Apply wavelet decomposition to the input tensor. + This function only returns the low frequency & the high frequency. + """ + high_freq = torch.zeros_like(image) + for i in range(levels): + radius = 2 ** i + low_freq = wavelet_blur(image, radius) + high_freq += (image - low_freq) + image = low_freq + + return high_freq, low_freq + +def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor): + """ + Apply wavelet decomposition, so that the content will have the same color as the style. + """ + # calculate the wavelet decomposition of the content feature + content_high_freq, content_low_freq = wavelet_decomposition(content_feat) + del content_low_freq + # calculate the wavelet decomposition of the style feature + style_high_freq, style_low_freq = wavelet_decomposition(style_feat) + del style_high_freq + # reconstruct the content feature with the style's high frequency + return content_high_freq + style_low_freq \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/conditioning.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/conditioning.py new file mode 100644 index 00000000..b9d9733a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/conditioning.py @@ -0,0 +1,57 @@ +from .utils import find_wildcards_seed, find_nearest_steps, is_linked_styles_selector +from .log import log_node_warn +from .translate import zh_to_en, has_chinese +from .wildcards import process_with_loras +from .adv_encode import advanced_encode + +from nodes import ConditioningConcat, ConditioningCombine, ConditioningAverage, ConditioningSetTimestepRange, CLIPTextEncode + +def prompt_to_cond(type, model, clip, clip_skip, lora_stack, text, prompt_token_normalization, prompt_weight_interpretation, a1111_prompt_style ,my_unique_id, prompt, easyCache, can_load_lora=True, steps=None, model_type=None): + styles_selector = is_linked_styles_selector(prompt, my_unique_id, type) + title = "Positive encoding" if type == 'positive' else "Negative encoding" + + # Translate cn to en + if model_type not in ['hydit'] and text is not None and has_chinese(text): + text = zh_to_en([text])[0] + + if model_type in ['hydit', 'flux', 'mochi']: + log_node_warn(title + "...") + embeddings_final, = CLIPTextEncode().encode(clip, text) if text is not None else (None,) + + return (embeddings_final, "", model, clip) + + log_node_warn(title + "...") + + positive_seed = find_wildcards_seed(my_unique_id, text, prompt) + model, clip, text, cond_decode, show_prompt, pipe_lora_stack = process_with_loras( + text, model, clip, type, positive_seed, can_load_lora, lora_stack, easyCache) + wildcard_prompt = cond_decode if show_prompt or styles_selector else "" + + clipped = clip.clone() + # 当clipζ¨‘εž‹δΈε­˜εœ¨t5xxlζ—ΆοΌŒε―ζ‰§θ‘Œθ·³θΏ‡ε±‚ + if not hasattr(clip.cond_stage_model, 't5xxl'): + if clip_skip != 0: + clipped.clip_layer(clip_skip) + + steps = steps if steps is not None else find_nearest_steps(my_unique_id, prompt) + return (advanced_encode(clipped, text, prompt_token_normalization, + prompt_weight_interpretation, w_max=1.0, + apply_to_pooled='enable', + a1111_prompt_style=a1111_prompt_style, steps=steps) if text is not None else None, wildcard_prompt, model, clipped) + +def set_cond(old_cond, new_cond, mode, average_strength, old_cond_start, old_cond_end, new_cond_start, new_cond_end): + if not old_cond: + return new_cond + else: + if mode == "replace": + return new_cond + elif mode == "concat": + return ConditioningConcat().concat(new_cond, old_cond)[0] + elif mode == "combine": + return ConditioningCombine().combine(old_cond, new_cond)[0] + elif mode == 'average': + return ConditioningAverage().addWeighted(new_cond, old_cond, average_strength)[0] + elif mode == 'timestep': + cond_1 = ConditioningSetTimestepRange().set_range(old_cond, old_cond_start, old_cond_end)[0] + cond_2 = ConditioningSetTimestepRange().set_range(new_cond, new_cond_start, new_cond_end)[0] + return ConditioningCombine().combine(cond_1, cond_2)[0] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/controlnet.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/controlnet.py new file mode 100644 index 00000000..ca7a1b8a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/controlnet.py @@ -0,0 +1,93 @@ +import folder_paths +import comfy.controlnet +import comfy.model_management +from nodes import NODE_CLASS_MAPPINGS + +union_controlnet_types = {"auto": -1, "openpose": 0, "depth": 1, "hed/pidi/scribble/ted": 2, "canny/lineart/anime_lineart/mlsd": 3, "normal": 4, "segment": 5, "tile": 6, "repaint": 7} + +class easyControlnet: + def __init__(self): + pass + + def apply(self, control_net_name, image, positive, negative, strength, start_percent=0, end_percent=1, control_net=None, scale_soft_weights=1, mask=None, union_type=None, easyCache=None, use_cache=True, model=None, vae=None): + if strength == 0: + return (positive, negative) + + # kolors controlnet patch + from ..modules.kolors.loader import is_kolors_model, applyKolorsUnet + if is_kolors_model(model): + from ..modules.kolors.model_patch import patch_controlnet + if control_net is None: + with applyKolorsUnet(): + control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights, use_cache) + control_net = patch_controlnet(model, control_net) + else: + if control_net is None: + if easyCache is not None: + control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights, use_cache) + else: + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + control_net = comfy.controlnet.load_controlnet(controlnet_path) + + # union controlnet + if union_type is not None: + control_net = control_net.copy() + type_number = union_controlnet_types[union_type] + if type_number >= 0: + control_net.set_extra_arg("control_type", [type_number]) + else: + control_net.set_extra_arg("control_type", []) + + if mask is not None: + mask = mask.to(self.device) + + if mask is not None and len(mask.shape) < 3: + mask = mask.unsqueeze(0) + + control_hint = image.movedim(-1, 1) + + is_cond = True + if negative is None: + p = [] + for t in positive: + n = [t[0], t[1].copy()] + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) + if 'control' in t[1]: + c_net.set_previous_controlnet(t[1]['control']) + n[1]['control'] = c_net + n[1]['control_apply_to_uncond'] = True + if mask is not None: + n[1]['mask'] = mask + n[1]['set_area_to_bounds'] = False + p.append(n) + positive = p + else: + cnets = {} + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + + if mask is not None: + d['mask'] = mask + d['set_area_to_bounds'] = False + + n = [t[0], d] + c.append(n) + out.append(c) + positive = out[0] + negative = out[1] + + return (positive, negative) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/dynthres_core.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/dynthres_core.py new file mode 100644 index 00000000..0c694503 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/dynthres_core.py @@ -0,0 +1,167 @@ +import torch, math + +######################### DynThresh Core ######################### + +class DynThresh: + + Modes = ["Constant", "Linear Down", "Cosine Down", "Half Cosine Down", "Linear Up", "Cosine Up", "Half Cosine Up", "Power Up", "Power Down", "Linear Repeating", "Cosine Repeating", "Sawtooth"] + Startpoints = ["MEAN", "ZERO"] + Variabilities = ["AD", "STD"] + + def __init__(self, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, experiment_mode, max_steps, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi): + self.mimic_scale = mimic_scale + self.threshold_percentile = threshold_percentile + self.mimic_mode = mimic_mode + self.cfg_mode = cfg_mode + self.max_steps = max_steps + self.cfg_scale_min = cfg_scale_min + self.mimic_scale_min = mimic_scale_min + self.experiment_mode = experiment_mode + self.sched_val = sched_val + self.sep_feat_channels = separate_feature_channels + self.scaling_startpoint = scaling_startpoint + self.variability_measure = variability_measure + self.interpolate_phi = interpolate_phi + + def interpret_scale(self, scale, mode, min): + scale -= min + max = self.max_steps - 1 + frac = self.step / max + if mode == "Constant": + pass + elif mode == "Linear Down": + scale *= 1.0 - frac + elif mode == "Half Cosine Down": + scale *= math.cos(frac) + elif mode == "Cosine Down": + scale *= math.cos(frac * 1.5707) + elif mode == "Linear Up": + scale *= frac + elif mode == "Half Cosine Up": + scale *= 1.0 - math.cos(frac) + elif mode == "Cosine Up": + scale *= 1.0 - math.cos(frac * 1.5707) + elif mode == "Power Up": + scale *= math.pow(frac, self.sched_val) + elif mode == "Power Down": + scale *= 1.0 - math.pow(frac, self.sched_val) + elif mode == "Linear Repeating": + portion = (frac * self.sched_val) % 1.0 + scale *= (0.5 - portion) * 2 if portion < 0.5 else (portion - 0.5) * 2 + elif mode == "Cosine Repeating": + scale *= math.cos(frac * 6.28318 * self.sched_val) * 0.5 + 0.5 + elif mode == "Sawtooth": + scale *= (frac * self.sched_val) % 1.0 + scale += min + return scale + + def dynthresh(self, cond, uncond, cfg_scale, weights): + mimic_scale = self.interpret_scale(self.mimic_scale, self.mimic_mode, self.mimic_scale_min) + cfg_scale = self.interpret_scale(cfg_scale, self.cfg_mode, self.cfg_scale_min) + # uncond shape is (batch, 4, height, width) + conds_per_batch = cond.shape[0] / uncond.shape[0] + assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches" + cond_stacked = cond.reshape((-1, int(conds_per_batch)) + uncond.shape[1:]) + + ### Normal first part of the CFG Scale logic, basically + diff = cond_stacked - uncond.unsqueeze(1) + if weights is not None: + diff = diff * weights + relative = diff.sum(1) + + ### Get the normal result for both mimic and normal scale + mim_target = uncond + relative * mimic_scale + cfg_target = uncond + relative * cfg_scale + ### If we weren't doing mimic scale, we'd just return cfg_target here + + ### Now recenter the values relative to their average rather than absolute, to allow scaling from average + mim_flattened = mim_target.flatten(2) + cfg_flattened = cfg_target.flatten(2) + mim_means = mim_flattened.mean(dim=2).unsqueeze(2) + cfg_means = cfg_flattened.mean(dim=2).unsqueeze(2) + mim_centered = mim_flattened - mim_means + cfg_centered = cfg_flattened - cfg_means + + if self.sep_feat_channels: + if self.variability_measure == 'STD': + mim_scaleref = mim_centered.std(dim=2).unsqueeze(2) + cfg_scaleref = cfg_centered.std(dim=2).unsqueeze(2) + else: # 'AD' + mim_scaleref = mim_centered.abs().max(dim=2).values.unsqueeze(2) + cfg_scaleref = torch.quantile(cfg_centered.abs(), self.threshold_percentile, dim=2).unsqueeze(2) + + else: + if self.variability_measure == 'STD': + mim_scaleref = mim_centered.std() + cfg_scaleref = cfg_centered.std() + else: # 'AD' + mim_scaleref = mim_centered.abs().max() + cfg_scaleref = torch.quantile(cfg_centered.abs(), self.threshold_percentile) + + if self.scaling_startpoint == 'ZERO': + scaling_factor = mim_scaleref / cfg_scaleref + result = cfg_flattened * scaling_factor + + else: # 'MEAN' + if self.variability_measure == 'STD': + cfg_renormalized = (cfg_centered / cfg_scaleref) * mim_scaleref + else: # 'AD' + ### Get the maximum value of all datapoints (with an optional threshold percentile on the uncond) + max_scaleref = torch.maximum(mim_scaleref, cfg_scaleref) + ### Clamp to the max + cfg_clamped = cfg_centered.clamp(-max_scaleref, max_scaleref) + ### Now shrink from the max to normalize and grow to the mimic scale (instead of the CFG scale) + cfg_renormalized = (cfg_clamped / max_scaleref) * mim_scaleref + + ### Now add it back onto the averages to get into real scale again and return + result = cfg_renormalized + cfg_means + + actual_res = result.unflatten(2, mim_target.shape[2:]) + + if self.interpolate_phi != 1.0: + actual_res = actual_res * self.interpolate_phi + cfg_target * (1.0 - self.interpolate_phi) + + if self.experiment_mode == 1: + num = actual_res.cpu().numpy() + for y in range(0, 64): + for x in range (0, 64): + if num[0][0][y][x] > 1.0: + num[0][1][y][x] *= 0.5 + if num[0][1][y][x] > 1.0: + num[0][1][y][x] *= 0.5 + if num[0][2][y][x] > 1.5: + num[0][2][y][x] *= 0.5 + actual_res = torch.from_numpy(num).to(device=uncond.device) + elif self.experiment_mode == 2: + num = actual_res.cpu().numpy() + for y in range(0, 64): + for x in range (0, 64): + over_scale = False + for z in range(0, 4): + if abs(num[0][z][y][x]) > 1.5: + over_scale = True + if over_scale: + for z in range(0, 4): + num[0][z][y][x] *= 0.7 + actual_res = torch.from_numpy(num).to(device=uncond.device) + elif self.experiment_mode == 3: + coefs = torch.tensor([ + # R G B W + [0.298, 0.207, 0.208, 0.0], # L1 + [0.187, 0.286, 0.173, 0.0], # L2 + [-0.158, 0.189, 0.264, 0.0], # L3 + [-0.184, -0.271, -0.473, 1.0], # L4 + ], device=uncond.device) + res_rgb = torch.einsum("laxy,ab -> lbxy", actual_res, coefs) + max_r, max_g, max_b, max_w = res_rgb[0][0].max(), res_rgb[0][1].max(), res_rgb[0][2].max(), res_rgb[0][3].max() + max_rgb = max(max_r, max_g, max_b) + print(f"test max = r={max_r}, g={max_g}, b={max_b}, w={max_w}, rgb={max_rgb}") + if self.step / (self.max_steps - 1) > 0.2: + if max_rgb < 2.0 and max_w < 3.0: + res_rgb /= max_rgb / 2.4 + else: + if max_rgb > 2.4 and max_w > 3.0: + res_rgb /= max_rgb / 2.4 + actual_res = torch.einsum("laxy,ab -> lbxy", res_rgb, coefs.inverse()) + + return actual_res \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/easing.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/easing.py new file mode 100644 index 00000000..db300c93 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/easing.py @@ -0,0 +1,27 @@ +@staticmethod +def easyIn(t: float)-> float: + return t*t +@staticmethod +def easyOut(t: float)-> float: + return -(t * (t - 2)) +@staticmethod +def easyInOut(t: float)-> float: + if t < 0.5: + return 2*t*t + else: + return (-2*t*t) + (4*t) - 1 + +class EasingBase: + + def easing(self, t: float, function='linear') -> float: + if function == 'easyIn': + return easyIn(t) + elif function == 'easyOut': + return easyOut(t) + elif function == 'easyInOut': + return easyInOut(t) + else: + return t + + def ease(self, start, end, t) -> float: + return end * t + start * (1 - t) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/gradual_latent_hires_fix.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/gradual_latent_hires_fix.py new file mode 100644 index 00000000..82d94704 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/gradual_latent_hires_fix.py @@ -0,0 +1,273 @@ +import torch +from torchvision.transforms.functional import gaussian_blur +from comfy.k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d, BrownianTreeNoiseSampler +from tqdm.auto import trange + +@torch.no_grad() +def sample_euler_ancestral( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + eta=1.0, + s_noise=1.0, + noise_sampler=None, + upscale_ratio=2.0, + start_step=5, + end_step=15, + upscale_n_step=3, + unsharp_kernel_size=3, + unsharp_sigma=0.5, + unsharp_strength=0.0, +): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + # make upscale info + upscale_steps = [] + step = start_step - 1 + while step < end_step - 1: + upscale_steps.append(step) + step += upscale_n_step + height, width = x.shape[2:] + upscale_shapes = [ + (int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1))) + for i in reversed(range(1, len(upscale_steps) + 1)) + ] + upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)} + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + # Resize + if i in upscale_info: + x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False) + if unsharp_strength > 0: + blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma) + x = x + unsharp_strength * (x - blurred) + + noise_sampler = default_noise_sampler(x) + noise = noise_sampler(sigmas[i], sigmas[i + 1]) + x = x + noise * sigma_up * s_noise + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + eta=1.0, + s_noise=1.0, + noise_sampler=None, + upscale_ratio=2.0, + start_step=5, + end_step=15, + upscale_n_step=3, + unsharp_kernel_size=3, + unsharp_sigma=0.5, + unsharp_strength=0.0, +): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + # make upscale info + upscale_steps = [] + step = start_step - 1 + while step < end_step - 1: + upscale_steps.append(step) + step += upscale_n_step + height, width = x.shape[2:] + upscale_shapes = [ + (int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1))) + for i in reversed(range(1, len(upscale_steps) + 1)) + ] + upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)} + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + # Resize + if i in upscale_info: + x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False) + if unsharp_strength > 0: + blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma) + x = x + unsharp_strength * (x - blurred) + noise_sampler = default_noise_sampler(x) + noise = noise_sampler(sigmas[i], sigmas[i + 1]) + x = x + noise * sigma_up * s_noise + return x + + +@torch.no_grad() +def sample_dpmpp_2m_sde( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + eta=1.0, + s_noise=1.0, + noise_sampler=None, + solver_type="midpoint", + upscale_ratio=2.0, + start_step=5, + end_step=15, + upscale_n_step=3, + unsharp_kernel_size=3, + unsharp_sigma=0.5, + unsharp_strength=0.0, +): + """DPM-Solver++(2M) SDE.""" + + if solver_type not in {"heun", "midpoint"}: + raise ValueError("solver_type must be 'heun' or 'midpoint'") + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + old_denoised = None + h_last = None + h = None + + # make upscale info + upscale_steps = [] + step = start_step - 1 + while step < end_step - 1: + upscale_steps.append(step) + step += upscale_n_step + height, width = x.shape[2:] + upscale_shapes = [ + (int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1))) + for i in reversed(range(1, len(upscale_steps) + 1)) + ] + upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)} + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++(2M) SDE + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + eta_h = eta * h + + x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised + + if old_denoised is not None: + r = h_last / h + if solver_type == "heun": + x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) + elif solver_type == "midpoint": + x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) + + if eta: + # Resize + if i in upscale_info: + x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False) + if unsharp_strength > 0: + blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma) + x = x + unsharp_strength * (x - blurred) + denoised = None # ζ¬‘γ‚Ήγƒ†γƒƒγƒ—γ¨γ‚΅γ‚€γ‚ΊγŒγ‚γ‚γͺいγγ§γ¨γ‚Šγ‚γˆγšNoneγ«γ—γ¦γŠγγ€‚ + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise + + old_denoised = denoised + h_last = h + return x + + +@torch.no_grad() +def sample_lcm( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + noise_sampler=None, + eta=None, + s_noise=None, + upscale_ratio=2.0, + start_step=5, + end_step=15, + upscale_n_step=3, + unsharp_kernel_size=3, + unsharp_sigma=0.5, + unsharp_strength=0.0, +): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + # make upscale info + upscale_steps = [] + step = start_step - 1 + while step < end_step - 1: + upscale_steps.append(step) + step += upscale_n_step + height, width = x.shape[2:] + upscale_shapes = [ + (int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1))) + for i in reversed(range(1, len(upscale_steps) + 1)) + ] + upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)} + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + + x = denoised + if sigmas[i + 1] > 0: + # Resize + if i in upscale_info: + x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False) + if unsharp_strength > 0: + blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma) + x = x + unsharp_strength * (x - blurred) + noise_sampler = default_noise_sampler(x) + x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1]) + + return x diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/image.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/image.py new file mode 100644 index 00000000..1d9dc818 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/image.py @@ -0,0 +1,227 @@ +import os +import base64 +import torch +import numpy as np +from enum import Enum +from PIL import Image +from io import BytesIO +from typing import List, Union + +import folder_paths +from .utils import install_package + +# PIL to Tensor +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) +# Tensor to PIL +def tensor2pil(image): + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) +# np to Tensor +def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor: + if isinstance(img_np, list): + return torch.cat([np2tensor(img) for img in img_np], dim=0) + return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0) +# Tensor to np +def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]: + if len(tensor.shape) == 3: # Single image + return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8) + else: # Batch of images + return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor] + +def pil2byte(pil_image, format='PNG'): + byte_arr = BytesIO() + pil_image.save(byte_arr, format=format) + byte_arr.seek(0) + return byte_arr + +def image2base64(image_base64): + image_bytes = base64.b64decode(image_base64) + image_data = Image.open(BytesIO(image_bytes)) + return image_data + +# Get new bounds +def get_new_bounds(width, height, left, right, top, bottom): + """Returns the new bounds for an image with inset crop data.""" + left = 0 + left + right = width - right + top = 0 + top + bottom = height - bottom + return (left, right, top, bottom) + +def RGB2RGBA(image: Image, mask: Image) -> Image: + (R, G, B) = image.convert('RGB').split() + return Image.merge('RGBA', (R, G, B, mask.convert('L'))) + +def image2mask(image: Image) -> torch.Tensor: + _image = image.convert('RGBA') + alpha = _image.split()[0] + bg = Image.new("L", _image.size) + _image = Image.merge('RGBA', (bg, bg, bg, alpha)) + ret_mask = torch.tensor([pil2tensor(_image)[0, :, :, 3].tolist()]) + return ret_mask + +def mask2image(mask: torch.Tensor) -> Image: + masks = tensor2np(mask) + for m in masks: + _mask = Image.fromarray(m).convert("L") + _image = Image.new("RGBA", _mask.size, color='white') + _image = Image.composite( + _image, Image.new("RGBA", _mask.size, color='black'), _mask) + return _image + +# ε›Ύεƒθžεˆ +class blendImage: + def g(self, x): + return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + elif mode == "multiply": + return img1 * img2 + elif mode == "screen": + return 1 - (1 - img1) * (1 - img2) + elif mode == "overlay": + return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) + elif mode == "soft_light": + return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), + img1 + (2 * img2 - 1) * (self.g(img1) - img1)) + elif mode == "difference": + return img1 - img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + + def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str = 'normal'): + image2 = image2.to(image1.device) + if image1.shape != image2.shape: + image2 = image2.permute(0, 3, 1, 2) + image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', + crop='center') + image2 = image2.permute(0, 2, 3, 1) + + blended_image = self.blend_mode(image1, image2, blend_mode) + blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor + blended_image = torch.clamp(blended_image, 0, 1) + return blended_image + + +def empty_image(width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return torch.cat((r, g, b), dim=-1) + + +class ResizeMode(Enum): + RESIZE = "Just Resize" + INNER_FIT = "Crop and Resize" + OUTER_FIT = "Resize and Fill" + def int_value(self): + if self == ResizeMode.RESIZE: + return 0 + elif self == ResizeMode.INNER_FIT: + return 1 + elif self == ResizeMode.OUTER_FIT: + return 2 + assert False, "NOTREACHED" + +# credit by https://github.com/chflame163/ComfyUI_LayerStyle/blob/main/py/imagefunc.py#L591C1-L617C22 +def fit_resize_image(image: Image, target_width: int, target_height: int, fit: str, resize_sampler: str, + background_color: str = '#000000') -> Image: + image = image.convert('RGB') + orig_width, orig_height = image.size + if image is not None: + if fit == 'letterbox': + if orig_width / orig_height > target_width / target_height: # ζ›΄ε½οΌŒδΈŠδΈ‹η•™ι»‘ + fit_width = target_width + fit_height = int(target_width / orig_width * orig_height) + else: # ζ›΄η˜¦οΌŒε·¦ε³η•™ι»‘ + fit_height = target_height + fit_width = int(target_height / orig_height * orig_width) + fit_image = image.resize((fit_width, fit_height), resize_sampler) + ret_image = Image.new('RGB', size=(target_width, target_height), color=background_color) + ret_image.paste(fit_image, box=((target_width - fit_width) // 2, (target_height - fit_height) // 2)) + elif fit == 'crop': + if orig_width / orig_height > target_width / target_height: # ζ›΄ε½οΌŒθ£ε·¦ε³ + fit_width = int(orig_height * target_width / target_height) + fit_image = image.crop( + ((orig_width - fit_width) // 2, 0, (orig_width - fit_width) // 2 + fit_width, orig_height)) + else: # ζ›΄η˜¦οΌŒθ£δΈŠδΈ‹ + fit_height = int(orig_width * target_height / target_width) + fit_image = image.crop( + (0, (orig_height - fit_height) // 2, orig_width, (orig_height - fit_height) // 2 + fit_height)) + ret_image = fit_image.resize((target_width, target_height), resize_sampler) + else: + ret_image = image.resize((target_width, target_height), resize_sampler) + return ret_image + +# CLIP反推 +import comfy.utils +from torchvision import transforms +Config, Interrogator = None, None +class CI_Inference: + ci_model = None + cache_path: str + + def __init__(self): + self.ci_model = None + self.low_vram = False + self.cache_path = os.path.join(folder_paths.models_dir, "clip_interrogator") + + def _load_model(self, model_name, low_vram=False): + if not (self.ci_model and model_name == self.ci_model.config.clip_model_name and self.low_vram == low_vram): + self.low_vram = low_vram + print(f"Load model: {model_name}") + + config = Config( + device="cuda" if torch.cuda.is_available() else "cpu", + download_cache=True, + clip_model_name=model_name, + clip_model_path=self.cache_path, + cache_path=self.cache_path, + caption_model_name='blip-large' + ) + + if low_vram: + config.apply_low_vram_defaults() + + self.ci_model = Interrogator(config) + + def _interrogate(self, image, mode, caption=None): + if mode == 'best': + prompt = self.ci_model.interrogate(image, caption=caption) + elif mode == 'classic': + prompt = self.ci_model.interrogate_classic(image, caption=caption) + elif mode == 'fast': + prompt = self.ci_model.interrogate_fast(image, caption=caption) + elif mode == 'negative': + prompt = self.ci_model.interrogate_negative(image) + else: + raise Exception(f"Unknown mode {mode}") + return prompt + + def image_to_prompt(self, image, mode, model_name='ViT-L-14/openai', low_vram=False): + try: + from clip_interrogator import Config, Interrogator + global Config, Interrogator + except: + install_package("clip_interrogator", "0.6.0") + from clip_interrogator import Config, Interrogator + + pbar = comfy.utils.ProgressBar(len(image)) + + self._load_model(model_name, low_vram) + prompt = [] + for i in range(len(image)): + im = image[i] + + im = tensor2pil(im) + im = im.convert('RGB') + + _prompt = self._interrogate(im, mode) + pbar.update(1) + prompt.append(_prompt) + + return prompt + +ci = CI_Inference() diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/lllite.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/lllite.py new file mode 100644 index 00000000..5fd686eb --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/lllite.py @@ -0,0 +1,237 @@ +import math +import torch +import comfy + + +def extra_options_to_module_prefix(extra_options): + # extra_options = {'transformer_index': 2, 'block_index': 8, 'original_shape': [2, 4, 128, 128], 'block': ('input', 7), 'n_heads': 20, 'dim_head': 64} + + # block is: [('input', 4), ('input', 5), ('input', 7), ('input', 8), ('middle', 0), + # ('output', 0), ('output', 1), ('output', 2), ('output', 3), ('output', 4), ('output', 5)] + # transformer_index is: [0, 1, 2, 3, 4, 5, 6, 7, 8], for each block + # block_index is: 0-1 or 0-9, depends on the block + # input 7 and 8, middle has 10 blocks + + # make module name from extra_options + block = extra_options["block"] + block_index = extra_options["block_index"] + if block[0] == "input": + module_pfx = f"lllite_unet_input_blocks_{block[1]}_1_transformer_blocks_{block_index}" + elif block[0] == "middle": + module_pfx = f"lllite_unet_middle_block_1_transformer_blocks_{block_index}" + elif block[0] == "output": + module_pfx = f"lllite_unet_output_blocks_{block[1]}_1_transformer_blocks_{block_index}" + else: + raise Exception("invalid block name") + return module_pfx + + +def load_control_net_lllite_patch(path, cond_image, multiplier, num_steps, start_percent, end_percent): + # calculate start and end step + start_step = math.floor(num_steps * start_percent * 0.01) if start_percent > 0 else 0 + end_step = math.floor(num_steps * end_percent * 0.01) if end_percent > 0 else num_steps + + # load weights + ctrl_sd = comfy.utils.load_torch_file(path, safe_load=True) + + # split each weights for each module + module_weights = {} + for key, value in ctrl_sd.items(): + fragments = key.split(".") + module_name = fragments[0] + weight_name = ".".join(fragments[1:]) + + if module_name not in module_weights: + module_weights[module_name] = {} + module_weights[module_name][weight_name] = value + + # load each module + modules = {} + for module_name, weights in module_weights.items(): + # ここγθ‡ͺε‹•εˆ€εšγ‚’δ½•γ¨γ‹γ—γŸγ„ + if "conditioning1.4.weight" in weights: + depth = 3 + elif weights["conditioning1.2.weight"].shape[-1] == 4: + depth = 2 + else: + depth = 1 + + module = LLLiteModule( + name=module_name, + is_conv2d=weights["down.0.weight"].ndim == 4, + in_dim=weights["down.0.weight"].shape[1], + depth=depth, + cond_emb_dim=weights["conditioning1.0.weight"].shape[0] * 2, + mlp_dim=weights["down.0.weight"].shape[0], + multiplier=multiplier, + num_steps=num_steps, + start_step=start_step, + end_step=end_step, + ) + info = module.load_state_dict(weights) + modules[module_name] = module + if len(modules) == 1: + module.is_first = True + + print(f"loaded {path} successfully, {len(modules)} modules") + + # cond imageγ‚’γ‚»γƒƒγƒˆγ™γ‚‹ + cond_image = cond_image.permute(0, 3, 1, 2) # b,h,w,3 -> b,3,h,w + cond_image = cond_image * 2.0 - 1.0 # 0-1 -> -1-+1 + + for module in modules.values(): + module.set_cond_image(cond_image) + + class control_net_lllite_patch: + def __init__(self, modules): + self.modules = modules + + def __call__(self, q, k, v, extra_options): + module_pfx = extra_options_to_module_prefix(extra_options) + + is_attn1 = q.shape[-1] == k.shape[-1] # self attention + if is_attn1: + module_pfx = module_pfx + "_attn1" + else: + module_pfx = module_pfx + "_attn2" + + module_pfx_to_q = module_pfx + "_to_q" + module_pfx_to_k = module_pfx + "_to_k" + module_pfx_to_v = module_pfx + "_to_v" + + if module_pfx_to_q in self.modules: + q = q + self.modules[module_pfx_to_q](q) + if module_pfx_to_k in self.modules: + k = k + self.modules[module_pfx_to_k](k) + if module_pfx_to_v in self.modules: + v = v + self.modules[module_pfx_to_v](v) + + return q, k, v + + def to(self, device): + for d in self.modules.keys(): + self.modules[d] = self.modules[d].to(device) + return self + + return control_net_lllite_patch(modules) + +class LLLiteModule(torch.nn.Module): + def __init__( + self, + name: str, + is_conv2d: bool, + in_dim: int, + depth: int, + cond_emb_dim: int, + mlp_dim: int, + multiplier: int, + num_steps: int, + start_step: int, + end_step: int, + ): + super().__init__() + self.name = name + self.is_conv2d = is_conv2d + self.multiplier = multiplier + self.num_steps = num_steps + self.start_step = start_step + self.end_step = end_step + self.is_first = False + + modules = [] + modules.append(torch.nn.Conv2d(3, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0)) # to latent (from VAE) size*2 + if depth == 1: + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0)) + elif depth == 2: + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=4, stride=4, padding=0)) + elif depth == 3: + # kernel size 8γ―ε€§γγ™γŽγ‚‹γγ§γ€4にする / kernel size 8 is too large, so set it to 4 + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0)) + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0)) + + self.conditioning1 = torch.nn.Sequential(*modules) + + if self.is_conv2d: + self.down = torch.nn.Sequential( + torch.nn.Conv2d(in_dim, mlp_dim, kernel_size=1, stride=1, padding=0), + torch.nn.ReLU(inplace=True), + ) + self.mid = torch.nn.Sequential( + torch.nn.Conv2d(mlp_dim + cond_emb_dim, mlp_dim, kernel_size=1, stride=1, padding=0), + torch.nn.ReLU(inplace=True), + ) + self.up = torch.nn.Sequential( + torch.nn.Conv2d(mlp_dim, in_dim, kernel_size=1, stride=1, padding=0), + ) + else: + self.down = torch.nn.Sequential( + torch.nn.Linear(in_dim, mlp_dim), + torch.nn.ReLU(inplace=True), + ) + self.mid = torch.nn.Sequential( + torch.nn.Linear(mlp_dim + cond_emb_dim, mlp_dim), + torch.nn.ReLU(inplace=True), + ) + self.up = torch.nn.Sequential( + torch.nn.Linear(mlp_dim, in_dim), + ) + + self.depth = depth + self.cond_image = None + self.cond_emb = None + self.current_step = 0 + + # @torch.inference_mode() + def set_cond_image(self, cond_image): + # print("set_cond_image", self.name) + self.cond_image = cond_image + self.cond_emb = None + self.current_step = 0 + + def forward(self, x): + if self.num_steps > 0: + if self.current_step < self.start_step: + self.current_step += 1 + return torch.zeros_like(x) + elif self.current_step >= self.end_step: + if self.is_first and self.current_step == self.end_step: + print(f"end LLLite: step {self.current_step}") + self.current_step += 1 + if self.current_step >= self.num_steps: + self.current_step = 0 # reset + return torch.zeros_like(x) + else: + if self.is_first and self.current_step == self.start_step: + print(f"start LLLite: step {self.current_step}") + self.current_step += 1 + if self.current_step >= self.num_steps: + self.current_step = 0 # reset + + if self.cond_emb is None: + # print(f"cond_emb is None, {self.name}") + cx = self.conditioning1(self.cond_image.to(x.device, dtype=x.dtype)) + if not self.is_conv2d: + # reshape / b,c,h,w -> b,h*w,c + n, c, h, w = cx.shape + cx = cx.view(n, c, h * w).permute(0, 2, 1) + self.cond_emb = cx + + cx = self.cond_emb + # print(f"forward {self.name}, {cx.shape}, {x.shape}") + + # uncond/condでxγ―γƒγƒƒγƒγ‚΅γ‚€γ‚ΊγŒ2倍 + if x.shape[0] != cx.shape[0]: + if self.is_conv2d: + cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1, 1) + else: + # print("x.shape[0] != cx.shape[0]", x.shape[0], cx.shape[0]) + cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1) + + cx = torch.cat([cx, self.down(x)], dim=1 if self.is_conv2d else 2) + cx = self.mid(cx) + cx = self.up(cx) + return cx * self.multiplier \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/loader.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/loader.py new file mode 100644 index 00000000..6702ed5a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/loader.py @@ -0,0 +1,570 @@ +import re, time, os, psutil +import folder_paths +import comfy.utils +import comfy.sd +import comfy.controlnet + +from comfy.model_patcher import ModelPatcher +from nodes import NODE_CLASS_MAPPINGS +from collections import defaultdict +from .log import log_node_info, log_node_error +from ..modules.dit.pixArt.loader import load_pixart + +diffusion_loaders = ["easy fullLoader", "easy a1111Loader", "easy fluxLoader", "easy comfyLoader", "easy hunyuanDiTLoader", "easy zero123Loader", "easy svdLoader"] +stable_cascade_loaders = ["easy cascadeLoader"] +dit_loaders = ['easy pixArtLoader'] +controlnet_loaders = ["easy controlnetLoader", "easy controlnetLoaderADV", "easy controlnetLoader++"] +instant_loaders = ["easy instantIDApply", "easy instantIDApplyADV"] +cascade_vae_node = ["easy preSamplingCascade", "easy fullCascadeKSampler"] +model_merge_node = ["easy XYInputs: ModelMergeBlocks"] +lora_widget = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy fluxLoader"] + +class easyLoader: + def __init__(self): + self.loaded_objects = { + "ckpt": defaultdict(tuple), # {ckpt_name: (model, ...)} + "unet": defaultdict(tuple), + "clip": defaultdict(tuple), + "clip_vision": defaultdict(tuple), + "bvae": defaultdict(tuple), + "vae": defaultdict(object), + "lora": defaultdict(dict), # {lora_name: {UID: (model_lora, clip_lora)}} + "controlnet": defaultdict(dict), + "t5": defaultdict(tuple), + "chatglm3": defaultdict(tuple), + } + self.memory_threshold = self.determine_memory_threshold(1) + self.lora_name_cache = [] + + def clean_values(self, values: str): + original_values = values.split("; ") + cleaned_values = [] + + for value in original_values: + cleaned_value = value.strip(';').strip() + if cleaned_value == "": + continue + try: + cleaned_value = int(cleaned_value) + except ValueError: + try: + cleaned_value = float(cleaned_value) + except ValueError: + pass + cleaned_values.append(cleaned_value) + + return cleaned_values + + def clear_unused_objects(self, desired_names: set, object_type: str): + keys = set(self.loaded_objects[object_type].keys()) + for key in keys - desired_names: + del self.loaded_objects[object_type][key] + + def get_input_value(self, entry, key, prompt=None): + val = entry["inputs"][key] + if isinstance(val, str): + return val + elif isinstance(val, list): + if prompt is not None and val[0]: + return prompt[val[0]]['inputs'][key] + else: + return val[0] + else: + return str(val) + + def process_pipe_loader(self, entry, desired_ckpt_names, desired_vae_names, desired_lora_names, desired_lora_settings, num_loras=3, suffix=""): + for idx in range(1, num_loras + 1): + lora_name_key = f"{suffix}lora{idx}_name" + desired_lora_names.add(self.get_input_value(entry, lora_name_key)) + setting = f'{self.get_input_value(entry, lora_name_key)};{entry["inputs"][f"{suffix}lora{idx}_model_strength"]};{entry["inputs"][f"{suffix}lora{idx}_clip_strength"]}' + desired_lora_settings.add(setting) + + desired_ckpt_names.add(self.get_input_value(entry, f"{suffix}ckpt_name")) + desired_vae_names.add(self.get_input_value(entry, f"{suffix}vae_name")) + + def update_loaded_objects(self, prompt): + desired_ckpt_names = set() + desired_unet_names = set() + desired_clip_names = set() + desired_vae_names = set() + desired_lora_names = set() + desired_lora_settings = set() + desired_controlnet_names = set() + desired_t5_names = set() + desired_glm3_names = set() + + for entry in prompt.values(): + class_type = entry["class_type"] + if class_type in lora_widget: + lora_name = self.get_input_value(entry, "lora_name") + desired_lora_names.add(lora_name) + setting = f'{lora_name};{entry["inputs"]["lora_model_strength"]};{entry["inputs"]["lora_clip_strength"]}' + desired_lora_settings.add(setting) + + if class_type in diffusion_loaders: + desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name", prompt)) + desired_vae_names.add(self.get_input_value(entry, "vae_name")) + + elif class_type in ['easy kolorsLoader']: + desired_unet_names.add(self.get_input_value(entry, "unet_name")) + desired_vae_names.add(self.get_input_value(entry, "vae_name")) + desired_glm3_names.add(self.get_input_value(entry, "chatglm3_name")) + + elif class_type in dit_loaders: + t5_name = self.get_input_value(entry, "mt5_name") if "mt5_name" in entry["inputs"] else None + clip_name = self.get_input_value(entry, "clip_name") if "clip_name" in entry["inputs"] else None + model_name = self.get_input_value(entry, "model_name") + ckpt_name = self.get_input_value(entry, "ckpt_name", prompt) + if t5_name: + desired_t5_names.add(t5_name) + if clip_name: + desired_clip_names.add(clip_name) + desired_ckpt_names.add(ckpt_name+'_'+model_name) + + elif class_type in stable_cascade_loaders: + desired_unet_names.add(self.get_input_value(entry, "stage_c")) + desired_unet_names.add(self.get_input_value(entry, "stage_b")) + desired_clip_names.add(self.get_input_value(entry, "clip_name")) + desired_vae_names.add(self.get_input_value(entry, "stage_a")) + + elif class_type in cascade_vae_node: + encode_vae_name = self.get_input_value(entry, "encode_vae_name") + decode_vae_name = self.get_input_value(entry, "decode_vae_name") + if encode_vae_name and encode_vae_name != 'None': + desired_vae_names.add(encode_vae_name) + if decode_vae_name and decode_vae_name != 'None': + desired_vae_names.add(decode_vae_name) + + elif class_type in controlnet_loaders: + control_net_name = self.get_input_value(entry, "control_net_name", prompt) + scale_soft_weights = self.get_input_value(entry, "scale_soft_weights") + desired_controlnet_names.add(f'{control_net_name};{scale_soft_weights}') + + elif class_type in instant_loaders: + control_net_name = self.get_input_value(entry, "control_net_name", prompt) + scale_soft_weights = self.get_input_value(entry, "cn_soft_weights") + desired_controlnet_names.add(f'{control_net_name};{scale_soft_weights}') + + elif class_type in model_merge_node: + desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_1")) + desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_2")) + vae_use = self.get_input_value(entry, "vae_use") + if vae_use != 'Use Model 1' and vae_use != 'Use Model 2': + desired_vae_names.add(vae_use) + + object_types = ["ckpt", "unet", "clip", "bvae", "vae", "lora", "controlnet", "t5"] + for object_type in object_types: + if object_type == 'unet': + desired_names = desired_unet_names + elif object_type in ["ckpt", "clip", "bvae"]: + if object_type == 'clip': + desired_names = desired_ckpt_names.union(desired_clip_names) + else: + desired_names = desired_ckpt_names + elif object_type == "vae": + desired_names = desired_vae_names + elif object_type == "controlnet": + desired_names = desired_controlnet_names + elif object_type == "t5": + desired_names = desired_t5_names + elif object_type == "chatglm3": + desired_names = desired_glm3_names + else: + desired_names = desired_lora_names + self.clear_unused_objects(desired_names, object_type) + + def add_to_cache(self, obj_type, key, value): + """ + Add an item to the cache with the current timestamp. + """ + timestamped_value = (value, time.time()) + self.loaded_objects[obj_type][key] = timestamped_value + + def determine_memory_threshold(self, percentage=0.8): + """ + Determines the memory threshold as a percentage of the total available memory. + Args: + - percentage (float): The fraction of total memory to use as the threshold. + Should be a value between 0 and 1. Default is 0.8 (80%). + Returns: + - memory_threshold (int): Memory threshold in bytes. + """ + total_memory = psutil.virtual_memory().total + memory_threshold = total_memory * percentage + return memory_threshold + + def get_memory_usage(self): + """ + Returns the memory usage of the current process in bytes. + """ + process = psutil.Process(os.getpid()) + return process.memory_info().rss + + def eviction_based_on_memory(self): + """ + Evicts objects from cache based on memory usage and priority. + """ + current_memory = self.get_memory_usage() + if current_memory < self.memory_threshold: + return + eviction_order = ["vae", "lora", "bvae", "clip", "ckpt", "controlnet", "unet", "t5", "chatglm3"] + for obj_type in eviction_order: + if current_memory < self.memory_threshold: + break + # Sort items based on age (using the timestamp) + items = list(self.loaded_objects[obj_type].items()) + items.sort(key=lambda x: x[1][1]) # Sorting by timestamp + + for item in items: + if current_memory < self.memory_threshold: + break + del self.loaded_objects[obj_type][item[0]] + current_memory = self.get_memory_usage() + + def load_checkpoint(self, ckpt_name, config_name=None, load_vision=False): + cache_name = ckpt_name + if config_name not in [None, "Default"]: + cache_name = ckpt_name + "_" + config_name + if cache_name in self.loaded_objects["ckpt"]: + clip_vision = self.loaded_objects["clip_vision"][cache_name][0] if load_vision else None + clip = self.loaded_objects["clip"][cache_name][0] if not load_vision else None + return self.loaded_objects["ckpt"][cache_name][0], clip, self.loaded_objects["bvae"][cache_name][0], clip_vision + + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + + output_clip = False if load_vision else True + output_clipvision = True if load_vision else False + if config_name not in [None, "Default"]: + config_path = folder_paths.get_full_path("configs", config_name) + loaded_ckpt = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + else: + model_options = {} + if re.search("nf4", ckpt_name): + from ..modules.bitsandbytes_NF4 import OPS + model_options = {"custom_operations": OPS} + loaded_ckpt = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=output_clip, output_clipvision=output_clipvision, embedding_directory=folder_paths.get_folder_paths("embeddings"), model_options=model_options) + + self.add_to_cache("ckpt", cache_name, loaded_ckpt[0]) + self.add_to_cache("bvae", cache_name, loaded_ckpt[2]) + + clip = loaded_ckpt[1] + clip_vision = loaded_ckpt[3] + if clip: + self.add_to_cache("clip", cache_name, clip) + if clip_vision: + self.add_to_cache("clip_vision", cache_name, clip_vision) + + self.eviction_based_on_memory() + + return loaded_ckpt[0], clip, loaded_ckpt[2], clip_vision + + def load_vae(self, vae_name): + if vae_name in self.loaded_objects["vae"]: + return self.loaded_objects["vae"][vae_name][0] + + vae_path = folder_paths.get_full_path("vae", vae_name) + sd = comfy.utils.load_torch_file(vae_path) + loaded_vae = comfy.sd.VAE(sd=sd) + self.add_to_cache("vae", vae_name, loaded_vae) + self.eviction_based_on_memory() + + return loaded_vae + + def load_unet(self, unet_name): + if unet_name in self.loaded_objects["unet"]: + log_node_info("Load UNet", f"{unet_name} cached") + return self.loaded_objects["unet"][unet_name][0] + + unet_path = folder_paths.get_full_path("unet", unet_name) + model = comfy.sd.load_unet(unet_path) + self.add_to_cache("unet", unet_name, model) + self.eviction_based_on_memory() + + return model + + def load_controlnet(self, control_net_name, scale_soft_weights=1, use_cache=True): + unique_id = f'{control_net_name};{str(scale_soft_weights)}' + if use_cache and unique_id in self.loaded_objects["controlnet"]: + return self.loaded_objects["controlnet"][unique_id][0] + if scale_soft_weights < 1: + if "ScaledSoftControlNetWeights" in NODE_CLASS_MAPPINGS: + soft_weight_cls = NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights'] + (weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False) + cn_adv_cls = NODE_CLASS_MAPPINGS['ControlNetLoaderAdvanced'] + control_net, = cn_adv_cls().load_controlnet(control_net_name, timestep_keyframe) + else: + raise Exception(f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'") + else: + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + control_net = comfy.controlnet.load_controlnet(controlnet_path) + if use_cache: + self.add_to_cache("controlnet", unique_id, control_net) + self.eviction_based_on_memory() + + return control_net + def load_clip(self, clip_name, type='stable_diffusion', load_clip=None): + if clip_name in self.loaded_objects["clip"]: + return self.loaded_objects["clip"][clip_name][0] + + if type == 'stable_diffusion': + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + elif type == 'stable_cascade': + clip_type = comfy.sd.CLIPType.STABLE_CASCADE + elif type == 'sd3': + clip_type = comfy.sd.CLIPType.SD3 + elif type == 'flux': + clip_type = comfy.sd.CLIPType.FLUX + elif type == 'stable_audio': + clip_type = comfy.sd.CLIPType.STABLE_AUDIO + clip_path = folder_paths.get_full_path("clip", clip_name) + load_clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) + self.add_to_cache("clip", clip_name, load_clip) + self.eviction_based_on_memory() + + return load_clip + + def load_lora(self, lora, model=None, clip=None, type=None , use_cache=True): + lora_name = lora["lora_name"] + model = model if model is not None else lora["model"] + clip = clip if clip is not None else lora["clip"] + model_strength = lora["model_strength"] + clip_strength = lora["clip_strength"] + lbw = lora["lbw"] if "lbw" in lora else None + lbw_a = lora["lbw_a"] if "lbw_a" in lora else None + lbw_b = lora["lbw_b"] if "lbw_b" in lora else None + + model_hash = str(model)[44:-1] + clip_hash = str(clip)[25:-1] if clip else '' + + unique_id = f'{model_hash};{clip_hash};{lora_name};{model_strength};{clip_strength}' + + if use_cache and unique_id in self.loaded_objects["lora"]: + log_node_info("Load LORA",f"{lora_name} cached") + return self.loaded_objects["lora"][unique_id][0] + + orig_lora_name = lora_name + lora_name = self.resolve_lora_name(lora_name) + + if lora_name is not None: + lora_path = folder_paths.get_full_path("loras", lora_name) + else: + lora_path = None + + if lora_path is not None: + log_node_info("Load LORA",f"{lora_name}: model={model_strength:.3f}, clip={clip_strength:.3f}, LBW={lbw}, A={lbw_a}, B={lbw_b}") + if lbw: + lbw = lora["lbw"] + lbw_a = lora["lbw_a"] + lbw_b = lora["lbw_b"] + if 'LoraLoaderBlockWeight //Inspire' not in NODE_CLASS_MAPPINGS: + raise Exception('[InspirePack Not Found] you need to install ComfyUI-Inspire-Pack') + cls = NODE_CLASS_MAPPINGS['LoraLoaderBlockWeight //Inspire'] + model, clip, _ = cls().doit(model, clip, lora_name, model_strength, clip_strength, False, 0, + lbw_a, lbw_b, "", lbw) + else: + _lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + keys = _lora.keys() + if "down_blocks.0.resnets.0.norm1.bias" in keys: + print('Using LORA for Resadapter') + key_map = {} + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + mapping_norm = {} + + for key in keys: + if ".weight" in key: + key_name_in_ori_sd = key_map[key.replace(".weight", "")] + mapping_norm[key_name_in_ori_sd] = _lora[key] + elif ".bias" in key: + key_name_in_ori_sd = key_map[key.replace(".bias", "")] + mapping_norm[key_name_in_ori_sd.replace(".weight", ".bias")] = _lora[ + key + ] + else: + print("===>Unexpected key", key) + mapping_norm[key] = _lora[key] + + for k in mapping_norm.keys(): + if k not in model.model.state_dict(): + print("===>Missing key:", k) + model.model.load_state_dict(mapping_norm, strict=False) + return (model, clip) + + # PixArt + if type is not None and type == 'PixArt': + from ..modules.dit.pixArt.loader import load_pixart_lora + model = load_pixart_lora(model, _lora, lora_path, model_strength) + else: + model, clip = comfy.sd.load_lora_for_models(model, clip, _lora, model_strength, clip_strength) + + if use_cache: + self.add_to_cache("lora", unique_id, (model, clip)) + self.eviction_based_on_memory() + else: + log_node_error(f"LORA NOT FOUND", orig_lora_name) + + return model, clip + + def resolve_lora_name(self, name): + if os.path.exists(name): + return name + else: + if len(self.lora_name_cache) == 0: + loras = folder_paths.get_filename_list("loras") + self.lora_name_cache.extend(loras) + for x in self.lora_name_cache: + if x.endswith(name): + return x + + # ε¦‚ζžœεˆ·ζ–°η½‘ι‘΅εŽζ–°ζ·»εŠ ηš„loraθ΅°θΏ™δΈͺι€»θΎ‘ + log_node_info("LORA NOT IN CACHE", f"{name}") + loras = folder_paths.get_filename_list("loras") + for x in loras: + if x.endswith(name): + self.lora_name_cache.append(x) + return x + + return None + + def load_main(self, ckpt_name, config_name, vae_name, lora_name, lora_model_strength, lora_clip_strength, optional_lora_stack, model_override, clip_override, vae_override, prompt, nf4=False): + model: ModelPatcher | None = None + clip: comfy.sd.CLIP | None = None + vae: comfy.sd.VAE | None = None + clip_vision = None + lora_stack = [] + + # Check for model override + can_load_lora = True + # εˆ€ζ–­ζ˜―ε¦ε­˜εœ¨ ζ¨‘εž‹ζˆ–Lora叠加xyplot, θ‹₯ε­˜εœ¨δΌ˜ε…ˆηΌ“ε­˜η¬¬δΈ€δΈͺζ¨‘εž‹ + # Determine whether there is a model or Lora overlapping xyplot, and if there is, prioritize caching the first model. + xy_model_id = next((x for x in prompt if str(prompt[x]["class_type"]) in ["easy XYInputs: ModelMergeBlocks", + "easy XYInputs: Checkpoint"]), None) + # This will find nodes that aren't actively connected to anything, and skip loading lora's for them. + xy_lora_id = next((x for x in prompt if str(prompt[x]["class_type"]) == "easy XYInputs: Lora"), None) + if xy_lora_id is not None: + can_load_lora = False + if xy_model_id is not None: + node = prompt[xy_model_id] + if "ckpt_name_1" in node["inputs"]: + ckpt_name_1 = node["inputs"]["ckpt_name_1"] + model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name_1) + can_load_lora = False + elif model_override is not None and clip_override is not None and vae_override is not None: + model = model_override + clip = clip_override + vae = vae_override + else: + model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name, config_name) + if model_override is not None: + model = model_override + if vae_override is not None: + vae = vae_override + elif clip_override is not None: + clip = clip_override + + + if optional_lora_stack is not None and can_load_lora: + for lora in optional_lora_stack: + # This is a subtle bit of code because it uses the model created by the last call, and passes it to the next call. + lora = {"lora_name": lora[0], "model": model, "clip": clip, "model_strength": lora[1], + "clip_strength": lora[2]} + model, clip = self.load_lora(lora) + lora['model'] = model + lora['clip'] = clip + lora_stack.append(lora) + + if lora_name != "None" and can_load_lora: + lora = {"lora_name": lora_name, "model": model, "clip": clip, "model_strength": lora_model_strength, + "clip_strength": lora_clip_strength} + model, clip = self.load_lora(lora) + lora_stack.append(lora) + + # Check for custom VAE + if vae_name not in ["Baked VAE", "Baked-VAE"]: + vae = self.load_vae(vae_name) + # CLIP skip + if not clip: + raise Exception("No CLIP found") + + return model, clip, vae, clip_vision, lora_stack + + # Kolors + def load_kolors_unet(self, unet_name): + if unet_name in self.loaded_objects["unet"]: + log_node_info("Load Kolors UNet", f"{unet_name} cached") + return self.loaded_objects["unet"][unet_name][0] + else: + from ..modules.kolors.loader import applyKolorsUnet + with applyKolorsUnet(): + unet_path = folder_paths.get_full_path("unet", unet_name) + sd = comfy.utils.load_torch_file(unet_path) + model = comfy.sd.load_unet_state_dict(sd) + if model is None: + raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + + self.add_to_cache("unet", unet_name, model) + self.eviction_based_on_memory() + + return model + + def load_chatglm3(self, chatglm3_name): + from ..modules.kolors.loader import load_chatglm3 + if chatglm3_name in self.loaded_objects["chatglm3"]: + log_node_info("Load ChatGLM3", f"{chatglm3_name} cached") + return self.loaded_objects["chatglm3"][chatglm3_name][0] + + chatglm_model = load_chatglm3(model_path=folder_paths.get_full_path("llm", chatglm3_name)) + self.add_to_cache("chatglm3", chatglm3_name, chatglm_model) + self.eviction_based_on_memory() + + return chatglm_model + + + # DiT + def load_dit_ckpt(self, ckpt_name, model_name, **kwargs): + if (ckpt_name+'_'+model_name) in self.loaded_objects["ckpt"]: + return self.loaded_objects["ckpt"][ckpt_name+'_'+model_name][0] + model = None + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + model_type = kwargs['model_type'] if "model_type" in kwargs else 'PixArt' + if model_type == 'PixArt': + pixart_conf = kwargs['pixart_conf'] + model_conf = pixart_conf[model_name] + model = load_pixart(ckpt_path, model_conf) + if model: + self.add_to_cache("ckpt", ckpt_name + '_' + model_name, model) + self.eviction_based_on_memory() + return model + + def load_t5_from_sd3_clip(self, sd3_clip, padding): + try: + from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel + except: + from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel + import copy + + clip = sd3_clip.clone() + assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!" + + # remove transformer + transformer = clip.cond_stage_model.t5xxl.transformer + clip.cond_stage_model.t5xxl.transformer = None + + # clone object + tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False) + tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl) + # put transformer back + clip.cond_stage_model.t5xxl.transformer = transformer + tmp.t5xxl.transformer = transformer + + # override special tokens + tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens) + tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match + + # tokenizer + tok = SD3Tokenizer() + tok.t5xxl.min_length = padding + + clip.cond_stage_model = tmp + clip.tokenizer = tok + + return clip diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/log.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/log.py new file mode 100644 index 00000000..8eea5883 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/log.py @@ -0,0 +1,77 @@ +COLORS_FG = { + 'BLACK': '\33[30m', + 'RED': '\33[31m', + 'GREEN': '\33[32m', + 'YELLOW': '\33[33m', + 'BLUE': '\33[34m', + 'MAGENTA': '\33[35m', + 'CYAN': '\33[36m', + 'WHITE': '\33[37m', + 'GREY': '\33[90m', + 'BRIGHT_RED': '\33[91m', + 'BRIGHT_GREEN': '\33[92m', + 'BRIGHT_YELLOW': '\33[93m', + 'BRIGHT_BLUE': '\33[94m', + 'BRIGHT_MAGENTA': '\33[95m', + 'BRIGHT_CYAN': '\33[96m', + 'BRIGHT_WHITE': '\33[97m', +} +COLORS_STYLE = { + 'RESET': '\33[0m', + 'BOLD': '\33[1m', + 'NORMAL': '\33[22m', + 'ITALIC': '\33[3m', + 'UNDERLINE': '\33[4m', + 'BLINK': '\33[5m', + 'BLINK2': '\33[6m', + 'SELECTED': '\33[7m', +} +COLORS_BG = { + 'BLACK': '\33[40m', + 'RED': '\33[41m', + 'GREEN': '\33[42m', + 'YELLOW': '\33[43m', + 'BLUE': '\33[44m', + 'MAGENTA': '\33[45m', + 'CYAN': '\33[46m', + 'WHITE': '\33[47m', + 'GREY': '\33[100m', + 'BRIGHT_RED': '\33[101m', + 'BRIGHT_GREEN': '\33[102m', + 'BRIGHT_YELLOW': '\33[103m', + 'BRIGHT_BLUE': '\33[104m', + 'BRIGHT_MAGENTA': '\33[105m', + 'BRIGHT_CYAN': '\33[106m', + 'BRIGHT_WHITE': '\33[107m', +} + +def log_node_success(node_name, message=None): + """Logs a success message.""" + _log_node(COLORS_FG["GREEN"], node_name, message) + +def log_node_info(node_name, message=None): + """Logs an info message.""" + _log_node(COLORS_FG["CYAN"], node_name, message) + + +def log_node_warn(node_name, message=None): + """Logs an warn message.""" + _log_node(COLORS_FG["YELLOW"], node_name, message) + +def log_node_error(node_name, message=None): + """Logs an warn message.""" + _log_node(COLORS_FG["RED"], node_name, message) + +def log_node(node_name, message=None): + """Logs a message.""" + _log_node(COLORS_FG["CYAN"], node_name, message) + + +def _log_node(color, node_name, message=None, prefix=''): + print(_get_log_msg(color, node_name, message, prefix=prefix)) + +def _get_log_msg(color, node_name, message=None, prefix=''): + msg = f'{COLORS_STYLE["BOLD"]}{color}{prefix}[EasyUse] {node_name.replace(" (EasyUse)", "")}' + msg += f':{COLORS_STYLE["RESET"]} {message}' if message is not None else f'{COLORS_STYLE["RESET"]}' + return msg + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/math.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/math.py new file mode 100644 index 00000000..07b9ec08 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/math.py @@ -0,0 +1,133 @@ +""" +Math utility functions for formula evaluation +""" +import math +import re + +def evaluate_formula(formula: str, a=0, b=0, c=0, d=0) -> float: + """ + θ‘η—字符串数学公式 + + ζ”―ζŒηš„θΏη—η¬¦ε’Œε‡½ζ•°οΌš + - 基本运η—οΌš+, -, *, /, //, %, ** + - 比较运η—οΌš>, <, >=, <=, ==, != + - ζ•°ε­¦ε‡½ζ•°οΌšabs, pow, round, ceil, floor, sqrt, exp, log, log10 + - δΈ‰θ§’ε‡½ζ•°οΌšsin, cos, tan, asin, acos, atan + - εΈΈι‡οΌšpi, e + + Args: + formula: ζ•°ε­¦ε…¬εΌε­—η¬¦δΈ²οΌŒε―δ»₯δ½Ώη”¨ε˜ι‡a、b、c、d + a: ε˜ι‡aηš„ε€Ό + b: ε˜ι‡bηš„ε€Ό + c: ε˜ι‡cηš„ε€Ό + d: ε˜ι‡dηš„ε€Ό + + Returns: + θ‘η—η»“ζžœ + + Examples: + >>> evaluate_formula("a + b", 1, 2) + 3.0 + >>> evaluate_formula("pow(a, 2)", 5) + 25.0 + >>> evaluate_formula("ceil(a / b)", 5, 2) + 3.0 + >>> evaluate_formula("(a>b)*b+(a<=b)*a", 5, 3) + 3.0 + >>> evaluate_formula("(a>b)*b+(a<=b)*a", 2, 3) + 2.0 + """ + # ε‰ε…¨ηš„数学函数白名单 + safe_dict = { + # εŸΊζœ¬θΏη— + 'abs': abs, + 'pow': pow, + 'round': round, + # 数学函数 + 'ceil': math.ceil, + 'floor': math.floor, + 'sqrt': math.sqrt, + 'exp': math.exp, + 'log': math.log, + 'log10': math.log10, + # 三角函数 + 'sin': math.sin, + 'cos': math.cos, + 'tan': math.tan, + 'asin': math.asin, + 'acos': math.acos, + 'atan': math.atan, + # 常量 + 'pi': math.pi, + 'e': math.e, + # ε˜ι‡ + 'a': float(a), + 'b': float(b), + 'c': float(c), + 'd': float(d), + } + + try: + # 使用evalθ‘η—ε…¬εΌοΌŒι™εˆΆε―η”¨ηš„ε‡½ζ•°ε’Œε˜ι‡ + result = eval(formula, {"__builtins__": {}}, safe_dict) + return float(result) + except Exception as e: + raise ValueError(f"公式θ‘η—ι”™θ――: {str(e)}") + + +def ceil_value(value: float) -> int: + """ε‘δΈŠε–ζ•΄""" + return math.ceil(value) + + +def floor_value(value: float) -> int: + """向下取整""" + return math.floor(value) + + +def round_value(value: float, decimals: int = 0) -> float: + """ + ε››θˆδΊ”ε…₯ + + Args: + value: θ¦ε–ζ•΄ηš„ε€Ό + decimals: 保留小数位数 + + Returns: + ε››θˆδΊ”ε…₯εŽηš„ε€Ό + """ + return round(value, decimals) + + +def power(base: float, exponent: float) -> float: + """θ‘η—幂运η—""" + return math.pow(base, exponent) + + +def sqrt_value(value: float) -> float: + """θ‘η—εΉ³ζ–Ήζ Ή""" + if value < 0: + raise ValueError("δΈθƒ½ε―Ήθ΄Ÿζ•°ζ±‚εΉ³ζ–Ήζ Ή") + return math.sqrt(value) + + +def add(a: float, b: float) -> float: + """εŠ ζ³•""" + return a + b + + +def subtract(a: float, b: float) -> float: + """减法""" + return a - b + + +def multiply(a: float, b: float) -> float: + """δΉ˜ζ³•""" + return a * b + + +def divide(a: float, b: float) -> float: + """陀法""" + if b == 0: + raise ValueError("陀数不能为離") + return a / b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/messages.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/messages.py new file mode 100644 index 00000000..a5ea47e5 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/messages.py @@ -0,0 +1,55 @@ +from server import PromptServer +from aiohttp import web +import time +import json + +class MessageCancelled(Exception): + pass + +class Message: + stash = {} + messages = {} + cancelled = False + + @classmethod + def addMessage(cls, id, message): + if message == '__cancel__': + cls.messages = {} + cls.cancelled = True + elif message == '__start__': + cls.messages = {} + cls.stash = {} + cls.cancelled = False + else: + cls.messages[str(id)] = message + + @classmethod + def waitForMessage(cls, id, period=0.1, asList=False): + sid = str(id) + while not (sid in cls.messages) and not ("-1" in cls.messages): + if cls.cancelled: + cls.cancelled = False + raise MessageCancelled() + time.sleep(period) + if cls.cancelled: + cls.cancelled = False + raise MessageCancelled() + message = cls.messages.pop(str(id), None) or cls.messages.pop("-1") + try: + if asList: + return [str(x.strip()) for x in message.split(",")] + else: + try: + return json.loads(message) + except ValueError: + return message + except ValueError: + print( f"ERROR IN MESSAGE - failed to parse '${message}' as ${'comma separated list of strings' if asList else 'string'}") + return [message] if asList else message + + +@PromptServer.instance.routes.post('/easyuse/message_callback') +async def message_callback(request): + post = await request.post() + Message.addMessage(post.get("id"), post.get("message")) + return web.json_response({}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/model.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/model.py new file mode 100644 index 00000000..41ccf51a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/model.py @@ -0,0 +1,58 @@ +import json +import os +import folder_paths +import server +from .utils import find_tags + +class easyModelManager: + + def __init__(self): + self.img_suffixes = [".png", ".jpg", ".jpeg", ".gif", ".webp", ".bmp", ".tiff", ".svg", ".tif", ".tiff"] + self.default_suffixes = [".ckpt", ".pt", ".bin", ".pth", ".safetensors"] + self.models_config = { + "checkpoints": {"suffix": self.default_suffixes}, + "loras": {"suffix": self.default_suffixes}, + "unet": {"suffix": self.default_suffixes}, + } + self.model_lists = {} + + def find_thumbnail(self, model_type, name): + file_no_ext = os.path.splitext(name)[0] + for ext in self.img_suffixes: + full_path = folder_paths.get_full_path(model_type, file_no_ext + ext) + if os.path.isfile(str(full_path)): + return full_path + return None + + def get_model_lists(self, model_type): + if model_type not in self.models_config: + return [] + filenames = folder_paths.get_filename_list(model_type) + model_lists = [] + for name in filenames: + model_suffix = os.path.splitext(name)[-1] + if model_suffix not in self.models_config[model_type]["suffix"]: + continue + else: + cfg = { + "name": os.path.basename(os.path.splitext(name)[0]), + "full_name": name, + "remark": '', + "file_path": folder_paths.get_full_path(model_type, name), + "type": model_type, + "suffix": model_suffix, + "dir_tags": find_tags(name), + "cover": self.find_thumbnail(model_type, name), + "metadata": None, + "sha256": None + } + model_lists.append(cfg) + + return model_lists + + def get_model_info(self, model_type, model_name): + pass + +# if __name__ == "__main__": +# manager = easyModelManager() +# print(manager.get_model_lists("checkpoints")) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/sampler.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/sampler.py new file mode 100644 index 00000000..70716efb --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/sampler.py @@ -0,0 +1,1053 @@ +import comfy +import comfy.model_management +import comfy.samplers +import torch +import numpy as np +import latent_preview +from nodes import MAX_RESOLUTION +from PIL import Image +from typing import Dict, List, Optional, Tuple, Union, Any +from ..modules.brushnet.model_patch import add_model_patch + +class easySampler: + def __init__(self): + self.last_helds: dict[str, list] = { + "results": [], + "pipe_line": [], + } + self.device = comfy.model_management.intermediate_device() + + @staticmethod + def tensor2pil(image: torch.Tensor) -> Image.Image: + """Convert a torch tensor to a PIL image.""" + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + + @staticmethod + def pil2tensor(image: Image.Image) -> torch.Tensor: + """Convert a PIL image to a torch tensor.""" + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + + @staticmethod + def enforce_mul_of_64(d): + d = int(d) + if d <= 7: + d = 8 + leftover = d % 8 # 8 is the number of pixels per byte + if leftover != 0: # if the number of pixels is not a multiple of 8 + if (leftover < 4): # if the number of pixels is less than 4 + d -= leftover # remove the leftover pixels + else: # if the number of pixels is more than 4 + d += 8 - leftover # add the leftover pixels + + return int(d) + + @staticmethod + def safe_split(to_split: str, delimiter: str) -> List[str]: + """Split the input string and return a list of non-empty parts.""" + parts = to_split.split(delimiter) + parts = [part for part in parts if part not in ('', ' ', ' ')] + + while len(parts) < 2: + parts.append('None') + return parts + + def emptyLatent(self, resolution, empty_latent_width, empty_latent_height, batch_size=1, compression=0, model_type='sd', video_length=25): + if resolution not in ["θ‡ͺεšδΉ‰ x θ‡ͺεšδΉ‰", 'width x height (custom)']: + try: + width, height = map(int, resolution.split(' x ')) + empty_latent_width = width + empty_latent_height = height + except ValueError: + raise ValueError("Invalid base_resolution format.") + if model_type == 'sd3': + latent = torch.ones([batch_size, 16, empty_latent_height // 8, empty_latent_width // 8], device=self.device) * 0.0609 + samples = {"samples": latent} + elif model_type == 'mochi': + latent = torch.zeros([batch_size, 12, ((video_length - 1) // 6) + 1, empty_latent_height // 8, empty_latent_width // 8], device=self.device) + samples = {"samples": latent} + elif compression == 0: + latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8], device=self.device) + samples = {"samples": latent} + else: + latent_c = torch.zeros( + [batch_size, 16, empty_latent_height // compression, empty_latent_width // compression]) + latent_b = torch.zeros([batch_size, 4, empty_latent_height // 4, empty_latent_width // 4]) + + samples = ({"samples": latent_c}, {"samples": latent_b}) + return samples + + def prepare_noise(self, latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", + variation_seed=None, variation_strength=None): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + + latent_size = latent_image.size() + latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]] + + if variation_strength is not None and variation_strength > 0 or incremental_seed_mode.startswith( + "variation str inc"): + if noise_device == "cpu": + variation_generator = torch.manual_seed(variation_seed) + else: + torch.cuda.manual_seed(variation_seed) + variation_generator = None + + variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=variation_generator, device=noise_device) + else: + variation_latent = None + + def apply_variation(input_latent, strength_up=None): + if variation_latent is None: + return input_latent + else: + strength = variation_strength + + if strength_up is not None: + strength += strength_up + + variation_noise = variation_latent.expand(input_latent.size()[0], -1, -1, -1) + result = (1 - strength) * input_latent + strength * variation_noise + return result + + # method: incremental seed batch noise + if noise_inds is None and incremental_seed_mode == "incremental": + batch_cnt = latent_size[0] + + latents = None + for i in range(batch_cnt): + if noise_device == "cpu": + generator = torch.manual_seed(seed + i) + else: + torch.cuda.manual_seed(seed + i) + generator = None + + latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + + latent = apply_variation(latent) + + if latents is None: + latents = latent + else: + latents = torch.cat((latents, latent), dim=0) + + return latents + + # method: incremental variation batch noise + elif noise_inds is None and incremental_seed_mode.startswith("variation str inc"): + batch_cnt = latent_size[0] + + latents = None + for i in range(batch_cnt): + if noise_device == "cpu": + generator = torch.manual_seed(seed) + else: + torch.cuda.manual_seed(seed) + generator = None + + latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + + step = float(incremental_seed_mode[18:]) + latent = apply_variation(latent, step * i) + + if latents is None: + latents = latent + else: + latents = torch.cat((latents, latent), dim=0) + + return latents + + # method: comfy batch noise + if noise_device == "cpu": + generator = torch.manual_seed(seed) + else: + torch.cuda.manual_seed(seed) + generator = None + + if noise_inds is None: + latents = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + latents = apply_variation(latents) + return latents + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1] + 1): + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, + layout=latent_image.layout, + generator=generator, device=noise_device) + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + + def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, + disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, + preview_latent=True, disable_pbar=False, noise_device='CPU'): + device = comfy.model_management.get_torch_device() + noise_device = 'cpu' if noise_device == 'CPU' else device + latent_image = latent["samples"] + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = False + + if preview_latent: + previewer = latent_preview.get_previewer(device, model.model.latent_format) + + pbar = comfy.utils.ProgressBar(steps) + + def callback(step, x0, x, total_steps): + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, + device=noise_device) + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = self.prepare_noise(latent_image, seed, batch_inds, noise_device=noise_device) + + ####################################################################################### + # add model patch + # brushnet + add_model_patch(model) + ####################################################################################### + samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, + latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, + last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, + callback=callback, + disable_pbar=disable_pbar, seed=seed) + out = latent.copy() + out["samples"] = samples + return out + + def custom_ksampler(self, model, seed, steps, cfg, _sampler, sigmas, positive, negative, latent, + disable_noise=False, preview_latent=True, disable_pbar=False, noise_device='CPU'): + + device = comfy.model_management.get_torch_device() + noise_device = 'cpu' if noise_device == 'CPU' else device + + latent_image = latent["samples"] + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device) + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = self.prepare_noise(latent_image, seed, batch_inds, noise_device=noise_device) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = False + + if preview_latent: + previewer = latent_preview.get_previewer(device, model.model.latent_format) + + pbar = comfy.utils.ProgressBar(steps) + + def callback(step, x0, x, total_steps): + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + + samples = comfy.samplers.sample(model, noise, positive, negative, cfg, device, _sampler, sigmas, latent_image=latent_image, model_options=model.model_options, + denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + + out = latent.copy() + out["samples"] = samples + return out + + def custom_advanced_ksampler(self, guider, sampler, sigmas, latent_image, add_noise='enable', seed=0, preview_latent=False): + latent = latent_image + latent_image = latent["samples"] + latent = latent.copy() + latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) + latent["samples"] = latent_image + + device = comfy.model_management.get_torch_device() + noise_device = device if add_noise == 'enable (GPU=A1111)' else 'cpu' + + if add_noise == 'disable': + noise = torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = self.prepare_noise(latent_image, seed, batch_inds, noise_device=noise_device) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + previewer = False + + model = guider.model_patcher + steps = sigmas.shape[-1] - 1 + if preview_latent: + previewer = latent_preview.get_previewer(model.load_device, model.model.latent_format) + + pbar = comfy.utils.ProgressBar(steps) + + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + def callback(step, x0, x, total_steps): + if x0_output is not None: + x0_output["x0"] = x0 + + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = guider.sample(noise, latent_image, sampler, sigmas, denoise_mask=noise_mask, + callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(comfy.model_management.intermediate_device()) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + + def get_value_by_id(self, key: str, my_unique_id: Any) -> Optional[Any]: + """Retrieve value by its associated ID.""" + try: + for value, id_ in self.last_helds[key]: + if id_ == my_unique_id: + return value + except KeyError: + return None + + def update_value_by_id(self, key: str, my_unique_id: Any, new_value: Any) -> Union[bool, None]: + """Update the value associated with a given ID. Return True if updated, False if appended, None if key doesn't exist.""" + try: + for i, (value, id_) in enumerate(self.last_helds[key]): + if id_ == my_unique_id: + self.last_helds[key][i] = (new_value, id_) + return True + self.last_helds[key].append((new_value, my_unique_id)) + return False + except KeyError: + return False + + def upscale(self, samples, upscale_method, scale_by, crop): + s = samples.copy() + width = self.enforce_mul_of_64(round(samples["samples"].shape[3] * scale_by)) + height = self.enforce_mul_of_64(round(samples["samples"].shape[2] * scale_by)) + + if (width > MAX_RESOLUTION): + width = MAX_RESOLUTION + if (height > MAX_RESOLUTION): + height = MAX_RESOLUTION + + s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, crop) + return (s,) + + def handle_upscale(self, samples: dict, upscale_method: str, factor: float, crop: bool) -> dict: + """Upscale the samples if the upscale_method is not set to 'None'.""" + if upscale_method != "None": + samples = self.upscale(samples, upscale_method, factor, crop)[0] + return samples + + def init_state(self, my_unique_id: Any, key: str, default: Any) -> Any: + """Initialize the state by either fetching the stored value or setting a default.""" + value = self.get_value_by_id(key, my_unique_id) + if value is not None: + return value + return default + + def get_output(self, pipe: dict,) -> Tuple: + """Return a tuple of various elements fetched from the input pipe dictionary.""" + return ( + pipe, + pipe.get("images"), + pipe.get("model"), + pipe.get("positive"), + pipe.get("negative"), + pipe.get("samples"), + pipe.get("vae"), + pipe.get("clip"), + pipe.get("seed"), + ) + + def get_output_sdxl(self, sdxl_pipe: dict) -> Tuple: + """Return a tuple of various elements fetched from the input sdxl_pipe dictionary.""" + return ( + sdxl_pipe, + sdxl_pipe.get("model"), + sdxl_pipe.get("positive"), + sdxl_pipe.get("negative"), + sdxl_pipe.get("vae"), + sdxl_pipe.get("refiner_model"), + sdxl_pipe.get("refiner_positive"), + sdxl_pipe.get("refiner_negative"), + sdxl_pipe.get("refiner_vae"), + sdxl_pipe.get("samples"), + sdxl_pipe.get("clip"), + sdxl_pipe.get("images"), + sdxl_pipe.get("seed") + ) + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +class alignYourStepsScheduler: + + NOISE_LEVELS = { + "SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, + 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], + "SDXL": [14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, + 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], + "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} + + def get_sigmas(self, model_type, steps, denoise): + + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + sigmas = self.NOISE_LEVELS[model_type][:] + if (steps + 1) != len(sigmas): + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas),) + + +class gitsScheduler: + + NOISE_LEVELS = { + 0.80: [ + [14.61464119, 7.49001646, 0.02916753], + [14.61464119, 11.54541874, 6.77309084, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 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0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, + 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, + 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, + 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, + 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, + 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, + 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, + 0.02916753], + [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, + 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, + 0.09824532, 0.02916753], + [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, + 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, + 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, + 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, + 0.13792117, 0.09824532, 0.02916753], + ], + } + + def get_sigmas(self, coeff, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + if steps <= 20: + sigmas = self.NOISE_LEVELS[round(coeff, 2)][steps-2][:] + else: + sigmas = self.NOISE_LEVELS[round(coeff, 2)][-1][:] + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/styleAlign.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/styleAlign.py new file mode 100644 index 00000000..fefae602 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/styleAlign.py @@ -0,0 +1,148 @@ +import torch +import torch.nn as nn +from comfy.model_patcher import ModelPatcher +from typing import Union + +T = torch.Tensor + + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d + + +class StyleAlignedArgs: + def __init__(self, share_attn: str) -> None: + self.adain_keys = "k" in share_attn + self.adain_values = "v" in share_attn + self.adain_queries = "q" in share_attn + + share_attention: bool = True + adain_queries: bool = True + adain_keys: bool = True + adain_values: bool = True + + +def expand_first( + feat: T, + scale=1.0, +) -> T: + """ + Expand the first element so it has the same shape as the rest of the batch. + """ + b = feat.shape[0] + feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1) + if scale == 1: + feat_style = feat_style.expand(2, b // 2, *feat.shape[1:]) + else: + feat_style = feat_style.repeat(1, b // 2, 1, 1, 1) + feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1) + return feat_style.reshape(*feat.shape) + + +def concat_first(feat: T, dim=2, scale=1.0) -> T: + """ + concat the the feature and the style feature expanded above + """ + feat_style = expand_first(feat, scale=scale) + return torch.cat((feat, feat_style), dim=dim) + + +def calc_mean_std(feat, eps: float = 1e-5) -> "tuple[T, T]": + feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt() + feat_mean = feat.mean(dim=-2, keepdims=True) + return feat_mean, feat_std + +def adain(feat: T) -> T: + feat_mean, feat_std = calc_mean_std(feat) + feat_style_mean = expand_first(feat_mean) + feat_style_std = expand_first(feat_std) + feat = (feat - feat_mean) / feat_std + feat = feat * feat_style_std + feat_style_mean + return feat + +class SharedAttentionProcessor: + def __init__(self, args: StyleAlignedArgs, scale: float): + self.args = args + self.scale = scale + + def __call__(self, q, k, v, extra_options): + if self.args.adain_queries: + q = adain(q) + if self.args.adain_keys: + k = adain(k) + if self.args.adain_values: + v = adain(v) + if self.args.share_attention: + k = concat_first(k, -2, scale=self.scale) + v = concat_first(v, -2) + + return q, k, v + + +def get_norm_layers( + layer: nn.Module, + norm_layers_: "dict[str, list[Union[nn.GroupNorm, nn.LayerNorm]]]", + share_layer_norm: bool, + share_group_norm: bool, +): + if isinstance(layer, nn.LayerNorm) and share_layer_norm: + norm_layers_["layer"].append(layer) + if isinstance(layer, nn.GroupNorm) and share_group_norm: + norm_layers_["group"].append(layer) + else: + for child_layer in layer.children(): + get_norm_layers( + child_layer, norm_layers_, share_layer_norm, share_group_norm + ) + + +def register_norm_forward( + norm_layer: Union[nn.GroupNorm, nn.LayerNorm], +) -> Union[nn.GroupNorm, nn.LayerNorm]: + if not hasattr(norm_layer, "orig_forward"): + setattr(norm_layer, "orig_forward", norm_layer.forward) + orig_forward = norm_layer.orig_forward + + def forward_(hidden_states: T) -> T: + n = hidden_states.shape[-2] + hidden_states = concat_first(hidden_states, dim=-2) + hidden_states = orig_forward(hidden_states) # type: ignore + return hidden_states[..., :n, :] + + norm_layer.forward = forward_ # type: ignore + return norm_layer + + +def register_shared_norm( + model: ModelPatcher, + share_group_norm: bool = True, + share_layer_norm: bool = True, +): + norm_layers = {"group": [], "layer": []} + get_norm_layers(model.model, norm_layers, share_layer_norm, share_group_norm) + print( + f"Patching {len(norm_layers['group'])} group norms, {len(norm_layers['layer'])} layer norms." + ) + return [register_norm_forward(layer) for layer in norm_layers["group"]] + [ + register_norm_forward(layer) for layer in norm_layers["layer"] + ] + + +SHARE_NORM_OPTIONS = ["both", "group", "layer", "disabled"] +SHARE_ATTN_OPTIONS = ["q+k", "q+k+v", "disabled"] + + +def styleAlignBatch(model, share_norm, share_attn, scale=1.0): + m = model.clone() + share_group_norm = share_norm in ["group", "both"] + share_layer_norm = share_norm in ["layer", "both"] + register_shared_norm(model, share_group_norm, share_layer_norm) + args = StyleAlignedArgs(share_attn) + m.set_model_attn1_patch(SharedAttentionProcessor(args, scale)) + return m \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/translate.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/translate.py new file mode 100644 index 00000000..89e304dc --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/translate.py @@ -0,0 +1,247 @@ +#credit to shadowcz007 for this module +#from https://github.com/shadowcz007/comfyui-mixlab-nodes/blob/main/nodes/TextGenerateNode.py +import re +import os +import folder_paths + +import comfy.utils +import torch +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer + +from .utils import install_package +try: + from lark import Lark, Transformer, v_args +except: + print('install lark...') + install_package('lark') + from lark import Lark, Transformer, v_args + +model_path = os.path.join(folder_paths.models_dir, 'prompt_generator') +zh_en_model_path = os.path.join(model_path, 'opus-mt-zh-en') +zh_en_model, zh_en_tokenizer = None, None + +def correct_prompt_syntax(prompt=""): + # print("input prompt",prompt) + corrected_elements = [] + # ε€„η†ζˆη»ŸδΈ€ηš„θ‹±ζ–‡ζ ‡η‚Ή + prompt = prompt.replace('（', '(').replace('οΌ‰', ')').replace('，', ',').replace(';', ',').replace('。', '.').replace('：',':').replace('\\',',') + # εˆ ι™€ε€šδ½™ηš„η©Ίζ Ό + prompt = re.sub(r'\s+', ' ', prompt).strip() + prompt = prompt.replace("< ","<").replace(" >",">").replace("( ","(").replace(" )",")").replace("[ ","[").replace(' ]',']') + + # εˆ†θ― + prompt_elements = prompt.split(',') + + def balance_brackets(element, open_bracket, close_bracket): + open_brackets_count = element.count(open_bracket) + close_brackets_count = element.count(close_bracket) + return element + close_bracket * (open_brackets_count - close_brackets_count) + + for element in prompt_elements: + element = element.strip() + + # 倄理空元素 + if not element: + continue + + # ζ£€ζŸ₯εΉΆε€„η†εœ†ζ‹¬ε·γ€ζ–Ήζ‹¬ε·γ€ε°–ζ‹¬ε· + if element[0] in '([': + corrected_element = balance_brackets(element, '(', ')') if element[0] == '(' else balance_brackets(element, '[', ']') + elif element[0] == '<': + corrected_element = balance_brackets(element, '<', '>') + else: + # εˆ ι™€εΌ€ε€΄ηš„ε³ζ‹¬ε·ζˆ–ε³ζ–Ήζ‹¬ε· + corrected_element = element.lstrip(')]') + + corrected_elements.append(corrected_element) + + # 重组δΏζ­£εŽηš„prompt + return ','.join(corrected_elements) + +def detect_language(input_str): + # 统θ‘δΈ­ζ–‡ε’Œθ‹±ζ–‡ε­—η¬¦ηš„ζ•°ι‡ + count_cn = count_en = 0 + for char in input_str: + if '\u4e00' <= char <= '\u9fff': + count_cn += 1 + elif char.isalpha(): + count_en += 1 + + # ζ Ήζη»Ÿθ‘ηš„ε­—η¬¦ζ•°ι‡εˆ€ζ–­δΈ»θ¦θ―­θ¨€ + if count_cn > count_en: + return "cn" + elif count_en > count_cn: + return "en" + else: + return "unknow" + +def has_chinese(text): + has_cn = False + _text = text + _text = re.sub(r'<.*?>', '', _text) + _text = re.sub(r'__.*?__', '', _text) + _text = re.sub(r'embedding:.*?$', '', _text) + for char in _text: + if '\u4e00' <= char <= '\u9fff': + has_cn = True + break + elif char.isalpha(): + continue + return has_cn + +def translate(text): + global zh_en_model_path, zh_en_model, zh_en_tokenizer + + if not os.path.exists(zh_en_model_path): + zh_en_model_path = 'Helsinki-NLP/opus-mt-zh-en' + + if zh_en_model is None: + + zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval() + zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path, padding=True, truncation=True) + + zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu") + with torch.no_grad(): + encoded = zh_en_tokenizer([text], return_tensors="pt") + encoded.to(zh_en_model.device) + sequences = zh_en_model.generate(**encoded) + return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0] + +@v_args(inline=True) # Decorator to flatten the tree directly into the function arguments +class ChinesePromptTranslate(Transformer): + + def sentence(self, *args): + return ", ".join(args) + + def phrase(self, *args): + return "".join(args) + + def emphasis(self, *args): + # Reconstruct the emphasis with translated content + return "(" + "".join(args) + ")" + + def weak_emphasis(self, *args): + print('weak_emphasis:', args) + return "[" + "".join(args) + "]" + + def embedding(self, *args): + print('prompt embedding', args[0]) + if len(args) == 1: + embedding_name = str(args[0]) + return f"embedding:{embedding_name}" + elif len(args) > 1: + embedding_name, *numbers = args + + if len(numbers) == 2: + return f"embedding:{embedding_name}:{numbers[0]}:{numbers[1]}" + elif len(numbers) == 1: + return f"embedding:{embedding_name}:{numbers[0]}" + else: + return f"embedding:{embedding_name}" + + def lora(self, *args): + if len(args) == 1: + return f"" + elif len(args) > 1: + # print('lora', args) + _, loar_name, *numbers = args + loar_name = str(loar_name).strip() + if len(numbers) == 2: + return f"" + elif len(numbers) == 1: + return f"" + else: + return f"" + + def weight(self, word, number): + translated_word = translate(str(word)).rstrip('.') + return f"({translated_word}:{str(number).strip()})" + + def schedule(self, *args): + print('prompt schedule', args) + data = [str(arg).strip() for arg in args] + + return f"[{':'.join(data)}]" + + def word(self, word): + # Translate each word using the dictionary + word = str(word) + match_cn = re.search(r'@.*?@', word) + if re.search(r'__.*?__', word): + return word.rstrip('.') + elif match_cn: + chinese = match_cn.group() + before = word.split('@', 1) + before = before[0] if len(before) > 0 else '' + before = translate(str(before)).rstrip('.') if before else '' + after = word.rsplit('@', 1) + after = after[len(after)-1] if len(after) > 1 else '' + after = translate(after).rstrip('.') if after else '' + return before + chinese.replace('@', '').rstrip('.') + after + elif detect_language(word) == "cn": + return translate(word).rstrip('.') + else: + return word.rstrip('.') + + +#εšδΉ‰Prompt文法 +grammar = r""" +start: sentence +sentence: phrase ("," phrase)* +phrase: emphasis | weight | word | lora | embedding | schedule +emphasis: "(" sentence ")" -> emphasis + | "[" sentence "]" -> weak_emphasis +weight: "(" word ":" NUMBER ")" +schedule: "[" word ":" word ":" NUMBER "]" +lora: "<" WORD ":" WORD (":" NUMBER)? (":" NUMBER)? ">" +embedding: "embedding" ":" WORD (":" NUMBER)? (":" NUMBER)? +word: WORD + +NUMBER: /\s*-?\d+(\.\d+)?\s*/ +WORD: /[^,:\(\)\[\]<>]+/ +""" +def zh_to_en(text): + global zh_en_model_path, zh_en_model, zh_en_tokenizer + # 进度村 + pbar = comfy.utils.ProgressBar(len(text) + 1) + texts = [correct_prompt_syntax(t) for t in text] + + install_package('sentencepiece', '0.2.0') + + if not os.path.exists(zh_en_model_path): + zh_en_model_path = 'Helsinki-NLP/opus-mt-zh-en' + + if zh_en_model is None: + zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval() + zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path, padding=True, truncation=True) + + zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu") + + prompt_result = [] + + en_texts = [] + + for t in texts: + if t: + # translated_text = translated_word = translate(zh_en_tokenizer,zh_en_model,str(t)) + parser = Lark(grammar, start="start", parser="lalr", transformer=ChinesePromptTranslate()) + # print('t',t) + result = parser.parse(t).children + # print('en_result',result) + # en_text=translate(zh_en_tokenizer,zh_en_model,text_without_syntax) + en_texts.append(result[0]) + + zh_en_model.to('cpu') + # print("test en_text", en_texts) + # en_text.to("cuda" if torch.cuda.is_available() else "cpu") + + pbar.update(1) + for t in en_texts: + prompt_result.append(t) + pbar.update(1) + + # print('prompt_result', prompt_result, ) + if len(prompt_result) == 0: + prompt_result = [""] + + return prompt_result \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/utils.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/utils.py new file mode 100644 index 00000000..122067e5 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/utils.py @@ -0,0 +1,282 @@ +class AlwaysEqualProxy(str): + def __eq__(self, _): + return True + + def __ne__(self, _): + return False + +class TautologyStr(str): + def __ne__(self, other): + return False + +class ByPassTypeTuple(tuple): + def __getitem__(self, index): + if index>0: + index=0 + item = super().__getitem__(index) + if isinstance(item, str): + return TautologyStr(item) + return item + +comfy_ui_revision = None +def get_comfyui_revision(): + try: + import git + import os + import folder_paths + repo = git.Repo(os.path.dirname(folder_paths.__file__)) + comfy_ui_revision = len(list(repo.iter_commits('HEAD'))) + except: + comfy_ui_revision = "Unknown" + return comfy_ui_revision + + +import sys +import importlib.util +import importlib.metadata +import comfy.model_management as mm +import gc +from packaging import version +from server import PromptServer +def is_package_installed(package): + try: + module = importlib.util.find_spec(package) + return module is not None + except ImportError as e: + print(e) + return False + +def install_package(package, v=None, compare=True, compare_version=None): + run_install = True + if is_package_installed(package): + try: + installed_version = importlib.metadata.version(package) + if v is not None: + if compare_version is None: + compare_version = v + if not compare or version.parse(installed_version) >= version.parse(compare_version): + run_install = False + else: + run_install = False + except: + run_install = False + + if run_install: + import subprocess + package_command = package + '==' + v if v is not None else package + PromptServer.instance.send_sync("easyuse-toast", {'content': f"Installing {package_command}...", 'duration': 5000}) + result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', package_command], capture_output=True, text=True) + if result.returncode == 0: + PromptServer.instance.send_sync("easyuse-toast", {'content': f"{package} installed successfully", 'type': 'success', 'duration': 5000}) + print(f"Package {package} installed successfully") + return True + else: + PromptServer.instance.send_sync("easyuse-toast", {'content': f"{package} installed failed", 'type': 'error', 'duration': 5000}) + print(f"Package {package} installed failed") + return False + else: + return False + +def compare_revision(num): + global comfy_ui_revision + if not comfy_ui_revision: + comfy_ui_revision = get_comfyui_revision() + return True if comfy_ui_revision == 'Unknown' or int(comfy_ui_revision) >= num else False + +def find_tags(string: str, sep="/") -> list[str]: + """ + find tags from string use the sep for split + Note: string may contain the \\ or / for path separator + """ + if not string: + return [] + string = string.replace("\\", "/") + while "//" in string: + string = string.replace("//", "/") + if string and sep in string: + return string.split(sep)[:-1] + return [] + + +from comfy.model_base import BaseModel +import comfy.supported_models +import comfy.supported_models_base +def get_sd_version(model): + base: BaseModel = model.model + model_config: comfy.supported_models.supported_models_base.BASE = base.model_config + if isinstance(model_config, comfy.supported_models.SDXL): + return 'sdxl' + elif isinstance(model_config, comfy.supported_models.SDXLRefiner): + return 'sdxl_refiner' + elif isinstance( + model_config, (comfy.supported_models.SD15, comfy.supported_models.SD20) + ): + return 'sd1' + elif isinstance( + model_config, (comfy.supported_models.SVD_img2vid) + ): + return 'svd' + elif isinstance(model_config, comfy.supported_models.SD3): + return 'sd3' + elif isinstance(model_config, comfy.supported_models.HunyuanDiT): + return 'hydit' + elif isinstance(model_config, comfy.supported_models.Flux): + return 'flux' + elif isinstance(model_config, comfy.supported_models.GenmoMochi): + return 'mochi' + else: + return 'unknown' + +def find_nearest_steps(clip_id, prompt): + """Find the nearest KSampler or preSampling node that references the given id.""" + def check_link_to_clip(node_id, clip_id, visited=None, node=None): + """Check if a given node links directly or indirectly to a loader node.""" + if visited is None: + visited = set() + + if node_id in visited: + return False + visited.add(node_id) + if "pipe" in node["inputs"]: + link_ids = node["inputs"]["pipe"] + for id in link_ids: + if id != 0 and id == str(clip_id): + return True + return False + + for id in prompt: + node = prompt[id] + if "Sampler" in node["class_type"] or "sampler" in node["class_type"] or "Sampling" in node["class_type"]: + # Check if this KSampler node directly or indirectly references the given CLIPTextEncode node + if check_link_to_clip(id, clip_id, None, node): + steps = node["inputs"]["steps"] if "steps" in node["inputs"] else 1 + return steps + return 1 + +def find_wildcards_seed(clip_id, text, prompt): + """ Find easy wildcards seed value""" + def find_link_clip_id(id, seed, wildcard_id): + node = prompt[id] + if "positive" in node['inputs']: + link_ids = node["inputs"]["positive"] + if type(link_ids) == list: + for id in link_ids: + if id != 0: + if id == wildcard_id: + wildcard_node = prompt[wildcard_id] + seed = wildcard_node["inputs"]["seed"] if "seed" in wildcard_node["inputs"] else None + if seed is None: + seed = wildcard_node["inputs"]["seed_num"] if "seed_num" in wildcard_node["inputs"] else None + return seed + else: + return find_link_clip_id(id, seed, wildcard_id) + else: + return None + else: + return None + if "__" in text: + seed = None + for id in prompt: + node = prompt[id] + if "wildcards" in node["class_type"]: + wildcard_id = id + return find_link_clip_id(str(clip_id), seed, wildcard_id) + return seed + else: + return None + +def is_linked_styles_selector(prompt, unique_id, prompt_type='positive'): + unique_id = unique_id.split('.')[len(unique_id.split('.')) - 1] if "." in unique_id else unique_id + inputs_values = prompt[unique_id]['inputs'][prompt_type] if prompt_type in prompt[unique_id][ + 'inputs'] else None + if type(inputs_values) == list and inputs_values != 'undefined' and inputs_values[0]: + return True if prompt[inputs_values[0]] and prompt[inputs_values[0]]['class_type'] == 'easy stylesSelector' else False + else: + return False + +use_mirror = False +def get_local_filepath(url, dirname, local_file_name=None): + """Get local file path when is already downloaded or download it""" + import os + from server import PromptServer + from urllib.parse import urlparse + from torch.hub import download_url_to_file + global use_mirror + if not os.path.exists(dirname): + os.makedirs(dirname) + if not local_file_name: + parsed_url = urlparse(url) + local_file_name = os.path.basename(parsed_url.path) + destination = os.path.join(dirname, local_file_name) + if not os.path.exists(destination): + try: + if use_mirror: + url = url.replace('huggingface.co', 'hf-mirror.com') + print(f'downloading {url} to {destination}') + PromptServer.instance.send_sync("easyuse-toast", {'content': f'Downloading model to {destination}, please wait...', 'duration': 10000}) + download_url_to_file(url, destination) + except Exception as e: + use_mirror = True + url = url.replace('huggingface.co', 'hf-mirror.com') + print(f'Unable to download from huggingface, trying mirror: {url}') + PromptServer.instance.send_sync("easyuse-toast", {'content': f'Unable to connect to huggingface, trying mirror: {url}', 'duration': 10000}) + try: + download_url_to_file(url, destination) + except Exception as err: + error_msg = str(err.args[0]) if err.args else str(err) + PromptServer.instance.send_sync("easyuse-toast", + {'content': f'Unable to download model from {url}', 'type':'error'}) + raise Exception(f'Download failed. Original URL and mirror both failed.\nError: {error_msg}') + return destination + +def to_lora_patch_dict(state_dict: dict) -> dict: + """ Convert raw lora state_dict to patch_dict that can be applied on + modelpatcher.""" + patch_dict = {} + for k, w in state_dict.items(): + model_key, patch_type, weight_index = k.split('::') + if model_key not in patch_dict: + patch_dict[model_key] = {} + if patch_type not in patch_dict[model_key]: + patch_dict[model_key][patch_type] = [None] * 16 + patch_dict[model_key][patch_type][int(weight_index)] = w + + patch_flat = {} + for model_key, v in patch_dict.items(): + for patch_type, weight_list in v.items(): + patch_flat[model_key] = (patch_type, weight_list) + + return patch_flat + +def easySave(images, filename_prefix, output_type, prompt=None, extra_pnginfo=None): + """Save or Preview Image""" + from nodes import PreviewImage, SaveImage + if output_type in ["Hide", "None"]: + return list() + elif output_type in ["Preview", "Preview&Choose"]: + filename_prefix = 'easyPreview' + results = PreviewImage().save_images(images, filename_prefix, prompt, extra_pnginfo) + return results['ui']['images'] + else: + results = SaveImage().save_images(images, filename_prefix, prompt, extra_pnginfo) + return results['ui']['images'] + +def getMetadata(filepath): + with open(filepath, "rb") as file: + # https://github.com/huggingface/safetensors#format + # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header + header_size = int.from_bytes(file.read(8), "little", signed=False) + + if header_size <= 0: + raise BufferError("Invalid header size") + + header = file.read(header_size) + if header_size <= 0: + raise BufferError("Invalid header") + + return header + +def cleanGPUUsedForce(): + gc.collect() + mm.unload_all_models() + mm.soft_empty_cache() diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/wildcards.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/wildcards.py new file mode 100644 index 00000000..384f2f8b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/wildcards.py @@ -0,0 +1,476 @@ +import json +import os +import random +import re +from math import prod + +import yaml + +import folder_paths + +from .log import log_node_info + +easy_wildcard_dict = {} + +def get_wildcard_list(): + return [f"__{x}__" for x in easy_wildcard_dict.keys()] + +def wildcard_normalize(x): + return x.replace("\\", "/").lower() + +def read_wildcard(k, v): + if isinstance(v, list): + k = wildcard_normalize(k) + easy_wildcard_dict[k] = v + elif isinstance(v, dict): + for k2, v2 in v.items(): + new_key = f"{k}/{k2}" + new_key = wildcard_normalize(new_key) + read_wildcard(new_key, v2) + +def read_wildcard_dict(wildcard_path): + global easy_wildcard_dict + for root, directories, files in os.walk(wildcard_path, followlinks=True): + for file in files: + if file.endswith('.txt'): + file_path = os.path.join(root, file) + rel_path = os.path.relpath(file_path, wildcard_path) + key = os.path.splitext(rel_path)[0].replace('\\', '/').lower() + + try: + with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f: + lines = f.read().splitlines() + easy_wildcard_dict[key] = lines + except UnicodeDecodeError: + with open(file_path, 'r', encoding="ISO-8859-1") as f: + lines = f.read().splitlines() + easy_wildcard_dict[key] = lines + elif file.endswith('.yaml'): + file_path = os.path.join(root, file) + with open(file_path, 'r') as f: + yaml_data = yaml.load(f, Loader=yaml.FullLoader) + + for k, v in yaml_data.items(): + read_wildcard(k, v) + elif file.endswith('.json'): + file_path = os.path.join(root, file) + try: + with open(file_path, 'r') as f: + json_data = json.load(f) + for key, value in json_data.items(): + key = wildcard_normalize(key) + easy_wildcard_dict[key] = value + except ValueError: + print('json files load error') + return easy_wildcard_dict + + +def process(text, seed=None): + + if seed is not None: + random.seed(seed) + + def replace_options(string): + replacements_found = False + + def replace_option(match): + nonlocal replacements_found + options = match.group(1).split('|') + + multi_select_pattern = options[0].split('$$') + select_range = None + select_sep = ' ' + range_pattern = r'(\d+)(-(\d+))?' + range_pattern2 = r'-(\d+)' + + if len(multi_select_pattern) > 1: + r = re.match(range_pattern, options[0]) + + if r is None: + r = re.match(range_pattern2, options[0]) + a = '1' + b = r.group(1).strip() + else: + a = r.group(1).strip() + b = r.group(3).strip() + + if r is not None: + if b is not None and is_numeric_string(a) and is_numeric_string(b): + # PATTERN: num1-num2 + select_range = int(a), int(b) + elif is_numeric_string(a): + # PATTERN: num + x = int(a) + select_range = (x, x) + + if select_range is not None and len(multi_select_pattern) == 2: + # PATTERN: count$$ + options[0] = multi_select_pattern[1] + elif select_range is not None and len(multi_select_pattern) == 3: + # PATTERN: count$$ sep $$ + select_sep = multi_select_pattern[1] + options[0] = multi_select_pattern[2] + + adjusted_probabilities = [] + + total_prob = 0 + + for option in options: + parts = option.split('::', 1) + if len(parts) == 2 and is_numeric_string(parts[0].strip()): + config_value = float(parts[0].strip()) + else: + config_value = 1 # Default value if no configuration is provided + + adjusted_probabilities.append(config_value) + total_prob += config_value + + normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities] + + if select_range is None: + select_count = 1 + else: + select_count = random.randint(select_range[0], select_range[1]) + + if select_count > len(options): + selected_items = options + else: + selected_items = random.choices(options, weights=normalized_probabilities, k=select_count) + selected_items = set(selected_items) + + try_count = 0 + while len(selected_items) < select_count and try_count < 10: + remaining_count = select_count - len(selected_items) + additional_items = random.choices(options, weights=normalized_probabilities, k=remaining_count) + selected_items |= set(additional_items) + try_count += 1 + + selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', x, 1) for x in selected_items] + replacement = select_sep.join(selected_items2) + if '::' in replacement: + pass + + replacements_found = True + return replacement + + pattern = r'{([^{}]*?)}' + replaced_string = re.sub(pattern, replace_option, string) + + return replaced_string, replacements_found + + def replace_wildcard(string): + global easy_wildcard_dict + pattern = r"__([\w\s.\-+/*\\]+?)__" + matches = re.findall(pattern, string) + replacements_found = False + + for match in matches: + keyword = match.lower() + keyword = wildcard_normalize(keyword) + if keyword in easy_wildcard_dict: + replacement = random.choice(easy_wildcard_dict[keyword]) + replacements_found = True + string = string.replace(f"__{match}__", replacement, 1) + elif '*' in keyword: + subpattern = keyword.replace('*', '.*').replace('+', r'\+') + total_patterns = [] + found = False + for k, v in easy_wildcard_dict.items(): + if re.match(subpattern, k) is not None: + total_patterns += v + found = True + + if found: + replacement = random.choice(total_patterns) + replacements_found = True + string = string.replace(f"__{match}__", replacement, 1) + elif '/' not in keyword: + string_fallback = string.replace(f"__{match}__", f"__*/{match}__", 1) + string, replacements_found = replace_wildcard(string_fallback) + + return string, replacements_found + + replace_depth = 100 + stop_unwrap = False + while not stop_unwrap and replace_depth > 1: + replace_depth -= 1 # prevent infinite loop + + # pass1: replace options + pass1, is_replaced1 = replace_options(text) + + while is_replaced1: + pass1, is_replaced1 = replace_options(pass1) + + # pass2: replace wildcards + text, is_replaced2 = replace_wildcard(pass1) + stop_unwrap = not is_replaced1 and not is_replaced2 + + return text + + +def is_numeric_string(input_str): + return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None + + +def safe_float(x): + if is_numeric_string(x): + return float(x) + else: + return 1.0 + + +def extract_lora_values(string): + pattern = r']+)>' + matches = re.findall(pattern, string) + + def touch_lbw(text): + return re.sub(r'LBW=[A-Za-z][A-Za-z0-9_-]*:', r'LBW=', text) + + items = [touch_lbw(match.strip(':')) for match in matches] + + added = set() + result = [] + for item in items: + item = item.split(':') + + lora = None + a = None + b = None + lbw = None + lbw_a = None + lbw_b = None + + if len(item) > 0: + lora = item[0] + + for sub_item in item[1:]: + if is_numeric_string(sub_item): + if a is None: + a = float(sub_item) + elif b is None: + b = float(sub_item) + elif sub_item.startswith("LBW="): + for lbw_item in sub_item[4:].split(';'): + if lbw_item.startswith("A="): + lbw_a = safe_float(lbw_item[2:].strip()) + elif lbw_item.startswith("B="): + lbw_b = safe_float(lbw_item[2:].strip()) + elif lbw_item.strip() != '': + lbw = lbw_item + + if a is None: + a = 1.0 + if b is None: + b = 1.0 + + if lora is not None and lora not in added: + result.append((lora, a, b, lbw, lbw_a, lbw_b)) + added.add(lora) + + return result + + +def remove_lora_tags(string): + pattern = r']+>' + result = re.sub(pattern, '', string) + + return result + +def process_with_loras(wildcard_opt, model, clip, title="Positive", seed=None, can_load_lora=True, pipe_lora_stack=[], easyCache=None): + pass1 = process(wildcard_opt, seed) + loras = extract_lora_values(pass1) + pass2 = remove_lora_tags(pass1) + + has_noodle_key = True if "__" in wildcard_opt else False + has_loras = True if loras != [] else False + show_wildcard_prompt = True if has_noodle_key or has_loras else False + + if can_load_lora and has_loras: + for lora_name, model_weight, clip_weight, lbw, lbw_a, lbw_b in loras: + if (lora_name.split('.')[-1]) not in folder_paths.supported_pt_extensions: + lora_name = lora_name+".safetensors" + lora = { + "lora_name": lora_name, "model": model, "clip": clip, "model_strength": model_weight, + "clip_strength": clip_weight, + "lbw_a": lbw_a, + "lbw_b": lbw_b, + "lbw": lbw + } + model, clip = easyCache.load_lora(lora) + lora["model"] = model + lora["clip"] = clip + pipe_lora_stack.append(lora) + + log_node_info("easy wildcards",f"{title}: {pass2}") + if pass1 != pass2: + log_node_info("easy wildcards",f'{title}_decode: {pass1}') + + return model, clip, pass2, pass1, show_wildcard_prompt, pipe_lora_stack + + +def expand_wildcard(keyword: str) -> tuple[str]: + """δΌ ε…₯ζ–‡δ»Άι€šι…η¬¦ηš„ε…³ι”θ―οΌŒδ»Ž easy_wildcard_dict δΈ­θŽ·ε–ι€šι…η¬¦ηš„ζ‰€ζœ‰ι€‰ι‘Ήγ€‚""" + global easy_wildcard_dict + if keyword in easy_wildcard_dict: + return tuple(easy_wildcard_dict[keyword]) + elif '*' in keyword: + subpattern = keyword.replace('*', '.*').replace('+', r"\+") + total_pattern = [] + for k, v in easy_wildcard_dict.items(): + if re.match(subpattern, k) is not None: + total_pattern.extend(v) + if total_pattern: + return tuple(total_pattern) + elif '/' not in keyword: + return expand_wildcard(f"*/{keyword}") + +def expand_options(options: str) -> tuple[str]: + """δΌ ε…₯εŽ»ζŽ‰ {} ηš„ι€‰ι‘Ήγ€‚ + ε±•εΌ€ι€‰ι‘Ήι€šι…η¬¦οΌŒθΏ”ε›žθ―₯ι€‰ι‘ΉδΈ­ηš„ζ―δΈ€ι‘ΉοΌŒθΏ™ι‡Œηš„ζ―δΈ€ι‘Ήιƒ½ζ˜―δΈ€δΈͺ替捒鑹。 + δΈδΌšε―Ήι€‰ι‘Ήε†…εΉθΏ›θ‘Œδ»»δ½•ε€„η†οΌŒε³δΎΏε­˜εœ¨η©Ίζ Όζˆ–η‰ΉζŠη¬¦ε·οΌŒδΉŸδΌšεŽŸζ ·θΏ”ε›žγ€‚""" + return tuple(options.split("|")) + + +def decimal_to_irregular(n, bases): + """ + ε°†εθΏ›εˆΆζ•°θ½¬ζ’δΈΊδΈθ§„εˆ™θΏ›εˆΆ + + :param n: εθΏ›εˆΆζ•° + :param bases: 各位η½ηš„εŸΊζ•°εˆ—θ‘¨οΌŒδ»Žδ½Žδ½εˆ°ι«˜δ½ + :return: δΈθ§„εˆ™θΏ›εˆΆθ‘¨η€Ίηš„εˆ—θ‘¨οΌŒδ»Žδ½Žδ½εˆ°ι«˜δ½ + """ + if n == 0: + return [0] * len(bases) if bases else [0] + + digits = [] + remaining = n + + # δ»Žδ½Žδ½εˆ°ι«˜δ½ε€„η† + for base in bases: + digit = remaining % base + digits.append(digit) + remaining = remaining // base + + return digits + + +class WildcardProcessor: + """ι€šι…η¬¦ε€„η†ε™¨ + + ι€šι…η¬¦ζ ΌεΌοΌš + + option : {a|b} + + wildcard: __keyword__ ι€šι…η¬¦ε†…εΉε°†δ»Ž Easy-Use ζ’δ»ΆζδΎ›ηš„ easy_wildcard_dict δΈ­θŽ·ε– + """ + + RE_OPTIONS = re.compile(r"{([^{}]*?)}") + RE_WILDCARD = re.compile(r"__([\w\s.\-+/*\\]+?)__") + RE_REPLACER = re.compile(r"{([^{}]*?)}|__([\w\s.\-+/*\\]+?)__") + + # ε°†θΎ“ε…₯ηš„ζη€Ίθ―θ½¬εŒ–ζˆη¬¦εˆ python str.format θ¦ζ±‚ζ ΌεΌηš„ζ¨‘ζΏοΌŒεΉΆε°† option ε’Œ wildcard ζŒ‰η…§ι‘ΊεΊεœ¨ζ¨‘ζΏδΈ­η•™δΈ‹ {0}, {1} 等占位符 + template: str + # option、wildcard ηš„ζ›Ώζ’ι‘Ήεˆ—θ‘¨οΌŒζŒ‰η…§εœ¨ζ¨‘ζΏδΈ­ε‡ΊηŽ°ηš„ι‘ΊεΊζŽ’εˆ—οΌŒη›ΈεŒηš„ζ›Ώζ’ι‘Ήεˆ—θ‘¨εͺ保留第一份 + replacers: dict[int, tuple[str]] + # ε δ½η¬¦ηš„ηΌ–ε·ε’Œζ›Ώζ’ι‘Ήεˆ—θ‘¨ηš„η΄’εΌ•ηš„ζ˜ ε°„οΌŒε δ½η¬¦ηΌ–ε·ζŒ‰η…§εœ¨ζ¨‘ζΏδΈ­ε‡ΊηŽ°ηš„ι‘ΊεΊζŽ’εˆ—οΌŒζ–ΉδΎΏε‡ε°‘ζ›Ώζ’ι‘Ήηš„ε­˜ε‚¨ε η”¨ + placeholder_mapping: dict[str, int] # placeholder_id => replacer_id + # ε„ζ›Ώζ’ι‘Ήεˆ—θ‘¨ηš„ι‘Ήζ•°οΌŒζŒ‰η…§εœ¨ζ¨‘ζΏδΈ­ε‡ΊηŽ°ηš„ι‘ΊεΊζŽ’εˆ—οΌŒζε‰θ‘η—οΌŒζ–ΉδΎΏεŽη»­δ½Ώη”¨ + placeholder_choices: dict[str, int] # placeholder_id => len(replacer) + + def __init__(self, text: str): + self.__make_template(text) + self.__total = None + + def random(self, seed=None) -> str: + "δ»Žζ‰€ζœ‰ε―θƒ½ζ€§δΈ­ιšζœΊθŽ·ε–δΈ€δΈͺ" + if seed is not None: + random.seed(seed) + return self.getn(random.randint(0, self.total() - 1)) + + def getn(self, n: int) -> str: + "δ»Žζ‰€ζœ‰ε―θƒ½ζ€§δΈ­θŽ·ε–η¬¬ n δΈͺ，δ»₯ self.total() δΈΊε‘¨ζœŸεΎͺ环" + n = n % self.total() + indice = decimal_to_irregular(n, self.placeholder_choices.values()) + replacements = { + placeholder_id: self.replacers[self.placeholder_mapping[placeholder_id]][i] + for placeholder_id, i in zip(self.placeholder_mapping.keys(), indice) + } + return self.template.format(**replacements) + + def getmany(self, limit: int, offset: int = 0) -> list[str]: + """θΏ”ε›žδΈ€η»„ε―θƒ½ζ€§η»„ζˆηš„εˆ—θ‘¨οΌŒδΈΊδΊ†ιΏε…η»“ζžœε€ͺι•Ώε―Όθ‡΄ε†…ε­˜ε η”¨θΆ…ι™οΌŒδ½Ώη”¨ limit ι™εˆΆεˆ—θ‘¨ηš„ι•ΏεΊ¦οΌŒδ½Ώη”¨ offset 调整偏移。 + θ‹₯ limit ε’Œ offset ηš„θΎη½ε―Όθ‡΄ι’„ζœŸηš„η»“ζžœι•ΏεΊ¦θΆ…θΏ‡ε‰©δΈ‹ηš„εžι™…ι•ΏεΊ¦οΌŒεˆ™δΌšε›žεˆ°εΌ€ε€΄γ€‚ + """ + return [self.getn(n) for n in range(offset, offset + limit)] + + def total(self) -> int: + "θ‘η—ε―θƒ½ζ€§ηš„ζ•°η›" + if self.__total is None: + self.__total = prod(self.placeholder_choices.values()) + return self.__total + + def __make_template(self, text: str): + """ε°†θΎ“ε…₯ηš„ζη€Ίθ―θ½¬εŒ–ζˆη¬¦εˆ python str.format θ¦ζ±‚ζ ΌεΌηš„ζ¨‘ζΏοΌŒ + εΉΆε°† option ε’Œ wildcard ζŒ‰η…§ι‘ΊεΊεœ¨ζ¨‘ζΏδΈ­η•™δΈ‹ {r0}, {r1} η­‰ε δ½η¬¦οΌŒ + ε³δ½Ώι‡εˆ°η›ΈεŒηš„ option ζˆ– wildcardοΌŒη•™δΈ‹ηš„ε δ½η¬¦ηΌ–ε·δΉŸδΈεŒοΌŒδ»Žθ€Œδ½Ώζ―ι‘Ήιƒ½η‹¬η«‹ε˜εŒ–γ€‚ + """ + self.placeholder_mapping = {} + placeholder_id = 0 + replacer_id = 0 + replacers_rev = {} # replacers => id + blocks = [] + # θ°ε½•ζ‰€ε€„η†θΏ‡ηš„ι€šι…η¬¦ζœ«ε°Ύεœ¨ζ–‡ζœ¬δΈ­ηš„δ½η½οΌŒη”¨δΊŽζ‹ΌζŽ₯εŒζ•΄ηš„樑板 + tail = 0 + for match in self.RE_REPLACER.finditer(text): + # ζε–εΉΆε±•εΌ€ι€šι…η¬¦ε†…εΉ + m = match.group(0) + if m.startswith("{"): + choices = expand_options(m[1:-1]) + elif m.startswith("__"): + keyword = m[2:-2].lower() + keyword = wildcard_normalize(keyword) + choices = expand_wildcard(keyword) + else: + raise ValueError(f"{m!r} is not a wildcard or option") + + # θ°ε½•ι€šι…η¬¦ηš„ζ›Ώζ’ι‘Ήεˆ—θ‘¨ε’ŒIDοΌŒη›ΈεŒηš„ι€šι…η¬¦εͺ保留第一δΈͺ + if choices not in replacers_rev: + replacers_rev[choices] = replacer_id + replacer_id += 1 + + # ζ‹ΌζŽ₯ι€šι…η¬¦ε‰ζ–Ήζ–‡ζœ¬ + start, end = match.span() + blocks.append(text[tail:start]) + tail = end + # ε°†ι€šι…η¬¦ζ›Ώζ’δΈΊε δ½η¬¦οΌŒεΉΆθ°ε½•ε δ½η¬¦ε’Œζ›Ώζ’ι‘Ήεˆ—θ‘¨ηš„η΄’εΌ•ηš„ζ˜ ε°„ + blocks.append(f"{{r{placeholder_id}}}") + self.placeholder_mapping[f"r{placeholder_id}"] = replacers_rev[choices] + placeholder_id += 1 + + if tail < len(text): + blocks.append(text[tail:]) + self.template = "".join(blocks) + self.replacers = {v: k for k, v in replacers_rev.items()} + self.placeholder_choices = { + placeholder_id: len(self.replacers[replacer_id]) + for placeholder_id, replacer_id in self.placeholder_mapping.items() + } + + +def test_option(): + text = "{|a|b|c}" + answer = ["", "a", "b", "c"] + p = WildcardProcessor(text) + assert p.total() == len(answer) + assert p.getn(0) == answer[0] + assert p.getmany(4) == answer + assert p.getmany(4, 1) == answer[1:] + + +def test_same(): + text = "{a|b},{a|b}" + answer = ["a,a", "b,a", "a,b", "b,b"] + p = WildcardProcessor(text) + assert p.total() == len(answer) + assert p.getn(0) == answer[0] + assert p.getmany(4) == answer + assert p.getmany(4, 1) == answer[1:] + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/libs/xyplot.py b/custom_nodes/ComfyUI-Easy-Use/py/libs/xyplot.py new file mode 100644 index 00000000..8cab23fa --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/libs/xyplot.py @@ -0,0 +1,697 @@ +import os, torch +from pathlib import Path +from PIL import Image, ImageDraw, ImageFont +from .utils import easySave, get_sd_version +from .adv_encode import advanced_encode +from .controlnet import easyControlnet +from .log import log_node_warn +from ..modules.layer_diffuse import LayerDiffuse +from ..config import RESOURCES_DIR +from nodes import CLIPTextEncode +import pprint +try: + from comfy_extras.nodes_flux import FluxGuidance +except: + FluxGuidance = None + +class easyXYPlot(): + + def __init__(self, xyPlotData, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id, sampler, easyCache): + self.x_node_type, self.x_type = sampler.safe_split(xyPlotData.get("x_axis"), ': ') + self.y_node_type, self.y_type = sampler.safe_split(xyPlotData.get("y_axis"), ': ') + self.x_values = xyPlotData.get("x_vals") if self.x_type != "None" else [] + self.y_values = xyPlotData.get("y_vals") if self.y_type != "None" else [] + self.custom_font = xyPlotData.get("custom_font") + + self.grid_spacing = xyPlotData.get("grid_spacing") + self.latent_id = 0 + self.output_individuals = xyPlotData.get("output_individuals") + + self.x_label, self.y_label = [], [] + self.max_width, self.max_height = 0, 0 + self.latents_plot = [] + self.image_list = [] + + self.num_cols = len(self.x_values) if len(self.x_values) > 0 else 1 + self.num_rows = len(self.y_values) if len(self.y_values) > 0 else 1 + + self.total = self.num_cols * self.num_rows + self.num = 0 + + self.save_prefix = save_prefix + self.image_output = image_output + self.prompt = prompt + self.extra_pnginfo = extra_pnginfo + self.my_unique_id = my_unique_id + + self.sampler = sampler + self.easyCache = easyCache + + # Helper Functions + @staticmethod + def define_variable(plot_image_vars, value_type, value, index): + + plot_image_vars[value_type] = value + if value_type in ["seed", "Seeds++ Batch"]: + value_label = f"seed: {value}" + else: + value_label = f"{value_type}: {value}" + + if "ControlNet" in value_type: + value_label = f"ControlNet {index + 1}" + + if value_type in ['Lora', 'Checkpoint']: + arr = value.split(',') + model_name = os.path.basename(os.path.splitext(arr[0])[0]) + trigger_words = ' ' + arr[3] if value_type == 'Lora' and len(arr[3]) > 2 else '' + lora_weight = float(arr[1]) if value_type == 'Lora' and len(arr) > 1 else 0 + lora_weight_desc = f"({lora_weight:.2f})" if lora_weight > 0 else '' + value_label = f"{model_name[:30]}{lora_weight_desc} {trigger_words}" + + if value_type in ["ModelMergeBlocks"]: + if ":" in value: + line = value.split(':') + value_label = f"{line[0]}" + elif len(value) > 16: + value_label = f"ModelMergeBlocks {index + 1}" + else: + value_label = f"MMB: {value}" + + if value_type in ["Pos Condition"]: + value_label = f"pos cond {index + 1}" if index>0 else f"pos cond" + if value_type in ["Neg Condition"]: + value_label = f"neg cond {index + 1}" if index>0 else f"neg cond" + + if value_type in ["Positive Prompt S/R"]: + value_label = f"pos prompt {index + 1}" if index>0 else f"pos prompt" + if value_type in ["Negative Prompt S/R"]: + value_label = f"neg prompt {index + 1}" if index>0 else f"neg prompt" + + if value_type in ["steps", "cfg", "denoise", "clip_skip", + "lora_model_strength", "lora_clip_strength"]: + value_label = f"{value_type}: {value}" + + if value_type == "positive": + value_label = f"pos prompt {index + 1}" + elif value_type == "negative": + value_label = f"neg prompt {index + 1}" + + return plot_image_vars, value_label + + @staticmethod + def get_font(font_size, font_path=None): + if font_path is None: + font_path = str(Path(os.path.join(RESOURCES_DIR, 'OpenSans-Medium.ttf'))) + return ImageFont.truetype(font_path, font_size) + + @staticmethod + def update_label(label, value, num_items): + if len(label) < num_items: + return [*label, value] + return label + + @staticmethod + def rearrange_tensors(latent, num_cols, num_rows): + new_latent = [] + for i in range(num_rows): + for j in range(num_cols): + index = j * num_rows + i + new_latent.append(latent[index]) + return new_latent + + def calculate_background_dimensions(self): + border_size = int((self.max_width // 8) * 1.5) if self.y_type != "None" or self.x_type != "None" else 0 + + bg_width = self.num_cols * (self.max_width + self.grid_spacing) - self.grid_spacing + border_size * ( + self.y_type != "None") + bg_height = self.num_rows * (self.max_height + self.grid_spacing) - self.grid_spacing + border_size * ( + self.x_type != "None") + + # Add space at the bottom of the image for common informaiton about the image + bg_height = bg_height + (border_size*2) +# print(f"Grid Size: width = {bg_width} height = {bg_height} border_size = {border_size}") + + x_offset_initial = border_size if self.y_type != "None" else 0 + y_offset = border_size if self.x_type != "None" else 0 + + return bg_width, bg_height, x_offset_initial, y_offset + + + def adjust_font_size(self, text, initial_font_size, label_width): + font = self.get_font(initial_font_size, self.custom_font) + text_width = font.getbbox(text) +# pprint.pp(f"Initial font size: {initial_font_size}, text: {text}, text_width: {text_width}") + if text_width and text_width[2]: + text_width = text_width[2] + + scaling_factor = 0.9 + if text_width > (label_width * scaling_factor): +# print(f"Adjusting font size from {initial_font_size} to fit text width {text_width} into label width {label_width} scaling_factor {scaling_factor}") + return int(initial_font_size * (label_width / text_width) * scaling_factor) + else: + return initial_font_size + + def textsize(self, d, text, font): + _, _, width, height = d.textbbox((0, 0), text=text, font=font) + return width, height + + def create_label(self, img, text, initial_font_size, is_x_label=True, max_font_size=70, min_font_size=10, label_width=0, label_height=0): + + # if the label_width is specified, leave it along. Otherwise do the old logic. + if label_width == 0: + label_width = img.width if is_x_label else img.height + + text_lines = text.split('\n') + longest_line = max(text_lines, key=len) + + # Adjust font size + font_size = self.adjust_font_size(longest_line, initial_font_size, label_width) + font_size = min(max_font_size, font_size) # Ensure font isn't too large + font_size = max(min_font_size, font_size) # Ensure font isn't too small + + if label_height == 0: + label_height = int(font_size * 1.5) if is_x_label else font_size + + label_bg = Image.new('RGBA', (label_width, label_height), color=(255, 255, 255, 0)) + d = ImageDraw.Draw(label_bg) + + font = self.get_font(font_size, self.custom_font) + + # Check if text will fit, if not insert ellipsis and reduce text + if self.textsize(d, text, font=font)[0] > label_width: + while self.textsize(d, text + '...', font=font)[0] > label_width and len(text) > 0: + text = text[:-1] + text = text + '...' + + # Compute text width and height for multi-line text + + text_widths, text_heights = zip(*[self.textsize(d, line, font=font) for line in text_lines]) + max_text_width = max(text_widths) + total_text_height = sum(text_heights) + + # Compute position for each line of text + lines_positions = [] + current_y = 0 + for line, line_width, line_height in zip(text_lines, text_widths, text_heights): + text_x = (label_width - line_width) // 2 + text_y = current_y + (label_height - total_text_height) // 2 + current_y += line_height + lines_positions.append((line, (text_x, text_y))) + + # Draw each line of text + for line, (text_x, text_y) in lines_positions: + d.text((text_x, text_y), line, fill='black', font=font) + + return label_bg + + def sample_plot_image(self, plot_image_vars, samples, preview_latent, latents_plot, image_list, disable_noise, + start_step, last_step, force_full_denoise, x_value=None, y_value=None): + model, clip, vae, positive, negative, seed, steps, cfg = None, None, None, None, None, None, None, None + sampler_name, scheduler, denoise = None, None, None + + a1111_prompt_style = plot_image_vars['a1111_prompt_style'] if "a1111_prompt_style" in plot_image_vars else False + clip = clip if clip is not None else plot_image_vars["clip"] + steps = plot_image_vars['steps'] if "steps" in plot_image_vars else 1 + + sd_version = get_sd_version(plot_image_vars['model']) + # ι«˜ηΊ§η”¨ζ³• + if plot_image_vars["x_node_type"] == "advanced" or plot_image_vars["y_node_type"] == "advanced": + if self.x_type == "Seeds++ Batch" or self.y_type == "Seeds++ Batch": + seed = int(x_value) if self.x_type == "Seeds++ Batch" else int(y_value) + if self.x_type == "Steps" or self.y_type == "Steps": + steps = int(x_value) if self.x_type == "Steps" else int(y_value) + if self.x_type == "StartStep" or self.y_type == "StartStep": + start_step = int(x_value) if self.x_type == "StartStep" else int(y_value) + if self.x_type == "EndStep" or self.y_type == "EndStep": + last_step = int(x_value) if self.x_type == "EndStep" else int(y_value) + if self.x_type == "CFG Scale" or self.y_type == "CFG Scale": + cfg = float(x_value) if self.x_type == "CFG Scale" else float(y_value) + if self.x_type == "Sampler" or self.y_type == "Sampler": + sampler_name = x_value if self.x_type == "Sampler" else y_value + if self.x_type == "Scheduler" or self.y_type == "Scheduler": + scheduler = x_value if self.x_type == "Scheduler" else y_value + if self.x_type == "Sampler&Scheduler" or self.y_type == "Sampler&Scheduler": + arr = x_value.split(',') if self.x_type == "Sampler&Scheduler" else y_value.split(',') + if arr[0] and arr[0]!= 'None': + sampler_name = arr[0] + if arr[1] and arr[1]!= 'None': + scheduler = arr[1] + if self.x_type == "Denoise" or self.y_type == "Denoise": + denoise = float(x_value) if self.x_type == "Denoise" else float(y_value) + if self.x_type == "Pos Condition" or self.y_type == "Pos Condition": + positive = plot_image_vars['positive_cond_stack'][int(x_value)] if self.x_type == "Pos Condition" else plot_image_vars['positive_cond_stack'][int(y_value)] + if self.x_type == "Neg Condition" or self.y_type == "Neg Condition": + negative = plot_image_vars['negative_cond_stack'][int(x_value)] if self.x_type == "Neg Condition" else plot_image_vars['negative_cond_stack'][int(y_value)] + # ζ¨‘εž‹ε εŠ  + if self.x_type == "ModelMergeBlocks" or self.y_type == "ModelMergeBlocks": + ckpt_name_1, ckpt_name_2 = plot_image_vars['models'] + model1, clip1, vae1, clip_vision = self.easyCache.load_checkpoint(ckpt_name_1) + model2, clip2, vae2, clip_vision = self.easyCache.load_checkpoint(ckpt_name_2) + xy_values = x_value if self.x_type == "ModelMergeBlocks" else y_value + if ":" in xy_values: + xy_line = xy_values.split(':') + xy_values = xy_line[1] + + xy_arrs = xy_values.split(',') + # ModelMergeBlocks + if len(xy_arrs) == 3: + input, middle, out = xy_arrs + kwargs = { + "input": input, + "middle": middle, + "out": out + } + elif len(xy_arrs) == 30: + kwargs = {} + kwargs["time_embed."] = xy_arrs[0] + kwargs["label_emb."] = xy_arrs[1] + + for i in range(12): + kwargs["input_blocks.{}.".format(i)] = xy_arrs[2+i] + + for i in range(3): + kwargs["middle_block.{}.".format(i)] = xy_arrs[14+i] + + for i in range(12): + kwargs["output_blocks.{}.".format(i)] = xy_arrs[17+i] + + kwargs["out."] = xy_arrs[29] + else: + raise Exception("ModelMergeBlocks weight length error") + default_ratio = next(iter(kwargs.values())) + + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + + for k in kp: + ratio = float(default_ratio) + k_unet = k[len("diffusion_model."):] + + last_arg_size = 0 + for arg in kwargs: + if k_unet.startswith(arg) and last_arg_size < len(arg): + ratio = float(kwargs[arg]) + last_arg_size = len(arg) + + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + + vae_use = plot_image_vars['vae_use'] + + clip = clip2 if vae_use == 'Use Model 2' else clip1 + if vae_use == 'Use Model 2': + vae = vae2 + elif vae_use == 'Use Model 1': + vae = vae1 + else: + vae = self.easyCache.load_vae(vae_use) + model = m + + # ε¦‚ζžœε­˜εœ¨lora_stack叠加lora + optional_lora_stack = plot_image_vars['lora_stack'] + if optional_lora_stack is not None and optional_lora_stack != []: + for lora in optional_lora_stack: + model, clip = self.easyCache.load_lora(lora) + + # 倄理clip + clip = clip.clone() + if plot_image_vars['clip_skip'] != 0: + clip.clip_layer(plot_image_vars['clip_skip']) + + # CheckPoint + if self.x_type == "Checkpoint" or self.y_type == "Checkpoint": + xy_values = x_value if self.x_type == "Checkpoint" else y_value + ckpt_name, clip_skip, vae_name = xy_values.split(",") + ckpt_name = ckpt_name.replace('*', ',') + vae_name = vae_name.replace('*', ',') + model, clip, vae, clip_vision = self.easyCache.load_checkpoint(ckpt_name) + if vae_name != 'None': + vae = self.easyCache.load_vae(vae_name) + + # ε¦‚ζžœε­˜εœ¨lora_stack叠加lora + optional_lora_stack = plot_image_vars['lora_stack'] + if optional_lora_stack is not None and optional_lora_stack != []: + for lora in optional_lora_stack: + lora['model'] = model + lora['clip'] = clip + model, clip = self.easyCache.load_lora(lora) + + # 倄理clip + clip = clip.clone() + if clip_skip != 'None': + clip.clip_layer(int(clip_skip)) + positive = plot_image_vars['positive'] + negative = plot_image_vars['negative'] + a1111_prompt_style = plot_image_vars['a1111_prompt_style'] + steps = plot_image_vars['steps'] + clip = clip if clip is not None else plot_image_vars["clip"] + positive = advanced_encode(clip, positive, + plot_image_vars['positive_token_normalization'], + plot_image_vars['positive_weight_interpretation'], + w_max=1.0, + apply_to_pooled="enable", + a1111_prompt_style=a1111_prompt_style, steps=steps) + + negative = advanced_encode(clip, negative, + plot_image_vars['negative_token_normalization'], + plot_image_vars['negative_weight_interpretation'], + w_max=1.0, + apply_to_pooled="enable", + a1111_prompt_style=a1111_prompt_style, steps=steps) + if "positive_cond" in plot_image_vars: + positive = positive + plot_image_vars["positive_cond"] + if "negative_cond" in plot_image_vars: + negative = negative + plot_image_vars["negative_cond"] + + # Lora + if self.x_type == "Lora" or self.y_type == "Lora": +# print(f"Lora: {x_value} {y_value}") + model = model if model is not None else plot_image_vars["model"] + clip = clip if clip is not None else plot_image_vars["clip"] + xy_values = x_value if self.x_type == "Lora" else y_value + lora_name, lora_model_strength, lora_clip_strength, _ = xy_values.split(",") + lora_stack = [{"lora_name": lora_name, "model": model, "clip" :clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)}] + +# print(f"new_lora_stack: {new_lora_stack}") + + + if 'lora_stack' in plot_image_vars: + lora_stack = lora_stack + plot_image_vars['lora_stack'] + + if lora_stack is not None and lora_stack != []: + for lora in lora_stack: + # Each generation of the model, must use the reference to previously created model / clip objects. + lora['model'] = model + lora['clip'] = clip + model, clip = self.easyCache.load_lora(lora) + + # 提瀺词 + if "Positive" in self.x_type or "Positive" in self.y_type: + if self.x_type == 'Positive Prompt S/R' or self.y_type == 'Positive Prompt S/R': + positive = x_value if self.x_type == "Positive Prompt S/R" else y_value + + if sd_version == 'flux': + positive, = CLIPTextEncode().encode(clip, positive) + else: + positive = advanced_encode(clip, positive, + plot_image_vars['positive_token_normalization'], + plot_image_vars['positive_weight_interpretation'], + w_max=1.0, + apply_to_pooled="enable", a1111_prompt_style=a1111_prompt_style, steps=steps) + + # if "positive_cond" in plot_image_vars: + # positive = positive + plot_image_vars["positive_cond"] + + if "Negative" in self.x_type or "Negative" in self.y_type: + if self.x_type == 'Negative Prompt S/R' or self.y_type == 'Negative Prompt S/R': + negative = x_value if self.x_type == "Negative Prompt S/R" else y_value + + if sd_version == 'flux': + negative, = CLIPTextEncode().encode(clip, negative) + else: + negative = advanced_encode(clip, negative, + plot_image_vars['negative_token_normalization'], + plot_image_vars['negative_weight_interpretation'], + w_max=1.0, + apply_to_pooled="enable", a1111_prompt_style=a1111_prompt_style, steps=steps) + # if "negative_cond" in plot_image_vars: + # negative = negative + plot_image_vars["negative_cond"] + + # ControlNet + if "ControlNet" in self.x_type or "ControlNet" in self.y_type: + cnet = plot_image_vars["cnet"] if "cnet" in plot_image_vars else None + positive = plot_image_vars["positive_cond"] if "positive" in plot_image_vars else None + negative = plot_image_vars["negative_cond"] if "negative" in plot_image_vars else None + if cnet: + index = x_value if "ControlNet" in self.x_type else y_value + controlnet = cnet[index] + for index, item in enumerate(controlnet): + control_net_name = item[0] + image = item[1] + strength = item[2] + start_percent = item[3] + end_percent = item[4] + provided_control_net = item[5] if len(item) > 5 else None + positive, negative = easyControlnet().apply(control_net_name, image, positive, negative, strength, start_percent, end_percent, provided_control_net, 1) + # Flux guidance + if self.x_type == "Flux Guidance" or self.y_type == "Flux Guidance": + positive = plot_image_vars["positive_cond"] if "positive" in plot_image_vars else None + flux_guidance = float(x_value) if self.x_type == "Flux Guidance" else float(y_value) + positive, = FluxGuidance().append(positive, flux_guidance) + + # η€ε•η”¨ζ³• + if plot_image_vars["x_node_type"] == "loader" or plot_image_vars["y_node_type"] == "loader": + if self.x_type == 'ckpt_name' or self.y_type == 'ckpt_name': + ckpt_name = x_value if self.x_type == "ckpt_name" else y_value + model, clip, vae, clip_vision = self.easyCache.load_checkpoint(ckpt_name) + + if self.x_type == 'lora_name' or self.y_type == 'lora_name': + model, clip, vae, clip_vision = self.easyCache.load_checkpoint(plot_image_vars['ckpt_name']) + lora_name = x_value if self.x_type == "lora_name" else y_value + lora = {"lora_name": lora_name, "model": model, "clip": clip, "model_strength": 1, "clip_strength": 1} + model, clip = self.easyCache.load_lora(lora) + + if self.x_type == 'lora_model_strength' or self.y_type == 'lora_model_strength': + model, clip, vae, clip_vision = self.easyCache.load_checkpoint(plot_image_vars['ckpt_name']) + lora_model_strength = float(x_value) if self.x_type == "lora_model_strength" else float(y_value) + lora = {"lora_name": plot_image_vars['lora_name'], "model": model, "clip": clip, "model_strength": lora_model_strength, "clip_strength": plot_image_vars['lora_clip_strength']} + model, clip = self.easyCache.load_lora(lora) + + if self.x_type == 'lora_clip_strength' or self.y_type == 'lora_clip_strength': + model, clip, vae, clip_vision = self.easyCache.load_checkpoint(plot_image_vars['ckpt_name']) + lora_clip_strength = float(x_value) if self.x_type == "lora_clip_strength" else float(y_value) + lora = {"lora_name": plot_image_vars['lora_name'], "model": model, "clip": clip, "model_strength": plot_image_vars['lora_model_strength'], "clip_strength": lora_clip_strength} + model, clip = self.easyCache.load_lora(lora) + + # Check for custom VAE + if self.x_type == 'vae_name' or self.y_type == 'vae_name': + vae_name = x_value if self.x_type == "vae_name" else y_value + vae = self.easyCache.load_vae(vae_name) + + # CLIP skip + if not clip: + raise Exception("No CLIP found") + clip = clip.clone() + clip.clip_layer(plot_image_vars['clip_skip']) + + if sd_version == 'flux': + positive, = CLIPTextEncode().encode(clip, positive) + else: + positive = advanced_encode(clip, plot_image_vars['positive'], + plot_image_vars['positive_token_normalization'], + plot_image_vars['positive_weight_interpretation'], w_max=1.0, + apply_to_pooled="enable",a1111_prompt_style=a1111_prompt_style, steps=steps) + + if sd_version == 'flux': + negative, = CLIPTextEncode().encode(clip, negative) + else: + negative = advanced_encode(clip, plot_image_vars['negative'], + plot_image_vars['negative_token_normalization'], + plot_image_vars['negative_weight_interpretation'], w_max=1.0, + apply_to_pooled="enable", a1111_prompt_style=a1111_prompt_style, steps=steps) + + + model = model if model is not None else plot_image_vars["model"] + vae = vae if vae is not None else plot_image_vars["vae"] + positive = positive if positive is not None else plot_image_vars["positive_cond"] + negative = negative if negative is not None else plot_image_vars["negative_cond"] + + seed = seed if seed is not None else plot_image_vars["seed"] + steps = steps if steps is not None else plot_image_vars["steps"] + cfg = cfg if cfg is not None else plot_image_vars["cfg"] + sampler_name = sampler_name if sampler_name is not None else plot_image_vars["sampler_name"] + scheduler = scheduler if scheduler is not None else plot_image_vars["scheduler"] + denoise = denoise if denoise is not None else plot_image_vars["denoise"] + + noise_device = plot_image_vars["noise_device"] if "noise_device" in plot_image_vars else 'cpu' + + # LayerDiffuse + layer_diffusion_method = plot_image_vars["layer_diffusion_method"] if "layer_diffusion_method" in plot_image_vars else None + empty_samples = plot_image_vars["empty_samples"] if "empty_samples" in plot_image_vars else None + + if layer_diffusion_method: + samp_blend_samples = plot_image_vars["blend_samples"] if "blend_samples" in plot_image_vars else None + additional_cond = plot_image_vars["layer_diffusion_cond"] if "layer_diffusion_cond" in plot_image_vars else None + + images = plot_image_vars["images"].movedim(-1, 1) if "images" in plot_image_vars else None + weight = plot_image_vars['layer_diffusion_weight'] if 'layer_diffusion_weight' in plot_image_vars else 1.0 + model, positive, negative = LayerDiffuse().apply_layer_diffusion(model, layer_diffusion_method, weight, samples, + samp_blend_samples, positive, + negative, images, additional_cond) + + samples = empty_samples if layer_diffusion_method is not None and empty_samples is not None else samples + # Sample + samples = self.sampler.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples, + denoise=denoise, disable_noise=disable_noise, preview_latent=preview_latent, + start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_device=noise_device) + + # Decode images and store + latent = samples["samples"] + + # Add the latent tensor to the tensors list + latents_plot.append(latent) + + # Decode the image + image = vae.decode(latent).cpu() + + if self.output_individuals in [True, "True"]: + easySave(image, self.save_prefix, self.image_output) + + # Convert the image from tensor to PIL Image and add it to the list + pil_image = self.sampler.tensor2pil(image) + image_list.append(pil_image) + + # Update max dimensions + self.max_width = max(self.max_width, pil_image.width) + self.max_height = max(self.max_height, pil_image.height) + + # Return the touched variables + return image_list, self.max_width, self.max_height, latents_plot + + # Process Functions + def validate_xy_plot(self): + if self.x_type == 'None' and self.y_type == 'None': + log_node_warn(f'#{self.my_unique_id}','No Valid Plot Types - Reverting to default sampling...') + return False + else: + return True + + def get_latent(self, samples): + # Extract the 'samples' tensor from the dictionary + latent_image_tensor = samples["samples"] + + # Split the tensor into individual image tensors + image_tensors = torch.split(latent_image_tensor, 1, dim=0) + + # Create a list of dictionaries containing the individual image tensors + latent_list = [{'samples': image} for image in image_tensors] + + # Set latent only to the first latent of batch + if self.latent_id >= len(latent_list): + log_node_warn(f'#{self.my_unique_id}',f'The selected latent_id ({self.latent_id}) is out of range.') + log_node_warn(f'#{self.my_unique_id}', f'Automatically setting the latent_id to the last image in the list (index: {len(latent_list) - 1}).') + + self.latent_id = len(latent_list) - 1 + + return latent_list[self.latent_id] + + def get_labels_and_sample(self, plot_image_vars, latent_image, preview_latent, start_step, last_step, + force_full_denoise, disable_noise): + for x_index, x_value in enumerate(self.x_values): + plot_image_vars, x_value_label = self.define_variable(plot_image_vars, self.x_type, x_value, + x_index) + self.x_label = self.update_label(self.x_label, x_value_label, len(self.x_values)) + if self.y_type != 'None': + for y_index, y_value in enumerate(self.y_values): + plot_image_vars, y_value_label = self.define_variable(plot_image_vars, self.y_type, y_value, + y_index) + self.y_label = self.update_label(self.y_label, y_value_label, len(self.y_values)) + # ttNl(f'{CC.GREY}X: {x_value_label}, Y: {y_value_label}').t( + # f'Plot Values {self.num}/{self.total} ->').p() + + self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image( + plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, + disable_noise, start_step, last_step, force_full_denoise, x_value, y_value) + self.num += 1 + else: + # ttNl(f'{CC.GREY}X: {x_value_label}').t(f'Plot Values {self.num}/{self.total} ->').p() + self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image( + plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, disable_noise, + start_step, last_step, force_full_denoise, x_value) + self.num += 1 + + # Rearrange latent array to match preview image grid + self.latents_plot = self.rearrange_tensors(self.latents_plot, self.num_cols, self.num_rows) + + # Concatenate the tensors along the first dimension (dim=0) + self.latents_plot = torch.cat(self.latents_plot, dim=0) + + return self.latents_plot + + def plot_images_and_labels(self, plot_image_vars): + + bg_width, bg_height, x_offset_initial, y_offset = self.calculate_background_dimensions() + + background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255)) + + output_image = [] + for row_index in range(self.num_rows): + x_offset = x_offset_initial + + for col_index in range(self.num_cols): + index = col_index * self.num_rows + row_index + img = self.image_list[index] + output_image.append(self.sampler.pil2tensor(img)) + background.paste(img, (x_offset, y_offset)) + + # Handle X label + if row_index == 0 and self.x_type != "None": + label_bg = self.create_label(img, self.x_label[col_index], int(48 * img.width / 512)) + label_y = (y_offset - label_bg.height) // 2 + background.alpha_composite(label_bg, (x_offset, label_y)) + + # Handle Y label + if col_index == 0 and self.y_type != "None": + label_bg = self.create_label(img, self.y_label[row_index], int(48 * img.height / 512), False) + label_bg = label_bg.rotate(90, expand=True) + + label_x = (x_offset - label_bg.width) // 2 + label_y = y_offset + (img.height - label_bg.height) // 2 + background.alpha_composite(label_bg, (label_x, label_y)) + + x_offset += img.width + self.grid_spacing + + y_offset += img.height + self.grid_spacing + + # lookup used models in the image + common_label = "" + # Update to add a function to do the heavy lifting. Parameters are plot_image_vars name, label to use, names of the axis, + + # pprint.pp(plot_image_vars) + + # We don't process LORAs here because there can be multiple of them. + labels = [ + {"id": "ckpt_name", "id_desc": "ckpt", "axis_type" : "Checkpoint"}, + {"id": "vae_name", "id_desc": '', "axis_type" : "vae_name"}, + {"id": "sampler_name", "id_desc": "sampler", "axis_type" : "Sampler"}, + {"id": "scheduler", "id_desc": '', "axis_type" : "Scheduler"}, + {"id": "steps", "id_desc": '', "axis_type" : "Steps"}, + {"id": "Flux Guidance", "id_desc": 'guidance', "axis_type" : "Flux Guidance"}, + {"id": "seed", "id_desc": '', "axis_type" : "Seeds++ Batch"} + ] + + for item in labels: + # Only add the label if it's not one of the axis + # print(f"Checking item: {item['id']} axis_type {item['axis_type']} x_type: {self.x_type} y_type: {self.y_type}") + if self.x_type != item['axis_type'] and self.y_type != item['axis_type']: + common_label += self.add_common_label(item['id'], plot_image_vars, item['id_desc']) + common_label += f"\n" + + if plot_image_vars['lora_stack'] is not None and plot_image_vars['lora_stack'] != []: +# print(f"lora_stack: {plot_image_vars['lora_stack']}") + for lora in plot_image_vars['lora_stack']: + + lora_name = lora['lora_name'] + lora_weight = lora['model_strength'] + if lora_name is not None and len(lora_name) > 0 and lora_weight > 0: + common_label += f"LORA: {lora_name} weight: {lora_weight:.2f} \n" + + common_label = common_label.strip() + + if len(common_label) > 0: + label_height = background.height - y_offset + label_bg = self.create_label(background, common_label, int(48 * background.width / 512), label_width=background.width, label_height=label_height) + label_x = (background.width - label_bg.width) // 2 + label_y = y_offset +# print(f"Adding common label: {common_label} x = {label_x} y = {label_y}") + background.alpha_composite(label_bg, (label_x, label_y)) + + return (self.sampler.pil2tensor(background), output_image) + + def add_common_label(self, tag, plot_image_vars, description = ''): + label = '' + if description == '': description = tag + if tag in plot_image_vars and plot_image_vars[tag] is not None and plot_image_vars[tag] != 'None': + label += f"{description}: {plot_image_vars[tag]} " +# print(f"add_common_label: {tag} description: {description} label: {label}" ) + return label diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ben/model.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ben/model.py new file mode 100644 index 00000000..df9685bd --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ben/model.py @@ -0,0 +1,1310 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ +from PIL import Image, ImageOps +from torchvision import transforms +import numpy as np +import random +import cv2 +import os +import subprocess +import time +import tempfile + + +def set_random_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +set_random_seed(9) + +torch.set_float32_matmul_precision('highest') + + +class Mlp(nn.Module): + """ Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """ Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """ Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """ Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """ Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + use_checkpoint=False): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2 ** i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint) + self.layers.append(layer) + + num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f'norm{i_layer}' + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def forward(self, x): + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') + x = (x + absolute_pos_embed) # B Wh*Ww C + + outs = [x.contiguous()] + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + return tuple(outs) + + +def get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "gelu": + return F.gelu + + raise RuntimeError(F"activation should be gelu, not {activation}.") + + +def make_cbr(in_dim, out_dim): + return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1), nn.InstanceNorm2d(out_dim), nn.GELU()) + + +def make_cbg(in_dim, out_dim): + return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1), nn.InstanceNorm2d(out_dim), nn.GELU()) + + +def rescale_to(x, scale_factor: float = 2, interpolation='nearest'): + return F.interpolate(x, scale_factor=scale_factor, mode=interpolation) + + +def resize_as(x, y, interpolation='bilinear'): + return F.interpolate(x, size=y.shape[-2:], mode=interpolation) + + +def image2patches(x): + """b c (hg h) (wg w) -> (hg wg b) c h w""" + x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) + return x + + +def patches2image(x): + """(hg wg b) c h w -> b c (hg h) (wg w)""" + x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2) + return x + + +class PositionEmbeddingSine: + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + self.dim_t = torch.arange(0, self.num_pos_feats, dtype=torch.float32) + + def __call__(self, b, h, w): + device = self.dim_t.device + mask = torch.zeros([b, h, w], dtype=torch.bool, device=device) + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(dim=1, dtype=torch.float32) + x_embed = not_mask.cumsum(dim=2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = self.temperature ** (2 * (self.dim_t.to(device) // 2) / self.num_pos_feats) + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + + return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + + +class PositionEmbeddingSine: + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + self.dim_t = torch.arange(0, self.num_pos_feats, dtype=torch.float32) + + def __call__(self, b, h, w): + device = self.dim_t.device + mask = torch.zeros([b, h, w], dtype=torch.bool, device=device) + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(dim=1, dtype=torch.float32) + x_embed = not_mask.cumsum(dim=2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = self.temperature ** (2 * (self.dim_t.to(device) // 2) / self.num_pos_feats) + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + + return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + + +class MCLM(nn.Module): + def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]): + super(MCLM, self).__init__() + self.attention = nn.ModuleList([ + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1) + ]) + + self.linear1 = nn.Linear(d_model, d_model * 2) + self.linear2 = nn.Linear(d_model * 2, d_model) + self.linear3 = nn.Linear(d_model, d_model * 2) + self.linear4 = nn.Linear(d_model * 2, d_model) + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(0.1) + self.dropout1 = nn.Dropout(0.1) + self.dropout2 = nn.Dropout(0.1) + self.activation = get_activation_fn('gelu') + self.pool_ratios = pool_ratios + self.p_poses = [] + self.g_pos = None + self.positional_encoding = PositionEmbeddingSine(num_pos_feats=d_model // 2, normalize=True) + + def forward(self, l, g): + """ + l: 4,c,h,w + g: 1,c,h,w + """ + self.p_poses = [] + self.g_pos = None + b, c, h, w = l.size() + # 4,c,h,w -> 1,c,2h,2w + concated_locs = rearrange(l, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2) + + pools = [] + for pool_ratio in self.pool_ratios: + # b,c,h,w + tgt_hw = (round(h / pool_ratio), round(w / pool_ratio)) + pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw) + pools.append(rearrange(pool, 'b c h w -> (h w) b c')) + if self.g_pos is None: + pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2], pool.shape[3]) + pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c') + self.p_poses.append(pos_emb) + pools = torch.cat(pools, 0) + if self.g_pos is None: + self.p_poses = torch.cat(self.p_poses, dim=0) + pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3]) + self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c') + + device = pools.device + self.p_poses = self.p_poses.to(device) + self.g_pos = self.g_pos.to(device) + + # attention between glb (q) & multisensory concated-locs (k,v) + g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c') + + g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0]) + g_hw_b_c = self.norm1(g_hw_b_c) + g_hw_b_c = g_hw_b_c + self.dropout2(self.linear2(self.dropout(self.activation(self.linear1(g_hw_b_c)).clone()))) + g_hw_b_c = self.norm2(g_hw_b_c) + + # attention between origin locs (q) & freashed glb (k,v) + l_hw_b_c = rearrange(l, "b c h w -> (h w) b c") + _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w) + _g_hw_b_c = rearrange(_g_hw_b_c, "(ng h) (nw w) b c -> (h w) (ng nw b) c", ng=2, nw=2) + outputs_re = [] + for i, (_l, _g) in enumerate(zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))): + outputs_re.append(self.attention[i + 1](_l, _g, _g)[0]) # (h w) 1 c + outputs_re = torch.cat(outputs_re, 1) # (h w) 4 c + + l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re) + l_hw_b_c = self.norm1(l_hw_b_c) + l_hw_b_c = l_hw_b_c + self.dropout2(self.linear4(self.dropout(self.activation(self.linear3(l_hw_b_c)).clone()))) + l_hw_b_c = self.norm2(l_hw_b_c) + + l = torch.cat((l_hw_b_c, g_hw_b_c), 1) # hw,b(5),c + return rearrange(l, "(h w) b c -> b c h w", h=h, w=w) ## (5,c,h*w) + + +class MCRM(nn.Module): + def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None): + super(MCRM, self).__init__() + self.attention = nn.ModuleList([ + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1) + ]) + self.linear3 = nn.Linear(d_model, d_model * 2) + self.linear4 = nn.Linear(d_model * 2, d_model) + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(0.1) + self.dropout1 = nn.Dropout(0.1) + self.dropout2 = nn.Dropout(0.1) + self.sigmoid = nn.Sigmoid() + self.activation = get_activation_fn('gelu') + self.sal_conv = nn.Conv2d(d_model, 1, 1) + self.pool_ratios = pool_ratios + + def forward(self, x): + device = x.device + b, c, h, w = x.size() + loc, glb = x.split([4, 1], dim=0) # 4,c,h,w; 1,c,h,w + + patched_glb = rearrange(glb, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) + + token_attention_map = self.sigmoid(self.sal_conv(glb)) + token_attention_map = F.interpolate(token_attention_map, size=patches2image(loc).shape[-2:], mode='nearest') + loc = loc * rearrange(token_attention_map, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) + + pools = [] + for pool_ratio in self.pool_ratios: + tgt_hw = (round(h / pool_ratio), round(w / pool_ratio)) + pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw) + pools.append(rearrange(pool, 'nl c h w -> nl c (h w)')) # nl(4),c,hw + + pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c") + loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c') + + outputs = [] + for i, q in enumerate(loc_.unbind(dim=0)): # traverse all local patches + v = pools[i] + k = v + outputs.append(self.attention[i](q, k, v)[0]) + + outputs = torch.cat(outputs, 1) + src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs) + src = self.norm1(src) + src = src + self.dropout2(self.linear4(self.dropout(self.activation(self.linear3(src)).clone()))) + src = self.norm2(src) + src = src.permute(1, 2, 0).reshape(4, c, h, w) # freshed loc + glb = glb + F.interpolate(patches2image(src), size=glb.shape[-2:], mode='nearest') # freshed glb + + return torch.cat((src, glb), 0), token_attention_map + + +class BEN_Base(nn.Module): + def __init__(self): + super().__init__() + + self.backbone = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12) + emb_dim = 128 + self.sideout5 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout4 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout3 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout2 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout1 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + + self.output5 = make_cbr(1024, emb_dim) + self.output4 = make_cbr(512, emb_dim) + self.output3 = make_cbr(256, emb_dim) + self.output2 = make_cbr(128, emb_dim) + self.output1 = make_cbr(128, emb_dim) + + self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8]) + self.conv1 = make_cbr(emb_dim, emb_dim) + self.conv2 = make_cbr(emb_dim, emb_dim) + self.conv3 = make_cbr(emb_dim, emb_dim) + self.conv4 = make_cbr(emb_dim, emb_dim) + self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8]) + self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8]) + self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8]) + self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8]) + + self.insmask_head = nn.Sequential( + nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1), + nn.InstanceNorm2d(384), + nn.GELU(), + nn.Conv2d(384, 384, kernel_size=3, padding=1), + nn.InstanceNorm2d(384), + nn.GELU(), + nn.Conv2d(384, emb_dim, kernel_size=3, padding=1) + ) + + self.shallow = nn.Sequential(nn.Conv2d(3, emb_dim, kernel_size=3, padding=1)) + self.upsample1 = make_cbg(emb_dim, emb_dim) + self.upsample2 = make_cbg(emb_dim, emb_dim) + self.output = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + + for m in self.modules(): + if isinstance(m, nn.GELU) or isinstance(m, nn.Dropout): + m.inplace = True + + @torch.inference_mode() + @torch.autocast(device_type="cuda", dtype=torch.float16) + def forward(self, x): + real_batch = x.size(0) + + shallow_batch = self.shallow(x) + glb_batch = rescale_to(x, scale_factor=0.5, interpolation='bilinear') + + final_input = None + for i in range(real_batch): + start = i * 4 + end = (i + 1) * 4 + loc_batch = image2patches(x[i, :, :, :].unsqueeze(dim=0)) + input_ = torch.cat((loc_batch, glb_batch[i, :, :, :].unsqueeze(dim=0)), dim=0) + + if final_input == None: + final_input = input_ + else: + final_input = torch.cat((final_input, input_), dim=0) + + features = self.backbone(final_input) + outputs = [] + + for i in range(real_batch): + start = i * 5 + end = (i + 1) * 5 + + f4 = features[4][start:end, :, :, :] # shape: [5, C, H, W] + f3 = features[3][start:end, :, :, :] + f2 = features[2][start:end, :, :, :] + f1 = features[1][start:end, :, :, :] + f0 = features[0][start:end, :, :, :] + e5 = self.output5(f4) + e4 = self.output4(f3) + e3 = self.output3(f2) + e2 = self.output2(f1) + e1 = self.output1(f0) + loc_e5, glb_e5 = e5.split([4, 1], dim=0) + e5 = self.multifieldcrossatt(loc_e5, glb_e5) # (4,128,16,16) + + e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4)) + e4 = self.conv4(e4) + e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3)) + e3 = self.conv3(e3) + e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2)) + e2 = self.conv2(e2) + e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1)) + e1 = self.conv1(e1) + + loc_e1, glb_e1 = e1.split([4, 1], dim=0) + + output1_cat = patches2image(loc_e1) # (1,128,256,256) + + # add glb feat in + output1_cat = output1_cat + resize_as(glb_e1, output1_cat) + # merge + final_output = self.insmask_head(output1_cat) # (1,128,256,256) + # shallow feature merge + shallow = shallow_batch[i, :, :, :].unsqueeze(dim=0) + final_output = final_output + resize_as(shallow, final_output) + final_output = self.upsample1(rescale_to(final_output)) + final_output = rescale_to(final_output + resize_as(shallow, final_output)) + final_output = self.upsample2(final_output) + final_output = self.output(final_output) + mask = final_output.sigmoid() + outputs.append(mask) + + return torch.cat(outputs, dim=0) + + def loadcheckpoints(self, model_path): + model_dict = torch.load(model_path, map_location="cpu", weights_only=True) + self.load_state_dict(model_dict['model_state_dict'], strict=True) + del model_path + + def inference(self, image, refine_foreground=False): + + set_random_seed(9) + # image = ImageOps.exif_transpose(image) + if isinstance(image, Image.Image): + image, h, w, original_image = rgb_loader_refiner(image) + if torch.cuda.is_available(): + + img_tensor = img_transform(image).unsqueeze(0).to(next(self.parameters()).device) + else: + img_tensor = img_transform32(image).unsqueeze(0).to(next(self.parameters()).device) + + with torch.no_grad(): + res = self.forward(img_tensor) + + # Show Results + if refine_foreground == True: + + pred_pil = transforms.ToPILImage()(res.squeeze()) + image_masked = refine_foreground_process(original_image, pred_pil) + mask = pred_pil.resize(original_image.size) + image_masked.putalpha(mask) + return mask, image_masked + + else: + alpha = postprocess_image(res, im_size=[w, h]) + pred_pil = transforms.ToPILImage()(alpha) + mask = pred_pil.resize(original_image.size) + original_image.putalpha(mask) + # mask = Image.fromarray(alpha) + + return mask, original_image + + def segment_video(self, video_path, output_path="./", fps=0, refine_foreground=False, batch=1, + print_frames_processed=True, webm=False, rgb_value=(0, 255, 0)): + + """ + Segments the given video to extract the foreground (with alpha) from each frame + and saves the result as either a WebM video (with alpha channel) or MP4 (with a + color background). + Args: + video_path (str): + Path to the input video file. + output_path (str, optional): + Directory (or full path) where the output video and/or files will be saved. + Defaults to "./". + fps (int, optional): + The frames per second (FPS) to use for the output video. If 0 (default), the + original FPS of the input video is used. Otherwise, overrides it. + refine_foreground (bool, optional): + Whether to run an additional β€œrefine foreground” process on each frame. + Defaults to False. + batch (int, optional): + Number of frames to process at once (inference batch size). Large batch sizes + may require more GPU memory. Defaults to 1. + print_frames_processed (bool, optional): + If True (default), prints progress (how many frames have been processed) to + the console. + webm (bool, optional): + If True (default), exports a WebM video with alpha channel (VP9 / yuva420p). + If False, exports an MP4 video composited over a solid color background. + rgb_value (tuple, optional): + The RGB background color (e.g., green screen) used to composite frames when + saving to MP4. Defaults to (0, 255, 0). + Returns: + None. Writes the output video(s) to disk in the specified format. + """ + + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + raise IOError(f"Cannot open video: {video_path}") + + original_fps = cap.get(cv2.CAP_PROP_FPS) + original_fps = 30 if original_fps == 0 else original_fps + fps = original_fps if fps == 0 else fps + + ret, first_frame = cap.read() + if not ret: + raise ValueError("No frames found in the video.") + height, width = first_frame.shape[:2] + cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + + foregrounds = [] + frame_idx = 0 + processed_count = 0 + batch_frames = [] + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + while True: + ret, frame = cap.read() + if not ret: + if batch_frames: + batch_results = self.inference(batch_frames, refine_foreground) + if isinstance(batch_results, Image.Image): + foregrounds.append(batch_results) + else: + foregrounds.extend(batch_results) + if print_frames_processed: + print(f"Processed frames {frame_idx - len(batch_frames) + 1} to {frame_idx} of {total_frames}") + break + + # Process every frame instead of using intervals + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + pil_frame = Image.fromarray(frame_rgb) + batch_frames.append(pil_frame) + + if len(batch_frames) == batch: + batch_results = self.inference(batch_frames, refine_foreground) + if isinstance(batch_results, Image.Image): + foregrounds.append(batch_results) + else: + foregrounds.extend(batch_results) + if print_frames_processed: + print(f"Processed frames {frame_idx - batch + 1} to {frame_idx} of {total_frames}") + batch_frames = [] + processed_count += batch + + frame_idx += 1 + + if webm: + alpha_webm_path = os.path.join(output_path, "foreground.webm") + pil_images_to_webm_alpha(foregrounds, alpha_webm_path, fps=original_fps) + + else: + cap.release() + fg_output = os.path.join(output_path, 'foreground.mp4') + + pil_images_to_mp4(foregrounds, fg_output, fps=original_fps, rgb_value=rgb_value) + cv2.destroyAllWindows() + + try: + fg_audio_output = os.path.join(output_path, 'foreground_output_with_audio.mp4') + add_audio_to_video(fg_output, video_path, fg_audio_output) + except Exception as e: + print("No audio found in the original video") + print(e) + + +def rgb_loader_refiner(original_image): + h, w = original_image.size + + image = original_image + # Convert to RGB if necessary + if image.mode != 'RGB': + image = image.convert('RGB') + + # Resize the image + image = image.resize((1024, 1024), resample=Image.LANCZOS) + + return image.convert('RGB'), h, w, original_image + + +# Define the image transformation +img_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float16), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +]) + +img_transform32 = transforms.Compose([ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float32), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +]) + + +def pil_images_to_mp4(images, output_path, fps=24, rgb_value=(0, 255, 0)): + """ + Converts an array of PIL images to an MP4 video. + + Args: + images: List of PIL images + output_path: Path to save the MP4 file + fps: Frames per second (default: 24) + rgb_value: Background RGB color tuple (default: green (0, 255, 0)) + """ + if not images: + raise ValueError("No images provided to convert to MP4.") + + width, height = images[0].size + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) + + for image in images: + # If image has alpha channel, composite onto the specified background color + if image.mode == 'RGBA': + # Create background image with specified RGB color + background = Image.new('RGB', image.size, rgb_value) + background = background.convert('RGBA') + # Composite the image onto the background + image = Image.alpha_composite(background, image) + image = image.convert('RGB') + else: + # Ensure RGB format for non-alpha images + image = image.convert('RGB') + + # Convert to OpenCV format and write + open_cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) + video_writer.write(open_cv_image) + + video_writer.release() + + +def pil_images_to_webm_alpha(images, output_path, fps=30): + """ + Converts a list of PIL RGBA images to a VP9 .webm video with alpha channel. + NOTE: Not all players will display alpha in WebM. + Browsers like Chrome/Firefox typically do support VP9 alpha. + """ + if not images: + raise ValueError("No images provided for WebM with alpha.") + + # Ensure output directory exists + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save frames as PNG (with alpha) + for idx, img in enumerate(images): + if img.mode != "RGBA": + img = img.convert("RGBA") + out_path = os.path.join(tmpdir, f"{idx:06d}.png") + img.save(out_path, "PNG") + + # Construct ffmpeg command + # -c:v libvpx-vp9 => VP9 encoder + # -pix_fmt yuva420p => alpha-enabled pixel format + # -auto-alt-ref 0 => helps preserve alpha frames (libvpx quirk) + ffmpeg_cmd = [ + "ffmpeg", "-y", + "-framerate", str(fps), + "-i", os.path.join(tmpdir, "%06d.png"), + "-c:v", "libvpx-vp9", + "-pix_fmt", "yuva420p", + "-auto-alt-ref", "0", + output_path + ] + + subprocess.run(ffmpeg_cmd, check=True) + + print(f"WebM with alpha saved to {output_path}") + + +def add_audio_to_video(video_without_audio_path, original_video_path, output_path): + """ + Check if the original video has an audio stream. If yes, add it. If not, skip. + """ + # 1) Probe original video for audio streams + probe_command = [ + 'ffprobe', '-v', 'error', + '-select_streams', 'a:0', + '-show_entries', 'stream=index', + '-of', 'csv=p=0', + original_video_path + ] + result = subprocess.run(probe_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + + # result.stdout is empty if no audio stream found + if not result.stdout.strip(): + print("No audio track found in original video, skipping audio addition.") + return + + print("Audio track detected; proceeding to mux audio.") + # 2) If audio found, run ffmpeg to add it + command = [ + 'ffmpeg', '-y', + '-i', video_without_audio_path, + '-i', original_video_path, + '-c', 'copy', + '-map', '0:v:0', + '-map', '1:a:0', # we know there's an audio track now + output_path + ] + subprocess.run(command, check=True) + print(f"Audio added successfully => {output_path}") + + +### Thanks to the source: https://huggingface.co/ZhengPeng7/BiRefNet/blob/main/handler.py +def refine_foreground_process(image, mask, r=90): + if mask.size != image.size: + mask = mask.resize(image.size) + image = np.array(image) / 255.0 + mask = np.array(mask) / 255.0 + estimated_foreground = FB_blur_fusion_foreground_estimator_2(image, mask, r=r) + image_masked = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8)) + return image_masked + + +def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90): + # Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation + alpha = alpha[:, :, None] + F, blur_B = FB_blur_fusion_foreground_estimator(image, image, image, alpha, r) + return FB_blur_fusion_foreground_estimator(image, F, blur_B, alpha, r=6)[0] + + +def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90): + if isinstance(image, Image.Image): + image = np.array(image) / 255.0 + blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None] + + blurred_FA = cv2.blur(F * alpha, (r, r)) + blurred_F = blurred_FA / (blurred_alpha + 1e-5) + + blurred_B1A = cv2.blur(B * (1 - alpha), (r, r)) + blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5) + F = blurred_F + alpha * \ + (image - alpha * blurred_F - (1 - alpha) * blurred_B) + F = np.clip(F, 0, 1) + return F, blurred_B + + +def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray: + result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear'), 0) + ma = torch.max(result) + mi = torch.min(result) + result = (result - mi) / (ma - mi) + im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8) + im_array = np.squeeze(im_array) + return im_array + + +def rgb_loader_refiner(original_image): + h, w = original_image.size + # # Apply EXIF orientation + + image = ImageOps.exif_transpose(original_image) + + if original_image.mode != 'RGB': + original_image = original_image.convert('RGB') + + image = original_image + # Convert to RGB if necessary + + # Resize the image + image = image.resize((1024, 1024), resample=Image.LANCZOS) + + return image, h, w, original_image + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/bitsandbytes_NF4/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/bitsandbytes_NF4/__init__.py new file mode 100644 index 00000000..51935968 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/bitsandbytes_NF4/__init__.py @@ -0,0 +1,167 @@ +#credit to comfyanonymous for this module +#from https://github.com/comfyanonymous/ComfyUI_bitsandbytes_NF4 +import comfy.ops +import torch +import folder_paths +from ...libs.utils import install_package + +try: + from bitsandbytes.nn.modules import Params4bit, QuantState +except ImportError: + Params4bit = torch.nn.Parameter + raise ImportError("Please install bitsandbytes>=0.43.3") + +def functional_linear_4bits(x, weight, bias): + try: + install_package("bitsandbytes", "0.43.3", True, "0.43.3") + import bitsandbytes as bnb + except ImportError: + raise ImportError("Please install bitsandbytes>=0.43.3") + + out = bnb.matmul_4bit(x, weight.t(), bias=bias, quant_state=weight.quant_state) + out = out.to(x) + return out + + +def copy_quant_state(state, device: torch.device = None): + if state is None: + return None + + device = device or state.absmax.device + + state2 = ( + QuantState( + absmax=state.state2.absmax.to(device), + shape=state.state2.shape, + code=state.state2.code.to(device), + blocksize=state.state2.blocksize, + quant_type=state.state2.quant_type, + dtype=state.state2.dtype, + ) + if state.nested + else None + ) + + return QuantState( + absmax=state.absmax.to(device), + shape=state.shape, + code=state.code.to(device), + blocksize=state.blocksize, + quant_type=state.quant_type, + dtype=state.dtype, + offset=state.offset.to(device) if state.nested else None, + state2=state2, + ) + + +class ForgeParams4bit(Params4bit): + + def to(self, *args, **kwargs): + device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs) + if device is not None and device.type == "cuda" and not self.bnb_quantized: + return self._quantize(device) + else: + n = ForgeParams4bit( + torch.nn.Parameter.to(self, device=device, dtype=dtype, non_blocking=non_blocking), + requires_grad=self.requires_grad, + quant_state=copy_quant_state(self.quant_state, device), + blocksize=self.blocksize, + compress_statistics=self.compress_statistics, + quant_type=self.quant_type, + quant_storage=self.quant_storage, + bnb_quantized=self.bnb_quantized, + module=self.module + ) + self.module.quant_state = n.quant_state + self.data = n.data + self.quant_state = n.quant_state + return n + +class ForgeLoader4Bit(torch.nn.Module): + def __init__(self, *, device, dtype, quant_type, **kwargs): + super().__init__() + self.dummy = torch.nn.Parameter(torch.empty(1, device=device, dtype=dtype)) + self.weight = None + self.quant_state = None + self.bias = None + self.quant_type = quant_type + + def _save_to_state_dict(self, destination, prefix, keep_vars): + super()._save_to_state_dict(destination, prefix, keep_vars) + quant_state = getattr(self.weight, "quant_state", None) + if quant_state is not None: + for k, v in quant_state.as_dict(packed=True).items(): + destination[prefix + "weight." + k] = v if keep_vars else v.detach() + return + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): + quant_state_keys = {k[len(prefix + "weight."):] for k in state_dict.keys() if k.startswith(prefix + "weight.")} + + if any('bitsandbytes' in k for k in quant_state_keys): + quant_state_dict = {k: state_dict[prefix + "weight." + k] for k in quant_state_keys} + + self.weight = ForgeParams4bit().from_prequantized( + data=state_dict[prefix + 'weight'], + quantized_stats=quant_state_dict, + requires_grad=False, + device=self.dummy.device, + module=self + ) + self.quant_state = self.weight.quant_state + + if prefix + 'bias' in state_dict: + self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy)) + + del self.dummy + elif hasattr(self, 'dummy'): + if prefix + 'weight' in state_dict: + self.weight = ForgeParams4bit( + state_dict[prefix + 'weight'].to(self.dummy), + requires_grad=False, + compress_statistics=True, + quant_type=self.quant_type, + quant_storage=torch.uint8, + module=self, + ) + self.quant_state = self.weight.quant_state + + if prefix + 'bias' in state_dict: + self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy)) + + del self.dummy + else: + super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) + +current_device = None +current_dtype = None +current_manual_cast_enabled = False +current_bnb_dtype = None + +class OPS(comfy.ops.manual_cast): + class Linear(ForgeLoader4Bit): + def __init__(self, *args, device=None, dtype=None, **kwargs): + super().__init__(device=device, dtype=dtype, quant_type=current_bnb_dtype) + self.parameters_manual_cast = current_manual_cast_enabled + + def forward(self, x): + self.weight.quant_state = self.quant_state + + if self.bias is not None and self.bias.dtype != x.dtype: + # Maybe this can also be set to all non-bnb ops since the cost is very low. + # And it only invokes one time, and most linear does not have bias + self.bias.data = self.bias.data.to(x.dtype) + + if not self.parameters_manual_cast: + return functional_linear_4bits(x, self.weight, self.bias) + elif not self.weight.bnb_quantized: + assert x.device.type == 'cuda', 'BNB Must Use CUDA as Computation Device!' + layer_original_device = self.weight.device + self.weight = self.weight._quantize(x.device) + bias = self.bias.to(x.device) if self.bias is not None else None + out = functional_linear_4bits(x, self.weight, bias) + self.weight = self.weight.to(layer_original_device) + return out + else: + weight, bias, signal = weights_manual_cast(self, x, skip_weight_dtype=True, skip_bias_dtype=True) + with main_stream_worker(weight, bias, signal): + return functional_linear_4bits(x, weight, bias) diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/briaai/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/briaai/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/briaai/rembg.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/briaai/rembg.py new file mode 100644 index 00000000..4689d149 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/briaai/rembg.py @@ -0,0 +1,475 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms.functional import normalize +import numpy as np + + +class REBNCONV(nn.Module): + def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1): + super(REBNCONV,self).__init__() + + self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride) + self.bn_s1 = nn.BatchNorm2d(out_ch) + self.relu_s1 = nn.ReLU(inplace=True) + + def forward(self,x): + + hx = x + xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) + + return xout + +## upsample tensor 'src' to have the same spatial size with tensor 'tar' +def _upsample_like(src,tar): + + src = F.interpolate(src,size=tar.shape[2:],mode='bilinear') + + return src + + +### RSU-7 ### +class RSU7(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512): + super(RSU7,self).__init__() + + self.in_ch = in_ch + self.mid_ch = mid_ch + self.out_ch = out_ch + + self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2 + + self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) + self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1) + + self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2) + + self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) + + def forward(self,x): + b, c, h, w = x.shape + + hx = x + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + hx = self.pool5(hx5) + + hx6 = self.rebnconv6(hx) + + hx7 = self.rebnconv7(hx6) + + hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1)) + hx6dup = _upsample_like(hx6d,hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1)) + hx5dup = _upsample_like(hx5d,hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1)) + hx4dup = _upsample_like(hx4d,hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1)) + hx3dup = _upsample_like(hx3d,hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) + hx2dup = _upsample_like(hx2d,hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) + + return hx1d + hxin + + +### RSU-6 ### +class RSU6(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU6,self).__init__() + + self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) + + self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) + self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1) + + self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2) + + self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) + + def forward(self,x): + + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + + hx6 = self.rebnconv6(hx5) + + + hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1)) + hx5dup = _upsample_like(hx5d,hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1)) + hx4dup = _upsample_like(hx4d,hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1)) + hx3dup = _upsample_like(hx3d,hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) + hx2dup = _upsample_like(hx2d,hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) + + return hx1d + hxin + +### RSU-5 ### +class RSU5(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU5,self).__init__() + + self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) + + self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) + self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1) + + self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2) + + self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) + + def forward(self,x): + + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + + hx5 = self.rebnconv5(hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1)) + hx4dup = _upsample_like(hx4d,hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1)) + hx3dup = _upsample_like(hx3d,hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) + hx2dup = _upsample_like(hx2d,hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) + + return hx1d + hxin + +### RSU-4 ### +class RSU4(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4,self).__init__() + + self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) + + self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) + self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) + self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) + + self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2) + + self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) + self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) + + def forward(self,x): + + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1)) + hx3dup = _upsample_like(hx3d,hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) + hx2dup = _upsample_like(hx2d,hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) + + return hx1d + hxin + +### RSU-4F ### +class RSU4F(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4F,self).__init__() + + self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) + + self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) + self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2) + self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4) + + self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8) + + self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4) + self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2) + self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) + + def forward(self,x): + + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx2 = self.rebnconv2(hx1) + hx3 = self.rebnconv3(hx2) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1)) + hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1)) + hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1)) + + return hx1d + hxin + + +class myrebnconv(nn.Module): + def __init__(self, in_ch=3, + out_ch=1, + kernel_size=3, + stride=1, + padding=1, + dilation=1, + groups=1): + super(myrebnconv,self).__init__() + + self.conv = nn.Conv2d(in_ch, + out_ch, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups) + self.bn = nn.BatchNorm2d(out_ch) + self.rl = nn.ReLU(inplace=True) + + def forward(self,x): + return self.rl(self.bn(self.conv(x))) + +def preprocess_image(im, model_input_size: list) -> torch.Tensor: + # im = im.resize(model_input_size, Image.BILINEAR) + im_np = np.array(im) + im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1) + im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear').type(torch.uint8) + image = torch.divide(im_tensor,255.0) + image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0]) + return image + +def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray: + result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0) + ma = torch.max(result) + mi = torch.min(result) + result = (result-mi)/(ma-mi) + im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8) + im_array = np.squeeze(im_array) + return im_array + +class BriaRMBG(nn.Module): + + def __init__(self, config:dict={"in_ch":3,"out_ch":1}): + super(BriaRMBG,self).__init__() + in_ch = config["in_ch"] + out_ch = config["out_ch"] + self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1) + self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.stage1 = RSU7(64,32,64) + self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.stage2 = RSU6(64,32,128) + self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.stage3 = RSU5(128,64,256) + self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.stage4 = RSU4(256,128,512) + self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.stage5 = RSU4F(512,256,512) + self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True) + + self.stage6 = RSU4F(512,256,512) + + # decoder + self.stage5d = RSU4F(1024,256,512) + self.stage4d = RSU4(1024,128,256) + self.stage3d = RSU5(512,64,128) + self.stage2d = RSU6(256,32,64) + self.stage1d = RSU7(128,16,64) + + self.side1 = nn.Conv2d(64,out_ch,3,padding=1) + self.side2 = nn.Conv2d(64,out_ch,3,padding=1) + self.side3 = nn.Conv2d(128,out_ch,3,padding=1) + self.side4 = nn.Conv2d(256,out_ch,3,padding=1) + self.side5 = nn.Conv2d(512,out_ch,3,padding=1) + self.side6 = nn.Conv2d(512,out_ch,3,padding=1) + + # self.outconv = nn.Conv2d(6*out_ch,out_ch,1) + + def forward(self,x): + + hx = x + + hxin = self.conv_in(hx) + #hx = self.pool_in(hxin) + + #stage 1 + hx1 = self.stage1(hxin) + hx = self.pool12(hx1) + + #stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + #stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + #stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + #stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + #stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6,hx5) + + #-------------------- decoder -------------------- + hx5d = self.stage5d(torch.cat((hx6up,hx5),1)) + hx5dup = _upsample_like(hx5d,hx4) + + hx4d = self.stage4d(torch.cat((hx5dup,hx4),1)) + hx4dup = _upsample_like(hx4d,hx3) + + hx3d = self.stage3d(torch.cat((hx4dup,hx3),1)) + hx3dup = _upsample_like(hx3d,hx2) + + hx2d = self.stage2d(torch.cat((hx3dup,hx2),1)) + hx2dup = _upsample_like(hx2d,hx1) + + hx1d = self.stage1d(torch.cat((hx2dup,hx1),1)) + + + #side output + d1 = self.side1(hx1d) + d1 = _upsample_like(d1,x) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2,x) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3,x) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4,x) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5,x) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6,x) + + return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)],[hx1d,hx2d,hx3d,hx4d,hx5d,hx6] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/__init__.py new file mode 100644 index 00000000..c35ea134 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/__init__.py @@ -0,0 +1,822 @@ +#credit to nullquant for this module +#from https://github.com/nullquant/ComfyUI-BrushNet + +import os +import types + +import torch +try: + from accelerate import init_empty_weights, load_checkpoint_and_dispatch +except: + init_empty_weights, load_checkpoint_and_dispatch = None, None + +import comfy + +try: + from .model import BrushNetModel, PowerPaintModel + from .model_patch import add_model_patch_option, patch_model_function_wrapper + from .powerpaint_utils import TokenizerWrapper, add_tokens +except: + BrushNetModel, PowerPaintModel = None, None + add_model_patch_option, patch_model_function_wrapper = None, None + TokenizerWrapper, add_tokens = None, None + +cwd_path = os.path.dirname(os.path.realpath(__file__)) +brushnet_config_file = os.path.join(cwd_path, 'config', 'brushnet.json') +brushnet_xl_config_file = os.path.join(cwd_path, 'config', 'brushnet_xl.json') +powerpaint_config_file = os.path.join(cwd_path, 'config', 'powerpaint.json') + +sd15_scaling_factor = 0.18215 +sdxl_scaling_factor = 0.13025 + +ModelsToUnload = [comfy.sd1_clip.SD1ClipModel, comfy.ldm.models.autoencoder.AutoencoderKL] + +class BrushNet: + + # Check models compatibility + def check_compatibilty(self, model, brushnet): + is_SDXL = False + is_PP = False + if isinstance(model.model.model_config, comfy.supported_models.SD15): + print('Base model type: SD1.5') + is_SDXL = False + if brushnet["SDXL"]: + raise Exception("Base model is SD15, but BrushNet is SDXL type") + if brushnet["PP"]: + is_PP = True + elif isinstance(model.model.model_config, comfy.supported_models.SDXL): + print('Base model type: SDXL') + is_SDXL = True + if not brushnet["SDXL"]: + raise Exception("Base model is SDXL, but BrushNet is SD15 type") + else: + print('Base model type: ', type(model.model.model_config)) + raise Exception("Unsupported model type: " + str(type(model.model.model_config))) + + return (is_SDXL, is_PP) + + def check_image_mask(self, image, mask, name): + if len(image.shape) < 4: + # image tensor shape should be [B, H, W, C], but batch somehow is missing + image = image[None, :, :, :] + + if len(mask.shape) > 3: + # mask tensor shape should be [B, H, W] but we get [B, H, W, C], image may be? + # take first mask, red channel + mask = (mask[:, :, :, 0])[:, :, :] + elif len(mask.shape) < 3: + # mask tensor shape should be [B, H, W] but batch somehow is missing + mask = mask[None, :, :] + + if image.shape[0] > mask.shape[0]: + print(name, "gets batch of images (%d) but only %d masks" % (image.shape[0], mask.shape[0])) + if mask.shape[0] == 1: + print(name, "will copy the mask to fill batch") + mask = torch.cat([mask] * image.shape[0], dim=0) + else: + print(name, "will add empty masks to fill batch") + empty_mask = torch.zeros([image.shape[0] - mask.shape[0], mask.shape[1], mask.shape[2]]) + mask = torch.cat([mask, empty_mask], dim=0) + elif image.shape[0] < mask.shape[0]: + print(name, "gets batch of images (%d) but too many (%d) masks" % (image.shape[0], mask.shape[0])) + mask = mask[:image.shape[0], :, :] + + return (image, mask) + + # Prepare image and mask + def prepare_image(self, image, mask): + + image, mask = self.check_image_mask(image, mask, 'BrushNet') + + print("BrushNet image.shape =", image.shape, "mask.shape =", mask.shape) + + if mask.shape[2] != image.shape[2] or mask.shape[1] != image.shape[1]: + raise Exception("Image and mask should be the same size") + + # As a suggestion of inferno46n2 (https://github.com/nullquant/ComfyUI-BrushNet/issues/64) + mask = mask.round() + + masked_image = image * (1.0 - mask[:, :, :, None]) + + return (masked_image, mask) + + # Get origin of the mask + def cut_with_mask(self, mask, width, height): + iy, ix = (mask == 1).nonzero(as_tuple=True) + + h0, w0 = mask.shape + + if iy.numel() == 0: + x_c = w0 / 2.0 + y_c = h0 / 2.0 + else: + x_min = ix.min().item() + x_max = ix.max().item() + y_min = iy.min().item() + y_max = iy.max().item() + + if x_max - x_min > width or y_max - y_min > height: + raise Exception("Mask is bigger than provided dimensions") + + x_c = (x_min + x_max) / 2.0 + y_c = (y_min + y_max) / 2.0 + + width2 = width / 2.0 + height2 = height / 2.0 + + if w0 <= width: + x0 = 0 + w = w0 + else: + x0 = max(0, x_c - width2) + w = width + if x0 + width > w0: + x0 = w0 - width + + if h0 <= height: + y0 = 0 + h = h0 + else: + y0 = max(0, y_c - height2) + h = height + if y0 + height > h0: + y0 = h0 - height + + return (int(x0), int(y0), int(w), int(h)) + + # Prepare conditioning_latents + @torch.inference_mode() + def get_image_latents(self, masked_image, mask, vae, scaling_factor): + processed_image = masked_image.to(vae.device) + image_latents = vae.encode(processed_image[:, :, :, :3]) * scaling_factor + processed_mask = 1. - mask[:, None, :, :] + interpolated_mask = torch.nn.functional.interpolate( + processed_mask, + size=( + image_latents.shape[-2], + image_latents.shape[-1] + ) + ) + interpolated_mask = interpolated_mask.to(image_latents.device) + + conditioning_latents = [image_latents, interpolated_mask] + + print('BrushNet CL: image_latents shape =', image_latents.shape, 'interpolated_mask shape =', + interpolated_mask.shape) + + return conditioning_latents + + def brushnet_blocks(self, sd): + brushnet_down_block = 0 + brushnet_mid_block = 0 + brushnet_up_block = 0 + for key in sd: + if 'brushnet_down_block' in key: + brushnet_down_block += 1 + if 'brushnet_mid_block' in key: + brushnet_mid_block += 1 + if 'brushnet_up_block' in key: + brushnet_up_block += 1 + return (brushnet_down_block, brushnet_mid_block, brushnet_up_block, len(sd)) + + def get_model_type(self, brushnet_file): + sd = comfy.utils.load_torch_file(brushnet_file) + brushnet_down_block, brushnet_mid_block, brushnet_up_block, keys = self.brushnet_blocks(sd) + del sd + if brushnet_down_block == 24 and brushnet_mid_block == 2 and brushnet_up_block == 30: + is_SDXL = False + if keys == 322: + is_PP = False + print('BrushNet model type: SD1.5') + else: + is_PP = True + print('PowerPaint model type: SD1.5') + elif brushnet_down_block == 18 and brushnet_mid_block == 2 and brushnet_up_block == 22: + print('BrushNet model type: Loading SDXL') + is_SDXL = True + is_PP = False + else: + raise Exception("Unknown BrushNet model") + return is_SDXL, is_PP + + def load_brushnet_model(self, brushnet_file, dtype='float16'): + is_SDXL, is_PP = self.get_model_type(brushnet_file) + with init_empty_weights(): + if is_SDXL: + brushnet_config = BrushNetModel.load_config(brushnet_xl_config_file) + brushnet_model = BrushNetModel.from_config(brushnet_config) + elif is_PP: + brushnet_config = PowerPaintModel.load_config(powerpaint_config_file) + brushnet_model = PowerPaintModel.from_config(brushnet_config) + else: + brushnet_config = BrushNetModel.load_config(brushnet_config_file) + brushnet_model = BrushNetModel.from_config(brushnet_config) + if is_PP: + print("PowerPaint model file:", brushnet_file) + else: + print("BrushNet model file:", brushnet_file) + + if dtype == 'float16': + torch_dtype = torch.float16 + elif dtype == 'bfloat16': + torch_dtype = torch.bfloat16 + elif dtype == 'float32': + torch_dtype = torch.float32 + else: + torch_dtype = torch.float64 + + brushnet_model = load_checkpoint_and_dispatch( + brushnet_model, + brushnet_file, + device_map="sequential", + max_memory=None, + offload_folder=None, + offload_state_dict=False, + dtype=torch_dtype, + force_hooks=False, + ) + + if is_PP: + print("PowerPaint model is loaded") + elif is_SDXL: + print("BrushNet SDXL model is loaded") + else: + print("BrushNet SD1.5 model is loaded") + + return ({"brushnet": brushnet_model, "SDXL": is_SDXL, "PP": is_PP, "dtype": torch_dtype},) + + def brushnet_model_update(self, model, vae, image, mask, brushnet, positive, negative, scale, start_at, end_at): + + is_SDXL, is_PP = self.check_compatibilty(model, brushnet) + + if is_PP: + raise Exception("PowerPaint model was loaded, please use PowerPaint node") + + # Make a copy of the model so that we're not patching it everywhere in the workflow. + model = model.clone() + + # prepare image and mask + # no batches for original image and mask + masked_image, mask = self.prepare_image(image, mask) + + batch = masked_image.shape[0] + width = masked_image.shape[2] + height = masked_image.shape[1] + + if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format, + 'scale_factor'): + scaling_factor = model.model.model_config.latent_format.scale_factor + elif is_SDXL: + scaling_factor = sdxl_scaling_factor + else: + scaling_factor = sd15_scaling_factor + + torch_dtype = brushnet['dtype'] + + # prepare conditioning latents + conditioning_latents = self.get_image_latents(masked_image, mask, vae, scaling_factor) + conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(brushnet['brushnet'].device) + conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(brushnet['brushnet'].device) + + # unload vae + del vae + # for loaded_model in comfy.model_management.current_loaded_models: + # if type(loaded_model.model.model) in ModelsToUnload: + # comfy.model_management.current_loaded_models.remove(loaded_model) + # loaded_model.model_unload() + # del loaded_model + + # prepare embeddings + prompt_embeds = positive[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device) + negative_prompt_embeds = negative[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device) + + max_tokens = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1]) + if prompt_embeds.shape[1] < max_tokens: + multiplier = max_tokens // 77 - prompt_embeds.shape[1] // 77 + prompt_embeds = torch.concat([prompt_embeds] + [prompt_embeds[:, -77:, :]] * multiplier, dim=1) + print('BrushNet: negative prompt more than 75 tokens:', negative_prompt_embeds.shape, + 'multiplying prompt_embeds') + if negative_prompt_embeds.shape[1] < max_tokens: + multiplier = max_tokens // 77 - negative_prompt_embeds.shape[1] // 77 + negative_prompt_embeds = torch.concat( + [negative_prompt_embeds] + [negative_prompt_embeds[:, -77:, :]] * multiplier, dim=1) + print('BrushNet: positive prompt more than 75 tokens:', prompt_embeds.shape, + 'multiplying negative_prompt_embeds') + + if len(positive[0]) > 1 and 'pooled_output' in positive[0][1] and positive[0][1]['pooled_output'] is not None: + pooled_prompt_embeds = positive[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device) + else: + print('BrushNet: positive conditioning has not pooled_output') + if is_SDXL: + print('BrushNet will not produce correct results') + pooled_prompt_embeds = torch.empty([2, 1280], device=brushnet['brushnet'].device).to(dtype=torch_dtype) + + if len(negative[0]) > 1 and 'pooled_output' in negative[0][1] and negative[0][1]['pooled_output'] is not None: + negative_pooled_prompt_embeds = negative[0][1]['pooled_output'].to(dtype=torch_dtype).to( + brushnet['brushnet'].device) + else: + print('BrushNet: negative conditioning has not pooled_output') + if is_SDXL: + print('BrushNet will not produce correct results') + negative_pooled_prompt_embeds = torch.empty([1, pooled_prompt_embeds.shape[1]], + device=brushnet['brushnet'].device).to(dtype=torch_dtype) + + time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(dtype=torch_dtype).to( + brushnet['brushnet'].device) + + if not is_SDXL: + pooled_prompt_embeds = None + negative_pooled_prompt_embeds = None + time_ids = None + + # apply patch to model + brushnet_conditioning_scale = scale + control_guidance_start = start_at + control_guidance_end = end_at + + add_brushnet_patch(model, + brushnet['brushnet'], + torch_dtype, + conditioning_latents, + (brushnet_conditioning_scale, control_guidance_start, control_guidance_end), + prompt_embeds, negative_prompt_embeds, + pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids, + False) + + latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]], + device=brushnet['brushnet'].device) + + return (model, positive, negative, {"samples": latent},) + + #powperpaint + def load_powerpaint_clip(self, base_clip_file, pp_clip_file): + pp_clip = comfy.sd.load_clip(ckpt_paths=[base_clip_file]) + + print('PowerPaint base CLIP file: ', base_clip_file) + + pp_tokenizer = TokenizerWrapper(pp_clip.tokenizer.clip_l.tokenizer) + pp_text_encoder = pp_clip.patcher.model.clip_l.transformer + + add_tokens( + tokenizer=pp_tokenizer, + text_encoder=pp_text_encoder, + placeholder_tokens=["P_ctxt", "P_shape", "P_obj"], + initialize_tokens=["a", "a", "a"], + num_vectors_per_token=10, + ) + + pp_text_encoder.load_state_dict(comfy.utils.load_torch_file(pp_clip_file), strict=False) + + print('PowerPaint CLIP file: ', pp_clip_file) + + pp_clip.tokenizer.clip_l.tokenizer = pp_tokenizer + pp_clip.patcher.model.clip_l.transformer = pp_text_encoder + + return (pp_clip,) + + def powerpaint_model_update(self, model, vae, image, mask, powerpaint, clip, positive, negative, fitting, function, scale, start_at, end_at, save_memory): + is_SDXL, is_PP = self.check_compatibilty(model, powerpaint) + if not is_PP: + raise Exception("BrushNet model was loaded, please use BrushNet node") + + # Make a copy of the model so that we're not patching it everywhere in the workflow. + model = model.clone() + + # prepare image and mask + # no batches for original image and mask + masked_image, mask = self.prepare_image(image, mask) + + batch = masked_image.shape[0] + # width = masked_image.shape[2] + # height = masked_image.shape[1] + + if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format, + 'scale_factor'): + scaling_factor = model.model.model_config.latent_format.scale_factor + else: + scaling_factor = sd15_scaling_factor + + torch_dtype = powerpaint['dtype'] + + # prepare conditioning latents + conditioning_latents = self.get_image_latents(masked_image, mask, vae, scaling_factor) + conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(powerpaint['brushnet'].device) + conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(powerpaint['brushnet'].device) + + # prepare embeddings + + if function == "object removal": + promptA = "P_ctxt" + promptB = "P_ctxt" + negative_promptA = "P_obj" + negative_promptB = "P_obj" + print('You should add to positive prompt: "empty scene blur"') + # positive = positive + " empty scene blur" + elif function == "context aware": + promptA = "P_ctxt" + promptB = "P_ctxt" + negative_promptA = "" + negative_promptB = "" + # positive = positive + " empty scene" + print('You should add to positive prompt: "empty scene"') + elif function == "shape guided": + promptA = "P_shape" + promptB = "P_ctxt" + negative_promptA = "P_shape" + negative_promptB = "P_ctxt" + elif function == "image outpainting": + promptA = "P_ctxt" + promptB = "P_ctxt" + negative_promptA = "P_obj" + negative_promptB = "P_obj" + # positive = positive + " empty scene" + print('You should add to positive prompt: "empty scene"') + else: + promptA = "P_obj" + promptB = "P_obj" + negative_promptA = "P_obj" + negative_promptB = "P_obj" + + tokens = clip.tokenize(promptA) + prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False) + + tokens = clip.tokenize(negative_promptA) + negative_prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False) + + tokens = clip.tokenize(promptB) + prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False) + + tokens = clip.tokenize(negative_promptB) + negative_prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False) + + prompt_embeds_pp = (prompt_embedsA * fitting + (1.0 - fitting) * prompt_embedsB).to(dtype=torch_dtype).to( + powerpaint['brushnet'].device) + negative_prompt_embeds_pp = (negative_prompt_embedsA * fitting + (1.0 - fitting) * negative_prompt_embedsB).to( + dtype=torch_dtype).to(powerpaint['brushnet'].device) + + # unload vae and CLIPs + del vae + del clip + # for loaded_model in comfy.model_management.current_loaded_models: + # if type(loaded_model.model.model) in ModelsToUnload: + # comfy.model_management.current_loaded_models.remove(loaded_model) + # loaded_model.model_unload() + # del loaded_model + + # apply patch to model + + brushnet_conditioning_scale = scale + control_guidance_start = start_at + control_guidance_end = end_at + + if save_memory != 'none': + powerpaint['brushnet'].set_attention_slice(save_memory) + + add_brushnet_patch(model, + powerpaint['brushnet'], + torch_dtype, + conditioning_latents, + (brushnet_conditioning_scale, control_guidance_start, control_guidance_end), + negative_prompt_embeds_pp, prompt_embeds_pp, + None, None, None, + False) + + latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]], + device=powerpaint['brushnet'].device) + + return (model, positive, negative, {"samples": latent},) +@torch.inference_mode() +def brushnet_inference(x, timesteps, transformer_options, debug): + if 'model_patch' not in transformer_options: + print('BrushNet inference: there is no model_patch key in transformer_options') + return ([], 0, []) + mp = transformer_options['model_patch'] + if 'brushnet' not in mp: + print('BrushNet inference: there is no brushnet key in mdel_patch') + return ([], 0, []) + bo = mp['brushnet'] + if 'model' not in bo: + print('BrushNet inference: there is no model key in brushnet') + return ([], 0, []) + brushnet = bo['model'] + if not (isinstance(brushnet, BrushNetModel) or isinstance(brushnet, PowerPaintModel)): + print('BrushNet model is not a BrushNetModel class') + return ([], 0, []) + + torch_dtype = bo['dtype'] + cl_list = bo['latents'] + brushnet_conditioning_scale, control_guidance_start, control_guidance_end = bo['controls'] + pe = bo['prompt_embeds'] + npe = bo['negative_prompt_embeds'] + ppe, nppe, time_ids = bo['add_embeds'] + + #do_classifier_free_guidance = mp['free_guidance'] + do_classifier_free_guidance = len(transformer_options['cond_or_uncond']) > 1 + + x = x.detach().clone() + x = x.to(torch_dtype).to(brushnet.device) + + timesteps = timesteps.detach().clone() + timesteps = timesteps.to(torch_dtype).to(brushnet.device) + + total_steps = mp['total_steps'] + step = mp['step'] + + added_cond_kwargs = {} + + if do_classifier_free_guidance and step == 0: + print('BrushNet inference: do_classifier_free_guidance is True') + + sub_idx = None + if 'ad_params' in transformer_options and 'sub_idxs' in transformer_options['ad_params']: + sub_idx = transformer_options['ad_params']['sub_idxs'] + + # we have batch input images + batch = cl_list[0].shape[0] + # we have incoming latents + latents_incoming = x.shape[0] + # and we already got some + latents_got = bo['latent_id'] + if step == 0 or batch > 1: + print('BrushNet inference, step = %d: image batch = %d, got %d latents, starting from %d' \ + % (step, batch, latents_incoming, latents_got)) + + image_latents = [] + masks = [] + prompt_embeds = [] + negative_prompt_embeds = [] + pooled_prompt_embeds = [] + negative_pooled_prompt_embeds = [] + if sub_idx: + # AnimateDiff indexes detected + if step == 0: + print('BrushNet inference: AnimateDiff indexes detected and applied') + + batch = len(sub_idx) + + if do_classifier_free_guidance: + for i in sub_idx: + image_latents.append(cl_list[0][i][None,:,:,:]) + masks.append(cl_list[1][i][None,:,:,:]) + prompt_embeds.append(pe) + negative_prompt_embeds.append(npe) + pooled_prompt_embeds.append(ppe) + negative_pooled_prompt_embeds.append(nppe) + for i in sub_idx: + image_latents.append(cl_list[0][i][None,:,:,:]) + masks.append(cl_list[1][i][None,:,:,:]) + else: + for i in sub_idx: + image_latents.append(cl_list[0][i][None,:,:,:]) + masks.append(cl_list[1][i][None,:,:,:]) + prompt_embeds.append(pe) + pooled_prompt_embeds.append(ppe) + else: + # do_classifier_free_guidance = 2 passes, 1st pass is cond, 2nd is uncond + continue_batch = True + for i in range(latents_incoming): + number = latents_got + i + if number < batch: + # 1st pass, cond + image_latents.append(cl_list[0][number][None,:,:,:]) + masks.append(cl_list[1][number][None,:,:,:]) + prompt_embeds.append(pe) + pooled_prompt_embeds.append(ppe) + elif do_classifier_free_guidance and number < batch * 2: + # 2nd pass, uncond + image_latents.append(cl_list[0][number-batch][None,:,:,:]) + masks.append(cl_list[1][number-batch][None,:,:,:]) + negative_prompt_embeds.append(npe) + negative_pooled_prompt_embeds.append(nppe) + else: + # latent batch + image_latents.append(cl_list[0][0][None,:,:,:]) + masks.append(cl_list[1][0][None,:,:,:]) + prompt_embeds.append(pe) + pooled_prompt_embeds.append(ppe) + latents_got = -i + continue_batch = False + + if continue_batch: + # we don't have full batch yet + if do_classifier_free_guidance: + if number < batch * 2 - 1: + bo['latent_id'] = number + 1 + else: + bo['latent_id'] = 0 + else: + if number < batch - 1: + bo['latent_id'] = number + 1 + else: + bo['latent_id'] = 0 + else: + bo['latent_id'] = 0 + + cl = [] + for il, m in zip(image_latents, masks): + cl.append(torch.concat([il, m], dim=1)) + cl2apply = torch.concat(cl, dim=0) + + conditioning_latents = cl2apply.to(torch_dtype).to(brushnet.device) + + prompt_embeds.extend(negative_prompt_embeds) + prompt_embeds = torch.concat(prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device) + + if ppe is not None: + added_cond_kwargs = {} + added_cond_kwargs['time_ids'] = torch.concat([time_ids] * latents_incoming, dim = 0).to(torch_dtype).to(brushnet.device) + + pooled_prompt_embeds.extend(negative_pooled_prompt_embeds) + pooled_prompt_embeds = torch.concat(pooled_prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device) + added_cond_kwargs['text_embeds'] = pooled_prompt_embeds + else: + added_cond_kwargs = None + + if x.shape[2] != conditioning_latents.shape[2] or x.shape[3] != conditioning_latents.shape[3]: + if step == 0: + print('BrushNet inference: image', conditioning_latents.shape, 'and latent', x.shape, 'have different size, resizing image') + conditioning_latents = torch.nn.functional.interpolate( + conditioning_latents, size=( + x.shape[2], + x.shape[3], + ), mode='bicubic', + ).to(torch_dtype).to(brushnet.device) + + if step == 0: + print('BrushNet inference: sample', x.shape, ', CL', conditioning_latents.shape, 'dtype', torch_dtype) + + if debug: print('BrushNet: step =', step) + + if step < control_guidance_start or step > control_guidance_end: + cond_scale = 0.0 + else: + cond_scale = brushnet_conditioning_scale + + return brushnet(x, + encoder_hidden_states=prompt_embeds, + brushnet_cond=conditioning_latents, + timestep = timesteps, + conditioning_scale=cond_scale, + guess_mode=False, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + debug=debug, + ) + +def add_brushnet_patch(model, brushnet, torch_dtype, conditioning_latents, + controls, + prompt_embeds, negative_prompt_embeds, + pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids, + debug): + + is_SDXL = isinstance(model.model.model_config, comfy.supported_models.SDXL) + + if model.model.model_config.custom_operations is None: + fp8 = model.model.model_config.optimizations.get("fp8", model.model.model_config.scaled_fp8 is not None) + operations = comfy.ops.pick_operations(model.model.model_config.unet_config.get("dtype", None), model.model.manual_cast_dtype, + fp8_optimizations=fp8, scaled_fp8=model.model.model_config.scaled_fp8) + else: + # such as gguf + operations = model.model.model_config.custom_operations + + if is_SDXL: + input_blocks = [[0, operations.Conv2d], + [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample], + [4, comfy.ldm.modules.attention.SpatialTransformer], + [5, comfy.ldm.modules.attention.SpatialTransformer], + [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample], + [7, comfy.ldm.modules.attention.SpatialTransformer], + [8, comfy.ldm.modules.attention.SpatialTransformer]] + middle_block = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock] + output_blocks = [[0, comfy.ldm.modules.attention.SpatialTransformer], + [1, comfy.ldm.modules.attention.SpatialTransformer], + [2, comfy.ldm.modules.attention.SpatialTransformer], + [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample], + [3, comfy.ldm.modules.attention.SpatialTransformer], + [4, comfy.ldm.modules.attention.SpatialTransformer], + [5, comfy.ldm.modules.attention.SpatialTransformer], + [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample], + [6, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [7, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [8, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]] + else: + input_blocks = [[0, operations.Conv2d], + [1, comfy.ldm.modules.attention.SpatialTransformer], + [2, comfy.ldm.modules.attention.SpatialTransformer], + [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample], + [4, comfy.ldm.modules.attention.SpatialTransformer], + [5, comfy.ldm.modules.attention.SpatialTransformer], + [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample], + [7, comfy.ldm.modules.attention.SpatialTransformer], + [8, comfy.ldm.modules.attention.SpatialTransformer], + [9, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample], + [10, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [11, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]] + middle_block = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock] + output_blocks = [[0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock], + [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample], + [3, comfy.ldm.modules.attention.SpatialTransformer], + [4, comfy.ldm.modules.attention.SpatialTransformer], + [5, comfy.ldm.modules.attention.SpatialTransformer], + [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample], + [6, comfy.ldm.modules.attention.SpatialTransformer], + [7, comfy.ldm.modules.attention.SpatialTransformer], + [8, comfy.ldm.modules.attention.SpatialTransformer], + [8, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample], + [9, comfy.ldm.modules.attention.SpatialTransformer], + [10, comfy.ldm.modules.attention.SpatialTransformer], + [11, comfy.ldm.modules.attention.SpatialTransformer]] + + def last_layer_index(block, tp): + layer_list = [] + for layer in block: + layer_list.append(type(layer)) + layer_list.reverse() + if tp not in layer_list: + return -1, layer_list.reverse() + return len(layer_list) - 1 - layer_list.index(tp), layer_list + + def brushnet_forward(model, x, timesteps, transformer_options, control): + if 'brushnet' not in transformer_options['model_patch']: + input_samples = [] + mid_sample = 0 + output_samples = [] + else: + # brushnet inference + input_samples, mid_sample, output_samples = brushnet_inference(x, timesteps, transformer_options, debug) + + # give additional samples to blocks + for i, tp in input_blocks: + idx, layer_list = last_layer_index(model.input_blocks[i], tp) + if idx < 0: + print("BrushNet can't find", tp, "layer in", i, "input block:", layer_list) + continue + model.input_blocks[i][idx].add_sample_after = input_samples.pop(0) if input_samples else 0 + + idx, layer_list = last_layer_index(model.middle_block, middle_block[1]) + if idx < 0: + print("BrushNet can't find", middle_block[1], "layer in middle block", layer_list) + model.middle_block[idx].add_sample_after = mid_sample + + for i, tp in output_blocks: + idx, layer_list = last_layer_index(model.output_blocks[i], tp) + if idx < 0: + print("BrushNet can't find", tp, "layer in", i, "outnput block:", layer_list) + continue + model.output_blocks[i][idx].add_sample_after = output_samples.pop(0) if output_samples else 0 + + patch_model_function_wrapper(model, brushnet_forward) + + to = add_model_patch_option(model) + mp = to['model_patch'] + if 'brushnet' not in mp: + mp['brushnet'] = {} + bo = mp['brushnet'] + + bo['model'] = brushnet + bo['dtype'] = torch_dtype + bo['latents'] = conditioning_latents + bo['controls'] = controls + bo['prompt_embeds'] = prompt_embeds + bo['negative_prompt_embeds'] = negative_prompt_embeds + bo['add_embeds'] = (pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids) + bo['latent_id'] = 0 + + # patch layers `forward` so we can apply brushnet + def forward_patched_by_brushnet(self, x, *args, **kwargs): + h = self.original_forward(x, *args, **kwargs) + if hasattr(self, 'add_sample_after') and type(self): + to_add = self.add_sample_after + if torch.is_tensor(to_add): + # interpolate due to RAUNet + if h.shape[2] != to_add.shape[2] or h.shape[3] != to_add.shape[3]: + to_add = torch.nn.functional.interpolate(to_add, size=(h.shape[2], h.shape[3]), mode='bicubic') + h += to_add.to(h.dtype).to(h.device) + else: + h += self.add_sample_after + self.add_sample_after = 0 + return h + + for i, block in enumerate(model.model.diffusion_model.input_blocks): + for j, layer in enumerate(block): + if not hasattr(layer, 'original_forward'): + layer.original_forward = layer.forward + layer.forward = types.MethodType(forward_patched_by_brushnet, layer) + layer.add_sample_after = 0 + + for j, layer in enumerate(model.model.diffusion_model.middle_block): + if not hasattr(layer, 'original_forward'): + layer.original_forward = layer.forward + layer.forward = types.MethodType(forward_patched_by_brushnet, layer) + layer.add_sample_after = 0 + + for i, block in enumerate(model.model.diffusion_model.output_blocks): + for j, layer in enumerate(block): + if not hasattr(layer, 'original_forward'): + layer.original_forward = layer.forward + layer.forward = types.MethodType(forward_patched_by_brushnet, layer) + layer.add_sample_after = 0 \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/brushnet.json b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/brushnet.json new file mode 100644 index 00000000..c22ae157 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/brushnet.json @@ -0,0 +1,58 @@ +{ + "_class_name": "BrushNetModel", + "_diffusers_version": "0.27.0.dev0", + "_name_or_path": "runs/logs/brushnet_randommask/checkpoint-100000", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "brushnet_conditioning_channel_order": "rgb", + "class_embed_type": null, + "conditioning_channels": 5, + "conditioning_embedding_out_channels": [ + 16, + 32, + 96, + 256 + ], + "cross_attention_dim": 768, + "down_block_types": [ + "DownBlock2D", + "DownBlock2D", + "DownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "global_pool_conditions": false, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "mid_block_type": "MidBlock2D", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "only_cross_attention": false, + "projection_class_embeddings_input_dim": null, + "resnet_time_scale_shift": "default", + "transformer_layers_per_block": 1, + "up_block_types": [ + "UpBlock2D", + "UpBlock2D", + "UpBlock2D", + "UpBlock2D" + ], + "upcast_attention": false, + "use_linear_projection": false +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/brushnet_xl.json b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/brushnet_xl.json new file mode 100644 index 00000000..1e0ebb7a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/brushnet_xl.json @@ -0,0 +1,63 @@ +{ + "_class_name": "BrushNetModel", + "_diffusers_version": "0.27.0.dev0", + "_name_or_path": "runs/logs/brushnetsdxl_randommask/checkpoint-80000", + "act_fn": "silu", + "addition_embed_type": "text_time", + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": 256, + "attention_head_dim": [ + 5, + 10, + 20 + ], + "block_out_channels": [ + 320, + 640, + 1280 + ], + "brushnet_conditioning_channel_order": "rgb", + "class_embed_type": null, + "conditioning_channels": 5, + "conditioning_embedding_out_channels": [ + 16, + 32, + 96, + 256 + ], + "cross_attention_dim": 2048, + "down_block_types": [ + "DownBlock2D", + "DownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "global_pool_conditions": false, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "mid_block_type": "MidBlock2D", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "only_cross_attention": false, + "projection_class_embeddings_input_dim": 2816, + "resnet_time_scale_shift": "default", + "transformer_layers_per_block": [ + 1, + 2, + 10 + ], + "up_block_types": [ + "UpBlock2D", + "UpBlock2D", + "UpBlock2D" + ], + "upcast_attention": null, + "use_linear_projection": true +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/powerpaint.json b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/powerpaint.json new file mode 100644 index 00000000..1ea97001 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/config/powerpaint.json @@ -0,0 +1,57 @@ +{ + "_class_name": "BrushNetModel", + "_diffusers_version": "0.27.2", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "brushnet_conditioning_channel_order": "rgb", + "class_embed_type": null, + "conditioning_channels": 5, + "conditioning_embedding_out_channels": [ + 16, + 32, + 96, + 256 + ], + "cross_attention_dim": 768, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "global_pool_conditions": false, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "only_cross_attention": false, + "projection_class_embeddings_input_dim": null, + "resnet_time_scale_shift": "default", + "transformer_layers_per_block": 1, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "upcast_attention": false, + "use_linear_projection": false +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/model.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/model.py new file mode 100644 index 00000000..ed256d21 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/model.py @@ -0,0 +1,1688 @@ +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from torch import nn + +from ...libs.utils import install_package +try: + install_package("diffusers", "0.27.2", True, "0.25.0") + + from diffusers.configuration_utils import ConfigMixin, register_to_config + from diffusers.utils import BaseOutput, logging + from diffusers.models.attention_processor import ( + ADDED_KV_ATTENTION_PROCESSORS, + CROSS_ATTENTION_PROCESSORS, + AttentionProcessor, + AttnAddedKVProcessor, + AttnProcessor, + ) + from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps + from diffusers.models.modeling_utils import ModelMixin + from diffusers.models.resnet import ResnetBlock2D + from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel + + from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel + from diffusers.models.transformers.transformer_2d import Transformer2DModel + + from .unet_2d_blocks import ( + CrossAttnDownBlock2D, + DownBlock2D, + get_down_block, + get_mid_block, + get_up_block, + ) + + from .unet_2d_condition import UNet2DConditionModel + + logger = logging.get_logger(__name__) + + def zero_module(module): + for p in module.parameters(): + nn.init.zeros_(p) + return module + + @dataclass + class BrushNetOutput(BaseOutput): + + up_block_res_samples: Tuple[torch.Tensor] + down_block_res_samples: Tuple[torch.Tensor] + mid_block_res_sample: torch.Tensor + + # BrushNetModel + class BrushNetModel(ModelMixin, ConfigMixin): + """A BrushNet model.""" + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + in_channels: int = 4, + conditioning_channels: int = 5, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str, ...] = ( + "DownBlock2D", + "DownBlock2D", + "DownBlock2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlock2D", + up_block_types: Tuple[str, ...] = ( + "UpBlock2D", + "UpBlock2D", + "UpBlock2D", + "UpBlock2D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int, ...]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + projection_class_embeddings_input_dim: Optional[int] = None, + brushnet_conditioning_channel_order: str = "rgb", + conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), + global_pool_conditions: bool = False, + addition_embed_type_num_heads: int = 64, + ): + super().__init__() + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + # input + conv_in_kernel = 3 + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in_condition = nn.Conv2d( + in_channels + conditioning_channels, block_out_channels[0], kernel_size=conv_in_kernel, + padding=conv_in_padding + ) + + # time + time_embed_dim = block_out_channels[0] * 4 + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + ) + + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + print("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + else: + self.class_embedding = None + + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, + time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + + elif addition_embed_type is not None: + raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") + + self.down_blocks = nn.ModuleList([]) + self.brushnet_down_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_down_blocks.append(brushnet_block) + + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads[i], + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + downsample_padding=downsample_padding, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + self.down_blocks.append(down_block) + + for _ in range(layers_per_block): + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_down_blocks.append(brushnet_block) + + if not is_final_block: + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_down_blocks.append(brushnet_block) + + # mid + mid_block_channel = block_out_channels[-1] + + brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_mid_block = brushnet_block + + self.mid_block = get_mid_block( + mid_block_type, + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=mid_block_channel, + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_transformer_layers_per_block = (list(reversed(transformer_layers_per_block))) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + + self.up_blocks = nn.ModuleList([]) + self.brushnet_up_blocks = nn.ModuleList([]) + + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resolution_idx=i, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=reversed_num_attention_heads[i], + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + for _ in range(layers_per_block + 1): + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_up_blocks.append(brushnet_block) + + if not is_final_block: + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_up_blocks.append(brushnet_block) + + @classmethod + def from_unet( + cls, + unet: UNet2DConditionModel, + brushnet_conditioning_channel_order: str = "rgb", + conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), + load_weights_from_unet: bool = True, + conditioning_channels: int = 5, + ): + r""" + Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`]. + + Parameters: + unet (`UNet2DConditionModel`): + The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied + where applicable. + """ + transformer_layers_per_block = ( + unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 + ) + encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None + encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None + addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None + addition_time_embed_dim = ( + unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None + ) + + down_block_types = ["DownBlock2D" for block_name in unet.config.down_block_types] + mid_block_type = "MidBlock2D" + up_block_types = ["UpBlock2D" for block_name in unet.config.down_block_types] + + brushnet = cls( + in_channels=unet.config.in_channels, + conditioning_channels=conditioning_channels, + flip_sin_to_cos=unet.config.flip_sin_to_cos, + freq_shift=unet.config.freq_shift, + down_block_types=down_block_types, + mid_block_type=mid_block_type, + up_block_types=up_block_types, + only_cross_attention=unet.config.only_cross_attention, + block_out_channels=unet.config.block_out_channels, + layers_per_block=unet.config.layers_per_block, + downsample_padding=unet.config.downsample_padding, + mid_block_scale_factor=unet.config.mid_block_scale_factor, + act_fn=unet.config.act_fn, + norm_num_groups=unet.config.norm_num_groups, + norm_eps=unet.config.norm_eps, + cross_attention_dim=unet.config.cross_attention_dim, + transformer_layers_per_block=transformer_layers_per_block, + encoder_hid_dim=encoder_hid_dim, + encoder_hid_dim_type=encoder_hid_dim_type, + attention_head_dim=unet.config.attention_head_dim, + num_attention_heads=unet.config.num_attention_heads, + use_linear_projection=unet.config.use_linear_projection, + class_embed_type=unet.config.class_embed_type, + addition_embed_type=addition_embed_type, + addition_time_embed_dim=addition_time_embed_dim, + num_class_embeds=unet.config.num_class_embeds, + upcast_attention=unet.config.upcast_attention, + resnet_time_scale_shift=unet.config.resnet_time_scale_shift, + projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, + brushnet_conditioning_channel_order=brushnet_conditioning_channel_order, + conditioning_embedding_out_channels=conditioning_embedding_out_channels, + ) + + if load_weights_from_unet: + conv_in_condition_weight = torch.zeros_like(brushnet.conv_in_condition.weight) + conv_in_condition_weight[:, :4, ...] = unet.conv_in.weight + conv_in_condition_weight[:, 4:8, ...] = unet.conv_in.weight + brushnet.conv_in_condition.weight = torch.nn.Parameter(conv_in_condition_weight) + brushnet.conv_in_condition.bias = unet.conv_in.bias + + brushnet.time_proj.load_state_dict(unet.time_proj.state_dict()) + brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) + + if brushnet.class_embedding: + brushnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) + + brushnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False) + brushnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False) + brushnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False) + + return brushnet + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, + processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnAddedKVProcessor() + elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice + def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None: + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value: bool = False) -> None: + if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + encoder_hidden_states: torch.Tensor, + brushnet_cond: torch.FloatTensor, + timestep=None, + time_emb=None, + conditioning_scale: float = 1.0, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guess_mode: bool = False, + return_dict: bool = True, + debug=False, + ) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]: + + # check channel order + channel_order = self.config.brushnet_conditioning_channel_order + + if channel_order == "rgb": + # in rgb order by default + ... + elif channel_order == "bgr": + brushnet_cond = torch.flip(brushnet_cond, dims=[1]) + else: + raise ValueError(f"unknown `brushnet_conditioning_channel_order`: {channel_order}") + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + if timestep is None and time_emb is None: + raise ValueError(f"`timestep` and `emb` are both None") + + # print("BN: sample.device", sample.device) + # print("BN: TE.device", self.time_embedding.linear_1.weight.device) + + if timestep is not None: + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + # print("t_emb.device =",t_emb.device) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + # print('emb.shape', emb.shape) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + if self.config.addition_embed_type is not None: + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + + elif self.config.addition_embed_type == "text_time": + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + + # print('text_embeds', text_embeds.shape, 'time_ids', time_ids.shape, 'time_embeds', time_embeds.shape, 'add__embeds', add_embeds.shape, 'aug_emb', aug_emb.shape) + + emb = emb + aug_emb if aug_emb is not None else emb + else: + emb = time_emb + + # 2. pre-process + + brushnet_cond = torch.concat([sample, brushnet_cond], 1) + sample = self.conv_in_condition(brushnet_cond) + + # 3. down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + down_block_res_samples += res_samples + + # 4. PaintingNet down blocks + brushnet_down_block_res_samples = () + for down_block_res_sample, brushnet_down_block in zip(down_block_res_samples, self.brushnet_down_blocks): + down_block_res_sample = brushnet_down_block(down_block_res_sample) + brushnet_down_block_res_samples = brushnet_down_block_res_samples + (down_block_res_sample,) + + # 5. mid + if self.mid_block is not None: + if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + ) + else: + sample = self.mid_block(sample, emb) + + # 6. BrushNet mid blocks + brushnet_mid_block_res_sample = self.brushnet_mid_block(sample) + + # 7. up + up_block_res_samples = () + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets):] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample, up_res_samples = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + return_res_samples=True + ) + else: + sample, up_res_samples = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + return_res_samples=True + ) + + up_block_res_samples += up_res_samples + + # 8. BrushNet up blocks + brushnet_up_block_res_samples = () + for up_block_res_sample, brushnet_up_block in zip(up_block_res_samples, self.brushnet_up_blocks): + up_block_res_sample = brushnet_up_block(up_block_res_sample) + brushnet_up_block_res_samples = brushnet_up_block_res_samples + (up_block_res_sample,) + + # 6. scaling + if guess_mode and not self.config.global_pool_conditions: + scales = torch.logspace(-1, 0, + len(brushnet_down_block_res_samples) + 1 + len(brushnet_up_block_res_samples), + device=sample.device) # 0.1 to 1.0 + scales = scales * conditioning_scale + + brushnet_down_block_res_samples = [sample * scale for sample, scale in + zip(brushnet_down_block_res_samples, + scales[:len(brushnet_down_block_res_samples)])] + brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * scales[ + len(brushnet_down_block_res_samples)] + brushnet_up_block_res_samples = [sample * scale for sample, scale in zip(brushnet_up_block_res_samples, + scales[ + len(brushnet_down_block_res_samples) + 1:])] + else: + brushnet_down_block_res_samples = [sample * conditioning_scale for sample in + brushnet_down_block_res_samples] + brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * conditioning_scale + brushnet_up_block_res_samples = [sample * conditioning_scale for sample in + brushnet_up_block_res_samples] + + if self.config.global_pool_conditions: + brushnet_down_block_res_samples = [ + torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_down_block_res_samples + ] + brushnet_mid_block_res_sample = torch.mean(brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True) + brushnet_up_block_res_samples = [ + torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_up_block_res_samples + ] + + if not return_dict: + return (brushnet_down_block_res_samples, brushnet_mid_block_res_sample, brushnet_up_block_res_samples) + + return BrushNetOutput( + down_block_res_samples=brushnet_down_block_res_samples, + mid_block_res_sample=brushnet_mid_block_res_sample, + up_block_res_samples=brushnet_up_block_res_samples + ) + + # PowerPaintModel + class PowerPaintModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + in_channels: int = 4, + conditioning_channels: int = 5, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str, ...] = ( + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", + up_block_types: Tuple[str, ...] = ( + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int, ...]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + projection_class_embeddings_input_dim: Optional[int] = None, + brushnet_conditioning_channel_order: str = "rgb", + conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), + global_pool_conditions: bool = False, + addition_embed_type_num_heads: int = 64, + ): + super().__init__() + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + # input + conv_in_kernel = 3 + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in_condition = nn.Conv2d( + in_channels + conditioning_channels, + block_out_channels[0], + kernel_size=conv_in_kernel, + padding=conv_in_padding, + ) + + # time + time_embed_dim = block_out_channels[0] * 4 + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + ) + + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + else: + self.class_embedding = None + + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + + elif addition_embed_type is not None: + raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") + + self.down_blocks = nn.ModuleList([]) + self.brushnet_down_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_down_blocks.append(brushnet_block) + + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads[i], + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + downsample_padding=downsample_padding, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + self.down_blocks.append(down_block) + + for _ in range(layers_per_block): + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_down_blocks.append(brushnet_block) + + if not is_final_block: + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_down_blocks.append(brushnet_block) + + # mid + mid_block_channel = block_out_channels[-1] + + brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_mid_block = brushnet_block + + self.mid_block = get_mid_block( + mid_block_type, + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=mid_block_channel, + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + + self.up_blocks = nn.ModuleList([]) + self.brushnet_up_blocks = nn.ModuleList([]) + + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resolution_idx=i, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=reversed_num_attention_heads[i], + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + for _ in range(layers_per_block + 1): + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_up_blocks.append(brushnet_block) + + if not is_final_block: + brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + brushnet_block = zero_module(brushnet_block) + self.brushnet_up_blocks.append(brushnet_block) + + @classmethod + def from_unet( + cls, + unet: UNet2DConditionModel, + brushnet_conditioning_channel_order: str = "rgb", + conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), + load_weights_from_unet: bool = True, + conditioning_channels: int = 5, + ): + r""" + Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`]. + + Parameters: + unet (`UNet2DConditionModel`): + The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied + where applicable. + """ + transformer_layers_per_block = ( + unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 + ) + encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None + encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None + addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None + addition_time_embed_dim = ( + unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None + ) + + brushnet = cls( + in_channels=unet.config.in_channels, + conditioning_channels=conditioning_channels, + flip_sin_to_cos=unet.config.flip_sin_to_cos, + freq_shift=unet.config.freq_shift, + # down_block_types=['DownBlock2D','DownBlock2D','DownBlock2D','DownBlock2D'], + down_block_types=[ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D", + ], + # mid_block_type='MidBlock2D', + mid_block_type="UNetMidBlock2DCrossAttn", + # up_block_types=['UpBlock2D','UpBlock2D','UpBlock2D','UpBlock2D'], + up_block_types=["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"], + only_cross_attention=unet.config.only_cross_attention, + block_out_channels=unet.config.block_out_channels, + layers_per_block=unet.config.layers_per_block, + downsample_padding=unet.config.downsample_padding, + mid_block_scale_factor=unet.config.mid_block_scale_factor, + act_fn=unet.config.act_fn, + norm_num_groups=unet.config.norm_num_groups, + norm_eps=unet.config.norm_eps, + cross_attention_dim=unet.config.cross_attention_dim, + transformer_layers_per_block=transformer_layers_per_block, + encoder_hid_dim=encoder_hid_dim, + encoder_hid_dim_type=encoder_hid_dim_type, + attention_head_dim=unet.config.attention_head_dim, + num_attention_heads=unet.config.num_attention_heads, + use_linear_projection=unet.config.use_linear_projection, + class_embed_type=unet.config.class_embed_type, + addition_embed_type=addition_embed_type, + addition_time_embed_dim=addition_time_embed_dim, + num_class_embeds=unet.config.num_class_embeds, + upcast_attention=unet.config.upcast_attention, + resnet_time_scale_shift=unet.config.resnet_time_scale_shift, + projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, + brushnet_conditioning_channel_order=brushnet_conditioning_channel_order, + conditioning_embedding_out_channels=conditioning_embedding_out_channels, + ) + + if load_weights_from_unet: + conv_in_condition_weight = torch.zeros_like(brushnet.conv_in_condition.weight) + conv_in_condition_weight[:, :4, ...] = unet.conv_in.weight + conv_in_condition_weight[:, 4:8, ...] = unet.conv_in.weight + brushnet.conv_in_condition.weight = torch.nn.Parameter(conv_in_condition_weight) + brushnet.conv_in_condition.bias = unet.conv_in.bias + + brushnet.time_proj.load_state_dict(unet.time_proj.state_dict()) + brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) + + if brushnet.class_embedding: + brushnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) + + brushnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False) + brushnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False) + brushnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False) + + return brushnet.to(unet.dtype) + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnAddedKVProcessor() + elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice + def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None: + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value: bool = False) -> None: + if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + brushnet_cond: torch.FloatTensor, + conditioning_scale: float = 1.0, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guess_mode: bool = False, + return_dict: bool = True, + debug=False, + ) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]: + """ + The [`BrushNetModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor. + timestep (`Union[torch.Tensor, float, int]`): + The number of timesteps to denoise an input. + encoder_hidden_states (`torch.Tensor`): + The encoder hidden states. + brushnet_cond (`torch.FloatTensor`): + The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`. + conditioning_scale (`float`, defaults to `1.0`): + The scale factor for BrushNet outputs. + class_labels (`torch.Tensor`, *optional*, defaults to `None`): + Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. + timestep_cond (`torch.Tensor`, *optional*, defaults to `None`): + Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the + timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep + embeddings. + attention_mask (`torch.Tensor`, *optional*, defaults to `None`): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + added_cond_kwargs (`dict`): + Additional conditions for the Stable Diffusion XL UNet. + cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`): + A kwargs dictionary that if specified is passed along to the `AttnProcessor`. + guess_mode (`bool`, defaults to `False`): + In this mode, the BrushNet encoder tries its best to recognize the input content of the input even if + you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended. + return_dict (`bool`, defaults to `True`): + Whether or not to return a [`~models.brushnet.BrushNetOutput`] instead of a plain tuple. + + Returns: + [`~models.brushnet.BrushNetOutput`] **or** `tuple`: + If `return_dict` is `True`, a [`~models.brushnet.BrushNetOutput`] is returned, otherwise a tuple is + returned where the first element is the sample tensor. + """ + # check channel order + channel_order = self.config.brushnet_conditioning_channel_order + + if channel_order == "rgb": + # in rgb order by default + ... + elif channel_order == "bgr": + brushnet_cond = torch.flip(brushnet_cond, dims=[1]) + else: + raise ValueError(f"unknown `brushnet_conditioning_channel_order`: {channel_order}") + + if debug: print('BrushNet CA: attn mask') + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + if debug: print('BrushNet CA: time') + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + if self.config.addition_embed_type is not None: + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + + elif self.config.addition_embed_type == "text_time": + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + + emb = emb + aug_emb if aug_emb is not None else emb + + if debug: print('BrushNet CA: pre-process') + + + # 2. pre-process + brushnet_cond = torch.concat([sample, brushnet_cond], 1) + sample = self.conv_in_condition(brushnet_cond) + + if debug: print('BrushNet CA: down') + + # 3. down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + if debug: print('BrushNet CA (down block with XA): ', type(downsample_block)) + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + debug=debug, + ) + else: + if debug: print('BrushNet CA (down block): ', type(downsample_block)) + sample, res_samples = downsample_block(hidden_states=sample, temb=emb, debug=debug) + + down_block_res_samples += res_samples + + if debug: print('BrushNet CA: PP down') + + # 4. PaintingNet down blocks + brushnet_down_block_res_samples = () + for down_block_res_sample, brushnet_down_block in zip(down_block_res_samples, self.brushnet_down_blocks): + down_block_res_sample = brushnet_down_block(down_block_res_sample) + brushnet_down_block_res_samples = brushnet_down_block_res_samples + (down_block_res_sample,) + + if debug: print('BrushNet CA: PP mid') + + # 5. mid + if self.mid_block is not None: + if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + ) + else: + sample = self.mid_block(sample, emb) + + if debug: print('BrushNet CA: mid') + + # 6. BrushNet mid blocks + brushnet_mid_block_res_sample = self.brushnet_mid_block(sample) + + if debug: print('BrushNet CA: PP up') + + # 7. up + up_block_res_samples = () + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample, up_res_samples = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + return_res_samples=True, + ) + else: + sample, up_res_samples = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + return_res_samples=True, + ) + + up_block_res_samples += up_res_samples + + if debug: print('BrushNet CA: up') + + # 8. BrushNet up blocks + brushnet_up_block_res_samples = () + for up_block_res_sample, brushnet_up_block in zip(up_block_res_samples, self.brushnet_up_blocks): + up_block_res_sample = brushnet_up_block(up_block_res_sample) + brushnet_up_block_res_samples = brushnet_up_block_res_samples + (up_block_res_sample,) + + if debug: print('BrushNet CA: scaling') + + # 6. scaling + if guess_mode and not self.config.global_pool_conditions: + scales = torch.logspace( + -1, + 0, + len(brushnet_down_block_res_samples) + 1 + len(brushnet_up_block_res_samples), + device=sample.device, + ) # 0.1 to 1.0 + scales = scales * conditioning_scale + + brushnet_down_block_res_samples = [ + sample * scale + for sample, scale in zip( + brushnet_down_block_res_samples, scales[: len(brushnet_down_block_res_samples)] + ) + ] + brushnet_mid_block_res_sample = ( + brushnet_mid_block_res_sample * scales[len(brushnet_down_block_res_samples)] + ) + brushnet_up_block_res_samples = [ + sample * scale + for sample, scale in zip( + brushnet_up_block_res_samples, scales[len(brushnet_down_block_res_samples) + 1 :] + ) + ] + else: + brushnet_down_block_res_samples = [ + sample * conditioning_scale for sample in brushnet_down_block_res_samples + ] + brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * conditioning_scale + brushnet_up_block_res_samples = [sample * conditioning_scale for sample in brushnet_up_block_res_samples] + + if self.config.global_pool_conditions: + brushnet_down_block_res_samples = [ + torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_down_block_res_samples + ] + brushnet_mid_block_res_sample = torch.mean(brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True) + brushnet_up_block_res_samples = [ + torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_up_block_res_samples + ] + + if debug: print('BrushNet CA: finish') + + if not return_dict: + return (brushnet_down_block_res_samples, brushnet_mid_block_res_sample, brushnet_up_block_res_samples) + + return BrushNetOutput( + down_block_res_samples=brushnet_down_block_res_samples, + mid_block_res_sample=brushnet_mid_block_res_sample, + up_block_res_samples=brushnet_up_block_res_samples, + ) + +except ImportError: + BrushNetModel = None + PowerPaintModel = None + # print("\33[33mModule 'diffusers' load failed. If you don't have it installed, do it:\033[0m") + # print("\33[33mpip install diffusers\033[0m") \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/model_patch.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/model_patch.py new file mode 100644 index 00000000..fe8d3f27 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/model_patch.py @@ -0,0 +1,137 @@ +import torch +import comfy + +# Check and add 'model_patch' to model.model_options['transformer_options'] +def add_model_patch_option(model): + if 'transformer_options' not in model.model_options: + model.model_options['transformer_options'] = {} + to = model.model_options['transformer_options'] + if "model_patch" not in to: + to["model_patch"] = {} + return to + + +# Patch model with model_function_wrapper +def patch_model_function_wrapper(model, forward_patch, remove=False): + def brushnet_model_function_wrapper(apply_model_method, options_dict): + to = options_dict['c']['transformer_options'] + + control = None + if 'control' in options_dict['c']: + control = options_dict['c']['control'] + + x = options_dict['input'] + timestep = options_dict['timestep'] + + # check if there are patches to execute + if 'model_patch' not in to or 'forward' not in to['model_patch']: + return apply_model_method(x, timestep, **options_dict['c']) + + mp = to['model_patch'] + unet = mp['unet'] + + all_sigmas = mp['all_sigmas'] + sigma = to['sigmas'][0].item() + total_steps = all_sigmas.shape[0] - 1 + step = torch.argmin((all_sigmas - sigma).abs()).item() + + mp['step'] = step + mp['total_steps'] = total_steps + + # comfy.model_base.apply_model + xc = model.model.model_sampling.calculate_input(timestep, x) + if 'c_concat' in options_dict['c'] and options_dict['c']['c_concat'] is not None: + xc = torch.cat([xc] + [options_dict['c']['c_concat']], dim=1) + t = model.model.model_sampling.timestep(timestep).float() + # execute all patches + for method in mp['forward']: + method(unet, xc, t, to, control) + + return apply_model_method(x, timestep, **options_dict['c']) + + if "model_function_wrapper" in model.model_options and model.model_options["model_function_wrapper"]: + print('BrushNet is going to replace existing model_function_wrapper:', + model.model_options["model_function_wrapper"]) + model.set_model_unet_function_wrapper(brushnet_model_function_wrapper) + + to = add_model_patch_option(model) + mp = to['model_patch'] + + if isinstance(model.model.model_config, comfy.supported_models.SD15): + mp['SDXL'] = False + elif isinstance(model.model.model_config, comfy.supported_models.SDXL): + mp['SDXL'] = True + else: + print('Base model type: ', type(model.model.model_config)) + raise Exception("Unsupported model type: ", type(model.model.model_config)) + + if 'forward' not in mp: + mp['forward'] = [] + + if remove: + if forward_patch in mp['forward']: + mp['forward'].remove(forward_patch) + else: + mp['forward'].append(forward_patch) + + mp['unet'] = model.model.diffusion_model + mp['step'] = 0 + mp['total_steps'] = 1 + + # apply patches to code + if comfy.samplers.sample.__doc__ is None or 'BrushNet' not in comfy.samplers.sample.__doc__: + comfy.samplers.original_sample = comfy.samplers.sample + comfy.samplers.sample = modified_sample + + if comfy.ldm.modules.diffusionmodules.openaimodel.apply_control.__doc__ is None or \ + 'BrushNet' not in comfy.ldm.modules.diffusionmodules.openaimodel.apply_control.__doc__: + comfy.ldm.modules.diffusionmodules.openaimodel.original_apply_control = comfy.ldm.modules.diffusionmodules.openaimodel.apply_control + comfy.ldm.modules.diffusionmodules.openaimodel.apply_control = modified_apply_control + + +# Model needs current step number and cfg at inference step. It is possible to write a custom KSampler but I'd like to use ComfyUI's one. +# The first versions had modified_common_ksampler, but it broke custom KSampler nodes +def modified_sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, + latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + ''' Modified by BrushNet nodes''' + cfg_guider = comfy.samplers.CFGGuider(model) + cfg_guider.set_conds(positive, negative) + cfg_guider.set_cfg(cfg) + + ### Modified part ###################################################################### + to = add_model_patch_option(model) + to['model_patch']['all_sigmas'] = sigmas + ####################################################################################### + + return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + +# To use Controlnet with RAUNet it is much easier to modify apply_control a little +def modified_apply_control(h, control, name): + '''Modified by BrushNet nodes''' + if control is not None and name in control and len(control[name]) > 0: + ctrl = control[name].pop() + if ctrl is not None: + if h.shape[2] != ctrl.shape[2] or h.shape[3] != ctrl.shape[3]: + ctrl = torch.nn.functional.interpolate(ctrl, size=(h.shape[2], h.shape[3]), mode='bicubic').to( + h.dtype).to(h.device) + try: + h += ctrl + except: + print.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) + return h + +def add_model_patch(model): + to = add_model_patch_option(model) + mp = to['model_patch'] + if "brushnet" in mp: + if isinstance(model.model.model_config, comfy.supported_models.SD15): + mp['SDXL'] = False + elif isinstance(model.model.model_config, comfy.supported_models.SDXL): + mp['SDXL'] = True + else: + print('Base model type: ', type(model.model.model_config)) + raise Exception("Unsupported model type: ", type(model.model.model_config)) + + mp['unet'] = model.model.diffusion_model + mp['step'] = 0 + mp['total_steps'] = 1 \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/powerpaint_utils.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/powerpaint_utils.py new file mode 100644 index 00000000..1cae2c13 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/powerpaint_utils.py @@ -0,0 +1,467 @@ +import copy +import random + +import torch +import torch.nn as nn +from transformers import CLIPTokenizer +from typing import Any, List, Optional, Union + + +class TokenizerWrapper: + """Tokenizer wrapper for CLIPTokenizer. Only support CLIPTokenizer + currently. This wrapper is modified from https://github.com/huggingface/dif + fusers/blob/e51f19aee82c8dd874b715a09dbc521d88835d68/src/diffusers/loaders. + py#L358 # noqa. + + Args: + from_pretrained (Union[str, os.PathLike], optional): The *model id* + of a pretrained model or a path to a *directory* containing + model weights and config. Defaults to None. + from_config (Union[str, os.PathLike], optional): The *model id* + of a pretrained model or a path to a *directory* containing + model weights and config. Defaults to None. + + *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs + will be passed to `from_pretrained` function. Otherwise, *args + and **kwargs will be used to initialize the model by + `self._module_cls(*args, **kwargs)`. + """ + + def __init__(self, tokenizer: CLIPTokenizer): + self.wrapped = tokenizer + self.token_map = {} + + def __getattr__(self, name: str) -> Any: + if name in self.__dict__: + return getattr(self, name) + # if name == "wrapped": + # return getattr(self, 'wrapped')#super().__getattr__("wrapped") + + try: + return getattr(self.wrapped, name) + except AttributeError: + raise AttributeError( + "'name' cannot be found in both " + f"'{self.__class__.__name__}' and " + f"'{self.__class__.__name__}.tokenizer'." + ) + + def try_adding_tokens(self, tokens: Union[str, List[str]], *args, **kwargs): + """Attempt to add tokens to the tokenizer. + + Args: + tokens (Union[str, List[str]]): The tokens to be added. + """ + num_added_tokens = self.wrapped.add_tokens(tokens, *args, **kwargs) + assert num_added_tokens != 0, ( + f"The tokenizer already contains the token {tokens}. Please pass " + "a different `placeholder_token` that is not already in the " + "tokenizer." + ) + + def get_token_info(self, token: str) -> dict: + """Get the information of a token, including its start and end index in + the current tokenizer. + + Args: + token (str): The token to be queried. + + Returns: + dict: The information of the token, including its start and end + index in current tokenizer. + """ + token_ids = self.__call__(token).input_ids + start, end = token_ids[1], token_ids[-2] + 1 + return {"name": token, "start": start, "end": end} + + def add_placeholder_token(self, placeholder_token: str, *args, num_vec_per_token: int = 1, **kwargs): + """Add placeholder tokens to the tokenizer. + + Args: + placeholder_token (str): The placeholder token to be added. + num_vec_per_token (int, optional): The number of vectors of + the added placeholder token. + *args, **kwargs: The arguments for `self.wrapped.add_tokens`. + """ + output = [] + if num_vec_per_token == 1: + self.try_adding_tokens(placeholder_token, *args, **kwargs) + output.append(placeholder_token) + else: + output = [] + for i in range(num_vec_per_token): + ith_token = placeholder_token + f"_{i}" + self.try_adding_tokens(ith_token, *args, **kwargs) + output.append(ith_token) + + for token in self.token_map: + if token in placeholder_token: + raise ValueError( + f"The tokenizer already has placeholder token {token} " + f"that can get confused with {placeholder_token} " + "keep placeholder tokens independent" + ) + self.token_map[placeholder_token] = output + + def replace_placeholder_tokens_in_text( + self, text: Union[str, List[str]], vector_shuffle: bool = False, prop_tokens_to_load: float = 1.0 + ) -> Union[str, List[str]]: + """Replace the keywords in text with placeholder tokens. This function + will be called in `self.__call__` and `self.encode`. + + Args: + text (Union[str, List[str]]): The text to be processed. + vector_shuffle (bool, optional): Whether to shuffle the vectors. + Defaults to False. + prop_tokens_to_load (float, optional): The proportion of tokens to + be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0. + + Returns: + Union[str, List[str]]: The processed text. + """ + if isinstance(text, list): + output = [] + for i in range(len(text)): + output.append(self.replace_placeholder_tokens_in_text(text[i], vector_shuffle=vector_shuffle)) + return output + + for placeholder_token in self.token_map: + if placeholder_token in text: + tokens = self.token_map[placeholder_token] + tokens = tokens[: 1 + int(len(tokens) * prop_tokens_to_load)] + if vector_shuffle: + tokens = copy.copy(tokens) + random.shuffle(tokens) + text = text.replace(placeholder_token, " ".join(tokens)) + return text + + def replace_text_with_placeholder_tokens(self, text: Union[str, List[str]]) -> Union[str, List[str]]: + """Replace the placeholder tokens in text with the original keywords. + This function will be called in `self.decode`. + + Args: + text (Union[str, List[str]]): The text to be processed. + + Returns: + Union[str, List[str]]: The processed text. + """ + if isinstance(text, list): + output = [] + for i in range(len(text)): + output.append(self.replace_text_with_placeholder_tokens(text[i])) + return output + + for placeholder_token, tokens in self.token_map.items(): + merged_tokens = " ".join(tokens) + if merged_tokens in text: + text = text.replace(merged_tokens, placeholder_token) + return text + + def __call__( + self, + text: Union[str, List[str]], + *args, + vector_shuffle: bool = False, + prop_tokens_to_load: float = 1.0, + **kwargs, + ): + """The call function of the wrapper. + + Args: + text (Union[str, List[str]]): The text to be tokenized. + vector_shuffle (bool, optional): Whether to shuffle the vectors. + Defaults to False. + prop_tokens_to_load (float, optional): The proportion of tokens to + be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0 + *args, **kwargs: The arguments for `self.wrapped.__call__`. + """ + replaced_text = self.replace_placeholder_tokens_in_text( + text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load + ) + + return self.wrapped.__call__(replaced_text, *args, **kwargs) + + def encode(self, text: Union[str, List[str]], *args, **kwargs): + """Encode the passed text to token index. + + Args: + text (Union[str, List[str]]): The text to be encode. + *args, **kwargs: The arguments for `self.wrapped.__call__`. + """ + replaced_text = self.replace_placeholder_tokens_in_text(text) + return self.wrapped(replaced_text, *args, **kwargs) + + def decode(self, token_ids, return_raw: bool = False, *args, **kwargs) -> Union[str, List[str]]: + """Decode the token index to text. + + Args: + token_ids: The token index to be decoded. + return_raw: Whether keep the placeholder token in the text. + Defaults to False. + *args, **kwargs: The arguments for `self.wrapped.decode`. + + Returns: + Union[str, List[str]]: The decoded text. + """ + text = self.wrapped.decode(token_ids, *args, **kwargs) + if return_raw: + return text + replaced_text = self.replace_text_with_placeholder_tokens(text) + return replaced_text + + def __repr__(self): + """The representation of the wrapper.""" + s = super().__repr__() + prefix = f"Wrapped Module Class: {self._module_cls}\n" + prefix += f"Wrapped Module Name: {self._module_name}\n" + if self._from_pretrained: + prefix += f"From Pretrained: {self._from_pretrained}\n" + s = prefix + s + return s + + +class EmbeddingLayerWithFixes(nn.Module): + """The revised embedding layer to support external embeddings. This design + of this class is inspired by https://github.com/AUTOMATIC1111/stable- + diffusion-webui/blob/22bcc7be428c94e9408f589966c2040187245d81/modules/sd_hi + jack.py#L224 # noqa. + + Args: + wrapped (nn.Emebdding): The embedding layer to be wrapped. + external_embeddings (Union[dict, List[dict]], optional): The external + embeddings added to this layer. Defaults to None. + """ + + def __init__(self, wrapped: nn.Embedding, external_embeddings: Optional[Union[dict, List[dict]]] = None): + super().__init__() + self.wrapped = wrapped + self.num_embeddings = wrapped.weight.shape[0] + + self.external_embeddings = [] + if external_embeddings: + self.add_embeddings(external_embeddings) + + self.trainable_embeddings = nn.ParameterDict() + + @property + def weight(self): + """Get the weight of wrapped embedding layer.""" + return self.wrapped.weight + + def check_duplicate_names(self, embeddings: List[dict]): + """Check whether duplicate names exist in list of 'external + embeddings'. + + Args: + embeddings (List[dict]): A list of embedding to be check. + """ + names = [emb["name"] for emb in embeddings] + assert len(names) == len(set(names)), ( + "Found duplicated names in 'external_embeddings'. Name list: " f"'{names}'" + ) + + def check_ids_overlap(self, embeddings): + """Check whether overlap exist in token ids of 'external_embeddings'. + + Args: + embeddings (List[dict]): A list of embedding to be check. + """ + ids_range = [[emb["start"], emb["end"], emb["name"]] for emb in embeddings] + ids_range.sort() # sort by 'start' + # check if 'end' has overlapping + for idx in range(len(ids_range) - 1): + name1, name2 = ids_range[idx][-1], ids_range[idx + 1][-1] + assert ids_range[idx][1] <= ids_range[idx + 1][0], ( + f"Found ids overlapping between embeddings '{name1}' " f"and '{name2}'." + ) + + def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]): + """Add external embeddings to this layer. + Use case: + Args: + embeddings (Union[dict, list[dict]]): The external embeddings to + be added. Each dict must contain the following 4 fields: 'name' + (the name of this embedding), 'embedding' (the embedding + tensor), 'start' (the start token id of this embedding), 'end' + (the end token id of this embedding). For example: + `{name: NAME, start: START, end: END, embedding: torch.Tensor}` + """ + if isinstance(embeddings, dict): + embeddings = [embeddings] + + self.external_embeddings += embeddings + self.check_duplicate_names(self.external_embeddings) + self.check_ids_overlap(self.external_embeddings) + + # set for trainable + added_trainable_emb_info = [] + for embedding in embeddings: + trainable = embedding.get("trainable", False) + if trainable: + name = embedding["name"] + embedding["embedding"] = torch.nn.Parameter(embedding["embedding"]) + self.trainable_embeddings[name] = embedding["embedding"] + added_trainable_emb_info.append(name) + + added_emb_info = [emb["name"] for emb in embeddings] + added_emb_info = ", ".join(added_emb_info) + print(f"Successfully add external embeddings: {added_emb_info}.", "current") + + if added_trainable_emb_info: + added_trainable_emb_info = ", ".join(added_trainable_emb_info) + print("Successfully add trainable external embeddings: " f"{added_trainable_emb_info}", "current") + + def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: + """Replace external input ids to 0. + + Args: + input_ids (torch.Tensor): The input ids to be replaced. + + Returns: + torch.Tensor: The replaced input ids. + """ + input_ids_fwd = input_ids.clone() + input_ids_fwd[input_ids_fwd >= self.num_embeddings] = 0 + return input_ids_fwd + + def replace_embeddings( + self, input_ids: torch.Tensor, embedding: torch.Tensor, external_embedding: dict + ) -> torch.Tensor: + """Replace external embedding to the embedding layer. Noted that, in + this function we use `torch.cat` to avoid inplace modification. + + Args: + input_ids (torch.Tensor): The original token ids. Shape like + [LENGTH, ]. + embedding (torch.Tensor): The embedding of token ids after + `replace_input_ids` function. + external_embedding (dict): The external embedding to be replaced. + + Returns: + torch.Tensor: The replaced embedding. + """ + new_embedding = [] + + name = external_embedding["name"] + start = external_embedding["start"] + end = external_embedding["end"] + target_ids_to_replace = [i for i in range(start, end)] + ext_emb = external_embedding["embedding"].to(embedding.device) + + # do not need to replace + if not (input_ids == start).any(): + return embedding + + # start replace + s_idx, e_idx = 0, 0 + while e_idx < len(input_ids): + if input_ids[e_idx] == start: + if e_idx != 0: + # add embedding do not need to replace + new_embedding.append(embedding[s_idx:e_idx]) + + # check if the next embedding need to replace is valid + actually_ids_to_replace = [int(i) for i in input_ids[e_idx: e_idx + end - start]] + assert actually_ids_to_replace == target_ids_to_replace, ( + f"Invalid 'input_ids' in position: {s_idx} to {e_idx}. " + f"Expect '{target_ids_to_replace}' for embedding " + f"'{name}' but found '{actually_ids_to_replace}'." + ) + + new_embedding.append(ext_emb) + + s_idx = e_idx + end - start + e_idx = s_idx + 1 + else: + e_idx += 1 + + if e_idx == len(input_ids): + new_embedding.append(embedding[s_idx:e_idx]) + + return torch.cat(new_embedding, dim=0) + + def forward(self, input_ids: torch.Tensor, external_embeddings: Optional[List[dict]] = None, out_dtype = None): + """The forward function. + + Args: + input_ids (torch.Tensor): The token ids shape like [bz, LENGTH] or + [LENGTH, ]. + external_embeddings (Optional[List[dict]]): The external + embeddings. If not passed, only `self.external_embeddings` + will be used. Defaults to None. + + input_ids: shape like [bz, LENGTH] or [LENGTH]. + """ + assert input_ids.ndim in [1, 2] + if input_ids.ndim == 1: + input_ids = input_ids.unsqueeze(0) + + if external_embeddings is None and not self.external_embeddings: + return self.wrapped(input_ids, out_dtype=out_dtype) + + input_ids_fwd = self.replace_input_ids(input_ids) + inputs_embeds = self.wrapped(input_ids_fwd) + + vecs = [] + + if external_embeddings is None: + external_embeddings = [] + elif isinstance(external_embeddings, dict): + external_embeddings = [external_embeddings] + embeddings = self.external_embeddings + external_embeddings + + for input_id, embedding in zip(input_ids, inputs_embeds): + new_embedding = embedding + for external_embedding in embeddings: + new_embedding = self.replace_embeddings(input_id, new_embedding, external_embedding) + vecs.append(new_embedding) + + return torch.stack(vecs).to(out_dtype) + + +def add_tokens( + tokenizer, text_encoder, placeholder_tokens: list, initialize_tokens: list = None, + num_vectors_per_token: int = 1 +): + """Add token for training. + + # TODO: support add tokens as dict, then we can load pretrained tokens. + """ + if initialize_tokens is not None: + assert len(initialize_tokens) == len( + placeholder_tokens + ), "placeholder_token should be the same length as initialize_token" + for ii in range(len(placeholder_tokens)): + tokenizer.add_placeholder_token(placeholder_tokens[ii], num_vec_per_token=num_vectors_per_token) + + # text_encoder.set_embedding_layer() + embedding_layer = text_encoder.text_model.embeddings.token_embedding + text_encoder.text_model.embeddings.token_embedding = EmbeddingLayerWithFixes(embedding_layer) + embedding_layer = text_encoder.text_model.embeddings.token_embedding + + assert embedding_layer is not None, ( + "Do not support get embedding layer for current text encoder. " "Please check your configuration." + ) + initialize_embedding = [] + if initialize_tokens is not None: + for ii in range(len(placeholder_tokens)): + init_id = tokenizer(initialize_tokens[ii]).input_ids[1] + temp_embedding = embedding_layer.weight[init_id] + initialize_embedding.append(temp_embedding[None, ...].repeat(num_vectors_per_token, 1)) + else: + for ii in range(len(placeholder_tokens)): + init_id = tokenizer("a").input_ids[1] + temp_embedding = embedding_layer.weight[init_id] + len_emb = temp_embedding.shape[0] + init_weight = (torch.rand(num_vectors_per_token, len_emb) - 0.5) / 2.0 + initialize_embedding.append(init_weight) + + # initialize_embedding = torch.cat(initialize_embedding,dim=0) + + token_info_all = [] + for ii in range(len(placeholder_tokens)): + token_info = tokenizer.get_token_info(placeholder_tokens[ii]) + token_info["embedding"] = initialize_embedding[ii] + token_info["trainable"] = True + token_info_all.append(token_info) + embedding_layer.add_embeddings(token_info_all) diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/unet_2d_blocks.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/unet_2d_blocks.py new file mode 100644 index 00000000..e332bb9c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/unet_2d_blocks.py @@ -0,0 +1,3908 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.utils import deprecate, is_torch_version, logging +from diffusers.utils.torch_utils import apply_freeu +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 +from diffusers.models.normalization import AdaGroupNorm +from diffusers.models.resnet import ( + Downsample2D, + FirDownsample2D, + FirUpsample2D, + KDownsample2D, + KUpsample2D, + ResnetBlock2D, + ResnetBlockCondNorm2D, + Upsample2D, +) +from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel +from diffusers.models.transformers.transformer_2d import Transformer2DModel + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def get_down_block( + down_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + temb_channels: int, + add_downsample: bool, + resnet_eps: float, + resnet_act_fn: str, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + downsample_padding: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + downsample_type: Optional[str] = None, + dropout: float = 0.0, +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warning( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock2D": + return DownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "ResnetDownsampleBlock2D": + return ResnetDownsampleBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + ) + elif down_block_type == "AttnDownBlock2D": + if add_downsample is False: + downsample_type = None + else: + downsample_type = downsample_type or "conv" # default to 'conv' + return AttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + downsample_type=downsample_type, + ) + elif down_block_type == "CrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") + return CrossAttnDownBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + ) + elif down_block_type == "SimpleCrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") + return SimpleCrossAttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif down_block_type == "SkipDownBlock2D": + return SkipDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "AttnSkipDownBlock2D": + return AttnSkipDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "DownEncoderBlock2D": + return DownEncoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "AttnDownEncoderBlock2D": + return AttnDownEncoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "KDownBlock2D": + return KDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + ) + elif down_block_type == "KCrossAttnDownBlock2D": + return KCrossAttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + add_self_attention=True if not add_downsample else False, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_mid_block( + mid_block_type: str, + temb_channels: int, + in_channels: int, + resnet_eps: float, + resnet_act_fn: str, + resnet_groups: int, + output_scale_factor: float = 1.0, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + mid_block_only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = 1, + dropout: float = 0.0, +): + if mid_block_type == "UNetMidBlock2DCrossAttn": + return UNetMidBlock2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + temb_channels=temb_channels, + dropout=dropout, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + output_scale_factor=output_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + resnet_groups=resnet_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": + return UNetMidBlock2DSimpleCrossAttn( + in_channels=in_channels, + temb_channels=temb_channels, + dropout=dropout, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + output_scale_factor=output_scale_factor, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + only_cross_attention=mid_block_only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif mid_block_type == "UNetMidBlock2D": + return UNetMidBlock2D( + in_channels=in_channels, + temb_channels=temb_channels, + dropout=dropout, + num_layers=0, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + output_scale_factor=output_scale_factor, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + add_attention=False, + ) + elif mid_block_type == "MidBlock2D": + return MidBlock2D( + in_channels=in_channels, + temb_channels=temb_channels, + dropout=dropout, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + output_scale_factor=output_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_groups=resnet_groups, + use_linear_projection=use_linear_projection, + ) + elif mid_block_type is None: + return None + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + +def get_up_block( + up_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + add_upsample: bool, + resnet_eps: float, + resnet_act_fn: str, + resolution_idx: Optional[int] = None, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + upsample_type: Optional[str] = None, + dropout: float = 0.0, +) -> nn.Module: + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warning( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock2D": + return UpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "ResnetUpsampleBlock2D": + return ResnetUpsampleBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + ) + elif up_block_type == "CrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") + return CrossAttnUpBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + ) + elif up_block_type == "SimpleCrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") + return SimpleCrossAttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif up_block_type == "AttnUpBlock2D": + if add_upsample is False: + upsample_type = None + else: + upsample_type = upsample_type or "conv" # default to 'conv' + + return AttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + upsample_type=upsample_type, + ) + elif up_block_type == "SkipUpBlock2D": + return SkipUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "AttnSkipUpBlock2D": + return AttnSkipUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "UpDecoderBlock2D": + return UpDecoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + elif up_block_type == "AttnUpDecoderBlock2D": + return AttnUpDecoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + elif up_block_type == "KUpBlock2D": + return KUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + ) + elif up_block_type == "KCrossAttnUpBlock2D": + return KCrossAttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + ) + + raise ValueError(f"{up_block_type} does not exist.") + + +class AutoencoderTinyBlock(nn.Module): + """ + Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU + blocks. + + Args: + in_channels (`int`): The number of input channels. + out_channels (`int`): The number of output channels. + act_fn (`str`): + ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`. + + Returns: + `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to + `out_channels`. + """ + + def __init__(self, in_channels: int, out_channels: int, act_fn: str): + super().__init__() + act_fn = get_activation(act_fn) + self.conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + act_fn, + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + act_fn, + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + ) + self.skip = ( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + if in_channels != out_channels + else nn.Identity() + ) + self.fuse = nn.ReLU() + + def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: + return self.fuse(self.conv(x) + self.skip(x)) + + +class UNetMidBlock2D(nn.Module): + """ + A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. + + Args: + in_channels (`int`): The number of input channels. + temb_channels (`int`): The number of temporal embedding channels. + dropout (`float`, *optional*, defaults to 0.0): The dropout rate. + num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. + resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. + resnet_time_scale_shift (`str`, *optional*, defaults to `default`): + The type of normalization to apply to the time embeddings. This can help to improve the performance of the + model on tasks with long-range temporal dependencies. + resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks. + resnet_groups (`int`, *optional*, defaults to 32): + The number of groups to use in the group normalization layers of the resnet blocks. + attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks. + resnet_pre_norm (`bool`, *optional*, defaults to `True`): + Whether to use pre-normalization for the resnet blocks. + add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks. + attention_head_dim (`int`, *optional*, defaults to 1): + Dimension of a single attention head. The number of attention heads is determined based on this value and + the number of input channels. + output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor. + + Returns: + `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, + in_channels, height, width)`. + + """ + + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + attn_groups: Optional[int] = None, + resnet_pre_norm: bool = True, + add_attention: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + ): + super().__init__() + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + self.add_attention = add_attention + + if attn_groups is None: + attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None + + # there is always at least one resnet + if resnet_time_scale_shift == "spatial": + resnets = [ + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm="spatial", + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + ) + ] + else: + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}." + ) + attention_head_dim = in_channels + + for _ in range(num_layers): + if self.add_attention: + attentions.append( + Attention( + in_channels, + heads=in_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=attn_groups, + spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + else: + attentions.append(None) + + if resnet_time_scale_shift == "spatial": + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm="spatial", + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + ) + ) + else: + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if attn is not None: + hidden_states = attn(hidden_states, temb=temb) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class UNetMidBlock2DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + output_scale_factor: float = 1.0, + cross_attention_dim: int = 1280, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # support for variable transformer layers per block + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for i in range(num_layers): + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + if cross_attention_kwargs is not None: + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + else: + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class UNetMidBlock2DSimpleCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + cross_attention_dim: int = 1280, + skip_time_act: bool = False, + only_cross_attention: bool = False, + cross_attention_norm: Optional[str] = None, + ): + super().__init__() + + self.has_cross_attention = True + + self.attention_head_dim = attention_head_dim + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + self.num_heads = in_channels // self.attention_head_dim + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + ) + ] + attentions = [] + + for _ in range(num_layers): + processor = ( + AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() + ) + + attentions.append( + Attention( + query_dim=in_channels, + cross_attention_dim=in_channels, + heads=self.num_heads, + dim_head=self.attention_head_dim, + added_kv_proj_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + bias=True, + upcast_softmax=True, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + processor=processor, + ) + ) + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + if attention_mask is None: + # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. + mask = None if encoder_hidden_states is None else encoder_attention_mask + else: + # when attention_mask is defined: we don't even check for encoder_attention_mask. + # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. + # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. + # then we can simplify this whole if/else block to: + # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask + mask = attention_mask + + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + # attn + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=mask, + **cross_attention_kwargs, + ) + + # resnet + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class MidBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + use_linear_projection: bool = False, + ): + super().__init__() + + self.has_cross_attention = False + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + + for i in range(num_layers): + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + lora_scale = 1.0 + hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) + for resnet in self.resnets[1:]: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + + return hidden_states + + +class AttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + downsample_padding: int = 1, + downsample_type: str = "conv", + ): + super().__init__() + resnets = [] + attentions = [] + self.downsample_type = downsample_type + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." + ) + attention_head_dim = out_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + attentions.append( + Attention( + out_channels, + heads=out_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=resnet_groups, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if downsample_type == "conv": + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + elif downsample_type == "resnet": + self.downsamplers = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + down=True, + ) + ] + ) + else: + self.downsamplers = None + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + output_states = () + + for resnet, attn in zip(self.resnets, self.attentions): + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, **cross_attention_kwargs) + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + if self.downsample_type == "resnet": + hidden_states = downsampler(hidden_states, temb=temb) + else: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + downsample_padding: int = 1, + add_downsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + additional_residuals: Optional[torch.FloatTensor] = None, + down_block_add_samples: Optional[torch.FloatTensor] = None, + debug=False, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + + if debug: print(' XAD2: forward') + + if cross_attention_kwargs is not None: + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + output_states = () + + blocks = list(zip(self.resnets, self.attentions)) + + for i, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + else: + if debug: print(' XAD2: resnet hs #', i, hidden_states.shape) + if debug and temb is not None: print(' XAD2: resnet temb #', i, temb.shape) + + hidden_states = resnet(hidden_states, temb) + + if debug: print(' XAD2: attn hs #', i, hidden_states.shape) + if debug and encoder_hidden_states is not None: print(' XAD2: attn ehs #', i, + encoder_hidden_states.shape) + + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + + if debug: print(' XAD2: add res', additional_residuals.shape) + + hidden_states = hidden_states + additional_residuals + + if down_block_add_samples is not None: + + if debug: print(' XAD2: add samples', down_block_add_samples.shape) + + hidden_states = hidden_states + down_block_add_samples.pop(0) + + if debug: print(' XAD2: output', hidden_states.shape) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + if down_block_add_samples is not None: + hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after + + output_states = output_states + (hidden_states,) + + if debug: + print(' XAD2: finish') + for st in output_states: + print(' XAD2: ', st.shape) + + return hidden_states, output_states + + +class DownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, + down_block_add_samples: Optional[torch.FloatTensor] = None, *args, **kwargs + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + output_states = () + + if kwargs.get("debug", False): print(' D2: forward', hidden_states.shape) + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + + if kwargs.get("debug", False): print(' D2: resnet', hidden_states.shape) + + hidden_states = resnet(hidden_states, temb) + + if down_block_add_samples is not None: + hidden_states = hidden_states + down_block_add_samples.pop(0) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + if down_block_add_samples is not None: + hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after + + output_states = output_states + (hidden_states,) + + if kwargs.get("debug", False): print(' D2: finish', hidden_states.shape) + + return hidden_states, output_states + + +class DownEncoderBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + if resnet_time_scale_shift == "spatial": + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=None, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm="spatial", + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + ) + ) + else: + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=None, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + def forward(self, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + for resnet in self.resnets: + hidden_states = resnet(hidden_states, temb=None) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + return hidden_states + + +class AttnDownEncoderBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + attentions = [] + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." + ) + attention_head_dim = out_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + if resnet_time_scale_shift == "spatial": + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=None, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm="spatial", + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + ) + ) + else: + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=None, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + attentions.append( + Attention( + out_channels, + heads=out_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=resnet_groups, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + def forward(self, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + for resnet, attn in zip(self.resnets, self.attentions): + hidden_states = resnet(hidden_states, temb=None) + hidden_states = attn(hidden_states) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + return hidden_states + + +class AttnSkipDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = np.sqrt(2.0), + add_downsample: bool = True, + ): + super().__init__() + self.attentions = nn.ModuleList([]) + self.resnets = nn.ModuleList([]) + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." + ) + attention_head_dim = out_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + self.resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(in_channels // 4, 32), + groups_out=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + self.attentions.append( + Attention( + out_channels, + heads=out_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=32, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + + if add_downsample: + self.resnet_down = ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_in_shortcut=True, + down=True, + kernel="fir", + ) + self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) + self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) + else: + self.resnet_down = None + self.downsamplers = None + self.skip_conv = None + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + skip_sample: Optional[torch.FloatTensor] = None, + *args, + **kwargs, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + output_states = () + + for resnet, attn in zip(self.resnets, self.attentions): + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states) + output_states += (hidden_states,) + + if self.downsamplers is not None: + hidden_states = self.resnet_down(hidden_states, temb) + for downsampler in self.downsamplers: + skip_sample = downsampler(skip_sample) + + hidden_states = self.skip_conv(skip_sample) + hidden_states + + output_states += (hidden_states,) + + return hidden_states, output_states, skip_sample + + +class SkipDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_pre_norm: bool = True, + output_scale_factor: float = np.sqrt(2.0), + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + self.resnets = nn.ModuleList([]) + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + self.resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(in_channels // 4, 32), + groups_out=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + if add_downsample: + self.resnet_down = ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_in_shortcut=True, + down=True, + kernel="fir", + ) + self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) + self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) + else: + self.resnet_down = None + self.downsamplers = None + self.skip_conv = None + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + skip_sample: Optional[torch.FloatTensor] = None, + *args, + **kwargs, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + output_states = () + + for resnet in self.resnets: + hidden_states = resnet(hidden_states, temb) + output_states += (hidden_states,) + + if self.downsamplers is not None: + hidden_states = self.resnet_down(hidden_states, temb) + for downsampler in self.downsamplers: + skip_sample = downsampler(skip_sample) + + hidden_states = self.skip_conv(skip_sample) + hidden_states + + output_states += (hidden_states,) + + return hidden_states, output_states, skip_sample + + +class ResnetDownsampleBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + skip_time_act: bool = False, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + down=True, + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, temb) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class SimpleCrossAttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + skip_time_act: bool = False, + only_cross_attention: bool = False, + cross_attention_norm: Optional[str] = None, + ): + super().__init__() + + self.has_cross_attention = True + + resnets = [] + attentions = [] + + self.attention_head_dim = attention_head_dim + self.num_heads = out_channels // self.attention_head_dim + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + ) + ) + + processor = ( + AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() + ) + + attentions.append( + Attention( + query_dim=out_channels, + cross_attention_dim=out_channels, + heads=self.num_heads, + dim_head=attention_head_dim, + added_kv_proj_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + bias=True, + upcast_softmax=True, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + processor=processor, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + down=True, + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + output_states = () + + if attention_mask is None: + # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. + mask = None if encoder_hidden_states is None else encoder_attention_mask + else: + # when attention_mask is defined: we don't even check for encoder_attention_mask. + # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. + # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. + # then we can simplify this whole if/else block to: + # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask + mask = attention_mask + + for resnet, attn in zip(self.resnets, self.attentions): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=mask, + **cross_attention_kwargs, + ) + else: + hidden_states = resnet(hidden_states, temb) + + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=mask, + **cross_attention_kwargs, + ) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, temb) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class KDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 4, + resnet_eps: float = 1e-5, + resnet_act_fn: str = "gelu", + resnet_group_size: int = 32, + add_downsample: bool = False, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + groups = in_channels // resnet_group_size + groups_out = out_channels // resnet_group_size + + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=out_channels, + dropout=dropout, + temb_channels=temb_channels, + groups=groups, + groups_out=groups_out, + eps=resnet_eps, + non_linearity=resnet_act_fn, + time_embedding_norm="ada_group", + conv_shortcut_bias=False, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + # YiYi's comments- might be able to use FirDownsample2D, look into details later + self.downsamplers = nn.ModuleList([KDownsample2D()]) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + return hidden_states, output_states + + +class KCrossAttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + cross_attention_dim: int, + dropout: float = 0.0, + num_layers: int = 4, + resnet_group_size: int = 32, + add_downsample: bool = True, + attention_head_dim: int = 64, + add_self_attention: bool = False, + resnet_eps: float = 1e-5, + resnet_act_fn: str = "gelu", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + groups = in_channels // resnet_group_size + groups_out = out_channels // resnet_group_size + + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=out_channels, + dropout=dropout, + temb_channels=temb_channels, + groups=groups, + groups_out=groups_out, + eps=resnet_eps, + non_linearity=resnet_act_fn, + time_embedding_norm="ada_group", + conv_shortcut_bias=False, + ) + ) + attentions.append( + KAttentionBlock( + out_channels, + out_channels // attention_head_dim, + attention_head_dim, + cross_attention_dim=cross_attention_dim, + temb_channels=temb_channels, + attention_bias=True, + add_self_attention=add_self_attention, + cross_attention_norm="layer_norm", + group_size=resnet_group_size, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.attentions = nn.ModuleList(attentions) + + if add_downsample: + self.downsamplers = nn.ModuleList([KDownsample2D()]) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + output_states = () + + for resnet, attn in zip(self.resnets, self.attentions): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + emb=temb, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + emb=temb, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + + if self.downsamplers is None: + output_states += (None,) + else: + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + return hidden_states, output_states + + +class AttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: int = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + upsample_type: str = "conv", + ): + super().__init__() + resnets = [] + attentions = [] + + self.upsample_type = upsample_type + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." + ) + attention_head_dim = out_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + attentions.append( + Attention( + out_channels, + heads=out_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=resnet_groups, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if upsample_type == "conv": + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + elif upsample_type == "resnet": + self.upsamplers = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + up=True, + ) + ] + ) + else: + self.upsamplers = None + + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + if self.upsample_type == "resnet": + hidden_states = upsampler(hidden_states, temb=temb) + else: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +class CrossAttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + return_res_samples: Optional[bool] = False, + up_block_add_samples: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + if cross_attention_kwargs is not None: + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + if return_res_samples: + output_states = () + + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + if return_res_samples: + output_states = output_states + (hidden_states,) + if up_block_add_samples is not None: + hidden_states = hidden_states + up_block_add_samples.pop(0) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + if return_res_samples: + output_states = output_states + (hidden_states,) + if up_block_add_samples is not None: + hidden_states = hidden_states + up_block_add_samples.pop(0) + + if return_res_samples: + return hidden_states, output_states + else: + return hidden_states + + +class UpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + return_res_samples: Optional[bool] = False, + up_block_add_samples: Optional[torch.FloatTensor] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + if return_res_samples: + output_states = () + + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + if return_res_samples: + output_states = output_states + (hidden_states,) + if up_block_add_samples is not None: + hidden_states = hidden_states + up_block_add_samples.pop(0) # todo: add before or after + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + if return_res_samples: + output_states = output_states + (hidden_states,) + if up_block_add_samples is not None: + hidden_states = hidden_states + up_block_add_samples.pop(0) # todo: add before or after + + if return_res_samples: + return hidden_states, output_states + else: + return hidden_states + + +class UpDecoderBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + temb_channels: Optional[int] = None, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + input_channels = in_channels if i == 0 else out_channels + + if resnet_time_scale_shift == "spatial": + resnets.append( + ResnetBlockCondNorm2D( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm="spatial", + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + ) + ) + else: + resnets.append( + ResnetBlock2D( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.resolution_idx = resolution_idx + + def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: + for resnet in self.resnets: + hidden_states = resnet(hidden_states, temb=temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +class AttnUpDecoderBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + temb_channels: Optional[int] = None, + ): + super().__init__() + resnets = [] + attentions = [] + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}." + ) + attention_head_dim = out_channels + + for i in range(num_layers): + input_channels = in_channels if i == 0 else out_channels + + if resnet_time_scale_shift == "spatial": + resnets.append( + ResnetBlockCondNorm2D( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm="spatial", + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + ) + ) + else: + resnets.append( + ResnetBlock2D( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + attentions.append( + Attention( + out_channels, + heads=out_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=resnet_groups if resnet_time_scale_shift != "spatial" else None, + spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.resolution_idx = resolution_idx + + def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: + for resnet, attn in zip(self.resnets, self.attentions): + hidden_states = resnet(hidden_states, temb=temb) + hidden_states = attn(hidden_states, temb=temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +class AttnSkipUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = np.sqrt(2.0), + add_upsample: bool = True, + ): + super().__init__() + self.attentions = nn.ModuleList([]) + self.resnets = nn.ModuleList([]) + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + self.resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(resnet_in_channels + res_skip_channels // 4, 32), + groups_out=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + if attention_head_dim is None: + logger.warning( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}." + ) + attention_head_dim = out_channels + + self.attentions.append( + Attention( + out_channels, + heads=out_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=32, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + + self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) + if add_upsample: + self.resnet_up = ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(out_channels // 4, 32), + groups_out=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_in_shortcut=True, + up=True, + kernel="fir", + ) + self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + self.skip_norm = torch.nn.GroupNorm( + num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True + ) + self.act = nn.SiLU() + else: + self.resnet_up = None + self.skip_conv = None + self.skip_norm = None + self.act = None + + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + skip_sample=None, + *args, + **kwargs, + ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + hidden_states = resnet(hidden_states, temb) + + hidden_states = self.attentions[0](hidden_states) + + if skip_sample is not None: + skip_sample = self.upsampler(skip_sample) + else: + skip_sample = 0 + + if self.resnet_up is not None: + skip_sample_states = self.skip_norm(hidden_states) + skip_sample_states = self.act(skip_sample_states) + skip_sample_states = self.skip_conv(skip_sample_states) + + skip_sample = skip_sample + skip_sample_states + + hidden_states = self.resnet_up(hidden_states, temb) + + return hidden_states, skip_sample + + +class SkipUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_pre_norm: bool = True, + output_scale_factor: float = np.sqrt(2.0), + add_upsample: bool = True, + upsample_padding: int = 1, + ): + super().__init__() + self.resnets = nn.ModuleList([]) + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + self.resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min((resnet_in_channels + res_skip_channels) // 4, 32), + groups_out=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) + if add_upsample: + self.resnet_up = ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=min(out_channels // 4, 32), + groups_out=min(out_channels // 4, 32), + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_in_shortcut=True, + up=True, + kernel="fir", + ) + self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + self.skip_norm = torch.nn.GroupNorm( + num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True + ) + self.act = nn.SiLU() + else: + self.resnet_up = None + self.skip_conv = None + self.skip_norm = None + self.act = None + + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + skip_sample=None, + *args, + **kwargs, + ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + hidden_states = resnet(hidden_states, temb) + + if skip_sample is not None: + skip_sample = self.upsampler(skip_sample) + else: + skip_sample = 0 + + if self.resnet_up is not None: + skip_sample_states = self.skip_norm(hidden_states) + skip_sample_states = self.act(skip_sample_states) + skip_sample_states = self.skip_conv(skip_sample_states) + + skip_sample = skip_sample + skip_sample_states + + hidden_states = self.resnet_up(hidden_states, temb) + + return hidden_states, skip_sample + + +class ResnetUpsampleBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + skip_time_act: bool = False, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + up=True, + ) + ] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, temb) + + return hidden_states + + +class SimpleCrossAttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attention_head_dim: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + skip_time_act: bool = False, + only_cross_attention: bool = False, + cross_attention_norm: Optional[str] = None, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.attention_head_dim = attention_head_dim + + self.num_heads = out_channels // self.attention_head_dim + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + ) + ) + + processor = ( + AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() + ) + + attentions.append( + Attention( + query_dim=out_channels, + cross_attention_dim=out_channels, + heads=self.num_heads, + dim_head=self.attention_head_dim, + added_kv_proj_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + bias=True, + upcast_softmax=True, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + processor=processor, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [ + ResnetBlock2D( + in_channels=out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + skip_time_act=skip_time_act, + up=True, + ) + ] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + if attention_mask is None: + # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. + mask = None if encoder_hidden_states is None else encoder_attention_mask + else: + # when attention_mask is defined: we don't even check for encoder_attention_mask. + # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. + # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. + # then we can simplify this whole if/else block to: + # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask + mask = attention_mask + + for resnet, attn in zip(self.resnets, self.attentions): + # resnet + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=mask, + **cross_attention_kwargs, + ) + else: + hidden_states = resnet(hidden_states, temb) + + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=mask, + **cross_attention_kwargs, + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, temb) + + return hidden_states + + +class KUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + resolution_idx: int, + dropout: float = 0.0, + num_layers: int = 5, + resnet_eps: float = 1e-5, + resnet_act_fn: str = "gelu", + resnet_group_size: Optional[int] = 32, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + k_in_channels = 2 * out_channels + k_out_channels = in_channels + num_layers = num_layers - 1 + + for i in range(num_layers): + in_channels = k_in_channels if i == 0 else out_channels + groups = in_channels // resnet_group_size + groups_out = out_channels // resnet_group_size + + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=k_out_channels if (i == num_layers - 1) else out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=groups, + groups_out=groups_out, + dropout=dropout, + non_linearity=resnet_act_fn, + time_embedding_norm="ada_group", + conv_shortcut_bias=False, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([KUpsample2D()]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + if len(args) > 0 or kwargs.get("scale", None) is not None: + deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." + deprecate("scale", "1.0.0", deprecation_message) + + res_hidden_states_tuple = res_hidden_states_tuple[-1] + if res_hidden_states_tuple is not None: + hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +class KCrossAttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + resolution_idx: int, + dropout: float = 0.0, + num_layers: int = 4, + resnet_eps: float = 1e-5, + resnet_act_fn: str = "gelu", + resnet_group_size: int = 32, + attention_head_dim: int = 1, # attention dim_head + cross_attention_dim: int = 768, + add_upsample: bool = True, + upcast_attention: bool = False, + ): + super().__init__() + resnets = [] + attentions = [] + + is_first_block = in_channels == out_channels == temb_channels + is_middle_block = in_channels != out_channels + add_self_attention = True if is_first_block else False + + self.has_cross_attention = True + self.attention_head_dim = attention_head_dim + + # in_channels, and out_channels for the block (k-unet) + k_in_channels = out_channels if is_first_block else 2 * out_channels + k_out_channels = in_channels + + num_layers = num_layers - 1 + + for i in range(num_layers): + in_channels = k_in_channels if i == 0 else out_channels + groups = in_channels // resnet_group_size + groups_out = out_channels // resnet_group_size + + if is_middle_block and (i == num_layers - 1): + conv_2d_out_channels = k_out_channels + else: + conv_2d_out_channels = None + + resnets.append( + ResnetBlockCondNorm2D( + in_channels=in_channels, + out_channels=out_channels, + conv_2d_out_channels=conv_2d_out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=groups, + groups_out=groups_out, + dropout=dropout, + non_linearity=resnet_act_fn, + time_embedding_norm="ada_group", + conv_shortcut_bias=False, + ) + ) + attentions.append( + KAttentionBlock( + k_out_channels if (i == num_layers - 1) else out_channels, + k_out_channels // attention_head_dim + if (i == num_layers - 1) + else out_channels // attention_head_dim, + attention_head_dim, + cross_attention_dim=cross_attention_dim, + temb_channels=temb_channels, + attention_bias=True, + add_self_attention=add_self_attention, + cross_attention_norm="layer_norm", + upcast_attention=upcast_attention, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.attentions = nn.ModuleList(attentions) + + if add_upsample: + self.upsamplers = nn.ModuleList([KUpsample2D()]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + res_hidden_states_tuple = res_hidden_states_tuple[-1] + if res_hidden_states_tuple is not None: + hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) + + for resnet, attn in zip(self.resnets, self.attentions): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + emb=temb, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + emb=temb, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +# can potentially later be renamed to `No-feed-forward` attention +class KAttentionBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + attention_bias (`bool`, *optional*, defaults to `False`): + Configure if the attention layers should contain a bias parameter. + upcast_attention (`bool`, *optional*, defaults to `False`): + Set to `True` to upcast the attention computation to `float32`. + temb_channels (`int`, *optional*, defaults to 768): + The number of channels in the token embedding. + add_self_attention (`bool`, *optional*, defaults to `False`): + Set to `True` to add self-attention to the block. + cross_attention_norm (`str`, *optional*, defaults to `None`): + The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. + group_size (`int`, *optional*, defaults to 32): + The number of groups to separate the channels into for group normalization. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout: float = 0.0, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + upcast_attention: bool = False, + temb_channels: int = 768, # for ada_group_norm + add_self_attention: bool = False, + cross_attention_norm: Optional[str] = None, + group_size: int = 32, + ): + super().__init__() + self.add_self_attention = add_self_attention + + # 1. Self-Attn + if add_self_attention: + self.norm1 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=None, + cross_attention_norm=None, + ) + + # 2. Cross-Attn + self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + cross_attention_norm=cross_attention_norm, + ) + + def _to_3d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor: + return hidden_states.permute(0, 2, 3, 1).reshape(hidden_states.shape[0], height * weight, -1) + + def _to_4d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor: + return hidden_states.permute(0, 2, 1).reshape(hidden_states.shape[0], -1, height, weight) + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + # TODO: mark emb as non-optional (self.norm2 requires it). + # requires assessing impact of change to positional param interface. + emb: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + if cross_attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") + + # 1. Self-Attention + if self.add_self_attention: + norm_hidden_states = self.norm1(hidden_states, emb) + + height, weight = norm_hidden_states.shape[2:] + norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + attn_output = self._to_4d(attn_output, height, weight) + + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention/None + norm_hidden_states = self.norm2(hidden_states, emb) + + height, weight = norm_hidden_states.shape[2:] + norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask if encoder_hidden_states is None else encoder_attention_mask, + **cross_attention_kwargs, + ) + attn_output = self._to_4d(attn_output, height, weight) + + hidden_states = attn_output + hidden_states + + return hidden_states diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/unet_2d_condition.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/unet_2d_condition.py new file mode 100644 index 00000000..103cd08d --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/unet_2d_condition.py @@ -0,0 +1,1359 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin +from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import ( + ADDED_KV_ATTENTION_PROCESSORS, + CROSS_ATTENTION_PROCESSORS, + Attention, + AttentionProcessor, + AttnAddedKVProcessor, + AttnProcessor, +) +from diffusers.models.embeddings import ( + GaussianFourierProjection, + GLIGENTextBoundingboxProjection, + ImageHintTimeEmbedding, + ImageProjection, + ImageTimeEmbedding, + TextImageProjection, + TextImageTimeEmbedding, + TextTimeEmbedding, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.modeling_utils import ModelMixin +from .unet_2d_blocks import ( + get_down_block, + get_mid_block, + get_up_block, +) + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet2DConditionOutput(BaseOutput): + """ + The output of [`UNet2DConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin): + r""" + A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. + flip_sin_to_cos (`bool`, *optional*, defaults to `True`): + Whether to flip the sin to cos in the time embedding. + freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): + The tuple of downsample blocks to use. + mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): + Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or + `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): + The tuple of upsample blocks to use. + only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): + Whether to include self-attention in the basic transformer blocks, see + [`~models.attention.BasicTransformerBlock`]. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. + mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. + norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. + If `None`, normalization and activation layers is skipped in post-processing. + norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling + blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + encoder_hid_dim (`int`, *optional*, defaults to None): + If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` + dimension to `cross_attention_dim`. + encoder_hid_dim_type (`str`, *optional*, defaults to `None`): + If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text + embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. + attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. + num_attention_heads (`int`, *optional*): + The number of attention heads. If not defined, defaults to `attention_head_dim` + resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config + for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. + class_embed_type (`str`, *optional*, defaults to `None`): + The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, + `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. + addition_embed_type (`str`, *optional*, defaults to `None`): + Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or + "text". "text" will use the `TextTimeEmbedding` layer. + addition_time_embed_dim: (`int`, *optional*, defaults to `None`): + Dimension for the timestep embeddings. + num_class_embeds (`int`, *optional*, defaults to `None`): + Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing + class conditioning with `class_embed_type` equal to `None`. + time_embedding_type (`str`, *optional*, defaults to `positional`): + The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. + time_embedding_dim (`int`, *optional*, defaults to `None`): + An optional override for the dimension of the projected time embedding. + time_embedding_act_fn (`str`, *optional*, defaults to `None`): + Optional activation function to use only once on the time embeddings before they are passed to the rest of + the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. + timestep_post_act (`str`, *optional*, defaults to `None`): + The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. + time_cond_proj_dim (`int`, *optional*, defaults to `None`): + The dimension of `cond_proj` layer in the timestep embedding. + conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. + conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. + projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when + `class_embed_type="projection"`. Required when `class_embed_type="projection"`. + class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time + embeddings with the class embeddings. + mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): + Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If + `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the + `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` + otherwise. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: Union[int, Tuple[int]] = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + dropout: float = 0.0, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: Union[int, Tuple[int]] = 1280, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int]]] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + time_embedding_type: str = "positional", + time_embedding_dim: Optional[int] = None, + time_embedding_act_fn: Optional[str] = None, + timestep_post_act: Optional[str] = None, + time_cond_proj_dim: Optional[int] = None, + conv_in_kernel: int = 3, + conv_out_kernel: int = 3, + projection_class_embeddings_input_dim: Optional[int] = None, + attention_type: str = "default", + class_embeddings_concat: bool = False, + mid_block_only_cross_attention: Optional[bool] = None, + cross_attention_norm: Optional[str] = None, + addition_embed_type_num_heads: int = 64, + ): + super().__init__() + + self.sample_size = sample_size + + if num_attention_heads is not None: + raise ValueError( + "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." + ) + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + self._check_config( + down_block_types=down_block_types, + up_block_types=up_block_types, + only_cross_attention=only_cross_attention, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + cross_attention_dim=cross_attention_dim, + transformer_layers_per_block=transformer_layers_per_block, + reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, + attention_head_dim=attention_head_dim, + num_attention_heads=num_attention_heads, + ) + + # input + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in = nn.Conv2d( + in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding + ) + + # time + time_embed_dim, timestep_input_dim = self._set_time_proj( + time_embedding_type, + block_out_channels=block_out_channels, + flip_sin_to_cos=flip_sin_to_cos, + freq_shift=freq_shift, + time_embedding_dim=time_embedding_dim, + ) + + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + post_act_fn=timestep_post_act, + cond_proj_dim=time_cond_proj_dim, + ) + + self._set_encoder_hid_proj( + encoder_hid_dim_type, + cross_attention_dim=cross_attention_dim, + encoder_hid_dim=encoder_hid_dim, + ) + + # class embedding + self._set_class_embedding( + class_embed_type, + act_fn=act_fn, + num_class_embeds=num_class_embeds, + projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, + time_embed_dim=time_embed_dim, + timestep_input_dim=timestep_input_dim, + ) + + self._set_add_embedding( + addition_embed_type, + addition_embed_type_num_heads=addition_embed_type_num_heads, + addition_time_embed_dim=addition_time_embed_dim, + cross_attention_dim=cross_attention_dim, + encoder_hid_dim=encoder_hid_dim, + flip_sin_to_cos=flip_sin_to_cos, + freq_shift=freq_shift, + projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, + time_embed_dim=time_embed_dim, + ) + + if time_embedding_act_fn is None: + self.time_embed_act = None + else: + self.time_embed_act = get_activation(time_embedding_act_fn) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = only_cross_attention + + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = False + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + if class_embeddings_concat: + # The time embeddings are concatenated with the class embeddings. The dimension of the + # time embeddings passed to the down, middle, and up blocks is twice the dimension of the + # regular time embeddings + blocks_time_embed_dim = time_embed_dim * 2 + else: + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + dropout=dropout, + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = get_mid_block( + mid_block_type, + temb_channels=blocks_time_embed_dim, + in_channels=block_out_channels[-1], + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + output_scale_factor=mid_block_scale_factor, + transformer_layers_per_block=transformer_layers_per_block[-1], + num_attention_heads=num_attention_heads[-1], + cross_attention_dim=cross_attention_dim[-1], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + mid_block_only_cross_attention=mid_block_only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[-1], + dropout=dropout, + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = ( + list(reversed(transformer_layers_per_block)) + if reverse_transformer_layers_per_block is None + else reverse_transformer_layers_per_block + ) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resolution_idx=i, + resnet_groups=norm_num_groups, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + dropout=dropout, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_num_groups is not None: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + + self.conv_act = get_activation(act_fn) + + else: + self.conv_norm_out = None + self.conv_act = None + + conv_out_padding = (conv_out_kernel - 1) // 2 + self.conv_out = nn.Conv2d( + block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding + ) + + self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim) + + def _check_config( + self, + down_block_types: Tuple[str], + up_block_types: Tuple[str], + only_cross_attention: Union[bool, Tuple[bool]], + block_out_channels: Tuple[int], + layers_per_block: Union[int, Tuple[int]], + cross_attention_dim: Union[int, Tuple[int]], + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]], + reverse_transformer_layers_per_block: bool, + attention_head_dim: int, + num_attention_heads: Optional[Union[int, Tuple[int]]], + ): + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: + for layer_number_per_block in transformer_layers_per_block: + if isinstance(layer_number_per_block, list): + raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") + + def _set_time_proj( + self, + time_embedding_type: str, + block_out_channels: int, + flip_sin_to_cos: bool, + freq_shift: float, + time_embedding_dim: int, + ) -> Tuple[int, int]: + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 + if time_embed_dim % 2 != 0: + raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") + self.time_proj = GaussianFourierProjection( + time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + return time_embed_dim, timestep_input_dim + + def _set_encoder_hid_proj( + self, + encoder_hid_dim_type: Optional[str], + cross_attention_dim: Union[int, Tuple[int]], + encoder_hid_dim: Optional[int], + ): + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 + self.encoder_hid_proj = ImageProjection( + image_embed_dim=encoder_hid_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + def _set_class_embedding( + self, + class_embed_type: Optional[str], + act_fn: str, + num_class_embeds: Optional[int], + projection_class_embeddings_input_dim: Optional[int], + time_embed_dim: int, + timestep_input_dim: int, + ): + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + elif class_embed_type == "simple_projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" + ) + self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) + else: + self.class_embedding = None + + def _set_add_embedding( + self, + addition_embed_type: str, + addition_embed_type_num_heads: int, + addition_time_embed_dim: Optional[int], + flip_sin_to_cos: bool, + freq_shift: float, + cross_attention_dim: Optional[int], + encoder_hid_dim: Optional[int], + projection_class_embeddings_input_dim: Optional[int], + time_embed_dim: int, + ): + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + elif addition_embed_type == "image": + # Kandinsky 2.2 + self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) + elif addition_embed_type == "image_hint": + # Kandinsky 2.2 ControlNet + self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) + elif addition_embed_type is not None: + raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") + + def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int): + if attention_type in ["gated", "gated-text-image"]: + positive_len = 768 + if isinstance(cross_attention_dim, int): + positive_len = cross_attention_dim + elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list): + positive_len = cross_attention_dim[0] + + feature_type = "text-only" if attention_type == "gated" else "text-image" + self.position_net = GLIGENTextBoundingboxProjection( + positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnAddedKVProcessor() + elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): + r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. + + The suffixes after the scaling factors represent the stage blocks where they are being applied. + + Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that + are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. + + Args: + s1 (`float`): + Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to + mitigate the "oversmoothing effect" in the enhanced denoising process. + s2 (`float`): + Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to + mitigate the "oversmoothing effect" in the enhanced denoising process. + b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. + b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. + """ + for i, upsample_block in enumerate(self.up_blocks): + setattr(upsample_block, "s1", s1) + setattr(upsample_block, "s2", s2) + setattr(upsample_block, "b1", b1) + setattr(upsample_block, "b2", b2) + + def disable_freeu(self): + """Disables the FreeU mechanism.""" + freeu_keys = {"s1", "s2", "b1", "b2"} + for i, upsample_block in enumerate(self.up_blocks): + for k in freeu_keys: + if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: + setattr(upsample_block, k, None) + + def fuse_qkv_projections(self): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) + are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is πŸ§ͺ experimental. + + + """ + self.original_attn_processors = None + + for _, attn_processor in self.attn_processors.items(): + if "Added" in str(attn_processor.__class__.__name__): + raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") + + self.original_attn_processors = self.attn_processors + + for module in self.modules(): + if isinstance(module, Attention): + module.fuse_projections(fuse=True) + + def unfuse_qkv_projections(self): + """Disables the fused QKV projection if enabled. + + + + This API is πŸ§ͺ experimental. + + + + """ + if self.original_attn_processors is not None: + self.set_attn_processor(self.original_attn_processors) + + def unload_lora(self): + """Unloads LoRA weights.""" + deprecate( + "unload_lora", + "0.28.0", + "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", + ) + for module in self.modules(): + if hasattr(module, "set_lora_layer"): + module.set_lora_layer(None) + + def get_time_embed( + self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int] + ) -> Optional[torch.Tensor]: + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + return t_emb + + def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]: + class_emb = None + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # there might be better ways to encapsulate this. + class_labels = class_labels.to(dtype=sample.dtype) + + class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) + return class_emb + + def get_aug_embed( + self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] + ) -> Optional[torch.Tensor]: + aug_emb = None + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + elif self.config.addition_embed_type == "text_image": + # Kandinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + + image_embs = added_cond_kwargs.get("image_embeds") + text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) + aug_emb = self.add_embedding(text_embs, image_embs) + elif self.config.addition_embed_type == "text_time": + # SDXL - style + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + elif self.config.addition_embed_type == "image": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + aug_emb = self.add_embedding(image_embs) + elif self.config.addition_embed_type == "image_hint": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + hint = added_cond_kwargs.get("hint") + aug_emb = self.add_embedding(image_embs, hint) + return aug_emb + + def process_encoder_hidden_states( + self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] + ) -> torch.Tensor: + if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": + # Kandinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(image_embeds) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + image_embeds = self.encoder_hid_proj(image_embeds) + encoder_hidden_states = (encoder_hidden_states, image_embeds) + return encoder_hidden_states + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + down_block_add_samples: Optional[Tuple[torch.Tensor]] = None, + mid_block_add_sample: Optional[Tuple[torch.Tensor]] = None, + up_block_add_samples: Optional[Tuple[torch.Tensor]] = None, + ) -> Union[UNet2DConditionOutput, Tuple]: + r""" + The [`UNet2DConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. + class_labels (`torch.Tensor`, *optional*, defaults to `None`): + Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. + timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): + Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed + through the `self.time_embedding` layer to obtain the timestep embeddings. + attention_mask (`torch.Tensor`, *optional*, defaults to `None`): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): + A tuple of tensors that if specified are added to the residuals of down unet blocks. + mid_block_additional_residual: (`torch.Tensor`, *optional*): + A tensor that if specified is added to the residual of the middle unet block. + down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): + additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) + encoder_attention_mask (`torch.Tensor`): + A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If + `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, + which adds large negative values to the attention scores corresponding to "discard" tokens. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, + otherwise a `tuple` is returned where the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2 ** self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + for dim in sample.shape[-2:]: + if dim % default_overall_up_factor != 0: + # Forward upsample size to force interpolation output size. + forward_upsample_size = True + break + + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None: + encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 0. center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # 1. time + t_emb = self.get_time_embed(sample=sample, timestep=timestep) + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + class_emb = self.get_class_embed(sample=sample, class_labels=class_labels) + if class_emb is not None: + if self.config.class_embeddings_concat: + emb = torch.cat([emb, class_emb], dim=-1) + else: + emb = emb + class_emb + + aug_emb = self.get_aug_embed( + emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs + ) + if self.config.addition_embed_type == "image_hint": + aug_emb, hint = aug_emb + sample = torch.cat([sample, hint], dim=1) + + emb = emb + aug_emb if aug_emb is not None else emb + + if self.time_embed_act is not None: + emb = self.time_embed_act(emb) + + encoder_hidden_states = self.process_encoder_hidden_states( + encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs + ) + + # 2. pre-process + sample = self.conv_in(sample) + + # 2.5 GLIGEN position net + if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None: + cross_attention_kwargs = cross_attention_kwargs.copy() + gligen_args = cross_attention_kwargs.pop("gligen") + cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} + + # 3. down + # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated + # to the internal blocks and will raise deprecation warnings. this will be confusing for our users. + if cross_attention_kwargs is not None: + cross_attention_kwargs = cross_attention_kwargs.copy() + lora_scale = cross_attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + + is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None + # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets + is_adapter = down_intrablock_additional_residuals is not None + # maintain backward compatibility for legacy usage, where + # T2I-Adapter and ControlNet both use down_block_additional_residuals arg + # but can only use one or the other + is_brushnet = down_block_add_samples is not None and mid_block_add_sample is not None and up_block_add_samples is not None + if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None: + deprecate( + "T2I should not use down_block_additional_residuals", + "1.3.0", + "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ + and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ + for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", + standard_warn=False, + ) + down_intrablock_additional_residuals = down_block_additional_residuals + is_adapter = True + + down_block_res_samples = (sample,) + + if is_brushnet: + sample = sample + down_block_add_samples.pop(0) + + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # For t2i-adapter CrossAttnDownBlock2D + additional_residuals = {} + if is_adapter and len(down_intrablock_additional_residuals) > 0: + additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) + + i = len(down_block_add_samples) + + if is_brushnet and len(down_block_add_samples) > 0: + additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) + for _ in range( + len(downsample_block.resnets) + (downsample_block.downsamplers != None))] + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + **additional_residuals, + ) + else: + additional_residuals = {} + + i = len(down_block_add_samples) + + if is_brushnet and len(down_block_add_samples) > 0: + additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) + for _ in range( + len(downsample_block.resnets) + (downsample_block.downsamplers != None))] + + sample, res_samples = downsample_block(hidden_states=sample, temb=emb, **additional_residuals) + if is_adapter and len(down_intrablock_additional_residuals) > 0: + sample += down_intrablock_additional_residuals.pop(0) + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + if self.mid_block is not None: + if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = self.mid_block(sample, emb) + + # To support T2I-Adapter-XL + if ( + is_adapter + and len(down_intrablock_additional_residuals) > 0 + and sample.shape == down_intrablock_additional_residuals[0].shape + ): + sample += down_intrablock_additional_residuals.pop(0) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + if is_brushnet: + sample = sample + mid_block_add_sample + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets):] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + additional_residuals = {} + + i = len(up_block_add_samples) + + if is_brushnet and len(up_block_add_samples) > 0: + additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) + for _ in range( + len(upsample_block.resnets) + (upsample_block.upsamplers != None))] + + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + **additional_residuals, + ) + else: + additional_residuals = {} + + i = len(up_block_add_samples) + + if is_brushnet and len(up_block_add_samples) > 0: + additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) + for _ in range( + len(upsample_block.resnets) + (upsample_block.upsamplers != None))] + + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + **additional_residuals, + ) + + # 6. post-process + if self.conv_norm_out: + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (sample,) + + return UNet2DConditionOutput(sample=sample) diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/__init__.py new file mode 100644 index 00000000..329a7347 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/__init__.py @@ -0,0 +1,2 @@ +#credit to city96 for this module +#from https://github.com/city96/ComfyUI_ExtraModels/ \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/config.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/config.py new file mode 100644 index 00000000..07d94105 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/config.py @@ -0,0 +1,120 @@ +""" +List of all DiT model types / settings +""" +sampling_settings = { + "beta_schedule" : "sqrt_linear", + "linear_start" : 0.0001, + "linear_end" : 0.02, + "timesteps" : 1000, +} + +dit_conf = { + "XL/2": { # DiT_XL_2 + "unet_config": { + "depth" : 28, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1152, + }, + "sampling_settings" : sampling_settings, + }, + "XL/4": { # DiT_XL_4 + "unet_config": { + "depth" : 28, + "num_heads" : 16, + "patch_size" : 4, + "hidden_size" : 1152, + }, + "sampling_settings" : sampling_settings, + }, + "XL/8": { # DiT_XL_8 + "unet_config": { + "depth" : 28, + "num_heads" : 16, + "patch_size" : 8, + "hidden_size" : 1152, + }, + "sampling_settings" : sampling_settings, + }, + "L/2": { # DiT_L_2 + "unet_config": { + "depth" : 24, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1024, + }, + "sampling_settings" : sampling_settings, + }, + "L/4": { # DiT_L_4 + "unet_config": { + "depth" : 24, + "num_heads" : 16, + "patch_size" : 4, + "hidden_size" : 1024, + }, + "sampling_settings" : sampling_settings, + }, + "L/8": { # DiT_L_8 + "unet_config": { + "depth" : 24, + "num_heads" : 16, + "patch_size" : 8, + "hidden_size" : 1024, + }, + "sampling_settings" : sampling_settings, + }, + "B/2": { # DiT_B_2 + "unet_config": { + "depth" : 12, + "num_heads" : 12, + "patch_size" : 2, + "hidden_size" : 768, + }, + "sampling_settings" : sampling_settings, + }, + "B/4": { # DiT_B_4 + "unet_config": { + "depth" : 12, + "num_heads" : 12, + "patch_size" : 4, + "hidden_size" : 768, + }, + "sampling_settings" : sampling_settings, + }, + "B/8": { # DiT_B_8 + "unet_config": { + "depth" : 12, + "num_heads" : 12, + "patch_size" : 8, + "hidden_size" : 768, + }, + "sampling_settings" : sampling_settings, + }, + "S/2": { # DiT_S_2 + "unet_config": { + "depth" : 12, + "num_heads" : 6, + "patch_size" : 2, + "hidden_size" : 384, + }, + "sampling_settings" : sampling_settings, + }, + "S/4": { # DiT_S_4 + "unet_config": { + "depth" : 12, + "num_heads" : 6, + "patch_size" : 4, + "hidden_size" : 384, + }, + "sampling_settings" : sampling_settings, + }, + "S/8": { # DiT_S_8 + "unet_config": { + "depth" : 12, + "num_heads" : 6, + "patch_size" : 8, + "hidden_size" : 384, + }, + "sampling_settings" : sampling_settings, + }, +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/LICENSE-Pixart b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/LICENSE-Pixart new file mode 100644 index 00000000..29ebfa54 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/LICENSE-Pixart @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/config.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/config.py new file mode 100644 index 00000000..47f521a7 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/config.py @@ -0,0 +1,139 @@ +""" +List of all PixArt model types / settings +""" +sampling_settings = { + "beta_schedule" : "sqrt_linear", + "linear_start" : 0.0001, + "linear_end" : 0.02, + "timesteps" : 1000, +} + +pixart_conf = { + "PixArtMS_XL_2": { # models/PixArtMS + "target": "PixArtMS", + "unet_config": { + "input_size" : 1024//8, + "depth" : 28, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1152, + "pe_interpolation": 2, + }, + "sampling_settings" : sampling_settings, + }, + "PixArtMS_Sigma_XL_2": { + "target": "PixArtMSSigma", + "unet_config": { + "input_size" : 1024//8, + "token_num" : 300, + "depth" : 28, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1152, + "micro_condition": False, + "pe_interpolation": 2, + "model_max_length": 300, + }, + "sampling_settings" : sampling_settings, + }, + "PixArtMS_Sigma_XL_2_900M": { + "target": "PixArtMSSigma", + "unet_config": { + "input_size": 1024 // 8, + "token_num": 300, + "depth": 42, + "num_heads": 16, + "patch_size": 2, + "hidden_size": 1152, + "micro_condition": False, + "pe_interpolation": 2, + "model_max_length": 300, + }, + "sampling_settings": sampling_settings, + }, + "PixArtMS_Sigma_XL_2_2K": { + "target": "PixArtMSSigma", + "unet_config": { + "input_size" : 2048//8, + "token_num" : 300, + "depth" : 28, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1152, + "micro_condition": False, + "pe_interpolation": 4, + "model_max_length": 300, + }, + "sampling_settings" : sampling_settings, + }, + "PixArt_XL_2": { # models/PixArt + "target": "PixArt", + "unet_config": { + "input_size" : 512//8, + "token_num" : 120, + "depth" : 28, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1152, + "pe_interpolation": 1, + }, + "sampling_settings" : sampling_settings, + }, +} + +pixart_conf.update({ # controlnet models + "ControlPixArtHalf": { + "target": "ControlPixArtHalf", + "unet_config": pixart_conf["PixArt_XL_2"]["unet_config"], + "sampling_settings": pixart_conf["PixArt_XL_2"]["sampling_settings"], + }, + "ControlPixArtMSHalf": { + "target": "ControlPixArtMSHalf", + "unet_config": pixart_conf["PixArtMS_XL_2"]["unet_config"], + "sampling_settings": pixart_conf["PixArtMS_XL_2"]["sampling_settings"], + } +}) + +pixart_res = { + "PixArtMS_XL_2": { # models/PixArtMS 1024x1024 + '0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856], + '0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600], + '0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344], + '0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152], + '0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024], + '1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896], + '1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768], + '1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640], + '2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576], + '3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512], + }, + "PixArt_XL_2": { # models/PixArt 512x512 + '0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928], + '0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800], + '0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672], + '0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576], + '0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512], + '1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448], + '1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384], + '1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320], + '2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288], + '3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256] + }, + "PixArtMS_Sigma_XL_2_2K": { + '0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712], + '0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200], + '0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688], + '0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304], + '0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048], + '1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792], + '1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536], + '1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280], + '2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152], + '3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024] + } +} +# These should be the same +pixart_res.update({ + "PixArtMS_Sigma_XL_2": pixart_res["PixArtMS_XL_2"], + "PixArtMS_Sigma_XL_2_512": pixart_res["PixArt_XL_2"], +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/diffusers_convert.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/diffusers_convert.py new file mode 100644 index 00000000..b5ca0ca0 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/diffusers_convert.py @@ -0,0 +1,216 @@ +# For using the diffusers format weights +# Based on the original ComfyUI function + +# https://github.com/PixArt-alpha/PixArt-alpha/blob/master/tools/convert_pixart_alpha_to_diffusers.py +import torch + +conversion_map_ms = [ # for multi_scale_train (MS) + # Resolution + ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"), + ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"), + ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"), + ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"), + # Aspect ratio + ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"), + ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"), + ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"), + ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"), +] + + +def get_depth(state_dict): + return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys()) + + +def get_lora_depth(state_dict): + return sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()) + + +def get_conversion_map(state_dict): + conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers) + # Patch embeddings + ("x_embedder.proj.weight", "pos_embed.proj.weight"), + ("x_embedder.proj.bias", "pos_embed.proj.bias"), + # Caption projection + ("y_embedder.y_embedding", "caption_projection.y_embedding"), + ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"), + ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"), + ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"), + ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"), + # AdaLN-single LN + ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"), + ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"), + ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"), + ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"), + # Shared norm + ("t_block.1.weight", "adaln_single.linear.weight"), + ("t_block.1.bias", "adaln_single.linear.bias"), + # Final block + ("final_layer.linear.weight", "proj_out.weight"), + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.scale_shift_table", "scale_shift_table"), + ] + + # Add actual transformer blocks + for depth in range(get_depth(state_dict)): + # Transformer blocks + conversion_map += [ + (f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"), + # Projection + (f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"), + (f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"), + # Feed-forward + (f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"), + (f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"), + (f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"), + (f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"), + # Cross-attention (proj) + (f"blocks.{depth}.cross_attn.proj.weight", f"transformer_blocks.{depth}.attn2.to_out.0.weight"), + (f"blocks.{depth}.cross_attn.proj.bias", f"transformer_blocks.{depth}.attn2.to_out.0.bias"), + ] + return conversion_map + + +def find_prefix(state_dict, target_key): + prefix = "" + for k in state_dict.keys(): + if k.endswith(target_key): + prefix = k.split(target_key)[0] + break + return prefix + + +def convert_state_dict(state_dict): + if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys(): + cmap = get_conversion_map(state_dict) + conversion_map_ms + else: + cmap = get_conversion_map(state_dict) + + missing = [k for k, v in cmap if v not in state_dict] + new_state_dict = {k: state_dict[v] for k, v in cmap if k not in missing} + matched = list(v for k, v in cmap if v in state_dict.keys()) + + for depth in range(get_depth(state_dict)): + for wb in ["weight", "bias"]: + # Self Attention + key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}" + new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat(( + state_dict[key('q')], state_dict[key('k')], state_dict[key('v')] + ), dim=0) + matched += [key('q'), key('k'), key('v')] + + # Cross-attention (linear) + key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}" + new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')] + new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat(( + state_dict[key('k')], state_dict[key('v')] + ), dim=0) + matched += [key('q'), key('k'), key('v')] + + if len(matched) < len(state_dict): + print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})") + print(list(set(state_dict.keys()) - set(matched))) + + if len(missing) > 0: + print(f"PixArt: UNET conversion has missing keys!") + print(missing) + + return new_state_dict + + +# Same as above but for LoRA weights: +def convert_lora_state_dict(state_dict, peft=True): + # koyha + rep_ak = lambda x: x.replace(".weight", ".lora_down.weight") + rep_bk = lambda x: x.replace(".weight", ".lora_up.weight") + rep_pk = lambda x: x.replace(".weight", ".alpha") + if peft: # peft + rep_ap = lambda x: x.replace(".weight", ".lora_A.weight") + rep_bp = lambda x: x.replace(".weight", ".lora_B.weight") + rep_pp = lambda x: x.replace(".weight", ".alpha") + + prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight") + state_dict = {k[len(prefix):]: v for k, v in state_dict.items()} + else: # OneTrainer + rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight" + rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight" + rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha" + + prefix = "lora_transformer_" + t5_marker = "lora_te_encoder" + t5_keys = [] + for key in list(state_dict.keys()): + if key.startswith(prefix): + state_dict[key[len(prefix):]] = state_dict.pop(key) + elif t5_marker in key: + t5_keys.append(state_dict.pop(key)) + if len(t5_keys) > 0: + print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys") + + cmap = [] + cmap_unet = get_conversion_map(state_dict) + conversion_map_ms # todo: 512 model + for k, v in cmap_unet: + if v.endswith(".weight"): + cmap.append((rep_ak(k), rep_ap(v))) + cmap.append((rep_bk(k), rep_bp(v))) + if not peft: + cmap.append((rep_pk(k), rep_pp(v))) + + missing = [k for k, v in cmap if v not in state_dict] + new_state_dict = {k: state_dict[v] for k, v in cmap if k not in missing} + matched = list(v for k, v in cmap if v in state_dict.keys()) + + lora_depth = get_lora_depth(state_dict) + for fp, fk in ((rep_ap, rep_ak), (rep_bp, rep_bk)): + for depth in range(lora_depth): + # Self Attention + key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight") + new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat(( + state_dict[key('q')], state_dict[key('k')], state_dict[key('v')] + ), dim=0) + + matched += [key('q'), key('k'), key('v')] + if not peft: + akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight") + new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")] + matched += [akey('q'), akey('k'), akey('v')] + + # Self Attention projection? + key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight") + new_state_dict[fk(f"blocks.{depth}.attn.proj.weight")] = state_dict[key('out.0')] + matched += [key('out.0')] + + # Cross-attention (linear) + key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight") + new_state_dict[fk(f"blocks.{depth}.cross_attn.q_linear.weight")] = state_dict[key('q')] + new_state_dict[fk(f"blocks.{depth}.cross_attn.kv_linear.weight")] = torch.cat(( + state_dict[key('k')], state_dict[key('v')] + ), dim=0) + matched += [key('q'), key('k'), key('v')] + if not peft: + akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight") + new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))] = state_dict[akey("q")] + new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")] + matched += [akey('q'), akey('k'), akey('v')] + + # Cross Attention projection? + key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight") + new_state_dict[fk(f"blocks.{depth}.cross_attn.proj.weight")] = state_dict[key('out.0')] + matched += [key('out.0')] + + key = fp(f"transformer_blocks.{depth}.ff.net.0.proj.weight") + new_state_dict[fk(f"blocks.{depth}.mlp.fc1.weight")] = state_dict[key] + matched += [key] + + key = fp(f"transformer_blocks.{depth}.ff.net.2.weight") + new_state_dict[fk(f"blocks.{depth}.mlp.fc2.weight")] = state_dict[key] + matched += [key] + + if len(matched) < len(state_dict): + print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})") + print(list(set(state_dict.keys()) - set(matched))) + + if len(missing) > 0: + print(f"PixArt: LoRA conversion has missing keys! (probably)") + print(missing) + + return new_state_dict \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/loader.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/loader.py new file mode 100644 index 00000000..d59a0fff --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/loader.py @@ -0,0 +1,331 @@ +import os +import json +import copy +import torch +import math +import comfy.supported_models_base +import comfy.latent_formats +import comfy.model_patcher +import comfy.model_base +import comfy.utils +import comfy.conds +from comfy import model_management +from .diffusers_convert import convert_state_dict, convert_lora_state_dict + +# checkpointbf +class EXM_PixArt(comfy.supported_models_base.BASE): + unet_config = {} + unet_extra_config = {} + latent_format = comfy.latent_formats.SD15 + + def __init__(self, model_conf): + self.model_target = model_conf.get("target") + self.unet_config = model_conf.get("unet_config", {}) + self.sampling_settings = model_conf.get("sampling_settings", {}) + self.latent_format = self.latent_format() + # UNET is handled by extension + self.unet_config["disable_unet_model_creation"] = True + + def model_type(self, state_dict, prefix=""): + return comfy.model_base.ModelType.EPS + + +class EXM_PixArt_Model(comfy.model_base.BaseModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + img_hw = kwargs.get("img_hw", None) + if img_hw is not None: + out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw)) + + aspect_ratio = kwargs.get("aspect_ratio", None) + if aspect_ratio is not None: + out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio)) + + cn_hint = kwargs.get("cn_hint", None) + if cn_hint is not None: + out["cn_hint"] = comfy.conds.CONDRegular(cn_hint) + + return out + + +def load_pixart(model_path, model_conf=None): + state_dict = comfy.utils.load_torch_file(model_path) + state_dict = state_dict.get("model", state_dict) + + # prefix + for prefix in ["model.diffusion_model.", ]: + if any(True for x in state_dict if x.startswith(prefix)): + state_dict = {k[len(prefix):]: v for k, v in state_dict.items()} + + # diffusers + if "adaln_single.linear.weight" in state_dict: + state_dict = convert_state_dict(state_dict) # Diffusers + + # guess auto config + if model_conf is None: + model_conf = guess_pixart_config(state_dict) + + parameters = comfy.utils.calculate_parameters(state_dict) + unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = comfy.model_management.get_torch_device() + offload_device = comfy.model_management.unet_offload_device() + + # ignore fp8/etc and use directly for now + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) + if manual_cast_dtype: + print(f"PixArt: falling back to {manual_cast_dtype}") + unet_dtype = manual_cast_dtype + + model_conf = EXM_PixArt(model_conf) # convert to object + model = EXM_PixArt_Model( # same as comfy.model_base.BaseModel + model_conf, + model_type=comfy.model_base.ModelType.EPS, + device=model_management.get_torch_device() + ) + + if model_conf.model_target == "PixArtMS": + from .models.PixArtMS import PixArtMS + model.diffusion_model = PixArtMS(**model_conf.unet_config) + elif model_conf.model_target == "PixArt": + from .models.PixArt import PixArt + model.diffusion_model = PixArt(**model_conf.unet_config) + elif model_conf.model_target == "PixArtMSSigma": + from .models.PixArtMS import PixArtMS + model.diffusion_model = PixArtMS(**model_conf.unet_config) + model.latent_format = comfy.latent_formats.SDXL() + elif model_conf.model_target == "ControlPixArtMSHalf": + from .models.PixArtMS import PixArtMS + from .models.pixart_controlnet import ControlPixArtMSHalf + model.diffusion_model = PixArtMS(**model_conf.unet_config) + model.diffusion_model = ControlPixArtMSHalf(model.diffusion_model) + elif model_conf.model_target == "ControlPixArtHalf": + from .models.PixArt import PixArt + from .models.pixart_controlnet import ControlPixArtHalf + model.diffusion_model = PixArt(**model_conf.unet_config) + model.diffusion_model = ControlPixArtHalf(model.diffusion_model) + else: + raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'") + + m, u = model.diffusion_model.load_state_dict(state_dict, strict=False) + if len(m) > 0: print("Missing UNET keys", m) + if len(u) > 0: print("Leftover UNET keys", u) + model.diffusion_model.dtype = unet_dtype + model.diffusion_model.eval() + model.diffusion_model.to(unet_dtype) + + model_patcher = comfy.model_patcher.ModelPatcher( + model, + load_device=load_device, + offload_device=offload_device, + ) + return model_patcher + + +def guess_pixart_config(sd): + """ + Guess config based on converted state dict. + """ + # Shared settings based on DiT_XL_2 - could be enumerated + config = { + "num_heads": 16, # get from attention + "patch_size": 2, # final layer I guess? + "hidden_size": 1152, # pos_embed.shape[2] + } + config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28 + + try: + # this is not present in the diffusers version for sigma? + config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0] + except KeyError: + # need better logic to guess this + config["model_max_length"] = 300 + + if "pos_embed" in sd: + config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"] + config["pe_interpolation"] = config["input_size"] // (512 // 8) # dumb guess + + target_arch = "PixArtMS" + if config["model_max_length"] == 300: + # Sigma + target_arch = "PixArtMSSigma" + config["micro_condition"] = False + if "input_size" not in config: + # The diffusers weights for 1K/2K are exactly the same...? + # replace patch embed logic with HyDiT? + print(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!") + config["input_size"] = 1024 // 8 + else: + # Alpha + if "csize_embedder.mlp.0.weight" in sd: + # MS (microconds) + target_arch = "PixArtMS" + config["micro_condition"] = True + if "input_size" not in config: + config["input_size"] = 1024 // 8 + config["pe_interpolation"] = 2 + else: + # PixArt + target_arch = "PixArt" + if "input_size" not in config: + config["input_size"] = 512 // 8 + config["pe_interpolation"] = 1 + + print("PixArt guessed config:", target_arch, config) + return { + "target": target_arch, + "unet_config": config, + "sampling_settings": { + "beta_schedule": "sqrt_linear", + "linear_start": 0.0001, + "linear_end": 0.02, + "timesteps": 1000, + } + } + +# lora +class EXM_PixArt_ModelPatcher(comfy.model_patcher.ModelPatcher): + def calculate_weight(self, patches, weight, key): + """ + This is almost the same as the comfy function, but stripped down to just the LoRA patch code. + The problem with the original code is the q/k/v keys being combined into one for the attention. + In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv). + This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict. + The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead. + + This is the best thing I could think of that would fix that, but it's very fragile. + - Check key shape to determine if it needs the fallback logic + - Cut the input into parts based on the shape (undoing the torch.cat) + - Do the matrix multiplication logic + - Recombine them to match the expected shape + """ + for p in patches: + alpha = p[0] + v = p[1] + strength_model = p[2] + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (self.calculate_weight(v[1:], v[0].clone(), key),) + + if len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "lora": + mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + try: + mat1 = mat1.flatten(start_dim=1) + mat2 = mat2.flatten(start_dim=1) + + ch1 = mat1.shape[0] // mat2.shape[1] + ch2 = mat2.shape[0] // mat1.shape[1] + ### Fallback logic for shape mismatch ### + if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0] / mat2.shape[1]) % 1 == 0: + mat1 = mat1.chunk(ch1, dim=0) + mat2 = mat2.chunk(ch1, dim=0) + weight += torch.cat( + [alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)], + dim=0, + ).reshape(weight.shape).type(weight.dtype) + else: + weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + return weight + + def clone(self): + n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, + weight_inplace_update=self.weight_inplace_update) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + + n.object_patches = self.object_patches.copy() + n.model_options = copy.deepcopy(self.model_options) + n.model_keys = self.model_keys + return n + + +def replace_model_patcher(model): + n = EXM_PixArt_ModelPatcher( + model=model.model, + size=model.size, + load_device=model.load_device, + offload_device=model.offload_device, + current_device=model.current_device, + weight_inplace_update=model.weight_inplace_update, + ) + n.patches = {} + for k in model.patches: + n.patches[k] = model.patches[k][:] + + n.object_patches = model.object_patches.copy() + n.model_options = copy.deepcopy(model.model_options) + return n + + +def find_peft_alpha(path): + def load_json(json_path): + with open(json_path) as f: + data = json.load(f) + alpha = data.get("lora_alpha") + alpha = alpha or data.get("alpha") + if not alpha: + print(" Found config but `lora_alpha` is missing!") + else: + print(f" Found config at {json_path} [alpha:{alpha}]") + return alpha + + # For some weird reason peft doesn't include the alpha in the actual model + print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...") + files = [ + f"{os.path.splitext(path)[0]}.json", + f"{os.path.splitext(path)[0]}.config.json", + os.path.join(os.path.dirname(path), "adapter_config.json"), + ] + for file in files: + if os.path.isfile(file): + return load_json(file) + + print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.") + return 8.0 + + +def load_pixart_lora(model, lora, lora_path, strength): + k_back = lambda x: x.replace(".lora_up.weight", "") + # need to convert the actual weights for this to work. + if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")): + lora = convert_lora_state_dict(lora, peft=True) + alpha = find_peft_alpha(lora_path) + lora.update({f"{k_back(x)}.alpha": torch.tensor(alpha) for x in lora.keys() if "lora_up" in x}) + else: # OneTrainer + lora = convert_lora_state_dict(lora, peft=False) + + key_map = {k_back(x): f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake + + loaded = comfy.lora.load_lora(lora, key_map) + if model is not None: + # switch to custom model patcher when using LoRAs + if isinstance(model, EXM_PixArt_ModelPatcher): + new_modelpatcher = model.clone() + else: + new_modelpatcher = replace_model_patcher(model) + k = new_modelpatcher.add_patches(loaded, strength) + else: + k = () + new_modelpatcher = None + + k = set(k) + for x in loaded: + if (x not in k): + print("NOT LOADED", x) + + return new_modelpatcher \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArt.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArt.py new file mode 100644 index 00000000..4d6cf93b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArt.py @@ -0,0 +1,250 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- +import math +import torch +import torch.nn as nn +import os +import numpy as np +from timm.models.layers import DropPath +from timm.models.vision_transformer import PatchEmbed, Mlp + + +from .utils import auto_grad_checkpoint, to_2tuple +from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer + + +class PixArtBlock(nn.Module): + """ + A PixArt block with adaptive layer norm (adaLN-single) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0, input_size=None, sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = AttentionKVCompress( + hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, + qk_norm=qk_norm, **block_kwargs + ) + self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + # to be compatible with lower version pytorch + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) + self.sampling = sampling + self.sr_ratio = sr_ratio + + def forward(self, x, y, t, mask=None, **kwargs): + B, N, C = x.shape + + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1) + x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C)) + x = x + self.cross_attn(x, y, mask) + x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) + + return x + + +### Core PixArt Model ### +class PixArt(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + def __init__( + self, + input_size=32, + patch_size=2, + in_channels=4, + hidden_size=1152, + depth=28, + num_heads=16, + mlp_ratio=4.0, + class_dropout_prob=0.1, + pred_sigma=True, + drop_path: float = 0., + caption_channels=4096, + pe_interpolation=1.0, + pe_precision=None, + config=None, + model_max_length=120, + qk_norm=False, + kv_compress_config=None, + **kwargs, + ): + super().__init__() + self.pred_sigma = pred_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if pred_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.pe_interpolation = pe_interpolation + self.pe_precision = pe_precision + self.depth = depth + + self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True) + self.t_embedder = TimestepEmbedder(hidden_size) + num_patches = self.x_embedder.num_patches + self.base_size = input_size // self.patch_size + # Will use fixed sin-cos embedding: + self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) + + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.t_block = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + self.y_embedder = CaptionEmbedder( + in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, + act_layer=approx_gelu, token_num=model_max_length + ) + drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule + self.kv_compress_config = kv_compress_config + if kv_compress_config is None: + self.kv_compress_config = { + 'sampling': None, + 'scale_factor': 1, + 'kv_compress_layer': [], + } + self.blocks = nn.ModuleList([ + PixArtBlock( + hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], + input_size=(input_size // patch_size, input_size // patch_size), + sampling=self.kv_compress_config['sampling'], + sr_ratio=int( + self.kv_compress_config['scale_factor'] + ) if i in self.kv_compress_config['kv_compress_layer'] else 1, + qk_norm=qk_norm, + ) + for i in range(depth) + ]) + self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) + + def forward_raw(self, x, t, y, mask=None, data_info=None): + """ + Original forward pass of PixArt. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, 1, 120, C) tensor of class labels + """ + x = x.to(self.dtype) + timestep = t.to(self.dtype) + y = y.to(self.dtype) + pos_embed = self.pos_embed.to(self.dtype) + self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size + x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 + t = self.t_embedder(timestep.to(x.dtype)) # (N, D) + t0 = self.t_block(t) + y = self.y_embedder(y, self.training) # (N, 1, L, D) + if mask is not None: + if mask.shape[0] != y.shape[0]: + mask = mask.repeat(y.shape[0] // mask.shape[0], 1) + mask = mask.squeeze(1).squeeze(1) + y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) + y_lens = mask.sum(dim=1).tolist() + else: + y_lens = [y.shape[2]] * y.shape[0] + y = y.squeeze(1).view(1, -1, x.shape[-1]) + for block in self.blocks: + x = auto_grad_checkpoint(block, x, y, t0, y_lens) # (N, T, D) #support grad checkpoint + x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) + x = self.unpatchify(x) # (N, out_channels, H, W) + return x + + def forward(self, x, timesteps, context, y=None, **kwargs): + """ + Forward pass that adapts comfy input to original forward function + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + timesteps: (N,) tensor of diffusion timesteps + context: (N, 1, 120, C) conditioning + y: extra conditioning. + """ + ## Still accepts the input w/o that dim but returns garbage + if len(context.shape) == 3: + context = context.unsqueeze(1) + + ## run original forward pass + out = self.forward_raw( + x = x.to(self.dtype), + t = timesteps.to(self.dtype), + y = context.to(self.dtype), + ) + + ## only return EPS + out = out.to(torch.float) + eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] + return eps + + def unpatchify(self, x): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) + return imgs + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, pe_interpolation=1.0, base_size=16): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + if isinstance(grid_size, int): + grid_size = to_2tuple(grid_size) + grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0]/base_size) / pe_interpolation + grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1]/base_size) / pe_interpolation + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + grid = grid.reshape([2, 1, grid_size[1], grid_size[0]]) + + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + return pos_embed.astype(np.float32) + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000 ** omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArtMS.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArtMS.py new file mode 100644 index 00000000..34ada90e --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArtMS.py @@ -0,0 +1,273 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- +import torch +import torch.nn as nn +from tqdm import tqdm +from timm.models.layers import DropPath +from timm.models.vision_transformer import Mlp + +from .utils import auto_grad_checkpoint, to_2tuple +from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder +from .PixArt import PixArt, get_2d_sincos_pos_embed + + +class PatchEmbed(nn.Module): + """ + 2D Image to Patch Embedding + """ + def __init__( + self, + patch_size=16, + in_chans=3, + embed_dim=768, + norm_layer=None, + flatten=True, + bias=True, + ): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + self.flatten = flatten + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x + + +class PixArtMSBlock(nn.Module): + """ + A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, + sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs): + super().__init__() + self.hidden_size = hidden_size + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = AttentionKVCompress( + hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, + qk_norm=qk_norm, **block_kwargs + ) + self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + # to be compatible with lower version pytorch + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) + + def forward(self, x, y, t, mask=None, HW=None, **kwargs): + B, N, C = x.shape + + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1) + x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) + x = x + self.cross_attn(x, y, mask) + x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) + + return x + + +### Core PixArt Model ### +class PixArtMS(PixArt): + """ + Diffusion model with a Transformer backbone. + """ + def __init__( + self, + input_size=32, + patch_size=2, + in_channels=4, + hidden_size=1152, + depth=28, + num_heads=16, + mlp_ratio=4.0, + class_dropout_prob=0.1, + learn_sigma=True, + pred_sigma=True, + drop_path: float = 0., + caption_channels=4096, + pe_interpolation=None, + pe_precision=None, + config=None, + model_max_length=120, + micro_condition=True, + qk_norm=False, + kv_compress_config=None, + **kwargs, + ): + super().__init__( + input_size=input_size, + patch_size=patch_size, + in_channels=in_channels, + hidden_size=hidden_size, + depth=depth, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + class_dropout_prob=class_dropout_prob, + learn_sigma=learn_sigma, + pred_sigma=pred_sigma, + drop_path=drop_path, + pe_interpolation=pe_interpolation, + config=config, + model_max_length=model_max_length, + qk_norm=qk_norm, + kv_compress_config=kv_compress_config, + **kwargs, + ) + self.dtype = torch.get_default_dtype() + self.h = self.w = 0 + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.t_block = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + self.x_embedder = PatchEmbed(patch_size, in_channels, hidden_size, bias=True) + self.y_embedder = CaptionEmbedder(in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length) + self.micro_conditioning = micro_condition + if self.micro_conditioning: + self.csize_embedder = SizeEmbedder(hidden_size//3) # c_size embed + self.ar_embedder = SizeEmbedder(hidden_size//3) # aspect ratio embed + drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule + if kv_compress_config is None: + kv_compress_config = { + 'sampling': None, + 'scale_factor': 1, + 'kv_compress_layer': [], + } + self.blocks = nn.ModuleList([ + PixArtMSBlock( + hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], + input_size=(input_size // patch_size, input_size // patch_size), + sampling=kv_compress_config['sampling'], + sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, + qk_norm=qk_norm, + ) + for i in range(depth) + ]) + self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) + + def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs): + """ + Original forward pass of PixArt. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, 1, 120, C) tensor of class labels + """ + bs = x.shape[0] + x = x.to(self.dtype) + timestep = t.to(self.dtype) + y = y.to(self.dtype) + + pe_interpolation = self.pe_interpolation + if pe_interpolation is None or self.pe_precision is not None: + # calculate pe_interpolation on-the-fly + pe_interpolation = round((x.shape[-1]+x.shape[-2])/2.0 / (512/8.0), self.pe_precision or 0) + + self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size + pos_embed = torch.from_numpy( + get_2d_sincos_pos_embed( + self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation, + base_size=self.base_size + ) + ).unsqueeze(0).to(device=x.device, dtype=self.dtype) + + x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 + t = self.t_embedder(timestep) # (N, D) + + if self.micro_conditioning: + c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype) + csize = self.csize_embedder(c_size, bs) # (N, D) + ar = self.ar_embedder(ar, bs) # (N, D) + t = t + torch.cat([csize, ar], dim=1) + + t0 = self.t_block(t) + y = self.y_embedder(y, self.training) # (N, D) + + if mask is not None: + if mask.shape[0] != y.shape[0]: + mask = mask.repeat(y.shape[0] // mask.shape[0], 1) + mask = mask.squeeze(1).squeeze(1) + y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) + y_lens = mask.sum(dim=1).tolist() + else: + y_lens = [y.shape[2]] * y.shape[0] + y = y.squeeze(1).view(1, -1, x.shape[-1]) + for block in self.blocks: + x = auto_grad_checkpoint(block, x, y, t0, y_lens, (self.h, self.w), **kwargs) # (N, T, D) #support grad checkpoint + + x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) + x = self.unpatchify(x) # (N, out_channels, H, W) + + return x + + def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs): + """ + Forward pass that adapts comfy input to original forward function + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + timesteps: (N,) tensor of diffusion timesteps + context: (N, 1, 120, C) conditioning + img_hw: height|width conditioning + aspect_ratio: aspect ratio conditioning + """ + ## size/ar from cond with fallback based on the latent image shape. + bs = x.shape[0] + data_info = {} + if img_hw is None: + data_info["img_hw"] = torch.tensor( + [[x.shape[2]*8, x.shape[3]*8]], + dtype=self.dtype, + device=x.device + ).repeat(bs, 1) + else: + data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device) + if aspect_ratio is None or True: + data_info["aspect_ratio"] = torch.tensor( + [[x.shape[2]/x.shape[3]]], + dtype=self.dtype, + device=x.device + ).repeat(bs, 1) + else: + data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device) + + ## Still accepts the input w/o that dim but returns garbage + if len(context.shape) == 3: + context = context.unsqueeze(1) + + ## run original forward pass + out = self.forward_raw( + x = x.to(self.dtype), + t = timesteps.to(self.dtype), + y = context.to(self.dtype), + data_info=data_info, + ) + + ## only return EPS + out = out.to(torch.float) + eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] + return eps + + def unpatchify(self, x): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + assert self.h * self.w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p)) + return imgs diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArt_blocks.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArt_blocks.py new file mode 100644 index 00000000..52cf847c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArt_blocks.py @@ -0,0 +1,477 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from timm.models.vision_transformer import Mlp, Attention as Attention_ +from einops import rearrange + +from comfy import model_management +if model_management.xformers_enabled(): + import xformers + import xformers.ops +else: + print(""" +######################################## + PixArt: Not using xformers! + Expect images to be non-deterministic! + Batch sizes > 1 are most likely broken +######################################## +""") + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + +def t2i_modulate(x, shift, scale): + return x * (1 + scale) + shift + +class MultiHeadCrossAttention(nn.Module): + def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwargs): + super(MultiHeadCrossAttention, self).__init__() + assert d_model % num_heads == 0, "d_model must be divisible by num_heads" + + self.d_model = d_model + self.num_heads = num_heads + self.head_dim = d_model // num_heads + + self.q_linear = nn.Linear(d_model, d_model) + self.kv_linear = nn.Linear(d_model, d_model*2) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, cond, mask=None): + # query/value: img tokens; key: condition; mask: if padding tokens + B, N, C = x.shape + + q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim) + kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim) + k, v = kv.unbind(2) + + if model_management.xformers_enabled(): + attn_bias = None + if mask is not None: + attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask) + x = xformers.ops.memory_efficient_attention( + q, k, v, + p=self.attn_drop.p, + attn_bias=attn_bias + ) + else: + q, k, v = map(lambda t: t.permute(0, 2, 1, 3),(q, k, v),) + attn_mask = None + if mask is not None and len(mask) > 1: + + # Create equivalent of xformer diagonal block mask, still only correct for square masks + # But depth doesn't matter as tensors can expand in that dimension + attn_mask_template = torch.ones( + [q.shape[2] // B, mask[0]], + dtype=torch.bool, + device=q.device + ) + attn_mask = torch.block_diag(attn_mask_template) + + # create a mask on the diagonal for each mask in the batch + for n in range(B - 1): + attn_mask = torch.block_diag(attn_mask, attn_mask_template) + + x = torch.nn.functional.scaled_dot_product_attention( + q, k, v, + attn_mask=attn_mask, + dropout_p=self.attn_drop.p + ).permute(0, 2, 1, 3).contiguous() + x = x.view(B, -1, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class AttentionKVCompress(Attention_): + """Multi-head Attention block with KV token compression and qk norm.""" + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + sampling='conv', + sr_ratio=1, + qk_norm=False, + **block_kwargs, + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool: If True, add a learnable bias to query, key, value. + """ + super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs) + + self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every'] + self.sr_ratio = sr_ratio + if sr_ratio > 1 and sampling == 'conv': + # Avg Conv Init. + self.sr = nn.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio) + self.sr.weight.data.fill_(1/sr_ratio**2) + self.sr.bias.data.zero_() + self.norm = nn.LayerNorm(dim) + if qk_norm: + self.q_norm = nn.LayerNorm(dim) + self.k_norm = nn.LayerNorm(dim) + else: + self.q_norm = nn.Identity() + self.k_norm = nn.Identity() + + def downsample_2d(self, tensor, H, W, scale_factor, sampling=None): + if sampling is None or scale_factor == 1: + return tensor + B, N, C = tensor.shape + + if sampling == 'uniform_every': + return tensor[:, ::scale_factor], int(N // scale_factor) + + tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2) + new_H, new_W = int(H / scale_factor), int(W / scale_factor) + new_N = new_H * new_W + + if sampling == 'ave': + tensor = F.interpolate( + tensor, scale_factor=1 / scale_factor, mode='nearest' + ).permute(0, 2, 3, 1) + elif sampling == 'uniform': + tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1) + elif sampling == 'conv': + tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1) + tensor = self.norm(tensor) + else: + raise ValueError + + return tensor.reshape(B, new_N, C).contiguous(), new_N + + def forward(self, x, mask=None, HW=None, block_id=None): + B, N, C = x.shape # 2 4096 1152 + new_N = N + if HW is None: + H = W = int(N ** 0.5) + else: + H, W = HW + qkv = self.qkv(x).reshape(B, N, 3, C) + + q, k, v = qkv.unbind(2) + dtype = q.dtype + q = self.q_norm(q) + k = self.k_norm(k) + + # KV compression + if self.sr_ratio > 1: + k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling) + v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling) + + q = q.reshape(B, N, self.num_heads, C // self.num_heads).to(dtype) + k = k.reshape(B, new_N, self.num_heads, C // self.num_heads).to(dtype) + v = v.reshape(B, new_N, self.num_heads, C // self.num_heads).to(dtype) + + attn_bias = None + if mask is not None: + attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device) + attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf')) + # Switch between torch / xformers attention + if model_management.xformers_enabled(): + x = xformers.ops.memory_efficient_attention( + q, k, v, + p=self.attn_drop.p, + attn_bias=attn_bias + ) + else: + q, k, v = map(lambda t: t.transpose(1, 2),(q, k, v),) + x = torch.nn.functional.scaled_dot_product_attention( + q, k, v, + dropout_p=self.attn_drop.p, + attn_mask=attn_bias + ).transpose(1, 2).contiguous() + x = x.view(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +################################################################################# +# AMP attention with fp32 softmax to fix loss NaN problem during training # +################################################################################# +class Attention(Attention_): + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) + use_fp32_attention = getattr(self, 'fp32_attention', False) + if use_fp32_attention: + q, k = q.float(), k.float() + with torch.cuda.amp.autocast(enabled=not use_fp32_attention): + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class FinalLayer(nn.Module): + """ + The final layer of PixArt. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class T2IFinalLayer(nn.Module): + """ + The final layer of PixArt. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5) + self.out_channels = out_channels + + def forward(self, x, t): + shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1) + x = t2i_modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class MaskFinalLayer(nn.Module): + """ + The final layer of PixArt. + """ + + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True) + ) + def forward(self, x, t): + shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class DecoderLayer(nn.Module): + """ + The final layer of PixArt. + """ + + def __init__(self, hidden_size, decoder_hidden_size): + super().__init__() + self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + def forward(self, x, t): + shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) + x = modulate(self.norm_decoder(x), shift, scale) + x = self.linear(x) + return x + + +################################################################################# +# Embedding Layers for Timesteps and Class Labels # +################################################################################# +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(t.dtype)) + return t_emb + + +class SizeEmbedder(TimestepEmbedder): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size) + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + self.outdim = hidden_size + + def forward(self, s, bs): + if s.ndim == 1: + s = s[:, None] + assert s.ndim == 2 + if s.shape[0] != bs: + s = s.repeat(bs//s.shape[0], 1) + assert s.shape[0] == bs + b, dims = s.shape[0], s.shape[1] + s = rearrange(s, "b d -> (b d)") + s_freq = self.timestep_embedding(s, self.frequency_embedding_size) + s_emb = self.mlp(s_freq.to(s.dtype)) + s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim) + return s_emb + + +class LabelEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = dropout_prob > 0 + self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + + +class CaptionEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + + def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120): + super().__init__() + self.y_proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0) + self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5)) + self.uncond_prob = uncond_prob + + def token_drop(self, caption, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob + else: + drop_ids = force_drop_ids == 1 + caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) + return caption + + def forward(self, caption, train, force_drop_ids=None): + if train: + assert caption.shape[2:] == self.y_embedding.shape + use_dropout = self.uncond_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + caption = self.token_drop(caption, force_drop_ids) + caption = self.y_proj(caption) + return caption + + +class CaptionEmbedderDoubleBr(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + + def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120): + super().__init__() + self.proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0) + self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5) + self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5) + self.uncond_prob = uncond_prob + + def token_drop(self, global_caption, caption, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob + else: + drop_ids = force_drop_ids == 1 + global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption) + caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) + return global_caption, caption + + def forward(self, caption, train, force_drop_ids=None): + assert caption.shape[2: ] == self.y_embedding.shape + global_caption = caption.mean(dim=2).squeeze() + use_dropout = self.uncond_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids) + y_embed = self.proj(global_caption) + return y_embed, caption diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/pixart_controlnet.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/pixart_controlnet.py new file mode 100644 index 00000000..37fa4c1d --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/pixart_controlnet.py @@ -0,0 +1,312 @@ +import re +import torch +import torch.nn as nn + +from copy import deepcopy +from torch import Tensor +from torch.nn import Module, Linear, init +from typing import Any, Mapping + +from .PixArt import PixArt, get_2d_sincos_pos_embed +from .PixArtMS import PixArtMSBlock, PixArtMS +from .utils import auto_grad_checkpoint + +# The implementation of ControlNet-Half architrecture +# https://github.com/lllyasviel/ControlNet/discussions/188 +class ControlT2IDitBlockHalf(Module): + def __init__(self, base_block: PixArtMSBlock, block_index: 0) -> None: + super().__init__() + self.copied_block = deepcopy(base_block) + self.block_index = block_index + + for p in self.copied_block.parameters(): + p.requires_grad_(True) + + self.copied_block.load_state_dict(base_block.state_dict()) + self.copied_block.train() + + self.hidden_size = hidden_size = base_block.hidden_size + if self.block_index == 0: + self.before_proj = Linear(hidden_size, hidden_size) + init.zeros_(self.before_proj.weight) + init.zeros_(self.before_proj.bias) + self.after_proj = Linear(hidden_size, hidden_size) + init.zeros_(self.after_proj.weight) + init.zeros_(self.after_proj.bias) + + def forward(self, x, y, t, mask=None, c=None): + + if self.block_index == 0: + # the first block + c = self.before_proj(c) + c = self.copied_block(x + c, y, t, mask) + c_skip = self.after_proj(c) + else: + # load from previous c and produce the c for skip connection + c = self.copied_block(c, y, t, mask) + c_skip = self.after_proj(c) + + return c, c_skip + + +# The implementation of ControlPixArtHalf net +class ControlPixArtHalf(Module): + # only support single res model + def __init__(self, base_model: PixArt, copy_blocks_num: int = 13) -> None: + super().__init__() + self.dtype = torch.get_default_dtype() + self.base_model = base_model.eval() + self.controlnet = [] + self.copy_blocks_num = copy_blocks_num + self.total_blocks_num = len(base_model.blocks) + for p in self.base_model.parameters(): + p.requires_grad_(False) + + # Copy first copy_blocks_num block + for i in range(copy_blocks_num): + self.controlnet.append(ControlT2IDitBlockHalf(base_model.blocks[i], i)) + self.controlnet = nn.ModuleList(self.controlnet) + + def __getattr__(self, name: str) -> Tensor or Module: + if name in ['forward', 'forward_with_dpmsolver', 'forward_with_cfg', 'forward_c', 'load_state_dict']: + return self.__dict__[name] + elif name in ['base_model', 'controlnet']: + return super().__getattr__(name) + else: + return getattr(self.base_model, name) + + def forward_c(self, c): + self.h, self.w = c.shape[-2]//self.patch_size, c.shape[-1]//self.patch_size + pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(c.device).to(self.dtype) + return self.x_embedder(c) + pos_embed if c is not None else c + + # def forward(self, x, t, c, **kwargs): + # return self.base_model(x, t, c=self.forward_c(c), **kwargs) + def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs): + # modify the original PixArtMS forward function + if c is not None: + c = c.to(self.dtype) + c = self.forward_c(c) + """ + Forward pass of PixArt. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, 1, 120, C) tensor of class labels + """ + x = x.to(self.dtype) + timestep = timestep.to(self.dtype) + y = y.to(self.dtype) + pos_embed = self.pos_embed.to(self.dtype) + self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size + x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 + t = self.t_embedder(timestep.to(x.dtype)) # (N, D) + t0 = self.t_block(t) + y = self.y_embedder(y, self.training) # (N, 1, L, D) + if mask is not None: + if mask.shape[0] != y.shape[0]: + mask = mask.repeat(y.shape[0] // mask.shape[0], 1) + mask = mask.squeeze(1).squeeze(1) + y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) + y_lens = mask.sum(dim=1).tolist() + else: + y_lens = [y.shape[2]] * y.shape[0] + y = y.squeeze(1).view(1, -1, x.shape[-1]) + + # define the first layer + x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint + + if c is not None: + # update c + for index in range(1, self.copy_blocks_num + 1): + c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs) + x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs) + + # update x + for index in range(self.copy_blocks_num + 1, self.total_blocks_num): + x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) + else: + for index in range(1, self.total_blocks_num): + x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) + + x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) + x = self.unpatchify(x) # (N, out_channels, H, W) + return x + + def forward(self, x, timesteps, context, cn_hint=None, **kwargs): + """ + Forward pass that adapts comfy input to original forward function + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + timesteps: (N,) tensor of diffusion timesteps + context: (N, 1, 120, C) conditioning + cn_hint: controlnet hint + """ + ## Still accepts the input w/o that dim but returns garbage + if len(context.shape) == 3: + context = context.unsqueeze(1) + + ## run original forward pass + out = self.forward_raw( + x = x.to(self.dtype), + timestep = timesteps.to(self.dtype), + y = context.to(self.dtype), + c = cn_hint, + ) + + ## only return EPS + out = out.to(torch.float) + eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] + return eps + + def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs): + model_out = self.forward_raw(x, t, y, data_info=data_info, c=c, **kwargs) + return model_out.chunk(2, dim=1)[0] + + # def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs): + # return self.base_model.forward_with_dpmsolver(x, t, y, data_info=data_info, c=self.forward_c(c), **kwargs) + + def forward_with_cfg(self, x, t, y, cfg_scale, data_info, c, **kwargs): + return self.base_model.forward_with_cfg(x, t, y, cfg_scale, data_info, c=self.forward_c(c), **kwargs) + + def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True): + if all((k.startswith('base_model') or k.startswith('controlnet')) for k in state_dict.keys()): + return super().load_state_dict(state_dict, strict) + else: + new_key = {} + for k in state_dict.keys(): + new_key[k] = re.sub(r"(blocks\.\d+)(.*)", r"\1.base_block\2", k) + for k, v in new_key.items(): + if k != v: + print(f"replace {k} to {v}") + state_dict[v] = state_dict.pop(k) + + return self.base_model.load_state_dict(state_dict, strict) + + def unpatchify(self, x): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + assert self.h * self.w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p)) + return imgs + + # @property + # def dtype(self): + ## θΏ”ε›žζ¨‘εž‹ε‚ζ•°ηš„ζ•°ζη±»εž‹ + # return next(self.parameters()).dtype + + +# The implementation for PixArtMS_Half + 1024 resolution +class ControlPixArtMSHalf(ControlPixArtHalf): + # support multi-scale res model (multi-scale model can also be applied to single reso training & inference) + def __init__(self, base_model: PixArtMS, copy_blocks_num: int = 13) -> None: + super().__init__(base_model=base_model, copy_blocks_num=copy_blocks_num) + + def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs): + # modify the original PixArtMS forward function + """ + Forward pass of PixArt. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, 1, 120, C) tensor of class labels + """ + if c is not None: + c = c.to(self.dtype) + c = self.forward_c(c) + bs = x.shape[0] + x = x.to(self.dtype) + timestep = timestep.to(self.dtype) + y = y.to(self.dtype) + c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype) + self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size + + pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(x.device).to(self.dtype) + x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 + t = self.t_embedder(timestep) # (N, D) + csize = self.csize_embedder(c_size, bs) # (N, D) + ar = self.ar_embedder(ar, bs) # (N, D) + t = t + torch.cat([csize, ar], dim=1) + t0 = self.t_block(t) + y = self.y_embedder(y, self.training) # (N, D) + if mask is not None: + if mask.shape[0] != y.shape[0]: + mask = mask.repeat(y.shape[0] // mask.shape[0], 1) + mask = mask.squeeze(1).squeeze(1) + y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) + y_lens = mask.sum(dim=1).tolist() + else: + y_lens = [y.shape[2]] * y.shape[0] + y = y.squeeze(1).view(1, -1, x.shape[-1]) + + # define the first layer + x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint + + if c is not None: + # update c + for index in range(1, self.copy_blocks_num + 1): + c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs) + x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs) + + # update x + for index in range(self.copy_blocks_num + 1, self.total_blocks_num): + x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) + else: + for index in range(1, self.total_blocks_num): + x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) + + x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) + x = self.unpatchify(x) # (N, out_channels, H, W) + return x + + def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, cn_hint=None, **kwargs): + """ + Forward pass that adapts comfy input to original forward function + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + timesteps: (N,) tensor of diffusion timesteps + context: (N, 1, 120, C) conditioning + img_hw: height|width conditioning + aspect_ratio: aspect ratio conditioning + cn_hint: controlnet hint + """ + ## size/ar from cond with fallback based on the latent image shape. + bs = x.shape[0] + data_info = {} + if img_hw is None: + data_info["img_hw"] = torch.tensor( + [[x.shape[2]*8, x.shape[3]*8]], + dtype=self.dtype, + device=x.device + ).repeat(bs, 1) + else: + data_info["img_hw"] = img_hw.to(x.dtype) + if aspect_ratio is None or True: + data_info["aspect_ratio"] = torch.tensor( + [[x.shape[2]/x.shape[3]]], + dtype=self.dtype, + device=x.device + ).repeat(bs, 1) + else: + data_info["aspect_ratio"] = aspect_ratio.to(x.dtype) + + ## Still accepts the input w/o that dim but returns garbage + if len(context.shape) == 3: + context = context.unsqueeze(1) + + ## run original forward pass + out = self.forward_raw( + x = x.to(self.dtype), + timestep = timesteps.to(self.dtype), + y = context.to(self.dtype), + c = cn_hint, + data_info=data_info, + ) + + ## only return EPS + out = out.to(torch.float) + eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] + return eps diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/utils.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/utils.py new file mode 100644 index 00000000..9f776214 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/utils.py @@ -0,0 +1,122 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.checkpoint import checkpoint, checkpoint_sequential +from collections.abc import Iterable +from itertools import repeat + +def _ntuple(n): + def parse(x): + if isinstance(x, Iterable) and not isinstance(x, str): + return x + return tuple(repeat(x, n)) + return parse + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) + +def set_grad_checkpoint(model, use_fp32_attention=False, gc_step=1): + assert isinstance(model, nn.Module) + + def set_attr(module): + module.grad_checkpointing = True + module.fp32_attention = use_fp32_attention + module.grad_checkpointing_step = gc_step + model.apply(set_attr) + +def auto_grad_checkpoint(module, *args, **kwargs): + if getattr(module, 'grad_checkpointing', False): + if isinstance(module, Iterable): + gc_step = module[0].grad_checkpointing_step + return checkpoint_sequential(module, gc_step, *args, **kwargs) + else: + return checkpoint(module, *args, **kwargs) + return module(*args, **kwargs) + +def checkpoint_sequential(functions, step, input, *args, **kwargs): + + # Hack for keyword-only parameter in a python 2.7-compliant way + preserve = kwargs.pop('preserve_rng_state', True) + if kwargs: + raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)) + + def run_function(start, end, functions): + def forward(input): + for j in range(start, end + 1): + input = functions[j](input, *args) + return input + return forward + + if isinstance(functions, torch.nn.Sequential): + functions = list(functions.children()) + + # the last chunk has to be non-volatile + end = -1 + segment = len(functions) // step + for start in range(0, step * (segment - 1), step): + end = start + step - 1 + input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve) + return run_function(end + 1, len(functions) - 1, functions)(input) + +def get_rel_pos(q_size, k_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + +def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + attn (Tensor): attention map. + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/utils.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/utils.py new file mode 100644 index 00000000..22bf8e9e --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/dit/utils.py @@ -0,0 +1,38 @@ +import torch +from comfy import model_management + +def string_to_dtype(s="none", mode=None): + s = s.lower().strip() + if s in ["default", "as-is"]: + return None + elif s in ["auto", "auto (comfy)"]: + if mode == "vae": + return model_management.vae_device() + elif mode == "text_encoder": + return model_management.text_encoder_dtype() + elif mode == "unet": + return model_management.unet_dtype() + else: + raise NotImplementedError(f"Unknown dtype mode '{mode}'") + elif s in ["none", "auto (hf)", "auto (hf/bnb)"]: + return None + elif s in ["fp32", "float32", "float"]: + return torch.float32 + elif s in ["bf16", "bfloat16"]: + return torch.bfloat16 + elif s in ["fp16", "float16", "half"]: + return torch.float16 + elif "fp8" in s or "float8" in s: + if "e5m2" in s: + return torch.float8_e5m2 + elif "e4m3" in s: + return torch.float8_e4m3fn + else: + raise NotImplementedError(f"Unknown 8bit dtype '{s}'") + elif "bnb" in s: + assert s in ["bnb8bit", "bnb4bit"], f"Unknown bnb mode '{s}'" + return s + elif s is None: + return None + else: + raise NotImplementedError(f"Unknown dtype '{s}'") \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/fooocus/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/fooocus/__init__.py new file mode 100644 index 00000000..4ab3c199 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/fooocus/__init__.py @@ -0,0 +1,139 @@ +#credit to Acly for this module +#from https://github.com/Acly/comfyui-inpaint-nodes +import torch +import torch.nn.functional as F +import comfy +from comfy.model_base import BaseModel +from comfy.model_patcher import ModelPatcher +from comfy.model_management import cast_to_device + +from ...libs.log import log_node_warn, log_node_error, log_node_info + +class InpaintHead(torch.nn.Module): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device="cpu")) + + def __call__(self, x): + x = F.pad(x, (1, 1, 1, 1), "replicate") + return F.conv2d(x, weight=self.head) + +# injected_model_patcher_calculate_weight = False +# original_calculate_weight = None + +class applyFooocusInpaint: + def calculate_weight_patched(self, patches, weight, key, intermediate_dtype=torch.float32): + remaining = [] + + for p in patches: + alpha = p[0] + v = p[1] + + is_fooocus_patch = isinstance(v, tuple) and len(v) == 2 and v[0] == "fooocus" + if not is_fooocus_patch: + remaining.append(p) + continue + + if alpha != 0.0: + v = v[1] + w1 = cast_to_device(v[0], weight.device, torch.float32) + if w1.shape == weight.shape: + w_min = cast_to_device(v[1], weight.device, torch.float32) + w_max = cast_to_device(v[2], weight.device, torch.float32) + w1 = (w1 / 255.0) * (w_max - w_min) + w_min + weight += alpha * cast_to_device(w1, weight.device, weight.dtype) + else: + print( + f"[ApplyFooocusInpaint] Shape mismatch {key}, weight not merged ({w1.shape} != {weight.shape})" + ) + + if len(remaining) > 0: + return self.original_calculate_weight(remaining, weight, key, intermediate_dtype) + return weight + + def __enter__(self): + try: + print("[comfyui-easy-use] Injecting patched comfy.lora.calculate_weight.calculate_weight") + self.original_calculate_weight = comfy.lora.calculate_weight + comfy.lora.calculate_weight = self.calculate_weight_patched + except AttributeError: + print("[comfyui-easy-use] Injecting patched comfy.model_patcher.ModelPatcher.calculate_weight") + self.original_calculate_weight = ModelPatcher.calculate_weight + ModelPatcher.calculate_weight = self.calculate_weight_patched + + def __exit__(self, exc_type, exc_value, traceback): + try: + comfy.lora.calculate_weight = self.original_calculate_weight + except: + ModelPatcher.calculate_weight = self.original_calculate_weight + +# def inject_patched_calculate_weight(): +# global injected_model_patcher_calculate_weight +# if not injected_model_patcher_calculate_weight: +# try: +# print("[comfyui-easy-use] Injecting patched comfy.lora.calculate_weight.calculate_weight") +# original_calculate_weight = comfy.lora.calculate_weight +# comfy.lora.original_calculate_weight = original_calculate_weight +# comfy.lora.calculate_weight = calculate_weight_patched +# except AttributeError: +# print("[comfyui-easy-use] Injecting patched comfy.model_patcher.ModelPatcher.calculate_weight") +# original_calculate_weight = ModelPatcher.calculate_weight +# ModelPatcher.original_calculate_weight = original_calculate_weight +# ModelPatcher.calculate_weight = calculate_weight_patched +# injected_model_patcher_calculate_weight = True + + +class InpaintWorker: + def __init__(self, node_name): + self.node_name = node_name if node_name is not None else "" + + def load_fooocus_patch(self, lora: dict, to_load: dict): + patch_dict = {} + loaded_keys = set() + for key in to_load.values(): + if value := lora.get(key, None): + patch_dict[key] = ("fooocus", value) + loaded_keys.add(key) + + not_loaded = sum(1 for x in lora if x not in loaded_keys) + if not_loaded > 0: + log_node_info(self.node_name, + f"{len(loaded_keys)} Lora keys loaded, {not_loaded} remaining keys not found in model." + ) + return patch_dict + + def _input_block_patch(self, h: torch.Tensor, transformer_options: dict): + if transformer_options["block"][1] == 0: + if self._inpaint_block is None or self._inpaint_block.shape != h.shape: + assert self._inpaint_head_feature is not None + batch = h.shape[0] // self._inpaint_head_feature.shape[0] + self._inpaint_block = self._inpaint_head_feature.to(h).repeat(batch, 1, 1, 1) + h = h + self._inpaint_block + return h + + def patch(self, model, latent, patch): + base_model: BaseModel = model.model + latent_pixels = base_model.process_latent_in(latent["samples"]) + noise_mask = latent["noise_mask"].round() + latent_mask = F.max_pool2d(noise_mask, (8, 8)).round().to(latent_pixels) + + inpaint_head_model, inpaint_lora = patch + feed = torch.cat([latent_mask, latent_pixels], dim=1) + inpaint_head_model.to(device=feed.device, dtype=feed.dtype) + self._inpaint_head_feature = inpaint_head_model(feed) + self._inpaint_block = None + + lora_keys = comfy.lora.model_lora_keys_unet(model.model, {}) + lora_keys.update({x: x for x in base_model.state_dict().keys()}) + loaded_lora = self.load_fooocus_patch(inpaint_lora, lora_keys) + + m = model.clone() + m.set_model_input_block_patch(self._input_block_patch) + patched = m.add_patches(loaded_lora, 1.0) + m.model_options['transformer_options']['fooocus'] = True + not_patched_count = sum(1 for x in loaded_lora if x not in patched) + if not_patched_count > 0: + log_node_error(self.node_name, f"Failed to patch {not_patched_count} keys") + + # inject_patched_calculate_weight() + return (m,) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/parsing_api.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/parsing_api.py new file mode 100644 index 00000000..610d1c3c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/parsing_api.py @@ -0,0 +1,156 @@ +import torch +import numpy as np +import cv2 +import torchvision.transforms as transforms +from torch.utils.data import DataLoader +from .simple_extractor_dataset import SimpleFolderDataset +from .transforms import transform_logits +from tqdm import tqdm +from PIL import Image + +def get_palette(num_cls): + """ Returns the color map for visualizing the segmentation mask. + Args: + num_cls: Number of classes + Returns: + The color map + """ + n = num_cls + palette = [0] * (n * 3) + for j in range(0, n): + lab = j + palette[j * 3 + 0] = 0 + palette[j * 3 + 1] = 0 + palette[j * 3 + 2] = 0 + i = 0 + while lab: + palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i)) + palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i)) + palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i)) + i += 1 + lab >>= 3 + return palette + + +def delete_irregular(logits_result): + parsing_result = np.argmax(logits_result, axis=2) + upper_cloth = np.where(parsing_result == 4, 255, 0) + contours, hierarchy = cv2.findContours(upper_cloth.astype(np.uint8), + cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1) + area = [] + for i in range(len(contours)): + a = cv2.contourArea(contours[i], True) + area.append(abs(a)) + if len(area) != 0: + top = area.index(max(area)) + M = cv2.moments(contours[top]) + cY = int(M["m01"] / M["m00"]) + + dresses = np.where(parsing_result == 7, 255, 0) + contours_dress, hierarchy_dress = cv2.findContours(dresses.astype(np.uint8), + cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1) + area_dress = [] + for j in range(len(contours_dress)): + a_d = cv2.contourArea(contours_dress[j], True) + area_dress.append(abs(a_d)) + if len(area_dress) != 0: + top_dress = area_dress.index(max(area_dress)) + M_dress = cv2.moments(contours_dress[top_dress]) + cY_dress = int(M_dress["m01"] / M_dress["m00"]) + wear_type = "dresses" + if len(area) != 0: + if len(area_dress) != 0 and cY_dress > cY: + irregular_list = np.array([4, 5, 6]) + logits_result[:, :, irregular_list] = -1 + else: + irregular_list = np.array([5, 6, 7, 8, 9, 10, 12, 13]) + logits_result[:cY, :, irregular_list] = -1 + wear_type = "cloth_pant" + parsing_result = np.argmax(logits_result, axis=2) + # pad border + parsing_result = np.pad(parsing_result, pad_width=1, mode='constant', constant_values=0) + return parsing_result, wear_type + + + +def hole_fill(img): + img_copy = img.copy() + mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8) + cv2.floodFill(img, mask, (0, 0), 255) + img_inverse = cv2.bitwise_not(img) + dst = cv2.bitwise_or(img_copy, img_inverse) + return dst + +def refine_mask(mask): + contours, hierarchy = cv2.findContours(mask.astype(np.uint8), + cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1) + area = [] + for j in range(len(contours)): + a_d = cv2.contourArea(contours[j], True) + area.append(abs(a_d)) + refine_mask = np.zeros_like(mask).astype(np.uint8) + if len(area) != 0: + i = area.index(max(area)) + cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1) + # keep large area in skin case + for j in range(len(area)): + if j != i and area[i] > 2000: + cv2.drawContours(refine_mask, contours, j, color=255, thickness=-1) + return refine_mask + +def refine_hole(parsing_result_filled, parsing_result, arm_mask): + filled_hole = cv2.bitwise_and(np.where(parsing_result_filled == 4, 255, 0), + np.where(parsing_result != 4, 255, 0)) - arm_mask * 255 + contours, hierarchy = cv2.findContours(filled_hole, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1) + refine_hole_mask = np.zeros_like(parsing_result).astype(np.uint8) + for i in range(len(contours)): + a = cv2.contourArea(contours[i], True) + # keep hole > 2000 pixels + if abs(a) > 2000: + cv2.drawContours(refine_hole_mask, contours, i, color=255, thickness=-1) + return refine_hole_mask + arm_mask + +def onnx_inference(lip_session, input_dir, mask_components=[0]): + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229]) + ]) + input_size = [473, 473] + + dataset_lip = SimpleFolderDataset(root=input_dir, input_size=input_size, transform=transform) + dataloader_lip = DataLoader(dataset_lip) + palette = get_palette(20) + with torch.no_grad(): + for _, batch in enumerate(tqdm(dataloader_lip)): + image, meta = batch + c = meta['center'].numpy()[0] + s = meta['scale'].numpy()[0] + w = meta['width'].numpy()[0] + h = meta['height'].numpy()[0] + + output = lip_session.run(None, {"input.1": image.numpy().astype(np.float32)}) + upsample = torch.nn.Upsample(size=input_size, mode='bilinear', align_corners=True) + upsample_output = upsample(torch.from_numpy(output[1][0]).unsqueeze(0)) + upsample_output = upsample_output.squeeze() + upsample_output = upsample_output.permute(1, 2, 0) # CHW -> HWC + logits_result_lip = transform_logits(upsample_output.data.cpu().numpy(), c, s, w, h, + input_size=input_size) + parsing_result = np.argmax(logits_result_lip, axis=2) + + output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8)) + output_img.putpalette(palette) + + mask = np.isin(output_img, mask_components).astype(np.uint8) + mask_image = Image.fromarray(mask * 255) + mask_image = mask_image.convert("RGB") + mask_image = torch.from_numpy(np.array(mask_image).astype(np.float32) / 255.0).unsqueeze(0) + + output_img = output_img.convert('RGB') + output_img = torch.from_numpy(np.array(output_img).astype(np.float32) / 255.0).unsqueeze(0) + + return output_img, mask_image + + + + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/run_parsing.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/run_parsing.py new file mode 100644 index 00000000..ebdf1556 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/run_parsing.py @@ -0,0 +1,109 @@ +import numpy as np +import torch +from PIL import Image +from .parsing_api import onnx_inference +from ...libs.utils import install_package + +class HumanParsing: + def __init__(self, model_path): + self.model_path = model_path + self.session = None + + def __call__(self, input_image, mask_components): + if self.session is None: + install_package('onnxruntime') + import onnxruntime as ort + + session_options = ort.SessionOptions() + session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + session_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + # session_options.add_session_config_entry('gpu_id', str(gpu_id)) + self.session = ort.InferenceSession(self.model_path, sess_options=session_options, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + + parsed_image, mask = onnx_inference(self.session, input_image, mask_components) + return parsed_image, mask + + +class HumanParts: + + def __init__(self, model_path): + self.model_path = model_path + self.session = None + # self.classes_dict = { + # "background": 0, + # "hair": 2, + # "glasses": 4, + # "top-clothes": 5, + # "bottom-clothes": 9, + # "torso-skin": 10, + # "face": 13, + # "left-arm": 14, + # "right-arm": 15, + # "left-leg": 16, + # "right-leg": 17, + # "left-foot": 18, + # "right-foot": 19, + # }, + self.classes = [0, 13, 2, 4, 5, 9, 10, 14, 15, 16, 17, 18, 19] + + + def __call__(self, input_image, mask_components): + if self.session is None: + install_package('onnxruntime') + import onnxruntime as ort + + self.session = ort.InferenceSession(self.model_path, providers=['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']) + + mask, = self.get_mask(self.session, input_image, 0, mask_components) + return mask + + def get_mask(self, model, image, rotation, mask_components): + image = image.squeeze(0) + image_np = image.numpy() * 255 + + pil_image = Image.fromarray(image_np.astype(np.uint8)) + original_size = pil_image.size # to resize the mask later + # resize to 512x512 as the model expects + pil_image = pil_image.resize((512, 512)) + center = (256, 256) + + if rotation != 0: + pil_image = pil_image.rotate(rotation, center=center) + + # normalize the image + image_np = np.array(pil_image).astype(np.float32) / 127.5 - 1 + image_np = np.expand_dims(image_np, axis=0) + + # use the onnx model to get the mask + input_name = model.get_inputs()[0].name + output_name = model.get_outputs()[0].name + result = model.run([output_name], {input_name: image_np}) + result = np.array(result[0]).argmax(axis=3).squeeze(0) + + score: int = 0 + + mask = np.zeros_like(result) + for class_index in mask_components: + detected = result == self.classes[class_index] + mask[detected] = 255 + score += mask.sum() + + # back to the original size + mask_image = Image.fromarray(mask.astype(np.uint8), mode="L") + if rotation != 0: + mask_image = mask_image.rotate(-rotation, center=center) + + mask_image = mask_image.resize(original_size) + + # and back to numpy... + mask = np.array(mask_image).astype(np.float32) / 255 + + # add 2 dimensions to match the expected output + mask = np.expand_dims(mask, axis=0) + mask = np.expand_dims(mask, axis=0) + # ensure to return a "binary mask_image" + + del image_np, result # free up memory, maybe not necessary + + return (torch.from_numpy(mask.astype(np.uint8)),) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/simple_extractor_dataset.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/simple_extractor_dataset.py new file mode 100644 index 00000000..5604b1ed --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/simple_extractor_dataset.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +""" +@Author : Peike Li +@Contact : peike.li@yahoo.com +@File : dataset.py +@Time : 8/30/19 9:12 PM +@Desc : Dataset Definition +@License : This source code is licensed under the license found in the + LICENSE file in the root directory of this source tree. +""" + +import os + +import cv2 +import numpy as np +from PIL import Image +from torch.utils import data +from .transforms import get_affine_transform + + +class SimpleFolderDataset(data.Dataset): + def __init__(self, root, input_size=[512, 512], transform=None): + self.root = root + self.input_size = input_size + self.transform = transform + self.aspect_ratio = input_size[1] * 1.0 / input_size[0] + self.input_size = np.asarray(input_size) + self.is_pil_image = False + if isinstance(root, Image.Image): + self.file_list = [root] + self.is_pil_image = True + elif os.path.isfile(root): + self.file_list = [os.path.basename(root)] + self.root = os.path.dirname(root) + else: + self.file_list = os.listdir(self.root) + + def __len__(self): + return len(self.file_list) + + def _box2cs(self, box): + x, y, w, h = box[:4] + return self._xywh2cs(x, y, w, h) + + def _xywh2cs(self, x, y, w, h): + center = np.zeros((2), dtype=np.float32) + center[0] = x + w * 0.5 + center[1] = y + h * 0.5 + if w > self.aspect_ratio * h: + h = w * 1.0 / self.aspect_ratio + elif w < self.aspect_ratio * h: + w = h * self.aspect_ratio + scale = np.array([w, h], dtype=np.float32) + return center, scale + + def __getitem__(self, index): + if self.is_pil_image: + img = np.asarray(self.file_list[index])[:, :, [2, 1, 0]] + else: + img_name = self.file_list[index] + img_path = os.path.join(self.root, img_name) + img = cv2.imread(img_path, cv2.IMREAD_COLOR) + h, w, _ = img.shape + + # Get person center and scale + person_center, s = self._box2cs([0, 0, w - 1, h - 1]) + r = 0 + trans = get_affine_transform(person_center, s, r, self.input_size) + input = cv2.warpAffine( + img, + trans, + (int(self.input_size[1]), int(self.input_size[0])), + flags=cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + borderValue=(0, 0, 0)) + + input = self.transform(input) + meta = { + 'center': person_center, + 'height': h, + 'width': w, + 'scale': s, + 'rotation': r + } + + return input, meta diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/transforms.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/transforms.py new file mode 100644 index 00000000..1442a728 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/human_parsing/transforms.py @@ -0,0 +1,167 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 +import torch + +class BRG2Tensor_transform(object): + def __call__(self, pic): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + +class BGR2RGB_transform(object): + def __call__(self, tensor): + return tensor[[2,1,0],:,:] + +def flip_back(output_flipped, matched_parts): + ''' + ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) + ''' + assert output_flipped.ndim == 4,\ + 'output_flipped should be [batch_size, num_joints, height, width]' + + output_flipped = output_flipped[:, :, :, ::-1] + + for pair in matched_parts: + tmp = output_flipped[:, pair[0], :, :].copy() + output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] + output_flipped[:, pair[1], :, :] = tmp + + return output_flipped + + +def fliplr_joints(joints, joints_vis, width, matched_parts): + """ + flip coords + """ + # Flip horizontal + joints[:, 0] = width - joints[:, 0] - 1 + + # Change left-right parts + for pair in matched_parts: + joints[pair[0], :], joints[pair[1], :] = \ + joints[pair[1], :], joints[pair[0], :].copy() + joints_vis[pair[0], :], joints_vis[pair[1], :] = \ + joints_vis[pair[1], :], joints_vis[pair[0], :].copy() + + return joints*joints_vis, joints_vis + + +def transform_preds(coords, center, scale, input_size): + target_coords = np.zeros(coords.shape) + trans = get_affine_transform(center, scale, 0, input_size, inv=1) + for p in range(coords.shape[0]): + target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) + return target_coords + +def transform_parsing(pred, center, scale, width, height, input_size): + + trans = get_affine_transform(center, scale, 0, input_size, inv=1) + target_pred = cv2.warpAffine( + pred, + trans, + (int(width), int(height)), #(int(width), int(height)), + flags=cv2.INTER_NEAREST, + borderMode=cv2.BORDER_CONSTANT, + borderValue=(0)) + + return target_pred + +def transform_logits(logits, center, scale, width, height, input_size): + + trans = get_affine_transform(center, scale, 0, input_size, inv=1) + channel = logits.shape[2] + target_logits = [] + for i in range(channel): + target_logit = cv2.warpAffine( + logits[:,:,i], + trans, + (int(width), int(height)), #(int(width), int(height)), + flags=cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + borderValue=(0)) + target_logits.append(target_logit) + target_logits = np.stack(target_logits,axis=2) + + return target_logits + + +def get_affine_transform(center, + scale, + rot, + output_size, + shift=np.array([0, 0], dtype=np.float32), + inv=0): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + print(scale) + scale = np.array([scale, scale]) + + scale_tmp = scale + + src_w = scale_tmp[0] + dst_w = output_size[1] + dst_h = output_size[0] + + rot_rad = np.pi * rot / 180 + src_dir = get_dir([0, src_w * -0.5], rot_rad) + dst_dir = np.array([0, (dst_w-1) * -0.5], np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale_tmp * shift + src[1, :] = center + src_dir + scale_tmp * shift + dst[0, :] = [(dst_w-1) * 0.5, (dst_h-1) * 0.5] + dst[1, :] = np.array([(dst_w-1) * 0.5, (dst_h-1) * 0.5]) + dst_dir + + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + + +def affine_transform(pt, t): + new_pt = np.array([pt[0], pt[1], 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2] + + +def get_3rd_point(a, b): + direct = a - b + return b + np.array([-direct[1], direct[0]], dtype=np.float32) + + +def get_dir(src_point, rot_rad): + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + + src_result = [0, 0] + src_result[0] = src_point[0] * cs - src_point[1] * sn + src_result[1] = src_point[0] * sn + src_point[1] * cs + + return src_result + + +def crop(img, center, scale, output_size, rot=0): + trans = get_affine_transform(center, scale, rot, output_size) + + dst_img = cv2.warpAffine(img, + trans, + (int(output_size[1]), int(output_size[0])), + flags=cv2.INTER_LINEAR) + + return dst_img diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ic_light/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ic_light/__init__.py new file mode 100644 index 00000000..a9a95abc --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ic_light/__init__.py @@ -0,0 +1,184 @@ +#credit to huchenlei for this module +#from https://github.com/huchenlei/ComfyUI-IC-Light-Native +import torch +import numpy as np +from typing import Tuple, TypedDict, Callable + +import comfy.model_management +from comfy.sd import load_unet +from comfy.ldm.models.autoencoder import AutoencoderKL +from comfy.model_base import BaseModel +from comfy.model_patcher import ModelPatcher +from PIL import Image +from nodes import VAEEncode +from ...libs.image import np2tensor, pil2tensor + +class UnetParams(TypedDict): + input: torch.Tensor + timestep: torch.Tensor + c: dict + cond_or_uncond: torch.Tensor + +class VAEEncodeArgMax(VAEEncode): + def encode(self, vae, pixels): + assert isinstance( + vae.first_stage_model, AutoencoderKL + ), "ArgMax only supported for AutoencoderKL" + original_sample_mode = vae.first_stage_model.regularization.sample + vae.first_stage_model.regularization.sample = False + ret = super().encode(vae, pixels) + vae.first_stage_model.regularization.sample = original_sample_mode + return ret + +class ICLight: + + @staticmethod + def apply_c_concat(params: UnetParams, concat_conds) -> UnetParams: + """Apply c_concat on unet call.""" + sample = params["input"] + params["c"]["c_concat"] = torch.cat( + ( + [concat_conds.to(sample.device)] + * (sample.shape[0] // concat_conds.shape[0]) + ), + dim=0, + ) + return params + + @staticmethod + def create_custom_conv( + original_conv: torch.nn.Module, + dtype: torch.dtype, + device=torch.device, + ) -> torch.nn.Module: + with torch.no_grad(): + new_conv_in = torch.nn.Conv2d( + 8, + original_conv.out_channels, + original_conv.kernel_size, + original_conv.stride, + original_conv.padding, + ) + new_conv_in.weight.zero_() + new_conv_in.weight[:, :4, :, :].copy_(original_conv.weight) + new_conv_in.bias = original_conv.bias + return new_conv_in.to(dtype=dtype, device=device) + + def generate_lighting_image(self, original_image, direction): + _, image_height, image_width, _ = original_image.shape + if direction == 'Left Light': + gradient = np.linspace(255, 0, image_width) + image = np.tile(gradient, (image_height, 1)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif direction == 'Right Light': + gradient = np.linspace(0, 255, image_width) + image = np.tile(gradient, (image_height, 1)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif direction == 'Top Light': + gradient = np.linspace(255, 0, image_height)[:, None] + image = np.tile(gradient, (1, image_width)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif direction == 'Bottom Light': + gradient = np.linspace(0, 255, image_height)[:, None] + image = np.tile(gradient, (1, image_width)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif direction == 'Circle Light': + x = np.linspace(-1, 1, image_width) + y = np.linspace(-1, 1, image_height) + x, y = np.meshgrid(x, y) + r = np.sqrt(x ** 2 + y ** 2) + r = r / r.max() + color1 = np.array([0, 0, 0])[np.newaxis, np.newaxis, :] + color2 = np.array([255, 255, 255])[np.newaxis, np.newaxis, :] + gradient = (color1 * r[..., np.newaxis] + color2 * (1 - r)[..., np.newaxis]).astype(np.uint8) + image = pil2tensor(Image.fromarray(gradient)) + return image + else: + image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) + return image + + def generate_source_image(self, original_image, source): + batch_size, image_height, image_width, _ = original_image.shape + if source == 'Use Flipped Background Image': + if batch_size < 2: + raise ValueError('Must be at least 2 image to use flipped background image.') + original_image = [img.unsqueeze(0) for img in original_image] + image = torch.flip(original_image[1], [2]) + return image + elif source == 'Ambient': + input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64 + return np2tensor(input_bg) + elif source == 'Left Light': + gradient = np.linspace(224, 32, image_width) + image = np.tile(gradient, (image_height, 1)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif source == 'Right Light': + gradient = np.linspace(32, 224, image_width) + image = np.tile(gradient, (image_height, 1)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif source == 'Top Light': + gradient = np.linspace(224, 32, image_height)[:, None] + image = np.tile(gradient, (1, image_width)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + elif source == 'Bottom Light': + gradient = np.linspace(32, 224, image_height)[:, None] + image = np.tile(gradient, (1, image_width)) + input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) + return np2tensor(input_bg) + else: + image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) + return image + + + def apply(self, ic_model_path, model, c_concat: dict, ic_model=None) -> Tuple[ModelPatcher]: + device = comfy.model_management.get_torch_device() + dtype = comfy.model_management.unet_dtype() + work_model = model.clone() + + # Apply scale factor. + base_model: BaseModel = work_model.model + scale_factor = base_model.model_config.latent_format.scale_factor + + # [B, 4, H, W] + concat_conds: torch.Tensor = c_concat["samples"] * scale_factor + # [1, 4 * B, H, W] + concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1) + + def unet_dummy_apply(unet_apply: Callable, params: UnetParams): + """A dummy unet apply wrapper serving as the endpoint of wrapper + chain.""" + return unet_apply(x=params["input"], t=params["timestep"], **params["c"]) + + existing_wrapper = work_model.model_options.get( + "model_function_wrapper", unet_dummy_apply + ) + + def wrapper_func(unet_apply: Callable, params: UnetParams): + return existing_wrapper(unet_apply, params=self.apply_c_concat(params, concat_conds)) + + work_model.set_model_unet_function_wrapper(wrapper_func) + if not ic_model: + ic_model = load_unet(ic_model_path) + ic_model_state_dict = ic_model.model.diffusion_model.state_dict() + + work_model.add_patches( + patches={ + ("diffusion_model." + key): ( + 'diff', + [ + value.to(dtype=dtype, device=device), + {"pad_weight": key == 'input_blocks.0.0.weight'} + ] + ) + for key, value in ic_model_state_dict.items() + } + ) + + return (work_model, ic_model) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/__init__.py new file mode 100644 index 00000000..a03e4fba --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/__init__.py @@ -0,0 +1,268 @@ +#credit to shakker-labs and instantX for this module +#from https://github.com/Shakker-Labs/ComfyUI-IPAdapter-Flux +import torch +from PIL import Image +import numpy as np +from .attention_processor import IPAFluxAttnProcessor2_0 +from .utils import is_model_pathched, FluxUpdateModules +from .sd3.resampler import TimeResampler +from .sd3.joinblock import JointBlockIPWrapper, IPAttnProcessor + +image_proj_model = None +class MLPProjModel(torch.nn.Module): + def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4): + super().__init__() + + self.cross_attention_dim = cross_attention_dim + self.num_tokens = num_tokens + + self.proj = torch.nn.Sequential( + torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2), + torch.nn.GELU(), + torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens), + ) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, id_embeds): + x = self.proj(id_embeds) + x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) + x = self.norm(x) + return x + +class InstantXFluxIpadapterApply: + def __init__(self, num_tokens=128): + self.device = None + self.dtype = torch.float16 + self.num_tokens = num_tokens + self.ip_ckpt = None + self.clip_vision = None + self.image_encoder = None + self.clip_image_processor = None + # state_dict + self.state_dict = None + self.joint_attention_dim = 4096 + self.hidden_size = 3072 + + def set_ip_adapter(self, flux_model, weight, timestep_percent_range=(0.0, 1.0)): + s = flux_model.model_sampling + percent_to_timestep_function = lambda a: s.percent_to_sigma(a) + timestep_range = (percent_to_timestep_function(timestep_percent_range[0]), + percent_to_timestep_function(timestep_percent_range[1])) + ip_attn_procs = {} # 19+38=57 + dsb_count = len(flux_model.diffusion_model.double_blocks) + for i in range(dsb_count): + name = f"double_blocks.{i}" + ip_attn_procs[name] = IPAFluxAttnProcessor2_0( + hidden_size=self.hidden_size, + cross_attention_dim=self.joint_attention_dim, + num_tokens=self.num_tokens, + scale=weight, + timestep_range=timestep_range + ).to(self.device, dtype=self.dtype) + ssb_count = len(flux_model.diffusion_model.single_blocks) + for i in range(ssb_count): + name = f"single_blocks.{i}" + ip_attn_procs[name] = IPAFluxAttnProcessor2_0( + hidden_size=self.hidden_size, + cross_attention_dim=self.joint_attention_dim, + num_tokens=self.num_tokens, + scale=weight, + timestep_range=timestep_range + ).to(self.device, dtype=self.dtype) + return ip_attn_procs + + def load_ip_adapter(self, flux_model, weight, timestep_percent_range=(0.0, 1.0)): + global image_proj_model + image_proj_model.load_state_dict(self.state_dict["image_proj"], strict=True) + ip_attn_procs = self.set_ip_adapter(flux_model, weight, timestep_percent_range) + ip_layers = torch.nn.ModuleList(ip_attn_procs.values()) + ip_layers.load_state_dict(self.state_dict["ip_adapter"], strict=True) + return ip_attn_procs + + def get_image_embeds(self, pil_image=None, clip_image_embeds=None): + # outputs = self.clip_vision.encode_image(pil_image) + # clip_image_embeds = outputs['image_embeds'] + # clip_image_embeds = clip_image_embeds.to(self.device, dtype=self.dtype) + # image_prompt_embeds = self.image_proj_model(clip_image_embeds) + if pil_image is not None: + if isinstance(pil_image, Image.Image): + pil_image = [pil_image] + clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values + clip_image_embeds = self.image_encoder( + clip_image.to(self.device, dtype=self.image_encoder.dtype)).pooler_output + clip_image_embeds = clip_image_embeds.to(dtype=self.dtype) + else: + clip_image_embeds = clip_image_embeds.to(self.device, dtype=self.dtype) + global image_proj_model + image_prompt_embeds = image_proj_model(clip_image_embeds) + return image_prompt_embeds + + def apply_ipadapter(self, model, ipadapter, image, weight, start_at, end_at, provider=None, use_tiled=False): + self.device = provider.lower() + if "clipvision" in ipadapter: + # self.clip_vision = ipadapter["clipvision"]['model'] + self.image_encoder = ipadapter["clipvision"]['model']['image_encoder'].to(self.device, dtype=self.dtype) + self.clip_image_processor = ipadapter["clipvision"]['model']['clip_image_processor'] + if "ipadapter" in ipadapter: + self.ip_ckpt = ipadapter["ipadapter"]['file'] + self.state_dict = ipadapter["ipadapter"]['model'] + + # process image + pil_image = image.numpy()[0] * 255.0 + pil_image = Image.fromarray(pil_image.astype(np.uint8)) + # initialize ipadapter + global image_proj_model + if image_proj_model is None: + image_proj_model = MLPProjModel( + cross_attention_dim=self.joint_attention_dim, # 4096 + id_embeddings_dim=1152, + num_tokens=self.num_tokens, + ) + image_proj_model.to(self.device, dtype=self.dtype) + ip_attn_procs = self.load_ip_adapter(model.model, weight, (start_at, end_at)) + # process control image + image_prompt_embeds = self.get_image_embeds(pil_image=pil_image, clip_image_embeds=None) + # set model + # is_patched = is_model_pathched(model.model) + bi = model.clone() + FluxUpdateModules(bi, ip_attn_procs, image_prompt_embeds) + + return (bi, image) + + +def patch_sd3( + patcher, + ip_procs, + resampler: TimeResampler, + clip_embeds, + weight=1.0, + start=0.0, + end=1.0, +): + """ + Patches a model_sampler to add the ipadapter + """ + mmdit = patcher.model.diffusion_model + timestep_schedule_max = patcher.model.model_config.sampling_settings.get( + "timesteps", 1000 + ) + # hook the model's forward function + # so that when it gets called, we can grab the timestep and send it to the resampler + ip_options = { + "hidden_states": None, + "t_emb": None, + "weight": weight, + } + + def ddit_wrapper(forward, args): + # this is between 0 and 1, so the adapters can calculate start_point and end_point + # actually, do we need to get the sigma value instead? + t_percent = 1 - args["timestep"].flatten()[0].cpu().item() + if start <= t_percent <= end: + batch_size = args["input"].shape[0] // len(args["cond_or_uncond"]) + # if we're only doing cond or only doing uncond, only pass one of them through the resampler + embeds = clip_embeds[args["cond_or_uncond"]] + # slight efficiency optimization todo: pass the embeds through and then afterwards + # repeat to the batch size + embeds = torch.repeat_interleave(embeds, batch_size, dim=0) + # the resampler wants between 0 and MAX_STEPS + timestep = args["timestep"] * timestep_schedule_max + image_emb, t_emb = resampler(embeds, timestep, need_temb=True) + # these will need to be accessible to the IPAdapters + ip_options["hidden_states"] = image_emb + ip_options["t_emb"] = t_emb + else: + ip_options["hidden_states"] = None + ip_options["t_emb"] = None + + return forward(args["input"], args["timestep"], **args["c"]) + + patcher.set_model_unet_function_wrapper(ddit_wrapper) + # patch each dit block + for i, block in enumerate(mmdit.joint_blocks): + wrapper = JointBlockIPWrapper(block, ip_procs[i], ip_options) + patcher.set_model_patch_replace(wrapper, "dit", "double_block", i) + +class InstantXSD3IpadapterApply: + def __init__(self): + self.device = None + self.dtype = torch.float16 + self.clip_image_processor = None + self.image_encoder = None + self.resampler = None + self.procs = None + + @torch.inference_mode() + def encode(self, image): + clip_image = self.clip_image_processor.image_processor(image, return_tensors="pt", do_rescale=False).pixel_values + clip_image_embeds = self.image_encoder( + clip_image.to(self.device, dtype=self.image_encoder.dtype), + output_hidden_states=True, + ).hidden_states[-2] + clip_image_embeds = torch.cat( + [clip_image_embeds, torch.zeros_like(clip_image_embeds)], dim=0 + ) + clip_image_embeds = clip_image_embeds.to(dtype=torch.float16) + return clip_image_embeds + + def apply_ipadapter(self, model, ipadapter, image, weight, start_at, end_at, provider=None, use_tiled=False): + self.device = provider.lower() + if "clipvision" in ipadapter: + self.image_encoder = ipadapter["clipvision"]['model']['image_encoder'].to(self.device, dtype=self.dtype) + self.clip_image_processor = ipadapter["clipvision"]['model']['clip_image_processor'] + if "ipadapter" in ipadapter: + self.ip_ckpt = ipadapter["ipadapter"]['file'] + self.state_dict = ipadapter["ipadapter"]['model'] + + self.resampler = TimeResampler( + dim=1280, + depth=4, + dim_head=64, + heads=20, + num_queries=64, + embedding_dim=1152, + output_dim=2432, + ff_mult=4, + timestep_in_dim=320, + timestep_flip_sin_to_cos=True, + timestep_freq_shift=0, + ) + self.resampler.eval() + self.resampler.to(self.device, dtype=self.dtype) + self.resampler.load_state_dict(self.state_dict["image_proj"]) + + # now we'll create the attention processors + # ip_adapter.keys looks like [0.proj, 0.to_k, ..., 1.proj, 1.to_k, ...] + n_procs = len( + set(x.split(".")[0] for x in self.state_dict["ip_adapter"].keys()) + ) + self.procs = torch.nn.ModuleList( + [ + # this is hardcoded for SD3.5L + IPAttnProcessor( + hidden_size=2432, + cross_attention_dim=2432, + ip_hidden_states_dim=2432, + ip_encoder_hidden_states_dim=2432, + head_dim=64, + timesteps_emb_dim=1280, + ).to(self.device, dtype=torch.float16) + for _ in range(n_procs) + ] + ) + self.procs.load_state_dict(self.state_dict["ip_adapter"]) + + work_model = model.clone() + embeds = self.encode(image) + + patch_sd3( + work_model, + self.procs, + self.resampler, + embeds, + weight, + start_at, + end_at, + ) + + return (work_model, image) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/attention_processor.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/attention_processor.py new file mode 100644 index 00000000..4eb1b153 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/attention_processor.py @@ -0,0 +1,87 @@ +import numbers +from typing import Dict, Optional, Tuple +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +class RMSNorm(nn.Module): + def __init__(self, dim, eps: float, elementwise_affine: bool = True): + super().__init__() + + self.eps = eps + + if isinstance(dim, numbers.Integral): + dim = (dim,) + + self.dim = torch.Size(dim) + + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim)) + else: + self.weight = None + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + if self.weight is not None: + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + hidden_states = hidden_states * self.weight + else: + hidden_states = hidden_states.to(input_dtype) + + return hidden_states + +class IPAFluxAttnProcessor2_0(nn.Module): + """Attention processor used typically in processing the SD3-like self-attention projections.""" + + def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, timestep_range=None): + super().__init__() + + self.hidden_size = hidden_size # 3072 + self.cross_attention_dim = cross_attention_dim # 4096 + self.scale = scale + self.num_tokens = num_tokens + + self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) + self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) + + self.norm_added_k = RMSNorm(128, eps=1e-5, elementwise_affine=False) + self.norm_added_v = RMSNorm(128, eps=1e-5, elementwise_affine=False) + self.timestep_range = timestep_range + + def __call__( + self, + num_heads, + query, + image_emb: torch.FloatTensor, + t: torch.FloatTensor + ) -> torch.FloatTensor: + # only apply IPA if timestep is within range + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + return None + # `ip-adapter` projections + ip_hidden_states = image_emb + ip_hidden_states_key_proj = self.to_k_ip(ip_hidden_states) + ip_hidden_states_value_proj = self.to_v_ip(ip_hidden_states) + + ip_hidden_states_key_proj = rearrange(ip_hidden_states_key_proj, 'B L (H D) -> B H L D', H=num_heads) + ip_hidden_states_value_proj = rearrange(ip_hidden_states_value_proj, 'B L (H D) -> B H L D', H=num_heads) + + ip_hidden_states_key_proj = self.norm_added_k(ip_hidden_states_key_proj) + ip_hidden_states_value_proj = self.norm_added_v(ip_hidden_states_value_proj) + + ip_hidden_states = F.scaled_dot_product_attention(query.to(image_emb.device).to(image_emb.dtype), + ip_hidden_states_key_proj, + ip_hidden_states_value_proj, + dropout_p=0.0, is_causal=False) + + ip_hidden_states = rearrange(ip_hidden_states, "B H L D -> B L (H D)", H=num_heads) + ip_hidden_states = ip_hidden_states.to(query.dtype).to(query.device) + + return self.scale * ip_hidden_states \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/layers.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/layers.py new file mode 100644 index 00000000..aa04e373 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/layers.py @@ -0,0 +1,153 @@ +import torch +from torch import Tensor, nn + +from .math import attention +from ..attention_processor import IPAFluxAttnProcessor2_0 +from comfy.ldm.flux.layers import DoubleStreamBlock, SingleStreamBlock +from comfy import model_management as mm + +class DoubleStreamBlockIPA(nn.Module): + def __init__(self, original_block: DoubleStreamBlock, ip_adapter, image_emb): + super().__init__() + + mlp_hidden_dim = original_block.img_mlp[0].out_features + mlp_ratio = mlp_hidden_dim / original_block.hidden_size + mlp_hidden_dim = int(original_block.hidden_size * mlp_ratio) + self.num_heads = original_block.num_heads + self.hidden_size = original_block.hidden_size + self.img_mod = original_block.img_mod + self.img_norm1 = original_block.img_norm1 + self.img_attn = original_block.img_attn + + self.img_norm2 = original_block.img_norm2 + self.img_mlp = original_block.img_mlp + + self.txt_mod = original_block.txt_mod + self.txt_norm1 = original_block.txt_norm1 + self.txt_attn = original_block.txt_attn + + self.txt_norm2 = original_block.txt_norm2 + self.txt_mlp = original_block.txt_mlp + self.flipped_img_txt = original_block.flipped_img_txt + + self.ip_adapter = ip_adapter + self.image_emb = image_emb + self.device = mm.get_torch_device() + + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, t: Tensor, attn_mask=None): + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, + 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, + 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + if self.flipped_img_txt: + # run actual attention + attn = attention(torch.cat((img_q, txt_q), dim=2), + torch.cat((img_k, txt_k), dim=2), + torch.cat((img_v, txt_v), dim=2), + pe=pe, mask=attn_mask) + + img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:] + else: + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, mask=attn_mask) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] + + for adapter, image in zip(self.ip_adapter, self.image_emb): + # this does a separate attention for each adapter + ip_hidden_states = adapter(self.num_heads, img_q, image, t) + if ip_hidden_states is not None: + ip_hidden_states = ip_hidden_states.to(self.device) + img_attn = img_attn + ip_hidden_states + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlockIPA(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__(self, original_block: SingleStreamBlock, ip_adapter, image_emb): + super().__init__() + self.hidden_dim = original_block.hidden_size + self.num_heads = original_block.num_heads + self.scale = original_block.scale + + self.mlp_hidden_dim = original_block.mlp_hidden_dim + # qkv and mlp_in + self.linear1 = original_block.linear1 + # proj and mlp_out + self.linear2 = original_block.linear2 + + self.norm = original_block.norm + + self.hidden_size = original_block.hidden_size + self.pre_norm = original_block.pre_norm + + self.mlp_act = original_block.mlp_act + self.modulation = original_block.modulation + + self.ip_adapter = ip_adapter + self.image_emb = image_emb + self.device = mm.get_torch_device() + + def add_adapter(self, ip_adapter: IPAFluxAttnProcessor2_0, image_emb): + self.ip_adapter.append(ip_adapter) + self.image_emb.append(image_emb) + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, t: Tensor, attn_mask=None) -> Tensor: + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe, mask=attn_mask) + + for adapter, image in zip(self.ip_adapter, self.image_emb): + # this does a separate attention for each adapter + # maybe we want a single joint attention call for all adapters? + ip_hidden_states = adapter(self.num_heads, q, image, t) + if ip_hidden_states is not None: + ip_hidden_states = ip_hidden_states.to(self.device) + attn = attn + ip_hidden_states + + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x += mod.gate * output + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/math.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/math.py new file mode 100644 index 00000000..1f58b181 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/flux/math.py @@ -0,0 +1,35 @@ +import torch +from einops import rearrange +from torch import Tensor +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: + q, k = apply_rope(q, k, pe) + + heads = q.shape[1] + x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/joinblock.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/joinblock.py new file mode 100644 index 00000000..95bbf26b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/joinblock.py @@ -0,0 +1,219 @@ +import torch +from torch import nn +from torch.nn import functional as F +from einops import rearrange + +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.modules.diffusionmodules.mmdit import (RMSNorm, JointBlock,) + + +class AdaLayerNorm(nn.Module): + """ + Norm layer adaptive layer norm zero (adaLN-Zero). + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + num_embeddings (`int`): The size of the embeddings dictionary. + """ + + def __init__(self, embedding_dim: int, time_embedding_dim=None, mode="normal"): + super().__init__() + + self.silu = nn.SiLU() + num_params_dict = dict( + zero=6, + normal=2, + ) + num_params = num_params_dict[mode] + self.linear = nn.Linear( + time_embedding_dim or embedding_dim, num_params * embedding_dim, bias=True + ) + self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) + self.mode = mode + + def forward( + self, + x, + hidden_dtype=None, + emb=None, + ): + emb = self.linear(self.silu(emb)) + if self.mode == "normal": + shift_msa, scale_msa = emb.chunk(2, dim=1) + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x + + elif self.mode == "zero": + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk( + 6, dim=1 + ) + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +class IPAttnProcessor(nn.Module): + + def __init__( + self, + hidden_size=None, + cross_attention_dim=None, + ip_hidden_states_dim=None, + ip_encoder_hidden_states_dim=None, + head_dim=None, + timesteps_emb_dim=1280, + ): + super().__init__() + + self.norm_ip = AdaLayerNorm( + ip_hidden_states_dim, time_embedding_dim=timesteps_emb_dim + ) + self.to_k_ip = nn.Linear(ip_hidden_states_dim, hidden_size, bias=False) + self.to_v_ip = nn.Linear(ip_hidden_states_dim, hidden_size, bias=False) + self.norm_q = RMSNorm(head_dim, 1e-6) + self.norm_k = RMSNorm(head_dim, 1e-6) + self.norm_ip_k = RMSNorm(head_dim, 1e-6) + + def forward( + self, + ip_hidden_states, + img_query, + img_key=None, + img_value=None, + t_emb=None, + n_heads=1, + ): + if ip_hidden_states is None: + return None + + if not hasattr(self, "to_k_ip") or not hasattr(self, "to_v_ip"): + return None + + # norm ip input + norm_ip_hidden_states = self.norm_ip(ip_hidden_states, emb=t_emb) + + # to k and v + ip_key = self.to_k_ip(norm_ip_hidden_states) + ip_value = self.to_v_ip(norm_ip_hidden_states) + + # reshape + img_query = rearrange(img_query, "b l (h d) -> b h l d", h=n_heads) + img_key = rearrange(img_key, "b l (h d) -> b h l d", h=n_heads) + # note that the image is in a different shape: b l h d + # so we transpose to b h l d + # or do we have to transpose here? + img_value = torch.transpose(img_value, 1, 2) + ip_key = rearrange(ip_key, "b l (h d) -> b h l d", h=n_heads) + ip_value = rearrange(ip_value, "b l (h d) -> b h l d", h=n_heads) + + # norm + img_query = self.norm_q(img_query) + img_key = self.norm_k(img_key) + ip_key = self.norm_ip_k(ip_key) + + # cat img + key = torch.cat([img_key, ip_key], dim=2) + value = torch.cat([img_value, ip_value], dim=2) + + # + ip_hidden_states = F.scaled_dot_product_attention( + img_query, key, value, dropout_p=0.0, is_causal=False + ) + ip_hidden_states = rearrange(ip_hidden_states, "b h l d -> b l (h d)") + ip_hidden_states = ip_hidden_states.to(img_query.dtype) + return ip_hidden_states + + +class JointBlockIPWrapper: + """To be used as a patch_replace with Comfy""" + + def __init__( + self, + original_block: JointBlock, + adapter: IPAttnProcessor, + ip_options=None, + ): + self.original_block = original_block + self.adapter = adapter + if ip_options is None: + ip_options = {} + self.ip_options = ip_options + + def block_mixing(self, context, x, context_block, x_block, c): + """ + Comes from mmdit.py. Modified to add ipadapter attention. + """ + context_qkv, context_intermediates = context_block.pre_attention(context, c) + + if x_block.x_block_self_attn: + x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c) + else: + x_qkv, x_intermediates = x_block.pre_attention(x, c) + + qkv = tuple(torch.cat((context_qkv[j], x_qkv[j]), dim=1) for j in range(3)) + + attn = optimized_attention( + qkv[0], + qkv[1], + qkv[2], + heads=x_block.attn.num_heads, + ) + context_attn, x_attn = ( + attn[:, : context_qkv[0].shape[1]], + attn[:, context_qkv[0].shape[1] :], + ) + # if the current timestep is not in the ipadapter enabling range, then the resampler wasn't run + # and the hidden states will be None + if ( + self.ip_options["hidden_states"] is not None + and self.ip_options["t_emb"] is not None + ): + # IP-Adapter + ip_attn = self.adapter( + self.ip_options["hidden_states"], + *x_qkv, + self.ip_options["t_emb"], + x_block.attn.num_heads, + ) + x_attn = x_attn + ip_attn * self.ip_options["weight"] + + # Everything else is unchanged + if not context_block.pre_only: + context = context_block.post_attention(context_attn, *context_intermediates) + + else: + context = None + if x_block.x_block_self_attn: + attn2 = optimized_attention( + x_qkv2[0], + x_qkv2[1], + x_qkv2[2], + heads=x_block.attn2.num_heads, + ) + x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) + else: + x = x_block.post_attention(x_attn, *x_intermediates) + return context, x + + def __call__(self, args, _): + # Code from mmdit.py: + # in this case, we're blocks_replace[("double_block", i)] + # note that although we're passed the original block, + # we can't actually get it from inside its wrapper + # (which would simplify the whole code...) + # ``` + # def block_wrap(args): + # out = {} + # out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"]) + # return out + # out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap}) + # context = out["txt"] + # x = out["img"] + # ``` + c, x = self.block_mixing( + args["txt"], + args["img"], + self.original_block.context_block, + self.original_block.x_block, + c=args["vec"], + ) + return {"txt": c, "img": x} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/resampler.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/resampler.py new file mode 100644 index 00000000..d8b68a07 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/sd3/resampler.py @@ -0,0 +1,385 @@ +# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +import math + +import torch +import torch.nn as nn +from typing import Optional + + +ACTIVATION_FUNCTIONS = { + "swish": nn.SiLU(), + "silu": nn.SiLU(), + "mish": nn.Mish(), + "gelu": nn.GELU(), + "relu": nn.ReLU(), +} +def get_activation(act_fn: str) -> nn.Module: + """Helper function to get activation function from string. + + Args: + act_fn (str): Name of activation function. + + Returns: + nn.Module: Activation function. + """ + + act_fn = act_fn.lower() + if act_fn in ACTIVATION_FUNCTIONS: + return ACTIVATION_FUNCTIONS[act_fn] + else: + raise ValueError(f"Unsupported activation function: {act_fn}") + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + Args + timesteps (torch.Tensor): + a 1-D Tensor of N indices, one per batch element. These may be fractional. + embedding_dim (int): + the dimension of the output. + flip_sin_to_cos (bool): + Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False) + downscale_freq_shift (float): + Controls the delta between frequencies between dimensions + scale (float): + Scaling factor applied to the embeddings. + max_period (int): + Controls the maximum frequency of the embeddings + Returns + torch.Tensor: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange( + start=0, end=half_dim, dtype=torch.float32, device=timesteps.device + ) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + +class Timesteps(nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + self.scale = scale + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + scale=self.scale, + ) + return t_emb + +class TimestepEmbedding(nn.Module): + def __init__( + self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + sample_proj_bias=True, + ): + super().__init__() + + self.linear_1 = nn.Linear(in_channels, time_embed_dim, sample_proj_bias) + + if cond_proj_dim is not None: + self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False) + else: + self.cond_proj = None + + self.act = get_activation(act_fn) + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias) + + if post_act_fn is None: + self.post_act = None + else: + self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents, shift=None, scale=None): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + if shift is not None and scale is not None: + latents = latents * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose( + -2, -1 + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + *args, + **kwargs, + ): + super().__init__() + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + + self.proj_in = nn.Linear(embedding_dim, dim) + + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, x): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.proj_in(x) + + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) + + +class TimeResampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + timestep_in_dim=320, + timestep_flip_sin_to_cos=True, + timestep_freq_shift=0, + ): + super().__init__() + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + + self.proj_in = nn.Linear(embedding_dim, dim) + + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + # msa + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + # ff + FeedForward(dim=dim, mult=ff_mult), + # adaLN + nn.Sequential(nn.SiLU(), nn.Linear(dim, 4 * dim, bias=True)), + ] + ) + ) + + # time + self.time_proj = Timesteps( + timestep_in_dim, timestep_flip_sin_to_cos, timestep_freq_shift + ) + self.time_embedding = TimestepEmbedding(timestep_in_dim, dim, act_fn="silu") + + # adaLN + # self.adaLN_modulation = nn.Sequential( + # nn.SiLU(), + # nn.Linear(timestep_out_dim, 6 * timestep_out_dim, bias=True) + # ) + + def forward(self, x, timestep, need_temb=False): + timestep_emb = self.embedding_time(x, timestep) # bs, dim + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.proj_in(x) + x = x + timestep_emb[:, None] + + for attn, ff, adaLN_modulation in self.layers: + shift_msa, scale_msa, shift_mlp, scale_mlp = adaLN_modulation( + timestep_emb + ).chunk(4, dim=1) + latents = attn(x, latents, shift_msa, scale_msa) + latents + + res = latents + for idx_ff in range(len(ff)): + layer_ff = ff[idx_ff] + latents = layer_ff(latents) + if idx_ff == 0 and isinstance(layer_ff, nn.LayerNorm): # adaLN + latents = latents * ( + 1 + scale_mlp.unsqueeze(1) + ) + shift_mlp.unsqueeze(1) + latents = latents + res + + # latents = ff(latents) + latents + + latents = self.proj_out(latents) + latents = self.norm_out(latents) + + if need_temb: + return latents, timestep_emb + else: + return latents + + def embedding_time(self, sample, timestep): + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, None) + return emb \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/utils.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/utils.py new file mode 100644 index 00000000..4556dde3 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/ipadapter/utils.py @@ -0,0 +1,136 @@ +import torch +from torch import Tensor +from .flux.layers import DoubleStreamBlockIPA, SingleStreamBlockIPA +from comfy.ldm.flux.layers import timestep_embedding +from types import MethodType + +def FluxUpdateModules(bi, ip_attn_procs, image_emb): + flux_model = bi.model + bi.add_object_patch(f"diffusion_model.forward_orig", MethodType(forward_orig_ipa, flux_model.diffusion_model)) + for i, original in enumerate(flux_model.diffusion_model.double_blocks): + patch_name = f"double_blocks.{i}" + maybe_patched_layer = bi.get_model_object(f"diffusion_model.{patch_name}") + # if there's already a patch there, collect its adapters and replace it + procs = [ip_attn_procs[patch_name]] + embs = [image_emb] + if isinstance(maybe_patched_layer, DoubleStreamBlockIPA): + procs = maybe_patched_layer.ip_adapter + procs + embs = maybe_patched_layer.image_emb + embs + # initial ipa models with image embeddings + new_layer = DoubleStreamBlockIPA(original, procs, embs) + # for example, ComfyUI internally uses model.add_patches to add loras + bi.add_object_patch(f"diffusion_model.{patch_name}", new_layer) + for i, original in enumerate(flux_model.diffusion_model.single_blocks): + patch_name = f"single_blocks.{i}" + maybe_patched_layer = bi.get_model_object(f"diffusion_model.{patch_name}") + procs = [ip_attn_procs[patch_name]] + embs = [image_emb] + if isinstance(maybe_patched_layer, SingleStreamBlockIPA): + procs = maybe_patched_layer.ip_adapter + procs + embs = maybe_patched_layer.image_emb + embs + # initial ipa models with image embeddings + new_layer = SingleStreamBlockIPA(original, procs, embs) + bi.add_object_patch(f"diffusion_model.{patch_name}", new_layer) + +def is_model_pathched(model): + def test(mod): + if isinstance(mod, DoubleStreamBlockIPA): + return True + else: + for p in mod.children(): + if test(p): + return True + return False + + result = test(model) + return result + + +def forward_orig_ipa( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor|None = None, + control=None, + transformer_options={}, + attn_mask: Tensor = None, +) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y[:,:self.params.vec_in_dim]) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + if isinstance(block, DoubleStreamBlockIPA): # ipadaper + out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], t=args["timesteps"], attn_mask=args.get("attn_mask")) + else: + out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask")) + return out + out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "timesteps": timesteps, "attn_mask": attn_mask}, {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + if isinstance(block, DoubleStreamBlockIPA): # ipadaper + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, t=timesteps, attn_mask=attn_mask) + else: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + if isinstance(block, SingleStreamBlockIPA): # ipadaper + out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], t=args["timesteps"], attn_mask=args.get("attn_mask")) + else: + out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "timesteps": timesteps, "attn_mask": attn_mask}, {"original_block": block_wrap}) + img = out["img"] + else: + if isinstance(block, SingleStreamBlockIPA): # ipadaper + img = block(img, vec=vec, pe=pe, t=timesteps, attn_mask=attn_mask) + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/config_chatglm.json b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/config_chatglm.json new file mode 100644 index 00000000..c6e19300 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/config_chatglm.json @@ -0,0 +1,42 @@ +{ + "_name_or_path": "THUDM/chatglm3-6b-base", + "model_type": "chatglm", + "architectures": [ + "ChatGLMModel" + ], + "auto_map": { + "AutoConfig": "configuration_chatglm.ChatGLMConfig", + "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration", + "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration", + "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration", + "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification" + }, + "add_bias_linear": false, + "add_qkv_bias": true, + "apply_query_key_layer_scaling": true, + "apply_residual_connection_post_layernorm": false, + "attention_dropout": 0.0, + "attention_softmax_in_fp32": true, + "bias_dropout_fusion": true, + "ffn_hidden_size": 13696, + "fp32_residual_connection": false, + "hidden_dropout": 0.0, + "hidden_size": 4096, + "kv_channels": 128, + "layernorm_epsilon": 1e-05, + "multi_query_attention": true, + "multi_query_group_num": 2, + "num_attention_heads": 32, + "num_layers": 28, + "original_rope": true, + "padded_vocab_size": 65024, + "post_layer_norm": true, + "rmsnorm": true, + "seq_length": 32768, + "use_cache": true, + "torch_dtype": "float16", + "transformers_version": "4.30.2", + "tie_word_embeddings": false, + "eos_token_id": 2, + "pad_token_id": 0 +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/configuration_chatglm.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/configuration_chatglm.py new file mode 100644 index 00000000..7a0e01bc --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/configuration_chatglm.py @@ -0,0 +1,60 @@ +from transformers import PretrainedConfig + +class ChatGLMConfig(PretrainedConfig): + model_type = "chatglm" + def __init__( + self, + num_layers=28, + padded_vocab_size=65024, + hidden_size=4096, + ffn_hidden_size=13696, + kv_channels=128, + num_attention_heads=32, + seq_length=2048, + hidden_dropout=0.0, + classifier_dropout=None, + attention_dropout=0.0, + layernorm_epsilon=1e-5, + rmsnorm=True, + apply_residual_connection_post_layernorm=False, + post_layer_norm=True, + add_bias_linear=False, + add_qkv_bias=False, + bias_dropout_fusion=True, + multi_query_attention=False, + multi_query_group_num=1, + apply_query_key_layer_scaling=True, + attention_softmax_in_fp32=True, + fp32_residual_connection=False, + quantization_bit=0, + pre_seq_len=None, + prefix_projection=False, + **kwargs + ): + self.num_layers = num_layers + self.vocab_size = padded_vocab_size + self.padded_vocab_size = padded_vocab_size + self.hidden_size = hidden_size + self.ffn_hidden_size = ffn_hidden_size + self.kv_channels = kv_channels + self.num_attention_heads = num_attention_heads + self.seq_length = seq_length + self.hidden_dropout = hidden_dropout + self.classifier_dropout = classifier_dropout + self.attention_dropout = attention_dropout + self.layernorm_epsilon = layernorm_epsilon + self.rmsnorm = rmsnorm + self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm + self.post_layer_norm = post_layer_norm + self.add_bias_linear = add_bias_linear + self.add_qkv_bias = add_qkv_bias + self.bias_dropout_fusion = bias_dropout_fusion + self.multi_query_attention = multi_query_attention + self.multi_query_group_num = multi_query_group_num + self.apply_query_key_layer_scaling = apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = attention_softmax_in_fp32 + self.fp32_residual_connection = fp32_residual_connection + self.quantization_bit = quantization_bit + self.pre_seq_len = pre_seq_len + self.prefix_projection = prefix_projection + super().__init__(**kwargs) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/modeling_chatglm.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/modeling_chatglm.py new file mode 100644 index 00000000..e25704c7 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/modeling_chatglm.py @@ -0,0 +1,1298 @@ +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any +from copy import deepcopy + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput + +try: + from .configuration_chatglm import ChatGLMConfig +except: + from .configuration_chatglm import ChatGLMConfig + + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm3-6b-base", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=torch.float, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask], dim=-1) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + is_first_forward: bool = True, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + if past_key_values is not None: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True, + "use_cache": use_cache + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, output, history): + content = "" + history = deepcopy(history) + for response in output.split("<|assistant|>"): + metadata, content = response.split("\n", maxsplit=1) + if not metadata.strip(): + content = content.strip() + history.append({"role": "assistant", "metadata": metadata, "content": content}) + content = content.replace("[[θ­η»ƒζ—Άι—΄]]", "2023εΉ΄") + else: + history.append({"role": "assistant", "metadata": metadata, "content": content}) + if history[0]["role"] == "system" and "tools" in history[0]: + content = "\n".join(content.split("\n")[1:-1]) + def tool_call(**kwargs): + return kwargs + parameters = eval(content) + content = {"name": metadata.strip(), "parameters": parameters} + else: + content = {"name": metadata.strip(), "content": content} + return content, history + + @torch.inference_mode() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, + **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = tokenizer.build_chat_input(query, history=history, role=role) + inputs = inputs.to(self.device) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + history.append({"role": role, "content": query}) + response, history = self.process_response(response, history) + return response, history + + @torch.inference_mode() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user", + past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, + logits_processor=None, return_past_key_values=False, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), + tokenizer.get_command("<|observation|>")] + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if past_key_values is None: + inputs = tokenizer.build_chat_input(query, history=history, role=role) + else: + inputs = tokenizer.build_chat_input(query, role=role) + inputs = inputs.to(self.device) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs['attention_mask'] = attention_mask + history.append({"role": role, "content": query}) + for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, + eos_token_id=eos_token_id, return_past_key_values=return_past_key_values, + **gen_kwargs): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + if response and response[-1] != "οΏ½": + response, new_history = self.process_response(response, history) + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.inference_mode() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + model_kwargs["use_cache"] = generation_config.use_cache + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul( + next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0) + ) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self + + +class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.num_labels = config.num_labels + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + + self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) + if config.classifier_dropout is not None: + self.dropout = nn.Dropout(config.classifier_dropout) + else: + self.dropout = None + self.config = config + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + full_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + full_attention_mask=full_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + pooled_hidden_states = hidden_states[-1] + if self.dropout is not None: + pooled_hidden_states = self.dropout(pooled_hidden_states) + logits = self.classifier_head(pooled_hidden_states) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze().float(), labels.squeeze()) + else: + loss = loss_fct(logits.float(), labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) + + if not return_dict: + output = (logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/quantization.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/quantization.py new file mode 100644 index 00000000..c3bb3926 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/quantization.py @@ -0,0 +1,190 @@ +from torch.nn import Linear +from torch.nn.parameter import Parameter + +import bz2 +import torch +import base64 +import ctypes +from transformers.utils import logging + +from typing import List +from functools import partial + +logger = logging.get_logger(__name__) + +try: + from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up + + class Kernel: + def __init__(self, code: bytes, function_names: List[str]): + self.code = code + self._function_names = function_names + self._cmodule = LazyKernelCModule(self.code) + + for name in self._function_names: + setattr(self, name, KernelFunction(self._cmodule, name)) + + quantization_code = "$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" + + kernels = Kernel( + bz2.decompress(base64.b64decode(quantization_code)), + [ + "int4WeightCompression", + "int4WeightExtractionFloat", + "int4WeightExtractionHalf", + "int8WeightExtractionFloat", + "int8WeightExtractionHalf", + ], + ) +except Exception as exception: + kernels = None + logger.warning("Failed to load cpm_kernels:" + str(exception)) + + +class W8A16Linear(torch.autograd.Function): + @staticmethod + def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width): + ctx.inp_shape = inp.size() + ctx.weight_bit_width = weight_bit_width + out_features = quant_w.size(0) + inp = inp.contiguous().view(-1, inp.size(-1)) + weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width) + ctx.weight_shape = weight.size() + output = inp.mm(weight.t()) + ctx.save_for_backward(inp, quant_w, scale_w) + return output.view(*(ctx.inp_shape[:-1] + (out_features,))) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + inp, quant_w, scale_w = ctx.saved_tensors + weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width) + grad_output = grad_output.contiguous().view(-1, weight.size(0)) + grad_input = grad_output.mm(weight) + grad_weight = grad_output.t().mm(inp) + return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None + + +def compress_int4_weight(weight: torch.Tensor): # (n, m) + with torch.cuda.device(weight.device): + n, m = weight.size(0), weight.size(1) + assert m % 2 == 0 + m = m // 2 + out = torch.empty(n, m, dtype=torch.int8, device="cuda") + stream = torch.cuda.current_stream() + + gridDim = (n, 1, 1) + blockDim = (min(round_up(m, 32), 1024), 1, 1) + + kernels.int4WeightCompression( + gridDim, + blockDim, + 0, + stream, + [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)], + ) + return out + + +def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int): + assert scale_list.dtype in [torch.half, torch.bfloat16] + assert weight.dtype in [torch.int8] + if source_bit_width == 8: + return weight.to(scale_list.dtype) * scale_list[:, None] + elif source_bit_width == 4: + func = ( + kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16 + ) + else: + assert False, "Unsupported bit-width" + + with torch.cuda.device(weight.device): + n, m = weight.size(0), weight.size(1) + out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda") + stream = torch.cuda.current_stream() + + gridDim = (n, 1, 1) + blockDim = (min(round_up(m, 32), 1024), 1, 1) + + func( + gridDim, + blockDim, + 0, + stream, + [ + ctypes.c_void_p(weight.data_ptr()), + ctypes.c_void_p(scale_list.data_ptr()), + ctypes.c_void_p(out.data_ptr()), + ctypes.c_int32(n), + ctypes.c_int32(m), + ], + ) + return out + + +class QuantizedLinear(torch.nn.Module): + def __init__(self, weight_bit_width: int, weight, bias=None, device="cpu", dtype=None, empty_init=False, *args, + **kwargs): + super().__init__() + self.weight_bit_width = weight_bit_width + + shape = weight.shape + + if weight is None or empty_init: + self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device) + self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device) + else: + self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1) + self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8) + if weight_bit_width == 4: + self.weight = compress_int4_weight(self.weight) + try: + self.weight = Parameter(self.weight.to(device), requires_grad=False) + except: + self.weight.to(device, dtype=self.weight.dtype) + self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False) + self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None + + def forward(self, input): + output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width) + if self.bias is not None: + output = output + self.bias + return output + + +def quantize(model, weight_bit_width, empty_init=False, device=None): + """Replace fp16 linear with quantized linear""" + for layer in model.layers: + layer.self_attention.query_key_value = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.self_attention.query_key_value.weight.to(torch.cuda.current_device()), + bias=layer.self_attention.query_key_value.bias, + dtype=layer.self_attention.query_key_value.weight.dtype, + device=layer.self_attention.query_key_value.weight.device if device is None else device, + empty_init=empty_init + ) + layer.self_attention.dense = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.self_attention.dense.weight.to(torch.cuda.current_device()), + bias=layer.self_attention.dense.bias, + dtype=layer.self_attention.dense.weight.dtype, + device=layer.self_attention.dense.weight.device if device is None else device, + empty_init=empty_init + ) + layer.mlp.dense_h_to_4h = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()), + bias=layer.mlp.dense_h_to_4h.bias, + dtype=layer.mlp.dense_h_to_4h.weight.dtype, + device=layer.mlp.dense_h_to_4h.weight.device if device is None else device, + empty_init=empty_init + ) + layer.mlp.dense_4h_to_h = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()), + bias=layer.mlp.dense_4h_to_h.bias, + dtype=layer.mlp.dense_4h_to_h.weight.dtype, + device=layer.mlp.dense_4h_to_h.weight.device if device is None else device, + empty_init=empty_init + ) + + return model \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenization_chatglm.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenization_chatglm.py new file mode 100644 index 00000000..0d8b7e56 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenization_chatglm.py @@ -0,0 +1,300 @@ +import json +import os +import re +from typing import List, Optional, Union, Dict +from sentencepiece import SentencePieceProcessor +from transformers import PreTrainedTokenizer +from transformers.utils import logging, PaddingStrategy +from transformers.tokenization_utils_base import EncodedInput, BatchEncoding + +class SPTokenizer: + def __init__(self, model_path: str): + # reload tokenizer + assert os.path.isfile(model_path), model_path + self.sp_model = SentencePieceProcessor(model_file=model_path) + + # BOS / EOS token IDs + self.n_words: int = self.sp_model.vocab_size() + self.bos_id: int = self.sp_model.bos_id() + self.eos_id: int = self.sp_model.eos_id() + self.pad_id: int = self.sp_model.unk_id() + assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() + + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + self.special_tokens = {} + self.index_special_tokens = {} + for token in special_tokens: + self.special_tokens[token] = self.n_words + self.index_special_tokens[self.n_words] = token + self.n_words += 1 + self.role_special_token_expression = "|".join([re.escape(token) for token in role_special_tokens]) + + def tokenize(self, s: str, encode_special_tokens=False): + if encode_special_tokens: + last_index = 0 + t = [] + for match in re.finditer(self.role_special_token_expression, s): + if last_index < match.start(): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()])) + t.append(s[match.start():match.end()]) + last_index = match.end() + if last_index < len(s): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:])) + return t + else: + return self.sp_model.EncodeAsPieces(s) + + def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: + assert type(s) is str + t = self.sp_model.encode(s) + if bos: + t = [self.bos_id] + t + if eos: + t = t + [self.eos_id] + return t + + def decode(self, t: List[int]) -> str: + text, buffer = "", [] + for token in t: + if token in self.index_special_tokens: + if buffer: + text += self.sp_model.decode(buffer) + buffer = [] + text += self.index_special_tokens[token] + else: + buffer.append(token) + if buffer: + text += self.sp_model.decode(buffer) + return text + + def decode_tokens(self, tokens: List[str]) -> str: + text = self.sp_model.DecodePieces(tokens) + return text + + def convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + if token in self.special_tokens: + return self.special_tokens[token] + return self.sp_model.PieceToId(token) + + def convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + if index in self.index_special_tokens: + return self.index_special_tokens[index] + if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0: + return "" + return self.sp_model.IdToPiece(index) + + +class ChatGLMTokenizer(PreTrainedTokenizer): + vocab_files_names = {"vocab_file": "tokenizer.model"} + + model_input_names = ["input_ids", "attention_mask", "position_ids"] + + def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False, + **kwargs): + self.name = "GLMTokenizer" + + self.vocab_file = vocab_file + self.tokenizer = SPTokenizer(vocab_file) + self.special_tokens = { + "": self.tokenizer.bos_id, + "": self.tokenizer.eos_id, + "": self.tokenizer.pad_id + } + self.encode_special_tokens = encode_special_tokens + super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, + encode_special_tokens=encode_special_tokens, + **kwargs) + + def get_command(self, token): + if token in self.special_tokens: + return self.special_tokens[token] + assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" + return self.tokenizer.special_tokens[token] + + @property + def unk_token(self) -> str: + return "" + + @property + def pad_token(self) -> str: + return "" + + @property + def pad_token_id(self): + return self.get_command("") + + @property + def eos_token(self) -> str: + return "" + + @property + def eos_token_id(self): + return self.get_command("") + + @property + def vocab_size(self): + return self.tokenizer.n_words + + def get_vocab(self): + """ Returns vocab as a dict """ + vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text, **kwargs): + return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens) + + def _convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + return self.tokenizer.convert_token_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.tokenizer.convert_id_to_token(index) + + def convert_tokens_to_string(self, tokens: List[str]) -> str: + return self.tokenizer.decode_tokens(tokens) + + def save_vocabulary(self, save_directory, filename_prefix=None): + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + filename_prefix (`str`, *optional*): + An optional prefix to add to the named of the saved files. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, self.vocab_files_names["vocab_file"] + ) + else: + vocab_file = save_directory + + with open(self.vocab_file, 'rb') as fin: + proto_str = fin.read() + + with open(vocab_file, "wb") as writer: + writer.write(proto_str) + + return (vocab_file,) + + def get_prefix_tokens(self): + prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] + return prefix_tokens + + def build_single_message(self, role, metadata, message): + assert role in ["system", "user", "assistant", "observation"], role + role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n") + message_tokens = self.tokenizer.encode(message) + tokens = role_tokens + message_tokens + return tokens + + def build_chat_input(self, query, history=None, role="user"): + if history is None: + history = [] + input_ids = [] + for item in history: + content = item["content"] + if item["role"] == "system" and "tools" in item: + content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False) + input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content)) + input_ids.extend(self.build_single_message(role, "", query)) + input_ids.extend([self.get_command("<|assistant|>")]) + return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True) + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + prefix_tokens = self.get_prefix_tokens() + token_ids_0 = prefix_tokens + token_ids_0 + if token_ids_1 is not None: + token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] + return token_ids_0 + + def _pad( + self, + encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], + max_length: Optional[int] = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + **kwargs + ) -> dict: + """ + Pad encoded inputs (on left/right and up to predefined length or max length in the batch) + + Args: + encoded_inputs: + Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). + max_length: maximum length of the returned list and optionally padding length (see below). + Will truncate by taking into account the special tokens. + padding_strategy: PaddingStrategy to use for padding. + + - PaddingStrategy.LONGEST Pad to the longest sequence in the batch + - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) + - PaddingStrategy.DO_NOT_PAD: Do not pad + The tokenizer padding sides are defined in self.padding_side: + + - 'left': pads on the left of the sequences + - 'right': pads on the right of the sequences + pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + return_attention_mask: + (optional) Set to False to avoid returning attention mask (default: set to model specifics) + """ + # Load from model defaults + assert self.padding_side == "left" + + required_input = encoded_inputs[self.model_input_names[0]] + seq_length = len(required_input) + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * seq_length + + if "position_ids" not in encoded_inputs: + encoded_inputs["position_ids"] = list(range(seq_length)) + + if needs_to_be_padded: + difference = max_length - len(required_input) + + if "attention_mask" in encoded_inputs: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "position_ids" in encoded_inputs: + encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + + return encoded_inputs \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/tokenizer.model b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/tokenizer.model new file mode 100644 index 00000000..c8336ad0 Binary files /dev/null and b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/tokenizer.model differ diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/tokenizer_config.json b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/tokenizer_config.json new file mode 100644 index 00000000..f6f13c88 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/tokenizer_config.json @@ -0,0 +1,12 @@ +{ + "name_or_path": "THUDM/chatglm3-6b-base", + "remove_space": false, + "do_lower_case": false, + "tokenizer_class": "ChatGLMTokenizer", + "auto_map": { + "AutoTokenizer": [ + "tokenization_chatglm.ChatGLMTokenizer", + null + ] + } +} diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/vocab.txt b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/vocab.txt new file mode 100644 index 00000000..c8336ad0 Binary files /dev/null and b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/chatglm/tokenizer/vocab.txt differ diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/clip_vision_config_vitl_336.json b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/clip_vision_config_vitl_336.json new file mode 100644 index 00000000..20a01914 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/clip_vision_config_vitl_336.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 336, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/loader.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/loader.py new file mode 100644 index 00000000..9902286a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/loader.py @@ -0,0 +1,303 @@ +import json +import os +import torch +import subprocess +import sys +import comfy.supported_models +import comfy.model_patcher +import comfy.model_management +import comfy.model_detection as model_detection +import comfy.model_base as model_base +from comfy.model_base import sdxl_pooled, CLIPEmbeddingNoiseAugmentation, Timestep, ModelType +from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel +from comfy.clip_vision import ClipVisionModel, Output +from comfy.utils import load_torch_file +from .chatglm.modeling_chatglm import ChatGLMModel, ChatGLMConfig +from .chatglm.tokenization_chatglm import ChatGLMTokenizer + +class KolorsUNetModel(UNetModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.encoder_hid_proj = torch.nn.Linear(4096, 2048, bias=True) + + def forward(self, *args, **kwargs): + with torch.cuda.amp.autocast(enabled=True): + if "context" in kwargs: + kwargs["context"] = self.encoder_hid_proj(kwargs["context"]) + result = super().forward(*args, **kwargs) + return result + +class KolorsSDXL(model_base.SDXL): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + model_base.BaseModel.__init__(self, model_config, model_type, device=device, unet_model=KolorsUNetModel) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([target_height]))) + out.append(self.embedder(torch.Tensor([target_width]))) + flat = torch.flatten(torch.cat(out)).unsqueeze( + dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class Kolors(comfy.supported_models.SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 5632, + "use_temporal_attention": False, + } + + def get_model(self, state_dict, prefix="", device=None): + out = KolorsSDXL(self, model_type=self.model_type(state_dict, prefix), device=device, ) + out.__class__ = model_base.SDXL + if self.inpaint_model(): + out.set_inpaint() + return out + +def kolors_unet_config_from_diffusers_unet(state_dict, dtype=None): + match = {} + transformer_depth = [] + + attn_res = 1 + count_blocks = model_detection.count_blocks + down_blocks = count_blocks(state_dict, "down_blocks.{}") + for i in range(down_blocks): + attn_blocks = count_blocks( + state_dict, "down_blocks.{}.attentions.".format(i) + '{}') + res_blocks = count_blocks( + state_dict, "down_blocks.{}.resnets.".format(i) + '{}') + for ab in range(attn_blocks): + transformer_count = count_blocks( + state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') + transformer_depth.append(transformer_count) + if transformer_count > 0: + match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format( + i, ab)].shape[1] + + attn_res *= 2 + if attn_blocks == 0: + for i in range(res_blocks): + transformer_depth.append(0) + + match["transformer_depth"] = transformer_depth + + match["model_channels"] = state_dict["conv_in.weight"].shape[0] + match["in_channels"] = state_dict["conv_in.weight"].shape[1] + match["adm_in_channels"] = None + if "class_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] + elif "add_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] + + Kolors = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + Kolors_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, + 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 9, + 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + Kolors_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, + 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 8, + 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, + 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, + 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, + 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, + 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, + 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, + 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 0, + 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, + 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + supported_models = [Kolors, Kolors_inpaint, + Kolors_ip2p, SDXL, SDXL_mid_cnet, SDXL_small_cnet] + + + for unet_config in supported_models: + matches = True + for k in match: + if match[k] != unet_config[k]: + # print("key {} does not match".format(k), match[k], "||", unet_config[k]) + matches = False + break + if matches: + return model_detection.convert_config(unet_config) + return None + +# chatglm3 model +class chatGLM3Model(torch.nn.Module): + def __init__(self, textmodel_json_config=None, device='cpu', offload_device='cpu', model_path=None): + super().__init__() + if model_path is None: + raise ValueError("model_path is required") + self.device = device + if textmodel_json_config is None: + textmodel_json_config = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "chatglm", + "config_chatglm.json" + ) + with open(textmodel_json_config, 'r') as file: + config = json.load(file) + textmodel_json_config = ChatGLMConfig(**config) + is_accelerate_available = False + try: + from accelerate import init_empty_weights + from accelerate.utils import set_module_tensor_to_device + is_accelerate_available = True + except: + pass + + from contextlib import nullcontext + with (init_empty_weights() if is_accelerate_available else nullcontext()): + with torch.no_grad(): + print('torch version:', torch.__version__) + self.text_encoder = ChatGLMModel(textmodel_json_config).eval() + if '4bit' in model_path: + try: + import cpm_kernels + except ImportError: + print("Installing cpm_kernels...") + subprocess.run([sys.executable, "-m", "pip", "install", "cpm_kernels"], check=True) + pass + self.text_encoder.quantize(4) + elif '8bit' in model_path: + self.text_encoder.quantize(8) + + sd = load_torch_file(model_path) + if is_accelerate_available: + for key in sd: + set_module_tensor_to_device(self.text_encoder, key, device=offload_device, value=sd[key]) + else: + print("WARNING: Accelerate not available, use load_state_dict load model") + self.text_encoder.load_state_dict() + +def load_chatglm3(model_path=None): + if model_path is None: + return + + load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + + glm3model = chatGLM3Model( + device=load_device, + offload_device=offload_device, + model_path=model_path + ) + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'chatglm', "tokenizer") + tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path) + text_encoder = glm3model.text_encoder + return {"text_encoder":text_encoder, "tokenizer":tokenizer} + + +# clipvision model +def load_clipvision_vitl_336(path): + sd = load_torch_file(path) + if "vision_model.encoder.layers.22.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") + else: + raise Exception("Unsupported clip vision model") + clip = ClipVisionModel(json_config) + m, u = clip.load_sd(sd) + if len(m) > 0: + print("missing clip vision: {}".format(m)) + u = set(u) + keys = list(sd.keys()) + for k in keys: + if k not in u: + t = sd.pop(k) + del t + return clip + +class applyKolorsUnet: + def __enter__(self): + import comfy.ldm.modules.diffusionmodules.openaimodel + import comfy.utils + import comfy.clip_vision + + self.original_UNET_MAP_BASIC = comfy.utils.UNET_MAP_BASIC.copy() + comfy.utils.UNET_MAP_BASIC.add(("encoder_hid_proj.weight", "encoder_hid_proj.weight"),) + comfy.utils.UNET_MAP_BASIC.add(("encoder_hid_proj.bias", "encoder_hid_proj.bias"),) + + self.original_unet_config_from_diffusers_unet = model_detection.unet_config_from_diffusers_unet + model_detection.unet_config_from_diffusers_unet = kolors_unet_config_from_diffusers_unet + + import comfy.supported_models + self.original_supported_models = comfy.supported_models.models + comfy.supported_models.models = [Kolors] + + self.original_load_clipvision_from_sd = comfy.clip_vision.load_clipvision_from_sd + comfy.clip_vision.load_clipvision_from_sd = load_clipvision_vitl_336 + + def __exit__(self, type, value, traceback): + import comfy.ldm.modules.diffusionmodules.openaimodel + import comfy.utils + import comfy.supported_models + import comfy.clip_vision + + comfy.utils.UNET_MAP_BASIC = self.original_UNET_MAP_BASIC + + model_detection.unet_config_from_diffusers_unet = self.original_unet_config_from_diffusers_unet + comfy.supported_models.models = self.original_supported_models + + comfy.clip_vision.load_clipvision_from_sd = self.original_load_clipvision_from_sd + + +def is_kolors_model(model): + unet_config = model.model.model_config.unet_config if hasattr(model, 'model') else None + if unet_config and "adm_in_channels" in unet_config and unet_config["adm_in_channels"] == 5632: + return True + else: + return False \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/model_patch.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/model_patch.py new file mode 100644 index 00000000..7900a0a1 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/model_patch.py @@ -0,0 +1,66 @@ +import torch +from torch.nn import Linear +from types import MethodType +import comfy.model_management +import comfy.samplers +from comfy.cldm.cldm import ControlNet +from comfy.controlnet import ControlLora + +def patch_controlnet(model, control_net): + import comfy.controlnet + if isinstance(control_net, ControlLora): + del_keys = [] + for k in control_net.control_weights: + if k.startswith("label_emb.0.0."): + del_keys.append(k) + + for k in del_keys: + control_net.control_weights.pop(k) + + super_pre_run = ControlLora.pre_run + super_copy = ControlLora.copy + + super_forward = ControlNet.forward + + def KolorsControlNet_forward(self, x, hint, timesteps, context, **kwargs): + with torch.cuda.amp.autocast(enabled=True): + context = model.model.diffusion_model.encoder_hid_proj(context) + return super_forward(self, x, hint, timesteps, context, **kwargs) + + def KolorsControlLora_pre_run(self, *args, **kwargs): + result = super_pre_run(self, *args, **kwargs) + + if hasattr(self, "control_model"): + self.control_model.forward = MethodType( + KolorsControlNet_forward, self.control_model) + return result + + control_net.pre_run = MethodType( + KolorsControlLora_pre_run, control_net) + + def KolorsControlLora_copy(self, *args, **kwargs): + c = super_copy(self, *args, **kwargs) + c.pre_run = MethodType( + KolorsControlLora_pre_run, c) + return c + + control_net.copy = MethodType(KolorsControlLora_copy, control_net) + + elif isinstance(control_net, comfy.controlnet.ControlNet): + model_label_emb = model.model.diffusion_model.label_emb + control_net.control_model.label_emb = model_label_emb + control_net.control_model_wrapped.model.label_emb = model_label_emb + super_forward = ControlNet.forward + + def KolorsControlNet_forward(self, x, hint, timesteps, context, **kwargs): + with torch.cuda.amp.autocast(enabled=True): + context = model.model.diffusion_model.encoder_hid_proj(context) + return super_forward(self, x, hint, timesteps, context, **kwargs) + + control_net.control_model.forward = MethodType( + KolorsControlNet_forward, control_net.control_model) + + else: + raise NotImplementedError(f"Type {control_net} not supported for KolorsControlNetPatch") + + return control_net diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/text_encode.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/text_encode.py new file mode 100644 index 00000000..b52e1bb2 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/kolors/text_encode.py @@ -0,0 +1,105 @@ +import re +import random +import gc +import comfy.model_management as mm +from nodes import ConditioningConcat, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine + +def chatglm3_text_encode(chatglm3_model, prompt, clean_gpu=False): + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + if clean_gpu: + mm.unload_all_models() + mm.soft_empty_cache() + # Function to randomly select an option from the brackets + + def choose_random_option(match): + options = match.group(1).split('|') + return random.choice(options) + + prompt = re.sub(r'\{([^{}]*)\}', choose_random_option, prompt) + + if "|" in prompt: + prompt = prompt.split("|") + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + + # Define tokenizers and text encoders + tokenizer = chatglm3_model['tokenizer'] + text_encoder = chatglm3_model['text_encoder'] + text_encoder.to(device) + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=256, + truncation=True, + return_tensors="pt", + ).to(device) + + output = text_encoder( + input_ids=text_inputs['input_ids'], + attention_mask=text_inputs['attention_mask'], + position_ids=text_inputs['position_ids'], + output_hidden_states=True) + + # [batch_size, 77, 4096] + prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone() + text_proj = output.hidden_states[-1][-1, :, :].clone() # [batch_size, 4096] + bs_embed, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, 1, 1) + prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1) + + bs_embed = text_proj.shape[0] + text_proj = text_proj.repeat(1, 1).view(bs_embed, -1) + text_encoder.to(offload_device) + if clean_gpu: + mm.soft_empty_cache() + gc.collect() + return [[prompt_embeds, {"pooled_output": text_proj},]] + +def chatglm3_adv_text_encode(chatglm3_model, text, clean_gpu=False): + time_start = 0 + time_end = 1 + match = re.search(r'TIMESTEP.*$', text) + if match: + timestep = match.group() + timestep = timestep.split(' ') + timestep = timestep[0] + text = text.replace(timestep, '') + value = timestep.split(':') + if len(value) >= 3: + time_start = float(value[1]) + time_end = float(value[2]) + elif len(value) == 2: + time_start = float(value[1]) + time_end = 1 + elif len(value) == 1: + time_start = 0.1 + time_end = 1 + + + pass3 = [x.strip() for x in text.split("BREAK")] + pass3 = [x for x in pass3 if x != ''] + + if len(pass3) == 0: + pass3 = [''] + + conditioning = None + + for text in pass3: + cond = chatglm3_text_encode(chatglm3_model, text, clean_gpu) + if conditioning is not None: + conditioning = ConditioningConcat().concat(conditioning, cond)[0] + else: + conditioning = cond + + # setTimeStepRange + if time_start > 0 or time_end < 1: + conditioning_2, = ConditioningSetTimestepRange().set_range(conditioning, 0, time_start) + conditioning_1, = ConditioningZeroOut().zero_out(conditioning) + conditioning_1, = ConditioningSetTimestepRange().set_range(conditioning_1, time_start, time_end) + conditioning, = ConditioningCombine().combine(conditioning_1, conditioning_2) + + return conditioning \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/__init__.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/__init__.py new file mode 100644 index 00000000..871daf0c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/__init__.py @@ -0,0 +1,219 @@ +#credit to huchenlei for this module +#from https://github.com/huchenlei/ComfyUI-layerdiffuse +import torch +import comfy.model_management +import comfy.lora +import copy +from typing import Optional +from enum import Enum +from comfy.utils import load_torch_file +from comfy.conds import CONDRegular +from comfy_extras.nodes_compositing import JoinImageWithAlpha +try: + from .model import ModelPatcher, TransparentVAEDecoder, calculate_weight_adjust_channel +except: + ModelPatcher, TransparentVAEDecoder, calculate_weight_adjust_channel = None, None, None +from .attension_sharing import AttentionSharingPatcher +from ...config import LAYER_DIFFUSION, LAYER_DIFFUSION_DIR, LAYER_DIFFUSION_VAE +from ...libs.utils import to_lora_patch_dict, get_local_filepath, get_sd_version + +load_layer_model_state_dict = load_torch_file +class LayerMethod(Enum): + FG_ONLY_ATTN = "Attention Injection" + FG_ONLY_CONV = "Conv Injection" + FG_TO_BLEND = "Foreground" + FG_BLEND_TO_BG = "Foreground to Background" + BG_TO_BLEND = "Background" + BG_BLEND_TO_FG = "Background to Foreground" + EVERYTHING = "Everything" + +class LayerDiffuse: + + def __init__(self) -> None: + self.vae_transparent_decoder = None + self.frames = 1 + + def get_layer_diffusion_method(self, method, has_blend_latent): + method = LayerMethod(method) + if method == LayerMethod.BG_TO_BLEND and has_blend_latent: + method = LayerMethod.BG_BLEND_TO_FG + elif method == LayerMethod.FG_TO_BLEND and has_blend_latent: + method = LayerMethod.FG_BLEND_TO_BG + return method + + def apply_layer_c_concat(self, cond, uncond, c_concat): + def write_c_concat(cond): + new_cond = [] + for t in cond: + n = [t[0], t[1].copy()] + if "model_conds" not in n[1]: + n[1]["model_conds"] = {} + n[1]["model_conds"]["c_concat"] = CONDRegular(c_concat) + new_cond.append(n) + return new_cond + + return (write_c_concat(cond), write_c_concat(uncond)) + + def apply_layer_diffusion(self, model, method, weight, samples, blend_samples, positive, negative, image=None, additional_cond=(None, None, None)): + control_img: Optional[torch.TensorType] = None + sd_version = get_sd_version(model) + model_url = LAYER_DIFFUSION[method.value][sd_version]["model_url"] + + if image is not None: + image = image.movedim(-1, 1) + + try: + if hasattr(comfy.lora, "calculate_weight"): + comfy.lora.calculate_weight = calculate_weight_adjust_channel(comfy.lora.calculate_weight) + else: + ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight) + except: + pass + + if method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN] and sd_version == 'sd1': + self.frames = 1 + elif method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG, LayerMethod.FG_BLEND_TO_BG] and sd_version == 'sd1': + self.frames = 2 + batch_size, _, height, width = samples['samples'].shape + if batch_size % 2 != 0: + raise Exception(f"The batch size should be a multiple of 2. ζ‰Ήζ¬‘ε€§ε°ιœ€δΈΊ2ηš„ε€ζ•°") + control_img = image + elif method == LayerMethod.EVERYTHING and sd_version == 'sd1': + batch_size, _, height, width = samples['samples'].shape + self.frames = 3 + if batch_size % 3 != 0: + raise Exception(f"The batch size should be a multiple of 3. ζ‰Ήζ¬‘ε€§ε°ιœ€δΈΊ3ηš„ε€ζ•°") + if model_url is None: + raise Exception(f"{method.value} is not supported for {sd_version} model") + + model_path = get_local_filepath(model_url, LAYER_DIFFUSION_DIR) + layer_lora_state_dict = load_layer_model_state_dict(model_path) + work_model = model.clone() + if sd_version == 'sd1': + patcher = AttentionSharingPatcher( + work_model, self.frames, use_control=control_img is not None + ) + patcher.load_state_dict(layer_lora_state_dict, strict=True) + if control_img is not None: + patcher.set_control(control_img) + else: + layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict) + work_model.add_patches(layer_lora_patch_dict, weight) + + # cond_contact + if method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.FG_ONLY_CONV]: + samp_model = work_model + elif sd_version == 'sdxl': + if method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND]: + c_concat = model.model.latent_format.process_in(samples["samples"]) + else: + c_concat = model.model.latent_format.process_in(torch.cat([samples["samples"], blend_samples["samples"]], dim=1)) + samp_model, positive, negative = (work_model,) + self.apply_layer_c_concat(positive, negative, c_concat) + elif sd_version == 'sd1': + if method in [LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG]: + additional_cond = (additional_cond[0], None) + elif method in [LayerMethod.FG_TO_BLEND, LayerMethod.FG_BLEND_TO_BG]: + additional_cond = (additional_cond[1], None) + + work_model.model_options.setdefault("transformer_options", {}) + work_model.model_options["transformer_options"]["cond_overwrite"] = [ + cond[0][0] if cond is not None else None + for cond in additional_cond + ] + samp_model = work_model + + return samp_model, positive, negative + + def join_image_with_alpha(self, image, alpha): + out = image.movedim(-1, 1) + if out.shape[1] == 3: # RGB + out = torch.cat([out, torch.ones_like(out[:, :1, :, :])], dim=1) + for i in range(out.shape[0]): + out[i, 3, :, :] = alpha + return out.movedim(1, -1) + + def image_to_alpha(self, image, latent): + pixel = image.movedim(-1, 1) # [B, H, W, C] => [B, C, H, W] + decoded = [] + sub_batch_size = 16 + for start_idx in range(0, latent.shape[0], sub_batch_size): + decoded.append( + self.vae_transparent_decoder.decode_pixel( + pixel[start_idx: start_idx + sub_batch_size], + latent[start_idx: start_idx + sub_batch_size], + ) + ) + pixel_with_alpha = torch.cat(decoded, dim=0) + # [B, C, H, W] => [B, H, W, C] + pixel_with_alpha = pixel_with_alpha.movedim(1, -1) + image = pixel_with_alpha[..., 1:] + alpha = pixel_with_alpha[..., 0] + + alpha = 1.0 - alpha + try: + new_images, = JoinImageWithAlpha().execute(image, alpha) + except: + new_images, = JoinImageWithAlpha().join_image_with_alpha(image, alpha) + return new_images, alpha + + def make_3d_mask(self, mask): + if len(mask.shape) == 4: + return mask.squeeze(0) + + elif len(mask.shape) == 2: + return mask.unsqueeze(0) + + return mask + + def masks_to_list(self, masks): + if masks is None: + empty_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + return ([empty_mask],) + + res = [] + + for mask in masks: + res.append(mask) + + return [self.make_3d_mask(x) for x in res] + + def layer_diffusion_decode(self, layer_diffusion_method, latent, blend_samples, samp_images, model): + alpha = [] + if layer_diffusion_method is not None: + sd_version = get_sd_version(model) + if sd_version not in ['sdxl', 'sd1']: + raise Exception(f"Only SDXL and SD1.5 model supported for Layer Diffusion") + method = self.get_layer_diffusion_method(layer_diffusion_method, blend_samples is not None) + sd15_allow = True if sd_version == 'sd1' and method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.EVERYTHING, LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG] else False + sdxl_allow = True if sd_version == 'sdxl' and method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN, LayerMethod.BG_BLEND_TO_FG] else False + if sdxl_allow or sd15_allow: + if self.vae_transparent_decoder is None: + model_url = LAYER_DIFFUSION_VAE['decode'][sd_version]["model_url"] + if model_url is None: + raise Exception(f"{method.value} is not supported for {sd_version} model") + decoder_file = get_local_filepath(model_url, LAYER_DIFFUSION_DIR) + self.vae_transparent_decoder = TransparentVAEDecoder( + load_torch_file(decoder_file), + device=comfy.model_management.get_torch_device(), + dtype=(torch.float16 if comfy.model_management.should_use_fp16() else torch.float32), + ) + if method in [LayerMethod.EVERYTHING, LayerMethod.BG_BLEND_TO_FG, LayerMethod.BG_TO_BLEND]: + new_images = [] + sliced_samples = copy.copy({"samples": latent}) + for index in range(len(samp_images)): + if index % self.frames == 0: + img = samp_images[index::self.frames] + alpha_images, _alpha = self.image_to_alpha(img, sliced_samples["samples"][index::self.frames]) + alpha.append(self.make_3d_mask(_alpha[0])) + new_images.append(alpha_images[0]) + else: + new_images.append(samp_images[index]) + else: + new_images, alpha = self.image_to_alpha(samp_images, latent) + else: + new_images = samp_images + else: + new_images = samp_images + + + return (new_images, samp_images, alpha) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/attension_sharing.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/attension_sharing.py new file mode 100644 index 00000000..89904eec --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/attension_sharing.py @@ -0,0 +1,359 @@ +# Currently only sd15 + +import functools +import torch +import einops + +from comfy import model_management, utils +from comfy.ldm.modules.attention import optimized_attention + +module_mapping_sd15 = { + 0: "input_blocks.1.1.transformer_blocks.0.attn1", + 1: "input_blocks.1.1.transformer_blocks.0.attn2", + 2: "input_blocks.2.1.transformer_blocks.0.attn1", + 3: "input_blocks.2.1.transformer_blocks.0.attn2", + 4: "input_blocks.4.1.transformer_blocks.0.attn1", + 5: "input_blocks.4.1.transformer_blocks.0.attn2", + 6: "input_blocks.5.1.transformer_blocks.0.attn1", + 7: "input_blocks.5.1.transformer_blocks.0.attn2", + 8: "input_blocks.7.1.transformer_blocks.0.attn1", + 9: "input_blocks.7.1.transformer_blocks.0.attn2", + 10: "input_blocks.8.1.transformer_blocks.0.attn1", + 11: "input_blocks.8.1.transformer_blocks.0.attn2", + 12: "output_blocks.3.1.transformer_blocks.0.attn1", + 13: "output_blocks.3.1.transformer_blocks.0.attn2", + 14: "output_blocks.4.1.transformer_blocks.0.attn1", + 15: "output_blocks.4.1.transformer_blocks.0.attn2", + 16: "output_blocks.5.1.transformer_blocks.0.attn1", + 17: "output_blocks.5.1.transformer_blocks.0.attn2", + 18: "output_blocks.6.1.transformer_blocks.0.attn1", + 19: "output_blocks.6.1.transformer_blocks.0.attn2", + 20: "output_blocks.7.1.transformer_blocks.0.attn1", + 21: "output_blocks.7.1.transformer_blocks.0.attn2", + 22: "output_blocks.8.1.transformer_blocks.0.attn1", + 23: "output_blocks.8.1.transformer_blocks.0.attn2", + 24: "output_blocks.9.1.transformer_blocks.0.attn1", + 25: "output_blocks.9.1.transformer_blocks.0.attn2", + 26: "output_blocks.10.1.transformer_blocks.0.attn1", + 27: "output_blocks.10.1.transformer_blocks.0.attn2", + 28: "output_blocks.11.1.transformer_blocks.0.attn1", + 29: "output_blocks.11.1.transformer_blocks.0.attn2", + 30: "middle_block.1.transformer_blocks.0.attn1", + 31: "middle_block.1.transformer_blocks.0.attn2", +} + + +def compute_cond_mark(cond_or_uncond, sigmas): + cond_or_uncond_size = int(sigmas.shape[0]) + + cond_mark = [] + for cx in cond_or_uncond: + cond_mark += [cx] * cond_or_uncond_size + + cond_mark = torch.Tensor(cond_mark).to(sigmas) + return cond_mark + + +class LoRALinearLayer(torch.nn.Module): + def __init__(self, in_features: int, out_features: int, rank: int = 256, org=None): + super().__init__() + self.down = torch.nn.Linear(in_features, rank, bias=False) + self.up = torch.nn.Linear(rank, out_features, bias=False) + self.org = [org] + + def forward(self, h): + org_weight = self.org[0].weight.to(h) + org_bias = self.org[0].bias.to(h) if self.org[0].bias is not None else None + down_weight = self.down.weight + up_weight = self.up.weight + final_weight = org_weight + torch.mm(up_weight, down_weight) + return torch.nn.functional.linear(h, final_weight, org_bias) + + +class AttentionSharingUnit(torch.nn.Module): + # `transformer_options` passed to the most recent BasicTransformerBlock.forward + # call. + transformer_options: dict = {} + + def __init__(self, module, frames=2, use_control=True, rank=256): + super().__init__() + + self.heads = module.heads + self.frames = frames + self.original_module = [module] + q_in_channels, q_out_channels = ( + module.to_q.in_features, + module.to_q.out_features, + ) + k_in_channels, k_out_channels = ( + module.to_k.in_features, + module.to_k.out_features, + ) + v_in_channels, v_out_channels = ( + module.to_v.in_features, + module.to_v.out_features, + ) + o_in_channels, o_out_channels = ( + module.to_out[0].in_features, + module.to_out[0].out_features, + ) + + hidden_size = k_out_channels + + self.to_q_lora = [ + LoRALinearLayer(q_in_channels, q_out_channels, rank, module.to_q) + for _ in range(self.frames) + ] + self.to_k_lora = [ + LoRALinearLayer(k_in_channels, k_out_channels, rank, module.to_k) + for _ in range(self.frames) + ] + self.to_v_lora = [ + LoRALinearLayer(v_in_channels, v_out_channels, rank, module.to_v) + for _ in range(self.frames) + ] + self.to_out_lora = [ + LoRALinearLayer(o_in_channels, o_out_channels, rank, module.to_out[0]) + for _ in range(self.frames) + ] + + self.to_q_lora = torch.nn.ModuleList(self.to_q_lora) + self.to_k_lora = torch.nn.ModuleList(self.to_k_lora) + self.to_v_lora = torch.nn.ModuleList(self.to_v_lora) + self.to_out_lora = torch.nn.ModuleList(self.to_out_lora) + + self.temporal_i = torch.nn.Linear( + in_features=hidden_size, out_features=hidden_size + ) + self.temporal_n = torch.nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6 + ) + self.temporal_q = torch.nn.Linear( + in_features=hidden_size, out_features=hidden_size + ) + self.temporal_k = torch.nn.Linear( + in_features=hidden_size, out_features=hidden_size + ) + self.temporal_v = torch.nn.Linear( + in_features=hidden_size, out_features=hidden_size + ) + self.temporal_o = torch.nn.Linear( + in_features=hidden_size, out_features=hidden_size + ) + + self.control_convs = None + + if use_control: + self.control_convs = [ + torch.nn.Sequential( + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + torch.nn.Conv2d(256, hidden_size, kernel_size=1), + ) + for _ in range(self.frames) + ] + self.control_convs = torch.nn.ModuleList(self.control_convs) + + self.control_signals = None + + def forward(self, h, context=None, value=None): + transformer_options = self.transformer_options + + modified_hidden_states = einops.rearrange( + h, "(b f) d c -> f b d c", f=self.frames + ) + + if self.control_convs is not None: + context_dim = int(modified_hidden_states.shape[2]) + control_outs = [] + for f in range(self.frames): + control_signal = self.control_signals[context_dim].to( + modified_hidden_states + ) + control = self.control_convs[f](control_signal) + control = einops.rearrange(control, "b c h w -> b (h w) c") + control_outs.append(control) + control_outs = torch.stack(control_outs, dim=0) + modified_hidden_states = modified_hidden_states + control_outs.to( + modified_hidden_states + ) + + if context is None: + framed_context = modified_hidden_states + else: + framed_context = einops.rearrange( + context, "(b f) d c -> f b d c", f=self.frames + ) + + framed_cond_mark = einops.rearrange( + compute_cond_mark( + transformer_options["cond_or_uncond"], + transformer_options["sigmas"], + ), + "(b f) -> f b", + f=self.frames, + ).to(modified_hidden_states) + + attn_outs = [] + for f in range(self.frames): + fcf = framed_context[f] + + if context is not None: + cond_overwrite = transformer_options.get("cond_overwrite", []) + if len(cond_overwrite) > f: + cond_overwrite = cond_overwrite[f] + else: + cond_overwrite = None + if cond_overwrite is not None: + cond_mark = framed_cond_mark[f][:, None, None] + fcf = cond_overwrite.to(fcf) * (1.0 - cond_mark) + fcf * cond_mark + + q = self.to_q_lora[f](modified_hidden_states[f]) + k = self.to_k_lora[f](fcf) + v = self.to_v_lora[f](fcf) + o = optimized_attention(q, k, v, self.heads) + o = self.to_out_lora[f](o) + o = self.original_module[0].to_out[1](o) + attn_outs.append(o) + + attn_outs = torch.stack(attn_outs, dim=0) + modified_hidden_states = modified_hidden_states + attn_outs.to( + modified_hidden_states + ) + modified_hidden_states = einops.rearrange( + modified_hidden_states, "f b d c -> (b f) d c", f=self.frames + ) + + x = modified_hidden_states + x = self.temporal_n(x) + x = self.temporal_i(x) + d = x.shape[1] + + x = einops.rearrange(x, "(b f) d c -> (b d) f c", f=self.frames) + + q = self.temporal_q(x) + k = self.temporal_k(x) + v = self.temporal_v(x) + + x = optimized_attention(q, k, v, self.heads) + x = self.temporal_o(x) + x = einops.rearrange(x, "(b d) f c -> (b f) d c", d=d) + + modified_hidden_states = modified_hidden_states + x + + return modified_hidden_states - h + + @classmethod + def hijack_transformer_block(cls): + def register_get_transformer_options(func): + @functools.wraps(func) + def forward(self, x, context=None, transformer_options={}): + cls.transformer_options = transformer_options + return func(self, x, context, transformer_options) + + return forward + + from comfy.ldm.modules.attention import BasicTransformerBlock + + BasicTransformerBlock.forward = register_get_transformer_options( + BasicTransformerBlock.forward + ) + + +AttentionSharingUnit.hijack_transformer_block() + + +class AdditionalAttentionCondsEncoder(torch.nn.Module): + def __init__(self): + super().__init__() + + self.blocks_0 = torch.nn.Sequential( + torch.nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2), + torch.nn.SiLU(), + torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2), + torch.nn.SiLU(), + torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2), + torch.nn.SiLU(), + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + ) # 64*64*256 + + self.blocks_1 = torch.nn.Sequential( + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2), + torch.nn.SiLU(), + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + ) # 32*32*256 + + self.blocks_2 = torch.nn.Sequential( + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2), + torch.nn.SiLU(), + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + ) # 16*16*256 + + self.blocks_3 = torch.nn.Sequential( + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2), + torch.nn.SiLU(), + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1), + torch.nn.SiLU(), + ) # 8*8*256 + + self.blks = [self.blocks_0, self.blocks_1, self.blocks_2, self.blocks_3] + + def __call__(self, h): + results = {} + for b in self.blks: + h = b(h) + results[int(h.shape[2]) * int(h.shape[3])] = h + return results + + +class HookerLayers(torch.nn.Module): + def __init__(self, layer_list): + super().__init__() + self.layers = torch.nn.ModuleList(layer_list) + + +class AttentionSharingPatcher(torch.nn.Module): + def __init__(self, unet, frames=2, use_control=True, rank=256): + super().__init__() + model_management.unload_model_clones(unet) + + units = [] + for i in range(32): + real_key = module_mapping_sd15[i] + attn_module = utils.get_attr(unet.model.diffusion_model, real_key) + u = AttentionSharingUnit( + attn_module, frames=frames, use_control=use_control, rank=rank + ) + units.append(u) + unet.add_object_patch("diffusion_model." + real_key, u) + + self.hookers = HookerLayers(units) + + if use_control: + self.kwargs_encoder = AdditionalAttentionCondsEncoder() + else: + self.kwargs_encoder = None + + self.dtype = torch.float32 + if model_management.should_use_fp16(model_management.get_torch_device()): + self.dtype = torch.float16 + self.hookers.half() + return + + def set_control(self, img): + img = img.cpu().float() * 2.0 - 1.0 + signals = self.kwargs_encoder(img) + for m in self.hookers.layers: + m.control_signals = signals + return diff --git a/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/model.py b/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/model.py new file mode 100644 index 00000000..14c9d1a6 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/model.py @@ -0,0 +1,390 @@ +import torch.nn as nn +import torch +import cv2 +import numpy as np +import comfy.model_management + +from comfy.model_patcher import ModelPatcher +from tqdm import tqdm +from typing import Optional, Tuple +from ...libs.utils import install_package +from packaging import version + +try: + install_package("diffusers", "0.27.2", True, "0.25.0") + + from diffusers.configuration_utils import ConfigMixin, register_to_config + from diffusers.models.modeling_utils import ModelMixin + from diffusers import __version__ + if __version__: + if version.parse(__version__) < version.parse("0.26.0"): + from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block + else: + from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block + + import functools + + def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + + class LatentTransparencyOffsetEncoder(torch.nn.Module): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.blocks = torch.nn.Sequential( + torch.nn.Conv2d(4, 32, kernel_size=3, padding=1, stride=1), + nn.SiLU(), + torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1), + nn.SiLU(), + torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2), + nn.SiLU(), + torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1), + nn.SiLU(), + torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2), + nn.SiLU(), + torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1), + nn.SiLU(), + torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2), + nn.SiLU(), + torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1), + nn.SiLU(), + zero_module(torch.nn.Conv2d(256, 4, kernel_size=3, padding=1, stride=1)), + ) + + def __call__(self, x): + return self.blocks(x) + + + # 1024 * 1024 * 3 -> 16 * 16 * 512 -> 1024 * 1024 * 3 + class UNet1024(ModelMixin, ConfigMixin): + @register_to_config + def __init__( + self, + in_channels: int = 3, + out_channels: int = 3, + down_block_types: Tuple[str] = ( + "DownBlock2D", + "DownBlock2D", + "DownBlock2D", + "DownBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + ), + up_block_types: Tuple[str] = ( + "AttnUpBlock2D", + "AttnUpBlock2D", + "AttnUpBlock2D", + "UpBlock2D", + "UpBlock2D", + "UpBlock2D", + "UpBlock2D", + ), + block_out_channels: Tuple[int] = (32, 32, 64, 128, 256, 512, 512), + layers_per_block: int = 2, + mid_block_scale_factor: float = 1, + downsample_padding: int = 1, + downsample_type: str = "conv", + upsample_type: str = "conv", + dropout: float = 0.0, + act_fn: str = "silu", + attention_head_dim: Optional[int] = 8, + norm_num_groups: int = 4, + norm_eps: float = 1e-5, + ): + super().__init__() + + # input + self.conv_in = nn.Conv2d( + in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1) + ) + self.latent_conv_in = zero_module( + nn.Conv2d(4, block_out_channels[2], kernel_size=1) + ) + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=None, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + attention_head_dim=( + attention_head_dim + if attention_head_dim is not None + else output_channel + ), + downsample_padding=downsample_padding, + resnet_time_scale_shift="default", + downsample_type=downsample_type, + dropout=dropout, + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlock2D( + in_channels=block_out_channels[-1], + temb_channels=None, + dropout=dropout, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift="default", + attention_head_dim=( + attention_head_dim + if attention_head_dim is not None + else block_out_channels[-1] + ), + resnet_groups=norm_num_groups, + attn_groups=None, + add_attention=True, + ) + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + is_final_block = i == len(block_out_channels) - 1 + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=None, + add_upsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + attention_head_dim=( + attention_head_dim + if attention_head_dim is not None + else output_channel + ), + resnet_time_scale_shift="default", + upsample_type=upsample_type, + dropout=dropout, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + self.conv_act = nn.SiLU() + self.conv_out = nn.Conv2d( + block_out_channels[0], out_channels, kernel_size=3, padding=1 + ) + + def forward(self, x, latent): + sample_latent = self.latent_conv_in(latent) + sample = self.conv_in(x) + emb = None + + down_block_res_samples = (sample,) + for i, downsample_block in enumerate(self.down_blocks): + if i == 3: + sample = sample + sample_latent + + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + down_block_res_samples += res_samples + + sample = self.mid_block(sample, emb) + + for upsample_block in self.up_blocks: + res_samples = down_block_res_samples[-len(upsample_block.resnets):] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] + sample = upsample_block(sample, res_samples, emb) + + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + return sample + + + def checkerboard(shape): + return np.indices(shape).sum(axis=0) % 2 + + + def fill_checkerboard_bg(y: torch.Tensor) -> torch.Tensor: + alpha = y[..., :1] + fg = y[..., 1:] + B, H, W, C = fg.shape + cb = checkerboard(shape=(H // 64, W // 64)) + cb = cv2.resize(cb, (W, H), interpolation=cv2.INTER_NEAREST) + cb = (0.5 + (cb - 0.5) * 0.1)[None, ..., None] + cb = torch.from_numpy(cb).to(fg) + vis = fg * alpha + cb * (1 - alpha) + return vis + + + class TransparentVAEDecoder: + def __init__(self, sd, device, dtype): + self.load_device = device + self.dtype = dtype + + model = UNet1024(in_channels=3, out_channels=4) + model.load_state_dict(sd, strict=True) + model.to(self.load_device, dtype=self.dtype) + model.eval() + self.model = model + + @torch.no_grad() + def estimate_single_pass(self, pixel, latent): + y = self.model(pixel, latent) + return y + + @torch.no_grad() + def estimate_augmented(self, pixel, latent): + args = [ + [False, 0], + [False, 1], + [False, 2], + [False, 3], + [True, 0], + [True, 1], + [True, 2], + [True, 3], + ] + + result = [] + + for flip, rok in tqdm(args): + feed_pixel = pixel.clone() + feed_latent = latent.clone() + + if flip: + feed_pixel = torch.flip(feed_pixel, dims=(3,)) + feed_latent = torch.flip(feed_latent, dims=(3,)) + + feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3)) + feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3)) + + eps = self.estimate_single_pass(feed_pixel, feed_latent).clip(0, 1) + eps = torch.rot90(eps, k=-rok, dims=(2, 3)) + + if flip: + eps = torch.flip(eps, dims=(3,)) + + result += [eps] + + result = torch.stack(result, dim=0) + median = torch.median(result, dim=0).values + return median + + @torch.no_grad() + def decode_pixel( + self, pixel: torch.TensorType, latent: torch.TensorType + ) -> torch.TensorType: + # pixel.shape = [B, C=3, H, W] + assert pixel.shape[1] == 3 + pixel_device = pixel.device + pixel_dtype = pixel.dtype + + pixel = pixel.to(device=self.load_device, dtype=self.dtype) + latent = latent.to(device=self.load_device, dtype=self.dtype) + # y.shape = [B, C=4, H, W] + y = self.estimate_augmented(pixel, latent) + y = y.clip(0, 1) + assert y.shape[1] == 4 + # Restore image to original device of input image. + return y.to(pixel_device, dtype=pixel_dtype) + + + def calculate_weight_adjust_channel(func): + """Patches ComfyUI's LoRA weight application to accept multi-channel inputs.""" + @functools.wraps(func) + def calculate_weight( + patches, weight: torch.Tensor, key: str, intermediate_type=torch.float32 + ) -> torch.Tensor: + weight = func(patches, weight, key, intermediate_type) + + for p in patches: + alpha = p[0] + v = p[1] + + # The recursion call should be handled in the main func call. + if isinstance(v, list): + continue + + if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": + w1 = v[0] + if all( + ( + alpha != 0.0, + w1.shape != weight.shape, + w1.ndim == weight.ndim == 4, + ) + ): + new_shape = [max(n, m) for n, m in zip(weight.shape, w1.shape)] + print( + f"Merged with {key} channel changed from {weight.shape} to {new_shape}" + ) + new_diff = alpha * comfy.model_management.cast_to_device( + w1, weight.device, weight.dtype + ) + new_weight = torch.zeros(size=new_shape).to(weight) + new_weight[ + : weight.shape[0], + : weight.shape[1], + : weight.shape[2], + : weight.shape[3], + ] = weight + new_weight[ + : new_diff.shape[0], + : new_diff.shape[1], + : new_diff.shape[2], + : new_diff.shape[3], + ] += new_diff + new_weight = new_weight.contiguous().clone() + weight = new_weight + return weight + + return calculate_weight + + +except ImportError: + ModelMixin = None + ConfigMixin = None + TransparentVAEDecoder = None + calculate_weight_adjust_channel = None + print("\33[33mModule 'diffusers' load failed. If you don't have it installed, do it:\033[0m") + print("\33[33mpip install diffusers\033[0m") + + + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/adapter.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/adapter.py new file mode 100644 index 00000000..8f28c2a9 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/adapter.py @@ -0,0 +1,1358 @@ +import re +import torch +import folder_paths +import comfy.utils, comfy.sample, comfy.samplers, comfy.controlnet, comfy.model_base, comfy.model_management, comfy.sampler_helpers, comfy.supported_models +from comfy_extras.nodes_compositing import JoinImageWithAlpha +from comfy.clip_vision import load as load_clip_vision + +from nodes import NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS +from ..config import * + +from ..libs.log import log_node_info, log_node_warn +from ..libs.utils import get_local_filepath, get_sd_version +from ..libs.wildcards import process_with_loras +from ..libs.controlnet import easyControlnet +from ..libs.conditioning import prompt_to_cond +from ..libs import cache as backend_cache + +from .. import easyCache + +class applyLoraPrompt: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "positive": ("STRING", {"default": "", "forceInput": True}), + }, + "optional": { + "negative": ("STRING", {"default": "", "forceInput": True}), + } + } + + RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING") + RETURN_NAMES = ("model", "clip", "positive", "negative") + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, model, clip, positive, negative=None): + model, clip, positive, _, _, _ = process_with_loras(positive, model, clip, 'Positive', easyCache=easyCache) + if negative is not None: + model, clip, negative, _, _, _ = process_with_loras(negative, model, clip, 'Negative', easyCache=easyCache) + + return (model, clip, positive, negative if negative is not None else "") + +class applyLoraStack: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_stack": ("LORA_STACK",), + "model": ("MODEL",), + }, + "optional": { + "optional_clip": ("CLIP",), + } + } + + RETURN_TYPES = ("MODEL", "CLIP") + RETURN_NAMES = ("model", "clip") + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, lora_stack, model, optional_clip=None): + clip = None + if lora_stack is not None and len(lora_stack) > 0: + for lora in lora_stack: + lora = {"lora_name": lora[0], "model": model, "clip": optional_clip, "model_strength": lora[1], + "clip_strength": lora[2]} + model, clip = easyCache.load_lora(lora, model, optional_clip, use_cache=False) + return (model, optional_clip if clip is None else clip) + +class applyControlnetStack: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "controlnet_stack": ("CONTROL_NET_STACK",), + "pipe": ("PIPE_LINE",), + }, + "optional": { + } + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, controlnet_stack, pipe): + + positive = pipe['positive'] + negative = pipe['negative'] + model = pipe['model'] + vae = pipe['vae'] + + if controlnet_stack is not None and len(controlnet_stack) >0: + for controlnet in controlnet_stack: + positive, negative = easyControlnet().apply(controlnet[0], controlnet[5], positive, negative, controlnet[1], start_percent=controlnet[2], end_percent=controlnet[3], control_net=None, scale_soft_weights=controlnet[4], mask=None, easyCache=easyCache, use_cache=False, model=model, vae=vae) + + new_pipe = { + **pipe, + "positive": positive, + "negetive": negative, + } + del pipe + + return (new_pipe,) + +# 风格对齐 +from ..libs.styleAlign import styleAlignBatch, SHARE_NORM_OPTIONS, SHARE_ATTN_OPTIONS +class styleAlignedBatchAlign: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL",), + "share_norm": (SHARE_NORM_OPTIONS,), + "share_attn": (SHARE_ATTN_OPTIONS,), + "scale": ("FLOAT", {"default": 1, "min": 0, "max": 1.0, "step": 0.1}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "align" + CATEGORY = "EasyUse/Adapter" + + def align(self, model, share_norm, share_attn, scale): + return (styleAlignBatch(model, share_norm, share_attn, scale),) + +# 光照对齐 +from ..modules.ic_light import ICLight, VAEEncodeArgMax +class icLightApply: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mode": (list(IC_LIGHT_MODELS.keys()),), + "model": ("MODEL",), + "image": ("IMAGE",), + "vae": ("VAE",), + "lighting": (['None', 'Left Light', 'Right Light', 'Top Light', 'Bottom Light', 'Circle Light'],{"default": "None"}), + "source": (['Use Background Image', 'Use Flipped Background Image', 'Left Light', 'Right Light', 'Top Light', 'Bottom Light', 'Ambient'],{"default": "Use Background Image"}), + "remove_bg": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("MODEL", "IMAGE") + RETURN_NAMES = ("model", "lighting_image") + FUNCTION = "apply" + CATEGORY = "EasyUse/Adapter" + + def batch(self, image1, image2): + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", + "center").movedim(1, -1) + s = torch.cat((image1, image2), dim=0) + return s + + def removebg(self, image): + if "easy imageRemBg" not in ALL_NODE_CLASS_MAPPINGS: + raise Exception("Please re-install ComfyUI-Easy-Use") + cls = ALL_NODE_CLASS_MAPPINGS['easy imageRemBg'] + results = cls().remove('RMBG-1.4', image, 'Hide', 'ComfyUI') + if "result" in results: + image, _ = results['result'] + return image + + def apply(self, mode, model, image, vae, lighting, source, remove_bg): + model_type = get_sd_version(model) + if model_type == 'sdxl': + raise Exception("IC Light model is not supported for SDXL now") + + batch_size, height, width, channel = image.shape + if channel == 3: + # remove bg + if mode == 'Foreground' or batch_size == 1: + if remove_bg: + image = self.removebg(image) + else: + mask = torch.full((1, height, width), 1.0, dtype=torch.float32, device="cpu") + try: + image, = JoinImageWithAlpha().execute(image, mask) + except: + image, = JoinImageWithAlpha().join_image_with_alpha(image, mask) + + iclight = ICLight() + if mode == 'Foreground': + lighting_image = iclight.generate_lighting_image(image, lighting) + else: + lighting_image = iclight.generate_source_image(image, source) + if source not in ['Use Background Image', 'Use Flipped Background Image']: + _, height, width, _ = lighting_image.shape + mask = torch.full((1, height, width), 1.0, dtype=torch.float32, device="cpu") + try: + lighting_image, = JoinImageWithAlpha().execute(lighting_image, mask) + except: + lighting_image, = JoinImageWithAlpha().join_image_with_alpha(lighting_image, mask) + if batch_size < 2: + image = self.batch(image, lighting_image) + else: + original_image = [img.unsqueeze(0) for img in image] + original_image = self.removebg(original_image[0]) + image = self.batch(original_image, lighting_image) + + latent, = VAEEncodeArgMax().encode(vae, image) + key = 'iclight_' + mode + '_' + model_type + model_path = get_local_filepath(IC_LIGHT_MODELS[mode]['sd1']["model_url"], + os.path.join(folder_paths.models_dir, "unet")) + ic_model = None + if key in backend_cache.cache: + log_node_info("easy icLightApply", f"Using icLightModel {mode+'_'+model_type} Cached") + _, ic_model = backend_cache.cache[key][1] + m, _ = iclight.apply(model_path, model, latent, ic_model) + else: + m, ic_model = iclight.apply(model_path, model, latent, ic_model) + backend_cache.update_cache(key, 'iclight', (False, ic_model)) + return (m, lighting_image) + + +def insightface_loader(provider, name='buffalo_l'): + try: + from insightface.app import FaceAnalysis + except ImportError as e: + raise Exception(e) + path = os.path.join(folder_paths.models_dir, "insightface") + model = FaceAnalysis(name=name, root=path, providers=[provider + 'ExecutionProvider', ]) + model.prepare(ctx_id=0, det_size=(640, 640)) + return model + +# Apply Ipadapter +class ipadapter: + + def __init__(self): + self.normal_presets = [ + 'LIGHT - SD1.5 only (low strength)', + 'STANDARD (medium strength)', + 'VIT-G (medium strength)', + 'PLUS (high strength)', + 'PLUS (kolors genernal)', + 'REGULAR - FLUX and SD3.5 only (high strength)', + 'PLUS FACE (portraits)', + 'FULL FACE - SD1.5 only (portraits stronger)', + 'COMPOSITION' + ] + self.faceid_presets = [ + 'FACEID', + 'FACEID PLUS - SD1.5 only', + "FACEID PLUS KOLORS", + 'FACEID PLUS V2', + 'FACEID PORTRAIT (style transfer)', + 'FACEID PORTRAIT UNNORM - SDXL only (strong)' + ] + self.weight_types = ["linear", "ease in", "ease out", 'ease in-out', 'reverse in-out', 'weak input', 'weak output', 'weak middle', 'strong middle', 'style transfer', 'composition', 'strong style transfer', 'style and composition', 'style transfer precise'] + self.presets = self.normal_presets + self.faceid_presets + + + def error(self): + raise Exception(f"[ERROR] To use ipadapterApply, you need to install 'ComfyUI_IPAdapter_plus'") + + def get_clipvision_file(self, preset, node_name): + preset = preset.lower() + clipvision_list = folder_paths.get_filename_list("clip_vision") + if preset.startswith("regular"): + # pattern = 'sigclip.vision.patch14.384' + pattern = 'siglip.so400m.patch14.384' + elif preset.startswith("plus (kolors") or preset.startswith("faceid plus kolors"): + pattern = 'Vit.Large.patch14.336.(bin|safetensors)$' + elif preset.startswith("vit-g"): + pattern = '(ViT.bigG.14.*39B.b160k|ipadapter.*sdxl|sdxl.*model.(bin|safetensors))' + else: + pattern = '(ViT.H.14.*s32B.b79K|ipadapter.*sd15|sd1.?5.*model.(bin|safetensors))' + clipvision_files = [e for e in clipvision_list if re.search(pattern, e, re.IGNORECASE)] + clipvision_name = clipvision_files[0] if len(clipvision_files)>0 else None + clipvision_file = folder_paths.get_full_path("clip_vision", clipvision_name) if clipvision_name else None + # if clipvision_name is not None: + # log_node_info(node_name, f"Using {clipvision_name}") + return clipvision_file, clipvision_name + + def get_ipadapter_file(self, preset, model_type, node_name): + preset = preset.lower() + ipadapter_list = folder_paths.get_filename_list("ipadapter") + is_insightface = False + lora_pattern = None + is_sdxl = model_type == 'sdxl' + is_flux = model_type == 'flux' + + if preset.startswith("light"): + if is_sdxl: + raise Exception("light model is not supported for SDXL") + pattern = 'sd15.light.v11.(safetensors|bin)$' + # if light model v11 is not found, try with the old version + if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]: + pattern = 'sd15.light.(safetensors|bin)$' + elif preset.startswith("standard"): + if is_sdxl: + pattern = 'ip.adapter.sdxl.vit.h.(safetensors|bin)$' + else: + pattern = 'ip.adapter.sd15.(safetensors|bin)$' + elif preset.startswith("vit-g"): + if is_sdxl: + pattern = 'ip.adapter.sdxl.(safetensors|bin)$' + else: + pattern = 'sd15.vit.g.(safetensors|bin)$' + elif preset.startswith("regular"): + if is_flux: + pattern = 'ip.adapter.flux.1.dev.(safetensors|bin)$' + else: + pattern = 'ip.adapter.sd35.(safetensors|bin)$' + elif preset.startswith("plus (high"): + if is_sdxl: + pattern = 'plus.sdxl.vit.h.(safetensors|bin)$' + else: + pattern = 'ip.adapter.plus.sd15.(safetensors|bin)$' + elif preset.startswith("plus (kolors"): + if is_sdxl: + pattern = 'plus.gener(nal|al).(safetensors|bin)$' + else: + raise Exception("kolors model is not supported for SD15") + elif preset.startswith("plus face"): + if is_sdxl: + pattern = 'plus.face.sdxl.vit.h.(safetensors|bin)$' + else: + pattern = 'plus.face.sd15.(safetensors|bin)$' + elif preset.startswith("full"): + if is_sdxl: + raise Exception("full face model is not supported for SDXL") + pattern = 'full.face.sd15.(safetensors|bin)$' + elif preset.startswith("composition"): + if is_sdxl: + pattern = 'plus.composition.sdxl.(safetensors|bin)$' + else: + pattern = 'plus.composition.sd15.(safetensors|bin)$' + elif preset.startswith("faceid portrait ("): + if is_sdxl: + pattern = 'portrait.sdxl.(safetensors|bin)$' + else: + pattern = 'portrait.v11.sd15.(safetensors|bin)$' + # if v11 is not found, try with the old version + if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]: + pattern = 'portrait.sd15.(safetensors|bin)$' + is_insightface = True + elif preset.startswith("faceid portrait unnorm"): + if is_sdxl: + pattern = r'portrait.sdxl.unnorm.(safetensors|bin)$' + else: + raise Exception("portrait unnorm model is not supported for SD1.5") + is_insightface = True + elif preset == "faceid": + if is_sdxl: + pattern = 'faceid.sdxl.(safetensors|bin)$' + lora_pattern = 'faceid.sdxl.lora.safetensors$' + else: + pattern = 'faceid.sd15.(safetensors|bin)$' + lora_pattern = 'faceid.sd15.lora.safetensors$' + is_insightface = True + elif preset.startswith("faceid plus kolors"): + if is_sdxl: + pattern = '(kolors.ip.adapter.faceid.plus|ipa.faceid.plus).(safetensors|bin)$' + else: + raise Exception("faceid plus kolors model is not supported for SD1.5") + is_insightface = True + elif preset.startswith("faceid plus -"): + if is_sdxl: + raise Exception("faceid plus model is not supported for SDXL") + pattern = 'faceid.plus.sd15.(safetensors|bin)$' + lora_pattern = 'faceid.plus.sd15.lora.safetensors$' + is_insightface = True + elif preset.startswith("faceid plus v2"): + if is_sdxl: + pattern = 'faceid.plusv2.sdxl.(safetensors|bin)$' + lora_pattern = 'faceid.plusv2.sdxl.lora.safetensors$' + else: + pattern = 'faceid.plusv2.sd15.(safetensors|bin)$' + lora_pattern = 'faceid.plusv2.sd15.lora.safetensors$' + is_insightface = True + else: + raise Exception(f"invalid type '{preset}'") + + ipadapter_files = [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)] + ipadapter_name = ipadapter_files[0] if len(ipadapter_files)>0 else None + ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_name) if ipadapter_name else None + # if ipadapter_name is not None: + # log_node_info(node_name, f"Using {ipadapter_name}") + + return ipadapter_file, ipadapter_name, is_insightface, lora_pattern + + def get_lora_pattern(self, file): + basename = os.path.basename(file) + lora_pattern = None + if re.search(r'faceid.sdxl.(safetensors|bin)$', basename, re.IGNORECASE): + lora_pattern = 'faceid.sdxl.lora.safetensors$' + elif re.search(r'faceid.sd15.(safetensors|bin)$', basename, re.IGNORECASE): + lora_pattern = 'faceid.sd15.lora.safetensors$' + elif re.search(r'faceid.plus.sd15.(safetensors|bin)$', basename, re.IGNORECASE): + lora_pattern = 'faceid.plus.sd15.lora.safetensors$' + elif re.search(r'faceid.plusv2.sdxl.(safetensors|bin)$', basename, re.IGNORECASE): + lora_pattern = 'faceid.plusv2.sdxl.lora.safetensors$' + elif re.search(r'faceid.plusv2.sd15.(safetensors|bin)$', basename, re.IGNORECASE): + lora_pattern = 'faceid.plusv2.sd15.lora.safetensors$' + + return lora_pattern + + def get_lora_file(self, preset, pattern, model_type, model, model_strength, clip_strength, clip=None): + lora_list = folder_paths.get_filename_list("loras") + lora_files = [e for e in lora_list if re.search(pattern, e, re.IGNORECASE)] + lora_name = lora_files[0] if lora_files else None + if lora_name: + return easyCache.load_lora({"model": model, "clip": clip, "lora_name": lora_name, "model_strength":model_strength, "clip_strength":clip_strength},) + else: + if "lora_url" in IPADAPTER_MODELS[preset][model_type]: + lora_name = get_local_filepath(IPADAPTER_MODELS[preset][model_type]["lora_url"], os.path.join(folder_paths.models_dir, "loras")) + return easyCache.load_lora({"model": model, "clip": clip, "lora_name": lora_name, "model_strength":model_strength, "clip_strength":clip_strength},) + return (model, clip) + + def ipadapter_model_loader(self, file): + model = comfy.utils.load_torch_file(file, safe_load=False) + + if file.lower().endswith(".safetensors"): + st_model = {"image_proj": {}, "ip_adapter": {}} + for key in model.keys(): + if key.startswith("image_proj."): + st_model["image_proj"][key.replace("image_proj.", "")] = model[key] + elif key.startswith("ip_adapter."): + st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key] + model = st_model + del st_model + + model_keys = model.keys() + if "adapter_modules" in model_keys: + model["ip_adapter"] = model["adapter_modules"] + model["faceidplusv2"] = True + del model['adapter_modules'] + + if not "ip_adapter" in model_keys or not model["ip_adapter"]: + raise Exception("invalid IPAdapter model {}".format(file)) + + if 'plusv2' in file.lower(): + model["faceidplusv2"] = True + + if 'unnorm' in file.lower(): + model["portraitunnorm"] = True + + return model + + def load_model(self, model, preset, lora_model_strength, provider="CPU", clip_vision=None, optional_ipadapter=None, cache_mode='none', node_name='easy ipadapterApply'): + pipeline = {"clipvision": {'file': None, 'model': None}, "ipadapter": {'file': None, 'model': None}, + "insightface": {'provider': None, 'model': None}} + ipadapter, insightface, is_insightface, lora_pattern = None, None, None, None + if optional_ipadapter is not None: + pipeline = optional_ipadapter + if not clip_vision: + clip_vision = pipeline['clipvision']['model'] + ipadapter = pipeline['ipadapter']['model'] + if 'insightface' in pipeline: + insightface = pipeline['insightface']['model'] + lora_pattern = self.get_lora_pattern(pipeline['ipadapter']['file']) + + # 1. Load the clipvision model + if not clip_vision: + clipvision_file, clipvision_name = self.get_clipvision_file(preset, node_name) + if clipvision_file is None: + if preset.lower().startswith("regular"): + # model_url = IPADAPTER_CLIPVISION_MODELS["sigclip_vision_patch14_384"]["model_url"] + # clipvision_file = get_local_filepath(model_url, IPADAPTER_DIR, "sigclip_vision_patch14_384.bin") + from huggingface_hub import snapshot_download + import shutil + CLIP_PATH = os.path.join(folder_paths.models_dir, "clip_vision", "google--siglip-so400m-patch14-384") + print("CLIP_VISION not found locally. Downloading google/siglip-so400m-patch14-384...") + try: + snapshot_download( + repo_id="google/siglip-so400m-patch14-384", + local_dir=os.path.join(folder_paths.models_dir, "clip_vision", + "cache--google--siglip-so400m-patch14-384"), + local_dir_use_symlinks=False, + resume_download=True + ) + shutil.move(os.path.join(folder_paths.models_dir, "clip_vision", + "cache--google--siglip-so400m-patch14-384"), CLIP_PATH) + print(f"CLIP_VISION has been downloaded to {CLIP_PATH}") + except Exception as e: + print(f"Error downloading CLIP model: {e}") + raise + clipvision_file = CLIP_PATH + elif preset.lower().startswith("plus (kolors"): + model_url = IPADAPTER_CLIPVISION_MODELS["clip-vit-large-patch14-336"]["model_url"] + clipvision_file = get_local_filepath(model_url, IPADAPTER_DIR, "clip-vit-large-patch14-336.bin") + else: + model_url = IPADAPTER_CLIPVISION_MODELS["clip-vit-h-14-laion2B-s32B-b79K"]["model_url"] + clipvision_file = get_local_filepath(model_url, IPADAPTER_DIR, "clip-vit-h-14-laion2B-s32B-b79K.safetensors") + clipvision_name = os.path.basename(model_url) + if clipvision_file == pipeline['clipvision']['file']: + clip_vision = pipeline['clipvision']['model'] + elif cache_mode in ["all", "clip_vision only"] and clipvision_name in backend_cache.cache: + log_node_info("easy ipadapterApply", f"Using ClipVisonModel {clipvision_name} Cached") + _, clip_vision = backend_cache.cache[clipvision_name][1] + else: + if preset.lower().startswith("regular"): + from transformers import SiglipVisionModel, AutoProcessor + image_encoder_path = os.path.dirname(clipvision_file) + image_encoder = SiglipVisionModel.from_pretrained(image_encoder_path) + clip_image_processor = AutoProcessor.from_pretrained(image_encoder_path) + clip_vision = { + 'image_encoder': image_encoder, + 'clip_image_processor': clip_image_processor + } + else: + clip_vision = load_clip_vision(clipvision_file) + log_node_info("easy ipadapterApply", f"Using ClipVisonModel {clipvision_name}") + if cache_mode in ["all", "clip_vision only"]: + backend_cache.update_cache(clipvision_name, 'clip_vision', (False, clip_vision)) + pipeline['clipvision']['file'] = clipvision_file + pipeline['clipvision']['model'] = clip_vision + # 2. Load the ipadapter model + model_type = get_sd_version(model) + if not ipadapter: + ipadapter_file, ipadapter_name, is_insightface, lora_pattern = self.get_ipadapter_file(preset, model_type, node_name) + if ipadapter_file is None: + model_url = IPADAPTER_MODELS[preset][model_type]["model_url"] + local_file_name = IPADAPTER_MODELS[preset][model_type]['model_file_name'] if "model_file_name" in IPADAPTER_MODELS[preset][model_type] else None + ipadapter_file = get_local_filepath(model_url, IPADAPTER_DIR, local_file_name) + ipadapter_name = os.path.basename(model_url) + if ipadapter_file == pipeline['ipadapter']['file']: + ipadapter = pipeline['ipadapter']['model'] + elif cache_mode in ["all", "ipadapter only"] and ipadapter_name in backend_cache.cache: + log_node_info("easy ipadapterApply", f"Using IpAdapterModel {ipadapter_name} Cached") + _, ipadapter = backend_cache.cache[ipadapter_name][1] + else: + ipadapter = self.ipadapter_model_loader(ipadapter_file) + pipeline['ipadapter']['file'] = ipadapter_file + log_node_info("easy ipadapterApply", f"Using IpAdapterModel {ipadapter_name}") + if cache_mode in ["all", "ipadapter only"]: + backend_cache.update_cache(ipadapter_name, 'ipadapter', (False, ipadapter)) + + pipeline['ipadapter']['model'] = ipadapter + + # 3. Load the lora model if needed + if lora_pattern is not None: + if lora_model_strength > 0: + model, _ = self.get_lora_file(preset, lora_pattern, model_type, model, lora_model_strength, 1) + + # 4. Load the insightface model if needed + if is_insightface: + if not insightface: + icache_key = 'insightface-' + provider + if provider == pipeline['insightface']['provider']: + insightface = pipeline['insightface']['model'] + elif cache_mode in ["all", "insightface only"] and icache_key in backend_cache.cache: + log_node_info("easy ipadapterApply", f"Using InsightFaceModel {icache_key} Cached") + _, insightface = backend_cache.cache[icache_key][1] + else: + insightface = insightface_loader(provider, 'antelopev2' if preset == 'FACEID PLUS KOLORS' else 'buffalo_l') + if cache_mode in ["all", "insightface only"]: + backend_cache.update_cache(icache_key, 'insightface',(False, insightface)) + pipeline['insightface']['provider'] = provider + pipeline['insightface']['model'] = insightface + + return (model, pipeline,) + +class ipadapterApply(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + presets = cls().presets + return { + "required": { + "model": ("MODEL",), + "image": ("IMAGE",), + "preset": (presets,), + "lora_strength": ("FLOAT", {"default": 0.6, "min": 0, "max": 1, "step": 0.01}), + "provider": (["CPU", "CUDA", "ROCM", "DirectML", "OpenVINO", "CoreML"], {"default": "CUDA"}), + "weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}), + "weight_faceidv2": ("FLOAT", { "default": 1.0, "min": -1, "max": 5.0, "step": 0.05 }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "cache_mode": (["insightface only", "clip_vision only", "ipadapter only", "all", "none"], {"default": "all"},), + "use_tiled": ("BOOLEAN", {"default": False},), + }, + + "optional": { + "attn_mask": ("MASK",), + "optional_ipadapter": ("IPADAPTER",), + } + } + + RETURN_TYPES = ("MODEL", "IMAGE", "MASK", "IPADAPTER",) + RETURN_NAMES = ("model", "images", "masks", "ipadapter", ) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, model, image, preset, lora_strength, provider, weight, weight_faceidv2, start_at, end_at, cache_mode, use_tiled, attn_mask=None, optional_ipadapter=None, weight_kolors=None): + images, masks = image, [None] + model, ipadapter = self.load_model(model, preset, lora_strength, provider, clip_vision=None, optional_ipadapter=optional_ipadapter, cache_mode=cache_mode) + if preset == 'REGULAR - FLUX and SD3.5 only (high strength)': + from ..modules.ipadapter import InstantXFluxIpadapterApply, InstantXSD3IpadapterApply + model_type = get_sd_version(model) + if model_type == 'flux': + model, images = InstantXFluxIpadapterApply().apply_ipadapter(model, ipadapter, image, weight, start_at, end_at, provider) + elif model_type == 'sd3': + model, images = InstantXSD3IpadapterApply().apply_ipadapter(model, ipadapter, image, weight, start_at, end_at, provider) + elif use_tiled and preset not in self.faceid_presets: + if "IPAdapterTiled" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterTiled"] + model, images, masks = cls().apply_tiled(model, ipadapter, image, weight, "linear", start_at, end_at, sharpening=0.0, combine_embeds="concat", image_negative=None, attn_mask=attn_mask, clip_vision=None, embeds_scaling='V only') + else: + if preset in ['FACEID PLUS KOLORS', 'FACEID PLUS V2', 'FACEID PORTRAIT (style transfer)']: + if "IPAdapterAdvanced" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterAdvanced"] + if weight_kolors is None: + weight_kolors = weight + model, images = cls().apply_ipadapter(model, ipadapter, start_at=start_at, end_at=end_at, weight=weight, weight_type="linear", combine_embeds="concat", weight_faceidv2=weight_faceidv2, image=image, image_negative=None, clip_vision=None, attn_mask=attn_mask, insightface=None, embeds_scaling='V only', weight_kolors=weight_kolors) + else: + if "IPAdapter" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapter"] + model, images = cls().apply_ipadapter(model, ipadapter, image, weight, start_at, end_at, weight_type='standard', attn_mask=attn_mask) + if images is None: + images = image + return (model, images, masks, ipadapter,) + +class ipadapterApplyAdvanced(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + presets = ipa_cls.presets + weight_types = ipa_cls.weight_types + return { + "required": { + "model": ("MODEL",), + "image": ("IMAGE",), + "preset": (presets,), + "lora_strength": ("FLOAT", {"default": 0.6, "min": 0, "max": 1, "step": 0.01}), + "provider": (["CPU", "CUDA", "ROCM", "DirectML", "OpenVINO", "CoreML"], {"default": "CUDA"}), + "weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}), + "weight_faceidv2": ("FLOAT", {"default": 1.0, "min": -1, "max": 5.0, "step": 0.05 }), + "weight_type": (weight_types,), + "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],), + "cache_mode": (["insightface only", "clip_vision only","ipadapter only", "all", "none"], {"default": "all"},), + "use_tiled": ("BOOLEAN", {"default": False},), + "use_batch": ("BOOLEAN", {"default": False},), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05}), + }, + + "optional": { + "image_negative": ("IMAGE",), + "attn_mask": ("MASK",), + "clip_vision": ("CLIP_VISION",), + "optional_ipadapter": ("IPADAPTER",), + "layer_weights": ("STRING", {"default": "", "multiline": True, "placeholder": "Mad Scientist Layer Weights"}), + } + } + + RETURN_TYPES = ("MODEL", "IMAGE", "MASK", "IPADAPTER",) + RETURN_NAMES = ("model", "images", "masks", "ipadapter", ) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, model, image, preset, lora_strength, provider, weight, weight_faceidv2, weight_type, combine_embeds, start_at, end_at, embeds_scaling, cache_mode, use_tiled, use_batch, sharpening, weight_style=1.0, weight_composition=1.0, image_style=None, image_composition=None, expand_style=False, image_negative=None, clip_vision=None, attn_mask=None, optional_ipadapter=None, layer_weights=None, weight_kolors=None): + images, masks = image, [None] + model, ipadapter = self.load_model(model, preset, lora_strength, provider, clip_vision=clip_vision, optional_ipadapter=optional_ipadapter, cache_mode=cache_mode) + + if weight_kolors is None: + weight_kolors = weight + + if layer_weights: + if "IPAdapterMS" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterAdvanced"] + model, images = cls().apply_ipadapter(model, ipadapter, weight=weight, weight_type=weight_type, start_at=start_at, end_at=end_at, combine_embeds=combine_embeds, weight_faceidv2=weight_faceidv2, image=image, image_negative=image_negative, weight_style=weight_style, weight_composition=weight_composition, image_style=image_style, image_composition=image_composition, expand_style=expand_style, clip_vision=clip_vision, attn_mask=attn_mask, insightface=None, embeds_scaling=embeds_scaling, layer_weights=layer_weights, weight_kolors=weight_kolors) + elif use_tiled: + if use_batch: + if "IPAdapterTiledBatch" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterTiledBatch"] + else: + if "IPAdapterTiled" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterTiled"] + model, images, masks = cls().apply_tiled(model, ipadapter, image=image, weight=weight, weight_type=weight_type, start_at=start_at, end_at=end_at, sharpening=sharpening, combine_embeds=combine_embeds, image_negative=image_negative, attn_mask=attn_mask, clip_vision=clip_vision, embeds_scaling=embeds_scaling) + else: + if use_batch: + if "IPAdapterBatch" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterBatch"] + else: + if "IPAdapterAdvanced" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterAdvanced"] + model, images = cls().apply_ipadapter(model, ipadapter, weight=weight, weight_type=weight_type, start_at=start_at, end_at=end_at, combine_embeds=combine_embeds, weight_faceidv2=weight_faceidv2, image=image, image_negative=image_negative, weight_style=1.0, weight_composition=1.0, image_style=image_style, image_composition=image_composition, expand_style=expand_style, clip_vision=clip_vision, attn_mask=attn_mask, insightface=None, embeds_scaling=embeds_scaling, weight_kolors=weight_kolors) + if images is None: + images = image + return (model, images, masks, ipadapter) + +class ipadapterApplyFaceIDKolors(ipadapterApplyAdvanced): + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + presets = ipa_cls.presets + weight_types = ipa_cls.weight_types + return { + "required": { + "model": ("MODEL",), + "image": ("IMAGE",), + "preset": (['FACEID PLUS KOLORS'], {"default":"FACEID PLUS KOLORS"}), + "lora_strength": ("FLOAT", {"default": 0.6, "min": 0, "max": 1, "step": 0.01}), + "provider": (["CPU", "CUDA", "ROCM", "DirectML", "OpenVINO", "CoreML"], {"default": "CUDA"}), + "weight": ("FLOAT", {"default": 0.8, "min": -1, "max": 3, "step": 0.05}), + "weight_faceidv2": ("FLOAT", {"default": 1.0, "min": -1, "max": 5.0, "step": 0.05}), + "weight_kolors": ("FLOAT", {"default": 0.8, "min": -1, "max": 5.0, "step": 0.05}), + "weight_type": (weight_types,), + "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],), + "cache_mode": (["insightface only", "clip_vision only", "ipadapter only", "all", "none"], {"default": "all"},), + "use_tiled": ("BOOLEAN", {"default": False},), + "use_batch": ("BOOLEAN", {"default": False},), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05}), + }, + + "optional": { + "image_negative": ("IMAGE",), + "attn_mask": ("MASK",), + "clip_vision": ("CLIP_VISION",), + "optional_ipadapter": ("IPADAPTER",), + } + } + + +class ipadapterStyleComposition(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + normal_presets = ipa_cls.normal_presets + weight_types = ipa_cls.weight_types + return { + "required": { + "model": ("MODEL",), + "image_style": ("IMAGE",), + "preset": (normal_presets,), + "weight_style": ("FLOAT", {"default": 1.0, "min": -1, "max": 5, "step": 0.05}), + "weight_composition": ("FLOAT", {"default": 1.0, "min": -1, "max": 5, "step": 0.05}), + "expand_style": ("BOOLEAN", {"default": False}), + "combine_embeds": (["concat", "add", "subtract", "average", "norm average"], {"default": "average"}), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],), + "cache_mode": (["insightface only", "clip_vision only", "ipadapter only", "all", "none"], + {"default": "all"},), + }, + "optional": { + "image_composition": ("IMAGE",), + "image_negative": ("IMAGE",), + "attn_mask": ("MASK",), + "clip_vision": ("CLIP_VISION",), + "optional_ipadapter": ("IPADAPTER",), + } + } + + CATEGORY = "EasyUse/Adapter" + + RETURN_TYPES = ("MODEL", "IPADAPTER",) + RETURN_NAMES = ("model", "ipadapter",) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, model, preset, weight_style, weight_composition, expand_style, combine_embeds, start_at, end_at, embeds_scaling, cache_mode, image_style=None , image_composition=None, image_negative=None, clip_vision=None, attn_mask=None, optional_ipadapter=None): + model, ipadapter = self.load_model(model, preset, 0, 'CPU', clip_vision=None, optional_ipadapter=optional_ipadapter, cache_mode=cache_mode) + + if "IPAdapterAdvanced" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterAdvanced"] + + model, image = cls().apply_ipadapter(model, ipadapter, start_at=start_at, end_at=end_at, weight_style=weight_style, weight_composition=weight_composition, weight_type='linear', combine_embeds=combine_embeds, weight_faceidv2=weight_composition, image_style=image_style, image_composition=image_composition, image_negative=image_negative, expand_style=expand_style, clip_vision=clip_vision, attn_mask=attn_mask, insightface=None, embeds_scaling=embeds_scaling) + return (model, ipadapter) + +class ipadapterApplyEncoder(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + normal_presets = ipa_cls.normal_presets + max_embeds_num = 4 + inputs = { + "required": { + "model": ("MODEL",), + "clip_vision": ("CLIP_VISION",), + "image1": ("IMAGE",), + "preset": (normal_presets,), + "num_embeds": ("INT", {"default": 2, "min": 1, "max": max_embeds_num}), + }, + "optional": {} + } + + for i in range(1, max_embeds_num + 1): + if i > 1: + inputs["optional"][f"image{i}"] = ("IMAGE",) + for i in range(1, max_embeds_num + 1): + inputs["optional"][f"mask{i}"] = ("MASK",) + inputs["optional"][f"weight{i}"] = ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}) + inputs["optional"]["combine_method"] = (["concat", "add", "subtract", "average", "norm average", "max", "min"],) + inputs["optional"]["optional_ipadapter"] = ("IPADAPTER",) + inputs["optional"]["pos_embeds"] = ("EMBEDS",) + inputs["optional"]["neg_embeds"] = ("EMBEDS",) + return inputs + + RETURN_TYPES = ("MODEL", "CLIP_VISION","IPADAPTER", "EMBEDS", "EMBEDS", ) + RETURN_NAMES = ("model", "clip_vision","ipadapter", "pos_embed", "neg_embed",) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def batch(self, embeds, method): + if method == 'concat' and len(embeds) == 1: + return (embeds[0],) + + embeds = [embed for embed in embeds if embed is not None] + embeds = torch.cat(embeds, dim=0) + + if method == "add": + embeds = torch.sum(embeds, dim=0).unsqueeze(0) + elif method == "subtract": + embeds = embeds[0] - torch.mean(embeds[1:], dim=0) + embeds = embeds.unsqueeze(0) + elif method == "average": + embeds = torch.mean(embeds, dim=0).unsqueeze(0) + elif method == "norm average": + embeds = torch.mean(embeds / torch.norm(embeds, dim=0, keepdim=True), dim=0).unsqueeze(0) + elif method == "max": + embeds = torch.max(embeds, dim=0).values.unsqueeze(0) + elif method == "min": + embeds = torch.min(embeds, dim=0).values.unsqueeze(0) + + return embeds + + def apply(self, **kwargs): + model = kwargs['model'] + clip_vision = kwargs['clip_vision'] + preset = kwargs['preset'] + if 'optional_ipadapter' in kwargs: + ipadapter = kwargs['optional_ipadapter'] + else: + model, ipadapter = self.load_model(model, preset, 0, 'CPU', clip_vision=clip_vision, optional_ipadapter=None, cache_mode='none') + + if "IPAdapterEncoder" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + encoder_cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterEncoder"] + pos_embeds = kwargs["pos_embeds"] if "pos_embeds" in kwargs else [] + neg_embeds = kwargs["neg_embeds"] if "neg_embeds" in kwargs else [] + for i in range(1, kwargs['num_embeds'] + 1): + if f"image{i}" not in kwargs: + raise Exception(f"image{i} is required") + kwargs[f"mask{i}"] = kwargs[f"mask{i}"] if f"mask{i}" in kwargs else None + kwargs[f"weight{i}"] = kwargs[f"weight{i}"] if f"weight{i}" in kwargs else 1.0 + + pos, neg = encoder_cls().encode(ipadapter, kwargs[f"image{i}"], kwargs[f"weight{i}"], kwargs[f"mask{i}"], clip_vision=clip_vision) + pos_embeds.append(pos) + neg_embeds.append(neg) + + pos_embeds = self.batch(pos_embeds, kwargs['combine_method']) + neg_embeds = self.batch(neg_embeds, kwargs['combine_method']) + + return (model,clip_vision, ipadapter, pos_embeds, neg_embeds) + +class ipadapterApplyEmbeds(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + weight_types = ipa_cls.weight_types + return { + "required": { + "model": ("MODEL",), + "clip_vision": ("CLIP_VISION",), + "ipadapter": ("IPADAPTER",), + "pos_embed": ("EMBEDS",), + "weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}), + "weight_type": (weight_types,), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],), + }, + + "optional": { + "neg_embed": ("EMBEDS",), + "attn_mask": ("MASK",), + } + } + + RETURN_TYPES = ("MODEL", "IPADAPTER",) + RETURN_NAMES = ("model", "ipadapter", ) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, model, ipadapter, clip_vision, pos_embed, weight, weight_type, start_at, end_at, embeds_scaling, attn_mask=None, neg_embed=None,): + if "IPAdapterEmbeds" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterEmbeds"] + model, image = cls().apply_ipadapter(model, ipadapter, pos_embed, weight, weight_type, start_at, end_at, neg_embed=neg_embed, attn_mask=attn_mask, clip_vision=clip_vision, embeds_scaling=embeds_scaling) + + return (model, ipadapter) + +class ipadapterApplyRegional(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + weight_types = ipa_cls.weight_types + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "positive": ("STRING", {"default": "", "placeholder": "positive", "multiline": True}), + "negative": ("STRING", {"default": "", "placeholder": "negative", "multiline": True}), + "image_weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 3.0, "step": 0.05}), + "prompt_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.05}), + "weight_type": (weight_types,), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }, + + "optional": { + "mask": ("MASK",), + "optional_ipadapter_params": ("IPADAPTER_PARAMS",), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "IPADAPTER_PARAMS", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("pipe", "ipadapter_params", "positive", "negative") + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, pipe, image, positive, negative, image_weight, prompt_weight, weight_type, start_at, end_at, mask=None, optional_ipadapter_params=None, prompt=None, my_unique_id=None): + model = pipe['model'] + + if positive == '': + positive = pipe['loader_settings']['positive'] + if negative == '': + negative = pipe['loader_settings']['negative'] + + if "clip" not in pipe or not pipe['clip']: + if "chatglm3_model" in pipe: + from ..modules.kolors.text_encode import chatglm3_adv_text_encode + chatglm3_model = pipe['chatglm3_model'] + # text encode + log_node_warn("Positive encoding...") + positive_embeddings_final = chatglm3_adv_text_encode(chatglm3_model, positive, False) + log_node_warn("Negative encoding...") + negative_embeddings_final = chatglm3_adv_text_encode(chatglm3_model, negative, False) + else: + clip = pipe['clip'] + clip_skip = pipe['loader_settings']['clip_skip'] + a1111_prompt_style = pipe['loader_settings']['a1111_prompt_style'] + pipe_lora_stack = pipe['loader_settings']['lora_stack'] + positive_token_normalization = pipe['loader_settings']['positive_token_normalization'] + positive_weight_interpretation = pipe['loader_settings']['positive_weight_interpretation'] + negative_token_normalization = pipe['loader_settings']['negative_token_normalization'] + negative_weight_interpretation = pipe['loader_settings']['negative_weight_interpretation'] + + positive_embeddings_final, positive_wildcard_prompt, model, clip = prompt_to_cond('positive', model, clip, clip_skip, pipe_lora_stack, positive, positive_token_normalization, positive_weight_interpretation, a1111_prompt_style, my_unique_id, prompt, easyCache) + negative_embeddings_final, negative_wildcard_prompt, model, clip = prompt_to_cond('negative', model, clip, clip_skip, pipe_lora_stack, negative, negative_token_normalization, negative_weight_interpretation, a1111_prompt_style, my_unique_id, prompt, easyCache) + + #ipadapter regional + if "IPAdapterRegionalConditioning" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterRegionalConditioning"] + ipadapter_params, new_positive_embeds, new_negative_embeds = cls().conditioning(image, image_weight, prompt_weight, weight_type, start_at, end_at, mask=mask, positive=positive_embeddings_final, negative=negative_embeddings_final) + + if optional_ipadapter_params is not None: + positive_embeds = pipe['positive'] + new_positive_embeds + negative_embeds = pipe['negative'] + new_negative_embeds + _ipadapter_params = { + "image": optional_ipadapter_params["image"] + ipadapter_params["image"], + "attn_mask": optional_ipadapter_params["attn_mask"] + ipadapter_params["attn_mask"], + "weight": optional_ipadapter_params["weight"] + ipadapter_params["weight"], + "weight_type": optional_ipadapter_params["weight_type"] + ipadapter_params["weight_type"], + "start_at": optional_ipadapter_params["start_at"] + ipadapter_params["start_at"], + "end_at": optional_ipadapter_params["end_at"] + ipadapter_params["end_at"], + } + ipadapter_params = _ipadapter_params + del _ipadapter_params + else: + positive_embeds = new_positive_embeds + negative_embeds = new_negative_embeds + + new_pipe = { + **pipe, + "positive": positive_embeds, + "negative": negative_embeds, + } + + del pipe + + return (new_pipe, ipadapter_params, positive_embeds, negative_embeds) + +class ipadapterApplyFromParams(ipadapter): + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + ipa_cls = cls() + normal_presets = ipa_cls.normal_presets + return { + "required": { + "model": ("MODEL",), + "preset": (normal_presets,), + "ipadapter_params": ("IPADAPTER_PARAMS",), + "combine_embeds": (["concat", "add", "subtract", "average", "norm average", "max", "min"],), + "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],), + "cache_mode": (["insightface only", "clip_vision only", "ipadapter only", "all", "none"], + {"default": "insightface only"}), + }, + + "optional": { + "optional_ipadapter": ("IPADAPTER",), + "image_negative": ("IMAGE",), + } + } + + RETURN_TYPES = ("MODEL", "IPADAPTER",) + RETURN_NAMES = ("model", "ipadapter", ) + CATEGORY = "EasyUse/Adapter" + FUNCTION = "apply" + + def apply(self, model, preset, ipadapter_params, combine_embeds, embeds_scaling, cache_mode, optional_ipadapter=None, image_negative=None,): + model, ipadapter = self.load_model(model, preset, 0, 'CPU', clip_vision=None, optional_ipadapter=optional_ipadapter, cache_mode=cache_mode) + if "IPAdapterFromParams" not in ALL_NODE_CLASS_MAPPINGS: + self.error() + cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterFromParams"] + model, image = cls().apply_ipadapter(model, ipadapter, clip_vision=None, combine_embeds=combine_embeds, embeds_scaling=embeds_scaling, image_negative=image_negative, ipadapter_params=ipadapter_params) + + return (model, ipadapter) + +#Apply InstantID +class instantID: + + def error(self): + raise Exception(f"[ERROR] To use instantIDApply, you need to install 'ComfyUI_InstantID'") + + def run(self, pipe, image, instantid_file, insightface, control_net_name, cn_strength, cn_soft_weights, weight, start_at, end_at, noise, image_kps=None, mask=None, control_net=None, positive=None, negative=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + instantid_model, insightface_model, face_embeds = None, None, None + model = pipe['model'] + # Load InstantID + cache_key = 'instantID' + if cache_key in backend_cache.cache: + log_node_info("easy instantIDApply","Using InstantIDModel Cached") + _, instantid_model = backend_cache.cache[cache_key][1] + if "InstantIDModelLoader" in ALL_NODE_CLASS_MAPPINGS: + load_instant_cls = ALL_NODE_CLASS_MAPPINGS["InstantIDModelLoader"] + instantid_model, = load_instant_cls().load_model(instantid_file) + backend_cache.update_cache(cache_key, 'instantid', (False, instantid_model)) + else: + self.error() + icache_key = 'insightface-' + insightface + if icache_key in backend_cache.cache: + log_node_info("easy instantIDApply", f"Using InsightFaceModel {insightface} Cached") + _, insightface_model = backend_cache.cache[icache_key][1] + elif "InstantIDFaceAnalysis" in ALL_NODE_CLASS_MAPPINGS: + load_insightface_cls = ALL_NODE_CLASS_MAPPINGS["InstantIDFaceAnalysis"] + insightface_model, = load_insightface_cls().load_insight_face(insightface) + backend_cache.update_cache(icache_key, 'insightface', (False, insightface_model)) + else: + self.error() + + # Apply InstantID + if "ApplyInstantID" in ALL_NODE_CLASS_MAPPINGS: + instantid_apply = ALL_NODE_CLASS_MAPPINGS['ApplyInstantID'] + if control_net is None: + control_net = easyCache.load_controlnet(control_net_name, cn_soft_weights) + model, positive, negative = instantid_apply().apply_instantid(instantid_model, insightface_model, control_net, image, model, positive, negative, start_at, end_at, weight=weight, ip_weight=None, cn_strength=cn_strength, noise=noise, image_kps=image_kps, mask=mask) + else: + self.error() + + new_pipe = { + "model": model, + "positive": positive, + "negative": negative, + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": pipe["samples"], + "images": pipe["images"], + "seed": 0, + + "loader_settings": pipe["loader_settings"] + } + + del pipe + + return (new_pipe, model, positive, negative) + +class instantIDApply(instantID): + + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required":{ + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "instantid_file": (folder_paths.get_filename_list("instantid"),), + "insightface": (["CPU", "CUDA", "ROCM"],), + "control_net_name": (folder_paths.get_filename_list("controlnet"),), + "cn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cn_soft_weights": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},), + "weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 5.0, "step": 0.01, }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }), + "noise": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.05, }), + }, + "optional": { + "image_kps": ("IMAGE",), + "mask": ("MASK",), + "control_net": ("CONTROL_NET",), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("pipe", "model", "positive", "negative") + + FUNCTION = "apply" + CATEGORY = "EasyUse/Adapter" + + + def apply(self, pipe, image, instantid_file, insightface, control_net_name, cn_strength, cn_soft_weights, weight, start_at, end_at, noise, image_kps=None, mask=None, control_net=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + positive = pipe['positive'] + negative = pipe['negative'] + return self.run(pipe, image, instantid_file, insightface, control_net_name, cn_strength, cn_soft_weights, weight, start_at, end_at, noise, image_kps, mask, control_net, positive, negative, prompt, extra_pnginfo, my_unique_id) + +#Apply InstantID Advanced +class instantIDApplyAdvanced(instantID): + + def __init__(self): + super().__init__() + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required":{ + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "instantid_file": (folder_paths.get_filename_list("instantid"),), + "insightface": (["CPU", "CUDA", "ROCM"],), + "control_net_name": (folder_paths.get_filename_list("controlnet"),), + "cn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cn_soft_weights": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},), + "weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 5.0, "step": 0.01, }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }), + "noise": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.05, }), + }, + "optional": { + "image_kps": ("IMAGE",), + "mask": ("MASK",), + "control_net": ("CONTROL_NET",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("pipe", "model", "positive", "negative") + + FUNCTION = "apply_advanced" + CATEGORY = "EasyUse/Adapter" + + def apply_advanced(self, pipe, image, instantid_file, insightface, control_net_name, cn_strength, cn_soft_weights, weight, start_at, end_at, noise, image_kps=None, mask=None, control_net=None, positive=None, negative=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + + positive = positive if positive is not None else pipe['positive'] + negative = negative if negative is not None else pipe['negative'] + + return self.run(pipe, image, instantid_file, insightface, control_net_name, cn_strength, cn_soft_weights, weight, start_at, end_at, noise, image_kps, mask, control_net, positive, negative, prompt, extra_pnginfo, my_unique_id) + +class applyPulID: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "pulid_file": (folder_paths.get_filename_list("pulid"),), + "insightface": (["CPU", "CUDA", "ROCM"],), + "image": ("IMAGE",), + "method": (["fidelity", "style", "neutral"],), + "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05}), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }, + "optional": { + "attn_mask": ("MASK",), + }, + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model",) + + FUNCTION = "run" + CATEGORY = "EasyUse/Adapter" + + def error(self): + raise Exception(f"[ERROR] To use pulIDApply, you need to install 'ComfyUI_PulID'") + + def run(self, model, image, pulid_file, insightface, weight, start_at, end_at, method=None, noise=0.0, fidelity=None, projection=None, attn_mask=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + pulid_model, insightface_model, eva_clip = None, None, None + # Load PulID + cache_key = 'pulID' + if cache_key in backend_cache.cache: + log_node_info("easy pulIDApply","Using InstantIDModel Cached") + _, pulid_model = backend_cache.cache[cache_key][1] + if "PulidModelLoader" in ALL_NODE_CLASS_MAPPINGS: + load_pulid_cls = ALL_NODE_CLASS_MAPPINGS["PulidModelLoader"] + pulid_model, = load_pulid_cls().load_model(pulid_file) + backend_cache.update_cache(cache_key, 'pulid', (False, pulid_model)) + else: + self.error() + # Load Insightface + icache_key = 'insightface-' + insightface + if icache_key in backend_cache.cache: + log_node_info("easy pulIDApply", f"Using InsightFaceModel {insightface} Cached") + _, insightface_model = backend_cache.cache[icache_key][1] + elif "PulidInsightFaceLoader" in ALL_NODE_CLASS_MAPPINGS: + load_insightface_cls = ALL_NODE_CLASS_MAPPINGS["PulidInsightFaceLoader"] + insightface_model, = load_insightface_cls().load_insightface(insightface) + backend_cache.update_cache(icache_key, 'insightface', (False, insightface_model)) + else: + self.error() + # Load Eva clip + ecache_key = 'eva_clip' + if ecache_key in backend_cache.cache: + log_node_info("easy pulIDApply", f"Using EVAClipModel Cached") + _, eva_clip = backend_cache.cache[ecache_key][1] + elif "PulidEvaClipLoader" in ALL_NODE_CLASS_MAPPINGS: + load_evaclip_cls = ALL_NODE_CLASS_MAPPINGS["PulidEvaClipLoader"] + eva_clip, = load_evaclip_cls().load_eva_clip() + backend_cache.update_cache(ecache_key, 'eva_clip', (False, eva_clip)) + else: + self.error() + + # Apply PulID + if method is not None: + if "ApplyPulid" in ALL_NODE_CLASS_MAPPINGS: + cls = ALL_NODE_CLASS_MAPPINGS['ApplyPulid'] + model, = cls().apply_pulid(model, pulid=pulid_model, eva_clip=eva_clip, face_analysis=insightface_model, image=image, weight=weight, method=method, start_at=start_at, end_at=end_at, attn_mask=attn_mask) + else: + self.error() + else: + if "ApplyPulidAdvanced" in ALL_NODE_CLASS_MAPPINGS: + cls = ALL_NODE_CLASS_MAPPINGS['ApplyPulidAdvanced'] + model, = cls().apply_pulid(model, pulid=pulid_model, eva_clip=eva_clip, face_analysis=insightface_model, image=image, weight=weight, projection=projection, fidelity=fidelity, noise=noise, start_at=start_at, end_at=end_at, attn_mask=attn_mask) + else: + self.error() + + return (model,) + +class applyPulIDADV(applyPulID): + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "pulid_file": (folder_paths.get_filename_list("pulid"),), + "insightface": (["CPU", "CUDA", "ROCM"],), + "image": ("IMAGE",), + "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05}), + "projection": (["ortho_v2", "ortho", "none"], {"default":"ortho_v2"}), + "fidelity": ("INT", {"default": 8, "min": 0, "max": 32, "step": 1}), + "noise": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }, + "optional": { + "attn_mask": ("MASK",), + }, + } + + + +NODE_CLASS_MAPPINGS = { + "easy loraPromptApply": applyLoraPrompt, + "easy loraStackApply": applyLoraStack, + "easy controlnetStackApply": applyControlnetStack, + "easy ipadapterApply": ipadapterApply, + "easy ipadapterApplyADV": ipadapterApplyAdvanced, + "easy ipadapterApplyFaceIDKolors": ipadapterApplyFaceIDKolors, + "easy ipadapterApplyEncoder": ipadapterApplyEncoder, + "easy ipadapterApplyEmbeds": ipadapterApplyEmbeds, + "easy ipadapterApplyRegional": ipadapterApplyRegional, + "easy ipadapterApplyFromParams": ipadapterApplyFromParams, + "easy ipadapterStyleComposition": ipadapterStyleComposition, + "easy instantIDApply": instantIDApply, + "easy instantIDApplyADV": instantIDApplyAdvanced, + "easy pulIDApply": applyPulID, + "easy pulIDApplyADV": applyPulIDADV, + "easy styleAlignedBatchAlign": styleAlignedBatchAlign, + "easy icLightApply": icLightApply +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy loraPromptApply": "Easy Apply LoraPrompt", + "easy loraStackApply": "Easy Apply LoraStack", + "easy controlnetStackApply": "Easy Apply CnetStack", + "easy ipadapterApply": "Easy Apply IPAdapter", + "easy ipadapterApplyADV": "Easy Apply IPAdapter (Advanced)", + "easy ipadapterApplyFaceIDKolors": "Easy Apply IPAdapter (FaceID Kolors)", + "easy ipadapterStyleComposition": "Easy Apply IPAdapter (StyleComposition)", + "easy ipadapterApplyEncoder": "Easy Apply IPAdapter (Encoder)", + "easy ipadapterApplyRegional": "Easy Apply IPAdapter (Regional)", + "easy ipadapterApplyEmbeds": "Easy Apply IPAdapter (Embeds)", + "easy ipadapterApplyFromParams": "Easy Apply IPAdapter (From Params)", + "easy instantIDApply": "Easy Apply InstantID", + "easy instantIDApplyADV": "Easy Apply InstantID (Advanced)", + "easy pulIDApply": "Easy Apply PuLID", + "easy pulIDApplyADV": "Easy Apply PuLID (Advanced)", + "easy styleAlignedBatchAlign": "Easy Apply StyleAlign", + "easy icLightApply": "Easy Apply ICLight" +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/api.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/api.py new file mode 100644 index 00000000..baee49ef --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/api.py @@ -0,0 +1,139 @@ +import comfy.utils +from ..libs.api.fluxai import fluxaiAPI +from ..libs.api.bizyair import bizyairAPI, encode_data +from nodes import NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS + +class joyCaption2API: + API_URL = f"/supernode/joycaption2" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "do_sample": ([True, False],), + "temperature": ( + "FLOAT", + { + "default": 0.5, + "min": 0.0, + "max": 2.0, + "step": 0.01, + "round": 0.001, + "display": "number", + }, + ), + "max_tokens": ( + "INT", + { + "default": 256, + "min": 16, + "max": 512, + "step": 16, + "display": "number", + }, + ), + "caption_type": ( + [ + "Descriptive", + "Descriptive (Informal)", + "Training Prompt", + "MidJourney", + "Booru tag list", + "Booru-like tag list", + "Art Critic", + "Product Listing", + "Social Media Post", + ], + ), + "caption_length": ( + ["any", "very short", "short", "medium-length", "long", "very long"] + + [str(i) for i in range(20, 261, 10)], + ), + "extra_options": ( + "STRING", + { + "placeholder": "Extra options(e.g):\nIf there is a person/character in the image you must refer to them as {name}.", + "tooltip": "Extra options for the model", + "multiline": True, + }, + ), + "name_input": ( + "STRING", + { + "default": "", + "tooltip": "Name input is only used if an Extra Option is selected that requires it.", + }, + ), + "custom_prompt": ( + "STRING", + { + "default": "", + "multiline": True, + }, + ), + }, + "optional":{ + "apikey_override": ("STRING", {"default": "", "forceInput": True, "tooltip":"Override the API key in the local config"}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("caption",) + + FUNCTION = "joycaption" + OUTPUT_NODE = False + + CATEGORY = "EasyUse/API" + + def joycaption( + self, + image, + do_sample, + temperature, + max_tokens, + caption_type, + caption_length, + extra_options, + name_input, + custom_prompt, + apikey_override=None + ): + pbar = comfy.utils.ProgressBar(100) + pbar.update_absolute(10) + SIZE_LIMIT = 1536 + _, w, h, c = image.shape + if w > SIZE_LIMIT or h > SIZE_LIMIT: + node_class = ALL_NODE_CLASS_MAPPINGS['easy imageScaleDownToSize'] + image, = node_class().image_scale_down_to_size(image, SIZE_LIMIT, True) + + payload = { + "image": None, + "do_sample": do_sample == True, + "temperature": temperature, + "max_new_tokens": max_tokens, + "caption_type": caption_type, + "caption_length": caption_length, + "extra_options": [extra_options], + "name_input": name_input, + "custom_prompt": custom_prompt, + } + + pbar.update_absolute(30) + caption = bizyairAPI.joyCaption(payload, image, apikey_override, API_URL=self.API_URL) + + pbar.update_absolute(100) + return (caption,) + +class joyCaption3API(joyCaption2API): + API_URL = f"/supernode/joycaption3" + +NODE_CLASS_MAPPINGS = { + "easy joyCaption2API": joyCaption2API, + "easy joyCaption3API": joyCaption3API, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy joyCaption2API": "JoyCaption2 (BizyAIR)", + "easy joyCaption3API": "JoyCaption3 (BizyAIR)", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/deprecated.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/deprecated.py new file mode 100644 index 00000000..ae89659c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/deprecated.py @@ -0,0 +1,521 @@ +import numpy as np +import os +import json +import torch +import folder_paths +import comfy +import comfy.model_management +from PIL import Image +from PIL.PngImagePlugin import PngInfo +from nodes import ConditioningSetMask, RepeatLatentBatch +from comfy_extras.nodes_mask import LatentCompositeMasked +from ..libs.log import log_node_info, log_node_warn +from ..libs.adv_encode import advanced_encode +from ..libs.utils import AlwaysEqualProxy +any_type = AlwaysEqualProxy("*") + + +class If: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any": (any_type,), + "if": (any_type,), + "else": (any_type,), + }, + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("?",) + FUNCTION = "execute" + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + + def execute(self, *args, **kwargs): + return (kwargs['if'] if kwargs['any'] else kwargs['else'],) + + +class poseEditor: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("STRING", {"default": ""}) + }} + + FUNCTION = "output_pose" + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + RETURN_TYPES = () + RETURN_NAMES = () + + def output_pose(self, image): + return () + + +class imageToMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "channel": (['red', 'green', 'blue'],), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "convert" + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + + def convert_to_single_channel(self, image, channel='red'): + from PIL import Image + # Convert to RGB mode to access individual channels + image = image.convert('RGB') + + # Extract the desired channel and convert to greyscale + if channel == 'red': + channel_img = image.split()[0].convert('L') + elif channel == 'green': + channel_img = image.split()[1].convert('L') + elif channel == 'blue': + channel_img = image.split()[2].convert('L') + else: + raise ValueError( + "Invalid channel option. Please choose 'red', 'green', or 'blue'.") + + # Convert the greyscale channel back to RGB mode + channel_img = Image.merge( + 'RGB', (channel_img, channel_img, channel_img)) + + return channel_img + + def convert(self, image, channel='red'): + from ..libs.image import pil2tensor, tensor2pil + image = self.convert_to_single_channel(tensor2pil(image), channel) + image = pil2tensor(image) + return (image.squeeze().mean(2),) + +# ζ˜Ύη€ΊζŽ¨η†ζ—Άι—΄ +class showSpentTime: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + FUNCTION = "notify" + OUTPUT_NODE = True + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + RETURN_TYPES = () + RETURN_NAMES = () + + def notify(self, pipe, spent_time=None, unique_id=None, extra_pnginfo=None): + if unique_id and extra_pnginfo and "workflow" in extra_pnginfo: + workflow = extra_pnginfo["workflow"] + node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id), None) + if node: + spent_time = pipe['loader_settings']['spent_time'] if 'spent_time' in pipe['loader_settings'] else '' + node["widgets_values"] = [spent_time] + + return {"ui": {"text": [spent_time]}, "result": {}} + + +# ζ½œη©Ίι—΄sigmaη›ΈδΉ˜ +class latentNoisy: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + "steps": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 1, "max": 10000}), + "source": (["CPU", "GPU"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "optional": { + "pipe": ("PIPE_LINE",), + "optional_model": ("MODEL",), + "optional_latent": ("LATENT",) + }} + + RETURN_TYPES = ("PIPE_LINE", "LATENT", "FLOAT",) + RETURN_NAMES = ("pipe", "latent", "sigma",) + FUNCTION = "run" + DEPRECATED = True + + CATEGORY = "EasyUse/🚫 Deprecated" + + def run(self, sampler_name, scheduler, steps, start_at_step, end_at_step, source, seed, pipe=None, optional_model=None, optional_latent=None): + model = optional_model if optional_model is not None else pipe["model"] + batch_size = pipe["loader_settings"]["batch_size"] + empty_latent_height = pipe["loader_settings"]["empty_latent_height"] + empty_latent_width = pipe["loader_settings"]["empty_latent_width"] + + if optional_latent is not None: + samples = optional_latent + else: + torch.manual_seed(seed) + if source == "CPU": + device = "cpu" + else: + device = comfy.model_management.get_torch_device() + noise = torch.randn((batch_size, 4, empty_latent_height // 8, empty_latent_width // 8), dtype=torch.float32, + device=device).cpu() + + samples = {"samples": noise} + + device = comfy.model_management.get_torch_device() + end_at_step = min(steps, end_at_step) + start_at_step = min(start_at_step, end_at_step) + comfy.model_management.load_model_gpu(model) + model_patcher = comfy.model_patcher.ModelPatcher(model.model, load_device=device, offload_device=comfy.model_management.unet_offload_device()) + sampler = comfy.samplers.KSampler(model_patcher, steps=steps, device=device, sampler=sampler_name, + scheduler=scheduler, denoise=1.0, model_options=model.model_options) + sigmas = sampler.sigmas + sigma = sigmas[start_at_step] - sigmas[end_at_step] + sigma /= model.model.latent_format.scale_factor + sigma = sigma.cpu().numpy() + + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1 * sigma + + if pipe is None: + pipe = {} + new_pipe = { + **pipe, + "samples": samples_out + } + del pipe + + return (new_pipe, samples_out, sigma) + +# Latentιη½©ε€εˆ +class latentCompositeMaskedWithCond: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + "text_combine": ("LIST",), + "source_latent": ("LATENT",), + "source_mask": ("MASK",), + "destination_mask": ("MASK",), + "text_combine_mode": (["add", "replace", "cover"], {"default": "add"}), + "replace_text": ("STRING", {"default": ""}) + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + OUTPUT_IS_LIST = (False, False, True) + + RETURN_TYPES = ("PIPE_LINE", "LATENT", "CONDITIONING") + RETURN_NAMES = ("pipe", "latent", "conditioning",) + FUNCTION = "run" + + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + + def run(self, pipe, text_combine, source_latent, source_mask, destination_mask, text_combine_mode, replace_text, prompt=None, extra_pnginfo=None, my_unique_id=None): + positive = None + clip = pipe["clip"] + destination_latent = pipe["samples"] + + conds = [] + + for text in text_combine: + if text_combine_mode == 'cover': + positive = text + elif text_combine_mode == 'replace' and replace_text != '': + positive = pipe["loader_settings"]["positive"].replace(replace_text, text) + else: + positive = pipe["loader_settings"]["positive"] + ',' + text + positive_token_normalization = pipe["loader_settings"]["positive_token_normalization"] + positive_weight_interpretation = pipe["loader_settings"]["positive_weight_interpretation"] + a1111_prompt_style = pipe["loader_settings"]["a1111_prompt_style"] + positive_cond = pipe["positive"] + + log_node_warn("Positive encoding...") + steps = pipe["loader_settings"]["steps"] if "steps" in pipe["loader_settings"] else 1 + positive_embeddings_final = advanced_encode(clip, positive, + positive_token_normalization, + positive_weight_interpretation, w_max=1.0, + apply_to_pooled='enable', a1111_prompt_style=a1111_prompt_style, steps=steps) + + # source cond + (cond_1,) = ConditioningSetMask().append(positive_cond, source_mask, "default", 1) + (cond_2,) = ConditioningSetMask().append(positive_embeddings_final, destination_mask, "default", 1) + positive_cond = cond_1 + cond_2 + + conds.append(positive_cond) + # latent composite masked + (samples,) = LatentCompositeMasked().composite(destination_latent, source_latent, 0, 0, False) + + new_pipe = { + **pipe, + "samples": samples, + "loader_settings": { + **pipe["loader_settings"], + "positive": positive, + } + } + + del pipe + + return (new_pipe, samples, conds) + +# ε™ͺ声注ε…₯εˆ°ζ½œη©Ίι—΄ +class injectNoiseToLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.0001}), + "normalize": ("BOOLEAN", {"default": False}), + "average": ("BOOLEAN", {"default": False}), + }, + "optional": { + "pipe_to_noise": ("PIPE_LINE",), + "image_to_latent": ("IMAGE",), + "latent": ("LATENT",), + "noise": ("LATENT",), + "mask": ("MASK",), + "mix_randn_amount": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.001}), + "seed": ("INT", {"default": 123, "min": 0, "max": 0xffffffffffffffff, "step": 1}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "inject" + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + + + def inject(self,strength, normalize, average, pipe_to_noise=None, noise=None, image_to_latent=None, latent=None, mix_randn_amount=0, mask=None, seed=None): + + vae = pipe_to_noise["vae"] if pipe_to_noise is not None else pipe_to_noise["vae"] + batch_size = pipe_to_noise["loader_settings"]["batch_size"] if pipe_to_noise is not None and "batch_size" in pipe_to_noise["loader_settings"] else 1 + if noise is None and pipe_to_noise is not None: + noise = pipe_to_noise["samples"] + elif noise is None: + raise Exception("InjectNoiseToLatent: No noise provided") + + if image_to_latent is not None and vae is not None: + samples = {"samples": vae.encode(image_to_latent[:, :, :, :3])} + latents = RepeatLatentBatch().repeat(samples, batch_size)[0] + elif latent is not None: + latents = latent + else: + latents = {"samples": noise["samples"].clone()} + + samples = latents.copy() + if latents["samples"].shape != noise["samples"].shape: + raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape") + if average: + noised = (samples["samples"].clone() + noise["samples"].clone()) / 2 + else: + noised = samples["samples"].clone() + noise["samples"].clone() * strength + if normalize: + noised = noised / noised.std() + if mask is not None: + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), + size=(noised.shape[2], noised.shape[3]), mode="bilinear") + mask = mask.expand((-1, noised.shape[1], -1, -1)) + if mask.shape[0] < noised.shape[0]: + mask = mask.repeat((noised.shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:noised.shape[0]] + noised = mask * noised + (1 - mask) * latents["samples"] + if mix_randn_amount > 0: + if seed is not None: + torch.manual_seed(seed) + rand_noise = torch.randn_like(noised) + noised = ((1 - mix_randn_amount) * noised + mix_randn_amount * + rand_noise) / ((mix_randn_amount ** 2 + (1 - mix_randn_amount) ** 2) ** 0.5) + samples["samples"] = noised + return (samples,) + + +from ..libs.api.stability import stableAPI +class stableDiffusion3API: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default": "", "placeholder": "Negative", "multiline": True}), + "model": (["sd3", "sd3-turbo"],), + "aspect_ratio": (['16:9', '1:1', '21:9', '2:3', '3:2', '4:5', '5:4', '9:16', '9:21'],), + "seed": ("INT", {"default": 0, "min": 0, "max": 4294967294}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}), + }, + "optional": { + "optional_image": ("IMAGE",), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + + FUNCTION = "generate" + OUTPUT_NODE = False + + CATEGORY = "EasyUse/🚫 Deprecated" + DEPRECATED = True + + + def generate(self, positive, negative, model, aspect_ratio, seed, denoise, optional_image=None, unique_id=None, extra_pnginfo=None): + stableAPI.getAPIKeys() + mode = 'text-to-image' + if optional_image is not None: + mode = 'image-to-image' + output_image = stableAPI.generate_sd3_image(positive, negative, aspect_ratio, seed=seed, mode=mode, model=model, strength=denoise, image=optional_image) + return (output_image,) + + +class saveImageLazy(): + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE",), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "save_metadata": ("BOOLEAN", {"default": True}), + }, + "optional":{}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + OUTPUT_NODE = False + FUNCTION = "save" + + DEPRECATED = True + CATEGORY = "EasyUse/🚫 Deprecated" + + def save(self, images, filename_prefix, save_metadata, prompt=None, extra_pnginfo=None): + extension = 'png' + + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + + results = list() + for (batch_number, image) in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + + filename_with_batch_num = filename.replace( + "%batch_num%", str(batch_number)) + + counter = 1 + + if os.path.exists(full_output_folder) and os.listdir(full_output_folder): + filtered_filenames = list(filter( + lambda filename: filename.startswith( + filename_with_batch_num + "_") + and filename[len(filename_with_batch_num) + 1:-4].isdigit(), + os.listdir(full_output_folder) + )) + + if filtered_filenames: + max_counter = max( + int(filename[len(filename_with_batch_num) + 1:-4]) + for filename in filtered_filenames + ) + counter = max_counter + 1 + + file = f"{filename_with_batch_num}_{counter:05}.{extension}" + + save_path = os.path.join(full_output_folder, file) + + if save_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text( + x, json.dumps(extra_pnginfo[x])) + + img.save(save_path, pnginfo=metadata) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + return {"ui": {"images": results} , "result": (images,)} + +from .logic import saveText, showAnything + +class showAnythingLazy(showAnything): + @classmethod + def INPUT_TYPES(s): + return {"required": {}, "optional": {"anything": (any_type, {}), }, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO", + }} + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ('output',) + INPUT_IS_LIST = True + OUTPUT_NODE = False + OUTPUT_IS_LIST = (False,) + DEPRECATED = True + FUNCTION = "log_input" + CATEGORY = "EasyUse/🚫 Deprecated" + +class saveTextLazy(saveText): + + RETURN_TYPES = ("STRING", "IMAGE") + RETURN_NAMES = ("text", 'image',) + + FUNCTION = "save_text" + OUTPUT_NODE = False + DEPRECATED = True + CATEGORY = "EasyUse/🚫 Deprecated" + +NODE_CLASS_MAPPINGS = { + "easy if": If, + "easy poseEditor": poseEditor, + "easy imageToMask": imageToMask, + "easy showSpentTime": showSpentTime, + "easy latentNoisy": latentNoisy, + "easy latentCompositeMaskedWithCond": latentCompositeMaskedWithCond, + "easy injectNoiseToLatent": injectNoiseToLatent, + "easy stableDiffusion3API": stableDiffusion3API, + "easy saveImageLazy": saveImageLazy, + "easy saveTextLazy": saveTextLazy, + "easy showAnythingLazy": showAnythingLazy, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy if": "If (🚫Deprecated)", + "easy poseEditor": "PoseEditor (🚫Deprecated)", + "easy imageToMask": "ImageToMask (🚫Deprecated)", + "easy showSpentTime": "Show Spent Time (🚫Deprecated)", + "easy latentNoisy": "LatentNoisy (🚫Deprecated)", + "easy latentCompositeMaskedWithCond": "LatentCompositeMaskedWithCond (🚫Deprecated)", + "easy injectNoiseToLatent": "InjectNoiseToLatent (🚫Deprecated)", + "easy stableDiffusion3API": "StableDiffusion3API (🚫Deprecated)", + "easy saveImageLazy": "SaveImageLazy (🚫Deprecated)", + "easy saveTextLazy": "SaveTextLazy (🚫Deprecated)", + "easy showAnythingLazy": "ShowAnythingLazy (🚫Deprecated)", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/fix.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/fix.py new file mode 100644 index 00000000..299110e8 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/fix.py @@ -0,0 +1,643 @@ +import sys +import time +import comfy +import torch +import folder_paths + +from comfy_extras.chainner_models import model_loading + +from server import PromptServer +from nodes import MAX_RESOLUTION, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS + +from ..libs.utils import easySave, get_sd_version +from ..libs.sampler import easySampler +from .. import easyCache, sampler + +class hiresFix: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos", "bislerp"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model_name": (folder_paths.get_filename_list("upscale_models"),), + "rescale_after_model": ([False, True], {"default": True}), + "rescale_method": (s.upscale_methods,), + "rescale": (["by percentage", "to Width/Height", 'to longer side - maintain aspect'],), + "percent": ("INT", {"default": 50, "min": 0, "max": 1000, "step": 1}), + "width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "longer_side": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "crop": (s.crop_methods,), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "vae": ("VAE",), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE", "LATENT", ) + RETURN_NAMES = ('pipe', 'image', "latent", ) + + FUNCTION = "upscale" + CATEGORY = "EasyUse/Fix" + OUTPUT_NODE = True + + def vae_encode_crop_pixels(self, pixels): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + return pixels + + def upscale(self, model_name, rescale_after_model, rescale_method, rescale, percent, width, height, + longer_side, crop, image_output, link_id, save_prefix, pipe=None, image=None, vae=None, prompt=None, + extra_pnginfo=None, my_unique_id=None): + + new_pipe = {} + if pipe is not None: + image = image if image is not None else pipe["images"] + vae = vae if vae is not None else pipe.get("vae") + elif image is None or vae is None: + raise ValueError("pipe or image or vae missing.") + # Load Model + model_path = folder_paths.get_full_path("upscale_models", model_name) + sd = comfy.utils.load_torch_file(model_path, safe_load=True) + upscale_model = model_loading.load_state_dict(sd).eval() + + # Model upscale + device = comfy.model_management.get_torch_device() + upscale_model.to(device) + in_img = image.movedim(-1, -3).to(device) + + tile = 128 + 64 + overlap = 8 + steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, + tile_y=tile, overlap=overlap) + pbar = comfy.utils.ProgressBar(steps) + s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, + upscale_amount=upscale_model.scale, pbar=pbar) + upscale_model.cpu() + s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0) + + # Post Model Rescale + if rescale_after_model == True: + samples = s.movedim(-1, 1) + orig_height = samples.shape[2] + orig_width = samples.shape[3] + if rescale == "by percentage" and percent != 0: + height = percent / 100 * orig_height + width = percent / 100 * orig_width + if (width > MAX_RESOLUTION): + width = MAX_RESOLUTION + if (height > MAX_RESOLUTION): + height = MAX_RESOLUTION + + width = easySampler.enforce_mul_of_64(width) + height = easySampler.enforce_mul_of_64(height) + elif rescale == "to longer side - maintain aspect": + longer_side = easySampler.enforce_mul_of_64(longer_side) + if orig_width > orig_height: + width, height = longer_side, easySampler.enforce_mul_of_64(longer_side * orig_height / orig_width) + else: + width, height = easySampler.enforce_mul_of_64(longer_side * orig_width / orig_height), longer_side + + s = comfy.utils.common_upscale(samples, width, height, rescale_method, crop) + s = s.movedim(1, -1) + + # vae encode + pixels = self.vae_encode_crop_pixels(s) + t = vae.encode(pixels[:, :, :, :3]) + + if pipe is not None: + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": vae, + "clip": pipe['clip'], + + "samples": {"samples": t}, + "images": s, + "seed": pipe['seed'], + + "loader_settings": { + **pipe["loader_settings"], + } + } + del pipe + else: + new_pipe = {} + + results = easySave(s, save_prefix, image_output, prompt, extra_pnginfo) + + if image_output in ("Sender", "Sender&Save"): + PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) + + if image_output in ("Hide", "Hide&Save"): + return (new_pipe, s, {"samples": t},) + + return {"ui": {"images": results}, + "result": (new_pipe, s, {"samples": t},)} + +# ι’„η»†θŠ‚δΏε€ +class preDetailerFix: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pipe": ("PIPE_LINE",), + "guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS + ['align_your_steps'],), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "bbox_segm_pipe": ("PIPE_LINE",), + "sam_pipe": ("PIPE_LINE",), + "optional_image": ("IMAGE",), + }, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + OUTPUT_IS_LIST = (False,) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Fix" + + def doit(self, pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, denoise, feather, noise_mask, force_inpaint, drop_size, wildcard, cycle, bbox_segm_pipe=None, sam_pipe=None, optional_image=None): + + model = pipe["model"] if "model" in pipe else None + if model is None: + raise Exception(f"[ERROR] pipe['model'] is missing") + clip = pipe["clip"] if"clip" in pipe else None + if clip is None: + raise Exception(f"[ERROR] pipe['clip'] is missing") + vae = pipe["vae"] if "vae" in pipe else None + if vae is None: + raise Exception(f"[ERROR] pipe['vae'] is missing") + if optional_image is not None: + images = optional_image + else: + images = pipe["images"] if "images" in pipe else None + if images is None: + raise Exception(f"[ERROR] pipe['image'] is missing") + positive = pipe["positive"] if "positive" in pipe else None + if positive is None: + raise Exception(f"[ERROR] pipe['positive'] is missing") + negative = pipe["negative"] if "negative" in pipe else None + if negative is None: + raise Exception(f"[ERROR] pipe['negative'] is missing") + bbox_segm_pipe = bbox_segm_pipe or (pipe["bbox_segm_pipe"] if pipe and "bbox_segm_pipe" in pipe else None) + if bbox_segm_pipe is None: + raise Exception(f"[ERROR] bbox_segm_pipe or pipe['bbox_segm_pipe'] is missing") + sam_pipe = sam_pipe or (pipe["sam_pipe"] if pipe and "sam_pipe" in pipe else None) + if sam_pipe is None: + raise Exception(f"[ERROR] sam_pipe or pipe['sam_pipe'] is missing") + + loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {} + + if(scheduler == 'align_your_steps'): + model_version = get_sd_version(model) + if model_version == 'sdxl': + scheduler = 'AYS SDXL' + elif model_version == 'svd': + scheduler = 'AYS SVD' + else: + scheduler = 'AYS SD1' + + new_pipe = { + "images": images, + "model": model, + "clip": clip, + "vae": vae, + "positive": positive, + "negative": negative, + "seed": seed, + + "bbox_segm_pipe": bbox_segm_pipe, + "sam_pipe": sam_pipe, + + "loader_settings": loader_settings, + + "detail_fix_settings": { + "guide_size": guide_size, + "guide_size_for": guide_size_for, + "max_size": max_size, + "seed": seed, + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "feather": feather, + "noise_mask": noise_mask, + "force_inpaint": force_inpaint, + "drop_size": drop_size, + "wildcard": wildcard, + "cycle": cycle + } + } + + + del bbox_segm_pipe + del sam_pipe + + return (new_pipe,) + +# ι’„ιη½©η»†θŠ‚δΏε€ +class preMaskDetailerFix: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pipe": ("PIPE_LINE",), + "mask": ("MASK",), + + "guide_size": ("FLOAT", {"default": 384, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "mask_mode": ("BOOLEAN", {"default": True, "label_on": "masked only", "label_off": "whole"}), + + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}), + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + # "patch": ("INPAINT_PATCH",), + "optional_image": ("IMAGE",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + }, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + OUTPUT_IS_LIST = (False,) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Fix" + + def doit(self, pipe, mask, guide_size, guide_size_for, max_size, mask_mode, seed, steps, cfg, sampler_name, scheduler, denoise, feather, crop_factor, drop_size,refiner_ratio, batch_size, cycle, optional_image=None, inpaint_model=False, noise_mask_feather=20): + + model = pipe["model"] if "model" in pipe else None + if model is None: + raise Exception(f"[ERROR] pipe['model'] is missing") + clip = pipe["clip"] if"clip" in pipe else None + if clip is None: + raise Exception(f"[ERROR] pipe['clip'] is missing") + vae = pipe["vae"] if "vae" in pipe else None + if vae is None: + raise Exception(f"[ERROR] pipe['vae'] is missing") + if optional_image is not None: + images = optional_image + else: + images = pipe["images"] if "images" in pipe else None + if images is None: + raise Exception(f"[ERROR] pipe['image'] is missing") + positive = pipe["positive"] if "positive" in pipe else None + if positive is None: + raise Exception(f"[ERROR] pipe['positive'] is missing") + negative = pipe["negative"] if "negative" in pipe else None + if negative is None: + raise Exception(f"[ERROR] pipe['negative'] is missing") + latent = pipe["samples"] if "samples" in pipe else None + if latent is None: + raise Exception(f"[ERROR] pipe['samples'] is missing") + + if 'noise_mask' not in latent: + if images is None: + raise Exception("No Images found") + if vae is None: + raise Exception("No VAE found") + x = (images.shape[1] // 8) * 8 + y = (images.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), + size=(images.shape[1], images.shape[2]), mode="bilinear") + + pixels = images.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset] + + mask_erosion = mask + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:, :, :, i] -= 0.5 + pixels[:, :, :, i] *= m + pixels[:, :, :, i] += 0.5 + t = vae.encode(pixels) + + latent = {"samples": t, "noise_mask": (mask_erosion[:, :, :x, :y].round())} + # when patch was linked + # if patch is not None: + # worker = InpaintWorker(node_name="easy kSamplerInpainting") + # model, = worker.patch(model, latent, patch) + + loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {} + + new_pipe = { + "images": images, + "model": model, + "clip": clip, + "vae": vae, + "positive": positive, + "negative": negative, + "seed": seed, + "mask": mask, + + "loader_settings": loader_settings, + + "detail_fix_settings": { + "guide_size": guide_size, + "guide_size_for": guide_size_for, + "max_size": max_size, + "seed": seed, + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "feather": feather, + "crop_factor": crop_factor, + "drop_size": drop_size, + "refiner_ratio": refiner_ratio, + "batch_size": batch_size, + "cycle": cycle + }, + + "mask_settings": { + "mask_mode": mask_mode, + "inpaint_model": inpaint_model, + "noise_mask_feather": noise_mask_feather + } + } + + del pipe + + return (new_pipe,) + +# η»†θŠ‚δΏε€ +class detailerFix: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pipe": ("PIPE_LINE",), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", } + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE", "IMAGE", "IMAGE") + RETURN_NAMES = ("pipe", "image", "cropped_refined", "cropped_enhanced_alpha") + OUTPUT_NODE = True + OUTPUT_IS_LIST = (False, False, True, True) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Fix" + + + def doit(self, pipe, image_output, link_id, save_prefix, model=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + + # Clean loaded_objects + easyCache.update_loaded_objects(prompt) + + my_unique_id = int(my_unique_id) + + model = model or (pipe["model"] if "model" in pipe else None) + if model is None: + raise Exception(f"[ERROR] model or pipe['model'] is missing") + + detail_fix_settings = pipe["detail_fix_settings"] if "detail_fix_settings" in pipe else None + if detail_fix_settings is None: + raise Exception(f"[ERROR] detail_fix_settings or pipe['detail_fix_settings'] is missing") + + mask = pipe["mask"] if "mask" in pipe else None + + image = pipe["images"] + clip = pipe["clip"] + vae = pipe["vae"] + seed = pipe["seed"] + positive = pipe["positive"] + negative = pipe["negative"] + loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {} + guide_size = pipe["detail_fix_settings"]["guide_size"] if "guide_size" in pipe["detail_fix_settings"] else 256 + guide_size_for = pipe["detail_fix_settings"]["guide_size_for"] if "guide_size_for" in pipe[ + "detail_fix_settings"] else True + max_size = pipe["detail_fix_settings"]["max_size"] if "max_size" in pipe["detail_fix_settings"] else 768 + steps = pipe["detail_fix_settings"]["steps"] if "steps" in pipe["detail_fix_settings"] else 20 + cfg = pipe["detail_fix_settings"]["cfg"] if "cfg" in pipe["detail_fix_settings"] else 1.0 + sampler_name = pipe["detail_fix_settings"]["sampler_name"] if "sampler_name" in pipe[ + "detail_fix_settings"] else None + scheduler = pipe["detail_fix_settings"]["scheduler"] if "scheduler" in pipe["detail_fix_settings"] else None + denoise = pipe["detail_fix_settings"]["denoise"] if "denoise" in pipe["detail_fix_settings"] else 0.5 + feather = pipe["detail_fix_settings"]["feather"] if "feather" in pipe["detail_fix_settings"] else 5 + crop_factor = pipe["detail_fix_settings"]["crop_factor"] if "crop_factor" in pipe["detail_fix_settings"] else 3.0 + drop_size = pipe["detail_fix_settings"]["drop_size"] if "drop_size" in pipe["detail_fix_settings"] else 10 + refiner_ratio = pipe["detail_fix_settings"]["refiner_ratio"] if "refiner_ratio" in pipe else 0.2 + batch_size = pipe["detail_fix_settings"]["batch_size"] if "batch_size" in pipe["detail_fix_settings"] else 1 + noise_mask = pipe["detail_fix_settings"]["noise_mask"] if "noise_mask" in pipe["detail_fix_settings"] else None + force_inpaint = pipe["detail_fix_settings"]["force_inpaint"] if "force_inpaint" in pipe["detail_fix_settings"] else False + wildcard = pipe["detail_fix_settings"]["wildcard"] if "wildcard" in pipe["detail_fix_settings"] else "" + cycle = pipe["detail_fix_settings"]["cycle"] if "cycle" in pipe["detail_fix_settings"] else 1 + + bbox_segm_pipe = pipe["bbox_segm_pipe"] if pipe and "bbox_segm_pipe" in pipe else None + sam_pipe = pipe["sam_pipe"] if "sam_pipe" in pipe else None + + # η»†θŠ‚δΏε€εˆε§‹ζ—Άι—΄ + start_time = int(time.time() * 1000) + if "mask_settings" in pipe: + mask_mode = pipe['mask_settings']["mask_mode"] if "inpaint_model" in pipe['mask_settings'] else True + inpaint_model = pipe['mask_settings']["inpaint_model"] if "inpaint_model" in pipe['mask_settings'] else False + noise_mask_feather = pipe['mask_settings']["noise_mask_feather"] if "noise_mask_feather" in pipe['mask_settings'] else 20 + cls = ALL_NODE_CLASS_MAPPINGS["MaskDetailerPipe"] + if "MaskDetailerPipe" not in ALL_NODE_CLASS_MAPPINGS: + raise Exception(f"[ERROR] To use MaskDetailerPipe, you need to install 'Impact Pack'") + basic_pipe = (model, clip, vae, positive, negative) + result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, basic_pipe, refiner_basic_pipe_opt = cls().doit(image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode, + seed, steps, cfg, sampler_name, scheduler, denoise, + feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1, + refiner_basic_pipe_opt=None, detailer_hook=None, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather) + result_mask = mask + result_cnet_images = () + else: + if bbox_segm_pipe is None: + raise Exception(f"[ERROR] bbox_segm_pipe or pipe['bbox_segm_pipe'] is missing") + if sam_pipe is None: + raise Exception(f"[ERROR] sam_pipe or pipe['sam_pipe'] is missing") + bbox_detector_opt, bbox_threshold, bbox_dilation, bbox_crop_factor, segm_detector_opt = bbox_segm_pipe + sam_model_opt, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative = sam_pipe + if "FaceDetailer" not in ALL_NODE_CLASS_MAPPINGS: + raise Exception(f"[ERROR] To use FaceDetailer, you need to install 'Impact Pack'") + cls = ALL_NODE_CLASS_MAPPINGS["FaceDetailer"] + + result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, pipe, result_cnet_images = cls().doit( + image, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, + scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, + bbox_threshold, bbox_dilation, bbox_crop_factor, + sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, + sam_mask_hint_use_negative, drop_size, bbox_detector_opt, wildcard, cycle, sam_model_opt, + segm_detector_opt, + detailer_hook=None) + + # η»†θŠ‚δΏε€η»“ζŸζ—Άι—΄ + end_time = int(time.time() * 1000) + + spent_time = 'Fix:' + str((end_time - start_time) / 1000) + '"' + + results = easySave(result_img, save_prefix, image_output, prompt, extra_pnginfo) + sampler.update_value_by_id("results", my_unique_id, results) + + # Clean loaded_objects + easyCache.update_loaded_objects(prompt) + + new_pipe = { + "samples": None, + "images": result_img, + "model": model, + "clip": clip, + "vae": vae, + "seed": seed, + "positive": positive, + "negative": negative, + "wildcard": wildcard, + "bbox_segm_pipe": bbox_segm_pipe, + "sam_pipe": sam_pipe, + + "loader_settings": { + **loader_settings, + "spent_time": spent_time + }, + "detail_fix_settings": detail_fix_settings + } + if "mask_settings" in pipe: + new_pipe["mask_settings"] = pipe["mask_settings"] + + sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe) + + del bbox_segm_pipe + del sam_pipe + del pipe + + if image_output in ("Hide", "Hide&Save"): + return (new_pipe, result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, result_cnet_images) + + if image_output in ("Sender", "Sender&Save"): + PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) + + return {"ui": {"images": results}, "result": (new_pipe, result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, result_cnet_images )} + +class ultralyticsDetectorForDetailerFix: + @classmethod + def INPUT_TYPES(s): + bboxs = ["bbox/" + x for x in folder_paths.get_filename_list("ultralytics_bbox")] + segms = ["segm/" + x for x in folder_paths.get_filename_list("ultralytics_segm")] + return {"required": + {"model_name": (bboxs + segms,), + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + "bbox_crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}), + } + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("bbox_segm_pipe",) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Fix" + + def doit(self, model_name, bbox_threshold, bbox_dilation, bbox_crop_factor): + if 'UltralyticsDetectorProvider' not in ALL_NODE_CLASS_MAPPINGS: + raise Exception(f"[ERROR] To use UltralyticsDetectorProvider, you need to install 'Impact Pack'") + cls = ALL_NODE_CLASS_MAPPINGS['UltralyticsDetectorProvider'] + bbox_detector, segm_detector = cls().doit(model_name) + pipe = (bbox_detector, bbox_threshold, bbox_dilation, bbox_crop_factor, segm_detector) + return (pipe,) + +class samLoaderForDetailerFix: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model_name": (folder_paths.get_filename_list("sams"),), + "device_mode": (["AUTO", "Prefer GPU", "CPU"],{"default": "AUTO"}), + "sam_detection_hint": ( + ["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", + "mask-point-bbox", "none"],), + "sam_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "sam_threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}), + "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam_mask_hint_use_negative": (["False", "Small", "Outter"],), + } + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("sam_pipe",) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Fix" + + def doit(self, model_name, device_mode, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative): + if 'SAMLoader' not in ALL_NODE_CLASS_MAPPINGS: + raise Exception(f"[ERROR] To use SAMLoader, you need to install 'Impact Pack'") + cls = ALL_NODE_CLASS_MAPPINGS['SAMLoader'] + (sam_model,) = cls().load_model(model_name, device_mode) + pipe = (sam_model, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative) + return (pipe,) + + +NODE_CLASS_MAPPINGS = { + "easy hiresFix": hiresFix, + "easy preDetailerFix": preDetailerFix, + "easy preMaskDetailerFix": preMaskDetailerFix, + "easy ultralyticsDetectorPipe": ultralyticsDetectorForDetailerFix, + "easy samLoaderPipe": samLoaderForDetailerFix, + "easy detailerFix": detailerFix +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy hiresFix": "HiresFix", + "easy preDetailerFix": "PreDetailerFix", + "easy preMaskDetailerFix": "preMaskDetailerFix", + "easy ultralyticsDetectorPipe": "UltralyticsDetector (Pipe)", + "easy samLoaderPipe": "SAMLoader (Pipe)", + "easy detailerFix": "DetailerFix", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/image.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/image.py new file mode 100644 index 00000000..ec188a2e --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/image.py @@ -0,0 +1,2205 @@ +import os +import folder_paths +import torch +import numpy as np +import comfy.utils +import comfy.model_management +import shutil +from comfy_extras.nodes_compositing import JoinImageWithAlpha +from server import PromptServer +from nodes import MAX_RESOLUTION, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS +from PIL import Image, ImageDraw, ImageFilter, ImageOps +import torch.nn.functional as F +from torchvision.transforms import Resize, CenterCrop, GaussianBlur, ToPILImage +from torchvision.transforms.functional import to_pil_image +from ..libs.log import log_node_info +from ..libs.utils import AlwaysEqualProxy, ByPassTypeTuple +from ..libs.cache import cache, update_cache, remove_cache +from ..libs.image import pil2tensor, tensor2pil, ResizeMode, get_new_bounds, RGB2RGBA, image2mask, empty_image, fit_resize_image +from ..libs.colorfix import adain_color_fix, wavelet_color_fix +from ..config import REMBG_DIR, REMBG_MODELS, HUMANPARSING_MODELS, MEDIAPIPE_MODELS, MEDIAPIPE_DIR + +any_type = AlwaysEqualProxy("*") +# 图像数量 +class imageCount: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + } + } + + CATEGORY = "EasyUse/Image" + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("count",) + FUNCTION = "get_count" + + def get_count(self, images): + return (images.size(0),) + +class imagesCountInDirectory: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "directory": ("STRING",), + "start_index": ("INT", {"default": 0, "min": 0, "step": 1}), + "limit": ("INT", {"default": -1, "min": -1, "max": 10000}), + } + } + + CATEGORY = "EasyUse/Image" + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("count",) + FUNCTION = "get_count" + + def get_count(self, directory, start_index, limit, **kwargs): + dir_files = os.listdir(directory) + valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] + dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] + if limit == -1: + files_length = len(dir_files) + total = files_length - start_index if start_index > 0 else files_length + else: + total = limit + return (total,) + +# ε›Ύεƒθ£εˆ‡ +class imageInsetCrop: + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "image": ("IMAGE",), + "measurement": (['Pixels', 'Percentage'],), + "left": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + "right": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + "top": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + "bottom": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crop" + + CATEGORY = "EasyUse/Image" + + # pylint: disable = too-many-arguments + def crop(self, measurement, left, right, top, bottom, image=None): + """Does the crop.""" + + _, height, width, _ = image.shape + + if measurement == 'Percentage': + left = int(width - (width * (100 - left) / 100)) + right = int(width - (width * (100 - right) / 100)) + top = int(height - (height * (100 - top) / 100)) + bottom = int(height - (height * (100 - bottom) / 100)) + + # Snap to 8 pixels + left = left // 8 * 8 + right = right // 8 * 8 + top = top // 8 * 8 + bottom = bottom // 8 * 8 + + if left == 0 and right == 0 and bottom == 0 and top == 0: + return (image,) + + inset_left, inset_right, inset_top, inset_bottom = get_new_bounds(width, height, left, right, + top, bottom) + if inset_top > inset_bottom: + raise ValueError( + f"Invalid cropping dimensions top ({inset_top}) exceeds bottom ({inset_bottom})") + if inset_left > inset_right: + raise ValueError( + f"Invalid cropping dimensions left ({inset_left}) exceeds right ({inset_right})") + + log_node_info("Image Inset Crop", f'Cropping image {width}x{height} width inset by {inset_left},{inset_right}, ' + + f'and height inset by {inset_top}, {inset_bottom}') + image = image[:, inset_top:inset_bottom, inset_left:inset_right, :] + + return (image,) + +# 图像尺寸 +class imageSize: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("width_int", "height_int") + OUTPUT_NODE = True + FUNCTION = "image_width_height" + + CATEGORY = "EasyUse/Image" + + def image_width_height(self, image): + _, raw_H, raw_W, _ = image.shape + + width = raw_W + height = raw_H + + if width is not None and height is not None: + result = (width, height) + else: + result = (0, 0) + return {"ui": {"text": "Width: "+str(width)+" , Height: "+str(height)}, "result": result} + +# ε›Ύεƒε°Ίε―ΈοΌˆζœ€ι•ΏθΎΉοΌ‰ +class imageSizeBySide: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "side": (["Longest", "Shortest"],) + } + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("resolution",) + OUTPUT_NODE = True + FUNCTION = "image_side" + + CATEGORY = "EasyUse/Image" + + def image_side(self, image, side): + _, raw_H, raw_W, _ = image.shape + + width = raw_W + height = raw_H + if width is not None and height is not None: + if side == "Longest": + result = (width,) if width > height else (height,) + elif side == 'Shortest': + result = (width,) if width < height else (height,) + else: + result = (0,) + return {"ui": {"text": str(result[0])}, "result": result} + +# ε›Ύεƒε°Ίε―ΈοΌˆζœ€ι•ΏθΎΉοΌ‰ +class imageSizeByLongerSide: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("resolution",) + OUTPUT_NODE = True + FUNCTION = "image_longer_side" + + CATEGORY = "EasyUse/Image" + + def image_longer_side(self, image): + _, raw_H, raw_W, _ = image.shape + + width = raw_W + height = raw_H + if width is not None and height is not None: + if width > height: + result = (width,) + else: + result = (height,) + else: + result = (0,) + return {"ui": {"text": str(result[0])}, "result": result} + +# 图像缩放 +class imageScaleDown: + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "width": ( + "INT", + {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}, + ), + "height": ( + "INT", + {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}, + ), + "crop": (s.crop_methods,), + } + } + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "EasyUse/Image" + FUNCTION = "image_scale_down" + + def image_scale_down(self, images, width, height, crop): + if crop == "center": + old_width = images.shape[2] + old_height = images.shape[1] + old_aspect = old_width / old_height + new_aspect = width / height + x = 0 + y = 0 + if old_aspect > new_aspect: + x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) + elif old_aspect < new_aspect: + y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) + s = images[:, y: old_height - y, x: old_width - x, :] + else: + s = images + + results = [] + for image in s: + img = tensor2pil(image).convert("RGB") + img = img.resize((width, height), Image.LANCZOS) + results.append(pil2tensor(img)) + + return (torch.cat(results, dim=0),) + +# 图像缩放比例 +class imageScaleDownBy(imageScaleDown): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "scale_by": ( + "FLOAT", + {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}, + ), + } + } + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "EasyUse/Image" + FUNCTION = "image_scale_down_by" + + def image_scale_down_by(self, images, scale_by): + width = images.shape[2] + height = images.shape[1] + new_width = int(width * scale_by) + new_height = int(height * scale_by) + return self.image_scale_down(images, new_width, new_height, "center") + +# 图像缩放尺寸 +class imageScaleDownToSize(imageScaleDownBy): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "size": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "mode": ("BOOLEAN", {"default": True, "label_on": "max", "label_off": "min"}), + } + } + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "EasyUse/Image" + FUNCTION = "image_scale_down_to_size" + + def image_scale_down_to_size(self, images, size, mode): + width = images.shape[2] + height = images.shape[1] + + if mode: + scale_by = size / max(width, height) + else: + scale_by = size / min(width, height) + + scale_by = min(scale_by, 1.0) + return self.image_scale_down_by(images, scale_by) + +class imageScaleToNormPixels: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "scale" + CATEGORY = "EasyUse/Image" + + def scale(self, image, upscale_method, scale_by): + height, width = image.shape[1:3] + width = int(width * scale_by - width * scale_by % 8) + height = int(height * scale_by - height * scale_by % 8) + upscale_image_cls = ALL_NODE_CLASS_MAPPINGS['ImageScale'] + image, = upscale_image_cls().upscale(image, upscale_method, width, height, "disabled") + return (image,) + +# ε›Ύεƒζ―”ηŽ‡ +class imageRatio: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("INT", "INT", "FLOAT", "FLOAT") + RETURN_NAMES = ("width_ratio_int", "height_ratio_int", "width_ratio_float", "height_ratio_float") + OUTPUT_NODE = True + FUNCTION = "image_ratio" + + CATEGORY = "EasyUse/Image" + + def gcf(self, a, b): + while b: + a, b = b, a % b + return a + + def image_ratio(self, image): + _, raw_H, raw_W, _ = image.shape + + width = raw_W + height = raw_H + + ratio = self.gcf(width, height) + + if width is not None and height is not None: + width_ratio = width // ratio + height_ratio = height // ratio + result = (width_ratio, height_ratio, width_ratio, height_ratio) + else: + width_ratio = 0 + height_ratio = 0 + result = (0, 0, 0.0, 0.0) + text = f"Image Ratio is {str(width_ratio)}:{str(height_ratio)}" + + return {"ui": {"text": text}, "result": result} + + +# 图像εŒηΎŽεƒη΄  +class imagePixelPerfect: + @classmethod + def INPUT_TYPES(s): + RESIZE_MODES = [ResizeMode.RESIZE.value, ResizeMode.INNER_FIT.value, ResizeMode.OUTER_FIT.value] + return { + "required": { + "image": ("IMAGE",), + "resize_mode": (RESIZE_MODES, {"default": ResizeMode.RESIZE.value}) + } + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("resolution",) + OUTPUT_NODE = True + FUNCTION = "execute" + + CATEGORY = "EasyUse/Image" + + def execute(self, image, resize_mode): + + _, raw_H, raw_W, _ = image.shape + + width = raw_W + height = raw_H + + k0 = float(height) / float(raw_H) + k1 = float(width) / float(raw_W) + + if resize_mode == ResizeMode.OUTER_FIT.value: + estimation = min(k0, k1) * float(min(raw_H, raw_W)) + else: + estimation = max(k0, k1) * float(min(raw_H, raw_W)) + + result = int(np.round(estimation)) + text = f"Width:{str(width)}\nHeight:{str(height)}\nPixelPerfect:{str(result)}" + + return {"ui": {"text": text}, "result": (result,)} + +# ε›ΎεƒδΏε­˜ (η€ζ˜“) +from nodes import PreviewImage, SaveImage +class imageSaveSimple: + + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "images": ("IMAGE",), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "only_preview": ("BOOLEAN", {"default": False}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + CATEGORY = "EasyUse/Image" + + def save(self, images, filename_prefix="ComfyUI", only_preview=False, prompt=None, extra_pnginfo=None): + if only_preview: + return PreviewImage().save_images(images, filename_prefix, prompt, extra_pnginfo) + else: + return SaveImage().save_images(images, filename_prefix, prompt, extra_pnginfo) + +# ε›Ύεƒζ‰Ήζ¬‘εˆεΉΆ +class JoinImageBatch: + """Turns an image batch into one big image.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "mode": (("horizontal", "vertical"), {"default": "horizontal"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "join" + CATEGORY = "EasyUse/Image" + + def join(self, images, mode): + n, h, w, c = images.shape + image = None + if mode == "vertical": + # for vertical we can just reshape + image = images.reshape(1, n * h, w, c) + elif mode == "horizontal": + # for horizontal we have to swap axes + image = torch.transpose(torch.transpose(images, 1, 2).reshape(1, n * w, h, c), 1, 2) + return (image,) + +class imageListToImageBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE",), + }} + + INPUT_IS_LIST = True + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Image" + + def doit(self, images): + if len(images) <= 1: + return (images[0],) + else: + image_shape = images[0].shape + for i, img in enumerate(images): + if image_shape[1:] == img[1:]: + continue + else: + images[i] = comfy.utils.common_upscale(img.movedim(-1, 1), img.shape[2], image_shape[1], "lanczos", + "center").movedim(1, -1) + images = torch.cat(images, dim=0) + return (images,) + + +class imageBatchToImageList: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), }} + + RETURN_TYPES = ("IMAGE",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Image" + + def doit(self, image): + images = [image[i:i + 1, ...] for i in range(image.shape[0])] + return (images,) + +# ε›Ύεƒζ‹†εˆ† +class imageSplitList: + @classmethod + + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE",) + RETURN_NAMES = ("images", "images", "images",) + FUNCTION = "doit" + CATEGORY = "EasyUse/Image" + + def doit(self, images): + length = len(images) + new_images = ([], [], []) + if length % 3 == 0: + for index, img in enumerate(images): + if index % 3 == 0: + new_images[0].append(img) + elif (index+1) % 3 == 0: + new_images[2].append(img) + else: + new_images[1].append(img) + elif length % 2 == 0: + for index, img in enumerate(images): + if index % 2 == 0: + new_images[0].append(img) + else: + new_images[1].append(img) + return new_images + +class imageSplitGrid: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "row": ("INT", {"default": 1,"min": 1,"max": 10,"step": 1,}), + "column": ("INT", {"default": 1,"min": 1,"max": 10,"step": 1,}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "doit" + CATEGORY = "EasyUse/Image" + + def crop(self, image, width, height, x, y): + x = min(x, image.shape[2] - 1) + y = min(y, image.shape[1] - 1) + to_x = width + x + to_y = height + y + img = image[:, y:to_y, x:to_x, :] + return img + + def doit(self, images, row, column): + _, height, width, _ = images.shape + sub_width = width // column + sub_height = height // row + new_images = [] + for i in range(row): + for j in range(column): + new_images.append(self.crop(images, sub_width, sub_height, j * sub_width, i * sub_height)) + + return (torch.cat(new_images, dim=0),) + +class imageSplitTiles: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "overlap_ratio": ("FLOAT", {"default": 0, "min": 0, "max": 0.5, "step": 0.01, }), + "overlap_offset": ("INT", {"default": 0, "min": - MAX_RESOLUTION // 2, "max": MAX_RESOLUTION // 2, "step": 1, }), + "tiles_rows": ("INT", {"default": 2, "min": 1, "max": 50, "step": 1}), + "tiles_cols": ("INT", {"default": 2, "min": 1, "max": 50, "step": 1}), + }, + "optional": { + "norm": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "OVERLAP", "INT") + RETURN_NAMES = ("tiles", "masks", "overlap", "total") + FUNCTION = "doit" + CATEGORY = "EasyUse/Image" + + def doit(self, image, overlap_ratio, overlap_offset, tiles_rows, tiles_cols, norm=True): + height, width = image.shape[1:3] + + total = tiles_rows * tiles_cols + tile_w = int(width // tiles_cols) + tile_h = int(height // tiles_rows) + + overlap_w = int(tile_w * overlap_ratio) + overlap_offset + overlap_h = int(tile_h * overlap_ratio) + overlap_offset + + overlap_w = min(tile_w // 2, overlap_w) + overlap_h = min(tile_h // 2, overlap_h) + + if norm: + overlap_w = int(overlap_w - overlap_w % 8) + overlap_h = int(overlap_h - overlap_h % 8) + + if tiles_rows == 1: + overlap_h = 0 + if tiles_cols == 1: + overlap_w = 0 + + solid_mask_cls = ALL_NODE_CLASS_MAPPINGS['SolidMask'] + feather_mask_cls = ALL_NODE_CLASS_MAPPINGS['FeatherMask'] + + tiles, masks = [], [] + + x, y = 0, 0 + for i in range(tiles_rows): + for j in range(tiles_cols): + y1 = i * tile_h + x1 = j * tile_w + + if i > 0: + y1 -= overlap_h + if j > 0: + x1 -= overlap_w + + y2 = y1 + tile_h + overlap_h + x2 = x1 + tile_w + overlap_w + + if y2 > height: + y2 = height + y1 = y2 - tile_h - overlap_h + if x2 > width: + x2 = width + x1 = x2 - tile_w - overlap_w + + tile = image[:, y1:y2, x1:x2, :] + h = tile.shape[1] + w = tile.shape[2] + tiles.append(tile) + + fearing_left = overlap_w if overlap_w * j > 0 else 0 + fearing_top = overlap_h if overlap_h * i > 0 else 0 + fearing_right = 0 + fearing_bottom = 0 + + mask, = solid_mask_cls().solid(1, w, h) + mask, = feather_mask_cls().feather(mask, fearing_left, fearing_top, fearing_right, fearing_bottom) + masks.append(mask) + + tiles = torch.cat(tiles, dim=0) + masks = torch.cat(masks, dim=0) + + return (tiles, masks, (overlap_w, overlap_h, tile_w, tile_h, tiles_rows, tiles_cols), total) + +class imageTilesFromBatch: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "tiles": ("IMAGE",), + "masks": ("MASK",), + "overlap": ("OVERLAP",), + "index":("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT") + RETURN_NAMES = ("image", "mask", "x", "y") + FUNCTION = "doit" + CATEGORY = "EasyUse/Image" + + def imageFromBatch(self, image, batch_index, length=1): + s_in = image + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s = s_in[batch_index:batch_index + length].clone() + return s + + def maskFromBatch(self, mask, start, length=1): + if length > mask.shape[0]: + length = mask.shape[0] + start = min(start, mask.shape[0]-1) + length = min(mask.shape[0]-start, length) + return mask[start:start + length] + + def doit(self, tiles, masks, overlap, index): + tile = self.imageFromBatch(tiles, index) + mask = self.maskFromBatch(masks, index) + overlap_w, overlap_h, tile_w, tile_h, tiles_rows, tiles_cols = overlap + + x = tile_w * (index % tiles_cols) - overlap_w if (index % tiles_cols) > 0 else 0 + y = tile_h * (index // tiles_cols) - overlap_h if tiles_rows > 1 and index > tiles_cols - 1 else 0 + + return (tile, mask, x, y) + + + +class imagesSplitImage: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE") + RETURN_NAMES = ("image1", "image2", "image3", "image4", "image5") + FUNCTION = "split" + CATEGORY = "EasyUse/Image" + + def split(self, images,): + new_images = torch.chunk(images, len(images), dim=0) + return new_images + +class imageConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "direction": (['right','down','left','up',],{"default": 'right'}), + "match_image_size": ("BOOLEAN", {"default": False}), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "concat" + CATEGORY = "EasyUse/Image" + + def concat(self, image1, image2, direction, match_image_size): + if image1 is None: + return (image2,) + elif image2 is None: + return (image1,) + if match_image_size: + # Convert tensor to PIL for proper aspect ratio resizing + pil_image2 = tensor2pil(image2) + if direction in ['right', 'left']: + aspect_ratio = pil_image2.width / pil_image2.height + new_height = image1.shape[1] + new_width = int(aspect_ratio * new_height) + pil_image2 = fit_resize_image(pil_image2, new_width, new_height, 'fill', Image.LANCZOS, '#000000') + else: # 'up' or 'down' + aspect_ratio = pil_image2.height / pil_image2.width + new_width = image1.shape[2] + new_height = int(aspect_ratio * new_width) + pil_image2 = fit_resize_image(pil_image2, new_width, new_height, 'fill', Image.LANCZOS, '#000000') + image2 = pil2tensor(pil_image2) + + if direction == 'right': + row = torch.cat((image1, image2), dim=2) + elif direction == 'down': + row = torch.cat((image1, image2), dim=1) + elif direction == 'left': + row = torch.cat((image2, image1), dim=2) + elif direction == 'up': + row = torch.cat((image2, image1), dim=1) + return (row,) + +# ε›Ύη‰‡θƒŒζ™―η§»ι™€ +from ..libs.utils import get_local_filepath, easySave, install_package +class imageRemBg: + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "images": ("IMAGE",), + "rem_mode": (("RMBG-2.0", "RMBG-1.4", "Inspyrenet", "BEN2"), {"default": "RMBG-1.4"}), + "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional":{ + "torchscript_jit": ("BOOLEAN", {"default": False}), + "add_background": (["none", "white", "black"], {"default": "none"}), + "refine_foreground": ("BOOLEAN", {"default": False}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("IMAGE", "MASK") + RETURN_NAMES = ("image", "mask") + FUNCTION = "remove" + OUTPUT_NODE = True + + CATEGORY = "EasyUse/Image" + + + def remove(self, rem_mode, images, image_output, save_prefix, torchscript_jit=False, add_background='none', refine_foreground=False, prompt=None, extra_pnginfo=None): + new_images = list() + masks = list() + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if rem_mode == "RMBG-2.0": + if rem_mode in cache: + _, model = cache[rem_mode][1] + else: + repo_id = REMBG_MODELS[rem_mode]['model_url'] + model_path = os.path.join(REMBG_DIR, 'RMBG-2.0') + if not os.path.exists(model_path): + from huggingface_hub import snapshot_download + snapshot_download(repo_id=repo_id, local_dir=model_path, ignore_patterns=["*.md", "*.txt"]) + from transformers import AutoModelForImageSegmentation + model = AutoModelForImageSegmentation.from_pretrained(model_path, trust_remote_code=True) + torch.set_float32_matmul_precision('high') + model.to(device) + model.eval() + update_cache(rem_mode, 'remove_background', (False, model)) + + from torchvision import transforms + transform_image = transforms.Compose([ + transforms.Resize((1024, 1024)), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]) + for image in images: + orig_im = tensor2pil(image) + input_image = transform_image(orig_im).unsqueeze(0).to(device) + + with torch.no_grad(): + preds = model(input_image)[-1].sigmoid().cpu() + pred = preds[0].squeeze() + + mask = transforms.ToPILImage()(pred) + mask = mask.resize(orig_im.size) + + new_im = orig_im.copy() + new_im.putalpha(mask) + + new_im_tensor = pil2tensor(new_im) + mask_tensor = pil2tensor(mask) + + new_images.append(new_im_tensor) + masks.append(mask_tensor) + + torch.cuda.empty_cache() + new_images = torch.cat(new_images, dim=0) + masks = torch.cat(masks, dim=0) + + elif rem_mode == "RMBG-1.4": + from ..modules.briaai.rembg import BriaRMBG, preprocess_image, postprocess_image + if rem_mode in cache: + _, net = cache[rem_mode][1] + else: + model_url = REMBG_MODELS[rem_mode]['model_url'] + suffix = model_url.split(".")[-1] + model_path = get_local_filepath(model_url, REMBG_DIR, rem_mode+'.'+suffix) + net = BriaRMBG() + net.load_state_dict(torch.load(model_path, map_location=device)) + net.to(device) + net.eval() + update_cache(rem_mode, 'remove_background', (False, net)) + + # prepare input + model_input_size = [1024, 1024] + for image in images: + orig_im = tensor2pil(image) + w, h = orig_im.size + image = preprocess_image(orig_im, model_input_size).to(device) + # inference + result = net(image) + result_image = postprocess_image(result[0][0], (h, w)) + mask_im = Image.fromarray(result_image) + new_im = Image.new("RGBA", mask_im.size, (0,0,0,0)) + new_im.paste(orig_im, mask=mask_im) + + new_images.append(pil2tensor(new_im)) + masks.append(pil2tensor(mask_im)) + + new_images = torch.cat(new_images, dim=0) + masks = torch.cat(masks, dim=0) + elif rem_mode == "BEN2": + if rem_mode in cache: + _, model = cache[rem_mode][1] + else: + from ..modules.ben.model import BEN_Base + model_url = REMBG_MODELS[rem_mode]['model_url'] + model_path = get_local_filepath(model_url, REMBG_DIR) + + model = BEN_Base().to(device).eval() + model.loadcheckpoints(model_path) + update_cache(rem_mode, 'remove_background', (False, model)) + + for image in images: + input_image = tensor2pil(image) + + if input_image.mode != 'RGBA': + input_image = input_image.convert("RGBA") + + mask, new_im = model.inference(input_image, refine_foreground) + + new_im_tensor = pil2tensor(new_im) + mask_tensor = pil2tensor(mask) + + new_images.append(new_im_tensor) + masks.append(mask_tensor) + + new_images = torch.cat(new_images, dim=0) + masks = torch.cat(masks, dim=0) + + elif rem_mode == "Inspyrenet": + from tqdm import tqdm + try: + from transparent_background import Remover + except: + install_package("transparent_background") + from transparent_background import Remover + + if rem_mode in cache: + _, remover = cache[rem_mode][1] + else: + remover = Remover(jit=torchscript_jit) + update_cache(rem_mode, 'remove_background', (False, remover)) + + for img in tqdm(images, "Inspyrenet Rembg"): + mid = remover.process(tensor2pil(img), type='rgba') + out = pil2tensor(mid) + new_images.append(out) + mask = out[:, :, :, 3] + masks.append(mask) + new_images = torch.cat(new_images, dim=0) + masks = torch.cat(masks, dim=0) + + if add_background != 'none': + + _layer = tensor2pil(new_images) + _canvas = Image.new('RGB', _layer.size, (255,255,255) if add_background == 'white' else (0, 0, 0)) + _canvas.paste(_layer, mask=_layer) + new_images = pil2tensor(_canvas) + + results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo) + + if image_output in ("Hide", "Hide/Save"): + return {"ui": {}, + "result": (new_images, masks)} + + return {"ui": {"images": results}, + "result": (new_images, masks)} + +# 图像选择器 +from ..libs.chooser import wait_for_chooser +class imageChooser(PreviewImage): + @classmethod + def INPUT_TYPES(self): + return { + "required":{ + "mode": (['Always Pause', 'Keep Last Selection'], {"default": "Always Pause"}), + "preview_rescale": ("FLOAT", {"default": 1.0, "min": 0.05, "max": 1.0, "step": 0.05}), + }, + "optional": { + "images": ("IMAGE",), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "chooser" + OUTPUT_NODE = True + INPUT_IS_LIST = True + CATEGORY = "EasyUse/Image" + + last_ic = {} + @classmethod + def IS_CHANGED(cls, my_unique_id, **kwargs): + return cls.last_ic[my_unique_id[0]] + + def tensor_bundle(self, tensor_in: torch.Tensor, picks): + if tensor_in is not None and len(picks): + batch = tensor_in.shape[0] + return torch.cat(tuple([tensor_in[(x) % batch].unsqueeze_(0) for x in picks])).reshape( + [-1] + list(tensor_in.shape[1:])) + else: + return None + + def chooser(self, prompt=None, my_unique_id=None, extra_pnginfo=None, **kwargs): + id = my_unique_id[0] + id = id.split('.')[len(id.split('.')) - 1] if "." in id else id + + if (kwargs.get('images') is None): + return (torch.zeros(1, 1, 1, 3),) + + images_in = torch.cat(kwargs.pop('images')) + for x in kwargs: kwargs[x] = kwargs[x][0] + + try: + pnginfo = extra_pnginfo[0] + except: + pnginfo = None + + preview_rescale = kwargs.pop('preview_rescale', 1.0) + if preview_rescale < 1.0: + images_preview, = imageScaleDownBy().image_scale_down_by(images_in, preview_rescale) + else: + images_preview = images_in + result = self.save_images(images=images_preview, prompt=prompt, extra_pnginfo=pnginfo) + if "ui" in result and "images" in result['ui']: + images = result["ui"]["images"] + else: + images = [] + try: + PromptServer.instance.send_sync("easyuse-image-choose", {"id": id, "urls": images}) + except Exception as e: + pass + + # θŽ·ε–δΈŠζ¬‘ι€‰ζ‹© + mode = kwargs.pop('mode', 'Always Pause') + return wait_for_chooser(id, images_in, mode) + +class imageColorMatch(PreviewImage): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_ref": ("IMAGE",), + "image_target": ("IMAGE",), + "method": (['wavelet', 'adain', 'mkl', 'hm', 'reinhard', 'mvgd', 'hm-mvgd-hm', 'hm-mkl-hm'],), + "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + CATEGORY = "EasyUse/Image" + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + OUTPUT_NODE = True + FUNCTION = "color_match" + + def color_match(self, image_ref, image_target, method, image_output, save_prefix, prompt=None, extra_pnginfo=None): + if method in ["wavelet", "adain"]: + result_images = wavelet_color_fix(tensor2pil(image_target), tensor2pil(image_ref)) if method == 'wavelet' else adain_color_fix(tensor2pil(image_target), tensor2pil(image_ref)) + new_images = pil2tensor(result_images) + else: + try: + from color_matcher import ColorMatcher + except: + install_package("color-matcher") + from color_matcher import ColorMatcher + image_ref = image_ref.cpu() + image_target = image_target.cpu() + batch_size = image_target.size(0) + out = [] + images_target = image_target.squeeze() + images_ref = image_ref.squeeze() + + image_ref_np = images_ref.numpy() + images_target_np = images_target.numpy() + if image_ref.size(0) > 1 and image_ref.size(0) != batch_size: + raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.") + cm = ColorMatcher() + for i in range(batch_size): + image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy() + image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy() + try: + image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method) + except BaseException as e: + print(f"Error occurred during transfer: {e}") + break + out.append(torch.from_numpy(image_result)) + + new_images = torch.stack(out, dim=0).to(torch.float32) + + results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo) + + if image_output in ("Hide", "Hide/Save"): + return {"ui": {}, + "result": (new_images,)} + + return {"ui": {"images": results}, + "result": (new_images,)} + +class imageDetailTransfer: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "target": ("IMAGE",), + "source": ("IMAGE",), + "mode": (["add", "multiply", "screen", "overlay", "soft_light", "hard_light", "color_dodge", "color_burn", "difference", "exclusion", "divide",],{"default": "add"}), + "blur_sigma": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 100.0, "step": 0.01}), + "blend_factor": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001, "round": 0.001}), + "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "mask": ("MASK",), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + OUTPUT_NODE = True + FUNCTION = "transfer" + CATEGORY = "EasyUse/Image" + + + + def transfer(self, target, source, mode, blur_sigma, blend_factor, image_output, save_prefix, mask=None, prompt=None, extra_pnginfo=None): + batch_size, height, width, _ = source.shape + device = comfy.model_management.get_torch_device() + target_tensor = target.permute(0, 3, 1, 2).clone().to(device) + source_tensor = source.permute(0, 3, 1, 2).clone().to(device) + + if target.shape[1:] != source.shape[1:]: + target_tensor = comfy.utils.common_upscale(target_tensor, width, height, "bilinear", "disabled") + if mask is not None and target.shape[1:] != mask.shape[1:]: + mask = mask.unsqueeze(1) + mask = F.interpolate(mask, size=(height, width), mode="bilinear") + mask = mask.squeeze(1) + + if source.shape[0] < batch_size: + source = source[0].unsqueeze(0).repeat(batch_size, 1, 1, 1) + + kernel_size = int(6 * int(blur_sigma) + 1) + + gaussian_blur = GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma)) + + blurred_target = gaussian_blur(target_tensor) + blurred_source = gaussian_blur(source_tensor) + + if mode == "add": + new_image = (source_tensor - blurred_source) + blurred_target + elif mode == "multiply": + new_image = source_tensor * blurred_target + elif mode == "screen": + new_image = 1 - (1 - source_tensor) * (1 - blurred_target) + elif mode == "overlay": + new_image = torch.where(blurred_target < 0.5, 2 * source_tensor * blurred_target, + 1 - 2 * (1 - source_tensor) * (1 - blurred_target)) + elif mode == "soft_light": + new_image = (1 - 2 * blurred_target) * source_tensor ** 2 + 2 * blurred_target * source_tensor + elif mode == "hard_light": + new_image = torch.where(source_tensor < 0.5, 2 * source_tensor * blurred_target, + 1 - 2 * (1 - source_tensor) * (1 - blurred_target)) + elif mode == "difference": + new_image = torch.abs(blurred_target - source_tensor) + elif mode == "exclusion": + new_image = 0.5 - 2 * (blurred_target - 0.5) * (source_tensor - 0.5) + elif mode == "color_dodge": + new_image = blurred_target / (1 - source_tensor) + elif mode == "color_burn": + new_image = 1 - (1 - blurred_target) / source_tensor + elif mode == "divide": + new_image = (source_tensor / blurred_source) * blurred_target + else: + new_image = source_tensor + + new_image = torch.lerp(target_tensor, new_image, blend_factor) + if mask is not None: + mask = mask.to(device) + new_image = torch.lerp(target_tensor, new_image, mask) + new_image = torch.clamp(new_image, 0, 1) + new_image = new_image.permute(0, 2, 3, 1).cpu().float() + + results = easySave(new_image, save_prefix, image_output, prompt, extra_pnginfo) + + if image_output in ("Hide", "Hide/Save"): + return {"ui": {}, + "result": (new_image,)} + + return {"ui": {"images": results}, + "result": (new_image,)} + +# ε›ΎεƒεζŽ¨ +from ..libs.image import ci +class imageInterrogator: + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "image": ("IMAGE",), + "mode": (['fast','classic','best','negative'],), + "use_lowvram": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("prompt",) + FUNCTION = "interrogate" + CATEGORY = "EasyUse/Image" + OUTPUT_NODE = False + OUTPUT_IS_LIST = (True,) + + def interrogate(self, image, mode, use_lowvram=False): + prompt = ci.image_to_prompt(image, mode, low_vram=use_lowvram) + return (prompt,) + +# δΊΊη±»εˆ†ε‰²ε™¨ +class humanSegmentation: + + @classmethod + def INPUT_TYPES(cls): + return { + "required":{ + "image": ("IMAGE",), + "method": (["selfie_multiclass_256x256", "human_parsing_lip", "human_parts (deeplabv3p)", "segformer_b3_clothes", "segformer_b3_fashion", "face_parsing"],), + "confidence": ("FLOAT", {"default": 0.4, "min": 0.05, "max": 0.95, "step": 0.01},), + "crop_multi": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001},), + "mask_components":( + "EASY_COMBO",{ + "options": [{'label':'Background','value':0}], + "multi_select": { + "placeholder": "select mask components", + "chip": True, + "max_selected_labels": 4, + } + } + ) + }, + "hidden": { + "prompt": "PROMPT", + "my_unique_id": "UNIQUE_ID", + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "BBOX") + RETURN_NAMES = ("image", "mask", "bbox") + FUNCTION = "parsing" + CATEGORY = "EasyUse/Segmentation" + + def get_mediapipe_image(self, image: Image): + import mediapipe as mp + # Convert image to NumPy array + numpy_image = np.asarray(image) + image_format = mp.ImageFormat.SRGB + # Convert BGR to RGB (if necessary) + if numpy_image.shape[-1] == 4: + image_format = mp.ImageFormat.SRGBA + elif numpy_image.shape[-1] == 3: + image_format = mp.ImageFormat.SRGB + numpy_image = cv2.cvtColor(numpy_image, cv2.COLOR_BGR2RGB) + return mp.Image(image_format=image_format, data=numpy_image) + + def parsing(self, image, confidence, method, crop_multi, mask_components, prompt=None, my_unique_id=None): + if isinstance(mask_components, str): + mask_components = [int(x) for x in mask_components.split(',') if x] + else: + mask_components = mask_components if mask_components else [] + + if method == 'selfie_multiclass_256x256': + try: + import mediapipe as mp + except: + install_package("mediapipe") + import mediapipe as mp + + from functools import reduce + + if method in cache: + _, model_asset_buffer = cache["selfie_multiclass_256x256"][1] + else: + model_path = get_local_filepath(MEDIAPIPE_MODELS['selfie_multiclass_256x256']['model_url'], MEDIAPIPE_DIR) + model_asset_buffer = None + with open(model_path, "rb") as f: + model_asset_buffer = f.read() + update_cache(method, 'human_segmentation', (False, model_asset_buffer)) + image_segmenter_base_options = mp.tasks.BaseOptions(model_asset_buffer=model_asset_buffer) + options = mp.tasks.vision.ImageSegmenterOptions( + base_options=image_segmenter_base_options, + running_mode=mp.tasks.vision.RunningMode.IMAGE, + output_category_mask=True) + # Create the image segmenter + ret_images = [] + ret_masks = [] + + if len(mask_components) == 0: + return (image, torch.zeros_like(image[:, :, :, 0:1]), torch.tensor([0,0,0,0])) + + with mp.tasks.vision.ImageSegmenter.create_from_options(options) as segmenter: + for img in image: + _image = torch.unsqueeze(img, 0) + orig_image = tensor2pil(_image).convert('RGB') + # Convert the Tensor to a PIL image + i = 255. * img.cpu().numpy() + image_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + # create our foreground and background arrays for storing the mask results + mask_background_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8) + mask_background_array[:] = (0, 0, 0, 255) + mask_foreground_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8) + mask_foreground_array[:] = (255, 255, 255, 255) + # Retrieve the masks for the segmented image + media_pipe_image = self.get_mediapipe_image(image=image_pil) + segmented_masks = segmenter.segment(media_pipe_image) + masks = [] + for i, com in enumerate(mask_components): + masks.append(segmented_masks.confidence_masks[com]) + + image_data = media_pipe_image.numpy_view() + image_shape = image_data.shape + # convert the image shape from "rgb" to "rgba" aka add the alpha channel + if image_shape[-1] == 3: + image_shape = (image_shape[0], image_shape[1], 4) + mask_background_array = np.zeros(image_shape, dtype=np.uint8) + mask_background_array[:] = (0, 0, 0, 255) + mask_foreground_array = np.zeros(image_shape, dtype=np.uint8) + mask_foreground_array[:] = (255, 255, 255, 255) + mask_arrays = [] + if len(masks) == 0: + mask_arrays.append(mask_background_array) + else: + for i, mask in enumerate(masks): + mask_2d = mask.numpy_view() + if mask_2d.ndim == 3 and mask_2d.shape[2] == 1: + mask_2d = mask_2d.squeeze(axis=2) + elif mask_2d.ndim != 2: + raise ValueError(f"Unexpected mask shape: {mask_2d.shape}") + condition = np.stack((mask_2d,) * image_shape[-1], axis=-1) > confidence + if condition.ndim == 4 and condition.shape[2] == 1: + condition = condition.squeeze(2) + mask_array = np.where(condition, mask_foreground_array, mask_background_array) + mask_arrays.append(mask_array) + # Merge our masks taking the maximum from each + merged_mask_arrays = reduce(np.maximum, mask_arrays) + # Create the image + mask_image = Image.fromarray(merged_mask_arrays) + # convert PIL image to tensor image + tensor_mask = mask_image.convert("RGB") + tensor_mask = np.array(tensor_mask).astype(np.float32) / 255.0 + tensor_mask = torch.from_numpy(tensor_mask)[None,] + _mask = tensor_mask.squeeze(3)[..., 0] + + _mask = tensor2pil(tensor_mask).convert('L') + + ret_image = RGB2RGBA(orig_image, _mask) + ret_images.append(pil2tensor(ret_image)) + ret_masks.append(image2mask(_mask)) + + output_image = torch.cat(ret_images, dim=0) + mask = torch.cat(ret_masks, dim=0) + + elif method == "human_parsing_lip": + if method in cache: + _, parsing = cache[method][1] + else: + from ..modules.human_parsing.run_parsing import HumanParsing + onnx_path = os.path.join(folder_paths.models_dir, 'onnx') + model_path = get_local_filepath(HUMANPARSING_MODELS['parsing_lip']['model_url'], onnx_path) + parsing = HumanParsing(model_path=model_path) + update_cache(method, 'human_segmentation', (False, parsing)) + + model_image = image.squeeze(0) + model_image = model_image.permute((2, 0, 1)) + model_image = to_pil_image(model_image) + + map_image, mask = parsing(model_image, mask_components) + + mask = mask[:, :, :, 0] + + alpha = 1.0 - mask + + try: + output_image, = JoinImageWithAlpha().execute(image, alpha) + except: + output_image, = JoinImageWithAlpha().join_image_with_alpha(image, alpha) + + + elif method == "human_parts (deeplabv3p)": + if method in cache: + _, parsing = cache[method][1] + else: + from ..modules.human_parsing.run_parsing import HumanParts + onnx_path = os.path.join(folder_paths.models_dir, 'onnx') + human_parts_path = os.path.join(onnx_path, 'human-parts') + model_path = get_local_filepath(HUMANPARSING_MODELS['human-parts']['model_url'], human_parts_path) + parsing = HumanParts(model_path=model_path) + update_cache(method, 'human_segmentation', (False, parsing)) + + ret_images = [] + ret_masks = [] + for img in image: + mask, = parsing(img, mask_components) + _mask = tensor2pil(mask).convert('L') + + ret_image = RGB2RGBA(tensor2pil(img).convert('RGB'), _mask.convert('L')) + ret_images.append(pil2tensor(ret_image)) + ret_masks.append(image2mask(_mask)) + + output_image = torch.cat(ret_images, dim=0) + mask = torch.cat(ret_masks, dim=0) + + elif method in ["segformer_b3_clothes", "segformer_b3_fashion", "face_parsing"]: + from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation + + # εˆ†ε‰² + def get_segmentation_from_model(tensor_image, model, processor): + cloth = tensor2pil(tensor_image) + inputs = processor(images=cloth, return_tensors="pt") + outputs = model(**inputs) + logits = outputs.logits.cpu() + upsampled_logits = F.interpolate(logits, size=cloth.size[::-1], mode="bilinear", + align_corners=False) + pred_seg = upsampled_logits.argmax(dim=1)[0].numpy() + return pred_seg, cloth + + + if method in cache: + _, (processor, model) = cache[method][1] + else: + model_folder_path = os.path.join(folder_paths.models_dir, method) + if os.path.exists(model_folder_path): + print(f"Start to load existing model...") + else: + from huggingface_hub import snapshot_download + PromptServer.instance.send_sync("easyuse-toast", {"content": f"Model not found locally. Downloading {method}...", "type": 'loading', "duration": 10000}) + print(f"Model not found locally. Downloading {method}...") + model_path_cache = os.path.join(folder_paths.models_dir, "cache-"+method) + snapshot_download( + repo_id=HUMANPARSING_MODELS[method]['model_name'], + local_dir=model_path_cache, + local_dir_use_symlinks=False, + resume_download=True + ) + shutil.move(model_path_cache, model_folder_path) + print(f"Model downloaded to {model_folder_path}...") + try: + model_folder_path = os.path.normpath(folder_paths.folder_names_and_paths[method][0][0]) + except: + pass + + processor = SegformerImageProcessor.from_pretrained(model_folder_path) + model = AutoModelForSemanticSegmentation.from_pretrained(model_folder_path) + update_cache(method, 'human_segmentation', (False, (processor, model))) + + ret_images = [] + ret_masks = [] + + if method == "face_parsing": + import matplotlib + import torchvision.transforms as T + transform = ToPILImage() + colormap = matplotlib.colormaps['viridis'] + device = model.device + results = [] + images = [] + for img in image: + size = img.shape[:2] + inputs = processor(images=transform(img.permute(2, 0, 1)), return_tensors="pt") + inputs = {k: v.to(device) for k, v in inputs.items()} + outputs = model(**inputs) + logits = outputs.logits + upsampled_logits = F.interpolate( + logits, + size=size, + mode="bilinear", + align_corners=False) + + pred_seg = upsampled_logits.argmax(dim=1)[0] + pred_seg_np = pred_seg.cpu().detach().numpy().astype(np.uint8) + results.append(torch.tensor(pred_seg_np)) + + results_out = torch.stack(results, dim=0) + for img, result_item in zip(image, results_out): + mask = torch.zeros(result_item.shape, dtype=torch.uint8) + for i in mask_components: + mask = mask | torch.where(result_item == i, 1, 0) + + # ε°†mask转捒为numpyζ•°η»„οΌŒεΉΆη‘保数ζη±»εž‹ζ­£η‘ + mask_np = (mask * 255).numpy().astype(np.uint8) + _mask = Image.fromarray(mask_np) + + # 倄理图像输出 + ret_image = RGB2RGBA(tensor2pil(img).convert('RGB'), _mask.convert('L')) + ret_images.append(pil2tensor(ret_image)) + ret_masks.append(image2mask(_mask)) + + else: + for img in image: + pred_seg, cloth = get_segmentation_from_model(img, model, processor) + i = torch.unsqueeze(img, 0) + i = pil2tensor(tensor2pil(i).convert('RGB')) + + mask = np.isin(pred_seg, mask_components).astype(np.uint8) + _mask = Image.fromarray(mask * 255) + + ret_image = RGB2RGBA(tensor2pil(img).convert('RGB'), _mask.convert('L')) + ret_images.append(pil2tensor(ret_image)) + ret_masks.append(image2mask(_mask)) + + output_image = torch.cat(ret_images, dim=0) + mask = torch.cat(ret_masks, dim=0) + + # use crop + bbox = [[0, 0, 0, 0]] + if crop_multi > 0.0: + output_image, mask, bbox = imageCropFromMask().crop(output_image, mask, crop_multi, crop_multi, 1.0) + + return (output_image, mask, bbox) + +class imageCropFromMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "image_crop_multi": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), + "mask_crop_multi": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), + "bbox_smooth_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK", "BBOX",) + RETURN_NAMES = ("crop_image", "crop_mask", "bbox",) + FUNCTION = "crop" + CATEGORY = "EasyUse/Image" + + def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): + if alpha == 0: + return prev_bbox_size + return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) + + def smooth_center(self, prev_center, curr_center, alpha=0.5): + if alpha == 0: + return prev_center + return ( + round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), + round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]) + ) + + def image2mask(self, image): + return image[:, :, :, 0] + + def mask2image(self, mask): + return mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + + def cropimage(self, original_images, masks, crop_size_mult, bbox_smooth_alpha): + + bounding_boxes = [] + cropped_images = [] + + self.max_bbox_width = 0 + self.max_bbox_height = 0 + + # First, calculate the maximum bounding box size across all masks + curr_max_bbox_width = 0 + curr_max_bbox_height = 0 + for mask in masks: + _mask = tensor2pil(mask) + non_zero_indices = np.nonzero(np.array(_mask)) + min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) + min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) + width = max_x - min_x + height = max_y - min_y + curr_max_bbox_width = max(curr_max_bbox_width, width) + curr_max_bbox_height = max(curr_max_bbox_height, height) + + # Smooth the changes in the bounding box size + self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha) + self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha) + + # Apply the crop size multiplier + self.max_bbox_width = round(self.max_bbox_width * crop_size_mult) + self.max_bbox_height = round(self.max_bbox_height * crop_size_mult) + bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height + + # Then, for each mask and corresponding image... + for i, (mask, img) in enumerate(zip(masks, original_images)): + _mask = tensor2pil(mask) + non_zero_indices = np.nonzero(np.array(_mask)) + min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) + min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) + + # Calculate center of bounding box + center_x = np.mean(non_zero_indices[1]) + center_y = np.mean(non_zero_indices[0]) + curr_center = (round(center_x), round(center_y)) + + # If this is the first frame, initialize prev_center with curr_center + if not hasattr(self, 'prev_center'): + self.prev_center = curr_center + + # Smooth the changes in the center coordinates from the second frame onwards + if i > 0: + center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha) + else: + center = curr_center + + # Update prev_center for the next frame + self.prev_center = center + + # Create bounding box using max_bbox_width and max_bbox_height + half_box_width = round(self.max_bbox_width / 2) + half_box_height = round(self.max_bbox_height / 2) + min_x = max(0, center[0] - half_box_width) + max_x = min(img.shape[1], center[0] + half_box_width) + min_y = max(0, center[1] - half_box_height) + max_y = min(img.shape[0], center[1] + half_box_height) + + # Append bounding box coordinates + bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y)) + + # Crop the image from the bounding box + cropped_img = img[min_y:max_y, min_x:max_x, :] + + # Calculate the new dimensions while maintaining the aspect ratio + new_height = min(cropped_img.shape[0], self.max_bbox_height) + new_width = round(new_height * bbox_aspect_ratio) + + # Resize the image + resize_transform = Resize((new_height, new_width)) + resized_img = resize_transform(cropped_img.permute(2, 0, 1)) + + # Perform the center crop to the desired size + crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary + cropped_resized_img = crop_transform(resized_img) + + cropped_images.append(cropped_resized_img.permute(1, 2, 0)) + + return cropped_images, bounding_boxes + + def crop(self, image, mask, image_crop_multi, mask_crop_multi, bbox_smooth_alpha): + cropped_images, bounding_boxes = self.cropimage(image, mask, image_crop_multi, bbox_smooth_alpha) + cropped_mask_image, _ = self.cropimage(self.mask2image(mask), mask, mask_crop_multi, bbox_smooth_alpha) + + cropped_image_out = torch.stack(cropped_images, dim=0) + cropped_mask_out = torch.stack(cropped_mask_image, dim=0) + + return (cropped_image_out, cropped_mask_out[:, :, :, 0], bounding_boxes) + + +class imageUncropFromBBOX: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_image": ("IMAGE",), + "crop_image": ("IMAGE",), + "bbox": ("BBOX",), + "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01},), + "use_square_mask": ("BOOLEAN", {"default": True}), + }, + "optional":{ + "optional_mask": ("MASK",) + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "uncrop" + CATEGORY = "EasyUse/Image" + + def bbox_check(self, bbox, target_size=None): + if not target_size: + return bbox + + new_bbox = ( + bbox[0], + bbox[1], + min(target_size[0] - bbox[0], bbox[2]), + min(target_size[1] - bbox[1], bbox[3]), + ) + return new_bbox + + def bbox_to_region(self, bbox, target_size=None): + bbox = self.bbox_check(bbox, target_size) + return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]) + + def uncrop(self, original_image, crop_image, bbox, border_blending, use_square_mask, optional_mask=None): + def inset_border(image, border_width=20, border_color=(0)): + width, height = image.size + bordered_image = Image.new(image.mode, (width, height), border_color) + bordered_image.paste(image, (0, 0)) + draw = ImageDraw.Draw(bordered_image) + draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width) + return bordered_image + + if len(original_image) != len(crop_image): + raise ValueError( + f"The number of original_images ({len(original_image)}) and cropped_images ({len(crop_image)}) should be the same") + + # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images + if len(bbox) > len(original_image): + print(f"Warning: Dropping excess bounding boxes. Expected {len(original_image)}, but got {len(bbox)}") + bbox = bbox[:len(original_image)] + elif len(bbox) < len(original_image): + raise ValueError("There should be at least as many bboxes as there are original and cropped images") + + + out_images = [] + + for i in range(len(original_image)): + img = tensor2pil(original_image[i]) + crop = tensor2pil(crop_image[i]) + _bbox = bbox[i] + + bb_x, bb_y, bb_width, bb_height = _bbox + paste_region = self.bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) + + # rescale the crop image to fit the paste_region + crop = crop.resize((round(paste_region[2] - paste_region[0]), round(paste_region[3] - paste_region[1]))) + crop_img = crop.convert("RGB") + + # border blending + if border_blending > 1.0: + border_blending = 1.0 + elif border_blending < 0.0: + border_blending = 0.0 + + blend_ratio = (max(crop_img.size) / 2) * float(border_blending) + blend = img.convert("RGBA") + + if use_square_mask: + mask = Image.new("L", img.size, 0) + mask_block = Image.new("L", (paste_region[2] - paste_region[0], paste_region[3] - paste_region[1]), 255) + mask_block = inset_border(mask_block, round(blend_ratio / 2), (0)) + mask.paste(mask_block, paste_region) + else: + if optional_mask is None: + raise ValueError("optional_mask is required when use_square_mask is False") + original_mask = tensor2pil(optional_mask) + original_mask = original_mask.resize((paste_region[2] - paste_region[0], paste_region[3] - paste_region[1])) + mask = Image.new("L", img.size, 0) + mask.paste(original_mask, paste_region) + + mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4)) + mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4)) + + blend.paste(crop_img, paste_region) + blend.putalpha(mask) + + img = Image.alpha_composite(img.convert("RGBA"), blend) + out_images.append(img.convert("RGB")) + + output_images = torch.cat([pil2tensor(img) for img in out_images], dim=0) + return (output_images,) + + + +import cv2 +import base64 +class loadImageBase64: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "base64_data": ("STRING", {"default": ""}), + "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("IMAGE", "MASK") + OUTPUT_NODE = True + FUNCTION = "load_image" + CATEGORY = "EasyUse/Image/LoadImage" + + def convert_color(self, image,): + if len(image.shape) > 2 and image.shape[2] >= 4: + return cv2.cvtColor(image, cv2.COLOR_BGRA2RGB) + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + def load_image(self, base64_data, image_output, save_prefix, prompt=None, extra_pnginfo=None): + nparr = np.frombuffer(base64.b64decode(base64_data), np.uint8) + + result = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED) + channels = cv2.split(result) + if len(channels) > 3: + mask = channels[3].astype(np.float32) / 255.0 + mask = torch.from_numpy(mask) + else: + mask = torch.ones(channels[0].shape, dtype=torch.float32, device="cpu") + + result = self.convert_color(result) + result = result.astype(np.float32) / 255.0 + new_images = torch.from_numpy(result)[None,] + + results = easySave(new_images, save_prefix, image_output, None, None) + mask = mask.unsqueeze(0) + + if image_output in ("Hide", "Hide/Save"): + return {"ui": {}, + "result": (new_images, mask)} + + return {"ui": {"images": results}, + "result": (new_images, mask)} + +class imageToBase64: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + }, + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "to_base64" + CATEGORY = "EasyUse/Image" + OUTPUT_NODE = True + + def to_base64(self, image, ): + import base64 + from io import BytesIO + + # 将张量图像转捒为PIL图像 + pil_image = tensor2pil(image) + + buffered = BytesIO() + pil_image.save(buffered, format="PNG") + image_bytes = buffered.getvalue() + + base64_str = base64.b64encode(image_bytes).decode("utf-8") + return {"result": (base64_str,)} + +class removeLocalImage: + + def __init__(self): + self.hasFile = False + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any": (any_type,), + "file_name": ("STRING",{"default":""}), + }, + } + + RETURN_TYPES = () + OUTPUT_NODE = True + FUNCTION = "remove" + CATEGORY = "EasyUse/Image" + + + + def remove(self, any, file_name): + self.hasFile = False + def listdir(path, dir_name=''): + for file in os.listdir(path): + file_path = os.path.join(path, file) + if os.path.isdir(file_path): + dir_name = os.path.basename(file_path) + listdir(file_path, dir_name) + else: + file = os.path.join(dir_name, file) + name_without_extension, file_extension = os.path.splitext(file) + if name_without_extension == file_name or file == file_name: + os.remove(os.path.join(folder_paths.input_directory, file)) + self.hasFile = True + break + + listdir(folder_paths.input_directory, '') + + if self.hasFile: + PromptServer.instance.send_sync("easyuse-toast", {"content": "Removed SuccessFully", "type":'success'}) + else: + PromptServer.instance.send_sync("easyuse-toast", {"content": "Removed Failed", "type": 'error'}) + return () + +try: + from comfy_execution.graph_utils import GraphBuilder, is_link +except: + GraphBuilder = None +class loadImagesForLoop: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "directory": ("STRING", {"default": ""}), + }, + "optional": { + "start_index": ("INT", {"default": 0, "min": 0, "step": 1}), + "limit": ("INT", {"default":-1, "min":-1, "max": 10000}), + "initial_value1": (any_type,), + "initial_value2": (any_type,), + }, + "hidden": { + "initial_value0": (any_type,), + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + "unique_id": "UNIQUE_ID" + } + } + + RETURN_TYPES = ByPassTypeTuple(tuple(["FLOW_CONTROL", "INT", "IMAGE", "MASK", "STRING", any_type, any_type])) + RETURN_NAMES = ByPassTypeTuple(tuple(["flow", "index", "image", "mask", "name", "value1", "value2"])) + + FUNCTION = "load_images" + + CATEGORY = "image" + + def load_images(self, directory: str, start_index: int = 0, limit: int =-1, prompt=None, extra_pnginfo=None, unique_id=None, **kwargs): + print(directory) + if not os.path.isdir(directory): + raise FileNotFoundError(f"Directory '{directory}' cannot be found.") + + dir_files = os.listdir(directory) + if len(dir_files) == 0: + raise FileNotFoundError(f"No files in directory '{directory}'.") + + # Filter files by extension + valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] + dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] + + dir_files = sorted(dir_files) + dir_files = [os.path.join(directory, x) for x in dir_files] + + graph = GraphBuilder() + index = 0 + # unique_id = unique_id.split('.')[len(unique_id.split('.')) - 1] if "." in unique_id else unique_id + # update_cache('forloop' + str(unique_id), 'forloop', total) + if "initial_value0" in kwargs: + index = kwargs["initial_value0"] + # start at start_index + image_path = dir_files[start_index+index] + + name = os.path.splitext(os.path.basename(image_path))[0] + + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + while_open = graph.node("easy whileLoopStart", condition=True, initial_value0=index, initial_value1=kwargs.get('initial_value1',None), initial_value2=kwargs.get('initial_value2',None)) + outputs = [kwargs.get('initial_value1',None), kwargs.get('initial_value2',None)] + + return { + "result": tuple(["stub", index, image, mask, name] + outputs), + "expand": graph.finalize(), + } + +class makeImageForICRepaint: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image_1": ("IMAGE",), + "direction": (["top-bottom", "left-right"], {"default": "left-right"}), + "pixels": ("INT", {"default": 0, "max": MAX_RESOLUTION, "min": 0, "step": 8, "tooltip": "The pixel of the output image is not set when it is 0"}), + "method": (["uniform height", "uniform width", "auto"],{"default": "auto"}), + }, + "optional": { + "image_2": ("IMAGE",), + "mask_1": ("MASK",), + "mask_2": ("MASK",), + }, + } + + DESCRIPTION = "make Image for ICLora to Re-paint" + CATEGORY = "EasyUse/Image" + FUNCTION = "make" + + RETURN_TYPES = ("IMAGE", "MASK", "MASK", "INT", "INT", "INT", "INT") + RETURN_NAMES = ("image", "mask", "context_mask", "width", "height", "x", "y") + + def fillMask(self, width, height, mask, box=(0, 0), color=0): + bg = Image.new("L", (width, height), color) + bg.paste(mask, box, mask) + return bg + + def emptyImage(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return torch.cat((r, g, b), dim=-1) + + def resize_image_and_mask(self, image, mask, w, h ,fit='fill'): + ret_images = [] + ret_masks = [] + _mask = Image.new('L', size=(w, h), color='black') + _image = Image.new('RGB', size=(w, h), color='black') + if image is not None and len(image) > 0: + for i in image: + _image = tensor2pil(i).convert('RGB') + _image = fit_resize_image(_image, w, h, fit, Image.LANCZOS, '#000000') + ret_images.append(pil2tensor(_image)) + if mask is not None and len(mask) > 0: + for m in mask: + _mask = tensor2pil(m).convert('L') + _mask = fit_resize_image(_mask, w, h, fit, Image.LANCZOS).convert('L') + ret_masks.append(image2mask(_mask)) + + if len(ret_images) > 0 and len(ret_masks) > 0: + return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),) + elif len(ret_images) > 0 and len(ret_masks) == 0: + return (torch.cat(ret_images, dim=0), None,) + elif len(ret_images) == 0 and len(ret_masks) > 0: + return (None, torch.cat(ret_masks, dim=0),) + else: + return (None, None) + + def make(self, image_1, direction, pixels, method, image_2=None, mask_1=None, mask_2=None): + if image_2 is None: + image_2 = self.emptyImage(image_1.shape[2], image_1.shape[1]) + mask_2 = torch.full((1, image_1.shape[1], image_1.shape[2]), 1, dtype=torch.float32, device="cpu") + + elif image_2 is not None and mask_2 is None: + mask_2 = torch.full((1, image_2.shape[1], image_2.shape[2]), 1, dtype=torch.float32, device="cpu") + + if pixels > 0: + _, img2_h, img2_w, _ = image_2.shape + if method == "uniform height": + h = pixels + w = int(img2_w * (pixels / img2_h)) + elif method == "uniform width": + w = pixels + h = int(img2_h * (pixels / img2_w)) + else: + h = pixels if direction == 'left-right' else int(img2_h * (pixels / img2_w)) + w = pixels if direction == 'top-bottom' else int(img2_w * (pixels / img2_h)) + + image_2, mask_2 = self.resize_image_and_mask(image_2, mask_2, w, h) + + _, img1_h, img1_w, _ = image_1.shape + _, img2_h, img2_w, _ = image_2.shape + + image, mask, context_mask = None, None, None + + # resize + if img1_h != img2_h and img1_w != img2_w: + width, height = img2_w, img2_h + fit = 'crop' + if method != 'uniform width': + if direction == 'left-right' and img1_h != img2_h: + scale_factor = img2_h / img1_h + width = round(img1_w * scale_factor) + elif direction == 'top-bottom' and img1_w != img2_w: + scale_factor = img2_w / img1_w + height = round(img1_h * scale_factor) + fit = 'fill' + image_1, mask_1 = self.resize_image_and_mask(image_1, mask_1, width, height, fit) + + if mask_1 is None: + mask_1 = torch.full((1, image_1.shape[1], image_1.shape[2]), 0, dtype=torch.float32, device="cpu") + + orig_image_1 = tensor2pil(image_1) + orig_mask_1 = tensor2pil(mask_1).convert('L') + + if orig_mask_1.size != orig_image_1.size: + orig_mask_1 = orig_mask_1.resize(orig_image_1.size) + + img1_w, img1_h = orig_image_1.size + image_1 = pil2tensor(orig_image_1) + image = torch.cat((image_1, image_2), dim=2) if direction == 'left-right' else torch.cat((image_1, image_2), + dim=1) + + context_mask = self.fillMask(image.shape[2], image.shape[1], orig_mask_1) + context_mask = pil2tensor(context_mask) + + orig_mask_2 = tensor2pil(mask_2).convert('L') + x = img1_w if direction == 'left-right' else 0 + y = img1_h if direction == 'top-bottom' else 0 + mask = self.fillMask(image.shape[2], image.shape[1], orig_mask_2, (x, y)) + mask = pil2tensor(mask) + + return (image, mask, context_mask, img2_w, img2_h, x, y) + + +NODE_CLASS_MAPPINGS = { + "easy imageInsetCrop": imageInsetCrop, + "easy imageCount": imageCount, + "easy imagesCountInDirectory": imagesCountInDirectory, + "easy imageSize": imageSize, + "easy imageSizeBySide": imageSizeBySide, + "easy imageSizeByLongerSide": imageSizeByLongerSide, + "easy imagePixelPerfect": imagePixelPerfect, + "easy imageScaleDown": imageScaleDown, + "easy imageScaleDownBy": imageScaleDownBy, + "easy imageScaleDownToSize": imageScaleDownToSize, + "easy imageScaleToNormPixels": imageScaleToNormPixels, + "easy imageRatio": imageRatio, + "easy imageConcat": imageConcat, + "easy imageListToImageBatch": imageListToImageBatch, + "easy imageBatchToImageList": imageBatchToImageList, + "easy imageSplitList": imageSplitList, + "easy imageSplitGrid": imageSplitGrid, + "easy imagesSplitImage": imagesSplitImage, + "easy imageSplitTiles": imageSplitTiles, + "easy imageTilesFromBatch": imageTilesFromBatch, + "easy imageCropFromMask": imageCropFromMask, + "easy imageUncropFromBBOX": imageUncropFromBBOX, + "easy imageSave": imageSaveSimple, + "easy imageRemBg": imageRemBg, + "easy imageChooser": imageChooser, + "easy imageColorMatch": imageColorMatch, + "easy imageDetailTransfer": imageDetailTransfer, + "easy imageInterrogator": imageInterrogator, + "easy loadImagesForLoop": loadImagesForLoop, + "easy loadImageBase64": loadImageBase64, + "easy imageToBase64": imageToBase64, + "easy joinImageBatch": JoinImageBatch, + "easy humanSegmentation": humanSegmentation, + "easy removeLocalImage": removeLocalImage, + "easy makeImageForICLora": makeImageForICRepaint +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy imageInsetCrop": "ImageInsetCrop", + "easy imageCount": "ImageCount", + "easy imagesCountInDirectory": "imagesCountInDirectory", + "easy imageSize": "ImageSize", + "easy imageSizeBySide": "ImageSize (Side)", + "easy imageSizeByLongerSide": "ImageSize (LongerSide)", + "easy imagePixelPerfect": "ImagePixelPerfect", + "easy imageScaleDown": "Image Scale Down", + "easy imageScaleDownBy": "Image Scale Down By", + "easy imageScaleDownToSize": "Image Scale Down To Size", + "easy imageScaleToNormPixels": "ImageScaleToNormPixels", + "easy imageRatio": "ImageRatio", + "easy imageHSVMask": "ImageHSVMask", + "easy imageConcat": "imageConcat", + "easy imageListToImageBatch": "Image List To Image Batch", + "easy imageBatchToImageList": "Image Batch To Image List", + "easy imageSplitList": "imageSplitList", + "easy imageSplitGrid": "imageSplitGrid", + "easy imageSplitTiles": "imageSplitTiles", + "easy imageTilesFromBatch": "imageTilesFromBatch", + "easy imagesSplitImage": "imagesSplitImage", + "easy imageCropFromMask": "imageCropFromMask", + "easy imageUncropFromBBOX": "imageUncropFromBBOX", + "easy imageSave": "Save Image (Simple)", + "easy imageRemBg": "Image Remove Bg", + "easy imageChooser": "Image Chooser", + "easy imageColorMatch": "Image Color Match", + "easy imageDetailTransfer": "Image Detail Transfer", + "easy imageInterrogator": "Image To Prompt", + "easy joinImageBatch": "JoinImageBatch", + "easy loadImageBase64": "Load Image (Base64)", + "easy loadImagesForLoop": "Load Images For Loop", + "easy imageToBase64": "Image To Base64", + "easy humanSegmentation": "Human Segmentation", + "easy removeLocalImage": "Remove Local Image", + "easy makeImageForICLora": "Make Image For ICLora" +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/inpaint.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/inpaint.py new file mode 100644 index 00000000..ab1b1d01 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/inpaint.py @@ -0,0 +1,356 @@ +import re +import torch +import comfy +from comfy_extras.nodes_mask import GrowMask +from nodes import VAEEncodeForInpaint, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS +from ..libs.utils import get_local_filepath +from ..libs.log import log_node_info +from ..libs import cache as backend_cache +from ..config import * + +# FooocusInpaint +class applyFooocusInpaint: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT",), + "head": (list(FOOOCUS_INPAINT_HEAD.keys()),), + "patch": (list(FOOOCUS_INPAINT_PATCH.keys()),), + }, + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model",) + CATEGORY = "EasyUse/Inpaint" + FUNCTION = "apply" + + def apply(self, model, latent, head, patch): + from ..modules.fooocus import InpaintHead, InpaintWorker + head_file = get_local_filepath(FOOOCUS_INPAINT_HEAD[head]["model_url"], INPAINT_DIR) + inpaint_head_model = InpaintHead() + sd = torch.load(head_file, map_location='cpu') + inpaint_head_model.load_state_dict(sd) + + patch_file = get_local_filepath(FOOOCUS_INPAINT_PATCH[patch]["model_url"], INPAINT_DIR) + inpaint_lora = comfy.utils.load_torch_file(patch_file, safe_load=True) + + patch = (inpaint_head_model, inpaint_lora) + worker = InpaintWorker(node_name="easy kSamplerInpainting") + cloned = model.clone() + + m, = worker.patch(cloned, latent, patch) + return (m,) + +# brushnet +from ..modules.brushnet import BrushNet +class applyBrushNet: + + def get_files_with_extension(folder='inpaint', extensions='.safetensors'): + return [file for file in folder_paths.get_filename_list(folder) if file.endswith(extensions)] + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "mask": ("MASK",), + "brushnet": (s.get_files_with_extension(),), + "dtype": (['float16', 'bfloat16', 'float32', 'float64'], ), + "scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), + "start_at": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}), + }, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + CATEGORY = "EasyUse/Inpaint" + FUNCTION = "apply" + + def apply(self, pipe, image, mask, brushnet, dtype, scale, start_at, end_at): + + model = pipe['model'] + vae = pipe['vae'] + positive = pipe['positive'] + negative = pipe['negative'] + cls = BrushNet() + if brushnet in backend_cache.cache: + log_node_info("easy brushnetApply", f"Using {brushnet} Cached") + _, brushnet_model = backend_cache.cache[brushnet][1] + else: + brushnet_file = os.path.join(folder_paths.get_full_path("inpaint", brushnet)) + brushnet_model, = cls.load_brushnet_model(brushnet_file, dtype) + backend_cache.update_cache(brushnet, 'brushnet', (False, brushnet_model)) + m, positive, negative, latent = cls.brushnet_model_update(model=model, vae=vae, image=image, mask=mask, + brushnet=brushnet_model, positive=positive, + negative=negative, scale=scale, start_at=start_at, + end_at=end_at) + new_pipe = { + **pipe, + "model": m, + "positive": positive, + "negative": negative, + "samples": latent, + } + del pipe + return (new_pipe,) + +# #powerpaint +class applyPowerPaint: + def get_files_with_extension(folder='inpaint', extensions='.safetensors'): + return [file for file in folder_paths.get_filename_list(folder) if file.endswith(extensions)] + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "mask": ("MASK",), + "powerpaint_model": (s.get_files_with_extension(),), + "powerpaint_clip": (s.get_files_with_extension(extensions='.bin'),), + "dtype": (['float16', 'bfloat16', 'float32', 'float64'],), + "fitting": ("FLOAT", {"default": 1.0, "min": 0.3, "max": 1.0}), + "function": (['text guided', 'shape guided', 'object removal', 'context aware', 'image outpainting'],), + "scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), + "start_at": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "save_memory": (['none', 'auto', 'max'],), + }, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + CATEGORY = "EasyUse/Inpaint" + FUNCTION = "apply" + + def apply(self, pipe, image, mask, powerpaint_model, powerpaint_clip, dtype, fitting, function, scale, start_at, end_at, save_memory='none'): + model = pipe['model'] + vae = pipe['vae'] + positive = pipe['positive'] + negative = pipe['negative'] + + cls = BrushNet() + # load powerpaint clip + if powerpaint_clip in backend_cache.cache: + log_node_info("easy powerpaintApply", f"Using {powerpaint_clip} Cached") + _, ppclip = backend_cache.cache[powerpaint_clip][1] + else: + model_url = POWERPAINT_MODELS['base_fp16']['model_url'] + base_clip = get_local_filepath(model_url, os.path.join(folder_paths.models_dir, 'clip')) + ppclip, = cls.load_powerpaint_clip(base_clip, os.path.join(folder_paths.get_full_path("inpaint", powerpaint_clip))) + backend_cache.update_cache(powerpaint_clip, 'ppclip', (False, ppclip)) + # load powerpaint model + if powerpaint_model in backend_cache.cache: + log_node_info("easy powerpaintApply", f"Using {powerpaint_model} Cached") + _, powerpaint = backend_cache.cache[powerpaint_model][1] + else: + powerpaint_file = os.path.join(folder_paths.get_full_path("inpaint", powerpaint_model)) + powerpaint, = cls.load_brushnet_model(powerpaint_file, dtype) + backend_cache.update_cache(powerpaint_model, 'powerpaint', (False, powerpaint)) + m, positive, negative, latent = cls.powerpaint_model_update(model=model, vae=vae, image=image, mask=mask, powerpaint=powerpaint, + clip=ppclip, positive=positive, + negative=negative, fitting=fitting, function=function, + scale=scale, start_at=start_at, end_at=end_at, save_memory=save_memory) + new_pipe = { + **pipe, + "model": m, + "positive": positive, + "negative": negative, + "samples": latent, + } + del pipe + return (new_pipe,) + +from node_helpers import conditioning_set_values +class applyInpaint: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "mask": ("MASK",), + "inpaint_mode": (('normal', 'fooocus_inpaint', 'brushnet_random', 'brushnet_segmentation', 'powerpaint'),), + "encode": (('none', 'vae_encode_inpaint', 'inpaint_model_conditioning', 'different_diffusion'), {"default": "none"}), + "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}), + "dtype": (['float16', 'bfloat16', 'float32', 'float64'],), + "fitting": ("FLOAT", {"default": 1.0, "min": 0.3, "max": 1.0}), + "function": (['text guided', 'shape guided', 'object removal', 'context aware', 'image outpainting'],), + "scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), + "start_at": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}), + }, + "optional":{ + "noise_mask": ("BOOLEAN", {"default": True}) + } + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + CATEGORY = "EasyUse/Inpaint" + FUNCTION = "apply" + + def inpaint_model_conditioning(self, pipe, image, vae, mask, grow_mask_by, noise_mask=True): + if grow_mask_by >0: + mask, = GrowMask().expand_mask(mask, grow_mask_by, False) + positive, negative, = pipe['positive'], pipe['negative'] + + pixels = image + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), + size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + orig_pixels = pixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset] + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:, :, :, i] -= 0.5 + pixels[:, :, :, i] *= m + pixels[:, :, :, i] += 0.5 + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + + out_latent = {} + + out_latent["samples"] = orig_latent + if noise_mask: + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, + "concat_mask": mask}) + out.append(c) + + pipe['positive'] = out[0] + pipe['negative'] = out[1] + pipe['samples'] = out_latent + + return pipe + + def get_brushnet_model(self, type, model): + model_type = 'sdxl' if isinstance(model.model.model_config, comfy.supported_models.SDXL) else 'sd1' + if type == 'brushnet_random': + brush_model = BRUSHNET_MODELS['random_mask'][model_type]['model_url'] + if model_type == 'sdxl': + pattern = 'brushnet.random.mask.sdxl.*.(safetensors|bin)$' + else: + pattern = 'brushnet.random.mask.*.(safetensors|bin)$' + elif type == 'brushnet_segmentation': + brush_model = BRUSHNET_MODELS['segmentation_mask'][model_type]['model_url'] + if model_type == 'sdxl': + pattern = 'brushnet.segmentation.mask.sdxl.*.(safetensors|bin)$' + else: + pattern = 'brushnet.segmentation.mask.*.(safetensors|bin)$' + + + brushfile = [e for e in folder_paths.get_filename_list('inpaint') if re.search(pattern, e, re.IGNORECASE)] + brushname = brushfile[0] if brushfile else None + if not brushname: + from urllib.parse import urlparse + get_local_filepath(brush_model, INPAINT_DIR) + parsed_url = urlparse(brush_model) + brushname = os.path.basename(parsed_url.path) + return brushname + + def get_powerpaint_model(self, model): + model_type = 'sdxl' if isinstance(model.model.model_config, comfy.supported_models.SDXL) else 'sd1' + if model_type == 'sdxl': + raise Exception("Powerpaint not supported for SDXL models") + + powerpaint_model = POWERPAINT_MODELS['v2.1']['model_url'] + powerpaint_clip = POWERPAINT_MODELS['v2.1']['clip_url'] + + from urllib.parse import urlparse + get_local_filepath(powerpaint_model, os.path.join(INPAINT_DIR, 'powerpaint')) + model_parsed_url = urlparse(powerpaint_model) + clip_parsed_url = urlparse(powerpaint_clip) + model_name = os.path.join("powerpaint",os.path.basename(model_parsed_url.path)) + clip_name = os.path.join("powerpaint",os.path.basename(clip_parsed_url.path)) + return model_name, clip_name + + def apply(self, pipe, image, mask, inpaint_mode, encode, grow_mask_by, dtype, fitting, function, scale, start_at, end_at, noise_mask=True): + new_pipe = { + **pipe, + } + del pipe + if inpaint_mode in ['brushnet_random', 'brushnet_segmentation']: + brushnet = self.get_brushnet_model(inpaint_mode, new_pipe['model']) + new_pipe, = applyBrushNet().apply(new_pipe, image, mask, brushnet, dtype, scale, start_at, end_at) + elif inpaint_mode == 'powerpaint': + powerpaint_model, powerpaint_clip = self.get_powerpaint_model(new_pipe['model']) + new_pipe, = applyPowerPaint().apply(new_pipe, image, mask, powerpaint_model, powerpaint_clip, dtype, fitting, function, scale, start_at, end_at) + + vae = new_pipe['vae'] + if encode == 'none': + if inpaint_mode == 'fooocus_inpaint': + model, = applyFooocusInpaint().apply(new_pipe['model'], new_pipe['samples'], + list(FOOOCUS_INPAINT_HEAD.keys())[0], + list(FOOOCUS_INPAINT_PATCH.keys())[0]) + new_pipe['model'] = model + elif encode == 'vae_encode_inpaint': + latent, = VAEEncodeForInpaint().encode(vae, image, mask, grow_mask_by) + new_pipe['samples'] = latent + if inpaint_mode == 'fooocus_inpaint': + model, = applyFooocusInpaint().apply(new_pipe['model'], new_pipe['samples'], + list(FOOOCUS_INPAINT_HEAD.keys())[0], + list(FOOOCUS_INPAINT_PATCH.keys())[0]) + new_pipe['model'] = model + elif encode == 'inpaint_model_conditioning': + if inpaint_mode == 'fooocus_inpaint': + latent, = VAEEncodeForInpaint().encode(vae, image, mask, grow_mask_by) + new_pipe['samples'] = latent + model, = applyFooocusInpaint().apply(new_pipe['model'], new_pipe['samples'], + list(FOOOCUS_INPAINT_HEAD.keys())[0], + list(FOOOCUS_INPAINT_PATCH.keys())[0]) + new_pipe['model'] = model + new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, 0, noise_mask=noise_mask) + else: + new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by, noise_mask=noise_mask) + elif encode == 'different_diffusion': + if inpaint_mode == 'fooocus_inpaint': + latent, = VAEEncodeForInpaint().encode(vae, image, mask, grow_mask_by) + new_pipe['samples'] = latent + model, = applyFooocusInpaint().apply(new_pipe['model'], new_pipe['samples'], + list(FOOOCUS_INPAINT_HEAD.keys())[0], + list(FOOOCUS_INPAINT_PATCH.keys())[0]) + new_pipe['model'] = model + new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, 0, noise_mask=noise_mask) + else: + new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by, noise_mask=noise_mask) + cls = ALL_NODE_CLASS_MAPPINGS['DifferentialDiffusion'] + if cls is not None: + try: + model, = cls().execute(new_pipe['model']) + except Exception: + model, = cls().apply(new_pipe['model']) + new_pipe['model'] = model + else: + raise Exception("Differential Diffusion not found,please update comfyui") + + return (new_pipe,) + +NODE_CLASS_MAPPINGS = { + "easy applyFooocusInpaint": applyFooocusInpaint, + "easy applyBrushNet": applyBrushNet, + "easy applyPowerPaint": applyPowerPaint, + "easy applyInpaint": applyInpaint +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy applyFooocusInpaint": "Easy Apply Fooocus Inpaint", + "easy applyBrushNet": "Easy Apply BrushNet", + "easy applyPowerPaint": "Easy Apply PowerPaint", + "easy applyInpaint": "Easy Apply Inpaint" +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/loaders.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/loaders.py new file mode 100644 index 00000000..deb82161 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/loaders.py @@ -0,0 +1,1565 @@ +import torch +import comfy.utils, comfy.sample, comfy.samplers, comfy.controlnet, comfy.model_base, comfy.model_management, comfy.sampler_helpers, comfy.supported_models +from comfy.sd import CLIP, VAE +from comfy.model_patcher import ModelPatcher +from PIL import Image + +from nodes import MAX_RESOLUTION, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS, ConditioningConcat, CLIPTextEncode, ConditioningZeroOut + +from ..libs.log import log_node_info, log_node_error, log_node_warn +from ..libs.wildcards import process_with_loras +from ..libs.utils import find_wildcards_seed, is_linked_styles_selector, get_sd_version, AlwaysEqualProxy +from ..libs.sampler import easySampler +from ..libs.controlnet import easyControlnet, union_controlnet_types +from ..libs.conditioning import prompt_to_cond +from ..libs.easing import EasingBase +from ..libs.translate import has_chinese, zh_to_en + +from ..config import * + +from .. import easyCache, sampler + +any_type = AlwaysEqualProxy("*") +# η€ζ˜“εŠ θ½½ε™¨εŒζ•΄ +resolution_strings = [f"{width} x {height} (custom)" if width == 'width' and height == 'height' else f"{width} x {height}" for width, height in BASE_RESOLUTIONS] +class fullLoader: + + @classmethod + def INPUT_TYPES(cls): + a1111_prompt_style_default = False + + return {"required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints") + ['None'],), + "config_name": (["Default", ] + folder_paths.get_filename_list("configs"), {"default": "Default"}), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + "clip_skip": ("INT", {"default": -2, "min": -24, "max": 0, "step": 1}), + + "lora_name": (["None"] + folder_paths.get_filename_list("loras"),), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "resolution": (resolution_strings, {"default": "512 x 512"}), + "empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + "positive_token_normalization": (["none", "mean", "length", "length+mean"],), + "positive_weight_interpretation": (["comfy", "A1111", "comfy++", "compel", "fixed attention"],), + + "negative": ("STRING", {"default": "", "placeholder": "Negative", "multiline": True}), + "negative_token_normalization": (["none", "mean", "length", "length+mean"],), + "negative_weight_interpretation": (["comfy", "A1111", "comfy++", "compel", "fixed attention"],), + + "batch_size": ( + "INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) + }, + "optional": {"model_override": ("MODEL",), "clip_override": ("CLIP",), "vae_override": ("VAE",), "optional_lora_stack": ("LORA_STACK",), "optional_controlnet_stack": ("CONTROL_NET_STACK",), "a1111_prompt_style": ("BOOLEAN", {"default": a1111_prompt_style_default})}, + "hidden": {"video_length": "INT", "prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE", "CLIP", "CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("pipe", "model", "vae", "clip", "positive", "negative", "latent") + + FUNCTION = "adv_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def adv_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, positive_token_normalization, positive_weight_interpretation, + negative, negative_token_normalization, negative_weight_interpretation, + batch_size, model_override=None, clip_override=None, vae_override=None, optional_lora_stack=None, optional_controlnet_stack=None, a1111_prompt_style=False, video_length=25, prompt=None, + my_unique_id=None + ): + + if ckpt_name == 'None' and model_override is None: + raise Exception("Please select a checkpoint or provide a model override.") + + # Clean models from loaded_objects + easyCache.update_loaded_objects(prompt) + + # Load models + log_node_warn("Loading models...") + model, clip, vae, clip_vision, lora_stack = easyCache.load_main(ckpt_name, config_name, vae_name, lora_name, lora_model_strength, lora_clip_strength, optional_lora_stack, model_override, clip_override, vae_override, prompt) + + # Create Empty Latent + model_type = get_sd_version(model) + samples = sampler.emptyLatent(resolution, empty_latent_width, empty_latent_height, batch_size, model_type=model_type, video_length=video_length) + + # Prompt to Conditioning + positive_embeddings_final, positive_wildcard_prompt, model, clip = prompt_to_cond('positive', model, clip, clip_skip, lora_stack, positive, positive_token_normalization, positive_weight_interpretation, a1111_prompt_style, my_unique_id, prompt, easyCache, model_type=model_type) + negative_embeddings_final, negative_wildcard_prompt, model, clip = prompt_to_cond('negative', model, clip, clip_skip, lora_stack, negative, negative_token_normalization, negative_weight_interpretation, a1111_prompt_style, my_unique_id, prompt, easyCache, model_type=model_type) + + if negative_embeddings_final is None: + negative_embeddings_final, = ConditioningZeroOut().zero_out(positive_embeddings_final) + + # Conditioning add controlnet + if optional_controlnet_stack is not None and len(optional_controlnet_stack) > 0: + for controlnet in optional_controlnet_stack: + positive_embeddings_final, negative_embeddings_final = easyControlnet().apply(controlnet[0], controlnet[5], positive_embeddings_final, negative_embeddings_final, controlnet[1], start_percent=controlnet[2], end_percent=controlnet[3], control_net=None, scale_soft_weights=controlnet[4], mask=None, easyCache=easyCache, use_cache=True, model=model, vae=vae) + + pipe = { + "model": model, + "positive": positive_embeddings_final, + "negative": negative_embeddings_final, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": None, + + "loader_settings": { + "ckpt_name": ckpt_name, + "vae_name": vae_name, + "lora_name": lora_name, + "lora_model_strength": lora_model_strength, + "lora_clip_strength": lora_clip_strength, + "lora_stack": lora_stack, + + "clip_skip": clip_skip, + "a1111_prompt_style": a1111_prompt_style, + "positive": positive, + "positive_token_normalization": positive_token_normalization, + "positive_weight_interpretation": positive_weight_interpretation, + "negative": negative, + "negative_token_normalization": negative_token_normalization, + "negative_weight_interpretation": negative_weight_interpretation, + "resolution": resolution, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": batch_size, + } + } + + return {"ui": {"positive": positive_wildcard_prompt, "negative": negative_wildcard_prompt}, "result": (pipe, model, vae, clip, positive_embeddings_final, negative_embeddings_final, samples)} + +# A1111η€ζ˜“εŠ θ½½ε™¨ +class a1111Loader(fullLoader): + @classmethod + def INPUT_TYPES(cls): + a1111_prompt_style_default = False + checkpoints = folder_paths.get_filename_list("checkpoints") + loras = ["None"] + folder_paths.get_filename_list("loras") + return { + "required": { + "ckpt_name": (checkpoints,), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + "clip_skip": ("INT", {"default": -2, "min": -24, "max": 0, "step": 1}), + + "lora_name": (loras,), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "resolution": (resolution_strings, {"default": "512 x 512"}), + "empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default":"", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default":"", "placeholder": "Negative", "multiline": True}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) + }, + "optional": { + "optional_lora_stack": ("LORA_STACK",), + "optional_controlnet_stack": ("CONTROL_NET_STACK",), + "a1111_prompt_style": ("BOOLEAN", {"default": a1111_prompt_style_default}), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "a1111loader" + CATEGORY = "EasyUse/Loaders" + + def a1111loader(self, ckpt_name, vae_name, clip_skip, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, negative, batch_size, optional_lora_stack=None, optional_controlnet_stack=None, a1111_prompt_style=False, prompt=None, + my_unique_id=None): + + return super().adv_pipeloader(ckpt_name, 'Default', vae_name, clip_skip, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, 'mean', 'A1111', + negative,'mean','A1111', + batch_size, None, None, None, optional_lora_stack=optional_lora_stack, optional_controlnet_stack=optional_controlnet_stack,a1111_prompt_style=a1111_prompt_style, prompt=prompt, + my_unique_id=my_unique_id + ) + +# Comfyη€ζ˜“εŠ θ½½ε™¨ +class comfyLoader(fullLoader): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + "clip_skip": ("INT", {"default": -2, "min": -24, "max": 0, "step": 1}), + + "lora_name": (["None"] + folder_paths.get_filename_list("loras"),), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "resolution": (resolution_strings, {"default": "512 x 512"}), + "empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default": "", "placeholder": "Negative", "multiline": True}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) + }, + "optional": {"optional_lora_stack": ("LORA_STACK",), "optional_controlnet_stack": ("CONTROL_NET_STACK",),}, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "comfyloader" + CATEGORY = "EasyUse/Loaders" + + def comfyloader(self, ckpt_name, vae_name, clip_skip, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, negative, batch_size, optional_lora_stack=None, optional_controlnet_stack=None, prompt=None, + my_unique_id=None): + return super().adv_pipeloader(ckpt_name, 'Default', vae_name, clip_skip, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, 'none', 'comfy', + negative, 'none', 'comfy', + batch_size, None, None, None, optional_lora_stack=optional_lora_stack, optional_controlnet_stack=optional_controlnet_stack, a1111_prompt_style=False, prompt=prompt, + my_unique_id=my_unique_id + ) + +# hyditη€ζ˜“εŠ θ½½ε™¨ +class hunyuanDiTLoader(fullLoader): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + + "lora_name": (["None"] + folder_paths.get_filename_list("loras"),), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "resolution": (resolution_strings, {"default": "1024 x 1024"}), + "empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default": "", "placeholder": "Negative", "multiline": True}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + }, + "optional": {"optional_lora_stack": ("LORA_STACK",), "optional_controlnet_stack": ("CONTROL_NET_STACK",),}, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "hyditloader" + CATEGORY = "EasyUse/Loaders" + + def hyditloader(self, ckpt_name, vae_name, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, negative, batch_size, optional_lora_stack=None, optional_controlnet_stack=None, prompt=None, + my_unique_id=None): + + return super().adv_pipeloader(ckpt_name, 'Default', vae_name, 0, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, 'none', 'comfy', + negative, 'none', 'comfy', + batch_size, None, None, None, optional_lora_stack=optional_lora_stack, optional_controlnet_stack=optional_controlnet_stack, a1111_prompt_style=False, prompt=prompt, + my_unique_id=my_unique_id + ) + +# stable Cascade +class cascadeLoader: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + + return {"required": { + "stage_c": (folder_paths.get_filename_list("unet") + folder_paths.get_filename_list("checkpoints"),), + "stage_b": (folder_paths.get_filename_list("unet") + folder_paths.get_filename_list("checkpoints"),), + "stage_a": (["Baked VAE"]+folder_paths.get_filename_list("vae"),), + "clip_name": (["None"] + folder_paths.get_filename_list("clip"),), + + "lora_name": (["None"] + folder_paths.get_filename_list("loras"),), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "resolution": (resolution_strings, {"default": "1024 x 1024"}), + "empty_latent_width": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "compression": ("INT", {"default": 42, "min": 32, "max": 64, "step": 1}), + + "positive": ("STRING", {"default":"", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default":"", "placeholder": "Negative", "multiline": True}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + }, + "optional": {"optional_lora_stack": ("LORA_STACK",), }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "LATENT", "VAE") + RETURN_NAMES = ("pipe", "model_c", "latent_c", "vae") + + FUNCTION = "adv_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def is_ckpt(self, name): + is_ckpt = False + path = folder_paths.get_full_path("checkpoints", name) + if path is not None: + is_ckpt = True + return is_ckpt + + def adv_pipeloader(self, stage_c, stage_b, stage_a, clip_name, lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, compression, + positive, negative, batch_size, optional_lora_stack=None,prompt=None, + my_unique_id=None): + + vae: VAE | None = None + model_c: ModelPatcher | None = None + model_b: ModelPatcher | None = None + clip: CLIP | None = None + can_load_lora = True + pipe_lora_stack = [] + + # Clean models from loaded_objects + easyCache.update_loaded_objects(prompt) + + # Create Empty Latent + samples = sampler.emptyLatent(resolution, empty_latent_width, empty_latent_height, batch_size, compression) + + if self.is_ckpt(stage_c): + model_c, clip, vae_c, clip_vision = easyCache.load_checkpoint(stage_c) + else: + model_c = easyCache.load_unet(stage_c) + vae_c = None + if self.is_ckpt(stage_b): + model_b, clip, vae_b, clip_vision = easyCache.load_checkpoint(stage_b) + else: + model_b = easyCache.load_unet(stage_b) + vae_b = None + + if optional_lora_stack is not None and can_load_lora: + for lora in optional_lora_stack: + lora = {"lora_name": lora[0], "model": model_c, "clip": clip, "model_strength": lora[1], "clip_strength": lora[2]} + model_c, clip = easyCache.load_lora(lora) + lora['model'] = model_c + lora['clip'] = clip + pipe_lora_stack.append(lora) + + if lora_name != "None" and can_load_lora: + lora = {"lora_name": lora_name, "model": model_c, "clip": clip, "model_strength": lora_model_strength, + "clip_strength": lora_clip_strength} + model_c, clip = easyCache.load_lora(lora) + pipe_lora_stack.append(lora) + + model = (model_c, model_b) + # Load clip + if clip_name != 'None': + clip = easyCache.load_clip(clip_name, "stable_cascade") + # Load vae + if stage_a not in ["Baked VAE", "Baked-VAE"]: + vae_b = easyCache.load_vae(stage_a) + + vae = (vae_c, vae_b) + # εˆ€ζ–­ζ˜―ε¦θΏžζŽ₯ styles selector + is_positive_linked_styles_selector = is_linked_styles_selector(prompt, my_unique_id, 'positive') + is_negative_linked_styles_selector = is_linked_styles_selector(prompt, my_unique_id, 'negative') + + positive_seed = find_wildcards_seed(my_unique_id, positive, prompt) + # Translate cn to en + if has_chinese(positive): + positive = zh_to_en([positive])[0] + model_c, clip, positive, positive_decode, show_positive_prompt, pipe_lora_stack = process_with_loras(positive, + model_c, clip, + "positive", + positive_seed, + can_load_lora, + pipe_lora_stack, + easyCache) + positive_wildcard_prompt = positive_decode if show_positive_prompt or is_positive_linked_styles_selector else "" + negative_seed = find_wildcards_seed(my_unique_id, negative, prompt) + # Translate cn to en + if has_chinese(negative): + negative = zh_to_en([negative])[0] + model_c, clip, negative, negative_decode, show_negative_prompt, pipe_lora_stack = process_with_loras(negative, + model_c, clip, + "negative", + negative_seed, + can_load_lora, + pipe_lora_stack, + easyCache) + negative_wildcard_prompt = negative_decode if show_negative_prompt or is_negative_linked_styles_selector else "" + + tokens = clip.tokenize(positive) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + positive_embeddings_final = [[cond, {"pooled_output": pooled}]] + + tokens = clip.tokenize(negative) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + negative_embeddings_final = [[cond, {"pooled_output": pooled}]] + + image = easySampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) + + pipe = { + "model": model, + "positive": positive_embeddings_final, + "negative": negative_embeddings_final, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": image, + "seed": 0, + + "loader_settings": { + "vae_name": stage_a, + "lora_name": lora_name, + "lora_model_strength": lora_model_strength, + "lora_clip_strength": lora_clip_strength, + "lora_stack": pipe_lora_stack, + + "positive": positive, + "positive_token_normalization": 'none', + "positive_weight_interpretation": 'comfy', + "negative": negative, + "negative_token_normalization": 'none', + "negative_weight_interpretation": 'comfy', + "resolution": resolution, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": batch_size, + "compression": compression + } + } + + return {"ui": {"positive": positive_wildcard_prompt, "negative": negative_wildcard_prompt}, + "result": (pipe, model_c, model_b, vae)} + +# Zero123η€ζ˜“εŠ θ½½ε™¨ (3D) +try: + from comfy_extras.nodes_stable3d import camera_embeddings +except FileNotFoundError: + log_node_error("EasyUse[zero123Loader]", "θ―·ζ›΄ζ–°ComfyUIεˆ°ζœ€ζ–°η‰ˆζœ¬") + +class zero123Loader: + + @classmethod + def INPUT_TYPES(cls): + def get_file_list(filenames): + return [file for file in filenames if file != "put_models_here.txt" and "zero123" in file.lower()] + + return {"required": { + "ckpt_name": (get_file_list(folder_paths.get_filename_list("checkpoints")),), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + + "init_image": ("IMAGE",), + "empty_latent_width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "adv_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def adv_pipeloader(self, ckpt_name, vae_name, init_image, empty_latent_width, empty_latent_height, batch_size, elevation, azimuth, prompt=None, my_unique_id=None): + model: ModelPatcher | None = None + vae: VAE | None = None + clip: CLIP | None = None + clip_vision = None + + # Clean models from loaded_objects + easyCache.update_loaded_objects(prompt) + + model, clip, vae, clip_vision = easyCache.load_checkpoint(ckpt_name, "Default", True) + + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1, 1), empty_latent_width, empty_latent_height, "bilinear", "center").movedim(1, -1) + encode_pixels = pixels[:, :, :, :3] + t = vae.encode(encode_pixels) + cam_embeds = camera_embeddings(elevation, azimuth) + cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]) + samples = {"samples": latent} + + image = easySampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) + + pipe = {"model": model, + "positive": positive, + "negative": negative, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": image, + "seed": 0, + + "loader_settings": {"ckpt_name": ckpt_name, + "vae_name": vae_name, + + "positive": positive, + "negative": negative, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": batch_size, + "seed": 0, + } + } + + return (pipe, model, vae) + +# SV3DεŠ θ½½ε™¨ +class sv3dLoader(EasingBase): + + def __init__(self): + super().__init__() + + @classmethod + def INPUT_TYPES(cls): + def get_file_list(filenames): + return [file for file in filenames if file != "put_models_here.txt" and "sv3d" in file] + + return {"required": { + "ckpt_name": (get_file_list(folder_paths.get_filename_list("checkpoints")),), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + + "init_image": ("IMAGE",), + "empty_latent_width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + + "batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}), + "interp_easing": (["linear", "ease_in", "ease_out", "ease_in_out"], {"default": "linear"}), + "easing_mode": (["azimuth", "elevation", "custom"], {"default": "azimuth"}), + }, + "optional": {"scheduler": ("STRING", {"default": "", "multiline": True})}, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "STRING") + RETURN_NAMES = ("pipe", "model", "interp_log") + + FUNCTION = "adv_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def adv_pipeloader(self, ckpt_name, vae_name, init_image, empty_latent_width, empty_latent_height, batch_size, interp_easing, easing_mode, scheduler='',prompt=None, my_unique_id=None): + model: ModelPatcher | None = None + vae: VAE | None = None + clip: CLIP | None = None + + # Clean models from loaded_objects + easyCache.update_loaded_objects(prompt) + + model, clip, vae, clip_vision = easyCache.load_checkpoint(ckpt_name, "Default", True) + + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1, 1), empty_latent_width, empty_latent_height, "bilinear", "center").movedim(1, + -1) + encode_pixels = pixels[:, :, :, :3] + t = vae.encode(encode_pixels) + + azimuth_points = [] + elevation_points = [] + if easing_mode == 'azimuth': + azimuth_points = [(0, 0), (batch_size-1, 360)] + elevation_points = [(0, 0)] * batch_size + elif easing_mode == 'elevation': + azimuth_points = [(0, 0)] * batch_size + elevation_points = [(0, -90), (batch_size-1, 90)] + else: + schedulers = scheduler.rstrip('\n') + for line in schedulers.split('\n'): + frame_str, point_str = line.split(':') + point_str = point_str.strip()[1:-1] + point = point_str.split(',') + azimuth_point = point[0] + elevation_point = point[1] if point[1] else 0.0 + frame = int(frame_str.strip()) + azimuth = float(azimuth_point) + azimuth_points.append((frame, azimuth)) + elevation_val = float(elevation_point) + elevation_points.append((frame, elevation_val)) + azimuth_points.sort(key=lambda x: x[0]) + elevation_points.sort(key=lambda x: x[0]) + + #interpolation + next_point = 1 + next_elevation_point = 1 + elevations = [] + azimuths = [] + # For azimuth interpolation + for i in range(batch_size): + # Find the interpolated azimuth for the current frame + while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: + next_point += 1 + if next_point == len(azimuth_points): + next_point -= 1 + prev_point = max(next_point - 1, 0) + + if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: + timing = (i - azimuth_points[prev_point][0]) / ( + azimuth_points[next_point][0] - azimuth_points[prev_point][0]) + interpolated_azimuth = self.ease(azimuth_points[prev_point][1], azimuth_points[next_point][1], self.easing(timing, interp_easing)) + else: + interpolated_azimuth = azimuth_points[prev_point][1] + + # Interpolate the elevation + next_elevation_point = 1 + while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: + next_elevation_point += 1 + if next_elevation_point == len(elevation_points): + next_elevation_point -= 1 + prev_elevation_point = max(next_elevation_point - 1, 0) + + if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: + timing = (i - elevation_points[prev_elevation_point][0]) / ( + elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) + interpolated_elevation = self.ease(elevation_points[prev_point][1], elevation_points[next_point][1], self.easing(timing, interp_easing)) + else: + interpolated_elevation = elevation_points[prev_elevation_point][1] + + azimuths.append(interpolated_azimuth) + elevations.append(interpolated_elevation) + + log_node_info("easy sv3dLoader", "azimuths:" + str(azimuths)) + log_node_info("easy sv3dLoader", "elevations:" + str(elevations)) + + log = 'azimuths:' + str(azimuths) + '\n\n' + "elevations:" + str(elevations) + # Structure the final output + positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] + negative = [[torch.zeros_like(pooled), + {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] + + latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]) + samples = {"samples": latent} + + image = easySampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) + + + pipe = {"model": model, + "positive": positive, + "negative": negative, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": image, + "seed": 0, + + "loader_settings": {"ckpt_name": ckpt_name, + "vae_name": vae_name, + + "positive": positive, + "negative": negative, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": batch_size, + "seed": 0, + } + } + + return (pipe, model, log) + +#svdεŠ θ½½ε™¨ +class svdLoader: + + @classmethod + def INPUT_TYPES(cls): + def get_file_list(filenames): + return [file for file in filenames if file != "put_models_here.txt" and "svd" in file.lower()] + + return {"required": { + "ckpt_name": (get_file_list(folder_paths.get_filename_list("checkpoints")),), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + "clip_name": (["None"] + folder_paths.get_filename_list("clip"),), + + "init_image": ("IMAGE",), + "resolution": (resolution_strings, {"default": "1024 x 576"}), + "empty_latent_width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + + "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), + "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), + "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), + "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }, + "optional": { + "optional_positive": ("STRING", {"default": "", "multiline": True}), + "optional_negative": ("STRING", {"default": "", "multiline": True}), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "adv_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def adv_pipeloader(self, ckpt_name, vae_name, clip_name, init_image, resolution, empty_latent_width, empty_latent_height, video_frames, motion_bucket_id, fps, augmentation_level, optional_positive=None, optional_negative=None, prompt=None, my_unique_id=None): + model: ModelPatcher | None = None + vae: VAE | None = None + clip: CLIP | None = None + clip_vision = None + + # resolution + if resolution != "θ‡ͺεšδΉ‰ x θ‡ͺεšδΉ‰": + try: + width, height = map(int, resolution.split(' x ')) + empty_latent_width = width + empty_latent_height = height + except ValueError: + raise ValueError("Invalid base_resolution format.") + + # Clean models from loaded_objects + easyCache.update_loaded_objects(prompt) + + model, clip, vae, clip_vision = easyCache.load_checkpoint(ckpt_name, "Default", True) + + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1, 1), empty_latent_width, empty_latent_height, "bilinear", "center").movedim(1, -1) + encode_pixels = pixels[:, :, :, :3] + if augmentation_level > 0: + encode_pixels += torch.randn_like(pixels) * augmentation_level + t = vae.encode(encode_pixels) + positive = [[pooled, + {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, + "concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), + {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, + "concat_latent_image": torch.zeros_like(t)}]] + if optional_positive is not None and optional_positive != '': + if clip_name == 'None': + raise Exception("You need choose a open_clip model when positive is not empty") + clip = easyCache.load_clip(clip_name) + if has_chinese(optional_positive): + optional_positive = zh_to_en([optional_positive])[0] + positive_embeddings_final, = CLIPTextEncode().encode(clip, optional_positive) + positive, = ConditioningConcat().concat(positive, positive_embeddings_final) + if optional_negative is not None and optional_negative != '': + if clip_name == 'None': + raise Exception("You need choose a open_clip model when negative is not empty") + if has_chinese(optional_negative): + optional_positive = zh_to_en([optional_negative])[0] + negative_embeddings_final, = CLIPTextEncode().encode(clip, optional_negative) + negative, = ConditioningConcat().concat(negative, negative_embeddings_final) + + latent = torch.zeros([video_frames, 4, empty_latent_height // 8, empty_latent_width // 8]) + samples = {"samples": latent} + + image = easySampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) + + pipe = {"model": model, + "positive": positive, + "negative": negative, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": image, + "seed": 0, + + "loader_settings": {"ckpt_name": ckpt_name, + "vae_name": vae_name, + + "positive": positive, + "negative": negative, + "resolution": resolution, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": 1, + "seed": 0, + } + } + + return (pipe, model, vae) + + +# kolors Loader +from ..modules.kolors.text_encode import chatglm3_adv_text_encode +class kolorsLoader: + + @classmethod + def INPUT_TYPES(cls): + return { + "required":{ + "unet_name": (folder_paths.get_filename_list("unet"),), + "vae_name": (folder_paths.get_filename_list("vae"),), + "chatglm3_name": (folder_paths.get_filename_list("llm"),), + "lora_name": (["None"] + folder_paths.get_filename_list("loras"),), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "resolution": (resolution_strings, {"default": "1024 x 576"}), + "empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default": "", "placeholder": "Negative", "multiline": True}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + }, + "optional": { + "model_override": ("MODEL",), + "vae_override": ("VAE",), + "optional_lora_stack": ("LORA_STACK",), + "auto_clean_gpu": ("BOOLEAN", {"default": False}), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "adv_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def adv_pipeloader(self, unet_name, vae_name, chatglm3_name, lora_name, lora_model_strength, lora_clip_strength, resolution, empty_latent_width, empty_latent_height, positive, negative, batch_size, model_override=None, optional_lora_stack=None, vae_override=None, auto_clean_gpu=False, prompt=None, my_unique_id=None): + # load unet + if model_override: + model = model_override + else: + model = easyCache.load_kolors_unet(unet_name) + # load vae + if vae_override: + vae = vae_override + else: + vae = easyCache.load_vae(vae_name) + # load chatglm3 + chatglm3_model = easyCache.load_chatglm3(chatglm3_name) + # load lora + lora_stack = [] + if optional_lora_stack is not None: + for lora in optional_lora_stack: + lora = {"lora_name": lora[0], "model": model, "clip": None, "model_strength": lora[1], + "clip_strength": lora[2]} + model, _ = easyCache.load_lora(lora) + lora['model'] = model + lora['clip'] = None + lora_stack.append(lora) + + if lora_name != "None": + lora = {"lora_name": lora_name, "model": model, "clip": None, "model_strength": lora_model_strength, + "clip_strength": lora_clip_strength} + model, _ = easyCache.load_lora(lora) + lora_stack.append(lora) + + + # text encode + log_node_warn("Positive encoding...") + positive_embeddings_final = chatglm3_adv_text_encode(chatglm3_model, positive, auto_clean_gpu) + log_node_warn("Negative encoding...") + negative_embeddings_final = chatglm3_adv_text_encode(chatglm3_model, negative, auto_clean_gpu) + + # empty latent + samples = sampler.emptyLatent(resolution, empty_latent_width, empty_latent_height, batch_size) + + pipe = { + "model": model, + "chatglm3_model": chatglm3_model, + "positive": positive_embeddings_final, + "negative": negative_embeddings_final, + "vae": vae, + "clip": None, + + "samples": samples, + "images": None, + + "loader_settings": { + "unet_name": unet_name, + "vae_name": vae_name, + "chatglm3_name": chatglm3_name, + + "lora_name": lora_name, + "lora_model_strength": lora_model_strength, + "lora_clip_strength": lora_clip_strength, + + "positive": positive, + "negative": negative, + "resolution": resolution, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": batch_size, + "auto_clean_gpu": auto_clean_gpu, + } + } + + return {"ui": {}, + "result": (pipe, model, vae, chatglm3_model, positive_embeddings_final, negative_embeddings_final, samples)} + + + return (chatglm3_model, None, None) + +# Flux Loader +class fluxLoader(fullLoader): + @classmethod + def INPUT_TYPES(cls): + checkpoints = folder_paths.get_filename_list("checkpoints") + loras = ["None"] + folder_paths.get_filename_list("loras") + return { + "required": { + "ckpt_name": (checkpoints + ['None'],), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), + "lora_name": (loras,), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "resolution": (resolution_strings, {"default": "1024 x 1024"}), + "empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + }, + "optional": { + "model_override": ("MODEL",), + "clip_override": ("CLIP",), + "vae_override": ("VAE",), + "optional_lora_stack": ("LORA_STACK",), + "optional_controlnet_stack": ("CONTROL_NET_STACK",), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "fluxloader" + CATEGORY = "EasyUse/Loaders" + + def fluxloader(self, ckpt_name, vae_name, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, batch_size, model_override=None, clip_override=None, vae_override=None, optional_lora_stack=None, optional_controlnet_stack=None, + a1111_prompt_style=False, prompt=None, + my_unique_id=None): + + if positive == '': + positive = None + + return super().adv_pipeloader(ckpt_name, 'Default', vae_name, 0, + lora_name, lora_model_strength, lora_clip_strength, + resolution, empty_latent_width, empty_latent_height, + positive, 'none', 'comfy', + None, 'none', 'comfy', + batch_size, model_override, clip_override, vae_override, optional_lora_stack=optional_lora_stack, + optional_controlnet_stack=optional_controlnet_stack, + a1111_prompt_style=a1111_prompt_style, prompt=prompt, + my_unique_id=my_unique_id) + + +# Dit Loader +from ..modules.dit.pixArt.config import pixart_conf, pixart_res + +class pixArtLoader: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), + "model_name":(list(pixart_conf.keys()),), + "vae_name": (folder_paths.get_filename_list("vae"),), + "t5_type": (['sd3'],), + "clip_name": (folder_paths.get_filename_list("clip"),), + "padding": ("INT", {"default": 1, "min": 1, "max": 300}), + "t5_name": (folder_paths.get_filename_list("t5"),), + "device": (["auto", "cpu", "gpu"], {"default": "cpu"}), + "dtype": (["default", "auto (comfy)", "FP32", "FP16", "BF16"],), + + "lora_name": (["None"] + folder_paths.get_filename_list("loras"),), + "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "ratio": (["custom"] + list(pixart_res["PixArtMS_XL_2"].keys()), {"default":"1.00"}), + "empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + + "positive": ("STRING", {"default": "", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default": "", "placeholder": "Negative", "multiline": True}), + + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + }, + "optional":{ + "optional_lora_stack": ("LORA_STACK",), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + FUNCTION = "pixart_pipeloader" + CATEGORY = "EasyUse/Loaders" + + def pixart_pipeloader(self, ckpt_name, model_name, vae_name, t5_type, clip_name, padding, t5_name, device, dtype, lora_name, lora_model_strength, ratio, empty_latent_width, empty_latent_height, positive, negative, batch_size, optional_lora_stack=None, prompt=None, my_unique_id=None): + # Clean models from loaded_objects + easyCache.update_loaded_objects(prompt) + + # load checkpoint + model = easyCache.load_dit_ckpt(ckpt_name=ckpt_name, model_name=model_name, pixart_conf=pixart_conf, + model_type='PixArt') + # load vae + vae = easyCache.load_vae(vae_name) + + # load t5 + if t5_type == 'sd3': + clip = easyCache.load_clip(clip_name=clip_name,type='sd3') + clip = easyCache.load_t5_from_sd3_clip(sd3_clip=clip, padding=padding) + lora_stack = None + if optional_lora_stack is not None: + for lora in optional_lora_stack: + lora = {"lora_name": lora[0], "model": model, "clip": clip, "model_strength": lora[1], + "clip_strength": lora[2]} + model, _ = easyCache.load_lora(lora, type='PixArt') + lora['model'] = model + lora['clip'] = clip + lora_stack.append(lora) + + if lora_name != "None": + lora = {"lora_name": lora_name, "model": model, "clip": clip, "model_strength": lora_model_strength, + "clip_strength": 1} + model, _ = easyCache.load_lora(lora, type='PixArt') + lora_stack.append(lora) + + positive_embeddings_final, = CLIPTextEncode().encode(clip, positive) + negative_embeddings_final, = CLIPTextEncode().encode(clip, negative) + else: + # todo t5v11 + positive_embeddings_final, negative_embeddings_final = None, None + clip = None + pass + + # Create Empty Latent + if ratio != 'custom': + if model_name in ['ControlPixArtMSHalf','PixArtMS_Sigma_XL_2_900M']: + res_name = 'PixArtMS_XL_2' + elif model_name in ['ControlPixArtHalf']: + res_name = 'PixArt_XL_2' + else: + res_name = model_name + width, height = pixart_res[res_name][ratio] + empty_latent_width = width + empty_latent_height = height + + latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8], device=sampler.device) + samples = {"samples": latent} + + log_node_warn("加载εŒζ―•...") + pipe = { + "model": model, + "positive": positive_embeddings_final, + "negative": negative_embeddings_final, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": None, + + "loader_settings": { + "ckpt_name": ckpt_name, + "clip_name": clip_name, + "vae_name": vae_name, + "t5_name": t5_name, + + "positive": positive, + "negative": negative, + "ratio": ratio, + "empty_latent_width": empty_latent_width, + "empty_latent_height": empty_latent_height, + "batch_size": batch_size, + } + } + + return {"ui": {}, + "result": (pipe, model, vae, clip, positive_embeddings_final, negative_embeddings_final, samples)} + + +# MochiεŠ θ½½ε™¨ +class mochiLoader(fullLoader): + @classmethod + def INPUT_TYPES(cls): + checkpoints = folder_paths.get_filename_list("checkpoints") + return { + "required": { + "ckpt_name": (checkpoints,), + "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"), {"default": "mochi_vae.safetensors"}), + + "positive": ("STRING", {"default":"", "placeholder": "Positive", "multiline": True}), + "negative": ("STRING", {"default":"", "placeholder": "Negative", "multiline": True}), + + "resolution": (resolution_strings, {"default": "width x height (custom)"}), + "empty_latent_width": ("INT", {"default": 848, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "empty_latent_height": ("INT", {"default": 480, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "length": ("INT", {"default": 25, "min": 7, "max": MAX_RESOLUTION, "step": 6}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) + }, + "optional": { + "model_override": ("MODEL",), "clip_override": ("CLIP",), "vae_override": ("VAE",), + }, + "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "VAE") + RETURN_NAMES = ("pipe", "model", "vae") + + FUNCTION = "mochiLoader" + CATEGORY = "EasyUse/Loaders" + + def mochiLoader(self, ckpt_name, vae_name, + positive, negative, + resolution, empty_latent_width, empty_latent_height, + length, batch_size, model_override=None, clip_override=None, vae_override=None, optional_lora_stack=None, optional_controlnet_stack=None, a1111_prompt_style=False, prompt=None, + my_unique_id=None): + + return super().adv_pipeloader(ckpt_name, 'Default', vae_name, 0, + "None", 1.0, 1.0, + resolution, empty_latent_width, empty_latent_height, + positive, 'none', 'comfy', + negative,'none','comfy', + batch_size, model_override, clip_override, vae_override, a1111_prompt_style=False, video_length=length, prompt=prompt, + my_unique_id=my_unique_id + ) +# lora +class loraSwitcher: + @classmethod + def INPUT_TYPES(s): + max_lora_num = 50 + inputs = { + "required": { + "toggle": ("BOOLEAN", {"label_on": "on", "label_off": "off"}), + "select": ("INT", {"default": 1, "min": 1, "max": max_lora_num}), + "num_loras": ("INT", {"default": 1, "min": 1, "max": max_lora_num}), + "lora_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}) + }, + "optional": { + "optional_lora_stack": ("LORA_STACK",), + }, + } + + for i in range(1, max_lora_num + 1): + inputs["optional"][f"lora_{i}_name"] = ( + ["None"] + folder_paths.get_filename_list("loras"), {"default": "None"}) + + return inputs + + RETURN_TYPES = ("LORA_STACK", any_type) + RETURN_NAMES = ("lora_stack", "lora_name") + FUNCTION = "stack" + + CATEGORY = "EasyUse/Loaders" + + def stack(self, toggle, select,num_loras, lora_strength, optional_lora_stack=None, **kwargs): + if (toggle in [False, None, "False"]) or not kwargs: + return (None,'') + + loras = [] + + # Import Stack values + if optional_lora_stack is not None: + loras.extend([l for l in optional_lora_stack if l[0] != "None"]) + + # Import Lora values + lora_name = kwargs.get(f"lora_{select}_name") + + if not lora_name or lora_name == "None": + return (None,'') + + loras.append((lora_name, lora_strength, lora_strength)) + + name = os.path.splitext(os.path.basename(str(lora_name)))[0] + return (loras, name) + + +class loraStack: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + max_lora_num = 10 + inputs = { + "required": { + "toggle": ("BOOLEAN", {"label_on": "on", "label_off": "off"}), + "mode": (["simple", "advanced"],), + "num_loras": ("INT", {"default": 1, "min": 1, "max": max_lora_num}), + }, + "optional": { + "optional_lora_stack": ("LORA_STACK",), + }, + } + + for i in range(1, max_lora_num+1): + inputs["optional"][f"lora_{i}_name"] = ( + ["None"] + folder_paths.get_filename_list("loras"), {"default": "None"}) + inputs["optional"][f"lora_{i}_strength"] = ( + "FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}) + inputs["optional"][f"lora_{i}_model_strength"] = ( + "FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}) + inputs["optional"][f"lora_{i}_clip_strength"] = ( + "FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}) + + return inputs + + RETURN_TYPES = ("LORA_STACK",) + RETURN_NAMES = ("lora_stack",) + FUNCTION = "stack" + + CATEGORY = "EasyUse/Loaders" + + def stack(self, toggle, mode, num_loras, optional_lora_stack=None, **kwargs): + if (toggle in [False, None, "False"]) or not kwargs: + return (None,) + + loras = [] + + # Import Stack values + if optional_lora_stack is not None: + loras.extend([l for l in optional_lora_stack if l[0] != "None"]) + + # Import Lora values + for i in range(1, num_loras + 1): + lora_name = kwargs.get(f"lora_{i}_name") + + if not lora_name or lora_name == "None": + continue + + if mode == "simple": + lora_strength = float(kwargs.get(f"lora_{i}_strength")) + loras.append((lora_name, lora_strength, lora_strength)) + elif mode == "advanced": + model_strength = float(kwargs.get(f"lora_{i}_model_strength")) + clip_strength = float(kwargs.get(f"lora_{i}_clip_strength")) + loras.append((lora_name, model_strength, clip_strength)) + return (loras,) + +class controlnetStack: + + + @classmethod + def INPUT_TYPES(s): + max_cn_num = 3 + inputs = { + "required": { + "toggle": ("BOOLEAN", {"label_on": "enabled", "label_off": "disabled"}), + "mode": (["simple", "advanced"],), + "num_controlnet": ("INT", {"default": 1, "min": 1, "max": max_cn_num}), + }, + "optional": { + "optional_controlnet_stack": ("CONTROL_NET_STACK",), + } + } + + for i in range(1, max_cn_num+1): + inputs["optional"][f"controlnet_{i}"] = (["None"] + folder_paths.get_filename_list("controlnet"), {"default": "None"}) + inputs["optional"][f"controlnet_{i}_strength"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01},) + inputs["optional"][f"start_percent_{i}"] = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},) + inputs["optional"][f"end_percent_{i}"] = ("FLOAT",{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},) + inputs["optional"][f"scale_soft_weight_{i}"] = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},) + inputs["optional"][f"image_{i}"] = ("IMAGE",) + return inputs + + RETURN_TYPES = ("CONTROL_NET_STACK",) + RETURN_NAMES = ("controlnet_stack",) + FUNCTION = "stack" + CATEGORY = "EasyUse/Loaders" + + def stack(self, toggle, mode, num_controlnet, optional_controlnet_stack=None, **kwargs): + if (toggle in [False, None, "False"]) or not kwargs: + return (None,) + + controlnets = [] + + # Import Stack values + if optional_controlnet_stack is not None: + controlnets.extend([l for l in optional_controlnet_stack if l[0] != "None"]) + + # Import Controlnet values + for i in range(1, num_controlnet+1): + controlnet_name = kwargs.get(f"controlnet_{i}") + + if not controlnet_name or controlnet_name == "None": + continue + + controlnet_strength = float(kwargs.get(f"controlnet_{i}_strength")) + start_percent = float(kwargs.get(f"start_percent_{i}")) if mode == "advanced" else 0 + end_percent = float(kwargs.get(f"end_percent_{i}")) if mode == "advanced" else 1.0 + scale_soft_weights = float(kwargs.get(f"scale_soft_weight_{i}")) + image = kwargs.get(f"image_{i}") + + controlnets.append((controlnet_name, controlnet_strength, start_percent, end_percent, scale_soft_weights, image, True)) + + return (controlnets,) +# controlnet +class controlnetSimple: + @classmethod + def INPUT_TYPES(s): + + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "control_net_name": (folder_paths.get_filename_list("controlnet"),), + }, + "optional": { + "control_net": ("CONTROL_NET",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "scale_soft_weights": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},), + } + } + + RETURN_TYPES = ("PIPE_LINE", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("pipe", "positive", "negative") + + FUNCTION = "controlnetApply" + CATEGORY = "EasyUse/Loaders" + + def controlnetApply(self, pipe, image, control_net_name, control_net=None, strength=1, scale_soft_weights=1, union_type=None): + + positive, negative = easyControlnet().apply(control_net_name, image, pipe["positive"], pipe["negative"], strength, 0, 1, control_net, scale_soft_weights, mask=None, easyCache=easyCache, model=pipe['model'], vae=pipe['vae']) + + new_pipe = { + "model": pipe['model'], + "positive": positive, + "negative": negative, + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": pipe["samples"], + "images": pipe["images"], + "seed": 0, + + "loader_settings": pipe["loader_settings"] + } + + del pipe + return (new_pipe, positive, negative) + +# controlnetADV +class controlnetAdvanced: + + @classmethod + def INPUT_TYPES(s): + + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "control_net_name": (folder_paths.get_filename_list("controlnet"),), + }, + "optional": { + "control_net": ("CONTROL_NET",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "scale_soft_weights": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},), + } + } + + RETURN_TYPES = ("PIPE_LINE", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("pipe", "positive", "negative") + + FUNCTION = "controlnetApply" + CATEGORY = "EasyUse/Loaders" + + + def controlnetApply(self, pipe, image, control_net_name, control_net=None, strength=1, start_percent=0, end_percent=1, scale_soft_weights=1): + positive, negative = easyControlnet().apply(control_net_name, image, pipe["positive"], pipe["negative"], + strength, start_percent, end_percent, control_net, scale_soft_weights, union_type=None, mask=None, easyCache=easyCache, model=pipe['model'], vae=pipe['vae']) + + new_pipe = { + "model": pipe['model'], + "positive": positive, + "negative": negative, + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": pipe["samples"], + "images": image, + "seed": 0, + + "loader_settings": pipe["loader_settings"] + } + + del pipe + + return (new_pipe, positive, negative) + +# controlnetPlusPlus +class controlnetPlusPlus: + + @classmethod + def INPUT_TYPES(s): + + return { + "required": { + "pipe": ("PIPE_LINE",), + "image": ("IMAGE",), + "control_net_name": (folder_paths.get_filename_list("controlnet"),), + }, + "optional": { + "control_net": ("CONTROL_NET",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "scale_soft_weights": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},), + "union_type": (list(union_controlnet_types.keys()),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("pipe", "positive", "negative") + + FUNCTION = "controlnetApply" + CATEGORY = "EasyUse/Loaders" + + + def controlnetApply(self, pipe, image, control_net_name, control_net=None, strength=1, start_percent=0, end_percent=1, scale_soft_weights=1, union_type=None): + if scale_soft_weights < 1: + if "ScaledSoftControlNetWeights" in ALL_NODE_CLASS_MAPPINGS: + soft_weight_cls = ALL_NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights'] + (weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False) + cn_adv_cls = ALL_NODE_CLASS_MAPPINGS['ACN_ControlNet++LoaderSingle'] + if union_type == 'auto': + union_type = 'none' + elif union_type == 'canny/lineart/anime_lineart/mlsd': + union_type = 'canny/lineart/mlsd' + elif union_type == 'repaint': + union_type = 'inpaint/outpaint' + control_net, = cn_adv_cls().load_controlnet_plusplus(control_net_name, union_type) + apply_adv_cls = ALL_NODE_CLASS_MAPPINGS['ACN_AdvancedControlNetApply'] + positive, negative, _ = apply_adv_cls().apply_controlnet(pipe["positive"], pipe["negative"], control_net, image, strength, start_percent, end_percent, timestep_kf=timestep_keyframe,) + else: + raise Exception( + f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'") + else: + positive, negative = easyControlnet().apply(control_net_name, image, pipe["positive"], pipe["negative"], + strength, start_percent, end_percent, control_net, scale_soft_weights, union_type=union_type, mask=None, easyCache=easyCache, model=pipe['model']) + + new_pipe = { + "model": pipe['model'], + "positive": positive, + "negative": negative, + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": pipe["samples"], + "images": pipe["images"], + "seed": 0, + + "loader_settings": pipe["loader_settings"] + } + + del pipe + + return (new_pipe, positive, negative) + +# LLLiteLoader +from ..libs.lllite import load_control_net_lllite_patch +class LLLiteLoader: + def __init__(self): + pass + @classmethod + def INPUT_TYPES(s): + def get_file_list(filenames): + return [file for file in filenames if file != "put_models_here.txt" and "lllite" in file] + + return { + "required": { + "model": ("MODEL",), + "model_name": (get_file_list(folder_paths.get_filename_list("controlnet")),), + "cond_image": ("IMAGE",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "steps": ("INT", {"default": 0, "min": 0, "max": 200, "step": 1}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "end_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lllite" + CATEGORY = "EasyUse/Loaders" + + def load_lllite(self, model, model_name, cond_image, strength, steps, start_percent, end_percent): + # cond_image is b,h,w,3, 0-1 + + model_path = os.path.join(folder_paths.get_full_path("controlnet", model_name)) + + model_lllite = model.clone() + patch = load_control_net_lllite_patch(model_path, cond_image, strength, steps, start_percent, end_percent) + if patch is not None: + model_lllite.set_model_attn1_patch(patch) + model_lllite.set_model_attn2_patch(patch) + + return (model_lllite,) + + +NODE_CLASS_MAPPINGS = { + "easy fullLoader": fullLoader, + "easy a1111Loader": a1111Loader, + "easy comfyLoader": comfyLoader, + "easy svdLoader": svdLoader, + "easy sv3dLoader": sv3dLoader, + "easy zero123Loader": zero123Loader, + "easy cascadeLoader": cascadeLoader, + "easy kolorsLoader": kolorsLoader, + "easy fluxLoader": fluxLoader, + "easy hunyuanDiTLoader": hunyuanDiTLoader, + "easy pixArtLoader": pixArtLoader, + "easy mochiLoader": mochiLoader, + "easy loraSwitcher": loraSwitcher, + "easy loraStack": loraStack, + "easy controlnetStack": controlnetStack, + "easy controlnetLoader": controlnetSimple, + "easy controlnetLoaderADV": controlnetAdvanced, + "easy controlnetLoader++": controlnetPlusPlus, + "easy LLLiteLoader": LLLiteLoader +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy fullLoader": "EasyLoader (Full)", + "easy a1111Loader": "EasyLoader (A1111)", + "easy comfyLoader": "EasyLoader (Comfy)", + "easy svdLoader": "EasyLoader (SVD)", + "easy sv3dLoader": "EasyLoader (SV3D)", + "easy zero123Loader": "EasyLoader (Zero123)", + "easy cascadeLoader": "EasyCascadeLoader", + "easy kolorsLoader": "EasyLoader (Kolors)", + "easy fluxLoader": "EasyLoader (Flux)", + "easy hunyuanDiTLoader": "EasyLoader (HunyuanDiT)", + "easy pixArtLoader": "EasyLoader (PixArt)", + "easy mochiLoader": "EasyLoader (Mochi)", + "easy loraSwitcher": "EasyLoraSwitcher", + "easy loraStack": "EasyLoraStack", + "easy controlnetStack": "EasyControlnetStack", + "easy controlnetLoader": "EasyControlnet", + "easy controlnetLoaderADV": "EasyControlnet (Advanced)", + "easy controlnetLoader++": "EasyControlnet++", + "easy LLLiteLoader": "EasyLLLite" +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/logic.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/logic.py new file mode 100755 index 00000000..b6f06a9a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/logic.py @@ -0,0 +1,2013 @@ +from typing import Iterator, List, Tuple, Dict, Any, Union, Optional +from _decimal import Context, getcontext +from decimal import Decimal +from nodes import PreviewImage, SaveImage, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS +from PIL import Image, ImageDraw, ImageFilter, ImageOps +from PIL.PngImagePlugin import PngInfo +from ..libs.utils import AlwaysEqualProxy, ByPassTypeTuple, cleanGPUUsedForce, compare_revision +from ..libs.cache import cache, update_cache, remove_cache +from ..libs.log import log_node_info, log_node_warn +from ..libs.math import evaluate_formula +import numpy as np +import time +import os +import re +import csv +import json +import torch +import comfy.utils +import folder_paths + +DEFAULT_FLOW_NUM = 2 +MAX_FLOW_NUM = 20 +lazy_options = {"lazy": True} + +any_type = AlwaysEqualProxy("*") + +def validate_list_args(args: Dict[str, List[Any]]) -> Tuple[bool, Optional[str], Optional[str]]: + """ + Checks that if there are multiple arguments, they are all the same length or 1 + :param args: + :return: Tuple (Status, mismatched_key_1, mismatched_key_2) + """ + # Only have 1 arg + if len(args) == 1: + return True, None, None + + len_to_match = None + matched_arg_name = None + for arg_name, arg in args.items(): + if arg_name == 'self': + # self is in locals() + continue + + if len(arg) != 1: + if len_to_match is None: + len_to_match = len(arg) + matched_arg_name = arg_name + elif len(arg) != len_to_match: + return False, arg_name, matched_arg_name + + return True, None, None + + +def error_if_mismatched_list_args(args: Dict[str, List[Any]]) -> None: + is_valid, failed_key1, failed_key2 = validate_list_args(args) + if not is_valid: + assert failed_key1 is not None + assert failed_key2 is not None + raise ValueError( + f"Mismatched list inputs received. {failed_key1}({len(args[failed_key1])}) !== {failed_key2}({len(args[failed_key2])})" + ) + + +def zip_with_fill(*lists: Union[List[Any], None]) -> Iterator[Tuple[Any, ...]]: + """ + Zips lists together, but if a list has 1 element, it will be repeated for each element in the other lists. + If a list is None, None will be used for that element. + (Not intended for use with lists of different lengths) + :param lists: + :return: Iterator of tuples of length len(lists) + """ + max_len = max(len(lst) if lst is not None else 0 for lst in lists) + for i in range(max_len): + yield tuple(None if lst is None else (lst[0] if len(lst) == 1 else lst[i]) for lst in lists) + + +# ---------------------------------------------------------------η±»εž‹ 开始----------------------------------------------------------------------# + +# 字符串 +class String: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"value": ("STRING", {"default": ""})}, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic/Type" + + def execute(self, value): + return (value,) + + +# ζ•΄ζ•° +class Int: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"value": ("INT", {"default": 0, "min": -999999, "max": 999999, })}, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("int",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic/Type" + + def execute(self, value): + return (value,) + + +# ζ•΄ζ•°θŒƒε›΄ +class RangeInt: + def __init__(self) -> None: + pass + + @classmethod + def INPUT_TYPES(s) -> Dict[str, Dict[str, Any]]: + return { + "required": { + "range_mode": (["step", "num_steps"], {"default": "step"}), + "start": ("INT", {"default": 0, "min": -4096, "max": 4096, "step": 1}), + "stop": ("INT", {"default": 0, "min": -4096, "max": 4096, "step": 1}), + "step": ("INT", {"default": 0, "min": -4096, "max": 4096, "step": 1}), + "num_steps": ("INT", {"default": 0, "min": -4096, "max": 4096, "step": 1}), + "end_mode": (["Inclusive", "Exclusive"], {"default": "Inclusive"}), + }, + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("range", "range_sizes") + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, True) + FUNCTION = "build_range" + + CATEGORY = "EasyUse/Logic/Type" + + def build_range( + self, range_mode, start, stop, step, num_steps, end_mode + ) -> Tuple[List[int], List[int]]: + error_if_mismatched_list_args(locals()) + + ranges = [] + range_sizes = [] + for range_mode, e_start, e_stop, e_num_steps, e_step, e_end_mode in zip_with_fill( + range_mode, start, stop, num_steps, step, end_mode + ): + if range_mode == 'step': + if e_end_mode == "Inclusive": + e_stop += 1 + vals = list(range(e_start, e_stop, e_step)) + ranges.extend(vals) + range_sizes.append(len(vals)) + elif range_mode == 'num_steps': + direction = 1 if e_stop > e_start else -1 + if e_end_mode == "Exclusive": + e_stop -= direction + vals = (np.rint(np.linspace(e_start, e_stop, e_num_steps)).astype(int).tolist()) + ranges.extend(vals) + range_sizes.append(len(vals)) + return ranges, range_sizes + + +# ζ΅η‚Ήζ•° +class Float: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"value": ("FLOAT", {"default": 0, "step": 0.01, "min":-0xffffffffffffffff, "max": 0xffffffffffffffff, })}, + } + + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("float",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic/Type" + + def execute(self, value): + return (round(value, 3),) + + +# ζ΅η‚Ήζ•°θŒƒε›΄ +class RangeFloat: + def __init__(self) -> None: + pass + + @classmethod + def INPUT_TYPES(s) -> Dict[str, Dict[str, Any]]: + return { + "required": { + "range_mode": (["step", "num_steps"], {"default": "step"}), + "start": ("FLOAT", {"default": 0, "min": -4096, "max": 4096, "step": 0.1}), + "stop": ("FLOAT", {"default": 0, "min": -4096, "max": 4096, "step": 0.1}), + "step": ("FLOAT", {"default": 0, "min": -4096, "max": 4096, "step": 0.1}), + "num_steps": ("INT", {"default": 0, "min": -4096, "max": 4096, "step": 1}), + "end_mode": (["Inclusive", "Exclusive"], {"default": "Inclusive"}), + }, + } + + RETURN_TYPES = ("FLOAT", "INT") + RETURN_NAMES = ("range", "range_sizes") + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, True) + FUNCTION = "build_range" + + CATEGORY = "EasyUse/Logic/Type" + + @staticmethod + def _decimal_range( + range_mode: String, start: Decimal, stop: Decimal, step: Decimal, num_steps: Int, inclusive: bool + ) -> Iterator[float]: + if range_mode == 'step': + ret_val = start + if inclusive: + stop = stop + step + direction = 1 if step > 0 else -1 + while (ret_val - stop) * direction < 0: + yield float(ret_val) + ret_val += step + elif range_mode == 'num_steps': + if num_steps is None or num_steps <= 0: + return + if num_steps == 1: + yield float(start) + return + # inclusive: include both endpoints, guaranteed length = num_steps + if inclusive: + step = (stop - start) / Decimal(num_steps - 1) + for i in range(num_steps): + # pin the last value exactly to stop to avoid drift + yield float(stop if i == num_steps - 1 else (start + step * Decimal(i))) + else: + # Exclusive: num_steps values starting at start, not including stop + step = (stop - start) / Decimal(num_steps) + for i in range(num_steps): + yield float(start + step * Decimal(i)) + + + def build_range( + self, + range_mode, + start, + stop, + step, + num_steps, + end_mode, + ) -> Tuple[List[float], List[int]]: + error_if_mismatched_list_args(locals()) + getcontext().prec = 12 + + start = [round(Decimal(s),2) for s in start] + stop = [round(Decimal(s),2) for s in stop] + step = [round(Decimal(s),2) for s in step] + + ranges = [] + range_sizes = [] + for range_mode, e_start, e_stop, e_step, e_num_steps, e_end_mode in zip_with_fill( + range_mode, start, stop, step, num_steps, end_mode + ): + vals = list( + self._decimal_range(range_mode, e_start, e_stop, e_step, e_num_steps, e_end_mode == 'Inclusive') + ) + ranges.extend(vals) + range_sizes.append(len(vals)) + + return ranges, range_sizes + + +# εΈƒε°” +class Boolean: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"value": ("BOOLEAN", {"default": False})}, + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic/Type" + + def execute(self, value): + return (value,) + + +# ---------------------------------------------------------------εΌ€ε…³ 开始----------------------------------------------------------------------# +class imageSwitch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "boolean": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_switch" + + CATEGORY = "EasyUse/Logic/Switch" + + def image_switch(self, image_a, image_b, boolean): + + if boolean: + return (image_a,) + else: + return (image_b,) + + +class textSwitch: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input": ("INT", {"default": 1, "min": 1, "max": 2}), + }, + "optional": { + "text1": ("STRING", {"forceInput": True}), + "text2": ("STRING", {"forceInput": True}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("STRING",) + CATEGORY = "EasyUse/Logic/Switch" + FUNCTION = "switch" + + def switch(self, input, text1=None, text2=None, ): + if input == 1: + return (text1,) + else: + return (text2,) + + +# ---------------------------------------------------------------Index Switch----------------------------------------------------------------------# + +class ab: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "A or B": ("BOOLEAN", {"default": True, "label_on": "A", "label_off": "B"}), + "in": (any_type,), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = (any_type, any_type,) + RETURN_NAMES = ("A", "B",) + FUNCTION = "switch" + + CATEGORY = "EasyUse/Logic" + + def blocker(self, value, block=False): + from comfy_execution.graph import ExecutionBlocker + return ExecutionBlocker(None) if block else value + + def switch(self, unique_id, **kwargs): + is_a = kwargs['A or B'] + a = self.blocker(kwargs['in'], not is_a) + b = self.blocker(kwargs['in'], is_a) + return (a, b) + + +class anythingInversedSwitch: + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "index": ("INT", {"default": 0, "min": 0, "max": 9, "step": 1}), + "in": (any_type,), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ByPassTypeTuple(tuple([any_type])) + RETURN_NAMES = ByPassTypeTuple(tuple(["out0"])) + FUNCTION = "switch" + + CATEGORY = "EasyUse/Logic" + + def switch(self, index, unique_id, **kwargs): + from comfy_execution.graph import ExecutionBlocker + res = [] + + for i in range(0, MAX_FLOW_NUM): + if index == i: + res.append(kwargs['in']) + else: + res.append(ExecutionBlocker(None)) + return res + + +class anythingIndexSwitch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "index": ("INT", {"default": 0, "min": 0, "max": 9, "step": 1}), + }, + "optional": { + } + } + for i in range(MAX_FLOW_NUM): + inputs["optional"]["value%d" % i] = (any_type, lazy_options) + return inputs + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("value",) + FUNCTION = "index_switch" + + CATEGORY = "EasyUse/Logic/Index Switch" + + def check_lazy_status(self, index, **kwargs): + key = "value%d" % index + if kwargs.get(key, None) is None: + return [key] + + def index_switch(self, index, **kwargs): + key = "value%d" % index + return (kwargs[key],) + + +class imageIndexSwitch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "index": ("INT", {"default": 0, "min": 0, "max": 9, "step": 1}), + }, + "optional": { + } + } + for i in range(MAX_FLOW_NUM): + inputs["optional"]["image%d" % i] = ("IMAGE", lazy_options) + return inputs + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "index_switch" + + CATEGORY = "EasyUse/Logic/Index Switch" + + def check_lazy_status(self, index, **kwargs): + key = "image%d" % index + if kwargs.get(key, None) is None: + return [key] + + def index_switch(self, index, **kwargs): + key = "image%d" % index + return (kwargs[key],) + + +class textIndexSwitch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "index": ("INT", {"default": 0, "min": 0, "max": 9, "step": 1}), + }, + "optional": { + } + } + for i in range(MAX_FLOW_NUM): + inputs["optional"]["text%d" % i] = ("STRING", {**lazy_options, "forceInput": True}) + return inputs + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("text",) + FUNCTION = "index_switch" + + CATEGORY = "EasyUse/Logic/Index Switch" + + def check_lazy_status(self, index, **kwargs): + key = "text%d" % index + if kwargs.get(key, None) is None: + return [key] + + def index_switch(self, index, **kwargs): + key = "text%d" % index + return (kwargs[key],) + + +class conditioningIndexSwitch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "index": ("INT", {"default": 0, "min": 0, "max": 9, "step": 1}), + }, + "optional": { + } + } + for i in range(MAX_FLOW_NUM): + inputs["optional"]["cond%d" % i] = ("CONDITIONING", lazy_options) + return inputs + + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("conditioning",) + FUNCTION = "index_switch" + + CATEGORY = "EasyUse/Logic/Index Switch" + + def check_lazy_status(self, index, **kwargs): + key = "cond%d" % index + if kwargs.get(key, None) is None: + return [key] + + def index_switch(self, index, **kwargs): + key = "cond%d" % index + return (kwargs[key],) + + +# ---------------------------------------------------------------Math----------------------------------------------------------------------# +class mathIntOperation: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), + "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), + "operation": (["add", "subtract", "multiply", "divide", "modulo", "power"],), + }, + } + + RETURN_TYPES = ("INT",) + FUNCTION = "int_math_operation" + + CATEGORY = "EasyUse/Logic/Math" + + def int_math_operation(self, a, b, operation): + if operation == "add": + return (a + b,) + elif operation == "subtract": + return (a - b,) + elif operation == "multiply": + return (a * b,) + elif operation == "divide": + return (a // b,) + elif operation == "modulo": + return (a % b,) + elif operation == "power": + return (a ** b,) + + +class mathFloatOperation: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 0.01}), + "b": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 0.01}), + "operation": (["add", "subtract", "multiply", "divide", "modulo", "power"],), + }, + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "float_math_operation" + + CATEGORY = "EasyUse/Logic/Math" + + def float_math_operation(self, a, b, operation): + if operation == "add": + return (round(a + b,3),) + elif operation == "subtract": + return (round(a - b,3),) + elif operation == "multiply": + return (round(a * b,3),) + elif operation == "divide": + return (round(a / b,3),) + elif operation == "modulo": + return (round(a % b,3),) + elif operation == "power": + return (round(a ** b,3),) + + +class mathStringOperation: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("STRING", {"multiline": False}), + "b": ("STRING", {"multiline": False}), + "operation": (["a == b", "a != b", "a IN b", "a MATCH REGEX(b)", "a BEGINSWITH b", "a ENDSWITH b"],), + "case_sensitive": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "string_math_operation" + + CATEGORY = "EasyUse/Logic/Math" + + def string_math_operation(self, a, b, operation, case_sensitive): + if not case_sensitive: + a = a.lower() + b = b.lower() + + if operation == "a == b": + return (a == b,) + elif operation == "a != b": + return (a != b,) + elif operation == "a IN b": + return (a in b,) + elif operation == "a MATCH REGEX(b)": + try: + return (re.match(b, a) is not None,) + except: + return (False,) + elif operation == "a BEGINSWITH b": + return (a.startswith(b),) + elif operation == "a ENDSWITH b": + return (a.endswith(b),) + + +class simpleMath: + """η€ε•θ‘η—ε™¨θŠ‚η‚ΉοΌŒζ”―ζŒε­—η¬¦δΈ²ζ•°ε­¦ε…¬εΌθ‘η—""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("STRING", { + "default": "", + "placeholder": "θΎ“ε…₯ζ•°ε­¦ε…¬εΌοΌŒε¦‚: a + b, pow(a, 2), ceil(a / b), floor(a * b), round(a / b, 2)" + }), + }, + "optional": { + "a": (any_type,), + "b": (any_type,), + "c": (any_type,), + }, + } + + RETURN_TYPES = ("INT","FLOAT", "BOOLEAN") + RETURN_NAMES = ("int", "float", "boolean") + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic/Math" + + def execute(self, value, a=0, b=0, c=0): + """ + ζ‰§θ‘Œε…¬εΌθ‘η— + + ζ”―ζŒηš„θΏη—οΌš + - 基本运η—οΌš+、-、*、/、**οΌˆεΉ‚οΌ‰γ€%οΌˆε–ζ¨‘οΌ‰ + - 比较运η—οΌš>, <, >=, <=, ==, != + - ε‡½ζ•°οΌšabs, pow, round, ceil, floor, sqrt, exp, log, log10 + - δΈ‰θ§’ε‡½ζ•°οΌšsin, cos, tan, asin, acos, atan + - εΈΈι‡οΌšpi, e + - ε˜ι‡οΌša, b, c + + η€ΊδΎ‹ε…¬εΌοΌš + - a + b + c + - (a>b)*b+(a<=b)*a + - pow(a, 2) + pow(b, 2) + - ceil(a / b) + - floor(a * b) + - round(a / b, 2) + - sqrt(a) + """ + try: + result = evaluate_formula(value, a, b, c) + result_int = int(result) + result_bool = result_int != 0 + return (result_int, result, result_bool) + except Exception as e: + error_msg = f"θ‘η—ι”™θ――: {str(e)}" + log_node_warn(error_msg) + # θΏ”ε›žι»˜θ€ε€Ό + return (0, 0.0, False) + + +class simpleMathDual: + """εŒε…¬εΌθ‘η—ε™¨θŠ‚η‚ΉοΌŒζ”―持一δΈͺη‹¬η«‹ηš„ζ•°ε­¦ε…¬εΌθ‘η—""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value1": ("STRING", { + "default": "", + "placeholder": "θΎ“ε…₯数学公式1οΌŒε¦‚: a + b, pow(a, 2), ceil(a / b)" + }), + "value2": ("STRING", { + "default": "", + "placeholder": "θΎ“ε…₯数学公式2οΌŒε¦‚: c * d, sqrt(c), floor(d / 2)" + }), + }, + "optional": { + "a": (any_type,), + "b": (any_type,), + "c": (any_type,), + "d": (any_type,), + }, + } + + RETURN_TYPES = ("INT", "FLOAT", "INT", "FLOAT") + RETURN_NAMES = ("int1", "float1", "int2", "float2") + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic/Math" + + def execute(self, value1, value2, a=0, b=0, c=0, d=0): + """ + ζ‰§θ‘ŒεŒε…¬εΌθ‘η— + + ζ”―ζŒηš„θΏη—οΌš + - 基本运η—οΌš+、-、*、/、**οΌˆεΉ‚οΌ‰γ€%οΌˆε–ζ¨‘οΌ‰ + - 比较运η—οΌš>, <, >=, <=, ==, != + - ε‡½ζ•°οΌšabs, pow, round, ceil, floor, sqrt, exp, log, log10 + - δΈ‰θ§’ε‡½ζ•°οΌšsin, cos, tan, asin, acos, atan + - εΈΈι‡οΌšpi, e + - ε˜ι‡οΌša, b, c, d + + η€ΊδΎ‹ε…¬εΌοΌš + - value1: a + b, value2: c + d + - value1: (a>b)*b+(a<=b)*a, value2: pow(c, 2) + pow(d, 2) + - value1: ceil(a / b), value2: floor(c * d) + - value1: sqrt(a), value2: round(c / d, 2) + """ + try: + result1 = evaluate_formula(value1, a, b, c, d) + result1_int = int(result1) + except Exception as e: + error_msg = f"公式1θ‘η—ι”™θ――: {str(e)}" + log_node_warn(error_msg) + result1 = 0.0 + result1_int = 0 + + try: + result2 = evaluate_formula(value2, a, b, c, d) + result2_int = int(result2) + except Exception as e: + error_msg = f"公式2θ‘η—ι”™θ――: {str(e)}" + log_node_warn(error_msg) + result2 = 0.0 + result2_int = 0 + + return (result1_int, result1, result2_int, result2) + + +# ---------------------------------------------------------------Flow----------------------------------------------------------------------# +try: + from comfy_execution.graph_utils import GraphBuilder, is_link + from comfy_execution.graph import ExecutionBlocker +except: + GraphBuilder = None + ExecutionBlocker = None + + +class whileLoopStart: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "condition": ("BOOLEAN", {"default": True}), + }, + "optional": { + }, + } + for i in range(MAX_FLOW_NUM): + inputs["optional"]["initial_value%d" % i] = (any_type,) + return inputs + + RETURN_TYPES = ByPassTypeTuple(tuple(["FLOW_CONTROL"] + [any_type] * MAX_FLOW_NUM)) + RETURN_NAMES = ByPassTypeTuple(tuple(["flow"] + ["value%d" % i for i in range(MAX_FLOW_NUM)])) + FUNCTION = "while_loop_open" + + CATEGORY = "EasyUse/Logic/While Loop" + + def while_loop_open(self, condition, **kwargs): + values = [] + for i in range(MAX_FLOW_NUM): + values.append(kwargs.get("initial_value%d" % i, None) if condition else ExecutionBlocker(None)) + return tuple(["stub"] + values) + + +class whileLoopEnd: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "flow": ("FLOW_CONTROL", {"rawLink": True}), + "condition": ("BOOLEAN", {}), + }, + "optional": { + }, + "hidden": { + "dynprompt": "DYNPROMPT", + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + } + } + for i in range(MAX_FLOW_NUM): + inputs["optional"]["initial_value%d" % i] = (any_type,) + return inputs + + RETURN_TYPES = ByPassTypeTuple(tuple([any_type] * MAX_FLOW_NUM)) + RETURN_NAMES = ByPassTypeTuple(tuple(["value%d" % i for i in range(MAX_FLOW_NUM)])) + FUNCTION = "while_loop_close" + + CATEGORY = "EasyUse/Logic/While Loop" + + def explore_dependencies(self, node_id, dynprompt, upstream, parent_ids): + node_info = dynprompt.get_node(node_id) + if "inputs" not in node_info: + return + + for k, v in node_info["inputs"].items(): + if is_link(v): + parent_id = v[0] + display_id = dynprompt.get_display_node_id(parent_id) + display_node = dynprompt.get_node(display_id) + class_type = display_node["class_type"] + if class_type not in ['easy forLoopEnd', 'easy whileLoopEnd']: + parent_ids.append(display_id) + if parent_id not in upstream: + upstream[parent_id] = [] + self.explore_dependencies(parent_id, dynprompt, upstream, parent_ids) + + upstream[parent_id].append(node_id) + + def explore_output_nodes(self, dynprompt, upstream, output_nodes, parent_ids): + for parent_id in upstream: + display_id = dynprompt.get_display_node_id(parent_id) + for output_id in output_nodes: + id = output_nodes[output_id][0] + if id in parent_ids and display_id == id and output_id not in upstream[parent_id]: + if '.' in parent_id: + arr = parent_id.split('.') + arr[len(arr)-1] = output_id + upstream[parent_id].append('.'.join(arr)) + else: + upstream[parent_id].append(output_id) + + def collect_contained(self, node_id, upstream, contained): + if node_id not in upstream: + return + for child_id in upstream[node_id]: + if child_id not in contained: + contained[child_id] = True + self.collect_contained(child_id, upstream, contained) + + def while_loop_close(self, flow, condition, dynprompt=None, unique_id=None,**kwargs): + if not condition: + # We're done with the loop + values = [] + for i in range(MAX_FLOW_NUM): + values.append(kwargs.get("initial_value%d" % i, None)) + return tuple(values) + + # We want to loop + this_node = dynprompt.get_node(unique_id) + upstream = {} + # Get the list of all nodes between the open and close nodes + parent_ids = [] + self.explore_dependencies(unique_id, dynprompt, upstream, parent_ids) + parent_ids = list(set(parent_ids)) + # Get the list of all output nodes between the open and close nodes + prompts = dynprompt.get_original_prompt() + output_nodes = {} + for id in prompts: + node = prompts[id] + if "inputs" not in node: + continue + class_type = node["class_type"] + class_def = ALL_NODE_CLASS_MAPPINGS[class_type] + if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: + for k, v in node['inputs'].items(): + if is_link(v): + output_nodes[id] = v + + graph = GraphBuilder() + self.explore_output_nodes(dynprompt, upstream, output_nodes, parent_ids) + contained = {} + open_node = flow[0] + self.collect_contained(open_node, upstream, contained) + contained[unique_id] = True + contained[open_node] = True + + for node_id in contained: + original_node = dynprompt.get_node(node_id) + node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id) + node.set_override_display_id(node_id) + for node_id in contained: + original_node = dynprompt.get_node(node_id) + node = graph.lookup_node("Recurse" if node_id == unique_id else node_id) + for k, v in original_node["inputs"].items(): + if is_link(v) and v[0] in contained: + parent = graph.lookup_node(v[0]) + node.set_input(k, parent.out(v[1])) + else: + node.set_input(k, v) + + new_open = graph.lookup_node(open_node) + for i in range(MAX_FLOW_NUM): + key = "initial_value%d" % i + new_open.set_input(key, kwargs.get(key, None)) + my_clone = graph.lookup_node("Recurse") + result = map(lambda x: my_clone.out(x), range(MAX_FLOW_NUM)) + return { + "result": tuple(result), + "expand": graph.finalize(), + } + + +class forLoopStart: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "total": ("INT", {"default": 1, "min": 1, "max": 100000, "step": 1}), + }, + "optional": { + "initial_value%d" % i: (any_type,) for i in range(1, MAX_FLOW_NUM) + }, + "hidden": { + "initial_value0": (any_type,), + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + "unique_id": "UNIQUE_ID" + } + } + + RETURN_TYPES = ByPassTypeTuple(tuple(["FLOW_CONTROL", "INT"] + [any_type] * (MAX_FLOW_NUM - 1))) + RETURN_NAMES = ByPassTypeTuple(tuple(["flow", "index"] + ["value%d" % i for i in range(1, MAX_FLOW_NUM)])) + FUNCTION = "for_loop_start" + + CATEGORY = "EasyUse/Logic/For Loop" + + def for_loop_start(self, total, prompt=None, extra_pnginfo=None, unique_id=None, **kwargs): + graph = GraphBuilder() + i = 0 + if "initial_value0" in kwargs: + i = kwargs["initial_value0"] + + initial_values = {("initial_value%d" % num): kwargs.get("initial_value%d" % num, None) for num in + range(1, MAX_FLOW_NUM)} + while_open = graph.node("easy whileLoopStart", condition=total, initial_value0=i, **initial_values) + outputs = [kwargs.get("initial_value%d" % num, None) for num in range(1, MAX_FLOW_NUM)] + return { + "result": tuple(["stub", i] + outputs), + "expand": graph.finalize(), + } + + +class forLoopEnd: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "flow": ("FLOW_CONTROL", {"rawLink": True}), + }, + "optional": { + "initial_value%d" % i: (any_type, {"rawLink": True}) for i in range(1, MAX_FLOW_NUM) + }, + "hidden": { + "dynprompt": "DYNPROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ByPassTypeTuple(tuple([any_type] * (MAX_FLOW_NUM - 1))) + RETURN_NAMES = ByPassTypeTuple(tuple(["value%d" % i for i in range(1, MAX_FLOW_NUM)])) + FUNCTION = "for_loop_end" + + CATEGORY = "EasyUse/Logic/For Loop" + + + + def for_loop_end(self, flow, dynprompt=None, extra_pnginfo=None, unique_id=None, **kwargs): + graph = GraphBuilder() + while_open = flow[0] + total = None + + # Using dynprompt to get the original node + forstart_node = dynprompt.get_node(while_open) + if forstart_node['class_type'] == 'easy forLoopStart': + inputs = forstart_node['inputs'] + total = inputs['total'] + elif forstart_node['class_type'] == 'easy loadImagesForLoop': + inputs = forstart_node['inputs'] + limit = inputs['limit'] + start_index = inputs['start_index'] + # Filter files by extension + directory = inputs['directory'] + total = graph.node('easy imagesCountInDirectory', directory=directory, limit=limit, start_index=start_index, extension='*').out(0) + + sub = graph.node("easy mathInt", operation="add", a=[while_open, 1], b=1) + cond = graph.node("easy compare", a=sub.out(0), b=total, comparison='a < b') + input_values = {("initial_value%d" % i): kwargs.get("initial_value%d" % i, None) for i in + range(1, MAX_FLOW_NUM)} + while_close = graph.node("easy whileLoopEnd", + flow=flow, + condition=cond.out(0), + initial_value0=sub.out(0), + **input_values) + return { + "result": tuple([while_close.out(i) for i in range(1, MAX_FLOW_NUM)]), + "expand": graph.finalize(), + } + + +COMPARE_FUNCTIONS = { + "a == b": lambda a, b: a == b, + "a != b": lambda a, b: a != b, + "a < b": lambda a, b: a < b, + "a > b": lambda a, b: a > b, + "a <= b": lambda a, b: a <= b, + "a >= b": lambda a, b: a >= b, + "a > 0": lambda a, b: a > 0, + "a <= 0": lambda a, b: a <= 0, + "b > 0": lambda a, b: b > 0, + "b <= 0": lambda a, b: b <= 0, +} + + +# ζ―”θΎƒ +class Compare: + @classmethod + def INPUT_TYPES(s): + compare_functions = list(COMPARE_FUNCTIONS.keys()) + return { + "optional": { + "a": (any_type, {"default": 0}), + "b": (any_type, {"default": 0}), + "comparison": (compare_functions, {"default": "a == b"}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + FUNCTION = "compare" + CATEGORY = "EasyUse/Logic/Math" + + def compare(self, a=0, b=0, comparison="a == b"): + return (COMPARE_FUNCTIONS[comparison](a, b),) + + +# εˆ€ζ–­ +class IfElse: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "boolean": ("BOOLEAN",), + "on_true": (any_type, lazy_options), + "on_false": (any_type, lazy_options), + }, + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("*",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def check_lazy_status(self, boolean=True, on_true=None, on_false=None): + if boolean and on_true is None: + return ["on_true"] + if not boolean and on_false is None: + return ["on_false"] + + def execute(self, *args, **kwargs): + return (kwargs['on_true'] if kwargs['boolean'] else kwargs['on_false'],) + + +class Blocker: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "continue": ("BOOLEAN", {"default": False}), + "in": (any_type, {"default": None}), + }, + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("out",) + CATEGORY = "EasyUse/Logic" + FUNCTION = "execute" + + def execute(self, **kwargs): + from comfy_execution.graph import ExecutionBlocker + return (kwargs['in'] if kwargs['continue'] else ExecutionBlocker(None),) + + +# 是否为SDXL +from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG + +class isMaskEmpty: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + }, + "optional": { + } + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, mask): + if mask is None: + return (True,) + if torch.all(mask == 0): + return (True,) + return (False,) + + +class isNone: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any": (any_type,) + }, + "optional": { + } + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, any): + return (True if (isinstance(any, str) and any == '') or (isinstance(any, (int, float)) and any == 0) or any is None else False,) + + +class isSDXL: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + "optional": { + "optional_pipe": ("PIPE_LINE",), + "optional_clip": ("CLIP",), + } + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, optional_pipe=None, optional_clip=None): + if optional_pipe is None and optional_clip is None: + raise Exception(f"[ERROR] optional_pipe or optional_clip is missing") + clip = optional_clip if optional_clip is not None else optional_pipe['clip'] + if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)): + return (True,) + else: + return (False,) + + +class isFileExist: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "file_path": ("STRING", {"default": ""}), + "file_name": ("STRING", {"default": ""}), + "file_extension": ("STRING", {"default": ""}), + }, + "optional": { + } + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("boolean",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, file_path, file_name, file_extension): + if not file_path: + raise Exception("file_path is missing") + + if file_name: + file_path = os.path.join(file_path, file_name) + if file_extension: + file_path = file_path + "." + file_extension + + if os.path.exists(file_path) and os.path.isfile(file_path): + return (True,) + else: + return (False,) + + +from nodes import MAX_RESOLUTION +from ..config import BASE_RESOLUTIONS + + +class pixels: + + @classmethod + def INPUT_TYPES(s): + resolution_strings = [ + f"{width} x {height} (custom)" if width == 'width' and height == 'height' else f"{width} x {height}" for + width, height in BASE_RESOLUTIONS] + return { + "required": { + "resolution": (resolution_strings,), + "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "scale": ("FLOAT", {"default": 2.000, "min": 0.001, "max": 10, "step": 0.001}), + "flip_w/h": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("INT", "INT", any_type, any_type, any_type) + RETURN_NAMES = ("width_norm", "height_norm", "width", "height", "scale_factor") + CATEGORY = "EasyUse/Logic" + FUNCTION = "create" + + def create(self, resolution, width, height, scale, **kwargs): + if resolution not in ["θ‡ͺεšδΉ‰ x θ‡ͺεšδΉ‰", 'width x height (custom)']: + try: + _width, _height = map(int, resolution.split(' x ')) + width = _width + height = _height + except ValueError: + raise ValueError("Invalid base_resolution format.") + + width = width * scale + height = height * scale + width_norm = int(width - width % 8) + height_norm = int(height - height % 8) + flip_wh = kwargs['flip_w/h'] + if flip_wh: + width, height = height, width + width_norm, height_norm = height_norm, width_norm + + return (width_norm, height_norm, width, height, scale) + + +# xy矩阡 +class xyAny: + + @classmethod + def INPUT_TYPES(s): + + return { + "required": { + "X": (any_type, {}), + "Y": (any_type, {}), + "direction": (["horizontal", "vertical"], {"default": "horizontal"}) + } + } + + RETURN_TYPES = (any_type, any_type) + RETURN_NAMES = ("X", "Y") + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, True) + CATEGORY = "EasyUse/Logic" + FUNCTION = "to_xy" + + def to_xy(self, X, Y, direction): + new_x = list() + new_y = list() + if direction[0] == "horizontal": + for y in Y: + for x in X: + new_x.append(x) + new_y.append(y) + else: + for x in X: + for y in Y: + new_x.append(x) + new_y.append(y) + + return (new_x, new_y) + + +class lengthAnything: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any": (any_type, {}), + }, + "hidden":{ + "prompt": "PROMPT", + "my_unique_id": "UNIQUE_ID" + } + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("length",) + + INPUT_IS_LIST = True + + FUNCTION = "getLength" + CATEGORY = "EasyUse/Logic" + + def getLength(self, any, prompt=None, my_unique_id=None): + prompt = prompt[0] + my_unique_id = my_unique_id[0] + my_unique_id = my_unique_id.split('.')[len(my_unique_id.split('.')) - 1] if "." in my_unique_id else my_unique_id + id, slot = prompt[my_unique_id]['inputs']['any'] + class_type = prompt[id]['class_type'] + node_class = ALL_NODE_CLASS_MAPPINGS[class_type] + output_is_list = node_class.OUTPUT_IS_LIST[slot] if hasattr(node_class, 'OUTPUT_IS_LIST') else False + + return (len(any) if output_is_list or len(any) > 1 else len(any[0]),) + +class indexAnything: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any": (any_type, {}), + "index": ("INT", {"default": 0, "min": -1000000, "max": 1000000, "step": 1}), + }, + "hidden":{ + "prompt": "PROMPT", + "my_unique_id": "UNIQUE_ID" + } + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("out",) + + INPUT_IS_LIST = True + + FUNCTION = "getIndex" + CATEGORY = "EasyUse/Logic" + + def getIndex(self, any, index, prompt=None, my_unique_id=None): + index = index[0] + prompt = prompt[0] + my_unique_id = my_unique_id[0] + my_unique_id = my_unique_id.split('.')[len(my_unique_id.split('.')) - 1] if "." in my_unique_id else my_unique_id + id, slot = prompt[my_unique_id]['inputs']['any'] + class_type = prompt[id]['class_type'] + node_class = ALL_NODE_CLASS_MAPPINGS[class_type] + output_is_list = node_class.OUTPUT_IS_LIST[slot] if hasattr(node_class, 'OUTPUT_IS_LIST') else False + + def normalize_index(index, length): + """ζ ‡ε‡†εŒ–η΄’εΌ•οΌŒε€„η†θ΄Ÿη΄’εΌ•εΉΆη‘δΏεœ¨ζœ‰ζ•ˆθŒƒε›΄ε†…""" + if index < 0: + index = length + index + if index < 0: + index = 0 + return min(max(0, index), length - 1) + + if output_is_list or len(any) > 1: + index = normalize_index(index, len(any)) + return (any[index],) + elif isinstance(any[0], torch.Tensor): + index = normalize_index(index, any[0].shape[0]) + s = any[0][index:index + 1].clone() + return (s,) + else: + if hasattr(any[0], '__len__') and len(any[0]) > 0: + index = normalize_index(index, len(any[0])) + return (any[0][index],) + return (any[0],) + + +class batchAnything: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any_1": (any_type, {}), + "any_2": (any_type, {}) + } + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("batch",) + + FUNCTION = "batch" + CATEGORY = "EasyUse/Logic" + + def latentBatch(self, any_1, any_2): + samples_out = any_1.copy() + s1 = any_1["samples"] + s2 = any_2["samples"] + + if s1.shape[1:] != s2.shape[1:]: + s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") + s = torch.cat((s1, s2), dim=0) + samples_out["samples"] = s + samples_out["batch_index"] = any_1.get("batch_index", + [x for x in range(0, s1.shape[0])]) + any_2.get( + "batch_index", [x for x in range(0, s2.shape[0])]) + + return samples_out + + def batch(self, any_1, any_2): + if isinstance(any_1, torch.Tensor) or isinstance(any_2, torch.Tensor): + if any_1 is None: + return (any_2,) + elif any_2 is None: + return (any_1,) + if any_1.shape[1:] != any_2.shape[1:]: + any_2 = comfy.utils.common_upscale(any_2.movedim(-1, 1), any_1.shape[2], any_1.shape[1], "bilinear", + "center").movedim(1, -1) + return (torch.cat((any_1, any_2), 0),) + elif isinstance(any_1, (str, float, int)): + if any_2 is None: + return (any_1,) + elif isinstance(any_2, tuple): + return (any_2 + (any_1,),) + elif isinstance(any_2, list): + return (any_2 + [any_1],) + return ([any_1, any_2],) + elif isinstance(any_2, (str, float, int)): + if any_1 is None: + return (any_2,) + elif isinstance(any_1, tuple): + return (any_1 + (any_2,),) + elif isinstance(any_1, list): + return (any_1 + [any_2],) + return ([any_2, any_1],) + elif isinstance(any_1, dict) and 'samples' in any_1: + if any_2 is None: + return (any_1,) + elif isinstance(any_2, dict) and 'samples' in any_2: + return (self.latentBatch(any_1, any_2),) + elif isinstance(any_2, dict) and 'samples' in any_2: + if any_1 is None: + return (any_2,) + elif isinstance(any_1, dict) and 'samples' in any_1: + return (self.latentBatch(any_2, any_1),) + else: + if any_1 is None: + return (any_2,) + elif any_2 is None: + return (any_1,) + return (any_1 + any_2,) + + +# θ½¬ζ’ζ‰€ζœ‰η±»εž‹ +class convertAnything: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "*": (any_type,), + "output_type": (["string", "int", "float", "boolean"], {"default": "string"}), + }} + + RETURN_TYPES = ByPassTypeTuple((any_type,)) + OUTPUT_NODE = True + FUNCTION = "convert" + CATEGORY = "EasyUse/Logic" + + def convert(self, *args, **kwargs): + anything = kwargs['*'] + output_type = kwargs['output_type'] + params = None + if output_type == 'string': + params = str(anything) + elif output_type == 'int': + params = int(anything) + elif output_type == 'float': + params = float(anything) + elif output_type == 'boolean': + params = bool(anything) + return (params,) + +class showAnything: + @classmethod + def INPUT_TYPES(s): + return {"required": {}, "optional": {"anything": (any_type, {}), }, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO", + }} + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ('output',) + INPUT_IS_LIST = True + OUTPUT_NODE = True + FUNCTION = "log_input" + CATEGORY = "EasyUse/Logic" + + def log_input(self, unique_id=None, extra_pnginfo=None, **kwargs): + + values = [] + if "anything" in kwargs: + for val in kwargs['anything']: + if isinstance(val, str): + values.append(val) + elif isinstance(val, (int, float, bool)): + values.append(str(val)) + elif isinstance(val, list) and len(val) <= 30: + values = val + elif val is not None: + try: + values.append(json.dumps(val, indent=4, ensure_ascii=False)) + except Exception: + try: + values.append(str(val)) + except Exception: + raise Exception('source exists, but could not be serialized.') + + if not extra_pnginfo: + pass + elif (not isinstance(extra_pnginfo[0], dict) or "workflow" not in extra_pnginfo[0]): + pass + else: + workflow = extra_pnginfo[0]["workflow"] + node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id[0]), None) + if node: + node["widgets_values"] = [values] + if isinstance(values, list) and len(values) == 1: + return {"ui": {"text": values}, "result": (values[0],), } + else: + return {"ui": {"text": values}, "result": (values,), } + +class showTensorShape: + @classmethod + def INPUT_TYPES(s): + return {"required": {"tensor": (any_type,)}, "optional": {}, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO" + }} + + RETURN_TYPES = () + RETURN_NAMES = () + OUTPUT_NODE = True + FUNCTION = "log_input" + CATEGORY = "EasyUse/Logic" + + def log_input(self, tensor, unique_id=None, extra_pnginfo=None): + shapes = [] + + def tensorShape(tensor): + if isinstance(tensor, dict): + for k in tensor: + tensorShape(tensor[k]) + elif isinstance(tensor, list): + for i in range(len(tensor)): + tensorShape(tensor[i]) + elif hasattr(tensor, 'shape'): + shapes.append(list(tensor.shape)) + + tensorShape(tensor) + + return {"ui": {"text": shapes}} + +class stringToIntList: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "string" :("STRING", {"default": "1, 2, 3", "multiline": True}), + } + } + RETURN_TYPES = ("INT",) + RETURN_NAMES = ('INT',) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, string): + int_list = [int(x.strip()) for x in string.split(',')] + return (int_list,) + +class stringToFloatList: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "string" :("STRING", {"default": "1, 2, 3", "multiline": True}), + } + } + + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ('FLOAT',) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, string): + float_list = [float(x.strip()) for x in string.split(',')] + return (float_list,) + + +class stringJoinLines: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "string" :("STRING", {"default": "", "multiline": True}), + "delimiter": ("STRING", {"default": " | "}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ('STRING',) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, string, delimiter): + # ε°†ε€šθ‘Œε­—η¬¦δΈ²ζŒ‰ζ’θ‘Œη¬¦εˆ†ε‰²ζˆεˆ—θ‘¨οΌŒεŽ»ι™€η©Ίθ‘Œε’Œζ―θ‘Œηš„ι¦–ε°Ύη©Ίζ Ό + lines = [line.strip() for line in string.split('\n') if line.strip()] + # η”¨ζŒ‡εšηš„εˆ†ιš”η¬¦θΏžζŽ₯ε„θ‘Œ + result = delimiter.join(lines) + return (result,) + + +class outputToList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "tuple": (any_type, {}), + }, "optional": {}, + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("list",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "output_to_List" + CATEGORY = "EasyUse/Logic" + + def output_to_List(self, tuple): + return (tuple,) + + +# cleanGpuUsed +class cleanGPUUsed: + @classmethod + def INPUT_TYPES(s): + return {"required": {"anything": (any_type, {})}, "optional": {}, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO", + }} + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("output",) + OUTPUT_NODE = True + FUNCTION = "empty_cache" + CATEGORY = "EasyUse/Logic" + + def empty_cache(self, anything, unique_id=None, extra_pnginfo=None): + cleanGPUUsedForce() + remove_cache('*') + return (anything,) + + +class clearCacheKey: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "anything": (any_type, {}), + "cache_key": ("STRING", {"default": "*"}), + }, "optional": {}, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO", } + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ('output',) + OUTPUT_NODE = True + FUNCTION = "empty_cache" + CATEGORY = "EasyUse/Logic" + + def empty_cache(self, anything, cache_name, unique_id=None, extra_pnginfo=None): + remove_cache(cache_name) + return (anything,) + + +class clearCacheAll: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "anything": (any_type, {}), + }, "optional": {}, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO", } + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("output",) + OUTPUT_NODE = True + FUNCTION = "empty_cache" + CATEGORY = "EasyUse/Logic" + + def empty_cache(self, anything, unique_id=None, extra_pnginfo=None): + remove_cache('*') + return (anything,) + + +class saveText: + + def __init__(self): + self.output_dir = folder_paths.output_directory + self.type = 'output' + + @classmethod + def INPUT_TYPES(s): + input_types = {} + input_types['required'] = { + "text": ("STRING", {"default": "", "forceInput": True}), + "output_file_path": ("STRING", {"multiline": False, "default": ""}), + "file_name": ("STRING", {"multiline": False, "default": ""}), + "file_extension": (["txt", "csv"],), + "overwrite": ("BOOLEAN", {"default": True}), + } + input_types['optional'] = { + "image": ("IMAGE",), + } + return input_types + + RETURN_TYPES = ("STRING", "IMAGE") + RETURN_NAMES = ("text", 'image',) + + FUNCTION = "save_text" + OUTPUT_NODE = True + CATEGORY = "EasyUse/Logic" + + def save_image(self, images, filename_prefix='', extension='png',quality=100, prompt=None, + extra_pnginfo=None, delimiter='_', filename_number_start='true', number_padding=4, + overwrite_mode='prefix_as_filename', output_path='', show_history='true', show_previews='true', + embed_workflow='true', lossless_webp=False, ): + results = list() + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + + # Delegate metadata/pnginfo + if extension == 'webp': + img_exif = img.getexif() + workflow_metadata = '' + prompt_str = '' + if prompt is not None: + prompt_str = json.dumps(prompt) + img_exif[0x010f] = "Prompt:" + prompt_str + if extra_pnginfo is not None: + for x in extra_pnginfo: + workflow_metadata += json.dumps(extra_pnginfo[x]) + img_exif[0x010e] = "Workflow:" + workflow_metadata + exif_data = img_exif.tobytes() + else: + metadata = PngInfo() + if embed_workflow == 'true': + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + exif_data = metadata + + file = f"{filename_prefix}.{extension}" + + # Save the images + try: + output_file = os.path.abspath(os.path.join(output_path, file)) + if extension in ["jpg", "jpeg"]: + img.save(output_file, + quality=quality, optimize=True) + elif extension == 'webp': + img.save(output_file, + quality=quality, lossless=lossless_webp, exif=exif_data) + elif extension == 'png': + img.save(output_file, + pnginfo=exif_data, optimize=True) + elif extension == 'bmp': + img.save(output_file) + elif extension == 'tiff': + img.save(output_file, + quality=quality, optimize=True) + else: + img.save(output_file, + pnginfo=exif_data, optimize=True) + + except OSError as e: + print(e) + except Exception as e: + print(e) + + def save_text(self, text, output_file_path, file_name, file_extension, overwrite, filename_number_start='true', image=None, prompt=None, + extra_pnginfo=None): + if isinstance(file_name, list): + file_name = file_name[0] + filepath = str(os.path.join(output_file_path, file_name)) + "." + file_extension + index = 1 + + if (output_file_path == "" or file_name == ""): + log_node_warn("Save Text", "No file details found. No file output.") + return () + + if not os.path.exists(output_file_path): + os.makedirs(output_file_path) + + if overwrite: + file_mode = "w" + else: + file_mode = "a" + + log_node_info("Save Text", f"Saving to {filepath}") + + if file_extension == "csv": + text_list = [] + for i in text.split("\n"): + text_list.append(i.strip()) + + with open(filepath, file_mode, newline="", encoding='utf-8') as csv_file: + csv_writer = csv.writer(csv_file) + # Write each line as a separate row in the CSV file + for line in text_list: + csv_writer.writerow([line]) + else: + with open(filepath, file_mode, newline="", encoding='utf-8') as text_file: + for line in text: + text_file.write(line) + + result = {"result": (text, None)} + + if image is not None: + imagepath = os.path.join(output_file_path, file_name) + image_index = 1 + if not overwrite: + while os.path.exists(filepath): + if os.path.exists(filepath): + imagepath = str(os.path.join(output_file_path, file_name)) + "_" + str(index) + index = index + 1 + else: + break + # result = self.save_images(image, imagepath, prompt, extra_pnginfo) + + delimiter = '_' + number_padding = 4 + lossless_webp = (False,) + + original_output = self.output_dir + + # Setup output path + if output_file_path in [None, '', "none", "."]: + output_path = self.output_dir + else: + output_path = '' + if not os.path.isabs(output_file_path): + output_path = os.path.join(self.output_dir, output_path) + base_output = os.path.basename(output_path) + if output_path.endswith("ComfyUI/output") or output_path.endswith(r"ComfyUI\output"): + base_output = "" + + # Check output destination + if output_path.strip() != '': + if not os.path.isabs(output_path): + output_path = os.path.join(folder_paths.output_directory, output_path) + if not os.path.exists(output_path.strip()): + print( + f'The path `{output_path.strip()}` specified doesn\'t exist! Creating directory.') + os.makedirs(output_path, exist_ok=True) + + images = [] + images.append(image) + images = torch.cat(images, dim=0) + self.save_image(images, imagepath, 'png', 100, prompt, extra_pnginfo, filename_number_start=filename_number_start, output_path=output_path, delimiter=delimiter, + number_padding=number_padding, lossless_webp=lossless_webp) + + log_node_info("Save Text", f"Saving Image to {imagepath}") + result['result'] = (text, image) + + return result + + +class sleep: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "any": (any_type, {}), + "delay": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1000000, "step": 0.1}), + }, + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("out",) + FUNCTION = "execute" + CATEGORY = "EasyUse/Logic" + + def execute(self, any, delay): + time.sleep(delay) + return (any,) + +NODE_CLASS_MAPPINGS = { + "easy string": String, + "easy int": Int, + "easy rangeInt": RangeInt, + "easy float": Float, + "easy rangeFloat": RangeFloat, + "easy boolean": Boolean, + "easy mathString": mathStringOperation, + "easy mathInt": mathIntOperation, + "easy mathFloat": mathFloatOperation, + "easy simpleMath": simpleMath, + "easy simpleMathDual": simpleMathDual, + "easy compare": Compare, + "easy imageSwitch": imageSwitch, + "easy textSwitch": textSwitch, + "easy imageIndexSwitch": imageIndexSwitch, + "easy textIndexSwitch": textIndexSwitch, + "easy conditioningIndexSwitch": conditioningIndexSwitch, + "easy anythingIndexSwitch": anythingIndexSwitch, + "easy ab": ab, + "easy anythingInversedSwitch": anythingInversedSwitch, + "easy whileLoopStart": whileLoopStart, + "easy whileLoopEnd": whileLoopEnd, + "easy forLoopStart": forLoopStart, + "easy forLoopEnd": forLoopEnd, + "easy blocker": Blocker, + "easy ifElse": IfElse, + "easy isMaskEmpty": isMaskEmpty, + "easy isNone": isNone, + "easy isSDXL": isSDXL, + "easy isFileExist": isFileExist, + "easy stringToIntList": stringToIntList, + "easy stringToFloatList": stringToFloatList, + "easy stringJoinLines": stringJoinLines, + "easy outputToList": outputToList, + "easy pixels": pixels, + "easy xyAny": xyAny, + "easy lengthAnything": lengthAnything, + "easy indexAnything": indexAnything, + "easy batchAnything": batchAnything, + "easy convertAnything": convertAnything, + "easy showAnything": showAnything, + "easy showTensorShape": showTensorShape, + "easy clearCacheKey": clearCacheKey, + "easy clearCacheAll": clearCacheAll, + "easy cleanGpuUsed": cleanGPUUsed, + "easy saveText": saveText, + "easy sleep": sleep +} +NODE_DISPLAY_NAME_MAPPINGS = { + "easy string": "String", + "easy int": "Int", + "easy rangeInt": "Range(Int)", + "easy float": "Float", + "easy rangeFloat": "Range(Float)", + "easy boolean": "Boolean", + "easy compare": "Compare", + "easy mathString": "Math String", + "easy mathInt": "Math Int", + "easy mathFloat": "Math Float", + "easy simpleMath": "Simple Math", + "easy simpleMathDual": "Simple Math Dual", + "easy imageSwitch": "Image Switch", + "easy textSwitch": "Text Switch", + "easy imageIndexSwitch": "Image Index Switch", + "easy textIndexSwitch": "Text Index Switch", + "easy conditioningIndexSwitch": "Conditioning Index Switch", + "easy anythingIndexSwitch": "Any Index Switch", + "easy ab": "A or B", + "easy anythingInversedSwitch": "Any Inversed Switch", + "easy whileLoopStart": "While Loop Start", + "easy whileLoopEnd": "While Loop End", + "easy forLoopStart": "For Loop Start", + "easy forLoopEnd": "For Loop End", + "easy ifElse": "If else", + "easy blocker": "Blocker", + "easy isMaskEmpty": "Is Mask Empty", + "easy isNone": "Is None", + "easy isSDXL": "Is SDXL", + "easy isFileExist": "Is File Exist", + "easy stringToIntList": "String to Int List", + "easy stringToFloatList":"String to Float List", + "easy stringJoinLines": "String Join Lines", + "easy outputToList": "Output to List", + "easy pixels": "Pixels W/H Norm", + "easy xyAny": "XY Any", + "easy lengthAnything": "Length Any", + "easy indexAnything": "Index Any", + "easy batchAnything": "Batch Any", + "easy convertAnything": "Convert Any", + "easy showAnything": "Show Any", + "easy showTensorShape": "Show Tensor Shape", + "easy clearCacheKey": "Clear Cache Key", + "easy clearCacheAll": "Clear Cache All", + "easy cleanGpuUsed": "Clean VRAM Used", + "easy saveText": "Save Text", + "easy sleep": "Sleep", +} + diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/pipe.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/pipe.py new file mode 100644 index 00000000..1d938dbe --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/pipe.py @@ -0,0 +1,778 @@ +import os +import folder_paths +import comfy.samplers, comfy.supported_models + +from nodes import LatentFromBatch, RepeatLatentBatch +from ..config import MAX_SEED_NUM + +from ..libs.log import log_node_warn +from ..libs.utils import get_sd_version +from ..libs.conditioning import prompt_to_cond, set_cond + +from .. import easyCache + +# θŠ‚η‚ΉζŸθΎ“ε…₯ +class pipeIn: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + "optional": { + "pipe": ("PIPE_LINE",), + "model": ("MODEL",), + "pos": ("CONDITIONING",), + "neg": ("CONDITIONING",), + "latent": ("LATENT",), + "vae": ("VAE",), + "clip": ("CLIP",), + "image": ("IMAGE",), + "xyPlot": ("XYPLOT",), + }, + "hidden": {"my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + FUNCTION = "flush" + + CATEGORY = "EasyUse/Pipe" + + def flush(self, pipe=None, model=None, pos=None, neg=None, latent=None, vae=None, clip=None, image=None, xyplot=None, my_unique_id=None): + + model = model if model is not None else pipe.get("model") + if model is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Model missing from pipeLine") + pos = pos if pos is not None else pipe.get("positive") + if pos is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Pos Conditioning missing from pipeLine") + neg = neg if neg is not None else pipe.get("negative") + if neg is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Neg Conditioning missing from pipeLine") + vae = vae if vae is not None else pipe.get("vae") + if vae is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "VAE missing from pipeLine") + clip = clip if clip is not None else pipe.get("clip") if pipe is not None and "clip" in pipe else None + # if clip is None: + # log_node_warn(f'pipeIn[{my_unique_id}]', "Clip missing from pipeLine") + if latent is not None: + samples = latent + elif image is None: + samples = pipe.get("samples") if pipe is not None else None + image = pipe.get("images") if pipe is not None else None + elif image is not None: + if pipe is None: + batch_size = 1 + else: + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + samples = {"samples": vae.encode(image[:, :, :, :3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + + if pipe is None: + pipe = {"loader_settings": {"positive": "", "negative": "", "xyplot": None}} + + xyplot = xyplot if xyplot is not None else pipe['loader_settings']['xyplot'] if xyplot in pipe['loader_settings'] else None + + new_pipe = { + **pipe, + "model": model, + "positive": pos, + "negative": neg, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": image, + "seed": pipe.get('seed') if pipe is not None and "seed" in pipe else None, + + "loader_settings": { + **pipe["loader_settings"], + "xyplot": xyplot + } + } + del pipe + + return (new_pipe,) + +# θŠ‚η‚ΉζŸθΎ“ε‡Ί +class pipeOut: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + }, + "hidden": {"my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE", "INT",) + RETURN_NAMES = ("pipe", "model", "pos", "neg", "latent", "vae", "clip", "image", "seed",) + FUNCTION = "flush" + + CATEGORY = "EasyUse/Pipe" + + def flush(self, pipe, my_unique_id=None): + model = pipe.get("model") + pos = pipe.get("positive") + neg = pipe.get("negative") + latent = pipe.get("samples") + vae = pipe.get("vae") + clip = pipe.get("clip") + image = pipe.get("images") + seed = pipe.get("seed") + + return pipe, model, pos, neg, latent, vae, clip, image, seed + +# ηΌ–θΎ‘θŠ‚η‚ΉζŸ +class pipeEdit: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip_skip": ("INT", {"default": -1, "min": -24, "max": 0, "step": 1}), + + "optional_positive": ("STRING", {"default": "", "multiline": True}), + "positive_token_normalization": (["none", "mean", "length", "length+mean"],), + "positive_weight_interpretation": (["comfy", "A1111", "comfy++", "compel", "fixed attention"],), + + "optional_negative": ("STRING", {"default": "", "multiline": True}), + "negative_token_normalization": (["none", "mean", "length", "length+mean"],), + "negative_weight_interpretation": (["comfy", "A1111", "comfy++", "compel", "fixed attention"],), + + "a1111_prompt_style": ("BOOLEAN", {"default": False}), + "conditioning_mode": (['replace', 'concat', 'combine', 'average', 'timestep'], {"default": "replace"}), + "average_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "old_cond_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "old_cond_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "new_cond_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "new_cond_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }, + "optional": { + "pipe": ("PIPE_LINE",), + "model": ("MODEL",), + "pos": ("CONDITIONING",), + "neg": ("CONDITIONING",), + "latent": ("LATENT",), + "vae": ("VAE",), + "clip": ("CLIP",), + "image": ("IMAGE",), + }, + "hidden": {"my_unique_id": "UNIQUE_ID", "prompt":"PROMPT"}, + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE") + RETURN_NAMES = ("pipe", "model", "pos", "neg", "latent", "vae", "clip", "image") + FUNCTION = "edit" + + CATEGORY = "EasyUse/Pipe" + + def edit(self, clip_skip, optional_positive, positive_token_normalization, positive_weight_interpretation, optional_negative, negative_token_normalization, negative_weight_interpretation, a1111_prompt_style, conditioning_mode, average_strength, old_cond_start, old_cond_end, new_cond_start, new_cond_end, pipe=None, model=None, pos=None, neg=None, latent=None, vae=None, clip=None, image=None, my_unique_id=None, prompt=None): + + model = model if model is not None else pipe.get("model") + if model is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Model missing from pipeLine") + vae = vae if vae is not None else pipe.get("vae") + if vae is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "VAE missing from pipeLine") + clip = clip if clip is not None else pipe.get("clip") + if clip is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Clip missing from pipeLine") + if image is None: + image = pipe.get("images") if pipe is not None else None + samples = latent if latent is not None else pipe.get("samples") + if samples is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Latent missing from pipeLine") + else: + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + samples = {"samples": vae.encode(image[:, :, :, :3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + + pipe_lora_stack = pipe.get("lora_stack") if pipe is not None and "lora_stack" in pipe else [] + + steps = pipe["loader_settings"]["steps"] if "steps" in pipe["loader_settings"] else 1 + if pos is None and optional_positive != '': + pos, positive_wildcard_prompt, model, clip = prompt_to_cond('positive', model, clip, clip_skip, + pipe_lora_stack, optional_positive, positive_token_normalization,positive_weight_interpretation, + a1111_prompt_style, my_unique_id, prompt, easyCache, True, steps) + pos = set_cond(pipe['positive'], pos, conditioning_mode, average_strength, old_cond_start, old_cond_end, new_cond_start, new_cond_end) + pipe['loader_settings']['positive'] = positive_wildcard_prompt + pipe['loader_settings']['positive_token_normalization'] = positive_token_normalization + pipe['loader_settings']['positive_weight_interpretation'] = positive_weight_interpretation + if a1111_prompt_style: + pipe['loader_settings']['a1111_prompt_style'] = True + else: + pos = pipe.get("positive") + if pos is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Pos Conditioning missing from pipeLine") + + if neg is None and optional_negative != '': + neg, negative_wildcard_prompt, model, clip = prompt_to_cond("negative", model, clip, clip_skip, pipe_lora_stack, optional_negative, + negative_token_normalization, negative_weight_interpretation, + a1111_prompt_style, my_unique_id, prompt, easyCache, True, steps) + neg = set_cond(pipe['negative'], neg, conditioning_mode, average_strength, old_cond_start, old_cond_end, new_cond_start, new_cond_end) + pipe['loader_settings']['negative'] = negative_wildcard_prompt + pipe['loader_settings']['negative_token_normalization'] = negative_token_normalization + pipe['loader_settings']['negative_weight_interpretation'] = negative_weight_interpretation + if a1111_prompt_style: + pipe['loader_settings']['a1111_prompt_style'] = True + else: + neg = pipe.get("negative") + if neg is None: + log_node_warn(f'pipeIn[{my_unique_id}]', "Neg Conditioning missing from pipeLine") + if pipe is None: + pipe = {"loader_settings": {"positive": "", "negative": "", "xyplot": None}} + + new_pipe = { + **pipe, + "model": model, + "positive": pos, + "negative": neg, + "vae": vae, + "clip": clip, + + "samples": samples, + "images": image, + "seed": pipe.get('seed') if pipe is not None and "seed" in pipe else None, + "loader_settings":{ + **pipe["loader_settings"] + } + } + del pipe + + return (new_pipe, model,pos, neg, latent, vae, clip, image) + +# ηΌ–θΎ‘θŠ‚η‚ΉζŸζη€Ίθ― +class pipeEditPrompt: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + "positive": ("STRING", {"default": "", "multiline": True}), + "negative": ("STRING", {"default": "", "multiline": True}), + }, + "hidden": {"my_unique_id": "UNIQUE_ID", "prompt": "PROMPT"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + FUNCTION = "edit" + + CATEGORY = "EasyUse/Pipe" + + def edit(self, pipe, positive, negative, my_unique_id=None, prompt=None): + model = pipe.get("model") + if model is None: + log_node_warn(f'pipeEdit[{my_unique_id}]', "Model missing from pipeLine") + + from ..modules.kolors.loader import is_kolors_model + model_type = get_sd_version(model) + if model_type == 'sdxl' and is_kolors_model(model): + from ..modules.kolors.text_encode import chatglm3_adv_text_encode + auto_clean_gpu = pipe["loader_settings"]["auto_clean_gpu"] if "auto_clean_gpu" in pipe["loader_settings"] else False + chatglm3_model = pipe["chatglm3_model"] if "chatglm3_model" in pipe else None + # text encode + log_node_warn("Positive encoding...") + positive_embeddings_final = chatglm3_adv_text_encode(chatglm3_model, positive, auto_clean_gpu) + log_node_warn("Negative encoding...") + negative_embeddings_final = chatglm3_adv_text_encode(chatglm3_model, negative, auto_clean_gpu) + else: + clip_skip = pipe["loader_settings"]["clip_skip"] if "clip_skip" in pipe["loader_settings"] else -1 + lora_stack = pipe.get("lora_stack") if pipe is not None and "lora_stack" in pipe else [] + clip = pipe.get("clip") if pipe is not None and "clip" in pipe else None + positive_token_normalization = pipe["loader_settings"]["positive_token_normalization"] if "positive_token_normalization" in pipe["loader_settings"] else "none" + positive_weight_interpretation = pipe["loader_settings"]["positive_weight_interpretation"] if "positive_weight_interpretation" in pipe["loader_settings"] else "comfy" + negative_token_normalization = pipe["loader_settings"]["negative_token_normalization"] if "negative_token_normalization" in pipe["loader_settings"] else "none" + negative_weight_interpretation = pipe["loader_settings"]["negative_weight_interpretation"] if "negative_weight_interpretation" in pipe["loader_settings"] else "comfy" + a1111_prompt_style = pipe["loader_settings"]["a1111_prompt_style"] if "a1111_prompt_style" in pipe["loader_settings"] else False + # Prompt to Conditioning + positive_embeddings_final, positive_wildcard_prompt, model, clip = prompt_to_cond('positive', model, clip, + clip_skip, lora_stack, + positive, + positive_token_normalization, + positive_weight_interpretation, + a1111_prompt_style, + my_unique_id, prompt, + easyCache, + model_type=model_type) + negative_embeddings_final, negative_wildcard_prompt, model, clip = prompt_to_cond('negative', model, clip, + clip_skip, lora_stack, + negative, + negative_token_normalization, + negative_weight_interpretation, + a1111_prompt_style, + my_unique_id, prompt, + easyCache, + model_type=model_type) + new_pipe = { + **pipe, + "model": model, + "positive": positive_embeddings_final, + "negative": negative_embeddings_final, + } + del pipe + + return (new_pipe,) + + +# θŠ‚η‚ΉζŸεˆ°εŸΊη‘€θŠ‚η‚ΉζŸοΌˆpipe to ComfyUI-Impack-pack's basic_pipeοΌ‰ +class pipeToBasicPipe: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + }, + "hidden": {"my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("basic_pipe",) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Pipe" + + def doit(self, pipe, my_unique_id=None): + new_pipe = (pipe.get('model'), pipe.get('clip'), pipe.get('vae'), pipe.get('positive'), pipe.get('negative')) + del pipe + return (new_pipe,) + +# 批欑紒引 +class pipeBatchIndex: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pipe": ("PIPE_LINE",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }, + "hidden": {"my_unique_id": "UNIQUE_ID"},} + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Pipe" + + def doit(self, pipe, batch_index, length, my_unique_id=None): + samples = pipe["samples"] + new_samples, = LatentFromBatch().frombatch(samples, batch_index, length) + new_pipe = { + **pipe, + "samples": new_samples + } + del pipe + return (new_pipe,) + +# pipeXYPlot +class pipeXYPlot: + lora_list = ["None"] + folder_paths.get_filename_list("loras") + lora_strengths = {"min": -4.0, "max": 4.0, "step": 0.01} + token_normalization = ["none", "mean", "length", "length+mean"] + weight_interpretation = ["comfy", "A1111", "compel", "comfy++"] + + loader_dict = { + "ckpt_name": folder_paths.get_filename_list("checkpoints"), + "vae_name": ["Baked-VAE"] + folder_paths.get_filename_list("vae"), + "clip_skip": {"min": -24, "max": -1, "step": 1}, + "lora_name": lora_list, + "lora_model_strength": lora_strengths, + "lora_clip_strength": lora_strengths, + "positive": [], + "negative": [], + } + + sampler_dict = { + "steps": {"min": 1, "max": 100, "step": 1}, + "cfg": {"min": 0.0, "max": 100.0, "step": 1.0}, + "sampler_name": comfy.samplers.KSampler.SAMPLERS, + "scheduler": comfy.samplers.KSampler.SCHEDULERS, + "denoise": {"min": 0.0, "max": 1.0, "step": 0.01}, + "seed": {"min": 0, "max": MAX_SEED_NUM}, + } + + plot_dict = {**sampler_dict, **loader_dict} + + plot_values = ["None", ] + plot_values.append("---------------------") + for k in sampler_dict: + plot_values.append(f'preSampling: {k}') + plot_values.append("---------------------") + for k in loader_dict: + plot_values.append(f'loader: {k}') + + def __init__(self): + pass + + rejected = ["None", "---------------------", "Nothing"] + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "grid_spacing": ("INT", {"min": 0, "max": 500, "step": 5, "default": 0, }), + "output_individuals": (["False", "True"], {"default": "False"}), + "flip_xy": (["False", "True"], {"default": "False"}), + "x_axis": (pipeXYPlot.plot_values, {"default": 'None'}), + "x_values": ( + "STRING", {"default": '', "multiline": True, "placeholder": 'insert values seperated by "; "'}), + "y_axis": (pipeXYPlot.plot_values, {"default": 'None'}), + "y_values": ( + "STRING", {"default": '', "multiline": True, "placeholder": 'insert values seperated by "; "'}), + }, + "optional": { + "pipe": ("PIPE_LINE",) + }, + "hidden": { + "plot_dict": (pipeXYPlot.plot_dict,), + }, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + FUNCTION = "plot" + + CATEGORY = "EasyUse/Pipe" + + def plot(self, grid_spacing, output_individuals, flip_xy, x_axis, x_values, y_axis, y_values, pipe=None, font_path=None): + def clean_values(values): + original_values = values.split("; ") + cleaned_values = [] + + for value in original_values: + # Strip the semi-colon + cleaned_value = value.strip(';').strip() + + if cleaned_value == "": + continue + + # Try to convert the cleaned_value back to int or float if possible + try: + cleaned_value = int(cleaned_value) + except ValueError: + try: + cleaned_value = float(cleaned_value) + except ValueError: + pass + + # Append the cleaned_value to the list + cleaned_values.append(cleaned_value) + + return cleaned_values + + if x_axis in self.rejected: + x_axis = "None" + x_values = [] + else: + x_values = clean_values(x_values) + + if y_axis in self.rejected: + y_axis = "None" + y_values = [] + else: + y_values = clean_values(y_values) + + if flip_xy == "True": + x_axis, y_axis = y_axis, x_axis + x_values, y_values = y_values, x_values + + + xy_plot = {"x_axis": x_axis, + "x_vals": x_values, + "y_axis": y_axis, + "y_vals": y_values, + "custom_font": font_path, + "grid_spacing": grid_spacing, + "output_individuals": output_individuals} + + if pipe is not None: + new_pipe = pipe.copy() + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "xyplot": xy_plot + } + del pipe + return (new_pipe, xy_plot,) + +# pipeXYPlotAdvanced +import platform +class pipeXYPlotAdvanced: + if platform.system() == "Windows": + system_root = os.environ.get("SystemRoot") + user_root = os.environ.get("USERPROFILE") + font_dir = os.path.join(system_root, "Fonts") if system_root else None + user_font_dir = os.path.join(user_root, "AppData","Local","Microsoft","Windows", "Fonts") if user_root else None + + # Default debian-based Linux & MacOS font dirs + elif platform.system() == "Linux": + font_dir = "/usr/share/fonts/truetype" + user_font_dir = None + elif platform.system() == "Darwin": + font_dir = "/System/Library/Fonts" + user_font_dir = None + else: + font_dir = None + user_font_dir = None + + @classmethod + def INPUT_TYPES(s): + files_list = [] + if s.font_dir and os.path.exists(s.font_dir): + font_dir = s.font_dir + files_list = files_list + [f for f in os.listdir(font_dir) if os.path.isfile(os.path.join(font_dir, f)) and f.lower().endswith(".ttf")] + + if s.user_font_dir and os.path.exists(s.user_font_dir): + files_list = files_list + [f for f in os.listdir(s.user_font_dir) if os.path.isfile(os.path.join(s.user_font_dir, f)) and f.lower().endswith(".ttf")] + + return { + "required": { + "pipe": ("PIPE_LINE",), + "grid_spacing": ("INT", {"min": 0, "max": 500, "step": 5, "default": 0, }), + "output_individuals": (["False", "True"], {"default": "False"}), + "flip_xy": (["False", "True"], {"default": "False"}), + }, + "optional": { + "X": ("X_Y",), + "Y": ("X_Y",), + "font": (["None"] + files_list,) + }, + "hidden": {"my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + FUNCTION = "plot" + + CATEGORY = "EasyUse/Pipe" + + def plot(self, pipe, grid_spacing, output_individuals, flip_xy, X=None, Y=None, font=None, my_unique_id=None): + font_path = os.path.join(self.font_dir, font) if font != "None" else None + if font_path and not os.path.exists(font_path): + font_path = os.path.join(self.user_font_dir, font) + + if X != None: + x_axis = X.get('axis') + x_values = X.get('values') + else: + x_axis = "Nothing" + x_values = [""] + if Y != None: + y_axis = Y.get('axis') + y_values = Y.get('values') + else: + y_axis = "Nothing" + y_values = [""] + + if pipe is not None: + new_pipe = pipe.copy() + positive = pipe["loader_settings"]["positive"] if "positive" in pipe["loader_settings"] else "" + negative = pipe["loader_settings"]["negative"] if "negative" in pipe["loader_settings"] else "" + + if x_axis == 'advanced: ModelMergeBlocks': + models = X.get('models') + vae_use = X.get('vae_use') + if models is None: + raise Exception("models is not found") + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "models": models, + "vae_use": vae_use + } + if y_axis == 'advanced: ModelMergeBlocks': + models = Y.get('models') + vae_use = Y.get('vae_use') + if models is None: + raise Exception("models is not found") + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "models": models, + "vae_use": vae_use + } + + if x_axis in ['advanced: Lora', 'advanced: Checkpoint']: + lora_stack = X.get('lora_stack') + _lora_stack = [] + if lora_stack is not None: + for lora in lora_stack: + _lora_stack.append( + {"lora_name": lora[0], "model": pipe['model'], "clip": pipe['clip'], "model_strength": lora[1], + "clip_strength": lora[2]}) + del lora_stack + x_values = "; ".join(x_values) + lora_stack = pipe['lora_stack'] + _lora_stack if 'lora_stack' in pipe else _lora_stack + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "lora_stack": lora_stack, + } + + if y_axis in ['advanced: Lora', 'advanced: Checkpoint']: + lora_stack = Y.get('lora_stack') + _lora_stack = [] + if lora_stack is not None: + for lora in lora_stack: + _lora_stack.append( + {"lora_name": lora[0], "model": pipe['model'], "clip": pipe['clip'], "model_strength": lora[1], + "clip_strength": lora[2]}) + del lora_stack + y_values = "; ".join(y_values) + lora_stack = pipe['lora_stack'] + _lora_stack if 'lora_stack' in pipe else _lora_stack + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "lora_stack": lora_stack, + } + + if x_axis == 'advanced: Seeds++ Batch': + if new_pipe['seed']: + value = x_values + x_values = [] + for index in range(value): + x_values.append(str(new_pipe['seed'] + index)) + x_values = "; ".join(x_values) + if y_axis == 'advanced: Seeds++ Batch': + if new_pipe['seed']: + value = y_values + y_values = [] + for index in range(value): + y_values.append(str(new_pipe['seed'] + index)) + y_values = "; ".join(y_values) + + if x_axis == 'advanced: Positive Prompt S/R': + if positive: + x_value = x_values + x_values = [] + for index, value in enumerate(x_value): + search_txt, replace_txt, replace_all = value + if replace_all: + txt = replace_txt if replace_txt is not None else positive + x_values.append(txt) + else: + txt = positive.replace(search_txt, replace_txt, 1) if replace_txt is not None else positive + x_values.append(txt) + x_values = "; ".join(x_values) + if y_axis == 'advanced: Positive Prompt S/R': + if positive: + y_value = y_values + y_values = [] + for index, value in enumerate(y_value): + search_txt, replace_txt, replace_all = value + if replace_all: + txt = replace_txt if replace_txt is not None else positive + y_values.append(txt) + else: + txt = positive.replace(search_txt, replace_txt, 1) if replace_txt is not None else positive + y_values.append(txt) + y_values = "; ".join(y_values) + + if x_axis == 'advanced: Negative Prompt S/R': + if negative: + x_value = x_values + x_values = [] + for index, value in enumerate(x_value): + search_txt, replace_txt, replace_all = value + if replace_all: + txt = replace_txt if replace_txt is not None else negative + x_values.append(txt) + else: + txt = negative.replace(search_txt, replace_txt, 1) if replace_txt is not None else negative + x_values.append(txt) + x_values = "; ".join(x_values) + if y_axis == 'advanced: Negative Prompt S/R': + if negative: + y_value = y_values + y_values = [] + for index, value in enumerate(y_value): + search_txt, replace_txt, replace_all = value + if replace_all: + txt = replace_txt if replace_txt is not None else negative + y_values.append(txt) + else: + txt = negative.replace(search_txt, replace_txt, 1) if replace_txt is not None else negative + y_values.append(txt) + y_values = "; ".join(y_values) + + if "advanced: ControlNet" in x_axis: + x_value = x_values + x_values = [] + cnet = [] + for index, value in enumerate(x_value): + cnet.append(value) + x_values.append(str(index)) + x_values = "; ".join(x_values) + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "cnet_stack": cnet, + } + + if "advanced: ControlNet" in y_axis: + y_value = y_values + y_values = [] + cnet = [] + for index, value in enumerate(y_value): + cnet.append(value) + y_values.append(str(index)) + y_values = "; ".join(y_values) + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "cnet_stack": cnet, + } + + if "advanced: Pos Condition" in x_axis: + x_values = "; ".join(x_values) + cond = X.get('cond') + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "positive_cond_stack": cond, + } + if "advanced: Pos Condition" in y_axis: + y_values = "; ".join(y_values) + cond = Y.get('cond') + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "positive_cond_stack": cond, + } + + if "advanced: Neg Condition" in x_axis: + x_values = "; ".join(x_values) + cond = X.get('cond') + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "negative_cond_stack": cond, + } + if "advanced: Neg Condition" in y_axis: + y_values = "; ".join(y_values) + cond = Y.get('cond') + new_pipe['loader_settings'] = { + **pipe['loader_settings'], + "negative_cond_stack": cond, + } + + del pipe + + return pipeXYPlot().plot(grid_spacing, output_individuals, flip_xy, x_axis, x_values, y_axis, y_values, new_pipe, font_path) + + +NODE_CLASS_MAPPINGS = { + "easy pipeIn": pipeIn, + "easy pipeOut": pipeOut, + "easy pipeEdit": pipeEdit, + "easy pipeEditPrompt": pipeEditPrompt, + "easy pipeToBasicPipe": pipeToBasicPipe, + "easy pipeBatchIndex": pipeBatchIndex, + "easy XYPlot": pipeXYPlot, + "easy XYPlotAdvanced": pipeXYPlotAdvanced +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy pipeIn": "Pipe In", + "easy pipeOut": "Pipe Out", + "easy pipeEdit": "Pipe Edit", + "easy pipeEditPrompt": "Pipe Edit Prompt", + "easy pipeBatchIndex": "Pipe Batch Index", + "easy pipeToBasicPipe": "Pipe -> BasicPipe", + "easy XYPlot": "XY Plot", + "easy XYPlotAdvanced": "XY Plot Advanced" +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/preSampling.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/preSampling.py new file mode 100644 index 00000000..80341078 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/preSampling.py @@ -0,0 +1,1002 @@ +import torch +import numpy as np +import comfy.utils, comfy.sample, comfy.samplers, comfy.controlnet, comfy.model_base, comfy.model_management, comfy.sampler_helpers, comfy.supported_models +from comfy.model_patcher import ModelPatcher + +from nodes import RepeatLatentBatch, CLIPTextEncode, VAEEncodeForInpaint +from ..modules.layer_diffuse import LayerMethod +from ..config import * + +from .. import easyCache, sampler + + +# 钄采样θΎη½οΌˆεŸΊη‘€οΌ‰ +class samplerSettings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS + NEW_SCHEDULERS,), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "optional": { + "image_to_latent": ("IMAGE",), + "latent": ("LATENT",), + }, + "hidden": + {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE", ) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def settings(self, pipe, steps, cfg, sampler_name, scheduler, denoise, seed, image_to_latent=None, latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + # ε›Ύη”Ÿε›Ύθ½¬ζ’ + vae = pipe["vae"] + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + if image_to_latent is not None: + _, height, width, _ = image_to_latent.shape + if height == 1 and width == 1: + samples = pipe["samples"] + images = pipe["images"] + else: + samples = {"samples": vae.encode(image_to_latent[:, :, :, :3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + images = image_to_latent + elif latent is not None: + samples = latent + images = pipe["images"] + else: + samples = pipe["samples"] + images = pipe["images"] + + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": samples, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "add_noise": "enabled" + } + } + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + +# 钄采样θΎη½οΌˆι«˜ηΊ§οΌ‰ +class samplerSettingsAdvanced: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS + NEW_SCHEDULERS,), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "add_noise": (["enable (CPU)", "enable (GPU=A1111)", "disable"], {"default": "enable (CPU)"}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + "return_with_leftover_noise": (["disable", "enable"], ), + }, + "optional": { + "image_to_latent": ("IMAGE",), + "latent": ("LATENT",) + }, + "hidden": + {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE", ) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def settings(self, pipe, steps, cfg, sampler_name, scheduler, start_at_step, end_at_step, add_noise, seed, return_with_leftover_noise, image_to_latent=None, latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + # ε›Ύη”Ÿε›Ύθ½¬ζ’ + vae = pipe["vae"] + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + if image_to_latent is not None: + _, height, width, _ = image_to_latent.shape + if height == 1 and width == 1: + samples = pipe["samples"] + images = pipe["images"] + else: + samples = {"samples": vae.encode(image_to_latent[:, :, :, :3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + images = image_to_latent + elif latent is not None: + samples = latent + images = pipe["images"] + else: + samples = pipe["samples"] + images = pipe["images"] + + force_full_denoise = True + if return_with_leftover_noise == "enable": + force_full_denoise = False + + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": samples, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "start_step": start_at_step, + "last_step": end_at_step, + "denoise": 1.0, + "add_noise": add_noise, + "force_full_denoise": force_full_denoise + } + } + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + +# 钄采样θΎη½οΌˆε™ͺ声注ε…₯οΌ‰ +class samplerSettingsNoiseIn: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "factor": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step":0.01, "round": 0.01}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS+NEW_SCHEDULERS,), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "optional": { + "optional_noise_seed": ("INT",{"forceInput": True}), + "optional_latent": ("LATENT",), + }, + "hidden": + {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE", ) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def slerp(self, val, low, high): + dims = low.shape + + low = low.reshape(dims[0], -1) + high = high.reshape(dims[0], -1) + + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + + low_norm[low_norm != low_norm] = 0.0 + high_norm[high_norm != high_norm] = 0.0 + + omega = torch.acos((low_norm * high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze( + 1) * high + + return res.reshape(dims) + + def prepare_mask(self, mask, shape): + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), + size=(shape[2], shape[3]), mode="bilinear") + mask = mask.expand((-1, shape[1], -1, -1)) + if mask.shape[0] < shape[0]: + mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] + return mask + + def expand_mask(self, mask, expand, tapered_corners): + try: + import scipy + + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = [] + for m in mask: + output = m.numpy() + for _ in range(abs(expand)): + if expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) + output = torch.from_numpy(output) + out.append(output) + + return torch.stack(out, dim=0) + except: + return None + + def settings(self, pipe, factor, steps, cfg, sampler_name, scheduler, denoise, seed, optional_noise_seed=None, optional_latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + latent = optional_latent if optional_latent is not None else pipe["samples"] + model = pipe["model"] + + # generate base noise + batch_size, _, height, width = latent["samples"].shape + generator = torch.manual_seed(seed) + base_noise = torch.randn((1, 4, height, width), dtype=torch.float32, device="cpu", generator=generator).repeat(batch_size, 1, 1, 1).cpu() + + # generate variation noise + if optional_noise_seed is None or optional_noise_seed == seed: + optional_noise_seed = seed+1 + generator = torch.manual_seed(optional_noise_seed) + variation_noise = torch.randn((batch_size, 4, height, width), dtype=torch.float32, device="cpu", + generator=generator).cpu() + + slerp_noise = self.slerp(factor, base_noise, variation_noise) + + end_at_step = steps # min(steps, end_at_step) + start_at_step = round(end_at_step - end_at_step * denoise) + + device = comfy.model_management.get_torch_device() + comfy.model_management.load_model_gpu(model) + model_patcher = comfy.model_patcher.ModelPatcher(model.model, load_device=device, offload_device=comfy.model_management.unet_offload_device()) + sampler = comfy.samplers.KSampler(model_patcher, steps=steps, device=device, sampler=sampler_name, + scheduler=scheduler, denoise=1.0, model_options=model.model_options) + sigmas = sampler.sigmas + sigma = sigmas[start_at_step] - sigmas[end_at_step] + sigma /= model.model.latent_format.scale_factor + sigma = sigma.cpu().numpy() + + work_latent = latent.copy() + work_latent["samples"] = latent["samples"].clone() + slerp_noise * sigma + + if "noise_mask" in latent: + noise_mask = self.prepare_mask(latent["noise_mask"], latent['samples'].shape) + work_latent["samples"] = noise_mask * work_latent["samples"] + (1-noise_mask) * latent["samples"] + work_latent['noise_mask'] = self.expand_mask(latent["noise_mask"].clone(), 5, True) + + if pipe is None: + pipe = {} + + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": work_latent, + "images": pipe['images'], + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "add_noise": "disable" + } + } + + return (new_pipe,) + +# 钄采样θΎη½οΌˆθ‡ͺεšδΉ‰οΌ‰ +class samplerCustomSettings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "pipe": ("PIPE_LINE",), + "guider": (['CFG','DualCFG','Basic', 'IP2P+CFG', 'IP2P+DualCFG','IP2P+Basic'],{"default":"Basic"}), + "cfg": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0}), + "cfg_negative": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS + ['inversed_euler'],), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS + ['karrasADV','exponentialADV','polyExponential', 'sdturbo', 'vp', 'alignYourSteps', 'gits'],), + "coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step": 0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step": 0.01, "round": False}), + "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False}), + "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step": 0.01, "round": False}), + "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step": 0.01, "round": False}), + "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step": 0.0001, "round": False}), + "flip_sigmas": ("BOOLEAN", {"default": False}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "add_noise": (["enable (CPU)", "enable (GPU=A1111)", "disable"], {"default": "enable (CPU)"}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "optional": { + "image_to_latent": ("IMAGE",), + "latent": ("LATENT",), + "optional_sampler":("SAMPLER",), + "optional_sigmas":("SIGMAS",), + }, + "hidden": + {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE", ) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def ip2p(self, positive, negative, vae, pixels, latent=None): + if latent is not None: + concat_latent = latent + else: + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + + concat_latent = vae.encode(pixels) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1], out_latent) + + + def settings(self, pipe, guider, cfg, cfg_negative, sampler_name, scheduler, coeff, steps, sigma_max, sigma_min, rho, beta_d, beta_min, eps_s, flip_sigmas, denoise, add_noise, seed, image_to_latent=None, latent=None, optional_sampler=None, optional_sigmas=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + + # ε›Ύη”Ÿε›Ύθ½¬ζ’ + vae = pipe["vae"] + model = pipe["model"] + positive = pipe['positive'] + negative = pipe['negative'] + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + + if image_to_latent is not None: + _, height, width, _ = image_to_latent.shape + if height == 1 and width == 1: + samples = pipe["samples"] + images = pipe["images"] + else: + if "IP2P" in guider: + positive, negative, latent = self.ip2p(pipe['positive'], pipe['negative'], vae, image_to_latent) + samples = latent + else: + samples = {"samples": vae.encode(image_to_latent[:, :, :, :3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + images = image_to_latent + elif latent is not None: + if "IP2P" in guider: + positive, negative, latent = self.ip2p(pipe['positive'], pipe['negative'], latent=latent) + samples = latent + else: + samples = latent + images = pipe["images"] + else: + samples = pipe["samples"] + images = pipe["images"] + + + new_pipe = { + "model": model, + "positive": positive, + "negative": negative, + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": samples, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "middle": pipe['negative'], + "steps": steps, + "cfg": cfg, + "cfg_negative": cfg_negative, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "add_noise": add_noise, + "custom": { + "guider": guider, + "coeff": coeff, + "sigma_max": sigma_max, + "sigma_min": sigma_min, + "rho": rho, + "beta_d": beta_d, + "beta_min": beta_min, + "eps_s": beta_min, + "flip_sigmas": flip_sigmas + }, + "optional_sampler": optional_sampler, + "optional_sigmas": optional_sigmas + } + } + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + +# 钄采样θΎη½οΌˆSDTurboοΌ‰ +from ..libs.gradual_latent_hires_fix import sample_dpmpp_2s_ancestral, sample_dpmpp_2m_sde, sample_lcm, sample_euler_ancestral +class sdTurboSettings: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "pipe": ("PIPE_LINE",), + "steps": ("INT", {"default": 1, "min": 1, "max": 10}), + "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.SAMPLER_NAMES,), + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False}), + "upscale_ratio": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 16.0, "step": 0.01, "round": False}), + "start_step": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}), + "end_step": ("INT", {"default": 15, "min": 0, "max": 1000, "step": 1}), + "upscale_n_step": ("INT", {"default": 3, "min": 0, "max": 1000, "step": 1}), + "unsharp_kernel_size": ("INT", {"default": 3, "min": 1, "max": 21, "step": 1}), + "unsharp_sigma": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01, "round": False}), + "unsharp_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def settings(self, pipe, steps, cfg, sampler_name, eta, s_noise, upscale_ratio, start_step, end_step, upscale_n_step, unsharp_kernel_size, unsharp_sigma, unsharp_strength, seed, prompt=None, extra_pnginfo=None, my_unique_id=None): + model = pipe['model'] + # sigma + timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps] + sigmas = model.model.model_sampling.sigma(timesteps) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + + #sampler + sample_function = None + extra_options = { + "eta": eta, + "s_noise": s_noise, + "upscale_ratio": upscale_ratio, + "start_step": start_step, + "end_step": end_step, + "upscale_n_step": upscale_n_step, + "unsharp_kernel_size": unsharp_kernel_size, + "unsharp_sigma": unsharp_sigma, + "unsharp_strength": unsharp_strength, + } + if sampler_name == "euler_ancestral": + sample_function = sample_euler_ancestral + elif sampler_name == "dpmpp_2s_ancestral": + sample_function = sample_dpmpp_2s_ancestral + elif sampler_name == "dpmpp_2m_sde": + sample_function = sample_dpmpp_2m_sde + elif sampler_name == "lcm": + sample_function = sample_lcm + + if sample_function is not None: + unsharp_kernel_size = unsharp_kernel_size if unsharp_kernel_size % 2 == 1 else unsharp_kernel_size + 1 + extra_options["unsharp_kernel_size"] = unsharp_kernel_size + _sampler = comfy.samplers.KSAMPLER(sample_function, extra_options) + else: + _sampler = comfy.samplers.sampler_object(sampler_name) + extra_options = None + + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": pipe["samples"], + "images": pipe["images"], + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "extra_options": extra_options, + "sampler": _sampler, + "sigmas": sigmas, + "steps": steps, + "cfg": cfg, + "add_noise": "enabled" + } + } + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + + +# cascade钄采样参数 +class cascadeSettings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "encode_vae_name": (["None"] + folder_paths.get_filename_list("vae"),), + "decode_vae_name": (["None"] + folder_paths.get_filename_list("vae"),), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default":"euler_ancestral"}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default":"simple"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "optional": { + "image_to_latent_c": ("IMAGE",), + "latent_c": ("LATENT",), + }, + "hidden":{"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def settings(self, pipe, encode_vae_name, decode_vae_name, steps, cfg, sampler_name, scheduler, denoise, seed, model=None, image_to_latent_c=None, latent_c=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + images, samples_c = None, None + samples = pipe['samples'] + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + + encode_vae_name = encode_vae_name if encode_vae_name is not None else pipe['loader_settings']['encode_vae_name'] + decode_vae_name = decode_vae_name if decode_vae_name is not None else pipe['loader_settings']['decode_vae_name'] + + if image_to_latent_c is not None: + if encode_vae_name != 'None': + encode_vae = easyCache.load_vae(encode_vae_name) + else: + encode_vae = pipe['vae'][0] + if "compression" not in pipe["loader_settings"]: + raise Exception("compression is not found") + compression = pipe["loader_settings"]['compression'] + width = image_to_latent_c.shape[-2] + height = image_to_latent_c.shape[-3] + out_width = (width // compression) * encode_vae.downscale_ratio + out_height = (height // compression) * encode_vae.downscale_ratio + + s = comfy.utils.common_upscale(image_to_latent_c.movedim(-1, 1), out_width, out_height, "bicubic", + "center").movedim(1, + -1) + c_latent = encode_vae.encode(s[:, :, :, :3]) + b_latent = torch.zeros([c_latent.shape[0], 4, height // 4, width // 4]) + + samples_c = {"samples": c_latent} + samples_c = RepeatLatentBatch().repeat(samples_c, batch_size)[0] + + samples_b = {"samples": b_latent} + samples_b = RepeatLatentBatch().repeat(samples_b, batch_size)[0] + samples = (samples_c, samples_b) + images = image_to_latent_c + elif latent_c is not None: + samples_c = latent_c + samples = (samples_c, samples[1]) + images = pipe["images"] + if samples_c is not None: + samples = (samples_c, samples[1]) + + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": samples, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "encode_vae_name": encode_vae_name, + "decode_vae_name": decode_vae_name, + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "add_noise": "enabled" + } + } + + sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe) + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + +# layerDiffusion钄采样参数 +class layerDiffusionSettings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + { + "pipe": ("PIPE_LINE",), + "method": ([LayerMethod.FG_ONLY_ATTN.value, LayerMethod.FG_ONLY_CONV.value, LayerMethod.EVERYTHING.value, LayerMethod.FG_TO_BLEND.value, LayerMethod.BG_TO_BLEND.value],), + "weight": ("FLOAT",{"default": 1.0, "min": -1, "max": 3, "step": 0.05},), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler"}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS+ NEW_SCHEDULERS, {"default": "normal"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "optional": { + "image": ("IMAGE",), + "blended_image": ("IMAGE",), + "mask": ("MASK",), + # "latent": ("LATENT",), + # "blended_latent": ("LATENT",), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def get_layer_diffusion_method(self, method, has_blend_latent): + method = LayerMethod(method) + if has_blend_latent: + if method == LayerMethod.BG_TO_BLEND: + method = LayerMethod.BG_BLEND_TO_FG + elif method == LayerMethod.FG_TO_BLEND: + method = LayerMethod.FG_BLEND_TO_BG + return method + + def settings(self, pipe, method, weight, steps, cfg, sampler_name, scheduler, denoise, seed, image=None, blended_image=None, mask=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + blend_samples = pipe['blend_samples'] if "blend_samples" in pipe else None + vae = pipe["vae"] + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + + method = self.get_layer_diffusion_method(method, blend_samples is not None or blended_image is not None) + + if image is not None or "image" in pipe: + image = image if image is not None else pipe['image'] + if mask is not None: + print('inpaint') + samples, = VAEEncodeForInpaint().encode(vae, image, mask) + else: + samples = {"samples": vae.encode(image[:,:,:,:3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + images = image + elif "samp_images" in pipe: + samples = {"samples": vae.encode(pipe["samp_images"][:,:,:,:3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + images = pipe["samp_images"] + else: + if method not in [LayerMethod.FG_ONLY_ATTN, LayerMethod.FG_ONLY_CONV, LayerMethod.EVERYTHING]: + raise Exception("image is missing") + + samples = pipe["samples"] + images = pipe["images"] + + if method in [LayerMethod.BG_BLEND_TO_FG, LayerMethod.FG_BLEND_TO_BG]: + if blended_image is None and blend_samples is None: + raise Exception("blended_image is missing") + elif blended_image is not None: + blend_samples = {"samples": vae.encode(blended_image[:,:,:,:3])} + blend_samples = RepeatLatentBatch().repeat(blend_samples, batch_size)[0] + + new_pipe = { + "model": pipe['model'], + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": samples, + "blend_samples": blend_samples, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "add_noise": "enabled", + "layer_diffusion_method": method, + "layer_diffusion_weight": weight, + } + } + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + +# 钄采样θΎη½οΌˆlayerDiffuseι™„εŠ οΌ‰ +class layerDiffusionSettingsADDTL: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + { + "pipe": ("PIPE_LINE",), + "foreground_prompt": ("STRING", {"default": "", "placeholder": "Foreground Additional Prompt", "multiline": True}), + "background_prompt": ("STRING", {"default": "", "placeholder": "Background Additional Prompt", "multiline": True}), + "blended_prompt": ("STRING", {"default": "", "placeholder": "Blended Additional Prompt", "multiline": True}), + }, + "optional": { + "optional_fg_cond": ("CONDITIONING",), + "optional_bg_cond": ("CONDITIONING",), + "optional_blended_cond": ("CONDITIONING",), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def settings(self, pipe, foreground_prompt, background_prompt, blended_prompt, optional_fg_cond=None, optional_bg_cond=None, optional_blended_cond=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + fg_cond, bg_cond, blended_cond = None, None, None + clip = pipe['clip'] + if optional_fg_cond is not None: + fg_cond = optional_fg_cond + elif foreground_prompt != "": + fg_cond, = CLIPTextEncode().encode(clip, foreground_prompt) + if optional_bg_cond is not None: + bg_cond = optional_bg_cond + elif background_prompt != "": + bg_cond, = CLIPTextEncode().encode(clip, background_prompt) + if optional_blended_cond is not None: + blended_cond = optional_blended_cond + elif blended_prompt != "": + blended_cond, = CLIPTextEncode().encode(clip, blended_prompt) + + new_pipe = { + **pipe, + "loader_settings": { + **pipe["loader_settings"], + "layer_diffusion_cond": (fg_cond, bg_cond, blended_cond) + } + } + + del pipe + + return (new_pipe,) + +# 钄采样θΎη½οΌˆεŠ¨ζ€CFGοΌ‰ +from ..libs.dynthres_core import DynThresh +class dynamicCFGSettings: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "cfg_mode": (DynThresh.Modes,), + "cfg_scale_min": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.5}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS+NEW_SCHEDULERS,), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "optional":{ + "image_to_latent": ("IMAGE",), + "latent": ("LATENT",) + }, + "hidden": + {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("PIPE_LINE",) + RETURN_NAMES = ("pipe",) + + FUNCTION = "settings" + CATEGORY = "EasyUse/PreSampling" + + def settings(self, pipe, steps, cfg, cfg_mode, cfg_scale_min,sampler_name, scheduler, denoise, seed, image_to_latent=None, latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None): + + + dynamic_thresh = DynThresh(7.0, 1.0,"CONSTANT", 0, cfg_mode, cfg_scale_min, 0, 0, 999, False, + "MEAN", "AD", 1) + + def sampler_dyn_thresh(args): + input = args["input"] + cond = input - args["cond"] + uncond = input - args["uncond"] + cond_scale = args["cond_scale"] + time_step = args["timestep"] + dynamic_thresh.step = 999 - time_step[0] + + return input - dynamic_thresh.dynthresh(cond, uncond, cond_scale, None) + + model = pipe['model'] + + m = model.clone() + m.set_model_sampler_cfg_function(sampler_dyn_thresh) + + # ε›Ύη”Ÿε›Ύθ½¬ζ’ + vae = pipe["vae"] + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + if image_to_latent is not None: + samples = {"samples": vae.encode(image_to_latent[:, :, :, :3])} + samples = RepeatLatentBatch().repeat(samples, batch_size)[0] + images = image_to_latent + elif latent is not None: + samples = RepeatLatentBatch().repeat(latent, batch_size)[0] + images = pipe["images"] + else: + samples = pipe["samples"] + images = pipe["images"] + + new_pipe = { + "model": m, + "positive": pipe['positive'], + "negative": pipe['negative'], + "vae": pipe['vae'], + "clip": pipe['clip'], + + "samples": samples, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise + }, + } + + del pipe + + return {"ui": {"value": [seed]}, "result": (new_pipe,)} + +# εŠ¨ζ€CFG +class dynamicThresholdingFull: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "mimic_scale": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.5}), + "threshold_percentile": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "mimic_mode": (DynThresh.Modes,), + "mimic_scale_min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.5}), + "cfg_mode": (DynThresh.Modes,), + "cfg_scale_min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.5}), + "sched_val": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + "separate_feature_channels": (["enable", "disable"],), + "scaling_startpoint": (DynThresh.Startpoints,), + "variability_measure": (DynThresh.Variabilities,), + "interpolate_phi": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "EasyUse/PreSampling" + + def patch(self, model, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, + sched_val, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi): + dynamic_thresh = DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, + cfg_scale_min, sched_val, 0, 999, separate_feature_channels == "enable", + scaling_startpoint, variability_measure, interpolate_phi) + + def sampler_dyn_thresh(args): + input = args["input"] + cond = input - args["cond"] + uncond = input - args["uncond"] + cond_scale = args["cond_scale"] + time_step = args["timestep"] + dynamic_thresh.step = 999 - time_step[0] + + return input - dynamic_thresh.dynthresh(cond, uncond, cond_scale, None) + + m = model.clone() + m.set_model_sampler_cfg_function(sampler_dyn_thresh) + return (m,) + + + +NODE_CLASS_MAPPINGS = { + "easy preSampling": samplerSettings, + "easy preSamplingAdvanced": samplerSettingsAdvanced, + "easy preSamplingNoiseIn": samplerSettingsNoiseIn, + "easy preSamplingCustom": samplerCustomSettings, + "easy preSamplingSdTurbo": sdTurboSettings, + "easy preSamplingDynamicCFG": dynamicCFGSettings, + "easy preSamplingCascade": cascadeSettings, + "easy preSamplingLayerDiffusion": layerDiffusionSettings, + "easy preSamplingLayerDiffusionADDTL": layerDiffusionSettingsADDTL, + "dynamicThresholdingFull": dynamicThresholdingFull, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy preSampling": "PreSampling", + "easy preSamplingAdvanced": "PreSampling (Advanced)", + "easy preSamplingNoiseIn": "PreSampling (NoiseIn)", + "easy preSamplingCustom": "PreSampling (Custom)", + "easy preSamplingSdTurbo": "PreSampling (SDTurbo)", + "easy preSamplingDynamicCFG": "PreSampling (DynamicCFG)", + "easy preSamplingCascade": "PreSampling (Cascade)", + "easy preSamplingLayerDiffusion": "PreSampling (LayerDiffuse)", + "easy preSamplingLayerDiffusionADDTL": "PreSampling (LayerDiffuse ADDTL)", + "dynamicThresholdingFull": "DynamicThresholdingFull", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/prompt.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/prompt.py new file mode 100644 index 00000000..bc941379 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/prompt.py @@ -0,0 +1,790 @@ +import json +import os +from urllib.request import urlopen +import folder_paths + +from .. import easyCache +from ..config import FOOOCUS_STYLES_DIR, MAX_SEED_NUM, PROMPT_TEMPLATE, RESOURCES_DIR +from ..libs.log import log_node_info +from ..libs.wildcards import WildcardProcessor, get_wildcard_list, process + +from comfy_api.latest import io + + +# 正青提瀺词 +class positivePrompt(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy positive", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("positive", default="", multiline=True, placeholder="Positive"), + ], + outputs=[ + io.String.Output(id="output_positive", display_name="positive"), + ], + ) + + @classmethod + def execute(cls, positive): + return io.NodeOutput(positive) + +# ι€šι…η¬¦ζη€Ίθ― +class wildcardsPrompt(io.ComfyNode): + + @classmethod + def define_schema(cls): + wildcard_list = get_wildcard_list() + return io.Schema( + node_id="easy wildcards", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("text", default="", multiline=True, dynamic_prompts=False, placeholder="(Support wildcard)"), + io.Combo.Input("Select to add LoRA", options=["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras")), + io.Combo.Input("Select to add Wildcard", options=["Select the Wildcard to add to the text"] + wildcard_list), + io.Int.Input("seed", default=0, min=0, max=MAX_SEED_NUM), + io.Boolean.Input("multiline_mode", default=False), + ], + outputs=[ + io.String.Output(id="output_text", display_name="text", is_output_list=True), + io.String.Output(id="populated_text", display_name="populated_text", is_output_list=True), + ], + hidden=[ + io.Hidden.prompt, + io.Hidden.extra_pnginfo, + io.Hidden.unique_id, + ], + ) + + @classmethod + def execute(cls, text, seed, multiline_mode, **kwargs): + prompt = cls.hidden.prompt + + # Clean loaded_objects + if prompt: + easyCache.update_loaded_objects(prompt) + + if multiline_mode: + populated_text = [] + _text = [] + text_lines = text.split("\n") + for t in text_lines: + _text.append(t) + populated_text.append(process(t, seed)) + text = _text + else: + populated_text = [process(text, seed)] + text = [text] + return io.NodeOutput(text, populated_text, ui={"value": [seed]}) + +# ι€šι…η¬¦ζη€Ίθ―ηŸ©ι˜΅οΌŒδΌšζŒ‰ι‘ΊεΊθΏ”ε›žεŒ…ε«ι€šι…η¬¦ηš„ζη€Ίθ―ζ‰€η”Ÿζˆηš„ζ‰€ζœ‰ε―θƒ½ +class wildcardsPromptMatrix(io.ComfyNode): + + @classmethod + def define_schema(cls): + wildcard_list = get_wildcard_list() + return io.Schema( + node_id="easy wildcardsMatrix", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("text", default="", multiline=True, dynamic_prompts=False, placeholder="(Support Lora Block Weight and wildcard)"), + io.Combo.Input("Select to add LoRA", options=["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras")), + io.Combo.Input("Select to add Wildcard", options=["Select the Wildcard to add to the text"] + wildcard_list), + io.Int.Input("offset", default=0, min=0, max=MAX_SEED_NUM, step=1, control_after_generate=True), + io.Int.Input("output_limit", default=1, min=-1, step=1, tooltip="Output All Probilities", optional=True), + ], + outputs=[ + io.String.Output("populated_text", is_output_list=True), + io.Int.Output("total"), + io.Int.Output("factors", is_output_list=True), + ], + hidden=[ + io.Hidden.prompt, + io.Hidden.extra_pnginfo, + io.Hidden.unique_id, + ], + ) + + @classmethod + def execute(cls, text, offset, output_limit=1, **kwargs): + prompt = cls.hidden.prompt + # Clean loaded_objects + if prompt: + easyCache.update_loaded_objects(prompt) + + p = WildcardProcessor(text) + total = p.total() + limit = total if output_limit > total or output_limit == -1 else output_limit + offset = 0 if output_limit == -1 else offset + populated_text = p.getmany(limit, offset) if output_limit != 1 else [p.getn(offset)] + return io.NodeOutput(populated_text, p.total(), list(p.placeholder_choices.values()), ui={"value": [offset]}) + +# 负青提瀺词 +class negativePrompt(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy negative", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("negative", default="", multiline=True, placeholder="Negative"), + ], + outputs=[ + io.String.Output(id="output_negative", display_name="negative"), + ], + ) + + @classmethod + def execute(cls, negative): + return io.NodeOutput(negative) + +# ι£Žζ Όζη€Ίθ―ι€‰ζ‹©ε™¨ +class stylesPromptSelector(io.ComfyNode): + + @classmethod + def define_schema(cls): + styles = ["fooocus_styles"] + styles_dir = FOOOCUS_STYLES_DIR + for file_name in os.listdir(styles_dir): + file = os.path.join(styles_dir, file_name) + if os.path.isfile(file) and file_name.endswith(".json"): + if file_name != "fooocus_styles.json": + styles.append(file_name.split(".")[0]) + + return io.Schema( + node_id="easy stylesSelector", + category="EasyUse/Prompt", + inputs=[ + io.Combo.Input("styles", options=styles, default="fooocus_styles"), + io.String.Input("positive", default="", force_input=True, optional=True), + io.String.Input("negative", default="", force_input=True, optional=True), + io.Custom(io_type="EASY_PROMPT_STYLES").Input("select_styles", optional=True), + ], + outputs=[ + io.String.Output(id="output_positive", display_name="positive"), + io.String.Output(id="output_negative", display_name="negative"), + ], + hidden=[ + io.Hidden.prompt, + io.Hidden.extra_pnginfo, + io.Hidden.unique_id, + ], + ) + + @classmethod + def execute(cls, styles, positive='', negative='', select_styles=None, **kwargs): + values = [] + all_styles = {} + positive_prompt, negative_prompt = '', negative + fooocus_custom_dir = os.path.join(FOOOCUS_STYLES_DIR, 'fooocus_styles.json') + if styles == "fooocus_styles" and not os.path.exists(fooocus_custom_dir): + file = os.path.join(RESOURCES_DIR, styles + '.json') + else: + file = os.path.join(FOOOCUS_STYLES_DIR, styles + '.json') + f = open(file, 'r', encoding='utf-8') + data = json.load(f) + f.close() + for d in data: + all_styles[d['name']] = d + # if my_unique_id in prompt: + # if prompt[my_unique_id]["inputs"]['select_styles']: + # values = prompt[my_unique_id]["inputs"]['select_styles'].split(',') + + if isinstance(select_styles, str): + values = select_styles.split(',') + else: + values = select_styles if select_styles else [] + + has_prompt = False + if len(values) == 0: + return io.NodeOutput(positive, negative) + + for index, val in enumerate(values): + if val not in all_styles: + continue + if 'prompt' in all_styles[val]: + if "{prompt}" in all_styles[val]['prompt'] and has_prompt == False: + positive_prompt = all_styles[val]['prompt'].replace('{prompt}', positive) + has_prompt = True + elif "{prompt}" in all_styles[val]['prompt']: + positive_prompt += ', ' + all_styles[val]['prompt'].replace(', {prompt}', '').replace('{prompt}', '') + else: + positive_prompt = all_styles[val]['prompt'] if positive_prompt == '' else positive_prompt + ', ' + all_styles[val]['prompt'] + if 'negative_prompt' in all_styles[val]: + negative_prompt += ', ' + all_styles[val]['negative_prompt'] if negative_prompt else all_styles[val]['negative_prompt'] + + if has_prompt == False and positive: + positive_prompt = positive + positive_prompt + ', ' + + return io.NodeOutput(positive_prompt, negative_prompt) + +#prompt +class prompt(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy prompt", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("text", default="", multiline=True, placeholder="Prompt"), + io.Combo.Input("prefix", options=["Select the prefix add to the text"] + PROMPT_TEMPLATE["prefix"], default="Select the prefix add to the text"), + io.Combo.Input("subject", options=["πŸ‘€Select the subject add to the text"] + PROMPT_TEMPLATE["subject"], default="πŸ‘€Select the subject add to the text"), + io.Combo.Input("action", options=["🎬Select the action add to the text"] + PROMPT_TEMPLATE["action"], default="🎬Select the action add to the text"), + io.Combo.Input("clothes", options=["πŸ‘šSelect the clothes add to the text"] + PROMPT_TEMPLATE["clothes"], default="πŸ‘šSelect the clothes add to the text"), + io.Combo.Input("environment", options=["β˜€οΈSelect the illumination environment add to the text"] + PROMPT_TEMPLATE["environment"], default="β˜€οΈSelect the illumination environment add to the text"), + io.Combo.Input("background", options=["🎞️Select the background add to the text"] + PROMPT_TEMPLATE["background"], default="🎞️Select the background add to the text"), + io.Combo.Input("nsfw", options=["πŸ”žSelect the nsfw add to the text"] + PROMPT_TEMPLATE["nsfw"], default="πŸ”žοΈSelect the nsfw add to the text"), + ], + outputs=[ + io.String.Output("prompt"), + ], + hidden=[ + io.Hidden.prompt, + io.Hidden.extra_pnginfo, + io.Hidden.unique_id, + ], + ) + + @classmethod + def execute(cls, text, **kwargs): + return io.NodeOutput(text) + +#promptList +class promptList(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy promptList", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("prompt_1", multiline=True, default=""), + io.String.Input("prompt_2", multiline=True, default=""), + io.String.Input("prompt_3", multiline=True, default=""), + io.String.Input("prompt_4", multiline=True, default=""), + io.String.Input("prompt_5", multiline=True, default=""), + io.Custom(io_type="LIST").Input("optional_prompt_list", optional=True), + ], + outputs=[ + io.Custom(io_type="LIST").Output("prompt_list"), + io.String.Output("prompt_strings", is_output_list=True), + ], + ) + + @classmethod + def execute(cls, prompt_1="", prompt_2="", prompt_3="", prompt_4="", prompt_5="", optional_prompt_list=None, **kwargs): + prompts = [] + + if optional_prompt_list: + for l in optional_prompt_list: + prompts.append(l) + + # Add individual prompts + for p in [prompt_1, prompt_2, prompt_3, prompt_4, prompt_5]: + if isinstance(p, str) and p != '': + prompts.append(p) + + return io.NodeOutput(prompts, prompts) + +#promptLine +class promptLine(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy promptLine", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("prompt", multiline=True, default="text"), + io.Int.Input("start_index", default=0, min=0, max=9999), + io.Int.Input("max_rows", default=1000, min=1, max=9999), + io.Boolean.Input("remove_empty_lines", default=True), + ], + outputs=[ + io.String.Output("STRING", is_output_list=True), + io.Combo.Output("COMBO", is_output_list=True), + ], + hidden=[ + io.Hidden.prompt, + io.Hidden.unique_id, + ], + ) + + @classmethod + def execute(cls, prompt, start_index, max_rows, remove_empty_lines=True, **kwargs): + lines = prompt.split('\n') + + if remove_empty_lines: + lines = [line for line in lines if line.strip()] + + start_index = max(0, min(start_index, len(lines) - 1)) + + end_index = min(start_index + max_rows, len(lines)) + + rows = lines[start_index:end_index] + + return io.NodeOutput(rows, rows) + +import comfy.utils +from server import PromptServer +from ..libs.messages import MessageCancelled, Message +class promptAwait(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy promptAwait", + category="EasyUse/Prompt", + inputs=[ + io.AnyType.Input("now"), + io.String.Input("prompt", multiline=True, default="", placeholder="Enter a prompt or use voice to enter to text"), + io.Custom(io_type="EASY_PROMPT_AWAIT_BAR").Input("toolbar"), + io.AnyType.Input("prev", optional=True), + ], + outputs=[ + io.AnyType.Output(id="output", display_name="output"), + io.String.Output(id="output_prompt", display_name="prompt"), + io.Boolean.Output("continue"), + io.Int.Output("seed"), + ], + hidden=[ + io.Hidden.prompt, + io.Hidden.unique_id, + io.Hidden.extra_pnginfo, + ], + ) + + @classmethod + def execute(cls, now, prompt, toolbar, prev=None, **kwargs): + id = cls.hidden.unique_id + id = id.split('.')[len(id.split('.')) - 1] if "." in id else id + if ":" in id: + id = id.split(":")[0] + pbar = comfy.utils.ProgressBar(100) + pbar.update_absolute(30) + PromptServer.instance.send_sync('easyuse_prompt_await', {"id": id}) + try: + res = Message.waitForMessage(id, asList=False) + if res is None or res == "-1": + result = (now, prompt, False, 0) + else: + input = now if res['select'] == 'now' or prev is None else prev + result = (input, res['prompt'], False if res['result'] == -1 else True, res['seed'] if res['unlock'] else res['last_seed']) + pbar.update_absolute(100) + return io.NodeOutput(*result) + except MessageCancelled: + pbar.update_absolute(100) + raise comfy.model_management.InterruptProcessingException() + +class promptConcat(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy promptConcat", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("prompt1", multiline=False, default="", force_input=True, optional=True), + io.String.Input("prompt2", multiline=False, default="", force_input=True, optional=True), + io.String.Input("separator", multiline=False, default="", optional=True), + ], + outputs=[ + io.String.Output("prompt"), + ], + ) + + @classmethod + def execute(cls, prompt1="", prompt2="", separator=""): + return io.NodeOutput(prompt1 + separator + prompt2) + +class promptReplace(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy promptReplace", + category="EasyUse/Prompt", + inputs=[ + io.String.Input("prompt", multiline=True, default="", force_input=True), + io.String.Input("find1", multiline=False, default="", optional=True), + io.String.Input("replace1", multiline=False, default="", optional=True), + io.String.Input("find2", multiline=False, default="", optional=True), + io.String.Input("replace2", multiline=False, default="", optional=True), + io.String.Input("find3", multiline=False, default="", optional=True), + io.String.Input("replace3", multiline=False, default="", optional=True), + ], + outputs=[ + io.String.Output(id="output_prompt",display_name="prompt"), + ], + ) + + @classmethod + def execute(cls, prompt, find1="", replace1="", find2="", replace2="", find3="", replace3=""): + prompt = prompt.replace(find1, replace1) + prompt = prompt.replace(find2, replace2) + prompt = prompt.replace(find3, replace3) + + return io.NodeOutput(prompt) + + +# θ‚–εƒε€§εΈˆ +# Created by AI Wiz Art (Stefano Flore) +# Version: 2.2 +# https://stefanoflore.it +# https://ai-wiz.art +class portraitMaster(io.ComfyNode): + + @classmethod + def define_schema(cls): + max_float_value = 1.95 + prompt_path = os.path.join(RESOURCES_DIR, 'portrait_prompt.json') + if not os.path.exists(prompt_path): + response = urlopen('https://raw.githubusercontent.com/yolain/ComfyUI-Easy-Use/main/resources/portrait_prompt.json') + temp_prompt = json.loads(response.read()) + prompt_serialized = json.dumps(temp_prompt, indent=4) + with open(prompt_path, "w") as f: + f.write(prompt_serialized) + del response, temp_prompt + # Load local + with open(prompt_path, 'r') as f: + data = json.load(f) + + inputs = [] + # Shot + inputs.append(io.Combo.Input("shot", options=['-'] + data['shot_list'])) + inputs.append(io.Float.Input("shot_weight", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + # Gender and age + inputs.append(io.Combo.Input("gender", options=['-'] + data['gender_list'], default="Woman")) + inputs.append(io.Int.Input("age", default=30, min=18, max=90, step=1, display_mode=io.NumberDisplay.slider)) + # Nationality + inputs.append(io.Combo.Input("nationality_1", options=['-'] + data['nationality_list'], default="Chinese")) + inputs.append(io.Combo.Input("nationality_2", options=['-'] + data['nationality_list'])) + inputs.append(io.Float.Input("nationality_mix", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + # Body + inputs.append(io.Combo.Input("body_type", options=['-'] + data['body_type_list'])) + inputs.append(io.Float.Input("body_type_weight", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Combo.Input("model_pose", options=['-'] + data['model_pose_list'])) + inputs.append(io.Combo.Input("eyes_color", options=['-'] + data['eyes_color_list'])) + # Face + inputs.append(io.Combo.Input("facial_expression", options=['-'] + data['face_expression_list'])) + inputs.append(io.Float.Input("facial_expression_weight", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Combo.Input("face_shape", options=['-'] + data['face_shape_list'])) + inputs.append(io.Float.Input("face_shape_weight", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("facial_asymmetry", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + # Hair + inputs.append(io.Combo.Input("hair_style", options=['-'] + data['hair_style_list'])) + inputs.append(io.Combo.Input("hair_color", options=['-'] + data['hair_color_list'])) + inputs.append(io.Float.Input("disheveled", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Combo.Input("beard", options=['-'] + data['beard_list'])) + # Skin details + inputs.append(io.Float.Input("skin_details", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("skin_pores", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("dimples", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("freckles", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("moles", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("skin_imperfections", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("skin_acne", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("tanned_skin", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + # Eyes + inputs.append(io.Float.Input("eyes_details", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("iris_details", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("circular_iris", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + inputs.append(io.Float.Input("circular_pupil", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + # Light + inputs.append(io.Combo.Input("light_type", options=['-'] + data['light_type_list'])) + inputs.append(io.Combo.Input("light_direction", options=['-'] + data['light_direction_list'])) + inputs.append(io.Float.Input("light_weight", default=0, step=0.05, min=0, max=max_float_value, display_mode=io.NumberDisplay.slider)) + # Additional + inputs.append(io.Combo.Input("photorealism_improvement", options=["enable", "disable"])) + inputs.append(io.String.Input("prompt_start", multiline=True, default="raw photo, (realistic:1.5)")) + inputs.append(io.String.Input("prompt_additional", multiline=True, default="")) + inputs.append(io.String.Input("prompt_end", multiline=True, default="")) + inputs.append(io.String.Input("negative_prompt", multiline=True, default="")) + + return io.Schema( + node_id="easy portraitMaster", + category="EasyUse/Prompt", + inputs=inputs, + outputs=[ + io.String.Output("positive"), + io.String.Output("negative"), + ], + ) + + @classmethod + def execute(cls, shot="-", shot_weight=1, gender="-", body_type="-", body_type_weight=0, eyes_color="-", + facial_expression="-", facial_expression_weight=0, face_shape="-", face_shape_weight=0, + nationality_1="-", nationality_2="-", nationality_mix=0.5, age=30, hair_style="-", hair_color="-", + disheveled=0, dimples=0, freckles=0, skin_pores=0, skin_details=0, moles=0, skin_imperfections=0, + wrinkles=0, tanned_skin=0, eyes_details=1, iris_details=1, circular_iris=1, circular_pupil=1, + facial_asymmetry=0, prompt_additional="", prompt_start="", prompt_end="", light_type="-", + light_direction="-", light_weight=0, negative_prompt="", photorealism_improvement="disable", beard="-", + model_pose="-", skin_acne=0): + + prompt = [] + + if gender == "-": + gender = "" + else: + if age <= 25 and gender == 'Woman': + gender = 'girl' + if age <= 25 and gender == 'Man': + gender = 'boy' + gender = " " + gender + " " + + if nationality_1 != '-' and nationality_2 != '-': + nationality = f"[{nationality_1}:{nationality_2}:{round(nationality_mix, 2)}]" + elif nationality_1 != '-': + nationality = nationality_1 + " " + elif nationality_2 != '-': + nationality = nationality_2 + " " + else: + nationality = "" + + if prompt_start != "": + prompt.append(f"{prompt_start}") + + if shot != "-" and shot_weight > 0: + prompt.append(f"({shot}:{round(shot_weight, 2)})") + + prompt.append(f"({nationality}{gender}{round(age)}-years-old:1.5)") + + if body_type != "-" and body_type_weight > 0: + prompt.append(f"({body_type}, {body_type} body:{round(body_type_weight, 2)})") + + if model_pose != "-": + prompt.append(f"({model_pose}:1.5)") + + if eyes_color != "-": + prompt.append(f"({eyes_color} eyes:1.25)") + + if facial_expression != "-" and facial_expression_weight > 0: + prompt.append( + f"({facial_expression}, {facial_expression} expression:{round(facial_expression_weight, 2)})") + + if face_shape != "-" and face_shape_weight > 0: + prompt.append(f"({face_shape} shape face:{round(face_shape_weight, 2)})") + + if hair_style != "-": + prompt.append(f"({hair_style} hairstyle:1.25)") + + if hair_color != "-": + prompt.append(f"({hair_color} hair:1.25)") + + if beard != "-": + prompt.append(f"({beard}:1.15)") + + if disheveled != "-" and disheveled > 0: + prompt.append(f"(disheveled:{round(disheveled, 2)})") + + if prompt_additional != "": + prompt.append(f"{prompt_additional}") + + if skin_details > 0: + prompt.append(f"(skin details, skin texture:{round(skin_details, 2)})") + + if skin_pores > 0: + prompt.append(f"(skin pores:{round(skin_pores, 2)})") + + if skin_imperfections > 0: + prompt.append(f"(skin imperfections:{round(skin_imperfections, 2)})") + + if skin_acne > 0: + prompt.append(f"(acne, skin with acne:{round(skin_acne, 2)})") + + if wrinkles > 0: + prompt.append(f"(skin imperfections:{round(wrinkles, 2)})") + + if tanned_skin > 0: + prompt.append(f"(tanned skin:{round(tanned_skin, 2)})") + + if dimples > 0: + prompt.append(f"(dimples:{round(dimples, 2)})") + + if freckles > 0: + prompt.append(f"(freckles:{round(freckles, 2)})") + + if moles > 0: + prompt.append(f"(skin pores:{round(moles, 2)})") + + if eyes_details > 0: + prompt.append(f"(eyes details:{round(eyes_details, 2)})") + + if iris_details > 0: + prompt.append(f"(iris details:{round(iris_details, 2)})") + + if circular_iris > 0: + prompt.append(f"(circular iris:{round(circular_iris, 2)})") + + if circular_pupil > 0: + prompt.append(f"(circular pupil:{round(circular_pupil, 2)})") + + if facial_asymmetry > 0: + prompt.append(f"(facial asymmetry, face asymmetry:{round(facial_asymmetry, 2)})") + + if light_type != '-' and light_weight > 0: + if light_direction != '-': + prompt.append(f"({light_type} {light_direction}:{round(light_weight, 2)})") + else: + prompt.append(f"({light_type}:{round(light_weight, 2)})") + + if prompt_end != "": + prompt.append(f"{prompt_end}") + + prompt = ", ".join(prompt) + prompt = prompt.lower() + + if photorealism_improvement == "enable": + prompt = prompt + ", (professional photo, balanced photo, balanced exposure:1.2), (film grain:1.15)" + + if photorealism_improvement == "enable": + negative_prompt = negative_prompt + ", (shinny skin, reflections on the skin, skin reflections:1.25)" + + log_node_info("Portrait Master as generate the prompt:", prompt) + + return io.NodeOutput(prompt, negative_prompt) + +# ε€šθ§’εΊ¦ +class multiAngle(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="easy multiAngle", + category="EasyUse/Prompt", + inputs=[ + io.Custom(io_type="EASY_MULTI_ANGLE").Input("multi_angle", optional=True), + ], + outputs=[ + io.String.Output("prompt", is_output_list=True), + io.Custom(io_type="EASY_MULTI_ANGLE").Output("params"), + ], + ) + + @classmethod + def execute(cls, multi_angle=None, **kwargs): + if multi_angle is None: + return io.NodeOutput([""]) + + if isinstance(multi_angle, str): + try: + multi_angle = json.loads(multi_angle) + except: + raise Exception(f"Invalid multi angle: {multi_angle}") + + prompts = [] + for angle_data in multi_angle: + rotate = angle_data.get("rotate", 0) + vertical = angle_data.get("vertical", 0) + zoom = angle_data.get("zoom", 5) + add_angle_prompt = angle_data.get("add_angle_prompt", True) + + # Validate input ranges + rotate = max(0, min(360, int(rotate))) + vertical = max(-90, min(90, int(vertical))) + zoom = max(0.0, min(10.0, float(zoom))) + + h_angle = rotate % 360 + + # Horizontal direction mapping + h_suffix = "" if add_angle_prompt else " quarter" + if h_angle < 22.5 or h_angle >= 337.5: h_direction = "front view" + elif h_angle < 67.5: h_direction = f"front-right{h_suffix} view" + elif h_angle < 112.5: h_direction = "right side view" + elif h_angle < 157.5: h_direction = f"back-right{h_suffix} view" + elif h_angle < 202.5: h_direction = "back view" + elif h_angle < 247.5: h_direction = f"back-left{h_suffix} view" + elif h_angle < 292.5: h_direction = "left side view" + else: h_direction = f"front-left{h_suffix} view" + + # Vertical direction mapping + if add_angle_prompt: + if vertical == -90: + v_direction = "bottom-looking-up perspective, extreme worm's eye view, focus subject bottom" + elif vertical < -75: + v_direction = "bottom-looking-up perspective, extreme worm's eye view" + elif vertical < -45: + v_direction = "ultra-low angle" + elif vertical < -15: + v_direction = "low angle" + elif vertical < 15: + v_direction = "eye level" + elif vertical < 45: + v_direction = "high angle" + elif vertical < 75: + v_direction = "bird's eye view" + elif vertical < 90: + v_direction = "top-down perspective, looking straight down at the top of the subject" + else: + v_direction = "top-down perspective, looking straight down at the top of the subject, face not visible, focus on subject head" + else: + if vertical < -15: + v_direction = "low-angle shot" + elif vertical < 15: + v_direction = "eye-level shot" + elif vertical < 45: + v_direction = "elevated shot" + elif vertical < 75: + v_direction = "high-angle shot" + elif vertical < 90: + v_direction = "top-down perspective, looking straight down at the top of the subject" + else: + v_direction = "top-down perspective, looking straight down at the top of the subject, face not visible, focus on subject head" + + # Distance/zoom mapping + if add_angle_prompt: + if zoom < 2: distance = "extreme wide shot" + elif zoom < 4: distance = "wide shot" + elif zoom < 6: distance = "medium shot" + elif zoom < 8: distance = "close-up" + else: distance = "extreme close-up" + else: + if zoom < 2: distance = "extreme wide shot" + elif zoom < 4: distance = "wide shot" + elif zoom < 6: distance = "medium shot" + elif zoom < 8: distance = "close-up" + else: distance = "extreme close-up" + + # Build prompt + if add_angle_prompt: + prompt = f"{h_direction}, {v_direction}, {distance} (horizontal: {rotate}, vertical: {vertical}, zoom: {zoom:.1f})" + else: + prompt = f"{h_direction} {v_direction} {distance}" + + prompts.append(prompt) + + return io.NodeOutput(prompts, multi_angle) + + +NODE_CLASS_MAPPINGS = { + "easy positive": positivePrompt, + "easy negative": negativePrompt, + "easy wildcards": wildcardsPrompt, + "easy wildcardsMatrix": wildcardsPromptMatrix, + "easy prompt": prompt, + "easy promptList": promptList, + "easy promptLine": promptLine, + "easy promptAwait": promptAwait, + "easy promptConcat": promptConcat, + "easy promptReplace": promptReplace, + "easy stylesSelector": stylesPromptSelector, + "easy portraitMaster": portraitMaster, + "easy multiAngle": multiAngle, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy positive": "Positive", + "easy negative": "Negative", + "easy wildcards": "Wildcards", + "easy wildcardsMatrix": "Wildcards Matrix", + "easy prompt": "Prompt", + "easy promptList": "PromptList", + "easy promptLine": "PromptLine", + "easy promptAwait": "PromptAwait", + "easy promptConcat": "PromptConcat", + "easy promptReplace": "PromptReplace", + "easy stylesSelector": "Styles Selector", + "easy portraitMaster": "Portrait Master", + "easy multiAngle": "Multi Angle", +} diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/samplers.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/samplers.py new file mode 100644 index 00000000..c8ac6dcf --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/samplers.py @@ -0,0 +1,1360 @@ +import sys, re, time +import torch +import comfy.utils, comfy.sample, comfy.samplers, comfy.controlnet, comfy.model_base, comfy.model_management, comfy.sampler_helpers, comfy.supported_models +import folder_paths +from comfy.model_patcher import ModelPatcher +from comfy_extras.nodes_mask import GrowMask +import comfy_extras.nodes_custom_sampler as custom_samplers +from tqdm import trange + +from server import PromptServer +from nodes import RepeatLatentBatch, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS, VAEEncodeForInpaint, InpaintModelConditioning +from ..modules.layer_diffuse import LayerDiffuse +from ..config import * + +from ..libs.log import log_node_warn +from ..libs.utils import easySave, get_local_filepath, get_sd_version +from ..libs.sampler import alignYourStepsScheduler, gitsScheduler +from ..libs.xyplot import easyXYPlot + +from .. import easyCache, sampler + +class samplerFull: + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS+NEW_SCHEDULERS,), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save", "None"],), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + "model": ("MODEL",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "latent": ("LATENT",), + "vae": ("VAE",), + "clip": ("CLIP",), + "xyPlot": ("XYPLOT",), + "image": ("IMAGE",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT",) + RETURN_NAMES = ("pipe", "image", "model", "positive", "negative", "latent", "vae", "clip", "seed",) + OUTPUT_NODE = True + FUNCTION = "run" + CATEGORY = "EasyUse/Sampler" + + def ip2p(self, positive, negative, vae=None, pixels=None, latent=None): + if latent is not None: + concat_latent = latent + else: + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + + concat_latent = vae.encode(pixels) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1], out_latent) + + def get_inversed_euler_sampler(self): + @torch.no_grad() + def sample_inversed_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0.,s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(1, len(sigmas), disable=disable): + sigma_in = sigmas[i - 1] + + if i == 1: + sigma_t = sigmas[i] + else: + sigma_t = sigma_in + + denoised = model(x, sigma_t * s_in, **extra_args) + + if i == 1: + d = (x - denoised) / (2 * sigmas[i]) + else: + d = (x - denoised) / sigmas[i - 1] + + dt = sigmas[i] - sigmas[i - 1] + x = x + d * dt + if callback is not None: + callback( + {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + return x / sigmas[-1] + + ksampler = comfy.samplers.KSAMPLER(sample_inversed_euler) + return (ksampler,) + + def get_custom_cls(self, sampler_name): + try: + cls = custom_samplers.__dict__[sampler_name] + cls = cls() + if hasattr(cls, "get_sigmas"): + cls.execute = cls.get_sigmas + elif hasattr(cls, "get_guider"): + cls.execute = cls.get_guider + elif hasattr(cls, "get_sampler"): + cls.execute = cls.get_sampler + return cls + except: + raise Exception(f"Custom sampler {sampler_name} not found, Please updated your ComfyUI") + + def add_model_patch_option(self, model): + if 'transformer_options' not in model.model_options: + model.model_options['transformer_options'] = {} + to = model.model_options['transformer_options'] + if "model_patch" not in to: + to["model_patch"] = {} + return to + + def get_sampler_custom(self, model, positive, negative, loader_settings): + _guider = None + middle = loader_settings['middle'] if "middle" in loader_settings else negative + steps = loader_settings['steps'] if "steps" in loader_settings else 20 + cfg = loader_settings['cfg'] if "cfg" in loader_settings else 8.0 + cfg_negative = loader_settings['cfg_negative'] if "cfg_negative" in loader_settings else 8.0 + sampler_name = loader_settings['sampler_name'] if "sampler_name" in loader_settings else "euler" + scheduler = loader_settings['scheduler'] if "scheduler" in loader_settings else "normal" + guider = loader_settings['custom']['guider'] if "guider" in loader_settings['custom'] else "CFG" + beta_d = loader_settings['custom']['beta_d'] if "beta_d" in loader_settings['custom'] else 0.1 + beta_min = loader_settings['custom']['beta_min'] if "beta_min" in loader_settings['custom'] else 0.1 + eps_s = loader_settings['custom']['eps_s'] if "eps_s" in loader_settings['custom'] else 0.1 + sigma_max = loader_settings['custom']['sigma_max'] if "sigma_max" in loader_settings['custom'] else 14.61 + sigma_min = loader_settings['custom']['sigma_min'] if "sigma_min" in loader_settings['custom'] else 0.03 + rho = loader_settings['custom']['rho'] if "rho" in loader_settings['custom'] else 7.0 + coeff = loader_settings['custom']['coeff'] if "coeff" in loader_settings['custom'] else 1.2 + flip_sigmas = loader_settings['custom']['flip_sigmas'] if "flip_sigmas" in loader_settings['custom'] else False + denoise = loader_settings['denoise'] if "denoise" in loader_settings else 1.0 + optional_sigmas = loader_settings['optional_sigmas'] if "optional_sigmas" in loader_settings else None + optional_sampler = loader_settings['optional_sampler'] if "optional_sampler" in loader_settings else None + + # sigmas + if optional_sigmas is not None: + sigmas = optional_sigmas + else: + if scheduler == 'vp': + sigmas, = self.get_custom_cls('VPScheduler').execute(steps, beta_d, beta_min, eps_s) + elif scheduler == 'karrasADV': + sigmas, = self.get_custom_cls('KarrasScheduler').execute(steps, sigma_max, sigma_min, rho) + elif scheduler == 'exponentialADV': + sigmas, = self.get_custom_cls('ExponentialScheduler').execute(steps, sigma_max, sigma_min) + elif scheduler == 'polyExponential': + sigmas, = self.get_custom_cls('PolyexponentialScheduler').execute(steps, sigma_max, sigma_min, rho) + elif scheduler == 'sdturbo': + sigmas, = self.get_custom_cls('SDTurboScheduler').execute(model, steps, denoise) + elif scheduler == 'alignYourSteps': + model_type = get_sd_version(model) + if model_type == 'unknown': + model_type = 'sdxl' + sigmas, = alignYourStepsScheduler().get_sigmas(model_type.upper(), steps, denoise) + elif scheduler == 'gits': + sigmas, = gitsScheduler().get_sigmas(coeff, steps, denoise) + else: + sigmas, = self.get_custom_cls('BasicScheduler').execute(model, scheduler, steps, denoise) + + # filp_sigmas + if flip_sigmas: + sigmas, = self.get_custom_cls('FlipSigmas').execute(sigmas) + + ####################################################################################### + # brushnet + to = None + transformer_options = model.model_options['transformer_options'] if "transformer_options" in model.model_options else {} + if 'model_patch' in transformer_options and 'brushnet' in transformer_options['model_patch']: + to = self.add_model_patch_option(model) + mp = to['model_patch'] + if isinstance(model.model.model_config, comfy.supported_models.SD15): + mp['SDXL'] = False + elif isinstance(model.model.model_config, comfy.supported_models.SDXL): + mp['SDXL'] = True + else: + print('Base model type: ', type(model.model.model_config)) + raise Exception("Unsupported model type: ", type(model.model.model_config)) + + mp['all_sigmas'] = sigmas + mp['unet'] = model.model.diffusion_model + mp['step'] = 0 + mp['total_steps'] = 1 + ####################################################################################### + # guider + if cfg > 0 and get_sd_version(model) == 'flux': + c = [] + for t in positive: + n = [t[0], t[1]] + n[1]['guidance'] = cfg + c.append(n) + positive = c + + if guider in ['CFG', 'IP2P+CFG']: + _guider, = self.get_custom_cls('CFGGuider').execute(model, positive, negative, cfg) + elif guider in ['DualCFG', 'IP2P+DualCFG']: + _guider, = self.get_custom_cls('DualCFGGuider').execute(model, positive, middle, + negative, cfg, cfg_negative) + else: + _guider, = self.get_custom_cls('BasicGuider').execute(model, positive) + + # sampler + if optional_sampler: + _sampler = optional_sampler + else: + if sampler_name == 'inversed_euler': + _sampler, = self.get_inversed_euler_sampler() + else: + _sampler, = self.get_custom_cls('KSamplerSelect').execute(sampler_name) + + + return (_guider, _sampler, sigmas) + + def run(self, pipe, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, seed=None, model=None, positive=None, negative=None, latent=None, vae=None, clip=None, xyPlot=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False, downscale_options=None, image=None): + + samp_model = model if model is not None else pipe["model"] + samp_positive = positive if positive is not None else pipe["positive"] + samp_negative = negative if negative is not None else pipe["negative"] + samp_samples = latent if latent is not None else pipe["samples"] + samp_vae = vae if vae is not None else pipe["vae"] + samp_clip = clip if clip is not None else pipe["clip"] + + samp_seed = seed if seed is not None else pipe['seed'] + + samp_custom = pipe["loader_settings"] if "custom" in pipe["loader_settings"] else None + + steps = steps if steps is not None else pipe['loader_settings']['steps'] + start_step = pipe['loader_settings']['start_step'] if 'start_step' in pipe['loader_settings'] else 0 + last_step = pipe['loader_settings']['last_step'] if 'last_step' in pipe['loader_settings'] else 10000 + cfg = cfg if cfg is not None else pipe['loader_settings']['cfg'] + sampler_name = sampler_name if sampler_name is not None else pipe['loader_settings']['sampler_name'] + scheduler = scheduler if scheduler is not None else pipe['loader_settings']['scheduler'] + denoise = denoise if denoise is not None else pipe['loader_settings']['denoise'] + add_noise = pipe['loader_settings']['add_noise'] if 'add_noise' in pipe['loader_settings'] else 'enabled' + force_full_denoise = pipe['loader_settings']['force_full_denoise'] if 'force_full_denoise' in pipe['loader_settings'] else True + noise_device = 'GPU' if ('a1111_prompt_style' in pipe['loader_settings'] and pipe['loader_settings']['a1111_prompt_style']) or add_noise == 'enable (GPU=A1111)' else 'CPU' + + if image is not None and latent is None: + samp_samples = {"samples": samp_vae.encode(image[:, :, :, :3])} + + disable_noise = False + if add_noise == "disable": + disable_noise = True + + def downscale_model_unet(samp_model): + # θŽ·ε–Unet参数 + if "PatchModelAddDownscale" in ALL_NODE_CLASS_MAPPINGS: + cls = ALL_NODE_CLASS_MAPPINGS['PatchModelAddDownscale'] + # θ‡ͺεŠ¨ζ”ΆηΌ©Unet + if downscale_options['downscale_factor'] is None: + unet_config = samp_model.model.model_config.unet_config + if unet_config is not None and "samples" in samp_samples: + height = samp_samples['samples'].shape[2] * 8 + width = samp_samples['samples'].shape[3] * 8 + context_dim = unet_config.get('context_dim') + longer_side = width if width > height else height + if context_dim is not None and longer_side > context_dim: + width_downscale_factor = float(width / context_dim) + height_downscale_factor = float(height / context_dim) + if width_downscale_factor > 1.75: + log_node_warn("Patch model unet add downscale...") + log_node_warn("Downscale factor:" + str(width_downscale_factor)) + (samp_model,) = cls().execute(samp_model, downscale_options['block_number'], width_downscale_factor, 0, 0.35, True, "bicubic", + "bicubic") + elif height_downscale_factor > 1.25: + log_node_warn("Patch model unet add downscale....") + log_node_warn("Downscale factor:" + str(height_downscale_factor)) + (samp_model,) = cls().execute(samp_model, downscale_options['block_number'], height_downscale_factor, 0, 0.35, True, "bicubic", + "bicubic") + else: + cls = ALL_NODE_CLASS_MAPPINGS['PatchModelAddDownscale'] + log_node_warn("Patch model unet add downscale....") + log_node_warn("Downscale factor:" + str(downscale_options['downscale_factor'])) + (samp_model,) = cls().execute(samp_model, downscale_options['block_number'], downscale_options['downscale_factor'], downscale_options['start_percent'], downscale_options['end_percent'], downscale_options['downscale_after_skip'], downscale_options['downscale_method'], downscale_options['upscale_method']) + return samp_model + + def process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, + samp_negative, + steps, start_step, last_step, cfg, sampler_name, scheduler, denoise, + image_output, link_id, save_prefix, tile_size, prompt, extra_pnginfo, my_unique_id, + preview_latent, force_full_denoise=force_full_denoise, disable_noise=disable_noise, samp_custom=None, noise_device='cpu'): + + # LayerDiffusion + layerDiffuse = None + samp_blend_samples = None + layer_diffusion_method = pipe['loader_settings']['layer_diffusion_method'] if 'layer_diffusion_method' in pipe['loader_settings'] else None + if layer_diffusion_method is not None: + layerDiffuse = LayerDiffuse() + samp_blend_samples = pipe["blend_samples"] if "blend_samples" in pipe else None + additional_cond = pipe["loader_settings"]['layer_diffusion_cond'] if "layer_diffusion_cond" in pipe[ + 'loader_settings'] else (None, None, None) + method = layerDiffuse.get_layer_diffusion_method(pipe['loader_settings']['layer_diffusion_method'], + samp_blend_samples is not None) + + images = pipe["images"] if "images" in pipe else None + weight = pipe['loader_settings']['layer_diffusion_weight'] if 'layer_diffusion_weight' in pipe[ + 'loader_settings'] else 1.0 + samp_model, samp_positive, samp_negative = layerDiffuse.apply_layer_diffusion(samp_model, method, weight, + samp_samples, samp_blend_samples, + samp_positive, samp_negative, + images, additional_cond) + resolution = pipe['loader_settings']['resolution'] if 'resolution' in pipe['loader_settings'] else "θ‡ͺεšδΉ‰ X θ‡ͺεšδΉ‰" + empty_latent_width = pipe['loader_settings']['empty_latent_width'] if 'empty_latent_width' in pipe['loader_settings'] else 512 + empty_latent_height = pipe['loader_settings']['empty_latent_height'] if 'empty_latent_height' in pipe['loader_settings'] else 512 + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + samp_samples = sampler.emptyLatent(resolution, empty_latent_width, empty_latent_height, batch_size) + + # Downscale Model Unet + if samp_model is not None and downscale_options is not None: + samp_model = downscale_model_unet(samp_model) + # ζŽ¨η†εˆε§‹ζ—Άι—΄ + start_time = int(time.time() * 1000) + # εΌ€ε§‹ζŽ¨η† + if samp_custom is not None: + _guider, _sampler, sigmas = self.get_sampler_custom(samp_model, samp_positive, samp_negative, samp_custom) + samp_samples, samp_blend_samples = sampler.custom_advanced_ksampler(_guider, _sampler, sigmas, samp_samples, add_noise, samp_seed, preview_latent=preview_latent) + elif scheduler == 'align_your_steps': + model_type = get_sd_version(samp_model) + if model_type == 'unknown': + model_type = 'sdxl' + sigmas, = alignYourStepsScheduler().get_sigmas(model_type.upper(), steps, denoise) + _sampler = comfy.samplers.sampler_object(sampler_name) + samp_samples = sampler.custom_ksampler(samp_model, samp_seed, steps, cfg, _sampler, sigmas, samp_positive, samp_negative, samp_samples, disable_noise=disable_noise, preview_latent=preview_latent, noise_device=noise_device) + elif scheduler == 'gits': + sigmas, = gitsScheduler().get_sigmas(coeff=1.2, steps=steps, denoise=denoise) + _sampler = comfy.samplers.sampler_object(sampler_name) + samp_samples = sampler.custom_ksampler(samp_model, samp_seed, steps, cfg, _sampler, sigmas, samp_positive, samp_negative, samp_samples, disable_noise=disable_noise, preview_latent=preview_latent, noise_device=noise_device) + else: + samp_samples = sampler.common_ksampler(samp_model, samp_seed, steps, cfg, sampler_name, scheduler, samp_positive, samp_negative, samp_samples, denoise=denoise, preview_latent=preview_latent, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, disable_noise=disable_noise, noise_device=noise_device) + # ζŽ¨η†η»“ζŸζ—Άι—΄ + end_time = int(time.time() * 1000) + latent = samp_samples["samples"] + + # 解码图片 + if image_output == 'None': + samp_images, new_images, alpha, results = None, None, None, None + spent_time = 'Diffusion:' + str((end_time - start_time) / 1000) + 'β€³' + else: + if tile_size is not None: + samp_images = samp_vae.decode_tiled(latent, tile_x=tile_size // 8, tile_y=tile_size // 8, ) + else: + samp_images = samp_vae.decode(latent).cpu() + if len(samp_images.shape) == 5: # Combine batches + samp_images = samp_images.reshape(-1, samp_images.shape[-3], samp_images.shape[-2], samp_images.shape[-1]) + # LayerDiffusion Decode + if layerDiffuse is not None: + new_images, samp_images, alpha = layerDiffuse.layer_diffusion_decode(layer_diffusion_method, latent, samp_blend_samples, samp_images, samp_model) + else: + new_images = samp_images + alpha = None + + # ζŽ¨η†ζ€»θ€—ζ—ΆοΌˆεŒ…ε«θ§£η οΌ‰ + end_decode_time = int(time.time() * 1000) + spent_time = 'Diffusion:' + str((end_time-start_time)/1000)+'β€³, VAEDecode:' + str((end_decode_time-end_time)/1000)+'β€³ ' + + results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo) + + new_pipe = { + **pipe, + "positive": samp_positive, + "negative": samp_negative, + "vae": samp_vae, + "clip": samp_clip, + + "samples": samp_samples, + "blend_samples": samp_blend_samples, + "images": new_images, + "samp_images": samp_images, + "alpha": alpha, + "seed": samp_seed, + + "loader_settings": { + **pipe["loader_settings"], + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "add_noise": add_noise, + "spent_time": spent_time + } + } + + del pipe + + if image_output in ("Hide", "Hide&Save", "None"): + return {"ui":{}, "result":sampler.get_output(new_pipe,)} + + if image_output in ("Sender", "Sender&Save"): + PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) + + return {"ui": {"images": results}, + "result": sampler.get_output(new_pipe,)} + + def process_xyPlot(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, + steps, cfg, sampler_name, scheduler, denoise, + image_output, link_id, save_prefix, tile_size, prompt, extra_pnginfo, my_unique_id, preview_latent, xyPlot, force_full_denoise, disable_noise, samp_custom, noise_device): + + sampleXYplot = easyXYPlot(xyPlot, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id, sampler, easyCache) + + if not sampleXYplot.validate_xy_plot(): + return process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, + samp_negative, steps, 0, 10000, cfg, + sampler_name, scheduler, denoise, image_output, link_id, save_prefix, tile_size, prompt, + extra_pnginfo, my_unique_id, preview_latent, samp_custom=samp_custom, noise_device=noise_device) + + # Downscale Model Unet + if samp_model is not None and downscale_options is not None: + samp_model = downscale_model_unet(samp_model) + + blend_samples = pipe['blend_samples'] if "blend_samples" in pipe else None + layer_diffusion_method = pipe['loader_settings']['layer_diffusion_method'] if 'layer_diffusion_method' in pipe['loader_settings'] else None + + plot_image_vars = { + "x_node_type": sampleXYplot.x_node_type, "y_node_type": sampleXYplot.y_node_type, + "lora_name": pipe["loader_settings"]["lora_name"] if "lora_name" in pipe["loader_settings"] else None, + "lora_model_strength": pipe["loader_settings"]["lora_model_strength"] if "lora_model_strength" in pipe["loader_settings"] else 1.0, + "lora_clip_strength": pipe["loader_settings"]["lora_clip_strength"] if "lora_clip_strength" in pipe["loader_settings"] else 1.0, + "lora_stack": pipe["loader_settings"]["lora_stack"] if "lora_stack" in pipe["loader_settings"] else None, + "steps": steps, + "cfg": cfg, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": denoise, + "seed": samp_seed, + "images": pipe['images'], + + "model": samp_model, "vae": samp_vae, "clip": samp_clip, "positive_cond": samp_positive, + "negative_cond": samp_negative, + "noise_device":noise_device, + + "ckpt_name": pipe['loader_settings']['ckpt_name'] if "ckpt_name" in pipe["loader_settings"] else None, + "vae_name": pipe['loader_settings']['vae_name'] if "vae_name" in pipe["loader_settings"] else None, + "clip_skip": pipe['loader_settings']['clip_skip'] if "clip_skip" in pipe["loader_settings"] else None, + "positive": pipe['loader_settings']['positive'] if "positive" in pipe["loader_settings"] else None, + "positive_token_normalization": pipe['loader_settings']['positive_token_normalization'] if "positive_token_normalization" in pipe["loader_settings"] else None, + "positive_weight_interpretation": pipe['loader_settings']['positive_weight_interpretation'] if "positive_weight_interpretation" in pipe["loader_settings"] else None, + "negative": pipe['loader_settings']['negative'] if "negative" in pipe["loader_settings"] else None, + "negative_token_normalization": pipe['loader_settings']['negative_token_normalization'] if "negative_token_normalization" in pipe["loader_settings"] else None, + "negative_weight_interpretation": pipe['loader_settings']['negative_weight_interpretation'] if "negative_weight_interpretation" in pipe["loader_settings"] else None, + } + + if "models" in pipe["loader_settings"]: + plot_image_vars["models"] = pipe["loader_settings"]["models"] + if "vae_use" in pipe["loader_settings"]: + plot_image_vars["vae_use"] = pipe["loader_settings"]["vae_use"] + if "a1111_prompt_style" in pipe["loader_settings"]: + plot_image_vars["a1111_prompt_style"] = pipe["loader_settings"]["a1111_prompt_style"] + if "cnet_stack" in pipe["loader_settings"]: + plot_image_vars["cnet"] = pipe["loader_settings"]["cnet_stack"] + if "positive_cond_stack" in pipe["loader_settings"]: + plot_image_vars["positive_cond_stack"] = pipe["loader_settings"]["positive_cond_stack"] + if "negative_cond_stack" in pipe["loader_settings"]: + plot_image_vars["negative_cond_stack"] = pipe["loader_settings"]["negative_cond_stack"] + if layer_diffusion_method: + plot_image_vars["layer_diffusion_method"] = layer_diffusion_method + if "layer_diffusion_weight" in pipe["loader_settings"]: + plot_image_vars["layer_diffusion_weight"] = pipe['loader_settings']['layer_diffusion_weight'] + if "layer_diffusion_cond" in pipe["loader_settings"]: + plot_image_vars["layer_diffusion_cond"] = pipe['loader_settings']['layer_diffusion_cond'] + if "empty_samples" in pipe["loader_settings"]: + plot_image_vars["empty_samples"] = pipe["loader_settings"]['empty_samples'] + + latent_image = sampleXYplot.get_latent(pipe["samples"]) + latents_plot = sampleXYplot.get_labels_and_sample(plot_image_vars, latent_image, preview_latent, start_step, + last_step, force_full_denoise, disable_noise) + + samp_samples = {"samples": latents_plot} + + images, image_list = sampleXYplot.plot_images_and_labels(plot_image_vars) + + # Generate output_images + output_images = torch.stack([tensor.squeeze() for tensor in image_list]) + + if layer_diffusion_method is not None: + layerDiffuse = LayerDiffuse() + new_images, samp_images, alpha = layerDiffuse.layer_diffusion_decode(layer_diffusion_method, latents_plot, blend_samples, + output_images, samp_model) + else: + new_images = output_images + samp_images = output_images + alpha = None + + results = easySave(images, save_prefix, image_output, prompt, extra_pnginfo) + + new_pipe = { + **pipe, + "positive": samp_positive, + "negative": samp_negative, + "vae": samp_vae, + "clip": samp_clip, + + "samples": samp_samples, + "blend_samples": blend_samples, + "samp_images": samp_images, + "images": new_images, + "seed": samp_seed, + "alpha": alpha, + + "loader_settings": pipe["loader_settings"], + } + + del pipe + + if image_output in ("Hide", "Hide&Save", "None"): + return {"ui": {}, "result": sampler.get_output(new_pipe,)} + + return {"ui": {"images": results}, "result": sampler.get_output(new_pipe)} + + preview_latent = True + if image_output in ("Hide", "Hide&Save", "None"): + preview_latent = False + + xyplot_id = next((x for x in prompt if "XYPlot" in str(prompt[x]["class_type"])), None) + if xyplot_id is None: + xyPlot = None + else: + xyPlot = pipe["loader_settings"]["xyplot"] if "xyplot" in pipe["loader_settings"] else xyPlot + + # Fooocus model patch + model_options = samp_model.model_options if samp_model.model_options else samp_model.model.model_options + transformer_options = model_options["transformer_options"] if "transformer_options" in model_options else {} + if "fooocus" in transformer_options: + from ..modules.fooocus import applyFooocusInpaint + del transformer_options["fooocus"] + with applyFooocusInpaint(): + if xyPlot is not None: + return process_xyPlot(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, + samp_negative, steps, cfg, sampler_name, scheduler, denoise, image_output, + link_id, save_prefix, tile_size, prompt, extra_pnginfo, my_unique_id, + preview_latent, xyPlot, force_full_denoise, disable_noise, samp_custom, noise_device) + else: + return process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, + samp_positive, samp_negative, steps, start_step, last_step, cfg, + sampler_name, scheduler, denoise, image_output, link_id, save_prefix, + tile_size, prompt, extra_pnginfo, my_unique_id, preview_latent, + force_full_denoise, disable_noise, samp_custom, noise_device) + else: + if xyPlot is not None: + return process_xyPlot(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, tile_size, prompt, extra_pnginfo, my_unique_id, preview_latent, xyPlot, force_full_denoise, disable_noise, samp_custom, noise_device) + else: + return process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive, samp_negative, steps, start_step, last_step, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, tile_size, prompt, extra_pnginfo, my_unique_id, preview_latent, force_full_denoise, disable_noise, samp_custom, noise_device) + +# η€ζ˜“采样器 +class samplerSimple(samplerFull): + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save", "None"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + + RETURN_TYPES = ("PIPE_LINE", "IMAGE",) + RETURN_NAMES = ("pipe", "image",) + OUTPUT_NODE = True + FUNCTION = "simple" + CATEGORY = "EasyUse/Sampler" + + def simple(self, pipe, image_output, link_id, save_prefix, model=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + + return super().run(pipe, None, None, None, None, None, image_output, link_id, save_prefix, + None, model, None, None, None, None, None, None, + None, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise) + +class samplerSimpleCustom(samplerFull): + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save", "None"],{"default": "None"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + + RETURN_TYPES = ("PIPE_LINE", "LATENT", "LATENT", "IMAGE") + RETURN_NAMES = ("pipe", "output", "denoised_output", "image") + OUTPUT_NODE = True + FUNCTION = "simple" + CATEGORY = "EasyUse/Sampler" + + def simple(self, pipe, image_output, link_id, save_prefix, model=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + + result = super().run(pipe, None, None, None, None, None, image_output, link_id, save_prefix, + None, model, None, None, None, None, None, None, + None, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise) + + pipe = result["result"][0] if "result" in result else None + + return ({"ui": result['ui'], "result": (pipe, pipe["samples"], pipe["blend_samples"], pipe["images"])}) + +# η€ζ˜“采样器 (Tiled) +class samplerSimpleTiled(samplerFull): + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save", "None"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}) + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE",) + RETURN_NAMES = ("pipe", "image",) + OUTPUT_NODE = True + FUNCTION = "tiled" + CATEGORY = "EasyUse/Sampler" + + def tiled(self, pipe, tile_size=512, image_output='preview', link_id=0, save_prefix='ComfyUI', model=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + + return super().run(pipe, None, None,None,None,None, image_output, link_id, save_prefix, + None, model, None, None, None, None, None, None, + tile_size, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise) + +# η€ζ˜“采样器 (LayerDiffusion) +class samplerSimpleLayerDiffusion(samplerFull): + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"], {"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}) + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE", "IMAGE", "MASK") + RETURN_NAMES = ("pipe", "final_image", "original_image", "alpha") + OUTPUT_NODE = True + OUTPUT_IS_LIST = (False, False, False, True) + FUNCTION = "layerDiffusion" + CATEGORY = "EasyUse/Sampler" + + def layerDiffusion(self, pipe, image_output='preview', link_id=0, save_prefix='ComfyUI', model=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + + result = super().run(pipe, None, None,None,None,None, image_output, link_id, save_prefix, + None, model, None, None, None, None, None, None, + None, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise) + pipe = result["result"][0] if "result" in result else None + return ({"ui":result['ui'], "result":(pipe, pipe["images"], pipe["samp_images"], pipe["alpha"])}) + +# η€ζ˜“采样器(ζ”ΆηΌ©Unet) +class samplerSimpleDownscaleUnet(samplerFull): + + upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"pipe": ("PIPE_LINE",), + "downscale_mode": (["None", "Auto", "Custom"],{"default": "Auto"}), + "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), + "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), + "downscale_after_skip": ("BOOLEAN", {"default": True}), + "downscale_method": (s.upscale_methods,), + "upscale_method": (s.upscale_methods,), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + + RETURN_TYPES = ("PIPE_LINE", "IMAGE",) + RETURN_NAMES = ("pipe", "image",) + OUTPUT_NODE = True + FUNCTION = "downscale_unet" + CATEGORY = "EasyUse/Sampler" + + def downscale_unet(self, pipe, downscale_mode, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method, image_output, link_id, save_prefix, model=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + downscale_options = None + if downscale_mode == 'Auto': + downscale_options = { + "block_number": block_number, + "downscale_factor": None, + "start_percent": 0, + "end_percent":0.35, + "downscale_after_skip": True, + "downscale_method": "bicubic", + "upscale_method": "bicubic" + } + elif downscale_mode == 'Custom': + downscale_options = { + "block_number": block_number, + "downscale_factor": downscale_factor, + "start_percent": start_percent, + "end_percent": end_percent, + "downscale_after_skip": downscale_after_skip, + "downscale_method": downscale_method, + "upscale_method": upscale_method + } + + return super().run(pipe, None, None,None,None,None, image_output, link_id, save_prefix, + None, model, None, None, None, None, None, None, + tile_size, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise, downscale_options) +# η€ζ˜“采样器 (ε†…θ‘₯) +class samplerSimpleInpainting(samplerFull): + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + "additional": (["None", "InpaintModelCond", "Differential Diffusion", "Fooocus Inpaint", "Fooocus Inpaint + DD", "Brushnet Random", "Brushnet Random + DD", "Brushnet Segmentation", "Brushnet Segmentation + DD"],{"default": "None"}) + }, + "optional": { + "model": ("MODEL",), + "mask": ("MASK",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE", "VAE") + RETURN_NAMES = ("pipe", "image", "vae") + OUTPUT_NODE = True + FUNCTION = "inpainting" + CATEGORY = "EasyUse/Sampler" + + def dd(self, model, positive, negative, pixels, vae, mask): + positive, negative, latent = InpaintModelConditioning().encode(positive, negative, pixels, vae, mask, noise_mask=True) + cls = ALL_NODE_CLASS_MAPPINGS['DifferentialDiffusion'] + if cls is not None: + model, = cls().apply(model) + else: + raise Exception("Differential Diffusion not found,please update comfyui") + return positive, negative, latent, model + + def get_brushnet_model(self, type, model): + model_type = 'sdxl' if isinstance(model.model.model_config, comfy.supported_models.SDXL) else 'sd1' + if type == 'random': + brush_model = BRUSHNET_MODELS['random_mask'][model_type]['model_url'] + if model_type == 'sdxl': + pattern = 'brushnet.random.mask.sdxl.*.(safetensors|bin)$' + else: + pattern = 'brushnet.random.mask.*.(safetensors|bin)$' + elif type == 'segmentation': + brush_model = BRUSHNET_MODELS['segmentation_mask'][model_type]['model_url'] + if model_type == 'sdxl': + pattern = 'brushnet.segmentation.mask.sdxl.*.(safetensors|bin)$' + else: + pattern = 'brushnet.segmentation.mask.*.(safetensors|bin)$' + + + brushfile = [e for e in folder_paths.get_filename_list('inpaint') if re.search(pattern, e, re.IGNORECASE)] + brushname = brushfile[0] if brushfile else None + if not brushname: + from urllib.parse import urlparse + get_local_filepath(brush_model, INPAINT_DIR) + parsed_url = urlparse(brush_model) + brushname = os.path.basename(parsed_url.path) + return brushname + + def apply_brushnet(self, brushname, model, vae, image, mask, positive, negative, scale=1.0, start_at=0, end_at=10000): + if "BrushNetLoader" not in ALL_NODE_CLASS_MAPPINGS: + raise Exception("BrushNetLoader not found,please install ComfyUI-BrushNet") + cls = ALL_NODE_CLASS_MAPPINGS['BrushNetLoader'] + brushnet, = cls().brushnet_loading(brushname, 'float16') + cls = ALL_NODE_CLASS_MAPPINGS['BrushNet'] + m, positive, negative, latent = cls().model_update(model=model, vae=vae, image=image, mask=mask, brushnet=brushnet, positive=positive, negative=negative, scale=scale, start_at=start_at, end_at=end_at) + return m, positive, negative, latent + + def inpainting(self, pipe, grow_mask_by, image_output, link_id, save_prefix, additional, model=None, mask=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + _model = model if model is not None else pipe['model'] + latent = pipe['samples'] if 'samples' in pipe else None + positive = pipe['positive'] + negative = pipe['negative'] + images = pipe["images"] if pipe and "images" in pipe else None + vae = pipe["vae"] if pipe and "vae" in pipe else None + if 'noise_mask' in latent and mask is None: + mask = latent['noise_mask'] + elif mask is not None: + if images is None: + raise Exception("No Images found") + if vae is None: + raise Exception("No VAE found") + + if additional == 'Differential Diffusion': + positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask) + elif additional == 'InpaintModelCond': + if mask is not None: + mask, = GrowMask().expand_mask(mask, grow_mask_by, False) + positive, negative, latent = InpaintModelConditioning().encode(positive, negative, images, vae, mask, True) + elif additional == 'Fooocus Inpaint': + head = list(FOOOCUS_INPAINT_HEAD.keys())[0] + patch = list(FOOOCUS_INPAINT_PATCH.keys())[0] + if mask is not None: + latent, = VAEEncodeForInpaint().encode(vae, images, mask, grow_mask_by) + _model, = ALL_NODE_CLASS_MAPPINGS['easy applyFooocusInpaint']().apply(_model, latent, head, patch) + elif additional == 'Fooocus Inpaint + DD': + head = list(FOOOCUS_INPAINT_HEAD.keys())[0] + patch = list(FOOOCUS_INPAINT_PATCH.keys())[0] + if mask is not None: + latent, = VAEEncodeForInpaint().encode(vae, images, mask, grow_mask_by) + _model, = ALL_NODE_CLASS_MAPPINGS['easy applyFooocusInpaint']().apply(_model, latent, head, patch) + positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask) + elif additional == 'Brushnet Random': + mask, = GrowMask().expand_mask(mask, grow_mask_by, False) + brush_name = self.get_brushnet_model('random', _model) + _model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, + negative) + elif additional == 'Brushnet Random + DD': + mask, = GrowMask().expand_mask(mask, grow_mask_by, False) + brush_name = self.get_brushnet_model('random', _model) + _model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, + negative) + positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask) + elif additional == 'Brushnet Segmentation': + mask, = GrowMask().expand_mask(mask, grow_mask_by, False) + brush_name = self.get_brushnet_model('segmentation', _model) + _model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, + negative) + elif additional == 'Brushnet Segmentation + DD': + mask, = GrowMask().expand_mask(mask, grow_mask_by, False) + brush_name = self.get_brushnet_model('segmentation', _model) + _model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, + negative) + positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask) + else: + latent, = VAEEncodeForInpaint().encode(vae, images, mask, grow_mask_by) + + results = super().run(pipe, None, None,None,None,None, image_output, link_id, save_prefix, + None, _model, positive, negative, latent, vae, None, None, + tile_size, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise) + + result = results['result'] + + return {"ui":results['ui'],"result":(result[0], result[1], result[0]['vae'],)} + +# SDTurbo采样器 +class samplerSDTurbo: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "model": ("MODEL",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", + "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "IMAGE",) + RETURN_NAMES = ("pipe", "image",) + OUTPUT_NODE = True + FUNCTION = "run" + + CATEGORY = "EasyUse/Sampler" + + def run(self, pipe, image_output, link_id, save_prefix, model=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None,): + # Clean loaded_objects + easyCache.update_loaded_objects(prompt) + + my_unique_id = int(my_unique_id) + + samp_model = pipe["model"] if model is None else model + samp_positive = pipe["positive"] + samp_negative = pipe["negative"] + samp_samples = pipe["samples"] + samp_vae = pipe["vae"] + samp_clip = pipe["clip"] + + samp_seed = pipe['seed'] + + samp_sampler = pipe['loader_settings']['sampler'] + + sigmas = pipe['loader_settings']['sigmas'] + cfg = pipe['loader_settings']['cfg'] + steps = pipe['loader_settings']['steps'] + + disable_noise = False + + preview_latent = True + if image_output in ("Hide", "Hide&Save"): + preview_latent = False + + # ζŽ¨η†εˆε§‹ζ—Άι—΄ + start_time = int(time.time() * 1000) + # εΌ€ε§‹ζŽ¨η† + samp_samples = sampler.custom_ksampler(samp_model, samp_seed, steps, cfg, samp_sampler, sigmas, samp_positive, samp_negative, samp_samples, + disable_noise, preview_latent) + # ζŽ¨η†η»“ζŸζ—Άι—΄ + end_time = int(time.time() * 1000) + + latent = samp_samples['samples'] + + # 解码图片 + if tile_size is not None: + samp_images = samp_vae.decode_tiled(latent, tile_x=tile_size // 8, tile_y=tile_size // 8, ) + else: + samp_images = samp_vae.decode(latent).cpu() + + # ζŽ¨η†ζ€»θ€—ζ—ΆοΌˆεŒ…ε«θ§£η οΌ‰ + end_decode_time = int(time.time() * 1000) + spent_time = 'Diffusion:' + str((end_time - start_time) / 1000) + 'β€³, VAEDecode:' + str( + (end_decode_time - end_time) / 1000) + 'β€³ ' + + # Clean loaded_objects + easyCache.update_loaded_objects(prompt) + + results = easySave(samp_images, save_prefix, image_output, prompt, extra_pnginfo) + sampler.update_value_by_id("results", my_unique_id, results) + + new_pipe = { + "model": samp_model, + "positive": samp_positive, + "negative": samp_negative, + "vae": samp_vae, + "clip": samp_clip, + + "samples": samp_samples, + "images": samp_images, + "seed": samp_seed, + + "loader_settings": { + **pipe["loader_settings"], + "spent_time": spent_time + } + } + + sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe) + + del pipe + + if image_output in ("Hide", "Hide&Save"): + return {"ui": {}, + "result": sampler.get_output(new_pipe, )} + + if image_output in ("Sender", "Sender&Save"): + PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) + + return {"ui": {"images": results}, + "result": sampler.get_output(new_pipe, )} + + +# CascadeεŒζ•΄ι‡‡ζ ·ε™¨ +class samplerCascadeFull: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "encode_vae_name": (["None"] + folder_paths.get_filename_list("vae"),), + "decode_vae_name": (["None"] + folder_paths.get_filename_list("vae"),), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default":"euler_ancestral"}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default":"simple"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + + "optional": { + "image_to_latent_c": ("IMAGE",), + "latent_c": ("LATENT",), + "model_c": ("MODEL",), + }, + "hidden":{"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + RETURN_TYPES = ("PIPE_LINE", "MODEL", "LATENT") + RETURN_NAMES = ("pipe", "model_b", "latent_b") + OUTPUT_NODE = True + + FUNCTION = "run" + CATEGORY = "EasyUse/Sampler" + + def run(self, pipe, encode_vae_name, decode_vae_name, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, seed, image_to_latent_c=None, latent_c=None, model_c=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + + encode_vae_name = encode_vae_name if encode_vae_name is not None else pipe['loader_settings']['encode_vae_name'] + decode_vae_name = decode_vae_name if decode_vae_name is not None else pipe['loader_settings']['decode_vae_name'] + + batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1 + if image_to_latent_c is not None: + if encode_vae_name != 'None': + encode_vae = easyCache.load_vae(encode_vae_name) + else: + encode_vae = pipe['vae'][0] + if "compression" not in pipe["loader_settings"]: + raise Exception("compression is not found") + + compression = pipe["loader_settings"]['compression'] + width = image_to_latent_c.shape[-2] + height = image_to_latent_c.shape[-3] + out_width = (width // compression) * encode_vae.downscale_ratio + out_height = (height // compression) * encode_vae.downscale_ratio + + s = comfy.utils.common_upscale(image_to_latent_c.movedim(-1, 1), out_width, out_height, "bicubic", + "center").movedim(1, -1) + latent_c = encode_vae.encode(s[:, :, :, :3]) + latent_b = torch.zeros([latent_c.shape[0], 4, height // 4, width // 4]) + + samples_c = {"samples": latent_c} + samples_c = RepeatLatentBatch().repeat(samples_c, batch_size)[0] + + samples_b = {"samples": latent_b} + samples_b = RepeatLatentBatch().repeat(samples_b, batch_size)[0] + images = image_to_latent_c + elif latent_c is not None: + samples_c = latent_c + samples_b = pipe["samples"][1] + images = pipe["images"] + else: + samples_c = pipe["samples"][0] + samples_b = pipe["samples"][1] + images = pipe["images"] + + # Clean loaded_objects + easyCache.update_loaded_objects(prompt) + samp_model = model_c if model_c else pipe["model"][0] + samp_positive = pipe["positive"] + samp_negative = pipe["negative"] + samp_samples = samples_c + + samp_seed = seed if seed is not None else pipe['seed'] + + steps = steps if steps is not None else pipe['loader_settings']['steps'] + start_step = pipe['loader_settings']['start_step'] if 'start_step' in pipe['loader_settings'] else 0 + last_step = pipe['loader_settings']['last_step'] if 'last_step' in pipe['loader_settings'] else 10000 + cfg = cfg if cfg is not None else pipe['loader_settings']['cfg'] + sampler_name = sampler_name if sampler_name is not None else pipe['loader_settings']['sampler_name'] + scheduler = scheduler if scheduler is not None else pipe['loader_settings']['scheduler'] + denoise = denoise if denoise is not None else pipe['loader_settings']['denoise'] + noise_device = 'gpu' if "a1111_prompt_style" in pipe['loader_settings'] and pipe['loader_settings']['a1111_prompt_style'] else 'cpu' + # ζŽ¨η†εˆε§‹ζ—Άι—΄ + start_time = int(time.time() * 1000) + # εΌ€ε§‹ζŽ¨η† + samp_samples = sampler.common_ksampler(samp_model, samp_seed, steps, cfg, sampler_name, scheduler, + samp_positive, samp_negative, samp_samples, denoise=denoise, + preview_latent=False, start_step=start_step, + last_step=last_step, force_full_denoise=False, + disable_noise=False, noise_device=noise_device) + # ζŽ¨η†η»“ζŸζ—Άι—΄ + end_time = int(time.time() * 1000) + stage_c = samp_samples["samples"] + results = None + + if image_output not in ['Hide', 'Hide&Save']: + if decode_vae_name != 'None': + decode_vae = easyCache.load_vae(decode_vae_name) + else: + decode_vae = pipe['vae'][0] + samp_images = decode_vae.decode(stage_c).cpu() + + results = easySave(samp_images, save_prefix, image_output, prompt, extra_pnginfo) + sampler.update_value_by_id("results", my_unique_id, results) + + # ζŽ¨η†ζ€»θ€—ζ—ΆοΌˆεŒ…ε«θ§£η οΌ‰ + end_decode_time = int(time.time() * 1000) + spent_time = 'Diffusion:' + str((end_time - start_time) / 1000) + 'β€³, VAEDecode:' + str( + (end_decode_time - end_time) / 1000) + 'β€³ ' + + # Clean loaded_objects + easyCache.update_loaded_objects(prompt) + # zero_out + c1 = [] + for t in samp_positive: + d = t[1].copy() + if "pooled_output" in d: + d["pooled_output"] = torch.zeros_like(d["pooled_output"]) + n = [torch.zeros_like(t[0]), d] + c1.append(n) + # stage_b_conditioning + c2 = [] + for t in c1: + d = t[1].copy() + d['stable_cascade_prior'] = stage_c + n = [t[0], d] + c2.append(n) + + + new_pipe = { + "model": pipe['model'][1], + "positive": c2, + "negative": c1, + "vae": pipe['vae'][1], + "clip": pipe['clip'], + + "samples": samples_b, + "images": images, + "seed": seed, + + "loader_settings": { + **pipe["loader_settings"], + "spent_time": spent_time + } + } + sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe) + + del pipe + + if image_output in ("Hide", "Hide&Save"): + return {"ui": {}, + "result": sampler.get_output(new_pipe, )} + + if image_output in ("Sender", "Sender&Save") and results is not None: + PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) + + return {"ui": {"images": results}, "result": (new_pipe, new_pipe['model'], new_pipe['samples'])} + +# η€ζ˜“采样器Cascade +class samplerCascadeSimple(samplerCascadeFull): + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"pipe": ("PIPE_LINE",), + "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"], {"default": "Preview"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "optional": { + "model_c": ("MODEL",), + }, + "hidden": + {"tile_size": "INT", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", + "embeddingsList": (folder_paths.get_filename_list("embeddings"),) + } + } + + + RETURN_TYPES = ("PIPE_LINE", "IMAGE",) + RETURN_NAMES = ("pipe", "image",) + OUTPUT_NODE = True + FUNCTION = "simple" + CATEGORY = "EasyUse/Sampler" + + def simple(self, pipe, image_output, link_id, save_prefix, model_c=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False): + + return super().run(pipe, None, None,None, None,None,None,None, image_output, link_id, save_prefix, + None, None, None, model_c, tile_size, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise) + +class unsampler: + @classmethod + def INPUT_TYPES(s): + return {"required":{ + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "end_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + "normalize": (["disable", "enable"],), + + }, + "optional": { + "pipe": ("PIPE_LINE",), + "optional_model": ("MODEL",), + "optional_positive": ("CONDITIONING",), + "optional_negative": ("CONDITIONING",), + "optional_latent": ("LATENT",), + } + } + + RETURN_TYPES = ("PIPE_LINE", "LATENT",) + RETURN_NAMES = ("pipe", "latent",) + FUNCTION = "unsampler" + + CATEGORY = "EasyUse/Sampler" + + def unsampler(self, cfg, sampler_name, steps, end_at_step, scheduler, normalize, pipe=None, optional_model=None, optional_positive=None, optional_negative=None, + optional_latent=None): + + model = optional_model if optional_model is not None else pipe["model"] + positive = optional_positive if optional_positive is not None else pipe["positive"] + negative = optional_negative if optional_negative is not None else pipe["negative"] + latent_image = optional_latent if optional_latent is not None else pipe["samples"] + + normalize = normalize == "enable" + device = comfy.model_management.get_torch_device() + latent = latent_image + latent_image = latent["samples"] + + end_at_step = min(end_at_step, steps - 1) + end_at_step = steps - end_at_step + + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + noise_mask = None + if "noise_mask" in latent: + noise_mask = comfy.sampler_helpers.prepare_mask(latent["noise_mask"], noise.shape, device) + + noise = noise.to(device) + latent_image = latent_image.to(device) + + _positive = comfy.sampler_helpers.convert_cond(positive) + _negative = comfy.sampler_helpers.convert_cond(negative) + models, inference_memory = comfy.sampler_helpers.get_additional_models({"positive": _positive, "negative": _negative}, model.model_dtype()) + + + comfy.model_management.load_models_gpu([model] + models, model.memory_required(noise.shape) + inference_memory) + + model_patcher = comfy.model_patcher.ModelPatcher(model.model, load_device=device, offload_device=comfy.model_management.unet_offload_device()) + + sampler = comfy.samplers.KSampler(model_patcher, steps=steps, device=device, sampler=sampler_name, + scheduler=scheduler, denoise=1.0, model_options=model.model_options) + + sigmas = sampler.sigmas.flip(0) + 0.0001 + + pbar = comfy.utils.ProgressBar(steps) + + def callback(step, x0, x, total_steps): + pbar.update_absolute(step + 1, total_steps) + + samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, + force_full_denoise=False, denoise_mask=noise_mask, sigmas=sigmas, start_step=0, + last_step=end_at_step, callback=callback) + if normalize: + # technically doesn't normalize because unsampling is not guaranteed to end at a std given by the schedule + samples -= samples.mean() + samples /= samples.std() + samples = samples.cpu() + + comfy.sample.cleanup_additional_models(models) + + out = latent.copy() + out["samples"] = samples + + if pipe is None: + pipe = {} + + new_pipe = { + **pipe, + "samples": out + } + + return (new_pipe, out,) + + +NODE_CLASS_MAPPINGS = { + # kSampler k采样器 + "easy fullkSampler": samplerFull, + "easy kSampler": samplerSimple, + "easy kSamplerCustom": samplerSimpleCustom, + "easy kSamplerTiled": samplerSimpleTiled, + "easy kSamplerLayerDiffusion": samplerSimpleLayerDiffusion, + "easy kSamplerInpainting": samplerSimpleInpainting, + "easy kSamplerDownscaleUnet": samplerSimpleDownscaleUnet, + "easy kSamplerSDTurbo": samplerSDTurbo, + "easy fullCascadeKSampler": samplerCascadeFull, + "easy cascadeKSampler": samplerCascadeSimple, + "easy unSampler": unsampler, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy kSampler": "EasyKSampler", + "easy kSamplerCustom": "EasyKSampler (Custom)", + "easy fullkSampler": "EasyKSampler (Full)", + "easy kSamplerTiled": "EasyKSampler (Tiled Decode)", + "easy kSamplerLayerDiffusion": "EasyKSampler (LayerDiffuse)", + "easy kSamplerInpainting": "EasyKSampler (Inpainting)", + "easy kSamplerDownscaleUnet": "EasyKsampler (Downscale Unet)", + "easy kSamplerSDTurbo": "EasyKSampler (SDTurbo)", + "easy cascadeKSampler": "EasyCascadeKsampler", + "easy fullCascadeKSampler": "EasyCascadeKsampler (Full)", + "easy unSampler": "EasyUnSampler", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/seed.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/seed.py new file mode 100644 index 00000000..e81b25f1 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/seed.py @@ -0,0 +1,106 @@ +from ..config import MAX_SEED_NUM +import hashlib +import random + +class easySeed: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("seed",) + FUNCTION = "doit" + + CATEGORY = "EasyUse/Seed" + + def doit(self, seed=0, prompt=None, extra_pnginfo=None, my_unique_id=None): + return seed, + +class seedList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "min_num": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + "max_num": ("INT", {"default": MAX_SEED_NUM, "max": MAX_SEED_NUM, "min": 0 }), + "method": (["random", "increment", "decrement"], {"default": "random"}), + "total": ("INT", {"default": 1, "min": 1, "max": 100000}), + "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM,}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("seed", "total") + FUNCTION = "doit" + DESCRIPTION = "Random number seed that can be used in a for loop, by connecting index and easy indexAny node to realize different seed values in the loop." + + CATEGORY = "EasyUse/Seed" + + def doit(self, min_num, max_num, method, total, seed=0, prompt=None, extra_pnginfo=None, my_unique_id=None): + random.seed(seed) + + seed_list = [] + if min_num > max_num: + min_num, max_num = max_num, min_num + for i in range(total): + if method == 'random': + s = random.randint(min_num, max_num) + elif method == 'increment': + s = min_num + i + if s > max_num: + s = max_num + elif method == 'decrement': + s = max_num - i + if s < min_num: + s = min_num + seed_list.append(s) + return seed_list, total + + @classmethod + def IS_CHANGED(s, seed, **kwargs): + m = hashlib.sha256() + m.update(seed) + return m.digest().hex() + +# ε…¨ε±€ιšζœΊη§ +class globalSeed: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "value": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}), + "mode": ("BOOLEAN", {"default": True, "label_on": "control_before_generate", "label_off": "control_after_generate"}), + "action": (["fixed", "increment", "decrement", "randomize", + "increment for each node", "decrement for each node", "randomize for each node"], ), + "last_seed": ("STRING", {"default": ""}), + } + } + + RETURN_TYPES = () + FUNCTION = "doit" + + CATEGORY = "EasyUse/Seed" + + OUTPUT_NODE = True + + def doit(self, **kwargs): + return {} + + +NODE_CLASS_MAPPINGS = { + "easy seed": easySeed, + "easy seedList": seedList, + "easy globalSeed": globalSeed, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy seed": "EasySeed", + "easy seedList": "EasySeedList", + "easy globalSeed": "EasyGlobalSeed", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/util.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/util.py new file mode 100644 index 00000000..f3e7602f --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/util.py @@ -0,0 +1,142 @@ +import os +import folder_paths +from ..libs.utils import AlwaysEqualProxy + +class showLoaderSettingsNames: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pipe": ("PIPE_LINE",), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + RETURN_TYPES = ("STRING", "STRING", "STRING",) + RETURN_NAMES = ("ckpt_name", "vae_name", "lora_name") + + FUNCTION = "notify" + OUTPUT_NODE = True + + CATEGORY = "EasyUse/Util" + + def notify(self, pipe, names=None, unique_id=None, extra_pnginfo=None): + if unique_id and extra_pnginfo and "workflow" in extra_pnginfo: + workflow = extra_pnginfo["workflow"] + node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id), None) + if node: + ckpt_name = pipe['loader_settings']['ckpt_name'] if 'ckpt_name' in pipe['loader_settings'] else '' + vae_name = pipe['loader_settings']['vae_name'] if 'vae_name' in pipe['loader_settings'] else '' + lora_name = pipe['loader_settings']['lora_name'] if 'lora_name' in pipe['loader_settings'] else '' + + if ckpt_name: + ckpt_name = os.path.basename(os.path.splitext(ckpt_name)[0]) + if vae_name: + vae_name = os.path.basename(os.path.splitext(vae_name)[0]) + if lora_name: + lora_name = os.path.basename(os.path.splitext(lora_name)[0]) + + names = "ckpt_name: " + ckpt_name + '\n' + "vae_name: " + vae_name + '\n' + "lora_name: " + lora_name + node["widgets_values"] = names + + return {"ui": {"text": [names]}, "result": (ckpt_name, vae_name, lora_name)} + +class sliderControl: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mode": (['ipadapter layer weights'],), + "model_type": (['sdxl', 'sd1'],), + }, + "hidden": { + "prompt": "PROMPT", + "my_unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("layer_weights",) + + FUNCTION = "control" + + CATEGORY = "EasyUse/Util" + + def control(self, mode, model_type, prompt=None, my_unique_id=None, extra_pnginfo=None): + values = '' + if my_unique_id in prompt: + if 'values' in prompt[my_unique_id]["inputs"]: + values = prompt[my_unique_id]["inputs"]['values'] + + return (values,) + +class setCkptName: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), + } + } + + RETURN_TYPES = (AlwaysEqualProxy('*'),) + RETURN_NAMES = ("ckpt_name",) + FUNCTION = "set_name" + CATEGORY = "EasyUse/Util" + + def set_name(self, ckpt_name): + return (ckpt_name,) + +class setControlName: + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "controlnet_name": (folder_paths.get_filename_list("controlnet"),), + } + } + + RETURN_TYPES = (AlwaysEqualProxy('*'),) + RETURN_NAMES = ("controlnet_name",) + FUNCTION = "set_name" + CATEGORY = "EasyUse/Util" + + def set_name(self, controlnet_name): + return (controlnet_name,) + +class setLoraName: + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "lora_name": (folder_paths.get_filename_list("loras"),), + } + } + + RETURN_TYPES = (AlwaysEqualProxy('*'),) + RETURN_NAMES = ("lora_name",) + FUNCTION = "set_name" + CATEGORY = "EasyUse/Util" + + def set_name(self, lora_name): + return (lora_name,) + + +NODE_CLASS_MAPPINGS = { + "easy showLoaderSettingsNames": showLoaderSettingsNames, + "easy sliderControl": sliderControl, + "easy ckptNames": setCkptName, + "easy controlnetNames": setControlName, + "easy loraNames": setLoraName, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy showLoaderSettingsNames": "Show Loader Settings Names", + "easy sliderControl": "Easy Slider Control", + "easy ckptNames": "Ckpt Names", + "easy controlnetNames": "ControlNet Names", + "easy loraNames": "Lora Names", +} diff --git a/custom_nodes/ComfyUI-Easy-Use/py/nodes/xyplot.py b/custom_nodes/ComfyUI-Easy-Use/py/nodes/xyplot.py new file mode 100644 index 00000000..5840a098 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/nodes/xyplot.py @@ -0,0 +1,699 @@ +import os +import json +import comfy +import folder_paths +from ..config import RESOURCES_DIR +from ..libs.utils import getMetadata +def load_preset(filename): + path = os.path.join(RESOURCES_DIR, filename) + path = os.path.abspath(path) + preset_list = [] + + if os.path.exists(path): + with open(path, 'r') as file: + for line in file: + preset_list.append(line.strip()) + + return preset_list + else: + return [] +def generate_floats(batch_count, first_float, last_float): + if batch_count > 1: + interval = (last_float - first_float) / (batch_count - 1) + values = [str(round(first_float + i * interval, 3)) for i in range(batch_count)] + else: + values = [str(first_float)] if batch_count == 1 else [] + return "; ".join(values) + +def generate_ints(batch_count, first_int, last_int): + if batch_count > 1: + interval = (last_int - first_int) / (batch_count - 1) + values = [str(int(first_int + i * interval)) for i in range(batch_count)] + else: + values = [str(first_int)] if batch_count == 1 else [] + # values = list(set(values)) # Remove duplicates + # values.sort() # Sort in ascending order + return "; ".join(values) + +# Seed++ Batch +class XYplot_SeedsBatch: + + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "batch_count": ("INT", {"default": 3, "min": 1, "max": 50}), }, + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, batch_count): + + axis = "advanced: Seeds++ Batch" + xy_values = {"axis": axis, "values": batch_count} + return (xy_values,) + +# Step Values +class XYplot_Steps: + parameters = ["steps", "start_at_step", "end_at_step",] + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "target_parameter": (cls.parameters,), + "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}), + "first_step": ("INT", {"default": 10, "min": 1, "max": 10000}), + "last_step": ("INT", {"default": 20, "min": 1, "max": 10000}), + "first_start_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "last_start_step": ("INT", {"default": 10, "min": 0, "max": 10000}), + "first_end_step": ("INT", {"default": 10, "min": 0, "max": 10000}), + "last_end_step": ("INT", {"default": 20, "min": 0, "max": 10000}), + } + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, target_parameter, batch_count, first_step, last_step, first_start_step, last_start_step, + first_end_step, last_end_step,): + + axis, xy_first, xy_last = None, None, None + + if target_parameter == "steps": + axis = "advanced: Steps" + xy_first = first_step + xy_last = last_step + elif target_parameter == "start_at_step": + axis = "advanced: StartStep" + xy_first = first_start_step + xy_last = last_start_step + elif target_parameter == "end_at_step": + axis = "advanced: EndStep" + xy_first = first_end_step + xy_last = last_end_step + + values = generate_ints(batch_count, xy_first, xy_last) + return ({"axis": axis, "values": values},) if values is not None else (None,) + +class XYplot_CFG: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}), + "first_cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}), + "last_cfg": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 100.0}), + } + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, batch_count, first_cfg, last_cfg): + axis = "advanced: CFG Scale" + values = generate_floats(batch_count, first_cfg, last_cfg) + return ({"axis": axis, "values": values},) if values else (None,) + +class XYplot_FluxGuidance: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}), + "first_guidance": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + "last_guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0}), + } + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, batch_count, first_guidance, last_guidance): + axis = "advanced: Flux Guidance" + values = generate_floats(batch_count, first_guidance, last_guidance) + return ({"axis": axis, "values": values},) if values else (None,) + +# Step Values +class XYplot_Sampler_Scheduler: + parameters = ["sampler", "scheduler", "sampler & scheduler"] + + @classmethod + def INPUT_TYPES(cls): + samplers = ["None"] + comfy.samplers.KSampler.SAMPLERS + schedulers = ["None"] + comfy.samplers.KSampler.SCHEDULERS + inputs = { + "required": { + "target_parameter": (cls.parameters,), + "input_count": ("INT", {"default": 1, "min": 1, "max": 30, "step": 1}) + } + } + for i in range(1, 30 + 1): + inputs["required"][f"sampler_{i}"] = (samplers,) + inputs["required"][f"scheduler_{i}"] = (schedulers,) + + return inputs + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, target_parameter, input_count, **kwargs): + axis, values, = None, None, + if target_parameter == "scheduler": + axis = "advanced: Scheduler" + schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)] + values = [scheduler for scheduler in schedulers if scheduler != "None"] + elif target_parameter == "sampler": + axis = "advanced: Sampler" + samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)] + values = [sampler for sampler in samplers if sampler != "None"] + else: + axis = "advanced: Sampler&Scheduler" + samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)] + schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)] + values = [] + for sampler, scheduler in zip(samplers, schedulers): + sampler = sampler if sampler else 'None' + scheduler = scheduler if scheduler else 'None' + values.append(sampler +','+ scheduler) + values = "; ".join(values) + return ({"axis": axis, "values": values},) if values else (None,) + +class XYplot_Denoise: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}), + "first_denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}), + "last_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}), + } + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, batch_count, first_denoise, last_denoise): + axis = "advanced: Denoise" + values = generate_floats(batch_count, first_denoise, last_denoise) + return ({"axis": axis, "values": values},) if values else (None,) + +# PromptSR +class XYplot_PromptSR: + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "target_prompt": (["positive", "negative"],), + "search_txt": ("STRING", {"default": "", "multiline": False}), + "replace_all_text": ("BOOLEAN", {"default": False}), + "replace_count": ("INT", {"default": 3, "min": 1, "max": 30 - 1}), + } + } + + # Dynamically add replace_X inputs + for i in range(1, 30): + replace_key = f"replace_{i}" + inputs["required"][replace_key] = ("STRING", {"default": "", "multiline": False, "placeholder": replace_key}) + + return inputs + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, target_prompt, search_txt, replace_all_text, replace_count, **kwargs): + axis = None + + if target_prompt == "positive": + axis = "advanced: Positive Prompt S/R" + elif target_prompt == "negative": + axis = "advanced: Negative Prompt S/R" + + # Create base entry + values = [(search_txt, None, replace_all_text)] + + if replace_count > 0: + # Append additional entries based on replace_count + values.extend([(search_txt, kwargs.get(f"replace_{i+1}"), replace_all_text) for i in range(replace_count)]) + return ({"axis": axis, "values": values},) if values is not None else (None,) + +# XYPlot Pos Condition +class XYplot_Positive_Cond: + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "optional": { + "positive_1": ("CONDITIONING",), + "positive_2": ("CONDITIONING",), + "positive_3": ("CONDITIONING",), + "positive_4": ("CONDITIONING",), + } + } + + return inputs + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, positive_1=None, positive_2=None, positive_3=None, positive_4=None): + axis = "advanced: Pos Condition" + values = [] + cond = [] + # Create base entry + if positive_1 is not None: + values.append("0") + cond.append(positive_1) + if positive_2 is not None: + values.append("1") + cond.append(positive_2) + if positive_3 is not None: + values.append("2") + cond.append(positive_3) + if positive_4 is not None: + values.append("3") + cond.append(positive_4) + + return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,) + +# XYPlot Neg Condition +class XYplot_Negative_Cond: + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "optional": { + "negative_1": ("CONDITIONING",), + "negative_2": ("CONDITIONING",), + "negative_3": ("CONDITIONING",), + "negative_4": ("CONDITIONING",), + } + } + + return inputs + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, negative_1=None, negative_2=None, negative_3=None, negative_4=None): + axis = "advanced: Neg Condition" + values = [] + cond = [] + # Create base entry + if negative_1 is not None: + values.append(0) + cond.append(negative_1) + if negative_2 is not None: + values.append(1) + cond.append(negative_2) + if negative_3 is not None: + values.append(2) + cond.append(negative_3) + if negative_4 is not None: + values.append(3) + cond.append(negative_4) + + return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,) + +# XYPlot Pos Condition List +class XYplot_Positive_Cond_List: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive": ("CONDITIONING",), + } + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, positive): + axis = "advanced: Pos Condition" + values = [] + cond = [] + for index, c in enumerate(positive): + values.append(str(index)) + cond.append(c) + + return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,) + +# XYPlot Neg Condition List +class XYplot_Negative_Cond_List: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "negative": ("CONDITIONING",), + } + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, negative): + axis = "advanced: Neg Condition" + values = [] + cond = [] + for index, c in enumerate(negative): + values.append(index) + cond.append(c) + + return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,) + +# XY Plot: ControlNet +class XYplot_Control_Net: + parameters = ["strength", "start_percent", "end_percent"] + @classmethod + def INPUT_TYPES(cls): + def get_file_list(filenames): + return [file for file in filenames if file != "put_models_here.txt" and "lllite" not in file] + + return { + "required": { + "control_net_name": (get_file_list(folder_paths.get_filename_list("controlnet")),), + "image": ("IMAGE",), + "target_parameter": (cls.parameters,), + "batch_count": ("INT", {"default": 3, "min": 1, "max": 30}), + "first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}), + "last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}), + "first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}), + "last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}), + "first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}), + "last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}), + }, + "optional": { + "control_net": ("CONTROL_NET",), + }, + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, control_net_name, image, target_parameter, batch_count, first_strength, last_strength, first_start_percent, + last_start_percent, first_end_percent, last_end_percent, strength, start_percent, end_percent, control_net=None): + + axis, = None, + + values = [] + + if target_parameter == "strength": + axis = "advanced: ControlNetStrength" + + values.append([(control_net_name, image, first_strength, start_percent, end_percent, control_net)]) + strength_increment = (last_strength - first_strength) / (batch_count - 1) if batch_count > 1 else 0 + for i in range(1, batch_count - 1): + values.append([(control_net_name, image, first_strength + i * strength_increment, start_percent, + end_percent, control_net)]) + if batch_count > 1: + values.append([(control_net_name, image, last_strength, start_percent, end_percent, control_net)]) + + elif target_parameter == "start_percent": + axis = "advanced: ControlNetStart%" + + percent_increment = (last_start_percent - first_start_percent) / (batch_count - 1) if batch_count > 1 else 0 + values.append([(control_net_name, image, strength, first_start_percent, end_percent, control_net)]) + for i in range(1, batch_count - 1): + values.append([(control_net_name, image, strength, first_start_percent + i * percent_increment, + end_percent, control_net)]) + + # Always add the last start_percent if batch_count is more than 1. + if batch_count > 1: + values.append([(control_net_name, image, strength, last_start_percent, end_percent, control_net)]) + + elif target_parameter == "end_percent": + axis = "advanced: ControlNetEnd%" + + percent_increment = (last_end_percent - first_end_percent) / (batch_count - 1) if batch_count > 1 else 0 + values.append([(control_net_name, image, strength, start_percent, first_end_percent, control_net)]) + for i in range(1, batch_count - 1): + values.append([(control_net_name, image, strength, start_percent, + first_end_percent + i * percent_increment, control_net)]) + + if batch_count > 1: + values.append([(control_net_name, image, strength, start_percent, last_end_percent, control_net)]) + + + return ({"axis": axis, "values": values},) + + +#Checkpoints +class XYplot_Checkpoint: + + modes = ["Ckpt Names", "Ckpt Names+ClipSkip", "Ckpt Names+ClipSkip+VAE"] + + @classmethod + def INPUT_TYPES(cls): + + checkpoints = ["None"] + folder_paths.get_filename_list("checkpoints") + vaes = ["Baked VAE"] + folder_paths.get_filename_list("vae") + + inputs = { + "required": { + "input_mode": (cls.modes,), + "ckpt_count": ("INT", {"default": 3, "min": 0, "max": 10, "step": 1}), + } + } + + for i in range(1, 10 + 1): + inputs["required"][f"ckpt_name_{i}"] = (checkpoints,) + inputs["required"][f"clip_skip_{i}"] = ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}) + inputs["required"][f"vae_name_{i}"] = (vaes,) + + inputs["optional"] = { + "optional_lora_stack": ("LORA_STACK",) + } + return inputs + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, input_mode, ckpt_count, **kwargs): + + axis = "advanced: Checkpoint" + + checkpoints = [kwargs.get(f"ckpt_name_{i}") for i in range(1, ckpt_count + 1)] + clip_skips = [kwargs.get(f"clip_skip_{i}") for i in range(1, ckpt_count + 1)] + vaes = [kwargs.get(f"vae_name_{i}") for i in range(1, ckpt_count + 1)] + + # Set None for Clip Skip and/or VAE if not correct modes + for i in range(ckpt_count): + if "ClipSkip" not in input_mode: + clip_skips[i] = 'None' + if "VAE" not in input_mode: + vaes[i] = 'None' + + # Extend each sub-array with lora_stack if it's not None + values = [checkpoint.replace(',', '*')+','+str(clip_skip)+','+vae.replace(',', '*') for checkpoint, clip_skip, vae in zip(checkpoints, clip_skips, vaes) if + checkpoint != "None"] + + optional_lora_stack = kwargs.get("optional_lora_stack") if "optional_lora_stack" in kwargs else [] + + xy_values = {"axis": axis, "values": values, "lora_stack": optional_lora_stack} + return (xy_values,) + +#Loras +class XYplot_Lora: + + modes = ["Lora Names", "Lora Names+Weights"] + + @classmethod + def INPUT_TYPES(cls): + loras = ["None"] + folder_paths.get_filename_list("loras") + + inputs = { + "required": { + "input_mode": (cls.modes,), + "lora_count": ("INT", {"default": 3, "min": 0, "max": 10, "step": 1}), + "model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + } + } + + for i in range(1, 10 + 1): + inputs["required"][f"lora_name_{i}"] = (loras,) + inputs["required"][f"model_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}) + inputs["required"][f"clip_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}) + + inputs["optional"] = { + "optional_lora_stack": ("LORA_STACK",), + "display_trigger_word": ("BOOLEAN", {"display_trigger_word": True, "tooltip": "Trigger words showing lora model pass through the model's metadata, but not necessarily accurately."}), + } + return inputs + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + + CATEGORY = "EasyUse/XY Inputs" + + def sort_tags_by_frequency(self, meta_tags): + if meta_tags is None: + return [] + if "ss_tag_frequency" in meta_tags: + meta_tags = meta_tags["ss_tag_frequency"] + meta_tags = json.loads(meta_tags) + sorted_tags = {} + for _, dataset in meta_tags.items(): + for tag, count in dataset.items(): + tag = str(tag).strip() + if tag in sorted_tags: + sorted_tags[tag] = sorted_tags[tag] + count + else: + sorted_tags[tag] = count + # sort tags by training frequency. Most seen tags firsts + sorted_tags = dict(sorted(sorted_tags.items(), key=lambda item: item[1], reverse=True)) + return list(sorted_tags.keys()) + else: + return [] + + def get_trigger_words(self, lora_name, display=False): + if not display: + return "" + + file_path = folder_paths.get_full_path('loras', lora_name) + if not file_path: + return '' + header = getMetadata(file_path) + header_json = json.loads(header) + meta = header_json["__metadata__"] if "__metadata__" in header_json else None + tags = self.sort_tags_by_frequency(meta) + return ' '+ tags[0] if len(tags) > 0 else '' + def xy_value(self, input_mode, lora_count, model_strength, clip_strength, display_trigger_words=True, **kwargs): + + axis = "advanced: Lora" + # Extract values from kwargs + loras = [kwargs.get(f"lora_name_{i}") for i in range(1, lora_count + 1)] + model_strs = [kwargs.get(f"model_str_{i}", model_strength) for i in range(1, lora_count + 1)] + clip_strs = [kwargs.get(f"clip_str_{i}", clip_strength) for i in range(1, lora_count + 1)] + + # Use model_strength and clip_strength for the loras where values are not provided + if "Weights" not in input_mode: + for i in range(lora_count): + model_strs[i] = model_strength + clip_strs[i] = clip_strength + + # Extend each sub-array with lora_stack if it's not None + values = [lora.replace(',', '*')+','+str(model_str)+','+str(clip_str) +',' + self.get_trigger_words(lora, display_trigger_words) for lora, model_str, clip_str + in zip(loras, model_strs, clip_strs) if lora != "None"] + + optional_lora_stack = kwargs.get("optional_lora_stack") if "optional_lora_stack" in kwargs else [] + + xy_values = {"axis": axis, "values": values, "lora_stack": optional_lora_stack} + return (xy_values,) + +# ζ¨‘εž‹ε εŠ  +class XYplot_ModelMergeBlocks: + + @classmethod + def INPUT_TYPES(s): + checkpoints = folder_paths.get_filename_list("checkpoints") + vae = ["Use Model 1", "Use Model 2"] + folder_paths.get_filename_list("vae") + + preset = ["Preset"] # 20 + preset += load_preset("mmb-preset.txt") + preset += load_preset("mmb-preset.custom.txt") + + default_vectors = "1,0,0; \n0,1,0; \n0,0,1; \n1,1,0; \n1,0,1; \n0,1,1; " + return { + "required": { + "ckpt_name_1": (checkpoints,), + "ckpt_name_2": (checkpoints,), + "vae_use": (vae, {"default": "Use Model 1"}), + "preset": (preset, {"default": "preset"}), + "values": ("STRING", {"default": default_vectors, "multiline": True, "placeholder": 'Support 2 methods:\n\n1.input, middle, out in same line and insert values seperated by "; "\n\n2.model merge block number seperated by ", " in same line and insert values seperated by "; "'}), + }, + "hidden": {"my_unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("X_Y",) + RETURN_NAMES = ("X or Y",) + FUNCTION = "xy_value" + + CATEGORY = "EasyUse/XY Inputs" + + def xy_value(self, ckpt_name_1, ckpt_name_2, vae_use, preset, values, my_unique_id=None): + + axis = "advanced: ModelMergeBlocks" + if ckpt_name_1 is None: + raise Exception("ckpt_name_1 is not found") + if ckpt_name_2 is None: + raise Exception("ckpt_name_2 is not found") + + models = (ckpt_name_1, ckpt_name_2) + + xy_values = {"axis":axis, "values":values, "models":models, "vae_use": vae_use} + return (xy_values,) + + +NODE_CLASS_MAPPINGS = { + "easy XYInputs: Seeds++ Batch": XYplot_SeedsBatch, + "easy XYInputs: Steps": XYplot_Steps, + "easy XYInputs: CFG Scale": XYplot_CFG, + "easy XYInputs: FluxGuidance": XYplot_FluxGuidance, + "easy XYInputs: Sampler/Scheduler": XYplot_Sampler_Scheduler, + "easy XYInputs: Denoise": XYplot_Denoise, + "easy XYInputs: Checkpoint": XYplot_Checkpoint, + "easy XYInputs: Lora": XYplot_Lora, + "easy XYInputs: ModelMergeBlocks": XYplot_ModelMergeBlocks, + "easy XYInputs: PromptSR": XYplot_PromptSR, + "easy XYInputs: ControlNet": XYplot_Control_Net, + "easy XYInputs: PositiveCond": XYplot_Positive_Cond, + "easy XYInputs: PositiveCondList": XYplot_Positive_Cond_List, + "easy XYInputs: NegativeCond": XYplot_Negative_Cond, + "easy XYInputs: NegativeCondList": XYplot_Negative_Cond_List, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy XYInputs: Seeds++ Batch": "XY Inputs: Seeds++ Batch //EasyUse", + "easy XYInputs: Steps": "XY Inputs: Steps //EasyUse", + "easy XYInputs: CFG Scale": "XY Inputs: CFG Scale //EasyUse", + "easy XYInputs: FluxGuidance": "XY Inputs: Flux Guidance //EasyUse", + "easy XYInputs: Sampler/Scheduler": "XY Inputs: Sampler/Scheduler //EasyUse", + "easy XYInputs: Denoise": "XY Inputs: Denoise //EasyUse", + "easy XYInputs: Checkpoint": "XY Inputs: Checkpoint //EasyUse", + "easy XYInputs: Lora": "XY Inputs: Lora //EasyUse", + "easy XYInputs: ModelMergeBlocks": "XY Inputs: ModelMergeBlocks //EasyUse", + "easy XYInputs: PromptSR": "XY Inputs: PromptSR //EasyUse", + "easy XYInputs: ControlNet": "XY Inputs: Controlnet //EasyUse", + "easy XYInputs: PositiveCond": "XY Inputs: PosCond //EasyUse", + "easy XYInputs: PositiveCondList": "XY Inputs: PosCondList //EasyUse", + "easy XYInputs: NegativeCond": "XY Inputs: NegCond //EasyUse", + "easy XYInputs: NegativeCondList": "XY Inputs: NegCondList //EasyUse", +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/py/routes.py b/custom_nodes/ComfyUI-Easy-Use/py/routes.py new file mode 100644 index 00000000..63a7528d --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/routes.py @@ -0,0 +1,300 @@ +import os +import hashlib +import sys +import json +import shutil +import folder_paths +from aiohttp import web +from server import PromptServer +from .config import RESOURCES_DIR, FOOOCUS_STYLES_DIR, FOOOCUS_STYLES_SAMPLES +from .libs.model import easyModelManager +from .libs.utils import getMetadata, cleanGPUUsedForce, get_local_filepath +from .libs.cache import remove_cache +from .libs.translate import has_chinese, zh_to_en + +@PromptServer.instance.routes.get('/easyuse/version') +def get_version(request): + try: + from .. import __version__ + return web.json_response({"version": __version__}) + except Exception as e: + print(e) + return web.Response(status=500) + +@PromptServer.instance.routes.post("/easyuse/cleangpu") +def cleanGPU(request): + try: + cleanGPUUsedForce() + remove_cache('*') + return web.Response(status=200) + except Exception as e: + return web.Response(status=500) + pass + +@PromptServer.instance.routes.post("/easyuse/removecache") +async def removecache(request): + post = await request.post() + key = post.get("key") + try: + remove_cache(key) + return web.Response(status=200) + except Exception as e: + return web.Response(status=500) + pass + +@PromptServer.instance.routes.post("/easyuse/translate") +async def translate(request): + post = await request.post() + text = post.get("text") + if has_chinese(text): + return web.json_response({"text": zh_to_en([text])[0]}) + else: + return web.json_response({"text": text}) + +@PromptServer.instance.routes.get("/easyuse/reboot") +def reboot(request): + try: + sys.stdout.close_log() + except Exception as e: + pass + + return os.execv(sys.executable, [sys.executable] + sys.argv) + +# parse csv +@PromptServer.instance.routes.post("/easyuse/upload/csv") +async def parse_csv(request): + post = await request.post() + csv = post.get("csv") + if csv and csv.file: + file = csv.file + text = '' + for line in file.readlines(): + line = str(line.strip()) + line = line.replace("'", "").replace("b",'') + text += line + '; \n' + return web.json_response(text) + +#get style list +@PromptServer.instance.routes.get("/easyuse/prompt/styles") +async def getStylesList(request): + if "name" in request.rel_url.query: + style_name = request.rel_url.query["name"] + fooocus_custom_dir = os.path.join(FOOOCUS_STYLES_DIR, 'fooocus_styles.json') + if style_name == 'fooocus_styles' and not os.path.exists(fooocus_custom_dir): + file = os.path.join(RESOURCES_DIR, style_name+'.json') + cn_file = os.path.join(RESOURCES_DIR, style_name + '_cn.json') + else: + file = os.path.join(FOOOCUS_STYLES_DIR, style_name+'.json') + cn_file = os.path.join(FOOOCUS_STYLES_DIR, style_name + '_cn.json') + cn_data = None + if os.path.isfile(cn_file): + f = open(cn_file, 'r', encoding='utf-8') + cn_data = json.load(f) + f.close() + if os.path.isfile(file): + f = open(file, 'r', encoding='utf-8') + data = json.load(f) + f.close() + if data: + ndata = [] + for d in data: + nd = {} + name = d['name'].replace('-', ' ') + words = name.split(' ') + key = ' '.join( + word.upper() if word.lower() in ['mre', 'sai', '3d'] else word.capitalize() for word in + words) + if "name_cn" in d: + nd['name_cn'] = d['name_cn'] + elif cn_data: + nd['name_cn'] = cn_data[key] if key in cn_data else key + nd["name"] = d['name'] + if "thumbnail" in d: + thumbnail = d['thumbnail'] + if isinstance(d['thumbnail'], str): + nd['thumbnail'] = thumbnail if "http" in thumbnail else f'/easyuse/prompt/styles/image?path={thumbnail}' + elif isinstance(d['thumbnail'], list): + nd['thumbnail'] = [thumb if "http" in thumb else f'/easyuse/prompt/styles/image?path={thumb}' for thumb in thumbnail] + else: + nd['thumbnail'] = f'/easyuse/prompt/styles/image?name={name}&styles_name={style_name}' + if "thumbnail_variant" in d: + nd['thumbnailVariant'] = d['thumbnail_variant'] + if "media_type" in d: + nd['mediaType'] = d['media_type'] + if "media_subtype" in d: + nd['mediaSubtype'] = d['media_subtype'] + if "prompt" in d: + nd['prompt'] = d['prompt'] + if "negative_prompt" in d: + nd['negative_prompt'] = d['negative_prompt'] + ndata.append(nd) + return web.json_response(ndata) + return web.Response(status=400) + +# get style preview image +@PromptServer.instance.routes.get("/easyuse/prompt/styles/image") +async def getStylesImage(request): + styles_name = request.rel_url.query["styles_name"] if "styles_name" in request.rel_url.query else None + if "path" in request.rel_url.query: + path = request.rel_url.query["path"] + file = os.path.join(FOOOCUS_STYLES_DIR, 'samples', path) + parent_file = os.path.join(FOOOCUS_STYLES_DIR, path) + if os.path.isfile(file): + return web.FileResponse(file) + elif os.path.isfile(parent_file): + return web.FileResponse(parent_file) + elif "name" in request.rel_url.query: + name = request.rel_url.query["name"] + if os.path.exists(os.path.join(FOOOCUS_STYLES_DIR, 'samples')): + file = os.path.join(FOOOCUS_STYLES_DIR, 'samples', name + '.jpg') + if os.path.isfile(file): + return web.FileResponse(file) + elif styles_name == 'fooocus_styles': + return web.Response(text=FOOOCUS_STYLES_SAMPLES + name + '.jpg') + elif styles_name == 'fooocus_styles': + return web.Response(text=FOOOCUS_STYLES_SAMPLES + name + '.jpg') + return web.Response(status=400) + +# get models lists +@PromptServer.instance.routes.get("/easyuse/models/list") +async def getModelsList(request): + if "type" in request.rel_url.query: + type = request.rel_url.query["type"] + if type not in ['checkpoints', 'loras']: + return web.Response(status=400) + manager = easyModelManager() + return web.json_response(manager.get_model_lists(type)) + else: + return web.Response(status=400) + +@PromptServer.instance.routes.post("/easyuse/metadata/notes/{name}") +async def save_notes(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = None + if type == "embeddings" or type == "loras": + name = name.lower() + files = folder_paths.get_filename_list(type) + for f in files: + lower_f = f.lower() + if lower_f == name: + file_path = folder_paths.get_full_path(type, f) + else: + n = os.path.splitext(f)[0].lower() + if n == name: + file_path = folder_paths.get_full_path(type, f) + + if file_path is not None: + break + else: + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + file_no_ext = os.path.splitext(file_path)[0] + info_file = file_no_ext + ".txt" + with open(info_file, "w") as f: + f.write(await request.text()) + + return web.Response(status=200) + +@PromptServer.instance.routes.get("/easyuse/metadata/{name}") +async def load_metadata(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = None + if type == "embeddings": + name = name.lower() + files = folder_paths.get_filename_list(type) + for f in files: + lower_f = f.lower() + if lower_f == name: + file_path = folder_paths.get_full_path(type, f) + else: + n = os.path.splitext(f)[0].lower() + if n == name: + file_path = folder_paths.get_full_path(type, f) + + if file_path is not None: + break + else: + file_path = folder_paths.get_full_path(type, name) + if not file_path: + return web.Response(status=404) + + try: + header = getMetadata(file_path) + header_json = json.loads(header) + meta = header_json["__metadata__"] if "__metadata__" in header_json else None + except: + meta = None + + if meta is None: + meta = {} + + file_no_ext = os.path.splitext(file_path)[0] + + info_file = file_no_ext + ".txt" + if os.path.isfile(info_file): + with open(info_file, "r") as f: + meta["easyuse.notes"] = f.read() + + hash_file = file_no_ext + ".sha256" + if os.path.isfile(hash_file): + with open(hash_file, "rt") as f: + meta["easyuse.sha256"] = f.read() + else: + with open(file_path, "rb") as f: + meta["easyuse.sha256"] = hashlib.sha256(f.read()).hexdigest() + with open(hash_file, "wt") as f: + f.write(meta["easyuse.sha256"]) + + return web.json_response(meta) + +@PromptServer.instance.routes.post("/easyuse/save/{name}") +async def save_preview(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + body = await request.json() + + dir = folder_paths.get_directory_by_type(body.get("type", "output")) + subfolder = body.get("subfolder", "") + full_output_folder = os.path.join(dir, os.path.normpath(subfolder)) + + if os.path.commonpath((dir, os.path.abspath(full_output_folder))) != dir: + return web.Response(status=400) + + filepath = os.path.join(full_output_folder, body.get("filename", "")) + image_path = folder_paths.get_full_path(type, name) + image_path = os.path.splitext( + image_path)[0] + os.path.splitext(filepath)[1] + + shutil.copyfile(filepath, image_path) + + return web.json_response({ + "image": type + "/" + os.path.basename(image_path) + }) + +@PromptServer.instance.routes.post("/easyuse/model/download") +async def download_model(request): + post = await request.post() + url = post.get("url") + local_dir = post.get("local_dir") + if local_dir not in ['checkpoints', 'loras', 'controlnet', 'onnx', 'instantid', 'ipadapter', 'dynamicrafter_models', 'mediapipe', 'rembg', 'layer_model']: + return web.Response(status=400) + local_path = os.path.join(folder_paths.models_dir, local_dir) + try: + get_local_filepath(url, local_path) + return web.Response(status=200) + except: + return web.Response(status=500) diff --git a/custom_nodes/ComfyUI-Easy-Use/py/server.py b/custom_nodes/ComfyUI-Easy-Use/py/server.py new file mode 100644 index 00000000..905ae983 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/py/server.py @@ -0,0 +1,166 @@ +import random +import server +from enum import Enum + +class SGmode(Enum): + FIX = 1 + INCR = 2 + DECR = 3 + RAND = 4 + + +class SeedGenerator: + def __init__(self, base_value, action): + self.base_value = base_value + + if action == "fixed" or action == "increment" or action == "decrement" or action == "randomize": + self.action = SGmode.FIX + elif action == 'increment for each node': + self.action = SGmode.INCR + elif action == 'decrement for each node': + self.action = SGmode.DECR + elif action == 'randomize for each node': + self.action = SGmode.RAND + + def next(self): + seed = self.base_value + + if self.action == SGmode.INCR: + self.base_value += 1 + if self.base_value > 1125899906842624: + self.base_value = 0 + elif self.action == SGmode.DECR: + self.base_value -= 1 + if self.base_value < 0: + self.base_value = 1125899906842624 + elif self.action == SGmode.RAND: + self.base_value = random.randint(0, 1125899906842624) + + return seed + + +def control_seed(v, action, seed_is_global): + action = v['inputs']['action'] if seed_is_global else action + value = v['inputs']['value'] if seed_is_global else v['inputs']['seed_num'] + + if action == 'increment' or action == 'increment for each node': + value = value + 1 + if value > 1125899906842624: + value = 0 + elif action == 'decrement' or action == 'decrement for each node': + value = value - 1 + if value < 0: + value = 1125899906842624 + elif action == 'randomize' or action == 'randomize for each node': + value = random.randint(0, 1125899906842624) + if seed_is_global: + v['inputs']['value'] = value + + return value + + +def prompt_seed_update(json_data): + try: + seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets'] + except: + return None + + workflow = json_data['extra_data']['extra_pnginfo']['workflow'] + seed_widget_map = workflow['seed_widgets'] + value = None + mode = None + node = None + action = None + seed_is_global = False + + for k, v in json_data['prompt'].items(): + if 'class_type' not in v: + continue + + cls = v['class_type'] + + if cls == 'easy globalSeed': + mode = v['inputs']['mode'] + action = v['inputs']['action'] + value = v['inputs']['value'] + node = k, v + seed_is_global = True + + # control before generated + if mode is not None and mode and seed_is_global: + value = control_seed(node[1], action, seed_is_global) + + if seed_is_global: + if value is not None: + seed_generator = SeedGenerator(value, action) + + for k, v in json_data['prompt'].items(): + for k2, v2 in v['inputs'].items(): + if isinstance(v2, str) and '$GlobalSeed.value$' in v2: + v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value)) + + if k not in seed_widget_map: + continue + + if 'seed_num' in v['inputs']: + if isinstance(v['inputs']['seed_num'], int): + v['inputs']['seed_num'] = seed_generator.next() + + if 'seed' in v['inputs']: + if isinstance(v['inputs']['seed'], int): + v['inputs']['seed'] = seed_generator.next() + + if 'noise_seed' in v['inputs']: + if isinstance(v['inputs']['noise_seed'], int): + v['inputs']['noise_seed'] = seed_generator.next() + + for k2, v2 in v['inputs'].items(): + if isinstance(v2, str) and '$GlobalSeed.value$' in v2: + v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value)) + # control after generated + if mode is not None and not mode: + control_seed(node[1], action, seed_is_global) + + return value is not None + + +def workflow_seed_update(json_data): + nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes'] + seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets'] + prompt = json_data['prompt'] + + updated_seed_map = {} + value = None + + for node in nodes: + node_id = str(node['id']) + if node_id in prompt: + if node['type'] == 'easy globalSeed': + value = prompt[node_id]['inputs']['value'] + length = len(node['widgets_values']) + node['widgets_values'][length-1] = node['widgets_values'][0] + node['widgets_values'][0] = value + elif node_id in seed_widget_map: + widget_idx = seed_widget_map[node_id] + + if 'seed_num' in prompt[node_id]['inputs']: + seed = prompt[node_id]['inputs']['seed_num'] + elif 'noise_seed' in prompt[node_id]['inputs']: + seed = prompt[node_id]['inputs']['noise_seed'] + else: + seed = prompt[node_id]['inputs']['seed'] + + node['widgets_values'][widget_idx] = seed + updated_seed_map[node_id] = seed + + server.PromptServer.instance.send_sync("easyuse-global-seed", {"id": node_id, "value": value, "seed_map": updated_seed_map}) + + +def onprompt(json_data): + is_changed = prompt_seed_update(json_data) + if is_changed: + workflow_seed_update(json_data) + + return json_data + +server.PromptServer.instance.add_on_prompt_handler(onprompt) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/pyproject.toml b/custom_nodes/ComfyUI-Easy-Use/pyproject.toml new file mode 100644 index 00000000..ae7142d5 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-easy-use" +description = "To enhance the usability of ComfyUI, optimizations and integrations have been implemented for several commonly used nodes." +version = "1.3.6" +license = { file = "LICENSE" } +dependencies = ["diffusers", "accelerate", "clip_interrogator", "sentencepiece", "lark", "onnxruntime", "spandrel", "opencv-python-headless", "matplotlib", "peft"] + +[project.urls] +Repository = "https://github.com/yolain/ComfyUI-Easy-Use" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "yolain" +DisplayName = "ComfyUI-Easy-Use" +Icon = "https://mintlify.s3.us-west-1.amazonaws.com/yolain/images/logo.svg" diff --git a/custom_nodes/ComfyUI-Easy-Use/repair_dependency_list.txt b/custom_nodes/ComfyUI-Easy-Use/repair_dependency_list.txt new file mode 100644 index 00000000..83d820ea --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/repair_dependency_list.txt @@ -0,0 +1,6 @@ +numpy>=1.19.0 +diffusers>=0.32.2 +huggingface_hub>=0.25.0 +transformers>=4.48.0 +peft>=0.14.0 +protobuf>=4.25.3 diff --git a/custom_nodes/ComfyUI-Easy-Use/requirements.txt b/custom_nodes/ComfyUI-Easy-Use/requirements.txt new file mode 100644 index 00000000..3f670335 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/requirements.txt @@ -0,0 +1,10 @@ +diffusers +accelerate +clip_interrogator>=0.6.0 +lark +onnxruntime +opencv-python-headless +sentencepiece +spandrel +matplotlib +peft diff --git a/custom_nodes/ComfyUI-Easy-Use/resources/OpenSans-Medium.ttf b/custom_nodes/ComfyUI-Easy-Use/resources/OpenSans-Medium.ttf new file mode 100644 index 00000000..6b419185 Binary files /dev/null and b/custom_nodes/ComfyUI-Easy-Use/resources/OpenSans-Medium.ttf differ diff --git a/custom_nodes/ComfyUI-Easy-Use/resources/fooocus_styles.json b/custom_nodes/ComfyUI-Easy-Use/resources/fooocus_styles.json new file mode 100644 index 00000000..b1db960a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/resources/fooocus_styles.json @@ -0,0 +1,1925 @@ +[ + { + "name": "Fooocus Enhance", + "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", + "name_cn": "Fooocus-δΌ˜εŒ–ε’žεΌΊ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fooocus_enhance.jpg" + }, + { + "name": "Fooocus Sharp", + "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", + "name_cn": "Fooocus-ι”εŒ–", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fooocus_sharp.jpg" + }, + { + "name": "Fooocus Masterpiece", + "prompt": "(masterpiece), (best quality), (ultra-detailed), {prompt}, illustration, disheveled hair, detailed eyes, perfect composition, moist skin, intricate details, earrings, by wlop", + "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality", + "name_cn": "Fooocus-杰作", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fooocus_masterpiece.jpg" + }, + { + "name": "Fooocus Photograph", + "prompt": "photograph {prompt}, 50mm . cinematic 4k epic detailed 4k epic detailed photograph shot on kodak detailed cinematic hbo dark moody, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", + "negative_prompt": "Brad Pitt, bokeh, depth of field, blurry, cropped, regular face, saturated, contrast, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", + "name_cn": "Fooocus-照片", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fooocus_photograph.jpg" + }, + { + "name": "Fooocus Negative", + "negative_prompt": "deformed, bad anatomy, disfigured, poorly drawn face, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, disconnected head, malformed hands, long neck, mutated hands and fingers, bad hands, missing fingers, cropped, worst quality, low quality, mutation, poorly drawn, huge calf, bad hands, fused hand, missing hand, disappearing arms, disappearing thigh, disappearing calf, disappearing legs, missing fingers, fused fingers, abnormal eye proportion, Abnormal hands, abnormal legs, abnormal feet, abnormal fingers, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch", + "name_cn": "Fooocus-反向提瀺词", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fooocus_negative.jpg" + }, + { + "name": "Fooocus Cinematic", + "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", + "name_cn": "Fooocus-η”΅ε½±ζ„Ÿ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fooocus_cinematic.jpg" + }, + { + "name": "sai-3d-model", + "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", + "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", + "name_cn": "SAI-3Dζ¨‘εž‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_3d_model.jpg" + }, + { + "name": "sai-analog film", + "prompt": "analog film photo {prompt} . faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", + "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", + "name_cn": "SAI-ζ¨‘ζ‹Ÿη”΅ε½±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_analog_film.jpg" + }, + { + "name": "sai-anime", + "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", + "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", + "name_cn": "SAI-动漫", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_anime.jpg" + }, + { + "name": "sai-cinematic", + "prompt": "cinematic film still {prompt} . shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", + "name_cn": "SAI-甡影片ζ΅", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_cinematic.jpg" + }, + { + "name": "sai-comic book", + "prompt": "comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed", + "negative_prompt": "photograph, deformed, glitch, noisy, realistic, stock photo", + "name_cn": "SAI-ζΌ«η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_comic_book.jpg" + }, + { + "name": "sai-craft clay", + "prompt": "play-doh style {prompt} . sculpture, clay art, centered composition, Claymation", + "negative_prompt": "sloppy, messy, grainy, highly detailed, ultra textured, photo", + "name_cn": "SAI-ε·₯θ‰Ίη²˜εœŸ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_craft_clay.jpg" + }, + { + "name": "sai-digital art", + "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", + "negative_prompt": "photo, photorealistic, realism, ugly", + "name_cn": "SAI-ζ•°ε­—θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_digital_art.jpg" + }, + { + "name": "sai-enhance", + "prompt": "breathtaking {prompt} . award-winning, professional, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy", + "name_cn": "SAI-咞强", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_enhance.jpg" + }, + { + "name": "sai-fantasy art", + "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", + "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", + "name_cn": "SAI-ε₯‡εΉ»θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_fantasy_art.jpg" + }, + { + "name": "sai-isometric", + "prompt": "isometric style {prompt} . vibrant, beautiful, crisp, detailed, ultra detailed, intricate", + "negative_prompt": "deformed, mutated, ugly, disfigured, blur, blurry, noise, noisy, realistic, photographic", + "name_cn": "SAI-η­‰θ·ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_isometric.jpg" + }, + { + "name": "sai-line art", + "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic", + "name_cn": "SAI-ηΊΏζ‘θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_line_art.jpg" + }, + { + "name": "sai-lowpoly", + "prompt": "low-poly style {prompt} . low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition", + "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo", + "name_cn": "SAI-低倚边归", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_lowpoly.jpg" + }, + { + "name": "sai-neonpunk", + "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", + "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", + "name_cn": "SAI-ιœ“θ™Ήζœ‹ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_neonpunk.jpg" + }, + { + "name": "sai-origami", + "prompt": "origami style {prompt} . paper art, pleated paper, folded, origami art, pleats, cut and fold, centered composition", + "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo", + "name_cn": "SAI-折纸", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_origami.jpg" + }, + { + "name": "sai-photographic", + "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", + "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", + "name_cn": "SAI-ζ‘„ε½±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_photographic.jpg" + }, + { + "name": "sai-pixel art", + "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", + "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", + "name_cn": "SAI-εƒη΄ θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_pixel_art.jpg" + }, + { + "name": "sai-texture", + "prompt": "texture {prompt} top down close-up", + "negative_prompt": "ugly, deformed, noisy, blurry", + "name_cn": "SAI-纹理", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sai_texture.jpg" + }, + { + "name": "mre-cinematic-dynamic", + "prompt": "epic cinematic shot of dynamic {prompt} in motion. main subject of high budget action movie. raw photo, motion blur. best quality, high resolution", + "negative_prompt": "static, still, motionless, sluggish. drawing, painting, illustration, rendered. low budget. low quality, low resolution", + "name_cn": "MRE-史诗甡影", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_cinematic_dynamic.jpg" + }, + { + "name": "mre-spontaneous-picture", + "prompt": "spontaneous picture of {prompt}, taken by talented amateur. best quality, high resolution. magical moment, natural look. simple but good looking", + "negative_prompt": "overthinked. low quality, low resolution", + "name_cn": "MRE-θ‡ͺη„Άηš„ζŠ“ζ‹η…§η‰‡", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_spontaneous_picture.jpg" + }, + { + "name": "mre-artistic-vision", + "prompt": "powerful artistic vision of {prompt}. breathtaking masterpiece made by great artist. best quality, high resolution", + "negative_prompt": "insignificant, flawed, made by bad artist. low quality, low resolution", + "name_cn": "MRE-θ‰Ίζœ―θ§†θ§‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_artistic_vision.jpg" + }, + { + "name": "mre-dark-dream", + "prompt": "dark and unsettling dream showing {prompt}. best quality, high resolution. created by genius but depressed mad artist. grim beauty", + "negative_prompt": "naive, cheerful. comfortable, casual, boring, cliche. low quality, low resolution", + "name_cn": "MRE-ι»‘ζš—ζ’¦ε’ƒ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_dark_dream.jpg" + }, + { + "name": "mre-gloomy-art", + "prompt": "astonishing gloomy art made mainly of shadows and lighting, forming {prompt}. masterful usage of lighting, shadows and chiaroscuro. made by black-hearted artist, drawing from darkness. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-ι˜΄ιƒθ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_gloomy_art.jpg" + }, + { + "name": "mre-bad-dream", + "prompt": "picture from really bad dream about terrifying {prompt}, true horror. bone-chilling vision. mad world that shouldn't exist. best quality, high resolution", + "negative_prompt": "nice dream, pleasant experience. low quality, low resolution", + "name_cn": "MRE-ε™©ζ’¦", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_bad_dream.jpg" + }, + { + "name": "mre-underground", + "prompt": "uncanny caliginous vision of {prompt}, created by remarkable underground artist. best quality, high resolution. raw and brutal art, careless but impressive style. inspired by darkness and chaos", + "negative_prompt": "photography, mainstream, civilized. low quality, low resolution", + "name_cn": "MRE-阴ζ£εœ°δΈ‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_underground.jpg" + }, + { + "name": "mre-surreal-painting", + "prompt": "surreal painting representing strange vision of {prompt}. harmonious madness, synergy with chance. unique artstyle, mindbending art, magical surrealism. best quality, high resolution", + "negative_prompt": "photography, illustration, drawing. realistic, possible. logical, sane. low quality, low resolution", + "name_cn": "MRE-θΆ…ηŽ°εžδΈ»δΉ‰η»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_surreal_painting.jpg" + }, + { + "name": "mre-dynamic-illustration", + "prompt": "insanely dynamic illustration of {prompt}. best quality, high resolution. crazy artstyle, careless brushstrokes, emotional and fun", + "negative_prompt": "photography, realistic. static, still, slow, boring. low quality, low resolution", + "name_cn": "MRE-εŠ¨ζ€ζ’η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_dynamic_illustration.jpg" + }, + { + "name": "mre-undead-art", + "prompt": "long forgotten art created by undead artist illustrating {prompt}, tribute to the death and decay. miserable art of the damned. wretched and decaying world. best quality, high resolution", + "negative_prompt": "alive, playful, living. low quality, low resolution", + "name_cn": "MRE-ι—εΏ˜θ‰Ίζœ―εΆδ½œε“", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_undead_art.jpg" + }, + { + "name": "mre-elemental-art", + "prompt": "art illustrating insane amounts of raging elemental energy turning into {prompt}, avatar of elements. magical surrealism, wizardry. best quality, high resolution", + "negative_prompt": "photography, realistic, real. low quality, low resolution", + "name_cn": "MRE-ε…ƒη΄ θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_elemental_art.jpg" + }, + { + "name": "mre-space-art", + "prompt": "winner of inter-galactic art contest illustrating {prompt}, symbol of the interstellar singularity. best quality, high resolution. artstyle previously unseen in the whole galaxy", + "negative_prompt": "created by human race, low quality, low resolution", + "name_cn": "MRE-η©Ίι—΄θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_space_art.jpg" + }, + { + "name": "mre-ancient-illustration", + "prompt": "sublime ancient illustration of {prompt}, predating human civilization. crude and simple, but also surprisingly beautiful artwork, made by genius primeval artist. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-叀代插图", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_ancient_illustration.jpg" + }, + { + "name": "mre-brave-art", + "prompt": "brave, shocking, and brutally true art showing {prompt}. inspired by courage and unlimited creativity. truth found in chaos. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-ε‹‡ζ•’θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_brave_art.jpg" + }, + { + "name": "mre-heroic-fantasy", + "prompt": "heroic fantasy painting of {prompt}, in the dangerous fantasy world. airbrush over oil on canvas. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-英雄幻想", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_heroic_fantasy.jpg" + }, + { + "name": "mre-dark-cyberpunk", + "prompt": "dark cyberpunk illustration of brutal {prompt} in a world without hope, ruled by ruthless criminal corporations. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-ι»‘ζš—θ΅›εšζœ‹ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_dark_cyberpunk.jpg" + }, + { + "name": "mre-lyrical-geometry", + "prompt": "geometric and lyrical abstraction painting presenting {prompt}. oil on metal. best quality, high resolution", + "negative_prompt": "photography, realistic, drawing, rendered. low quality, low resolution", + "name_cn": "MRE-ζŠ’ζƒ…ε‡ δ½•ζŠ½θ±‘η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_lyrical_geometry.jpg" + }, + { + "name": "mre-sumi-e-symbolic", + "prompt": "big long brushstrokes of deep black sumi-e turning into symbolic painting of {prompt}. master level raw art. best quality, high resolution", + "negative_prompt": "photography, rendered. low quality, low resolution", + "name_cn": "MRE-ε’¨η»˜ι•Ώη¬”η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_sumi_e_symbolic.jpg" + }, + { + "name": "mre-sumi-e-detailed", + "prompt": "highly detailed black sumi-e painting of {prompt}. in-depth study of perfection, created by a master. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-η²Ύη»†ε’¨η»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_sumi_e_detailed.jpg" + }, + { + "name": "mre-manga", + "prompt": "manga artwork presenting {prompt}. created by japanese manga artist. highly emotional. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-ζ—₯ζœ¬ζΌ«η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_manga.jpg" + }, + { + "name": "mre-anime", + "prompt": "anime artwork illustrating {prompt}. created by japanese anime studio. highly emotional. best quality, high resolution", + "negative_prompt": "low quality, low resolution", + "name_cn": "MRE-ζ—₯ζœ¬εŠ¨η”»η‰‡", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_anime.jpg" + }, + { + "name": "mre-comic", + "prompt": "breathtaking illustration from adult comic book presenting {prompt}. fabulous artwork. best quality, high resolution", + "negative_prompt": "deformed, ugly, low quality, low resolution", + "name_cn": "MRE-ζˆδΊΊζΌ«η”»δΉ¦ζ’η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mre_comic.jpg" + }, + { + "name": "ads-advertising", + "prompt": "advertising poster style {prompt} . Professional, modern, product-focused, commercial, eye-catching, highly detailed", + "negative_prompt": "noisy, blurry, amateurish, sloppy, unattractive", + "name_cn": "εΉΏε‘Š-εΉΏε‘Š", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_advertising.jpg" + }, + { + "name": "ads-automotive", + "prompt": "automotive advertisement style {prompt} . sleek, dynamic, professional, commercial, vehicle-focused, high-resolution, highly detailed", + "negative_prompt": "noisy, blurry, unattractive, sloppy, unprofessional", + "name_cn": "εΉΏε‘Š-汽车", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_automotive.jpg" + }, + { + "name": "ads-corporate", + "prompt": "corporate branding style {prompt} . professional, clean, modern, sleek, minimalist, business-oriented, highly detailed", + "negative_prompt": "noisy, blurry, grungy, sloppy, cluttered, disorganized", + "name_cn": "εΉΏε‘Š-δΌδΈšε“η‰Œ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_corporate.jpg" + }, + { + "name": "ads-fashion editorial", + "prompt": "fashion editorial style {prompt} . high fashion, trendy, stylish, editorial, magazine style, professional, highly detailed", + "negative_prompt": "outdated, blurry, noisy, unattractive, sloppy", + "name_cn": "εΉΏε‘Š-ζ—Άε°šηΌ–θΎ‘", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_fashion_editorial.jpg" + }, + { + "name": "ads-food photography", + "prompt": "food photography style {prompt} . appetizing, professional, culinary, high-resolution, commercial, highly detailed", + "negative_prompt": "unappetizing, sloppy, unprofessional, noisy, blurry", + "name_cn": "εΉΏε‘Š-ι£Ÿε“ζ‘„ε½±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_food_photography.jpg" + }, + { + "name": "ads-gourmet food photography", + "prompt": "gourmet food photo of {prompt} . soft natural lighting, macro details, vibrant colors, fresh ingredients, glistening textures, bokeh background, styled plating, wooden tabletop, garnished, tantalizing, editorial quality", + "negative_prompt": "cartoon, anime, sketch, grayscale, dull, overexposed, cluttered, messy plate, deformed", + "name_cn": "εΉΏε‘Š-ι‘ΆηΊ§ηΎŽι£Ÿζ‘„ε½±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_gourmet_food_photography.jpg" + }, + { + "name": "ads-luxury", + "prompt": "luxury product style {prompt} . elegant, sophisticated, high-end, luxurious, professional, highly detailed", + "negative_prompt": "cheap, noisy, blurry, unattractive, amateurish", + "name_cn": "εΉΏε‘Š-ε₯’δΎˆε“", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_luxury.jpg" + }, + { + "name": "ads-real estate", + "prompt": "real estate photography style {prompt} . professional, inviting, well-lit, high-resolution, property-focused, commercial, highly detailed", + "negative_prompt": "dark, blurry, unappealing, noisy, unprofessional", + "name_cn": "εΉΏε‘Š-房地产", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_real_estate.jpg" + }, + { + "name": "ads-retail", + "prompt": "retail packaging style {prompt} . vibrant, enticing, commercial, product-focused, eye-catching, professional, highly detailed", + "negative_prompt": "noisy, blurry, amateurish, sloppy, unattractive", + "name_cn": "εΉΏε‘Š-ι›Άε”", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ads_retail.jpg" + }, + { + "name": "artstyle-abstract", + "prompt": "abstract style {prompt} . non-representational, colors and shapes, expression of feelings, imaginative, highly detailed", + "negative_prompt": "realistic, photographic, figurative, concrete", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-抽豑", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_abstract.jpg" + }, + { + "name": "artstyle-abstract expressionism", + "prompt": "abstract expressionist painting {prompt} . energetic brushwork, bold colors, abstract forms, expressive, emotional", + "negative_prompt": "realistic, photorealistic, low contrast, plain, simple, monochrome", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-ζŠ½θ±‘θ‘¨ηŽ°δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_abstract_expressionism.jpg" + }, + { + "name": "artstyle-art deco", + "prompt": "art deco style {prompt} . geometric shapes, bold colors, luxurious, elegant, decorative, symmetrical, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-θ£…ι₯°θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_art_deco.jpg" + }, + { + "name": "artstyle-art nouveau", + "prompt": "art nouveau style {prompt} . elegant, decorative, curvilinear forms, nature-inspired, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-ζ–°θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_art_nouveau.jpg" + }, + { + "name": "artstyle-constructivist", + "prompt": "constructivist style {prompt} . geometric shapes, bold colors, dynamic composition, propaganda art style", + "negative_prompt": "realistic, photorealistic, low contrast, plain, simple, abstract expressionism", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-ζž„ι€ δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_constructivist.jpg" + }, + { + "name": "artstyle-cubist", + "prompt": "cubist artwork {prompt} . geometric shapes, abstract, innovative, revolutionary", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-立体主义", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_cubist.jpg" + }, + { + "name": "artstyle-expressionist", + "prompt": "expressionist {prompt} . raw, emotional, dynamic, distortion for emotional effect, vibrant, use of unusual colors, detailed", + "negative_prompt": "realism, symmetry, quiet, calm, photo", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-θ‘¨ηŽ°δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_expressionist.jpg" + }, + { + "name": "artstyle-graffiti", + "prompt": "graffiti style {prompt} . street art, vibrant, urban, detailed, tag, mural", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-ζΆ‚ιΈ¦", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_graffiti.jpg" + }, + { + "name": "artstyle-hyperrealism", + "prompt": "hyperrealistic art {prompt} . extremely high-resolution details, photographic, realism pushed to extreme, fine texture, incredibly lifelike", + "negative_prompt": "simplified, abstract, unrealistic, impressionistic, low resolution", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-袅写εžδΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_hyperrealism.jpg" + }, + { + "name": "artstyle-impressionist", + "prompt": "impressionist painting {prompt} . loose brushwork, vibrant color, light and shadow play, captures feeling over form", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-印豑派", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_impressionist.jpg" + }, + { + "name": "artstyle-pointillism", + "prompt": "pointillism style {prompt} . composed entirely of small, distinct dots of color, vibrant, highly detailed", + "negative_prompt": "line drawing, smooth shading, large color fields, simplistic", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-点彩派", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_pointillism.jpg" + }, + { + "name": "artstyle-pop art", + "prompt": "pop Art style {prompt} . bright colors, bold outlines, popular culture themes, ironic or kitsch", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, minimalist", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-ζ³’ζ™θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_pop_art.jpg" + }, + { + "name": "artstyle-psychedelic", + "prompt": "psychedelic style {prompt} . vibrant colors, swirling patterns, abstract forms, surreal, trippy", + "negative_prompt": "monochrome, black and white, low contrast, realistic, photorealistic, plain, simple", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-θΏ·εΉ»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_psychedelic.jpg" + }, + { + "name": "artstyle-renaissance", + "prompt": "renaissance style {prompt} . realistic, perspective, light and shadow, religious or mythological themes, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, modernist, minimalist, abstract", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-文艺倍兴", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_renaissance.jpg" + }, + { + "name": "artstyle-steampunk", + "prompt": "steampunk style {prompt} . antique, mechanical, brass and copper tones, gears, intricate, detailed", + "negative_prompt": "deformed, glitch, noisy, low contrast, anime, photorealistic", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-θ’Έζ±½ζœ‹ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_steampunk.jpg" + }, + { + "name": "artstyle-surrealist", + "prompt": "surrealist art {prompt} . dreamlike, mysterious, provocative, symbolic, intricate, detailed", + "negative_prompt": "anime, photorealistic, realistic, deformed, glitch, noisy, low contrast", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-θΆ…ηŽ°εžδΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_surrealist.jpg" + }, + { + "name": "artstyle-typography", + "prompt": "typographic art {prompt} . stylized, intricate, detailed, artistic, text-based", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-字体θΎθ‘", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_typography.jpg" + }, + { + "name": "artstyle-watercolor", + "prompt": "watercolor painting {prompt} . vibrant, beautiful, painterly, detailed, textural, artistic", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy", + "name_cn": "θ‰Ίζœ―ι£Žζ Ό-水彩", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/artstyle_watercolor.jpg" + }, + { + "name": "futuristic-biomechanical", + "prompt": "biomechanical style {prompt} . blend of organic and mechanical elements, futuristic, cybernetic, detailed, intricate", + "negative_prompt": "natural, rustic, primitive, organic, simplistic", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-η”Ÿη‰©ζœΊζ’°", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_biomechanical.jpg" + }, + { + "name": "futuristic-biomechanical cyberpunk", + "prompt": "biomechanical cyberpunk {prompt} . cybernetics, human-machine fusion, dystopian, organic meets artificial, dark, intricate, highly detailed", + "negative_prompt": "natural, colorful, deformed, sketch, low contrast, watercolor", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-η”Ÿη‰©ζœΊζ’°-θ΅›εšζœ‹ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_biomechanical_cyberpunk.jpg" + }, + { + "name": "futuristic-cybernetic", + "prompt": "cybernetic style {prompt} . futuristic, technological, cybernetic enhancements, robotics, artificial intelligence themes", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-人机融合", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_cybernetic.jpg" + }, + { + "name": "futuristic-cybernetic robot", + "prompt": "cybernetic robot {prompt} . android, AI, machine, metal, wires, tech, futuristic, highly detailed", + "negative_prompt": "organic, natural, human, sketch, watercolor, low contrast", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-人机融合-ζœΊε™¨δΊΊ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_cybernetic_robot.jpg" + }, + { + "name": "futuristic-cyberpunk cityscape", + "prompt": "cyberpunk cityscape {prompt} . neon lights, dark alleys, skyscrapers, futuristic, vibrant colors, high contrast, highly detailed", + "negative_prompt": "natural, rural, deformed, low contrast, black and white, sketch, watercolor", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-θ΅›εšζœ‹ε…‹εŸŽεΈ‚", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_cyberpunk_cityscape.jpg" + }, + { + "name": "futuristic-futuristic", + "prompt": "futuristic style {prompt} . sleek, modern, ultramodern, high tech, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vintage, antique", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-ζœͺζ₯δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_futuristic.jpg" + }, + { + "name": "futuristic-retro cyberpunk", + "prompt": "retro cyberpunk {prompt} . 80's inspired, synthwave, neon, vibrant, detailed, retro futurism", + "negative_prompt": "modern, desaturated, black and white, realism, low contrast", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-ε€ε€θ΅›εšζœ‹ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_retro_cyberpunk.jpg" + }, + { + "name": "futuristic-retro futurism", + "prompt": "retro-futuristic {prompt} . vintage sci-fi, 50s and 60s style, atomic age, vibrant, highly detailed", + "negative_prompt": "contemporary, realistic, rustic, primitive", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-倍叀ζœͺζ₯δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_retro_futurism.jpg" + }, + { + "name": "futuristic-sci-fi", + "prompt": "sci-fi style {prompt} . futuristic, technological, alien worlds, space themes, advanced civilizations", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-科幻", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_sci_fi.jpg" + }, + { + "name": "futuristic-vaporwave", + "prompt": "vaporwave style {prompt} . retro aesthetic, cyberpunk, vibrant, neon colors, vintage 80s and 90s style, highly detailed", + "negative_prompt": "monochrome, muted colors, realism, rustic, minimalist, dark", + "name_cn": "ζœͺζ₯δΈ»δΉ‰-θ’Έζ±½ζ³’", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futuristic_vaporwave.jpg" + }, + { + "name": "game-bubble bobble", + "prompt": "Bubble Bobble style {prompt} . 8-bit, cute, pixelated, fantasy, vibrant, reminiscent of Bubble Bobble game", + "negative_prompt": "realistic, modern, photorealistic, violent, horror", + "name_cn": "游戏-泑泑龙", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_bubble_bobble.jpg" + }, + { + "name": "game-cyberpunk game", + "prompt": "cyberpunk game style {prompt} . neon, dystopian, futuristic, digital, vibrant, detailed, high contrast, reminiscent of cyberpunk genre video games", + "negative_prompt": "historical, natural, rustic, low detailed", + "name_cn": "游戏-θ΅›εšζœ‹ε…‹ζΈΈζˆ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_cyberpunk_game.jpg" + }, + { + "name": "game-fighting game", + "prompt": "fighting game style {prompt} . dynamic, vibrant, action-packed, detailed character design, reminiscent of fighting video games", + "negative_prompt": "peaceful, calm, minimalist, photorealistic", + "name_cn": "游戏-ζ Όζ–—ζΈΈζˆ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_fighting_game.jpg" + }, + { + "name": "game-gta", + "prompt": "GTA-style artwork {prompt} . satirical, exaggerated, pop art style, vibrant colors, iconic characters, action-packed", + "negative_prompt": "realistic, black and white, low contrast, impressionist, cubist, noisy, blurry, deformed", + "name_cn": "游戏-δΎ η›—ηŒŽθ½¦ζ‰‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_gta.jpg" + }, + { + "name": "game-mario", + "prompt": "Super Mario style {prompt} . vibrant, cute, cartoony, fantasy, playful, reminiscent of Super Mario series", + "negative_prompt": "realistic, modern, horror, dystopian, violent", + "name_cn": "游戏-ι©¬ι‡Œε₯₯", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_mario.jpg" + }, + { + "name": "game-minecraft", + "prompt": "Minecraft style {prompt} . blocky, pixelated, vibrant colors, recognizable characters and objects, game assets", + "negative_prompt": "smooth, realistic, detailed, photorealistic, noise, blurry, deformed", + "name_cn": "游戏-ζˆ‘ηš„δΈ–η•Œ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_minecraft.jpg" + }, + { + "name": "game-pokemon", + "prompt": "PokΓ©mon style {prompt} . vibrant, cute, anime, fantasy, reminiscent of PokΓ©mon series", + "negative_prompt": "realistic, modern, horror, dystopian, violent", + "name_cn": "游戏-εε―ζ’¦", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_pokemon.jpg" + }, + { + "name": "game-retro arcade", + "prompt": "retro arcade style {prompt} . 8-bit, pixelated, vibrant, classic video game, old school gaming, reminiscent of 80s and 90s arcade games", + "negative_prompt": "modern, ultra-high resolution, photorealistic, 3D", + "name_cn": "游戏-ε€ε€θ‘—ζœΊ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_retro_arcade.jpg" + }, + { + "name": "game-retro game", + "prompt": "retro game art {prompt} . 16-bit, vibrant colors, pixelated, nostalgic, charming, fun", + "negative_prompt": "realistic, photorealistic, 35mm film, deformed, glitch, low contrast, noisy", + "name_cn": "游戏-倍叀游戏", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_retro_game.jpg" + }, + { + "name": "game-rpg fantasy game", + "prompt": "role-playing game (RPG) style fantasy {prompt} . detailed, vibrant, immersive, reminiscent of high fantasy RPG games", + "negative_prompt": "sci-fi, modern, urban, futuristic, low detailed", + "name_cn": "游戏-角色ζ‰ζΌ”εΉ»ζƒ³ζΈΈζˆ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_rpg_fantasy_game.jpg" + }, + { + "name": "game-strategy game", + "prompt": "strategy game style {prompt} . overhead view, detailed map, units, reminiscent of real-time strategy video games", + "negative_prompt": "first-person view, modern, photorealistic", + "name_cn": "游戏-η­–η•₯游戏", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_strategy_game.jpg" + }, + { + "name": "game-streetfighter", + "prompt": "Street Fighter style {prompt} . vibrant, dynamic, arcade, 2D fighting game, highly detailed, reminiscent of Street Fighter series", + "negative_prompt": "3D, realistic, modern, photorealistic, turn-based strategy", + "name_cn": "游戏-θ‘—ε€΄ιœΈηŽ‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_streetfighter.jpg" + }, + { + "name": "game-zelda", + "prompt": "Legend of Zelda style {prompt} . vibrant, fantasy, detailed, epic, heroic, reminiscent of The Legend of Zelda series", + "negative_prompt": "sci-fi, modern, realistic, horror", + "name_cn": "游戏-ε‘žε°”θΎΎδΌ θ―΄", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/game_zelda.jpg" + }, + { + "name": "misc-architectural", + "prompt": "architectural style {prompt} . clean lines, geometric shapes, minimalist, modern, architectural drawing, highly detailed", + "negative_prompt": "curved lines, ornate, baroque, abstract, grunge", + "name_cn": "ε…Άδ»–-ε»Ίη­‘", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_architectural.jpg" + }, + { + "name": "misc-disco", + "prompt": "disco-themed {prompt} . vibrant, groovy, retro 70s style, shiny disco balls, neon lights, dance floor, highly detailed", + "negative_prompt": "minimalist, rustic, monochrome, contemporary, simplistic", + "name_cn": "ε…Άδ»–-θΏͺ斯科", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_disco.jpg" + }, + { + "name": "misc-dreamscape", + "prompt": "dreamscape {prompt} . surreal, ethereal, dreamy, mysterious, fantasy, highly detailed", + "negative_prompt": "realistic, concrete, ordinary, mundane", + "name_cn": "ε…Άδ»–-ζ’¦ε’ƒ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_dreamscape.jpg" + }, + { + "name": "misc-dystopian", + "prompt": "dystopian style {prompt} . bleak, post-apocalyptic, somber, dramatic, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, cheerful, optimistic, vibrant, colorful", + "name_cn": "ε…Άδ»–-εδΉŒζ‰˜ι‚¦", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_dystopian.jpg" + }, + { + "name": "misc-fairy tale", + "prompt": "fairy tale {prompt} . magical, fantastical, enchanting, storybook style, highly detailed", + "negative_prompt": "realistic, modern, ordinary, mundane", + "name_cn": "ε…Άδ»–-η«₯话故事", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_fairy_tale.jpg" + }, + { + "name": "misc-gothic", + "prompt": "gothic style {prompt} . dark, mysterious, haunting, dramatic, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, cheerful, optimistic", + "name_cn": "ε…Άδ»–-ε“₯η‰Ήι£Ž", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_gothic.jpg" + }, + { + "name": "misc-grunge", + "prompt": "grunge style {prompt} . textured, distressed, vintage, edgy, punk rock vibe, dirty, noisy", + "negative_prompt": "smooth, clean, minimalist, sleek, modern, photorealistic", + "name_cn": "ε…Άδ»–-εžζŽ‰ηš„", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_grunge.jpg" + }, + { + "name": "misc-horror", + "prompt": "horror-themed {prompt} . eerie, unsettling, dark, spooky, suspenseful, grim, highly detailed", + "negative_prompt": "cheerful, bright, vibrant, light-hearted, cute", + "name_cn": "ε…Άδ»–-恐怖", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_horror.jpg" + }, + { + "name": "misc-kawaii", + "prompt": "kawaii style {prompt} . cute, adorable, brightly colored, cheerful, anime influence, highly detailed", + "negative_prompt": "dark, scary, realistic, monochrome, abstract", + "name_cn": "ε…Άδ»–-可爱", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_kawaii.jpg" + }, + { + "name": "misc-lovecraftian", + "prompt": "lovecraftian horror {prompt} . eldritch, cosmic horror, unknown, mysterious, surreal, highly detailed", + "negative_prompt": "light-hearted, mundane, familiar, simplistic, realistic", + "name_cn": "ε…Άδ»–-洛倫克拉倫特", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_lovecraftian.jpg" + }, + { + "name": "misc-macabre", + "prompt": "macabre style {prompt} . dark, gothic, grim, haunting, highly detailed", + "negative_prompt": "bright, cheerful, light-hearted, cartoonish, cute", + "name_cn": "ε…Άδ»–-恐怖", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_macabre.jpg" + }, + { + "name": "misc-manga", + "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", + "name_cn": "ε…Άδ»–-ζΌ«η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_manga.jpg" + }, + { + "name": "misc-metropolis", + "prompt": "metropolis-themed {prompt} . urban, cityscape, skyscrapers, modern, futuristic, highly detailed", + "negative_prompt": "rural, natural, rustic, historical, simple", + "name_cn": "ε…Άδ»–-倧都市", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_metropolis.jpg" + }, + { + "name": "misc-minimalist", + "prompt": "minimalist style {prompt} . simple, clean, uncluttered, modern, elegant", + "negative_prompt": "ornate, complicated, highly detailed, cluttered, disordered, messy, noisy", + "name_cn": "ε…Άδ»–-极η€δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_minimalist.jpg" + }, + { + "name": "misc-monochrome", + "prompt": "monochrome {prompt} . black and white, contrast, tone, texture, detailed", + "negative_prompt": "colorful, vibrant, noisy, blurry, deformed", + "name_cn": "ε…Άδ»–-单色", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_monochrome.jpg" + }, + { + "name": "misc-nautical", + "prompt": "nautical-themed {prompt} . sea, ocean, ships, maritime, beach, marine life, highly detailed", + "negative_prompt": "landlocked, desert, mountains, urban, rustic", + "name_cn": "ε…Άδ»–-θˆͺζ΅·", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_nautical.jpg" + }, + { + "name": "misc-space", + "prompt": "space-themed {prompt} . cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed", + "negative_prompt": "earthly, mundane, ground-based, realism", + "name_cn": "ε…Άδ»–-ε€ͺη©Ί", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_space.jpg" + }, + { + "name": "misc-stained glass", + "prompt": "stained glass style {prompt} . vibrant, beautiful, translucent, intricate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic", + "name_cn": "ε…Άδ»–-ε½©θ‰²ηŽ»η’ƒ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_stained_glass.jpg" + }, + { + "name": "misc-techwear fashion", + "prompt": "techwear fashion {prompt} . futuristic, cyberpunk, urban, tactical, sleek, dark, highly detailed", + "negative_prompt": "vintage, rural, colorful, low contrast, realism, sketch, watercolor", + "name_cn": "ε…Άδ»–-η§‘ζŠ€ζ—Άε°š", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_techwear_fashion.jpg" + }, + { + "name": "misc-tribal", + "prompt": "tribal style {prompt} . indigenous, ethnic, traditional patterns, bold, natural colors, highly detailed", + "negative_prompt": "modern, futuristic, minimalist, pastel", + "name_cn": "ε…Άδ»–-部落", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_tribal.jpg" + }, + { + "name": "misc-zentangle", + "prompt": "zentangle {prompt} . intricate, abstract, monochrome, patterns, meditative, highly detailed", + "negative_prompt": "colorful, representative, simplistic, large fields of color", + "name_cn": "ε…Άδ»–-η¦…η»•η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/misc_zentangle.jpg" + }, + { + "name": "papercraft-collage", + "prompt": "collage style {prompt} . mixed media, layered, textural, detailed, artistic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic", + "name_cn": "手ε·₯θ‰Ί-ζ‹Όθ΄΄", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_collage.jpg" + }, + { + "name": "papercraft-flat papercut", + "prompt": "flat papercut style {prompt} . silhouette, clean cuts, paper, sharp edges, minimalist, color block", + "negative_prompt": "3D, high detail, noise, grainy, blurry, painting, drawing, photo, disfigured", + "name_cn": "手ε·₯θ‰Ί-平青ε‰ͺηΊΈ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_flat_papercut.jpg" + }, + { + "name": "papercraft-kirigami", + "prompt": "kirigami representation of {prompt} . 3D, paper folding, paper cutting, Japanese, intricate, symmetrical, precision, clean lines", + "negative_prompt": "painting, drawing, 2D, noisy, blurry, deformed", + "name_cn": "手ε·₯θ‰Ί-εˆ‡ηΊΈ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_kirigami.jpg" + }, + { + "name": "papercraft-paper mache", + "prompt": "paper mache representation of {prompt} . 3D, sculptural, textured, handmade, vibrant, fun", + "negative_prompt": "2D, flat, photo, sketch, digital art, deformed, noisy, blurry", + "name_cn": "手ε·₯θ‰Ί-纸框呑造", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_paper_mache.jpg" + }, + { + "name": "papercraft-paper quilling", + "prompt": "paper quilling art of {prompt} . intricate, delicate, curling, rolling, shaping, coiling, loops, 3D, dimensional, ornamental", + "negative_prompt": "photo, painting, drawing, 2D, flat, deformed, noisy, blurry", + "name_cn": "手ε·₯θ‰Ί-纸艺卷轴", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_paper_quilling.jpg" + }, + { + "name": "papercraft-papercut collage", + "prompt": "papercut collage of {prompt} . mixed media, textured paper, overlapping, asymmetrical, abstract, vibrant", + "negative_prompt": "photo, 3D, realistic, drawing, painting, high detail, disfigured", + "name_cn": "手ε·₯θ‰Ί-ε‰ͺηΊΈζ‹Όθ΄΄", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_papercut_collage.jpg" + }, + { + "name": "papercraft-papercut shadow box", + "prompt": "3D papercut shadow box of {prompt} . layered, dimensional, depth, silhouette, shadow, papercut, handmade, high contrast", + "negative_prompt": "painting, drawing, photo, 2D, flat, high detail, blurry, noisy, disfigured", + "name_cn": "手ε·₯θ‰Ί-ε‰ͺηΊΈε½±η±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_papercut_shadow_box.jpg" + }, + { + "name": "papercraft-stacked papercut", + "prompt": "stacked papercut art of {prompt} . 3D, layered, dimensional, depth, precision cut, stacked layers, papercut, high contrast", + "negative_prompt": "2D, flat, noisy, blurry, painting, drawing, photo, deformed", + "name_cn": "手ε·₯θ‰Ί-层叠ε‰ͺηΊΈ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_stacked_papercut.jpg" + }, + { + "name": "papercraft-thick layered papercut", + "prompt": "thick layered papercut art of {prompt} . deep 3D, volumetric, dimensional, depth, thick paper, high stack, heavy texture, tangible layers", + "negative_prompt": "2D, flat, thin paper, low stack, smooth texture, painting, drawing, photo, deformed", + "name_cn": "手ε·₯θ‰Ί-εŽšε±‚ε‰ͺηΊΈ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/papercraft_thick_layered_papercut.jpg" + }, + { + "name": "photo-alien", + "prompt": "alien-themed {prompt} . extraterrestrial, cosmic, otherworldly, mysterious, sci-fi, highly detailed", + "negative_prompt": "earthly, mundane, common, realistic, simple", + "name_cn": "ζ‘„ε½±-ε€–ζ˜ŸδΊΊ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_alien.jpg" + }, + { + "name": "photo-film noir", + "prompt": "film noir style {prompt} . monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful", + "name_cn": "ζ‘„ε½±-黑色甡影", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_film_noir.jpg" + }, + { + "name": "photo-glamour", + "prompt": "glamorous photo {prompt} . high fashion, luxurious, extravagant, stylish, sensual, opulent, elegance, stunning beauty, professional, high contrast, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy, sketch, low contrast, dull, plain, modest", + "name_cn": "ζ‘„ε½±-ι­…εŠ›", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_glamour.jpg" + }, + { + "name": "photo-hdr", + "prompt": "HDR photo of {prompt} . High dynamic range, vivid, rich details, clear shadows and highlights, realistic, intense, enhanced contrast, highly detailed", + "negative_prompt": "flat, low contrast, oversaturated, underexposed, overexposed, blurred, noisy", + "name_cn": "ζ‘„ε½±-ι«˜εŠ¨ζ€θŒƒε›΄", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_hdr.jpg" + }, + { + "name": "photo-iphone photographic", + "prompt": "iphone photo {prompt} . large depth of field, deep depth of field, highly detailed", + "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, shallow depth of field, bokeh", + "name_cn": "ζ‘„ε½±-θ‹Ήζžœζ‰‹ζœΊζ‘„ε½±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_iphone_photographic.jpg" + }, + { + "name": "photo-long exposure", + "prompt": "long exposure photo of {prompt} . Blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed", + "negative_prompt": "static, noisy, deformed, shaky, abrupt, flat, low contrast", + "name_cn": "ζ‘„ε½±-长曝光", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_long_exposure.jpg" + }, + { + "name": "photo-neon noir", + "prompt": "neon noir {prompt} . cyberpunk, dark, rainy streets, neon signs, high contrast, low light, vibrant, highly detailed", + "negative_prompt": "bright, sunny, daytime, low contrast, black and white, sketch, watercolor", + "name_cn": "ζ‘„ε½±-ιœ“θ™Ήι»‘θ‰²", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_neon_noir.jpg" + }, + { + "name": "photo-silhouette", + "prompt": "silhouette style {prompt} . high contrast, minimalistic, black and white, stark, dramatic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, color, realism, photorealistic", + "name_cn": "ζ‘„ε½±-θ½ε»“", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_silhouette.jpg" + }, + { + "name": "photo-tilt-shift", + "prompt": "tilt-shift photo of {prompt} . selective focus, miniature effect, blurred background, highly detailed, vibrant, perspective control", + "negative_prompt": "blurry, noisy, deformed, flat, low contrast, unrealistic, oversaturated, underexposed", + "name_cn": "ζ‘„ε½±-移轴", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/photo_tilt_shift.jpg" + }, + { + "name": "cinematic-diva", + "prompt": "UHD, 8K, ultra detailed, a cinematic photograph of {prompt}, beautiful lighting, great composition", + "negative_prompt": "ugly, deformed, noisy, blurry, NSFW", + "name_cn": "η”΅ε½±ε₯³δΈ»θ§’", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/cinematic_diva.jpg" + }, + { + "name": "Abstract Expressionism", + "prompt": "Abstract Expressionism Art, {prompt}, High contrast, minimalistic, colorful, stark, dramatic, expressionism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic", + "name_cn": "ζŠ½θ±‘θ‘¨ηŽ°δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/abstract_expressionism.jpg" + }, + { + "name": "Academia", + "prompt": "Academia, {prompt}, preppy Ivy League style, stark, dramatic, chic boarding school, academia", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt", + "name_cn": "学术", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/academia.jpg" + }, + { + "name": "Action Figure", + "prompt": "Action Figure, {prompt}, plastic collectable action figure, collectable toy action figure", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "动作人偢", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/action_figure.jpg" + }, + { + "name": "Adorable 3D Character", + "prompt": "Adorable 3D Character, {prompt}, 3D render, adorable character, 3D art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic", + "name_cn": "ε―ηˆ±ηš„3D角色", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/adorable_3d_character.jpg" + }, + { + "name": "Adorable Kawaii", + "prompt": "Adorable Kawaii, {prompt}, pretty, cute, adorable, kawaii", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, gothic, dark, moody, monochromatic", + "name_cn": "ε―ηˆ±ηš„ε‘ε“‡δΌŠ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/adorable_kawaii.jpg" + }, + { + "name": "Art Deco", + "prompt": "Art Deco, {prompt}, sleek, geometric forms, art deco style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θ£…ι₯°θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/art_deco.jpg" + }, + { + "name": "Art Nouveau", + "prompt": "Art Nouveau, beautiful art, {prompt}, sleek, organic forms, long, sinuous, art nouveau style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, industrial, mechanical", + "name_cn": "ζ–°θ‰Ίζœ―οΌŒηΎŽδΈ½θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/art_nouveau.jpg" + }, + { + "name": "Astral Aura", + "prompt": "Astral Aura, {prompt}, astral, colorful aura, vibrant energy", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ˜Ÿδ½“ε…‰ηŽ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/astral_aura.jpg" + }, + { + "name": "Avant-garde", + "prompt": "Avant-garde, {prompt}, unusual, experimental, avant-garde art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "前卫", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/avant_garde.jpg" + }, + { + "name": "Baroque", + "prompt": "Baroque, {prompt}, dramatic, exuberant, grandeur, baroque art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε·΄ζ΄›ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/baroque.jpg" + }, + { + "name": "Bauhaus-Style Poster", + "prompt": "Bauhaus-Style Poster, {prompt}, simple geometric shapes, clean lines, primary colors, Bauhaus-Style Poster", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "εŒ…θ±ͺζ–―ι£Žζ Όζ΅·ζŠ₯", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/bauhaus_style_poster.jpg" + }, + { + "name": "Blueprint Schematic Drawing", + "prompt": "Blueprint Schematic Drawing, {prompt}, technical drawing, blueprint, schematic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "蓝图瀺意图", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/blueprint_schematic_drawing.jpg" + }, + { + "name": "Caricature", + "prompt": "Caricature, {prompt}, exaggerated, comical, caricature", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic", + "name_cn": "ζΌ«η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/caricature.jpg" + }, + { + "name": "Cel Shaded Art", + "prompt": "Cel Shaded Art, {prompt}, 2D, flat color, toon shading, cel shaded style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε‘ι€šζΈ²ζŸ“", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/cel_shaded_art.jpg" + }, + { + "name": "Character Design Sheet", + "prompt": "Character Design Sheet, {prompt}, character reference sheet, character turn around", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "角色θΎθ‘葨", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/character_design_sheet.jpg" + }, + { + "name": "Classicism Art", + "prompt": "Classicism Art, {prompt}, inspired by Roman and Greek culture, clarity, harmonious, classicism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε€ε…ΈδΈ»δΉ‰θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/classicism_art.jpg" + }, + { + "name": "Color Field Painting", + "prompt": "Color Field Painting, {prompt}, abstract, simple, geometic, color field painting style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θ‰²ε½©ι’†εŸŸη»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/color_field_painting.jpg" + }, + { + "name": "Colored Pencil Art", + "prompt": "Colored Pencil Art, {prompt}, colored pencil strokes, light color, visible paper texture, colored pencil art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε½©θ‰²ι“…η¬”θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/colored_pencil_art.jpg" + }, + { + "name": "Conceptual Art", + "prompt": "Conceptual Art, {prompt}, concept art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ¦‚εΏ΅θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/conceptual_art.jpg" + }, + { + "name": "Constructivism", + "prompt": "Constructivism Art, {prompt}, minimalistic, geometric forms, constructivism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε»Ίζž„δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/constructivism.jpg" + }, + { + "name": "Cubism", + "prompt": "Cubism Art, {prompt}, flat geometric forms, cubism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "立体主义", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/cubism.jpg" + }, + { + "name": "Dadaism", + "prompt": "Dadaism Art, {prompt}, satirical, nonsensical, dadaism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θΎΎθΎΎδΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/dadaism.jpg" + }, + { + "name": "Dark Fantasy", + "prompt": "Dark Fantasy Art, {prompt}, dark, moody, dark fantasy style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, bright, sunny", + "name_cn": "ι»‘ζš—ε₯‡εΉ»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/dark_fantasy.jpg" + }, + { + "name": "Dark Moody Atmosphere", + "prompt": "Dark Moody Atmosphere, {prompt}, dramatic, mysterious, dark moody atmosphere", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, vibrant, colorful, bright", + "name_cn": "ι»‘ζš—εΏ§ιƒζ°”ζ°›", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/dark_moody_atmosphere.jpg" + }, + { + "name": "DMT Art Style", + "prompt": "DMT Art Style, {prompt}, bright colors, surreal visuals, swirling patterns, DMT art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θΏ·εΉ»θ‰Ίζœ―ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/dmt_art_style.jpg" + }, + { + "name": "Doodle Art", + "prompt": "Doodle Art Style, {prompt}, drawing, freeform, swirling patterns, doodle art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζΆ‚ιΈ¦θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/doodle_art.jpg" + }, + { + "name": "Double Exposure", + "prompt": "Double Exposure Style, {prompt}, double image ghost effect, image combination, double exposure style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "εŒι‡ζ›ε…‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/double_exposure.jpg" + }, + { + "name": "Dripping Paint Splatter Art", + "prompt": "Dripping Paint Splatter Art, {prompt}, dramatic, paint drips, splatters, dripping paint", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ»΄ζΌ†ι£žζΊ…θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/dripping_paint_splatter_art.jpg" + }, + { + "name": "Expressionism", + "prompt": "Expressionism Art Style, {prompt}, movement, contrast, emotional, exaggerated forms, expressionism art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θ‘¨ηŽ°δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/expressionism.jpg" + }, + { + "name": "Faded Polaroid Photo", + "prompt": "Faded Polaroid Photo, {prompt}, analog, old faded photo, old polaroid", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, vibrant, colorful", + "name_cn": "θ€ͺθ‰²ηš„εδΈ½ζ₯照片", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/faded_polaroid_photo.jpg" + }, + { + "name": "Fauvism", + "prompt": "Fauvism Art, {prompt}, painterly, bold colors, textured brushwork, fauvism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ι‡Žε…½ζ΄Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fauvism.jpg" + }, + { + "name": "Flat 2D Art", + "prompt": "Flat 2D Art, {prompt}, simple flat color, 2-dimensional, Flat 2D Art Style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic", + "name_cn": "平青 2D θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/flat_2d_art.jpg" + }, + { + "name": "Fortnite Art Style", + "prompt": "Fortnite Art Style, {prompt}, 3D cartoon, colorful, Fortnite Art Style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic", + "name_cn": "ε ‘εž’δΉ‹ε€œθ‰Ίζœ―ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/fortnite_art_style.jpg" + }, + { + "name": "Futurism", + "prompt": "Futurism Art Style, {prompt}, dynamic, dramatic, Futurism Art Style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζœͺζ₯ζ΄Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/futurism.jpg" + }, + { + "name": "Glitchcore", + "prompt": "Glitchcore Art Style, {prompt}, dynamic, dramatic, distorted, vibrant colors, glitchcore art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ•…ιšœζ ΈεΏƒ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/glitchcore.jpg" + }, + { + "name": "Glo-fi", + "prompt": "Glo-fi Art Style, {prompt}, dynamic, dramatic, vibrant colors, glo-fi art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε…‰ζ˜Žι«˜δΏηœŸ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/glo_fi.jpg" + }, + { + "name": "Googie Art Style", + "prompt": "Googie Art Style, {prompt}, dynamic, dramatic, 1950's futurism, bold boomerang angles, Googie art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε€ε‰θ‰Ίζœ―ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/googie_art_style.jpg" + }, + { + "name": "Graffiti Art", + "prompt": "Graffiti Art Style, {prompt}, dynamic, dramatic, vibrant colors, graffiti art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζΆ‚ιΈ¦θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/graffiti_art.jpg" + }, + { + "name": "Harlem Renaissance Art", + "prompt": "Harlem Renaissance Art Style, {prompt}, dynamic, dramatic, 1920s African American culture, Harlem Renaissance art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε“ˆθŽ±ε§†ζ–‡θ‰Ίε€ε…΄θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/harlem_renaissance_art.jpg" + }, + { + "name": "High Fashion", + "prompt": "High Fashion, {prompt}, dynamic, dramatic, haute couture, elegant, ornate clothing, High Fashion", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ι«˜ηΊ§ζ—Άθ£…", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/high_fashion.jpg" + }, + { + "name": "Idyllic", + "prompt": "Idyllic, {prompt}, peaceful, happy, pleasant, happy, harmonious, picturesque, charming", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "η”°ε›­θ―—θˆ¬", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/idyllic.jpg" + }, + { + "name": "Impressionism", + "prompt": "Impressionism, {prompt}, painterly, small brushstrokes, visible brushstrokes, impressionistic style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "印豑派", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/impressionism.jpg" + }, + { + "name": "Infographic Drawing", + "prompt": "Infographic Drawing, {prompt}, diagram, infographic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "δΏ‘ζ―ε›Ύθ‘¨η»˜ε›Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/infographic_drawing.jpg" + }, + { + "name": "Ink Dripping Drawing", + "prompt": "Ink Dripping Drawing, {prompt}, ink drawing, dripping ink", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, colorful, vibrant", + "name_cn": "ζ»΄ε’¨η»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ink_dripping_drawing.jpg" + }, + { + "name": "Japanese Ink Drawing", + "prompt": "Japanese Ink Drawing, {prompt}, ink drawing, inkwash, Japanese Ink Drawing", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, colorful, vibrant", + "name_cn": "ζ—₯式水咨画", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/japanese_ink_drawing.jpg" + }, + { + "name": "Knolling Photography", + "prompt": "Knolling Photography, {prompt}, flat lay photography, object arrangment, knolling photography", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "规律摆放摄影", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/knolling_photography.jpg" + }, + { + "name": "Light Cheery Atmosphere", + "prompt": "Light Cheery Atmosphere, {prompt}, happy, joyful, cheerful, carefree, gleeful, lighthearted, pleasant atmosphere", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, monochromatic, dark, moody", + "name_cn": "θ½»ζΎζ„‰εΏ«ηš„ζ°”ζ°›", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/light_cheery_atmosphere.jpg" + }, + { + "name": "Logo Design", + "prompt": "Logo Design, {prompt}, dynamic graphic art, vector art, minimalist, professional logo design", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ ‡εΏ—θΎθ‘", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/logo_design.jpg" + }, + { + "name": "Luxurious Elegance", + "prompt": "Luxurious Elegance, {prompt}, extravagant, ornate, designer, opulent, picturesque, lavish", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε₯’εŽδΌ˜ι›…", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/luxurious_elegance.jpg" + }, + { + "name": "Macro Photography", + "prompt": "Macro Photography, {prompt}, close-up, macro 100mm, macro photography", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "εΎθ·ζ‘„ε½±", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/macro_photography.jpg" + }, + { + "name": "Mandola Art", + "prompt": "Mandola art style, {prompt}, complex, circular design, mandola", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ›Όι™€η½—θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mandola_art.jpg" + }, + { + "name": "Marker Drawing", + "prompt": "Marker Drawing, {prompt}, bold marker lines, visibile paper texture, marker drawing", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photograph, realistic", + "name_cn": "ι©¬ε…‹η¬”η»˜ε›Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/marker_drawing.jpg" + }, + { + "name": "Medievalism", + "prompt": "Medievalism, {prompt}, inspired by The Middle Ages, medieval art, elaborate patterns and decoration, Medievalism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "δΈ­δΈ–ηΊͺδΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/medievalism.jpg" + }, + { + "name": "Minimalism", + "prompt": "Minimalism, {prompt}, abstract, simple geometic shapes, hard edges, sleek contours, Minimalism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "极η€δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/minimalism.jpg" + }, + { + "name": "Neo-Baroque", + "prompt": "Neo-Baroque, {prompt}, ornate and elaborate, dynaimc, Neo-Baroque", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ–°ε·΄ζ΄›ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/neo_baroque.jpg" + }, + { + "name": "Neo-Byzantine", + "prompt": "Neo-Byzantine, {prompt}, grand decorative religious style, Orthodox Christian inspired, Neo-Byzantine", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ–°ζ‹œε εΊ­", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/neo_byzantine.jpg" + }, + { + "name": "Neo-Futurism", + "prompt": "Neo-Futurism, {prompt}, high-tech, curves, spirals, flowing lines, idealistic future, Neo-Futurism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ζ–°ζœͺζ₯ζ΄Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/neo_futurism.jpg" + }, + { + "name": "Neo-Impressionism", + "prompt": "Neo-Impressionism, {prompt}, tiny dabs of color, Pointillism, painterly, Neo-Impressionism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photograph, realistic", + "name_cn": "新印豑派", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/neo_impressionism.jpg" + }, + { + "name": "Neo-Rococo", + "prompt": "Neo-Rococo, {prompt}, curved forms, naturalistic ornamentation, elaborate, decorative, gaudy, Neo-Rococo", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "新洛可可", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/neo_rococo.jpg" + }, + { + "name": "Neoclassicism", + "prompt": "Neoclassicism, {prompt}, ancient Rome and Greece inspired, idealic, sober colors, Neoclassicism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "新叀典主义", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/neoclassicism.jpg" + }, + { + "name": "Op Art", + "prompt": "Op Art, {prompt}, optical illusion, abstract, geometric pattern, impression of movement, Op Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "欧ζ™θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/op_art.jpg" + }, + { + "name": "Ornate and Intricate", + "prompt": "Ornate and Intricate, {prompt}, decorative, highly detailed, elaborate, ornate, intricate", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "εŽδΈ½θ€Œε€ζ‚", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/ornate_and_intricate.jpg" + }, + { + "name": "Pencil Sketch Drawing", + "prompt": "Pencil Sketch Drawing, {prompt}, black and white drawing, graphite drawing", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "铅笔素描", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/pencil_sketch_drawing.jpg" + }, + { + "name": "Pop Art 2", + "prompt": "Pop Art, {prompt}, vivid colors, flat color, 2D, strong lines, Pop Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic", + "name_cn": "ζ΅θ‘Œθ‰Ίζœ―2", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/pop_art_2.jpg" + }, + { + "name": "Rococo", + "prompt": "Rococo, {prompt}, flamboyant, pastel colors, curved lines, elaborate detail, Rococo", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "洛可可", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/rococo.jpg" + }, + { + "name": "Silhouette Art", + "prompt": "Silhouette Art, {prompt}, high contrast, well defined, Silhouette Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "ε‰ͺε½±θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/silhouette_art.jpg" + }, + { + "name": "Simple Vector Art", + "prompt": "Simple Vector Art, {prompt}, 2D flat, simple shapes, minimalistic, professional graphic, flat color, high contrast, Simple Vector Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic", + "name_cn": "η€ε•ηŸ’ι‡θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/simple_vector_art.jpg" + }, + { + "name": "Sketchup", + "prompt": "Sketchup, {prompt}, CAD, professional design, Sketchup", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, photograph", + "name_cn": "草图", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sketchup.jpg" + }, + { + "name": "Steampunk 2", + "prompt": "Steampunk, {prompt}, retrofuturistic science fantasy, steam-powered tech, vintage industry, gears, neo-victorian, steampunk", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θ΅›εšζœ‹ε…‹2", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/steampunk_2.jpg" + }, + { + "name": "Surrealism", + "prompt": "Surrealism, {prompt}, expressive, dramatic, organic lines and forms, dreamlike and mysterious, Surrealism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic", + "name_cn": "θΆ…ηŽ°εžδΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/surrealism.jpg" + }, + { + "name": "Suprematism", + "prompt": "Suprematism, {prompt}, abstract, limited color palette, geometric forms, Suprematism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic", + "name_cn": "θ‡³δΈŠδΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/suprematism.jpg" + }, + { + "name": "Terragen", + "prompt": "Terragen, {prompt}, beautiful massive landscape, epic scenery, Terragen", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "εœ°θ‘¨ι£Žζ™―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/terragen.jpg" + }, + { + "name": "Tranquil Relaxing Atmosphere", + "prompt": "Tranquil Relaxing Atmosphere, {prompt}, calming style, soothing colors, peaceful, idealic, Tranquil Relaxing Atmosphere", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, oversaturated", + "name_cn": "ει™θ½»ζΎηš„ζ°›ε›΄", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/tranquil_relaxing_atmosphere.jpg" + }, + { + "name": "Sticker Designs", + "prompt": "Vector Art Stickers, {prompt}, professional vector design, sticker designs, Sticker Sheet", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "θ΄΄ηΊΈθΎθ‘", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/sticker_designs.jpg" + }, + { + "name": "Vibrant Rim Light", + "prompt": "Vibrant Rim Light, {prompt}, bright rim light, high contrast, bold edge light", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "η”ŸεŠ¨ηš„θΎΉηΌ˜ε…‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/vibrant_rim_light.jpg" + }, + { + "name": "Volumetric Lighting", + "prompt": "Volumetric Lighting, {prompt}, light depth, dramatic atmospheric lighting, Volumetric Lighting", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast", + "name_cn": "δ½“η§―η…§ζ˜Ž", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/volumetric_lighting.jpg" + }, + { + "name": "Watercolor 2", + "prompt": "Watercolor style painting, {prompt}, visible paper texture, colorwash, watercolor", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic", + "name_cn": "水彩2", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/watercolor_2.jpg" + }, + { + "name": "Whimsical and Playful", + "prompt": "Whimsical and Playful, {prompt}, imaginative, fantastical, bight colors, stylized, happy, Whimsical and Playful", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, drab, boring, moody", + "name_cn": "异想倩开、俏ηš", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/whimsical_and_playful.jpg" + }, + { + "name": "MK Chromolithography", + "prompt": "Chromolithograph {prompt}. Vibrant colors, intricate details, rich color saturation, meticulous registration, multi-layered printing, decorative elements, historical charm, artistic reproductions, commercial posters, nostalgic, ornate compositions.", + "negative_prompt": "monochromatic, simple designs, limited color palette, imprecise registration, minimalistic, modern aesthetic, digital appearance.", + "name_cn": "MK θ‰²ε½©η‰ˆη”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_chromolithography.jpg" + }, + { + "name": "MK Cross Processing Print", + "prompt": "Cross processing print {prompt}. Experimental color shifts, unconventional tonalities, vibrant and surreal hues, heightened contrasts, unpredictable results, artistic unpredictability, retro and vintage feel, dynamic color interplay, abstract and dreamlike.", + "negative_prompt": "predictable color tones, traditional processing, realistic color representation, subdued contrasts, standard photographic aesthetics.", + "name_cn": "MK 亀叉过程打印", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_cross_processing_print.jpg" + }, + { + "name": "MK Dufaycolor Photograph", + "prompt": "Dufaycolor photograph {prompt}. Vintage color palette, distinctive color rendering, soft and dreamy atmosphere, historical charm, unique color process, grainy texture, evocative mood, nostalgic aesthetic, hand-tinted appearance, artistic patina.", + "negative_prompt": "modern color reproduction, hyperrealistic tones, sharp and clear details, digital precision, contemporary aesthetic.", + "name_cn": "MK ζœζ³•θ‰²ε½©η…§η‰‡", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_dufaycolor_photograph.jpg" + }, + { + "name": "MK Herbarium", + "prompt": "Herbarium drawing{prompt}. Botanical accuracy, old botanical book illustration, detailed illustrations, pressed plants, delicate and precise linework, scientific documentation, meticulous presentation, educational purpose, organic compositions, timeless aesthetic, naturalistic beauty.", + "negative_prompt": "abstract representation, vibrant colors, artistic interpretation, chaotic compositions, fantastical elements, digital appearance.", + "name_cn": "MK ζ€η‰©ζ ‡ζœ¬ι¦†", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_herbarium.jpg" + }, + { + "name": "MK Punk Collage", + "prompt": "punk collage style {prompt} . mixed media, papercut,textured paper, overlapping, ripped posters, safety pins, chaotic layers, graffiti-style elements, anarchy symbols, vintage photos, cut-and-paste aesthetic, bold typography, distorted images, political messages, urban decay, distressed textures, newspaper clippings, spray paint, rebellious icons, DIY spirit, vivid colors, punk band logos, edgy and raw compositions, ", + "negative_prompt": "conventional,blurry, noisy, low contrast", + "name_cn": "MK ζœ‹ε…‹ζ‹Όθ΄΄η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_punk_collage.jpg" + }, + { + "name": "MK mosaic", + "prompt": "mosaic style {prompt} . fragmented, assembled, colorful, highly detailed", + "negative_prompt": "whole, unbroken, monochrome", + "name_cn": "MK ι•Άε΅Œε›Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_mosaic.jpg" + }, + { + "name": "MK Van Gogh", + "prompt": "Oil painting by Van Gogh {prompt} . Expressive, impasto, swirling brushwork, vibrant, brush strokes, Brushstroke-heavy, Textured, Impasto, Colorful, Dynamic, Bold, Distinctive, Vibrant, Whirling, Expressive, Dramatic, Swirling, Layered, Intense, Contrastive, Atmospheric, Luminous, Textural, Evocative, SpiraledVan Gogh style", + "negative_prompt": "realistic, photorealistic, calm, straight lines, signature, frame, text, watermark", + "name_cn": "MK 撡高", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_van_gogh.jpg" + }, + { + "name": "MK Coloring Book", + "prompt": "centered black and white high contrast line drawing, coloring book style,{prompt} . monochrome, blank white background", + "negative_prompt": "greyscale, gradients,shadows,shadow, colored, Red, Blue, Yellow, Green, Orange, Purple, Pink, Brown, Gray, Beige, Turquoise, Lavender, Cyan, Magenta, Olive, Indigo, black background", + "name_cn": "MK 色彩书", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_coloring_book.jpg" + }, + { + "name": "MK Singer Sargent", + "prompt": "Oil painting by John Singer Sargent {prompt}. Elegant, refined, masterful technique,realistic portrayal, subtle play of light, captivating expression, rich details, harmonious colors, skillful composition, brush strokes, chiaroscuro.", + "negative_prompt": "realistic, photorealistic, abstract, overly stylized, excessive contrasts, distorted,bright colors,disorder.", + "name_cn": "MK θΎ›ζ Ό Β· θ¨ε°”η”Ÿη‰Ή", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_singer_sargent.jpg" + }, + { + "name": "MK Pollock", + "prompt": "Oil painting by Jackson Pollock {prompt}. Abstract expressionism, drip painting, chaotic composition, energetic, spontaneous, unconventional technique, dynamic, bold, distinctive, vibrant, intense, expressive, energetic, layered, non-representational, gestural.", + "negative_prompt": "(realistic:1.5), (photorealistic:1.5), representational, calm, ordered composition, precise lines, detailed forms, subdued colors, quiet, static, traditional, figurative.", + "name_cn": "MK ζ³’ζ΄›ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_pollock.jpg" + }, + { + "name": "MK Basquiat", + "prompt": "Artwork by Jean-Michel Basquiat {prompt}. Neo-expressionism, street art influence, graffiti-inspired, raw, energetic, bold colors, dynamic composition, chaotic, layered, textural, expressive, spontaneous, distinctive, symbolic,energetic brushstrokes.", + "negative_prompt": "(realistic:1.5), (photorealistic:1.5), calm, precise lines, conventional composition, subdued", + "name_cn": "MK ε·΄ζ–―ε₯Žη‰Ή", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_basquiat.jpg" + }, + { + "name": "MK Andy Warhol", + "prompt": "Artwork in the style of Andy Warhol {prompt}. Pop art, vibrant colors, bold compositions, repetition of iconic imagery, celebrity culture, commercial aesthetics, mass production influence, stylized simplicity, cultural commentary, graphical elements, distinctive portraits.", + "negative_prompt": "subdued colors, realistic, lack of repetition, minimalistic.", + "name_cn": "MK ε‰θΏͺ Β· ζ²ƒιœε°”", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_andy_warhol.jpg" + }, + { + "name": "MK Halftone print", + "prompt": "Halftone print of {prompt}. Dot matrix pattern, grayscale tones, vintage aesthetic, newspaper print vibe, stylized dots, visual texture, black and white contrasts, retro appearance, artistic pointillism,pop culture, (Roy Lichtenstein style:1.5).", + "negative_prompt": "smooth gradients, continuous tones, vibrant colors.", + "name_cn": "MK εŠθ‰²η‰ˆη”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_halftone_print.jpg" + }, + { + "name": "MK Gond Painting", + "prompt": "Gond painting {prompt}. Intricate patterns, vibrant colors, detailed motifs, nature-inspired themes, tribal folklore, fine lines, intricate detailing, storytelling compositions, mystical and folkloric, cultural richness.", + "negative_prompt": "monochromatic, abstract shapes, minimalistic.", + "name_cn": "MK θ΄‘εΎ·η»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_gond_painting.jpg" + }, + { + "name": "MK Albumen Print", + "prompt": "Albumen print {prompt}. Sepia tones, fine details, subtle tonal gradations, delicate highlights, vintage aesthetic, soft and muted atmosphere, historical charm, rich textures, meticulous craftsmanship, classic photographic technique, vignetting.", + "negative_prompt": "vibrant colors, high contrast, modern, digital appearance, sharp details, contemporary style.", + "name_cn": "MK η™½θ›‹ζΈ…ε°εˆ·", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_albumen_print.jpg" + }, + { + "name": "MK Aquatint Print", + "prompt": "Aquatint print {prompt}. Soft tonal gradations, atmospheric effects, velvety textures, rich contrasts, fine details, etching process, delicate lines, nuanced shading, expressive and moody atmosphere, artistic depth.", + "negative_prompt": "sharp contrasts, bold lines, minimalistic.", + "name_cn": "MK ζ°΄θš€εˆ»ε°εˆ·", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_aquatint_print.jpg" + }, + { + "name": "MK Anthotype Print", + "prompt": "Anthotype print {prompt}. Monochrome dye, soft and muted colors, organic textures, ephemeral and delicate appearance, low details, watercolor canvas, low contrast, overexposed, silhouette, textured paper.", + "negative_prompt": "vibrant synthetic dyes, bold and saturated colors.", + "name_cn": "MK θŠ±ηΊΉη‰ˆη”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_anthotype_print.jpg" + }, + { + "name": "MK Inuit Carving", + "prompt": "A sculpture made of ivory, {prompt} made of . Sculptures, Inuit art style, intricate carvings, natural materials, storytelling motifs, arctic wildlife themes, symbolic representations, cultural traditions, earthy tones, harmonious compositions, spiritual and mythological elements.", + "negative_prompt": "abstract, vibrant colors.", + "name_cn": "MK ε› ηΊ½η‰Ήι›•ε‘‘", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_inuit_carving.jpg" + }, + { + "name": "MK Bromoil Print", + "prompt": "Bromoil print {prompt}. Painterly effects, sepia tones, textured surfaces, rich contrasts, expressive brushwork, tonal variations, vintage aesthetic, atmospheric mood, handmade quality, artistic experimentation, darkroom craftsmanship, vignetting.", + "negative_prompt": "smooth surfaces, minimal brushwork, contemporary digital appearance.", + "name_cn": "MK 溴油印刷", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_bromoil_print.jpg" + }, + { + "name": "MK Calotype Print", + "prompt": "Calotype print {prompt}. Soft focus, subtle tonal range, paper negative process, fine details, vintage aesthetic, artistic experimentation, atmospheric mood, early photographic charm, handmade quality, vignetting.", + "negative_prompt": "sharp focus, bold contrasts, modern aesthetic, digital photography.", + "name_cn": "MK ε‘ζ΄›ι›…ε›Ύε°εˆ·", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_calotype_print.jpg" + }, + { + "name": "MK Color Sketchnote", + "prompt": "Color sketchnote {prompt}. Hand-drawn elements, vibrant colors, visual hierarchy, playful illustrations, varied typography, graphic icons, organic and dynamic layout, personalized touches, creative expression, engaging storytelling.", + "negative_prompt": "monochromatic, geometric layout.", + "name_cn": "MK色彩素描笔θ°", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_color_sketchnote.jpg" + }, + { + "name": "MK Cibulak Porcelain", + "prompt": "A sculpture made of blue pattern porcelain of {prompt}. Classic design, blue and white color scheme, intricate detailing, floral motifs, onion-shaped elements, historical charm, rococo, white ware, cobalt blue, underglaze pattern, fine craftsmanship, traditional elegance, delicate patterns, vintage aesthetic, Meissen, Blue Onion pattern, Cibulak.", + "negative_prompt": "tea, teapot, cup, teacup,bright colors, bold and modern design, absence of intricate detailing, lack of floral motifs, non-traditional shapes.", + "name_cn": "MK θ₯ΏεΈƒζ‹‰η“·ε™¨", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_cibulak_porcelain.jpg" + }, + { + "name": "MK Alcohol Ink Art", + "prompt": "Alcohol ink art {prompt}. Fluid and vibrant colors, unpredictable patterns, organic textures, translucent layers, abstract compositions, ethereal and dreamy effects, free-flowing movement, expressive brushstrokes, contemporary aesthetic, wet textured paper.", + "negative_prompt": "monochromatic, controlled patterns.", + "name_cn": "MK ι…’η²Ύζ°΄ε½©θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_alcohol_ink_art.jpg" + }, + { + "name": "MK One Line Art", + "prompt": "One line art {prompt}. Continuous and unbroken black line, minimalistic, simplicity, economical use of space, flowing and dynamic, symbolic representations, contemporary aesthetic, evocative and abstract, white background.", + "negative_prompt": "disjointed lines, complexity, complex detailing.", + "name_cn": "MK δΈ€ηΊΏη”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_one_line_art.jpg" + }, + { + "name": "MK Blacklight Paint", + "prompt": "Blacklight paint {prompt}. Fluorescent pigments, vibrant and surreal colors, ethereal glow, otherworldly effects, dynamic and psychedelic compositions, neon aesthetics, transformative in ultraviolet light, contemporary and experimental.", + "negative_prompt": "muted colors, traditional and realistic compositions.", + "name_cn": "MK ι»‘ε…‰ζ²ΉζΌ†", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_blacklight_paint.jpg" + }, + { + "name": "MK Carnival Glass", + "prompt": "A sculpture made of Carnival glass, {prompt}. Iridescent surfaces, vibrant colors, intricate patterns, opalescent hues, reflective and prismatic effects, Art Nouveau and Art Deco influences, vintage charm, intricate detailing, lustrous and luminous appearance, Carnival Glass style.", + "negative_prompt": "non-iridescent surfaces, muted colors, absence of intricate patterns, lack of opalescent hues, modern and minimalist aesthetic.", + "name_cn": "MK ε˜‰εΉ΄εŽηŽ»η’ƒ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_carnival_glass.jpg" + }, + { + "name": "MK Cyanotype Print", + "prompt": "Cyanotype print {prompt}. Prussian blue tones, distinctive coloration, high contrast, blueprint aesthetics, atmospheric mood, sun-exposed paper, silhouette effects, delicate details, historical charm, handmade and experimental quality.", + "negative_prompt": "vibrant colors, low contrast, modern and polished appearance.", + "name_cn": "MK ι’θ‰²η‰ˆη”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_cyanotype_print.jpg" + }, + { + "name": "MK Cross-Stitching", + "prompt": "Cross-stitching {prompt}. Intricate patterns, embroidery thread, sewing, fine details, precise stitches, textile artistry, symmetrical designs, varied color palette, traditional and contemporary motifs, handmade and crafted,canvas, nostalgic charm.", + "negative_prompt": "paper, paint, ink, photography.", + "name_cn": "MK δΊ€ε‰ι’ˆη»‡", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_cross_stitching.jpg" + }, + { + "name": "MK Encaustic Paint", + "prompt": "Encaustic paint {prompt}. Textured surfaces, translucent layers, luminous quality, wax medium, rich color saturation, fluid and organic shapes, contemporary and historical influences, mixed media elements, atmospheric depth.", + "negative_prompt": "flat surfaces, opaque layers, lack of wax medium, muted color palette, absence of textured surfaces, non-mixed media.", + "name_cn": "MK θœ‘ζΌ†", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_encaustic_paint.jpg" + }, + { + "name": "MK Embroidery", + "prompt": "Embroidery {prompt}. Intricate stitching, embroidery thread, fine details, varied thread textures, textile artistry, embellished surfaces, diverse color palette, traditional and contemporary motifs, handmade and crafted, tactile and ornate.", + "negative_prompt": "minimalist, monochromatic.", + "name_cn": "MK 刺绣", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_embroidery.jpg" + }, + { + "name": "MK Gyotaku", + "prompt": "Gyotaku {prompt}. Fish impressions, realistic details, ink rubbings, textured surfaces, traditional Japanese art form, nature-inspired compositions, artistic representation of marine life, black and white contrasts, cultural significance.", + "negative_prompt": "photography.", + "name_cn": "MK ι±Όζ‹“η‰ˆη”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_gyotaku.jpg" + }, + { + "name": "MK Luminogram", + "prompt": "Luminogram {prompt}. Photogram technique, ethereal and abstract effects, light and shadow interplay, luminous quality, experimental process, direct light exposure, unique and unpredictable results, artistic experimentation.", + "negative_prompt": "", + "name_cn": "MK ε…‰ζ„Ÿε½±εƒ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_luminogram.jpg" + }, + { + "name": "MK Lite Brite Art", + "prompt": "Lite Brite art {prompt}. Luminous and colorful designs, pixelated compositions, retro aesthetic, glowing effects, creative patterns, interactive and playful, nostalgic charm, vibrant and dynamic arrangements.", + "negative_prompt": "monochromatic.", + "name_cn": "MK ε½©θ‰²η―ζ³‘θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_lite_brite_art.jpg" + }, + { + "name": "MK Mokume-gane", + "prompt": "Mokume-gane {prompt}. Wood-grain patterns, mixed metal layers, intricate and organic designs, traditional Japanese metalwork, harmonious color combinations, artisanal craftsmanship, unique and layered textures, cultural and historical significance.", + "negative_prompt": "uniform metal surfaces.", + "name_cn": "MK ζœ¨ι‡‘ε·₯θ‰Ί", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_mokume_gane.jpg" + }, + { + "name": "Pebble Art", + "prompt": "a sculpture made of peebles, {prompt}. Pebble art style,natural materials, textured surfaces, balanced compositions, organic forms, harmonious arrangements, tactile and 3D effects, beach-inspired aesthetic, creative storytelling, artisanal craftsmanship.", + "negative_prompt": "non-natural materials, lack of textured surfaces, imbalanced compositions, absence of organic forms, non-tactile appearance.", + "name_cn": "MK ιΉ…ε΅ηŸ³θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/pebble_art.jpg" + }, + { + "name": "MK Palekh", + "prompt": "Palekh art {prompt}. Miniature paintings, intricate details, vivid colors, folkloric themes, lacquer finish, storytelling compositions, symbolic elements, Russian folklore influence, cultural and historical significance.", + "negative_prompt": "large-scale paintings.", + "name_cn": "MK εΈ•εˆ—θ΅«", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_palekh.jpg" + }, + { + "name": "MK Suminagashi", + "prompt": "Suminagashi {prompt}. Floating ink patterns, marbled effects, delicate and ethereal designs, water-based ink, fluid and unpredictable compositions, meditative process, monochromatic or subtle color palette, Japanese artistic tradition.", + "negative_prompt": "vibrant and bold color palette.", + "name_cn": "MK ζΎ„ζ΄—η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_suminagashi.jpg" + }, + { + "name": "MK Scrimshaw", + "prompt": "A Scrimshaw engraving of {prompt}. Intricate engravings on a spermwhale's teeth, marine motifs, detailed scenes, nautical themes, black and white contrasts, historical craftsmanship, artisanal carving, storytelling compositions, maritime heritage.", + "negative_prompt": "colorful, modern.", + "name_cn": "MK 丝线绣", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_scrimshaw.jpg" + }, + { + "name": "MK Shibori", + "prompt": "Shibori {prompt}. Textured fabric, intricate patterns, resist-dyeing technique, indigo or vibrant colors, organic and flowing designs, Japanese textile art, cultural tradition, tactile and visual interest.", + "negative_prompt": "monochromatic.", + "name_cn": "MK ζΉΏεΈƒι›•η‰ˆε°εˆ·", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_shibori.jpg" + }, + { + "name": "MK Vitreous Enamel", + "prompt": "A sculpture made of Vitreous enamel {prompt}. Smooth and glossy surfaces, vibrant colors, glass-like finish, durable and resilient, intricate detailing, traditional and contemporary applications, artistic craftsmanship, jewelry and decorative objects, , Vitreous enamel, colored glass.", + "negative_prompt": "rough surfaces, muted colors.", + "name_cn": "MK ηŽ»η’ƒηη…", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_vitreous_enamel.jpg" + }, + { + "name": "MK Ukiyo-e", + "prompt": "Ukiyo-e {prompt}. Woodblock prints, vibrant colors, intricate details, depictions of landscapes, kabuki actors, beautiful women, cultural scenes, traditional Japanese art, artistic craftsmanship, historical significance.", + "negative_prompt": "absence of woodblock prints, muted colors, lack of intricate details, non-traditional Japanese themes, absence of cultural scenes.", + "name_cn": "MK ζ΅δΈ–η»˜", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_ukiyo_e.jpg" + }, + { + "name": "MK vintage-airline-poster", + "prompt": "vintage airline poster {prompt} . classic aviation fonts, pastel colors, elegant aircraft illustrations, scenic destinations, distressed textures, retro travel allure", + "negative_prompt": "modern fonts, bold colors, hyper-realistic, sleek design", + "name_cn": "MK 叀董θˆͺ空公司桷ζŠ₯", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_vintage_airline_poster.jpg" + }, + { + "name": "MK vintage-travel-poster", + "prompt": "vintage travel poster {prompt} . retro fonts, muted colors, scenic illustrations, iconic landmarks, distressed textures, nostalgic vibes", + "negative_prompt": "modern fonts, vibrant colors, hyper-realistic, sleek design", + "name_cn": "MK ε€θ‘£ζ—…θ‘Œζ΅·ζŠ₯", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_vintage_travel_poster.jpg" + }, + { + "name": "MK bauhaus-style", + "prompt": "Bauhaus-inspired {prompt} . minimalism, geometric precision, primary colors, sans-serif typography, asymmetry, functional design", + "negative_prompt": "ornate, intricate, excessive detail, complex patterns, serif typography", + "name_cn": "Mk εŒ…θ±ͺζ–―ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_bauhaus_style.jpg" + }, + { + "name": "MK afrofuturism", + "prompt": "Afrofuturism illustration {prompt} . vibrant colors, futuristic elements, cultural symbolism, cosmic imagery, dynamic patterns, empowering narratives", + "negative_prompt": "monochromatic", + "name_cn": "Mk 非洲ζœͺζ₯δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_afrofuturism.jpg" + }, + { + "name": "MK atompunk", + "prompt": "Atompunk illustation, {prompt} . retro-futuristic, atomic age aesthetics, sleek lines, metallic textures, futuristic technology, optimism, energy", + "negative_prompt": "organic, natural textures, rustic, dystopian", + "name_cn": "Mk εŽŸε­ζœ‹ε…‹", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_atompunk.jpg" + }, + { + "name": "MK constructivism", + "prompt": "Constructivism {prompt} . geometric abstraction, bold colors, industrial aesthetics, dynamic compositions, utilitarian design, revolutionary spirit", + "negative_prompt": "organic shapes, muted colors, ornate elements, traditional", + "name_cn": "Mk ζž„ζˆζ΄Ύ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_constructivism.jpg" + }, + { + "name": "MK chicano-art", + "prompt": "Chicano art {prompt} . bold colors, cultural symbolism, muralism, lowrider aesthetics, barrio life, political messages, social activism, Mexico", + "negative_prompt": "monochromatic, minimalist, mainstream aesthetics", + "name_cn": "Mk θ₯Ώη­η‰™θ£”ηΎŽε›½θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_chicano_art.jpg" + }, + { + "name": "MK de-stijl", + "prompt": "De Stijl Art {prompt} . neoplasticism, primary colors, geometric abstraction, horizontal and vertical lines, simplicity, harmony, utopian ideals", + "negative_prompt": "complex patterns, muted colors, ornate elements, asymmetry", + "name_cn": "Mk 去风格派", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_de_stijl.jpg" + }, + { + "name": "MK dayak-art", + "prompt": "Dayak art sculpture of {prompt} . intricate patterns, nature-inspired motifs, vibrant colors, traditional craftsmanship, cultural symbolism, storytelling", + "negative_prompt": "minimalist, monochromatic, modern", + "name_cn": "Mk θΎΎι›…ε…‹θ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_dayak_art.jpg" + }, + { + "name": "MK fayum-portrait", + "prompt": "Fayum portrait {prompt} . encaustic painting, realistic facial features, warm earth tones, serene expressions, ancient Egyptian influences", + "negative_prompt": "abstract, vibrant colors, exaggerated features, modern", + "name_cn": "Mk 法尀姆肖像画", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_fayum_portrait.jpg" + }, + { + "name": "MK illuminated-manuscript", + "prompt": "Illuminated manuscript {prompt} . intricate calligraphy, rich colors, detailed illustrations, gold leaf accents, ornate borders, religious, historical, medieval", + "negative_prompt": "modern typography, minimalist design, monochromatic, abstract themes", + "name_cn": "Mk ε½©η»˜ζ‰‹η¨Ώ", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_illuminated_manuscript.jpg" + }, + { + "name": "MK kalighat-painting", + "prompt": "Kalighat painting {prompt} . bold lines, vibrant colors, narrative storytelling, cultural motifs, flat compositions, expressive characters", + "negative_prompt": "subdued colors, intricate details, realistic portrayal, modern aesthetics", + "name_cn": "Mk ε‘εˆ©εŠ η‰Ήη»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_kalighat_painting.jpg" + }, + { + "name": "MK madhubani-painting", + "prompt": "Madhubani painting {prompt} . intricate patterns, vibrant colors, nature-inspired motifs, cultural storytelling, symmetry, folk art aesthetics", + "negative_prompt": "abstract, muted colors, minimalistic design, modern aesthetics", + "name_cn": "Mk ι©¬ζœη­ε°Όη»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_madhubani_painting.jpg" + }, + { + "name": "MK pictorialism", + "prompt": "Pictorialism illustration{prompt} . soft focus, atmospheric effects, artistic interpretation, tonality, muted colors, evocative storytelling", + "negative_prompt": "sharp focus, high contrast, realistic depiction, vivid colors", + "name_cn": "Mk ζη»˜δΈ»δΉ‰", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_pictorialism.jpg" + }, + { + "name": "MK pichwai-painting", + "prompt": "Pichwai painting {prompt} . intricate detailing, vibrant colors, religious themes, nature motifs, devotional storytelling, gold leaf accents", + "negative_prompt": "minimalist, subdued colors, abstract design", + "name_cn": "Mk ηšε₯‡η“¦δΌŠη»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_pichwai_painting.jpg" + }, + { + "name": "MK patachitra-painting", + "prompt": "Patachitra painting {prompt} . bold outlines, vibrant colors, intricate detailing, mythological themes, storytelling, traditional craftsmanship", + "negative_prompt": "subdued colors, minimalistic, abstract, modern aesthetics", + "name_cn": "Mk εΈ•ε‘”εŸΊη‰Ήζ‹‰η»˜η”»", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_patachitra_painting.jpg" + }, + { + "name": "MK samoan-art-inspired", + "prompt": "Samoan art-inspired wooden sculpture {prompt} . traditional motifs, natural elements, bold colors, cultural symbolism, storytelling, craftsmanship", + "negative_prompt": "modern aesthetics, minimalist, abstract", + "name_cn": "Mk θ¨θŽ«δΊšθ‰Ίζœ―ε―ε‘ηš„", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_samoan_art_inspired.jpg" + }, + { + "name": "MK tlingit-art", + "prompt": "Tlingit art {prompt} . formline design, natural elements, animal motifs, bold colors, cultural storytelling, traditional craftsmanship, Alaska traditional art, (totem:1.5)", + "negative_prompt": "", + "name_cn": "Mk η‰Ήζž—ε‰η‰Ήθ‰Ίζœ―", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_tlingit_art.jpg" + }, + { + "name": "MK adnate-style", + "prompt": "Painting by Adnate {prompt} . realistic portraits, street art, large-scale murals, subdued color palette, social narratives", + "negative_prompt": "abstract, vibrant colors, small-scale art", + "name_cn": "Mk ι˜ΏθΎΎε†…η‰Ήι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_adnate_style.jpg" + }, + { + "name": "MK ron-english-style", + "prompt": "Painting by Ron English {prompt} . pop-surrealism, cultural subversion, iconic mash-ups, vibrant and bold colors, satirical commentary", + "negative_prompt": "traditional, monochromatic", + "name_cn": "Mk η½—ζ©θ‹±ε›½ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_ron_english_style.jpg" + }, + { + "name": "MK shepard-fairey-style", + "prompt": "Painting by Shepard Fairey {prompt} . street art, political activism, iconic stencils, bold typography, high contrast, red, black, and white color palette", + "negative_prompt": "traditional, muted colors", + "name_cn": "Mk θˆ’εΈ•εΎ· Β· θ΄Ήε°”εˆ©ι£Žζ Ό", + "thumbnail": "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/mk_shepard_fairey_style.jpg" + } +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/resources/mmb-preset.custom.txt.example b/custom_nodes/ComfyUI-Easy-Use/resources/mmb-preset.custom.txt.example new file mode 100644 index 00000000..eb113a50 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/resources/mmb-preset.custom.txt.example @@ -0,0 +1,8 @@ +MMB-ALL:1,1,1 +MMB-IN:1,0,0 +MMB-MID:0,1,0 +MMB-OUT:0,0,1 +MMB-INMID:1,1,0 +MMB-INOUT:1,0,1 +MMB-MIDOUT:0,1,1 +MMB-NONE:0,0,0 \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/resources/mmb-preset.txt b/custom_nodes/ComfyUI-Easy-Use/resources/mmb-preset.txt new file mode 100644 index 00000000..7403808c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/resources/mmb-preset.txt @@ -0,0 +1,9 @@ +MMB-ALL:1,1,1 +MMB-IN:1,0,0 +MMB-MID:0,1,0 +MMB-OUT:0,0,1 +MMB-INMID:1,1,0 +MMB-INOUT:1,0,1 +MMB-MIDOUT:0,1,1 +MMB-NONE:0,0,0 +@MMBN-FULL-TEST:27 diff --git a/custom_nodes/ComfyUI-Easy-Use/resources/portrait_prompt.json b/custom_nodes/ComfyUI-Easy-Use/resources/portrait_prompt.json new file mode 100644 index 00000000..7c77a392 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/resources/portrait_prompt.json @@ -0,0 +1,407 @@ +{ + "beard_list": [ + "Stubble Beard", + "Goatee", + "Full Beard", + "Van Dyke Beard", + "Soul Patch", + "Garibaldi Beard", + "Mutton Chops", + "Circle Beard", + "Corporate Beard", + "Balbo Beard", + "Ducktail Beard", + "Chinstrap Beard", + "Anchor Beard", + "Chevron Mustache", + "Horseshoe Mustache", + "Handlebar Mustache", + "Imperial Mustache", + "Pencil Mustache", + "Friendly Mutton Chops", + "Zappa Mustache" + ], + "body_type_list": [ + "Underweight", + "Normal weight", + "Overweight", + "Obese" + ], + "eyes_color_list": [ + "Brown", + "Blue", + "Green", + "Hazel", + "Gray", + "Amber", + "Red", + "Violet" + ], + "face_expression_list": [ + "Happy", + "Sad", + "Angry", + "Surprised", + "Fearful", + "Disgusted", + "Contemptuous", + "Excited", + "Nervous", + "Confused", + "Amused", + "Content", + "Disappointed", + "Bored", + "Relieved", + "In love", + "Shy", + "Envious", + "Proud", + "Cautious", + "Serious", + "Serene", + "Peaceful", + "Calm" + ], + "face_shape_list": [ + "Oval", + "Round", + "Square", + "Heart-shaped", + "Long", + "Rectangle", + "Triangle", + "Inverted Triangle", + "Pear-shaped", + "Oblong", + "Square Round", + "Square Oval" + ], + "gender_list": [ + "Man", + "Woman" + ], + "hair_color_list": [ + "Black", + "Brown", + "Blonde", + "Red", + "Auburn", + "Chestnut", + "Gray", + "White", + "Salt and pepper" + ], + "hair_style_list": [ + "Asymmetrical cut", + "Blunt cut", + "Bob cut", + "Braided bob", + "Buzz cut", + "Choppy cut", + "Curly bob", + "Curtain bangs", + "Faux hawk", + "Feathered cut", + "French bob", + "Layered cut", + "Long bob", + "Mohawk", + "Pixie cut", + "Shag cut", + "Side-swept bangs", + "Textured cut", + "Undercut", + "Wavy bob", + "Faux hawk short pixie", + "Brave short haircut with shaved sides", + "Tapered haricut wuth shaved side", + "Stacked bob", + "Lemonade braids", + "Middle part ponytails", + "Stitch braids", + "Deep side part", + "French braids", + "Box braids", + "Two dutch braids", + "Wavy cut with curtains bangs", + "Right side shaved", + "Sweeping pixie", + "Smooth lob", + "Long pixie", + "Sideswept pixie", + "Italian bob", + "Shullet" + ], + "light_direction_list": [ + "top", + "bottom", + "right", + "left", + "front", + "rear", + "top-right", + "top-left", + "bottom-right", + "bottom-left" + ], + "light_type_list": [ + "Natural sunlight", + "Soft ambient light", + "Harsh sunlight", + "Overcast sky", + "Sunset glow", + "Sunrise warmth", + "Twilight hues", + "Candlelight", + "Incandescent lighting", + "Fluorescent lighting", + "Moonlight", + "Dappled sunlight", + "Backlit silhouette", + "Spotlight", + "Rim lighting", + "Firelight", + "City streetlights", + "Studio lighting", + "Lantern light", + "Tungsten lighting", + "Cloudy day diffused light", + "Skylight", + "Golden hour light", + "Blue hour light", + "Flash photography", + "Stage lighting", + "Neon lights", + "Torchlight", + "Softbox lighting", + "Rim light", + "Lightning", + "Abstract light patterns" + ], + "model_pose_list": [ + "Power Pose", + "Walking Pose", + "The Over-the-Shoulder Look", + "S-curve Pose", + "Sitting Pose", + "Close-Up Beauty Shot Pose", + "Leaning Pose", + "Arms Up Pose", + "Casual Stroll Pose", + "Headshot Pose", + "Sitting Cross-Legged Pose", + "Back Arch Pose", + "Hand-on-Hip Pose", + "Gazing into the Distance Pose", + "Candid Laugh Pose", + "Dynamic Action Pose", + "Contrapposto Pose", + "High Fashion Pose" + ], + "nationality_list": [ + "Afghan", + "Albanian", + "Algerian", + "Andorran", + "Angolan", + "Antiguans Barbudans", + "Argentine", + "Armenian", + "Australian", + "Austrian", + "Azerbaijani", + "Bahamian", + "Bahraini", + "Bangladeshi", + "Barbadian", + "Belarusian", + "Belgian", + "Belizean", + "Beninese", + "Bhutanese", + "Bolivian", + "Bosnian Herzegovinian", + "Brazilian", + "British", + "Bruneian", + "Bulgarian", + "Burkinabe", + "Burundian", + "Cambodian", + "Cameroonian", + "Canadian", + "Cape Verdian", + "Central African", + "Chadian", + "Chilean", + "Chinese", + "Colombian", + "Comoran", + "Congolese", + "Costa Rican", + "Croatian", + "Cuban", + "Cypriot", + "Czech", + "Danish", + "Djibouti", + "Dominican", + "Dutch", + "East Timorese", + "Ecuadorean", + "Egyptian", + "Emirian", + "Equatorial Guinean", + "Eritrean", + "Estonian", + "Ethiopian", + "Fijian", + "Filipino", + "Finnish", + "French", + "Gabonese", + "Gambian", + "Georgian", + "German", + "Ghanaian", + "Greek", + "Grenadian", + "Guatemalan", + "Guinean", + "Guyanese", + "Haitian", + "Herzegovinian", + "Honduran", + "Hungarian", + "Icelander", + "Indian", + "Indonesian", + "Iranian", + "Iraqi", + "Irish", + "Israeli", + "Italian", + "Ivorian", + "Jamaican", + "Japanese", + "Jordanian", + "Kazakhstani", + "Kenyan", + "Kiribati", + "North Korean", + "South Korean", + "Kuwaiti", + "Kyrgyz", + "Laotian", + "Latvian", + "Lebanese", + "Liberian", + "Libyan", + "Liechtensteiner", + "Lithuanian", + "Luxembourgish", + "Macedonian", + "Malagasy", + "Malawian", + "Malaysian", + "Maldivan", + "Malian", + "Maltese", + "Marshallese", + "Mauritanian", + "Mauritian", + "Mexican", + "Micronesian", + "Moldovan", + "Monegasque", + "Mongolian", + "Montenegrin", + "Moroccan", + "Mosotho", + "Motswana", + "Mozambican", + "Namibian", + "Nauruan", + "Nepalese", + "New Zealander", + "Ni-Vanuatu", + "Nicaraguan", + "Nigerian", + "Nigerien", + "North Korean", + "Northern Irish", + "Norwegian", + "Omani", + "Pakistani", + "Palauan", + "Palestinian", + "Panamanian", + "Papua New Guinean", + "Paraguayan", + "Peruvian", + "Polish", + "Portuguese", + "Qatari", + "Romanian", + "Russian", + "Rwandan", + "Saint Lucian", + "Salvadoran", + "Samoan", + "San Marinese", + "Sao Tomean", + "Saudi", + "Scottish", + "Senegalese", + "Serbian", + "Seychellois", + "Sierra Leonean", + "Singaporean", + "Slovakian", + "Slovenian", + "Solomon Islander", + "Somali", + "South African", + "South Korean", + "South Sudanese", + "Spanish", + "Sri Lankan", + "Sudanese", + "Surinamer", + "Swazi", + "Swedish", + "Swiss", + "Syrian", + "Tajikistani", + "Tanzanian", + "Thai", + "Togolese", + "Tongan", + "Trinidadian Tobagonian", + "Tunisian", + "Turkish", + "Turkmen", + "Tuvaluan", + "Ugandan", + "Ukrainian", + "Uruguayan", + "Uzbekistani", + "Venezuelan", + "Vietnamese", + "Welsh", + "Yemeni", + "Zambian", + "Zimbabwean" + ], + "shot_list": [ + "Head portrait", + "Head and shoulders portrait", + "Half-length portrait", + "Full-length portrait", + "Face", + "Portrait", + "Full body", + "Close-up" + ] +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/tools/combine_autocomplete.py b/custom_nodes/ComfyUI-Easy-Use/tools/combine_autocomplete.py new file mode 100644 index 00000000..d0245371 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/tools/combine_autocomplete.py @@ -0,0 +1,50 @@ +# θ‡ͺεšδΉ‰ζη€Ίθ―θ‡ͺ动θ‘₯ε…¨ε·₯ε…· + +import os, sys +import glob +import shutil + +output_file = None +cwd_path = os.path.dirname(os.path.realpath(__file__)) +pyssss_path = os.path.join(cwd_path, "..", "ComfyUI-Custom-Scripts", "user") +combine_folder = os.path.join(cwd_path, "autocomplete") + +def backup_autocomplete(): + bak_file = os.path.join(pyssss_path, "autocomplete.txt.bak") + if os.path.exists(bak_file): + pass + elif os.path.exists(output_file): + shutil.copy(output_file, bak_file) + +def combine_autocomplete(): + if os.path.exists(combine_folder): + pass + else: + os.mkdir(combine_folder) + if os.path.exists(pyssss_path): + output_file = os.path.join(pyssss_path, "autocomplete.txt") + # ιεŽ† combine η›ε½•δΈ‹ηš„ζ‰€ζœ‰ txt ζ–‡δ»ΆοΌŒθ―»ε–ε†…εΉεΉΆεˆεΉΆ + merged_content = '' + for file_path in glob.glob(os.path.join(combine_folder, '*.txt')): + with open(file_path, 'r', encoding='utf-8', errors='ignore') as file: + try: + file_content = file.read() + merged_content += file_content + '\n' + except UnicodeDecodeError: + pass + if merged_content != '': + # ε°†εˆεΉΆηš„ε†…εΉε†™ε…₯η›ζ ‡ζ–‡δ»Ά autocomplete.txtοΌŒεΉΆζŒ‡εšηΌ–码为 utf-8 + with open(output_file, 'w', encoding='utf-8') as target_file: + target_file.write(merged_content) + +if __name__ == "__main__": + arg = sys.argv[0] + if 'combine_autocomplete' in arg: + arg = sys.argv[1] + if arg == 'backup': + backup_autocomplete() + elif arg == 'combine': + combine_autocomplete() + else: + print("Usage: python combine_autocomplete.py [backup|combine]") + sys.exit(1) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/account.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/account.css new file mode 100644 index 00000000..626aa07c --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/account.css @@ -0,0 +1,93 @@ +.easyuse-account{ + +} +.easyuse-account-user{ + font-size: 10px; + color:var(--descrip-text); + text-align: center; +} +.easyuse-account-user-info{ + display: flex; + justify-content: space-between; + align-items: center; + padding-bottom:10px; + cursor: pointer; +} +.easyuse-account-user-info .user{ + display: flex; + align-items: center; +} +.easyuse-account-user-info .edit{ + padding:5px 10px; + background: var(--comfy-menu-bg); + border-radius:4px; +} +.easyuse-account-user-info:hover{ + filter:brightness(110%); +} +.easyuse-account-user-info h5{ + margin:0; + font-size: 10px; + text-align: left; +} +.easyuse-account-user-info h6{ + margin:0; + font-size: 8px; + text-align: left; + font-weight: 300; +} +.easyuse-account-user-info .remark{ + margin-top: 4px; +} +.easyuse-account-user-info .avatar{ + width: 36px; + height: 36px; + background: var(--comfy-input-bg); + border-radius: 50%; + margin-right: 5px; + display: flex; + justify-content: center; + align-items: center; + font-size: 16px; + overflow: hidden; +} +.easyuse-account-user-info .avatar img{ + width: 100%; + height: 100%; +} +.easyuse-account-dialog{ + width: 600px; +} +.easyuse-account-dialog-main a, .easyuse-account-dialog-main a:visited{ + font-weight: 400; + color: var(--theme-color-light); +} +.easyuse-account-dialog-item{ + display: flex; + justify-content: flex-start; + align-items: center; + padding: 10px 0; + border-bottom: 1px solid var(--border-color); +} +.easyuse-account-dialog-item input{ + padding:5px; + margin-right:5px; +} +.easyuse-account-dialog-item input.key{ + flex:1; +} +.easyuse-account-dialog-item button{ + cursor: pointer; + margin-left:5px!important; + padding:5px!important; + font-size: 16px!important; +} +.easyuse-account-dialog-item button:hover{ + filter:brightness(120%); +} +.easyuse-account-dialog-item button.choose { + background: var(--theme-color); +} +.easyuse-account-dialog-item button.delete{ + background: var(--error-color); +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/chooser.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/chooser.css new file mode 100644 index 00000000..0c1696bf --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/chooser.css @@ -0,0 +1,35 @@ +.easyuse-chooser-dialog{ + max-width: 600px; +} +.easyuse-chooser-dialog-title{ + font-size: 18px; + font-weight: 700; + text-align: center; + color:var(--input-text); + margin:0; +} +.easyuse-chooser-dialog-images{ + margin-top:10px; + display: flex; + flex-wrap: wrap; + width: 100%; + box-sizing: border-box; +} +.easyuse-chooser-dialog-images img{ + width: 50%; + height: auto; + cursor: pointer; + box-sizing: border-box; + filter:brightness(80%); +} +.easyuse-chooser-dialog-images img:hover{ + filter:brightness(100%); +} +.easyuse-chooser-dialog-images img.selected{ + border: 4px solid var(--success-color); +} + +.easyuse-chooser-hidden{ + display: none; + height:0; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/contextmenu.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/contextmenu.css new file mode 100644 index 00000000..9fc0e958 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/contextmenu.css @@ -0,0 +1,20 @@ +.easyuse-model{ + position:relative; +} +.easyuse-model:hover img{ + display: block; + opacity: 1; +} +.easyuse-model img{ + position: absolute; + z-index:1; + right:-155px; + top:0; + width:150px; + height:auto; + display: none; + filter:brightness(70%); + -webkit-filter: brightness(70%); + opacity: 0; + transition:all 0.5s ease-in-out; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/dropdown.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/dropdown.css new file mode 100644 index 00000000..176ff618 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/dropdown.css @@ -0,0 +1,68 @@ +.easy-dropdown, .easy-nested-dropdown { + position: relative; + box-sizing: border-box; + background-color: #171717; + box-shadow: 0 4px 4px rgba(255, 255, 255, .25); + padding: 0; + margin: 0; + list-style: none; + z-index: 1000; + overflow: visible; + max-height: fit-content; + max-width: fit-content; +} + +.easy-dropdown { + position: absolute; + border-radius: 0; +} + +/* Style for final items */ +.easy-dropdown li.item, .easy-nested-dropdown li.item { + font-weight: normal; + min-width: max-content; +} + +/* Style for folders (parent items) */ +.easy-dropdown li.folder, .easy-nested-dropdown li.folder { + cursor: default; + position: relative; + border-right: 3px solid cyan; +} + +.easy-dropdown li.folder::after, .easy-nested-dropdown li.folder::after { + content: ">"; + position: absolute; + right: 2px; + font-weight: normal; +} + +.easy-dropdown li, .easy-nested-dropdown li { + padding: 4px 10px; + cursor: pointer; + font-family: system-ui; + font-size: 0.7rem; + position: relative; +} + +/* Style for nested dropdowns */ +.easy-nested-dropdown { + position: absolute; + top: 0; + left: 100%; + margin: 0; + border: none; + display: none; +} + +.easy-dropdown li.selected > .easy-nested-dropdown, +.easy-nested-dropdown li.selected > .easy-nested-dropdown { + display: block; + border: none; +} + +.easy-dropdown li.selected, +.easy-nested-dropdown li.selected { + background-color: #e5e5e5; + border: none; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/easy.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/easy.css new file mode 100644 index 00000000..b2f83537 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/easy.css @@ -0,0 +1,130 @@ + +.pysssss-workflow-popup{ + min-width:220px!important; + /*right:0px!important;*/ + /*left:auto!important;*/ +} +body{ + font-family: var(--font-family)!important; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +textarea{ + font-family: var(--font-family)!important; +} + +.comfy-multiline-input{ + background-color: transparent; + border:1px solid var(--border-color); + border-radius:8px; + padding: 8px; + font-size: 12px; +} +.comfy-modal { + border:1px solid var(--border-color); + box-shadow:none; + backdrop-filter: blur(8px) brightness(120%); +} +.comfy-menu{ + border-radius:16px; + box-shadow:0 0 1px var(--descrip-text); + backdrop-filter: blur(8px) brightness(120%); +} +.comfy-menu button,.comfy-modal button { + font-size: 14px; + padding:4px 0; + margin-bottom:4px; +} +.comfy-menu button.comfy-settings-btn{ + font-size: 12px; +} +.comfy-menu-btns { + margin-bottom: 4px; +} +.comfy-menu-btns button,.comfy-list-actions button{ + font-size: 10px; +} +.comfy-menu > button, +.comfy-menu-btns button, +.comfy-menu .comfy-list button, +.comfy-modal button { + border-width:1px; +} +.comfy-modal-content{ + width: 100%; +} + + +dialog{ + border:1px solid var(--border-color); + background:transparent; + backdrop-filter: blur(8px) brightness(120%); + box-shadow:none; +} +.cm-title{ + background-color:transparent!important; +} +.cm-notice-board{ + border-radius:10px!important; + border:1px solid var(--border-color)!important; +} +.cm-menu-container{ + margin-bottom:50px!important; +} +hr{ + border:1px solid var(--border-color); +} +#comfy-dev-save-api-button{ + justify-content: center; +} +#shareButton{ + background:linear-gradient(to left,var(--theme-color),var(--theme-color-light))!important; + color:white!important; +} +#queue-button{ + position:relative; + overflow:hidden; + min-height:30px; + z-index:1; +} + +#queue-button:after{ + clear: both; + content:attr(data-attr); + background:green; + color:#FFF; + width:var(--process-bar-width); + height:100%; + position:absolute; + top:0; + left:0; + z-index:0; + text-align:center; + display:flex; + justify-content:center; + align-items:center; +} + +.litegraph .litemenu-entry.has_submenu { + border-right: 2px solid var(--theme-color); +} +::-webkit-scrollbar { + width: 0em; +} +::-webkit-scrollbar-track { + background-color: transparent; +} +::-webkit-scrollbar-thumb { + background-color: transparent; + border-radius: 2px; +} +::-webkit-scrollbar-thumb:hover { + background-color: transparent; +} + +[data-theme="dark"] .workspace_manager .chakra-card{ + background-color:var(--comfy-menu-bg)!important; +} +.workspace_manager .chakra-card{ + width: 400px; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/groupmap.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/groupmap.css new file mode 100644 index 00000000..75aa8679 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/groupmap.css @@ -0,0 +1,34 @@ +#easyuse_groups_map{ + flex-direction: column; + align-items: end; + display:flex;position: absolute; + top: 50px; left: 10px; width: 180px; + border-radius:12px; + min-height:100px; + max-height:400px; + color: var(--descrip-text); + background-color: var(--comfy-menu-bg); + padding: 10px 4px; + border: 1px solid var(--border-color); + z-index: 399; + padding-top: 0; +} +#easyuse_groups_map .icon{ + width: 12px; + height:12px; +} +#easyuse_groups_map .closeBtn{ + float: right; + color: var(--input-text); + border-radius:30px; + background-color: var(--comfy-input-bg); + border: 1px solid var(--border-color); + cursor: pointer; + aspect-ratio: 1 / 1; + display: flex; + justify-content: center; + align-items: center; +} +#easyuse_groups_map .closeBtn:hover{ + filter:brightness(120%); +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/index.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/index.css new file mode 100644 index 00000000..5c148a21 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/index.css @@ -0,0 +1,11 @@ +@import "theme.css"; +@import "dropdown.css"; +@import "selector.css"; +@import "groupmap.css"; +@import "contextmenu.css"; +@import "modelinfo.css"; +@import "toast.css"; +@import "account.css"; +@import "chooser.css"; +@import "toolbar.css"; +@import "sliderControl.css"; \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/modelinfo.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/modelinfo.css new file mode 100644 index 00000000..370c21f9 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/modelinfo.css @@ -0,0 +1,265 @@ +.easyuse-model-info { + color: white; + max-width: 90vw; + font-family: var(--font-family); +} +.easyuse-model-content { + display: flex; + flex-direction: column; + overflow: hidden; +} +.easyuse-model-header{ + margin:0 0 15px 0; +} +.easyuse-model-header-remark{ + display: flex; + align-items: center; + margin-top:5px; +} +.easyuse-model-info h2 { + text-align: left; + margin:0; +} +.easyuse-model-info h5 { + text-align: left; + margin:0 15px 0 0px; + font-weight: 400; + color:var(--descrip-text); +} +.easyuse-model-info p { + margin: 5px 0; +} +.easyuse-model-info a { + color: var(--theme-color-light); +} +.easyuse-model-info a:hover { + text-decoration: underline; +} +.easyuse-model-tags-list { + display: flex; + flex-wrap: wrap; + list-style: none; + gap: 10px; + max-height: 200px; + overflow: auto; + margin: 10px 0; + padding: 0; +} +.easyuse-model-tag { + background-color: var(--comfy-input-bg); + border: 2px solid var(--border-color); + color: var(--input-text); + display: flex; + align-items: center; + gap: 5px; + border-radius: 5px; + padding: 2px 5px; + cursor: pointer; +} +.easyuse-model-tag--selected span::before { + content: "βœ…"; + position: absolute; + background-color: var(--theme-color-light); + left: 0; + top: 0; + right: 0; + bottom: 0; + text-align: center; +} +.easyuse-model-tag:hover { + border: 2px solid var(--theme-color-light); +} +.easyuse-model-tag p { + margin: 0; +} +.easyuse-model-tag span { + text-align: center; + border-radius: 5px; + background-color: var(--theme-color-light); + padding: 2px; + position: relative; + min-width: 20px; + overflow: hidden; + color: #fff; +} + +.easyuse-model-metadata .comfy-modal-content { + max-width: 100%; +} +.easyuse-model-metadata label { + margin-right: 1ch; + color: #ccc; +} + +.easyuse-model-metadata span { + color: var(--theme-color-light); +} + +.easyuse-preview { + max-width:660px; + margin-right: 15px; + position: relative; +} +.easyuse-preview-group{ + position: relative; + overflow: hidden; + border-radius:.5rem; + width: 660px; +} +.easyuse-preview-list{ + display: flex; + flex-wrap: nowrap; + width: 100%; + transition: all .5s ease-in-out; +} +.easyuse-preview-list.no-transition{ + transition: none; +} +.easyuse-preview-slide{ + display: flex; + flex-basis: calc(50% - 5px); + flex-grow: 0; + flex-shrink: 0; + position: relative; + justify-content: center; + align-items: center; + padding-right:5px; + padding-left:0; +} +.easyuse-preview-slide:nth-child(even){ + padding-left:5px; + padding-right:0; +} +.easyuse-preview-slide-content{ + position: relative; + min-height:150px; + width: 100%; +} +.easyuse-preview-slide-content .save{ + position: absolute; + right: 6px; + z-index: 12; + bottom: 6px; + display: flex; + align-items: center; + height: 26px; + padding: 0 9px; + color: var(--input-text); + font-size: 12px; + line-height: 26px; + background: rgba(0, 0, 0, .5); + border-radius: 13px; + cursor: pointer; + min-width:80px; + text-align: center; +} +.easyuse-preview-slide-content .save:hover{ + filter: brightness(120%); + will-change: auto; +} + +.easyuse-preview-slide-content img { + border-radius: 14px; + object-position: center center; + max-width: 100%; + max-height:700px; + border-style: none; + vertical-align: middle; +} +.easyuse-preview button { + position: absolute; + z-index:10; + top: 50%; + display: flex; + align-items: center; + justify-content: center; + width:30px; + height:30px; + border-radius:15px; + border:1px solid rgba(66, 63, 78, .15); + background-color: rgba(66, 63, 78, .5); + color:hsla(0, 0%, 100%, .8); + transition-property: color, background-color, border-color, text-decoration-color, fill, stroke; + transition-timing-function: cubic-bezier(.4,0,.2,1); + transition-duration: .15s; + transform: translateY(-50%); +} +.easyuse-preview button.left{ + left:10px; +} +.easyuse-preview button.right{ + right:10px; +} + +.easyuse-model-detail{ + margin-top: 16px; + overflow: hidden; + border: 1px solid var(--border-color); + border-radius: 8px; + width:300px; +} +.easyuse-model-detail-head{ + height: 40px; + padding: 0 10px; + font-weight: 500; + font-size: 14px; + font-style: normal; + line-height: 40px; +} +.easyuse-model-detail-body{ + box-sizing: border-box; + font-size: 12px; +} +.easyuse-model-detail-item{ + display: flex; + justify-content: flex-start; + border-top: 1px solid var(--border-color); +} +.easyuse-model-detail-item-label{ + flex-shrink: 0; + width: 88px; + padding-top: 5px; + padding-bottom: 5px; + padding-left: 10px; + border-right: 1px solid var(--border-color); + color: var(--input-text); + font-weight: 400; +} +.easyuse-model-detail-item-value{ + display: flex; + flex-wrap: wrap; + padding: 5px 10px 5px 10px; + color: var(--input-text); +} +.easyuse-model-detail-textarea{ + border-top:1px solid var(--border-color); + padding:10px; + height:100px; + overflow-y: auto; + font-size: 12px; +} +.easyuse-model-detail-textarea textarea{ + width:100%; + height:100%; + border:0; + background-color:transparent; + color: var(--input-text); +} +.easyuse-model-detail-textarea textarea::placeholder{ + color:var(--descrip-text); +} +.easyuse-model-detail-textarea.empty{ + display: flex; + justify-content: center; + align-items: center; + color: var(--descrip-text); +} + +.easyuse-model-notes { + background-color: rgba(0, 0, 0, 0.25); + padding: 5px; + margin-top: 5px; +} +.easyuse-model-notes:empty { + display: none; +} diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/selector.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/selector.css new file mode 100644 index 00000000..4ecce1e4 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/selector.css @@ -0,0 +1,108 @@ +.easyuse-prompt-styles{ + overflow: auto; +} +.easyuse-prompt-styles .tools{ + display:flex; + justify-content:space-between; + height:30px; + padding-bottom:10px; + border-bottom:2px solid var(--border-color); +} +.easyuse-prompt-styles .tools button.delete{ + height:30px; + border-radius: 8px; + border: 2px solid var(--border-color); + font-size:11px; + background:var(--comfy-input-bg); + color:var(--error-text); + box-shadow:none; + cursor:pointer; +} +.easyuse-prompt-styles .tools button.delete:hover{ + filter: brightness(1.2); +} +.easyuse-prompt-styles .tools textarea.search{ + flex:1; + margin-left:10px; + height:20px; + line-height:20px; + border-radius: 8px; + border: 2px solid var(--border-color); + font-size:11px; + background:var(--comfy-input-bg); + color:var(--input-text); + box-shadow:none; + padding:4px 10px; + outline: none; + resize: none; + appearance:none; +} +.easyuse-prompt-styles-list{ + list-style: none; + padding: 0; + margin: 0; + min-height: 150px; + height: calc(100% - 40px); + overflow: auto; + /*display: flex;*/ + /*flex-wrap: wrap;*/ +} +.easyuse-prompt-styles-list.no-top{ + height: auto; +} + +.easyuse-prompt-styles-tag{ + display: inline-block; + vertical-align: middle; + margin-top: 8px; + margin-right: 8px; + padding:4px; + color: var(--input-text); + background-color: var(--comfy-input-bg); + border-radius: 8px; + border: 2px solid var(--border-color); + font-size:11px; + cursor:pointer; +} +.easyuse-prompt-styles-tag.hide{ + display:none; +} +.easyuse-prompt-styles-tag:hover{ + filter: brightness(1.2); +} +.easyuse-prompt-styles-tag input{ + --ring-color: transparent; + position: relative; + box-shadow: none; + border: 2px solid var(--border-color); + border-radius: 2px; + background: linear-gradient(135deg, var(--comfy-menu-bg) 0%, var(--comfy-input-bg) 60%); +} +.easyuse-prompt-styles-tag input[type=checkbox]:checked{ + border: 1px solid var(--theme-color-light); + background-color: var(--theme-color-light); + background-image: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e"); +} +.easyuse-prompt-styles-tag input[type=checkbox]{ + color-adjust: exact; + display: inline-block; + flex-shrink: 0; + vertical-align: middle; + appearance: none; + border: 2px solid var(--border-color); + background-origin: border-box; + padding: 0; + width: 1rem; + height: 1rem; + border-radius:4px; + color:var(--theme-color-light); + user-select: none; +} +.easyuse-prompt-styles-tag span{ + margin:0 4px; + vertical-align: middle; +} +#show_image_id{ + width:128px; + height:128px; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/sliderControl.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/sliderControl.css new file mode 100644 index 00000000..42e749b4 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/sliderControl.css @@ -0,0 +1,66 @@ +.easyuse-slider{ + width:100%; + height:100%; + display: flex; + flex-direction: row; + justify-content: space-between; + position: relative; +} +.easyuse-slider-item{ + height: inherit; + min-width: 25px; + justify-content: center; + display: flex; + flex-direction: column; + align-items: center; +} +.easyuse-slider-item.positive .easyuse-slider-item-label{ + color: var(--success-color); +} +.easyuse-slider-item.negative .easyuse-slider-item-label{ + color: var(--error-color); +} +.easyuse-slider-item-input{ + height:15px; + font-size: 10px; + color: var(--input-text); +} +.easyuse-slider-item-label{ + height:15px; + border: none; + color: var(--descrip-text); + font-size: 8px; +} +.easyuse-slider-item-scroll { + width: 5px; + height: calc(100% - 30px); + background: var(--comfy-input-bg); + border-radius: 10px; + position: relative; +} +.easyuse-slider-item-bar{ + width: 10px; + height: 10px; + background: linear-gradient(to bottom, var(--input-text), var(--descrip-text)); + border-radius:100%; + box-shadow: 0 2px 10px var(--bg-color); + position: absolute; + top: 0; + left:-2.5px; + cursor: pointer; + z-index:1; +} +.easyuse-slider-item-area{ + width: 100%; + border-radius:20px; + position: absolute; + bottom: 0; + background: var(--input-text); + z-index:0; +} +.easyuse-slider-item.positive .easyuse-slider-item-area{ + background: var(--success-color); +} +.easyuse-slider-item.negative .easyuse-slider-item-area{ + background: var(--error-color); +} diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/theme.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/theme.css new file mode 100644 index 00000000..d2754c8f --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/theme.css @@ -0,0 +1,10 @@ +:root { + /*--theme-color:#3f3eed;*/ + /*--theme-color-light: #008ecb;*/ + --theme-color:#236692; + --theme-color-light: #3485bb; + --success-color: #52c41a; + --error-color: #ff4d4f; + --warning-color: #faad14; + --font-family: Inter, -apple-system, BlinkMacSystemFont, Helvetica Neue, sans-serif; +} diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/toast.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/toast.css new file mode 100644 index 00000000..b0166fb4 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/toast.css @@ -0,0 +1,110 @@ +.easyuse-toast-container{ + position: fixed; + z-index: 99999; + top: 0; + left: 0; + width: 100%; + height: 0; + display: flex; + flex-direction: column; + align-items: center; + justify-content: start; + padding:10px 0; +} +.easyuse-toast-container > div { + position: relative; + height: fit-content; + padding: 4px; + margin-top: -100px; /* re-set by JS */ + opacity: 0; + transition: all 0.33s ease-in-out; + z-index: 3; +} + +.easyuse-toast-container > div:last-child { + z-index: 2; +} + +.easyuse-toast-container > div:not(.-show) { + z-index: 1; +} + +.easyuse-toast-container > div.-show { + opacity: 1; + margin-top: 0px !important; +} + +.easyuse-toast-container > div.-show { + opacity: 1; + transform: translateY(0%); +} + +.easyuse-toast-container > div > div { + position: relative; + background: var(--comfy-menu-bg); + color: var(--input-text); + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; + height: fit-content; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.88); + padding: 9px 12px; + border-radius: 8px; + font-family: Arial, sans-serif; + font-size: 14px; + pointer-events: all; +} + +.easyuse-toast-container > div > div > span { + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; +} + +.easyuse-toast-container > div > div > span svg { + width: 16px; + height: auto; + margin-right: 8px; +} + +.easyuse-toast-container > div > div > span svg[data-icon=info-circle]{ + fill: var(--theme-color-light); +} +.easyuse-toast-container > div > div > span svg[data-icon=check-circle]{ + fill: var(--success-color); +} +.easyuse-toast-container > div > div > span svg[data-icon=close-circle]{ + fill: var(--error-color); +} +.easyuse-toast-container > div > div > span svg[data-icon=exclamation-circle]{ + fill: var(--warning-color); +} +/*rotate animation*/ +@keyframes rotate { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(360deg); + } +} +.easyuse-toast-container > div > div > span svg[data-icon=loading]{ + fill: var(--theme-color); + animation: rotate 1s linear infinite; +} + +.easyuse-toast-container a { + cursor: pointer; + text-decoration: underline; + color: var(--theme-color-light); + margin-left: 4px; + display: inline-block; + line-height: 1; +} + +.easyuse-toast-container a:hover { + color: var(--theme-color-light); + text-decoration: none; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/toolbar.css b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/toolbar.css new file mode 100644 index 00000000..89a72439 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/css/toolbar.css @@ -0,0 +1,230 @@ +.easyuse-toolbar{ + background: rgba(15,15,15,.5); + backdrop-filter: blur(4px) brightness(120%); + border-radius:0 12px 12px 0; + min-width:50px; + height:24px; + position: fixed; + bottom:85px; + left:0px; + display: flex; + align-items: center; + z-index:10000; +} +.easyuse-toolbar.disable-render-info{ + bottom: 55px; +} +.easyuse-toolbar-item{ + border-radius:20px; + height: 20px; + width:20px; + cursor: pointer; + display: flex; + justify-content: center; + align-items: center; + transition: all 0.3s ease-in-out; + margin-left:2.5px; +} +.easyuse-toolbar-icon{ + width: 14px; + height: 14px; + display: flex; + justify-content: center; + align-items: center; + font-size: 12px; + color:white; + transition: all 0.3s ease-in-out; +} +.easyuse-toolbar-icon svg{ + width: 14px; + height: 14px; +} +.easyuse-toolbar-tips{ + visibility: hidden; + opacity: 0; + position: absolute; + top: -25px; + left: 0; + color: var(--descrip-text); + padding: 2px 5px; + border-radius: 5px; + font-size: 11px; + min-width:100px; + transition: all 0.3s ease-in-out; +} +.easyuse-toolbar-item:hover{ + background:rgba(12,12,12,1); +} +.easyuse-toolbar-item:hover .easyuse-toolbar-tips{ + opacity: 1; + visibility: visible; +} +.easyuse-toolbar-item:hover .easyuse-toolbar-icon.group{ + color:var(--warning-color); +} +.easyuse-toolbar-item:hover .easyuse-toolbar-icon.rocket{ + color:var(--theme-color-light); +} +.easyuse-toolbar-item:hover .easyuse-toolbar-icon.question{ + color:var(--success-color); +} + + +.easyuse-guide-dialog{ + max-width: 300px; + font-family: var(--font-family); + position: absolute; + z-index:100; + left:0; + bottom:140px; + background: rgba(25,25,25,.85); + backdrop-filter: blur(8px) brightness(120%); + border-radius:0 12px 12px 0; + padding:10px; + transition: .5s all ease-in-out; + visibility: visible; + opacity: 1; + transform: translateX(0%); +} +.easyuse-guide-dialog.disable-render-info{ + bottom:110px; +} +.easyuse-guide-dialog-top{ + display: flex; + justify-content: space-between; + align-items: center; +} +.easyuse-guide-dialog-top .icon{ + width: 12px; + height:12px; +} +.easyuse-guide-dialog.hidden{ + opacity: 0; + transform: translateX(-50%); + visibility: hidden; +} +.easyuse-guide-dialog .closeBtn{ + float: right; + color: var(--input-text); + border-radius:30px; + background-color: var(--comfy-input-bg); + border: 1px solid var(--border-color); + cursor: pointer; + aspect-ratio: 1 / 1; + display: flex; + justify-content: center; + align-items: center; +} +.easyuse-guide-dialog .closeBtn:hover{ + filter:brightness(120%); +} +.easyuse-guide-dialog-title{ + color:var(--input-text); + font-size: 16px; + font-weight: bold; + margin-bottom: 5px; +} +.easyuse-guide-dialog-remark{ + color: var(--input-text); + font-size: 12px; + margin-top: 5px; +} +.easyuse-guide-dialog-content{ + max-height: 600px; + overflow: auto; +} +.easyuse-guide-dialog a, .easyuse-guide-dialog a:visited{ + color: var(--theme-color-light); + cursor: pointer; +} +.easyuse-guide-dialog-note{ + margin-top: 20px; + color:white; +} +.easyuse-guide-dialog p{ + margin:4px 0; + font-size: 12px; + font-weight: 300; +} +.markdown-body h1, .markdown-body h2, .markdown-body h3, .markdown-body h4, .markdown-body h5, .markdown-body h6 { + margin-top: 12px; + margin-bottom: 8px; + font-weight: 600; + line-height: 1.25; + padding-bottom: 5px; + border-bottom: 1px solid var(--border-color); + color: var(--input-text); +} +.markdown-body h1{ + font-size: 18px; +} +.markdown-body h2{ + font-size: 16px; +} +.markdown-body h3{ + font-size: 14px; +} +.markdown-body h4{ + font-size: 13px; +} +.markdown-body table { + display: block; + /*width: 100%;*/ + /*width: max-content;*/ + max-width: 300px; + overflow: auto; + color:var(--input-text); + box-sizing: border-box; + border: 1px solid var(--border-color); + text-align: left; + width: 100%; +} +.markdown-body table th, .markdown-body table td { + padding: 6px 13px; + font-size: 12px; + margin:0; + border-right: 1px solid var(--border-color); + border-bottom: 1px solid var(--border-color); +} +.markdown-body table td { + font-size: 12px; +} +.markdown-body table th:last-child, .markdown-body table td:last-child{ + border-right: none; +} +.markdown-body table tr:last-child td{ + border-bottom: none; +} +.markdown-body table th{ + font-weight: bold; + width: auto; + min-width: 70px; +} +.markdown-body table th:last-child{ + width:100%; +} +.markdown-body .warning{ + color:var(--warning-color) +} +.markdown-body .error{ + color:var(--error-color) +} +.markdown-body .success{ + color:var(--success-color) +} +.markdown-body .link{ + color:var(--theme-color-light) +} + +#comfyui-menu-monitor{ + width:120px; +} +#comfyui-menu-monitor #crystools-monitor-container{ + margin:0 auto!important; +} +#comfyui-menu-monitor #crystools-monitor-container > div{ + margin:2px 0!important; +} +#comfyui-menu-monitor #crystools-monitor-container > div > div > div{ + padding:0 4px!important; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/bookmark.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/bookmark.js new file mode 100644 index 00000000..554b484f --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/bookmark.js @@ -0,0 +1,101 @@ +import { app } from "../../../scripts/app.js"; + + +app.registerExtension({ + name: "easy bookmark", + registerCustomNodes() { + class Bookmark { + type = 'easy bookmark' + title = "πŸ”–"; + + slot_start_y = -20; + + ___collapsed_width = 0; + + get _collapsed_width() { + return this.___collapsed_width; + } + + set _collapsed_width(width){ + const canvas = app.canvas ; + const ctx = canvas.canvas.getContext('2d'); + if(ctx){ + const oldFont = ctx.font; + ctx.font = canvas.title_text_font; + this.___collapsed_width = 40 + ctx.measureText(this.title).width; + ctx.font = oldFont; + } + } + + isVirtualNode = true; + serialize_widgets = true; + keypressBound = null; + + constructor() { + + this.addWidget('text', 'shortcut_key', '1', (value) => { + value = value.trim()[0] || '1'; + if(value !== ''){ + this.title = "πŸ”– " + value; + } + },{ + y: 8, + }); + this.addWidget('number', 'zoom', 1, (value) => {}, { + y: 8 + LiteGraph.NODE_WIDGET_HEIGHT + 4, + max: 2, + min: 0.5, + precision: 2, + }); + this.keypressBound = this.onKeypress.bind(this); + } + + onAdded(){ + setTimeout(_=>{ + const value = this.widgets[0].value + if(value){ + this.title = "πŸ”– " + value; + } + },1) + window.addEventListener("keydown", this.keypressBound); + } + + onRemoved() { + window.removeEventListener("keydown", this.keypressBound); + } + + onKeypress(event){ + const target = event.target; + if (['input','textarea'].includes(target.localName)) { + return; + } + if (this.widgets[0] && event.key.toLocaleLowerCase() === this.widgets[0].value.toLocaleLowerCase()) { + this.canvasToBookmark(); + } + } + + canvasToBookmark() { + const canvas = app.canvas; + // ComfyUI seemed to break us again, but couldn't repro. No reason to not check, I guess. + // https://github.com/rgthree/rgthree-comfy/issues/71 + if (canvas?.ds?.offset) { + canvas.ds.offset[0] = -this.pos[0] + 16; + canvas.ds.offset[1] = -this.pos[1] + 40; + } + if (canvas?.ds?.scale != null) { + canvas.ds.scale = Number(this.widgets[1].value || 1); + } + canvas.setDirty(true, true); + } + } + + LiteGraph.registerNodeType( + "easy bookmark", + Object.assign(Bookmark,{ + title: "Bookmark πŸ”–", + }) + ); + + Bookmark.category = "EasyUse/Util" + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/dropdown.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/dropdown.js new file mode 100644 index 00000000..f1fd485f --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/dropdown.js @@ -0,0 +1,218 @@ +let activeDropdown = null; + +export function removeDropdown() { + if (activeDropdown) { + activeDropdown.removeEventListeners(); + activeDropdown.dropdown.remove(); + activeDropdown = null; + } +} +export function createDropdown(inputEl, suggestions, onSelect, isDict = false) { + removeDropdown(); + new Dropdown(inputEl, suggestions, onSelect, isDict); +} + +class Dropdown { + constructor(inputEl, suggestions, onSelect, isDict = false) { + this.dropdown = document.createElement('ul'); + this.dropdown.setAttribute('role', 'listbox'); + this.dropdown.classList.add('easy-dropdown'); + this.selectedIndex = -1; + this.inputEl = inputEl; + this.suggestions = suggestions; + this.onSelect = onSelect; + this.isDict = isDict; + + this.focusedDropdown = this.dropdown; + + this.buildDropdown(); + + this.onKeyDownBound = this.onKeyDown.bind(this); + this.onWheelBound = this.onWheel.bind(this); + this.onClickBound = this.onClick.bind(this); + + this.addEventListeners(); + } + + buildDropdown() { + if (this.isDict) { + this.buildNestedDropdown(this.suggestions, this.dropdown); + } else { + this.suggestions.forEach((suggestion, index) => { + this.addListItem(suggestion, index, this.dropdown); + }); + } + + const inputRect = this.inputEl.getBoundingClientRect(); + this.dropdown.style.top = (inputRect.top + inputRect.height - 10) + 'px'; + this.dropdown.style.left = inputRect.left + 'px'; + + document.body.appendChild(this.dropdown); + activeDropdown = this; + } + + buildNestedDropdown(dictionary, parentElement) { + let index = 0; + Object.keys(dictionary).forEach((key) => { + const item = dictionary[key]; + if (typeof item === "object" && item !== null) { + const nestedDropdown = document.createElement('ul'); + nestedDropdown.setAttribute('role', 'listbox'); + nestedDropdown.classList.add('easy-nested-dropdown'); + const parentListItem = document.createElement('li'); + parentListItem.classList.add('folder'); + parentListItem.textContent = key; + parentListItem.appendChild(nestedDropdown); + parentListItem.addEventListener('mouseover', this.onMouseOver.bind(this, index, parentElement)); + parentElement.appendChild(parentListItem); + this.buildNestedDropdown(item, nestedDropdown); + index = index + 1; + } else { + const listItem = document.createElement('li'); + listItem.classList.add('item'); + listItem.setAttribute('role', 'option'); + listItem.textContent = key; + listItem.addEventListener('mouseover', this.onMouseOver.bind(this, index, parentElement)); + listItem.addEventListener('mousedown', this.onMouseDown.bind(this, key)); + parentElement.appendChild(listItem); + index = index + 1; + } + }); + } + + addListItem(item, index, parentElement) { + const listItem = document.createElement('li'); + listItem.setAttribute('role', 'option'); + listItem.textContent = item; + listItem.addEventListener('mouseover', this.onMouseOver.bind(this, index)); + listItem.addEventListener('mousedown', this.onMouseDown.bind(this, item)); + parentElement.appendChild(listItem); + } + + addEventListeners() { + document.addEventListener('keydown', this.onKeyDownBound); + this.dropdown.addEventListener('wheel', this.onWheelBound); + document.addEventListener('click', this.onClickBound); + } + + removeEventListeners() { + document.removeEventListener('keydown', this.onKeyDownBound); + this.dropdown.removeEventListener('wheel', this.onWheelBound); + document.removeEventListener('click', this.onClickBound); + } + + onMouseOver(index, parentElement) { + if (parentElement) { + this.focusedDropdown = parentElement; + } + this.selectedIndex = index; + this.updateSelection(); + } + + onMouseOut() { + this.selectedIndex = -1; + this.updateSelection(); + } + + onMouseDown(suggestion, event) { + event.preventDefault(); + this.onSelect(suggestion); + this.dropdown.remove(); + this.removeEventListeners(); + } + + onKeyDown(event) { + const enterKeyCode = 13; + const escKeyCode = 27; + const arrowUpKeyCode = 38; + const arrowDownKeyCode = 40; + const arrowRightKeyCode = 39; + const arrowLeftKeyCode = 37; + const tabKeyCode = 9; + + const items = Array.from(this.focusedDropdown.children); + const selectedItem = items[this.selectedIndex]; + + if (activeDropdown) { + if (event.keyCode === arrowUpKeyCode) { + event.preventDefault(); + this.selectedIndex = Math.max(0, this.selectedIndex - 1); + this.updateSelection(); + } + + else if (event.keyCode === arrowDownKeyCode) { + event.preventDefault(); + this.selectedIndex = Math.min(items.length - 1, this.selectedIndex + 1); + this.updateSelection(); + } + + else if (event.keyCode === arrowRightKeyCode) { + event.preventDefault(); + if (selectedItem && selectedItem.classList.contains('folder')) { + const nestedDropdown = selectedItem.querySelector('.easy-nested-dropdown'); + if (nestedDropdown) { + this.focusedDropdown = nestedDropdown; + this.selectedIndex = 0; + this.updateSelection(); + } + } + } + + else if (event.keyCode === arrowLeftKeyCode && this.focusedDropdown !== this.dropdown) { + const parentDropdown = this.focusedDropdown.closest('.easy-dropdown, .easy-nested-dropdown').parentNode.closest('.easy-dropdown, .easy-nested-dropdown'); + if (parentDropdown) { + this.focusedDropdown = parentDropdown; + this.selectedIndex = Array.from(parentDropdown.children).indexOf(this.focusedDropdown.parentNode); + this.updateSelection(); + } + } + + else if ((event.keyCode === enterKeyCode || event.keyCode === tabKeyCode) && this.selectedIndex >= 0) { + event.preventDefault(); + if (selectedItem.classList.contains('item')) { + this.onSelect(items[this.selectedIndex].textContent); + this.dropdown.remove(); + this.removeEventListeners(); + } + + const nestedDropdown = selectedItem.querySelector('.easy-nested-dropdown'); + if (nestedDropdown) { + this.focusedDropdown = nestedDropdown; + this.selectedIndex = 0; + this.updateSelection(); + } + } + + else if (event.keyCode === escKeyCode) { + this.dropdown.remove(); + this.removeEventListeners(); + } + } + } + + onWheel(event) { + const top = parseInt(this.dropdown.style.top); + if (localStorage.getItem("Comfy.Settings.Comfy.InvertMenuScrolling")) { + this.dropdown.style.top = (top + (event.deltaY < 0 ? 10 : -10)) + "px"; + } else { + this.dropdown.style.top = (top + (event.deltaY < 0 ? -10 : 10)) + "px"; + } + } + + onClick(event) { + if (!this.dropdown.contains(event.target) && event.target !== this.inputEl) { + this.dropdown.remove(); + this.removeEventListeners(); + } + } + + updateSelection() { + Array.from(this.focusedDropdown.children).forEach((li, index) => { + if (index === this.selectedIndex) { + li.classList.add('selected'); + } else { + li.classList.remove('selected'); + } + }); + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/i18n.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/i18n.js new file mode 100644 index 00000000..643a1101 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/i18n.js @@ -0,0 +1,101 @@ +import {getLocale} from './utils.js' +const locale = getLocale() + +const zhCN = { + "Workflow created by": "ε·₯δ½œζ΅εˆ›ε»Ίθ€…", + "Watch more video content": "θ§‚ηœ‹ζ›΄ε€šθ§†ι’‘ε†…εΉ", + "Workflow Guide":"ε·₯δ½œζ΅ζŒ‡ε—", + // ExtraMenu + "πŸ’Ž View Checkpoint Info...": "πŸ’Ž ζŸ₯ηœ‹ Checkpoint 俑息...", + "πŸ’Ž View Lora Info...": "πŸ’Ž ζŸ₯ηœ‹ Lora 俑息...", + "πŸ”ƒ Reload Node": "πŸ”ƒ εˆ·ζ–°θŠ‚η‚Ή", + // ModelInfo + "Updated At:": "ζœ€θΏ‘ζ›΄ζ–°οΌš", + "Created At:": "ι¦–ζ¬‘ε‘εΈƒοΌš", + "✏️ Edit": "✏️ ηΌ–θΎ‘", + "πŸ’Ύ Save": "πŸ’Ύ 保存", + "No notes": "ε½“ε‰θΏ˜ζ²‘ζœ‰ε€‡ζ³¨ε†…εΉ", + "Saving Notes...": "ζ­£εœ¨δΏε­˜ε€‡ζ³¨...", + "Type your notes here":"εœ¨θΏ™ι‡ŒθΎ“ε…₯倇注内εΉ", + "ModelName":"ζ¨‘εž‹εη§°", + "Models Required":"ζ‰€ιœ€ζ¨‘εž‹", + "Download Model": "δΈ‹θ½½ζ¨‘εž‹", + "Source Url": "ζ¨‘εž‹ζΊεœ°ε€", + "Notes": "倇注", + "Type": "η±»εž‹", + "Trained Words": "θ­η»ƒθ―", + "BaseModel": "εŸΊη‘€η—法", + "Details": "θ―¦ζƒ…", + "Description": "描述", + "Download": "下载量", + "Source": "ζ₯源", + "Saving Preview...": "ζ­£εœ¨δΏε­˜ι’„θ§ˆε›Ύ...", + "Saving Succeed":"保存成功", + "Clean SuccessFully":"ζΈ…η†ζˆεŠŸ", + "Clean Failed": "清理倱θ΄₯", + "Saving Failed":"保存倱θ΄₯", + "No COMBO link": "ζ²’ζœ‰ζ‰Ύεˆ°COMBO连ζŽ₯", + "Reboot ComfyUI":"重启ComfyUI", + "Are you sure you'd like to reboot the server?": "ζ˜―ε¦θ¦ι‡ε―ComfyUI？", + // GroupMap + "Groups Map": "η‘理组", + "Cleanup Of GPU Usage": "清理GPU占用", + "Please stop all running tasks before cleaning GPU": "θ―·εœ¨ζΈ…η†GPUδΉ‹ε‰εœζ­’ζ‰€ζœ‰θΏθ‘ŒδΈ­ηš„δ»»εŠ‘", + "Always": "启用中", + "Bypass": "ε·²εΏ½η•₯", + "Never": "ε·²εœη”¨", + "Auto Sorting": "θ‡ͺεŠ¨ζŽ’εΊ", + "Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never": "点击`启用中/ε·²εΏ½η•₯`可θΎη½η»„樑式, ι•ΏζŒ‰ε―εœη”¨θ―₯η»„θŠ‚η‚Ή", + // Quick + "Enable ALT+1~9 to paste nodes from nodes template (ComfyUI-Easy-Use)": "启用ALT1~9δ»ŽθŠ‚η‚Ήζ¨‘ζΏη²˜θ΄΄εˆ°ε·₯作桁 (ComfyUI-Easy-Use)", + "Enable process bar in queue button (ComfyUI-Easy-Use)": "ε―η”¨ζη€Ίθ―ι˜Ÿεˆ—θΏ›εΊ¦ζ˜Ύη€Ίζ‘ (ComfyUI-Easy-UseοΌ‰", + "Enable ContextMenu Auto Nest Subdirectories (ComfyUI-Easy-Use)": "ε―η”¨δΈŠδΈ‹ζ–‡θœε•θ‡ͺ动塌ε₯—子η›ε½• (ComfyUI-Easy-Use)", + "Enable tool bar fixed on the left-bottom (ComfyUI-Easy-Use)": "启用ε·₯具栏固εšεœ¨ε·¦δΈ‹θ§’ (ComfyUI-Easy-Use)", + "Too many thumbnails, have closed the display": "ζ¨‘εž‹ηΌ©η•₯ε›Ύε€ͺε€šε•¦οΌŒδΈΊζ‚¨ε…³ι—­δΊ†ζ˜Ύη€Ί", + // selector + "Empty All": "ζΈ…η©Ίζ‰€ζœ‰", + "πŸ”Ž Type here to search styles ...": "πŸ”Ž εœ¨ζ­€ε€„θΎ“ε…₯δ»₯搜紒样式 ...", + // account + "Loading UserInfo...": "ζ­£εœ¨θŽ·ε–η”¨ζˆ·δΏ‘ζ―...", + "Please set the APIKEY first": "θ―·ε…ˆθΎη½APIKEY", + "Setting APIKEY": "θΎη½APIKEY", + "Save Account Info": "保存账号俑息", + "Choose": "选择", + "Delete": "εˆ ι™€", + "Edit": "ηΌ–θΎ‘", + "At least one account is required": "εˆ ι™€ε€±θ΄₯: θ‡³ε°‘ιœ€θ¦δΈ€δΈͺ账户", + "APIKEY is not Empty": "APIKEY 不能为空", + "Add Account": "添加账号", + "Getting Your APIKEY": "θŽ·ε–ζ‚¨ηš„APIKEY", + // choosers + "Choose Selected Images": "ι€‰ζ‹©ι€‰δΈ­ηš„ε›Ύη‰‡", + "Choose images to continue": "选择图片δ»₯继续", + // seg + "Background": "θƒŒζ™―", + "Hat": "帽子", + "Hair": "倴发", + "Body": "身体", + "Face": "脸部", + "Clothes": "董服", + "Others": "ε…Άδ»–", + "Glove": "手ε₯—", + "Sunglasses": "ε€ͺι˜³ι•œ", + "Upper-clothes": "上董", + "Dress": "θΏžθ‘£θ£™", + "Coat": "ε€–ε₯—", + "Socks": "蒜子", + "Pants": "裀子", + "Jumpsuits": "θΏžδ½“θ‘£", + "Scarf": "ε›΄ε·Ύ", + "Skirt": "裙子", + "Left-arm": "左臂", + "Right-arm": "右臂", + "Left-leg": "ε·¦θ…Ώ", + "Right-leg": "右腿", + "Left-shoe": "ε·¦ιž‹", + "Right-shoe": "ε³ιž‹", +} +export const $t = (key) => { + const cn = zhCN[key] + return locale === 'zh-CN' && cn ? cn : key +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/icon.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/icon.js new file mode 100644 index 00000000..e71a0a34 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/icon.js @@ -0,0 +1,25 @@ +export const logoIcon = ` + + + + + + + + + + + + + + + + + +` + +export const quesitonIcon = `` +export const rocketIcon = `` +export const groupIcon = `` +export const rebootIcon = `` +export const closeIcon = `` \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/model.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/model.js new file mode 100644 index 00000000..b9492883 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/model.js @@ -0,0 +1,683 @@ +import { $el, ComfyDialog } from "../../../../scripts/ui.js"; +import { api } from "../../../../scripts/api.js"; +import {formatTime} from './utils.js'; +import {$t} from "./i18n.js"; +import {toast} from "./toast.js"; + +class MetadataDialog extends ComfyDialog { + constructor() { + super(); + this.element.classList.add("easyuse-model-metadata"); + } + show(metadata) { + super.show( + $el( + "div", + Object.keys(metadata).map((k) => + $el("div", [$el("label", { textContent: k }), $el("span", { textContent: metadata[k] })]) + ) + ) + ); + } +} + +export class ModelInfoDialog extends ComfyDialog { + constructor(name) { + super(); + this.name = name; + this.element.classList.add("easyuse-model-info"); + } + + get customNotes() { + return this.metadata["easyuse.notes"]; + } + + set customNotes(v) { + this.metadata["easyuse.notes"] = v; + } + + get hash() { + return this.metadata["easyuse.sha256"]; + } + + async show(type, value) { + this.type = type; + + const req = api.fetchApi("/easyuse/metadata/" + encodeURIComponent(`${type}/${value}`)); + this.info = $el("div", { style: { flex: "auto" } }); + // this.img = $el("img", { style: { display: "none" } }); + this.imgCurrent = 0 + this.imgList = $el("div.easyuse-preview-list",{ + style: { display: "none" } + }) + this.imgWrapper = $el("div.easyuse-preview", [ + $el("div.easyuse-preview-group",[ + this.imgList + ]), + ]); + this.main = $el("main", { style: { display: "flex" } }, [this.imgWrapper, this.info]); + this.content = $el("div.easyuse-model-content", [ + $el("div.easyuse-model-header",[$el("h2", { textContent: this.name })]) + , this.main]); + + const loading = $el("div", { textContent: "ℹ️ Loading...", parent: this.content }); + + super.show(this.content); + + this.metadata = await (await req).json(); + this.viewMetadata.style.cursor = this.viewMetadata.style.opacity = ""; + this.viewMetadata.removeAttribute("disabled"); + + loading.remove(); + this.addInfo(); + } + + createButtons() { + const btns = super.createButtons(); + this.viewMetadata = $el("button", { + type: "button", + textContent: "View raw metadata", + disabled: "disabled", + style: { + opacity: 0.5, + cursor: "not-allowed", + }, + onclick: (e) => { + if (this.metadata) { + new MetadataDialog().show(this.metadata); + } + }, + }); + + btns.unshift(this.viewMetadata); + return btns; + } + + parseNote() { + if (!this.customNotes) return []; + + let notes = []; + // Extract links from notes + const r = new RegExp("(\\bhttps?:\\/\\/[^\\s]+)", "g"); + let end = 0; + let m; + do { + m = r.exec(this.customNotes); + let pos; + let fin = 0; + if (m) { + pos = m.index; + fin = m.index + m[0].length; + } else { + pos = this.customNotes.length; + } + + let pre = this.customNotes.substring(end, pos); + if (pre) { + pre = pre.replaceAll("\n", "
"); + notes.push( + $el("span", { + innerHTML: pre, + }) + ); + } + if (m) { + notes.push( + $el("a", { + href: m[0], + textContent: m[0], + target: "_blank", + }) + ); + } + + end = fin; + } while (m); + return notes; + } + + addInfoEntry(name, value) { + return $el( + "p", + { + parent: this.info, + }, + [ + typeof name === "string" ? $el("label", { textContent: name + ": " }) : name, + typeof value === "string" ? $el("span", { textContent: value }) : value, + ] + ); + } + + async getCivitaiDetails() { + const req = await fetch("https://civitai.com/api/v1/model-versions/by-hash/" + this.hash); + if (req.status === 200) { + return await req.json(); + } else if (req.status === 404) { + throw new Error("Model not found"); + } else { + throw new Error(`Error loading info (${req.status}) ${req.statusText}`); + } + } + + addCivitaiInfo() { + const promise = this.getCivitaiDetails(); + const content = $el("span", { textContent: "ℹ️ Loading..." }); + + this.addInfoEntry( + $el("label", [ + $el("img", { + style: { + width: "18px", + position: "relative", + top: "3px", + margin: "0 5px 0 0", + }, + src: "https://civitai.com/favicon.ico", + }), + $el("span", { textContent: "Civitai: " }), + ]), + content + ); + + return promise + .then((info) => { + this.imgWrapper.style.display = 'block' + // ε˜ζ›΄ζ ‡ι’˜δΏ‘ζ― + let header = this.element.querySelector('.easyuse-model-header') + if(header){ + header.replaceChildren( + $el("h2", { textContent: this.name }), + $el("div.easyuse-model-header-remark",[ + $el("h5", { textContent: $t("Updated At:") + formatTime(new Date(info.updatedAt),'yyyy/MM/dd')}), + $el("h5", { textContent: $t("Created At:") + formatTime(new Date(info.updatedAt),'yyyy/MM/dd')}), + ]) + ) + } + // ζ›Ώζ’ε†…εΉ + let textarea = null + let notes = this.parseNote.call(this) + let editText = $t("✏️ Edit") + console.log(notes) + let textarea_div = $el("div.easyuse-model-detail-textarea",[ + $el("p",notes?.length>0 ? notes : {textContent:$t('No notes')}), + ]) + if(!notes || notes.length == 0) textarea_div.classList.add('empty') + else textarea_div.classList.remove('empty') + this.info.replaceChildren( + $el("div.easyuse-model-detail",[ + $el("div.easyuse-model-detail-head.flex-b",[ + $el('span',$t("Notes")), + $el("a", { + textContent: editText, + href: "#", + style: { + fontSize: "12px", + float: "right", + color: "var(--warning-color)", + textDecoration: "none", + }, + onclick: async (e) => { + e.preventDefault(); + + if (textarea) { + if(textarea.value != this.customNotes){ + toast.showLoading($t('Saving Notes...')) + this.customNotes = textarea.value; + const resp = await api.fetchApi( + "/easyuse/metadata/notes/" + encodeURIComponent(`${this.type}/${this.name}`), + { + method: "POST", + body: this.customNotes, + } + ); + toast.hideLoading() + if (resp.status !== 200) { + toast.error($t('Saving Failed')) + console.error(resp); + alert(`Error saving notes (${resp.status}) ${resp.statusText}`); + return; + } + toast.success($t('Saving Succeed')) + notes = this.parseNote.call(this) + console.log(notes) + textarea_div.replaceChildren($el("p",notes?.length>0 ? notes : {textContent:$t('No notes')})); + if(textarea.value) textarea_div.classList.remove('empty') + else textarea_div.classList.add('empty') + }else { + textarea_div.replaceChildren($el("p",{textContent:$t('No notes')})); + textarea_div.classList.add('empty') + } + e.target.textContent = editText; + textarea.remove(); + textarea = null; + + } else { + e.target.textContent = "πŸ’Ύ Save"; + textarea = $el("textarea", { + placeholder: $t("Type your notes here"), + style: { + width: "100%", + minWidth: "200px", + minHeight: "50px", + height:"100px" + }, + textContent: this.customNotes, + }); + textarea_div.replaceChildren(textarea); + textarea.focus() + } + } + }) + ]), + textarea_div + ]), + $el("div.easyuse-model-detail",[ + $el("div.easyuse-model-detail-head",{textContent:$t("Details")}), + $el("div.easyuse-model-detail-body",[ + $el("div.easyuse-model-detail-item",[ + $el("div.easyuse-model-detail-item-label",{textContent:$t("Type")}), + $el("div.easyuse-model-detail-item-value",{textContent:info.model.type}), + ]), + $el("div.easyuse-model-detail-item",[ + $el("div.easyuse-model-detail-item-label",{textContent:$t("BaseModel")}), + $el("div.easyuse-model-detail-item-value",{textContent:info.baseModel}), + ]), + $el("div.easyuse-model-detail-item",[ + $el("div.easyuse-model-detail-item-label",{textContent:$t("Download")}), + $el("div.easyuse-model-detail-item-value",{textContent:info.stats?.downloadCount || 0}), + ]), + $el("div.easyuse-model-detail-item",[ + $el("div.easyuse-model-detail-item-label",{textContent:$t("Trained Words")}), + $el("div.easyuse-model-detail-item-value",{textContent:info?.trainedWords.join(',') || '-'}), + ]), + $el("div.easyuse-model-detail-item",[ + $el("div.easyuse-model-detail-item-label",{textContent:$t("Source")}), + $el("div.easyuse-model-detail-item-value",[ + $el("label", [ + $el("img", { + style: { + width: "14px", + position: "relative", + top: "3px", + margin: "0 5px 0 0", + }, + src: "https://civitai.com/favicon.ico", + }), + $el("a", { + href: "https://civitai.com/models/" + info.modelId, + textContent: "View " + info.model.name, + target: "_blank", + }) + ]) + ]), + ]) + ]), + ]) + ); + + if (info.images?.length) { + this.imgCurrent = 0 + this.isSaving = false + info.images.map(cate=> + cate.url && + this.imgList.appendChild( + $el('div.easyuse-preview-slide',[ + $el('div.easyuse-preview-slide-content',[ + $el('img',{src:(cate.url)}), + $el("div.save", { + textContent: "Save as preview", + onclick: async () => { + if(this.isSaving) return + this.isSaving = true + toast.showLoading($t('Saving Preview...')) + // Convert the preview to a blob + const blob = await (await fetch(cate.url)).blob(); + + // Store it in temp + const name = "temp_preview." + new URL(cate.url).pathname.split(".")[1]; + const body = new FormData(); + body.append("image", new File([blob], name)); + body.append("overwrite", "true"); + body.append("type", "temp"); + + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status !== 200) { + this.isSaving = false + toast.error($t('Saving Failed')) + toast.hideLoading() + console.error(resp); + alert(`Error saving preview (${req.status}) ${req.statusText}`); + return; + } + + // Use as preview + await api.fetchApi("/easyuse/save/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: JSON.stringify({ + filename: name, + type: "temp", + }), + headers: { + "content-type": "application/json", + }, + }).then(_=>{ + toast.success($t('Saving Succeed')) + toast.hideLoading() + }); + this.isSaving = false + app.refreshComboInNodes(); + }, + }) + ]) + ]) + ) + ) + let _this = this + this.imgDistance = (-660 * this.imgCurrent).toString() + this.imgList.style.display = '' + this.imgList.style.transform = 'translate3d(' + this.imgDistance +'px, 0px, 0px)' + this.slides = this.imgList.querySelectorAll('.easyuse-preview-slide') + // ζ·»εŠ ζŒ‰ι’ + this.slideLeftButton = $el("button.left",{ + parent: this.imgWrapper, + style:{ + display:info.images.length <= 2 ? 'none' : 'block' + }, + innerHTML:``, + onclick: ()=>{ + if(info.images.length <= 2) return + _this.imgList.classList.remove("no-transition") + if(_this.imgCurrent == 0){ + _this.imgCurrent = (info.images.length/2)-1 + this.slides[this.slides.length-1].style.transform = 'translate3d(' + (-660 * (this.imgCurrent+1)).toString()+'px, 0px, 0px)' + this.slides[this.slides.length-2].style.transform = 'translate3d(' + (-660 * (this.imgCurrent+1)).toString()+'px, 0px, 0px)' + _this.imgList.style.transform = 'translate3d(660px, 0px, 0px)' + setTimeout(_=>{ + this.slides[this.slides.length-1].style.transform = 'translate3d(0px, 0px, 0px)' + this.slides[this.slides.length-2].style.transform = 'translate3d(0px, 0px, 0px)' + _this.imgDistance = (-660 * this.imgCurrent).toString() + _this.imgList.style.transform = 'translate3d(' + _this.imgDistance +'px, 0px, 0px)' + _this.imgList.classList.add("no-transition") + },500) + } + else { + _this.imgCurrent = _this.imgCurrent-1 + _this.imgDistance = (-660 * this.imgCurrent).toString() + _this.imgList.style.transform = 'translate3d(' + _this.imgDistance +'px, 0px, 0px)' + } + } + }) + this.slideRightButton = $el("button.right",{ + parent: this.imgWrapper, + style:{ + display:info.images.length <= 2 ? 'none' : 'block' + }, + innerHTML:``, + onclick: ()=>{ + if(info.images.length <= 2) return + _this.imgList.classList.remove("no-transition") + + if( _this.imgCurrent >= (info.images.length/2)-1){ + _this.imgCurrent = 0 + const max = info.images.length/2 + this.slides[0].style.transform = 'translate3d(' + (660 * max).toString()+'px, 0px, 0px)' + this.slides[1].style.transform = 'translate3d(' + (660 * max).toString()+'px, 0px, 0px)' + _this.imgList.style.transform = 'translate3d(' + (-660 * max).toString()+'px, 0px, 0px)' + setTimeout(_=>{ + this.slides[0].style.transform = 'translate3d(0px, 0px, 0px)' + this.slides[1].style.transform = 'translate3d(0px, 0px, 0px)' + _this.imgDistance = (-660 * this.imgCurrent).toString() + _this.imgList.style.transform = 'translate3d(' + _this.imgDistance +'px, 0px, 0px)' + _this.imgList.classList.add("no-transition") + },500) + } + else { + _this.imgCurrent = _this.imgCurrent+1 + _this.imgDistance = (-660 * this.imgCurrent).toString() + _this.imgList.style.transform = 'translate3d(' + _this.imgDistance +'px, 0px, 0px)' + } + + } + }) + + } + + if(info.description){ + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + marginTop: "10px", + }, + }); + } + + return info; + }) + .catch((err) => { + this.imgWrapper.style.display = 'none' + content.textContent = "⚠️ " + err.message; + }) + .finally(_=>{ + }) + } +} + + +export class CheckpointInfoDialog extends ModelInfoDialog { + async addInfo() { + // super.addInfo(); + await this.addCivitaiInfo(); + } +} + +const MAX_TAGS = 500 +export class LoraInfoDialog extends ModelInfoDialog { + getTagFrequency() { + if (!this.metadata.ss_tag_frequency) return []; + + const datasets = JSON.parse(this.metadata.ss_tag_frequency); + const tags = {}; + for (const setName in datasets) { + const set = datasets[setName]; + for (const t in set) { + if (t in tags) { + tags[t] += set[t]; + } else { + tags[t] = set[t]; + } + } + } + + return Object.entries(tags).sort((a, b) => b[1] - a[1]); + } + + getResolutions() { + let res = []; + if (this.metadata.ss_bucket_info) { + const parsed = JSON.parse(this.metadata.ss_bucket_info); + if (parsed?.buckets) { + for (const { resolution, count } of Object.values(parsed.buckets)) { + res.push([count, `${resolution.join("x")} * ${count}`]); + } + } + } + res = res.sort((a, b) => b[0] - a[0]).map((a) => a[1]); + let r = this.metadata.ss_resolution; + if (r) { + const s = r.split(","); + const w = s[0].replace("(", ""); + const h = s[1].replace(")", ""); + res.push(`${w.trim()}x${h.trim()} (Base res)`); + } else if ((r = this.metadata["modelspec.resolution"])) { + res.push(r + " (Base res"); + } + if (!res.length) { + res.push("⚠️ Unknown"); + } + return res; + } + + getTagList(tags) { + return tags.map((t) => + $el( + "li.easyuse-model-tag", + { + dataset: { + tag: t[0], + }, + $: (el) => { + el.onclick = () => { + el.classList.toggle("easyuse-model-tag--selected"); + }; + }, + }, + [ + $el("p", { + textContent: t[0], + }), + $el("span", { + textContent: t[1], + }), + ] + ) + ); + } + + addTags() { + let tags = this.getTagFrequency(); + let hasMore; + if (tags?.length) { + const c = tags.length; + let list; + if (c > MAX_TAGS) { + tags = tags.slice(0, MAX_TAGS); + hasMore = $el("p", [ + $el("span", { textContent: `⚠️ Only showing first ${MAX_TAGS} tags ` }), + $el("a", { + href: "#", + textContent: `Show all ${c}`, + onclick: () => { + list.replaceChildren(...this.getTagList(this.getTagFrequency())); + hasMore.remove(); + }, + }), + ]); + } + list = $el("ol.easyuse-model-tags-list", this.getTagList(tags)); + this.tags = $el("div", [list]); + } else { + this.tags = $el("p", { textContent: "⚠️ No tag frequency metadata found" }); + } + + this.content.append(this.tags); + + if (hasMore) { + this.content.append(hasMore); + } + } + + async addInfo() { + // this.addInfoEntry("Name", this.metadata.ss_output_name || "⚠️ Unknown"); + // this.addInfoEntry("Base Model", this.metadata.ss_sd_model_name || "⚠️ Unknown"); + // this.addInfoEntry("Clip Skip", this.metadata.ss_clip_skip || "⚠️ Unknown"); + // + // this.addInfoEntry( + // "Resolution", + // $el( + // "select", + // this.getResolutions().map((r) => $el("option", { textContent: r })) + // ) + // ); + + // super.addInfo(); + const p = this.addCivitaiInfo(); + this.addTags(); + + const info = await p; + if (info) { + // $el( + // "p", + // { + // parent: this.content, + // textContent: "Trained Words: ", + // }, + // [ + // $el("pre", { + // textContent: info.trainedWords.join(", "), + // style: { + // whiteSpace: "pre-wrap", + // margin: "10px 0", + // background: "#222", + // padding: "5px", + // borderRadius: "5px", + // maxHeight: "250px", + // overflow: "auto", + // }, + // }), + // ] + // ); + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } + + createButtons() { + const btns = super.createButtons(); + + function copyTags(e, tags) { + const textarea = $el("textarea", { + parent: document.body, + style: { + position: "fixed", + }, + textContent: tags.map((el) => el.dataset.tag).join(", "), + }); + textarea.select(); + try { + document.execCommand("copy"); + if (!e.target.dataset.text) { + e.target.dataset.text = e.target.textContent; + } + e.target.textContent = "Copied " + tags.length + " tags"; + setTimeout(() => { + e.target.textContent = e.target.dataset.text; + }, 1000); + } catch (ex) { + prompt("Copy to clipboard: Ctrl+C, Enter", text); + } finally { + document.body.removeChild(textarea); + } + } + + btns.unshift( + $el("button", { + type: "button", + textContent: "Copy Selected", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".easyuse-model-tag--selected")]); + }, + }), + $el("button", { + type: "button", + textContent: "Copy All", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".easyuse-model-tag")]); + }, + }) + ); + + return btns; + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/toast.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/toast.js new file mode 100644 index 00000000..f32ab5e4 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/toast.js @@ -0,0 +1,127 @@ +import {sleep} from "./utils.js"; +import {$t} from "./i18n.js"; + +class Toast{ + + constructor() { + this.info_icon = `` + this.success_icon = `` + this.error_icon = `` + this.warn_icon = `` + this.loading_icon = `` + } + + async showToast(data){ + let container = document.querySelector(".easyuse-toast-container"); + if (!container) { + container = document.createElement("div"); + container.classList.add("easyuse-toast-container"); + document.body.appendChild(container); + } + await this.hideToast(data.id); + const toastContainer = document.createElement("div"); + const content = document.createElement("span"); + content.innerHTML = data.content; + toastContainer.appendChild(content); + for (let a = 0; a < (data.actions || []).length; a++) { + const action = data.actions[a]; + if (a > 0) { + const sep = document.createElement("span"); + sep.innerHTML = " | "; + toastContainer.appendChild(sep); + } + const actionEl = document.createElement("a"); + actionEl.innerText = action.label; + if (action.href) { + actionEl.target = "_blank"; + actionEl.href = action.href; + } + if (action.callback) { + actionEl.onclick = (e) => { + return action.callback(e); + }; + } + toastContainer.appendChild(actionEl); + } + const animContainer = document.createElement("div"); + animContainer.setAttribute("toast-id", data.id); + animContainer.appendChild(toastContainer); + container.appendChild(animContainer); + await sleep(64); + animContainer.style.marginTop = `-${animContainer.offsetHeight}px`; + await sleep(64); + animContainer.classList.add("-show"); + if (data.duration) { + await sleep(data.duration); + this.hideToast(data.id); + } + } + async hideToast(id) { + const msg = document.querySelector(`.easyuse-toast-container > [toast-id="${id}"]`); + if (msg === null || msg === void 0 ? void 0 : msg.classList.contains("-show")) { + msg.classList.remove("-show"); + await sleep(750); + } + msg && msg.remove(); + } + async clearAllMessages() { + let container = document.querySelector(".easyuse-toast-container"); + container && (container.innerHTML = ""); + } + + async copyright(duration = 5000, actions = []) { + this.showToast({ + id: `toast-info`, + content: `${this.info_icon} ${$t('Workflow created by')} Yolain , ${$t('Watch more video content')} Bη«™δΉ±δΉ±ε‘€`, + duration, + actions + }); + } + async info(content, duration = 3000, actions = []) { + this.showToast({ + id: `toast-info`, + content: `${this.info_icon} ${content}`, + duration, + actions + }); + } + async success(content, duration = 3000, actions = []) { + this.showToast({ + id: `toast-success`, + content: `${this.success_icon} ${content}`, + duration, + actions + }); + } + async error(content, duration = 3000, actions = []) { + this.showToast({ + id: `toast-error`, + content: `${this.error_icon} ${content}`, + duration, + actions + }); + } + async warn(content, duration = 3000, actions = []) { + this.showToast({ + id: `toast-warn`, + content: `${this.warn_icon} ${content}`, + duration, + actions + }); + } + async showLoading(content, duration = 0, actions = []) { + this.showToast({ + id: `toast-loading`, + content: `${this.loading_icon} ${content}`, + duration, + actions + }); + } + + async hideLoading() { + this.hideToast("toast-loading"); + } + +} + +export const toast = new Toast(); diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/utils.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/utils.js new file mode 100644 index 00000000..9a988a7a --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/common/utils.js @@ -0,0 +1,187 @@ +export function sleep(ms = 100, value) { + return new Promise((resolve) => { + setTimeout(() => { + resolve(value); + }, ms); + }); +} +export function addPreconnect(href, crossorigin=false){ + const preconnect = document.createElement("link"); + preconnect.rel = 'preconnect' + preconnect.href = href + if(crossorigin) preconnect.crossorigin = '' + document.head.appendChild(preconnect); +} +export function addCss(href, base=true) { + const link = document.createElement("link"); + link.rel = "stylesheet"; + link.type = "text/css"; + link.href = base ? "extensions/ComfyUI-Easy-Use/"+href : href; + document.head.appendChild(link); +} + +export function addMeta(name, content) { + const meta = document.createElement("meta"); + meta.setAttribute("name", name); + meta.setAttribute('content', content); + document.head.appendChild(meta); +} + +export function deepEqual(obj1, obj2) { + if (typeof obj1 !== typeof obj2) { + return false + } + if (typeof obj1 !== 'object' || obj1 === null || obj2 === null) { + return obj1 === obj2 + } + const keys1 = Object.keys(obj1) + const keys2 = Object.keys(obj2) + if (keys1.length !== keys2.length) { + return false + } + for (let key of keys1) { + if (!deepEqual(obj1[key], obj2[key])) { + return false + } + } + return true +} + + +export function getLocale(){ + const locale = localStorage['AGL.Locale'] || localStorage['Comfy.Settings.AGL.Locale'] || 'en-US' + return locale +} + +export function spliceExtension(fileName){ + return fileName.substring(0,fileName.lastIndexOf('.')) +} +export function getExtension(fileName){ + return fileName.substring(fileName.lastIndexOf('.') + 1) +} + +export function formatTime(time, format) { + time = typeof (time) === "number" ? time : (time instanceof Date ? time.getTime() : parseInt(time)); + if (isNaN(time)) return null; + if (typeof (format) !== 'string' || !format) format = 'yyyy-MM-dd hh:mm:ss'; + let _time = new Date(time); + time = _time.toString().split(/[\s\:]/g).slice(0, -2); + time[1] = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'][_time.getMonth()]; + let _mapping = { + MM: 1, + dd: 2, + yyyy: 3, + hh: 4, + mm: 5, + ss: 6 + }; + return format.replace(/([Mmdhs]|y{2})\1/g, (key) => time[_mapping[key]]); +} + + +let origProps = {}; +export const findWidgetByName = (node, name) => node.widgets.find((w) => w.name === name); + +export const doesInputWithNameExist = (node, name) => node.inputs ? node.inputs.some((input) => input.name === name) : false; + +export function updateNodeHeight(node) {node.setSize([node.size[0], node.computeSize()[1]]);} + +export function toggleWidget(node, widget, show = false, suffix = "") { + if (!widget || doesInputWithNameExist(node, widget.name)) return; + if (!origProps[widget.name]) { + origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize }; + } + const origSize = node.size; + + widget.type = show ? origProps[widget.name].origType : "easyHidden" + suffix; + widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4]; + + widget.linkedWidgets?.forEach(w => toggleWidget(node, w, ":" + widget.name, show)); + + const height = show ? Math.max(node.computeSize()[1], origSize[1]) : node.size[1]; + node.setSize([node.size[0], height]); +} + +export function isLocalNetwork(ip) { + const localNetworkRanges = [ + '192.168.', + '10.', + '127.', + /^172\.((1[6-9]|2[0-9]|3[0-1])\.)/ + ]; + + return localNetworkRanges.some(range => { + if (typeof range === 'string') { + return ip.startsWith(range); + } else { + return range.test(ip); + } + }); +} + + +/** +* accAdd ι«˜η²ΎεΊ¦εŠ ζ³• +* @since 1.0.10 +* @param {Number} arg1 +* @param {Number} arg2 +* @return {Number} +*/ +export function accAdd(arg1, arg2) { + let r1, r2, s1, s2,max; + s1 = typeof arg1 == 'string' ? arg1 : arg1.toString() + s2 = typeof arg2 == 'string' ? arg2 : arg2.toString() + try { r1 = s1.split(".")[1].length } catch (e) { r1 = 0 } + try { r2 = s2.split(".")[1].length } catch (e) { r2 = 0 } + max = Math.pow(10, Math.max(r1, r2)) + return (arg1 * max + arg2 * max) / max +} +/** + * accSub ι«˜η²ΎεΊ¦ε‡ζ³• + * @since 1.0.10 + * @param {Number} arg1 + * @param {Number} arg2 + * @return {Number} + */ +export function accSub(arg1, arg2) { + let r1, r2, max, min,s1,s2; + s1 = typeof arg1 == 'string' ? arg1 : arg1.toString() + s2 = typeof arg2 == 'string' ? arg2 : arg2.toString() + try { r1 = s1.split(".")[1].length } catch (e) { r1 = 0 } + try { r2 = s2.split(".")[1].length } catch (e) { r2 = 0 } + max = Math.pow(10, Math.max(r1, r2)); + //εŠ¨ζ€ζŽ§εˆΆη²ΎεΊ¦ι•ΏεΊ¦ + min = (r1 >= r2) ? r1 : r2; + return ((arg1 * max - arg2 * max) / max).toFixed(min) +} +/** + * accMul ι«˜η²ΎεΊ¦δΉ˜ζ³• + * @since 1.0.10 + * @param {Number} arg1 + * @param {Number} arg2 + * @return {Number} + */ +export function accMul(arg1, arg2) { + let max = 0, s1 = typeof arg1 == 'string' ? arg1 : arg1.toString(), s2 = typeof arg2 == 'string' ? arg2 : arg2.toString(); + try { max += s1.split(".")[1].length } catch (e) { } + try { max += s2.split(".")[1].length } catch (e) { } + return Number(s1.replace(".", "")) * Number(s2.replace(".", "")) / Math.pow(10, max) +} +/** + * accDiv ι«˜η²ΎεΊ¦ι™€ζ³• + * @since 1.0.10 + * @param {Number} arg1 + * @param {Number} arg2 + * @return {Number} + */ +export function accDiv(arg1, arg2) { + let t1 = 0, t2 = 0, r1, r2,s1 = typeof arg1 == 'string' ? arg1 : arg1.toString(), s2 = typeof arg2 == 'string' ? arg2 : arg2.toString(); + try { t1 = s1.toString().split(".")[1].length } catch (e) { } + try { t2 = s2.toString().split(".")[1].length } catch (e) { } + r1 = Number(s1.toString().replace(".", "")) + r2 = Number(s2.toString().replace(".", "")) + return (r1 / r2) * Math.pow(10, t2 - t1) +} +Number.prototype.div = function (arg) { + return accDiv(this, arg); +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easy.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easy.js new file mode 100644 index 00000000..b22d2fb2 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easy.js @@ -0,0 +1,610 @@ +import { api } from "../../../../scripts/api.js"; +import { app } from "../../../../scripts/app.js"; +import {deepEqual, addCss, addMeta, isLocalNetwork} from "../common/utils.js"; +import {logoIcon, quesitonIcon, rocketIcon, groupIcon, rebootIcon, closeIcon} from "../common/icon.js"; +import {$t} from '../common/i18n.js'; +import {toast} from "../common/toast.js"; +import {$el, ComfyDialog} from "../../../../scripts/ui.js"; + + +addCss('css/index.css') + +api.addEventListener("easyuse-toast",event=>{ + const content = event.detail.content + const type = event.detail.type + const duration = event.detail.duration + if(!type){ + toast.info(content, duration) + } + else{ + toast.showToast({ + id: `toast-${type}`, + content: `${toast[type+"_icon"]} ${content}`, + duration: duration || 3000, + }) + } +}) + + +let draggerEl = null +let isGroupMapcanMove = true +function createGroupMap(){ + let div = document.querySelector('#easyuse_groups_map') + if(div){ + div.style.display = div.style.display == 'none' ? 'flex' : 'none' + return + } + let groups = app.canvas.graph._groups + let nodes = app.canvas.graph._nodes + let old_nodes = groups.length + div = document.createElement('div') + div.id = 'easyuse_groups_map' + div.innerHTML = '' + let btn = document.createElement('div') + btn.style = `display: flex; + width: calc(100% - 8px); + justify-content: space-between; + align-items: center; + padding: 0 6px; + height: 44px;` + let hideBtn = $el('button.closeBtn',{ + innerHTML:closeIcon, + onclick:_=>div.style.display = 'none' + }) + let textB = document.createElement('p') + btn.appendChild(textB) + btn.appendChild(hideBtn) + textB.style.fontSize = '11px' + textB.innerHTML = `${$t('Groups Map')} (EasyUse)` + div.appendChild(btn) + + div.addEventListener('mousedown', function (e) { + var startX = e.clientX + var startY = e.clientY + var offsetX = div.offsetLeft + var offsetY = div.offsetTop + + function moveBox (e) { + var newX = e.clientX + var newY = e.clientY + var deltaX = newX - startX + var deltaY = newY - startY + div.style.left = offsetX + deltaX + 'px' + div.style.top = offsetY + deltaY + 'px' + } + + function stopMoving () { + document.removeEventListener('mousemove', moveBox) + document.removeEventListener('mouseup', stopMoving) + } + + if(isGroupMapcanMove){ + document.addEventListener('mousemove', moveBox) + document.addEventListener('mouseup', stopMoving) + } + }) + + function updateGroups(groups, groupsDiv, autoSortDiv){ + if(groups.length>0){ + autoSortDiv.style.display = 'block' + }else autoSortDiv.style.display = 'none' + for (let index in groups) { + const group = groups[index] + const title = group.title + const show_text = $t('Always') + const hide_text = $t('Bypass') + const mute_text = $t('Never') + let group_item = document.createElement('div') + let group_item_style = `justify-content: space-between;display:flex;background-color: var(--comfy-input-bg);border-radius: 5px;border:1px solid var(--border-color);margin-top:5px;` + group_item.addEventListener("mouseover",event=>{ + event.preventDefault() + group_item.style = group_item_style + "filter:brightness(1.2);" + }) + group_item.addEventListener("mouseleave",event=>{ + event.preventDefault() + group_item.style = group_item_style + "filter:brightness(1);" + }) + group_item.addEventListener("dragstart",e=>{ + draggerEl = e.currentTarget; + e.currentTarget.style.opacity = "0.6"; + e.currentTarget.style.border = "1px dashed yellow"; + e.dataTransfer.effectAllowed = 'move'; + e.dataTransfer.setDragImage(emptyImg, 0, 0); + }) + group_item.addEventListener("dragend",e=>{ + e.target.style.opacity = "1"; + e.currentTarget.style.border = "1px dashed transparent"; + e.currentTarget.removeAttribute("draggable"); + document.querySelectorAll('.easyuse-group-item').forEach((el,i) => { + var prev_i = el.dataset.id; + if (el == draggerEl && prev_i != i ) { + groups.splice(i, 0, groups.splice(prev_i, 1)[0]); + } + el.dataset.id = i; + }); + isGroupMapcanMove = true + }) + group_item.addEventListener("dragover",e=>{ + e.preventDefault(); + if (e.currentTarget == draggerEl) return; + let rect = e.currentTarget.getBoundingClientRect(); + if (e.clientY > rect.top + rect.height / 2) { + e.currentTarget.parentNode.insertBefore(draggerEl, e.currentTarget.nextSibling); + } else { + e.currentTarget.parentNode.insertBefore(draggerEl, e.currentTarget); + } + isGroupMapcanMove = true + }) + + + group_item.setAttribute('data-id',index) + group_item.className = 'easyuse-group-item' + group_item.style = group_item_style + // ζ ‡ι’˜ + let text_group_title = document.createElement('div') + text_group_title.style = `flex:1;font-size:12px;color:var(--input-text);padding:4px;white-space: nowrap;overflow: hidden;text-overflow: ellipsis;cursor:pointer` + text_group_title.innerHTML = `${title}` + text_group_title.addEventListener('mousedown',e=>{ + isGroupMapcanMove = false + e.currentTarget.parentNode.draggable = 'true'; + }) + text_group_title.addEventListener('mouseleave',e=>{ + setTimeout(_=>{ + isGroupMapcanMove = true + },150) + }) + group_item.append(text_group_title) + // ζŒ‰ι’η»„ + let buttons = document.createElement('div') + group.recomputeInsideNodes(); + const nodesInGroup = group._nodes; + let isGroupShow = nodesInGroup && nodesInGroup.length>0 && nodesInGroup[0].mode == 0 + let isGroupMute = nodesInGroup && nodesInGroup.length>0 && nodesInGroup[0].mode == 2 + let go_btn = document.createElement('button') + go_btn.style = "margin-right:6px;cursor:pointer;font-size:10px;padding:2px 4px;color:var(--input-text);background-color: var(--comfy-input-bg);border: 1px solid var(--border-color);border-radius:4px;" + go_btn.innerText = "Go" + go_btn.addEventListener('click', () => { + app.canvas.ds.offset[0] = -group.pos[0] - group.size[0] * 0.5 + (app.canvas.canvas.width * 0.5) / app.canvas.ds.scale; + app.canvas.ds.offset[1] = -group.pos[1] - group.size[1] * 0.5 + (app.canvas.canvas.height * 0.5) / app.canvas.ds.scale; + app.canvas.setDirty(true, true); + app.canvas.setZoom(1) + }) + buttons.append(go_btn) + let see_btn = document.createElement('button') + let defaultStyle = `cursor:pointer;font-size:10px;;padding:2px;border: 1px solid var(--border-color);border-radius:4px;width:36px;` + see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:var(--theme-color);color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle) + see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text) + let pressTimer + let firstTime =0, lastTime =0 + let isHolding = false + see_btn.addEventListener('click', () => { + if(isHolding){ + isHolding = false + return + } + for (const node of nodesInGroup) { + node.mode = isGroupShow ? 4 : 0; + node.graph.change(); + } + isGroupShow = nodesInGroup[0].mode == 0 ? true : false + isGroupMute = nodesInGroup[0].mode == 2 ? true : false + see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle) + see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text) + }) + see_btn.addEventListener('mousedown', () => { + firstTime = new Date().getTime(); + clearTimeout(pressTimer); + pressTimer = setTimeout(_=>{ + for (const node of nodesInGroup) { + node.mode = isGroupMute ? 0 : 2; + node.graph.change(); + } + isGroupShow = nodesInGroup[0].mode == 0 ? true : false + isGroupMute = nodesInGroup[0].mode == 2 ? true : false + see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle) + see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text) + },500) + }) + see_btn.addEventListener('mouseup', () => { + lastTime = new Date().getTime(); + if(lastTime - firstTime > 500) isHolding = true + clearTimeout(pressTimer); + }) + buttons.append(see_btn) + group_item.append(buttons) + + groupsDiv.append(group_item) + } + + } + + let groupsDiv = document.createElement('div') + groupsDiv.id = 'easyuse-groups-items' + groupsDiv.style = `overflow-y: auto;max-height: 400px;height:100%;width: 100%;` + + let autoSortDiv = document.createElement('button') + autoSortDiv.style = `cursor:pointer;font-size:10px;padding:2px 4px;color:var(--input-text);background-color: var(--comfy-input-bg);border: 1px solid var(--border-color);border-radius:4px;` + autoSortDiv.innerText = $t('Auto Sorting') + autoSortDiv.addEventListener('click',e=>{ + e.preventDefault() + groupsDiv.innerHTML = `` + let new_groups = groups.sort((a,b)=> a['pos'][0] - b['pos'][0]).sort((a,b)=> a['pos'][1] - b['pos'][1]) + updateGroups(new_groups, groupsDiv, autoSortDiv) + }) + + updateGroups(groups, groupsDiv, autoSortDiv) + + div.appendChild(groupsDiv) + + let remarkDiv = document.createElement('p') + remarkDiv.style = `text-align:center; font-size:10px; padding:0 10px;color:var(--descrip-text)` + remarkDiv.innerText = $t('Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never') + div.appendChild(groupsDiv) + div.appendChild(remarkDiv) + div.appendChild(autoSortDiv) + + let graphDiv = document.getElementById("graph-canvas") + graphDiv.addEventListener('mouseover', async () => { + groupsDiv.innerHTML = `` + let new_groups = app.canvas.graph._groups + updateGroups(new_groups, groupsDiv, autoSortDiv) + old_nodes = nodes + }) + + if (!document.querySelector('#easyuse_groups_map')){ + document.body.appendChild(div) + }else{ + div.style.display = 'flex' + } + +} + +async function cleanup(){ + try { + const {Running, Pending} = await api.getQueue() + if(Running.length>0 || Pending.length>0){ + toast.error($t("Clean Failed")+ ":"+ $t("Please stop all running tasks before cleaning GPU")) + return + } + api.fetchApi("/easyuse/cleangpu",{ + method:"POST" + }).then(res=>{ + if(res.status == 200){ + toast.success($t("Clean SuccessFully")) + }else{ + toast.error($t("Clean Failed")) + } + }) + + } catch (exception) {} +} + + +let guideDialog = null +let isDownloading = false +function download_model(url,local_dir){ + if(isDownloading || !url || !local_dir) return + isDownloading = true + let body = new FormData(); + body.append('url', url); + body.append('local_dir', local_dir); + api.fetchApi("/easyuse/model/download",{ + method:"POST", + body + }).then(res=>{ + if(res.status == 200){ + toast.success($t("Download SuccessFully")) + }else{ + toast.error($t("Download Failed")) + } + isDownloading = false + }) + +} +class GuideDialog { + + constructor(note, need_models){ + this.dialogDiv = null + this.modelsDiv = null + + if(need_models?.length>0){ + let tbody = [] + + for(let i=0;idownload_model(need_models[i]['download_url'],need_models[i]['local_dir']), target:"_blank", textContent:$t('Download Model')}) : '', + need_models[i]['source_url'] ? $el('a',{href:need_models[i]['source_url'], target:"_blank", textContent:$t('Source Url')}) : '', + need_models[i]['desciption'] ? $el('span',{textContent:need_models[i]['desciption']}) : '', + ]), + ])) + } + this.modelsDiv = $el('div.easyuse-guide-dialog-models.markdown-body',[ + $el('h3',{textContent:$t('Models Required')}), + $el('table',{cellpadding:0,cellspacing:0},[ + $el('thead',[ + $el('tr',[ + $el('th',{innerHTML:$t('ModelName')}), + $el('th',{innerHTML:$t('Description')}), + ]) + ]), + $el('tbody',tbody) + ]) + ]) + } + + this.dialogDiv = $el('div.easyuse-guide-dialog.hidden',[ + $el('div.easyuse-guide-dialog-header',[ + $el('div.easyuse-guide-dialog-top',[ + $el('div.easyuse-guide-dialog-title',{ + innerHTML:$t('Workflow Guide') + }), + $el('button.closeBtn',{innerHTML:closeIcon,onclick:_=>this.close()}) + ]), + + $el('div.easyuse-guide-dialog-remark',{ + innerHTML:`${$t('Workflow created by')} Yolain , ${$t('Watch more video content')} Bη«™δΉ±δΉ±ε‘€` + }) + ]), + $el('div.easyuse-guide-dialog-content.markdown-body',[ + $el('div.easyuse-guide-dialog-note',{ + innerHTML:note + }), + ...this.modelsDiv ? [this.modelsDiv] : [] + ]) + ]) + + if(disableRenderInfo){ + this.dialogDiv.classList.add('disable-render-info') + } + document.body.appendChild(this.dialogDiv) + } + show(){ + if(this.dialogDiv) this.dialogDiv.classList.remove('hidden') + } + + close(){ + if(this.dialogDiv){ + this.dialogDiv.classList.add('hidden') + } + } + toggle(){ + if(this.dialogDiv){ + if(this.dialogDiv.classList.contains('hidden')){ + this.show() + }else{ + this.close() + } + } + } + + remove(){ + if(this.dialogDiv) document.body.removeChild(this.dialogDiv) + } +} + +// toolbar +const toolBarId = "Comfy.EasyUse.toolBar" +const getEnableToolBar = _ => app.ui.settings.getSettingValue(toolBarId, true) +const getNewMenuPosition = _ => { + try{ + return app.ui.settings.getSettingValue('Comfy.UseNewMenu', 'Disabled') + }catch (e){ + return 'Disabled' + } +} + +let note = null +let toolbar = null +let enableToolBar = getEnableToolBar() && getNewMenuPosition() == 'Disabled' +let disableRenderInfo = localStorage['Comfy.Settings.Comfy.EasyUse.disableRenderInfo'] ? true : false +export function addToolBar(app) { + app.ui.settings.addSetting({ + id: toolBarId, + name: $t("Enable tool bar fixed on the left-bottom (ComfyUI-Easy-Use)"), + type: "boolean", + defaultValue: enableToolBar, + onChange(value) { + enableToolBar = !!value; + if(enableToolBar){ + showToolBar() + }else hideToolBar() + }, + }); +} +function showToolBar(){ + if(toolbar) toolbar.style.display = 'flex' +} +function hideToolBar(){ + if(toolbar) toolbar.style.display = 'none' +} +let monitor = null +function setCrystoolsUI(position){ + const crystools = document.getElementById('crystools-root')?.children || null + if(crystools?.length>0){ + if(!monitor){ + for (let i = 0; i < crystools.length; i++) { + if (crystools[i].id === 'crystools-monitor-container') { + monitor = crystools[i]; + break; + } + } + } + if(monitor){ + if(position == 'Disabled'){ + let replace = true + for (let i = 0; i < crystools.length; i++) { + if (crystools[i].id === 'crystools-monitor-container') { + replace = false + break; + } + } + document.getElementById('crystools-root').appendChild(monitor) + } + else { + let monitor_div = document.getElementById('comfyui-menu-monitor') + if(!monitor_div) app.menu.settingsGroup.element.before($el('div',{id:'comfyui-menu-monitor'},monitor)) + else monitor_div.appendChild(monitor) + } + } + } +} +const changeNewMenuPosition = app.ui.settings.settingsLookup?.['Comfy.UseNewMenu'] +if(changeNewMenuPosition) changeNewMenuPosition.onChange = v => { + v == 'Disabled' ? showToolBar() : hideToolBar() + setCrystoolsUI(v) +} + + + +app.registerExtension({ + name: "comfy.easyUse", + init() { + // Canvas Menu + const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = getCanvasMenuOptions.apply(this, arguments); + let emptyImg = new Image() + emptyImg.src = "data:image/gif;base64,R0lGODlhAQABAIAAAAUEBAAAACwAAAAAAQABAAACAkQBADs="; + + options.push(null, + // Groups Map + { + content: groupIcon.replace('currentColor','var(--warning-color)') + ' '+ $t('Groups Map') + ' (EasyUse)', + callback: async() => { + createGroupMap() + } + }, + // Force clean ComfyUI GPU Used εΌΊεˆΆεΈθ½½ζ¨‘εž‹GPU占用 + { + content: rocketIcon.replace('currentColor','var(--theme-color-light)') + ' '+ $t('Cleanup Of GPU Usage') + ' (EasyUse)', + callback: async() =>{ + await cleanup() + } + }, + // Only show the reboot option if the server is running on a local network δ»…εœ¨ζœ¬εœ°ζˆ–ε±€εŸŸη½‘ηŽ―ε’ƒε―ι‡ε―ζœεŠ‘ + isLocalNetwork(window.location.host) ? { + content: rebootIcon.replace('currentColor','var(--error-color)') + ' '+ $t('Reboot ComfyUI') + ' (EasyUse)', + callback: _ =>{ + if (confirm($t("Are you sure you'd like to reboot the server?"))){ + try { + api.fetchApi("/easyuse/reboot"); + } catch (exception) {} + } + } + } : null, + ); + return options; + }; + + let renderInfoEvent = LGraphCanvas.prototype.renderInfo + if(disableRenderInfo){ + LGraphCanvas.prototype.renderInfo = function (ctx, x, y) {} + } + + if(!toolbar){ + toolbar = $el('div.easyuse-toolbar',[ + $el('div.easyuse-toolbar-item',{ + onclick:_=>{ + createGroupMap() + } + },[ + $el('div.easyuse-toolbar-icon.group', {innerHTML:groupIcon}), + $el('div.easyuse-toolbar-tips',$t('Groups Map')) + ]), + $el('div.easyuse-toolbar-item',{ + onclick:async()=>{ + await cleanup() + } + },[ + $el('div.easyuse-toolbar-icon.rocket',{innerHTML:rocketIcon}), + $el('div.easyuse-toolbar-tips',$t('Cleanup Of GPU Usage')) + ]), + ]) + if(disableRenderInfo){ + toolbar.classList.add('disable-render-info') + }else{ + toolbar.classList.remove('disable-render-info') + } + document.body.appendChild(toolbar) + } + + // rewrite handleFile + let loadGraphDataEvent = app.loadGraphData + app.loadGraphData = async function (data, clean=true) { + // if(data?.extra?.cpr){ + // toast.copyright() + // } + if(data?.extra?.note){ + if(guideDialog) { + guideDialog.remove() + guideDialog = null + } + if(note && toolbar) toolbar.removeChild(note) + const need_models = data.extra?.need_models || null + guideDialog = new GuideDialog(data.extra.note, need_models) + note = $el('div.easyuse-toolbar-item',{ + onclick:async()=>{ + guideDialog.toggle() + } + },[ + $el('div.easyuse-toolbar-icon.question',{innerHTML:quesitonIcon}), + $el('div.easyuse-toolbar-tips',$t('Workflow Guide')) + ]) + if(toolbar) toolbar.insertBefore(note, toolbar.firstChild) + } + else{ + if(note) { + toolbar.removeChild(note) + note = null + } + } + return await loadGraphDataEvent.apply(this, [...arguments]) + } + + addToolBar(app) + }, + async setup() { + // New style menu button + if(app.menu?.actionsGroup){ + const groupMap = new (await import('../../../../scripts/ui/components/button.js')).ComfyButton({ + icon:'list-box', + action:()=> createGroupMap(), + tooltip: "EasyUse Group Map", + // content: "EasyUse Group Map", + classList: "comfyui-button comfyui-menu-mobile-collapse" + }); + app.menu.actionsGroup.element.after(groupMap.element); + const position = getNewMenuPosition() + setCrystoolsUI(position) + if(position == 'Disabled') showToolBar() + else hideToolBar() + // const easyNewMenu = $el('div.easyuse-new-menu',[ + // $el('div.easyuse-new-menu-intro',[ + // $el('div.easyuse-new-menu-logo',{innerHTML:logoIcon}), + // $el('div.easyuse-new-menu-title',[ + // $el('div.title',{textContent:'ComfyUI-Easy-Use'}), + // $el('div.desc',{textContent:'Version:'}) + // ]) + // ]) + // ]) + // app.menu?.actionsGroup.element.after(new (await import('../../../../scripts/ui/components/splitButton.js')).ComfySplitButton({ + // primary: groupMap, + // mode:'click', + // position:'absolute', + // horizontal: 'right' + // },easyNewMenu).element); + } + + }, + beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name.startsWith("easy")) { + const origOnConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : undefined; + return r; + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyAccount.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyAccount.js new file mode 100644 index 00000000..421432fa --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyAccount.js @@ -0,0 +1,283 @@ +import { app } from "../../../../scripts/app.js"; +import { api } from "../../../../scripts/api.js"; +import { $el, ComfyDialog } from "../../../../scripts/ui.js"; +import { $t } from '../common/i18n.js' +import { toast } from "../common/toast.js"; +import {sleep, accSub} from "../common/utils.js"; + +let api_keys = [] +let api_current = 0 +let user_info = {} + +const api_cost = { + 'sd3': 6.5, + 'sd3-turbo': 4, +} + +class AccountDialog extends ComfyDialog { + constructor() { + super(); + this.lists = [] + this.dialog_div = null + this.user_div = null + } + + addItem(index, user_div){ + return $el('div.easyuse-account-dialog-item',[ + $el('input',{type:'text',placeholder:'Enter name',oninput: e=>{ + const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode) + api_keys[dataIndex]['name'] = e.target.value + },value:api_keys[index]['name']}), + $el('input.key',{type:'text',oninput: e=>{ + const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode) + api_keys[dataIndex]['key'] = e.target.value + },placeholder:'Enter APIKEY', value:api_keys[index]['key']}), + $el('button.choose',{textContent:$t('Choose'),onclick:async(e)=>{ + const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode) + let name = api_keys[dataIndex]['name'] + let key = api_keys[dataIndex]['key'] + if(!name){ + toast.error($t('Please enter the account name')) + return + } + else if(!key){ + toast.error($t('Please enter the APIKEY')) + return + } + let missing = true + for(let i=0;i{ + new AccountDialog().show(user_div); + } + },[ + $el('div.user',[ + $el('div.avatar', avatar ? [$el('img',{src:avatar})] : 'πŸ˜€'), + $el('div.info', [ + $el('h5.name', email), + $el('h6.remark','Credits: '+ credits) + ]) + ]), + $el('div.edit', {textContent:$t('Edit')}) + ]) + ) + toast.success($t('Save Succeed')) + } + else toast.success($t('Save Succeed')) + this.close() + } else { + toast.error($t('Save Failed')) + } + }}), + $el('button.delete',{textContent:$t('Delete'),onclick:e=>{ + const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode) + if(api_keys.length<=1){ + toast.error($t('At least one account is required')) + return + } + api_keys.splice(dataIndex,1) + this.dialog_div.removeChild(e.target.parentNode) + }}), + ]) + } + + show(userdiv) { + api_keys.forEach((item,index)=>{ + this.lists.push(this.addItem(index,userdiv)) + }) + this.dialog_div = $el("div.easyuse-account-dialog", this.lists) + super.show( + $el('div.easyuse-account-dialog-main',[ + $el('div',[ + $el('a',{href:'https://platform.stability.ai/account/keys',target:'_blank',textContent:$t('Getting Your APIKEY')}), + ]), + this.dialog_div, + ]) + ); + } + + createButtons() { + const btns = super.createButtons(); + btns.unshift($el('button',{ + type:'button', + textContent:$t('Save Account Info'), + onclick:_=>{ + let missing = true + for(let i=0;i { + if (res.status == 200) { + toast.success($t('Save Succeed')) + + } else { + toast.error($t('Save Failed')) + } + }) + } + } + })) + btns.unshift($el('button',{ + type:'button', + textContent:$t('Add Account'), + onclick:_=>{ + const name = 'Account '+(api_keys.length).toString() + api_keys.push({name,key:''}) + const item = this.addItem(api_keys.length - 1) + this.lists.push(item) + this.dialog_div.appendChild(item) + } + })) + return btns + } +} + + +app.registerExtension({ + name: 'comfy.easyUse.account', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if(nodeData.name == 'easy stableDiffusion3API'){ + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = async function() { + onNodeCreated ? onNodeCreated?.apply(this, arguments) : undefined; + const seed_widget = this.widgets.find(w => ['seed_num','seed'].includes(w.name)) + const seed_control = this.widgets.find(w=> ['control_before_generate','control_after_generate'].includes(w.name)) + let model_widget = this.widgets.find(w => w.name == 'model') + model_widget.callback = value =>{ + cost_widget.value = '-'+api_cost[value] + } + const cost_widget = this.addWidget('text', 'cost_credit', '0', _=>{ + },{ + serialize:false, + }) + cost_widget.disabled = true + setTimeout(_=>{ + if(seed_control.name == 'control_before_generate' && seed_widget.value === 0){ + seed_widget.value = Math.floor(Math.random() * 4294967294) + } + cost_widget.value = '-'+api_cost[model_widget.value] + },100) + let user_div = $el('div.easyuse-account-user', [$t('Loading UserInfo...')]) + let account = this.addDOMWidget('account',"btn",$el('div.easyuse-account',user_div)); + // ζ›΄ζ–°balance俑息 + api.addEventListener('stable-diffusion-api-generate-succeed', async ({detail}) => { + let remarkDiv = user_div.querySelectorAll('.remark') + if(remarkDiv && remarkDiv[0]){ + const credits = detail?.model ? api_cost[detail.model] : 0 + if(credits) { + let balance = accSub(parseFloat(remarkDiv[0].innerText.replace(/Credits: /g,'')),credits) + if(balance>0){ + remarkDiv[0].innerText = 'Credits: '+ balance.toString() + } + } + } + await sleep(10000) + const res = await api.fetchApi('/easyuse/stability/balance') + if(res.status == 200){ + const data = await res.json() + if(data?.balance){ + const credits = data.balance?.credits || 0 + if(remarkDiv && remarkDiv[0]){ + remarkDiv[0].innerText = 'Credits: ' + credits + } + } + } + }) + // θŽ·ε–api_keys + const res = await api.fetchApi('/easyuse/stability/api_keys') + if (res.status == 200){ + let data = await res.json() + api_keys = data.keys + api_current = data.current + if (api_keys.length > 0 && api_current!==undefined){ + const api_key = api_keys[api_current]['key'] + const api_name = api_keys[api_current]['name'] + if(!api_key){ + user_div.replaceChildren( + $el('div.easyuse-account-user-info', { + onclick:_=>{ + new AccountDialog().show(user_div); + } + },[ + $el('div.user',[ + $el('div.avatar', 'πŸ˜€'), + $el('div.info', [ + $el('h5.name', api_name), + $el('h6.remark',$t('Click to set the APIKEY first')) + ]) + ]), + $el('div.edit', {textContent:$t('Edit')}) + ]) + ) + }else{ + // θŽ·ε–θ΄¦ε·δΏ‘ζ― + const res = await api.fetchApi('/easyuse/stability/user_info') + if(res.status == 200){ + const data = await res.json() + if(data?.account && data?.balance){ + const avatar = data.account?.profile_picture || null + const email = data.account?.email || null + const credits = data.balance?.credits || 0 + user_div.replaceChildren( + $el('div.easyuse-account-user-info', { + onclick:_=>{ + new AccountDialog().show(user_div); + } + },[ + $el('div.user',[ + $el('div.avatar', avatar ? [$el('img',{src:avatar})] : 'πŸ˜€'), + $el('div.info', [ + $el('h5.name', email), + $el('h6.remark','Credits: '+ credits) + ]) + ]), + $el('div.edit', {textContent:$t('Edit')}) + ]) + ) + } + } + } + } + } + } + } + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyContextMenu.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyContextMenu.js new file mode 100644 index 00000000..0abbdd68 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyContextMenu.js @@ -0,0 +1,174 @@ +import {app} from "../../../../scripts/app.js"; +import {api} from "../../../../scripts/api.js"; +import {$el} from "../../../../scripts/ui.js"; +import {$t} from "../common/i18n.js"; +import {getExtension, spliceExtension} from '../common/utils.js' +import {toast} from "../common/toast.js"; + +const setting_id = "Comfy.EasyUse.MenuNestSub" +let enableMenuNestSub = false +let thumbnails = [] + +export function addMenuNestSubSetting(app) { + app.ui.settings.addSetting({ + id: setting_id, + name: $t("Enable ContextMenu Auto Nest Subdirectories (ComfyUI-Easy-Use)"), + type: "boolean", + defaultValue: enableMenuNestSub, + onChange(value) { + enableMenuNestSub = !!value; + }, + }); +} + +const getEnableMenuNestSub = _ => app.ui.settings.getSettingValue(setting_id, enableMenuNestSub) + + +const Loaders = ['easy fullLoader','easy a1111Loader','easy comfyLoader'] +app.registerExtension({ + name:"comfy.easyUse.contextMenu", + async setup(app){ + addMenuNestSubSetting(app) + // θŽ·ε–ζ‰€ζœ‰ζ¨‘εž‹ε›Ύεƒ + const imgRes = await api.fetchApi(`/easyuse/models/thumbnail`) + if (imgRes.status === 200) { + let data = await imgRes.json(); + thumbnails = data + } + else if(getEnableMenuNestSub()){ + toast.error($t("Too many thumbnails, have closed the display")) + } + const existingContextMenu = LiteGraph.ContextMenu; + LiteGraph.ContextMenu = function(values,options){ + const threshold = 10; + const enabled = getEnableMenuNestSub(); + if(!enabled || (values?.length || 0) <= threshold || !(options?.callback) || values.some(i => typeof i !== 'string')){ + if(enabled){ + // console.log('Skipping context menu auto nesting for incompatible menu.'); + } + return existingContextMenu.apply(this,[...arguments]); + } + const compatValues = values; + const originalValues = [...compatValues]; + const folders = {}; + const specialOps = []; + const folderless = []; + for(const value of compatValues){ + const splitBy = value.indexOf('/') > -1 ? '/' : '\\'; + const valueSplit = value.split(splitBy); + if(valueSplit.length > 1){ + const key = valueSplit.shift(); + folders[key] = folders[key] || []; + folders[key].push(valueSplit.join(splitBy)); + }else if(value === 'CHOOSE' || value.startsWith('DISABLE ')){ + specialOps.push(value); + }else{ + folderless.push(value); + } + } + const foldersCount = Object.values(folders).length; + if(foldersCount > 0){ + const oldcallback = options.callback; + options.callback = null; + const newCallback = (item,options) => { + if(['None','ζ— ','η„‘','γͺし'].includes(item.content)) oldcallback('None',options) + else oldcallback(originalValues.find(i => i.endsWith(item.content),options)); + }; + const addContent = (content, folderName='') => { + const name = folderName ? folderName + '\\' + spliceExtension(content) : spliceExtension(content); + const ext = getExtension(content) + const time = new Date().getTime() + let thumbnail = '' + if(['ckpt', 'pt', 'bin', 'pth', 'safetensors'].includes(ext)){ + for(let i=0;i { + let subs = [] + let less = [] + const b = folder.map(name=> { + const _folders = {}; + const splitBy = name.indexOf('/') > -1 ? '/' : '\\'; + const valueSplit = name.split(splitBy); + if(valueSplit.length > 1){ + const key = valueSplit.shift(); + _folders[key] = _folders[key] || []; + _folders[key].push(valueSplit.join(splitBy)); + } + const foldersCount = Object.values(folders).length; + if(foldersCount > 0){ + let key = Object.keys(_folders)[0] + if(key && _folders[key]) subs.push({key, value:_folders[key][0]}) + else{ + less.push(addContent(name,key)) + } + } + return addContent(name,folderName) + }) + if(subs.length>0){ + let subs_obj = {} + subs.forEach(item => { + subs_obj[item.key] = subs_obj[item.key] || [] + subs_obj[item.key].push(item.value) + }) + return [...Object.entries(subs_obj).map(f => { + return { + content: f[0], + has_submenu: true, + callback: () => {}, + submenu: { + options: add_sub_folder(f[1], f[0]), + } + } + }),...less] + } + else return b + } + + for(const [folderName,folder] of Object.entries(folders)){ + newValues.push({ + content:folderName, + has_submenu:true, + callback:() => {}, + submenu:{ + options:add_sub_folder(folder,folderName), + } + }); + } + newValues.push(...folderless.map(f => addContent(f, ''))); + if(specialOps.length > 0) newValues.push(...specialOps.map(f => addContent(f, ''))); + return existingContextMenu.call(this,newValues,options); + } + return existingContextMenu.apply(this,[...arguments]); + } + LiteGraph.ContextMenu.prototype = existingContextMenu.prototype; + }, + +}) + diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyDynamicWidgets.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyDynamicWidgets.js new file mode 100644 index 00000000..7ab5a5af --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyDynamicWidgets.js @@ -0,0 +1,1274 @@ +import { app } from "../../../../scripts/app.js"; +import { api } from "../../../../scripts/api.js"; +import { ComfyWidgets } from "../../../../scripts/widgets.js"; +import { toast} from "../common/toast.js"; +import { $t } from '../common/i18n.js'; + +import { findWidgetByName, toggleWidget, updateNodeHeight} from "../common/utils.js"; + +const seedNodes = ["easy seed", "easy latentNoisy", "easy wildcards", "easy preSampling", "easy preSamplingAdvanced", "easy preSamplingNoiseIn", "easy preSamplingSdTurbo", "easy preSamplingCascade", "easy preSamplingDynamicCFG", "easy preSamplingLayerDiffusion", "easy fullkSampler", "easy fullCascadeKSampler"] +const loaderNodes = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy fluxLoader", "easy hunyuanDiTLoader", "easy pixArtLoader"] + +function widgetLogic(node, widget) { + if (widget.name === 'lora_name') { + if (widget.value === "None") { + toggleWidget(node, findWidgetByName(node, 'lora_model_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_clip_strength')) + } else { + toggleWidget(node, findWidgetByName(node, 'lora_model_strength'), true) + toggleWidget(node, findWidgetByName(node, 'lora_clip_strength'), true) + } + } + if (widget.name === 'rescale') { + let rescale_after_model = findWidgetByName(node, 'rescale_after_model').value + if (widget.value === 'by percentage' && rescale_after_model) { + toggleWidget(node, findWidgetByName(node, 'width')) + toggleWidget(node, findWidgetByName(node, 'height')) + toggleWidget(node, findWidgetByName(node, 'longer_side')) + toggleWidget(node, findWidgetByName(node, 'percent'), true) + } else if (widget.value === 'to Width/Height' && rescale_after_model) { + toggleWidget(node, findWidgetByName(node, 'width'), true) + toggleWidget(node, findWidgetByName(node, 'height'), true) + toggleWidget(node, findWidgetByName(node, 'percent')) + toggleWidget(node, findWidgetByName(node, 'longer_side')) + } else if (rescale_after_model) { + toggleWidget(node, findWidgetByName(node, 'longer_side'), true) + toggleWidget(node, findWidgetByName(node, 'width')) + toggleWidget(node, findWidgetByName(node, 'height')) + toggleWidget(node, findWidgetByName(node, 'percent')) + } + updateNodeHeight(node) + } + if (widget.name === 'upscale_method') { + if (widget.value === "None") { + toggleWidget(node, findWidgetByName(node, 'factor')) + toggleWidget(node, findWidgetByName(node, 'crop')) + } else { + toggleWidget(node, findWidgetByName(node, 'factor'), true) + toggleWidget(node, findWidgetByName(node, 'crop'), true) + } + updateNodeHeight(node) + } + if (widget.name === 'image_output') { + if (widget.value === 'Sender' || widget.value === 'Sender&Save'){ + toggleWidget(node, findWidgetByName(node, 'link_id'), true) + }else { + toggleWidget(node, findWidgetByName(node, 'link_id')) + } + if (widget.value === 'Hide' || widget.value === 'Preview' || widget.value == 'Preview&Choose' || widget.value === 'Sender') { + toggleWidget(node, findWidgetByName(node, 'save_prefix')) + toggleWidget(node, findWidgetByName(node, 'output_path')) + toggleWidget(node, findWidgetByName(node, 'embed_workflow')) + toggleWidget(node, findWidgetByName(node, 'number_padding')) + toggleWidget(node, findWidgetByName(node, 'overwrite_existing')) + } else if (widget.value === 'Save' || widget.value === 'Hide&Save' || widget.value === 'Sender&Save') { + toggleWidget(node, findWidgetByName(node, 'save_prefix'), true) + toggleWidget(node, findWidgetByName(node, 'output_path'), true) + toggleWidget(node, findWidgetByName(node, 'embed_workflow'), true) + toggleWidget(node, findWidgetByName(node, 'number_padding'), true) + toggleWidget(node, findWidgetByName(node, 'overwrite_existing'), true) + } + + if(widget.value === 'Hide' || widget.value === 'Hide&Save'){ + toggleWidget(node, findWidgetByName(node, 'decode_vae_name')) + }else{ + toggleWidget(node, findWidgetByName(node, 'decode_vae_name'), true) + } + } + if (widget.name === 'add_noise') { + let control_before_widget = findWidgetByName(node, 'control_before_generate') + let control_after_widget = findWidgetByName(node, 'control_after_generate') + if (widget.value === "disable") { + toggleWidget(node, findWidgetByName(node, 'seed')) + if(control_before_widget){ + control_before_widget.last_value = control_before_widget.value + control_before_widget.value = 'fixed' + toggleWidget(node, control_before_widget) + } + if(control_after_widget){ + control_after_widget.last_value = control_after_widget.value + control_after_widget.value = 'fixed' + toggleWidget(node, control_after_widget) + } + } else { + toggleWidget(node, findWidgetByName(node, 'seed'), true) + if(control_before_widget){ + if(control_before_widget?.last_value) control_before_widget.value = control_before_widget.last_value + toggleWidget(node, control_before_widget, true) + } + if(control_after_widget) { + if(control_after_widget?.last_value) control_after_widget.value = control_after_widget.last_value + toggleWidget(node, findWidgetByName(node, control_after_widget, true)) + } + } + updateNodeHeight(node) + } + if (widget.name === 'num_loras') { + let number_to_show = widget.value + 1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_name'), true) + if (findWidgetByName(node, 'mode').value === "simple") { + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength')) + } else { + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true) + } + } + for (let i = number_to_show; i < 21; i++) { + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_name')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength')) + } + updateNodeHeight(node) + } + if (widget.name === 'num_controlnet') { + let number_to_show = widget.value + 1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'controlnet_'+i), true) + toggleWidget(node, findWidgetByName(node, 'controlnet_'+i+'_strength'), true) + toggleWidget(node, findWidgetByName(node, 'scale_soft_weight_'+i),true) + if (findWidgetByName(node, 'mode').value === "simple") { + toggleWidget(node, findWidgetByName(node, 'start_percent_'+i)) + toggleWidget(node, findWidgetByName(node, 'end_percent_'+i)) + } else { + toggleWidget(node, findWidgetByName(node, 'start_percent_'+i),true) + toggleWidget(node, findWidgetByName(node, 'end_percent_'+i), true) + } + } + for (let i = number_to_show; i < 10; i++) { + toggleWidget(node, findWidgetByName(node, 'controlnet_'+i)) + toggleWidget(node, findWidgetByName(node, 'controlnet_'+i+'_strength')) + toggleWidget(node, findWidgetByName(node, 'start_percent_'+i)) + toggleWidget(node, findWidgetByName(node, 'end_percent_'+i)) + toggleWidget(node, findWidgetByName(node, 'scale_soft_weight_'+i)) + } + updateNodeHeight(node) + } + + if (widget.name === 'mode') { + switch (node.comfyClass) { + case 'easy loraStack': + for (let i = 0; i < (findWidgetByName(node, 'num_loras').value + 1); i++) { + if (widget.value === "simple") { + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength')) + } else { + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength')) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true) + toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)} + } + updateNodeHeight(node) + break + case 'easy controlnetStack': + for (let i = 0; i < (findWidgetByName(node, 'num_controlnet').value + 1); i++) { + if (widget.value === "simple") { + toggleWidget(node, findWidgetByName(node, 'start_percent_'+i)) + toggleWidget(node, findWidgetByName(node, 'end_percent_'+i)) + } else { + toggleWidget(node, findWidgetByName(node, 'start_percent_' + i), true) + toggleWidget(node, findWidgetByName(node, 'end_percent_' + i), true) + } + } + updateNodeHeight(node) + break + case 'easy icLightApply': + if (widget.value === "Foreground") { + toggleWidget(node, findWidgetByName(node, 'lighting'), true) + toggleWidget(node, findWidgetByName(node, 'remove_bg'), true) + toggleWidget(node, findWidgetByName(node, 'source')) + } else { + toggleWidget(node, findWidgetByName(node, 'lighting')) + toggleWidget(node, findWidgetByName(node, 'source'), true) + toggleWidget(node, findWidgetByName(node, 'remove_bg')) + } + updateNodeHeight(node) + break + } + } + + if (widget.name === 'resolution') { + if(widget.value === "θ‡ͺεšδΉ‰ x θ‡ͺεšδΉ‰"){ + widget.value = 'width x height (custom)' + } + if (widget.value === "θ‡ͺεšδΉ‰ x θ‡ͺεšδΉ‰" || widget.value === 'width x height (custom)') { + toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), true) + toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), true) + } else { + toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), false) + toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), false) + } + } + if (widget.name === 'ratio') { + if (widget.value === "custom") { + toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), true) + toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), true) + } else { + toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), false) + toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), false) + } + } + if (widget.name === 'downscale_mode') { + const widget_names = ['block_number', 'downscale_factor', 'start_percent', 'end_percent', 'downscale_after_skip', 'downscale_method', 'upscale_method'] + if (widget.value === "None") widget_names.map(name=> toggleWidget(node, findWidgetByName(node, name))) + else if(widget.value == 'Auto') widget_names.map(name =>toggleWidget(node, findWidgetByName(node, name),name == 'block_number' ? true : false)) + else widget_names.map(name=> toggleWidget(node, findWidgetByName(node, name), true)) + updateNodeHeight(node) + } + + if (widget.name == 'range_mode'){ + if(widget.value == 'step'){ + toggleWidget(node, findWidgetByName(node, 'step'), true) + toggleWidget(node, findWidgetByName(node, 'num_steps')) + }else if(widget.value == 'num_steps'){ + toggleWidget(node, findWidgetByName(node, 'step')) + toggleWidget(node, findWidgetByName(node, 'num_steps'), true) + } + updateNodeHeight(node) + } + + if (widget.name === 'toggle') { + widget.type = 'toggle' + widget.options = {on: 'Enabled', off: 'Disabled'} + } + + if(widget.name == 'text_combine_mode'){ + if(widget.value == 'replace'){ + toggleWidget(node, findWidgetByName(node, 'replace_text'), true) + }else{ + toggleWidget(node, findWidgetByName(node, 'replace_text')) + } + updateNodeHeight(node) + } + + if (widget.name === 'conditioning_mode') { + if (["replace", "concat", "combine"].includes(widget.value)) { + toggleWidget(node, findWidgetByName(node, 'average_strength')) + toggleWidget(node, findWidgetByName(node, 'old_cond_start')) + toggleWidget(node, findWidgetByName(node, 'old_cond_end')) + toggleWidget(node, findWidgetByName(node, 'new_cond_start')) + toggleWidget(node, findWidgetByName(node, 'new_cond_end')) + } else if(widget.value == 'average'){ + toggleWidget(node, findWidgetByName(node, 'average_strength'), true) + toggleWidget(node, findWidgetByName(node, 'old_cond_start')) + toggleWidget(node, findWidgetByName(node, 'old_cond_end')) + toggleWidget(node, findWidgetByName(node, 'new_cond_start')) + toggleWidget(node, findWidgetByName(node, 'new_cond_end')) + }else if(widget.value == 'timestep'){ + toggleWidget(node, findWidgetByName(node, 'average_strength')) + toggleWidget(node, findWidgetByName(node, 'old_cond_start'), true) + toggleWidget(node, findWidgetByName(node, 'old_cond_end'), true) + toggleWidget(node, findWidgetByName(node, 'new_cond_start'), true) + toggleWidget(node, findWidgetByName(node, 'new_cond_end'), true) + } + } + + if (widget.name === 'preset') { + const normol_presets = [ + 'LIGHT - SD1.5 only (low strength)', + 'STANDARD (medium strength)', + 'VIT-G (medium strength)', + 'PLUS (high strength)', 'PLUS FACE (portraits)', + 'FULL FACE - SD1.5 only (portraits stronger)', + ] + const faceid_presets = [ + 'FACEID', + 'FACEID PLUS - SD1.5 only', + 'FACEID PLUS KOLORS', + 'FACEID PLUS V2', + 'FACEID PORTRAIT (style transfer)', + 'FACEID PORTRAIT UNNORM - SDXL only (strong)' + ] + if(normol_presets.includes(widget.value)){ + toggleWidget(node, findWidgetByName(node, 'lora_strength')) + toggleWidget(node, findWidgetByName(node, 'provider')) + toggleWidget(node, findWidgetByName(node, 'weight_faceidv2')) + toggleWidget(node, findWidgetByName(node, 'weight_kolors')) + toggleWidget(node, findWidgetByName(node, 'use_tiled'), true) + let use_tiled = findWidgetByName(node, 'use_tiled') + if(use_tiled && use_tiled.value){ + toggleWidget(node, findWidgetByName(node, 'sharpening'), true) + }else { + toggleWidget(node, findWidgetByName(node, 'sharpening')) + } + + } + else if(faceid_presets.includes(widget.value)){ + toggleWidget(node, findWidgetByName(node, 'weight_faceidv2'), ['FACEID PLUS V2','FACEID PLUS KOLORS'].includes(widget.value) ? true : false); + toggleWidget(node, findWidgetByName(node, 'weight_kolors'), ['FACEID PLUS KOLORS'].includes(widget.value) ? true : false); + if(['FACEID PLUS KOLORS','FACEID PORTRAIT (style transfer)','FACEID PORTRAIT UNNORM - SDXL only (strong)'].includes(widget.value)){ + toggleWidget(node, findWidgetByName(node, 'lora_strength'), false) + } + else{ + toggleWidget(node, findWidgetByName(node, 'lora_strength'), true) + } + toggleWidget(node, findWidgetByName(node, 'provider'), true) + toggleWidget(node, findWidgetByName(node, 'use_tiled')) + toggleWidget(node, findWidgetByName(node, 'sharpening')) + } + updateNodeHeight(node) + } + + if (widget.name === 'use_tiled') { + if(widget.value) + toggleWidget(node, findWidgetByName(node, 'sharpening'), true) + else + toggleWidget(node, findWidgetByName(node, 'sharpening')) + updateNodeHeight(node) + } + + if (widget.name === 'num_embeds') { + let number_to_show = widget.value + 1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'weight'+i), true) + } + for (let i = number_to_show; i < 6; i++) { + toggleWidget(node, findWidgetByName(node, 'weight'+i)) + } + updateNodeHeight(node) + } + + if (widget.name === 'guider'){ + switch (widget.value){ + case 'Basic': + toggleWidget(node, findWidgetByName(node, 'cfg')) + toggleWidget(node, findWidgetByName(node, 'cfg_negative')) + break + case 'CFG': + toggleWidget(node, findWidgetByName(node, 'cfg'),true) + toggleWidget(node, findWidgetByName(node, 'cfg_negative')) + break + case 'IP2P+DualCFG': + case 'DualCFG': + toggleWidget(node, findWidgetByName(node, 'cfg'),true) + toggleWidget(node, findWidgetByName(node, 'cfg_negative'), true) + break + + } + updateNodeHeight(node) + } + + if (widget.name === 'scheduler'){ + if (['karrasADV','exponentialADV','polyExponential'].includes(widget.value)){ + toggleWidget(node, findWidgetByName(node, 'sigma_max'), true) + toggleWidget(node, findWidgetByName(node, 'sigma_min'), true) + toggleWidget(node, findWidgetByName(node, 'denoise')) + toggleWidget(node, findWidgetByName(node, 'beta_d')) + toggleWidget(node, findWidgetByName(node, 'beta_min')) + toggleWidget(node, findWidgetByName(node, 'eps_s')) + toggleWidget(node, findWidgetByName(node, 'coeff')) + if(widget.value != 'exponentialADV'){ + toggleWidget(node, findWidgetByName(node, 'rho'), true) + }else{ + toggleWidget(node, findWidgetByName(node, 'rho')) + } + }else if(widget.value == 'vp'){ + toggleWidget(node, findWidgetByName(node, 'sigma_max')) + toggleWidget(node, findWidgetByName(node, 'sigma_min')) + toggleWidget(node, findWidgetByName(node, 'denoise')) + toggleWidget(node, findWidgetByName(node, 'rho')) + toggleWidget(node, findWidgetByName(node, 'beta_d'),true) + toggleWidget(node, findWidgetByName(node, 'beta_min'),true) + toggleWidget(node, findWidgetByName(node, 'eps_s'),true) + toggleWidget(node, findWidgetByName(node, 'coeff')) + } + else{ + toggleWidget(node, findWidgetByName(node, 'denoise'),true) + toggleWidget(node, findWidgetByName(node, 'sigma_max')) + toggleWidget(node, findWidgetByName(node, 'sigma_min')) + toggleWidget(node, findWidgetByName(node, 'beta_d')) + toggleWidget(node, findWidgetByName(node, 'beta_min')) + toggleWidget(node, findWidgetByName(node, 'eps_s')) + toggleWidget(node, findWidgetByName(node, 'rho')) + if(widget.value == 'gits') toggleWidget(node, findWidgetByName(node, 'coeff'), true) + else toggleWidget(node, findWidgetByName(node, 'coeff')) + } + updateNodeHeight(node) + } + + if(widget.name === 'inpaint_mode'){ + switch (widget.value){ + case 'normal': + case 'fooocus_inpaint': + toggleWidget(node, findWidgetByName(node, 'dtype')) + toggleWidget(node, findWidgetByName(node, 'fitting')) + toggleWidget(node, findWidgetByName(node, 'function')) + toggleWidget(node, findWidgetByName(node, 'scale')) + toggleWidget(node, findWidgetByName(node, 'start_at')) + toggleWidget(node, findWidgetByName(node, 'end_at')) + break + case 'brushnet_random': + case 'brushnet_segmentation': + toggleWidget(node, findWidgetByName(node, 'dtype'), true) + toggleWidget(node, findWidgetByName(node, 'fitting')) + toggleWidget(node, findWidgetByName(node, 'function')) + toggleWidget(node, findWidgetByName(node, 'scale'), true) + toggleWidget(node, findWidgetByName(node, 'start_at'), true) + toggleWidget(node, findWidgetByName(node, 'end_at'), true) + break + case 'powerpaint': + toggleWidget(node, findWidgetByName(node, 'dtype'), true) + toggleWidget(node, findWidgetByName(node, 'fitting'),true) + toggleWidget(node, findWidgetByName(node, 'function'),true) + toggleWidget(node, findWidgetByName(node, 'scale'), true) + toggleWidget(node, findWidgetByName(node, 'start_at'), true) + toggleWidget(node, findWidgetByName(node, 'end_at'), true) + break + } + updateNodeHeight(node) + } + + if(widget.name == 't5_type'){ + switch (widget.value){ + case 'sd3': + toggleWidget(node, findWidgetByName(node, 'clip_name'), true) + toggleWidget(node, findWidgetByName(node, 'padding'), true) + toggleWidget(node, findWidgetByName(node, 't5_name')) + toggleWidget(node, findWidgetByName(node, 'device')) + toggleWidget(node, findWidgetByName(node, 'dtype')) + break + case 't5v11': + toggleWidget(node, findWidgetByName(node, 'clip_name')) + toggleWidget(node, findWidgetByName(node, 'padding')) + toggleWidget(node, findWidgetByName(node, 't5_name'),true) + toggleWidget(node, findWidgetByName(node, 'device'),true) + toggleWidget(node, findWidgetByName(node, 'dtype'),true) + } + updateNodeHeight(node) + } + + if(widget.name == 'rem_mode'){ + switch (widget.value){ + case 'Inspyrenet': + toggleWidget(node, findWidgetByName(node, 'torchscript_jit'), true) + break + default: + toggleWidget(node, findWidgetByName(node, 'torchscript_jit'), false) + break + } + } +} + +function widgetLogic2(node, widget) { + if (widget.name === 'sampler_name') { + const widget_names = ['eta','s_noise','upscale_ratio','start_step','end_step','upscale_n_step','unsharp_kernel_size','unsharp_sigma','unsharp_strength'] + if (["euler_ancestral", "dpmpp_2s_ancestral", "dpmpp_2m_sde", "lcm"].includes(widget.value)) { + widget_names.map(name=> toggleWidget(node, findWidgetByName(node, name)), true) + } else { + widget_names.map(name=> toggleWidget(node, findWidgetByName(node, name))) + } + updateNodeHeight(node) + } +} + +function widgetLogic3(node, widget){ + if (widget.name === 'target_parameter') { + if (node.comfyClass == 'easy XYInputs: Steps'){ + switch (widget.value){ + case "steps": + toggleWidget(node, findWidgetByName(node, 'first_step'), true) + toggleWidget(node, findWidgetByName(node, 'last_step'), true) + toggleWidget(node, findWidgetByName(node, 'first_start_step')) + toggleWidget(node, findWidgetByName(node, 'last_start_step')) + toggleWidget(node, findWidgetByName(node, 'first_end_step')) + toggleWidget(node, findWidgetByName(node, 'last_end_step')) + break + case "start_at_step": + toggleWidget(node, findWidgetByName(node, 'first_step')) + toggleWidget(node, findWidgetByName(node, 'last_step')) + toggleWidget(node, findWidgetByName(node, 'first_start_step'), true) + toggleWidget(node, findWidgetByName(node, 'last_start_step'), true) + toggleWidget(node, findWidgetByName(node, 'first_end_step')) + toggleWidget(node, findWidgetByName(node, 'last_end_step')) + break + case "end_at_step": + toggleWidget(node, findWidgetByName(node, 'first_step')) + toggleWidget(node, findWidgetByName(node, 'last_step')) + toggleWidget(node, findWidgetByName(node, 'first_start_step')) + toggleWidget(node, findWidgetByName(node, 'last_start_step')) + toggleWidget(node, findWidgetByName(node, 'first_end_step'),true) + toggleWidget(node, findWidgetByName(node, 'last_end_step'),true) + break + } + } + if (node.comfyClass == 'easy XYInputs: Sampler/Scheduler'){ + let number_to_show = findWidgetByName(node, 'input_count').value + 1 + for (let i = 0; i < number_to_show; i++) { + switch (widget.value) { + case "sampler": + toggleWidget(node, findWidgetByName(node, 'sampler_'+i), true) + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i)) + break + case "scheduler": + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i), true) + toggleWidget(node, findWidgetByName(node, 'sampler_'+i)) + break + default: + toggleWidget(node, findWidgetByName(node, 'sampler_'+i), true) + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i), true) + break + } + } + updateNodeHeight(node) + } + if (node.comfyClass == 'easy XYInputs: ControlNet'){ + switch (widget.value){ + case "strength": + toggleWidget(node, findWidgetByName(node, 'first_strength'), true) + toggleWidget(node, findWidgetByName(node, 'last_strength'), true) + toggleWidget(node, findWidgetByName(node, 'strength')) + toggleWidget(node, findWidgetByName(node, 'start_percent'), true) + toggleWidget(node, findWidgetByName(node, 'end_percent'), true) + toggleWidget(node, findWidgetByName(node, 'first_start_percent')) + toggleWidget(node, findWidgetByName(node, 'last_start_percent')) + toggleWidget(node, findWidgetByName(node, 'first_end_percent')) + toggleWidget(node, findWidgetByName(node, 'last_end_percent')) + break + case "start_percent": + toggleWidget(node, findWidgetByName(node, 'first_strength')) + toggleWidget(node, findWidgetByName(node, 'last_strength')) + toggleWidget(node, findWidgetByName(node, 'strength'), true) + toggleWidget(node, findWidgetByName(node, 'start_percent')) + toggleWidget(node, findWidgetByName(node, 'end_percent'), true) + toggleWidget(node, findWidgetByName(node, 'first_start_percent'), true) + toggleWidget(node, findWidgetByName(node, 'last_start_percent'), true) + toggleWidget(node, findWidgetByName(node, 'first_end_percent')) + toggleWidget(node, findWidgetByName(node, 'last_end_percent')) + break + case "end_percent": + toggleWidget(node, findWidgetByName(node, 'first_strength')) + toggleWidget(node, findWidgetByName(node, 'last_strength')) + toggleWidget(node, findWidgetByName(node, 'strength'), true) + toggleWidget(node, findWidgetByName(node, 'start_percent'), true) + toggleWidget(node, findWidgetByName(node, 'end_percent')) + toggleWidget(node, findWidgetByName(node, 'first_start_percent')) + toggleWidget(node, findWidgetByName(node, 'last_start_percent')) + toggleWidget(node, findWidgetByName(node, 'first_end_percent'), true) + toggleWidget(node, findWidgetByName(node, 'last_end_percent'), true) + break + } + updateNodeHeight(node) + } + + } + if (node.comfyClass == 'easy XYInputs: PromptSR'){ + let number_to_show = findWidgetByName(node, 'replace_count').value + 1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'replace_'+i), true) + } + for (let i = number_to_show; i < 31; i++) { + toggleWidget(node, findWidgetByName(node, 'replace_'+i)) + } + updateNodeHeight(node) + } + + if(widget.name == 'input_count'){ + let number_to_show = widget.value + 1 + for (let i = 0; i < number_to_show; i++) { + if (findWidgetByName(node, 'target_parameter').value === "sampler") { + toggleWidget(node, findWidgetByName(node, 'sampler_'+i), true) + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i)) + } + else if (findWidgetByName(node, 'target_parameter').value === "scheduler") { + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i), true) + toggleWidget(node, findWidgetByName(node, 'sampler_'+i)) + } else { + toggleWidget(node, findWidgetByName(node, 'sampler_'+i), true) + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i), true) + } + } + for (let i = number_to_show; i < 31; i++) { + toggleWidget(node, findWidgetByName(node, 'sampler_'+i)) + toggleWidget(node, findWidgetByName(node, 'scheduler_'+i)) + } + updateNodeHeight(node) + } + if (widget.name === 'lora_count') { + let number_to_show = widget.value + 1 + const isWeight = findWidgetByName(node, 'input_mode').value.indexOf("Weights") == -1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'lora_name_'+i), true) + if (isWeight) { + toggleWidget(node, findWidgetByName(node, 'lora_name_'+i), true) + toggleWidget(node, findWidgetByName(node, 'model_str_'+i)) + toggleWidget(node, findWidgetByName(node, 'clip_str_'+i)) + } else { + toggleWidget(node, findWidgetByName(node, 'lora_name_'+i), true) + toggleWidget(node, findWidgetByName(node, 'model_str_'+i),true) + toggleWidget(node, findWidgetByName(node, 'clip_str_'+i), true) + } + } + for (let i = number_to_show; i < 11; i++) { + toggleWidget(node, findWidgetByName(node, 'lora_name_'+i)) + toggleWidget(node, findWidgetByName(node, 'model_str_'+i)) + toggleWidget(node, findWidgetByName(node, 'clip_str_'+i)) + } + updateNodeHeight(node) + } + if (widget.name === 'ckpt_count') { + let number_to_show = widget.value + 1 + const hasClipSkip = findWidgetByName(node, 'input_mode').value.indexOf("ClipSkip") != -1 + const hasVae = findWidgetByName(node, 'input_mode').value.indexOf("VAE") != -1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'ckpt_name_'+i), true) + if (hasClipSkip && hasVae) { + toggleWidget(node, findWidgetByName(node, 'clip_skip_'+i), true) + toggleWidget(node, findWidgetByName(node, 'vae_name_'+i), true) + } else if (hasVae){ + toggleWidget(node, findWidgetByName(node, 'clip_skip_' + i)) + toggleWidget(node, findWidgetByName(node, 'vae_name_' + i), true) + }else{ + toggleWidget(node, findWidgetByName(node, 'clip_skip_' + i)) + toggleWidget(node, findWidgetByName(node, 'vae_name_' + i)) + } + } + for (let i = number_to_show; i < 11; i++) { + toggleWidget(node, findWidgetByName(node, 'ckpt_name_'+i)) + toggleWidget(node, findWidgetByName(node, 'clip_skip_'+i)) + toggleWidget(node, findWidgetByName(node, 'vae_name_'+i)) + } + updateNodeHeight(node) + } + + if (widget.name === 'input_mode') { + if(node.comfyClass == 'easy XYInputs: Lora'){ + let number_to_show = findWidgetByName(node, 'lora_count').value + 1 + const hasWeight = widget.value.indexOf("Weights") != -1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'lora_name_'+i), true) + if (hasWeight) { + toggleWidget(node, findWidgetByName(node, 'model_str_'+i), true) + toggleWidget(node, findWidgetByName(node, 'clip_str_'+i), true) + } else { + toggleWidget(node, findWidgetByName(node, 'model_str_' + i)) + toggleWidget(node, findWidgetByName(node, 'clip_str_' + i)) + } + } + if(hasWeight){ + toggleWidget(node, findWidgetByName(node, 'model_strength')) + toggleWidget(node, findWidgetByName(node, 'clip_strength')) + }else{ + toggleWidget(node, findWidgetByName(node, 'model_strength'), true) + toggleWidget(node, findWidgetByName(node, 'clip_strength'),true) + } + } + else if(node.comfyClass == 'easy XYInputs: Checkpoint'){ + let number_to_show = findWidgetByName(node, 'ckpt_count').value + 1 + const hasClipSkip = widget.value.indexOf("ClipSkip") != -1 + const hasVae = widget.value.indexOf("VAE") != -1 + for (let i = 0; i < number_to_show; i++) { + toggleWidget(node, findWidgetByName(node, 'ckpt_name_'+i), true) + if (hasClipSkip && hasVae) { + toggleWidget(node, findWidgetByName(node, 'clip_skip_'+i), true) + toggleWidget(node, findWidgetByName(node, 'vae_name_'+i), true) + } else if (hasClipSkip){ + toggleWidget(node, findWidgetByName(node, 'clip_skip_' + i), true) + toggleWidget(node, findWidgetByName(node, 'vae_name_' + i)) + }else{ + toggleWidget(node, findWidgetByName(node, 'clip_skip_' + i)) + toggleWidget(node, findWidgetByName(node, 'vae_name_' + i)) + } + } + } + + updateNodeHeight(node) + } + + // if(widget.name == 'replace_count'){ + // let number_to_show = widget.value + 1 + // for (let i = 0; i < number_to_show; i++) { + // toggleWidget(node, findWidgetByName(node, 'replace_'+i), true) + // } + // for (let i = number_to_show; i < 31; i++) { + // toggleWidget(node, findWidgetByName(node, 'replace_'+i)) + // } + // updateNodeHeight(node) + // } +} + +app.registerExtension({ + name: "comfy.easyUse.dynamicWidgets", + + nodeCreated(node) { + switch (node.comfyClass){ + case "easy fullLoader": + case "easy a1111Loader": + case "easy fluxLoader": + case "easy comfyLoader": + case "easy cascadeLoader": + case "easy svdLoader": + case "easy dynamiCrafterLoader": + case "easy hunyuanDiTLoader": + case "easy pixArtLoader": + case "easy kolorsLoader": + case "easy loraStack": + case "easy controlnetStack": + case "easy latentNoisy": + case "easy preSampling": + case "easy preSamplingAdvanced": + case "easy preSamplingNoiseIn": + case "easy preSamplingCustom": + case "easy preSamplingSdTurbo": + case "easy preSamplingCascade": + case "easy preSamplingLayerDiffusion": + case "easy fullkSampler": + case "easy kSampler": + case "easy kSamplerSDTurbo": + case "easy kSamplerTiled": + case "easy kSamplerLayerDiffusion": + case "easy kSamplerInpainting": + case "easy kSamplerDownscaleUnet": + case "easy fullCascadeKSampler": + case "easy cascadeKSampler": + case "easy hiresFix": + case "easy detailerFix": + case "easy imageRemBg": + case "easy imageColorMatch": + case "easy imageDetailTransfer": + case "easy loadImageBase64": + case "easy XYInputs: Steps": + case "easy XYInputs: Sampler/Scheduler": + case 'easy XYInputs: Checkpoint': + case "easy XYInputs: Lora": + case "easy XYInputs: PromptSR": + case "easy XYInputs: ControlNet": + case "easy rangeInt": + case "easy rangeFloat": + case 'easy latentCompositeMaskedWithCond': + case 'easy pipeEdit': + case 'easy icLightApply': + case 'easy ipadapterApply': + case 'easy ipadapterApplyADV': + case 'easy ipadapterApplyFaceIDKolors': + case 'easy ipadapterApplyEncoder': + case 'easy applyInpaint': + getSetters(node) + break + case "easy wildcards": + const wildcard_text_widget_index = node.widgets.findIndex((w) => w.name == 'text'); + const wildcard_text_widget = node.widgets[wildcard_text_widget_index]; + + // lora selector, wildcard selector + let combo_id = 1; + + Object.defineProperty(node.widgets[combo_id], "value", { + set: (value) => { + const stackTrace = new Error().stack; + if(stackTrace.includes('inner_value_change')) { + if(value != "Select the LoRA to add to the text") { + let lora_name = value; + if (lora_name.endsWith('.safetensors')) { + lora_name = lora_name.slice(0, -12); + } + + wildcard_text_widget.value += ``; + } + } + }, + get: () => { return "Select the LoRA to add to the text"; } + }); + + Object.defineProperty(node.widgets[combo_id+1], "value", { + set: (value) => { + const stackTrace = new Error().stack; + if(stackTrace.includes('inner_value_change')) { + if(value != "Select the Wildcard to add to the text") { + if(wildcard_text_widget.value != '') + wildcard_text_widget.value += ', ' + + wildcard_text_widget.value += value; + } + } + }, + get: () => { return "Select the Wildcard to add to the text"; } + }); + + // Preventing validation errors from occurring in any situation. + node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; } + node.widgets[combo_id+1].serializeValue = () => { return "Select the Wildcard to add to the text"; } + break + case "easy detailerFix": + const textarea_widget_index = node.widgets.findIndex((w) => w.type === "customtext"); + if(textarea_widget_index == -1) return + node.widgets[textarea_widget_index].dynamicPrompts = false + node.widgets[textarea_widget_index].inputEl.placeholder = "wildcard spec: if kept empty, this option will be ignored"; + node.widgets[textarea_widget_index].serializeValue = () => {return node.widgets[textarea_widget_index].value}; + break + case "easy XYInputs: ModelMergeBlocks": + let preset_i = 3; + let vector_i = 4; + let file_i = 5; + node._value = "Preset"; + + let valuesWidget = node.widgets[vector_i] + Object.defineProperty(node.widgets[preset_i], "value", { + set: (value) => { + const stackTrace = new Error().stack; + if(stackTrace.includes('inner_value_change')) { + if(value != "Preset") { + if(!value.startsWith('@') && valuesWidget.value) + valuesWidget.value += "\n"; + if(value.startsWith('@')) { + let spec = value.split(':')[1]; + var n; + var sub_n = null; + var block = null; + + if(isNaN(spec)) { + let sub_spec = spec.split(','); + + if(sub_spec.length != 3) { + valuesWidget = '!! SPEC ERROR !!'; + node._value = ''; + return; + } + + n = parseInt(sub_spec[0].trim()); + sub_n = parseInt(sub_spec[1].trim()); + block = parseInt(sub_spec[2].trim()); + } + else { + n = parseInt(spec.trim()); + } + + valuesWidget.value = ""; + if(sub_n == null) { + for(let i=1; i<=n; i++) { + var temp = "1,1"; + for(let j=1; j<=n; j++) { + if(temp!='') + temp += ','; + if(j==i) + temp += '1'; + else + temp += '0'; + } + temp += ',1; '; + + valuesWidget.value += `B${i}:${temp}\n`; + } + } + else { + for(let i=1; i<=sub_n; i++) { + var temp = ""; + for(let j=1; j<=n; j++) { + if(temp!='') + temp += ','; + + if(block!=j) + temp += '0'; + else { + temp += ' '; + for(let k=1; k<=sub_n; k++) { + if(k==i) + temp += '1 '; + else + temp += '0 '; + } + } + } + + valuesWidget.value += `B${block}.SUB${i}:${temp}\n`; + } + } + } + else { + valuesWidget.value += `${value}; `; + } + // if(node.widgets_values) { + // valuesWidget.value = node.widgets[preset_i].value+ `; `; + // } + } + } + + node._value = value; + }, + get: () => { + return node._value; + } + }); + + const cb = node.callback; + valuesWidget.callback = function () { + if (cb) { + return cb.apply(this, arguments); + } + }; + + // upload .csv + async function uploadFile(file) { + try { + const body = new FormData(); + body.append("csv", file); + const resp = await api.fetchApi("/easyuse/upload/csv", { + method: "POST", + body, + }); + + if (resp.status === 200) { + const data = await resp.json(); + node.widgets[vector_i].value = data + } else { + alert(resp.status + " - " + resp.statusText); + } + } catch (error) { + alert(error); + } + } + + const fileInput = document.createElement("input"); + Object.assign(fileInput, { + type: "file", + accept: "text/csv", + style: "show: none", + onchange: async (event) => { + if (fileInput.files.length) { + await uploadFile(fileInput.files[0], true); + event.target.value = '' + } + }, + }); + document.body.append(fileInput); + + const name = "choose .csv file into values" + let uploadWidget = node.addWidget("button", name, "csv", () => { + fileInput.click(); + }); + uploadWidget.label = name; + uploadWidget.serialize = false; + + break + } + + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + function addText(arr_text) { + var text = ''; + for (let i = 0; i < arr_text.length; i++) { + text += arr_text[i]; + } + return text + } + + if (["easy showSpentTime"].includes(nodeData.name)) { + function populate(text) { + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "spent_time"); + if (pos !== -1 && this.widgets[pos]) { + const w = this.widgets[pos] + console.log(text) + w.value = text; + } + } + } + + // When the node is executed we will be sent the input text, show this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + const text = addText(message.text) + populate.call(this, text); + }; + } + + if (["easy showLoaderSettingsNames"].includes(nodeData.name)) { + function populate(text) { + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "names"); + if (pos !== -1 && this.widgets[pos]) { + const w = this.widgets[pos] + w.value = text; + } + } + } + + // When the node is executed we will be sent the input text, show this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + const text = addText(message.text) + populate.call(this, text); + }; + } + + if (loaderNodes.includes(nodeData.name)) { + function populate(text, type = 'positive') { + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === type + "_prompt"); + const className = "comfy-multiline-input wildcard_" + type + '_' + this.id.toString() + if (pos == -1 && text) { + const inputEl = document.createElement("textarea"); + inputEl.className = className; + inputEl.placeholder = "Wildcard Prompt (" + type + ")" + const widget = this.addDOMWidget(type + "_prompt", "customtext", inputEl, { + getValue() { + return inputEl.value; + }, + setValue(v) { + inputEl.value = v; + }, + serialize: false, + }); + widget.inputEl = inputEl; + widget.inputEl.readOnly = true + inputEl.addEventListener("input", () => { + widget.callback?.(widget.value); + }); + widget.value = text; + } else if (this.widgets[pos]) { + if (text) { + const w = this.widgets[pos] + w.value = text; + } else { + this.widgets.splice(pos, 1); + const element = document.getElementsByClassName(className) + if (element && element[0]) element[0].remove() + } + } + } + } + + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + const positive = addText(message.positive) + const negative = addText(message.negative) + populate.call(this, positive, "positive"); + populate.call(this, negative, "negative"); + }; + } + + if(["easy sv3dLoader"].includes(nodeData.name)){ + function changeSchedulerText(mode, batch_size, inputEl) { + console.log(mode) + switch (mode){ + case 'azimuth': + inputEl.readOnly = true + inputEl.style.opacity = 0.6 + return `0:(0.0,0.0)` + (batch_size > 1 ? `\n${batch_size-1}:(360.0,0.0)` : '') + case 'elevation': + inputEl.readOnly = true + inputEl.style.opacity = 0.6 + return `0:(-90.0,0.0)` + (batch_size > 1 ? `\n${batch_size-1}:(90.0,0.0)` : '') + case 'custom': + inputEl.readOnly = false + inputEl.style.opacity = 1 + return `0:(0.0,0.0)\n9:(180.0,0.0)\n20:(360.0,0.0)` + } + } + + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = async function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + const easing_mode_widget = this.widgets.find(w => w.name == 'easing_mode') + const batch_size = this.widgets.find(w => w.name == 'batch_size') + const scheduler = this.widgets.find(w => w.name == 'scheduler') + setTimeout(_=>{ + if(!scheduler.value) scheduler.value = changeSchedulerText(easing_mode_widget.value, batch_size.value, scheduler.inputEl) + },1) + easing_mode_widget.callback = value=>{ + scheduler.value = changeSchedulerText(value, batch_size.value, scheduler.inputEl) + } + batch_size.callback = value =>{ + scheduler.value = changeSchedulerText(easing_mode_widget.value, value, scheduler.inputEl) + } + } + } + + if (seedNodes.includes(nodeData.name)) { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = async function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + const seed_widget = this.widgets.find(w => ['seed_num','seed'].includes(w.name)) + const seed_control = this.widgets.find(w=> ['control_before_generate','control_after_generate'].includes(w.name)) + if(nodeData.name == 'easy seed'){ + const randomSeedButton = this.addWidget("button", "🎲 Manual Random Seed", null, _=>{ + if(seed_control.value != 'fixed') seed_control.value = 'fixed' + seed_widget.value = Math.floor(Math.random() * 1125899906842624) + app.queuePrompt(0, 1) + },{ serialize:false}) + seed_widget.linkedWidgets = [randomSeedButton, seed_control]; + } + } + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = async function () { + onAdded ? onAdded.apply(this, []) : undefined; + const seed_widget = this.widgets.find(w => ['seed_num','seed'].includes(w.name)) + const seed_control = this.widgets.find(w=> ['control_before_generate','control_after_generate'].includes(w.name)) + setTimeout(_=>{ + if(seed_control.name == 'control_before_generate' && seed_widget.value === 0) { + seed_widget.value = Math.floor(Math.random() * 1125899906842624) + } + },1) + } + } + + if (nodeData.name == 'easy imageInsetCrop') { + function setWidgetStep(a) { + const measurementWidget = a.widgets[0] + for (let i = 1; i <= 4; i++) { + if (measurementWidget.value === 'Pixels') { + a.widgets[i].options.step = 80; + a.widgets[i].options.max = 8192; + } else { + a.widgets[i].options.step = 10; + a.widgets[i].options.max = 99; + } + } + } + + nodeType.prototype.onAdded = async function (graph) { + const measurementWidget = this.widgets[0]; + let callback = measurementWidget.callback; + measurementWidget.callback = (...args) => { + setWidgetStep(this); + callback && callback.apply(measurementWidget, [...args]); + }; + setTimeout(_=>{ + setWidgetStep(this); + },1) + } + } + + if(['easy showAnything', 'easy showTensorShape', 'easy imageInterrogator'].includes(nodeData.name)){ + function populate(text) { + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "text"); + if (pos !== -1) { + for (let i = pos; i < this.widgets.length; i++) { + this.widgets[i].onRemove?.(); + } + this.widgets.length = pos; + } + } + + for (const list of text) { + const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget; + w.inputEl.readOnly = true; + w.inputEl.style.opacity = 0.6; + w.value = list; + } + + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + }); + } + + // When the node is executed we will be sent the input text, display this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + populate.call(this, message.text); + }; + + if(!['easy imageInterrogator'].includes(nodeData.name)) { + const onConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + onConfigure?.apply(this, arguments); + if (this.widgets_values?.length) { + populate.call(this, this.widgets_values); + } + }; + } + } + + if(nodeData.name == 'easy convertAnything'){ + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = async function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + setTimeout(_=>{ + const type_control = this.widgets[this.widgets.findIndex((w) => w.name === "output_type")] + let _this = this + type_control.callback = async() => { + _this.outputs[0].type = (type_control.value).toUpperCase() + _this.outputs[0].name = type_control.value + _this.outputs[0].label = type_control.value + } + },300) + + } + } + + if (nodeData.name == 'easy promptLine') { + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = async function () { + onAdded ? onAdded.apply(this, []) : undefined; + let prompt_widget = this.widgets.find(w => w.name == "prompt") + const button = this.addWidget("button", "get values from COMBO link", '', () => { + const output_link = this.outputs[1]?.links?.length>0 ? this.outputs[1]['links'][0] : null + const all_nodes = app.graph._nodes + const node = all_nodes.find(cate=> cate.inputs?.find(input=> input.link == output_link)) + if(!output_link || !node){ + toast.error($t('No COMBO link'), 3000) + return + } + else{ + const input = node.inputs.find(input=> input.link == output_link) + const widget_name = input.widget.name + const widgets = node.widgets + const widget = widgets.find(cate=> cate.name == widget_name) + let values = widget?.options.values || null + if(values){ + values = values.join('\n') + prompt_widget.value = values + } + } + }, { + serialize: false + }) + } + } + } +}); + + +const getSetWidgets = ['rescale_after_model', 'rescale', + 'lora_name', 'lora1_name', 'lora2_name', 'lora3_name', + 'refiner_lora1_name', 'refiner_lora2_name', 'upscale_method', + 'image_output', 'add_noise', 'info', 'sampler_name', + 'ckpt_B_name', 'ckpt_C_name', 'save_model', 'refiner_ckpt_name', + 'num_loras', 'num_controlnet', 'mode', 'toggle', 'resolution', 'ratio', 'target_parameter', + 'input_count', 'replace_count', 'downscale_mode', 'range_mode','text_combine_mode', 'input_mode', + 'lora_count','ckpt_count', 'conditioning_mode', 'preset', 'use_tiled', 'use_batch', 'num_embeds', + "easing_mode", "guider", "scheduler", "inpaint_mode", 't5_type', 'rem_mode' +] + +function getSetters(node) { + if (node.widgets) + for (const w of node.widgets) { + if (getSetWidgets.includes(w.name)) { + if(node.comfyClass.indexOf("easy XYInputs:") != -1) widgetLogic3(node, w) + else if(w.name == 'sampler_name' && node.comfyClass == 'easy preSamplingSdTurbo') widgetLogic2(node, w); + else widgetLogic(node, w); + let widgetValue = w.value; + + // Define getters and setters for widget values + Object.defineProperty(w, 'value', { + get() { + return widgetValue; + }, + set(newVal) { + if (newVal !== widgetValue) { + widgetValue = newVal; + if(node.comfyClass.indexOf("easy XYInputs:") != -1) widgetLogic3(node, w) + else if(w.name == 'sampler_name' && node.comfyClass == 'easy preSamplingSdTurbo') widgetLogic2(node, w); + else widgetLogic(node, w); + } + } + }); + } + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyExtraMenu.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyExtraMenu.js new file mode 100644 index 00000000..b4ea61fc --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyExtraMenu.js @@ -0,0 +1,593 @@ +import {app} from "../../../../scripts/app.js"; +import {$t} from '../common/i18n.js' +import {CheckpointInfoDialog, LoraInfoDialog} from "../common/model.js"; + +const loaders = ['easy fullLoader', 'easy a1111Loader', 'easy comfyLoader', 'easy kolorsLoader', 'easy hunyuanDiTLoader', 'easy pixArtLoader'] +const preSampling = ['easy preSampling', 'easy preSamplingAdvanced', 'easy preSamplingDynamicCFG', 'easy preSamplingNoiseIn', 'easy preSamplingCustom', 'easy preSamplingLayerDiffusion', 'easy fullkSampler'] +const kSampler = ['easy kSampler', 'easy kSamplerTiled', 'easy kSamplerInpainting', 'easy kSamplerDownscaleUnet', 'easy kSamplerLayerDiffusion'] +const controlnet = ['easy controlnetLoader', 'easy controlnetLoaderADV', 'easy controlnetLoader++', 'easy instantIDApply', 'easy instantIDApplyADV'] +const ipadapter = ['easy ipadapterApply', 'easy ipadapterApplyADV', 'easy ipadapterApplyFaceIDKolors', 'easy ipadapterStyleComposition', 'easy ipadapterApplyFromParams', 'easy pulIDApply', 'easy pulIDApplyADV'] +const positive_prompt = ['easy positive', 'easy wildcards'] +const imageNode = ['easy loadImageBase64', 'LoadImage', 'LoadImageMask'] +const inpaint = ['easy applyBrushNet', 'easy applyPowerPaint', 'easy applyInpaint'] +const widgetMapping = { + "positive_prompt":{ + "text": "positive", + "positive": "text" + }, + "loaders":{ + "ckpt_name": "ckpt_name", + "vae_name": "vae_name", + "clip_skip": "clip_skip", + "lora_name": "lora_name", + "resolution": "resolution", + "empty_latent_width": "empty_latent_width", + "empty_latent_height": "empty_latent_height", + "positive": "positive", + "negative": "negative", + "batch_size": "batch_size", + "a1111_prompt_style": "a1111_prompt_style" + }, + "preSampling":{ + "steps": "steps", + "cfg": "cfg", + "cfg_scale_min": "cfg", + "sampler_name": "sampler_name", + "scheduler": "scheduler", + "denoise": "denoise", + "seed_num": "seed_num", + "seed": "seed" + }, + "kSampler":{ + "image_output": "image_output", + "save_prefix": "save_prefix", + "link_id": "link_id" + }, + "controlnet":{ + "control_net_name":"control_net_name", + "strength": ["strength", "cn_strength"], + "scale_soft_weights": ["scale_soft_weights","cn_soft_weights"], + "cn_strength": ["strength", "cn_strength"], + "cn_soft_weights": ["scale_soft_weights","cn_soft_weights"], + }, + "ipadapter":{ + "preset":"preset", + "lora_strength": "lora_strength", + "provider": "provider", + "weight":"weight", + "weight_faceidv2": "weight_faceidv2", + "start_at": "start_at", + "end_at": "end_at", + "cache_mode": "cache_mode", + "use_tiled": "use_tiled", + "insightface": "insightface", + "pulid_file": "pulid_file" + }, + "load_image":{ + "image":"image", + "base64_data":"base64_data", + "channel": "channel" + }, + "inpaint":{ + "dtype": "dtype", + "fitting": "fitting", + "function": "function", + "scale": "scale", + "start_at": "start_at", + "end_at": "end_at" + } +} +const inputMapping = { + "loaders":{ + "optional_lora_stack": "optional_lora_stack", + "positive": "positive", + "negative": "negative" + }, + "preSampling":{ + "pipe": "pipe", + "image_to_latent": "image_to_latent", + "latent": "latent" + }, + "kSampler":{ + "pipe": "pipe", + "model": "model" + }, + "controlnet":{ + "pipe": "pipe", + "image": "image", + "image_kps": "image_kps", + "control_net": "control_net", + "positive": "positive", + "negative": "negative", + "mask": "mask" + }, + "positive_prompt":{ + + }, + "ipadapter":{ + "model":"model", + "image":"image", + "image_style": "image", + "attn_mask":"attn_mask", + "optional_ipadapter":"optional_ipadapter" + }, + "inpaint":{ + "pipe": "pipe", + "image": "image", + "mask": "mask" + } +}; + +const outputMapping = { + "loaders":{ + "pipe": "pipe", + "model": "model", + "vae": "vae", + "clip": null, + "positive": null, + "negative": null, + "latent": null, + }, + "preSampling":{ + "pipe":"pipe" + }, + "kSampler":{ + "pipe": "pipe", + "image": "image" + }, + "controlnet":{ + "pipe": "pipe", + "positive": "positive", + "negative": "negative" + }, + "positive_prompt":{ + "text": "positive", + "positive": "text" + }, + "load_image":{ + "IMAGE":"IMAGE", + "MASK": "MASK" + }, + "ipadapter":{ + "model":"model", + "tiles":"tiles", + "masks":"masks", + "ipadapter":"ipadapter" + }, + "inpaint":{ + "pipe": "pipe", + } +}; + +// ζ›Ώζ’θŠ‚η‚Ή +function replaceNode(oldNode, newNodeName, type) { + const newNode = LiteGraph.createNode(newNodeName); + if (!newNode) { + return; + } + app.graph.add(newNode); + + newNode.pos = oldNode.pos.slice(); + newNode.size = oldNode.size.slice(); + + oldNode.widgets.forEach(widget => { + if(widgetMapping[type][widget.name]){ + const newName = widgetMapping[type][widget.name]; + if (newName) { + const newWidget = findWidgetByName(newNode, newName); + if (newWidget) { + newWidget.value = widget.value; + if(widget.name == 'seed_num'){ + newWidget.linkedWidgets[0].value = widget.linkedWidgets[0].value + } + if(widget.type == 'converted-widget'){ + convertToInput(newNode, newWidget, widget); + } + } + } + } + + }); + + if(oldNode.inputs){ + oldNode.inputs.forEach((input, index) => { + if (input && input.link && inputMapping[type][input.name]) { + const newInputName = inputMapping[type][input.name]; + // If the new node does not have this output, skip + if (newInputName === null) { + return; + } + const newInputIndex = newNode.findInputSlot(newInputName); + if (newInputIndex !== -1) { + const originLinkInfo = oldNode.graph.links[input.link]; + if (originLinkInfo) { + const originNode = oldNode.graph.getNodeById(originLinkInfo.origin_id); + if (originNode) { + originNode.connect(originLinkInfo.origin_slot, newNode, newInputIndex); + } + } + } + } + }); + } + + if(oldNode.outputs){ + oldNode.outputs.forEach((output, index) => { + if (output && output.links && outputMapping[type] && outputMapping[type][output.name]) { + const newOutputName = outputMapping[type][output.name]; + // If the new node does not have this output, skip + if (newOutputName === null) { + return; + } + const newOutputIndex = newNode.findOutputSlot(newOutputName); + if (newOutputIndex !== -1) { + output.links.forEach(link => { + const targetLinkInfo = oldNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(newOutputIndex, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + } + }); + } + + + // Remove old node + app.graph.remove(oldNode); + + // Remove others + if(newNode.type == 'easy fullkSampler'){ + const link_output_id = newNode.outputs[0].links + if(link_output_id && link_output_id[0]){ + const nodes = app.graph._nodes + const node = nodes.find(cate=> cate.inputs && cate.inputs[0] && cate.inputs[0]['link'] == link_output_id[0]) + if(node){ + app.graph.remove(node); + } + } + }else if(preSampling.includes(newNode.type)){ + const link_output_id = newNode.outputs[0].links + if(!link_output_id || !link_output_id[0]){ + const ksampler = LiteGraph.createNode('easy kSampler'); + app.graph.add(ksampler); + ksampler.pos = newNode.pos.slice(); + ksampler.pos[0] = ksampler.pos[0] + newNode.size[0] + 20; + const newInputIndex = newNode.findInputSlot('pipe'); + if (newInputIndex !== -1) { + if (newNode) { + newNode.connect(0, ksampler, newInputIndex); + } + } + } + } + + // autoHeight + newNode.setSize([newNode.size[0], newNode.computeSize()[1]]); +} + +export function findWidgetByName(node, widgetName) { + return node.widgets.find(widget => typeof widgetName == 'object' ? widgetName.includes(widget.name) : widget.name === widgetName); +} +function replaceNodeMenuCallback(currentNode, targetNodeName, type) { + return function() { + replaceNode(currentNode, targetNodeName, type); + }; +} +const addMenuHandler = (nodeType, cb)=> { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} +const addMenu = (content, type, nodes_include, nodeType, has_submenu=true) => { + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: content, + has_submenu: has_submenu, + callback: (value, options, e, menu, node) => showSwapMenu(value, options, e, menu, node, type, nodes_include) + }) + if(type == 'loaders') { + options.unshift({ + content: $t("πŸ’Ž View Lora Info..."), + callback: (value, options, e, menu, node) => { + const widget = node.widgets.find(cate => cate.name == 'lora_name') + let name = widget.value; + if (!name || name == 'None') return + new LoraInfoDialog(name).show('loras', name); + } + }) + options.unshift({ + content: $t("πŸ’Ž View Checkpoint Info..."), + callback: (value, options, e, menu, node) => { + let name = node.widgets[0].value; + if (!name || name == 'None') return + new CheckpointInfoDialog(name).show('checkpoints', name); + } + }) + } + }) +} +const showSwapMenu = (value, options, e, menu, node, type, nodes_include) => { + const swapOptions = []; + nodes_include.map(cate=>{ + if (node.type !== cate) { + swapOptions.push({ + content: `${cate}`, + callback: replaceNodeMenuCallback(node, cate, type) + }); + } + }) + new LiteGraph.ContextMenu(swapOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + return false; +} + +// ι‡θ½½θŠ‚η‚Ή +const CONVERTED_TYPE = "converted-widget"; +const GET_CONFIG = Symbol(); + +function hideWidget(node, widget, suffix = "") { + widget.origType = widget.type; + widget.origComputeSize = widget.computeSize; + widget.origSerializeValue = widget.serializeValue; + widget.computeSize = () => [0, -4]; // -4 is due to the gap litegraph adds between widgets automatically + widget.type = CONVERTED_TYPE + suffix; + widget.serializeValue = () => { + // Prevent serializing the widget if we have no input linked + if (!node.inputs) { + return undefined; + } + let node_input = node.inputs.find((i) => i.widget?.name === widget.name); + + if (!node_input || !node_input.link) { + return undefined; + } + return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; + }; + + // Hide any linked widgets, e.g. seed+seedControl + if (widget.linkedWidgets) { + for (const w of widget.linkedWidgets) { + hideWidget(node, w, ":" + widget.name); + } + } +} +function convertToInput(node, widget, config) { + console.log('config:', config) + hideWidget(node, widget); + + const { type } = getWidgetType(config); + + // Add input and store widget config for creating on primitive node + const sz = node.size; + if(!widget.options || !widget.options.forceInput){ + node.addInput(widget.name, type, { + widget: { name: widget.name, [GET_CONFIG]: () => config }, + }); + } + + for (const widget of node.widgets) { + widget.last_y += LiteGraph.NODE_SLOT_HEIGHT; + } + + // Restore original size but grow if needed + node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]); +} + +function getWidgetType(config) { + // Special handling for COMBO so we restrict links based on the entries + let type = config[0]; + if (type instanceof Array) { + type = "COMBO"; + } + return { type }; +} + +const reloadNode = function (node) { + const nodeType = node.constructor.type; + const origVals = node.properties.origVals || {}; + + const nodeTitle = origVals.title || node.title; + const nodeColor = origVals.color || node.color; + const bgColor = origVals.bgcolor || node.bgcolor; + const oldNode = node + const options = { + 'size': [...node.size], + 'color': nodeColor, + 'bgcolor': bgColor, + 'pos': [...node.pos] + } + + let inputLinks = [] + let outputLinks = [] + if(node.inputs){ + for (const input of node.inputs) { + if (input.link) { + const input_name = input.name + const input_slot = node.findInputSlot(input_name) + const input_node = node.getInputNode(input_slot) + const input_link = node.getInputLink(input_slot) + inputLinks.push([input_link.origin_slot, input_node, input_name]) + } + } + } + if(node.outputs) { + for (const output of node.outputs) { + if (output.links) { + const output_name = output.name + + for (const linkID of output.links) { + const output_link = graph.links[linkID] + const output_node = graph._nodes_by_id[output_link.target_id] + outputLinks.push([output_name, output_node, output_link.target_slot]) + } + } + } + } + + app.graph.remove(node) + const newNode = app.graph.add(LiteGraph.createNode(nodeType, nodeTitle, options)); + + function handleLinks() { + // re-convert inputs + if(oldNode.widgets) { + for (let w of oldNode.widgets) { + if (w.type === 'converted-widget') { + const WidgetToConvert = newNode.widgets.find((nw) => nw.name === w.name); + for (let i of oldNode.inputs) { + if (i.name === w.name) { + convertToInput(newNode, WidgetToConvert, i.widget); + } + } + } + } + } + // replace input and output links + for (let input of inputLinks) { + const [output_slot, output_node, input_name] = input; + output_node.connect(output_slot, newNode.id, input_name) + } + for (let output of outputLinks) { + const [output_name, input_node, input_slot] = output; + newNode.connect(output_name, input_node, input_slot) + } + } + + // fix widget values + let values = oldNode.widgets_values; + if (!values && newNode.widgets?.length>0) { + newNode.widgets.forEach((newWidget, index) => { + const oldWidget = oldNode.widgets[index]; + if (newWidget.name === oldWidget.name && newWidget.type === oldWidget.type) { + newWidget.value = oldWidget.value; + } + }); + handleLinks(); + return; + } + let pass = false + const isIterateForwards = values?.length <= newNode.widgets?.length; + let vi = isIterateForwards ? 0 : values.length - 1; + function evalWidgetValues(testValue, newWidg) { + if (testValue === true || testValue === false) { + if (newWidg.options?.on && newWidg.options?.off) { + return { value: testValue, pass: true }; + } + } else if (typeof testValue === "number") { + if (newWidg.options?.min <= testValue && testValue <= newWidg.options?.max) { + return { value: testValue, pass: true }; + } + } else if (newWidg.options?.values?.includes(testValue)) { + return { value: testValue, pass: true }; + } else if (newWidg.inputEl && typeof testValue === "string") { + return { value: testValue, pass: true }; + } + return { value: newWidg.value, pass: false }; + } + const updateValue = (wi) => { + const oldWidget = oldNode.widgets[wi]; + let newWidget = newNode.widgets[wi]; + if (newWidget.name === oldWidget.name && newWidget.type === oldWidget.type) { + while ((isIterateForwards ? vi < values.length : vi >= 0) && !pass) { + let { value, pass } = evalWidgetValues(values[vi], newWidget); + if (pass && value !== null) { + newWidget.value = value; + break; + } + vi += isIterateForwards ? 1 : -1; + } + vi++ + if (!isIterateForwards) { + vi = values.length - (newNode.widgets?.length - 1 - wi); + } + } + }; + if (isIterateForwards && newNode.widgets?.length>0) { + for (let wi = 0; wi < newNode.widgets.length; wi++) { + updateValue(wi); + } + } else if(newNode.widgets?.length>0){ + for (let wi = newNode.widgets.length - 1; wi >= 0; wi--) { + updateValue(wi); + } + } + handleLinks(); +}; + + +app.registerExtension({ + name: "comfy.easyUse.extraMenu", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + // εˆ·ζ–°θŠ‚η‚Ή + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: $t("πŸ”ƒ Reload Node"), + callback: (value, options, e, menu, node) => { + let graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) { + reloadNode(node); + } else { + for (let i in graphcanvas.selected_nodes) { + reloadNode(graphcanvas.selected_nodes[i]); + } + } + } + }) + // ckptNames + if(nodeData.name == 'easy ckptNames'){ + options.unshift({ + content: $t("πŸ’Ž View Checkpoint Info..."), + callback: (value, options, e, menu, node) => { + let name = node.widgets[0].value; + if (!name || name == 'None') return + new CheckpointInfoDialog(name).show('checkpoints', name); + } + }) + } + }) + + // Swap提瀺词 + if (positive_prompt.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap EasyPrompt", 'positive_prompt', positive_prompt, nodeType) + } + // SwapεŠ θ½½ε™¨ + if (loaders.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap EasyLoader", 'loaders', loaders, nodeType) + } + // Swap钄采样器 + if (preSampling.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap EasyPreSampling", 'preSampling', preSampling, nodeType) + } + // Swap kSampler + if (kSampler.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap EasyKSampler", 'preSampling', kSampler, nodeType) + } + // Swap ControlNet + if (controlnet.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap EasyControlnet", 'controlnet', controlnet, nodeType) + } + // Swap IPAdapater + if (ipadapter.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap EasyAdapater", 'ipadapter', ipadapter, nodeType) + } + // Swap Image + if (imageNode.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap LoadImage", 'load_image', imageNode, nodeType) + } + // Swap inpaint + if (inpaint.includes(nodeData.name)) { + addMenu("β†ͺ️ Swap InpaintNode", 'inpaint', inpaint, nodeType) + } + } +}); + diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyInterface.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyInterface.js new file mode 100644 index 00000000..5f73e339 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyInterface.js @@ -0,0 +1,788 @@ +import { app } from "../../../../scripts/app.js"; +import { api } from "../../../../scripts/api.js"; +import { $el } from "../../../../scripts/ui.js"; +import {addPreconnect, addCss} from "../common/utils.js"; + +const locale = localStorage['AGL.Locale'] || localStorage['Comfy.Settings.AGL.Locale'] || 'en-US' + +const customThemeColor = "#236692" +const customThemeColorLight = "#3485bb" +// 咞加Slotι’œθ‰² +const customPipeLineLink = "#7737AA" +const customPipeLineSDXLLink = "#7737AA" +const customIntLink = "#29699C" +const customXYPlotLink = "#74DA5D" +const customLoraStackLink = "#94dccd" +const customXYLink = "#38291f" + +var customLinkColors = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.customLinkColors')) || {}; +if (!customLinkColors["PIPE_LINE"] || !LGraphCanvas.link_type_colors["PIPE_LINE"]) {customLinkColors["PIPE_LINE"] = customPipeLineLink;} +if (!customLinkColors["PIPE_LINE_SDXL"] || !LGraphCanvas.link_type_colors["PIPE_LINE_SDXL"]) {customLinkColors["PIPE_LINE_SDXL"] = customPipeLineSDXLLink;} +if (!customLinkColors["INT"] || !LGraphCanvas.link_type_colors["INT"]) {customLinkColors["INT"] = customIntLink;} +if (!customLinkColors["XYPLOT"] || !LGraphCanvas.link_type_colors["XYPLOT"]) {customLinkColors["XYPLOT"] = customXYPlotLink;} +if (!customLinkColors["X_Y"] || !LGraphCanvas.link_type_colors["X_Y"]) {customLinkColors["X_Y"] = customXYLink;} +if (!customLinkColors["LORA_STACK"] || !LGraphCanvas.link_type_colors["LORA_STACK"]) {customLinkColors["LORA_STACK"] = customLoraStackLink;} +if (!customLinkColors["CONTROL_NET_STACK"] || !LGraphCanvas.link_type_colors["CONTROL_NET_STACK"]) {customLinkColors["CONTROL_NET_STACK"] = customLoraStackLink;} + +localStorage.setItem('Comfy.Settings.easyUse.customLinkColors', JSON.stringify(customLinkColors)); + +// 咞加θ‡ͺεšδΉ‰δΈ»ι’˜ +const ui = { + "version": 102, + "id": "obsidian", + "name": "Obsidian", + "colors": { + "node_slot": { + "CLIP": "#FFD500", + "CLIP_VISION": "#A8DADC", + "CLIP_VISION_OUTPUT": "#ad7452", + "CONDITIONING": "#FFA931", + "CONTROL_NET": "#6EE7B7", + "IMAGE": "#64B5F6", + "LATENT": "#FF9CF9", + "MASK": "#81C784", + "MODEL": "#B39DDB", + "STYLE_MODEL": "#C2FFAE", + "VAE": "#FF6E6E", + "TAESD": "#DCC274", + "PIPE_LINE": customPipeLineLink, + "PIPE_LINE_SDXL": customPipeLineSDXLLink, + "INT": customIntLink, + "XYPLOT": customXYPlotLink, + "X_Y": customXYLink + }, + "litegraph_base": { + "BACKGROUND_IMAGE": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII=", + "CLEAR_BACKGROUND_COLOR": "#222222", + "NODE_TITLE_COLOR": "rgba(255,255,255,.75)", + "NODE_SELECTED_TITLE_COLOR": "#FFF", + "NODE_TEXT_SIZE": 14, + "NODE_TEXT_COLOR": "#b8b8b8", + "NODE_SUBTEXT_SIZE": 12, + "NODE_DEFAULT_COLOR": "rgba(0,0,0,.8)", + "NODE_DEFAULT_BGCOLOR": "rgba(22,22,22,.8)", + "NODE_DEFAULT_BOXCOLOR": "rgba(255,255,255,.75)", + "NODE_DEFAULT_SHAPE": "box", + "NODE_BOX_OUTLINE_COLOR": customThemeColor, + "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0)", + "DEFAULT_GROUP_FONT": 24, + + "WIDGET_BGCOLOR": "#242424", + "WIDGET_OUTLINE_COLOR": "#333", + "WIDGET_TEXT_COLOR": "#a3a3a8", + "WIDGET_SECONDARY_TEXT_COLOR": "#97979c", + + "LINK_COLOR": "#9A9", + "EVENT_LINK_COLOR": "#A86", + "CONNECTING_LINK_COLOR": "#AFA" + }, + "comfy_base": { + "fg-color": "#fff", + "bg-color": "#242424", + "comfy-menu-bg": "rgba(24,24,24,.9)", + "comfy-input-bg": "#262626", + "input-text": "#ddd", + "descrip-text": "#999", + "drag-text": "#ccc", + "error-text": "#ff4444", + "border-color": "#29292c", + "tr-even-bg-color": "rgba(28,28,28,.9)", + "tr-odd-bg-color": "rgba(19,19,19,.9)" + } + } +} + +let custom_theme = null +let control_mode = null +try{ + custom_theme = localStorage.getItem('Comfy.Settings.Comfy.CustomColorPalettes') ? JSON.parse(localStorage.getItem('Comfy.Settings.Comfy.CustomColorPalettes')) : {}; +} +catch (e) {custom_theme = {}} +try{ + const dark_bg = "data:image/png;base64,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" + // δΏζ”Ήθ‡ͺεšδΉ‰δΈ»ι’˜ + if(!custom_theme || !custom_theme.obsidian || !custom_theme.obsidian.version || custom_theme.obsidian.version0) node.execute_triggered--; + if (node.action_triggered>0) node.action_triggered--; + }; + LGraphCanvas.prototype.drawNodeWidgets = function( + node, + posY, + ctx, + active_widget + ) { + if (!node.widgets || !node.widgets.length) { + return 0; + } + var width = node.size[0]; + var widgets = node.widgets; + posY += 2; + var H = LiteGraph.NODE_WIDGET_HEIGHT; + var show_text = this.ds.scale > 0.5; + ctx.save(); + ctx.globalAlpha = this.editor_alpha; + var outline_color = LiteGraph.WIDGET_OUTLINE_COLOR; + var background_color = LiteGraph.WIDGET_BGCOLOR; + var text_color = LiteGraph.WIDGET_TEXT_COLOR; + var secondary_text_color = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + var margin = 12; + + for (var i = 0; i < widgets.length; ++i) { + var w = widgets[i]; + var y = posY; + if (w.y) { + y = w.y; + } + w.last_y = y; + ctx.strokeStyle = outline_color; + ctx.fillStyle = "#222"; + ctx.textAlign = "left"; + ctx.lineWidth = 1; + if(w.disabled) + ctx.globalAlpha *= 0.5; + var widget_width = w.width || width; + + switch (w.type) { + case "button": + ctx.font = "10px Inter" + ctx.fillStyle = background_color; + if (w.clicked) { + ctx.fillStyle = "#AAA"; + w.clicked = false; + this.dirty_canvas = true; + } + ctx.beginPath(); + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.25]); + ctx.fill(); + if(show_text && !w.disabled) + ctx.stroke(); + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText(w.label || w.name, widget_width * 0.5, y + H * 0.7); + } + break; + case "toggle": + ctx.font = "10px Inter" + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.25]); + else + ctx.rect(margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if(show_text && !w.disabled) + ctx.stroke(); + ctx.fillStyle = w.value ? customThemeColor : "#333"; + ctx.beginPath(); + ctx.arc( widget_width - margin * 2, y + H * 0.5, H * 0.25, 0, Math.PI * 2 ); + ctx.fill(); + if (show_text) { + ctx.fillStyle = secondary_text_color; + const label = w.label || w.name; + if (label != null) { + ctx.fillText(label, margin * 1.6, y + H * 0.7); + } + ctx.font = "10px Inter" + ctx.fillStyle = w.value ? text_color : secondary_text_color; + ctx.textAlign = "right"; + ctx.fillText( + w.value + ? w.options.on || "true" + : w.options.off || "false", + widget_width - 35, + y + H * 0.7 + ); + } + break; + case "slider": + ctx.font = "10px Inter" + ctx.fillStyle = background_color; + ctx.strokeStyle = outline_color; + ctx.beginPath(); + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H*0.25]); + ctx.fill(); + ctx.stroke() + var range = w.options.max - w.options.min; + var nvalue = (w.value - w.options.min) / range; + if(nvalue < 0.0) nvalue = 0.0; + if(nvalue > 1.0) nvalue = 1.0; + ctx.fillStyle = w.options.hasOwnProperty("slider_color") ? w.options.slider_color : (active_widget == w ? "#333" : customThemeColor); + ctx.beginPath(); + ctx.roundRect(margin, y, nvalue * (widget_width - margin * 2), H, [H*0.25]); + ctx.fill(); + if (w.marker) { + var marker_nvalue = (w.marker - w.options.min) / range; + if(marker_nvalue < 0.0) marker_nvalue = 0.0; + if(marker_nvalue > 1.0) marker_nvalue = 1.0; + ctx.fillStyle = w.options.hasOwnProperty("marker_color") ? w.options.marker_color : "#AA9"; + ctx.roundRect( margin + marker_nvalue * (widget_width - margin * 2), y, 2, H , [H * 0.25] ); + } + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + var text = (w.label || w.name) + ": " + (Number(w.value).toFixed(w.options.precision != null ? w.options.precision : 3)).toString() + ctx.fillText( + text, + widget_width * 0.5, + y + H * 0.7 + ); + + } + break; + case "number": + case "combo": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if(show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.25] ); + else + ctx.rect(margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if (show_text) { + if(!w.disabled) + ctx.stroke(); + ctx.fillStyle = text_color; + if(!w.disabled) + { + ctx.beginPath(); + ctx.moveTo(margin + 12, y + 6.5); + ctx.lineTo(margin + 6, y + H * 0.5); + ctx.lineTo(margin + 12, y + H - 6.5); + ctx.fill(); + ctx.beginPath(); + ctx.moveTo(widget_width - margin - 12, y + 6.5); + ctx.lineTo(widget_width - margin - 6, y + H * 0.5); + ctx.lineTo(widget_width - margin - 12, y + H - 6.5); + ctx.fill(); + } + ctx.fillStyle = secondary_text_color; + ctx.font = "10px Inter" + ctx.fillText(w.label || w.name, margin * 2 + 5, y + H * 0.7); + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + var rightDistance = 6 + if (w.type == "number") { + ctx.font = "10px Inter,JetBrains Mono,monospace" + ctx.fillText( + Number(w.value).toFixed( + w.options.precision !== undefined + ? w.options.precision + : 3 + ), + widget_width - margin * 2 - rightDistance, + y + H * 0.7 + ); + } else { + var v = w.value; + if( w.options.values ) + { + var values = w.options.values; + if( values.constructor === Function ) + values = values(); + if(values && values.constructor !== Array) + v = values[ w.value ]; + } + ctx.fillText( + v, + widget_width - margin * 2 - rightDistance, + y + H * 0.7 + ); + } + } + break; + case "string": + case "text": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.25]); + else + ctx.rect( margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if (show_text) { + if(!w.disabled) + ctx.stroke(); + ctx.save(); + ctx.beginPath(); + ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.clip(); + + //ctx.stroke(); + ctx.fillStyle = secondary_text_color; + const label = w.label || w.name; + ctx.font = "10px Inter" + if (label != null) { + ctx.fillText(label, margin * 2, y + H * 0.7); + } + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + ctx.fillText(String(w.value).substr(0,30), widget_width - margin * 2, y + H * 0.7); //30 chars max + ctx.restore(); + } + break; + default: + if (w.draw) { + w.draw(ctx, node, widget_width, y, H); + } + break; + } + posY += (w.computeSize ? w.computeSize(widget_width)[1] : H) + 4; + ctx.globalAlpha = this.editor_alpha; + + } + ctx.restore(); + ctx.textAlign = "left"; + }; + } +}catch(e){ + console.error(e) +} + +function updateControlWidgetLabel(widget, controlValueRunBefore=false) { + let replacement = "after"; + let find = "before"; + if (controlValueRunBefore) { + [find, replacement] = [replacement, find] + } + widget.label = (widget.label ?? widget.name).replace(find, replacement); + widget.name = widget.label; +} + +// θŠ‚η‚Ήι’œθ‰² +const COLOR_THEMES = LGraphCanvas.node_colors +const NODE_COLORS = { + "easy positive":"green", + "easy negative":"red", + "easy promptList":"cyan", + "easy promptLine":"cyan", + "easy promptConcat":"cyan", + "easy promptReplace":"cyan", + "easy XYInputs: Seeds++ Batch": customXYLink, + "easy XYInputs: ModelMergeBlocks": customXYLink, + 'easy textSwitch': "pale_blue" +} + +function setNodeColors(node, theme) { + if (!theme) {return;} + if(theme.color) node.color = theme.color; + if(theme.bgcolor) node.bgcolor = theme.bgcolor; +} + + +app.registerExtension({ + name: "comfy.easyUse.interface", + setup() { + Object.assign(app.canvas.default_connection_color_byType, customLinkColors); + Object.assign(LGraphCanvas.link_type_colors, customLinkColors); + }, + + async nodeCreated(node) { + if (NODE_COLORS.hasOwnProperty(node.comfyClass)) { + const colorKey = NODE_COLORS[node.comfyClass] + const theme = COLOR_THEMES[colorKey]; + setNodeColors(node, theme); + } + // δΏε€ε˜ζ–Ήbug: εΊ”θ―₯εˆε§‹εŒ–δΏζ”ΉθŠ‚η‚Ήηš„control_mode name + if(control_mode && control_mode == 'before'){ + const controlValueRunBefore = control_mode == 'before' + if(node.widgets && node.widgets.length>0) { + for (const w of node.widgets) { + if (['control_before_generate', 'control_after_generate'].includes(w.name)) { + await updateControlWidgetLabel(w, controlValueRunBefore); + if (w.linkedWidgets) { + for (const l of w.linkedWidgets) { + await updateControlWidgetLabel(l, controlValueRunBefore); + } + } + } + } + } + } + }, +}) diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyQuick.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyQuick.js new file mode 100644 index 00000000..394bd8a3 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyQuick.js @@ -0,0 +1,250 @@ +// 1.0.2 +import { app } from "../../../../scripts/app.js"; +import { GroupNodeConfig } from "../../../../extensions/core/groupNode.js"; +import { api } from "../../../../scripts/api.js"; +import { $t } from "../common/i18n.js" + +const nodeTemplateShortcutId = "Comfy.EasyUse.NodeTemplateShortcut" +const processBarId = "Comfy.EasyUse.queueProcessBar" + +let enableNodeTemplateShortcut = true +let enableQueueProcess = false + +export function addNodeTemplateShortcutSetting(app) { + app.ui.settings.addSetting({ + id: nodeTemplateShortcutId, + name: $t("Enable ALT+1~9 to paste nodes from nodes template (ComfyUI-Easy-Use)"), + type: "boolean", + defaultValue: enableNodeTemplateShortcut, + onChange(value) { + enableNodeTemplateShortcut = !!value; + }, + }); +} +export function addQueueProcessSetting(app) { + app.ui.settings.addSetting({ + id: processBarId, + name: $t("Enable process bar in queue button (ComfyUI-Easy-Use)"), + type: "boolean", + defaultValue: enableQueueProcess, + onChange(value) { + enableQueueProcess = !!value; + }, + }); +} +const getEnableNodeTemplateShortcut = _ => app.ui.settings.getSettingValue(nodeTemplateShortcutId, true) +const getQueueProcessSetting = _ => app.ui.settings.getSettingValue(processBarId, false) + +function loadTemplate(){ + return localStorage['Comfy.NodeTemplates'] ? JSON.parse(localStorage['Comfy.NodeTemplates']) : null +} +const clipboardAction = async (cb) => { + const old = localStorage.getItem("litegrapheditor_clipboard"); + await cb(); + localStorage.setItem("litegrapheditor_clipboard", old); +}; +async function addTemplateToCanvas(t){ + const data = JSON.parse(t.data); + await GroupNodeConfig.registerFromWorkflow(data.groupNodes, {}); + localStorage.setItem("litegrapheditor_clipboard", t.data); + app.canvas.pasteFromClipboard(); +} + +app.registerExtension({ + name: 'comfy.easyUse.quick', + init() { + const keybindListener = async function (event) { + let modifierPressed = event.altKey; + const isEnabled = getEnableNodeTemplateShortcut() + if(isEnabled){ + const mac_alt_nums = ['Β‘','β„’','Β£','Β’','∞','Β§','ΒΆ','β€’','Βͺ'] + const nums = ['1','2','3','4','5','6','7','8','9'] + let key = event.key + if(mac_alt_nums.includes(key)){ + const idx = mac_alt_nums.findIndex(cate=> cate == key) + key = nums[idx] + modifierPressed = true + } + if(['1','2','3','4','5','6','7','8','9'].includes(key) && modifierPressed) { + const template = loadTemplate() + const idx = parseInt(key) - 1 + if (template && template[idx]) { + let t = template[idx] + try{ + let data = JSON.parse(t.data) + data.title = t.name + t.data = JSON.stringify(data) + clipboardAction(_ => { + addTemplateToCanvas(t) + }) + }catch (e){ + console.error(e) + } + + } + if (event.ctrlKey || event.altKey || event.metaKey) { + return; + } + } + } + + } + window.addEventListener("keydown", keybindListener, true); + }, + + setup(app) { + addNodeTemplateShortcutSetting(app) + addQueueProcessSetting(app) + registerListeners() + } +}); + +const registerListeners = () => { + const queue_button = document.getElementById("queue-button") + const old_queue_button_text = queue_button.innerText + api.addEventListener('progress', ({ + detail, + }) => { + const isEnabled = getQueueProcessSetting() + if(isEnabled){ + const { + value, max, node, + } = detail; + const progress = Math.floor((value / max) * 100); + // console.log(progress) + if (!isNaN(progress) && progress >= 0 && progress <= 100) { + queue_button.innerText = progress ==0 || progress == 100 ? old_queue_button_text : "γ…€ " + const width = progress ==0 || progress == 100 ? '0%' : progress.toString() + '%' + let bar = document.createElement("div") + queue_button.setAttribute('data-attr', progress ==0 || progress == 100 ? "" : progress.toString() + '%') + document.documentElement.style.setProperty('--process-bar-width', width) + } + } + + }, false); + + api.addEventListener('status', ({ + detail, + }) => { + const queueRemaining = detail?.exec_info.queue_remaining; + if(queueRemaining === 0){ + let queue_button = document.getElementById("queue-button") + queue_button.innerText = old_queue_button_text + queue_button.setAttribute('data-attr', "") + document.documentElement.style.setProperty('--process-bar-width', '0%') + } + }, false); +}; + + +// δΏζ”Ήη²˜θ΄΄ζŒ‡δ»€ +LGraphCanvas.prototype.pasteFromClipboard = function(isConnectUnselected = false) { + // if ctrl + shift + v is off, return when isConnectUnselected is true (shift is pressed) to maintain old behavior + if (!LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && isConnectUnselected) { + return; + } + var data = localStorage.getItem("litegrapheditor_clipboard"); + if (!data) { + return; + } + + this.graph.beforeChange(); + + //create nodes + var clipboard_info = JSON.parse(data); + // calculate top-left node, could work without this processing but using diff with last node pos :: clipboard_info.nodes[clipboard_info.nodes.length-1].pos + var posMin = false; + var posMinIndexes = false; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + if (posMin){ + if(posMin[0]>clipboard_info.nodes[i].pos[0]){ + posMin[0] = clipboard_info.nodes[i].pos[0]; + posMinIndexes[0] = i; + } + if(posMin[1]>clipboard_info.nodes[i].pos[1]){ + posMin[1] = clipboard_info.nodes[i].pos[1]; + posMinIndexes[1] = i; + } + } + else{ + posMin = [clipboard_info.nodes[i].pos[0], clipboard_info.nodes[i].pos[1]]; + posMinIndexes = [i, i]; + } + } + var nodes = []; + var left_arr = [], right_arr = [], top_arr =[], bottom_arr =[]; + + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + var node_data = clipboard_info.nodes[i]; + var node = LiteGraph.createNode(node_data.type); + if (node) { + + node.configure(node_data); + //paste in last known mouse position + node.pos[0] += this.graph_mouse[0] - posMin[0]; //+= 5; + node.pos[1] += this.graph_mouse[1] - posMin[1]; //+= 5; + + left_arr.push(node.pos[0]) + right_arr.push(node.pos[0] + node.size[0]) + top_arr.push(node.pos[1]) + bottom_arr.push(node.pos[1] + node.size[1]) + + this.graph.add(node,{doProcessChange:false}); + + nodes.push(node); + + } + } + + if(clipboard_info.title){ + var l = Math.min(...left_arr) - 15; + var r = Math.max(...right_arr) - this.graph_mouse[0] + 30; + var t = Math.min(...top_arr) - 100; + var b = Math.max(...bottom_arr) - this.graph_mouse[1] + 130; + + // create group + const groups = [ + { + "title": clipboard_info.title, + "bounding": [ + l, + t, + r, + b + ], + "color": "#3f789e", + "font_size": 24, + "locked": false + } + ] + + for (var i = 0; i < groups.length; ++i) { + var group = new LiteGraph.LGraphGroup(); + group.configure(groups[i]); + this.graph.add(group); + } + } + + //create links + for (var i = 0; i < clipboard_info.links.length; ++i) { + var link_info = clipboard_info.links[i]; + var origin_node; + var origin_node_relative_id = link_info[0]; + if (origin_node_relative_id != null) { + origin_node = nodes[origin_node_relative_id]; + } else if (LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && isConnectUnselected) { + var origin_node_id = link_info[4]; + if (origin_node_id) { + origin_node = this.graph.getNodeById(origin_node_id); + } + } + var target_node = nodes[link_info[2]]; + if( origin_node && target_node ) + origin_node.connect(link_info[1], target_node, link_info[3]); + else + console.warn("Warning, nodes missing on pasting"); + } + + this.selectNodes(nodes); + this.graph.afterChange(); +}; \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySaveImage.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySaveImage.js new file mode 100644 index 00000000..35adb752 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySaveImage.js @@ -0,0 +1,36 @@ +import { app } from "../../../../scripts/app.js"; +import { applyTextReplacements } from "../../../../scripts/utils.js"; + +const extraNodes = ["easy imageSave", "easy fullkSampler", "easy kSampler", "easy kSamplerTiled","easy kSamplerInpainting", "easy kSamplerDownscaleUnet", "easy kSamplerSDTurbo","easy detailerFix"] + +app.registerExtension({ + name: "Comfy.Easy.SaveImageExtraOutput", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (extraNodes.includes(nodeData.name)) { + const onNodeCreated = nodeType.prototype.onNodeCreated; + // When the SaveImage node is created we want to override the serialization of the output name widget to run our S&R + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + + const widget = this.widgets.find((w) => w.name === "filename_prefix" || w.name === 'save_prefix'); + widget.serializeValue = () => { + return applyTextReplacements(app, widget.value); + }; + + return r; + }; + } else { + // When any other node is created add a property to alias the node + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + + if (!this.properties || !("Node name for S&R" in this.properties)) { + this.addProperty("Node name for S&R", this.constructor.type, "string"); + } + + return r; + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySeg.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySeg.js new file mode 100644 index 00000000..11e8feb6 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySeg.js @@ -0,0 +1,136 @@ +import {app} from "../../../../scripts/app.js"; +import {$el} from "../../../../scripts/ui.js"; +import {$t} from "../common/i18n.js"; +import {findWidgetByName, toggleWidget} from "../common/utils.js"; + + +const tags = { + "selfie_multiclass_256x256": ["Background", "Hair", "Body", "Face", "Clothes", "Others",], + "human_parsing_lip":["Background","Hat","Hair","Glove","Sunglasses","Upper-clothes","Dress","Coat","Socks","Pants","Jumpsuits","Scarf","Skirt","Face","Left-arm","Right-arm","Left-leg","Right-leg","Left-shoe","Right-shoe"], +} +function getTagList(tags) { + let rlist=[] + tags.forEach((k,i) => { + rlist.push($el( + "label.easyuse-prompt-styles-tag", + { + dataset: { + tag: i, + name: $t(k), + index: i + }, + $: (el) => { + el.children[0].onclick = () => { + el.classList.toggle("easyuse-prompt-styles-tag-selected"); + }; + }, + }, + [ + $el("input",{ + type: 'checkbox', + name: i + }), + $el("span",{ + textContent: $t(k), + }) + ] + )) + }); + return rlist +} + + +app.registerExtension({ + name: 'comfy.easyUse.seg', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + + if (nodeData.name == 'easy humanSegmentation') { + // εˆ›ε»Ίζ—Ά + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated ? onNodeCreated?.apply(this, arguments) : undefined; + const method = this.widgets.findIndex((w) => w.name == 'method'); + const list = $el("ul.easyuse-prompt-styles-list.no-top", []); + let method_values = '' + this.setProperty("values", []) + + let selector = this.addDOMWidget('mask_components',"btn",$el('div.easyuse-prompt-styles',[list])) + + Object.defineProperty(this.widgets[method],'value',{ + set:(value)=>{ + method_values = value + if(method_values){ + selector.element.children[0].innerHTML = '' + if(method_values == 'selfie_multiclass_256x256'){ + toggleWidget(this, findWidgetByName(this, 'confidence'), true) + this.setSize([300, 260]); + }else{ + toggleWidget(this, findWidgetByName(this, 'confidence')) + this.setSize([300, 500]); + } + let list = getTagList(tags[method_values]); + selector.element.children[0].append(...list) + } + }, + get: () => { + return method_values + } + }) + + let mask_select_values = '' + + Object.defineProperty(selector, "value", { + set: (value) => { + setTimeout(_=>{ + selector.element.children[0].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + let arr = value.split(',') + if (arr.includes(el.dataset.tag)) { + el.classList.add("easyuse-prompt-styles-tag-selected"); + el.children[0].checked = true + } + }) + },100) + }, + get: () => { + selector.element.children[0].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + if(el.classList.value.indexOf("easyuse-prompt-styles-tag-selected")>=0){ + if(!this.properties["values"].includes(el.dataset.tag)){ + this.properties["values"].push(el.dataset.tag); + } + }else{ + if(this.properties["values"].includes(el.dataset.tag)){ + this.properties["values"]= this.properties["values"].filter(v=>v!=el.dataset.tag); + } + } + }); + mask_select_values = this.properties["values"].join(','); + return mask_select_values; + } + }); + + let old_values = '' + let mask_lists_dom = selector.element.children[0] + + // εˆε§‹εŒ– + setTimeout(_=>{ + if(!method_values) { + method_values = 'selfie_multiclass_256x256' + selector.element.children[0].innerHTML = '' + // ι‡ζ–°ζŽ’εΊ + let list = getTagList(tags[method_values]); + selector.element.children[0].append(...list) + } + if(method_values == 'selfie_multiclass_256x256'){ + toggleWidget(this, findWidgetByName(this, 'confidence'), true) + this.setSize([300, 260]); + }else{ + toggleWidget(this, findWidgetByName(this, 'confidence')) + this.setSize([300, 500]); + } + },1) + + return onNodeCreated; + } + } + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySelector.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySelector.js new file mode 100644 index 00000000..c9dde34d --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySelector.js @@ -0,0 +1,296 @@ +// 1.0.3 +import { app } from "../../../../scripts/app.js"; +import { api } from "../../../../scripts/api.js"; +import { $el } from "../../../../scripts/ui.js"; +import { $t } from "../common/i18n.js"; + +// θŽ·ε–ι£Žζ Όεˆ—θ‘¨ +let styles_list_cache = {} +let styles_image_cache = {} +async function getStylesList(name){ + if(styles_list_cache[name]) return styles_list_cache[name] + else{ + const resp = await api.fetchApi(`/easyuse/prompt/styles?name=${name}`); + if (resp.status === 200) { + let data = await resp.json(); + styles_list_cache[name] = data; + return data; + } + return undefined; + } +} +async function getStylesImage(name, styles_name){ + if(!styles_image_cache[styles_name]) styles_image_cache[styles_name] = {} + if(styles_image_cache[styles_name][name]) return styles_image_cache[styles_name][name] + else{ + const resp = await api.fetchApi(`/easyuse/prompt/styles/image?name=${name}&styles_name=${styles_name}`); + if (resp.status === 200) { + const text = await resp.text() + if(text.startsWith('http')){ + styles_image_cache[styles_name][name] = text + return text + } + const url = `/easyuse/prompt/styles/image?name=${name}&styles_name=${styles_name}` + styles_image_cache[styles_name][name] = url + return url + } + return undefined; + } +} + +function getTagList(tags, styleName, language='en-US') { + let rlist=[] + tags.forEach((k,i) => { + rlist.push($el( + "label.easyuse-prompt-styles-tag", + { + dataset: { + tag: k['name'], + name: language == 'zh-CN' && k['name_cn'] ? k['name_cn'] : k['name'], + imgName: k['imgName'], + index: i + }, + $: (el) => { + el.children[0].onclick = () => { + el.classList.toggle("easyuse-prompt-styles-tag-selected"); + }; + el.onmousemove = (e) => { + displayImage(el.dataset.imgName, styleName, e) + }; + el.onmouseout = () => { + hiddenImage() + }; + el.onmouseover = (e) => { + displayImage(el.dataset.imgName, styleName) + }; + }, + }, + [ + $el("input",{ + type: 'checkbox', + name: k['name'] + }), + $el("span",{ + textContent: language == 'zh-CN' && k['name_cn'] ? k['name_cn'] : k['name'], + }) + ] + )) + }); + return rlist +} + +const foocus_base_path = "https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/" +const empty_img = "data:image/jpeg;base64,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" +async function displayImage(imgName, styleName) { + var e = event || window.event; + var img = document.getElementById("show_image_id"); + var pxy= img.parentElement.getBoundingClientRect(); + if(imgName) { + const url = await getStylesImage(imgName, styleName) + img.src = url + img.onerror = _ =>{ + img.src = empty_img + } + } + var scale = app?.canvas?.ds?.scale || 1; + var x = (e.pageX-pxy.x-100)/scale; + var y = (e.pageY-pxy.y+25)/scale; + img.style.left = x+"px"; + img.style.top = y+"px"; + img.style.display = "block"; + img.style.borderRadius = "10px"; + img.style.borderColor = "var(--fg-color)" + img.style.borderWidth = "1px"; + img.style.borderStyle = "solid"; +} +function hiddenImage(){ //theEvent用ζ₯δΌ ε…₯δΊ‹δ»ΆοΌŒFirefoxηš„ζ–ΉεΌ + var img = document.getElementById('show_image_id'); + img.style.display = "none"; +} + +// StylePromptSelector +app.registerExtension({ + name: 'comfy.easyUse.styleSelector', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + + if(nodeData.name == 'easy stylesSelector'){ + // εˆ›ε»Ίζ—Ά + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function() { + onNodeCreated ? onNodeCreated?.apply(this, arguments) : undefined; + const styles_id = this.widgets.findIndex((w) => w.name == 'styles'); + const language = localStorage['AGL.Locale'] || localStorage['Comfy.Settings.AGL.Locale'] || 'en-US' + const list = $el("ul.easyuse-prompt-styles-list",[]); + let styles_values = '' + this.setProperty("values", []) + + let selector = this.addDOMWidget('select_styles',"btn",$el('div.easyuse-prompt-styles',[$el('div.tools', [ + $el('button.delete',{ + textContent: $t('Empty All'), + style:{}, + onclick:()=>{ + selector.element.children[0].querySelectorAll(".search").forEach(el=>{ + el.value = '' + }) + selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag-selected").forEach(el => { + el.classList.remove("easyuse-prompt-styles-tag-selected"); + el.children[0].checked = false + }) + selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + el.classList.remove('hide') + }) + this.setProperty("values", []) + }} + ), + $el('textarea.search',{ + dir:"ltr", + style:{"overflow-y": "scroll"}, + rows:1, + placeholder:$t("πŸ”Ž Type here to search styles ..."), + oninput:(e)=>{ + let value = e.target.value + selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + const name = el.dataset.name.toLowerCase() + const tag = el.dataset.tag.toLowerCase() + const lower_value = value.toLowerCase() + if(name.indexOf(lower_value) != -1 || tag.indexOf(lower_value) != -1 || el.classList.value.indexOf("easyuse-prompt-styles-tag-selected")!=-1){ + el.classList.remove('hide') + } + else{ + el.classList.add('hide') + } + }) + } + }) + ]),list, + $el('img',{id:'show_image_id', + style:{display:'none',position:'absolute'}, + src:``, + onerror:()=>{ + this.src = empty_img + } + }) + ])); + + Object.defineProperty(this.widgets[styles_id],'value',{ + set:(value)=>{ + styles_values = value + if(styles_values){ + getStylesList(styles_values).then(_=>{ + selector.element.children[1].innerHTML='' + if(styles_list_cache[styles_values]){ + let tags = styles_list_cache[styles_values] + // ι‡ζ–°ζŽ’εΊ + if(selector.value) tags = tags.sort((a,b)=> selector.value.includes(b.name) - selector.value.includes(a.name)) + this.properties["values"] = [] + let list = getTagList(tags, value, language); + selector.element.children[1].append(...list) + selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + if (this.properties["values"].includes(el.dataset.tag)) { + el.classList.add("easyuse-prompt-styles-tag-selected"); + } + if(this.size?.[0]<150 || this.size?.[1]<150) this.setSize([425, 500]); + }) + } + }) + } + }, + get: () => { + return styles_values + } + }) + + + let style_select_values = '' + Object.defineProperty(selector, "value", { + set: (value) => { + setTimeout(_=>{ + selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + let arr = value.split(',') + if (arr.includes(el.dataset.tag)) { + el.classList.add("easyuse-prompt-styles-tag-selected"); + el.children[0].checked = true + } + }) + },300) + }, + get: () => { + selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + if(el.classList.value.indexOf("easyuse-prompt-styles-tag-selected")>=0){ + if(!this.properties["values"].includes(el.dataset.tag)){ + this.properties["values"].push(el.dataset.tag); + } + }else{ + if(this.properties["values"].includes(el.dataset.tag)){ + this.properties["values"]= this.properties["values"].filter(v=>v!=el.dataset.tag); + } + } + }); + style_select_values = this.properties["values"].join(','); + return style_select_values; + } + }); + + let old_values = '' + let style_lists_dom = selector.element.children[1] + style_lists_dom.addEventListener('mouseenter', function (e) { + let value = '' + style_lists_dom.querySelectorAll(".easyuse-prompt-styles-tag-selected").forEach(el=> value+=el.dataset.tag) + old_values = value + }) + style_lists_dom.addEventListener('mouseleave', function (e) { + let value = '' + style_lists_dom.querySelectorAll(".easyuse-prompt-styles-tag-selected").forEach(el=> value+=el.dataset.tag) + let new_values = value + if(old_values != new_values){ + // console.log("ι€‰ι‘Ήε‘η”ŸδΊ†ε˜εŒ–") + // θŽ·ε–ζœη΄’ε€Ό + const search_value = document.getElementsByClassName('search')[0]['value'] + // ι‡ζ–°ζŽ’εΊ + const tags = styles_list_cache[styles_values].sort((a,b)=> new_values.includes(b.name) - new_values.includes(a.name)) + style_lists_dom.innerHTML = '' + let list = getTagList(tags, styles_values, language); + style_lists_dom.append(...list) + style_lists_dom.querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => { + if (new_values.includes(el.dataset.tag)) { + el.classList.add("easyuse-prompt-styles-tag-selected"); + el.children[0].checked = true; + } + if(search_value){ + if(el.dataset.name.indexOf(search_value) != -1 || el.dataset.tag.indexOf(search_value) != -1 || el.classList.value.indexOf("easyuse-prompt-styles-tag-selected")!=-1){ + el.classList.remove('hide') + } + else{ + el.classList.add('hide') + } + } + + }) + } + }) + + + // εˆε§‹εŒ– + setTimeout(_=>{ + if(!styles_values) { + styles_values = 'fooocus_styles' + getStylesList(styles_values).then(_=>{ + selector.element.children[1].innerHTML='' + if(styles_list_cache[styles_values]){ + let tags = styles_list_cache[styles_values] + // ι‡ζ–°ζŽ’εΊ + if(selector.value) tags = tags.sort((a,b)=> selector.value.includes(b.name) - selector.value.includes(a.name)) + let list = getTagList(tags, styles_values, language); + selector.element.children[1].append(...list) + } + }) + } + if(this.size?.[0]<150 || this.size?.[1]<150) this.setSize([425, 500]); + // + },100) + + return onNodeCreated; + } + } + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySliderControl.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySliderControl.js new file mode 100644 index 00000000..17fc5094 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySliderControl.js @@ -0,0 +1,173 @@ +import { app } from "../../../../scripts/app.js"; +import { api } from "../../../../scripts/api.js"; +import { $el } from "../../../../scripts/ui.js"; +import { $t } from "../common/i18n.js"; +import { sleep } from "../common/utils.js"; + + +const calculatePercent = (value, min, max) => ((value-min)/(max-min)*100) + +const getLayerDefaultValue = (index) => { + switch (index){ + case 3: + return 2.5 + case 6: + return 1 + default: + return 0 + } +} + +const addLayer = (_this, layer_total, arrays, sliders, i) => { + let scroll = $el('div.easyuse-slider-item-scroll') + let value = $el('div.easyuse-slider-item-input', {textContent: arrays[i]['value']}) + let label = $el('div.easyuse-slider-item-label', {textContent: 'L'+i}) + let girdTotal = (arrays[i]['max'] - arrays[i]['min']) / arrays[i]['step'] + let area = $el('div.easyuse-slider-item-area', {style:{ height: calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max']) + '%'}}) + let bar = $el('div.easyuse-slider-item-bar', { + style:{ top: (100-calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max'])) + '%'}, + onmousedown: (e) => { + let event = e || window.event; + var y = event.clientY - bar.offsetTop; + document.onmousemove = (e) => { + let event = e || window.event; + let top = event.clientY - y; + if(top < 0){ + top = 0; + } + else if(top > scroll.offsetHeight - bar.offsetHeight){ + top = scroll.offsetHeight - bar.offsetHeight; + } + // topεˆ°ζœ€θΏ‘ηš„girdHeightε€Ό + let girlHeight = (scroll.offsetHeight - bar.offsetHeight)/ girdTotal + top = Math.round(top / girlHeight) * girlHeight; + bar.style.top = Math.floor(top/(scroll.offsetHeight - bar.offsetHeight)* 100) + '%'; + area.style.height = Math.floor((scroll.offsetHeight - bar.offsetHeight - top)/(scroll.offsetHeight - bar.offsetHeight)* 100) + '%'; + value.innerText = parseFloat(parseFloat(arrays[i]['max'] - (arrays[i]['max']-arrays[i]['min']) * (top/(scroll.offsetHeight - bar.offsetHeight))).toFixed(2)) + arrays[i]['value'] = value.innerText + _this.properties['values'][i] = i+':'+value.innerText + window.getSelection ? window.getSelection().removeAllRanges() : document.selection.empty(); + } + }, + ondblclick:_=>{ + bar.style.top = (100-calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max'])) + '%' + area.style.height = calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max']) + '%' + value.innerText = arrays[i]['default'] + arrays[i]['value'] = arrays[i]['default'] + _this.properties['values'][i] = i+':'+value.innerText + } + }) + document.onmouseup = _=> document.onmousemove = null; + + scroll.replaceChildren(bar,area) + let item_div = $el('div.easyuse-slider-item',[ + value, + scroll, + label + ]) + if(i == 3 ) layer_total == 12 ? item_div.classList.add('negative') : item_div.classList.remove('negative') + else if(i == 6) layer_total == 12 ? item_div.classList.add('positive') : item_div.classList.remove('positive') + sliders.push(item_div) + return item_div +} + +const setSliderValue = (_this, type, refresh=false, values_div, sliders_value) => { + let layer_total = type == 'sdxl' ? 12 : 16 + let sliders = [] + let arrays = Array.from({length: layer_total}, (v, i) => ({default: layer_total == 12 ? getLayerDefaultValue(i) : 0, min: -1, max: 3, step: 0.05, value:layer_total == 12 ? getLayerDefaultValue(i) : 0})) + _this.setProperty("values", Array.from({length: layer_total}, (v, i) => i+':'+arrays[i]['value'])) + for (let i = 0; i < layer_total; i++) { + addLayer(_this, layer_total, arrays, sliders, i) + } + if(refresh) values_div.replaceChildren(...sliders) + else{ + values_div = $el('div.easyuse-slider', sliders) + sliders_value = _this.addDOMWidget('values',"btn",values_div) + } + + Object.defineProperty(sliders_value, 'value', { + set: function() {}, + get: function() { + return _this.properties.values.join(','); + } + }); + return {sliders, arrays, values_div, sliders_value} +} + + +app.registerExtension({ + name: 'comfy.easyUse.sliderControl', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if(nodeData.name == 'easy sliderControl'){ + // εˆ›ε»Ίζ—Ά + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function() { + onNodeCreated && onNodeCreated.call(this); + const mode = this.widgets[0]; + const model_type = this.widgets[1]; + let layer_total = model_type.value == 'sdxl' ? 12 : 16 + let _this = this + let values_div = null + let sliders_value = null + mode.callback = async()=>{ + switch (mode.value) { + case 'ipadapter layer weights': + nodeData.output_name = ['layer_weights'] + _this.outputs[0]['name'] = 'layer_weights' + _this.outputs[0]['label'] = 'layer_weights' + break + } + } + + model_type.callback = async()=>{ + if(values_div) { + let r2 = setSliderValue(_this, model_type.value, true, values_div, sliders_value) + values_div = r2.values_div + sliders_value = r2.sliders_value + } + _this.setSize(model_type.value == 'sdxl' ? [375,320] : [455,320]) + } + + let r1 = setSliderValue(_this, model_type.value, false, values_div, sliders_value) + let sliders = r1.sliders + let arrays = r1.arrays + values_div = r1.values_div + sliders_value = r1.sliders_value + setTimeout(_=>{ + let values_widgets_index = this.widgets.findIndex((w) => w.name == 'values'); + if(values_widgets_index != -1){ + let old_values_widget = this.widgets[values_widgets_index]; + let old_value = old_values_widget.value.split(',') + let layer_total = _this.widgets[1].value == 'sdxl' ? 12 : 16 + for (let i = 0; i < layer_total; i++) { + let value = parseFloat(parseFloat(old_value[i].split(':')[1]).toFixed(2)) + let item_div = sliders[i] || null + // ε­˜εœ¨ε±‚ε³δΏζ”Ή + if(arrays[i]){ + arrays[i]['value'] = value + _this.properties['values'][i] = old_value[i] + }else{ + arrays.push({default: layer_total == 12 ? getLayerDefaultValue(i) : 0, min: -1, max: 3, step: 0.05, value:layer_total == 12 ? getLayerDefaultValue(i) : 0}) + _this.properties['values'].push(i+':'+arrays[i]['value']) + // ζ·»εŠ ηΌΊε€±ε±‚ + item_div = addLayer(_this, layer_total, arrays, sliders, i) + values_div.appendChild(item_div) + } + // todo: δΏζ”Ήbar位η½η­‰ + let input = item_div.getElementsByClassName('easyuse-slider-item-input')[0] + let bar = item_div.getElementsByClassName('easyuse-slider-item-bar')[0] + let area = item_div.getElementsByClassName('easyuse-slider-item-area')[0] + if(i == 3 ) layer_total == 12 ? item_div.classList.add('negative') : item_div.classList.remove('negative') + else if(i == 6) layer_total == 12 ? item_div.classList.add('positive') : item_div.classList.remove('positive') + input.textContent = value + bar.style.top = (100-calculatePercent(value,arrays[i]['min'],arrays[i]['max'])) + '%' + area.style.height = calculatePercent(value,arrays[i]['min'],arrays[i]['max']) + '%' + } + } + _this.setSize(model_type.value == 'sdxl' ? [375,320] : [455,320]) + },1) + return onNodeCreated; + } + } + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySuggestion.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySuggestion.js new file mode 100644 index 00000000..49891b01 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easySuggestion.js @@ -0,0 +1,554 @@ +import {app} from "../../../../scripts/app.js"; +import {api} from "../../../../scripts/api.js"; +import {$el} from "../../../../scripts/ui.js"; + +const propmts = ["easy wildcards", "easy positive", "easy negative", "easy stylesSelector", "easy promptConcat", "easy promptReplace"] +const loaders = ["easy a1111Loader", "easy comfyLoader", "easy fullLoader", "easy svdLoader", "easy cascadeLoader", "easy sv3dLoader"] +const preSamplingNodes = ["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingNoiseIn", "easy preSamplingCustom", "easy preSamplingDynamicCFG","easy preSamplingSdTurbo", "easy preSamplingLayerDiffusion"] +const kSampler = ["easy kSampler", "easy kSamplerTiled","easy kSamplerInpainting", "easy kSamplerDownscaleUnet", "easy kSamplerSDTurbo"] +const controlNetNodes = ["easy controlnetLoader", "easy controlnetLoaderADV"] +const instantIDNodes = ["easy instantIDApply", "easy instantIDApplyADV"] +const ipadapterNodes = ["easy ipadapterApply", "easy ipadapterApplyADV" ,"easy ipadapterApplyFaceIDKolors", "easy ipadapterStyleComposition"] +const pipeNodes = ['easy pipeIn','easy pipeOut', 'easy pipeEdit'] +const xyNodes = ['easy XYPlot', 'easy XYPlotAdvanced'] +const extraNodes = ['easy setNode'] +const modelNormalNodes = [...['RescaleCFG','LoraLoaderModelOnly','LoraLoader','FreeU','FreeU_v2'],...ipadapterNodes,...extraNodes] +const suggestions = { + // prompt + "easy seed":{ + "from":{ + "INT": [...preSamplingNodes,...['easy fullkSampler']] + } + }, + "easy positive":{ + "from":{ + "STRING": [...propmts] + } + }, + "easy negative":{ + "from":{ + "STRING": [...propmts] + } + }, + "easy wildcards":{ + "from":{ + "STRING": [...["Reroute","easy showAnything"],...propmts,] + } + }, + "easy stylesSelector":{ + "from":{ + "STRING": [...["Reroute","easy showAnything"],...propmts,] + } + }, + "easy promptConcat":{ + "from":{ + "STRING": [...["Reroute","easy showAnything"],...propmts,] + } + }, + "easy promptReplace":{ + "from":{ + "STRING": [...["Reroute","easy showAnything"],...propmts,] + } + }, + // sdη›Έε…³ + "easy fullLoader": { + "from":{ + "PIPE_LINE": [...preSamplingNodes,...['easy fullkSampler'],...pipeNodes,...extraNodes], + "MODEL":modelNormalNodes + }, + "to":{ + "STRING": [...propmts] + } + }, + "easy a1111Loader": { + "from": { + "PIPE_LINE": [ ...preSamplingNodes, ...controlNetNodes, ...instantIDNodes, ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "STRING": [...propmts] + } + }, + "easy comfyLoader": { + "from": { + "PIPE_LINE": [ ...preSamplingNodes, ...controlNetNodes, ...instantIDNodes, ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "STRING": [...propmts] + } + }, + "easy svdLoader":{ + "from": { + "PIPE_LINE": [ ...["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingDynamicCFG"], ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "STRING": [...propmts] + } + }, + "easy zero123Loader":{ + "from": { + "PIPE_LINE": [ ...["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingDynamicCFG"], ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "STRING": [...propmts] + } + }, + "easy sv3dLoader":{ + "from": { + "PIPE_LINE": [ ...["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingDynamicCFG"], ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "STRING": [...propmts] + } + }, + "easy preSampling": { + "from": { + "PIPE_LINE": [ ...kSampler, ...pipeNodes, ...controlNetNodes, ...xyNodes, ...extraNodes] + }, + }, + "easy preSamplingAdvanced": { + "from": { + "PIPE_LINE": [ ...kSampler, ...pipeNodes, ...controlNetNodes, ...xyNodes, ...extraNodes] + } + }, + "easy preSamplingDynamicCFG": { + "from": { + "PIPE_LINE": [ ...kSampler, ...pipeNodes, ...controlNetNodes, ...xyNodes, ...extraNodes] + } + }, + "easy preSamplingCustom": { + "from": { + "PIPE_LINE": [ ...kSampler, ...pipeNodes, ...controlNetNodes, ...xyNodes, ...extraNodes] + } + }, + "easy preSamplingLayerDiffusion": { + "from": { + "PIPE_LINE": [...["easy kSamplerLayerDiffusion"], ...kSampler, ...pipeNodes, ...controlNetNodes, ...xyNodes, ...extraNodes] + } + }, + "easy preSamplingNoiseIn": { + "from": { + "PIPE_LINE": [ ...kSampler, ...pipeNodes, ...controlNetNodes, ...xyNodes, ...extraNodes] + } + }, + // ksampler + "easy fullkSampler": { + "from": { + "PIPE_LINE": [ ...pipeNodes.reverse(), ...['easy preDetailerFix', 'easy preMaskDetailerFix'], ...preSamplingNodes, ...extraNodes] + } + }, + "easy kSampler": { + "from": { + "PIPE_LINE": [ ...pipeNodes.reverse(), ...['easy preDetailerFix', 'easy preMaskDetailerFix', 'easy hiresFix'], ...preSamplingNodes, ...extraNodes], + } + }, + // cn + "easy controlnetLoader": { + "from": { + "PIPE_LINE": [ ...preSamplingNodes, ...controlNetNodes, ...instantIDNodes, ...pipeNodes, ...extraNodes] + } + }, + "easy controlnetLoaderADV":{ + "from": { + "PIPE_LINE": [ ...preSamplingNodes, ...controlNetNodes, ...instantIDNodes, ...pipeNodes, ...extraNodes] + } + }, + // instant + "easy instantIDApply": { + "from": { + "PIPE_LINE": [ ...preSamplingNodes, ...controlNetNodes, ...instantIDNodes, ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "COMBO": [...["easy promptLine"]] + } + }, + "easy instantIDApplyADV":{ + "from": { + "PIPE_LINE": [ ...preSamplingNodes, ...controlNetNodes, ...instantIDNodes, ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes + }, + "to":{ + "COMBO": [...["easy promptLine"]] + } + }, + "easy ipadapterApply":{ + "to":{ + "COMBO": [...["easy promptLine"]] + } + }, + "easy ipadapterApplyADV":{ + "to":{ + "STRING": [...["easy sliderControl"], ...propmts], + "COMBO": [...["easy promptLine"]] + } + }, + "easy ipadapterStyleComposition":{ + "to":{ + "COMBO": [...["easy promptLine"]] + } + }, + // fix + "easy preDetailerFix":{ + "from": { + "PIPE_LINE": [...["easy detailerFix"], ...pipeNodes, ...extraNodes] + }, + "to":{ + "PIPE_LINE": [...["easy ultralyticsDetectorPipe", "easy samLoaderPipe", "easy kSampler", "easy fullkSampler"]] + } + }, + "easy preMaskDetailerFix":{ + "from": { + "PIPE_LINE": [...["easy detailerFix"], ...pipeNodes, ...extraNodes] + } + }, + "easy samLoaderPipe": { + "from":{ + "PIPE_LINE": [...["easy preDetailerFix"], ...pipeNodes, ...extraNodes] + } + }, + "easy ultralyticsDetectorPipe": { + "from":{ + "PIPE_LINE": [...["easy preDetailerFix"], ...pipeNodes, ...extraNodes] + } + }, + // cascadeη›Έε…³ + "easy cascadeLoader":{ + "from": { + "PIPE_LINE": [ ...["easy fullCascadeKSampler", 'easy preSamplingCascade'], ...controlNetNodes, ...pipeNodes, ...extraNodes], + "MODEL": modelNormalNodes.filter(cate => !ipadapterNodes.includes(cate)) + } + }, + "easy fullCascadeKSampler":{ + "from": { + "PIPE_LINE": [ ...["easy preSampling", "easy preSamplingAdvanced"], ...pipeNodes, ...extraNodes] + } + }, + "easy preSamplingCascade":{ + "from": { + "PIPE_LINE": [ ...["easy cascadeKSampler",], ...pipeNodes, ...extraNodes] + } + }, + "easy cascadeKSampler": { + "from": { + "PIPE_LINE": [ ...["easy preSampling", "easy preSamplingAdvanced"], ...pipeNodes, ...extraNodes] + } + }, +} + +class NullGraphError extends Error { + constructor(message="Attempted to access LGraph reference that was null or undefined.", cause) { + super(message, {cause}) + this.name = "NullGraphError" + } +} +app.registerExtension({ + name: "comfy.easyuse.suggestions", + async setup() { + const createDefaultNodeForSlot = LGraphCanvas.prototype.createDefaultNodeForSlot; + LGraphCanvas.prototype.createDefaultNodeForSlot = function(optPass) { // addNodeMenu for connection + const opts = Object.assign({ nodeFrom: null // input + ,slotFrom: null // input + ,nodeTo: null // output + ,slotTo: null // output + ,position: [] // pass the event coords + ,nodeType: null // choose a nodetype to add, AUTO to set at first good + ,posAdd:[0,0] // adjust x,y + ,posSizeFix:[0,0] // alpha, adjust the position x,y based on the new node size w,h + } + , optPass || {} + ); + const { afterRerouteId } = opts + const that = this; + + const isFrom = opts.nodeFrom && opts.slotFrom!==null; + const isTo = !isFrom && opts.nodeTo && opts.slotTo!==null; + const node = isFrom ? opts.nodeFrom : opts.nodeTo + // Not an Easy Use node, skip showConnectionMenu hijack + if(!node || !Object.keys(suggestions).includes(node.type)){ + return createDefaultNodeForSlot.call(this, optPass) + } + + if (!isFrom && !isTo){ + console.warn("No data passed to createDefaultNodeForSlot "+opts.nodeFrom+" "+opts.slotFrom+" "+opts.nodeTo+" "+opts.slotTo); + return false; + } + if (!opts.nodeType){ + console.warn("No type to createDefaultNodeForSlot"); + return false; + } + + const nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + const nodeType = nodeX.type + let slotX = isFrom ? opts.slotFrom : opts.slotTo; + + let iSlotConn = false; + switch (typeof slotX){ + case "string": + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX,false) : nodeX.findInputSlot(slotX,false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "undefined": + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information "+slotX); + return false; + } + + if (slotX===false || iSlotConn===false){ + console.warn("createDefaultNodeForSlot bad slotX "+slotX+" "+iSlotConn); + } + + // check for defaults nodes for this slottype + var fromSlotType = slotX.type==LiteGraph.EVENT?"_event_":slotX.type; + var slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in; + if(slotTypesDefault && slotTypesDefault[fromSlotType]){ + if (slotX.link !== null) { + // is connected + }else{ + // is not not connected + } + let nodeNewType = false; + const fromOrTo = isFrom ? 'from' : 'to' + if(suggestions[nodeType] && suggestions[nodeType][fromOrTo] && suggestions[nodeType][fromOrTo][fromSlotType]?.length>0){ + for(var typeX in suggestions[nodeType][fromOrTo][fromSlotType]){ + if (opts.nodeType == suggestions[nodeType][fromOrTo][fromSlotType][typeX] || opts.nodeType == "AUTO") { + nodeNewType = suggestions[nodeType][fromOrTo][fromSlotType][typeX]; + break + } + } + } + else if(typeof slotTypesDefault[fromSlotType] == "object" || typeof slotTypesDefault[fromSlotType] == "array"){ + for(var typeX in slotTypesDefault[fromSlotType]){ + if (opts.nodeType == slotTypesDefault[fromSlotType][typeX] || opts.nodeType == "AUTO"){ + nodeNewType = slotTypesDefault[fromSlotType][typeX]; + break; + } + } + }else{ + if (opts.nodeType == slotTypesDefault[fromSlotType] || opts.nodeType == "AUTO") nodeNewType = slotTypesDefault[fromSlotType]; + } + if (nodeNewType) { + var nodeNewOpts = false; + if (typeof nodeNewType == "object" && nodeNewType.node){ + nodeNewOpts = nodeNewType; + nodeNewType = nodeNewType.node; + } + + //that.graph.beforeChange(); + + var newNode = LiteGraph.createNode(nodeNewType); + if(newNode){ + // if is object pass options + if (nodeNewOpts){ + if (nodeNewOpts.properties) { + for (var i in nodeNewOpts.properties) { + newNode.addProperty( i, nodeNewOpts.properties[i] ); + } + } + if (nodeNewOpts.inputs) { + newNode.inputs = []; + for (var i in nodeNewOpts.inputs) { + newNode.addOutput( + nodeNewOpts.inputs[i][0], + nodeNewOpts.inputs[i][1] + ); + } + } + if (nodeNewOpts.outputs) { + newNode.outputs = []; + for (var i in nodeNewOpts.outputs) { + newNode.addOutput( + nodeNewOpts.outputs[i][0], + nodeNewOpts.outputs[i][1] + ); + } + } + if (nodeNewOpts.title) { + newNode.title = nodeNewOpts.title; + } + if (nodeNewOpts.json) { + newNode.configure(nodeNewOpts.json); + } + + } + + // add the node + that.graph.add(newNode); + newNode.pos = [ opts.position[0]+opts.posAdd[0]+(opts.posSizeFix[0]?opts.posSizeFix[0]*newNode.size[0]:0) + ,opts.position[1]+opts.posAdd[1]+(opts.posSizeFix[1]?opts.posSizeFix[1]*newNode.size[1]:0)]; //that.last_click_position; //[e.canvasX+30, e.canvasX+5];*/ + + // connect the two! + if (isFrom){ + opts.nodeFrom.connectByType( iSlotConn, newNode, fromSlotType ); + }else{ + opts.nodeTo.connectByTypeOutput( iSlotConn, newNode, fromSlotType ); + } + + return true; + + }else{ + console.log("failed creating "+nodeNewType); + } + } + } + return false; + } + + let showConnectionMenu = LGraphCanvas.prototype.showConnectionMenu + LGraphCanvas.prototype.showConnectionMenu = function(optPass) { // addNodeMenu for connection + const opts = Object.assign({ + nodeFrom: null, // input + slotFrom: null, // input + nodeTo: null, // output + slotTo: null, // output + e: undefined, + allow_searchbox: this.allow_searchbox, + showSearchBox: this.showSearchBox, + } + ,optPass || {} + ); + const that = this; + const { graph } = this + const { afterRerouteId } = opts + const isFrom = opts.nodeFrom && opts.slotFrom; + const isTo = !isFrom && opts.nodeTo && opts.slotTo; + const node = isFrom ? opts.nodeFrom : opts.nodeTo + + // Not an Easy Use node, skip showConnectionMenu hijack + if(!node || !Object.keys(suggestions).includes(node.type)){ + return showConnectionMenu.call(this, optPass) + } + + if (!isFrom && !isTo){ + console.warn("No data passed to showConnectionMenu"); + return false; + } + + const nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + if (!nodeX) throw new TypeError("nodeX was null when creating default node for slot.") + let slotX = isFrom ? opts.slotFrom : opts.slotTo; + + let iSlotConn = false; + switch (typeof slotX){ + case "string": + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX,false) : nodeX.findInputSlot(slotX,false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information "+slotX); + return false; + } + + const options = ["Add Node", "Add Reroute", null] + if (opts.allow_searchbox){ + options.push("Search"); + options.push(null); + } + + // get defaults nodes for this slottype + var fromSlotType = slotX.type==LiteGraph.EVENT?"_event_":slotX.type; + var slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in; + var nodeType = nodeX.type + if(slotTypesDefault && slotTypesDefault[fromSlotType]){ + const fromOrTo = isFrom ? 'from' : 'to' + if(suggestions[nodeType] && suggestions[nodeType][fromOrTo] && suggestions[nodeType][fromOrTo][fromSlotType]?.length>0){ + for(var typeX in suggestions[nodeType][fromOrTo][fromSlotType]){ + options.push(suggestions[nodeType][fromOrTo][fromSlotType][typeX]); + } + } + else if(typeof slotTypesDefault[fromSlotType] == "object" || typeof slotTypesDefault[fromSlotType] == "array"){ + for(var typeX in slotTypesDefault[fromSlotType]){ + options.push(slotTypesDefault[fromSlotType][typeX]); + } + }else{ + options.push(slotTypesDefault[fromSlotType]); + } + } + + // build menu + var menu = new LiteGraph.ContextMenu(options, { + event: opts.e, + extra: slotX, + title: + (slotX && slotX.name != "" + ? slotX.name + (fromSlotType ? " | " : "") + : "") + (slotX && fromSlotType ? fromSlotType : ""), + callback: inner_clicked, + }); + + // callback + function inner_clicked(v,options,e) { + //console.log("Process showConnectionMenu selection"); + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function (node) { + if (!node) return + + if (isFrom) { + opts.nodeFrom?.connectByType(iSlotConn, node, fromSlotType, { afterRerouteId }) + } else { + opts.nodeTo?.connectByTypeOutput(iSlotConn, node, fromSlotType, { afterRerouteId }) + } + }) + break; + case "Add Reroute": + const node = isFrom ? opts.nodeFrom : opts.nodeTo + const slot = options.extra + if (!graph) throw new NullGraphError() + if (!node) throw new TypeError("Cannot add reroute: node was null") + if (!slot) throw new TypeError("Cannot add reroute: slot was null") + if (!opts.e) throw new TypeError("Cannot add reroute: CanvasPointerEvent was null") + + const reroute = node.connectFloatingReroute([opts.e.canvasX, opts.e.canvasY], slot, afterRerouteId) + if (!reroute) throw new Error("Failed to create reroute") + + that.dirty_canvas = true + that.dirty_bgcanvas = true + break + case "Search": + if(isFrom){ + opts.showSearchBox(e,{node_from: opts.nodeFrom, slot_from: slotX, type_filter_in: fromSlotType}); + }else{ + opts.showSearchBox(e,{node_to: opts.nodeTo, slot_from: slotX, type_filter_out: fromSlotType}); + } + break; + default: + const customProps = { + position: [opts.e?.canvasX ?? 0, opts.e?.canvasY ?? 0], + nodeType: v, + afterRerouteId, + } + // check for defaults nodes for this slottype + that.createDefaultNodeForSlot(Object.assign(opts, customProps)) + break; + } + } + + return false; + }; + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyWidgets.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyWidgets.js new file mode 100644 index 00000000..899befed --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyWidgets.js @@ -0,0 +1,400 @@ +import { app } from "../../../../scripts/app.js"; +import { ComfyWidgets } from "../../../../scripts/widgets.js"; + +const KEY_CODES = { ENTER: 13, ESC: 27, ARROW_DOWN: 40, ARROW_UP: 38 }; +const WIDGET_GAP = -4; + +function hideInfoWidget(e, node, widget) { + let dropdownShouldBeRemoved = false; + let selectionIndex = -1; + + if (e) { + e.preventDefault(); + e.stopPropagation(); + displayDropdown(widget); + } else { + hideWidget(widget, node); + } + + function createDropdownElement() { + const dropdown = document.createElement('ul'); + dropdown.id = 'hideinfo-dropdown'; + dropdown.setAttribute('role', 'listbox'); + dropdown.classList.add('hideInfo-dropdown'); + return dropdown; + } + + function createDropdownItem(textContent, action) { + const listItem = document.createElement('li'); + listItem.id = `hideInfo-item-${textContent.replace(/ /g, '')}`; + listItem.classList.add('hideInfo-item'); + listItem.setAttribute('role', 'option'); + listItem.textContent = textContent; + listItem.addEventListener('mousedown', (event) => { + event.preventDefault(); + action(widget, node); // perform the action when dropdown item is clicked + removeDropdown(); + dropdownShouldBeRemoved = false; + }); + listItem.dataset.action = textContent.replace(/ /g, ''); // store the action in a data attribute + return listItem; + } + + function displayDropdown(widget) { + removeDropdown(); + + const dropdown = createDropdownElement(); + const listItemHide = createDropdownItem('Hide info Widget', hideWidget); + const listItemHideAll = createDropdownItem('Hide for all of this node-type', hideWidgetForNodetype); + + dropdown.appendChild(listItemHide); + dropdown.appendChild(listItemHideAll); + + const inputRect = widget.inputEl.getBoundingClientRect(); + dropdown.style.top = `${inputRect.top + inputRect.height}px`; + dropdown.style.left = `${inputRect.left}px`; + dropdown.style.width = `${inputRect.width}px`; + + document.body.appendChild(dropdown); + dropdownShouldBeRemoved = true; + + widget.inputEl.removeEventListener('keydown', handleKeyDown); + widget.inputEl.addEventListener('keydown', handleKeyDown); + document.addEventListener('click', handleDocumentClick); + } + + function removeDropdown() { + const dropdown = document.getElementById('hideinfo-dropdown'); + if (dropdown) { + dropdown.remove(); + widget.inputEl.removeEventListener('keydown', handleKeyDown); + } + document.removeEventListener('click', handleDocumentClick); + + } + + function handleKeyDown(event) { + const dropdownItems = document.querySelectorAll('.hideInfo-item'); + + if (event.keyCode === KEY_CODES.ENTER && dropdownShouldBeRemoved) { + event.preventDefault(); + if (selectionIndex !== -1) { + const selectedAction = dropdownItems[selectionIndex].dataset.action; + if (selectedAction === 'HideinfoWidget') { + hideWidget(widget, node); + } else if (selectedAction === 'Hideforall') { + hideWidgetForNodetype(widget, node); + } + removeDropdown(); + dropdownShouldBeRemoved = false; + } + } else if (event.keyCode === KEY_CODES.ARROW_DOWN && dropdownShouldBeRemoved) { + event.preventDefault(); + if (selectionIndex !== -1) { + dropdownItems[selectionIndex].classList.remove('selected'); + } + selectionIndex = (selectionIndex + 1) % dropdownItems.length; + dropdownItems[selectionIndex].classList.add('selected'); + } else if (event.keyCode === KEY_CODES.ARROW_UP && dropdownShouldBeRemoved) { + event.preventDefault(); + if (selectionIndex !== -1) { + dropdownItems[selectionIndex].classList.remove('selected'); + } + selectionIndex = (selectionIndex - 1 + dropdownItems.length) % dropdownItems.length; + dropdownItems[selectionIndex].classList.add('selected'); + } else if (event.keyCode === KEY_CODES.ESC && dropdownShouldBeRemoved) { + event.preventDefault(); + removeDropdown(); + } + } + + function hideWidget(widget, node) { + node.properties['infoWidgetHidden'] = true; + widget.type = "esayHidden"; + widget.computeSize = () => [0, WIDGET_GAP]; + node.setSize([node.size[0], node.size[1]]); + } + + function hideWidgetForNodetype(widget, node) { + hideWidget(widget, node) + const hiddenNodeTypes = JSON.parse(localStorage.getItem('hiddenWidgetNodeTypes') || "[]"); + if (!hiddenNodeTypes.includes(node.constructor.type)) { + hiddenNodeTypes.push(node.constructor.type); + } + localStorage.setItem('hiddenWidgetNodeTypes', JSON.stringify(hiddenNodeTypes)); + } + + function handleDocumentClick(event) { + const dropdown = document.getElementById('hideinfo-dropdown'); + + // If the click was outside the dropdown and the dropdown should be removed, remove it + if (dropdown && !dropdown.contains(event.target) && dropdownShouldBeRemoved) { + removeDropdown(); + dropdownShouldBeRemoved = false; + } + } +} + + +var styleElement = document.createElement("style"); +const cssCode = ` +.easy-info_widget { + background-color: var(--comfy-input-bg); + color: var(--input-text); + overflow: hidden; + padding: 2px; + resize: none; + border: none; + box-sizing: border-box; + font-size: 10px; + border-radius: 7px; + text-align: center; + text-wrap: balance; +} +.hideInfo-dropdown { + position: absolute; + box-sizing: border-box; + background-color: #121212; + border-radius: 7px; + box-shadow: 0 2px 4px rgba(255, 255, 255, .25); + padding: 0; + margin: 0; + list-style: none; + z-index: 1000; + overflow: auto; + max-height: 200px; +} + +.hideInfo-dropdown li { + padding: 4px 10px; + cursor: pointer; + font-family: system-ui; + font-size: 0.7rem; +} + +.hideInfo-dropdown li:hover, +.hideInfo-dropdown li.selected { + background-color: #e5e5e5; + border-radius: 7px; +} +` +styleElement.innerHTML = cssCode +document.head.appendChild(styleElement); + +const InfoSymbol = Symbol(); +const InfoResizeSymbol = Symbol(); + + + + +// WIDGET FUNCTIONS +function addInfoWidget(node, name, opts, app) { + const INFO_W_SIZE = 50; + + node.addProperty('infoWidgetHidden', false) + + function computeSize(size) { + if (node.widgets[0].last_y == null) return; + + let y = node.widgets[0].last_y; + + // Compute the height of all non easyInfo widgets + let widgetHeight = 0; + const infoWidges = []; + for (let i = 0; i < node.widgets.length; i++) { + const w = node.widgets[i]; + if (w.type === "easyInfo") { + infoWidges.push(w); + } else { + if (w.computeSize) { + widgetHeight += w.computeSize()[1] + 4; + } else { + widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + } + } + + let infoWidgetSpace = infoWidges.length * INFO_W_SIZE; // Height for all info widgets + + // Check if there's enough space for all widgets + if (size[1] < y + widgetHeight + infoWidgetSpace) { + // There isn't enough space for all the widgets, increase the size of the node + node.size[1] = y + widgetHeight + infoWidgetSpace; + node.graph.setDirtyCanvas(true); + } + + // Position each of the widgets + for (const w of node.widgets) { + w.y = y; + if (w.type === "easyInfo") { + y += INFO_W_SIZE; + } else if (w.computeSize) { + y += w.computeSize()[1] + 4; + } else { + y += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + } + } + + const widget = { + type: "easyInfo", + name, + get value() { + return this.inputEl.value; + }, + set value(x) { + this.inputEl.value = x; + }, + draw: function (ctx, _, widgetWidth, y, widgetHeight) { + if (!this.parent.inputHeight) { + // If we are initially offscreen when created we wont have received a resize event + // Calculate it here instead + computeSize(node.size); + } + const visible = app.canvas.ds.scale > 0.5 && this.type === "easyInfo"; + const margin = 10; + const elRect = ctx.canvas.getBoundingClientRect(); + const transform = new DOMMatrix() + .scaleSelf(elRect.width / ctx.canvas.width, elRect.height / ctx.canvas.height) + .multiplySelf(ctx.getTransform()) + .translateSelf(margin, margin + y); + + Object.assign(this.inputEl.style, { + transformOrigin: "0 0", + transform: transform, + left: "0px", + top: "0px", + width: `${widgetWidth - (margin * 2)}px`, + height: `${this.parent.inputHeight - (margin * 2)}px`, + position: "absolute", + background: (!node.color)?'':node.color, + color: (!node.color)?'':'white', + zIndex: app.graph._nodes.indexOf(node), + }); + this.inputEl.hidden = !visible; + }, + }; + widget.inputEl = document.createElement("textarea"); + widget.inputEl.className = "easy-info_widget"; + widget.inputEl.value = opts.defaultVal; + widget.inputEl.placeholder = opts.placeholder || ""; + widget.inputEl.readOnly = true; + widget.parent = node; + + document.body.appendChild(widget.inputEl); + + node.addCustomWidget(widget); + + app.canvas.onDrawBackground = function () { + // Draw node isnt fired once the node is off the screen + // if it goes off screen quickly, the input may not be removed + // this shifts it off screen so it can be moved back if the node is visible. + for (let n in app.graph._nodes) { + n = app.graph._nodes[n]; + for (let w in n.widgets) { + let wid = n.widgets[w]; + if (Object.hasOwn(wid, "inputEl")) { + wid.inputEl.style.left = -8000 + "px"; + wid.inputEl.style.position = "absolute"; + } + } + } + }; + + node.onRemoved = function () { + // When removing this node we need to remove the input from the DOM + for (let y in this.widgets) { + if (this.widgets[y].inputEl) { + this.widgets[y].inputEl.remove(); + } + } + }; + + widget.onRemove = () => { + widget.inputEl?.remove(); + + // Restore original size handler if we are the last + if (!--node[InfoSymbol]) { + node.onResize = node[InfoResizeSymbol]; + delete node[InfoSymbol]; + delete node[InfoResizeSymbol]; + } + }; + + if (node[InfoSymbol]) { + node[InfoSymbol]++; + } else { + node[InfoSymbol] = 1; + const onResize = (node[InfoResizeSymbol] = node.onResize); + + node.onResize = function (size) { + computeSize(size); + + // Call original resizer handler + if (onResize) { + console.log(this, arguments) + onResize.apply(this, arguments); + } + }; + } + + return { widget }; +} + +// WIDGETS +const easyCustomWidgets = { + INFO(node, inputName, inputData, app) { + const defaultVal = inputData[1].default || ""; + return addInfoWidget(node, inputName, { defaultVal, ...inputData[1] }, app); + }, +} + + + +app.registerExtension({ + name: "comfy.easy.widgets", + getCustomWidgets(app) { + return easyCustomWidgets; + }, + nodeCreated(node) { + if (node.widgets) { + // Locate info widgets + const widgets = node.widgets.filter((n) => (n.type === "easyInfo")); + for (const widget of widgets) { + widget.inputEl.addEventListener('contextmenu', function(e) { + hideInfoWidget(e, node, widget); + }); + widget.inputEl.addEventListener('click', function(e) { + hideInfoWidget(e, node, widget); + }); + } + } + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + const hiddenNodeTypes = JSON.parse(localStorage.getItem('hiddenWidgetNodeTypes') || "[]"); + const origOnConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : undefined; + if (this.properties['infoWidgetHidden']) { + for (let i in this.widgets) { + if (this.widgets[i].type == "easyInfo") { + hideInfoWidget(null, this, this.widgets[i]); + } + } + } + return r; + }; + const origOnAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = function () { + const r = origOnAdded ? origOnAdded.apply(this, arguments) : undefined; + if (hiddenNodeTypes.includes(this.type)) { + for (let i in this.widgets) { + if (this.widgets[i].type == "easyInfo") { + this.properties['infoWidgetHidden'] = true; + } + } + } + return r; + } + } +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyXYPlot.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyXYPlot.js new file mode 100644 index 00000000..6f27007b --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/easy/easyXYPlot.js @@ -0,0 +1,212 @@ +import { app } from "../../../../scripts/app.js"; +import {removeDropdown, createDropdown} from "../common/dropdown.js"; + +function generateNumList(dictionary) { + const minimum = dictionary["min"] || 0; + const maximum = dictionary["max"] || 0; + const step = dictionary["step"] || 1; + + if (step === 0) { + return []; + } + + const result = []; + let currentValue = minimum; + + while (currentValue <= maximum) { + if (Number.isInteger(step)) { + result.push(Math.round(currentValue) + '; '); + } else { + let formattedValue = currentValue.toFixed(3); + if(formattedValue == -0.000){ + formattedValue = '0.000'; + } + if (!/\.\d{3}$/.test(formattedValue)) { + formattedValue += "0"; + } + result.push(formattedValue + "; "); + } + currentValue += step; + } + + if (maximum >= 0 && minimum >= 0) { + //low to high + return result; + } + else { + //high to low + return result.reverse(); + } +} + +let plotDict = {}; +let currentOptionsDict = {}; + +function getCurrentOptionLists(node, widget) { + const nodeId = String(node.id); + const widgetName = widget.name; + const widgetValue = widget.value.replace(/^(loader|preSampling):\s/, ''); + + if (!currentOptionsDict[widgetName]) { + currentOptionsDict = {...currentOptionsDict, [widgetName]: plotDict[widgetValue]}; + } else if (currentOptionsDict[widgetName] != plotDict[widgetValue]) { + currentOptionsDict[widgetName] = plotDict[widgetValue]; + } +} + +function addGetSetters(node) { + if (node.widgets) + for (const w of node.widgets) { + if (w.name === "x_axis" || + w.name === "y_axis") { + let widgetValue = w.value; + + // Define getters and setters for widget values + Object.defineProperty(w, 'value', { + + get() { + return widgetValue; + }, + set(newVal) { + if (newVal !== widgetValue) { + widgetValue = newVal; + getCurrentOptionLists(node, w); + } + } + }); + } + } +} + +function dropdownCreator(node) { + if (node.widgets) { + const widgets = node.widgets.filter( + (n) => (n.type === "customtext" && n.dynamicPrompts !== false) || n.dynamicPrompts + ); + + for (const w of widgets) { + function replaceOptionSegments(selectedOption, inputSegments, cursorSegmentIndex, optionsList) { + if (selectedOption) { + inputSegments[cursorSegmentIndex] = selectedOption; + } + + return inputSegments.map(segment => verifySegment(segment, optionsList)) + .filter(item => item !== '') + .join(''); + } + + function verifySegment(segment, optionsList) { + segment = cleanSegment(segment); + + if (isInOptionsList(segment, optionsList)) { + return segment + '; '; + } + + let matchedOptions = findMatchedOptions(segment, optionsList); + + if (matchedOptions.length === 1 || matchedOptions.length === 2) { + return matchedOptions[0]; + } + + if (isInOptionsList(formatNumberSegment(segment), optionsList)) { + return formatNumberSegment(segment) + '; '; + } + + return ''; + } + + function cleanSegment(segment) { + return segment.replace(/(\n|;| )/g, ''); + } + + function isInOptionsList(segment, optionsList) { + return optionsList.includes(segment + '; '); + } + + function findMatchedOptions(segment, optionsList) { + return optionsList.filter(option => option.toLowerCase().includes(segment.toLowerCase())); + } + + function formatNumberSegment(segment) { + if (Number(segment)) { + return Number(segment).toFixed(3); + } + + if (['0', '0.', '0.0', '0.00', '00'].includes(segment)) { + return '0.000'; + } + return segment; + } + + + const onInput = function () { + const axisWidgetName = w.name[0] + '_axis'; + let optionsList = currentOptionsDict?.[axisWidgetName] || []; + if (optionsList.length === 0) {return} + + const inputText = w.inputEl.value; + const cursorPosition = w.inputEl.selectionStart; + let inputSegments = inputText.split('; '); + + const cursorSegmentIndex = inputText.substring(0, cursorPosition).split('; ').length - 1; + const currentSegment = inputSegments[cursorSegmentIndex]; + const currentSegmentLower = currentSegment.replace(/\n/g, '').toLowerCase(); + const filteredOptionsList = optionsList.filter(option => option.toLowerCase().includes(currentSegmentLower)).map(option => option.replace(/; /g, '')); + + if (filteredOptionsList.length > 0) { + createDropdown(w.inputEl, filteredOptionsList, (selectedOption) => { + const verifiedText = replaceOptionSegments(selectedOption, inputSegments, cursorSegmentIndex, optionsList); + w.inputEl.value = verifiedText; + }); + } + else { + removeDropdown(); + const verifiedText = replaceOptionSegments(null, inputSegments, cursorSegmentIndex, optionsList); + w.inputEl.value = verifiedText; + } + }; + + w.inputEl.removeEventListener('input', onInput); + w.inputEl.addEventListener('input', onInput); + w.inputEl.removeEventListener('mouseup', onInput); + w.inputEl.addEventListener('mouseup', onInput); + } + } +} + +app.registerExtension({ + name: "comfy.easy.xyPlot", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "easy XYPlot") { + plotDict = nodeData.input.hidden.plot_dict[0]; + + for (const key in plotDict) { + const value = plotDict[key]; + if (Array.isArray(value)) { + let updatedValues = []; + for (const v of value) { + updatedValues.push(v + '; '); + } + plotDict[key] = updatedValues; + } else if (typeof(value) === 'object') { + if(key == 'seed'){ + plotDict[key] = value + '; '; + } + else { + plotDict[key] = generateNumList(value); + } + } else { + plotDict[key] = value + '; '; + } + } + plotDict["None"] = []; + plotDict["---------------------"] = []; + } + }, + nodeCreated(node) { + if (node.comfyClass === "easy XYPlot") { + addGetSetters(node); + dropdownCreator(node); + } + } +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/getset.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/getset.js new file mode 100644 index 00000000..78957b28 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/getset.js @@ -0,0 +1,311 @@ +import { app } from "../../../scripts/app.js"; + +// Node that allows you to tunnel connections for cleaner graphs + +app.registerExtension({ + name: "easy setNode", + registerCustomNodes() { + class SetNode { + defaultVisibility = true; + serialize_widgets = true; + constructor() { + if (!this.properties) { + this.properties = { + "previousName": "" + }; + } + this.properties.showOutputText = SetNode.defaultVisibility; + + const node = this; + node.color = LGraphCanvas.node_colors.blue.color; + + this.addWidget( + "text", + "Constant", + '', + (s, t, u, v, x) => { + node.validateName(node.graph); + if(this.widgets[0].value !== ''){ + this.title = "Set_" + this.widgets[0].value; + } + this.update(); + this.properties.previousName = this.widgets[0].value; + }, + {} + ) + + this.addInput("*", "*"); + + + this.onConnectionsChange = function( + slotType, //1 = input, 2 = output + slot, + isChangeConnect, + link_info, + output + ) { + // console.log("onConnectionsChange"); + //On Disconnect + if (slotType == 1 && !isChangeConnect) { + this.inputs[slot].type = '*'; + this.inputs[slot].name = '*'; + } + + //On Connect + if (link_info && node.graph && slotType == 1 && isChangeConnect) { + const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id); + const type = fromNode.outputs[link_info.origin_slot].type; + + if (this.title === "Set"){ + this.title = "Set_" + type; + } + if (this.widgets[0].value === '*'){ + this.widgets[0].value = type + } + + this.validateName(node.graph); + this.inputs[0].type = type; + this.inputs[0].name = type; + + setTimeout(_=>{ + if(type != this.widgets[0].value){ + this.title = "Set_" + this.widgets[0].value; + } + },1) + } + + //Update either way + this.update(); + } + + this.validateName = function(graph) { + let widgetValue = node.widgets[0].value; + if (widgetValue != '') { + let tries = 0; + let collisions = []; + + do { + collisions = graph._nodes.filter((otherNode) => { + if (otherNode == this) { + return false; + } + if (otherNode.type == 'easy setNode' && otherNode.widgets[0].value === widgetValue) { + return true; + } + return false; + }) + if (collisions.length > 0) { + widgetValue = node.widgets[0].value + "_" + tries; + } + tries++; + } while (collisions.length > 0) + node.widgets[0].value = widgetValue; + this.update(); + } + } + + this.clone = function () { + const cloned = SetNode.prototype.clone.apply(this); + cloned.inputs[0].name = '*'; + cloned.inputs[0].type = '*'; + cloned.properties.previousName = ''; + cloned.size = cloned.computeSize(); + return cloned; + }; + + this.onAdded = function(graph) { + this.validateName(graph); + } + + + this.update = function() { + if (node.graph) { + this.findGetters(node.graph).forEach((getter) => { + getter.setType(this.inputs[0].type); + }); + if (this.widgets[0].value) { + this.findGetters(node.graph, true).forEach((getter) => { + getter.setName(this.widgets[0].value) + }); + } + + const allGetters = node.graph._nodes.filter((otherNode) => otherNode.type == "easy getNode"); + allGetters.forEach((otherNode) => { + if (otherNode.setComboValues) { + otherNode.setComboValues(); + } + }) + } + } + + + this.findGetters = function(graph, checkForPreviousName) { + const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value; + return graph._nodes.filter((otherNode) => { + if (otherNode.type == 'easy getNode' && otherNode.widgets[0].value === name && name != '') { + return true; + } + return false; + }) + } + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + onRemoved() { + const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "easy getNode"); + allGetters.forEach((otherNode) => { + if (otherNode.setComboValues) { + otherNode.setComboValues([this]); + } + }) + } + } + + + LiteGraph.registerNodeType( + "easy setNode", + Object.assign(SetNode, { + title: "Set", + }) + ); + + SetNode.category = "EasyUse/Util"; + }, +}); + + +app.registerExtension({ + name: "easy getNode", + registerCustomNodes() { + class GetNode { + + defaultVisibility = true; + serialize_widgets = true; + + constructor() { + if (!this.properties) { + this.properties = {}; + } + this.properties.showOutputText = GetNode.defaultVisibility; + + const node = this; + node.color = LGraphCanvas.node_colors.blue.color; + this.addWidget( + "combo", + "Constant", + "", + (e) => { + this.onRename(); + }, + { + values: () => { + const setterNodes = node.graph._nodes.filter((otherNode) => otherNode.type == 'easy setNode'); + return setterNodes.map((otherNode) => otherNode.widgets[0].value).sort(); + } + } + ) + + + this.addOutput("*", '*'); + + + this.onConnectionsChange = function( + slotType, //0 = output, 1 = input + slot, //self-explanatory + isChangeConnect, + link_info, + output + ) { + this.validateLinks(); + setTimeout(_=>{ + this.title = 'Get_' + this.widgets[0].value + },1) + } + + + this.setName = function(name) { + node.widgets[0].value = name; + node.onRename(); + node.serialize(); + } + + + this.onRename = function() { + const setter = this.findSetter(node.graph); + if (setter) { + this.setType(setter.inputs[0].type); + this.title = "Get_" + setter.widgets[0].value; + } else { + this.setType('*'); + } + } + + this.clone = function () { + const cloned = GetNode.prototype.clone.apply(this); + cloned.size = cloned.computeSize(); + return cloned; + }; + + this.validateLinks = function() { + if (this.outputs[0].type != '*' && this.outputs[0].links) { + this.outputs[0].links.forEach((linkId) => { + const link = node.graph.links[linkId]; + if (link && link.type != this.outputs[0].type && link.type != '*') { + node.graph.removeLink(linkId) + } + }) + } + } + + this.setType = function(type) { + this.outputs[0].name = type; + this.outputs[0].type = type; + this.validateLinks(); + } + + this.findSetter = function(graph) { + const name = this.widgets[0].value; + return graph._nodes.find((otherNode) => { + if (otherNode.type == 'easy setNode' && otherNode.widgets[0].value === name && name != '') { + return true; + } + return false; + }) + } + + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + + getInputLink(slot) { + const setter = this.findSetter(this.graph); + + if (setter) { + const slot_info = setter.inputs[slot]; + const link = this.graph.links[ slot_info.link ]; + return link; + } else { + throw new Error("No setter found for " + this.widgets[0].value + "(" + this.type + ")"); + } + + } + onAdded(graph) { + //this.setComboValues(); + //this.validateName(graph); + } + + } + + + LiteGraph.registerNodeType( + "easy getNode", + Object.assign(GetNode, { + title: "Get", + }) + ); + + GetNode.category = "EasyUse/Util"; + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image.js new file mode 100644 index 00000000..7d8b45ca --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image.js @@ -0,0 +1,66 @@ +import { app } from "../../../scripts/app.js"; + + +app.registerExtension({ + name: "comfy.easyUse.imageWidgets", + + nodeCreated(node) { + if (["easy imageSize","easy imageSizeBySide","easy imageSizeByLongerSide","easy imageSizeShow", "easy imageRatio", "easy imagePixelPerfect"].includes(node.comfyClass)) { + + const inputEl = document.createElement("textarea"); + inputEl.className = "comfy-multiline-input"; + inputEl.readOnly = true + + const widget = node.addDOMWidget("info", "customtext", inputEl, { + getValue() { + return inputEl.value; + }, + setValue(v) { + inputEl.value = v; + }, + serialize: false + }); + widget.inputEl = inputEl; + + inputEl.addEventListener("input", () => { + widget.callback?.(widget.value); + }); + } + }, + + beforeRegisterNodeDef(nodeType, nodeData, app) { + if (["easy imageSize","easy imageSizeBySide","easy imageSizeByLongerSide", "easy imageSizeShow", "easy imageRatio", "easy imagePixelPerfect"].includes(nodeData.name)) { + function populate(arr_text) { + var text = ''; + for (let i = 0; i < arr_text.length; i++){ + text += arr_text[i]; + } + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "info"); + if (pos !== -1 && this.widgets[pos]) { + const w = this.widgets[pos] + w.value = text; + } + } + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + }); + } + + // When the node is executed we will be sent the input text, display this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + populate.call(this, message.text); + }; + } + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/chooser.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/chooser.js new file mode 100644 index 00000000..e1c153dc --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/chooser.js @@ -0,0 +1,237 @@ +import { app } from "../../../../scripts/app.js"; +import { api } from "../../../../scripts/api.js"; +import { ComfyDialog, $el } from "../../../../scripts/ui.js"; + +import { restart_from_here } from "./prompt.js"; +import { hud, FlowState } from "./state.js"; +import { send_cancel, send_message, send_onstart, skip_next_restart_message } from "./messaging.js"; +import { display_preview_images, additionalDrawBackground, click_is_in_image } from "./preview.js"; +import {$t} from "../common/i18n.js"; + + +class chooserImageDialog extends ComfyDialog { + + constructor() { + super(); + this.node = null + this.select_index = [] + this.dialog_div = null + } + + show(image,node){ + this.select_index = [] + this.node = node + + const images_div = image.map((img, index) => { + const imgEl = $el('img', { + src: img.src, + onclick: _ => { + if(this.select_index.includes(index)){ + this.select_index = this.select_index.filter(i => i !== index) + imgEl.classList.remove('selected') + } else { + this.select_index.push(index) + imgEl.classList.add('selected') + } + if (node.selected.has(index)) node.selected.delete(index); + else node.selected.add(index); + } + }) + return imgEl + }) + super.show($el('div.easyuse-chooser-dialog',[ + $el('h5.easyuse-chooser-dialog-title', $t('Choose images to continue')), + $el('div.easyuse-chooser-dialog-images',images_div) + ])) + } + createButtons() { + const btns = super.createButtons(); + btns[0].onclick = _ => { + if (FlowState.running()) { send_cancel();} + super.close() + } + btns.unshift($el('button', { + type: 'button', + textContent: $t('Choose Selected Images'), + onclick: _ => { + if (FlowState.paused()) { + send_message(this.node.id, [...this.node.selected, -1, ...this.node.anti_selected]); + } + if (FlowState.idle()) { + skip_next_restart_message(); + restart_from_here(this.node.id).then(() => { send_message(this.node.id, [...this.node.selected, -1, ...this.node.anti_selected]); }); + } + super.close() + } + })) + return btns + } + +} + +function progressButtonPressed() { + const node = app.graph._nodes_by_id[this.node_id]; + if (node) { + const selected = [...node.selected] + if(selected?.length>0){ + node.setProperty('values',selected) + } + if (FlowState.paused()) { + send_message(node.id, [...node.selected, -1, ...node.anti_selected]); + } + if (FlowState.idle()) { + skip_next_restart_message(); + restart_from_here(node.id).then(() => { send_message(node.id, [...node.selected, -1, ...node.anti_selected]); }); + } + } +} + +function cancelButtonPressed() { + + if (FlowState.running()) { send_cancel();} +} + +function enable_disabling(button) { + Object.defineProperty(button, 'clicked', { + get : function() { return this._clicked; }, + set : function(v) { this._clicked = (v && this.name!=''); } + }) +} + +function disable_serialize(widget) { + if (!widget.options) widget.options = { }; + widget.options.serialize = false; +} + +app.registerExtension({ + name:'comfy.easyuse.imageChooser', + init() { + window.addEventListener("beforeunload", send_cancel, true); + }, + setup(app) { + + const draw = LGraphCanvas.prototype.draw; + LGraphCanvas.prototype.draw = function() { + if (hud.update()) { + app.graph._nodes.forEach((node)=> { if (node.update) { node.update(); } }) + } + draw.apply(this,arguments); + } + + + function easyuseImageChooser(event) { + const {node,image,isKSampler} = display_preview_images(event); + if(isKSampler) { + const dialog = new chooserImageDialog(); + dialog.show(image,node) + } + } + api.addEventListener("easyuse-image-choose", easyuseImageChooser); + + /* + If a run is interrupted, send a cancel message (unless we're doing the cancelling, to avoid infinite loop) + */ + const original_api_interrupt = api.interrupt; + api.interrupt = function () { + if (FlowState.paused() && !FlowState.cancelling) send_cancel(); + original_api_interrupt.apply(this, arguments); + } + + /* + At the start of execution + */ + function on_execution_start() { + if (send_onstart()) { + app.graph._nodes.forEach((node)=> { + if (node.selected || node.anti_selected) { + node.selected.clear(); + node.anti_selected.clear(); + node.update(); + } + }) + } + } + api.addEventListener("execution_start", on_execution_start); + }, + + async nodeCreated(node, app) { + + if(node.comfyClass == 'easy imageChooser'){ + node.setProperty('values',[]) + + /* A property defining the top of the image when there is just one */ + if(node?.imageIndex === undefined){ + Object.defineProperty(node, 'imageIndex', { + get : function() { return null; }, + set: function (v) {node.overIndex= v}, + }) + } + if(node?.imagey === undefined){ + Object.defineProperty(node, 'imagey', { + get : function() { return null; }, + set: function (v) {return node.widgets[node.widgets.length-1].last_y+LiteGraph.NODE_WIDGET_HEIGHT;}, + }) + } + + /* Capture clicks */ + const org_onMouseDown = node.onMouseDown; + node.onMouseDown = function( e, pos, canvas ) { + if (e.isPrimary) { + const i = click_is_in_image(node, pos); + if (i>=0) { this.imageClicked(i); } + } + return (org_onMouseDown && org_onMouseDown.apply(this, arguments)); + } + + node.send_button_widget = node.addWidget("button", "", "", progressButtonPressed); + node.cancel_button_widget = node.addWidget("button", "", "", cancelButtonPressed); + enable_disabling(node.cancel_button_widget); + enable_disabling(node.send_button_widget); + disable_serialize(node.cancel_button_widget); + disable_serialize(node.send_button_widget); + + } + }, + + beforeRegisterNodeDef(nodeType, nodeData, app) { + if(nodeData?.name == 'easy imageChooser'){ + + const onDrawBackground = nodeType.prototype.onDrawBackground; + nodeType.prototype.onDrawBackground = function(ctx) { + onDrawBackground.apply(this, arguments); + additionalDrawBackground(this, ctx); + } + + nodeType.prototype.imageClicked = function (imageIndex) { + if (nodeType?.comfyClass==="easy imageChooser") { + if (this.selected.has(imageIndex)) this.selected.delete(imageIndex); + else this.selected.add(imageIndex); + this.update(); + } + } + + const update = nodeType.prototype.update; + nodeType.prototype.update = function() { + if (update) update.apply(this,arguments); + if (this.send_button_widget) { + this.send_button_widget.node_id = this.id; + const selection = ( this.selected ? this.selected.size : 0 ) + ( this.anti_selected ? this.anti_selected.size : 0 ) + const maxlength = this.imgs?.length || 0; + if (FlowState.paused_here(this.id) && selection>0) { + this.send_button_widget.name = (selection>1) ? "Progress selected (" + selection + '/' + maxlength +")" : "Progress selected image"; + } else if (selection>0) { + this.send_button_widget.name = (selection>1) ? "Progress selected (" + selection + '/' + maxlength +")" : "Progress selected image as restart"; + } + else { + this.send_button_widget.name = ""; + } + } + if (this.cancel_button_widget) { + const isRunning = FlowState.running() + this.cancel_button_widget.name = isRunning ? "Cancel current run" : ""; + } + this.setDirtyCanvas(true,true); + } + } + } +}) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/messaging.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/messaging.js new file mode 100644 index 00000000..b5864462 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/messaging.js @@ -0,0 +1,34 @@ +import { api } from "../../../../scripts/api.js"; +import { FlowState } from "./state.js"; + +function send_message_from_pausing_node(message) { + const id = app.runningNodeId; + send_message(id, message); +} + +function send_message(id, message) { + const body = new FormData(); + body.append('message',message); + body.append('id', id); + api.fetchApi("/easyuse/image_chooser_message", { method: "POST", body, }); +} + +function send_cancel() { + send_message(-1,'__cancel__'); + FlowState.cancelling = true; + api.interrupt(); + FlowState.cancelling = false; +} + +var skip_next = 0; +function skip_next_restart_message() { skip_next += 1; } +function send_onstart() { + if (skip_next>0) { + skip_next -= 1; + return false; + } + send_message(-1,'__start__'); + return true; +} + +export { send_message_from_pausing_node, send_cancel, send_message, send_onstart, skip_next_restart_message } \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/preview.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/preview.js new file mode 100644 index 00000000..6f8fe440 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/preview.js @@ -0,0 +1,90 @@ +import { app } from "../../../../scripts/app.js"; + +const kSampler = ['easy kSampler', 'easy kSamplerTiled', 'easy fullkSampler'] + +function display_preview_images(event) { + const node = app.graph._nodes_by_id[event.detail.id]; + if (node) { + node.selected = new Set(); + node.anti_selected = new Set(); + const image = showImages(node, event.detail.urls); + return {node,image,isKSampler:kSampler.includes(node.type)} + } else { + console.log(`Image Chooser Preview - failed to find ${event.detail.id}`) + } +} + +function showImages(node, urls) { + node.imgs = []; + urls.forEach((u)=> { + const img = new Image(); + node.imgs.push(img); + img.onload = () => { app.graph.setDirtyCanvas(true); }; + img.src = `/view?filename=${encodeURIComponent(u.filename)}&type=temp&subfolder=${app.getPreviewFormatParam()}` + }) + node.setSizeForImage?.(); + return node.imgs +} + +function drawRect(node, s, ctx) { + const padding = 1; + var rect; + if (node.imageRects) { + rect = node.imageRects[s]; + } else { + const y = node.imagey; + rect = [padding,y+padding,node.size[0]-2*padding,node.size[1]-y-2*padding]; + } + ctx.strokeRect(rect[0]+padding, rect[1]+padding, rect[2]-padding*2, rect[3]-padding*2); +} + +function additionalDrawBackground(node, ctx) { + if (!node.imgs) return; + if (node.imageRects) { + for (let i = 0; i < node.imgs.length; i++) { + // delete underlying image + ctx.fillStyle = "#000"; + ctx.fillRect(...node.imageRects[i]) + // draw the new one + const img = node.imgs[i]; + const cellWidth = node.imageRects[i][2]; + const cellHeight = node.imageRects[i][3]; + + let wratio = cellWidth/img.width; + let hratio = cellHeight/img.height; + var ratio = Math.min(wratio, hratio); + + let imgHeight = ratio * img.height; + let imgWidth = ratio * img.width; + + const imgX = node.imageRects[i][0] + (cellWidth - imgWidth)/2; + const imgY = node.imageRects[i][1] + (cellHeight - imgHeight)/2; + const cell_padding = 2; + ctx.drawImage(img, imgX+cell_padding, imgY+cell_padding, imgWidth-cell_padding*2, imgHeight-cell_padding*2); + + } + } + ctx.lineWidth = 2; + ctx.strokeStyle = "green"; + node?.selected?.forEach((s) => { drawRect(node,s, ctx) }) + ctx.strokeStyle = "#F88"; + node?.anti_selected?.forEach((s) => { drawRect(node,s, ctx) }) +} + +function click_is_in_image(node, pos) { + if (node.imgs?.length>1) { + for (var i = 0; i 0 && dx < node.imageRects[i][2] && + dy > 0 && dy < node.imageRects[i][3] ) { + return i; + } + } + } else if (node.imgs?.length==1) { + if (pos[1]>node.imagey) return 0; + } + return -1; +} + +export { display_preview_images, additionalDrawBackground, click_is_in_image } \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/prompt.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/prompt.js new file mode 100644 index 00000000..c9c89b62 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/prompt.js @@ -0,0 +1,114 @@ +import { app } from "../../../../scripts/app.js"; + +function links_with(p, node_id, down, up) { + const links_with = []; + p.workflow.links.forEach((l) => { + if (down && l[1]===node_id && !links_with.includes(l[3])) links_with.push(l[3]) + if (up && l[3]===node_id && !links_with.includes(l[1])) links_with.push(l[1]) + }); + return links_with; +} + +function _all_v_nodes(p, here_id) { + /* + Make a list of all downstream nodes. + */ + const downstream = []; + const to_process = [here_id] + while(to_process.length>0) { + const id = to_process.pop(); + downstream.push(id); + to_process.push( + ...links_with(p,id,true,false).filter((nid)=>{ + return !(downstream.includes(nid) || to_process.includes(nid)) + }) + ) + } + + /* + Now all upstream nodes from any of the downstream nodes (except us). + Put us on the result list so we don't flow up through us + */ + to_process.push(...downstream.filter((n)=>{ return n!=here_id})); + const back_upstream = [here_id]; + while(to_process.length>0) { + const id = to_process.pop(); + back_upstream.push(id); + to_process.push( + ...links_with(p,id,false,true).filter((nid)=>{ + return !(back_upstream.includes(nid) || to_process.includes(nid)) + }) + ) + } + + const keep = []; + keep.push(...downstream); + keep.push(...back_upstream.filter((n)=>{return !keep.includes(n)})); + + console.log(`Nodes to keep: ${keep}`); + return keep; +} + +async function all_v_nodes(here_id) { + const p = structuredClone(await app.graphToPrompt()); + const all_nodes = []; + p.workflow.nodes.forEach((node)=>{all_nodes.push(node.id)}) + p.workflow.links = p.workflow.links.filter((l)=>{ return (all_nodes.includes(l[1]) && all_nodes.includes(l[3]))} ) + return _all_v_nodes(p,here_id); +} + +async function restart_from_here(here_id, go_down_to_chooser=false) { + const p = structuredClone(await app.graphToPrompt()); + /* + Make a list of all nodes, and filter out links that are no longer valid + */ + const all_nodes = []; + p.workflow.nodes.forEach((node)=>{all_nodes.push(node.id)}) + p.workflow.links = p.workflow.links.filter((l)=>{ return (all_nodes.includes(l[1]) && all_nodes.includes(l[3]))} ) + + /* Move downstream to a chooser */ + if (go_down_to_chooser) { + while (!app.graph._nodes_by_id[here_id].isChooser) { + here_id = links_with(p, here_id, true, false)[0]; + } + } + + const keep = _all_v_nodes(p, here_id); + + /* + Filter p.workflow.nodes and p.workflow.links + */ + p.workflow.nodes = p.workflow.nodes.filter((node) => { + if (node.id===here_id) node.inputs.forEach((i)=>{i.link=null}) // remove our upstream links + return (keep.includes(node.id)) // only keep keepers + }) + p.workflow.links = p.workflow.links.filter((l) => {return (keep.includes(l[1]) && keep.includes(l[3]))}) + + /* + Filter the p.output object to only include nodes we're keeping + */ + const new_output = {} + for (const [key, value] of Object.entries(p.output)) { + if (keep.includes(parseInt(key))) new_output[key] = value; + } + /* + Filter the p.output entry for the start node to remove any list (ie link) inputs + */ + const new_inputs = {}; + for (const [key, value] of Object.entries(new_output[here_id.toString()].inputs)) { + if (!Array.isArray(value)) new_inputs[key] = value; + } + new_output[here_id.toString()].inputs = new_inputs; + + p.output = new_output; + + // temporarily hijack graph_to_prompt with a version that restores the old one but returns this prompt + const gtp_was = app.graphToPrompt; + app.graphToPrompt = () => { + app.graphToPrompt = gtp_was; + return p; + } + app.queuePrompt(0); +} + +export { restart_from_here, all_v_nodes } \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/state.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/state.js new file mode 100644 index 00000000..69437d72 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/image_chooser/state.js @@ -0,0 +1,55 @@ +import { app } from "../../../../scripts/app.js"; + + +class HUD { + constructor() { + this.current_node_id = undefined; + this.class_of_current_node = null; + this.current_node_is_chooser = false; + } + + update() { + if (app.runningNodeId==this.current_node_id) return false; + + this.current_node_id = app.runningNodeId; + + if (this.current_node_id) { + this.class_of_current_node = app.graph?._nodes_by_id[app.runningNodeId.toString()]?.comfyClass; + this.current_node_is_chooser = this.class_of_current_node === "easy imageChooser" + } else { + this.class_of_current_node = undefined; + this.current_node_is_chooser = false; + } + return true; + } +} + +const hud = new HUD(); + + +class FlowState { + constructor(){} + static idle() { + return (!app.runningNodeId); + } + static paused() { + return true; + } + static paused_here(node_id) { + return (FlowState.paused() && FlowState.here(node_id)) + } + static running() { + return (!FlowState.idle()); + } + static here(node_id) { + return (app.runningNodeId==node_id); + } + static state() { + if (FlowState.paused()) return "Paused"; + if (FlowState.running()) return "Running"; + return "Idle"; + } + static cancelling = false; +} + +export { hud, FlowState} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/poseEditor.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/poseEditor.js new file mode 100644 index 00000000..bfd6813e --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/poseEditor.js @@ -0,0 +1,666 @@ +import { app } from "../../../scripts/app.js"; +import { fabric } from "../lib/fabric.js"; + +fabric.Object.prototype.transparentCorners = false; +fabric.Object.prototype.cornerColor = "#108ce6"; +fabric.Object.prototype.borderColor = "#108ce6"; +fabric.Object.prototype.cornerSize = 10; + +let connect_keypoints = [ + [0, 1], + [1, 2], + [2, 3], + [3, 4], + [1, 5], + [5, 6], + [6, 7], + [1, 8], + [8, 9], + [9, 10], + [1, 11], + [11, 12], + [12, 13], + [0, 14], + [14, 16], + [0, 15], + [15, 17], +]; + +let connect_color = [ + [0, 0, 255], + [255, 0, 0], + [255, 170, 0], + [255, 255, 0], + [255, 85, 0], + [170, 255, 0], + [85, 255, 0], + [0, 255, 0], + [0, 255, 85], + [0, 255, 170], + [0, 255, 255], + [0, 170, 255], + [0, 85, 255], + [85, 0, 255], + [170, 0, 255], + [255, 0, 255], + [255, 0, 170], + [255, 0, 85], +]; + +const default_keypoints = [ + [241, 77], + [241, 120], + [191, 118], + [177, 183], + [163, 252], + [298, 118], + [317, 182], + [332, 245], + [225, 241], + [213, 359], + [215, 454], + [270, 240], + [282, 360], + [286, 456], + [232, 59], + [253, 60], + [225, 70], + [260, 72], +]; + +class OpenPose { + constructor(node, canvasElement) { + this.lockMode = false; + this.visibleEyes = true; + this.flipped = false; + this.node = node; + this.undo_history = LS_Poses[node.name].undo_history || []; + this.redo_history = LS_Poses[node.name].redo_history || []; + this.history_change = false; + this.canvas = this.initCanvas(canvasElement); + this.image = node.widgets.find((w) => w.name === "image"); + } + + setPose(keypoints) { + this.canvas.clear(); + + this.canvas.backgroundColor = "#000"; + + const res = []; + for (let i = 0; i < keypoints.length; i += 18) { + const chunk = keypoints.slice(i, i + 18); + res.push(chunk); + } + + for (let item of res) { + this.addPose(item); + this.canvas.discardActiveObject(); + } + } + + addPose(keypoints = undefined) { + if (keypoints === undefined) { + keypoints = default_keypoints; + } + + const group = new fabric.Group(); + + const makeCircle = ( + color, + left, + top, + line1, + line2, + line3, + line4, + line5 + ) => { + let c = new fabric.Circle({ + left: left, + top: top, + strokeWidth: 1, + radius: 5, + fill: color, + stroke: color, + }); + + c.hasControls = c.hasBorders = false; + c.line1 = line1; + c.line2 = line2; + c.line3 = line3; + c.line4 = line4; + c.line5 = line5; + + return c; + }; + + const makeLine = (coords, color) => { + return new fabric.Line(coords, { + fill: color, + stroke: color, + strokeWidth: 10, + selectable: false, + evented: false, + }); + }; + + const lines = []; + const circles = []; + + for (let i = 0; i < connect_keypoints.length; i++) { + // ζŽ₯ηΆšγ•γ‚Œγ‚‹idxγ‚’ζŒ‡εšγ€€[0, 1]γͺら0と1぀γͺぐ + const item = connect_keypoints[i]; + const line = makeLine( + keypoints[item[0]].concat(keypoints[item[1]]), + `rgba(${connect_color[i].join(", ")}, 0.7)` + ); + lines.push(line); + this.canvas.add(line); + } + + for (let i = 0; i < keypoints.length; i++) { + let list = []; + + connect_keypoints.filter((item, idx) => { + if (item.includes(i)) { + list.push(lines[idx]); + return idx; + } + }); + const circle = makeCircle( + `rgb(${connect_color[i].join(", ")})`, + keypoints[i][0], + keypoints[i][1], + ...list + ); + circle["id"] = i; + circles.push(circle); + group.addWithUpdate(circle); + } + + this.canvas.discardActiveObject(); + this.canvas.setActiveObject(group); + this.canvas.add(group); + group.toActiveSelection(); + this.canvas.requestRenderAll(); + } + + initCanvas() { + this.canvas = new fabric.Canvas(this.canvas, { + backgroundColor: "#000", + preserveObjectStacking: true, + }); + + const updateLines = (target) => { + if ("_objects" in target) { + const flipX = target.flipX ? -1 : 1; + const flipY = target.flipY ? -1 : 1; + this.flipped = flipX * flipY === -1; + const showEyes = this.flipped ? !this.visibleEyes : this.visibleEyes; + + if (target.angle === 0) { + const rtop = target.top; + const rleft = target.left; + for (const item of target._objects) { + let p = item; + p.scaleX = 1; + p.scaleY = 1; + const top = + rtop + + p.top * target.scaleY * flipY + + (target.height * target.scaleY) / 2; + const left = + rleft + + p.left * target.scaleX * flipX + + (target.width * target.scaleX) / 2; + p["_top"] = top; + p["_left"] = left; + if (p["id"] === 0) { + p.line1 && p.line1.set({ x1: left, y1: top }); + } else { + p.line1 && p.line1.set({ x2: left, y2: top }); + } + if (p["id"] === 14 || p["id"] === 15) { + p.radius = showEyes ? 5 : 0; + if (p.line1) p.line1.strokeWidth = showEyes ? 10 : 0; + if (p.line2) p.line2.strokeWidth = showEyes ? 10 : 0; + } + p.line2 && p.line2.set({ x1: left, y1: top }); + p.line3 && p.line3.set({ x1: left, y1: top }); + p.line4 && p.line4.set({ x1: left, y1: top }); + p.line5 && p.line5.set({ x1: left, y1: top }); + } + } else { + const aCoords = target.aCoords; + const center = { + x: (aCoords.tl.x + aCoords.br.x) / 2, + y: (aCoords.tl.y + aCoords.br.y) / 2, + }; + const rad = (target.angle * Math.PI) / 180; + const sin = Math.sin(rad); + const cos = Math.cos(rad); + + for (const item of target._objects) { + let p = item; + const p_top = p.top * target.scaleY * flipY; + const p_left = p.left * target.scaleX * flipX; + const left = center.x + p_left * cos - p_top * sin; + const top = center.y + p_left * sin + p_top * cos; + p["_top"] = top; + p["_left"] = left; + if (p["id"] === 0) { + p.line1 && p.line1.set({ x1: left, y1: top }); + } else { + p.line1 && p.line1.set({ x2: left, y2: top }); + } + if (p["id"] === 14 || p["id"] === 15) { + p.radius = showEyes ? 5 : 0.3; + if (p.line1) p.line1.strokeWidth = showEyes ? 10 : 0; + if (p.line2) p.line2.strokeWidth = showEyes ? 10 : 0; + } + p.line2 && p.line2.set({ x1: left, y1: top }); + p.line3 && p.line3.set({ x1: left, y1: top }); + p.line4 && p.line4.set({ x1: left, y1: top }); + p.line5 && p.line5.set({ x1: left, y1: top }); + } + } + } else { + var p = target; + if (p["id"] === 0) { + p.line1 && p.line1.set({ x1: p.left, y1: p.top }); + } else { + p.line1 && p.line1.set({ x2: p.left, y2: p.top }); + } + p.line2 && p.line2.set({ x1: p.left, y1: p.top }); + p.line3 && p.line3.set({ x1: p.left, y1: p.top }); + p.line4 && p.line4.set({ x1: p.left, y1: p.top }); + p.line5 && p.line5.set({ x1: p.left, y1: p.top }); + } + this.canvas.renderAll(); + }; + + this.canvas.on("object:moving", (e) => { + updateLines(e.target); + }); + + this.canvas.on("object:scaling", (e) => { + updateLines(e.target); + this.canvas.renderAll(); + }); + + this.canvas.on("object:rotating", (e) => { + updateLines(e.target); + this.canvas.renderAll(); + }); + + this.canvas.on("object:modified", () => { + if ( + this.lockMode || + this.canvas.getActiveObject().type == "activeSelection" + ) + return; + this.undo_history.push(this.getJSON()); + this.redo_history.length = 0; + this.history_change = true; + this.uploadPoseFile(this.node.name); + }); + + if (!LS_Poses[this.node.name].undo_history.length) { + this.setPose(default_keypoints); + this.undo_history.push(this.getJSON()); + } + return this.canvas; + } + + undo() { + if (this.undo_history.length > 0) { + this.lockMode = true; + if (this.undo_history.length > 1) + this.redo_history.push(this.undo_history.pop()); + + const content = this.undo_history[this.undo_history.length - 1]; + this.loadPreset(content); + this.canvas.renderAll(); + this.lockMode = false; + this.history_change = true; + this.uploadPoseFile(this.node.name); + } + } + + redo() { + if (this.redo_history.length > 0) { + this.lockMode = true; + const content = this.redo_history.pop(); + this.undo_history.push(content); + this.loadPreset(content); + this.canvas.renderAll(); + this.lockMode = false; + this.history_change = true; + this.uploadPoseFile(this.node.name); + } + } + + resetCanvas() { + this.canvas.clear(); + this.canvas.backgroundColor = "#000"; + this.addPose(); + } + + updateHistoryData() { + if (this.history_change) { + LS_Poses[this.node.name].undo_history = this.undo_history; + LS_Poses[this.node.name].redo_history = this.redo_history; + LS_Save(); + this.history_change = false; + } + } + + uploadPoseFile(fileName) { + // Upload pose to temp folder ComfyUI + + const uploadFile = async (blobFile) => { + try { + const resp = await fetch("/upload/image", { + method: "POST", + body: blobFile, + }); + + if (resp.status === 200) { + const data = await resp.json(); + + if (!this.image.options.values.includes(data.name)) { + this.image.options.values.push(data.name); + } + + this.image.value = data.name; + this.updateHistoryData(); + } else { + alert(resp.status + " - " + resp.statusText); + } + } catch (error) { + console.error(error); + } + }; + + this.canvas.lowerCanvasEl.toBlob(function (blob) { + let formData = new FormData(); + formData.append("image", blob, fileName); + formData.append("overwrite", "true"); + formData.append("type", "temp"); + uploadFile(formData); + }, "image/png"); + // - end + + const callb = this.node.callback, + self = this; + this.image.callback = function () { + this.image.value = self.node.name; + if (callb) { + return callb.apply(this, arguments); + } + }; + } + + getJSON() { + const json = { + keypoints: this.canvas + .getObjects() + .filter((item) => { + if (item.type === "circle") return item; + }) + .map((item) => { + return [Math.round(item.left), Math.round(item.top)]; + }), + }; + + return json; + } + + loadPreset(json) { + try { + if (json["keypoints"].length % 18 === 0) { + this.setPose(json["keypoints"]); + } else { + throw new Error("keypoints is invalid"); + } + } catch (e) { + console.error(e); + } + } +} + +// Create OpenPose widget +function createOpenPose(node, inputName, inputData, app) { + node.name = inputName; + const widget = { + type: "openpose", + name: `w${inputName}`, + + draw: function (ctx, _, widgetWidth, y, widgetHeight) { + const margin = 10, + visible = app.canvas.ds.scale > 0.5 && this.type === "openpose", + clientRectBound = ctx.canvas.getBoundingClientRect(), + transform = new DOMMatrix() + .scaleSelf( + clientRectBound.width / ctx.canvas.width, + clientRectBound.height / ctx.canvas.height + ) + .multiplySelf(ctx.getTransform()) + .translateSelf(margin, margin + y), + w = (widgetWidth - margin * 2 - 3) * transform.a; + + Object.assign(this.openpose.style, { + left: `${transform.a * margin + transform.e}px`, + top: `${transform.d + transform.f}px`, + width: w + "px", + height: w + "px", + position: "absolute", + zIndex: app.graph._nodes.indexOf(node), + }); + + Object.assign(this.openpose.children[0].style, { + width: w + "px", + height: w + "px", + }); + + Object.assign(this.openpose.children[1].style, { + width: w + "px", + height: w + "px", + }); + + Array.from(this.openpose.children[2].children).forEach((element) => { + Object.assign(element.style, { + width: `${28.0 * transform.a}px`, + height: `${22.0 * transform.d}px`, + fontSize: `${transform.d * 10.0}px`, + }); + element.hidden = !visible; + }); + }, + }; + + // Fabric canvas + let canvasOpenPose = document.createElement("canvas"); + node.openPose = new OpenPose(node, canvasOpenPose); + + node.openPose.canvas.setWidth(512); + node.openPose.canvas.setHeight(512); + + let widgetCombo = node.widgets.filter((w) => w.type === "combo"); + widgetCombo[0].value = node.name; + + widget.openpose = node.openPose.canvas.wrapperEl; + widget.parent = node; + + // Create elements undo, redo, clear history + let panelButtons = document.createElement("div"), + undoButton = document.createElement("button"), + redoButton = document.createElement("button"), + historyClearButton = document.createElement("button"); + + panelButtons.className = "panelButtons comfy-menu-btns"; + undoButton.textContent = "⟲"; + redoButton.textContent = "⟳"; + historyClearButton.textContent = "βœ–"; + undoButton.title = "Undo"; + redoButton.title = "Redo"; + historyClearButton.title = "Clear History"; + + undoButton.addEventListener("click", () => node.openPose.undo()); + redoButton.addEventListener("click", () => node.openPose.redo()); + historyClearButton.addEventListener("click", () => { + if (confirm(`Delete all pose history of a node "${node.name}"?`)) { + node.openPose.undo_history = []; + node.openPose.redo_history = []; + node.openPose.setPose(default_keypoints); + node.openPose.undo_history.push(node.openPose.getJSON()); + node.openPose.history_change = true; + node.openPose.updateHistoryData(); + } + }); + + panelButtons.appendChild(undoButton); + panelButtons.appendChild(redoButton); + panelButtons.appendChild(historyClearButton); + node.openPose.canvas.wrapperEl.appendChild(panelButtons); + + document.body.appendChild(widget.openpose); + + // Add buttons add, reset, undo, redo poses + node.addWidget("button", "Add pose", "add_pose", () => { + node.openPose.addPose(); + }); + + node.addWidget("button", "Reset pose", "reset_pose", () => { + node.openPose.resetCanvas(); + }); + + // Add customWidget to node + node.addCustomWidget(widget); + + node.onRemoved = () => { + if (Object.hasOwn(LS_Poses, node.name)) { + delete LS_Poses[node.name]; + LS_Save(); + } + + // When removing this node we need to remove the input from the DOM + for (let y in node.widgets) { + if (node.widgets[y].openpose) { + node.widgets[y].openpose.remove(); + } + } + }; + + widget.onRemove = () => { + widget.openpose?.remove(); + }; + + app.canvas.onDrawBackground = function () { + // Draw node isnt fired once the node is off the screen + // if it goes off screen quickly, the input may not be removed + // this shifts it off screen so it can be moved back if the node is visible. + for (let n in app.graph._nodes) { + n = graph._nodes[n]; + for (let w in n.widgets) { + let wid = n.widgets[w]; + if (Object.hasOwn(wid, "openpose")) { + wid.openpose.style.left = -8000 + "px"; + wid.openpose.style.position = "absolute"; + } + } + } + }; + return { widget: widget }; +} + +window.LS_Poses = {}; +function LS_Save() { + ///console.log("Save:", LS_Poses); + localStorage.setItem("ComfyUI_Poses", JSON.stringify(LS_Poses)); +} + +app.registerExtension({ + name: "comfy.easyuse.poseEditor", + async init(app) { + // Any initial setup to run as soon as the page loads + let style = document.createElement("style"); + style.innerText = `.panelButtons{ + position: absolute; + padding: 4px; + display: flex; + gap: 4px; + flex-direction: column; + width: fit-content; + } + .panelButtons button:last-child{ + border-color: var(--error-text); + color: var(--error-text) !important; + } + + `; + document.head.appendChild(style); + }, + async setup(app) { + let openPoseNode = app.graph._nodes.filter((wi) => wi.type == "easy poseEditor"); + + if (openPoseNode.length) { + openPoseNode.map((n) => { + console.log(`Setup PoseNode: ${n.name}`); + let widgetImage = n.widgets.find((w) => w.name == "image"); + if (widgetImage && Object.hasOwn(LS_Poses, n.name)) { + let pose_ls = LS_Poses[n.name].undo_history; + n.openPose.loadPreset( + pose_ls.length > 0 + ? pose_ls[pose_ls.length - 1] + : { keypoints: default_keypoints } + ); + } + }); + } + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "easy poseEditor") { + const onNodeCreated = nodeType.prototype.onNodeCreated; + + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated + ? onNodeCreated.apply(this, arguments) + : undefined; + + let openPoseNode = app.graph._nodes.filter( + (wi) => {wi.type == "easy poseEditor"} + ), + nodeName = `Pose_${openPoseNode.length}`, + nodeNamePNG = `${nodeName}.png`; + + console.log(`Create PoseNode: ${nodeName}`); + + LS_Poses = + localStorage.getItem("ComfyUI_Poses") && + JSON.parse(localStorage.getItem("ComfyUI_Poses")); + if (!LS_Poses) { + localStorage.setItem("ComfyUI_Poses", JSON.stringify({})); + LS_Poses = JSON.parse(localStorage.getItem("ComfyUI_Poses")); + } + + if (!Object.hasOwn(LS_Poses, nodeNamePNG)) { + LS_Poses[nodeNamePNG] = { + undo_history: [], + redo_history: [], + }; + LS_Save(); + } + + createOpenPose.apply(this, [this, nodeNamePNG, {}, app]); + setTimeout(() => { + this.openPose.uploadPoseFile(nodeNamePNG); + }, 1); + + this.setSize([530, 620]); + + return r; + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/seed.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/seed.js new file mode 100644 index 00000000..dc6c17e0 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/js/seed.js @@ -0,0 +1,47 @@ +import { api } from "../../../scripts/api.js"; + +// ε…¨ε±€Seed +function globalSeedHandler(event) { + let nodes = app.graph._nodes_by_id; + for(let i in nodes) { + let node = nodes[i]; + if(node.type == 'easy globalSeed') { + if(node.widgets) { + const w = node.widgets.find((w) => w.name == 'value'); + const last_w = node.widgets.find((w) => w.name == 'last_seed'); + last_w.value = w.value; + w.value = event.detail.value; + } + } + else{ + if(node.widgets) { + const w = node.widgets.find((w) => w.name == 'seed_num' || w.name == 'seed' || w.name == 'noise_seed'); + if(w && event.detail.seed_map[node.id] != undefined) { + w.value = event.detail.seed_map[node.id]; + } + } + } + + } +} + +api.addEventListener("easyuse-global-seed", globalSeedHandler); + +const original_queuePrompt = api.queuePrompt; +async function queuePrompt_with_seed(number, { output, workflow }) { + workflow.seed_widgets = {}; + + for(let i in app.graph._nodes_by_id) { + let widgets = app.graph._nodes_by_id[i].widgets; + if(widgets) { + for(let j in widgets) { + if((widgets[j].name == 'seed_num' || widgets[j].name == 'seed' || widgets[j].name == 'noise_seed') && widgets[j].type != 'converted-widget') + workflow.seed_widgets[i] = parseInt(j); + } + } + } + + return await original_queuePrompt.call(api, number, { output, workflow }); +} + +api.queuePrompt = queuePrompt_with_seed; diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v1/lib/fabric.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/lib/fabric.js new file mode 100644 index 00000000..fa7bd3ce --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v1/lib/fabric.js @@ -0,0 +1,10 @@ +/** + * Bundled by jsDelivr using Rollup v2.79.1 and Terser v5.17.1. + * Original file: /npm/fabric-with-all-modules@4.6.1/dist/fabric.js + * + * Do NOT use SRI with dynamically generated files! 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i,r,n,s,a,h,c=t.getAttribute("viewBox"),l=1,u=1,f=t.getAttribute("width"),d=t.getAttribute("height"),g=t.getAttribute("x")||0,p=t.getAttribute("y")||0,v=t.getAttribute("preserveAspectRatio")||"",m=!c||!(c=c.match(y)),_=!f||!d||"100%"===f||"100%"===d,b=m&&_,x={},C="",w=0,S=0;if(x.width=0,x.height=0,x.toBeParsed=b,m&&(g||p)&&t.parentNode&&"#document"!==t.parentNode.nodeName&&(C=" translate("+o(g)+" "+o(p)+") ",a=(t.getAttribute("transform")||"")+C,t.setAttribute("transform",a),t.removeAttribute("x"),t.removeAttribute("y")),b)return x;if(m)return 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S=Math.abs(o.x)+Math.abs(o.y),T=e.original,O=S/(Math.abs(h.x*T.scaleX/d.scaleX)+Math.abs(h.y*T.scaleY/d.scaleY));s=T.scaleX*O,a=T.scaleY*O}else s=Math.abs(o.x*d.scaleX/h.x),a=Math.abs(o.y*d.scaleY/h.y);v(e)&&(s*=2,a*=2),e.signX!==c&&"y"!==_&&(e.originX=l[e.originX],s*=-1,e.signX=c),e.signY!==u&&"x"!==_&&(e.originY=l[e.originY],a*=-1,e.signY=u)}var P=d.scaleX,E=d.scaleY;return _?("x"===_&&d.set("scaleX",s),"y"===_&&d.set("scaleY",a)):(!g&&d.set("scaleX",s),!y&&d.set("scaleY",a)),P!==d.scaleX||E!==d.scaleY}n.scaleCursorStyleHandler=function(t,e,r){var n=p(t,r),o="";if(0!==e.x&&0===e.y?o="x":0===e.x&&0!==e.y&&(o="y"),m(r,o,n))return"not-allowed";var s=d(r,e);return i[s]+"-resize"},n.skewCursorStyleHandler=function(t,e,i){var n="not-allowed";if(0!==e.x&&i.lockSkewingY)return n;if(0!==e.y&&i.lockSkewingX)return n;var o=d(i,e)%4;return r[o]+"-resize"},n.scaleSkewCursorStyleHandler=function(t,e,i){return 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in t)this[e]=t[e]},e.Control.prototype={visible:!0,actionName:"scale",angle:0,x:0,y:0,offsetX:0,offsetY:0,sizeX:null,sizeY:null,touchSizeX:null,touchSizeY:null,cursorStyle:"crosshair",withConnection:!1,actionHandler:function(){},mouseDownHandler:function(){},mouseUpHandler:function(){},getActionHandler:function(){return this.actionHandler},getMouseDownHandler:function(){return this.mouseDownHandler},getMouseUpHandler:function(){return this.mouseUpHandler},cursorStyleHandler:function(t,e){return e.cursorStyle},getActionName:function(t,e){return e.actionName},getVisibility:function(t,e){var i=t._controlsVisibility;return i&&void 0!==i[e]?i[e]:this.visible},setVisibility:function(t){this.visible=t},positionHandler:function(t,i){return e.util.transformPoint({x:this.x*t.x+this.offsetX,y:this.y*t.y+this.offsetY},i)},calcCornerCoords:function(t,i,r,n,o){var s,a,h,c,l=o?this.touchSizeX:this.sizeX,u=o?this.touchSizeY:this.sizeY;if(l&&u&&l!==u){var f=Math.atan2(u,l),d=Math.sqrt(l*l+u*u)/2,g=f-e.util.degreesToRadians(t),p=Math.PI/2-f-e.util.degreesToRadians(t);s=d*e.util.cos(g),a=d*e.util.sin(g),h=d*e.util.cos(p),c=d*e.util.sin(p)}else{d=.7071067812*(l&&u?l:i);g=e.util.degreesToRadians(45-t);s=h=d*e.util.cos(g),a=c=d*e.util.sin(g)}return{tl:{x:r-c,y:n-h},tr:{x:r+s,y:n-a},bl:{x:r-s,y:n+a},br:{x:r+c,y:n+h}}},render:function(t,i,r,n,o){if("circle"===((n=n||{}).cornerStyle||o.cornerStyle))e.controlsUtils.renderCircleControl.call(this,t,i,r,n,o);else e.controlsUtils.renderSquareControl.call(this,t,i,r,n,o)}}}(t),function(){function t(t,e){var i,r,n,o,s=t.getAttribute("style"),a=t.getAttribute("offset")||0;if(a=(a=parseFloat(a)/(/%$/.test(a)?100:1))<0?0:a>1?1:a,s){var h=s.split(/\s*;\s*/);for(""===h[h.length-1]&&h.pop(),o=h.length;o--;){var c=h[o].split(/\s*:\s*/),l=c[0].trim(),u=c[1].trim();"stop-color"===l?i=u:"stop-opacity"===l&&(n=u)}}return i||(i=t.getAttribute("stop-color")||"rgb(0,0,0)"),n||(n=t.getAttribute("stop-opacity")),r=(i=new T.Color(i)).getAlpha(),n=isNaN(parseFloat(n))?1:parseFloat(n),n*=r*e,{offset:a,color:i.toRgb(),opacity:n}}var e=T.util.object.clone;T.Gradient=T.util.createClass({offsetX:0,offsetY:0,gradientTransform:null,gradientUnits:"pixels",type:"linear",initialize:function(t){t||(t={}),t.coords||(t.coords={});var e,i=this;Object.keys(t).forEach((function(e){i[e]=t[e]})),this.id?this.id+="_"+T.Object.__uid++:this.id=T.Object.__uid++,e={x1:t.coords.x1||0,y1:t.coords.y1||0,x2:t.coords.x2||0,y2:t.coords.y2||0},"radial"===this.type&&(e.r1=t.coords.r1||0,e.r2=t.coords.r2||0),this.coords=e,this.colorStops=t.colorStops.slice()},addColorStop:function(t){for(var e in t){var i=new T.Color(t[e]);this.colorStops.push({offset:parseFloat(e),color:i.toRgb(),opacity:i.getAlpha()})}return this},toObject:function(t){var e={type:this.type,coords:this.coords,colorStops:this.colorStops,offsetX:this.offsetX,offsetY:this.offsetY,gradientUnits:this.gradientUnits,gradientTransform:this.gradientTransform?this.gradientTransform.concat():this.gradientTransform};return T.util.populateWithProperties(this,e,t),e},toSVG:function(t,i){var r,n,o,s,a=e(this.coords,!0),h=(i=i||{},e(this.colorStops,!0)),c=a.r1>a.r2,l=this.gradientTransform?this.gradientTransform.concat():T.iMatrix.concat(),u=-this.offsetX,f=-this.offsetY,d=!!i.additionalTransform,g="pixels"===this.gradientUnits?"userSpaceOnUse":"objectBoundingBox";if(h.sort((function(t,e){return t.offset-e.offset})),"objectBoundingBox"===g?(u/=t.width,f/=t.height):(u+=t.width/2,f+=t.height/2),"path"===t.type&&"percentage"!==this.gradientUnits&&(u-=t.pathOffset.x,f-=t.pathOffset.y),l[4]-=u,l[5]-=f,s='id="SVGID_'+this.id+'" gradientUnits="'+g+'"',s+=' gradientTransform="'+(d?i.additionalTransform+" ":"")+T.util.matrixToSVG(l)+'" ',"linear"===this.type?o=["\n']:"radial"===this.type&&(o=["\n']),"radial"===this.type){if(c)for((h=h.concat()).reverse(),r=0,n=h.length;r0){var v=p/Math.max(a.r1,a.r2);for(r=0,n=h.length;r\n')}return o.push("linear"===this.type?"\n":"\n"),o.join("")},toLive:function(t){var e,i,r,n=T.util.object.clone(this.coords);if(this.type){for("linear"===this.type?e=t.createLinearGradient(n.x1,n.y1,n.x2,n.y2):"radial"===this.type&&(e=t.createRadialGradient(n.x1,n.y1,n.r1,n.x2,n.y2,n.r2)),i=0,r=this.colorStops.length;i1?1:o,isNaN(o)&&(o=1);var s,a,h,c,l=e.getElementsByTagName("stop"),u="userSpaceOnUse"===e.getAttribute("gradientUnits")?"pixels":"percentage",f=e.getAttribute("gradientTransform")||"",d=[],g=0,p=0;for("linearGradient"===e.nodeName||"LINEARGRADIENT"===e.nodeName?(s="linear",a=function(t){return{x1:t.getAttribute("x1")||0,y1:t.getAttribute("y1")||0,x2:t.getAttribute("x2")||"100%",y2:t.getAttribute("y2")||0}}(e)):(s="radial",a=function(t){return{x1:t.getAttribute("fx")||t.getAttribute("cx")||"50%",y1:t.getAttribute("fy")||t.getAttribute("cy")||"50%",r1:0,x2:t.getAttribute("cx")||"50%",y2:t.getAttribute("cy")||"50%",r2:t.getAttribute("r")||"50%"}}(e)),h=l.length;h--;)d.push(t(l[h],o));return c=T.parseTransformAttribute(f),function(t,e,i,r){var n,o;Object.keys(e).forEach((function(t){"Infinity"===(n=e[t])?o=1:"-Infinity"===n?o=0:(o=parseFloat(e[t],10),"string"==typeof n&&/^(\d+\.\d+)%|(\d+)%$/.test(n)&&(o*=.01,"pixels"===r&&("x1"!==t&&"x2"!==t&&"r2"!==t||(o*=i.viewBoxWidth||i.width),"y1"!==t&&"y2"!==t||(o*=i.viewBoxHeight||i.height)))),e[t]=o}))}(0,a,n,u),"pixels"===u&&(g=-i.left,p=-i.top),new T.Gradient({id:e.getAttribute("id"),type:s,coords:a,colorStops:d,gradientUnits:u,gradientTransform:c,offsetX:g,offsetY:p})}})}(),_=T.util.toFixed,T.Pattern=T.util.createClass({repeat:"repeat",offsetX:0,offsetY:0,crossOrigin:"",patternTransform:null,initialize:function(t,e){if(t||(t={}),this.id=T.Object.__uid++,this.setOptions(t),!t.source||t.source&&"string"!=typeof t.source)e&&e(this);else{var i=this;this.source=T.util.createImage(),T.util.loadImage(t.source,(function(t,r){i.source=t,e&&e(i,r)}),null,this.crossOrigin)}},toObject:function(t){var e,i,r=T.Object.NUM_FRACTION_DIGITS;return"string"==typeof this.source.src?e=this.source.src:"object"==typeof this.source&&this.source.toDataURL&&(e=this.source.toDataURL()),i={type:"pattern",source:e,repeat:this.repeat,crossOrigin:this.crossOrigin,offsetX:_(this.offsetX,r),offsetY:_(this.offsetY,r),patternTransform:this.patternTransform?this.patternTransform.concat():null},T.util.populateWithProperties(this,i,t),i},toSVG:function(t){var e="function"==typeof this.source?this.source():this.source,i=e.width/t.width,r=e.height/t.height,n=this.offsetX/t.width,o=this.offsetY/t.height,s="";return"repeat-x"!==this.repeat&&"no-repeat"!==this.repeat||(r=1,o&&(r+=Math.abs(o))),"repeat-y"!==this.repeat&&"no-repeat"!==this.repeat||(i=1,n&&(i+=Math.abs(n))),e.src?s=e.src:e.toDataURL&&(s=e.toDataURL()),'\n\n\n'},setOptions:function(t){for(var e in t)this[e]=t[e]},toLive:function(t){var e=this.source;if(!e)return"";if(void 0!==e.src){if(!e.complete)return"";if(0===e.naturalWidth||0===e.naturalHeight)return""}return t.createPattern(e,this.repeat)}}),function(t){var e=t.fabric||(t.fabric={}),i=e.util.toFixed;e.Shadow?e.warn("fabric.Shadow is already defined."):(e.Shadow=e.util.createClass({color:"rgb(0,0,0)",blur:0,offsetX:0,offsetY:0,affectStroke:!1,includeDefaultValues:!0,nonScaling:!1,initialize:function(t){for(var i in"string"==typeof t&&(t=this._parseShadow(t)),t)this[i]=t[i];this.id=e.Object.__uid++},_parseShadow:function(t){var i=t.trim(),r=e.Shadow.reOffsetsAndBlur.exec(i)||[];return{color:(i.replace(e.Shadow.reOffsetsAndBlur,"")||"rgb(0,0,0)").trim(),offsetX:parseFloat(r[1],10)||0,offsetY:parseFloat(r[2],10)||0,blur:parseFloat(r[3],10)||0}},toString:function(){return[this.offsetX,this.offsetY,this.blur,this.color].join("px ")},toSVG:function(t){var r=40,n=40,o=e.Object.NUM_FRACTION_DIGITS,s=e.util.rotateVector({x:this.offsetX,y:this.offsetY},e.util.degreesToRadians(-t.angle)),a=new e.Color(this.color);return t.width&&t.height&&(r=100*i((Math.abs(s.x)+this.blur)/t.width,o)+20,n=100*i((Math.abs(s.y)+this.blur)/t.height,o)+20),t.flipX&&(s.x*=-1),t.flipY&&(s.y*=-1),'\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\n\n'},toObject:function(){if(this.includeDefaultValues)return{color:this.color,blur:this.blur,offsetX:this.offsetX,offsetY:this.offsetY,affectStroke:this.affectStroke,nonScaling:this.nonScaling};var t={},i=e.Shadow.prototype;return["color","blur","offsetX","offsetY","affectStroke","nonScaling"].forEach((function(e){this[e]!==i[e]&&(t[e]=this[e])}),this),t}}),e.Shadow.reOffsetsAndBlur=/(?:\s|^)(-?\d+(?:\.\d*)?(?:px)?(?:\s?|$))?(-?\d+(?:\.\d*)?(?:px)?(?:\s?|$))?(\d+(?:\.\d*)?(?:px)?)?(?:\s?|$)(?:$|\s)/)}(t),function(){if(T.StaticCanvas)T.warn("fabric.StaticCanvas is already defined.");else{var t=T.util.object.extend,e=T.util.getElementOffset,i=T.util.removeFromArray,r=T.util.toFixed,n=T.util.transformPoint,o=T.util.invertTransform,s=T.util.getNodeCanvas,a=T.util.createCanvasElement,h=new Error("Could not initialize `canvas` element");T.StaticCanvas=T.util.createClass(T.CommonMethods,{initialize:function(t,e){e||(e={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(t,e)},backgroundColor:"",backgroundImage:null,overlayColor:"",overlayImage:null,includeDefaultValues:!0,stateful:!1,renderOnAddRemove:!0,controlsAboveOverlay:!1,allowTouchScrolling:!1,imageSmoothingEnabled:!0,viewportTransform:T.iMatrix.concat(),backgroundVpt:!0,overlayVpt:!0,enableRetinaScaling:!0,vptCoords:{},skipOffscreen:!0,clipPath:void 0,_initStatic:function(t,e){var i=this.requestRenderAllBound;this._objects=[],this._createLowerCanvas(t),this._initOptions(e),this.interactive||this._initRetinaScaling(),e.overlayImage&&this.setOverlayImage(e.overlayImage,i),e.backgroundImage&&this.setBackgroundImage(e.backgroundImage,i),e.backgroundColor&&this.setBackgroundColor(e.backgroundColor,i),e.overlayColor&&this.setOverlayColor(e.overlayColor,i),this.calcOffset()},_isRetinaScaling:function(){return 1!==T.devicePixelRatio&&this.enableRetinaScaling},getRetinaScaling:function(){return this._isRetinaScaling()?T.devicePixelRatio:1},_initRetinaScaling:function(){if(this._isRetinaScaling()){var t=T.devicePixelRatio;this.__initRetinaScaling(t,this.lowerCanvasEl,this.contextContainer),this.upperCanvasEl&&this.__initRetinaScaling(t,this.upperCanvasEl,this.contextTop)}},__initRetinaScaling:function(t,e,i){e.setAttribute("width",this.width*t),e.setAttribute("height",this.height*t),i.scale(t,t)},calcOffset:function(){return this._offset=e(this.lowerCanvasEl),this},setOverlayImage:function(t,e,i){return this.__setBgOverlayImage("overlayImage",t,e,i)},setBackgroundImage:function(t,e,i){return this.__setBgOverlayImage("backgroundImage",t,e,i)},setOverlayColor:function(t,e){return this.__setBgOverlayColor("overlayColor",t,e)},setBackgroundColor:function(t,e){return this.__setBgOverlayColor("backgroundColor",t,e)},__setBgOverlayImage:function(t,e,i,r){return"string"==typeof e?T.util.loadImage(e,(function(e,n){if(e){var o=new T.Image(e,r);this[t]=o,o.canvas=this}i&&i(e,n)}),this,r&&r.crossOrigin):(r&&e.setOptions(r),this[t]=e,e&&(e.canvas=this),i&&i(e,!1)),this},__setBgOverlayColor:function(t,e,i){return this[t]=e,this._initGradient(e,t),this._initPattern(e,t,i),this},_createCanvasElement:function(){var t=a();if(!t)throw h;if(t.style||(t.style={}),void 0===t.getContext)throw h;return t},_initOptions:function(t){var e=this.lowerCanvasEl;this._setOptions(t),this.width=this.width||parseInt(e.width,10)||0,this.height=this.height||parseInt(e.height,10)||0,this.lowerCanvasEl.style&&(e.width=this.width,e.height=this.height,e.style.width=this.width+"px",e.style.height=this.height+"px",this.viewportTransform=this.viewportTransform.slice())},_createLowerCanvas:function(t){t&&t.getContext?this.lowerCanvasEl=t:this.lowerCanvasEl=T.util.getById(t)||this._createCanvasElement(),T.util.addClass(this.lowerCanvasEl,"lower-canvas"),this._originalCanvasStyle=this.lowerCanvasEl.style,this.interactive&&this._applyCanvasStyle(this.lowerCanvasEl),this.contextContainer=this.lowerCanvasEl.getContext("2d")},getWidth:function(){return this.width},getHeight:function(){return this.height},setWidth:function(t,e){return this.setDimensions({width:t},e)},setHeight:function(t,e){return this.setDimensions({height:t},e)},setDimensions:function(t,e){var i;for(var r in e=e||{},t)i=t[r],e.cssOnly||(this._setBackstoreDimension(r,t[r]),i+="px",this.hasLostContext=!0),e.backstoreOnly||this._setCssDimension(r,i);return this._isCurrentlyDrawing&&this.freeDrawingBrush&&this.freeDrawingBrush._setBrushStyles(),this._initRetinaScaling(),this.calcOffset(),e.cssOnly||this.requestRenderAll(),this},_setBackstoreDimension:function(t,e){return this.lowerCanvasEl[t]=e,this.upperCanvasEl&&(this.upperCanvasEl[t]=e),this.cacheCanvasEl&&(this.cacheCanvasEl[t]=e),this[t]=e,this},_setCssDimension:function(t,e){return this.lowerCanvasEl.style[t]=e,this.upperCanvasEl&&(this.upperCanvasEl.style[t]=e),this.wrapperEl&&(this.wrapperEl.style[t]=e),this},getZoom:function(){return this.viewportTransform[0]},setViewportTransform:function(t){var e,i,r,n=this._activeObject,o=this.backgroundImage,s=this.overlayImage;for(this.viewportTransform=t,i=0,r=this._objects.length;i\n'),this._setSVGBgOverlayColor(i,"background"),this._setSVGBgOverlayImage(i,"backgroundImage",e),this._setSVGObjects(i,e),this.clipPath&&i.push("\n"),this._setSVGBgOverlayColor(i,"overlay"),this._setSVGBgOverlayImage(i,"overlayImage",e),i.push(""),i.join("")},_setSVGPreamble:function(t,e){e.suppressPreamble||t.push('\n','\n')},_setSVGHeader:function(t,e){var i,n=e.width||this.width,o=e.height||this.height,s='viewBox="0 0 '+this.width+" "+this.height+'" ',a=T.Object.NUM_FRACTION_DIGITS;e.viewBox?s='viewBox="'+e.viewBox.x+" "+e.viewBox.y+" "+e.viewBox.width+" "+e.viewBox.height+'" ':this.svgViewportTransformation&&(i=this.viewportTransform,s='viewBox="'+r(-i[4]/i[0],a)+" "+r(-i[5]/i[3],a)+" "+r(this.width/i[0],a)+" "+r(this.height/i[3],a)+'" '),t.push("\n',"Created with Fabric.js ",T.version,"\n","\n",this.createSVGFontFacesMarkup(),this.createSVGRefElementsMarkup(),this.createSVGClipPathMarkup(e),"\n")},createSVGClipPathMarkup:function(t){var e=this.clipPath;return e?(e.clipPathId="CLIPPATH_"+T.Object.__uid++,'\n'+this.clipPath.toClipPathSVG(t.reviver)+"\n"):""},createSVGRefElementsMarkup:function(){var t=this;return["background","overlay"].map((function(e){var i=t[e+"Color"];if(i&&i.toLive){var r=t[e+"Vpt"],n=t.viewportTransform,o={width:t.width/(r?n[0]:1),height:t.height/(r?n[3]:1)};return i.toSVG(o,{additionalTransform:r?T.util.matrixToSVG(n):""})}})).join("")},createSVGFontFacesMarkup:function(){var t,e,i,r,n,o,s,a,h="",c={},l=T.fontPaths,u=[];for(this._objects.forEach((function t(e){u.push(e),e._objects&&e._objects.forEach(t)})),s=0,a=u.length;s',"\n",h,"","\n"].join("")),h},_setSVGObjects:function(t,e){var i,r,n,o=this._objects;for(r=0,n=o.length;r\n")}else t.push('\n")},sendToBack:function(t){if(!t)return this;var e,r,n,o=this._activeObject;if(t===o&&"activeSelection"===t.type)for(e=(n=o._objects).length;e--;)r=n[e],i(this._objects,r),this._objects.unshift(r);else i(this._objects,t),this._objects.unshift(t);return this.renderOnAddRemove&&this.requestRenderAll(),this},bringToFront:function(t){if(!t)return this;var e,r,n,o=this._activeObject;if(t===o&&"activeSelection"===t.type)for(n=o._objects,e=0;e0+c&&(s=o-1,i(this._objects,n),this._objects.splice(s,0,n)),c++;else 0!==(o=this._objects.indexOf(t))&&(s=this._findNewLowerIndex(t,o,e),i(this._objects,t),this._objects.splice(s,0,t));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewLowerIndex:function(t,e,i){var r,n;if(i)for(r=e,n=e-1;n>=0;--n){if(t.intersectsWithObject(this._objects[n])||t.isContainedWithinObject(this._objects[n])||this._objects[n].isContainedWithinObject(t)){r=n;break}}else r=e-1;return r},bringForward:function(t,e){if(!t)return this;var r,n,o,s,a,h=this._activeObject,c=0;if(t===h&&"activeSelection"===t.type)for(r=(a=h._objects).length;r--;)n=a[r],(o=this._objects.indexOf(n))"}}),t(T.StaticCanvas.prototype,T.Observable),t(T.StaticCanvas.prototype,T.Collection),t(T.StaticCanvas.prototype,T.DataURLExporter),t(T.StaticCanvas,{EMPTY_JSON:'{"objects": [], "background": "white"}',supports:function(t){var e=a();if(!e||!e.getContext)return null;var i=e.getContext("2d");return i&&"setLineDash"===t?void 0!==i.setLineDash:null}}),T.StaticCanvas.prototype.toJSON=T.StaticCanvas.prototype.toObject,T.isLikelyNode&&(T.StaticCanvas.prototype.createPNGStream=function(){var t=s(this.lowerCanvasEl);return t&&t.createPNGStream()},T.StaticCanvas.prototype.createJPEGStream=function(t){var e=s(this.lowerCanvasEl);return e&&e.createJPEGStream(t)})}}(),T.BaseBrush=T.util.createClass({color:"rgb(0, 0, 0)",width:1,shadow:null,strokeLineCap:"round",strokeLineJoin:"round",strokeMiterLimit:10,strokeDashArray:null,limitedToCanvasSize:!1,_setBrushStyles:function(){var t=this.canvas.contextTop;t.strokeStyle=this.color,t.lineWidth=this.width,t.lineCap=this.strokeLineCap,t.miterLimit=this.strokeMiterLimit,t.lineJoin=this.strokeLineJoin,t.setLineDash(this.strokeDashArray||[])},_saveAndTransform:function(t){var e=this.canvas.viewportTransform;t.save(),t.transform(e[0],e[1],e[2],e[3],e[4],e[5])},_setShadow:function(){if(this.shadow){var t=this.canvas,e=this.shadow,i=t.contextTop,r=t.getZoom();t&&t._isRetinaScaling()&&(r*=T.devicePixelRatio),i.shadowColor=e.color,i.shadowBlur=e.blur*r,i.shadowOffsetX=e.offsetX*r,i.shadowOffsetY=e.offsetY*r}},needsFullRender:function(){return new T.Color(this.color).getAlpha()<1||!!this.shadow},_resetShadow:function(){var t=this.canvas.contextTop;t.shadowColor="",t.shadowBlur=t.shadowOffsetX=t.shadowOffsetY=0},_isOutSideCanvas:function(t){return t.x<0||t.x>this.canvas.getWidth()||t.y<0||t.y>this.canvas.getHeight()}}),T.PencilBrush=T.util.createClass(T.BaseBrush,{decimate:.4,initialize:function(t){this.canvas=t,this._points=[]},_drawSegment:function(t,e,i){var r=e.midPointFrom(i);return t.quadraticCurveTo(e.x,e.y,r.x,r.y),r},onMouseDown:function(t,e){this.canvas._isMainEvent(e.e)&&(this._prepareForDrawing(t),this._captureDrawingPath(t),this._render())},onMouseMove:function(t,e){if(this.canvas._isMainEvent(e.e)&&(!0!==this.limitedToCanvasSize||!this._isOutSideCanvas(t))&&this._captureDrawingPath(t)&&this._points.length>1)if(this.needsFullRender())this.canvas.clearContext(this.canvas.contextTop),this._render();else{var i=this._points,r=i.length,n=this.canvas.contextTop;this._saveAndTransform(n),this.oldEnd&&(n.beginPath(),n.moveTo(this.oldEnd.x,this.oldEnd.y)),this.oldEnd=this._drawSegment(n,i[r-2],i[r-1],!0),n.stroke(),n.restore()}},onMouseUp:function(t){return!this.canvas._isMainEvent(t.e)||(this.oldEnd=void 0,this._finalizeAndAddPath(),!1)},_prepareForDrawing:function(t){var e=new T.Point(t.x,t.y);this._reset(),this._addPoint(e),this.canvas.contextTop.moveTo(e.x,e.y)},_addPoint:function(t){return!(this._points.length>1&&t.eq(this._points[this._points.length-1])||(this._points.push(t),0))},_reset:function(){this._points=[],this._setBrushStyles(),this._setShadow()},_captureDrawingPath:function(t){var e=new T.Point(t.x,t.y);return this._addPoint(e)},_render:function(){var t,e,i=this.canvas.contextTop,r=this._points[0],n=this._points[1];if(this._saveAndTransform(i),i.beginPath(),2===this._points.length&&r.x===n.x&&r.y===n.y){var o=this.width/1e3;r=new T.Point(r.x,r.y),n=new T.Point(n.x,n.y),r.x-=o,n.x+=o}for(i.moveTo(r.x,r.y),t=1,e=this._points.length;t=n&&(s=t[i],a.push(s));return a.push(t[o]),a},_finalizeAndAddPath:function(){this.canvas.contextTop.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate));var t=this.convertPointsToSVGPath(this._points);if(this._isEmptySVGPath(t))this.canvas.requestRenderAll();else{var e=this.createPath(t);this.canvas.clearContext(this.canvas.contextTop),this.canvas.fire("before:path:created",{path:e}),this.canvas.add(e),this.canvas.requestRenderAll(),e.setCoords(),this._resetShadow(),this.canvas.fire("path:created",{path:e})}}}),T.CircleBrush=T.util.createClass(T.BaseBrush,{width:10,initialize:function(t){this.canvas=t,this.points=[]},drawDot:function(t){var e=this.addPoint(t),i=this.canvas.contextTop;this._saveAndTransform(i),this.dot(i,e),i.restore()},dot:function(t,e){t.fillStyle=e.fill,t.beginPath(),t.arc(e.x,e.y,e.radius,0,2*Math.PI,!1),t.closePath(),t.fill()},onMouseDown:function(t){this.points.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.drawDot(t)},_render:function(){var t,e,i=this.canvas.contextTop,r=this.points;for(this._saveAndTransform(i),t=0,e=r.length;t0&&!this.preserveObjectStacking){e=[],i=[];for(var n=0,o=this._objects.length;n1&&(this._activeObject._objects=i),e.push.apply(e,i)}else e=this._objects;return e},renderAll:function(){!this.contextTopDirty||this._groupSelector||this.isDrawingMode||(this.clearContext(this.contextTop),this.contextTopDirty=!1),this.hasLostContext&&this.renderTopLayer(this.contextTop);var t=this.contextContainer;return this.renderCanvas(t,this._chooseObjectsToRender()),this},renderTopLayer:function(t){t.save(),this.isDrawingMode&&this._isCurrentlyDrawing&&(this.freeDrawingBrush&&this.freeDrawingBrush._render(),this.contextTopDirty=!0),this.selection&&this._groupSelector&&(this._drawSelection(t),this.contextTopDirty=!0),t.restore()},renderTop:function(){var t=this.contextTop;return this.clearContext(t),this.renderTopLayer(t),this.fire("after:render"),this},_normalizePointer:function(t,e){var i=t.calcTransformMatrix(),r=T.util.invertTransform(i),n=this.restorePointerVpt(e);return T.util.transformPoint(n,r)},isTargetTransparent:function(t,e,i){if(t.shouldCache()&&t._cacheCanvas&&t!==this._activeObject){var r=this._normalizePointer(t,{x:e,y:i}),n=Math.max(t.cacheTranslationX+r.x*t.zoomX,0),o=Math.max(t.cacheTranslationY+r.y*t.zoomY,0);return T.util.isTransparent(t._cacheContext,Math.round(n),Math.round(o),this.targetFindTolerance)}var s=this.contextCache,a=t.selectionBackgroundColor,h=this.viewportTransform;return t.selectionBackgroundColor="",this.clearContext(s),s.save(),s.transform(h[0],h[1],h[2],h[3],h[4],h[5]),t.render(s),s.restore(),t.selectionBackgroundColor=a,T.util.isTransparent(s,e,i,this.targetFindTolerance)},_isSelectionKeyPressed:function(t){return"[object Array]"===Object.prototype.toString.call(this.selectionKey)?!!this.selectionKey.find((function(e){return!0===t[e]})):t[this.selectionKey]},_shouldClearSelection:function(t,e){var i=this.getActiveObjects(),r=this._activeObject;return!e||e&&r&&i.length>1&&-1===i.indexOf(e)&&r!==e&&!this._isSelectionKeyPressed(t)||e&&!e.evented||e&&!e.selectable&&r&&r!==e},_shouldCenterTransform:function(t,e,i){var r;if(t)return"scale"===e||"scaleX"===e||"scaleY"===e||"resizing"===e?r=this.centeredScaling||t.centeredScaling:"rotate"===e&&(r=this.centeredRotation||t.centeredRotation),r?!i:i},_getOriginFromCorner:function(t,e){var i={x:t.originX,y:t.originY};return"ml"===e||"tl"===e||"bl"===e?i.x="right":"mr"!==e&&"tr"!==e&&"br"!==e||(i.x="left"),"tl"===e||"mt"===e||"tr"===e?i.y="bottom":"bl"!==e&&"mb"!==e&&"br"!==e||(i.y="top"),i},_getActionFromCorner:function(t,e,i,r){if(!e||!t)return"drag";var n=r.controls[e];return n.getActionName(i,n,r)},_setupCurrentTransform:function(t,i,r){if(i){var n=this.getPointer(t),o=i.__corner,s=i.controls[o],a=r&&o?s.getActionHandler(t,i,s):T.controlsUtils.dragHandler,h=this._getActionFromCorner(r,o,t,i),c=this._getOriginFromCorner(i,o),l=t[this.centeredKey],u={target:i,action:h,actionHandler:a,corner:o,scaleX:i.scaleX,scaleY:i.scaleY,skewX:i.skewX,skewY:i.skewY,offsetX:n.x-i.left,offsetY:n.y-i.top,originX:c.x,originY:c.y,ex:n.x,ey:n.y,lastX:n.x,lastY:n.y,theta:e(i.angle),width:i.width*i.scaleX,shiftKey:t.shiftKey,altKey:l,original:T.util.saveObjectTransform(i)};this._shouldCenterTransform(i,h,l)&&(u.originX="center",u.originY="center"),u.original.originX=c.x,u.original.originY=c.y,this._currentTransform=u,this._beforeTransform(t)}},setCursor:function(t){this.upperCanvasEl.style.cursor=t},_drawSelection:function(t){var e=this._groupSelector,i=new T.Point(e.ex,e.ey),r=T.util.transformPoint(i,this.viewportTransform),n=new T.Point(e.ex+e.left,e.ey+e.top),o=T.util.transformPoint(n,this.viewportTransform),s=Math.min(r.x,o.x),a=Math.min(r.y,o.y),h=Math.max(r.x,o.x),c=Math.max(r.y,o.y),l=this.selectionLineWidth/2;this.selectionColor&&(t.fillStyle=this.selectionColor,t.fillRect(s,a,h-s,c-a)),this.selectionLineWidth&&this.selectionBorderColor&&(t.lineWidth=this.selectionLineWidth,t.strokeStyle=this.selectionBorderColor,s+=l,a+=l,h-=l,c-=l,T.Object.prototype._setLineDash.call(this,t,this.selectionDashArray),t.strokeRect(s,a,h-s,c-a))},findTarget:function(t,e){if(!this.skipTargetFind){var r,n,o=this.getPointer(t,!0),s=this._activeObject,a=this.getActiveObjects(),h=i(t),c=a.length>1&&!e||1===a.length;if(this.targets=[],c&&s._findTargetCorner(o,h))return s;if(a.length>1&&!e&&s===this._searchPossibleTargets([s],o))return s;if(1===a.length&&s===this._searchPossibleTargets([s],o)){if(!this.preserveObjectStacking)return s;r=s,n=this.targets,this.targets=[]}var l=this._searchPossibleTargets(this._objects,o);return t[this.altSelectionKey]&&l&&r&&l!==r&&(l=r,this.targets=n),l}},_checkTarget:function(t,e,i){if(e&&e.visible&&e.evented&&e.containsPoint(t)){if(!this.perPixelTargetFind&&!e.perPixelTargetFind||e.isEditing)return!0;if(!this.isTargetTransparent(e,i.x,i.y))return!0}},_searchPossibleTargets:function(t,e){for(var i,r,n=t.length;n--;){var o=t[n],s=o.group?this._normalizePointer(o.group,e):e;if(this._checkTarget(s,o,e)){(i=t[n]).subTargetCheck&&i instanceof T.Group&&(r=this._searchPossibleTargets(i._objects,e))&&this.targets.push(r);break}}return i},restorePointerVpt:function(t){return T.util.transformPoint(t,T.util.invertTransform(this.viewportTransform))},getPointer:function(e,i){if(this._absolutePointer&&!i)return this._absolutePointer;if(this._pointer&&i)return this._pointer;var r,n=t(e),o=this.upperCanvasEl,s=o.getBoundingClientRect(),a=s.width||0,h=s.height||0;a&&h||("top"in s&&"bottom"in s&&(h=Math.abs(s.top-s.bottom)),"right"in s&&"left"in s&&(a=Math.abs(s.right-s.left))),this.calcOffset(),n.x=n.x-this._offset.left,n.y=n.y-this._offset.top,i||(n=this.restorePointerVpt(n));var c=this.getRetinaScaling();return 1!==c&&(n.x/=c,n.y/=c),r=0===a||0===h?{width:1,height:1}:{width:o.width/a,height:o.height/h},{x:n.x*r.width,y:n.y*r.height}},_createUpperCanvas:function(){var t=this.lowerCanvasEl.className.replace(/\s*lower-canvas\s*/,""),e=this.lowerCanvasEl,i=this.upperCanvasEl;i?i.className="":(i=this._createCanvasElement(),this.upperCanvasEl=i),T.util.addClass(i,"upper-canvas "+t),this.wrapperEl.appendChild(i),this._copyCanvasStyle(e,i),this._applyCanvasStyle(i),this.contextTop=i.getContext("2d")},_createCacheCanvas:function(){this.cacheCanvasEl=this._createCanvasElement(),this.cacheCanvasEl.setAttribute("width",this.width),this.cacheCanvasEl.setAttribute("height",this.height),this.contextCache=this.cacheCanvasEl.getContext("2d")},_initWrapperElement:function(){this.wrapperEl=T.util.wrapElement(this.lowerCanvasEl,"div",{class:this.containerClass}),T.util.setStyle(this.wrapperEl,{width:this.width+"px",height:this.height+"px",position:"relative"}),T.util.makeElementUnselectable(this.wrapperEl)},_applyCanvasStyle:function(t){var e=this.width||t.width,i=this.height||t.height;T.util.setStyle(t,{position:"absolute",width:e+"px",height:i+"px",left:0,top:0,"touch-action":this.allowTouchScrolling?"manipulation":"none","-ms-touch-action":this.allowTouchScrolling?"manipulation":"none"}),t.width=e,t.height=i,T.util.makeElementUnselectable(t)},_copyCanvasStyle:function(t,e){e.style.cssText=t.style.cssText},getSelectionContext:function(){return this.contextTop},getSelectionElement:function(){return this.upperCanvasEl},getActiveObject:function(){return this._activeObject},getActiveObjects:function(){var t=this._activeObject;return t?"activeSelection"===t.type&&t._objects?t._objects.slice(0):[t]:[]},_onObjectRemoved:function(t){t===this._activeObject&&(this.fire("before:selection:cleared",{target:t}),this._discardActiveObject(),this.fire("selection:cleared",{target:t}),t.fire("deselected")),t===this._hoveredTarget&&(this._hoveredTarget=null,this._hoveredTargets=[]),this.callSuper("_onObjectRemoved",t)},_fireSelectionEvents:function(t,e){var i=!1,r=this.getActiveObjects(),n=[],o=[];t.forEach((function(t){-1===r.indexOf(t)&&(i=!0,t.fire("deselected",{e:e,target:t}),o.push(t))})),r.forEach((function(r){-1===t.indexOf(r)&&(i=!0,r.fire("selected",{e:e,target:r}),n.push(r))})),t.length>0&&r.length>0?i&&this.fire("selection:updated",{e:e,selected:n,deselected:o,updated:n[0]||o[0],target:this._activeObject}):r.length>0?this.fire("selection:created",{e:e,selected:n,target:this._activeObject}):t.length>0&&this.fire("selection:cleared",{e:e,deselected:o})},setActiveObject:function(t,e){var i=this.getActiveObjects();return this._setActiveObject(t,e),this._fireSelectionEvents(i,e),this},_setActiveObject:function(t,e){return this._activeObject!==t&&(!!this._discardActiveObject(e,t)&&(!t.onSelect({e:e})&&(this._activeObject=t,!0)))},_discardActiveObject:function(t,e){var i=this._activeObject;if(i){if(i.onDeselect({e:t,object:e}))return!1;this._activeObject=null}return!0},discardActiveObject:function(t){var e=this.getActiveObjects(),i=this.getActiveObject();return e.length&&this.fire("before:selection:cleared",{target:i,e:t}),this._discardActiveObject(t),this._fireSelectionEvents(e,t),this},dispose:function(){var t=this.wrapperEl;return this.removeListeners(),t.removeChild(this.upperCanvasEl),t.removeChild(this.lowerCanvasEl),this.contextCache=null,this.contextTop=null,["upperCanvasEl","cacheCanvasEl"].forEach(function(t){T.util.cleanUpJsdomNode(this[t]),this[t]=void 0}.bind(this)),t.parentNode&&t.parentNode.replaceChild(this.lowerCanvasEl,this.wrapperEl),delete this.wrapperEl,T.StaticCanvas.prototype.dispose.call(this),this},clear:function(){return this.discardActiveObject(),this.clearContext(this.contextTop),this.callSuper("clear")},drawControls:function(t){var e=this._activeObject;e&&e._renderControls(t)},_toObject:function(t,e,i){var r=this._realizeGroupTransformOnObject(t),n=this.callSuper("_toObject",t,e,i);return this._unwindGroupTransformOnObject(t,r),n},_realizeGroupTransformOnObject:function(t){if(t.group&&"activeSelection"===t.group.type&&this._activeObject===t.group){var e={};return["angle","flipX","flipY","left","scaleX","scaleY","skewX","skewY","top"].forEach((function(i){e[i]=t[i]})),T.util.addTransformToObject(t,this._activeObject.calcOwnMatrix()),e}return null},_unwindGroupTransformOnObject:function(t,e){e&&t.set(e)},_setSVGObject:function(t,e,i){var r=this._realizeGroupTransformOnObject(e);this.callSuper("_setSVGObject",t,e,i),this._unwindGroupTransformOnObject(e,r)},setViewportTransform:function(t){this.renderOnAddRemove&&this._activeObject&&this._activeObject.isEditing&&this._activeObject.clearContextTop(),T.StaticCanvas.prototype.setViewportTransform.call(this,t)}}),T.StaticCanvas)"prototype"!==r&&(T.Canvas[r]=T.StaticCanvas[r])}(),function(){var t=T.util.addListener,e=T.util.removeListener,i={passive:!1};function r(t,e){return t.button&&t.button===e-1}T.util.object.extend(T.Canvas.prototype,{mainTouchId:null,_initEventListeners:function(){this.removeListeners(),this._bindEvents(),this.addOrRemove(t,"add")},_getEventPrefix:function(){return this.enablePointerEvents?"pointer":"mouse"},addOrRemove:function(t,e){var r=this.upperCanvasEl,n=this._getEventPrefix();t(T.window,"resize",this._onResize),t(r,n+"down",this._onMouseDown),t(r,n+"move",this._onMouseMove,i),t(r,n+"out",this._onMouseOut),t(r,n+"enter",this._onMouseEnter),t(r,"wheel",this._onMouseWheel),t(r,"contextmenu",this._onContextMenu),t(r,"dblclick",this._onDoubleClick),t(r,"dragover",this._onDragOver),t(r,"dragenter",this._onDragEnter),t(r,"dragleave",this._onDragLeave),t(r,"drop",this._onDrop),this.enablePointerEvents||t(r,"touchstart",this._onTouchStart,i),void 0!==P&&e in P&&(P[e](r,"gesture",this._onGesture),P[e](r,"drag",this._onDrag),P[e](r,"orientation",this._onOrientationChange),P[e](r,"shake",this._onShake),P[e](r,"longpress",this._onLongPress))},removeListeners:function(){this.addOrRemove(e,"remove");var t=this._getEventPrefix();e(T.document,t+"up",this._onMouseUp),e(T.document,"touchend",this._onTouchEnd,i),e(T.document,t+"move",this._onMouseMove,i),e(T.document,"touchmove",this._onMouseMove,i)},_bindEvents:function(){this.eventsBound||(this._onMouseDown=this._onMouseDown.bind(this),this._onTouchStart=this._onTouchStart.bind(this),this._onMouseMove=this._onMouseMove.bind(this),this._onMouseUp=this._onMouseUp.bind(this),this._onTouchEnd=this._onTouchEnd.bind(this),this._onResize=this._onResize.bind(this),this._onGesture=this._onGesture.bind(this),this._onDrag=this._onDrag.bind(this),this._onShake=this._onShake.bind(this),this._onLongPress=this._onLongPress.bind(this),this._onOrientationChange=this._onOrientationChange.bind(this),this._onMouseWheel=this._onMouseWheel.bind(this),this._onMouseOut=this._onMouseOut.bind(this),this._onMouseEnter=this._onMouseEnter.bind(this),this._onContextMenu=this._onContextMenu.bind(this),this._onDoubleClick=this._onDoubleClick.bind(this),this._onDragOver=this._onDragOver.bind(this),this._onDragEnter=this._simpleEventHandler.bind(this,"dragenter"),this._onDragLeave=this._simpleEventHandler.bind(this,"dragleave"),this._onDrop=this._simpleEventHandler.bind(this,"drop"),this.eventsBound=!0)},_onGesture:function(t,e){this.__onTransformGesture&&this.__onTransformGesture(t,e)},_onDrag:function(t,e){this.__onDrag&&this.__onDrag(t,e)},_onMouseWheel:function(t){this.__onMouseWheel(t)},_onMouseOut:function(t){var e=this._hoveredTarget;this.fire("mouse:out",{target:e,e:t}),this._hoveredTarget=null,e&&e.fire("mouseout",{e:t});var i=this;this._hoveredTargets.forEach((function(r){i.fire("mouse:out",{target:e,e:t}),r&&e.fire("mouseout",{e:t})})),this._hoveredTargets=[],this._iTextInstances&&this._iTextInstances.forEach((function(t){t.isEditing&&t.hiddenTextarea.focus()}))},_onMouseEnter:function(t){this._currentTransform||this.findTarget(t)||(this.fire("mouse:over",{target:null,e:t}),this._hoveredTarget=null,this._hoveredTargets=[])},_onOrientationChange:function(t,e){this.__onOrientationChange&&this.__onOrientationChange(t,e)},_onShake:function(t,e){this.__onShake&&this.__onShake(t,e)},_onLongPress:function(t,e){this.__onLongPress&&this.__onLongPress(t,e)},_onDragOver:function(t){t.preventDefault();var e=this._simpleEventHandler("dragover",t);this._fireEnterLeaveEvents(e,t)},_onContextMenu:function(t){return this.stopContextMenu&&(t.stopPropagation(),t.preventDefault()),!1},_onDoubleClick:function(t){this._cacheTransformEventData(t),this._handleEvent(t,"dblclick"),this._resetTransformEventData(t)},getPointerId:function(t){var e=t.changedTouches;return e?e[0]&&e[0].identifier:this.enablePointerEvents?t.pointerId:-1},_isMainEvent:function(t){return!0===t.isPrimary||!1!==t.isPrimary&&("touchend"===t.type&&0===t.touches.length||(!t.changedTouches||t.changedTouches[0].identifier===this.mainTouchId))},_onTouchStart:function(r){r.preventDefault(),null===this.mainTouchId&&(this.mainTouchId=this.getPointerId(r)),this.__onMouseDown(r),this._resetTransformEventData();var n=this.upperCanvasEl,o=this._getEventPrefix();t(T.document,"touchend",this._onTouchEnd,i),t(T.document,"touchmove",this._onMouseMove,i),e(n,o+"down",this._onMouseDown)},_onMouseDown:function(r){this.__onMouseDown(r),this._resetTransformEventData();var n=this.upperCanvasEl,o=this._getEventPrefix();e(n,o+"move",this._onMouseMove,i),t(T.document,o+"up",this._onMouseUp),t(T.document,o+"move",this._onMouseMove,i)},_onTouchEnd:function(r){if(!(r.touches.length>0)){this.__onMouseUp(r),this._resetTransformEventData(),this.mainTouchId=null;var n=this._getEventPrefix();e(T.document,"touchend",this._onTouchEnd,i),e(T.document,"touchmove",this._onMouseMove,i);var o=this;this._willAddMouseDown&&clearTimeout(this._willAddMouseDown),this._willAddMouseDown=setTimeout((function(){t(o.upperCanvasEl,n+"down",o._onMouseDown),o._willAddMouseDown=0}),400)}},_onMouseUp:function(r){this.__onMouseUp(r),this._resetTransformEventData();var n=this.upperCanvasEl,o=this._getEventPrefix();this._isMainEvent(r)&&(e(T.document,o+"up",this._onMouseUp),e(T.document,o+"move",this._onMouseMove,i),t(n,o+"move",this._onMouseMove,i))},_onMouseMove:function(t){!this.allowTouchScrolling&&t.preventDefault&&t.preventDefault(),this.__onMouseMove(t)},_onResize:function(){this.calcOffset()},_shouldRender:function(t){var e=this._activeObject;return!!(!!e!=!!t||e&&t&&e!==t)||(e&&e.isEditing,!1)},__onMouseUp:function(t){var e,i=this._currentTransform,n=this._groupSelector,o=!1,s=!n||0===n.left&&0===n.top;if(this._cacheTransformEventData(t),e=this._target,this._handleEvent(t,"up:before"),r(t,3))this.fireRightClick&&this._handleEvent(t,"up",3,s);else{if(r(t,2))return this.fireMiddleClick&&this._handleEvent(t,"up",2,s),void this._resetTransformEventData();if(this.isDrawingMode&&this._isCurrentlyDrawing)this._onMouseUpInDrawingMode(t);else if(this._isMainEvent(t)){if(i&&(this._finalizeCurrentTransform(t),o=i.actionPerformed),!s){var a=e===this._activeObject;this._maybeGroupObjects(t),o||(o=this._shouldRender(e)||!a&&e===this._activeObject)}if(e){if(e.selectable&&e!==this._activeObject&&"up"===e.activeOn)this.setActiveObject(e,t),o=!0;else{var h=e._findTargetCorner(this.getPointer(t,!0),T.util.isTouchEvent(t)),c=e.controls[h],l=c&&c.getMouseUpHandler(t,e,c);if(l){var u=this.getPointer(t);l(t,i,u.x,u.y)}}e.isMoving=!1}this._setCursorFromEvent(t,e),this._handleEvent(t,"up",1,s),this._groupSelector=null,this._currentTransform=null,e&&(e.__corner=0),o?this.requestRenderAll():s||this.renderTop()}}},_simpleEventHandler:function(t,e){var i=this.findTarget(e),r=this.targets,n={e:e,target:i,subTargets:r};if(this.fire(t,n),i&&i.fire(t,n),!r)return i;for(var o=0;o1&&(e=new T.ActiveSelection(i.reverse(),{canvas:this}),this.setActiveObject(e,t))},_collectObjects:function(t){for(var e,i=[],r=this._groupSelector.ex,n=this._groupSelector.ey,o=r+this._groupSelector.left,s=n+this._groupSelector.top,a=new T.Point(b(r,o),b(n,s)),h=new T.Point(x(r,o),x(n,s)),c=!this.selectionFullyContained,l=r===o&&n===s,u=this._objects.length;u--&&!((e=this._objects[u])&&e.selectable&&e.visible&&(c&&e.intersectsWithRect(a,h,!0)||e.isContainedWithinRect(a,h,!0)||c&&e.containsPoint(a,null,!0)||c&&e.containsPoint(h,null,!0))&&(i.push(e),l)););return i.length>1&&(i=i.filter((function(e){return!e.onSelect({e:t})}))),i},_maybeGroupObjects:function(t){this.selection&&this._groupSelector&&this._groupSelectedObjects(t),this.setCursor(this.defaultCursor),this._groupSelector=null}}),T.util.object.extend(T.StaticCanvas.prototype,{toDataURL:function(t){t||(t={});var e=t.format||"png",i=t.quality||1,r=(t.multiplier||1)*(t.enableRetinaScaling?this.getRetinaScaling():1),n=this.toCanvasElement(r,t);return T.util.toDataURL(n,e,i)},toCanvasElement:function(t,e){t=t||1;var i=((e=e||{}).width||this.width)*t,r=(e.height||this.height)*t,n=this.getZoom(),o=this.width,s=this.height,a=n*t,h=this.viewportTransform,c=(h[4]-(e.left||0))*t,l=(h[5]-(e.top||0))*t,u=this.interactive,f=[a,0,0,a,c,l],d=this.enableRetinaScaling,g=T.util.createCanvasElement(),p=this.contextTop;return g.width=i,g.height=r,this.contextTop=null,this.enableRetinaScaling=!1,this.interactive=!1,this.viewportTransform=f,this.width=i,this.height=r,this.calcViewportBoundaries(),this.renderCanvas(g.getContext("2d"),this._objects),this.viewportTransform=h,this.width=o,this.height=s,this.calcViewportBoundaries(),this.interactive=u,this.enableRetinaScaling=d,this.contextTop=p,g}}),T.util.object.extend(T.StaticCanvas.prototype,{loadFromJSON:function(t,e,i){if(t){var r="string"==typeof t?JSON.parse(t):T.util.object.clone(t),n=this,o=r.clipPath,s=this.renderOnAddRemove;return this.renderOnAddRemove=!1,delete r.clipPath,this._enlivenObjects(r.objects,(function(t){n.clear(),n._setBgOverlay(r,(function(){o?n._enlivenObjects([o],(function(i){n.clipPath=i[0],n.__setupCanvas.call(n,r,t,s,e)})):n.__setupCanvas.call(n,r,t,s,e)}))}),i),this}},__setupCanvas:function(t,e,i,r){var n=this;e.forEach((function(t,e){n.insertAt(t,e)})),this.renderOnAddRemove=i,delete t.objects,delete t.backgroundImage,delete t.overlayImage,delete t.background,delete t.overlay,this._setOptions(t),this.renderAll(),r&&r()},_setBgOverlay:function(t,e){var i={backgroundColor:!1,overlayColor:!1,backgroundImage:!1,overlayImage:!1};if(t.backgroundImage||t.overlayImage||t.background||t.overlay){var r=function(){i.backgroundImage&&i.overlayImage&&i.backgroundColor&&i.overlayColor&&e&&e()};this.__setBgOverlay("backgroundImage",t.backgroundImage,i,r),this.__setBgOverlay("overlayImage",t.overlayImage,i,r),this.__setBgOverlay("backgroundColor",t.background,i,r),this.__setBgOverlay("overlayColor",t.overlay,i,r)}else e&&e()},__setBgOverlay:function(t,e,i,r){var n=this;if(!e)return i[t]=!0,void(r&&r());"backgroundImage"===t||"overlayImage"===t?T.util.enlivenObjects([e],(function(e){n[t]=e[0],i[t]=!0,r&&r()})):this["set"+T.util.string.capitalize(t,!0)](e,(function(){i[t]=!0,r&&r()}))},_enlivenObjects:function(t,e,i){t&&0!==t.length?T.util.enlivenObjects(t,(function(t){e&&e(t)}),null,i):e&&e([])},_toDataURL:function(t,e){this.clone((function(i){e(i.toDataURL(t))}))},_toDataURLWithMultiplier:function(t,e,i){this.clone((function(r){i(r.toDataURLWithMultiplier(t,e))}))},clone:function(t,e){var i=JSON.stringify(this.toJSON(e));this.cloneWithoutData((function(e){e.loadFromJSON(i,(function(){t&&t(e)}))}))},cloneWithoutData:function(t){var e=T.util.createCanvasElement();e.width=this.width,e.height=this.height;var i=new T.Canvas(e);this.backgroundImage?(i.setBackgroundImage(this.backgroundImage.src,(function(){i.renderAll(),t&&t(i)})),i.backgroundImageOpacity=this.backgroundImageOpacity,i.backgroundImageStretch=this.backgroundImageStretch):t&&t(i)}}),C=T.util.degreesToRadians,w=T.util.radiansToDegrees,T.util.object.extend(T.Canvas.prototype,{__onTransformGesture:function(t,e){if(!this.isDrawingMode&&t.touches&&2===t.touches.length&&"gesture"===e.gesture){var i=this.findTarget(t);void 0!==i&&(this.__gesturesParams={e:t,self:e,target:i},this.__gesturesRenderer()),this.fire("touch:gesture",{target:i,e:t,self:e})}},__gesturesParams:null,__gesturesRenderer:function(){if(null!==this.__gesturesParams&&null!==this._currentTransform){var t=this.__gesturesParams.self,e=this._currentTransform,i=this.__gesturesParams.e;e.action="scale",e.originX=e.originY="center",this._scaleObjectBy(t.scale,i),0!==t.rotation&&(e.action="rotate",this._rotateObjectByAngle(t.rotation,i)),this.requestRenderAll(),e.action="drag"}},__onDrag:function(t,e){this.fire("touch:drag",{e:t,self:e})},__onOrientationChange:function(t,e){this.fire("touch:orientation",{e:t,self:e})},__onShake:function(t,e){this.fire("touch:shake",{e:t,self:e})},__onLongPress:function(t,e){this.fire("touch:longpress",{e:t,self:e})},_scaleObjectBy:function(t,e){var i=this._currentTransform,r=i.target;return i.gestureScale=t,r._scaling=!0,T.controlsUtils.scalingEqually(e,i,0,0)},_rotateObjectByAngle:function(t,e){var i=this._currentTransform;i.target.get("lockRotation")||(i.target.rotate(w(C(t)+i.theta)),this._fire("rotating",{target:i.target,e:e,transform:i}))}}),function(t){var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.util.object.clone,n=e.util.toFixed,o=e.util.string.capitalize,s=e.util.degreesToRadians,a=!e.isLikelyNode;e.Object||(e.Object=e.util.createClass(e.CommonMethods,{type:"object",originX:"left",originY:"top",top:0,left:0,width:0,height:0,scaleX:1,scaleY:1,flipX:!1,flipY:!1,opacity:1,angle:0,skewX:0,skewY:0,cornerSize:13,touchCornerSize:24,transparentCorners:!0,hoverCursor:null,moveCursor:null,padding:0,borderColor:"rgb(178,204,255)",borderDashArray:null,cornerColor:"rgb(178,204,255)",cornerStrokeColor:null,cornerStyle:"rect",cornerDashArray:null,centeredScaling:!1,centeredRotation:!0,fill:"rgb(0,0,0)",fillRule:"nonzero",globalCompositeOperation:"source-over",backgroundColor:"",selectionBackgroundColor:"",stroke:null,strokeWidth:1,strokeDashArray:null,strokeDashOffset:0,strokeLineCap:"butt",strokeLineJoin:"miter",strokeMiterLimit:4,shadow:null,borderOpacityWhenMoving:.4,borderScaleFactor:1,minScaleLimit:0,selectable:!0,evented:!0,visible:!0,hasControls:!0,hasBorders:!0,perPixelTargetFind:!1,includeDefaultValues:!0,lockMovementX:!1,lockMovementY:!1,lockRotation:!1,lockScalingX:!1,lockScalingY:!1,lockSkewingX:!1,lockSkewingY:!1,lockScalingFlip:!1,excludeFromExport:!1,objectCaching:a,statefullCache:!1,noScaleCache:!0,strokeUniform:!1,dirty:!0,__corner:0,paintFirst:"fill",activeOn:"down",stateProperties:"top left width height scaleX scaleY flipX flipY originX originY transformMatrix stroke strokeWidth strokeDashArray strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit angle opacity fill globalCompositeOperation shadow visible backgroundColor skewX skewY fillRule paintFirst clipPath strokeUniform".split(" "),cacheProperties:"fill stroke strokeWidth strokeDashArray width height paintFirst strokeUniform strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit backgroundColor clipPath".split(" "),colorProperties:"fill stroke backgroundColor".split(" "),clipPath:void 0,inverted:!1,absolutePositioned:!1,initialize:function(t){t&&this.setOptions(t)},_createCacheCanvas:function(){this._cacheProperties={},this._cacheCanvas=e.util.createCanvasElement(),this._cacheContext=this._cacheCanvas.getContext("2d"),this._updateCacheCanvas(),this.dirty=!0},_limitCacheSize:function(t){var i=e.perfLimitSizeTotal,r=t.width,n=t.height,o=e.maxCacheSideLimit,s=e.minCacheSideLimit;if(r<=o&&n<=o&&r*n<=i)return rl&&(t.zoomX/=r/l,t.width=l,t.capped=!0),n>u&&(t.zoomY/=n/u,t.height=u,t.capped=!0),t},_getCacheCanvasDimensions:function(){var t=this.getTotalObjectScaling(),e=this._getTransformedDimensions(0,0),i=e.x*t.scaleX/this.scaleX,r=e.y*t.scaleY/this.scaleY;return{width:i+2,height:r+2,zoomX:t.scaleX,zoomY:t.scaleY,x:i,y:r}},_updateCacheCanvas:function(){var t=this.canvas;if(this.noScaleCache&&t&&t._currentTransform){var i=t._currentTransform.target,r=t._currentTransform.action;if(this===i&&r.slice&&"scale"===r.slice(0,5))return!1}var n,o,s=this._cacheCanvas,a=this._limitCacheSize(this._getCacheCanvasDimensions()),h=e.minCacheSideLimit,c=a.width,l=a.height,u=a.zoomX,f=a.zoomY,d=c!==this.cacheWidth||l!==this.cacheHeight,g=this.zoomX!==u||this.zoomY!==f,p=d||g,v=0,m=0,y=!1;if(d){var _=this._cacheCanvas.width,b=this._cacheCanvas.height,x=c>_||l>b;y=x||(c<.9*_||l<.9*b)&&_>h&&b>h,x&&!a.capped&&(c>h||l>h)&&(v=.1*c,m=.1*l)}return this instanceof e.Text&&this.path&&(p=!0,y=!0,v+=this.getHeightOfLine(0)*this.zoomX,m+=this.getHeightOfLine(0)*this.zoomY),!!p&&(y?(s.width=Math.ceil(c+v),s.height=Math.ceil(l+m)):(this._cacheContext.setTransform(1,0,0,1,0,0),this._cacheContext.clearRect(0,0,s.width,s.height)),n=a.x/2,o=a.y/2,this.cacheTranslationX=Math.round(s.width/2-n)+n,this.cacheTranslationY=Math.round(s.height/2-o)+o,this.cacheWidth=c,this.cacheHeight=l,this._cacheContext.translate(this.cacheTranslationX,this.cacheTranslationY),this._cacheContext.scale(u,f),this.zoomX=u,this.zoomY=f,!0)},setOptions:function(t){this._setOptions(t),this._initGradient(t.fill,"fill"),this._initGradient(t.stroke,"stroke"),this._initPattern(t.fill,"fill"),this._initPattern(t.stroke,"stroke")},transform:function(t){var e=this.group&&!this.group._transformDone||this.group&&this.canvas&&t===this.canvas.contextTop,i=this.calcTransformMatrix(!e);t.transform(i[0],i[1],i[2],i[3],i[4],i[5])},toObject:function(t){var i=e.Object.NUM_FRACTION_DIGITS,r={type:this.type,version:e.version,originX:this.originX,originY:this.originY,left:n(this.left,i),top:n(this.top,i),width:n(this.width,i),height:n(this.height,i),fill:this.fill&&this.fill.toObject?this.fill.toObject():this.fill,stroke:this.stroke&&this.stroke.toObject?this.stroke.toObject():this.stroke,strokeWidth:n(this.strokeWidth,i),strokeDashArray:this.strokeDashArray?this.strokeDashArray.concat():this.strokeDashArray,strokeLineCap:this.strokeLineCap,strokeDashOffset:this.strokeDashOffset,strokeLineJoin:this.strokeLineJoin,strokeUniform:this.strokeUniform,strokeMiterLimit:n(this.strokeMiterLimit,i),scaleX:n(this.scaleX,i),scaleY:n(this.scaleY,i),angle:n(this.angle,i),flipX:this.flipX,flipY:this.flipY,opacity:n(this.opacity,i),shadow:this.shadow&&this.shadow.toObject?this.shadow.toObject():this.shadow,visible:this.visible,backgroundColor:this.backgroundColor,fillRule:this.fillRule,paintFirst:this.paintFirst,globalCompositeOperation:this.globalCompositeOperation,skewX:n(this.skewX,i),skewY:n(this.skewY,i)};return this.clipPath&&!this.clipPath.excludeFromExport&&(r.clipPath=this.clipPath.toObject(t),r.clipPath.inverted=this.clipPath.inverted,r.clipPath.absolutePositioned=this.clipPath.absolutePositioned),e.util.populateWithProperties(this,r,t),this.includeDefaultValues||(r=this._removeDefaultValues(r)),r},toDatalessObject:function(t){return this.toObject(t)},_removeDefaultValues:function(t){var i=e.util.getKlass(t.type).prototype;return i.stateProperties.forEach((function(e){"left"!==e&&"top"!==e&&(t[e]===i[e]&&delete t[e],"[object Array]"===Object.prototype.toString.call(t[e])&&"[object Array]"===Object.prototype.toString.call(i[e])&&0===t[e].length&&0===i[e].length&&delete t[e])})),t},toString:function(){return"#"},getObjectScaling:function(){if(!this.group)return{scaleX:this.scaleX,scaleY:this.scaleY};var t=e.util.qrDecompose(this.calcTransformMatrix());return{scaleX:Math.abs(t.scaleX),scaleY:Math.abs(t.scaleY)}},getTotalObjectScaling:function(){var t=this.getObjectScaling(),e=t.scaleX,i=t.scaleY;if(this.canvas){var r=this.canvas.getZoom(),n=this.canvas.getRetinaScaling();e*=r*n,i*=r*n}return{scaleX:e,scaleY:i}},getObjectOpacity:function(){var t=this.opacity;return this.group&&(t*=this.group.getObjectOpacity()),t},_set:function(t,i){var r="scaleX"===t||"scaleY"===t,n=this[t]!==i,o=!1;return r&&(i=this._constrainScale(i)),"scaleX"===t&&i<0?(this.flipX=!this.flipX,i*=-1):"scaleY"===t&&i<0?(this.flipY=!this.flipY,i*=-1):"shadow"!==t||!i||i instanceof e.Shadow?"dirty"===t&&this.group&&this.group.set("dirty",i):i=new e.Shadow(i),this[t]=i,n&&(o=this.group&&this.group.isOnACache(),this.cacheProperties.indexOf(t)>-1?(this.dirty=!0,o&&this.group.set("dirty",!0)):o&&this.stateProperties.indexOf(t)>-1&&this.group.set("dirty",!0)),this},setOnGroup:function(){},getViewportTransform:function(){return this.canvas&&this.canvas.viewportTransform?this.canvas.viewportTransform:e.iMatrix.concat()},isNotVisible:function(){return 0===this.opacity||!this.width&&!this.height&&0===this.strokeWidth||!this.visible},render:function(t){this.isNotVisible()||this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(t.save(),this._setupCompositeOperation(t),this.drawSelectionBackground(t),this.transform(t),this._setOpacity(t),this._setShadow(t,this),this.shouldCache()?(this.renderCache(),this.drawCacheOnCanvas(t)):(this._removeCacheCanvas(),this.dirty=!1,this.drawObject(t),this.objectCaching&&this.statefullCache&&this.saveState({propertySet:"cacheProperties"})),t.restore())},renderCache:function(t){t=t||{},this._cacheCanvas||this._createCacheCanvas(),this.isCacheDirty()&&(this.statefullCache&&this.saveState({propertySet:"cacheProperties"}),this.drawObject(this._cacheContext,t.forClipping),this.dirty=!1)},_removeCacheCanvas:function(){this._cacheCanvas=null,this.cacheWidth=0,this.cacheHeight=0},hasStroke:function(){return this.stroke&&"transparent"!==this.stroke&&0!==this.strokeWidth},hasFill:function(){return this.fill&&"transparent"!==this.fill},needsItsOwnCache:function(){return!("stroke"!==this.paintFirst||!this.hasFill()||!this.hasStroke()||"object"!=typeof this.shadow)||!!this.clipPath},shouldCache:function(){return this.ownCaching=this.needsItsOwnCache()||this.objectCaching&&(!this.group||!this.group.isOnACache()),this.ownCaching},willDrawShadow:function(){return!!this.shadow&&(0!==this.shadow.offsetX||0!==this.shadow.offsetY)},drawClipPathOnCache:function(t){var i=this.clipPath;if(t.save(),i.inverted?t.globalCompositeOperation="destination-out":t.globalCompositeOperation="destination-in",i.absolutePositioned){var r=e.util.invertTransform(this.calcTransformMatrix());t.transform(r[0],r[1],r[2],r[3],r[4],r[5])}i.transform(t),t.scale(1/i.zoomX,1/i.zoomY),t.drawImage(i._cacheCanvas,-i.cacheTranslationX,-i.cacheTranslationY),t.restore()},drawObject:function(t,e){var i=this.fill,r=this.stroke;e?(this.fill="black",this.stroke="",this._setClippingProperties(t)):this._renderBackground(t),this._render(t),this._drawClipPath(t),this.fill=i,this.stroke=r},_drawClipPath:function(t){var e=this.clipPath;e&&(e.canvas=this.canvas,e.shouldCache(),e._transformDone=!0,e.renderCache({forClipping:!0}),this.drawClipPathOnCache(t))},drawCacheOnCanvas:function(t){t.scale(1/this.zoomX,1/this.zoomY),t.drawImage(this._cacheCanvas,-this.cacheTranslationX,-this.cacheTranslationY)},isCacheDirty:function(t){if(this.isNotVisible())return!1;if(this._cacheCanvas&&!t&&this._updateCacheCanvas())return!0;if(this.dirty||this.clipPath&&this.clipPath.absolutePositioned||this.statefullCache&&this.hasStateChanged("cacheProperties")){if(this._cacheCanvas&&!t){var e=this.cacheWidth/this.zoomX,i=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-e/2,-i/2,e,i)}return!0}return!1},_renderBackground:function(t){if(this.backgroundColor){var e=this._getNonTransformedDimensions();t.fillStyle=this.backgroundColor,t.fillRect(-e.x/2,-e.y/2,e.x,e.y),this._removeShadow(t)}},_setOpacity:function(t){this.group&&!this.group._transformDone?t.globalAlpha=this.getObjectOpacity():t.globalAlpha*=this.opacity},_setStrokeStyles:function(t,e){var i=e.stroke;i&&(t.lineWidth=e.strokeWidth,t.lineCap=e.strokeLineCap,t.lineDashOffset=e.strokeDashOffset,t.lineJoin=e.strokeLineJoin,t.miterLimit=e.strokeMiterLimit,i.toLive?"percentage"===i.gradientUnits||i.gradientTransform||i.patternTransform?this._applyPatternForTransformedGradient(t,i):(t.strokeStyle=i.toLive(t,this),this._applyPatternGradientTransform(t,i)):t.strokeStyle=e.stroke)},_setFillStyles:function(t,e){var i=e.fill;i&&(i.toLive?(t.fillStyle=i.toLive(t,this),this._applyPatternGradientTransform(t,e.fill)):t.fillStyle=i)},_setClippingProperties:function(t){t.globalAlpha=1,t.strokeStyle="transparent",t.fillStyle="#000000"},_setLineDash:function(t,e){e&&0!==e.length&&(1&e.length&&e.push.apply(e,e),t.setLineDash(e))},_renderControls:function(t,i){var r,n,o,a=this.getViewportTransform(),h=this.calcTransformMatrix();n=void 0!==(i=i||{}).hasBorders?i.hasBorders:this.hasBorders,o=void 0!==i.hasControls?i.hasControls:this.hasControls,h=e.util.multiplyTransformMatrices(a,h),r=e.util.qrDecompose(h),t.save(),t.translate(r.translateX,r.translateY),t.lineWidth=1*this.borderScaleFactor,this.group||(t.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1),t.rotate(s(r.angle)),i.forActiveSelection||this.group?n&&this.drawBordersInGroup(t,r,i):n&&this.drawBorders(t,i),o&&this.drawControls(t,i),t.restore()},_setShadow:function(t){if(this.shadow){var i,r=this.shadow,n=this.canvas,o=n&&n.viewportTransform[0]||1,s=n&&n.viewportTransform[3]||1;i=r.nonScaling?{scaleX:1,scaleY:1}:this.getObjectScaling(),n&&n._isRetinaScaling()&&(o*=e.devicePixelRatio,s*=e.devicePixelRatio),t.shadowColor=r.color,t.shadowBlur=r.blur*e.browserShadowBlurConstant*(o+s)*(i.scaleX+i.scaleY)/4,t.shadowOffsetX=r.offsetX*o*i.scaleX,t.shadowOffsetY=r.offsetY*s*i.scaleY}},_removeShadow:function(t){this.shadow&&(t.shadowColor="",t.shadowBlur=t.shadowOffsetX=t.shadowOffsetY=0)},_applyPatternGradientTransform:function(t,e){if(!e||!e.toLive)return{offsetX:0,offsetY:0};var i=e.gradientTransform||e.patternTransform,r=-this.width/2+e.offsetX||0,n=-this.height/2+e.offsetY||0;return"percentage"===e.gradientUnits?t.transform(this.width,0,0,this.height,r,n):t.transform(1,0,0,1,r,n),i&&t.transform(i[0],i[1],i[2],i[3],i[4],i[5]),{offsetX:r,offsetY:n}},_renderPaintInOrder:function(t){"stroke"===this.paintFirst?(this._renderStroke(t),this._renderFill(t)):(this._renderFill(t),this._renderStroke(t))},_render:function(){},_renderFill:function(t){this.fill&&(t.save(),this._setFillStyles(t,this),"evenodd"===this.fillRule?t.fill("evenodd"):t.fill(),t.restore())},_renderStroke:function(t){if(this.stroke&&0!==this.strokeWidth){if(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(t),t.save(),this.strokeUniform&&this.group){var e=this.getObjectScaling();t.scale(1/e.scaleX,1/e.scaleY)}else this.strokeUniform&&t.scale(1/this.scaleX,1/this.scaleY);this._setLineDash(t,this.strokeDashArray),this._setStrokeStyles(t,this),t.stroke(),t.restore()}},_applyPatternForTransformedGradient:function(t,i){var r,n=this._limitCacheSize(this._getCacheCanvasDimensions()),o=e.util.createCanvasElement(),s=this.canvas.getRetinaScaling(),a=n.x/this.scaleX/s,h=n.y/this.scaleY/s;o.width=a,o.height=h,(r=o.getContext("2d")).beginPath(),r.moveTo(0,0),r.lineTo(a,0),r.lineTo(a,h),r.lineTo(0,h),r.closePath(),r.translate(a/2,h/2),r.scale(n.zoomX/this.scaleX/s,n.zoomY/this.scaleY/s),this._applyPatternGradientTransform(r,i),r.fillStyle=i.toLive(t),r.fill(),t.translate(-this.width/2-this.strokeWidth/2,-this.height/2-this.strokeWidth/2),t.scale(s*this.scaleX/n.zoomX,s*this.scaleY/n.zoomY),t.strokeStyle=r.createPattern(o,"no-repeat")},_findCenterFromElement:function(){return{x:this.left+this.width/2,y:this.top+this.height/2}},_assignTransformMatrixProps:function(){if(this.transformMatrix){var t=e.util.qrDecompose(this.transformMatrix);this.flipX=!1,this.flipY=!1,this.set("scaleX",t.scaleX),this.set("scaleY",t.scaleY),this.angle=t.angle,this.skewX=t.skewX,this.skewY=0}},_removeTransformMatrix:function(t){var i=this._findCenterFromElement();this.transformMatrix&&(this._assignTransformMatrixProps(),i=e.util.transformPoint(i,this.transformMatrix)),this.transformMatrix=null,t&&(this.scaleX*=t.scaleX,this.scaleY*=t.scaleY,this.cropX=t.cropX,this.cropY=t.cropY,i.x+=t.offsetLeft,i.y+=t.offsetTop,this.width=t.width,this.height=t.height),this.setPositionByOrigin(i,"center","center")},clone:function(t,i){var r=this.toObject(i);this.constructor.fromObject?this.constructor.fromObject(r,t):e.Object._fromObject("Object",r,t)},cloneAsImage:function(t,i){var r=this.toCanvasElement(i);return t&&t(new e.Image(r)),this},toCanvasElement:function(t){t||(t={});var i=e.util,r=i.saveObjectTransform(this),n=this.group,o=this.shadow,s=Math.abs,a=(t.multiplier||1)*(t.enableRetinaScaling?e.devicePixelRatio:1);delete this.group,t.withoutTransform&&i.resetObjectTransform(this),t.withoutShadow&&(this.shadow=null);var h,c,l,u,f=e.util.createCanvasElement(),d=this.getBoundingRect(!0,!0),g=this.shadow,p={x:0,y:0};g&&(c=g.blur,h=g.nonScaling?{scaleX:1,scaleY:1}:this.getObjectScaling(),p.x=2*Math.round(s(g.offsetX)+c)*s(h.scaleX),p.y=2*Math.round(s(g.offsetY)+c)*s(h.scaleY)),l=d.width+p.x,u=d.height+p.y,f.width=Math.ceil(l),f.height=Math.ceil(u);var v=new e.StaticCanvas(f,{enableRetinaScaling:!1,renderOnAddRemove:!1,skipOffscreen:!1});"jpeg"===t.format&&(v.backgroundColor="#fff"),this.setPositionByOrigin(new e.Point(v.width/2,v.height/2),"center","center");var m=this.canvas;v.add(this);var y=v.toCanvasElement(a||1,t);return this.shadow=o,this.set("canvas",m),n&&(this.group=n),this.set(r).setCoords(),v._objects=[],v.dispose(),v=null,y},toDataURL:function(t){return t||(t={}),e.util.toDataURL(this.toCanvasElement(t),t.format||"png",t.quality||1)},isType:function(t){return this.type===t},complexity:function(){return 1},toJSON:function(t){return this.toObject(t)},rotate:function(t){var e=("center"!==this.originX||"center"!==this.originY)&&this.centeredRotation;return e&&this._setOriginToCenter(),this.set("angle",t),e&&this._resetOrigin(),this},centerH:function(){return this.canvas&&this.canvas.centerObjectH(this),this},viewportCenterH:function(){return this.canvas&&this.canvas.viewportCenterObjectH(this),this},centerV:function(){return this.canvas&&this.canvas.centerObjectV(this),this},viewportCenterV:function(){return this.canvas&&this.canvas.viewportCenterObjectV(this),this},center:function(){return this.canvas&&this.canvas.centerObject(this),this},viewportCenter:function(){return this.canvas&&this.canvas.viewportCenterObject(this),this},getLocalPointer:function(t,i){i=i||this.canvas.getPointer(t);var r=new e.Point(i.x,i.y),n=this._getLeftTopCoords();return this.angle&&(r=e.util.rotatePoint(r,n,s(-this.angle))),{x:r.x-n.x,y:r.y-n.y}},_setupCompositeOperation:function(t){this.globalCompositeOperation&&(t.globalCompositeOperation=this.globalCompositeOperation)}}),e.util.createAccessors&&e.util.createAccessors(e.Object),i(e.Object.prototype,e.Observable),e.Object.NUM_FRACTION_DIGITS=2,e.Object._fromObject=function(t,i,n,o){var s=e[t];i=r(i,!0),e.util.enlivenPatterns([i.fill,i.stroke],(function(t){void 0!==t[0]&&(i.fill=t[0]),void 0!==t[1]&&(i.stroke=t[1]),e.util.enlivenObjects([i.clipPath],(function(t){i.clipPath=t[0];var e=o?new s(i[o],i):new s(i);n&&n(e)}))}))},e.Object.__uid=0)}(t),function(){var t=T.util.degreesToRadians,e={left:-.5,center:0,right:.5},i={top:-.5,center:0,bottom:.5};T.util.object.extend(T.Object.prototype,{translateToGivenOrigin:function(t,r,n,o,s){var a,h,c,l=t.x,u=t.y;return"string"==typeof r?r=e[r]:r-=.5,"string"==typeof o?o=e[o]:o-=.5,"string"==typeof n?n=i[n]:n-=.5,"string"==typeof s?s=i[s]:s-=.5,h=s-n,((a=o-r)||h)&&(c=this._getTransformedDimensions(),l=t.x+a*c.x,u=t.y+h*c.y),new T.Point(l,u)},translateToCenterPoint:function(e,i,r){var n=this.translateToGivenOrigin(e,i,r,"center","center");return this.angle?T.util.rotatePoint(n,e,t(this.angle)):n},translateToOriginPoint:function(e,i,r){var n=this.translateToGivenOrigin(e,"center","center",i,r);return this.angle?T.util.rotatePoint(n,e,t(this.angle)):n},getCenterPoint:function(){var t=new T.Point(this.left,this.top);return this.translateToCenterPoint(t,this.originX,this.originY)},getPointByOrigin:function(t,e){var i=this.getCenterPoint();return this.translateToOriginPoint(i,t,e)},toLocalPoint:function(e,i,r){var n,o,s=this.getCenterPoint();return n=void 0!==i&&void 0!==r?this.translateToGivenOrigin(s,"center","center",i,r):new T.Point(this.left,this.top),o=new T.Point(e.x,e.y),this.angle&&(o=T.util.rotatePoint(o,s,-t(this.angle))),o.subtractEquals(n)},setPositionByOrigin:function(t,e,i){var r=this.translateToCenterPoint(t,e,i),n=this.translateToOriginPoint(r,this.originX,this.originY);this.set("left",n.x),this.set("top",n.y)},adjustPosition:function(i){var r,n,o=t(this.angle),s=this.getScaledWidth(),a=T.util.cos(o)*s,h=T.util.sin(o)*s;r="string"==typeof this.originX?e[this.originX]:this.originX-.5,n="string"==typeof i?e[i]:i-.5,this.left+=a*(n-r),this.top+=h*(n-r),this.setCoords(),this.originX=i},_setOriginToCenter:function(){this._originalOriginX=this.originX,this._originalOriginY=this.originY;var t=this.getCenterPoint();this.originX="center",this.originY="center",this.left=t.x,this.top=t.y},_resetOrigin:function(){var t=this.translateToOriginPoint(this.getCenterPoint(),this._originalOriginX,this._originalOriginY);this.originX=this._originalOriginX,this.originY=this._originalOriginY,this.left=t.x,this.top=t.y,this._originalOriginX=null,this._originalOriginY=null},_getLeftTopCoords:function(){return this.translateToOriginPoint(this.getCenterPoint(),"left","top")}})}(),function(){var t=T.util,e=t.degreesToRadians,i=t.multiplyTransformMatrices,r=t.transformPoint;t.object.extend(T.Object.prototype,{oCoords:null,aCoords:null,lineCoords:null,ownMatrixCache:null,matrixCache:null,controls:{},_getCoords:function(t,e){return e?t?this.calcACoords():this.calcLineCoords():(this.aCoords&&this.lineCoords||this.setCoords(!0),t?this.aCoords:this.lineCoords)},getCoords:function(t,e){return i=this._getCoords(t,e),[new T.Point(i.tl.x,i.tl.y),new T.Point(i.tr.x,i.tr.y),new T.Point(i.br.x,i.br.y),new T.Point(i.bl.x,i.bl.y)];var i},intersectsWithRect:function(t,e,i,r){var n=this.getCoords(i,r);return"Intersection"===T.Intersection.intersectPolygonRectangle(n,t,e).status},intersectsWithObject:function(t,e,i){return"Intersection"===T.Intersection.intersectPolygonPolygon(this.getCoords(e,i),t.getCoords(e,i)).status||t.isContainedWithinObject(this,e,i)||this.isContainedWithinObject(t,e,i)},isContainedWithinObject:function(t,e,i){for(var r=this.getCoords(e,i),n=e?t.aCoords:t.lineCoords,o=0,s=t._getImageLines(n);o<4;o++)if(!t.containsPoint(r[o],s))return!1;return!0},isContainedWithinRect:function(t,e,i,r){var n=this.getBoundingRect(i,r);return n.left>=t.x&&n.left+n.width<=e.x&&n.top>=t.y&&n.top+n.height<=e.y},containsPoint:function(t,e,i,r){var n=this._getCoords(i,r),o=(e=e||this._getImageLines(n),this._findCrossPoints(t,e));return 0!==o&&o%2==1},isOnScreen:function(t){if(!this.canvas)return!1;var e=this.canvas.vptCoords.tl,i=this.canvas.vptCoords.br;return!!this.getCoords(!0,t).some((function(t){return t.x<=i.x&&t.x>=e.x&&t.y<=i.y&&t.y>=e.y}))||(!!this.intersectsWithRect(e,i,!0,t)||this._containsCenterOfCanvas(e,i,t))},_containsCenterOfCanvas:function(t,e,i){var r={x:(t.x+e.x)/2,y:(t.y+e.y)/2};return!!this.containsPoint(r,null,!0,i)},isPartiallyOnScreen:function(t){if(!this.canvas)return!1;var e=this.canvas.vptCoords.tl,i=this.canvas.vptCoords.br;return!!this.intersectsWithRect(e,i,!0,t)||this.getCoords(!0,t).every((function(t){return(t.x>=i.x||t.x<=e.x)&&(t.y>=i.y||t.y<=e.y)}))&&this._containsCenterOfCanvas(e,i,t)},_getImageLines:function(t){return{topline:{o:t.tl,d:t.tr},rightline:{o:t.tr,d:t.br},bottomline:{o:t.br,d:t.bl},leftline:{o:t.bl,d:t.tl}}},_findCrossPoints:function(t,e){var i,r,n,o=0;for(var s in e)if(!((n=e[s]).o.y=t.y&&n.d.y>=t.y||(n.o.x===n.d.x&&n.o.x>=t.x?r=n.o.x:(0,i=(n.d.y-n.o.y)/(n.d.x-n.o.x),r=-(t.y-0*t.x-(n.o.y-i*n.o.x))/(0-i)),r>=t.x&&(o+=1),2!==o)))break;return o},getBoundingRect:function(e,i){var r=this.getCoords(e,i);return t.makeBoundingBoxFromPoints(r)},getScaledWidth:function(){return this._getTransformedDimensions().x},getScaledHeight:function(){return this._getTransformedDimensions().y},_constrainScale:function(t){return Math.abs(t)\n')}},toSVG:function(t){return this._createBaseSVGMarkup(this._toSVG(t),{reviver:t})},toClipPathSVG:function(t){return"\t"+this._createBaseClipPathSVGMarkup(this._toSVG(t),{reviver:t})},_createBaseClipPathSVGMarkup:function(t,e){var i=(e=e||{}).reviver,r=e.additionalTransform||"",n=[this.getSvgTransform(!0,r),this.getSvgCommons()].join(""),o=t.indexOf("COMMON_PARTS");return t[o]=n,i?i(t.join("")):t.join("")},_createBaseSVGMarkup:function(t,e){var i,r,n=(e=e||{}).noStyle,o=e.reviver,s=n?"":'style="'+this.getSvgStyles()+'" ',a=e.withShadow?'style="'+this.getSvgFilter()+'" ':"",h=this.clipPath,c=this.strokeUniform?'vector-effect="non-scaling-stroke" ':"",l=h&&h.absolutePositioned,u=this.stroke,f=this.fill,d=this.shadow,g=[],p=t.indexOf("COMMON_PARTS"),v=e.additionalTransform;return h&&(h.clipPathId="CLIPPATH_"+T.Object.__uid++,r='\n'+h.toClipPathSVG(o)+"\n"),l&&g.push("\n"),g.push("\n"),i=[s,c,n?"":this.addPaintOrder()," ",v?'transform="'+v+'" ':""].join(""),t[p]=i,f&&f.toLive&&g.push(f.toSVG(this)),u&&u.toLive&&g.push(u.toSVG(this)),d&&g.push(d.toSVG(this)),h&&g.push(r),g.push(t.join("")),g.push("\n"),l&&g.push("\n"),o?o(g.join("")):g.join("")},addPaintOrder:function(){return"fill"!==this.paintFirst?' paint-order="'+this.paintFirst+'" ':""}})}(),function(){var t=T.util.object.extend,e="stateProperties";function i(e,i,r){var n={};r.forEach((function(t){n[t]=e[t]})),t(e[i],n,!0)}function r(t,e,i){if(t===e)return!0;if(Array.isArray(t)){if(!Array.isArray(e)||t.length!==e.length)return!1;for(var n=0,o=t.length;n=0;h--)if(n=a[h],this.isControlVisible(n)&&(r=this._getImageLines(e?this.oCoords[n].touchCorner:this.oCoords[n].corner),0!==(i=this._findCrossPoints({x:o,y:s},r))&&i%2==1))return this.__corner=n,n;return!1},forEachControl:function(t){for(var e in this.controls)t(this.controls[e],e,this)},_setCornerCoords:function(){var t=this.oCoords;for(var e in t){var i=this.controls[e];t[e].corner=i.calcCornerCoords(this.angle,this.cornerSize,t[e].x,t[e].y,!1),t[e].touchCorner=i.calcCornerCoords(this.angle,this.touchCornerSize,t[e].x,t[e].y,!0)}},drawSelectionBackground:function(e){if(!this.selectionBackgroundColor||this.canvas&&!this.canvas.interactive||this.canvas&&this.canvas._activeObject!==this)return this;e.save();var i=this.getCenterPoint(),r=this._calculateCurrentDimensions(),n=this.canvas.viewportTransform;return e.translate(i.x,i.y),e.scale(1/n[0],1/n[3]),e.rotate(t(this.angle)),e.fillStyle=this.selectionBackgroundColor,e.fillRect(-r.x/2,-r.y/2,r.x,r.y),e.restore(),this},drawBorders:function(t,e){e=e||{};var i=this._calculateCurrentDimensions(),r=this.borderScaleFactor,n=i.x+r,o=i.y+r,s=void 0!==e.hasControls?e.hasControls:this.hasControls,a=!1;return t.save(),t.strokeStyle=e.borderColor||this.borderColor,this._setLineDash(t,e.borderDashArray||this.borderDashArray),t.strokeRect(-n/2,-o/2,n,o),s&&(t.beginPath(),this.forEachControl((function(e,i,r){e.withConnection&&e.getVisibility(r,i)&&(a=!0,t.moveTo(e.x*n,e.y*o),t.lineTo(e.x*n+e.offsetX,e.y*o+e.offsetY))})),a&&t.stroke()),t.restore(),this},drawBordersInGroup:function(t,e,i){i=i||{};var r=T.util.sizeAfterTransform(this.width,this.height,e),n=this.strokeWidth,o=this.strokeUniform,s=this.borderScaleFactor,a=r.x+n*(o?this.canvas.getZoom():e.scaleX)+s,h=r.y+n*(o?this.canvas.getZoom():e.scaleY)+s;return t.save(),this._setLineDash(t,i.borderDashArray||this.borderDashArray),t.strokeStyle=i.borderColor||this.borderColor,t.strokeRect(-a/2,-h/2,a,h),t.restore(),this},drawControls:function(t,e){e=e||{},t.save();var i,r,n=this.canvas.getRetinaScaling();return t.setTransform(n,0,0,n,0,0),t.strokeStyle=t.fillStyle=e.cornerColor||this.cornerColor,this.transparentCorners||(t.strokeStyle=e.cornerStrokeColor||this.cornerStrokeColor),this._setLineDash(t,e.cornerDashArray||this.cornerDashArray),this.setCoords(),this.group&&(i=this.group.calcTransformMatrix()),this.forEachControl((function(n,o,s){r=s.oCoords[o],n.getVisibility(s,o)&&(i&&(r=T.util.transformPoint(r,i)),n.render(t,r.x,r.y,e,s))})),t.restore(),this},isControlVisible:function(t){return this.controls[t]&&this.controls[t].getVisibility(this,t)},setControlVisible:function(t,e){return this._controlsVisibility||(this._controlsVisibility={}),this._controlsVisibility[t]=e,this},setControlsVisibility:function(t){for(var e in t||(t={}),t)this.setControlVisible(e,t[e]);return this},onDeselect:function(){},onSelect:function(){}})}(),T.util.object.extend(T.StaticCanvas.prototype,{FX_DURATION:500,fxCenterObjectH:function(t,e){var i=function(){},r=(e=e||{}).onComplete||i,n=e.onChange||i,o=this;return T.util.animate({startValue:t.left,endValue:this.getCenter().left,duration:this.FX_DURATION,onChange:function(e){t.set("left",e),o.requestRenderAll(),n()},onComplete:function(){t.setCoords(),r()}}),this},fxCenterObjectV:function(t,e){var i=function(){},r=(e=e||{}).onComplete||i,n=e.onChange||i,o=this;return T.util.animate({startValue:t.top,endValue:this.getCenter().top,duration:this.FX_DURATION,onChange:function(e){t.set("top",e),o.requestRenderAll(),n()},onComplete:function(){t.setCoords(),r()}}),this},fxRemove:function(t,e){var i=function(){},r=(e=e||{}).onComplete||i,n=e.onChange||i,o=this;return T.util.animate({startValue:t.opacity,endValue:0,duration:this.FX_DURATION,onChange:function(e){t.set("opacity",e),o.requestRenderAll(),n()},onComplete:function(){o.remove(t),r()}}),this}}),T.util.object.extend(T.Object.prototype,{animate:function(){if(arguments[0]&&"object"==typeof arguments[0]){var t,e,i=[];for(t in arguments[0])i.push(t);for(var r=0,n=i.length;r-1||n&&o.colorProperties.indexOf(n[1])>-1,a=n?this.get(n[0])[n[1]]:this.get(t);"from"in i||(i.from=a),s||(e=~e.indexOf("=")?a+parseFloat(e.replace("=","")):parseFloat(e));var h={startValue:i.from,endValue:e,byValue:i.by,easing:i.easing,duration:i.duration,abort:i.abort&&function(t,e,r){return i.abort.call(o,t,e,r)},onChange:function(e,s,a){n?o[n[0]][n[1]]=e:o.set(t,e),r||i.onChange&&i.onChange(e,s,a)},onComplete:function(t,e,n){r||(o.setCoords(),i.onComplete&&i.onComplete(t,e,n))}};return s?T.util.animateColor(h.startValue,h.endValue,h.duration,h):T.util.animate(h)}}),function(t){var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.util.object.clone,n={x1:1,x2:1,y1:1,y2:1};function o(t,e){var i=t.origin,r=t.axis1,n=t.axis2,o=t.dimension,s=e.nearest,a=e.center,h=e.farthest;return function(){switch(this.get(i)){case s:return Math.min(this.get(r),this.get(n));case a:return Math.min(this.get(r),this.get(n))+.5*this.get(o);case h:return Math.max(this.get(r),this.get(n))}}}e.Line?e.warn("fabric.Line is already defined"):(e.Line=e.util.createClass(e.Object,{type:"line",x1:0,y1:0,x2:0,y2:0,cacheProperties:e.Object.prototype.cacheProperties.concat("x1","x2","y1","y2"),initialize:function(t,e){t||(t=[0,0,0,0]),this.callSuper("initialize",e),this.set("x1",t[0]),this.set("y1",t[1]),this.set("x2",t[2]),this.set("y2",t[3]),this._setWidthHeight(e)},_setWidthHeight:function(t){t||(t={}),this.width=Math.abs(this.x2-this.x1),this.height=Math.abs(this.y2-this.y1),this.left="left"in t?t.left:this._getLeftToOriginX(),this.top="top"in t?t.top:this._getTopToOriginY()},_set:function(t,e){return this.callSuper("_set",t,e),void 0!==n[t]&&this._setWidthHeight(),this},_getLeftToOriginX:o({origin:"originX",axis1:"x1",axis2:"x2",dimension:"width"},{nearest:"left",center:"center",farthest:"right"}),_getTopToOriginY:o({origin:"originY",axis1:"y1",axis2:"y2",dimension:"height"},{nearest:"top",center:"center",farthest:"bottom"}),_render:function(t){t.beginPath();var e=this.calcLinePoints();t.moveTo(e.x1,e.y1),t.lineTo(e.x2,e.y2),t.lineWidth=this.strokeWidth;var i=t.strokeStyle;t.strokeStyle=this.stroke||t.fillStyle,this.stroke&&this._renderStroke(t),t.strokeStyle=i},_findCenterFromElement:function(){return{x:(this.x1+this.x2)/2,y:(this.y1+this.y2)/2}},toObject:function(t){return i(this.callSuper("toObject",t),this.calcLinePoints())},_getNonTransformedDimensions:function(){var t=this.callSuper("_getNonTransformedDimensions");return"butt"===this.strokeLineCap&&(0===this.width&&(t.y-=this.strokeWidth),0===this.height&&(t.x-=this.strokeWidth)),t},calcLinePoints:function(){var t=this.x1<=this.x2?-1:1,e=this.y1<=this.y2?-1:1,i=t*this.width*.5,r=e*this.height*.5;return{x1:i,x2:t*this.width*-.5,y1:r,y2:e*this.height*-.5}},_toSVG:function(){var t=this.calcLinePoints();return["\n']}}),e.Line.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("x1 y1 x2 y2".split(" ")),e.Line.fromElement=function(t,r,n){n=n||{};var o=e.parseAttributes(t,e.Line.ATTRIBUTE_NAMES),s=[o.x1||0,o.y1||0,o.x2||0,o.y2||0];r(new e.Line(s,i(o,n)))},e.Line.fromObject=function(t,i){var n=r(t,!0);n.points=[t.x1,t.y1,t.x2,t.y2],e.Object._fromObject("Line",n,(function(t){delete t.points,i&&i(t)}),"points")})}(t),function(t){var e=t.fabric||(t.fabric={}),i=Math.PI;e.Circle?e.warn("fabric.Circle is already defined."):(e.Circle=e.util.createClass(e.Object,{type:"circle",radius:0,startAngle:0,endAngle:2*i,cacheProperties:e.Object.prototype.cacheProperties.concat("radius","startAngle","endAngle"),_set:function(t,e){return this.callSuper("_set",t,e),"radius"===t&&this.setRadius(e),this},toObject:function(t){return this.callSuper("toObject",["radius","startAngle","endAngle"].concat(t))},_toSVG:function(){var t,r=(this.endAngle-this.startAngle)%(2*i);if(0===r)t=["\n'];else{var n=e.util.cos(this.startAngle)*this.radius,o=e.util.sin(this.startAngle)*this.radius,s=e.util.cos(this.endAngle)*this.radius,a=e.util.sin(this.endAngle)*this.radius,h=r>i?"1":"0";t=['\n"]}return t},_render:function(t){t.beginPath(),t.arc(0,0,this.radius,this.startAngle,this.endAngle,!1),this._renderPaintInOrder(t)},getRadiusX:function(){return this.get("radius")*this.get("scaleX")},getRadiusY:function(){return this.get("radius")*this.get("scaleY")},setRadius:function(t){return this.radius=t,this.set("width",2*t).set("height",2*t)}}),e.Circle.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("cx cy r".split(" ")),e.Circle.fromElement=function(t,i){var r,n=e.parseAttributes(t,e.Circle.ATTRIBUTE_NAMES);if(!("radius"in(r=n)&&r.radius>=0))throw new Error("value of `r` attribute is required and can not be negative");n.left=(n.left||0)-n.radius,n.top=(n.top||0)-n.radius,i(new e.Circle(n))},e.Circle.fromObject=function(t,i){e.Object._fromObject("Circle",t,i)})}(t),function(t){var e=t.fabric||(t.fabric={});e.Triangle?e.warn("fabric.Triangle is already defined"):(e.Triangle=e.util.createClass(e.Object,{type:"triangle",width:100,height:100,_render:function(t){var e=this.width/2,i=this.height/2;t.beginPath(),t.moveTo(-e,i),t.lineTo(0,-i),t.lineTo(e,i),t.closePath(),this._renderPaintInOrder(t)},_toSVG:function(){var t=this.width/2,e=this.height/2;return["']}}),e.Triangle.fromObject=function(t,i){return e.Object._fromObject("Triangle",t,i)})}(t),function(t){var e=t.fabric||(t.fabric={}),i=2*Math.PI;e.Ellipse?e.warn("fabric.Ellipse is already defined."):(e.Ellipse=e.util.createClass(e.Object,{type:"ellipse",rx:0,ry:0,cacheProperties:e.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(t){this.callSuper("initialize",t),this.set("rx",t&&t.rx||0),this.set("ry",t&&t.ry||0)},_set:function(t,e){switch(this.callSuper("_set",t,e),t){case"rx":this.rx=e,this.set("width",2*e);break;case"ry":this.ry=e,this.set("height",2*e)}return this},getRx:function(){return this.get("rx")*this.get("scaleX")},getRy:function(){return this.get("ry")*this.get("scaleY")},toObject:function(t){return this.callSuper("toObject",["rx","ry"].concat(t))},_toSVG:function(){return["\n']},_render:function(t){t.beginPath(),t.save(),t.transform(1,0,0,this.ry/this.rx,0,0),t.arc(0,0,this.rx,0,i,!1),t.restore(),this._renderPaintInOrder(t)}}),e.Ellipse.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("cx cy rx ry".split(" ")),e.Ellipse.fromElement=function(t,i){var r=e.parseAttributes(t,e.Ellipse.ATTRIBUTE_NAMES);r.left=(r.left||0)-r.rx,r.top=(r.top||0)-r.ry,i(new e.Ellipse(r))},e.Ellipse.fromObject=function(t,i){e.Object._fromObject("Ellipse",t,i)})}(t),function(t){var e=t.fabric||(t.fabric={}),i=e.util.object.extend;e.Rect?e.warn("fabric.Rect is already defined"):(e.Rect=e.util.createClass(e.Object,{stateProperties:e.Object.prototype.stateProperties.concat("rx","ry"),type:"rect",rx:0,ry:0,cacheProperties:e.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(t){this.callSuper("initialize",t),this._initRxRy()},_initRxRy:function(){this.rx&&!this.ry?this.ry=this.rx:this.ry&&!this.rx&&(this.rx=this.ry)},_render:function(t){var e=this.rx?Math.min(this.rx,this.width/2):0,i=this.ry?Math.min(this.ry,this.height/2):0,r=this.width,n=this.height,o=-this.width/2,s=-this.height/2,a=0!==e||0!==i,h=.4477152502;t.beginPath(),t.moveTo(o+e,s),t.lineTo(o+r-e,s),a&&t.bezierCurveTo(o+r-h*e,s,o+r,s+h*i,o+r,s+i),t.lineTo(o+r,s+n-i),a&&t.bezierCurveTo(o+r,s+n-h*i,o+r-h*e,s+n,o+r-e,s+n),t.lineTo(o+e,s+n),a&&t.bezierCurveTo(o+h*e,s+n,o,s+n-h*i,o,s+n-i),t.lineTo(o,s+i),a&&t.bezierCurveTo(o,s+h*i,o+h*e,s,o+e,s),t.closePath(),this._renderPaintInOrder(t)},toObject:function(t){return this.callSuper("toObject",["rx","ry"].concat(t))},_toSVG:function(){return["\n']}}),e.Rect.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("x y rx ry width height".split(" ")),e.Rect.fromElement=function(t,r,n){if(!t)return r(null);n=n||{};var o=e.parseAttributes(t,e.Rect.ATTRIBUTE_NAMES);o.left=o.left||0,o.top=o.top||0,o.height=o.height||0,o.width=o.width||0;var s=new e.Rect(i(n?e.util.object.clone(n):{},o));s.visible=s.visible&&s.width>0&&s.height>0,r(s)},e.Rect.fromObject=function(t,i){return e.Object._fromObject("Rect",t,i)})}(t),function(t){var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.util.array.min,n=e.util.array.max,o=e.util.toFixed;e.Polyline?e.warn("fabric.Polyline is already defined"):(e.Polyline=e.util.createClass(e.Object,{type:"polyline",points:null,cacheProperties:e.Object.prototype.cacheProperties.concat("points"),initialize:function(t,e){e=e||{},this.points=t||[],this.callSuper("initialize",e),this._setPositionDimensions(e)},_setPositionDimensions:function(t){var e,i=this._calcDimensions(t);this.width=i.width,this.height=i.height,t.fromSVG||(e=this.translateToGivenOrigin({x:i.left-this.strokeWidth/2,y:i.top-this.strokeWidth/2},"left","top",this.originX,this.originY)),void 0===t.left&&(this.left=t.fromSVG?i.left:e.x),void 0===t.top&&(this.top=t.fromSVG?i.top:e.y),this.pathOffset={x:i.left+this.width/2,y:i.top+this.height/2}},_calcDimensions:function(){var t=this.points,e=r(t,"x")||0,i=r(t,"y")||0;return{left:e,top:i,width:(n(t,"x")||0)-e,height:(n(t,"y")||0)-i}},toObject:function(t){return i(this.callSuper("toObject",t),{points:this.points.concat()})},_toSVG:function(){for(var t=[],i=this.pathOffset.x,r=this.pathOffset.y,n=e.Object.NUM_FRACTION_DIGITS,s=0,a=this.points.length;s\n']},commonRender:function(t){var e,i=this.points.length,r=this.pathOffset.x,n=this.pathOffset.y;if(!i||isNaN(this.points[i-1].y))return!1;t.beginPath(),t.moveTo(this.points[0].x-r,this.points[0].y-n);for(var o=0;o"},toObject:function(t){return n(this.callSuper("toObject",t),{path:this.path.map((function(t){return t.slice()}))})},toDatalessObject:function(t){var e=this.toObject(["sourcePath"].concat(t));return e.sourcePath&&delete e.path,e},_toSVG:function(){return["\n"]},_getOffsetTransform:function(){var t=e.Object.NUM_FRACTION_DIGITS;return" translate("+s(-this.pathOffset.x,t)+", "+s(-this.pathOffset.y,t)+")"},toClipPathSVG:function(t){var e=this._getOffsetTransform();return"\t"+this._createBaseClipPathSVGMarkup(this._toSVG(),{reviver:t,additionalTransform:e})},toSVG:function(t){var e=this._getOffsetTransform();return this._createBaseSVGMarkup(this._toSVG(),{reviver:t,additionalTransform:e})},complexity:function(){return this.path.length},_calcDimensions:function(){for(var t,n,o=[],s=[],a=0,h=0,c=0,l=0,u=0,f=this.path.length;u"},addWithUpdate:function(t){var i=!!this.group;return this._restoreObjectsState(),e.util.resetObjectTransform(this),t&&(i&&e.util.removeTransformFromObject(t,this.group.calcTransformMatrix()),this._objects.push(t),t.group=this,t._set("canvas",this.canvas)),this._calcBounds(),this._updateObjectsCoords(),this.dirty=!0,i?this.group.addWithUpdate():this.setCoords(),this},removeWithUpdate:function(t){return this._restoreObjectsState(),e.util.resetObjectTransform(this),this.remove(t),this._calcBounds(),this._updateObjectsCoords(),this.setCoords(),this.dirty=!0,this},_onObjectAdded:function(t){this.dirty=!0,t.group=this,t._set("canvas",this.canvas)},_onObjectRemoved:function(t){this.dirty=!0,delete t.group},_set:function(t,i){var r=this._objects.length;if(this.useSetOnGroup)for(;r--;)this._objects[r].setOnGroup(t,i);if("canvas"===t)for(;r--;)this._objects[r]._set(t,i);e.Object.prototype._set.call(this,t,i)},toObject:function(t){var i=this.includeDefaultValues,r=this._objects.filter((function(t){return!t.excludeFromExport})).map((function(e){var r=e.includeDefaultValues;e.includeDefaultValues=i;var n=e.toObject(t);return e.includeDefaultValues=r,n})),n=e.Object.prototype.toObject.call(this,t);return n.objects=r,n},toDatalessObject:function(t){var i,r=this.sourcePath;if(r)i=r;else{var n=this.includeDefaultValues;i=this._objects.map((function(e){var i=e.includeDefaultValues;e.includeDefaultValues=n;var r=e.toDatalessObject(t);return e.includeDefaultValues=i,r}))}var o=e.Object.prototype.toDatalessObject.call(this,t);return o.objects=i,o},render:function(t){this._transformDone=!0,this.callSuper("render",t),this._transformDone=!1},shouldCache:function(){var t=e.Object.prototype.shouldCache.call(this);if(t)for(var i=0,r=this._objects.length;i\n"],i=0,r=this._objects.length;i\n"),e},getSvgStyles:function(){var t=void 0!==this.opacity&&1!==this.opacity?"opacity: "+this.opacity+";":"",e=this.visible?"":" visibility: hidden;";return[t,this.getSvgFilter(),e].join("")},toClipPathSVG:function(t){for(var e=[],i=0,r=this._objects.length;i"},shouldCache:function(){return!1},isOnACache:function(){return!1},_renderControls:function(t,e,i){t.save(),t.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1,this.callSuper("_renderControls",t,e),void 0===(i=i||{}).hasControls&&(i.hasControls=!1),i.forActiveSelection=!0;for(var r=0,n=this._objects.length;r\n','\t\n',"\n"),s=' clip-path="url(#imageCrop_'+h+')" '}if(this.imageSmoothing||(a='" image-rendering="optimizeSpeed'),i.push("\t\n"),this.stroke||this.strokeDashArray){var c=this.fill;this.fill=null,t=["\t\n'],this.fill=c}return e="fill"!==this.paintFirst?e.concat(t,i):e.concat(i,t)},getSrc:function(t){var e=t?this._element:this._originalElement;return e?e.toDataURL?e.toDataURL():this.srcFromAttribute?e.getAttribute("src"):e.src:this.src||""},setSrc:function(t,e,i){return T.util.loadImage(t,(function(t,r){this.setElement(t,i),this._setWidthHeight(),e&&e(this,r)}),this,i&&i.crossOrigin),this},toString:function(){return'#'},applyResizeFilters:function(){var t=this.resizeFilter,e=this.minimumScaleTrigger,i=this.getTotalObjectScaling(),r=i.scaleX,n=i.scaleY,o=this._filteredEl||this._originalElement;if(this.group&&this.set("dirty",!0),!t||r>e&&n>e)return this._element=o,this._filterScalingX=1,this._filterScalingY=1,this._lastScaleX=r,void(this._lastScaleY=n);T.filterBackend||(T.filterBackend=T.initFilterBackend());var s=T.util.createCanvasElement(),a=this._filteredEl?this.cacheKey+"_filtered":this.cacheKey,h=o.width,c=o.height;s.width=h,s.height=c,this._element=s,this._lastScaleX=t.scaleX=r,this._lastScaleY=t.scaleY=n,T.filterBackend.applyFilters([t],o,h,c,this._element,a),this._filterScalingX=s.width/this._originalElement.width,this._filterScalingY=s.height/this._originalElement.height},applyFilters:function(t){if(t=(t=t||this.filters||[]).filter((function(t){return t&&!t.isNeutralState()})),this.set("dirty",!0),this.removeTexture(this.cacheKey+"_filtered"),0===t.length)return this._element=this._originalElement,this._filteredEl=null,this._filterScalingX=1,this._filterScalingY=1,this;var e=this._originalElement,i=e.naturalWidth||e.width,r=e.naturalHeight||e.height;if(this._element===this._originalElement){var n=T.util.createCanvasElement();n.width=i,n.height=r,this._element=n,this._filteredEl=n}else this._element=this._filteredEl,this._filteredEl.getContext("2d").clearRect(0,0,i,r),this._lastScaleX=1,this._lastScaleY=1;return T.filterBackend||(T.filterBackend=T.initFilterBackend()),T.filterBackend.applyFilters(t,this._originalElement,i,r,this._element,this.cacheKey),this._originalElement.width===this._element.width&&this._originalElement.height===this._element.height||(this._filterScalingX=this._element.width/this._originalElement.width,this._filterScalingY=this._element.height/this._originalElement.height),this},_render:function(t){T.util.setImageSmoothing(t,this.imageSmoothing),!0!==this.isMoving&&this.resizeFilter&&this._needsResize()&&this.applyResizeFilters(),this._stroke(t),this._renderPaintInOrder(t)},drawCacheOnCanvas:function(t){T.util.setImageSmoothing(t,this.imageSmoothing),T.Object.prototype.drawCacheOnCanvas.call(this,t)},shouldCache:function(){return this.needsItsOwnCache()},_renderFill:function(t){var e=this._element;if(e){var i=this._filterScalingX,r=this._filterScalingY,n=this.width,o=this.height,s=Math.min,a=Math.max,h=a(this.cropX,0),c=a(this.cropY,0),l=e.naturalWidth||e.width,u=e.naturalHeight||e.height,f=h*i,d=c*r,g=s(n*i,l-f),p=s(o*r,u-d),v=-n/2,m=-o/2,y=s(n,l/i-h),_=s(o,u/r-c);e&&t.drawImage(e,f,d,g,p,v,m,y,_)}},_needsResize:function(){var t=this.getTotalObjectScaling();return t.scaleX!==this._lastScaleX||t.scaleY!==this._lastScaleY},_resetWidthHeight:function(){this.set(this.getOriginalSize())},_initElement:function(t,e){this.setElement(T.util.getById(t),e),T.util.addClass(this.getElement(),T.Image.CSS_CANVAS)},_initConfig:function(t){t||(t={}),this.setOptions(t),this._setWidthHeight(t)},_initFilters:function(t,e){t&&t.length?T.util.enlivenObjects(t,(function(t){e&&e(t)}),"fabric.Image.filters"):e&&e()},_setWidthHeight:function(t){t||(t={});var e=this.getElement();this.width=t.width||e.naturalWidth||e.width||0,this.height=t.height||e.naturalHeight||e.height||0},parsePreserveAspectRatioAttribute:function(){var t,e=T.util.parsePreserveAspectRatioAttribute(this.preserveAspectRatio||""),i=this._element.width,r=this._element.height,n=1,o=1,s=0,a=0,h=0,c=0,l=this.width,u=this.height,f={width:l,height:u};return!e||"none"===e.alignX&&"none"===e.alignY?(n=l/i,o=u/r):("meet"===e.meetOrSlice&&(t=(l-i*(n=o=T.util.findScaleToFit(this._element,f)))/2,"Min"===e.alignX&&(s=-t),"Max"===e.alignX&&(s=t),t=(u-r*o)/2,"Min"===e.alignY&&(a=-t),"Max"===e.alignY&&(a=t)),"slice"===e.meetOrSlice&&(t=i-l/(n=o=T.util.findScaleToCover(this._element,f)),"Mid"===e.alignX&&(h=t/2),"Max"===e.alignX&&(h=t),t=r-u/o,"Mid"===e.alignY&&(c=t/2),"Max"===e.alignY&&(c=t),i=l/n,r=u/o)),{width:i,height:r,scaleX:n,scaleY:o,offsetLeft:s,offsetTop:a,cropX:h,cropY:c}}}),T.Image.CSS_CANVAS="canvas-img",T.Image.prototype.getSvgSrc=T.Image.prototype.getSrc,T.Image.fromObject=function(t,e){var i=T.util.object.clone(t);T.util.loadImage(i.src,(function(t,r){r?e&&e(null,!0):T.Image.prototype._initFilters.call(i,i.filters,(function(r){i.filters=r||[],T.Image.prototype._initFilters.call(i,[i.resizeFilter],(function(r){i.resizeFilter=r[0],T.util.enlivenObjects([i.clipPath],(function(r){i.clipPath=r[0];var n=new T.Image(t,i);e(n,!1)}))}))}))}),null,i.crossOrigin)},T.Image.fromURL=function(t,e,i){T.util.loadImage(t,(function(t,r){e&&e(new T.Image(t,i),r)}),null,i&&i.crossOrigin)},T.Image.ATTRIBUTE_NAMES=T.SHARED_ATTRIBUTES.concat("x y width height preserveAspectRatio xlink:href crossOrigin image-rendering".split(" ")),T.Image.fromElement=function(t,i,r){var n=T.parseAttributes(t,T.Image.ATTRIBUTE_NAMES);T.Image.fromURL(n["xlink:href"],i,e(r?T.util.object.clone(r):{},n))})}(t),T.util.object.extend(T.Object.prototype,{_getAngleValueForStraighten:function(){var t=this.angle%360;return t>0?90*Math.round((t-1)/90):90*Math.round(t/90)},straighten:function(){return this.rotate(this._getAngleValueForStraighten()),this},fxStraighten:function(t){var e=function(){},i=(t=t||{}).onComplete||e,r=t.onChange||e,n=this;return T.util.animate({startValue:this.get("angle"),endValue:this._getAngleValueForStraighten(),duration:this.FX_DURATION,onChange:function(t){n.rotate(t),r()},onComplete:function(){n.setCoords(),i()}}),this}}),T.util.object.extend(T.StaticCanvas.prototype,{straightenObject:function(t){return t.straighten(),this.requestRenderAll(),this},fxStraightenObject:function(t){return t.fxStraighten({onChange:this.requestRenderAllBound}),this}}),function(){function t(t,e){var i="precision "+e+" float;\nvoid main(){}",r=t.createShader(t.FRAGMENT_SHADER);return t.shaderSource(r,i),t.compileShader(r),!!t.getShaderParameter(r,t.COMPILE_STATUS)}function e(t){t&&t.tileSize&&(this.tileSize=t.tileSize),this.setupGLContext(this.tileSize,this.tileSize),this.captureGPUInfo()}T.isWebglSupported=function(e){if(T.isLikelyNode)return!1;e=e||T.WebglFilterBackend.prototype.tileSize;var i=document.createElement("canvas"),r=i.getContext("webgl")||i.getContext("experimental-webgl"),n=!1;if(r){T.maxTextureSize=r.getParameter(r.MAX_TEXTURE_SIZE),n=T.maxTextureSize>=e;for(var o=["highp","mediump","lowp"],s=0;s<3;s++)if(t(r,o[s])){T.webGlPrecision=o[s];break}}return this.isSupported=n,n},T.WebglFilterBackend=e,e.prototype={tileSize:2048,resources:{},setupGLContext:function(t,e){this.dispose(),this.createWebGLCanvas(t,e),this.aPosition=new Float32Array([0,0,0,1,1,0,1,1]),this.chooseFastestCopyGLTo2DMethod(t,e)},chooseFastestCopyGLTo2DMethod:function(t,e){var i,r=void 0!==window.performance;try{new ImageData(1,1),i=!0}catch(t){i=!1}var n="undefined"!=typeof ArrayBuffer,o="undefined"!=typeof Uint8ClampedArray;if(r&&i&&n&&o){var s=T.util.createCanvasElement(),a=new ArrayBuffer(t*e*4);if(T.forceGLPutImageData)return this.imageBuffer=a,void(this.copyGLTo2D=A);var h,c,l={imageBuffer:a,destinationWidth:t,destinationHeight:e,targetCanvas:s};s.width=t,s.height=e,h=window.performance.now(),k.call(l,this.gl,l),c=window.performance.now()-h,h=window.performance.now(),A.call(l,this.gl,l),c>window.performance.now()-h?(this.imageBuffer=a,this.copyGLTo2D=A):this.copyGLTo2D=k}},createWebGLCanvas:function(t,e){var i=T.util.createCanvasElement();i.width=t,i.height=e;var r={alpha:!0,premultipliedAlpha:!1,depth:!1,stencil:!1,antialias:!1},n=i.getContext("webgl",r);n||(n=i.getContext("experimental-webgl",r)),n&&(n.clearColor(0,0,0,0),this.canvas=i,this.gl=n)},applyFilters:function(t,e,i,r,n,o){var s,a=this.gl;o&&(s=this.getCachedTexture(o,e));var h={originalWidth:e.width||e.originalWidth,originalHeight:e.height||e.originalHeight,sourceWidth:i,sourceHeight:r,destinationWidth:i,destinationHeight:r,context:a,sourceTexture:this.createTexture(a,i,r,!s&&e),targetTexture:this.createTexture(a,i,r),originalTexture:s||this.createTexture(a,i,r,!s&&e),passes:t.length,webgl:!0,aPosition:this.aPosition,programCache:this.programCache,pass:0,filterBackend:this,targetCanvas:n},c=a.createFramebuffer();return a.bindFramebuffer(a.FRAMEBUFFER,c),t.forEach((function(t){t&&t.applyTo(h)})),function(t){var e=t.targetCanvas,i=e.width,r=e.height,n=t.destinationWidth,o=t.destinationHeight;i===n&&r===o||(e.width=n,e.height=o)}(h),this.copyGLTo2D(a,h),a.bindTexture(a.TEXTURE_2D,null),a.deleteTexture(h.sourceTexture),a.deleteTexture(h.targetTexture),a.deleteFramebuffer(c),n.getContext("2d").setTransform(1,0,0,1,0,0),h},dispose:function(){this.canvas&&(this.canvas=null,this.gl=null),this.clearWebGLCaches()},clearWebGLCaches:function(){this.programCache={},this.textureCache={}},createTexture:function(t,e,i,r){var n=t.createTexture();return t.bindTexture(t.TEXTURE_2D,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,r):t.texImage2D(t.TEXTURE_2D,0,t.RGBA,e,i,0,t.RGBA,t.UNSIGNED_BYTE,null),n},getCachedTexture:function(t,e){if(this.textureCache[t])return this.textureCache[t];var i=this.createTexture(this.gl,e.width,e.height,e);return this.textureCache[t]=i,i},evictCachesForKey:function(t){this.textureCache[t]&&(this.gl.deleteTexture(this.textureCache[t]),delete this.textureCache[t])},copyGLTo2D:k,captureGPUInfo:function(){if(this.gpuInfo)return this.gpuInfo;var t=this.gl,e={renderer:"",vendor:""};if(!t)return e;var i=t.getExtension("WEBGL_debug_renderer_info");if(i){var r=t.getParameter(i.UNMASKED_RENDERER_WEBGL),n=t.getParameter(i.UNMASKED_VENDOR_WEBGL);r&&(e.renderer=r.toLowerCase()),n&&(e.vendor=n.toLowerCase())}return this.gpuInfo=e,e}}}(),function(){var t=function(){};function e(){}T.Canvas2dFilterBackend=e,e.prototype={evictCachesForKey:t,dispose:t,clearWebGLCaches:t,resources:{},applyFilters:function(t,e,i,r,n){var o=n.getContext("2d");o.drawImage(e,0,0,i,r);var s={sourceWidth:i,sourceHeight:r,imageData:o.getImageData(0,0,i,r),originalEl:e,originalImageData:o.getImageData(0,0,i,r),canvasEl:n,ctx:o,filterBackend:this};return t.forEach((function(t){t.applyTo(s)})),s.imageData.width===i&&s.imageData.height===r||(n.width=s.imageData.width,n.height=s.imageData.height),o.putImageData(s.imageData,0,0),s}}}(),T.Image=T.Image||{},T.Image.filters=T.Image.filters||{},T.Image.filters.BaseFilter=T.util.createClass({type:"BaseFilter",vertexSource:"attribute vec2 aPosition;\nvarying vec2 vTexCoord;\nvoid main() {\nvTexCoord = aPosition;\ngl_Position = vec4(aPosition * 2.0 - 1.0, 0.0, 1.0);\n}",fragmentSource:"precision highp float;\nvarying vec2 vTexCoord;\nuniform sampler2D uTexture;\nvoid main() {\ngl_FragColor = texture2D(uTexture, vTexCoord);\n}",initialize:function(t){t&&this.setOptions(t)},setOptions:function(t){for(var e in t)this[e]=t[e]},createProgram:function(t,e,i){e=e||this.fragmentSource,i=i||this.vertexSource,"highp"!==T.webGlPrecision&&(e=e.replace(/precision highp float/g,"precision "+T.webGlPrecision+" float"));var r=t.createShader(t.VERTEX_SHADER);if(t.shaderSource(r,i),t.compileShader(r),!t.getShaderParameter(r,t.COMPILE_STATUS))throw new Error("Vertex shader compile error for "+this.type+": "+t.getShaderInfoLog(r));var n=t.createShader(t.FRAGMENT_SHADER);if(t.shaderSource(n,e),t.compileShader(n),!t.getShaderParameter(n,t.COMPILE_STATUS))throw new Error("Fragment shader compile error for "+this.type+": "+t.getShaderInfoLog(n));var o=t.createProgram();if(t.attachShader(o,r),t.attachShader(o,n),t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS))throw new Error('Shader link error for "${this.type}" '+t.getProgramInfoLog(o));var s=this.getAttributeLocations(t,o),a=this.getUniformLocations(t,o)||{};return a.uStepW=t.getUniformLocation(o,"uStepW"),a.uStepH=t.getUniformLocation(o,"uStepH"),{program:o,attributeLocations:s,uniformLocations:a}},getAttributeLocations:function(t,e){return{aPosition:t.getAttribLocation(e,"aPosition")}},getUniformLocations:function(){return{}},sendAttributeData:function(t,e,i){var r=e.aPosition,n=t.createBuffer();t.bindBuffer(t.ARRAY_BUFFER,n),t.enableVertexAttribArray(r),t.vertexAttribPointer(r,2,t.FLOAT,!1,0,0),t.bufferData(t.ARRAY_BUFFER,i,t.STATIC_DRAW)},_setupFrameBuffer:function(t){var e,i,r=t.context;t.passes>1?(e=t.destinationWidth,i=t.destinationHeight,t.sourceWidth===e&&t.sourceHeight===i||(r.deleteTexture(t.targetTexture),t.targetTexture=t.filterBackend.createTexture(r,e,i)),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t.targetTexture,0)):(r.bindFramebuffer(r.FRAMEBUFFER,null),r.finish())},_swapTextures:function(t){t.passes--,t.pass++;var e=t.targetTexture;t.targetTexture=t.sourceTexture,t.sourceTexture=e},isNeutralState:function(){var t=this.mainParameter,e=T.Image.filters[this.type].prototype;if(t){if(Array.isArray(e[t])){for(var i=e[t].length;i--;)if(this[t][i]!==e[t][i])return!1;return!0}return e[t]===this[t]}return!1},applyTo:function(t){t.webgl?(this._setupFrameBuffer(t),this.applyToWebGL(t),this._swapTextures(t)):this.applyTo2d(t)},retrieveShader:function(t){return t.programCache.hasOwnProperty(this.type)||(t.programCache[this.type]=this.createProgram(t.context)),t.programCache[this.type]},applyToWebGL:function(t){var e=t.context,i=this.retrieveShader(t);0===t.pass&&t.originalTexture?e.bindTexture(e.TEXTURE_2D,t.originalTexture):e.bindTexture(e.TEXTURE_2D,t.sourceTexture),e.useProgram(i.program),this.sendAttributeData(e,i.attributeLocations,t.aPosition),e.uniform1f(i.uniformLocations.uStepW,1/t.sourceWidth),e.uniform1f(i.uniformLocations.uStepH,1/t.sourceHeight),this.sendUniformData(e,i.uniformLocations),e.viewport(0,0,t.destinationWidth,t.destinationHeight),e.drawArrays(e.TRIANGLE_STRIP,0,4)},bindAdditionalTexture:function(t,e,i){t.activeTexture(i),t.bindTexture(t.TEXTURE_2D,e),t.activeTexture(t.TEXTURE0)},unbindAdditionalTexture:function(t,e){t.activeTexture(e),t.bindTexture(t.TEXTURE_2D,null),t.activeTexture(t.TEXTURE0)},getMainParameter:function(){return this[this.mainParameter]},setMainParameter:function(t){this[this.mainParameter]=t},sendUniformData:function(){},createHelpLayer:function(t){if(!t.helpLayer){var e=document.createElement("canvas");e.width=t.sourceWidth,e.height=t.sourceHeight,t.helpLayer=e}},toObject:function(){var t={type:this.type},e=this.mainParameter;return e&&(t[e]=this[e]),t},toJSON:function(){return this.toObject()}}),T.Image.filters.BaseFilter.fromObject=function(t,e){var i=new T.Image.filters[t.type](t);return e&&e(i),i},function(t){var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.ColorMatrix=r(i.BaseFilter,{type:"ColorMatrix",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nvarying vec2 vTexCoord;\nuniform mat4 uColorMatrix;\nuniform vec4 uConstants;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\ncolor *= uColorMatrix;\ncolor += uConstants;\ngl_FragColor = color;\n}",matrix:[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0],mainParameter:"matrix",colorsOnly:!0,initialize:function(t){this.callSuper("initialize",t),this.matrix=this.matrix.slice(0)},applyTo2d:function(t){var e,i,r,n,o,s=t.imageData.data,a=s.length,h=this.matrix,c=this.colorsOnly;for(o=0;o=b||s<0||s>=_||(h=4*(a*_+s),c=v[d*m+f],e+=p[h]*c,i+=p[h+1]*c,r+=p[h+2]*c,w||(n+=p[h+3]*c));C[o]=e,C[o+1]=i,C[o+2]=r,C[o+3]=w?p[o+3]:n}t.imageData=x},getUniformLocations:function(t,e){return{uMatrix:t.getUniformLocation(e,"uMatrix"),uOpaque:t.getUniformLocation(e,"uOpaque"),uHalfSize:t.getUniformLocation(e,"uHalfSize"),uSize:t.getUniformLocation(e,"uSize")}},sendUniformData:function(t,e){t.uniform1fv(e.uMatrix,this.matrix)},toObject:function(){return i(this.callSuper("toObject"),{opaque:this.opaque,matrix:this.matrix})}}),e.Image.filters.Convolute.fromObject=e.Image.filters.BaseFilter.fromObject}(t),function(t){var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Grayscale=r(i.BaseFilter,{type:"Grayscale",fragmentSource:{average:"precision highp float;\nuniform sampler2D uTexture;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat average = (color.r + color.b + color.g) / 3.0;\ngl_FragColor = vec4(average, average, average, color.a);\n}",lightness:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uMode;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 col = texture2D(uTexture, vTexCoord);\nfloat average = (max(max(col.r, col.g),col.b) + min(min(col.r, col.g),col.b)) / 2.0;\ngl_FragColor = vec4(average, average, average, col.a);\n}",luminosity:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uMode;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 col = texture2D(uTexture, vTexCoord);\nfloat average = 0.21 * col.r + 0.72 * col.g + 0.07 * col.b;\ngl_FragColor = vec4(average, average, average, col.a);\n}"},mode:"average",mainParameter:"mode",applyTo2d:function(t){var e,i,r=t.imageData.data,n=r.length,o=this.mode;for(e=0;ec[0]&&n>c[1]&&o>c[2]&&r 0.0) {\n"+this.fragmentSource[t]+"}\n}"},retrieveShader:function(t){var e,i=this.type+"_"+this.mode;return t.programCache.hasOwnProperty(i)||(e=this.buildSource(this.mode),t.programCache[i]=this.createProgram(t.context,e)),t.programCache[i]},applyTo2d:function(t){var i,r,n,o,s,a,h,c=t.imageData.data,l=c.length,u=1-this.alpha;i=(h=new e.Color(this.color).getSource())[0]*this.alpha,r=h[1]*this.alpha,n=h[2]*this.alpha;for(var f=0;f=t||e<=-t)return 0;if(e<1.1920929e-7&&e>-1.1920929e-7)return 1;var i=(e*=Math.PI)/t;return a(e)/e*a(i)/i}},applyTo2d:function(t){var e=t.imageData,i=this.scaleX,r=this.scaleY;this.rcpScaleX=1/i,this.rcpScaleY=1/r;var n,o=e.width,a=e.height,h=s(o*i),c=s(a*r);"sliceHack"===this.resizeType?n=this.sliceByTwo(t,o,a,h,c):"hermite"===this.resizeType?n=this.hermiteFastResize(t,o,a,h,c):"bilinear"===this.resizeType?n=this.bilinearFiltering(t,o,a,h,c):"lanczos"===this.resizeType&&(n=this.lanczosResize(t,o,a,h,c)),t.imageData=n},sliceByTwo:function(t,i,n,o,s){var a,h,c=t.imageData,l=.5,u=!1,f=!1,d=i*l,g=n*l,p=e.filterBackend.resources,v=0,m=0,y=i,_=0;for(p.sliceByTwo||(p.sliceByTwo=document.createElement("canvas")),((a=p.sliceByTwo).width<1.5*i||a.height=e)){M=r(1e3*o(S-x.x)),b[M]||(b[M]={});for(var F=C.y-_;F<=C.y+_;F++)F<0||F>=s||(j=r(1e3*o(F-x.y)),b[M][j]||(b[M][j]=d(n(i(M*v,2)+i(j*m,2))/1e3)),(T=b[M][j])>0&&(P+=T,E+=T*l[O=4*(F*e+S)],k+=T*l[O+1],A+=T*l[O+2],D+=T*l[O+3]))}f[O=4*(w*a+h)]=E/P,f[O+1]=k/P,f[O+2]=A/P,f[O+3]=D/P}return++h1&&j<-1||(_=2*j*j*j-3*j*j+1)>0&&(T+=_*d[(M=4*(D+P*e))+3],x+=_,d[M+3]<255&&(_=_*d[M+3]/250),C+=_*d[M],w+=_*d[M+1],S+=_*d[M+2],b+=_)}p[y]=C/b,p[y+1]=w/b,p[y+2]=S/b,p[y+3]=T/x}return g},toObject:function(){return{type:this.type,scaleX:this.scaleX,scaleY:this.scaleY,resizeType:this.resizeType,lanczosLobes:this.lanczosLobes}}}),e.Image.filters.Resize.fromObject=e.Image.filters.BaseFilter.fromObject}(t),function(t){var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Contrast=r(i.BaseFilter,{type:"Contrast",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uContrast;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat contrastF = 1.015 * (uContrast + 1.0) / (1.0 * (1.015 - uContrast));\ncolor.rgb = contrastF * (color.rgb - 0.5) + 0.5;\ngl_FragColor = color;\n}",contrast:0,mainParameter:"contrast",applyTo2d:function(t){if(0!==this.contrast){var e,i=t.imageData.data,r=i.length,n=Math.floor(255*this.contrast),o=259*(n+255)/(255*(259-n));for(e=0;e1&&(e=1/this.aspectRatio):this.aspectRatio<1&&(e=this.aspectRatio),t=e*this.blur*.12,this.horizontal?i[0]=t:i[1]=t,i}}),i.Blur.fromObject=e.Image.filters.BaseFilter.fromObject}(t),function(t){var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Gamma=r(i.BaseFilter,{type:"Gamma",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform vec3 uGamma;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nvec3 correction = (1.0 / uGamma);\ncolor.r = pow(color.r, correction.r);\ncolor.g = pow(color.g, correction.g);\ncolor.b = pow(color.b, correction.b);\ngl_FragColor = color;\ngl_FragColor.rgb *= color.a;\n}",gamma:[1,1,1],mainParameter:"gamma",initialize:function(t){this.gamma=[1,1,1],i.BaseFilter.prototype.initialize.call(this,t)},applyTo2d:function(t){var e,i=t.imageData.data,r=this.gamma,n=i.length,o=1/r[0],s=1/r[1],a=1/r[2];for(this.rVals||(this.rVals=new Uint8Array(256),this.gVals=new Uint8Array(256),this.bVals=new Uint8Array(256)),e=0,n=256;e'},_getCacheCanvasDimensions:function(){var t=this.callSuper("_getCacheCanvasDimensions"),e=this.fontSize;return t.width+=e*t.zoomX,t.height+=e*t.zoomY,t},_render:function(t){var e=this.path;e&&!e.isNotVisible()&&e._render(t),this._setTextStyles(t),this._renderTextLinesBackground(t),this._renderTextDecoration(t,"underline"),this._renderText(t),this._renderTextDecoration(t,"overline"),this._renderTextDecoration(t,"linethrough")},_renderText:function(t){"stroke"===this.paintFirst?(this._renderTextStroke(t),this._renderTextFill(t)):(this._renderTextFill(t),this._renderTextStroke(t))},_setTextStyles:function(t,e,i){t.textBaseline="alphabetic",t.font=this._getFontDeclaration(e,i)},calcTextWidth:function(){for(var t=this.getLineWidth(0),e=1,i=this._textLines.length;et&&(t=r)}return t},_renderTextLine:function(t,e,i,r,n,o){this._renderChars(t,e,i,r,n,o)},_renderTextLinesBackground:function(t){if(this.textBackgroundColor||this.styleHas("textBackgroundColor")){for(var e,i,r,n,o,s,a,h=t.fillStyle,c=this._getLeftOffset(),l=this._getTopOffset(),u=0,f=0,d=this.path,g=0,p=this._textLines.length;g=0:ia?u%=a:u<0&&(u+=a),this._setGraphemeOnPath(u,o,s),u+=o.kernedWidth}return{width:h,numOfSpaces:0}},_setGraphemeOnPath:function(t,i,r){var n=t+i.kernedWidth/2,o=this.path,s=e.util.getPointOnPath(o.path,n,o.segmentsInfo);i.renderLeft=s.x-r.x,i.renderTop=s.y-r.y,i.angle=s.angle+("right"===this.pathSide?Math.PI:0)},_getGraphemeBox:function(t,e,i,r,n){var o,s=this.getCompleteStyleDeclaration(e,i),a=r?this.getCompleteStyleDeclaration(e,i-1):{},h=this._measureChar(t,s,r,a),c=h.kernedWidth,l=h.width;0!==this.charSpacing&&(l+=o=this._getWidthOfCharSpacing(),c+=o);var u={width:l,left:0,height:s.fontSize,kernedWidth:c,deltaY:s.deltaY};if(i>0&&!n){var f=this.__charBounds[e][i-1];u.left=f.left+f.width+h.kernedWidth-h.width}return u},getHeightOfLine:function(t){if(this.__lineHeights[t])return this.__lineHeights[t];for(var e=this._textLines[t],i=this.getHeightOfChar(t,0),r=1,n=e.length;r0){var P=y+o+u;"rtl"===this.direction&&(P=this.width-P-f),c&&m&&(t.fillStyle=m,t.fillRect(P,l+C*r+s,f,this.fontSize/15)),u=d.left,f=d.width,c=g,m=v,r=n,s=a}else f+=d.kernedWidth;P=y+o+u;"rtl"===this.direction&&(P=this.width-P-f),t.fillStyle=v,g&&v&&t.fillRect(P,l+C*r+s,f-x,this.fontSize/15),_+=i}else _+=i;this._removeShadow(t)}},_getFontDeclaration:function(t,i){var r=t||this,n=this.fontFamily,o=e.Text.genericFonts.indexOf(n.toLowerCase())>-1,s=void 0===n||n.indexOf("'")>-1||n.indexOf(",")>-1||n.indexOf('"')>-1||o?r.fontFamily:'"'+r.fontFamily+'"';return[e.isLikelyNode?r.fontWeight:r.fontStyle,e.isLikelyNode?r.fontStyle:r.fontWeight,i?this.CACHE_FONT_SIZE+"px":r.fontSize+"px",s].join(" ")},render:function(t){this.visible&&(this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(this._shouldClearDimensionCache()&&this.initDimensions(),this.callSuper("render",t)))},_splitTextIntoLines:function(t){for(var i=t.split(this._reNewline),r=new Array(i.length),n=["\n"],o=[],s=0;s-1&&(t.underline=!0),t.textDecoration.indexOf("line-through")>-1&&(t.linethrough=!0),t.textDecoration.indexOf("overline")>-1&&(t.overline=!0),delete t.textDecoration)}T.IText=T.util.createClass(T.Text,T.Observable,{type:"i-text",selectionStart:0,selectionEnd:0,selectionColor:"rgba(17,119,255,0.3)",isEditing:!1,editable:!0,editingBorderColor:"rgba(102,153,255,0.25)",cursorWidth:2,cursorColor:"",cursorDelay:1e3,cursorDuration:600,caching:!0,hiddenTextareaContainer:null,_reSpace:/\s|\n/,_currentCursorOpacity:0,_selectionDirection:null,_abortCursorAnimation:!1,__widthOfSpace:[],inCompositionMode:!1,initialize:function(t,e){this.callSuper("initialize",t,e),this.initBehavior()},setSelectionStart:function(t){t=Math.max(t,0),this._updateAndFire("selectionStart",t)},setSelectionEnd:function(t){t=Math.min(t,this.text.length),this._updateAndFire("selectionEnd",t)},_updateAndFire:function(t,e){this[t]!==e&&(this._fireSelectionChanged(),this[t]=e),this._updateTextarea()},_fireSelectionChanged:function(){this.fire("selection:changed"),this.canvas&&this.canvas.fire("text:selection:changed",{target:this})},initDimensions:function(){this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this.callSuper("initDimensions")},render:function(t){this.clearContextTop(),this.callSuper("render",t),this.cursorOffsetCache={},this.renderCursorOrSelection()},_render:function(t){this.callSuper("_render",t)},clearContextTop:function(t){if(this.isEditing&&this.canvas&&this.canvas.contextTop){var e=this.canvas.contextTop,i=this.canvas.viewportTransform;e.save(),e.transform(i[0],i[1],i[2],i[3],i[4],i[5]),this.transform(e),this._clearTextArea(e),t||e.restore()}},renderCursorOrSelection:function(){if(this.isEditing&&this.canvas&&this.canvas.contextTop){var t=this._getCursorBoundaries(),e=this.canvas.contextTop;this.clearContextTop(!0),this.selectionStart===this.selectionEnd?this.renderCursor(t,e):this.renderSelection(t,e),e.restore()}},_clearTextArea:function(t){var e=this.width+4,i=this.height+4;t.clearRect(-e/2,-i/2,e,i)},_getCursorBoundaries:function(t){void 0===t&&(t=this.selectionStart);var e=this._getLeftOffset(),i=this._getTopOffset(),r=this._getCursorBoundariesOffsets(t);return{left:e,top:i,leftOffset:r.left,topOffset:r.top}},_getCursorBoundariesOffsets:function(t){if(this.cursorOffsetCache&&"top"in this.cursorOffsetCache)return this.cursorOffsetCache;var e,i,r,n,o=0,s=0,a=this.get2DCursorLocation(t);r=a.charIndex,i=a.lineIndex;for(var h=0;h0?s:0)},"rtl"===this.direction&&(n.left*=-1),this.cursorOffsetCache=n,this.cursorOffsetCache},renderCursor:function(t,e){var i=this.get2DCursorLocation(),r=i.lineIndex,n=i.charIndex>0?i.charIndex-1:0,o=this.getValueOfPropertyAt(r,n,"fontSize"),s=this.scaleX*this.canvas.getZoom(),a=this.cursorWidth/s,h=t.topOffset,c=this.getValueOfPropertyAt(r,n,"deltaY");h+=(1-this._fontSizeFraction)*this.getHeightOfLine(r)/this.lineHeight-o*(1-this._fontSizeFraction),this.inCompositionMode&&this.renderSelection(t,e),e.fillStyle=this.cursorColor||this.getValueOfPropertyAt(r,n,"fill"),e.globalAlpha=this.__isMousedown?1:this._currentCursorOpacity,e.fillRect(t.left+t.leftOffset-a/2,h+t.top+c,a,o)},renderSelection:function(t,e){for(var i=this.inCompositionMode?this.hiddenTextarea.selectionStart:this.selectionStart,r=this.inCompositionMode?this.hiddenTextarea.selectionEnd:this.selectionEnd,n=-1!==this.textAlign.indexOf("justify"),o=this.get2DCursorLocation(i),s=this.get2DCursorLocation(r),a=o.lineIndex,h=s.lineIndex,c=o.charIndex<0?0:o.charIndex,l=s.charIndex<0?0:s.charIndex,u=a;u<=h;u++){var f,d=this._getLineLeftOffset(u)||0,g=this.getHeightOfLine(u),p=0,v=0;if(u===a&&(p=this.__charBounds[a][c].left),u>=a&&u1)&&(g/=this.lineHeight);var y=t.left+d+p,_=v-p,b=g,x=0;this.inCompositionMode?(e.fillStyle=this.compositionColor||"black",b=1,x=g):e.fillStyle=this.selectionColor,"rtl"===this.direction&&(y=this.width-y-_),e.fillRect(y,t.top+t.topOffset+x,_,b),t.topOffset+=f}},getCurrentCharFontSize:function(){var t=this._getCurrentCharIndex();return this.getValueOfPropertyAt(t.l,t.c,"fontSize")},getCurrentCharColor:function(){var t=this._getCurrentCharIndex();return this.getValueOfPropertyAt(t.l,t.c,"fill")},_getCurrentCharIndex:function(){var t=this.get2DCursorLocation(this.selectionStart,!0),e=t.charIndex>0?t.charIndex-1:0;return{l:t.lineIndex,c:e}}}),T.IText.fromObject=function(e,i){if(t(e),e.styles)for(var r in e.styles)for(var n in e.styles[r])t(e.styles[r][n]);T.Object._fromObject("IText",e,i,"text")}}(),S=T.util.object.clone,T.util.object.extend(T.IText.prototype,{initBehavior:function(){this.initAddedHandler(),this.initRemovedHandler(),this.initCursorSelectionHandlers(),this.initDoubleClickSimulation(),this.mouseMoveHandler=this.mouseMoveHandler.bind(this)},onDeselect:function(){this.isEditing&&this.exitEditing(),this.selected=!1},initAddedHandler:function(){var t=this;this.on("added",(function(){var e=t.canvas;e&&(e._hasITextHandlers||(e._hasITextHandlers=!0,t._initCanvasHandlers(e)),e._iTextInstances=e._iTextInstances||[],e._iTextInstances.push(t))}))},initRemovedHandler:function(){var t=this;this.on("removed",(function(){var e=t.canvas;e&&(e._iTextInstances=e._iTextInstances||[],T.util.removeFromArray(e._iTextInstances,t),0===e._iTextInstances.length&&(e._hasITextHandlers=!1,t._removeCanvasHandlers(e)))}))},_initCanvasHandlers:function(t){t._mouseUpITextHandler=function(){t._iTextInstances&&t._iTextInstances.forEach((function(t){t.__isMousedown=!1}))},t.on("mouse:up",t._mouseUpITextHandler)},_removeCanvasHandlers:function(t){t.off("mouse:up",t._mouseUpITextHandler)},_tick:function(){this._currentTickState=this._animateCursor(this,1,this.cursorDuration,"_onTickComplete")},_animateCursor:function(t,e,i,r){var n;return n={isAborted:!1,abort:function(){this.isAborted=!0}},t.animate("_currentCursorOpacity",e,{duration:i,onComplete:function(){n.isAborted||t[r]()},onChange:function(){t.canvas&&t.selectionStart===t.selectionEnd&&t.renderCursorOrSelection()},abort:function(){return n.isAborted}}),n},_onTickComplete:function(){var t=this;this._cursorTimeout1&&clearTimeout(this._cursorTimeout1),this._cursorTimeout1=setTimeout((function(){t._currentTickCompleteState=t._animateCursor(t,0,this.cursorDuration/2,"_tick")}),100)},initDelayedCursor:function(t){var e=this,i=t?0:this.cursorDelay;this.abortCursorAnimation(),this._currentCursorOpacity=1,this._cursorTimeout2=setTimeout((function(){e._tick()}),i)},abortCursorAnimation:function(){var t=this._currentTickState||this._currentTickCompleteState,e=this.canvas;this._currentTickState&&this._currentTickState.abort(),this._currentTickCompleteState&&this._currentTickCompleteState.abort(),clearTimeout(this._cursorTimeout1),clearTimeout(this._cursorTimeout2),this._currentCursorOpacity=0,t&&e&&e.clearContext(e.contextTop||e.contextContainer)},selectAll:function(){return this.selectionStart=0,this.selectionEnd=this._text.length,this._fireSelectionChanged(),this._updateTextarea(),this},getSelectedText:function(){return this._text.slice(this.selectionStart,this.selectionEnd).join("")},findWordBoundaryLeft:function(t){var e=0,i=t-1;if(this._reSpace.test(this._text[i]))for(;this._reSpace.test(this._text[i]);)e++,i--;for(;/\S/.test(this._text[i])&&i>-1;)e++,i--;return t-e},findWordBoundaryRight:function(t){var e=0,i=t;if(this._reSpace.test(this._text[i]))for(;this._reSpace.test(this._text[i]);)e++,i++;for(;/\S/.test(this._text[i])&&i-1;)e++,i--;return t-e},findLineBoundaryRight:function(t){for(var e=0,i=t;!/\n/.test(this._text[i])&&i0&&rthis.__selectionStartOnMouseDown?(this.selectionStart=this.__selectionStartOnMouseDown,this.selectionEnd=e):(this.selectionStart=e,this.selectionEnd=this.__selectionStartOnMouseDown),this.selectionStart===i&&this.selectionEnd===r||(this.restartCursorIfNeeded(),this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()))}},_setEditingProps:function(){this.hoverCursor="text",this.canvas&&(this.canvas.defaultCursor=this.canvas.moveCursor="text"),this.borderColor=this.editingBorderColor,this.hasControls=this.selectable=!1,this.lockMovementX=this.lockMovementY=!0},fromStringToGraphemeSelection:function(t,e,i){var r=i.slice(0,t),n=T.util.string.graphemeSplit(r).length;if(t===e)return{selectionStart:n,selectionEnd:n};var o=i.slice(t,e);return{selectionStart:n,selectionEnd:n+T.util.string.graphemeSplit(o).length}},fromGraphemeToStringSelection:function(t,e,i){var r=i.slice(0,t).join("").length;return t===e?{selectionStart:r,selectionEnd:r}:{selectionStart:r,selectionEnd:r+i.slice(t,e).join("").length}},_updateTextarea:function(){if(this.cursorOffsetCache={},this.hiddenTextarea){if(!this.inCompositionMode){var t=this.fromGraphemeToStringSelection(this.selectionStart,this.selectionEnd,this._text);this.hiddenTextarea.selectionStart=t.selectionStart,this.hiddenTextarea.selectionEnd=t.selectionEnd}this.updateTextareaPosition()}},updateFromTextArea:function(){if(this.hiddenTextarea){this.cursorOffsetCache={},this.text=this.hiddenTextarea.value,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords());var t=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value);this.selectionEnd=this.selectionStart=t.selectionEnd,this.inCompositionMode||(this.selectionStart=t.selectionStart),this.updateTextareaPosition()}},updateTextareaPosition:function(){if(this.selectionStart===this.selectionEnd){var t=this._calcTextareaPosition();this.hiddenTextarea.style.left=t.left,this.hiddenTextarea.style.top=t.top}},_calcTextareaPosition:function(){if(!this.canvas)return{x:1,y:1};var t=this.inCompositionMode?this.compositionStart:this.selectionStart,e=this._getCursorBoundaries(t),i=this.get2DCursorLocation(t),r=i.lineIndex,n=i.charIndex,o=this.getValueOfPropertyAt(r,n,"fontSize")*this.lineHeight,s=e.leftOffset,a=this.calcTransformMatrix(),h={x:e.left+s,y:e.top+e.topOffset+o},c=this.canvas.getRetinaScaling(),l=this.canvas.upperCanvasEl,u=l.width/c,f=l.height/c,d=u-o,g=f-o,p=l.clientWidth/u,v=l.clientHeight/f;return h=T.util.transformPoint(h,a),(h=T.util.transformPoint(h,this.canvas.viewportTransform)).x*=p,h.y*=v,h.x<0&&(h.x=0),h.x>d&&(h.x=d),h.y<0&&(h.y=0),h.y>g&&(h.y=g),h.x+=this.canvas._offset.left,h.y+=this.canvas._offset.top,{left:h.x+"px",top:h.y+"px",fontSize:o+"px",charHeight:o}},_saveEditingProps:function(){this._savedProps={hasControls:this.hasControls,borderColor:this.borderColor,lockMovementX:this.lockMovementX,lockMovementY:this.lockMovementY,hoverCursor:this.hoverCursor,selectable:this.selectable,defaultCursor:this.canvas&&this.canvas.defaultCursor,moveCursor:this.canvas&&this.canvas.moveCursor}},_restoreEditingProps:function(){this._savedProps&&(this.hoverCursor=this._savedProps.hoverCursor,this.hasControls=this._savedProps.hasControls,this.borderColor=this._savedProps.borderColor,this.selectable=this._savedProps.selectable,this.lockMovementX=this._savedProps.lockMovementX,this.lockMovementY=this._savedProps.lockMovementY,this.canvas&&(this.canvas.defaultCursor=this._savedProps.defaultCursor,this.canvas.moveCursor=this._savedProps.moveCursor))},exitEditing:function(){var t=this._textBeforeEdit!==this.text,e=this.hiddenTextarea;return this.selected=!1,this.isEditing=!1,this.selectionEnd=this.selectionStart,e&&(e.blur&&e.blur(),e.parentNode&&e.parentNode.removeChild(e)),this.hiddenTextarea=null,this.abortCursorAnimation(),this._restoreEditingProps(),this._currentCursorOpacity=0,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this.fire("editing:exited"),t&&this.fire("modified"),this.canvas&&(this.canvas.off("mouse:move",this.mouseMoveHandler),this.canvas.fire("text:editing:exited",{target:this}),t&&this.canvas.fire("object:modified",{target:this})),this},_removeExtraneousStyles:function(){for(var t in this.styles)this._textLines[t]||delete this.styles[t]},removeStyleFromTo:function(t,e){var i,r,n=this.get2DCursorLocation(t,!0),o=this.get2DCursorLocation(e,!0),s=n.lineIndex,a=n.charIndex,h=o.lineIndex,c=o.charIndex;if(s!==h){if(this.styles[s])for(i=a;i=c&&(r[l-f]=r[u],delete r[u])}},shiftLineStyles:function(t,e){var i=S(this.styles);for(var r in this.styles){var n=parseInt(r,10);n>t&&(this.styles[n+e]=i[n],i[n-e]||delete this.styles[n])}},restartCursorIfNeeded:function(){this._currentTickState&&!this._currentTickState.isAborted&&this._currentTickCompleteState&&!this._currentTickCompleteState.isAborted||this.initDelayedCursor()},insertNewlineStyleObject:function(t,e,i,r){var n,o={},s=!1,a=this._unwrappedTextLines[t].length===e;for(var h in i||(i=1),this.shiftLineStyles(t,i),this.styles[t]&&(n=this.styles[t][0===e?e:e-1]),this.styles[t]){var c=parseInt(h,10);c>=e&&(s=!0,o[c-e]=this.styles[t][h],a&&0===e||delete this.styles[t][h])}var l=!1;for(s&&!a&&(this.styles[t+i]=o,l=!0),l&&i--;i>0;)r&&r[i-1]?this.styles[t+i]={0:S(r[i-1])}:n?this.styles[t+i]={0:S(n)}:delete this.styles[t+i],i--;this._forceClearCache=!0},insertCharStyleObject:function(t,e,i,r){this.styles||(this.styles={});var n=this.styles[t],o=n?S(n):{};for(var s in i||(i=1),o){var a=parseInt(s,10);a>=e&&(n[a+i]=o[a],o[a-i]||delete n[a])}if(this._forceClearCache=!0,r)for(;i--;)Object.keys(r[i]).length&&(this.styles[t]||(this.styles[t]={}),this.styles[t][e+i]=S(r[i]));else if(n)for(var h=n[e?e-1:1];h&&i--;)this.styles[t][e+i]=S(h)},insertNewStyleBlock:function(t,e,i){for(var r=this.get2DCursorLocation(e,!0),n=[0],o=0,s=0;s0&&(this.insertCharStyleObject(r.lineIndex,r.charIndex,n[0],i),i=i&&i.slice(n[0]+1)),o&&this.insertNewlineStyleObject(r.lineIndex,r.charIndex+n[0],o),s=1;s0?this.insertCharStyleObject(r.lineIndex+s,0,n[s],i):i&&(this.styles[r.lineIndex+s][0]=i[0]),i=i&&i.slice(n[s]+1);n[s]>0&&this.insertCharStyleObject(r.lineIndex+s,0,n[s],i)},setSelectionStartEndWithShift:function(t,e,i){i<=t?(e===t?this._selectionDirection="left":"right"===this._selectionDirection&&(this._selectionDirection="left",this.selectionEnd=t),this.selectionStart=i):i>t&&it?this.selectionStart=t:this.selectionStart<0&&(this.selectionStart=0),this.selectionEnd>t?this.selectionEnd=t:this.selectionEnd<0&&(this.selectionEnd=0)}}),T.util.object.extend(T.IText.prototype,{initDoubleClickSimulation:function(){this.__lastClickTime=+new Date,this.__lastLastClickTime=+new Date,this.__lastPointer={},this.on("mousedown",this.onMouseDown)},onMouseDown:function(t){if(this.canvas){this.__newClickTime=+new Date;var e=t.pointer;this.isTripleClick(e)&&(this.fire("tripleclick",t),this._stopEvent(t.e)),this.__lastLastClickTime=this.__lastClickTime,this.__lastClickTime=this.__newClickTime,this.__lastPointer=e,this.__lastIsEditing=this.isEditing,this.__lastSelected=this.selected}},isTripleClick:function(t){return this.__newClickTime-this.__lastClickTime<500&&this.__lastClickTime-this.__lastLastClickTime<500&&this.__lastPointer.x===t.x&&this.__lastPointer.y===t.y},_stopEvent:function(t){t.preventDefault&&t.preventDefault(),t.stopPropagation&&t.stopPropagation()},initCursorSelectionHandlers:function(){this.initMousedownHandler(),this.initMouseupHandler(),this.initClicks()},doubleClickHandler:function(t){this.isEditing&&this.selectWord(this.getSelectionStartFromPointer(t.e))},tripleClickHandler:function(t){this.isEditing&&this.selectLine(this.getSelectionStartFromPointer(t.e))},initClicks:function(){this.on("mousedblclick",this.doubleClickHandler),this.on("tripleclick",this.tripleClickHandler)},_mouseDownHandler:function(t){!this.canvas||!this.editable||t.e.button&&1!==t.e.button||(this.__isMousedown=!0,this.selected&&(this.inCompositionMode=!1,this.setCursorByClick(t.e)),this.isEditing&&(this.__selectionStartOnMouseDown=this.selectionStart,this.selectionStart===this.selectionEnd&&this.abortCursorAnimation(),this.renderCursorOrSelection()))},_mouseDownHandlerBefore:function(t){!this.canvas||!this.editable||t.e.button&&1!==t.e.button||(this.selected=this===this.canvas._activeObject)},initMousedownHandler:function(){this.on("mousedown",this._mouseDownHandler),this.on("mousedown:before",this._mouseDownHandlerBefore)},initMouseupHandler:function(){this.on("mouseup",this.mouseUpHandler)},mouseUpHandler:function(t){if(this.__isMousedown=!1,!(!this.editable||this.group||t.transform&&t.transform.actionPerformed||t.e.button&&1!==t.e.button)){if(this.canvas){var e=this.canvas._activeObject;if(e&&e!==this)return}this.__lastSelected&&!this.__corner?(this.selected=!1,this.__lastSelected=!1,this.enterEditing(t.e),this.selectionStart===this.selectionEnd?this.initDelayedCursor(!0):this.renderCursorOrSelection()):this.selected=!0}},setCursorByClick:function(t){var e=this.getSelectionStartFromPointer(t),i=this.selectionStart,r=this.selectionEnd;t.shiftKey?this.setSelectionStartEndWithShift(i,r,e):(this.selectionStart=e,this.selectionEnd=e),this.isEditing&&(this._fireSelectionChanged(),this._updateTextarea())},getSelectionStartFromPointer:function(t){for(var e,i=this.getLocalPointer(t),r=0,n=0,o=0,s=0,a=0,h=0,c=this._textLines.length;h0&&(s+=this._textLines[h-1].length+this.missingNewlineOffset(h-1));n=this._getLineLeftOffset(a)*this.scaleX,e=this._textLines[a],"rtl"===this.direction&&(i.x=this.width*this.scaleX-i.x+n);for(var l=0,u=e.length;lo||s<0?0:1);return this.flipX&&(a=n-a),a>this._text.length&&(a=this._text.length),a}}),T.util.object.extend(T.IText.prototype,{initHiddenTextarea:function(){this.hiddenTextarea=T.document.createElement("textarea"),this.hiddenTextarea.setAttribute("autocapitalize","off"),this.hiddenTextarea.setAttribute("autocorrect","off"),this.hiddenTextarea.setAttribute("autocomplete","off"),this.hiddenTextarea.setAttribute("spellcheck","false"),this.hiddenTextarea.setAttribute("data-fabric-hiddentextarea",""),this.hiddenTextarea.setAttribute("wrap","off");var t=this._calcTextareaPosition();this.hiddenTextarea.style.cssText="position: absolute; top: "+t.top+"; left: "+t.left+"; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px; paddingο½°top: "+t.fontSize+";",this.hiddenTextareaContainer?this.hiddenTextareaContainer.appendChild(this.hiddenTextarea):T.document.body.appendChild(this.hiddenTextarea),T.util.addListener(this.hiddenTextarea,"keydown",this.onKeyDown.bind(this)),T.util.addListener(this.hiddenTextarea,"keyup",this.onKeyUp.bind(this)),T.util.addListener(this.hiddenTextarea,"input",this.onInput.bind(this)),T.util.addListener(this.hiddenTextarea,"copy",this.copy.bind(this)),T.util.addListener(this.hiddenTextarea,"cut",this.copy.bind(this)),T.util.addListener(this.hiddenTextarea,"paste",this.paste.bind(this)),T.util.addListener(this.hiddenTextarea,"compositionstart",this.onCompositionStart.bind(this)),T.util.addListener(this.hiddenTextarea,"compositionupdate",this.onCompositionUpdate.bind(this)),T.util.addListener(this.hiddenTextarea,"compositionend",this.onCompositionEnd.bind(this)),!this._clickHandlerInitialized&&this.canvas&&(T.util.addListener(this.canvas.upperCanvasEl,"click",this.onClick.bind(this)),this._clickHandlerInitialized=!0)},keysMap:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorRight",36:"moveCursorLeft",37:"moveCursorLeft",38:"moveCursorUp",39:"moveCursorRight",40:"moveCursorDown"},keysMapRtl:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorLeft",36:"moveCursorRight",37:"moveCursorRight",38:"moveCursorUp",39:"moveCursorLeft",40:"moveCursorDown"},ctrlKeysMapUp:{67:"copy",88:"cut"},ctrlKeysMapDown:{65:"selectAll"},onClick:function(){this.hiddenTextarea&&this.hiddenTextarea.focus()},onKeyDown:function(t){if(this.isEditing){var e="rtl"===this.direction?this.keysMapRtl:this.keysMap;if(t.keyCode in e)this[e[t.keyCode]](t);else{if(!(t.keyCode in this.ctrlKeysMapDown)||!t.ctrlKey&&!t.metaKey)return;this[this.ctrlKeysMapDown[t.keyCode]](t)}t.stopImmediatePropagation(),t.preventDefault(),t.keyCode>=33&&t.keyCode<=40?(this.inCompositionMode=!1,this.clearContextTop(),this.renderCursorOrSelection()):this.canvas&&this.canvas.requestRenderAll()}},onKeyUp:function(t){!this.isEditing||this._copyDone||this.inCompositionMode?this._copyDone=!1:t.keyCode in this.ctrlKeysMapUp&&(t.ctrlKey||t.metaKey)&&(this[this.ctrlKeysMapUp[t.keyCode]](t),t.stopImmediatePropagation(),t.preventDefault(),this.canvas&&this.canvas.requestRenderAll())},onInput:function(t){var e=this.fromPaste;if(this.fromPaste=!1,t&&t.stopPropagation(),this.isEditing){var i,r,n,o,s,a=this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,h=this._text.length,c=a.length,l=c-h,u=this.selectionStart,f=this.selectionEnd,d=u!==f;if(""===this.hiddenTextarea.value)return this.styles={},this.updateFromTextArea(),this.fire("changed"),void(this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll()));var g=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value),p=u>g.selectionStart;d?(i=this._text.slice(u,f),l+=f-u):c0&&(r+=(i=this.__charBounds[t][e-1]).left+i.width),r},getDownCursorOffset:function(t,e){var i=this._getSelectionForOffset(t,e),r=this.get2DCursorLocation(i),n=r.lineIndex;if(n===this._textLines.length-1||t.metaKey||34===t.keyCode)return this._text.length-i;var o=r.charIndex,s=this._getWidthBeforeCursor(n,o),a=this._getIndexOnLine(n+1,s);return this._textLines[n].slice(o).length+a+1+this.missingNewlineOffset(n)},_getSelectionForOffset:function(t,e){return t.shiftKey&&this.selectionStart!==this.selectionEnd&&e?this.selectionEnd:this.selectionStart},getUpCursorOffset:function(t,e){var i=this._getSelectionForOffset(t,e),r=this.get2DCursorLocation(i),n=r.lineIndex;if(0===n||t.metaKey||33===t.keyCode)return-i;var o=r.charIndex,s=this._getWidthBeforeCursor(n,o),a=this._getIndexOnLine(n-1,s),h=this._textLines[n].slice(0,o),c=this.missingNewlineOffset(n-1);return-this._textLines[n-1].length+a-h.length+(1-c)},_getIndexOnLine:function(t,e){for(var i,r,n=this._textLines[t],o=this._getLineLeftOffset(t),s=0,a=0,h=n.length;ae){r=!0;var c=o-i,l=o,u=Math.abs(c-e);s=Math.abs(l-e)=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorUpOrDown("Down",t)},moveCursorUp:function(t){0===this.selectionStart&&0===this.selectionEnd||this._moveCursorUpOrDown("Up",t)},_moveCursorUpOrDown:function(t,e){var i=this["get"+t+"CursorOffset"](e,"right"===this._selectionDirection);e.shiftKey?this.moveCursorWithShift(i):this.moveCursorWithoutShift(i),0!==i&&(this.setSelectionInBoundaries(),this.abortCursorAnimation(),this._currentCursorOpacity=1,this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorWithShift:function(t){var e="left"===this._selectionDirection?this.selectionStart+t:this.selectionEnd+t;return this.setSelectionStartEndWithShift(this.selectionStart,this.selectionEnd,e),0!==t},moveCursorWithoutShift:function(t){return t<0?(this.selectionStart+=t,this.selectionEnd=this.selectionStart):(this.selectionEnd+=t,this.selectionStart=this.selectionEnd),0!==t},moveCursorLeft:function(t){0===this.selectionStart&&0===this.selectionEnd||this._moveCursorLeftOrRight("Left",t)},_move:function(t,e,i){var r;if(t.altKey)r=this["findWordBoundary"+i](this[e]);else{if(!t.metaKey&&35!==t.keyCode&&36!==t.keyCode)return this[e]+="Left"===i?-1:1,!0;r=this["findLineBoundary"+i](this[e])}if(void 0!==typeof r&&this[e]!==r)return this[e]=r,!0},_moveLeft:function(t,e){return this._move(t,e,"Left")},_moveRight:function(t,e){return this._move(t,e,"Right")},moveCursorLeftWithoutShift:function(t){var e=!0;return this._selectionDirection="left",this.selectionEnd===this.selectionStart&&0!==this.selectionStart&&(e=this._moveLeft(t,"selectionStart")),this.selectionEnd=this.selectionStart,e},moveCursorLeftWithShift:function(t){return"right"===this._selectionDirection&&this.selectionStart!==this.selectionEnd?this._moveLeft(t,"selectionEnd"):0!==this.selectionStart?(this._selectionDirection="left",this._moveLeft(t,"selectionStart")):void 0},moveCursorRight:function(t){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorLeftOrRight("Right",t)},_moveCursorLeftOrRight:function(t,e){var i="moveCursor"+t+"With";this._currentCursorOpacity=1,e.shiftKey?i+="Shift":i+="outShift",this[i](e)&&(this.abortCursorAnimation(),this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorRightWithShift:function(t){return"left"===this._selectionDirection&&this.selectionStart!==this.selectionEnd?this._moveRight(t,"selectionStart"):this.selectionEnd!==this._text.length?(this._selectionDirection="right",this._moveRight(t,"selectionEnd")):void 0},moveCursorRightWithoutShift:function(t){var e=!0;return this._selectionDirection="right",this.selectionStart===this.selectionEnd?(e=this._moveRight(t,"selectionStart"),this.selectionEnd=this.selectionStart):this.selectionStart=this.selectionEnd,e},removeChars:function(t,e){void 0===e&&(e=t+1),this.removeStyleFromTo(t,e),this._text.splice(t,e-t),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()},insertChars:function(t,e,i,r){void 0===r&&(r=i),r>i&&this.removeStyleFromTo(i,r);var n=T.util.string.graphemeSplit(t);this.insertNewStyleBlock(n,i,e),this._text=[].concat(this._text.slice(0,i),n,this._text.slice(r)),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()}}),function(){var t=T.util.toFixed,e=/ +/g;T.util.object.extend(T.Text.prototype,{_toSVG:function(){var t=this._getSVGLeftTopOffsets(),e=this._getSVGTextAndBg(t.textTop,t.textLeft);return this._wrapSVGTextAndBg(e)},toSVG:function(t){return this._createBaseSVGMarkup(this._toSVG(),{reviver:t,noStyle:!0,withShadow:!0})},_getSVGLeftTopOffsets:function(){return{textLeft:-this.width/2,textTop:-this.height/2,lineTop:this.getHeightOfLine(0)}},_wrapSVGTextAndBg:function(t){var e=this.getSvgTextDecoration(this);return[t.textBgRects.join(""),'\t\t",t.textSpans.join(""),"\n"]},_getSVGTextAndBg:function(t,e){var i,r=[],n=[],o=t;this._setSVGBg(n);for(var s=0,a=this._textLines.length;s",T.util.string.escapeXml(i),""].join("")},_setSVGTextLineText:function(t,e,i,r){var n,o,s,a,h,c=this.getHeightOfLine(e),l=-1!==this.textAlign.indexOf("justify"),u="",f=0,d=this._textLines[e];r+=c*(1-this._fontSizeFraction)/this.lineHeight;for(var g=0,p=d.length-1;g<=p;g++)h=g===p||this.charSpacing,u+=d[g],s=this.__charBounds[e][g],0===f?(i+=s.kernedWidth-s.width,f+=s.width):f+=s.kernedWidth,l&&!h&&this._reSpaceAndTab.test(d[g])&&(h=!0),h||(n=n||this.getCompleteStyleDeclaration(e,g),o=this.getCompleteStyleDeclaration(e,g+1),h=this._hasStyleChangedForSvg(n,o)),h&&(a=this._getStyleDeclaration(e,g)||{},t.push(this._createTextCharSpan(u,a,i,r)),u="",n=o,i+=f,f=0)},_pushTextBgRect:function(e,i,r,n,o,s){var a=T.Object.NUM_FRACTION_DIGITS;e.push("\t\t\n')},_setSVGTextLineBg:function(t,e,i,r){for(var n,o,s=this._textLines[e],a=this.getHeightOfLine(e)/this.lineHeight,h=0,c=0,l=this.getValueOfPropertyAt(e,0,"textBackgroundColor"),u=0,f=s.length;uthis.width&&this._set("width",this.dynamicMinWidth),-1!==this.textAlign.indexOf("justify")&&this.enlargeSpaces(),this.height=this.calcTextHeight(),this.saveState({propertySet:"_dimensionAffectingProps"}))},_generateStyleMap:function(t){for(var e=0,i=0,r=0,n={},o=0;o0?(i=0,r++,e++):!this.splitByGrapheme&&this._reSpaceAndTab.test(t.graphemeText[r])&&o>0&&(i++,r++),n[o]={line:e,offset:i},r+=t.graphemeLines[o].length,i+=t.graphemeLines[o].length;return n},styleHas:function(t,i){if(this._styleMap&&!this.isWrapping){var r=this._styleMap[i];r&&(i=r.line)}return e.Text.prototype.styleHas.call(this,t,i)},isEmptyStyles:function(t){if(!this.styles)return!0;var e,i,r=0,n=!1,o=this._styleMap[t],s=this._styleMap[t+1];for(var a in o&&(t=o.line,r=o.offset),s&&(n=s.line===t,e=s.offset),i=void 0===t?this.styles:{line:this.styles[t]})for(var h in i[a])if(h>=r&&(!n||hr&&!v?(a.push(h),h=[],o=d,v=!0):o+=m,v||s||h.push(f),h=h.concat(l),g=s?0:this._measureWord([f],i,u),u++,v=!1,d>p&&(p=d);return y&&a.push(h),p+n>this.dynamicMinWidth&&(this.dynamicMinWidth=p-m+n),a},isEndOfWrapping:function(t){return!this._styleMap[t+1]||this._styleMap[t+1].line!==this._styleMap[t].line},missingNewlineOffset:function(t){return this.splitByGrapheme?this.isEndOfWrapping(t)?1:0:1},_splitTextIntoLines:function(t){for(var i=e.Text.prototype._splitTextIntoLines.call(this,t),r=this._wrapText(i.lines,this.width),n=new Array(r.length),o=0;o","",e.toSVG(t.reviver),"",""];return e._objects[0].fill=i,r.join("\n")}return""},_createBaseSVGMarkup:function(t,e){var i=this.getEraser();if(i){var n=this.eraserToSVG(e);this.clipPath=null;var o=r.call(this,t,e);return this.clipPath=i,[n,o.replace(">",'mask="url(#'+i.clipPathId+')" >')].join("\n")}return r.call(this,t,e)}});var n=T.Group.prototype._restoreObjectsState,o=T.Group.prototype.toObject,s=T.Group.prototype._getBounds;T.util.object.extend(T.Group.prototype,{_getBounds:function(t,e,i){if(this.eraser)return this.width=this._objects[0].width,void(this.height=this._objects[0].height);s.call(this,t,e,i)},_addEraserPathToObjects:function(t){this._objects.forEach((function(e){T.EraserBrush.prototype._addPathToObjectEraser.call(T.EraserBrush.prototype,e,t)}))},applyEraserToObjects:function(){var t=this;if(this.getEraser()){var e=t.calcTransformMatrix();t.getEraser().clone((function(i){var r=i._objects[0].clipPath;t.clipPath=r||void 0,i.getObjects("path").forEach((function(i){var n=T.util.multiplyTransformMatrices(e,i.calcTransformMatrix());T.util.applyTransformToObject(i,n),r?r.clone((function(r){T.EraserBrush.prototype.applyClipPathToPath.call(T.EraserBrush.prototype,i,r,e),t._addEraserPathToObjects(i)})):t._addEraserPathToObjects(i)}))}))}},_restoreObjectsState:function(){return!0===this.erasable&&this.applyEraserToObjects(),n.call(this)},toObject:function(t){return o.call(this,["eraser"].concat(t))}}),T.util.object.extend(T.Canvas.prototype,{isErasing:function(){return this.isDrawingMode&&this.freeDrawingBrush&&"eraser"===this.freeDrawingBrush.type&&this.freeDrawingBrush._isErasing},renderAll:function(){if(!this.contextTopDirty||this._groupSelector||this.isDrawingMode||(this.clearContext(this.contextTop),this.contextTopDirty=!1),!this.isErasing()){this.hasLostContext&&this.renderTopLayer(this.contextTop);var t=this.contextContainer;return this.renderCanvas(t,this._chooseObjectsToRender()),this}this.freeDrawingBrush._render()}}),T.EraserBrush=T.util.createClass(T.PencilBrush,{type:"eraser",_ready:!1,_drawOverlayOnTop:!1,_isErasing:!1,initialize:function(t){this.callSuper("initialize",t),this._renderBound=this._render.bind(this),this.render=this.render.bind(this)},hideObject:function(t){t&&(t._originalOpacity=t.opacity,t.set({opacity:0}))},restoreObjectVisibility:function(t){t&&t._originalOpacity&&(t.set({opacity:t._originalOpacity}),t._originalOpacity=void 0)},_isErasable:function(t){return!1!==t.erasable},prepareCanvasBackgroundForLayer:function(t){if("overlay"!==t){var e=this.canvas.backgroundImage,i="top"===t;e&&this._isErasable(e)===!i&&this.hideObject(e)}},prepareCanvasOverlayForLayer:function(t){var e=this.canvas,i=e.overlayImage,r=!!e.overlayColor;if(e.overlayColor&&"overlay"!==t&&(this.__overlayColor=e.overlayColor,delete e.overlayColor),"bottom"===t)return this.hideObject(i),!1;var n="top"===t,o=i&&!this._isErasable(i)||r;return i&&this._isErasable(i)===!n&&this.hideObject(i),o},restoreCanvasDrawables:function(){var t=this.canvas;this.__overlayColor&&(t.overlayColor=this.__overlayColor,delete this.__overlayColor),this.restoreObjectVisibility(t.backgroundImage),this.restoreObjectVisibility(t.overlayImage)},prepareCollectionTraversal:function(t){var e=this;t.forEachObject((function(t){t.forEachObject&&"deep"===t.erasable?e.prepareCollectionTraversal(t):t.erasable&&e.hideObject(t)}))},restoreCollectionTraversal:function(t){var e=this;t.forEachObject((function(t){t.forEachObject&&"deep"===t.erasable?e.restoreCollectionTraversal(t):e.restoreObjectVisibility(t)}))},prepareCanvasObjectsForLayer:function(t){"bottom"===t&&this.prepareCollectionTraversal(this.canvas)},restoreCanvasObjectsFromLayer:function(t){"bottom"===t&&this.restoreCollectionTraversal(this.canvas)},prepareCanvasForLayer:function(t){return this.prepareCanvasBackgroundForLayer(t),this.prepareCanvasObjectsForLayer(t),this.prepareCanvasOverlayForLayer(t)},restoreCanvasFromLayer:function(t){this.restoreCanvasDrawables(),this.restoreCanvasObjectsFromLayer(t)},renderBottomLayer:function(){var t=this.canvas;this.prepareCanvasForLayer("bottom"),t.renderCanvas(t.getContext(),t.getObjects().filter((function(t){return!t.erasable||t.forEachObject}))),this.restoreCanvasFromLayer("bottom")},renderTopLayer:function(){var t=this.canvas;this._drawOverlayOnTop=this.prepareCanvasForLayer("top"),t.renderCanvas(t.contextTop,t.getObjects()),this.callSuper("_render"),this.restoreCanvasFromLayer("top")},renderOverlay:function(){this.prepareCanvasForLayer("overlay");var t=this.canvas,e=t.contextTop;t._renderOverlay(e),this.restoreCanvasFromLayer("overlay")},_saveAndTransform:function(t){this.callSuper("_saveAndTransform",t),t.globalCompositeOperation="destination-out"},needsFullRender:function(){return this.callSuper("needsFullRender")||this._drawOverlayOnTop},onMouseDown:function(t,e){this.canvas._isMainEvent(e.e)&&(this._prepareForDrawing(t),this._captureDrawingPath(t),this._isErasing=!0,this.canvas.fire("erasing:start"),this._ready=!0,this._render())},_render:function(){this._ready&&(this.isRendering=1,this.renderBottomLayer(),this.renderTopLayer(),this.renderOverlay(),this.isRendering=0)},render:function(){return!!this._isErasing&&(this.isRendering?this.isRendering=T.util.requestAnimFrame(this._renderBound):this._render(),!0)},applyClipPathToPath:function(t,e,i){var r=t.calcTransformMatrix(),n=e.calcTransformMatrix(),o=T.util.multiplyTransformMatrices(T.util.invertTransform(r),i);return T.util.applyTransformToObject(e,T.util.multiplyTransformMatrices(o,n)),t.clipPath=e,t},clonePathWithClipPath:function(t,e,i){var r=e.calcTransformMatrix(),n=e.getClipPath(),o=this;t.clone((function(t){n.clone((function(e){i(o.applyClipPathToPath(t,e,r))}))}))},_addPathToObjectEraser:function(t,e){var i,r=this;if(t.forEachObject&&"deep"===t.erasable){var n=t._objects.filter((function(t){return t.erasable}));n.length>0&&t.clipPath?this.clonePathWithClipPath(e,t,(function(t){n.forEach((function(e){r._addPathToObjectEraser(e,t)}))})):n.length>0&&n.forEach((function(t){r._addPathToObjectEraser(t,e)}))}else{if(t.getEraser())i=t.clipPath;else{var o=t._getNonTransformedDimensions(),s=new T.Rect({fill:"rgb(0,0,0)",width:o.x,height:o.y,clipPath:t.clipPath,originX:"center",originY:"center"});i=new T.Group([s],{eraser:!0})}e.clone((function(e){e.globalCompositeOperation="destination-out";var n=T.util.multiplyTransformMatrices(T.util.invertTransform(t.calcTransformMatrix()),e.calcTransformMatrix());T.util.applyTransformToObject(e,n),i.addWithUpdate(e),t.set({clipPath:i,dirty:!0}),t.fire("erasing:end",{path:e}),t.group&&Array.isArray(r.__subTargets)&&r.__subTargets.push(t)}))}},applyEraserToCanvas:function(t){var e=this.canvas,i={};return["backgroundImage","overlayImage"].forEach((function(r){var n=e[r];n&&n.erasable&&(this._addPathToObjectEraser(n,t),i[r]=n)}),this),i},_finalizeAndAddPath:function(){var t=this.canvas.contextTop,e=this.canvas;t.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate)),e.clearContext(e.contextTop),this._isErasing=!1;var i=this._points&&this._points.length>1?this.convertPointsToSVGPath(this._points):null;if(!i||this._isEmptySVGPath(i))return e.fire("erasing:end"),void e.requestRenderAll();var r=this.createPath(i);r.setCoords(),e.fire("before:path:created",{path:r});var n=this.applyEraserToCanvas(r),o=this;this.__subTargets=[];var s=[];e.forEachObject((function(t){t.erasable&&t.intersectsWithObject(r,!0,!0)&&(o._addPathToObjectEraser(t,r),s.push(t))})),e.fire("erasing:end",{path:r,targets:s,subTargets:this.__subTargets,drawables:n}),delete this.__subTargets,e.requestRenderAll(),r.setCoords(),this._resetShadow(),e.fire("path:created",{path:r})}})}()}(tt);var rt=tt.fabric;export{tt as default,rt as fabric}; +//# sourceMappingURL=/sm/4595deba885e6b95e012cf1ed9deed8a7b30edf3ae4c7d4bd5cc11d619b75c11.map \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Easy-Use/web_version/v2/assets/extensions-kN7l-EJZ.js b/custom_nodes/ComfyUI-Easy-Use/web_version/v2/assets/extensions-kN7l-EJZ.js new file mode 100644 index 00000000..e4985aa5 --- /dev/null +++ b/custom_nodes/ComfyUI-Easy-Use/web_version/v2/assets/extensions-kN7l-EJZ.js @@ -0,0 +1 @@ +var e,t,i,s,a,n,o,l,r,d,u,A,c,p,h,m,f=Object.defineProperty,g=e=>{throw TypeError(e)},v=(e,t,i)=>((e,t,i)=>t in e?f(e,t,{enumerable:!0,configurable:!0,writable:!0,value:i}):e[t]=i)(e,"symbol"!=typeof t?t+"":t,i),y=(e,t,i)=>t.has(e)||g("Cannot "+i),b=(e,t,i)=>(y(e,t,"read from private field"),i?i.call(e):t.get(e)),w=(e,t,i)=>t.has(e)?g("Cannot add the same private member more than once"):t instanceof WeakSet?t.add(e):t.set(e,i),_=(e,t,i,s)=>(y(e,t,"write to private field"),s?s.call(e,i):t.set(e,i),i);import{d as S,h as L,s as x,a as k,T as E,b as N,c as C,e as I,f as T,g as O,i as M,j as D}from"./vendor-D0iX-Vke.js";import{c as R}from"./lodash-DkvwM0Z9.js";import{r as P,m as G,w as z,o as V,a as j,c as U,b as W,d as F,u as X,e as Z,f as H,g as q,h as Y,F as K,i as J,n as B,j as Q,v as $,t as ee,k as te,l as ie,p as se,q as ae,s as ne,x as oe}from"./vue-Cu3MA6TM.js";import{u as le,a as re,b as de,c as ue}from"./vueuse-Chow9bSK.js";import{P as Ae}from"./primevue-CWKvsPkZ.js";let ce=(null==(t=null==(e=window.comfyAPI)?void 0:e.app)?void 0:t.app)||null,pe=(null==(s=null==(i=window.comfyAPI)?void 0:i.api)?void 0:s.api)||null,he=(null==(n=null==(a=window.comfyAPI)?void 0:a.ui)?void 0:n.$el)||null,me=(null==(l=null==(o=window.comfyAPI)?void 0:o.dialog)?void 0:l.ComfyDialog)||null,fe=(null==(d=null==(r=window.comfyAPI)?void 0:r.widgets)?void 0:d.ComfyWidgets)||null,ge=(null==(A=null==(u=window.comfyAPI)?void 0:u.utils)?void 0:A.applyTextReplacements)||null,ve=(null==(p=null==(c=window.comfyAPI)?void 0:c.groupNode)?void 0:p.GroupNodeConfig)||null;const ye=e=>{var t,i;return e?null==(i=null==(t=null==ce?void 0:ce.ui)?void 0:t.settings)?void 0:i.getSettingValue(e):null};function be(e,t=null){try{let i=e?ye(e):null;return null==i&&(i=t?localStorage[t]:localStorage[e]||null),i}catch(i){return null}}function we(e,t=e=>{}){var i;const s=null==(i=ce.ui.settings.settingsLookup)?void 0:i[e];s&&(s.onChange=e=>t(e))}async function _e(e,t,i=null){var s,a;if(!e)throw new Error("Invalid arguments");try{(null==(a=null==(s=null==ce?void 0:ce.ui)?void 0:s.settings)?void 0:a.setSettingValue)?ce.ui.settings.setSettingValue(e,t):await pe.storeSetting(e,t),i&&(localStorage[i]="object"==typeof t?JSON.stringify(t):t)}catch(n){}}const Se={"Workflow created by":"ε·₯δ½œζ΅εˆ›ε»Ίθ€…","Watch more video content":"θ§‚ηœ‹ζ›΄ε€šθ§†ι’‘ε†…εΉ","Workflow Guide":"ε·₯δ½œζ΅ζŒ‡ε—","πŸ’Ž View Checkpoint Info...":"πŸ’Ž ζŸ₯ηœ‹ Checkpoint 俑息...","πŸ’Ž View Lora Info...":"πŸ’Ž ζŸ₯ηœ‹ Lora 俑息...","πŸ”ƒ Reload Node":"πŸ”ƒ εˆ·ζ–°θŠ‚η‚Ή","Updated At:":"ζœ€θΏ‘ζ›΄ζ–°οΌš","Created At:":"ι¦–ζ¬‘ε‘εΈƒοΌš","✏️ Edit":"✏️ ηΌ–θΎ‘","πŸ’Ύ Save":"πŸ’Ύ 保存","No notes":"ε½“ε‰θΏ˜ζ²‘ζœ‰ε€‡ζ³¨ε†…εΉ","Saving Notes...":"ζ­£εœ¨δΏε­˜ε€‡ζ³¨...","Type your notes here":"εœ¨θΏ™ι‡ŒθΎ“ε…₯倇注内εΉ",ModelName:"ζ¨‘εž‹εη§°","Models Required":"ζ‰€ιœ€ζ¨‘εž‹","Download Model":"δΈ‹θ½½ζ¨‘εž‹","Source Url":"ζ¨‘εž‹ζΊεœ°ε€",Notes:"倇注",Type:"η±»εž‹","Trained Words":"θ­η»ƒθ―",BaseModel:"εŸΊη‘€η—法",Details:"θ―¦ζƒ…",Description:"描述",Download:"下载量",Source:"ζ₯源","Saving Preview...":"ζ­£εœ¨δΏε­˜ι’„θ§ˆε›Ύ...","Saving Succeed":"保存成功","Clean SuccessFully":"ζΈ…η†ζˆεŠŸ","Clean Failed":"清理倱θ΄₯","Saving Failed":"保存倱θ΄₯","No COMBO link":"ζ²’ζœ‰ζ‰Ύεˆ°COMBO连ζŽ₯","Reboot ComfyUI":"重启ComfyUI","Are you sure you'd like to reboot the server?":"ζ˜―ε¦θ¦ι‡ε―ComfyUI？","NODES MAP":"η‘η†θŠ‚η‚ΉδΈŽη»„",NodesMap:"θŠ‚η‚ΉδΈŽη»„","Nodes map sorting mode":"η‘η†θŠ‚η‚Ήη»„ζŽ’εΊζ¨‘εΌ","Search by Node ID/Name...":"ι€šθΏ‡θŠ‚η‚ΉID/名称搜紒...","No Nodes":"ζœͺζ‰Ύεˆ°θŠ‚η‚Ή","No nodes found in the map":"在ε·₯δ½œζ΅η¨‹δΈ­ζ²‘ζœ‰ζ‰Ύεˆ°θŠ‚η‚Ή","No nodes found in the search":"ζœͺζ‰Ύεˆ°δΈŽζœη΄’εŒΉι…ηš„θŠ‚η‚Ή","Jump to this node":"跳转到θ―₯θŠ‚η‚Ή","Rename node":"ι‡ε‘½εθŠ‚η‚Ή","Delete node":"εˆ ι™€θŠ‚η‚Ή","Rename group":"重命名组","Delete group":"εˆ ι™€η»„","Expand All":"ε±•εΌ€ζ‰€ζœ‰η»„","Collapse All":"ζŠ˜ε ζ‰€ζœ‰η»„",Close:"ε…³ι—­","Default automatic sorting, if set to manual, groups can be dragged and dropped and the sorting results saved.":"默θ€θ‡ͺεŠ¨ζŽ’εΊοΌŒε¦‚ζžœθΎη½δΈΊζ‰‹εŠ¨οΌŒη»„可δ»₯ζ‹–ζ”ΎεΉΆδΏε­˜ζŽ’εΊη»“ζžœγ€‚","For drag and drop sorting, please find Nodes map sorting mode in Settings->EasyUse and change it to manual":"ε¦‚ιœ€ζ‹–ζ‹½ζŽ’εΊθ―·εœ¨θΎη½->EasyUseθŠ‚η‚ΉδΈ­ζ‰Ύεˆ°η‘η†θŠ‚η‚Ήη»„ζŽ’εΊζ¨‘εΌεΉΆδΏζ”Ήζˆ manual",Queue:"ι˜Ÿεˆ—","Cleanup Of VRAM Usage":"ζΈ…η†ζ˜Ύε­˜ε η”¨","Please stop all running tasks before cleaning GPU":"θ―·εœ¨ζΈ…η†GPUδΉ‹ε‰εœζ­’ζ‰€ζœ‰θΏθ‘ŒδΈ­ηš„δ»»εŠ‘",Always:"启用中",Bypass:"ε·²εΏ½η•₯",Never:"ε·²εœη”¨","Auto Sorting":"θ‡ͺεŠ¨ζŽ’εΊ","Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never":"点击`启用中/ε·²εΏ½η•₯`可θΎη½η»„樑式, ι•ΏζŒ‰ε―εœη”¨θ―₯η»„θŠ‚η‚Ή","Enable Shift+Up/Down/Left/Right key and Shift+Ctrl+Alt+Left/Right to align selected nodes":"启用 Shift+上/δΈ‹/ε·¦/右 ε’Œ Shift+Ctrl+Alt+ε·¦/右 ι”ε―Ήι½ι€‰δΈ­ηš„θŠ‚η‚Ή","Enable Shift+Ctrl+Left/Right key to normalize selected nodes":"启用 Shift+Ctrl+ε·¦/右 ι”θ§„θŒƒεŒ–ι€‰δΈ­ηš„θŠ‚η‚Ή","Enable Shift+g to add selected nodes to a group":"启用 Shift+g ι”ε°†ι€‰δΈ­ηš„θŠ‚η‚Ήζ·»εŠ δΈ€δΈͺη»„","Enable Shift+r to unload models and node cache":"启用 Shift+r ι”εΈθ½½ζ¨‘εž‹ε’ŒθŠ‚η‚ΉηΌ“ε­˜","Enable Shift+m to toggle nodes map":"启用 Shift+m ι”ζ˜Ύιšη‘η†θŠ‚η‚Ήη»„","Enable Up/Down/Left/Right key to jump nearest nodes":"启用 上/δΈ‹/ε·¦/右 ι”θ·³θ½¬εˆ°ζœ€θΏ‘ηš„ε‰εŽθŠ‚η‚Ή","Enable Alt+1~9 to paste nodes from nodes template":"启用 Alt+1~9 δ»ŽθŠ‚η‚Ήζ¨‘ζΏη²˜θ΄΄εˆ°ε·₯作桁中","Enable contextMenu auto nest subdirectories":"ε―η”¨δΈŠδΈ‹ζ–‡θœε•θ‡ͺ动塌ε₯—子η›ε½•","Enable right-click menu to add node A~Z sorting":"启用右ι”θœε•δΈ­ζ–°ε»ΊθŠ‚η‚ΉA~ZζŽ’εΊ","Enable model thumbnails display":"ε―εŠ¨ζ¨‘εž‹ι’„θ§ˆε›Ύζ˜Ύη€Ί","Enable nodes runtime display":"ε―εŠ¨θŠ‚η‚ΉθΏθ‘Œζ—Άι—΄ζ˜Ύη€Ί","Enable chain get node and set node with parent nodes":"ε―η”¨ε°†θŽ·ε–η‚Ήε’ŒθΎη½η‚ΉδΈŽηˆΆθŠ‚η‚Ήι“Ύεœ¨δΈ€θ΅·","Maximum number of model thumbnails displayed":"ζ˜Ύη€Ίηš„ζ¨‘εž‹ηΌ©η•₯ε›Ύηš„ζœ€ε€§ζ•°ι‡","Too many thumbnails will affect the first loading time, set the maximum value to not load the thumbnail function when there are too many models's thumbnail":"ε€ͺε€šηš„ηΌ©η•₯ε›ΎδΌšε½±ε“ι¦–ζ¬‘εŠ θ½½ζ—Άι—΄οΌŒε½“ζ¨‘εž‹ηΌ©η•₯ε›Ύε€ͺε€šζ—ΆοΌŒθΎη½ζœ€ε€§ε€Όδ»₯不加载缩η•₯ε›ΎεŠŸθƒ½","Too many thumbnails, have closed the display":"ζ¨‘εž‹ηΌ©η•₯ε›Ύε€ͺε€šε•¦οΌŒδΈΊζ‚¨ε…³ι—­δΊ†ζ˜Ύη€Ί","Shift+Up/Down/Left/Right can align selected nodes, Shift+Ctrl+Alt+Left/Right can distribute horizontal/vertical nodes":"Shift+上/δΈ‹/ε·¦/右 可δ»₯ε―Ήι½ι€‰δΈ­ηš„θŠ‚η‚Ή, Shift+Ctrl+Alt+ε·¦/右 可δ»₯ζ°΄εΉ³/εž‚η›΄εˆ†εΈƒθŠ‚η‚Ή","Enable Shift+Ctrl+Left key to normalize width and Shift+Ctrl+Right key to normalize height":"启用 Shift+Ctrl+ε·¦ ι”θ§„θŒƒεŒ–ε½εΊ¦ε’Œ Shift+Ctrl+右 ι”θ§„θŒƒεŒ–ι«˜εΊ¦","After v1.2.39, Ctrl+g can be used instead of it":"从v1.2.39εΌ€ε§‹οΌŒε―δ»₯使用Ctrl+g代替","Styles Selector Display Type":"ζ ·εΌι€‰ζ‹©ε™¨ζ˜Ύη€Ίη±»εž‹","Styles Selector Display Type, if set to 'Grid', it will display as a Grid, if set to 'List', it will display as a list":"ζ ·εΌι€‰ζ‹©ε™¨ζ˜Ύη€Ίη±»εž‹οΌŒε¦‚ζžœθΎη½δΈΊβ€œη½‘ζ Όβ€οΌŒεˆ™ζ˜Ύη€ΊδΈΊη½‘ζ ΌοΌŒε¦‚ζžœθΎη½δΈΊβ€œεˆ—θ‘¨β€οΌŒεˆ™ζ˜Ύη€ΊδΈΊεˆ—θ‘¨","Use three shortcut buttons in the right-click menu":"在右ι”θœε•δΈ­δ½Ώη”¨δΈ‰δΈͺεΏ«ζ·ζŒ‰ι’","Enable Nodes Map":"ε―η”¨θŠ‚η‚Ήη»„η‘理","Display Groups Only":"δ»…ζ˜Ύη€Ίη»„","Display Node ID":"ζ˜Ύη€ΊθŠ‚η‚ΉID","You need to refresh the page to update successfully":"ζ‚¨ιœ€θ¦εˆ·ζ–°ι‘΅ι’δ»₯ζˆεŠŸζ›΄ζ–°","Get styles list Failed":"θŽ·ε–ζ ·εΌεˆ—θ‘¨ε€±θ΄₯","Get style image Failed":"θŽ·ε–ζ ·εΌε›Ύη‰‡ε€±θ΄₯","Empty All":"ζΈ…η©Ίζ‰€ζœ‰","Type here to search styles ...":"εœ¨ζ­€ε€„θΎ“ε…₯δ»₯搜紒样式 ...","Loading UserInfo...":"ζ­£εœ¨θŽ·ε–η”¨ζˆ·δΏ‘ζ―...","Please set the APIKEY first":"θ―·ε…ˆθΎη½APIKEY","Setting APIKEY":"θΎη½APIKEY","Save Account Info":"保存账号俑息",Choose:"选择",Delete:"εˆ ι™€",Edit:"ηΌ–θΎ‘","At least one account is required":"εˆ ι™€ε€±θ΄₯: θ‡³ε°‘ιœ€θ¦δΈ€δΈͺ账户","APIKEY is not Empty":"APIKEY 不能为空","Add Account":"添加账号","Getting Your APIKEY":"θŽ·ε–ζ‚¨ηš„APIKEY",Canceling:"ζ­£εœ¨ε–ζΆˆ","Cancel current run":"ε–ζΆˆε½“ε‰θΏθ‘Œ","Waiting for selection...":"等待选择中...","Using last selection":"δ½Ώη”¨δΈŠζ¬‘ι€‰ζ‹©","Pass Through":"跳过选择","Unknown Mode":"ζœͺηŸ₯樑式","Choose Selected Images":"ι€‰ζ‹©ι€‰δΈ­ηš„ε›Ύη‰‡","Choose images to continue":"选择图片δ»₯继续","Please run first":"θ―·ε…ˆθΏθ‘Œι˜Ÿεˆ—","Progress selected":"ε€„η†ι€‰δΈ­ηš„ε›Ύη‰‡","Progress selected image":"ε€„η†ι€‰δΈ­ηš„ε›Ύη‰‡","Please select images to continue":"请选择图片δ»₯继续",Background:"θƒŒζ™―",Hat:"帽子",Hair:"倴发",Body:"身体",Face:"脸部",Clothes:"董服",Others:"ε…Άδ»–",Glove:"手ε₯—",Glasses:"ηœΌι•œ",Sunglasses:"ε€ͺι˜³ι•œ","Upper-clothes":"上董","Top-clothes":"上董","Bottom-clothes":"δΈ‹θΊ«θ£…","Torso-skin":"ηšθ‚€",Dress:"θΏžθ‘£θ£™",Belt:"θ…°εΈ¦",Coat:"ε€–ε₯—",Socks:"蒜子",Pants:"裀子",Jumpsuits:"θΏžδ½“θ‘£",Bag:"εŒ…",Scarf:"ε›΄ε·Ύ",Skirt:"裙子","Left-arm":"左臂","Right-arm":"右臂","Left-leg":"ε·¦θ…Ώ","Right-leg":"右腿","Left-foot":"ε·¦θ„š","Right-foot":"ε³θ„š","Left-shoe":"ε·¦ιž‹","Right-shoe":"ε³ιž‹","select mask components":"ι€‰ζ‹©εˆ†ε‰²ιƒ¨δ½","select items":"选择鑹",Unlabelled:"ζœͺζ ‡θ°","shirt, blouse":"葬葫，ε₯³εΌθ‘¬θ‘«","top, t-shirt, sweatshirt":"上董，T恀，运动葫",sweater:"ζ―›θ‘£",cardigan:"εΌ€θ₯Ÿζ―›θ‘£",jacket:"ε€Ήε…‹",vest:"θƒŒεΏƒ",pants:"ι•Ώθ£€",shorts:"短裀",skirt:"裙子",coat:"ε€–ε₯—",dress:"θΏžθ‘£θ£™",jumpsuit:"θΏžδ½“θ‘£",cape:"ζ–—η―·",glasses:"ηœΌι•œ",hat:"帽子","headband, head covering, hair accessory":"倴带，倴ι₯°οΌŒε‘ι₯°",tie:"ι’†εΈ¦",glove:"手ε₯—",watch:"手葨",belt:"θ…°εΈ¦","leg warmer":"ζŠ€θ…Ώ","tights, stockings":"紧身蒜，丝蒜",sock:"蒜子",shoe:"ιž‹ε­","bag, wallet":"εŒ…οΌŒι’±εŒ…",scarf:"ε›΄ε·Ύ",umbrella:"ι›¨δΌž",hood:"εΈ½ε…œ",collar:"钆子",lapel:"ηΏ»ι’†",epaulette:"θ‚©η« ",sleeve:"蒖子",pocket:"口蒋",neckline:"钆口",buckle:"ζ‰£ηŽ―",zipper:"拉链",applique:"贴花",bead:"珠子",bow:"蝴蝢结",flower:"θŠ±ζœ΅θ£…ι₯°",fringe:"桁苏",ribbon:"丝带",rivet:"铆钉",ruffle:"θ€ΆθΎΉ",sequin:"δΊη‰‡",tassel:"桁苏穗",background:"θƒŒζ™―",skin:"ηšθ‚€",nose:"鼻子",eyeglasses:"ηœΌι•œ",left_eye:"左眼",right_eye:"右眼",left_eyebrow:"ε·¦ηœ‰ζ―›",right_eyebrow:"ε³ηœ‰ζ―›",left_ear:"ε·¦θ€³",right_ear:"右耳",mouth:"嘴巴",upper_lip:"δΈŠε”‡",lower_lip:"下唇",hair:"倴发",earring:"θ€³ηŽ―",necklace:"ι‘Ήι“Ύ",neck:"脖子",clothing:"ζœθ£…",s:"η§’","No Node Templates Found":"ζœͺζ‰Ύεˆ°θŠ‚η‚Ήζ¨‘ζΏι’„θΎ","Get Node Templates File Failed":"θŽ·ε–θŠ‚η‚Ήζ¨‘ζΏζ–‡δ»Άε€±θ΄₯","Node template with {key} not set":"ζœͺθΎη½εΏ«ζ·ι”δΈΊ{key}ηš„θŠ‚η‚Ήι’„θΎ","ComfyUI Basic":"ComfyUI εŸΊη‘€θŠ‚η‚Ή","Recommend Nodes":"ζŽ¨θθŠ‚η‚Ή","Others A~Z":"ε…Άδ»–θŠ‚η‚Ή A~Z","The maximum number of inputs is 20":"θΎ“ε…₯ζ•°ι‡ζœ€ε€§20δΈͺ","The maximum number of outputs is 20":"θΎ“ε‡Ίζ•°ι‡ζœ€ε€§20δΈͺ","Stop recording":"εœζ­’ε½•ιŸ³","Voice input":"θ―­ιŸ³θΎ“ε…₯",Continue:"继续",Stop:"停歒",now:"当前",prev:"δΈŠδΈ€ζ­₯","Add New Tab":"ζ·»εŠ ζ–°ζ ‡η­Ύ","Add Angle Prompt":"ζ·»εŠ θ§’εΊ¦ζη€Ίθ―","Invert Rotate Mode":"反转旋转",Hollow:"镂空展瀺",Reset:"重η½","Angle Prompt":"角度词",Image:"图像",F:"前",B:"后",U:"ι‘Ά",D:"εΊ•",L:"ε·¦",R:"右",Rotate:"旋转",Vertical:"εž‚η›΄",Zoom:"ηΌ©ζ”Ύ"},Le=(e,t=!1)=>{switch(be("Comfy.Locale")){case"zh-CN":case"zh":return Se[e]||e;default:return e}};const xe="comfyui-easyuse-",ke="dark-theme",Ee={PIPE_LINE:"#7737AA",PIPE_LINE_SDXL:"#7737AA",INT:"#29699C",X_Y:"#38291f",XYPLOT:"#74DA5D",LORA_STACK:"#94dccd",CONTROL_NET_STACK:"#94dccd",FLOW_CONTROL:"#373780"},Ne=0x4000000000000,Ce=["loaders","latent","image","mask","sampling","_for_testing","advanced","utils","api","api node","εŠ θ½½ε™¨","Latent","图像","ιη½©","采样","_η”¨δΊŽζ΅‹θ―•","高级","ε·₯ε…·","API","api θŠ‚η‚Ή","chargeurs","latent","image","masque","Γ©chantillonnage","_pour_test","avancΓ©","outils","nΕ“ud api","Π·Π°Π³Ρ€ΡƒΠ·Ρ‡ΠΈΠΊΠΈ","Π»Π°Ρ‚Π΅Π½Ρ‚Π½Ρ‹ΠΉ","ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅","маска","Π²Ρ‹Π±ΠΎΡ€ΠΊΠ°","_для_тСстирования","Ρ€Π°ΡΡˆΠΈΡ€Π΅Π½Π½Ρ‹ΠΉ","инструмСнты","api","api ΡƒΠ·Π΅Π»","λ‘œλ”","잠재","이미지","마슀크","μƒ˜ν”Œλ§","_ν…ŒμŠ€νŠΈμš©","κ³ κΈ‰","도ꡬ","api","api λ…Έλ“œ","ローダー","潜在","画像","γƒžγ‚Ήγ‚―","ァンプγƒͺング","_γƒ†γ‚Ήγƒˆη”¨","高度γͺ","ツール","api","apiγƒŽγƒΌγƒ‰","cargadores","latente","imagen","mΓ‘scara","muestreo","_para_pruebas","avanzado","herramientas","api","nodo 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Pe=JSON.parse(JSON.stringify(Re));delete Pe.NODE_COLORS,Pe.ColorPalette.id="obsidian_dark",Pe.ColorPalette.name="Obsidian 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Ge="#FFFFFF",ze={ColorPalette:{id:"milk_white",name:"Milk 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e=LiteGraph.ContextMenu;LiteGraph.ContextMenu=function(t,i){if(!(null==i?void 0:i.callback)||t.some((e=>"string"!=typeof e))){if(i.parentMenu);else if(i.extra);else if(i.scale);else{const e=be("EasyUse.ContextMenu.QuickOptions",null);if(i.hasOwnProperty("extra")&&"Disable"!==e){if("At the forefront"==e?t.unshift(null):t.push(null),s=window.location.host,["192.168.","10.","127.",/^172\.((1[6-9]|2[0-9]|3[0-1])\.)/].some((e=>"string"==typeof e?s.startsWith(e):e.test(s)))){const i={content:`${Le("Reboot ComfyUI")}`,callback:e=>(async()=>{if(confirm(Le("Are you sure you'd like to reboot the server?")))try{pe.fetchApi("/easyuse/reboot")}catch(e){}})()};"At the forefront"==e?t.unshift(i):t.push(i)}const i=be("EasyUse.Hotkeys.cleanVRAMUsed",null)?"("+Fe("Shift+r")+")":"",a={content:`${Le("Cleanup Of VRAM Usage")} ${i}`,callback:e=>hi()};"At the forefront"==e?t.unshift(a):t.push(a);const n=be("EasyUse.Hotkeys.toggleNodesMap",null)?"("+Fe("Shift+m")+")":"",o={content:`${Le("Nodes Map")} 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n=window.location.protocol,o=window.location.host;let l=s?`${`${n}//${o}`}/api/experiment/models/preview/${s}/${a}/${vi(e)}`:"";return{folder:s,content:e,fullName:e,title:he("div.comfyui-easyuse-contextmenu-model",{},[he("span",{},e)]).outerHTML,thumbnail:l,callback:r}}));const d=e,u={},A=[],c=[];for(const f of d){const e=f.indexOf("/")>-1?"/":"\\",t=f.split(e);if(t.length>1){const i=t.shift();u[i]=u[i]||[],u[i].push({value:t.join(e),fullValue:f})}else"CHOOSE"===f||f.startsWith("DISABLE ")?A.push({value:f,fullValue:f}):c.push({value:f,fullValue:f})}const p=Object.values(u).length,h=[],m=(e,t="",s)=>{var a,n;let o;o=he("div.comfyui-easyuse-contextmenu-model",{},[he("span",{},e)]);let l=null==(a=mi[s])?void 0:a.folder,d=null==(n=mi[s])?void 0:n.pathIndex;const u=window.location.protocol,A=window.location.host;let c=l?`${`${u}//${A}`}/api/experiment/models/preview/${l}/${d}/${vi(s)}`:"";return{folder:l,content:e,fullName:s,thumbnail:i?c:null,title:o.outerHTML,callback:r}};if(p>0){const e=(t,i)=>{let s=[],a=[];const n=t.map((({value:e,fullValue:t})=>{const n={},o=e.indexOf("/")>-1?"/":"\\",l=e.split(o);if(l.length>1){const e=l.shift();n[e]=n[e]||[],n[e].push({value:l.join(o),fullValue:t})}if(Object.values(u).length>0){let i=Object.keys(n)[0];i&&n[i]?s.push({key:i,value:n[i][0]}):a.push(m(e,i,t))}return m(e,i,t)}));if(s.length>0){let t={};return s.forEach((e=>{t[e.key]=t[e.key]||[],t[e.key].push(e.value)})),[...Object.entries(t).map((t=>({content:t[0],has_submenu:!0,callback:()=>{},submenu:{options:e(t[1],t[0])}}))),...a]}return n};for(const[t,i]of Object.entries(u))h.push({content:t,has_submenu:!0,callback:()=>{},submenu:{options:e(i,t)}})}h.push(...c.map((e=>m(e.value,"",e.fullValue)))),A.length>0&&h.push(...A.map((e=>m(e.value,"",e.fullValue))));return h}(t,i);return s?e.call(this,s,i):e.apply(this,[...arguments])}var s},LiteGraph.ContextMenu.prototype=e.prototype,be("EasyUse.ContextMenu.NodesSort")&&(LiteGraph.ContextMenu.prototype.addItem=yi),document.getElementById("graph-canvas").addEventListener("mouseenter",(e=>{setTimeout((e=>{const t=document.getElementById("easyuse-model-thumbnail");t&&0!=t.style.opacity&&(t.style.display="none",t.style.opacity=0,t.style.left="0px",t.style.top="0px")}),100)}))},async beforeRegisterNodeDef(e,t,i){const s=e.prototype.onNodeCreated;["CheckpointLoader|pysssss","LoraLoader|pysssss"].includes(t.name)&&(e.prototype.onNodeCreated=async function(){s&&s.apply(this,[]);let e=ft(this,"lora_name")||ft(this,"ckpt_name");if(e){let t=e.onClick;e.onClick=function(e){return fi=!0,setTimeout((e=>{fi=!1}),500),t.call(this,e)}}})}});const bi=async()=>{["checkpoints","loras","diffusion_models"].map((async e=>{const t=await pe.getModels(e);(null==t?void 0:t.length)>0&&t.map((t=>{mi[t.name]={folder:e,pathIndex:t.pathIndex}}))}))},wi=(e,t,i)=>s=>{var a;const n=t=>{setTimeout((s=>{const a=i.getBoundingClientRect();if(!a)return;const n=document.body.getBoundingClientRect();if(!n)return;const{left:o,top:l}=((e,t,i)=>{const{x:s}=e.getBoundingClientRect();let{top:a,left:n}=t;const{width:o,height:l}=i;return s<=o&&(n+=t.width),t.top<=l||(a=l),{left:n,top:a}})(e,a,n),r=document.getElementById("easyuse-model-thumbnail");r.src=t,r.style.left=`${o}px`,r.style.top=`${l}px`,r.style.display="block",r.style.opacity=1,r.onerror=e=>{r.src=Me}}),10)};if(null==(a=null==mi?void 0:mi[t.fullName])?void 0:a.img){let e=mi[t.fullName].img;n("no_preview_image"==e?Me:e.src)}else{let e=new Image;e.src=t.thumbnail,e.onload=i=>{mi[t.fullName].img=e,n(t.thumbnail)},e.onerror=i=>{e=null,mi[t.fullName].img="no_preview_image",n(Me)}}},_i=()=>e=>{const t=document.getElementById("easyuse-model-thumbnail");t&&0!=t.style.opacity&&(t.style.display="none",t.style.opacity=0,t.style.left="0px",t.style.top="0px")};class Si extends me{constructor(){super(),this.element.classList.add("easyuse-model-metadata")}show(e){super.show(he("div",Object.keys(e).map((t=>he("div",[he("label",{textContent:t}),he("span",{textContent:e[t]})])))))}}class Li extends me{constructor(e){super(),this.name=e,this.element.classList.add("easyuse-model-info")}get customNotes(){return this.metadata["easyuse.notes"]}set customNotes(e){this.metadata["easyuse.notes"]=e}get hash(){return this.metadata["easyuse.sha256"]}async show(e,t){this.type=e;const i=pe.fetchApi("/easyuse/metadata/"+encodeURIComponent(`${e}/${t}`));this.info=he("div",{style:{flex:"auto"}}),this.imgCurrent=0,this.imgList=he("div.easyuse-preview-list",{style:{display:"none"}}),this.imgWrapper=he("div.easyuse-preview",[he("div.easyuse-preview-group",[this.imgList])]),this.main=he("main",{style:{display:"flex"}},[this.imgWrapper,this.info]),this.content=he("div.easyuse-model-content",[he("div.easyuse-model-header",[he("h2",{textContent:this.name})]),this.main]);const s=he("div",{textContent:"ℹ️ Loading...",parent:this.content});super.show(this.content),this.metadata=await(await i).json(),this.viewMetadata.style.cursor=this.viewMetadata.style.opacity="",this.viewMetadata.removeAttribute("disabled"),s.remove(),this.addInfo()}createButtons(){const e=super.createButtons();return this.viewMetadata=he("button",{type:"button",textContent:"View raw metadata",disabled:"disabled",style:{opacity:.5,cursor:"not-allowed"},onclick:e=>{this.metadata&&(new Si).show(this.metadata)}}),e.unshift(this.viewMetadata),e}parseNote(){if(!this.customNotes)return[];let e=[];const t=new RegExp("(\\bhttps?:\\/\\/[^\\s]+)","g");let i,s=0;do{let a;i=t.exec(this.customNotes);let n=0;i?(a=i.index,n=i.index+i[0].length):a=this.customNotes.length;let o=this.customNotes.substring(s,a);o&&(o=o.replaceAll("\n","
"),e.push(he("span",{innerHTML:o}))),i&&e.push(he("a",{href:i[0],textContent:i[0],target:"_blank"})),s=n}while(i);return e}addInfoEntry(e,t){return he("p",{parent:this.info},["string"==typeof e?he("label",{textContent:e+": "}):e,"string"==typeof t?he("span",{textContent:t}):t])}async getCivitaiDetails(){const e=await fetch("https://civitai.com/api/v1/model-versions/by-hash/"+this.hash);if(200===e.status)return await e.json();throw 404===e.status?new Error("Model not found"):new Error(`Error loading info (${e.status}) ${e.statusText}`)}addCivitaiInfo(){const e=this.getCivitaiDetails(),t=he("span",{textContent:"ℹ️ Loading..."});return this.addInfoEntry(he("label",[he("img",{style:{width:"18px",position:"relative",top:"3px",margin:"0 5px 0 0"},src:"https://civitai.com/favicon.ico"}),he("span",{textContent:"Civitai: "})]),t),e.then((e=>{var t,i;this.imgWrapper.style.display="block";let s=this.element.querySelector(".easyuse-model-header");s&&s.replaceChildren(he("h2",{textContent:this.name}),he("div.easyuse-model-header-remark",[he("h5",{textContent:Le("Updated At:")+Ue(new Date(e.updatedAt),"yyyy/MM/dd")}),he("h5",{textContent:Le("Created At:")+Ue(new Date(e.updatedAt),"yyyy/MM/dd")})]));let a=null,n=this.parseNote.call(this),o=Le("✏️ Edit"),l=he("div.easyuse-model-detail-textarea",[he("p",(null==n?void 0:n.length)>0?n:{textContent:Le("No notes")})]);if(n&&0!=n.length?l.classList.remove("empty"):l.classList.add("empty"),this.info.replaceChildren(he("div.easyuse-model-detail",[he("div.easyuse-model-detail-head.flex-b",[he("span",Le("Notes")),he("a",{textContent:o,href:"#",style:{fontSize:"12px",float:"right",color:"var(--warning-color)",textDecoration:"none"},onclick:async e=>{if(e.preventDefault(),a){if(a.value!=this.customNotes){Xe.showLoading(Le("Saving Notes...")),this.customNotes=a.value;const e=await pe.fetchApi("/easyuse/metadata/notes/"+encodeURIComponent(`${this.type}/${this.name}`),{method:"POST",body:this.customNotes});if(Xe.hideLoading(),200!==e.status)return Xe.error(Le("Saving Failed")),void alert(`Error saving notes (${e.status}) ${e.statusText}`);Xe.success(Le("Saving Succeed")),n=this.parseNote.call(this),l.replaceChildren(he("p",(null==n?void 0:n.length)>0?n:{textContent:Le("No notes")})),a.value?l.classList.remove("empty"):l.classList.add("empty")}else l.replaceChildren(he("p",{textContent:Le("No notes")})),l.classList.add("empty");e.target.textContent=o,a.remove(),a=null}else e.target.textContent="πŸ’Ύ Save",a=he("textarea",{placeholder:Le("Type your notes here"),style:{width:"100%",minWidth:"200px",minHeight:"50px",height:"100px"},textContent:this.customNotes}),l.replaceChildren(a),a.focus()}})]),l]),he("div.easyuse-model-detail",[he("div.easyuse-model-detail-head",{textContent:Le("Details")}),he("div.easyuse-model-detail-body",[he("div.easyuse-model-detail-item",[he("div.easyuse-model-detail-item-label",{textContent:Le("Type")}),he("div.easyuse-model-detail-item-value",{textContent:e.model.type})]),he("div.easyuse-model-detail-item",[he("div.easyuse-model-detail-item-label",{textContent:Le("BaseModel")}),he("div.easyuse-model-detail-item-value",{textContent:e.baseModel})]),he("div.easyuse-model-detail-item",[he("div.easyuse-model-detail-item-label",{textContent:Le("Download")}),he("div.easyuse-model-detail-item-value",{textContent:(null==(t=e.stats)?void 0:t.downloadCount)||0})]),he("div.easyuse-model-detail-item",[he("div.easyuse-model-detail-item-label",{textContent:Le("Trained Words")}),he("div.easyuse-model-detail-item-value",{textContent:(null==e?void 0:e.trainedWords.join(","))||"-"})]),he("div.easyuse-model-detail-item",[he("div.easyuse-model-detail-item-label",{textContent:Le("Source")}),he("div.easyuse-model-detail-item-value",[he("label",[he("img",{style:{width:"14px",position:"relative",top:"3px",margin:"0 5px 0 0"},src:"https://civitai.com/favicon.ico"}),he("a",{href:"https://civitai.com/models/"+e.modelId,textContent:"View "+e.model.name,target:"_blank"})])])])])])),null==(i=e.images)?void 0:i.length){this.imgCurrent=0,this.isSaving=!1,e.images.map((e=>e.url&&this.imgList.appendChild(he("div.easyuse-preview-slide",[he("div.easyuse-preview-slide-content",[he("img",{src:e.url}),he("div.save",{textContent:"Save as preview",onclick:async()=>{if(this.isSaving)return;this.isSaving=!0,Xe.showLoading(Le("Saving Preview..."));const t=await(await fetch(e.url)).blob(),i="temp_preview."+new URL(e.url).pathname.split(".")[1],s=new FormData;s.append("image",new File([t],i)),s.append("overwrite","true"),s.append("type","temp");if(200!==(await pe.fetchApi("/upload/image",{method:"POST",body:s})).status)return this.isSaving=!1,Xe.error(Le("Saving Failed")),Xe.hideLoading(),void alert(`Error saving preview (${req.status}) ${req.statusText}`);await pe.fetchApi("/easyuse/save/"+encodeURIComponent(`${this.type}/${this.name}`),{method:"POST",body:JSON.stringify({filename:i,type:"temp"}),headers:{"content-type":"application/json"}}).then((e=>{Xe.success(Le("Saving Succeed")),Xe.hideLoading()})),this.isSaving=!1,app.refreshComboInNodes()}})])]))));let t=this;this.imgDistance=(-660*this.imgCurrent).toString(),this.imgList.style.display="",this.imgList.style.transform="translate3d("+this.imgDistance+"px, 0px, 0px)",this.slides=this.imgList.querySelectorAll(".easyuse-preview-slide"),this.slideLeftButton=he("button.left",{parent:this.imgWrapper,style:{display:e.images.length<=2?"none":"block"},innerHTML:'',onclick:()=>{e.images.length<=2||(t.imgList.classList.remove("no-transition"),0==t.imgCurrent?(t.imgCurrent=e.images.length/2-1,this.slides[this.slides.length-1].style.transform="translate3d("+(-660*(this.imgCurrent+1)).toString()+"px, 0px, 0px)",this.slides[this.slides.length-2].style.transform="translate3d("+(-660*(this.imgCurrent+1)).toString()+"px, 0px, 0px)",t.imgList.style.transform="translate3d(660px, 0px, 0px)",setTimeout((e=>{this.slides[this.slides.length-1].style.transform="translate3d(0px, 0px, 0px)",this.slides[this.slides.length-2].style.transform="translate3d(0px, 0px, 0px)",t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)",t.imgList.classList.add("no-transition")}),500)):(t.imgCurrent=t.imgCurrent-1,t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)"))}}),this.slideRightButton=he("button.right",{parent:this.imgWrapper,style:{display:e.images.length<=2?"none":"block"},innerHTML:'',onclick:()=>{if(!(e.images.length<=2))if(t.imgList.classList.remove("no-transition"),t.imgCurrent>=e.images.length/2-1){t.imgCurrent=0;const i=e.images.length/2;this.slides[0].style.transform="translate3d("+(660*i).toString()+"px, 0px, 0px)",this.slides[1].style.transform="translate3d("+(660*i).toString()+"px, 0px, 0px)",t.imgList.style.transform="translate3d("+(-660*i).toString()+"px, 0px, 0px)",setTimeout((e=>{this.slides[0].style.transform="translate3d(0px, 0px, 0px)",this.slides[1].style.transform="translate3d(0px, 0px, 0px)",t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)",t.imgList.classList.add("no-transition")}),500)}else t.imgCurrent=t.imgCurrent+1,t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)"}})}return e.description&&he("div",{parent:this.content,innerHTML:e.description,style:{marginTop:"10px"}}),e})).catch((e=>{this.imgWrapper.style.display="none",t.textContent="⚠️ "+e.message})).finally((e=>{}))}}class xi extends Li{async addInfo(){await this.addCivitaiInfo()}}class ki extends Li{getTagFrequency(){if(!this.metadata.ss_tag_frequency)return[];const e=JSON.parse(this.metadata.ss_tag_frequency),t={};for(const i in e){const s=e[i];for(const e in s)e in t?t[e]+=s[e]:t[e]=s[e]}return Object.entries(t).sort(((e,t)=>t[1]-e[1]))}getResolutions(){let e=[];if(this.metadata.ss_bucket_info){const t=JSON.parse(this.metadata.ss_bucket_info);if(null==t?void 0:t.buckets)for(const{resolution:i,count:s}of Object.values(t.buckets))e.push([s,`${i.join("x")} * ${s}`])}e=e.sort(((e,t)=>t[0]-e[0])).map((e=>e[1]));let t=this.metadata.ss_resolution;if(t){const i=t.split(","),s=i[0].replace("(",""),a=i[1].replace(")","");e.push(`${s.trim()}x${a.trim()} (Base res)`)}else(t=this.metadata["modelspec.resolution"])&&e.push(t+" (Base res");return e.length||e.push("⚠️ Unknown"),e}getTagList(e){return e.map((e=>he("li.easyuse-model-tag",{dataset:{tag:e[0]},$:e=>{e.onclick=()=>{e.classList.toggle("easyuse-model-tag--selected")}}},[he("p",{textContent:e[0]}),he("span",{textContent:e[1]})])))}addTags(){let e,t=this.getTagFrequency();if(null==t?void 0:t.length){const i=t.length;let s;i>500&&(t=t.slice(0,500),e=he("p",[he("span",{textContent:"⚠️ Only showing first 500 tags "}),he("a",{href:"#",textContent:`Show all ${i}`,onclick:()=>{s.replaceChildren(...this.getTagList(this.getTagFrequency())),e.remove()}})])),s=he("ol.easyuse-model-tags-list",this.getTagList(t)),this.tags=he("div",[s])}else this.tags=he("p",{textContent:"⚠️ No tag frequency metadata found"});this.content.append(this.tags),e&&this.content.append(e)}async addInfo(){const e=this.addCivitaiInfo();this.addTags();const t=await e;t&&he("div",{parent:this.content,innerHTML:t.description,style:{maxHeight:"250px",overflow:"auto"}})}createButtons(){const e=super.createButtons();function t(e,t){const i=he("textarea",{parent:document.body,style:{position:"fixed"},textContent:t.map((e=>e.dataset.tag)).join(", ")});i.select();try{document.execCommand("copy"),e.target.dataset.text||(e.target.dataset.text=e.target.textContent),e.target.textContent="Copied "+t.length+" tags",setTimeout((()=>{e.target.textContent=e.target.dataset.text}),1e3)}catch(s){prompt("Copy to clipboard: Ctrl+C, Enter",text)}finally{document.body.removeChild(i)}}return e.unshift(he("button",{type:"button",textContent:"Copy Selected",onclick:e=>{t(e,[...this.tags.querySelectorAll(".easyuse-model-tag--selected")])}}),he("button",{type:"button",textContent:"Copy 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preSamplingAdvanced",...qi,...Ki]}},"easy preSamplingCascade":{from:{PIPE_LINE:["easy cascadeKSampler",...qi,...Ki]}},"easy cascadeKSampler":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced",...qi,...Ki]}},"easy pipeEdit":{from:{PIPE_LINE:[...Wi,...Xi,...Zi,...Ki]},to:{PIPE_LINE:[...Wi,...Xi,...Zi,...qi,...Ki]}},"easy pipeEditPrompt":{from:{PIPE_LINE:[...Wi,...Xi,...Zi,...Ki]},to:{PIPE_LINE:[...Wi,...Xi,...Zi,...qi,...Ki]}}};class Qi extends Error{constructor(e="Attempted to access LGraph reference that was null or undefined.",t){super(e,{cause:t}),this.name="NullGraphError"}}ce.registerExtension({name:"Comfy.EasyUse.Suggestions",async setup(e){const t=LGraphCanvas.prototype.createDefaultNodeForSlot;LGraphCanvas.prototype.createDefaultNodeForSlot=function(e){var i;const s=Object.assign({nodeFrom:null,slotFrom:null,nodeTo:null,slotTo:null,position:[],nodeType:null,posAdd:[0,0],posSizeFix:[0,0]},e||{}),a=this,n=s.nodeFrom&&null!==s.slotFrom,o=!n&&s.nodeTo&&null!==s.slotTo,l=n?s.nodeFrom:s.nodeTo;if(!l||!Object.keys(Bi).includes(l.type))return t.call(this,e);if(!n&&!o)return!1;if(!s.nodeType)return!1;const r=n?s.nodeFrom:s.nodeTo;if(!r)throw new TypeError("nodeX was null when creating default node for slot.");const d=r.type;let u=n?s.slotFrom:s.slotTo,A=!1;switch(typeof u){case"string":A=n?r.findOutputSlot(u,!1):r.findInputSlot(u,!1),u=n?r.outputs[u]:r.inputs[u];break;case"object":if(null===u)return!1;A=n?r.findOutputSlot(u.name):r.findInputSlot(u.name);break;case"number":A=u,u=n?r.outputs[u]:r.inputs[u];break;default:return!1}var c=u.type==LiteGraph.EVENT?"_event_":u.type,p=n?LiteGraph.slot_types_default_out:LiteGraph.slot_types_default_in;if(null==p?void 0:p[c]){let e=!1;const t=n?"from":"to";if(Bi[d]&&Bi[d][t]&&(null==(i=Bi[d][t][c])?void 0:i.length)>0){for(var h in Bi[d][t][c])if(s.nodeType==Bi[d][t][c][h]||"AUTO"==s.nodeType){e=Bi[d][t][c][h];break}}else if("object"==typeof p[c]||"array"==typeof p[c]){for(var h in p[c])if(s.nodeType==p[c][h]||"AUTO"==s.nodeType){e=p[c][h];break}}else s.nodeType!=p[c]&&"AUTO"!=s.nodeType||(e=p[c]);if(e){var m=!1;"object"==typeof e&&e.node&&(m=e,e=e.node);var f=LiteGraph.createNode(e);if(f){if(m){if(m.properties)for(var g in m.properties)f.addProperty(g,m.properties[g]);if(m.inputs)for(var g in f.inputs=[],m.inputs)f.addOutput(m.inputs[g][0],m.inputs[g][1]);if(m.outputs)for(var g in f.outputs=[],m.outputs)f.addOutput(m.outputs[g][0],m.outputs[g][1]);m.title&&(f.title=m.title),m.json&&f.configure(m.json)}a.graph.add(f),f.pos=[s.position[0]+s.posAdd[0]+(s.posSizeFix[0]?s.posSizeFix[0]*f.size[0]:0),s.position[1]+s.posAdd[1]+(s.posSizeFix[1]?s.posSizeFix[1]*f.size[1]:0)];const e={node:f,opts:s};if(!this.canvas.dispatchEvent(new CustomEvent("connect-new-default-node",{detail:e,cancelable:!0})))return!0;if(n){if(!s.nodeFrom)throw new TypeError("createDefaultNodeForSlot - nodeFrom was null");s.nodeFrom.connectByType(A,f,c)}else{if(!s.nodeTo)throw new TypeError("createDefaultNodeForSlot - nodeTo was null");s.nodeTo.connectByTypeOutput(A,f,c)}return!0}}}return!1};let i=LGraphCanvas.prototype.showConnectionMenu;LGraphCanvas.prototype.showConnectionMenu=function(e){var t;const s=Object.assign({nodeFrom:null,slotFrom:null,nodeTo:null,slotTo:null,e:void 0,allow_searchbox:this.allow_searchbox,showSearchBox:this.showSearchBox},e||{}),a=this,{graph:n}=this,{afterRerouteId:o}=s,l=s.nodeFrom&&s.slotFrom,r=!l&&s.nodeTo&&s.slotTo,d=l?s.nodeFrom:s.nodeTo;if(!d||!Object.keys(Bi).includes(d.type))return i.call(this,e);if(!l&&!r)return!1;const u=l?s.nodeFrom:s.nodeTo;if(!u)throw new TypeError("nodeX was null when creating default node for slot.");let A=l?s.slotFrom:s.slotTo,c=!1;switch(typeof A){case"string":c=l?u.findOutputSlot(A,!1):u.findInputSlot(A,!1),A=l?u.outputs[A]:u.inputs[A];break;case"object":if(null===A)return;c=l?u.findOutputSlot(A.name):u.findInputSlot(A.name);break;case"number":c=A,A=l?u.outputs[A]:u.inputs[A];break;default:return!1}const p=["Add Node","Add Reroute",null];s.allow_searchbox&&p.push("Search",null);var h=A.type==LiteGraph.EVENT?"_event_":A.type,m=l?LiteGraph.slot_types_default_out:LiteGraph.slot_types_default_in,f=u.type;if(m&&m[h]){const e=l?"from":"to";if(Bi[f]&&Bi[f][e]&&(null==(t=Bi[f][e][h])?void 0:t.length)>0)for(var g in Bi[f][e][h])p.push(Bi[f][e][h][g]);else if("object"==typeof m[h]||"array"==typeof m[h])for(var g in m[h])p.push(m[h][g]);else p.push(m[h])}var v=new LiteGraph.ContextMenu(p,{event:s.e,extra:A,title:(A&&""!=A.name?A.name+(h?" | ":""):"")+(A&&h?h:""),callback:function(e,t,i){var r,d;switch(e){case"Add Node":LGraphCanvas.onMenuAdd(null,null,i,v,(function(e){var t,i;e&&(l?null==(t=s.nodeFrom)||t.connectByType(c,e,h,{afterRerouteId:o}):null==(i=s.nodeTo)||i.connectByTypeOutput(c,e,h,{afterRerouteId:o}))}));break;case"Add Reroute":const u=l?s.nodeFrom:s.nodeTo,p=t.extra;if(!n)throw new Qi;if(!u)throw new TypeError("Cannot add reroute: node was null");if(!p)throw new TypeError("Cannot add reroute: slot was null");if(!s.e)throw new TypeError("Cannot add reroute: CanvasPointerEvent was null");if(!u.connectFloatingReroute([s.e.canvasX,s.e.canvasY],p,o))throw new Error("Failed to create reroute");a.dirty_canvas=!0,a.dirty_bgcanvas=!0;break;case"Search":l?s.showSearchBox(i,{node_from:s.nodeFrom,slot_from:A,type_filter_in:h}):s.showSearchBox(i,{node_to:s.nodeTo,slot_from:A,type_filter_out:h});break;default:const m={position:[(null==(r=s.e)?void 0:r.canvasX)??0,(null==(d=s.e)?void 0:d.canvasY)??0],nodeType:e,afterRerouteId:o};a.createDefaultNodeForSlot(Object.assign(s,m))}}});return v}}}),ce.registerExtension({name:"Comfy.EasyUse.TimeTaken",setup(){let e=new Map,t=0;pe.addEventListener("execution_start",(e=>{graph&&graph._nodes.forEach((e=>{e.executionDuration&&delete e.executionDuration}))})),pe.addEventListener("executing",(i=>{if(!be("EasyUse.Nodes.Runtime",null))return;const s=(null==i?void 0:i.node)||(null==i?void 0:i.detail)||null,a=e.get(t);if(e.delete(t),t&&a){const e=Date.now()-a,i=bt(t);i&&(i.executionDuration||(i.executionDuration=0),i.executionDuration=i.executionDuration+e/1e3)}t=s,e.set(s,Date.now())}))},beforeRegisterNodeDef(e,t){const i=e.prototype.onDrawForeground;e.prototype.onDrawForeground=function(...e){const[t]=e;return function(e,t){if(!t)return;t=parseFloat(t).toFixed(3)+Le("s"),e.save(),e.fillStyle=LiteGraph.NODE_DEFAULT_BGCOLOR,function(e,t,i,s,a,n){e.beginPath(),e.moveTo(t+n,i),e.lineTo(t+s-n,i),e.arcTo(t+s,i,t+s,i+n,n),e.lineTo(t+s,i+a-n),e.arcTo(t+s,i+a,t+s-n,i+a,n),e.lineTo(t+n,i+a),e.arcTo(t,i+a,t,i+a-n,n),e.lineTo(t,i+n),e.arcTo(t,i,t+n,i,n),e.closePath()}(e,0,-LiteGraph.NODE_TITLE_HEIGHT-20,e.measureText(t).width+10,LiteGraph.NODE_TITLE_HEIGHT-10,4),e.fill(),function(e,t,i,s,a="#000",n=12,o="Inter"){e.font=`${n}px ${o}`,e.fillStyle=a,e.fillText(t,i,s)}(e,t,8,-LiteGraph.NODE_TITLE_HEIGHT-6,LiteGraph.NODE_TITLE_COLOR),e.restore()}(t,this.executionDuration),null==i?void 0:i.apply(this,e)}}});let $i=null;ce.registerExtension({name:"Comfy.EasyUse.HotKeys",setup(){if(void 0!==L){L("up,down,left,right",(function(e,t){var i,s,a,n,o,l,r,d,u,A,c,p,h,m,f;e.preventDefault();if(!be("EasyUse.Hotkeys.JumpNearestNodes",null))return;const g=wt();if(0===g.length)return;const v=g[0];switch(t.key){case"up":case"left":let e=null;if(Et(v)){const e=null==(i=v.widgets_values)?void 0:i[0],t=null==(s=v.graph)?void 0:s._nodes,a=null==t?void 0:t.find((t=>{var i;if(Nt(t)){if((null==(i=t.widgets_values)?void 0:i[0])===e)return t}return null}));a&&St(a)}else if((null==(a=v.inputs)?void 0:a.length)>0){for(let t=0;t{var i;if(Et(t)){if((null==(i=t.widgets_values)?void 0:i[0])===e)return t}return null}));i&&St(i)}else if((null==(A=v.outputs)?void 0:A.length)>0){for(let e=0;e0&&v.outputs[e].links[0]){t=v.outputs[e].links[0];break}if(t){const e=null==(p=v.graph)?void 0:p.links;if(e[t]){const i=null==(h=e[t])?void 0:h.target_id,s=null==(f=null==(m=v.graph)?void 0:m._nodes_by_id)?void 0:f[i];s&&St(s)}}}}})),L("shift+up,shift+down,shift+left,shift+right,shift+alt+⌘+left,shift+alt+⌘+right,shift+alt+ctrl+left,shift+alt+ctrl+right",(function(e,t){e.preventDefault();if(!be("EasyUse.Hotkeys.AlignSelectedNodes",null))return;const i=wt();if(i.length<=1)return;const 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s=i;switch(t.key){case"shift+ctrl+left":case"shift+⌘+left":zt(s,"width");break;case"shift+ctrl+right":case"shift+⌘+right":zt(s,"height")}$i||($i=Ze()),$i&&$i.update()})),L("shift+g",(function(e,t){e.preventDefault();be("EasyUse.Hotkeys.AddGroup",null)&&(ts(),$i||($i=Ze()),$i&&$i.update())})),L("shift+r",(function(e,t){e.preventDefault();be("EasyUse.Hotkeys.cleanVRAMused",null)&&hi()})),L("shift+m",(function(e,t){var i,s,a;if(!be("EasyUse.Hotkeys.toggleNodesMap",null))return;let n=(null==(i=ce.extensionManager)?void 0:i.sidebarTab)||ce.extensionManager,o=(null==(s=ce.extensionManager.sidebarTab)?void 0:s.activeSidebarTabId)||(null==(a=ce.extensionManager)?void 0:a.activeSidebarTab);n.activeSidebarTabId=o==Oe?null:Oe}));const e=[];Array.from(Array(10).keys()).forEach((t=>e.push(`alt+${t}`))),L(e.join(","),(async function(e,t){e.preventDefault();if(!be("EasyUse.Hotkeys.NodesTemplate",null))return;const i=t.key;let s=parseInt(i.split("+")[1]);const a=await pe.getUserData("comfy.templates.json");let n=null;if(200==a.status)try{n=await a.json()}catch(l){Xe.error(Le("Get Node Templates File Failed"))}else localStorage["Comfy.NodeTemplates"]?n=JSON.parse(localStorage["Comfy.NodeTemplates"]):Xe.warn(Le("No Node Templates Found"));if(!n)return void Xe.warn(Le("No Node Templates Found"));s=0===s?9:s-1;const o=n[s];if(o)try{const e=(null==o?void 0:o.name)||"Group",t=(null==o?void 0:o.data)?JSON.parse(o.data):[];es((async()=>{await ve.registerFromWorkflow(t.groupNodes,{}),localStorage.litegrapheditor_clipboard=o.data,ce.canvas.pasteFromClipboard(),t.groupNodes||ts(e)}))}catch(l){Xe.error(l)}else Xe.warn(Le("Node template with {key} not set").replace("{key}",i))}));const t=async function(e){if(("b"===e.key||"m"==e.key)&&(e.metaKey||e.ctrlKey)){if(0===wt().length)return;$i||($i=Ze()),$i&&$i.update()}};window.addEventListener("keydown",t,!0)}}});const es=async e=>{const t=localStorage.litegrapheditor_clipboard;await 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0:a.length)||0)};(this.lazyLoadState.first!==c.first||this.lazyLoadState.last!==c.last)&&this.$emit("lazy-load",c),this.lazyLoadState=c}}},onScroll:function(t){var e=this;if(this.$emit("scroll",t),this.delay){if(this.scrollTimeout&&clearTimeout(this.scrollTimeout),this.isPageChanged()){if(!this.d_loading&&this.showLoader)(this.onScrollPositionChange(t).isRangeChanged||!!this.step&&this.isPageChanged())&&(this.d_loading=!0);this.scrollTimeout=setTimeout((function(){e.onScrollChange(t),!e.d_loading||!e.showLoader||e.lazy&&void 0!==e.loading||(e.d_loading=!1,e.page=e.getPageByFirst())}),this.delay)}}else this.onScrollChange(t)},onResize:function(){var t=this;this.resizeTimeout&&clearTimeout(this.resizeTimeout),this.resizeTimeout=setTimeout((function(){if(Z(t.element)){var e=t.isBoth(),n=t.isVertical(),o=t.isHorizontal(),i=[R(t.element),j(t.element)],r=i[0],s=i[1],l=r!==t.defaultWidth,a=s!==t.defaultHeight;(e?l||a:o?l:!!n&&a)&&(t.d_numToleratedItems=t.numToleratedItems,t.defaultWidth=r,t.defaultHeight=s,t.defaultContentWidth=R(t.content),t.defaultContentHeight=j(t.content),t.init())}}),this.resizeDelay)},bindResizeListener:function(){this.resizeListener||(this.resizeListener=this.onResize.bind(this),window.addEventListener("resize",this.resizeListener),window.addEventListener("orientationchange",this.resizeListener))},unbindResizeListener:function(){this.resizeListener&&(window.removeEventListener("resize",this.resizeListener),window.removeEventListener("orientationchange",this.resizeListener),this.resizeListener=null)},getOptions:function(t){var e=(this.items||[]).length,n=this.isBoth()?this.first.rows+t:this.first+t;return{index:n,count:e,first:0===n,last:n===e-1,even:n%2==0,odd:n%2!=0}},getLoaderOptions:function(t,e){var n=this.loaderArr.length;return tn({index:t,count:n,first:0===t,last:t===n-1,even:t%2==0,odd:t%2!=0},e)},getPageByFirst:function(t){return Math.floor(((null!=t?t:this.first)+4*this.d_numToleratedItems)/(this.step||1))},isPageChanged:function(t){return!(this.step&&!this.lazy)||this.page!==this.getPageByFirst(null!=t?t:this.first)},setContentEl:function(t){this.content=t||this.content||J(this.element,'[data-pc-section="content"]')},elementRef:function(t){this.element=t},contentRef:function(t){this.content=t}},computed:{containerClass:function(){return["p-virtualscroller",this.class,{"p-virtualscroller-inline":this.inline,"p-virtualscroller-both p-both-scroll":this.isBoth(),"p-virtualscroller-horizontal p-horizontal-scroll":this.isHorizontal()}]},contentClass:function(){return["p-virtualscroller-content",{"p-virtualscroller-loading":this.d_loading}]},loaderClass:function(){return["p-virtualscroller-loader",{"p-virtualscroller-loader-mask":!this.$slots.loader}]},loadedItems:function(){var t=this;return this.items&&!this.d_loading?this.isBoth()?this.items.slice(this.appendOnly?0:this.first.rows,this.last.rows).map((function(e){return t.columns?e:e.slice(t.appendOnly?0:t.first.cols,t.last.cols)})):this.isHorizontal()&&this.columns?this.items:this.items.slice(this.appendOnly?0:this.first,this.last):[]},loadedRows:function(){return this.d_loading?this.loaderDisabled?this.loaderArr:[]:this.loadedItems},loadedColumns:function(){if(this.columns){var t=this.isBoth(),e=this.isHorizontal();if(t||e)return this.d_loading&&this.loaderDisabled?t?this.loaderArr[0]:this.loaderArr:this.columns.slice(t?this.first.cols:this.first,t?this.last.cols:this.last)}return this.columns}},components:{SpinnerIcon:Ct}},on=["tabindex"];nn.render=function(t,e,n,o,i,r){var s=k("SpinnerIcon");return t.disabled?(m(),g(_,{key:1},[v(t.$slots,"default"),v(t.$slots,"content",{items:t.items,rows:t.items,columns:r.loadedColumns})],64)):(m(),g("div",I({key:0,ref:r.elementRef,class:r.containerClass,tabindex:t.tabindex,style:t.style,onScroll:e[0]||(e[0]=function(){return r.onScroll&&r.onScroll.apply(r,arguments)})},t.ptmi("root")),[v(t.$slots,"content",{styleClass:r.contentClass,items:r.loadedItems,getItemOptions:r.getOptions,loading:i.d_loading,getLoaderOptions:r.getLoaderOptions,itemSize:t.itemSize,rows:r.loadedRows,columns:r.loadedColumns,contentRef:r.contentRef,spacerStyle:i.spacerStyle,contentStyle:i.contentStyle,vertical:r.isVertical(),horizontal:r.isHorizontal(),both:r.isBoth()},(function(){return[C("div",I({ref:r.contentRef,class:r.contentClass,style:i.contentStyle},t.ptm("content")),[(m(!0),g(_,null,M(r.loadedItems,(function(e,n){return v(t.$slots,"item",{key:n,item:e,options:r.getOptions(n)})})),128))],16)]})),t.showSpacer?(m(),g("div",I({key:0,class:"p-virtualscroller-spacer",style:i.spacerStyle},t.ptm("spacer")),null,16)):$("",!0),!t.loaderDisabled&&t.showLoader&&i.d_loading?(m(),g("div",I({key:1,class:r.loaderClass},t.ptm("loader")),[t.$slots&&t.$slots.loader?(m(!0),g(_,{key:0},M(i.loaderArr,(function(e,n){return v(t.$slots,"loader",{key:n,options:r.getLoaderOptions(n,r.isBoth()&&{numCols:t.d_numItemsInViewport.cols})})})),128)):$("",!0),v(t.$slots,"loadingicon",{},(function(){return[P(s,I({spin:"",class:"p-virtualscroller-loading-icon"},t.ptm("loadingIcon")),null,16)]}))],16)):$("",!0)],16,on))};var rn=St.extend({name:"select",theme:function(t){var e=t.dt;return"\n.p-select {\n display: inline-flex;\n cursor: pointer;\n position: relative;\n user-select: none;\n background: ".concat(e("select.background"),";\n border: 1px solid ").concat(e("select.border.color"),";\n transition: background ").concat(e("select.transition.duration"),", color ").concat(e("select.transition.duration"),", border-color ").concat(e("select.transition.duration"),",\n outline-color ").concat(e("select.transition.duration"),", box-shadow ").concat(e("select.transition.duration"),";\n border-radius: ").concat(e("select.border.radius"),";\n outline-color: transparent;\n box-shadow: ").concat(e("select.shadow"),";\n}\n\n.p-select:not(.p-disabled):hover {\n border-color: ").concat(e("select.hover.border.color"),";\n}\n\n.p-select:not(.p-disabled).p-focus {\n border-color: ").concat(e("select.focus.border.color"),";\n box-shadow: ").concat(e("select.focus.ring.shadow"),";\n outline: ").concat(e("select.focus.ring.width")," ").concat(e("select.focus.ring.style")," ").concat(e("select.focus.ring.color"),";\n outline-offset: ").concat(e("select.focus.ring.offset"),";\n}\n\n.p-select.p-variant-filled {\n background: ").concat(e("select.filled.background"),";\n}\n\n.p-select.p-variant-filled:not(.p-disabled):hover {\n background: ").concat(e("select.filled.hover.background"),";\n}\n\n.p-select.p-variant-filled:not(.p-disabled).p-focus {\n background: ").concat(e("select.filled.focus.background"),";\n}\n\n.p-select.p-invalid {\n border-color: ").concat(e("select.invalid.border.color"),";\n}\n\n.p-select.p-disabled {\n opacity: 1;\n background: ").concat(e("select.disabled.background"),";\n}\n\n.p-select-clear-icon {\n position: absolute;\n top: 50%;\n margin-top: -0.5rem;\n color: ").concat(e("select.clear.icon.color"),";\n inset-inline-end: ").concat(e("select.dropdown.width"),";\n}\n\n.p-select-dropdown {\n display: flex;\n align-items: center;\n justify-content: center;\n flex-shrink: 0;\n background: transparent;\n color: ").concat(e("select.dropdown.color"),";\n width: ").concat(e("select.dropdown.width"),";\n border-start-end-radius: ").concat(e("select.border.radius"),";\n border-end-end-radius: ").concat(e("select.border.radius"),";\n}\n\n.p-select-label {\n display: block;\n white-space: nowrap;\n overflow: hidden;\n flex: 1 1 auto;\n width: 1%;\n padding: ").concat(e("select.padding.y")," ").concat(e("select.padding.x"),";\n text-overflow: ellipsis;\n cursor: pointer;\n color: ").concat(e("select.color"),";\n background: transparent;\n border: 0 none;\n outline: 0 none;\n}\n\n.p-select-label.p-placeholder {\n color: ").concat(e("select.placeholder.color"),";\n}\n\n.p-select.p-invalid .p-select-label.p-placeholder {\n color: ").concat(e("select.invalid.placeholder.color"),";\n}\n\n.p-select:has(.p-select-clear-icon) .p-select-label {\n padding-inline-end: calc(1rem + ").concat(e("select.padding.x"),");\n}\n\n.p-select.p-disabled .p-select-label {\n color: ").concat(e("select.disabled.color"),";\n}\n\n.p-select-label-empty {\n overflow: hidden;\n opacity: 0;\n}\n\ninput.p-select-label {\n cursor: default;\n}\n\n.p-select .p-select-overlay {\n min-width: 100%;\n}\n\n.p-select-overlay {\n position: absolute;\n top: 0;\n left: 0;\n background: ").concat(e("select.overlay.background"),";\n color: ").concat(e("select.overlay.color"),";\n border: 1px solid ").concat(e("select.overlay.border.color"),";\n border-radius: ").concat(e("select.overlay.border.radius"),";\n box-shadow: ").concat(e("select.overlay.shadow"),";\n}\n\n.p-select-header {\n padding: ").concat(e("select.list.header.padding"),";\n}\n\n.p-select-filter {\n width: 100%;\n}\n\n.p-select-list-container {\n overflow: auto;\n}\n\n.p-select-option-group {\n cursor: auto;\n margin: 0;\n padding: ").concat(e("select.option.group.padding"),";\n background: ").concat(e("select.option.group.background"),";\n color: ").concat(e("select.option.group.color"),";\n font-weight: ").concat(e("select.option.group.font.weight"),";\n}\n\n.p-select-list {\n margin: 0;\n padding: 0;\n list-style-type: none;\n padding: ").concat(e("select.list.padding"),";\n gap: ").concat(e("select.list.gap"),";\n display: flex;\n flex-direction: column;\n}\n\n.p-select-option {\n cursor: pointer;\n font-weight: normal;\n white-space: nowrap;\n position: relative;\n overflow: hidden;\n display: flex;\n align-items: center;\n padding: ").concat(e("select.option.padding"),";\n border: 0 none;\n color: ").concat(e("select.option.color"),";\n background: transparent;\n transition: background ").concat(e("select.transition.duration"),", color ").concat(e("select.transition.duration"),", border-color ").concat(e("select.transition.duration"),",\n box-shadow ").concat(e("select.transition.duration"),", outline-color ").concat(e("select.transition.duration"),";\n border-radius: ").concat(e("select.option.border.radius"),";\n}\n\n.p-select-option:not(.p-select-option-selected):not(.p-disabled).p-focus {\n background: ").concat(e("select.option.focus.background"),";\n color: ").concat(e("select.option.focus.color"),";\n}\n\n.p-select-option.p-select-option-selected {\n background: ").concat(e("select.option.selected.background"),";\n color: ").concat(e("select.option.selected.color"),";\n}\n\n.p-select-option.p-select-option-selected.p-focus {\n background: ").concat(e("select.option.selected.focus.background"),";\n color: ").concat(e("select.option.selected.focus.color"),";\n}\n\n.p-select-option-check-icon {\n position: relative;\n margin-inline-start: ").concat(e("select.checkmark.gutter.start"),";\n margin-inline-end: ").concat(e("select.checkmark.gutter.end"),";\n color: ").concat(e("select.checkmark.color"),";\n}\n\n.p-select-empty-message {\n padding: ").concat(e("select.empty.message.padding"),";\n}\n\n.p-select-fluid {\n display: flex;\n width: 100%;\n}\n\n.p-select-sm .p-select-label {\n font-size: ").concat(e("select.sm.font.size"),";\n padding-block: ").concat(e("select.sm.padding.y"),";\n padding-inline: ").concat(e("select.sm.padding.x"),";\n}\n\n.p-select-sm .p-select-dropdown .p-icon {\n font-size: ").concat(e("select.sm.font.size"),";\n width: ").concat(e("select.sm.font.size"),";\n height: ").concat(e("select.sm.font.size"),";\n}\n\n.p-select-lg .p-select-label {\n font-size: ").concat(e("select.lg.font.size"),";\n padding-block: ").concat(e("select.lg.padding.y"),";\n padding-inline: ").concat(e("select.lg.padding.x"),";\n}\n\n.p-select-lg .p-select-dropdown .p-icon {\n font-size: ").concat(e("select.lg.font.size"),";\n width: ").concat(e("select.lg.font.size"),";\n height: ").concat(e("select.lg.font.size"),";\n}\n")},classes:{root:function(t){var e=t.instance,n=t.props,o=t.state;return["p-select p-component p-inputwrapper",{"p-disabled":n.disabled,"p-invalid":e.$invalid,"p-variant-filled":"filled"===e.$variant,"p-focus":o.focused,"p-inputwrapper-filled":e.$filled,"p-inputwrapper-focus":o.focused||o.overlayVisible,"p-select-open":o.overlayVisible,"p-select-fluid":e.$fluid,"p-select-sm p-inputfield-sm":"small"===n.size,"p-select-lg p-inputfield-lg":"large"===n.size}]},label:function(t){var e=t.instance,n=t.props;return["p-select-label",{"p-placeholder":!n.editable&&e.label===n.placeholder,"p-select-label-empty":!(n.editable||e.$slots.value||"p-emptylabel"!==e.label&&0!==e.label.length)}]},clearIcon:"p-select-clear-icon",dropdown:"p-select-dropdown",loadingicon:"p-select-loading-icon",dropdownIcon:"p-select-dropdown-icon",overlay:"p-select-overlay p-component",header:"p-select-header",pcFilter:"p-select-filter",listContainer:"p-select-list-container",list:"p-select-list",optionGroup:"p-select-option-group",optionGroupLabel:"p-select-option-group-label",option:function(t){var e=t.instance,n=t.props,o=t.state,i=t.option,r=t.focusedOption;return["p-select-option",{"p-select-option-selected":e.isSelected(i)&&n.highlightOnSelect,"p-focus":o.focusedOptionIndex===r,"p-disabled":e.isOptionDisabled(i)}]},optionLabel:"p-select-option-label",optionCheckIcon:"p-select-option-check-icon",optionBlankIcon:"p-select-option-blank-icon",emptyMessage:"p-select-empty-message"}});function sn(t){return(sn="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function ln(t){return function(t){if(Array.isArray(t))return an(t)}(t)||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(t)||function(t,e){if(t){if("string"==typeof t)return an(t,e);var n={}.toString.call(t).slice(8,-1);return"Object"===n&&t.constructor&&(n=t.constructor.name),"Map"===n||"Set"===n?Array.from(t):"Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n)?an(t,e):void 0}}(t)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function an(t,e){(null==e||e>t.length)&&(e=t.length);for(var n=0,o=Array(e);n2&&void 0!==arguments[2])||arguments[2],o=this.getOptionValue(e);this.updateModel(t,o),n&&this.hide(!0)},onOptionMouseMove:function(t,e){this.focusOnHover&&this.changeFocusedOptionIndex(t,e)},onFilterChange:function(t){var e=t.target.value;this.filterValue=e,this.focusedOptionIndex=-1,this.$emit("filter",{originalEvent:t,value:e}),!this.virtualScrollerDisabled&&this.virtualScroller.scrollToIndex(0)},onFilterKeyDown:function(t){if(!t.isComposing)switch(t.code){case"ArrowDown":this.onArrowDownKey(t);break;case"ArrowUp":this.onArrowUpKey(t,!0);break;case"ArrowLeft":case"ArrowRight":this.onArrowLeftKey(t,!0);break;case"Home":this.onHomeKey(t,!0);break;case"End":this.onEndKey(t,!0);break;case"Enter":case"NumpadEnter":this.onEnterKey(t);break;case"Escape":this.onEscapeKey(t);break;case"Tab":this.onTabKey(t,!0)}},onFilterBlur:function(){this.focusedOptionIndex=-1},onFilterUpdated:function(){this.overlayVisible&&this.alignOverlay()},onOverlayClick:function(t){Xe.emit("overlay-click",{originalEvent:t,target:this.$el})},onOverlayKeyDown:function(t){if("Escape"===t.code)this.onEscapeKey(t)},onArrowDownKey:function(t){if(this.overlayVisible){var e=-1!==this.focusedOptionIndex?this.findNextOptionIndex(this.focusedOptionIndex):this.clicked?this.findFirstOptionIndex():this.findFirstFocusedOptionIndex();this.changeFocusedOptionIndex(t,e)}else this.show(),this.editable&&this.changeFocusedOptionIndex(t,this.findSelectedOptionIndex());t.preventDefault()},onArrowUpKey:function(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(t.altKey&&!e)-1!==this.focusedOptionIndex&&this.onOptionSelect(t,this.visibleOptions[this.focusedOptionIndex]),this.overlayVisible&&this.hide(),t.preventDefault();else{var n=-1!==this.focusedOptionIndex?this.findPrevOptionIndex(this.focusedOptionIndex):this.clicked?this.findLastOptionIndex():this.findLastFocusedOptionIndex();this.changeFocusedOptionIndex(t,n),!this.overlayVisible&&this.show(),t.preventDefault()}},onArrowLeftKey:function(t){arguments.length>1&&void 0!==arguments[1]&&arguments[1]&&(this.focusedOptionIndex=-1)},onHomeKey:function(t){if(arguments.length>1&&void 0!==arguments[1]&&arguments[1]){var e=t.currentTarget;t.shiftKey?e.setSelectionRange(0,t.target.selectionStart):(e.setSelectionRange(0,0),this.focusedOptionIndex=-1)}else this.changeFocusedOptionIndex(t,this.findFirstOptionIndex()),!this.overlayVisible&&this.show();t.preventDefault()},onEndKey:function(t){if(arguments.length>1&&void 0!==arguments[1]&&arguments[1]){var e=t.currentTarget;if(t.shiftKey)e.setSelectionRange(t.target.selectionStart,e.value.length);else{var n=e.value.length;e.setSelectionRange(n,n),this.focusedOptionIndex=-1}}else this.changeFocusedOptionIndex(t,this.findLastOptionIndex()),!this.overlayVisible&&this.show();t.preventDefault()},onPageUpKey:function(t){this.scrollInView(0),t.preventDefault()},onPageDownKey:function(t){this.scrollInView(this.visibleOptions.length-1),t.preventDefault()},onEnterKey:function(t){this.overlayVisible?(-1!==this.focusedOptionIndex&&this.onOptionSelect(t,this.visibleOptions[this.focusedOptionIndex]),this.hide()):(this.focusedOptionIndex=-1,this.onArrowDownKey(t)),t.preventDefault()},onSpaceKey:function(t){!(arguments.length>1&&void 0!==arguments[1]&&arguments[1])&&this.onEnterKey(t)},onEscapeKey:function(t){this.overlayVisible&&this.hide(!0),t.preventDefault(),t.stopPropagation()},onTabKey:function(t){arguments.length>1&&void 0!==arguments[1]&&arguments[1]||(this.overlayVisible&&this.hasFocusableElements()?(et(this.$refs.firstHiddenFocusableElementOnOverlay),t.preventDefault()):(-1!==this.focusedOptionIndex&&this.onOptionSelect(t,this.visibleOptions[this.focusedOptionIndex]),this.overlayVisible&&this.hide(this.filter)))},onBackspaceKey:function(t){arguments.length>1&&void 0!==arguments[1]&&arguments[1]&&!this.overlayVisible&&this.show()},onOverlayEnter:function(t){var e=this;Q.set("overlay",t,this.$primevue.config.zIndex.overlay),st(t,{position:"absolute",top:"0",left:"0"}),this.alignOverlay(),this.scrollInView(),setTimeout((function(){e.autoFilterFocus&&e.filter&&et(e.$refs.filterInput.$el)}),1)},onOverlayAfterEnter:function(){this.bindOutsideClickListener(),this.bindScrollListener(),this.bindResizeListener(),this.$emit("show")},onOverlayLeave:function(){var t=this;this.unbindOutsideClickListener(),this.unbindScrollListener(),this.unbindResizeListener(),this.autoFilterFocus&&this.filter&&!this.editable&&this.$nextTick((function(){et(t.$refs.filterInput.$el)})),this.$emit("hide"),this.overlay=null},onOverlayAfterLeave:function(t){Q.clear(t)},alignOverlay:function(){"self"===this.appendTo?lt(this.overlay,this.$el):(this.overlay.style.minWidth=G(this.$el)+"px",at(this.overlay,this.$el))},bindOutsideClickListener:function(){var t=this;this.outsideClickListener||(this.outsideClickListener=function(e){t.overlayVisible&&t.overlay&&!t.$el.contains(e.target)&&!t.overlay.contains(e.target)&&t.hide()},document.addEventListener("click",this.outsideClickListener))},unbindOutsideClickListener:function(){this.outsideClickListener&&(document.removeEventListener("click",this.outsideClickListener),this.outsideClickListener=null)},bindScrollListener:function(){var t=this;this.scrollHandler||(this.scrollHandler=new 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").concat(e("checkbox.checked.hover.background"),";\n border-color: ").concat(e("checkbox.checked.hover.border.color"),";\n}\n\n.p-checkbox-checked:not(.p-disabled):has(.p-checkbox-input:hover) .p-checkbox-icon {\n color: ").concat(e("checkbox.icon.checked.hover.color"),";\n}\n\n.p-checkbox:not(.p-disabled):has(.p-checkbox-input:focus-visible) .p-checkbox-box {\n border-color: ").concat(e("checkbox.focus.border.color"),";\n box-shadow: ").concat(e("checkbox.focus.ring.shadow"),";\n outline: ").concat(e("checkbox.focus.ring.width")," ").concat(e("checkbox.focus.ring.style")," ").concat(e("checkbox.focus.ring.color"),";\n outline-offset: ").concat(e("checkbox.focus.ring.offset"),";\n}\n\n.p-checkbox-checked:not(.p-disabled):has(.p-checkbox-input:focus-visible) .p-checkbox-box {\n border-color: ").concat(e("checkbox.checked.focus.border.color"),";\n}\n\n.p-checkbox.p-invalid > .p-checkbox-box {\n border-color: ").concat(e("checkbox.invalid.border.color"),";\n}\n\n.p-checkbox.p-variant-filled .p-checkbox-box {\n background: ").concat(e("checkbox.filled.background"),";\n}\n\n.p-checkbox-checked.p-variant-filled .p-checkbox-box {\n background: ").concat(e("checkbox.checked.background"),";\n}\n\n.p-checkbox-checked.p-variant-filled:not(.p-disabled):has(.p-checkbox-input:hover) .p-checkbox-box {\n background: ").concat(e("checkbox.checked.hover.background"),";\n}\n\n.p-checkbox.p-disabled {\n opacity: 1;\n}\n\n.p-checkbox.p-disabled .p-checkbox-box {\n background: ").concat(e("checkbox.disabled.background"),";\n border-color: ").concat(e("checkbox.checked.disabled.border.color"),";\n}\n\n.p-checkbox.p-disabled .p-checkbox-box .p-checkbox-icon {\n color: ").concat(e("checkbox.icon.disabled.color"),";\n}\n\n.p-checkbox-sm,\n.p-checkbox-sm .p-checkbox-box {\n width: ").concat(e("checkbox.sm.width"),";\n height: ").concat(e("checkbox.sm.height"),";\n}\n\n.p-checkbox-sm .p-checkbox-icon {\n font-size: ").concat(e("checkbox.icon.sm.size"),";\n width: ").concat(e("checkbox.icon.sm.size"),";\n height: ").concat(e("checkbox.icon.sm.size"),";\n}\n\n.p-checkbox-lg,\n.p-checkbox-lg .p-checkbox-box {\n width: ").concat(e("checkbox.lg.width"),";\n height: ").concat(e("checkbox.lg.height"),";\n}\n\n.p-checkbox-lg .p-checkbox-icon {\n font-size: ").concat(e("checkbox.icon.lg.size"),";\n width: ").concat(e("checkbox.icon.lg.size"),";\n height: ").concat(e("checkbox.icon.lg.size"),";\n}\n")},classes:{root:function(t){var e=t.instance,n=t.props;return["p-checkbox p-component",{"p-checkbox-checked":e.checked,"p-disabled":n.disabled,"p-invalid":e.$pcCheckboxGroup?e.$pcCheckboxGroup.$invalid:e.$invalid,"p-variant-filled":"filled"===e.$variant,"p-checkbox-sm p-inputfield-sm":"small"===n.size,"p-checkbox-lg p-inputfield-lg":"large"===n.size}]},box:"p-checkbox-box",input:"p-checkbox-input",icon:"p-checkbox-icon"}});function Ln(t){return function(t){if(Array.isArray(t))return $n(t)}(t)||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(t)||function(t,e){if(t){if("string"==typeof t)return $n(t,e);var n={}.toString.call(t).slice(8,-1);return"Object"===n&&t.constructor&&(n=t.constructor.name),"Map"===n||"Set"===n?Array.from(t):"Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n)?$n(t,e):void 0}}(t)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function $n(t,e){(null==e||e>t.length)&&(e=t.length);for(var n=0,o=Array(e);nt.length)&&(e=t.length);for(var n=0,o=Array(e);n2&&void 0!==arguments[2]?arguments[2]:-1,i=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(!this.disabled&&!this.isOptionDisabled(e)){var r=null;r=this.isSelected(e)?this.d_value.filter((function(t){return!dt(t,n.getOptionValue(e),n.equalityKey)})):[].concat(Bn(this.d_value||[]),[this.getOptionValue(e)]),this.updateModel(t,r),-1!==o&&(this.focusedOptionIndex=o),i&&et(this.$refs.focusInput)}},onOptionMouseMove:function(t,e){this.focusOnHover&&this.changeFocusedOptionIndex(t,e)},onOptionSelectRange:function(t){var e=this,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(-1===n&&(n=this.findNearestSelectedOptionIndex(o,!0)),-1===o&&(o=this.findNearestSelectedOptionIndex(n)),-1!==n&&-1!==o){var i=Math.min(n,o),r=Math.max(n,o),s=this.visibleOptions.slice(i,r+1).filter((function(t){return e.isValidOption(t)})).map((function(t){return e.getOptionValue(t)}));this.updateModel(t,s)}},onFilterChange:function(t){var e=t.target.value;this.filterValue=e,this.focusedOptionIndex=-1,this.$emit("filter",{originalEvent:t,value:e}),!this.virtualScrollerDisabled&&this.virtualScroller.scrollToIndex(0)},onFilterKeyDown:function(t){switch(t.code){case"ArrowDown":this.onArrowDownKey(t);break;case"ArrowUp":this.onArrowUpKey(t,!0);break;case"ArrowLeft":case"ArrowRight":this.onArrowLeftKey(t,!0);break;case"Home":this.onHomeKey(t,!0);break;case"End":this.onEndKey(t,!0);break;case"Enter":case"NumpadEnter":this.onEnterKey(t);break;case"Escape":this.onEscapeKey(t);break;case"Tab":this.onTabKey(t,!0)}},onFilterBlur:function(){this.focusedOptionIndex=-1},onFilterUpdated:function(){this.overlayVisible&&this.alignOverlay()},onOverlayClick:function(t){Xe.emit("overlay-click",{originalEvent:t,target:this.$el})},onOverlayKeyDown:function(t){if("Escape"===t.code)this.onEscapeKey(t)},onArrowDownKey:function(t){if(this.overlayVisible){var e=-1!==this.focusedOptionIndex?this.findNextOptionIndex(this.focusedOptionIndex):this.clicked?this.findFirstOptionIndex():this.findFirstFocusedOptionIndex();t.shiftKey&&this.onOptionSelectRange(t,this.startRangeIndex,e),this.changeFocusedOptionIndex(t,e)}else this.show();t.preventDefault()},onArrowUpKey:function(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(t.altKey&&!e)-1!==this.focusedOptionIndex&&this.onOptionSelect(t,this.visibleOptions[this.focusedOptionIndex]),this.overlayVisible&&this.hide(),t.preventDefault();else{var n=-1!==this.focusedOptionIndex?this.findPrevOptionIndex(this.focusedOptionIndex):this.clicked?this.findLastOptionIndex():this.findLastFocusedOptionIndex();t.shiftKey&&this.onOptionSelectRange(t,n,this.startRangeIndex),this.changeFocusedOptionIndex(t,n),!this.overlayVisible&&this.show(),t.preventDefault()}},onArrowLeftKey:function(t){arguments.length>1&&void 0!==arguments[1]&&arguments[1]&&(this.focusedOptionIndex=-1)},onHomeKey:function(t){if(arguments.length>1&&void 0!==arguments[1]&&arguments[1]){var e=t.currentTarget;t.shiftKey?e.setSelectionRange(0,t.target.selectionStart):(e.setSelectionRange(0,0),this.focusedOptionIndex=-1)}else{var n=t.metaKey||t.ctrlKey,o=this.findFirstOptionIndex();t.shiftKey&&n&&this.onOptionSelectRange(t,o,this.startRangeIndex),this.changeFocusedOptionIndex(t,o),!this.overlayVisible&&this.show()}t.preventDefault()},onEndKey:function(t){if(arguments.length>1&&void 0!==arguments[1]&&arguments[1]){var e=t.currentTarget;if(t.shiftKey)e.setSelectionRange(t.target.selectionStart,e.value.length);else{var n=e.value.length;e.setSelectionRange(n,n),this.focusedOptionIndex=-1}}else{var o=t.metaKey||t.ctrlKey,i=this.findLastOptionIndex();t.shiftKey&&o&&this.onOptionSelectRange(t,this.startRangeIndex,i),this.changeFocusedOptionIndex(t,i),!this.overlayVisible&&this.show()}t.preventDefault()},onPageUpKey:function(t){this.scrollInView(0),t.preventDefault()},onPageDownKey:function(t){this.scrollInView(this.visibleOptions.length-1),t.preventDefault()},onEnterKey:function(t){this.overlayVisible?-1!==this.focusedOptionIndex&&(t.shiftKey?this.onOptionSelectRange(t,this.focusedOptionIndex):this.onOptionSelect(t,this.visibleOptions[this.focusedOptionIndex])):(this.focusedOptionIndex=-1,this.onArrowDownKey(t)),t.preventDefault()},onEscapeKey:function(t){this.overlayVisible&&this.hide(!0),t.preventDefault()},onTabKey:function(t){arguments.length>1&&void 0!==arguments[1]&&arguments[1]||(this.overlayVisible&&this.hasFocusableElements()?(et(t.shiftKey?this.$refs.lastHiddenFocusableElementOnOverlay:this.$refs.firstHiddenFocusableElementOnOverlay),t.preventDefault()):(-1!==this.focusedOptionIndex&&this.onOptionSelect(t,this.visibleOptions[this.focusedOptionIndex]),this.overlayVisible&&this.hide(this.filter)))},onShiftKey:function(){this.startRangeIndex=this.focusedOptionIndex},onOverlayEnter:function(t){Q.set("overlay",t,this.$primevue.config.zIndex.overlay),st(t,{position:"absolute",top:"0",left:"0"}),this.alignOverlay(),this.scrollInView(),this.autoFilterFocus&&et(this.$refs.filterInput.$el)},onOverlayAfterEnter:function(){this.bindOutsideClickListener(),this.bindScrollListener(),this.bindResizeListener(),this.$emit("show")},onOverlayLeave:function(){this.unbindOutsideClickListener(),this.unbindScrollListener(),this.unbindResizeListener(),this.$emit("hide"),this.overlay=null},onOverlayAfterLeave:function(t){Q.clear(t)},alignOverlay:function(){"self"===this.appendTo?lt(this.overlay,this.$el):(this.overlay.style.minWidth=G(this.$el)+"px",at(this.overlay,this.$el))},bindOutsideClickListener:function(){var t=this;this.outsideClickListener||(this.outsideClickListener=function(e){t.overlayVisible&&t.isOutsideClicked(e)&&t.hide()},document.addEventListener("click",this.outsideClickListener))},unbindOutsideClickListener:function(){this.outsideClickListener&&(document.removeEventListener("click",this.outsideClickListener),this.outsideClickListener=null)},bindScrollListener:function(){var t=this;this.scrollHandler||(this.scrollHandler=new Tt(this.$refs.container,(function(){t.overlayVisible&&t.hide()}))),this.scrollHandler.bindScrollListener()},unbindScrollListener:function(){this.scrollHandler&&this.scrollHandler.unbindScrollListener()},bindResizeListener:function(){var t=this;this.resizeListener||(this.resizeListener=function(){t.overlayVisible&&!ct()&&t.hide()},window.addEventListener("resize",this.resizeListener))},unbindResizeListener:function(){this.resizeListener&&(window.removeEventListener("resize",this.resizeListener),this.resizeListener=null)},isOutsideClicked:function(t){return!(this.$el.isSameNode(t.target)||this.$el.contains(t.target)||this.overlay&&this.overlay.contains(t.target))},getLabelByValue:function(t){var e=this,n=(this.optionGroupLabel?this.flatOptions(this.options):this.options||[]).find((function(n){return!e.isOptionGroup(n)&&dt(e.getOptionValue(n),t,e.equalityKey)}));return n?this.getOptionLabel(n):null},getSelectedItemsLabel:function(){var t=/{(.*?)}/,e=this.selectedItemsLabel||this.$primevue.config.locale.selectionMessage;return t.test(e)?e.replace(e.match(t)[0],this.d_value.length+""):e},onToggleAll:function(t){var e=this;if(null!==this.selectAll)this.$emit("selectall-change",{originalEvent:t,checked:!this.allSelected});else{var n=this.allSelected?[]:this.visibleOptions.filter((function(t){return e.isValidOption(t)})).map((function(t){return e.getOptionValue(t)}));this.updateModel(t,n)}},removeOption:function(t,e){var n=this;t.stopPropagation();var o=this.d_value.filter((function(t){return!dt(t,e,n.equalityKey)}));this.updateModel(t,o)},clearFilter:function(){this.filterValue=null},hasFocusableElements:function(){return ut(this.overlay,':not([data-p-hidden-focusable="true"])').length>0},isOptionMatched:function(t){var e;return this.isValidOption(t)&&"string"==typeof this.getOptionLabel(t)&&(null===(e=this.getOptionLabel(t))||void 0===e?void 0:e.toLocaleLowerCase(this.filterLocale).startsWith(this.searchValue.toLocaleLowerCase(this.filterLocale)))},isValidOption:function(t){return K(t)&&!(this.isOptionDisabled(t)||this.isOptionGroup(t))},isValidSelectedOption:function(t){return this.isValidOption(t)&&this.isSelected(t)},isEquals:function(t,e){return dt(t,e,this.equalityKey)},isSelected:function(t){var e=this,n=this.getOptionValue(t);return(this.d_value||[]).some((function(t){return e.isEquals(t,n)}))},findFirstOptionIndex:function(){var t=this;return this.visibleOptions.findIndex((function(e){return t.isValidOption(e)}))},findLastOptionIndex:function(){var t=this;return pt(this.visibleOptions,(function(e){return t.isValidOption(e)}))},findNextOptionIndex:function(t){var e=this,n=t-1?n+t+1:t},findPrevOptionIndex:function(t){var e=this,n=t>0?pt(this.visibleOptions.slice(0,t),(function(t){return e.isValidOption(t)})):-1;return n>-1?n:t},findSelectedOptionIndex:function(){var t=this;if(this.$filled)for(var e,n=function(){var e=t.d_value[o],n=t.visibleOptions.findIndex((function(n){return t.isValidSelectedOption(n)&&t.isEquals(e,t.getOptionValue(n))}));if(n>-1)return{v:n}},o=this.d_value.length-1;o>=0;o--)if(e=n())return e.v;return-1},findFirstSelectedOptionIndex:function(){var t=this;return this.$filled?this.visibleOptions.findIndex((function(e){return t.isValidSelectedOption(e)})):-1},findLastSelectedOptionIndex:function(){var t=this;return this.$filled?pt(this.visibleOptions,(function(e){return t.isValidSelectedOption(e)})):-1},findNextSelectedOptionIndex:function(t){var e=this,n=this.$filled&&t-1?n+t+1:-1},findPrevSelectedOptionIndex:function(t){var e=this,n=this.$filled&&t>0?pt(this.visibleOptions.slice(0,t),(function(t){return e.isValidSelectedOption(t)})):-1;return n>-1?n:-1},findNearestSelectedOptionIndex:function(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=-1;return this.$filled&&(n=e?-1===(n=this.findPrevSelectedOptionIndex(t))?this.findNextSelectedOptionIndex(t):n:-1===(n=this.findNextSelectedOptionIndex(t))?this.findPrevSelectedOptionIndex(t):n),n>-1?n:t},findFirstFocusedOptionIndex:function(){var t=this.findSelectedOptionIndex();return t<0?this.findFirstOptionIndex():t},findLastFocusedOptionIndex:function(){var t=this.findSelectedOptionIndex();return t<0?this.findLastOptionIndex():t},searchOptions:function(t){var e=this;this.searchValue=(this.searchValue||"")+t.key;var n=-1;K(this.searchValue)&&(-1===(n=-1!==this.focusedOptionIndex?-1===(n=this.visibleOptions.slice(this.focusedOptionIndex).findIndex((function(t){return e.isOptionMatched(t)})))?this.visibleOptions.slice(0,this.focusedOptionIndex).findIndex((function(t){return e.isOptionMatched(t)})):n+this.focusedOptionIndex:this.visibleOptions.findIndex((function(t){return e.isOptionMatched(t)})))&&-1===this.focusedOptionIndex&&(n=this.findFirstFocusedOptionIndex()),-1!==n&&this.changeFocusedOptionIndex(t,n)),this.searchTimeout&&clearTimeout(this.searchTimeout),this.searchTimeout=setTimeout((function(){e.searchValue="",e.searchTimeout=null}),500)},changeFocusedOptionIndex:function(t,e){this.focusedOptionIndex!==e&&(this.focusedOptionIndex=e,this.scrollInView(),this.selectOnFocus&&this.onOptionSelect(t,this.visibleOptions[e]))},scrollInView:function(){var t=this,e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1;this.$nextTick((function(){var n=-1!==e?"".concat(t.id,"_").concat(e):t.focusedOptionId,o=J(t.list,'li[id="'.concat(n,'"]'));o?o.scrollIntoView&&o.scrollIntoView({block:"nearest",inline:"nearest"}):t.virtualScrollerDisabled||t.virtualScroller&&t.virtualScroller.scrollToIndex(-1!==e?e:t.focusedOptionIndex)}))},autoUpdateModel:function(){if(this.selectOnFocus&&this.autoOptionFocus&&!this.$filled){this.focusedOptionIndex=this.findFirstFocusedOptionIndex();var t=this.getOptionValue(this.visibleOptions[this.focusedOptionIndex]);this.updateModel(null,[t])}},updateModel:function(t,e){this.writeValue(e,t),this.$emit("change",{originalEvent:t,value:e})},flatOptions:function(t){var e=this;return(t||[]).reduce((function(t,n,o){t.push({optionGroup:n,group:!0,index:o});var i=e.getOptionGroupChildren(n);return i&&i.forEach((function(e){return t.push(e)})),t}),[])},overlayRef:function(t){this.overlay=t},listRef:function(t,e){this.list=t,e&&e(t)},virtualScrollerRef:function(t){this.virtualScroller=t}},computed:{visibleOptions:function(){var t=this,e=this.optionGroupLabel?this.flatOptions(this.options):this.options||[];if(this.filterValue){var n=_t.filter(e,this.searchFields,this.filterValue,this.filterMatchMode,this.filterLocale);if(this.optionGroupLabel){var o=this.options||[],i=[];return o.forEach((function(e){var o=t.getOptionGroupChildren(e).filter((function(t){return n.includes(t)}));o.length>0&&i.push(Kn(Kn({},e),{},Dn({},"string"==typeof t.optionGroupChildren?t.optionGroupChildren:"items",Bn(o))))})),this.flatOptions(i)}return n}return e},label:function(){var t;if(this.d_value&&this.d_value.length){if(K(this.maxSelectedLabels)&&this.d_value.length>this.maxSelectedLabels)return this.getSelectedItemsLabel();t="";for(var e=0;ethis.maxSelectedLabels},allSelected:function(){var t=this;return null!==this.selectAll?this.selectAll:K(this.visibleOptions)&&this.visibleOptions.every((function(e){return t.isOptionGroup(e)||t.isOptionDisabled(e)||t.isSelected(e)}))},hasSelectedOption:function(){return this.$filled},equalityKey:function(){return this.optionValue?null:this.dataKey},searchFields:function(){return this.filterFields||[this.optionLabel]},maxSelectionLimitReached:function(){return this.selectionLimit&&this.d_value&&this.d_value.length===this.selectionLimit},filterResultMessageText:function(){return K(this.visibleOptions)?this.filterMessageText.replaceAll("{0}",this.visibleOptions.length):this.emptyFilterMessageText},filterMessageText:function(){return this.filterMessage||this.$primevue.config.locale.searchMessage||""},emptyFilterMessageText:function(){return this.emptyFilterMessage||this.$primevue.config.locale.emptySearchMessage||this.$primevue.config.locale.emptyFilterMessage||""},emptyMessageText:function(){return this.emptyMessage||this.$primevue.config.locale.emptyMessage||""},selectionMessageText:function(){return this.selectionMessage||this.$primevue.config.locale.selectionMessage||""},emptySelectionMessageText:function(){return this.emptySelectionMessage||this.$primevue.config.locale.emptySelectionMessage||""},selectedMessageText:function(){return this.$filled?this.selectionMessageText.replaceAll("{0}",this.d_value.length):this.emptySelectionMessageText},focusedOptionId:function(){return-1!==this.focusedOptionIndex?"".concat(this.id,"_").concat(this.focusedOptionIndex):null},ariaSetSize:function(){var t=this;return this.visibleOptions.filter((function(e){return!t.isOptionGroup(e)})).length},toggleAllAriaLabel:function(){return this.$primevue.config.locale.aria?this.$primevue.config.locale.aria[this.allSelected?"selectAll":"unselectAll"]:void 0},listAriaLabel:function(){return this.$primevue.config.locale.aria?this.$primevue.config.locale.aria.listLabel:void 0},virtualScrollerDisabled:function(){return!this.virtualScrollerOptions},hasFluid:function(){return D(this.fluid)?!!this.$pcFluid:this.fluid},isClearIconVisible:function(){return this.showClear&&null!=this.d_value&&K(this.options)}},directives:{ripple:De},components:{InputText:We,Checkbox:Cn,VirtualScroller:nn,Portal:Ye,Chip:_n,IconField:Ue,InputIcon:Ne,TimesIcon:Mt,SearchIcon:Vt,ChevronDownIcon:Pt,SpinnerIcon:Ct,CheckIcon:Ft}};function Rn(t){return(Rn="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function Gn(t,e,n){return(e=function(t){var e=function(t,e){if("object"!=Rn(t)||!t)return t;var n=t[Symbol.toPrimitive];if(void 0!==n){var o=n.call(t,e||"default");if("object"!=Rn(o))return o;throw new TypeError("@@toPrimitive must return a primitive 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r.onBlur(t,0)}),tabindex:t.tabindex,role:"slider","aria-valuemin":t.min,"aria-valuenow":t.d_value?t.d_value[0]:null,"aria-valuemax":t.max,"aria-labelledby":t.ariaLabelledby,"aria-label":t.ariaLabel,"aria-orientation":t.orientation},t.ptm("startHandler")),null,16,eo)):$("",!0),t.range?(m(),g("span",I({key:2,class:t.cx("handle"),style:[t.sx("handle"),r.rangeEndHandleStyle()],onTouchstartPassive:e[12]||(e[12]=function(t){return r.onDragStart(t,1)}),onTouchmovePassive:e[13]||(e[13]=function(t){return r.onDrag(t)}),onTouchend:e[14]||(e[14]=function(t){return r.onDragEnd(t)}),onMousedown:e[15]||(e[15]=function(t){return r.onMouseDown(t,1)}),onKeydown:e[16]||(e[16]=function(t){return r.onKeyDown(t,1)}),onBlur:e[17]||(e[17]=function(t){return r.onBlur(t,1)}),tabindex:t.tabindex,role:"slider","aria-valuemin":t.min,"aria-valuenow":t.d_value?t.d_value[1]:null,"aria-valuemax":t.max,"aria-labelledby":t.ariaLabelledby,"aria-label":t.ariaLabel,"aria-orientation":t.orientation},t.ptm("endHandler")),null,16,no)):$("",!0)],16)};var oo={name:"Card",extends:{name:"BaseCard",extends:Lt,style:St.extend({name:"card",theme:function(t){var e=t.dt;return"\n.p-card {\n background: ".concat(e("card.background"),";\n color: ").concat(e("card.color"),";\n box-shadow: ").concat(e("card.shadow"),";\n border-radius: ").concat(e("card.border.radius"),";\n display: flex;\n flex-direction: column;\n}\n\n.p-card-caption {\n display: flex;\n flex-direction: column;\n gap: ").concat(e("card.caption.gap"),";\n}\n\n.p-card-body {\n padding: ").concat(e("card.body.padding"),";\n display: flex;\n flex-direction: column;\n gap: ").concat(e("card.body.gap"),";\n}\n\n.p-card-title {\n font-size: ").concat(e("card.title.font.size"),";\n font-weight: ").concat(e("card.title.font.weight"),";\n}\n\n.p-card-subtitle {\n color: ").concat(e("card.subtitle.color"),";\n}\n")},classes:{root:"p-card p-component",header:"p-card-header",body:"p-card-body",caption:"p-card-caption",title:"p-card-title",subtitle:"p-card-subtitle",content:"p-card-content",footer:"p-card-footer"}}),provide:function(){return{$pcCard:this,$parentInstance:this}}},inheritAttrs:!1};oo.render=function(t,e,n,o,i,r){return m(),g("div",I({class:t.cx("root")},t.ptmi("root")),[t.$slots.header?(m(),g("div",I({key:0,class:t.cx("header")},t.ptm("header")),[v(t.$slots,"header")],16)):$("",!0),C("div",I({class:t.cx("body")},t.ptm("body")),[t.$slots.title||t.$slots.subtitle?(m(),g("div",I({key:0,class:t.cx("caption")},t.ptm("caption")),[t.$slots.title?(m(),g("div",I({key:0,class:t.cx("title")},t.ptm("title")),[v(t.$slots,"title")],16)):$("",!0),t.$slots.subtitle?(m(),g("div",I({key:1,class:t.cx("subtitle")},t.ptm("subtitle")),[v(t.$slots,"subtitle")],16)):$("",!0)],16)):$("",!0),C("div",I({class:t.cx("content")},t.ptm("content")),[v(t.$slots,"content")],16),t.$slots.footer?(m(),g("div",I({key:1,class:t.cx("footer")},t.ptm("footer")),[v(t.$slots,"footer")],16)):$("",!0)],16)],16)};var io=St.extend({name:"contextmenu",theme:function(t){var e=t.dt;return"\n.p-contextmenu {\n background: ".concat(e("contextmenu.background"),";\n color: ").concat(e("contextmenu.color"),";\n border: 1px solid ").concat(e("contextmenu.border.color"),";\n border-radius: ").concat(e("contextmenu.border.radius"),";\n box-shadow: ").concat(e("contextmenu.shadow"),";\n min-width: 12.5rem;\n}\n\n.p-contextmenu-root-list,\n.p-contextmenu-submenu {\n margin: 0;\n padding: ").concat(e("contextmenu.list.padding"),";\n list-style: none;\n outline: 0 none;\n display: flex;\n flex-direction: column;\n gap: ").concat(e("contextmenu.list.gap"),";\n}\n\n.p-contextmenu-submenu {\n position: absolute;\n display: flex;\n flex-direction: column;\n min-width: 100%;\n z-index: 1;\n background: ").concat(e("contextmenu.background"),";\n color: ").concat(e("contextmenu.color"),";\n border: 1px solid ").concat(e("contextmenu.border.color"),";\n border-radius: ").concat(e("contextmenu.border.radius"),";\n box-shadow: ").concat(e("contextmenu.shadow"),";\n}\n\n.p-contextmenu-item {\n position: relative;\n}\n\n.p-contextmenu-item-content {\n transition: background ").concat(e("contextmenu.transition.duration"),", color ").concat(e("contextmenu.transition.duration"),";\n border-radius: ").concat(e("contextmenu.item.border.radius"),";\n color: ").concat(e("contextmenu.item.color"),";\n}\n\n.p-contextmenu-item-link {\n cursor: pointer;\n display: flex;\n align-items: center;\n text-decoration: none;\n overflow: hidden;\n position: relative;\n color: inherit;\n padding: ").concat(e("contextmenu.item.padding"),";\n gap: ").concat(e("contextmenu.item.gap"),";\n user-select: none;\n}\n\n.p-contextmenu-item-label {\n line-height: 1;\n}\n\n.p-contextmenu-item-icon {\n color: ").concat(e("contextmenu.item.icon.color"),";\n}\n\n.p-contextmenu-submenu-icon {\n color: ").concat(e("contextmenu.submenu.icon.color"),";\n margin-left: auto;\n font-size: ").concat(e("contextmenu.submenu.icon.size"),";\n width: ").concat(e("contextmenu.submenu.icon.size"),";\n height: ").concat(e("contextmenu.submenu.icon.size"),";\n}\n\n.p-contextmenu-submenu-icon:dir(rtl) {\n margin-left: 0;\n margin-right: auto;\n}\n\n.p-contextmenu-item.p-focus > .p-contextmenu-item-content {\n color: ").concat(e("contextmenu.item.focus.color"),";\n background: ").concat(e("contextmenu.item.focus.background"),";\n}\n\n.p-contextmenu-item.p-focus > .p-contextmenu-item-content .p-contextmenu-item-icon {\n color: ").concat(e("contextmenu.item.icon.focus.color"),";\n}\n\n.p-contextmenu-item.p-focus > .p-contextmenu-item-content .p-contextmenu-submenu-icon {\n color: ").concat(e("contextmenu.submenu.icon.focus.color"),";\n}\n\n.p-contextmenu-item:not(.p-disabled) > .p-contextmenu-item-content:hover {\n color: ").concat(e("contextmenu.item.focus.color"),";\n background: ").concat(e("contextmenu.item.focus.background"),";\n}\n\n.p-contextmenu-item:not(.p-disabled) > .p-contextmenu-item-content:hover .p-contextmenu-item-icon {\n color: ").concat(e("contextmenu.item.icon.focus.color"),";\n}\n\n.p-contextmenu-item:not(.p-disabled) > .p-contextmenu-item-content:hover .p-contextmenu-submenu-icon {\n color: ").concat(e("contextmenu.submenu.icon.focus.color"),";\n}\n\n.p-contextmenu-item-active > .p-contextmenu-item-content {\n color: ").concat(e("contextmenu.item.active.color"),";\n background: ").concat(e("contextmenu.item.active.background"),";\n}\n\n.p-contextmenu-item-active > .p-contextmenu-item-content .p-contextmenu-item-icon {\n color: ").concat(e("contextmenu.item.icon.active.color"),";\n}\n\n.p-contextmenu-item-active > .p-contextmenu-item-content .p-contextmenu-submenu-icon {\n color: ").concat(e("contextmenu.submenu.icon.active.color"),";\n}\n\n.p-contextmenu-separator {\n border-block-start: 1px solid ").concat(e("contextmenu.separator.border.color"),";\n}\n\n.p-contextmenu-enter-from,\n.p-contextmenu-leave-active {\n opacity: 0;\n}\n\n.p-contextmenu-enter-active {\n transition: opacity 250ms;\n}\n\n.p-contextmenu-mobile .p-contextmenu-submenu {\n position: static;\n box-shadow: none;\n border: 0 none;\n padding-inline-start: ").concat(e("tieredmenu.submenu.mobile.indent"),";\n padding-inline-end: 0;\n}\n\n.p-contextmenu-mobile .p-contextmenu-submenu-icon {\n transition: transform 0.2s;\n transform: rotate(90deg);\n}\n\n.p-contextmenu-mobile .p-contextmenu-item-active > .p-contextmenu-item-content .p-contextmenu-submenu-icon {\n transform: rotate(-90deg);\n}\n")},classes:{root:function(t){return["p-contextmenu p-component",{"p-contextmenu-mobile":t.instance.queryMatches}]},rootList:"p-contextmenu-root-list",item:function(t){var e=t.instance,n=t.processedItem;return["p-contextmenu-item",{"p-contextmenu-item-active":e.isItemActive(n),"p-focus":e.isItemFocused(n),"p-disabled":e.isItemDisabled(n)}]},itemContent:"p-contextmenu-item-content",itemLink:"p-contextmenu-item-link",itemIcon:"p-contextmenu-item-icon",itemLabel:"p-contextmenu-item-label",submenuIcon:"p-contextmenu-submenu-icon",submenu:"p-contextmenu-submenu",separator:"p-contextmenu-separator"}}),ro={name:"BaseContextMenu",extends:Lt,props:{model:{type:Array,default:null},appendTo:{type:[String,Object],default:"body"},autoZIndex:{type:Boolean,default:!0},baseZIndex:{type:Number,default:0},global:{type:Boolean,default:!1},breakpoint:{type:String,default:"960px"},tabindex:{type:Number,default:0},ariaLabelledby:{type:String,default:null},ariaLabel:{type:String,default:null}},style:io,provide:function(){return{$pcContextMenu:this,$parentInstance:this}}},so={name:"ContextMenuSub",hostName:"ContextMenu",extends:Lt,emits:["item-click","item-mouseenter","item-mousemove"],props:{items:{type:Array,default:null},menuId:{type:String,default:null},focusedItemId:{type:String,default:null},root:{type:Boolean,default:!1},visible:{type:Boolean,default:!1},level:{type:Number,default:0},templates:{type:Object,default:null},activeItemPath:{type:Object,default:null},tabindex:{type:Number,default:0}},methods:{getItemId:function(t){return"".concat(this.menuId,"_").concat(t.key)},getItemKey:function(t){return this.getItemId(t)},getItemProp:function(t,e,n){return t&&t.item?It(t.item[e],n):void 0},getItemLabel:function(t){return this.getItemProp(t,"label")},getItemLabelId:function(t){return"".concat(this.menuId,"_").concat(t.key,"_label")},getPTOptions:function(t,e,n){return this.ptm(t,{context:{item:e.item,active:this.isItemActive(e),focused:this.isItemFocused(e),disabled:this.isItemDisabled(e),index:n}})},isItemActive:function(t){return this.activeItemPath.some((function(e){return e.key===t.key}))},isItemVisible:function(t){return!1!==this.getItemProp(t,"visible")},isItemDisabled:function(t){return this.getItemProp(t,"disabled")},isItemFocused:function(t){return this.focusedItemId===this.getItemId(t)},isItemGroup:function(t){return K(t.items)},onItemClick:function(t,e){this.getItemProp(e,"command",{originalEvent:t,item:e.item}),this.$emit("item-click",{originalEvent:t,processedItem:e,isFocus:!0})},onItemMouseEnter:function(t,e){this.$emit("item-mouseenter",{originalEvent:t,processedItem:e})},onItemMouseMove:function(t,e){this.$emit("item-mousemove",{originalEvent:t,processedItem:e,isFocus:!0})},getAriaSetSize:function(){var t=this;return this.items.filter((function(e){return t.isItemVisible(e)&&!t.getItemProp(e,"separator")})).length},getAriaPosInset:function(t){var e=this;return t-this.items.slice(0,t).filter((function(t){return e.isItemVisible(t)&&e.getItemProp(t,"separator")})).length+1},onEnter:function(){Ot(this.$refs.container,this.level)},getMenuItemProps:function(t,e){return{action:I({class:this.cx("itemLink"),tabindex:-1},this.getPTOptions("itemLink",t,e)),icon:I({class:[this.cx("itemIcon"),this.getItemProp(t,"icon")]},this.getPTOptions("itemIcon",t,e)),label:I({class:this.cx("itemLabel")},this.getPTOptions("itemLabel",t,e)),submenuicon:I({class:this.cx("submenuIcon")},this.getPTOptions("submenuicon",t,e))}}},components:{AngleRightIcon:Ht},directives:{ripple:De}},lo=["tabindex"],ao=["id","aria-label","aria-disabled","aria-expanded","aria-haspopup","aria-level","aria-setsize","aria-posinset","data-p-active","data-p-focused","data-p-disabled"],co=["onClick","onMouseenter","onMousemove"],uo=["href","target"],po=["id"],ho=["id"];so.render=function(t,e,n,o,i,r){var s=k("AngleRightIcon"),l=k("ContextMenuSub",!0),a=w("ripple");return m(),S(V,I({name:"p-contextmenusub",onEnter:r.onEnter},t.ptm("menu.transition")),{default:L((function(){return[n.root||n.visible?(m(),g("ul",I({key:0,ref:"container",tabindex:n.tabindex},t.ptm("rootList")),[(m(!0),g(_,null,M(n.items,(function(o,i){return m(),g(_,{key:r.getItemKey(o)},[r.isItemVisible(o)&&!r.getItemProp(o,"separator")?(m(),g("li",I({key:0,id:r.getItemId(o),style:r.getItemProp(o,"style"),class:[t.cx("item",{processedItem:o}),r.getItemProp(o,"class")],role:"menuitem","aria-label":r.getItemLabel(o),"aria-disabled":r.isItemDisabled(o)||void 0,"aria-expanded":r.isItemGroup(o)?r.isItemActive(o):void 0,"aria-haspopup":r.isItemGroup(o)&&!r.getItemProp(o,"to")?"menu":void 0,"aria-level":n.level+1,"aria-setsize":r.getAriaSetSize(),"aria-posinset":r.getAriaPosInset(i),ref_for:!0},r.getPTOptions("item",o,i),{"data-p-active":r.isItemActive(o),"data-p-focused":r.isItemFocused(o),"data-p-disabled":r.isItemDisabled(o)}),[C("div",I({class:t.cx("itemContent"),onClick:function(t){return r.onItemClick(t,o)},onMouseenter:function(t){return r.onItemMouseEnter(t,o)},onMousemove:function(t){return r.onItemMouseMove(t,o)},ref_for:!0},r.getPTOptions("itemContent",o,i)),[n.templates.item?(m(),S(E(n.templates.item),{key:1,item:o.item,hasSubmenu:r.getItemProp(o,"items"),label:r.getItemLabel(o),props:r.getMenuItemProps(o,i)},null,8,["item","hasSubmenu","label","props"])):O((m(),g("a",I({key:0,href:r.getItemProp(o,"url"),class:t.cx("itemLink"),target:r.getItemProp(o,"target"),tabindex:"-1",ref_for:!0},r.getPTOptions("itemLink",o,i)),[n.templates.itemicon?(m(),S(E(n.templates.itemicon),{key:0,item:o.item,class:z(t.cx("itemIcon"))},null,8,["item","class"])):r.getItemProp(o,"icon")?(m(),g("span",I({key:1,class:[t.cx("itemIcon"),r.getItemProp(o,"icon")],ref_for:!0},r.getPTOptions("itemIcon",o,i)),null,16)):$("",!0),C("span",I({id:r.getItemLabelId(o),class:t.cx("itemLabel"),ref_for:!0},r.getPTOptions("itemLabel",o,i)),x(r.getItemLabel(o)),17,po),r.getItemProp(o,"items")?(m(),g(_,{key:2},[n.templates.submenuicon?(m(),S(E(n.templates.submenuicon),{key:0,active:r.isItemActive(o),class:z(t.cx("submenuIcon"))},null,8,["active","class"])):(m(),S(s,I({key:1,class:t.cx("submenuIcon"),ref_for:!0},r.getPTOptions("submenuicon",o,i)),null,16,["class"]))],64)):$("",!0)],16,uo)),[[a]])],16,co),r.isItemVisible(o)&&r.isItemGroup(o)?(m(),S(l,I({key:0,id:r.getItemId(o)+"_list",role:"menu",class:t.cx("submenu"),menuId:n.menuId,focusedItemId:n.focusedItemId,items:o.items,templates:n.templates,activeItemPath:n.activeItemPath,level:n.level+1,visible:r.isItemActive(o)&&r.isItemGroup(o),pt:t.pt,unstyled:t.unstyled,onItemClick:e[0]||(e[0]=function(e){return t.$emit("item-click",e)}),onItemMouseenter:e[1]||(e[1]=function(e){return t.$emit("item-mouseenter",e)}),onItemMousemove:e[2]||(e[2]=function(e){return t.$emit("item-mousemove",e)}),"aria-labelledby":r.getItemLabelId(o),ref_for:!0},t.ptm("submenu")),null,16,["id","class","menuId","focusedItemId","items","templates","activeItemPath","level","visible","pt","unstyled","aria-labelledby"])):$("",!0)],16,ao)):$("",!0),r.isItemVisible(o)&&r.getItemProp(o,"separator")?(m(),g("li",I({key:1,id:r.getItemId(o),style:r.getItemProp(o,"style"),class:[t.cx("separator"),r.getItemProp(o,"class")],role:"separator",ref_for:!0},t.ptm("separator")),null,16,ho)):$("",!0)],64)})),128))],16,lo)):$("",!0)]})),_:1},16,["onEnter"])};var fo={name:"ContextMenu",extends:ro,inheritAttrs:!1,emits:["focus","blur","show","hide","before-show","before-hide"],target:null,outsideClickListener:null,resizeListener:null,documentContextMenuListener:null,matchMediaListener:null,pageX:null,pageY:null,container:null,list:null,data:function(){return{id:this.$attrs.id,focused:!1,focusedItemInfo:{index:-1,level:0,parentKey:""},activeItemPath:[],visible:!1,submenuVisible:!1,query:null,queryMatches:!1}},watch:{"$attrs.id":function(t){this.id=t||Et()},activeItemPath:function(t){K(t)?(this.bindOutsideClickListener(),this.bindResizeListener()):this.visible||(this.unbindOutsideClickListener(),this.unbindResizeListener())}},mounted:function(){this.id=this.id||Et(),this.bindMatchMediaListener(),this.global&&this.bindDocumentContextMenuListener()},beforeUnmount:function(){this.unbindResizeListener(),this.unbindOutsideClickListener(),this.unbindDocumentContextMenuListener(),this.unbindMatchMediaListener(),this.container&&this.autoZIndex&&Q.clear(this.container),this.target=null,this.container=null},methods:{getItemProp:function(t,e){return t?It(t[e]):void 0},getItemLabel:function(t){return this.getItemProp(t,"label")},isItemDisabled:function(t){return this.getItemProp(t,"disabled")},isItemVisible:function(t){return!1!==this.getItemProp(t,"visible")},isItemGroup:function(t){return K(this.getItemProp(t,"items"))},isItemSeparator:function(t){return this.getItemProp(t,"separator")},getProccessedItemLabel:function(t){return t?this.getItemLabel(t.item):void 0},isProccessedItemGroup:function(t){return t&&K(t.items)},toggle:function(t){this.visible?this.hide():this.show(t)},show:function(t){this.$emit("before-show"),this.activeItemPath=[],this.focusedItemInfo={index:-1,level:0,parentKey:""},et(this.list),this.pageX=t.pageX,this.pageY=t.pageY,this.visible?this.position():this.visible=!0,t.stopPropagation(),t.preventDefault()},hide:function(){this.$emit("before-hide"),this.visible=!1,this.activeItemPath=[],this.focusedItemInfo={index:-1,level:0,parentKey:""}},onFocus:function(t){this.focused=!0,this.focusedItemInfo=-1!==this.focusedItemInfo.index?this.focusedItemInfo:{index:-1,level:0,parentKey:""},this.$emit("focus",t)},onBlur:function(t){this.focused=!1,this.focusedItemInfo={index:-1,level:0,parentKey:""},this.searchValue="",this.$emit("blur",t)},onKeyDown:function(t){var e=t.metaKey||t.ctrlKey;switch(t.code){case"ArrowDown":this.onArrowDownKey(t);break;case"ArrowUp":this.onArrowUpKey(t);break;case"ArrowLeft":this.onArrowLeftKey(t);break;case"ArrowRight":this.onArrowRightKey(t);break;case"Home":this.onHomeKey(t);break;case"End":this.onEndKey(t);break;case"Space":this.onSpaceKey(t);break;case"Enter":case"NumpadEnter":this.onEnterKey(t);break;case"Escape":this.onEscapeKey(t);break;case"Tab":this.onTabKey(t);break;case"PageDown":case"PageUp":case"Backspace":case"ShiftLeft":case"ShiftRight":break;default:!e&&ot(t.key)&&this.searchItems(t,t.key)}},onItemChange:function(t,e){var n=t.processedItem,o=t.isFocus;if(!D(n)){var i=n.index,r=n.key,s=n.level,l=n.parentKey,a=n.items,c=K(a),u=this.activeItemPath.filter((function(t){return t.parentKey!==l&&t.parentKey!==r}));c&&(u.push(n),this.submenuVisible=!0),this.focusedItemInfo={index:i,level:s,parentKey:l},o&&et(this.list),"hover"===e&&this.queryMatches||(this.activeItemPath=u)}},onItemClick:function(t){var e=t.processedItem,n=this.isProccessedItemGroup(e);if(this.isSelected(e)){var o=e.index,i=e.key,r=e.level,s=e.parentKey;this.activeItemPath=this.activeItemPath.filter((function(t){return i!==t.key&&i.startsWith(t.key)})),this.focusedItemInfo={index:o,level:r,parentKey:s},et(this.list)}else n?this.onItemChange(t):this.hide()},onItemMouseEnter:function(t){this.onItemChange(t,"hover")},onItemMouseMove:function(t){this.focused&&this.changeFocusedItemIndex(t,t.processedItem.index)},onArrowDownKey:function(t){var e=-1!==this.focusedItemInfo.index?this.findNextItemIndex(this.focusedItemInfo.index):this.findFirstFocusedItemIndex();this.changeFocusedItemIndex(t,e),t.preventDefault()},onArrowUpKey:function(t){if(t.altKey){if(-1!==this.focusedItemInfo.index){var e=this.visibleItems[this.focusedItemInfo.index];!this.isProccessedItemGroup(e)&&this.onItemChange({originalEvent:t,processedItem:e})}this.popup&&this.hide(),t.preventDefault()}else{var n=-1!==this.focusedItemInfo.index?this.findPrevItemIndex(this.focusedItemInfo.index):this.findLastFocusedItemIndex();this.changeFocusedItemIndex(t,n),t.preventDefault()}},onArrowLeftKey:function(t){var e=this,n=this.visibleItems[this.focusedItemInfo.index],o=this.activeItemPath.find((function(t){return t.key===n.parentKey}));D(n.parent)||(this.focusedItemInfo={index:-1,parentKey:o?o.parentKey:""},this.searchValue="",this.onArrowDownKey(t)),this.activeItemPath=this.activeItemPath.filter((function(t){return t.parentKey!==e.focusedItemInfo.parentKey})),t.preventDefault()},onArrowRightKey:function(t){var 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relative;\n transition: none;\n}\n\n.p-togglebutton::before {\n content: "";\n background: transparent;\n transition: background ').concat(e("togglebutton.transition.duration"),", color ").concat(e("togglebutton.transition.duration"),", border-color ").concat(e("togglebutton.transition.duration"),",\n outline-color ").concat(e("togglebutton.transition.duration"),", box-shadow ").concat(e("togglebutton.transition.duration"),";\n position: absolute;\n inset-inline-start: ").concat(e("togglebutton.content.left"),";\n inset-block-start: ").concat(e("togglebutton.content.top"),";\n width: calc(100% - calc(2 * ").concat(e("togglebutton.content.left"),"));\n height: calc(100% - calc(2 * ").concat(e("togglebutton.content.top"),"));\n border-radius: ").concat(e("togglebutton.border.radius"),";\n}\n\n.p-togglebutton.p-togglebutton-checked::before {\n background: ").concat(e("togglebutton.content.checked.background"),";\n box-shadow: 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e=t.instance,n=t.props;return["p-togglebutton p-component",{"p-togglebutton-checked":e.active,"p-invalid":e.$invalid,"p-togglebutton-sm p-inputfield-sm":"small"===n.size,"p-togglebutton-lg 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0===e||e.call(n,t)}},computed:{active:function(){return!0===this.d_value},hasLabel:function(){return K(this.onLabel)&&K(this.offLabel)},label:function(){return this.hasLabel?this.d_value?this.onLabel:this.offLabel:"Β "}},directives:{ripple:De}},go=["tabindex","disabled","aria-pressed","aria-labelledby","data-p-checked","data-p-disabled"];mo.render=function(t,e,n,o,i,r){var s=w("ripple");return O((m(),g("button",I({type:"button",class:t.cx("root"),tabindex:t.tabindex,disabled:t.disabled,"aria-pressed":t.d_value,onClick:e[0]||(e[0]=function(){return r.onChange&&r.onChange.apply(r,arguments)}),onBlur:e[1]||(e[1]=function(){return r.onBlur&&r.onBlur.apply(r,arguments)})},r.getPTOptions("root"),{"aria-labelledby":t.ariaLabelledby,"data-p-checked":r.active,"data-p-disabled":t.disabled}),[C("span",I({class:t.cx("content")},r.getPTOptions("content")),[v(t.$slots,"default",{},(function(){return[v(t.$slots,"icon",{value:t.d_value,class:z(t.cx("icon"))},(function(){return[t.onIcon||t.offIcon?(m(),g("span",I({key:0,class:[t.cx("icon"),t.d_value?t.onIcon:t.offIcon]},r.getPTOptions("icon")),null,16)):$("",!0)]})),C("span",I({class:t.cx("label")},r.getPTOptions("label")),x(r.label),17)]}))],16)],16,go)),[[s]])};var vo=St.extend({name:"selectbutton",theme:function(t){var e=t.dt;return"\n.p-selectbutton {\n display: inline-flex;\n user-select: none;\n vertical-align: bottom;\n outline-color: transparent;\n border-radius: ".concat(e("selectbutton.border.radius"),";\n}\n\n.p-selectbutton .p-togglebutton {\n border-radius: 0;\n border-width: 1px 1px 1px 0;\n}\n\n.p-selectbutton .p-togglebutton:focus-visible {\n position: relative;\n z-index: 1;\n}\n\n.p-selectbutton .p-togglebutton:first-child {\n border-inline-start-width: 1px;\n border-start-start-radius: ").concat(e("selectbutton.border.radius"),";\n border-end-start-radius: ").concat(e("selectbutton.border.radius"),";\n}\n\n.p-selectbutton .p-togglebutton:last-child {\n border-start-end-radius: ").concat(e("selectbutton.border.radius"),";\n border-end-end-radius: ").concat(e("selectbutton.border.radius"),";\n}\n\n.p-selectbutton.p-invalid {\n outline: 1px solid ").concat(e("selectbutton.invalid.border.color"),";\n outline-offset: 0;\n}\n")},classes:{root:function(t){return["p-selectbutton p-component",{"p-invalid":t.instance.$invalid}]}}});function yo(t){return function(t){if(Array.isArray(t))return Io(t)}(t)||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(t)||xo(t)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array 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--- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/.gitignore @@ -0,0 +1,40 @@ +__pycache__/ +.idea/ +.vscode/ +.tmp +.cache +src/.cache/ +models/ +*.bin +*.safetensors +*.ckpt +*.pth +*.pt +config.ini +snapshots/** +startup-scripts/** +.openart_key +.youml +matrix_auth +channels.list +comfyworkflows_sharekey +github-stats-cache.json +pip_overrides.json +check2.sh + +.DS_Store +*pyc +.vscode +__pycache__ +*.egg-info +*.bak +checkpoints +results +backup +.python-version +node.zip +CLAUDE.md +.claude +last_session.yaml +ideas.yaml +test_film_emulation_fix.py \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/AI_DETECTION_IMPROVEMENTS.md b/custom_nodes/ComfyUI-EasyColorCorrector/AI_DETECTION_IMPROVEMENTS.md new file mode 100644 index 00000000..8c0ac1c2 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/AI_DETECTION_IMPROVEMENTS.md @@ -0,0 +1,150 @@ +# AI Detection Improvements for EasyColorCorrector + +## Overview +The AI scene detection has been significantly enhanced to better handle modern AI-generated imagery. The previous system used basic edge density and saturation thresholds that weren't suitable for sophisticated AI art. + +## Key Improvements + +### 1. Multi-Pass Analysis System +- **Pass 1**: Basic metrics (saturation, edge density, texture contrast) +- **Pass 2**: Advanced feature analysis with scoring system +- **Pass 3**: Confidence-based classification with fallbacks + +### 2. Enhanced Detection Features + +#### Saturation Pattern Analysis +- **High saturation pixels**: Detects vibrant AI art styles +- **Medium saturation pixels**: Identifies realistic skin tones and natural colors +- **Low saturation pixels**: Recognizes muted/desaturated styles + +#### Color Coherence Detection +- Analyzes relationships between dominant colors in LAB color space +- AI art typically has more coherent color schemes than photos +- Helps distinguish between stylized art and natural photography + +#### Edge Smoothness Analysis +- Multi-scale edge detection to identify AI rendering characteristics +- AI art often has smoother gradients compared to photographed textures +- Distinguishes between painted/rendered vs. captured content + +#### Frequency Domain Analysis +- FFT-based analysis of spatial frequency distribution +- AI content often lacks high-frequency natural textures +- Helps identify smooth, digitally-created content + +### 3. Improved Scene Type Scoring + +Each scene type now uses multiple weighted indicators: + +#### Anime Detection +- High saturation pixels (>15% of image) +- Smooth edges (smoothness > 0.7) +- Coherent color schemes (coherence > 0.6) +- High average saturation (>120) +- Low high-frequency content + +#### Concept Art Detection +- High color variance (>45) +- Medium-high saturation (>80) +- Moderate edge detail (0.1-0.3 density) +- Good color coherence (>0.5) +- Reasonable texture contrast (>35) + +#### Stylized Art Detection +- Good edge smoothness (>0.6) +- Moderate saturation (70-150) +- Strong color coherence (>0.55) +- Medium saturation pixel distribution (>30%) +- Smooth frequency characteristics + +#### Portrait Detection +- Face detection (40% weight) +- Soft edges (not too harsh) +- Appropriate skin tone saturation +- Some color coherence +- Reasonable detail/texture + +### 4. Enhanced Face Detection +- Multi-scale detection optimized for AI-generated faces +- More lenient parameters for stylized/anime portraits +- Post-processing to filter false positives +- Aspect ratio validation for realistic faces + +### 5. Improved Lighting Analysis +- Pixel distribution analysis (dark, bright, mid-tone concentration) +- New "moody" category for dramatic AI art +- Additional metrics stored for fine-tuning +- Better handling of AI-specific lighting characteristics + +## Expected Results for Your Test Prompts + +### Auto Mode Testing + +| Prompt Type | Expected Detection | Key Indicators | +|-------------|-------------------|----------------| +| **Fantasy Dragon** | `concept_art` or `detailed_illustration` | High color variance, artistic coherence, moderate edge detail | +| **Anime Girl** | `anime` | High saturation, smooth edges, coherent colors | +| **Stylized Portrait** | `stylized_art` or `portrait` | Face detection + artistic smoothness | +| **Realistic Portrait** | `portrait` | Face detection + natural characteristics | +| **Night Street** | `realistic_photo` + `low_light` | Natural textures + dark pixel analysis | + +### Preset Mode Testing + +| Prompt Type | Expected Scene Type | Lighting | Notes | +|-------------|-------------------|----------|--------| +| **Desert Biker** | `concept_art` | `good` or `bright` | Cinematic composition | +| **Epic Fantasy** | `concept_art` | Varies | High color variance | +| **Anime Moody** | `anime` | `moody` or `low_light` | New moody category | +| **Cyberpunk** | `stylized_art` | `low_light` | Neon characteristics | +| **Vintage Film** | `stylized_art` | Varies | Artistic coherence | + +### Manual Mode Testing + +| Prompt Type | Expected Detection | Color Characteristics | +|-------------|-------------------|---------------------| +| **Neutral Portrait** | `portrait` | Good for grading baseline | +| **Golden Hour** | `realistic_photo` | Warm color cast detection | +| **Studio Still Life** | `detailed_illustration` | Controlled lighting | +| **High Contrast City** | `realistic_photo` | Natural edge patterns | + +## Debugging Features + +The improved system now provides: + +- **Scene scores**: Individual scores for each scene type +- **Detection confidence**: Overall confidence level (0.0-1.0) +- **Lighting metrics**: Detailed brightness/contrast analysis +- **Feature analysis**: Edge smoothness, color coherence, frequency characteristics + +## Testing Your Improvements + +1. **Run the test script**: `python test_ai_detection.py` +2. **Add your AI images** to the `test_images/` directory +3. **Check confidence scores** - should be >0.6 for good detection +4. **Verify scene types** match your expectations +5. **Look at detailed scores** to understand classification reasoning + +## Troubleshooting Common Issues + +### Low Confidence Scores +- Image may be edge case between categories +- Try adjusting detection thresholds +- Check if image has mixed characteristics + +### Wrong Scene Type +- Review the feature analysis output +- Consider if image has unusual characteristics +- May need additional training on specific art styles + +### Missing Face Detection +- Check if face is stylized (anime/cartoon) +- Verify face size and orientation +- Consider if artistic style affects detection + +## Future Improvements + +1. **Machine Learning Classification**: Train a CNN on AI art categories +2. **Style Transfer Detection**: Identify specific AI models/styles +3. **Temporal Consistency**: For video/animation content +4. **User Feedback Loop**: Learn from user corrections + diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/LICENSE b/custom_nodes/ComfyUI-EasyColorCorrector/LICENSE new file mode 100644 index 00000000..9cf10627 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/LICENSE @@ -0,0 +1,19 @@ +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/README.md b/custom_nodes/ComfyUI-EasyColorCorrector/README.md new file mode 100644 index 00000000..36dfe145 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/README.md @@ -0,0 +1,472 @@ +# ComfyUI-EasyColorCorrector 🎨 + +**Professional-grade AI-powered color correction and enhancement for ComfyUI** + +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/) +[![ComfyUI](https://img.shields.io/badge/ComfyUI-Compatible-brightgreen.svg)](https://github.com/comfyanonymous/ComfyUI) + + +>[!WARNING] +> Release v1.2.1 introduces **major refactoring** and **enhancements** and a need name change to **ComfyUI-EasyColorCorrector**. This is a **breaking change** and you will may need to uninstall the old node before installing the new one. + +> [!NOTE] +> **Disclaimer**: I'm obviously joking about the "$1000+ plugin" claims and competing with Photoshop - this is a ComfyUI custom node, not industry software! πŸ˜„ **BUT** it does have legit AI-powered features, face detection, and professional color science. And hey, it beats paying Adobe! πŸ’° + +--- + +## πŸ†• **Latest Updates (Major Update)** + +### ✨ **Major Professional Improvements & Fixes** +- **Manual Mode is now truly professional**: Real temperature + tint separation (LAB color space), not just hue sliders +- **Preset Mode is transparent**: Sliders update live to show exactly what each preset does +- **Slider ranges are realistic**: No more wild valuesβ€”contrast/brightness are now in sensible ranges +- **White Balance is professional**: -1.0 (cool/blue) to +1.0 (warm/orange), like real color grading software +- **Skin tone processing fixed**: No more green tint on light skinβ€”portraits look natural +- **Smart UI**: AI analysis auto-disables in Manual mode for pure manual control +- **Live Preset Updates**: See exactly what each preset does to your sliders +- **2-Axis Color Control**: Separate temperature and tint like the pros use +- **Working 3-Way Correction**: Lift/Gamma/Gain actually works now + +### πŸ†• **New Nodes** +- **VAE Color Corrector**: Fix VAE-induced color shifts in inpainting/img2img workflows with advanced bias analysis +- **Batch Color Corrector**: Process entire video sequences with the same AI-powered corrections +- **RAW Image Processor**: Direct RAW file processing with professional color science +- **Color Corrector Viewer**: Real-time preview and analysis for video workflows +- **Color Palette Extractor**: Standalone palette extraction for reference workflows +- **Film Emulation**: Professional film stock emulation with highlight rolloff + +> The main Color Corrector is professional-grade and rock solid. The specialized nodes target specific workflows and advanced use cases. + +--- + +## πŸš€ What is this? + **ComfyUI-EasyColorCorrector** *is a professional-grade, AI-powered color correction suite that democratizes advanced color grading for the ComfyUI ecosystem.* + +**That’s the fancy version.** + +**Real talk:** I’m not trying to take over the color correction world. It’s just a node. + + I built it to bridge the gap between **β€œAI, *do the thing*”** and **β€œlet me tweak this like I’m grading a Netflix series.”** +If it helps artists, creators, and chaos-powered ComfyUI users get better color without paying Adobe or wiring 12 nodes together? Mission accomplished. + +If not… hey, at least now you have histograms. + + +**This node is *opinionated* about color science** - it leverages computer vision, perceptual color spaces, and advanced algorithms to deliver results that rival industry-standard color grading tools. + +### 🀯 The TL;DR + +- **πŸ€– AI-Powered**: Face detection, scene analysis, content-aware enhancement +- **🎨 Professional**: LAB color space, 3-way color correction, perceptual curves +- **⚑ Real-time**: Selective node execution for instant feedback +- **πŸŽ›οΈ Three Modes**: Auto (AI magic), Preset (curated looks), Manual (full control) + +--- + +![alt text](screen.png) + +## ✨ Key Features + +### πŸ€– **AI-Powered Intelligence** + +- **Face Detection**: Automatic skin tone preservation using OpenCV with advanced segmentation +- **Advanced Scene Analysis**: Detects anime, concept art, stylized art, detailed illustrations, portraits, and realistic photos +- **Lighting Detection**: Low-light, bright, flat, optimal lighting analysis +- **Content-Aware Enhancement**: Specialized profiles for artistic content vs. photographic content +- **Edge-Aware Processing**: Multi-scale edge detection with detail preservation +- **Advanced Segmentation**: SLIC-based skin region detection for precise face enhancement + +### 🎨 **Professional Color Science** + +- **LAB Color Space**: Perceptual white balance and color corrections with Bradford chromatic adaptation +- **K-means Analysis**: Intelligent dominant color extraction in both RGB and LAB spaces +- **3-Way Color Corrector**: Professional lift/gamma/gain controls +- **Perceptual Curves**: Gamma-aware brightness and contrast +- **Advanced Color Matching**: Robust reference image color matching with histogram and LAB methods +- **Professional Color Extraction**: Brightness-sorted palette generation with perceptual spacing + +### ⚑ **Performance & Usability** + +- **Real-time Preview**: Selective node execution (no full workflow runs) +- **Graceful Fallbacks**: Works without advanced libraries installed +- **Three Distinct Modes**: Each optimized for different use cases +- **Smart UI**: Mode-specific controls with intelligent tooltips +- **Visual Outputs**: Built-in histogram and color palette image generation +- **Multiple Output Types**: Processed image, palette data, histogram image, and palette image + +--- + +## πŸŽ›οΈ The Three Modes + +### πŸ€– **Auto Mode: AI Magic** + +Just make it look amazing + +The AI analyzes your image and applies: + +- **Scene-specific enhancement profiles** for 6 content types (anime, concept art, stylized art, detailed illustrations, portraits, realistic photos) +- **Intelligent white balance** in LAB color space +- **Face-aware skin tone preservation** using OpenCV detection +- **Adaptive contrast and saturation** with up to 50% boost for concept art +- **Lighting-aware brightness adjustments** for low-light, bright, flat, and optimal conditions +- **Enhanced glow effects** for artistic content with stronger highlight enhancement + +**Perfect for**: Quick enhancement, artistic content, concept art, anime, batch processing + +### 🎨 **Preset Mode: Curated Looks** + +Give me that specific vibe + +30 professional presets with intelligent slider updates: + +**Portrait Presets (5)**: Natural, Warm, Cool, High Key, Dramatic +**Concept Art & Illustration (6)**: Epic Fantasy, Sci-Fi Chrome, Dark Fantasy, Vibrant Concept, Matte Painting, Digital Art +**Artistic & Stylized (6)**: Anime Bright, Anime Moody, Cyberpunk, Pastel Dreams, Neon Nights, Comic Book +**Cinematic (5)**: Cinematic, Teal & Orange, Film Noir, Vintage Film, Bleach Bypass +**Natural (4)**: Golden Hour, Blue Hour, Sunny Day, Overcast +**Classic (4)**: Sepia, Black & White, Faded, Moody + +Enhanced Features: + +- **Live slider updates**: Presets automatically update parameter sliders for full transparency +- **Content-aware adaptation**: Presets automatically adjust based on detected scene type +- **Artistic content boost**: Up to 40% enhancement for concept art and illustrations +- **Smart variation**: Intelligent randomization that respects the original artistic intent + +**Perfect for**: Consistent styling, concept art workflows, artistic looks, client work + +### πŸŽ›οΈ **Manual Mode: Professional Control** + +*"I know exactly what I want"* + +Professional-grade tools with pure manual control: +- **Professional Temperature & Tint**: Separate LAB color space controls (blue↔orange, green↔magenta) +- **3-way color corrector**: Lift/Gamma/Gain with balanced ranges for precise control +- **Intelligent UI**: AI analysis auto-disabled for pure manual workflow +- **Balanced parameter ranges**: Realistic slider ranges that don't break at low values +- **Professional color grading**: Works like Lightroom/DaVinci Resolve +- **Film-like grain** with luminance preservation +- **Enhanced sliders**: Fine-tuned steps for precise adjustments + +**Perfect for**: Precision work, professional color grading, learning, complex corrections + +--- + +## πŸ”§ VAE Color Corrector + +### **Fix VAE-Induced Color Shifts in Inpainting Workflows** + +A specialized node designed to solve a common ComfyUI problem: when images go through VAE encoding/decoding (especially in inpainting workflows), they often develop noticeable color shifts compared to the original input. + +### ✨ **Key Features** + +- **Four Correction Methods**: + - **Luminance Zones**: Professional shadows/midtones/highlights correction (recommended) + - **Histogram Matching**: Match color distributions between original and processed + - **Statistical Matching**: Align color statistics (mean/std) across channels + - **Advanced 3D LUT**: Precise 3D color mapping using k-means clustering + +- **VAE-Aware Analysis**: + - Analyzes color bias patterns across luminance zones + - Detects high/medium/low VAE color bias automatically + - Applies intelligent strength adjustments based on bias severity + +- **Smart Preservation**: + - **Auto-detect**: Automatically identifies and preserves inpainted areas + - **Mask-based**: Use custom masks (white=preserve, black=correct) + - **Edge feathering**: Smooth transitions between corrected/preserved areas + +- **Performance Features**: + - **Lock Input**: Prevents upstream reprocessing when adjusting parameters + - **Smart Caching**: Preserves expensive VAE analysis between adjustments + - **Safe Color Clamping**: Prevents quantization artifacts (black squares) + +### 🎯 **Perfect For** + +- **Inpainting workflows** where color shifts occur after VAE processing +- **Img2img workflows** with noticeable color deviations +- **Batch processing** with consistent VAE color correction +- **Professional workflows** requiring precise color matching + +### πŸš€ **Usage** + +1. Connect **original image** (before VAE encoding) +2. Connect **processed image** (after VAE decoding) +3. Choose correction method (start with "luminance_zones") +4. Adjust **correction strength** (0.8 is usually good) +5. Enable **lock_input_image** for real-time parameter tuning + +``` +Original Image β†’ VAE Encode β†’ [Inpainting/Processing] β†’ VAE Decode β†’ Processed Image + ↓ ↓ + └──────────────────── VAE Color Corrector β†β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + ↓ + Color-Corrected Result +``` + +--- + +## πŸ“Š Visual Outputs & Analysis + +### πŸ–ΌοΈ **Four Output Types** + +The node provides four distinct outputs for maximum workflow flexibility: + +1. **πŸ“Έ Processed Image**: Your enhanced/corrected image +2. **🎨 Palette Data**: Comma-separated hex color string for other nodes +3. **πŸ“Š Histogram Image**: RGB histogram visualization (512Γ—768px) +4. **🎨 Palette Image**: Clean color swatches visualization (600Γ—120px) + +### πŸ“ˆ **Histogram Visualization** +- **Professional RGB histogram** with proper scaling and contrast +- **Multi-channel overlay** showing red, green, and blue distributions +- **Dark background** with subtle grid lines for better readability +- **Connect to Preview Image** node to view during processing + +### 🌈 **Color Palette Extraction** +- **6 dominant colors** extracted using advanced K-means clustering +- **Perceptual LAB-space analysis** for better color representation +- **Brightness-sorted arrangement** for natural palette flow +- **Clean swatch design** without borders or text clutter +- **Connect to Preview Image** node for instant palette viewing + +### πŸ”§ **Reference Image Color Matching** +- **Robust histogram matching** prevents black spots and artifacts +- **Conservative LAB adjustments** for natural color transitions +- **Multi-method approach** with safe fallbacks +- **Strength control** from 0.0 (no effect) to 1.0 (full matching) + +--- + +## πŸ› οΈ Installation + +### Quick Install (Recommended) + +```bash +cd ComfyUI/custom_nodes +git clone https://github.com/regiellis/ComfyUI-EasyColorCorrector.git +cd ComfyUI-EasyColorCorrector +pip install -r requirements.txt +``` + +### Manual Install + +1. Download and extract to `ComfyUI/custom_nodes/ComfyUI-EasyColorCorrector` +2. Install dependencies: + +```bash +pip install \ + "torch>=1.12.0" \ + "numpy>=1.21.0" \ + "opencv-python>=4.8.0" \ + "scikit-learn>=1.3.0" \ + "scikit-image>=0.21.0" \ + "colour-science>=0.4.3" \ + "scipy>=1.11.0" \ + "rawpy>=0.18.0" \ + "imageio>=2.28.0" \ + "OpenEXR>=1.3.9" \ + "Pillow>=9.0.0" \ + "torchvision>=0.13.0" \ + "huggingface_hub>=0.15.0" \ + "timm>=0.9.2" +``` + +3. Restart ComfyUI + +> **Note**: The node works without the advanced libraries, but you'll miss the AI-powered features. Install them for the full experience! + +--- + +## 🎠Usage + +### Basic Workflow + +#### **Main Color Corrector** +1. Add **"Easy Color Corrector"** node to your workflow +2. Connect an image input +3. Choose your mode: + - **Auto**: Enable AI analysis and let it work its magic + - **Preset**: Pick a style and optionally enable AI adaptation + - **Manual**: Full control with professional tools +4. **Connect outputs as needed**: + - Main **image output** β†’ Save Image or further processing + - **Histogram output** β†’ Preview Image (to see RGB analysis) + - **Palette output** β†’ Preview Image (to see color swatches) + - **Palette data** β†’ Text nodes or other color-aware nodes + +#### **VAE Color Corrector** +1. Add **"VAE Color Corrector"** node to your workflow +2. Connect **original image** (before VAE) and **processed image** (after VAE) +3. Choose correction method (start with "luminance_zones") +4. Adjust correction strength (0.8 recommended) +5. Optionally provide a mask or enable auto-preserve for inpainted areas + +### πŸ“Š **Enabling Visual Analysis** + +Enable **"Extract Palette"** to generate: +- βœ… RGB histogram visualization +- βœ… Color palette extraction and visualization +- βœ… Dominant color analysis in the console +- βœ… Advanced K-means clustering + +When disabled, histogram and palette outputs will be black images. + +### πŸ”₯ Pro Tips + +- **Enable Real-time Preview** for instant feedback (uses selective execution) +- **Manual Mode auto-disables AI** for pure manual control (can re-enable if needed) +- **Preset sliders update automatically** showing exactly what the preset does +- **White Balance**: -1.0 = cooler/blue, +1.0 = warmer/orange (professional range) +- **Temperature & Tint**: Separate controls in Manual mode for professional color grading +- **Face detection works best** with clear, well-lit portraits +- **Try presets first**, then fine-tune with manual controls +- **Enable Extract Palette** to see histogram and color analysis +- **Use Reference Image** for mood board color matching +- **Connect histogram/palette outputs** to Preview Image nodes for visual feedback + +### Example Console Output + +``` +πŸ€– AI Analysis: concept_art scene, good lighting, 0 faces detected +🎨 AI-Enhanced Preset: Epic Fantasy adapted for concept_art/good +πŸŽ›οΈ Manual Mode: Auto-disabled AI analysis for pure manual control +🌑️ Applied professional color adjustments: temperature: 0.3 (warmer), tint: -0.1 (green) +🎨 Extracted color palette: #2A1810,#8B4513,#D2691E,#F4A460,#DEB887,#F5DEB3 +πŸ–ΌοΈ Histogram and palette images available as separate node outputs +``` + +--- + +## βš™οΈ Advanced Configuration + +### AI Analysis Control + +```python +ai_analysis: bool = True # Enable AI-powered features +adjust_for_skin_tone: bool = True # Face detection + skin preservation +white_balance_strength: 0.0 # Temperature: -1.0 (cool) to +1.0 (warm) +enhancement_strength: 0.2 # Overall AI enhancement power (balanced default) +extract_palette: bool = False # Generate histogram and palette images +``` + +### Lock Input Image + +```python +lock_input_image: bool = False # Lock input to prevent upstream reprocessing +# When enabled: Caches input image so upstream nodes don't reprocess +# Result: Faster parameter adjustments without full workflow regeneration +``` + +### Performance Modes + +- **Full AI**: All features enabled (default) +- **Basic Mode**: AI analysis disabled for maximum speed +- **Real-time**: Selective execution for instant feedback + +--- + +## 🧠 The Science Behind It + +### Computer Vision Pipeline + +1. **OpenCV Face Detection**: Haar cascade classifiers with SLIC segmentation +2. **K-means Color Analysis**: 5-cluster dominant color extraction in RGB and LAB spaces +3. **Edge Density Analysis**: Multi-scale edge detection for anime vs. photo classification +4. **Histogram Analysis**: Lighting condition detection and RGB distribution analysis +5. **Advanced Segmentation**: SLIC superpixel segmentation for precise skin tone detection + +### Color Science + +1. **LAB Color Space**: Perceptual uniformity for white balance with Bradford chromatic adaptation +2. **Perceptual Curves**: Gamma-aware brightness/contrast with edge preservation +3. **Selective Saturation**: Vibrancy that preserves skin tones using advanced masking +4. **3-Way Correction**: Professional lift/gamma/gain masking with artistic content awareness +5. **Advanced Color Matching**: Histogram-based and LAB color space reference matching +6. **Professional Palette Extraction**: Brightness-sorted dominant colors with perceptual spacing + +### AI Enhancement Profiles + +```python +if scene_type == "concept_art": + contrast_boost = 0.35 * enhancement_strength + saturation_boost = 0.5 * enhancement_strength +elif scene_type == "anime": + contrast_boost = 0.25 * enhancement_strength + saturation_boost = 0.45 * enhancement_strength +elif scene_type == "portrait": + # More conservative for faces + contrast_boost = 0.15 * enhancement_strength + # + automatic skin warmth boost +``` + +--- + +## 🀝 Contributing + +Found a bug? Have an idea? Want to add more presets? + +1. **Fork** the repository +2. **Create** a feature branch (`git checkout -b amazing-feature`) +3. **Commit** your changes (`git commit -m 'Add amazing feature'`) +4. **Push** to the branch (`git push origin amazing-feature`) +5. **Open** a Pull Request + +### 🎨 Adding New Presets + +```python +"Your Preset Name": { + "warmth": 0.1, # -1.0 to 1.0 + "vibrancy": 0.3, # -1.0 to 1.0 + "contrast": 0.2, # -1.0 to 1.0 + "brightness": 0.05 # -1.0 to 1.0 +} +``` + +--- + +## πŸ“ License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +--- + +## πŸ™ Acknowledgments + +- **ComfyUI Community** for the amazing framework +- **OpenCV** for computer vision capabilities +- **Colour Science** for professional color space operations +- **scikit-learn** for machine learning algorithms + +--- + +## πŸ’‘ FAQ + +**Q: Do I need all the dependencies?** +A: The node works without them, but you'll miss the AI features. Install them for the full experience! + +**Q: Is this better than Photoshop?** +A: For color correction? It's competitive. For everything else? No. πŸ˜„ + +**Q: Can I use this commercially?** +A: Yes! MIT license means you can use it however you want. + +**Q: Why is it called "Easy" when it's so advanced?** +A: Because the AI makes professional color grading *easy*. The complexity is hidden behind intelligent automation. + +**Q: What's the difference between the main node and VAE Color Corrector?** +A: The main node is for general color correction and enhancement. VAE Color Corrector specifically fixes color shifts that occur when images go through VAE encoding/decoding in ComfyUI workflows. + +**Q: When should I use the VAE Color Corrector?** +A: Use it when you notice color shifts after inpainting, img2img, or any workflow where images pass through a VAE. Connect the original (pre-VAE) and processed (post-VAE) images. + +**Q: Why am I getting black squares in my images?** +A: This was a quantization artifact issue that's now fixed with safe color clamping. Update to the latest version for the fix. + +--- + +**Made with ❀️ and way too much caffeine** + +*Author: Regi E.* +*"Making AI-powered color grading accessible to everyone"* diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/__init__.py b/custom_nodes/ComfyUI-EasyColorCorrector/__init__.py new file mode 100644 index 00000000..3c47c6da --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/__init__.py @@ -0,0 +1,32 @@ +import os + +from .src import EasyColorCorrection, BatchColorCorrection, RawImageProcessor, ColorCorrectionViewer, ColorPaletteExtractor, FilmEmulation, VAEColorCorrector + +WEB_DIRECTORY = os.path.join(os.path.dirname(__file__), "js") +CSS_DIRECTORY = os.path.join(os.path.dirname(__file__), "css") + +NODE_CLASS_MAPPINGS = { + "EasyColorCorrection": EasyColorCorrection, + "BatchColorCorrection": BatchColorCorrection, + "RawImageProcessor": RawImageProcessor, + "ColorCorrectionViewer": ColorCorrectionViewer, + "ColorPaletteExtractor": ColorPaletteExtractor, + "FilmEmulation": FilmEmulation, + "VAEColorCorrector": VAEColorCorrector, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "EasyColorCorrection": "Color Corrector", + "BatchColorCorrection": "Batch Color Corrector (beta)", + "RawImageProcessor": "RAW Image Processor (beta)", + "ColorCorrectionViewer": "Color Correction Viewer (beta)", + "ColorPaletteExtractor": "Color Palette Extractor", + "FilmEmulation": "Film Emulation", + "VAEColorCorrector": "VAE Color Corrector", +} + +__all__ = [ + "NODE_CLASS_MAPPINGS", + "NODE_DISPLAY_NAME_MAPPINGS", + "WEB_DIRECTORY", + "CSS_DIRECTORY", +] diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/example_workflows/Easy Color Corrector.json b/custom_nodes/ComfyUI-EasyColorCorrector/example_workflows/Easy Color Corrector.json new file mode 100755 index 00000000..815bfd74 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/example_workflows/Easy Color Corrector.json @@ -0,0 +1,812 @@ +{ + "id": "d377023b-95db-4fab-8535-8ffd78f79dba", + "revision": 0, + 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a dynamic UI for three powerful modes with optional real-time preview: + * 1. Auto: πŸ€– AI-POWERED ENHANCEMENT - Face detection, scene analysis, perceptual color science + * 2. Preset: 🎨 STYLE-BASED PRESETS - Curated looks with intelligent variation + * 3. Manual: πŸŽ›οΈ PROFESSIONAL GRADING - Full manual control with advanced tools + */ + +// EasyColorCorrection Global Settings +const SETTINGS = { + NOTIFICATIONS: { + NONE: "none", + MINIMAL: "minimal", + FULL: "full" + }, + PREVIEW: { + AUTO_ENABLE: "auto_enable_preview", + QUALITY: "preview_quality" + }, + PROCESSING: { + AUTO_GPU: "auto_gpu_detection", + PREFER_CPU: "prefer_cpu_processing", + BATCH_SIZE: "batch_processing_size" + } +}; + +// Helper function to show notifications based on settings using ComfyUI toast API +function showNotification(message, type = "info") { + const notificationLevel = app.ui.settings.getSettingValue("EasyColorCorrection.notifications", SETTINGS.NOTIFICATIONS.FULL); + + // Debug: Log the current notification level + debugLog(`Notification level: ${notificationLevel}, message type: ${type}, message: ${message}`); + + switch (notificationLevel) { + case SETTINGS.NOTIFICATIONS.NONE: + // No notifications + break; + case SETTINGS.NOTIFICATIONS.MINIMAL: + // Only show errors and important messages + if (type === "error" || type === "important") { + app.extensionManager.toast.add({ + severity: type === "error" ? "error" : "warn", + summary: "Easy Color Corrector", + detail: message, + life: 5000 + }); + } + break; + case SETTINGS.NOTIFICATIONS.FULL: + default: + // Show all notifications using toast + let severity = "info"; + if (type === "error") severity = "error"; + else if (type === "important") severity = "warn"; + + app.extensionManager.toast.add({ + severity: severity, + summary: "Easy Color Correction", + detail: message, + life: 4000 + }); + break; + } + + // Always log to console for debugging + debugLog(message); +} + +// Helper function for debug logging +function debugLog(message, level = "info") { + const debugMode = app.ui.settings.getSettingValue("EasyColorCorrection.debug_mode", false); + + // Always log for now to debug settings issues + const timestamp = new Date().toISOString().substr(11, 8); + console.log(`[${timestamp}] [EasyColorCorrection] [${level}] ${message}`); +} + +// Helper function to check batch size warnings +function checkBatchSizeWarning(frameCount) { + const threshold = app.ui.settings.getSettingValue("EasyColorCorrection.batch_processing_size", 100); + + debugLog(`Checking batch size: ${frameCount} frames vs threshold: ${threshold}`); + + if (frameCount > threshold) { + showNotification( + `⚠️ Large batch detected: ${frameCount} frames (>${threshold}). Consider CPU processing to save VRAM.`, + "important" + ); + return true; + } + return false; +} + +app.registerExtension({ + name: "comfyui-easycolorcorrection", + + async setup() { + // Register settings when extension loads + app.ui.settings.addSetting({ + id: "EasyColorCorrection.notifications", + name: "🎨 Easy Color Corrector: Notification Level", + type: "combo", + options: [ + { value: SETTINGS.NOTIFICATIONS.NONE, text: "None - No notifications" }, + { value: SETTINGS.NOTIFICATIONS.MINIMAL, text: "Minimal - Errors and important only" }, + { value: SETTINGS.NOTIFICATIONS.FULL, text: "Full - All notifications (default)" } + ], + defaultValue: SETTINGS.NOTIFICATIONS.FULL, + tooltip: "Control how many notifications Easy Color Corrector shows" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.auto_enable_preview", + name: "🎨 Easy Color Corrector: Auto-Enable Preview", + type: "boolean", + defaultValue: true, + tooltip: "Automatically enable real-time preview for new nodes" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.preview_quality", + name: "🎨 Easy Color Corrector: Preview Quality", + type: "combo", + options: [ + { value: "low", text: "Low - Faster preview" }, + { value: "medium", text: "Medium - Balanced (default)" }, + { value: "high", text: "High - Best quality" } + ], + defaultValue: "medium", + tooltip: "Quality level for real-time preview rendering" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.auto_gpu_detection", + name: "🎨 Easy Color Corrector: Auto GPU Detection", + type: "boolean", + defaultValue: true, + tooltip: "Automatically detect and suggest GPU usage for batch processing" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.prefer_cpu_processing", + name: "🎨 Easy Color Corrector: Prefer CPU Processing", + type: "boolean", + defaultValue: false, + tooltip: "Default to CPU processing for new batch nodes (saves VRAM)" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.batch_processing_size", + name: "🎨 Easy Color Corrector: Batch Size Warning Threshold", + type: "number", + defaultValue: 100, + min: 10, + max: 1000, + step: 10, + tooltip: "Show VRAM warning when batch size exceeds this number of frames" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.show_processing_time", + name: "🎨 Easy Color Corrector: Show Processing Time", + type: "boolean", + defaultValue: true, + tooltip: "Display processing time in notifications" + }); + + app.ui.settings.addSetting({ + id: "EasyColorCorrection.debug_mode", + name: "🎨 Easy Color Corrector: Debug Mode", + type: "boolean", + defaultValue: false, + tooltip: "Enable detailed console logging for debugging" + }); + }, + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "ColorCorrectionViewer") { + // Use VideoHelperSuite pattern - simplified implementation + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + if (onNodeCreated) { + onNodeCreated.apply(this, arguments); + } + + const node = this; + + // Simple info display + node.infoWidget = node.addWidget("text", "πŸ“Š Status", "Waiting for media...", function(v) {}, { + multiline: false, + readonly: true + }); + + // File location button + node.addWidget("button", "πŸ“ Show Files", null, function() { + if (node.videoData && node.videoData.subfolder) { + showNotification(`πŸ“ Files: ComfyUI/output/${node.videoData.subfolder}/`, "info"); + } else { + showNotification("No files generated yet", "info"); + } + }); + + // Initialize data + node.videoData = { subfolder: null, totalFrames: 0, fps: 24.0 }; + + // Handle execution results (VideoHelperSuite compatible) + node.onExecuted = function(message) { + debugLog("ColorViewer onExecuted: " + JSON.stringify(Object.keys(message || {}))); + + if (!message) return; + + // Handle animated GIF (VideoHelperSuite format) + if (message.gifs && Array.isArray(message.gifs) && message.gifs.length > 0) { + const gif = message.gifs[0]; + this.videoData.subfolder = gif.subfolder; + this.videoData.totalFrames = gif.frame_count || 0; + this.videoData.fps = gif.frame_rate || 24.0; + + this.infoWidget.value = `🎬 GIF: ${this.videoData.totalFrames} frames @ ${this.videoData.fps} FPS`; + showNotification(`🎬 Animated preview: ${gif.filename}`, "info"); + debugLog(`GIF created: ${gif.filename} (${this.videoData.totalFrames} frames)`); + } + // Handle image sequence + else if (message.images && Array.isArray(message.images)) { + this.videoData.totalFrames = message.images.length; + + if (message.images.length > 0 && message.images[0].subfolder) { + this.videoData.subfolder = message.images[0].subfolder; + } + + this.infoWidget.value = `πŸ“Έ Images: ${this.videoData.totalFrames} frames`; + + if (this.videoData.totalFrames > 100) { + showNotification(`⚠️ Large sequence: ${this.videoData.totalFrames} frames`, "important"); + } else { + showNotification(`πŸ“Έ Sequence: ${this.videoData.totalFrames} frames`, "info"); + } + + debugLog(`Image sequence: ${this.videoData.totalFrames} frames`); + } + else { + this.infoWidget.value = "No media found"; + debugLog("No compatible media in message", "error"); + } + + this.setDirtyCanvas(true, true); + }; + }; + } + else if (nodeData.name === "EasyColorCorrection") { + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + if (onNodeCreated) { + onNodeCreated.apply(this, arguments); + } + + const node = this; + const modeWidget = node.widgets.find(w => w.name === "mode"); + const lockInputWidget = node.widgets.find(w => w.name === "lock_input_image"); + const useGpuWidget = node.widgets.find(w => w.name === "use_gpu"); + + // Apply global settings to new nodes + if (lockInputWidget) { + const autoEnablePreview = app.ui.settings.getSettingValue("EasyColorCorrection.auto_enable_preview", false); + if (autoEnablePreview) { + lockInputWidget.value = true; + } + } + + if (useGpuWidget) { + const preferCpu = app.ui.settings.getSettingValue("EasyColorCorrection.prefer_cpu_processing", false); + if (preferCpu) { + useGpuWidget.value = false; + } + } + + // A clear map of all widgets we need to control + const allWidgets = { + reference_strength: node.widgets.find(w => w.name === "reference_strength"), + extract_palette: node.widgets.find(w => w.name === "extract_palette"), + lock_input_image: node.widgets.find(w => w.name === "lock_input_image"), + ai_analysis: node.widgets.find(w => w.name === "ai_analysis"), + adjust_for_skin_tone: node.widgets.find(w => w.name === "adjust_for_skin_tone"), + white_balance_strength: node.widgets.find(w => w.name === "white_balance_strength"), + enhancement_strength: node.widgets.find(w => w.name === "enhancement_strength"), + pop_factor: node.widgets.find(w => w.name === "pop_factor"), + warmth: node.widgets.find(w => w.name === "warmth"), + vibrancy: node.widgets.find(w => w.name === "vibrancy"), + contrast: node.widgets.find(w => w.name === "contrast"), + brightness: node.widgets.find(w => w.name === "brightness"), + tint: node.widgets.find(w => w.name === "tint"), + preset: node.widgets.find(w => w.name === "preset"), + variation: node.widgets.find(w => w.name === "variation"), + lift: node.widgets.find(w => w.name === "lift"), + gamma: node.widgets.find(w => w.name === "gamma"), + gain: node.widgets.find(w => w.name === "gain"), + noise: node.widgets.find(w => w.name === "noise"), + effect_strength: node.widgets.find(w => w.name === "effect_strength"), + // Colorize mode widgets + colorize_strength: node.widgets.find(w => w.name === "colorize_strength"), + skin_warmth: node.widgets.find(w => w.name === "skin_warmth"), + sky_saturation: node.widgets.find(w => w.name === "sky_saturation"), + vegetation_green: node.widgets.find(w => w.name === "vegetation_green"), + sepia_tone: node.widgets.find(w => w.name === "sepia_tone"), + colorize_mode: node.widgets.find(w => w.name === "colorize_mode"), + // Semantic Selective Color widgets + skin_tone_adjustment: node.widgets.find(w => w.name === "skin_tone_adjustment"), + sky_adjustment: node.widgets.find(w => w.name === "sky_adjustment"), + foliage_adjustment: node.widgets.find(w => w.name === "foliage_adjustment"), + selective_hue_shift: node.widgets.find(w => w.name === "selective_hue_shift"), + selective_saturation: node.widgets.find(w => w.name === "selective_saturation"), + selective_strength: node.widgets.find(w => w.name === "selective_strength"), + force_colorize: node.widgets.find(w => w.name === "force_colorize"), + use_gpu: node.widgets.find(w => w.name === "use_gpu"), + }; + + // Mode-specific AI analysis tooltips to clarify how it works in each mode + const aiAnalysisTooltips = { + "Auto": "πŸš€ GPU-accelerated AI: tensor-based scene detection, lighting analysis, edge detection", + "Preset": "🎨 GPU-optimized presets: fast content type detection (anime/concept art/portrait)", + "Manual": "πŸŽ›οΈ GPU-enhanced guidance: advanced masking and controls with tensor operations", + "Colorize": "🎨 Smart colorization: automatically skips art/anime, only colorizes photos and realistic content" + }; + + // Configuration defining which widgets are visible in each mode + const visibilityConfig = { + "Auto": ["reference_strength", "extract_palette", "lock_input_image", "ai_analysis", "adjust_for_skin_tone", "white_balance_strength", "enhancement_strength", "pop_factor", "effect_strength", "use_gpu"], + "Preset": ["reference_strength", "extract_palette", "lock_input_image", "ai_analysis", "adjust_for_skin_tone", "white_balance_strength", "enhancement_strength", "preset", "variation", "effect_strength", "use_gpu"], + "Manual": ["reference_strength", "extract_palette", "lock_input_image", "ai_analysis", "adjust_for_skin_tone", "white_balance_strength", "enhancement_strength", "warmth", "vibrancy", "contrast", "brightness", "tint", "lift", "gamma", "gain", "noise", "skin_tone_adjustment", "sky_adjustment", "foliage_adjustment", "selective_hue_shift", "selective_saturation", "selective_strength", "effect_strength", "use_gpu"], + "Colorize": ["reference_strength", "extract_palette", "lock_input_image", "ai_analysis", "adjust_for_skin_tone", "colorize_strength", "skin_warmth", "sky_saturation", "vegetation_green", "sepia_tone", "colorize_mode", "force_colorize", "effect_strength", "use_gpu"], + }; + + // Real-time preview functionality + let realtimeTimeout = null; + const REALTIME_DELAY = 150; // ms debounce + + function scheduleRealtimeUpdate() { + if (!lockInputWidget || !lockInputWidget.value) return; + + // Clear existing timeout + if (realtimeTimeout) { + clearTimeout(realtimeTimeout); + } + + // Schedule new update + realtimeTimeout = setTimeout(() => { + triggerNodeExecution(); + }, REALTIME_DELAY); + } + + async function triggerNodeExecution() { + try { + // Check if the node has inputs connected + if (!node.inputs || !node.inputs[0] || !node.inputs[0].link) { + return; // No image input connected + } + + // Build selective workflow - only this node and its dependencies + const workflow = app.graph.serialize(); + const executionNodes = buildSelectiveWorkflow(workflow, node.id); + + if (Object.keys(executionNodes).length > 0) { + await api.queuePrompt(0, executionNodes); + } + } catch (error) { + console.warn("Real-time preview error:", error); + } + } + + function buildSelectiveWorkflow(workflow, targetNodeId) { + try { + // Convert to ComfyUI prompt format + const fullPrompt = app.graphToPrompt(); + if (!fullPrompt || !fullPrompt.workflow) { + console.warn("Could not build workflow prompt"); + return {}; + } + + const allNodes = fullPrompt.workflow; + const selectedNodes = {}; + const visited = new Set(); + + // Recursively collect dependencies + function collectDependencies(nodeId) { + const nodeIdStr = String(nodeId); + + if (visited.has(nodeIdStr) || !allNodes[nodeIdStr]) { + return; + } + + visited.add(nodeIdStr); + const nodeData = allNodes[nodeIdStr]; + + // Add this node to selection + selectedNodes[nodeIdStr] = { ...nodeData }; + + // Find input dependencies + if (nodeData.inputs) { + Object.values(nodeData.inputs).forEach(input => { + if (Array.isArray(input) && input.length >= 2) { + // This is a node connection [nodeId, outputIndex] + const dependencyNodeId = input[0]; + collectDependencies(dependencyNodeId); + } + }); + } + } + + // Start from target node + collectDependencies(targetNodeId); + + console.log(`πŸš€ AI Color Corrector: executing ${Object.keys(selectedNodes).length} nodes instead of ${Object.keys(allNodes).length}`); + console.log(`πŸ€– AI Analysis will detect: faces, scene type, lighting conditions, dominant colors`); + return selectedNodes; + + } catch (error) { + console.warn("Error building selective workflow:", error); + return {}; + } + } + + // Widgets that should trigger real-time updates when changed + const realtimeWidgets = [ + "reference_strength", "white_balance_strength", "enhancement_strength", "pop_factor", "effect_strength", + "warmth", "vibrancy", "contrast", "brightness", "tint", "preset", "variation", + "lift", "gamma", "gain", "noise" + ]; + + // === HISTOGRAM FUNCTIONALITY === + function generateHistogramData(imageData) { + const data = imageData.data; + const rHist = new Array(256).fill(0); + const gHist = new Array(256).fill(0); + const bHist = new Array(256).fill(0); + + for (let i = 0; i < data.length; i += 4) { + const r = Math.floor(data[i]); + const g = Math.floor(data[i + 1]); + const b = Math.floor(data[i + 2]); + + rHist[r]++; + gHist[g]++; + bHist[b]++; + } + + return { red: rHist, green: gHist, blue: bHist }; + } + + function createHistogramWidget() { + // Create proper LiteGraph widget + const histogramWidget = { + type: "histogram_display", + name: "πŸ“Š RGB Histogram", + value: null, + size: [280, 120], + + draw: function(ctx, nodeWidth, widgetY, height) { + if (!this.value) return; + + const margin = 10; + const w = Math.min(280, nodeWidth - margin * 2); + const h = 100; + const x = margin; + const y = widgetY; + + // Background + ctx.fillStyle = "#1a1a1a"; + ctx.fillRect(x, y, w, h); + ctx.strokeStyle = "#444"; + ctx.strokeRect(x, y, w, h); + + if (this.value && this.value.red) { + const histData = this.value; + const maxVal = Math.max( + Math.max(...histData.red), + Math.max(...histData.green), + Math.max(...histData.blue) + ); + + if (maxVal > 0) { + const scaleX = w / 256; + const scaleY = h / maxVal; + + // Red channel + ctx.strokeStyle = 'rgba(255, 100, 100, 0.8)'; + ctx.lineWidth = 1; + ctx.beginPath(); + for (let i = 0; i < 256; i++) { + const plotX = x + i * scaleX; + const plotY = y + h - (histData.red[i] * scaleY); + if (i === 0) ctx.moveTo(plotX, plotY); + else ctx.lineTo(plotX, plotY); + } + ctx.stroke(); + + // Green channel + ctx.strokeStyle = 'rgba(100, 255, 100, 0.8)'; + ctx.beginPath(); + for (let i = 0; i < 256; i++) { + const plotX = x + i * scaleX; + const plotY = y + h - (histData.green[i] * scaleY); + if (i === 0) ctx.moveTo(plotX, plotY); + else ctx.lineTo(plotX, plotY); + } + ctx.stroke(); + + // Blue channel + ctx.strokeStyle = 'rgba(100, 100, 255, 0.8)'; + ctx.beginPath(); + for (let i = 0; i < 256; i++) { + const plotX = x + i * scaleX; + const plotY = y + h - (histData.blue[i] * scaleY); + if (i === 0) ctx.moveTo(plotX, plotY); + else ctx.lineTo(plotX, plotY); + } + ctx.stroke(); + } + } + }, + + updateHistogram: function(imageElement) { + if (!imageElement) return; + + const tempCanvas = document.createElement('canvas'); + const tempCtx = tempCanvas.getContext('2d'); + tempCanvas.width = Math.min(imageElement.width, 512); // Limit size for performance + tempCanvas.height = Math.min(imageElement.height, 512); + + try { + tempCtx.drawImage(imageElement, 0, 0, tempCanvas.width, tempCanvas.height); + const imageData = tempCtx.getImageData(0, 0, tempCanvas.width, tempCanvas.height); + this.value = generateHistogramData(imageData); + } catch (error) { + console.log("Histogram update failed:", error); + this.value = null; + } + } + }; + + return histogramWidget; + } + + // === COLOR PALETTE DISPLAY === + function displayColorPalette(paletteString) { + if (!paletteString) return; + + const colors = paletteString.split(','); + console.log('🎨 Color Palette:', colors.map(c => `%c●%c ${c}`).join(' '), + ...colors.flatMap(c => [`color: ${c}; font-size: 16px`, 'color: inherit'])); + } + + function createColorPaletteWidget() { + // Create proper LiteGraph widget + const paletteWidget = { + type: "color_palette_display", + name: "🎨 Color Palette", + value: [], + size: [320, 80], + + draw: function(ctx, nodeWidth, widgetY, height) { + if (!this.value || this.value.length === 0) return; + + const margin = 10; + const w = Math.min(320, nodeWidth - margin * 2); + const h = 60; + const x = margin; + const y = widgetY; + + // Background + ctx.fillStyle = "#1a1a1a"; + ctx.fillRect(x, y, w, h); + ctx.strokeStyle = "#444"; + ctx.strokeRect(x, y, w, h); + + // Draw color swatches + const swatchWidth = w / this.value.length; + const swatchHeight = h - 20; + + this.value.forEach((color, index) => { + const swatchX = x + (index * swatchWidth) + 2; + const swatchY = y + 2; + + // Draw color swatch + ctx.fillStyle = color; + ctx.fillRect(swatchX, swatchY, swatchWidth - 4, swatchHeight); + + // Draw subtle border + ctx.strokeStyle = '#666'; + ctx.lineWidth = 1; + ctx.strokeRect(swatchX, swatchY, swatchWidth - 4, swatchHeight); + + // Draw hex label + ctx.fillStyle = '#ffffff'; + ctx.font = '9px monospace'; + ctx.textAlign = 'center'; + const textX = swatchX + (swatchWidth - 4) / 2; + const textY = y + h - 5; + ctx.fillText(color.toUpperCase(), textX, textY); + }); + }, + + updatePalette: function(colorsArray) { + this.value = colorsArray || []; + } + }; + + return paletteWidget; + } + + function updateWidgetVisibility() { + if (!modeWidget) return; + const currentMode = modeWidget.value; + const visibleWidgets = visibilityConfig[currentMode] || []; + const aiAnalysisEnabled = allWidgets.ai_analysis ? allWidgets.ai_analysis.value : true; + + // Use ComfyUI's built-in 'hidden' property + for (const name in allWidgets) { + const widget = allWidgets[name]; + if (widget) { + let shouldShow = visibleWidgets.includes(name); + + // Note: enhancement_strength works with or without AI analysis + // No conditional hiding needed + + widget.hidden = !shouldShow; + } + } + + // Update AI analysis tooltip based on current mode + if (allWidgets.ai_analysis && aiAnalysisTooltips[currentMode]) { + allWidgets.ai_analysis.tooltip = aiAnalysisTooltips[currentMode]; + + // Helpful console message to clarify AI analysis works in all modes + if (allWidgets.ai_analysis.value) { + console.log(`🎯 ${currentMode} Mode: AI Analysis enhances this mode! ${aiAnalysisTooltips[currentMode]}`); + } + } + + // Trigger a redraw to ensure the node resizes correctly + node.computeSize(); + node.setDirtyCanvas(true, true); + } + + function attachRealtimeCallbacks() { + // Attach real-time callbacks to relevant widgets + for (const widgetName of realtimeWidgets) { + const widget = allWidgets[widgetName]; + if (widget && widget.type === "number") { + const originalCallback = widget.callback; + widget.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + scheduleRealtimeUpdate(); + }; + } + } + } + + // --- Attach Callback to Mode Widget --- + if (modeWidget) { + const originalModeCallback = modeWidget.callback; + modeWidget.callback = function() { + if (originalModeCallback) originalModeCallback.apply(this, arguments); + + // Auto-disable AI analysis when switching to Manual mode + if (this.value === "Manual" && allWidgets.ai_analysis) { + allWidgets.ai_analysis.value = false; + console.log("πŸŽ›οΈ Manual Mode: Auto-disabled AI analysis for pure manual control"); + } + + updateWidgetVisibility(); + }; + } + + // --- Attach Callback to Preset Widget --- + if (allWidgets.preset) { + const originalPresetCallback = allWidgets.preset.callback; + allWidgets.preset.callback = function() { + if (originalPresetCallback) originalPresetCallback.apply(this, arguments); + updateSlidersFromPreset(this.value); + }; + } + + // --- Attach Callback to AI Analysis Widget --- + if (allWidgets.ai_analysis) { + const originalAiCallback = allWidgets.ai_analysis.callback; + allWidgets.ai_analysis.callback = function() { + if (originalAiCallback) originalAiCallback.apply(this, arguments); + updateWidgetVisibility(); // Update visibility when AI analysis changes + }; + } + + // === PRESET VALUE DEFINITIONS === + const PRESETS = { + "Natural Portrait": { warmth: 0.08, vibrancy: 0.12, contrast: 0.08, brightness: 0.03 }, + "Warm Portrait": { warmth: 0.18, vibrancy: 0.15, contrast: 0.06, brightness: 0.05 }, + "Cool Portrait": { warmth: -0.12, vibrancy: 0.08, contrast: 0.10, brightness: 0.02 }, + "High Key Portrait": { warmth: 0.05, vibrancy: 0.08, contrast: -0.05, brightness: 0.20 }, + "Dramatic Portrait": { warmth: 0.02, vibrancy: 0.20, contrast: 0.25, brightness: -0.05 }, + "Epic Fantasy": { warmth: 0.1, vibrancy: 0.4, contrast: 0.3, brightness: 0.05 }, + "Sci-Fi Chrome": { warmth: -0.2, vibrancy: 0.3, contrast: 0.35, brightness: 0.1 }, + "Dark Fantasy": { warmth: -0.1, vibrancy: 0.25, contrast: 0.4, brightness: -0.15 }, + "Vibrant Concept": { warmth: 0.05, vibrancy: 0.5, contrast: 0.25, brightness: 0.08 }, + "Matte Painting": { warmth: 0.08, vibrancy: 0.3, contrast: 0.2, brightness: 0.03 }, + "Digital Art": { warmth: 0.0, vibrancy: 0.45, contrast: 0.28, brightness: 0.05 }, + "Anime Bright": { warmth: 0.12, vibrancy: 0.45, contrast: 0.2, brightness: 0.12 }, + "Anime Moody": { warmth: -0.05, vibrancy: 0.35, contrast: 0.25, brightness: -0.05 }, + "Cyberpunk": { warmth: -0.15, vibrancy: 0.45, contrast: 0.25, brightness: -0.03 }, + "Pastel Dreams": { warmth: 0.12, vibrancy: -0.08, contrast: -0.08, brightness: 0.12 }, + "Neon Nights": { warmth: -0.18, vibrancy: 0.40, contrast: 0.20, brightness: -0.05 }, + "Comic Book": { warmth: 0.05, vibrancy: 0.5, contrast: 0.35, brightness: 0.08 }, + "Cinematic": { warmth: 0.12, vibrancy: 0.15, contrast: 0.18, brightness: 0.02 }, + "Teal & Orange": { warmth: -0.08, vibrancy: 0.25, contrast: 0.15, brightness: 0.0 }, + "Film Noir": { warmth: -0.05, vibrancy: -0.80, contrast: 0.35, brightness: -0.08 }, + "Vintage Film": { warmth: 0.15, vibrancy: -0.10, contrast: 0.12, brightness: 0.03 }, + "Bleach Bypass": { warmth: -0.02, vibrancy: -0.25, contrast: 0.30, brightness: 0.05 }, + "Golden Hour": { warmth: 0.25, vibrancy: 0.18, contrast: 0.08, brightness: 0.08 }, + "Blue Hour": { warmth: -0.20, vibrancy: 0.15, contrast: 0.12, brightness: 0.02 }, + "Sunny Day": { warmth: 0.15, vibrancy: 0.20, contrast: 0.10, brightness: 0.08 }, + "Overcast": { warmth: -0.08, vibrancy: 0.05, contrast: 0.08, brightness: 0.05 }, + "Sepia": { warmth: 0.30, vibrancy: -0.35, contrast: 0.08, brightness: 0.03 }, + "Black & White": { warmth: 0.0, vibrancy: -1.0, contrast: 0.15, brightness: 0.0 }, + "Faded": { warmth: 0.05, vibrancy: -0.15, contrast: -0.12, brightness: 0.08 }, + "Moody": { warmth: -0.08, vibrancy: 0.12, contrast: 0.20, brightness: -0.08 } + }; + + // Function to update sliders when preset changes + function updateSlidersFromPreset(presetName) { + const presetValues = PRESETS[presetName]; + if (!presetValues) return; + + // Update warmth, vibrancy, contrast, brightness sliders + if (allWidgets.warmth && allWidgets.warmth.type === "number") { + allWidgets.warmth.value = presetValues.warmth; + } + if (allWidgets.vibrancy && allWidgets.vibrancy.type === "number") { + allWidgets.vibrancy.value = presetValues.vibrancy; + } + if (allWidgets.contrast && allWidgets.contrast.type === "number") { + allWidgets.contrast.value = presetValues.contrast; + } + if (allWidgets.brightness && allWidgets.brightness.type === "number") { + allWidgets.brightness.value = presetValues.brightness; + } + + // Trigger realtime update if enabled + scheduleRealtimeUpdate(); + + // Force node redraw to show updated slider values + node.setDirtyCanvas(true, true); + + console.log(`🎨 Applied preset "${presetName}":`, presetValues); + } + + // === ADVANCED FEATURES SETUP === + let histogramWidget = null; + let paletteWidget = null; + + // Add widgets if extract_palette is enabled + function updateAdvancedFeatures() { + const extractPalette = allWidgets.extract_palette; + + if (extractPalette && extractPalette.value && !histogramWidget) { + // Add histogram widget + histogramWidget = createHistogramWidget(); + node.widgets.push(histogramWidget); + console.log("πŸ“Š Histogram display enabled"); + + // Add color palette widget + paletteWidget = createColorPaletteWidget(); + node.widgets.push(paletteWidget); + console.log("🎨 Color palette display enabled"); + + // Try to get current image and update histogram + setTimeout(() => { + const imageInput = node.inputs && node.inputs[0]; + if (imageInput && imageInput.link) { + // Find connected image node and update histogram + const connectedNode = app.graph.getNodeById(imageInput.link.origin_id); + if (connectedNode && connectedNode.imgs && connectedNode.imgs[0]) { + histogramWidget.updateHistogram(connectedNode.imgs[0]); + } + } + }, 100); + + } else if (extractPalette && !extractPalette.value && histogramWidget) { + // Remove histogram widget + const histogramIndex = node.widgets.indexOf(histogramWidget); + if (histogramIndex !== -1) { + node.widgets.splice(histogramIndex, 1); + } + histogramWidget = null; + console.log("πŸ“Š Histogram display disabled"); + + // Remove palette widget + const paletteIndex = node.widgets.indexOf(paletteWidget); + if (paletteIndex !== -1) { + node.widgets.splice(paletteIndex, 1); + } + paletteWidget = null; + console.log("🎨 Color palette display disabled"); + } + + node.computeSize(); + node.setDirtyCanvas(true, true); + } + + // Monitor extract_palette changes + if (allWidgets.extract_palette) { + const originalCallback = allWidgets.extract_palette.callback; + allWidgets.extract_palette.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + setTimeout(updateAdvancedFeatures, 10); + }; + } + + // Listen for execution completion to update histogram and display palette + const originalOnExecuted = node.onExecuted; + node.onExecuted = function(message) { + if (originalOnExecuted) originalOnExecuted.call(this, message); + + // Display color palette if extracted + if (message.palette_data && message.palette_data.length > 0) { + displayColorPalette(message.palette_data); + + // Update palette widget if enabled + if (paletteWidget && message.palette_data) { + const colors = message.palette_data.split(','); + paletteWidget.updatePalette(colors); + node.setDirtyCanvas(true, true); + } + } + + // Update histogram if enabled + if (histogramWidget && node.imgs && node.imgs[0]) { + histogramWidget.updateHistogram(node.imgs[0]); + } + + // Enhanced image preview integration for outputs + // Check if we have histogram and palette image outputs + if (extract_palette && extractPalette && extractPalette.value) { + try { + // Try to create preview for histogram and palette images if available + // This leverages ComfyUI's built-in image preview system + const outputImages = []; + + // Add main processed image + if (node.imgs && node.imgs[0]) { + outputImages.push(node.imgs[0]); + } + + // Note: Histogram and palette images are now available as separate outputs + // Users can connect them to Preview Image nodes for visualization + console.log("πŸ–ΌοΈ Histogram and palette images available as separate node outputs"); + + } catch (error) { + console.log("Preview integration note:", error); + } + } + }; + + // --- Initial UI Setup --- + setTimeout(() => { + updateWidgetVisibility(); + attachRealtimeCallbacks(); + updateAdvancedFeatures(); + }, 10); + }; + } + + // === BATCH COLOR CORRECTION NODE === + if (nodeData.name === "BatchColorCorrection") { + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + if (onNodeCreated) { + onNodeCreated.apply(this, arguments); + } + + const node = this; + const modeWidget = node.widgets.find(w => w.name === "mode"); + const framesPerBatchWidget = node.widgets.find(w => w.name === "frames_per_batch"); + const useGpuWidget = node.widgets.find(w => w.name === "use_gpu"); + + // A clear map of all widgets we need to control for batch processing + const allWidgets = { + frames_per_batch: node.widgets.find(w => w.name === "frames_per_batch"), + use_gpu: node.widgets.find(w => w.name === "use_gpu"), + reference_strength: node.widgets.find(w => w.name === "reference_strength"), + extract_palette: node.widgets.find(w => w.name === "extract_palette"), + ai_analysis: node.widgets.find(w => w.name === "ai_analysis"), + adjust_for_skin_tone: node.widgets.find(w => w.name === "adjust_for_skin_tone"), + white_balance_strength: node.widgets.find(w => w.name === "white_balance_strength"), + enhancement_strength: node.widgets.find(w => w.name === "enhancement_strength"), + warmth: node.widgets.find(w => w.name === "warmth"), + vibrancy: node.widgets.find(w => w.name === "vibrancy"), + contrast: node.widgets.find(w => w.name === "contrast"), + brightness: node.widgets.find(w => w.name === "brightness"), + preset: node.widgets.find(w => w.name === "preset"), + lift: node.widgets.find(w => w.name === "lift"), + gamma: node.widgets.find(w => w.name === "gamma"), + gain: node.widgets.find(w => w.name === "gain"), + noise: node.widgets.find(w => w.name === "noise"), + effect_strength: node.widgets.find(w => w.name === "effect_strength"), + }; + + // Apply global settings to new BatchColorCorrection nodes + if (useGpuWidget) { + const preferCpu = app.ui.settings.getSettingValue("EasyColorCorrection.prefer_cpu_processing", false); + if (preferCpu) { + useGpuWidget.value = false; + console.log("🎬 BatchColorCorrection: Applied CPU preference setting - GPU disabled by default"); + } + } + + // Mode-specific tooltips for batch processing + const aiAnalysisTooltips = { + "Auto": "πŸš€ GPU-accelerated AI analysis: fast tensor-based scene detection, lighting analysis, edge detection", + "Preset": "🎨 GPU-optimized presets: consistent style across frames with efficient tensor operations", + "Manual": "πŸŽ›οΈ GPU-enhanced controls: advanced color grading with full GPU acceleration" + }; + + // Batch-specific configuration defining which widgets are visible in each mode + const visibilityConfig = { + "Auto": ["frames_per_batch", "use_gpu", "reference_strength", "extract_palette", "ai_analysis", "adjust_for_skin_tone", "white_balance_strength", "enhancement_strength", "effect_strength"], + "Preset": ["frames_per_batch", "use_gpu", "reference_strength", "extract_palette", "ai_analysis", "adjust_for_skin_tone", "white_balance_strength", "enhancement_strength", "preset", "effect_strength"], + "Manual": ["frames_per_batch", "use_gpu", "reference_strength", "extract_palette", "ai_analysis", "adjust_for_skin_tone", "white_balance_strength", "enhancement_strength", "warmth", "vibrancy", "contrast", "brightness", "lift", "gamma", "gain", "noise", "effect_strength"], + }; + + // Progress tracking for batch processing + let batchProgress = { + currentBatch: 0, + totalBatches: 0, + totalFrames: 0, + isProcessing: false + }; + + function createBatchProgressWidget() { + const progressWidget = { + type: "batch_progress_display", + name: "🎬 Batch Progress", + value: batchProgress, + size: [320, 60], + + draw: function(ctx, nodeWidth, widgetY, height) { + const margin = 10; + const w = Math.min(320, nodeWidth - margin * 2); + const h = 50; + const x = margin; + const y = widgetY; + + // Background + ctx.fillStyle = "#1a1a1a"; + ctx.fillRect(x, y, w, h); + ctx.strokeStyle = "#444"; + ctx.strokeRect(x, y, w, h); + + if (this.value.totalFrames > 0) { + // Progress text + ctx.fillStyle = "#ffffff"; + ctx.font = "12px Arial"; + ctx.textAlign = "left"; + + const progressText = this.value.isProcessing + ? `πŸš€ GPU Processing: Batch ${this.value.currentBatch}/${this.value.totalBatches} β€’ ${this.value.totalFrames} frames` + : `πŸš€ GPU Ready: ${this.value.totalFrames} frames β€’ ${this.value.totalBatches} batches`; + + ctx.fillText(progressText, x + 5, y + 20); + + // Progress bar + if (this.value.isProcessing && this.value.totalBatches > 0) { + const progressWidth = w - 10; + const progressHeight = 8; + const progressX = x + 5; + const progressY = y + 30; + + // Background bar + ctx.fillStyle = "#333"; + ctx.fillRect(progressX, progressY, progressWidth, progressHeight); + + // Progress bar + const progress = this.value.currentBatch / this.value.totalBatches; + ctx.fillStyle = "#4CAF50"; + ctx.fillRect(progressX, progressY, progressWidth * progress, progressHeight); + + // Border + ctx.strokeStyle = "#666"; + ctx.strokeRect(progressX, progressY, progressWidth, progressHeight); + } + } + }, + + updateProgress: function(current, total, frames, isProcessing = false) { + this.value.currentBatch = current; + this.value.totalBatches = total; + this.value.totalFrames = frames; + this.value.isProcessing = isProcessing; + } + }; + + return progressWidget; + } + + // Frame count estimation based on frames_per_batch + function updateBatchEstimation() { + if (!framesPerBatchWidget) return; + + const framesPerBatch = framesPerBatchWidget.value || 16; + + // Try to estimate frame count from connected input + if (node.inputs && node.inputs[0] && node.inputs[0].link) { + const connectedNode = app.graph.getNodeById(node.inputs[0].link.origin_id); + if (connectedNode && connectedNode.imgs) { + const estimatedFrames = connectedNode.imgs.length || 1; + const estimatedBatches = Math.ceil(estimatedFrames / framesPerBatch); + + if (progressWidget) { + progressWidget.updateProgress(0, estimatedBatches, estimatedFrames, false); + node.setDirtyCanvas(true, true); + } + } + } + } + + function updateWidgetVisibility() { + if (!modeWidget) return; + const currentMode = modeWidget.value; + const visibleWidgets = visibilityConfig[currentMode] || []; + + // Use ComfyUI's built-in 'hidden' property + for (const name in allWidgets) { + const widget = allWidgets[name]; + if (widget) { + widget.hidden = !visibleWidgets.includes(name); + } + } + + // Update AI analysis tooltip based on current mode + if (allWidgets.ai_analysis && aiAnalysisTooltips[currentMode]) { + allWidgets.ai_analysis.tooltip = aiAnalysisTooltips[currentMode]; + + // Helpful console message for batch processing + if (allWidgets.ai_analysis.value) { + console.log(`🎬 Batch ${currentMode} Mode: AI Analysis optimized for video sequences! ${aiAnalysisTooltips[currentMode]}`); + } + } + + // Trigger a redraw to ensure the node resizes correctly + node.computeSize(); + node.setDirtyCanvas(true, true); + } + + // --- Attach Callback to Mode Widget --- + if (modeWidget) { + const originalModeCallback = modeWidget.callback; + modeWidget.callback = function() { + if (originalModeCallback) originalModeCallback.apply(this, arguments); + updateWidgetVisibility(); + }; + } + + // --- Attach Callback to Frames Per Batch Widget --- + if (framesPerBatchWidget) { + const originalFramesCallback = framesPerBatchWidget.callback; + framesPerBatchWidget.callback = function() { + if (originalFramesCallback) originalFramesCallback.apply(this, arguments); + updateBatchEstimation(); + + // Toast notification guidance for batch sizing + const frames = this.value; + let message, type; + + if (frames <= 4) { + message = `🎯 Batch Size: ${frames} frames - BEST QUALITY but SLOW (high memory per frame, detailed processing)`; + type = "info"; + } else if (frames <= 16) { + message = `βš–οΈ Batch Size: ${frames} frames - BALANCED Speed & Quality (recommended for most workflows)`; + type = "info"; + } else if (frames <= 32) { + message = `⚑ Batch Size: ${frames} frames - FAST processing but RESOURCE HEAVY (requires more VRAM)`; + type = "important"; + } else { + message = `πŸ”₯ Batch Size: ${frames} frames - MAXIMUM SPEED but VERY RESOURCE HEAVY (8GB+ VRAM recommended)`; + type = "important"; + } + + showNotification(message, type); + }; + } + + // === BATCH PROGRESS WIDGET SETUP === + let progressWidget = null; + + function addProgressWidget() { + if (!progressWidget) { + progressWidget = createBatchProgressWidget(); + node.widgets.push(progressWidget); + console.log("🎬 Batch progress display enabled"); + updateBatchEstimation(); + } + } + + // === BATCH PREVIEW WIDGET === + let previewWidget = null; + + function createBatchPreviewWidget() { + const previewWidget = { + type: "batch_preview_display", + name: "🎞️ Frame Preview", + value: { + frames: [], + currentFrame: 0, + isPlaying: false, + frameRate: 4, // fps for preview playback + lastUpdate: 0 + }, + size: [400, 320], + + draw: function(ctx, nodeWidth, widgetY, height) { + const margin = 10; + const w = Math.min(400, nodeWidth - margin * 2); + const h = 300; + const x = margin; + const y = widgetY; + + // Main preview area background + ctx.fillStyle = "#1a1a1a"; + ctx.fillRect(x, y, w, h); + ctx.strokeStyle = "#444"; + ctx.strokeRect(x, y, w, h); + + if (this.value.frames.length > 0) { + const currentFrame = this.value.frames[this.value.currentFrame]; + if (currentFrame) { + // Calculate aspect ratio and fit image + const imgAspect = currentFrame.width / currentFrame.height; + const previewAspect = w / (h - 80); // Leave space for controls + + let drawWidth, drawHeight, drawX, drawY; + if (imgAspect > previewAspect) { + drawWidth = w - 20; + drawHeight = drawWidth / imgAspect; + drawX = x + 10; + drawY = y + 10 + (h - 80 - drawHeight) / 2; + } else { + drawHeight = h - 100; + drawWidth = drawHeight * imgAspect; + drawX = x + (w - drawWidth) / 2; + drawY = y + 10; + } + + // Draw current frame + try { + ctx.drawImage(currentFrame, drawX, drawY, drawWidth, drawHeight); + + // Frame border + ctx.strokeStyle = "#666"; + ctx.lineWidth = 1; + ctx.strokeRect(drawX, drawY, drawWidth, drawHeight); + } catch (error) { + // Fallback if image can't be drawn + ctx.fillStyle = "#333"; + ctx.fillRect(drawX, drawY, drawWidth, drawHeight); + ctx.fillStyle = "#999"; + ctx.font = "14px Arial"; + ctx.textAlign = "center"; + ctx.fillText("Frame Preview", drawX + drawWidth/2, drawY + drawHeight/2); + } + } + + // Controls area + const controlsY = y + h - 70; + + // Progress bar + const progressWidth = w - 120; + const progressHeight = 6; + const progressX = x + 10; + const progressBarY = controlsY + 5; + + // Progress background + ctx.fillStyle = "#333"; + ctx.fillRect(progressX, progressBarY, progressWidth, progressHeight); + + // Progress fill + if (this.value.frames.length > 1) { + const progress = this.value.currentFrame / (this.value.frames.length - 1); + ctx.fillStyle = "#4CAF50"; + ctx.fillRect(progressX, progressBarY, progressWidth * progress, progressHeight); + } + + // Progress border + ctx.strokeStyle = "#666"; + ctx.strokeRect(progressX, progressBarY, progressWidth, progressHeight); + + // Frame counter + ctx.fillStyle = "#ffffff"; + ctx.font = "12px Arial"; + ctx.textAlign = "right"; + ctx.fillText(`${this.value.currentFrame + 1}/${this.value.frames.length}`, x + w - 10, controlsY + 15); + + // Help text (small) + ctx.fillStyle = "#888"; + ctx.font = "10px Arial"; + ctx.textAlign = "left"; + ctx.fillText("Space: play/pause, ←→: frame, Del: clear, +/-: speed", x + 10, controlsY + 35); + + // Play/Pause button area + const buttonSize = 30; + const buttonX = x + progressWidth + 20; + const buttonY = controlsY; + + ctx.fillStyle = this.value.isPlaying ? "#ff6b6b" : "#4CAF50"; + ctx.fillRect(buttonX, buttonY, buttonSize, buttonSize); + ctx.strokeStyle = "#fff"; + ctx.strokeRect(buttonX, buttonY, buttonSize, buttonSize); + + // Play/Pause icon + ctx.fillStyle = "#fff"; + ctx.font = "16px Arial"; + ctx.textAlign = "center"; + const iconText = this.value.isPlaying ? "⏸" : "β–Ά"; + ctx.fillText(iconText, buttonX + buttonSize/2, buttonY + buttonSize/2 + 5); + + // Auto-play animation + if (this.value.isPlaying && this.value.frames.length > 1) { + const now = Date.now(); + if (now - this.value.lastUpdate > (1000 / this.value.frameRate)) { + this.value.currentFrame = (this.value.currentFrame + 1) % this.value.frames.length; + this.value.lastUpdate = now; + node.setDirtyCanvas(true, true); + } + } + + } else { + // No frames message + ctx.fillStyle = "#666"; + ctx.font = "16px Arial"; + ctx.textAlign = "center"; + ctx.fillText("No frames to preview", x + w/2, y + h/2); + ctx.font = "12px Arial"; + ctx.fillText("Process some frames to see preview", x + w/2, y + h/2 + 25); + } + }, + + mouse: function(event, pos, node) { + if (event.type === "pointerdown" && this.value.frames.length > 0) { + const margin = 10; + const w = Math.min(400, node.size[0] - margin * 2); + const h = 300; + const x = margin; + const y = this.widgetY || 0; + const controlsY = y + h - 70; + + // Check if clicked on progress bar + const progressWidth = w - 120; + const progressX = x + 10; + const progressBarY = controlsY + 5; + + if (pos[0] >= progressX && pos[0] <= progressX + progressWidth && + pos[1] >= progressBarY && pos[1] <= progressBarY + 16) { + // Clicked on progress bar - seek to position + const clickProgress = (pos[0] - progressX) / progressWidth; + this.value.currentFrame = Math.floor(clickProgress * (this.value.frames.length - 1)); + this.value.currentFrame = Math.max(0, Math.min(this.value.currentFrame, this.value.frames.length - 1)); + node.setDirtyCanvas(true, true); + console.log(`🎞️ Seeked to frame ${this.value.currentFrame + 1}/${this.value.frames.length}`); + return true; + } + + // Check if clicked on play/pause button + const buttonSize = 30; + const buttonX = x + progressWidth + 20; + const buttonY = controlsY; + + if (pos[0] >= buttonX && pos[0] <= buttonX + buttonSize && + pos[1] >= buttonY && pos[1] <= buttonY + buttonSize) { + // Toggle play/pause + this.value.isPlaying = !this.value.isPlaying; + if (this.value.isPlaying) { + this.value.lastUpdate = Date.now(); + console.log("🎞️ Preview playback started"); + } else { + console.log("🎞️ Preview playback paused"); + } + node.setDirtyCanvas(true, true); + return true; + } + + // Double-click on preview area to clear frames + if (pos[1] < controlsY - 20) { + const now = Date.now(); + if (this.lastClickTime && (now - this.lastClickTime) < 300) { + // Double click - clear frames + this.clearFrames(); + node.setDirtyCanvas(true, true); + console.log("🎞️ Preview cleared (double-click)"); + return true; + } + this.lastClickTime = now; + } + } + return false; + }, + + onKeyDown: function(event, node) { + if (this.value.frames.length === 0) return false; + + switch(event.key) { + case ' ': // Spacebar - play/pause + this.value.isPlaying = !this.value.isPlaying; + if (this.value.isPlaying) { + this.value.lastUpdate = Date.now(); + } + node.setDirtyCanvas(true, true); + return true; + + case 'ArrowLeft': // Previous frame + this.value.currentFrame = Math.max(0, this.value.currentFrame - 1); + this.value.isPlaying = false; + node.setDirtyCanvas(true, true); + return true; + + case 'ArrowRight': // Next frame + this.value.currentFrame = Math.min(this.value.frames.length - 1, this.value.currentFrame + 1); + this.value.isPlaying = false; + node.setDirtyCanvas(true, true); + return true; + + case 'Home': // First frame + this.value.currentFrame = 0; + this.value.isPlaying = false; + node.setDirtyCanvas(true, true); + return true; + + case 'End': // Last frame + this.value.currentFrame = this.value.frames.length - 1; + this.value.isPlaying = false; + node.setDirtyCanvas(true, true); + return true; + + case 'Delete': // Clear frames + case 'Backspace': + this.clearFrames(); + node.setDirtyCanvas(true, true); + console.log("🎞️ Preview cleared (keyboard)"); + return true; + + case '+': // Increase frame rate + case '=': + this.value.frameRate = Math.min(30, this.value.frameRate + 1); + console.log(`🎞️ Frame rate: ${this.value.frameRate} fps`); + return true; + + case '-': // Decrease frame rate + this.value.frameRate = Math.max(1, this.value.frameRate - 1); + console.log(`🎞️ Frame rate: ${this.value.frameRate} fps`); + return true; + } + return false; + }, + + addFrame: function(imageElement) { + if (imageElement && this.value.frames.length < 50) { // Limit to 50 frames for performance + this.value.frames.push(imageElement); + console.log(`🎞️ Added frame ${this.value.frames.length} to preview`); + + // Auto-advance to newest frame if not playing + if (!this.value.isPlaying) { + this.value.currentFrame = this.value.frames.length - 1; + } + } + }, + + clearFrames: function() { + this.value.frames = []; + this.value.currentFrame = 0; + this.value.isPlaying = false; + console.log("🎞️ Preview frames cleared"); + }, + + setFrameRate: function(fps) { + this.value.frameRate = Math.max(1, Math.min(fps, 30)); + } + }; + + return previewWidget; + } + + function addPreviewWidget() { + if (!previewWidget) { + previewWidget = createBatchPreviewWidget(); + node.widgets.push(previewWidget); + console.log("🎞️ Batch preview display enabled"); + } + } + + // Enhanced execution tracking for batch processing + const originalOnExecuted = node.onExecuted; + node.onExecuted = function(message) { + if (originalOnExecuted) originalOnExecuted.call(this, message); + + // Update progress when execution completes + if (progressWidget && message.frame_count) { + const frameCount = parseInt(message.frame_count); + const framesPerBatch = framesPerBatchWidget ? framesPerBatchWidget.value : 16; + const totalBatches = Math.ceil(frameCount / framesPerBatch); + + progressWidget.updateProgress(totalBatches, totalBatches, frameCount, false); + node.setDirtyCanvas(true, true); + + console.log(`βœ… Batch processing complete: ${frameCount} frames processed in ${totalBatches} batches`); + } + + // Update preview widget with processed frames + if (previewWidget && node.imgs && node.imgs.length > 0) { + // Clear previous frames + previewWidget.clearFrames(); + + // Add new frames (limit to 50 for performance) + const maxFrames = Math.min(50, node.imgs.length); + const step = Math.max(1, Math.floor(node.imgs.length / maxFrames)); + + for (let i = 0; i < node.imgs.length; i += step) { + if (previewWidget.value.frames.length < maxFrames) { + previewWidget.addFrame(node.imgs[i]); + } + } + + console.log(`🎞️ Preview updated with ${previewWidget.value.frames.length} frames`); + node.setDirtyCanvas(true, true); + } + + // Display representative color palette for video + if (message.palette_data && message.palette_data.length > 0) { + console.log('🎨 Representative Video Palette:', message.palette_data.split(',').map(c => `%c●%c ${c}`).join(' '), + ...message.palette_data.split(',').flatMap(c => [`color: ${c}; font-size: 16px`, 'color: inherit'])); + } + }; + + // Monitor input connections for frame estimation + const originalOnConnectionsChange = node.onConnectionsChange; + node.onConnectionsChange = function(type, index, connected, link_info) { + if (originalOnConnectionsChange) originalOnConnectionsChange.call(this, type, index, connected, link_info); + + if (type === 1 && index === 0) { // Input connection change on first input + setTimeout(updateBatchEstimation, 100); + } + }; + + // --- Initial UI Setup --- + setTimeout(() => { + addProgressWidget(); + addPreviewWidget(); + updateWidgetVisibility(); + updateBatchEstimation(); + }, 10); + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/js/histogram_widget_examples.js b/custom_nodes/ComfyUI-EasyColorCorrector/js/histogram_widget_examples.js new file mode 100644 index 00000000..ea95de26 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/js/histogram_widget_examples.js @@ -0,0 +1,354 @@ +/** + * EXAMPLES: Proper ComfyUI Custom Widget Implementation + * Based on analysis of working extensions in the ComfyUI ecosystem + */ + +// Example 1: Histogram Widget (Fixed Implementation) +function createHistogramWidget() { + const widget = { + type: "HISTOGRAM_DISPLAY", + name: "histogram_display", + size: [320, 120], + value: null, // Store histogram data here + + // Main drawing function - ComfyUI calls this during node rendering + draw: function(ctx, nodeWidth, widgetY, height) { + const margin = 15; + const widgetWidth = nodeWidth - (margin * 2); + const widgetHeight = height || 100; + + // Draw widget background + ctx.save(); + ctx.fillStyle = LiteGraph.WIDGET_BGCOLOR || '#1a1a1a'; + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR || '#444'; + ctx.lineWidth = 1; + + ctx.beginPath(); + ctx.roundRect(margin, widgetY, widgetWidth, widgetHeight, [4]); + ctx.fill(); + ctx.stroke(); + + // Draw histogram if data exists + if (this.value && this.value.red) { + this.drawHistogramData(ctx, margin + 5, widgetY + 5, widgetWidth - 10, widgetHeight - 10); + } else { + // Draw placeholder text + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR || '#888'; + ctx.font = '12px Arial'; + ctx.textAlign = 'center'; + ctx.fillText('No image data', margin + widgetWidth/2, widgetY + widgetHeight/2); + } + + ctx.restore(); + }, + + // Helper function to draw histogram data + drawHistogramData: function(ctx, x, y, width, height) { + if (!this.value) return; + + const { red, green, blue } = this.value; + const maxVal = Math.max( + Math.max(...red), + Math.max(...green), + Math.max(...blue) + ); + + if (maxVal === 0) return; + + ctx.save(); + ctx.globalCompositeOperation = 'screen'; + + // Draw RGB channels + const channels = [ + { data: red, color: 'rgba(255, 100, 100, 0.8)' }, + { data: green, color: 'rgba(100, 255, 100, 0.8)' }, + { data: blue, color: 'rgba(100, 100, 255, 0.8)' } + ]; + + channels.forEach(channel => { + ctx.strokeStyle = channel.color; + ctx.lineWidth = 1; + ctx.beginPath(); + + for (let i = 0; i < 256; i++) { + const px = x + (i / 255) * width; + const py = y + height - (channel.data[i] / maxVal) * height; + + if (i === 0) ctx.moveTo(px, py); + else ctx.lineTo(px, py); + } + ctx.stroke(); + }); + + ctx.restore(); + }, + + // Update histogram data + updateHistogram: function(imageData) { + if (!imageData) return; + + this.value = this.generateHistogramData(imageData); + }, + + // Generate histogram from image data + generateHistogramData: function(imageData) { + const data = imageData.data; + const rHist = new Array(256).fill(0); + const gHist = new Array(256).fill(0); + const bHist = new Array(256).fill(0); + + for (let i = 0; i < data.length; i += 4) { + rHist[Math.floor(data[i])]++; + gHist[Math.floor(data[i + 1])]++; + bHist[Math.floor(data[i + 2])]++; + } + + return { red: rHist, green: gHist, blue: bHist }; + }, + + // Optional: Handle mouse events + mouse: function(event, pos, node) { + // Return true if event was handled, false otherwise + return false; + }, + + // Optional: Compute widget size + computeSize: function(width) { + return [width, 120]; // [width, height] + } + }; + + return widget; +} + +// Example 2: Color Palette Widget (Fixed Implementation) +function createColorPaletteWidget() { + const widget = { + type: "COLOR_PALETTE_DISPLAY", + name: "color_palette_display", + size: [320, 80], + value: [], // Store color array here + + draw: function(ctx, nodeWidth, widgetY, height) { + const margin = 15; + const widgetWidth = nodeWidth - (margin * 2); + const widgetHeight = height || 60; + + // Draw widget background + ctx.save(); + ctx.fillStyle = LiteGraph.WIDGET_BGCOLOR || '#1a1a1a'; + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR || '#444'; + ctx.lineWidth = 1; + + ctx.beginPath(); + ctx.roundRect(margin, widgetY, widgetWidth, widgetHeight, [4]); + ctx.fill(); + ctx.stroke(); + + // Draw color palette if data exists + if (this.value && this.value.length > 0) { + this.drawColorPalette(ctx, margin + 5, widgetY + 5, widgetWidth - 10, widgetHeight - 10); + } else { + // Draw placeholder + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR || '#888'; + ctx.font = '12px Arial'; + ctx.textAlign = 'center'; + ctx.fillText('No palette extracted', margin + widgetWidth/2, widgetY + widgetHeight/2); + } + + ctx.restore(); + }, + + drawColorPalette: function(ctx, x, y, width, height) { + if (!this.value || this.value.length === 0) return; + + const swatchWidth = width / this.value.length; + const swatchHeight = height - 20; // Leave space for labels + + ctx.save(); + + this.value.forEach((color, index) => { + const swatchX = x + index * swatchWidth; + const swatchY = y; + + // Draw color swatch + ctx.fillStyle = color; + ctx.fillRect(swatchX + 1, swatchY, swatchWidth - 2, swatchHeight); + + // Draw border + ctx.strokeStyle = '#666'; + ctx.lineWidth = 1; + ctx.strokeRect(swatchX + 1, swatchY, swatchWidth - 2, swatchHeight); + + // Draw label if space allows + if (swatchWidth > 30) { + ctx.fillStyle = '#fff'; + ctx.font = '8px monospace'; + ctx.textAlign = 'center'; + ctx.fillText( + color.substring(0, 7), // Show #RRGGBB + swatchX + swatchWidth/2, + y + height - 5 + ); + } + }); + + ctx.restore(); + }, + + updatePalette: function(colorsArray) { + this.value = colorsArray || []; + } + }; + + return widget; +} + +// Example 3: Interactive Button Widget (Based on rgthree-comfy) +function createButtonWidget(label, callback) { + const widget = { + type: "CUSTOM_BUTTON", + name: "custom_button", + size: [200, 30], + value: label, + isPressed: false, + + draw: function(ctx, nodeWidth, widgetY, height) { + const margin = 15; + const buttonWidth = Math.min(200, nodeWidth - margin * 2); + const buttonHeight = height || 25; + const buttonX = margin + (nodeWidth - margin * 2 - buttonWidth) / 2; // Center button + + ctx.save(); + + // Button background + ctx.fillStyle = this.isPressed ? '#555' : LiteGraph.WIDGET_BGCOLOR || '#333'; + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR || '#666'; + ctx.lineWidth = 1; + + ctx.beginPath(); + ctx.roundRect(buttonX, widgetY, buttonWidth, buttonHeight, [buttonHeight * 0.2]); + ctx.fill(); + ctx.stroke(); + + // Button text + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR || '#fff'; + ctx.font = '12px Arial'; + ctx.textAlign = 'center'; + ctx.textBaseline = 'middle'; + ctx.fillText(this.value, buttonX + buttonWidth/2, widgetY + buttonHeight/2); + + ctx.restore(); + + // Store button bounds for mouse detection + this._bounds = [buttonX, widgetY, buttonWidth, buttonHeight]; + }, + + mouse: function(event, pos, node) { + if (!this._bounds) return false; + + const [x, y, width, height] = this._bounds; + const isInside = pos[0] >= x && pos[0] <= x + width && + pos[1] >= y && pos[1] <= y + height; + + if (event.type === "pointerdown" && isInside) { + this.isPressed = true; + node.setDirtyCanvas(true, true); + return true; + } + + if (event.type === "pointerup") { + if (this.isPressed && isInside && callback) { + callback(); + } + this.isPressed = false; + node.setDirtyCanvas(true, true); + return this.isPressed; + } + + return false; + } + }; + + return widget; +} + +// Example 4: Progress Bar Widget (Based on ComfyUI-Crystools) +function createProgressBarWidget() { + const widget = { + type: "PROGRESS_BAR", + name: "progress_bar", + size: [300, 25], + value: 0, // Progress value 0-100 + + draw: function(ctx, nodeWidth, widgetY, height) { + const margin = 15; + const barWidth = nodeWidth - margin * 2; + const barHeight = height || 20; + + ctx.save(); + + // Background + ctx.fillStyle = LiteGraph.WIDGET_BGCOLOR || '#1a1a1a'; + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR || '#444'; + ctx.lineWidth = 1; + + ctx.beginPath(); + ctx.roundRect(margin, widgetY, barWidth, barHeight, [barHeight * 0.3]); + ctx.fill(); + ctx.stroke(); + + // Progress fill + if (this.value > 0) { + const fillWidth = (barWidth - 4) * (this.value / 100); + ctx.fillStyle = '#4a9eff'; + ctx.beginPath(); + ctx.roundRect(margin + 2, widgetY + 2, fillWidth, barHeight - 4, [barHeight * 0.2]); + ctx.fill(); + } + + // Progress text + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR || '#fff'; + ctx.font = '10px Arial'; + ctx.textAlign = 'center'; + ctx.textBaseline = 'middle'; + ctx.fillText(`${Math.round(this.value)}%`, margin + barWidth/2, widgetY + barHeight/2); + + ctx.restore(); + }, + + updateProgress: function(percentage) { + this.value = Math.max(0, Math.min(100, percentage)); + } + }; + + return widget; +} + +// Usage Examples: +/* +// In your node's onNodeCreated function: + +// 1. Add histogram widget +const histogramWidget = createHistogramWidget(); +node.addCustomWidget(histogramWidget); + +// 2. Add color palette widget +const paletteWidget = createColorPaletteWidget(); +node.addCustomWidget(paletteWidget); + +// 3. Add button widget +const buttonWidget = createButtonWidget("Process Image", () => { + console.log("Button clicked!"); +}); +node.addCustomWidget(buttonWidget); + +// 4. Add progress bar +const progressWidget = createProgressBarWidget(); +node.addCustomWidget(progressWidget); + +// Update widgets: +histogramWidget.updateHistogram(imageData); +paletteWidget.updatePalette(['#ff0000', '#00ff00', '#0000ff']); +progressWidget.updateProgress(75); +*/ \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/project_goals.md b/custom_nodes/ComfyUI-EasyColorCorrector/project_goals.md new file mode 100644 index 00000000..ec9f9d55 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/project_goals.md @@ -0,0 +1,145 @@ +# ComfyUI-Easy Color Corrector Project Goals + +## Project Vision + + **ComfyUI-EasyColorCorrector** is a professional-grade, AI-powered color correction suite that democratizes advanced color grading for the ComfyUI ecosystem. + + That’s the fancy version. + + Real talk: I’m not trying to take over the color correction world. It’s just a node. + + I built it to bridge the gap between β€œAI, do the thing” and β€œlet me tweak this like I’m grading a Netflix series.” + + If it helps artists, creators, and chaos-powered ComfyUI users get better color without paying Adobe or wiring 12 nodes together? Mission accomplished. + + If not… hey, at least now you have histograms. + + + +--- + +## Core Objectives + +### 1. **AI-Powered Automation** +Make professional color grading accessible through intelligent automation that understands content, lighting, and artistic intent. + +### 2. **Artistic Content Focus** +Specialize in enhancing concept art, anime, illustrations, and stylized content while maintaining excellent photographic support. + +### 3. **Real-Time Workflow Integration** +Seamlessly integrate with ComfyUI workflows with instant feedback and visual analysis tools. + +--- + +## Current Achievement Status + +### βœ… **Completed Goals** + +#### **AI Analysis Pipeline** +- [x] OpenCV-based face detection with SLIC segmentation +- [x] Scene classification (6 types: anime, concept art, portraits, etc.) +- [x] Lighting condition analysis (4 conditions) +- [x] Edge-aware content type detection +- [x] K-means color clustering in RGB and LAB spaces + +#### **Professional Color Science** +- [x] LAB color space operations with Bradford chromatic adaptation +- [x] 3-way color corrector (lift/gamma/gain) +- [x] Perceptual gamma curves with edge preservation +- [x] Advanced color matching with histogram and LAB methods +- [x] Professional palette extraction with brightness sorting + +#### **Three Operation Modes** +- [x] Auto Mode: AI-driven scene analysis and enhancement +- [x] Preset Mode: 30 curated professional looks with AI adaptation +- [x] Manual Mode: Full professional control with AI assistance + +#### **Advanced UI/UX** +- [x] Real-time preview with selective node execution +- [x] Dynamic mode-specific UI controls +- [x] Visual outputs (histogram, color palette images) +- [x] Graceful fallbacks for missing dependencies + +#### **Content-Aware Enhancement** +- [x] Artistic content detection and specialized processing +- [x] Skin tone preservation during face detection +- [x] Enhanced processing for concept art and illustrations +- [x] Adaptive strength based on content type + +--- + +## Current Roadmap & Future Goals + +### 🎯 **Short-Term Goals (v1.1-1.2)** + +#### **Performance & Optimization** +- [ ] Memory usage optimization for large images +- [ ] GPU utilization improvements for complex operations +- [ ] Batch processing optimization for multiple images +- [ ] Caching system for repeated AI analysis + +#### **Enhanced AI Capabilities** +- [ ] Improved anime/manga style detection +- [ ] Advanced artistic style classification (oil painting, watercolor, digital art, etc.) +- [ ] Content-aware masking for selective enhancements +- [ ] Intelligent auto-cropping suggestion for composition + +#### **User Experience Improvements** +- [ ] Preset favorites and custom preset saving +- [ ] One-click preset application with live preview +- [ ] Enhanced tooltips with before/after examples +- [ ] Keyboard shortcuts for common operations + +### πŸš€ **Medium-Term Goals (v1.3-1.5)** + +#### **Advanced Color Science** +- [ ] ACES color space support for professional workflows +- [ ] Advanced tone mapping for HDR content +- [ ] Color harmony analysis and suggestions +- [ ] Perceptual color difference calculations + +#### **Professional Features** +- [ ] Advanced masking tools (luminosity, color range, gradient) +- [ ] Multi-point curve adjustments +- [ ] Professional LUT import/export +- [ ] Color matching from reference images with region selection + +#### **AI Enhancement** +- [ ] Deep learning-based enhancement models +- [ ] Style transfer integration for artistic looks +- [ ] Intelligent auto-exposure and contrast +- [ ] Scene-aware noise reduction + +#### **Workflow Integration** +- [x] Batch processing node for multiple images +- [ ] Integration with other ComfyUI custom nodes +- [ ] API for external tool integration +- [ ] Cloud-based AI processing options + +### 🌟 **Long-Term Vision (v2.0+)** + +#### **Next-Generation AI** +- [ ] Custom-trained models for artistic content enhancement +- [ ] Real-time style adaptation based on artistic movements +- [ ] Intelligent composition analysis and suggestions +- [ ] Advanced facial feature enhancement with artistic consideration + +#### **Professional Studio Features** +- [ ] Multi-layer adjustment system +- [ ] Professional color grading workflows +- [ ] Client review and approval system +- [ ] Advanced color accuracy tools and calibration + +#### **Community & Ecosystem** +- [ ] Community preset sharing platform +- [ ] Plugin system for third-party enhancements +- [ ] Educational resources and tutorials +- [ ] Professional certification program + + + +--- + +*Project: ComfyUI-EasyColorCorrector* +*Current Version: 1.2.0* +*Updated: June 2025* \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/pyproject.toml b/custom_nodes/ComfyUI-EasyColorCorrector/pyproject.toml new file mode 100644 index 00000000..900d1e9c --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/pyproject.toml @@ -0,0 +1,30 @@ +# pyproject.tom +[project] +name = "ComfyUI-EasyColorCorrector" +description = "ComfyUI custom node for flexible and efficient image color correction and post-processing." +version = "1.2.5" + +license = {text = "MIT"} +dependencies = [ + "torch>=1.12.0", + "numpy>=1.21.0", + "opencv-python>=4.8.0", + "scikit-learn>=1.3.0", + "scikit-image>=0.21.0", + "colour-science>=0.4.3", + "scipy>=1.11.0", + "rawpy>=0.18.0", + "imageio>=2.28.0", + "OpenEXR>=1.3.9", + "Pillow>=9.0.0", + "torchvision>=0.13.0", + "huggingface_hub>=0.15.0", + "timm>=0.9.2", +] +[project.urls] +Repository = "https://github.com/regiellis/ComfyUI-EasyColorCorrector" + +[tool.comfy] +PublisherId = "itsjustregi" +DisplayName = "Easy Color Corrector" +Icon = "" diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/requirements.txt b/custom_nodes/ComfyUI-EasyColorCorrector/requirements.txt new file mode 100644 index 00000000..a1f0866b --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/requirements.txt @@ -0,0 +1,15 @@ +torch>=1.12.0 +numpy>=1.21.0 +opencv-python>=4.8.0 +scikit-learn>=1.3.0 +scikit-image>=0.21.0 +colour-science>=0.4.3 +scipy>=1.11.0 +rawpy>=0.18.0 +imageio>=2.28.0 +OpenEXR>=1.3.9 +Pillow>=9.0.0 +torchvision>=0.13.0 +huggingface_hub>=0.15.0 +timm>=0.9.2 + diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/screen.png b/custom_nodes/ComfyUI-EasyColorCorrector/screen.png new file mode 100755 index 00000000..ddfccbd9 Binary files /dev/null and b/custom_nodes/ComfyUI-EasyColorCorrector/screen.png differ diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/__init__.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/__init__.py new file mode 100644 index 00000000..053f5e0b --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/__init__.py @@ -0,0 +1,5345 @@ +# STANDARD LIBRARY IMPORTS +import typing +import os + +# THIRD-PARTY IMPORTS +import torch +import torch.nn.functional as F +import numpy as np + +# ADVANCED COMPUTER VISION AND COLOR SCIENCE +try: + import cv2 + from sklearn.cluster import KMeans + from skimage import filters, segmentation, measure + import colour + from scipy import ndimage + + ADVANCED_LIBS_AVAILABLE = True +except ImportError as e: + print(f"EasyColorCorrection: Advanced features disabled - missing library: {e}") + ADVANCED_LIBS_AVAILABLE = False + +# RAW IMAGE PROCESSING +try: + import rawpy + + RAW_PROCESSING_AVAILABLE = True +except ImportError as e: + print(f"EasyColorCorrection: RAW processing disabled - missing rawpy library: {e}") + RAW_PROCESSING_AVAILABLE = False + +# DEEP LEARNING COLORIZATION +try: + from PIL import Image + import torchvision.transforms as transforms + import urllib.request + import os + from huggingface_hub import hf_hub_download, snapshot_download + import timm + + DL_COLORIZATION_AVAILABLE = True +except ImportError as e: + print(f"EasyColorCorrection: Deep learning colorization disabled - missing libraries: {e}") + DL_COLORIZATION_AVAILABLE = False + +# HDR/EXR IMAGE PROCESSING +try: + import OpenEXR + import Imath + + EXR_PROCESSING_AVAILABLE = True +except ImportError as e: + print( + f"EasyColorCorrection: EXR processing disabled - missing OpenEXR library: {e}" + ) + EXR_PROCESSING_AVAILABLE = False + +try: + import imageio + + IMAGEIO_AVAILABLE = True +except ImportError as e: + print( + f"EasyColorCorrection: Extended format support disabled - missing imageio library: {e}" + ) + IMAGEIO_AVAILABLE = False + + +# --- Helper functions for color space conversion --- +def rgb_to_hsv(rgb: torch.Tensor) -> torch.Tensor: + """Converts an RGB image tensor to HSV. Expects input shape [B, H, W, C] with values 0-1.""" + cmax, cmax_indices = torch.max(rgb, dim=-1) + cmin = torch.min(rgb, dim=-1)[0] + delta = cmax - cmin + + h = torch.zeros_like(cmax) + h[cmax_indices == 0] = (((rgb[..., 1] - rgb[..., 2]) / (delta + 1e-8)) % 6)[ + cmax_indices == 0 + ] + h[cmax_indices == 1] = (((rgb[..., 2] - rgb[..., 0]) / (delta + 1e-8)) + 2)[ + cmax_indices == 1 + ] + h[cmax_indices == 2] = (((rgb[..., 0] - rgb[..., 1]) / (delta + 1e-8)) + 4)[ + cmax_indices == 2 + ] + + h = h / 6.0 + h[delta == 0] = 0.0 + + s = torch.where( + cmax == 0, torch.tensor(0.0, device=rgb.device), delta / (cmax + 1e-8) + ) + v = cmax + + return torch.stack([h, s, v], dim=-1) + + +def hsv_to_rgb(hsv: torch.Tensor) -> torch.Tensor: + """Converts an HSV image tensor to RGB. Expects input shape [B, H, W, C] with values 0-1.""" + h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] + i = (h * 6.0).floor() + f = (h * 6.0) - i + p = v * (1.0 - s) + q = v * (1.0 - s * f) + t = v * (1.0 - s * (1.0 - f)) + + rgb = torch.zeros_like(hsv) + + mask0, mask1, mask2 = (i % 6) == 0, (i % 6) == 1, (i % 6) == 2 + mask3, mask4, mask5 = (i % 6) == 3, (i % 6) == 4, (i % 6) == 5 + + rgb[mask0] = torch.stack([v, t, p], dim=-1)[mask0] + rgb[mask1] = torch.stack([q, v, p], dim=-1)[mask1] + rgb[mask2] = torch.stack([p, v, t], dim=-1)[mask2] + rgb[mask3] = torch.stack([p, q, v], dim=-1)[mask3] + rgb[mask4] = torch.stack([t, p, v], dim=-1)[mask4] + rgb[mask5] = torch.stack([v, p, q], dim=-1)[mask5] + + rgb[s == 0] = torch.stack([v, v, v], dim=-1)[s == 0] + + return rgb + + +def create_color_range_mask(hsv: torch.Tensor, target_hue: float, hue_range: float = 60.0) -> torch.Tensor: + """Create a mask for a specific color range in HSV space.""" + h = hsv[..., 0] # Hue channel (0-1) + s = hsv[..., 1] # Saturation channel (0-1) + + # Convert target hue from degrees to 0-1 range + target_h = (target_hue % 360) / 360.0 + range_h = hue_range / 360.0 + + # Calculate hue distance (handling wrap-around) + hue_diff = torch.abs(h - target_h) + hue_diff = torch.min(hue_diff, 1.0 - hue_diff) # Handle wrap-around at 0/1 + + # Create smooth falloff mask based on hue distance and saturation + hue_mask = torch.exp(-((hue_diff / range_h) ** 2) * 3.0) # Gaussian falloff + saturation_mask = torch.clamp(s * 2.0, 0.0, 1.0) # Favor more saturated colors + + return hue_mask * saturation_mask + + +def apply_selective_color_adjustment( + image: torch.Tensor, + target_hue: float, + hue_adjustment: float, + saturation_adjustment: float, + lightness_adjustment: float, + hue_range: float = 60.0 +) -> torch.Tensor: + """Apply selective color adjustments to a specific color range.""" + if abs(hue_adjustment) < 0.001 and abs(saturation_adjustment) < 0.001 and abs(lightness_adjustment) < 0.001: + return image + + # Convert to HSV for processing + hsv = rgb_to_hsv(image) + + # Create mask for target color range + mask = create_color_range_mask(hsv, target_hue, hue_range) + + # Apply adjustments + adjusted_hsv = hsv.clone() + + # Hue adjustment (wrap around 0-1) + if abs(hue_adjustment) > 0.001: + hue_shift = hue_adjustment * 0.1 # Scale adjustment + adjusted_hsv[..., 0] = (adjusted_hsv[..., 0] + hue_shift * mask.unsqueeze(-1)) % 1.0 + + # Saturation adjustment + if abs(saturation_adjustment) > 0.001: + sat_factor = 1.0 + saturation_adjustment + adjusted_hsv[..., 1] = torch.clamp( + adjusted_hsv[..., 1] * (1.0 + (sat_factor - 1.0) * mask.unsqueeze(-1)), + 0.0, 1.0 + ) + + # Lightness adjustment (applied to Value in HSV) + if abs(lightness_adjustment) > 0.001: + light_factor = 1.0 + lightness_adjustment * 0.5 # Scale adjustment + adjusted_hsv[..., 2] = torch.clamp( + adjusted_hsv[..., 2] * (1.0 + (light_factor - 1.0) * mask.unsqueeze(-1)), + 0.0, 1.0 + ) + + # Convert back to RGB + return hsv_to_rgb(adjusted_hsv) + + +class DDColorColorization: + """DDColor Deep Learning Colorization using pre-trained models.""" + + def __init__(self): + self.model = None + self.device = None + self.transform = None + self.model_name = "piddnad/ddcolor_modelscope" + + def _load_fallback_model(self, device): + """Load a simple fallback model if DDColor fails.""" + try: + import torch.nn as nn + + class FallbackColorizationNet(nn.Module): + def __init__(self): + super().__init__() + self.encoder = nn.Sequential( + nn.Conv2d(3, 64, 3, padding=1), + nn.ReLU(), + nn.Conv2d(64, 128, 3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(128, 256, 3, padding=1), + nn.ReLU(), + ) + self.decoder = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), + nn.Conv2d(256, 128, 3, padding=1), + nn.ReLU(), + nn.Conv2d(128, 64, 3, padding=1), + nn.ReLU(), + nn.Conv2d(64, 2, 3, padding=1), + nn.Tanh(), + ) + + def forward(self, x): + encoded = self.encoder(x) + decoded = self.decoder(encoded) + return decoded + + self.model = FallbackColorizationNet() + self.model.to(device) + self.model.eval() + self.device = device + + print("βœ… Fallback colorization model loaded") + return True + + except Exception as e: + print(f"❌ Failed to load fallback model: {e}") + return False + + + def load_model(self, device): + """Load the DDColor model from Hugging Face.""" + if not DL_COLORIZATION_AVAILABLE: + return False + + try: + print("πŸ”„ Loading DDColor model...") + from huggingface_hub import snapshot_download + import torch.nn as nn + + # Try to download the model files (this will be cached) + try: + model_path = snapshot_download(repo_id=self.model_name, cache_dir=os.path.join(os.path.dirname(__file__), ".cache")) + print(f"βœ… DDColor model files downloaded to {model_path}") + except Exception as e: + print(f"⚠️ Could not download DDColor model: {e}") + print("⚠️ Falling back to simplified colorization") + return self._load_fallback_model(device) + + # Load DDColor architecture (improved version with proper upsampling) + class DDColorNet(nn.Module): + def __init__(self): + super().__init__() + # Simple but effective encoder + self.encoder = nn.Sequential( + # Input: 3 x 256 x 256 + nn.Conv2d(3, 64, 3, padding=1), # 64 x 256 x 256 + nn.ReLU(), + nn.Conv2d(64, 64, 3, padding=1), # 64 x 256 x 256 + nn.ReLU(), + nn.MaxPool2d(2), # 64 x 128 x 128 + + nn.Conv2d(64, 128, 3, padding=1), # 128 x 128 x 128 + nn.ReLU(), + nn.Conv2d(128, 128, 3, padding=1), # 128 x 128 x 128 + nn.ReLU(), + nn.MaxPool2d(2), # 128 x 64 x 64 + + nn.Conv2d(128, 256, 3, padding=1), # 256 x 64 x 64 + nn.ReLU(), + nn.Conv2d(256, 256, 3, padding=1), # 256 x 64 x 64 + nn.ReLU(), + nn.MaxPool2d(2), # 256 x 32 x 32 + + nn.Conv2d(256, 512, 3, padding=1), # 512 x 32 x 32 + nn.ReLU(), + ) + + # Decoder with proper upsampling to match input size + self.decoder = nn.Sequential( + # 512 x 32 x 32 -> 256 x 64 x 64 + nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1), + nn.ReLU(), + + # 256 x 64 x 64 -> 128 x 128 x 128 + nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1), + nn.ReLU(), + + # 128 x 128 x 128 -> 64 x 256 x 256 + nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1), + nn.ReLU(), + + # Final layer to produce AB channels: 64 x 256 x 256 -> 2 x 256 x 256 + nn.Conv2d(64, 2, 3, padding=1), + nn.Tanh(), + ) + + def forward(self, x): + # Encode + encoded = self.encoder(x) + # Decode to AB channels + ab = self.decoder(encoded) + return ab + + self.model = DDColorNet() + self.model.to(device) + self.model.eval() + self.device = device + + # Set up transforms + self.transform = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + ]) + + print("βœ… DDColor model loaded successfully") + return True + + except Exception as e: + print(f"❌ Failed to load DDColor model: {e}") + print("⚠️ Falling back to basic colorization") + return self._load_fallback_model(device) + + def colorize_image(self, image_tensor, strength=1.0): + """Colorize a grayscale or low-saturation image using DDColor.""" + if self.model is None: + print("❌ DDColor model not loaded") + return image_tensor + + try: + print(f"πŸ€– Applying DDColor colorization with strength {strength:.2f}") + device = image_tensor.device + original_size = image_tensor.shape + print(f"πŸ”§ Input image size: {original_size}") + + # Prepare input - convert to proper format + input_rgb = image_tensor.squeeze(0) # Remove batch dimension + + # Convert to grayscale for DDColor input (expects single channel) + if ADVANCED_LIBS_AVAILABLE: + # Convert to grayscale using proper weights + gray = 0.299 * input_rgb[:,:,0] + 0.587 * input_rgb[:,:,1] + 0.114 * input_rgb[:,:,2] + gray_3ch = gray.unsqueeze(-1).repeat(1, 1, 3) # Convert to 3-channel for model + else: + # Simple average + gray = torch.mean(input_rgb, dim=2, keepdim=True) + gray_3ch = gray.repeat(1, 1, 3) + + # Resize for model input and add batch dimension + input_resized = F.interpolate(gray_3ch.unsqueeze(0).permute(0, 3, 1, 2), + size=(256, 256), mode='bilinear', align_corners=False) + + print(f"πŸ”§ Model input size: {input_resized.shape}") + + # Run through model + with torch.no_grad(): + ab_pred = self.model(input_resized) + print(f"πŸ”§ Model output size: {ab_pred.shape}") + + # Resize back to original size + if ab_pred.shape[-2:] != original_size[1:3]: + ab_pred = F.interpolate(ab_pred, size=original_size[1:3], + mode='bilinear', align_corners=False) + print(f"πŸ”§ Resized output to: {ab_pred.shape}") + + # Convert to LAB and combine with L channel + if ADVANCED_LIBS_AVAILABLE: + print("πŸ”§ Using advanced LAB color space conversion") + # Convert RGB input to LAB to get L channel + rgb_np = (input_rgb.cpu().numpy() * 255).astype(np.uint8) + lab_image = cv2.cvtColor(rgb_np, cv2.COLOR_RGB2LAB) + l_channel = lab_image[:, :, 0].astype(np.float32) + + # Scale AB predictions - be more conservative with scaling + ab_np = ab_pred.squeeze(0).permute(1, 2, 0).cpu().numpy() + + # DDColor typically outputs in [-1,1], scale more conservatively + ab_np = ab_np * 50.0 # Reduced from 127 to 50 for less aggressive coloring + + # Combine L and AB channels + lab_pred = np.zeros((original_size[1], original_size[2], 3), dtype=np.float32) + lab_pred[:,:,0] = l_channel # L channel from original + lab_pred[:,:,1] = ab_np[:,:,0] # A channel from prediction + lab_pred[:,:,2] = ab_np[:,:,1] # B channel from prediction + + # Ensure values are in valid LAB range + lab_pred[:,:,0] = np.clip(lab_pred[:,:,0], 0, 100) # L channel range + lab_pred[:,:,1:] = np.clip(lab_pred[:,:,1:], -127, 127) # AB channel range + + print(f"πŸ”§ LAB stats - L: [{lab_pred[:,:,0].min():.1f}, {lab_pred[:,:,0].max():.1f}], A: [{lab_pred[:,:,1].min():.1f}, {lab_pred[:,:,1].max():.1f}], B: [{lab_pred[:,:,2].min():.1f}, {lab_pred[:,:,2].max():.1f}]") + + # Convert back to RGB - ensure proper data type + lab_uint8 = lab_pred.astype(np.uint8) + rgb_pred = cv2.cvtColor(lab_uint8, cv2.COLOR_LAB2RGB) + result_tensor = torch.from_numpy(rgb_pred.astype(np.float32) / 255.0).unsqueeze(0).to(device) + print("βœ… LAB to RGB conversion completed") + else: + print("πŸ”§ Using fallback RGB colorization") + # Simple fallback - blend AB predictions with grayscale + ab_pred_scaled = ab_pred * 0.5 # Reduce intensity + + # Create colorized version + colorized = torch.zeros_like(input_rgb) + gray_normalized = (gray - gray.min()) / (gray.max() - gray.min() + 1e-8) + + # Use AB predictions to modulate RG channels + ab_resized = ab_pred.squeeze(0).permute(1, 2, 0) + colorized[:,:,0] = gray_normalized.squeeze(-1) # Keep grayscale as red + colorized[:,:,1] = torch.clamp(gray_normalized.squeeze(-1) + ab_resized[:,:,0] * 0.3, 0, 1) # A -> Green + colorized[:,:,2] = torch.clamp(gray_normalized.squeeze(-1) + ab_resized[:,:,1] * 0.3, 0, 1) # B -> Blue + + result_tensor = colorized.unsqueeze(0) + + # Blend with original based on strength + result = torch.lerp(image_tensor, result_tensor, strength) + print(f"βœ… DDColor colorization completed, blended with strength {strength:.2f}") + + return result + + except Exception as e: + print(f"❌ DDColor colorization failed: {e}") + import traceback + traceback.print_exc() + return image_tensor + + +# Global colorization instance +_deep_colorizer = None + +def get_deep_colorizer(): + """Get or create the global deep colorization instance.""" + global _deep_colorizer + if _deep_colorizer is None: + _deep_colorizer = DDColorColorization() + return _deep_colorizer + + +def apply_semantic_color_adjustments( + image: torch.Tensor, + skin_tone_adjustment: float = 0.0, + sky_adjustment: float = 0.0, + foliage_adjustment: float = 0.0, + selective_hue_shift: float = 0.0, + selective_saturation: float = 0.0, + selective_strength: float = 1.0, +) -> torch.Tensor: + """Apply semantic color adjustments targeting skin tones, sky, and foliage.""" + result = image.clone() + + # Early exit if no adjustments + if (abs(skin_tone_adjustment) < 0.001 and abs(sky_adjustment) < 0.001 and + abs(foliage_adjustment) < 0.001 and abs(selective_hue_shift) < 0.001 and + abs(selective_saturation) < 0.001): + return result + + # Define semantic color targets with broader, more natural ranges + semantic_targets = [] + + # Skin tones: warm oranges/yellows (20-50 degrees) + if abs(skin_tone_adjustment) > 0.001: + semantic_targets.append((35, selective_hue_shift, selective_saturation + skin_tone_adjustment, skin_tone_adjustment * 0.3, 80.0)) + + # Sky/water: blues and cyans (200-260 degrees) + if abs(sky_adjustment) > 0.001: + semantic_targets.append((230, selective_hue_shift, selective_saturation + sky_adjustment, sky_adjustment * 0.2, 70.0)) + + # Foliage: greens (90-150 degrees) + if abs(foliage_adjustment) > 0.001: + semantic_targets.append((120, selective_hue_shift, selective_saturation + foliage_adjustment, foliage_adjustment * 0.3, 80.0)) + + # Apply semantic adjustments + for target_hue, hue_adj, sat_adj, light_adj, hue_range in semantic_targets: + if abs(hue_adj) > 0.001 or abs(sat_adj) > 0.001 or abs(light_adj) > 0.001: + adjustment = apply_selective_color_adjustment( + result, target_hue, hue_adj * selective_strength, + sat_adj * selective_strength, light_adj * selective_strength, hue_range + ) + # Blend with original based on selective_strength + result = torch.lerp(result, adjustment, selective_strength) + + return result + + +# --- ADVANCED ANALYSIS FUNCTIONS --- +def extract_color_palette( + image_np: np.ndarray, num_colors: int = 6 +) -> typing.List[str]: + """Extract perceptually-spaced dominant colors using advanced color science.""" + if not ADVANCED_LIBS_AVAILABLE: + return [] + + try: + pixels = image_np.reshape(-1, 3) + sample_size = min(10000, len(pixels)) + sample_indices = np.random.choice(len(pixels), sample_size, replace=False) + sample_pixels = pixels[sample_indices] + + kmeans = KMeans(n_clusters=num_colors, random_state=42, n_init=5) + kmeans.fit(sample_pixels) + dominant_colors = kmeans.cluster_centers_ + + try: + rgb_norm = sample_pixels.astype(np.float32) / 255.0 + lab_pixels = colour.sRGB_to_Lab(rgb_norm) + + lab_kmeans = KMeans(n_clusters=num_colors, random_state=42, n_init=5) + lab_kmeans.fit(lab_pixels) + lab_centers = lab_kmeans.cluster_centers_ + + perceptual_rgb = colour.Lab_to_sRGB(lab_centers) + perceptual_rgb = np.clip(perceptual_rgb * 255, 0, 255) + + blended_colors = dominant_colors * 0.6 + perceptual_rgb * 0.4 + final_colors = np.clip(blended_colors, 0, 255) + + except Exception: + final_colors = dominant_colors + + brightness_values = np.sum(final_colors * [0.299, 0.587, 0.114], axis=1) + sorted_indices = np.argsort(brightness_values) + final_colors = final_colors[sorted_indices] + + hex_colors = [] + for color in final_colors: + r, g, b = [int(c) for c in color] + hex_colors.append(f"#{r:02x}{g:02x}{b:02x}") + + return hex_colors + except Exception: + return [] + + +def match_to_reference_colors( + image_np: np.ndarray, reference_np: np.ndarray, strength: float = 0.5 +) -> np.ndarray: + """ + Match image colors to reference using highlights/midtones/shadows LUT approach. + Works like professional color grading - matches colors in different luminance zones. + """ + if not ADVANCED_LIBS_AVAILABLE: + return image_np + + try: + if reference_np is None or image_np is None: + return image_np + + if reference_np.size == 0 or image_np.size == 0: + return image_np + + strength = np.clip(strength, 0.0, 1.0) + if strength == 0.0: + return image_np + + # Convert to float for processing + image_float = image_np.astype(np.float32) / 255.0 + reference_float = reference_np.astype(np.float32) / 255.0 + matched_image = image_float.copy() + + # Calculate luminance for zone segmentation + # Use proper luminance weights: 0.299*R + 0.587*G + 0.114*B + img_luminance = ( + 0.299 * image_float[:, :, 0] + + 0.587 * image_float[:, :, 1] + + 0.114 * image_float[:, :, 2] + ) + ref_luminance = ( + 0.299 * reference_float[:, :, 0] + + 0.587 * reference_float[:, :, 1] + + 0.114 * reference_float[:, :, 2] + ) + + # Define luminance zones with smooth transitions + # Shadows: 0.0-0.33, Midtones: 0.33-0.66, Highlights: 0.66-1.0 + shadow_mask = np.clip(1.0 - img_luminance / 0.33, 0.0, 1.0) + highlight_mask = np.clip((img_luminance - 0.66) / 0.34, 0.0, 1.0) + midtone_mask = 1.0 - shadow_mask - highlight_mask + midtone_mask = np.clip(midtone_mask, 0.0, 1.0) + + # For each zone, calculate average color shift from reference + for zone_name, zone_mask in [ + ("shadows", shadow_mask), + ("midtones", midtone_mask), + ("highlights", highlight_mask) + ]: + if np.sum(zone_mask) > 0: # Only process if zone has pixels + + # Get pixels in this luminance zone + img_zone_mask = zone_mask > 0.1 # Threshold to avoid near-zero weights + ref_zone_mask = None + + # Find corresponding luminance zone in reference image + if zone_name == "shadows": + ref_zone_mask = ref_luminance < 0.4 + elif zone_name == "midtones": + ref_zone_mask = (ref_luminance >= 0.3) & (ref_luminance <= 0.7) + else: # highlights + ref_zone_mask = ref_luminance > 0.6 + + if np.sum(ref_zone_mask) > 0: # Reference zone has pixels + + # Calculate average colors in this zone + for channel in range(3): + # Image zone average + img_zone_pixels = matched_image[:, :, channel][img_zone_mask] + img_zone_avg = np.mean(img_zone_pixels) if len(img_zone_pixels) > 0 else 0 + + # Reference zone average + ref_zone_pixels = reference_float[:, :, channel][ref_zone_mask] + ref_zone_avg = np.mean(ref_zone_pixels) if len(ref_zone_pixels) > 0 else 0 + + # Calculate color shift for this zone + color_shift = ref_zone_avg - img_zone_avg + + # Apply shift with zone mask and strength + shift_amount = color_shift * strength * zone_mask[:, :, np.newaxis] + matched_image[:, :, channel] += shift_amount.squeeze() + + # Clamp values and convert back to uint8 + matched_image = np.clip(matched_image, 0.0, 1.0) + return (matched_image * 255.0).astype(np.uint8) + + except Exception: + # Return original image if anything fails + return image_np + + +def get_preferred_device(use_gpu: bool = True): + """Get the preferred device considering user settings.""" + if not use_gpu: + return torch.device("cpu") + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def analyze_image_content( + image_np: np.ndarray, device: torch.device = None +) -> typing.Dict[str, typing.Any]: + """Advanced image analysis using computer vision with GPU acceleration.""" + if not ADVANCED_LIBS_AVAILABLE: + return { + "faces": [], + "dominant_colors": [], + "scene_type": "unknown", + "lighting": "auto", + } + + analysis = {} + + if device is None: + device = get_preferred_device() + try: + gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) + face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + "haarcascade_frontalface_default.xml" + ) + # Balanced parameters for reliable face detection + faces = face_cascade.detectMultiScale( + gray, + scaleFactor=1.1, # Standard scale factor + minNeighbors=3, # Balanced between sensitivity and accuracy + minSize=(30, 30), # Reasonable minimum size + flags=cv2.CASCADE_SCALE_IMAGE + ) + analysis["faces"] = faces.tolist() if len(faces) > 0 else [] + except Exception: + analysis["faces"] = [] + + try: + pixels = image_np.reshape(-1, 3) + sample_size = min(10000, len(pixels)) + sample_indices = np.random.choice(len(pixels), sample_size, replace=False) + sample_pixels = pixels[sample_indices] + + try: + sample_tensor = torch.from_numpy(sample_pixels.astype(np.float32)).to( + device + ) + _ = sample_tensor + + kmeans = KMeans(n_clusters=5, random_state=42, n_init=5, init="k-means++") + kmeans.fit(sample_pixels) + + dominant_colors = kmeans.cluster_centers_ + analysis["dominant_colors"] = dominant_colors.tolist() + except Exception: + kmeans = KMeans(n_clusters=5, random_state=42, n_init=3) + kmeans.fit(sample_pixels) + dominant_colors = kmeans.cluster_centers_ + analysis["dominant_colors"] = dominant_colors.tolist() + except Exception: + analysis["dominant_colors"] = [] + + try: + gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) + + try: + gray_tensor = torch.from_numpy(gray.astype(np.float32)).to(device) + sobel_x = ( + torch.tensor( + [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], + dtype=torch.float32, + device=device, + ) + .unsqueeze(0) + .unsqueeze(0) + ) + sobel_y = ( + torch.tensor( + [[-1, -2, -1], [0, 0, 0], [1, 2, 1]], + dtype=torch.float32, + device=device, + ) + .unsqueeze(0) + .unsqueeze(0) + ) + + gray_padded = torch.nn.functional.pad( + gray_tensor.unsqueeze(0).unsqueeze(0), (1, 1, 1, 1), mode="reflect" + ) + edges_x = torch.nn.functional.conv2d(gray_padded, sobel_x).squeeze() + edges_y = torch.nn.functional.conv2d(gray_padded, sobel_y).squeeze() + edges = torch.sqrt(edges_x**2 + edges_y**2) + edge_density = torch.mean(edges).cpu().item() + except Exception: + edges = filters.sobel(gray) + edge_density = np.mean(edges) + + hsv = cv2.cvtColor(image_np, cv2.COLOR_RGB2HSV) + try: + hsv_tensor = torch.from_numpy(hsv.astype(np.float32)).to(device) + avg_saturation = torch.mean(hsv_tensor[:, :, 1]).cpu().item() + color_variance = torch.std(hsv_tensor[:, :, 1]).cpu().item() + texture_contrast = ( + torch.std(torch.from_numpy(gray.astype(np.float32)).to(device)) + .cpu() + .item() + ) + except Exception: + avg_saturation = np.mean(hsv[:, :, 1]) + color_variance = np.std(hsv[:, :, 1]) + texture_contrast = np.std(gray) + if edge_density < 0.08 and avg_saturation > 120 and color_variance > 40: + analysis["scene_type"] = "anime" + elif edge_density < 0.12 and avg_saturation > 80 and texture_contrast < 35: + analysis["scene_type"] = "stylized_art" + elif avg_saturation > 100 and color_variance > 50 and texture_contrast > 40: + analysis["scene_type"] = "concept_art" + elif edge_density > 0.25 and texture_contrast > 65 and avg_saturation > 90: + analysis["scene_type"] = "detailed_illustration" + elif len(analysis["faces"]) > 0 and edge_density < 0.15: + analysis["scene_type"] = "portrait" + elif edge_density > 0.15 and avg_saturation < 80: + analysis["scene_type"] = "realistic_photo" + else: + analysis["scene_type"] = "general" + except Exception: + analysis["scene_type"] = "general" + + try: + lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB) + l_channel = lab[:, :, 0] + + brightness = np.mean(l_channel) + contrast = np.std(l_channel) + + if brightness < 85: + analysis["lighting"] = "low_light" + elif brightness > 170: + analysis["lighting"] = "bright" + elif contrast < 20: + analysis["lighting"] = "flat" + else: + analysis["lighting"] = "good" + except Exception: + analysis["lighting"] = "auto" + + return analysis + + +def edge_aware_enhancement(image_np: np.ndarray, strength: float = 0.5) -> np.ndarray: + """Apply edge-aware enhancement using advanced filtering.""" + if not ADVANCED_LIBS_AVAILABLE: + return image_np + + try: + lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB) + l_channel = lab[:, :, 0].astype(np.float32) + + edges_small = ndimage.gaussian_laplace(l_channel, sigma=0.5) + edges_medium = ndimage.gaussian_laplace(l_channel, sigma=1.0) + edges_large = ndimage.gaussian_laplace(l_channel, sigma=2.0) + + combined_edges = ( + np.abs(edges_small) + + np.abs(edges_medium) * 0.5 + + np.abs(edges_large) * 0.25 + ) + + edge_map = combined_edges / (np.max(combined_edges) + 1e-8) + + enhancement_mask = 1.0 - np.clip(edge_map * 2.0, 0.0, 1.0) + + enhancement_mask = ndimage.gaussian_filter(enhancement_mask, sigma=1.0) + + enhanced_l = l_channel.copy() + + mean_filtered = ndimage.uniform_filter(l_channel, size=5) + local_contrast = l_channel - mean_filtered + + enhanced_l = l_channel + (local_contrast * strength * enhancement_mask) + enhanced_l = np.clip(enhanced_l, 0, 255) + + lab[:, :, 0] = enhanced_l.astype(np.uint8) + + enhanced = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) + return enhanced + + except Exception: + return image_np + + +def intelligent_white_balance( + image_np: np.ndarray, strength: float = 0.6 +) -> np.ndarray: + """Advanced white balance using professional color science. + + Args: + strength: -1.0 to 1.0, where negative values enhance cool/blue tones, + positive values enhance warm/orange tones, 0.0 = neutral + """ + if not ADVANCED_LIBS_AVAILABLE: + return image_np + + try: + lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB) + + a_channel = lab[:, :, 1].astype(np.float32) - 128 + b_channel = lab[:, :, 2].astype(np.float32) - 128 + + if strength >= 0.0: + # Positive strength: traditional white balance (focus on temperature) + a_shift = np.median(a_channel) + b_shift = np.median(b_channel) + # Reduce tint influence since we have separate tint control + lab[:, :, 1] = np.clip( + lab[:, :, 1] - a_shift * strength * 0.3, 0, 255 + ) # Reduced tint correction + lab[:, :, 2] = np.clip( + lab[:, :, 2] - b_shift * strength, 0, 255 + ) # Full temperature correction + else: + # Negative strength: enhance cool tones (blue/cyan) + abs_strength = abs(strength) + # Focus primarily on b channel (temperature) for cool adjustment + cool_b_shift = -25 * abs_strength # Toward blue (cooler temperature) + # Minimal a channel adjustment since we have separate tint control + cool_a_shift = -5 * abs_strength # Slight toward green (cooler) + + lab[:, :, 1] = np.clip(lab[:, :, 1] + cool_a_shift, 0, 255) + lab[:, :, 2] = np.clip(lab[:, :, 2] + cool_b_shift, 0, 255) + + corrected = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) + + try: + rgb_norm = image_np.astype(np.float32) / 255.0 + + grey_world_rgb = np.mean(rgb_norm.reshape(-1, 3), axis=0) + + xyz = colour.sRGB_to_XYZ(rgb_norm) + + illuminant_xy = colour.XYZ_to_xy(grey_world_rgb.reshape(1, -1)) + + d65_xy = colour.CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"][ + "D65" + ] + + if np.linalg.norm(illuminant_xy - d65_xy) > 0.01: + adapted_xyz = colour.chromatic_adaptation_VonKries( + xyz, illuminant_xy[0], d65_xy, transform="Bradford" + ) + + adapted_rgb = colour.XYZ_to_sRGB(adapted_xyz) + adapted_rgb = np.clip(adapted_rgb, 0, 1) + + colour_corrected = (adapted_rgb * 255).astype(np.uint8) + corrected = corrected * (1 - strength * 0.3) + colour_corrected * ( + strength * 0.3 + ) + corrected = np.clip(corrected, 0, 255).astype(np.uint8) + + except Exception: + pass + + return corrected + + except Exception: + return image_np + + +def enhance_faces( + image_np: np.ndarray, faces: typing.List, strength: float = 0.3 +) -> np.ndarray: + """Intelligently enhance detected faces using advanced segmentation with heavy feathering.""" + if not ADVANCED_LIBS_AVAILABLE or not faces: + return image_np + + try: + enhanced = image_np.copy() + + for x, y, w, h in faces: + pad = max(30, int(min(w, h) * 0.3)) + x_start = max(0, x - pad) + y_start = max(0, y - pad) + x_end = min(image_np.shape[1], x + w + pad) + y_end = min(image_np.shape[0], y + h + pad) + + face_region = enhanced[y_start:y_end, x_start:x_end] + original_face_region = image_np[y_start:y_end, x_start:x_end].copy() + + segments = segmentation.slic( + face_region, n_segments=50, compactness=10, sigma=1 + ) + + face_lab = cv2.cvtColor(face_region, cv2.COLOR_RGB2LAB) + + skin_segments = set() + for segment_id in np.unique(segments): + segment_mask = segments == segment_id + segment_lab = face_lab[segment_mask] + + if len(segment_lab) > 10: + l_mean = np.mean(segment_lab[:, 0]) + a_mean = np.mean(segment_lab[:, 1]) + b_mean = np.mean(segment_lab[:, 2]) + + l_valid = 40 < l_mean < 200 + a_valid = 120 < a_mean < 142 + b_valid = 130 < b_mean < 165 + + is_dark_skin = l_mean < 90 + dark_skin_valid = ( + is_dark_skin + and 25 < l_mean < 90 + and 118 < a_mean < 148 + and 128 < b_mean < 168 + ) + + is_very_light = l_mean > 160 + light_skin_valid = ( + is_very_light + and 160 < l_mean < 220 + and 118 < a_mean < 135 + and 130 < b_mean < 160 + ) + + if ( + (l_valid and a_valid and b_valid) + or dark_skin_valid + or light_skin_valid + ): + skin_segments.add(segment_id) + + skin_mask = np.zeros(segments.shape, dtype=bool) + for segment_id in skin_segments: + skin_mask |= segments == segment_id + + skin_mask_float = skin_mask.astype(float) + + skin_mask_float = ndimage.gaussian_filter(skin_mask_float, sigma=1.5) + + skin_mask_float = ndimage.gaussian_filter(skin_mask_float, sigma=4.0) + + skin_mask_float = ndimage.gaussian_filter(skin_mask_float, sigma=8.0) + + face_center_x = (x + w // 2) - x_start + face_center_y = (y + h // 2) - y_start + face_radius = max(w, h) * 0.6 + + coords_y, coords_x = np.ogrid[ + : face_region.shape[0], : face_region.shape[1] + ] + + distance_from_center = np.sqrt( + (coords_x - face_center_x) ** 2 + (coords_y - face_center_y) ** 2 + ) + + falloff_mask = 1.0 / ( + 1.0 + np.exp((distance_from_center - face_radius) / (face_radius * 0.3)) + ) + + final_mask = skin_mask_float * falloff_mask + + kernel = np.ones((5, 5), np.float32) / 25 + final_mask = cv2.filter2D(final_mask, -1, kernel) + + final_mask = ndimage.gaussian_filter(final_mask, sigma=6.0) + + # CRITICAL FIX: Prevent mask from affecting bright highlights (>85% luminance) + # This prevents green tints in bright non-skin areas + face_gray = cv2.cvtColor(face_region, cv2.COLOR_RGB2GRAY).astype(np.float32) / 255.0 + highlight_protection = np.where(face_gray > 0.85, 0.0, 1.0) + final_mask = final_mask * highlight_protection + + final_mask = np.clip(final_mask, 0.0, 1.0) + + enhanced_face = face_region.copy() + + face_lab = cv2.cvtColor(enhanced_face, cv2.COLOR_RGB2LAB) + l_channel = face_lab[:, :, 0].astype(np.float32) + a_channel = face_lab[:, :, 1].astype(np.float32) + b_channel = face_lab[:, :, 2].astype(np.float32) + + very_dark_skin = (l_channel < 60) & (a_channel > 132) + dark_skin = (l_channel < 90) & (l_channel >= 60) & (a_channel > 128) + very_light_skin = l_channel > 180 + + brightness_adjustment = np.where( + very_dark_skin, + strength * 0.18, + np.where( + dark_skin, + strength * 0.22, + np.where( + very_light_skin, + strength * 0.15, + strength * 0.25, + ), + ), + ) + l_enhanced = l_channel * (1.0 + brightness_adjustment * final_mask) + face_lab[:, :, 0] = np.clip(l_enhanced, 0, 255).astype(np.uint8) + + green_reduction = np.where( + very_dark_skin, + strength * 0.28, + np.where( + dark_skin, + strength * 0.18, + np.where( + very_light_skin, + 0.0, # No green reduction for very light skin to avoid green tint + strength * 0.12, + ), + ), + ) + a_enhanced = a_channel * (1.0 - green_reduction * final_mask) + face_lab[:, :, 1] = np.clip(a_enhanced, 0, 255).astype(np.uint8) + + warming_adjustment = np.where( + very_dark_skin, + strength * 0.15, + np.where( + dark_skin, + strength * 0.12, + np.where( + very_light_skin, + strength * 0.05, + strength * 0.08, + ), + ), + ) + b_enhanced = b_channel * (1.0 + warming_adjustment * final_mask) + face_lab[:, :, 2] = np.clip(b_enhanced, 0, 255).astype(np.uint8) + + enhanced_face = cv2.cvtColor(face_lab, cv2.COLOR_LAB2RGB) + + final_mask_3d = np.stack([final_mask, final_mask, final_mask], axis=2) + blended_face = ( + original_face_region * (1.0 - final_mask_3d) + + enhanced_face * final_mask_3d + ).astype(np.uint8) + + enhanced[y_start:y_end, x_start:x_end] = blended_face + + return enhanced + + except Exception: + return image_np + + +def generate_histogram_image(image_np: np.ndarray) -> np.ndarray: + """Generate a well-scaled histogram visualization as an image.""" + try: + hist_r = cv2.calcHist([image_np], [0], None, [256], [0, 256]) + hist_g = cv2.calcHist([image_np], [1], None, [256], [0, 256]) + hist_b = cv2.calcHist([image_np], [2], None, [256], [0, 256]) + + hist_height = 512 + hist_width = 768 + hist_image = np.zeros((hist_height, hist_width, 3), dtype=np.uint8) + + hist_image.fill(20) + + margin = 40 + draw_height = hist_height - (margin * 2) + draw_width = hist_width - (margin * 2) + + hist_r = cv2.normalize(hist_r, hist_r, 0, draw_height, cv2.NORM_MINMAX) + hist_g = cv2.normalize(hist_g, hist_g, 0, draw_height, cv2.NORM_MINMAX) + hist_b = cv2.normalize(hist_b, hist_b, 0, draw_height, cv2.NORM_MINMAX) + + bin_width = max(1, draw_width // 256) + + for i in range(256): + x = margin + (i * draw_width // 256) + + if hist_r[i][0] > 0: + cv2.rectangle( + hist_image, + (x, hist_height - margin), + (x + bin_width, hist_height - margin - int(hist_r[i][0])), + (80, 80, 255), + -1, + ) + + if hist_g[i][0] > 0: + cv2.rectangle( + hist_image, + (x, hist_height - margin), + (x + bin_width, hist_height - margin - int(hist_g[i][0])), + (80, 255, 80), + -1, + ) + + if hist_b[i][0] > 0: + cv2.rectangle( + hist_image, + (x, hist_height - margin), + (x + bin_width, hist_height - margin - int(hist_b[i][0])), + (255, 80, 80), + -1, + ) + + grid_spacing = draw_width // 8 + for i in range(1, 8): + x = margin + (i * grid_spacing) + cv2.line( + hist_image, (x, margin), (x, hist_height - margin), (60, 60, 60), 1 + ) + + grid_spacing = draw_height // 4 + for i in range(1, 4): + y = margin + (i * grid_spacing) + cv2.line(hist_image, (margin, y), (hist_width - margin, y), (60, 60, 60), 1) + + cv2.rectangle( + hist_image, + (margin, margin), + (hist_width - margin, hist_height - margin), + (100, 100, 100), + 2, + ) + + hist_image_rgb = cv2.cvtColor(hist_image, cv2.COLOR_BGR2RGB) + return hist_image_rgb + + except Exception: + return np.zeros((512, 768, 3), dtype=np.uint8) + + +def generate_palette_image(hex_colors: typing.List[str]) -> np.ndarray: + """Generate a clean color palette visualization as an image.""" + try: + if not hex_colors: + return np.zeros((120, 600, 3), dtype=np.uint8) + + palette_height = 120 + palette_width = 600 + palette_image = np.zeros((palette_height, palette_width, 3), dtype=np.uint8) + + swatch_width = palette_width // len(hex_colors) + + for i, hex_color in enumerate(hex_colors): + hex_color = hex_color.lstrip("#") + try: + r = int(hex_color[0:2], 16) + g = int(hex_color[2:4], 16) + b = int(hex_color[4:6], 16) + except (ValueError, IndexError): + r, g, b = 128, 128, 128 + + x_start = i * swatch_width + x_end = (i + 1) * swatch_width + palette_image[:, x_start:x_end] = [r, g, b] + + return palette_image + + except Exception: + return np.zeros((120, 600, 3), dtype=np.uint8) + + +class EasyColorCorrection: + PRESETS: typing.Dict[str, typing.Dict[str, float]] = { + "Natural Portrait": { + "warmth": 0.08, + "vibrancy": 0.12, + "contrast": 0.08, + "brightness": 0.03, + }, + "Warm Portrait": { + "warmth": 0.18, + "vibrancy": 0.15, + "contrast": 0.06, + "brightness": 0.05, + }, + "Cool Portrait": { + "warmth": -0.12, + "vibrancy": 0.08, + "contrast": 0.10, + "brightness": 0.02, + }, + "High Key Portrait": { + "warmth": 0.05, + "vibrancy": 0.08, + "contrast": -0.05, + "brightness": 0.20, + }, + "Dramatic Portrait": { + "warmth": 0.02, + "vibrancy": 0.20, + "contrast": 0.25, + "brightness": -0.05, + }, + "Epic Fantasy": { + "warmth": 0.1, + "vibrancy": 0.4, + "contrast": 0.3, + "brightness": 0.05, + }, + "Sci-Fi Chrome": { + "warmth": -0.2, + "vibrancy": 0.3, + "contrast": 0.35, + "brightness": 0.1, + }, + "Dark Fantasy": { + "warmth": -0.1, + "vibrancy": 0.25, + "contrast": 0.4, + "brightness": -0.15, + }, + "Vibrant Concept": { + "warmth": 0.05, + "vibrancy": 0.5, + "contrast": 0.25, + "brightness": 0.08, + }, + "Matte Painting": { + "warmth": 0.08, + "vibrancy": 0.3, + "contrast": 0.2, + "brightness": 0.03, + }, + "Digital Art": { + "warmth": 0.0, + "vibrancy": 0.45, + "contrast": 0.28, + "brightness": 0.05, + }, + "Anime Bright": { + "warmth": 0.12, + "vibrancy": 0.45, + "contrast": 0.2, + "brightness": 0.12, + }, + "Anime Moody": { + "warmth": -0.05, + "vibrancy": 0.35, + "contrast": 0.25, + "brightness": -0.05, + }, + "Cyberpunk": { + "warmth": -0.15, + "vibrancy": 0.45, + "contrast": 0.25, + "brightness": -0.03, + }, + "Pastel Dreams": { + "warmth": 0.12, + "vibrancy": -0.08, + "contrast": -0.08, + "brightness": 0.12, + }, + "Neon Nights": { + "warmth": -0.18, + "vibrancy": 0.40, + "contrast": 0.20, + "brightness": -0.05, + }, + "Comic Book": { + "warmth": 0.05, + "vibrancy": 0.5, + "contrast": 0.35, + "brightness": 0.08, + }, + "Cinematic": { + "warmth": 0.12, + "vibrancy": 0.15, + "contrast": 0.18, + "brightness": 0.02, + }, + "Teal & Orange": { + "warmth": -0.08, + "vibrancy": 0.25, + "contrast": 0.15, + "brightness": 0.0, + }, + "Film Noir": { + "warmth": -0.05, + "vibrancy": -0.80, + "contrast": 0.35, + "brightness": -0.08, + }, + "Vintage Film": { + "warmth": 0.15, + "vibrancy": -0.10, + "contrast": 0.12, + "brightness": 0.03, + }, + "Bleach Bypass": { + "warmth": -0.02, + "vibrancy": -0.25, + "contrast": 0.30, + "brightness": 0.05, + }, + "Golden Hour": { + "warmth": 0.25, + "vibrancy": 0.18, + "contrast": 0.08, + "brightness": 0.08, + }, + "Blue Hour": { + "warmth": -0.20, + "vibrancy": 0.15, + "contrast": 0.12, + "brightness": 0.02, + }, + "Sunny Day": { + "warmth": 0.15, + "vibrancy": 0.20, + "contrast": 0.10, + "brightness": 0.08, + }, + "Overcast": { + "warmth": -0.08, + "vibrancy": 0.05, + "contrast": 0.08, + "brightness": 0.05, + }, + "Sepia": { + "warmth": 0.30, + "vibrancy": -0.35, + "contrast": 0.08, + "brightness": 0.03, + }, + "Black & White": { + "warmth": 0.0, + "vibrancy": -1.0, + "contrast": 0.15, + "brightness": 0.0, + }, + "Faded": { + "warmth": 0.05, + "vibrancy": -0.15, + "contrast": -0.12, + "brightness": 0.08, + }, + "Moody": { + "warmth": -0.08, + "vibrancy": 0.12, + "contrast": 0.20, + "brightness": -0.08, + }, + } + + @classmethod + def INPUT_TYPES(cls) -> typing.Dict: + return { + "required": { + "image": ("IMAGE", {}), + "mode": (["Auto", "Preset", "Manual", "Colorize"], {"default": "Auto"}), + }, + "optional": { + "reference_image": ( + "IMAGE", + { + "tooltip": "🎨 Optional reference image for color matching (concept art/mood boards)" + }, + ), + "reference_strength": ( + "FLOAT", + { + "default": 0.3, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "Strength of color matching to reference image", + }, + ), + "extract_palette": ( + "BOOLEAN", + { + "default": False, + "tooltip": "πŸ“Š Extract and display dominant color palette", + }, + ), + "lock_input_image": ( + "BOOLEAN", + { + "default": False, + "tooltip": "πŸ”’ Lock input image to prevent upstream nodes from reprocessing when adjusting color parameters", + }, + ), + "ai_analysis": ( + "BOOLEAN", + { + "default": True, + "tooltip": "πŸ€– Enable AI-powered face detection, scene analysis & content-aware enhancement", + }, + ), + "adjust_for_skin_tone": ( + "BOOLEAN", + { + "default": False, + "tooltip": "AI face detection + skin tone preservation (requires ai_analysis)", + }, + ), + "white_balance_strength": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "White balance adjustment: -1.0 = cooler/blue, +1.0 = warmer/orange", + }, + ), + "enhancement_strength": ( + "FLOAT", + { + "default": 0.2, + "min": 0.0, + "max": 2.5, + "step": 0.05, + "tooltip": "Overall strength of AI-powered enhancements", + }, + ), + "pop_factor": ( + "FLOAT", + { + "default": 0.7, + "min": 0.0, + "max": 2.0, + "step": 0.05, + "tooltip": "Extra pop factor for artistic content (anime, detailed photos)", + }, + ), + "effect_strength": ( + "FLOAT", + { + "default": 0.6, + "min": 0.0, + "max": 3.0, + "step": 0.05, + "tooltip": "Strength of overall color correction effect", + }, + ), + "warmth": ( + "FLOAT", + { + "default": 0.0, + "min": -2.0, + "max": 2.0, + "step": 0.05, + "tooltip": "Green/Magenta warmth adjustment", + }, + ), + "vibrancy": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "Saturation boost for less saturated areas", + }, + ), + "contrast": ( + "FLOAT", + { + "default": 0.0, + "min": -0.5, + "max": 0.5, + "step": 0.02, + "tooltip": "Contrast adjustment for overall image", + }, + ), + "brightness": ( + "FLOAT", + { + "default": 0.0, + "min": -0.5, + "max": 0.5, + "step": 0.02, + "tooltip": "Brightness adjustment for overall image", + }, + ), + "tint": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "Tint adjustment: -1.0 = green, +1.0 = magenta (Manual mode only)", + }, + ), + "preset": (list(cls.PRESETS.keys()), {}), + "variation": ( + "FLOAT", + { + "default": 0.0, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "Variation strength for preset adjustments", + }, + ), + "lift": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.10, + "tooltip": "Lift adjustment for shadows in the image", + }, + ), + "gamma": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.10, + "tooltip": "Gamma adjustment for midtones in the image", + }, + ), + "gain": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.10, + "tooltip": "Gain adjustment for highlights in the image", + }, + ), + "noise": ( + "FLOAT", + { + "default": 0.0, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "Noise reduction strength (0.0 = no noise reduction, 1.0 = maximum noise reduction)", + }, + ), + # Semantic Selective Color Controls + "skin_tone_adjustment": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "πŸ§‘ Adjust skin tones and flesh colors (targets warm oranges/yellows)", + }, + ), + "sky_adjustment": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "πŸŒ… Adjust sky and blue regions (targets blues/cyans)", + }, + ), + "foliage_adjustment": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "🌿 Adjust foliage and vegetation (targets greens)", + }, + ), + "selective_hue_shift": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "🎨 Universal hue shift for targeted colors", + }, + ), + "selective_saturation": ( + "FLOAT", + { + "default": 0.0, + "min": -1.0, + "max": 1.0, + "step": 0.05, + "tooltip": "🎨 Universal saturation adjustment for targeted colors", + }, + ), + "selective_strength": ( + "FLOAT", + { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.05, + "tooltip": "πŸ’ͺ Overall strength of selective color adjustments", + }, + ), + # Colorize mode specific parameters + "colorize_strength": ( + "FLOAT", + { + "default": 0.8, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "🎨 Overall colorization strength (only for photos, not art/anime)", + }, + ), + "skin_warmth": ( + "FLOAT", + { + "default": 0.3, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "πŸ§‘ Warmth applied to detected skin tones", + }, + ), + "sky_saturation": ( + "FLOAT", + { + "default": 0.6, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "πŸŒ… Saturation boost for sky and blue regions", + }, + ), + "vegetation_green": ( + "FLOAT", + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "🌿 Green enhancement for vegetation and foliage", + }, + ), + "sepia_tone": ( + "FLOAT", + { + "default": 0.0, + "min": 0.0, + "max": 1.0, + "step": 0.10, + "tooltip": "🟀 Sepia tone blend for vintage photo colorization", + }, + ), + "colorize_mode": ( + ["deep_learning", "classic", "portrait", "landscape", "vintage"], + { + "default": "deep_learning", + "tooltip": "🎯 Colorization method: deep_learning (AI), classic (HSV), portrait, landscape, or vintage", + }, + ), + "force_colorize": ( + "BOOLEAN", + { + "default": False, + "tooltip": "πŸ”„ Force colorization even for art/anime content (overrides AI detection)", + }, + ), + "use_gpu": ( + "BOOLEAN", + { + "default": True, + "tooltip": "πŸš€ Use GPU acceleration for processing (disable to force CPU)", + }, + ), + "mask": ("MASK", {}), + }, + } + + RETURN_TYPES = ("IMAGE", "STRING", "IMAGE", "IMAGE") + RETURN_NAMES = ("image", "palette_data", "histogram", "palette_image") + FUNCTION = "run" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "Easy Color Corrector" + + def run( + self, + image: torch.Tensor, + mode: str, + reference_image: typing.Optional[torch.Tensor] = None, + reference_strength: float = 0.3, + extract_palette: bool = False, + lock_input_image: bool = False, + ai_analysis: bool = True, + adjust_for_skin_tone: bool = False, + white_balance_strength: float = 0.0, + enhancement_strength: float = 0.2, + pop_factor: float = 0.7, + effect_strength: float = 0.6, + warmth: float = 0.0, + vibrancy: float = 0.0, + contrast: float = 0.0, + brightness: float = 0.0, + tint: float = 0.0, + preset: str = "Anime", + variation: float = 0.0, + lift: float = 0.0, + gamma: float = 0.0, + gain: float = 0.0, + noise: float = 0.0, + # Semantic Selective Color parameters + skin_tone_adjustment: float = 0.0, + sky_adjustment: float = 0.0, + foliage_adjustment: float = 0.0, + selective_hue_shift: float = 0.0, + selective_saturation: float = 0.0, + selective_strength: float = 1.0, + colorize_strength: float = 0.8, + skin_warmth: float = 0.3, + sky_saturation: float = 0.6, + vegetation_green: float = 0.5, + sepia_tone: float = 0.0, + colorize_mode: str = "auto", + force_colorize: bool = False, + use_gpu: bool = True, + mask: typing.Optional[torch.Tensor] = None, + ) -> tuple: + + # Input validation and type conversion for backward compatibility + # Handle cases where old workflows pass wrong types due to parameter reordering + + # Validate and fix float parameters that might receive None or wrong types + def safe_float(value, default=0.0, param_name="unknown"): + """Convert value to float, handling None, lists, and strings gracefully.""" + if value is None: + print(f"⚠️ Parameter '{param_name}' is None, using default: {default}") + return default + if isinstance(value, (list, tuple)): + print(f"⚠️ Parameter '{param_name}' is a list/tuple, using default: {default}") + return default + if isinstance(value, str): + # Check if it's a preset name that was misplaced + if value in self.PRESETS: + print(f"⚠️ Parameter '{param_name}' received preset name '{value}', using default: {default}") + return default + try: + return float(value) + except (ValueError, TypeError): + print(f"⚠️ Parameter '{param_name}' could not convert '{value}' to float, using default: {default}") + return default + try: + return float(value) + except (ValueError, TypeError): + print(f"⚠️ Parameter '{param_name}' conversion failed for {type(value).__name__}, using default: {default}") + return default + + # Validate preset parameter + if preset is None or preset == '0' or (isinstance(preset, str) and preset not in self.PRESETS): + # Use first preset as default if invalid + preset = list(self.PRESETS.keys())[0] + print(f"⚠️ Invalid preset value, using default: {preset}") + + # Apply safe_float to all float parameters that might have issues + reference_strength = safe_float(reference_strength, 0.3, "reference_strength") + white_balance_strength = safe_float(white_balance_strength, 0.0, "white_balance_strength") + enhancement_strength = safe_float(enhancement_strength, 0.2, "enhancement_strength") + pop_factor = safe_float(pop_factor, 0.7, "pop_factor") + effect_strength = safe_float(effect_strength, 0.6, "effect_strength") + warmth = safe_float(warmth, 0.0, "warmth") + vibrancy = safe_float(vibrancy, 0.0, "vibrancy") + contrast = safe_float(contrast, 0.0, "contrast") + brightness = safe_float(brightness, 0.0, "brightness") + tint = safe_float(tint, 0.0, "tint") + variation = safe_float(variation, 0.0, "variation") + lift = safe_float(lift, 0.0, "lift") + gamma = safe_float(gamma, 0.0, "gamma") + gain = safe_float(gain, 0.0, "gain") + noise = safe_float(noise, 0.0, "noise") + skin_tone_adjustment = safe_float(skin_tone_adjustment, 0.0, "skin_tone_adjustment") + sky_adjustment = safe_float(sky_adjustment, 0.0, "sky_adjustment") + foliage_adjustment = safe_float(foliage_adjustment, 0.0, "foliage_adjustment") + selective_hue_shift = safe_float(selective_hue_shift, 0.0, "selective_hue_shift") + selective_saturation = safe_float(selective_saturation, 0.0, "selective_saturation") + selective_strength = safe_float(selective_strength, 1.0, "selective_strength") + colorize_strength = safe_float(colorize_strength, 0.8, "colorize_strength") + skin_warmth = safe_float(skin_warmth, 0.3, "skin_warmth") + sky_saturation = safe_float(sky_saturation, 0.6, "sky_saturation") + vegetation_green = safe_float(vegetation_green, 0.5, "vegetation_green") + sepia_tone = safe_float(sepia_tone, 0.0, "sepia_tone") + + # Validate boolean parameters + if not isinstance(extract_palette, bool): + extract_palette = bool(extract_palette) if extract_palette not in [None, [], ""] else False + if not isinstance(lock_input_image, bool): + lock_input_image = bool(lock_input_image) if lock_input_image not in [None, [], ""] else False + if not isinstance(ai_analysis, bool): + ai_analysis = bool(ai_analysis) if ai_analysis not in [None, [], ""] else True + if not isinstance(adjust_for_skin_tone, bool): + adjust_for_skin_tone = bool(adjust_for_skin_tone) if adjust_for_skin_tone not in [None, [], ""] else False + if not isinstance(force_colorize, bool): + force_colorize = bool(force_colorize) if force_colorize not in [None, [], ""] else False + if not isinstance(use_gpu, bool): + use_gpu = bool(use_gpu) if use_gpu not in [None, [], ""] else True + + # Validate colorize_mode + valid_colorize_modes = ["deep_learning", "classic", "portrait", "landscape", "vintage"] + if colorize_mode not in valid_colorize_modes: + colorize_mode = "deep_learning" + print(f"⚠️ Invalid colorize_mode, using default: {colorize_mode}") + + original_image = image.clone() + _, height, width, _ = image.shape + + # Handle GPU processing based on user choice + device = get_preferred_device(use_gpu) + print(f"πŸ”§ EasyColorCorrection Debug: use_gpu={use_gpu}, CUDA available={torch.cuda.is_available()}") + print(f"πŸ”§ Input image device: {image.device}, target device: {device}") + + if use_gpu and torch.cuda.is_available(): + if not str(image.device).startswith("cuda"): + print(f"πŸš€ Moving image to GPU: {device}") + image = image.to(device) + original_image = original_image.to(device) + if mask is not None: + mask = mask.to(device) + print(f"πŸš€ Moved mask to GPU: {device}") + if reference_image is not None: + reference_image = reference_image.to(device) + print(f"πŸš€ Moved reference image to GPU: {device}") + print(f"βœ… Image now on device: {image.device}") + else: + print(f"βœ… Image already on GPU: {image.device}") + elif use_gpu and not torch.cuda.is_available(): + print("❌ GPU requested but CUDA not available - using CPU") + else: + print(f"πŸ’» CPU processing selected - device: {device}") + + # Cache for locked input - avoid reprocessing upstream when enabled + if lock_input_image: + # Check if the input image has changed (cache invalidation) + image_changed = False + if ( + not hasattr(self, "_cached_original_image") + or self._cached_original_image is None + ): + image_changed = True + else: + # Compare image tensors to detect changes from upstream + try: + if not torch.equal(self._cached_original_image, original_image): + image_changed = True + except: + # Different shapes or other comparison issues = definitely changed + image_changed = True + + if image_changed: + self._cached_original_image = original_image.clone() + self._cached_analysis = None + print("πŸ”„ Locked Input: New image detected, updating cache") + + processed_image = self._cached_original_image.clone() + else: + processed_image = image.clone() + # Clear cache when input is not locked + if hasattr(self, "_cached_original_image"): + self._cached_original_image = None + self._cached_analysis = None + + # --- SHARED AI ANALYSIS (available to all modes) --- + analysis = None + if ai_analysis and ADVANCED_LIBS_AVAILABLE: + # Only create image_np when AI analysis is actually needed + image_np = (processed_image[0].cpu().numpy() * 255).astype(np.uint8) + # Use cached analysis when input is locked + if ( + lock_input_image + and hasattr(self, "_cached_analysis") + and self._cached_analysis is not None + ): + analysis = self._cached_analysis + else: + analysis = analyze_image_content( + image_np, get_preferred_device(use_gpu) + ) + if lock_input_image: + self._cached_analysis = analysis + + print( + f"πŸ€– AI Analysis{'(cached)' if lock_input_image and hasattr(self, '_cached_analysis') else ''} for {mode} Mode: {analysis['scene_type']} scene, {analysis['lighting']} lighting, {len(analysis['faces'])} faces detected" + ) + else: + # Provide fallback analysis for non-AI modes + analysis = { + "scene_type": "general", + "lighting": "auto", + "faces": [], + "dominant_colors": [], + } + if not ai_analysis: + print(f"πŸ”§ {mode} Mode: AI analysis disabled, using fallback values") + + # --- AUTO MODE --- + if mode == "Auto": + if ai_analysis and ADVANCED_LIBS_AVAILABLE: + if white_balance_strength != 0.0: + wb_corrected = intelligent_white_balance( + image_np, white_balance_strength + ) + processed_image = ( + torch.from_numpy(wb_corrected.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + + if enhancement_strength > 0.2: + edge_enhanced = edge_aware_enhancement( + image_np, enhancement_strength * 0.3 + ) + processed_image = ( + torch.from_numpy(edge_enhanced.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + image_np = edge_enhanced + if analysis["faces"] and adjust_for_skin_tone: + # Warning for overdriven enhancement + if enhancement_strength > 1.0: + print(f"⚠️ WARNING: Enhancement strength ({enhancement_strength:.1f}) > 1.0 with skin tone adjustment may cause color artifacts in highlights. Recommended: ≀ 1.0") + face_enhanced = enhance_faces( + image_np, analysis["faces"], enhancement_strength * 0.5 + ) + processed_image = ( + torch.from_numpy(face_enhanced.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + else: + analysis = { + "scene_type": "general", + "lighting": "auto", + "faces": [], + "dominant_colors": [], + } + print("πŸ”§ Basic Auto Mode: AI analysis disabled") + # Use consistent white balance regardless of AI setting + if white_balance_strength != 0.0 and ADVANCED_LIBS_AVAILABLE: + image_np = (processed_image[0].cpu().numpy() * 255).astype(np.uint8) + wb_corrected = intelligent_white_balance(image_np, white_balance_strength) + processed_image = ( + torch.from_numpy(wb_corrected.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + print(f"🌑️ Applied white balance: {white_balance_strength:.2f} ({'warmer' if white_balance_strength > 0 else 'cooler' if white_balance_strength < 0 else 'neutral'})") + hsv_enhanced = rgb_to_hsv(processed_image) + h_enh, s_enh, v_enh = ( + hsv_enhanced[..., 0], + hsv_enhanced[..., 1], + hsv_enhanced[..., 2], + ) + + scene_type = analysis["scene_type"] + lighting = analysis["lighting"] + if scene_type == "anime": + contrast_boost = 0.18 * enhancement_strength + saturation_boost = 0.55 * enhancement_strength + vibrancy_strength = 0.45 * pop_factor + elif scene_type == "concept_art": + contrast_boost = 0.25 * enhancement_strength + saturation_boost = 0.6 * enhancement_strength + vibrancy_strength = 0.5 * pop_factor + elif scene_type == "stylized_art": + contrast_boost = 0.22 * enhancement_strength + saturation_boost = 0.5 * enhancement_strength + vibrancy_strength = 0.4 * pop_factor + elif scene_type == "detailed_illustration": + contrast_boost = 0.2 * enhancement_strength + saturation_boost = 0.45 * enhancement_strength + vibrancy_strength = 0.35 * pop_factor + elif scene_type == "portrait": + contrast_boost = 0.12 * enhancement_strength + saturation_boost = 0.3 * enhancement_strength + vibrancy_strength = 0.25 * pop_factor + elif scene_type == "realistic_photo": + contrast_boost = 0.15 * enhancement_strength + saturation_boost = 0.35 * enhancement_strength + vibrancy_strength = 0.3 * pop_factor + else: + contrast_boost = 0.16 * enhancement_strength + saturation_boost = 0.42 * enhancement_strength + vibrancy_strength = 0.35 * pop_factor + + if lighting == "low_light": + contrast_boost *= 1.3 + v_enh = v_enh * (1.0 + 0.15 * enhancement_strength) + elif lighting == "bright": + contrast_boost *= 0.8 + v_enh = v_enh * (1.0 - 0.1 * enhancement_strength) + elif lighting == "flat": + contrast_boost *= 1.5 + v_min, v_max = torch.quantile(v_enh, 0.01), torch.quantile(v_enh, 0.99) + if v_max > v_min: + v_enh = torch.clamp((v_enh - v_min) / (v_max - v_min), 0.0, 1.0) + + v_enh = 0.5 + (v_enh - 0.5) * (1.0 + contrast_boost) + if analysis["dominant_colors"] and ADVANCED_LIBS_AVAILABLE: + avg_dom_sat = np.mean( + [ + np.max(color) - np.min(color) + for color in analysis["dominant_colors"] + ] + ) + saturation_factor = max(0.5, 1.0 - avg_dom_sat / 255.0) + saturation_boost *= saturation_factor + + s_enh = s_enh * (1.0 + saturation_boost) + + saturation_mask = 1.0 - s_enh + s_enh = s_enh + (vibrancy_strength * saturation_mask * s_enh) + if pop_factor > 0.3 and scene_type in [ + "anime", + "concept_art", + "stylized_art", + "detailed_illustration", + ]: + highlight_mask = torch.clamp((v_enh - 0.7) * 3.33, 0.0, 1.0) + if scene_type == "concept_art": + glow_strength = 0.25 * pop_factor * enhancement_strength + elif scene_type in ["anime", "stylized_art"]: + glow_strength = 0.2 * pop_factor * enhancement_strength + else: + glow_strength = 0.15 * pop_factor * enhancement_strength + + v_enh = v_enh + (glow_strength * highlight_mask) + s_enh = torch.clamp(s_enh, 0.0, 1.0) + v_enh = torch.clamp(v_enh, 0.0, 1.0) + processed_image = hsv_to_rgb(torch.stack([h_enh, s_enh, v_enh], dim=-1)) + processed_image = torch.clamp(processed_image, 0.0, 1.0) + + # --- PRESET MODE --- + if mode == "Preset": + p_vals = self.PRESETS.get(preset, {}).copy() + if analysis: + scene_type = analysis["scene_type"] + lighting = analysis["lighting"] + + if scene_type == "concept_art": + p_vals["vibrancy"] = p_vals.get("vibrancy", 0.0) * 1.4 + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.3 + p_vals["brightness"] = p_vals.get("brightness", 0.0) + 0.05 + elif scene_type == "anime": + p_vals["vibrancy"] = p_vals.get("vibrancy", 0.0) * 1.3 + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.2 + elif scene_type == "stylized_art": + p_vals["vibrancy"] = p_vals.get("vibrancy", 0.0) * 1.25 + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.15 + elif scene_type == "detailed_illustration": + p_vals["vibrancy"] = p_vals.get("vibrancy", 0.0) * 1.2 + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.1 + elif scene_type == "portrait" and analysis["faces"]: + p_vals["vibrancy"] = p_vals.get("vibrancy", 0.0) * 0.8 + p_vals["warmth"] = p_vals.get("warmth", 0.0) + 0.05 + elif scene_type == "realistic_photo": + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.1 + + if lighting == "low_light": + p_vals["brightness"] = p_vals.get("brightness", 0.0) + 0.1 + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.4 + elif lighting == "bright": + p_vals["brightness"] = p_vals.get("brightness", 0.0) - 0.05 + elif lighting == "flat": + p_vals["contrast"] = p_vals.get("contrast", 0.0) * 1.5 + + print( + f"🎨 AI-Enhanced Preset: {preset} adapted for {scene_type}/{lighting}" + ) + if analysis and analysis["dominant_colors"]: + # Reduce variation if image is already very colorful + avg_color_range = np.mean( + [ + np.max(color) - np.min(color) + for color in analysis["dominant_colors"] + ] + ) + variation_factor = max(0.3, 1.0 - avg_color_range / 200.0) + v_factor = variation * 0.05 * variation_factor + else: + v_factor = variation * 0.05 + + # Apply preset values with intelligent variation (only for Preset mode) + if mode == "Preset": + warmth = p_vals.get("warmth", 0.0) + (torch.randn(1).item() * v_factor) + vibrancy = p_vals.get("vibrancy", 0.0) + ( + torch.randn(1).item() * v_factor + ) + contrast = p_vals.get("contrast", 0.0) + ( + torch.randn(1).item() * v_factor + ) + brightness = p_vals.get("brightness", 0.0) + ( + torch.randn(1).item() * v_factor + ) + # For Manual mode, use the direct parameter values (warmth, vibrancy, contrast, brightness are already set as function parameters) + + # --- ADVANCED COLOR PROCESSING (Preset and Manual modes) --- + if mode != "Auto": + # === INTELLIGENT WHITE BALANCE (if enabled) === + if white_balance_strength != 0.0 and ADVANCED_LIBS_AVAILABLE: + # Create image_np if not already available (when AI analysis is disabled) + if not ai_analysis or 'image_np' not in locals(): + image_np = (processed_image[0].cpu().numpy() * 255).astype(np.uint8) + wb_corrected = intelligent_white_balance( + image_np, white_balance_strength + ) + processed_image = ( + torch.from_numpy(wb_corrected.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + print(f"🌑️ Applied white balance: {white_balance_strength:.2f} ({'warmer' if white_balance_strength > 0 else 'cooler' if white_balance_strength < 0 else 'neutral'})") + + # === FACE-AWARE PROCESSING === + if ( + analysis + and analysis["faces"] + and adjust_for_skin_tone + and ADVANCED_LIBS_AVAILABLE + ): + # Warning for overdriven enhancement + if enhancement_strength > 1.0: + print(f"⚠️ WARNING: Enhancement strength ({enhancement_strength:.1f}) > 1.0 with skin tone adjustment may cause color artifacts in highlights. Recommended: ≀ 1.0") + face_enhanced = enhance_faces( + image_np, analysis["faces"], enhancement_strength * 0.3 + ) + processed_image = ( + torch.from_numpy(face_enhanced.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + + # Manual mode controls only apply when mode == "Manual" + # Move HSV processing inside Manual mode + + # --- MANUAL MODE --- + if mode == "Manual": + # Convert to HSV for manual adjustments + hsv_image = rgb_to_hsv(processed_image) + h, s, v = hsv_image[..., 0], hsv_image[..., 1], hsv_image[..., 2] + + ai_guidance = "" + if analysis: + scene_type = analysis["scene_type"] + if scene_type in [ + "concept_art", + "anime", + "stylized_art", + "detailed_illustration", + ]: + ai_guidance = f" + AI Guidance ({scene_type})" + else: + ai_guidance = " + AI Guidance" + print(f"πŸŽ›οΈ Professional Manual Mode{ai_guidance}") + + # Basic HSV adjustments + if vibrancy != 0.0: + saturation_mask = 1.0 - s + s = s * (1.0 + vibrancy) + (vibrancy * 0.3 * saturation_mask * s) + + if brightness != 0.0: + v = v + brightness * (1.0 - v * 0.5) + + if contrast != 0.0: + v = 0.5 + (v - 0.5) * (1.0 + contrast) + + processed_image = hsv_to_rgb(torch.stack([h, s, v], dim=-1)) + processed_image = torch.clamp(processed_image, 0.0, 1.0) + + # === TEMPERATURE & TINT PROCESSING (LAB color space) === + if (warmth != 0.0 or tint != 0.0): + # Convert to numpy for LAB processing + image_np_for_color = (processed_image[0].cpu().numpy() * 255).astype( + np.uint8 + ) + + if ADVANCED_LIBS_AVAILABLE: + try: + # Convert to LAB color space + lab = cv2.cvtColor(image_np_for_color, cv2.COLOR_RGB2LAB) + + # Apply temperature adjustment to 'b' channel (blue-yellow axis) + if warmth != 0.0: + # 'b' channel: values around 128 are neutral, <128 is blue, >128 is yellow + temperature_shift = ( + warmth * 35 + ) # Scale factor for visible temperature effect + lab[:, :, 2] = np.clip( + lab[:, :, 2] + temperature_shift, 0, 255 + ) + + # Apply tint adjustment to 'a' channel (green-magenta axis) + if tint != 0.0: + # 'a' channel: values around 128 are neutral, <128 is green, >128 is magenta + tint_shift = ( + tint * 30 + ) # Scale factor for visible tint effect + lab[:, :, 1] = np.clip(lab[:, :, 1] + tint_shift, 0, 255) + + # Convert back to RGB + color_corrected_rgb = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) + processed_image = ( + torch.from_numpy( + color_corrected_rgb.astype(np.float32) / 255.0 + ) + .unsqueeze(0) + .to(processed_image.device) + ) + processed_image = torch.clamp(processed_image, 0.0, 1.0) + + adjustments = [] + if warmth != 0.0: + adjustments.append( + f"temperature: {warmth:.2f} ({'warmer' if warmth > 0 else 'cooler'})" + ) + if tint != 0.0: + adjustments.append( + f"tint: {tint:.2f} ({'magenta' if tint > 0 else 'green'})" + ) + print( + f"🌑️ Applied professional color adjustments: {', '.join(adjustments)}" + ) + + except Exception as e: + print( + f"⚠️ Temperature/tint processing failed, using original: {e}" + ) + else: + print( + "⚠️ Professional temperature/tint requires advanced libraries (OpenCV), skipping" + ) + + # Update HSV after temperature/tint adjustments + hsv_image = rgb_to_hsv(processed_image) + h, s, v = hsv_image[..., 0], hsv_image[..., 1], hsv_image[..., 2] + + if analysis and analysis["scene_type"] in [ + "concept_art", + "anime", + "stylized_art", + "detailed_illustration", + ]: + shadows_mask = 1.0 - torch.clamp(v * 2.5, 0.0, 1.0) + midtones_mask = 1.0 - torch.abs(v - 0.5) * 1.8 + highlights_mask = torch.clamp((v - 0.6) * 2.5, 0.0, 1.0) + else: + shadows_mask = 1.0 - torch.clamp(v * 3.0, 0.0, 1.0) + midtones_mask = 1.0 - torch.abs(v - 0.5) * 2.0 + highlights_mask = torch.clamp((v - 0.66) * 3.0, 0.0, 1.0) + + # === 3-WAY COLOR CORRECTION (Lift/Gamma/Gain) === + corrections_applied = [] + if lift != 0.0: + # Shadows (lift) - increased strength for noticeable effect + lift_strength = 0.8 # Increased for more visible control + v = v + (lift * lift_strength * shadows_mask) + corrections_applied.append(f"lift: {lift:.2f}") + + if gamma != 0.0: + # Midtones (gamma) - increased strength for noticeable effect + gamma_exp = 1.0 / ( + 1.0 + gamma * 1.2 + ) # Increased for more visible control + v_gamma = torch.pow(torch.clamp(v, 0.001, 1.0), gamma_exp) + v = torch.lerp(v, v_gamma, midtones_mask) + corrections_applied.append(f"gamma: {gamma:.2f}") + + if gain != 0.0: + # Highlights (gain) - increased strength for noticeable effect + gain_strength = 0.8 # Increased for more visible control + v = v + (gain * gain_strength * highlights_mask) + corrections_applied.append(f"gain: {gain:.2f}") + + if corrections_applied: + print(f"πŸŽ›οΈ 3-way color correction applied: {', '.join(corrections_applied)}") + + # Convert modified HSV back to RGB after lift/gamma/gain processing + processed_image = hsv_to_rgb(torch.stack([h, s, v], dim=-1)) + processed_image = torch.clamp(processed_image, 0.0, 1.0) + + if noise > 0.0: + mono_noise = torch.randn( + processed_image.shape[:-1], device=processed_image.device + ).unsqueeze(-1) + luminance_mask = 1.0 - torch.abs(v - 0.5) * 2.0 + luminance_mask = torch.clamp(luminance_mask, 0.0, 1.0).unsqueeze(-1) + + rgb_temp = hsv_to_rgb(torch.stack([h, s, v], dim=-1)) + rgb_temp += mono_noise * noise * 0.15 * luminance_mask + rgb_temp = torch.clamp(rgb_temp, 0.0, 1.0) + + hsv_temp = rgb_to_hsv(rgb_temp) + h, s, v = hsv_temp[..., 0], hsv_temp[..., 1], hsv_temp[..., 2] + + # --- COLORIZE MODE --- + if mode == "Colorize": + # Always apply colorization in colorize mode - user knows what they're doing + if analysis: + scene_type = analysis["scene_type"] + print(f"🎨 Colorize Mode: Processing {scene_type} content") + print(f"πŸ”§ Calling _apply_colorization with mode: {colorize_mode}") + print(f"πŸ”§ Colorization parameters: strength={colorize_strength}, skin_warmth={skin_warmth}, sky_saturation={sky_saturation}") + processed_image = self._apply_colorization( + original_image, + processed_image, + analysis, + colorize_strength, + skin_warmth, + sky_saturation, + vegetation_green, + sepia_tone, + colorize_mode, + ) + else: + print( + "🎨 Colorize Mode: Applying general colorization (no AI analysis)" + ) + processed_image = self._apply_colorization( + original_image, + processed_image, + None, + colorize_strength, + skin_warmth, + sky_saturation, + vegetation_green, + sepia_tone, + colorize_mode, + ) + + # Note: Skin tone protection is now handled within the colorization methods + + if mode in ["Auto", "Preset"]: + processed_image = torch.lerp( + original_image, processed_image, effect_strength + ) + + if mode == "Manual" and noise > 0.0: + mono_noise = torch.randn( + processed_image.shape[:-1], device=image.device + ).unsqueeze(-1) + hsv_for_noise = rgb_to_hsv(processed_image) + v_for_noise = hsv_for_noise[..., 2] + luminance_mask = 1.0 - torch.abs(v_for_noise - 0.5) * 2.0 + luminance_mask = torch.clamp(luminance_mask, 0.0, 1.0).unsqueeze(-1) + + processed_image += mono_noise * noise * 0.2 * luminance_mask + + processed_image = torch.clamp(processed_image, 0.0, 1.0) + + # --- COLOR REFERENCE MATCHING --- + if reference_image is not None and reference_strength > 0.0: + image_np = (processed_image[0].cpu().numpy() * 255).astype(np.uint8) + ref_np = (reference_image[0].cpu().numpy() * 255).astype(np.uint8) + + matched_image = match_to_reference_colors( + image_np, ref_np, reference_strength + ) + processed_image = ( + torch.from_numpy(matched_image.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + processed_image = torch.clamp(processed_image, 0.0, 1.0) + print( + f"🎨 Applied color reference matching with {reference_strength:.1f} strength" + ) + + # --- COLOR PALETTE EXTRACTION --- + palette_data = "" + hex_colors = [] + if extract_palette: + image_for_palette = (processed_image[0].cpu().numpy() * 255).astype( + np.uint8 + ) + hex_colors = extract_color_palette(image_for_palette, num_colors=6) + if hex_colors: + palette_data = ",".join(hex_colors) + print(f"🎨 Extracted color palette: {palette_data}") + + if mask is not None: + if mask.shape[1:] != (height, width): + mask = F.interpolate( + mask.unsqueeze(1), + size=(height, width), + mode="bilinear", + align_corners=False, + ).squeeze(1) + mask = mask.unsqueeze(-1) + processed_image = torch.lerp(original_image, processed_image, mask) + + # --- GENERATE VISUALIZATION IMAGES --- + final_image_np = (processed_image[0].cpu().numpy() * 255).astype(np.uint8) + + if extract_palette: + histogram_image_np = generate_histogram_image(final_image_np) + histogram_tensor = ( + torch.from_numpy(histogram_image_np.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + else: + histogram_tensor = torch.zeros( + (1, 512, 768, 3), device=processed_image.device + ) + + if extract_palette and hex_colors: + palette_image_np = generate_palette_image(hex_colors) + palette_image_tensor = ( + torch.from_numpy(palette_image_np.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(processed_image.device) + ) + else: + palette_image_tensor = torch.zeros( + (1, 120, 600, 3), device=processed_image.device + ) + + return (processed_image, palette_data, histogram_tensor, palette_image_tensor) + + def _apply_colorization( + self, + original_image, + processed_image, + analysis, + colorize_strength, + skin_warmth, + sky_saturation, + vegetation_green, + sepia_tone, + colorize_mode, + ): + """ + Advanced deep learning colorization with fallback to classic methods. + Supports multiple colorization modes including state-of-the-art AI models. + """ + device = original_image.device + print(f"πŸ”§ _apply_colorization called with mode: {colorize_mode}") + print(f"πŸ”§ DL_COLORIZATION_AVAILABLE: {DL_COLORIZATION_AVAILABLE}") + + # Check if image needs colorization + avg_saturation = torch.mean(rgb_to_hsv(processed_image)[..., 1]).item() + print(f"πŸ”§ Average saturation: {avg_saturation:.3f}") + if avg_saturation > 0.3: + print(f"⚠️ Image already has color (avg saturation: {avg_saturation:.2f}), applying gentle enhancement") + colorize_strength *= 0.3 + + if colorize_mode == "deep_learning": + # Use deep learning colorization (primary method) + if DL_COLORIZATION_AVAILABLE: + try: + print("πŸ€– Initializing DDColor deep learning colorization...") + colorizer = get_deep_colorizer() + + # Load model if not already loaded + if colorizer.model is None: + print("πŸ“₯ Loading DDColor model...") + success = colorizer.load_model(device) + if not success: + print("⚠️ DDColor model failed to load, falling back to classic colorization") + return self._apply_classic_colorization( + original_image, processed_image, analysis, + colorize_strength, skin_warmth, sky_saturation, + vegetation_green, sepia_tone + ) + else: + print("βœ… DDColor model already loaded") + + # Apply deep learning colorization + print(f"🎨 Applying DDColor colorization (strength: {colorize_strength:.2f})...") + colorized_image = colorizer.colorize_image(processed_image, colorize_strength) + + # Check if colorization actually happened + if torch.equal(colorized_image, processed_image): + print("⚠️ DDColor returned unchanged image, falling back to classic colorization") + return self._apply_classic_colorization( + original_image, processed_image, analysis, + colorize_strength, skin_warmth, sky_saturation, + vegetation_green, sepia_tone + ) + + # Apply additional adjustments if needed + if skin_warmth != 0.0 or sky_saturation != 0.0 or vegetation_green != 0.0: + print("πŸ”§ Applying additional color adjustments...") + colorized_image = self._apply_color_adjustments( + colorized_image, analysis, skin_warmth, + sky_saturation, vegetation_green, colorize_strength + ) + + print("βœ… DDColor deep learning colorization completed successfully") + return colorized_image + + except Exception as e: + print(f"❌ DDColor deep learning colorization failed: {e}") + print("⚠️ Falling back to classic colorization") + import traceback + traceback.print_exc() + else: + print("⚠️ Deep learning libraries not available, using classic colorization") + + # Fallback to classic colorization for other modes or when DL fails + print(f"πŸ”§ Using classic colorization fallback for mode: {colorize_mode}") + return self._apply_classic_colorization( + original_image, processed_image, analysis, + colorize_strength, skin_warmth, sky_saturation, + vegetation_green, sepia_tone, colorize_mode + ) + + def _apply_classic_colorization( + self, + original_image, + processed_image, + analysis, + colorize_strength, + skin_warmth, + sky_saturation, + vegetation_green, + sepia_tone, + colorize_mode="classic" + ): + """Classic HSV-based colorization for fallback and specific modes.""" + device = original_image.device + print(f"πŸ”§ Classic colorization: mode={colorize_mode}, strength={colorize_strength}") + print(f"πŸ”§ Parameters: skin_warmth={skin_warmth}, sky_saturation={sky_saturation}, vegetation_green={vegetation_green}") + + # Convert to HSV for color manipulation + hsv_image = rgb_to_hsv(processed_image) + h, s, v = hsv_image[..., 0], hsv_image[..., 1], hsv_image[..., 2] + + # Create region masks + luminance = torch.mean(processed_image, dim=-1) + height = processed_image.shape[1] + + # Sky detection (upper regions with high luminance) + sky_region = torch.zeros_like(luminance, device=device) + sky_upper_third = height // 3 + sky_region[:, :sky_upper_third, :] = 1.0 + sky_luminance_mask = (luminance > 0.7).float() + sky_mask = sky_region * sky_luminance_mask + + # Vegetation detection (mid-luminance areas) + vegetation_mask = ((luminance > 0.2) & (luminance < 0.8)).float() + vegetation_mask = vegetation_mask * (1.0 - sky_mask) + + # Skin tone detection + skin_mask = torch.zeros_like(luminance, device=device) + if analysis and analysis.get("faces"): + skin_mask = ((luminance > 0.25) & (luminance < 0.85)).float() + skin_mask = skin_mask * (1.0 - sky_mask) * (1.0 - vegetation_mask) + + # Apply colorization based on mode + if colorize_mode == "vintage": + base_hue = 0.08 # Warm sepia hue + h = torch.full_like(h, base_hue) + s = s + sepia_tone * 0.4 * colorize_strength + + elif colorize_mode == "portrait": + # Portrait-focused colorization + skin_hue = 0.08 + h = torch.where(skin_mask > 0.3, skin_hue, h) + s = torch.where(skin_mask > 0.3, s + skin_warmth * colorize_strength, s) + + elif colorize_mode == "landscape": + # Landscape-focused colorization + sky_hue = 0.58 # Blue + h = torch.where(sky_mask > 0.5, sky_hue, h) + s = torch.where(sky_mask > 0.5, s + sky_saturation * colorize_strength, s) + + vegetation_hue = 0.25 # Green + h = torch.where(vegetation_mask > 0.4, vegetation_hue, h) + s = torch.where(vegetation_mask > 0.4, s + vegetation_green * colorize_strength, s) + + else: # classic or auto mode + # Basic intelligent colorization + if sky_saturation > 0: + sky_hue = 0.58 + h = torch.where(sky_mask > 0.5, sky_hue, h) + s = torch.where(sky_mask > 0.5, s + sky_saturation * colorize_strength, s) + + if vegetation_green > 0: + vegetation_hue = 0.25 + h = torch.where(vegetation_mask > 0.4, vegetation_hue, h) + s = torch.where(vegetation_mask > 0.4, s + vegetation_green * colorize_strength, s) + + if skin_warmth > 0 and analysis and analysis.get("faces"): + skin_hue = 0.08 + h = torch.where(skin_mask > 0.3, skin_hue, h) + s = torch.where(skin_mask > 0.3, s + skin_warmth * colorize_strength, s) + + # Apply sepia tone if specified + if sepia_tone > 0: + sepia_hue = 0.08 + h = torch.lerp(h, torch.full_like(h, sepia_hue), sepia_tone) + s = s + sepia_tone * 0.3 + + # Clamp and convert back to RGB + h = h % 1.0 + s = torch.clamp(s, 0.0, 1.0) + v = torch.clamp(v, 0.0, 1.0) + + colorized_hsv = torch.stack([h, s, v], dim=-1) + colorized_rgb = hsv_to_rgb(colorized_hsv) + + # Blend with original + final_image = torch.lerp(processed_image, colorized_rgb, colorize_strength) + return torch.clamp(final_image, 0.0, 1.0) + + def _apply_color_adjustments( + self, + image, + analysis, + skin_warmth, + sky_saturation, + vegetation_green, + strength + ): + """Apply additional color adjustments to deep learning colorized image.""" + if skin_warmth == 0.0 and sky_saturation == 0.0 and vegetation_green == 0.0: + return image + + print(f"πŸ”§ Applying targeted color adjustments: skin_warmth={skin_warmth:.2f}, sky_saturation={sky_saturation:.2f}, vegetation_green={vegetation_green:.2f}") + + # Apply targeted adjustments without full classic colorization pipeline + result = image.clone() + + # Convert to HSV for targeted adjustments + hsv = rgb_to_hsv(result) + h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] + + # Skin tone adjustments (warm oranges/peaches) + if skin_warmth != 0.0: + # Target skin tone hues (roughly 0.02-0.08 in HSV) + skin_mask = ((h >= 0.01) & (h <= 0.1)) & (s > 0.1) + if skin_mask.any(): + # Warm up skin tones + h = torch.where(skin_mask, h + skin_warmth * 0.02, h) + s = torch.where(skin_mask, torch.clamp(s + skin_warmth * 0.1, 0, 1), s) + print(f" βœ… Applied skin warming to {skin_mask.sum().item()} pixels") + + # Sky saturation adjustments (blues) + if sky_saturation != 0.0: + # Target sky/blue hues (roughly 0.55-0.75 in HSV) + sky_mask = ((h >= 0.5) & (h <= 0.8)) & (s > 0.1) + if sky_mask.any(): + s = torch.where(sky_mask, torch.clamp(s + sky_saturation * 0.2, 0, 1), s) + print(f" βœ… Applied sky saturation to {sky_mask.sum().item()} pixels") + + # Vegetation adjustments (greens) + if vegetation_green != 0.0: + # Target green hues (roughly 0.25-0.45 in HSV) + green_mask = ((h >= 0.2) & (h <= 0.5)) & (s > 0.1) + if green_mask.any(): + s = torch.where(green_mask, torch.clamp(s + vegetation_green * 0.15, 0, 1), s) + h = torch.where(green_mask, h + vegetation_green * 0.01, h) + print(f" βœ… Applied vegetation enhancement to {green_mask.sum().item()} pixels") + + # Convert back to RGB + adjusted_hsv = torch.stack([h, s, v], dim=-1) + result = hsv_to_rgb(adjusted_hsv) + + # Blend with original based on reduced strength + adjustment_strength = strength * 0.5 # Reduce strength for subtle adjustments + result = torch.lerp(image, result, adjustment_strength) + + print(f" βœ… Color adjustments applied with strength {adjustment_strength:.2f}") + return result + + +class BatchColorCorrection: + """ + Batch Color Corrector node for processing video frame sequences from VHS upload nodes. + Processes multiple frames efficiently while maintaining consistency across the sequence. + """ + + # Share presets with main EasyColorCorrection class + PRESETS = EasyColorCorrection.PRESETS + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "mode": (["Auto", "Preset", "Manual"], {"default": "Auto"}), + "frames_per_batch": ( + "INT", + { + "default": 16, + "min": 1, + "max": 64, + "step": 1, + "tooltip": "🎯 Batch Size Guide:\n1-4: Best Quality but Slow (high memory per frame)\n8-16: Balanced Speed & Quality (recommended)\n32-64: Fastest but Resource Heavy (requires more VRAM)" + }, + ), + "use_gpu": ( + "BOOLEAN", + { + "default": True, + "tooltip": "⚠️ GPU: Faster processing but uses significant VRAM (2-8GB+ for large batches). CPU: Slower but uses system RAM instead of VRAM.", + }, + ), + }, + "optional": { + "ai_analysis": ("BOOLEAN", {"default": True}), + "preset": ( + [ + "Natural", + "Warm", + "Cool", + "High Key", + "Dramatic", + "Epic Fantasy", + "Sci-Fi Chrome", + "Dark Fantasy", + "Vibrant Concept", + "Matte Painting", + "Digital Art", + "Anime Bright", + "Anime Moody", + "Cyberpunk", + "Pastel Dreams", + "Neon Nights", + "Comic Book", + "Cinematic", + "Teal & Orange", + "Film Noir", + "Vintage Film", + "Bleach Bypass", + "Golden Hour", + "Blue Hour", + "Sunny Day", + "Overcast", + "Sepia", + "Black & White", + "Faded", + "Moody", + ], + {"default": "Natural"}, + ), + "effect_strength": ( + "FLOAT", + {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.1}, + ), + "enhancement_strength": ( + "FLOAT", + {"default": 0.8, "min": 0.0, "max": 1.5, "step": 0.1}, + ), + "adjust_for_skin_tone": ("BOOLEAN", {"default": True}), + "white_balance_strength": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "warmth": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "vibrancy": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "brightness": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "contrast": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "tint": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "lift": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "gamma": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "gain": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "noise": ( + "FLOAT", + {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1}, + ), + "extract_palette": ("BOOLEAN", {"default": False}), + "reference_image": ("IMAGE",), + "reference_strength": ( + "FLOAT", + {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}, + ), + "mask": ("MASK",), + }, + } + + RETURN_TYPES = ("IMAGE", "STRING", "IMAGE", "IMAGE", "INT") + RETURN_NAMES = ( + "images", + "palette_data", + "histogram", + "palette_image", + "frame_count", + ) + FUNCTION = "batch_color_correct" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "Easy Batch Color Corrector (Beta)" + OUTPUT_NODE = True + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") # Always update for video previews + + DISPLAY_NAME = "Batch Color Corrector" + + def batch_color_correct( + self, + images, + mode="Auto", + frames_per_batch=16, + use_gpu=True, + ai_analysis=True, + preset="Natural", + effect_strength=0.4, + enhancement_strength=0.8, + adjust_for_skin_tone=True, + white_balance_strength=0.6, + warmth=0.0, + vibrancy=0.0, + brightness=0.0, + contrast=0.0, + tint=0.0, + lift=0.0, + gamma=0.0, + gain=0.0, + noise=0.0, + extract_palette=False, + reference_image=None, + reference_strength=0.5, + mask=None, + ): + """ + GPU-optimized batch processing for video frame sequences. + Processes multiple frames efficiently while keeping tensors on GPU. + """ + + # Get batch dimensions and device + total_frames = images.shape[0] + frame_height = images.shape[1] + frame_width = images.shape[2] + device = images.device + + # Debug device and user preferences + print(f"πŸ”§ CUDA Available: {torch.cuda.is_available()}") + print(f"πŸ”§ User GPU preference: {use_gpu}") + print(f"πŸ”§ Initial device: {device}") + print(f"πŸ”§ Input tensor device: {images.device} | dtype: {images.dtype}") + + # Handle GPU processing based on user choice + if use_gpu and torch.cuda.is_available(): + gpu_memory_before = torch.cuda.memory_allocated() / 1024**3 # GB + print(f"πŸš€ GPU Memory Before: {gpu_memory_before:.2f} GB") + + if not str(device).startswith("cuda"): + print("πŸš€ User enabled GPU - moving tensors to GPU...") + images = images.cuda() + device = images.device + print(f"βœ… Images moved to: {device}") + if mask is not None: + mask = mask.cuda() + print(f"βœ… Mask moved to: {device}") + else: + print(f"βœ… Tensors already on GPU: {device}") + + elif use_gpu and not torch.cuda.is_available(): + print("❌ User requested GPU but CUDA not available - falling back to CPU") + gpu_memory_before = 0 + + else: + print("πŸ’» User selected CPU processing - keeping tensors on CPU") + gpu_memory_before = 0 + + print( + f"🎬 GPU Batch Color Corrector: Processing {total_frames} frames ({frame_width}x{frame_height}) on {device}" + ) + print(f"πŸ“Š Processing in batches of {frames_per_batch} frames") + + # Pre-allocate output tensor to avoid OOM from torch.cat + _, height, width, channels = images.shape + final_images = torch.zeros((total_frames, height, width, channels), device=device, dtype=images.dtype) + + # Lists for metadata (much smaller memory footprint) + all_palette_data = [] + all_histograms = [] + all_palette_images = [] + processed_count = 0 + + # Process frames in GPU-optimized batches with interruption support + try: + for batch_start in range(0, total_frames, frames_per_batch): + # Check for interruption requests + import comfy.model_management as model_management + + if model_management.interrupt_processing: + print("πŸ›‘ Batch processing interrupted by user") + # Return partially processed results + if processed_count > 0: + partial_images = final_images[:processed_count] + print( + f"⚠️ Partial result: {processed_count}/{total_frames} frames processed" + ) + return ( + partial_images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + processed_count, + ) + else: + return ( + images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + 0, + ) + + batch_end = min(batch_start + frames_per_batch, total_frames) + + print( + f"πŸ”„ GPU Processing batch {batch_start//frames_per_batch + 1}/{(total_frames + frames_per_batch - 1)//frames_per_batch}: frames {batch_start}-{batch_end-1}" + ) + + # Extract current batch - keep on GPU + batch_frames = images[ + batch_start:batch_end + ] # Shape: (batch_size, H, W, C) + batch_masks = mask[batch_start:batch_end] if mask is not None else None + + # Process entire batch on GPU using vectorized operations + batch_processed = self._process_batch_gpu( + batch_frames=batch_frames, + batch_masks=batch_masks, + mode=mode, + ai_analysis=ai_analysis, + preset=preset, + effect_strength=effect_strength, + enhancement_strength=enhancement_strength, + adjust_for_skin_tone=adjust_for_skin_tone, + white_balance_strength=white_balance_strength, + warmth=warmth, + vibrancy=vibrancy, + brightness=brightness, + contrast=contrast, + lift=lift, + gamma=gamma, + gain=gain, + noise=noise, + device=device, + ) + + # Copy batch results directly into pre-allocated tensor + final_images[batch_start:batch_end] = batch_processed + processed_count = batch_end + + # Only extract palette from middle frame to avoid CPU overhead + if extract_palette and batch_start <= total_frames // 2 < batch_end: + middle_frame_idx = (total_frames // 2) - batch_start + middle_frame = batch_processed[ + middle_frame_idx : middle_frame_idx + 1 + ] + + # Generate GPU-based simplified palette without CPU transfer + palette_data = "GPU_BATCH_MODE" # Simplified for performance + histogram_tensor = torch.zeros((1, 512, 768, 3), device=device) + palette_img_tensor = torch.zeros((1, 120, 600, 3), device=device) + + all_palette_data.append(palette_data) + all_histograms.append(histogram_tensor) + all_palette_images.append(palette_img_tensor) + + except KeyboardInterrupt: + print("πŸ›‘ Batch processing interrupted by KeyboardInterrupt") + if processed_count > 0: + partial_images = final_images[:processed_count] + print( + f"⚠️ Partial result: {processed_count}/{total_frames} frames processed" + ) + return ( + partial_images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + processed_count, + ) + else: + return ( + images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + 0, + ) + except Exception as e: + print(f"❌ Error during batch processing: {e}") + if processed_count > 0: + partial_images = final_images[:processed_count] + print( + f"⚠️ Partial result after error: {processed_count}/{total_frames} frames processed" + ) + return ( + partial_images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + processed_count, + ) + else: + return ( + images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + 0, + ) + + # Processing completed successfully + if processed_count == total_frames: + + # Use middle frame data for representation + representative_palette = all_palette_data[0] if all_palette_data else "" + representative_histogram = ( + all_histograms[0] + if all_histograms + else torch.zeros((1, 512, 768, 3), device=device) + ) + representative_palette_img = ( + all_palette_images[0] + if all_palette_images + else torch.zeros((1, 120, 600, 3), device=device) + ) + + # Memory cleanup and final GPU status + if use_gpu and torch.cuda.is_available(): + gpu_memory_after = torch.cuda.memory_allocated(device) / 1024**3 # GB + print( + f"πŸš€ GPU Memory After: {gpu_memory_after:.2f} GB (Delta: {gpu_memory_after - gpu_memory_before:.2f} GB)" + ) + + # Force garbage collection to free memory + import gc + + gc.collect() + torch.cuda.empty_cache() + + gpu_memory_final = torch.cuda.memory_allocated(device) / 1024**3 # GB + print(f"🧹 GPU Memory After Cleanup: {gpu_memory_final:.2f} GB") + + print( + f"βœ… GPU Batch processing complete: {total_frames} frames processed on {device}" + ) + if representative_palette: + print(f"🎨 Representative palette: {representative_palette}") + + return ( + final_images, + representative_palette, + representative_histogram, + representative_palette_img, + total_frames, + ) + else: + # Fallback if no frames processed + return ( + images, + "", + torch.zeros((1, 512, 768, 3), device=device), + torch.zeros((1, 120, 600, 3), device=device), + 0, + ) + + def _process_batch_gpu( + self, + batch_frames, + batch_masks, + mode, + ai_analysis, + preset, + effect_strength, + enhancement_strength, + adjust_for_skin_tone, + white_balance_strength, + warmth, + vibrancy, + brightness, + contrast, + lift, + gamma, + gain, + noise, + device, + ): + """ + GPU-optimized batch processing that processes multiple frames simultaneously. + """ + batch_size = batch_frames.shape[0] + + # Debug GPU utilization in core processing + print(f"πŸ”₯ Processing batch of {batch_size} frames on {device}") + print(f"🎯 Batch tensor device: {batch_frames.device}") + print( + f"πŸŽ›οΈ Parameters: warmth={warmth}, vibrancy={vibrancy}, brightness={brightness}, contrast={contrast}" + ) + + # Ensure batch is on correct GPU device + if str(batch_frames.device) != str(device): + print(f"⚠️ Moving batch from {batch_frames.device} to {device}") + batch_frames = batch_frames.to(device) + if batch_masks is not None: + batch_masks = batch_masks.to(device) + + original_batch = batch_frames.clone() + + # Process all frames in batch simultaneously using vectorized operations + processed_batch = batch_frames.clone() + + # AI Analysis (only on first frame to save computation) + analysis = None + if ai_analysis: + # Use GPU-based analysis instead of CPU-heavy OpenCV operations + analysis = self._analyze_image_gpu(batch_frames[0], device) + + # Apply preset modifications if in Preset mode + if mode == "Preset": + # Map simplified batch preset names to full preset names + preset_mapping = { + "Natural": "Natural Portrait", + "Warm": "Warm Portrait", + "Cool": "Cool Portrait", + "High Key": "High Key Portrait", + "Dramatic": "Dramatic Portrait", + } + + full_preset_name = preset_mapping.get(preset, preset) + if full_preset_name in self.PRESETS: + preset_values = self.PRESETS[full_preset_name] + warmth += preset_values.get("warmth", 0.0) + vibrancy += preset_values.get("vibrancy", 0.0) + brightness += preset_values.get("brightness", 0.0) + contrast += preset_values.get("contrast", 0.0) + print(f"🎨 Applied batch preset: {preset} -> {full_preset_name}") + else: + print(f"⚠️ Preset '{preset}' not found in PRESETS dictionary") + + if analysis and analysis["scene_type"] in [ + "concept_art", + "anime", + "stylized_art", + ]: + vibrancy *= 1.4 + contrast *= 1.25 + + # Auto mode specific processing + if mode == "Auto": + print( + f"πŸ€– Batch Auto Mode: Applying intelligent enhancements to {batch_size} frames" + ) + + # Apply white balance to entire batch if enabled + if white_balance_strength > 0.0: + if ai_analysis and ADVANCED_LIBS_AVAILABLE: + # AI-based white balance on first frame, apply to all + first_frame_np = (batch_frames[0].cpu().numpy() * 255).astype( + np.uint8 + ) + wb_corrected_first = intelligent_white_balance( + first_frame_np, white_balance_strength + ) + + # Calculate the correction factors from first frame + original_mean = torch.mean(batch_frames[0], dim=(0, 1)) + corrected_mean = torch.mean( + torch.from_numpy( + wb_corrected_first.astype(np.float32) / 255.0 + ).to(device), + dim=(0, 1), + ) + wb_factors = corrected_mean / (original_mean + 1e-6) + + # Apply same factors to entire batch + processed_batch = processed_batch * wb_factors.view(1, 1, 1, 3) + processed_batch = torch.clamp(processed_batch, 0.0, 1.0) + print( + f"πŸ”§ Applied AI white balance to batch (factors: {wb_factors})" + ) + else: + # Tensor-based white balance for entire batch + B, H, W, C = processed_batch.shape + flat_batch = processed_batch.view(B, -1, C) + percentile_40 = torch.quantile( + flat_batch, 0.40, dim=1, keepdim=True + ) + percentile_60 = torch.quantile( + flat_batch, 0.60, dim=1, keepdim=True + ) + midtone_mean = (percentile_40 + percentile_60) / 2.0 + avg_gray = torch.mean(midtone_mean, dim=-1, keepdim=True) + scale = avg_gray / (midtone_mean + 1e-6) + scale = torch.lerp( + torch.ones_like(scale), scale, white_balance_strength + ) + scale = scale.view(B, 1, 1, C) + processed_batch = processed_batch * scale + processed_batch = torch.clamp(processed_batch, 0.0, 1.0) + print(f"πŸ”§ Applied tensor white balance to batch") + + # Apply enhancement based on scene analysis + if enhancement_strength > 0.2: + hsv_temp = rgb_to_hsv(processed_batch) + h_temp, s_temp, v_temp = ( + hsv_temp[..., 0], + hsv_temp[..., 1], + hsv_temp[..., 2], + ) + + # Initialize variables with defaults + scene_type = "general" + lighting = "auto" + + if analysis: + scene_type = analysis["scene_type"] + lighting = analysis["lighting_condition"] + + # Scene-specific enhancements + if scene_type == "anime": + contrast_boost = 0.18 * enhancement_strength + saturation_boost = 0.55 * enhancement_strength + v_temp = 0.5 + (v_temp - 0.5) * (1.0 + contrast_boost) + s_temp = s_temp * (1.0 + saturation_boost) + print( + f"🎨 Applied anime enhancement (contrast: {contrast_boost:.3f}, saturation: {saturation_boost:.3f})" + ) + elif scene_type == "concept_art": + contrast_boost = 0.25 * enhancement_strength + saturation_boost = 0.40 * enhancement_strength + v_temp = 0.5 + (v_temp - 0.5) * (1.0 + contrast_boost) + s_temp = s_temp * (1.0 + saturation_boost) + print(f"🎨 Applied concept art enhancement") + elif scene_type == "portrait": + warmth += 0.05 * enhancement_strength + contrast_boost = 0.12 * enhancement_strength + v_temp = 0.5 + (v_temp - 0.5) * (1.0 + contrast_boost) + print(f"🎨 Applied portrait enhancement") + else: + # General enhancement + contrast_boost = 0.15 * enhancement_strength + saturation_boost = 0.20 * enhancement_strength + v_temp = 0.5 + (v_temp - 0.5) * (1.0 + contrast_boost) + s_temp = s_temp * (1.0 + saturation_boost) + print(f"🎨 Applied general enhancement") + + # Lighting adjustments + if lighting == "low_light": + brightness += 0.1 * enhancement_strength + contrast += 0.4 * enhancement_strength + print(f"πŸ’‘ Applied low-light enhancement") + elif lighting == "bright": + brightness -= 0.05 * enhancement_strength + print(f"πŸ’‘ Applied bright lighting adjustment") + elif lighting == "flat": + contrast += 0.5 * enhancement_strength + print(f"πŸ’‘ Applied flat lighting enhancement") + + s_temp = torch.clamp(s_temp, 0.0, 1.0) + v_temp = torch.clamp(v_temp, 0.0, 1.0) + processed_batch = hsv_to_rgb( + torch.stack([h_temp, s_temp, v_temp], dim=-1) + ) + + # Face enhancement for Auto mode (if faces detected and adjust_for_skin_tone enabled) + if ( + analysis + and analysis.get("faces") + and adjust_for_skin_tone + and ADVANCED_LIBS_AVAILABLE + ): + print( + f"πŸ‘€ Applying face enhancement to batch with {len(analysis['faces'])} faces detected" + ) + # Warning for overdriven enhancement + if enhancement_strength > 1.0: + print(f"⚠️ WARNING: Enhancement strength ({enhancement_strength:.1f}) > 1.0 with skin tone adjustment may cause color artifacts in highlights. Recommended: ≀ 1.0") + face_enhanced_frames = [] + + for i in range(batch_size): + frame_np = (processed_batch[i].cpu().numpy() * 255).astype(np.uint8) + enhanced_frame_np = enhance_faces( + frame_np, analysis["faces"], enhancement_strength * 0.5 + ) + enhanced_frame_tensor = torch.from_numpy( + enhanced_frame_np.astype(np.float32) / 255.0 + ).to(device) + face_enhanced_frames.append(enhanced_frame_tensor) + + processed_batch = torch.stack(face_enhanced_frames, dim=0) + print(f"βœ… Applied face enhancement to {batch_size} frames") + + # Convert to HSV for batch processing + hsv_batch = rgb_to_hsv(processed_batch) + h, s, v = hsv_batch[..., 0], hsv_batch[..., 1], hsv_batch[..., 2] + + # Track changes to confirm processing is happening + original_mean = torch.mean(processed_batch).item() + print(f"πŸ“Š Original batch mean: {original_mean:.4f}") + + # Manual mode color corrections (applied to entire batch) + if mode == "Manual": + # Apply basic color corrections first + if warmth != 0.0: + h = (h + warmth * 0.1) % 1.0 + + if vibrancy != 0.0: + saturation_mask = 1.0 - s + s = s * (1.0 + vibrancy) + (vibrancy * 0.3 * saturation_mask * s) + + if brightness != 0.0: + v = v + brightness * (1.0 - v * 0.5) + + if contrast != 0.0: + v = 0.5 + (v - 0.5) * (1.0 + contrast) + + # Apply tint adjustment (requires LAB color space) + if tint != 0.0 and ADVANCED_LIBS_AVAILABLE: + try: + # Convert current HSV back to RGB then to numpy for LAB processing + temp_hsv = torch.stack([h, s, v], dim=-1) + temp_rgb = hsv_to_rgb(temp_hsv) + image_np_for_tint = (temp_rgb.squeeze(0).cpu().numpy() * 255).astype(np.uint8) + + # Convert to LAB and apply tint + lab = cv2.cvtColor(image_np_for_tint, cv2.COLOR_RGB2LAB) + tint_shift = tint * 30 # Scale factor for visible tint effect + lab[:, :, 1] = np.clip(lab[:, :, 1] + tint_shift, 0, 255) + + # Convert back to RGB then to tensors + corrected_rgb = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) + corrected_tensor = torch.from_numpy(corrected_rgb.astype(np.float32) / 255.0).unsqueeze(0).to(device) + + # Convert back to HSV for continued processing + corrected_hsv = rgb_to_hsv(corrected_tensor) + h, s, v = corrected_hsv[..., 0], corrected_hsv[..., 1], corrected_hsv[..., 2] + + except Exception as e: + print(f"⚠️ Tint processing failed: {e}") + + # Create masks for 3-way color correction + shadows_mask = 1.0 - torch.clamp(v * 3.0, 0.0, 1.0) + midtones_mask = 1.0 - torch.abs(v - 0.5) * 2.0 + highlights_mask = torch.clamp((v - 0.66) * 3.0, 0.0, 1.0) + + if lift != 0.0: + v = v + (lift * 0.8 * shadows_mask) + + if gamma != 0.0: + gamma_exp = 1.0 / (1.0 + gamma * 1.2) + v_gamma = torch.pow(torch.clamp(v, 0.001, 1.0), gamma_exp) + v = torch.lerp(v, v_gamma, midtones_mask) + + if gain != 0.0: + v = v + (gain * 0.8 * highlights_mask) + + # Semantic selective color adjustments not available in BatchColorCorrection + # (use main EasyColorCorrection node for selective controls) + + # Add noise if specified (Manual mode only) + if noise > 0.0: + mono_noise = torch.randn( + (batch_size, processed_batch.shape[1], processed_batch.shape[2], 1), + device=device, + ) + luminance_mask = 1.0 - torch.abs(v - 0.5) * 2.0 + luminance_mask = torch.clamp(luminance_mask, 0.0, 1.0).unsqueeze(-1) + + rgb_temp = hsv_to_rgb(torch.stack([h, s, v], dim=-1)) + rgb_temp += mono_noise * noise * 0.15 * luminance_mask + rgb_temp = torch.clamp(rgb_temp, 0.0, 1.0) + + hsv_temp = rgb_to_hsv(rgb_temp) + h, s, v = hsv_temp[..., 0], hsv_temp[..., 1], hsv_temp[..., 2] + + # Clamp and convert back to RGB + s = torch.clamp(s, 0.0, 1.0) + v = torch.clamp(v, 0.0, 1.0) + processed_hsv = torch.stack([h, s, v], dim=-1) + processed_batch = hsv_to_rgb(processed_hsv) + + # Apply effect strength to entire batch + if mode in ["Auto", "Preset"]: + processed_batch = torch.lerp( + original_batch, processed_batch, effect_strength + ) + + # Apply masks to entire batch if provided + if batch_masks is not None: + if batch_masks.shape[1:] != ( + processed_batch.shape[1], + processed_batch.shape[2], + ): + batch_masks = F.interpolate( + batch_masks.unsqueeze(1), + size=(processed_batch.shape[1], processed_batch.shape[2]), + mode="bilinear", + align_corners=False, + ).squeeze(1) + batch_masks = batch_masks.unsqueeze(-1) + processed_batch = torch.lerp(original_batch, processed_batch, batch_masks) + + processed_batch = torch.clamp(processed_batch, 0.0, 1.0) + + # Confirm processing happened + final_mean = torch.mean(processed_batch).item() + change = abs(final_mean - original_mean) + print(f"πŸ“Š Final batch mean: {final_mean:.4f} | Change: {change:.6f}") + if change > 0.001: + print("βœ… Color correction applied successfully") + else: + print("⚠️ Minimal/no changes detected") + + return processed_batch + + def _analyze_image_gpu(self, image_tensor, device): + """ + GPU-based image analysis without CPU bottlenecks. + Replaces CPU-heavy OpenCV operations with GPU tensor operations. + """ + # Convert to HSV for analysis + hsv = rgb_to_hsv(image_tensor.unsqueeze(0))[0] # Remove batch dim + h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] + + # Analyze brightness distribution (replaces histogram analysis) + brightness_mean = torch.mean(v).item() + brightness_std = torch.std(v).item() + + # Determine lighting condition based on brightness stats + if brightness_mean < 0.3: + lighting_condition = "low_light" + elif brightness_mean > 0.8: + lighting_condition = "bright" + elif brightness_std < 0.15: + lighting_condition = "flat" + else: + lighting_condition = "optimal" + + # Analyze saturation for scene type detection + saturation_mean = torch.mean(s).item() + saturation_std = torch.std(s).item() + + # Simple scene classification based on color statistics + if saturation_mean > 0.6 and saturation_std > 0.25: + scene_type = "concept_art" + elif saturation_mean > 0.5: + scene_type = "stylized_art" + elif saturation_mean < 0.3: + scene_type = "portrait" + else: + scene_type = "realistic_photo" + + # Edge detection using Sobel filters on GPU + gray = torch.mean(image_tensor, dim=-1, keepdim=True) + + # Sobel kernels + sobel_x = torch.tensor( + [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32, device=device + ).view(1, 1, 3, 3) + sobel_y = torch.tensor( + [[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32, device=device + ).view(1, 1, 3, 3) + + # Apply convolution for edge detection + gray_padded = F.pad( + gray.permute(2, 0, 1).unsqueeze(0), (1, 1, 1, 1), mode="reflect" + ) + edges_x = F.conv2d(gray_padded, sobel_x) + edges_y = F.conv2d(gray_padded, sobel_y) + edges = torch.sqrt(edges_x**2 + edges_y**2) + + edge_density = torch.mean(edges).item() + + return { + "scene_type": scene_type, + "lighting_condition": lighting_condition, + "brightness_mean": brightness_mean, + "saturation_mean": saturation_mean, + "edge_density": edge_density, + "has_faces": False, # Simplified - no CPU face detection + "skin_tone_areas": [], # Simplified - avoid CPU processing + } + + +class RawImageProcessor: + """ + Advanced Image Processor for camera raw, HDR, and lossless formats. + Handles DNG, ARW, CR2, NEF, EXR, HDR, TIFF 16-bit, and other high-quality formats. + Outputs processed IMAGE data compatible with EasyColorCorrection. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "file_path": ("STRING", {"default": "", "multiline": False}), + "format_type": ( + ["auto", "raw", "exr", "hdr", "tiff16"], + {"default": "auto"}, + ), + }, + "optional": { + # RAW-specific controls + "white_balance": ( + [ + "auto", + "camera", + "daylight", + "cloudy", + "shade", + "tungsten", + "fluorescent", + "flash", + ], + {"default": "auto"}, + ), + "demosaic_algorithm": ( + ["AHD", "VNG", "PPG", "AAHD"], + {"default": "AHD"}, + ), + # HDR/EXR tone mapping controls + "tone_mapping": ( + ["none", "reinhard", "drago", "aces"], + {"default": "aces"}, + ), + "hdr_exposure": ( + "FLOAT", + {"default": 0.0, "min": -5.0, "max": 5.0, "step": 0.1}, + ), + "hdr_gamma": ( + "FLOAT", + {"default": 2.2, "min": 0.5, "max": 4.0, "step": 0.1}, + ), + # Universal controls + "exposure": ( + "FLOAT", + {"default": 0.0, "min": -3.0, "max": 3.0, "step": 0.1}, + ), + "highlights": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "shadows": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "brightness": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "contrast": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "saturation": ( + "FLOAT", + {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.1}, + ), + "noise_reduction": ( + "FLOAT", + {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1}, + ), + "output_colorspace": ( + ["sRGB", "Adobe RGB", "ProPhoto RGB"], + {"default": "sRGB"}, + ), + "output_gamma": (["sRGB", "linear", "1.8", "2.2"], {"default": "sRGB"}), + }, + } + + RETURN_TYPES = ("IMAGE", "STRING") + RETURN_NAMES = ("image", "metadata") + FUNCTION = "process_raw_image" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "Raw Image Processor" + + def process_raw_image( + self, + file_path, + format_type="auto", + white_balance="auto", + demosaic_algorithm="AHD", + tone_mapping="aces", + hdr_exposure=0.0, + hdr_gamma=2.2, + exposure=0.0, + highlights=0.0, + shadows=0.0, + brightness=0.0, + contrast=0.0, + saturation=0.0, + noise_reduction=0.0, + output_colorspace="sRGB", + output_gamma="sRGB", + ): + """ + Process advanced image formats (RAW, EXR, HDR, TIFF 16-bit) with professional controls. + """ + if not file_path or not file_path.strip(): + raise ValueError("File path is required") + + # Auto-detect format based on file extension + file_extension = file_path.lower().split(".")[-1] + if format_type == "auto": + if file_extension in ["dng", "arw", "cr2", "nef", "orf", "rw2", "raf"]: + format_type = "raw" + elif file_extension == "exr": + format_type = "exr" + elif file_extension == "hdr": + format_type = "hdr" + elif file_extension in ["tiff", "tif"]: + format_type = "tiff16" + else: + format_type = "raw" # Default fallback + + try: + if format_type == "raw": + rgb_array, metadata_info = self._process_raw_format( + file_path, + white_balance, + demosaic_algorithm, + exposure, + highlights, + shadows, + brightness, + noise_reduction, + output_colorspace, + output_gamma, + ) + + elif format_type == "exr": + rgb_array, metadata_info = self._process_exr_format( + file_path, + tone_mapping, + hdr_exposure, + hdr_gamma, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ) + + elif format_type == "hdr": + rgb_array, metadata_info = self._process_hdr_format( + file_path, + tone_mapping, + hdr_exposure, + hdr_gamma, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ) + + elif format_type == "tiff16": + rgb_array, metadata_info = self._process_tiff16_format( + file_path, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ) + + else: + raise ValueError(f"Unsupported format type: {format_type}") + + except Exception as e: + error_msg = f"Error processing {format_type.upper()} file: {str(e)}" + print(error_msg) + # Return a black image as fallback + rgb_array = np.zeros((512, 512, 3), dtype=np.uint8) + metadata_info = {"error": error_msg} + + # Convert to PyTorch tensor format expected by ComfyUI + if rgb_array.dtype != np.uint8: + # Normalize HDR data to 0-1 range before converting to uint8 + if rgb_array.max() > 1.0: + rgb_array = rgb_array / rgb_array.max() + rgb_array = (rgb_array * 255).astype(np.uint8) + + # Convert to float and normalize to 0-1 range + image_tensor = torch.from_numpy(rgb_array.astype(np.float32) / 255.0) + + # Add batch dimension: (H, W, C) -> (1, H, W, C) + image_tensor = image_tensor.unsqueeze(0) + + # Format metadata as string + metadata_str = ", ".join([f"{k}: {v}" for k, v in metadata_info.items()]) + + print( + f"βœ… {format_type.upper()} Image Processed: {rgb_array.shape[1]}x{rgb_array.shape[0]} from {file_path}" + ) + if metadata_str: + print(f"πŸ“· Metadata: {metadata_str}") + + return (image_tensor, metadata_str) + + def _process_raw_format( + self, + file_path, + white_balance, + demosaic_algorithm, + exposure, + highlights, + shadows, + brightness, + noise_reduction, + output_colorspace, + output_gamma, + ): + """Process RAW camera formats using rawpy.""" + if not RAW_PROCESSING_AVAILABLE: + raise ValueError( + "RAW processing not available. Install rawpy: pip install rawpy" + ) + + with rawpy.imread(file_path) as raw: + params = rawpy.Params() + + # Demosaic algorithm + demosaic_map = { + "AHD": rawpy.DemosaicAlgorithm.AHD, + "VNG": rawpy.DemosaicAlgorithm.VNG, + "PPG": rawpy.DemosaicAlgorithm.PPG, + "AAHD": rawpy.DemosaicAlgorithm.AAHD, + } + params.demosaic_algorithm = demosaic_map.get( + demosaic_algorithm, rawpy.DemosaicAlgorithm.AHD + ) + + # White balance + if white_balance == "auto": + params.use_auto_wb = True + elif white_balance == "camera": + params.use_camera_wb = True + else: + wb_presets = { + "daylight": [1.0, 1.0, 1.0, 1.0], + "cloudy": [1.2, 1.0, 0.8, 1.0], + "shade": [1.4, 1.0, 0.7, 1.0], + "tungsten": [0.6, 1.0, 1.8, 1.0], + "fluorescent": [0.8, 1.0, 1.4, 1.0], + "flash": [1.1, 1.0, 0.9, 1.0], + } + if white_balance in wb_presets: + params.user_wb = wb_presets[white_balance] + + # Exposure and processing + params.exp_correc = True + params.exp_shift = exposure + params.highlight_mode = ( + rawpy.HighlightMode.Clip + if highlights == 0 + else rawpy.HighlightMode.Reconstruct + ) + params.bright = 1.0 + brightness + params.output_color = ( + rawpy.ColorSpace.sRGB + if output_colorspace == "sRGB" + else rawpy.ColorSpace.Adobe + ) + params.gamma = (1.0, 1.0) if output_gamma == "linear" else (2.2, 4.5) + + if noise_reduction > 0: + params.median_filter_passes = int(noise_reduction * 5) + + rgb_array = raw.postprocess(params) + + metadata_info = { + "format": "RAW", + "width": rgb_array.shape[1], + "height": rgb_array.shape[0], + "demosaic": demosaic_algorithm, + "white_balance": white_balance, + } + + return rgb_array, metadata_info + + def _process_exr_format( + self, + file_path, + tone_mapping, + hdr_exposure, + hdr_gamma, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ): + """Process EXR HDR format using OpenEXR.""" + if not EXR_PROCESSING_AVAILABLE: + # Fallback to imageio if OpenEXR not available + if IMAGEIO_AVAILABLE: + return self._process_with_imageio( + file_path, + "EXR", + tone_mapping, + hdr_exposure, + hdr_gamma, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ) + else: + raise ValueError("EXR processing requires OpenEXR or imageio library") + + exr_file = OpenEXR.InputFile(file_path) + header = exr_file.header() + + # Get image dimensions + dw = header["displayWindow"] + width = dw.max.x - dw.min.x + 1 + height = dw.max.y - dw.min.y + 1 + + # Read RGB channels + channels = exr_file.channels( + ["R", "G", "B"], Imath.PixelType(Imath.PixelType.FLOAT) + ) + + # Convert to numpy arrays + r_channel = np.frombuffer(channels[0], dtype=np.float32).reshape(height, width) + g_channel = np.frombuffer(channels[1], dtype=np.float32).reshape(height, width) + b_channel = np.frombuffer(channels[2], dtype=np.float32).reshape(height, width) + + # Combine channels + rgb_array = np.stack([r_channel, g_channel, b_channel], axis=2) + + # Apply HDR exposure + if hdr_exposure != 0: + rgb_array = rgb_array * (2**hdr_exposure) + + # Apply tone mapping + rgb_array = self._apply_tone_mapping(rgb_array, tone_mapping, hdr_gamma) + + metadata_info = { + "format": "EXR", + "width": width, + "height": height, + "tone_mapping": tone_mapping, + "hdr_exposure": hdr_exposure, + } + + return rgb_array, metadata_info + + def _process_hdr_format( + self, + file_path, + tone_mapping, + hdr_exposure, + hdr_gamma, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ): + """Process HDR format using imageio.""" + if not IMAGEIO_AVAILABLE: + raise ValueError("HDR processing requires imageio library") + + # Read HDR image + rgb_array = imageio.imread(file_path, format="HDR-FI") + + # Apply HDR exposure + if hdr_exposure != 0: + rgb_array = rgb_array * (2**hdr_exposure) + + # Apply tone mapping + rgb_array = self._apply_tone_mapping(rgb_array, tone_mapping, hdr_gamma) + + metadata_info = { + "format": "HDR", + "width": rgb_array.shape[1], + "height": rgb_array.shape[0], + "tone_mapping": tone_mapping, + "hdr_exposure": hdr_exposure, + } + + return rgb_array, metadata_info + + def _process_tiff16_format( + self, file_path, exposure, highlights, shadows, brightness, contrast, saturation + ): + """Process 16-bit TIFF format.""" + if not IMAGEIO_AVAILABLE: + raise ValueError("TIFF 16-bit processing requires imageio library") + + # Read 16-bit TIFF + rgb_array = imageio.imread(file_path) + + # Convert to float and normalize + if rgb_array.dtype == np.uint16: + rgb_array = rgb_array.astype(np.float32) / 65535.0 + elif rgb_array.dtype == np.uint8: + rgb_array = rgb_array.astype(np.float32) / 255.0 + + # Apply basic adjustments + if exposure != 0: + rgb_array = rgb_array * (2**exposure) + + rgb_array = np.clip(rgb_array, 0, 1) + + metadata_info = { + "format": "TIFF 16-bit", + "width": rgb_array.shape[1], + "height": rgb_array.shape[0], + "bit_depth": "16-bit" if rgb_array.dtype == np.uint16 else "8-bit", + } + + return rgb_array, metadata_info + + def _process_with_imageio( + self, + file_path, + format_name, + tone_mapping, + hdr_exposure, + hdr_gamma, + exposure, + highlights, + shadows, + brightness, + contrast, + saturation, + ): + """Fallback processing using imageio.""" + rgb_array = imageio.imread(file_path) + + # Convert to float if needed + if rgb_array.dtype == np.uint8: + rgb_array = rgb_array.astype(np.float32) / 255.0 + elif rgb_array.dtype == np.uint16: + rgb_array = rgb_array.astype(np.float32) / 65535.0 + + # Apply HDR exposure if it's an HDR format + if hdr_exposure != 0: + rgb_array = rgb_array * (2**hdr_exposure) + + # Apply tone mapping for HDR formats + if format_name in ["EXR", "HDR"]: + rgb_array = self._apply_tone_mapping(rgb_array, tone_mapping, hdr_gamma) + + metadata_info = { + "format": f"{format_name} (imageio fallback)", + "width": rgb_array.shape[1], + "height": rgb_array.shape[0], + } + + return rgb_array, metadata_info + + def _apply_tone_mapping(self, rgb_array, tone_mapping, hdr_gamma): + """Apply tone mapping to HDR image data.""" + if tone_mapping == "none": + return np.clip(rgb_array, 0, 1) + + elif tone_mapping == "reinhard": + # Simple Reinhard tone mapping + return rgb_array / (1.0 + rgb_array) + + elif tone_mapping == "drago": + # Drago tone mapping approximation + luminance = np.dot(rgb_array, [0.299, 0.587, 0.114]) + max_lum = np.max(luminance) + if max_lum > 0: + scale = np.log10(max_lum + 1) / np.log10( + 2.0 + + 8.0 * ((luminance / max_lum) ** (np.log10(0.5) / np.log10(0.85))) + ) + return rgb_array * scale[..., np.newaxis] + return rgb_array + + elif tone_mapping == "aces": + # ACES tone mapping curve approximation + a = 2.51 + b = 0.03 + c = 2.43 + d = 0.59 + e = 0.14 + return np.clip( + (rgb_array * (a * rgb_array + b)) + / (rgb_array * (c * rgb_array + d) + e), + 0, + 1, + ) + + # Apply gamma correction + if hdr_gamma != 2.2: + rgb_array = np.power(np.clip(rgb_array, 0, 1), 1.0 / hdr_gamma) + + return np.clip(rgb_array, 0, 1) + + +class ColorCorrectionViewer: + """ + Video Viewer for batch color-corrected image sequences. + Provides playback controls with adjustable framerate. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "fps": ( + "FLOAT", + {"default": 24.0, "min": 1.0, "max": 60.0, "step": 0.1}, + ), + }, + "optional": { + "auto_play": ("BOOLEAN", {"default": True}), + "loop": ("BOOLEAN", {"default": True}), + "frame_skip": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + } + + RETURN_TYPES = () + FUNCTION = "view_sequence" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "Color Corrector Image Viewer" + OUTPUT_NODE = True + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") # Always update for video display + + def view_sequence(self, images, fps=24.0, auto_play=True, loop=True, frame_skip=1): + """ + Display image sequence with video playback controls. + """ + if images is None or images.shape[0] == 0: + print("⚠️ No images provided to viewer") + return {"ui": {"text": "No images provided"}} + + total_frames = images.shape[0] + + # Apply frame skipping if specified + if frame_skip > 1: + selected_indices = torch.arange(0, total_frames, frame_skip) + images = images[selected_indices] + total_frames = images.shape[0] + + duration = total_frames / fps + + # For ComfyUI OUTPUT_NODE, we need to save images to ComfyUI's output directory + import os + import time + from PIL import Image + import folder_paths + + # Create subfolder first so we can reference it + output_dir = folder_paths.get_output_directory() + subfolder = f"colorviewer_{int(time.time())}" + full_output_dir = os.path.join(output_dir, subfolder) + os.makedirs(full_output_dir, exist_ok=True) + + print(f"🎬 Color Corrector Viewer: {total_frames} frames at {fps} FPS") + print(f"⏱️ Duration: {duration:.2f} seconds") + print(f"πŸ”„ Auto-play: {auto_play}, Loop: {loop}") + if frame_skip > 1: + print(f"⏭️ Frame skip: every {frame_skip} frames") + + # Store cleanup info for the deletion endpoint + self._last_subfolder = subfolder + + results = [] + + for i in range(total_frames): + # Convert tensor to numpy and ensure proper format + img_tensor = images[i] # Single image + img_np = img_tensor.cpu().numpy() + + # Ensure values are in 0-255 range and uint8 + if img_np.max() <= 1.0: + img_np = (img_np * 255).astype(np.uint8) + else: + img_np = img_np.astype(np.uint8) + + # Save image to ComfyUI output directory + img_pil = Image.fromarray(img_np, 'RGB') + filename = f"frame_{i:04d}.png" + img_path = os.path.join(full_output_dir, filename) + img_pil.save(img_path) + + # Add image info in ComfyUI format + results.append({ + "filename": filename, + "subfolder": subfolder, + "type": "output" + }) + + # Create a simple GIF for video preview (following VideoHelperSuite pattern) + if total_frames > 1: + # Create animated GIF from frames + gif_filename = f"preview_{int(time.time())}.gif" + gif_path = os.path.join(full_output_dir, gif_filename) + + try: + # Convert images to PIL Images for GIF creation + pil_images = [] + for filename_info in results: + img_path = os.path.join(full_output_dir, filename_info["filename"]) + pil_img = Image.open(img_path) + pil_images.append(pil_img) + + # Create animated GIF + if pil_images: + duration_ms = int(1000 / fps) # Convert to milliseconds + pil_images[0].save( + gif_path, + save_all=True, + append_images=pil_images[1:], + duration=duration_ms, + loop=0 if loop else 1 + ) + + # Use VideoHelperSuite format for video preview + preview = { + "filename": gif_filename, + "subfolder": subfolder, + "type": "output", + "format": "gif", + "frame_rate": fps, + "frame_count": total_frames, + "duration": duration + } + + print(f"🎬 Created animated preview: {gif_filename}") + + return { + "ui": { + "gifs": [preview], # Use VideoHelperSuite's "gifs" format + "images": results, # Keep individual frames for compatibility + } + } + + except Exception as e: + print(f"⚠️ Failed to create GIF preview: {e}") + + # Fallback for single images or if GIF creation fails + return { + "ui": { + "images": results, + "frame_count": [total_frames], + "duration": [duration], + "fps": [fps], + "subfolder": [subfolder] + } + } + + +class ColorPaletteExtractor: + """ + Extracts color palette from an image for use as reference input. + Creates the same palette format used by the main EasyColorCorrection node. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "palette_size": ("INT", {"default": 8, "min": 3, "max": 16, "step": 1}), + "extract_palette": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("STRING", "IMAGE", "IMAGE") + RETURN_NAMES = ("palette_data", "histogram", "palette_image") + FUNCTION = "extract_palette" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "Color Palette Extractor" + + def extract_palette(self, image, palette_size=8, extract_palette=True): + """ + Extract color palette from input image. + Returns palette data string, histogram visualization, and palette image. + """ + if not extract_palette: + # Return empty/default outputs when disabled + empty_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32) + return ("No palette extracted", empty_image, empty_image) + + try: + # Convert image to numpy for processing + image_np = (image[0].cpu().numpy() * 255).astype(np.uint8) + + # Extract hex color palette + hex_colors = extract_color_palette(image_np, num_colors=palette_size) + + # Create palette data string (same format as main node) + if hex_colors: + palette_data = ", ".join(hex_colors) + else: + palette_data = "No palette extracted" + + # Generate histogram image + if ADVANCED_LIBS_AVAILABLE: + histogram_image = generate_histogram_image(image_np) + histogram_tensor = torch.from_numpy(histogram_image.astype(np.float32) / 255.0).unsqueeze(0) + else: + # Create simple fallback histogram + histogram_tensor = torch.zeros((1, 512, 768, 3), dtype=torch.float32) + print("⚠️ Histogram generation disabled - missing OpenCV") + + # Generate palette image + if hex_colors: + palette_image = generate_palette_image(hex_colors) + palette_tensor = torch.from_numpy(palette_image.astype(np.float32) / 255.0).unsqueeze(0) + else: + # Create empty palette image if no colors extracted + palette_tensor = torch.zeros((1, 120, 600, 3), dtype=torch.float32) + + print(f"🎨 Extracted {len(hex_colors)}-color palette: {palette_data}") + + return (palette_data, histogram_tensor, palette_tensor) + + except Exception as e: + print(f"❌ ColorPaletteExtractor error: {e}") + import traceback + traceback.print_exc() + # Return safe fallback + empty_histogram = torch.zeros((1, 512, 768, 3), dtype=torch.float32) + empty_palette = torch.zeros((1, 120, 600, 3), dtype=torch.float32) + return ("Palette extraction failed", empty_histogram, empty_palette) + + @classmethod + def cleanup_images(cls, subfolder): + """Clean up generated images from a specific subfolder""" + try: + import folder_paths + import shutil + + output_dir = folder_paths.get_output_directory() + full_path = os.path.join(output_dir, subfolder) + + if os.path.exists(full_path): + shutil.rmtree(full_path) + print(f"πŸ—‘οΈ Cleaned up {subfolder} directory") + return True + else: + print(f"⚠️ Directory {subfolder} not found") + return False + except Exception as e: + print(f"❌ Cleanup failed: {e}") + return False + + +class FilmEmulation: + """ + Professional film stock emulation node for creative styling. + + Simulates the characteristics of classic analog film stocks including: + - Color response curves specific to each film type + - Film grain patterns and intensity + - Contrast curves and saturation response + - Temperature/tint characteristics + - Push/pull processing simulation + """ + + # Film stock definitions with their characteristic parameters + FILM_STOCKS = { + "Kodak Portra 400": { + "description": "Warm, natural skin tones with subtle grain", + "temperature_bias": 0.15, # Warm bias + "tint_bias": 0.05, # Slight magenta + "saturation_curve": "smooth", + "contrast_curve": "soft", + "grain_type": "fine", + "color_shifts": {"shadows": (0.02, 0.1, 0.05), "highlights": (0.1, 0.05, 0.0)}, + "gamma_curve": 0.95, + }, + "Kodak Portra 800": { + "description": "Higher speed version with more pronounced grain", + "temperature_bias": 0.18, + "tint_bias": 0.08, + "saturation_curve": "smooth", + "contrast_curve": "soft", + "grain_type": "medium", + "color_shifts": {"shadows": (0.03, 0.12, 0.08), "highlights": (0.12, 0.08, 0.02)}, + "gamma_curve": 0.92, + }, + "Fuji Velvia 50": { + "description": "Saturated, punchy colors with cool shadows", + "temperature_bias": -0.1, # Cool bias + "tint_bias": -0.05, # Slight green + "saturation_curve": "punchy", + "contrast_curve": "high", + "grain_type": "ultra_fine", + "color_shifts": {"shadows": (-0.05, 0.0, 0.1), "highlights": (0.05, 0.15, 0.05)}, + "gamma_curve": 1.1, + }, + "Fuji Velvia 100": { + "description": "Slightly more subdued than Velvia 50", + "temperature_bias": -0.08, + "tint_bias": -0.03, + "saturation_curve": "punchy", + "contrast_curve": "medium_high", + "grain_type": "fine", + "color_shifts": {"shadows": (-0.03, 0.0, 0.08), "highlights": (0.03, 0.12, 0.03)}, + "gamma_curve": 1.05, + }, + "Kodak Vision3 500T": { + "description": "Cinematic tungsten-balanced stock", + "temperature_bias": 0.25, # Strong tungsten bias + "tint_bias": 0.1, + "saturation_curve": "cinematic", + "contrast_curve": "medium", + "grain_type": "medium", + "color_shifts": {"shadows": (0.05, 0.2, 0.1), "highlights": (0.15, 0.1, 0.0)}, + "gamma_curve": 0.9, + }, + "Kodak Gold 200": { + "description": "Vintage warm look with yellow/orange cast", + "temperature_bias": 0.2, + "tint_bias": 0.15, # Strong yellow/orange + "saturation_curve": "vintage", + "contrast_curve": "medium", + "grain_type": "medium", + "color_shifts": {"shadows": (0.08, 0.15, 0.05), "highlights": (0.2, 0.12, 0.0)}, + "gamma_curve": 0.88, + }, + "Fuji Pro 400H": { + "description": "Soft, pastel tones, overexposure-friendly", + "temperature_bias": 0.05, + "tint_bias": 0.02, + "saturation_curve": "soft", + "contrast_curve": "low", + "grain_type": "fine", + "color_shifts": {"shadows": (0.02, 0.05, 0.08), "highlights": (0.08, 0.08, 0.05)}, + "gamma_curve": 0.85, + }, + "Kodak Tri-X 400": { + "description": "Classic B&W with distinctive grain", + "temperature_bias": 0.0, # B&W + "tint_bias": 0.0, + "saturation_curve": "bw", + "contrast_curve": "high", + "grain_type": "heavy", + "color_shifts": {"shadows": (0.0, 0.0, 0.0), "highlights": (0.0, 0.0, 0.0)}, + "gamma_curve": 1.2, + }, + "Ilford HP5 Plus": { + "description": "High contrast B&W with fine grain", + "temperature_bias": 0.0, + "tint_bias": 0.0, + "saturation_curve": "bw", + "contrast_curve": "very_high", + "grain_type": "medium", + "color_shifts": {"shadows": (0.0, 0.0, 0.0), "highlights": (0.0, 0.0, 0.0)}, + "gamma_curve": 1.3, + }, + } + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE", {}), + "film_stock": (list(cls.FILM_STOCKS.keys()), {"default": "Kodak Portra 400"}), + "strength": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.05, + "tooltip": "Blend strength between original and film emulation" + }), + "grain_intensity": ("FLOAT", { + "default": 0.3, + "min": 0.0, + "max": 1.0, + "step": 0.05, + "tooltip": "Film grain intensity (0.0 = no grain, 1.0 = maximum grain)" + }), + "exposure_compensation": ("FLOAT", { + "default": 0.0, + "min": -2.0, + "max": 2.0, + "step": 0.1, + "tooltip": "Exposure compensation in stops (simulates over/under exposure response)" + }), + "push_pull": ("FLOAT", { + "default": 0.0, + "min": -2.0, + "max": 2.0, + "step": 0.5, + "tooltip": "Push/pull processing simulation (affects contrast and grain)" + }), + "highlight_rolloff": ("FLOAT", { + "default": 0.8, + "min": 0.0, + "max": 1.0, + "step": 0.05, + "tooltip": "Film highlight rolloff characteristics" + }), + } + } + + RETURN_TYPES = ("IMAGE", "STRING") + RETURN_NAMES = ("image", "film_info") + FUNCTION = "apply_film_emulation" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "Film Emulation" + + def apply_film_emulation(self, image, film_stock, strength=1.0, grain_intensity=0.3, + exposure_compensation=0.0, push_pull=0.0, highlight_rolloff=0.8): + """Apply film stock emulation to the input image.""" + + original_image = image.clone() + processed_image = image.clone() + + # Get film stock characteristics + stock_params = self.FILM_STOCKS[film_stock] + + print(f"🎞️ Applying {film_stock} emulation (strength: {strength:.2f})") + print(f"πŸ“ {stock_params['description']}") + + # Apply exposure compensation first (simulates film response to different exposures) + if exposure_compensation != 0.0: + processed_image = self._apply_exposure_compensation( + processed_image, exposure_compensation, stock_params + ) + + # Apply film color characteristics + processed_image = self._apply_color_response(processed_image, stock_params, push_pull) + + # Apply film grain + if grain_intensity > 0.0: + processed_image = self._apply_film_grain( + processed_image, stock_params, grain_intensity, push_pull + ) + + # Apply highlight rolloff + processed_image = self._apply_highlight_rolloff( + processed_image, highlight_rolloff, stock_params + ) + + # Blend with original based on strength + if strength < 1.0: + processed_image = torch.lerp(original_image, processed_image, strength) + + # Generate film info string + film_info = f"Film: {film_stock}, Strength: {strength:.2f}, Grain: {grain_intensity:.2f}" + if exposure_compensation != 0.0: + film_info += f", Exposure: {exposure_compensation:+.1f} stops" + if push_pull != 0.0: + film_info += f", Push/Pull: {push_pull:+.1f}" + + print(f"βœ… Film emulation applied: {film_info}") + + return (processed_image, film_info) + + def _apply_exposure_compensation(self, image, exposure_stops, stock_params): + """Simulate film response to different exposure levels.""" + # Convert stops to linear multiplier + exposure_multiplier = 2.0 ** exposure_stops + + # Apply exposure with film-specific response + exposed_image = image * exposure_multiplier + + # Film has non-linear response to overexposure (shoulder curve) + if exposure_stops > 0: + # Simulate film shoulder - highlights compress gracefully + shoulder_strength = min(exposure_stops / 2.0, 1.0) + exposed_image = torch.where( + exposed_image > 0.8, + 0.8 + (exposed_image - 0.8) * (1.0 - shoulder_strength * 0.7), + exposed_image + ) + + return torch.clamp(exposed_image, 0.0, 1.0) + + def _apply_color_response(self, image, stock_params, push_pull): + """Apply film-specific color response curves and characteristics.""" + + # Convert to HSV for easier manipulation + if ADVANCED_LIBS_AVAILABLE: + hsv_image = rgb_to_hsv(image) + h, s, v = hsv_image[..., 0], hsv_image[..., 1], hsv_image[..., 2] + else: + # Fallback to simple processing + return self._apply_simple_film_response(image, stock_params, push_pull) + + # Apply temperature and tint bias + temp_bias = stock_params["temperature_bias"] + tint_bias = stock_params["tint_bias"] + + if temp_bias != 0.0: + # Adjust hue for temperature (blue-yellow axis) + h = (h + temp_bias * 0.1) % 1.0 + + # Apply saturation curve based on film type + saturation_curve = stock_params["saturation_curve"] + s = self._apply_saturation_curve(s, saturation_curve, push_pull) + + # Apply contrast curve (gamma) + gamma = stock_params["gamma_curve"] + if push_pull != 0.0: + # Push processing increases contrast, pull decreases it + gamma = gamma + (push_pull * 0.15) + + v = torch.pow(torch.clamp(v, 0.001, 1.0), gamma) + + # Apply color shifts to shadows and highlights + color_shifts = stock_params["color_shifts"] + processed_image = hsv_to_rgb(torch.stack([h, s, v], dim=-1)) + processed_image = self._apply_color_shifts(processed_image, color_shifts) + + return torch.clamp(processed_image, 0.0, 1.0) + + def _apply_saturation_curve(self, saturation, curve_type, push_pull): + """Apply film-specific saturation response curves.""" + + if curve_type == "bw": + # Black and white films + return torch.zeros_like(saturation) + + # Saturation multipliers based on film type + multipliers = { + "soft": 0.85, + "smooth": 0.95, + "cinematic": 1.0, + "vintage": 1.1, + "punchy": 1.3, + } + + multiplier = multipliers.get(curve_type, 1.0) + + # Push processing affects saturation + if push_pull != 0.0: + multiplier += push_pull * 0.15 + + # Apply film-specific saturation curve + if curve_type == "punchy": + # Velvia-style: boost mid-saturation, compress high saturation + s_adjusted = saturation * multiplier + s_adjusted = torch.where( + saturation > 0.7, + 0.7 + (s_adjusted - 0.7) * 0.6, # Compress high saturation + s_adjusted + ) + elif curve_type == "soft": + # Pro 400H style: gentle saturation with smooth rolloff + s_adjusted = saturation * multiplier + s_adjusted = torch.pow(s_adjusted, 1.1) # Slight curve + else: + # Linear adjustment for other types + s_adjusted = saturation * multiplier + + return torch.clamp(s_adjusted, 0.0, 1.0) + + def _apply_color_shifts(self, image, color_shifts): + """Apply film-specific color shifts to shadows and highlights.""" + + # Calculate luminance for masking + luminance = 0.299 * image[..., 0] + 0.587 * image[..., 1] + 0.114 * image[..., 2] + + # Create shadow and highlight masks + shadow_mask = torch.clamp(1.0 - luminance * 3.0, 0.0, 1.0) + highlight_mask = torch.clamp((luminance - 0.6) * 2.5, 0.0, 1.0) + + # Apply shadow color shifts + shadow_shift = color_shifts["shadows"] + for i, shift in enumerate(shadow_shift): + if shift != 0.0: + image[..., i] += shift * shadow_mask + + # Apply highlight color shifts + highlight_shift = color_shifts["highlights"] + for i, shift in enumerate(highlight_shift): + if shift != 0.0: + image[..., i] += shift * highlight_mask + + return torch.clamp(image, 0.0, 1.0) + + def _apply_simple_film_response(self, image, stock_params, push_pull): + """Simplified film response when advanced libraries aren't available.""" + + # Basic gamma adjustment + gamma = stock_params["gamma_curve"] + if push_pull != 0.0: + gamma = gamma + (push_pull * 0.15) + + processed = torch.pow(torch.clamp(image, 0.001, 1.0), gamma) + + # Basic temperature/tint adjustment + temp_bias = stock_params["temperature_bias"] + tint_bias = stock_params["tint_bias"] + + if temp_bias != 0.0: + # Simple temperature shift (blue-yellow) + processed[..., 0] += temp_bias * 0.1 # Red + processed[..., 2] -= temp_bias * 0.05 # Blue + + if tint_bias != 0.0: + # Simple tint shift (green-magenta) + processed[..., 0] += tint_bias * 0.05 # Red (magenta) + processed[..., 1] -= tint_bias * 0.05 # Green + + return torch.clamp(processed, 0.0, 1.0) + + def _apply_film_grain(self, image, stock_params, grain_intensity, push_pull): + """Apply film-specific grain patterns.""" + + grain_type = stock_params["grain_type"] + device = image.device + + # Grain characteristics based on type + grain_params = { + "ultra_fine": {"size": 0.5, "strength": 0.3}, + "fine": {"size": 0.7, "strength": 0.4}, + "medium": {"size": 1.0, "strength": 0.6}, + "heavy": {"size": 1.5, "strength": 0.8}, + } + + params = grain_params.get(grain_type, grain_params["medium"]) + + # Push processing increases grain + grain_strength = params["strength"] * grain_intensity + if push_pull > 0: + grain_strength *= (1.0 + push_pull * 0.5) + + # Generate film grain noise + noise_shape = image.shape + grain_noise = torch.randn(noise_shape, device=device) * grain_strength * 0.02 + + # Apply grain with luminance dependency (more grain in shadows for most films) + luminance = 0.299 * image[..., 0] + 0.587 * image[..., 1] + 0.114 * image[..., 2] + grain_mask = 1.0 - luminance * 0.5 # More grain in darker areas + grain_mask = grain_mask.unsqueeze(-1) + + grained_image = image + (grain_noise * grain_mask) + + return torch.clamp(grained_image, 0.0, 1.0) + + def _apply_highlight_rolloff(self, image, rolloff_strength, stock_params): + """Apply film-specific highlight rolloff characteristics.""" + + # Film naturally compresses highlights + luminance = 0.299 * image[..., 0] + 0.587 * image[..., 1] + 0.114 * image[..., 2] + + # Create highlight mask + highlight_threshold = 0.8 + highlight_mask = torch.clamp((luminance - highlight_threshold) / (1.0 - highlight_threshold), 0.0, 1.0) + + # Apply rolloff based on film characteristics + contrast_curve = stock_params["contrast_curve"] + + if contrast_curve in ["high", "very_high"]: + # High contrast films have harder rolloff + rolloff_curve = torch.pow(highlight_mask, 1.5) + else: + # Softer films have gentler rolloff + rolloff_curve = torch.pow(highlight_mask, 0.8) + + # Apply the rolloff + rolloff_factor = 1.0 - (rolloff_curve * rolloff_strength * 0.3) + rolloff_factor = rolloff_factor.unsqueeze(-1) + + return image * rolloff_factor + + +class VAEColorCorrector: + """ + Specialized color correction for VAE artifacts in inpainting/img2img workflows. + Fixes color shifts in unmasked areas by referencing the original input image. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_image": ("IMAGE", {"tooltip": "πŸ”΄ Original input image (before VAE encoding)"}), + "processed_image": ("IMAGE", {"tooltip": "🟑 Image after VAE decode (with color shifts)"}), + "correction_strength": ( + "FLOAT", + { + "default": 0.8, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "🎚️ How strongly to correct VAE color shifts (0.0 = no correction, 1.0 = full correction)" + } + ), + "method": ( + ["luminance_zones", "histogram_matching", "statistical_matching", "advanced_3d_lut"], + { + "default": "luminance_zones", + "tooltip": "πŸ”§ Color correction method:\nβ€’ luminance_zones: Professional shadows/midtones/highlights (recommended)\nβ€’ histogram_matching: Match color distributions\nβ€’ statistical_matching: Match color statistics\nβ€’ advanced_3d_lut: Most accurate but slower 3D color mapping" + } + ), + "auto_preserve": ( + "BOOLEAN", + { + "default": True, + "tooltip": "πŸ€– Auto-detect and preserve inpainted areas when no mask is provided" + } + ), + "lock_input_image": ( + "BOOLEAN", + { + "default": False, + "tooltip": "πŸ”’ Lock input images to prevent upstream nodes from reprocessing when adjusting correction parameters", + }, + ), + }, + "optional": { + "vae": ("VAE", {"tooltip": "πŸ”§ VAE model used for encoding/decoding (helps with VAE-specific color corrections)"}), + "mask": ("MASK", {"tooltip": "⚫ Optional mask - white areas will be preserved (inpainted areas)"}), + "edge_feather": ( + "INT", + { + "default": 5, + "min": 0, + "max": 50, + "step": 1, + "tooltip": "🌟 Feather edges between corrected/preserved areas (pixels)" + } + ), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("corrected_image",) + FUNCTION = "correct_vae_colors" + CATEGORY = "itsjustregi / Easy Color Corrector" + DISPLAY_NAME = "VAE Color Corrector" + + def correct_vae_colors( + self, + original_image, + processed_image, + correction_strength=0.8, + method="luminance_zones", + auto_preserve=True, + lock_input_image=False, + vae=None, + mask=None, + edge_feather=5 + ): + """ + Correct VAE-induced color shifts by referencing the original image. + """ + device = original_image.device + + # Handle input image locking to prevent upstream reprocessing + if lock_input_image: + # Check if input images have changed (cache invalidation) + images_changed = False + if ( + not hasattr(self, "_cached_original_image") + or self._cached_original_image is None + or not hasattr(self, "_cached_processed_image") + or self._cached_processed_image is None + ): + images_changed = True + else: + # Compare image tensors to detect changes from upstream + try: + if not torch.equal(self._cached_original_image, original_image) or \ + not torch.equal(self._cached_processed_image, processed_image): + images_changed = True + except: + # Different shapes or other comparison issues = definitely changed + images_changed = True + + if images_changed: + self._cached_original_image = original_image.clone() + self._cached_processed_image = processed_image.clone() + self._cached_vae_analysis = None + print("πŸ”„ Locked Input: New images detected, updating cache") + + # Use cached images for processing + original_image = self._cached_original_image.clone() + processed_image = self._cached_processed_image.clone() + + print(f"πŸ”§ VAE Color Correction: method={method}, strength={correction_strength:.2f}") + print(f"πŸ“ Original: {original_image.shape}, Processed: {processed_image.shape}") + if lock_input_image: + print("πŸ”’ Input images locked - preventing upstream reprocessing") + if vae is not None: + print("🎯 VAE model provided for enhanced correction") + + # Ensure images are same size + if original_image.shape != processed_image.shape: + print("⚠️ Image size mismatch - resizing processed to match original") + processed_image = F.interpolate( + processed_image.permute(0, 3, 1, 2), + size=(original_image.shape[1], original_image.shape[2]), + mode='bilinear', + align_corners=False + ).permute(0, 2, 3, 1) + + # Process each image in batch + corrected_batch = [] + + for i in range(original_image.shape[0]): + orig_img = original_image[i] + proc_img = processed_image[i] + current_mask = mask[i] if mask is not None else None + + # Apply color correction + corrected_img = self._apply_vae_color_correction( + orig_img, proc_img, method, correction_strength, + auto_preserve, vae, current_mask, edge_feather, device, lock_input_image + ) + + corrected_batch.append(corrected_img) + + result = torch.stack(corrected_batch, dim=0) + print(f"βœ… VAE color correction completed for {len(corrected_batch)} images") + + return (result,) + + def _apply_vae_color_correction( + self, original_img, processed_img, method, strength, + auto_preserve, vae, mask, edge_feather, device, lock_input_image + ): + """Apply the actual color correction.""" + + # Convert to numpy for processing + orig_np = (original_img.cpu().numpy() * 255).astype(np.uint8) + proc_np = (processed_img.cpu().numpy() * 255).astype(np.uint8) + + # VAE-specific adjustments with caching + vae_adjustment = 1.0 + vae_color_bias = None + if vae is not None: + # Use cached VAE analysis when input is locked + if ( + lock_input_image + and hasattr(self, "_cached_vae_analysis") + and self._cached_vae_analysis is not None + ): + vae_adjustment, vae_color_bias = self._cached_vae_analysis + print("🎯 Using cached VAE analysis for enhanced correction") + else: + print("🎯 VAE model detected - analyzing VAE characteristics for enhanced correction") + vae_adjustment, vae_color_bias = self._analyze_vae_characteristics(vae, orig_np, proc_np) + # Cache the analysis if input is locked + if lock_input_image: + self._cached_vae_analysis = (vae_adjustment, vae_color_bias) + + print(f"πŸ”§ Applying {method} color correction...") + + # Balance strength across different methods + adjusted_strength = self._balance_correction_strength(method, strength) * vae_adjustment + + # Additional safety: limit maximum strength to prevent quantization artifacts + if adjusted_strength > 1.0: + print(f"⚠️ Strength {adjusted_strength:.2f} > 1.0, clamping to 1.0 to prevent artifacts") + adjusted_strength = 1.0 + + print(f"πŸ”§ Adjusted strength: {strength:.2f} β†’ {adjusted_strength:.2f} for {method}") + + # Apply color correction based on method with VAE bias compensation + if method == "advanced_3d_lut": + corrected_np = self._advanced_3d_lut_correction(orig_np, proc_np, adjusted_strength, vae_color_bias) + elif method == "luminance_zones": + if vae_color_bias is not None: + corrected_np = self._vae_aware_luminance_correction(proc_np, orig_np, adjusted_strength, vae_color_bias) + else: + corrected_np = match_to_reference_colors(proc_np, orig_np, adjusted_strength) + elif method == "histogram_matching": + corrected_np = self._histogram_matching_correction(orig_np, proc_np, adjusted_strength, vae_color_bias) + else: # statistical_matching + corrected_np = self._statistical_matching_correction(orig_np, proc_np, adjusted_strength, vae_color_bias) + + # Convert back to tensor + corrected_tensor = torch.from_numpy(corrected_np.astype(np.float32) / 255.0).to(device) + + # Handle mask-based preservation + if mask is not None: + # Always use mask consistently: white pixels = preserve, black pixels = correct + corrected_tensor = self._apply_mask_preservation( + processed_img, corrected_tensor, mask, edge_feather, device + ) + elif auto_preserve: + # Auto-detect changed areas if no mask provided and auto_preserve is True + corrected_tensor = self._auto_preserve_inpainted( + original_img, processed_img, corrected_tensor, edge_feather, device + ) + + return corrected_tensor + + def _safe_clamp_colors(self, image_np, preserve_gradients=True): + """Safely clamp colors to prevent quantization artifacts (black squares).""" + # Diagnostic: Check for extreme values + min_val = np.min(image_np) + max_val = np.max(image_np) + if min_val < -10 or max_val > 265: + print(f"⚠️ Extreme color values detected: min={min_val:.1f}, max={max_val:.1f} - applying safe clamping") + + if preserve_gradients: + # Soft clamping with sigmoid-like curve to preserve gradients + image_float = image_np.astype(np.float32) + + # Apply soft clamping for values outside normal range + below_zero = image_float < 0 + above_255 = image_float > 255 + + if np.any(below_zero): + # Soft approach to zero for negative values + negative_values = image_float[below_zero] + image_float[below_zero] = -5 * np.log(1 + np.exp(-negative_values / 5)) + + if np.any(above_255): + # Soft approach to 255 for values above + high_values = image_float[above_255] + image_float[above_255] = 255 + 5 * np.log(1 + np.exp((high_values - 255) / 5)) + + # Final gentle clamp + return np.clip(image_float, 0, 255).astype(np.uint8) + else: + # Standard hard clamping + return np.clip(image_np, 0, 255).astype(np.uint8) + + def _analyze_vae_characteristics(self, vae, original_np, processed_np): + """Analyze VAE-specific color characteristics and biases.""" + try: + # Note: VAE model object could be used for future model-specific analysis + # Currently we analyze empirically by comparing original vs processed images + # Calculate per-channel color bias introduced by the VAE + orig_float = original_np.astype(np.float32) + proc_float = processed_np.astype(np.float32) + + # Analyze color bias in different luminance zones + gray_orig = np.mean(orig_float, axis=2) + + # Create luminance-based masks + shadows_mask = gray_orig < 85 + midtones_mask = (gray_orig >= 85) & (gray_orig <= 170) + highlights_mask = gray_orig > 170 + + vae_bias = {} + + for zone_name, mask in [("shadows", shadows_mask), ("midtones", midtones_mask), ("highlights", highlights_mask)]: + if np.sum(mask) > 100: # Ensure enough pixels for reliable statistics + orig_zone = orig_float[mask] + proc_zone = proc_float[mask] + + # Calculate color bias per channel + bias_r = np.mean(proc_zone[:, 0]) - np.mean(orig_zone[:, 0]) + bias_g = np.mean(proc_zone[:, 1]) - np.mean(orig_zone[:, 1]) + bias_b = np.mean(proc_zone[:, 2]) - np.mean(orig_zone[:, 2]) + + vae_bias[zone_name] = np.array([bias_r, bias_g, bias_b]) + else: + vae_bias[zone_name] = np.array([0.0, 0.0, 0.0]) + + # Calculate overall bias strength + total_bias = np.mean([np.abs(bias).sum() for bias in vae_bias.values()]) + + # Adjust correction strength based on VAE bias severity + if total_bias > 15: # High bias + vae_adjustment = 0.85 + print(f"πŸ”΄ High VAE color bias detected ({total_bias:.1f}) - reducing correction strength") + elif total_bias > 8: # Medium bias + vae_adjustment = 0.92 + print(f"🟑 Medium VAE color bias detected ({total_bias:.1f}) - slight adjustment") + else: # Low bias + vae_adjustment = 0.98 + print(f"🟒 Low VAE color bias detected ({total_bias:.1f}) - minimal adjustment") + + return vae_adjustment, vae_bias + + except Exception as e: + print(f"⚠️ VAE analysis failed: {e}, using default settings") + return 0.9, None + + def _vae_aware_luminance_correction(self, processed_np, original_np, strength, vae_color_bias): + """Enhanced luminance zone correction that compensates for VAE-specific color biases.""" + try: + # Start with standard luminance correction + corrected_np = match_to_reference_colors(processed_np, original_np, strength) + + # Apply VAE bias compensation + corrected_float = corrected_np.astype(np.float32) + gray = np.mean(corrected_float, axis=2) + + # Apply zone-specific bias corrections + shadows_mask = gray < 85 + midtones_mask = (gray >= 85) & (gray <= 170) + highlights_mask = gray > 170 + + bias_strength = strength * 0.3 # Gentle bias correction + + for zone_name, mask in [("shadows", shadows_mask), ("midtones", midtones_mask), ("highlights", highlights_mask)]: + if zone_name in vae_color_bias and np.sum(mask) > 0: + bias = vae_color_bias[zone_name] + # Apply inverse bias correction with limiting + for c in range(3): + bias_correction = bias[c] * bias_strength + # Limit bias correction to prevent extreme shifts + bias_correction = np.clip(bias_correction, -30, 30) + corrected_float[mask, c] -= bias_correction + + # Safe clamping to prevent quantization artifacts + corrected_np = self._safe_clamp_colors(corrected_float, preserve_gradients=True) + print(f"βœ… VAE-aware luminance correction applied with bias compensation") + + return corrected_np + + except Exception as e: + print(f"❌ VAE-aware correction failed: {e}, falling back to standard") + return match_to_reference_colors(processed_np, original_np, strength) + + def _balance_correction_strength(self, method, strength): + """Balance correction strength across different methods for consistent results.""" + # Different methods have different sensitivities, so we adjust accordingly + if method == "luminance_zones": + # Luminance zones method is well-balanced, use as-is + return strength + elif method == "histogram_matching": + # Histogram matching can be aggressive, reduce slightly + return strength * 0.85 + elif method == "statistical_matching": + # Statistical matching is gentler, increase slightly + return min(1.0, strength * 1.15) + elif method == "advanced_3d_lut": + # 3D LUT can be very aggressive, reduce significantly + return strength * 0.7 + else: + return strength + + def _advanced_3d_lut_correction(self, original_np, processed_np, strength, vae_color_bias=None): + """Advanced 3D LUT-based color correction for precise VAE artifact fixing.""" + if not ADVANCED_LIBS_AVAILABLE: + return match_to_reference_colors(processed_np, original_np, strength) + + try: + print("πŸ”§ Building 3D color mapping...") + + # Create color mapping using k-means clustering + from sklearn.cluster import KMeans + + # Sample colors for mapping (use every 4th pixel for speed) + orig_samples = original_np[::4, ::4].reshape(-1, 3) + proc_samples = processed_np[::4, ::4].reshape(-1, 3) + + # Use k-means to find representative color pairs + n_clusters = min(64, len(orig_samples) // 10) # Adaptive cluster count + + if n_clusters < 8: + # Too few samples, fall back to zone-based matching + return match_to_reference_colors(processed_np, original_np, strength) + + # Cluster processed colors + kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10) + proc_clusters = kmeans.fit_predict(proc_samples) + proc_centers = kmeans.cluster_centers_ + + # Find corresponding original colors for each cluster + orig_centers = np.zeros_like(proc_centers) + for i in range(n_clusters): + cluster_mask = proc_clusters == i + if np.sum(cluster_mask) > 0: + # Average original colors that correspond to this processed cluster + orig_centers[i] = np.mean(orig_samples[cluster_mask], axis=0) + else: + orig_centers[i] = proc_centers[i] # Fallback + + # Apply color mapping to full image + corrected_np = processed_np.astype(np.float32) + + # For each pixel, find closest processed center and map to original center + proc_flat = processed_np.reshape(-1, 3).astype(np.float32) + + # Vectorized distance calculation + distances = np.linalg.norm( + proc_flat[:, np.newaxis, :] - proc_centers[np.newaxis, :, :], axis=2 + ) + closest_clusters = np.argmin(distances, axis=1) + + # Apply color shifts with distance-based blending and VAE bias compensation + min_distances = np.min(distances, axis=1) + max_distance = np.percentile(min_distances, 90) # Use 90th percentile for normalization + + for i in range(n_clusters): + cluster_mask = closest_clusters == i + if np.sum(cluster_mask) > 0: + # Calculate color shift for this cluster + color_shift = orig_centers[i] - proc_centers[i] + + # Apply VAE bias compensation if available + if vae_color_bias is not None: + # Determine luminance zone for this cluster + cluster_luminance = np.mean(proc_centers[i]) + if cluster_luminance < 85: + zone_bias = vae_color_bias.get("shadows", np.array([0.0, 0.0, 0.0])) + elif cluster_luminance <= 170: + zone_bias = vae_color_bias.get("midtones", np.array([0.0, 0.0, 0.0])) + else: + zone_bias = vae_color_bias.get("highlights", np.array([0.0, 0.0, 0.0])) + + # Add VAE bias compensation to color shift + color_shift -= zone_bias * 0.5 # Gentle bias compensation + + # Apply with distance-based falloff and strength + cluster_distances = min_distances[cluster_mask] + distance_weights = np.clip(1.0 - cluster_distances / max_distance, 0.1, 1.0) + + for c in range(3): + shift_amount = color_shift[c] * strength * distance_weights + # Limit maximum shift to prevent extreme corrections + shift_amount = np.clip(shift_amount, -50, 50) + proc_flat[cluster_mask, c] += shift_amount + + # Reshape back and apply safe clamping + corrected_np = proc_flat.reshape(processed_np.shape) + corrected_np = self._safe_clamp_colors(corrected_np, preserve_gradients=True) + + print(f"βœ… 3D LUT correction applied using {n_clusters} color clusters") + return corrected_np + + except Exception as e: + print(f"❌ 3D LUT correction failed: {e}, falling back to zone matching") + return match_to_reference_colors(processed_np, original_np, strength) + + def _histogram_matching_correction(self, original_np, processed_np, strength, vae_color_bias=None): + """Histogram-based color matching.""" + if not ADVANCED_LIBS_AVAILABLE: + return match_to_reference_colors(processed_np, original_np, strength) + + try: + from skimage import exposure + + corrected_np = processed_np.astype(np.float32) + original_float = original_np.astype(np.float32) + + # Match histogram for each channel + for c in range(3): + matched_channel = exposure.match_histograms( + corrected_np[:,:,c], original_float[:,:,c] + ) + # Blend with original based on strength + corrected_np[:,:,c] = ( + processed_np[:,:,c] * (1 - strength) + + matched_channel * strength + ) + + # Apply VAE bias compensation if available + if vae_color_bias is not None: + gray = np.mean(corrected_np, axis=2) + bias_strength = strength * 0.2 # Gentle bias correction for histogram method + + # Apply zone-specific bias corrections + shadows_mask = gray < 85 + midtones_mask = (gray >= 85) & (gray <= 170) + highlights_mask = gray > 170 + + for zone_name, mask in [("shadows", shadows_mask), ("midtones", midtones_mask), ("highlights", highlights_mask)]: + if zone_name in vae_color_bias and np.sum(mask) > 0: + bias = vae_color_bias[zone_name] + for c in range(3): + bias_correction = bias[c] * bias_strength + # Limit bias correction to prevent extreme shifts + bias_correction = np.clip(bias_correction, -30, 30) + corrected_np[mask, c] -= bias_correction + + print("βœ… VAE bias compensation applied to histogram matching") + + return self._safe_clamp_colors(corrected_np, preserve_gradients=True) + + except Exception as e: + print(f"❌ Histogram matching failed: {e}") + return match_to_reference_colors(processed_np, original_np, strength) + + def _statistical_matching_correction(self, original_np, processed_np, strength, vae_color_bias=None): + """Statistical moment matching (mean and std).""" + corrected_np = processed_np.astype(np.float32) + original_float = original_np.astype(np.float32) + + for c in range(3): + # Calculate statistics + proc_mean = np.mean(corrected_np[:,:,c]) + proc_std = np.std(corrected_np[:,:,c]) + orig_mean = np.mean(original_float[:,:,c]) + orig_std = np.std(original_float[:,:,c]) + + # Normalize and rescale + if proc_std > 0: + normalized = (corrected_np[:,:,c] - proc_mean) / proc_std + rescaled = normalized * orig_std + orig_mean + + # Blend with strength + corrected_np[:,:,c] = ( + corrected_np[:,:,c] * (1 - strength) + + rescaled * strength + ) + + # Apply VAE bias compensation if available + if vae_color_bias is not None: + gray = np.mean(corrected_np, axis=2) + bias_strength = strength * 0.25 # Gentle bias correction for statistical method + + # Apply zone-specific bias corrections + shadows_mask = gray < 85 + midtones_mask = (gray >= 85) & (gray <= 170) + highlights_mask = gray > 170 + + for zone_name, mask in [("shadows", shadows_mask), ("midtones", midtones_mask), ("highlights", highlights_mask)]: + if zone_name in vae_color_bias and np.sum(mask) > 0: + bias = vae_color_bias[zone_name] + for c in range(3): + bias_correction = bias[c] * bias_strength + # Limit bias correction to prevent extreme shifts + bias_correction = np.clip(bias_correction, -30, 30) + corrected_np[mask, c] -= bias_correction + + print("βœ… VAE bias compensation applied to statistical matching") + + return self._safe_clamp_colors(corrected_np, preserve_gradients=True) + + def _apply_mask_preservation(self, processed_img, corrected_img, mask, edge_feather, device): + """Apply mask to preserve areas. White pixels in mask = preserve, black pixels = correct.""" + # Mask convention: white (1.0) = preserve, black (0.0) = correct + # We need correction_mask where 1.0 = correct, 0.0 = preserve + correction_mask = 1.0 - mask.to(device) # Invert: white becomes 0 (preserve), black becomes 1 (correct) + + if edge_feather > 0 and ADVANCED_LIBS_AVAILABLE: + # Apply gaussian blur for soft edges + correction_mask_np = correction_mask.cpu().numpy() + correction_mask_np = cv2.GaussianBlur( + correction_mask_np, (edge_feather*2+1, edge_feather*2+1), edge_feather/3 + ) + correction_mask = torch.from_numpy(correction_mask_np).to(device) + + # Apply mask: blend between processed and corrected + correction_mask = correction_mask.unsqueeze(-1) # Add channel dimension + result = processed_img * (1 - correction_mask) + corrected_img * correction_mask + + print(f"βœ… Mask-based preservation applied with {edge_feather}px feather") + return result + + def _auto_preserve_inpainted(self, original_img, processed_img, corrected_img, edge_feather, device): + """Auto-detect inpainted areas and preserve them.""" + # Calculate difference between original and processed to find changed areas + diff = torch.abs(original_img - processed_img) + diff_magnitude = torch.mean(diff, dim=-1) # Average across RGB + + # Threshold to find significantly changed areas (likely inpainted) + threshold = torch.quantile(diff_magnitude, 0.7) # Top 30% of changes + inpainted_mask = (diff_magnitude > threshold).float() + + # Correction mask: 1 = correct, 0 = preserve + correction_mask = 1.0 - inpainted_mask + + if edge_feather > 0 and ADVANCED_LIBS_AVAILABLE: + # Smooth the mask + correction_mask_np = correction_mask.cpu().numpy() + correction_mask_np = cv2.GaussianBlur( + correction_mask_np, (edge_feather*2+1, edge_feather*2+1), edge_feather/3 + ) + correction_mask = torch.from_numpy(correction_mask_np).to(device) + + correction_mask = correction_mask.unsqueeze(-1) # Add channel dimension + result = processed_img * (1 - correction_mask) + corrected_img * correction_mask + + preserved_pixels = torch.sum(1 - correction_mask).item() + print(f"βœ… Auto-preserved {preserved_pixels:.0f} pixels (likely inpainted areas)") + return result + + +# Export all classes +__all__ = [ + "EasyColorCorrection", + "BatchColorCorrection", + "RawImageProcessor", + "ColorCorrectionViewer", + "ColorPaletteExtractor", + "FilmEmulation", + "VAEColorCorrector", +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/__version__.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/__version__.py new file mode 100644 index 00000000..b7e19904 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/__version__.py @@ -0,0 +1 @@ +__version__ = "1.2.5" diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/nodes/__init__.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/nodes/__init__.py new file mode 100644 index 00000000..fc9e85f0 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/nodes/__init__.py @@ -0,0 +1,18 @@ +# Nodes package for EasyColorCorrection +from src import EasyColorCorrection +from src import BatchColorCorrection +from src import RawImageProcessor +from src import ColorCorrectionViewer +from src import ColorPaletteExtractor +from src import FilmEmulation +from src import VAEColorCorrector + +__all__ = [ + "EasyColorCorrection", + "BatchColorCorrection", + "RawImageProcessor", + "ColorCorrectionViewer", + "ColorPaletteExtractor", + "FilmEmulation", + "VAEColorCorrector", +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/nodes/vae_color_corrector.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/nodes/vae_color_corrector.py new file mode 100644 index 00000000..7bc37fba --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/nodes/vae_color_corrector.py @@ -0,0 +1,328 @@ +"""VAE Color Corrector node for fixing VAE-induced color shifts in inpainting workflows.""" + +import torch +import torch.nn.functional as F +import numpy as np + +from ..utils import ADVANCED_LIBS_AVAILABLE, match_to_reference_colors + +if ADVANCED_LIBS_AVAILABLE: + import cv2 + from sklearn.cluster import KMeans + from skimage import exposure + + +class VAEColorCorrector: + """ + Specialized color correction for VAE artifacts in inpainting/img2img workflows. + Fixes color shifts in unmasked areas by referencing the original input image. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_image": ("IMAGE", {"tooltip": "πŸ”΄ Original input image (before VAE encoding)"}), + "processed_image": ("IMAGE", {"tooltip": "🟑 Image after VAE decode (with color shifts)"}), + "correction_strength": ( + "FLOAT", + { + "default": 0.8, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "🎚️ How strongly to correct VAE color shifts (0.0 = no correction, 1.0 = full correction)" + } + ), + "method": ( + ["luminance_zones", "histogram_matching", "statistical_matching", "advanced_3d_lut"], + { + "default": "advanced_3d_lut", + "tooltip": "πŸ”§ Color correction method:\nβ€’ luminance_zones: Professional shadows/midtones/highlights\nβ€’ histogram_matching: Match color distributions\nβ€’ statistical_matching: Match color statistics\nβ€’ advanced_3d_lut: Most accurate 3D color mapping" + } + ), + "preserve_inpainted": ( + "BOOLEAN", + { + "default": True, + "tooltip": "🎨 Only correct areas that existed in original (preserve new inpainted content)" + } + ), + }, + "optional": { + "mask": ("MASK", {"tooltip": "⚫ Optional mask - white areas will be preserved (inpainted areas)"}), + "edge_feather": ( + "INT", + { + "default": 5, + "min": 0, + "max": 50, + "step": 1, + "tooltip": "🌟 Feather edges between corrected/preserved areas (pixels)" + } + ), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("corrected_image",) + FUNCTION = "correct_vae_colors" + CATEGORY = "EasyColorCorrection" + + def correct_vae_colors( + self, + original_image, + processed_image, + correction_strength=0.8, + method="advanced_3d_lut", + preserve_inpainted=True, + mask=None, + edge_feather=5 + ): + """ + Correct VAE-induced color shifts by referencing the original image. + """ + device = original_image.device + + print(f"πŸ”§ VAE Color Correction: method={method}, strength={correction_strength:.2f}") + print(f"πŸ“ Original: {original_image.shape}, Processed: {processed_image.shape}") + + # Ensure images are same size + if original_image.shape != processed_image.shape: + print("⚠️ Image size mismatch - resizing processed to match original") + processed_image = F.interpolate( + processed_image.permute(0, 3, 1, 2), + size=(original_image.shape[1], original_image.shape[2]), + mode='bilinear', + align_corners=False + ).permute(0, 2, 3, 1) + + # Process each image in batch + corrected_batch = [] + + for i in range(original_image.shape[0]): + orig_img = original_image[i] + proc_img = processed_image[i] + current_mask = mask[i] if mask is not None else None + + # Apply color correction + corrected_img = self._apply_vae_color_correction( + orig_img, proc_img, method, correction_strength, + preserve_inpainted, current_mask, edge_feather, device + ) + + corrected_batch.append(corrected_img) + + result = torch.stack(corrected_batch, dim=0) + print(f"βœ… VAE color correction completed for {len(corrected_batch)} images") + + return (result,) + + def _apply_vae_color_correction( + self, original_img, processed_img, method, strength, + preserve_inpainted, mask, edge_feather, device + ): + """Apply the actual color correction.""" + + # Convert to numpy for processing + orig_np = (original_img.cpu().numpy() * 255).astype(np.uint8) + proc_np = (processed_img.cpu().numpy() * 255).astype(np.uint8) + + print(f"πŸ”§ Applying {method} color correction...") + + # Apply color correction based on method + if method == "advanced_3d_lut": + corrected_np = self._advanced_3d_lut_correction(orig_np, proc_np, strength) + elif method == "luminance_zones": + corrected_np = match_to_reference_colors(proc_np, orig_np, strength) + elif method == "histogram_matching": + corrected_np = self._histogram_matching_correction(orig_np, proc_np, strength) + else: # statistical_matching + corrected_np = self._statistical_matching_correction(orig_np, proc_np, strength) + + # Convert back to tensor + corrected_tensor = torch.from_numpy(corrected_np.astype(np.float32) / 255.0).to(device) + + # Handle mask-based preservation + if preserve_inpainted and mask is not None: + corrected_tensor = self._apply_mask_preservation( + processed_img, corrected_tensor, mask, edge_feather, device + ) + elif preserve_inpainted: + # Auto-detect changed areas if no mask provided + corrected_tensor = self._auto_preserve_inpainted( + original_img, processed_img, corrected_tensor, edge_feather, device + ) + + return corrected_tensor + + def _advanced_3d_lut_correction(self, original_np, processed_np, strength): + """Advanced 3D LUT-based color correction for precise VAE artifact fixing.""" + if not ADVANCED_LIBS_AVAILABLE: + return match_to_reference_colors(processed_np, original_np, strength) + + try: + print("πŸ”§ Building 3D color mapping...") + + # Sample colors for mapping (use every 4th pixel for speed) + orig_samples = original_np[::4, ::4].reshape(-1, 3) + proc_samples = processed_np[::4, ::4].reshape(-1, 3) + + # Use k-means to find representative color pairs + n_clusters = min(64, len(orig_samples) // 10) # Adaptive cluster count + + if n_clusters < 8: + # Too few samples, fall back to zone-based matching + return match_to_reference_colors(processed_np, original_np, strength) + + # Cluster processed colors + kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10) + proc_clusters = kmeans.fit_predict(proc_samples) + proc_centers = kmeans.cluster_centers_ + + # Find corresponding original colors for each cluster + orig_centers = np.zeros_like(proc_centers) + for i in range(n_clusters): + cluster_mask = proc_clusters == i + if np.sum(cluster_mask) > 0: + # Average original colors that correspond to this processed cluster + orig_centers[i] = np.mean(orig_samples[cluster_mask], axis=0) + else: + orig_centers[i] = proc_centers[i] # Fallback + + # Apply color mapping to full image + proc_flat = processed_np.reshape(-1, 3).astype(np.float32) + + # Vectorized distance calculation + distances = np.linalg.norm( + proc_flat[:, np.newaxis, :] - proc_centers[np.newaxis, :, :], axis=2 + ) + closest_clusters = np.argmin(distances, axis=1) + + # Apply color shifts with distance-based blending + min_distances = np.min(distances, axis=1) + max_distance = np.percentile(min_distances, 90) # Use 90th percentile for normalization + + for i in range(n_clusters): + cluster_mask = closest_clusters == i + if np.sum(cluster_mask) > 0: + # Calculate color shift for this cluster + color_shift = orig_centers[i] - proc_centers[i] + + # Apply with distance-based falloff and strength + cluster_distances = min_distances[cluster_mask] + distance_weights = np.clip(1.0 - cluster_distances / max_distance, 0.1, 1.0) + + for c in range(3): + shift_amount = color_shift[c] * strength * distance_weights + proc_flat[cluster_mask, c] += shift_amount + + # Reshape back and clamp + corrected_np = proc_flat.reshape(processed_np.shape) + corrected_np = np.clip(corrected_np, 0, 255).astype(np.uint8) + + print(f"βœ… 3D LUT correction applied using {n_clusters} color clusters") + return corrected_np + + except Exception as e: + print(f"❌ 3D LUT correction failed: {e}, falling back to zone matching") + return match_to_reference_colors(processed_np, original_np, strength) + + def _histogram_matching_correction(self, original_np, processed_np, strength): + """Histogram-based color matching.""" + if not ADVANCED_LIBS_AVAILABLE: + return match_to_reference_colors(processed_np, original_np, strength) + + try: + corrected_np = processed_np.astype(np.float32) + original_float = original_np.astype(np.float32) + + # Match histogram for each channel + for c in range(3): + matched_channel = exposure.match_histograms( + corrected_np[:,:,c], original_float[:,:,c] + ) + # Blend with original based on strength + corrected_np[:,:,c] = ( + processed_np[:,:,c] * (1 - strength) + + matched_channel * strength + ) + + return np.clip(corrected_np, 0, 255).astype(np.uint8) + + except Exception as e: + print(f"❌ Histogram matching failed: {e}") + return match_to_reference_colors(processed_np, original_np, strength) + + def _statistical_matching_correction(self, original_np, processed_np, strength): + """Statistical moment matching (mean and std).""" + corrected_np = processed_np.astype(np.float32) + original_float = original_np.astype(np.float32) + + for c in range(3): + # Calculate statistics + proc_mean = np.mean(corrected_np[:,:,c]) + proc_std = np.std(corrected_np[:,:,c]) + orig_mean = np.mean(original_float[:,:,c]) + orig_std = np.std(original_float[:,:,c]) + + # Normalize and rescale + if proc_std > 0: + normalized = (corrected_np[:,:,c] - proc_mean) / proc_std + rescaled = normalized * orig_std + orig_mean + + # Blend with strength + corrected_np[:,:,c] = ( + corrected_np[:,:,c] * (1 - strength) + + rescaled * strength + ) + + return np.clip(corrected_np, 0, 255).astype(np.uint8) + + def _apply_mask_preservation(self, processed_img, corrected_img, mask, edge_feather, device): + """Apply mask to preserve inpainted areas.""" + # Invert mask: 0 = preserve (inpainted), 1 = correct (original areas) + correction_mask = 1.0 - mask.to(device) + + if edge_feather > 0 and ADVANCED_LIBS_AVAILABLE: + # Apply gaussian blur for soft edges + correction_mask_np = correction_mask.cpu().numpy() + correction_mask_np = cv2.GaussianBlur( + correction_mask_np, (edge_feather*2+1, edge_feather*2+1), edge_feather/3 + ) + correction_mask = torch.from_numpy(correction_mask_np).to(device) + + # Apply mask: blend between processed and corrected + correction_mask = correction_mask.unsqueeze(-1) # Add channel dimension + result = processed_img * (1 - correction_mask) + corrected_img * correction_mask + + print(f"βœ… Mask-based preservation applied with {edge_feather}px feather") + return result + + def _auto_preserve_inpainted(self, original_img, processed_img, corrected_img, edge_feather, device): + """Auto-detect inpainted areas and preserve them.""" + # Calculate difference between original and processed to find changed areas + diff = torch.abs(original_img - processed_img) + diff_magnitude = torch.mean(diff, dim=-1) # Average across RGB + + # Threshold to find significantly changed areas (likely inpainted) + threshold = torch.quantile(diff_magnitude, 0.7) # Top 30% of changes + inpainted_mask = (diff_magnitude > threshold).float() + + # Correction mask: 1 = correct, 0 = preserve + correction_mask = 1.0 - inpainted_mask + + if edge_feather > 0 and ADVANCED_LIBS_AVAILABLE: + # Smooth the mask + correction_mask_np = correction_mask.cpu().numpy() + correction_mask_np = cv2.GaussianBlur( + correction_mask_np, (edge_feather*2+1, edge_feather*2+1), edge_feather/3 + ) + correction_mask = torch.from_numpy(correction_mask_np).to(device) + + correction_mask = correction_mask.unsqueeze(-1) # Add channel dimension + result = processed_img * (1 - correction_mask) + corrected_img * correction_mask + + preserved_pixels = torch.sum(1 - correction_mask).item() + print(f"βœ… Auto-preserved {preserved_pixels:.0f} pixels (likely inpainted areas)") + return result \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/__init__.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/__init__.py new file mode 100644 index 00000000..c416fd2c --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/__init__.py @@ -0,0 +1,22 @@ +# Utils package for EasyColorCorrection +from .imports import * +from .color_functions import * +from .image_analysis import * +from .device_utils import * + +__all__ = [ + # Imports + "ADVANCED_LIBS_AVAILABLE", + "RAW_PROCESSING_AVAILABLE", + "DL_COLORIZATION_AVAILABLE", + "EXR_PROCESSING_AVAILABLE", + "IMAGEIO_AVAILABLE", + # Color functions + "rgb_to_hsv", + "hsv_to_rgb", + "match_to_reference_colors", + # Image analysis + "analyze_image_content", + # Device utils + "get_preferred_device", +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/color_functions.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/color_functions.py new file mode 100644 index 00000000..07ba1904 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/color_functions.py @@ -0,0 +1,276 @@ +"""Color space conversion and color manipulation functions.""" + +import torch +import numpy as np +from .imports import ADVANCED_LIBS_AVAILABLE + +if ADVANCED_LIBS_AVAILABLE: + import cv2 + + +def rgb_to_hsv(rgb: torch.Tensor) -> torch.Tensor: + """Converts an RGB image tensor to HSV. Expects input shape [B, H, W, C] with values 0-1.""" + cmax, cmax_indices = torch.max(rgb, dim=-1) + cmin = torch.min(rgb, dim=-1)[0] + delta = cmax - cmin + + h = torch.zeros_like(cmax) + h[cmax_indices == 0] = (((rgb[..., 1] - rgb[..., 2]) / (delta + 1e-8)) % 6)[ + cmax_indices == 0 + ] + h[cmax_indices == 1] = (((rgb[..., 2] - rgb[..., 0]) / (delta + 1e-8)) + 2)[ + cmax_indices == 1 + ] + h[cmax_indices == 2] = (((rgb[..., 0] - rgb[..., 1]) / (delta + 1e-8)) + 4)[ + cmax_indices == 2 + ] + + h = h / 6.0 + h[delta == 0] = 0.0 + + s = torch.where( + cmax == 0, torch.tensor(0.0, device=rgb.device), delta / (cmax + 1e-8) + ) + v = cmax + + return torch.stack([h, s, v], dim=-1) + + +def hsv_to_rgb(hsv: torch.Tensor) -> torch.Tensor: + """Converts an HSV image tensor to RGB. Expects input shape [B, H, W, C] with values 0-1.""" + h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] + i = (h * 6.0).floor() + f = (h * 6.0) - i + p = v * (1.0 - s) + q = v * (1.0 - s * f) + t = v * (1.0 - s * (1.0 - f)) + + rgb = torch.zeros_like(hsv) + + mask0, mask1, mask2 = (i % 6) == 0, (i % 6) == 1, (i % 6) == 2 + mask3, mask4, mask5 = (i % 6) == 3, (i % 6) == 4, (i % 6) == 5 + + rgb[mask0] = torch.stack([v, t, p], dim=-1)[mask0] + rgb[mask1] = torch.stack([q, v, p], dim=-1)[mask1] + rgb[mask2] = torch.stack([p, v, t], dim=-1)[mask2] + rgb[mask3] = torch.stack([p, q, v], dim=-1)[mask3] + rgb[mask4] = torch.stack([t, p, v], dim=-1)[mask4] + rgb[mask5] = torch.stack([v, p, q], dim=-1)[mask5] + + rgb[s == 0] = torch.stack([v, v, v], dim=-1)[s == 0] + + return rgb + + +def create_color_range_mask(hsv: torch.Tensor, target_hue: float, hue_range: float = 60.0) -> torch.Tensor: + """Create a mask for a specific color range in HSV space.""" + h = hsv[..., 0] # Hue channel (0-1) + s = hsv[..., 1] # Saturation channel (0-1) + + # Convert target hue from degrees to 0-1 range + target_h = (target_hue % 360) / 360.0 + range_h = hue_range / 360.0 + + # Calculate hue distance (handling wrap-around) + hue_diff = torch.abs(h - target_h) + hue_diff = torch.min(hue_diff, 1.0 - hue_diff) # Handle wrap-around at 0/1 + + # Create smooth falloff mask based on hue distance and saturation + hue_mask = torch.exp(-((hue_diff / range_h) ** 2) * 3.0) # Gaussian falloff + saturation_mask = torch.clamp(s * 2.0, 0.0, 1.0) # Favor more saturated colors + + return hue_mask * saturation_mask + + +def apply_selective_color_adjustment( + image: torch.Tensor, + target_hue: float, + hue_adjustment: float, + saturation_adjustment: float, + lightness_adjustment: float, + hue_range: float = 60.0 +) -> torch.Tensor: + """Apply selective color adjustments to a specific color range.""" + if abs(hue_adjustment) < 0.001 and abs(saturation_adjustment) < 0.001 and abs(lightness_adjustment) < 0.001: + return image + + hsv = rgb_to_hsv(image) + h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] + + # Create mask for target color range + mask = create_color_range_mask(hsv, target_hue, hue_range) + mask = mask.unsqueeze(-1) # Add channel dimension for broadcasting + + # Apply adjustments + if abs(hue_adjustment) > 0.001: + h_adjusted = (h + hue_adjustment / 360.0) % 1.0 + h = torch.lerp(h, h_adjusted, mask.squeeze(-1)) + + if abs(saturation_adjustment) > 0.001: + s_adjusted = torch.clamp(s + saturation_adjustment, 0.0, 1.0) + s = torch.lerp(s, s_adjusted, mask.squeeze(-1)) + + if abs(lightness_adjustment) > 0.001: + v_adjusted = torch.clamp(v + lightness_adjustment, 0.0, 1.0) + v = torch.lerp(v, v_adjusted, mask.squeeze(-1)) + + # Convert back to RGB + adjusted_hsv = torch.stack([h, s, v], dim=-1) + return hsv_to_rgb(adjusted_hsv) + + +def match_to_reference_colors( + image_np: np.ndarray, reference_np: np.ndarray, strength: float = 0.5 +) -> np.ndarray: + """ + Match image colors to reference using highlights/midtones/shadows LUT approach. + Works like professional color grading - matches colors in different luminance zones. + """ + if not ADVANCED_LIBS_AVAILABLE: + return image_np + + try: + if reference_np is None or image_np is None: + return image_np + + if reference_np.size == 0 or image_np.size == 0: + return image_np + + strength = np.clip(strength, 0.0, 1.0) + if strength == 0.0: + return image_np + + # Convert to float for processing + image_float = image_np.astype(np.float32) / 255.0 + reference_float = reference_np.astype(np.float32) / 255.0 + matched_image = image_float.copy() + + # Calculate luminance for zone segmentation + # Use proper luminance weights: 0.299*R + 0.587*G + 0.114*B + img_luminance = ( + 0.299 * image_float[:, :, 0] + + 0.587 * image_float[:, :, 1] + + 0.114 * image_float[:, :, 2] + ) + ref_luminance = ( + 0.299 * reference_float[:, :, 0] + + 0.587 * reference_float[:, :, 1] + + 0.114 * reference_float[:, :, 2] + ) + + # Define luminance zones with smooth transitions + # Shadows: 0.0-0.33, Midtones: 0.33-0.66, Highlights: 0.66-1.0 + shadow_mask = np.clip(1.0 - img_luminance / 0.33, 0.0, 1.0) + highlight_mask = np.clip((img_luminance - 0.66) / 0.34, 0.0, 1.0) + midtone_mask = 1.0 - shadow_mask - highlight_mask + midtone_mask = np.clip(midtone_mask, 0.0, 1.0) + + # For each zone, calculate average color shift from reference + for zone_name, zone_mask in [ + ("shadows", shadow_mask), + ("midtones", midtone_mask), + ("highlights", highlight_mask) + ]: + if np.sum(zone_mask) > 0: # Only process if zone has pixels + + # Get pixels in this luminance zone + img_zone_mask = zone_mask > 0.1 # Threshold to avoid near-zero weights + ref_zone_mask = None + + # Find corresponding luminance zone in reference image + if zone_name == "shadows": + ref_zone_mask = ref_luminance < 0.4 + elif zone_name == "midtones": + ref_zone_mask = (ref_luminance >= 0.3) & (ref_luminance <= 0.7) + else: # highlights + ref_zone_mask = ref_luminance > 0.6 + + if np.sum(ref_zone_mask) > 0: # Reference zone has pixels + + # Calculate average colors in this zone + for channel in range(3): + # Image zone average + img_zone_pixels = matched_image[:, :, channel][img_zone_mask] + img_zone_avg = np.mean(img_zone_pixels) if len(img_zone_pixels) > 0 else 0 + + # Reference zone average + ref_zone_pixels = reference_float[:, :, channel][ref_zone_mask] + ref_zone_avg = np.mean(ref_zone_pixels) if len(ref_zone_pixels) > 0 else 0 + + # Calculate color shift for this zone + color_shift = ref_zone_avg - img_zone_avg + + # Apply shift with zone mask and strength + shift_amount = color_shift * strength * zone_mask[:, :, np.newaxis] + matched_image[:, :, channel] += shift_amount.squeeze() + + # Clamp values and convert back to uint8 + matched_image = np.clip(matched_image, 0.0, 1.0) + return (matched_image * 255.0).astype(np.uint8) + + except Exception: + # Return original image if anything fails + return image_np + + +def apply_semantic_color_adjustments( + image: torch.Tensor, + skin_tone_adjustment: float = 0.0, + sky_adjustment: float = 0.0, + foliage_adjustment: float = 0.0, + selective_hue_shift: float = 0.0, + selective_saturation: float = 0.0, + selective_strength: float = 1.0, +) -> torch.Tensor: + """Apply semantic color adjustments targeting skin tones, sky, and foliage.""" + result = image.clone() + + # Early exit if no adjustments + if (abs(skin_tone_adjustment) < 0.001 and abs(sky_adjustment) < 0.001 and + abs(foliage_adjustment) < 0.001 and abs(selective_hue_shift) < 0.001): + return result + + # Convert to HSV for adjustments + hsv = rgb_to_hsv(result) + h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] + + # Skin tone adjustments (orange-red hues, ~15-45 degrees) + if abs(skin_tone_adjustment) > 0.001: + result = apply_selective_color_adjustment( + result, 30.0, 0.0, skin_tone_adjustment * 0.3, skin_tone_adjustment * 0.1, 40.0 + ) + + # Sky adjustments (blue hues, ~200-240 degrees) + if abs(sky_adjustment) > 0.001: + result = apply_selective_color_adjustment( + result, 220.0, 0.0, sky_adjustment * 0.4, 0.0, 50.0 + ) + + # Foliage adjustments (green hues, ~80-140 degrees) + if abs(foliage_adjustment) > 0.001: + result = apply_selective_color_adjustment( + result, 110.0, 0.0, foliage_adjustment * 0.3, foliage_adjustment * 0.05, 60.0 + ) + + # Custom selective adjustments + if abs(selective_hue_shift) > 0.001 or abs(selective_saturation) > 0.001: + # Apply to mid-range colors (avoiding extreme darks/lights) + hsv_current = rgb_to_hsv(result) + h_curr, s_curr, v_curr = hsv_current[..., 0], hsv_current[..., 1], hsv_current[..., 2] + + # Create mask for mid-tones with decent saturation + mask = ((v_curr > 0.2) & (v_curr < 0.8) & (s_curr > 0.1)).float() + mask = mask.unsqueeze(-1) + + if abs(selective_hue_shift) > 0.001: + h_adjusted = (h_curr + selective_hue_shift / 360.0) % 1.0 + h_curr = torch.lerp(h_curr, h_adjusted, mask.squeeze(-1) * selective_strength) + + if abs(selective_saturation) > 0.001: + s_adjusted = torch.clamp(s_curr + selective_saturation, 0.0, 1.0) + s_curr = torch.lerp(s_curr, s_adjusted, mask.squeeze(-1) * selective_strength) + + adjusted_hsv = torch.stack([h_curr, s_curr, v_curr], dim=-1) + result = hsv_to_rgb(adjusted_hsv) + + return result \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/device_utils.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/device_utils.py new file mode 100644 index 00000000..ae83393c --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/device_utils.py @@ -0,0 +1,10 @@ +"""Device and GPU management utilities.""" + +import torch + + +def get_preferred_device(use_gpu: bool = True): + """Get the preferred device considering user settings.""" + if not use_gpu: + return torch.device("cpu") + return torch.device("cuda" if torch.cuda.is_available() else "cpu") \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/image_analysis.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/image_analysis.py new file mode 100644 index 00000000..4ace49bd --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/image_analysis.py @@ -0,0 +1,110 @@ +"""Image analysis and content detection functions.""" + +import typing +import numpy as np +import torch +from .imports import ADVANCED_LIBS_AVAILABLE +from .device_utils import get_preferred_device + +if ADVANCED_LIBS_AVAILABLE: + import cv2 + from sklearn.cluster import KMeans + from skimage import filters + from scipy import ndimage + + +def analyze_image_content( + image_np: np.ndarray, device: torch.device = None +) -> typing.Dict[str, typing.Any]: + """Advanced image analysis using computer vision with GPU acceleration.""" + if not ADVANCED_LIBS_AVAILABLE: + return { + "faces": [], + "dominant_colors": [], + "scene_type": "unknown", + "lighting": "auto", + } + + analysis = {} + + if device is None: + device = get_preferred_device() + + # Face detection + try: + gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) + face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + "haarcascade_frontalface_default.xml" + ) + faces = face_cascade.detectMultiScale( + gray, + scaleFactor=1.1, + minNeighbors=3, + minSize=(30, 30), + flags=cv2.CASCADE_SCALE_IMAGE + ) + analysis["faces"] = faces.tolist() if len(faces) > 0 else [] + except Exception: + analysis["faces"] = [] + + # Color analysis + try: + pixels = image_np.reshape(-1, 3) + sample_size = min(10000, len(pixels)) + sample_indices = np.random.choice(len(pixels), sample_size, replace=False) + sample_pixels = pixels[sample_indices] + + kmeans = KMeans(n_clusters=5, random_state=42, n_init=5, init="k-means++") + kmeans.fit(sample_pixels) + dominant_colors = kmeans.cluster_centers_ + analysis["dominant_colors"] = dominant_colors.tolist() + except Exception: + analysis["dominant_colors"] = [] + + # Scene type classification + try: + gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) + edges = filters.sobel(gray) + edge_density = np.mean(edges) + + hsv = cv2.cvtColor(image_np, cv2.COLOR_RGB2HSV) + avg_saturation = np.mean(hsv[:, :, 1]) + color_variance = np.std(hsv[:, :, 1]) + texture_contrast = np.std(gray) + + if edge_density < 0.08 and avg_saturation > 120 and color_variance > 40: + analysis["scene_type"] = "anime" + elif edge_density < 0.12 and avg_saturation > 80 and texture_contrast < 35: + analysis["scene_type"] = "stylized_art" + elif avg_saturation > 100 and color_variance > 50 and texture_contrast > 40: + analysis["scene_type"] = "concept_art" + elif edge_density > 0.25 and texture_contrast > 65 and avg_saturation > 90: + analysis["scene_type"] = "detailed_illustration" + elif len(analysis["faces"]) > 0 and edge_density < 0.15: + analysis["scene_type"] = "portrait" + elif edge_density > 0.15 and avg_saturation < 80: + analysis["scene_type"] = "realistic_photo" + else: + analysis["scene_type"] = "general" + except Exception: + analysis["scene_type"] = "general" + + # Lighting analysis + try: + lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB) + l_channel = lab[:, :, 0] + brightness = np.mean(l_channel) + contrast = np.std(l_channel) + + if brightness < 85: + analysis["lighting"] = "low_light" + elif brightness > 170: + analysis["lighting"] = "bright" + elif contrast < 20: + analysis["lighting"] = "flat" + else: + analysis["lighting"] = "good" + except Exception: + analysis["lighting"] = "auto" + + return analysis \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/imports.py b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/imports.py new file mode 100644 index 00000000..4ef6a8cf --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/src/utils/imports.py @@ -0,0 +1,66 @@ +# STANDARD LIBRARY IMPORTS +import typing +import os + +# THIRD-PARTY IMPORTS +import torch +import torch.nn.functional as F +import numpy as np + +# ADVANCED COMPUTER VISION AND COLOR SCIENCE +try: + import cv2 + from sklearn.cluster import KMeans + from skimage import filters, segmentation, measure + import colour + from scipy import ndimage + + ADVANCED_LIBS_AVAILABLE = True +except ImportError as e: + print(f"EasyColorCorrection: Advanced features disabled - missing library: {e}") + ADVANCED_LIBS_AVAILABLE = False + +# RAW IMAGE PROCESSING +try: + import rawpy + + RAW_PROCESSING_AVAILABLE = True +except ImportError as e: + print(f"EasyColorCorrection: RAW processing disabled - missing rawpy library: {e}") + RAW_PROCESSING_AVAILABLE = False + +# DEEP LEARNING COLORIZATION +try: + from PIL import Image + import torchvision.transforms as transforms + import urllib.request + import os + from huggingface_hub import hf_hub_download, snapshot_download + import timm + + DL_COLORIZATION_AVAILABLE = True +except ImportError as e: + print(f"EasyColorCorrection: Deep learning colorization disabled - missing libraries: {e}") + DL_COLORIZATION_AVAILABLE = False + +# HDR/EXR IMAGE PROCESSING +try: + import OpenEXR + import Imath + + EXR_PROCESSING_AVAILABLE = True +except ImportError as e: + print( + f"EasyColorCorrection: EXR processing disabled - missing OpenEXR library: {e}" + ) + EXR_PROCESSING_AVAILABLE = False + +try: + import imageio + + IMAGEIO_AVAILABLE = True +except ImportError as e: + print( + f"EasyColorCorrection: Extended format support disabled - missing imageio library: {e}" + ) + IMAGEIO_AVAILABLE = False \ No newline at end of file diff --git a/custom_nodes/ComfyUI-EasyColorCorrector/test_validation_fix.py b/custom_nodes/ComfyUI-EasyColorCorrector/test_validation_fix.py new file mode 100644 index 00000000..771867a5 --- /dev/null +++ b/custom_nodes/ComfyUI-EasyColorCorrector/test_validation_fix.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python3 +"""Test script to verify input validation fixes for EasyColorCorrection node.""" + +import sys +import torch +import numpy as np + +# Add src to path +sys.path.insert(0, 'src') + +from src import EasyColorCorrection + +def test_invalid_inputs(): + """Test that the node handles invalid inputs gracefully.""" + print("=" * 60) + print("Testing EasyColorCorrection with invalid inputs") + print("=" * 60) + + # Create a simple test image (1 batch, 64x64, RGB) + test_image = torch.rand(1, 64, 64, 3) + + node = EasyColorCorrection() + + # Test case 1: None values for float parameters + print("\n1. Testing None values for float parameters...") + try: + result = node.run( + image=test_image, + mode="Auto", + noise=None, # Should use default 0.0 + tint=None, # Should use default 0.0 + skin_tone_adjustment=None, # Should use default 0.0 + ) + print("βœ… Test 1 passed: None values handled correctly") + except Exception as e: + print(f"❌ Test 1 failed: {e}") + return False + + # Test case 2: Empty list for float parameter + print("\n2. Testing empty list for float parameter...") + try: + result = node.run( + image=test_image, + mode="Auto", + skin_tone_adjustment=[], # Should use default 0.0 + ) + print("βœ… Test 2 passed: Empty list handled correctly") + except Exception as e: + print(f"❌ Test 2 failed: {e}") + return False + + # Test case 3: String preset name in float parameter + print("\n3. Testing string preset name in float parameter...") + try: + result = node.run( + image=test_image, + mode="Auto", + tint="Anime Bright", # Should use default 0.0 + ) + print("βœ… Test 3 passed: String preset name handled correctly") + except Exception as e: + print(f"❌ Test 3 failed: {e}") + return False + + # Test case 4: Invalid preset value + print("\n4. Testing invalid preset value...") + try: + result = node.run( + image=test_image, + mode="Preset", + preset='0', # Should use first valid preset + ) + print("βœ… Test 4 passed: Invalid preset handled correctly") + except Exception as e: + print(f"❌ Test 4 failed: {e}") + return False + + # Test case 5: Valid inputs (regression test) + print("\n5. Testing valid inputs (regression)...") + try: + result = node.run( + image=test_image, + mode="Manual", + warmth=0.1, + contrast=0.2, + brightness=0.05, + tint=0.0, + preset="Anime Bright", + noise=0.0, + skin_tone_adjustment=0.0, + ) + print("βœ… Test 5 passed: Valid inputs work correctly") + except Exception as e: + print(f"❌ Test 5 failed: {e}") + return False + + print("\n" + "=" * 60) + print("All tests passed! βœ…") + print("=" * 60) + return True + +if __name__ == "__main__": + success = test_invalid_inputs() + sys.exit(0 if success else 1) diff --git a/custom_nodes/ComfyUI-Florence2/.gitignore b/custom_nodes/ComfyUI-Florence2/.gitignore new file mode 100644 index 00000000..bd13e807 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/.gitignore @@ -0,0 +1,9 @@ +.DS_Store +*pyc +.vscode +__pycache__ +*.egg-info +*.bak +checkpoints +results +backup \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Florence2/LICENSE b/custom_nodes/ComfyUI-Florence2/LICENSE new file mode 100644 index 00000000..061b4951 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Jukka SeppΓ€nen + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/ComfyUI-Florence2/README.md b/custom_nodes/ComfyUI-Florence2/README.md new file mode 100644 index 00000000..6cbfafc8 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/README.md @@ -0,0 +1,70 @@ +# Florence2 in ComfyUI + +> Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. +Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. +It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. +The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. + +## New Feature: Document Visual Question Answering (DocVQA) + +This fork includes support for Document Visual Question Answering (DocVQA) using the Florence2 model. DocVQA allows you to ask questions about the content of document images, and the model will provide answers based on the visual and textual information in the document. This feature is particularly useful for extracting information from scanned documents, forms, receipts, and other text-heavy images. + +## Installation: + +Clone this repository to 'ComfyUI/custom_nodes` folder. + +Install the dependencies in requirements.txt, transformers version 4.38.0 minimum is required: + +`pip install -r requirements.txt` + +or if you use portable (run this in ComfyUI_windows_portable -folder): + +`python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-Florence2\requirements.txt` + +![image](https://github.com/kijai/ComfyUI-Florence2/assets/40791699/4d537ac7-5490-470f-92f5-3007da7b9cc7) +![image](https://github.com/kijai/ComfyUI-Florence2/assets/40791699/512357b7-39ee-43ee-bb63-7347b0a8d07d) + +Supports most Florence2 models, which can be automatically downloaded with the `DownloadAndLoadFlorence2Model` to `ComfyUI/models/LLM`: + +Official: + +https://huggingface.co/microsoft/Florence-2-base + +https://huggingface.co/microsoft/Florence-2-base-ft + +https://huggingface.co/microsoft/Florence-2-large + +https://huggingface.co/microsoft/Florence-2-large-ft + +https://huggingface.co/HuggingFaceM4/Florence-2-DocVQA + +Tested finetunes: + +https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v1.5 + +https://huggingface.co/MiaoshouAI/Florence-2-large-PromptGen-v1.5 + +https://huggingface.co/thwri/CogFlorence-2.2-Large + +https://huggingface.co/HuggingFaceM4/Florence-2-DocVQA + +https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner + +https://huggingface.co/gokaygokay/Florence-2-Flux-Large + +https://huggingface.co/NikshepShetty/Florence-2-pixelpros + +## Using DocVQA + +To use the DocVQA feature: +1. Load a document image into ComfyUI. +2. Connect the image to the Florence2 DocVQA node. +3. Input your question about the document. +4. The node will output the answer based on the document's content. + +Example questions: +- "What is the total amount on this receipt?" +- "What is the date mentioned in this form?" +- "Who is the sender of this letter?" + +Note: The accuracy of answers depends on the quality of the input image and the complexity of the question. diff --git a/custom_nodes/ComfyUI-Florence2/__init__.py b/custom_nodes/ComfyUI-Florence2/__init__.py new file mode 100644 index 00000000..2e96bd6a --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/__init__.py @@ -0,0 +1,3 @@ +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Florence2/configuration_florence2.py b/custom_nodes/ComfyUI-Florence2/configuration_florence2.py new file mode 100644 index 00000000..f230565a --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/configuration_florence2.py @@ -0,0 +1,341 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import warnings +""" Florence-2 configuration""" + +from typing import Optional + +from transformers import AutoConfig +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +class Florence2VisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel + according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the Florence2VisionModel architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + drop_path_rate (`float`, *optional*, defaults to 0.1): + The dropout rate of the drop path layer. + patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]): + The patch size of the image. + patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]): + The patch stride of the image. + patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]): + The patch padding of the image. + patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]): + Whether to apply layer normalization before the patch embedding layer. + enable_checkpoint (`bool`, *optional*, defaults to False): + Whether to enable checkpointing. + dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]): + The dimension of the embedding layer. + num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]): + The number of attention heads. + num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]): + The number of groups. + depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]): + The depth of the model. + window_size (`int`, *optional*, defaults to 12): + The window size of the model. + projection_dim (`int`, *optional*, defaults to 1024): + The dimension of the projection layer. + visual_temporal_embedding (`dict`, *optional*): + The configuration of the visual temporal embedding. + image_pos_embed (`dict`, *optional*): + The configuration of the image position embedding. + image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]): + The source of the image feature. + Example: + + ```python + >>> from transformers import Florence2VisionConfig, Florence2VisionModel + + >>> # Initializing a Florence2 Vision style configuration + >>> configuration = Florence2VisionConfig() + + >>> # Initializing a model (with random weights) + >>> model = Florence2VisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "florence2_vision" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + drop_path_rate=0.1, + patch_size=[7, 3, 3, 3], + patch_stride=[4, 2, 2, 2], + patch_padding=[3, 1, 1, 1], + patch_prenorm=[False, True, True, True], + enable_checkpoint=False, + dim_embed=[256, 512, 1024, 2048], + num_heads=[8, 16, 32, 64], + num_groups=[8, 16, 32, 64], + depths=[1, 1, 9, 1], + window_size=12, + projection_dim=1024, + visual_temporal_embedding=None, + image_pos_embed=None, + image_feature_source=["spatial_avg_pool", "temporal_avg_pool"], + **kwargs, + ): + self.drop_path_rate = drop_path_rate + self.patch_size = patch_size + self.patch_stride = patch_stride + self.patch_padding = patch_padding + self.patch_prenorm = patch_prenorm + self.enable_checkpoint = enable_checkpoint + self.dim_embed = dim_embed + self.num_heads = num_heads + self.num_groups = num_groups + self.depths = depths + self.window_size = window_size + self.projection_dim = projection_dim + self.visual_temporal_embedding = visual_temporal_embedding + self.image_pos_embed = image_pos_embed + self.image_feature_source = image_feature_source + + super().__init__(**kwargs) + + + +class Florence2LanguageConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Florence2LanguagePreTrainedModel`]. It is used to instantiate a BART + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the BART + [facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 51289): + Vocabulary size of the Florence2Language model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Florence2LanguageModel`]. + d_model (`int`, *optional*, defaults to 1024): + Dimensionality of the layers and the pooler layer. + encoder_layers (`int`, *optional*, defaults to 12): + Number of encoder layers. + decoder_layers (`int`, *optional*, defaults to 12): + Number of decoder layers. + encoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer encoder. + decoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer decoder. + decoder_ffn_dim (`int`, *optional*, defaults to 4096): + Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. + encoder_ffn_dim (`int`, *optional*, defaults to 4096): + Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. + activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + activation_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for activations inside the fully connected layer. + classifier_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for classifier. + max_position_embeddings (`int`, *optional*, defaults to 1024): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + init_std (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + encoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + decoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + scale_embedding (`bool`, *optional*, defaults to `False`): + Scale embeddings by diving by sqrt(d_model). + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). + num_labels (`int`, *optional*, defaults to 3): + The number of labels to use in [`Florence2LanguageForSequenceClassification`]. + forced_eos_token_id (`int`, *optional*, defaults to 2): + The id of the token to force as the last generated token when `max_length` is reached. Usually set to + `eos_token_id`. + + Example: + + ```python + >>> from transformers import Florence2LanguageConfig, Florence2LanguageModel + + >>> # Initializing a Florence2 Language style configuration + >>> configuration = Florence2LanguageConfig() + + >>> # Initializing a model (with random weights) + >>> model = Florence2LangaugeModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "florence2_language" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} + + def __init__( + self, + vocab_size=51289, + max_position_embeddings=1024, + encoder_layers=12, + encoder_ffn_dim=4096, + encoder_attention_heads=16, + decoder_layers=12, + decoder_ffn_dim=4096, + decoder_attention_heads=16, + encoder_layerdrop=0.0, + decoder_layerdrop=0.0, + activation_function="gelu", + d_model=1024, + dropout=0.1, + attention_dropout=0.0, + activation_dropout=0.0, + init_std=0.02, + classifier_dropout=0.0, + scale_embedding=False, + use_cache=True, + num_labels=3, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + is_encoder_decoder=True, + decoder_start_token_id=2, + forced_eos_token_id=2, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.d_model = d_model + self.encoder_ffn_dim = encoder_ffn_dim + self.encoder_layers = encoder_layers + self.encoder_attention_heads = encoder_attention_heads + self.decoder_ffn_dim = decoder_ffn_dim + self.decoder_layers = decoder_layers + self.decoder_attention_heads = decoder_attention_heads + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.init_std = init_std + self.encoder_layerdrop = encoder_layerdrop + self.decoder_layerdrop = decoder_layerdrop + self.classifier_dropout = classifier_dropout + self.use_cache = use_cache + self.num_hidden_layers = encoder_layers + self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True + self.forced_bos_token_id = bos_token_id + + super().__init__( + num_labels=num_labels, + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + is_encoder_decoder=is_encoder_decoder, + decoder_start_token_id=decoder_start_token_id, + forced_eos_token_id=forced_eos_token_id, + **kwargs, + ) + + # ensure backward compatibility for BART CNN models + # if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): + # self.forced_bos_token_id = self.bos_token_id + # warnings.warn( + # f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " + # "The config can simply be saved and uploaded again to be fixed." + # ) + +class Florence2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Florence2ForConditionalGeneration`]. It is used to instantiate an + Florence-2 model according to the specified arguments, defining the model architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vision_config (`Florence2VisionConfig`, *optional*): + Custom vision config or dict + text_config (`Union[AutoConfig, dict]`, *optional*): + The config object of the text backbone. + ignore_index (`int`, *optional*, defaults to -100): + The ignore index for the loss function. + vocab_size (`int`, *optional*, defaults to 51289): + Vocabulary size of the Florence2model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`~Florence2ForConditionalGeneration`] + projection_dim (`int`, *optional*, defaults to 1024): + Dimension of the multimodal projection space. + + Example: + + ```python + >>> from transformers import Florence2ForConditionalGeneration, Florence2Config, CLIPVisionConfig, BartConfig + + >>> # Initializing a clip-like vision config + >>> vision_config = CLIPVisionConfig() + + >>> # Initializing a Bart config + >>> text_config = BartConfig() + + >>> # Initializing a Florence-2 configuration + >>> configuration = Florence2Config(vision_config, text_config) + + >>> # Initializing a model from the florence-2 configuration + >>> model = Florence2ForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "florence2" + is_composition = False + + def __init__( + self, + vision_config=None, + text_config=None, + ignore_index=-100, + vocab_size=51289, + projection_dim=1024, + **kwargs, + ): + self.ignore_index = ignore_index + self.vocab_size = vocab_size + self.projection_dim = projection_dim + if vision_config is not None: + vision_config = PretrainedConfig(**vision_config) + self.vision_config = vision_config + self.vocab_size = self.vocab_size + + self.text_config = text_config + if text_config is not None: + self.text_config = Florence2LanguageConfig(**text_config) + + + super().__init__(**kwargs) + diff --git a/custom_nodes/ComfyUI-Florence2/modeling_florence2.py b/custom_nodes/ComfyUI-Florence2/modeling_florence2.py new file mode 100644 index 00000000..c10b8a0b --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/modeling_florence2.py @@ -0,0 +1,2863 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" PyTorch Florence-2 model.""" +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import math +import torch +import torch.utils.checkpoint +from torch import nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from torch.nn import CrossEntropyLoss +from collections import OrderedDict +from einops import rearrange +try: + from timm.models.layers import DropPath, trunc_normal_ +except: + from timm.layers import DropPath, trunc_normal_ + +from transformers.modeling_utils import PreTrainedModel +from transformers.generation.utils import GenerationMixin +from transformers.utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + logging, + replace_return_docstrings, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, +) +from .configuration_florence2 import Florence2Config +from .configuration_florence2 import Florence2LanguageConfig +from .configuration_florence2 import Florence2VisionConfig + + +from transformers.activations import ACT2FN +from transformers.modeling_attn_mask_utils import ( + _prepare_4d_attention_mask, + _prepare_4d_attention_mask_for_sdpa, + _prepare_4d_causal_attention_mask, + _prepare_4d_causal_attention_mask_for_sdpa, +) +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +import transformers +from packaging import version + + +if is_flash_attn_2_available(): + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "Florence2Config" + +class LearnedAbsolutePositionEmbedding2D(nn.Module): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, embedding_dim=256, num_pos=50): + super().__init__() + self.row_embeddings = nn.Embedding(num_pos, embedding_dim // 2) + self.column_embeddings = nn.Embedding(num_pos, embedding_dim - (embedding_dim // 2)) + + def forward(self, pixel_values): + """ + pixel_values: (batch_size, height, width, num_channels) + returns: (batch_size, height, width, embedding_dim * 2) + """ + if len(pixel_values.shape) != 4: + raise ValueError('pixel_values must be a 4D tensor') + height, width = pixel_values.shape[1:3] + width_values = torch.arange(width, device=pixel_values.device) + height_values = torch.arange(height, device=pixel_values.device) + x_emb = self.column_embeddings(width_values) + y_emb = self.row_embeddings(height_values) + # (height, width, embedding_dim * 2) + pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) + # (embedding_dim * 2, height, width) + pos = pos.permute(2, 0, 1) + pos = pos.unsqueeze(0) + # (batch_size, embedding_dim * 2, height, width) + pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) + # (batch_size, height, width, embedding_dim * 2) + pos = pos.permute(0, 2, 3, 1) + return pos + +class PositionalEmbeddingCosine1D(nn.Module): + """ + This class implements a very simple positional encoding. It follows closely + the encoder from the link below: + https://pytorch.org/tutorials/beginner/translation_transformer.html + + Args: + embed_dim: The dimension of the embeddings. + dropout_prob: The dropout probability. + max_seq_len: The maximum length to precompute the positional encodings. + """ + def __init__( + self, + embed_dim: int = 512, + max_seq_len: int = 1024) -> None: + super(PositionalEmbeddingCosine1D, self).__init__() + self.embed_dim = embed_dim + self.max_seq_len = max_seq_len + # Generate the sinusoidal arrays. + factor = math.log(10000) + denominator = torch.exp( + -factor * torch.arange(0, self.embed_dim, 2) / self.embed_dim) + # Matrix where rows correspond to a positional embedding as a function + # of the position index (i.e., the row index). + frequencies = \ + torch.arange(0, self.max_seq_len) \ + .reshape(self.max_seq_len, 1) * denominator + pos_idx_to_embed = torch.zeros((self.max_seq_len, self.embed_dim)) + # Populate uneven entries. + pos_idx_to_embed[:, 0::2] = torch.sin(frequencies) + pos_idx_to_embed[:, 1::2] = torch.cos(frequencies) + # Save the positional embeddings in a constant buffer. + self.register_buffer("pos_idx_to_embed", pos_idx_to_embed) + + def forward(self, seq_embeds: torch.Tensor) -> torch.Tensor: + """ + Args: + seq_embeds: The sequence embeddings in order. Allowed size: + 1. [T, D], where T is the length of the sequence, and D is the + frame embedding dimension. + 2. [B, T, D], where B is the batch size and T and D are the + same as above. + + Returns a tensor of with the same dimensions as the input: i.e., + [1, T, D] or [T, D]. + """ + shape_len = len(seq_embeds.shape) + assert 2 <= shape_len <= 3 + len_seq = seq_embeds.size(-2) + assert len_seq <= self.max_seq_len + pos_embeds = self.pos_idx_to_embed[0:seq_embeds.size(-2), :] + # Adapt pre-computed positional embeddings to the input. + if shape_len == 3: + pos_embeds = pos_embeds.view( + (1, pos_embeds.size(0), pos_embeds.size(1))) + return pos_embeds + + +class LearnedAbsolutePositionEmbedding1D(nn.Module): + """ + Learnable absolute positional embeddings for 1D sequences. + + Args: + embed_dim: The dimension of the embeddings. + max_seq_len: The maximum length to precompute the positional encodings. + """ + def __init__( + self, + embedding_dim: int = 512, + num_pos: int = 1024) -> None: + super(LearnedAbsolutePositionEmbedding1D, self).__init__() + self.embeddings = nn.Embedding(num_pos, embedding_dim) + self.num_pos = num_pos + + def forward(self, seq_embeds: torch.Tensor) -> torch.Tensor: + """ + Args: + seq_embeds: The sequence embeddings in order. Allowed size: + 1. [T, D], where T is the length of the sequence, and D is the + frame embedding dimension. + 2. [B, T, D], where B is the batch size and T and D are the + same as above. + + Returns a tensor of with the same dimensions as the input: i.e., + [1, T, D] or [T, D]. + """ + shape_len = len(seq_embeds.shape) + assert 2 <= shape_len <= 3 + len_seq = seq_embeds.size(-2) + assert len_seq <= self.num_pos + # [T, D] + pos_embeds = self.embeddings(torch.arange(len_seq).to(seq_embeds.device)) + # Adapt pre-computed positional embeddings to the input. + if shape_len == 3: + pos_embeds = pos_embeds.view( + (1, pos_embeds.size(0), pos_embeds.size(1))) + return pos_embeds + + + +class MySequential(nn.Sequential): + def forward(self, *inputs): + for module in self._modules.values(): + if type(inputs) == tuple: + inputs = module(*inputs) + else: + inputs = module(inputs) + return inputs + + +class PreNorm(nn.Module): + def __init__(self, norm, fn, drop_path=None): + super().__init__() + self.norm = norm + self.fn = fn + self.drop_path = drop_path + + def forward(self, x, *args, **kwargs): + shortcut = x + if self.norm != None: + x, size = self.fn(self.norm(x), *args, **kwargs) + else: + x, size = self.fn(x, *args, **kwargs) + + if self.drop_path: + x = self.drop_path(x) + + x = shortcut + x + + return x, size + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.net = nn.Sequential(OrderedDict([ + ("fc1", nn.Linear(in_features, hidden_features)), + ("act", act_layer()), + ("fc2", nn.Linear(hidden_features, out_features)) + ])) + + def forward(self, x, size): + return self.net(x), size + + +class DepthWiseConv2d(nn.Module): + def __init__( + self, + dim_in, + kernel_size, + padding, + stride, + bias=True, + ): + super().__init__() + self.dw = nn.Conv2d( + dim_in, dim_in, + kernel_size=kernel_size, + padding=padding, + groups=dim_in, + stride=stride, + bias=bias + ) + + def forward(self, x, size): + B, N, C = x.shape + H, W = size + assert N == H * W + + x = self.dw(x.transpose(1, 2).view(B, C, H, W)) + size = (x.size(-2), x.size(-1)) + x = x.flatten(2).transpose(1, 2) + return x, size + + +class ConvEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__( + self, + patch_size=7, + in_chans=3, + embed_dim=64, + stride=4, + padding=2, + norm_layer=None, + pre_norm=True + ): + super().__init__() + self.patch_size = patch_size + + self.proj = nn.Conv2d( + in_chans, embed_dim, + kernel_size=patch_size, + stride=stride, + padding=padding + ) + + dim_norm = in_chans if pre_norm else embed_dim + self.norm = norm_layer(dim_norm) if norm_layer else None + + self.pre_norm = pre_norm + + def forward(self, x, size): + H, W = size + if len(x.size()) == 3: + if self.norm and self.pre_norm: + x = self.norm(x) + x = rearrange( + x, 'b (h w) c -> b c h w', + h=H, w=W + ) + + x = self.proj(x) + + _, _, H, W = x.shape + x = rearrange(x, 'b c h w -> b (h w) c') + if self.norm and not self.pre_norm: + x = self.norm(x) + + return x, (H, W) + + +class ChannelAttention(nn.Module): + + def __init__(self, dim, groups=8, qkv_bias=True): + super().__init__() + + self.groups = groups + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + def forward(self, x, size): + B, N, C = x.shape + + qkv = self.qkv(x).reshape(B, N, 3, self.groups, C // self.groups).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q * (float(N) ** -0.5) + attention = q.transpose(-1, -2) @ k + attention = attention.softmax(dim=-1) + x = (attention @ v.transpose(-1, -2)).transpose(-1, -2) + x = x.transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + return x, size + + +class ChannelBlock(nn.Module): + + def __init__(self, dim, groups, mlp_ratio=4., qkv_bias=True, + drop_path_rate=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, + conv_at_attn=True, conv_at_ffn=True): + super().__init__() + + drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + self.conv1 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_attn else None + self.channel_attn = PreNorm( + norm_layer(dim), + ChannelAttention(dim, groups=groups, qkv_bias=qkv_bias), + drop_path + ) + self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_ffn else None + self.ffn = PreNorm( + norm_layer(dim), + Mlp(in_features=dim, hidden_features=int(dim*mlp_ratio), act_layer=act_layer), + drop_path + ) + + def forward(self, x, size): + if self.conv1: + x, size = self.conv1(x, size) + x, size = self.channel_attn(x, size) + + if self.conv2: + x, size = self.conv2(x, size) + x, size = self.ffn(x, size) + + return x, size + + +def window_partition(x, window_size: int): + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, batch_size: int, window_size: int, H: int, W: int): + B = batch_size + # this will cause onnx conversion failed for dynamic axis, because treated as constant + # int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + def __init__(self, dim, num_heads, window_size, qkv_bias=True): + + super().__init__() + self.dim = dim + self.window_size = window_size + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = float(head_dim) ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, size): + + H, W = size + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + x = window_partition(x, self.window_size) + x = x.view(-1, self.window_size * self.window_size, C) + + # W-MSA/SW-MSA + # attn_windows = self.attn(x_windows) + + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + attn = self.softmax(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + + # merge windows + x = x.view( + -1, self.window_size, self.window_size, C + ) + x = window_reverse(x, B, self.window_size, Hp, Wp) + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + return x, size + + +class SpatialBlock(nn.Module): + + def __init__(self, dim, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop_path_rate=0., act_layer=nn.GELU, + norm_layer=nn.LayerNorm, conv_at_attn=True, conv_at_ffn=True): + super().__init__() + + drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + self.conv1 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_attn else None + self.window_attn = PreNorm( + norm_layer(dim), + WindowAttention(dim, num_heads, window_size, qkv_bias=qkv_bias), + drop_path + ) + self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_ffn else None + self.ffn = PreNorm( + norm_layer(dim), + Mlp(in_features=dim, hidden_features=int(dim*mlp_ratio), act_layer=act_layer), + drop_path + ) + + def forward(self, x, size): + if self.conv1: + x, size = self.conv1(x, size) + x, size = self.window_attn(x, size) + + if self.conv2: + x, size = self.conv2(x, size) + x, size = self.ffn(x, size) + return x, size + + +class DaViT(nn.Module): + """ DaViT: Dual-Attention Transformer + + Args: + in_chans (int): Number of input image channels. Default: 3. + num_classes (int): Number of classes for classification head. Default: 1000. + patch_size (tuple(int)): Patch size of convolution in different stages. Default: (7, 2, 2, 2). + patch_stride (tuple(int)): Patch stride of convolution in different stages. Default: (4, 2, 2, 2). + patch_padding (tuple(int)): Patch padding of convolution in different stages. Default: (3, 0, 0, 0). + patch_prenorm (tuple(bool)): If True, perform norm before convlution layer. Default: (True, False, False, False). + embed_dims (tuple(int)): Patch embedding dimension in different stages. Default: (64, 128, 192, 256). + num_heads (tuple(int)): Number of spatial attention heads in different stages. Default: (4, 8, 12, 16). + num_groups (tuple(int)): Number of channel groups in different stages. Default: (4, 8, 12, 16). + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True. + drop_path_rate (float): Stochastic depth rate. Default: 0.1. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + enable_checkpoint (bool): If True, enable checkpointing. Default: False. + conv_at_attn (bool): If True, performe depthwise convolution before attention layer. Default: True. + conv_at_ffn (bool): If True, performe depthwise convolution before ffn layer. Default: True. + """ + + def __init__( + self, + in_chans=3, + num_classes=1000, + depths=(1, 1, 3, 1), + patch_size=(7, 2, 2, 2), + patch_stride=(4, 2, 2, 2), + patch_padding=(3, 0, 0, 0), + patch_prenorm=(False, False, False, False), + embed_dims=(64, 128, 192, 256), + num_heads=(3, 6, 12, 24), + num_groups=(3, 6, 12, 24), + window_size=7, + mlp_ratio=4., + qkv_bias=True, + drop_path_rate=0.1, + norm_layer=nn.LayerNorm, + enable_checkpoint=False, + conv_at_attn=True, + conv_at_ffn=True, + ): + super().__init__() + + self.num_classes = num_classes + self.embed_dims = embed_dims + self.num_heads = num_heads + self.num_groups = num_groups + self.num_stages = len(self.embed_dims) + self.enable_checkpoint = enable_checkpoint + assert self.num_stages == len(self.num_heads) == len(self.num_groups) + + num_stages = len(embed_dims) + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)*2)] + + depth_offset = 0 + convs = [] + blocks = [] + for i in range(num_stages): + conv_embed = ConvEmbed( + patch_size=patch_size[i], + stride=patch_stride[i], + padding=patch_padding[i], + in_chans=in_chans if i == 0 else self.embed_dims[i - 1], + embed_dim=self.embed_dims[i], + norm_layer=norm_layer, + pre_norm=patch_prenorm[i] + ) + convs.append(conv_embed) + + block = MySequential( + *[ + MySequential(OrderedDict([ + ( + 'spatial_block', SpatialBlock( + embed_dims[i], + num_heads[i], + window_size, + drop_path_rate=dpr[depth_offset+j*2], + qkv_bias=qkv_bias, + mlp_ratio=mlp_ratio, + conv_at_attn=conv_at_attn, + conv_at_ffn=conv_at_ffn, + ) + ), + ( + 'channel_block', ChannelBlock( + embed_dims[i], + num_groups[i], + drop_path_rate=dpr[depth_offset+j*2+1], + qkv_bias=qkv_bias, + mlp_ratio=mlp_ratio, + conv_at_attn=conv_at_attn, + conv_at_ffn=conv_at_ffn, + ) + ) + ])) for j in range(depths[i]) + ] + ) + blocks.append(block) + depth_offset += depths[i]*2 + + self.convs = nn.ModuleList(convs) + self.blocks = nn.ModuleList(blocks) + + self.norms = norm_layer(self.embed_dims[-1]) + self.avgpool = nn.AdaptiveAvgPool1d(1) + self.head = nn.Linear(self.embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity() + + @property + def dim_out(self): + return self.embed_dims[-1] + + def forward_features_unpool(self, x): + """ + forward until avg pooling + Args: + x (_type_): input image tensor + """ + input_size = (x.size(2), x.size(3)) + for conv, block in zip(self.convs, self.blocks): + x, input_size = conv(x, input_size) + if self.enable_checkpoint: + x, input_size = checkpoint.checkpoint(block, x, input_size) + else: + x, input_size = block(x, input_size) + return x + + def forward_features(self, x): + x = self.forward_features_unpool(x) + + # (batch_size, num_tokens, token_dim) + x = self.avgpool(x.transpose(1, 2)) + # (batch_size, 1, num_tokens) + x = torch.flatten(x, 1) + x = self.norms(x) + + return x + + def forward(self, x): + x = self.forward_features(x) + x = self.head(x) + return x + + @classmethod + def from_config(cls, config): + return cls( + depths=config.depths, + embed_dims=config.dim_embed, + num_heads=config.num_heads, + num_groups=config.num_groups, + patch_size=config.patch_size, + patch_stride=config.patch_stride, + patch_padding=config.patch_padding, + patch_prenorm=config.patch_prenorm, + drop_path_rate=config.drop_path_rate, + window_size=config.window_size, + ) + + + + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +class Florence2LearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # Florence2 is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): + """`input_ids' shape is expected to be [bsz x seqlen].""" + + bsz, seq_len = input_ids.shape[:2] + positions = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ).expand(bsz, -1) + + return super().forward(positions + self.offset) + + +class Florence2ScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +class Florence2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: Optional[Florence2LanguageConfig] = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +class Florence2FlashAttention2(Florence2Attention): + """ + Florence2 flash attention module. This module inherits from `Florence2Attention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # Florence2FlashAttention2 attention does not support output_attentions + if output_attentions: + raise ValueError("Florence2FlashAttention2 attention does not support output_attentions") + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, q_len, _ = hidden_states.size() + + # get query proj + query_states = self._reshape(self.q_proj(hidden_states), -1, bsz) + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0].transpose(1, 2) + value_states = past_key_value[1].transpose(1, 2) + elif is_cross_attention: + # cross_attentions + key_states = self._reshape(self.k_proj(key_value_states), -1, bsz) + value_states = self._reshape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) + value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1) + value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1) + else: + # self_attention + key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) + value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2)) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (LlamaRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, key_states, value_states, attention_mask, q_len, dropout=self.dropout + ) + + attn_output = attn_output.reshape(bsz, q_len, -1) + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward + def _flash_attention_forward( + self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`float`): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + attn_output = flash_attn_func( + query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal + ) + + return attn_output + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +class Florence2SdpaAttention(Florence2Attention): + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + if output_attentions or layer_head_mask is not None: + # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Florence2Model is using Florence2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention" + ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states, + key_value_states=key_value_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + query_states = self._shape(query_states, tgt_len, bsz) + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1. + is_causal = True if self.is_causal and attention_mask is None and tgt_len > 1 else False + + # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask, + # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577 + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.dropout if self.training else 0.0, + is_causal=is_causal, + ) + + if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, None, past_key_value + + +FLORENCE2_ATTENTION_CLASSES = { + "eager": Florence2Attention, + "sdpa": Florence2SdpaAttention, + "flash_attention_2": Florence2FlashAttention2, +} + + +class Florence2EncoderLayer(nn.Module): + def __init__(self, config: Florence2LanguageConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = FLORENCE2_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: torch.FloatTensor, + layer_head_mask: torch.FloatTensor, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states, attn_weights, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + if hidden_states.dtype == torch.float16 and ( + torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() + ): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class Florence2DecoderLayer(nn.Module): + def __init__(self, config: Florence2LanguageConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = FLORENCE2_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + is_causal=True, + config=config, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = FLORENCE2_ATTENTION_CLASSES[config._attn_implementation]( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + cross_attn_layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = True, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of + size `(decoder_attention_heads,)`. + past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + # Self Attention + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + # add present self-attn cache to positions 1,2 of present_key_value tuple + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=self_attn_past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Cross-Attention Block + cross_attn_present_key_value = None + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + + # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + layer_head_mask=cross_attn_layer_head_mask, + past_key_value=cross_attn_past_key_value, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # add cross-attn to positions 3,4 of present_key_value tuple + present_key_value = present_key_value + cross_attn_present_key_value + + # Fully Connected + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + + +class Florence2LanguagePreTrainedModel(PreTrainedModel): + config_class = Florence2LanguageConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _keys_to_ignore_on_load_unexpected = ["encoder.version", "decoder.version"] + _no_split_modules = [r"Florence2EncoderLayer", r"Florence2DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + + def _init_weights(self, module): + std = self.config.init_std + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.Conv2d): + nn.init.normal_(module.weight, std=0.02) + for name, _ in module.named_parameters(): + if name == "bias": + nn.init.constant_(module.bias, 0) + elif isinstance(module, nn.LayerNorm): + nn.init.constant_(module.weight, 1.0) + nn.init.constant_(module.bias, 0) + elif isinstance(module, nn.BatchNorm2d): + nn.init.constant_(module.weight, 1.0) + nn.init.constant_(module.bias, 0) + + @property + def dummy_inputs(self): + pad_token = self.config.pad_token_id + input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) + dummy_inputs = { + "attention_mask": input_ids.ne(pad_token), + "input_ids": input_ids, + } + return dummy_inputs + + +class Florence2Encoder(Florence2LanguagePreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`Florence2EncoderLayer`]. + + Args: + config: Florence2LanguageConfig + embed_tokens (nn.Embedding): output embedding + """ + + def __init__(self, config: Florence2LanguageConfig, embed_tokens: Optional[nn.Embedding] = None): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = Florence2ScaledWordEmbedding( + config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale + ) + + if embed_tokens is not None: + self.embed_tokens.weight = embed_tokens.weight + + self.embed_positions = Florence2LearnedPositionalEmbedding( + config.max_position_embeddings, + embed_dim, + ) + self.layers = nn.ModuleList([Florence2EncoderLayer(config) for _ in range(config.encoder_layers)]) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self._use_sdpa = config._attn_implementation == "sdpa" + self.layernorm_embedding = nn.LayerNorm(embed_dim) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_ids = input_ids.view(-1, input_ids.shape[-1]) + elif inputs_embeds is not None: + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + embed_pos = self.embed_positions(input) + embed_pos = embed_pos.to(inputs_embeds.device) + + hidden_states = inputs_embeds + embed_pos + hidden_states = self.layernorm_embedding(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # expand attention_mask + if attention_mask is not None: + if self._use_flash_attention_2: + attention_mask = attention_mask if 0 in attention_mask else None + elif self._use_sdpa and head_mask is None and not output_attentions: + # output_attentions=True & head_mask can not be supported when using SDPA, fall back to + # the manual implementation that requires a 4D causal mask in all cases. + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype) + else: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + # check if head_mask has a correct number of layers specified if desired + if head_mask is not None: + if head_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The head_mask should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + attention_mask, + (head_mask[idx] if head_mask is not None else None), + output_attentions, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +class Florence2Decoder(Florence2LanguagePreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Florence2DecoderLayer`] + + Args: + config: Florence2LanguageConfig + embed_tokens (nn.Embedding): output embedding + """ + + def __init__(self, config: Florence2LanguageConfig, embed_tokens: Optional[nn.Embedding] = None): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = Florence2ScaledWordEmbedding( + config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale + ) + + if embed_tokens is not None: + self.embed_tokens.weight = embed_tokens.weight + + self.embed_positions = Florence2LearnedPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + ) + self.layers = nn.ModuleList([Florence2DecoderLayer(config) for _ in range(config.decoder_layers)]) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self._use_sdpa = config._attn_implementation == "sdpa" + + self.layernorm_embedding = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing + cross-attention on hidden heads. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.shape + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input) + + if self._use_flash_attention_2: + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._use_sdpa and not output_attentions and cross_attn_head_mask is None: + # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + input_shape, + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + if self._use_flash_attention_2: + encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None + elif self._use_sdpa and cross_attn_head_mask is None and not output_attentions: + # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa( + encoder_attention_mask, + inputs_embeds.dtype, + tgt_len=input_shape[-1], + ) + else: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask( + encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ) + + # embed positions + positions = self.embed_positions(input, past_key_values_length) + positions = positions.to(inputs_embeds.device) + + hidden_states = inputs_embeds + positions + hidden_states = self.layernorm_embedding(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + next_decoder_cache = () if use_cache else None + + # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + head_mask[idx] if head_mask is not None else None, + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, + None, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + cross_attn_layer_head_mask=( + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None + ), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +class Florence2LanguageModel(Florence2LanguagePreTrainedModel): + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] + + def __init__(self, config: Florence2LanguageConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) + + self.encoder = Florence2Encoder(config, self.shared) + self.decoder = Florence2Decoder(config, self.shared) + + # Initialize weights and apply final processing + self.post_init() + + def _tie_weights(self): + if self.config.tie_word_embeddings: + self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared) + self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared) + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Seq2SeqModelOutput]: + # different to other models, Florence2 automatically creates decoder_input_ids from + # input_ids if no decoder_input_ids are provided + if decoder_input_ids is None and decoder_inputs_embeds is None: + if input_ids is None: + raise ValueError( + "If no `decoder_input_ids` or `decoder_inputs_embeds` are " + "passed, `input_ids` cannot be `None`. Please pass either " + "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`." + ) + + decoder_input_ids = shift_tokens_right( + input_ids, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel, GenerationMixin): + base_model_prefix = "model" + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] + _keys_to_ignore_on_load_missing = ["final_logits_bias"] + + def __init__(self, config: Florence2LanguageConfig): + super().__init__(config) + self.model = Florence2LanguageModel(config) + self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + # Initialize weights and apply final processing + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + self.post_init() + + def _tie_weights(self): + if self.config.tie_word_embeddings: + self._tie_or_clone_weights(self.model.encoder.embed_tokens, self.model.shared) + self._tie_or_clone_weights(self.model.decoder.embed_tokens, self.model.shared) + self._tie_or_clone_weights(self.lm_head, self.model.shared) + + def tie_weights(self): + self.model.encoder.embed_tokens.weight = self.model.shared.weight + self.model.decoder.embed_tokens.weight = self.model.shared.weight + self.lm_head.weight = self.model.shared.weight + + def get_encoder(self): + return self.model.get_encoder() + + def get_decoder(self): + return self.model.get_decoder() + + def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of) + self._resize_final_logits_bias(new_embeddings.weight.shape[0]) + return new_embeddings + + def _resize_final_logits_bias(self, new_num_tokens: int) -> None: + old_num_tokens = self.final_logits_bias.shape[-1] + if new_num_tokens <= old_num_tokens: + new_bias = self.final_logits_bias[:, :new_num_tokens] + else: + extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) + new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) + self.register_buffer("final_logits_bias", new_bias) + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Seq2SeqLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(outputs[0]) + lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) + + masked_lm_loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + past_key_values=None, + attention_mask=None, + decoder_attention_mask=None, + head_mask=None, + decoder_head_mask=None, + cross_attn_head_mask=None, + use_cache=None, + encoder_outputs=None, + **kwargs, + ): + # cut decoder_input_ids if past_key_values is used + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if decoder_input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = decoder_input_ids.shape[1] - 1 + + decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] + + return { + "input_ids": None, # encoder_outputs is defined. input_ids not needed + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "head_mask": head_mask, + "decoder_head_mask": decoder_head_mask, + "cross_attn_head_mask": cross_attn_head_mask, + "use_cache": use_cache, # change this to avoid caching (presumably for debugging) + } + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + # cached cross_attention states don't have to be reordered -> they are always the same + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + + layer_past[2:], + ) + return reordered_past + +@dataclass +class Florence2Seq2SeqLMOutput(ModelOutput): + """ + Base class for Florence-2 model's outputs that also contains : pre-computed hidden states that can speed up sequential + decoding. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the + weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, + num_image_tokens, hidden_size)`. + + image_hidden_states of the model produced by the vision encoder + """ + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + last_hidden_state: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None + decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None + encoder_last_hidden_state: Optional[torch.FloatTensor] = None + encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None + encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None + image_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None + + +FLORENCE2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Florence2Config`] or [`Florence2VisionConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Florence-2 Model outputting raw hidden-states without any specific head on top.", + FLORENCE2_START_DOCSTRING, +) +class Florence2PreTrainedModel(PreTrainedModel): + config_class = Florence2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _skip_keys_device_placement = "past_key_values" + + @property + def _supports_flash_attn_2(self): + """ + Retrieve language_model's attribute to check whether the model supports + Flash Attention 2 or not. + """ + if hasattr(self, 'language_model') and self.language_model is not None: + return self.language_model._supports_flash_attn_2 + return True # Default to True during initialization + + @property + def _supports_sdpa(self): + """ + Retrieve language_model's attribute to check whether the model supports + SDPA or not. + """ + if hasattr(self, 'language_model') and self.language_model is not None: + return self.language_model._supports_sdpa + return True # Default to True during initialization + + +FLORENCE2_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): + The tensors corresponding to the input images. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`Florence2Processor`] uses + [`CLIPImageProcessor`] for processing images). + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +@add_start_docstrings( + """The FLORENCE2 vision model without any head""", + FLORENCE2_START_DOCSTRING, +) +class Florence2VisionModel(Florence2PreTrainedModel): + def __init__(self, config: Florence2VisionConfig): + super().__init__(config) + assert config.model_type == 'davit', 'only DaViT is supported for now' + self.vision_tower = DaViT.from_config(config=config) + + self.post_init() + + def forward(self, pixel_values): + if len(pixel_values.shape) == 4: + x = self.vision_tower.forward_features_unpool(pixel_values) + else: + raise ValueError(f'invalid image shape {pixel_values.shape}') + return x + + +@add_start_docstrings( + """The FLORENCE2 vision model with projection layer""", + FLORENCE2_START_DOCSTRING, +) +class Florence2VisionModelWithProjection(Florence2PreTrainedModel): + def __init__(self, config: Florence2VisionConfig): + super().__init__(config) + assert config.model_type == 'davit', 'only DaViT is supported for now' + self.vision_tower = DaViT.from_config(config=config) + + self._build_image_projection_layers(config) + + self.post_init() + + def _build_image_projection_layers(self, config): + image_dim_out = config.dim_embed[-1] + dim_projection = config.projection_dim + self.image_projection = nn.Parameter( + torch.empty(image_dim_out, dim_projection) + ) + self.image_proj_norm = nn.LayerNorm(dim_projection) + image_pos_embed_config = config.image_pos_embed + if image_pos_embed_config['type'] == 'learned_abs_2d': + self.image_pos_embed = LearnedAbsolutePositionEmbedding2D( + embedding_dim=image_dim_out, + num_pos=image_pos_embed_config['max_pos_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + self.image_feature_source = config.image_feature_source + + # temporal embedding + visual_temporal_embedding_config = config.visual_temporal_embedding + if visual_temporal_embedding_config['type'] == 'COSINE': + self.visual_temporal_embed = PositionalEmbeddingCosine1D( + embed_dim=image_dim_out, + max_seq_len=visual_temporal_embedding_config['max_temporal_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + def forward(self, pixel_values): + if len(pixel_values.shape) == 4: + batch_size, C, H, W = pixel_values.shape + T = 1 + x = self.vision_tower.forward_features_unpool(pixel_values) + else: + raise ValueError(f'invalid image shape {pixel_values.shape}') + + if self.image_pos_embed is not None: + x = x.view(batch_size * T, -1, x.shape[-1]) + num_tokens = x.shape[-2] + h, w = int(num_tokens ** 0.5), int(num_tokens ** 0.5) + assert h * w == num_tokens, 'only support square feature maps for now' + x = x.view(batch_size * T, h, w, x.shape[-1]) + pos_embed = self.image_pos_embed(x) + x = x + pos_embed + x = x.view(batch_size, T * h*w, x.shape[-1]) + + if self.visual_temporal_embed is not None: + visual_temporal_embed = self.visual_temporal_embed(x.view(batch_size, T, -1, x.shape[-1])[:, :, 0]) + x = x.view(batch_size, T, -1, x.shape[-1]) + visual_temporal_embed.view(1, T, 1, x.shape[-1]) + + x_feat_dict = {} + + spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2) + x_feat_dict['spatial_avg_pool'] = spatial_avg_pool_x + + temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1) + x_feat_dict['temporal_avg_pool'] = temporal_avg_pool_x + + x = x.view(batch_size, T, -1, x.shape[-1])[:, -1] + x_feat_dict['last_frame'] = x + + new_x = [] + for _image_feature_source in self.image_feature_source: + if _image_feature_source not in x_feat_dict: + raise ValueError('invalid image feature source: {}'.format(_image_feature_source)) + new_x.append(x_feat_dict[_image_feature_source]) + + x = torch.cat(new_x, dim=1) + + x = x @ self.image_projection + x = self.image_proj_norm(x) + + + return x + + + +@add_start_docstrings( + """The FLORENCE2 model which consists of a vision backbone and a language model.""", + FLORENCE2_START_DOCSTRING, +) +class Florence2ForConditionalGeneration(Florence2PreTrainedModel, GenerationMixin): + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + _tied_weights_keys = ["language_model.encoder.embed_tokens.weight", "language_model.decoder.embed_tokens.weight", "language_model.lm_head.weight"] + def __init__(self, config: Florence2Config): + super().__init__(config) + assert config.vision_config.model_type == 'davit', 'only DaViT is supported for now' + self.vision_tower = DaViT.from_config(config=config.vision_config) + # remove unused layers + del self.vision_tower.head + del self.vision_tower.norms + + self.vocab_size = config.vocab_size + self._attn_implementation = config._attn_implementation + self._build_image_projection_layers(config) + + language_model = Florence2LanguageForConditionalGeneration(config=config.text_config) + + self.language_model = language_model + + self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1 + self.post_init() + + def _build_image_projection_layers(self, config): + image_dim_out = config.vision_config.dim_embed[-1] + dim_projection = config.vision_config.projection_dim + self.image_projection = nn.Parameter( + torch.empty(image_dim_out, dim_projection) + ) + self.image_proj_norm = nn.LayerNorm(dim_projection) + image_pos_embed_config = config.vision_config.image_pos_embed + if image_pos_embed_config['type'] == 'learned_abs_2d': + self.image_pos_embed = LearnedAbsolutePositionEmbedding2D( + embedding_dim=image_dim_out, + num_pos=image_pos_embed_config['max_pos_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + self.image_feature_source = config.vision_config.image_feature_source + + # temporal embedding + visual_temporal_embedding_config = config.vision_config.visual_temporal_embedding + if visual_temporal_embedding_config['type'] == 'COSINE': + self.visual_temporal_embed = PositionalEmbeddingCosine1D( + embed_dim=image_dim_out, + max_seq_len=visual_temporal_embedding_config['max_temporal_embeddings'] + ) + else: + raise NotImplementedError('Not implemented yet') + + def get_encoder(self): + return self.language_model.get_encoder() + + def get_decoder(self): + return self.language_model.get_decoder() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding: + model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) + # update vocab size + self.config.text_config.vocab_size = model_embeds.num_embeddings + self.config.vocab_size = model_embeds.num_embeddings + self.vocab_size = model_embeds.num_embeddings + return model_embeds + + def _encode_image(self, pixel_values): + if len(pixel_values.shape) == 4: + batch_size, C, H, W = pixel_values.shape + T = 1 + x = self.vision_tower.forward_features_unpool(pixel_values) + else: + raise ValueError(f'invalid image shape {pixel_values.shape}') + + if self.image_pos_embed is not None: + x = x.view(batch_size * T, -1, x.shape[-1]) + num_tokens = x.shape[-2] + h, w = int(num_tokens ** 0.5), int(num_tokens ** 0.5) + assert h * w == num_tokens, 'only support square feature maps for now' + x = x.view(batch_size * T, h, w, x.shape[-1]) + pos_embed = self.image_pos_embed(x) + x = x + pos_embed + x = x.view(batch_size, T * h*w, x.shape[-1]) + + if self.visual_temporal_embed is not None: + visual_temporal_embed = self.visual_temporal_embed(x.view(batch_size, T, -1, x.shape[-1])[:, :, 0]) + x = x.view(batch_size, T, -1, x.shape[-1]) + visual_temporal_embed.view(1, T, 1, x.shape[-1]) + + x_feat_dict = {} + + spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2) + x_feat_dict['spatial_avg_pool'] = spatial_avg_pool_x + + temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1) + x_feat_dict['temporal_avg_pool'] = temporal_avg_pool_x + + x = x.view(batch_size, T, -1, x.shape[-1])[:, -1] + x_feat_dict['last_frame'] = x + + new_x = [] + for _image_feature_source in self.image_feature_source: + if _image_feature_source not in x_feat_dict: + raise ValueError('invalid image feature source: {}'.format(_image_feature_source)) + new_x.append(x_feat_dict[_image_feature_source]) + + x = torch.cat(new_x, dim=1) + + x = x @ self.image_projection + x = self.image_proj_norm(x) + + return x + + def _merge_input_ids_with_image_features( + self, image_features, inputs_embeds + ): + batch_size, image_token_length = image_features.size()[:-1] + device = image_features.device + image_attention_mask = torch.ones(batch_size, image_token_length, device=device) + + # task_prefix_embeds: [batch_size, padded_context_length, hidden_size] + # task_prefix_attention_mask: [batch_size, context_length] + if inputs_embeds is None: + return image_features, image_attention_mask + + task_prefix_embeds = inputs_embeds + task_prefix_attention_mask = torch.ones(batch_size, task_prefix_embeds.size(1), device=device) + + if len(task_prefix_attention_mask.shape) == 3: + task_prefix_attention_mask = task_prefix_attention_mask[:, 0] + + # concat [image embeds, task prefix embeds] + inputs_embeds = torch.cat([image_features, task_prefix_embeds], dim=1) + attention_mask = torch.cat([image_attention_mask, task_prefix_attention_mask], dim=1) + + return inputs_embeds, attention_mask + + + @add_start_docstrings_to_model_forward(FLORENCE2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=Florence2Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + pixel_values: torch.FloatTensor = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Florence2Seq2SeqLMOutput]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, Florence2ForConditionalGeneration + + >>> model = Florence2ForConditionalGeneration.from_pretrained("microsoft/Florence-2-large") + >>> processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large") + + >>> prompt = "" + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(text=prompt, images=image, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_length=100) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "A green car parked in front of a yellow building." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + image_features = None + if inputs_embeds is None: + # 1. Extra the input embeddings + if input_ids is not None: + inputs_embeds = self.get_input_embeddings()(input_ids) + # 2. Merge text and images + if pixel_values is not None: + # (batch_size, num_image_tokens, hidden_size) + image_features = self._encode_image(pixel_values) + inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds) + + if inputs_embeds is not None: + attention_mask = attention_mask.to(inputs_embeds.dtype) + outputs = self.language_model( + attention_mask=attention_mask, + labels=labels, + inputs_embeds=inputs_embeds, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = outputs.logits + logits = logits.float() + loss = outputs.loss + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return Florence2Seq2SeqLMOutput( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + image_hidden_states=image_features + ) + + def generate( + self, + input_ids, + inputs_embeds=None, + pixel_values=None, + **kwargs + ): + + if inputs_embeds is None: + # 1. Extra the input embeddings + if input_ids is not None: + inputs_embeds = self.get_input_embeddings()(input_ids) + # 2. Merge text and images + if pixel_values is not None: + image_features = self._encode_image(pixel_values) + inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds) + + return self.language_model.generate( + input_ids=None, + inputs_embeds=inputs_embeds, + **kwargs + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + past_key_values=None, + attention_mask=None, + pixel_values=None, + decoder_attention_mask=None, + head_mask=None, + decoder_head_mask=None, + cross_attn_head_mask=None, + use_cache=None, + encoder_outputs=None, + **kwargs, + ): + # cut decoder_input_ids if past_key_values is used + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if decoder_input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = decoder_input_ids.shape[1] - 1 + + decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] + + return { + "input_ids": None, # encoder_outputs is defined. input_ids not needed + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "attention_mask": attention_mask, + "pixel_values": pixel_values, + "decoder_attention_mask": decoder_attention_mask, + "head_mask": head_mask, + "decoder_head_mask": decoder_head_mask, + "cross_attn_head_mask": cross_attn_head_mask, + "use_cache": use_cache, # change this to avoid caching (presumably for debugging) + } + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return self.language_model.shift_tokens_right(labels) + + def _reorder_cache(self, *args, **kwargs): + return self.language_model._reorder_cache(*args, **kwargs) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Florence2/nodes.py b/custom_nodes/ComfyUI-Florence2/nodes.py new file mode 100644 index 00000000..f6017bb6 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/nodes.py @@ -0,0 +1,753 @@ +from collections.abc import Callable +import torch +import torchvision.transforms.functional as F +import io +import os +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from PIL import Image, ImageDraw, ImageColor, ImageFont +import random +import numpy as np +import re +from pathlib import Path + +from transformers.dynamic_module_utils import get_imports +import transformers +from packaging import version + +from safetensors.torch import save_file + +def load_model(model_path: str, attention: str, dtype: torch.dtype, offload_device: torch.device): + from .modeling_florence2 import Florence2ForConditionalGeneration, Florence2Config + from transformers import CLIPImageProcessor, BartTokenizerFast + from .processing_florence2 import Florence2Processor + from accelerate import init_empty_weights + from accelerate.utils import set_module_tensor_to_device + + config = Florence2Config.from_pretrained(model_path) + config._attn_implementation = attention + with init_empty_weights(): + model = Florence2ForConditionalGeneration(config) + + checkpoint_path = os.path.join(model_path, "model.safetensors") + if not os.path.exists(checkpoint_path): + checkpoint_path = os.path.join(model_path, "pytorch_model.bin") + if os.path.exists(checkpoint_path): + state_dict = load_torch_file(checkpoint_path) + else: + raise FileNotFoundError(f"No model weights found at {model_path}") + + key_mapping = {} + if "language_model.model.shared.weight" in state_dict: + key_mapping["language_model.model.encoder.embed_tokens.weight"] = "language_model.model.shared.weight" + key_mapping["language_model.model.decoder.embed_tokens.weight"] = "language_model.model.shared.weight" + + for name, param in model.named_parameters(): + # Check if we need to remap the key + actual_key = key_mapping.get(name, name) + + if actual_key in state_dict: + set_module_tensor_to_device(model, name, offload_device, value=state_dict[actual_key].to(dtype)) + else: + print(f"Parameter {name} not found in state_dict.") + + # Tie embeddings + model.language_model.tie_weights() + model = model.eval().to(dtype).to(offload_device) + + # Create image processor + image_processor = CLIPImageProcessor( + do_resize=True, + size={"height": 768, "width": 768}, + resample=3, # BICUBIC + do_center_crop=False, + do_rescale=True, + rescale_factor=1/255.0, + do_normalize=True, + image_mean=[0.485, 0.456, 0.406], + image_std=[0.229, 0.224, 0.225], + ) + image_processor.image_seq_length = 577 + + # Create tokenizer - Florence2 uses BART tokenizer + tokenizer = BartTokenizerFast.from_pretrained(model_path) + processor = Florence2Processor(image_processor=image_processor, tokenizer=tokenizer) + return model, processor + +def fixed_get_imports(filename: str | os.PathLike) -> list[str]: + try: + if not str(filename).endswith("modeling_florence2.py"): + return get_imports(filename) + imports = get_imports(filename) + imports.remove("flash_attn") + except: + print(f"No flash_attn import to remove") + pass + return imports + + +def create_path_dict(paths: list[str], predicate: Callable[[Path], bool] = lambda _: True) -> dict[str, str]: + """ + Creates a flat dictionary of the contents of all given paths: ``{name: absolute_path}``. + + Non-recursive. Optionally takes a predicate to filter items. Duplicate names overwrite (the last one wins). + + Args: + paths (list[str]): + The paths to search for items. + predicate (Callable[[Path], bool]): + (Optional) If provided, each path is tested against this filter. + Returns ``True`` to include a path. + + Default: Include everything + """ + + flattened_paths = [item for path in paths if Path(path).exists() for item in Path(path).iterdir() if predicate(item)] + + return {item.name: str(item.absolute()) for item in flattened_paths} + + +import comfy.model_management as mm +from comfy.utils import ProgressBar, load_torch_file + +device = mm.get_torch_device() +offload_device = mm.unet_offload_device() + +import folder_paths + +script_directory = os.path.dirname(os.path.abspath(__file__)) +model_directory = os.path.join(folder_paths.models_dir, "LLM") +os.makedirs(model_directory, exist_ok=True) + +# Ensure ComfyUI knows about the LLM model path +folder_paths.add_model_folder_path("LLM", model_directory) + +from transformers import AutoProcessor, set_seed + +model_list = [ + 'microsoft/Florence-2-base', + 'microsoft/Florence-2-base-ft', + 'microsoft/Florence-2-large', + 'microsoft/Florence-2-large-ft', + 'HuggingFaceM4/Florence-2-DocVQA', + 'thwri/CogFlorence-2.1-Large', + 'thwri/CogFlorence-2.2-Large', + 'gokaygokay/Florence-2-SD3-Captioner', + 'gokaygokay/Florence-2-Flux-Large', + 'MiaoshouAI/Florence-2-base-PromptGen-v1.5', + 'MiaoshouAI/Florence-2-large-PromptGen-v1.5', + 'MiaoshouAI/Florence-2-base-PromptGen-v2.0', + 'MiaoshouAI/Florence-2-large-PromptGen-v2.0', + 'PJMixers-Images/Florence-2-base-Castollux-v0.5' + ] + +class DownloadAndLoadFlorence2Model: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": (model_list, {"default": 'microsoft/Florence-2-base'}), + "precision": ([ 'fp16','bf16','fp32'], + { + "default": 'fp16' + }), + "attention": ( + [ 'flash_attention_2', 'sdpa', 'eager'], + { + "default": 'sdpa' + }), + }, + "optional": { + "lora": ("PEFTLORA",), + "convert_to_safetensors": ("BOOLEAN", {"default": False, "tooltip": "Some of the older model weights are not saved in .safetensors format, which seem to cause longer loading times, this option converts the .bin weights to .safetensors"}), + } + } + + RETURN_TYPES = ("FL2MODEL",) + RETURN_NAMES = ("florence2_model",) + FUNCTION = "loadmodel" + CATEGORY = "Florence2" + + def loadmodel(self, model, precision, attention, lora=None, convert_to_safetensors=False): + if model not in model_list: + raise ValueError(f"Model {model} is not in the supported model list.") + + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + + model_name = model.rsplit('/', 1)[-1] + model_path = os.path.join(model_directory, model_name) + + if not os.path.exists(model_path): + print(f"Downloading Florence2 model to: {model_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id=model, + local_dir=model_path, + local_dir_use_symlinks=False) + + print(f"Florence2 using {attention} for attention") + + if convert_to_safetensors: + model_weight_path = os.path.join(model_path, 'pytorch_model.bin') + if os.path.exists(model_weight_path): + safetensors_weight_path = os.path.join(model_path, 'model.safetensors') + print(f"Converting {model_weight_path} to {safetensors_weight_path}") + if not os.path.exists(safetensors_weight_path): + sd = torch.load(model_weight_path, map_location=offload_device) + sd_new = {} + for k, v in sd.items(): + sd_new[k] = v.clone() + save_file(sd_new, safetensors_weight_path) + if os.path.exists(safetensors_weight_path): + print(f"Conversion successful. Deleting original file: {model_weight_path}") + os.remove(model_weight_path) + print(f"Original {model_weight_path} file deleted.") + + if version.parse(transformers.__version__) >= version.parse('5.0.0'): + model, processor = load_model(model_path, attention, dtype, offload_device) + else: + from .modeling_florence2 import Florence2ForConditionalGeneration + model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, dtype=dtype).to(offload_device) + processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) + + if lora is not None: + from peft import PeftModel + adapter_name = lora + model = PeftModel.from_pretrained(model, adapter_name, trust_remote_code=True) + + florence2_model = { + 'model': model, + 'processor': processor, + 'dtype': dtype + } + + return (florence2_model,) + +class DownloadAndLoadFlorence2Lora: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ( + [ + 'NikshepShetty/Florence-2-pixelprose', + ], + ), + }, + + } + + RETURN_TYPES = ("PEFTLORA",) + RETURN_NAMES = ("lora",) + FUNCTION = "loadmodel" + CATEGORY = "Florence2" + + def loadmodel(self, model): + if model not in ['NikshepShetty/Florence-2-pixelprose']: + raise ValueError(f"Lora Model {model} is not in the supported lora model list.") + model_name = model.rsplit('/', 1)[-1] + model_path = os.path.join(model_directory, model_name) + + if not os.path.exists(model_path): + print(f"Downloading Florence2 lora model to: {model_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id=model, + local_dir=model_path, + local_dir_use_symlinks=False) + return (model_path,) + +class Florence2ModelLoader: + + @classmethod + def INPUT_TYPES(s): + all_llm_paths = folder_paths.get_folder_paths("LLM") + s.model_paths = create_path_dict(all_llm_paths, lambda x: x.is_dir()) + + return {"required": { + "model": ([*s.model_paths], {"tooltip": "models are expected to be in Comfyui/models/LLM folder"}), + "precision": (['fp16','bf16','fp32'],), + "attention": ( + [ 'flash_attention_2', 'sdpa', 'eager'], + { + "default": 'sdpa' + }), + }, + "optional": { + "lora": ("PEFTLORA",), + "convert_to_safetensors": ("BOOLEAN", {"default": False, "tooltip": "Some of the older model weights are not saved in .safetensors format, which seem to cause longer loading times, this option converts the .bin weights to .safetensors"}), + } + } + + RETURN_TYPES = ("FL2MODEL",) + RETURN_NAMES = ("florence2_model",) + FUNCTION = "loadmodel" + CATEGORY = "Florence2" + + def loadmodel(self, model, precision, attention, lora=None, convert_to_safetensors=False): + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + model_path = Florence2ModelLoader.model_paths.get(model) + print(f"Loading model from {model_path}") + print(f"Florence2 using {attention} for attention") + if convert_to_safetensors: + model_weight_path = os.path.join(model_path, 'pytorch_model.bin') + if os.path.exists(model_weight_path): + safetensors_weight_path = os.path.join(model_path, 'model.safetensors') + print(f"Converting {model_weight_path} to {safetensors_weight_path}") + if not os.path.exists(safetensors_weight_path): + sd = torch.load(model_weight_path, map_location=offload_device) + sd_new = {} + for k, v in sd.items(): + sd_new[k] = v.clone() + save_file(sd_new, safetensors_weight_path) + if os.path.exists(safetensors_weight_path): + print(f"Conversion successful. Deleting original file: {model_weight_path}") + os.remove(model_weight_path) + print(f"Original {model_weight_path} file deleted.") + + if version.parse(transformers.__version__) >= version.parse('5.0.0'): + model, processor = load_model(model_path, attention, dtype, offload_device) + else: + from .modeling_florence2 import Florence2ForConditionalGeneration + model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, dtype=dtype).to(offload_device) + processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) + + if lora is not None: + from peft import PeftModel + adapter_name = lora + model = PeftModel.from_pretrained(model, adapter_name, trust_remote_code=True) + + florence2_model = { + 'model': model, + 'processor': processor, + 'dtype': dtype + } + + return (florence2_model,) + +class Florence2Run: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE", ), + "florence2_model": ("FL2MODEL", ), + "text_input": ("STRING", {"default": "", "multiline": True}), + "task": ( + [ + 'region_caption', + 'dense_region_caption', + 'region_proposal', + 'caption', + 'detailed_caption', + 'more_detailed_caption', + 'caption_to_phrase_grounding', + 'referring_expression_segmentation', + 'ocr', + 'ocr_with_region', + 'docvqa', + 'prompt_gen_tags', + 'prompt_gen_mixed_caption', + 'prompt_gen_analyze', + 'prompt_gen_mixed_caption_plus', + ], + ), + "fill_mask": ("BOOLEAN", {"default": True}), + }, + "optional": { + "keep_model_loaded": ("BOOLEAN", {"default": False}), + "max_new_tokens": ("INT", {"default": 1024, "min": 1, "max": 4096}), + "num_beams": ("INT", {"default": 3, "min": 1, "max": 64}), + "do_sample": ("BOOLEAN", {"default": True}), + "output_mask_select": ("STRING", {"default": ""}), + "seed": ("INT", {"default": 1, "min": 1, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "JSON") + RETURN_NAMES =("image", "mask", "caption", "data") + FUNCTION = "encode" + CATEGORY = "Florence2" + + def hash_seed(self, seed): + import hashlib + # Convert the seed to a string and then to bytes + seed_bytes = str(seed).encode('utf-8') + # Create a SHA-256 hash of the seed bytes + hash_object = hashlib.sha256(seed_bytes) + # Convert the hash to an integer + hashed_seed = int(hash_object.hexdigest(), 16) + # Ensure the hashed seed is within the acceptable range for set_seed + return hashed_seed % (2**32) + + def encode(self, image, text_input, florence2_model, task, fill_mask, keep_model_loaded=False, + num_beams=3, max_new_tokens=1024, do_sample=True, output_mask_select="", seed=None): + _, height, width, _ = image.shape + annotated_image_tensor = None + mask_tensor = None + processor = florence2_model['processor'] + model = florence2_model['model'] + dtype = florence2_model['dtype'] + model.to(device) + + if seed: + set_seed(self.hash_seed(seed)) + + colormap = ['blue','orange','green','purple','brown','pink','olive','cyan','red', + 'lime','indigo','violet','aqua','magenta','gold','tan','skyblue'] + + prompts = { + 'region_caption': '', + 'dense_region_caption': '', + 'region_proposal': '', + 'caption': '', + 'detailed_caption': '', + 'more_detailed_caption': '', + 'caption_to_phrase_grounding': '', + 'referring_expression_segmentation': '', + 'ocr': '', + 'ocr_with_region': '', + 'docvqa': '', + 'prompt_gen_tags': '', + 'prompt_gen_mixed_caption': '', + 'prompt_gen_analyze': '', + 'prompt_gen_mixed_caption_plus': '', + } + task_prompt = prompts.get(task, '') + + if (task not in ['referring_expression_segmentation', 'caption_to_phrase_grounding', 'docvqa']) and text_input: + raise ValueError("Text input (prompt) is only supported for 'referring_expression_segmentation', 'caption_to_phrase_grounding', and 'docvqa'") + + if text_input != "": + prompt = task_prompt + " " + text_input + else: + prompt = task_prompt + + image = image.permute(0, 3, 1, 2) + + out = [] + out_masks = [] + out_results = [] + out_data = [] + pbar = ProgressBar(len(image)) + for img in image: + image_pil = F.to_pil_image(img) + inputs = processor(text=prompt, images=image_pil, return_tensors="pt", do_rescale=False).to(dtype).to(device) + + generated_ids = model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=max_new_tokens, + do_sample=do_sample, + num_beams=num_beams, + use_cache=False, + ) + + results = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + print(results) + # cleanup the special tokens from the final list + if task == 'ocr_with_region': + clean_results = str(results) + cleaned_string = re.sub(r'|<[^>]*>', '\n', clean_results) + clean_results = re.sub(r'\n+', '\n', cleaned_string) + else: + clean_results = str(results) + clean_results = clean_results.replace('', '') + clean_results = clean_results.replace('', '') + + #return single string if only one image for compatibility with nodes that can't handle string lists + if len(image) == 1: + out_results = clean_results + else: + out_results.append(clean_results) + + W, H = image_pil.size + + parsed_answer = processor.post_process_generation(results, task=task_prompt, image_size=(W, H)) + + if task == 'region_caption' or task == 'dense_region_caption' or task == 'caption_to_phrase_grounding' or task == 'region_proposal': + fig, ax = plt.subplots(figsize=(W / 100, H / 100), dpi=100) + fig.subplots_adjust(left=0, right=1, top=1, bottom=0) + ax.imshow(image_pil) + bboxes = parsed_answer[task_prompt]['bboxes'] + labels = parsed_answer[task_prompt]['labels'] + + mask_indexes = [] + # Determine mask indexes outside the loop + if output_mask_select != "": + mask_indexes = [n for n in output_mask_select.split(",")] + print(mask_indexes) + else: + mask_indexes = [str(i) for i in range(len(bboxes))] + + # Initialize mask_layer only if needed + if fill_mask: + mask_layer = Image.new('RGB', image_pil.size, (0, 0, 0)) + mask_draw = ImageDraw.Draw(mask_layer) + + for index, (bbox, label) in enumerate(zip(bboxes, labels)): + # Modify the label to include the index + indexed_label = f"{index}.{label}" + + if fill_mask: + # Ensure y1 is greater than or equal to y0 for mask drawing + x0, y0, x1, y1 = bbox[0], bbox[1], bbox[2], bbox[3] + if y1 < y0: + y0, y1 = y1, y0 + if x1 < x0: + x0, x1 = x1, x0 + + if str(index) in mask_indexes: + print("match index:", str(index), "in mask_indexes:", mask_indexes) + mask_draw.rectangle([x0, y0, x1, y1], fill=(255, 255, 255)) + if label in mask_indexes: + print("match label") + mask_draw.rectangle([x0, y0, x1, y1], fill=(255, 255, 255)) + + # Create a Rectangle patch + # Ensure y1 is greater than or equal to y0 + y0, y1 = bbox[1], bbox[3] + if y1 < y0: + y0, y1 = y1, y0 + + rect = patches.Rectangle( + (bbox[0], y0), # (x,y) - lower left corner + bbox[2] - bbox[0], # Width + y1 - y0, # Height + linewidth=1, + edgecolor='r', + facecolor='none', + label=indexed_label + ) + # Calculate text width with a rough estimation + text_width = len(label) * 6 # Adjust multiplier based on your font size + text_height = 12 # Adjust based on your font size + + # Get corrected coordinates + x0, y0, x1, y1 = bbox[0], bbox[1], bbox[2], bbox[3] + if y1 < y0: + y0, y1 = y1, y0 + if x1 < x0: + x0, x1 = x1, x0 + + # Initial text position + text_x = x0 + text_y = y0 - text_height # Position text above the top-left of the bbox + + # Adjust text_x if text is going off the left or right edge + if text_x < 0: + text_x = 0 + elif text_x + text_width > W: + text_x = W - text_width + + # Adjust text_y if text is going off the top edge + if text_y < 0: + text_y = y1 # Move text below the bottom-left of the bbox if it doesn't overlap with bbox + + # Add the rectangle to the plot + ax.add_patch(rect) + facecolor = random.choice(colormap) if len(image) == 1 else 'red' + # Add the label + plt.text( + text_x, + text_y, + indexed_label, + color='white', + fontsize=12, + bbox=dict(facecolor=facecolor, alpha=0.5) + ) + if fill_mask: + mask_tensor = F.to_tensor(mask_layer) + mask_tensor = mask_tensor.unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + mask_tensor = mask_tensor.mean(dim=0, keepdim=True) + mask_tensor = mask_tensor.repeat(1, 1, 1, 3) + mask_tensor = mask_tensor[:, :, :, 0] + out_masks.append(mask_tensor) + + # Remove axis and padding around the image + ax.axis('off') + ax.margins(0,0) + ax.get_xaxis().set_major_locator(plt.NullLocator()) + ax.get_yaxis().set_major_locator(plt.NullLocator()) + fig.canvas.draw() + buf = io.BytesIO() + plt.savefig(buf, format='png', pad_inches=0) + buf.seek(0) + annotated_image_pil = Image.open(buf) + + annotated_image_tensor = F.to_tensor(annotated_image_pil) + out_tensor = annotated_image_tensor[:3, :, :].unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + out.append(out_tensor) + + if task == 'caption_to_phrase_grounding': + out_data.append(parsed_answer[task_prompt]) + else: + out_data.append(bboxes) + + + pbar.update(1) + + plt.close(fig) + + elif task == 'referring_expression_segmentation': + # Create a new black image + mask_image = Image.new('RGB', (W, H), 'black') + mask_draw = ImageDraw.Draw(mask_image) + + predictions = parsed_answer[task_prompt] + + # Iterate over polygons and labels + for polygons, label in zip(predictions['polygons'], predictions['labels']): + color = random.choice(colormap) + for _polygon in polygons: + _polygon = np.array(_polygon).reshape(-1, 2) + # Clamp polygon points to image boundaries + _polygon = np.clip(_polygon, [0, 0], [W - 1, H - 1]) + if len(_polygon) < 3: + print('Invalid polygon:', _polygon) + continue + + _polygon = _polygon.reshape(-1).tolist() + + # Draw the polygon + if fill_mask: + overlay = Image.new('RGBA', image_pil.size, (255, 255, 255, 0)) + image_pil = image_pil.convert('RGBA') + draw = ImageDraw.Draw(overlay) + color_with_opacity = ImageColor.getrgb(color) + (180,) + draw.polygon(_polygon, outline=color, fill=color_with_opacity, width=3) + image_pil = Image.alpha_composite(image_pil, overlay) + else: + draw = ImageDraw.Draw(image_pil) + draw.polygon(_polygon, outline=color, width=3) + + #draw mask + mask_draw.polygon(_polygon, outline="white", fill="white") + + image_tensor = F.to_tensor(image_pil) + image_tensor = image_tensor[:3, :, :].unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + out.append(image_tensor) + + mask_tensor = F.to_tensor(mask_image) + mask_tensor = mask_tensor.unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + mask_tensor = mask_tensor.mean(dim=0, keepdim=True) + mask_tensor = mask_tensor.repeat(1, 1, 1, 3) + mask_tensor = mask_tensor[:, :, :, 0] + out_masks.append(mask_tensor) + pbar.update(1) + + elif task == 'ocr_with_region': + try: + font = ImageFont.load_default().font_variant(size=24) + except: + font = ImageFont.load_default() + predictions = parsed_answer[task_prompt] + scale = 1 + image_pil = image_pil.convert('RGBA') + overlay = Image.new('RGBA', image_pil.size, (255, 255, 255, 0)) + draw = ImageDraw.Draw(overlay) + bboxes, labels = predictions['quad_boxes'], predictions['labels'] + + # Create a new black image for the mask + mask_image = Image.new('RGB', (W, H), 'black') + mask_draw = ImageDraw.Draw(mask_image) + + for box, label in zip(bboxes, labels): + scaled_box = [v / (width if idx % 2 == 0 else height) for idx, v in enumerate(box)] + out_data.append({"label": label, "box": scaled_box}) + + color = random.choice(colormap) + new_box = (np.array(box) * scale).tolist() + + # Ensure polygon coordinates are valid + # For polygons, we need to make sure the points form a valid shape + # This is a simple check to ensure the polygon has at least 3 points + if len(new_box) >= 6: # At least 3 points (x,y pairs) + if fill_mask: + color_with_opacity = ImageColor.getrgb(color) + (180,) + draw.polygon(new_box, outline=color, fill=color_with_opacity, width=3) + else: + draw.polygon(new_box, outline=color, width=3) + + # Get the first point for text positioning + text_x, text_y = new_box[0]+8, new_box[1]+2 + + draw.text((text_x, text_y), + "{}".format(label), + align="right", + font=font, + fill=color) + + # Draw the mask + mask_draw.polygon(new_box, outline="white", fill="white") + + image_pil = Image.alpha_composite(image_pil, overlay) + image_pil = image_pil.convert('RGB') + + image_tensor = F.to_tensor(image_pil) + image_tensor = image_tensor[:3, :, :].unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + out.append(image_tensor) + + # Process the mask + mask_tensor = F.to_tensor(mask_image) + mask_tensor = mask_tensor.unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + mask_tensor = mask_tensor.mean(dim=0, keepdim=True) + mask_tensor = mask_tensor.repeat(1, 1, 1, 3) + mask_tensor = mask_tensor[:, :, :, 0] + out_masks.append(mask_tensor) + + pbar.update(1) + + elif task == 'docvqa': + if text_input == "": + raise ValueError("Text input (prompt) is required for 'docvqa'") + prompt = " " + text_input + + inputs = processor(text=prompt, images=image_pil, return_tensors="pt", do_rescale=False).to(dtype).to(device) + generated_ids = model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=max_new_tokens, + do_sample=do_sample, + num_beams=num_beams, + use_cache=False, + ) + + results = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + clean_results = results.replace('', '').replace('', '') + + if len(image) == 1: + out_results = clean_results + else: + out_results.append(clean_results) + + out.append(F.to_tensor(image_pil).unsqueeze(0).permute(0, 2, 3, 1).cpu().float()) + + pbar.update(1) + + if len(out) > 0: + out_tensor = torch.cat(out, dim=0) + else: + out_tensor = torch.zeros((1, 64,64, 3), dtype=torch.float32, device="cpu") + if len(out_masks) > 0: + out_mask_tensor = torch.cat(out_masks, dim=0) + else: + out_mask_tensor = torch.zeros((1,64,64), dtype=torch.float32, device="cpu") + + if not keep_model_loaded: + print("Offloading model...") + model.to(offload_device) + mm.soft_empty_cache() + + return (out_tensor, out_mask_tensor, out_results, out_data) + +NODE_CLASS_MAPPINGS = { + "DownloadAndLoadFlorence2Model": DownloadAndLoadFlorence2Model, + "DownloadAndLoadFlorence2Lora": DownloadAndLoadFlorence2Lora, + "Florence2ModelLoader": Florence2ModelLoader, + "Florence2Run": Florence2Run, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DownloadAndLoadFlorence2Model": "DownloadAndLoadFlorence2Model", + "DownloadAndLoadFlorence2Lora": "DownloadAndLoadFlorence2Lora", + "Florence2ModelLoader": "Florence2ModelLoader", + "Florence2Run": "Florence2Run", +} diff --git a/custom_nodes/ComfyUI-Florence2/processing_florence2.py b/custom_nodes/ComfyUI-Florence2/processing_florence2.py new file mode 100644 index 00000000..f368b710 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/processing_florence2.py @@ -0,0 +1,1053 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Florence-2. +""" + +import re +import logging +from typing import List, Optional, Union +import numpy as np + +import torch + +from transformers.feature_extraction_utils import BatchFeature +from transformers.image_utils import ImageInput, is_valid_image +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils_base import ( + PaddingStrategy, + PreTokenizedInput, + TextInput, + TruncationStrategy, +) +from transformers.utils import TensorType + + +logger = logging.getLogger(__name__) + +# Copied from transformers.models.idefics2.processing_idefics2.is_url +def is_url(val) -> bool: + return isinstance(val, str) and val.startswith("http") + +# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url +def is_image_or_image_url(elem): + return is_url(elem) or is_valid_image(elem) + + +def _is_str_or_image(elem): + return isinstance(elem, (str)) or is_image_or_image_url(elem) + + +class Florence2Processor(ProcessorMixin): + r""" + Constructs a Florence2 processor which wraps a Florence2 image processor and a Florence2 tokenizer into a single processor. + + [`Florence2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BartTokenizerFast`]. See the + [`~Florence2Processor.__call__`] and [`~Florence2Processor.decode`] for more information. + + Args: + image_processor ([`CLIPImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`BartTokenizerFast`], *optional*): + The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "CLIPImageProcessor" + tokenizer_class = ("BartTokenizer", "BartTokenizerFast") + + def __init__( + self, + image_processor=None, + tokenizer=None, + ): + if image_processor is None: + raise ValueError("You need to specify an `image_processor`.") + if tokenizer is None: + raise ValueError("You need to specify a `tokenizer`.") + if not hasattr(image_processor, "image_seq_length"): + raise ValueError("Image processor is missing an `image_seq_length` attribute.") + + self.image_seq_length = image_processor.image_seq_length + + # Get existing additional_special_tokens safely (works with both Roberta and BART tokenizers) + existing_special_tokens = list(getattr(tokenizer, 'additional_special_tokens', []) or []) + + tokens_to_add = { + 'additional_special_tokens': \ + existing_special_tokens + \ + ['', '', '', ''] + \ + [f'' for x in range(1000)] + \ + ['', '', '', '','', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''] + } + tokenizer.add_special_tokens(tokens_to_add) + + self.tasks_answer_post_processing_type = { + '': 'pure_text', + '': 'ocr', + '': 'pure_text', + '': 'pure_text', + '': 'pure_text', + '': 'description_with_bboxes', + '': 'description_with_bboxes', + '': "phrase_grounding", + '': 'polygons', + '': 'polygons', + '': 'description_with_bboxes_or_polygons', + '': 'pure_text', + '': 'pure_text', + '': 'pure_text', + '': 'bboxes' + } + + self.task_prompts_without_inputs = { + '': 'What is the text in the image?', + '': 'What is the text in the image, with regions?', + '': 'What does the image describe?', + '': 'Describe in detail what is shown in the image.', + '': 'Describe with a paragraph what is shown in the image.', + '': 'Locate the objects with category name in the image.', + '': 'Locate the objects in the image, with their descriptions.', + '': 'Locate the region proposals in the image.' + } + + self.task_prompts_with_input = { + '': "Locate the phrases in the caption: {input}", + '': 'Locate {input} in the image with mask', + '': 'What is the polygon mask of region {input}', + '': 'Locate {input} in the image.', + '': 'What is the region {input}?', + '': 'What does the region {input} describe?', + '': 'What text is in the region {input}?', + } + + self.post_processor = Florence2PostProcesser(tokenizer=tokenizer) + + + super().__init__(image_processor, tokenizer) + + def _construct_prompts(self, text): + # replace the task tokens with the task prompts if task token is in the text + prompts = [] + for _text in text: + # 1. fixed task prompts without additional inputs + for task_token, task_prompt in self.task_prompts_without_inputs.items(): + if task_token in _text: + assert _text == task_token, f"Task token {task_token} should be the only token in the text." + _text = task_prompt + break + # 2. task prompts with additional inputs + for task_token, task_prompt in self.task_prompts_with_input.items(): + if task_token in _text: + _text = task_prompt.format(input=_text.replace(task_token, '')) + break + prompts.append(_text) + return prompts + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + images: ImageInput = None, + tokenize_newline_separately: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length=None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + do_resize: bool = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821 + input_data_format: Optional[ + Union[str, "ChannelDimension"] # noqa: F821 + ] = None, + resample: "PILImageResampling" = None, # noqa: F821 + do_convert_rgb: bool = None, + do_thumbnail: bool = None, + do_align_long_axis: bool = None, + do_rescale: bool = None, + ) -> BatchFeature: + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to BartTokenizerFast's [`~BartTokenizerFast.__call__`] if `text` is not `None` to encode + the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to + CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring + of the above two methods for more information. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a + number of channels, H and W are image height and width. + tokenize_newline_separately (`bool`, defaults to `True`): + Adds a separately tokenized '\n' at the end of the prompt. + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`, *optional*): + Activates truncation to cut input sequences longer than `max_length` to `max_length`. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` + is provided, the `input_ids` will also contain the suffix input ids. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **labels** -- Labels compatible with training if `suffix` is not None + """ + + return_token_type_ids = False + + if images is None: + raise ValueError("`images` are expected as arguments to a `Florence2Processor` instance.") + if text is None: + logger.warning_once( + "You are using Florence-2 without a text prompt." + ) + text = "" + + if isinstance(text, List) and isinstance(images, List): + if len(images) < len(text): + raise ValueError( + f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image." + ) + if _is_str_or_image(text): + text = [text] + elif isinstance(text, list) and _is_str_or_image(text[0]): + pass + + pixel_values = self.image_processor( + images, + do_resize=do_resize, + do_normalize=do_normalize, + return_tensors=return_tensors, + image_mean=image_mean, + image_std=image_std, + input_data_format=input_data_format, + data_format=data_format, + resample=resample, + do_convert_rgb=do_convert_rgb, + )["pixel_values"] + + if max_length is not None: + max_length -= self.image_seq_length # max_length has to account for the image tokens + + text = self._construct_prompts(text) + + inputs = self.tokenizer( + text, + return_tensors=return_tensors, + padding=padding, + max_length=max_length, + truncation=truncation, + return_token_type_ids=return_token_type_ids, + ) + + return_data = {**inputs, "pixel_values": pixel_values} + + if return_token_type_ids: + labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100) + return_data.update({"labels": labels}) + return BatchFeature(data=return_data) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2 + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Florence2 + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Florence2 + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) + + def post_process_generation(self, text, task, image_size): + """ + Post-process the output of the model to each of the task outputs. + + Args: + text (`str`): The text to post-process. + task (`str`): The task to post-process the text for. + image_size (`Tuple[int, int]`): The size of the image. height x width. + """ + + task_answer_post_processing_type = self.tasks_answer_post_processing_type.get(task, 'pure_text') + task_answer = self.post_processor( + text=text, + image_size=image_size, + parse_tasks=task_answer_post_processing_type, + )[task_answer_post_processing_type] + + if task_answer_post_processing_type == 'pure_text': + final_answer = task_answer + # remove the special tokens + final_answer = final_answer.replace('', '').replace('', '') + elif task_answer_post_processing_type in ['od', 'description_with_bboxes', 'bboxes']: + od_instances = task_answer + bboxes_od = [_od_instance['bbox'] for _od_instance in od_instances] + labels_od = [str(_od_instance['cat_name']) for _od_instance in od_instances] + final_answer = {'bboxes': bboxes_od, 'labels': labels_od} + elif task_answer_post_processing_type in ['ocr']: + bboxes = [_od_instance['quad_box'] for _od_instance in task_answer] + labels = [str(_od_instance['text']) for _od_instance in task_answer] + final_answer = {'quad_boxes': bboxes, 'labels': labels} + elif task_answer_post_processing_type in ['phrase_grounding']: + bboxes = [] + labels = [] + for _grounded_phrase in task_answer: + for _bbox in _grounded_phrase['bbox']: + bboxes.append(_bbox) + labels.append(_grounded_phrase['cat_name']) + final_answer = {'bboxes': bboxes, 'labels': labels} + elif task_answer_post_processing_type in ['description_with_polygons', 'polygons']: + labels = [] + polygons = [] + for result in task_answer: + label = result['cat_name'] + _polygons = result['polygons'] + labels.append(label) + polygons.append(_polygons) + final_answer = {'polygons': polygons, 'labels': labels} + elif task_answer_post_processing_type in ['description_with_bboxes_or_polygons']: + bboxes = [] + bboxes_labels = [] + polygons = [] + polygons_labels = [] + for result in task_answer: + label = result['cat_name'] + if 'polygons' in result: + _polygons = result['polygons'] + polygons.append(_polygons) + polygons_labels.append(label) + else: + _bbox = result['bbox'] + bboxes.append(_bbox) + bboxes_labels.append(label) + final_answer = {'bboxes': bboxes, 'bboxes_labels': bboxes_labels, 'polygons': polygons, 'polygons_labels': polygons_labels} + else: + raise ValueError('Unknown task answer post processing type: {}'.format(task_answer_post_processing_type)) + + final_answer = { + task: final_answer} + return final_answer + +class BoxQuantizer(object): + def __init__(self, mode, bins): + self.mode = mode + self.bins = bins + + def quantize(self, boxes: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + quantized_xmin = ( + xmin / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_ymin = ( + ymin / size_per_bin_h).floor().clamp(0, bins_h - 1) + quantized_xmax = ( + xmax / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_ymax = ( + ymax / size_per_bin_h).floor().clamp(0, bins_h - 1) + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + quantized_boxes = torch.cat( + (quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1 + ).int() + + return quantized_boxes + + def dequantize(self, boxes: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + # Add 0.5 to use the center position of the bin as the coordinate. + dequantized_xmin = (xmin + 0.5) * size_per_bin_w + dequantized_ymin = (ymin + 0.5) * size_per_bin_h + dequantized_xmax = (xmax + 0.5) * size_per_bin_w + dequantized_ymax = (ymax + 0.5) * size_per_bin_h + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + dequantized_boxes = torch.cat( + (dequantized_xmin, dequantized_ymin, + dequantized_xmax, dequantized_ymax), dim=-1 + ) + + return dequantized_boxes + + +class CoordinatesQuantizer(object): + """ + Quantize coornidates (Nx2) + """ + + def __init__(self, mode, bins): + self.mode = mode + self.bins = bins + + def quantize(self, coordinates: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)' + x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + quantized_x = (x / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_y = (y / size_per_bin_h).floor().clamp(0, bins_h - 1) + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + quantized_coordinates = torch.cat( + (quantized_x, quantized_y), dim=-1 + ).int() + + return quantized_coordinates + + def dequantize(self, coordinates: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)' + x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + # Add 0.5 to use the center position of the bin as the coordinate. + dequantized_x = (x + 0.5) * size_per_bin_w + dequantized_y = (y + 0.5) * size_per_bin_h + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + dequantized_coordinates = torch.cat( + (dequantized_x, dequantized_y), dim=-1 + ) + + return dequantized_coordinates + + +class Florence2PostProcesser(object): + """ + Florence-2 post process for converting text prediction to various tasks results. + + Args: + config: A dict of configs. + tokenizer: A tokenizer for decoding text to spans. + sample config: + UNIFIED_POST_PROCESS: + # commom configs + NUM_BBOX_HEIGHT_BINS: 1000 + NUM_BBOX_WIDTH_BINS: 1000 + COORDINATES_HEIGHT_BINS: 1000 + COORDINATES_WIDTH_BINS: 1000 + # task specific configs, override the common configs + PRASE_TASKS: + - TASK_NAME: 'video_dense_caption' + PATTERN: 'r([a-zA-Z0-9 ]+)' + SCORE_MODE: 'avg_cat_name_scores' + NUM_BINS: 100 + - TASK_NAME: 'od' + PATTERN: 'r([a-zA-Z0-9 ]+)' + SCORE_MODE: 'avg_cat_name_scores' + + Returns: + parsed_dict (dict): A dict of parsed results. + """ + def __init__( + self, + tokenizer=None + ): + parse_tasks = [] + parse_task_configs = {} + config = self._create_default_config() + for task in config['PARSE_TASKS']: + parse_tasks.append(task['TASK_NAME']) + parse_task_configs[task['TASK_NAME']] = task + + self.config = config + self.parse_tasks = parse_tasks + self.parse_tasks_configs = parse_task_configs + + self.tokenizer = tokenizer + if self.tokenizer is not None: + self.all_special_tokens = set(self.tokenizer.all_special_tokens) + + self.init_quantizers() + self.black_list_of_phrase_grounding = self._create_black_list_of_phrase_grounding() + + def _create_black_list_of_phrase_grounding(self): + black_list = {} + + if 'phrase_grounding' in self.parse_tasks and self.parse_tasks_configs['phrase_grounding']['FILTER_BY_BLACK_LIST']: + black_list = set( + ['it', 'I', 'me', 'mine', + 'you', 'your', 'yours', + 'he', 'him', 'his', + 'she', 'her', 'hers', + 'they', 'them', 'their', 'theirs', + 'one', 'oneself', + 'we', 'us', 'our', 'ours', + 'you', 'your', 'yours', + 'they', 'them', 'their', 'theirs', + 'mine', 'yours', 'his', 'hers', 'its', + 'ours', 'yours', 'theirs', + 'myself', 'yourself', 'himself', 'herself', 'itself', + 'ourselves', 'yourselves', 'themselves', + 'this', 'that', + 'these', 'those', + 'who', 'whom', 'whose', 'which', 'what', + 'who', 'whom', 'whose', 'which', 'that', + 'all', 'another', 'any', 'anybody', 'anyone', 'anything', + 'each', 'everybody', 'everyone', 'everything', + 'few', 'many', 'nobody', 'none', 'one', 'several', + 'some', 'somebody', 'someone', 'something', + 'each other', 'one another', + 'myself', 'yourself', 'himself', 'herself', 'itself', + 'ourselves', 'yourselves', 'themselves', + 'the image', 'image', 'images', 'the', 'a', 'an', 'a group', + 'other objects', 'lots', 'a set', + ] + ) + + return black_list + + def _create_default_config(self): + config = { + 'NUM_BBOX_HEIGHT_BINS': 1000, + 'NUM_BBOX_WIDTH_BINS': 1000, + 'BOX_QUANTIZATION_MODE': 'floor', + 'COORDINATES_HEIGHT_BINS': 1000, + 'COORDINATES_WIDTH_BINS': 1000, + 'COORDINATES_QUANTIZATION_MODE': 'floor', + 'PARSE_TASKS': [ + { + 'TASK_NAME': 'od', + 'PATTERN': r'([a-zA-Z0-9 ]+)' + }, + { + 'TASK_NAME': 'ocr', + 'PATTERN': r'(.+?)', + 'AREA_THRESHOLD': 0.00 + }, + { + 'TASK_NAME': 'phrase_grounding', + 'FILTER_BY_BLACK_LIST': True + }, + { + 'TASK_NAME': 'pure_text', + }, + { + 'TASK_NAME': 'description_with_bboxes', + }, + { + 'TASK_NAME': 'description_with_polygons', + }, + { + 'TASK_NAME': 'polygons', + }, + { + 'TASK_NAME': 'bboxes', + }, + { + 'TASK_NAME': 'description_with_bboxes_or_polygons', + } + ] + } + + return config + + def init_quantizers(self): + # we have box_quantizer (od, grounding) and coordinates_quantizer (ocr, referring_segmentation) + num_bbox_height_bins = self.config.get('NUM_BBOX_HEIGHT_BINS', 1000) + num_bbox_width_bins = self.config.get('NUM_BBOX_WIDTH_BINS', 1000) + box_quantization_mode = self.config.get('BOX_QUANTIZATION_MODE', 'floor') + self.box_quantizer = BoxQuantizer( + box_quantization_mode, + (num_bbox_width_bins, num_bbox_height_bins), + ) + + num_bbox_height_bins = self.config['COORDINATES_HEIGHT_BINS'] if 'COORDINATES_HEIGHT_BINS' in self.config else self.config.get('NUM_BBOX_HEIGHT_BINS', 1000) + num_bbox_width_bins = self.config['COORDINATES_WIDTH_BINS'] if 'COORDINATES_WIDTH_BINS' in self.config else self.config.get('NUM_BBOX_WIDTH_BINS', 1000) + box_quantization_mode = self.config.get('COORDINATES_QUANTIZATION_MODE') if 'COORDINATES_QUANTIZATION_MODE' in self.config else self.config.get('BOX_QUANTIZATION_MODE', 'floor') + self.coordinates_quantizer = CoordinatesQuantizer( + box_quantization_mode, + (num_bbox_width_bins, num_bbox_height_bins), + ) + + + def parse_od_from_text_and_spans( + self, + text, + pattern, + image_size, + phrase_centric=False + ): + parsed = list(re.finditer(pattern, text)) + + instances = [] + for i in range(len(parsed)): + # Prepare instance. + instance = {} + + if phrase_centric: + bbox_bins = [int(parsed[i].group(j)) for j in range(2, 6)] + else: + bbox_bins = [int(parsed[i].group(j)) for j in range(1, 5)] + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + if phrase_centric: + instance['cat_name'] = parsed[i].group(1).lower().strip() + else: + instance['cat_name'] = parsed[i].group(5).lower().strip() + instances.append(instance) + + return instances + + def parse_ocr_from_text_and_spans(self, + text, + pattern, + image_size, + area_threshold=-1.0, + ): + bboxes = [] + labels = [] + text = text.replace('', '') + # ocr with regions + parsed = re.findall(pattern, text) + instances = [] + image_width, image_height = image_size + + for ocr_line in parsed: + ocr_content = ocr_line[0] + quad_box = ocr_line[1:] + quad_box = [int(i) for i in quad_box] + quad_box = self.coordinates_quantizer.dequantize( + torch.tensor(np.array(quad_box).reshape(-1, 2)), + size=image_size + ).reshape(-1).tolist() + + if area_threshold > 0: + x_coords = [i for i in quad_box[0::2]] + y_coords = [i for i in quad_box[1::2]] + + # apply the Shoelace formula + area = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i] for i in range(4 - 1))) + + if area < (image_width * image_height) * area_threshold: + continue + + bboxes.append(quad_box) + labels.append(ocr_content) + instances.append({ + 'quad_box': quad_box, + 'text': ocr_content, + }) + return instances + + def parse_phrase_grounding_from_text_and_spans(self, text, pattern, image_size): + # ignore and + cur_span = 0 + if text.startswith(''): + cur_span += 3 + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + pattern = r"([^<]+(?:){4,})" + phrases = re.findall(pattern, text) + + # pattern should be text pattern and od pattern + pattern = r'^\s*(.*?)(?=||||||' + + instances = [] + for pharse_text in phrases: + phrase_text_strip = pharse_text.replace('', '', 1) + phrase_text_strip = pharse_text.replace('', '', 1) + + if phrase_text_strip == '': + cur_span += len(pharse_text) + continue + + # Prepare instance. + instance = {} + + # parse phrase, get string + phrase = re.search(pattern, phrase_text_strip) + if phrase is None: + cur_span += len(pharse_text) + continue + + # parse bboxes by box_pattern + bboxes_parsed = list(re.finditer(box_pattern, pharse_text)) + if len(bboxes_parsed) == 0: + cur_span += len(pharse_text) + continue + + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + if phrase in self.black_list_of_phrase_grounding: + cur_span += len(pharse_text) + continue + + # a list of list + bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed] + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + # exclude non-ascii characters + phrase = phrase.encode('ascii',errors='ignore').decode('ascii') + instance['cat_name'] = phrase + + instances.append(instance) + + return instances + + def parse_description_with_bboxes_from_text_and_spans(self, text, pattern, image_size, allow_empty_phrase=False): + # temporary parse solution, split by '.' + # ignore and + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + if allow_empty_phrase: + pattern = r"(?:(?:){{4,}})" + else: + pattern = r"([^<]+(?:){4,})" + phrases = re.findall(pattern, text) + + # pattern should be text pattern and od pattern + pattern = r'^\s*(.*?)(?=||||||' + + instances = [] + for pharse_text in phrases: + phrase_text_strip = pharse_text.replace('', '', 1) + phrase_text_strip = pharse_text.replace('', '', 1) + + if phrase_text_strip == '' and not allow_empty_phrase: + continue + + # parse phrase, get string + phrase = re.search(pattern, phrase_text_strip) + if phrase is None: + continue + + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + # parse bboxes by box_pattern + bboxes_parsed = list(re.finditer(box_pattern, pharse_text)) + if len(bboxes_parsed) == 0: + continue + + # a list of list + bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed] + + bboxes = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + phrase = phrase.encode('ascii',errors='ignore').decode('ascii') + for _bboxes in bboxes: + # Prepare instance. + instance = {} + instance['bbox'] = _bboxes + # exclude non-ascii characters + instance['cat_name'] = phrase + instances.append(instance) + + return instances + + def parse_description_with_polygons_from_text_and_spans(self, text, pattern, image_size, + allow_empty_phrase=False, + polygon_sep_token='', + polygon_start_token='', + polygon_end_token='', + with_box_at_start=False, + ): + + # ref_seg format: '<><><><><><>' + # ignore and + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + if allow_empty_phrase: + pattern = rf"(?:(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})" + else: + # [^<]+: This part matches one or more characters that are not the < symbol. + # The ^ inside the square brackets [] is a negation, meaning it matches anything except <. + # + pattern = rf"([^<]+(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})" + phrases = re.findall(pattern, text) + + phrase_string_pattern = r'^\s*(.*?)(?=||||||)' + box_pattern = rf'((?:)+)(?:{re.escape(polygon_sep_token)}|$)' + + # one polygons instance is separated by polygon_start_token and polygon_end_token + polygons_instance_pattern = rf'{re.escape(polygon_start_token)}(.*?){re.escape(polygon_end_token)}' + + instances = [] + for phrase_text in phrases: + + # exclude loc_\d+> + # need to get span if want to include category score + phrase_text_strip = re.sub(r'^loc_\d+>', '', phrase_text, count=1) + + # phrase = phrase.replace('', '') + # phrase = phrase.replace('poly>', '') + + if phrase_text_strip == '' and not allow_empty_phrase: + continue + + + # parse phrase, get string + phrase = re.search(phrase_string_pattern, phrase_text_strip) + if phrase is None: + continue + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + # parse bboxes by box_pattern + + # split by polygon_start_token and polygon_end_token first using polygons_instance_pattern + if polygon_start_token in phrase_text and polygon_end_token in phrase_text: + polygons_instances_parsed = list(re.finditer(polygons_instance_pattern, phrase_text)) + else: + polygons_instances_parsed = [phrase_text] + + for _polygons_instances_parsed in polygons_instances_parsed: + # Prepare instance. + instance = {} + + # polygons_parsed= list(re.finditer(box_pattern, phrase_text)) + if isinstance(_polygons_instances_parsed, str): + polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed)) + else: + polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed.group(1))) + if len(polygons_parsed) == 0: + continue + + # a list of list (polygon) + bbox = [] + polygons = [] + for _polygon_parsed in polygons_parsed: + # group 1: whole ... + _polygon = _polygon_parsed.group(1) + # parse into list of int + _polygon = [int(_loc_parsed.group(1)) for _loc_parsed in re.finditer(r'', _polygon)] + if with_box_at_start and len(bbox) == 0: + if len(_polygon) > 4: + # no valid bbox prediction + bbox = _polygon[:4] + _polygon = _polygon[4:] + else: + bbox = [0, 0, 0, 0] + # abandon last element if is not paired + if len(_polygon) % 2 == 1: + _polygon = _polygon[:-1] + + # reshape into (n, 2) + _polygon = self.coordinates_quantizer.dequantize( + torch.tensor(np.array(_polygon).reshape(-1, 2)), + size=image_size + ).reshape(-1).tolist() + # reshape back + polygons.append(_polygon) + + instance['cat_name'] = phrase + instance['polygons'] = polygons + if len(bbox) != 0: + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor([bbox]), + size=image_size + ).tolist()[0] + + instances.append(instance) + + return instances + + def __call__( + self, + text=None, + image_size=None, + parse_tasks=None, + ): + """ + Args: + text: model outputs + image_size: (width, height) + parse_tasks: a list of tasks to parse, if None, parse all tasks. + + """ + if parse_tasks is not None: + if isinstance(parse_tasks, str): + parse_tasks = [parse_tasks] + for _parse_task in parse_tasks: + assert _parse_task in self.parse_tasks, f'parse task {_parse_task} not supported' + + # sequence or text should be provided + assert text is not None, 'text should be provided' + + parsed_dict = { + 'text': text + } + + for task in self.parse_tasks: + if parse_tasks is not None and task not in parse_tasks: + continue + + pattern = self.parse_tasks_configs[task].get('PATTERN', None) + + if task == 'ocr': + instances = self.parse_ocr_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + area_threshold=self.parse_tasks_configs[task].get('AREA_THRESHOLD', 0.0), + ) + parsed_dict['ocr'] = instances + elif task == 'phrase_grounding': + instances = self.parse_phrase_grounding_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['phrase_grounding'] = instances + elif task == 'pure_text': + parsed_dict['pure_text'] = text + elif task == 'description_with_bboxes': + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_bboxes'] = instances + elif task == 'description_with_polygons': + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_polygons'] = instances + elif task == 'polygons': + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + allow_empty_phrase=True, + ) + parsed_dict['polygons'] = instances + elif task == 'bboxes': + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + allow_empty_phrase=True, + ) + parsed_dict['bboxes'] = instances + elif task == 'description_with_bboxes_or_polygons': + if '' in text: + # only support either polygons or bboxes, not both at the same time + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + else: + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_bboxes_or_polygons'] = instances + else: + raise ValueError("task {} is not supported".format(task)) + + return parsed_dict diff --git a/custom_nodes/ComfyUI-Florence2/pyproject.toml b/custom_nodes/ComfyUI-Florence2/pyproject.toml new file mode 100644 index 00000000..ff081658 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-florence2" +description = "Nodes to use Florence2 VLM for image vision tasks: object detection, captioning, segmentation and ocr" +version = "1.0.8" +license = "MIT" +dependencies = ["transformers>=4.39.0,!=4.50.*"] + +[project.urls] +Repository = "https://github.com/kijai/ComfyUI-Florence2" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "kijai" +DisplayName = "ComfyUI-Florence2" +Icon = "" diff --git a/custom_nodes/ComfyUI-Florence2/requirements.txt b/custom_nodes/ComfyUI-Florence2/requirements.txt new file mode 100644 index 00000000..73e89b54 --- /dev/null +++ b/custom_nodes/ComfyUI-Florence2/requirements.txt @@ -0,0 +1,6 @@ +transformers>=4.39.0,!=4.50.* +matplotlib +timm +pillow>=10.2.0 +peft +accelerate>=0.26.0 diff --git a/custom_nodes/ComfyUI-Impact-Pack/.gitignore b/custom_nodes/ComfyUI-Impact-Pack/.gitignore new file mode 100644 index 00000000..4282eafb --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/.gitignore @@ -0,0 +1,12 @@ +__pycache__ +*.ini +wildcards/** +.vscode/ +.idea/ +subpack +impact_subpack +*.txt +*.yaml +!requirements.txt +!LICENSE.txt +.claude/ \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Impact-Pack/LICENSE.txt b/custom_nodes/ComfyUI-Impact-Pack/LICENSE.txt new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/LICENSE.txt @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/ComfyUI-Impact-Pack/README.md b/custom_nodes/ComfyUI-Impact-Pack/README.md new file mode 100644 index 00000000..5832a2b7 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/README.md @@ -0,0 +1,519 @@ +[![Youtube Badge](https://img.shields.io/badge/Youtube-FF0000?style=for-the-badge&logo=Youtube&logoColor=white&link=https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)](https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP) + +# ComfyUI-Impact-Pack + +**Custom node pack for ComfyUI** +This node pack helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more. + +NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pack. To use the UltralyticsDetectorProvider node, please install the ComfyUI-Impact-Subpack separately. + +## NOTICE +* V8.24: This compatibility patch requires ComfyUI version 0.3.63 or higher due to structural changes in DifferentialDiffusion. +* V8.19: legacy nodes (mmdet and etc.) are removed +* V8.18: Support [facebookresearch/sam2](https://github.com/facebookresearch/sam2) models +* V8.0: The `Impact Subpack` is no longer installed automatically. To use `UltralyticsDetectorProvider` nodes, please install the `Impact Subpack` separately. +* V7.6: Automatic installation is no longer supported. Please install using ComfyUI-Manager, or manually install requirements.txt and run install.py to complete the installation. +* V7.0: Supports Switch based on Execution Model Inversion. +* V6.0: Supports FLUX.1 model in Impact KSampler, Detailers, PreviewBridgeLatent +* V5.0: It is no longer compatible with versions of ComfyUI before 2024.04.08. +* V4.87.4: Update to a version of ComfyUI after 2024.04.08 for proper functionality. +* V4.85: Incompatible with the outdated **ComfyUI IPAdapter Plus**. (A version dated March 24th or later is required.) +* V4.77: Compatibility patch applied. Requires ComfyUI version (Oct. 8th) or later. +* V4.73.3: ControlNetApply (SEGS) supports AnimateDiff +* V4.20.1: Due to the feature update in `RegionalSampler`, the parameter order has changed, causing malfunctions in previously created `RegionalSamplers`. Please adjust the parameters accordingly. +* V4.12: `MASKS` is changed to `MASK`. +* V4.7.2 isn't compatible with old version of `ControlNet Auxiliary Preprocessor`. If you will use `MediaPipe FaceMesh to SEGS` update to latest version(Sep. 17th). +* Selection weight syntax is changed(: -> ::) since V3.16. ([tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md)) +* Starting from V3.6, requires latest version(Aug 8, 9ccc965) of ComfyUI. +* **In versions below V3.3.1, there was an issue with the image quality generated after using the UltralyticsDetectorProvider. Please make sure to upgrade to a newer version.** +* Starting from V3.0, nodes related to `mmdet` are optional nodes that are activated only based on the configuration settings. + - Through ComfyUI-Impact-Subpack, you can utilize UltralyticsDetectorProvider to access various detection models. +* Between versions 2.22 and 2.21, there is partial compatibility loss regarding the Detailer workflow. If you continue to use the existing workflow, errors may occur during execution. An additional output called "enhanced_alpha_list" has been added to Detailer-related nodes. +* The permission error related to cv2 that occurred during the installation of Impact Pack has been patched in version 2.21.4. However, please note that the latest versions of ComfyUI and ComfyUI-Manager are required. +* The "PreviewBridge" feature may not function correctly on ComfyUI versions released before July 1, 2023. +* Attempting to load the "ComfyUI-Impact-Pack" on ComfyUI versions released before June 27, 2023, will result in a failure. +* With the addition of wildcard support in FaceDetailer, the structure of DETAILER_PIPE-related nodes and Detailer nodes has changed. There may be malfunctions when using the existing workflow. + + +## How To Install + +### **Recommended** +* Install via [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager). + +### **Manual** +* Navigate to `ComfyUI/custom_nodes` in your terminal (cmd). +* Clone the repository under the `custom_nodes` directory using the following command: + ``` + git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack comfyui-impact-pack + cd comfyui-impact-pack + ``` +* Install dependencies in your Python environment. + * For Windows Portable, run the following command inside `ComfyUI\custom_nodes\comfyui-impact-pack`: + ``` + ..\..\..\python_embeded\python.exe -m pip install -r requirements.txt + ``` + * If using venv or conda, activate your Python environment first, then run: + ``` + pip install -r requirements.txt + ``` + +### Companion Pack +* If you need the `Ultralytics Detector Provider` to use various YOLO detection models, you should also install [ComfyUI-Impact-Subpack](https://github.com/ltdrdata/ComfyUI-Impact-Subpack). + + +## Custom Nodes +### [Detector nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md) + * `SAMLoader (Impact)` - Loads the SAM model. + * `ONNXDetectorProvider` - Loads the ONNX model to provide BBOX_DETECTOR. + * `CLIPSegDetectorProvider` - Wrapper for CLIPSeg to provide BBOX_DETECTOR. + * You need to install the ComfyUI-CLIPSeg node extension. + * `SEGM Detector (combined)` - Detects segmentation and returns a mask from the input image. + * `BBOX Detector (combined)` - Detects bounding boxes and returns a mask from the input image. + * `SAMDetector (combined)` - Utilizes the SAM technology to extract the segment at the location indicated by the input SEGS on the input image and outputs it as a unified mask. + * `SAMDetector (Segmented)` - It is similar to `SAMDetector (combined)`, but it separates and outputs the detected segments. Multiple segments can be found for the same detected area, and currently, a policy is in place to group them arbitrarily in sets of three. This aspect is expected to be improved in the future. + * As a result, it outputs the `combined_mask`, which is a unified mask, and `batch_masks`, which are multiple masks grouped together in batch form. + * While `batch_masks` may not be completely separated, it provides functionality to perform some level of segmentation. + * `Simple Detector (SEGS)` - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow. + * `Simple Detector for Video (SEGS)` – Performs detection on videos composed of image frames. Instead of using a single mask, it performs detection individually on each image frame and generates a SEGS object with a batch of masks. + * `SAM2 Video Detector (SEGS)` – Similar to `Simple Detector for Video (SEGS)`, but utilizes SAM2’s video tracking technology to generate a SEGS object with a batch of masks. + * To use this node, you must select a SAM2 model in the SAMLoader. + + +### ControlNet, IPAdapter + * `ControlNetApply (SEGS)` - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node. + * `segs_preprocessor` and `control_image` can be selectively applied. If a `control_image` is given, `segs_preprocessor` will be ignored. + * If set to `control_image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `segs_preprocessor` should be verified through the `cnet_images` output of each Detailer. + * The `segs_preprocessor` operates by applying preprocessing on-the-fly based on the cropped image during the detailing process, while `control_image` will be cropped and used as input to `ControlNetApply (SEGS)`. + * `ControlNetClear (SEGS)` - Clear applied ControlNet in SEGS + * `IPAdapterApply (SEGS)` - To apply IPAdapter in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node. + + +### Mask operation + * `Pixelwise(SEGS & SEGS)` - Performs a 'pixelwise and' operation between two SEGS. + * `Pixelwise(SEGS - SEGS)` - Subtracts one SEGS from another. + * `Pixelwise(SEGS & MASK)` - Performs a pixelwise AND operation between SEGS and MASK. + * `Pixelwise(SEGS & MASKS ForEach)` - Performs a pixelwise AND operation between SEGS and MASKS. + * Please note that this operation is performed with batches of MASKS, not just a single MASK. + * `Pixelwise(MASK & MASK)` - Performs a 'pixelwise and' operation between two masks. + * `Pixelwise(MASK - MASK)` - Subtracts one mask from another. + * `Pixelwise(MASK + MASK)` - Combine two masks. + * `SEGM Detector (SEGS)` - Detects segmentation and returns SEGS from the input image. + * `BBOX Detector (SEGS)` - Detects bounding boxes and returns SEGS from the input image. + * `Dilate Mask` - Dilate Mask. + * Support erosion for negative value. + * `Gaussian Blur Mask` - Apply Gaussian Blur to Mask. You can utilize this for mask feathering. + * `Mask Rect Area` - Create a rectangular mask defined by percentages with preview canvas. + * `Mask Rect Area (Advanced)` - Create a rectangular mask defined by pixels and image size. + + +### [Detailer nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detailers.md) + * `Detailer (SEGS)` - Refines the image based on SEGS. + * `Detailer (SEGS) with auto retry` - Refines the image based on SEGS and will automatically retry if the patch is all black. + * `DetailerDebug (SEGS)` - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image. + * To prevent regeneration caused by the seed that does not change every time when using 'external_seed', please disable the 'seed random generate' option in the 'Detailer...' node. + * `MASK to SEGS` - Generates SEGS based on the mask. + * `MASK to SEGS For Video` - Generates SEGS based on the mask for Video. (Renamed from `MASK to SEGS For AnimateDiff`) + * When using a single mask, convert it to SEGS to apply it to the entire frame. + * When using a batch mask, the contour fill feature is disabled. + * `MediaPipe FaceMesh to SEGS` - Separate each landmark from the mediapipe facemesh image to create labeled SEGS. + * Usually, the size of images created through the MediaPipe facemesh preprocessor is downscaled. It resizes the MediaPipe facemesh image to the original size given as reference_image_opt for matching sizes during processing. + * `ToBinaryMask` - Separates the mask generated with alpha values between 0 and 255 into 0 and 255. The non-zero parts are always set to 255. + * `Masks to Mask List` - This node converts the MASKS in batch form to a list of individual masks. + * `Mask List to Masks` - This node converts the MASK list to MASK batch form. + * `EmptySEGS` - Provides an empty SEGS. + * `MaskPainter` - Provides a feature to draw masks. + * `FaceDetailer` - Easily detects faces and improves them. + * `FaceDetailer (pipe)` - Easily detects faces and improves them (for multipass). + * `MaskDetailer (pipe)` - This is a simple inpaint node that applies the Detailer to the mask area. + + * `FromDetailer (SDXL/pipe)`, `BasicPipe -> DetailerPipe (SDXL)`, `Edit DetailerPipe (SDXL)` - These are pipe functions used in Detailer for utilizing the refiner model of SDXL. + * `Any PIPE -> BasicPipe` - Convert the PIPE Value of other custom nodes that are not BASIC_PIPE but internally have the same structure as BASIC_PIPE to BASIC_PIPE. If an incompatible type is applied, it may cause runtime errors. + + +### SEGS Manipulation nodes + * `SEGSDetailer` - Performs detailed work on SEGS without pasting it back onto the original image. + * `SEGSPaste` - Pastes the results of SEGS onto the original image. + * If `ref_image_opt` is present, the images contained within SEGS are ignored. Instead, the image within `ref_image_opt` corresponding to the crop area of SEGS is taken and pasted. The size of the image in `ref_image_opt` should be the same as the original image size. + * This node can be used in conjunction with the processing results of AnimateDiff. + * `SEGSPreview` - Provides a preview of SEGS. + * This option is used to preview the improved image through `SEGSDetailer` before merging it into the original. Prior to going through ```SEGSDetailer```, SEGS only contains mask information without image information. If fallback_image_opt is connected to the original image, SEGS without image information will generate a preview using the original image. However, if SEGS already contains image information, fallback_image_opt will be ignored. + * This node can be used in conjunction with the processing results of AnimateDiff. + * `SEGSPreview (CNET Image)` - Show images configured with `ControlNetApply (SEGS)` for debugging purposes. + * `SEGSToImageList` - Convert SEGS To Image List + * `SEGSToMaskList` - Convert SEGS To Mask List + * `SEGS Filter (label)` - This node filters SEGS based on the label of the detected areas. + * `SEGS Filter (ordered)` - This node sorts SEGS based on size and position and retrieves SEGs within a certain range. + * `SEGS Filter (range)` - This node retrieves only SEGs from SEGS that have a size and position within a certain range. + * `SEGS Filter (non max suppression)` - This node filters SEGS by removing those with high overlap based on the Intersection over Union (IoU) threshold, keeping only the most confident detections. + * `SEGS Filter (intersection)` - This node filters segs1, keeping only the SEGS that do not significantly overlap with any SEGS in segs2, based on the Intersection over Area (IoA) threshold. + * `SEGS Assign (label)` - Assign labels sequentially to SEGS. This node is useful when used with `[LAB]` of FaceDetailer. + * `SEGSConcat` - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different from segs2 will be ignored. + * `SEGS Merge` - SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed. + * `Picker (SEGS)` - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates. + * `Set Default Image For SEGS` - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved. + * `Remove Image from SEGS` - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS. + * `Make Tile SEGS` - [experimental] Create SEGS in the form of tiles from an image to facilitate experiments for Tiled Upscale using the Detailer. + * The `filter_in_segs_opt` and `filter_out_segs_opt` are optional inputs. If these inputs are provided, when creating the tiles, the mask for each tile is generated by overlapping with the mask of `filter_in_segs_opt` and excluding the overlap with the mask of `filter_out_segs_opt`. Tiles with an empty mask will not be created as SEGS. + * `Dilate Mask (SEGS)` - Dilate/Erosion Mask in SEGS + * `Gaussian Blur Mask (SEGS)` - Apply Gaussian Blur to Mask in SEGS + * `SEGS_ELT Manipulation` - experimental nodes + * `DecomposeSEGS` - Decompose SEGS to allow for detailed manipulation. + * `AssembleSEGS` - Reassemble the decomposed SEGS. + * `From SEG_ELT` - Extract detailed information from SEG_ELT. + * `Edit SEG_ELT` - Modify some of the information in SEG_ELT. + * `Dilate SEG_ELT` - Dilate the mask of SEG_ELT. + * `From SEG_ELT` bbox - Extract coordinate from bbox in SEG_ELT + * `From SEG_ELT` crop_region - Extract coordinate from crop_region in SEG_ELT + * `Count Elt in SEGS` - Number of Elts ins SEGS + + +### Pipe nodes + * `ToDetailerPipe`, `FromDetailerPipe` - These nodes are used to bundle multiple inputs used in the detailer, such as models and vae, ..., into a single DETAILER_PIPE or extract the elements that are bundled in the DETAILER_PIPE. + * `ToBasicPipe`, `FromBasicPipe` - These nodes are used to bundle model, clip, vae, positive conditioning, and negative conditioning into a single BASIC_PIPE, or extract each element from the BASIC_PIPE. + * `EditBasicPipe`, `EditDetailerPipe` - These nodes are used to replace some elements in BASIC_PIPE or DETAILER_PIPE. + * `FromDetailerPipe_v2`, `FromBasicPipe_v2` - It has the same functionality as `FromDetailerPipe` and `FromBasicPipe`, but it has an additional output that directly exports the input pipe. It is useful when editing EditBasicPipe and EditDetailerPipe. +* `Latent Scale (on Pixel Space)` - This node converts latent to pixel space, upscales it, and then converts it back to latent. + * If upscale_model_opt is provided, it uses the model to upscale the pixel and then downscales it using the interpolation method provided in scale_method to the target resolution. +* `PixelKSampleUpscalerProvider` - An upscaler is provided that converts latent to pixels using VAEDecode, performs upscaling, converts back to latent using VAEEncode, and then performs k-sampling. This upscaler can be attached to nodes such as `Iterative Upscale` for use. + * Similar to `Latent Scale (on Pixel Space)`, if upscale_model_opt is provided, it performs pixel upscaling using the model. +* `PixelTiledKSampleUpscalerProvider` - It is similar to `PixelKSampleUpscalerProvider`, but it uses `ComfyUI_TiledKSampler` and Tiled VAE Decoder/Encoder to avoid GPU VRAM issues at high resolutions. + * You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension. + + +### PK_HOOK + * `DenoiseScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the denoise to target_denoise as the iterative-step progresses. + * `CfgScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the cfg to target_cfg as the iterative-step progresses. + * `StepsScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the sampling-steps to target_steps as the iterative-step progresses. + * `NoiseInjectionHookProvider` - During each iteration of IterativeUpscale, noise is injected into the latent space while varying the strength according to a schedule. + * You need to install the [BlenderNeko/ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) node extension. + * The seed serves as the initial value required for generating noise, and it increments by 1 with each iteration as the process unfolds. + * The source determines the types of CPU noise and GPU noise to be configured. + * Currently, there is only a simple schedule available, where the strength of the noise varies from start_strength to end_strength during the progression of each iteration. + * `UnsamplerHookProvider` - Apply Unsampler during each iteration. To use this node, ComfyUI_Noise must be installed. + * `PixelKSampleHookCombine` - This is used to connect two PK_HOOKs. hook1 is executed first and then hook2 is executed. + * If you want to simultaneously change cfg and denoise, you can combine the PK_HOOKs of CfgScheduleHookProvider and PixelKSampleHookCombine. + + +### DETAILER_HOOK + * `NoiseInjectionDetailerHookProvider` - The `detailer_hook` is a hook in the `Detailer` that injects noise during the processing of each SEGS. + * `UnsamplerDetailerHookProvider` - Apply Unsampler during each cycle. To use this node, ComfyUI_Noise must be installed. + * `DenoiseSchedulerDetailerHookProvider` - During the progress of the cycle, the detailer's denoise is altered up to the `target_denoise`. + * `CoreMLDetailerHookProvider` - CoreML supports only 512x512, 512x768, 768x512, 768x768 size sampling. CoreMLDetailerHookProvider precisely fixes the upscale of the crop_region to this size. When using this hook, it will always be selected size, regardless of the guide_size. However, if the guide_size is too small, skipping will occur. + * `DetailerHookCombine` - This is used to connect two DETAILER_HOOKs. Similar to PixelKSampleHookCombine. + * `SEGSOrderedFilterDetailerHook`, SEGSRangeFilterDetailerHook, SEGSLabelFilterDetailerHook - There are a wrapper node that provides SEGSFilter nodes to be applied in FaceDetailer or Detector by creating DETAILER_HOOK. + * `PreviewDetailerHook` - Connecting this hook node helps provide assistance for viewing previews whenever SEGS Detailing tasks are completed. When working with a large number of SEGS, such as Make Tile SEGS, it allows for monitoring the situation as improvements progress incrementally. + * Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`. + * `VariationNoiseDetailerHookProvider` - Apply variation seed to the detailer. It can be applied in multiple stages through combine. + * `CustomSamplerDetailerHookProvider` - Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied. + * `LamaRemoverDetailerHookProvider` – Applies Lama Remover to the upscaled image during the detailing stage. If `skip_sampling` is set to True, Lama Remover can be used alone without the detailing stage, allowing it to simply remove detected regions. + * Not applicable for **AnimateDiff** detailers. When using `DetailerHookCombine`, `skip_sampling` is only applied if it is set to `True` for all hooks. + * To use this node, the node pack at [Layer-norm/comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) must be installed. + + +### Iterative Upscale nodes + * `Iterative Upscale (Latent/on Pixel Space)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. + This takes latent as input and outputs latent as the result. + * `Iterative Upscale (Image)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. This takes image as input and outputs image as the result. + * Internally, this node uses 'Iterative Upscale (Latent)'. + + +### TwoSamplers nodes +* `TwoSamplersForMask` - This node can apply two samplers depending on the mask area. The base_sampler is applied to the area where the mask is 0, while the mask_sampler is applied to the area where the mask is 1. + * Note: The latent encoded through VAEEncodeForInpaint cannot be used. +* `KSamplerProvider` - This is a wrapper that enables KSampler to be used in TwoSamplersForMask TwoSamplersForMaskUpscalerProvider. +* `TiledKSamplerProvider` - ComfyUI_TiledKSampler is a wrapper that provides KSAMPLER. + * You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension. + +* `TwoAdvancedSamplersForMask` - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step. +* `KSamplerAdvancedProvider` - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask, RegionalSampler. + * sigma_factor: By multiplying the denoise schedule by the sigma_factor, you can adjust the amount of denoising based on the configured denoise. + +* `TwoSamplersForMaskUpscalerProvider` - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale. + * TwoSamplersForMaskUpscalerProviderPipe - pipe version of TwoSamplersForMaskUpscalerProvider. + + +### Image Utils + * `PreviewBridge (image)` - This custom node can be used with a bridge for image when using the MaskEditor feature of Clipspace. + * `PreviewBridge (latent)` - This custom node can be used with a bridge for latent image when using the MaskEditor feature of Clipspace. + * If a latent with a mask is provided as input, it displays the mask. Additionally, the mask output provides the mask set in the latent. + * If a latent without a mask is provided as input, it outputs the original latent as is, but the mask output provides an output with the entire region set as a mask. + * When set mask through MaskEditor, a mask is applied to the latent, and the output includes the stored mask. The same mask is also output as the mask output. + * When connected to `vae_opt`, it takes higher priority than the `preview_method`. + * `ImageSender`, `ImageReceiver` - The images generated in ImageSender are automatically sent to the ImageReceiver with the same link_id. + * `LatentSender`, `LatentReceiver` - The latent generated in LatentSender are automatically sent to the LatentReceiver with the same link_id. + * Furthermore, LatentSender is implemented with PreviewLatent, which stores the latent in payload form within the image thumbnail. + * Due to the current structure of ComfyUI, it is unable to distinguish between SDXL latent and SD1.5/SD2.1 latent. Therefore, it generates thumbnails by decoding them using the SD1.5 method. + + +### Switch nodes + * `Switch (image,mask)`, `Switch (latent)`, `Switch (SEGS)` - Among multiple inputs, it selects the input designated by the selector and outputs it. The first input must be provided, while the others are optional. However, if the input specified by the selector is not connected, an error may occur. + * `Switch (Any)` - This is a Switch node that takes an arbitrary number of inputs and produces a single output. Its type is determined when connected to any node, and connecting inputs increases the available slots for connections. + * `Inversed Switch (Any)` - In contrast to `Switch (Any)`, it takes a single input and outputs one of many. + * NOTE: See this [tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/switch.md) + + +### [Wildcards](http://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) nodes + * These are nodes that supports syntax in the form of `__wildcard-name__` and dynamic prompt syntax like `{a|b|c}`. + * Wildcard files can be used by placing `.txt` or `.yaml` files under either `ComfyUI-Impact-Pack/wildcards` or `ComfyUI-Impact-Pack/custom_wildcards` paths. + * You can download and use [Wildcard YAML](https://civitai.com/models/138970/billions-of-wildcards-all-in-one) files in this format. + * After the first execution, you can change the custom wildcards path in the `custom_wildcards` entry within the `ComfyUI-Impact-Pack/impact-pack.ini` file created. + * `ImpactWildcardProcessor` - The text is generated by processing the wildcard in the Text. If the mode is set to "populate", a dynamic prompt is generated with each execution and the input is filled in the second textbox. If the mode is set to "fixed", the content of the second textbox remains unchanged. + * When an image is generated with the "fixed" mode, the prompt used for that particular generation is stored in the metadata. + * `ImpactWildcardEncode` - Similar to ImpactWildcardProcessor, this provides the loading functionality of LoRAs (e.g. ``). Populated prompts are encoded using the clip after all the lora loading is done. + * If the `Inspire Pack` is installed, you can use **Lora Block Weight** in the form of `LBW=lbw spec;` + * ``, ``, `` + + +### Regional Sampling + * These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step. + * `RegionalPrompt` - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`. + * `CombineRegionalPrompts` - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`. + * `RegionalSampler` - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region. + * overlap_factor - Specifies the amount of overlap for each region to blend well with the area outside the mask. + * restore_latent - When sampling each region, restore the areas outside the mask to the base latent, preventing additional noise from being introduced outside the mask during region sampling. + * `RegionalSamplerAdvanced` - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`. + > NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure. + + +### Impact KSampler + * These samplers support basic_pipe and AYS/OSS/GITS scheduler + * `KSampler (pipe)` - pipe version of KSampler + * `KSampler (advanced/pipe)` - pipe version of KSamplerAdvacned + * When converting the scheduler widget to input, refer to the `Impact Scheduler Adapter` node to resolve compatibility issues. + * `GITSScheduler Func Provider` - provider scheduler function for GITSScheduler + + +### Batch/List Util + * `Image Batch to Image List` - Convert Image batch to Image List + - You can use images generated in a multi batch to handle them + * `Image List to Image Batch` - Convert Image List to Image Batch + * `Make Image List` - Convert multiple images into a single image list + * `Make Image Batch` - Convert multiple images into a single image batch + - The input of images can be scaled up as needed + * `Masks to Mask List`, `Mask List to Masks`, `Make Mask List`, `Make Mask Batch` - It has the same functionality as the nodes above, but uses mask as input instead of image. + * `Flatten Mask Batch` - Flattens a Mask Batch into a single Mask. Normal operation is not guaranteed for non-binary masks. + * `Make List (Any)` - Create a list with arbitrary values. + * `Select Nth Item (Any list)` - Selects the Nth item from a list. If the index is out of range, it returns the last item in the list. + + +### Logics (experimental) + * These nodes are experimental nodes designed to implement the logic for loops and dynamic switching. + * `ImpactCompare`, `ImpactConditionalBranch`, `ImpactConditionalBranchSelMode`, `ImpactInt`, `ImpactBoolean`, `ImpactValueSender`, `ImpactValueReceiver`, `ImpactImageInfo`, `ImpactMinMax`, `ImpactNeg`, `ImpactConditionalStopIteration` + * `ImpactIsNotEmptySEGS` - This node returns `true` only if the input SEGS is not empty. + * `ImpactIfNone` - Returns `true` if any_input is None, and returns `false` if it is not None. + * `Queue Trigger` - When this node is executed, it adds a new queue to assist with repetitive tasks. It will only execute if the signal's status changes. + * `Queue Trigger (Countdown)` - Like the Queue Trigger, it adds a queue, but only adds it if it's greater than 1, and decrements the count by one each time it runs. + * `Sleep` - Waits for the specified time (in seconds). + * `Set Widget Value` - This node sets one of the optional inputs to the specified node's widget. An error may occur if the types do not match. + * `Set Mute State` - This node changes the mute state of a specific node. + * `Control Bridge` - This node modifies the state of the connected control nodes based on the `mode` and `behavior` . If there are nodes that require a change, the current execution is paused, the mute status is updated, and a new prompt queue is inserted. + * When the `mode` is `active`, it makes the connected control nodes active regardless of the behavior. + * When the `mode` is `Bypass/Mute`, it changes the state of the connected nodes based on whether the behavior is `Bypass` or `Mute`. + * **Limitation**: Due to these characteristics, it does not function correctly when the batch count exceeds 1. Additionally, it does not guarantee proper operation when the seed is randomized or when the state of nodes is altered by actions such as `Queue Trigger`, `Set Widget Value`, `Set Mute`, before the Control Bridge. + * When utilizing this node, please structure the workflow in such a way that `Queue Trigger`, `Set Widget Value`, `Set Mute State`, and similar actions are executed at the end of the workflow. + * If you want to change the value of the seed at each iteration, please ensure that Set Widget Value is executed at the end of the workflow instead of using randomization. + * It is not a problem if the seed changes due to randomization as long as it occurs after the Control Bridge section. + * `Remote Boolean (on prompt)`, `Remote Int (on prompt)` - At the start of the prompt, this node forcibly sets the `widget_value` of `node_id`. It is disregarded if the target widget type is different. + * You can find the `node_id` by checking through [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) using the format `Badge: #ID Nickname`. + * Experimental set of nodes for implementing loop functionality (tutorial to be prepared later / [example workflow](test/loop-test.json)). + + +### Limitation +* Many nodes in the `Impact Pack` use a wildcard type to allow arbitrary input/output connections. This approach will be replaced once ComfyUI officially supports **dynamic types**. Until then, while it functions without issues, type validation may still produce error messages. + + +### HuggingFace nodes + * These nodes provide functionalities based on HuggingFace repository models. + * The path where the HuggingFace model cache is stored can be changed through the `HF_HOME` environment variable. + * `HF Transformers Classifier Provider` - This is a node that provides a classifier based on HuggingFace's transformers models. + * The 'repo id' parameter should contain HuggingFace's repo id. When `preset_repo_id` is set to `Manual repo id`, use the manually entered repo id in `manual_repo_id`. + * e.g. 'rizvandwiki/gender-classification-2' is a repository that provides a model for gender classification. + * `SEGS Classify` - This node utilizes the `TRANSFORMERS_CLASSIFIER` loaded with 'HF Transformers Classifier Provider' to classify `SEGS`. + * The 'expr' allows for forms like `label > number`, and in the case of `preset_expr` being `Manual expr`, it uses the expression entered in `manual_expr`. + * For example, in the case of `male <= 0.4`, if the score of the `male` label in the classification result is less than or equal to 0.4, it is categorized as `filtered_SEGS`, otherwise, it is categorized as `remained_SEGS`. + * For supported labels, please refer to the `config.json` of the respective HuggingFace repository. + * `#Female` and `#Male` are symbols that group multiple labels such as `Female, women, woman, ...`, for convenience, rather than being single labels. + + +### Etc nodes + * `Impact Scheduler Adapter` - With the addition of AYS to the scheduler of the Impact Pack and Inspire Pack, there is an issue of incompatibility when the existing scheduler widget is converted to input. The Impact Scheduler Adapter allows for an indirect connection to be possible. + * `StringListToString` - Convert String List to String + * `WildcardPromptFromString` - Create labeled wildcard for detailer from string. + * This node works well when used with MakeTileSEGS. [[Link](https://github.com/ltdrdata/ComfyUI-Impact-Pack/pull/536#discussion_r1586060779)] + + * `String Selector` - It selects and returns a portion of the string. When `multiline` mode is disabled, it simply returns the string of the line pointed to by the selector. When `multiline` mode is enabled, it divides the string based on lines that start with `#` and returns them. If the `select` value is larger than the number of items, it will start counting from the first line again and return accordingly. + * `Combine Conditionings` - It takes multiple conditionings as input and combines them into a single conditioning. + * `Concat Conditionings` - It takes multiple conditionings as input and concat them into a single conditioning. + * `Negative Cond Placeholder` - Models like FLUX.1 do not use Negative Conditioning. This is a placeholder node for them. You can use FLUX.1 by replacing the Negative Conditioning used in Impact KSampler, KSampler (Inspire), and Detailer with this node. + * `Execution Order Controller` - A helper node that can forcibly control the execution order of nodes. + * Connect the output of the node that should be executed first to the signal, and make the input of the node that should be executed later pass through this node. + * `List Bridge` - When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed. + + +## Feature +* `Interactive SAM Detector (Clipspace)` - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'. +* Providing a feature to detect errors that occur when mixing models and clips from checkpoints such as `SDXL Base`, `SDXL Refiner`, `SD1.x`, `SD2.x` during sample execution, and reporting appropriate errors. + + +## How To Install? + +### Install via ComfyUI-Manager (Recommended) +* Search `ComfyUI Impact Pack` in ComfyUI-Manager and click `Install` button. + +### Manual Install (Not Recommended) +1. `cd custom_nodes` +2. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack` +3. `cd ComfyUI-Impact-Pack` +4. `pip install -r requirements.txt` + * **IMPORTANT**: + * You must install it within the Python environment where ComfyUI is running. + * For the portable version, use `\python_embeded\python.exe -m pip` instead of `pip`. For a `venv`, activate the `venv` first and then use `pip`. +5. Restart ComfyUI + +* NOTE1: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance. +* NOTE2: You can use this colab notebook [colab notebook](https://colab.research.google.com/github/ltdrdata/ComfyUI-Impact-Pack/blob/Main/notebook/comfyui_colab_impact_pack.ipynb) to launch it. This notebook automatically downloads the impact pack to the custom_nodes directory, installs the tested dependencies, and runs it. +* NOTE3: If you create an empty file named `skip_download_model` in the `ComfyUI/custom_nodes/` directory, it will skip the model download step during the installation of the impact pack. + + +## Package Dependencies (If you need to manual setup.) + +* pip install + * segment-anything + * scikit-image + * piexif + * opencv-python + * scipy + * numpy<2 + * dill + * matplotlib + * (optional) onnxruntime + * (deprecated) openmim # for mim + * (deprecated) pycocotools # for mim + +* linux packages (ubuntu) + * libgl1-mesa-glx + * libglib2.0-0 + + +## Config example +* Once you run the Impact Pack for the first time, an `impact-pack.ini` file will be automatically generated in the Impact Pack directory. You can modify this configuration file to customize the default behavior. + * `dependency_version` - don't touch this + * `sam_editor_cpu` - use cpu for `SAM editor` instead of gpu + * sam_editor_model: Specify the SAM model for the SAM editor. + * You can download various SAM models using ComfyUI-Manager. + * Path to SAM model: `ComfyUI/models/sams` +``` +[default] +sam_editor_cpu = False +sam_editor_model = sam_vit_b_01ec64.pth +``` + + +## Other Materials (auto-download when installing) + +* ComfyUI/models/sams <= https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth + + +## Troubleshooting page +* [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) + + +## How To Use (DDetailer feature) + +#### 1. Basic auto face detection and refine exapmle. +![simple](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple.png) +* The face that has been damaged due to low resolution is restored with high resolution by generating and synthesizing it, in order to restore the details. +* The FaceDetailer node is a combination of a Detector node for face detection and a Detailer node for image enhancement. See the [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/tutorial/advanced.md) for a more detailed explanation. +* The MASK output of FaceDetailer provides a visualization of where the detected and enhanced areas are. + +![simple-orig](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-original.png) ![simple-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-refined.png) +* You can see that the face in the image on the left has increased detail as in the image on the right. + +#### 2. 2Pass refine (restore a severely damaged face) +![2pass-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-simple.png) +* Although two FaceDetailers can be attached together for a 2-pass configuration, various common inputs used in KSampler can be passed through DETAILER_PIPE, so FaceDetailerPipe can be used to configure easily. +* In 1pass, only rough outline recovery is required, so restore with a reasonable resolution and low options. However, if you increase the dilation at this time, not only the face but also the surrounding parts are included in the recovery range, so it is useful when you need to reshape the face other than the facial part. + +![2pass-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-original.png) ![2pass-example-middle](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-1pass.png) ![2pass-example-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-2pass.png) +* In the first stage, the severely damaged face is restored to some extent, and in the second stage, the details are restored + +#### 3. Face Bbox(bounding box) + Person silhouette segmentation (prevent distortion of the background.) +![combination-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination.jpg) +![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-refined.png) + +* Facial synthesis that emphasizes details is delicately aligned with the contours of the face, and it can be observed that it does not affect the image outside of the face. + +* The BBoxDetectorForEach node is used to detect faces, and the SAMDetectorCombined node is used to find the segment related to the detected face. By using the Segs & Mask node with the two masks obtained in this way, an accurate mask that intersects based on segs can be generated. If this generated mask is input to the DetailerForEach node, only the target area can be created in high resolution from the image and then composited. + +#### 4. Iterative Upscale +![upscale-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-workflow.png) + +* The IterativeUpscale node is a node that enlarges an image/latent by a scale_factor. In this process, the upscale is carried out progressively by dividing it into steps. +* IterativeUpscale takes an Upscaler as an input, similar to a plugin, and uses it during each iteration. PixelKSampleUpscalerProvider is an Upscaler that converts the latent representation to pixel space and applies ksampling. + * The upscale_model_opt is an optional parameter that determines whether to use the upscale function of the model base if available. Using the upscale function of the model base can significantly reduce the number of iterative steps required. If an x2 upscaler is used, the image/latent is first upscaled by a factor of 2 and then downscaled to the target scale at each step before further processing is done. + +* The following image is an image of 304x512 pixels and the same image scaled up to three times its original size using IterativeUpscale. + +![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-3x.png) + + +#### 5. Interactive SAM Detector (Clipspace) + +* When you right-click on the node that outputs 'MASK' and 'IMAGE', a menu called "Open in SAM Detector" appears, as shown in the following picture. Clicking on the menu opens a dialog in SAM's functionality, allowing you to generate a segment mask. +![samdetector-menu](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-menu.png) + +* By clicking the left mouse button on a coordinate, a positive prompt in blue color is entered, indicating the area that should be included. Clicking the right mouse button on a coordinate enters a negative prompt in red color, indicating the area that should be excluded. Positive prompts represent the areas that should be included, while negative prompts represent the areas that should be excluded. +* You can remove the points that were added by using the "undo" button. After selecting the points, pressing the "detect" button generates the mask. Additionally, you can adjust the fidelity slider to determine the extent to which the mask belongs to the confidence region. + +![samdetector-dialog](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-dialog.jpg) + +* If you opened the dialog through "Open in SAM Detector" from the node, you can directly apply the changes by clicking the "Save to node" button. However, if you opened the dialog through the "clipspace" menu, you can save it to clipspace by clicking the "Save" button. + +![samdetector-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-result.jpg) + +* When you execute using the reflected mask in the node, you can observe that the image and mask are displayed separately. + + +## Others Tutorials +* [ComfyUI-extension-tutorials/ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-extension-tutorials/tree/Main/ComfyUI-Impact-Pack) - You can find various tutorials and workflows on this page. +* [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/advanced.md) +* [SAM Application](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam.md) +* [PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/previewbridge.md) +* [Mask Pointer](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/maskpointer.md) +* [ONNX Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ONNX.md) +* [CLIPSeg Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/clipseg.md) +* [Extreme Highresolution Upscale](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/extreme-upscale.md) +* [TwoSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplers.md) +* [TwoAdvancedSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoAdvancedSamplers.md) +* [Advanced Iterative Upscale: PK_HOOK](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/pk_hook.md) +* [Advanced Iterative Upscale: TwoSamplersForMask Upscale Provider](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplersUpscale.md) +* [Interactive SAM + PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam_with_preview_bridge.md) +* [ImageSender/ImageReceiver/LatentSender/LatentReceiver](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sender_receiver.md) +* [ImpactWildcardProcessor](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md) + + +## Credits + +ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI. + +dustysys/[ddetailer](https://github.com/dustysys/ddetailer) - DDetailer for Stable-diffusion-webUI extension. + +Bing-su/[dddetailer](https://github.com/Bing-su/dddetailer) - The anime-face-detector used in ddetailer has been updated to be compatible with mmdet 3.0.0, and we have also applied a patch to the pycocotools dependency for Windows environment in ddetailer. + +facebook/[segment-anything](https://github.com/facebookresearch/segment-anything) - Segmentation Anything! + +hysts/[anime-face-detector](https://github.com/hysts/anime-face-detector) - Creator of `anime-face_yolov3`, which has impressive performance on a variety of art styles. + +open-mmlab/[mmdetection](https://github.com/open-mmlab/mmdetection) - Object detection toolset. `dd-person_mask2former` was trained via transfer learning using their [R-50 Mask2Former instance segmentation model](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former#instance-segmentation) as a base. + +biegert/[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg) - This is a custom node that enables the use of CLIPSeg technology, which can find segments through prompts, in ComfyUI. + +BlenderNeok/[ComfyUI-TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) - The tile sampler allows high-resolution sampling even in places with low GPU VRAM. + +BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The noise injection feature relies on this function and slerp code for noise variation + +WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI. + +Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack! + +Layer-norm/[comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) - Required for using `LamaRemoverDetailerHook`. diff --git a/custom_nodes/ComfyUI-Impact-Pack/__init__.py b/custom_nodes/ComfyUI-Impact-Pack/__init__.py new file mode 100644 index 00000000..ee98acab --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/__init__.py @@ -0,0 +1,456 @@ +""" +@author: Dr.Lt.Data +@title: Impact Pack +@nickname: Impact Pack +@description: This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler. +""" + +import folder_paths +import os +import sys +import logging + +comfy_path = os.path.dirname(folder_paths.__file__) +impact_path = os.path.join(os.path.dirname(__file__)) +modules_path = os.path.join(os.path.dirname(__file__), "modules") + +sys.path.append(modules_path) + +import impact.config +logging.info(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})") + +# Core +# recheck dependencies for colab +try: + import folder_paths + import torch # noqa: F401 + import cv2 # noqa: F401 + from cv2 import setNumThreads # noqa: F401 + import numpy as np # noqa: F401 + import comfy.samplers + import comfy.sd # noqa: F401 + from PIL import Image, ImageFilter # noqa: F401 + from skimage.measure import label, regionprops # noqa: F401 + from collections import namedtuple # noqa: F401 + import piexif # noqa: F401 + import nodes +except Exception as e: + import logging + logging.error("[Impact Pack] Failed to import due to several dependencies are missing!!!!") + raise e + + +import impact.impact_server # to load server api + +from .modules.impact.impact_pack import * # noqa: F403 +from .modules.impact.detectors import * # noqa: F403 +from .modules.impact.pipe import * # noqa: F403 +from .modules.impact.logics import * # noqa: F403 +from .modules.impact.util_nodes import * # noqa: F403 +from .modules.impact.segs_nodes import * # noqa: F403 +from .modules.impact.special_samplers import * # noqa: F403 +from .modules.impact.hf_nodes import * # noqa: F403 +from .modules.impact.bridge_nodes import * # noqa: F403 +from .modules.impact.hook_nodes import * # noqa: F403 +from .modules.impact.animatediff_nodes import * # noqa: F403 +from .modules.impact.segs_upscaler import * # noqa: F403 + +import threading + + +threading.Thread(target=impact.wildcards.wildcard_load).start() + + +NODE_CLASS_MAPPINGS = { + "SAMLoader": SAMLoader, # noqa: F405 + "CLIPSegDetectorProvider": CLIPSegDetectorProvider, # noqa: F405 + "ONNXDetectorProvider": ONNXDetectorProvider, # noqa: F405 + + "BitwiseAndMaskForEach": BitwiseAndMaskForEach, # noqa: F405 + "SubtractMaskForEach": SubtractMaskForEach, # noqa: F405 + + "DetailerForEach": DetailerForEach, # noqa: F405 + "DetailerForEachAutoRetry": DetailerForEachAutoRetry, # noqa: F405 + "DetailerForEachDebug": DetailerForEachTest, # noqa: F405 + "DetailerForEachPipe": DetailerForEachPipe, # noqa: F405 + "DetailerForEachDebugPipe": DetailerForEachTestPipe, # noqa: F405 + "DetailerForEachPipeForAnimateDiff": DetailerForEachPipeForAnimateDiff, # noqa: F405 + + "SAMDetectorCombined": SAMDetectorCombined, # noqa: F405 + "SAMDetectorSegmented": SAMDetectorSegmented, # noqa: F405 + + "FaceDetailer": FaceDetailer, # noqa: F405 + "FaceDetailerPipe": FaceDetailerPipe, # noqa: F405 + "MaskDetailerPipe": MaskDetailerPipe, # noqa: F405 + + "ToDetailerPipe": ToDetailerPipe, # noqa: F405 + "ToDetailerPipeSDXL": ToDetailerPipeSDXL, # noqa: F405 + "FromDetailerPipe": FromDetailerPipe, # noqa: F405 + "FromDetailerPipe_v2": FromDetailerPipe_v2, # noqa: F405 + "FromDetailerPipeSDXL": FromDetailerPipe_SDXL, # noqa: F405 + "AnyPipeToBasic": AnyPipeToBasic, # noqa: F405 + "ToBasicPipe": ToBasicPipe, # noqa: F405 + "FromBasicPipe": FromBasicPipe, # noqa: F405 + "FromBasicPipe_v2": FromBasicPipe_v2, # noqa: F405 + "BasicPipeToDetailerPipe": BasicPipeToDetailerPipe, # noqa: F405 + "BasicPipeToDetailerPipeSDXL": BasicPipeToDetailerPipeSDXL, # noqa: F405 + "DetailerPipeToBasicPipe": DetailerPipeToBasicPipe, # noqa: F405 + "EditBasicPipe": EditBasicPipe, # noqa: F405 + "EditDetailerPipe": EditDetailerPipe, # noqa: F405 + "EditDetailerPipeSDXL": EditDetailerPipeSDXL, # noqa: F405 + + "LatentPixelScale": LatentPixelScale, # noqa: F405 + "PixelKSampleUpscalerProvider": PixelKSampleUpscalerProvider, # noqa: F405 + "PixelKSampleUpscalerProviderPipe": PixelKSampleUpscalerProviderPipe, # noqa: F405 + "IterativeLatentUpscale": IterativeLatentUpscale, # noqa: F405 + "IterativeImageUpscale": IterativeImageUpscale, # noqa: F405 + "PixelTiledKSampleUpscalerProvider": PixelTiledKSampleUpscalerProvider, # noqa: F405 + "PixelTiledKSampleUpscalerProviderPipe": PixelTiledKSampleUpscalerProviderPipe, # noqa: F405 + "TwoSamplersForMaskUpscalerProvider": TwoSamplersForMaskUpscalerProvider, # noqa: F405 + "TwoSamplersForMaskUpscalerProviderPipe": TwoSamplersForMaskUpscalerProviderPipe, # noqa: F405 + + "PixelKSampleHookCombine": PixelKSampleHookCombine, # noqa: F405 + "DenoiseScheduleHookProvider": DenoiseScheduleHookProvider, # noqa: F405 + "StepsScheduleHookProvider": StepsScheduleHookProvider, # noqa: F405 + "CfgScheduleHookProvider": CfgScheduleHookProvider, # noqa: F405 + "NoiseInjectionHookProvider": NoiseInjectionHookProvider, # noqa: F405 + "UnsamplerHookProvider": UnsamplerHookProvider, # noqa: F405 + "CoreMLDetailerHookProvider": CoreMLDetailerHookProvider, # noqa: F405 + "PreviewDetailerHookProvider": PreviewDetailerHookProvider, # noqa: F405 + "BlackPatchRetryHookProvider": BlackPatchRetryHookProvider, # noqa: F405 + "CustomSamplerDetailerHookProvider": CustomSamplerDetailerHookProvider, # noqa: F405 + "LamaRemoverDetailerHookProvider": LamaRemoverDetailerHookProvider, # noqa: F405 + + "DetailerHookCombine": DetailerHookCombine, # noqa: F405 + "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider, # noqa: F405 + "UnsamplerDetailerHookProvider": UnsamplerDetailerHookProvider, # noqa: F405 + "DenoiseSchedulerDetailerHookProvider": DenoiseSchedulerDetailerHookProvider, # noqa: F405 + "SEGSOrderedFilterDetailerHookProvider": SEGSOrderedFilterDetailerHookProvider, # noqa: F405 + "SEGSRangeFilterDetailerHookProvider": SEGSRangeFilterDetailerHookProvider, # noqa: F405 + "SEGSLabelFilterDetailerHookProvider": SEGSLabelFilterDetailerHookProvider, # noqa: F405 + "VariationNoiseDetailerHookProvider": VariationNoiseDetailerHookProvider, # noqa: F405 + # "CustomNoiseDetailerHookProvider": CustomNoiseDetailerHookProvider, + + "BitwiseAndMask": BitwiseAndMask, # noqa: F405 + "SubtractMask": SubtractMask, # noqa: F405 + "AddMask": AddMask, # noqa: F405 + "MaskRectArea": MaskRectArea, # noqa: F405 + "MaskRectAreaAdvanced": MaskRectAreaAdvanced, # noqa: F405 + "ImpactSegsAndMask": SegsBitwiseAndMask, # noqa: F405 + "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach, # noqa: F405 + "EmptySegs": EmptySEGS, # noqa: F405 + "ImpactFlattenMask": FlattenMask, # noqa: F405 + + "MediaPipeFaceMeshToSEGS": MediaPipeFaceMeshToSEGS, # noqa: F405 + "MaskToSEGS": MaskToSEGS, # noqa: F405 + "MaskToSEGS_for_AnimateDiff": MaskToSEGS_for_AnimateDiff, # noqa: F405 + "ToBinaryMask": ToBinaryMask, # noqa: F405 + "MasksToMaskList": MasksToMaskList, # noqa: F405 + "MaskListToMaskBatch": MaskListToMaskBatch, # noqa: F405 + "ImageListToImageBatch": ImageListToImageBatch, # noqa: F405 + "SetDefaultImageForSEGS": DefaultImageForSEGS, # noqa: F405 + "RemoveImageFromSEGS": RemoveImageFromSEGS, # noqa: F405 + + "BboxDetectorSEGS": BboxDetectorForEach, # noqa: F405 + "SegmDetectorSEGS": SegmDetectorForEach, # noqa: F405 + "ONNXDetectorSEGS": BboxDetectorForEach, # noqa: F405 + "ImpactSimpleDetectorSEGS_for_AD": SimpleDetectorForAnimateDiff, # noqa: F405 + "ImpactSAM2VideoDetectorSEGS": SAM2VideoDetectorSEGS, # noqa: F405 + "ImpactSimpleDetectorSEGS": SimpleDetectorForEach, # noqa: F405 + "ImpactSimpleDetectorSEGSPipe": SimpleDetectorForEachPipe, # noqa: F405 + "ImpactControlNetApplySEGS": ControlNetApplySEGS, # noqa: F405 + "ImpactControlNetApplyAdvancedSEGS": ControlNetApplyAdvancedSEGS, # noqa: F405 + "ImpactControlNetClearSEGS": ControlNetClearSEGS, # noqa: F405 + "ImpactIPAdapterApplySEGS": IPAdapterApplySEGS, # noqa: F405 + + "ImpactDecomposeSEGS": DecomposeSEGS, # noqa: F405 + "ImpactAssembleSEGS": AssembleSEGS, # noqa: F405 + "ImpactFrom_SEG_ELT": From_SEG_ELT, # noqa: F405 + "ImpactEdit_SEG_ELT": Edit_SEG_ELT, # noqa: F405 + "ImpactDilate_Mask_SEG_ELT": Dilate_SEG_ELT, # noqa: F405 + "ImpactDilateMask": DilateMask, # noqa: F405 + "ImpactGaussianBlurMask": GaussianBlurMask, # noqa: F405 + "ImpactDilateMaskInSEGS": DilateMaskInSEGS, # noqa: F405 + "ImpactGaussianBlurMaskInSEGS": GaussianBlurMaskInSEGS, # noqa: F405 + "ImpactScaleBy_BBOX_SEG_ELT": SEG_ELT_BBOX_ScaleBy, # noqa: F405 + "ImpactFrom_SEG_ELT_bbox": From_SEG_ELT_bbox, # noqa: F405 + "ImpactFrom_SEG_ELT_crop_region": From_SEG_ELT_crop_region, # noqa: F405 + "ImpactCount_Elts_in_SEGS": Count_Elts_in_SEGS, # noqa: F405 + + "BboxDetectorCombined_v2": BboxDetectorCombined, # noqa: F405 + "SegmDetectorCombined_v2": SegmDetectorCombined, # noqa: F405 + "SegsToCombinedMask": SegsToCombinedMask, # noqa: F405 + + "KSamplerProvider": KSamplerProvider, # noqa: F405 + "TwoSamplersForMask": TwoSamplersForMask, # noqa: F405 + "TiledKSamplerProvider": TiledKSamplerProvider, # noqa: F405 + + "KSamplerAdvancedProvider": KSamplerAdvancedProvider, # noqa: F405 + "TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask, # noqa: F405 + + "ImpactNegativeConditioningPlaceholder": NegativeConditioningPlaceholder, # noqa: F405 + + "PreviewBridge": PreviewBridge, # noqa: F405 + "PreviewBridgeLatent": PreviewBridgeLatent, # noqa: F405 + "ImageSender": ImageSender, # noqa: F405 + "ImageReceiver": ImageReceiver, # noqa: F405 + "LatentSender": LatentSender, # noqa: F405 + "LatentReceiver": LatentReceiver, # noqa: F405 + "ImageMaskSwitch": ImageMaskSwitch, # noqa: F405 + "LatentSwitch": GeneralSwitch, # noqa: F405 + "SEGSSwitch": GeneralSwitch, # noqa: F405 + "ImpactSwitch": GeneralSwitch, # noqa: F405 + "ImpactInversedSwitch": GeneralInversedSwitch, # noqa: F405 + + "ImpactWildcardProcessor": ImpactWildcardProcessor, # noqa: F405 + "ImpactWildcardEncode": ImpactWildcardEncode, # noqa: F405 + + "SEGSUpscaler": SEGSUpscaler, # noqa: F405 + "SEGSUpscalerPipe": SEGSUpscalerPipe, # noqa: F405 + "SEGSDetailer": SEGSDetailer, # noqa: F405 + "SEGSPaste": SEGSPaste, # noqa: F405 + "SEGSPreview": SEGSPreview, # noqa: F405 + "SEGSPreviewCNet": SEGSPreviewCNet, # noqa: F405 + "SEGSToImageList": SEGSToImageList, # noqa: F405 + "ImpactSEGSToMaskList": SEGSToMaskList, # noqa: F405 + "ImpactSEGSToMaskBatch": SEGSToMaskBatch, # noqa: F405 + "ImpactSEGSConcat": SEGSConcat, # noqa: F405 + "ImpactSEGSPicker": SEGSPicker, # noqa: F405 + "ImpactMakeTileSEGS": MakeTileSEGS, # noqa: F405 + "ImpactSEGSMerge": SEGSMerge, # noqa: F405 + + "SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff, # noqa: F405 + + "ImpactKSamplerBasicPipe": KSamplerBasicPipe, # noqa: F405 + "ImpactKSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipe, # noqa: F405 + + "ReencodeLatent": ReencodeLatent, # noqa: F405 + "ReencodeLatentPipe": ReencodeLatentPipe, # noqa: F405 + + "ImpactImageBatchToImageList": ImageBatchToImageList, # noqa: F405 + "ImpactMakeImageList": MakeImageList, # noqa: F405 + "ImpactMakeImageBatch": MakeImageBatch, # noqa: F405 + "ImpactMakeAnyList": MakeAnyList, # noqa: F405 + "ImpactMakeMaskList": MakeMaskList, # noqa: F405 + "ImpactMakeMaskBatch": MakeMaskBatch, # noqa: F405 + "ImpactSelectNthItemOfAnyList": NthItemOfAnyList, # noqa: F405 + + "RegionalSampler": RegionalSampler, # noqa: F405 + "RegionalSamplerAdvanced": RegionalSamplerAdvanced, # noqa: F405 + "CombineRegionalPrompts": CombineRegionalPrompts, # noqa: F405 + "RegionalPrompt": RegionalPrompt, # noqa: F405 + + "ImpactCombineConditionings": CombineConditionings, # noqa: F405 + "ImpactConcatConditionings": ConcatConditionings, # noqa: F405 + + "ImpactSEGSLabelAssign": SEGSLabelAssign, # noqa: F405 + "ImpactSEGSLabelFilter": SEGSLabelFilter, # noqa: F405 + "ImpactSEGSRangeFilter": SEGSRangeFilter, # noqa: F405 + "ImpactSEGSOrderedFilter": SEGSOrderedFilter, # noqa: F405 + "ImpactSEGSIntersectionFilter": SEGSIntersectionFilter, # noqa: F405 + "ImpactSEGSNMSFilter": SEGSNMSFilter, # noqa: F405 + + "ImpactCompare": ImpactCompare, # noqa: F405 + "ImpactConditionalBranch": ImpactConditionalBranch, # noqa: F405 + "ImpactConditionalBranchSelMode": ImpactConditionalBranchSelMode, # noqa: F405 + "ImpactIfNone": ImpactIfNone, # noqa: F405 + "ImpactConvertDataType": ImpactConvertDataType, # noqa: F405 + "ImpactLogicalOperators": ImpactLogicalOperators, # noqa: F405 + "ImpactInt": ImpactInt, # noqa: F405 + "ImpactFloat": ImpactFloat, # noqa: F405 + "ImpactBoolean": ImpactBoolean, # noqa: F405 + "ImpactValueSender": ImpactValueSender, # noqa: F405 + "ImpactValueReceiver": ImpactValueReceiver, # noqa: F405 + "ImpactImageInfo": ImpactImageInfo, # noqa: F405 + "ImpactLatentInfo": ImpactLatentInfo, # noqa: F405 + "ImpactMinMax": ImpactMinMax, # noqa: F405 + "ImpactNeg": ImpactNeg, # noqa: F405 + "ImpactConditionalStopIteration": ImpactConditionalStopIteration, # noqa: F405 + "ImpactStringSelector": StringSelector, # noqa: F405 + "StringListToString": StringListToString, # noqa: F405 + "WildcardPromptFromString": WildcardPromptFromString, # noqa: F405 + "ImpactExecutionOrderController": ImpactExecutionOrderController, # noqa: F405 + "ImpactListBridge": ImpactListBridge, # noqa: F405 + + "RemoveNoiseMask": RemoveNoiseMask, # noqa: F405 + + "ImpactLogger": ImpactLogger, # noqa: F405 + "ImpactDummyInput": ImpactDummyInput, # noqa: F405 + + "ImpactQueueTrigger": ImpactQueueTrigger, # noqa: F405 + "ImpactQueueTriggerCountdown": ImpactQueueTriggerCountdown, # noqa: F405 + "ImpactSetWidgetValue": ImpactSetWidgetValue, # noqa: F405 + "ImpactNodeSetMuteState": ImpactNodeSetMuteState, # noqa: F405 + "ImpactControlBridge": ImpactControlBridge, # noqa: F405 + "ImpactIsNotEmptySEGS": ImpactNotEmptySEGS, # noqa: F405 + "ImpactSleep": ImpactSleep, # noqa: F405 + "ImpactRemoteBoolean": ImpactRemoteBoolean, # noqa: F405 + "ImpactRemoteInt": ImpactRemoteInt, # noqa: F405 + + "ImpactHFTransformersClassifierProvider": HF_TransformersClassifierProvider, # noqa: F405 + "ImpactSEGSClassify": SEGS_Classify, # noqa: F405 + + "ImpactSchedulerAdapter": ImpactSchedulerAdapter, # noqa: F405 + "GITSSchedulerFuncProvider": GITSSchedulerFuncProvider # noqa: F405 +} + + +NODE_DISPLAY_NAME_MAPPINGS = { + "SAMLoader": "SAMLoader (Impact)", + + "BboxDetectorSEGS": "BBOX Detector (SEGS)", + "SegmDetectorSEGS": "SEGM Detector (SEGS)", + "ONNXDetectorSEGS": "ONNX Detector (SEGS/legacy) - use BBOXDetector", + "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for Video (SEGS)", + "ImpactSAM2VideoDetectorSEGS": "SAM2 Video Detector (SEGS)", + "ImpactSimpleDetectorSEGS": "Simple Detector (SEGS)", + "ImpactSimpleDetectorSEGSPipe": "Simple Detector (SEGS/pipe)", + "ImpactControlNetApplySEGS": "ControlNetApply (SEGS) - DEPRECATED", + "ImpactControlNetApplyAdvancedSEGS": "ControlNetApply (SEGS)", + "ImpactIPAdapterApplySEGS": "IPAdapterApply (SEGS)", + + "BboxDetectorCombined_v2": "BBOX Detector (combined)", + "SegmDetectorCombined_v2": "SEGM Detector (combined)", + "SegsToCombinedMask": "SEGS to MASK (combined)", + "MediaPipeFaceMeshToSEGS": "MediaPipe FaceMesh to SEGS", + "MaskToSEGS": "MASK to SEGS", + "MaskToSEGS_for_AnimateDiff": "MASK to SEGS for Video", + "BitwiseAndMaskForEach": "Pixelwise(SEGS & SEGS)", + "SubtractMaskForEach": "Pixelwise(SEGS - SEGS)", + "ImpactSegsAndMask": "Pixelwise(SEGS & MASK)", + "ImpactSegsAndMaskForEach": "Pixelwise(SEGS & MASKS ForEach)", + "BitwiseAndMask": "Pixelwise(MASK & MASK)", + "SubtractMask": "Pixelwise(MASK - MASK)", + "AddMask": "Pixelwise(MASK + MASK)", + "MaskRectArea": "Mask Rect Area", + "MaskRectAreaAdvanced": "Mask Rect Area (Advanced)", + "ImpactFlattenMask": "Flatten Mask Batch", + "DetailerForEach": "Detailer (SEGS)", + "DetailerForEachAutoRetry": "Detailer (SEGS) with auto retry", + "DetailerForEachPipe": "Detailer (SEGS/pipe)", + "DetailerForEachDebug": "DetailerDebug (SEGS)", + "DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)", + "SEGSDetailerForAnimateDiff": "SEGSDetailer For Video (SEGS/pipe)", + "DetailerForEachPipeForAnimateDiff": "Detailer For Video (SEGS/pipe)", + "SEGSUpscaler": "Upscaler (SEGS)", + "SEGSUpscalerPipe": "Upscaler (SEGS/pipe)", + + "SAMDetectorCombined": "SAMDetector (combined)", + "SAMDetectorSegmented": "SAMDetector (segmented)", + "FaceDetailerPipe": "FaceDetailer (pipe)", + "MaskDetailerPipe": "MaskDetailer (pipe)", + + "FromDetailerPipeSDXL": "FromDetailer (SDXL/pipe)", + "BasicPipeToDetailerPipeSDXL": "BasicPipe -> DetailerPipe (SDXL)", + "EditDetailerPipeSDXL": "Edit DetailerPipe (SDXL)", + + "BasicPipeToDetailerPipe": "BasicPipe -> DetailerPipe", + "DetailerPipeToBasicPipe": "DetailerPipe -> BasicPipe", + "EditBasicPipe": "Edit BasicPipe", + "EditDetailerPipe": "Edit DetailerPipe", + "AnyPipeToBasic": "Any PIPE -> BasicPipe", + + "LatentPixelScale": "Latent Scale (on Pixel Space)", + "IterativeLatentUpscale": "Iterative Upscale (Latent/on Pixel Space)", + "IterativeImageUpscale": "Iterative Upscale (Image)", + + "TwoSamplersForMaskUpscalerProvider": "TwoSamplersForMask Upscaler Provider", + "TwoSamplersForMaskUpscalerProviderPipe": "TwoSamplersForMask Upscaler Provider (pipe)", + + "ReencodeLatent": "Reencode Latent", + "ReencodeLatentPipe": "Reencode Latent (pipe)", + + "ImpactKSamplerBasicPipe": "KSampler (pipe)", + "ImpactKSamplerAdvancedBasicPipe": "KSampler (Advanced/pipe)", + "ImpactSEGSLabelAssign": "SEGS Assign (label)", + "ImpactSEGSLabelFilter": "SEGS Filter (label)", + "ImpactSEGSRangeFilter": "SEGS Filter (range)", + "ImpactSEGSOrderedFilter": "SEGS Filter (ordered)", + "ImpactSEGSIntersectionFilter": "SEGS Filter (intersection)", + "ImpactSEGSNMSFilter": "SEGS Filter (non max suppression)", + "ImpactSEGSConcat": "SEGS Concat", + "ImpactSEGSToMaskList": "SEGS to Mask List", + "ImpactSEGSToMaskBatch": "SEGS to Mask Batch", + "ImpactSEGSPicker": "Picker (SEGS)", + "ImpactMakeTileSEGS": "Make Tile SEGS", + "ImpactSEGSMerge": "SEGS Merge", + + "ImpactDecomposeSEGS": "Decompose (SEGS)", + "ImpactAssembleSEGS": "Assemble (SEGS)", + "ImpactFrom_SEG_ELT": "From SEG_ELT", + "ImpactEdit_SEG_ELT": "Edit SEG_ELT", + "ImpactFrom_SEG_ELT_bbox": "From SEG_ELT bbox", + "ImpactFrom_SEG_ELT_crop_region": "From SEG_ELT crop_region", + "ImpactDilate_Mask_SEG_ELT": "Dilate Mask (SEG_ELT)", + "ImpactScaleBy_BBOX_SEG_ELT": "ScaleBy BBOX (SEG_ELT)", + "ImpactCount_Elts_in_SEGS": "Count Elts in SEGS", + "ImpactDilateMask": "Dilate Mask", + "ImpactGaussianBlurMask": "Gaussian Blur Mask", + "ImpactDilateMaskInSEGS": "Dilate Mask (SEGS)", + "ImpactGaussianBlurMaskInSEGS": "Gaussian Blur Mask (SEGS)", + + "PreviewBridge": "Preview Bridge (Image)", + "PreviewBridgeLatent": "Preview Bridge (Latent)", + "ImageSender": "Image Sender", + "ImageReceiver": "Image Receiver", + "ImageMaskSwitch": "Switch (images, mask)", + "ImpactSwitch": "Switch (Any)", + "ImpactInversedSwitch": "Inversed Switch (Any)", + "ImpactExecutionOrderController": "Execution Order Controller", + "ImpactListBridge": "List Bridge", + + "MasksToMaskList": "Mask Batch to Mask List", + "MaskListToMaskBatch": "Mask List to Mask Batch", + "ImpactImageBatchToImageList": "Image Batch to Image List", + "ImageListToImageBatch": "Image List to Image Batch", + + "ImpactMakeImageList": "Make Image List", + "ImpactMakeImageBatch": "Make Image Batch", + "ImpactMakeMaskList": "Make Mask List", + "ImpactMakeMaskBatch": "Make Mask Batch", + "ImpactMakeAnyList": "Make List (Any)", + "ImpactSelectNthItemOfAnyList": "Select Nth Item (Any list)", + + "ImpactStringSelector": "String Selector", + "StringListToString": "String List to String", + "WildcardPromptFromString": "Wildcard Prompt from String", + "ImpactIsNotEmptySEGS": "SEGS isn't Empty", + "SetDefaultImageForSEGS": "Set Default Image for SEGS", + "RemoveImageFromSEGS": "Remove Image from SEGS", + + "RemoveNoiseMask": "Remove Noise Mask", + + "ImpactCombineConditionings": "Combine Conditionings", + "ImpactConcatConditionings": "Concat Conditionings", + + "ImpactQueueTrigger": "Queue Trigger", + "ImpactQueueTriggerCountdown": "Queue Trigger (Countdown)", + "ImpactSetWidgetValue": "Set Widget Value", + "ImpactNodeSetMuteState": "Set Mute State", + "ImpactControlBridge": "Control Bridge", + "ImpactSleep": "Sleep", + "ImpactRemoteBoolean": "Remote Boolean (on prompt)", + "ImpactRemoteInt": "Remote Int (on prompt)", + + "ImpactHFTransformersClassifierProvider": "HF Transformers Classifier Provider", + "ImpactSEGSClassify": "SEGS Classify", + + "LatentSwitch": "Switch (latent/legacy)", + "SEGSSwitch": "Switch (SEGS/legacy)", + + "SEGSPreviewCNet": "SEGSPreview (CNET Image)", + + "ImpactSchedulerAdapter": "Impact Scheduler Adapter", + "GITSSchedulerFuncProvider": "GITSScheduler Func Provider", + "ImpactNegativeConditioningPlaceholder": "Negative Cond Placeholder" +} + + +# NOTE: Inject directly into EXTENSION_WEB_DIRS instead of WEB_DIRECTORY +# Provide the js path fixed as ComfyUI-Impact-Pack instead of the path name, making it available for external use + +# WEB_DIRECTORY = "js" -- deprecated method +nodes.EXTENSION_WEB_DIRS["ComfyUI-Impact-Pack"] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'js') + + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards/put_wildcards_here b/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards/put_wildcards_here new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/README.md b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/README.md new file mode 100644 index 00000000..fd5da5c3 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/README.md @@ -0,0 +1,39 @@ +# Wildcard System Documentation + +Progressive on-demand wildcard loading system for ComfyUI Impact Pack. + +## Documentation Structure + +- **[WILDCARD_SYSTEM_PRD.md](WILDCARD_SYSTEM_PRD.md)** - Product requirements and specifications +- **[WILDCARD_SYSTEM_DESIGN.md](WILDCARD_SYSTEM_DESIGN.md)** - Technical architecture and implementation +- **[WILDCARD_TESTING_GUIDE.md](WILDCARD_TESTING_GUIDE.md)** - Testing procedures and validation + +## Quick Links + +- Test Suite: `../../tests/` +- Test Samples: `../../tests/wildcards/samples/` +- Implementation: `../../modules/impact/wildcards.py` +- Server API: `../../modules/impact/impact_server.py` + +## Test Execution + +```bash +cd tests/ + +# Run all test suites +bash test_encoding.sh # UTF-8 multi-language (15 tests) +bash test_error_handling.sh # Error handling (10 tests) +bash test_edge_cases.sh # Edge cases (20 tests) +bash test_deep_nesting.sh # 7-level nesting (15 tests) +bash test_ondemand_loading.sh # On-demand loading (8 tests) +bash test_config_quotes.sh # Config quotes (5 tests) +``` + +## Status + +βœ… **Production Ready** +- 73 tests, 100% pass rate (6 test suites) +- Complete PRD coverage +- Zero implementation bugs +- UTF-8 encoding verified +- Error handling validated diff --git a/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/SUMMARY.md b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/SUMMARY.md new file mode 100644 index 00000000..b0742a0a --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/SUMMARY.md @@ -0,0 +1,151 @@ +# Wildcard System - Project Summary + +## Overview + +Progressive on-demand wildcard loading system for ComfyUI Impact Pack with dynamic prompt support, UTF-8 encoding, and comprehensive testing. + +**Status**: βœ… Production Ready +**Test Coverage**: 86 tests, 100% pass rate +**Documentation**: Complete PRD, design docs, and testing guide + +--- + +## Core Features + +- **Wildcard Expansion**: `__wildcard__` syntax with transitive multi-level expansion +- **Dynamic Prompts**: + - Basic selection: `{option1|option2|option3}` + - Weighted selection: `{10::common|1::rare}` (weight comes first) + - Multi-select: `{2$$, $$red|blue|green}` with custom separators +- **UTF-8 Support**: Korean, Chinese, Arabic, emoji, special characters +- **Pattern Matching**: Depth-agnostic `__*/name__` syntax +- **On-Demand Loading**: Progressive lazy loading with configurable cache limits +- **Error Handling**: Circular reference detection, graceful fallbacks + +--- + +## Architecture + +### Implementation +- `modules/impact/wildcards.py` - Core LazyWildcardLoader and expansion engine +- `modules/impact/impact_server.py` - Server API endpoint (/impact/wildcards) +- `modules/impact/config.py` - Configuration with quoted path support + +### Key Design Decisions +- **Lazy Loading**: Memory-efficient progressive loading strategy +- **Transitive Expansion**: Multi-level wildcard references through directory hierarchy +- **Case-Insensitive Matching**: Fuzzy matching for user convenience +- **Circular Reference Detection**: Max 100 iterations with clear error messages + +--- + +## Testing + +### Test Suites (86 tests) +1. **UTF-8 Encoding** (15 tests) - Multi-language support validation +2. **Error Handling** (10 tests) - Graceful error recovery +3. **Edge Cases** (20 tests) - Boundary conditions and special scenarios +4. **Deep Nesting** (17 tests) - 7-level transitive expansion + pattern matching +5. **On-Demand Loading** (8 tests) - Progressive loading with cache limits +6. **Config Quotes** (5 tests) - Configuration path handling +7. **Dynamic Prompts** (11 tests) - Statistical validation of dynamic features + +### Test Infrastructure +- Dedicated ports per suite (8188-8198) +- Automated server lifecycle management +- Comprehensive logging in `/tmp/` +- 100% pass rate with statistical validation + +--- + +## Documentation + +- **[README](README.md)** - Quick start and feature overview +- **[PRD](WILDCARD_SYSTEM_PRD.md)** - Complete product requirements +- **[Design](WILDCARD_SYSTEM_DESIGN.md)** - Technical architecture +- **[Testing Guide](WILDCARD_TESTING_GUIDE.md)** - Test procedures and validation + +--- + +## Quick Start + +### Basic Usage +```python +# Simple wildcard +"a photo of __animal__" + +# Dynamic prompt +"a {red|blue|green} __vehicle__" + +# Weighted selection (weight comes FIRST) +"{10::common|1::rare} scene" + +# Multi-select +"{2$$, $$happy|sad|angry|excited} person" +``` + +### Running Tests +```bash +cd tests/ +bash test_encoding.sh +bash test_error_handling.sh +bash test_edge_cases.sh +bash test_deep_nesting.sh +bash test_ondemand_loading.sh +bash test_config_quotes.sh +bash test_dynamic_prompts_full.sh +``` + +--- + +## Key Implementations + +### Weighted Selection Syntax +**Correct**: `{weight::option}` - Weight comes FIRST +- `{10::common|1::rare}` β†’ 91% common, 9% rare βœ… +- `{5::red|3::green|2::blue}` β†’ 50%, 30%, 20% βœ… + +**Incorrect**: `{option::weight}` - Treated as equal weights +- `{common::10|rare::1}` β†’ 50% each ❌ + +### Empty Line Filtering +Filter empty lines AND comment lines: +```python +[x for x in lines if x.strip() and not x.strip().startswith('#')] +``` + +### Config Path Quotes +Strip quotes from configuration paths: +```python +custom_wildcards_path = default_conf.get('custom_wildcards', '').strip('\'"') +``` + +--- + +## Limitations + +- Weighted selection supports integers and simple decimals only +- Complex decimal weights may conflict with multiselect pattern detection +- Circular references limited to 100 iterations +- Prefer integer weight ratios for clarity + +--- + +## Performance + +- **Lazy Loading**: Only load wildcards when needed +- **On-Demand Mode**: Progressive loading based on cache limits +- **Memory Efficient**: Configurable cache size (0.5MB - 100MB) +- **Fast Lookup**: Optimized directory traversal with pattern matching + +--- + +## Production Ready + +βœ… Zero known bugs +βœ… Complete PRD coverage +βœ… 100% test pass rate +βœ… Statistical validation +βœ… Comprehensive documentation +βœ… Multi-language support +βœ… Graceful error handling diff --git a/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_SYSTEM_DESIGN.md b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_SYSTEM_DESIGN.md new file mode 100644 index 00000000..fa9f8da6 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_SYSTEM_DESIGN.md @@ -0,0 +1,817 @@ +# Wildcard System - Design Document + +**Document Type**: Technical Design Document +**Product**: ComfyUI Impact Pack Wildcard System +**Version**: 2.0 (Depth-Agnostic Matching) +**Last Updated**: 2025-11-18 +**Status**: Released + +--- + +## 1. System Architecture + +### 1.1 High-Level Architecture + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ ComfyUI Frontend β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ ImpactWildcardProcessor / ImpactWildcardEncode β”‚ β”‚ +β”‚ β”‚ - Wildcard Prompt (editable) β”‚ β”‚ +β”‚ β”‚ - Populated Prompt (read-only in Populate mode) β”‚ β”‚ +β”‚ β”‚ - Mode: Populate / Fixed β”‚ β”‚ +β”‚ β”‚ - UI Indicator: 🟒 Full Cache / πŸ”΅ On-Demand β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Impact Server (API) β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ POST /impact/wildcards β”‚ β”‚ +β”‚ β”‚ GET /impact/wildcards/list β”‚ β”‚ +β”‚ β”‚ GET /impact/wildcards/list/loaded β”‚ β”‚ +β”‚ β”‚ GET /impact/wildcards/refresh β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Wildcard Processing Engine β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ process() - Main entry point β”‚ β”‚ +β”‚ β”‚ β”œβ”€ process_comment_out() β”‚ β”‚ +β”‚ β”‚ β”œβ”€ replace_options() - {a|b|c} β”‚ β”‚ +β”‚ β”‚ └─ replace_wildcard() - __wildcard__ β”‚ β”‚ +β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ get_wildcard_value() β”‚ β”‚ +β”‚ β”‚ β”œβ”€ Direct lookup β”‚ β”‚ +β”‚ β”‚ β”œβ”€ Depth-agnostic fallback ⭐ NEW β”‚ β”‚ +β”‚ β”‚ └─ On-demand file loading β”‚ β”‚ +β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ get_wildcard_options() - {option1|__wild__|option3} β”‚ β”‚ +β”‚ β”‚ └─ Pattern matching for wildcards in options β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Loading System β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Startup Phase β”‚ β”‚ +β”‚ β”‚ β”œβ”€ calculate_directory_size() - Early termination β”‚ β”‚ +β”‚ β”‚ β”œβ”€ Determine mode (Full Cache / On-Demand) β”‚ β”‚ +β”‚ β”‚ └─ scan_wildcard_metadata() - TXT metadata only β”‚ β”‚ +β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ Full Cache Mode β”‚ β”‚ +β”‚ β”‚ └─ load_wildcards() - Load all data β”‚ β”‚ +β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ On-Demand Mode ⭐ NEW β”‚ β”‚ +β”‚ β”‚ β”œβ”€ Pre-load: YAML files (keys in content) β”‚ β”‚ +β”‚ β”‚ └─ On-demand: TXT files (path = key) β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Data Storage β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ wildcard_dict = {} β”‚ β”‚ +β”‚ β”‚ - Full cache: All wildcard data β”‚ β”‚ +β”‚ β”‚ - On-demand: Not used β”‚ β”‚ +β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ available_wildcards = {} ⭐ NEW β”‚ β”‚ +β”‚ β”‚ - On-demand only: Metadata (path β†’ file) β”‚ β”‚ +β”‚ β”‚ - Example: {"dragon": "/path/dragon.txt"} β”‚ β”‚ +β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ loaded_wildcards = {} ⭐ NEW β”‚ β”‚ +β”‚ β”‚ - On-demand only: Loaded data cache β”‚ β”‚ +β”‚ β”‚ - Example: {"dragon": ["red dragon", "blue..."]} β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ File System β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ wildcards/ (bundled) β”‚ β”‚ +β”‚ β”‚ custom_wildcards/ (user-defined) β”‚ β”‚ +β”‚ β”‚ β”œβ”€ *.txt files (one option per line) β”‚ β”‚ +β”‚ β”‚ └─ *.yaml files (nested structure) β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ +``` + +--- + +## 2. Core Components + +### 2.1 Processing Engine + +#### 2.1.1 process() + +**Purpose**: Main entry point for wildcard text processing + +**Flow**: +```python +def process(text, seed=None): + 1. process_comment_out(text) # Remove # comments + 2. random.seed(seed) # Deterministic generation + 3. replace_options(text) # Process {a|b|c} + 4. replace_wildcard(text) # Process __wildcard__ + 5. return processed_text +``` + +**Features**: +- Maximum 100 iterations for nested expansion +- Deterministic with seed +- Supports transitive wildcards + +--- + +#### 2.1.2 replace_options() + +**Purpose**: Process dynamic prompts `{option1|option2}` + +**Supported Syntax**: +```python +{a|b|c} # Random selection +{3::a|2::b|c} # Weighted (3:2:1 ratio) +{2$$, $$a|b|c|d} # Multi-select 2, comma-separated +{2-4$$; $$a|b|c|d} # Multi-select 2-4, semicolon-separated +{a|{b|c}|d} # Nested options +``` + +**Algorithm**: +1. Parse weight prefix (`::`) +2. Calculate normalized probabilities +3. Use `np.random.choice()` with probabilities +4. Handle multi-select with custom separators + +--- + +#### 2.1.3 replace_wildcard() + +**Purpose**: Process wildcard references `__wildcard__` + +**Flow**: +```python +def replace_wildcard(string): + for each __match__: + 1. keyword = normalize(match) + 2. options = get_wildcard_value(keyword) + 3. if options: + random select from options + elif '*' in keyword: + pattern matching (for __*/name__) + else: + keep unchanged + 4. replace in string +``` + +**Pattern Matching** (`__*/name__`): +```python +if keyword.startswith('*/'): + base_name = keyword[2:] # "*/dragon" β†’ "dragon" + for k in wildcards: + if matches_pattern(k, base_name): + collect options + combine all options +``` + +--- + +### 2.2 Depth-Agnostic Matching ⭐ NEW + +#### 2.2.1 get_wildcard_value() + +**Purpose**: Retrieve wildcard data with automatic depth-agnostic fallback + +**Algorithm**: +```python +def get_wildcard_value(key): + # Phase 1: Direct lookup + if key in loaded_wildcards: + return loaded_wildcards[key] + + # Phase 2: File discovery + file_path = find_wildcard_file(key) + if file_path: + load and cache + return data + + # Phase 3: Depth-agnostic fallback ⭐ NEW + matched_keys = [] + for k in available_wildcards: + if matches_depth_agnostic(k, key): + matched_keys.append(k) + + if matched_keys: + # Combine all matched wildcards + all_options = [] + for mk in matched_keys: + all_options.extend(get_wildcard_value(mk)) + + # Cache combined result + loaded_wildcards[key] = all_options + return all_options + + return None +``` + +**Pattern Matching Logic**: +```python +def matches_depth_agnostic(stored_key, search_key): + """ + Examples: + search_key = "dragon" + stored_key = "dragon" β†’ True (exact) + stored_key = "custom_wildcards/dragon" β†’ True (ends with) + stored_key = "dragon/wizard" β†’ True (starts with) + stored_key = "a/b/dragon/c/d" β†’ True (contains) + """ + return (stored_key == search_key or + stored_key.endswith('/' + search_key) or + stored_key.startswith(search_key + '/') or + ('/' + search_key + '/') in stored_key) +``` + +**Benefits**: +- Works with any directory structure +- No configuration needed +- Combines multiple sources for variety +- Cached for performance + +--- + +### 2.3 Loading System + +#### 2.3.1 Mode Detection + +**Decision Algorithm**: +```python +def determine_loading_mode(): + total_size = calculate_directory_size() + cache_limit = config.wildcard_cache_limit_mb * 1024 * 1024 + + if total_size >= cache_limit: + return ON_DEMAND_MODE + else: + return FULL_CACHE_MODE +``` + +**Early Termination**: +```python +def calculate_directory_size(): + size = 0 + for file in walk(directory): + size += file_size + if size >= cache_limit: + return size # Early termination + return size +``` + +**Performance**: < 1 second for 10GB+ collections + +--- + +#### 2.3.2 Metadata Scanning ⭐ NEW + +**Purpose**: Discover TXT wildcards without loading data + +**Algorithm**: +```python +def scan_wildcard_metadata(path): + for file in walk(path): + if file.endswith('.txt'): + rel_path = relpath(file, path) + key = normalize(remove_extension(rel_path)) + available_wildcards[key] = file # Store path only +``` + +**Storage**: +```python +available_wildcards = { + "dragon": "/path/custom_wildcards/dragon.txt", + "custom_wildcards/dragon": "/path/custom_wildcards/dragon.txt", + "dragon/wizard": "/path/dragon/wizard.txt", + ... +} +``` + +**Memory**: ~50 bytes per file (path string) + +--- + +#### 2.3.3 On-Demand Loading ⭐ NEW + +**Purpose**: Load wildcard data only when accessed + +**Flow**: +``` +User request: __dragon__ + ↓ +get_wildcard_value("dragon") + ↓ +Not in cache β†’ find_wildcard_file("dragon") + ↓ +File not found β†’ Depth-agnostic fallback + ↓ +Pattern match: ["custom_wildcards/dragon", "dragon/wizard", ...] + ↓ +Load each matched file + ↓ +Combine all options + ↓ +Cache result: loaded_wildcards["dragon"] = combined_options + ↓ +Return combined_options +``` + +**YAML Pre-Loading**: +```python +def load_yaml_wildcards(): + """ + YAML wildcards CANNOT be on-demand because: + - Keys are inside file content, not file path + - Must parse entire file to discover keys + + Example: + File: colors.yaml + Content: + warm: [red, orange, yellow] + cold: [blue, green, purple] + + To know "__colors/warm__" exists, must parse entire file. + """ + for yaml_file in find_yaml_files(): + data = yaml.load(yaml_file) + for key, value in data.items(): + loaded_wildcards[key] = value +``` + +--- + +### 2.4 Data Structures + +#### 2.4.1 Global State + +```python +# Configuration +_on_demand_mode = False # True if on-demand mode active +wildcard_dict = {} # Full cache mode storage +available_wildcards = {} # On-demand metadata (key β†’ file path) +loaded_wildcards = {} # On-demand loaded data (key β†’ options) + +# Thread safety +wildcard_lock = threading.Lock() +``` + +#### 2.4.2 Key Normalization + +```python +def wildcard_normalize(x): + """ + Normalize wildcard keys for consistent lookup + + Examples: + "Dragon" β†’ "dragon" (lowercase) + "dragon.txt" β†’ "dragon" (remove extension) + "folder/Dragon" β†’ "folder/dragon" (lowercase) + """ + return x.lower().replace('\\', '/') +``` + +--- + +## 3. API Design + +### 3.1 POST /impact/wildcards + +**Purpose**: Process wildcard text + +**Request**: +```json +{ + "text": "a {red|blue} __flowers__", + "seed": 42 +} +``` + +**Response**: +```json +{ + "text": "a red rose" +} +``` + +**Implementation**: +```python +@app.post("/impact/wildcards") +def process_wildcards(request): + text = request.json["text"] + seed = request.json.get("seed") + result = process(text, seed) + return {"text": result} +``` + +--- + +### 3.2 GET /impact/wildcards/list/loaded ⭐ NEW + +**Purpose**: Track progressive loading + +**Response**: +```json +{ + "data": ["__dragon__", "__flowers__"], + "on_demand_mode": true, + "total_available": 1000 +} +``` + +**Implementation**: +```python +@app.get("/impact/wildcards/list/loaded") +def get_loaded_wildcards(): + with wildcard_lock: + if _on_demand_mode: + return { + "data": [f"__{k}__" for k in loaded_wildcards.keys()], + "on_demand_mode": True, + "total_available": len(available_wildcards) + } + else: + return { + "data": [f"__{k}__" for k in wildcard_dict.keys()], + "on_demand_mode": False, + "total_available": len(wildcard_dict) + } +``` + +--- + +### 3.3 GET /impact/wildcards/refresh + +**Purpose**: Reload all wildcards + +**Implementation**: +```python +@app.get("/impact/wildcards/refresh") +def refresh_wildcards(): + global wildcard_dict, loaded_wildcards, available_wildcards + + with wildcard_lock: + # Clear all caches + wildcard_dict.clear() + loaded_wildcards.clear() + available_wildcards.clear() + + # Re-initialize + wildcard_load() + + return {"status": "ok"} +``` + +--- + +## 4. File Format Support + +### 4.1 TXT Format + +**Structure**: +``` +# flowers.txt +rose +tulip +# Comments start with # +sunflower +``` + +**Parsing**: +```python +def load_txt_wildcard(file_path): + with open(file_path) as f: + lines = f.read().splitlines() + return [x for x in lines if not x.strip().startswith('#')] +``` + +**On-Demand**: βœ… Fully supported + +--- + +### 4.2 YAML Format + +**Structure**: +```yaml +# colors.yaml +warm: + - red + - orange + - yellow + +cold: + - blue + - green + - purple +``` + +**Usage**: `__colors/warm__`, `__colors/cold__` + +**Parsing**: +```python +def load_yaml_wildcard(file_path): + data = yaml.load(file_path) + for key, value in data.items(): + if isinstance(value, list): + loaded_wildcards[key] = value + elif isinstance(value, dict): + # Recursive for nested structure + load_nested(key, value) +``` + +**On-Demand**: ⚠️ Always pre-loaded (keys in content) + +--- + +## 5. UI Integration + +### 5.1 ImpactWildcardProcessor Node + +**Features**: +- **Wildcard Prompt**: User input with wildcard syntax +- **Populated Prompt**: Processed result +- **Mode Selector**: Populate / Fixed + - **Populate**: Process wildcards on queue, populate result + - **Fixed**: Use populated text as-is (for saved images) + +**UI Indicator**: +- 🟒 **Full Cache**: All wildcards loaded +- πŸ”΅ **On-Demand**: Progressive loading active (shows count) + +--- + +### 5.2 ImpactWildcardEncode Node + +**Additional Features**: +- **LoRA Loading**: `` +- **LoRA Block Weight**: `` +- **BREAK Syntax**: Separate encoding with Concat +- **Clip Integration**: Returns processed model + clip + +**Special Syntax**: +``` + +``` + +--- + +### 5.3 Detailer Wildcard Features + +**Ordering**: +- `[ASC]`: Ascending order (x, y) +- `[DSC]`: Descending order (x, y) +- `[ASC-SIZE]`: Ascending by area +- `[DSC-SIZE]`: Descending by area +- `[RND]`: Random order + +**Control**: +- `[SEP]`: Separate prompts per detection area +- `[SKIP]`: Skip detailing for this area +- `[STOP]`: Stop detailing (including current area) +- `[LAB]`: Label-based application +- `[CONCAT]`: Concatenate with positive conditioning + +**Example**: +``` +[ASC] +1girl, blue eyes, smile [SEP] +1boy, brown eyes [SEP] +``` + +--- + +## 6. Performance Optimization + +### 6.1 Startup Optimization + +**Techniques**: +1. **Early Termination**: Stop size calculation at cache limit +2. **Metadata Only**: Don't load TXT file content +3. **YAML Pre-loading**: Small files, pre-load is acceptable + +**Results**: +- 10GB collection: 20-60 min β†’ < 1 min (95%+ improvement) + +--- + +### 6.2 Runtime Optimization + +**Techniques**: +1. **Caching**: Store loaded wildcards in memory +2. **Depth-Agnostic Caching**: Cache combined pattern results +3. **NumPy Random**: Fast random generation + +**Results**: +- First access: < 50ms +- Cached access: < 1ms + +--- + +### 6.3 Memory Optimization + +**Techniques**: +1. **Progressive Loading**: Load only accessed wildcards +2. **Metadata Storage**: Store paths, not data +3. **Combined Caching**: Cache pattern match results + +**Results**: +- Initial: < 100MB (vs 1GB+ in old implementation) +- Growth: Linear with usage, not total size + +--- + +## 7. Error Handling + +### 7.1 File Not Found + +**Scenario**: Wildcard file doesn't exist + +**Handling**: +```python +def get_wildcard_value(key): + file_path = find_wildcard_file(key) + if file_path is None: + # Try depth-agnostic fallback + matched = find_pattern_matches(key) + if matched: + return combine_matched(matched) + + # No match found - log warning, return None + logging.warning(f"Wildcard not found: {key}") + return None +``` + +**User Impact**: Wildcard remains unexpanded + +--- + +### 7.2 File Read Error + +**Scenario**: Cannot read file (permissions, encoding, etc.) + +**Handling**: +```python +def load_txt_wildcard(file_path): + try: + with open(file_path, 'r', encoding="ISO-8859-1") as f: + return f.read().splitlines() + except Exception as e: + logging.error(f"Failed to load {file_path}: {e}") + return None +``` + +**User Impact**: Wildcard not loaded, error logged + +--- + +### 7.3 Infinite Loop Protection + +**Scenario**: Circular wildcard references + +**Protection**: +```python +def process(text, seed=None): + max_iterations = 100 + for i in range(max_iterations): + new_text = process_one_pass(text) + if new_text == text: + break # No changes, done + text = new_text + + if i == max_iterations - 1: + logging.warning("Max iterations reached") + + return text +``` + +**User Impact**: Processing stops after 100 iterations + +--- + +## 8. Testing Strategy + +### 8.1 Unit Tests + +**Coverage**: +- `process()`: All syntax variations +- `replace_options()`: Weight, multi-select, nested +- `replace_wildcard()`: Direct, pattern, depth-agnostic +- `get_wildcard_value()`: Direct, fallback, caching + +--- + +### 8.2 Integration Tests + +**Scenarios**: +- Full cache mode activation +- On-demand mode activation +- Progressive loading tracking +- Depth-agnostic matching +- API endpoints + +**Test Suite**: `tests/test_dragon_wildcard_expansion.sh` + +--- + +### 8.3 Performance Tests + +**Metrics**: +- Startup time (10GB collection) +- Memory usage (initial, after 100 accesses) +- First access latency +- Cached access latency +- Pattern matching latency + +**Test Tool**: `/tmp/test_depth_agnostic.sh` + +--- + +## 9. Security Considerations + +### 9.1 Path Traversal + +**Risk**: Malicious wildcard names could access files outside wildcard directory + +**Mitigation**: +```python +def find_wildcard_file(key): + # Normalize and validate path + safe_key = os.path.normpath(key) + if '..' in safe_key or safe_key.startswith('/'): + logging.error(f"Invalid wildcard path: {key}") + return None + + # Ensure result is within wildcard directory + file_path = os.path.join(wildcards_path, safe_key) + if not file_path.startswith(wildcards_path): + logging.error(f"Path traversal attempt: {key}") + return None + + return file_path +``` + +--- + +### 9.2 Resource Exhaustion + +**Risk**: Very large wildcards or infinite loops + +**Mitigation**: +1. **Iteration Limit**: Max 100 expansions +2. **File Size Limit**: Reasonable file size checks +3. **Memory Monitoring**: Track loaded wildcard count + +--- + +## 10. Future Enhancements + +### 10.1 Planned Features + +1. **LRU Cache**: Automatic eviction of least-used wildcards +2. **Background Preloading**: Preload frequently-used wildcards +3. **Persistent Cache**: Save loaded wildcards across restarts +4. **Usage Statistics**: Track wildcard access patterns +5. **Compression**: Compress infrequently-used wildcards + +### 10.2 Performance Improvements + +1. **Parallel Loading**: Load multiple wildcards concurrently +2. **Index Structure**: B-tree for faster lookups +3. **Memory Pooling**: Reduce allocation overhead + +--- + +## 11. References + +### 11.1 External Documentation + +- [Product Requirements Document](WILDCARD_SYSTEM_PRD.md) +- [User Guide](WILDCARD_SYSTEM_OVERVIEW.md) +- [Testing Guide](WILDCARD_TESTING_GUIDE.md) +- [Tutorial](../../ComfyUI-extension-tutorials/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) + +### 11.2 Code References + +- **Core Engine**: `modules/impact/wildcards.py` +- **API Server**: `modules/impact/impact_server.py` +- **UI Nodes**: `nodes.py` (ImpactWildcardProcessor, ImpactWildcardEncode) + +--- + +**Document Approval**: +- Engineering Lead: βœ… Approved +- Architecture Review: βœ… Approved +- Security Review: βœ… Approved + +**Last Review**: 2025-11-18 diff --git a/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_SYSTEM_PRD.md b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_SYSTEM_PRD.md new file mode 100644 index 00000000..811c47b2 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_SYSTEM_PRD.md @@ -0,0 +1,435 @@ +# Wildcard System - Product Requirements Document + +**Product**: ComfyUI Impact Pack Wildcard System +**Version**: 2.0 (Depth-Agnostic Matching) +**Status**: Released +**Last Updated**: 2025-11-18 + +--- + +## 1. Overview + +### 1.1 Product Vision + +The Wildcard System provides **dynamic text generation** for AI prompts, enabling users to create rich, varied prompts with minimal manual effort. + +### 1.2 Target Users + +- **AI Artists**: Creating varied prompts for image generation +- **Content Creators**: Generating diverse text content +- **Game Designers**: Dynamic NPC dialogue and procedural content +- **ComfyUI Users**: Workflow automation with dynamic text + +--- + +## 2. Core Features + +> **Note**: For detailed syntax examples and usage guides, see the [ImpactWildcard Tutorial](../../../ComfyUI-extension-tutorials/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md). + +### 2.1 Wildcard Syntax + +**Basic Wildcards**: +- `__wildcard_name__` - Simple text replacement (e.g., `__flower__` β†’ random flower from flower.txt) +- `__category/subcategory__` - Hierarchical organization with subdirectories (e.g., `__obj/person__`) +- Transitive wildcards - Wildcards can reference other wildcards +- Case-insensitive matching - `__Jewel__` and `__jewel__` are identical +- `*` aggregation pattern (V4.15.1+) - Groups all items from path and subdirectories into one collection + +**Quantifiers**: +- `N#__wildcard__` - Repeat wildcard N times +- Example: `5#__wildcards__` expands to `__wildcards__|__wildcards__|__wildcards__|__wildcards__|__wildcards__` +- Can be combined with multi-select: `{2$$, $$5#__wildcards__}` + +**Comments**: +- Lines starting with `#` are treated as comments and removed +- Text following a comment is separated by single blank space from text before comment +- Example: + ``` + first {a|b|c} second # not a comment, + # this is a comment + trailing text + ``` + Becomes: `first a second # not a comment, trailing text` + +**Pattern Matching**: +- `__*/wildcard__` - Depth-agnostic pattern matching at any directory level +- Automatic fallback when direct lookup fails + +--- + +### 2.2 Dynamic Prompts + +**Basic Selection**: +- `{option1|option2|option3}` - Random selection from options +- Unlimited nesting: `{a|{d|e|f}|c}` - Nested options are evaluated +- Example: `{blue apple|red {cherry|berry}|green melon}` β†’ `blue apple`, `red cherry`, `red berry`, or `green melon` +- Complex nesting: `1{girl is holding {blue pencil|red __fruit__|colorful __flower__}|boy is riding __vehicle__}` + +**Weighted Selection**: +- `{weight::option}` - Control selection probability +- **Syntax**: Weight comes FIRST, then `::`, then the option value +- **Correct**: `{10::common|1::rare}` β†’ 10:1 ratio (β‰ˆ91% vs β‰ˆ9%) +- **Incorrect**: `{common::10|rare::1}` β†’ Will be treated as equal weights (50% vs 50%) +- Weights are normalized: `{5::red|3::green|2::blue}` β†’ 50% red, 30% green, 20% blue +- Unweighted options default to weight 1: `{5::red|green|2::blue}` β†’ 5:1:2 ratio + +**Limitations**: +- Weights must be integers or simple decimals (e.g., `5`, `10`, `0.5`) +- Complex decimal weights may cause parsing issues due to multiselect pattern conflicts +- For decimal ratios, prefer integer equivalents: use `{5::a|3::b|2::c}` instead of `{0.5::a|0.3::b|0.2::c}` + +**Multi-Select**: +- `{n$$opt1|opt2|opt3}` - Select exactly n items +- `{n1-n2$$opt1|opt2|opt3}` - Select between n1 and n2 items (excess ignored if range exceeds options) +- `{-n$$opt1|opt2|opt3}` - Select between 1 and n items +- **Custom separator**: `{n$$ separator $$opt1|opt2|opt3}` + - Example: `{2$$ and $$red|blue|green}` β†’ "red and blue" + - Example: `{1-2$$ or $$apple|orange|banana}` β†’ "apple" or "apple or orange" + +--- + +### 2.3 ComfyUI Nodes + +**ImpactWildcardProcessor**: +- **Purpose**: Browser-level wildcard processing for prompt generation +- **Dual Input Fields**: + - Upper field: Wildcard Prompt (accepts wildcard syntax) + - Lower field: Populated Prompt (displays generated result) +- **Mode Control**: + - **Populate**: Processes wildcards on queue prompt, populates result (read-only) + - **Fixed**: Ignores wildcard prompt, allows manual editing of populated prompt +- **Seed Input**: + - Supports seed-based deterministic generation + - Compatible seed inputs: `ImpactInt`, `Seed (rgthree)` only + - Limitation: Reads superficial input only, does not use execution results from other nodes +- **UI Indicator**: + - 🟒 Full Cache: All wildcards pre-loaded + - πŸ”΅ On-Demand: Shows count of loaded wildcards + +**ImpactWildcardEncode**: +- All features of ImpactWildcardProcessor +- **LoRA Loading**: `` syntax + - If `clip_weight` omitted, uses same value as `model_weight` + - All loaded LoRAs applied to both `model` and `clip` outputs +- **LoRA Block Weight (LBW)** (requires Inspire Pack): + - Syntax: `` + - Use `;` as separator within spec, recommended to end with `;` + - Specs without `A=` or `B=` β†’ used in `Lora Loader (Block Weight)` node + - Specs with `A=` or `B=` β†’ parameters for `A` and `B` in loader node + - Examples: + - `` + - `` + - `` +- **BREAK Syntax**: Separately encode prompts and connect using `Conditioning (Concat)` +- **Output**: Returns processed conditioning with all LoRAs applied + +--- + +### 2.4 Detailer Integration + +Special syntax for Detailer Wildcard nodes (region-specific prompt application). + +**Ordering Control** (place at very beginning of prompt): +- `[ASC]` - Ascending order by (x, y) coordinates (left takes precedence, then top) +- `[DSC]` - Descending order by (x, y) coordinates +- `[ASC-SIZE]` - Ascending order by area size +- `[DSC-SIZE]` - Descending order by area size +- `[RND]` - Random order +- Example: `[ASC]\n1girl, blue eyes, smile [SEP]\n1boy, brown eyes [SEP]` + +**Area Control**: +- `[SEP]` - Separator for different prompts per detection area (SEG) +- `[SKIP]` - Skip detailing for current SEG +- `[STOP]` - Stop detailing, including current SEG +- `[CONCAT]` - Concatenate wildcard conditioning with positive conditioning (instead of replacing) + +**Label-Based Application**: +- `[LAB]` - Apply prompts based on labels (each label appears once) +- `[ALL]` - Prefix that applies to all labels +- Example: + ``` + [LAB] + [ALL] laugh, detailed eyes + [Female] blue eyes + [Male] brown eyes + ``` + Female labels get: "laugh, detailed eyes, blue eyes" + Male labels get: "laugh, detailed eyes, brown eyes" + +**Complete Example**: +``` +[DSC-SIZE] +sun glasses[SEP] +[SKIP][SEP] +blue glasses[SEP] +[STOP] +``` +Result: Faces sorted by size descending, largest gets "sun glasses", second largest skipped, third gets "blue glasses", rest not detailed. + +--- + +### 2.5 File Formats + +**TXT Files**: +- **Format**: One option per line (comma-separated on single line = one item) +- **Comments**: Lines starting with `#` are comments +- **Encoding**: UTF-8 +- **Loading**: Supports on-demand loading (loaded only when used) +- **Subfolder Support**: Use path in wildcard name (e.g., `custom_wildcards/obj/person.txt` β†’ `__obj/person__`) +- **Example** (flower.txt): + ``` + rose + orchid + iris + carnation + lily + ``` + +**YAML Files** (V4.18.4+): +- **Format**: Nested hierarchical structure with multiple levels +- **Usage**: Keys become wildcard paths (e.g., `astronomy.Celestial-Bodies` β†’ `__astronomy/Celestial-Bodies__`) +- **Loading**: Always pre-loaded at startup (keys exist in file content, not path) +- **Example**: + ```yaml + astronomy: + Celestial-Bodies: + - Star + - Planet + surface-swap: + - swap the surfaces for + - replace the surfaces with + ``` +- **Performance Note**: For large collections with on-demand loading, prefer TXT file structure over YAML + +**Wildcard Directories**: +- Default directories: `ComfyUI-Impact-Pack/wildcards/` and `ComfyUI-Impact-Pack/custom_wildcards/` +- Recommendation: Use `custom_wildcards/` to avoid conflicts during updates +- Custom path: Configure via `impact-pack.ini` β†’ `custom_wildcards` setting + +--- + +### 2.6 System Features + +**Progressive On-Demand Loading** ⭐: +- **Automatic Mode Detection**: System chooses optimal loading strategy based on collection size +- **Full Cache Mode** (total size < 50MB): + - All wildcards loaded into memory at startup + - Instant access with no load delays + - UI Indicator: 🟒 `Select Wildcard 🟒 Full Cache` + - Startup log: `Using full cache mode.` +- **On-Demand Mode** (total size β‰₯ 50MB): + - Only metadata scanned at startup (< 1 minute for 10GB+) + - Actual wildcard data loaded progressively as accessed + - Low initial memory (< 100MB) + - UI Indicator: πŸ”΅ `Select Wildcard πŸ”΅ On-Demand: X loaded` + - Startup log: `Using on-demand loading mode (metadata scan only).` +- **Configuration**: Adjust threshold via `impact-pack.ini` β†’ `wildcard_cache_limit_mb = 50` +- **File Type Behavior**: + - TXT files: Full on-demand loading support + - YAML files: Always pre-loaded (keys embedded in content) +- **Refresh Behavior**: Clears all cached data, re-scans directories, re-determines mode + +**Depth-Agnostic Matching** ⭐: +- **Automatic Fallback**: When direct lookup fails, searches for pattern matches at any depth +- **Pattern Matching**: Finds keys that end with, start with, or contain the wildcard name +- **Multi-Source Combination**: Combines all matched wildcards into single selection pool +- **Zero Configuration**: Works automatically with any directory structure +- **Performance**: Results cached for subsequent access + +**Wildcard Refresh API**: +- `GET /impact/wildcards/refresh` - Reload wildcards without restarting ComfyUI +- Clears all cached data (full cache and on-demand loaded) +- Re-scans wildcard directories +- Re-determines loading mode + +**Other APIs**: +- `POST /impact/wildcards` - Process wildcard text with seed +- `GET /impact/wildcards/list` - List all available wildcards +- `GET /impact/wildcards/list/loaded` - Show currently loaded wildcards (on-demand mode) + +**Deterministic Generation**: +- Seed-based random selection ensures reproducibility +- Same seed + same wildcard = same result +- Compatible with ImpactInt and Seed(rgthree) nodes + +--- + +## 3. Requirements + +### 3.1 Functional Requirements + +**FR-1: Wildcard Processing** +- Support all documented syntax patterns +- Deterministic results with seed control +- Up to 100 levels of nested expansion +- Graceful error handling + +**FR-2: Dynamic Prompts** +- Random, weighted, and multi-select +- Unlimited nesting depth +- Custom separators + +**FR-3: Progressive Loading** +- Automatic mode detection +- On-demand loading for large collections +- Real-time tracking + +**FR-4: Depth-Agnostic Matching** +- Automatic fallback pattern matching +- Combine all matched wildcards +- Support any directory structure + +**FR-5: ComfyUI Integration** +- ImpactWildcardProcessor node +- ImpactWildcardEncode node with LoRA +- Detailer special syntax + +--- + +### 3.2 Non-Functional Requirements + +**NFR-1: Usability** +- Time to first success: < 5 minutes +- Zero configuration for basic use +- Clear error messages + +**NFR-2: Reliability** +- 100% deterministic with same seed +- Graceful error handling +- No data loss on refresh + +**NFR-3: Compatibility** +- Python 3.8+ +- Windows, Linux, macOS +- Backward compatible with v1.x + +**NFR-4: Scalability** +- Collections up to 100GB +- Up to 1M wildcard files +- Concurrent multi-user access + +--- + +## 4. Configuration + +**File**: `impact-pack.ini` (in ComfyUI-Impact-Pack directory) + +```ini +[default] +# Custom wildcard directory (optional) +# Use this to specify additional wildcard directory path +custom_wildcards = /path/to/wildcards + +# Cache size limit in MB (default: 50) +# Determines threshold for Full Cache vs On-Demand mode +wildcard_cache_limit_mb = 50 +``` + +**Default Wildcard Directories**: +- `ComfyUI-Impact-Pack/wildcards/` - System wildcards (avoid modifying) +- `ComfyUI-Impact-Pack/custom_wildcards/` - User wildcards (recommended) +- Custom path via `custom_wildcards` setting (optional) + +**Configuration Best Practices**: +- No configuration required for basic use +- Use `custom_wildcards/` to avoid conflicts during updates +- Adjust `wildcard_cache_limit_mb` based on system memory and collection size: + - Lower limit β†’ More likely to use on-demand mode (slower first access, lower memory) + - Higher limit β†’ More likely to use full cache mode (faster access, higher memory) +- For large collections (10GB+), consider organizing into subdirectories for better performance + +--- + +## 5. User Workflows + +### 5.1 Getting Started + +**Goal**: First wildcard in < 5 minutes + +1. Create file: `custom_wildcards/flower.txt` +2. Add content (one per line): + ``` + rose + orchid + iris + carnation + lily + ``` +3. Use in ImpactWildcardProcessor: `a beautiful __flower__` +4. Set mode to Populate and run queue prompt +5. Result: Random selection like "a beautiful rose" + +### 5.2 Reusable Prompt Templates + +**Goal**: Save frequently used prompts + +1. Create `custom_wildcards/ppos.txt` with: + ``` + photorealistic:1.4, best quality:1.4 + ``` +2. Use concise prompt: `__ppos__, beautiful nature` +3. Result: "photorealistic:1.4, best quality:1.4, beautiful nature" + +### 5.3 Large Collections + +**Goal**: Import 10GB+ seamlessly + +1. Copy large wildcard collection to directory +2. Start ComfyUI (< 1 minute startup with on-demand mode) +3. Check UI indicator: πŸ”΅ On-Demand mode active +4. Use wildcards immediately (loaded on first access) +5. Subsequent uses are cached for speed + +### 5.4 LoRA + Wildcards + +**Goal**: Dynamic character with LoRA + +1. Create `custom_wildcards/characters.txt`: + ``` + young girl with blue dress + warrior with armor + mage with robe + ``` +2. Use ImpactWildcardEncode node +3. Prompt: `__characters__, {day|night} scene, detailed face` +4. Result: Random character with LoRA loaded + random time of day + +### 5.5 Multi-Face Detailing + +**Goal**: Different prompts for multiple detected faces + +1. Create Detailer Wildcard prompt: + ``` + [DSC-SIZE] + blue eyes, smile[SEP] + brown eyes, serious[SEP] + green eyes, laugh + ``` +2. Result: Largest face gets "blue eyes, smile", second gets "brown eyes, serious", third gets "green eyes, laugh" + +--- + +## 6. References + +### User Documentation +- **[ImpactWildcard Tutorial](../../../ComfyUI-extension-tutorials/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md)** - Complete feature documentation + +### Technical Documentation +- **[Design Document](WILDCARD_SYSTEM_DESIGN.md)** - Architecture details +- **[Testing Guide](WILDCARD_TESTING_GUIDE.md)** - Test procedures + +--- + +## Appendix: Glossary + +- **Wildcard**: Reusable text snippet (`__name__`) +- **Dynamic Prompt**: Inline options (`{a|b|c}`) +- **Pattern Matching**: Finding wildcards by partial match +- **Depth-Agnostic**: Works with any directory structure +- **On-Demand Loading**: Load data when accessed +- **LoRA**: Low-Rank Adaptation models +- **Detailer**: Node for region-specific processing + +--- + +**Last Updated**: 2025-11-18 diff --git a/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_TESTING_GUIDE.md b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_TESTING_GUIDE.md new file mode 100644 index 00000000..495a8956 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/docs/wildcards/WILDCARD_TESTING_GUIDE.md @@ -0,0 +1,381 @@ +# Wildcard System Testing Guide + +Complete testing guide for the ComfyUI Impact Pack wildcard system. + +--- + +## πŸ“‹ Table of Contents + +1. [Test Overview](#test-overview) +2. [Test Suites](#test-suites) +3. [Quick Start](#quick-start) +4. [Running Tests](#running-tests) +5. [Test Validation](#test-validation) + +--- + +## Test Overview + +### Test Statistics +- **Total Tests**: 86 tests across 7 suites +- **Coverage**: 100% of PRD core requirements +- **Pass Rate**: 100% +- **Test Types**: UTF-8, error handling, edge cases, nesting, on-demand, config, dynamic prompts + +### Test Structure + +``` +tests/ +β”œβ”€β”€ Test Suites (7 suites, 86 tests) +β”‚ β”œβ”€β”€ test_encoding.sh # 15 tests - UTF-8 multi-language support +β”‚ β”œβ”€β”€ test_error_handling.sh # 10 tests - Error recovery and graceful handling +β”‚ β”œβ”€β”€ test_edge_cases.sh # 20 tests - Boundary conditions and special cases +β”‚ β”œβ”€β”€ test_deep_nesting.sh # 17 tests - 7-level transitive expansion + pattern matching +β”‚ β”œβ”€β”€ test_ondemand_loading.sh # 8 tests - Progressive lazy loading with cache limits +β”‚ β”œβ”€β”€ test_config_quotes.sh # 5 tests - Configuration path handling +β”‚ └── test_dynamic_prompts_full.sh # 11 tests - Weighted/multiselect with statistical validation +β”‚ +β”œβ”€β”€ Documentation +β”‚ β”œβ”€β”€ README.md # Test suite overview +β”‚ └── RUN_ALL_TESTS.md # Execution guide +β”‚ +β”œβ”€β”€ Test Samples +β”‚ └── wildcards/samples/ # Test wildcard files +β”‚ β”œβ”€β”€ level1/.../level7/ # 7-level nesting structure +β”‚ β”œβ”€β”€ *.txt # Various test wildcards +β”‚ └── μ•„λ¦„λ‹€μš΄μƒ‰.txt # Korean UTF-8 sample +β”‚ +└── Utilities + └── restart_test_server.sh # Server management utility +``` + +--- + +## Test Suites + +### 1. UTF-8 Encoding Tests (15 tests) +**File**: `test_encoding.sh` +**Port**: 8188 +**Purpose**: Multi-language support validation + +**Test Coverage**: +- Korean text (ν•œκΈ€) +- Chinese text (δΈ­ζ–‡) +- Arabic text (Ψ§Ω„ΨΉΨ±Ψ¨ΩŠΨ©) +- Emoji support (πŸ‰πŸ”₯⚑) +- Special characters +- Mixed multi-language content +- Case-insensitive Korean matching + +**Key Validations**: +- All non-ASCII characters preserved +- UTF-8 encoding consistency +- No character corruption +- Proper string comparison + +--- + +### 2. Error Handling Tests (10 tests) +**File**: `test_error_handling.sh` +**Port**: 8189 +**Purpose**: Graceful error recovery + +**Test Coverage**: +- Non-existent wildcards +- Missing files +- Circular reference detection (direct and indirect) +- Malformed dynamic prompt syntax +- Deep nesting without crashes +- Invalid quantifiers + +**Key Validations**: +- No server crashes +- Clear error messages +- Original text preserved on error +- Circular detection within 100 iterations + +--- + +### 3. Edge Cases Tests (20 tests) +**File**: `test_edge_cases.sh` +**Port**: 8190 +**Purpose**: Boundary conditions and special scenarios + +**Test Coverage**: +- Empty lines and comments in wildcard files +- Very long lines (>1000 chars) +- Basic wildcard expansion +- Case-insensitive matching +- Quantifiers (1-10 repetitions) +- Pattern matching (`__*/name__`) + +**Key Validations**: +- Empty lines filtered correctly +- Comments ignored properly +- Long text handling +- Quantifier accuracy +- Pattern matching at any depth + +--- + +### 4. Deep Nesting Tests (17 tests) +**File**: `test_deep_nesting.sh` +**Port**: 8194 +**Purpose**: 7-level transitive expansion and pattern matching + +**Test Coverage**: +- Direct level access (Level 1-7) +- Transitive expansion through all levels +- Multiple wildcard nesting +- Mixed depth combinations +- Quantifiers with nesting +- Weighted selection with nesting +- Depth-agnostic pattern matching + +**Key Validations**: +- All 7 levels fully expanded +- No unexpanded wildcards remain +- Pattern matching ignores directory depth +- Complex combinations work correctly + +**Directory Structure**: +``` +samples/level1/level2/level3/level4/level5/level6/level7/ +``` + +--- + +### 5. On-Demand Loading Tests (8 tests) +**File**: `test_ondemand_loading.sh` +**Port**: 8191 +**Purpose**: Progressive lazy loading with configurable cache limits + +**Test Coverage**: +- Small cache (1MB) - On-demand mode +- Medium cache (10MB) - Hybrid mode +- Large cache (100MB) - Full cache mode +- Aggressive lazy (0.5MB) +- Various thresholds (5MB, 20MB, 50MB) + +**Key Validations**: +- Correct loading mode selection +- Progressive loading functionality +- Cache limit enforcement +- No performance degradation + +**Note**: Uses temporary samples in `/tmp/` with auto-cleanup + +--- + +### 6. Config Quotes Tests (5 tests) +**File**: `test_config_quotes.sh` +**Port**: 8192 +**Purpose**: Configuration path handling with quotes + +**Test Coverage**: +- Paths with single quotes +- Paths with double quotes +- Paths with spaces (quoted) +- Mixed quote scenarios +- Unquoted baseline + +**Key Validations**: +- Quotes stripped correctly +- Paths with spaces handled +- Wildcards loaded from quoted paths + +--- + +### 7. Dynamic Prompts Tests (11 tests) +**File**: `test_dynamic_prompts_full.sh` +**Port**: 8193 +**Purpose**: Statistical validation of weighted and multiselect features + +**Test Coverage**: +- Multiselect (2-5 items) with custom separators +- Weighted selection (various ratios: 10:1, 1:1:1, 5:3:2) +- Nested dynamic prompts +- Basic random selection +- Seed variation validation + +**Statistical Validation**: +- 100 iterations for weighted selection +- 20 iterations for multiselect +- Distribution verification (Β±15% tolerance) +- Duplicate detection +- Separator validation + +**Key Validations**: +- Exact item count for multiselect +- No duplicates in multiselect +- Correct separators +- Statistical distribution matches weight ratios +- Nested prompt expansion + +--- + +## Quick Start + +### Run All Tests +```bash +cd tests/ +bash test_encoding.sh && \ +bash test_error_handling.sh && \ +bash test_edge_cases.sh && \ +bash test_deep_nesting.sh && \ +bash test_ondemand_loading.sh && \ +bash test_config_quotes.sh && \ +bash test_dynamic_prompts_full.sh +``` + +### Run Individual Suite +```bash +cd tests/ +bash test_encoding.sh +``` + +### Check Test Results +All tests output: +- βœ… PASS - Test succeeded with validation +- ❌ FAIL - Test failed (should not occur) +- ⚠️ WARNING - Partial success or non-critical issue + +--- + +## Running Tests + +### Prerequisites +- ComfyUI server must be installable +- Port availability (8188-8194) +- Network access to 127.0.0.1 +- Python 3 with json module + +### Automatic Server Management +All test suites automatically: +1. Kill any existing server on target port +2. Create temporary configuration file +3. Start ComfyUI server +4. Wait for server ready (up to 60s) +5. Execute tests +6. Clean up (kill server, remove config) + +### Test Execution Flow +``` +1. Setup + β”œβ”€ Kill existing server on port + β”œβ”€ Create impact-pack.ini config + └─ Start ComfyUI server + +2. Wait for Ready + β”œβ”€ Poll server every second + β”œβ”€ Max 60 seconds timeout + └─ Log tail on failure + +3. Execute Tests + β”œβ”€ Call /impact/wildcards API + β”œβ”€ Validate responses + └─ Check behavior + +4. Cleanup + β”œβ”€ Kill server process + └─ Remove config file +``` + +--- + +## Test Validation + +### What Tests Validate + +**Behavioral Validation** (Not just "no errors"): +- **Weighted Selection**: Statistical distribution matches weight ratios +- **Multiselect**: Exact count, no duplicates, correct separator +- **Nesting**: All levels fully expanded, no remaining wildcards +- **Pattern Matching**: Depth-agnostic matching works correctly +- **UTF-8**: Character preservation and proper encoding +- **Error Handling**: Graceful recovery with meaningful messages + +### Success Criteria +- All 86 tests must pass (100% pass rate) +- No server crashes or hangs +- API responses within expected format +- Statistical distributions within Β±15% tolerance +- No unexpanded wildcards in final output + +### Validation Examples + +**Weighted Selection**: +```bash +# Test 10:1 ratio with 100 iterations +# Expected: ~91% common, ~9% rare +# Actual: Count distribution within Β±15% +``` + +**Multiselect**: +```bash +# Test {2$$, $$red|blue|green} +# Expected: Exactly 2 items, comma-space separator, no duplicates +# Validation: Count words, check separator, detect duplicates +``` + +**Pattern Matching**: +```bash +# Test __*/dragon__ +# Expected: Matches dragon.txt, fantasy/dragon.txt, dragon/fire.txt +# Validation: No unexpanded wildcards remain +``` + +--- + +## Troubleshooting + +### Common Issues + +**Server Fails to Start**: +```bash +# Check log file +tail -20 /tmp/{test_name}_test.log + +# Check port availability +lsof -i :8188 + +# Kill conflicting process +pkill -f "python.*main.py.*--port 8188" +``` + +**Tests Timeout**: +- Increase wait time in test script (default 60s) +- Check server performance and resources +- Verify network connectivity to 127.0.0.1 + +**Statistical Tests Fail**: +- Expected for very small sample sizes +- Β±15% tolerance accounts for randomness +- Rerun test to verify consistency + +**UTF-8 Issues**: +- Ensure terminal supports UTF-8 +- Check file encoding: `file -i tests/wildcards/samples/*.txt` +- Verify locale: `locale | grep UTF-8` + +--- + +## Test Maintenance + +### Adding New Tests +1. Create new test function in appropriate suite +2. Follow existing test patterns (setup, execute, validate, cleanup) +3. Update test counts in README.md and SUMMARY.md +4. Update this guide with new test description + +### Modifying Existing Tests +1. Preserve behavioral validation (not just "no errors") +2. Maintain statistical rigor for dynamic prompt tests +3. Update documentation if test purpose changes +4. Verify all 86 tests still pass after modification + +### Test Philosophy +- **Tests validate behavior**, not just execution success +- **Statistical validation** for probabilistic features +- **Real-world scenarios** with production-like setup +- **Comprehensive coverage** of all PRD requirements diff --git a/custom_nodes/ComfyUI-Impact-Pack/example_workflows/1-FaceDetailer.jpg b/custom_nodes/ComfyUI-Impact-Pack/example_workflows/1-FaceDetailer.jpg new file mode 100644 index 00000000..3ca20f5b Binary files /dev/null and b/custom_nodes/ComfyUI-Impact-Pack/example_workflows/1-FaceDetailer.jpg differ diff --git a/custom_nodes/ComfyUI-Impact-Pack/example_workflows/1-FaceDetailer.json b/custom_nodes/ComfyUI-Impact-Pack/example_workflows/1-FaceDetailer.json new file mode 100644 index 00000000..0801a0bb --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/example_workflows/1-FaceDetailer.json @@ -0,0 +1,1269 @@ +{ + "last_node_id": 61, + "last_link_id": 170, + "nodes": [ + { + "id": 28, + "type": "KSampler", + "pos": [ + 530, + 840 + ], + "size": [ + 320, + 600 + ], + "flags": {}, + "order": 14, + "mode": 0, + "inputs": [ + { + "name": "model", + "type": "MODEL", + "link": 65 + }, + { + "name": "positive", + "type": "CONDITIONING", + "link": 57 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 170 + }, + { + "name": "latent_image", + "type": "LATENT", + "link": 59, + "slot_index": 3 + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 60 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "KSampler" + }, + "widgets_values": [ + 431433362471142, + "fixed", + 20, + 8, + "euler", + "normal", + 1 + ], + "color": "#322", + "bgcolor": "#533" + }, + { + "id": 43, + "type": "PreviewImage", + "pos": [ + 2390, + -140 + ], + "size": [ + 230, + 300 + ], + "flags": {}, + "order": 19, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 142 + } + ], + "outputs": [], + "title": "Cropped (refined)", + "properties": { + "Node name for S&R": "PreviewImage" + }, + "widgets_values": [], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 52, + "type": "PreviewImage", + "pos": [ + 2390, + 210 + ], + "size": [ + 230, + 310 + ], + "flags": {}, + "order": 20, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 146 + } + ], + "outputs": [], + "properties": { + "Node name for S&R": "PreviewImage" + }, + "widgets_values": [], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 53, + "type": "UltralyticsDetectorProvider", + "pos": [ + 1290, + 200 + ], + "size": [ + 315, + 78 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "BBOX_DETECTOR", + "type": "BBOX_DETECTOR", + "shape": 3, + "links": [ + 150 + ], + "slot_index": 0 + }, + { + "name": "SEGM_DETECTOR", + "type": "SEGM_DETECTOR", + "shape": 3, + "links": null + } + ], + "properties": { + "Node name for S&R": "UltralyticsDetectorProvider" + }, + "widgets_values": [ + "bbox/face_yolov8m.pt" + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 16, + "type": "SAMLoader", + "pos": [ + 1290, + 340 + ], + "size": [ + 320, + 82 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "SAM_MODEL", + "type": "SAM_MODEL", + "links": [ + 151 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "SAMLoader" + }, + "widgets_values": [ + "sam_vit_b_01ec64.pth", + "AUTO" + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 30, + "type": "VAEDecode", + "pos": [ + 1010, + 840 + ], + "size": [ + 140, + 50 + ], + "flags": {}, + "order": 15, + "mode": 0, + "inputs": [ + { + "name": "samples", + "type": "LATENT", + "link": 60 + }, + { + "name": "vae", + "type": "VAE", + "link": 164 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 78, + 152 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "VAEDecode" + }, + "widgets_values": [], + "color": "#322", + "bgcolor": "#533" + }, + { + "id": 4, + "type": "CheckpointLoaderSimple", + "pos": [ + -640, + 190 + ], + "size": [ + 312.0885314941406, + 98 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "MODEL", + "type": "MODEL", + "links": [ + 64, + 157 + ], + "slot_index": 0 + }, + { + "name": "CLIP", + "type": "CLIP", + "links": [ + 148, + 149, + 159 + ], + "slot_index": 1 + }, + { + "name": "VAE", + "type": "VAE", + "links": [ + 161, + 163 + ], + "slot_index": 2 + } + ], + "properties": { + "Node name for S&R": "CheckpointLoaderSimple" + }, + "widgets_values": [ + "SD1.5/fantexiRealistic_v10.safetensors" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 58, + "type": "Reroute", + "pos": [ + 850, + 220 + ], + "size": [ + 75, + 26 + ], + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [ + { + "name": "", + "type": "*", + "link": 163 + } + ], + "outputs": [ + { + "name": "", + "type": "VAE", + "links": [ + 164 + ], + "slot_index": 0 + } + ], + "properties": { + "showOutputText": false, + "horizontal": false + } + }, + { + "id": 5, + "type": "CLIPTextEncode", + "pos": [ + -120, + 300 + ], + "size": [ + 310, + 180 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 148 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "links": [ + 57, + 165 + ], + "slot_index": 0 + } + ], + "title": "Positive", + "properties": { + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "(photorealistic:1.4), best quality, masterpiece, 1girl, (detailed eyes), perfect anatomy, smile, details, perfect eyes, perfect face, (SpringGreen+letter_printed_sleeveless_turtleneck), ((white_low_waist_jeans)), (thigh_gap:1.2), at_the_top_of_mountain, snow, daytime, windy, path, mountain_villa, sky_view, slender, looking_away, (small breast:1.2)" + ], + "color": "#322", + "bgcolor": "#533" + }, + { + "id": 6, + "type": 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"inputs": [ + { + "name": "segs", + "type": "SEGS", + "link": 105, + "slot_index": 0 + }, + { + "name": "fallback_image_opt", + "type": "IMAGE", + "shape": 7, + "link": 10, + "slot_index": 1 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "shape": 6, + "links": [], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "SEGSPreview" + }, + "widgets_values": [ + true, + 0.1 + ] + }, + { + "id": 60, + "type": "Note", + "pos": [ + -1033, + 292 + ], + "size": [ + 638.3837890625, + 178.84756469726562 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "1.Intro", + "properties": { + "text": "" + }, + "widgets_values": [ + "This video demonstrates how to apply the newly added \"Make Tile SEGS\" in the Impact Pack to upscale using the upscale method.\n\n\"Make Tile SEGS\" node splits the image into tiles and creates SEGS.\n\nBy using this, you can mimic the tile-based upscale function and, if the detected SEGS is too large, you can also split it for detailing." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 2, + "type": "LoadImage", + "pos": [ + -160, + 290 + ], + "size": [ + 315, + 314 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "shape": 3, + "links": [ + 8, + 34 + ], + "slot_index": 0 + }, + { + "name": "MASK", + "type": "MASK", + "shape": 3, + "links": null + } + ], + "properties": { + "Node name for S&R": "LoadImage" + }, + "widgets_values": [ + "20240107_013.webp", + "image" + ] + }, + { + "id": 62, + "type": "Note", + "pos": [ + 190, + 60 + ], + "size": [ + 396.33758544921875, + 127.46672821044922 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "2. Simple Upscale", + "properties": { + "text": "" + }, + "widgets_values": [ + "First, let's upscale the original 1024x1536 image to double its size.\n\nSimply upscale by 2x using the \"Upscale Image Scale By\". \nThe result will, of course, be blurry.\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 61, + "type": "Note", + "pos": [ + 780, + 35 + ], + "size": [ + 677.756591796875, + 157.3253173828125 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "3. Make Tile SEGS", + "properties": { + "text": "" + }, + "widgets_values": [ + "Let's process this image into SEGS using \"Make Tile SEGS\".\n\nYou can see that SEGS is structured so that every part of the image can be included in the mask area.\n\nUnlike the traditional tile upscaler, this method uses Detailer, so you can improve tile heterogeneity using the 'crop_factor'.\n\nAlso, setting 'mask_irregularity' to 0.7 will make the mask border irregular, improving the heterogeneity of the junctions." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 63, + "type": "Note", + "pos": [ + -108, + 1056 + ], + "size": [ + 709.2979736328125, + 143.4364013671875 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "4.Human SEGS", + "properties": { + "text": "" + }, + "widgets_values": [ + "Next, let's separate the background and the person to alleviate the noticeable artifacts, especially in the case of humans.\n\nApply the person ultralytics model to the \"Simple Detector\" to create SEGS containing the entire person.\n\nConnect the SEGS to the 'filter_out_segs_opt' in one \"Make Tile SEGS\" node, \nand in the other \"Make Tile SEGS\" node, connect it to the 'filter_in_segs_opt'." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 65, + "type": "Note", + "pos": [ + 776, + 803 + ], + "size": [ + 620.825927734375, + 163.94039916992188 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "5. filter_out_segs_opt", + "properties": { + "text": "" + }, + "widgets_values": [ + "The node connected to 'filter_out_segs_opt' creates SEGS excluding the mask of the input SEGS, allowing you to detail the background tiles.\n\n'min_overlap' determines how much the masks of each SEGS should overlap, and 'filter_segs_dilation' dilates the mask of the input SEGS.\n\nIncreasing 'filter_segs_dilation' in 'filter_out_segs_opt' generates masks further away from the person." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 66, + "type": "Note", + "pos": [ + 814, + 2007 + ], + "size": [ + 620.825927734375, + 163.94039916992188 + ], + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "6. filter_in_segs_opt", + "properties": { + "text": "" + }, + "widgets_values": [ + "On the other hand, the node connected to 'filter_in_segs_opt' creates SEGS with masks overlapping the input SEGS, allowing you to detail the person.\n\nSince detailing the person requires more attention than the background, increase 'bbox_size' to avoid creating small pieces, and increase 'min_overlap' to reduce junction artifacts and allow overlapping detailing.\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 67, + "type": "Note", + "pos": [ + 1955, + 1805 + ], + "size": [ + 620.825927734375, + 163.94039916992188 + ], + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "7. Detailing", + "properties": { + "text": "" + }, + "widgets_values": [ + "Now, using the SEGS created in this way, let's improve the upscaled image using two Detailer nodes.\n\nAlthough you can handle this with \"SEGS Concat\", separating into two Detailer nodes allows for separate options for background and person detailing.\n\nThis way, when modifying the detailing options for a person, you can prevent the recalculation of background detailing.\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 64, + "type": "Note", + "pos": [ + 2994, + 500 + ], + "size": [ + 620.825927734375, + 163.94039916992188 + ], + "flags": {}, + "order": 11, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "8. 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+ print(f"\nWARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{os.path.dirname(__file__)}/../../` as the ComfyUI path.", file=sys.stderr) + comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + +model_path = os.environ.get('COMFYUI_MODEL_PATH') +if model_path is None: + try: + import folder_paths + model_path = folder_paths.models_dir + except: + pass + + if model_path is None: + model_path = os.path.abspath(os.path.join(comfy_path, 'models')) + print(f"\nWARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.", file=sys.stderr) + + +sys.path.append(impact_path) +sys.path.append(comfy_path) + + +# --- +def handle_stream(stream, is_stdout): + stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace') + + for msg in stream: + if is_stdout: + print(msg, end="", file=sys.stdout) + else: + print(msg, end="", file=sys.stderr) + + +def process_wrap(cmd_str, cwd=None, handler=None, env=None): + print(f"[Impact Pack] EXECUTE: {cmd_str} in '{cwd}'") + process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env, text=True, bufsize=1) + + if handler is None: + handler = handle_stream + + stdout_thread = threading.Thread(target=handler, args=(process.stdout, True)) + stderr_thread = threading.Thread(target=handler, args=(process.stderr, False)) + + stdout_thread.start() + stderr_thread.start() + + stdout_thread.join() + stderr_thread.join() + + return process.wait() +# --- + + +try: + from torchvision.datasets.utils import download_url + import impact.config + + print("### ComfyUI-Impact-Pack: Check dependencies") + def install(): + new_env = os.environ.copy() + new_env["COMFYUI_PATH"] = comfy_path + new_env["COMFYUI_MODEL_PATH"] = model_path + + # Download model + print("### ComfyUI-Impact-Pack: Check basic models") + sam_path = os.path.join(model_path, "sams") + onnx_path = os.path.join(model_path, "onnx") + + if not os.path.exists(os.path.join(os.path.dirname(__file__), '..', 'skip_download_model')): + try: + if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")): + download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path) + except: + print("[Impact Pack] Failed to auto-download model files. Please download them manually.") + + if not os.path.exists(onnx_path): + print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})") + os.mkdir(onnx_path) + + impact.config.write_config() + + # Remove legacy subpack + try: + subpack_path = os.path.join(os.path.dirname(__file__), 'impact_subpack') + if os.path.exists(subpack_path): + shutil.rmtree(subpack_path) + print(f"Legacy subpack is detected. '{subpack_path}' is removed.") + + subpack_path = os.path.join(os.path.dirname(__file__), 'subpack') + if os.path.exists(subpack_path): + shutil.rmtree(subpack_path) + print(f"Legacy subpack is detected. '{subpack_path}' is removed.") + except: + print(f"ERROT: Failed to delete legacy subpack '{subpack_path}'\nPlease delete the folder after terminate ComfyUI.") + + install() + +except Exception: + print("[ERROR] ComfyUI-Impact-Pack: Dependency installation has failed. Please install manually.") + traceback.print_exc() diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/common.js b/custom_nodes/ComfyUI-Impact-Pack/js/common.js new file mode 100644 index 00000000..f2244e3f --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/common.js @@ -0,0 +1,281 @@ +import { api } from "../../scripts/api.js"; +import { app } from "../../scripts/app.js"; + +let original_show = app.ui.dialog.show; + +export function customAlert(message) { + try { + app.extensionManager.toast.addAlert(message); + } + catch { + alert(message); + } +} + +export function isBeforeFrontendVersion(compareVersion) { + try { + const frontendVersion = window['__COMFYUI_FRONTEND_VERSION__']; + if (typeof frontendVersion !== 'string') { + return false; + } + + function parseVersion(versionString) { + const parts = versionString.split('.').map(Number); + return parts.length === 3 && parts.every(part => !isNaN(part)) ? parts : null; + } + + const currentVersion = parseVersion(frontendVersion); + const comparisonVersion = parseVersion(compareVersion); + + if (!currentVersion || !comparisonVersion) { + return false; + } + + for (let i = 0; i < 3; i++) { + if (currentVersion[i] > comparisonVersion[i]) { + return false; + } else if (currentVersion[i] < comparisonVersion[i]) { + return true; + } + } + + return false; + } catch { + return true; + } +} + +function dialog_show_wrapper(html) { + if (typeof html === "string") { + if(html.includes("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE")) { + return; + } + + this.textElement.innerHTML = html; + } else { + this.textElement.replaceChildren(html); + } + this.element.style.display = "flex"; +} + +app.ui.dialog.show = dialog_show_wrapper; + + +function nodeFeedbackHandler(event) { + let nodes = app.graph._nodes_by_id; + let node = nodes[event.detail.node_id]; + if(node) { + const w = node.widgets.find((w) => event.detail.widget_name === w.name); + if(w) { + w.value = event.detail.value; + } + } +} + +api.addEventListener("impact-node-feedback", nodeFeedbackHandler); + + +function setMuteState(event) { + let nodes = app.graph._nodes_by_id; + let node = nodes[event.detail.node_id]; + if(node) { + if(event.detail.is_active) + node.mode = 0; + else + node.mode = 2; + } +} + +api.addEventListener("impact-node-mute-state", setMuteState); + + +async function bridgeContinue(event) { + let nodes = app.graph._nodes_by_id; + let node = nodes[event.detail.node_id]; + if(node) { + const mutes = new Set(event.detail.mutes); + const actives = new Set(event.detail.actives); + const bypasses = new Set(event.detail.bypasses); + + for(let i in app.graph._nodes_by_id) { + let this_node = app.graph._nodes_by_id[i]; + if(mutes.has(i)) { + this_node.mode = 2; + } + else if(actives.has(i)) { + this_node.mode = 0; + } + else if(bypasses.has(i)) { + this_node.mode = 4; + } + } + + await app.queuePrompt(0, 1); + } +} + +api.addEventListener("impact-bridge-continue", bridgeContinue); + + +function addQueue(event) { + app.queuePrompt(0, 1); +} + +api.addEventListener("impact-add-queue", addQueue); + + +function refreshPreview(event) { + let node_id = event.detail.node_id; + let item = event.detail.item; + let img = new Image(); + img.src = `/view?filename=${item.filename}&subfolder=${item.subfolder}&type=${item.type}&no-cache=${Date.now()}`; + let node = app.graph._nodes_by_id[node_id]; + if(node) + node.imgs = [img]; +} + +api.addEventListener("impact-preview", refreshPreview); + + +// ============================================================================ +// MaskRectArea Shared Utilities +// ============================================================================ + +/** + * Reads a numeric value from a connected link by inspecting the origin node widget. + * More reliable than getInputData() in ComfyUI's frontend execution model. + * + * @param {LGraphNode} node - LiteGraph node instance + * @param {string} inputName - Name of the input to read + * @returns {number|null} The numeric value or null if not available + */ +export function readLinkedNumber(node, inputName) { + try { + if (!node || !node.graph || !Array.isArray(node.inputs)) { + return null; + } + const inp = node.inputs.find(i => i && i.name === inputName); + if (!inp || inp.link == null) { + return null; + } + + const link = node.graph.links && node.graph.links[inp.link]; + if (!link) { + return null; + } + + const originNode = node.graph.getNodeById + ? node.graph.getNodeById(link.origin_id) + : null; + if (!originNode || !Array.isArray(originNode.widgets) || originNode.widgets.length === 0) { + return null; + } + + const w = originNode.widgets.find(ww => ww && ww.name === "value") + || originNode.widgets[0]; + const v = w ? w.value : null; + + return (typeof v === "number") ? v : null; + } catch (e) { + return null; + } +} + +/** + * Generates a color based on percentage using HSL color space. + * + * @param {number} percent - Value between 0 and 1 + * @param {string} alpha - Hex alpha value (e.g., "ff", "80") + * @returns {string} Hex color string with alpha (e.g., "#ff8040ff") + */ +export function getDrawColor(percent, alpha) { + let h = 360 * percent; + let s = 50; + let l = 50; + l /= 100; + const a = s * Math.min(l, 1 - l) / 100; + const f = n => { + const k = (n + h / 30) % 12; + const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1); + return Math.round(255 * color).toString(16).padStart(2, '0'); + }; + return `#${f(0)}${f(8)}${f(4)}${alpha}`; +} + +/** + * Computes and adjusts canvas size for preview widgets. + * + * @param {LGraphNode} node - LiteGraph node instance + * @param {[number, number]} size - [width, height] array + * @param {number} minHeight - Minimum canvas height (REQUIRED) + * @param {number} minWidth - Minimum canvas width (REQUIRED) + * @returns {void} + */ +export function computeCanvasSize(node, size, minHeight, minWidth) { + // Validate required parameters + if (typeof minHeight !== 'number' || typeof minWidth !== 'number') { + console.warn('[computeCanvasSize] minHeight and minWidth are required parameters'); + return; + } + + // Null safety check for widgets array + if (!node.widgets?.length || node.widgets[0].last_y == null) { + return; + } + + // LiteGraph global availability check + const NODE_WIDGET_HEIGHT = (typeof LiteGraph !== 'undefined' && LiteGraph.NODE_WIDGET_HEIGHT) + ? LiteGraph.NODE_WIDGET_HEIGHT + : 20; + + let y = node.widgets[0].last_y + 5; + let freeSpace = size[1] - y; + + // Compute the height of all non-customCanvas widgets + let widgetHeight = 0; + for (let i = 0; i < node.widgets.length; i++) { + const w = node.widgets[i]; + if (w.type !== "customCanvas") { + if (w.computeSize) { + widgetHeight += w.computeSize()[1] + 4; + } else { + widgetHeight += NODE_WIDGET_HEIGHT + 5; + } + } + } + + // Ensure there is enough vertical space + freeSpace -= widgetHeight; + + // Clamp minimum canvas height + if (freeSpace < minHeight) { + freeSpace = minHeight; + } + + // Allow both grow and shrink to fit content + const targetHeight = y + widgetHeight + freeSpace; + if (node.size[1] !== targetHeight) { + node.size[1] = targetHeight; + node.graph.setDirtyCanvas(true); + } + + // Ensure the node width meets the minimum width requirement + if (node.size[0] < minWidth) { + node.size[0] = minWidth; + node.graph.setDirtyCanvas(true); + } + + // Position each of the widgets + for (const w of node.widgets) { + w.y = y; + if (w.type === "customCanvas") { + y += freeSpace; + } else if (w.computeSize) { + y += w.computeSize()[1] + 4; + } else { + y += NODE_WIDGET_HEIGHT + 4; + } + } + + node.canvasHeight = freeSpace; +} diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/impact-image-util.js b/custom_nodes/ComfyUI-Impact-Pack/js/impact-image-util.js new file mode 100644 index 00000000..4985a9e5 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/impact-image-util.js @@ -0,0 +1,229 @@ +import { ComfyApp, app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +function load_image(str) { + let base64String = canvas.toDataURL('image/png'); + let img = new Image(); + img.src = base64String; +} + +function getFileItem(baseType, path) { + try { + let pathType = baseType; + + if (path.endsWith("[output]")) { + pathType = "output"; + path = path.slice(0, -9); + } else if (path.endsWith("[input]")) { + pathType = "input"; + path = path.slice(0, -8); + } else if (path.endsWith("[temp]")) { + pathType = "temp"; + path = path.slice(0, -7); + } + + const subfolder = path.substring(0, path.lastIndexOf('/')); + const filename = path.substring(path.lastIndexOf('/') + 1); + + return { + filename: filename, + subfolder: subfolder, + type: pathType + }; + } + catch(exception) { + return null; + } +} + +async function loadImageFromUrl(image, node_id, v, need_to_load) { + let item = getFileItem('temp', v); + + if(item) { + let params = `?node_id=${node_id}&filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`; + + let res = await api.fetchApi('/impact/set/pb_id_image'+params, { cache: "no-store" }); + if(res.status == 200) { + let pb_id = await res.text(); + if(need_to_load) {; + image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`); + } + return pb_id; + } + else { + return `$${node_id}-0`; + } + } + else { + return `$${node_id}-0`; + } +} + +async function loadImageFromId(image, v) { + let res = await api.fetchApi('/impact/get/pb_id_image?id='+v, { cache: "no-store" }); + if(res.status == 200) { + let item = await res.json(); + image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`); + return true; + } + + return false; +} + +app.registerExtension({ + name: "Comfy.Impact.img", + + nodeCreated(node, app) { + if(node.comfyClass == "PreviewBridge" || node.comfyClass == "PreviewBridgeLatent") { + let w = node.widgets.find(obj => obj.name === 'image'); + node._imgs = [new Image()]; + node.imageIndex = 0; + + Object.defineProperty(w, 'value', { + async set(v) { + if(w._lock) + return; + + const stackTrace = new Error().stack; + if(stackTrace.includes('presetText.js')) + return; + + var image = new Image(); + if(v && v.constructor == String && v.startsWith('$')) { + // from node feedback + let need_to_load = node._imgs[0].src == ''; + if(await loadImageFromId(image, v, need_to_load)) { + w._value = v; + if(node._imgs[0].src == '') { + node._imgs = [image]; + } + } + else { + w._value = `$${node.id}-0`; + } + } + else { + // from clipspace + w._lock = true; + w._value = await loadImageFromUrl(image, node.id, v, false); + w._lock = false; + } + }, + get() { + if(w._value == undefined) { + w._value = `$${node.id}-0`; + } + return w._value; + } + }); + + Object.defineProperty(node, 'imgs', { + set(v) { + const stackTrace = new Error().stack; + if(v && v.length == 0) + return; + else if(stackTrace.includes('pasteFromClipspace')) { + let sp = new URLSearchParams(v[0].src.split("?")[1]); + let str = ""; + if(sp.get('subfolder')) { + str += sp.get('subfolder') + '/'; + } + str += `${sp.get("filename")} [${sp.get("type")}]`; + + w.value = str; + } + + node._imgs = v; + }, + get() { + return node._imgs; + } + }); + } + + if(node.comfyClass == "ImageReceiver") { + let path_widget = node.widgets.find(obj => obj.name === 'image'); + let w = node.widgets.find(obj => obj.name === 'image_data'); + let stw_widget = node.widgets.find(obj => obj.name === 'save_to_workflow'); + w._value = ""; + + Object.defineProperty(w, 'value', { + set(v) { + if(v != '[IMAGE DATA]') + w._value = v; + }, + get() { + const stackTrace = new Error().stack; + if(!stackTrace.includes('draw') && !stackTrace.includes('graphToPrompt') && stackTrace.includes('app.js')) { + return "[IMAGE DATA]"; + } + else { + if(stw_widget.value) + return w._value; + else + return ""; + } + } + }); + + let set_img_act = (v) => { + node._img = v; + var canvas = document.createElement('canvas'); + canvas.width = v[0].width; + canvas.height = v[0].height; + + var context = canvas.getContext('2d'); + context.drawImage(v[0], 0, 0, v[0].width, v[0].height); + + var base64Image = canvas.toDataURL('image/png'); + w.value = base64Image; + }; + + Object.defineProperty(node, 'imgs', { + set(v) { + if (v && !v[0].complete) { + let orig_onload = v[0].onload; + v[0].onload = function(v2) { + if(orig_onload) + orig_onload(); + set_img_act(v); + }; + } + else { + set_img_act(v); + } + }, + get() { + if(this._img == undefined && w.value != '') { + this._img = [new Image()]; + if(stw_widget.value && w.value != '[IMAGE DATA]') + this._img[0].src = w.value; + } + else if(this._img == undefined && path_widget.value) { + let image = new Image(); + image.src = path_widget.value; + + try { + let item = getFileItem('temp', path_widget.value); + let params = `?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`; + + let res = api.fetchApi('/view/validate'+params, { cache: "no-store" }).then(response => response); + if(res.status == 200) { + image.src = api.apiURL('/view'+params); + } + + this._img = [new Image()]; // placeholder + image.onload = function(v) { + set_img_act([image]); + }; + } + catch { + + } + } + return this._img; + } + }); + } + } +}) diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/impact-pack.js b/custom_nodes/ComfyUI-Impact-Pack/js/impact-pack.js new file mode 100644 index 00000000..4d7d1db4 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/impact-pack.js @@ -0,0 +1,988 @@ +import { ComfyApp, app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { api } from "../../scripts/api.js"; +import { customAlert, isBeforeFrontendVersion } from "./common.js"; + +const is_legacy_front = () => isBeforeFrontendVersion('1.16.9'); + +if(is_legacy_front()) { + customAlert("An outdated version(<1.16.9) of the `comfyui-frontend-package` is installed. It is not compatible with the current version of the Impact Pack."); +} + +let wildcards_list = []; +let wildcard_status = { + on_demand_mode: false, + total_available: 0, + loaded_count: 0, + last_update: null +}; + +async function load_wildcards() { + let res = await api.fetchApi('/impact/wildcards/list'); + let data = await res.json(); + wildcards_list = data.data; +} + +async function load_wildcard_status() { + try { + let res = await api.fetchApi('/impact/wildcards/list/loaded'); + let data = await res.json(); + wildcard_status = { + on_demand_mode: data.on_demand_mode || false, + total_available: data.total_available || 0, + loaded_count: data.data ? data.data.length : 0, + last_update: new Date() + }; + } catch (error) { + console.error('Failed to load wildcard status:', error); + } +} + +export function get_wildcard_label() { + if (wildcard_status.on_demand_mode) { + return `Select Wildcard πŸ”΅ On-Demand: ${wildcard_status.loaded_count} loaded`; + } else { + return `Select Wildcard 🟒 Full Cache`; + } +} + +export function is_wildcard_label(value) { + // Check if value is a label (not an actual wildcard selection) + return value === "Select the Wildcard to add to the text" || + value.startsWith("Select Wildcard πŸ”΅ On-Demand:") || + value === "Select Wildcard 🟒 Full Cache"; +} + +Promise.all([load_wildcards(), load_wildcard_status()]); + +export function get_wildcards_list() { + return wildcards_list; +} + +export { load_wildcard_status }; + +// temporary implementation (copying from https://github.com/pythongosssss/ComfyUI-WD14-Tagger) +// I think this should be included into master!! +class ImpactProgressBadge { + constructor() { + if (!window.__progress_badge__) { + window.__progress_badge__ = Symbol("__impact_progress_badge__"); + } + this.symbol = window.__progress_badge__; + } + + getState(node) { + return node[this.symbol] || {}; + } + + setState(node, state) { + node[this.symbol] = state; + app.canvas.setDirty(true); + } + + addStatusHandler(nodeType) { + if (nodeType[this.symbol]?.statusTagHandler) { + return; + } + if (!nodeType[this.symbol]) { + nodeType[this.symbol] = {}; + } + nodeType[this.symbol] = { + statusTagHandler: true, + }; + + api.addEventListener("impact/update_status", ({ detail }) => { + let { node, progress, text } = detail; + const n = app.graph.getNodeById(+(node || app.runningNodeId)); + if (!n) return; + const state = this.getState(n); + state.status = Object.assign(state.status || {}, { progress: text ? progress : null, text: text || null }); + this.setState(n, state); + }); + + const self = this; + const onDrawForeground = nodeType.prototype.onDrawForeground; + nodeType.prototype.onDrawForeground = function (ctx) { + const r = onDrawForeground?.apply?.(this, arguments); + const state = self.getState(this); + if (!state?.status?.text) { + return r; + } + + const { fgColor, bgColor, text, progress, progressColor } = { ...state.status }; + + ctx.save(); + ctx.font = "12px sans-serif"; + const sz = ctx.measureText(text); + ctx.fillStyle = bgColor || "dodgerblue"; + ctx.beginPath(); + ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, sz.width + 12, 20, 5); + ctx.fill(); + + if (progress) { + ctx.fillStyle = progressColor || "green"; + ctx.beginPath(); + ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, (sz.width + 12) * progress, 20, 5); + ctx.fill(); + } + + ctx.fillStyle = fgColor || "#fff"; + ctx.fillText(text, 6, -LiteGraph.NODE_TITLE_HEIGHT - 6); + ctx.restore(); + return r; + }; + } +} + +const input_tracking = {}; +const input_dirty = {}; +const output_tracking = {}; + +function progressExecuteHandler(event) { + if(event.detail?.output?.aux){ + const id = event.detail.node; + if(input_tracking.hasOwnProperty(id)) { + if(input_tracking.hasOwnProperty(id) && input_tracking[id][0] != event.detail.output.aux[0]) { + input_dirty[id] = true; + } + else{ + + } + } + + input_tracking[id] = event.detail.output.aux; + } +} + +function imgSendHandler(event) { + if(event.detail.images.length > 0){ + let data = event.detail.images[0]; + let filename = `${data.filename} [${data.type}]`; + + let nodes = app.graph._nodes; + for(let i in nodes) { + if(nodes[i].type == 'ImageReceiver') { + let is_linked = false; + + if(nodes[i].widgets[1].type == 'converted-widget') { + for(let j in nodes[i].inputs) { + let input = nodes[i].inputs[j]; + if(input.name === 'link_id') { + if(input.link) { + let src_node = app.graph._nodes_by_id[app.graph.links[input.link].origin_id]; + if(src_node.type == 'ImpactInt' || src_node.type == 'PrimitiveNode') { + is_linked = true; + } + } + break; + } + } + } + else if(nodes[i].widgets[1].value == event.detail.link_id) { + is_linked = true; + } + + if(is_linked) { + if(data.subfolder) + nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`; + else + nodes[i].widgets[0].value = `${data.filename} [${data.type}]`; + + let img = new Image(); + img.onload = (event) => { + nodes[i].imgs = [img]; + nodes[i].size[1] = Math.max(200, nodes[i].size[1]); + app.canvas.setDirty(true); + }; + img.src = `/view?filename=${data.filename}&type=${data.type}&subfolder=${data.subfolder}`+app.getPreviewFormatParam(); + } + } + } + } +} + + +function latentSendHandler(event) { + if(event.detail.images.length > 0){ + let data = event.detail.images[0]; + let filename = `${data.filename} [${data.type}]`; + + let nodes = app.graph._nodes; + for(let i in nodes) { + if(nodes[i].type == 'LatentReceiver') { + if(nodes[i].widgets[1].value == event.detail.link_id) { + if(data.subfolder) + nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`; + else + nodes[i].widgets[0].value = `${data.filename} [${data.type}]`; + + let img = new Image(); + img.src = `/view?filename=${data.filename}&type=${data.type}&subfolder=${data.subfolder}`+app.getPreviewFormatParam(); + nodes[i].imgs = [img]; + nodes[i].size[1] = Math.max(200, nodes[i].size[1]); + } + } + } + } +} + + +function valueSendHandler(event) { + let nodes = app.graph._nodes; + for(let i in nodes) { + if(nodes[i].type == 'ImpactValueReceiver') { + if(nodes[i].widgets[2].value == event.detail.link_id) { + nodes[i].widgets[1].value = event.detail.value; + + let typ = typeof event.detail.value; + if(typ == 'string') { + nodes[i].widgets[0].value = "STRING"; + } + else if(typ == "boolean") { + nodes[i].widgets[0].value = "BOOLEAN"; + } + else if(typ != "number") { + nodes[i].widgets[0].value = typeof event.detail.value; + } + else if(Number.isInteger(event.detail.value)) { + nodes[i].widgets[0].value = "INT"; + } + else { + nodes[i].widgets[0].value = "FLOAT"; + } + } + } + } +} + + +const impactProgressBadge = new ImpactProgressBadge(); + +api.addEventListener("stop-iteration", () => { + document.getElementById("autoQueueCheckbox").checked = false; +}); +api.addEventListener("value-send", valueSendHandler); +api.addEventListener("img-send", imgSendHandler); +api.addEventListener("latent-send", latentSendHandler); +api.addEventListener("executed", progressExecuteHandler); + +// Update wildcard status after workflow execution (on-demand mode) +api.addEventListener("executed", async (event) => { + if (wildcard_status.on_demand_mode) { + await load_wildcard_status(); + await load_wildcards(); + app.canvas.setDirty(true); + } +}); + +app.registerExtension({ + name: "Comfy.Impack", + + commands: [ + { + id: 'refresh-impact-wildcard', + label: 'Impact: Refresh Wildcard', + function: async () => { + await api.fetchApi('/impact/wildcards/refresh'); + await Promise.all([load_wildcards(), load_wildcard_status()]); + app.extensionManager.toast.add({ + severity: 'info', + summary: 'Refreshed!', + detail: 'Impact Wildcard List is refreshed!!', + life: 3000 + }); + } + } + ], + + menuCommands: [ + { + path: ['Edit'], + commands: ['refresh-impact-wildcard'] + } + ], + + loadedGraphNode(node, app) { + if (node.comfyClass == "MaskPainter") { + input_dirty[node.id + ""] = true; + } + }, + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name == "IterativeLatentUpscale" || nodeData.name == "IterativeImageUpscale" + || nodeData.name == "RegionalSampler"|| nodeData.name == "RegionalSamplerAdvanced") { + impactProgressBadge.addStatusHandler(nodeType); + } + + if(nodeData.name == "ImpactControlBridge") { + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + if(index != 0 || !link_info || this.inputs[0].type != '*') + return; + + // assign type + let slot_type = '*'; + + if(type == 2) { + slot_type = link_info.type; + } + else { + const node = app.graph.getNodeById(link_info.origin_id); + slot_type = node?.outputs[link_info.origin_slot]?.type; + } + + this.inputs[0].type = slot_type; + this.outputs[0].type = slot_type; + this.outputs[0].label = slot_type; + } + } + + if(nodeData.name == "ImpactConditionalBranch" || nodeData.name == "ImpactConditionalBranchSelMode") { + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + if(!link_info || this.inputs[0].type != '*') + return; + + if(index >= 2) + return; + + // assign type + let slot_type = '*'; + + if(type == 2) { + slot_type = link_info.type; + } + else { + const node = app.graph.getNodeById(link_info.origin_id); + slot_type = node?.outputs[link_info.origin_slot].type; + } + + this.inputs[0].type = slot_type; + this.inputs[1].type = slot_type; + this.outputs[0].type = slot_type; + this.outputs[0].label = slot_type; + } + } + + if(nodeData.name == "ImpactCompare") { + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + if(!link_info || this.inputs[0].type != '*' || type == 2) + return; + + // assign type + const node = app.graph.getNodeById(link_info.origin_id); + let slot_type = node?.outputs[link_info.origin_slot].type; + + this.inputs[0].type = slot_type; + this.inputs[1].type = slot_type; + } + } + + if(nodeData.name == "ImpactSelectNthItemOfAnyList") { + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + if(!link_info || this.inputs[0].type != '*') + return; + + if(index >= 2) + return; + + // assign type + let slot_type = '*'; + + if(type == 2) { + slot_type = link_info.type; + } + else { + const node = app.graph.getNodeById(link_info.origin_id); + slot_type = node?.outputs[link_info.origin_slot].type; + } + + this.inputs[0].type = slot_type; + this.outputs[0].type = slot_type; + this.outputs[0].label = slot_type; + } + } + + if(nodeData.name === 'ImpactInversedSwitch') { + nodeData.output = ['*']; + nodeData.output_is_list = [false]; + nodeData.output_name = ['output1']; + + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + if(!link_info) + return; + + // HOTFIX: subgraph + const stackTrace = new Error().stack; + + if(stackTrace.includes('convertToSubgraph') || stackTrace.includes('Subgraph.configure')) { + return; + } + + if(type == 2) { + // connect output + if(connected){ + if(app.graph._nodes_by_id[link_info.target_id]?.type == 'Reroute') { + app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot); + } + + if(this.outputs[0].type == '*'){ + if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') { + app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot); + } + else { + // propagate type + this.outputs[0].type = link_info.type; + this.outputs[0].name = link_info.type; + + for(let i in this.inputs) { + if(this.inputs[i].name != 'select') + this.inputs[i].type = link_info.type; + } + } + } + } + } + else { + if(app.graph._nodes_by_id[link_info.origin_id]?.type == 'Reroute') + this.disconnectInput(link_info.target_slot); + + // connect input + if(this.inputs[0].type == '*'){ + const node = app.graph.getNodeById(link_info.origin_id); + let origin_type = node?.outputs[link_info.origin_slot]?.type; + + if(origin_type==undefined) { + return; // fallback + } + + if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') { + this.disconnectInput(link_info.target_slot); + return; + } + + for(let i in this.inputs) { + if(this.inputs[i].name != 'select') + this.inputs[i].type = origin_type; + } + + this.outputs[0].type = origin_type; + this.outputs[0].name = 'output1'; + } + + return; + } + + if (!connected && this.outputs.length > 1) { + const stackTrace = new Error().stack; + + if( + !stackTrace.includes('LGraphNode.prototype.connect') && // for touch device + !stackTrace.includes('LGraphNode.connect') && // for mouse device + !stackTrace.includes('loadGraphData')) { + if(this.outputs[link_info.origin_slot].links.length == 0) { + this.removeOutput(link_info.origin_slot); + } + } + } + + let slot_i = 1; + for (let i = 0; i < this.outputs.length; i++) { + this.outputs[i].name = `output${slot_i}` + if (this.outputs[i].slot_index === undefined) { + this.outputs[i].slot_index = i; + } + slot_i++; + } + + if(connected) { + // NOTE: node.slot_index is different with link_info.origin_slot + let last_slot_index = this.outputs.length - 1; + if (last_slot_index == link_info.origin_slot) { + this.addOutput(`output${slot_i}`, this.outputs[0].type); + } + } + + let select_slot = this.inputs.find(x => x.name == "select"); + if(this.widgets?.length) { + this.widgets[0].options.max = select_slot?this.outputs.length-1:this.outputs.length; + this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max); + if(this.widgets[0].options.max > 0 && this.widgets[0].value == 0) + this.widgets[0].value = 1; + } + } + } + + if (nodeData.name === 'ImpactMakeImageList' || nodeData.name === 'ImpactMakeImageBatch' || + nodeData.name === 'ImpactMakeMaskList' || nodeData.name === 'ImpactMakeMaskBatch' || + nodeData.name === 'ImpactMakeAnyList' || nodeData.name === 'CombineRegionalPrompts' || + nodeData.name === 'ImpactCombineConditionings' || nodeData.name === 'ImpactConcatConditionings' || + nodeData.name === 'ImpactSEGSConcat' || + nodeData.name === 'ImpactSwitch' || nodeData.name === 'LatentSwitch' || nodeData.name == 'SEGSSwitch') { + var input_name = "input"; + + switch(nodeData.name) { + case 'ImpactMakeImageList': + case 'ImpactMakeImageBatch': + input_name = "image"; + break; + + case 'ImpactMakeMaskList': + case 'ImpactMakeMaskBatch': + input_name = "mask"; + break; + + case 'ImpactMakeAnyList': + input_name = "value"; + break; + + case 'ImpactSEGSConcat': + input_name = "segs"; + break; + + case 'CombineRegionalPrompts': + input_name = "regional_prompts"; + break; + + case 'ImpactCombineConditionings': + case 'ImpactConcatConditionings': + input_name = "conditioning"; + break; + + case 'LatentSwitch': + input_name = "input"; + break; + + case 'SEGSSwitch': + input_name = "input"; + break; + + case 'ImpactSwitch': + input_name = "input"; + } + + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + const stackTrace = new Error().stack; + + // HOTFIX: subgraph + if(stackTrace.includes('convertToSubgraph') || stackTrace.includes('Subgraph.configure')) { + return; + } + + if(stackTrace.includes('loadGraphData')) { + if(this.widgets?.[0]) { + this.widgets[0].options.max = this.inputs.length-3; + this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max); + } + return; + } + + if(stackTrace.includes('pasteFromClipboard')) { + if(this.widgets?.[0]) { + this.widgets[0].options.max = this.inputs.length-3; + this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max); + } + return; + } + + if(!link_info) + return; + + if(type == 2) { + // connect output + if(connected && index == 0){ + if(nodeData.name == 'ImpactSwitch' && app.graph._nodes_by_id[link_info.target_id]?.type == 'Reroute') { + app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot); + } + + if(this.outputs[0].type == '*'){ + if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') { + app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot); + } + else { + // propagate type + this.outputs[0].type = link_info.type; + this.outputs[0].label = link_info.type; + this.outputs[0].name = link_info.type; + + for(let i in this.inputs) { + let input_i = this.inputs[i]; + if(input_i.name != 'select' && input_i.name != 'sel_mode') + input_i.type = link_info.type; + } + } + } + } + + return; + } + else { + if(nodeData.name == 'ImpactSwitch' && app.graph._nodes_by_id[link_info.origin_id]?.type == 'Reroute') + this.disconnectInput(link_info.target_slot); + + // connect input + if(this.inputs[index].name == 'select' || this.inputs[index].name == 'sel_mode') + return; + + if(this.inputs[0].type == '*'){ + const node = app.graph.getNodeById(link_info.origin_id); + + // NOTE: node is undefined when subgraph editing mode + if(node) { + let origin_type = node.outputs[link_info.origin_slot]?.type; + if(link_info.target_slot == 0 && this.inputs.length > 3) { // NOTE: widgets are regarded as input since new front + origin_type = this.inputs[1].type; + node.connect(link_info.origin_slot, node.id, 'input1'); + } + + if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') { + this.disconnectInput(link_info.target_slot); + return; + } + + for(let i in this.inputs) { + let input_i = this.inputs[i]; + if(input_i.name != 'select' && input_i.name != 'sel_mode') + input_i.type = origin_type; + } + + this.outputs[0].type = origin_type; + this.outputs[0].label = origin_type; + this.outputs[0].name = origin_type; + } + } + } + + let widget_count = 0; + if(nodeData.name == 'ImpactSwitch' || nodeData.name == 'LatentSwitch' || nodeData.name == 'SEGSSwitch') { + widget_count += 1; + } + + if (!connected && (this.inputs.length > widget_count+1)) { + if( + !stackTrace.includes('LGraphNode.prototype.connect') && // for touch device + !stackTrace.includes('LGraphNode.connect') && // for mouse device + !stackTrace.includes('loadGraphData') && + this.inputs[index].name != 'select') { + this.removeInput(index); + } + } + + let slot_i = 1; + for (let i = 0; i < this.inputs.length; i++) { + let input_i = this.inputs[i]; + if(input_i.name != 'select'&& input_i.name != 'sel_mode') { + input_i.name = `${input_name}${slot_i}` + slot_i++; + } + } + + if(connected) { + this.addInput(`${input_name}${slot_i}`, this.outputs[0].type); + } + + if(this.widgets?.[0]) { + this.widgets[0].options.max = this.inputs.length-3; + this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max); + } + } + } + }, + + nodeCreated(node, app) { + if(node.comfyClass == "MaskPainter") { + node.addWidget("button", "Edit mask", null, () => { + ComfyApp.copyToClipspace(node); + ComfyApp.clipspace_return_node = node; + ComfyApp.open_maskeditor(); + }); + } + + switch(node.comfyClass) { + case "ToDetailerPipe": + case "ToDetailerPipeSDXL": + case "BasicPipeToDetailerPipe": + case "BasicPipeToDetailerPipeSDXL": + case "EditDetailerPipe": + case "FaceDetailer": + case "DetailerForEach": + case "DetailerForEachDebug": + case "DetailerForEachPipe": + case "DetailerForEachDebugPipe": + { + for(let i in node.widgets) { + let widget = node.widgets[i]; + if(widget.type === "customtext") { + widget.dynamicPrompts = false; + widget.inputEl.placeholder = "wildcard spec: if kept empty, this option will be ignored"; + widget.serializeValue = () => { + return node.widgets[i].value; + }; + } + } + } + break; + } + + if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") { + node.widgets[0].callback = (value, canvas, node, pos, e) => { + if(node) { + if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(",")) + node.widgets[1].value += ", " + + node.widgets[1].value += value; + if(node.widgets_values) + node.widgets_values[1] = node.widgets[1].value; + } + } + + Object.defineProperty(node.widgets[0], "value", { + set: (value) => { + node._value = value; + }, + get: () => { + return node._value; + } + }); + } + + if(node.comfyClass == "UltralyticsDetectorProvider") { + let model_name_widget = node.widgets.find((w) => w.name === "model_name"); + let orig_draw = node.onDrawForeground; + node.onDrawForeground = function (ctx) { + const r = orig_draw?.apply?.(this, arguments); + + let is_seg = model_name_widget.value?.startsWith('segm/') || model_name_widget.value?.includes('-seg'); + if(!is_seg) { + var slot_pos = new Float32Array(2); + var pos = node.getConnectionPos(false, 1, slot_pos); + + pos[0] -= node.pos[0] - 10; + pos[1] -= node.pos[1]; + + ctx.beginPath(); + ctx.strokeStyle = "red"; + ctx.lineWidth = 4; + ctx.moveTo(pos[0] - 5, pos[1] - 5); + ctx.lineTo(pos[0] + 5, pos[1] + 5); + ctx.moveTo(pos[0] + 5, pos[1] - 5); + ctx.lineTo(pos[0] - 5, pos[1] + 5); + ctx.stroke(); + } + } + } + + if( + node.comfyClass == "ImpactWildcardEncode" || node.comfyClass == "ImpactWildcardProcessor" + || node.comfyClass == "ToDetailerPipe" || node.comfyClass == "ToDetailerPipeSDXL" + || node.comfyClass == "EditDetailerPipe" || node.comfyClass == "EditDetailerPipeSDXL" + || node.comfyClass == "BasicPipeToDetailerPipe" || node.comfyClass == "BasicPipeToDetailerPipeSDXL") { + node._value = "Select the LoRA to add to the text"; + node._wvalue = "Select the Wildcard to add to the text"; + + var tbox_id = 0; + var combo_id = 3; + var has_lora = true; + + switch(node.comfyClass){ + case "ImpactWildcardEncode": + tbox_id = 0; + combo_id = 3; + break; + + case "ImpactWildcardProcessor": + tbox_id = 0; + combo_id = 4; + has_lora = false; + break; + + case "ToDetailerPipe": + case "ToDetailerPipeSDXL": + case "EditDetailerPipe": + case "EditDetailerPipeSDXL": + case "BasicPipeToDetailerPipe": + case "BasicPipeToDetailerPipeSDXL": + tbox_id = 0; + combo_id = 1; + break; + } + + node.widgets[combo_id+1].callback = async (value, canvas, node, pos, e) => { + if(node) { + if(node.widgets[tbox_id].value != '') + node.widgets[tbox_id].value += ', ' + + node.widgets[tbox_id].value += node._wildcard_value; + + // Reload wildcard status to update loaded count + if (wildcard_status.on_demand_mode) { + await load_wildcard_status(); + await load_wildcards(); + app.canvas.setDirty(true); + } + } + } + + Object.defineProperty(node.widgets[combo_id+1], "value", { + set: (value) => { + if (!is_wildcard_label(value)) + node._wildcard_value = value; + }, + get: () => { return get_wildcard_label(); } + }); + + Object.defineProperty(node.widgets[combo_id+1].options, "values", { + set: (x) => {}, + get: () => { + return wildcards_list; + } + }); + + if(has_lora) { + node.widgets[combo_id].callback = (value, canvas, node, pos, e) => { + if(node) { + let lora_name = node._value; + if(lora_name.endsWith('.safetensors')) { + lora_name = lora_name.slice(0, -12); + } + + node.widgets[tbox_id].value += ``; + if(node.widgets_values) { + node.widgets_values[tbox_id] = node.widgets[tbox_id].value; + } + } + } + + Object.defineProperty(node.widgets[combo_id], "value", { + set: (value) => { + if (value !== "Select the LoRA to add to the text") + node._value = value; + }, + + get: () => { return "Select the LoRA to add to the text"; } + }); + } + + // Preventing validation errors from occurring in any situation. + if(has_lora) { + node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; } + } + node.widgets[combo_id+1].serializeValue = () => { return "Select the Wildcard to add to the text"; } + } + + if(node.comfyClass == "ImpactWildcardProcessor" || node.comfyClass == "ImpactWildcardEncode") { + node.widgets[0].inputEl.placeholder = "Wildcard Prompt (User input)"; + node.widgets[1].inputEl.placeholder = "Populated Prompt (Will be generated automatically)"; + node.widgets[1].inputEl.disabled = true; + + const populated_text_widget = node.widgets.find((w) => w.name == 'populated_text'); + const mode_widget = node.widgets.find((w) => w.name == 'mode'); + + // mode combo + Object.defineProperty(mode_widget, "value", { + set: (value) => { + if(value == true) + node._mode_value = "populate"; + else if(value == false) + node._mode_value = "fixed"; + else + node._mode_value = value; // combo value + + populated_text_widget.inputEl.disabled = node._mode_value == 'populate'; + }, + get: () => { + if(node._mode_value != undefined) + return node._mode_value; + else + return 'populate'; + } + }); + } + + if (node.comfyClass == "MaskPainter") { + node.widgets[0].value = '#placeholder'; + + Object.defineProperty(node, "images", { + set: function(value) { + node._images = value; + }, + get: function() { + const id = node.id+""; + if(node.widgets[0].value != '#placeholder') { + var need_invalidate = false; + + if(input_dirty.hasOwnProperty(id) && input_dirty[id]) { + node.widgets[0].value = {...input_tracking[id][1]}; + input_dirty[id] = false; + need_invalidate = true + this._images = app.nodeOutputs[id].images; + } + + let filename = app.nodeOutputs[id]['aux'][1][0]['filename']; + let subfolder = app.nodeOutputs[id]['aux'][1][0]['subfolder']; + let type = app.nodeOutputs[id]['aux'][1][0]['type']; + + let item = + { + image_hash: app.nodeOutputs[id]['aux'][0], + forward_filename: app.nodeOutputs[id]['aux'][1][0]['filename'], + forward_subfolder: app.nodeOutputs[id]['aux'][1][0]['subfolder'], + forward_type: app.nodeOutputs[id]['aux'][1][0]['type'] + }; + + if(node._images) { + app.nodeOutputs[id].images = [{ + ...node._images[0], + ...item + }]; + + node.widgets[0].value = + { + ...node._images[0], + ...item + }; + } + else { + app.nodeOutputs[id].images = [{ + ...item + }]; + + node.widgets[0].value = + { + ...item + }; + } + + if(need_invalidate) { + Promise.all( + app.nodeOutputs[id].images.map((src) => { + return new Promise((r) => { + const img = new Image(); + img.onload = () => r(img); + img.onerror = () => r(null); + img.src = "/view?" + new URLSearchParams(src).toString(); + }); + }) + ).then((imgs) => { + this.imgs = imgs.filter(Boolean); + this.setSizeForImage?.(); + app.graph.setDirtyCanvas(true); + }); + + app.nodeOutputs[id].images[0] = { ...node.widgets[0].value }; + } + + return app.nodeOutputs[id].images; + } + else { + return node._images; + } + } + }); + } + } +}); diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/impact-sam-editor.js b/custom_nodes/ComfyUI-Impact-Pack/js/impact-sam-editor.js new file mode 100644 index 00000000..371987c0 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/impact-sam-editor.js @@ -0,0 +1,641 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { ComfyApp } from "../../scripts/app.js"; +import { ClipspaceDialog } from "../../extensions/core/clipspace.js"; + +function addMenuHandler(nodeType, cb) { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} + +// Helper function to convert a data URL to a Blob object +function dataURLToBlob(dataURL) { + const parts = dataURL.split(';base64,'); + const contentType = parts[0].split(':')[1]; + const byteString = atob(parts[1]); + const arrayBuffer = new ArrayBuffer(byteString.length); + const uint8Array = new Uint8Array(arrayBuffer); + for (let i = 0; i < byteString.length; i++) { + uint8Array[i] = byteString.charCodeAt(i); + } + return new Blob([arrayBuffer], { type: contentType }); +} + +function loadedImageToBlob(image) { + const canvas = document.createElement('canvas'); + + canvas.width = image.width; + canvas.height = image.height; + + const ctx = canvas.getContext('2d'); + + ctx.drawImage(image, 0, 0); + + const dataURL = canvas.toDataURL('image/png', 1); + const blob = dataURLToBlob(dataURL); + + return blob; +} + +async function uploadMask(filepath, formData) { + await api.fetchApi('/upload/mask', { + method: 'POST', + body: formData + }).then(response => {}).catch(error => { + console.error('Error:', error); + }); + + ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']] = new Image(); + ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src = `view?filename=${filepath.filename}&type=${filepath.type}`; + + if(ComfyApp.clipspace.images) + ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']] = filepath; + + ClipspaceDialog.invalidatePreview(); +} + +class ImpactSamEditorDialog extends ComfyDialog { + static instance = null; + + static getInstance() { + if(!ImpactSamEditorDialog.instance) { + ImpactSamEditorDialog.instance = new ImpactSamEditorDialog(); + } + + return ImpactSamEditorDialog.instance; + } + + constructor() { + super(); + this.element = $el("div.comfy-modal", { parent: document.body }, + [ $el("div.comfy-modal-content", + [...this.createButtons()]), + ]); + } + + createButtons() { + return []; + } + + createButton(name, callback) { + var button = document.createElement("button"); + button.innerText = name; + button.addEventListener("click", callback); + return button; + } + + createLeftButton(name, callback) { + var button = this.createButton(name, callback); + button.style.cssFloat = "left"; + button.style.marginRight = "4px"; + return button; + } + + createRightButton(name, callback) { + var button = this.createButton(name, callback); + button.style.cssFloat = "right"; + button.style.marginLeft = "4px"; + return button; + } + + createLeftSlider(self, name, callback) { + const divElement = document.createElement('div'); + divElement.id = "sam-confidence-slider"; + divElement.style.cssFloat = "left"; + divElement.style.fontFamily = "sans-serif"; + divElement.style.marginRight = "4px"; + divElement.style.color = "var(--input-text)"; + divElement.style.backgroundColor = "var(--comfy-input-bg)"; + divElement.style.borderRadius = "8px"; + divElement.style.borderColor = "var(--border-color)"; + divElement.style.borderStyle = "solid"; + divElement.style.fontSize = "15px"; + divElement.style.height = "21px"; + divElement.style.padding = "1px 6px"; + divElement.style.display = "flex"; + divElement.style.position = "relative"; + divElement.style.top = "2px"; + self.confidence_slider_input = document.createElement('input'); + self.confidence_slider_input.setAttribute('type', 'range'); + self.confidence_slider_input.setAttribute('min', '0'); + self.confidence_slider_input.setAttribute('max', '100'); + self.confidence_slider_input.setAttribute('value', '70'); + const labelElement = document.createElement("label"); + labelElement.textContent = name; + + divElement.appendChild(labelElement); + divElement.appendChild(self.confidence_slider_input); + + self.confidence_slider_input.addEventListener("change", callback); + + return divElement; + } + + async detect_and_invalidate_mask_canvas(self) { + const mask_img = await self.detect(self); + + const canvas = self.maskCtx.canvas; + const ctx = self.maskCtx; + + ctx.clearRect(0, 0, canvas.width, canvas.height); + + await new Promise((resolve, reject) => { + self.mask_image = new Image(); + self.mask_image.onload = function() { + ctx.drawImage(self.mask_image, 0, 0, canvas.width, canvas.height); + resolve(); + }; + self.mask_image.onerror = reject; + self.mask_image.src = mask_img.src; + }); + } + + setlayout(imgCanvas, maskCanvas, pointsCanvas) { + const self = this; + + // If it is specified as relative, using it only as a hidden placeholder for padding is recommended + // to prevent anomalies where it exceeds a certain size and goes outside of the window. + var placeholder = document.createElement("div"); + placeholder.style.position = "relative"; + placeholder.style.height = "50px"; + + var bottom_panel = document.createElement("div"); + bottom_panel.style.position = "absolute"; + bottom_panel.style.bottom = "0px"; + bottom_panel.style.left = "20px"; + bottom_panel.style.right = "20px"; + bottom_panel.style.height = "50px"; + + var brush = document.createElement("div"); + brush.id = "sam-brush"; + brush.style.backgroundColor = "blue"; + brush.style.outline = "2px solid pink"; + brush.style.borderRadius = "50%"; + brush.style.MozBorderRadius = "50%"; + brush.style.WebkitBorderRadius = "50%"; + brush.style.position = "absolute"; + brush.style.zIndex = 100; + brush.style.pointerEvents = "none"; + this.brush = brush; + this.element.appendChild(imgCanvas); + this.element.appendChild(maskCanvas); + this.element.appendChild(pointsCanvas); + this.element.appendChild(placeholder); // must below z-index than bottom_panel to avoid covering button + this.element.appendChild(bottom_panel); + document.body.appendChild(brush); + this.brush_size = 5; + + var confidence_slider = this.createLeftSlider(self, "Confidence", (event) => { + self.confidence = event.target.value; + }); + + var clearButton = this.createLeftButton("Clear", () => { + self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height); + self.pointsCtx.clearRect(0, 0, self.pointsCanvas.width, self.pointsCanvas.height); + + self.prompt_points = []; + + self.invalidatePointsCanvas(self); + }); + + var detectButton = this.createLeftButton("Detect", () => self.detect_and_invalidate_mask_canvas(self)); + + var cancelButton = this.createRightButton("Cancel", () => { + document.removeEventListener("mouseup", ImpactSamEditorDialog.handleMouseUp); + document.removeEventListener("keydown", ImpactSamEditorDialog.handleKeyDown); + self.close(); + }); + + self.saveButton = this.createRightButton("Save", () => { + document.removeEventListener("mouseup", ImpactSamEditorDialog.handleMouseUp); + document.removeEventListener("keydown", ImpactSamEditorDialog.handleKeyDown); + self.save(self); + }); + + var undoButton = this.createLeftButton("Undo", () => { + if(self.prompt_points.length > 0) { + self.prompt_points.pop(); + self.pointsCtx.clearRect(0, 0, self.pointsCanvas.width, self.pointsCanvas.height); + self.invalidatePointsCanvas(self); + } + }); + + bottom_panel.appendChild(clearButton); + bottom_panel.appendChild(detectButton); + bottom_panel.appendChild(self.saveButton); + bottom_panel.appendChild(cancelButton); + bottom_panel.appendChild(confidence_slider); + bottom_panel.appendChild(undoButton); + + imgCanvas.style.position = "relative"; + imgCanvas.style.top = "200"; + imgCanvas.style.left = "0"; + + maskCanvas.style.position = "absolute"; + maskCanvas.style.opacity = 0.5; + pointsCanvas.style.position = "absolute"; + } + + show() { + this.mask_image = null; + self.prompt_points = []; + + this.message_box = $el("p", ["Please wait a moment while the SAM model and the image are being loaded."]); + this.element.appendChild(this.message_box); + + if(self.imgCtx) { + self.imgCtx.clearRect(0, 0, self.imageCanvas.width, self.imageCanvas.height); + } + + const target_image_path = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src; + this.load_sam(target_image_path); + + if(!this.is_layout_created) { + // layout + const imgCanvas = document.createElement('canvas'); + const maskCanvas = document.createElement('canvas'); + const pointsCanvas = document.createElement('canvas'); + + imgCanvas.id = "imageCanvas"; + maskCanvas.id = "samEditorMaskCanvas"; + pointsCanvas.id = "pointsCanvas"; + + this.setlayout(imgCanvas, maskCanvas, pointsCanvas); + + // prepare content + this.imgCanvas = imgCanvas; + this.maskCanvas = maskCanvas; + this.pointsCanvas = pointsCanvas; + this.maskCtx = maskCanvas.getContext('2d'); + this.pointsCtx = pointsCanvas.getContext('2d'); + + this.is_layout_created = true; + + // replacement of onClose hook since close is not real close + const self = this; + const observer = new MutationObserver(function(mutations) { + mutations.forEach(function(mutation) { + if (mutation.type === 'attributes' && mutation.attributeName === 'style') { + if(self.last_display_style && self.last_display_style != 'none' && self.element.style.display == 'none') { + ComfyApp.onClipspaceEditorClosed(); + } + + self.last_display_style = self.element.style.display; + } + }); + }); + + const config = { attributes: true }; + observer.observe(this.element, config); + } + + this.setImages(target_image_path, this.imgCanvas, this.pointsCanvas); + + if(ComfyApp.clipspace_return_node) { + this.saveButton.innerText = "Save to node"; + } + else { + this.saveButton.innerText = "Save"; + } + this.saveButton.disabled = true; + + this.element.style.display = "block"; + this.element.style.zIndex = 8888; // NOTE: alert dialog must be high priority. + } + + updateBrushPreview(self, event) { + event.preventDefault(); + + const centerX = event.pageX; + const centerY = event.pageY; + + const brush = self.brush; + + brush.style.width = self.brush_size * 2 + "px"; + brush.style.height = self.brush_size * 2 + "px"; + brush.style.left = (centerX - self.brush_size) + "px"; + brush.style.top = (centerY - self.brush_size) + "px"; + } + + setImages(target_image_path, imgCanvas, pointsCanvas) { + const imgCtx = imgCanvas.getContext('2d'); + const maskCtx = this.maskCtx; + const maskCanvas = this.maskCanvas; + + const self = this; + + // image load + const orig_image = new Image(); + window.addEventListener("resize", () => { + // repositioning + imgCanvas.width = window.innerWidth - 250; + imgCanvas.height = window.innerHeight - 200; + + // redraw image + let drawWidth = orig_image.width; + let drawHeight = orig_image.height; + + if (orig_image.width > imgCanvas.width) { + drawWidth = imgCanvas.width; + drawHeight = (drawWidth / orig_image.width) * orig_image.height; + } + + if (drawHeight > imgCanvas.height) { + drawHeight = imgCanvas.height; + drawWidth = (drawHeight / orig_image.height) * orig_image.width; + } + + imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight); + + // update mask + let w = (drawWidth * imgCanvas.clientWidth/imgCanvas.width) + "px"; + let h = (drawHeight * imgCanvas.clientHeight/imgCanvas.height) + "px"; + + pointsCanvas.width = drawWidth * imgCanvas.clientWidth/imgCanvas.width; + pointsCanvas.height = drawHeight * imgCanvas.clientHeight/imgCanvas.height; + pointsCanvas.style.top = imgCanvas.offsetTop + "px"; + pointsCanvas.style.left = imgCanvas.offsetLeft + "px"; + + maskCanvas.width = pointsCanvas.width; + maskCanvas.height = pointsCanvas.height; + maskCanvas.style.top = imgCanvas.offsetTop + "px"; + maskCanvas.style.left = imgCanvas.offsetLeft + "px"; + + self.invalidateMaskCanvas(self); + self.invalidatePointsCanvas(self); + }); + + // original image load + orig_image.onload = () => self.onLoaded(self); + const rgb_url = new URL(target_image_path); + rgb_url.searchParams.delete('channel'); + rgb_url.searchParams.set('channel', 'rgb'); + orig_image.src = rgb_url; + self.image = orig_image; + } + + onLoaded(self) { + if(self.message_box) { + self.element.removeChild(self.message_box); + self.message_box = null; + } + + window.dispatchEvent(new Event('resize')); + + self.setEventHandler(pointsCanvas); + self.saveButton.disabled = false; + } + + setEventHandler(targetCanvas) { + targetCanvas.addEventListener("contextmenu", (event) => { + event.preventDefault(); + }); + + const self = this; + targetCanvas.addEventListener('pointermove', (event) => this.updateBrushPreview(self,event)); + targetCanvas.addEventListener('pointerdown', (event) => this.handlePointerDown(self,event)); + targetCanvas.addEventListener('pointerover', (event) => { this.brush.style.display = "block"; }); + targetCanvas.addEventListener('pointerleave', (event) => { this.brush.style.display = "none"; }); + document.addEventListener('keydown', ImpactSamEditorDialog.handleKeyDown); + } + + static handleKeyDown(event) { + const self = ImpactSamEditorDialog.instance; + if (event.key === '=') { // positive + brush.style.backgroundColor = "blue"; + brush.style.outline = "2px solid pink"; + self.is_positive_mode = true; + } else if (event.key === '-') { // negative + brush.style.backgroundColor = "red"; + brush.style.outline = "2px solid skyblue"; + self.is_positive_mode = false; + } + } + + is_positive_mode = true; + prompt_points = []; + confidence = 70; + + invalidatePointsCanvas(self) { + const ctx = self.pointsCtx; + + for (const i in self.prompt_points) { + const [is_positive, x, y] = self.prompt_points[i]; + + const scaledX = x * ctx.canvas.width / self.image.width; + const scaledY = y * ctx.canvas.height / self.image.height; + + if(is_positive) + ctx.fillStyle = "blue"; + else + ctx.fillStyle = "red"; + ctx.beginPath(); + ctx.arc(scaledX, scaledY, 3, 0, 3 * Math.PI); + ctx.fill(); + } + } + + invalidateMaskCanvas(self) { + if(self.mask_image) { + self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height); + self.maskCtx.drawImage(self.mask_image, 0, 0, self.maskCanvas.width, self.maskCanvas.height); + } + } + + async load_sam(url) { + const parsedUrl = new URL(url); + const searchParams = new URLSearchParams(parsedUrl.search); + + const filename = searchParams.get("filename") || ""; + const fileType = searchParams.get("type") || ""; + const subfolder = searchParams.get("subfolder") || ""; + + const data = { + sam_model_name: "auto", + filename: filename, + type: fileType, + subfolder: subfolder + }; + + api.fetchApi('/sam/prepare', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(data) + }); + } + + async detect(self) { + const positive_points = []; + const negative_points = []; + + for(const i in self.prompt_points) { + const [is_positive, x, y] = self.prompt_points[i]; + const point = [x,y]; + if(is_positive) { + positive_points.push(point); + } + else + negative_points.push(point); + } + + const data = { + positive_points: positive_points, + negative_points: negative_points, + threshold: self.confidence/100 + }; + + const response = await api.fetchApi('/sam/detect', { + method: 'POST', + headers: { 'Content-Type': 'image/png' }, + body: JSON.stringify(data) + }); + + const blob = await response.blob(); + const url = URL.createObjectURL(blob); + + return new Promise((resolve, reject) => { + const image = new Image(); + image.onload = () => resolve(image); + image.onerror = reject; + image.src = url; + }); + } + + handlePointerDown(self, event) { + if ([0, 2, 5].includes(event.button)) { + event.preventDefault(); + const x = event.offsetX || event.targetTouches[0].clientX - maskRect.left; + const y = event.offsetY || event.targetTouches[0].clientY - maskRect.top; + + const originalX = x * self.image.width / self.pointsCanvas.clientWidth; + const originalY = y * self.image.height / self.pointsCanvas.clientHeight; + + var point = null; + if (event.button == 0) { + // positive + point = [true, originalX, originalY]; + } else { + // negative + point = [false, originalX, originalY]; + } + + self.prompt_points.push(point); + + self.invalidatePointsCanvas(self); + } + } + + async save(self) { + if(!self.mask_image) { + this.close(); + return; + } + + const save_canvas = document.createElement('canvas'); + + const save_ctx = save_canvas.getContext('2d', {willReadFrequently:true}); + save_canvas.width = self.mask_image.width; + save_canvas.height = self.mask_image.height; + + save_ctx.drawImage(self.mask_image, 0, 0, save_canvas.width, save_canvas.height); + + const save_data = save_ctx.getImageData(0, 0, save_canvas.width, save_canvas.height); + + // refine mask image + for (let i = 0; i < save_data.data.length; i += 4) { + if(save_data.data[i]) { + save_data.data[i+3] = 0; + } + else { + save_data.data[i+3] = 255; + } + + save_data.data[i] = 0; + save_data.data[i+1] = 0; + save_data.data[i+2] = 0; + } + + save_ctx.globalCompositeOperation = 'source-over'; + save_ctx.putImageData(save_data, 0, 0); + + const formData = new FormData(); + const filename = "clipspace-mask-" + performance.now() + ".png"; + + const item = + { + "filename": filename, + "subfolder": "", + "type": "temp", + }; + + if(ComfyApp.clipspace.images) + ComfyApp.clipspace.images[0] = item; + + if(ComfyApp.clipspace.widgets) { + const index = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image'); + + if(index >= 0) + ComfyApp.clipspace.widgets[index].value = `${filename} [temp]`; + } + + const dataURL = save_canvas.toDataURL(); + const blob = dataURLToBlob(dataURL); + + let original_url = new URL(this.image.src); + + const original_ref = { filename: original_url.searchParams.get('filename') }; + + let original_subfolder = original_url.searchParams.get("subfolder"); + if(original_subfolder) + original_ref.subfolder = original_subfolder; + + let original_type = original_url.searchParams.get("type"); + if(original_type) + original_ref.type = original_type; + + formData.append('image', blob, filename); + formData.append('original_ref', JSON.stringify(original_ref)); + formData.append('type', "temp"); + + await uploadMask(item, formData); + ComfyApp.onClipspaceEditorSave(); + this.close(); + } +} + +app.registerExtension({ + name: "Comfy.Impact.SAMEditor", + init(app) { + const callback = + function () { + let dlg = ImpactSamEditorDialog.getInstance(); + dlg.show(); + }; + + const context_predicate = () => ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0 + ClipspaceDialog.registerButton("Impact SAM Detector", context_predicate, callback); + }, + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (Array.isArray(nodeData.output) && (nodeData.output.includes("MASK") || nodeData.output.includes("IMAGE"))) { + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: "Open in SAM Detector", + callback: () => { + ComfyApp.copyToClipspace(this); + ComfyApp.clipspace_return_node = this; + + let dlg = ImpactSamEditorDialog.getInstance(); + dlg.show(); + }, + }); + }); + } + } +}); + diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/impact-segs-picker.js b/custom_nodes/ComfyUI-Impact-Pack/js/impact-segs-picker.js new file mode 100644 index 00000000..01319f07 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/impact-segs-picker.js @@ -0,0 +1,182 @@ +import { ComfyApp, app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { api } from "../../scripts/api.js"; + +async function open_picker(node) { + const resp = await api.fetchApi(`/impact/segs/picker/count?id=${node.id}`); + const body = await resp.text(); + + let cnt = parseInt(body); + + var existingPicker = document.getElementById('impact-picker'); + if (existingPicker) { + existingPicker.parentNode.removeChild(existingPicker); + } + + var gallery = document.createElement('div'); + gallery.id = 'impact-picker'; + + gallery.style.position = "absolute"; + gallery.style.height = "80%"; + gallery.style.width = "80%"; + gallery.style.top = "10%"; + gallery.style.left = "10%"; + gallery.style.display = 'flex'; + gallery.style.flexWrap = 'wrap'; + gallery.style.maxHeight = '600px'; + gallery.style.overflow = 'auto'; + gallery.style.backgroundColor = 'rgba(0,0,0,0.3)'; + gallery.style.padding = '20px'; + gallery.draggable = false; + gallery.style.zIndex = 5000; + + var doneButton = document.createElement('button'); + doneButton.textContent = 'Done'; + doneButton.style.padding = '10px 10px'; + doneButton.style.border = 'none'; + doneButton.style.borderRadius = '5px'; + doneButton.style.fontFamily = 'Arial, sans-serif'; + doneButton.style.fontSize = '16px'; + doneButton.style.fontWeight = 'bold'; + doneButton.style.color = '#fff'; + doneButton.style.background = 'linear-gradient(to bottom, #0070B8, #003D66)'; + doneButton.style.boxShadow = '0 2px 4px rgba(0, 0, 0, 0.4)'; + doneButton.style.margin = "20px"; + doneButton.style.height = "40px"; + + var cancelButton = document.createElement('button'); + cancelButton.textContent = 'Cancel'; + cancelButton.style.padding = '10px 10px'; + cancelButton.style.border = 'none'; + cancelButton.style.borderRadius = '5px'; + cancelButton.style.fontFamily = 'Arial, sans-serif'; + cancelButton.style.fontSize = '16px'; + cancelButton.style.fontWeight = 'bold'; + cancelButton.style.color = '#fff'; + cancelButton.style.background = 'linear-gradient(to bottom, #ff70B8, #ff3D66)'; + cancelButton.style.boxShadow = '0 2px 4px rgba(0, 0, 0, 0.4)'; + cancelButton.style.margin = "20px"; + cancelButton.style.height = "40px"; + + const w = node.widgets.find((w) => w.name == 'picks'); + let prev_selected = w.value.split(',').map(function(item) { + return parseInt(item, 10); + }); + + let images = []; + doneButton.onclick = () => { + var result = ''; + for(let i in images) { + if(images[i].isSelected) { + if(result != '') + result += ', '; + + result += (parseInt(i)+1); + } + } + + w.value = result; + + gallery.parentNode.removeChild(gallery); + } + + cancelButton.onclick = () => { + gallery.parentNode.removeChild(gallery); + } + + var panel = document.createElement('div'); + panel.style.clear = 'both'; + panel.style.width = '100%'; + panel.style.height = '40px'; + panel.style.justifyContent = 'center'; + panel.style.alignItems = 'center'; + panel.style.display = 'flex'; + panel.appendChild(doneButton); + panel.appendChild(cancelButton); + gallery.appendChild(panel); + + var hint = document.createElement('label'); + hint.style.position = 'absolute'; + hint.innerHTML = 'Click: Toggle Selection
Ctrl-click: Single Selection'; + gallery.appendChild(hint); + + let max_size = 300; + + for(let i=0; i image.naturalHeight) { + ratio = max_size/image.naturalWidth; + } + else { + ratio = max_size/image.naturalHeight; + } + + let width = image.naturalWidth * ratio; + let height = image.naturalHeight * ratio; + + if(width < height) { + this.style.marginLeft = (200-width)/2+"px"; + } + else{ + this.style.marginTop = (200-height)/2+"px"; + } + + this.style.width = width+"px"; + this.style.height = height+"px"; + this.style.objectFit = 'cover'; + } + + image.addEventListener('click', function(event) { + if(event.ctrlKey) { + for(let i in images) { + if(images[i].isSelected) { + images[i].style.border = 'none'; + images[i].isSelected = false; + } + } + + image.style.border = '2px solid #006699'; + image.isSelected = true; + + return; + } + + if(image.isSelected) { + image.style.border = 'none'; + image.isSelected = false; + } + else { + image.style.border = '2px solid #006699'; + image.isSelected = true; + } + }); + + gallery.appendChild(image); + } + + document.body.appendChild(gallery); +} + + +app.registerExtension({ + name: "Comfy.Impack.Picker", + + nodeCreated(node, app) { + if(node.comfyClass == "ImpactSEGSPicker") { + node.addWidget("button", "pick", "image", () => { + open_picker(node); + }); + } + } +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area-advanced.js b/custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area-advanced.js new file mode 100644 index 00000000..985b92c5 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area-advanced.js @@ -0,0 +1,459 @@ +import { app } from "../../scripts/app.js"; +import { readLinkedNumber, getDrawColor, computeCanvasSize } from "./common.js"; +function showPreviewCanvas(node, app) { + + const widget = { + type: "customCanvas", + name: "mask-rect-area-canvas", + get value() { + return this.canvas.value; + }, + set value(x) { + this.canvas.value = x; + }, + draw: function (ctx, node, widgetWidth, widgetY) { + + // If we are initially offscreen when created we wont have received a resize event + // Calculate it here instead + if (!node.canvasHeight) { + computeCanvasSize(node, node.size, 220, 240); + } + + const visible = true; + const t = ctx.getTransform(); + const margin = 12; + const border = 2; + const widgetHeight = node.canvasHeight; + + // Keep preview in sync when inputs are driven by links. + syncLinkedInputsToPropertiesAdvanced(node); + + const width = Math.max(1, Math.round(node.properties["width"])); + const height = Math.max(1, Math.round(node.properties["height"])); + const scale = Math.min( + (widgetWidth - margin * 3) / width, + (widgetHeight - margin * 3) / height + ); + const blurRadius = node.properties["blur_radius"] || 0; + const index = 0; + + Object.assign(this.canvas.style, { + left: `${t.e}px`, + top: `${t.f + (widgetY * t.d)}px`, + width: `${widgetWidth * t.a}px`, + height: `${widgetHeight * t.d}px`, + position: "absolute", + zIndex: 1, + fontSize: `${t.d * 10.0}px`, + pointerEvents: "none" + }); + + this.canvas.hidden = !visible; + + let backgroundWidth = width * scale; + let backgroundHeight = height * scale; + + let xOffset = margin; + if (backgroundWidth < widgetWidth) { + xOffset += (widgetWidth - backgroundWidth) / 2 - margin; + } + let yOffset = (margin / 2); + if (backgroundHeight < widgetHeight) { + yOffset += (widgetHeight - backgroundHeight) / 2 - margin; + } + + let widgetX = xOffset; + widgetY = widgetY + yOffset; + + // Draw the background border + ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2) + + // Draw the main background area + ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; + ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight); + + // Draw the conditioning zone + let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight); + + ctx.fillStyle = getDrawColor(0, "80"); + ctx.fillRect(widgetX + x, widgetY + y, w, h); + ctx.beginPath(); + ctx.lineWidth = 1; + + // Draw grid lines + for (let x = 0; x <= width / 64; x += 1) { + ctx.moveTo(widgetX + x * 64 * scale, widgetY); + ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight); + } + + for (let y = 0; y <= height / 64; y += 1) { + ctx.moveTo(widgetX, widgetY + y * 64 * scale); + ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale); + } + + ctx.strokeStyle = "#66666650"; + ctx.stroke(); + ctx.closePath(); + + // Draw current zone + let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight); + + ctx.fillStyle = getDrawColor(0, "80"); + ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh); + + ctx.fillStyle = getDrawColor(0, "40"); + ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2); + + // Draw white border around the current zone + ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR; + ctx.lineWidth = 2; + ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh); + + // Display + ctx.beginPath(); + + ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2); + ctx.fill(); + + ctx.lineWidth = 1; + ctx.strokeStyle = "white"; + ctx.stroke(); + + ctx.lineWidth = 1; + ctx.closePath(); + + // Draw progress bar canvas + if (backgroundWidth < widgetWidth) { + xOffset += (widgetWidth - backgroundWidth) / 2 - margin; + } + + // Adjust X and Y coordinates + const barHeight = 8; + let widgetYBar = widgetY + backgroundHeight + margin; + + // Draw the border around the progress bar + ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.fillRect( + widgetX - border, + widgetYBar - border, + backgroundWidth + border * 2, + barHeight + border * 2 + ); + + // Draw the main bar area (background) + ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; + ctx.fillRect( + widgetX, + widgetYBar, + backgroundWidth, + barHeight + ); + + // Draw progress bar grid + ctx.beginPath(); + ctx.lineWidth = 1; + ctx.strokeStyle = "#66666650"; + + // Calculate the number of grid lines based on the bar size + const numLines = Math.floor(backgroundWidth / 64); + + // Draw grid lines + for (let x = 0; x <= width / 64; x += 1) { + ctx.moveTo(widgetX + x * 64 * scale, widgetYBar); + ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight); + } + ctx.stroke(); + ctx.closePath(); + + // Draw progress (based on blur_radius) + const progress = Math.min(blurRadius / 255, 1); + ctx.fillStyle = "rgba(0, 120, 255, 0.5)"; + + ctx.fillRect( + widgetX, + widgetYBar, + backgroundWidth * progress, + barHeight + ); + } + }; + + widget.canvas = document.createElement("canvas"); + widget.canvas.className = "mask-rect-area-canvas"; + widget.parent = node; + + widget.computeLayoutSize = function (node) { + return { + minHeight: 200, + maxHeight: 300 + }; + }; + + document.body.appendChild(widget.canvas); + node.addCustomWidget(widget); + + app.canvas.onDrawBackground = function () { + // Draw node isnt fired once the node is off the screen + // if it goes off screen quickly, the input may not be removed + // this shifts it off screen so it can be moved back if the node is visible. + for (let n in app.graph._nodes) { + n = app.graph._nodes[n]; + for (let w in n.widgets) { + let wid = n.widgets[w]; + if (Object.hasOwn(wid, "canvas")) { + wid.canvas.style.left = -8000 + "px"; + wid.canvas.style.position = "absolute"; + } + } + } + }; + + node.onResize = function (size) { + computeCanvasSize(node, size, 220, 240); + }; + + return {minWidth: 200, minHeight: 200, widget}; +} + +app.registerExtension({ + name: "drltdata.MaskRectAreaAdvanced", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name !== "MaskRectAreaAdvanced") { + return; + } + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + + this.setProperty("width", 512); + this.setProperty("height", 512); + this.setProperty("x", 0); + this.setProperty("y", 0); + this.setProperty("w", 256); + this.setProperty("h", 256); + this.setProperty("blur_radius", 0); + + this.selected = false; + this.index = 3; + this.serialize_widgets = true; + + // If the node already provides widgets from Python/ComfyUI, do NOT recreate them + const hasExisting = Array.isArray(this.widgets) && this.widgets.some(w => w && w.name === "x"); + + // Helper: attach callbacks to existing widgets to keep node.properties in sync (canvas preview). + const hookWidget = (node, widgetName, propName, opts) => { + if (!Array.isArray(node.widgets)) { + return; + } + const w = node.widgets.find(ww => ww && ww.name === widgetName); + if (!w) { + return; + } + + const min = (opts && typeof opts.min === "number") ? opts.min : undefined; + const max = (opts && typeof opts.max === "number") ? opts.max : undefined; + const step = (opts && typeof opts.step === "number") ? opts.step : undefined; + + if (node.properties && Object.prototype.hasOwnProperty.call(node.properties, propName)) { + w.value = node.properties[propName]; + } else { + node.properties[propName] = w.value; + } + + const prevCb = w.callback; + w.callback = function (v, ...args) { + let val = v; + if (typeof val === "number") { + if (typeof step === "number" && step > 0) { + const s = step / 10; + val = Math.round(val / s) * s; + } else { + val = Math.round(val); + } + if (typeof min === "number") { + val = Math.max(min, val); + } + if (typeof max === "number") { + val = Math.min(max, val); + } + } + this.value = val; + node.properties[propName] = val; + if (prevCb) { + return prevCb.call(this, val, ...args); + } + }; + }; + + if (hasExisting) { + hookWidget(this, "x", "x", {"step": 10}); + hookWidget(this, "y", "y", {"step": 10}); + hookWidget(this, "width", "w", {"step": 10}); + hookWidget(this, "height", "h", {"step": 10}); + hookWidget(this, "image_width", "width", {"step": 10}); + hookWidget(this, "image_height", "height", {"step": 10}); + hookWidget(this, "blur_radius", "blur_radius", {"min": 0, "max": 255, "step": 10}); + } else { + CUSTOM_INT(this, "x", 0, function (v, _, node) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + node.properties["x"] = this.value; + }); + CUSTOM_INT(this, "y", 0, function (v, _, node) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + node.properties["y"] = this.value; + }); + CUSTOM_INT(this, "width", 256, function (v, _, node) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + node.properties["w"] = this.value; + }); + CUSTOM_INT(this, "height", 256, function (v, _, node) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + node.properties["h"] = this.value; + }); + CUSTOM_INT(this, "image_width", 512, function (v, _, node) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + node.properties["width"] = this.value; + }); + CUSTOM_INT(this, "image_height", 512, function (v, _, node) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + node.properties["height"] = this.value; + }); + CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) { + this.value = Math.round(v) || 0; + node.properties["blur_radius"] = this.value; + }, + {"min": 0, "max": 255, "step": 10} + ); + } + + showPreviewCanvas(this, app); + + this.onSelected = function () { + this.selected = true; + }; + this.onDeselected = function () { + this.selected = false; + }; + + return r; + }; + } +}); + +// Calculate the drawing area using individual properties. +function getDrawArea(node, backgroundWidth, backgroundHeight) { + let x = node.properties["x"] * backgroundWidth / node.properties["width"]; + let y = node.properties["y"] * backgroundHeight / node.properties["height"]; + let w = node.properties["w"] * backgroundWidth / node.properties["width"]; + let h = node.properties["h"] * backgroundHeight / node.properties["height"]; + + if (x > backgroundWidth) { + x = backgroundWidth; + } + if (y > backgroundHeight) { + y = backgroundHeight; + } + + if (x + w > backgroundWidth) { + w = Math.max(0, backgroundWidth - x); + } + + if (y + h > backgroundHeight) { + h = Math.max(0, backgroundHeight - y); + } + + return [x, y, w, h]; +} + +function CUSTOM_INT(node, inputName, val, func, config = {}) { + return { + widget: node.addWidget( + "number", + inputName, + val, + func, + Object.assign({}, {min: 0, max: 4096, step: 640, precision: 0}, config) + ) + }; +} + +function syncLinkedInputsToPropertiesAdvanced(node) { + let changed = false; + + const vx = readLinkedNumber(node, "x"); + if (vx != null) { + const nv = Math.max(0, Math.round(vx)); + if (node.properties["x"] !== nv) { + node.properties["x"] = nv; + changed = true; + } + } + + const vy = readLinkedNumber(node, "y"); + if (vy != null) { + const nv = Math.max(0, Math.round(vy)); + if (node.properties["y"] !== nv) { + node.properties["y"] = nv; + changed = true; + } + } + + // Input "width" is the rectangle width in px -> property "w" + const vw = readLinkedNumber(node, "width"); + if (vw != null) { + const nv = Math.max(0, Math.round(vw)); + if (node.properties["w"] !== nv) { + node.properties["w"] = nv; + changed = true; + } + } + + // Input "height" is the rectangle height in px -> property "h" + const vh = readLinkedNumber(node, "height"); + if (vh != null) { + const nv = Math.max(0, Math.round(vh)); + if (node.properties["h"] !== nv) { + node.properties["h"] = nv; + changed = true; + } + } + + // Image size (must be >=1 to avoid division by zero in getDrawArea) + const viw = readLinkedNumber(node, "image_width"); + if (viw != null) { + const nv = Math.max(1, Math.round(viw)); + if (node.properties["width"] !== nv) { + node.properties["width"] = nv; + changed = true; + } + } + + const vih = readLinkedNumber(node, "image_height"); + if (vih != null) { + const nv = Math.max(1, Math.round(vih)); + if (node.properties["height"] !== nv) { + node.properties["height"] = nv; + changed = true; + } + } + + const vbr = readLinkedNumber(node, "blur_radius"); + if (vbr != null) { + const nv = Math.max(0, Math.min(255, Math.round(vbr))); + if (node.properties["blur_radius"] !== nv) { + node.properties["blur_radius"] = nv; + changed = true; + } + } + + return changed; +} + diff --git a/custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area.js b/custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area.js new file mode 100644 index 00000000..309096bb --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area.js @@ -0,0 +1,494 @@ +import { app } from "../../scripts/app.js"; +import { readLinkedNumber, getDrawColor, computeCanvasSize } from "./common.js"; +function showPreviewCanvas(node, app) { + + const widget = { + type: "customCanvas", + name: "mask-rect-area-canvas", + get value() { + return this.canvas.value; + }, + set value(x) { + this.canvas.value = x; + }, + draw: function (ctx, node, widgetWidth, widgetY) { + + // If we are initially offscreen when created we wont have received a resize event + // Calculate it here instead + if (!node.canvasHeight) { + computeCanvasSize(node, node.size, 200, 200); + } + + const visible = true; + const t = ctx.getTransform(); + const margin = 12; + const border = 2; + const widgetHeight = node.canvasHeight; + const width = 512; + const height = 512; + const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height); + const blurRadius = node.properties["blur_radius"] || 0; + const index = 0; + + Object.assign(this.canvas.style, { + left: `${t.e}px`, + top: `${t.f + (widgetY * t.d)}px`, + width: `${widgetWidth * t.a}px`, + height: `${widgetHeight * t.d}px`, + position: "absolute", + zIndex: 1, + fontSize: `${t.d * 10.0}px`, + pointerEvents: "none" + }); + + this.canvas.hidden = !visible; + + let backgroundWidth = width * scale; + let backgroundHeight = height * scale; + let xOffset = margin; + if (backgroundWidth < widgetWidth) { + xOffset += (widgetWidth - backgroundWidth) / 2 - margin; + } + let yOffset = (margin / 2); + if (backgroundHeight < widgetHeight) { + yOffset += (widgetHeight - backgroundHeight) / 2 - margin; + } + + let widgetX = xOffset; + widgetY = widgetY + yOffset; + + // Draw the background border + ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2); + + // Draw the main background area + ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; + ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight); + + // Keep preview in sync when inputs are driven by links. + syncLinkedInputsToProperties(node); + + // Draw the conditioning zone + let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight); + + ctx.fillStyle = getDrawColor(0, "80"); + ctx.fillRect(widgetX + x, widgetY + y, w, h); + ctx.beginPath(); + ctx.lineWidth = 1; + + // Draw grid lines + for (let x = 0; x <= width / 64; x += 1) { + ctx.moveTo(widgetX + x * 64 * scale, widgetY); + ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight); + } + + for (let y = 0; y <= height / 64; y += 1) { + ctx.moveTo(widgetX, widgetY + y * 64 * scale); + ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale); + } + + ctx.strokeStyle = "#66666650"; + ctx.stroke(); + ctx.closePath(); + + // Draw current zone + let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight); + + ctx.fillStyle = getDrawColor(0, "80"); + ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh); + + ctx.fillStyle = getDrawColor(0, "40"); + ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2); + + // Draw white border around the current zone + ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR; + ctx.lineWidth = 2; + ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh); + + // Display + ctx.beginPath(); + + ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2); + ctx.fill(); + + ctx.lineWidth = 1; + ctx.strokeStyle = "white"; + ctx.stroke(); + ctx.lineWidth = 1; + ctx.closePath(); + + // Draw progress bar canvas + if (backgroundWidth < widgetWidth) { + xOffset += (widgetWidth - backgroundWidth) / 2 - margin; + } + + const barHeight = 8; + let widgetYBar = widgetY + backgroundHeight + margin; + + // Draw progress bar border + ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.fillRect( + widgetX - border, + widgetYBar - border, + backgroundWidth + border * 2, + barHeight + border * 2 + ); + + // Draw progress bar area + ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas + ctx.fillRect( + widgetX, + widgetYBar, + backgroundWidth, + barHeight + ); + + // Draw progress bar grid + ctx.beginPath(); + ctx.lineWidth = 1; + ctx.strokeStyle = "#66666650"; + + // Determine max lines + const numLines = Math.floor(backgroundWidth / 64); + + // Draw progress bar grid + for (let x = 0; x <= width / 64; x += 1) { + ctx.moveTo(widgetX + x * 64 * scale, widgetYBar); + ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight); + } + ctx.stroke(); + ctx.closePath(); + + // Draw progress bar + const progress = Math.min(blurRadius / 255, 1); + ctx.fillStyle = "rgba(0, 120, 255, 0.5)"; + + ctx.fillRect( + widgetX, + widgetYBar, + backgroundWidth * progress, + barHeight + ); + } + }; + + widget.canvas = document.createElement("canvas"); + widget.canvas.className = "mask-rect-area-canvas"; + widget.parent = node; + + widget.computeLayoutSize = function (node) { + return { + minHeight: 200, + maxHeight: 300 + }; + }; + + document.body.appendChild(widget.canvas); + node.addCustomWidget(widget); + + app.canvas.onDrawBackground = function () { + // Draw node isnt fired once the node is off the screen + // if it goes off screen quickly, the input may not be removed + // this shifts it off screen so it can be moved back if the node is visible. + for (let n in app.graph._nodes) { + n = app.graph._nodes[n]; + for (let w in n.widgets) { + let wid = n.widgets[w]; + if (Object.hasOwn(wid, "canvas")) { + wid.canvas.style.left = -8000 + "px"; + wid.canvas.style.position = "absolute"; + } + } + } + }; + + node.onResize = function (size) { + computeCanvasSize(node, size, 200, 200); + }; + + return {minWidth: 200, minHeight: 200, widget}; +} + +app.registerExtension({ + name: 'drltdata.MaskRectArea', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name !== "MaskRectArea") { + return; + } + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + + this.setProperty("width", 512); + this.setProperty("height", 512); + this.setProperty("x", 0); + this.setProperty("y", 0); + this.setProperty("w", 50); + this.setProperty("h", 50); + this.setProperty("blur_radius", 0); + + this.selected = false; + this.index = 3; + this.serialize_widgets = true; + + // If Python/ComfyUI already created typed widgets, do not recreate them (avoid duplicates). + const hasExisting = Array.isArray(this.widgets) && this.widgets.some(w => w && w.name === "x"); + + // Hook existing widgets to keep node.properties in sync (canvas uses properties). + const hookWidget = (node, widgetName, propName, opts) => { + if (!Array.isArray(node.widgets)) { + return; + } + const w = node.widgets.find(ww => ww && ww.name === widgetName); + if (!w) { + return; + } + + const min = (opts && typeof opts.min === "number") ? opts.min : undefined; + const max = (opts && typeof opts.max === "number") ? opts.max : undefined; + + if (node.properties && Object.prototype.hasOwnProperty.call(node.properties, propName)) { + w.value = node.properties[propName]; + } else { + node.properties[propName] = w.value; + } + + const prevCb = w.callback; + w.callback = function (v, ...args) { + let val = v; + + if (typeof val === "number") { + val = Math.round(val); + + if (typeof min === "number") { + val = Math.max(min, val); + } + if (typeof max === "number") { + val = Math.min(max, val); + } + } + + this.value = val; + node.properties[propName] = val; + + if (prevCb) { + return prevCb.call(this, val, ...args); + } + }; + }; + + if (hasExisting) { + // Note: "width"/"height" widgets map to "w"/"h" properties (percent-based). + hookWidget(this, "x", "x", {"min": 0, "max": 100}); + hookWidget(this, "y", "y", {"min": 0, "max": 100}); + hookWidget(this, "width", "w", {"min": 0, "max": 100}); + hookWidget(this, "height", "h", {"min": 0, "max": 100}); + hookWidget(this, "blur_radius", "blur_radius", {"min": 0, "max": 255}); + } else { + CUSTOM_INT(this, "x", 0, function (v, _, node) { + this.value = Math.max(0, Math.min(100, Math.round(v))); + node.properties["x"] = this.value; + }); + CUSTOM_INT(this, "y", 0, function (v, _, node) { + this.value = Math.max(0, Math.min(100, Math.round(v))); + node.properties["y"] = this.value; + }); + CUSTOM_INT(this, "w", 50, function (v, _, node) { + this.value = Math.max(0, Math.min(100, Math.round(v))); + node.properties["w"] = this.value; + }); + CUSTOM_INT(this, "h", 50, function (v, _, node) { + this.value = Math.max(0, Math.min(100, Math.round(v))); + node.properties["h"] = this.value; + }); + CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) { + this.value = Math.round(v) || 0; + node.properties["blur_radius"] = this.value; + }, {"min": 0, "max": 255, "step": 10}); + + // If Python widgets exist, they will be used instead; this is back-compat only. + } + + showPreviewCanvas(this, app); + + // Sync linked input values -> node.properties so the preview updates when driven by connections. + const prevOnExecute = this.onExecute; + this.onExecute = function () { + const rr = prevOnExecute ? prevOnExecute.apply(this, arguments) : undefined; + + const readLinkedInt = (inputName) => { + if (!Array.isArray(this.inputs)) { + return null; + } + const inp = this.inputs.find(i => i && i.name === inputName); + if (!inp || !inp.link) { + return null; + } + try { + const v = this.getInputData(inputName); + return (typeof v === "number") ? v : null; + } catch (e) { + return null; + } + }; + + let changed = false; + + const vx = readLinkedInt("x"); + if (vx != null) { + const nv = Math.max(0, Math.min(100, Math.round(vx))); + if (this.properties["x"] !== nv) { + this.properties["x"] = nv; + changed = true; + } + } + + const vy = readLinkedInt("y"); + if (vy != null) { + const nv = Math.max(0, Math.min(100, Math.round(vy))); + if (this.properties["y"] !== nv) { + this.properties["y"] = nv; + changed = true; + } + } + + const vw = readLinkedInt("width"); + if (vw != null) { + const nv = Math.max(0, Math.min(100, Math.round(vw))); + if (this.properties["w"] !== nv) { + this.properties["w"] = nv; + changed = true; + } + } + + const vh = readLinkedInt("height"); + if (vh != null) { + const nv = Math.max(0, Math.min(100, Math.round(vh))); + if (this.properties["h"] !== nv) { + this.properties["h"] = nv; + changed = true; + } + } + + const vbr = readLinkedInt("blur_radius"); + if (vbr != null) { + const nv = Math.max(0, Math.min(255, Math.round(vbr))); + if (this.properties["blur_radius"] !== nv) { + this.properties["blur_radius"] = nv; + changed = true; + } + } + + if (changed) { + this.setDirtyCanvas(true, true); + if (this.graph) { + this.graph.setDirtyCanvas(true, true); + } + } + + return rr; + }; + + this.onSelected = function () { + this.selected = true; + }; + this.onDeselected = function () { + this.selected = false; + }; + + return r; + }; + } +}); + + +// Calculate the drawing area using percentage-based properties. +function getDrawArea(node, backgroundWidth, backgroundHeight) { + // Convert percentages to actual pixel values based on the background dimensions + let x = (node.properties["x"] / 100) * backgroundWidth; + let y = (node.properties["y"] / 100) * backgroundHeight; + let w = (node.properties["w"] / 100) * backgroundWidth; + let h = (node.properties["h"] / 100) * backgroundHeight; + + // Ensure the values do not exceed the background boundaries + if (x > backgroundWidth) { + x = backgroundWidth; + } + if (y > backgroundHeight) { + y = backgroundHeight; + } + + // Adjust width and height to fit within the background dimensions + if (x + w > backgroundWidth) { + w = Math.max(0, backgroundWidth - x); + } + if (y + h > backgroundHeight) { + h = Math.max(0, backgroundHeight - y); + } + + return [x, y, w, h]; +} + +function CUSTOM_INT(node, inputName, val, func, config = {}) { + return { + widget: node.addWidget( + "number", + inputName, + val, + func, + Object.assign({}, {min: 0, max: 100, step: 10, precision: 0}, config) + ) + }; +} + +function syncLinkedInputsToProperties(node) { + let changed = false; + + const vx = readLinkedNumber(node, "x"); + if (vx != null) { + const nv = Math.max(0, Math.min(100, Math.round(vx))); + if (node.properties["x"] !== nv) { + node.properties["x"] = nv; + changed = true; + } + } + + const vy = readLinkedNumber(node, "y"); + if (vy != null) { + const nv = Math.max(0, Math.min(100, Math.round(vy))); + if (node.properties["y"] !== nv) { + node.properties["y"] = nv; + changed = true; + } + } + + const vw = readLinkedNumber(node, "width"); + if (vw != null) { + const nv = Math.max(0, Math.min(100, Math.round(vw))); + if (node.properties["w"] !== nv) { + node.properties["w"] = nv; + changed = true; + } + } + + const vh = readLinkedNumber(node, "height"); + if (vh != null) { + const nv = Math.max(0, Math.min(100, Math.round(vh))); + if (node.properties["h"] !== nv) { + node.properties["h"] = nv; + changed = true; + } + } + + const vbr = readLinkedNumber(node, "blur_radius"); + if (vbr != null) { + const nv = Math.max(0, Math.min(255, Math.round(vbr))); + if (node.properties["blur_radius"] !== nv) { + node.properties["blur_radius"] = nv; + changed = true; + } + } + + return changed; +} diff --git a/custom_nodes/ComfyUI-Impact-Pack/latent.png b/custom_nodes/ComfyUI-Impact-Pack/latent.png new file mode 100644 index 00000000..19fed324 Binary files /dev/null and b/custom_nodes/ComfyUI-Impact-Pack/latent.png differ diff --git a/custom_nodes/ComfyUI-Impact-Pack/locales/ko/nodeDefs.json b/custom_nodes/ComfyUI-Impact-Pack/locales/ko/nodeDefs.json new file mode 100644 index 00000000..1626d6cb --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/locales/ko/nodeDefs.json @@ -0,0 +1,1241 @@ +{ + "FaceDetailer": { + "description": "감지 λͺ¨λΈ(bbox, segm, sam) λͺ¨λΈμ„ μ΄μš©ν•΄μ„œ μž…λ ₯ μ΄λ―Έμ§€μ—μ„œ μžλ™μœΌλ‘œ νŠΉμ • 객체λ₯Ό κ°μ§€ν•˜κ³ , 감지 μ˜μ—­μ„ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.\nμ‚¬μš©μžλ“€μ΄ 자주 μ‚¬μš©ν•˜λŠ” μ–Όκ΅΄ λ””ν…ŒμΌ κ°•ν™” μ›Œν¬ν”Œλ‘œλ₯Ό λ‹¨μˆœν™”ν•˜κΈ° μœ„ν•΄ νŠΉν™” μ‹œν‚¨ λ…Έλ“œμ΄κΈ΄ ν•˜μ§€λ§Œ, 감지 λͺ¨λΈμ— λ”°λΌμ„œ λ‹€μ–‘ν•œ μžλ™ 인페인트 μš©λ„λ‘œ μ‚¬μš© κ°€λŠ₯ν•©λ‹ˆλ‹€.", + "display_name": "μ–Όκ΅΄ λ””ν…ŒμΌλŸ¬", + "inputs": { + "image": { + "name": "이미지" + }, + "model": { + "name": "λͺ¨λΈ", + "tooltip": "λ§Œμ•½ `ImpactDummyInput` 을 μ—°κ²° ν•˜λ©΄, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "positive": { + "name": "긍정 쑰건" + }, + "negative": { + "name": "λΆ€μ • 쑰건" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "bbox_threshold": { + "name": "bbox 감지 μž„κ³„μΉ˜", + "tooltip": "사각 μ˜μ—­(bbox) 감지 λͺ¨λΈμ˜ μ΅œμ†Œ 감지 μž„κ³„μΉ˜λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. μž„κ³„μΉ˜κ°€ λ†’μ„μˆ˜λ‘ ν™•μ‹€ν•œ 객체만 κ°μ§€ν•˜μ§€λ§Œ, 객체λ₯Ό κ°μ§€ν•˜μ§€ λͺ»ν•  ν™•λ₯ μ΄ μ¦κ°€ν•©λ‹ˆλ‹€." + }, + "bbox_dilation": { + "name": "bbox ν™•μž₯", + "tooltip": "κ°μ§€λœ 사각 μ˜μ—­(bbox)을 ν™•μž₯ ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ€ κ°μ§€λœ μ˜μ—­λ³΄λ‹€ 더 넓은 μ˜μ—­μ„ 인페인트 ν•  경우 μ‚¬μš©ν•©λ‹ˆλ‹€.\n주의: sam λͺ¨λΈμ„ μ‚¬μš©ν•  경우 μž˜λΌλ‚Έ μ˜μ—­λ‚΄μ—μ„œ bbox ν™•μž₯을 ν•˜λ”λΌλ„, sam 감지 μ˜μ—­μ΄ μž‘μœΌλ©΄ μ—¬μ „νžˆ μ œν•œλ©λ‹ˆλ‹€." + }, + "bbox_crop_factor": { + "name": "bbox 자λ₯΄κΈ° 배율", + "tooltip": "κ°μ§€λœ 사각 μ˜μ—­(bbox)의 λͺ‡λ°° 크기의 μ˜μ—­μ„ μž˜λΌλ‚Ό 것 인지λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. 이 크기가 λ„ˆλ¬΄ μž‘μœΌλ©΄, 인페인트 ν•  μ΄λ―Έμ§€μ˜ μ£Όλ³€ 정보가 λΆ€μ‘±ν•΄μ„œ μœ„ν™”κ°μ΄ κ°•ν•œ 이미지가 μƒμ„±λ©λ‹ˆλ‹€. 이 크기가 λ„ˆλ¬΄ 크면, μΈνŽ˜μΈνŒ…μ— λ„ˆλ¬΄ 였랜 μ‹œκ°„μ΄ 걸릴 수 있으며, λͺ¨λΈμ˜ μ—­λŸ‰μ„ μ΄ˆκ³Όν•  μ •λ„λ‘œ 클 경우 μ˜¬λ°”λ₯΄μ§€ μ•Šμ€ 이미지λ₯Ό μƒμ„±ν•˜κ²Œ λ©λ‹ˆλ‹€." + }, + "sam_detection_hint": { + "name": "sam 감지 힌트", + "tooltip": "[μ‹€ν—˜κΈ°λŠ₯] 였래된 μ‹€ν—˜ κΈ°λŠ₯으둜, sam λͺ¨λΈμ˜ 감지 힌트λ₯Ό μ œκ³΅ν•˜λŠ” λ°©μ‹μž…λ‹ˆλ‹€. cetner-1 (쀑앙점 1개) μ™Έμ—λŠ” μ‚¬μš©ν•˜μ§€ μ•Šμ„ 것을 ꢌμž₯ν•©λ‹ˆλ‹€." + }, + "sam_dilation": { + "name": "sam 마슀크 ν™•μž₯", + "tooltip": "sam λͺ¨λΈλ‘œ κ°μ§€λœ 싀루엣 마슀크λ₯Ό ν™•μž₯ν•©λ‹ˆλ‹€." + }, + "sam_threshold": { + "name": "sam 감지 μž„κ³„μΉ˜", + "tooltip": "sam λͺ¨λΈμ˜ μ΅œμ†Œ 감지 μž„κ³„μΉ˜λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. μž„κ³„μΉ˜κ°€ λ†’μ„μˆ˜λ‘ ν™•μ‹€ν•œ 객체만 κ°μ§€ν•˜μ§€λ§Œ, 객체λ₯Ό κ°μ§€ν•˜μ§€ λͺ»ν•  ν™•λ₯ μ΄ μ¦κ°€ν•©λ‹ˆλ‹€." + }, + "sam_bbox_expansion": { + "name": "sam μ˜μ—­ ν™•μž₯", + "tooltip": "sam 의 감지 μ˜μ—­μ„ ν™•μž₯ν•©λ‹ˆλ‹€. 감지 μ˜μ—­μ€ 마슀크λ₯Ό ν¬ν•¨ν•˜λŠ” 전체 사각 μ˜μ—­μž…λ‹ˆλ‹€.\n주의1:sam 마슀크λ₯Ό ν™•μž₯ν•˜λ”λΌλ„, 감지 μ˜μ—­μ„ λ²—μ–΄λ‚  수 μ—†μŠ΅λ‹ˆλ‹€.\n주의2: bbox ν™•μž₯을 ν•˜λ”λΌλ„, sam 감지 μ˜μ—­μ΄ μž‘μœΌλ©΄ μ—¬μ „νžˆ μ œν•œλ©λ‹ˆλ‹€." + }, + "sam_mask_hint_threshold": { + "name": "sam 마슀크 힌트 μž„κ³„μΉ˜", + "tooltip": "[μ‹€ν—˜κΈ°λŠ₯] 였래된 μ‹€ν—˜ κΈ°λŠ₯으둜, mask-hint λͺ¨λ“œμ—μ„œλ§Œ μ‚¬μš©λ˜λŠ” μ˜΅μ…˜μœΌλ‘œ, λ§ˆμŠ€ν¬μ—μ„œ 이 크기 μ΄μƒμ˜ 점 마슀크λ₯Ό sam의 힌트둜 μ‚¬μš©ν•©λ‹ˆλ‹€." + }, + "sam_mask_hint_use_negative": { + "name": "sam 마슀크 νžŒνŠΈμ— μ œμ™Έ 힌트 μ‚¬μš©", + "tooltip": "[μ‹€ν—˜κΈ°λŠ₯] 였래된 μ‹€ν—˜ κΈ°λŠ₯으둜, mask-hitn λͺ¨λ“œμ—μ„œλ§Œ μ‚¬μš©λ˜λŠ” μ˜΅μ…˜μœΌλ‘œ, sam 마슀크 힌트 μž„κ³„μΉ˜λ³΄λ‹€ μž‘μ€ 점 마슀크λ₯Ό sam의 μ œμ™Έ 힌트둜 μ‚¬μš©ν•©λ‹ˆλ‹€." + }, + "drop_size": { + "name": "감지 μ΅œμ†Œ 크기", + "tooltip": "사각 μ˜μ—­(bbox) κ°μ§€κΈ°λ‘œ κ°μ§€ν•œ 크기가 이 μ„€μ •κ°’ 보닀 μž‘μ„ 경우 λ¬΄μ‹œν•©λ‹ˆλ‹€." + }, + "bbox_detector": { + "name": "bbox 감지기", + "tooltip": "λ””ν…ŒμΌ κ°œμ„  λŒ€μƒμ„ μžλ™μœΌλ‘œ κ°μ§€ν•΄μ£ΌλŠ” 사각 μ˜μ—­(bbox) 감지기 μž…λ ₯.\n이 κ°μ§€κΈ°λ‘œ κ°μ§€λœ 감지 정보가 κΈ°μ€€ μ •λ³΄μž…λ‹ˆλ‹€." + }, + "wildcard": { + "name": "μ™€μΌλ“œμΉ΄λ“œ ν”„λ‘¬ν”„νŠΈ", + "tooltip": "'μ™€μΌλ“œμΉ΄λ“œ 인코더 (Impact)'와 μœ μ‚¬ν•œ κΈ°λŠ₯을 μˆ˜ν–‰ν•˜μ—¬, μ™€μΌλ“œμΉ΄λ“œ κΈ°λŠ₯κ³Ό 둜라 λ‘œλ”© κΈ°λŠ₯을 μ œκ³΅ν•©λ‹ˆλ‹€. λ˜ν•œ, κ°μ§€λœ μ˜μ—­λ³„λ‘œ λ‹€λ₯Έ ν”„λ‘¬ν”„νŠΈλ₯Ό μ μš©ν•˜λŠ” κΈ°λŠ₯듀을 μ œκ³΅ν•©λ‹ˆλ‹€.\n더 μžμ„Έν•œ μ •λ³΄λŠ” νŠœν† λ¦¬μ–Ό νŽ˜μ΄μ§€λ₯Ό μ°Έκ³ ν•˜μ„Έμš”.\n주의:이 μž…λ ₯을 λΉ„μ›Œλ‘λ©΄, 이 μž…λ ₯은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "sam_model_opt": { + "name": "sam λͺ¨λΈ", + "tooltip": "이 λͺ¨λΈμ„ μ œκ³΅ν•  경우 sam λͺ¨λΈμ„ 감지 보쑰 λͺ¨λΈλ‘œ μ‚¬μš©ν•©λ‹ˆλ‹€. bbox κ°μ§€κΈ°λ‘œ κ°μ§€λœ 사각 μ˜μ—­μ— sam λͺ¨λΈμ„ μ μš©ν•΄μ„œ μ •κ΅ν•œ 싀루엣 마슀크λ₯Ό μƒμ„±ν•©λ‹ˆλ‹€.\n주의: 이 μž…λ ₯이 연결될 경우 segm κ°μ§€κΈ°λŠ” λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "segm_detector_opt": { + "name": "segm 감지기", + "tooltip": "이 λͺ¨λΈμ„ μ œκ³΅ν•  경우 segm λͺ¨λΈμ„ 감지 보쑰 λͺ¨λΈλ‘œ μ‚¬μš©ν•©λ‹ˆλ‹€. bbox κ°μ§€κΈ°λ‘œ κ°μ§€λœ 사각 μ˜μ—­μ— segm κ°μ§€κΈ°λ‘œ κ°μ§€λœ 싀루엣 마슀크λ₯Ό μƒμ„±ν•©λ‹ˆλ‹€.\n주의: 이 μž…λ ₯은 sam λͺ¨λΈμ΄ 연결될 경우 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "tiled_encode": { + "name": "타일 μΈμ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE μΈμ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE μΈμ½”λ“œ' λŒ€μ‹  'VAE μΈμ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + }, + "tiled_decode": { + "name": "타일 λ””μ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE λ‹€μ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE λ””μ½”λ“œ' λŒ€μ‹  'VAE λ””μ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미" + }, + "1": { + "name": "μž˜λΌλ‚Έ 이미지" + }, + "2": { + "name": "μž˜λΌλ‚Έ 투λͺ… 이미지" + }, + "3": { + "name": "마슀크" + }, + "4": { + "name": "λ””ν…ŒμΌλŸ¬ νŒŒμ΄ν”„" + }, + "5": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "FaceDetailerPipe": { + "description": "감지 λͺ¨λΈ(bbox, segm, sam) λͺ¨λΈμ„ μ΄μš©ν•΄μ„œ μž…λ ₯ μ΄λ―Έμ§€μ—μ„œ μžλ™μœΌλ‘œ νŠΉμ • 객체λ₯Ό κ°μ§€ν•˜κ³ , 감지 μ˜μ—­μ„ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.\nμ‚¬μš©μžλ“€μ΄ 자주 μ‚¬μš©ν•˜λŠ” μ–Όκ΅΄ λ””ν…ŒμΌ κ°•ν™” μ›Œν¬ν”Œλ‘œλ₯Ό λ‹¨μˆœν™”ν•˜κΈ° μœ„ν•΄ νŠΉν™” μ‹œν‚¨ λ…Έλ“œμ΄κΈ΄ ν•˜μ§€λ§Œ, 감지 λͺ¨λΈμ— λ”°λΌμ„œ λ‹€μ–‘ν•œ μžλ™ 인페인트 μš©λ„λ‘œ μ‚¬μš© κ°€λŠ₯ν•©λ‹ˆλ‹€.", + "display_name": "μ–Όκ΅΄ λ””ν…ŒμΌλŸ¬ (νŒŒμ΄ν”„)", + "inputs": { + "image": { + "name": "이미지" + }, + "detailer_pipe": { + "name": "λ””ν…ŒμΌλŸ¬ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ λ””ν…ŒμΌλŸ¬ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "bbox_threshold": { + "name": "bbox 감지 μž„κ³„μΉ˜", + "tooltip": "사각 μ˜μ—­(bbox) 감지 λͺ¨λΈμ˜ μ΅œμ†Œ 감지 μž„κ³„μΉ˜λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. μž„κ³„μΉ˜κ°€ λ†’μ„μˆ˜λ‘ ν™•μ‹€ν•œ 객체만 κ°μ§€ν•˜μ§€λ§Œ, 객체λ₯Ό κ°μ§€ν•˜μ§€ λͺ»ν•  ν™•λ₯ μ΄ μ¦κ°€ν•©λ‹ˆλ‹€." + }, + "bbox_dilation": { + "name": "bbox ν™•μž₯", + "tooltip": "κ°μ§€λœ 사각 μ˜μ—­(bbox)을 ν™•μž₯ ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ€ κ°μ§€λœ μ˜μ—­λ³΄λ‹€ 더 넓은 μ˜μ—­μ„ 인페인트 ν•  경우 μ‚¬μš©ν•©λ‹ˆλ‹€.\n주의: sam λͺ¨λΈμ„ μ‚¬μš©ν•  경우 μž˜λΌλ‚Έ μ˜μ—­λ‚΄μ—μ„œ bbox ν™•μž₯을 ν•˜λ”λΌλ„, sam 감지 μ˜μ—­μ΄ μž‘μœΌλ©΄ μ—¬μ „νžˆ μ œν•œλ©λ‹ˆλ‹€." + }, + "bbox_crop_factor": { + "name": "bbox 자λ₯΄κΈ° 배율", + "tooltip": "κ°μ§€λœ 사각 μ˜μ—­(bbox)의 λͺ‡λ°° 크기의 μ˜μ—­μ„ μž˜λΌλ‚Ό 것 인지λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. 이 크기가 λ„ˆλ¬΄ μž‘μœΌλ©΄, 인페인트 ν•  μ΄λ―Έμ§€μ˜ μ£Όλ³€ 정보가 λΆ€μ‘±ν•΄μ„œ μœ„ν™”κ°μ΄ κ°•ν•œ 이미지가 μƒμ„±λ©λ‹ˆλ‹€. 이 크기가 λ„ˆλ¬΄ 크면, μΈνŽ˜μΈνŒ…μ— λ„ˆλ¬΄ 였랜 μ‹œκ°„μ΄ 걸릴 수 있으며, λͺ¨λΈμ˜ μ—­λŸ‰μ„ μ΄ˆκ³Όν•  μ •λ„λ‘œ 클 경우 μ˜¬λ°”λ₯΄μ§€ μ•Šμ€ 이미지λ₯Ό μƒμ„±ν•˜κ²Œ λ©λ‹ˆλ‹€." + }, + "sam_detection_hint": { + "name": "sam 감지 힌트", + "tooltip": "[μ‹€ν—˜κΈ°λŠ₯] 였래된 μ‹€ν—˜ κΈ°λŠ₯으둜, sam λͺ¨λΈμ˜ 감지 힌트λ₯Ό μ œκ³΅ν•˜λŠ” λ°©μ‹μž…λ‹ˆλ‹€. cetner-1 (쀑앙점 1개) μ™Έμ—λŠ” μ‚¬μš©ν•˜μ§€ μ•Šμ„ 것을 ꢌμž₯ν•©λ‹ˆλ‹€." + }, + "sam_dilation": { + "name": "sam 마슀크 ν™•μž₯", + "tooltip": "sam λͺ¨λΈλ‘œ κ°μ§€λœ 싀루엣 마슀크λ₯Ό ν™•μž₯ν•©λ‹ˆλ‹€." + }, + "sam_threshold": { + "name": "sam 감지 μž„κ³„μΉ˜", + "tooltip": "sam λͺ¨λΈμ˜ μ΅œμ†Œ 감지 μž„κ³„μΉ˜λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. μž„κ³„μΉ˜κ°€ λ†’μ„μˆ˜λ‘ ν™•μ‹€ν•œ 객체만 κ°μ§€ν•˜μ§€λ§Œ, 객체λ₯Ό κ°μ§€ν•˜μ§€ λͺ»ν•  ν™•λ₯ μ΄ μ¦κ°€ν•©λ‹ˆλ‹€." + }, + "sam_bbox_expansion": { + "name": "sam μ˜μ—­ ν™•μž₯", + "tooltip": "sam 의 감지 μ˜μ—­μ„ ν™•μž₯ν•©λ‹ˆλ‹€. 감지 μ˜μ—­μ€ 마슀크λ₯Ό ν¬ν•¨ν•˜λŠ” 전체 사각 μ˜μ—­μž…λ‹ˆλ‹€.\n주의1:sam 마슀크λ₯Ό ν™•μž₯ν•˜λ”λΌλ„, 감지 μ˜μ—­μ„ λ²—μ–΄λ‚  수 μ—†μŠ΅λ‹ˆλ‹€.\n주의2: bbox ν™•μž₯을 ν•˜λ”λΌλ„, sam 감지 μ˜μ—­μ΄ μž‘μœΌλ©΄ μ—¬μ „νžˆ μ œν•œλ©λ‹ˆλ‹€." + }, + "sam_mask_hint_threshold": { + "name": "sam 마슀크 힌트 μž„κ³„μΉ˜", + "tooltip": "[μ‹€ν—˜κΈ°λŠ₯] 였래된 μ‹€ν—˜ κΈ°λŠ₯으둜, mask-hint λͺ¨λ“œμ—μ„œλ§Œ μ‚¬μš©λ˜λŠ” μ˜΅μ…˜μœΌλ‘œ, λ§ˆμŠ€ν¬μ—μ„œ 이 크기 μ΄μƒμ˜ 점 마슀크λ₯Ό sam의 힌트둜 μ‚¬μš©ν•©λ‹ˆλ‹€." + }, + "sam_mask_hint_use_negative": { + "name": "sam 마슀크 νžŒνŠΈμ— μ œμ™Έ 힌트 μ‚¬μš©", + "tooltip": "[μ‹€ν—˜κΈ°λŠ₯] 였래된 μ‹€ν—˜ κΈ°λŠ₯으둜, mask-hitn λͺ¨λ“œμ—μ„œλ§Œ μ‚¬μš©λ˜λŠ” μ˜΅μ…˜μœΌλ‘œ, sam 마슀크 힌트 μž„κ³„μΉ˜λ³΄λ‹€ μž‘μ€ 점 마슀크λ₯Ό sam의 μ œμ™Έ 힌트둜 μ‚¬μš©ν•©λ‹ˆλ‹€." + }, + "drop_size": { + "name": "감지 μ΅œμ†Œ 크기", + "tooltip": "사각 μ˜μ—­(bbox) κ°μ§€κΈ°λ‘œ κ°μ§€ν•œ 크기가 이 μ„€μ •κ°’ 보닀 μž‘μ„ 경우 λ¬΄μ‹œν•©λ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "tiled_encode": { + "name": "타일 μΈμ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE μΈμ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE μΈμ½”λ“œ' λŒ€μ‹  'VAE μΈμ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + }, + "tiled_decode": { + "name": "타일 λ””μ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE λ‹€μ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE λ””μ½”λ“œ' λŒ€μ‹  'VAE λ””μ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미" + }, + "1": { + "name": "μž˜λΌλ‚Έ 이미지" + }, + "2": { + "name": "μž˜λΌλ‚Έ 투λͺ… 이미지" + }, + "3": { + "name": "마슀크" + }, + "4": { + "name": "λ””ν…ŒμΌλŸ¬ νŒŒμ΄ν”„" + }, + "5": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "DetailerForEach": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.", + "display_name": "λ””ν…ŒμΌλŸ¬ (SEGS)", + "inputs": { + "image": { + "name": "이미지" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "model": { + "name": "λͺ¨λΈ", + "tooltip": "λ§Œμ•½ `ImpactDummyInput` 을 μ—°κ²° ν•˜λ©΄, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "positive": { + "name": "긍정 쑰건" + }, + "negative": { + "name": "λΆ€μ • 쑰건" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "wildcard": { + "name": "μ™€μΌλ“œμΉ΄λ“œ ν”„λ‘¬ν”„νŠΈ", + "tooltip": "'μ™€μΌλ“œμΉ΄λ“œ 인코더 (Impact)'와 μœ μ‚¬ν•œ κΈ°λŠ₯을 μˆ˜ν–‰ν•˜μ—¬, μ™€μΌλ“œμΉ΄λ“œ κΈ°λŠ₯κ³Ό 둜라 λ‘œλ”© κΈ°λŠ₯을 μ œκ³΅ν•©λ‹ˆλ‹€. λ˜ν•œ, κ°μ§€λœ μ˜μ—­λ³„λ‘œ λ‹€λ₯Έ ν”„λ‘¬ν”„νŠΈλ₯Ό μ μš©ν•˜λŠ” κΈ°λŠ₯듀을 μ œκ³΅ν•©λ‹ˆλ‹€.\n더 μžμ„Έν•œ μ •λ³΄λŠ” νŠœν† λ¦¬μ–Ό νŽ˜μ΄μ§€λ₯Ό μ°Έκ³ ν•˜μ„Έμš”.\n주의:이 μž…λ ₯을 λΉ„μ›Œλ‘λ©΄, 이 μž…λ ₯은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "tiled_encode": { + "name": "타일 μΈμ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE μΈμ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE μΈμ½”λ“œ' λŒ€μ‹  'VAE μΈμ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + }, + "tiled_decode": { + "name": "타일 λ””μ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE λ‹€μ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE λ””μ½”λ“œ' λŒ€μ‹  'VAE λ””μ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미지" + } + } + }, + "DetailerForEachPipe": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.", + "display_name": "λ””ν…ŒμΌλŸ¬ (상세/SEGS/νŒŒμ΄ν”„)", + "inputs": { + "image": { + "name": "이미지" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "basic_pipe": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ κΈ°λ³Έ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "refiner_basic_pipe_opt": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ 단계에 μ μš©ν•  κΈ°λ³Έ νŒŒμ΄ν”„λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "tiled_encode": { + "name": "타일 μΈμ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE μΈμ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE μΈμ½”λ“œ' λŒ€μ‹  'VAE μΈμ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + }, + "tiled_decode": { + "name": "타일 λ””μ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE λ‹€μ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE λ””μ½”λ“œ' λŒ€μ‹  'VAE λ””μ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미지" + }, + "1": { + "name": "segs" + }, + "2": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„" + }, + "3": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "DetailerForEachDebug": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.", + "display_name": "λ””ν…ŒμΌλŸ¬ (상세/SEGS)", + "inputs": { + "image": { + "name": "이미지" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "model": { + "name": "λͺ¨λΈ", + "tooltip": "λ§Œμ•½ `ImpactDummyInput` 을 μ—°κ²° ν•˜λ©΄, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "positive": { + "name": "긍정 쑰건" + }, + "negative": { + "name": "λΆ€μ • 쑰건" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "wildcard": { + "name": "μ™€μΌλ“œμΉ΄λ“œ ν”„λ‘¬ν”„νŠΈ", + "tooltip": "'μ™€μΌλ“œμΉ΄λ“œ 인코더 (Impact)'와 μœ μ‚¬ν•œ κΈ°λŠ₯을 μˆ˜ν–‰ν•˜μ—¬, μ™€μΌλ“œμΉ΄λ“œ κΈ°λŠ₯κ³Ό 둜라 λ‘œλ”© κΈ°λŠ₯을 μ œκ³΅ν•©λ‹ˆλ‹€. λ˜ν•œ, κ°μ§€λœ μ˜μ—­λ³„λ‘œ λ‹€λ₯Έ ν”„λ‘¬ν”„νŠΈλ₯Ό μ μš©ν•˜λŠ” κΈ°λŠ₯듀을 μ œκ³΅ν•©λ‹ˆλ‹€.\n더 μžμ„Έν•œ μ •λ³΄λŠ” νŠœν† λ¦¬μ–Ό νŽ˜μ΄μ§€λ₯Ό μ°Έκ³ ν•˜μ„Έμš”.\n주의:이 μž…λ ₯을 λΉ„μ›Œλ‘λ©΄, 이 μž…λ ₯은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "tiled_encode": { + "name": "타일 μΈμ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE μΈμ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE μΈμ½”λ“œ' λŒ€μ‹  'VAE μΈμ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + }, + "tiled_decode": { + "name": "타일 λ””μ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE λ‹€μ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE λ””μ½”λ“œ' λŒ€μ‹  'VAE λ””μ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미지" + }, + "1": { + "name": "μž˜λΌλ‚Έ 이미지" + }, + "2": { + "name": "μž˜λΌλ‚Έ κ°œμ„  이미지" + }, + "3": { + "name": "μž˜λΌλ‚Έ 투λͺ… κ°œμ„  이미지" + }, + "4": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "DetailerForEachDebugPipe": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.", + "display_name": "λ””ν…ŒμΌλŸ¬ (상세/SEGS/νŒŒμ΄ν”„)", + "inputs": { + "image": { + "name": "이미지" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "basic_pipe": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ κΈ°λ³Έ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "refiner_basic_pipe_opt": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ 단계에 μ μš©ν•  κΈ°λ³Έ νŒŒμ΄ν”„λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + }, + "tiled_encode": { + "name": "타일 μΈμ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE μΈμ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE μΈμ½”λ“œ' λŒ€μ‹  'VAE μΈμ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + }, + "tiled_decode": { + "name": "타일 λ””μ½”λ“œ μ‚¬μš©", + "tooltip": "이 μ˜΅μ…˜μ„ 켜면, λ‚΄λΆ€μ μœΌλ‘œ 'VAE λ‹€μ½”λ“œ'λ₯Ό μ‚¬μš©ν•  경우, κΈ°λ³Έ 'VAE λ””μ½”λ“œ' λŒ€μ‹  'VAE λ””μ½”λ“œ (타일)' 을 μ μš©ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미지" + }, + "1": { + "name": "κ°œμ„  SEGS" + }, + "2": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„" + }, + "3": { + "name": "μž˜λΌλ‚Έ 이미지" + }, + "4": { + "name": "μž˜λΌλ‚Έ κ°œμ„  이미지" + }, + "5": { + "name": "μž˜λΌλ‚Έ 투λͺ… κ°œμ„  이미지" + }, + "6": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "DetailerForEachPipeForAnimateDiff": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.\n이 λ…Έλ“œλŠ” AnimateDiff와 같은 λ™μ˜μƒμ˜ λ””ν…ŒμΌ κ°œμ„ μ„ μœ„ν•œ 특수 λ””ν…ŒμΌλŸ¬ λ…Έλ“œλ‘œμ¨, SEGSκ°€ λ‹΄κ³  μžˆλŠ” λ§ˆμŠ€ν¬κ°€ μ—¬λŸ¬ ν”„λ ˆμž„μ— 걸친 배치 λ§ˆμŠ€ν¬κ°€ λ˜λŠ” 경우λ₯Ό μ²˜λ¦¬ν•  수 μžˆμŠ΅λ‹ˆλ‹€.", + "display_name": "λ””ν…ŒμΌλŸ¬ (AnimateDiff/νŒŒμ΄ν”„)", + "inputs": { + "image_frames": { + "name": "이미지 ν”„λ ˆμž„ 묢음" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "basic_pipe": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ κΈ°λ³Έ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "refiner_basic_pipe_opt": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ 단계에 μ μš©ν•  κΈ°λ³Έ νŒŒμ΄ν”„λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  SEGS" + }, + "1": { + "name": "κ°œμ„  이미지" + }, + "2": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„" + }, + "3": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "SEGSDetailerForAnimateDiff": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.\n이 λ…Έλ“œλŠ” 원본 이미지가 μ•„λ‹Œ SEGSλ₯Ό λŒ€μƒμœΌλ‘œ μ μš©λ˜λŠ” λ…Έλ“œλ‘œ 원본 이미지에 μ μš©ν•˜λ €λ©΄ 'SEGS λΆ™μ—¬λ„£κΈ°' λ…Έλ“œλ₯Ό μ‚¬μš©ν•˜μ„Έμš”.\n이 λ…Έλ“œλŠ” AnimateDiff와 같은 λ™μ˜μƒμ˜ λ””ν…ŒμΌ κ°œμ„ μ„ μœ„ν•œ 특수 λ””ν…ŒμΌλŸ¬ λ…Έλ“œλ‘œμ¨, SEGSκ°€ λ‹΄κ³  μžˆλŠ” λ§ˆμŠ€ν¬κ°€ μ—¬λŸ¬ ν”„λ ˆμž„μ— 걸친 배치 λ§ˆμŠ€ν¬κ°€ λ˜λŠ” 경우λ₯Ό μ²˜λ¦¬ν•  수 μžˆμŠ΅λ‹ˆλ‹€.", + "display_name": "SEGS λ””ν…ŒμΌλŸ¬ (AnimateDiff/νŒŒμ΄ν”„)", + "inputs": { + "image_frames": { + "name": "이미지 ν”„λ ˆμž„ 묢음" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "basic_pipe": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ κΈ°λ³Έ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "refiner_basic_pipe_opt": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ 단계에 μ μš©ν•  κΈ°λ³Έ νŒŒμ΄ν”„λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  SEGS" + }, + "1": { + "name": "κ°œμ„  이미지" + } + } + }, + "SEGSDetailer": { + "description": "감지 μ˜μ—­ 정보 묢음(SEGS)λ‚΄μ˜ 각 μ˜μ—­λ“€μ— λŒ€ν•΄ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μ΄νŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.\n이 λ…Έλ“œλŠ” 원본 이미지가 μ•„λ‹Œ SEGSλ₯Ό λŒ€μƒμœΌλ‘œ μ μš©λ˜λŠ” λ…Έλ“œλ‘œ 원본 이미지에 μ μš©ν•˜λ €λ©΄ 'SEGS λΆ™μ—¬λ„£κΈ°' λ…Έλ“œλ₯Ό μ‚¬μš©ν•˜μ„Έμš”.", + "display_name": "SEGS λ””ν…ŒμΌλŸ¬ (νŒŒμ΄ν”„)", + "inputs": { + "image": { + "name": "이미지" + }, + "segs": { + "name": "segs", + "tooltip": "감지 μ˜μ—­ 정보λ₯Ό λ‹΄κ³  μžˆλŠ” 묢음.\n이 μ˜μ—­λ“€μ„ λŒ€μƒμœΌλ‘œ μΈνŽ˜μΈνŠΈκ°€ μ μš©λ©λ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "noise_mask": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 μ‚¬μš©", + "tooltip": "인페인트 ν•  λ•Œ, 마슀크λ₯Ό μ μš©ν•΄μ„œ 마슀크 μ˜μ—­λ§Œ μΈνŽ˜μΈνŠΈν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ„ μ μš©ν•˜μ§€ μ•ŠμœΌλ©΄, μž˜λΌλ‚Έ 이미지 전체가 μž¬μƒμ„±λ˜μ–΄ λ…Έμ΄μ¦ˆ μ œκ±°μ–‘μ΄ 클 λ•Œ μœ„ν™”κ°μ΄ λ‚˜νƒ€λ‚˜κ²Œ λ©λ‹ˆλ‹€." + }, + "force_inpaint": { + "name": "인페인트 κ°•μ œ 적용", + "tooltip": "κ°€μ΄λ“œ 크기와 상관 없이 μΈνŽ˜μΈνŒ…μ„ 무쑰건 적용 ν•©λ‹ˆλ‹€. 이 μ˜΅μ…˜μ΄ κΊΌμ ΈμžˆλŠ” 경우 κ°€μ΄λ“œ 크기보닀 이미 큰 감지 μ˜μ—­μ€ μΈνŽ˜μΈνŒ…μ„ κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "basic_pipe": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ κΈ°λ³Έ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "batch_size": { + "name": "배치 갯수", + "tooltip": "λŒ€μƒ SEGS 에 λŒ€ν•΄μ„œ 배치 갯수만큼 μ—¬λŸ¬κ°œμ˜ 후보λ₯Ό μƒμ„±ν•©λ‹ˆλ‹€. μ—¬λŸ¬κ°œλ₯Ό 생성할 경우 'κ³ λ₯΄κΈ° (SEGS)'와 ν•¨κ»˜ μ‚¬μš©ν•˜μ„Έμš”." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "refiner_basic_pipe_opt": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ 단계에 μ μš©ν•  κΈ°λ³Έ νŒŒμ΄ν”„λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  SEGS" + }, + "1": { + "name": "μ»¨νŠΈλ‘€λ„· 이미지" + } + } + }, + "MaskDetailerPipe": { + "description": "이 λ””ν…ŒμΌλŸ¬ λ…Έλ“œλŠ” 마슀크둜 μ„€μ •λœ μ˜μ—­μ„ ν™•λŒ€ν•΄μ„œ κ°€μ΄λ“œ 크기λ₯Ό 기반으둜 ν™•λŒ€ν•΄μ„œ μΈνŽ˜μΈνŠΈν•˜λŠ” λ°©λ²•μœΌλ‘œ λ””ν…ŒμΌμ„ κ°•ν™”ν•©λ‹ˆλ‹€.", + "display_name": "마슀크 λ””ν…ŒμΌλŸ¬ (νŒŒμ΄ν”„)", + "inputs": { + "image": { + "name": "이미지" + }, + "mask": { + "name": "마슀크", + "tooltip": "λ””ν…ŒμΌμ„ κ°•ν™”ν•˜κ³  싢은 λŒ€μƒ μ˜μ—­μ΄ μ„€μ •λœ 마슀크. λΆ„λ¦¬λœ 마슀크 μ˜μ—­μ€ κ°œλ³„μ μœΌλ‘œ λ””ν…ŒμΌ κ°•ν™”κ°€ μ΄λ£¨μ–΄μ§‘λ‹ˆλ‹€." + }, + "basic_pipe": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "λ§Œμ•½ κΈ°λ³Έ νŒŒμ΄ν”„ λ‚΄μ˜ λͺ¨λΈμ— `ImpactDummyInput` κ°€ μ„€μ •λœ 경우, 인페인트 단계λ₯Ό κ±΄λ„ˆ λœλ‹ˆλ‹€." + }, + "guide_size": { + "name": "κ°€μ΄λ“œ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기 λŒ€μƒ'으둜 μ§€μ •λœ 크기의 κ°€μž₯ 짧은면을 이 ν¬κΈ°κΉŒμ§€ ν™•λŒ€ν•©λ‹ˆλ‹€." + }, + "guide_size_for": { + "name": "κ°€μ΄λ“œ 크기 λŒ€μƒ", + "tooltip": "bbox: κ°μ§€λœ 사각 μ˜μ—­(bbox)\ncrop_region: μž˜λΌλ‚Έ μ˜μ—­" + }, + "max_size": { + "name": "μ΅œλŒ€ 크기", + "tooltip": "'κ°€μ΄λ“œ 크기'둜 ν™•λŒ€ ν•  λ•Œ, κ°€μž₯ κΈ΄ 면의 길이λ₯Ό 이 크기둜 μ œν•œν•©λ‹ˆλ‹€. λ„ˆλ¬΄ 크게 ν™•λŒ€ λ˜λŠ” 것을 λ§‰μ•„μ€λ‹ˆλ‹€." + }, + "mask_mode": { + "name": "마슀크 λͺ¨λ“œ", + "tooltip": "마슀크둜 μ„€μ •λœ μ˜μ—­λ§Œμ„ 인페인트 할지, μž˜λΌλ‚Έ μ˜μ—­ 전체λ₯Ό 인페인트 ν•  것인지λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ" + }, + "steps": { + "name": "μŠ€ν…μˆ˜" + }, + "sampler_name": { + "name": "μƒ˜ν”ŒλŸ¬ 이름" + }, + "scheduler": { + "name": "μŠ€μΌ€μ₯΄λŸ¬" + }, + "denoise": { + "name": "λ…Έμ΄μ¦ˆ μ œκ±°μ–‘" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "ν™•λŒ€ ν•΄μ„œ 인페인트 된 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "crop_factor": { + "name": "자λ₯΄κΈ° 배율", + "tooltip": "각 마슀크둜 μ„€μ •λœ μ˜μ—­μ— λŒ€ν•΄μ„œ λͺ‡λ°° 크기λ₯Ό μž˜λΌλ‚΄μ–΄μ„œ μΈνŽ˜μΈνŠΈμ— μ‚¬μš©ν• μ§€λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€. 이 크기가 λ„ˆλ¬΄ μž‘μœΌλ©΄, 인페인트 ν•  μ΄λ―Έμ§€μ˜ μ£Όλ³€ 정보가 λΆ€μ‘±ν•΄μ„œ μœ„ν™”κ°μ΄ κ°•ν•œ 이미지가 μƒμ„±λ©λ‹ˆλ‹€. 이 크기가 λ„ˆλ¬΄ 크면, μΈνŽ˜μΈνŒ…μ— λ„ˆλ¬΄ 였랜 μ‹œκ°„μ΄ 걸릴 수 있으며, λͺ¨λΈμ˜ μ—­λŸ‰μ„ μ΄ˆκ³Όν•  μ •λ„λ‘œ 클 경우 μ˜¬λ°”λ₯΄μ§€ μ•Šμ€ 이미지λ₯Ό μƒμ„±ν•˜κ²Œ λ©λ‹ˆλ‹€." + }, + "drop_size": { + "name": "감지 μ΅œμ†Œ 크기", + "tooltip": "사각 μ˜μ—­(bbox) κ°μ§€κΈ°λ‘œ κ°μ§€ν•œ 크기가 이 μ„€μ •κ°’ 보닀 μž‘μ„ 경우 λ¬΄μ‹œν•©λ‹ˆλ‹€." + }, + "refiner_ratio": { + "name": "λΌνŒŒμ΄λ„ˆ 적용 λΉ„μœ¨", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ λͺ¨λΈμ„ μ‚¬μš©ν•  경우 적용될 ν›„λ°˜ μŠ€ν…μˆ˜ λΉ„μœ¨μ„ μ„€μ •ν•©λ‹ˆλ‹€." + }, + "batch_size": { + "name": "배치 갯수", + "tooltip": "λŒ€μƒ SEGS 에 λŒ€ν•΄μ„œ 배치 갯수만큼 μ—¬λŸ¬κ°œμ˜ 후보λ₯Ό μƒμ„±ν•©λ‹ˆλ‹€. μ—¬λŸ¬κ°œλ₯Ό 생성할 경우 'κ³ λ₯΄κΈ° (SEGS)'와 ν•¨κ»˜ μ‚¬μš©ν•˜μ„Έμš”." + }, + "cycle": { + "name": "반볡수", + "tooltip": "μ„€μ •λœ κ°’λ§ŒνΌ μΈνŽ˜μΈνŒ…μ„ 반볡 μ μš©ν•©λ‹ˆλ‹€. 인코딩/λ””μ½”λ”© 없이 ν™•λŒ€λœ 잠재 이미지 λ‹¨κ³„μ—μ„œ λ°˜λ³΅λ©λ‹ˆλ‹€." + }, + "refiner_basic_pipe_opt": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„", + "tooltip": "SDXL λ¦¬νŒŒμ΄λ„ˆ 단계에 μ μš©ν•  κΈ°λ³Έ νŒŒμ΄ν”„λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "detailer_hook": { + "name": "λ””ν…ŒμΌλŸ¬ 후크", + "tooltip": "이 λ…Έλ“œμ˜ μ‹€ν–‰ μ€‘κ°„λ‹¨κ³„μ—μ„œ μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μˆ˜ν–‰ν•  수 μžˆλŠ” 후크λ₯Ό μ—°κ²°ν•©λ‹ˆλ‹€." + }, + "inpaint_model": { + "name": "인페인트 λͺ¨λΈ λͺ¨λ“œ", + "tooltip": "인페인트 μ „μš© λͺ¨λΈμ„ μ‚¬μš©ν•  경우 이 μ˜΅μ…˜μ„ 켜면, μΈνŽ˜μΈνŒ…μ‹œμ— '인페인트 λͺ¨λΈ 쑰건 μ„€μ •'이 μ μš©λ˜μ–΄ μˆ˜ν–‰λ©λ‹ˆλ‹€." + }, + "noise_mask_feather": { + "name": "λ…Έμ΄μ¦ˆ 마슀크 κ°€μž₯자리 흐림", + "tooltip": "μΈνŽ˜μΈνŠΈμ‹œμ— μ μš©λ˜λŠ” λ…Έμ΄μ¦ˆ 마슀크의 κ°€μž₯자리λ₯Ό 흐리게 ν•©λ‹ˆλ‹€. 이 섀정값이 0을 μ΄ˆκ³Όν•  경우, λ‚΄λΆ€μ μœΌλ‘œ μžλ™μœΌλ‘œ 'μ°¨λ“± ν™•μ‚°' λ…Έλ“œλ₯Ό μ μš©ν•©λ‹ˆλ‹€." + }, + "bbox_fill": { + "name": "bbox μ±„μš°κΈ°", + "tooltip": "각 마슀크 쑰각듀을 ν•΄λ‹Ή 마슀크λ₯Ό ν¬ν•¨ν•˜λŠ” κ°€μž₯ μž‘μ€ 사각 μ˜μ—­μ˜ 마슀크둜 κ°„μ£Όν•©λ‹ˆλ‹€." + }, + "contour_fill": { + "name": "윀곽 λ‚΄λΆ€ μ±„μš°κΈ°", + "tooltip": "μœ€κ³½μ„  ν˜•νƒœμ˜ 마슀크 μ‘°κ°λ“€μ˜ 경우 마슀크 λ‚΄λΆ€κ°€ λͺ¨λ‘ μ±„μ›Œμ§„ κ²ƒμœΌλ‘œ κ°„μ£Όν•©λ‹ˆλ‹€." + }, + "scheduler_func_opt": { + "name": "μŠ€μΌ€μ₯΄λŸ¬ ν•¨μˆ˜", + "tooltip": "GITS μŠ€μΌ€μ₯΄λŸ¬ 처럼 κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ λ¦¬μŠ€νŠΈμ—μ„œ 선택할 수 μ—†λŠ” 특수 μŠ€μΌ€μ₯΄λŸ¬λ₯Ό μ‚¬μš©ν•  수 있게 ν•΄μ€λ‹ˆλ‹€. 이 μž…λ ₯이 μ—°κ²°λ˜λ©΄, κΈ°λ³Έ μŠ€μΌ€μ₯΄λŸ¬ 선택은 λ¬΄μ‹œλ©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  이미지" + }, + "1": { + "name": "μž˜λΌλ‚Έ κ°œμ„  이미지" + }, + "2": { + "name": "μž˜λΌλ‚Έ 투λͺ… κ°œμ„  이미지" + }, + "3": { + "name": "κΈ°λ³Έ νŒŒμ΄ν”„" + }, + "4": { + "name": "λ¦¬νŒŒμ΄λ„ˆ κΈ°λ³Έ νŒŒμ΄ν”„" + } + } + }, + + "SEGSPaste": { + "description": "SEGS λ””ν…ŒμΌλŸ¬λ₯Ό 톡해 κ°œμ„ λœ SEGSλ₯Ό 원본 이미지에 λΆ™μ—¬λ„£λŠ” κΈ°λŠ₯을 μ œκ³΅ν•˜κΈ° μœ„ν•œ λ…Έλ“œμž…λ‹ˆλ‹€.", + "display_name": "SEGS λΆ™μ—¬λ„£κΈ°", + "inputs": { + "image": { + "name": "원본 이미지" + }, + "segs": { + "name": "segs" + }, + "feather": { + "name": "κ°€μž₯자리 흐림", + "tooltip": "κ°œμ„ λœ SEGS의 이미지λ₯Ό 원본 이미지에 뢙여넣을 λ•Œ, 이 수치둜 마슀크의 κ°€μž₯ 자리λ₯Ό 흐리게 ν•΄μ„œ λΆ™μ—¬λ„£μ–΄ 이음맀의 μœ„ν™”κ°μ„ μ€„μ—¬μ€λ‹ˆλ‹€." + }, + "alpha": { + "name": "투λͺ…도", + "tooltip": "원본에 λΆ™μ—¬λ„£λŠ” 이미지에 투λͺ…도λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€." + }, + "ref_image_opt": { + "name": "μ°Έμ‘° 이미지", + "tooltip": "λ””ν…ŒμΌλŸ¬λ₯Ό ν†΅κ³Όμ‹œν‚€κ±°λ‚˜ 'SEGS에 κΈ°λ³Έ 이미지 μ„€μ •'을 ν•œ κ²½μš°κ°€ μ•„λ‹ˆλΌλ©΄, SEGSλŠ” 이미지가 없이 감지 μ˜μ—­ μ •λ³΄λ§Œ μžˆμŠ΅λ‹ˆλ‹€. 이 λ•Œ κ°μ§€μ˜μ—­μ΄ μ°Έμ‘°ν•  원본 이미지λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "κ°œμ„  SEGS" + } + } + }, + + "ImpactSEGSPicker": { + "description": "μž…λ ₯된 SEGS μ€‘μ—μ„œ μ„ νƒλœ SEGSλ§Œμ„ κ³ λ₯Ό 수 μžˆλŠ” μžˆλŠ” κΈ°λŠ₯을 μ œκ³΅ν•©λ‹ˆλ‹€.", + "display_name": "κ³ λ₯΄κΈ° (SEGS)", + "inputs": { + "picks": { + "name": "선택 λͺ©λ‘", + "tooltip": "좜λ ₯ν•  SEGS 번호 λͺ©λ‘μ„ λ‚˜μ—΄ν•©λ‹ˆλ‹€. 'pick' λ²„νŠΌμ„ λˆŒλŸ¬μ„œ μ„ νƒν•˜μ„Έμš”." + }, + "segs": { + "name": "segs" + }, + "fallback_image_opt": { + "name": "μ°Έμ‘° 이미지", + "tooltip": "λ””ν…ŒμΌλŸ¬λ₯Ό ν†΅κ³Όμ‹œν‚€κ±°λ‚˜ 'SEGS에 κΈ°λ³Έ 이미지 μ„€μ •'을 ν•œ κ²½μš°κ°€ μ•„λ‹ˆλΌλ©΄, SEGSλŠ” 이미지가 없이 감지 μ˜μ—­ μ •λ³΄λ§Œ μžˆμŠ΅λ‹ˆλ‹€. 이 λ•Œ κ°μ§€μ˜μ—­μ΄ μ°Έμ‘°ν•  원본 이미지λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "μ„ νƒλœ SEGS" + } + } + }, + + "SetDefaultImageForSEGS": { + "description": "λ””ν…ŒμΌλŸ¬λ₯Ό ν†΅κ³Όμ‹œν‚¨ κ²½μš°κ°€ μ•„λ‹ˆλΌλ©΄, SEGSλŠ” 이미지가 없이 감지 μ˜μ—­ μ •λ³΄λ§Œ μžˆμŠ΅λ‹ˆλ‹€. 이 λ…Έλ“œλŠ” SEGS에 κΈ°λ³Έ 이미지λ₯Ό μ„€μ •ν•΄ μ€λ‹ˆλ‹€.", + "display_name": "SEGS에 κΈ°λ³Έ 이미지 μ„€μ •", + "inputs": { + "segs": { + "name": "segs" + }, + "image": { + "name": "이미지" + }, + "override": { + "name": "λμ–΄μ“°κΈ°", + "tooltip": "이미 μ„€μ •λœ 이미지가 μžˆλŠ” 경우 λμ–΄μ“Έμ§€ μ—¬λΆ€λ₯Ό μ„€μ •ν•©λ‹ˆλ‹€." + } + }, + "outputs": { + "0": { + "name": "segs" + } + } + }, + + "ImpactWildcardProcessor": { + "description": "이 λ…Έλ“œλŠ” μ™€μΌλ“œμΉ΄λ“œ ꡬ문으둜 μž‘μ„±λœ ν…μŠ€νŠΈ ν”„λ‘¬ν”„νŠΈλ₯Ό μ²˜λ¦¬ν•˜κ³ , 처리된 ν…μŠ€νŠΈ ν”„λ‘¬ν”„νŠΈλ₯Ό 좜λ ₯ν•©λ‹ˆλ‹€.\n\nTIP: μ›Œν¬ν”Œλ‘œκ°€ μ‹€ν–‰λ˜κΈ° 전에 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ'의 처리 κ²°κ³Όκ°€ 'μ±„μ›Œμ§„(populated) ν…μŠ€νŠΈ'에 ν‘œμ‹œλ˜λ©°, 이 값은 μ›Œν¬ν”Œλ‘œμ™€ ν•¨κ»˜ μ €μž₯λ©λ‹ˆλ‹€. μž…λ ₯으둜 λ³€ν™˜λœ μ‹œλ“œλ₯Ό μ‚¬μš©ν•˜λ €λ©΄ 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ' λŒ€μ‹  'μ±„μ›Œμ§„(populated) ν…μŠ€νŠΈ'에 직접 ν”„λ‘¬ν”„νŠΈλ₯Ό μž‘μ„±ν•˜κ³ , λͺ¨λ“œλ₯Ό 'κ³ μ •(fixed)'둜 μ„€μ •ν•˜μ„Έμš”.", + "display_name": "μ™€μΌλ“œμΉ΄λ“œ 처리기 (Impact)", + "inputs": { + "wildcard_text": { + "name": "μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ", + "tooltip": "μ™€μΌλ“œμΉ΄λ“œ λ¬Έλ²•μœΌλ‘œ μž‘μ„±λœ ν…μŠ€νŠΈ ν”„λ‘¬ν”„νŠΈλ₯Ό μž…λ ₯ν•˜μ„Έμš”." + }, + "populated_text": { + "name": "μ±„μ›Œμ§„ ν…μŠ€νŠΈ", + "tooltip": "이 λ…Έλ“œμ— μ‹€ν–‰ 쀑에 μ „λ‹¬λ˜λŠ” μ‹€μ œ 값은 μ—¬κΈ° ν‘œμ‹œλœ κ°’μž…λ‹ˆλ‹€. λ™μž‘μ€ λͺ¨λ“œμ— 따라 μ•½κ°„ λ‹€λ₯Ό 수 있으며, 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'μ—μ„œλ„ μ™€μΌλ“œμΉ΄λ“œ ꡬ문을 μ‚¬μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€." + }, + "mode": { + "name": "λͺ¨λ“œ", + "tooltip": "μ±„μš°κΈ°(populate): μ›Œν¬ν”Œλ‘œλ₯Ό μ‹€ν–‰ν•˜κΈ° 전에 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ'μ—μ„œ 처리된 ν”„λ‘¬ν”„νŠΈλ‘œ 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'의 κΈ°μ‘΄ 값을 λμ–΄μ”λ‹ˆλ‹€. 이 λͺ¨λ“œμ—μ„œλŠ” 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'λ₯Ό μˆ˜μ •ν•  수 μ—†μŠ΅λ‹ˆλ‹€.\n\nκ³ μ •(fixed): 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ'λ₯Ό λ¬΄μ‹œν•˜κ³  'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'의 값을 κ·ΈλŒ€λ‘œ μœ μ§€ν•©λ‹ˆλ‹€. 이 λͺ¨λ“œμ—μ„œλŠ” 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'λ₯Ό μˆ˜μ •ν•  수 μžˆμŠ΅λ‹ˆλ‹€.\n\nμž¬ν˜„(reproduce): 이 λͺ¨λ“œλŠ” ν•œ 번만 'κ³ μ •(fixed)' λͺ¨λ“œλ‘œ μž‘λ™ν•˜μ—¬ μž¬ν˜„ν•œ ν›„, μ΄ν›„μ—λŠ” 'μ±„μš°κΈ°(populate)' λͺ¨λ“œλ‘œ μ „ν™˜λ©λ‹ˆλ‹€." + }, + "seed": { + "name": "μ‹œλ“œ", + "tooltip": "μ™€μΌλ“œμΉ΄λ“œμ˜ λ¬΄μž‘μœ„ 선택에 μ‚¬μš©ν•  μ‹œλ“œ μž…λ‹ˆλ‹€" + }, + "Select to add Wildcard": { + "name": "μΆ”κ°€ν•  μ™€μΌλ“œμΉ΄λ“œ 선택" + } + }, + "outputs": { + "0": { + "name": "처리된 ν…μŠ€νŠΈ" + } + } + }, + + "ImpactWildcardEncode": { + "description": "이 λ…Έλ“œλŠ” μ™€μΌλ“œμΉ΄λ“œ ꡬ문으둜 μž‘μ„±λœ ν…μŠ€νŠΈ ν”„λ‘¬ν”„νŠΈλ₯Ό μ²˜λ¦¬ν•˜κ³  이λ₯Ό 쑰건으둜 좜λ ₯ν•©λ‹ˆλ‹€. λ˜ν•œ LoRA ꡬ문을 μ§€μ›ν•˜λ©°, 적용된 LoRAλŠ” λͺ¨λΈ 좜λ ₯에 λ°˜μ˜λ©λ‹ˆλ‹€.\n\nTIP1: μ›Œν¬ν”Œλ‘œκ°€ μ‹€ν–‰λ˜κΈ° 전에 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ'의 처리 κ²°κ³Όκ°€ 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'에 ν‘œμ‹œλ˜λ©°, 이 값은 μ›Œν¬ν”Œλ‘œμ™€ ν•¨κ»˜ μ €μž₯λ©λ‹ˆλ‹€. μž…λ ₯으둜 λ³€ν™˜λœ μ‹œλ“œλ₯Ό μ‚¬μš©ν•˜λ €λ©΄ 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ' λŒ€μ‹  'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'에 직접 ν”„λ‘¬ν”„νŠΈλ₯Ό μž‘μ„±ν•˜κ³ , λͺ¨λ“œλ₯Ό 'κ³ μ •(fixed)'둜 μ„€μ •ν•˜μ„Έμš”.\nTIP2: 'Inspire Pack'이 μ„€μΉ˜λ˜μ–΄ 있으면 LBW(둜라 블둝 μ›¨μ΄νŠΈ) ꡬ문도 μ μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€.", + "display_name": "μ™€μΌλ“œμΉ΄λ“œ 인코딩 (Impact)", + "inputs": { + "wildcard_text": { + "name": "μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ", + "tooltip": "μ™€μΌλ“œμΉ΄λ“œ λ¬Έλ²•μœΌλ‘œ μž‘μ„±λœ ν…μŠ€νŠΈ ν”„λ‘¬ν”„νŠΈλ₯Ό μž…λ ₯ν•˜μ„Έμš”." + }, + "populated_text": { + "name": "μ±„μ›Œμ§„ ν…μŠ€νŠΈ", + "tooltip": "이 λ…Έλ“œμ— μ‹€ν–‰ 쀑에 μ „λ‹¬λ˜λŠ” μ‹€μ œ 값은 μ—¬κΈ° ν‘œμ‹œλœ κ°’μž…λ‹ˆλ‹€. λ™μž‘μ€ λͺ¨λ“œμ— 따라 μ•½κ°„ λ‹€λ₯Ό 수 있으며, 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'μ—μ„œλ„ μ™€μΌλ“œμΉ΄λ“œ ꡬ문을 μ‚¬μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€." + }, + "mode": { + "name": "λͺ¨λ“œ", + "tooltip": "μ±„μš°κΈ°(populate): μ›Œν¬ν”Œλ‘œλ₯Ό μ‹€ν–‰ν•˜κΈ° 전에 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ'μ—μ„œ 처리된 ν”„λ‘¬ν”„νŠΈλ‘œ 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'의 κΈ°μ‘΄ 값을 λμ–΄μ”λ‹ˆλ‹€. 이 λͺ¨λ“œμ—μ„œλŠ” 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'λ₯Ό μˆ˜μ •ν•  수 μ—†μŠ΅λ‹ˆλ‹€.\n\nκ³ μ •(fixed): 'μ™€μΌλ“œμΉ΄λ“œ ν…μŠ€νŠΈ'λ₯Ό λ¬΄μ‹œν•˜κ³  'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'의 값을 κ·ΈλŒ€λ‘œ μœ μ§€ν•©λ‹ˆλ‹€. 이 λͺ¨λ“œμ—μ„œλŠ” 'μ±„μ›Œμ§„ ν…μŠ€νŠΈ'λ₯Ό μˆ˜μ •ν•  수 μžˆμŠ΅λ‹ˆλ‹€.\n\nμž¬ν˜„(reproduce): 이 λͺ¨λ“œλŠ” ν•œ 번만 'κ³ μ •(fixed)' λͺ¨λ“œλ‘œ μž‘λ™ν•˜μ—¬ μž¬ν˜„ν•œ ν›„, μ΄ν›„μ—λŠ” 'μ±„μš°κΈ°(populate)' λͺ¨λ“œλ‘œ μ „ν™˜λ©λ‹ˆλ‹€." + }, + "Select to add LoRA": { + "name": "μΆ”κ°€ν•  LoRA 선택" + }, + "Select to add Wildcard": { + "name": "μΆ”κ°€ν•  μ™€μΌλ“œμΉ΄λ“œ 선택" + }, + "seed": { + "name": "μ‹œλ“œ", + "tooltip": "μ™€μΌλ“œμΉ΄λ“œμ˜ λ¬΄μž‘μœ„ 선택에 μ‚¬μš©ν•  μ‹œλ“œ μž…λ‹ˆλ‹€" + } + }, + "outputs": { + "0": { + "name": "model", + "tooltip": "LoRA 적용 문법이 μ‚¬μš©λœ 경우, LoRA κ°€ 적용된 model이 좜λ ₯λ©λ‹ˆλ‹€." + }, + "1": { + "name": "clip", + "tooltip": "LoRA 적용 문법이 μ‚¬μš©λœ 경우, LoRA κ°€ 적용된 clip이 좜λ ₯λ©λ‹ˆλ‹€." + }, + "2": { + "name": "쑰건" + }, + "3": { + "name": "μ±„μ›Œμ§„ ν…μŠ€νŠΈ" + } + } + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/additional_dependencies.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/additional_dependencies.py new file mode 100644 index 00000000..904c729f --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/additional_dependencies.py @@ -0,0 +1,12 @@ +import sys +import subprocess + + +def ensure_onnx_package(): + try: + import onnxruntime # noqa: F401 + except Exception: + if "python_embeded" in sys.executable or "python_embedded" in sys.executable: + subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'onnxruntime']) + else: + subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'onnxruntime']) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/animatediff_nodes.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/animatediff_nodes.py new file mode 100644 index 00000000..5b45e82f --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/animatediff_nodes.py @@ -0,0 +1,200 @@ +from nodes import MAX_RESOLUTION +import impact.core as core +from impact.core import SEG +from impact.segs_nodes import SEGSPaste +import comfy +from impact import utils +import torch +import nodes +import logging + +try: + from comfy_extras import nodes_differential_diffusion +except Exception: + logging.warning("\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n") + raise Exception("[Impact Pack] ComfyUI is an outdated version.") + + +class SEGSDetailerForAnimateDiff: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "image_frames": ("IMAGE", ), + "segs": ("SEGS", ), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + }, + "optional": { + "refiner_basic_pipe_opt": ("BASIC_PIPE",), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("SEGS", "IMAGE") + RETURN_NAMES = ("segs", "cnet_images") + OUTPUT_IS_LIST = (False, True) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.\nAs a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames." + + @staticmethod + def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None): + + model, clip, vae, positive, negative = basic_pipe + if refiner_basic_pipe_opt is None: + refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None + else: + refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt + + segs = core.segs_scale_match(segs, image_frames.shape) + + new_segs = [] + cnet_image_list = [] + + if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + for seg in segs[1]: + cropped_image_frames = None + + for image in image_frames: + image = image.unsqueeze(0) + cropped_image = seg.cropped_image if seg.cropped_image is not None else utils.crop_tensor4(image, seg.crop_region) + cropped_image = utils.to_tensor(cropped_image) + if cropped_image_frames is None: + cropped_image_frames = cropped_image + else: + cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0) + + cropped_image_frames = cropped_image_frames.cpu().numpy() + + # It is assumed that AnimateDiff does not support conditioning masks based on test results, but it will be added for future consideration. + cropped_positive = [ + [condition, { + k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in positive + ] + + cropped_negative = [ + [condition, { + k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in negative + ] + + if not (isinstance(model, str) and model == "DUMMY"): + enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size, + seg.bbox, seed, steps, cfg, sampler_name, scheduler, + cropped_positive, cropped_negative, denoise, seg.cropped_mask, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, + refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper, + noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt) + else: + enhanced_image_tensor = cropped_image_frames + cnet_images = None + + if cnet_images is not None: + cnet_image_list.extend(cnet_images) + + if enhanced_image_tensor is None: + new_cropped_image = cropped_image_frames + else: + new_cropped_image = enhanced_image_tensor.cpu().numpy() + + new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None) + new_segs.append(new_seg) + + return (segs[0], new_segs), cnet_image_list + + def doit(self, image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None): + + segs, cnet_images = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, + scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, + noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt) + + if len(cnet_images) == 0: + cnet_images = [utils.empty_pil_tensor()] + + return (segs, cnet_images) + + +class DetailerForEachPipeForAnimateDiff: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "image_frames": ("IMAGE", ), + "segs": ("SEGS", ), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + }, + "optional": { + "detailer_hook": ("DETAILER_HOOK",), + "refiner_basic_pipe_opt": ("BASIC_PIPE",), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE") + RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images") + OUTPUT_IS_LIST = (False, False, False, True) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames." + + @staticmethod + def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, + noise_mask_feather=0, scheduler_func_opt=None): + + enhanced_segs = [] + cnet_image_list = [] + + for sub_seg in segs[1]: + single_seg = segs[0], [sub_seg] + enhanced_seg, cnet_images = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather, scheduler_func_opt=scheduler_func_opt) + + image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0] + + if cnet_images is not None: + cnet_image_list.extend(cnet_images) + + if detailer_hook is not None: + image_frames = detailer_hook.post_paste(image_frames) + + enhanced_segs += enhanced_seg[1] + + new_segs = segs[0], enhanced_segs + return image_frames, new_segs, basic_pipe, cnet_image_list diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/bridge_nodes.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/bridge_nodes.py new file mode 100644 index 00000000..b3923b58 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/bridge_nodes.py @@ -0,0 +1,490 @@ +import os +from PIL import ImageOps +import logging +import folder_paths +import torch +import nodes +from PIL import Image +import numpy as np +from impact import utils + +# NOTE: this should not be `from . import core`. +# I don't know why but... 'from .' and 'from impact' refer to different core modules. +# This separates global variables of the core module and breaks the preview bridge. +from impact import core +# <-- +import random + + +class PreviewBridge: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE",), + "image": ("STRING", {"default": ""}), + }, + "optional": { + "block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped."}), + "restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input image is the same size as the previous image, restore using the last saved mask\nalways: Whenever the input image changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`"}), + }, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("IMAGE", "MASK", ) + + FUNCTION = "doit" + + OUTPUT_NODE = True + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty." + + def __init__(self): + super().__init__() + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prev_hash = None + + @staticmethod + def load_image(pb_id): + is_fail = False + if pb_id not in core.preview_bridge_image_id_map: + is_fail = True + + if not is_fail: + image_path, ui_item = core.preview_bridge_image_id_map[pb_id] + if not os.path.isfile(image_path): + is_fail = True + + if not is_fail: + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + else: + image = utils.empty_pil_tensor() + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + ui_item = { + "filename": 'empty.png', + "subfolder": '', + "type": 'temp' + } + + return image, mask.unsqueeze(0), ui_item + + @staticmethod + def register_clipspace_image(clipspace_path, node_id): + """Register a clipspace image file in the preview bridge system. + + This handles the case where ComfyUI's mask editor creates clipspace files + that need to be integrated with the preview bridge system. + """ + # Remove [input] suffix if present + clean_path = clipspace_path.replace(" [input]", "").replace("[input]", "") + + # Try to find the actual clipspace file + input_dir = folder_paths.get_input_directory() + potential_paths = [ + clean_path, + os.path.join(input_dir, clean_path), + os.path.join(input_dir, "clipspace", os.path.basename(clean_path)), + os.path.abspath(clean_path), + ] + + actual_file = None + for path in potential_paths: + if os.path.isfile(path): + actual_file = path + break + + if not actual_file: + return False + + # Create ui_item for the clipspace file + ui_item = { + 'filename': os.path.basename(actual_file), + 'subfolder': 'clipspace', + 'type': 'input' + } + + # Register it using the preview bridge system + core.set_previewbridge_image(node_id, actual_file, ui_item) + # Also register under the original clipspace path for compatibility + core.preview_bridge_image_id_map[clipspace_path] = (actual_file, ui_item) + + return True + + def doit(self, images, image, unique_id, block=False, restore_mask="never", prompt=None, extra_pnginfo=None): + need_refresh = False + images_changed = False + + # Check if images have changed (this determines if we start fresh) + if unique_id not in core.preview_bridge_cache: + need_refresh = True + images_changed = True + elif core.preview_bridge_cache[unique_id][0] is not images: + need_refresh = True + images_changed = True + + # If images changed, clear the mask cache to ensure fresh start behavior + # This restores the original behavior where new images start with empty masks + # unless restore_mask is set to "always" or "if_same_size" + if images_changed and restore_mask not in ["always", "if_same_size"] and unique_id in core.preview_bridge_last_mask_cache: + del core.preview_bridge_last_mask_cache[unique_id] + + # Handle clipspace files that aren't registered in the preview bridge system + # This only applies when images haven't changed (same image, new mask scenario) + if not need_refresh and image not in core.preview_bridge_image_id_map: + # Check if this is a clipspace file that needs to be registered + is_clipspace = image and ("clipspace" in image.lower() or "[input]" in image) + if is_clipspace: + if not PreviewBridge.register_clipspace_image(image, unique_id): + need_refresh = True + else: + need_refresh = True + + if not need_refresh: + pixels, mask, path_item = PreviewBridge.load_image(image) + image = [path_item] + else: + # For new images (images_changed=True), we want to start fresh regardless of restore_mask + # For same image with refresh needed, respect the restore_mask setting + # Exception: when restore_mask is "always", restore even with new images + # Exception: when restore_mask is "if_same_size", allow restoration to check size compatibility + if restore_mask != "never" and (not images_changed or restore_mask in ["always", "if_same_size"]): + mask = core.preview_bridge_last_mask_cache.get(unique_id) + if mask is None: + mask = None + elif restore_mask == "if_same_size" and mask.shape[1:] != images.shape[1:3]: + # For if_same_size, clear mask if dimensions don't match + mask = None + # For "always", keep the mask regardless of size + else: + mask = None + + if mask is None: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo) + else: + masked_images = utils.tensor_convert_rgba(images) + resized_mask = utils.resize_mask(mask, (images.shape[1], images.shape[2])).unsqueeze(3) + resized_mask = 1 - resized_mask + utils.tensor_putalpha(masked_images, resized_mask) + res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo) + + image2 = res['ui']['images'] + pixels = images + + path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename']) + core.set_previewbridge_image(unique_id, path, image2[0]) + core.preview_bridge_image_id_map[image] = (path, image2[0]) + core.preview_bridge_image_name_map[unique_id, path] = (image, image2[0]) + core.preview_bridge_cache[unique_id] = (images, image2) + + image = image2 + + is_empty_mask = torch.all(mask == 0) + + if block and is_empty_mask and core.is_execution_model_version_supported(): + from comfy_execution.graph import ExecutionBlocker + result = ExecutionBlocker(None), ExecutionBlocker(None) + elif block and is_empty_mask: + logging.warning("[Impact Pack] PreviewBridge: ComfyUI is outdated - blocking feature is disabled.") + result = pixels, mask + else: + result = pixels, mask + + if not is_empty_mask: + core.preview_bridge_last_mask_cache[unique_id] = mask + + return { + "ui": {"images": image}, + "result": result, + } + + +def decode_latent(latent, preview_method, vae_opt=None): + if vae_opt is not None: + image = nodes.VAEDecode().decode(vae_opt, latent)[0] + return image + + from comfy.cli_args import LatentPreviewMethod + import comfy.latent_formats as latent_formats + + if preview_method.startswith("TAE"): + decoder_name = None + + if preview_method == "TAESD15": + decoder_name = "taesd" + elif preview_method == 'TAESDXL': + decoder_name = "taesdxl" + elif preview_method == 'TAESD3': + decoder_name = "taesd3" + elif preview_method == 'TAEF1': + decoder_name = "taef1" + + if decoder_name: + vae = nodes.VAELoader().load_vae(decoder_name)[0] + image = nodes.VAEDecode().decode(vae, latent)[0] + return image + + if preview_method == "Latent2RGB-SD15": + latent_format = latent_formats.SD15() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SDXL": + latent_format = latent_formats.SDXL() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SD3": + latent_format = latent_formats.SD3() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SD-X4": + latent_format = latent_formats.SD_X4() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-Playground-2.5": + latent_format = latent_formats.SDXL_Playground_2_5() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SC-Prior": + latent_format = latent_formats.SC_Prior() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SC-B": + latent_format = latent_formats.SC_B() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-FLUX.1": + latent_format = latent_formats.Flux() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-LTXV": + latent_format = latent_formats.LTXV() + method = LatentPreviewMethod.Latent2RGB + else: + logging.warning(f"[Impact Pack] PreviewBridgeLatent: '{preview_method}' is unsupported preview method.") + latent_format = latent_formats.SD15() + method = LatentPreviewMethod.Latent2RGB + + previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method) + samples = latent_format.process_in(latent['samples']) + + pil_image = previewer.decode_latent_to_preview(samples) + pixels_size = pil_image.size[0]*8, pil_image.size[1]*8 + resized_image = pil_image.resize(pixels_size, resample=utils.LANCZOS) + + return utils.to_tensor(resized_image).unsqueeze(0) + + +class PreviewBridgeLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "latent": ("LATENT",), + "image": ("STRING", {"default": ""}), + "preview_method": (["Latent2RGB-FLUX.1", + "Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3", + "Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5", + "Latent2RGB-SC-Prior", "Latent2RGB-SC-B", + "Latent2RGB-LTXV", + "TAEF1", "TAESDXL", "TAESD15", "TAESD3"],), + }, + "optional": { + "vae_opt": ("VAE", ), + "block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped. Instead, it returns a white mask."}), + "restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input latent is the same size as the previous latent, restore using the last saved mask\nalways: Whenever the input latent changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`\nIf the input latent already has a mask, do not restore mask."}), + }, + "hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("LATENT", "MASK", ) + + FUNCTION = "doit" + + OUTPUT_NODE = True + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a latent image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty." + + def __init__(self): + super().__init__() + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prev_hash = None + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + + @staticmethod + def load_image(pb_id): + is_fail = False + if pb_id not in core.preview_bridge_image_id_map: + is_fail = True + + if not is_fail: + image_path, ui_item = core.preview_bridge_image_id_map[pb_id] + if not os.path.isfile(image_path): + is_fail = True + + if not is_fail: + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = None + else: + image = utils.empty_pil_tensor() + mask = None + ui_item = { + "filename": 'empty.png', + "subfolder": '', + "type": 'temp' + } + + return image, mask, ui_item + + def doit(self, latent, image, preview_method, vae_opt=None, block=False, unique_id=None, restore_mask='never', prompt=None, extra_pnginfo=None): + latent_channels = latent['samples'].shape[1] + + if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method: + preview_method_channels = 16 + elif 'LTXV' in preview_method: + preview_method_channels = 128 + else: + preview_method_channels = 4 + + if vae_opt is None and latent_channels != preview_method_channels: + logging.warning("[PreviewBridgeLatent] The version of latent is not compatible with preview_method.\nSD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.") + raise Exception("The version of latent is not compatible with preview_method.
SD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.") + + need_refresh = False + latent_changed = False + + # Check if latent has changed + if unique_id not in core.preview_bridge_cache: + need_refresh = True + latent_changed = True + elif (core.preview_bridge_cache[unique_id][0] is not latent + or (vae_opt is None and core.preview_bridge_cache[unique_id][2] is not None) + or (vae_opt is None and core.preview_bridge_cache[unique_id][1] != preview_method) + or (vae_opt is not None and core.preview_bridge_cache[unique_id][2] is not vae_opt)): + need_refresh = True + latent_changed = True + + # If latent changed, clear the mask cache to ensure fresh start behavior + # unless restore_mask is set to "always" or "if_same_size" + if latent_changed and restore_mask not in ["always", "if_same_size"] and unique_id in core.preview_bridge_last_mask_cache: + del core.preview_bridge_last_mask_cache[unique_id] + + # Handle clipspace files that aren't registered in the preview bridge system + # This only applies when latent hasn't changed (same latent, new mask scenario) + if not need_refresh and image not in core.preview_bridge_image_id_map: + is_clipspace = image and ("clipspace" in image.lower() or "[input]" in image) + if is_clipspace: + if not PreviewBridge.register_clipspace_image(image, unique_id): + need_refresh = True + else: + need_refresh = True + + if not need_refresh: + pixels, mask, path_item = PreviewBridge.load_image(image) + + if mask is None: + mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0) + if 'noise_mask' in latent: + res_latent = latent.copy() + del res_latent['noise_mask'] + else: + res_latent = latent + + is_empty_mask = True + else: + res_latent = latent.copy() + res_latent['noise_mask'] = mask + + is_empty_mask = torch.all(mask == 1) + + res_image = [path_item] + else: + decoded_image = decode_latent(latent, preview_method, vae_opt) + + if 'noise_mask' in latent: + mask = latent['noise_mask'].squeeze(0) # 4D mask -> 3D mask + + decoded_pil = utils.to_pil(decoded_image) + + inverted_mask = 1 - mask # invert + resized_mask = utils.resize_mask(inverted_mask, (decoded_image.shape[1], decoded_image.shape[2])) + result_pil = utils.apply_mask_alpha_to_pil(decoded_pil, resized_mask) + + full_output_folder, filename, counter, _, _ = folder_paths.get_save_image_path("PreviewBridge/PBL-"+self.prefix_append, folder_paths.get_temp_directory(), result_pil.size[0], result_pil.size[1]) + file = f"{filename}_{counter}.png" + result_pil.save(os.path.join(full_output_folder, file), compress_level=4) + res_image = [{ + 'filename': file, + 'subfolder': 'PreviewBridge', + 'type': 'temp', + }] + + is_empty_mask = False + else: + # For new latents (latent_changed=True), start fresh regardless of restore_mask + # For same latent with refresh needed, respect the restore_mask setting + # Exception: when restore_mask is "always", restore even with new latents + # Exception: when restore_mask is "if_same_size", allow restoration to check size compatibility + if restore_mask != "never" and (not latent_changed or restore_mask in ["always", "if_same_size"]): + mask = core.preview_bridge_last_mask_cache.get(unique_id) + if mask is None: + mask = None + elif restore_mask == "if_same_size" and mask.shape[1:] != decoded_image.shape[1:3]: + # For if_same_size, clear mask if dimensions don't match + mask = None + # For "always", keep the mask regardless of size + else: + mask = None + + if mask is None: + mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0) + res = nodes.PreviewImage().save_images(decoded_image, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo) + else: + masked_images = utils.tensor_convert_rgba(decoded_image) + resized_mask = utils.resize_mask(mask, (decoded_image.shape[1], decoded_image.shape[2])).unsqueeze(3) + resized_mask = 1 - resized_mask + utils.tensor_putalpha(masked_images, resized_mask) + res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo) + + res_image = res['ui']['images'] + + is_empty_mask = torch.all(mask == 1) + + path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', res_image[0]['filename']) + core.set_previewbridge_image(unique_id, path, res_image[0]) + core.preview_bridge_image_id_map[image] = (path, res_image[0]) + core.preview_bridge_image_name_map[unique_id, path] = (image, res_image[0]) + core.preview_bridge_cache[unique_id] = (latent, preview_method, vae_opt, res_image) + + res_latent = latent + + if block and is_empty_mask and core.is_execution_model_version_supported(): + from comfy_execution.graph import ExecutionBlocker + result = ExecutionBlocker(None), ExecutionBlocker(None) + elif block and is_empty_mask: + logging.warning("[Impact Pack] PreviewBridgeLatent: ComfyUI is outdated - blocking feature is disabled.") + result = res_latent, mask + else: + result = res_latent, mask + + if not is_empty_mask: + core.preview_bridge_last_mask_cache[unique_id] = mask + + return { + "ui": {"images": res_image}, + "result": result, + } \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/config.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/config.py new file mode 100644 index 00000000..19d5278c --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/config.py @@ -0,0 +1,78 @@ +import configparser +import logging +import os + +version_code = [8, 28, 2] +version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '') + +my_path = os.path.dirname(__file__) +old_config_path = os.path.join(my_path, "impact-pack.ini") +config_path = os.path.join(my_path, "..", "..", "impact-pack.ini") +latent_letter_path = os.path.join(my_path, "..", "..", "latent.png") + + +def write_config(): + config = configparser.ConfigParser() + config['default'] = { + 'sam_editor_cpu': str(get_config()['sam_editor_cpu']), + 'sam_editor_model': get_config()['sam_editor_model'], + 'custom_wildcards': get_config()['custom_wildcards'], + 'disable_gpu_opencv': get_config()['disable_gpu_opencv'], + 'wildcard_cache_limit_mb': str(get_config()['wildcard_cache_limit_mb']), + } + with open(config_path, 'w') as configfile: + config.write(configfile) + + +def read_config(): + try: + config = configparser.ConfigParser() + config.read(config_path) + default_conf = config['default'] + + # Strip quotes from custom_wildcards path if present + custom_wildcards_path = default_conf.get('custom_wildcards', '').strip('\'"') + + if not os.path.exists(custom_wildcards_path): + logging.warning(f"[Impact Pack] custom_wildcards path not found: {custom_wildcards_path}. Using default path.") + custom_wildcards_path = os.path.join(my_path, "..", "..", "custom_wildcards") + + default_conf['custom_wildcards'] = custom_wildcards_path + + # Parse wildcard_cache_limit_mb with default value of 50MB + cache_limit_mb = 50 + if 'wildcard_cache_limit_mb' in default_conf: + try: + cache_limit_mb = float(default_conf['wildcard_cache_limit_mb']) + except ValueError: + logging.warning(f"[Impact Pack] Invalid wildcard_cache_limit_mb value: {default_conf['wildcard_cache_limit_mb']}. Using default: 50") + cache_limit_mb = 50 + + return { + 'sam_editor_cpu': default_conf['sam_editor_cpu'].lower() == 'true' if 'sam_editor_cpu' in default_conf else False, + 'sam_editor_model': default_conf['sam_editor_model'].lower() if 'sam_editor_model' else 'sam_vit_b_01ec64.pth', + 'custom_wildcards': default_conf['custom_wildcards'] if 'custom_wildcards' in default_conf else os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "custom_wildcards")), + 'disable_gpu_opencv': default_conf['disable_gpu_opencv'].lower() == 'true' if 'disable_gpu_opencv' in default_conf else True, + 'wildcard_cache_limit_mb': cache_limit_mb + } + + except Exception: + return { + 'sam_editor_cpu': False, + 'sam_editor_model': 'sam_vit_b_01ec64.pth', + 'custom_wildcards': os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "custom_wildcards")), + 'disable_gpu_opencv': True, + 'wildcard_cache_limit_mb': 50 + } + + +cached_config = None + + +def get_config(): + global cached_config + + if cached_config is None: + cached_config = read_config() + + return cached_config diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/core.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/core.py new file mode 100644 index 00000000..801b71fb --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/core.py @@ -0,0 +1,2417 @@ +import os +import warnings + +import torch +from segment_anything import SamPredictor + +from comfy_extras.nodes_custom_sampler import Noise_RandomNoise +from collections import namedtuple +import numpy as np +from PIL import ImageOps, Image + +import nodes +from server import PromptServer +import comfy +import impact.wildcards as wildcards +import math +import cv2 +import time +from comfy import model_management +from impact import utils +from impact import impact_sampling +from concurrent.futures import ThreadPoolExecutor +import inspect +from collections import OrderedDict +import torch.nn.functional as F +import logging +import sys +import importlib + + +is_sam2_available = importlib.util.find_spec("sam2") +sam2_unavailable_message = f"\n----------------------------------------------------------------------------\n[Impact Pack] The SAM2 functionality is unavailable because the `facebook/sam2` dependency is not installed.\n\nInstallation command:\n{sys.executable} -m pip install git+https://github.com/facebookresearch/sam2\n----------------------------------------------------------------------------\n" +if is_sam2_available: + from sam2.sam2_image_predictor import SAM2ImagePredictor + from sam2.build_sam import build_sam2, build_sam2_video_predictor +else: + logging.warning(sam2_unavailable_message) + +try: + from comfy_extras import nodes_differential_diffusion +except Exception: + logging.warning("\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n") + raise Exception("[Impact Pack] ComfyUI is an outdated version.") + + +SEG = namedtuple("SEG", + ['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label', 'control_net_wrapper'], + defaults=[None]) + +pb_id_cnt = time.time() +preview_bridge_image_id_map = {} +preview_bridge_image_name_map = {} + +preview_bridge_cache = {} +preview_bridge_last_mask_cache = {} + +current_prompt = None + +ADDITIONAL_SCHEDULERS = ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]', 'OSS FLUX', 'OSS Wan', 'OSS Chroma'] + +def get_schedulers(): + return list(comfy.samplers.SCHEDULER_HANDLERS) + ADDITIONAL_SCHEDULERS + +def is_execution_model_version_supported(): + try: + import comfy_execution # noqa: F401 + return True + except Exception: + return False + + +def set_previewbridge_image(node_id, file, item): + global pb_id_cnt + + if file in preview_bridge_image_name_map: + pb_id = preview_bridge_image_name_map[node_id, file] + if pb_id.startswith(f"${node_id}"): + return pb_id + + pb_id = f"${node_id}-{pb_id_cnt}" + preview_bridge_image_id_map[pb_id] = (file, item) + preview_bridge_image_name_map[node_id, file] = (pb_id, item) + if os.path.isfile(file): + i = Image.open(file) + i = ImageOps.exif_transpose(i) + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + preview_bridge_last_mask_cache[node_id] = mask.unsqueeze(0) + pb_id_cnt += 1 + + return pb_id + + +def erosion_mask(mask, grow_mask_by): + mask = utils.make_2d_mask(mask) + + w = mask.shape[1] + h = mask.shape[0] + + device = comfy.model_management.get_torch_device() + mask = mask.clone().to(device) + mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode="bilinear").to(device) + if grow_mask_by == 0: + mask_erosion = mask2 + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)).to(device) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1) + + return mask_erosion[:, :, :w, :h].round().cpu() + + +# CREDIT: https://github.com/BlenderNeko/ComfyUI_Noise/blob/afb14757216257b12268c91845eac248727a55e2/nodes.py#L68 +# https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 +def slerp(val, low, high): + dims = low.shape + + low = low.reshape(dims[0], -1) + high = high.reshape(dims[0], -1) + + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + + low_norm[low_norm != low_norm] = 0.0 + high_norm[high_norm != high_norm] = 0.0 + + omega = torch.acos((low_norm*high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + + return res.reshape(dims) + + +def mix_noise(from_noise, to_noise, strength, variation_method): + if variation_method == 'slerp': + mixed_noise = slerp(strength, from_noise, to_noise) + else: + # linear + mixed_noise = (1 - strength) * from_noise + strength * to_noise + + # NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling. + scale_factor = math.sqrt((1 - strength) ** 2 + strength ** 2) + mixed_noise /= scale_factor + + return mixed_noise + + +class REGIONAL_PROMPT: + def __init__(self, mask, sampler, variation_seed=0, variation_strength=0.0, variation_method='linear'): + mask = utils.make_2d_mask(mask) + + self.mask = mask + self.sampler = sampler + self.mask_erosion = None + self.erosion_factor = None + self.variation_seed = variation_seed + self.variation_strength = variation_strength + self.variation_method = variation_method + + def clone_with_sampler(self, sampler): + rp = REGIONAL_PROMPT(self.mask, sampler) + rp.mask_erosion = self.mask_erosion + rp.erosion_factor = self.erosion_factor + rp.variation_seed = self.variation_seed + rp.variation_strength = self.variation_strength + rp.variation_method = self.variation_method + return rp + + def get_mask_erosion(self, factor): + if self.mask_erosion is None or self.erosion_factor != factor: + self.mask_erosion = erosion_mask(self.mask, factor) + self.erosion_factor = factor + + return self.mask_erosion + + def touch_noise(self, noise): + if self.variation_strength > 0.0: + mask = utils.make_3d_mask(self.mask) + mask = utils.resize_mask(mask, (noise.shape[2], noise.shape[3])).unsqueeze(0) + + regional_noise = Noise_RandomNoise(self.variation_seed).generate_noise({'samples': noise}) + mixed_noise = mix_noise(noise, regional_noise, self.variation_strength, variation_method=self.variation_method) + + return (mask == 1).float() * mixed_noise + (mask == 0).float() * noise + + return noise + + +class NO_BBOX_DETECTOR: + pass + + +class NO_SEGM_DETECTOR: + pass + + +def create_segmasks(results): + bboxs = results[1] + segms = results[2] + confidence = results[3] + + results = [] + for i in range(len(segms)): + item = (bboxs[i], segms[i].astype(np.float32), confidence[i]) + results.append(item) + return results + + +def gen_detection_hints_from_mask_area(x, y, mask, threshold, use_negative): + mask = utils.make_2d_mask(mask) + + points = [] + plabs = [] + + # minimum sampling step >= 3 + y_step = max(3, int(mask.shape[0] / 20)) + x_step = max(3, int(mask.shape[1] / 20)) + + for i in range(0, len(mask), y_step): + for j in range(0, len(mask[i]), x_step): + if mask[i][j] > threshold: + points.append((x + j, y + i)) + plabs.append(1) + elif use_negative and mask[i][j] == 0: + points.append((x + j, y + i)) + plabs.append(0) + + return points, plabs + + +def gen_negative_hints(w, h, x1, y1, x2, y2): + npoints = [] + nplabs = [] + + # minimum sampling step >= 3 + y_step = max(3, int(w / 20)) + x_step = max(3, int(h / 20)) + + for i in range(10, h - 10, y_step): + for j in range(10, w - 10, x_step): + if not (x1 - 10 <= j and j <= x2 + 10 and y1 - 10 <= i and i <= y2 + 10): + npoints.append((j, i)) + nplabs.append(0) + + return npoints, nplabs + + +def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, bbox, seed, steps, cfg, + sampler_name, + scheduler, positive, negative, denoise, noise_mask, force_inpaint, + wildcard_opt=None, wildcard_opt_concat_mode=None, + detailer_hook=None, + refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, + refiner_negative=None, control_net_wrapper=None, cycle=1, + inpaint_model=False, noise_mask_feather=0, scheduler_func=None, + vae_tiled_encode=False, vae_tiled_decode=False): + + if noise_mask is not None: + noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather) + noise_mask = noise_mask.squeeze(3) + + if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + if wildcard_opt is not None and wildcard_opt != "": + model, _, wildcard_positive = wildcards.process_with_loras(wildcard_opt, model, clip) + + if wildcard_opt_concat_mode == "concat": + positive = nodes.ConditioningConcat().concat(positive, wildcard_positive)[0] + else: + positive = wildcard_positive + positive = [positive[0].copy()] + if 'pooled_output' in wildcard_positive[0][1]: + positive[0][1]['pooled_output'] = wildcard_positive[0][1]['pooled_output'] + elif 'pooled_output' in positive[0][1]: + del positive[0][1]['pooled_output'] + + h = image.shape[1] + w = image.shape[2] + + bbox_h = bbox[3] - bbox[1] + bbox_w = bbox[2] - bbox[0] + + # Skip processing if the detected bbox is already larger than the guide_size + if not force_inpaint and bbox_h >= guide_size and bbox_w >= guide_size: + logging.info("Detailer: segment skip (enough big)") + return None, None + + if guide_size_for_bbox: # == "bbox" + # Scale up based on the smaller dimension between width and height. + upscale = guide_size / min(bbox_w, bbox_h) + else: + # for cropped_size + upscale = guide_size / min(w, h) + + new_w = int(w * upscale) + new_h = int(h * upscale) + + # safeguard + if 'aitemplate_keep_loaded' in model.model_options: + max_size = min(4096, max_size) + + if new_w > max_size or new_h > max_size: + upscale *= max_size / max(new_w, new_h) + new_w = int(w * upscale) + new_h = int(h * upscale) + + if not force_inpaint: + if upscale <= 1.0: + logging.info(f"Detailer: segment skip [determined upscale factor={upscale}]") + return None, None + + if new_w == 0 or new_h == 0: + logging.info(f"Detailer: segment skip [zero size={new_w, new_h}]") + return None, None + else: + if upscale <= 1.0 or new_w == 0 or new_h == 0: + logging.info("Detailer: force inpaint") + upscale = 1.0 + new_w = w + new_h = h + + if detailer_hook is not None: + new_w, new_h = detailer_hook.touch_scaled_size(new_w, new_h) + + logging.info(f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}") + + # upscale + upscaled_image = utils.tensor_resize(image, new_w, new_h) + + if detailer_hook is not None: + upscaled_image = detailer_hook.post_upscale(upscaled_image, noise_mask) + + cnet_pils = None + if control_net_wrapper is not None: + positive, negative, cnet_pils = control_net_wrapper.apply(positive, negative, upscaled_image, noise_mask) + model, cnet_pils2 = control_net_wrapper.doit_ipadapter(model) + cnet_pils.extend(cnet_pils2) + + # prepare mask + if detailer_hook is None or not detailer_hook.get_skip_sampling(): + if noise_mask is not None and inpaint_model: + imc_encode = nodes.InpaintModelConditioning().encode + if 'noise_mask' in inspect.signature(imc_encode).parameters: + positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, mask=noise_mask, noise_mask=True) + else: + logging.warning("[Impact Pack] ComfyUI is an outdated version.") + positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask) + else: + latent_image = utils.to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode) + if noise_mask is not None: + latent_image['noise_mask'] = noise_mask + + if detailer_hook is not None: + latent_image = detailer_hook.post_encode(latent_image) + + refined_latent = latent_image + + sampler_opt=None + if detailer_hook is not None: + sampler_opt = detailer_hook.get_custom_sampler() + + # ksampler + for i in range(0, cycle): + if detailer_hook is not None: + if detailer_hook is not None: + detailer_hook.set_steps((i, cycle)) + + refined_latent = detailer_hook.cycle_latent(refined_latent) + + model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \ + detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise) + noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False) + if not is_touched: + noise = None + else: + model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, _, denoise2 = \ + model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise + noise = None + + refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, + refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, + noise=noise, scheduler_func=scheduler_func, sampler_opt=sampler_opt) + + if detailer_hook is not None: + refined_latent = detailer_hook.pre_decode(refined_latent) + + # non-latent downscale - latent downscale cause bad quality + start = time.time() + if vae_tiled_decode: + (refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings + logging.info(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s") + else: + try: + refined_image = vae.decode(refined_latent['samples']) + except Exception: + # usually an out-of-memory exception from the decode, so try a tiled approach + logging.warning(f"[Impact Pack] failed after {time.time() - start:.1f}s, doing vae.decode_tiled 64...") + refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, ) + logging.info(f"[Impact Pack] vae decoded in {time.time() - start:.1f}s") + else: + # skipped + refined_image = upscaled_image + + if detailer_hook is not None: + refined_image = detailer_hook.post_decode(refined_image) + + # downscale + + # workaround: support WAN as an i2i model + if len(refined_image.shape) == 5: + refined_image = refined_image.squeeze(0) + + refined_image = utils.tensor_resize(refined_image, w, h) + + # prevent mixing of device + refined_image = refined_image.cpu() + + # don't convert to latent - latent break image + # preserving pil is much better + return refined_image, cnet_pils + + +def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, guide_size_for_bbox, max_size, bbox, seed, steps, cfg, + sampler_name, + scheduler, positive, negative, denoise, noise_mask, + wildcard_opt=None, wildcard_opt_concat_mode=None, + detailer_hook=None, + refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, + refiner_negative=None, control_net_wrapper=None, noise_mask_feather=0, scheduler_func=None): + if noise_mask is not None: + noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather) + noise_mask = noise_mask.squeeze(3) + + if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + if wildcard_opt is not None and wildcard_opt != "": + model, _, wildcard_positive = wildcards.process_with_loras(wildcard_opt, model, clip) + + if wildcard_opt_concat_mode == "concat": + positive = nodes.ConditioningConcat().concat(positive, wildcard_positive)[0] + else: + positive = wildcard_positive + + h = image_frames.shape[1] + w = image_frames.shape[2] + + bbox_h = bbox[3] - bbox[1] + bbox_w = bbox[2] - bbox[0] + + # Skip processing if the detected bbox is already larger than the guide_size + if guide_size_for_bbox: # == "bbox" + # Scale up based on the smaller dimension between width and height. + upscale = guide_size / min(bbox_w, bbox_h) + else: + # for cropped_size + upscale = guide_size / min(w, h) + + new_w = int(w * upscale) + new_h = int(h * upscale) + + # safeguard + if 'aitemplate_keep_loaded' in model.model_options: + max_size = min(4096, max_size) + + if new_w > max_size or new_h > max_size: + upscale *= max_size / max(new_w, new_h) + new_w = int(w * upscale) + new_h = int(h * upscale) + + if upscale <= 1.0 or new_w == 0 or new_h == 0: + logging.info("Detailer: force inpaint") + upscale = 1.0 + new_w = w + new_h = h + + if detailer_hook is not None: + new_w, new_h = detailer_hook.touch_scaled_size(new_w, new_h) + + logging.info(f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}") + + # upscale the mask tensor by a factor of 2 using bilinear interpolation + if isinstance(noise_mask, np.ndarray): + noise_mask = torch.from_numpy(noise_mask) + + if len(noise_mask.shape) == 2: + noise_mask = noise_mask.unsqueeze(0) + else: # == 3 + noise_mask = noise_mask + + upscaled_mask = None + + for single_mask in noise_mask: + single_mask = single_mask.unsqueeze(0).unsqueeze(0) + upscaled_single_mask = torch.nn.functional.interpolate(single_mask, size=(new_h, new_w), mode='bilinear', align_corners=False) + upscaled_single_mask = upscaled_single_mask.squeeze(0) + + if upscaled_mask is None: + upscaled_mask = upscaled_single_mask + else: + upscaled_mask = torch.cat((upscaled_mask, upscaled_single_mask), dim=0) + + latent_frames = None + for image in image_frames: + image = torch.from_numpy(image).unsqueeze(0) + + # upscale + upscaled_image = utils.tensor_resize(image, new_w, new_h) + + # ksampler + samples = utils.to_latent_image(upscaled_image, vae)['samples'] + + if latent_frames is None: + latent_frames = samples + else: + latent_frames = torch.concat((latent_frames, samples), dim=0) + + cnet_images = None + if control_net_wrapper is not None: + positive, negative, cnet_images = control_net_wrapper.apply(positive, negative, torch.from_numpy(image_frames), noise_mask, use_acn=True) + + if len(upscaled_mask) != len(image_frames) and len(upscaled_mask) > 1: + logging.warning(f"[Impact Pack] DetailerForAnimateDiff: The number of the mask frames({len(upscaled_mask)}) and the image frames({len(image_frames)}) are different. Combine the mask frames and apply.") + combined_mask = upscaled_mask[0].to(torch.uint8) + + for frame_mask in upscaled_mask[1:]: + combined_mask |= (frame_mask * 255).to(torch.uint8) + + combined_mask = (combined_mask/255.0).to(torch.float32) + + upscaled_mask = combined_mask.expand(len(image_frames), -1, -1) + upscaled_mask = utils.to_binary_mask(upscaled_mask, 0.1) + + latent = { + 'noise_mask': upscaled_mask, + 'samples': latent_frames + } + + + sampler_opt=None + if detailer_hook is not None: + sampler_opt = detailer_hook.get_custom_sampler() + + if detailer_hook is not None: + latent = detailer_hook.post_encode(latent) + + refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, + latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func, sampler_opt=sampler_opt) + + if detailer_hook is not None: + refined_latent = detailer_hook.pre_decode(refined_latent) + + refined_image_frames = None + for refined_sample in refined_latent['samples']: + refined_sample = refined_sample.unsqueeze(0) + + # non-latent downscale - latent downscale cause bad quality + refined_image = vae.decode(refined_sample) + + if refined_image_frames is None: + refined_image_frames = refined_image + else: + refined_image_frames = torch.concat((refined_image_frames, refined_image), dim=0) + + if detailer_hook is not None: + refined_image_frames = detailer_hook.post_decode(refined_image_frames) + + refined_image_frames = nodes.ImageScale().upscale(image=refined_image_frames, upscale_method='lanczos', width=w, height=h, crop='disabled')[0] + + return refined_image_frames, cnet_images + + +def composite_to(dest_latent, crop_region, src_latent): + x1 = crop_region[0] + y1 = crop_region[1] + + # composite to original latent + lc = nodes.LatentComposite() + orig_image = lc.composite(dest_latent, src_latent, x1, y1) + + return orig_image[0] + + +def sam_predict(predictor, points, plabs, bbox, threshold): + point_coords = None if not points else np.array(points) + point_labels = None if not plabs else np.array(plabs) + + box = np.array([bbox]) if bbox is not None else None + + cur_masks, scores, _ = predictor.predict(point_coords=point_coords, point_labels=point_labels, box=box) + + total_masks = [] + + selected = False + max_score = 0 + max_mask = None + for idx in range(len(scores)): + if scores[idx] > max_score: + max_score = scores[idx] + max_mask = cur_masks[idx] + + if scores[idx] >= threshold: + selected = True + total_masks.append(cur_masks[idx]) + else: + pass + + if not selected and max_mask is not None: + total_masks.append(max_mask) + + return total_masks + + +class SAMWrapper: + def __init__(self, model, is_auto_mode, safe_to_gpu=None): + self.model = model + self.safe_to_gpu = safe_to_gpu if safe_to_gpu is not None else SafeToGPU_stub() + self.is_auto_mode = is_auto_mode + + def prepare_device(self): + if self.is_auto_mode: + device = comfy.model_management.get_torch_device() + self.safe_to_gpu.to_device(self.model, device=device) + + def release_device(self): + if self.is_auto_mode: + self.model.to(device="cpu") + + def predict(self, image, points, plabs, bbox, threshold): + predictor = SamPredictor(self.model) + predictor.set_image(image, "RGB") + + return sam_predict(predictor, points, plabs, bbox, threshold) + + +class SAM2Wrapper: + def __init__(self, config, modelname, is_auto_mode, safe_to_gpu=None, device_mode="AUTO"): + self.config = config + self.modelname = modelname + self.image_predictor = None + self.video_predictor = None + self.device_mode = device_mode + self.safe_to_gpu = safe_to_gpu if safe_to_gpu is not None else SafeToGPU_stub() + self.is_auto_mode = is_auto_mode + + def prepare_device(self): + pass + + def prepare_image_device(self): + if self.is_auto_mode: + device = comfy.model_management.get_torch_device() + self.safe_to_gpu.to_device(self.image_predictor.model, device=device) + + def prepare_video_device(self): + if self.is_auto_mode: + device = comfy.model_management.get_torch_device() + self.safe_to_gpu.to_device(self.video_predictor, device=device) + + def release_device(self): + if self.is_auto_mode: + if self.image_predictor: + self.image_predictor.model.to(device="cpu") + if self.video_predictor: + self.video_predictor.to(device="cpu") + + def predict(self, image, points, plabs, bbox, threshold): + if not is_sam2_available: + raise Exception(sam2_unavailable_message) + + if self.image_predictor is None: + self.image_predictor = SAM2ImagePredictor(build_sam2(self.config, self.modelname)) + + self.prepare_image_device() + + self.image_predictor.set_image(image) + + return sam_predict(self.image_predictor, points, plabs, bbox, threshold) + + def predict_video_segs(self, image_frames, segs): + if not is_sam2_available: + raise Exception(sam2_unavailable_message) + + if self.video_predictor is None: + self.video_predictor = build_sam2_video_predictor(self.config, self.modelname) + + self.prepare_video_device() + + orig_video_height = image_frames.shape[1] + orig_video_width = image_frames.shape[2] + + image_frames, padding = utils.resize_with_padding(image_frames, self.video_predictor.image_size, self.video_predictor.image_size) + image_frames = image_frames.permute(0, 3, 1, 2) + + inference_state = {} + inference_state["images"] = image_frames + inference_state["num_frames"] = len(image_frames) + inference_state["video_height"] = self.video_predictor.image_size + inference_state["video_width"] = self.video_predictor.image_size + inference_state["offload_video_to_cpu"] = True + inference_state["offload_state_to_cpu"] = self.device_mode == "CPU" + inference_state["device"] = self.video_predictor.device + + if inference_state["offload_state_to_cpu"]: + inference_state["storage_device"] = torch.device("cpu") + else: + inference_state["storage_device"] = self.video_predictor.device + + inference_state["point_inputs_per_obj"] = {} + inference_state["mask_inputs_per_obj"] = {} + inference_state["cached_features"] = {} + inference_state["constants"] = {} + + inference_state["obj_id_to_idx"] = OrderedDict() + inference_state["obj_idx_to_id"] = OrderedDict() + inference_state["obj_ids"] = [] + + inference_state["output_dict_per_obj"] = {} + inference_state["temp_output_dict_per_obj"] = {} + inference_state["frames_tracked_per_obj"] = {} + self.video_predictor._get_image_feature(inference_state, frame_idx=0, batch_size=1) + + temp_masks = {} + for i in range(0, len(segs[1])): + bbox = segs[1][i].bbox + + adjusted_bbox = utils.adjust_bbox_after_resize( + bbox, + (orig_video_height, orig_video_width), + (self.video_predictor.image_size, self.video_predictor.image_size), + padding + ) + + points = [utils.center_of_bbox(adjusted_bbox)] + plabs = [1] + self.video_predictor.add_new_points_or_box(inference_state=inference_state, frame_idx=0, obj_id=i, points=points, labels=plabs, box=adjusted_bbox) + temp_masks[i] = [] + + for frame_idx, object_ids, masks in self.video_predictor.propagate_in_video(inference_state): + for i in object_ids: + m = masks[i] + m = m.permute(1, 2, 0) + temp_masks[i].append(m) + + result = {} + for k, v in temp_masks.items(): + m = torch.stack(v, dim=0) + m = utils.remove_padding(m, padding) + result[k] = utils.resize_with_padding(m, orig_video_width, orig_video_height)[0] + + return result + +class ESAMWrapper: + def __init__(self, model, device): + self.model = model + self.func_inference = nodes.NODE_CLASS_MAPPINGS['Yoloworld_ESAM_Zho'] + self.device = device + + def prepare_device(self): + pass + + def release_device(self): + pass + + def predict(self, image, points, plabs, bbox, threshold): + if self.device == 'CPU': + self.device = 'cpu' + else: + self.device = 'cuda' + + detected_masks = self.func_inference.inference_sam_with_boxes(image=image, xyxy=[bbox], model=self.model, device=self.device) + return [detected_masks.squeeze(0)] + + +def make_sam_mask(sam, segs, image, detection_hint, dilation, + threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative): + + if not hasattr(sam, 'sam_wrapper') and not isinstance(sam, SAM2Wrapper): + raise Exception("[Impact Pack] Invalid SAMLoader is connected. Make sure 'SAMLoader (Impact)'.\nKnown issue: The ComfyUI-YOLO node overrides the SAMLoader (Impact), making it unusable. You need to uninstall ComfyUI-YOLO.\n\n\n") + + + if isinstance(sam, SAM2Wrapper): + sam_obj = sam + else: + sam_obj = sam.sam_wrapper + + sam_obj.prepare_device() + + try: + image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + + total_masks = [] + + use_small_negative = mask_hint_use_negative == "Small" + + # seg_shape = segs[0] + segs = segs[1] + if detection_hint == "mask-points": + points = [] + plabs = [] + + for i in range(len(segs)): + bbox = segs[i].bbox + center = utils.center_of_bbox(segs[i].bbox) + points.append(center) + + # small point is background, big point is foreground + if use_small_negative and bbox[2] - bbox[0] < 10: + plabs.append(0) + else: + plabs.append(1) + + detected_masks = sam_obj.predict(image, points, plabs, None, threshold) + total_masks += detected_masks + + else: + for i in range(len(segs)): + bbox = segs[i].bbox + center = utils.center_of_bbox(bbox) + + x1 = max(bbox[0] - bbox_expansion, 0) + y1 = max(bbox[1] - bbox_expansion, 0) + x2 = min(bbox[2] + bbox_expansion, image.shape[1]) + y2 = min(bbox[3] + bbox_expansion, image.shape[0]) + + dilated_bbox = [x1, y1, x2, y2] + + points = [] + plabs = [] + if detection_hint == "center-1": + points.append(center) + plabs = [1] # 1 = foreground point, 0 = background point + + elif detection_hint == "horizontal-2": + gap = (x2 - x1) / 3 + points.append((x1 + gap, center[1])) + points.append((x1 + gap * 2, center[1])) + plabs = [1, 1] + + elif detection_hint == "vertical-2": + gap = (y2 - y1) / 3 + points.append((center[0], y1 + gap)) + points.append((center[0], y1 + gap * 2)) + plabs = [1, 1] + + elif detection_hint == "rect-4": + x_gap = (x2 - x1) / 3 + y_gap = (y2 - y1) / 3 + points.append((x1 + x_gap, center[1])) + points.append((x1 + x_gap * 2, center[1])) + points.append((center[0], y1 + y_gap)) + points.append((center[0], y1 + y_gap * 2)) + plabs = [1, 1, 1, 1] + + elif detection_hint == "diamond-4": + x_gap = (x2 - x1) / 3 + y_gap = (y2 - y1) / 3 + points.append((x1 + x_gap, y1 + y_gap)) + points.append((x1 + x_gap * 2, y1 + y_gap)) + points.append((x1 + x_gap, y1 + y_gap * 2)) + points.append((x1 + x_gap * 2, y1 + y_gap * 2)) + plabs = [1, 1, 1, 1] + + elif detection_hint == "mask-point-bbox": + center = utils.center_of_bbox(segs[i].bbox) + points.append(center) + plabs = [1] + + elif detection_hint == "mask-area": + points, plabs = gen_detection_hints_from_mask_area(segs[i].crop_region[0], segs[i].crop_region[1], + segs[i].cropped_mask, + mask_hint_threshold, use_small_negative) + + if mask_hint_use_negative == "Outter": + npoints, nplabs = gen_negative_hints(image.shape[0], image.shape[1], + segs[i].crop_region[0], segs[i].crop_region[1], + segs[i].crop_region[2], segs[i].crop_region[3]) + + points += npoints + plabs += nplabs + + detected_masks = sam_obj.predict(image, points, plabs, dilated_bbox, threshold) + total_masks += detected_masks + + # merge every collected masks + mask = utils.combine_masks2(total_masks) + + finally: + sam_obj.release_device() + + if mask is not None: + mask = mask.float() + mask = utils.dilate_mask(mask.cpu().numpy(), dilation) + mask = torch.from_numpy(mask) + else: + size = image.shape[0], image.shape[1] + mask = torch.zeros(size, dtype=torch.float32, device="cpu") # empty mask + + mask = utils.make_3d_mask(mask) + return mask + + +def generate_detection_hints(image, seg, center, detection_hint, dilated_bbox, mask_hint_threshold, use_small_negative, + mask_hint_use_negative): + [x1, y1, x2, y2] = dilated_bbox + + points = [] + plabs = [] + if detection_hint == "center-1": + points.append(center) + plabs = [1] # 1 = foreground point, 0 = background point + + elif detection_hint == "horizontal-2": + gap = (x2 - x1) / 3 + points.append((x1 + gap, center[1])) + points.append((x1 + gap * 2, center[1])) + plabs = [1, 1] + + elif detection_hint == "vertical-2": + gap = (y2 - y1) / 3 + points.append((center[0], y1 + gap)) + points.append((center[0], y1 + gap * 2)) + plabs = [1, 1] + + elif detection_hint == "rect-4": + x_gap = (x2 - x1) / 3 + y_gap = (y2 - y1) / 3 + points.append((x1 + x_gap, center[1])) + points.append((x1 + x_gap * 2, center[1])) + points.append((center[0], y1 + y_gap)) + points.append((center[0], y1 + y_gap * 2)) + plabs = [1, 1, 1, 1] + + elif detection_hint == "diamond-4": + x_gap = (x2 - x1) / 3 + y_gap = (y2 - y1) / 3 + points.append((x1 + x_gap, y1 + y_gap)) + points.append((x1 + x_gap * 2, y1 + y_gap)) + points.append((x1 + x_gap, y1 + y_gap * 2)) + points.append((x1 + x_gap * 2, y1 + y_gap * 2)) + plabs = [1, 1, 1, 1] + + elif detection_hint == "mask-point-bbox": + center = utils.center_of_bbox(seg.bbox) + points.append(center) + plabs = [1] + + elif detection_hint == "mask-area": + points, plabs = gen_detection_hints_from_mask_area(seg.crop_region[0], seg.crop_region[1], + seg.cropped_mask, + mask_hint_threshold, use_small_negative) + + if mask_hint_use_negative == "Outter": + npoints, nplabs = gen_negative_hints(image.shape[0], image.shape[1], + seg.crop_region[0], seg.crop_region[1], + seg.crop_region[2], seg.crop_region[3]) + + points += npoints + plabs += nplabs + + return points, plabs + + +def convert_and_stack_masks(masks): + if len(masks) == 0: + return None + + mask_tensors = [] + for mask in masks: + mask_array = np.array(mask, dtype=np.uint8) + mask_tensor = torch.from_numpy(mask_array) + mask_tensors.append(mask_tensor) + + stacked_masks = torch.stack(mask_tensors, dim=0) + stacked_masks = stacked_masks.unsqueeze(1) + + return stacked_masks + + +def merge_and_stack_masks(stacked_masks, group_size): + if stacked_masks is None: + return None + + num_masks = stacked_masks.size(0) + merged_masks = [] + + for i in range(0, num_masks, group_size): + subset_masks = stacked_masks[i:i + group_size] + merged_mask = torch.any(subset_masks, dim=0) + merged_masks.append(merged_mask) + + if len(merged_masks) > 0: + merged_masks = torch.stack(merged_masks, dim=0) + + return merged_masks + + +def segs_scale_match(segs, target_shape): + h = segs[0][0] + w = segs[0][1] + + th = target_shape[1] + tw = target_shape[2] + + if (h == th and w == tw) or h == 0 or w == 0: + return segs + + rh = th / h + rw = tw / w + + new_segs = [] + for seg in segs[1]: + cropped_image = seg.cropped_image + cropped_mask = seg.cropped_mask + x1, y1, x2, y2 = seg.crop_region + bx1, by1, bx2, by2 = seg.bbox + + crop_region = int(x1*rw), int(y1*rw), int(x2*rh), int(y2*rh) + bbox = int(bx1*rw), int(by1*rw), int(bx2*rh), int(by2*rh) + new_w = crop_region[2] - crop_region[0] + new_h = crop_region[3] - crop_region[1] + + if isinstance(cropped_mask, np.ndarray): + cropped_mask = torch.from_numpy(cropped_mask) + + if isinstance(cropped_mask, torch.Tensor) and len(cropped_mask.shape) == 3: + cropped_mask = torch.nn.functional.interpolate(cropped_mask.unsqueeze(0), size=(new_h, new_w), mode='bilinear', align_corners=False) + cropped_mask = cropped_mask.squeeze(0) + else: + cropped_mask = torch.nn.functional.interpolate(cropped_mask.unsqueeze(0).unsqueeze(0), size=(new_h, new_w), mode='bilinear', align_corners=False) + cropped_mask = cropped_mask.squeeze(0).squeeze(0).numpy() + + if cropped_image is not None: + cropped_image = utils.tensor_resize(cropped_image if isinstance(cropped_image, torch.Tensor) else torch.from_numpy(cropped_image), new_w, new_h) + cropped_image = cropped_image.numpy() + + new_seg = SEG(cropped_image, cropped_mask, seg.confidence, crop_region, bbox, seg.label, seg.control_net_wrapper) + new_segs.append(new_seg) + + return (th, tw), new_segs + + +# Used Python's slicing feature. stacked_masks[2::3] means starting from index 2, selecting every third tensor with a step size of 3. +# This allows for quickly obtaining the last tensor of every three tensors in stacked_masks. +def every_three_pick_last(stacked_masks): + selected_masks = stacked_masks[2::3] + return selected_masks + + +def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation, + threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative): + + if not hasattr(sam, 'sam_wrapper'): + raise Exception("[Impact Pack] Invalid SAMLoader is connected. Make sure 'SAMLoader (Impact)'.") + + sam_obj = sam.sam_wrapper + sam_obj.prepare_device() + + try: + image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + + total_masks = [] + + use_small_negative = mask_hint_use_negative == "Small" + + # seg_shape = segs[0] + segs = segs[1] + if detection_hint == "mask-points": + points = [] + plabs = [] + + for i in range(len(segs)): + bbox = segs[i].bbox + center = utils.center_of_bbox(bbox) + points.append(center) + + # small point is background, big point is foreground + if use_small_negative and bbox[2] - bbox[0] < 10: + plabs.append(0) + else: + plabs.append(1) + + detected_masks = sam_obj.predict(image, points, plabs, None, threshold) + total_masks += detected_masks + + else: + for i in range(len(segs)): + bbox = segs[i].bbox + center = utils.center_of_bbox(bbox) + x1 = max(bbox[0] - bbox_expansion, 0) + y1 = max(bbox[1] - bbox_expansion, 0) + x2 = min(bbox[2] + bbox_expansion, image.shape[1]) + y2 = min(bbox[3] + bbox_expansion, image.shape[0]) + + dilated_bbox = [x1, y1, x2, y2] + + points, plabs = generate_detection_hints(image, segs[i], center, detection_hint, dilated_bbox, + mask_hint_threshold, use_small_negative, + mask_hint_use_negative) + + detected_masks = sam_obj.predict(image, points, plabs, dilated_bbox, threshold) + + total_masks += detected_masks + + # merge every collected masks + mask = utils.combine_masks2(total_masks) + + finally: + sam_obj.release_device() + + mask_working_device = torch.device("cpu") + + if mask is not None: + mask = mask.float() + mask = utils.dilate_mask(mask.cpu().numpy(), dilation) + mask = torch.from_numpy(mask) + mask = mask.to(device=mask_working_device) + else: + # Extracting batch, height and width + height, width, _ = image.shape + mask = torch.zeros( + (height, width), dtype=torch.float32, device=mask_working_device + ) # empty mask + + stacked_masks = convert_and_stack_masks(total_masks) + + return (mask, merge_and_stack_masks(stacked_masks, group_size=3)) + # return every_three_pick_last(stacked_masks) + + +def segs_bitwise_and_mask(segs, mask): + mask = utils.make_2d_mask(mask) + + if mask is None: + logging.warning("[SegsBitwiseAndMask] Cannot operate: MASK is empty.") + return ([],) + + items = [] + + mask = (mask.cpu().numpy() * 255).astype(np.uint8) + + for seg in segs[1]: + cropped_mask = (seg.cropped_mask * 255).astype(np.uint8) + crop_region = seg.crop_region + + cropped_mask2 = mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] + + new_mask = np.bitwise_and(cropped_mask.astype(np.uint8), cropped_mask2) + new_mask = new_mask.astype(np.float32) / 255.0 + + item = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None) + items.append(item) + + return segs[0], items + + +def segs_bitwise_subtract_mask(segs, mask): + mask = utils.make_2d_mask(mask) + + if mask is None: + logging.warning("[SegsBitwiseSubtractMask] Cannot operate: MASK is empty.") + return ([],) + + items = [] + + mask = (mask.cpu().numpy() * 255).astype(np.uint8) + + for seg in segs[1]: + cropped_mask = (seg.cropped_mask * 255).astype(np.uint8) + crop_region = seg.crop_region + + cropped_mask2 = mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] + + new_mask = cv2.subtract(cropped_mask.astype(np.uint8), cropped_mask2) + new_mask = new_mask.astype(np.float32) / 255.0 + + item = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None) + items.append(item) + + return segs[0], items + + +def apply_mask_to_each_seg(segs, masks): + if masks is None: + logging.warning("[SegsBitwiseAndMask] Cannot operate: MASK is empty.") + return (segs[0], [],) + + items = [] + + masks = masks.squeeze(1) + + for seg, mask in zip(segs[1], masks): + cropped_mask = (seg.cropped_mask * 255).astype(np.uint8) + crop_region = seg.crop_region + + cropped_mask2 = (mask.cpu().numpy() * 255).astype(np.uint8) + cropped_mask2 = cropped_mask2[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] + + new_mask = np.bitwise_and(cropped_mask.astype(np.uint8), cropped_mask2) + new_mask = new_mask.astype(np.float32) / 255.0 + + item = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None) + items.append(item) + + return segs[0], items + + +def dilate_segs(segs, factor): + if factor == 0: + return segs + + new_segs = [] + for seg in segs[1]: + new_mask = utils.dilate_mask(seg.cropped_mask, factor) + new_seg = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + new_segs.append(new_seg) + + return (segs[0], new_segs) + + +class ONNXDetector: + onnx_model = None + + def __init__(self, onnx_model): + self.onnx_model = onnx_model + + def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None): + drop_size = max(drop_size, 1) + try: + import impact.impact_onnx as onnx + + h = image.shape[1] + w = image.shape[2] + + labels, scores, boxes = onnx.onnx_inference(image, self.onnx_model) + + # collect feasible item + result = [] + + for i in range(len(labels)): + if scores[i] > threshold: + item_bbox = boxes[i] + x1, y1, x2, y2 = item_bbox + + if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue + crop_region = utils.make_crop_region(w, h, item_bbox, crop_factor) + + if detailer_hook is not None: + crop_region = item_bbox.post_crop_region(w, h, item_bbox, crop_region) + + crop_x1, crop_y1, crop_x2, crop_y2, = crop_region + + # prepare cropped mask + cropped_mask = np.zeros((crop_y2 - crop_y1, crop_x2 - crop_x1)) + cropped_mask[y1 - crop_y1:y2 - crop_y1, x1 - crop_x1:x2 - crop_x1] = 1 + cropped_mask = utils.dilate_mask(cropped_mask, dilation) + + # make items. just convert the integer label to a string + item = SEG(None, cropped_mask, scores[i], crop_region, item_bbox, str(labels[i]), None) + result.append(item) + + shape = h, w + segs = shape, result + + if detailer_hook is not None and hasattr(detailer_hook, "post_detection"): + segs = detailer_hook.post_detection(segs) + + return segs + except Exception as e: + logging.error(f"ONNXDetector: unable to execute.\n{e}") + + def detect_combined(self, image, threshold, dilation): + return segs_to_combined_mask(self.detect(image, threshold, dilation, 1)) + + def setAux(self, x): + pass + + +def batch_mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A', crop_min_size=None, detailer_hook=None): + combined_mask = mask.max(dim=0).values + + segs = mask_to_segs(combined_mask, combined, crop_factor, bbox_fill, drop_size, label, crop_min_size, detailer_hook) + + new_segs = [] + for seg in segs[1]: + x1, y1, x2, y2 = seg.crop_region + cropped_mask = mask[:, y1:y2, x1:x2] + item = SEG(None, cropped_mask, 1.0, seg.crop_region, seg.bbox, label, None) + new_segs.append(item) + + return segs[0], new_segs + + +def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A', crop_min_size=None, detailer_hook=None, is_contour=True): + drop_size = max(drop_size, 1) + if mask is None: + logging.info("[mask_to_segs] Cannot operate: MASK is empty.") + return ([],) + + if isinstance(mask, np.ndarray): + pass # `mask` is already a NumPy array + else: + try: + mask = mask.numpy() + except AttributeError: + logging.info("[mask_to_segs] Cannot operate: MASK is not a NumPy array or Tensor.") + return ([],) + + if mask is None: + logging.info("[mask_to_segs] Cannot operate: MASK is empty.") + return ([],) + + result = [] + + if len(mask.shape) == 2: + mask = np.expand_dims(mask, axis=0) + + for i in range(mask.shape[0]): + mask_i = mask[i] + + if combined: + indices = np.nonzero(mask_i) + if len(indices[0]) > 0 and len(indices[1]) > 0: + bbox = ( + np.min(indices[1]), + np.min(indices[0]), + np.max(indices[1]), + np.max(indices[0]), + ) + crop_region = utils.make_crop_region( + mask_i.shape[1], mask_i.shape[0], bbox, crop_factor + ) + x1, y1, x2, y2 = crop_region + + if detailer_hook is not None: + crop_region = detailer_hook.post_crop_region(mask_i.shape[1], mask_i.shape[0], bbox, crop_region) + + if x2 - x1 > 0 and y2 - y1 > 0: + cropped_mask = mask_i[y1:y2, x1:x2] + + if bbox_fill: + bx1, by1, bx2, by2 = bbox + cropped_mask = cropped_mask.copy() + cropped_mask[by1:by2, bx1:bx2] = 1.0 + + if cropped_mask is not None: + item = SEG(None, cropped_mask, 1.0, crop_region, bbox, label, None) + result.append(item) + + else: + mask_i_uint8 = (mask_i * 255.0).astype(np.uint8) + contours, ctree = cv2.findContours(mask_i_uint8, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + for j, contour in enumerate(contours): + hierarchy = ctree[0][j] + if hierarchy[3] != -1: + continue + + separated_mask = np.zeros_like(mask_i_uint8) + cv2.drawContours(separated_mask, [contour], 0, 255, -1) + separated_mask = np.array(separated_mask / 255.0).astype(np.float32) + + x, y, w, h = cv2.boundingRect(contour) + bbox = x, y, x + w, y + h + crop_region = utils.make_crop_region( + mask_i.shape[1], mask_i.shape[0], bbox, crop_factor, crop_min_size + ) + + if detailer_hook is not None: + crop_region = detailer_hook.post_crop_region(mask_i.shape[1], mask_i.shape[0], bbox, crop_region) + + if w > drop_size and h > drop_size: + if is_contour: + mask_src = separated_mask + else: + mask_src = mask_i * separated_mask + + cropped_mask = np.array( + mask_src[ + crop_region[1]: crop_region[3], + crop_region[0]: crop_region[2], + ] + ) + + if bbox_fill: + cx1, cy1, _, _ = crop_region + bx1 = x - cx1 + bx2 = x+w - cx1 + by1 = y - cy1 + by2 = y+h - cy1 + cropped_mask[by1:by2, bx1:bx2] = 1.0 + + if cropped_mask is not None: + cropped_mask = torch.clip(torch.from_numpy(cropped_mask), 0, 1.0) + item = SEG(None, cropped_mask.numpy(), 1.0, crop_region, bbox, label, None) + result.append(item) + + if not result: + logging.info("[mask_to_segs] Empty mask.") + + logging.info(f"# of Detected SEGS: {len(result)}") + # for r in result: + # print(f"\tbbox={r.bbox}, crop={r.crop_region}, label={r.label}") + + # shape: (b,h,w) -> (h,w) + return (mask.shape[1], mask.shape[2]), result + + +def mediapipe_facemesh_to_segs(image, crop_factor, bbox_fill, crop_min_size, drop_size, dilation, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil): + parts = { + "face": np.array([0x0A, 0xC8, 0x0A]), + "mouth": np.array([0x0A, 0xB4, 0x0A]), + "left_eyebrow": np.array([0xB4, 0xDC, 0x0A]), + "left_eye": np.array([0xB4, 0xC8, 0x0A]), + "left_pupil": np.array([0xFA, 0xC8, 0x0A]), + "right_eyebrow": np.array([0x0A, 0xDC, 0xB4]), + "right_eye": np.array([0x0A, 0xC8, 0xB4]), + "right_pupil": np.array([0x0A, 0xC8, 0xFA]), + } + + def create_segments(image, color): + image = (image * 255).to(torch.uint8) + image = image.squeeze(0).numpy() + mask = cv2.inRange(image, color, color) + + contours, ctree = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + mask_list = [] + for i, contour in enumerate(contours): + hierarchy = ctree[0][i] + if hierarchy[3] == -1: + convex_hull = cv2.convexHull(contour) + convex_segment = np.zeros_like(image) + cv2.fillPoly(convex_segment, [convex_hull], (255, 255, 255)) + + convex_segment = np.expand_dims(convex_segment, axis=0).astype(np.float32) / 255.0 + tensor = torch.from_numpy(convex_segment) + mask_tensor = torch.any(tensor != 0, dim=-1).float() + mask_tensor = mask_tensor.squeeze(0) + mask_tensor = torch.from_numpy(utils.dilate_mask(mask_tensor.numpy(), dilation)) + mask_list.append(mask_tensor.unsqueeze(0)) + + return mask_list + + segs = [] + + def create_seg(label): + mask_list = create_segments(image, parts[label]) + for mask in mask_list: + seg = mask_to_segs(mask, False, crop_factor, bbox_fill, drop_size=drop_size, label=label, crop_min_size=crop_min_size) + if len(seg[1]) > 0: + segs.extend(seg[1]) + + if face: + create_seg('face') + + if mouth: + create_seg('mouth') + + if left_eyebrow: + create_seg('left_eyebrow') + + if left_eye: + create_seg('left_eye') + + if left_pupil: + create_seg('left_pupil') + + if right_eyebrow: + create_seg('right_eyebrow') + + if right_eye: + create_seg('right_eye') + + if right_pupil: + create_seg('right_pupil') + + return (image.shape[1], image.shape[2]), segs + + +def segs_to_combined_mask(segs): + shape = segs[0] + h = shape[0] + w = shape[1] + + mask = np.zeros((h, w), dtype=np.uint8) + + for seg in segs[1]: + cropped_mask = seg.cropped_mask + crop_region = seg.crop_region + mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] |= (cropped_mask * 255).astype(np.uint8) + + return torch.from_numpy(mask.astype(np.float32) / 255.0) + + +def segs_to_masklist(segs): + shape = segs[0] + h = shape[0] + w = shape[1] + + masks = [] + for seg in segs[1]: + if isinstance(seg.cropped_mask, np.ndarray): + cropped_mask = torch.from_numpy(seg.cropped_mask) + else: + cropped_mask = seg.cropped_mask + + if cropped_mask.ndim == 2: + cropped_mask = cropped_mask.unsqueeze(0) + + n = len(cropped_mask) + + mask = torch.zeros((n, h, w), dtype=torch.uint8) + crop_region = seg.crop_region + mask[:, crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] |= (cropped_mask * 255).to(torch.uint8) + mask = (mask / 255.0).to(torch.float32) + + for x in mask: + masks.append(x) + + if len(masks) == 0: + empty_mask = torch.zeros((h, w), dtype=torch.float32, device="cpu") + masks = [empty_mask] + + return masks + + +def vae_decode(vae, samples, use_tile, hook, tile_size=512, overlap=64): + if use_tile: + decoder = nodes.VAEDecodeTiled() + if 'overlap' in inspect.signature(decoder.decode).parameters: + pixels = decoder.decode(vae, samples, tile_size, overlap=overlap)[0] + else: + logging.warning("[Impact Pack] Your ComfyUI is outdated.") + pixels = decoder.decode(vae, samples, tile_size)[0] + else: + pixels = nodes.VAEDecode().decode(vae, samples)[0] + + if hook is not None: + pixels = hook.post_decode(pixels) + + return pixels + + +def vae_encode(vae, pixels, use_tile, hook, tile_size=512, overlap=64): + if use_tile: + encoder = nodes.VAEEncodeTiled() + if 'overlap' in inspect.signature(encoder.encode).parameters: + samples = encoder.encode(vae, pixels, tile_size, overlap=overlap)[0] + else: + logging.warning("[Impact Pack] Your ComfyUI is outdated.") + samples = encoder.encode(vae, pixels, tile_size)[0] + else: + samples = nodes.VAEEncode().encode(vae, pixels)[0] + + if hook is not None: + samples = hook.post_encode(samples) + + return samples + + +def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + return latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0] + + +def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap) + + if save_temp_prefix is not None: + nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix) + + pixels = nodes.ImageScale().upscale(pixels, scale_method, int(w), int(h), False)[0] + + old_pixels = pixels + if hook is not None: + pixels = hook.post_upscale(pixels) + + return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels + + +def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0] + + +def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap) + + if save_temp_prefix is not None: + nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix) + + w = pixels.shape[2] * scale_factor + h = pixels.shape[1] * scale_factor + pixels = nodes.ImageScale().upscale(pixels, scale_method, int(w), int(h), False)[0] + + old_pixels = pixels + if hook is not None: + pixels = hook.post_upscale(pixels) + + return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels + + +def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + return latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0] + + +def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap) + + if save_temp_prefix is not None: + nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix) + + w = pixels.shape[2] + + # upscale by model upscaler + current_w = w + while current_w < new_w: + model_upscaler = nodes.NODE_CLASS_MAPPINGS['ImageUpscaleWithModel']() + if hasattr(model_upscaler, 'execute'): + pixels = model_upscaler.execute(upscale_model, pixels)[0] + else: + pixels = model_upscaler.upscale(upscale_model, pixels)[0] + + current_w = pixels.shape[2] + if current_w == w: + logging.info("[latent_upscale_on_pixel_space_with_model] x1 upscale model selected") + break + + # downscale to target scale + pixels = nodes.ImageScale().upscale(pixels, scale_method, int(new_w), int(new_h), False)[0] + + old_pixels = pixels + if hook is not None: + pixels = hook.post_upscale(pixels) + + return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels + + +def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False, + tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0] + +def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False, + tile_size=512, save_temp_prefix=None, hook=None, overlap=64): + pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap) + + if save_temp_prefix is not None: + nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix) + + w = pixels.shape[2] + h = pixels.shape[1] + + new_w = w * scale_factor + new_h = h * scale_factor + + # upscale by model upscaler + current_w = w + while current_w < new_w: + model_upscaler = nodes.NODE_CLASS_MAPPINGS['ImageUpscaleWithModel']() + if hasattr(model_upscaler, 'execute'): + pixels = model_upscaler.execute(upscale_model, pixels)[0] + else: + pixels = model_upscaler.upscale(upscale_model, pixels)[0] + + current_w = pixels.shape[2] + if current_w == w: + logging.info("[latent_upscale_on_pixel_space_with_model] x1 upscale model selected") + break + + # downscale to target scale + pixels = nodes.ImageScale().upscale(pixels, scale_method, int(new_w), int(new_h), False)[0] + + old_pixels = pixels + if hook is not None: + pixels = hook.post_upscale(pixels) + + return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels + + +class TwoSamplersForMaskUpscaler: + def __init__(self, scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae, + full_sampler_opt=None, upscale_model_opt=None, hook_base_opt=None, hook_mask_opt=None, + hook_full_opt=None, + tile_size=512): + + mask = utils.make_2d_mask(mask) + + mask = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + + self.params = scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae + self.upscale_model = upscale_model_opt + self.full_sampler = full_sampler_opt + self.hook_base = hook_base_opt + self.hook_mask = hook_mask_opt + self.hook_full = hook_full_opt + self.use_tiled_vae = use_tiled_vae + self.tile_size = tile_size + self.is_tiled = False + self.vae = vae + + def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None): + scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae = self.params + + mask = utils.make_2d_mask(mask) + + self.prepare_hook(step_info) + + # upscale latent + if self.upscale_model is None: + upscaled_latent = latent_upscale_on_pixel_space(samples, scale_method, upscale_factor, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, + hook=self.hook_base, tile_size=self.tile_size) + else: + upscaled_latent = latent_upscale_on_pixel_space_with_model(samples, scale_method, self.upscale_model, + upscale_factor, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, + hook=self.hook_mask, tile_size=self.tile_size) + + return self.do_samples(step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent) + + def prepare_hook(self, step_info): + if self.hook_base is not None: + self.hook_base.set_steps(step_info) + if self.hook_mask is not None: + self.hook_mask.set_steps(step_info) + if self.hook_full is not None: + self.hook_full.set_steps(step_info) + + def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None): + scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae = self.params + + mask = utils.make_2d_mask(mask) + + self.prepare_hook(step_info) + + # upscale latent + if self.upscale_model is None: + upscaled_latent = latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, + hook=self.hook_base, + tile_size=self.tile_size) + else: + upscaled_latent = latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, self.upscale_model, + w, h, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, + hook=self.hook_mask, + tile_size=self.tile_size) + + return self.do_samples(step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent) + + def is_full_sample_time(self, step_info, sample_schedule): + cur_step, total_step = step_info + + # make start from 1 instead of zero + cur_step += 1 + total_step += 1 + + if sample_schedule == "none": + return False + + elif sample_schedule == "interleave1": + return cur_step % 2 == 0 + + elif sample_schedule == "interleave2": + return cur_step % 3 == 0 + + elif sample_schedule == "interleave3": + return cur_step % 4 == 0 + + elif sample_schedule == "last1": + return cur_step == total_step + + elif sample_schedule == "last2": + return cur_step >= total_step - 1 + + elif sample_schedule == "interleave1+last1": + return cur_step % 2 == 0 or cur_step >= total_step - 1 + + elif sample_schedule == "interleave2+last1": + return cur_step % 2 == 0 or cur_step >= total_step - 1 + + elif sample_schedule == "interleave3+last1": + return cur_step % 2 == 0 or cur_step >= total_step - 1 + + def do_samples(self, step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent): + mask = utils.make_2d_mask(mask) + + if self.is_full_sample_time(step_info, sample_schedule): + logging.info(f"step_info={step_info} / full time") + + upscaled_latent = base_sampler.sample(upscaled_latent, self.hook_base) + sampler = self.full_sampler if self.full_sampler is not None else base_sampler + return sampler.sample(upscaled_latent, self.hook_full) + + else: + logging.info(f"step_info={step_info} / non-full time") + # upscale mask + if mask.ndim == 2: + mask = mask[None, :, :, None] + upscaled_mask = F.interpolate(mask, size=(upscaled_latent['samples'].shape[2], upscaled_latent['samples'].shape[3]), mode='bilinear', align_corners=True) + upscaled_mask = upscaled_mask[:, :, :upscaled_latent['samples'].shape[2], :upscaled_latent['samples'].shape[3]] + + # base sampler + upscaled_inv_mask = torch.where(upscaled_mask != 1.0, torch.tensor(1.0), torch.tensor(0.0)) + upscaled_latent['noise_mask'] = upscaled_inv_mask + upscaled_latent = base_sampler.sample(upscaled_latent, self.hook_base) + + # mask sampler + upscaled_latent['noise_mask'] = upscaled_mask + upscaled_latent = mask_sampler.sample(upscaled_latent, self.hook_mask) + + # remove mask + del upscaled_latent['noise_mask'] + return upscaled_latent + + +class PixelKSampleUpscaler: + def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, + use_tiled_vae, upscale_model_opt=None, hook_opt=None, tile_size=512, scheduler_func=None, + tile_cnet_opt=None, tile_cnet_strength=1.0): + self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise + self.upscale_model = upscale_model_opt + self.hook = hook_opt + self.use_tiled_vae = use_tiled_vae + self.tile_size = tile_size + self.is_tiled = False + self.vae = vae + self.scheduler_func = scheduler_func + self.tile_cnet = tile_cnet_opt + self.tile_cnet_strength = tile_cnet_strength + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, images): + if self.tile_cnet is not None: + image_batch, image_w, image_h, _ = images.shape + if image_batch > 1: + warnings.warn('Multiple latents in batch, Tile ControlNet being ignored') + else: + if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + raise RuntimeError("'TilePreprocessor' node (from comfyui_controlnet_aux) isn't installed.") + preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']() + # might add capacity to set pyrUp_iters later, not needed for now though + preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0] + positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive, + negative=negative, + control_net=self.tile_cnet, + image=preprocessed, + strength=self.tile_cnet_strength, + start_percent=0, + end_percent=1.0, + vae=self.vae) + + refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler, + positive, negative, upscaled_latent, denoise, scheduler_func=self.scheduler_func) + + return refined_latent + + def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None): + scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params + + if self.hook is not None: + self.hook.set_steps(step_info) + + if self.upscale_model is None: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space2(samples, scale_method, upscale_factor, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, hook=self.hook, tile_size=512) + else: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space_with_model2(samples, scale_method, self.upscale_model, + upscale_factor, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, + hook=self.hook, + tile_size=self.tile_size) + + if self.hook is not None: + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \ + self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, + upscaled_latent, denoise) + + if 'noise_mask' in samples: + upscaled_latent['noise_mask'] = samples['noise_mask'] + + refined_latent = self.sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, upscaled_images) + return refined_latent + + def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None): + scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params + + if self.hook is not None: + self.hook.set_steps(step_info) + + if self.upscale_model is None: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, hook=self.hook, + tile_size=self.tile_size) + else: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, self.upscale_model, + w, h, vae, + use_tile=self.use_tiled_vae, + save_temp_prefix=save_temp_prefix, + hook=self.hook, + tile_size=self.tile_size) + + if self.hook is not None: + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \ + self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, + upscaled_latent, denoise) + + if 'noise_mask' in samples: + upscaled_latent['noise_mask'] = samples['noise_mask'] + + refined_latent = self.sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, upscaled_images) + return refined_latent + + +class IPAdapterWrapper: + def __init__(self, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, weight_v2, reference_image, neg_image=None, prev_control_net=None, combine_embeds='concat'): + self.reference_image = reference_image + self.ipadapter_pipe = ipadapter_pipe + self.weight = weight + self.weight_type = weight_type + self.noise = noise + self.start_at = start_at + self.end_at = end_at + self.unfold_batch = unfold_batch + self.prev_control_net = prev_control_net + self.weight_v2 = weight_v2 + self.image = reference_image + self.neg_image = neg_image + self.combine_embeds = combine_embeds + + # name 'apply_ipadapter' isn't allowed + def doit_ipadapter(self, model): + cnet_image_list = [self.image] + prev_cnet_images = [] + + if 'IPAdapterAdvanced' not in nodes.NODE_CLASS_MAPPINGS: + if 'IPAdapterApply' in nodes.NODE_CLASS_MAPPINGS: + raise Exception("[ERROR] 'ComfyUI IPAdapter Plus' is outdated.") + + utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus', + "To use 'IPAdapterApplySEGS' node, 'ComfyUI IPAdapter Plus' extension is required.") + raise Exception("[ERROR] To use IPAdapterApplySEGS, you need to install 'ComfyUI IPAdapter Plus'") + + obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced'] + + ipadapter, _, clip_vision, insightface, lora_loader = self.ipadapter_pipe + model = lora_loader(model) + + if self.prev_control_net is not None: + model, prev_cnet_images = self.prev_control_net.doit_ipadapter(model) + + model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, weight=self.weight, weight_type=self.weight_type, + start_at=self.start_at, end_at=self.end_at, combine_embeds=self.combine_embeds, + clip_vision=clip_vision, image=self.image, image_negative=self.neg_image, attn_mask=None, + insightface=insightface, weight_faceidv2=self.weight_v2)[0] + + cnet_image_list.extend(prev_cnet_images) + + return model, cnet_image_list + + def apply(self, positive, negative, image, mask=None, use_acn=False): + if self.prev_control_net is not None: + return self.prev_control_net.apply(positive, negative, image, mask, use_acn=use_acn) + else: + return positive, negative, [] + + +class ControlNetWrapper: + def __init__(self, control_net, strength, preprocessor, prev_control_net=None, original_size=None, crop_region=None, control_image=None): + self.control_net = control_net + self.strength = strength + self.preprocessor = preprocessor + self.prev_control_net = prev_control_net + + if original_size is not None and crop_region is not None and control_image is not None: + self.control_image = utils.tensor_resize(control_image, original_size[1], original_size[0]) + self.control_image = torch.tensor(utils.tensor_crop(self.control_image, crop_region)) + else: + self.control_image = None + + def apply(self, positive, negative, image, mask=None, use_acn=False): + cnet_image_list = [] + prev_cnet_images = [] + + if self.prev_control_net is not None: + positive, negative, prev_cnet_images = self.prev_control_net.apply(positive, negative, image, mask, use_acn=use_acn) + + if self.control_image is not None: + cnet_image = self.control_image + elif self.preprocessor is not None: + cnet_image = self.preprocessor.apply(image, mask) + else: + cnet_image = image + + cnet_image_list.extend(prev_cnet_images) + cnet_image_list.append(cnet_image) + + if use_acn: + if "ACN_AdvancedControlNetApply" in nodes.NODE_CLASS_MAPPINGS: + acn = nodes.NODE_CLASS_MAPPINGS['ACN_AdvancedControlNetApply']() + positive, negative, _ = acn.apply_controlnet(positive=positive, negative=negative, control_net=self.control_net, image=cnet_image, + strength=self.strength, start_percent=0.0, end_percent=1.0) + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler', + "To use 'ControlNetWrapper' for AnimateDiff, 'ComfyUI-Advanced-ControlNet' extension is required.") + raise Exception("'ACN_AdvancedControlNetApply' node isn't installed.") + else: + positive = nodes.ControlNetApply().apply_controlnet(positive, self.control_net, cnet_image, self.strength)[0] + + return positive, negative, cnet_image_list + + def doit_ipadapter(self, model): + if self.prev_control_net is not None: + return self.prev_control_net.doit_ipadapter(model) + else: + return model, [] + + +class ControlNetAdvancedWrapper: + def __init__(self, control_net, strength, start_percent, end_percent, preprocessor, prev_control_net=None, + original_size=None, crop_region=None, control_image=None, vae=None): + self.control_net = control_net + self.strength = strength + self.preprocessor = preprocessor + self.prev_control_net = prev_control_net + self.start_percent = start_percent + self.end_percent = end_percent + self.vae = vae + + if original_size is not None and crop_region is not None and control_image is not None: + self.control_image = utils.tensor_resize(control_image, original_size[1], original_size[0]) + self.control_image = torch.tensor(utils.tensor_crop(self.control_image, crop_region)) + else: + self.control_image = None + + def doit_ipadapter(self, model): + if self.prev_control_net is not None: + return self.prev_control_net.doit_ipadapter(model) + else: + return model, [] + + def apply(self, positive, negative, image, mask=None, use_acn=False): + cnet_image_list = [] + prev_cnet_images = [] + + if self.prev_control_net is not None: + positive, negative, prev_cnet_images = self.prev_control_net.apply(positive, negative, image, mask) + + if self.control_image is not None: + cnet_image = self.control_image + elif self.preprocessor is not None: + cnet_image = self.preprocessor.apply(image, mask) + else: + cnet_image = image + + cnet_image_list.extend(prev_cnet_images) + cnet_image_list.append(cnet_image) + + if use_acn: + if "ACN_AdvancedControlNetApply" in nodes.NODE_CLASS_MAPPINGS: + acn = nodes.NODE_CLASS_MAPPINGS['ACN_AdvancedControlNetApply']() + positive, negative, _ = acn.apply_controlnet(positive=positive, negative=negative, control_net=self.control_net, image=cnet_image, + strength=self.strength, start_percent=self.start_percent, end_percent=self.end_percent) + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler', + "To use 'ControlNetAdvancedWrapper' for AnimateDiff, 'ComfyUI-Advanced-ControlNet' extension is required.") + raise Exception("'ACN_AdvancedControlNetApply' node isn't installed.") + else: + if self.vae is not None: + apply_controlnet = nodes.ControlNetApplyAdvanced().apply_controlnet + signature = inspect.signature(apply_controlnet) + + if 'vae' in signature.parameters: + positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent, vae=self.vae) + else: + logging.error("[Impact Pack] ERROR: The ComfyUI version is outdated. VAE cannot be used in ApplyControlNet.") + raise Exception("[Impact Pack] ERROR: The ComfyUI version is outdated. VAE cannot be used in ApplyControlNet.") + else: + positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent) + + return positive, negative, cnet_image_list + + +# REQUIREMENTS: BlenderNeko/ComfyUI_TiledKSampler +class TiledKSamplerWrapper: + params = None + + def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, + tile_width, tile_height, tiling_strategy): + self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy + + def sample(self, latent_image, hook=None): + if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS: + TiledKSampler = nodes.NODE_CLASS_MAPPINGS['BNK_TiledKSampler'] + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler', + "To use 'TiledKSamplerProvider', 'Tiled sampling for ComfyUI' extension is required.") + raise Exception("'BNK_TiledKSampler' node isn't installed.") + + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy = self.params + + if hook is not None: + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \ + hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise) + + return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, + scheduler, positive, negative, latent_image, denoise)[0] + + +class PixelTiledKSampleUpscaler: + def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, + denoise, + tile_width, tile_height, tiling_strategy, + upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0, overlap=64): + self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise + self.vae = vae + self.tile_params = tile_width, tile_height, tiling_strategy + self.upscale_model = upscale_model_opt + self.hook = hook_opt + self.tile_cnet = tile_cnet_opt + self.tile_size = tile_size + self.is_tiled = True + self.tile_cnet_strength = tile_cnet_strength + self.overlap = overlap + + def tiled_ksample(self, latent, images): + if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS: + TiledKSampler = nodes.NODE_CLASS_MAPPINGS['BNK_TiledKSampler'] + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler', + "To use 'PixelTiledKSampleUpscalerProvider', 'Tiled sampling for ComfyUI' extension is required.") + raise RuntimeError("'BNK_TiledKSampler' node isn't installed.") + + scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params + tile_width, tile_height, tiling_strategy = self.tile_params + + if self.tile_cnet is not None: + image_batch, image_w, image_h, _ = images.shape + if image_batch > 1: + warnings.warn('Multiple latents in batch, Tile ControlNet being ignored') + else: + if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + raise RuntimeError("'TilePreprocessor' node (from comfyui_controlnet_aux) isn't installed.") + preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']() + # might add capacity to set pyrUp_iters later, not needed for now though + preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0] + + positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive, + negative=negative, + control_net=self.tile_cnet, + image=preprocessed, + strength=self.tile_cnet_strength, + start_percent=0, end_percent=1.0, + vae=self.vae) + + return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, + scheduler, positive, negative, latent, denoise)[0] + + def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None): + scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params + + if self.hook is not None: + self.hook.set_steps(step_info) + + if self.upscale_model is None: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space2(samples, scale_method, upscale_factor, vae, + use_tile=True, save_temp_prefix=save_temp_prefix, + hook=self.hook, tile_size=self.tile_size) + else: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space_with_model2(samples, scale_method, self.upscale_model, + upscale_factor, vae, use_tile=True, + save_temp_prefix=save_temp_prefix, + hook=self.hook, tile_size=self.tile_size) + + refined_latent = self.tiled_ksample(upscaled_latent, upscaled_images) + + return refined_latent + + def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None): + scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params + + if self.hook is not None: + self.hook.set_steps(step_info) + + if self.upscale_model is None: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, + use_tile=True, save_temp_prefix=save_temp_prefix, + hook=self.hook, tile_size=self.tile_size) + else: + upscaled_latent, upscaled_images = \ + latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, + self.upscale_model, w, h, vae, + use_tile=True, + save_temp_prefix=save_temp_prefix, + hook=self.hook, + tile_size=self.tile_size) + + refined_latent = self.tiled_ksample(upscaled_latent, upscaled_images) + + return refined_latent + + +# REQUIREMENTS: biegert/ComfyUI-CLIPSeg +class BBoxDetectorBasedOnCLIPSeg: + prompt = None + blur = None + threshold = None + dilation_factor = None + aux = None + + def __init__(self, prompt, blur, threshold, dilation_factor): + self.prompt = prompt + self.blur = blur + self.threshold = threshold + self.dilation_factor = dilation_factor + + def detect(self, image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size=1, detailer_hook=None): + mask = self.detect_combined(image, bbox_threshold, bbox_dilation) + + mask = utils.make_2d_mask(mask) + + segs = mask_to_segs(mask, False, bbox_crop_factor, True, drop_size, detailer_hook=detailer_hook) + + if detailer_hook is not None and hasattr(detailer_hook, "post_detection"): + segs = detailer_hook.post_detection(segs) + + return segs + + def detect_combined(self, image, bbox_threshold, bbox_dilation): + if "CLIPSeg" in nodes.NODE_CLASS_MAPPINGS: + CLIPSeg = nodes.NODE_CLASS_MAPPINGS['CLIPSeg'] + else: + utils.try_install_custom_node('https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py', + "To use 'CLIPSegDetectorProvider', 'CLIPSeg' extension is required.") + raise Exception("'CLIPSeg' node isn't installed.") + + if self.threshold is None: + threshold = bbox_threshold + else: + threshold = self.threshold + + if self.dilation_factor is None: + dilation_factor = bbox_dilation + else: + dilation_factor = self.dilation_factor + + prompt = self.aux if self.prompt == '' and self.aux is not None else self.prompt + + mask, _, _ = CLIPSeg().segment_image(image, prompt, self.blur, threshold, dilation_factor) + mask = utils.to_binary_mask(mask) + return mask + + def setAux(self, x): + self.aux = x + + +def update_node_status(node, text, progress=None): + if PromptServer.instance.client_id is None: + return + + PromptServer.instance.send_sync("impact/update_status", { + "node": node, + "progress": progress, + "text": text + }, PromptServer.instance.client_id) + + +def random_mask_raw(mask, bbox, factor): + x1, y1, x2, y2 = bbox + w = x2 - x1 + h = y2 - y1 + + factor = max(6, int(min(w, h) * factor / 4)) + + def draw_random_circle(center, radius): + i, j = center + for x in range(int(i - radius), int(i + radius)): + for y in range(int(j - radius), int(j + radius)): + if np.linalg.norm(np.array([x, y]) - np.array([i, j])) <= radius: + mask[x, y] = 1 + + def draw_irregular_line(start, end, pivot, is_vertical): + i = start + while i < end: + base_radius = np.random.randint(5, factor) + radius = int(base_radius) + + if is_vertical: + draw_random_circle((i, pivot), radius) + else: + draw_random_circle((pivot, i), radius) + + i += radius + + def draw_irregular_line_parallel(start, end, pivot, is_vertical): + with ThreadPoolExecutor(max_workers=16) as executor: + futures = [] + step = (end - start) // 16 + for i in range(start, end, step): + future = executor.submit(draw_irregular_line, i, min(i + step, end), pivot, is_vertical) + futures.append(future) + + for future in futures: + future.result() + + draw_irregular_line_parallel(y1 + factor, y2 - factor, x1 + factor, True) + draw_irregular_line_parallel(y1 + factor, y2 - factor, x2 - factor, True) + draw_irregular_line_parallel(x1 + factor, x2 - factor, y1 + factor, False) + draw_irregular_line_parallel(x1 + factor, x2 - factor, y2 - factor, False) + + mask[y1 + factor:y2 - factor, x1 + factor:x2 - factor] = 1.0 + + +def random_mask(mask, bbox, factor, size=128): + small_mask = np.zeros((size, size)).astype(np.float32) + random_mask_raw(small_mask, (0, 0, size, size), factor) + + x1, y1, x2, y2 = bbox + small_mask = torch.tensor(small_mask).unsqueeze(0).unsqueeze(0) + bbox_mask = torch.nn.functional.interpolate(small_mask, size=(y2 - y1, x2 - x1), mode='bilinear', align_corners=False) + bbox_mask = bbox_mask.squeeze(0).squeeze(0) + mask[y1:y2, x1:x2] = bbox_mask + + +def adaptive_mask_paste(dest_mask, src_mask, bbox): + x1, y1, x2, y2 = bbox + small_mask = torch.tensor(src_mask).unsqueeze(0).unsqueeze(0) + bbox_mask = torch.nn.functional.interpolate(small_mask, size=(y2 - y1, x2 - x1), mode='bilinear', align_corners=False) + bbox_mask = bbox_mask.squeeze(0).squeeze(0) + dest_mask[y1:y2, x1:x2] = bbox_mask + + +def crop_condition_mask(mask, image, crop_region): + cond_scale = (mask.shape[1] / image.shape[1], mask.shape[2] / image.shape[2]) + mask_region = [round(v * cond_scale[i % 2]) for i, v in enumerate(crop_region)] + return utils.crop_ndarray3(mask, mask_region) + + +class SafeToGPU: + def __init__(self, size): + self.size = size + + def to_device(self, obj, device): + if utils.is_same_device(device, 'cpu'): + obj.to(device) + else: + if utils.is_same_device(obj.device, 'cpu'): # cpu to gpu + model_management.free_memory(self.size * 1.3, device) + if model_management.get_free_memory(device) > self.size * 1.3: + try: + obj.to(device) + except Exception: + logging.warning(f"[Impact Pack] The model is not moved to the '{device}' due to insufficient memory. [1]") + else: + logging.warning(f"[Impact Pack] The model is not moved to the '{device}' due to insufficient memory. [2]") + + +class SafeToGPU_stub(): + def to_device(self, obj, device): + pass + + +from comfy.cli_args import args, LatentPreviewMethod +import folder_paths +from latent_preview import TAESD, TAESDPreviewerImpl, Latent2RGBPreviewer + +try: + import comfy.latent_formats as latent_formats + + + def get_previewer(device, latent_format=latent_formats.SD15(), force=False, method=None): + previewer = None + + if method is None: + method = args.preview_method + + if method != LatentPreviewMethod.NoPreviews or force: + # TODO previewer methods + taesd_decoder_path = None + + if hasattr(latent_format, "taesd_decoder_path"): + taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name) + + if method == LatentPreviewMethod.Auto: + method = LatentPreviewMethod.Latent2RGB + if taesd_decoder_path: + method = LatentPreviewMethod.TAESD + + if method == LatentPreviewMethod.TAESD: + if taesd_decoder_path: + taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device) + previewer = TAESDPreviewerImpl(taesd) + else: + logging.warning("[Impact Pack] TAESD previews enabled, but could not find models/vae_approx/{}".format( + latent_format.taesd_decoder_name)) + + if previewer is None: + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors) + return previewer + +except Exception: + logging.error("#########################################################################") + logging.error("[ERROR] ComfyUI-Impact-Pack: Please update ComfyUI to the latest version.") + logging.error("#########################################################################") diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/defs.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/defs.py new file mode 100644 index 00000000..39b099cf --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/defs.py @@ -0,0 +1,17 @@ +detection_labels = [ + 'hand', 'face', 'mouth', 'eyes', 'eyebrows', 'pupils', + 'left_eyebrow', 'left_eye', 'left_pupil', 'right_eyebrow', 'right_eye', 'right_pupil', + 'short_sleeved_shirt', 'long_sleeved_shirt', 'short_sleeved_outwear', 'long_sleeved_outwear', + 'vest', 'sling', 'shorts', 'trousers', 'skirt', 'short_sleeved_dress', 'long_sleeved_dress', 'vest_dress', 'sling_dress', + "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", + "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", + "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", + "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", + "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", + "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", + "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", + "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", + "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", + "hair drier", "toothbrush" + ] diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/detectors.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/detectors.py new file mode 100644 index 00000000..05a9a8d6 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/detectors.py @@ -0,0 +1,560 @@ +import logging + +import impact.core as core +from nodes import MAX_RESOLUTION +import impact.segs_nodes as segs_nodes +import impact.utils as utils +import torch +from impact.core import SEG + +SAM_MODEL_TOOLTIP = {"tooltip": "Segment Anything Model for Silhouette Detection.\nBe sure to use the SAM_MODEL loaded through the SAMLoader (Impact) node as input."} +SAM_MODEL_TOOLTIP_OPTIONAL = {"tooltip": "[OPTIONAL]\nSegment Anything Model for Silhouette Detection.\nBe sure to use the SAM_MODEL loaded through the SAMLoader (Impact) node as input.\nGiven this input, it refines the rectangular areas detected by BBOX_DETECTOR into silhouette shapes through SAM.\nsam_model_opt takes priority over segm_detector_opt."} + +MASK_HINT_THRESHOLD_TOOLTIP = "When detection_hint is mask-area, the mask of SEGS is used as a point hint for SAM (Segment Anything).\nIn this case, only the areas of the mask with brightness values equal to or greater than mask_hint_threshold are used as hints." +MASK_HINT_USE_NEGATIVE_TOOLTIP = "When detecting with SAM (Segment Anything), negative hints are applied as follows:\nSmall: When the SEGS is smaller than 10 pixels in size\nOuter: Sampling the image area outside the SEGS region at regular intervals" + +DILATION_TOOLTIP = "Set the value to dilate the result mask. If the value is negative, it erodes the mask." +DETECTION_HINT_TOOLTIP = {"tooltip": "It is recommended to use only center-1.\nWhen refining the mask of SEGS with the SAM (Segment Anything) model, center-1 uses only the rectangular area of SEGS and a single point at the exact center as hints.\nOther options were added during the experimental stage and do not work well."} + +BBOX_EXPANSION_TOOLTIP = "When performing SAM (Segment Anything) detection within the SEGS area, the rectangular area of SEGS is expanded and used as a hint." + +class SAMDetectorCombined: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sam_model": ("SAM_MODEL", SAM_MODEL_TOOLTIP), + "segs": ("SEGS", {"tooltip": "This is the segment information detected by the detector.\nIt refines the Mask through the SAM (Segment Anything) detector for all areas pointed to by SEGS, and combines all Masks to return as a single Mask."}), + "image": ("IMAGE", {"tooltip": "It is assumed that segs contains only the information about the detected areas, and does not include the image. SAM (Segment Anything) operates by referencing this image."}), + "detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", + "mask-points", "mask-point-bbox", "none"], DETECTION_HINT_TOOLTIP), + "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1, "tooltip": DILATION_TOOLTIP}), + "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Set the sensitivity threshold for the mask detected by SAM (Segment Anything). A higher value generates a more specific mask with a narrower range. For example, when pointing to a person's area, it might detect clothes, which is a narrower range, instead of the entire person."}), + "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": BBOX_EXPANSION_TOOLTIP}), + "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": MASK_HINT_THRESHOLD_TOOLTIP}), + "mask_hint_use_negative": (["False", "Small", "Outter"], {"tooltip": MASK_HINT_USE_NEGATIVE_TOOLTIP}) + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + def doit(self, sam_model, segs, image, detection_hint, dilation, + threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative): + return (core.make_sam_mask(sam_model, segs, image, detection_hint, dilation, + threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative), ) + + +class SAMDetectorSegmented: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sam_model": ("SAM_MODEL", SAM_MODEL_TOOLTIP), + "segs": ("SEGS", {"tooltip": "This is the segment information detected by the detector.\nFor the SEGS region, the masks detected by SAM (Segment Anything) are created as a unified mask and a batch of individual masks."}), + "image": ("IMAGE", {"tooltip": "It is assumed that segs contains only the information about the detected areas, and does not include the image. SAM (Segment Anything) operates by referencing this image."}), + "detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", + "mask-points", "mask-point-bbox", "none"], DETECTION_HINT_TOOLTIP), + "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1, "tooltip": DILATION_TOOLTIP}), + "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": BBOX_EXPANSION_TOOLTIP}), + "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": MASK_HINT_THRESHOLD_TOOLTIP}), + "mask_hint_use_negative": (["False", "Small", "Outter"], {"tooltip": MASK_HINT_USE_NEGATIVE_TOOLTIP}) + } + } + + RETURN_TYPES = ("MASK", "MASK") + RETURN_NAMES = ("combined_mask", "batch_masks") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + def doit(self, sam_model, segs, image, detection_hint, dilation, + threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative): + combined_mask, batch_masks = core.make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation, + threshold, bbox_expansion, mask_hint_threshold, + mask_hint_use_negative) + return (combined_mask, batch_masks, ) + + +class BboxDetectorForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "bbox_detector": ("BBOX_DETECTOR", ), + "image": ("IMAGE", ), + "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}), + }, + "optional": {"detailer_hook": ("DETAILER_HOOK",), } + } + + RETURN_TYPES = ("SEGS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + def doit(self, bbox_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None): + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: BboxDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook) + + if labels is not None and labels != '': + labels = labels.split(',') + if len(labels) > 0: + segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels) + + return (segs, ) + + +class SegmDetectorForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segm_detector": ("SEGM_DETECTOR", ), + "image": ("IMAGE", ), + "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}), + }, + "optional": {"detailer_hook": ("DETAILER_HOOK",), } + } + + RETURN_TYPES = ("SEGS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + def doit(self, segm_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None): + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: SegmDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook) + + if labels is not None and labels != '': + labels = labels.split(',') + if len(labels) > 0: + segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels) + + return (segs, ) + + +class SegmDetectorCombined: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segm_detector": ("SEGM_DETECTOR", ), + "image": ("IMAGE", ), + "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + def doit(self, segm_detector, image, threshold, dilation): + mask = segm_detector.detect_combined(image, threshold, dilation) + + if mask is None: + mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu") + + return (mask.unsqueeze(0),) + + +class BboxDetectorCombined(SegmDetectorCombined): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "bbox_detector": ("BBOX_DETECTOR", ), + "image": ("IMAGE", ), + "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "dilation": ("INT", {"default": 4, "min": -512, "max": 512, "step": 1}), + } + } + + def doit(self, bbox_detector, image, threshold, dilation): + mask = bbox_detector.detect_combined(image, threshold, dilation) + + if mask is None: + mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu") + + return (mask.unsqueeze(0),) + + +class SimpleDetectorForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "bbox_detector": ("BBOX_DETECTOR", ), + "image": ("IMAGE", ), + + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + + "sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}), + + "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL), + "segm_detector_opt": ("SEGM_DETECTOR", ), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + @staticmethod + def detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, + sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None, + detailer_hook=None): + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + if segm_detector_opt is not None and hasattr(segm_detector_opt, 'bbox_detector') and segm_detector_opt.bbox_detector == bbox_detector: + # Better segm support for YOLO-World detector + segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook) + else: + segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size, detailer_hook=detailer_hook) + + if sam_model_opt is not None: + mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation, + sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False) + segs = core.segs_bitwise_and_mask(segs, mask) + elif segm_detector_opt is not None: + segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook) + mask = core.segs_to_combined_mask(segm_segs) + segs = core.segs_bitwise_and_mask(segs, mask) + + segs = core.dilate_segs(segs, post_dilation) + + return (segs,) + + def doit(self, bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, + sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None): + + return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, + sam_mask_hint_threshold, post_dilation=post_dilation, + sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt) + + +class SimpleDetectorForEachPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "detailer_pipe": ("DETAILER_PIPE", ), + "image": ("IMAGE", ), + + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + + "sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}), + + "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + def doit(self, detailer_pipe, image, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, post_dilation=0): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe + + return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, + sam_mask_hint_threshold, post_dilation=post_dilation, sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt, + detailer_hook=detailer_hook) + +class SAM2VideoDetectorSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image_frames": ("IMAGE", ), + + "bbox_detector": ("BBOX_DETECTOR", ), + "sam2_model": ("SAM_MODEL", ), + + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam2_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + } + } + + RETURN_TYPES = ("SEGS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + @staticmethod + def doit(bbox_detector, sam2_model, image_frames, bbox_threshold, sam2_threshold, crop_factor, drop_size): + # ---- Check SAM2 model ---- + if not isinstance(sam2_model, core.SAM2Wrapper): + logging.error("[Impact Pack] To use the SAM2VideoDetectorSEGS node, a valid SAM2 model must be provided as input to `sam2_model`.") + raise Exception("To use the SAM2VideoDetectorSEGS node, a SAM2 model must be provided as input to `sam2_model`.") + + # ---- Detect bboxes ---- + segs = bbox_detector.detect(image_frames[0].unsqueeze(0), bbox_threshold, 0, 0, drop_size) + + # ---- If no detections, try reversed frames before giving up ---- + if len(segs[1]) == 0: + reversed_frames = torch.flip(image_frames, dims=[0]) + segs_rev = bbox_detector.detect(reversed_frames[0].unsqueeze(0), bbox_threshold, 0, 0, drop_size) + + if len(segs_rev[1]) == 0: + # No Bboxes when reversed -> Give up + h, w = image_frames.shape[1:3] + return (((h, w), []), ) + + # ---- Predict masks in reversed mode ---- + segs_masks = sam2_model.predict_video_segs(reversed_frames, segs_rev) + + # segs_masks wieder umdrehen, damit sie mit Originalframes matchen + for k in segs_masks.keys(): + segs_masks[k] = torch.flip(segs_masks[k], dims=[0]) + else: + # ---- Predict masks if BBOXES were found in forward pass---- + segs_masks = sam2_model.predict_video_segs(image_frames, segs) + + def get_whole_merged_mask(all_masks): + merged_mask = (all_masks[0] * 255).to(torch.uint8) + for mask in all_masks[1:]: + merged_mask |= (mask * 255).to(torch.uint8) + + merged_mask = (merged_mask / 255.0).to(torch.float32) + merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0] + return merged_mask + + new_segs = [] + for k, v in segs_masks.items(): + v = v.squeeze(3) + m = get_whole_merged_mask(v) + seg = segs_nodes.MaskToSEGS.doit(m, False, crop_factor, False, drop_size, contour_fill=True)[0][1] + + if len(seg) == 0: + continue + + seg = seg[0] + + x1, y1, x2, y2 = seg.crop_region + masks = [] + for mask in v: + masks.append(mask[y1:y2, x1:x2]) + cropped_mask = torch.stack(masks) + cropped_mask = (cropped_mask >= (sam2_threshold * 100 - 50)).to(torch.uint8).cpu() + + new_seg = SEG( + seg.cropped_image, + cropped_mask, + seg.confidence, + seg.crop_region, + seg.bbox, + seg.label, + seg.control_net_wrapper + ) + new_segs.append(new_seg) + + return ((segs[0], new_segs), ) + + + +class SimpleDetectorForAnimateDiff: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "bbox_detector": ("BBOX_DETECTOR", ), + "image_frames": ("IMAGE", ), + + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}), + + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + + "sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "sub_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}), + "sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}), + + "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "masking_mode": (["Pivot SEGS", "Combine neighboring frames", "Don't combine"],), + "segs_pivot": (["Combined mask", "1st frame mask"],), + "sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL), + "segm_detector_opt": ("SEGM_DETECTOR", ), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + @staticmethod + def detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, + masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None): + + h = image_frames.shape[1] + w = image_frames.shape[2] + + # gather segs for all frames + segs_by_frames = [] + for image in image_frames: + image = image.unsqueeze(0) + segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size) + + if sam_model_opt is not None: + mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation, + sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False) + segs = core.segs_bitwise_and_mask(segs, mask) + elif segm_detector_opt is not None: + segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size) + mask = core.segs_to_combined_mask(segm_segs) + segs = core.segs_bitwise_and_mask(segs, mask) + + segs_by_frames.append(segs) + + def get_masked_frames(): + masks_by_frame = [] + for i, segs in enumerate(segs_by_frames): + masks_in_frame = segs_nodes.SEGSToMaskList().doit(segs)[0] + current_frame_mask = (masks_in_frame[0] * 255).to(torch.uint8) + + for mask in masks_in_frame[1:]: + current_frame_mask |= (mask * 255).to(torch.uint8) + + current_frame_mask = (current_frame_mask/255.0).to(torch.float32) + current_frame_mask = utils.to_binary_mask(current_frame_mask, 0.1)[0] + + masks_by_frame.append(current_frame_mask) + + return masks_by_frame + + def get_empty_mask(): + return torch.zeros((h, w), dtype=torch.float32, device="cpu") + + def get_neighboring_mask_at(i, masks_by_frame): + prv = masks_by_frame[i-1] if i > 1 else get_empty_mask() + cur = masks_by_frame[i] + nxt = masks_by_frame[i-1] if i > 1 else get_empty_mask() + + prv = prv if prv is not None else get_empty_mask() + cur = cur.clone() if cur is not None else get_empty_mask() + nxt = nxt if nxt is not None else get_empty_mask() + + return prv, cur, nxt + + def get_merged_neighboring_mask(masks_by_frame): + if len(masks_by_frame) <= 1: + return masks_by_frame + + result = [] + for i in range(0, len(masks_by_frame)): + prv, cur, nxt = get_neighboring_mask_at(i, masks_by_frame) + cur = (cur * 255).to(torch.uint8) + cur |= (prv * 255).to(torch.uint8) + cur |= (nxt * 255).to(torch.uint8) + cur = (cur / 255.0).to(torch.float32) + cur = utils.to_binary_mask(cur, 0.1)[0] + result.append(cur) + + return result + + def get_whole_merged_mask(): + all_masks = [] + for segs in segs_by_frames: + all_masks += segs_nodes.SEGSToMaskList().doit(segs)[0] + + merged_mask = (all_masks[0] * 255).to(torch.uint8) + for mask in all_masks[1:]: + merged_mask |= (mask * 255).to(torch.uint8) + + merged_mask = (merged_mask / 255.0).to(torch.float32) + merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0] + return merged_mask + + def get_pivot_segs(): + if segs_pivot == "1st frame mask": + return segs_by_frames[0][1] + else: + merged_mask = get_whole_merged_mask() + return segs_nodes.MaskToSEGS.doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0] + + def get_segs(merged_neighboring=False): + pivot_segs = get_pivot_segs() + + masks_by_frame = get_masked_frames() + if merged_neighboring: + masks_by_frame = get_merged_neighboring_mask(masks_by_frame) + + new_segs = [] + for seg in pivot_segs[1]: + cropped_mask = torch.zeros(seg.cropped_mask.shape, dtype=torch.float32, device="cpu").unsqueeze(0) + pivot_mask = torch.from_numpy(seg.cropped_mask) + x1, y1, x2, y2 = seg.crop_region + for mask in masks_by_frame: + cropped_mask_at_frame = (mask[y1:y2, x1:x2] * pivot_mask).unsqueeze(0) + cropped_mask = torch.cat((cropped_mask, cropped_mask_at_frame), dim=0) + + if len(cropped_mask) > 1: + cropped_mask = cropped_mask[1:] + + new_seg = SEG(seg.cropped_image, cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + new_segs.append(new_seg) + + return pivot_segs[0], new_segs + + # create result mask + if masking_mode == "Pivot SEGS": + return (get_pivot_segs(), ) + + elif masking_mode == "Combine neighboring frames": + return (get_segs(merged_neighboring=True), ) + + else: # elif masking_mode == "Don't combine": + return (get_segs(merged_neighboring=False), ) + + def doit(self, bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, + masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None): + + return SimpleDetectorForAnimateDiff.detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size, + sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, + masking_mode, segs_pivot, sam_model_opt, segm_detector_opt) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hf_nodes.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hf_nodes.py new file mode 100644 index 00000000..9c8e4208 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hf_nodes.py @@ -0,0 +1,189 @@ +import comfy +import re +from impact import utils + + +hf_transformer_model_urls = [ + "rizvandwiki/gender-classification-2", + "NTQAI/pedestrian_gender_recognition", + "Leilab/gender_class", + "ProjectPersonal/GenderClassifier", + "crangana/trained-gender", + "cledoux42/GenderNew_v002", + "ivensamdh/genderage2" +] + + +class HF_TransformersClassifierProvider: + @classmethod + def INPUT_TYPES(s): + global hf_transformer_model_urls + return {"required": { + "preset_repo_id": (hf_transformer_model_urls + ['Manual repo id'],), + "manual_repo_id": ("STRING", {"multiline": False}), + "device_mode": (["AUTO", "Prefer GPU", "CPU"],), + }, + } + + RETURN_TYPES = ("TRANSFORMERS_CLASSIFIER",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/HuggingFace" + + def doit(self, preset_repo_id, manual_repo_id, device_mode): + from transformers import pipeline + + if preset_repo_id == 'Manual repo id': + url = manual_repo_id + else: + url = preset_repo_id + + if device_mode != 'CPU': + device = comfy.model_management.get_torch_device() + else: + device = "cpu" + + classifier = pipeline('image-classification', model=url, device=device) + + return (classifier,) + + +preset_classify_expr = [ + '#Female > #Male', + '#Female < #Male', + 'female > 0.5', + 'male > 0.5', + 'Age16to25 > 0.1', + 'Age50to69 > 0.1', +] + +symbolic_label_map = { + '#Female': {'female', 'Female', 'Human Female', 'woman', 'women', 'girl'}, + '#Male': {'male', 'Male', 'Human Male', 'man', 'men', 'boy'} +} + +def is_numeric_string(input_str): + return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None + + +classify_expr_pattern = r'([^><= ]+)\s*(>|<|>=|<=|=)\s*([^><= ]+)' + + +class SEGS_Classify: + @classmethod + def INPUT_TYPES(s): + global preset_classify_expr + return {"required": { + "classifier": ("TRANSFORMERS_CLASSIFIER",), + "segs": ("SEGS",), + "preset_expr": (preset_classify_expr + ['Manual expr'],), + "manual_expr": ("STRING", {"multiline": False}), + }, + "optional": { + "ref_image_opt": ("IMAGE", ), + } + } + + RETURN_TYPES = ("SEGS", "SEGS", "STRING") + RETURN_NAMES = ("filtered_SEGS", "remained_SEGS", "detected_labels") + OUTPUT_IS_LIST = (False, False, True) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/HuggingFace" + + @staticmethod + def lookup_classified_label_score(score_infos, label): + global symbolic_label_map + + if label.startswith('#'): + if label not in symbolic_label_map: + return None + else: + label = symbolic_label_map[label] + else: + label = {label} + + for x in score_infos: + if x['label'] in label: + return x['score'] + + return None + + def doit(self, classifier, segs, preset_expr, manual_expr, ref_image_opt=None): + if preset_expr == 'Manual expr': + expr_str = manual_expr + else: + expr_str = preset_expr + + match = re.match(classify_expr_pattern, expr_str) + + if match is None: + return (segs[0], []), segs, [] + + a = match.group(1) + op = match.group(2) + b = match.group(3) + + a_is_lab = not is_numeric_string(a) + b_is_lab = not is_numeric_string(b) + + classified = [] + remained_SEGS = [] + provided_labels = set() + + for seg in segs[1]: + cropped_image = None + + if seg.cropped_image is not None: + cropped_image = seg.cropped_image + elif ref_image_opt is not None: + # take from original image + cropped_image = utils.crop_image(ref_image_opt, seg.crop_region) + + if cropped_image is not None: + cropped_image = utils.to_pil(cropped_image) + res = classifier(cropped_image) + classified.append((seg, res)) + + for x in res: + provided_labels.add(x['label']) + else: + remained_SEGS.append(seg) + + filtered_SEGS = [] + for seg, res in classified: + if a_is_lab: + avalue = SEGS_Classify.lookup_classified_label_score(res, a) + else: + avalue = a + + if b_is_lab: + bvalue = SEGS_Classify.lookup_classified_label_score(res, b) + else: + bvalue = b + + if avalue is None or bvalue is None: + remained_SEGS.append(seg) + continue + + avalue = float(avalue) + bvalue = float(bvalue) + + if op == '>': + cond = avalue > bvalue + elif op == '<': + cond = avalue < bvalue + elif op == '>=': + cond = avalue >= bvalue + elif op == '<=': + cond = avalue <= bvalue + else: + cond = avalue == bvalue + + if cond: + filtered_SEGS.append(seg) + else: + remained_SEGS.append(seg) + + return (segs[0], filtered_SEGS), (segs[0], remained_SEGS), list(provided_labels) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hook_nodes.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hook_nodes.py new file mode 100644 index 00000000..f1716697 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hook_nodes.py @@ -0,0 +1,128 @@ +import sys +from . import hooks +from . import defs + + +class SEGSOrderedFilterDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2"],), + "order": ("BOOLEAN", {"default": True, "label_on": "descending", "label_off": "ascending"}), + "take_start": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "take_count": ("INT", {"default": 1, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, target, order, take_start, take_count): + hook = hooks.SEGSOrderedFilterDetailerHook(target, order, take_start, take_count) + return (hook, ) + + +class SEGSRangeFilterDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "length_percent"],), + "mode": ("BOOLEAN", {"default": True, "label_on": "inside", "label_off": "outside"}), + "min_value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "max_value": ("INT", {"default": 67108864, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, target, mode, min_value, max_value): + hook = hooks.SEGSRangeFilterDetailerHook(target, mode, min_value, max_value) + return (hook, ) + + +class SEGSLabelFilterDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "preset": (['all'] + defs.detection_labels,), + "labels": ("STRING", {"multiline": True, "placeholder": "List the types of segments to be allowed, separated by commas"}), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, preset, labels): + hook = hooks.SEGSLabelFilterDetailerHook(labels) + return (hook, ) + + +class PreviewDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"quality": ("INT", {"default": 95, "min": 20, "max": 100})}, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("DETAILER_HOOK", "UPSCALER_HOOK") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + NOT_IDEMPOTENT = True + + def doit(self, quality, unique_id): + hook = hooks.PreviewDetailerHook(unique_id, quality) + return hook, hook + + +class LamaRemoverDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask_threshold":("INT", {"default": 250, "min": 0, "max": 255, "step": 1, "display": "slider"}), + "gaussblur_radius": ("INT", {"default": 8, "min": 0, "max": 20, "step": 1, "display": "slider"}), + "skip_sampling": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("DETAILER_HOOK", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, mask_threshold, gaussblur_radius, skip_sampling): + hook = hooks.LamaRemoverDetailerHook(mask_threshold, gaussblur_radius, skip_sampling) + return (hook, ) + + +class BlackPatchRetryHookProvider: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mean_thresh": ("INT", {"default": 10, "min": 0, "max": 255}), + "var_thresh": ("INT", {"default": 5, "min": 0, "max": 255}) + }, + } + + RETURN_TYPES = ("DETAILER_HOOK", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + NOT_IDEMPOTENT = True + + def doit(self, mean_thresh, var_thresh): + hook = hooks.BlackPatchRetryHook(mean_thresh, var_thresh) + return hook, diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hooks.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hooks.py new file mode 100644 index 00000000..2a8c14df --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/hooks.py @@ -0,0 +1,595 @@ +import copy +import torch +import nodes +from impact import utils +from . import segs_nodes +from thirdparty import noise_nodes +from server import PromptServer +import asyncio +import folder_paths +import os +from comfy_extras import nodes_custom_sampler +import math +import logging + + +class PixelKSampleHook: + cur_step = 0 + total_step = 0 + + def __init__(self): + pass + + def set_steps(self, info): + self.cur_step, self.total_step = info + + def post_decode(self, pixels): + return pixels + + def post_upscale(self, pixels, mask=None): + return pixels + + def post_encode(self, samples): + return samples + + def pre_decode(self, samples): + return samples + + def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, + denoise): + return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise + + def post_crop_region(self, w, h, item_bbox, crop_region): + return crop_region + + def touch_scaled_size(self, w, h): + return w, h + + +class PixelKSampleHookCombine(PixelKSampleHook): + hook1 = None + hook2 = None + + def __init__(self, hook1, hook2): + super().__init__() + self.hook1 = hook1 + self.hook2 = hook2 + + def set_steps(self, info): + self.hook1.set_steps(info) + self.hook2.set_steps(info) + + def pre_decode(self, samples): + return self.hook2.pre_decode(self.hook1.pre_decode(samples)) + + def post_decode(self, pixels): + return self.hook2.post_decode(self.hook1.post_decode(pixels)) + + def post_upscale(self, pixels, mask=None): + return self.hook2.post_upscale(self.hook1.post_upscale(pixels, mask), mask) + + def post_encode(self, samples): + return self.hook2.post_encode(self.hook1.post_encode(samples)) + + def post_crop_region(self, w, h, item_bbox, crop_region): + crop_region = self.hook1.post_crop_region(w, h, item_bbox, crop_region) + return self.hook2.post_crop_region(w, h, item_bbox, crop_region) + + def touch_scaled_size(self, w, h): + w, h = self.hook1.touch_scaled_size(w, h) + return self.hook2.touch_scaled_size(w, h) + + def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, + denoise): + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \ + self.hook1.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, + upscaled_latent, denoise) + + return self.hook2.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, + upscaled_latent, denoise) + + +class DetailerHookCombine(PixelKSampleHookCombine): + def cycle_latent(self, latent): + latent = self.hook1.cycle_latent(latent) + latent = self.hook2.cycle_latent(latent) + return latent + + def post_detection(self, segs): + segs = self.hook1.post_detection(segs) + segs = self.hook2.post_detection(segs) + return segs + + def post_paste(self, image): + image = self.hook1.post_paste(image) + image = self.hook2.post_paste(image) + return image + + def get_custom_noise(self, seed, noise, is_touched): + noise_1st, is_touched = self.hook1.get_custom_noise(seed, noise, is_touched) + noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched) + return noise, is_touched + + def get_custom_sampler(self): + if self.hook1.get_custom_sampler() is not None: + return self.hook1.get_custom_sampler() + else: + return self.hook2.get_custom_sampler() + + def get_skip_sampling(self): + return self.hook1.get_skip_sampling() and self.hook2.get_skip_sampling() + + def should_retry_patch(self, patch): + return self.hook1.should_retry_patch(patch) or self.hook2.should_retry_patch(patch) + + +class SimpleCfgScheduleHook(PixelKSampleHook): + target_cfg = 0 + + def __init__(self, target_cfg): + super().__init__() + self.target_cfg = target_cfg + + def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise): + if self.total_step > 1: + progress = self.cur_step / (self.total_step - 1) + gap = self.target_cfg - cfg + current_cfg = int(cfg + gap * progress) + else: + current_cfg = self.target_cfg + + return model, seed, steps, current_cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise + + +class SimpleDenoiseScheduleHook(PixelKSampleHook): + def __init__(self, target_denoise): + super().__init__() + self.target_denoise = target_denoise + + def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise): + if self.total_step > 1: + progress = self.cur_step / (self.total_step - 1) + gap = self.target_denoise - denoise + current_denoise = denoise + gap * progress + else: + current_denoise = self.target_denoise + + return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, current_denoise + + +class SimpleStepsScheduleHook(PixelKSampleHook): + def __init__(self, target_steps): + super().__init__() + self.target_steps = target_steps + + def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise): + if self.total_step > 1: + progress = self.cur_step / (self.total_step - 1) + gap = self.target_steps - steps + current_steps = int(steps + gap * progress) + else: + current_steps = self.target_steps + + return model, seed, current_steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise + + +class DetailerHook(PixelKSampleHook): + def cycle_latent(self, latent): + return latent + + def post_detection(self, segs): + return segs + + def post_paste(self, image): + return image + + def get_custom_noise(self, seed, noise, is_touched): + return noise, is_touched + + def get_custom_sampler(self): + return None + + def get_skip_sampling(self): + return False + + def should_retry_patch(self, patch): + return False + + +class CustomSamplerDetailerHookProvider(DetailerHook): + def __init__(self, sampler): + super().__init__() + self.sampler = sampler + + def get_custom_sampler(self): + return self.sampler + + +# class CustomNoiseDetailerHookProvider(DetailerHook): +# def __init__(self, noise): +# super().__init__() +# self.noise = noise +# +# def get_custom_noise(self, seed, noise, is_start): +# return self.noise + + +class VariationNoiseDetailerHookProvider(DetailerHook): + def __init__(self, variation_seed, variation_strength): + super().__init__() + self.variation_seed = variation_seed + self.variation_strength = variation_strength + + def get_custom_noise(self, seed, noise, is_touched): + empty_noise = {'samples': torch.zeros(noise.size())} + if not is_touched: + noise = nodes_custom_sampler.Noise_RandomNoise(seed).generate_noise(empty_noise) + noise_2nd = nodes_custom_sampler.Noise_RandomNoise(self.variation_seed).generate_noise(empty_noise) + + mixed_noise = ((1 - self.variation_strength) * noise + self.variation_strength * noise_2nd) + + # NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling. + scale_factor = math.sqrt((1 - self.variation_strength) ** 2 + self.variation_strength ** 2) + corrected_noise = mixed_noise / scale_factor # Scale the noise to maintain variance of 1 + + return corrected_noise, True + + +class SimpleDetailerDenoiseSchedulerHook(DetailerHook): + def __init__(self, target_denoise): + super().__init__() + self.target_denoise = target_denoise + + def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise): + if self.total_step > 1: + progress = self.cur_step / (self.total_step - 1) + gap = self.target_denoise - denoise + current_denoise = denoise + gap * progress + else: + # ignore hook if total cycle <= 1 + current_denoise = denoise + + return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, current_denoise + + +class CoreMLHook(DetailerHook): + def __init__(self, mode): + super().__init__() + resolution = mode.split('x') + + self.w = int(resolution[0]) + self.h = int(resolution[1]) + + self.override_bbox_by_segm = False + + def pre_decode(self, samples): + new_samples = copy.deepcopy(samples) + new_samples['samples'] = samples['samples'][0].unsqueeze(0) + return new_samples + + def post_encode(self, samples): + new_samples = copy.deepcopy(samples) + new_samples['samples'] = samples['samples'].repeat(2, 1, 1, 1) + return new_samples + + def post_crop_region(self, w, h, item_bbox, crop_region): + x1, y1, x2, y2 = crop_region + bx1, by1, bx2, by2 = item_bbox + crop_w = x2-x1 + crop_h = y2-y1 + + crop_ratio = crop_w/crop_h + target_ratio = self.w/self.h + if crop_ratio < target_ratio: + # shrink height + top_gap = by1 - y1 + bottom_gap = y2 - by2 + + gap_ratio = top_gap / bottom_gap + + target_height = 1/target_ratio*crop_w + delta_height = crop_h - target_height + + new_y1 = int(y1 + delta_height*gap_ratio) + new_y2 = int(new_y1 + target_height) + crop_region = x1, new_y1, x2, new_y2 + + elif crop_ratio > target_ratio: + # shrink width + left_gap = bx1 - x1 + right_gap = x2 - bx2 + + gap_ratio = left_gap / right_gap + + target_width = target_ratio*crop_h + delta_width = crop_w - target_width + + new_x1 = int(x1 + delta_width*gap_ratio) + new_x2 = int(new_x1 + target_width) + crop_region = new_x1, y1, new_x2, y2 + + return crop_region + + def touch_scaled_size(self, w, h): + return self.w, self.h + + +# REQUIREMENTS: BlenderNeko/ComfyUI Noise +class InjectNoiseHook(PixelKSampleHook): + def __init__(self, source, seed, start_strength, end_strength): + super().__init__() + self.source = source + self.seed = seed + self.start_strength = start_strength + self.end_strength = end_strength + + def post_encode(self, samples): + cur_step = self.cur_step + + size = samples['samples'].shape + seed = cur_step + self.seed + cur_step + + if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS: + NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"] + InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"] + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise', + "To use 'NoiseInjectionHookProvider', 'ComfyUI Noise' extension is required.") + raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.") + + noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0] + + # inj noise + mask = None + if 'noise_mask' in samples: + mask = samples['noise_mask'] + + strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / self.total_step + samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0] + logging.info(f"[Impact Pack] InjectNoiseHook: strength = {strength}") + + if mask is not None: + samples['noise_mask'] = mask + + return samples + + +class UnsamplerHook(PixelKSampleHook): + def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name, + scheduler, normalize, positive, negative): + super().__init__() + self.model = model + self.cfg = cfg + self.sampler_name = sampler_name + self.steps = steps + self.start_end_at_step = start_end_at_step + self.end_end_at_step = end_end_at_step + self.scheduler = scheduler + self.normalize = normalize + self.positive = positive + self.negative = negative + + def post_encode(self, samples): + cur_step = self.cur_step + + Unsampler = noise_nodes.Unsampler + + end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / self.total_step + end_at_step = int(end_at_step) + + logging.info(f"[Impact Pack] UnsamplerHook: end_at_step = {end_at_step}") + + # inj noise + mask = None + if 'noise_mask' in samples: + mask = samples['noise_mask'] + + samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step, + self.scheduler, self.normalize, self.positive, self.negative, samples)[0] + + if mask is not None: + samples['noise_mask'] = mask + + return samples + + +class InjectNoiseHookForDetailer(DetailerHook): + def __init__(self, source, seed, start_strength, end_strength, from_start=False): + super().__init__() + self.source = source + self.seed = seed + self.start_strength = start_strength + self.end_strength = end_strength + self.from_start = from_start + + def inject_noise(self, samples): + cur_step = self.cur_step if self.from_start else self.cur_step - 1 + total_step = self.total_step if self.from_start else self.total_step - 1 + + size = samples['samples'].shape + seed = cur_step + self.seed + cur_step + + if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS: + NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"] + InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"] + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise', + "To use 'NoiseInjectionDetailerHookProvider', 'ComfyUI Noise' extension is required.") + raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.") + + noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0] + + # inj noise + mask = None + if 'noise_mask' in samples: + mask = samples['noise_mask'] + + strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / total_step + samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0] + + if mask is not None: + samples['noise_mask'] = mask + + return samples + + def cycle_latent(self, latent): + if self.cur_step == 0 and not self.from_start: + return latent + else: + return self.inject_noise(latent) + + +class UnsamplerDetailerHook(DetailerHook): + def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name, + scheduler, normalize, positive, negative, from_start=False): + super().__init__() + self.model = model + self.cfg = cfg + self.sampler_name = sampler_name + self.steps = steps + self.start_end_at_step = start_end_at_step + self.end_end_at_step = end_end_at_step + self.scheduler = scheduler + self.normalize = normalize + self.positive = positive + self.negative = negative + self.from_start = from_start + + def unsample(self, samples): + cur_step = self.cur_step if self.from_start else self.cur_step - 1 + total_step = self.total_step if self.from_start else self.total_step - 1 + + Unsampler = noise_nodes.Unsampler + + end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / total_step + end_at_step = int(end_at_step) + + # inj noise + mask = None + if 'noise_mask' in samples: + mask = samples['noise_mask'] + + samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step, + self.scheduler, self.normalize, self.positive, self.negative, samples)[0] + + if mask is not None: + samples['noise_mask'] = mask + + return samples + + def cycle_latent(self, latent): + if self.cur_step == 0 and not self.from_start: + return latent + else: + return self.unsample(latent) + + +class SEGSOrderedFilterDetailerHook(DetailerHook): + def __init__(self, target, order, take_start, take_count): + super().__init__() + self.target = target + self.order = order + self.take_start = take_start + self.take_count = take_count + + def post_detection(self, segs): + return segs_nodes.SEGSOrderedFilter().doit(segs, self.target, self.order, self.take_start, self.take_count)[0] + + +class SEGSRangeFilterDetailerHook(DetailerHook): + def __init__(self, target, mode, min_value, max_value): + super().__init__() + self.target = target + self.mode = mode + self.min_value = min_value + self.max_value = max_value + + def post_detection(self, segs): + return segs_nodes.SEGSRangeFilter().doit(segs, self.target, self.mode, self.min_value, self.max_value)[0] + + +class SEGSLabelFilterDetailerHook(DetailerHook): + def __init__(self, labels): + super().__init__() + self.labels = labels + + def post_detection(self, segs): + return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0] + + +class LamaRemoverDetailerHook(DetailerHook): + def __init__(self, mask_threshold, gaussblur_radius, skip_sampling): + super().__init__() + self.mask_threshold = mask_threshold + self.gaussblur_radius = gaussblur_radius + self.skip_sampling = skip_sampling + + def post_upscale(self, img, mask=None): + if "LamaRemover" in nodes.NODE_CLASS_MAPPINGS: + lama_remover_obj = nodes.NODE_CLASS_MAPPINGS['LamaRemover']() + else: + utils.try_install_custom_node('https://github.com/Layer-norm/comfyui-lama-remover', + "To use 'LAMARemoverDetailerHookProvider', 'comfyui-lama-remover' nodepack is required.") + raise Exception("'LamaRemover' node is not installed.") + + return lama_remover_obj.lama_remover(img, masks=mask, mask_threshold=self.mask_threshold, gaussblur_radius=self.gaussblur_radius, invert_mask=False)[0] + + def get_skip_sampling(self): + return self.skip_sampling + + +class PreviewDetailerHook(DetailerHook): + def __init__(self, node_id, quality): + super().__init__() + self.node_id = node_id + self.quality = quality + + async def send(self, image): + if len(image) > 0: + image = image[0].unsqueeze(0) + img = utils.tensor2pil(image) + + temp_path = os.path.join(folder_paths.get_temp_directory(), 'pvhook') + + if not os.path.exists(temp_path): + os.makedirs(temp_path) + + fullpath = os.path.join(temp_path, f"{self.node_id}.webp") + img.save(fullpath, quality=self.quality) + + item = { + "filename": f"{self.node_id}.webp", + "subfolder": 'pvhook', + "type": 'temp' + } + + PromptServer.instance.send_sync("impact-preview", {'node_id': self.node_id, 'item': item}) + + def post_paste(self, image): + loop = asyncio.get_running_loop() + loop.create_task(self.send(image)) + return image + + +class BlackPatchRetryHook(DetailerHook): + def __init__(self, mean_thresh, var_thresh): + super().__init__() + assert 0 <= mean_thresh <= 255 and 0 <= var_thresh <= 255 + self.mean_thresh = mean_thresh + self.var_thresh = var_thresh + + def should_retry_patch(self, cropped_region): + # remove the first dimension (batch_size) + if cropped_region.ndim == 4: + assert cropped_region.shape[0] == 1 + cropped_region = cropped_region.squeeze(0) + + # turn image to grayscape + if cropped_region.ndim == 3: + assert cropped_region.shape[-1] in [1, 3] + cropped_region = cropped_region.mean(axis=-1) # simple average grayscale + + mean = cropped_region.mean() + var = cropped_region.var() + + return (mean <= self.mean_thresh/255) and (var <= self.var_thresh/255) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_onnx.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_onnx.py new file mode 100644 index 00000000..77c859fd --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_onnx.py @@ -0,0 +1,39 @@ +import impact.additional_dependencies +import numpy as np +from impact import utils +import logging + +impact.additional_dependencies.ensure_onnx_package() + +try: + import onnxruntime + + def onnx_inference(image, onnx_model): + # prepare image + pil = utils.tensor2pil(image) + image = np.ascontiguousarray(pil) + image = image[:, :, ::-1] # to BGR image + image = image.astype(np.float32) + image -= [103.939, 116.779, 123.68] # 'caffe' mode image preprocessing + + # do detection + onnx_model = onnxruntime.InferenceSession(onnx_model, providers=["CPUExecutionProvider"]) + outputs = onnx_model.run( + [s_i.name for s_i in onnx_model.get_outputs()], + {onnx_model.get_inputs()[0].name: np.expand_dims(image, axis=0)}, + ) + + labels = [op for op in outputs if op.dtype == "int32"][0] + scores = [op for op in outputs if isinstance(op[0][0], np.float32)][0] + boxes = [op for op in outputs if isinstance(op[0][0], np.ndarray)][0] + + # filter-out useless item + idx = np.where(labels[0] == -1)[0][0] + + labels = labels[0][:idx] + scores = scores[0][:idx] + boxes = boxes[0][:idx].astype(np.uint32) + + return labels, scores, boxes +except Exception: + logging.error("[Impact Pack] ComfyUI-Impact-Pack: 'onnxruntime' package doesn't support 'python 3.11', yet.\t{e}") diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_pack.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_pack.py new file mode 100644 index 00000000..9e25c0ba --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_pack.py @@ -0,0 +1,2767 @@ +import os +import sys + +import comfy.samplers +import comfy.sd +import warnings +from segment_anything import sam_model_registry +from io import BytesIO +import piexif +import zipfile +import re + +import impact.wildcards + +import impact.core as core +from impact.core import SEG +from impact.config import latent_letter_path +from nodes import MAX_RESOLUTION +from PIL import Image, ImageOps +import numpy as np +import hashlib +import json +import safetensors.torch +from PIL.PngImagePlugin import PngInfo +import comfy.model_management +import base64 +import impact.wildcards as wildcards +from . import hooks +from . import utils +import inspect +import folder_paths +import torch +import nodes +import cv2 +import logging + + +try: + from comfy_extras import nodes_differential_diffusion +except Exception: + logging.warning("\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n") + raise Exception("[Impact Pack] ComfyUI is an outdated version.") + + +warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') + +model_path = folder_paths.models_dir + + +# folder_paths.supported_pt_extensions +utils.add_folder_path_and_extensions("sams", [os.path.join(model_path, "sams")], folder_paths.supported_pt_extensions) +utils.add_folder_path_and_extensions("onnx", [os.path.join(model_path, "onnx")], {'.onnx'}) + + +# Nodes +class ONNXDetectorProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model_name": (folder_paths.get_filename_list("onnx"), )}} + + RETURN_TYPES = ("BBOX_DETECTOR", ) + FUNCTION = "load_onnx" + + CATEGORY = "ImpactPack" + + def load_onnx(self, model_name): + model = folder_paths.get_full_path("onnx", model_name) + return (core.ONNXDetector(model), ) + + +class CLIPSegDetectorProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "text": ("STRING", {"multiline": False, "tooltip": "Enter the targets to be detected, separated by commas"}), + "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 7, "tooltip": "Blurs the detected mask"}), + "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.4, "tooltip": "Detects only areas that are certain above the threshold."}), + "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4, "tooltip": "Dilates the detected mask."}), + } + } + + RETURN_TYPES = ("BBOX_DETECTOR", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "Provides a detection function using CLIPSeg, which generates masks based on text prompts.\nTo use this node, the CLIPSeg custom node must be installed." + + def doit(self, text, blur, threshold, dilation_factor): + if "CLIPSeg" in nodes.NODE_CLASS_MAPPINGS: + return (core.BBoxDetectorBasedOnCLIPSeg(text, blur, threshold, dilation_factor), ) + else: + logging.error("[ERROR] CLIPSegToBboxDetector: CLIPSeg custom node isn't installed. You must install biegert/ComfyUI-CLIPSeg extension to use this node.") + raise Exception("[ERROR] CLIPSegToBboxDetector: CLIPSeg custom node isn't installed. You must install biegert/ComfyUI-CLIPSeg extension to use this node.") + + +sam2_config_table = { + 'sam2.1_hiera_base_plus.pt': 'configs/sam2.1/sam2.1_hiera_b+.yaml', + 'sam2.1_hiera_large.pt': 'configs/sam2.1/sam2.1_hiera_l.yaml', + 'sam2.1_hiera_small.pt': 'configs/sam2.1/sam2.1_hiera_s.yaml', + 'sam2.1_hiera_tiny.pt': 'configs/sam2.1/sam2.1_hiera_t.yaml', + 'sam2_hiera_tiny.pt': 'configs/sam2/sam2_hiera_t.yaml', + 'sam2_hiera_small.pt': 'configs/sam2/sam2_hiera_s.yaml', + 'sam2_hiera_base_plus.pt': 'configs/sam2/sam2_hiera_b+.yaml', + 'sam2_hiera_large.pt': 'configs/sam2/sam2_hiera_l.yaml' +} + +class SAMLoader: + @classmethod + def INPUT_TYPES(cls): + models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x and (x.endswith('.pt') or x.endswith('.pth') or x.endswith('.safetensors'))] + + if 'ESAM_ModelLoader_Zho' in nodes.NODE_CLASS_MAPPINGS: + models.append('ESAM') + + return { + "required": { + "model_name": (models, {"tooltip": "The detection accuracy varies depending on the SAM model. ESAM can only be used if ComfyUI-YoloWorld-EfficientSAM is installed."}), + "device_mode": (["AUTO", "Prefer GPU", "CPU"], {"tooltip": "AUTO: Only applicable when a GPU is available. It temporarily loads the SAM_MODEL into VRAM only when the detection function is used.\n" + "Prefer GPU: Tries to keep the SAM_MODEL on the GPU whenever possible. This can be used when there is sufficient VRAM available.\n" + "CPU: Always loads only on the CPU."}), + } + } + + RETURN_TYPES = ("SAM_MODEL", ) + FUNCTION = "load_model" + + CATEGORY = "ImpactPack" + + DESCRIPTION = "Load the SAM (Segment Anything) model. This can be used in places that utilize SAM detection functionality, such as SAMDetector or SimpleDetector.\nThe SAM detection functionality in Impact Pack must use the SAM_MODEL loaded through this node." + + def load_model(self, model_name, device_mode="auto"): + if model_name == 'ESAM': + if 'ESAM_ModelLoader_Zho' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/ZHO-ZHO-ZHO/ComfyUI-YoloWorld-EfficientSAM', + "To use 'ESAM' model, 'ComfyUI-YoloWorld-EfficientSAM' extension is required.") + raise Exception("'ComfyUI-YoloWorld-EfficientSAM' node isn't installed.") + + esam_loader = nodes.NODE_CLASS_MAPPINGS['ESAM_ModelLoader_Zho']() + + if device_mode == 'CPU': + esam = esam_loader.load_esam_model('CPU')[0] + else: + device_mode = 'CUDA' + esam = esam_loader.load_esam_model('CUDA')[0] + + sam_obj = core.ESAMWrapper(esam, device_mode) + esam.sam_wrapper = sam_obj + + logging.info(f"Loads EfficientSAM model: (device:{device_mode})") + return (esam, ) + elif model_name in sam2_config_table: + model_kind = 'sam2' + config = sam2_config_table[model_name] + modelname = folder_paths.get_full_path("sams", model_name) + else: + modelname = folder_paths.get_full_path("sams", model_name) + + if 'vit_h' in model_name: + model_kind = 'vit_h' + elif 'vit_l' in model_name: + model_kind = 'vit_l' + else: + model_kind = 'vit_b' + + sam = sam_model_registry[model_kind](checkpoint=modelname) + + size = os.path.getsize(modelname) + safe_to = core.SafeToGPU(size) + + # Unless user explicitly wants to use CPU, we use GPU + device = comfy.model_management.get_torch_device() if device_mode == "Prefer GPU" else "CPU" + + if device_mode == "Prefer GPU": + safe_to.to_device(sam, device) + + is_auto_mode = device_mode == "AUTO" + + if model_kind == 'sam2': + sam = core.SAM2Wrapper(config=config, modelname=modelname, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to, device_mode=device_mode) + logging.info(f"Loads SAM2 model: {modelname} (device:{device_mode})") + else: + sam_obj = core.SAMWrapper(sam, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to) + sam.sam_wrapper = sam_obj + logging.info(f"Loads SAM model: {modelname} (device:{device_mode})") + + return (sam, ) + + +class ONNXDetectorForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "onnx_detector": ("ONNX_DETECTOR",), + "image": ("IMAGE",), + "threshold": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}), + "dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + "crop_factor": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + } + } + + RETURN_TYPES = ("SEGS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detector" + + OUTPUT_NODE = True + + def doit(self, onnx_detector, image, threshold, dilation, crop_factor, drop_size): + segs = onnx_detector.detect(image, threshold, dilation, crop_factor, drop_size) + return (segs, ) + + +class DetailerForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "segs": ("SEGS", ), + "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}), + "clip": ("CLIP",), + "vae": ("VAE",), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "detailer_hook": ("DETAILER_HOOK",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + } + } + + RETURN_TYPES = ("IMAGE", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size." + + @staticmethod + def get_core_module(): + return core + + @staticmethod + def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None, + refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, + cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + image = image.clone() + enhanced_alpha_list = [] + enhanced_list = [] + cropped_list = [] + cnet_pil_list = [] + + segs = core.segs_scale_match(segs, image.shape) + new_segs = [] + + wildcard_concat_mode = None + if wildcard_opt is not None: + if wildcard_opt.startswith('[CONCAT]'): + wildcard_concat_mode = 'concat' + wildcard_opt = wildcard_opt[8:] + wmode, wildcard_chooser = wildcards.process_wildcard_for_segs(wildcard_opt) + else: + wmode, wildcard_chooser = None, None + + if wmode in ['ASC', 'DSC', 'ASC-SIZE', 'DSC-SIZE']: + if wmode == 'ASC': + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1])) + elif wmode == 'DSC': + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]), reverse=True) + elif wmode == 'ASC-SIZE': + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[2]-x.bbox[0]) * (x.bbox[3]-x.bbox[1])) + + else: # wmode == 'DSC-SIZE' + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[2]-x.bbox[0]) * (x.bbox[3]-x.bbox[1]), reverse=True) + else: + ordered_segs = segs[1] + + if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + for i, seg in enumerate(ordered_segs): + cropped_image = utils.crop_ndarray4(image.cpu().numpy(), seg.crop_region) # Never use seg.cropped_image to handle overlapping area + cropped_image = utils.to_tensor(cropped_image) + mask = utils.to_tensor(seg.cropped_mask) + mask = utils.tensor_gaussian_blur_mask(mask, feather) + + is_mask_all_zeros = (seg.cropped_mask == 0).all().item() + if is_mask_all_zeros: + logging.info("Detailer: segment skip [empty mask]") + continue + + if noise_mask: + cropped_mask = seg.cropped_mask + else: + cropped_mask = None + + if wildcard_chooser is not None and wmode != "LAB": + seg_seed, wildcard_item = wildcard_chooser.get(seg) + elif wildcard_chooser is not None and wmode == "LAB": + seg_seed, wildcard_item = None, wildcard_chooser.get(seg) + else: + seg_seed, wildcard_item = None, None + + seg_seed = seed + i if seg_seed is None else seg_seed + + if not isinstance(positive, str): + cropped_positive = [ + [condition, { + k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in positive + ] + else: + cropped_positive = positive + + if not isinstance(negative, str): + cropped_negative = [ + [condition, { + k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in negative + ] + else: + # Negative Conditioning is placeholder such as FLUX.1 + cropped_negative = negative + + if wildcard_item and wildcard_item.strip() == '[SKIP]': + continue + + if wildcard_item and wildcard_item.strip() == '[STOP]': + break + + orig_cropped_image = cropped_image.clone() + if not (isinstance(model, str) and model == "DUMMY"): + enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, + seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler, + cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint, + wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode, + detailer_hook=detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, + refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode, + vae_tiled_decode=tiled_decode) + else: + enhanced_image = cropped_image + cnet_pils = None + + if cnet_pils is not None: + cnet_pil_list.extend(cnet_pils) + + if enhanced_image is not None: + # don't latent composite-> converting to latent caused poor quality + # use image paste + image = image.cpu() + enhanced_image = enhanced_image.cpu() + utils.tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask) # this code affecting to `cropped_image`. + enhanced_list.append(enhanced_image) + + if detailer_hook is not None: + image = detailer_hook.post_paste(image) + + if enhanced_image is not None: + # Convert enhanced_pil_alpha to RGBA mode + enhanced_image_alpha = utils.tensor_convert_rgba(enhanced_image) + new_seg_image = enhanced_image.numpy() # alpha should not be applied to seg_image + + # Apply the mask + mask = utils.tensor_resize(mask, *utils.tensor_get_size(enhanced_image)) + utils.tensor_putalpha(enhanced_image_alpha, mask) + enhanced_alpha_list.append(enhanced_image_alpha) + else: + new_seg_image = None + + cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image` + + new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + new_segs.append(new_seg) + + image_tensor = utils.tensor_convert_rgb(image) + + cropped_list.sort(key=lambda x: x.shape, reverse=True) + enhanced_list.sort(key=lambda x: x.shape, reverse=True) + enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True) + + return image_tensor, cropped_list, enhanced_list, enhanced_alpha_list, cnet_pil_list, (segs[0], new_segs) + + def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, + scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1, + detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, + tiled_encode=False, tiled_decode=False): + + enhanced_img, *_ = \ + DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, + cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, + force_inpaint, wildcard, detailer_hook, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode) + + return (enhanced_img, ) + + +class DetailerForEachAutoRetry: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "segs": ("SEGS", ), + "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}), + "clip": ("CLIP",), + "vae": ("VAE",), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + "max_retries": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "detailer_hook": ("DETAILER_HOOK",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + } + } + + RETURN_TYPES = ("IMAGE", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size." + + @staticmethod + def get_core_module(): + return core + + @staticmethod + def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None, + refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, + cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False, max_retries=1): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + image = image.clone() + enhanced_alpha_list = [] + enhanced_list = [] + cropped_list = [] + cnet_pil_list = [] + + segs = core.segs_scale_match(segs, image.shape) + new_segs = [] + + wildcard_concat_mode = None + if wildcard_opt is not None: + if wildcard_opt.startswith('[CONCAT]'): + wildcard_concat_mode = 'concat' + wildcard_opt = wildcard_opt[8:] + wmode, wildcard_chooser = wildcards.process_wildcard_for_segs(wildcard_opt) + else: + wmode, wildcard_chooser = None, None + + if wmode in ['ASC', 'DSC', 'ASC-SIZE', 'DSC-SIZE']: + if wmode == 'ASC': + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1])) + elif wmode == 'DSC': + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]), reverse=True) + elif wmode == 'ASC-SIZE': + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[2]-x.bbox[0]) * (x.bbox[3]-x.bbox[1])) + + else: # wmode == 'DSC-SIZE' + ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[2]-x.bbox[0]) * (x.bbox[3]-x.bbox[1]), reverse=True) + else: + ordered_segs = segs[1] + + if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + for i, seg in enumerate(ordered_segs): + cropped_image = utils.crop_ndarray4(image.cpu().numpy(), seg.crop_region) # Never use seg.cropped_image to handle overlapping area + cropped_image = utils.to_tensor(cropped_image) + mask = utils.to_tensor(seg.cropped_mask) + mask = utils.tensor_gaussian_blur_mask(mask, feather) + + is_mask_all_zeros = (seg.cropped_mask == 0).all().item() + if is_mask_all_zeros: + print("Detailer: segment skip [empty mask]") + continue + + if noise_mask: + cropped_mask = seg.cropped_mask + else: + cropped_mask = None + + if wildcard_chooser is not None and wmode != "LAB": + seg_seed, wildcard_item = wildcard_chooser.get(seg) + elif wildcard_chooser is not None and wmode == "LAB": + seg_seed, wildcard_item = None, wildcard_chooser.get(seg) + else: + seg_seed, wildcard_item = None, None + + seg_seed = seed + i if seg_seed is None else seg_seed + + if not isinstance(positive, str): + cropped_positive = [ + [condition, { + k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in positive + ] + else: + cropped_positive = positive + + if not isinstance(negative, str): + cropped_negative = [ + [condition, { + k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in negative + ] + else: + # Negative Conditioning is placeholder such as FLUX.1 + cropped_negative = negative + + if wildcard_item and wildcard_item.strip() == '[SKIP]': + continue + + if wildcard_item and wildcard_item.strip() == '[STOP]': + break + + orig_cropped_image = cropped_image.clone() + + # initialize + enhanced_image = cropped_image + cnet_pils = None + + if not (isinstance(model, str) and model == "DUMMY"): + for retry in range(max_retries): + enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, + seg.bbox, seg_seed + retry, steps, cfg, sampler_name, scheduler, + cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint, + wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode, + detailer_hook=detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, + refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode, + vae_tiled_decode=tiled_decode) + + if detailer_hook is None or not detailer_hook.should_retry_patch(enhanced_image): + break + + if retry + 1 == max_retries: + raise Exception("Max retries reached") + else: + print("Detect bad patch, retrying...") + + if cnet_pils is not None: + cnet_pil_list.extend(cnet_pils) + + if enhanced_image is not None: + # don't latent composite-> converting to latent caused poor quality + # use image paste + image = image.cpu() + enhanced_image = enhanced_image.cpu() + utils.tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask) # this code affecting to `cropped_image`. + enhanced_list.append(enhanced_image) + + if detailer_hook is not None: + image = detailer_hook.post_paste(image) + + if enhanced_image is not None: + # Convert enhanced_pil_alpha to RGBA mode + enhanced_image_alpha = utils.tensor_convert_rgba(enhanced_image) + new_seg_image = enhanced_image.numpy() # alpha should not be applied to seg_image + + # Apply the mask + mask = utils.tensor_resize(mask, *utils.tensor_get_size(enhanced_image)) + utils.tensor_putalpha(enhanced_image_alpha, mask) + enhanced_alpha_list.append(enhanced_image_alpha) + else: + new_seg_image = None + + cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image` + + new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + new_segs.append(new_seg) + + image_tensor = utils.tensor_convert_rgb(image) + + cropped_list.sort(key=lambda x: x.shape, reverse=True) + enhanced_list.sort(key=lambda x: x.shape, reverse=True) + enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True) + + return image_tensor, cropped_list, enhanced_list, enhanced_alpha_list, cnet_pil_list, (segs[0], new_segs) + + def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, + scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1, + detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, + tiled_encode=False, tiled_decode=False, max_retries=1): + + enhanced_img, *_ = \ + DetailerForEachAutoRetry.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, + cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, + force_inpaint, wildcard, detailer_hook, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode, max_retries=max_retries) + + return (enhanced_img, ) + + +class DetailerForEachPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "segs": ("SEGS", ), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "detailer_hook": ("DETAILER_HOOK",), + "refiner_basic_pipe_opt": ("BASIC_PIPE",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + } + } + + RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE") + RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images") + OUTPUT_IS_LIST = (False, False, False, True) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = DetailerForEach.DESCRIPTION + + def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard, + refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, + cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, + tiled_encode=False, tiled_decode=False): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + model, clip, vae, positive, negative = basic_pipe + + if refiner_basic_pipe_opt is None: + refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None + else: + refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt + + enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \ + DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, + sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, + force_inpaint, wildcard, detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt, + tiled_encode=tiled_encode, tiled_decode=tiled_decode) + + # set fallback image + if len(cnet_pil_list) == 0: + cnet_pil_list = [utils.empty_pil_tensor()] + + return enhanced_img, new_segs, basic_pipe, cnet_pil_list + + +class FaceDetailer: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}), + "clip": ("CLIP",), + "vae": ("VAE",), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + "bbox_crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}), + + "sam_detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", "mask-point-bbox", "none"],), + "sam_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "sam_threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}), + "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam_mask_hint_use_negative": (["False", "Small", "Outter"],), + + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + + "bbox_detector": ("BBOX_DETECTOR", ), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "sam_model_opt": ("SAM_MODEL", ), + "segm_detector_opt": ("SEGM_DETECTOR", ), + "detailer_hook": ("DETAILER_HOOK",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + }} + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE") + RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "mask", "detailer_pipe", "cnet_images") + OUTPUT_IS_LIST = (False, True, True, False, False, True) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Simple" + + DESCRIPTION = "This node enhances details by automatically detecting specific objects in the input image using detection models (bbox, segm, sam) and regenerating the image by enlarging the detected area based on the guide size.\nAlthough this node is specialized to simplify the commonly used facial detail enhancement workflow, it can also be used for various automatic inpainting purposes depending on the detection model." + + @staticmethod + def enhance_face(image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, + bbox_threshold, bbox_dilation, bbox_crop_factor, + sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, + sam_mask_hint_use_negative, drop_size, + bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None, + refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1, + inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False): + + # make default prompt as 'face' if empty prompt for CLIPSeg + bbox_detector.setAux('face') + segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size, detailer_hook=detailer_hook) + bbox_detector.setAux(None) + + # bbox + sam combination + if sam_model_opt is not None: + sam_mask = core.make_sam_mask(sam_model_opt, segs, image, sam_detection_hint, sam_dilation, + sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, + sam_mask_hint_use_negative, ) + segs = core.segs_bitwise_and_mask(segs, sam_mask) + + elif segm_detector is not None: + segm_segs = segm_detector.detect(image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size) + + if (hasattr(segm_detector, 'override_bbox_by_segm') and segm_detector.override_bbox_by_segm and + not (detailer_hook is not None and not hasattr(detailer_hook, 'override_bbox_by_segm'))): + segs = segm_segs + else: + segm_mask = core.segs_to_combined_mask(segm_segs) + segs = core.segs_bitwise_and_mask(segs, segm_mask) + + if len(segs[1]) > 0: + enhanced_img, _, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \ + DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, + sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, + force_inpaint, wildcard_opt, detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, + refiner_negative=refiner_negative, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode) + else: + enhanced_img = image + cropped_enhanced = [] + cropped_enhanced_alpha = [] + cnet_pil_list = [] + + # Mask Generator + mask = core.segs_to_combined_mask(segs) + + if len(cropped_enhanced) == 0: + cropped_enhanced = [utils.empty_pil_tensor()] + + if len(cropped_enhanced_alpha) == 0: + cropped_enhanced_alpha = [utils.empty_pil_tensor()] + + if len(cnet_pil_list) == 0: + cnet_pil_list = [utils.empty_pil_tensor()] + + return enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list + + def doit(self, image, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, + bbox_threshold, bbox_dilation, bbox_crop_factor, + sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, + sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1, + sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, + scheduler_func_opt=None, tiled_encode=False, tiled_decode=False): + + result_img = None + result_mask = None + result_cropped_enhanced = [] + result_cropped_enhanced_alpha = [] + result_cnet_images = [] + + if len(image) > 1: + logging.warning("[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.") + + for i, single_image in enumerate(image): + enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face( + single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, + bbox_threshold, bbox_dilation, bbox_crop_factor, + sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, + sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt, + tiled_encode=tiled_encode, tiled_decode=tiled_decode) + + result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img + result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask + result_cropped_enhanced.extend(cropped_enhanced) + result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha) + result_cnet_images.extend(cnet_pil_list) + + pipe = (model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None) + return result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, pipe, result_cnet_images + + +class LatentPixelScale: + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples": ("LATENT", ), + "scale_method": (s.upscale_methods,), + "scale_factor": ("FLOAT", {"default": 1.5, "min": 0.1, "max": 10000, "step": 0.05}), + "vae": ("VAE", ), + "use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL", ), + } + } + + RETURN_TYPES = ("LATENT", "IMAGE") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, samples, scale_method, scale_factor, vae, use_tiled_vae, upscale_model_opt=None): + if upscale_model_opt is None: + latimg = core.latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=use_tiled_vae) + else: + latimg = core.latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model_opt, scale_factor, vae, use_tile=use_tiled_vae) + return latimg + + +class NoiseInjectionDetailerHookProvider: + schedules = ["skip_start", "from_start"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "schedule_for_cycle": (s.schedules,), + "source": (["CPU", "GPU"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "start_strength": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 200.0, "step": 0.01}), + "end_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + def doit(self, schedule_for_cycle, source, seed, start_strength, end_strength): + try: + hook = hooks.InjectNoiseHookForDetailer(source, seed, start_strength, end_strength, + from_start=('from_start' in schedule_for_cycle)) + return (hook, ) + except Exception as e: + logging.error(f"[Impact Pack] NoiseInjectionDetailerHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.\t{e}") + + +# class CustomNoiseDetailerHookProvider: +# @classmethod +# def INPUT_TYPES(s): +# return {"required": { +# "noise": ("NOISE",)}, +# } +# +# RETURN_TYPES = ("DETAILER_HOOK",) +# FUNCTION = "doit" +# +# CATEGORY = "ImpactPack/Detailer" +# +# def doit(self, noise): +# hook = hooks.CustomNoiseDetailerHookProvider(noise) +# return (hook, ) + + +class VariationNoiseDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01})} + } + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + def doit(self, seed, strength): + hook = hooks.VariationNoiseDetailerHookProvider(seed, strength) + return (hook, ) + + +class UnsamplerDetailerHookProvider: + schedules = ["skip_start", "from_start"] + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 25, "min": 1, "max": 10000}), + "start_end_at_step": ("INT", {"default": 21, "min": 0, "max": 10000}), + "end_end_at_step": ("INT", {"default": 24, "min": 0, "max": 10000}), + "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "normalize": (["disable", "enable"], ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "schedule_for_cycle": (s.schedules,), + }} + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + def doit(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name, + scheduler, normalize, positive, negative, schedule_for_cycle): + try: + hook = hooks.UnsamplerDetailerHook(model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name, + scheduler, normalize, positive, negative, + from_start=('from_start' in schedule_for_cycle)) + + return (hook, ) + except Exception as e: + logging.error(f"[Impact Pack] UnsamplerDetailerHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.\t{e}") + pass + + +class DenoiseSchedulerDetailerHookProvider: + schedules = ["simple"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "schedule_for_cycle": (s.schedules,), + "target_denoise": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + def doit(self, schedule_for_cycle, target_denoise): + hook = hooks.SimpleDetailerDenoiseSchedulerHook(target_denoise) + return (hook, ) + + +class CoreMLDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": {"mode": (["512x512", "768x768", "512x768", "768x512"], )}, } + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + def doit(self, mode): + hook = hooks.CoreMLHook(mode) + return (hook, ) + + +class CustomSamplerDetailerHookProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sampler": ("SAMPLER", ), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied." + + def doit(self, sampler): + hook = hooks.CustomSamplerDetailerHookProvider(sampler) + return (hook, ) + + +class CfgScheduleHookProvider: + schedules = ["simple"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "schedule_for_iteration": (s.schedules,), + "target_cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0}), + }, + } + + RETURN_TYPES = ("PK_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, schedule_for_iteration, target_cfg): + hook = None + if schedule_for_iteration == "simple": + hook = hooks.SimpleCfgScheduleHook(target_cfg) + + return (hook, ) + + +class UnsamplerHookProvider: + schedules = ["simple"] + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 25, "min": 1, "max": 10000}), + "start_end_at_step": ("INT", {"default": 21, "min": 0, "max": 10000}), + "end_end_at_step": ("INT", {"default": 24, "min": 0, "max": 10000}), + "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "normalize": (["disable", "enable"], ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "schedule_for_iteration": (s.schedules,), + }} + + RETURN_TYPES = ("PK_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name, + scheduler, normalize, positive, negative, schedule_for_iteration): + try: + hook = None + if schedule_for_iteration == "simple": + hook = hooks.UnsamplerHook(model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name, + scheduler, normalize, positive, negative) + + return (hook, ) + except Exception as e: + logging.error(f"[Impact Pack] UnsamplerHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.\t{e}") + + +class NoiseInjectionHookProvider: + schedules = ["simple"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "schedule_for_iteration": (s.schedules,), + "source": (["CPU", "GPU"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "start_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}), + "end_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("PK_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, schedule_for_iteration, source, seed, start_strength, end_strength): + try: + hook = None + if schedule_for_iteration == "simple": + hook = hooks.InjectNoiseHook(source, seed, start_strength, end_strength) + + return (hook, ) + except Exception as e: + logging.error(f"[Impact Pack] NoiseInjectionHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.\t{e}") + + +class DenoiseScheduleHookProvider: + schedules = ["simple"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "schedule_for_iteration": (s.schedules,), + "target_denoise": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("PK_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, schedule_for_iteration, target_denoise): + hook = None + if schedule_for_iteration == "simple": + hook = hooks.SimpleDenoiseScheduleHook(target_denoise) + + return (hook, ) + + +class StepsScheduleHookProvider: + schedules = ["simple"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "schedule_for_iteration": (s.schedules,), + "target_steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + }, + } + + RETURN_TYPES = ("PK_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, schedule_for_iteration, target_steps): + hook = None + if schedule_for_iteration == "simple": + hook = hooks.SimpleStepsScheduleHook(target_steps) + + return (hook, ) + + +class DetailerHookCombine: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "hook1": ("DETAILER_HOOK",), + "hook2": ("DETAILER_HOOK",), + }, + } + + RETURN_TYPES = ("DETAILER_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, hook1, hook2): + hook = hooks.DetailerHookCombine(hook1, hook2) + return (hook, ) + + +class PixelKSampleHookCombine: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "hook1": ("PK_HOOK",), + "hook2": ("PK_HOOK",), + }, + } + + RETURN_TYPES = ("PK_HOOK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, hook1, hook2): + hook = hooks.PixelKSampleHookCombine(hook1, hook2) + return (hook, ) + + +class PixelTiledKSampleUpscalerProvider: + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scale_method": (s.upscale_methods,), + "model": ("MODEL",), + "vae": ("VAE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}), + "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}), + "tiling_strategy": (["random", "padded", 'simple'], ), + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL", ), + "pk_hook_opt": ("PK_HOOK", ), + "tile_cnet_opt": ("CONTROL_NET", ), + "tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), + } + } + + RETURN_TYPES = ("UPSCALER",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy, upscale_model_opt=None, + pk_hook_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0, overlap=64): + if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS: + upscaler = core.PixelTiledKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, + tile_width, tile_height, tiling_strategy, upscale_model_opt, pk_hook_opt, tile_cnet_opt, + tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength, overlap=overlap) + return (upscaler, ) + else: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler', + "To use 'PixelTiledKSampleUpscalerProvider' node, 'BlenderNeko/ComfyUI_TiledKSampler' extension is required.") + + raise Exception("[ERROR] PixelTiledKSampleUpscalerProvider: ComfyUI_TiledKSampler custom node isn't installed. You must install BlenderNeko/ComfyUI_TiledKSampler extension to use this node.") + + +class PixelTiledKSampleUpscalerProviderPipe: + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scale_method": (s.upscale_methods,), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}), + "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}), + "tiling_strategy": (["random", "padded", 'simple'], ), + "basic_pipe": ("BASIC_PIPE",) + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL", ), + "pk_hook_opt": ("PK_HOOK", ), + "tile_cnet_opt": ("CONTROL_NET", ), + "tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("UPSCALER",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, scale_method, seed, steps, cfg, sampler_name, scheduler, denoise, tile_width, tile_height, tiling_strategy, basic_pipe, upscale_model_opt=None, pk_hook_opt=None, + tile_cnet_opt=None, tile_cnet_strength=1.0): + if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS: + model, _, vae, positive, negative = basic_pipe + upscaler = core.PixelTiledKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, + tile_width, tile_height, tiling_strategy, upscale_model_opt, pk_hook_opt, tile_cnet_opt, + tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength) + return (upscaler, ) + else: + logging.error("[Impact Pack] PixelTiledKSampleUpscalerProviderPipe: ComfyUI_TiledKSampler custom node isn't installed. You must install BlenderNeko/ComfyUI_TiledKSampler extension to use this node.") + raise Exception("[Impact Pack] PixelTiledKSampleUpscalerProviderPipe: ComfyUI_TiledKSampler custom node isn't installed. You must install BlenderNeko/ComfyUI_TiledKSampler extension to use this node.") + + +class PixelKSampleUpscalerProvider: + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scale_method": (s.upscale_methods,), + "model": ("MODEL",), + "vae": ("VAE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (core.get_schedulers(), ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}), + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL", ), + "pk_hook_opt": ("PK_HOOK", ), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("UPSCALER",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, + use_tiled_vae, upscale_model_opt=None, pk_hook_opt=None, tile_size=512, scheduler_func_opt=None): + upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, use_tiled_vae, upscale_model_opt, pk_hook_opt, + tile_size=tile_size, scheduler_func=scheduler_func_opt) + return (upscaler, ) + + +class PixelKSampleUpscalerProviderPipe(PixelKSampleUpscalerProvider): + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scale_method": (s.upscale_methods,), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (core.get_schedulers(), ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "basic_pipe": ("BASIC_PIPE",), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}), + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL", ), + "pk_hook_opt": ("PK_HOOK", ), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "tile_cnet_opt": ("CONTROL_NET", ), + "tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("UPSCALER",) + FUNCTION = "doit_pipe" + + CATEGORY = "ImpactPack/Upscale" + + def doit_pipe(self, scale_method, seed, steps, cfg, sampler_name, scheduler, denoise, + use_tiled_vae, basic_pipe, upscale_model_opt=None, pk_hook_opt=None, + tile_size=512, scheduler_func_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0): + model, _, vae, positive, negative = basic_pipe + upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, use_tiled_vae, upscale_model_opt, pk_hook_opt, + tile_size=tile_size, scheduler_func=scheduler_func_opt, + tile_cnet_opt=tile_cnet_opt, tile_cnet_strength=tile_cnet_strength) + return (upscaler, ) + + +class TwoSamplersForMaskUpscalerProvider: + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scale_method": (s.upscale_methods,), + "full_sample_schedule": ( + ["none", "interleave1", "interleave2", "interleave3", + "last1", "last2", + "interleave1+last1", "interleave2+last1", "interleave3+last1", + ],), + "use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "base_sampler": ("KSAMPLER", ), + "mask_sampler": ("KSAMPLER", ), + "mask": ("MASK", ), + "vae": ("VAE",), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}), + }, + "optional": { + "full_sampler_opt": ("KSAMPLER",), + "upscale_model_opt": ("UPSCALE_MODEL", ), + "pk_hook_base_opt": ("PK_HOOK", ), + "pk_hook_mask_opt": ("PK_HOOK", ), + "pk_hook_full_opt": ("PK_HOOK", ), + } + } + + RETURN_TYPES = ("UPSCALER", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, scale_method, full_sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae, + full_sampler_opt=None, upscale_model_opt=None, + pk_hook_base_opt=None, pk_hook_mask_opt=None, pk_hook_full_opt=None, tile_size=512): + upscaler = core.TwoSamplersForMaskUpscaler(scale_method, full_sample_schedule, use_tiled_vae, + base_sampler, mask_sampler, mask, vae, full_sampler_opt, upscale_model_opt, + pk_hook_base_opt, pk_hook_mask_opt, pk_hook_full_opt, tile_size=tile_size) + return (upscaler, ) + + +class TwoSamplersForMaskUpscalerProviderPipe: + upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scale_method": (s.upscale_methods,), + "full_sample_schedule": ( + ["none", "interleave1", "interleave2", "interleave3", + "last1", "last2", + "interleave1+last1", "interleave2+last1", "interleave3+last1", + ],), + "use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "base_sampler": ("KSAMPLER", ), + "mask_sampler": ("KSAMPLER", ), + "mask": ("MASK", ), + "basic_pipe": ("BASIC_PIPE",), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}), + }, + "optional": { + "full_sampler_opt": ("KSAMPLER",), + "upscale_model_opt": ("UPSCALE_MODEL", ), + "pk_hook_base_opt": ("PK_HOOK", ), + "pk_hook_mask_opt": ("PK_HOOK", ), + "pk_hook_full_opt": ("PK_HOOK", ), + } + } + + RETURN_TYPES = ("UPSCALER", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, scale_method, full_sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, basic_pipe, + full_sampler_opt=None, upscale_model_opt=None, + pk_hook_base_opt=None, pk_hook_mask_opt=None, pk_hook_full_opt=None, tile_size=512): + + mask = utils.make_2d_mask(mask) + + _, _, vae, _, _ = basic_pipe + upscaler = core.TwoSamplersForMaskUpscaler(scale_method, full_sample_schedule, use_tiled_vae, + base_sampler, mask_sampler, mask, vae, full_sampler_opt, upscale_model_opt, + pk_hook_base_opt, pk_hook_mask_opt, pk_hook_full_opt, tile_size=tile_size) + return (upscaler, ) + + +class IterativeLatentUpscale: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples": ("LATENT", ), + "upscale_factor": ("FLOAT", {"default": 1.5, "min": 1, "max": 10000, "step": 0.1}), + "steps": ("INT", {"default": 3, "min": 1, "max": 10000, "step": 1}), + "temp_prefix": ("STRING", {"default": ""}), + "upscaler": ("UPSCALER",), + "step_mode": (["simple", "geometric"], {"default": "simple"}), + "vae_compression": ("INT", {"default": 8, "min": 0, "max": 256, "step": 8}) + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("LATENT", "VAE") + RETURN_NAMES = ("latent", "vae") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + # dim_reduction_factor=8 for SD1/SDXL, used to calculate actual dims from latents based on VAE + def doit(self, samples, upscale_factor, steps, temp_prefix, upscaler, step_mode="simple", vae_compression=8, unique_id=None): + h, w = samples['samples'].shape[-2:] + w, h = w * vae_compression, h * vae_compression + + if temp_prefix == "": + temp_prefix = None + + if step_mode == "geometric": + upscale_factor_unit = pow(upscale_factor, 1.0/steps) + else: # simple + upscale_factor_unit = max(0, (upscale_factor - 1.0) / steps) + + current_latent = samples + noise_mask = current_latent.get('noise_mask') + scale = 1 + + for i in range(steps-1): + if step_mode == "geometric": + scale *= upscale_factor_unit + else: # simple + scale += upscale_factor_unit + + new_w = w*scale + new_h = h*scale + core.update_node_status(unique_id, f"{i+1}/{steps} steps | x{scale:.2f}", (i+1)/steps) + logging.info(f"IterativeLatentUpscale[{i+1}/{steps}]: {new_w:.1f}x{new_h:.1f} (scale:{scale:.2f}) ") + step_info = i, steps + current_latent = upscaler.upscale_shape(step_info, current_latent, new_w, new_h, temp_prefix) + if noise_mask is not None: + current_latent['noise_mask'] = noise_mask + + if scale < upscale_factor: + new_w = w*upscale_factor + new_h = h*upscale_factor + core.update_node_status(unique_id, f"Final step | x{upscale_factor:.2f}", 1.0) + logging.info(f"IterativeLatentUpscale[Final]: {new_w:.1f}x{new_h:.1f} (scale:{upscale_factor:.2f}) ") + step_info = steps-1, steps + current_latent = upscaler.upscale_shape(step_info, current_latent, new_w, new_h, temp_prefix) + + core.update_node_status(unique_id, "", None) + + return current_latent, upscaler.vae + + +class IterativeImageUpscale: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pixels": ("IMAGE", ), + "upscale_factor": ("FLOAT", {"default": 1.5, "min": 1, "max": 10000, "step": 0.1}), + "steps": ("INT", {"default": 3, "min": 1, "max": 10000, "step": 1}), + "temp_prefix": ("STRING", {"default": ""}), + "upscaler": ("UPSCALER",), + "vae": ("VAE",), + "step_mode": (["simple", "geometric"], {"default": "simple"}), + "vae_compression": ("INT", {"default": 8, "min": 0, "max": 256, "step": 8}) + }, + "hidden": {"unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + def doit(self, pixels, upscale_factor, steps, temp_prefix, upscaler, vae, step_mode="simple", vae_compression=8, unique_id=None): + if temp_prefix == "": + temp_prefix = None + + core.update_node_status(unique_id, "VAEEncode (first)", 0) + if upscaler.is_tiled: + encoder = nodes.VAEEncodeTiled() + if 'overlap' in inspect.signature(encoder.encode).parameters: + latent = encoder.encode(vae, pixels, upscaler.tile_size, overlap=upscaler.overlap)[0] + else: + latent = encoder.encode(vae, pixels, upscaler.tile_size)[0] + else: + latent = nodes.VAEEncode().encode(vae, pixels)[0] + + refined_latent = IterativeLatentUpscale().doit(latent, upscale_factor, steps, temp_prefix, upscaler, step_mode, vae_compression, unique_id) + + core.update_node_status(unique_id, "VAEDecode (final)", 1.0) + if upscaler.is_tiled: + pixels = nodes.VAEDecodeTiled().decode(vae, refined_latent[0], upscaler.tile_size)[0] + else: + pixels = nodes.VAEDecode().decode(vae, refined_latent[0])[0] + + core.update_node_status(unique_id, "", None) + + return (pixels, ) + + +class FaceDetailerPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "detailer_pipe": ("DETAILER_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the detailer_pipe, the inference stage is skipped."}), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + + "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "bbox_dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + "bbox_crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}), + + "sam_detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", "mask-point-bbox", "none"],), + "sam_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "sam_threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}), + "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "sam_mask_hint_use_negative": (["False", "Small", "Outter"],), + + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE") + RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "mask", "detailer_pipe", "cnet_images") + OUTPUT_IS_LIST = (False, True, True, False, False, True) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Simple" + + DESCRIPTION = FaceDetailer.DESCRIPTION + + def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor, + sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, + sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None, + cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, + tiled_encode=False, tiled_decode=False): + + result_img = None + result_mask = None + result_cropped_enhanced = [] + result_cropped_enhanced_alpha = [] + result_cnet_images = [] + + if len(image) > 1: + logging.warning("[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.") + + model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector, sam_model_opt, detailer_hook, \ + refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe + + for i, single_image in enumerate(image): + enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face( + single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, feather, noise_mask, force_inpaint, + bbox_threshold, bbox_dilation, bbox_crop_factor, + sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, + sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt, + tiled_encode=tiled_encode, tiled_decode=tiled_decode) + + result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img + result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask + result_cropped_enhanced.extend(cropped_enhanced) + result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha) + result_cnet_images.extend(cnet_pil_list) + + if len(result_cropped_enhanced) == 0: + result_cropped_enhanced = [utils.empty_pil_tensor()] + + if len(result_cropped_enhanced_alpha) == 0: + result_cropped_enhanced_alpha = [utils.empty_pil_tensor()] + + if len(result_cnet_images) == 0: + result_cnet_images = [utils.empty_pil_tensor()] + + return result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, detailer_pipe, result_cnet_images + + +class MaskDetailerPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "mask": ("MASK", ), + "basic_pipe": ("BASIC_PIPE",), + + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "mask bbox", "label_off": "crop region"}), + "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}), + "mask_mode": ("BOOLEAN", {"default": True, "label_on": "masked only", "label_off": "whole"}), + + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "refiner_basic_pipe_opt": ("BASIC_PIPE", ), + "detailer_hook": ("DETAILER_HOOK",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "bbox_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "contour_fill": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "BASIC_PIPE", "BASIC_PIPE") + RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "basic_pipe", "refiner_basic_pipe_opt") + OUTPUT_IS_LIST = (False, True, True, False, False) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "" + + def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode, + seed, steps, cfg, sampler_name, scheduler, denoise, + feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1, + refiner_basic_pipe_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, + bbox_fill=False, contour_fill=True, scheduler_func_opt=None): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: MaskDetailer does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + model, clip, vae, positive, negative = basic_pipe + + if refiner_basic_pipe_opt is None: + refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None + else: + refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt + + # create segs + if mask is not None: + mask = utils.make_2d_mask(mask) + segs = core.mask_to_segs(mask, False, crop_factor, bbox_fill, drop_size, is_contour=contour_fill) + else: + segs = ((image.shape[1], image.shape[2]), []) + + enhanced_img_batch = None + cropped_enhanced_list = [] + cropped_enhanced_alpha_list = [] + + for i in range(batch_size): + if mask is not None: + enhanced_img, _, cropped_enhanced, cropped_enhanced_alpha, _, _ = \ + DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed+i, steps, + cfg, sampler_name, scheduler, positive, negative, denoise, feather, mask_mode, + force_inpaint=True, wildcard_opt=None, detailer_hook=detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, refiner_clip=refiner_clip, + refiner_positive=refiner_positive, refiner_negative=refiner_negative, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt) + else: + enhanced_img, cropped_enhanced, cropped_enhanced_alpha = image, [], [] + + if enhanced_img_batch is None: + enhanced_img_batch = enhanced_img + else: + enhanced_img_batch = torch.cat((enhanced_img_batch, enhanced_img), dim=0) + + cropped_enhanced_list += cropped_enhanced + cropped_enhanced_alpha_list += cropped_enhanced_alpha + + # set fallback image + if len(cropped_enhanced_list) == 0: + cropped_enhanced_list = [utils.empty_pil_tensor()] + + if len(cropped_enhanced_alpha_list) == 0: + cropped_enhanced_alpha_list = [utils.empty_pil_tensor()] + + return enhanced_img_batch, cropped_enhanced_list, cropped_enhanced_alpha_list, basic_pipe, refiner_basic_pipe_opt + + +class DetailerForEachTest(DetailerForEach): + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE") + RETURN_NAMES = ("image", "cropped", "cropped_refined", "cropped_refined_alpha", "cnet_images") + OUTPUT_IS_LIST = (False, True, True, True, True) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, + scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, detailer_hook=None, + cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \ + DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, + cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, + force_inpaint, wildcard, detailer_hook, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode) + + # set fallback image + if len(cropped) == 0: + cropped = [utils.empty_pil_tensor()] + + if len(cropped_enhanced) == 0: + cropped_enhanced = [utils.empty_pil_tensor()] + + if len(cropped_enhanced_alpha) == 0: + cropped_enhanced_alpha = [utils.empty_pil_tensor()] + + if len(cnet_pil_list) == 0: + cnet_pil_list = [utils.empty_pil_tensor()] + + return enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list + + +class DetailerForEachTestPipe(DetailerForEachPipe): + RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE", "IMAGE", "IMAGE", "IMAGE", ) + RETURN_NAMES = ("image", "segs", "basic_pipe", "cropped", "cropped_refined", "cropped_refined_alpha", 'cnet_images') + OUTPUT_IS_LIST = (False, False, False, True, True, True, True) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = DetailerForEach.DESCRIPTION + + def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard, cycle=1, + refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, + scheduler_func_opt=None, tiled_encode=False, tiled_decode=False): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + model, clip, vae, positive, negative = basic_pipe + + if refiner_basic_pipe_opt is None: + refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None + else: + refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt + + enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \ + DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, + sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, + force_inpaint, wildcard, detailer_hook, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, + refiner_negative=refiner_negative, + cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, + scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode) + + # set fallback image + if len(cropped) == 0: + cropped = [utils.empty_pil_tensor()] + + if len(cropped_enhanced) == 0: + cropped_enhanced = [utils.empty_pil_tensor()] + + if len(cropped_enhanced_alpha) == 0: + cropped_enhanced_alpha = [utils.empty_pil_tensor()] + + if len(cnet_pil_list) == 0: + cnet_pil_list = [utils.empty_pil_tensor()] + + return enhanced_img, new_segs, basic_pipe, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list + + +class SegsBitwiseAndMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS",), + "mask": ("MASK",), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, segs, mask): + return (core.segs_bitwise_and_mask(segs, mask), ) + + +class SegsBitwiseAndMaskForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS",), + "masks": ("MASK",), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, segs, masks): + return (core.apply_mask_to_each_seg(segs, masks), ) + + +class BitwiseAndMaskForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "base_segs": ("SEGS",), + "mask_segs": ("SEGS",), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + DESCRIPTION = "Retains only the overlapping areas between the masks included in base_segs and the mask regions of mask_segs. SEGS with no overlapping mask areas are filtered out." + + def doit(self, base_segs, mask_segs): + mask = core.segs_to_combined_mask(mask_segs) + mask = utils.make_3d_mask(mask) + + return SegsBitwiseAndMask().doit(base_segs, mask) + + +class SubtractMaskForEach: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "base_segs": ("SEGS",), + "mask_segs": ("SEGS",), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + DESCRIPTION = "Removes only the overlapping areas between the masks included in base_segs and the mask regions of mask_segs. SEGS with no overlapping mask areas are filtered out." + + def doit(self, base_segs, mask_segs): + mask = core.segs_to_combined_mask(mask_segs) + mask = utils.make_3d_mask(mask) + return (core.segs_bitwise_subtract_mask(base_segs, mask), ) + + +class ToBinaryMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK",), + "threshold": ("INT", {"default": 20, "min": 1, "max": 255}), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, mask, threshold): + mask = utils.to_binary_mask(mask, threshold/255.0) + return (mask,) + + +class FlattenMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "masks": ("MASK",), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, masks): + masks = utils.make_3d_mask(masks) + masks = utils.flatten_mask(masks) + return (masks,) + + +class BitwiseAndMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask1": ("MASK",), + "mask2": ("MASK",), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, mask1, mask2): + mask = utils.bitwise_and_masks(mask1, mask2) + return (mask,) + + +class SubtractMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask1": ("MASK", ), + "mask2": ("MASK", ), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, mask1, mask2): + mask = utils.subtract_masks(mask1, mask2) + return (mask,) + + +class AddMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask1": ("MASK",), + "mask2": ("MASK",), + } + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, mask1, mask2): + mask = utils.add_masks(mask1, mask2) + return (mask,) + + +def get_image_hash(arr): + split_index1 = arr.shape[0] // 2 + split_index2 = arr.shape[1] // 2 + part1 = arr[:split_index1, :split_index2] + part2 = arr[:split_index1, split_index2:] + part3 = arr[split_index1:, :split_index2] + part4 = arr[split_index1:, split_index2:] + + # 각 뢀뢄을 ν•©μ‚° + sum1 = np.sum(part1) + sum2 = np.sum(part2) + sum3 = np.sum(part3) + sum4 = np.sum(part4) + + return hash((sum1, sum2, sum3, sum4)) + + +def get_file_item(base_type, path): + path_type = base_type + + if path == "[output]": + path_type = "output" + path = path[:-9] + elif path == "[input]": + path_type = "input" + path = path[:-8] + elif path == "[temp]": + path_type = "temp" + path = path[:-7] + + subfolder = os.path.dirname(path) + filename = os.path.basename(path) + + return { + "filename": filename, + "subfolder": subfolder, + "type": path_type + } + + +class MaskRectArea: + # Creates a rectangle mask using percentage. + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + # Added typed INT inputs so this node can be driven by other INT nodes. + "x": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), + "width": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), + "height": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), + "blur_radius": ("INT", {"default": 0, "min": 0, "step": 1}) + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("MASK",) + + CATEGORY = "ImpactPack/Operation" + FUNCTION = "create_mask" + + def create_mask(self, x, y, width, height, blur_radius, extra_pnginfo, unique_id): + # Backward-compat: if node properties exist in workflow, prefer them. + try: + for node in extra_pnginfo["workflow"]["nodes"]: + if str(node["id"]) == str(unique_id): + props = node.get("properties", {}) + x = int(props.get("x", x)) + y = int(props.get("y", y)) + width = int(props.get("w", width)) + height = int(props.get("h", height)) + blur_radius = int(props.get("blur_radius", blur_radius)) + break + except Exception: + pass + + # Clamp percent inputs + if x < 0: + x = 0 + if y < 0: + y = 0 + if width < 0: + width = 0 + if height < 0: + height = 0 + if x > 100: + x = 100 + if y > 100: + y = 100 + if width > 100: + width = 100 + if height > 100: + height = 100 + + # Convert percent to ratio + min_x = x / 100.0 + min_y = y / 100.0 + w_ratio = width / 100.0 + h_ratio = height / 100.0 + + # Create a mask with standard resolution (e.g., 512x512) + resolution = 512 + mask = torch.zeros((resolution, resolution), dtype=torch.float32) + + # Calculate pixel coordinates + min_x_px = int(min_x * resolution) + min_y_px = int(min_y * resolution) + max_x_px = int((min_x + w_ratio) * resolution) + max_y_px = int((min_y + h_ratio) * resolution) + + # Clamp pixel bounds + if min_x_px < 0: + min_x_px = 0 + if min_y_px < 0: + min_y_px = 0 + if max_x_px > resolution: + max_x_px = resolution + if max_y_px > resolution: + max_y_px = resolution + + # Draw the rectangle on the mask + if max_x_px > min_x_px and max_y_px > min_y_px: + mask[min_y_px:max_y_px, min_x_px:max_x_px] = 1.0 + + # Apply blur if the radii are greater than 0 + if blur_radius > 0: + dx = blur_radius * 2 + 1 + dy = blur_radius * 2 + 1 + + # Convert the mask to a format compatible with OpenCV (numpy array) + mask_np = mask.cpu().numpy().astype("float32") + + # Apply Gaussian Blur + blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0) + + # Convert back to tensor + mask = torch.from_numpy(blurred_mask) + + # Return the mask as a tensor with an additional channel + return (mask.unsqueeze(0),) + + +class MaskRectAreaAdvanced: + # Creates a rectangle mask using pixels relative to image size. + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "x": ("INT", {"default": 0, "min": 0, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "step": 1}), + "width": ("INT", {"default": 256, "min": 0, "step": 1}), + "height": ("INT", {"default": 320, "min": 0, "step": 1}), + "image_width": ("INT", {"default": 512, "min": 1, "step": 1}), + "image_height": ("INT", {"default": 320, "min": 1, "step": 1}), + "blur_radius": ("INT", {"default": 0, "min": 0, "step": 1}) + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("MASK",) + + CATEGORY = "ImpactPack/Operation" + FUNCTION = "create_mask_advanced" + + def create_mask_advanced(self, x, y, width, height, image_width, image_height, blur_radius, extra_pnginfo, unique_id): + # Backward-compat fallback: if node properties exist in workflow, prefer them + try: + for node in extra_pnginfo["workflow"]["nodes"]: + if node["id"] == int(unique_id): + props = node.get("properties", {}) + x = int(props.get("x", x)) + y = int(props.get("y", y)) + width = int(props.get("w", width)) + height = int(props.get("h", height)) + image_width = int(props.get("width", image_width)) + image_height = int(props.get("height", image_height)) + blur_radius = int(props.get("blur_radius", blur_radius)) + break + except Exception: + pass + + # Clamp to safe bounds + if image_width < 1: + image_width = 1 + if image_height < 1: + image_height = 1 + if width < 0: + width = 0 + if height < 0: + height = 0 + if x < 0: + x = 0 + if y < 0: + y = 0 + + max_x = min(x + width, image_width) + max_y = min(y + height, image_height) + + mask = torch.zeros((image_height, image_width), dtype=torch.float32) + + if max_x > x and max_y > y: + mask[y:max_y, x:max_x] = 1.0 + + # Apply blur if the radii are greater than 0 + if blur_radius > 0: + k = blur_radius * 2 + 1 + mask_np = mask.cpu().numpy().astype("float32") + blurred_mask = cv2.GaussianBlur(mask_np, (k, k), 0) + mask = torch.from_numpy(blurred_mask) + + # Return the mask as a tensor with an additional channel + return (mask.unsqueeze(0),) + + +class ImageReceiver: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": { + "image": (sorted(files), ), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "save_to_workflow": ("BOOLEAN", {"default": False}), + "image_data": ("STRING", {"multiline": False}), + "trigger_always": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + }, + } + + FUNCTION = "doit" + + RETURN_TYPES = ("IMAGE", "MASK") + + CATEGORY = "ImpactPack/Util" + + def doit(self, image, link_id, save_to_workflow, image_data, trigger_always): + if save_to_workflow: + try: + image_data = base64.b64decode(image_data.split(",")[1]) + i = Image.open(BytesIO(image_data)) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + return image, mask.unsqueeze(0) + except Exception: + logging.warning("[WARN] ComfyUI-Impact-Pack: ImageReceiver - invalid 'image_data'") + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + return utils.empty_pil_tensor(64, 64), mask + else: + return nodes.LoadImage().load_image(image) + + @classmethod + def VALIDATE_INPUTS(s, image, link_id, save_to_workflow, image_data, trigger_always): + if image != '#DATA' and not folder_paths.exists_annotated_filepath(image) or image.startswith("/") or ".." in image: + return "Invalid image file: {}".format(image) + + return True + + @classmethod + def IS_CHANGED(s, image, link_id, save_to_workflow, image_data, trigger_always): + if trigger_always: + return float("NaN") + else: + if save_to_workflow: + return hash(image_data) + else: + return hash(image) + + +from server import PromptServer + +class ImageSender(nodes.PreviewImage): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ImgSender"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + OUTPUT_NODE = True + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, images, filename_prefix="ImgSender", link_id=0, prompt=None, extra_pnginfo=None): + result = nodes.PreviewImage().save_images(images, filename_prefix, prompt, extra_pnginfo) + PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": result['ui']['images']}) + return result + + +class LatentReceiver: + def __init__(self): + self.input_dir = folder_paths.get_input_directory() + self.type = "input" + + @classmethod + def INPUT_TYPES(s): + def check_file_extension(x): + return x.endswith(".latent") or x.endswith(".latent.png") + + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and check_file_extension(f)] + return {"required": { + "latent": (sorted(files), ), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "trigger_always": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + }, + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + RETURN_TYPES = ("LATENT",) + + @staticmethod + def load_preview_latent(image_path): + if not os.path.exists(image_path): + return None + + image = Image.open(image_path) + exif_data = piexif.load(image.info["exif"]) + + if piexif.ExifIFD.UserComment in exif_data["Exif"]: + compressed_data = exif_data["Exif"][piexif.ExifIFD.UserComment] + compressed_data_io = BytesIO(compressed_data) + with zipfile.ZipFile(compressed_data_io, mode='r') as archive: + tensor_bytes = archive.read("latent") + tensor = safetensors.torch.load(tensor_bytes) + return {"samples": tensor['latent_tensor']} + return None + + def parse_filename(self, filename): + pattern = r"^(.*)/(.*?)\[(.*)\]\s*$" + match = re.match(pattern, filename) + if match: + subfolder = match.group(1) + filename = match.group(2).rstrip() + file_type = match.group(3) + else: + subfolder = '' + file_type = self.type + + return {'filename': filename, 'subfolder': subfolder, 'type': file_type} + + def doit(self, **kwargs): + if 'latent' not in kwargs: + return (torch.zeros([1, 4, 8, 8]), ) + + latent = kwargs['latent'] + + latent_name = latent + latent_path = folder_paths.get_annotated_filepath(latent_name) + + if latent.endswith(".latent"): + latent = safetensors.torch.load_file(latent_path, device="cpu") + multiplier = 1.0 + if "latent_format_version_0" not in latent: + multiplier = 1.0 / 0.18215 + samples = {"samples": latent["latent_tensor"].float() * multiplier} + else: + samples = LatentReceiver.load_preview_latent(latent_path) + + if samples is None: + samples = {'samples': torch.zeros([1, 4, 8, 8])} + + preview = self.parse_filename(latent_name) + + return { + 'ui': {"images": [preview]}, + 'result': (samples, ) + } + + @classmethod + def IS_CHANGED(s, latent, link_id, trigger_always): + if trigger_always: + return float("NaN") + else: + image_path = folder_paths.get_annotated_filepath(latent) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, latent, link_id, trigger_always): + if not folder_paths.exists_annotated_filepath(latent) or latent.startswith("/") or ".." in latent: + return "Invalid latent file: {}".format(latent) + return True + + +class LatentSender(nodes.SaveLatent): + def __init__(self): + super().__init__() + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples": ("LATENT", ), + "filename_prefix": ("STRING", {"default": "latents/LatentSender"}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "preview_method": (["Latent2RGB-FLUX.1", + "Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3", + "Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5", + "Latent2RGB-SC-Prior", "Latent2RGB-SC-B", + "Latent2RGB-LTXV", + "TAEF1", "TAESDXL", "TAESD15", "TAESD3"],) + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + OUTPUT_NODE = True + + RETURN_TYPES = () + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def save_to_file(tensor_bytes, prompt, extra_pnginfo, image, image_path): + compressed_data = BytesIO() + with zipfile.ZipFile(compressed_data, mode='w') as archive: + archive.writestr("latent", tensor_bytes) + image = image.copy() + exif_data = {"Exif": {piexif.ExifIFD.UserComment: compressed_data.getvalue()}} + + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + exif_bytes = piexif.dump(exif_data) + image.save(image_path, format='png', exif=exif_bytes, pnginfo=metadata, optimize=True) + + @staticmethod + def prepare_preview(latent_tensor, preview_method): + from comfy.cli_args import LatentPreviewMethod + import comfy.latent_formats as latent_formats + + lower_bound = 128 + upper_bound = 256 + + if preview_method == "Latent2RGB-SD15": + latent_format = latent_formats.SD15() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SDXL": + latent_format = latent_formats.SDXL() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SD3": + latent_format = latent_formats.SD3() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SD-X4": + latent_format = latent_formats.SD_X4() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-Playground-2.5": + latent_format = latent_formats.SDXL_Playground_2_5() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SC-Prior": + latent_format = latent_formats.SC_Prior() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-SC-B": + latent_format = latent_formats.SC_B() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-FLUX.1": + latent_format = latent_formats.Flux() + method = LatentPreviewMethod.Latent2RGB + elif preview_method == "Latent2RGB-LTXV": + latent_format = latent_formats.LTXV() + method = LatentPreviewMethod.Latent2RGB + else: + logging.warning(f"[Impact Pack] LatentSender: '{preview_method}' is unsupported preview method.") + latent_format = latent_formats.SD15() + method = LatentPreviewMethod.Latent2RGB + + previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method) + + image = previewer.decode_latent_to_preview(latent_tensor) + min_size = min(image.size[0], image.size[1]) + max_size = max(image.size[0], image.size[1]) + + scale_factor = 1 + if max_size > upper_bound: + scale_factor = upper_bound/max_size + + # prevent too small preview + if min_size*scale_factor < lower_bound: + scale_factor = lower_bound/min_size + + w = int(image.size[0] * scale_factor) + h = int(image.size[1] * scale_factor) + + image = image.resize((w, h), resample=Image.NEAREST) + + return LatentSender.attach_format_text(image) + + @staticmethod + def attach_format_text(image): + width_a, height_a = image.size + + letter_image = Image.open(latent_letter_path) + width_b, height_b = letter_image.size + + new_width = max(width_a, width_b) + new_height = height_a + height_b + + new_image = Image.new('RGB', (new_width, new_height), (0, 0, 0)) + + offset_x = (new_width - width_b) // 2 + offset_y = (height_a + (new_height - height_a - height_b) // 2) + new_image.paste(letter_image, (offset_x, offset_y)) + + new_image.paste(image, (0, 0)) + + return new_image + + def doit(self, samples, filename_prefix="latents/LatentSender", link_id=0, preview_method="Latent2RGB-SDXL", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + # load preview + preview = LatentSender.prepare_preview(samples['samples'], preview_method) + + # support save metadata for latent sharing + file = f"{filename}_{counter:05}_.latent.png" + fullpath = os.path.join(full_output_folder, file) + + output = {"latent_tensor": samples["samples"]} + + tensor_bytes = safetensors.torch.save(output) + LatentSender.save_to_file(tensor_bytes, prompt, extra_pnginfo, preview, fullpath) + + latent_path = { + 'filename': file, + 'subfolder': subfolder, + 'type': self.type + } + + PromptServer.instance.send_sync("latent-send", {"link_id": link_id, "images": [latent_path]}) + + return {'ui': {'images': [latent_path]}} + + +class ImpactWildcardProcessor: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}), + "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardProcessor' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}), + "mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip": + "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n" + "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n" + "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode." + }), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + }, + } + + CATEGORY = "ImpactPack/Prompt" + + DESCRIPTION = ("The 'ImpactWildcardProcessor' processes text prompts written in wildcard syntax and outputs the processed text prompt.\n\n" + "TIP: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'.") + + RETURN_TYPES = ("STRING", ) + RETURN_NAMES = ("processed text",) + FUNCTION = "doit" + + @staticmethod + def process(**kwargs): + return impact.wildcards.process(**kwargs) + + def doit(self, *args, **kwargs): + populated_text = ImpactWildcardProcessor.process(text=kwargs['populated_text'], seed=kwargs['seed']) + return (populated_text, ) + + +class ImpactWildcardEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}), + "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardEncode' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}), + "mode": (["populate", "fixed", "reproduce"], {"tooltip": + "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n" + "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode\n." + "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ), + "Select to add Wildcard": (["Select the Wildcard to add to the text"], ), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}), + }, + } + + CATEGORY = "ImpactPack/Prompt" + + DESCRIPTION = ("The 'ImpactWildcardEncode' node processes text prompts written in wildcard syntax and outputs them as conditioning. It also supports LoRA syntax, with the applied LoRA reflected in the model's output.\n\n" + "TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'.\n" + "TIP2: If the 'Inspire Pack' is installed, LBW(LoRA Block Weight) syntax can also be applied.") + + RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING") + RETURN_NAMES = ("model", "clip", "conditioning", "populated_text") + FUNCTION = "doit" + + @staticmethod + def process_with_loras(**kwargs): + return impact.wildcards.process_with_loras(**kwargs) + + @staticmethod + def get_wildcard_list(): + return impact.wildcards.get_wildcard_list() + + def doit(self, *args, **kwargs): + populated = kwargs['populated_text'] + processed = [] + model, clip, conditioning = impact.wildcards.process_with_loras(wildcard_opt=populated, model=kwargs['model'], clip=kwargs['clip'], seed=kwargs['seed'], processed=processed) + return model, clip, conditioning, processed[0] + + +class ImpactSchedulerAdapter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"defaultInput": True, }), + "extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]', 'OSS FLUX', 'OSS Wan', 'OSS Chroma'],), + }} + + CATEGORY = "ImpactPack/Util" + + RETURN_TYPES = (core.get_schedulers(),) + RETURN_NAMES = ("scheduler",) + + FUNCTION = "doit" + + def doit(self, scheduler, extra_scheduler): + if extra_scheduler != 'None': + return (extra_scheduler,) + + return (scheduler,) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_sampling.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_sampling.py new file mode 100644 index 00000000..06de0b57 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_sampling.py @@ -0,0 +1,323 @@ +import logging + +import nodes +from comfy.k_diffusion import sampling as k_diffusion_sampling +from comfy import samplers +from comfy_extras import nodes_custom_sampler +import latent_preview +import comfy +import torch +import math +import comfy.model_management as mm + + +try: + from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise + import node_helpers +except Exception: + logging.warning("\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n") + raise Exception("[Impact Pack] ComfyUI is an outdated version.") + + +def calculate_sigmas(model, sampler, scheduler, steps): + discard_penultimate_sigma = False + if sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']: + steps += 1 + discard_penultimate_sigma = True + + if scheduler.startswith('AYS'): + sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0] + elif scheduler.startswith('GITS[coeff='): + sigmas = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']().execute(float(scheduler[11:-1]), steps, denoise=1.0)[0] + elif scheduler == 'LTXV[default]': + sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().execute(20, 2.05, 0.95, True, 0.1)[0] + elif scheduler.startswith('OSS'): + sigmas = nodes.NODE_CLASS_MAPPINGS['OptimalStepsScheduler']().execute(scheduler[4:], steps, denoise=1.0)[0] + else: + sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + +def get_noise_sampler(x, cpu, total_sigmas, **kwargs): + if 'extra_args' in kwargs and 'seed' in kwargs['extra_args']: + sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max() + seed = kwargs['extra_args'].get("seed", None) + return k_diffusion_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=cpu) + return None + + +def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}): + if sampler_name in ["dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu"]: + if sampler_name == "dpmpp_sde": + orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde + elif sampler_name == "dpmpp_sde_gpu": + orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde_gpu + elif sampler_name == "dpmpp_2m_sde": + orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde + elif sampler_name == "dpmpp_2m_sde_gpu": + orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde_gpu + elif sampler_name == "dpmpp_3m_sde": + orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde + elif sampler_name == "dpmpp_3m_sde_gpu": + orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde_gpu + + def sampler_function_wrapper(model, x, sigmas, **kwargs): + if 'noise_sampler' not in kwargs: + kwargs['noise_sampler'] = get_noise_sampler(x, 'gpu' not in sampler_name, total_sigmas, **kwargs) + + return orig_sampler_function(model, x, sigmas, **kwargs) + + sampler_function = sampler_function_wrapper + + else: + return comfy.samplers.sampler_object(sampler_name) + + return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options) + + +# modified version of SamplerCustom.sample +def sample_with_custom_noise(model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image, noise=None, callback=None): + latent = latent_image + latent_image = latent["samples"] + + if hasattr(comfy.sample, 'fix_empty_latent_channels'): + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + out = latent.copy() + out['samples'] = latent_image + + if noise is None: + if not add_noise: + noise = Noise_EmptyNoise().generate_noise(out) + else: + noise = Noise_RandomNoise(noise_seed).generate_noise(out) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + preview_callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) + + if callback is not None: + def touched_callback(step, x0, x, total_steps): + callback(step, x0, x, total_steps) + preview_callback(step, x0, x, total_steps) + else: + touched_callback = preview_callback + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + + device = mm.get_torch_device() + + noise = noise.to(device) + latent_image = latent_image.to(device) + if noise_mask is not None: + noise_mask = noise_mask.to(device) + + if negative != 'NegativePlaceholder': + # This way is incompatible with Advanced ControlNet, yet. + # guider = comfy.samplers.CFGGuider(model) + # guider.set_conds(positive, negative) + # guider.set_cfg(cfg) + samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, + noise_mask=noise_mask, callback=touched_callback, + disable_pbar=disable_pbar, seed=noise_seed) + else: + guider = nodes_custom_sampler.Guider_Basic(model) + positive = node_helpers.conditioning_set_values(positive, {"guidance": cfg}) + guider.set_conds(positive) + samples = guider.sample(noise, latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=touched_callback, disable_pbar=disable_pbar, seed=noise_seed) + + samples = samples.to(comfy.model_management.intermediate_device()) + + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return out, out_denoised + + +# When sampling one step at a time, it mitigates the problem. (especially for _sde series samplers) +def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative, + latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None, noise=None, callback=None, scheduler_func=None): + + if scheduler_func is not None: + total_sigmas = scheduler_func(model, sampler_name, steps) + else: + if sampler_opt is None: + total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps) + else: + total_sigmas = calculate_sigmas(model, "", scheduler, steps) + + sigmas = total_sigmas + + if end_at_step is not None and end_at_step < (len(total_sigmas) - 1): + sigmas = total_sigmas[:end_at_step + 1] + if not return_with_leftover_noise: + sigmas[-1] = 0 + + if start_at_step is not None: + if start_at_step < (len(sigmas) - 1): + sigmas = sigmas[start_at_step:] * sigma_ratio + else: + if latent_image is not None: + return latent_image + else: + return {'samples': torch.zeros_like(noise)} + + if sampler_opt is None: + impact_sampler = ksampler(sampler_name, total_sigmas) + else: + impact_sampler = sampler_opt + + if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0): + return latent_image + + res = sample_with_custom_noise(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image, noise=noise, callback=callback) + + if return_with_leftover_noise: + return res[0] + else: + return res[1] + + +def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None, scheduler_func=None): + advanced_steps = math.floor(steps / denoise) + start_at_step = advanced_steps - steps + end_at_step = start_at_step + steps + return separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + start_at_step, end_at_step, False, scheduler_func=scheduler_func) + + +def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, + refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None, sampler_opt=None): + + if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None: + # Use separated_sample instead of KSampler for `AYS scheduler` + # refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0] + + advanced_steps = math.floor(steps / denoise) + start_at_step = advanced_steps - steps + end_at_step = start_at_step + steps + + refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, start_at_step, end_at_step, False, + sigma_ratio=sigma_factor, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func) + else: + advanced_steps = math.floor(steps / denoise) + start_at_step = advanced_steps - steps + end_at_step = start_at_step + math.floor(steps * (1.0 - refiner_ratio)) + + # print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}") + temp_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, start_at_step, end_at_step, True, + sigma_ratio=sigma_factor, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func) + + if 'noise_mask' in latent_image: + # noise_latent = \ + # impact_sampling.separated_sample(refiner_model, "enable", seed, advanced_steps, cfg, sampler_name, + # scheduler, refiner_positive, refiner_negative, latent_image, end_at_step, + # end_at_step, "enable") + + latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']() + temp_latent = latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0] + + # print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}") + refined_latent = separated_sample(refiner_model, False, seed, advanced_steps, cfg, sampler_name, scheduler, + refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False, + sigma_ratio=sigma_factor, sampler_opt=sampler_opt, scheduler_func=scheduler_func) + + return refined_latent + + +class KSamplerAdvancedWrapper: + params = None + + def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0, scheduler_func=None): + self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor + self.sampler_opt = sampler_opt + self.scheduler_func = scheduler_func + + def clone_with_conditionings(self, positive, negative): + model, cfg, sampler_name, scheduler, _, _, _ = self.params + return KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, self.sampler_opt) + + def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step, return_with_leftover_noise, hook=None, + recovery_mode="ratio additional", recovery_sampler="AUTO", recovery_sigma_ratio=1.0, noise=None): + + model, cfg, sampler_name, scheduler, positive, negative, sigma_factor = self.params + # steps, start_at_step, end_at_step = self.compensate_denoise(steps, start_at_step, end_at_step) + + if hook is not None: + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent = hook.pre_ksample_advanced(model, add_noise, seed, steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, start_at_step, end_at_step, + return_with_leftover_noise) + + if recovery_mode != 'DISABLE' and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']: + base_image = latent_image.copy() + if recovery_mode == "ratio between": + sigma_ratio = 1.0 - recovery_sigma_ratio + else: + sigma_ratio = 1.0 + else: + base_image = None + sigma_ratio = 1.0 + + try: + if sigma_ratio > 0: + latent_image = separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, start_at_step, end_at_step, + return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor, + sampler_opt=self.sampler_opt, noise=noise, scheduler_func=self.scheduler_func) + except ValueError as e: + if str(e) == 'sigma_min and sigma_max must not be 0': + logging.warning("\nWARN: sampling skipped - sigma_min and sigma_max are 0") + return latent_image + + if (recovery_sigma_ratio > 0 and recovery_mode != 'DISABLE' and + sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']): + compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu'] else 2 + if recovery_sampler == "AUTO": + recovery_sampler = 'dpm_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m' + + latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']() + + noise_mask = latent_image['noise_mask'] + + if len(noise_mask.shape) == 4: + noise_mask = noise_mask.squeeze(0).squeeze(0) + + latent_image = latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0] + + try: + latent_image = separated_sample(model, add_noise, seed, steps, cfg, recovery_sampler, scheduler, + positive, negative, latent_image, start_at_step-compensate, end_at_step, return_with_leftover_noise, + sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt, scheduler_func=self.scheduler_func) + except ValueError as e: + if str(e) == 'sigma_min and sigma_max must not be 0': + logging.warning("\nWARN: sampling skipped - sigma_min and sigma_max are 0") + + return latent_image + + +class KSamplerWrapper: + params = None + + def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=None): + self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise + self.scheduler_func = scheduler_func + + def sample(self, latent_image, hook=None): + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params + + if hook is not None: + model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \ + hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise) + + return impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=self.scheduler_func) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_server.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_server.py new file mode 100644 index 00000000..af20f116 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_server.py @@ -0,0 +1,619 @@ +import io +import logging +import os +import random +import threading +import traceback +from io import BytesIO + +import comfy +import folder_paths +import impact +import impact.core as core +import impact.impact_pack as impact_pack +import impact.utils as utils +import nodes +import numpy as np +import torchvision +from aiohttp import web +from impact.utils import to_tensor +from PIL import Image +from segment_anything import SamPredictor, sam_model_registry +from server import PromptServer + +sam_predictor = None +default_sam_model_name = os.path.join(impact_pack.model_path, "sams", "sam_vit_b_01ec64.pth") + +sam_lock = threading.Condition() + +last_prepare_data = None + + +def async_prepare_sam(image_dir, model_name, filename): + with sam_lock: + global sam_predictor + + if 'vit_h' in model_name: + model_kind = 'vit_h' + elif 'vit_l' in model_name: + model_kind = 'vit_l' + else: + model_kind = 'vit_b' + + sam_model = sam_model_registry[model_kind](checkpoint=model_name) + sam_predictor = SamPredictor(sam_model) + + image_path = os.path.join(image_dir, filename) + image = nodes.LoadImage().load_image(image_path)[0] + image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + + if impact.config.get_config()['sam_editor_cpu']: + device = 'cpu' + else: + device = comfy.model_management.get_torch_device() + + sam_predictor.model.to(device=device) + sam_predictor.set_image(image, "RGB") + sam_predictor.model.cpu() + + +@PromptServer.instance.routes.post("/sam/prepare") +async def sam_prepare(request): + global sam_predictor + global last_prepare_data + data = await request.json() + + with sam_lock: + if last_prepare_data is not None and last_prepare_data == data: + # already loaded: skip -- prevent redundant loading + return web.Response(status=200) + + last_prepare_data = data + + model_name = 'sam_vit_b_01ec64.pth' + if data['sam_model_name'] == 'auto': + model_name = impact.config.get_config()['sam_editor_model'] + + model_path = folder_paths.get_full_path("sams", model_name) + + if model_path is None: + logging.error(f"[Impact Pack] The '{model_name}' model file cannot be found in any sams model path.") + return web.Response(status=400) + + logging.info(f"[Impact Pack] Loading SAM model '{model_path}'") + + filename, image_dir = folder_paths.annotated_filepath(data["filename"]) + + if image_dir is None: + typ = data['type'] if data['type'] != '' else 'output' + image_dir = folder_paths.get_directory_by_type(typ) + if data['subfolder'] is not None and data['subfolder'] != '': + image_dir += f"/{data['subfolder']}" + + if image_dir is None: + return web.Response(status=400) + + thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_path, filename,)) + thread.start() + + logging.info("[Impact Pack] SAM model loaded. ") + return web.Response(status=200) + + +@PromptServer.instance.routes.post("/sam/release") +async def release_sam(request): + global sam_predictor + + with sam_lock: + temp = sam_predictor + del temp + sam_predictor = None + + logging.info("[Impact Pack]: unloading SAM model") + + +@PromptServer.instance.routes.post("/sam/detect") +async def sam_detect(request): + global sam_predictor + with sam_lock: + if sam_predictor is not None: + if impact.config.get_config()['sam_editor_cpu']: + device = 'cpu' + else: + device = comfy.model_management.get_torch_device() + + sam_predictor.model.to(device=device) + try: + data = await request.json() + + positive_points = data['positive_points'] + negative_points = data['negative_points'] + threshold = data['threshold'] + + points = [] + plabs = [] + + for p in positive_points: + points.append(p) + plabs.append(1) + + for p in negative_points: + points.append(p) + plabs.append(0) + + detected_masks = core.sam_predict(sam_predictor, points, plabs, None, threshold) + mask = utils.combine_masks2(detected_masks) + + if mask is None: + return web.Response(status=400) + + image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + i = 255. * image.cpu().numpy() + + img = Image.fromarray(np.clip(i[0], 0, 255).astype(np.uint8)) + + img_buffer = io.BytesIO() + img.save(img_buffer, format='png') + + headers = {'Content-Type': 'image/png'} + finally: + sam_predictor.model.to(device="cpu") + + return web.Response(body=img_buffer.getvalue(), headers=headers) + + else: + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/impact/wildcards/refresh") +async def wildcards_refresh(request): + impact.wildcards.wildcard_load() + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/impact/wildcards/list") +async def wildcards_list(request): + data = {'data': impact.wildcards.get_wildcard_list()} + return web.json_response(data) + + +@PromptServer.instance.routes.get("/impact/wildcards/list/loaded") +async def wildcards_list_loaded(request): + """ + Get list of actually loaded wildcards (progressive loading in on-demand mode). + + Returns: + - In on-demand mode: only wildcards that have been loaded into memory + - In full cache mode: same as /wildcards/list (all wildcards) + """ + data = { + 'data': impact.wildcards.get_loaded_wildcard_list(), + 'on_demand_mode': impact.wildcards.is_on_demand_mode(), + 'total_available': len(impact.wildcards.available_wildcards) if impact.wildcards.is_on_demand_mode() else len(impact.wildcards.wildcard_dict) + } + return web.json_response(data) + + +@PromptServer.instance.routes.post("/impact/wildcards") +async def populate_wildcards(request): + data = await request.json() + populated = impact.wildcards.process(data['text'], data.get('seed', None)) + return web.json_response({"text": populated}) + + +segs_picker_map = {} + +@PromptServer.instance.routes.get("/impact/segs/picker/count") +async def segs_picker_count(request): + node_id = request.rel_url.query.get('id', '') + + if node_id in segs_picker_map: + res = len(segs_picker_map[node_id]) + return web.Response(status=200, text=str(res)) + + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/impact/segs/picker/view") +async def segs_picker(request): + node_id = request.rel_url.query.get('id', '') + idx = int(request.rel_url.query.get('idx', '')) + + if node_id in segs_picker_map and idx < len(segs_picker_map[node_id]): + img = to_tensor(segs_picker_map[node_id][idx]).permute(0, 3, 1, 2).squeeze(0) + pil = torchvision.transforms.ToPILImage('RGB')(img) + + image_bytes = BytesIO() + pil.save(image_bytes, format="PNG") + image_bytes.seek(0) + return web.Response(status=200, body=image_bytes, content_type='image/png', headers={"Content-Disposition": f"filename={node_id}{idx}.png"}) + + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/view/validate") +async def view_validate(request): + if "filename" in request.rel_url.query: + filename = request.rel_url.query["filename"] + subfolder = request.rel_url.query["subfolder"] + filename, base_dir = folder_paths.annotated_filepath(filename) + + if filename == '' or filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if base_dir is None: + base_dir = folder_paths.get_input_directory() + + file = os.path.join(base_dir, subfolder, filename) + + if os.path.isfile(file): + return web.Response(status=200) + + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/impact/validate/pb_id_image") +async def view_pb_id_image(request): + if "id" in request.rel_url.query: + pb_id = request.rel_url.query["id"] + + if pb_id not in core.preview_bridge_image_id_map: + return web.Response(status=400) + + file = core.preview_bridge_image_id_map[pb_id] + if os.path.isfile(file): + return web.Response(status=200) + + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/impact/set/pb_id_image") +async def set_previewbridge_image(request): + try: + if "filename" in request.rel_url.query: + node_id = request.rel_url.query["node_id"] + filename = request.rel_url.query["filename"] + path_type = request.rel_url.query["type"] + subfolder = request.rel_url.query["subfolder"] + filename, output_dir = folder_paths.annotated_filepath(filename) + + if filename == '' or filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + if path_type == 'input': + output_dir = folder_paths.get_input_directory() + elif path_type == 'output': + output_dir = folder_paths.get_output_directory() + else: + output_dir = folder_paths.get_temp_directory() + + file = os.path.join(output_dir, subfolder, filename) + item = { + 'filename': filename, + 'type': path_type, + 'subfolder': subfolder, + } + pb_id = core.set_previewbridge_image(node_id, file, item) + + return web.Response(status=200, text=pb_id) + except Exception: + traceback.print_exc() + + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/impact/get/pb_id_image") +async def get_previewbridge_image(request): + if "id" in request.rel_url.query: + pb_id = request.rel_url.query["id"] + + if pb_id in core.preview_bridge_image_id_map: + _, path_item = core.preview_bridge_image_id_map[pb_id] + return web.json_response(path_item) + + return web.Response(status=400) + + +@PromptServer.instance.routes.get("/impact/view/pb_id_image") +async def view_previewbridge_image(request): + if "id" in request.rel_url.query: + pb_id = request.rel_url.query["id"] + + if pb_id in core.preview_bridge_image_id_map: + file = core.preview_bridge_image_id_map[pb_id] + + with Image.open(file): + filename = os.path.basename(file) + return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""}) + + return web.Response(status=400) + + +def onprompt_for_switch(json_data): + inversed_switch_info = {} + onprompt_switch_info = {} + onprompt_cond_branch_info = {} + disabled_switch = set() + + + for k, v in json_data['prompt'].items(): + if 'class_type' not in v: + continue + + cls = v['class_type'] + if cls == 'ImpactInversedSwitch': + # if 'sel_mode' is 'select_on_prompt' + if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']: + select_input = v['inputs']['select'] + # if 'select' is converted input + if isinstance(select_input, list) and len(select_input) == 2: + input_node = json_data['prompt'][select_input[0]] + if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']: + inversed_switch_info[k] = input_node['inputs']['value'] + else: + logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactInversedSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n") + else: + inversed_switch_info[k] = select_input + + elif cls in ['ImpactSwitch', 'LatentSwitch', 'SEGSSwitch', 'ImpactMakeImageList']: + # if 'sel_mode' is 'select_on_prompt' + if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']: + select_input = v['inputs']['select'] + # if 'select' is converted input + if isinstance(select_input, list) and len(select_input) == 2: + input_node = json_data['prompt'][select_input[0]] + if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']: + onprompt_switch_info[k] = input_node['inputs']['value'] + if input_node['class_type'] == 'ImpactSwitch' and 'inputs' in input_node and 'select' in input_node['inputs']: + if isinstance(input_node['inputs']['select'], int): + onprompt_switch_info[k] = input_node['inputs']['select'] + else: + logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n") + else: + onprompt_switch_info[k] = select_input + + if k in onprompt_switch_info and f'input{onprompt_switch_info[k]}' not in v['inputs']: + # disconnect output + disabled_switch.add(k) + + elif cls == 'ImpactConditionalBranchSelMode': + if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'cond' in v['inputs']: + cond_input = v['inputs']['cond'] + if isinstance(cond_input, list) and len(cond_input) == 2: + input_node = json_data['prompt'][cond_input[0]] + if (input_node['class_type'] == 'ImpactValueReceiver' and 'inputs' in input_node + and 'value' in input_node['inputs'] and 'typ' in input_node['inputs']): + if 'BOOLEAN' == input_node['inputs']['typ']: + try: + onprompt_cond_branch_info[k] = input_node['inputs']['value'].lower() == "true" + except Exception: + pass + else: + onprompt_cond_branch_info[k] = cond_input + + for k, v in json_data['prompt'].items(): + disable_targets = set() + + for kk, vv in v['inputs'].items(): + if isinstance(vv, list) and len(vv) == 2: + if vv[0] in inversed_switch_info: + if vv[1] + 1 != inversed_switch_info[vv[0]]: + disable_targets.add(kk) + else: + del inversed_switch_info[k] + + if vv[0] in disabled_switch: + disable_targets.add(kk) + + if k in onprompt_switch_info: + selected_slot_name = f"input{onprompt_switch_info[k]}" + for kk, vv in v['inputs'].items(): + if kk != selected_slot_name and kk.startswith('input'): + disable_targets.add(kk) + + if k in onprompt_cond_branch_info: + selected_slot_name = "tt_value" if onprompt_cond_branch_info[k] else "ff_value" + for kk, vv in v['inputs'].items(): + if kk in ['tt_value', 'ff_value'] and kk != selected_slot_name: + disable_targets.add(kk) + + for kk in disable_targets: + del v['inputs'][kk] + + # inversed_switch - select out of range + for target in inversed_switch_info.keys(): + del json_data['prompt'][target]['inputs']['input'] + + +def onprompt_for_pickers(json_data): + detected_pickers = set() + + for k, v in json_data['prompt'].items(): + if 'class_type' not in v: + continue + + cls = v['class_type'] + if cls == 'ImpactSEGSPicker': + detected_pickers.add(k) + + # garbage collection + keys_to_remove = [key for key in segs_picker_map if key not in detected_pickers] + for key in keys_to_remove: + del segs_picker_map[key] + + +def gc_preview_bridge_cache(json_data): + prompt_keys = json_data['prompt'].keys() + + for key in list(core.preview_bridge_cache.keys()): + if key not in prompt_keys: + # print(f"key deleted [PB]: {key}") + del core.preview_bridge_cache[key] + + for key in list(core.preview_bridge_last_mask_cache.keys()): + if key not in prompt_keys: + # print(f"key deleted [PB_last_mask]: {key}") + del core.preview_bridge_last_mask_cache[key] + + +def workflow_imagereceiver_update(json_data): + prompt = json_data['prompt'] + + for v in prompt.values(): + if 'class_type' in v and v['class_type'] == 'ImageReceiver': + if v['inputs']['save_to_workflow']: + v['inputs']['image'] = "#DATA" + + +def regional_sampler_seed_update(json_data): + prompt = json_data['prompt'] + + for k, v in prompt.items(): + if 'class_type' in v and v['class_type'] == 'RegionalSampler': + seed_2nd_mode = v['inputs']['seed_2nd_mode'] + + new_seed = None + if seed_2nd_mode == 'increment': + new_seed = v['inputs']['seed_2nd']+1 + if new_seed > 1125899906842624: + new_seed = 0 + elif seed_2nd_mode == 'decrement': + new_seed = v['inputs']['seed_2nd']-1 + if new_seed < 0: + new_seed = 1125899906842624 + elif seed_2nd_mode == 'randomize': + new_seed = random.randint(0, 1125899906842624) + + if new_seed is not None: + PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed}) + + +def find_input_value(input_node, prompt, input_type=int, input_keys=('value',)): + input_val = None + + try: + for n in input_keys: + input_val = input_node['inputs'].get(n, None) + if isinstance(input_val, input_type): + break + elif isinstance(input_val, list) and len(input_val): + input_val = find_input_value(prompt[input_val[0]], prompt=prompt, input_type=input_type, input_keys=input_keys) + if input_val is not None: + break + + except Exception as e : + logging.warning(f"[Impact Pack] Error encountered on find {input_type} value - {e}") + + return input_val + + +def onprompt_populate_wildcards(json_data): + prompt = json_data['prompt'] + + updated_widget_values = {} + for k, v in prompt.items(): + if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'): + inputs = v['inputs'] + + # legacy adapter + if isinstance(inputs['mode'], bool): + if inputs['mode']: + new_mode = 'populate' + else: + new_mode = 'fixed' + + inputs['mode'] = new_mode + + if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str): + if isinstance(inputs['seed'], list): + try: + input_node = prompt[inputs['seed'][0]] + if input_node['class_type'] == 'ImpactInt': + input_seed = int(input_node['inputs']['value']) + if not isinstance(input_seed, int): + continue + elif input_node['class_type'] == 'Seed (rgthree)': + input_seed = int(input_node['inputs']['seed']) + if not isinstance(input_seed, int): + continue + else: + input_seed = find_input_value(input_node, prompt=prompt, input_type=int, input_keys=('int', 'seed', 'value')) + if input_seed is None: + logging.info(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ") + continue + except Exception: + continue + else: + input_seed = int(inputs['seed']) + + inputs['populated_text'] = impact.wildcards.process(inputs['wildcard_text'], input_seed) + inputs['mode'] = 'reproduce' + + PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']}) + updated_widget_values[k] = inputs['populated_text'] + + if inputs['mode'] == 'reproduce': + PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "mode", "type": "STRING", "value": 'populate'}) + + + + match json_data: + case {"extra_data": {"extra_pnginfo": {"workflow": {"nodes": nodes}}}}: + for node in nodes: + match node: + case {"id": id, "widgets_values": widgets_values}: + key = str(id) + if key in updated_widget_values: + widgets_values[1] = updated_widget_values[key] + widgets_values[2] = "reproduce" + + +def onprompt_for_remote(json_data): + prompt = json_data['prompt'] + + for v in prompt.values(): + if 'class_type' in v: + cls = v['class_type'] + if cls == 'ImpactRemoteBoolean' or cls == 'ImpactRemoteInt': + inputs = v['inputs'] + node_id = str(inputs['node_id']) + + if node_id not in prompt: + continue + + target_inputs = prompt[node_id]['inputs'] + + widget_name = inputs['widget_name'] + if widget_name in target_inputs: + widget_type = None + if cls == 'ImpactRemoteBoolean' and isinstance(target_inputs[widget_name], bool): + widget_type = 'BOOLEAN' + + elif cls == 'ImpactRemoteInt' and (isinstance(target_inputs[widget_name], int) or isinstance(target_inputs[widget_name], float)): + widget_type = 'INT' + + if widget_type is None: + break + + target_inputs[widget_name] = inputs['value'] + PromptServer.instance.send_sync("impact-node-feedback", {"node_id": node_id, "widget_name": widget_name, "type": widget_type, "value": inputs['value']}) + + +def onprompt(json_data): + try: + onprompt_for_remote(json_data) # NOTE: top priority + onprompt_for_switch(json_data) + onprompt_for_pickers(json_data) + onprompt_populate_wildcards(json_data) + gc_preview_bridge_cache(json_data) + workflow_imagereceiver_update(json_data) + regional_sampler_seed_update(json_data) + core.current_prompt = json_data + except Exception: + logging.exception("[Impact Pack] ComfyUI-Impact-Pack: Error on prompt - several features will not work.") + + return json_data + + +PromptServer.instance.add_on_prompt_handler(onprompt) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/logics.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/logics.py new file mode 100644 index 00000000..413ba494 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/logics.py @@ -0,0 +1,779 @@ +import sys +import time + +import execution +import impact.impact_server +from server import PromptServer +from impact.utils import any_typ +import impact.core as core +import re +import nodes +import logging + + +class ImpactCompare: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "cmp": (['a = b', 'a <> b', 'a > b', 'a < b', 'a >= b', 'a <= b', 'tt', 'ff'],), + "a": (any_typ, ), + "b": (any_typ, ), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("BOOLEAN", ) + + def doit(self, cmp, a, b): + if cmp == "a = b": + return (a == b, ) + elif cmp == "a <> b": + return (a != b, ) + elif cmp == "a > b": + return (a > b, ) + elif cmp == "a < b": + return (a < b, ) + elif cmp == "a >= b": + return (a >= b, ) + elif cmp == "a <= b": + return (a <= b, ) + elif cmp == 'tt': + return (True, ) + else: + return (False, ) + + +class ImpactNotEmptySEGS: + @classmethod + def INPUT_TYPES(cls): + return {"required": {"segs": ("SEGS",)}} + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("BOOLEAN", ) + + def doit(self, segs): + return (segs[1] != [], ) + + +class ImpactConditionalBranch: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "cond": ("BOOLEAN",), + "tt_value": (any_typ,{"lazy": True}), + "ff_value": (any_typ,{"lazy": True}), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = (any_typ, ) + + def check_lazy_status(self, cond, tt_value=None, ff_value=None): + if cond and tt_value is None: + return ["tt_value"] + if not cond and ff_value is None: + return ["ff_value"] + + def doit(self, cond, tt_value=None, ff_value=None): + if cond: + return (tt_value,) + else: + return (ff_value,) + + +class ImpactConditionalBranchSelMode: + @classmethod + def INPUT_TYPES(cls): + if not core.is_execution_model_version_supported(): + required_inputs = { + "cond": ("BOOLEAN",), + "sel_mode": ("BOOLEAN", {"default": True, "label_on": "select_on_prompt", "label_off": "select_on_execution"}), + } + else: + required_inputs = { + "cond": ("BOOLEAN",), + } + + return { + "required": required_inputs, + "optional": { + "tt_value": (any_typ,), + "ff_value": (any_typ,), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = (any_typ, ) + + def doit(self, cond, tt_value=None, ff_value=None, **kwargs): + if cond: + return (tt_value,) + else: + return (ff_value,) + + +class ImpactConvertDataType: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": {"value": (any_typ,)}} + + RETURN_TYPES = ("STRING", "FLOAT", "INT", "BOOLEAN") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic" + + @staticmethod + def is_number(string): + pattern = re.compile(r'^[-+]?[0-9]*\.?[0-9]+$') + return bool(pattern.match(string)) + + def doit(self, value): + if self.is_number(str(value)): + num = value + else: + if str.lower(str(value)) != "false": + num = 1 + else: + num = 0 + return (str(value), float(num), int(float(num)), bool(float(num)), ) + + +class ImpactIfNone: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": {"signal": (any_typ,), "any_input": (any_typ,), } + } + + RETURN_TYPES = (any_typ, "BOOLEAN") + RETURN_NAMES = ("signal_opt", "bool") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic" + + def doit(self, signal=None, any_input=None): + if any_input is None: + return (signal, False, ) + else: + return (signal, True, ) + + +class ImpactLogicalOperators: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "operator": (['and', 'or', 'xor'],), + "bool_a": ("BOOLEAN", {"forceInput": True}), + "bool_b": ("BOOLEAN", {"forceInput": True}), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("BOOLEAN", ) + + def doit(self, operator, bool_a, bool_b): + if operator == "and": + return (bool_a and bool_b, ) + elif operator == "or": + return (bool_a or bool_b, ) + else: + return (bool_a != bool_b, ) + + +class ImpactConditionalStopIteration: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { "cond": ("BOOLEAN", {"forceInput": True}), }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = () + + OUTPUT_NODE = True + + def doit(self, cond): + if cond: + PromptServer.instance.send_sync("stop-iteration", {}) + return {} + + +class ImpactNeg: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { "value": ("BOOLEAN", {"forceInput": True}), }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("BOOLEAN", ) + + def doit(self, value): + return (not value, ) + + +class ImpactInt: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("INT", ) + + def doit(self, value): + return (value, ) + + +class ImpactFloat: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 1.0, "min": -3.402823466e+38, "max": 3.402823466e+38}), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("FLOAT", ) + + def doit(self, value): + return (value, ) + + +class ImpactBoolean: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("BOOLEAN", {"default": False}), + }, + } + + FUNCTION = "doit" + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = ("BOOLEAN", ) + + def doit(self, value): + return (value, ) + + +class ImpactValueSender: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "value": (any_typ, ), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + }, + "optional": { + "signal_opt": (any_typ,), + } + } + + OUTPUT_NODE = True + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = (any_typ, ) + RETURN_NAMES = ("signal", ) + + def doit(self, value, link_id=0, signal_opt=None): + PromptServer.instance.send_sync("value-send", {"link_id": link_id, "value": value}) + return (signal_opt, ) + + +class ImpactIntConstSender: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "signal": (any_typ, ), + "value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + OUTPUT_NODE = True + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = () + + def doit(self, signal, value, link_id=0): + PromptServer.instance.send_sync("value-send", {"link_id": link_id, "value": value}) + return {} + + +class ImpactValueReceiver: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "typ": (["STRING", "INT", "FLOAT", "BOOLEAN"], ), + "value": ("STRING", {"default": ""}), + "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic" + + RETURN_TYPES = (any_typ, ) + + def doit(self, typ, value, link_id=0): + if typ == "INT": + return (int(value), ) + elif typ == "FLOAT": + return (float(value), ) + elif typ == "BOOLEAN": + return (value.lower() == "true", ) + else: + return (value, ) + + +class ImpactImageInfo: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "value": ("IMAGE", ), + }, + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + + RETURN_TYPES = ("INT", "INT", "INT", "INT") + RETURN_NAMES = ("batch", "height", "width", "channel") + + def doit(self, value): + return (value.shape[0], value.shape[1], value.shape[2], value.shape[3]) + + +class ImpactLatentInfo: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "value": ("LATENT", ), + }, + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + + RETURN_TYPES = ("INT", "INT", "INT", "INT") + RETURN_NAMES = ("batch", "height", "width", "channel") + + def doit(self, value): + shape = value['samples'].shape + return (shape[0], shape[2] * 8, shape[3] * 8, shape[1]) + + +class ImpactMinMax: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "mode": ("BOOLEAN", {"default": True, "label_on": "max", "label_off": "min"}), + "a": (any_typ,), + "b": (any_typ,), + }, + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + + RETURN_TYPES = ("INT", ) + + def doit(self, mode, a, b): + if mode: + return (max(a, b), ) + else: + return (min(a, b),) + + +class ImpactQueueTrigger: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "signal": (any_typ,), + "mode": ("BOOLEAN", {"default": True, "label_on": "Trigger", "label_off": "Don't trigger"}), + } + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("signal_opt",) + OUTPUT_NODE = True + + def doit(self, signal, mode): + if(mode): + PromptServer.instance.send_sync("impact-add-queue", {}) + + return (signal,) + + +class ImpactQueueTriggerCountdown: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "count": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "total": ("INT", {"default": 10, "min": 1, "max": 0xffffffffffffffff}), + "mode": ("BOOLEAN", {"default": True, "label_on": "Trigger", "label_off": "Don't trigger"}), + }, + "optional": {"signal": (any_typ,),}, + "hidden": {"unique_id": "UNIQUE_ID"} + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = (any_typ, "INT", "INT") + RETURN_NAMES = ("signal_opt", "count", "total") + OUTPUT_NODE = True + + def doit(self, count, total, mode, unique_id, signal=None): + if (mode): + if count < total - 1: + PromptServer.instance.send_sync("impact-node-feedback", + {"node_id": unique_id, "widget_name": "count", "type": "int", "value": count+1}) + PromptServer.instance.send_sync("impact-add-queue", {}) + if count >= total - 1: + PromptServer.instance.send_sync("impact-node-feedback", + {"node_id": unique_id, "widget_name": "count", "type": "int", "value": 0}) + + return (signal, count, total) + + + +class ImpactSetWidgetValue: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "signal": (any_typ,), + "node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "widget_name": ("STRING", {"multiline": False}), + }, + "optional": { + "boolean_value": ("BOOLEAN", {"forceInput": True}), + "int_value": ("INT", {"forceInput": True}), + "float_value": ("FLOAT", {"forceInput": True}), + "string_value": ("STRING", {"forceInput": True}), + } + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("signal_opt",) + OUTPUT_NODE = True + + def doit(self, signal, node_id, widget_name, boolean_value=None, int_value=None, float_value=None, string_value=None, ): + kind = None + if boolean_value is not None: + value = boolean_value + kind = "BOOLEAN" + elif int_value is not None: + value = int_value + kind = "INT" + elif float_value is not None: + value = float_value + kind = "FLOAT" + elif string_value is not None: + value = string_value + kind = "STRING" + else: + value = None + + if value is not None: + PromptServer.instance.send_sync("impact-node-feedback", + {"node_id": node_id, "widget_name": widget_name, "type": kind, "value": value}) + + return (signal,) + + +class ImpactNodeSetMuteState: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "signal": (any_typ,), + "node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "set_state": ("BOOLEAN", {"default": True, "label_on": "active", "label_off": "mute"}), + } + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("signal_opt",) + OUTPUT_NODE = True + + def doit(self, signal, node_id, set_state): + PromptServer.instance.send_sync("impact-node-mute-state", {"node_id": node_id, "is_active": set_state}) + return (signal,) + + +class ImpactSleep: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "signal": (any_typ,), + "seconds": ("FLOAT", {"default": 0.5, "min": 0, "max": 3600}), + } + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("signal_opt",) + OUTPUT_NODE = True + + def doit(self, signal, seconds): + time.sleep(seconds) + return (signal,) + + +def workflow_to_map(workflow): + nodes = {} + links = {} + for link in workflow['links']: + links[link[0]] = link[1:] + for node in workflow['nodes']: + nodes[str(node['id'])] = node + + return nodes, links + + +class ImpactRemoteBoolean: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "widget_name": ("STRING", {"multiline": False}), + "value": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}), + }} + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = () + OUTPUT_NODE = True + + def doit(self, **kwargs): + return {} + + +class ImpactRemoteInt: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "widget_name": ("STRING", {"multiline": False}), + "value": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}), + }} + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic/_for_test" + RETURN_TYPES = () + OUTPUT_NODE = True + + def doit(self, **kwargs): + return {} + +class ImpactControlBridge: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "value": (any_typ,), + "mode": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Stop/Mute/Bypass"}), + "behavior": (["Stop", "Mute", "Bypass"], ), + }, + "hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"} + } + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Logic" + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("value",) + OUTPUT_NODE = True + + DESCRIPTION = ("When behavior is Stop and mode is active, the input value is passed directly to the output.\n" + "When behavior is Mute/Bypass and mode is active, the node connected to the output is changed to active state.\n" + "When behavior is Stop and mode is Stop/Mute/Bypass, the workflow execution of the current node is halted.\n" + "When behavior is Mute/Bypass and mode is Stop/Mute/Bypass, the node connected to the output is changed to Mute/Bypass state.") + + @classmethod + def IS_CHANGED(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None): + if behavior == "Stop": + return value, mode, behavior + else: + # NOTE: extra_pnginfo is not populated for IS_CHANGED. + # so extra_pnginfo is useless in here + try: + workflow = core.current_prompt['extra_data']['extra_pnginfo']['workflow'] + except Exception: + logging.info("[Impact Pack] core.current_prompt['extra_data']['extra_pnginfo']['workflow']") + return 0 + + nodes, links = workflow_to_map(workflow) + next_nodes = [] + + for link in nodes[unique_id]['outputs'][0]['links']: + node_id = str(links[link][2]) + impact.utils.collect_non_reroute_nodes(nodes, links, next_nodes, node_id) + + return next_nodes + + def doit(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None): + global error_skip_flag + + if core.is_execution_model_version_supported(): + from comfy_execution.graph import ExecutionBlocker + else: + logging.info("[Impact Pack] ImpactControlBridge: ComfyUI is outdated. The 'Stop' behavior cannot function properly.") + + if behavior == "Stop": + if mode: + return (value, ) + else: + return (ExecutionBlocker(None), ) + elif extra_pnginfo is None: + logging.warning(f"[Impact Pack] limitation: '{behavior}' behavior cannot be used in API execution.") + return (value,) + else: + workflow_nodes, links = workflow_to_map(extra_pnginfo['workflow']) + + active_nodes = [] + mute_nodes = [] + bypass_nodes = [] + + for link in workflow_nodes[unique_id]['outputs'][0]['links']: + node_id = str(links[link][2]) + + next_nodes = [] + impact.utils.collect_non_reroute_nodes(workflow_nodes, links, next_nodes, node_id) + + for next_node_id in next_nodes: + node_mode = workflow_nodes[next_node_id]['mode'] + + if node_mode == 0: + active_nodes.append(next_node_id) + elif node_mode == 2: + mute_nodes.append(next_node_id) + elif node_mode == 4: + bypass_nodes.append(next_node_id) + + if mode: + # active + should_be_active_nodes = mute_nodes + bypass_nodes + if len(should_be_active_nodes) > 0: + PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'actives': list(should_be_active_nodes)}) + nodes.interrupt_processing() + + elif behavior == "Mute" or behavior == True: # noqa: E712 + # mute + should_be_mute_nodes = active_nodes + bypass_nodes + if len(should_be_mute_nodes) > 0: + PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'mutes': list(should_be_mute_nodes)}) + nodes.interrupt_processing() + + else: + # bypass + should_be_bypass_nodes = active_nodes + mute_nodes + if len(should_be_bypass_nodes) > 0: + PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'bypasses': list(should_be_bypass_nodes)}) + nodes.interrupt_processing() + + return (value, ) + + +class ImpactExecutionOrderController: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "signal": (any_typ,), + "value": (any_typ,), + }} + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + RETURN_TYPES = (any_typ, any_typ) + RETURN_NAMES = ("signal", "value") + + def doit(self, signal, value): + return signal, value + + +class ImpactListBridge: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "list_input": (any_typ,), + }} + + FUNCTION = "doit" + + DESCRIPTION = "When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed." + + CATEGORY = "ImpactPack/Util" + RETURN_TYPES = (any_typ, ) + RETURN_NAMES = ("list_output", ) + + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + @staticmethod + def doit(list_input): + return (list_input,) + + +original_handle_execution = execution.PromptExecutor.handle_execution_error + + +def handle_execution_error(**kwargs): + execution.PromptExecutor.handle_execution_error(**kwargs) + diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/pipe.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/pipe.py new file mode 100644 index 00000000..4e782f6a --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/pipe.py @@ -0,0 +1,440 @@ +import folder_paths +from impact.utils import any_typ + + +class ToDetailerPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "bbox_detector": ("BBOX_DETECTOR", ), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"], ), + }, + "optional": { + "sam_model_opt": ("SAM_MODEL",), + "segm_detector_opt": ("SEGM_DETECTOR",), + "detailer_hook": ("DETAILER_HOOK",), + }} + + RETURN_TYPES = ("DETAILER_PIPE", ) + RETURN_NAMES = ("detailer_pipe", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, *args, **kwargs): + pipe = (kwargs['model'], kwargs['clip'], kwargs['vae'], kwargs['positive'], kwargs['negative'], kwargs['wildcard'], kwargs['bbox_detector'], + kwargs.get('segm_detector_opt', None), kwargs.get('sam_model_opt', None), kwargs.get('detailer_hook', None), + kwargs.get('refiner_model', None), kwargs.get('refiner_clip', None), + kwargs.get('refiner_positive', None), kwargs.get('refiner_negative', None)) + return (pipe, ) + + +class ToDetailerPipeSDXL(ToDetailerPipe): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "refiner_model": ("MODEL",), + "refiner_clip": ("CLIP",), + "refiner_positive": ("CONDITIONING",), + "refiner_negative": ("CONDITIONING",), + "bbox_detector": ("BBOX_DETECTOR", ), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + }, + "optional": { + "sam_model_opt": ("SAM_MODEL",), + "segm_detector_opt": ("SEGM_DETECTOR",), + "detailer_hook": ("DETAILER_HOOK",), + }} + + +class FromDetailerPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, } + + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR", "DETAILER_HOOK") + RETURN_NAMES = ("model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt", "detailer_hook") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, detailer_pipe): + model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, _, _, _, _ = detailer_pipe + return model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook + + +class FromDetailerPipe_v2: + @classmethod + def INPUT_TYPES(s): + return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, } + + RETURN_TYPES = ("DETAILER_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR", "DETAILER_HOOK") + RETURN_NAMES = ("detailer_pipe", "model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt", "detailer_hook") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, detailer_pipe): + model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, _, _, _, _ = detailer_pipe + return detailer_pipe, model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook + + +class FromDetailerPipe_SDXL: + @classmethod + def INPUT_TYPES(s): + return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, } + + RETURN_TYPES = ("DETAILER_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR", "DETAILER_HOOK", "MODEL", "CLIP", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("detailer_pipe", "model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt", "detailer_hook", "refiner_model", "refiner_clip", "refiner_positive", "refiner_negative") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, detailer_pipe): + model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe + return detailer_pipe, model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative + + +class AnyPipeToBasic: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"any_pipe": (any_typ,)}, + } + + RETURN_TYPES = ("BASIC_PIPE", ) + RETURN_NAMES = ("basic_pipe", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, any_pipe): + return (any_pipe[:5], ) + + +class ToBasicPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + }, + } + + RETURN_TYPES = ("BASIC_PIPE", ) + RETURN_NAMES = ("basic_pipe", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, model, clip, vae, positive, negative): + pipe = (model, clip, vae, positive, negative) + return (pipe, ) + + +class FromBasicPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": {"basic_pipe": ("BASIC_PIPE",), }, } + + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("model", "clip", "vae", "positive", "negative") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, basic_pipe): + model, clip, vae, positive, negative = basic_pipe + return model, clip, vae, positive, negative + + +class FromBasicPipe_v2: + @classmethod + def INPUT_TYPES(s): + return {"required": {"basic_pipe": ("BASIC_PIPE",), }, } + + RETURN_TYPES = ("BASIC_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("basic_pipe", "model", "clip", "vae", "positive", "negative") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, basic_pipe): + model, clip, vae, positive, negative = basic_pipe + return basic_pipe, model, clip, vae, positive, negative + + +class BasicPipeToDetailerPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": {"basic_pipe": ("BASIC_PIPE",), + "bbox_detector": ("BBOX_DETECTOR", ), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + }, + "optional": { + "sam_model_opt": ("SAM_MODEL", ), + "segm_detector_opt": ("SEGM_DETECTOR",), + "detailer_hook": ("DETAILER_HOOK",), + }, + } + + RETURN_TYPES = ("DETAILER_PIPE", ) + RETURN_NAMES = ("detailer_pipe", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, *args, **kwargs): + basic_pipe = kwargs['basic_pipe'] + bbox_detector = kwargs['bbox_detector'] + wildcard = kwargs['wildcard'] + sam_model_opt = kwargs.get('sam_model_opt', None) + segm_detector_opt = kwargs.get('segm_detector_opt', None) + detailer_hook = kwargs.get('detailer_hook', None) + + model, clip, vae, positive, negative = basic_pipe + pipe = model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None + return (pipe, ) + + +class BasicPipeToDetailerPipeSDXL: + @classmethod + def INPUT_TYPES(s): + return {"required": {"base_basic_pipe": ("BASIC_PIPE",), + "refiner_basic_pipe": ("BASIC_PIPE",), + "bbox_detector": ("BBOX_DETECTOR", ), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + }, + "optional": { + "sam_model_opt": ("SAM_MODEL", ), + "segm_detector_opt": ("SEGM_DETECTOR",), + "detailer_hook": ("DETAILER_HOOK",), + }, + } + + RETURN_TYPES = ("DETAILER_PIPE", ) + RETURN_NAMES = ("detailer_pipe", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, *args, **kwargs): + base_basic_pipe = kwargs['base_basic_pipe'] + refiner_basic_pipe = kwargs['refiner_basic_pipe'] + bbox_detector = kwargs['bbox_detector'] + wildcard = kwargs['wildcard'] + sam_model_opt = kwargs.get('sam_model_opt', None) + segm_detector_opt = kwargs.get('segm_detector_opt', None) + detailer_hook = kwargs.get('detailer_hook', None) + + model, clip, vae, positive, negative = base_basic_pipe + refiner_model, refiner_clip, refiner_vae, refiner_positive, refiner_negative = refiner_basic_pipe + pipe = model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative + return (pipe, ) + + +class DetailerPipeToBasicPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }} + + RETURN_TYPES = ("BASIC_PIPE", "BASIC_PIPE") + RETURN_NAMES = ("base_basic_pipe", "refiner_basic_pipe") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, detailer_pipe): + model, clip, vae, positive, negative, _, _, _, _, _, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe + pipe = model, clip, vae, positive, negative + refiner_pipe = refiner_model, refiner_clip, vae, refiner_positive, refiner_negative + return (pipe, refiner_pipe) + + +class EditBasicPipe: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"basic_pipe": ("BASIC_PIPE",), }, + "optional": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + }, + } + + RETURN_TYPES = ("BASIC_PIPE", ) + RETURN_NAMES = ("basic_pipe", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, basic_pipe, model=None, clip=None, vae=None, positive=None, negative=None): + res_model, res_clip, res_vae, res_positive, res_negative = basic_pipe + + if model is not None: + res_model = model + + if clip is not None: + res_clip = clip + + if vae is not None: + res_vae = vae + + if positive is not None: + res_positive = positive + + if negative is not None: + res_negative = negative + + pipe = res_model, res_clip, res_vae, res_positive, res_negative + + return (pipe, ) + + +class EditDetailerPipe: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "detailer_pipe": ("DETAILER_PIPE",), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + }, + "optional": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "bbox_detector": ("BBOX_DETECTOR",), + "sam_model": ("SAM_MODEL",), + "segm_detector": ("SEGM_DETECTOR",), + "detailer_hook": ("DETAILER_HOOK",), + }, + } + + RETURN_TYPES = ("DETAILER_PIPE",) + RETURN_NAMES = ("detailer_pipe",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Pipe" + + def doit(self, *args, **kwargs): + detailer_pipe = kwargs['detailer_pipe'] + wildcard = kwargs['wildcard'] + model = kwargs.get('model', None) + clip = kwargs.get('clip', None) + vae = kwargs.get('vae', None) + positive = kwargs.get('positive', None) + negative = kwargs.get('negative', None) + bbox_detector = kwargs.get('bbox_detector', None) + sam_model = kwargs.get('sam_model', None) + segm_detector = kwargs.get('segm_detector', None) + detailer_hook = kwargs.get('detailer_hook', None) + refiner_model = kwargs.get('refiner_model', None) + refiner_clip = kwargs.get('refiner_clip', None) + refiner_positive = kwargs.get('refiner_positive', None) + refiner_negative = kwargs.get('refiner_negative', None) + + res_model, res_clip, res_vae, res_positive, res_negative, res_wildcard, res_bbox_detector, res_segm_detector, res_sam_model, res_detailer_hook, res_refiner_model, res_refiner_clip, res_refiner_positive, res_refiner_negative = detailer_pipe + + if model is not None: + res_model = model + + if clip is not None: + res_clip = clip + + if vae is not None: + res_vae = vae + + if positive is not None: + res_positive = positive + + if negative is not None: + res_negative = negative + + if bbox_detector is not None: + res_bbox_detector = bbox_detector + + if segm_detector is not None: + res_segm_detector = segm_detector + + if wildcard != "": + res_wildcard = wildcard + + if sam_model is not None: + res_sam_model = sam_model + + if detailer_hook is not None: + res_detailer_hook = detailer_hook + + if refiner_model is not None: + res_refiner_model = refiner_model + + if refiner_clip is not None: + res_refiner_clip = refiner_clip + + if refiner_positive is not None: + res_refiner_positive = refiner_positive + + if refiner_negative is not None: + res_refiner_negative = refiner_negative + + pipe = (res_model, res_clip, res_vae, res_positive, res_negative, res_wildcard, + res_bbox_detector, res_segm_detector, res_sam_model, res_detailer_hook, + res_refiner_model, res_refiner_clip, res_refiner_positive, res_refiner_negative) + + return (pipe, ) + + +class EditDetailerPipeSDXL(EditDetailerPipe): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "detailer_pipe": ("DETAILER_PIPE",), + "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + }, + "optional": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "refiner_model": ("MODEL",), + "refiner_clip": ("CLIP",), + "refiner_positive": ("CONDITIONING",), + "refiner_negative": ("CONDITIONING",), + "bbox_detector": ("BBOX_DETECTOR",), + "sam_model": ("SAM_MODEL",), + "segm_detector": ("SEGM_DETECTOR",), + "detailer_hook": ("DETAILER_HOOK",), + }, + } diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/segs_nodes.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/segs_nodes.py new file mode 100644 index 00000000..ee46df1a --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/segs_nodes.py @@ -0,0 +1,2029 @@ +import os +import sys + +import impact.impact_server +from nodes import MAX_RESOLUTION + +from . import core +from .core import SEG +import impact.utils as utils +from . import defs +from . import segs_upscaler +from comfy.cli_args import args +import math +from PIL import Image +import comfy +import numpy as np +import torch +import folder_paths +import logging + + +from typing import Callable, Union + +try: + from comfy_extras import nodes_differential_diffusion +except Exception: + logging.info("\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n") + raise Exception("[Impact Pack] ComfyUI is an outdated version.") + + +class SEGSDetailer: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "segs": ("SEGS", ), + "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), + "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}), + "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}), + + "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": { + "refiner_basic_pipe_opt": ("BASIC_PIPE",), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("SEGS", "IMAGE") + RETURN_NAMES = ("segs", "cnet_images") + OUTPUT_IS_LIST = (False, True) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node." + + @staticmethod + def do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1, + refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None): + + model, clip, vae, positive, negative = basic_pipe + if refiner_basic_pipe_opt is None: + refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None + else: + refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt + + segs = core.segs_scale_match(segs, image.shape) + + new_segs = [] + cnet_pil_list = [] + + if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + for i in range(batch_size): + seed += 1 + for seg in segs[1]: + cropped_image = seg.cropped_image if seg.cropped_image is not None \ + else utils.crop_ndarray4(image.numpy(), seg.crop_region) + cropped_image = utils.to_tensor(cropped_image) + + is_mask_all_zeros = (seg.cropped_mask == 0).all().item() + if is_mask_all_zeros: + logging.info("Detailer: segment skip [empty mask]") + new_segs.append(seg) + continue + + if noise_mask: + cropped_mask = seg.cropped_mask + else: + cropped_mask = None + + cropped_positive = [ + [condition, { + k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in positive + ] + + cropped_negative = [ + [condition, { + k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v + for k, v in details.items() + }] + for condition, details in negative + ] + + if not (isinstance(model, str) and model == "DUMMY"): + enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size, + seg.bbox, seed, steps, cfg, sampler_name, scheduler, + cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint, + refiner_ratio=refiner_ratio, refiner_model=refiner_model, + refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative, + control_net_wrapper=seg.control_net_wrapper, cycle=cycle, + inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt) + else: + enhanced_image = cropped_image + cnet_pils = None + + if cnet_pils is not None: + cnet_pil_list.extend(cnet_pils) + + if enhanced_image is None: + new_cropped_image = cropped_image + else: + new_cropped_image = enhanced_image + + new_seg = SEG(utils.to_numpy(new_cropped_image), seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None) + new_segs.append(new_seg) + + return (segs[0], new_segs), cnet_pil_list + + def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, + denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1, + refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None): + + if len(image) > 1: + raise Exception('[Impact Pack] ERROR: SEGSDetailer does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.') + + segs, cnet_pil_list = SEGSDetailer.do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, + scheduler, denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio, batch_size, cycle=cycle, + refiner_basic_pipe_opt=refiner_basic_pipe_opt, + inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt) + + # set fallback image + if len(cnet_pil_list) == 0: + cnet_pil_list = [utils.empty_pil_tensor()] + + return segs, cnet_pil_list + + + +class SEGSPaste: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "segs": ("SEGS", ), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "alpha": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + }, + "optional": {"ref_image_opt": ("IMAGE", ), } + } + + RETURN_TYPES = ("IMAGE", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Detailer" + + DESCRIPTION = "This node provides a function to paste the enhanced SEGS, improved through the SEGS detailer, back onto the original image." + + @staticmethod + def doit(image, segs, feather, alpha=255, ref_image_opt=None): + # Optimized SEGS paste node: preallocates result and avoids repeated concat. + segs = core.segs_scale_match(segs, image.shape) + + batch_size, _, _, _ = image.shape + result = torch.empty_like(image) + + with torch.no_grad(): + for i in range(batch_size): + # avoid extra clone/unsqueeze + image_i = image[i].unsqueeze(0).clone() + + for seg in segs[1]: + ref_image = None + + # ref_image handling + if ref_image_opt is None and seg.cropped_image is not None: + cropped_image = seg.cropped_image + if isinstance(cropped_image, np.ndarray): + cropped_image = torch.from_numpy(cropped_image) + ref_image = cropped_image[i].unsqueeze(0) + elif ref_image_opt is not None: + ref_tensor = ref_image_opt[i].unsqueeze(0) + ref_image = utils.crop_image(ref_tensor, seg.crop_region) + + if ref_image is None: + continue + + # mask handling + cmask = seg.cropped_mask + if cmask.ndim == 3 and len(cmask) == batch_size: + mask = cmask[i] + elif cmask.ndim == 3 and len(cmask) > 1: + # statt OR-Schleife β†’ vektorisiert + mask = torch.any(cmask > 0.1, dim=0).float() + else: # ndim == 2 + mask = cmask + + # blur + alpha + mask = utils.tensor_gaussian_blur_mask(mask, feather) * (alpha / 255.0) + + # ensure same device + mask = mask.to(image_i.device) + ref_image = ref_image.to(image_i.device) + + x, y, *_ = seg.crop_region + utils.tensor_paste(image_i, ref_image, (x, y), mask) + + result[i] = image_i[0] + + if not args.highvram and not args.gpu_only: + result = result.cpu() + + return (result,) + + +class SEGSPreviewCNet: + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + + @classmethod + def INPUT_TYPES(s): + return {"required": {"segs": ("SEGS", ),}, } + + RETURN_TYPES = ("IMAGE", ) + OUTPUT_IS_LIST = (True, ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + OUTPUT_NODE = True + + def doit(self, segs): + full_output_folder, filename, counter, subfolder, filename_prefix = \ + folder_paths.get_save_image_path("impact_seg_preview", self.output_dir, segs[0][1], segs[0][0]) + + results = list() + result_image_list = [] + + for seg in segs[1]: + file = f"{filename}_{counter:05}_.webp" + + if seg.control_net_wrapper is not None and seg.control_net_wrapper.control_image is not None: + cnet_image = seg.control_net_wrapper.control_image + result_image_list.append(cnet_image) + else: + cnet_image = utils.empty_pil_tensor(64, 64) + + cnet_pil = utils.tensor2pil(cnet_image) + cnet_pil.save(os.path.join(full_output_folder, file)) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + counter += 1 + + return {"ui": {"images": results}, "result": (result_image_list,)} + + +class SEGSPreview: + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "alpha_mode": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "min_alpha": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "fallback_image_opt": ("IMAGE", ), + } + } + + RETURN_TYPES = ("IMAGE", ) + OUTPUT_IS_LIST = (True, ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + OUTPUT_NODE = True + + def doit(self, segs, alpha_mode=True, min_alpha=0.0, fallback_image_opt=None): + full_output_folder, filename, counter, subfolder, filename_prefix = \ + folder_paths.get_save_image_path("impact_seg_preview", self.output_dir, segs[0][1], segs[0][0]) + + results = list() + result_image_list = [] + + if fallback_image_opt is not None: + segs = core.segs_scale_match(segs, fallback_image_opt.shape) + + if min_alpha != 0: + min_alpha = int(255 * min_alpha) + + if len(segs[1]) > 0: + if segs[1][0].cropped_image is not None: + batch_count = len(segs[1][0].cropped_image) + elif fallback_image_opt is not None: + batch_count = len(fallback_image_opt) + else: + return {"ui": {"images": results}} + + for seg in segs[1]: + result_image_batch = None + cached_mask = None + + def get_combined_mask(): + nonlocal cached_mask + + if cached_mask is not None: + return cached_mask + else: + if isinstance(seg.cropped_mask, np.ndarray): + masks = torch.tensor(seg.cropped_mask) + else: + masks = seg.cropped_mask + + cached_mask = (masks[0] * 255).to(torch.uint8) + for x in masks[1:]: + cached_mask |= (x * 255).to(torch.uint8) + cached_mask = (cached_mask/255.0).to(torch.float32) + cached_mask = utils.to_binary_mask(cached_mask, 0.1) + cached_mask = cached_mask.numpy() + + return cached_mask + + def stack_image(image, mask=None): + nonlocal result_image_batch + + if isinstance(image, np.ndarray): + image = torch.from_numpy(image) + + if mask is not None: + image *= torch.tensor(mask)[None, ..., None] + + if result_image_batch is None: + result_image_batch = image + else: + result_image_batch = torch.concat((result_image_batch, image), dim=0) + + for i in range(batch_count): + cropped_image = None + + if seg.cropped_image is not None: + cropped_image = seg.cropped_image[i, None] + elif fallback_image_opt is not None: + # take from original image + ref_image = fallback_image_opt[i].unsqueeze(0) + cropped_image = utils.crop_image(ref_image, seg.crop_region) + + if cropped_image is not None: + if isinstance(cropped_image, np.ndarray): + cropped_image = torch.from_numpy(cropped_image) + + cropped_image = cropped_image.clone() + cropped_pil = utils.to_pil(cropped_image) + + if alpha_mode: + if isinstance(seg.cropped_mask, np.ndarray): + cropped_mask = seg.cropped_mask + else: + if seg.cropped_image is not None and len(seg.cropped_image) != len(seg.cropped_mask): + cropped_mask = get_combined_mask() + else: + cropped_mask = seg.cropped_mask[i].numpy() + + mask_array = (cropped_mask * 255).astype(np.uint8) + + if min_alpha != 0: + mask_array[mask_array < min_alpha] = min_alpha + + mask_pil = Image.fromarray(mask_array, mode='L').resize(cropped_pil.size) + cropped_pil.putalpha(mask_pil) + stack_image(cropped_image, cropped_mask) + else: + stack_image(cropped_image) + + file = f"{filename}_{counter:05}_.webp" + cropped_pil.save(os.path.join(full_output_folder, file)) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + counter += 1 + + if result_image_batch is not None: + result_image_list.append(result_image_batch) + + return {"ui": {"images": results}, "result": (result_image_list,) } + + +class SEGSLabelFilter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "preset": (['all'] + defs.detection_labels, ), + "labels": ("STRING", {"multiline": True, "placeholder": "List the types of segments to be allowed, separated by commas"}), + }, + } + + RETURN_TYPES = ("SEGS", "SEGS",) + RETURN_NAMES = ("filtered_SEGS", "remained_SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def filter(segs, labels): + labels = set([label.strip() for label in labels]) + + if 'all' in labels: + return (segs, (segs[0], []), ) + else: + res_segs = [] + remained_segs = [] + + for x in segs[1]: + if x.label in labels: + res_segs.append(x) + elif 'eyes' in labels and x.label in ['left_eye', 'right_eye']: + res_segs.append(x) + elif 'eyebrows' in labels and x.label in ['left_eyebrow', 'right_eyebrow']: + res_segs.append(x) + elif 'pupils' in labels and x.label in ['left_pupil', 'right_pupil']: + res_segs.append(x) + else: + remained_segs.append(x) + + return ((segs[0], res_segs), (segs[0], remained_segs), ) + + def doit(self, segs, preset, labels): + labels = labels.split(',') + return SEGSLabelFilter.filter(segs, labels) + + +class SEGSLabelAssign: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "labels": ("STRING", {"multiline": True, "placeholder": "List the label to be assigned in order of segs, separated by commas"}), + }, + } + + RETURN_TYPES = ("SEGS",) + RETURN_NAMES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def assign(segs, labels): + labels = [label.strip() for label in labels] + + if len(labels) != len(segs[1]): + logging.warning(f'[Impact Pack] SEGSLabelAssign: length of labels ({len(labels)}) != length of segs ({len(segs[1])})') + + labeled_segs = [] + + idx = 0 + for x in segs[1]: + if len(labels) > idx: + x = x._replace(label=labels[idx]) + labeled_segs.append(x) + idx += 1 + + return ((segs[0], labeled_segs), ) + + def doit(self, segs, labels): + labels = labels.split(',') + return SEGSLabelAssign.assign(segs, labels) + + +class SEGSOrderedFilter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "confidence", "none"],), + "order": ("BOOLEAN", {"default": True, "label_on": "descending", "label_off": "ascending"}), + "take_start": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "take_count": ("INT", {"default": 1, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + RETURN_TYPES = ("SEGS", "SEGS",) + RETURN_NAMES = ("filtered_SEGS", "remained_SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def get_sort_key_fn(target: str) -> Union[Callable, None]: + if target == "none": + return None + + def sort_key_fn(seg): + x1, y1, x2, y2 = seg.crop_region + if target == "confidence": return seg.confidence + if target == "area(=w*h)": return (x2 - x1) * (y2 - y1) + if target == "width": return x2 - x1 + if target == "height": return y2 - y1 + if target == "x1": return x1 + if target == "y1": return y1 + if target == "x2": return x2 + if target == "y2": return y2 + raise Exception(f"[Impact Pack] SEGSOrderedFilter - Unexpected target '{target}'") + + return sort_key_fn + + def doit(self, segs, target, order, take_start, take_count): + sort_key_fn = SEGSOrderedFilter.get_sort_key_fn(target) + + sorted_list = list(segs[1]) # make a shallow copy, so it does not mutate the original list when sort + if sort_key_fn is not None: + sorted_list.sort(key=sort_key_fn, reverse=order) + + take_stop = take_start + take_count + return (segs[0], sorted_list[take_start:take_stop]), \ + (segs[0], sorted_list[:take_start] + sorted_list[take_stop:]), + + +class SEGSRangeFilter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "length_percent", "confidence(0-100)"],), + "mode": ("BOOLEAN", {"default": True, "label_on": "inside", "label_off": "outside"}), + "min_value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), + "max_value": ("INT", {"default": 67108864, "min": 0, "max": sys.maxsize, "step": 1}), + }, + } + + RETURN_TYPES = ("SEGS", "SEGS",) + RETURN_NAMES = ("filtered_SEGS", "remained_SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs, target, mode, min_value, max_value): + new_segs = [] + remained_segs = [] + + for seg in segs[1]: + x1 = seg.crop_region[0] + y1 = seg.crop_region[1] + x2 = seg.crop_region[2] + y2 = seg.crop_region[3] + + if target == "area(=w*h)": + value = (y2 - y1) * (x2 - x1) + elif target == "length_percent": + h = y2 - y1 + w = x2 - x1 + value = max(h/w, w/h)*100 + elif target == "width": + value = x2 - x1 + elif target == "height": + value = y2 - y1 + elif target == "x1": + value = x1 + elif target == "x2": + value = x2 + elif target == "y1": + value = y1 + elif target == "y2": + value = y2 + elif target == "confidence(0-100)": + value = seg.confidence*100 + else: + raise Exception(f"[Impact Pack] SEGSRangeFilter - Unexpected target '{target}'") + + if mode and min_value <= value <= max_value: + logging.info(f"[in] value={value} / {mode}, {min_value}, {max_value}") + new_segs.append(seg) + elif not mode and (value < min_value or value > max_value): + logging.info(f"[out] value={value} / {mode}, {min_value}, {max_value}") + new_segs.append(seg) + else: + remained_segs.append(seg) + logging.info(f"[filter] value={value} / {mode}, {min_value}, {max_value}") + + return (segs[0], new_segs), (segs[0], remained_segs), + + +class SEGSIntersectionFilter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs1": ("SEGS", ), + "segs2": ("SEGS", ), + "ioa_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("SEGS",) + RETURN_NAMES = ("filtered_SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def compute_ioa(self, mask1, mask2): + """Compute Intersection over Area (IoA) between two boxes.""" + inter_mask = utils.bitwise_and_masks(mask1, mask2) + + inter_area = (inter_mask > 0).sum() + area1 = (mask1 > 0).sum() + + return inter_area / area1 if area1 > 0 else 0 + + def doit(self, segs1, segs2, ioa_threshold): + """Remove segments from segs1 if their IoA with any segment in segs2 exceeds the threshold.""" + # Extract bounding boxes for all segments in segs1 and segs2 + keep = [] + + # Iterate over all segments in segs1 + for idx1, seg1 in enumerate(segs1[1]): + keep_segment = True # Assume the segment should be kept + mask1 = core.segs_to_combined_mask((segs1[0], [seg1])) + + # Compare with every segment in segs2 + for seg2 in segs2[1]: + mask2 = core.segs_to_combined_mask((segs2[0], [seg2])) + ioa = self.compute_ioa(mask1, mask2) # IoA between segment 1 and segment 2 + + if ioa > ioa_threshold: # If IoA exceeds the threshold, mark the segment for removal + keep_segment = False + break # If one overlap exceeds threshold, break early and mark for removal + + # Keep the segment if it did not exceed the threshold with any other segment + if keep_segment: + keep.append(segs1[1][idx1]) + + return (segs1[0], keep), # Return the updated SEGS + + +class SEGSNMSFilter: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "segs": ("SEGS",), + "iou_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("SEGS",) + RETURN_NAMES = ("filtered_SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def compute_iou(self, mask1, mask2): + """Compute IoU between two bounding boxes (x1, y1, x2, y2).""" + inter_mask = utils.bitwise_and_masks(mask1, mask2) + union_mask = utils.add_masks(mask1, mask2) + + inter_area = (inter_mask > 0).sum() + union_area = (union_mask > 0).sum() + + return inter_area / union_area if union_area > 0 else 0 + + def doit(self, segs, iou_threshold): + """Perform NMS to filter overlapping segments.""" + confidences = np.ndarray.flatten(np.array([seg.confidence for seg in segs[1]])) + + # Sort boxes by confidence (high to low) + sorted_indices = np.argsort(confidences)[::-1].tolist() + keep = [] + + while len(sorted_indices) > 0: + idx = sorted_indices[0] + mask1 = core.segs_to_combined_mask((segs[0], [segs[1][idx]])) + keep.append(idx) + sorted_indices = sorted_indices[1:] + + # Filter indices only contain the indices where the bbox does not intersect + filtered_indices = [] + for i in sorted_indices: + mask2 = core.segs_to_combined_mask((segs[0], [segs[1][i]])) + iou = self.compute_iou(mask1, mask2) + if iou < iou_threshold: + filtered_indices.append(i) + + sorted_indices = np.array(filtered_indices) + + filtered_segs = [segs[1][i] for i in keep] + return (segs[0], filtered_segs), + + +class SEGSToImageList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + }, + "optional": { + "fallback_image_opt": ("IMAGE", ), + } + } + + RETURN_TYPES = ("IMAGE",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs, fallback_image_opt=None): + results = list() + + if fallback_image_opt is not None: + segs = core.segs_scale_match(segs, fallback_image_opt.shape) + + for seg in segs[1]: + if seg.cropped_image is not None: + cropped_image = utils.to_tensor(seg.cropped_image) + elif fallback_image_opt is not None: + # take from original image + cropped_image = utils.to_tensor(utils.crop_image(fallback_image_opt, seg.crop_region)) + else: + cropped_image = utils.empty_pil_tensor() + + results.append(cropped_image) + + if len(results) == 0: + results.append(utils.empty_pil_tensor()) + + return (results,) + + +class SEGSToMaskList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + }, + } + + RETURN_TYPES = ("MASK",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs): + masks = core.segs_to_masklist(segs) + if len(masks) == 0: + empty_mask = torch.zeros(segs[0], dtype=torch.float32, device="cpu") + masks = [empty_mask] + masks = [utils.make_3d_mask(mask) for mask in masks] + return (masks,) + + +class SEGSToMaskBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + }, + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs): + masks = core.segs_to_masklist(segs) + masks = [utils.make_3d_mask(mask) for mask in masks] + mask_batch = torch.concat(masks) + return (mask_batch,) + + +class SEGSMerge: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + }, + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed." + + def doit(self, segs): + crop_left = sys.maxsize + crop_right = 0 + crop_top = sys.maxsize + crop_bottom = 0 + + bbox_left = sys.maxsize + bbox_right = 0 + bbox_top = sys.maxsize + bbox_bottom = 0 + + min_confidence = 1.0 + + for seg in segs[1]: + cx1 = seg.crop_region[0] + cy1 = seg.crop_region[1] + cx2 = seg.crop_region[2] + cy2 = seg.crop_region[3] + + bx1 = seg.bbox[0] + by1 = seg.bbox[1] + bx2 = seg.bbox[2] + by2 = seg.bbox[3] + + crop_left = min(crop_left, cx1) + crop_top = min(crop_top, cy1) + crop_right = max(crop_right, cx2) + crop_bottom = max(crop_bottom, cy2) + + bbox_left = min(bbox_left, bx1) + bbox_top = min(bbox_top, by1) + bbox_right = max(bbox_right, bx2) + bbox_bottom = max(bbox_bottom, by2) + + min_confidence = min(min_confidence, seg.confidence) + + combined_mask = core.segs_to_combined_mask(segs) + cropped_mask = combined_mask[crop_top:crop_bottom, crop_left:crop_right] + cropped_mask = cropped_mask.unsqueeze(0) + + crop_region = [crop_left, crop_top, crop_right, crop_bottom] + bbox = [bbox_left, bbox_top, bbox_right, bbox_bottom] + + seg = SEG(None, cropped_mask, min_confidence, crop_region, bbox, 'merged', None) + return ((segs[0], [seg]),) + + +class SEGSConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs1": ("SEGS", ), + }, + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, **kwargs): + dim = None + res = None + + for k, v in list(kwargs.items()): + if v[0] == (0, 0) or len(v[1]) == 0: + continue + + if dim is None: + dim = v[0] + res = v[1] + else: + if v[0] == dim: + res = res + v[1] + else: + logging.error(f"[Impact Pack] source shape of 'segs1'{dim} and '{k}'{v[0]} are different. '{k}' will be ignored") + + if dim is None: + empty_segs = ((0, 0), []) + return (empty_segs, ) + else: + return ((dim, res), ) + + +class Count_Elts_in_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + }, + } + + RETURN_TYPES = ("INT",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs): + return (len(segs[1]), ) + + +class DecomposeSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + }, + } + + RETURN_TYPES = ("SEGS_HEADER", "SEG_ELT",) + OUTPUT_IS_LIST = (False, True, ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs): + return segs + + +class AssembleSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seg_header": ("SEGS_HEADER", ), + "seg_elt": ("SEG_ELT", ), + }, + } + + INPUT_IS_LIST = True + + RETURN_TYPES = ("SEGS", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, seg_header, seg_elt): + return ((seg_header[0], seg_elt), ) + + +class From_SEG_ELT: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seg_elt": ("SEG_ELT", ), + }, + } + + RETURN_TYPES = ("SEG_ELT", "IMAGE", "MASK", "SEG_ELT_crop_region", "SEG_ELT_bbox", "SEG_ELT_control_net_wrapper", "FLOAT", "STRING") + RETURN_NAMES = ("seg_elt", "cropped_image", "cropped_mask", "crop_region", "bbox", "control_net_wrapper", "confidence", "label") + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, seg_elt): + cropped_image = utils.to_tensor(seg_elt.cropped_image) if seg_elt.cropped_image is not None else None + return (seg_elt, cropped_image, utils.to_tensor(seg_elt.cropped_mask), seg_elt.crop_region, seg_elt.bbox, seg_elt.control_net_wrapper, seg_elt.confidence, seg_elt.label,) + + +class From_SEG_ELT_bbox: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "bbox": ("SEG_ELT_bbox", ), + }, + } + + RETURN_TYPES = ("INT", "INT", "INT", "INT") + RETURN_NAMES = ("left", "top", "right", "bottom") + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, bbox): + return [int(c) for c in bbox] + + +class From_SEG_ELT_crop_region: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "crop_region": ("SEG_ELT_crop_region", ), + }, + } + + RETURN_TYPES = ("INT", "INT", "INT", "INT") + RETURN_NAMES = ("left", "top", "right", "bottom") + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, crop_region): + return crop_region + + +class Edit_SEG_ELT: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seg_elt": ("SEG_ELT", ), + }, + "optional": { + "cropped_image_opt": ("IMAGE", ), + "cropped_mask_opt": ("MASK", ), + "crop_region_opt": ("SEG_ELT_crop_region", ), + "bbox_opt": ("SEG_ELT_bbox", ), + "control_net_wrapper_opt": ("SEG_ELT_control_net_wrapper", ), + "confidence_opt": ("FLOAT", {"min": 0, "max": 1.0, "step": 0.1, "forceInput": True}), + "label_opt": ("STRING", {"multiline": False, "forceInput": True}), + } + } + + RETURN_TYPES = ("SEG_ELT", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, seg_elt, cropped_image_opt=None, cropped_mask_opt=None, confidence_opt=None, crop_region_opt=None, + bbox_opt=None, label_opt=None, control_net_wrapper_opt=None): + + cropped_image = seg_elt.cropped_image if cropped_image_opt is None else cropped_image_opt + cropped_mask = seg_elt.cropped_mask if cropped_mask_opt is None else cropped_mask_opt + confidence = seg_elt.confidence if confidence_opt is None else confidence_opt + crop_region = seg_elt.crop_region if crop_region_opt is None else crop_region_opt + bbox = seg_elt.bbox if bbox_opt is None else bbox_opt + label = seg_elt.label if label_opt is None else label_opt + control_net_wrapper = seg_elt.control_net_wrapper if control_net_wrapper_opt is None else control_net_wrapper_opt + + cropped_image = cropped_image.numpy() if cropped_image is not None else None + + if isinstance(cropped_mask, torch.Tensor): + if len(cropped_mask.shape) == 3: + cropped_mask = cropped_mask.squeeze(0) + + cropped_mask = cropped_mask.numpy() + + seg = SEG(cropped_image, cropped_mask, confidence, crop_region, bbox, label, control_net_wrapper) + + return (seg,) + + +class DilateMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK", ), + "dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + }} + + RETURN_TYPES = ("MASK", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, mask, dilation): + mask = utils.dilate_mask(mask.numpy(), dilation) + mask = torch.from_numpy(mask) + mask = utils.make_3d_mask(mask) + return (mask, ) + + +class GaussianBlurMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK", ), + "kernel_size": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}), + "sigma": ("FLOAT", {"default": 10.0, "min": 0.1, "max": 100.0, "step": 0.1}), + }} + + RETURN_TYPES = ("MASK", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, mask, kernel_size, sigma): + # Some custom nodes use abnormal 4-dimensional masks in the format of b, c, h, w. In the impact pack, internal 4-dimensional masks are required in the format of b, h, w, c. Therefore, normalization is performed using the normal mask format, which is 3-dimensional, before proceeding with the operation. + mask = utils.make_3d_mask(mask) + mask = torch.unsqueeze(mask, dim=-1) + mask = utils.tensor_gaussian_blur_mask(mask, kernel_size, sigma) + mask = torch.squeeze(mask, dim=-1) + return (mask, ) + + +class DilateMaskInSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + }} + + RETURN_TYPES = ("SEGS", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs, dilation): + new_segs = [] + for seg in segs[1]: + mask = utils.dilate_mask(seg.cropped_mask, dilation) + seg = SEG(seg.cropped_image, mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + new_segs.append(seg) + + return ((segs[0], new_segs), ) + + +class GaussianBlurMaskInSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "kernel_size": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}), + "sigma": ("FLOAT", {"default": 10.0, "min": 0.1, "max": 100.0, "step": 0.1}), + }} + + RETURN_TYPES = ("SEGS", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, segs, kernel_size, sigma): + new_segs = [] + for seg in segs[1]: + mask = utils.tensor_gaussian_blur_mask(seg.cropped_mask, kernel_size, sigma) + mask = torch.squeeze(mask, dim=-1).squeeze(0).numpy() + seg = SEG(seg.cropped_image, mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + new_segs.append(seg) + + return ((segs[0], new_segs), ) + + +class Dilate_SEG_ELT: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seg_elt": ("SEG_ELT", ), + "dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}), + }} + + RETURN_TYPES = ("SEG_ELT", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, seg, dilation): + mask = utils.dilate_mask(seg.cropped_mask, dilation) + seg = SEG(seg.cropped_image, mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + return (seg,) + + +class SEG_ELT_BBOX_ScaleBy: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seg": ("SEG_ELT", ), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}), } + } + + RETURN_TYPES = ("SEG_ELT", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def fill_zero_outside_bbox(mask, crop_region, bbox): + cx1, cy1, _, _ = crop_region + x1, y1, x2, y2 = bbox + x1, y1, x2, y2 = x1-cx1, y1-cy1, x2-cx1, y2-cy1 + h, w = mask.shape + + x1 = int(min(w-1, max(0, x1))) + x2 = int(min(w-1, max(0, x2))) + y1 = int(min(h-1, max(0, y1))) + y2 = int(min(h-1, max(0, y2))) + + mask_cropped = mask.copy() + mask_cropped[:, :x1] = 0 # zero fill left side + mask_cropped[:, x2:] = 0 # zero fill right side + mask_cropped[:y1, :] = 0 # zero fill top side + mask_cropped[y2:, :] = 0 # zero fill bottom side + return mask_cropped + + def doit(self, seg, scale_by): + x1, y1, x2, y2 = seg.bbox + w = x2-x1 + h = y2-y1 + + dw = int((w * scale_by - w)/2) + dh = int((h * scale_by - h)/2) + + bbox = (x1-dw, y1-dh, x2+dw, y2+dh) + + cropped_mask = SEG_ELT_BBOX_ScaleBy.fill_zero_outside_bbox(seg.cropped_mask, seg.crop_region, bbox) + seg = SEG(seg.cropped_image, cropped_mask, seg.confidence, seg.crop_region, bbox, seg.label, seg.control_net_wrapper) + return (seg,) + + +class EmptySEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {}, } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self): + shape = 0, 0 + return ((shape, []),) + + +class SegsToCombinedMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"segs": ("SEGS",), }} + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, segs): + mask = core.segs_to_combined_mask(segs) + mask = utils.make_3d_mask(mask) + return (mask,) + + +class MediaPipeFaceMeshToSEGS: + @classmethod + def INPUT_TYPES(s): + bool_true_widget = ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"}) + bool_false_widget = ("BOOLEAN", {"default": False, "label_on": "Enabled", "label_off": "Disabled"}) + return {"required": { + "image": ("IMAGE",), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "bbox_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "crop_min_size": ("INT", {"min": 10, "max": MAX_RESOLUTION, "step": 1, "default": 50}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 1}), + "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "face": bool_true_widget, + "mouth": bool_false_widget, + "left_eyebrow": bool_false_widget, + "left_eye": bool_false_widget, + "left_pupil": bool_false_widget, + "right_eyebrow": bool_false_widget, + "right_eye": bool_false_widget, + "right_pupil": bool_false_widget, + }, + # "optional": {"reference_image_opt": ("IMAGE", ), } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, image, crop_factor, bbox_fill, crop_min_size, drop_size, dilation, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil): + # padding is obsolete now + # https://github.com/Fannovel16/comfyui_controlnet_aux/blob/1ec41fceff1ee99596445a0c73392fd91df407dc/utils.py#L33 + # def calc_pad(h_raw, w_raw): + # resolution = normalize_size_base_64(h_raw, w_raw) + # + # def pad64(x): + # return int(np.ceil(float(x) / 64.0) * 64 - x) + # + # k = float(resolution) / float(min(h_raw, w_raw)) + # h_target = int(np.round(float(h_raw) * k)) + # w_target = int(np.round(float(w_raw) * k)) + # + # return pad64(h_target), pad64(w_target) + + # if reference_image_opt is not None: + # if image.shape[1:] != reference_image_opt.shape[1:]: + # scale_by1 = reference_image_opt.shape[1] / image.shape[1] + # scale_by2 = reference_image_opt.shape[2] / image.shape[2] + # scale_by = min(scale_by1, scale_by2) + # + # # padding is obsolete now + # # h_pad, w_pad = calc_pad(reference_image_opt.shape[1], reference_image_opt.shape[2]) + # # if h_pad != 0: + # # # height padded + # # image = image[:, :-h_pad, :, :] + # # elif w_pad != 0: + # # # width padded + # # image = image[:, :, :-w_pad, :] + # + # image = nodes.ImageScaleBy().upscale(image, "bilinear", scale_by)[0] + + result = core.mediapipe_facemesh_to_segs(image, crop_factor, bbox_fill, crop_min_size, drop_size, dilation, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil) + return (result, ) + + +class MaskToSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK",), + "combined": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "bbox_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "contour_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + @staticmethod + def doit(mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False): + mask = utils.make_2d_mask(mask) + result = core.mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size, is_contour=contour_fill) + + return (result, ) + + +class MaskToSEGS_for_AnimateDiff: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK",), + "combined": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}), + "bbox_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), + "contour_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + @staticmethod + def doit(mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False): + if (len(mask.shape) == 4 and mask.shape[1] > 1) or (len(mask.shape) == 3 and mask.shape[0] > 1): + mask = utils.make_3d_mask(mask) + if contour_fill: + logging.info("[Impact Pack] MaskToSEGS_for_AnimateDiff: 'contour_fill' is ignored because batch mask 'contour_fill' is not supported.") + result = core.batch_mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size) + return (result, ) + + mask = utils.make_2d_mask(mask) + segs = core.mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size, is_contour=contour_fill) + all_masks = SEGSToMaskList().doit(segs)[0] + + result_mask = (all_masks[0] * 255).to(torch.uint8) + for mask in all_masks[1:]: + result_mask |= (mask * 255).to(torch.uint8) + + result_mask = (result_mask/255.0).to(torch.float32) + result_mask = utils.to_binary_mask(result_mask, 0.1)[0] + + return MaskToSEGS.doit(result_mask, False, crop_factor, False, drop_size, contour_fill) + + +class IPAdapterApplySEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS",), + "ipadapter_pipe": ("IPADAPTER_PIPE",), + "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}), + "noise": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01}), + "weight_type": (["original", "linear", "channel penalty"], {"default": 'channel penalty'}), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.001}), + "unfold_batch": ("BOOLEAN", {"default": False}), + "faceid_v2": ("BOOLEAN", {"default": False}), + "weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}), + "context_crop_factor": ("FLOAT", {"default": 1.2, "min": 1.0, "max": 100, "step": 0.1}), + "reference_image": ("IMAGE",), + }, + "optional": { + "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],), + "neg_image": ("IMAGE",), + }, + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(segs, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, context_crop_factor, reference_image, combine_embeds="concat", neg_image=None): + + if len(ipadapter_pipe) == 4: + logging.info("[Impact Pack] IPAdapterApplySEGS: Installed Inspire Pack is outdated.") + raise Exception("Inspire Pack is outdated.") + + new_segs = [] + + h, w = segs[0] + + if reference_image.shape[2] != w or reference_image.shape[1] != h: + reference_image = utils.tensor_resize(reference_image, w, h) + + for seg in segs[1]: + # The context_crop_region sets how much wider the IPAdapter context will reflect compared to the crop_region, not the bbox + context_crop_region = utils.make_crop_region(w, h, seg.crop_region, context_crop_factor) + cropped_image = utils.crop_image(reference_image, context_crop_region) + + control_net_wrapper = core.IPAdapterWrapper(ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, weight_v2, cropped_image, neg_image=neg_image, prev_control_net=seg.control_net_wrapper, combine_embeds=combine_embeds) + new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper) + new_segs.append(new_seg) + + return ((segs[0], new_segs), ) + + +class ControlNetApplySEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS",), + "control_net": ("CONTROL_NET",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }, + "optional": { + "segs_preprocessor": ("SEGS_PREPROCESSOR",), + "control_image": ("IMAGE",) + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + DEPRECATED = True + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(segs, control_net, strength, segs_preprocessor=None, control_image=None): + new_segs = [] + + for seg in segs[1]: + control_net_wrapper = core.ControlNetWrapper(control_net, strength, segs_preprocessor, seg.control_net_wrapper, + original_size=segs[0], crop_region=seg.crop_region, control_image=control_image) + new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper) + new_segs.append(new_seg) + + return ((segs[0], new_segs), ) + + +class ControlNetApplyAdvancedSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS",), + "control_net": ("CONTROL_NET",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }, + "optional": { + "segs_preprocessor": ("SEGS_PREPROCESSOR",), + "control_image": ("IMAGE",), + "vae": ("VAE",) + } + } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None, vae=None): + new_segs = [] + + for seg in segs[1]: + control_net_wrapper = core.ControlNetAdvancedWrapper(control_net, strength, start_percent, end_percent, segs_preprocessor, + seg.control_net_wrapper, original_size=segs[0], crop_region=seg.crop_region, + control_image=control_image, vae=vae) + new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper) + new_segs.append(new_seg) + + return ((segs[0], new_segs), ) + + +class ControlNetClearSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {"segs": ("SEGS",), }, } + + RETURN_TYPES = ("SEGS",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(segs): + new_segs = [] + + for seg in segs[1]: + new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None) + new_segs.append(new_seg) + + return ((segs[0], new_segs), ) + + +class SEGSSwitch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "select": ("INT", {"default": 1, "min": 1, "max": 99999, "step": 1}), + "segs1": ("SEGS",), + }, + } + + RETURN_TYPES = ("SEGS", ) + + OUTPUT_NODE = True + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, *args, **kwargs): + input_name = f"segs{int(kwargs['select'])}" + + if input_name in kwargs: + return (kwargs[input_name],) + else: + logging.info("SEGSSwitch: invalid select index ('segs1' is selected)") + return (kwargs['segs1'],) + + +class SEGSPicker: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "picks": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}), + "segs": ("SEGS",), + }, + "optional": { + "fallback_image_opt": ("IMAGE", ), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("SEGS", ) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "This node provides a function to select only the chosen SEGS from the input SEGS." + + @staticmethod + def doit(picks, segs, fallback_image_opt=None, unique_id=None): + if fallback_image_opt is not None: + segs = core.segs_scale_match(segs, fallback_image_opt.shape) + + # generate candidates image + cands = [] + for seg in segs[1]: + if seg.cropped_image is not None: + cropped_image = seg.cropped_image + elif fallback_image_opt is not None: + # take from original image + cropped_image = utils.crop_image(fallback_image_opt, seg.crop_region) + else: + cropped_image = utils.empty_pil_tensor() + + mask_array = seg.cropped_mask.copy() + mask_array[mask_array < 0.3] = 0.3 + mask_array = mask_array[None, ..., None] + cropped_image = cropped_image * mask_array + + cands.append(cropped_image) + + impact.impact_server.segs_picker_map[unique_id] = cands + + # pass only selected + pick_ids = set() + + for pick in picks.split(","): + try: + pick_ids.add(int(pick)-1) + except Exception: + pass + + new_segs = [] + for i in pick_ids: + if 0 <= i < len(segs[1]): + new_segs.append(segs[1][i]) + + return ((segs[0], new_segs),) + + +class DefaultImageForSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "segs": ("SEGS", ), + "image": ("IMAGE", ), + "override": ("BOOLEAN", {"default": True}), + }} + + RETURN_TYPES = ("SEGS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "If the SEGS have not passed through the detailer, they contain only detection area information without an image. This node sets a default image for the SEGS." + + @staticmethod + def doit(segs, image, override): + results = [] + + segs = core.segs_scale_match(segs, image.shape) + + if len(segs[1]) > 0: + if segs[1][0].cropped_image is not None: + batch_count = len(segs[1][0].cropped_image) + else: + batch_count = len(image) + + for seg in segs[1]: + if seg.cropped_image is not None and not override: + cropped_image = seg.cropped_image + else: + cropped_image = None + for i in range(0, batch_count): + # take from original image + ref_image = image[i].unsqueeze(0) + cropped_image2 = utils.crop_image(ref_image, seg.crop_region) + + if cropped_image is None: + cropped_image = cropped_image2 + else: + cropped_image = torch.cat((cropped_image, cropped_image2), dim=0) + + new_seg = SEG(cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + results.append(new_seg) + + return ((segs[0], results), ) + else: + return (segs, ) + + +class RemoveImageFromSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {"segs": ("SEGS", ), }} + + RETURN_TYPES = ("SEGS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(segs): + results = [] + + if len(segs[1]) > 0: + for seg in segs[1]: + new_seg = SEG(None, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper) + results.append(new_seg) + + return ((segs[0], results), ) + else: + return (segs, ) + + +class MakeTileSEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE", ), + "bbox_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 8}), + "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.01}), + "min_overlap": ("INT", {"default": 5, "min": 0, "max": 512, "step": 1}), + "filter_segs_dilation": ("INT", {"default": 20, "min": -255, "max": 255, "step": 1}), + "mask_irregularity": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "irregular_mask_mode": (["Reuse fast", "Reuse quality", "All random fast", "All random quality"],) + }, + "optional": { + "filter_in_segs_opt": ("SEGS", ), + "filter_out_segs_opt": ("SEGS", ), + } + } + + RETURN_TYPES = ("SEGS",) + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/__for_testing" + + @staticmethod + def doit(images, bbox_size, crop_factor, min_overlap, filter_segs_dilation, mask_irregularity=0, irregular_mask_mode="Reuse fast", filter_in_segs_opt=None, filter_out_segs_opt=None): + if bbox_size <= 2*min_overlap: + new_min_overlap = bbox_size / 2 + logging.info(f"[MakeTileSEGS] min_overlap should be greater than bbox_size. (value changed: {min_overlap} => {new_min_overlap})") + min_overlap = new_min_overlap + + _, ih, iw, _ = images.size() + + mask_cache = None + mask_quality = 512 + if mask_irregularity > 0: + if irregular_mask_mode == "Reuse fast": + mask_quality = 128 + mask_cache = np.zeros((128, 128)).astype(np.float32) + core.random_mask(mask_cache, (0, 0, 128, 128), factor=mask_irregularity, size=mask_quality) + elif irregular_mask_mode == "Reuse quality": + mask_quality = 512 + mask_cache = np.zeros((512, 512)).astype(np.float32) + core.random_mask(mask_cache, (0, 0, 512, 512), factor=mask_irregularity, size=mask_quality) + elif irregular_mask_mode == "All random fast": + mask_quality = 512 + + # compensate overlap/bbox_size for irregular mask + if mask_irregularity > 0: + compensate = max(6, int(mask_quality * mask_irregularity / 4)) + min_overlap += compensate + bbox_size += compensate*2 + + # create exclusion mask + if filter_out_segs_opt is not None: + exclusion_mask = core.segs_to_combined_mask(filter_out_segs_opt) + exclusion_mask = utils.make_3d_mask(exclusion_mask) + exclusion_mask = utils.resize_mask(exclusion_mask, (ih, iw)) + exclusion_mask = utils.dilate_mask(exclusion_mask.cpu().numpy(), filter_segs_dilation) + else: + exclusion_mask = None + + if filter_in_segs_opt is not None: + and_mask = core.segs_to_combined_mask(filter_in_segs_opt) + and_mask = utils.make_3d_mask(and_mask) + and_mask = utils.resize_mask(and_mask, (ih, iw)) + and_mask = utils.dilate_mask(and_mask.cpu().numpy(), filter_segs_dilation) + + a, b = core.mask_to_segs(and_mask, True, 1.0, False, 0) + if len(b) == 0: + return ((a, b),) + + start_x, start_y, c, d = b[0].crop_region + w = c - start_x + h = d - start_y + else: + start_x = 0 + start_y = 0 + h, w = ih, iw + and_mask = None + + # calculate tile factors + if bbox_size > h or bbox_size > w: + new_bbox_size = min(bbox_size, min(w, h)) + logging.info(f"[MaskTileSEGS] bbox_size is greater than resolution (value changed: {bbox_size} => {new_bbox_size}") + bbox_size = new_bbox_size + + n_horizontal = math.ceil(w / (bbox_size - min_overlap)) + n_vertical = math.ceil(h / (bbox_size - min_overlap)) + + w_overlap_sum = (bbox_size * n_horizontal) - w + if w_overlap_sum < 0: + n_horizontal += 1 + w_overlap_sum = (bbox_size * n_horizontal) - w + + w_overlap_size = 0 if n_horizontal == 1 else int(w_overlap_sum/(n_horizontal-1)) + + h_overlap_sum = (bbox_size * n_vertical) - h + if h_overlap_sum < 0: + n_vertical += 1 + h_overlap_sum = (bbox_size * n_vertical) - h + + h_overlap_size = 0 if n_vertical == 1 else int(h_overlap_sum/(n_vertical-1)) + + new_segs = [] + + if w_overlap_size == bbox_size: + n_horizontal = 1 + + if h_overlap_size == bbox_size: + n_vertical = 1 + + y = start_y + for j in range(0, n_vertical): + x = start_x + for i in range(0, n_horizontal): + x1 = x + y1 = y + + if x+bbox_size < iw-1: + x2 = x+bbox_size + else: + x2 = iw + x1 = iw-bbox_size + + if y+bbox_size < ih-1: + y2 = y+bbox_size + else: + y2 = ih + y1 = ih-bbox_size + + bbox = x1, y1, x2, y2 + crop_region = utils.make_crop_region(iw, ih, bbox, crop_factor) + cx1, cy1, cx2, cy2 = crop_region + + mask = np.zeros((cy2 - cy1, cx2 - cx1)).astype(np.float32) + + rel_left = x1 - cx1 + rel_top = y1 - cy1 + rel_right = x2 - cx1 + rel_bot = y2 - cy1 + + if mask_irregularity > 0: + if mask_cache is not None: + core.adaptive_mask_paste(mask, mask_cache, (rel_left, rel_top, rel_right, rel_bot)) + else: + core.random_mask(mask, (rel_left, rel_top, rel_right, rel_bot), factor=mask_irregularity, size=mask_quality) + + # corner filling + if rel_left == 0: + pad = int((x2 - x1) / 8) + mask[rel_top:rel_bot, :pad] = 1.0 + + if rel_top == 0: + pad = int((y2 - y1) / 8) + mask[:pad, rel_left:rel_right] = 1.0 + + if rel_right == mask.shape[1]: + pad = int((x2 - x1) / 8) + mask[rel_top:rel_bot, -pad:] = 1.0 + + if rel_bot == mask.shape[0]: + pad = int((y2 - y1) / 8) + mask[-pad:, rel_left:rel_right] = 1.0 + else: + mask[rel_top:rel_bot, rel_left:rel_right] = 1.0 + + mask = torch.tensor(mask) + + if exclusion_mask is not None: + exclusion_mask_cropped = exclusion_mask[cy1:cy2, cx1:cx2] + mask[exclusion_mask_cropped != 0] = 0.0 + + if and_mask is not None: + and_mask_cropped = and_mask[cy1:cy2, cx1:cx2] + mask[and_mask_cropped == 0] = 0.0 + + is_mask_zero = torch.all(mask == 0.0).item() + + if not is_mask_zero: + item = SEG(None, mask.numpy(), 1.0, crop_region, bbox, "", None) + new_segs.append(item) + + x += bbox_size - w_overlap_size + y += bbox_size - h_overlap_size + + res = (ih, iw), new_segs # segs + return (res,) + + +class SEGSUpscaler: + @classmethod + def INPUT_TYPES(s): + resampling_methods = ["lanczos", "nearest", "bilinear", "bicubic"] + + return {"required": { + "image": ("IMAGE",), + "segs": ("SEGS",), + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 100.0, "step": 0.01}), + "resampling_method": (resampling_methods,), + "supersample": (["true", "false"],), + "rounding_modulus": ("INT", {"default": 8, "min": 8, "max": 1024, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL",), + "upscaler_hook_opt": ("UPSCALER_HOOK",), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + @staticmethod + def doit(image, segs, model, clip, vae, rescale_factor, resampling_method, supersample, rounding_modulus, + seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, inpaint_model, noise_mask_feather, + upscale_model_opt=None, upscaler_hook_opt=None, scheduler_func_opt=None): + + new_image = segs_upscaler.upscaler(image, upscale_model_opt, rescale_factor, resampling_method, supersample, rounding_modulus) + + segs = core.segs_scale_match(segs, new_image.shape) + + ordered_segs = segs[1] + + for i, seg in enumerate(ordered_segs): + cropped_image = utils.crop_ndarray4(new_image.numpy(), seg.crop_region) + cropped_image = utils.to_tensor(cropped_image) + mask = utils.to_tensor(seg.cropped_mask) + mask = utils.tensor_gaussian_blur_mask(mask, feather) + + is_mask_all_zeros = (seg.cropped_mask == 0).all().item() + if is_mask_all_zeros: + logging.info("SEGSUpscaler: segment skip [empty mask]") + continue + + cropped_mask = seg.cropped_mask + + seg_seed = seed + i + + enhanced_image = segs_upscaler.img2img_segs(cropped_image, model, clip, vae, seg_seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, + noise_mask=cropped_mask, control_net_wrapper=seg.control_net_wrapper, + inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt) + if enhanced_image is not None: + new_image = new_image.cpu() + enhanced_image = enhanced_image.cpu() + left = seg.crop_region[0] + top = seg.crop_region[1] + utils.tensor_paste(new_image, enhanced_image, (left, top), mask) + + if upscaler_hook_opt is not None: + new_image = upscaler_hook_opt.post_paste(new_image) + + enhanced_img = utils.tensor_convert_rgb(new_image) + + return (enhanced_img,) + + +class SEGSUpscalerPipe: + @classmethod + def INPUT_TYPES(s): + resampling_methods = ["lanczos", "nearest", "bilinear", "bicubic"] + + return {"required": { + "image": ("IMAGE",), + "segs": ("SEGS",), + "basic_pipe": ("BASIC_PIPE",), + "rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 100.0, "step": 0.01}), + "resampling_method": (resampling_methods,), + "supersample": (["true", "false"],), + "rounding_modulus": ("INT", {"default": 8, "min": 8, "max": 1024, "step": 8}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (core.get_schedulers(),), + "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), + "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), + }, + "optional": { + "upscale_model_opt": ("UPSCALE_MODEL",), + "upscaler_hook_opt": ("UPSCALER_HOOK",), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Upscale" + + @staticmethod + def doit(image, segs, basic_pipe, rescale_factor, resampling_method, supersample, rounding_modulus, + seed, steps, cfg, sampler_name, scheduler, denoise, feather, inpaint_model, noise_mask_feather, + upscale_model_opt=None, upscaler_hook_opt=None, scheduler_func_opt=None): + + model, clip, vae, positive, negative = basic_pipe + + return SEGSUpscaler.doit(image, segs, model, clip, vae, rescale_factor, resampling_method, supersample, rounding_modulus, + seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, inpaint_model, noise_mask_feather, + upscale_model_opt=upscale_model_opt, upscaler_hook_opt=upscaler_hook_opt, scheduler_func_opt=scheduler_func_opt) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/segs_upscaler.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/segs_upscaler.py new file mode 100644 index 00000000..fb3cd304 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/segs_upscaler.py @@ -0,0 +1,140 @@ +from impact import impact_sampling +from comfy import model_management +from impact import utils +from PIL import Image +import nodes +import torch +import inspect +import logging +import comfy + +try: + from comfy_extras import nodes_differential_diffusion +except Exception: + logging.info("[Impact Pack] ComfyUI is an outdated version. The DifferentialDiffusion feature will be disabled.") + + +# Implementation based on `https://github.com/lingondricka2/Upscaler-Detailer` + +# code from comfyroll ---> +# https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/blob/main/nodes/functions_upscale.py + +def upscale_with_model(upscale_model, image): + device = model_management.get_torch_device() + upscale_model.to(device) + in_img = image.movedim(-1, -3).to(device) + + tile = 512 + overlap = 32 + + oom = True + while oom: + try: + steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) + pbar = comfy.utils.ProgressBar(steps) + s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + oom = False + except model_management.OOM_EXCEPTION as e: + tile //= 2 + if tile < 128: + raise e + + s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0) + return s + + +def apply_resize_image(image: Image.Image, original_width, original_height, rounding_modulus, mode='scale', supersample='true', factor: int = 2, width: int = 1024, height: int = 1024, + resample='bicubic'): + # Calculate the new width and height based on the given mode and parameters + if mode == 'rescale': + new_width, new_height = int(original_width * factor), int(original_height * factor) + else: + m = rounding_modulus + original_ratio = original_height / original_width + height = int(width * original_ratio) + + new_width = width if width % m == 0 else width + (m - width % m) + new_height = height if height % m == 0 else height + (m - height % m) + + # Define a dictionary of resampling filters + resample_filters = {'nearest': 0, 'bilinear': 2, 'bicubic': 3, 'lanczos': 1} + + # Apply supersample + if supersample == 'true': + image = image.resize((new_width * 8, new_height * 8), resample=Image.Resampling(resample_filters[resample])) + + # Resize the image using the given resampling filter + resized_image = image.resize((new_width, new_height), resample=Image.Resampling(resample_filters[resample])) + + return resized_image + + +def upscaler(image, upscale_model, rescale_factor, resampling_method, supersample, rounding_modulus): + if upscale_model is not None: + up_image = upscale_with_model(upscale_model, image) + else: + up_image = image + + pil_img = utils.tensor2pil(image) + original_width, original_height = pil_img.size + scaled_image = utils.pil2tensor(apply_resize_image(utils.tensor2pil(up_image), original_width, original_height, rounding_modulus, 'rescale', + supersample, rescale_factor, 1024, resampling_method)) + return scaled_image + +# <--- + + +def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, scheduler, + positive, negative, denoise, noise_mask, control_net_wrapper=None, + inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None): + + original_image_size = image.shape[1:3] + + # Match to original image size + if original_image_size[0] % 8 > 0 or original_image_size[1] % 8 > 0: + scale = 8/min(original_image_size[0], original_image_size[1]) + 1 + w = int(original_image_size[1] * scale) + h = int(original_image_size[0] * scale) + image = utils.tensor_resize(image, w, h) + + if noise_mask is not None: + noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather) + noise_mask = noise_mask.squeeze(3) + + if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options: + model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0] + + if control_net_wrapper is not None: + positive, negative, _ = control_net_wrapper.apply(positive, negative, image, noise_mask) + + # prepare mask + if noise_mask is not None and inpaint_model: + imc_encode = nodes.InpaintModelConditioning().encode + if 'noise_mask' in inspect.signature(imc_encode).parameters: + positive, negative, latent_image = imc_encode(positive, negative, image, vae, mask=noise_mask, noise_mask=True) + else: + logging.info("[Impact Pack] ComfyUI is an outdated version.") + positive, negative, latent_image = imc_encode(positive, negative, image, vae, noise_mask) + else: + latent_image = utils.to_latent_image(image, vae) + if noise_mask is not None: + latent_image['noise_mask'] = noise_mask + + refined_latent = latent_image + + # ksampler + refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, refined_latent, denoise, scheduler_func=scheduler_func_opt) + + # non-latent downscale - latent downscale cause bad quality + refined_image = vae.decode(refined_latent['samples']) + + # prevent mixing of device + refined_image = refined_image.cpu() + + # Match to original image size + if refined_image.shape[1:3] != original_image_size: + refined_image = utils.tensor_resize(refined_image, original_image_size[1], original_image_size[0]) + + # don't convert to latent - latent break image + # preserving pil is much better + return refined_image diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/special_samplers.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/special_samplers.py new file mode 100644 index 00000000..ffa1c768 --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/special_samplers.py @@ -0,0 +1,686 @@ +import math +import impact.core as core +from comfy_extras.nodes_custom_sampler import Noise_RandomNoise +from nodes import MAX_RESOLUTION +import nodes +from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper, separated_sample, impact_sample +import comfy +import torch +import numpy as np +import logging + + +class TiledKSamplerProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "noise schedule"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}), + "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64, "tooltip": "Sets the width of the tile to be used in TiledKSampler."}), + "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64, "tooltip": "Sets the height of the tile to be used in TiledKSampler."}), + "tiling_strategy": (["random", "padded", 'simple'], {"tooltip": "Sets the tiling strategy for TiledKSampler."} ), + "basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}) + }} + + OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)", ) + + RETURN_TYPES = ("KSAMPLER",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Sampler" + + @staticmethod + def doit(seed, steps, cfg, sampler_name, scheduler, denoise, + tile_width, tile_height, tiling_strategy, basic_pipe): + model, _, _, positive, negative = basic_pipe + sampler = core.TiledKSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, + tile_width, tile_height, tiling_strategy) + return (sampler, ) + + +class KSamplerProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}), + "scheduler": (core.get_schedulers(), {"tooltip": "noise schedule"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}), + "basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}) + }, + "optional": { + "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}), + } + } + + OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)",) + + RETURN_TYPES = ("KSAMPLER",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Sampler" + + @staticmethod + def doit(seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe, scheduler_func_opt=None): + model, _, _, positive, negative = basic_pipe + sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=scheduler_func_opt) + return (sampler, ) + + +class KSamplerAdvancedProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "toolip": "classifier free guidance value"}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"toolip": "sampler"}), + "scheduler": (core.get_schedulers(), {"toolip": "noise schedule"}), + "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "toolip": "Multiplier of noise schedule"}), + "basic_pipe": ("BASIC_PIPE", {"toolip": "basic_pipe input for sampling"}) + }, + "optional": { + "sampler_opt": ("SAMPLER", {"toolip": "[OPTIONAL] Uses the passed sampler instead of internal impact_sampler."}), + "scheduler_func_opt": ("SCHEDULER_FUNC", {"toolip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}), + } + } + + OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)", ) + + RETURN_TYPES = ("KSAMPLER_ADVANCED",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Sampler" + + @staticmethod + def doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None, scheduler_func_opt=None): + model, _, _, positive, negative = basic_pipe + sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor, scheduler_func=scheduler_func_opt) + return (sampler, ) + + +class TwoSamplersForMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "latent_image": ("LATENT", {"tooltip": "input latent image"}), + "base_sampler": ("KSAMPLER", {"tooltip": "Sampler to apply to the region outside the mask."}), + "mask_sampler": ("KSAMPLER", {"tooltip": "Sampler to apply to the masked region."}), + "mask": ("MASK", {"tooltip": "region mask"}) + }, + } + + OUTPUT_TOOLTIPS = ("result latent", ) + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Sampler" + + @staticmethod + def doit(latent_image, base_sampler, mask_sampler, mask): + inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0)) + + latent_image['noise_mask'] = inv_mask + new_latent_image = base_sampler.sample(latent_image) + + new_latent_image['noise_mask'] = mask + new_latent_image = mask_sampler.sample(new_latent_image) + + del new_latent_image['noise_mask'] + + return (new_latent_image, ) + + +class TwoAdvancedSamplersForMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}), + "samples": ("LATENT", {"tooltip": "input latent image"}), + "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "Sampler to apply to the region outside the mask."}), + "mask_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "Sampler to apply to the masked region."}), + "mask": ("MASK", {"tooltip": "region mask"}), + "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask by the overlap_factor amount to overlap with other regions."}) + }, + } + + OUTPUT_TOOLTIPS = ("result latent", ) + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Sampler" + + @staticmethod + def doit(seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor): + regional_prompts = RegionalPrompt().doit(mask=mask, advanced_sampler=mask_sampler)[0] + + return RegionalSampler().doit(seed=seed, seed_2nd=0, seed_2nd_mode="ignore", steps=steps, base_only_steps=1, + denoise=denoise, samples=samples, base_sampler=base_sampler, + regional_prompts=regional_prompts, overlap_factor=overlap_factor, + restore_latent=True, additional_mode="ratio between", + additional_sampler="AUTO", additional_sigma_ratio=0.3) + + +class RegionalPrompt: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK", {"tooltip": "region mask"}), + "advanced_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "sampler for specified region"}), + }, + "optional": { + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Sets the extra seed to be used for noise variation."}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Sets the strength of the noise variation."}), + "variation_method": (["linear", "slerp"], {"tooltip": "Sets how the original noise and extra noise are blended together."}), + } + } + + OUTPUT_TOOLTIPS = ("regional prompts. (Can be used in the RegionalSampler.)", ) + + RETURN_TYPES = ("REGIONAL_PROMPTS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Regional" + + @staticmethod + def doit(mask, advanced_sampler, variation_seed=0, variation_strength=0.0, variation_method="linear"): + regional_prompt = core.REGIONAL_PROMPT(mask, advanced_sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method) + return ([regional_prompt], ) + + +class CombineRegionalPrompts: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "regional_prompts1": ("REGIONAL_PROMPTS", {"tooltip": "input regional_prompts. (Connecting to the input slot increases the number of additional slots.)"}), + }, + } + + OUTPUT_TOOLTIPS = ("Combined REGIONAL_PROMPTS", ) + + RETURN_TYPES = ("REGIONAL_PROMPTS", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Regional" + + @staticmethod + def doit(**kwargs): + res = [] + for k, v in kwargs.items(): + res += v + + return (res, ) + + +class CombineConditionings: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning1": ("CONDITIONING", { "tooltip": "input conditionings. (Connecting to the input slot increases the number of additional slots.)" }), + }, + } + + OUTPUT_TOOLTIPS = ("Combined conditioning", ) + + RETURN_TYPES = ("CONDITIONING", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(**kwargs): + res = [] + for k, v in kwargs.items(): + res += v + + return (res, ) + + +class ConcatConditionings: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning1": ("CONDITIONING", { "tooltip": "input conditionings. (Connecting to the input slot increases the number of additional slots.)" }), + }, + } + + OUTPUT_TOOLTIPS = ("Concatenated conditioning", ) + + RETURN_TYPES = ("CONDITIONING", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + @staticmethod + def doit(**kwargs): + conditioning_to = list(kwargs.values())[0] + + for k, conditioning_from in list(kwargs.items())[1:]: + out = [] + if len(conditioning_from) > 1: + logging.warning("Warning: ConcatConditionings {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.") + + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from), 1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + conditioning_to = out + + return (out, ) + + +class RegionalSampler: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "seed_2nd": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Additional noise seed. The behavior is determined by seed_2nd_mode."}), + "seed_2nd_mode": (["ignore", "fixed", "seed+seed_2nd", "seed-seed_2nd", "increment", "decrement", "randomize"], {"tooltip": "application method of seed_2nd. 1) ignore: Do not use seed_2nd. In the base only sampling stage, the seed is applied as a noise seed, and in the regional sampling stage, denoising is performed as it is without additional noise. 2) Others: In the base only sampling stage, the seed is applied as a noise seed, and once it is closed so that there is no leftover noise, new noise is added with seed_2nd and the regional samping stage is performed. a) fixed: Use seed_2nd as it is as an additional noise seed. b) seed+seed_2nd: Apply the value of seed+seed_2nd as an additional noise seed. c) seed-seed_2nd: Apply the value of seed-seed_2nd as an additional noise seed. d) increment: Not implemented yet. Same with fixed. e) decrement: Not implemented yet. Same with fixed. f) randomize: Not implemented yet. Same with fixed."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "base_only_steps": ("INT", {"default": 2, "min": 0, "max": 10000, "tooltip": "total sampling steps"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}), + "samples": ("LATENT", {"tooltip": "input latent image"}), + "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "The sampler applied outside the area set by the regional_prompt."}), + "regional_prompts": ("REGIONAL_PROMPTS", {"tooltip": "The prompt applied to each region"}), + "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask set in regional_prompts by the overlap_factor amount to overlap with other regions."}), + "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "At each step, restore the noise outside the mask area to its original state, as per the principle of inpainting. This option is provided for backward compatibility, and it is recommended to always set it to true."}), + "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between", "tooltip": "..._sde or uni_pc and other special samplers are used, the region is not properly denoised, and it causes a phenomenon that destroys the overall harmony. To compensate for this, a recovery operation is performed using another sampler. This requires a longer time for sampling because a second sampling is performed at each step in each region using a special sampler. 1) DISABLE: Disable this feature. 2) ratio additional: After performing the denoise amount to be performed in the step with the sampler set in the region, the recovery sampler is additionally applied by the additional_sigma_ratio. If you use this option, the total denoise amount increases by additional_sigma_ratio. 3) ratio between: The denoise amount to be performed in the step with the sampler set in the region and the denoise amount to be applied to the recovery sampler are divided by additional_sigma_ratio, and denoise is performed for each denoise amount. If you use this option, the total denoise amount does not change."}), + "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"], {"tooltip": "1) AUTO: Automatically set the recovery sampler. If the sampler is uni_pc, uni_pc_bh2, dpmpp_sde, dpmpp_sde_gpu, the dpm_fast sampler is selected If the sampler is dpmpp_2m_sde, dpmpp_2m_sde_gpu, dpmpp_3m_sde, dpmpp_3m_sde_gpu, the dpmpp_2m sampler is selected. 2) Others: Manually set the recovery sampler."}), + "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Multiplier of noise schedule to be applied according to additional_mode."}), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + OUTPUT_TOOLTIPS = ("result latent", ) + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Regional" + + @staticmethod + def separated_sample(*args, **kwargs): + return separated_sample(*args, **kwargs) + + @staticmethod + def mask_erosion(samples, mask, grow_mask_by): + mask = mask.clone() + + w = samples['samples'].shape[3] + h = samples['samples'].shape[2] + + mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode="bilinear") + if grow_mask_by == 0: + mask_erosion = mask2 + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1) + + return mask_erosion[:, :, :w, :h].round() + + @staticmethod + def doit(seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent, + additional_mode, additional_sampler, additional_sigma_ratio, unique_id=None): + + samples = samples.copy() + samples['samples'] = comfy.sample.fix_empty_latent_channels(base_sampler.params[0], samples['samples']) + + if restore_latent: + latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']() + else: + latent_compositor = None + + masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts] + masks = [np.ceil(mask).astype(np.int32) for mask in masks] + combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks)) + + inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0)) + + adv_steps = int(steps / denoise) + start_at_step = adv_steps - steps + + region_len = len(regional_prompts) + total = steps*region_len + + leftover_noise = False + if base_only_steps > 0: + if seed_2nd_mode == 'ignore': + leftover_noise = True + + noise = Noise_RandomNoise(seed).generate_noise(samples) + + for rp in regional_prompts: + noise = rp.touch_noise(noise) + + samples = base_sampler.sample_advanced(True, seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recovery_mode="DISABLE", noise=noise) + + if seed_2nd_mode == "seed+seed_2nd": + seed += seed_2nd + if seed > 1125899906842624: + seed = seed - 1125899906842624 + elif seed_2nd_mode == "seed-seed_2nd": + seed -= seed_2nd + if seed < 0: + seed += 1125899906842624 + elif seed_2nd_mode != 'ignore': + seed = seed_2nd + + new_latent_image = samples.copy() + base_latent_image = None + + if not leftover_noise: + add_noise = True + noise = Noise_RandomNoise(seed).generate_noise(samples) + + for rp in regional_prompts: + noise = rp.touch_noise(noise) + else: + add_noise = False + noise = None + + for i in range(start_at_step+base_only_steps, adv_steps): + core.update_node_status(unique_id, f"{i}/{steps} steps | ", ((i-start_at_step)*region_len)/total) + + new_latent_image['noise_mask'] = inv_mask + new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, + start_at_step=i, end_at_step=i + 1, return_with_leftover_noise=True, + recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise) + + if restore_latent: + if 'noise_mask' in new_latent_image: + del new_latent_image['noise_mask'] + base_latent_image = new_latent_image.copy() + + j = 1 + for regional_prompt in regional_prompts: + if restore_latent: + new_latent_image = base_latent_image.copy() + + core.update_node_status(unique_id, f"{i}/{steps} steps | {j}/{region_len}", ((i-start_at_step)*region_len + j)/total) + + region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0) + + new_latent_image['noise_mask'] = region_mask + new_latent_image = regional_prompt.sampler.sample_advanced(False, seed, adv_steps, new_latent_image, i, i + 1, True, + recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) + + if restore_latent: + del new_latent_image['noise_mask'] + base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0] + new_latent_image = base_latent_image + + j += 1 + + add_noise = False + + # finalize + core.update_node_status(unique_id, "finalize") + if base_latent_image is not None: + new_latent_image = base_latent_image + else: + base_latent_image = new_latent_image + + new_latent_image['noise_mask'] = inv_mask + new_latent_image = base_sampler.sample_advanced(False, seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, False, + recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) + + core.update_node_status(unique_id, f"{steps}/{steps} steps", total) + core.update_node_status(unique_id, "", None) + + if restore_latent: + new_latent_image = base_latent_image + + if 'noise_mask' in new_latent_image: + del new_latent_image['noise_mask'] + + return (new_latent_image, ) + + +class RegionalSamplerAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "Whether to add noise"}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "tooltip": "The starting step of the sampling to be applied at this node within the range of 'steps'."}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "tooltip": "The step at which sampling applied at this node will stop within the range of steps (if greater than steps, sampling will continue only up to steps)."}), + "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask set in regional_prompts by the overlap_factor amount to overlap with other regions."}), + "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "At each step, restore the noise outside the mask area to its original state, as per the principle of inpainting. This option is provided for backward compatibility, and it is recommended to always set it to true."}), + "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled", "tooltip": "Whether to return the latent with noise remaining if the noise has not been completely removed according to the noise schedule, or to completely remove the noise before returning it."}), + "latent_image": ("LATENT", {"tooltip": "input latent image"}), + "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "The sampler applied outside the area set by the regional_prompt."}), + "regional_prompts": ("REGIONAL_PROMPTS", {"tooltip": "The prompt applied to each region"}), + "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between", "tooltip": "..._sde or uni_pc and other special samplers are used, the region is not properly denoised, and it causes a phenomenon that destroys the overall harmony. To compensate for this, a recovery operation is performed using another sampler. This requires a longer time for sampling because a second sampling is performed at each step in each region using a special sampler. 1) DISABLE: Disable this feature. 2) ratio additional: After performing the denoise amount to be performed in the step with the sampler set in the region, the recovery sampler is additionally applied by the additional_sigma_ratio. If you use this option, the total denoise amount increases by additional_sigma_ratio. 3) ratio between: The denoise amount to be performed in the step with the sampler set in the region and the denoise amount to be applied to the recovery sampler are divided by additional_sigma_ratio, and denoise is performed for each denoise amount. If you use this option, the total denoise amount does not change."}), + "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"], {"tooltip": "1) AUTO: Automatically set the recovery sampler. If the sampler is uni_pc, uni_pc_bh2, dpmpp_sde, dpmpp_sde_gpu, the dpm_fast sampler is selected If the sampler is dpmpp_2m_sde, dpmpp_2m_sde_gpu, dpmpp_3m_sde, dpmpp_3m_sde_gpu, the dpmpp_2m sampler is selected. 2) Others: Manually set the recovery sampler."}), + "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Multiplier of noise schedule to be applied according to additional_mode."}), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + OUTPUT_TOOLTIPS = ("result latent", ) + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Regional" + + @staticmethod + def doit(add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent, return_with_leftover_noise, latent_image, base_sampler, regional_prompts, + additional_mode, additional_sampler, additional_sigma_ratio, unique_id): + + new_latent_image = latent_image.copy() + new_latent_image['samples'] = comfy.sample.fix_empty_latent_channels(base_sampler.params[0], new_latent_image['samples']) + + if restore_latent: + latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']() + else: + latent_compositor = None + + masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts] + masks = [np.ceil(mask).astype(np.int32) for mask in masks] + combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks)) + + inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0)) + + region_len = len(regional_prompts) + end_at_step = min(steps, end_at_step) + total = (end_at_step - start_at_step) * region_len + + base_latent_image = None + region_masks = {} + + for i in range(start_at_step, end_at_step-1): + core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | ", ((i-start_at_step)*region_len)/total) + + cur_add_noise = True if i == start_at_step and add_noise else False + + if cur_add_noise: + noise = Noise_RandomNoise(noise_seed).generate_noise(new_latent_image) + for rp in regional_prompts: + noise = rp.touch_noise(noise) + else: + noise = None + + new_latent_image['noise_mask'] = inv_mask + new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, True, + recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise) + + if restore_latent: + del new_latent_image['noise_mask'] + base_latent_image = new_latent_image.copy() + + j = 1 + for regional_prompt in regional_prompts: + if restore_latent: + new_latent_image = base_latent_image.copy() + + core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | {j}/{region_len}", ((i-start_at_step)*region_len + j)/total) + + if j not in region_masks: + region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0) + region_masks[j] = region_mask + else: + region_mask = region_masks[j] + + new_latent_image['noise_mask'] = region_mask + new_latent_image = regional_prompt.sampler.sample_advanced(False, noise_seed, steps, new_latent_image, i, i + 1, True, + recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) + + if restore_latent: + del new_latent_image['noise_mask'] + base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0] + new_latent_image = base_latent_image + + j += 1 + + # finalize + core.update_node_status(unique_id, "finalize") + if base_latent_image is not None: + new_latent_image = base_latent_image + else: + base_latent_image = new_latent_image + + new_latent_image['noise_mask'] = inv_mask + new_latent_image = base_sampler.sample_advanced(False, noise_seed, steps, new_latent_image, end_at_step-1, end_at_step, return_with_leftover_noise, + recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio) + + core.update_node_status(unique_id, f"{end_at_step}/{end_at_step} steps", total) + core.update_node_status(unique_id, "", None) + + if restore_latent: + new_latent_image = base_latent_image + + if 'noise_mask' in new_latent_image: + del new_latent_image['noise_mask'] + + return (new_latent_image, ) + + +class KSamplerBasicPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}), + "scheduler": (core.get_schedulers(), {"tooltip": "noise schedule"}), + "latent_image": ("LATENT", {"tooltip": "input latent image"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}), + }, + "optional": + { + "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}), + } + } + + OUTPUT_TOOLTIPS = ("passthrough input basic_pipe", "result latent", "VAE in basic_pipe") + + RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE") + FUNCTION = "sample" + + CATEGORY = "ImpactPack/sampling" + + @staticmethod + def sample(basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0, scheduler_func_opt=None): + model, clip, vae, positive, negative = basic_pipe + latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=scheduler_func_opt) + return basic_pipe, latent, vae + + +class KSamplerAdvancedBasicPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}), + "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable", "tooltip": "Whether to add noise"}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}), + "scheduler": (core.get_schedulers(), {"tooltip": "noise schedule"}), + "latent_image": ("LATENT", {"tooltip": "input latent image"}), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "tooltip": "The starting step of the sampling to be applied at this node within the range of 'steps'."}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "tooltip": "The step at which sampling applied at this node will stop within the range of steps (if greater than steps, sampling will continue only up to steps)."}), + "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable", "tooltip": "Whether to return the latent with noise remaining if the noise has not been completely removed according to the noise schedule, or to completely remove the noise before returning it."}), + }, + "optional": + { + "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}), + } + } + + OUTPUT_TOOLTIPS = ("passthrough input basic_pipe", "result latent", "VAE in basic_pipe") + + RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE") + FUNCTION = "sample" + + CATEGORY = "ImpactPack/sampling" + + @staticmethod + def sample(basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, scheduler_func_opt=None): + model, clip, vae, positive, negative = basic_pipe + + latent = separated_sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, scheduler_func=scheduler_func_opt) + return basic_pipe, latent, vae + + +class GITSSchedulerFuncProvider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05, "tooltip": "coeff factor of GITS Scheduler"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "denoise amount for noise schedule"}), + } + } + + OUTPUT_TOOLTIPS = ("Returns a function that generates a noise schedule using GITSScheduler. This can be used in place of a predetermined noise schedule to dynamically generate a noise schedule based on the steps.",) + + RETURN_TYPES = ("SCHEDULER_FUNC",) + CATEGORY = "ImpactPack/sampling" + + FUNCTION = "doit" + + @staticmethod + def doit(coeff, denoise): + def f(model, sampler, steps): + if 'GITSScheduler' not in nodes.NODE_CLASS_MAPPINGS: + raise Exception("[Impact Pack] ComfyUI is an outdated version. Cannot use GITSScheduler.") + + scheduler = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']() + return scheduler.get_sigmas(coeff, steps, denoise)[0] + + return (f, ) + + +class NegativeConditioningPlaceholder: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + + OUTPUT_TOOLTIPS = ("This is a Placeholder for the FLUX model that does not use Negative Conditioning.",) + + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "ImpactPack/sampling" + + FUNCTION = "doit" + + @staticmethod + def doit(): + return ("NegativePlaceholder", ) diff --git a/custom_nodes/ComfyUI-Impact-Pack/modules/impact/util_nodes.py b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/util_nodes.py new file mode 100644 index 00000000..1c94c48f --- /dev/null +++ b/custom_nodes/ComfyUI-Impact-Pack/modules/impact/util_nodes.py @@ -0,0 +1,775 @@ +from impact.utils import any_typ, ByPassTypeTuple, make_3d_mask +import comfy_extras.nodes_mask +from nodes import MAX_RESOLUTION +import torch +import comfy +import sys +import nodes +import re +import impact.core as core +from server import PromptServer +import inspect +import logging + + +class GeneralSwitch: + @classmethod + def INPUT_TYPES(s): + dyn_inputs = {"input1": (any_typ, {"lazy": True, "tooltip": "Any input. When connected, one more input slot is added."}), } + if core.is_execution_model_version_supported(): + stack = inspect.stack() + if stack[2].function == 'get_input_info': + # bypass validation + class AllContainer: + def __contains__(self, item): + return True + + def __getitem__(self, key): + return any_typ, {"lazy": True} + + dyn_inputs = AllContainer() + + inputs = {"required": { + "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1, "tooltip": "The input number you want to output among the inputs"}), + "sel_mode": ("BOOLEAN", {"default": False, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False, + "tooltip": "In the case of 'select_on_execution', the selection is dynamically determined at the time of workflow execution. 'select_on_prompt' is an option that exists for older versions of ComfyUI, and it makes the decision before the workflow execution."}), + }, + "optional": dyn_inputs, + "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"} + } + + return inputs + + RETURN_TYPES = (any_typ, "STRING", "INT") + RETURN_NAMES = ("selected_value", "selected_label", "selected_index") + OUTPUT_TOOLTIPS = ("Output is generated only from the input chosen by the 'select' value.", "Slot label of the selected input slot", "Outputs the select value as is") + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def check_lazy_status(self, *args, **kwargs): + selected_index = int(kwargs['select']) + input_name = f"input{selected_index}" + + logging.info(f"SELECTED: {input_name}") + + if input_name in kwargs: + return [input_name] + else: + return [] + + @staticmethod + def doit(*args, **kwargs): + selected_index = int(kwargs['select']) + input_name = f"input{selected_index}" + + selected_label = input_name + node_id = kwargs['unique_id'] + + if 'extra_pnginfo' in kwargs and kwargs['extra_pnginfo'] is not None: + nodelist = kwargs['extra_pnginfo']['workflow']['nodes'] + for node in nodelist: + if str(node['id']) == node_id: + inputs = node['inputs'] + + for slot in inputs: + if slot['name'] == input_name and 'label' in slot: + selected_label = slot['label'] + + break + else: + logging.info("[Impact-Pack] The switch node does not guarantee proper functioning in API mode.") + + if input_name in kwargs: + return kwargs[input_name], selected_label, selected_index + else: + logging.info("ImpactSwitch: invalid select index (ignored)") + return None, "", selected_index + +class LatentSwitch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "select": ("INT", {"default": 1, "min": 1, "max": 99999, "step": 1}), + "latent1": ("LATENT",), + }, + } + + RETURN_TYPES = ("LATENT", ) + + OUTPUT_NODE = True + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, *args, **kwargs): + input_name = f"latent{int(kwargs['select'])}" + + if input_name in kwargs: + return (kwargs[input_name],) + else: + logging.info("LatentSwitch: invalid select index ('latent1' is selected)") + return (kwargs['latent1'],) + + +class ImageMaskSwitch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "select": ("INT", {"default": 1, "min": 1, "max": 4, "step": 1}), + "images1": ("IMAGE",), + }, + + "optional": { + "mask1_opt": ("MASK",), + "images2_opt": ("IMAGE",), + "mask2_opt": ("MASK",), + "images3_opt": ("IMAGE",), + "mask3_opt": ("MASK",), + "images4_opt": ("IMAGE",), + "mask4_opt": ("MASK",), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK",) + + OUTPUT_NODE = True + + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, select, images1, mask1_opt=None, images2_opt=None, mask2_opt=None, images3_opt=None, mask3_opt=None, + images4_opt=None, mask4_opt=None): + if select == 1: + return images1, mask1_opt, + elif select == 2: + return images2_opt, mask2_opt, + elif select == 3: + return images3_opt, mask3_opt, + else: + return images4_opt, mask4_opt, + + +class GeneralInversedSwitch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1, "tooltip": "The output number you want to send from the input"}), + "input": (any_typ, {"tooltip": "Any input. When connected, one more input slot is added."}), + + }, + "optional": { + "sel_mode": ("BOOLEAN", {"default": False, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False, + "tooltip": "In the case of 'select_on_execution', the selection is dynamically determined at the time of workflow execution. 'select_on_prompt' is an option that exists for older versions of ComfyUI, and it makes the decision before the workflow execution."}), + }, + "hidden": {"prompt": "PROMPT", "unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ByPassTypeTuple((any_typ, )) + OUTPUT_TOOLTIPS = ("Output occurs only from the output selected by the 'select' value.\nWhen slots are connected, additional slots are created.", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, select, prompt, unique_id, input, **kwargs): + if core.is_execution_model_version_supported(): + from comfy_execution.graph import ExecutionBlocker + else: + logging.warning("[Impact Pack] InversedSwitch: ComfyUI is outdated. The 'select_on_execution' mode cannot function properly.") + + res = [] + + # search max output count in prompt + cnt = 0 + for x in prompt.values(): + for y in x.get('inputs', {}).values(): + if isinstance(y, list) and len(y) == 2: + if y[0] == unique_id: + cnt = max(cnt, y[1]) + + for i in range(0, cnt + 1): + if select == i+1: + res.append(input) + elif core.is_execution_model_version_supported(): + res.append(ExecutionBlocker(None)) + else: + res.append(None) + + return res + + +class RemoveNoiseMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, samples): + res = {key: value for key, value in samples.items() if key != 'noise_mask'} + return (res, ) + + +class ImagePasteMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("IMAGE",), + "source": ("IMAGE",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "composite" + + CATEGORY = "image" + + def composite(self, destination, source, x, y, resize_source, mask = None): + destination = destination.clone().movedim(-1, 1) + output = comfy_extras.nodes_mask.composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) + return (output,) + + +from impact.utils import any_typ + +class ImpactLogger: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "data": (any_typ,), + "text": ("STRING", {"multiline": True}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}, + } + + CATEGORY = "ImpactPack/Debug" + + OUTPUT_NODE = True + + RETURN_TYPES = () + FUNCTION = "doit" + + def doit(self, data, text, prompt, extra_pnginfo, unique_id): + shape = "" + if hasattr(data, "shape"): + shape = f"{data.shape} / " + + logging.info(f"[IMPACT LOGGER]: {shape}{data}") + + logging.info(f" PROMPT: {prompt}") + + # for x in prompt: + # if 'inputs' in x and 'populated_text' in x['inputs']: + # print(f"PROMPT: {x['10']['inputs']['populated_text']}") + # + # for x in extra_pnginfo['workflow']['nodes']: + # if x['type'] == 'ImpactWildcardProcessor': + # print(f" WV : {x['widgets_values'][1]}\n") + + PromptServer.instance.send_sync("impact-node-feedback", {"node_id": unique_id, "widget_name": "text", "type": "TEXT", "value": f"{data}"}) + return {} + + +class ImpactDummyInput: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + + CATEGORY = "ImpactPack/Debug" + + RETURN_TYPES = (any_typ,) + FUNCTION = "doit" + + def doit(self): + return ("DUMMY",) + + +class MasksToMaskList: + @classmethod + def INPUT_TYPES(s): + return {"optional": { + "masks": ("MASK", ), + } + } + + RETURN_TYPES = ("MASK", ) + OUTPUT_IS_LIST = (True, ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, masks): + if masks is None: + empty_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + return ([empty_mask], ) + + res = [] + + for mask in masks: + res.append(mask) + + res = [make_3d_mask(x) for x in res] + + return (res, ) + + +class MaskListToMaskBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK", ), + } + } + + INPUT_IS_LIST = True + + RETURN_TYPES = ("MASK", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, mask): + if len(mask) == 0: + empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu").unsqueeze(0) + return (empty_mask,) + + masks_3d = [make_3d_mask(m) for m in mask] + target_shape = masks_3d[0].shape[1:] + upscaled_masks = [] + for m in masks_3d: + if m.shape[1:] != target_shape: + m = m.unsqueeze(1).repeat(1, 3, 1, 1) + m = comfy.utils.common_upscale(m, target_shape[1], target_shape[0], "lanczos", "center") + m = m[:, 0, :, :] + + upscaled_masks.append(m) + # Concatenate all at once + result = torch.cat(upscaled_masks, dim=0) + return (result,) + + +class ImageListToImageBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE", ), + } + } + + INPUT_IS_LIST = True + + RETURN_TYPES = ("IMAGE", ) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Operation" + + def doit(self, images): + if len(images) == 0: + return () + if len(images) == 1: + img = images[0] + if img.ndim == 3: # add batch dim if missing + img = img.unsqueeze(0) + return (img,) + + # Start with the first image + image1 = images[0] + if image1.ndim == 3: + image1 = image1.unsqueeze(0) + + for image2 in images[1:]: + # Ensure batch dim + if image2.ndim == 3: + image2 = image2.unsqueeze(0) + + # Ensure same device + if image2.device != image1.device: + image2 = image2.to(image1.device) + + # Ensure HxW match exactly + H, W = image1.shape[1], image1.shape[2] + if image2.shape[1] != H or image2.shape[2] != W: + image2 = comfy.utils.common_upscale( + image2.movedim(-1, 1), # move channels first + W, # width + H, # height + "lanczos", + "center" + ).movedim(1, -1) # move channels back last + + # Ensure channels match + if image2.shape[3] != image1.shape[3]: + # simple fix: truncate or pad channels + min_C = min(image1.shape[3], image2.shape[3]) + image1 = image1[:, :, :, :min_C] + image2 = image2[:, :, :, :min_C] + + # Concatenate along batch dimension + image1 = torch.cat((image1, image2), dim=0) + + return (image1,) + + +class ImageBatchToImageList: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), }} + + RETURN_TYPES = ("IMAGE",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, image): + images = [image[i:i + 1, ...] for i in range(image.shape[0])] + return (images, ) + + +class MakeAnyList: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + "optional": {"value1": (any_typ,), } + } + + RETURN_TYPES = (any_typ,) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, **kwargs): + values = [] + + for k, v in kwargs.items(): + if v is not None: + values.append(v) + + return (values, ) + + +class MakeMaskList: + @classmethod + def INPUT_TYPES(s): + return {"required": {"mask1": ("MASK",), }} + + RETURN_TYPES = ("MASK",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, **kwargs): + masks = [] + + for k, v in kwargs.items(): + masks.append(v) + + return (masks, ) + + +class NthItemOfAnyList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "any_list": (any_typ,), + "index": ("INT", {"default": 0, "min": -sys.maxsize, "max": sys.maxsize, "step": 1, "tooltip": "The index of the item you want to select from the list. Use negative values to select from the end (e.g., -1 for last item, -2 for second to last)."}), + } + } + + RETURN_TYPES = (any_typ,) + INPUT_IS_LIST = True + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + DESCRIPTION = "Selects the Nth item from a list. If the index is out of range, it returns the last item in the list." + + def doit(self, any_list, index): + i = index[0] + list_len = len(any_list) + if i >= list_len or i < -list_len: + return (any_list[-1],) + else: + return (any_list[i],) + + +class MakeImageList: + @classmethod + def INPUT_TYPES(s): + return {"optional": {"image1": ("IMAGE",), }} + + RETURN_TYPES = ("IMAGE",) + OUTPUT_IS_LIST = (True,) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, **kwargs): + images = [] + + for k, v in kwargs.items(): + images.append(v) + + return (images, ) + + +class MakeImageBatch: + @classmethod + def INPUT_TYPES(s): + return {"optional": {"image1": ("IMAGE",), }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, **kwargs): + images = [value for value in kwargs.values()] + + if len(images) == 1: + return (images[0],) + else: + image1 = images[0] + for image2 in images[1:]: + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "lanczos", "center").movedim(1, -1) + image1 = torch.cat((image1, image2), dim=0) + return (image1,) + + +class MakeMaskBatch: + @classmethod + def INPUT_TYPES(s): + return {"optional": {"mask1": ("MASK",), }} + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, **kwargs): + masks = [make_3d_mask(value) for value in kwargs.values()] + + if len(masks) == 1: + return (masks[0],) + else: + mask1 = masks[0] + for mask2 in masks[1:]: + if mask1.shape[1:] != mask2.shape[1:]: + mask2 = comfy.utils.common_upscale(mask2.movedim(-1, 1), mask1.shape[2], mask1.shape[1], "lanczos", "center").movedim(1, -1) + mask1 = torch.cat((mask1, mask2), dim=0) + return (mask1,) + + +class ReencodeLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples": ("LATENT", ), + "tile_mode": (["None", "Both", "Decode(input) only", "Encode(output) only"],), + "input_vae": ("VAE", ), + "output_vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}), + }, + "optional": { + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "tooltip": "This setting applies when 'tile_mode' is enabled."}), + } + } + + CATEGORY = "ImpactPack/Util" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512, overlap=64): + if tile_mode in ["Both", "Decode(input) only"]: + decoder = nodes.VAEDecodeTiled() + if 'overlap' in inspect.signature(decoder.decode).parameters: + pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0] + else: + pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0] + else: + pixels = nodes.VAEDecode().decode(input_vae, samples)[0] + + if tile_mode in ["Both", "Encode(output) only"]: + encoder = nodes.VAEEncodeTiled() + if 'overlap' in inspect.signature(encoder.encode).parameters: + return encoder.encode(output_vae, pixels, tile_size, overlap=overlap) + else: + return encoder.encode(output_vae, pixels, tile_size) + else: + return nodes.VAEEncode().encode(output_vae, pixels) + + +class ReencodeLatentPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples": ("LATENT", ), + "tile_mode": (["None", "Both", "Decode(input) only", "Encode(output) only"],), + "input_basic_pipe": ("BASIC_PIPE", ), + "output_basic_pipe": ("BASIC_PIPE", ), + }, + } + + CATEGORY = "ImpactPack/Util" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + def doit(self, samples, tile_mode, input_basic_pipe, output_basic_pipe): + _, _, input_vae, _, _ = input_basic_pipe + _, _, output_vae, _, _ = output_basic_pipe + return ReencodeLatent().doit(samples, tile_mode, input_vae, output_vae) + + +class StringSelector: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "strings": ("STRING", {"multiline": True}), + "multiline": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "select": ("INT", {"min": 0, "max": sys.maxsize, "step": 1, "default": 0}), + }} + + RETURN_TYPES = ("STRING",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, strings, multiline, select): + lines = strings.split('\n') + + if multiline: + result = [] + current_string = "" + + for line in lines: + if line.startswith("#"): + if current_string: + result.append(current_string.strip()) + current_string = "" + current_string += line + "\n" + + if current_string: + result.append(current_string.strip()) + + if len(result) == 0: + selected = strings + else: + selected = result[select % len(result)] + + if selected.startswith('#'): + selected = selected[1:] + else: + if len(lines) == 0: + selected = strings + else: + selected = lines[select % len(lines)] + + return (selected, ) + + +class StringListToString: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "join_with": ("STRING", {"default": "\\n"}), + "string_list": ("STRING", {"forceInput": True}), + } + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("STRING",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, join_with, string_list): + # convert \\n to newline character + if join_with[0] == "\\n": + join_with[0] = "\n" + + joined_text = join_with[0].join(string_list) + + return (joined_text,) + + +class WildcardPromptFromString: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": ("STRING", {"forceInput": True}), + "delimiter": ("STRING", {"multiline": False, "default": "\\n" }), + "prefix_all": ("STRING", {"multiline": False}), + "postfix_all": ("STRING", {"multiline": False}), + "restrict_to_tags": ("STRING", {"multiline": False}), + "exclude_tags": ("STRING", {"multiline": False}) + }, + } + + RETURN_TYPES = ("STRING", "STRING",) + RETURN_NAMES = ("wildcard", "segs_labels",) + FUNCTION = "doit" + + CATEGORY = "ImpactPack/Util" + + def doit(self, string, delimiter, prefix_all, postfix_all, restrict_to_tags, exclude_tags): + # convert \\n to newline character + if delimiter == "\\n": + delimiter = "\n" + + # some sanity checks and normalization for later processing + if prefix_all is None: + prefix_all = "" + if postfix_all is None: + postfix_all = "" + if restrict_to_tags is None: + restrict_to_tags = "" + if exclude_tags is None: + exclude_tags = "" + + restrict_to_tags = restrict_to_tags.split(", ") + exclude_tags = exclude_tags.split(", ") + + # build the wildcard prompt per list entry + output = ["[LAB]"] + labels = [] + for x in string.split(delimiter): + label = str(len(labels) + 1) + labels.append(label) + x = x.split(", ") + # restrict to tags + if restrict_to_tags != [""]: + x = list(set(x) & set(restrict_to_tags)) + # remove tags + if exclude_tags != [""]: + x = list(set(x) - set(exclude_tags)) + # next row: ', '') + + return (out, ) + + +class CameraPoseVisualizer: + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pose_file_path": ("STRING", {"default": '', "multiline": False}), + "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}), + "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}), + "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}), + "use_exact_fx": ("BOOLEAN", {"default": False}), + "relative_c2w": ("BOOLEAN", {"default": True}), + "use_viewer": ("BOOLEAN", {"default": False}), + }, + "optional": { + "cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "plot" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ +Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose +or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. +""" + + def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None): + import matplotlib as mpl + import matplotlib.pyplot as plt + from torchvision.transforms import ToTensor + + x_min = -2.0 * scale + x_max = 2.0 * scale + y_min = -2.0 * scale + y_max = 2.0 * scale + z_min = -2.0 * scale + z_max = 2.0 * scale + plt.rcParams['text.color'] = '#999999' + self.fig = plt.figure(figsize=(18, 7)) + self.fig.patch.set_facecolor('#353535') + self.ax = self.fig.add_subplot(projection='3d') + self.ax.set_facecolor('#353535') # Set the background color here + self.ax.grid(color='#999999', linestyle='-', linewidth=0.5) + self.plotly_data = None # plotly data traces + self.ax.set_aspect("auto") + self.ax.set_xlim(x_min, x_max) + self.ax.set_ylim(y_min, y_max) + self.ax.set_zlim(z_min, z_max) + self.ax.set_xlabel('x', color='#999999') + self.ax.set_ylabel('y', color='#999999') + self.ax.set_zlabel('z', color='#999999') + for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels(): + text.set_color('#999999') + print('initialize camera pose visualizer') + + if pose_file_path != "": + with open(pose_file_path, 'r') as f: + poses = f.readlines() + w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]] + fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]] + #print(poses) + elif cameractrl_poses is not None: + poses = cameractrl_poses + w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses] + fxs = [pose[1] for pose in cameractrl_poses] + else: + raise ValueError("Please provide either pose_file_path or cameractrl_poses") + + total_frames = len(w2cs) + transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4) + last_row = np.zeros((1, 4)) + last_row[0, -1] = 1.0 + + w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs] + c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w) + + for frame_idx, c2w in enumerate(c2ws): + self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval, + zval=(fxs[frame_idx] if use_exact_fx else zval)) + + # Create the colorbar + cmap = mpl.cm.rainbow + norm = mpl.colors.Normalize(vmin=0, vmax=total_frames) + colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical') + + # Change the colorbar label + colorbar.set_label('Frame', color='#999999') # Change the label and its color + + # Change the tick colors + colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color + + # Change the tick frequency + # Assuming you want to set the ticks at every 10th frame + ticks = np.arange(0, total_frames, 10) + colorbar.ax.yaxis.set_ticks(ticks) + + plt.title('') + plt.draw() + buf = BytesIO() + plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) + buf.seek(0) + img = Image.open(buf) + tensor_img = ToTensor()(img) + buf.close() + tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0) + if use_viewer: + time.sleep(1) + plt.show() + return (tensor_img,) + + def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3): + import matplotlib.pyplot as plt + from mpl_toolkits.mplot3d.art3d import Poly3DCollection + vertex_std = np.array([[0, 0, 0, 1], + [base_xval, -base_xval * hw_ratio, zval, 1], + [base_xval, base_xval * hw_ratio, zval, 1], + [-base_xval, base_xval * hw_ratio, zval, 1], + [-base_xval, -base_xval * hw_ratio, zval, 1]]) + vertex_transformed = vertex_std @ extrinsic.T + meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], + [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] + + color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) + + self.ax.add_collection3d( + Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25)) + + def customize_legend(self, list_label): + from matplotlib.patches import Patch + import matplotlib.pyplot as plt + list_handle = [] + for idx, label in enumerate(list_label): + color = plt.cm.rainbow(idx / len(list_label)) + patch = Patch(color=color, label=label) + list_handle.append(patch) + plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) + + def get_c2w(self, w2cs, transform_matrix, relative_c2w): + if relative_c2w: + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] + else: + ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] + ret_poses = [transform_matrix @ x for x in ret_poses] + return np.array(ret_poses, dtype=np.float32) + + + +class CheckpointPerturbWeights: + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "joint_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), + "final_layer": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), + "rest_of_the_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "mod" + OUTPUT_NODE = True + + CATEGORY = "KJNodes/experimental" + + def mod(self, seed, model, joint_blocks, final_layer, rest_of_the_blocks): + import copy + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + device = model_management.get_torch_device() + model_copy = copy.deepcopy(model) + model_copy.model.to(device) + keys = model_copy.model.diffusion_model.state_dict().keys() + + dict = {} + for key in keys: + dict[key] = model_copy.model.diffusion_model.state_dict()[key] + + pbar = ProgressBar(len(keys)) + for k in keys: + v = dict[k] + print(f'{k}: {v.std()}') + if k.startswith('joint_blocks'): + multiplier = joint_blocks + elif k.startswith('final_layer'): + multiplier = final_layer + else: + multiplier = rest_of_the_blocks + dict[k] += torch.normal(torch.zeros_like(v) * v.mean(), torch.ones_like(v) * v.std() * multiplier).to(device) + pbar.update(1) + model_copy.model.diffusion_model.load_state_dict(dict) + return model_copy, + +class DifferentialDiffusionAdvanced(): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "samples": ("LATENT",), + "mask": ("MASK",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), + }} + RETURN_TYPES = ("MODEL", "LATENT") + FUNCTION = "apply" + CATEGORY = "_for_testing" + INIT = False + + def apply(self, model, samples, mask, multiplier): + self.multiplier = multiplier + model = model.clone() + model.set_model_denoise_mask_function(self.forward) + s = samples.copy() + s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + return (model, s) + + def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): + model = extra_options["model"] + step_sigmas = extra_options["sigmas"] + sigma_to = model.inner_model.model_sampling.sigma_min + if step_sigmas[-1] > sigma_to: + sigma_to = step_sigmas[-1] + sigma_from = step_sigmas[0] + + ts_from = model.inner_model.model_sampling.timestep(sigma_from) + ts_to = model.inner_model.model_sampling.timestep(sigma_to) + current_ts = model.inner_model.model_sampling.timestep(sigma[0]) + + threshold = (current_ts - ts_to) / (ts_from - ts_to) / self.multiplier + + return (denoise_mask >= threshold).to(denoise_mask.dtype) + +class FluxBlockLoraSelect: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(19): + arg_dict["double_blocks.{}.".format(i)] = argument + + for i in range(38): + arg_dict["single_blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + +class HunyuanVideoBlockLoraSelect: + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(20): + arg_dict["double_blocks.{}.".format(i)] = argument + + for i in range(40): + arg_dict["single_blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + +class Wan21BlockLoraSelect: + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(40): + arg_dict["blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + +class LTX2BlockLoraSelect: + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(48): + arg_dict["blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + + +class DiTBlockLoraLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), + + }, + "optional": { + "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}), + "opt_lora_path": ("STRING", {"forceInput": True, "tooltip": "Absolute path of the LoRA."}), + "blocks": ("SELECTEDDITBLOCKS",), + } + } + + RETURN_TYPES = ("MODEL", "STRING", ) + RETURN_NAMES = ("model", "rank", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.", "possible rank of the LoRA.") + FUNCTION = "load_lora" + CATEGORY = "KJNodes/experimental" + + def load_lora(self, model, strength_model, lora_name=None, opt_lora_path=None, blocks=None): + + import comfy.lora + + if opt_lora_path: + lora_path = opt_lora_path + else: + lora_path = folder_paths.get_full_path("loras", lora_name) + + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + self.loaded_lora = None + + if lora is None: + lora = load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + # Find the first key that ends with "weight" + rank = "unknown" + weight_key = next((key for key in lora.keys() if key.endswith('weight')), None) + # Print the shape of the value corresponding to the key + if weight_key: + print(f"Shape of the first 'weight' key ({weight_key}): {lora[weight_key].shape}") + rank = str(lora[weight_key].shape[0]) + else: + print("No key ending with 'weight' found.") + rank = "Couldn't find rank" + self.loaded_lora = (lora_path, lora) + + key_map = {} + if model is not None: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + + loaded = comfy.lora.load_lora(lora, key_map) + + if blocks is not None: + keys_to_delete = [] + + for block in blocks: + for key in list(loaded.keys()): + match = False + if isinstance(key, str) and block in key: + match = True + elif isinstance(key, tuple): + for k in key: + if block in k: + match = True + break + + if match: + ratio = blocks[block] + if ratio == 0: + keys_to_delete.append(key) + else: + # Only modify LoRA adapters, skip diff tuples + value = loaded[key] + if hasattr(value, 'weights'): + print(f"Modifying LoRA adapter for key: {key}") + weights_list = list(value.weights) + weights_list[2] = ratio + loaded[key].weights = tuple(weights_list) + else: + print(f"Skipping non-LoRA entry for key: {key}") + + for key in keys_to_delete: + del loaded[key] + + print("loading lora keys:") + for key, value in loaded.items(): + if hasattr(value, 'weights'): + print(f"Key: {key}, Alpha: {value.weights[2]}") + else: + print(f"Key: {key}, Type: {type(value)}") + + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + + k = set(k) + for x in loaded: + if (x not in k): + print("NOT LOADED {}".format(x)) + + return (new_modelpatcher, rank) + +class CustomControlNetWeightsFluxFromList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "list_of_floats": ("FLOAT", {"forceInput": True}, ), + }, + "optional": { + "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), + "cn_extras": ("CN_WEIGHTS_EXTRAS",), + "autosize": ("ACNAUTOSIZE", {"padding": 0}), + } + } + + RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) + RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") + FUNCTION = "load_weights" + DESCRIPTION = "Creates controlnet weights from a list of floats for Advanced-ControlNet" + + CATEGORY = "KJNodes/controlnet" + + def load_weights(self, list_of_floats: list[float], + uncond_multiplier: float=1.0, cn_extras: dict[str]={}): + + adv_control = importlib.import_module("ComfyUI-Advanced-ControlNet.adv_control") + ControlWeights = adv_control.utils.ControlWeights + TimestepKeyframeGroup = adv_control.utils.TimestepKeyframeGroup + TimestepKeyframe = adv_control.utils.TimestepKeyframe + + weights = ControlWeights.controlnet(weights_input=list_of_floats, uncond_multiplier=uncond_multiplier, extras=cn_extras) + print(weights.weights_input) + return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) + +SHAKKERLABS_UNION_CONTROLNET_TYPES = { + "canny": 0, + "tile": 1, + "depth": 2, + "blur": 3, + "pose": 4, + "gray": 5, + "low quality": 6, +} + +class SetShakkerLabsUnionControlNetType: + @classmethod + def INPUT_TYPES(s): + return {"required": {"control_net": ("CONTROL_NET", ), + "type": (["auto"] + list(SHAKKERLABS_UNION_CONTROLNET_TYPES.keys()),) + }} + + CATEGORY = "conditioning/controlnet" + RETURN_TYPES = ("CONTROL_NET",) + + FUNCTION = "set_controlnet_type" + + def set_controlnet_type(self, control_net, type): + control_net = control_net.copy() + type_number = SHAKKERLABS_UNION_CONTROLNET_TYPES.get(type, -1) + if type_number >= 0: + control_net.set_extra_arg("control_type", [type_number]) + else: + control_net.set_extra_arg("control_type", []) + + return (control_net,) + +class ModelSaveKJ: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}), + "model_key_prefix": ("STRING", {"default": "model.diffusion_model."}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, filename_prefix, model_key_prefix, prompt=None, extra_pnginfo=None): + from comfy.utils import save_torch_file + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + load_models = [model] + + model_management.load_models_gpu(load_models, force_patch_weights=True) + default_prefix = "model.diffusion_model." + + sd = model.model.state_dict_for_saving(None, None, None) + + new_sd = {} + for k in sd: + if k.startswith(default_prefix): + new_key = model_key_prefix + k[len(default_prefix):] + else: + new_key = k # In case the key doesn't start with the default prefix, keep it unchanged + t = sd[k] + if not t.is_contiguous(): + t = t.contiguous() + new_sd[new_key] = t + print(full_output_folder) + if not os.path.exists(full_output_folder): + os.makedirs(full_output_folder) + save_torch_file(new_sd, os.path.join(full_output_folder, output_checkpoint)) + return {} + +class StyleModelApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "style_model": ("STYLE_MODEL", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_stylemodel" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "StyleModelApply but with strength parameter" + + def apply_stylemodel(self, clip_vision_output, style_model, conditioning, strength=1.0): + cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + cond = strength * cond + c = [] + for t in conditioning: + n = [torch.cat((t[0], cond), dim=1), t[1].copy()] + c.append(n) + return (c, ) + +class AudioConcatenate: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "audio1": ("AUDIO",), + "audio2": ("AUDIO",), + "direction": ( + [ 'right', + 'left', + ], + { + "default": 'right' + }), + }} + + RETURN_TYPES = ("AUDIO",) + FUNCTION = "concanate" + CATEGORY = "KJNodes/audio" + DESCRIPTION = """ +Concatenates the audio1 to audio2 in the specified direction. +""" + + def concanate(self, audio1, audio2, direction): + sample_rate_1 = audio1["sample_rate"] + sample_rate_2 = audio2["sample_rate"] + if sample_rate_1 != sample_rate_2: + raise Exception("Sample rates of the two audios do not match") + + waveform_1 = audio1["waveform"] + print(waveform_1.shape) + waveform_2 = audio2["waveform"] + + # Concatenate based on the specified direction + if direction == 'right': + concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2) # Concatenate along width + elif direction == 'left': + concatenated_audio= torch.cat((waveform_2, waveform_1), dim=2) # Concatenate along width + return ({"waveform": concatenated_audio, "sample_rate": sample_rate_1},) + +class LeapfusionHunyuanI2V: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT",), + "index": ("INT", {"default": 0, "min": -1, "max": 1000, "step": 1,"tooltip": "The index of the latent to be replaced. 0 for first frame and -1 for last"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of steps to apply"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of steps to apply"}), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/experimental" + + def patch(self, model, latent, index, strength, start_percent, end_percent): + + def outer_wrapper(samples, index, start_percent, end_percent): + def unet_wrapper(apply_model, args): + steps = args["c"]["transformer_options"]["sample_sigmas"] + inp, timestep, c = args["input"], args["timestep"], args["c"] + matched_step_index = (steps == timestep).nonzero() + if len(matched_step_index) > 0: + current_step_index = matched_step_index.item() + else: + for i in range(len(steps) - 1): + # walk from beginning of steps until crossing the timestep + if (steps[i] - timestep[0]) * (steps[i + 1] - timestep[0]) <= 0: + current_step_index = i + break + else: + current_step_index = 0 + current_percent = current_step_index / (len(steps) - 1) + if samples is not None: + if start_percent <= current_percent <= end_percent: + inp[:, :, [index], :, :] = samples[:, :, [0], :, :].to(inp) + else: + inp[:, :, [index], :, :] = torch.zeros(1) + return apply_model(inp, timestep, **c) + return unet_wrapper + + samples = latent["samples"] * 0.476986 * strength + m = model.clone() + m.set_model_unet_function_wrapper(outer_wrapper(samples, index, start_percent, end_percent)) + + return (m,) + +class ImageNoiseAugmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "noise_aug_strength": ("FLOAT", {"default": None, "min": 0.0, "max": 100.0, "step": 0.001}), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "add_noise" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ + Add noise to an image. + """ + + def add_noise(self, image, noise_aug_strength, seed): + torch.manual_seed(seed) + sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * noise_aug_strength + image_noise = torch.randn_like(image) * sigma[:, None, None, None] + image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise) + image_out = image + image_noise + return image_out, + +class VAELoaderKJ: + video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5"] + image_taes = ["taesd", "taesdxl", "taesd3", "taef1"] + @staticmethod + def vae_list(s): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + sd3_taesd_enc = False + sd3_taesd_dec = False + f1_taesd_enc = False + f1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + elif v.startswith("taesd3_decoder."): + sd3_taesd_dec = True + elif v.startswith("taesd3_encoder."): + sd3_taesd_enc = True + elif v.startswith("taef1_encoder."): + f1_taesd_dec = True + elif v.startswith("taef1_decoder."): + f1_taesd_enc = True + else: + for tae in s.video_taes: + if v.startswith(tae): + vaes.append(v) + + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + if sd3_taesd_dec and sd3_taesd_enc: + vaes.append("taesd3") + if f1_taesd_dec and f1_taesd_enc: + vaes.append("taef1") + vaes.append("pixel_space") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesd3": + sd["vae_scale"] = torch.tensor(1.5305) + sd["vae_shift"] = torch.tensor(0.0609) + elif name == "taef1": + sd["vae_scale"] = torch.tensor(0.3611) + sd["vae_shift"] = torch.tensor(0.1159) + return sd + + @classmethod + def INPUT_TYPES(s): + return { + "required": { "vae_name": (s.vae_list(s), ), + "device": (["main_device", "cpu"],), + "weight_dtype": (["bf16", "fp16", "fp32" ],), + } + } + + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + CATEGORY = "KJNodes/vae" + + def load_vae(self, vae_name, device, weight_dtype): + from comfy.sd import VAE + metadata = None + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[weight_dtype] + if device == "main_device": + device = model_management.get_torch_device() + elif device == "cpu": + device = torch.device("cpu") + + if vae_name == "pixel_space": + sd = {} + sd["pixel_space_vae"] = torch.tensor(1.0) + elif vae_name in self.image_taes: + sd = self.load_taesd(vae_name) + else: + if os.path.splitext(vae_name)[0] in self.video_taes: + vae_path = folder_paths.get_full_path_or_raise("vae_approx", vae_name) + else: + vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) + sd, metadata = comfy.utils.load_torch_file(vae_path, return_metadata=True) + + if "vocoder.conv_post.weight" in sd: + from comfy.ldm.lightricks.vae.audio_vae import AudioVAE + vae = AudioVAE(sd, metadata) + else: + vae = VAE(sd=sd, device=device, dtype=dtype, metadata=metadata) + vae.throw_exception_if_invalid() + return (vae,) + +from comfy.samplers import sampling_function, CFGGuider +class Guider_ScheduledCFG(CFGGuider): + + def set_cfg(self, cfg, start_percent, end_percent): + self.cfg = cfg + self.start_percent = start_percent + self.end_percent = end_percent + + def predict_noise(self, x, timestep, model_options={}, seed=None): + steps = model_options["transformer_options"]["sample_sigmas"] + if isinstance(timestep, torch.Tensor): + timestep_value = timestep.reshape(-1)[0].to(steps) + else: + timestep_value = torch.tensor(timestep, device=steps.device, dtype=steps.dtype) + matched_step_index = torch.isclose(steps, timestep_value).nonzero() + assert not (isinstance(self.cfg, list) and len(self.cfg) != (len(steps) - 1)), "cfg list length must match step count" + if len(matched_step_index) > 0: + current_step_index = matched_step_index.item() + else: + for i in range(len(steps) - 1): + # walk from beginning of steps until crossing the timestep + if (steps[i] - timestep_value) * (steps[i + 1] - timestep_value) <= 0: + current_step_index = i + break + else: + current_step_index = 0 + current_percent = current_step_index / (len(steps) - 1) + + if self.start_percent <= current_percent <= self.end_percent: + if isinstance(self.cfg, list): + cfg = self.cfg[current_step_index] + else: + cfg = self.cfg + uncond = self.conds.get("negative", None) + else: + uncond = None + cfg = 1.0 + + return sampling_function(self.inner_model, x, timestep, uncond, self.conds.get("positive", None), cfg, model_options=model_options, seed=seed) + +class ScheduledCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step":0.01}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01}), + }, + } + RETURN_TYPES = ("GUIDER",) + FUNCTION = "get_guider" + CATEGORY = "KJNodes/experimental" + DESCRiPTION = """ +CFG Guider that allows for scheduled CFG changes over steps, the steps outside the range will use CFG 1.0 thus being processed faster. +cfg input can be a list of floats matching step count, or a single float for all steps. +""" + + def get_guider(self, model, cfg, positive, negative, start_percent, end_percent): + guider = Guider_ScheduledCFG(model) + guider.set_conds(positive, negative) + guider.set_cfg(cfg, start_percent, end_percent) + return (guider, ) + + +class ApplyRifleXRoPE_WanVideo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), + "k": ("INT", {"default": 6, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "KJNodes/experimental" + EXPERIMENTAL = True + DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx" + + def patch(self, model, latent, k): + model_class = model.model.diffusion_model + + model_clone = model.clone() + num_frames = latent["samples"].shape[2] + d = model_class.dim // model_class.num_heads + + rope_embedder = EmbedND_RifleX( + d, + 10000.0, + [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)], + num_frames, + k + ) + + model_clone.add_object_patch(f"diffusion_model.rope_embedder", rope_embedder) + + return (model_clone, ) + +class ApplyRifleXRoPE_HunuyanVideo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), + "k": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "KJNodes/experimental" + EXPERIMENTAL = True + DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx" + + def patch(self, model, latent, k): + model_class = model.model.diffusion_model + + model_clone = model.clone() + num_frames = latent["samples"].shape[2] + + pe_embedder = EmbedND_RifleX( + model_class.params.hidden_size // model_class.params.num_heads, + model_class.params.theta, + model_class.params.axes_dim, + num_frames, + k + ) + + model_clone.add_object_patch(f"diffusion_model.pe_embedder", pe_embedder) + + return (model_clone, ) + +def rope_riflex(pos, dim, theta, L_test, k): + from einops import rearrange + assert dim % 2 == 0 + if model_management.is_device_mps(pos.device) or model_management.is_intel_xpu() or model_management.is_directml_enabled(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + + # RIFLEX modification - adjust last frequency component if L_test and k are provided + if k and L_test: + omega[k-1] = 0.9 * 2 * torch.pi / L_test + + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + +class EmbedND_RifleX(nn.Module): + def __init__(self, dim, theta, axes_dim, num_frames, k): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + self.num_frames = num_frames + self.k = k + + def forward(self, ids): + n_axes = ids.shape[-1] + emb = torch.cat( + [rope_riflex(ids[..., i], self.axes_dim[i], self.theta, self.num_frames, self.k if i == 0 else 0) for i in range(n_axes)], + dim=-3, + ) + return emb.unsqueeze(1) + + +class Timer: + def __init__(self, name): + self.name = name + self.start_time = None + self.elapsed = 0 + +class TimerNodeKJ: + @classmethod + + def INPUT_TYPES(s): + return { + "required": { + "any_input": (IO.ANY, ), + "mode": (["start", "stop"],), + "name": ("STRING", {"default": "Timer"}), + }, + "optional": { + "timer": ("TIMER",), + }, + } + + RETURN_TYPES = (IO.ANY, "TIMER", "INT", ) + RETURN_NAMES = ("any_output", "timer", "time") + FUNCTION = "timer" + CATEGORY = "KJNodes/misc" + + def timer(self, mode, name, any_input=None, timer=None): + if timer is None: + if mode == "start": + timer = Timer(name=name) + timer.start_time = time.time() + return {"ui": { + "text": [f"{timer.start_time}"]}, + "result": (any_input, timer, 0) + } + elif mode == "stop" and timer is not None: + end_time = time.time() + timer.elapsed = int((end_time - timer.start_time) * 1000) + timer.start_time = None + return (any_input, timer, timer.elapsed) + +class HunyuanVideoEncodeKeyframesToCond: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "positive": ("CONDITIONING", ), + "vae": ("VAE", ), + "start_frame": ("IMAGE", ), + "end_frame": ("IMAGE", ), + "num_frames": ("INT", {"default": 33, "min": 2, "max": 4096, "step": 1}), + "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), + "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}), + "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), + }, + "optional": { + "negative": ("CONDITIONING", ), + } + } + + RETURN_TYPES = ("MODEL", "CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("model", "positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "KJNodes/videomodels" + + def encode(self, model, positive, start_frame, end_frame, num_frames, vae, tile_size, overlap, temporal_size, temporal_overlap, negative=None): + + model_clone = model.clone() + + model_clone.add_object_patch("concat_keys", ("concat_image",)) + + + x = (start_frame.shape[1] // 8) * 8 + y = (start_frame.shape[2] // 8) * 8 + + if start_frame.shape[1] != x or start_frame.shape[2] != y: + x_offset = (start_frame.shape[1] % 8) // 2 + y_offset = (start_frame.shape[2] % 8) // 2 + start_frame = start_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + if end_frame.shape[1] != x or end_frame.shape[2] != y: + x_offset = (start_frame.shape[1] % 8) // 2 + y_offset = (start_frame.shape[2] % 8) // 2 + end_frame = end_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + + video_frames = torch.zeros(num_frames-2, start_frame.shape[1], start_frame.shape[2], start_frame.shape[3], device=start_frame.device, dtype=start_frame.dtype) + video_frames = torch.cat([start_frame, video_frames, end_frame], dim=0) + + concat_latent = vae.encode_tiled(video_frames[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative if negative is not None else []]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + if len(out) == 1: + out.append(out[0]) + return (model_clone, out[0], out[1], out_latent) + + +class LazySwitchKJ: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "switch": ("BOOLEAN",), + "on_false": (IO.ANY, {"lazy": True}), + "on_true": (IO.ANY, {"lazy": True}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "switch" + CATEGORY = "KJNodes/misc" + DESCRIPTION = "Controls flow of execution based on a boolean switch." + + def check_lazy_status(self, switch, on_false=None, on_true=None): + if switch and on_true is None: + return ["on_true"] + if not switch and on_false is None: + return ["on_false"] + + def switch(self, switch, on_false = None, on_true=None): + value = on_true if switch else on_false + return (value,) + + +from comfy.patcher_extension import WrappersMP +from comfy.sampler_helpers import prepare_mask +class TTM_OuterSampleWrapper: + def __init__(self, mask, steps): + self.mask = mask + self.steps = steps + + def __call__(self, executor, noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes): + guider = executor.class_obj + guider.model_options + wrappers = guider.model_options["transformer_options"]["wrappers"] + w = wrappers.setdefault(WrappersMP.APPLY_MODEL, {}) + + if self.mask is not None: + motion_mask = self.mask.reshape((-1, 1, self.mask.shape[-2], self.mask.shape[-1])) + shape = latent_shapes[0] + motion_mask = prepare_mask(motion_mask, shape, noise.device) + + scale_latent_inpaint = guider.model_patcher.model.scale_latent_inpaint + w["TTM_ApplyModel_Wrapper"] = [TTM_ApplyModel_Wrapper(latent_image, noise, motion_mask, self.steps, scale_latent_inpaint)] + + out = executor(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) + + return out + + +class TTM_ApplyModel_Wrapper: + def __init__(self, reference_samples, noise, motion_mask, steps, scale_latent_inpaint): + self.reference_samples = reference_samples + self.noise = noise + self.motion_mask = motion_mask + self.steps = steps + self.scale_latent_inpaint = scale_latent_inpaint + + def __call__(self, executor, x, t, c_concat, c_crossattn, control, transformer_options, **kwargs): + sigmas = transformer_options["sample_sigmas"] + + matched = (sigmas == t).nonzero(as_tuple=True)[0] + if matched.numel() > 0: + current_step_index = matched.item() + else: + crossing = ((sigmas[:-1] - t) * (sigmas[1:] - t) <= 0).nonzero(as_tuple=True)[0] + current_step_index = crossing.item() if crossing.numel() > 0 else 0 + + next_sigma = sigmas[current_step_index + 1] if current_step_index < len(sigmas) - 1 else sigmas[current_step_index] + + if current_step_index != 0 and current_step_index < self.steps: + noisy_latent = self.scale_latent_inpaint(x=x, sigma=torch.tensor([next_sigma]), noise=self.noise.to(x), latent_image=self.reference_samples.to(x)) + if self.motion_mask is not None: + x = x * (1-self.motion_mask).to(x) + noisy_latent * self.motion_mask.to(x) + else: + x = noisy_latent + + return executor(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) + + +class LatentInpaintTTM: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "steps": ("INT", {"default": 7, "min": 0, "max": 888, "step": 1, "tooltip": "Number of steps to apply TTM inpainting for."}), + }, + "optional": { + "mask": ("MASK", {"tooltip": "Latent mask where white (1.0) is the area to inpaint and black (0.0) is the area to keep unchanged."}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + EXPERIMENTAL = True + DESCRIPTION = "https://github.com/time-to-move/TTM" + SEARCH_ALIASES = ["time to move"] + CATEGORY = "KJNodes/experimental" + + def patch(self, model, steps, mask=None): + m = model.clone() + m.add_wrapper_with_key(WrappersMP.OUTER_SAMPLE, "TTM_OuterSampleWrapper", TTM_OuterSampleWrapper(mask, steps)) + return (m, ) + + +class SimpleCalculatorKJ(io.ComfyNode): + @classmethod + def define_schema(cls): + template = io.Autogrow.TemplateNames(input=io.MultiType.Input("var", [io.Int, io.Float, io.Boolean], optional=True), names=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"], min=2) + return io.Schema( + node_id="SimpleCalculatorKJ", + category="KJNodes/misc", + description=""" +Calculator node that evaluates a mathematical expression using inputs a and b. + Supported operations: +, -, *, /, //, %, **, <<, >>, unary +/- + Supported comparisons: ==, !=, <, <=, >, >= + Supported logic: and, or, not + Supported functions: abs(), round(), min(), max(), pow(), sqrt(), sin(), cos(), tan(), log(), log10(), exp(), floor(), ceil() + Supported constants: pi, euler, True, False +""", + search_aliases=["math", "arithmetic", "expression", "logic"], + inputs=[ + io.String.Input("expression", default="a + b", multiline=True), + io.Autogrow.Input("variables", template=template), + ], + outputs=[ + io.Float.Output(), + io.Int.Output(), + io.Boolean.Output(), + ], + ) + + @classmethod + def execute(cls, variables, expression, a=None, b=None) -> io.NodeOutput: + import ast + import operator + import math + + # Allowed operations + allowed_operators = { + ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul, ast.Div: operator.truediv, + ast.FloorDiv: operator.floordiv, ast.Mod: operator.mod, ast.Pow: operator.pow, + ast.USub: operator.neg, ast.UAdd: operator.pos, ast.LShift: operator.lshift, + ast.RShift: operator.rshift, ast.Eq: operator.eq, ast.NotEq: operator.ne, ast.Lt: operator.lt, + ast.LtE: operator.le, ast.Gt: operator.gt, ast.GtE: operator.ge, ast.And: operator.and_, + ast.Or: operator.or_, ast.Not: operator.not_, + } + + # Allowed functions + allowed_functions = { + 'abs': abs, 'round': round, 'min': min, 'max': max, + 'pow': pow, 'sqrt': math.sqrt, 'sin': math.sin, + 'cos': math.cos, 'tan': math.tan, 'log': math.log, + 'log10': math.log10, 'exp': math.exp, 'floor': math.floor, + 'ceil': math.ceil + } + + # Allowed constants - start with pi, e, True, False + allowed_names = {'pi': math.pi, 'euler': math.e, 'True': True, 'False': False} + + # Add all variables from autogrow to allowed_names + for var_name, var_value in variables.items(): + allowed_names[var_name] = var_value + + # Backwards compatibility: add a and b if they're provided (for old workflows) + if a is not None: + allowed_names['a'] = a + if b is not None: + allowed_names['b'] = b + + def eval_node(node): + if isinstance(node, ast.Constant): # Numbers and booleans + return node.value + elif isinstance(node, ast.Name): # Variables + if node.id in allowed_names: + return allowed_names[node.id] + raise ValueError(f"Name '{node.id}' is not allowed") + elif isinstance(node, ast.BinOp): # Binary operations + if type(node.op) not in allowed_operators: + raise ValueError(f"Operator {type(node.op).__name__} is not allowed") + left = eval_node(node.left) + right = eval_node(node.right) + return allowed_operators[type(node.op)](left, right) + elif isinstance(node, ast.UnaryOp): # Unary operations + if type(node.op) not in allowed_operators: + raise ValueError(f"Operator {type(node.op).__name__} is not allowed") + operand = eval_node(node.operand) + return allowed_operators[type(node.op)](operand) + elif isinstance(node, ast.Compare): # Comparison operations + left = eval_node(node.left) + for op, comparator in zip(node.ops, node.comparators): + if type(op) not in allowed_operators: + raise ValueError(f"Operator {type(op).__name__} is not allowed") + right = eval_node(comparator) + result = allowed_operators[type(op)](left, right) + if not result: + return False + left = right + return True + elif isinstance(node, ast.BoolOp): # Boolean operations (and, or) + if type(node.op) not in allowed_operators: + raise ValueError(f"Operator {type(node.op).__name__} is not allowed") + values = [eval_node(value) for value in node.values] + if isinstance(node.op, ast.And): + return all(values) + elif isinstance(node.op, ast.Or): + return any(values) + elif isinstance(node, ast.Call): # Function calls + if not isinstance(node.func, ast.Name): + raise ValueError("Only simple function calls are allowed") + if node.func.id not in allowed_functions: + raise ValueError(f"Function '{node.func.id}' is not allowed") + args = [eval_node(arg) for arg in node.args] + return allowed_functions[node.func.id](*args) + else: + raise ValueError(f"Node type {type(node).__name__} is not allowed") + + try: + tree = ast.parse(expression, mode='eval') + result = eval_node(tree.body) + return io.NodeOutput(float(result), int(result), bool(result)) + except Exception as e: + print(f"CalculatorKJ Error: {str(e)}") + return io.NodeOutput(0.0, 0, False) + + +class GetTrackRange(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="GetTrackRange", + category="conditioning/video_models", + inputs=[ + io.Tracks.Input("tracks"), + io.Int.Input("start_index", default=24, min=-10000, max=10000, step=1), + io.Int.Input("num_frames", default=10, min=1, max=10000, step=1), + ], + outputs=[ + io.Tracks.Output(), + ], + ) + + @classmethod + def execute(cls, tracks, start_index, num_frames) -> io.NodeOutput: + track_path = tracks["track_path"] + mask = tracks["track_visibility"] + total_frames = track_path.shape[0] + + if start_index < 0: + start_index = total_frames + start_index + start_index = max(0, min(start_index, total_frames)) + + # Clamp end_index + end_index = max(0, min(start_index + num_frames, total_frames)) + + tracks_out = track_path[start_index:end_index, ...] + mask_out = mask[start_index:end_index, ...] + + out_track = { + "track_path": tracks_out, + "track_visibility": mask_out, + } + return io.NodeOutput(out_track) + +class AddNoiseToTrackPath(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="AddNoiseToTrackPath", + category="conditioning/video_models", + inputs=[ + io.Tracks.Input("tracks"), + io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01), + io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, step=1), + io.Float.Input("noise_x_ratio", default=1.0, min=0.0, max=100.0, step=0.01, + tooltip="Multiplier for horizontal noise component"), + io.Float.Input("noise_y_ratio", default=1.0, min=0.0, max=100.0, step=0.01, + tooltip="Multiplier for vertical noise component"), + io.Float.Input("noise_temporal_ratio", default=1.0, min=0.0, max=100.0, step=0.01, + tooltip="Multiplier for temporal (frame-to-frame) noise"), + ], + outputs=[ + io.Tracks.Output(), + ], + ) + + @classmethod + def execute(cls, tracks, strength, seed, noise_x_ratio, noise_y_ratio, noise_temporal_ratio) -> io.NodeOutput: + track_path = tracks["track_path"].clone() + mask = tracks["track_visibility"] + + torch.manual_seed(seed) + noise = torch.randn_like(track_path) * strength + + # Apply directional scaling to noise + noise[..., 0] *= noise_x_ratio # X coordinate noise + noise[..., 1] *= noise_y_ratio # Y coordinate noise + + # Apply temporal smoothing if temporal ratio is less than 1 + if noise_temporal_ratio < 1.0: + num_frames = track_path.shape[0] + smoothed_noise = noise.clone() + kernel_size = max(1, int((1.0 - noise_temporal_ratio) * 10)) + + for i in range(num_frames): + start_idx = max(0, i - kernel_size // 2) + end_idx = min(num_frames, i + kernel_size // 2 + 1) + smoothed_noise[i] = noise[start_idx:end_idx].mean(dim=0) + + noise = smoothed_noise * noise_temporal_ratio + noise * (1 - noise_temporal_ratio) + + track_path = track_path + noise + + out_track = { + "track_path": track_path, + "track_visibility": mask, + } + return io.NodeOutput(out_track) + + +class VAEDecodeLoopKJ: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "samples": ("LATENT", {"tooltip": "The latent to be decoded."}), + "vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}), + "overlap_latent_frames": ("INT", {"default": 2, "min": 2, "max": 8, "step": 1, "tooltip": "Number of frames to blend for seamless loop, for Wan 2 works and HunyuanVideo 1.5 should use 4"}), + } + } + RETURN_TYPES = ("IMAGE",) + OUTPUT_TOOLTIPS = ("The decoded images.",) + FUNCTION = "decode" + CATEGORY = "KJNodes/vae" + DESCRIPTION = "Video latent VAE decoding to fix artifacts on loop seams." + + def decode(self, vae, samples, overlap_latent_frames): + latents = samples["samples"] + + images = vae.decode(latents) + if overlap_latent_frames <= 0: + if len(images.shape) == 5: + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + return (images, ) + + end_frames = overlap_latent_frames + 1 + start_frames = overlap_latent_frames + + temp_images = vae.decode(torch.cat([latents[:, :, -end_frames:]] + [latents[:, :, :start_frames]], dim=2)).cpu().float() + + total_concat = end_frames + start_frames + temp_start = total_concat * 2 - 1 + main_start = total_concat + (overlap_latent_frames if overlap_latent_frames > 2 else 0) + + images = torch.cat([temp_images[:, temp_start:].to(images), images[:, main_start:]], dim=1) + if len(images.shape) == 5: + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + + return (images, ) + +import comfy.latent_formats +class WanImageToVideoSVIPro(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanImageToVideoSVIPro", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Int.Input("length", default=81, min=1, max=MAX_RESOLUTION, step=4), + io.Latent.Input("anchor_samples"), + io.Latent.Input("prev_samples", optional=True), + io.Int.Input("motion_latent_count", default=1, min=0, max=128, step=1), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, length, motion_latent_count, anchor_samples, prev_samples=None) -> io.NodeOutput: + anchor_latent = anchor_samples["samples"].clone() + + B, C, T, H, W = anchor_latent.shape + empty_latent = torch.zeros([B, 16, ((length - 1) // 4) + 1, H, W], device=model_management.intermediate_device()) + + total_latents = (length - 1) // 4 + 1 + device = anchor_latent.device + dtype = anchor_latent.dtype + + if prev_samples is None or motion_latent_count == 0: + padding_size = total_latents - anchor_latent.shape[2] + image_cond_latent = anchor_latent + else: + motion_latent = prev_samples["samples"][:, :, -motion_latent_count:].clone() + padding_size = total_latents - anchor_latent.shape[2] - motion_latent.shape[2] + image_cond_latent = torch.cat([anchor_latent, motion_latent], dim=2) + + padding = torch.zeros(1, C, padding_size, H, W, dtype=dtype, device=device) + padding = comfy.latent_formats.Wan21().process_out(padding) + image_cond_latent = torch.cat([image_cond_latent, padding], dim=2) + + mask = torch.ones((1, 1, empty_latent.shape[2], H, W), device=device, dtype=dtype) + mask[:, :, :1] = 0.0 + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": image_cond_latent, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": image_cond_latent, "concat_mask": mask}) + + out_latent = {} + out_latent["samples"] = empty_latent + return io.NodeOutput(positive, negative, out_latent) + +class DeprecatedCompileNodeKJ: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (IO.ANY,), + }, + } + RETURN_TYPES = (IO.ANY,) + FUNCTION = "passthrough" + CATEGORY = "KJNodes/deprecated" + DESCRIPTION = "This node has been replaced with TorchCompileModelAdvanced node, please use that instead." + def passthrough(self, model): + return (model,) + + +class VisualizeSigmasKJ(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="VisualizeSigmasKJ", + category="KJNodes/misc", + inputs=[ + io.Sigmas.Input("sigmas"), + io.Int.Input("start_step", default=0, min=-1, max=1000, step=1, + tooltip="Step index to mark as the start of a range (inclusive). Set to -1 to disable."), + io.Int.Input("end_step", default=-1, min=-1, max=1000, step=1, + tooltip="Step index to mark as the end of a range (inclusive). Set to - 1 to disable."), + ], + outputs=[ + io.Sigmas.Output(display_name="sigmas_out"), + io.Image.Output(display_name="image"), + ], + ) + + @classmethod + def execute(cls, sigmas, start_step=0, end_step=-1) -> io.NodeOutput: + + start_idx = 0 + end_idx = len(sigmas) - 1 + + if isinstance(start_step, float): + idxs = (sigmas <= start_step).nonzero(as_tuple=True)[0] + if len(idxs) > 0: + start_idx = idxs[0].item() + elif isinstance(start_step, int): + if start_step > 0: + start_idx = start_step + + if isinstance(end_step, float): + idxs = (sigmas >= end_step).nonzero(as_tuple=True)[0] + if len(idxs) > 0: + end_idx = idxs[-1].item() + elif isinstance(end_step, int): + if end_step != -1: + end_idx = end_step - 1 + + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + sigmas_np = sigmas.cpu().numpy() + if not np.isclose(sigmas_np[-1], 0.0, atol=1e-6): + sigmas_np = np.append(sigmas_np, 0.0) + buf = BytesIO() + fig = plt.figure(facecolor='#353535') + ax = fig.add_subplot(111) + ax.set_facecolor('#353535') # Set axes background color + x_values = range(0, len(sigmas_np)) + ax.plot(x_values, sigmas_np) + # Annotate each sigma value + ax.scatter(x_values, sigmas_np, color='white', s=20, zorder=3) # Small dots at each sigma + for x, y in zip(x_values, sigmas_np): + # Show all annotations if few steps, or just show split step annotations + show_annotation = len(sigmas_np) <= 10 + is_split_step = (start_idx > 0 and x == start_idx) or (end_idx != -1 and x == end_idx + 1) + + if show_annotation or is_split_step: + color = 'orange' + if is_split_step: + color = 'yellow' + ax.annotate(f"{y:.3f}", (x, y), textcoords="offset points", xytext=(10, 1), ha='center', color=color, fontsize=12) + ax.set_xticks(x_values) + ax.set_title("Sigmas", color='white') # Title font color + ax.set_xlabel("Step", color='white') # X label font color + ax.set_ylabel("Sigma Value", color='white') # Y label font color + ax.tick_params(axis='x', colors='white', labelsize=10) # X tick color + ax.tick_params(axis='y', colors='white', labelsize=10) # Y tick color + # Add split point if end_step is defined + end_idx += 1 + if end_idx != -1 and 0 <= end_idx < len(sigmas_np) - 1: + ax.axvline(end_idx, color='red', linestyle='--', linewidth=2, label='end_step split') + # Add split point if start_step is defined + if start_idx > 0 and 0 <= start_idx < len(sigmas_np): + ax.axvline(start_idx, color='green', linestyle='--', linewidth=2, label='start_step split') + if (end_idx != -1 and 0 <= end_idx < len(sigmas_np)) or (start_idx > 0 and 0 <= start_idx < len(sigmas_np)): + handles, labels = ax.get_legend_handles_labels() + if labels: + ax.legend() + # Draw shaded range + range_start_idx = start_idx if start_idx > 0 else 0 + range_end_idx = end_idx if end_idx > 0 and end_idx < len(sigmas_np) else len(sigmas_np) - 1 + if range_start_idx < range_end_idx: + ax.axvspan(range_start_idx, range_end_idx, color='lightblue', alpha=0.1, label='Sampled Range') + + + plt.tight_layout() + fig.canvas.draw() + w, h = fig.canvas.get_width_height() + try: + buf = np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8) + buf = buf.reshape(h, w, 4) + buf = buf[:, :, [1, 2, 3]] # Convert ARGB to RGB + except: + buf = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) + buf = buf.reshape(h, w, 3).copy() + image = torch.from_numpy(buf).float() / 255.0 + image = image.unsqueeze(0) #(H, W, C) -> (1, H, W, C) + plt.close(fig) + + sigmas_out = sigmas[start_idx:end_idx + 1] if end_idx != -1 else sigmas[start_idx:] + + return io.NodeOutput(sigmas_out,image) + +class PreviewLatentNoiseMask(io.ComfyNode): + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="PreviewLatentNoiseMask", + category="KJNodes/latent", + description="Previews the latent noise mask", + inputs=[ + io.Latent.Input("latent",), + ], + outputs=[ + io.Mask.Output(display_name="mask"), + ], + ) + + @classmethod + def execute(cls, latent) -> io.NodeOutput: + noise_mask = latent.get("noise_mask", None) + if noise_mask is None: + return io.NodeOutput(torch.zeros((1, 64, 64))) + noise_mask = noise_mask.clone() + + if noise_mask.ndim == 5: + noise_mask = noise_mask[0, 0] + + return io.NodeOutput(noise_mask) diff --git a/custom_nodes/ComfyUI-KJNodes/pyproject.toml b/custom_nodes/ComfyUI-KJNodes/pyproject.toml new file mode 100644 index 00000000..5993ba58 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-kjnodes" +description = "Various quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability." +version = "1.2.9" +license = {file = "LICENSE"} +dependencies = ["numpy", "pillow>=10.3.0", "scipy", "color-matcher", "matplotlib", "huggingface_hub"] + +[project.urls] +Repository = "https://github.com/kijai/ComfyUI-KJNodes" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "kijai" +DisplayName = "ComfyUI-KJNodes" +Icon = "https://avatars.githubusercontent.com/u/40791699" diff --git a/custom_nodes/ComfyUI-KJNodes/requirements.txt b/custom_nodes/ComfyUI-KJNodes/requirements.txt new file mode 100644 index 00000000..3a339e26 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/requirements.txt @@ -0,0 +1,7 @@ +pillow>=10.3.0 +scipy +color-matcher +matplotlib +huggingface_hub +mss +opencv-python-headless diff --git a/custom_nodes/ComfyUI-KJNodes/utility/fluid.py b/custom_nodes/ComfyUI-KJNodes/utility/fluid.py new file mode 100644 index 00000000..c0691987 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/fluid.py @@ -0,0 +1,67 @@ +import numpy as np +from scipy.ndimage import map_coordinates, spline_filter +from scipy.sparse.linalg import factorized + +from .numerical import difference, operator + + +class Fluid: + def __init__(self, shape, *quantities, pressure_order=1, advect_order=3): + self.shape = shape + self.dimensions = len(shape) + + # Prototyping is simplified by dynamically + # creating advected quantities as needed. + self.quantities = quantities + for q in quantities: + setattr(self, q, np.zeros(shape)) + + self.indices = np.indices(shape) + self.velocity = np.zeros((self.dimensions, *shape)) + + laplacian = operator(shape, difference(2, pressure_order)) + self.pressure_solver = factorized(laplacian) + + self.advect_order = advect_order + + def step(self): + # Advection is computed backwards in time as described in Stable Fluids. + advection_map = self.indices - self.velocity + + # SciPy's spline filter introduces checkerboard divergence. + # A linear blend of the filtered and unfiltered fields based + # on some value epsilon eliminates this error. + def advect(field, filter_epsilon=10e-2, mode='constant'): + filtered = spline_filter(field, order=self.advect_order, mode=mode) + field = filtered * (1 - filter_epsilon) + field * filter_epsilon + return map_coordinates(field, advection_map, prefilter=False, order=self.advect_order, mode=mode) + + # Apply advection to each axis of the + # velocity field and each user-defined quantity. + for d in range(self.dimensions): + self.velocity[d] = advect(self.velocity[d]) + + for q in self.quantities: + setattr(self, q, advect(getattr(self, q))) + + # Compute the jacobian at each point in the + # velocity field to extract curl and divergence. + jacobian_shape = (self.dimensions,) * 2 + partials = tuple(np.gradient(d) for d in self.velocity) + jacobian = np.stack(partials).reshape(*jacobian_shape, *self.shape) + + divergence = jacobian.trace() + + # If this curl calculation is extended to 3D, the y-axis value must be negated. + # This corresponds to the coefficients of the levi-civita symbol in that dimension. + # Higher dimensions do not have a vector -> scalar, or vector -> vector, + # correspondence between velocity and curl due to differing isomorphisms + # between exterior powers in dimensions != 2 or 3 respectively. + curl_mask = np.triu(np.ones(jacobian_shape, dtype=bool), k=1) + curl = (jacobian[curl_mask] - jacobian[curl_mask.T]).squeeze() + + # Apply the pressure correction to the fluid's velocity field. + pressure = self.pressure_solver(divergence.flatten()).reshape(self.shape) + self.velocity -= np.gradient(pressure) + + return divergence, curl, pressure \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/magictex.py b/custom_nodes/ComfyUI-KJNodes/utility/magictex.py new file mode 100644 index 00000000..e6d426f7 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/magictex.py @@ -0,0 +1,95 @@ +"""Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy + +https://github.com/cheind/magic-texture +""" +from typing import Tuple, Optional +import numpy as np + + +def coordinate_grid(shape: Tuple[int, int], dtype=np.float32): + """Returns a three-dimensional coordinate grid of given shape for use in `magic`.""" + x = np.linspace(-1, 1, shape[1], endpoint=True, dtype=dtype) + y = np.linspace(-1, 1, shape[0], endpoint=True, dtype=dtype) + X, Y = np.meshgrid(x, y) + XYZ = np.stack((X, Y, np.ones_like(X)), -1) + return XYZ + + +def random_transform(coords: np.ndarray, rng: np.random.Generator = None): + """Returns randomly transformed coordinates""" + H, W = coords.shape[:2] + rng = rng or np.random.default_rng() + m = rng.uniform(-1.0, 1.0, size=(3, 3)).astype(coords.dtype) + return (coords.reshape(-1, 3) @ m.T).reshape(H, W, 3) + + +def magic( + coords: np.ndarray, + depth: Optional[int] = None, + distortion: Optional[int] = None, + rng: np.random.Generator = None, +): + """Returns color magic color texture. + + The implementation is based on Blender's (https://www.blender.org/) magic + texture shader. The following adaptions have been made: + - we exchange the nested if-cascade by a probabilistic iterative approach + + Kwargs + ------ + coords: HxWx3 array + Coordinates transformed into colors by this method. See + `magictex.coordinate_grid` to generate the default. + depth: int (optional) + Number of transformations applied. Higher numbers lead to more + nested patterns. If not specified, randomly sampled. + distortion: float (optional) + Distortion of patterns. Larger values indicate more distortion, + lower values tend to generate smoother patterns. If not specified, + randomly sampled. + rng: np.random.Generator + Optional random generator to draw samples from. + + Returns + ------- + colors: HxWx3 array + Three channel color image in range [0,1] + """ + rng = rng or np.random.default_rng() + if distortion is None: + distortion = rng.uniform(1, 4) + if depth is None: + depth = rng.integers(1, 5) + + H, W = coords.shape[:2] + XYZ = coords + x = np.sin((XYZ[..., 0] + XYZ[..., 1] + XYZ[..., 2]) * distortion) + y = np.cos((-XYZ[..., 0] + XYZ[..., 1] - XYZ[..., 2]) * distortion) + z = -np.cos((-XYZ[..., 0] - XYZ[..., 1] + XYZ[..., 2]) * distortion) + + if depth > 0: + x *= distortion + y *= distortion + z *= distortion + y = -np.cos(x - y + z) + y *= distortion + + xyz = [x, y, z] + fns = [np.cos, np.sin] + for _ in range(1, depth): + axis = rng.choice(3) + fn = fns[rng.choice(2)] + signs = rng.binomial(n=1, p=0.5, size=4) * 2 - 1 + + xyz[axis] = signs[-1] * fn( + signs[0] * xyz[0] + signs[1] * xyz[1] + signs[2] * xyz[2] + ) + xyz[axis] *= distortion + + x, y, z = xyz + x /= 2 * distortion + y /= 2 * distortion + z /= 2 * distortion + c = 0.5 - np.stack((x, y, z), -1) + np.clip(c, 0, 1.0) + return c \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/numerical.py b/custom_nodes/ComfyUI-KJNodes/utility/numerical.py new file mode 100644 index 00000000..b5b88bc6 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/numerical.py @@ -0,0 +1,25 @@ +from functools import reduce +from itertools import cycle +from math import factorial + +import numpy as np +import scipy.sparse as sp + + +def difference(derivative, accuracy=1): + # Central differences implemented based on the article here: + # http://web.media.mit.edu/~crtaylor/calculator.html + derivative += 1 + radius = accuracy + derivative // 2 - 1 + points = range(-radius, radius + 1) + coefficients = np.linalg.inv(np.vander(points)) + return coefficients[-derivative] * factorial(derivative - 1), points + + +def operator(shape, *differences): + # Credit to Philip Zucker for figuring out + # that kronsum's argument order is reversed. + # Without that bit of wisdom I'd have lost it. + differences = zip(shape, cycle(differences)) + factors = (sp.diags(*diff, shape=(dim,) * 2) for dim, diff in differences) + return reduce(lambda a, f: sp.kronsum(f, a, format='csc'), factors) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/utility.py b/custom_nodes/ComfyUI-KJNodes/utility/utility.py new file mode 100644 index 00000000..5b7ea444 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/utility.py @@ -0,0 +1,88 @@ +import torch +import numpy as np +from PIL import Image, ImageColor +from typing import Union, List +import logging + +# Utility functions from mtb nodes: https://github.com/melMass/comfy_mtb +def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor: + if isinstance(image, list): + return torch.cat([pil2tensor(img) for img in image], dim=0) + + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + + +def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor: + if isinstance(img_np, list): + return torch.cat([np2tensor(img) for img in img_np], dim=0) + + return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0) + + +def tensor2np(tensor: torch.Tensor): + if len(tensor.shape) == 3: # Single image + return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8) + else: # Batch of images + return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor] + +def tensor2pil(image: torch.Tensor) -> List[Image.Image]: + batch_count = image.size(0) if len(image.shape) > 3 else 1 + if batch_count > 1: + out = [] + for i in range(batch_count): + out.extend(tensor2pil(image[i])) + return out + + return [ + Image.fromarray( + np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + ) + ] + +def string_to_color(color_string: str) -> List[int]: + color_list = [0, 0, 0] # Default fallback (black) + + if ',' in color_string: + # Handle CSV format (e.g., "255, 0, 0" or "255, 0, 0, 128" or "1.0, 0.5, 0.0") + try: + values = [float(channel.strip()) for channel in color_string.split(',')] + # Convert to 0-255 range if values are in 0-1 range + if all(0 <= v <= 1 for v in values): + color_list = [int(v * 255) for v in values] + else: + color_list = [int(v) for v in values] + except ValueError: + logging.warning(f"Invalid color format: {color_string}. Using default black.") + elif color_string.lstrip('#').isalnum() and not color_string.lstrip('#').replace('.', '', 1).isdigit(): + # Could be Hex format or color name + color_string_stripped = color_string.lstrip('#') + # Try hex first + if len(color_string_stripped) in [6, 8] and all(c in '0123456789ABCDEFabcdef' for c in color_string_stripped): + if len(color_string_stripped) == 6: # #RRGGBB + color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4)] + elif len(color_string_stripped) == 8: # #RRGGBBAA + color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4, 6)] + else: + # Try color name (e.g., "red", "blue", "cyan") + try: + rgb = ImageColor.getrgb(color_string) + color_list = list(rgb) + except ValueError: + logging.warning(f"Invalid color name or hex format: {color_string}. Using default black.") + else: + # Handle single value (grayscale) - can be int or float + try: + value = float(color_string.strip()) + # Convert to 0-255 range if it's a float between 0-1 + if 0 <= value <= 1: + value = int(value * 255) + else: + value = int(value) + color_list = [value, value, value] + except ValueError: + logging.warning(f"Invalid color format: {color_string}. Using default black.") + + # Clip values to valid range + color_list = np.clip(color_list, 0, 255).tolist() + + return color_list diff --git a/custom_nodes/ComfyUI-KJNodes/web/green.png b/custom_nodes/ComfyUI-KJNodes/web/green.png new file mode 100644 index 00000000..900964e4 Binary files /dev/null and b/custom_nodes/ComfyUI-KJNodes/web/green.png differ diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js b/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js new file mode 100644 index 00000000..9069207e --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js @@ -0,0 +1,23 @@ +const { app } = window.comfyAPI.app; + +app.registerExtension({ + name: "KJNodes.appearance", + nodeCreated(node) { + switch (node.comfyClass) { + case "INTConstant": + node.setSize([200, 58]); + node.color = "#1b4669"; + node.bgcolor = "#29699c"; + break; + case "FloatConstant": + node.setSize([200, 58]); + node.color = LGraphCanvas.node_colors.green.color; + node.bgcolor = LGraphCanvas.node_colors.green.bgcolor; + break; + case "ConditioningMultiCombine": + node.color = LGraphCanvas.node_colors.brown.color; + node.bgcolor = LGraphCanvas.node_colors.brown.bgcolor; + break; + } + } +}); diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/browserstatus.js b/custom_nodes/ComfyUI-KJNodes/web/js/browserstatus.js new file mode 100644 index 00000000..7ef12a18 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/browserstatus.js @@ -0,0 +1,55 @@ +const { api } = window.comfyAPI.api; +const { app } = window.comfyAPI.app; + +app.registerExtension({ + name: "KJNodes.browserstatus", + setup() { + if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) { + return; + } + api.addEventListener("status", ({ detail }) => { + let title = "ComfyUI"; + let favicon = "green"; + let queueRemaining = detail && detail.exec_info.queue_remaining; + + if (queueRemaining) { + favicon = "red"; + title = `00% - ${queueRemaining} | ${title}`; + } + let link = document.querySelector("link[rel~='icon']"); + if (!link) { + link = document.createElement("link"); + link.rel = "icon"; + document.head.appendChild(link); + } + link.href = new URL(`../${favicon}.png`, import.meta.url); + document.title = title; + }); + //add progress to the title + api.addEventListener("progress", ({ detail }) => { + const { value, max } = detail; + const progress = Math.floor((value / max) * 100); + let title = document.title; + + if (!isNaN(progress) && progress >= 0 && progress <= 100) { + const paddedProgress = String(progress).padStart(2, '0'); + title = `${paddedProgress}% ${title.replace(/^\d+%\s/, '')}`; + } + document.title = title; + }); + }, + init() { + if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) { + return; + } + const pythongossFeed = app.extensions.find( + (e) => e.name === 'pysssss.FaviconStatus', + ) + if (pythongossFeed) { + console.warn("KJNodes - Overriding pysssss.FaviconStatus") + pythongossFeed.setup = function() { + console.warn("Disabled by KJNodes") + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js b/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js new file mode 100644 index 00000000..02017e15 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js @@ -0,0 +1,175 @@ +const { app } = window.comfyAPI.app; + +// Adds context menu entries, code partly from pyssssscustom-scripts + +function addMenuHandler(nodeType, cb) { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} + +function addNode(name, nextTo, options) { + console.log("name:", name); + console.log("nextTo:", nextTo); + options = { side: "left", select: true, shiftY: 0, shiftX: 0, ...(options || {}) }; + const node = LiteGraph.createNode(name); + app.graph.add(node); + + node.pos = [ + options.side === "left" ? nextTo.pos[0] - (node.size[0] + options.offset): nextTo.pos[0] + nextTo.size[0] + options.offset, + + nextTo.pos[1] + options.shiftY, + ]; + + // Automatically connect nodes + if (options.side === "left") { + // New node on left: connect new node's output to nextTo's first available input + if (node.outputs && node.outputs.length > 0 && nextTo.inputs && nextTo.inputs.length > 0) { + for (let i = 0; i < nextTo.inputs.length; i++) { + if (!nextTo.inputs[i].link) { + node.connect(0, nextTo, i); + break; + } + } + } + } else { + // New node on right: connect nextTo's output to new node's first available input + if (nextTo.outputs && nextTo.outputs.length > 0 && node.inputs && node.inputs.length > 0) { + for (let i = 0; i < node.inputs.length; i++) { + if (!node.inputs[i].link) { + nextTo.connect(0, node, i); + break; + } + } + } + } + + if (options.select) { + app.canvas.selectNode(node, false); + } + return node; +} + +app.registerExtension({ + name: "KJNodesContextmenu", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.input && nodeData.input.required) { + addMenuHandler(nodeType, function (_, options) { + options.unshift( + { + content: "Add GetNode", + callback: () => {addNode("GetNode", this, { side:"left", offset: 30});} + }, + { + content: "Add SetNode", + callback: () => {addNode("SetNode", this, { side:"right", offset: 30 });} + }, + { + content: "Add PreviewAsTextNode", + callback: () => {addNode("PreviewAny", this, { side:"right", offset: 30 }); + }, + }); + }); + } + }, + async setup(app) { + const updateSlots = (value) => { + const valuesToAddToIn = ["GetNode"]; + const valuesToAddToOut = ["SetNode"]; + // Remove entries if they exist + for (const arr of Object.values(LiteGraph.slot_types_default_in)) { + for (const valueToAdd of valuesToAddToIn) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + } + } + + for (const arr of Object.values(LiteGraph.slot_types_default_out)) { + for (const valueToAdd of valuesToAddToOut) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + } + } + if (value!="disabled") { + for (const arr of Object.values(LiteGraph.slot_types_default_in)) { + for (const valueToAdd of valuesToAddToIn) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + if (value === "top") { + arr.unshift(valueToAdd); + } else { + arr.push(valueToAdd); + } + } + } + + for (const arr of Object.values(LiteGraph.slot_types_default_out)) { + for (const valueToAdd of valuesToAddToOut) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + if (value === "top") { + arr.unshift(valueToAdd); + } else { + arr.push(valueToAdd); + } + } + } + } + }; + + app.ui.settings.addSetting({ + id: "KJNodes.SetGetMenu", + name: "KJNodes: Make Set/Get -nodes defaults", + tooltip: 'Adds Set/Get nodes to the top or bottom of the list of available node suggestions.', + options: ['disabled', 'top', 'bottom'], + defaultValue: 'disabled', + type: "combo", + onChange: updateSlots, + + }); + app.ui.settings.addSetting({ + id: "KJNodes.MiddleClickDefault", + name: "KJNodes: Middle click default node adding", + defaultValue: false, + type: "boolean", + onChange: (value) => { + LiteGraph.middle_click_slot_add_default_node = value; + }, + }); + app.ui.settings.addSetting({ + id: "KJNodes.nodeAutoColor", + name: "KJNodes: Automatically set node colors", + type: "boolean", + defaultValue: true, + }); + app.ui.settings.addSetting({ + id: "KJNodes.helpPopup", + name: "KJNodes: Help popups", + defaultValue: true, + type: "boolean", + }); + app.ui.settings.addSetting({ + id: "KJNodes.disablePrefix", + name: "KJNodes: Disable automatic Set_ and Get_ prefix", + defaultValue: true, + type: "boolean", + }); + app.ui.settings.addSetting({ + id: "KJNodes.browserStatus", + name: "KJNodes: 🟒 Stoplight browser status icon πŸ”΄", + defaultValue: false, + type: "boolean", + }); +} +}); diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js new file mode 100644 index 00000000..27178449 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js @@ -0,0 +1,95 @@ +const { app } = window.comfyAPI.app; + +//from melmass +export function makeUUID() { + let dt = new Date().getTime() + const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { + const r = ((dt + Math.random() * 16) % 16) | 0 + dt = Math.floor(dt / 16) + return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) + }) + return uuid +} + +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} +app.registerExtension({ + name: 'KJNodes.FastPreview', + + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === 'FastPreview') { + chainCallback(nodeType.prototype, "onNodeCreated", function () { + + var element = document.createElement("div"); + this.uuid = makeUUID() + element.id = `fast-preview-${this.uuid}` + + this.previewWidget = this.addDOMWidget(nodeData.name, "FastPreviewWidget", element, { + serialize: false, + hideOnZoom: false, + }); + + this.previewer = new Previewer(this); + + this.setSize([550, 550]); + this.resizable = false; + this.previewWidget.parentEl = document.createElement("div"); + this.previewWidget.parentEl.className = "fast-preview"; + this.previewWidget.parentEl.id = `fast-preview-${this.uuid}` + element.appendChild(this.previewWidget.parentEl); + + chainCallback(this, "onExecuted", function (message) { + let bg_image = message["bg_image"]; + this.properties.imgData = { + name: "bg_image", + base64: bg_image + }; + this.previewer.refreshBackgroundImage(this); + }); + + + }); // onAfterGraphConfigured + }//node created + } //before register +})//register + +class Previewer { + constructor(context) { + this.node = context; + this.previousWidth = null; + this.previousHeight = null; + } + refreshBackgroundImage = () => { + const imgData = this.node?.properties?.imgData; + if (imgData?.base64) { + const base64String = imgData.base64; + const imageUrl = `data:${imgData.type};base64,${base64String}`; + const img = new Image(); + img.src = imageUrl; + img.onload = () => { + const { width, height } = img; + if (width !== this.previousWidth || height !== this.previousHeight) { + this.node.setSize([width, height]); + this.previousWidth = width; + this.previousHeight = height; + } + this.node.previewWidget.element.style.backgroundImage = `url(${imageUrl})`; + }; + } + }; + } \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js b/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js new file mode 100644 index 00000000..9ea71359 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js @@ -0,0 +1,326 @@ +const { app } = window.comfyAPI.app; + +// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/ +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', +) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) +} + +loadScript('kjweb_async/marked.min.js').catch((e) => { + console.log(e) +}) +loadScript('kjweb_async/purify.min.js').catch((e) => { + console.log(e) +}) + +const categories = ["KJNodes", "SUPIR", "VoiceCraft", "Marigold", "IC-Light", "WanVideoWrapper"]; +app.registerExtension({ + name: "KJNodes.HelpPopup", + async beforeRegisterNodeDef(nodeType, nodeData) { + + if (app.ui.settings.getSettingValue("KJNodes.helpPopup") === false) { + return; + } + try { + categories.forEach(category => { + if (nodeData?.category?.startsWith(category)) { + addDocumentation(nodeData, nodeType); + } + else return + }); + } catch (error) { + console.error("Error in registering KJNodes.HelpPopup", error); + } + }, +}); + +const create_documentation_stylesheet = () => { + const tag = 'kj-documentation-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .kj-documentation-popup { + background: var(--comfy-menu-bg); + position: absolute; + color: var(--fg-color); + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + border-radius: 10px; + border-style: solid; + border-width: medium; + border-color: var(--border-color); + z-index: 5; + overflow: hidden; + } + .content-wrapper { + overflow: auto; + max-height: 100%; + /* Scrollbar styling for Chrome */ + &::-webkit-scrollbar { + width: 6px; + } + &::-webkit-scrollbar-track { + background: var(--bg-color); + } + &::-webkit-scrollbar-thumb { + background-color: var(--fg-color); + border-radius: 6px; + border: 3px solid var(--bg-color); + } + + /* Scrollbar styling for Firefox */ + scrollbar-width: thin; + scrollbar-color: var(--fg-color) var(--bg-color); + a { + color: yellow; + } + a:visited { + color: orange; + } + a:hover { + color: red; + } + } + ` + document.head.appendChild(styleTag) + } + } + + /** Add documentation widget to the selected node */ + export const addDocumentation = ( + nodeData, + nodeType, + opts = { icon_size: 14, icon_margin: 4 },) => { + + opts = opts || {} + const iconSize = opts.icon_size ? opts.icon_size : 14 + const iconMargin = opts.icon_margin ? opts.icon_margin : 4 + let docElement = null + let contentWrapper = null + //if no description in the node python code, don't do anything + if (!nodeData.description) { + return + } + + const drawFg = nodeType.prototype.onDrawForeground + nodeType.prototype.onDrawForeground = function (ctx) { + const r = drawFg ? drawFg.apply(this, arguments) : undefined + if (this.flags.collapsed) return r + + // icon position + const x = this.size[0] - iconSize - iconMargin + + // create the popup + if (this.show_doc && docElement === null) { + docElement = document.createElement('div') + contentWrapper = document.createElement('div'); + docElement.appendChild(contentWrapper); + + create_documentation_stylesheet() + contentWrapper.classList.add('content-wrapper'); + docElement.classList.add('kj-documentation-popup') + + //parse the string from the python node code to html with marked, and sanitize the html with DOMPurify + contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(nodeData.description,)) + + // resize handle + const resizeHandle = document.createElement('div'); + resizeHandle.style.width = '0'; + resizeHandle.style.height = '0'; + resizeHandle.style.position = 'absolute'; + resizeHandle.style.bottom = '0'; + resizeHandle.style.right = '0'; + resizeHandle.style.cursor = 'se-resize'; + + // Add pseudo-elements to create a triangle shape + const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim(); + resizeHandle.style.borderTop = '10px solid transparent'; + resizeHandle.style.borderLeft = '10px solid transparent'; + resizeHandle.style.borderBottom = `10px solid ${borderColor}`; + resizeHandle.style.borderRight = `10px solid ${borderColor}`; + + docElement.appendChild(resizeHandle) + let isResizing = false + let startX, startY, startWidth, startHeight + + resizeHandle.addEventListener('mousedown', function (e) { + e.preventDefault(); + e.stopPropagation(); + isResizing = true; + startX = e.clientX; + startY = e.clientY; + startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10); + startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10); + }, + { signal: this.docCtrl.signal }, + ); + + // close button + const closeButton = document.createElement('div'); + closeButton.textContent = '❌'; + closeButton.style.position = 'absolute'; + closeButton.style.top = '0'; + closeButton.style.right = '0'; + closeButton.style.cursor = 'pointer'; + closeButton.style.padding = '5px'; + closeButton.style.color = 'red'; + closeButton.style.fontSize = '12px'; + + docElement.appendChild(closeButton) + + closeButton.addEventListener('mousedown', (e) => { + e.stopPropagation(); + this.show_doc = !this.show_doc + docElement.parentNode.removeChild(docElement) + docElement = null + if (contentWrapper) { + contentWrapper.remove() + contentWrapper = null + } + }, + { signal: this.docCtrl.signal }, + ); + + document.addEventListener('mousemove', function (e) { + if (!isResizing) return; + const scale = app.canvas.ds.scale; + const newWidth = startWidth + (e.clientX - startX) / scale; + const newHeight = startHeight + (e.clientY - startY) / scale;; + docElement.style.width = `${newWidth}px`; + docElement.style.height = `${newHeight}px`; + }, + { signal: this.docCtrl.signal }, + ); + + document.addEventListener('mouseup', function () { + isResizing = false + }, + { signal: this.docCtrl.signal }, + ) + + document.body.appendChild(docElement) + } + // close the popup + else if (!this.show_doc && docElement !== null) { + docElement.parentNode.removeChild(docElement) + docElement = null + } + // update position of the popup + if (this.show_doc && docElement !== null) { + const rect = ctx.canvas.getBoundingClientRect() + const scaleX = rect.width / ctx.canvas.width + const scaleY = rect.height / ctx.canvas.height + + const transform = new DOMMatrix() + .scaleSelf(scaleX, scaleY) + .multiplySelf(ctx.getTransform()) + .translateSelf(this.size[0] * scaleX * Math.max(1.0,window.devicePixelRatio) , 0) + .translateSelf(10, -32) + + const scale = new DOMMatrix() + .scaleSelf(transform.a, transform.d); + const bcr = app.canvas.canvas.getBoundingClientRect() + + const styleObject = { + transformOrigin: '0 0', + transform: scale, + left: `${transform.a + bcr.x + transform.e}px`, + top: `${transform.d + bcr.y + transform.f}px`, + }; + Object.assign(docElement.style, styleObject); + } + + ctx.save() + ctx.translate(x - 2, iconSize - 34) + ctx.scale(iconSize / 32, iconSize / 32) + ctx.strokeStyle = 'rgba(255,255,255,0.3)' + ctx.lineCap = 'round' + ctx.lineJoin = 'round' + ctx.lineWidth = 2.4 + ctx.font = 'bold 36px monospace' + ctx.fillStyle = 'orange'; + ctx.fillText('?', 0, 24) + ctx.restore() + return r + } + // handle clicking of the icon + const mouseDown = nodeType.prototype.onMouseDown + nodeType.prototype.onMouseDown = function (e, localPos, canvas) { + const r = mouseDown ? mouseDown.apply(this, arguments) : undefined + const iconX = this.size[0] - iconSize - iconMargin + const iconY = iconSize - 34 + if ( + localPos[0] > iconX && + localPos[0] < iconX + iconSize && + localPos[1] > iconY && + localPos[1] < iconY + iconSize + ) { + if (this.show_doc === undefined) { + this.show_doc = true + } else { + this.show_doc = !this.show_doc + } + if (this.show_doc) { + this.docCtrl = new AbortController() + } else { + this.docCtrl.abort() + } + return true; + } + return r; + } + const onRem = nodeType.prototype.onRemoved + + nodeType.prototype.onRemoved = function () { + const r = onRem ? onRem.apply(this, []) : undefined + + if (docElement) { + docElement.remove() + docElement = null + } + + if (contentWrapper) { + contentWrapper.remove() + contentWrapper = null + } + return r + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js b/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js new file mode 100644 index 00000000..6ab10760 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js @@ -0,0 +1,416 @@ +const { app } = window.comfyAPI.app; +const { applyTextReplacements } = window.comfyAPI.utils; + +app.registerExtension({ + name: "KJNodes.jsnodes", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if(!nodeData?.category?.startsWith("KJNodes")) { + return; + } + switch (nodeData.name) { + case "ConditioningMultiCombine": + nodeType.prototype.onNodeCreated = function () { + this._type = "CONDITIONING" + this.inputs_offset = nodeData.name.includes("selective")?1:0 + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.type === this._type).length + if(target_number_of_inputs===num_inputs)return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`conditioning_${i}`, this._type) + } + }); + } + break; + case "ImageBatchMulti": + case "ImageAddMulti": + case "ImageConcatMulti": + case "CrossFadeImagesMulti": + case "TransitionImagesMulti": + nodeType.prototype.onNodeCreated = function () { + this._type = "IMAGE" + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.type === this._type).length + if(target_number_of_inputs===num_inputs)return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`image_${i}`, this._type, {shape: 7}); + } + + }); + } + break; + case "MaskBatchMulti": + nodeType.prototype.onNodeCreated = function () { + this._type = "MASK" + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.type === this._type).length + if(target_number_of_inputs===num_inputs)return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`mask_${i}`, this._type) + } + }); + } + break; + + case "FluxBlockLoraSelect": + case "HunyuanVideoBlockLoraSelect": + case "Wan21BlockLoraSelect": + case "LTX2BlockLoraSelect": + nodeType.prototype.onNodeCreated = function () { + this.addWidget("button", "Set all", null, () => { + const userInput = prompt("Enter the values to set for widgets (e.g., s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0):", ""); + if (userInput) { + const regex = /([sd])?(\d+(?:,\d+|-?\d+)*?)?=(\d+(\.\d+)?)/; + const match = userInput.match(regex); + if (match) { + const type = match[1]; + const indicesPart = match[2]; + const value = parseFloat(match[3]); + + let targetWidgets = []; + if (type === 's') { + targetWidgets = this.widgets.filter(widget => widget.name.includes("single")); + } else if (type === 'd') { + targetWidgets = this.widgets.filter(widget => widget.name.includes("double")); + } else { + targetWidgets = this.widgets; // No type specified, all widgets + } + + if (indicesPart) { + const indices = indicesPart.split(',').flatMap(part => { + if (part.includes('-')) { + const [start, end] = part.split('-').map(Number); + return Array.from({ length: end - start + 1 }, (_, i) => start + i); + } + return Number(part); + }); + + for (const index of indices) { + if (index < targetWidgets.length) { + targetWidgets[index].value = value; + } + } + } else { + // No indices provided, set value for all target widgets + for (const widget of targetWidgets) { + widget.value = value; + } + } + } else if (!isNaN(parseFloat(userInput))) { + // Single value provided, set it for all widgets + const value = parseFloat(userInput); + for (const widget of this.widgets) { + widget.value = value; + } + } else { + alert("Invalid input format. Please use the format s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0"); + } + } else { + alert("Invalid input. Please enter a value."); + } + }); + }; + break; + + case "GetMaskSizeAndCount": + const onGetMaskSizeConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + const v = onGetMaskSizeConnectInput? onGetMaskSizeConnectInput.apply(this, arguments): undefined + this.outputs[1]["label"] = "width" + this.outputs[2]["label"] = "height" + this.outputs[3]["label"] = "count" + return v; + } + const onGetMaskSizeExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + const r = onGetMaskSizeExecuted? onGetMaskSizeExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x').map(Number); + this.outputs[1]["label"] = values[1] + " width" + this.outputs[2]["label"] = values[2] + " height" + this.outputs[3]["label"] = values[0] + " count" + return r + } + break; + + case "GetImageSizeAndCount": + const onGetImageSizeConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + console.log(this) + const v = onGetImageSizeConnectInput? onGetImageSizeConnectInput.apply(this, arguments): undefined + //console.log(this) + this.outputs[1]["label"] = "width" + this.outputs[2]["label"] = "height" + this.outputs[3]["label"] = "count" + return v; + } + //const onGetImageSizeExecuted = nodeType.prototype.onExecuted; + const onGetImageSizeExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + console.log(this) + const r = onGetImageSizeExecuted? onGetImageSizeExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x').map(Number); + console.log(values) + this.outputs[1]["label"] = values[1] + " width" + this.outputs[2]["label"] = values[2] + " height" + this.outputs[3]["label"] = values[0] + " count" + return r + } + break; + + case "GetLatentSizeAndCount": + const onGetLatentConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + console.log(this) + const v = onGetLatentConnectInput? onGetLatentConnectInput.apply(this, arguments): undefined + //console.log(this) + this.outputs[1]["label"] = "batch_size" + this.outputs[2]["label"] = "channels" + this.outputs[3]["label"] = "frames" + this.outputs[4]["label"] = "height" + this.outputs[5]["label"] = "width" + return v; + } + //const onGetImageSizeExecuted = nodeType.prototype.onExecuted; + const onGetLatentSizeExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + console.log(this) + const r = onGetLatentSizeExecuted? onGetLatentSizeExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x').map(Number); + console.log(values) + this.outputs[1]["label"] = values[0] + " batch" + this.outputs[2]["label"] = values[1] + " channels" + this.outputs[3]["label"] = values[2] + " frames" + this.outputs[4]["label"] = values[3] + " height" + this.outputs[5]["label"] = values[4] + " width" + return r + } + break; + + case "PreviewAnimation": + const onPreviewAnimationConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + const v = onPreviewAnimationConnectInput? onPreviewAnimationConnectInput.apply(this, arguments): undefined + this.title = "Preview Animation" + return v; + } + const onPreviewAnimationExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + const r = onPreviewAnimationExecuted? onPreviewAnimationExecuted.apply(this,arguments): undefined + let values = message["text"].toString(); + this.title = "Preview Animation " + values + return r + } + break; + + case "VRAM_Debug": + const onVRAM_DebugConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + const v = onVRAM_DebugConnectInput? onVRAM_DebugConnectInput.apply(this, arguments): undefined + this.outputs[3]["label"] = "freemem_before" + this.outputs[4]["label"] = "freemem_after" + return v; + } + const onVRAM_DebugExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + const r = onVRAM_DebugExecuted? onVRAM_DebugExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x'); + this.outputs[3]["label"] = values[0] + " freemem_before" + this.outputs[4]["label"] = values[1] + " freemem_after" + return r + } + break; + + case "JoinStringMulti": + const originalOnNodeCreated = nodeType.prototype.onNodeCreated || function() {}; + nodeType.prototype.onNodeCreated = function () { + originalOnNodeCreated.apply(this, arguments); + + this._type = "STRING"; + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.name && input.name.toLowerCase().includes("string_")).length + if (target_number_of_inputs === num_inputs) return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`string_${i}`, this._type, {shape: 7}); + } + }); + } + break; + case "SoundReactive": + nodeType.prototype.onNodeCreated = function () { + let audioContext; + let microphoneStream; + let animationFrameId; + let analyser; + let dataArray; + let startRangeHz; + let endRangeHz; + let smoothingFactor = 0.5; + let smoothedSoundLevel = 0; + + // Function to update the widget value in real-time + const updateWidgetValueInRealTime = () => { + // Ensure analyser and dataArray are defined before using them + if (analyser && dataArray) { + analyser.getByteFrequencyData(dataArray); + + const startRangeHzWidget = this.widgets.find(w => w.name === "start_range_hz"); + if (startRangeHzWidget) startRangeHz = startRangeHzWidget.value; + const endRangeHzWidget = this.widgets.find(w => w.name === "end_range_hz"); + if (endRangeHzWidget) endRangeHz = endRangeHzWidget.value; + const smoothingFactorWidget = this.widgets.find(w => w.name === "smoothing_factor"); + if (smoothingFactorWidget) smoothingFactor = smoothingFactorWidget.value; + + // Calculate frequency bin width (frequency resolution) + const frequencyBinWidth = audioContext.sampleRate / analyser.fftSize; + // Convert the widget values from Hz to indices + const startRangeIndex = Math.floor(startRangeHz / frequencyBinWidth); + const endRangeIndex = Math.floor(endRangeHz / frequencyBinWidth); + + // Function to calculate the average value for a frequency range + const calculateAverage = (start, end) => { + const sum = dataArray.slice(start, end).reduce((acc, val) => acc + val, 0); + const average = sum / (end - start); + + // Apply exponential moving average smoothing + smoothedSoundLevel = (average * (1 - smoothingFactor)) + (smoothedSoundLevel * smoothingFactor); + return smoothedSoundLevel; + }; + // Calculate the average levels for each frequency range + const soundLevel = calculateAverage(startRangeIndex, endRangeIndex); + + // Update the widget values + + const lowLevelWidget = this.widgets.find(w => w.name === "sound_level"); + if (lowLevelWidget) lowLevelWidget.value = soundLevel; + + animationFrameId = requestAnimationFrame(updateWidgetValueInRealTime); + } + }; + + // Function to start capturing audio from the microphone + const startMicrophoneCapture = () => { + // Only create the audio context and analyser once + if (!audioContext) { + audioContext = new (window.AudioContext || window.webkitAudioContext)(); + // Access the sample rate of the audio context + console.log(`Sample rate: ${audioContext.sampleRate}Hz`); + analyser = audioContext.createAnalyser(); + analyser.fftSize = 2048; + dataArray = new Uint8Array(analyser.frequencyBinCount); + // Get the range values from widgets (assumed to be in Hz) + const lowRangeWidget = this.widgets.find(w => w.name === "low_range_hz"); + if (lowRangeWidget) startRangeHz = lowRangeWidget.value; + + const midRangeWidget = this.widgets.find(w => w.name === "mid_range_hz"); + if (midRangeWidget) endRangeHz = midRangeWidget.value; + } + + navigator.mediaDevices.getUserMedia({ audio: true }).then(stream => { + microphoneStream = stream; + const microphone = audioContext.createMediaStreamSource(stream); + microphone.connect(analyser); + updateWidgetValueInRealTime(); + }).catch(error => { + console.error('Access to microphone was denied or an error occurred:', error); + }); + }; + + // Function to stop capturing audio from the microphone + const stopMicrophoneCapture = () => { + if (animationFrameId) { + cancelAnimationFrame(animationFrameId); + } + if (microphoneStream) { + microphoneStream.getTracks().forEach(track => track.stop()); + } + if (audioContext) { + audioContext.close(); + // Reset audioContext to ensure it can be created again when starting + audioContext = null; + } + }; + + // Add start button + this.addWidget("button", "Start mic capture", null, startMicrophoneCapture); + + // Add stop button + this.addWidget("button", "Stop mic capture", null, stopMicrophoneCapture); + }; + break; + case "SaveImageKJ": + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function() { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0; + const widget = this.widgets.find((w) => w.name === "filename_prefix"); + widget.serializeValue = () => { + return applyTextReplacements(app, widget.value); + }; + return r; + }; + break; + + } + + }, + async setup() { + // to keep Set/Get node virtual connections visible when offscreen + const originalComputeVisibleNodes = LGraphCanvas.prototype.computeVisibleNodes; + LGraphCanvas.prototype.computeVisibleNodes = function () { + const visibleNodesSet = new Set(originalComputeVisibleNodes.apply(this, arguments)); + for (const node of this.graph._nodes) { + if ((node.type === "SetNode" || node.type === "GetNode") && node.drawConnection) { + visibleNodesSet.add(node); + } + } + return Array.from(visibleNodesSet); + }; + + } +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/point_editor.js b/custom_nodes/ComfyUI-KJNodes/web/js/point_editor.js new file mode 100644 index 00000000..a96abfec --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/point_editor.js @@ -0,0 +1,744 @@ +const { app } = window.comfyAPI.app; +import { getLocalMouse } from './protovisUtil.js'; + +//from melmass +export function makeUUID() { + let dt = new Date().getTime() + const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { + const r = ((dt + Math.random() * 16) % 16) | 0 + dt = Math.floor(dt / 16) + return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) + }) + return uuid +} + +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', +) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) +} +const create_documentation_stylesheet = () => { + const tag = 'kj-pointseditor-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .points-editor { + + position: absolute; + + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + z-index: 0; + overflow: hidden; + } + ` + document.head.appendChild(styleTag) + } +} + +loadScript('kjweb_async/svg-path-properties.min.js').catch((e) => { + console.log(e) +}) +loadScript('kjweb_async/protovis.min.js').catch((e) => { + console.log(e) +}) +create_documentation_stylesheet() + +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} +app.registerExtension({ + name: 'KJNodes.PointEditor', + + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === 'PointsEditor') { + chainCallback(nodeType.prototype, "onNodeCreated", function () { + + hideWidgetForGood(this, this.widgets.find(w => w.name === "coordinates")) + hideWidgetForGood(this, this.widgets.find(w => w.name === "neg_coordinates")) + hideWidgetForGood(this, this.widgets.find(w => w.name === "bboxes")) + + var element = document.createElement("div"); + this.uuid = makeUUID() + element.id = `points-editor-${this.uuid}` + + this.previewMediaType = 'image' + + this.pointsEditor = this.addDOMWidget(nodeData.name, "PointsEditorWidget", element, { + serialize: false, + hideOnZoom: false, + }); + + // context menu + this.contextMenu = document.createElement("div"); + this.contextMenu.id = "context-menu"; + this.contextMenu.style.display = "none"; + this.contextMenu.style.position = "absolute"; + this.contextMenu.style.backgroundColor = "#202020"; + this.contextMenu.style.minWidth = "100px"; + this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)"; + this.contextMenu.style.zIndex = "100"; + this.contextMenu.style.padding = "5px"; + + function styleMenuItem(menuItem) { + menuItem.style.display = "block"; + menuItem.style.padding = "5px"; + menuItem.style.color = "#FFF"; + menuItem.style.fontFamily = "Arial, sans-serif"; + menuItem.style.fontSize = "16px"; + menuItem.style.textDecoration = "none"; + menuItem.style.marginBottom = "5px"; + } + function createMenuItem(id, textContent) { + let menuItem = document.createElement("a"); + menuItem.href = "#"; + menuItem.id = `menu-item-${id}`; + menuItem.textContent = textContent; + styleMenuItem(menuItem); + return menuItem; + } + + // Create an array of menu items using the createMenuItem function + this.menuItems = [ + createMenuItem(0, "Load Image"), + createMenuItem(1, "Clear Image"), + ]; + + // Add mouseover and mouseout event listeners to each menu item for styling + this.menuItems.forEach(menuItem => { + menuItem.addEventListener('mouseover', function () { + this.style.backgroundColor = "gray"; + }); + + menuItem.addEventListener('mouseout', function () { + this.style.backgroundColor = "#202020"; + }); + }); + + // Append each menu item to the context menu + this.menuItems.forEach(menuItem => { + this.contextMenu.appendChild(menuItem); + }); + + document.body.appendChild(this.contextMenu); + + this.addWidget("button", "New canvas", null, () => { + if (!this.properties || !("points" in this.properties)) { + this.editor = new PointsEditor(this); + this.addProperty("points", this.constructor.type, "string"); + this.addProperty("neg_points", this.constructor.type, "string"); + + } + else { + this.editor = new PointsEditor(this, true); + } + }); + + this.setSize([550, 550]); + this.resizable = false; + this.pointsEditor.parentEl = document.createElement("div"); + this.pointsEditor.parentEl.className = "points-editor"; + this.pointsEditor.parentEl.id = `points-editor-${this.uuid}` + element.appendChild(this.pointsEditor.parentEl); + + chainCallback(this, "onConfigure", function () { + try { + this.editor = new PointsEditor(this); + } catch (error) { + console.error("An error occurred while configuring the editor:", error); + } + }); + chainCallback(this, "onExecuted", function (message) { + let bg_image = message["bg_image"]; + this.properties.imgData = { + name: "bg_image", + base64: bg_image + }; + this.editor.refreshBackgroundImage(this); + }); + + }); // onAfterGraphConfigured + }//node created + } //before register +})//register + +class PointsEditor { + constructor(context, reset = false) { + this.node = context; + this.reset = reset; + const self = this; // Keep a reference to the main class context + + console.log("creatingPointEditor") + + this.node.pasteFile = (file) => { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + return true; + } + return false; + }; + + this.node.onDragOver = function (e) { + if (e.dataTransfer && e.dataTransfer.items) { + return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/")); + } + return false; + }; + + // On drop upload files + this.node.onDragDrop = (e) => { + console.log("onDragDrop called"); + let handled = false; + for (const file of e.dataTransfer.files) { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + handled = true; + } + } + return handled; + }; + + // context menu + this.createContextMenu(); + + if (reset && context.pointsEditor.element) { + context.pointsEditor.element.innerHTML = ''; // Clear the container + } + this.pos_coordWidget = context.widgets.find(w => w.name === "coordinates"); + this.neg_coordWidget = context.widgets.find(w => w.name === "neg_coordinates"); + this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store"); + this.widthWidget = context.widgets.find(w => w.name === "width"); + this.heightWidget = context.widgets.find(w => w.name === "height"); + this.bboxStoreWidget = context.widgets.find(w => w.name === "bbox_store"); + this.bboxWidget = context.widgets.find(w => w.name === "bboxes"); + + //widget callbacks + this.widthWidget.callback = () => { + this.width = this.widthWidget.value; + if (this.width > 256) { + context.setSize([this.width + 45, context.size[1]]); + } + this.vis.width(this.width); + this.updateData(); + } + this.heightWidget.callback = () => { + this.height = this.heightWidget.value + this.vis.height(this.height) + context.setSize([context.size[0], this.height + 300]); + this.updateData(); + } + this.pointsStoreWidget.callback = () => { + this.points = JSON.parse(pointsStoreWidget.value).positive; + this.neg_points = JSON.parse(pointsStoreWidget.value).negative; + this.updateData(); + } + this.bboxStoreWidget.callback = () => { + this.bbox = JSON.parse(bboxStoreWidget.value) + this.updateData(); + } + + this.width = this.widthWidget.value; + this.height = this.heightWidget.value; + var i = 3; + this.points = []; + this.neg_points = []; + this.bbox = [{}]; + var drawing = false; + + // Initialize or reset points array + if (!reset && this.pointsStoreWidget.value != "") { + this.points = JSON.parse(this.pointsStoreWidget.value).positive; + this.neg_points = JSON.parse(this.pointsStoreWidget.value).negative; + this.bbox = JSON.parse(this.bboxStoreWidget.value); + console.log(this.bbox) + } else { + this.points = [ + { + x: this.width / 2, // Middle point horizontally centered + y: this.height / 2 // Middle point vertically centered + } + ]; + this.neg_points = [ + { + x: 0, // Middle point horizontally centered + y: 0 // Middle point vertically centered + } + ]; + const combinedPoints = { + positive: this.points, + negative: this.neg_points, + }; + this.pointsStoreWidget.value = JSON.stringify(combinedPoints); + this.bboxStoreWidget.value = JSON.stringify(this.bbox); + } + + //create main canvas panel + this.vis = new pv.Panel() + .width(this.width) + .height(this.height) + .fillStyle("#222") + .strokeStyle("gray") + .lineWidth(2) + .antialias(false) + .margin(10) + .event("mousedown", function () { + let mouse = getLocalMouse(this); + if (pv.event.shiftKey && pv.event.button === 2) { // Use pv.event to access the event object + let scaledMouse = { + x: mouse.x / app.canvas.ds.scale, + y: mouse.y / app.canvas.ds.scale + }; + i = self.neg_points.push(scaledMouse) - 1; + self.updateData(); + return this; + } + else if (pv.event.shiftKey) { + let scaledMouse = { + x: mouse.x / app.canvas.ds.scale, + y: mouse.y / app.canvas.ds.scale + }; + i = self.points.push(scaledMouse) - 1; + self.updateData(); + return this; + } + else if (pv.event.ctrlKey) { + console.log("start drawing at " + mouse.x / app.canvas.ds.scale + ", " + mouse.y / app.canvas.ds.scale); + drawing = true; + self.bbox[0].startX = mouse.x / app.canvas.ds.scale; + self.bbox[0].startY = mouse.y / app.canvas.ds.scale; + } + else if (pv.event.button === 2) { + self.node.contextMenu.style.display = 'block'; + self.node.contextMenu.style.left = `${pv.event.clientX}px`; + self.node.contextMenu.style.top = `${pv.event.clientY}px`; + } + }) + .event("mousemove", function () { + if (drawing) { + let mouse = getLocalMouse(this); + self.bbox[0].endX = mouse.x / app.canvas.ds.scale; + self.bbox[0].endY = mouse.y / app.canvas.ds.scale; + self.vis.render(); + } + }) + .event("mouseup", function () { + let mouse = getLocalMouse(this); + console.log("end drawing at " + mouse.x / app.canvas.ds.scale + ", " + mouse.y / app.canvas.ds.scale); + drawing = false; + self.updateData(); + }); + + this.backgroundImage = this.vis.add(pv.Image).visible(false) + + //create bounding box + this.bounding_box = this.vis.add(pv.Area) + .data(function () { + if (drawing || (self.bbox && self.bbox[0] && Object.keys(self.bbox[0]).length > 0)) { + return [self.bbox[0].startX, self.bbox[0].endX]; + } else { + return []; + } + }) + .bottom(function () {return self.height - Math.max(self.bbox[0].startY, self.bbox[0].endY); }) + .left(function (d) {return d; }) + .height(function () {return Math.abs(self.bbox[0].startY - self.bbox[0].endY);}) + .fillStyle("rgba(70, 130, 180, 0.5)") + .strokeStyle("steelblue") + .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; }) + .add(pv.Dot) + .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; }) + .data(() => { + if (self.bbox && Object.keys(self.bbox[0]).length > 0) { + return [{ + x: self.bbox[0].endX, + y: self.bbox[0].endY + }]; + } else { + return []; + } + }) + .left(d => d.x) + .top(d => d.y) + .radius(Math.log(Math.min(self.width, self.height)) * 1) + .shape("square") + .cursor("move") + .strokeStyle("steelblue") + .lineWidth(2) + .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("drag", function () { + let mouse = getLocalMouse(this); + let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new position by the inverse of the scale factor + let adjustedY = mouse.y / app.canvas.ds.scale; + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(self.vis.width(), adjustedX)); + adjustedY = Math.max(0, Math.min(self.vis.height(), adjustedY)); + self.bbox[0].endX = adjustedX; + self.bbox[0].endY = adjustedY; + self.vis.render(); + }) + .event("dragend", function () { + self.updateData(); + }); + + //create positive points + this.vis.add(pv.Dot) + .data(() => this.points) + .left(d => d.x) + .top(d => d.y) + .radius(Math.log(Math.min(self.width, self.height)) * 4) + .shape("circle") + .cursor("move") + .strokeStyle(function () { return i == this.index ? "#07f907" : "#139613"; }) + .lineWidth(4) + .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function () { + i = this.index; + }) + .event("dragend", function () { + if (pv.event.button === 2 && i !== 0 && i !== self.points.length - 1) { + this.index = i; + self.points.splice(i--, 1); + } + self.updateData(); + + }) + .event("drag", function () { + let mouse = getLocalMouse(this); + let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor + let adjustedY = mouse.y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + self.points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position + self.vis.render(); // Re-render the visualization to reflect the new position + }) + + .anchor("center") + .add(pv.Label) + .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left + .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25) // Shift label down if on top half, otherwise shift up + .font(25 + "px sans-serif") + .text(d => {return this.points.indexOf(d); }) + .textStyle("#139613") + .textShadow("2px 2px 2px black") + .add(pv.Dot) // Add smaller point in the center + .data(() => this.points) + .left(d => d.x) + .top(d => d.y) + .radius(2) // Smaller radius for the center point + .shape("circle") + .fillStyle("red") // Color for the center point + .lineWidth(1); // Stroke thickness for the center point + + //create negative points + this.vis.add(pv.Dot) + .data(() => this.neg_points) + .left(d => d.x) + .top(d => d.y) + .radius(Math.log(Math.min(self.width, self.height)) * 4) + .shape("circle") + .cursor("move") + .strokeStyle(function () { return i == this.index ? "#f91111" : "#891616"; }) + .lineWidth(4) + .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function () { + i = this.index; + }) + .event("dragend", function () { + if (pv.event.button === 2 && i !== 0 && i !== self.neg_points.length - 1) { + this.index = i; + self.neg_points.splice(i--, 1); + } + self.updateData(); + + }) + .event("drag", function () { + let mouse = getLocalMouse(this); + let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor + let adjustedY = mouse.y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + self.neg_points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position + self.vis.render(); // Re-render the visualization to reflect the new position + }) + .anchor("center") + .add(pv.Label) + .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left + .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25) // Shift label down if on top half, otherwise shift up + .font(25 + "px sans-serif") + .text(d => {return this.neg_points.indexOf(d); }) + .textStyle("red") + .textShadow("2px 2px 2px black") + .add(pv.Dot) // Add smaller point in the center + .data(() => this.neg_points) + .left(d => d.x) + .top(d => d.y) + .radius(2) // Smaller radius for the center point + .shape("circle") + .fillStyle("red") // Color for the center point + .lineWidth(1); // Stroke thickness for the center point + + if (this.points.length != 0) { + this.vis.render(); + } + + var svgElement = this.vis.canvas(); + svgElement.style['zIndex'] = "2" + svgElement.style['position'] = "relative" + this.node.pointsEditor.element.appendChild(svgElement); + + if (this.width > 256) { + this.node.setSize([this.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], this.height + 300]); + this.updateData(); + this.refreshBackgroundImage(); + + }//end constructor + + updateData = () => { + if (!this.points || this.points.length === 0) { + console.log("no points"); + return; + } + const combinedPoints = { + positive: this.points, + negative: this.neg_points, + }; + this.pointsStoreWidget.value = JSON.stringify(combinedPoints); + this.pos_coordWidget.value = JSON.stringify(this.points); + this.neg_coordWidget.value = JSON.stringify(this.neg_points); + + if (this.bbox.length != 0) { + let bboxString = JSON.stringify(this.bbox); + this.bboxStoreWidget.value = bboxString; + this.bboxWidget.value = bboxString; + } + + this.vis.render(); + }; + + handleImageLoad = (img, file, base64String) => { + console.log(img.width, img.height); // Access width and height here + this.widthWidget.value = img.width; + this.heightWidget.value = img.height; + + if (img.width != this.vis.width() || img.height != this.vis.height()) { + if (img.width > 256) { + this.node.setSize([img.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], img.height + 300]); + this.vis.width(img.width); + this.vis.height(img.height); + this.height = img.height; + this.width = img.width; + this.updateData(); + } + this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render(); + }; + + processImage = (img, file) => { + const canvas = document.createElement('canvas'); + const ctx = canvas.getContext('2d'); + + const maxWidth = 800; // maximum width + const maxHeight = 600; // maximum height + let width = img.width; + let height = img.height; + + // Calculate the new dimensions while preserving the aspect ratio + if (width > height) { + if (width > maxWidth) { + height *= maxWidth / width; + width = maxWidth; + } + } else { + if (height > maxHeight) { + width *= maxHeight / height; + height = maxHeight; + } + } + + canvas.width = width; + canvas.height = height; + ctx.drawImage(img, 0, 0, width, height); + + // Get the compressed image data as a Base64 string + const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1 + + this.node.properties.imgData = { + name: file.name, + lastModified: file.lastModified, + size: file.size, + type: file.type, + base64: base64String + }; + handleImageLoad(img, file, base64String); +}; + + handleImageFile = (file) => { + const reader = new FileReader(); + reader.onloadend = () => { + const img = new Image(); + img.src = reader.result; + img.onload = () => processImage(img, file); + }; + reader.readAsDataURL(file); + + const imageUrl = URL.createObjectURL(file); + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, file, null); + }; + + refreshBackgroundImage = () => { + if (this.node.properties.imgData && this.node.properties.imgData.base64) { + const base64String = this.node.properties.imgData.base64; + const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`; + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, null, base64String); + } + }; + + createContextMenu = () => { + self = this; + document.addEventListener('contextmenu', function (e) { + if (e.target.closest(`#points-editor-${self.node.uuid}`) || + e.target.closest('#context-menu')) { + e.preventDefault(); + } + }); + + document.addEventListener('click', function (e) { + if (!self.node.contextMenu.contains(e.target)) { + self.node.contextMenu.style.display = 'none'; + } + }); + + this.node.menuItems.forEach((menuItem, index) => { + self = this; + menuItem.addEventListener('click', function (e) { + e.preventDefault(); + switch (index) { + case 0: + // Create file input element + const fileInput = document.createElement('input'); + fileInput.type = 'file'; + fileInput.accept = 'image/*'; // Accept only image files + + // Listen for file selection + fileInput.addEventListener('change', function (event) { + const file = event.target.files[0]; // Get the selected file + + if (file) { + const imageUrl = URL.createObjectURL(file); + let img = new Image(); + img.src = imageUrl; + img.onload = () => self.handleImageLoad(img, file, null); + } + }); + + fileInput.click(); + + self.node.contextMenu.style.display = 'none'; + break; + case 1: + self.backgroundImage.visible(false).root.render(); + self.node.properties.imgData = null; + self.node.contextMenu.style.display = 'none'; + break; + } + }); + }); + }//end createContextMenu +}//end class + + +//from melmass +export function hideWidgetForGood(node, widget, suffix = '') { + widget.origType = widget.type + widget.origComputeSize = widget.computeSize + widget.origSerializeValue = widget.serializeValue + widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically + widget.type = "converted-widget" + suffix + // widget.serializeValue = () => { + // // Prevent serializing the widget if we have no input linked + // const w = node.inputs?.find((i) => i.widget?.name === widget.name); + // if (w?.link == null) { + // return undefined; + // } + // return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; + // }; + + // Hide any linked widgets, e.g. seed+seedControl + if (widget.linkedWidgets) { + for (const w of widget.linkedWidgets) { + hideWidgetForGood(node, w, ':' + widget.name) + } + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/protovisUtil.js b/custom_nodes/ComfyUI-KJNodes/web/js/protovisUtil.js new file mode 100644 index 00000000..2b857241 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/protovisUtil.js @@ -0,0 +1,25 @@ +/** + * Utility functions for protovis in ComfyUI. + */ + +/** + * Get correct local coordinates for protovis in transformed containers. + * Uses getBoundingClientRect() which properly accounts for CSS transforms. + * + * This fixes coordinate calculation issues when protovis widgets are rendered + * inside ComfyUI's vueNodes mode, which uses CSS transforms for panning/zooming. + * + * @param {pv.Mark} mark - The protovis mark instance + * @returns {{x: number, y: number}} Local coordinates relative to the canvas + */ +export function getLocalMouse(mark) { + const e = pv.event + if (!e) return { x: 0, y: 0 } + const canvas = mark.root.canvas() + if (!canvas) return { x: 0, y: 0 } + const rect = canvas.getBoundingClientRect() + return { + x: e.clientX - rect.left, + y: e.clientY - rect.top + } +} diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js b/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js new file mode 100644 index 00000000..c1936837 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js @@ -0,0 +1,567 @@ +const { app } = window.comfyAPI.app; + +//based on diffus3's SetGet: https://github.com/diffus3/ComfyUI-extensions + +// Nodes that allow you to tunnel connections for cleaner graphs +function setColorAndBgColor(type) { + const colorMap = { + "DEFAULT": LGraphCanvas.node_colors.gray, + "MODEL": LGraphCanvas.node_colors.blue, + "LATENT": LGraphCanvas.node_colors.purple, + "VAE": LGraphCanvas.node_colors.red, + "WANVAE": LGraphCanvas.node_colors.red, + "CONDITIONING": LGraphCanvas.node_colors.brown, + "IMAGE": LGraphCanvas.node_colors.pale_blue, + "CLIP": LGraphCanvas.node_colors.yellow, + "FLOAT": LGraphCanvas.node_colors.green, + "MASK": { color: "#1c5715", bgcolor: "#1f401b"}, + "INT": { color: "#1b4669", bgcolor: "#29699c"}, + "CONTROL_NET": { color: "#156653", bgcolor: "#1c453b"}, + "NOISE": { color: "#2e2e2e", bgcolor: "#242121"}, + "GUIDER": { color: "#3c7878", bgcolor: "#1c453b"}, + "SAMPLER": { color: "#614a4a", bgcolor: "#3b2c2c"}, + "SIGMAS": { color: "#485248", bgcolor: "#272e27"}, + + }; + console.log("Setting color for type:", colorMap[type]); + const colors = colorMap[type]; + if (colors) { + this.color = colors.color; + this.bgcolor = colors.bgcolor; + } + else{ + // Default color + this.color = LGraphCanvas.node_colors.gray; + this.bgcolor = LGraphCanvas.node_colors.gray; + } +} +let disablePrefix = app.ui.settings.getSettingValue("KJNodes.disablePrefix") +const LGraphNode = LiteGraph.LGraphNode + +function showAlert(message) { + app.extensionManager.toast.add({ + severity: 'warn', + summary: "KJ Get/Set", + detail: `${message}. Most likely you're missing custom nodes`, + life: 5000, + }) +} +app.registerExtension({ + name: "SetNode", + registerCustomNodes() { + class SetNode extends LGraphNode { + defaultVisibility = true; + serialize_widgets = true; + drawConnection = false; + currentGetters = null; + slotColor = "#FFF"; + canvas = app.canvas; + menuEntry = "Show connections"; + + constructor(title) { + super(title) + if (!this.properties) { + this.properties = { + "previousName": "" + }; + } + this.properties.showOutputText = SetNode.defaultVisibility; + + const node = this; + + this.addWidget( + "text", + "Constant", + '', + (s, t, u, v, x) => { + node.validateName(node.graph); + if(this.widgets[0].value !== ''){ + this.title = (!disablePrefix ? "Set_" : "") + this.widgets[0].value; + } + this.update(); + this.properties.previousName = this.widgets[0].value; + }, + {} + ) + + this.addInput("*", "*"); + this.addOutput("*", '*'); + + this.onConnectionsChange = function( + slotType, //1 = input, 2 = output + slot, + isChangeConnect, + link_info, + output + ) { + //On Disconnect + if (slotType == 1 && !isChangeConnect) { + if(this.inputs[slot].name === ''){ + this.inputs[slot].type = '*'; + this.inputs[slot].name = '*'; + this.title = "Set" + } + } + if (slotType == 2 && !isChangeConnect) { + if (this.outputs && this.outputs[slot]) { + this.outputs[slot].type = '*'; + this.outputs[slot].name = '*'; + } + } + //On Connect + if (link_info && node.graph && slotType == 1 && isChangeConnect) { + const resolve = link_info.resolve(node.graph) + const type = (resolve?.subgraphInput ?? resolve?.output)?.type + if (type) { + if (this.title === "Set"){ + this.title = (!disablePrefix ? "Set_" : "") + type; + } + if (this.widgets[0].value === '*'){ + this.widgets[0].value = type + } + + this.validateName(node.graph); + this.inputs[0].type = type; + this.inputs[0].name = type; + + if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){ + setColorAndBgColor.call(this, type); + } + } else { + showAlert("node input undefined.") + } + } + if (link_info && node.graph && slotType == 2 && isChangeConnect) { + const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id); + + if (fromNode && fromNode.inputs && fromNode.inputs[link_info.origin_slot]) { + const type = fromNode.inputs[link_info.origin_slot].type; + + this.outputs[0].type = type; + this.outputs[0].name = type; + } else { + showAlert('node output undefined'); + } + } + + + //Update either way + this.update(); + } + + this.validateName = function(graph) { + let widgetValue = node.widgets[0].value; + + if (widgetValue !== '') { + let tries = 0; + const existingValues = new Set(); + + graph._nodes.forEach(otherNode => { + if (otherNode !== this && otherNode.type === 'SetNode') { + existingValues.add(otherNode.widgets[0].value); + } + }); + + while (existingValues.has(widgetValue)) { + widgetValue = node.widgets[0].value + "_" + tries; + tries++; + } + + node.widgets[0].value = widgetValue; + this.update(); + } + } + + this.clone = function () { + const cloned = SetNode.prototype.clone.apply(this); + cloned.inputs[0].name = '*'; + cloned.inputs[0].type = '*'; + cloned.value = ''; + cloned.properties.previousName = ''; + cloned.size = cloned.computeSize(); + return cloned; + }; + + this.onAdded = function(graph) { + this.validateName(graph); + } + + + this.update = function() { + if (!node.graph) { + return; + } + + const getters = this.findGetters(node.graph); + getters.forEach(getter => { + getter.setType(this.inputs[0].type); + }); + + if (this.widgets[0].value) { + const gettersWithPreviousName = this.findGetters(node.graph, true); + gettersWithPreviousName.forEach(getter => { + getter.setName(this.widgets[0].value); + }); + } + + const allGetters = node.graph._nodes.filter(otherNode => otherNode.type === "GetNode"); + allGetters.forEach(otherNode => { + if (otherNode.setComboValues) { + otherNode.setComboValues(); + } + }); + } + + + this.findGetters = function(graph, checkForPreviousName) { + const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value; + return graph._nodes.filter(otherNode => otherNode.type === 'GetNode' && otherNode.widgets[0].value === name && name !== ''); + } + + + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + + onRemoved() { + const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "GetNode"); + allGetters.forEach((otherNode) => { + if (otherNode.setComboValues) { + otherNode.setComboValues([this]); + } + }) + } + getExtraMenuOptions(_, options) { + this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + options.unshift( + { + content: this.menuEntry, + callback: () => { + this.currentGetters = this.findGetters(this.graph); + if (this.currentGetters.length == 0) return; + let linkType = (this.currentGetters[0].outputs[0].type); + this.slotColor = this.canvas.default_connection_color_byType[linkType] + this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + this.drawConnection = !this.drawConnection; + this.canvas.setDirty(true, true); + + }, + has_submenu: true, + submenu: { + title: "Color", + options: [ + { + content: "Highlight", + callback: () => { + this.slotColor = "orange" + this.canvas.setDirty(true, true); + } + } + ], + }, + }, + { + content: "Hide all connections", + callback: () => { + const allGetters = this.graph._nodes.filter(otherNode => otherNode.type === "GetNode" || otherNode.type === "SetNode"); + allGetters.forEach(otherNode => { + otherNode.drawConnection = false; + console.log(otherNode); + }); + + this.menuEntry = "Show connections"; + this.drawConnection = false + this.canvas.setDirty(true, true); + + }, + + }, + ); + // Dynamically add a submenu for all getters + this.currentGetters = this.findGetters(this.graph); + if (this.currentGetters) { + + let gettersSubmenu = this.currentGetters.map(getter => ({ + + content: `${getter.title} id: ${getter.id}`, + callback: () => { + this.canvas.centerOnNode(getter); + this.canvas.selectNode(getter, false); + this.canvas.setDirty(true, true); + + }, + })); + + options.unshift({ + content: "Getters", + has_submenu: true, + submenu: { + title: "GetNodes", + options: gettersSubmenu, + } + }); + } + } + + + onDrawForeground(ctx, lGraphCanvas) { + if (this.drawConnection) { + this._drawVirtualLinks(lGraphCanvas, ctx); + } + } + // onDrawCollapsed(ctx, lGraphCanvas) { + // if (this.drawConnection) { + // this._drawVirtualLinks(lGraphCanvas, ctx); + // } + // } + _drawVirtualLinks(lGraphCanvas, ctx) { + if (!this.currentGetters?.length) return; + var title = this.getTitle ? this.getTitle() : this.title; + var title_width = ctx.measureText(title).width; + if (!this.flags.collapsed) { + var start_node_slotpos = [ + this.size[0], + LiteGraph.NODE_TITLE_HEIGHT * 0.5, + ]; + } + else { + + var start_node_slotpos = [ + title_width + 55, + -15, + + ]; + } + // Provide a default link object with necessary properties, to avoid errors as link can't be null anymore + const defaultLink = { type: 'default', color: this.slotColor }; + + for (const getter of this.currentGetters) { + if (!this.flags.collapsed) { + var end_node_slotpos = this.getConnectionPos(false, 0); + end_node_slotpos = [ + getter.pos[0] - end_node_slotpos[0] + this.size[0], + getter.pos[1] - end_node_slotpos[1] + ]; + } + else { + var end_node_slotpos = this.getConnectionPos(false, 0); + end_node_slotpos = [ + getter.pos[0] - end_node_slotpos[0] + title_width + 50, + getter.pos[1] - end_node_slotpos[1] - 30 + ]; + } + lGraphCanvas.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + defaultLink, + false, + null, + this.slotColor, + LiteGraph.RIGHT, + LiteGraph.LEFT + ); + } + } + } + + LiteGraph.registerNodeType( + "SetNode", + Object.assign(SetNode, { + title: "Set", + }) + ); + + SetNode.category = "KJNodes"; + }, +}); + +app.registerExtension({ + name: "GetNode", + registerCustomNodes() { + class GetNode extends LGraphNode { + + defaultVisibility = true; + serialize_widgets = true; + drawConnection = false; + slotColor = "#FFF"; + currentSetter = null; + canvas = app.canvas; + + constructor(title) { + super(title) + if (!this.properties) { + this.properties = {}; + } + this.properties.showOutputText = GetNode.defaultVisibility; + const node = this; + this.addWidget( + "combo", + "Constant", + "", + (e) => { + this.onRename(); + }, + { + values: () => { + const setterNodes = node.graph._nodes.filter((otherNode) => otherNode.type == 'SetNode'); + return setterNodes.map((otherNode) => otherNode.widgets[0].value).sort(); + } + } + ) + + this.addOutput("*", '*'); + this.onConnectionsChange = function( + slotType, //0 = output, 1 = input + slot, //self-explanatory + isChangeConnect, + link_info, + output + ) { + this.validateLinks(); + } + + this.setName = function(name) { + node.widgets[0].value = name; + node.onRename(); + node.serialize(); + } + + this.onRename = function() { + const setter = this.findSetter(node.graph); + if (setter) { + let linkType = (setter.inputs[0].type); + + this.setType(linkType); + this.title = (!disablePrefix ? "Get_" : "") + setter.widgets[0].value; + + if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){ + setColorAndBgColor.call(this, linkType); + } + + } else { + this.setType('*'); + } + } + + this.clone = function () { + const cloned = GetNode.prototype.clone.apply(this); + cloned.size = cloned.computeSize(); + return cloned; + }; + + this.validateLinks = function() { + if (this.outputs[0].type !== '*' && this.outputs[0].links) { + this.outputs[0].links.filter(linkId => { + const link = node.graph.links[linkId]; + return link && (!link.type.split(",").includes(this.outputs[0].type) && link.type !== '*'); + }).forEach(linkId => { + node.graph.removeLink(linkId); + }); + } + }; + + this.setType = function(type) { + this.outputs[0].name = type; + this.outputs[0].type = type; + this.validateLinks(); + } + + this.findSetter = function(graph) { + const name = this.widgets[0].value; + const foundNode = graph._nodes.find(otherNode => otherNode.type === 'SetNode' && otherNode.widgets[0].value === name && name !== ''); + return foundNode; + }; + + this.goToSetter = function() { + this.canvas.centerOnNode(this.currentSetter); + this.canvas.selectNode(this.currentSetter, false); + }; + + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + getInputLink(slot) { + const setter = this.findSetter(this.graph); + + if (setter) { + const slotInfo = setter.inputs[slot]; + const link = this.graph.links[slotInfo.link]; + return link; + } else { + const errorMessage = "No SetNode found for " + this.widgets[0].value + "(" + this.type + ")"; + showAlert(errorMessage); + //throw new Error(errorMessage); + } + } + onAdded(graph) { + } + getExtraMenuOptions(_, options) { + let menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + this.currentSetter = this.findSetter(this.graph) + if (!this.currentSetter) return + options.unshift( + { + content: "Go to setter", + callback: () => { + this.goToSetter(); + }, + }, + { + content: menuEntry, + callback: () => { + let linkType = (this.currentSetter.inputs[0].type); + this.drawConnection = !this.drawConnection; + this.slotColor = this.canvas.default_connection_color_byType[linkType] + this.canvas.setDirty(true, true); + }, + }, + ); + } + + onDrawForeground(ctx, lGraphCanvas) { + if (this.drawConnection) { + this._drawVirtualLink(lGraphCanvas, ctx); + } + } + // onDrawCollapsed(ctx, lGraphCanvas) { + // if (this.drawConnection) { + // this._drawVirtualLink(lGraphCanvas, ctx); + // } + // } + _drawVirtualLink(lGraphCanvas, ctx) { + if (!this.currentSetter) return; + + // Provide a default link object with necessary properties, to avoid errors as link can't be null anymore + const defaultLink = { type: 'default', color: this.slotColor }; + + let start_node_slotpos = this.currentSetter.getConnectionPos(false, 0); + start_node_slotpos = [ + start_node_slotpos[0] - this.pos[0], + start_node_slotpos[1] - this.pos[1], + ]; + let end_node_slotpos = [0, -LiteGraph.NODE_TITLE_HEIGHT * 0.5]; + lGraphCanvas.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + defaultLink, + false, + null, + this.slotColor + ); + } + } + + LiteGraph.registerNodeType( + "GetNode", + Object.assign(GetNode, { + title: "Get", + }) + ); + + GetNode.category = "KJNodes"; + }, +}); diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/spline_editor.js b/custom_nodes/ComfyUI-KJNodes/web/js/spline_editor.js new file mode 100644 index 00000000..4acf6362 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/spline_editor.js @@ -0,0 +1,1386 @@ +const { app } = window.comfyAPI.app; +import { getLocalMouse } from './protovisUtil.js'; + +//from melmass +export function makeUUID() { + let dt = new Date().getTime() + const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { + const r = ((dt + Math.random() * 16) % 16) | 0 + dt = Math.floor(dt / 16) + return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) + }) + return uuid +} + +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', + ) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) + } + const create_documentation_stylesheet = () => { + const tag = 'kj-splineditor-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .spline-editor { + + position: absolute; + + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + z-index: 0; + overflow: hidden; + } + ` + document.head.appendChild(styleTag) + } + } + +loadScript('kjweb_async/svg-path-properties.min.js').catch((e) => { + console.log(e) +}) +loadScript('kjweb_async/protovis.min.js').catch((e) => { + console.log(e) +}) +create_documentation_stylesheet() + +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} +app.registerExtension({ + name: 'KJNodes.SplineEditor', + + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === 'SplineEditor') { + chainCallback(nodeType.prototype, "onNodeCreated", function () { + + this.widgets.find(w => w.name === "coordinates").hidden = true + + var element = document.createElement("div"); + this.uuid = makeUUID() + element.id = `spline-editor-${this.uuid}` + + this.previewMediaType = 'image' + + this.splineEditor = this.addDOMWidget(nodeData.name, "SplineEditorWidget", element, { + serialize: false, + hideOnZoom: false, + }); + + // context menu + this.contextMenu = document.createElement("div"); + this.contextMenu.className = 'spline-editor-context-menu'; + this.contextMenu.id = "context-menu"; + this.contextMenu.style.display = "none"; + this.contextMenu.style.position = "absolute"; + this.contextMenu.style.backgroundColor = "#202020"; + this.contextMenu.style.minWidth = "100px"; + this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)"; + this.contextMenu.style.zIndex = "100"; + this.contextMenu.style.padding = "5px"; + + function styleMenuItem(menuItem) { + menuItem.style.display = "block"; + menuItem.style.padding = "5px"; + menuItem.style.color = "#FFF"; + menuItem.style.fontFamily = "Arial, sans-serif"; + menuItem.style.fontSize = "16px"; + menuItem.style.textDecoration = "none"; + menuItem.style.marginBottom = "5px"; + } + function createMenuItem(id, textContent) { + let menuItem = document.createElement("a"); + menuItem.href = "#"; + menuItem.id = `menu-item-${id}`; + menuItem.textContent = textContent; + styleMenuItem(menuItem); + return menuItem; + } + + // Create an array of menu items using the createMenuItem function + this.menuItems = [ + createMenuItem(0, "Toggle handles"), + createMenuItem(1, "Display sample points"), + createMenuItem(2, "Switch point shape"), + createMenuItem(3, "Background image"), + createMenuItem(4, "Invert point order"), + createMenuItem(5, "Clear Image"), + createMenuItem(6, "Add new spline"), + createMenuItem(7, "Add new single point"), + createMenuItem(8, "Delete current spline"), + createMenuItem(9, "Next spline"), + ]; + + // Add mouseover and mouseout event listeners to each menu item for styling + this.menuItems.forEach(menuItem => { + menuItem.addEventListener('mouseover', function() { + this.style.backgroundColor = "gray"; + }); + + menuItem.addEventListener('mouseout', function() { + this.style.backgroundColor = "#202020"; + }); + }); + + // Append each menu item to the context menu + this.menuItems.forEach(menuItem => { + this.contextMenu.appendChild(menuItem); + }); + + document.body.appendChild(this.contextMenu); + + this.addWidget("button", "New canvas", null, () => { + if (!this.properties || !("points" in this.properties)) { + this.editor = new SplineEditor(this); + this.addProperty("points", this.constructor.type, "string"); + } + else { + this.editor = new SplineEditor(this, true); + } + }); + + this.setSize([550, 1000]); + this.resizable = false; + this.splineEditor.parentEl = document.createElement("div"); + this.splineEditor.parentEl.className = "spline-editor"; + this.splineEditor.parentEl.id = `spline-editor-${this.uuid}` + element.appendChild(this.splineEditor.parentEl); + + chainCallback(this, "onConfigure", function () { + try { + this.editor = new SplineEditor(this); + } catch (error) { + console.error("An error occurred while configuring the editor:", error); + } + }); + chainCallback(this, "onExecuted", function (message) { + let bg_image = message["bg_image"]; + this.properties.imgData = { + name: "bg_image", + base64: bg_image + }; + this.editor.refreshBackgroundImage(this); + }); + + }); // onAfterGraphConfigured + }//node created + } //before register +})//register + + +class SplineEditor{ + constructor(context, reset = false) { + this.node = context; + this.reset=reset; + const self = this; + console.log("creatingSplineEditor") + + this.node.pasteFile = (file) => { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + return true; + } + return false; + }; + + this.node.onDragOver = function (e) { + if (e.dataTransfer && e.dataTransfer.items) { + return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/")); + } + return false; + }; + + // On drop upload files + this.node.onDragDrop = (e) => { + console.log("onDragDrop called"); + let handled = false; + for (const file of e.dataTransfer.files) { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + handled = true; + } + } + return handled; + }; + + // context menu + this.createContextMenu(); + + + this.dotShape = "circle"; + this.drawSamplePoints = false; + + if (reset && context.splineEditor.element) { + context.splineEditor.element.innerHTML = ''; // Clear the container + } + this.coordWidget = context.widgets.find(w => w.name === "coordinates"); + this.interpolationWidget = context.widgets.find(w => w.name === "interpolation"); + this.pointsWidget = context.widgets.find(w => w.name === "points_to_sample"); + this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store"); + this.tensionWidget = context.widgets.find(w => w.name === "tension"); + this.minValueWidget = context.widgets.find(w => w.name === "min_value"); + this.maxValueWidget = context.widgets.find(w => w.name === "max_value"); + this.samplingMethodWidget = context.widgets.find(w => w.name === "sampling_method"); + this.widthWidget = context.widgets.find(w => w.name === "mask_width"); + this.heightWidget = context.widgets.find(w => w.name === "mask_height"); + + this.interpolation = this.interpolationWidget.value + this.tension = this.tensionWidget.value + this.points_to_sample = this.pointsWidget.value + this.rangeMin = this.minValueWidget.value + this.rangeMax = this.maxValueWidget.value + this.pointsLayer = null; + this.samplingMethod = this.samplingMethodWidget.value + + if (this.samplingMethod == "path"||this.samplingMethod == "speed") { + this.dotShape = "triangle" + } + + + this.interpolationWidget.callback = () => { + this.interpolation = this.interpolationWidget.value + this.updatePath(); + } + this.samplingMethodWidget.callback = () => { + this.samplingMethod = this.samplingMethodWidget.value + if (this.samplingMethod == "path") { + this.dotShape = "triangle" + } + else if (this.samplingMethod == "controlpoints") { + this.dotShape = "circle" + this.drawSamplePoints = true; + } + this.updatePath(); + } + this.tensionWidget.callback = () => { + this.tension = this.tensionWidget.value + this.updatePath(); + } + this.pointsWidget.callback = () => { + this.points_to_sample = this.pointsWidget.value + this.updatePath(); + } + this.minValueWidget.callback = () => { + this.rangeMin = this.minValueWidget.value + this.updatePath(); + } + this.maxValueWidget.callback = () => { + this.rangeMax = this.maxValueWidget.value + this.updatePath(); + } + this.widthWidget.callback = () => { + this.width = this.widthWidget.value; + if (this.width > 256) { + context.setSize([this.width + 45, context.size[1]]); + } + this.vis.width(this.width); + this.updatePath(); +} +this.heightWidget.callback = () => { + this.height = this.heightWidget.value + this.vis.height(this.height) + context.setSize([context.size[0], this.height + 450]); + this.updatePath(); + } + this.pointsStoreWidget.callback = () => { + points = JSON.parse(this.pointsStoreWidget.value); + this.updatePath(); + } + + // Initialize or reset points array + this.drawHandles = false; + this.drawRuler = true; + var hoverIndex = -1; + var isDragging = false; + this.width = this.widthWidget.value; + this.height = this.heightWidget.value; + var i = 3; + this.splines = []; + this.activeSplineIndex = 0; // Track which spline is being edited + // init mouse position +this.lastMousePosition = { x: this.width/2, y: this.height/2 }; + + if (!reset && this.pointsStoreWidget.value != "") { + try { + const parsedData = JSON.parse(this.pointsStoreWidget.value); + // Check if it's already in the new format (array of splines) + if (Array.isArray(parsedData) && parsedData.length > 0 && parsedData[0].hasOwnProperty('points')) { + this.splines = parsedData; + } else { + // Convert old format (single array of points) to new format + this.splines = [{ + points: parsedData, + color: "#1f77b4", + name: "Spline 1" + }]; + } + } catch (e) { + console.error("Error parsing spline data:", e); + this.initializeDefaultSplines(); + } +} else { + this.initializeDefaultSplines(); + this.pointsStoreWidget.value = JSON.stringify(this.splines); +} + + this.vis = new pv.Panel() + .width(this.width) + .height(this.height) + .fillStyle("#222") + .strokeStyle("gray") + .lineWidth(2) + .antialias(false) + .margin(10) + .event("mousedown", function () { + let mouse = getLocalMouse(this); + if (pv.event.shiftKey) { // Use pv.event to access the event object + let scaledMouse = { + x: mouse.x / app.canvas.ds.scale, + y: mouse.y / app.canvas.ds.scale + }; + i = self.splines[self.activeSplineIndex].points.push(scaledMouse) - 1; + self.updatePath(); + return this; + } + else if (pv.event.ctrlKey) { + // Capture the clicked location + let clickedPoint = { + x: mouse.x / app.canvas.ds.scale, + y: mouse.y / app.canvas.ds.scale + }; + + // Find the two closest points to the clicked location + const activePoints = self.splines[self.activeSplineIndex].points; + let { point1Index, point2Index } = self.findClosestPoints(self.splines[self.activeSplineIndex].points, clickedPoint); + + // Calculate the midpoint between the two closest points + let midpoint = { + x: (activePoints[point1Index].x + activePoints[point2Index].x) / 2, + y: (activePoints[point1Index].y + activePoints[point2Index].y) / 2 + }; + + // Insert the midpoint into the array + activePoints.splice(point2Index, 0, midpoint); + i = point2Index; + self.updatePath(); + } + else if (pv.event.button === 2) { + // Store the current mouse position adjusted for scale + self.lastMousePosition = { + x: mouse.x / app.canvas.ds.scale, + y: mouse.y / app.canvas.ds.scale + }; + + self.node.contextMenu.style.display = 'block'; + self.node.contextMenu.style.left = `${pv.event.clientX}px`; + self.node.contextMenu.style.top = `${pv.event.clientY}px`; + } + }) + this.backgroundImage = this.vis.add(pv.Image).visible(false) + + this.vis.add(pv.Rule) + .data(pv.range(0, this.height, 64)) + .bottom(d => d) + .strokeStyle("gray") + .lineWidth(3) + .visible(() => self.drawRuler) + + this.hoverSplineIndex = -1; + + this.splines.forEach((spline, splineIndex) => { + const strokeObj = this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle("black") // Stroke color + .lineWidth(() => { + // Make stroke slightly wider than the main line + if (splineIndex === this.activeSplineIndex) return 5; + if (splineIndex === this.hoverSplineIndex) return 4; + return 3.5; + }); + + this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle(spline.color) + .lineWidth(() => { + // Change line width based on active or hover state + if (splineIndex === this.activeSplineIndex) return 3; + if (splineIndex === this.hoverSplineIndex) return 2; + return 1.5; + }) + .event("mouseover", () => { + this.hoverSplineIndex = splineIndex; + this.vis.render(); + }) + .event("mouseout", () => { + this.hoverSplineIndex = -1; + this.vis.render(); + }) + .event("mousedown", () => { + if (this.activeSplineIndex !== splineIndex) { + this.activeSplineIndex = splineIndex; + this.refreshSplineElements(); + } + }); + }); + + this.vis.add(pv.Dot) + .data(() => { + const activeSpline = this.splines[this.activeSplineIndex]; + // If this is a single point, don't show it in the main visualization + if (activeSpline.isSinglePoint || (activeSpline.points && activeSpline.points.length === 1)) { + return []; // Return empty array to hide in main visualization + } + return activeSpline.points; + }) + .left(d => d.x) + .top(d => d.y) + .radius(12) + .shape(function() { + return self.dotShape; + }) + .angle(function() { + const index = this.index; + let angle = 0; + + if (self.dotShape === "triangle") { + const activePoints = self.splines[self.activeSplineIndex].points; + let dxNext = 0, dyNext = 0; + if (index < activePoints.length - 1) { + dxNext = activePoints[index + 1].x - activePoints[index].x; + dyNext = activePoints[index + 1].y - activePoints[index].y; + } + + let dxPrev = 0, dyPrev = 0; + if (index > 0) { + dxPrev = activePoints[index].x - activePoints[index - 1].x; + dyPrev = activePoints[index].y - activePoints[index - 1].y; + } + + const dx = (dxNext + dxPrev) / 2; + const dy = (dyNext + dyPrev) / 2; + + angle = Math.atan2(dy, dx); + angle -= Math.PI / 2; + angle = (angle + 2 * Math.PI) % (2 * Math.PI); + } + + return angle; + }) + .cursor("move") + .strokeStyle(function () { return i == this.index ? "#ff7f0e" : "#1f77b4"; }) + .fillStyle(function () { return "rgba(100, 100, 100, 0.3)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function () { + i = this.index; + hoverIndex = this.index; + isDragging = true; + const activePoints = self.splines[self.activeSplineIndex].points; + if (pv.event.button === 2 && i !== 0 && i !== activePoints.length - 1) { + activePoints.splice(i--, 1); + self.vis.render(); + } + return this; + }) + .event("dragend", function() { + if (this.pathElements !== null) { + self.updatePath(); + } + isDragging = false; + }) + .event("drag", function () { + let mouse = getLocalMouse(this); + let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor + let adjustedY = mouse.y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + self.splines[self.activeSplineIndex].points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position + self.vis.render(); // Re-render the visualization to reflect the new position + }) + .event("mouseover", function() { + hoverIndex = this.index; // Set the hover index to the index of the hovered dot + self.vis.render(); // Re-render the visualization + }) + .event("mouseout", function() { + !isDragging && (hoverIndex = -1); // Reset the hover index when the mouse leaves the dot + self.vis.render(); // Re-render the visualization + }) + .anchor("center") + .add(pv.Label) + .visible(function() { + return hoverIndex === this.index; // Only show the label for the hovered dot + }) + .left(d => d.x < this.width / 2 ? d.x + 80 : d.x - 70) // Shift label to right if on left half, otherwise shift to left + .top(d => d.y < this.height / 2 ? d.y + 20 : d.y - 20) // Shift label down if on top half, otherwise shift up + .font(12 + "px sans-serif") + .text(d => { + if (this.samplingMethod == "path") { + return `X: ${Math.round(d.x)}, Y: ${Math.round(d.y)}`; + } else { + let frame = Math.round((d.x / self.width) * self.points_to_sample); + let normalizedY = (1.0 - (d.y / self.height) - 0.0) * (self.rangeMax - self.rangeMin) + self.rangeMin; + let normalizedX = (d.x / self.width); + return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`; + } + }) + .textStyle("orange") + + // single points + this.vis.add(pv.Dot) + .data(() => { + // Collect all single points from all splines + const singlePoints = []; + this.splines.forEach((spline, splineIndex) => { + if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) { + singlePoints.push({ + x: spline.points[0].x, + y: spline.points[0].y, + splineIndex: splineIndex, + color: spline.color + }); + } + }); + return singlePoints; + }) + .left(d => d.x) + .top(d => d.y) + .radius(6) + .shape("square") + .strokeStyle(d => d.splineIndex === this.activeSplineIndex ? "#ff7f0e" : d.color) + .fillStyle(d => "rgba(100, 100, 100, 0.9)") + .lineWidth(d => d.splineIndex === this.activeSplineIndex ? 3 : 1.5) + .cursor("move") + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function(d) { + self.activeSplineIndex = d.splineIndex; + self.refreshSplineElements(); + return this; + }) + .event("drag", function(d) { + let mouse = getLocalMouse(this); + let adjustedX = mouse.x / app.canvas.ds.scale; + let adjustedY = mouse.y / app.canvas.ds.scale; + + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + + // Update the point position + const spline = self.splines[d.splineIndex]; + spline.points[0] = { x: adjustedX, y: adjustedY }; + + // For single points, we need to refresh the entire spline element + // to prevent the line-drawing effect + + }) + .event("dragend", function(d) { + self.refreshSplineElements(); + self.updatePath(); + }) + .visible(d => true); // Make always visible + + if (this.splines.length != 0) { + this.vis.render(); + } + var svgElement = this.vis.canvas(); + svgElement.style['zIndex'] = "2" + svgElement.style['position'] = "relative" + this.node.splineEditor.element.appendChild(svgElement); + this.pathElements = svgElement.getElementsByTagName('path'); // Get all path elements + + if (this.width > 256) { + this.node.setSize([this.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], this.height + 450]); + this.updatePath(); + this.refreshBackgroundImage(); + } + + updatePath = () => { + if (!this.splines || this.splines.length === 0) { + console.log("no splines"); + return; + } + // Get active spline points + console.log("this.activeSplineIndex", this.activeSplineIndex); + const activeSpline = this.splines[this.activeSplineIndex]; + const activePoints = activeSpline.points; + + if (!activePoints || activePoints.length === 0) { + console.log("no points in active spline"); + return; + } + + + let coords; + if (this.samplingMethod != "controlpoints") { + coords = this.samplePoints(this.pathElements[this.activeSplineIndex], this.points_to_sample, this.samplingMethod, this.width, this.activeSplineIndex); + } else { + coords = activePoints; + } + + let allSplineCoords = []; + for (let i = 0; i < this.splines.length; i++) { + // Use the same sampling method for all splines + let splineCoords; + const pathElement = this.pathElements[i]; + + if (this.samplingMethod != "controlpoints" && pathElement) { + splineCoords = this.samplePoints(pathElement, this.points_to_sample, this.samplingMethod, this.width, i); + } else { + // Fall back to control points if no path element or sampling method is "controlpoints" + splineCoords = this.splines[i].points; + } + + allSplineCoords.push(splineCoords); + } + + if (this.drawSamplePoints) { + if (this.pointsLayer) { + // Update the data of the existing points layer + this.pointsLayer.data(coords); + } else { + // Create the points layer if it doesn't exist + this.pointsLayer = this.vis.add(pv.Dot) + .data(coords) + .left(function(d) { return d.x; }) + .top(function(d) { return d.y; }) + .radius(5) // Adjust the radius as needed + .fillStyle("red") // Change the color as needed + .strokeStyle("black") // Change the stroke color as needed + .lineWidth(1); // Adjust the line width as needed + } + } else { + if (this.pointsLayer) { + // Remove the points layer + this.pointsLayer.data([]); + this.vis.render(); + } + } + this.pointsStoreWidget.value = JSON.stringify(this.splines); + if (this.coordWidget) { + this.coordWidget.value = JSON.stringify(allSplineCoords); + } + this.vis.render(); + }; + + handleImageLoad = (img, file, base64String) => { + //console.log(img.width, img.height); // Access width and height here + this.widthWidget.value = img.width; + this.heightWidget.value = img.height; + this.drawRuler = false; + + if (img.width != this.vis.width() || img.height != this.vis.height()) { + if (img.width > 256) { + this.node.setSize([img.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], img.height + 520]); + this.vis.width(img.width); + this.vis.height(img.height); + this.height = img.height; + this.width = img.width; + + this.updatePath(); + } + this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render(); + }; + + processImage = (img, file) => { + const canvas = document.createElement('canvas'); + const ctx = canvas.getContext('2d'); + + const maxWidth = 800; // maximum width + const maxHeight = 600; // maximum height + let width = img.width; + let height = img.height; + + // Calculate the new dimensions while preserving the aspect ratio + if (width > height) { + if (width > maxWidth) { + height *= maxWidth / width; + width = maxWidth; + } + } else { + if (height > maxHeight) { + width *= maxHeight / height; + height = maxHeight; + } + } + + canvas.width = width; + canvas.height = height; + ctx.drawImage(img, 0, 0, width, height); + + // Get the compressed image data as a Base64 string + const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1 + + this.node.properties.imgData = { + name: file.name, + lastModified: file.lastModified, + size: file.size, + type: file.type, + base64: base64String + }; + handleImageLoad(img, file, base64String); + }; + + handleImageFile = (file) => { + const reader = new FileReader(); + reader.onloadend = () => { + const img = new Image(); + img.src = reader.result; + img.onload = () => processImage(img, file); + }; + reader.readAsDataURL(file); + + const imageUrl = URL.createObjectURL(file); + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, file, null); + }; + + refreshBackgroundImage = () => { + if (this.node.properties.imgData && this.node.properties.imgData.base64) { + const base64String = this.node.properties.imgData.base64; + const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`; + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, null, base64String); + } + }; + + refreshSplineElements = () => { + // Clear existing line elements and recreate them + const svgElement = this.vis.canvas(); + + // Remove all existing line elements + const oldLines = svgElement.querySelectorAll('path'); + oldLines.forEach(line => line.remove()); + + this.pathElements = []; + this.lineObjects = []; + + const originalChildren = [...this.vis.children]; + + // Find line objects to remove (those that represent splines) + const linesToRemove = originalChildren.filter(child => + child instanceof pv.Line + ); + linesToRemove.forEach(line => line.visible(false)); + + // Re-add all spline lines and store references to them + this.splines.forEach((spline, splineIndex) => { + // For single points, we need a special handling + if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) { + const point = spline.points[0]; + // For single points, create a tiny line at the same point + // This ensures we have a path element for the point + const lineObj = this.vis.add(pv.Line) + .data([point, {x: point.x + 0.001, y: point.y + 0.001}]) + .left(d => d.x) + .top(d => d.y) + .strokeStyle(spline.color) + .lineWidth(() => { + if (splineIndex === this.activeSplineIndex) return 3; + if (splineIndex === this.hoverSplineIndex) return 2; + return 1.5; + }) + .event("mouseover", () => { + this.hoverSplineIndex = splineIndex; + this.vis.render(); + }) + .event("mouseout", () => { + this.hoverSplineIndex = -1; + this.vis.render(); + }) + .event("mousedown", () => { + if (this.activeSplineIndex !== splineIndex) { + this.activeSplineIndex = splineIndex; + this.refreshSplineElements(); + } + }); + this.lineObjects.push(lineObj); + } else { + // For normal multi-point splines + const strokeObj = this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle("black") // Stroke color + .lineWidth(() => { + // Make stroke slightly wider than the main line + if (splineIndex === this.activeSplineIndex) return 5; + if (splineIndex === this.hoverSplineIndex) return 4; + return 3.5; + }); + const lineObj = this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle(spline.color) + .lineWidth(() => { + if (splineIndex === this.activeSplineIndex) return 3; + if (splineIndex === this.hoverSplineIndex) return 2; + return 1.5; + }) + .event("mouseover", () => { + this.hoverSplineIndex = splineIndex; + this.vis.render(); + }) + .event("mouseout", () => { + this.hoverSplineIndex = -1; + this.vis.render(); + }) + .event("mousedown", () => { + if (this.activeSplineIndex !== splineIndex) { + this.activeSplineIndex = splineIndex; + this.refreshSplineElements(); + } + }); + + // // Add invisible wider hit area for easier selection + // this.vis.add(pv.Line) + // .data(() => spline.points) + // .left(d => d.x) + // .top(d => d.y) + // .interpolate(() => this.interpolation) + // .tension(() => this.tension) + // .segmented(() => false) + // .strokeStyle("rgba(0,0,0,0.01)") // Nearly invisible + // .lineWidth(15) // Much wider hit area + // .event("mouseover", () => { + // this.hoverSplineIndex = splineIndex; + // this.vis.render(); + // }) + // .event("mouseout", () => { + // this.hoverSplineIndex = -1; + // this.vis.render(); + // }) + // .event("mousedown", () => { + // if (pv.event.shiftKey) { + // if (this.activeSplineIndex !== splineIndex) { + // this.activeSplineIndex = splineIndex; + // this.refreshSplineElements(); + // } + // }} + // ); + + this.lineObjects.push(lineObj); + } + }); + + this.vis.render(); + + requestAnimationFrame(() => { + const allPaths = Array.from(svgElement.querySelectorAll('path')); + this.pathElements = []; + + // First try: look at paths with specific childIndex values + this.lineObjects.forEach((lineObj, i) => { + // Find paths that correspond to our line objects + const childIndex = lineObj.childIndex; + const matchingPath = allPaths.find(path => + path.$scene && path.$scene.scenes && + path.$scene.scenes.childIndex === childIndex + ); + + if (matchingPath) { + //console.log("matchingPath:", matchingPath); + this.pathElements[i] = matchingPath; + } + }); + + // Check if we found all paths + if (this.pathElements.filter(p => p).length !== this.splines.length) { + // Fallback to color matching + this.pathElements = []; + for (let i = 0; i < this.splines.length; i++) { + const color = this.splines[i].color; + const matchingPath = allPaths.find(p => + p.getAttribute('style')?.includes(color) && + !this.pathElements.includes(p) + ); + + if (matchingPath) { + this.pathElements[i] = matchingPath; + } + } + } + + // If we still don't have the right number of paths, use the first N paths + if (this.pathElements.filter(p => p).length !== this.splines.length) { + this.pathElements = allPaths.slice(0, this.splines.length); + } + + this.updatePath(); + }); + }; + + + initializeDefaultSplines() { + this.splines = [{ + points: pv.range(1, 4).map((i, index) => { + if (index === 0) { + return { x: 0, y: this.height }; + } else if (index === 2) { + return { x: this.width, y: 0 }; + } else { + return { + x: i * this.width / 5, + y: 50 + Math.random() * (this.height - 100) + }; + } + }), + color: this.getSplineColor(0), + name: "Spline 1" + }]; + } + + getSplineColor(index) { + const colors = [ + "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", + "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", + "#bcbd22", "#17becf" + ]; + return colors[index % colors.length]; + } + + createContextMenu = () => { + const self = this; + const oldMenu = this.node.contextMenu; + const newMenu = oldMenu.cloneNode(true); + oldMenu.parentNode.replaceChild(newMenu, oldMenu); + this.node.contextMenu = newMenu; + + document.addEventListener('contextmenu', function (e) { + if (e.target.closest(`#points-editor-${self.node.uuid}`) || + e.target.closest('#context-menu')) { + e.preventDefault(); + } + }); + + document.addEventListener('click', function (e) { + document.querySelectorAll('.spline-editor-context-menu').forEach(menu => { + menu.style.display = 'none'; + }); + }); + + this.node.contextMenu.addEventListener('click', function(e) { + e.preventDefault(); + if (e.target.tagName === 'A') { + const id = parseInt(e.target.id.split('-')[2]); + + switch(id) { + case 0: + e.preventDefault(); + if (!self.drawHandles) { + self.drawHandles = true + self.vis.add(pv.Line) + .data(() => self.splines[self.activeSplineIndex].points.map((point, index) => ({ + start: point, + end: [index] + }))) + .left(d => d.start.x) + .top(d => d.start.y) + .interpolate("linear") + .tension(0) // Straight lines + .strokeStyle("#ff7f0e") // Same color as control points + .lineWidth(1) + .visible(() => self.drawHandles); + self.vis.render(); + } else { + self.drawHandles = false + self.vis.render(); + } + self.node.contextMenu.style.display = 'none'; + break; + case 1: + + self.drawSamplePoints = !self.drawSamplePoints; + self.updatePath(); + break; + case 2: + if (self.dotShape == "circle"){ + self.dotShape = "triangle" + } + else { + self.dotShape = "circle" + } + self.updatePath(); + break; + case 3: + // Create file input element + const fileInput = document.createElement('input'); + fileInput.type = 'file'; + fileInput.accept = 'image/*'; // Accept only image files + + // Listen for file selection + fileInput.addEventListener('change', function (event) { + const file = event.target.files[0]; // Get the selected file + + if (file) { + const imageUrl = URL.createObjectURL(file); + let img = new Image(); + img.src = imageUrl; + img.onload = () => self.handleImageLoad(img, file, null); + } + }); + + fileInput.click(); + + self.node.contextMenu.style.display = 'none'; + break; + case 4: + self.splines[self.activeSplineIndex].points.reverse(); + self.updatePath(); + break; + case 5: + self.backgroundImage.visible(false).root.render(); + self.node.properties.imgData = null; + self.node.contextMenu.style.display = 'none'; + break; + case 6: // Add new spline + const newSplineIndex = self.splines.length; + self.splines.push({ + points: [ + // Create default points for the new spline + { x: 0, y: self.height }, + { x: self.width/2, y: self.height/2 }, + { x: self.width, y: 0 } + ], + color: self.getSplineColor(newSplineIndex), + name: `Spline ${newSplineIndex + 1}` + }); + self.activeSplineIndex = newSplineIndex; + self.refreshSplineElements(); + self.node.contextMenu.style.display = 'none'; + break; + case 7: // Add new single point + const newSingleSplineIndex = self.splines.length; + self.splines.push({ + points: [ + { x: self.lastMousePosition.x, y: self.lastMousePosition.y }, + ], + color: self.getSplineColor(newSingleSplineIndex), + name: `Spline ${newSingleSplineIndex + 1}`, + isSinglePoint: true + }); + self.activeSplineIndex = newSingleSplineIndex; + self.refreshSplineElements(); + self.node.contextMenu.style.display = 'none'; + break; + case 8: // Delete current spline + if (self.splines.length > 1) { + self.splines.splice(self.activeSplineIndex, 1); + self.activeSplineIndex = Math.min(self.activeSplineIndex, self.splines.length - 1); + self.refreshSplineElements(); + } + self.node.contextMenu.style.display = 'none'; + break; + case 9: // Next spline + self.activeSplineIndex = (self.activeSplineIndex + 1) % self.splines.length; + self.refreshSplineElements(); + self.node.contextMenu.style.display = 'none'; + break; + } + } + }); + } + + samplePoints(svgPathElement, numSamples, samplingMethod, width, splineIndex) { + const spline = this.splines[splineIndex]; + + // Check if this is a single point spline + if (spline && (spline.isSinglePoint || (spline.points && spline.points.length === 1))) { + // For a single point, return an array with the same coordinates repeated + const point = spline.points[0]; + return Array(numSamples).fill().map(() => ({ x: point.x, y: point.y })); + } + + if (!svgPathElement) { + console.warn(`Path element not found for spline index: ${splineIndex}. Available paths: ${this.pathElements.length}`); + + + const splinePoints = this.splines[splineIndex].points; + + // If we have no points, return an empty array + if (!splinePoints || splinePoints.length === 0) { + return []; + } + + // Create a simple interpolation between control points + const result = []; + for (let i = 0; i < numSamples; i++) { + const t = i / (numSamples - 1); + const idx = Math.min( + Math.floor(t * (splinePoints.length - 1)), + splinePoints.length - 2 + ); + const fraction = (t * (splinePoints.length - 1)) - idx; + + const x = splinePoints[idx].x + fraction * (splinePoints[idx + 1].x - splinePoints[idx].x); + const y = splinePoints[idx].y + fraction * (splinePoints[idx + 1].y - splinePoints[idx].y); + + result.push({ x, y }); + } + return result; + } + + var svgWidth = width; // Fixed width of the SVG element + var pathLength = svgPathElement.getTotalLength(); + var points = []; + + if (samplingMethod === "speed") { + // Calculate control point distances along the path + const controlPoints = this.splines[splineIndex].points; + const pathPositions = []; + + // Find approximate path positions for each control point + for (const cp of controlPoints) { + let bestDist = Infinity; + let bestPos = 0; + + // Sample the path to find closest point to each control point + for (let pos = 0; pos <= pathLength; pos += pathLength / 100) { + const pt = svgPathElement.getPointAtLength(pos); + const dist = Math.sqrt(Math.pow(pt.x - cp.x, 2) + Math.pow(pt.y - cp.y, 2)); + + if (dist < bestDist) { + bestDist = dist; + bestPos = pos; + } + } + pathPositions.push(bestPos); + } + + // Sort positions along path + pathPositions.sort((a, b) => a - b); + + // Create a smooth speed mapping function with synchronization + const createSynchronizedMapping = () => { + // Calculate segment lengths and densities + const segments = []; + let totalLength = pathPositions[pathPositions.length - 1] - pathPositions[0]; + + for (let i = 0; i < pathPositions.length - 1; i++) { + const segLength = pathPositions[i+1] - pathPositions[i]; + // Inverse relationship - shorter segments = higher density = slower speed + const density = 1 / Math.max(segLength, 0.0001); + segments.push({ + position: pathPositions[i], + length: segLength, + density: density + }); + } + + // Create mapping function with forced synchronization at endpoints + return t => { + // Force synchronization at t=0 and t=1 + if (t === 0) return 0; + if (t === 1) return pathLength; + + // For intermediate points, use the speed control + // Scale t to fit between first and last control points + const firstPos = pathPositions[0]; + const lastPos = pathPositions[pathPositions.length - 1]; + + // Create a density-weighted position mapping + let totalWeight = 0; + let weights = []; + + for (let i = 0; i < segments.length; i++) { + totalWeight += segments[i].density; + weights.push(segments[i].density); + } + + // Normalize weights + const normalizedWeights = weights.map(w => w / totalWeight); + + // Calculate cumulative weights + let cumulativeWeight = 0; + const cumulativeWeights = normalizedWeights.map(w => { + cumulativeWeight += w; + return cumulativeWeight; + }); + + // Find the segment for this t value + let segmentIndex = 0; + for (let i = 0; i < cumulativeWeights.length; i++) { + if (t <= cumulativeWeights[i]) { + segmentIndex = i; + break; + } + } + + // Calculate position within segment + const segmentStart = segmentIndex > 0 ? cumulativeWeights[segmentIndex - 1] : 0; + const segmentEnd = cumulativeWeights[segmentIndex]; + const segmentT = (t - segmentStart) / (segmentEnd - segmentStart); + + // Map to path position + const pathStart = pathPositions[segmentIndex]; + const pathEnd = pathPositions[segmentIndex + 1]; + const pos = pathStart + segmentT * (pathEnd - pathStart); + + // Scale to fill entire path + return pos; + }; + }; + + const mapToPath = createSynchronizedMapping(); + + // Sample using the synchronized mapping function + for (let i = 0; i < numSamples; i++) { + const t = i / (numSamples - 1); + const pathPos = mapToPath(t); + const point = svgPathElement.getPointAtLength(pathPos); + points.push({ x: point.x, y: point.y }); + } + + return points; + + } + else{ + for (var i = 0; i < numSamples; i++) { + if (samplingMethod === "time") { + // Calculate the x-coordinate for the current sample based on the SVG's width + var x = (svgWidth / (numSamples - 1)) * i; + // Find the point on the path that intersects the vertical line at the calculated x-coordinate + var point = this.findPointAtX(svgPathElement, x, pathLength); + } + else if (samplingMethod === "path") { + // Calculate the distance along the path for the current sample + var distance = (pathLength / (numSamples - 1)) * i; + // Get the point at the current distance + var point = svgPathElement.getPointAtLength(distance); + } + + // Add the point to the array of points + points.push({ x: point.x, y: point.y }); + } + return points; + } + } + + findClosestPoints(points, clickedPoint) { + // Calculate distances from clickedPoint to each point in the array + let distances = points.map(point => { + let dx = clickedPoint.x - point.x; + let dy = clickedPoint.y - point.y; + return { index: points.indexOf(point), distance: Math.sqrt(dx * dx + dy * dy) }; + }); + // Sort distances and get the indices of the two closest points + let sortedDistances = distances.sort((a, b) => a.distance - b.distance); + let closestPoint1Index = sortedDistances[0].index; + let closestPoint2Index = sortedDistances[1].index; + // Ensure point1Index is always the smaller index + if (closestPoint1Index > closestPoint2Index) { + [closestPoint1Index, closestPoint2Index] = [closestPoint2Index, closestPoint1Index]; + } + return { point1Index: closestPoint1Index, point2Index: closestPoint2Index }; + } + + findPointAtX(svgPathElement, targetX, pathLength) { + let low = 0; + let high = pathLength; + let bestPoint = svgPathElement.getPointAtLength(0); + + while (low <= high) { + let mid = low + (high - low) / 2; + let point = svgPathElement.getPointAtLength(mid); + + if (Math.abs(point.x - targetX) < 1) { + return point; // The point is close enough to the target + } + + if (point.x < targetX) { + low = mid + 1; + } else { + high = mid - 1; + } + + // Keep track of the closest point found so far + if (Math.abs(point.x - targetX) < Math.abs(bestPoint.x - targetX)) { + bestPoint = point; + } + } + + // Return the closest point found + return bestPoint; + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/red.png b/custom_nodes/ComfyUI-KJNodes/web/red.png new file mode 100644 index 00000000..4352c118 Binary files /dev/null and b/custom_nodes/ComfyUI-KJNodes/web/red.png differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/LICENSE b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/LICENSE new file mode 100644 index 00000000..29f81d81 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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When using the train_weight method, the prior image will act as the main id image, which will lead the other id images to sum up to an optimized id embedding. + +This prior was randomly choosen previously, now we can assign it. + +Leaving the prior image input empty is OK just as previous. + +Please choose the best id image in your mind as the prior, or just experiment around and see what happens. +![oct28](https://github.com/user-attachments/assets/6a481cd9-2836-4f6f-9ad5-7458356c332a) + +## new features +### common fusion methods for multi-image input +mean(official), concat, max...etc + +### some further experimental fusion methods. +using the norm of the conditions to weight them + +using the max norm token among images + +a novel very fast embeddings self-training methods(explained here: https://github.com/balazik/ComfyUI-PuLID-Flux/issues/28) + +### switch between using gray image (official) and rgb. +in some cases, using gray image will bring detail loss + +![2024-10-12_204047](https://github.com/user-attachments/assets/0ae96170-2eff-44e9-a53a-6a7447dbc0f1) + +## tricks make your generation better +### fusion method leverages many id images to enhance fidelity +1. Besides mean fusion, you can try max or max_token, which can boost some major feature of a face (like large eyes, special nose or sth). it can go distortion beyond fidelity though. +2. With train_weight method, you can train with less than 2000 steps to make a deeper fusion than the non-training methods. Be aware too many training steps will make the training crash to the prior image. + +### additional notes +1. Flux is a high capacity base model, it even can cognize the input image in some super human way. +for example, you can resize your high quality input image with lanczos method rather than nearest area or billinear. you get finer texture. Keep in mind that taking care of your input image is the thing when the base model is strong. +2. The best pulid weight is around 0.8-0.95 for flux pulid 0.9.0. 1.0 is not good. For 0.9.1, it's higher towards around 0.9-1.0. Nonetheless the 0.9.1 is not always better than 0.9.0. +3. The base model is flux-dev or its finetuning, and the precision does mean the thing. fp16 should always be sound. fp8 is OK. I won't recommend gguf or nf4 things. +4. Some of the finetuned flux dev model may have strong bias. for example, it may sway the faces to a certain human race. +5. Euler simple is always working. Euler beta give you higher quality especially if your input image is somewhat low quality. +6. If you wanna use 3rd party flux-d weight, better to use a merged one or with a lora weight, rather than a finetuned one. Full finetuning can hurt the connection between pulid and original flux-d base model. You can test by yourself though. + +## basic notes for common users +This is an experimental node. It can give enhanced result but I'm not promising basic instructions for users who barely know about python developing or AI developing. + +Please follow the comfyui instructions or https://github.com/balazik/ComfyUI-PuLID-Flux to enable usage. + +If you are just using SDXL pulid, you can use https://github.com/cubiq/PuLID_ComfyUI. Some of the installation instructions there may also help. diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/__init__.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/__init__.py new file mode 100644 index 00000000..c452677d --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/__init__.py @@ -0,0 +1,3 @@ +from .pulidflux import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/encoders_flux.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/encoders_flux.py new file mode 100644 index 00000000..cbe5fb21 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/encoders_flux.py @@ -0,0 +1,207 @@ +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttentionCA(nn.Module): + def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + k, v = self.to_kv(x).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class IDFormer(nn.Module): + """ + - perceiver resampler like arch (compared with previous MLP-like arch) + - we concat id embedding (generated by arcface) and query tokens as latents + - latents will attend each other and interact with vit features through cross-attention + - vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two + IDFormer layers + """ + def __init__( + self, + dim=1024, + depth=10, + dim_head=64, + heads=16, + num_id_token=5, + num_queries=32, + output_dim=2048, + ff_mult=4, + ): + super().__init__() + + self.num_id_token = num_id_token + self.dim = dim + self.num_queries = num_queries + assert depth % 5 == 0 + self.depth = depth // 5 + scale = dim ** -0.5 + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale) + self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim)) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + for i in range(5): + setattr( + self, + f'mapping_{i}', + nn.Sequential( + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim), + ), + ) + + self.id_embedding_mapping = nn.Sequential( + nn.Linear(1280, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim * num_id_token), + ) + + def forward(self, x, y): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.id_embedding_mapping(x) + x = x.reshape(-1, self.num_id_token, self.dim) + + latents = torch.cat((latents, x), dim=1) + + for i in range(5): + vit_feature = getattr(self, f'mapping_{i}')(y[i]) + ctx_feature = torch.cat((x, vit_feature), dim=1) + for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]: + latents = attn(ctx_feature, latents) + latents + latents = ff(latents) + latents + + latents = latents[:, :self.num_queries] + latents = latents @ self.proj_out + return latents diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/__init__.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/__init__.py new file mode 100644 index 00000000..7551b459 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/__init__.py @@ -0,0 +1,11 @@ +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_transforms +from .factory import list_models, add_model_config, get_model_config, load_checkpoint +from .loss import ClipLoss +from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg,\ + convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\ + get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +from .tokenizer import SimpleTokenizer, tokenize +from .transform import image_transform \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/bpe_simple_vocab_16e6.txt.gz b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 00000000..7b5088a5 Binary files /dev/null and b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/bpe_simple_vocab_16e6.txt.gz differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/constants.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/constants.py new file mode 100644 index 00000000..a7f12d55 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/constants.py @@ -0,0 +1,2 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/eva_vit_model.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/eva_vit_model.py new file mode 100644 index 00000000..f6cbd62b --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/eva_vit_model.py @@ -0,0 +1,548 @@ +# -------------------------------------------------------- +# Adapted from https://github.com/microsoft/unilm/tree/master/beit +# -------------------------------------------------------- +import math +import os +from functools import partial +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +except: + from timm.layers import drop_path, to_2tuple, trunc_normal_ + +from .transformer import PatchDropout +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers + import xformers.ops as xops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + drop=0., + subln=False, + + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.ffn_ln(x) + + x = self.fc2(x) + x = self.drop(x) + return x + +class SwiGLU(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., + norm_layer=nn.LayerNorm, subln=False): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.subln = subln + if self.subln: + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + else: + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() + # self.proj = nn.Linear(all_head_dim, all_head_dim) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + self.rope = rope + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + B, N, C = x.shape + if self.subln: + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + else: + + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C + q, k, v = qkv[0], qkv[1], qkv[2] + + if self.rope: + # slightly fast impl + q_t = q[:, :, 1:, :] + ro_q_t = self.rope(q_t) + q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) + + k_t = k[:, :, 1:, :] + ro_k_t = self.rope(k_t) + k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) + + if self.xattn: + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale, + ) + x = x.reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias.type_as(attn) + + if attn_mask is not None: + attn_mask = attn_mask.bool() + attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf")) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False, + subln=False, naiveswiglu=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, + xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + if naiveswiglu: + self.mlp = SwiGLU( + in_features=dim, + hidden_features=mlp_hidden_dim, + subln=subln, + norm_layer=norm_layer, + ) + else: + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + subln=subln, + drop=drop + ) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + self.postnorm = postnorm + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + if self.gamma_1 is None: + if self.postnorm: + x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + if self.postnorm: + x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class EVAVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0., + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False, + use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False, + pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False): + super().__init__() + + if not XFORMERS_IS_AVAILBLE: + xattn = False + + self.image_size = img_size + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + + if rope: + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + self.rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + # patch_dropout=patch_dropout + ) + else: + self.rope = None + + self.naiveswiglu = naiveswiglu + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, + xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu) + for i in range(depth)]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) + + self.apply(self._init_weights) + self.fix_init_weight() + + if isinstance(self.head, nn.Linear): + trunc_normal_(self.head.weight, std=.02) + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.grad_checkpointing = grad_checkpointing + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + if self.naiveswiglu: + rescale(layer.mlp.w3.weight.data, layer_id + 1) + else: + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def get_cast_dtype(self) -> torch.dtype: + return self.blocks[0].mlp.fc2.weight.dtype + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, return_all_features=False, return_hidden=False, shuffle=False): + + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + if shuffle: + idx = torch.randperm(x.shape[1]) + 1 + zero = torch.LongTensor([0, ]) + idx = torch.cat([zero, idx]) + pos_embed = self.pos_embed[:, idx] + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if shuffle: + x = x + pos_embed + elif self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + if os.getenv('RoPE') == '1': + if self.training and not isinstance(self.patch_dropout, nn.Identity): + x, patch_indices_keep = self.patch_dropout(x) + self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep) + else: + self.rope.forward = partial(self.rope.forward, patch_indices_keep=None) + x = self.patch_dropout(x) + else: + x = self.patch_dropout(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + hidden_states = [] + for idx, blk in enumerate(self.blocks): + if (0 < idx <= 20) and (idx % 4 == 0) and return_hidden: + hidden_states.append(x) + if self.grad_checkpointing: + x = checkpoint(blk, x, (rel_pos_bias,)) + else: + x = blk(x, rel_pos_bias=rel_pos_bias) + + if not return_all_features: + x = self.norm(x) + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)), hidden_states + else: + return x[:, 0], hidden_states + return x + + def forward(self, x, return_all_features=False, return_hidden=False, shuffle=False): + if return_all_features: + return self.forward_features(x, return_all_features, return_hidden, shuffle) + x, hidden_states = self.forward_features(x, return_all_features, return_hidden, shuffle) + x = self.head(x) + if return_hidden: + return x, hidden_states + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/factory.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/factory.py new file mode 100644 index 00000000..5c25632b --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/factory.py @@ -0,0 +1,517 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +from typing import Optional, Tuple, Union, Dict, Any +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + get_cast_dtype +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model +from .transform import image_transform +from .tokenizer import HFTokenizer, tokenize +from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed + + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, "r", encoding="utf8") as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))) + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name): + config = get_model_config(model_name) + tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize + return tokenizer + + +# loading openai CLIP weights when is_openai=True for training +def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]): + if is_openai: + model = torch.jit.load(checkpoint_path, map_location="cpu").eval() + state_dict = model.state_dict() + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + else: + checkpoint = torch.load(checkpoint_path, map_location=map_location) + for mk in model_key.split('|'): + if isinstance(checkpoint, dict) and mk in checkpoint: + state_dict = checkpoint[mk] + break + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + for k in skip_list: + if k in list(state_dict.keys()): + logging.info(f"Removing key {k} from pretrained checkpoint") + del state_dict[k] + + if os.getenv('RoPE') == '1': + for k in list(state_dict.keys()): + if 'freqs_cos' in k or 'freqs_sin' in k: + del state_dict[k] + return state_dict + + + +def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True): + state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'): + state_dict['logit_scale'] = state_dict['text.logit_scale'] + del state_dict['text.logit_scale'] + + # resize_clip_pos_embed for CLIP and open CLIP + if 'visual.positional_embedding' in state_dict: + resize_clip_pos_embed(state_dict, model) + # specified to eva_vit_model + elif 'visual.pos_embed' in state_dict: + resize_evaclip_pos_embed(state_dict, model) + + # resize_clip_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}") + return incompatible_keys + +def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if not k.startswith('visual.'): + del state_dict[k] + for k in list(state_dict.keys()): + if k.startswith('visual.'): + new_k = k[7:] + state_dict[new_k] = state_dict[k] + del state_dict[k] + return state_dict + +def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if k.startswith('visual.'): + del state_dict[k] + return state_dict + +def get_pretrained_tag(pretrained_model): + pretrained_model = pretrained_model.lower() + if "laion" in pretrained_model or "open_clip" in pretrained_model: + return "open_clip" + elif "openai" in pretrained_model: + return "clip" + elif "eva" in pretrained_model and "clip" in pretrained_model: + return "eva_clip" + else: + return "other" + +def load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=True, + visual_model=None, + text_model=None, + model_key="model|module|state_dict", + skip_list=[]): + visual_tag = get_pretrained_tag(visual_model) + text_tag = get_pretrained_tag(text_model) + + logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}") + visual_incompatible_keys, text_incompatible_keys = None, None + if visual_checkpoint_path: + if visual_tag == "eva_clip" or visual_tag == "open_clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list) + elif visual_tag == "clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + # resize_clip_pos_embed for CLIP and open CLIP + if 'positional_embedding' in visual_state_dict: + resize_visual_pos_embed(visual_state_dict, model) + # specified to EVA model + elif 'pos_embed' in visual_state_dict: + resize_eva_pos_embed(visual_state_dict, model) + + visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict) + logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}") + logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}") + + if text_checkpoint_path: + if text_tag == "eva_clip" or text_tag == "open_clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list) + elif text_tag == "clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict) + + logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}") + logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}") + + return visual_incompatible_keys, text_incompatible_keys + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + else: + model_cfg = get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if 'rope' in model_cfg.get('vision_cfg', {}): + if model_cfg['vision_cfg']['rope']: + os.environ['RoPE'] = "1" + else: + os.environ['RoPE'] = "0" + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout + + cast_dtype = get_cast_dtype(precision) + custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg']) + + + if custom_clip: + if 'hf_model_name' in model_cfg.get('text_cfg', {}): + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_cfg = {} + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, + checkpoint_path, + model_key="model|module|state_dict", + strict=False + ) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + else: + visual_checkpoint_path = '' + text_checkpoint_path = '' + + if pretrained_image: + pretrained_visual_model = pretrained_visual_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image) + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + elif pretrained_image_cfg: + visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_image): + visual_checkpoint_path = pretrained_image + else: + logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + + if pretrained_text: + pretrained_text_model = pretrained_text_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text) + if pretrained_image_cfg: + text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_text): + text_checkpoint_path = pretrained_text + else: + logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + + if visual_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).') + if text_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).') + + if visual_checkpoint_path or text_checkpoint_path: + load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=False, + visual_model=pretrained_visual_model, + text_model=pretrained_text_model, + model_key="model|module|state_dict", + skip_list=skip_list + ) + + if "fp16" in precision or "bf16" in precision: + logging.info(f'convert precision to {precision}') + model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16) + + model.to(device=device) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + if jit: + model = torch.jit.script(model) + + return model + + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + + +def create_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + del model + + return preprocess_train, preprocess_val + +def create_model_from_pretrained( + model_name: str, + pretrained: str, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + is_frozen: bool = False, +): + if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained): + raise RuntimeError( + f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.' + f' Use open_clip.list_pretrained() to find one.') + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + cache_dir=cache_dir, + ) + + if is_frozen: + for param in model.parameters(): + param.requires_grad = False + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/hf_configs.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/hf_configs.py new file mode 100644 index 00000000..dad38698 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/hf_configs.py @@ -0,0 +1,57 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, + "bert": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + } +} diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/hf_model.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/hf_model.py new file mode 100644 index 00000000..1384109d --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/hf_model.py @@ -0,0 +1,248 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch import TensorType +try: + import transformers + from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + +# utils +def _camel2snake(s): + return re.sub(r'(? TensorType: + # image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(x.device) + # attn_mask = (x != self.config.pad_token_id).long() + # out = self.transformer( + # input_ids=x, + # attention_mask=attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # ) + # pooled_out = self.pooler(out, attn_mask) + + # return self.itm_proj(pooled_out) + + def mask(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None): + if masked_indices is None: + masked_indices = torch.bernoulli(probability_matrix).bool() + + masked_indices[input_ids == self.tokenizer.pad_token_id] = False + masked_indices[input_ids == self.tokenizer.cls_token_id] = False + + if targets is not None: + targets[~masked_indices] = -100 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(input_ids.shape, 0.8)).bool() & masked_indices + input_ids[indices_replaced] = self.tokenizer.mask_token_id + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(input_ids.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(vocab_size, input_ids.shape, dtype=torch.long).to(device) + input_ids[indices_random] = random_words[indices_random] + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + + if targets is not None: + return input_ids, targets + else: + return input_ids + + def forward_mlm(self, input_ids, image_embeds, mlm_probability=0.25): + labels = input_ids.clone() + attn_mask = (input_ids != self.config.pad_token_id).long() + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(input_ids.device) + vocab_size = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["vocab_size"]) + probability_matrix = torch.full(labels.shape, mlm_probability) + input_ids, labels = self.mask(input_ids, vocab_size, input_ids.device, targets=labels, + probability_matrix = probability_matrix) + mlm_output = self.transformer(input_ids, + attention_mask = attn_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + labels = labels, + ) + return mlm_output.loss + # mlm_output = self.transformer(input_ids, + # attention_mask = attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # return_dict = True, + # ).last_hidden_state + # logits = self.mlm_proj(mlm_output) + + # # logits = logits[:, :-1, :].contiguous().view(-1, vocab_size) + # logits = logits[:, 1:, :].contiguous().view(-1, vocab_size) + # labels = labels[:, 1:].contiguous().view(-1) + + # mlm_loss = F.cross_entropy( + # logits, + # labels, + # # label_smoothing=0.1, + # ) + # return mlm_loss + + + def forward(self, x:TensorType) -> TensorType: + attn_mask = (x != self.config.pad_token_id).long() + out = self.transformer(input_ids=x, attention_mask=attn_mask) + pooled_out = self.pooler(out, attn_mask) + + return self.proj(pooled_out) + + def lock(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + if not unlocked_layers: # full freezing + for n, p in self.transformer.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + return + + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model") + embeddings = getattr( + self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"]) + modules = [embeddings, *layer_list][:-unlocked_layers] + # freeze layers + for module in modules: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.gradient_checkpointing_enable() + + def get_num_layers(self): + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + return len(layer_list) + + def init_parameters(self): + pass diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/loss.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/loss.py new file mode 100644 index 00000000..c8f7b8db --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/loss.py @@ -0,0 +1,138 @@ +import math +import torch +import torch.nn as nn +from torch.nn import functional as F + +try: + import torch.distributed.nn + from torch import distributed as dist + has_distributed = True +except ImportError: + has_distributed = False + +try: + import horovod.torch as hvd +except ImportError: + hvd = None + +from timm.loss import LabelSmoothingCrossEntropy + + +def gather_features( + image_features, + text_features, + local_loss=False, + gather_with_grad=False, + rank=0, + world_size=1, + use_horovod=False +): + assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' + if use_horovod: + assert hvd is not None, 'Please install horovod' + if gather_with_grad: + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + else: + with torch.no_grad(): + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) + gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + else: + # We gather tensors from all gpus + if gather_with_grad: + all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0) + # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0) + else: + gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] + dist.all_gather(gathered_image_features, image_features) + dist.all_gather(gathered_text_features, text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + + return all_image_features, all_text_features + + +class ClipLoss(nn.Module): + + def __init__( + self, + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + smoothing=0., + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + self.use_horovod = use_horovod + self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None + + # cache state + self.prev_num_logits = 0 + self.labels = {} + + def forward(self, image_features, text_features, logit_scale=1.): + device = image_features.device + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + # calculated ground-truth and cache if enabled + num_logits = logits_per_image.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + if self.label_smoothing_cross_entropy: + total_loss = ( + self.label_smoothing_cross_entropy(logits_per_image, labels) + + self.label_smoothing_cross_entropy(logits_per_text, labels) + ) / 2 + else: + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + acc = None + i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image) + t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text) + acc = {"i2t": i2t_acc, "t2i": t2i_acc} + return total_loss, acc \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model.py new file mode 100644 index 00000000..438559d5 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model.py @@ -0,0 +1,439 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union +from functools import partial + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +try: + from .hf_model import HFTextEncoder +except: + HFTextEncoder = None +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .eva_vit_model import EVAVisionTransformer +from .transformer import LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + print("Nvidia APEX normalization not installed, using PyTorch LayerNorm") + +try: + import xformers.ops as xops +except ImportError: + xops = None + #print("Please 'pip install xformers'") + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + drop_path_rate: Optional[float] = None # drop path rate + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size + qkv_bias: bool = True + fusedLN: bool = False + xattn: bool = False + postnorm: bool = False + rope: bool = False + pt_hw_seq_len: int = 16 # 224/14 + intp_freq: bool = False + naiveswiglu: bool = False + subln: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + masked_language_modeling: bool = False + fusedLN: bool = False + xattn: bool = False + attn_mask: bool = True + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.eva_model_name: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNorm + + visual = EVAVisionTransformer( + img_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + num_classes=embed_dim, + use_mean_pooling=vision_cfg.global_average_pool, #False + init_values=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + embed_dim=vision_cfg.width, + depth=vision_cfg.layers, + num_heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + qkv_bias=vision_cfg.qkv_bias, + drop_path_rate=vision_cfg.drop_path_rate, + norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6), + xattn=vision_cfg.xattn, + rope=vision_cfg.rope, + postnorm=vision_cfg.postnorm, + pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14 + intp_freq= vision_cfg.intp_freq, + naiveswiglu= vision_cfg.naiveswiglu, + subln= vision_cfg.subln + ) + elif vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + embed_dim=embed_dim, + image_size=vision_cfg.image_size + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + global_average_pool=vision_cfg.global_average_pool, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + tokenizer_name=text_cfg.hf_tokenizer_name, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + masked_language_modeling=text_cfg.masked_language_modeling + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer= FusedLayerNorm if text_cfg.fusedLN else norm_layer, + xattn=text_cfg.xattn, + attn_mask=text_cfg.attn_mask, + ) + return text + +class CLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +class CustomCLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + itm_task: bool = False, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr, None) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + if isinstance(l, nn.Parameter): + l.data = l.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name) and isinstance(l, nn.Parameter): + attr = getattr(l, name, None) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + 'logit_scale' + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-B-16.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-B-16.json new file mode 100644 index 00000000..cedbc312 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-B-16.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16, + "eva_model_name": "eva-clip-b-16", + "ls_init_value": 0.1, + "drop_path_rate": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json new file mode 100644 index 00000000..624aad63 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-g-14.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-g-14.json new file mode 100644 index 00000000..c4698467 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA01-CLIP-g-14.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0.4, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-B-16.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-B-16.json new file mode 100644 index 00000000..3f0b684d --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-B-16.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "head_width": 64, + "patch_size": 16, + "mlp_ratio": 2.6667, + "eva_model_name": "eva-clip-b-16-X", + "drop_path_rate": 0.0, + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "xattn": true, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-L-14-336.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-L-14-336.json new file mode 100644 index 00000000..08bba368 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-L-14-336.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14-336", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-L-14.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-L-14.json new file mode 100644 index 00000000..8079344b --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-L-14.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json new file mode 100644 index 00000000..0d5a867d --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32, + "xattn": false, + "fusedLN": true + } +} diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-bigE-14.json b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-bigE-14.json new file mode 100644 index 00000000..fe9fd138 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/model_configs/EVA02-CLIP-bigE-14.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/modified_resnet.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/modified_resnet.py new file mode 100644 index 00000000..96aad296 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/openai.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/openai.py new file mode 100644 index 00000000..feebd6c0 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/pretrained.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/pretrained.py new file mode 100644 index 00000000..f22d34c9 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/pretrained.py @@ -0,0 +1,332 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +try: + from huggingface_hub import hf_hub_download + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_EVAB16 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_EVAL14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_EVAL14_336 = dict( + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), + eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_EVAg14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), +) + +_EVAg14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_EVAbigE14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), +) + +_EVAbigE14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), +) + + +_PRETRAINED = { + # "ViT-B-32": _VITB32, + "OpenaiCLIP-B-32": _VITB32, + "OpenCLIP-B-32": _VITB32, + + # "ViT-B-32-quickgelu": _VITB32_quickgelu, + "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, + "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, + + # "ViT-B-16": _VITB16, + "OpenaiCLIP-B-16": _VITB16, + "OpenCLIP-B-16": _VITB16, + + "EVA02-B-16": _EVAB16, + "EVA02-CLIP-B-16": _EVAB16, + + # "ViT-B-16-plus-240": _VITB16_PLUS_240, + "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, + + # "ViT-L-14": _VITL14, + "OpenaiCLIP-L-14": _VITL14, + "OpenCLIP-L-14": _VITL14, + + "EVA02-L-14": _EVAL14, + "EVA02-CLIP-L-14": _EVAL14, + + # "ViT-L-14-336": _VITL14_336, + "OpenaiCLIP-L-14-336": _VITL14_336, + + "EVA02-CLIP-L-14-336": _EVAL14_336, + + # "ViT-H-14": _VITH14, + # "ViT-g-14": _VITg14, + "OpenCLIP-H-14": _VITH14, + "OpenCLIP-g-14": _VITg14, + + "EVA01-CLIP-g-14": _EVAg14, + "EVA01-CLIP-g-14-plus": _EVAg14_PLUS, + + # "ViT-bigG-14": _VITbigG14, + "OpenCLIP-bigG-14": _VITbigG14, + + "EVA02-CLIP-bigE-14": _EVAbigE14, + "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, +): + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + + return target diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/rope.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/rope.py new file mode 100644 index 00000000..3274df1f --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/rope.py @@ -0,0 +1,137 @@ +from math import pi +import torch +from torch import nn +from einops import rearrange, repeat +import logging + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + + return torch.cat((t_left, t, t_right), dim = -1) + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + patch_dropout = 0. + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) + freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) + + self.patch_dropout = patch_dropout + + self.register_buffer("freqs_cos", freqs_cos) + self.register_buffer("freqs_sin", freqs_sin) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, patch_indices_keep=None): + if patch_indices_keep is not None: + batch = t.size()[0] + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + + freqs_cos = freqs_cos[batch_indices, patch_indices_keep] + freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j') + freqs_sin = freqs_sin[batch_indices, patch_indices_keep] + freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j') + + return t * freqs_cos + rotate_half(t) * freqs_sin + + return t * self.freqs_cos + rotate_half(t) * self.freqs_sin \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/timm_model.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/timm_model.py new file mode 100644 index 00000000..214375cb --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/timm_model.py @@ -0,0 +1,122 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + pretrained=False): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + self.trunk = timm.create_model(model_name, pretrained=pretrained) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/tokenizer.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/tokenizer.py new file mode 100644 index 00000000..4f74c9d1 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/tokenizer.py @@ -0,0 +1,201 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("Β‘"), ord("Β¬")+1))+list(range(ord("Β"), ord("ΓΏ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + +_tokenizer = SimpleTokenizer() + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder[""] + eot_token = _tokenizer.encoder[""] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + +class HFTokenizer: + "HuggingFace tokenizer wrapper" + def __init__(self, tokenizer_name:str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids + return input_ids diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/transform.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/transform.py new file mode 100644 index 00000000..86d856cb --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/transform.py @@ -0,0 +1,103 @@ +from typing import Optional, Sequence, Tuple + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F + +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[:2] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +# class CatGen(nn.Module): +# def __init__(self, num=4): +# self.num = num +# def mixgen_batch(image, text): +# batch_size = image.shape[0] +# index = np.random.permutation(batch_size) + +# cat_images = [] +# for i in range(batch_size): +# # image mixup +# image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:] +# # text concat +# text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0] +# text = torch.stack(text) +# return image, text + + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + normalize = Normalize(mean=mean, std=std) + if is_train: + return Compose([ + RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), + _convert_to_rgb, + ToTensor(), + normalize, + ]) + else: + if resize_longest_max: + transforms = [ + ResizeMaxSize(image_size, fill=fill_color) + ] + else: + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend([ + _convert_to_rgb, + ToTensor(), + normalize, + ]) + return Compose(transforms) diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/transformer.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/transformer.py new file mode 100644 index 00000000..51321cee --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/transformer.py @@ -0,0 +1,737 @@ +import os +import logging +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +try: + from timm.models.layers import trunc_normal_ +except: + from timm.layers import trunc_normal_ + +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast +from .utils import to_2tuple + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + import deepspeed + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + print("Please 'pip install deepspeed'") + deepspeed = None + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: torch.Tensor): + output = F.layer_norm( + x.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(x) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}") + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + if self.training and os.getenv('RoPE') == '1': + return x, patch_indices_keep + + return x + + +def _in_projection_packed( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + w: torch.Tensor, + b: Optional[torch.Tensor] = None, + ): + """ + https://github.com/pytorch/pytorch/blob/db2a237763eb8693a20788be94f8c192e762baa8/torch/nn/functional.py#L4726 + """ + E = q.size(-1) + if k is v: + if q is k: + # self-attention + return F.linear(q, w, b).chunk(3, dim=-1) + else: + # encoder-decoder attention + w_q, w_kv = w.split([E, E * 2]) + if b is None: + b_q = b_kv = None + else: + b_q, b_kv = b.split([E, E * 2]) + return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1) + else: + w_q, w_k, w_v = w.chunk(3) + if b is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = b.chunk(3) + return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v) + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False, + rope=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + self.rope = rope + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + if self.xattn: + q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None, + ) + else: + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=True, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias) + N_q, B_q, C_q = q.shape + N_k, B_k, C_k = k.shape + N_v, B_v, C_v = v.shape + if self.xattn: + # B, N, C -> B, N, num_heads, C + q = q.permute(1, 0, 2).reshape(B_q, N_q, self.num_heads, -1) + k = k.permute(1, 0, 2).reshape(B_k, N_k, self.num_heads, -1) + v = v.permute(1, 0, 2).reshape(B_v, N_v, self.num_heads, -1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None + ) + else: + # B*H, L, C + q = q.contiguous().view(N_q, B_q * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(N_k, B_k * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(N_v, B_v * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + # B*H, N_q, N_k + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(B_q, self.num_heads, N_q, N_k) * logit_scale + attn = attn.view(-1, N_q, N_k) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(B_q, self.num_heads, N_q, C_q) * self.head_scale + x = x.view(-1, N_q, C_q) + x = x.transpose(0, 1).reshape(N_q, B_q, C_q) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.ln_1_k = norm_layer(d_model) if cross_attn else self.ln_1 + self.ln_1_v = norm_layer(d_model) if cross_attn else self.ln_1 + self.attn = CustomAttention( + d_model, n_head, + qkv_bias=True, + attn_drop=0., + proj_drop=0., + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + xattn=xattn + ) + + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q = q + self.ls_1(self.ln_attn(self.attn(self.ln_1(q), self.ln_1_k(k), self.ln_1_v(v), attn_mask=attn_mask))) + q = q + self.ls_2(self.mlp(self.ln_2(q))) + return q + +class CustomTransformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = True, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + self.xattn = xattn + + self.resblocks = nn.ModuleList([ + CustomResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + scale_cosine_attn=scale_cosine_attn, + scale_heads=scale_heads, + scale_attn=scale_attn, + scale_fc=scale_fc, + cross_attn=cross_attn, + xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, q: torch.Tensor, k: torch.Tensor = None, v: torch.Tensor = None, attn_mask: Optional[torch.Tensor] = None): + if k is None and v is None: + k = v = q + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + q = checkpoint(r, q, k, v, attn_mask) + else: + q = r(q, k, v, attn_mask=attn_mask) + return q + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + if xattn: + self.attn = Attention(d_model, n_head, xattn=True) + else: + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + self.xattn = xattn + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None + if self.xattn: + return self.attn(x, attn_mask=attn_mask) + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + patch_dropout: float = 0., + global_average_pool: bool = False, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.image_size = to_2tuple(image_size) + self.patch_size = to_2tuple(patch_size) + self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1]) + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + self.ln_pre = norm_layer(width) + + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.global_average_pool = global_average_pool + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def get_num_layers(self): + return self.transformer.layers + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'positional_embedding', 'class_embedding'} + + def forward(self, x: torch.Tensor, return_all_features: bool=False): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if not return_all_features: + if self.global_average_pool: + x = x.mean(dim=1) #x = x[:,1:,:].mean(dim=1) + else: + x = x[:, 0] + + x = self.ln_post(x) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class TextTransformer(nn.Module): + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool= False, + attn_mask: bool = True + ): + super().__init__() + self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.xattn = xattn + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if attn_mask: + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + else: + self.attn_mask = None + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + # return {'positional_embedding', 'token_embedding'} + return {'positional_embedding'} + + def get_num_layers(self): + return self.transformer.layers + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text, return_all_features: bool=False): + cast_dtype = self.transformer.get_cast_dtype() + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + # x = self.transformer(x) # no attention mask is applied + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if not return_all_features: + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/utils.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/utils.py new file mode 100644 index 00000000..c7631d75 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/eva_clip/utils.py @@ -0,0 +1,326 @@ +from itertools import repeat +import collections.abc +import logging +import math +import numpy as np + +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d +import torch.nn.functional as F + +# open CLIP +def resize_clip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed + + +def resize_visual_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['positional_embedding'] = new_pos_embed + +def resize_evaclip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'visual.pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['visual.pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['visual.pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['visual.patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['visual.patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_eva_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + for key in all_keys: + if "relative_position_index" in key: + state_dict.pop(key) + + if "relative_position_bias_table" in key: + rel_pos_bias = state_dict[key] + src_num_pos, num_attn_heads = rel_pos_bias.size() + dst_num_pos, _ = model.visual.state_dict()[key].size() + dst_patch_shape = model.visual.patch_embed.patch_shape + if dst_patch_shape[0] != dst_patch_shape[1]: + raise NotImplementedError() + num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) + src_size = int((src_num_pos - num_extra_tokens) ** 0.5) + dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) + if src_size != dst_size: + print("Position interpolate for %s from %dx%d to %dx%d" % ( + key, src_size, src_size, dst_size, dst_size)) + extra_tokens = rel_pos_bias[-num_extra_tokens:, :] + rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] + + def geometric_progression(a, r, n): + return a * (1.0 - r ** n) / (1.0 - r) + + left, right = 1.01, 1.5 + while right - left > 1e-6: + q = (left + right) / 2.0 + gp = geometric_progression(1, q, src_size // 2) + if gp > dst_size // 2: + right = q + else: + left = q + + # if q > 1.090307: + # q = 1.090307 + + dis = [] + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + + x = r_ids + [0] + dis + y = r_ids + [0] + dis + + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + dy = np.arange(-t, t + 0.1, 1.0) + + print("Original positions = %s" % str(x)) + print("Target positions = %s" % str(dx)) + + all_rel_pos_bias = [] + + for i in range(num_attn_heads): + z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() + f = F.interpolate.interp2d(x, y, z, kind='cubic') + all_rel_pos_bias.append( + torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) + + rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) + + new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) + state_dict[key] = new_rel_pos_bias + + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) + + +def is_logging(args): + def is_global_master(args): + return args.rank == 0 + + def is_local_master(args): + return args.local_rank == 0 + + def is_master(args, local=False): + return is_local_master(args) if local else is_global_master(args) + return is_master + + +class AllGather(torch.autograd.Function): + """An autograd function that performs allgather on a tensor. + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + + @staticmethod + def forward(ctx, tensor, rank, world_size): + tensors_gather = [torch.empty_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(tensors_gather, tensor) + ctx.rank = rank + ctx.batch_size = tensor.shape[0] + return torch.cat(tensors_gather, 0) + + @staticmethod + def backward(ctx, grad_output): + return ( + grad_output[ctx.batch_size * ctx.rank: ctx.batch_size * (ctx.rank + 1)], + None, + None + ) + +allgather = AllGather.apply \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/einstein.jpg b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/einstein.jpg new file mode 100644 index 00000000..e7182c13 Binary files /dev/null and b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/einstein.jpg differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/flux_pulid_multi.json 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newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/pulid_flux_einstein.png b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/pulid_flux_einstein.png new file mode 100644 index 00000000..b3a7cec9 Binary files /dev/null and b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/examples/pulid_flux_einstein.png differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/online_train1.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/online_train1.py new file mode 100644 index 00000000..4f8e6f10 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/online_train1.py @@ -0,0 +1,88 @@ +# supervised by a global average embedding, which is a biased estimation of the true embedding +# use projection to enable a complex decoding +# makes no big difference than mean so far, the decoding may not work πŸ€¦β€ + +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch +from tqdm import tqdm +import random + +class Transform(nn.Module): + def __init__(self, n=2, token_size=32, input_dim=2048): + super().__init__() + + self.n=n + self.dim= input_dim*token_size + self.token_size=token_size + self.input_dim=input_dim + + self.weight = nn.Parameter(torch.ones(self.n,1),requires_grad=True) + + self.projections = nn.ModuleList([nn.Sequential( + nn.Linear(self.dim, 512), + nn.ReLU(), + nn.Linear(512, self.dim) + ) for _ in range(self.n)]) + + def encode(self, x): + x = x.view(-1, self.dim) + x = self.weight*x + return x + + def decode(self, x): + out=[] + for i in range(self.n): + t = self.projections[i](x[i]) + out.append(t) + x = torch.stack(out, dim=0) + x=x.view(self.n,self.token_size,self.input_dim) + x=torch.mean(x,dim=0) + return x + + def forward(self, x): + x = self.encode(x) + x = self.decode(x) + return x + +def online_train(cond, device="cuda:1",step=1000): + old_device=cond.device + dtype=cond.dtype + cond = cond.clone().to(device,torch.float32) + cond.requires_grad=False + torch.set_grad_enabled(True) + + print("online training, initializing model...") + n=cond.shape[0] + model=Transform(n=n) + optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.0001) + criterion = nn.MSELoss() + model.to(device) + model.train() + + y=torch.mean(cond,dim=0) + + random.seed(42) + bar=tqdm(range(step)) + for s in bar: + optimizer.zero_grad() + attack_weight=[random.uniform(0.5,1.5) for _ in range(n)] + attack_weight=torch.tensor(attack_weight)[:,None,None].to(device) + x=attack_weight*cond + output = model(x) + loss = criterion(output, y) + loss.backward() + optimizer.step() + bar.set_postfix(loss=loss.item()) + + weight=model.weight + cond=weight[:,:,None]*cond + print(weight) + + print("online training, ending...") + del model + del optimizer + + cond=torch.mean(cond,dim=0).unsqueeze(0) + return cond.to(old_device,dtype=dtype) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/online_train2.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/online_train2.py new file mode 100644 index 00000000..77277f6e --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/online_train2.py @@ -0,0 +1,71 @@ +# self-supervised learning, one of the embedding acts as the target, the other as the support +# works nicely + +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch +from tqdm import tqdm +import random + +class Transform(nn.Module): + def __init__(self, n=2, token_size=32, input_dim=2048): + super().__init__() + + self.n=n + self.token_size=token_size + + self.weight = nn.Parameter(torch.ones(self.n,self.token_size),requires_grad=True) + + def encode(self, x): + x = torch.einsum('bij,bi->ij', x, self.weight) + return x + + def forward(self, x): + x = self.encode(x) + return x + +def criterion(output, target, token_sample_rate=0.25): + t=target-output + t=torch.norm(t,dim=1) + s=random.sample(range(t.shape[0]),int(token_sample_rate*t.shape[0])) + return torch.mean(t[s]) + +def online_train(cond, device="cuda:1",step=1000): + old_device=cond.device + dtype=cond.dtype + cond = cond.clone().to(device,torch.float32) + # cond.requires_grad=False + # torch.set_grad_enabled(True) + + y=cond[0,:,:] + cond=cond[1:,:,:] + + print("online training, initializing model...") + n=cond.shape[0] + model=Transform(n=n) + optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.0001) + model.to(device) + model.train() + + random.seed(42) + bar=tqdm(range(step)) + for s in bar: + optimizer.zero_grad() + x=cond + output = model(x) + loss = criterion(output, y) + loss.backward() + optimizer.step() + bar.set_postfix(loss=loss.item()) + + weight=model.weight + print(weight) + cond=weight[:,:,None]*cond+y[None,:,:]*(1.0/n) + + print("online training, ending...") + del model + del optimizer + + cond=torch.mean(cond,dim=0).unsqueeze(0) + return cond.to(old_device,dtype=dtype) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/pulidflux.py b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/pulidflux.py new file mode 100644 index 00000000..4946fc31 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/pulidflux.py @@ -0,0 +1,554 @@ + +import torch +from torch import nn, Tensor +from torchvision import transforms +from torchvision.transforms import functional +import os +import logging +import folder_paths +import comfy.utils +from comfy.ldm.flux.layers import timestep_embedding +import comfy.model_management +from insightface.app import FaceAnalysis +from facexlib.parsing import init_parsing_model +from facexlib.utils.face_restoration_helper import FaceRestoreHelper + +import torch.nn.functional as F + +from .eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .encoders_flux import IDFormer, PerceiverAttentionCA + +INSIGHTFACE_DIR = os.path.join(folder_paths.models_dir, "insightface") + +MODELS_DIR = os.path.join(folder_paths.models_dir, "pulid") +if "pulid" not in folder_paths.folder_names_and_paths: + current_paths = [MODELS_DIR] +else: + current_paths, _ = folder_paths.folder_names_and_paths["pulid"] +folder_paths.folder_names_and_paths["pulid"] = (current_paths, folder_paths.supported_pt_extensions) + +from .online_train2 import online_train + +class PulidFluxModel(nn.Module): + def __init__(self): + super().__init__() + + self.double_interval = 2 + self.single_interval = 4 + + # Init encoder + self.pulid_encoder = IDFormer() + + # Init attention + num_ca = 19 // self.double_interval + 38 // self.single_interval + if 19 % self.double_interval != 0: + num_ca += 1 + if 38 % self.single_interval != 0: + num_ca += 1 + self.pulid_ca = nn.ModuleList([ + PerceiverAttentionCA() for _ in range(num_ca) + ]) + + def from_pretrained(self, path: str): + state_dict = comfy.utils.load_torch_file(path, safe_load=True) + state_dict_dict = {} + for k, v in state_dict.items(): + module = k.split('.')[0] + state_dict_dict.setdefault(module, {}) + new_k = k[len(module) + 1:] + state_dict_dict[module][new_k] = v + + for module in state_dict_dict: + getattr(self, module).load_state_dict(state_dict_dict[module], strict=True) + + del state_dict + del state_dict_dict + + def get_embeds(self, face_embed, clip_embeds): + return self.pulid_encoder(face_embed, clip_embeds) + +def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control=None, + transformer_options={}, + attn_mask: Tensor = None, + **kwargs # so it won't break if we add more stuff in the future +) -> Tensor: + device = comfy.model_management.get_torch_device() + patches_replace = transformer_options.get("patches_replace", {}) + + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + ca_idx = 0 + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + # PuLID attention + if self.pulid_data: + if i % self.pulid_double_interval == 0: + # Will calculate influence of all pulid nodes at once + for _, node_data in self.pulid_data.items(): + condition_start = node_data['sigma_start'] >= timesteps + condition_end = timesteps >= node_data['sigma_end'] + condition = torch.logical_and( + condition_start, condition_end).all() + + if condition: + img = img + node_data['weight'] * self.pulid_ca[ca_idx].to(device)(node_data['embedding'], img) + ca_idx += 1 + + img = torch.cat((txt, img), 1) + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + + # PuLID attention + if self.pulid_data: + real_img, txt = img[:, txt.shape[1]:, ...], img[:, :txt.shape[1], ...] + if i % self.pulid_single_interval == 0: + # Will calculate influence of all nodes at once + for _, node_data in self.pulid_data.items(): + condition_start = node_data['sigma_start'] >= timesteps + condition_end = timesteps >= node_data['sigma_end'] + + # Combine conditions and reduce to a single boolean + condition = torch.logical_and(condition_start, condition_end).all() + + if condition: + real_img = real_img + node_data['weight'] * self.pulid_ca[ca_idx].to(device)(node_data['embedding'], real_img) + ca_idx += 1 + img = torch.cat((txt, real_img), 1) + + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img + +def tensor_to_image(tensor): + image = tensor.mul(255).clamp(0, 255).byte().cpu() + image = image[..., [2, 1, 0]].numpy() + return image + +def image_to_tensor(image): + tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1) + tensor = tensor[..., [2, 1, 0]] + return tensor + +def resize_with_pad(img, target_size): # image: 1, h, w, 3 + img = img.permute(0, 3, 1, 2) + H, W = target_size + + h, w = img.shape[2], img.shape[3] + scale_h = H / h + scale_w = W / w + scale = min(scale_h, scale_w) + + new_h = int(min(h * scale,H)) + new_w = int(min(w * scale,W)) + new_size = (new_h, new_w) + + img = F.interpolate(img, size=new_size, mode='bicubic', align_corners=False) + + pad_top = (H - new_h) // 2 + pad_bottom = (H - new_h) - pad_top + pad_left = (W - new_w) // 2 + pad_right = (W - new_w) - pad_left + img = F.pad(img, pad=(pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0) + + return img.permute(0, 2, 3, 1) + +def to_gray(img): + x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3] + x = x.repeat(1, 3, 1, 1) + return x + +""" +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Nodes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +class PulidFluxModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pulid_file": (folder_paths.get_filename_list("pulid"), )}} + + RETURN_TYPES = ("PULIDFLUX",) + FUNCTION = "load_model" + CATEGORY = "pulid" + + def load_model(self, pulid_file): + model_path = folder_paths.get_full_path("pulid", pulid_file) + + # Also initialize the model, takes longer to load but then it doesn't have to be done every time you change parameters in the apply node + model = PulidFluxModel() + + logging.info("Loading PuLID-Flux model.") + model.from_pretrained(path=model_path) + + return (model,) + +class PulidFluxInsightFaceLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "provider": (["CPU", "CUDA", "ROCM"], ), + }, + } + + RETURN_TYPES = ("FACEANALYSIS",) + FUNCTION = "load_insightface" + CATEGORY = "pulid" + + def load_insightface(self, provider): + model = FaceAnalysis(name="antelopev2", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',]) # alternative to buffalo_l + model.prepare(ctx_id=0, det_size=(640, 640)) + + return (model,) + +class PulidFluxEvaClipLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + } + + RETURN_TYPES = ("EVA_CLIP",) + FUNCTION = "load_eva_clip" + CATEGORY = "pulid" + + def load_eva_clip(self): + from .eva_clip.factory import create_model_and_transforms + + model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True) + + model = model.visual + + eva_transform_mean = getattr(model, 'image_mean', OPENAI_DATASET_MEAN) + eva_transform_std = getattr(model, 'image_std', OPENAI_DATASET_STD) + if not isinstance(eva_transform_mean, (list, tuple)): + model["image_mean"] = (eva_transform_mean,) * 3 + if not isinstance(eva_transform_std, (list, tuple)): + model["image_std"] = (eva_transform_std,) * 3 + + return (model,) + +class ApplyPulidFlux: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", ), + "pulid_flux": ("PULIDFLUX", ), + "eva_clip": ("EVA_CLIP", ), + "face_analysis": ("FACEANALYSIS", ), + "image": ("IMAGE", ), + "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05 }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + "fusion": (["mean","concat","max","norm_id","max_token","auto_weight","train_weight"],), + "fusion_weight_max": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 20.0, "step": 0.1 }), + "fusion_weight_min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 20.0, "step": 0.1 }), + "train_step": ("INT", {"default": 1000, "min": 0, "max": 20000, "step": 1 }), + "use_gray": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}), + }, + "optional": { + "attn_mask": ("MASK", ), + "prior_image": ("IMAGE",), # for train weight, as the target + }, + "hidden": { + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply_pulid_flux" + CATEGORY = "pulid" + + def __init__(self): + self.pulid_data_dict = None + + def apply_pulid_flux(self, model, pulid_flux, eva_clip, face_analysis, image, weight, start_at, end_at, prior_image=None,fusion="mean", fusion_weight_max=1.0, fusion_weight_min=0.0, train_step=1000, use_gray=True, attn_mask=None, unique_id=None): + device = comfy.model_management.get_torch_device() + # Why should I care what args say, when the unet model has a different dtype?! + # Am I missing something?! + #dtype = comfy.model_management.unet_dtype() + dtype = model.model.diffusion_model.dtype + # For 8bit use bfloat16 (because ufunc_add_CUDA is not implemented) + if dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: + dtype = torch.bfloat16 + + eva_clip.to(device, dtype=dtype) + pulid_flux.to(device, dtype=dtype) + + # TODO: Add masking support! + if attn_mask is not None: + if attn_mask.dim() > 3: + attn_mask = attn_mask.squeeze(-1) + elif attn_mask.dim() < 3: + attn_mask = attn_mask.unsqueeze(0) + attn_mask = attn_mask.to(device, dtype=dtype) + + if prior_image is not None: + prior_image = resize_with_pad(prior_image.to(image.device, dtype=image.dtype), target_size=(image.shape[1], image.shape[2])) + image=torch.cat((prior_image,image),dim=0) + image = tensor_to_image(image) + + face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + device=device, + ) + + face_helper.face_parse = None + face_helper.face_parse = init_parsing_model(model_name='bisenet', device=device) + + bg_label = [0, 16, 18, 7, 8, 9, 14, 15] + cond = [] + + # Analyse multiple images at multiple sizes and combine largest area embeddings + for i in range(image.shape[0]): + # get insightface embeddings + iface_embeds = None + for size in [(size, size) for size in range(640, 256, -64)]: + face_analysis.det_model.input_size = size + face_info = face_analysis.get(image[i]) + if face_info: + # Only use the maximum face + # Removed the reverse=True from original code because we need the largest area not the smallest one! + # Sorts the list in ascending order (smallest to largest), + # then selects the last element, which is the largest face + face_info = sorted(face_info, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[-1] + iface_embeds = torch.from_numpy(face_info.embedding).unsqueeze(0).to(device, dtype=dtype) + break + else: + # No face detected, skip this image + logging.warning(f'Warning: No face detected in image {str(i)}') + continue + + # get eva_clip embeddings + face_helper.clean_all() + face_helper.read_image(image[i]) + face_helper.get_face_landmarks_5(only_center_face=True) + face_helper.align_warp_face() + + if len(face_helper.cropped_faces) == 0: + # No face detected, skip this image + continue + + # Get aligned face image + align_face = face_helper.cropped_faces[0] + # Convert bgr face image to tensor + align_face = image_to_tensor(align_face).unsqueeze(0).permute(0, 3, 1, 2).to(device) + parsing_out = face_helper.face_parse(functional.normalize(align_face, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] + parsing_out = parsing_out.argmax(dim=1, keepdim=True) + bg = sum(parsing_out == i for i in bg_label).bool() + white_image = torch.ones_like(align_face) + # Only keep the face features + if use_gray: + _align_face = to_gray(align_face) + else: + _align_face = align_face + face_features_image = torch.where(bg, white_image, _align_face) + + # Transform img before sending to eva_clip + # Apparently MPS only supports NEAREST interpolation? + face_features_image = functional.resize(face_features_image, eva_clip.image_size, transforms.InterpolationMode.BICUBIC if 'cuda' in device.type else transforms.InterpolationMode.NEAREST).to(device, dtype=dtype) + face_features_image = functional.normalize(face_features_image, eva_clip.image_mean, eva_clip.image_std) + + # eva_clip + id_cond_vit, id_vit_hidden = eva_clip(face_features_image, return_all_features=False, return_hidden=True, shuffle=False) + id_cond_vit = id_cond_vit.to(device, dtype=dtype) + for idx in range(len(id_vit_hidden)): + id_vit_hidden[idx] = id_vit_hidden[idx].to(device, dtype=dtype) + + id_cond_vit = torch.div(id_cond_vit, torch.norm(id_cond_vit, 2, 1, True)) + + # Combine embeddings + id_cond = torch.cat([iface_embeds, id_cond_vit], dim=-1) + + # Pulid_encoder + cond.append(pulid_flux.get_embeds(id_cond, id_vit_hidden)) + + if not cond: + # No faces detected, return the original model + logging.warning("PuLID warning: No faces detected in any of the given images, returning unmodified model.") + return (model,) + + # fusion embeddings + if fusion == "mean": + cond = torch.cat(cond).to(device, dtype=dtype) # N,32,2048 + if cond.shape[0] > 1: + cond = torch.mean(cond, dim=0, keepdim=True) + elif fusion == "concat": + cond = torch.cat(cond, dim=1).to(device, dtype=dtype) + elif fusion == "max": + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + cond = torch.max(cond, dim=0, keepdim=True)[0] + elif fusion == "norm_id": + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + norm=torch.norm(cond,dim=(1,2)) + norm=norm/torch.sum(norm) + cond=torch.einsum("wij,w->ij",cond,norm).unsqueeze(0) + elif fusion == "max_token": + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + norm=torch.norm(cond,dim=2) + _,idx=torch.max(norm,dim=0) + cond=torch.stack([cond[j,i] for i,j in enumerate(idx)]).unsqueeze(0) + elif fusion == "auto_weight": # πŸ€” + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + norm=torch.norm(cond,dim=2) + order=torch.argsort(norm,descending=False,dim=0) + regular_weight=torch.linspace(fusion_weight_min,fusion_weight_max,norm.shape[0]).to(device, dtype=dtype) + + _cond=[] + for i in range(cond.shape[1]): + o=order[:,i] + _cond.append(torch.einsum('ij,i->j',cond[:,i,:],regular_weight[o])) + cond=torch.stack(_cond,dim=0).unsqueeze(0) + elif fusion == "train_weight": + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + if train_step > 0: + with torch.inference_mode(False): + cond = online_train(cond, device=cond.device, step=train_step) + else: + cond = torch.mean(cond, dim=0, keepdim=True) + + sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at) + sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at) + + # Patch the Flux model (original diffusion_model) + # Nah, I don't care for the official ModelPatcher because it's undocumented! + # I want the end result now, and I don’t mind if I break other custom nodes in the process. πŸ˜„ + flux_model = model.model.diffusion_model + # Let's see if we already patched the underlying flux model, if not apply patch + if not hasattr(flux_model, "pulid_ca"): + # Add perceiver attention, variables and current node data (weight, embedding, sigma_start, sigma_end) + # The pulid_data is stored in Dict by unique node index, + # so we can chain multiple ApplyPulidFlux nodes! + flux_model.pulid_ca = pulid_flux.pulid_ca + flux_model.pulid_double_interval = pulid_flux.double_interval + flux_model.pulid_single_interval = pulid_flux.single_interval + flux_model.pulid_data = {} + # Replace model forward_orig with our own + new_method = forward_orig.__get__(flux_model, flux_model.__class__) + setattr(flux_model, 'forward_orig', new_method) + + # Patch is already in place, add data (weight, embedding, sigma_start, sigma_end) under unique node index + flux_model.pulid_data[unique_id] = { + 'weight': weight, + 'embedding': cond, + 'sigma_start': sigma_start, + 'sigma_end': sigma_end, + } + + # Keep a reference for destructor (if node is deleted the data will be deleted as well) + self.pulid_data_dict = {'data': flux_model.pulid_data, 'unique_id': unique_id} + + return (model,) + + def __del__(self): + # Destroy the data for this node + if self.pulid_data_dict: + del self.pulid_data_dict['data'][self.pulid_data_dict['unique_id']] + del self.pulid_data_dict + + +NODE_CLASS_MAPPINGS = { + "PulidFluxModelLoader": PulidFluxModelLoader, + "PulidFluxInsightFaceLoader": PulidFluxInsightFaceLoader, + "PulidFluxEvaClipLoader": PulidFluxEvaClipLoader, + "ApplyPulidFlux": ApplyPulidFlux, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PulidFluxModelLoader": "Load PuLID Flux Model", + "PulidFluxInsightFaceLoader": "Load InsightFace (PuLID Flux)", + "PulidFluxEvaClipLoader": "Load Eva Clip (PuLID Flux)", + "ApplyPulidFlux": "Apply PuLID Flux", +} diff --git a/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/requirements.txt b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/requirements.txt new file mode 100644 index 00000000..ded0538b --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux-Enhanced/requirements.txt @@ -0,0 +1,6 @@ +facexlib +insightface +onnxruntime +onnxruntime-gpu +ftfy +timm diff --git a/custom_nodes/ComfyUI-PuLID-Flux/.gitignore b/custom_nodes/ComfyUI-PuLID-Flux/.gitignore new file mode 100644 index 00000000..1a823f56 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/.gitignore @@ -0,0 +1,3 @@ +# ignore specific directories +__pycache__/ +\!misc/ diff --git a/custom_nodes/ComfyUI-PuLID-Flux/LICENSE b/custom_nodes/ComfyUI-PuLID-Flux/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/ComfyUI-PuLID-Flux/README.md b/custom_nodes/ComfyUI-PuLID-Flux/README.md new file mode 100644 index 00000000..828e0e7b --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/README.md @@ -0,0 +1,47 @@ +# PuLID-Flux for ComfyUI +[PuLID-Flux](https://github.com/ToTheBeginning/PuLID) ComfyUI implementation (Alpha version) + +![pulid_flux_einstein](examples/pulid_flux_einstein.png) + +### :new: Version Updates +* V0.1.0: Working node with weight, start_at, end_at support (attn_mask not working) + +## Notes +This project was heavily inspired by [cubiq/PuLID_ComfyUI](https://github.com/cubiq/PuLID_ComfyUI). It is just a prototype that uses some convenient model `hacks` for the encoder section. I wanted to test the model’s quality before reimplementing it in a more formal manner. For better results I recommend the `16bit` or `8bit GGUF` model version of Flux1-dev (the 8e5m2 returns blurry backgrounds). +In the `examples` directory you'll find some basic workflows. + +## Supported Flux models: +##### 32bit/16bit (~22GB VRAM): [model](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors) +##### 8bit gguf (~12GB VRAM): [model](https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q8_0.gguf), [encoder](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/blob/main/t5-v1_1-xxl-encoder-Q8_0.gguf) +##### 8 bit FP8 e5m2 (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e5m2.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +##### 8 bit FP8 e4m3fn (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e4m3fn.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +##### Clip and VAE (for all models): [clip](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors), [vae](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors) + +For GGUF models you will need to install [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) + + +## Installation + - Install this repo into `ComfyUI/custom_nodes` +``` +git clone https://github.com/balazik/ComfyUI-PuLID-Flux.git +``` +- Install all the packages listed in the `requirements.txt` file into the Python environment where you run ComfyUI. I prefer not to use automatic installation scripts, as I dislike when scripts install software without my knowledge. :wink: +- You need one of the mentioned `Flux.1-dev` models. Download the model into `ComfyUI/models/unet`, clip and encoder into `ComfyUI/models/clip`, VAE into `ComfyUI/models/vae`. +- [PuLID Flux pre-trained model](https://huggingface.co/guozinan/PuLID/blob/main/pulid_flux_v0.9.0.safetensors?download=true) goes in `ComfyUI/models/pulid/`. +- The EVA CLIP is EVA02-CLIP-L-14-336, should be downloaded automatically (will be located in the huggingface directory). If for some reason the auto-download fails (and you get face_analysis.py, **init assert 'detection' in self.models exception**), download this [EVA-CLIP](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt?download=true) model manually, put the file to your `ComfyUI/models/clip`and restart ComfyUI. + +- `facexlib` dependency needs to be installed, the models are downloaded at first use. +- Finally you need InsightFace with [AntelopeV2](https://huggingface.co/MonsterMMORPG/tools/tree/main), the unzipped models should be placed in `ComfyUI/models/insightface/models/antelopev2`. + +## Known issues +- ApplyPulidFlux doesn't work on HW with CUDA compute < v8.0, (when Flux FP8 it needs bfloat16). +- When the ApplyPulidFlux node is disconnected after first run, the Flux model is still influenced by the node. +- ApplyPulidFlux attn_mask is not working (in progress). + +## Credits + +ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI. + +[PuLID for Flux](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md) - tuning-free ID customization solution for FLUX.1-dev + +cubiq [PuLID_ComfyUI](https://github.com/cubiq/PuLID_ComfyUI) - PuLID ComfyUI native implementation (Thanks for the awesome work what you do Matteo :wink: ). diff --git a/custom_nodes/ComfyUI-PuLID-Flux/__init__.py b/custom_nodes/ComfyUI-PuLID-Flux/__init__.py new file mode 100644 index 00000000..7ca69bca --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/__init__.py @@ -0,0 +1,3 @@ +from .pulidflux import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/ComfyUI-PuLID-Flux/encoders_flux.py b/custom_nodes/ComfyUI-PuLID-Flux/encoders_flux.py new file mode 100644 index 00000000..7891fb31 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/encoders_flux.py @@ -0,0 +1,207 @@ +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttentionCA(nn.Module): + def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + k, v = self.to_kv(x).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class IDFormer(nn.Module): + """ + - perceiver resampler like arch (compared with previous MLP-like arch) + - we concat id embedding (generated by arcface) and query tokens as latents + - latents will attend each other and interact with vit features through cross-attention + - vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two + IDFormer layers + """ + def __init__( + self, + dim=1024, + depth=10, + dim_head=64, + heads=16, + num_id_token=5, + num_queries=32, + output_dim=2048, + ff_mult=4, + ): + super().__init__() + + self.num_id_token = num_id_token + self.dim = dim + self.num_queries = num_queries + assert depth % 5 == 0 + self.depth = depth // 5 + scale = dim ** -0.5 + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale) + self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim)) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + for i in range(5): + setattr( + self, + f'mapping_{i}', + nn.Sequential( + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim), + ), + ) + + self.id_embedding_mapping = nn.Sequential( + nn.Linear(1280, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim * num_id_token), + ) + + def forward(self, x, y): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.id_embedding_mapping(x) + x = x.reshape(-1, self.num_id_token, self.dim) + + latents = torch.cat((latents, x), dim=1) + + for i in range(5): + vit_feature = getattr(self, f'mapping_{i}')(y[i]) + ctx_feature = torch.cat((x, vit_feature), dim=1) + for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]: + latents = attn(ctx_feature, latents) + latents + latents = ff(latents) + latents + + latents = latents[:, :self.num_queries] + latents = latents @ self.proj_out + return latents diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/__init__.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/__init__.py new file mode 100644 index 00000000..fa2d014b --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/__init__.py @@ -0,0 +1,11 @@ +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_transforms +from .factory import list_models, add_model_config, get_model_config, load_checkpoint +from .loss import ClipLoss +from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg,\ + convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\ + get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +from .tokenizer import SimpleTokenizer, tokenize +from .transform import image_transform \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/bpe_simple_vocab_16e6.txt.gz b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 00000000..7b5088a5 Binary files /dev/null and b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/bpe_simple_vocab_16e6.txt.gz differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/constants.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/constants.py new file mode 100644 index 00000000..a670bb3f --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/constants.py @@ -0,0 +1,2 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/eva_vit_model.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/eva_vit_model.py new file mode 100644 index 00000000..51db88cf --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/eva_vit_model.py @@ -0,0 +1,548 @@ +# -------------------------------------------------------- +# Adapted from https://github.com/microsoft/unilm/tree/master/beit +# -------------------------------------------------------- +import math +import os +from functools import partial +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +except: + from timm.layers import drop_path, to_2tuple, trunc_normal_ + +from .transformer import PatchDropout +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers + import xformers.ops as xops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + drop=0., + subln=False, + + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.ffn_ln(x) + + x = self.fc2(x) + x = self.drop(x) + return x + +class SwiGLU(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., + norm_layer=nn.LayerNorm, subln=False): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.subln = subln + if self.subln: + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + else: + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() + # self.proj = nn.Linear(all_head_dim, all_head_dim) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + self.rope = rope + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + B, N, C = x.shape + if self.subln: + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + else: + + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C + q, k, v = qkv[0], qkv[1], qkv[2] + + if self.rope: + # slightly fast impl + q_t = q[:, :, 1:, :] + ro_q_t = self.rope(q_t) + q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) + + k_t = k[:, :, 1:, :] + ro_k_t = self.rope(k_t) + k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) + + if self.xattn: + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale, + ) + x = x.reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias.type_as(attn) + + if attn_mask is not None: + attn_mask = attn_mask.bool() + attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf")) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False, + subln=False, naiveswiglu=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, + xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + if naiveswiglu: + self.mlp = SwiGLU( + in_features=dim, + hidden_features=mlp_hidden_dim, + subln=subln, + norm_layer=norm_layer, + ) + else: + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + subln=subln, + drop=drop + ) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + self.postnorm = postnorm + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + if self.gamma_1 is None: + if self.postnorm: + x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + if self.postnorm: + x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class EVAVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0., + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False, + use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False, + pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False): + super().__init__() + + if not XFORMERS_IS_AVAILBLE: + xattn = False + + self.image_size = img_size + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + + if rope: + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + self.rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + # patch_dropout=patch_dropout + ) + else: + self.rope = None + + self.naiveswiglu = naiveswiglu + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, + xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu) + for i in range(depth)]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) + + self.apply(self._init_weights) + self.fix_init_weight() + + if isinstance(self.head, nn.Linear): + trunc_normal_(self.head.weight, std=.02) + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.grad_checkpointing = grad_checkpointing + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + if self.naiveswiglu: + rescale(layer.mlp.w3.weight.data, layer_id + 1) + else: + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def get_cast_dtype(self) -> torch.dtype: + return self.blocks[0].mlp.fc2.weight.dtype + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, return_all_features=False, return_hidden=False, shuffle=False): + + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + if shuffle: + idx = torch.randperm(x.shape[1]) + 1 + zero = torch.LongTensor([0, ]) + idx = torch.cat([zero, idx]) + pos_embed = self.pos_embed[:, idx] + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if shuffle: + x = x + pos_embed + elif self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + if os.getenv('RoPE') == '1': + if self.training and not isinstance(self.patch_dropout, nn.Identity): + x, patch_indices_keep = self.patch_dropout(x) + self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep) + else: + self.rope.forward = partial(self.rope.forward, patch_indices_keep=None) + x = self.patch_dropout(x) + else: + x = self.patch_dropout(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + hidden_states = [] + for idx, blk in enumerate(self.blocks): + if (0 < idx <= 20) and (idx % 4 == 0) and return_hidden: + hidden_states.append(x) + if self.grad_checkpointing: + x = checkpoint(blk, x, (rel_pos_bias,)) + else: + x = blk(x, rel_pos_bias=rel_pos_bias) + + if not return_all_features: + x = self.norm(x) + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)), hidden_states + else: + return x[:, 0], hidden_states + return x + + def forward(self, x, return_all_features=False, return_hidden=False, shuffle=False): + if return_all_features: + return self.forward_features(x, return_all_features, return_hidden, shuffle) + x, hidden_states = self.forward_features(x, return_all_features, return_hidden, shuffle) + x = self.head(x) + if return_hidden: + return x, hidden_states + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/factory.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/factory.py new file mode 100644 index 00000000..ced89999 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/factory.py @@ -0,0 +1,517 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +from typing import Optional, Tuple, Union, Dict, Any +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + get_cast_dtype +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model +from .transform import image_transform +from .tokenizer import HFTokenizer, tokenize +from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed + + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, "r", encoding="utf8") as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))) + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name): + config = get_model_config(model_name) + tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize + return tokenizer + + +# loading openai CLIP weights when is_openai=True for training +def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]): + if is_openai: + model = torch.jit.load(checkpoint_path, map_location="cpu").eval() + state_dict = model.state_dict() + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + else: + checkpoint = torch.load(checkpoint_path, map_location=map_location) + for mk in model_key.split('|'): + if isinstance(checkpoint, dict) and mk in checkpoint: + state_dict = checkpoint[mk] + break + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + for k in skip_list: + if k in list(state_dict.keys()): + logging.info(f"Removing key {k} from pretrained checkpoint") + del state_dict[k] + + if os.getenv('RoPE') == '1': + for k in list(state_dict.keys()): + if 'freqs_cos' in k or 'freqs_sin' in k: + del state_dict[k] + return state_dict + + + +def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True): + state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'): + state_dict['logit_scale'] = state_dict['text.logit_scale'] + del state_dict['text.logit_scale'] + + # resize_clip_pos_embed for CLIP and open CLIP + if 'visual.positional_embedding' in state_dict: + resize_clip_pos_embed(state_dict, model) + # specified to eva_vit_model + elif 'visual.pos_embed' in state_dict: + resize_evaclip_pos_embed(state_dict, model) + + # resize_clip_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}") + return incompatible_keys + +def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if not k.startswith('visual.'): + del state_dict[k] + for k in list(state_dict.keys()): + if k.startswith('visual.'): + new_k = k[7:] + state_dict[new_k] = state_dict[k] + del state_dict[k] + return state_dict + +def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if k.startswith('visual.'): + del state_dict[k] + return state_dict + +def get_pretrained_tag(pretrained_model): + pretrained_model = pretrained_model.lower() + if "laion" in pretrained_model or "open_clip" in pretrained_model: + return "open_clip" + elif "openai" in pretrained_model: + return "clip" + elif "eva" in pretrained_model and "clip" in pretrained_model: + return "eva_clip" + else: + return "other" + +def load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=True, + visual_model=None, + text_model=None, + model_key="model|module|state_dict", + skip_list=[]): + visual_tag = get_pretrained_tag(visual_model) + text_tag = get_pretrained_tag(text_model) + + logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}") + visual_incompatible_keys, text_incompatible_keys = None, None + if visual_checkpoint_path: + if visual_tag == "eva_clip" or visual_tag == "open_clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list) + elif visual_tag == "clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + # resize_clip_pos_embed for CLIP and open CLIP + if 'positional_embedding' in visual_state_dict: + resize_visual_pos_embed(visual_state_dict, model) + # specified to EVA model + elif 'pos_embed' in visual_state_dict: + resize_eva_pos_embed(visual_state_dict, model) + + visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict) + logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}") + logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}") + + if text_checkpoint_path: + if text_tag == "eva_clip" or text_tag == "open_clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list) + elif text_tag == "clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict) + + logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}") + logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}") + + return visual_incompatible_keys, text_incompatible_keys + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + else: + model_cfg = get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if 'rope' in model_cfg.get('vision_cfg', {}): + if model_cfg['vision_cfg']['rope']: + os.environ['RoPE'] = "1" + else: + os.environ['RoPE'] = "0" + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout + + cast_dtype = get_cast_dtype(precision) + custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg']) + + + if custom_clip: + if 'hf_model_name' in model_cfg.get('text_cfg', {}): + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_cfg = {} + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, + checkpoint_path, + model_key="model|module|state_dict", + strict=False + ) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + else: + visual_checkpoint_path = '' + text_checkpoint_path = '' + + if pretrained_image: + pretrained_visual_model = pretrained_visual_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image) + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + elif pretrained_image_cfg: + visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_image): + visual_checkpoint_path = pretrained_image + else: + logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + + if pretrained_text: + pretrained_text_model = pretrained_text_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text) + if pretrained_image_cfg: + text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_text): + text_checkpoint_path = pretrained_text + else: + logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + + if visual_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).') + if text_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).') + + if visual_checkpoint_path or text_checkpoint_path: + load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=False, + visual_model=pretrained_visual_model, + text_model=pretrained_text_model, + model_key="model|module|state_dict", + skip_list=skip_list + ) + + if "fp16" in precision or "bf16" in precision: + logging.info(f'convert precision to {precision}') + model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16) + + model.to(device=device) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + if jit: + model = torch.jit.script(model) + + return model + + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + + +def create_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + del model + + return preprocess_train, preprocess_val + +def create_model_from_pretrained( + model_name: str, + pretrained: str, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + is_frozen: bool = False, +): + if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained): + raise RuntimeError( + f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.' + f' Use open_clip.list_pretrained() to find one.') + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + cache_dir=cache_dir, + ) + + if is_frozen: + for param in model.parameters(): + param.requires_grad = False + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/hf_configs.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/hf_configs.py new file mode 100644 index 00000000..a8c9b704 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/hf_configs.py @@ -0,0 +1,57 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, + "bert": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + } +} diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/hf_model.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/hf_model.py new file mode 100644 index 00000000..c4b9fd85 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/hf_model.py @@ -0,0 +1,248 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch import TensorType +try: + import transformers + from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + +# utils +def _camel2snake(s): + return re.sub(r'(? TensorType: + # image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(x.device) + # attn_mask = (x != self.config.pad_token_id).long() + # out = self.transformer( + # input_ids=x, + # attention_mask=attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # ) + # pooled_out = self.pooler(out, attn_mask) + + # return self.itm_proj(pooled_out) + + def mask(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None): + if masked_indices is None: + masked_indices = torch.bernoulli(probability_matrix).bool() + + masked_indices[input_ids == self.tokenizer.pad_token_id] = False + masked_indices[input_ids == self.tokenizer.cls_token_id] = False + + if targets is not None: + targets[~masked_indices] = -100 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(input_ids.shape, 0.8)).bool() & masked_indices + input_ids[indices_replaced] = self.tokenizer.mask_token_id + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(input_ids.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(vocab_size, input_ids.shape, dtype=torch.long).to(device) + input_ids[indices_random] = random_words[indices_random] + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + + if targets is not None: + return input_ids, targets + else: + return input_ids + + def forward_mlm(self, input_ids, image_embeds, mlm_probability=0.25): + labels = input_ids.clone() + attn_mask = (input_ids != self.config.pad_token_id).long() + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(input_ids.device) + vocab_size = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["vocab_size"]) + probability_matrix = torch.full(labels.shape, mlm_probability) + input_ids, labels = self.mask(input_ids, vocab_size, input_ids.device, targets=labels, + probability_matrix = probability_matrix) + mlm_output = self.transformer(input_ids, + attention_mask = attn_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + labels = labels, + ) + return mlm_output.loss + # mlm_output = self.transformer(input_ids, + # attention_mask = attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # return_dict = True, + # ).last_hidden_state + # logits = self.mlm_proj(mlm_output) + + # # logits = logits[:, :-1, :].contiguous().view(-1, vocab_size) + # logits = logits[:, 1:, :].contiguous().view(-1, vocab_size) + # labels = labels[:, 1:].contiguous().view(-1) + + # mlm_loss = F.cross_entropy( + # logits, + # labels, + # # label_smoothing=0.1, + # ) + # return mlm_loss + + + def forward(self, x:TensorType) -> TensorType: + attn_mask = (x != self.config.pad_token_id).long() + out = self.transformer(input_ids=x, attention_mask=attn_mask) + pooled_out = self.pooler(out, attn_mask) + + return self.proj(pooled_out) + + def lock(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + if not unlocked_layers: # full freezing + for n, p in self.transformer.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + return + + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model") + embeddings = getattr( + self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"]) + modules = [embeddings, *layer_list][:-unlocked_layers] + # freeze layers + for module in modules: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.gradient_checkpointing_enable() + + def get_num_layers(self): + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + return len(layer_list) + + def init_parameters(self): + pass diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/loss.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/loss.py new file mode 100644 index 00000000..473f60d9 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/loss.py @@ -0,0 +1,138 @@ +import math +import torch +import torch.nn as nn +from torch.nn import functional as F + +try: + import torch.distributed.nn + from torch import distributed as dist + has_distributed = True +except ImportError: + has_distributed = False + +try: + import horovod.torch as hvd +except ImportError: + hvd = None + +from timm.loss import LabelSmoothingCrossEntropy + + +def gather_features( + image_features, + text_features, + local_loss=False, + gather_with_grad=False, + rank=0, + world_size=1, + use_horovod=False +): + assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' + if use_horovod: + assert hvd is not None, 'Please install horovod' + if gather_with_grad: + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + else: + with torch.no_grad(): + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) + gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + else: + # We gather tensors from all gpus + if gather_with_grad: + all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0) + # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0) + else: + gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] + dist.all_gather(gathered_image_features, image_features) + dist.all_gather(gathered_text_features, text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + + return all_image_features, all_text_features + + +class ClipLoss(nn.Module): + + def __init__( + self, + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + smoothing=0., + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + self.use_horovod = use_horovod + self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None + + # cache state + self.prev_num_logits = 0 + self.labels = {} + + def forward(self, image_features, text_features, logit_scale=1.): + device = image_features.device + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + # calculated ground-truth and cache if enabled + num_logits = logits_per_image.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + if self.label_smoothing_cross_entropy: + total_loss = ( + self.label_smoothing_cross_entropy(logits_per_image, labels) + + self.label_smoothing_cross_entropy(logits_per_text, labels) + ) / 2 + else: + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + acc = None + i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image) + t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text) + acc = {"i2t": i2t_acc, "t2i": t2i_acc} + return total_loss, acc \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model.py new file mode 100644 index 00000000..90f34b91 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model.py @@ -0,0 +1,439 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union +from functools import partial + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +try: + from .hf_model import HFTextEncoder +except: + HFTextEncoder = None +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .eva_vit_model import EVAVisionTransformer +from .transformer import LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + print("Nvidia APEX normalization not installed, using PyTorch LayerNorm") + +try: + import xformers.ops as xops +except ImportError: + xops = None + #print("Please 'pip install xformers'") + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + drop_path_rate: Optional[float] = None # drop path rate + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size + qkv_bias: bool = True + fusedLN: bool = False + xattn: bool = False + postnorm: bool = False + rope: bool = False + pt_hw_seq_len: int = 16 # 224/14 + intp_freq: bool = False + naiveswiglu: bool = False + subln: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + masked_language_modeling: bool = False + fusedLN: bool = False + xattn: bool = False + attn_mask: bool = True + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.eva_model_name: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNorm + + visual = EVAVisionTransformer( + img_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + num_classes=embed_dim, + use_mean_pooling=vision_cfg.global_average_pool, #False + init_values=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + embed_dim=vision_cfg.width, + depth=vision_cfg.layers, + num_heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + qkv_bias=vision_cfg.qkv_bias, + drop_path_rate=vision_cfg.drop_path_rate, + norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6), + xattn=vision_cfg.xattn, + rope=vision_cfg.rope, + postnorm=vision_cfg.postnorm, + pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14 + intp_freq= vision_cfg.intp_freq, + naiveswiglu= vision_cfg.naiveswiglu, + subln= vision_cfg.subln + ) + elif vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + embed_dim=embed_dim, + image_size=vision_cfg.image_size + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + global_average_pool=vision_cfg.global_average_pool, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + tokenizer_name=text_cfg.hf_tokenizer_name, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + masked_language_modeling=text_cfg.masked_language_modeling + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer= FusedLayerNorm if text_cfg.fusedLN else norm_layer, + xattn=text_cfg.xattn, + attn_mask=text_cfg.attn_mask, + ) + return text + +class CLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +class CustomCLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + itm_task: bool = False, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr, None) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + if isinstance(l, nn.Parameter): + l.data = l.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name) and isinstance(l, nn.Parameter): + attr = getattr(l, name, None) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + 'logit_scale' + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-B-16.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-B-16.json new file mode 100644 index 00000000..aad20580 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-B-16.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16, + "eva_model_name": "eva-clip-b-16", + "ls_init_value": 0.1, + "drop_path_rate": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json new file mode 100644 index 00000000..10027957 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-g-14.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-g-14.json new file mode 100644 index 00000000..5d338b4e --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA01-CLIP-g-14.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0.4, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-B-16.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-B-16.json new file mode 100644 index 00000000..e4a6e723 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-B-16.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "head_width": 64, + "patch_size": 16, + "mlp_ratio": 2.6667, + "eva_model_name": "eva-clip-b-16-X", + "drop_path_rate": 0.0, + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "xattn": true, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-L-14-336.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-L-14-336.json new file mode 100644 index 00000000..3e1d124e --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-L-14-336.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14-336", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-L-14.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-L-14.json new file mode 100644 index 00000000..03b22ad3 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-L-14.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json new file mode 100644 index 00000000..aa04e254 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32, + "xattn": false, + "fusedLN": true + } +} diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-bigE-14.json b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-bigE-14.json new file mode 100644 index 00000000..747ffccc --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/model_configs/EVA02-CLIP-bigE-14.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/modified_resnet.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/modified_resnet.py new file mode 100644 index 00000000..6a8d3aed --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/openai.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/openai.py new file mode 100644 index 00000000..cc4e13e8 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/pretrained.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/pretrained.py new file mode 100644 index 00000000..a1e55dcf --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/pretrained.py @@ -0,0 +1,332 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +try: + from huggingface_hub import hf_hub_download + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_EVAB16 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_EVAL14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_EVAL14_336 = dict( + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), + eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_EVAg14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), +) + +_EVAg14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_EVAbigE14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), +) + +_EVAbigE14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), +) + + +_PRETRAINED = { + # "ViT-B-32": _VITB32, + "OpenaiCLIP-B-32": _VITB32, + "OpenCLIP-B-32": _VITB32, + + # "ViT-B-32-quickgelu": _VITB32_quickgelu, + "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, + "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, + + # "ViT-B-16": _VITB16, + "OpenaiCLIP-B-16": _VITB16, + "OpenCLIP-B-16": _VITB16, + + "EVA02-B-16": _EVAB16, + "EVA02-CLIP-B-16": _EVAB16, + + # "ViT-B-16-plus-240": _VITB16_PLUS_240, + "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, + + # "ViT-L-14": _VITL14, + "OpenaiCLIP-L-14": _VITL14, + "OpenCLIP-L-14": _VITL14, + + "EVA02-L-14": _EVAL14, + "EVA02-CLIP-L-14": _EVAL14, + + # "ViT-L-14-336": _VITL14_336, + "OpenaiCLIP-L-14-336": _VITL14_336, + + "EVA02-CLIP-L-14-336": _EVAL14_336, + + # "ViT-H-14": _VITH14, + # "ViT-g-14": _VITg14, + "OpenCLIP-H-14": _VITH14, + "OpenCLIP-g-14": _VITg14, + + "EVA01-CLIP-g-14": _EVAg14, + "EVA01-CLIP-g-14-plus": _EVAg14_PLUS, + + # "ViT-bigG-14": _VITbigG14, + "OpenCLIP-bigG-14": _VITbigG14, + + "EVA02-CLIP-bigE-14": _EVAbigE14, + "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, +): + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + + return target diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/rope.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/rope.py new file mode 100644 index 00000000..69030c35 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/rope.py @@ -0,0 +1,137 @@ +from math import pi +import torch +from torch import nn +from einops import rearrange, repeat +import logging + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + + return torch.cat((t_left, t, t_right), dim = -1) + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + patch_dropout = 0. + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) + freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) + + self.patch_dropout = patch_dropout + + self.register_buffer("freqs_cos", freqs_cos) + self.register_buffer("freqs_sin", freqs_sin) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, patch_indices_keep=None): + if patch_indices_keep is not None: + batch = t.size()[0] + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + + freqs_cos = freqs_cos[batch_indices, patch_indices_keep] + freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j') + freqs_sin = freqs_sin[batch_indices, patch_indices_keep] + freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j') + + return t * freqs_cos + rotate_half(t) * freqs_sin + + return t * self.freqs_cos + rotate_half(t) * self.freqs_sin \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/timm_model.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/timm_model.py new file mode 100644 index 00000000..b58122c0 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/timm_model.py @@ -0,0 +1,122 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + pretrained=False): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + self.trunk = timm.create_model(model_name, pretrained=pretrained) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/tokenizer.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/tokenizer.py new file mode 100644 index 00000000..41482f82 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/tokenizer.py @@ -0,0 +1,201 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("Β‘"), ord("Β¬")+1))+list(range(ord("Β"), ord("ΓΏ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + +_tokenizer = SimpleTokenizer() + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder[""] + eot_token = _tokenizer.encoder[""] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + +class HFTokenizer: + "HuggingFace tokenizer wrapper" + def __init__(self, tokenizer_name:str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids + return input_ids diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/transform.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/transform.py new file mode 100644 index 00000000..39f3e4cf --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/transform.py @@ -0,0 +1,103 @@ +from typing import Optional, Sequence, Tuple + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F + +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[:2] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +# class CatGen(nn.Module): +# def __init__(self, num=4): +# self.num = num +# def mixgen_batch(image, text): +# batch_size = image.shape[0] +# index = np.random.permutation(batch_size) + +# cat_images = [] +# for i in range(batch_size): +# # image mixup +# image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:] +# # text concat +# text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0] +# text = torch.stack(text) +# return image, text + + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + normalize = Normalize(mean=mean, std=std) + if is_train: + return Compose([ + RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), + _convert_to_rgb, + ToTensor(), + normalize, + ]) + else: + if resize_longest_max: + transforms = [ + ResizeMaxSize(image_size, fill=fill_color) + ] + else: + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend([ + _convert_to_rgb, + ToTensor(), + normalize, + ]) + return Compose(transforms) diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/transformer.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/transformer.py new file mode 100644 index 00000000..33e89ff7 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/transformer.py @@ -0,0 +1,737 @@ +import os +import logging +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +try: + from timm.models.layers import trunc_normal_ +except: + from timm.layers import trunc_normal_ + +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast +from .utils import to_2tuple + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + import deepspeed + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + print("Please 'pip install deepspeed'") + deepspeed = None + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: torch.Tensor): + output = F.layer_norm( + x.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(x) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}") + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + if self.training and os.getenv('RoPE') == '1': + return x, patch_indices_keep + + return x + + +def _in_projection_packed( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + w: torch.Tensor, + b: Optional[torch.Tensor] = None, + ): + """ + https://github.com/pytorch/pytorch/blob/db2a237763eb8693a20788be94f8c192e762baa8/torch/nn/functional.py#L4726 + """ + E = q.size(-1) + if k is v: + if q is k: + # self-attention + return F.linear(q, w, b).chunk(3, dim=-1) + else: + # encoder-decoder attention + w_q, w_kv = w.split([E, E * 2]) + if b is None: + b_q = b_kv = None + else: + b_q, b_kv = b.split([E, E * 2]) + return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1) + else: + w_q, w_k, w_v = w.chunk(3) + if b is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = b.chunk(3) + return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v) + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False, + rope=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + self.rope = rope + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + if self.xattn: + q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None, + ) + else: + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=True, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias) + N_q, B_q, C_q = q.shape + N_k, B_k, C_k = k.shape + N_v, B_v, C_v = v.shape + if self.xattn: + # B, N, C -> B, N, num_heads, C + q = q.permute(1, 0, 2).reshape(B_q, N_q, self.num_heads, -1) + k = k.permute(1, 0, 2).reshape(B_k, N_k, self.num_heads, -1) + v = v.permute(1, 0, 2).reshape(B_v, N_v, self.num_heads, -1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None + ) + else: + # B*H, L, C + q = q.contiguous().view(N_q, B_q * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(N_k, B_k * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(N_v, B_v * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + # B*H, N_q, N_k + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(B_q, self.num_heads, N_q, N_k) * logit_scale + attn = attn.view(-1, N_q, N_k) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(B_q, self.num_heads, N_q, C_q) * self.head_scale + x = x.view(-1, N_q, C_q) + x = x.transpose(0, 1).reshape(N_q, B_q, C_q) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.ln_1_k = norm_layer(d_model) if cross_attn else self.ln_1 + self.ln_1_v = norm_layer(d_model) if cross_attn else self.ln_1 + self.attn = CustomAttention( + d_model, n_head, + qkv_bias=True, + attn_drop=0., + proj_drop=0., + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + xattn=xattn + ) + + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q = q + self.ls_1(self.ln_attn(self.attn(self.ln_1(q), self.ln_1_k(k), self.ln_1_v(v), attn_mask=attn_mask))) + q = q + self.ls_2(self.mlp(self.ln_2(q))) + return q + +class CustomTransformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = True, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + self.xattn = xattn + + self.resblocks = nn.ModuleList([ + CustomResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + scale_cosine_attn=scale_cosine_attn, + scale_heads=scale_heads, + scale_attn=scale_attn, + scale_fc=scale_fc, + cross_attn=cross_attn, + xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, q: torch.Tensor, k: torch.Tensor = None, v: torch.Tensor = None, attn_mask: Optional[torch.Tensor] = None): + if k is None and v is None: + k = v = q + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + q = checkpoint(r, q, k, v, attn_mask) + else: + q = r(q, k, v, attn_mask=attn_mask) + return q + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + if xattn: + self.attn = Attention(d_model, n_head, xattn=True) + else: + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + self.xattn = xattn + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None + if self.xattn: + return self.attn(x, attn_mask=attn_mask) + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + patch_dropout: float = 0., + global_average_pool: bool = False, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.image_size = to_2tuple(image_size) + self.patch_size = to_2tuple(patch_size) + self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1]) + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + self.ln_pre = norm_layer(width) + + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.global_average_pool = global_average_pool + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def get_num_layers(self): + return self.transformer.layers + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'positional_embedding', 'class_embedding'} + + def forward(self, x: torch.Tensor, return_all_features: bool=False): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if not return_all_features: + if self.global_average_pool: + x = x.mean(dim=1) #x = x[:,1:,:].mean(dim=1) + else: + x = x[:, 0] + + x = self.ln_post(x) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class TextTransformer(nn.Module): + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool= False, + attn_mask: bool = True + ): + super().__init__() + self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.xattn = xattn + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if attn_mask: + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + else: + self.attn_mask = None + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + # return {'positional_embedding', 'token_embedding'} + return {'positional_embedding'} + + def get_num_layers(self): + return self.transformer.layers + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text, return_all_features: bool=False): + cast_dtype = self.transformer.get_cast_dtype() + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + # x = self.transformer(x) # no attention mask is applied + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if not return_all_features: + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x diff --git a/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/utils.py b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/utils.py new file mode 100644 index 00000000..bdc5a7a4 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/eva_clip/utils.py @@ -0,0 +1,326 @@ +from itertools import repeat +import collections.abc +import logging +import math +import numpy as np + +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d +import torch.nn.functional as F + +# open CLIP +def resize_clip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed + + +def resize_visual_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['positional_embedding'] = new_pos_embed + +def resize_evaclip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'visual.pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['visual.pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['visual.pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['visual.patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['visual.patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_eva_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + for key in all_keys: + if "relative_position_index" in key: + state_dict.pop(key) + + if "relative_position_bias_table" in key: + rel_pos_bias = state_dict[key] + src_num_pos, num_attn_heads = rel_pos_bias.size() + dst_num_pos, _ = model.visual.state_dict()[key].size() + dst_patch_shape = model.visual.patch_embed.patch_shape + if dst_patch_shape[0] != dst_patch_shape[1]: + raise NotImplementedError() + num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) + src_size = int((src_num_pos - num_extra_tokens) ** 0.5) + dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) + if src_size != dst_size: + print("Position interpolate for %s from %dx%d to %dx%d" % ( + key, src_size, src_size, dst_size, dst_size)) + extra_tokens = rel_pos_bias[-num_extra_tokens:, :] + rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] + + def geometric_progression(a, r, n): + return a * (1.0 - r ** n) / (1.0 - r) + + left, right = 1.01, 1.5 + while right - left > 1e-6: + q = (left + right) / 2.0 + gp = geometric_progression(1, q, src_size // 2) + if gp > dst_size // 2: + right = q + else: + left = q + + # if q > 1.090307: + # q = 1.090307 + + dis = [] + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + + x = r_ids + [0] + dis + y = r_ids + [0] + dis + + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + dy = np.arange(-t, t + 0.1, 1.0) + + print("Original positions = %s" % str(x)) + print("Target positions = %s" % str(dx)) + + all_rel_pos_bias = [] + + for i in range(num_attn_heads): + z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() + f = F.interpolate.interp2d(x, y, z, kind='cubic') + all_rel_pos_bias.append( + torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) + + rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) + + new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) + state_dict[key] = new_rel_pos_bias + + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) + + +def is_logging(args): + def is_global_master(args): + return args.rank == 0 + + def is_local_master(args): + return args.local_rank == 0 + + def is_master(args, local=False): + return is_local_master(args) if local else is_global_master(args) + return is_master + + +class AllGather(torch.autograd.Function): + """An autograd function that performs allgather on a tensor. + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + + @staticmethod + def forward(ctx, tensor, rank, world_size): + tensors_gather = [torch.empty_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(tensors_gather, tensor) + ctx.rank = rank + ctx.batch_size = tensor.shape[0] + return torch.cat(tensors_gather, 0) + + @staticmethod + def backward(ctx, grad_output): + return ( + grad_output[ctx.batch_size * ctx.rank: ctx.batch_size * (ctx.rank + 1)], + None, + None + ) + +allgather = AllGather.apply \ No newline at end of file diff --git a/custom_nodes/ComfyUI-PuLID-Flux/examples/einstein.jpg b/custom_nodes/ComfyUI-PuLID-Flux/examples/einstein.jpg new file mode 100644 index 00000000..e7182c13 Binary files /dev/null and b/custom_nodes/ComfyUI-PuLID-Flux/examples/einstein.jpg differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux/examples/pulid_flux_16bit_simple.json 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a/custom_nodes/ComfyUI-PuLID-Flux/examples/pulid_flux_einstein.png b/custom_nodes/ComfyUI-PuLID-Flux/examples/pulid_flux_einstein.png new file mode 100644 index 00000000..b3a7cec9 Binary files /dev/null and b/custom_nodes/ComfyUI-PuLID-Flux/examples/pulid_flux_einstein.png differ diff --git a/custom_nodes/ComfyUI-PuLID-Flux/pulidflux.py b/custom_nodes/ComfyUI-PuLID-Flux/pulidflux.py new file mode 100644 index 00000000..50c08182 --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/pulidflux.py @@ -0,0 +1,419 @@ + +import torch +from torch import nn, Tensor +from torchvision import transforms +from torchvision.transforms import functional +import os +import logging +import folder_paths +import comfy.utils +from comfy.ldm.flux.layers import timestep_embedding +from insightface.app import FaceAnalysis +from facexlib.parsing import init_parsing_model +from facexlib.utils.face_restoration_helper import FaceRestoreHelper + +from .eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .encoders_flux import IDFormer, PerceiverAttentionCA + +INSIGHTFACE_DIR = os.path.join(folder_paths.models_dir, "insightface") + +MODELS_DIR = os.path.join(folder_paths.models_dir, "pulid") +if "pulid" not in folder_paths.folder_names_and_paths: + current_paths = [MODELS_DIR] +else: + current_paths, _ = folder_paths.folder_names_and_paths["pulid"] +folder_paths.folder_names_and_paths["pulid"] = (current_paths, folder_paths.supported_pt_extensions) + +class PulidFluxModel(nn.Module): + def __init__(self): + super().__init__() + + self.double_interval = 2 + self.single_interval = 4 + + # Init encoder + self.pulid_encoder = IDFormer() + + # Init attention + num_ca = 19 // self.double_interval + 38 // self.single_interval + if 19 % self.double_interval != 0: + num_ca += 1 + if 38 % self.single_interval != 0: + num_ca += 1 + self.pulid_ca = nn.ModuleList([ + PerceiverAttentionCA() for _ in range(num_ca) + ]) + + def from_pretrained(self, path: str): + state_dict = comfy.utils.load_torch_file(path, safe_load=True) + state_dict_dict = {} + for k, v in state_dict.items(): + module = k.split('.')[0] + state_dict_dict.setdefault(module, {}) + new_k = k[len(module) + 1:] + state_dict_dict[module][new_k] = v + + for module in state_dict_dict: + getattr(self, module).load_state_dict(state_dict_dict[module], strict=True) + + del state_dict + del state_dict_dict + + def get_embeds(self, face_embed, clip_embeds): + return self.pulid_encoder(face_embed, clip_embeds) + +def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control=None, +) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + ca_idx = 0 + for i, block in enumerate(self.double_blocks): + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + # PuLID attention + if self.pulid_data: + if i % self.pulid_double_interval == 0: + # Will calculate influence of all pulid nodes at once + for _, node_data in self.pulid_data.items(): + if torch.any((node_data['sigma_start'] >= timesteps) & (timesteps >= node_data['sigma_end'])): + img = img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], img) + ca_idx += 1 + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + img = block(img, vec=vec, pe=pe) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + # PuLID attention + if self.pulid_data: + real_img, txt = img[:, txt.shape[1]:, ...], img[:, :txt.shape[1], ...] + if i % self.pulid_single_interval == 0: + # Will calculate influence of all nodes at once + for _, node_data in self.pulid_data.items(): + if torch.any((node_data['sigma_start'] >= timesteps) & (timesteps >= node_data['sigma_end'])): + real_img = real_img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], real_img) + ca_idx += 1 + img = torch.cat((txt, real_img), 1) + + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img + +def tensor_to_image(tensor): + image = tensor.mul(255).clamp(0, 255).byte().cpu() + image = image[..., [2, 1, 0]].numpy() + return image + +def image_to_tensor(image): + tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1) + tensor = tensor[..., [2, 1, 0]] + return tensor + +def to_gray(img): + x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3] + x = x.repeat(1, 3, 1, 1) + return x + +""" +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Nodes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +class PulidFluxModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pulid_file": (folder_paths.get_filename_list("pulid"), )}} + + RETURN_TYPES = ("PULIDFLUX",) + FUNCTION = "load_model" + CATEGORY = "pulid" + + def load_model(self, pulid_file): + model_path = folder_paths.get_full_path("pulid", pulid_file) + + # Also initialize the model, takes longer to load but then it doesn't have to be done every time you change parameters in the apply node + model = PulidFluxModel() + + logging.info("Loading PuLID-Flux model.") + model.from_pretrained(path=model_path) + + return (model,) + +class PulidFluxInsightFaceLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "provider": (["CPU", "CUDA", "ROCM"], ), + }, + } + + RETURN_TYPES = ("FACEANALYSIS",) + FUNCTION = "load_insightface" + CATEGORY = "pulid" + + def load_insightface(self, provider): + model = FaceAnalysis(name="antelopev2", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',]) # alternative to buffalo_l + model.prepare(ctx_id=0, det_size=(640, 640)) + + return (model,) + +class PulidFluxEvaClipLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + } + + RETURN_TYPES = ("EVA_CLIP",) + FUNCTION = "load_eva_clip" + CATEGORY = "pulid" + + def load_eva_clip(self): + from .eva_clip.factory import create_model_and_transforms + + model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True) + + model = model.visual + + eva_transform_mean = getattr(model, 'image_mean', OPENAI_DATASET_MEAN) + eva_transform_std = getattr(model, 'image_std', OPENAI_DATASET_STD) + if not isinstance(eva_transform_mean, (list, tuple)): + model["image_mean"] = (eva_transform_mean,) * 3 + if not isinstance(eva_transform_std, (list, tuple)): + model["image_std"] = (eva_transform_std,) * 3 + + return (model,) + +class ApplyPulidFlux: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", ), + "pulid_flux": ("PULIDFLUX", ), + "eva_clip": ("EVA_CLIP", ), + "face_analysis": ("FACEANALYSIS", ), + "image": ("IMAGE", ), + "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05 }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + }, + "optional": { + "attn_mask": ("MASK", ), + }, + "hidden": { + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply_pulid_flux" + CATEGORY = "pulid" + + def __init__(self): + self.pulid_data_dict = None + + def apply_pulid_flux(self, model, pulid_flux, eva_clip, face_analysis, image, weight, start_at, end_at, attn_mask=None, unique_id=None): + device = comfy.model_management.get_torch_device() + # Why should I care what args say, when the unet model has a different dtype?! + # Am I missing something?! + #dtype = comfy.model_management.unet_dtype() + dtype = model.model.diffusion_model.dtype + # Because of 8bit models we must check what cast type does the unet uses + # ZLUDA (Intel, AMD) & GPUs with compute capability < 8.0 don't support bfloat16 etc. + # Issue: https://github.com/balazik/ComfyUI-PuLID-Flux/issues/6 + if model.model.manual_cast_dtype is not None: + dtype = model.model.manual_cast_dtype + + eva_clip.to(device, dtype=dtype) + pulid_flux.to(device, dtype=dtype) + + # TODO: Add masking support! + if attn_mask is not None: + if attn_mask.dim() > 3: + attn_mask = attn_mask.squeeze(-1) + elif attn_mask.dim() < 3: + attn_mask = attn_mask.unsqueeze(0) + attn_mask = attn_mask.to(device, dtype=dtype) + + image = tensor_to_image(image) + + face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + device=device, + ) + + face_helper.face_parse = None + face_helper.face_parse = init_parsing_model(model_name='bisenet', device=device) + + bg_label = [0, 16, 18, 7, 8, 9, 14, 15] + cond = [] + + # Analyse multiple images at multiple sizes and combine largest area embeddings + for i in range(image.shape[0]): + # get insightface embeddings + iface_embeds = None + for size in [(size, size) for size in range(640, 256, -64)]: + face_analysis.det_model.input_size = size + face_info = face_analysis.get(image[i]) + if face_info: + # Only use the maximum face + # Removed the reverse=True from original code because we need the largest area not the smallest one! + # Sorts the list in ascending order (smallest to largest), + # then selects the last element, which is the largest face + face_info = sorted(face_info, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[-1] + iface_embeds = torch.from_numpy(face_info.embedding).unsqueeze(0).to(device, dtype=dtype) + break + else: + # No face detected, skip this image + logging.warning(f'Warning: No face detected in image {str(i)}') + continue + + # get eva_clip embeddings + face_helper.clean_all() + face_helper.read_image(image[i]) + face_helper.get_face_landmarks_5(only_center_face=True) + face_helper.align_warp_face() + + if len(face_helper.cropped_faces) == 0: + # No face detected, skip this image + continue + + # Get aligned face image + align_face = face_helper.cropped_faces[0] + # Convert bgr face image to tensor + align_face = image_to_tensor(align_face).unsqueeze(0).permute(0, 3, 1, 2).to(device) + parsing_out = face_helper.face_parse(functional.normalize(align_face, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] + parsing_out = parsing_out.argmax(dim=1, keepdim=True) + bg = sum(parsing_out == i for i in bg_label).bool() + white_image = torch.ones_like(align_face) + # Only keep the face features + face_features_image = torch.where(bg, white_image, to_gray(align_face)) + + # Transform img before sending to eva_clip + # Apparently MPS only supports NEAREST interpolation? + face_features_image = functional.resize(face_features_image, eva_clip.image_size, transforms.InterpolationMode.BICUBIC if 'cuda' in device.type else transforms.InterpolationMode.NEAREST).to(device, dtype=dtype) + face_features_image = functional.normalize(face_features_image, eva_clip.image_mean, eva_clip.image_std) + + # eva_clip + id_cond_vit, id_vit_hidden = eva_clip(face_features_image, return_all_features=False, return_hidden=True, shuffle=False) + id_cond_vit = id_cond_vit.to(device, dtype=dtype) + for idx in range(len(id_vit_hidden)): + id_vit_hidden[idx] = id_vit_hidden[idx].to(device, dtype=dtype) + + id_cond_vit = torch.div(id_cond_vit, torch.norm(id_cond_vit, 2, 1, True)) + + # Combine embeddings + id_cond = torch.cat([iface_embeds, id_cond_vit], dim=-1) + + # Pulid_encoder + cond.append(pulid_flux.get_embeds(id_cond, id_vit_hidden)) + + if not cond: + # No faces detected, return the original model + logging.warning("PuLID warning: No faces detected in any of the given images, returning unmodified model.") + return (model,) + + # average embeddings + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + cond = torch.mean(cond, dim=0, keepdim=True) + + sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at) + sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at) + + # Patch the Flux model (original diffusion_model) + # Nah, I don't care for the official ModelPatcher because it's undocumented! + # I want the end result now, and I don’t mind if I break other custom nodes in the process. πŸ˜„ + flux_model = model.model.diffusion_model + # Let's see if we already patched the underlying flux model, if not apply patch + if not hasattr(flux_model, "pulid_ca"): + # Add perceiver attention, variables and current node data (weight, embedding, sigma_start, sigma_end) + # The pulid_data is stored in Dict by unique node index, + # so we can chain multiple ApplyPulidFlux nodes! + flux_model.pulid_ca = pulid_flux.pulid_ca + flux_model.pulid_double_interval = pulid_flux.double_interval + flux_model.pulid_single_interval = pulid_flux.single_interval + flux_model.pulid_data = {} + # Replace model forward_orig with our own + new_method = forward_orig.__get__(flux_model, flux_model.__class__) + setattr(flux_model, 'forward_orig', new_method) + + # Patch is already in place, add data (weight, embedding, sigma_start, sigma_end) under unique node index + flux_model.pulid_data[unique_id] = { + 'weight': weight, + 'embedding': cond, + 'sigma_start': sigma_start, + 'sigma_end': sigma_end, + } + + # Keep a reference for destructor (if node is deleted the data will be deleted as well) + self.pulid_data_dict = {'data': flux_model.pulid_data, 'unique_id': unique_id} + + return (model,) + + def __del__(self): + # Destroy the data for this node + if self.pulid_data_dict: + del self.pulid_data_dict['data'][self.pulid_data_dict['unique_id']] + del self.pulid_data_dict + + +NODE_CLASS_MAPPINGS = { + "PulidFluxModelLoader": PulidFluxModelLoader, + "PulidFluxInsightFaceLoader": PulidFluxInsightFaceLoader, + "PulidFluxEvaClipLoader": PulidFluxEvaClipLoader, + "ApplyPulidFlux": ApplyPulidFlux, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PulidFluxModelLoader": "Load PuLID Flux Model", + "PulidFluxInsightFaceLoader": "Load InsightFace (PuLID Flux)", + "PulidFluxEvaClipLoader": "Load Eva Clip (PuLID Flux)", + "ApplyPulidFlux": "Apply PuLID Flux", +} diff --git a/custom_nodes/ComfyUI-PuLID-Flux/requirements.txt b/custom_nodes/ComfyUI-PuLID-Flux/requirements.txt new file mode 100644 index 00000000..ed8eed5c --- /dev/null +++ b/custom_nodes/ComfyUI-PuLID-Flux/requirements.txt @@ -0,0 +1,6 @@ +facexlib +insightface +onnxruntime +onnxruntime-gpu +ftfy +timm diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/.gitignore b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/.gitignore new file mode 100644 index 00000000..15201acc --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/.gitignore @@ -0,0 +1,171 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# PyPI configuration file +.pypirc diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/LICENSE b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/LICENSE new file mode 100644 index 00000000..f570ca0c --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Joel Trauger + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/README.md b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/README.md new file mode 100644 index 00000000..af4b85f7 --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/README.md @@ -0,0 +1,2 @@ +# ComfyUI-UltimateSDUpscale-GGUF +GGUF implementation for the ComfyUI Ultimate SD Upscale node. diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/__init__.py b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/__init__.py new file mode 100644 index 00000000..bae9d3ef --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/__init__.py @@ -0,0 +1,13 @@ +""" +ComfyUI Ultimate SD Upscale Node for GGUF models +""" + +from .ultimate_sd_upscale_gguf import UltimateSDUpscaleGGUF + +NODE_CLASS_MAPPINGS = { + "UltimateSDUpscaleGGUF": UltimateSDUpscaleGGUF +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "UltimateSDUpscaleGGUF": "Ultimate SD Upscale (GGUF)" +} diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/pyproject.toml b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/pyproject.toml new file mode 100644 index 00000000..1a07f059 --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "ComfyUI-UltimateSDUpscale-GGUF" +description = "Flux (GGUF) implementation for the ComfyUI Ultimate SD Upscale node." +version = "1.0.0" +license = {file = "LICENSE"} + +[project.urls] +Repository = "https://github.com/traugdor/ComfyUI-UltimateSDUpscale-GGUF" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "traugdor" +DisplayName = "ComfyUI-UltimateSDUpscale-GGUF" +Icon = "" \ No newline at end of file diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/sampler.py b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/sampler.py new file mode 100644 index 00000000..f33ca96b --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/sampler.py @@ -0,0 +1,175 @@ +import os +import sys +import torch + +# Add ComfyUI path to sys.path +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +COMFY_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, "..", "..")) +if COMFY_DIR not in sys.path: + sys.path.append(COMFY_DIR) + +from comfy_extras.nodes_custom_sampler import SamplerCustomAdvanced +import comfy.sample +import comfy.model_management +import latent_preview + +# VRAM Management +import torch.cuda +from comfy.cli_args import args +import comfy.model_management + +# Get the current device +device = comfy.model_management.get_torch_device() + +class SamplerHelper: + @staticmethod + def force_memory_cleanup(unload_models=False): + comfy.model_management.cleanup_models() + if unload_models: + comfy.model_management.unload_all_models() + comfy.model_management.soft_empty_cache(True) + torch.cuda.empty_cache() + + @staticmethod + def create_gaussian_blend_kernel(overlap, device): + """Create a gaussian blend kernel that matches BHWC format.""" + try: + y = torch.arange(overlap, device=device).float() + x = torch.arange(overlap, device=device).float() + + # Calculate 1D gaussian weights + center = overlap / 2 + sigma = overlap / 4 # Controls the spread of the gaussian + + # Create 1D gaussians + y_kernel = torch.exp(-((y - center) ** 2) / (2 * sigma ** 2)) + x_kernel = torch.exp(-((x - center) ** 2) / (2 * sigma ** 2)) + + # Expand to match dimensions properly + y_kernel = y_kernel.view(-1, 1) # Shape: [overlap, 1] + x_kernel = x_kernel.view(1, -1) # Shape: [1, overlap] + + # Create 2D kernel through outer product + kernel = y_kernel @ x_kernel # Shape: [overlap, overlap] + + # Normalize kernel + kernel = kernel / kernel.max() + + # Add batch dimension and move to BHWC format + # [overlap, overlap] -> [1, overlap, overlap, 1] + kernel = kernel.view(1, overlap, overlap, 1) + + return kernel + finally: + # Cleanup temporary tensors + del y, x, y_kernel, x_kernel + + @staticmethod + def blend_tile_edges(tile, existing_output, kernel): + """Blend tile edges using gaussian kernel. Expects BHWC format.""" + overlap = kernel.shape[1] # Kernel is [B,H,W,C] + + # Extract overlap regions + left = existing_output[:, :, :overlap, :] if tile.shape[2] > overlap else None + right = existing_output[:, :, -overlap:, :] if tile.shape[2] > overlap else None + top = existing_output[:, :overlap, :, :] if tile.shape[1] > overlap else None + bottom = existing_output[:, -overlap:, :, :] if tile.shape[1] > overlap else None + + # Create kernels for each direction + kernel_horizontal = kernel # Already in BHWC format + kernel_vertical = kernel.permute(0, 2, 1, 3) # Swap H,W for vertical blending + + # Blend edges + if left is not None: + tile[:, :, :overlap, :] = tile[:, :, :overlap, :] * kernel_horizontal + left * (1 - kernel_horizontal) + if right is not None: + tile[:, :, -overlap:, :] = tile[:, :, -overlap:, :] * kernel_horizontal.flip(2) + right * (1 - kernel_horizontal.flip(2)) + if top is not None: + tile[:, :overlap, :, :] = tile[:, :overlap, :, :] * kernel_vertical + top * (1 - kernel_vertical) + if bottom is not None: + tile[:, -overlap:, :, :] = tile[:, -overlap:, :, :] * kernel_vertical.flip(1) + bottom * (1 - kernel_vertical.flip(1)) + + return tile + + @staticmethod + def process_latent_batch(latents, noise, guider, sampler, sigmas): + processed_latents = [] + + for latent in latents: + # Process single latent + processed = Sampler.sample(noise, guider, sampler, sigmas, latent) + processed_latents.append(processed) + + # Clear current latent + del latent + + # Aggressive memory cleanup after function completes + SamplerHelper.force_memory_cleanup() + + return processed_latents + +class OptimizedSampler: + def __init__(self): + self.last_samples = None + self.callback_count = 0 + + def sample(self, noise, guider, sampler, sigmas, latent_image): + # Process latent + latent_image["samples"] = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image["samples"]) + + # Handle noise mask + noise_mask = None + if "noise_mask" in latent_image: + noise_mask = latent_image["noise_mask"] + + # Setup callback for progress + x0_output = {} + callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) + + try: + # Generate noise + noise_tensor = noise.generate_noise(latent_image) + + # Sample + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = guider.sample( + noise_tensor, + latent_image["samples"], + sampler, + sigmas, + denoise_mask=noise_mask, + callback=callback, + disable_pbar=disable_pbar, + seed=noise.seed + ) + + return {"samples": samples} + + finally: + # Just cleanup tensors we created + if 'noise_tensor' in locals(): + del noise_tensor + if noise_mask is not None: + del noise_mask + +class Sampler: + @staticmethod + def sample(noise, guider, sampler, sigmas, latent): + # Create optimized sampler instance + opt_sampler = OptimizedSampler() + + # Sample using optimized sampler + samples = opt_sampler.sample(noise, guider, sampler, sigmas, latent) + del opt_sampler + + # Package output + output = {"samples": samples["samples"]} + if "noise_mask" in latent: + output["noise_mask"] = latent["noise_mask"] + + return output + + @staticmethod + def encode(image, vae): + t = vae.encode(image[:,:,:,:3]) + return {"samples": t} diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/seam_fixer.py b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/seam_fixer.py new file mode 100644 index 00000000..5b492640 --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/seam_fixer.py @@ -0,0 +1,265 @@ +import torch +import os +import sys + +# Add ComfyUI path to sys.path +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +COMFY_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, "..", "..")) +if COMFY_DIR not in sys.path: + sys.path.append(COMFY_DIR) + +from .upscale_settings import UpscaleSettings +from .sampler import Sampler + +class SeamFixer: + VALID_MODES = ["None", "Band Pass", "Half Tile", "Half Tile + Intersections"] + + def __init__(self, mode, width, mask_blur, padding, transition_sharpness, settings, device): + if mode not in self.VALID_MODES: + raise ValueError(f"Invalid seam fix mode: {mode}. Must be one of {self.VALID_MODES}") + + self.mode = mode + self.width = width + self.mask_blur = mask_blur + self.padding = padding + self.upscale_settings = settings + self.transition_sharpness = transition_sharpness + self.device = device + + def get_band_coordinates(self): + vertical_bands = [] + horizontal_bands = [] + + # Vertical bands (along tile columns) + for x in range(1, self.upscale_settings.num_tiles_x): + # Calculate x position where tiles meet + seam_x = x * self.upscale_settings.tile_width + start_x = max(0, seam_x - self.width) + end_x = min(self.upscale_settings.target_width, seam_x + self.width) + # Band goes full height + vertical_bands.append((start_x, end_x, 0, self.upscale_settings.target_height)) + + # Horizontal bands (along tile rows) + for y in range(1, self.upscale_settings.num_tiles_y): + # Calculate y position where tiles meet + seam_y = y * self.upscale_settings.tile_height + start_y = max(0, seam_y - self.width) + end_y = min(self.upscale_settings.target_height, seam_y + self.width) + # Band goes full width + horizontal_bands.append((0, self.upscale_settings.target_width, start_y, end_y)) + + return vertical_bands, horizontal_bands + + def get_half_tile_coordinates(self): + vertical_halves = [] + horizontal_halves = [] + + # Vertical seams (process right half of left tile and left half of right tile) + for x in range(1, self.upscale_settings.num_tiles_x): + seam_x = x * self.upscale_settings.tile_width + + # Right half of left tile + left_half = ( + seam_x - self.upscale_settings.tile_width//2, # start at middle of left tile + seam_x + self.padding, # extend slightly into right tile + 0, # full height + self.upscale_settings.target_height + ) + + # Left half of right tile + right_half = ( + seam_x - self.padding, # start slightly in left tile + seam_x + self.upscale_settings.tile_width//2, # end at middle of right tile + 0, # full height + self.upscale_settings.target_height + ) + + vertical_halves.extend([left_half, right_half]) + + # Horizontal seams (process bottom half of top tile and top half of bottom tile) + for y in range(1, self.upscale_settings.num_tiles_y): + seam_y = y * self.upscale_settings.tile_height + + # Bottom half of top tile + top_half = ( + 0, # full width + self.upscale_settings.target_width, + seam_y - self.upscale_settings.tile_height//2, # start at middle of top tile + seam_y + self.padding # extend slightly into bottom tile + ) + + # Top half of bottom tile + bottom_half = ( + 0, # full width + self.upscale_settings.target_width, + seam_y - self.padding, # start slightly in top tile + seam_y + self.upscale_settings.tile_height//2 # end at middle of bottom tile + ) + + horizontal_halves.extend([top_half, bottom_half]) + + return vertical_halves, horizontal_halves + + def get_intersection_coordinates(self): + intersections = [] + + # For each internal tile corner (where 4 tiles meet) + for y in range(1, self.upscale_settings.num_tiles_y): + for x in range(1, self.upscale_settings.num_tiles_x): + seam_x = x * self.upscale_settings.tile_width + seam_y = y * self.upscale_settings.tile_height + + # Calculate the intersection region centered on the seam intersection + # This creates a square region that overlaps with the half-tiles + half_width = self.upscale_settings.tile_width // 4 # Quarter tile width + half_height = self.upscale_settings.tile_height // 4 # Quarter tile height + + intersection = ( + seam_x - half_width, # start quarter tile left of seam + seam_x + half_width, # end quarter tile right of seam + seam_y - half_height, # start quarter tile above seam + seam_y + half_height # end quarter tile below seam + ) + + intersections.append(intersection) + + return intersections + + def process_band(self, upscaled_image, band, vae, sampler, noise, guider, sigmas): + start_x, end_x, start_y, end_y = band + + # Extract band region + band_image = upscaled_image[:, start_y:end_y, start_x:end_x, :] + + # Create mask for blending (in BCHW for conv2d) + mask = torch.zeros((1, 1, end_y - start_y, end_x - start_x), device=self.device) + mask[:, :, :, :] = 1 + + # Apply mask blur if specified + if self.mask_blur > 0: + adjusted_blur = self.mask_blur * self.transition_sharpness + # Ensure kernel size is odd and not larger than input + kernel_size = min( + int(adjusted_blur * 2 + 1), + min(end_y - start_y, end_x - start_x) - 1 # Leave at least 1 pixel + ) + if kernel_size % 2 == 0: # Make odd + kernel_size -= 1 + if kernel_size > 0: # Only apply if we have a valid kernel size + kernel = torch.ones(1, 1, kernel_size, kernel_size, device=self.device) + kernel = kernel / kernel.numel() + mask = torch.nn.functional.conv2d( + mask, + kernel, + padding=kernel_size//2 + ) + mask = torch.clamp(mask, 0, 1) + + # Process through VAE and sampling (VAE expects BHWC format) + latent = Sampler.encode(band_image, vae) + latent["noise_mask"] = mask # Noise mask stays in BCHW format + + sampled = Sampler.sample(noise, guider, sampler, sigmas, latent) + processed_band = vae.decode(sampled["samples"]) + + # Convert mask to BHWC for blending + mask = mask.permute(0, 2, 3, 1) + + return processed_band, mask + + def fix_seams(self, upscaled_image, vae, sampler, noise, guider, sigmas): + if self.mode == "None": + return upscaled_image + + result_image = upscaled_image.clone() + + if self.mode == "Band Pass": + vertical_bands, horizontal_bands = self.get_band_coordinates() + + # Process vertical bands + for band in vertical_bands: + processed_band, mask = self.process_band( + upscaled_image, band, vae, sampler, noise, guider, sigmas + ) + start_x, end_x, start_y, end_y = band + + # Blend band back into image + for c in range(upscaled_image.shape[-1]): + result_image[:, start_y:end_y, start_x:end_x, c] = \ + processed_band[:, :, :, c] * mask[:, :, :, 0] + \ + result_image[:, start_y:end_y, start_x:end_x, c] * (1 - mask[:, :, :, 0]) + + # Process horizontal bands + for band in horizontal_bands: + processed_band, mask = self.process_band( + result_image, band, vae, sampler, noise, guider, sigmas + ) + start_x, end_x, start_y, end_y = band + + # Blend band back into image + for c in range(upscaled_image.shape[-1]): + result_image[:, start_y:end_y, start_x:end_x, c] = \ + processed_band[:, :, :, c] * mask[:, :, :, 0] + \ + result_image[:, start_y:end_y, start_x:end_x, c] * (1 - mask[:, :, :, 0]) + + elif self.mode in ["Half Tile", "Half Tile + Intersections"]: + vertical_halves, horizontal_halves = self.get_half_tile_coordinates() + + # Process vertical half-tiles + for half_tile in vertical_halves: + processed_half, mask = self.process_band( + upscaled_image, half_tile, vae, sampler, noise, guider, sigmas + ) + start_x, end_x, start_y, end_y = half_tile + + # Blend half-tile back into image + for c in range(upscaled_image.shape[-1]): + result_image[:, start_y:end_y, start_x:end_x, c] = \ + processed_half[:, :, :, c] * mask[:, :, :, 0] + \ + result_image[:, start_y:end_y, start_x:end_x, c] * (1 - mask[:, :, :, 0]) + + # Process horizontal half-tiles + for half_tile in horizontal_halves: + processed_half, mask = self.process_band( + result_image, half_tile, vae, sampler, noise, guider, sigmas + ) + start_x, end_x, start_y, end_y = half_tile + + # Blend half-tile back into image + for c in range(upscaled_image.shape[-1]): + result_image[:, start_y:end_y, start_x:end_x, c] = \ + processed_half[:, :, :, c] * mask[:, :, :, 0] + \ + result_image[:, start_y:end_y, start_x:end_x, c] * (1 - mask[:, :, :, 0]) + + # Process intersections if in intersection mode + if self.mode == "Half Tile + Intersections": + intersections = self.get_intersection_coordinates() + + # Process each intersection region + for intersection in intersections: + processed_intersection, mask = self.process_band( + result_image, intersection, vae, sampler, noise, guider, sigmas + ) + start_x, end_x, start_y, end_y = intersection + + # Use radial gradient for intersection mask + # This creates a circular blend that smoothly transitions in all directions + center_x = (end_x - start_x) // 2 + center_y = (end_y - start_y) // 2 + y, x = torch.meshgrid( + torch.arange(end_y - start_y, device=self.device), + torch.arange(end_x - start_x, device=self.device), + indexing='ij' + ) + radius = torch.sqrt((x - center_x)**2 + (y - center_y)**2) + max_radius = min(center_x, center_y) + radial_mask = torch.clamp(1 - radius / max_radius, 0, 1) + + # Blend intersection back into image + radial_mask = radial_mask.unsqueeze(-1) # Add channel dimension + for c in range(upscaled_image.shape[-1]): + result_image[:, start_y:end_y, start_x:end_x, c] = \ + processed_intersection[:, :, :, c] * radial_mask[:, :, :, 0] + \ + result_image[:, start_y:end_y, start_x:end_x, c] * (1 - radial_mask[:, :, :, 0]) + + return result_image diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/ultimate_sd_upscale_gguf.py b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/ultimate_sd_upscale_gguf.py new file mode 100644 index 00000000..5299dc9d --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/ultimate_sd_upscale_gguf.py @@ -0,0 +1,171 @@ +import math +import torch +import os +import sys +import time +import logging +import torch.cuda +import torch.nn.functional as F + +# Add ComfyUI path to sys.path +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +COMFY_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, "..", "..")) +if COMFY_DIR not in sys.path: + sys.path.append(COMFY_DIR) + +import comfy.utils +import comfy_extras.nodes_upscale_model as numodel +from .upscale_settings import UpscaleSettings +from .sampler import SamplerHelper, Sampler +from .seam_fixer import SeamFixer + +class UltimateSDUpscaleGGUF: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "image": ("IMAGE", ), + "noise": ("NOISE", ), + "guider": ("GUIDER", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "vae": ("VAE", ), + "upscale_model": ("UPSCALE_MODEL",), + "upscale_by": ("FLOAT", { "default": 2.0, "min": 1.0, "max": 8.0, "step": 0.1 }), + "max_tile_size": ("INT", { "default": 512, "min": 256, "max": 2048, "step": 64 }), + "mask_blur": ("INT", { "default": 8, "min": 0, "max": 64, "step": 1 }), + "transition_sharpness": ("FLOAT", { "default": 0.333, "min": 0.125, "max": 1.0, "step": 0.001 }), + "tile_padding": ("INT", { "default": 32, "min": 0, "max": 128, "step": 8 }), + "seam_fix_mode": ("STRING", { "default": "None", "options": ["None", "Band Pass", "Half Tile", "Half Tile + Intersections"] }), + "seam_fix_width": ("INT", { "default": 64, "min": 0, "max": 8192, "step": 8 }), + "seam_fix_mask_blur": ("INT", { "default": 8, "min": 0, "max": 64, "step": 1 }), + "seam_fix_padding": ("INT", { "default": 16, "min": 0, "max": 128, "step": 8 }), + "force_uniform_tiles": ("BOOLEAN", { "default": True }) + }} + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE") # (upscaled_image, tiles, masks) + RETURN_NAMES = ("upscaled", "tiles", "masks") + OUTPUT_IS_LIST = (False, True, True) + FUNCTION = "upscale" + CATEGORY = "image/upscaling" + + def upscale( + self, image, noise, guider, sampler, sigmas, vae, upscale_model, upscale_by, max_tile_size, + mask_blur, transition_sharpness, tile_padding, seam_fix_mode, seam_fix_width, + seam_fix_mask_blur, seam_fix_padding, force_uniform_tiles + ): + settings = UpscaleSettings( + target_width=int(image.shape[2] * upscale_by), + target_height=int(image.shape[1] * upscale_by), + max_tile_size=max_tile_size, + tile_padding=tile_padding, + force_uniform_tiles=force_uniform_tiles + ) + + upScalerWithModel = numodel.ImageUpscaleWithModel() + image_tuple = upScalerWithModel.upscale(upscale_model, image) + image = image_tuple[0] + samples = image.movedim(-1,1) + image = comfy.utils.common_upscale(samples, settings.sampling_width, settings.sampling_height, "area", "disabled") + image = image.movedim(1,-1) + + output = image.to('cpu') + latents = [] + tile_positions = [] + tile_masks = [] + all_tiles = [] + all_masks = [] + + SamplerHelper.force_memory_cleanup(True) + + for tile_y in range(settings.num_tiles_y): + for tile_x in range(settings.num_tiles_x): + x1, x2, y1, y2, pad_x1, pad_x2, pad_y1, pad_y2 = settings.get_tile_coordinates(tile_x, tile_y, tile_padding) + + if tile_padding > 0: + pad = tile_padding + full_h, full_w = (y2-y1) + pad*2, (x2-x1) + pad*2 + mask = torch.zeros((1, 1, full_h, full_w), device=image.device) + y_coords = torch.arange(full_h, device=image.device).view(-1, 1) + x_coords = torch.arange(full_w, device=image.device).view(1, -1) + tile_y1, tile_y2 = pad, pad + (y2-y1) + tile_x1, tile_x2 = pad, pad + (x2-x1) + dist_from_y1 = torch.abs(y_coords - tile_y1) + dist_from_y2 = torch.abs(y_coords - tile_y2) + dist_from_x1 = torch.abs(x_coords - tile_x1) + dist_from_x2 = torch.abs(x_coords - tile_x2) + y_dist = torch.minimum(dist_from_y1, dist_from_y2) + x_dist = torch.minimum(dist_from_x1, dist_from_x2) + y_dist = torch.where((y_coords >= tile_y1) & (y_coords <= tile_y2), 0, y_dist) + x_dist = torch.where((x_coords >= tile_x1) & (x_coords <= tile_x2), 0, x_dist) + dist = torch.sqrt(y_dist**2 + x_dist**2) + falloff = 1.0 - torch.clamp(dist / pad, min=0, max=1) + mask[0, 0] = falloff + + if mask_blur > 0: + kernel_size = min(pad * 2 - 1, 63) + sigma = pad / 2 * math.ceil(1.0 / transition_sharpness) / 4 + x = torch.arange(-(kernel_size//2), kernel_size//2 + 1, device=image.device).float() + gaussian = torch.exp(-(x**2)/(2*sigma**2)) + gaussian = gaussian / gaussian.sum() + kernel = gaussian.view(1, 1, -1, 1) @ gaussian.view(1, 1, 1, -1) + mask = F.conv2d(mask, kernel, padding=(kernel_size-1) // 2) + + mask[:, :, pad:pad+(y2-y1), pad:pad+(x2-x1)] = 1.0 + + x_start = 0 if tile_x > 0 else pad + x_end = full_w if tile_x < settings.num_tiles_x - 1 else full_w - pad + y_start = 0 if tile_y > 0 else pad + y_end = full_h if tile_y < settings.num_tiles_y - 1 else full_h - pad + + mask = mask[:, :, y_start:y_end, x_start:x_end] + else: + mask = torch.ones((1, 1, y2-y1, x2-x1), device=image.device) + + mask = torch.clamp(mask, 0, 1) + tile = image[:, pad_y1:pad_y2, pad_x1:pad_x2, :].clone() + latent = Sampler.encode(tile, vae) + latent_h, latent_w = latent["samples"].shape[2:4] + mask_latent = F.interpolate(mask, size=(latent_h, latent_w), mode='bilinear') + latents.append(latent) + tile_positions.append((x1, x2, y1, y2, pad_x1, pad_x2, pad_y1, pad_y2)) + tile_masks.append(mask) + del tile, mask, mask_latent + torch.cuda.empty_cache() + + SamplerHelper.force_memory_cleanup(True) + + processed_latents = SamplerHelper.process_latent_batch(latents, noise, guider, sampler, sigmas) + #processed_latents = latents + del latents + SamplerHelper.force_memory_cleanup(True) + decoded_tiles = [] + for processed_latent in processed_latents: + tile = vae.decode(processed_latent["samples"]) + decoded_tiles.append(tile.cpu()) + del processed_latent + torch.cuda.empty_cache() + + del processed_latents + SamplerHelper.force_memory_cleanup(True) + + for tile, pos, mask in zip(decoded_tiles, tile_positions, tile_masks): + x1, x2, y1, y2, tpad_x1, tpad_x2, tpad_y1, tpad_y2 = pos + output_slice = output[:, tpad_y1:tpad_y2, tpad_x1:tpad_x2, :] + tile_gpu = tile.to(image.device) + mask = mask.to(image.device) + mask = mask.movedim(1, -1) + mask = mask.expand(-1, -1, -1, 3) + blended = tile_gpu * mask + output_slice.to(image.device) * (1 - mask) + output[:, tpad_y1:tpad_y2, tpad_x1:tpad_x2, :] = blended + all_tiles.append(tile_gpu.cpu()) + all_masks.append(mask.cpu()) + del tile_gpu, mask + torch.cuda.empty_cache() + + del decoded_tiles, tile_positions, tile_masks + SamplerHelper.force_memory_cleanup(True) + samples = output.movedim(-1,1) + output = comfy.utils.common_upscale(samples, settings.target_width, settings.target_height, "lanczos", "disabled") + output = output.movedim(1,-1) + + return (output, all_tiles, all_masks) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/upscale_settings.py b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/upscale_settings.py new file mode 100644 index 00000000..bac1082e --- /dev/null +++ b/custom_nodes/ComfyUI-UltimateSDUpscale-GGUF/upscale_settings.py @@ -0,0 +1,92 @@ +import math + +class UpscaleSettings: + MIN_TILE_SIZE = 256 + MINIMUM_BENEFICIAL_UPSCALE = 1.5 + NEARLY_SQUARE_THRESHOLD = 0.01 # 1% difference threshold + + def __init__(self, target_width, target_height, max_tile_size, tile_padding, force_uniform_tiles=False): + self.target_width = target_width + self.target_height = target_height + self.tile_padding = tile_padding + self.force_uniform = force_uniform_tiles + self.max_tile_size = max_tile_size + + # Calculate final tile dimensions + self.tile_width, self.tile_height, self.sampling_width, self.sampling_height = self._calculate_tile_dimensions() + + def _calculate_uniform_tile_dimensions(self): + # Calculate initial number of tiles + # x = width, y = height + # find longest side first and divide by max tile size + if self.target_width > self.target_height: + self.num_tiles_x = math.ceil(self.target_width / self.max_tile_size) + max_tile_width = self.max_tile_size + max_tile_height = math.ceil(self.max_tile_size * (self.target_height / self.target_width) / 8 ) * 8 + self.num_tiles_y = math.ceil(self.target_height / max_tile_height) + else: + self.num_tiles_y = math.ceil(self.target_height / self.max_tile_size) + max_tile_width = math.ceil(self.max_tile_size * (self.target_width / self.target_height) / 8 ) * 8 + max_tile_height = self.max_tile_size + self.num_tiles_x = math.ceil(self.target_width / max_tile_width) + + sampling_width = self.num_tiles_x * max_tile_width + sampling_height = self.num_tiles_y * max_tile_height + + return max_tile_width, max_tile_height, sampling_width, sampling_height + + def _calculate_tile_dimensions(self): + # consume self.tile_ratio + if self.force_uniform: + tile_width, tile_height, sampling_width, sampling_height = self._calculate_uniform_tile_dimensions() + if tile_width is not None and tile_height is not None: + return tile_width, tile_height, sampling_width, sampling_height + + # Double check that image dimensions are a multiple of 8 and adjust if necessary + # Return as sampling dimensions + sampling_width = max(8, ((self.target_width + 7) // 8) * 8) + sampling_height = max(8, ((self.target_height + 7) // 8) * 8) + + # For non-uniform tiles, use square tiles with minimum size constraint + tile_size = max(64, math.ceil(self.max_tile_size / 8) * 8) + tile_size = min(tile_size, max(8, min(sampling_width, sampling_height))) + + # Calculate number of full tiles needed, ensuring at least 1 tile in each dimension + self.num_tiles_x = max(1, math.ceil(sampling_width / tile_size)) + self.num_tiles_y = max(1, math.ceil(sampling_height / tile_size)) + + return tile_size, tile_size, sampling_width, sampling_height + + def get_tile_coordinates(self, tile_x, tile_y, tile_padding): + if not (0 <= tile_x < self.num_tiles_x and 0 <= tile_y < self.num_tiles_y): + return None, None, None, None + + # Calculate base tile coordinates in sampling space + x1 = tile_x * self.tile_width + x2 = min((tile_x + 1) * self.tile_width, self.sampling_width) + y1 = tile_y * self.tile_height + y2 = min((tile_y + 1) * self.tile_height, self.sampling_height) + + # Add padding only at seams + pad_left = tile_padding if tile_x > 0 else 0 + pad_right = tile_padding if tile_x < self.num_tiles_x - 1 else 0 + pad_top = tile_padding if tile_y > 0 else 0 + pad_bottom = tile_padding if tile_y < self.num_tiles_y - 1 else 0 + + # Apply padding to coordinates + pad_x1 = max(0, x1 - pad_left) + pad_x2 = min(self.sampling_width, x2 + pad_right) + pad_y1 = max(0, y1 - pad_top) + pad_y2 = min(self.sampling_height, y2 + pad_bottom) + + # All coordinates are already multiples of 8 due to how sampling_width/height + # and tile_width/height are calculated + return x1, x2, y1, y2, pad_x1, pad_x2, pad_y1, pad_y2 + + def get_tile_mask_area(self, tile_width, tile_height, tile_padding): + tile_start_x = max(0, tile_padding) + tile_end_x = min(tile_width, self.tile_width + tile_padding) + tile_start_y = max(0, tile_padding) + tile_end_y = min(tile_height, self.tile_height + tile_padding) + + return tile_start_x, tile_end_x, tile_start_y, tile_end_y diff --git a/custom_nodes/ComfyUI-segment-anything-2/.gitignore b/custom_nodes/ComfyUI-segment-anything-2/.gitignore new file mode 100644 index 00000000..bd13e807 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/.gitignore @@ -0,0 +1,9 @@ +.DS_Store +*pyc +.vscode +__pycache__ +*.egg-info +*.bak +checkpoints +results +backup \ No newline at end of file diff --git a/custom_nodes/ComfyUI-segment-anything-2/LICENSE b/custom_nodes/ComfyUI-segment-anything-2/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/ComfyUI-segment-anything-2/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/__init__.py new file mode 100644 index 00000000..2e96bd6a --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/__init__.py @@ -0,0 +1,3 @@ +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-segment-anything-2/example_workflows/florence_segment_2.json b/custom_nodes/ComfyUI-segment-anything-2/example_workflows/florence_segment_2.json new file mode 100644 index 00000000..93b5aad3 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/example_workflows/florence_segment_2.json @@ -0,0 +1,579 @@ +{ + "last_node_id": 102, + "last_link_id": 239, + "nodes": [ + { + "id": 83, + "type": "LoadImage", + "pos": [ + -6, + 40 + ], + "size": { + "0": 315, + "1": 314 + }, + "flags": {}, + "order": 0, + "mode": 0, + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 196 + ], + "shape": 3, + "slot_index": 0 + }, + { + "name": "MASK", + "type": "MASK", + "links": null, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "LoadImage" + }, + "widgets_values": [ + "truck.jpg", + "image" + ] + }, + { + "id": 66, + "type": "DownloadAndLoadSAM2Model", + "pos": [ + -34, + -171 + ], + "size": { + "0": 351.7801513671875, + "1": 130 + }, + "flags": {}, + "order": 1, + "mode": 0, + "outputs": [ + { + "name": "sam2_model", + "type": "SAM2MODEL", + "links": [ + 236 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "DownloadAndLoadSAM2Model" + }, + "widgets_values": [ + "sam2_hiera_small.safetensors", + "single_image", + "cuda", + "bf16" + ] + }, + { + "id": 84, + "type": "ImageAndMaskPreview", + "pos": [ + 958, + -293 + ], + "size": { + "0": 667.9199829101562, + "1": 541.2733154296875 + }, + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [ + { + "name": "image", + "type": "IMAGE", + "link": 192 + }, + { + "name": "mask", + "type": "MASK", + "link": 238, + "slot_index": 1 + } + ], + "outputs": [ + { + "name": "composite", + "type": "IMAGE", + 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"JSON", + "link": 204 + } + ], + "outputs": [ + { + "name": "coordinates", + "type": "STRING", + "links": [], + "shape": 3, + "slot_index": 0 + }, + { + "name": "bboxes", + "type": "BBOX", + "links": [ + 239 + ], + "shape": 3, + "slot_index": 1 + } + ], + "properties": { + "Node name for S&R": "Florence2toCoordinates" + }, + "widgets_values": [ + "" + ] + }, + { + "id": 87, + "type": "Florence2Run", + "pos": [ + -85, + -796 + ], + "size": { + "0": 400, + "1": 304 + }, + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "name": "image", + "type": "IMAGE", + "link": 210, + "slot_index": 0 + }, + { + "name": "florence2_model", + "type": "FL2MODEL", + "link": 197, + "slot_index": 1 + } + ], + "outputs": [ + { + "name": "image", + "type": "IMAGE", + "links": [ + 200 + ], + "shape": 3, + "slot_index": 0 + }, + { + "name": "mask", + "type": "MASK", + "links": null, + "shape": 3 + }, + { + "name": "caption", + "type": "STRING", + "links": null, + "shape": 3, + "slot_index": 2 + }, + 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+ ] + } + }, + "widgets_values": [ + "{\"positive\":[{\"x\":620.2460000000001,\"y\":359.37000000000006},{\"x\":620.73,\"y\":245.63000000000002}],\"negative\":[{\"x\":0,\"y\":0}]}", + "[{\"x\":620.2460000000001,\"y\":359.37000000000006},{\"x\":620.73,\"y\":245.63000000000002}]", + "[{\"x\":0,\"y\":0}]", + "[{}]", + "[{}]", + "xyxy", + 768, + 768, + false, + null, + null, + null + ] + } + ], + "links": [ + [ + 40, + 106, + 0, + 105, + 0, + "SAM2MODEL" + ], + [ + 41, + 102, + 0, + 105, + 1, + "IMAGE" + ], + [ + 42, + 105, + 0, + 107, + 1, + "MASK" + ], + [ + 43, + 102, + 0, + 107, + 0, + "IMAGE" + ], + [ + 52, + 102, + 0, + 114, + 0, + "IMAGE" + ], + [ + 53, + 114, + 0, + 112, + 0, + "STRING" + ], + [ + 54, + 114, + 0, + 105, + 3, + "STRING" + ] + ], + "groups": [], + "config": {}, + "extra": { + "ds": { + "scale": 0.7513148009015777, + "offset": { + "0": 226.08052057760656, + "1": 820.3321624947772 + } + } + }, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-segment-anything-2/load_model.py b/custom_nodes/ComfyUI-segment-anything-2/load_model.py new file mode 100644 index 00000000..03d6e05c --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/load_model.py @@ -0,0 +1,194 @@ +import yaml +from .sam2.modeling.sam2_base import SAM2Base +from .sam2.modeling.backbones.image_encoder import ImageEncoder +from .sam2.modeling.backbones.hieradet import Hiera +from .sam2.modeling.backbones.image_encoder import FpnNeck +from .sam2.modeling.position_encoding import PositionEmbeddingSine +from .sam2.modeling.memory_attention import MemoryAttention, MemoryAttentionLayer +from .sam2.modeling.sam.transformer import RoPEAttention +from .sam2.modeling.memory_encoder import MemoryEncoder, MaskDownSampler, Fuser, CXBlock + +from .sam2.sam2_image_predictor import SAM2ImagePredictor +from .sam2.sam2_video_predictor import SAM2VideoPredictor +from .sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator +from comfy.utils import load_torch_file + +def load_model(model_path, model_cfg_path, segmentor, dtype, device): + # Load the YAML configuration + with open(model_cfg_path, 'r') as file: + config = yaml.safe_load(file) + + # Extract the model configuration + model_config = config['model'] + + # Instantiate the image encoder components + trunk_config = model_config['image_encoder']['trunk'] + neck_config = model_config['image_encoder']['neck'] + position_encoding_config = neck_config['position_encoding'] + + position_encoding = PositionEmbeddingSine( + num_pos_feats=position_encoding_config['num_pos_feats'], + normalize=position_encoding_config['normalize'], + scale=position_encoding_config['scale'], + temperature=position_encoding_config['temperature'] + ) + + neck = FpnNeck( + position_encoding=position_encoding, + d_model=neck_config['d_model'], + backbone_channel_list=neck_config['backbone_channel_list'], + fpn_top_down_levels=neck_config['fpn_top_down_levels'], + fpn_interp_model=neck_config['fpn_interp_model'] + ) + + keys_to_include = ['embed_dim', 'num_heads', 'global_att_blocks', 'window_pos_embed_bkg_spatial_size', 'stages'] + trunk_kwargs = {key: trunk_config[key] for key in keys_to_include if key in trunk_config} + trunk = Hiera(**trunk_kwargs) + + image_encoder = ImageEncoder( + scalp=model_config['image_encoder']['scalp'], + trunk=trunk, + neck=neck + ) + # Instantiate the memory attention components + memory_attention_layer_config = config['model']['memory_attention']['layer'] + self_attention_config = memory_attention_layer_config['self_attention'] + cross_attention_config = memory_attention_layer_config['cross_attention'] + + self_attention = RoPEAttention( + rope_theta=self_attention_config['rope_theta'], + feat_sizes=self_attention_config['feat_sizes'], + embedding_dim=self_attention_config['embedding_dim'], + num_heads=self_attention_config['num_heads'], + downsample_rate=self_attention_config['downsample_rate'], + dropout=self_attention_config['dropout'] + ) + + cross_attention = RoPEAttention( + rope_theta=cross_attention_config['rope_theta'], + feat_sizes=cross_attention_config['feat_sizes'], + rope_k_repeat=cross_attention_config['rope_k_repeat'], + embedding_dim=cross_attention_config['embedding_dim'], + num_heads=cross_attention_config['num_heads'], + downsample_rate=cross_attention_config['downsample_rate'], + dropout=cross_attention_config['dropout'], + kv_in_dim=cross_attention_config['kv_in_dim'] + ) + + memory_attention_layer = MemoryAttentionLayer( + activation=memory_attention_layer_config['activation'], + dim_feedforward=memory_attention_layer_config['dim_feedforward'], + dropout=memory_attention_layer_config['dropout'], + pos_enc_at_attn=memory_attention_layer_config['pos_enc_at_attn'], + self_attention=self_attention, + d_model=memory_attention_layer_config['d_model'], + pos_enc_at_cross_attn_keys=memory_attention_layer_config['pos_enc_at_cross_attn_keys'], + pos_enc_at_cross_attn_queries=memory_attention_layer_config['pos_enc_at_cross_attn_queries'], + cross_attention=cross_attention + ) + + memory_attention = MemoryAttention( + d_model=config['model']['memory_attention']['d_model'], + pos_enc_at_input=config['model']['memory_attention']['pos_enc_at_input'], + layer=memory_attention_layer, + num_layers=config['model']['memory_attention']['num_layers'] + ) + + # Instantiate the memory encoder components + memory_encoder_config = config['model']['memory_encoder'] + position_encoding_mem_enc_config = memory_encoder_config['position_encoding'] + mask_downsampler_config = memory_encoder_config['mask_downsampler'] + fuser_layer_config = memory_encoder_config['fuser']['layer'] + + position_encoding_mem_enc = PositionEmbeddingSine( + num_pos_feats=position_encoding_mem_enc_config['num_pos_feats'], + normalize=position_encoding_mem_enc_config['normalize'], + scale=position_encoding_mem_enc_config['scale'], + temperature=position_encoding_mem_enc_config['temperature'] + ) + + mask_downsampler = MaskDownSampler( + kernel_size=mask_downsampler_config['kernel_size'], + stride=mask_downsampler_config['stride'], + padding=mask_downsampler_config['padding'] + ) + + fuser_layer = CXBlock( + dim=fuser_layer_config['dim'], + kernel_size=fuser_layer_config['kernel_size'], + padding=fuser_layer_config['padding'], + layer_scale_init_value=float(fuser_layer_config['layer_scale_init_value']) + ) + fuser = Fuser( + num_layers=memory_encoder_config['fuser']['num_layers'], + layer=fuser_layer + ) + + memory_encoder = MemoryEncoder( + position_encoding=position_encoding_mem_enc, + mask_downsampler=mask_downsampler, + fuser=fuser, + out_dim=memory_encoder_config['out_dim'] + ) + + sam_mask_decoder_extra_args = { + "dynamic_multimask_via_stability": True, + "dynamic_multimask_stability_delta": 0.05, + "dynamic_multimask_stability_thresh": 0.98, + } + + def initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device): + return model_class( + image_encoder=image_encoder, + memory_attention=memory_attention, + memory_encoder=memory_encoder, + sam_mask_decoder_extra_args=sam_mask_decoder_extra_args, + num_maskmem=model_config['num_maskmem'], + image_size=model_config['image_size'], + sigmoid_scale_for_mem_enc=model_config['sigmoid_scale_for_mem_enc'], + sigmoid_bias_for_mem_enc=model_config['sigmoid_bias_for_mem_enc'], + use_mask_input_as_output_without_sam=model_config['use_mask_input_as_output_without_sam'], + directly_add_no_mem_embed=model_config['directly_add_no_mem_embed'], + use_high_res_features_in_sam=model_config['use_high_res_features_in_sam'], + multimask_output_in_sam=model_config['multimask_output_in_sam'], + iou_prediction_use_sigmoid=model_config['iou_prediction_use_sigmoid'], + use_obj_ptrs_in_encoder=model_config['use_obj_ptrs_in_encoder'], + add_tpos_enc_to_obj_ptrs=model_config['add_tpos_enc_to_obj_ptrs'], + only_obj_ptrs_in_the_past_for_eval=model_config['only_obj_ptrs_in_the_past_for_eval'], + pred_obj_scores=model_config['pred_obj_scores'], + pred_obj_scores_mlp=model_config['pred_obj_scores_mlp'], + fixed_no_obj_ptr=model_config['fixed_no_obj_ptr'], + multimask_output_for_tracking=model_config['multimask_output_for_tracking'], + use_multimask_token_for_obj_ptr=model_config['use_multimask_token_for_obj_ptr'], + compile_image_encoder=model_config['compile_image_encoder'], + multimask_min_pt_num=model_config['multimask_min_pt_num'], + multimask_max_pt_num=model_config['multimask_max_pt_num'], + use_mlp_for_obj_ptr_proj=model_config['use_mlp_for_obj_ptr_proj'], + proj_tpos_enc_in_obj_ptrs=model_config['proj_tpos_enc_in_obj_ptrs'], + no_obj_embed_spatial=model_config['no_obj_embed_spatial'], + use_signed_tpos_enc_to_obj_ptrs=model_config['use_signed_tpos_enc_to_obj_ptrs'], + binarize_mask_from_pts_for_mem_enc=True if segmentor == 'video' else False, + ).to(dtype).to(device).eval() + + # Load the state dictionary + sd = load_torch_file(model_path) + + # Initialize model based on segmentor type + if segmentor == 'single_image': + model_class = SAM2Base + model = initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device) + model.load_state_dict(sd) + model = SAM2ImagePredictor(model) + elif segmentor == 'video': + model_class = SAM2VideoPredictor + model = initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device) + model.load_state_dict(sd) + elif segmentor == 'automaskgenerator': + model_class = SAM2Base + model = initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device) + model.load_state_dict(sd) + model = SAM2AutomaticMaskGenerator(model) + else: + raise ValueError(f"Segmentor {segmentor} not supported") + + return model \ No newline at end of file diff --git a/custom_nodes/ComfyUI-segment-anything-2/nodes.py b/custom_nodes/ComfyUI-segment-anything-2/nodes.py new file mode 100644 index 00000000..c5ac4064 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/nodes.py @@ -0,0 +1,771 @@ +import torch +from torch.functional import F +import os +import numpy as np +import json +import random + +from tqdm import tqdm +from contextlib import nullcontext + +from .load_model import load_model + +import comfy.model_management as mm +from comfy.utils import ProgressBar, common_upscale +import folder_paths + +script_directory = os.path.dirname(os.path.abspath(__file__)) + +class DownloadAndLoadSAM2Model: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ([ + 'sam2_hiera_base_plus.safetensors', + 'sam2_hiera_large.safetensors', + 'sam2_hiera_small.safetensors', + 'sam2_hiera_tiny.safetensors', + 'sam2.1_hiera_base_plus.safetensors', + 'sam2.1_hiera_large.safetensors', + 'sam2.1_hiera_small.safetensors', + 'sam2.1_hiera_tiny.safetensors', + ],), + "segmentor": ( + ['single_image','video', 'automaskgenerator'], + ), + "device": (['cuda', 'cpu', 'mps'], ), + "precision": ([ 'fp16','bf16','fp32'], + { + "default": 'fp16' + }), + + }, + } + + RETURN_TYPES = ("SAM2MODEL",) + RETURN_NAMES = ("sam2_model",) + FUNCTION = "loadmodel" + CATEGORY = "SAM2" + + def loadmodel(self, model, segmentor, device, precision): + if precision != 'fp32' and device == 'cpu': + raise ValueError("fp16 and bf16 are not supported on cpu") + + if device == "cuda": + if torch.cuda.get_device_properties(0).major >= 8: + # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + device = {"cuda": torch.device("cuda"), "cpu": torch.device("cpu"), "mps": torch.device("mps")}[device] + + download_path = os.path.join(folder_paths.models_dir, "sam2") + if precision != 'fp32' and "2.1" in model: + base_name, extension = model.rsplit('.', 1) + model = f"{base_name}-fp16.{extension}" + model_path = os.path.join(download_path, model) + print("model_path: ", model_path) + + if not os.path.exists(model_path): + print(f"Downloading SAM2 model to: {model_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id="Kijai/sam2-safetensors", + allow_patterns=[f"*{model}*"], + local_dir=download_path, + local_dir_use_symlinks=False) + + model_mapping = { + "2.0": { + "base": "sam2_hiera_b+.yaml", + "large": "sam2_hiera_l.yaml", + "small": "sam2_hiera_s.yaml", + "tiny": "sam2_hiera_t.yaml" + }, + "2.1": { + "base": "sam2.1_hiera_b+.yaml", + "large": "sam2.1_hiera_l.yaml", + "small": "sam2.1_hiera_s.yaml", + "tiny": "sam2.1_hiera_t.yaml" + } + } + version = "2.1" if "2.1" in model else "2.0" + + model_cfg_path = next( + (os.path.join(script_directory, "sam2_configs", cfg) + for key, cfg in model_mapping[version].items() if key in model), + None + ) + print(f"Using model config: {model_cfg_path}") + + model = load_model(model_path, model_cfg_path, segmentor, dtype, device) + + sam2_model = { + 'model': model, + 'dtype': dtype, + 'device': device, + 'segmentor' : segmentor, + 'version': version + } + + return (sam2_model,) + + +class Florence2toCoordinates: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "data": ("JSON", ), + "index": ("STRING", {"default": "0"}), + "batch": ("BOOLEAN", {"default": False}), + }, + + } + + RETURN_TYPES = ("STRING", "BBOX") + RETURN_NAMES =("center_coordinates", "bboxes") + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, data, index, batch=False): + try: + coordinates = coordinates.replace("'", '"') + coordinates = json.loads(coordinates) + except: + coordinates = data + + if len(data)==0: + return (json.dumps([{'x': 0, 'y': 0}]),) + center_points = [] + + def get_bboxes(item): + return item["bboxes"] if isinstance(item, dict) else item + + if index.strip(): # Check if index is not empty + indexes = [int(i) for i in index.split(",")] + else: # If index is empty, use all indices from data[0] + indexes = list(range(len(get_bboxes(data[0])))) + + print("Indexes:", indexes) + bboxes = [] + + if batch: + for idx in indexes: + if 0 <= idx < len(get_bboxes(data[0])): + for i in range(len(data)): + bbox = get_bboxes(data[i])[idx] + min_x, min_y, max_x, max_y = bbox + center_x = int((min_x + max_x) / 2) + center_y = int((min_y + max_y) / 2) + center_points.append({"x": center_x, "y": center_y}) + bboxes.append(bbox) + else: + for idx in indexes: + if 0 <= idx < len(get_bboxes(data[0])): + bbox = get_bboxes(data[0])[idx] + min_x, min_y, max_x, max_y = bbox + center_x = int((min_x + max_x) / 2) + center_y = int((min_y + max_y) / 2) + center_points.append({"x": center_x, "y": center_y}) + bboxes.append(bbox) + else: + raise ValueError(f"There's nothing in index: {idx}") + + coordinates = json.dumps(center_points) + print("Coordinates:", coordinates) + return (coordinates, bboxes) + +class Sam2Segmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "image": ("IMAGE", ), + "keep_model_loaded": ("BOOLEAN", {"default": False}), + }, + "optional": { + "coordinates_positive": ("STRING", {"forceInput": True}), + "coordinates_negative": ("STRING", {"forceInput": True}), + "bboxes": ("BBOX", ), + "individual_objects": ("BOOLEAN", {"default": False}), + "mask": ("MASK", ), + + }, + } + + RETURN_TYPES = ("MASK", ) + RETURN_NAMES =("mask", ) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, image, sam2_model, keep_model_loaded, coordinates_positive=None, coordinates_negative=None, + individual_objects=False, bboxes=None, mask=None): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + B, H, W, C = image.shape + + if mask is not None: + input_mask = mask.clone().unsqueeze(1) + input_mask = F.interpolate(input_mask, size=(256, 256), mode="bilinear") + input_mask = input_mask.squeeze(1) + + if segmentor == 'automaskgenerator': + raise ValueError("For automaskgenerator use Sam2AutoMaskSegmentation -node") + if segmentor == 'single_image' and B > 1: + print("Segmenting batch of images with single_image segmentor") + + if segmentor == 'video' and bboxes is not None and "2.1" not in sam2_model["version"]: + raise ValueError("2.0 model doesn't support bboxes with video segmentor") + + if segmentor == 'video': # video model needs images resized first thing + model_input_image_size = model.image_size + print("Resizing to model input image size: ", model_input_image_size) + image = common_upscale(image.movedim(-1,1), model_input_image_size, model_input_image_size, "bilinear", "disabled").movedim(1,-1) + + #handle point coordinates + if coordinates_positive is not None: + try: + coordinates_positive = json.loads(coordinates_positive.replace("'", '"')) + coordinates_positive = [(coord['x'], coord['y']) for coord in coordinates_positive] + if coordinates_negative is not None: + coordinates_negative = json.loads(coordinates_negative.replace("'", '"')) + coordinates_negative = [(coord['x'], coord['y']) for coord in coordinates_negative] + except: + pass + + if not individual_objects: + positive_point_coords = np.atleast_2d(np.array(coordinates_positive)) + else: + positive_point_coords = np.array([np.atleast_2d(coord) for coord in coordinates_positive]) + + if coordinates_negative is not None: + negative_point_coords = np.array(coordinates_negative) + # Ensure both positive and negative coords are lists of 2D arrays if individual_objects is True + if individual_objects: + assert negative_point_coords.shape[0] <= positive_point_coords.shape[0], "Can't have more negative than positive points in individual_objects mode" + if negative_point_coords.ndim == 2: + negative_point_coords = negative_point_coords[:, np.newaxis, :] + # Extend negative coordinates to match the number of positive coordinates + while negative_point_coords.shape[0] < positive_point_coords.shape[0]: + negative_point_coords = np.concatenate((negative_point_coords, negative_point_coords[:1, :, :]), axis=0) + final_coords = np.concatenate((positive_point_coords, negative_point_coords), axis=1) + else: + final_coords = np.concatenate((positive_point_coords, negative_point_coords), axis=0) + else: + final_coords = positive_point_coords + + # Handle possible bboxes + if bboxes is not None: + boxes_np_batch = [] + for bbox_list in bboxes: + boxes_np = [] + for bbox in bbox_list: + boxes_np.append(bbox) + boxes_np = np.array(boxes_np) + boxes_np_batch.append(boxes_np) + if individual_objects: + final_box = np.array(boxes_np_batch) + else: + final_box = np.array(boxes_np) + final_labels = None + + #handle labels + if coordinates_positive is not None: + if not individual_objects: + positive_point_labels = np.ones(len(positive_point_coords)) + else: + positive_labels = [] + for point in positive_point_coords: + positive_labels.append(np.array([1])) # 1) + positive_point_labels = np.stack(positive_labels, axis=0) + + if coordinates_negative is not None: + if not individual_objects: + negative_point_labels = np.zeros(len(negative_point_coords)) # 0 = negative + final_labels = np.concatenate((positive_point_labels, negative_point_labels), axis=0) + else: + negative_labels = [] + for point in positive_point_coords: + negative_labels.append(np.array([0])) # 1) + negative_point_labels = np.stack(negative_labels, axis=0) + #combine labels + final_labels = np.concatenate((positive_point_labels, negative_point_labels), axis=1) + else: + final_labels = positive_point_labels + print("combined labels: ", final_labels) + print("combined labels shape: ", final_labels.shape) + + mask_list = [] + try: + model.to(device) + except: + model.model.to(device) + + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + if segmentor == 'single_image': + image_np = (image.contiguous() * 255).byte().numpy() + comfy_pbar = ProgressBar(len(image_np)) + tqdm_pbar = tqdm(total=len(image_np), desc="Processing Images") + for i in range(len(image_np)): + model.set_image(image_np[i]) + if bboxes is None: + input_box = None + else: + if len(image_np) > 1: + input_box = final_box[i] + input_box = final_box + + out_masks, scores, logits = model.predict( + point_coords=final_coords if coordinates_positive is not None else None, + point_labels=final_labels if coordinates_positive is not None else None, + box=input_box, + multimask_output=True if not individual_objects else False, + mask_input = input_mask[i].unsqueeze(0) if mask is not None else None, + ) + + if out_masks.ndim == 3: + sorted_ind = np.argsort(scores)[::-1] + out_masks = out_masks[sorted_ind][0] #choose only the best result for now + scores = scores[sorted_ind] + logits = logits[sorted_ind] + mask_list.append(np.expand_dims(out_masks, axis=0)) + else: + _, _, H, W = out_masks.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=bool) + for out_mask in out_masks: + combined_mask = np.logical_or(combined_mask, out_mask) + combined_mask = combined_mask.astype(np.uint8) + mask_list.append(combined_mask) + comfy_pbar.update(1) + tqdm_pbar.update(1) + + elif segmentor == 'video': + mask_list = [] + if hasattr(self, 'inference_state') and self.inference_state is not None: + model.reset_state(self.inference_state) + self.inference_state = model.init_state(image.permute(0, 3, 1, 2).contiguous(), H, W, device=device) + if bboxes is None: + input_box = None + else: + input_box = bboxes[0] + + if individual_objects and bboxes is not None: + raise ValueError("bboxes not supported with individual_objects") + + + if individual_objects: + for i, (coord, label) in enumerate(zip(final_coords, final_labels)): + _, out_obj_ids, out_mask_logits = model.add_new_points_or_box( + inference_state=self.inference_state, + frame_idx=0, + obj_id=i, + points=final_coords[i], + labels=final_labels[i], + clear_old_points=True, + box=input_box + ) + else: + _, out_obj_ids, out_mask_logits = model.add_new_points_or_box( + inference_state=self.inference_state, + frame_idx=0, + obj_id=1, + points=final_coords if coordinates_positive is not None else None, + labels=final_labels if coordinates_positive is not None else None, + clear_old_points=True, + box=input_box + ) + + pbar = ProgressBar(B) + video_segments = {} + for out_frame_idx, out_obj_ids, out_mask_logits in model.propagate_in_video(self.inference_state): + video_segments[out_frame_idx] = { + out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() + for i, out_obj_id in enumerate(out_obj_ids) + } + pbar.update(1) + if individual_objects: + _, _, H, W = out_mask_logits.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=np.uint8) + for i, out_obj_id in enumerate(out_obj_ids): + out_mask = (out_mask_logits[i] > 0.0).cpu().numpy() + combined_mask = np.logical_or(combined_mask, out_mask) + video_segments[out_frame_idx] = combined_mask + + if individual_objects: + for frame_idx, combined_mask in video_segments.items(): + mask_list.append(combined_mask) + else: + for frame_idx, obj_masks in video_segments.items(): + for out_obj_id, out_mask in obj_masks.items(): + mask_list.append(out_mask) + + if not keep_model_loaded: + try: + model.to(offload_device) + except: + model.model.to(offload_device) + if hasattr(self, 'inference_state') and self.inference_state is not None and hasattr(model, "reset_state"): + model.reset_state(self.inference_state) + self.inference_state = None + mm.soft_empty_cache() + + out_list = [] + for mask in mask_list: + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.permute(1, 2, 0) + mask_tensor = mask_tensor[:, :, 0] + out_list.append(mask_tensor) + mask_tensor = torch.stack(out_list, dim=0).cpu().float() + return (mask_tensor,) + +class Sam2VideoSegmentationAddPoints: + @classmethod + def IS_CHANGED(s): # TODO: smarter reset? + return "" + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "coordinates_positive": ("STRING", {"forceInput": True}), + "frame_index": ("INT", {"default": 0}), + "object_index": ("INT", {"default": 0}), + }, + "optional": { + "image": ("IMAGE", ), + "coordinates_negative": ("STRING", {"forceInput": True}), + "prev_inference_state": ("SAM2INFERENCESTATE", ), + }, + } + + RETURN_TYPES = ("SAM2MODEL", "SAM2INFERENCESTATE", ) + RETURN_NAMES =("sam2_model", "inference_state", ) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, sam2_model, coordinates_positive, frame_index, object_index, image=None, coordinates_negative=None, prev_inference_state=None): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + + + if segmentor != 'video': + raise ValueError("Loaded model is not SAM2Video") + if image is not None: + B, H, W, C = image.shape + model_input_image_size = model.image_size + print("Resizing to model input image size: ", model_input_image_size) + image = common_upscale(image.movedim(-1,1), model_input_image_size, model_input_image_size, "bilinear", "disabled").movedim(1,-1) + + try: + coordinates_positive = json.loads(coordinates_positive.replace("'", '"')) + coordinates_positive = [(coord['x'], coord['y']) for coord in coordinates_positive] + if coordinates_negative is not None: + coordinates_negative = json.loads(coordinates_negative.replace("'", '"')) + coordinates_negative = [(coord['x'], coord['y']) for coord in coordinates_negative] + except: + pass + + positive_point_coords = np.array(coordinates_positive) + positive_point_labels = [1] * len(positive_point_coords) # 1 = positive + positive_point_labels = np.array(positive_point_labels) + print("positive coordinates: ", positive_point_coords) + + if coordinates_negative is not None: + negative_point_coords = np.array(coordinates_negative) + negative_point_labels = [0] * len(negative_point_coords) # 0 = negative + negative_point_labels = np.array(negative_point_labels) + print("negative coordinates: ", negative_point_coords) + + # Combine coordinates and labels + else: + negative_point_coords = np.empty((0, 2)) + negative_point_labels = np.array([]) + # Ensure both positive and negative coordinates are 2D arrays + positive_point_coords = np.atleast_2d(positive_point_coords) + negative_point_coords = np.atleast_2d(negative_point_coords) + + # Ensure both positive and negative labels are 1D arrays + positive_point_labels = np.atleast_1d(positive_point_labels) + negative_point_labels = np.atleast_1d(negative_point_labels) + + combined_coords = np.concatenate((positive_point_coords, negative_point_coords), axis=0) + combined_labels = np.concatenate((positive_point_labels, negative_point_labels), axis=0) + + model.to(device) + + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(model.device), dtype=dtype) if autocast_condition else nullcontext(): + if prev_inference_state is None: + print("Initializing inference state") + if hasattr(self, 'inference_state'): + model.reset_state(self.inference_state) + self.inference_state = model.init_state(image.permute(0, 3, 1, 2).contiguous(), H, W, device=device) + else: + print("Using previous inference state") + B = prev_inference_state['num_frames'] + self.inference_state = prev_inference_state['inference_state'] + _, out_obj_ids, out_mask_logits = model.add_new_points( + inference_state=self.inference_state, + frame_idx=frame_index, + obj_id=object_index, + points=combined_coords, + labels=combined_labels, + ) + inference_state = { + "inference_state": self.inference_state, + "num_frames": B, + } + sam2_model = { + 'model': model, + 'dtype': dtype, + 'device': device, + 'segmentor' : segmentor + } + return (sam2_model, inference_state,) + +class Sam2VideoSegmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "inference_state": ("SAM2INFERENCESTATE", ), + "keep_model_loaded": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("MASK", ) + RETURN_NAMES =("mask", ) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, sam2_model, inference_state, keep_model_loaded): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + inference_state = inference_state["inference_state"] + B = inference_state["num_frames"] + + if segmentor != 'video': + raise ValueError("Loaded model is not SAM2Video") + + model.to(device) + + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + + #if hasattr(self, 'inference_state'): + # model.reset_state(self.inference_state) + + pbar = ProgressBar(B) + video_segments = {} + for out_frame_idx, out_obj_ids, out_mask_logits in model.propagate_in_video(inference_state): + print("out_mask_logits",out_mask_logits.shape) + _, _, H, W = out_mask_logits.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=np.uint8) + for i, out_obj_id in enumerate(out_obj_ids): + out_mask = (out_mask_logits[i] > 0.0).cpu().numpy() + combined_mask = np.logical_or(combined_mask, out_mask) + video_segments[out_frame_idx] = combined_mask + pbar.update(1) + + mask_list = [] + # Collect the combined masks + for frame_idx, combined_mask in video_segments.items(): + mask_list.append(combined_mask) + print(f"Total masks collected: {len(mask_list)}") + + if not keep_model_loaded: + model.to(offload_device) + + out_list = [] + for mask in mask_list: + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.permute(1, 2, 0) + mask_tensor = mask_tensor[:, :, 0] + out_list.append(mask_tensor) + mask_tensor = torch.stack(out_list, dim=0).cpu().float() + return (mask_tensor,) + +class Sam2AutoSegmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "image": ("IMAGE", ), + "points_per_side": ("INT", {"default": 32}), + "points_per_batch": ("INT", {"default": 64}), + "pred_iou_thresh": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}), + "stability_score_thresh": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01}), + "stability_score_offset": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "mask_threshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_n_layers": ("INT", {"default": 0}), + "box_nms_thresh": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_nms_thresh": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_overlap_ratio": ("FLOAT", {"default": 0.34, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_n_points_downscale_factor": ("INT", {"default": 1}), + "min_mask_region_area": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "use_m2m": ("BOOLEAN", {"default": False}), + "keep_model_loaded": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("MASK", "IMAGE", "BBOX",) + RETURN_NAMES =("mask", "segmented_image", "bbox" ,) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, image, sam2_model, points_per_side, points_per_batch, pred_iou_thresh, stability_score_thresh, + stability_score_offset, crop_n_layers, box_nms_thresh, crop_n_points_downscale_factor, min_mask_region_area, + use_m2m, mask_threshold, crop_nms_thresh, crop_overlap_ratio, keep_model_loaded): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + + if segmentor != 'automaskgenerator': + raise ValueError("Loaded model is not SAM2AutomaticMaskGenerator") + + model.points_per_side=points_per_side + model.points_per_batch=points_per_batch + model.pred_iou_thresh=pred_iou_thresh + model.stability_score_thresh=stability_score_thresh + model.stability_score_offset=stability_score_offset + model.crop_n_layers=crop_n_layers + model.box_nms_thresh=box_nms_thresh + model.crop_n_points_downscale_factor=crop_n_points_downscale_factor + model.crop_nms_thresh=crop_nms_thresh + model.crop_overlap_ratio=crop_overlap_ratio + model.min_mask_region_area=min_mask_region_area + model.use_m2m=use_m2m + model.mask_threshold=mask_threshold + + model.predictor.model.to(device) + + B, H, W, C = image.shape + image_np = (image.contiguous() * 255).byte().numpy() + + out_list = [] + segment_out_list = [] + mask_list=[] + + pbar = ProgressBar(B) + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + for img_np in image_np: + result_dict = model.generate(img_np) + mask_list = [item['segmentation'] for item in result_dict] + bbox_list = [item['bbox'] for item in result_dict] + + # Generate random colors for each mask + num_masks = len(mask_list) + colors = [tuple(random.choices(range(256), k=3)) for _ in range(num_masks)] + + # Create a blank image to overlay masks + overlay_image = np.zeros((H, W, 3), dtype=np.uint8) + + # Create a combined mask initialized to zeros + combined_mask = np.zeros((H, W), dtype=np.uint8) + + # Iterate through masks and color them + for mask, color in zip(mask_list, colors): + + # Combine masks using logical OR + combined_mask = np.logical_or(combined_mask, mask).astype(np.uint8) + + # Convert mask to numpy array + mask_np = mask.astype(np.uint8) + + # Color the mask + colored_mask = np.zeros_like(overlay_image) + for i in range(3): # Apply color channel-wise + colored_mask[:, :, i] = mask_np * color[i] + + # Blend the colored mask with the overlay image + overlay_image = np.where(colored_mask > 0, colored_mask, overlay_image) + out_list.append(torch.from_numpy(combined_mask)) + segment_out_list.append(overlay_image) + pbar.update(1) + + stacked_array = np.stack(segment_out_list, axis=0) + segment_image_tensor = torch.from_numpy(stacked_array).float() / 255 + + if not keep_model_loaded: + model.predictor.model.to(offload_device) + + mask_tensor = torch.stack(out_list, dim=0) + return (mask_tensor.cpu().float(), segment_image_tensor.cpu().float(), bbox_list) + +#WIP +# class OwlV2Detector: +# @classmethod +# def INPUT_TYPES(s): +# return { +# "required": { +# "image": ("IMAGE", ), +# }, +# } + +# RETURN_TYPES = ("MASK", ) +# RETURN_NAMES =("mask", ) +# FUNCTION = "segment" +# CATEGORY = "SAM2" + +# def segment(self, image): +# from transformers import Owlv2Processor, Owlv2ForObjectDetection +# device = mm.get_torch_device() +# offload_device = mm.unet_offload_device() +# processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble") +# model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") + +# url = "http://images.cocodataset.org/val2017/000000039769.jpg" +# image = Image.open(requests.get(url, stream=True).raw) +# texts = [["a photo of a cat", "a photo of a dog"]] +# inputs = processor(text=texts, images=image, return_tensors="pt") +# outputs = model(**inputs) + +# # Target image sizes (height, width) to rescale box predictions [batch_size, 2] +# target_sizes = torch.Tensor([image.size[::-1]]) +# # Convert outputs (bounding boxes and class logits) to Pascal VOC Format (xmin, ymin, xmax, ymax) +# results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1) +# i = 0 # Retrieve predictions for the first image for the corresponding text queries +# text = texts[i] +# boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"] +# for box, score, label in zip(boxes, scores, labels): +# box = [round(i, 2) for i in box.tolist()] +# print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}") + + +# return (mask_tensor,) + +NODE_CLASS_MAPPINGS = { + "DownloadAndLoadSAM2Model": DownloadAndLoadSAM2Model, + "Sam2Segmentation": Sam2Segmentation, + "Florence2toCoordinates": Florence2toCoordinates, + "Sam2AutoSegmentation": Sam2AutoSegmentation, + "Sam2VideoSegmentationAddPoints": Sam2VideoSegmentationAddPoints, + "Sam2VideoSegmentation": Sam2VideoSegmentation +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DownloadAndLoadSAM2Model": "(Down)Load SAM2Model", + "Sam2Segmentation": "Sam2Segmentation", + "Florence2toCoordinates": "Florence2 Coordinates", + "Sam2AutoSegmentation": "Sam2AutoSegmentation", + "Sam2VideoSegmentationAddPoints": "Sam2VideoSegmentationAddPoints", + "Sam2VideoSegmentation": "Sam2VideoSegmentation" +} diff --git a/custom_nodes/ComfyUI-segment-anything-2/pyproject.toml b/custom_nodes/ComfyUI-segment-anything-2/pyproject.toml new file mode 100644 index 00000000..6bcc0611 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-segment-anything-2" +description = "Nodes to use [a/segment-anything-2](https://github.com/facebookresearch/segment-anything-2) for image or video segmentation." +version = "1.0.2" +license = {file = "LICENSE"} +dependencies = [] + +[project.urls] +Repository = "https://github.com/kijai/ComfyUI-segment-anything-2" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "kijai" +DisplayName = "ComfyUI-segment-anything-2" +Icon = "" diff --git a/custom_nodes/ComfyUI-segment-anything-2/readme.md b/custom_nodes/ComfyUI-segment-anything-2/readme.md new file mode 100644 index 00000000..b13c435a --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/readme.md @@ -0,0 +1,25 @@ +# WORK IN PROGRESS + +PointsEditor is now available for testing in KJNodes: https://github.com/kijai/ComfyUI-KJNodes + +https://github.com/user-attachments/assets/c4a88647-679f-4cf2-ba1f-4fa8c7308c1e + +https://github.com/user-attachments/assets/f15fafe8-72e8-41cc-b246-e947b1efe5ec + +https://github.com/user-attachments/assets/c1efb595-0fb1-4ae7-b4fa-2def08eda0a8 + +For testing only currently. + +Functional, but needs better coordinate selector. + +For now mask postprocessing is disabled due to it needing cuda extension compilation. We can use other nodes for this purpose anyway, so might leave it that way, we'll see. + +Models are automatically downloade from https://huggingface.co/Kijai/sam2-safetensors/tree/main + +to `ComfyUI/models/sam2` + + + +Original repo: + +https://github.com/facebookresearch/segment-anything-2 diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/automatic_mask_generator.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/automatic_mask_generator.py new file mode 100644 index 00000000..40208358 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/automatic_mask_generator.py @@ -0,0 +1,436 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +from torchvision.ops.boxes import batched_nms, box_area # type: ignore + +from ..sam2.modeling.sam2_base import SAM2Base +from ..sam2.sam2_image_predictor import SAM2ImagePredictor +from ..sam2.utils.amg import ( + area_from_rle, + batch_iterator, + batched_mask_to_box, + box_xyxy_to_xywh, + build_all_layer_point_grids, + calculate_stability_score, + coco_encode_rle, + generate_crop_boxes, + is_box_near_crop_edge, + mask_to_rle_pytorch, + MaskData, + remove_small_regions, + rle_to_mask, + uncrop_boxes_xyxy, + uncrop_masks, + uncrop_points, +) + + +class SAM2AutomaticMaskGenerator: + def __init__( + self, + model: SAM2Base, + points_per_side: Optional[int] = 32, + points_per_batch: int = 64, + pred_iou_thresh: float = 0.8, + stability_score_thresh: float = 0.95, + stability_score_offset: float = 1.0, + mask_threshold: float = 0.0, + box_nms_thresh: float = 0.7, + crop_n_layers: int = 0, + crop_nms_thresh: float = 0.7, + crop_overlap_ratio: float = 512 / 1500, + crop_n_points_downscale_factor: int = 1, + point_grids: Optional[List[np.ndarray]] = None, + min_mask_region_area: int = 0, + output_mode: str = "binary_mask", + use_m2m: bool = False, + multimask_output: bool = True, + ) -> None: + """ + Using a SAM 2 model, generates masks for the entire image. + Generates a grid of point prompts over the image, then filters + low quality and duplicate masks. The default settings are chosen + for SAM 2 with a HieraL backbone. + + Arguments: + model (Sam): The SAM 2 model to use for mask prediction. + points_per_side (int or None): The number of points to be sampled + along one side of the image. The total number of points is + points_per_side**2. If None, 'point_grids' must provide explicit + point sampling. + points_per_batch (int): Sets the number of points run simultaneously + by the model. Higher numbers may be faster but use more GPU memory. + pred_iou_thresh (float): A filtering threshold in [0,1], using the + model's predicted mask quality. + stability_score_thresh (float): A filtering threshold in [0,1], using + the stability of the mask under changes to the cutoff used to binarize + the model's mask predictions. + stability_score_offset (float): The amount to shift the cutoff when + calculated the stability score. + mask_threshold (float): Threshold for binarizing the mask logits + box_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks. + crop_n_layers (int): If >0, mask prediction will be run again on + crops of the image. Sets the number of layers to run, where each + layer has 2**i_layer number of image crops. + crop_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks between different crops. + crop_overlap_ratio (float): Sets the degree to which crops overlap. + In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + crop_n_points_downscale_factor (int): The number of points-per-side + sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + point_grids (list(np.ndarray) or None): A list over explicit grids + of points used for sampling, normalized to [0,1]. The nth grid in the + list is used in the nth crop layer. Exclusive with points_per_side. + min_mask_region_area (int): If >0, postprocessing will be applied + to remove disconnected regions and holes in masks with area smaller + than min_mask_region_area. Requires opencv. + output_mode (str): The form masks are returned in. Can be 'binary_mask', + 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. + For large resolutions, 'binary_mask' may consume large amounts of + memory. + use_m2m (bool): Whether to add a one step refinement using previous mask predictions. + multimask_output (bool): Whether to output multimask at each point of the grid. + """ + + assert (points_per_side is None) != ( + point_grids is None + ), "Exactly one of points_per_side or point_grid must be provided." + if points_per_side is not None: + self.point_grids = build_all_layer_point_grids( + points_per_side, + crop_n_layers, + crop_n_points_downscale_factor, + ) + elif point_grids is not None: + self.point_grids = point_grids + else: + raise ValueError("Can't have both points_per_side and point_grid be None.") + + assert output_mode in [ + "binary_mask", + "uncompressed_rle", + "coco_rle", + ], f"Unknown output_mode {output_mode}." + if output_mode == "coco_rle": + try: + from pycocotools import mask as mask_utils # type: ignore # noqa: F401 + except ImportError as e: + print("Please install pycocotools") + raise e + + self.predictor = SAM2ImagePredictor( + model, + max_hole_area=min_mask_region_area, + max_sprinkle_area=min_mask_region_area, + ) + self.points_per_batch = points_per_batch + self.pred_iou_thresh = pred_iou_thresh + self.stability_score_thresh = stability_score_thresh + self.stability_score_offset = stability_score_offset + self.mask_threshold = mask_threshold + self.box_nms_thresh = box_nms_thresh + self.crop_n_layers = crop_n_layers + self.crop_nms_thresh = crop_nms_thresh + self.crop_overlap_ratio = crop_overlap_ratio + self.crop_n_points_downscale_factor = crop_n_points_downscale_factor + self.min_mask_region_area = min_mask_region_area + self.output_mode = output_mode + self.use_m2m = use_m2m + self.multimask_output = multimask_output + + @torch.no_grad() + def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: + """ + Generates masks for the given image. + + Arguments: + image (np.ndarray): The image to generate masks for, in HWC uint8 format. + + Returns: + list(dict(str, any)): A list over records for masks. Each record is + a dict containing the following keys: + segmentation (dict(str, any) or np.ndarray): The mask. If + output_mode='binary_mask', is an array of shape HW. Otherwise, + is a dictionary containing the RLE. + bbox (list(float)): The box around the mask, in XYWH format. + area (int): The area in pixels of the mask. + predicted_iou (float): The model's own prediction of the mask's + quality. This is filtered by the pred_iou_thresh parameter. + point_coords (list(list(float))): The point coordinates input + to the model to generate this mask. + stability_score (float): A measure of the mask's quality. This + is filtered on using the stability_score_thresh parameter. + crop_box (list(float)): The crop of the image used to generate + the mask, given in XYWH format. + """ + + # Generate masks + mask_data = self._generate_masks(image) + + # Encode masks + if self.output_mode == "coco_rle": + mask_data["segmentations"] = [ + coco_encode_rle(rle) for rle in mask_data["rles"] + ] + elif self.output_mode == "binary_mask": + mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]] + else: + mask_data["segmentations"] = mask_data["rles"] + + # Write mask records + curr_anns = [] + for idx in range(len(mask_data["segmentations"])): + ann = { + "segmentation": mask_data["segmentations"][idx], + "area": area_from_rle(mask_data["rles"][idx]), + "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(), + "predicted_iou": mask_data["iou_preds"][idx].item(), + "point_coords": [mask_data["points"][idx].tolist()], + "stability_score": mask_data["stability_score"][idx].item(), + "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(), + } + curr_anns.append(ann) + + return curr_anns + + def _generate_masks(self, image: np.ndarray) -> MaskData: + orig_size = image.shape[:2] + crop_boxes, layer_idxs = generate_crop_boxes( + orig_size, self.crop_n_layers, self.crop_overlap_ratio + ) + + # Iterate over image crops + data = MaskData() + for crop_box, layer_idx in zip(crop_boxes, layer_idxs): + crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) + data.cat(crop_data) + + # Remove duplicate masks between crops + if len(crop_boxes) > 1: + # Prefer masks from smaller crops + scores = 1 / box_area(data["crop_boxes"]) + scores = scores.to(data["boxes"].device) + keep_by_nms = batched_nms( + data["boxes"].float(), + scores, + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.crop_nms_thresh, + ) + data.filter(keep_by_nms) + data.to_numpy() + return data + + def _process_crop( + self, + image: np.ndarray, + crop_box: List[int], + crop_layer_idx: int, + orig_size: Tuple[int, ...], + ) -> MaskData: + # Crop the image and calculate embeddings + x0, y0, x1, y1 = crop_box + cropped_im = image[y0:y1, x0:x1, :] + cropped_im_size = cropped_im.shape[:2] + self.predictor.set_image(cropped_im) + + # Get points for this crop + points_scale = np.array(cropped_im_size)[None, ::-1] + points_for_image = self.point_grids[crop_layer_idx] * points_scale + + # Generate masks for this crop in batches + data = MaskData() + for (points,) in batch_iterator(self.points_per_batch, points_for_image): + batch_data = self._process_batch( + points, cropped_im_size, crop_box, orig_size, normalize=True + ) + data.cat(batch_data) + del batch_data + self.predictor.reset_predictor() + + # Remove duplicates within this crop. + keep_by_nms = batched_nms( + data["boxes"].float(), + data["iou_preds"], + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.box_nms_thresh, + ) + data.filter(keep_by_nms) + + # Return to the original image frame + data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box) + data["points"] = uncrop_points(data["points"], crop_box) + data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))]) + + return data + + def _process_batch( + self, + points: np.ndarray, + im_size: Tuple[int, ...], + crop_box: List[int], + orig_size: Tuple[int, ...], + normalize=False, + ) -> MaskData: + orig_h, orig_w = orig_size + + # Run model on this batch + points = torch.as_tensor( + points, dtype=torch.float32, device=self.predictor.device + ) + in_points = self.predictor._transforms.transform_coords( + points, normalize=normalize, orig_hw=im_size + ) + in_labels = torch.ones( + in_points.shape[0], dtype=torch.int, device=in_points.device + ) + masks, iou_preds, low_res_masks = self.predictor._predict( + in_points[:, None, :], + in_labels[:, None], + multimask_output=self.multimask_output, + return_logits=True, + ) + + # Serialize predictions and store in MaskData + data = MaskData( + masks=masks.flatten(0, 1), + iou_preds=iou_preds.flatten(0, 1), + points=points.repeat_interleave(masks.shape[1], dim=0), + low_res_masks=low_res_masks.flatten(0, 1), + ) + del masks + + if not self.use_m2m: + # Filter by predicted IoU + if self.pred_iou_thresh > 0.0: + keep_mask = data["iou_preds"] > self.pred_iou_thresh + data.filter(keep_mask) + + # Calculate and filter by stability score + data["stability_score"] = calculate_stability_score( + data["masks"], self.mask_threshold, self.stability_score_offset + ) + if self.stability_score_thresh > 0.0: + keep_mask = data["stability_score"] >= self.stability_score_thresh + data.filter(keep_mask) + else: + # One step refinement using previous mask predictions + in_points = self.predictor._transforms.transform_coords( + data["points"], normalize=normalize, orig_hw=im_size + ) + labels = torch.ones( + in_points.shape[0], dtype=torch.int, device=in_points.device + ) + masks, ious = self.refine_with_m2m( + in_points, labels, data["low_res_masks"], self.points_per_batch + ) + data["masks"] = masks.squeeze(1) + data["iou_preds"] = ious.squeeze(1) + + if self.pred_iou_thresh > 0.0: + keep_mask = data["iou_preds"] > self.pred_iou_thresh + data.filter(keep_mask) + + data["stability_score"] = calculate_stability_score( + data["masks"], self.mask_threshold, self.stability_score_offset + ) + if self.stability_score_thresh > 0.0: + keep_mask = data["stability_score"] >= self.stability_score_thresh + data.filter(keep_mask) + + # Threshold masks and calculate boxes + data["masks"] = data["masks"] > self.mask_threshold + data["boxes"] = batched_mask_to_box(data["masks"]) + + # Filter boxes that touch crop boundaries + keep_mask = ~is_box_near_crop_edge( + data["boxes"], crop_box, [0, 0, orig_w, orig_h] + ) + if not torch.all(keep_mask): + data.filter(keep_mask) + + # Compress to RLE + data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) + data["rles"] = mask_to_rle_pytorch(data["masks"]) + del data["masks"] + + return data + + @staticmethod + def postprocess_small_regions( + mask_data: MaskData, min_area: int, nms_thresh: float + ) -> MaskData: + """ + Removes small disconnected regions and holes in masks, then reruns + box NMS to remove any new duplicates. + + Edits mask_data in place. + + Requires open-cv as a dependency. + """ + if len(mask_data["rles"]) == 0: + return mask_data + + # Filter small disconnected regions and holes + new_masks = [] + scores = [] + for rle in mask_data["rles"]: + mask = rle_to_mask(rle) + + mask, changed = remove_small_regions(mask, min_area, mode="holes") + unchanged = not changed + mask, changed = remove_small_regions(mask, min_area, mode="islands") + unchanged = unchanged and not changed + + new_masks.append(torch.as_tensor(mask).unsqueeze(0)) + # Give score=0 to changed masks and score=1 to unchanged masks + # so NMS will prefer ones that didn't need postprocessing + scores.append(float(unchanged)) + + # Recalculate boxes and remove any new duplicates + masks = torch.cat(new_masks, dim=0) + boxes = batched_mask_to_box(masks) + keep_by_nms = batched_nms( + boxes.float(), + torch.as_tensor(scores), + torch.zeros_like(boxes[:, 0]), # categories + iou_threshold=nms_thresh, + ) + + # Only recalculate RLEs for masks that have changed + for i_mask in keep_by_nms: + if scores[i_mask] == 0.0: + mask_torch = masks[i_mask].unsqueeze(0) + mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0] + mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly + mask_data.filter(keep_by_nms) + + return mask_data + + def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch): + new_masks = [] + new_iou_preds = [] + + for cur_points, cur_point_labels, low_res_mask in batch_iterator( + points_per_batch, points, point_labels, low_res_masks + ): + best_masks, best_iou_preds, _ = self.predictor._predict( + cur_points[:, None, :], + cur_point_labels[:, None], + mask_input=low_res_mask[:, None, :], + multimask_output=False, + return_logits=True, + ) + new_masks.append(best_masks) + new_iou_preds.append(best_iou_preds) + masks = torch.cat(new_masks, dim=0) + return masks, torch.cat(new_iou_preds, dim=0) diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/hieradet.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/hieradet.py new file mode 100644 index 00000000..90033097 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/hieradet.py @@ -0,0 +1,316 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from functools import partial +from typing import List, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +#from iopath.common.file_io import g_pathmgr + +from ....sam2.modeling.backbones.utils import ( + PatchEmbed, + window_partition, + window_unpartition, +) + +from ....sam2.modeling.sam2_utils import DropPath, MLP + + +def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor: + if pool is None: + return x + # (B, H, W, C) -> (B, C, H, W) + x = x.permute(0, 3, 1, 2) + x = pool(x) + # (B, C, H', W') -> (B, H', W', C) + x = x.permute(0, 2, 3, 1) + if norm: + x = norm(x) + + return x + + +class MultiScaleAttention(nn.Module): + def __init__( + self, + dim: int, + dim_out: int, + num_heads: int, + q_pool: nn.Module = None, + ): + super().__init__() + + self.dim = dim + self.dim_out = dim_out + self.num_heads = num_heads + self.q_pool = q_pool + self.qkv = nn.Linear(dim, dim_out * 3) + self.proj = nn.Linear(dim_out, dim_out) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, H, W, _ = x.shape + # qkv with shape (B, H * W, 3, nHead, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1) + # q, k, v with shape (B, H * W, nheads, C) + q, k, v = torch.unbind(qkv, 2) + + # Q pooling (for downsample at stage changes) + if self.q_pool: + q = do_pool(q.reshape(B, H, W, -1), self.q_pool) + H, W = q.shape[1:3] # downsampled shape + q = q.reshape(B, H * W, self.num_heads, -1) + + # Torch's SDPA expects [B, nheads, H*W, C] so we transpose + x = F.scaled_dot_product_attention( + q.transpose(1, 2), + k.transpose(1, 2), + v.transpose(1, 2), + ) + # Transpose back + x = x.transpose(1, 2) + x = x.reshape(B, H, W, -1) + + x = self.proj(x) + + return x + + +class MultiScaleBlock(nn.Module): + def __init__( + self, + dim: int, + dim_out: int, + num_heads: int, + mlp_ratio: float = 4.0, + drop_path: float = 0.0, + norm_layer: Union[nn.Module, str] = "LayerNorm", + q_stride: Tuple[int, int] = None, + act_layer: nn.Module = nn.GELU, + window_size: int = 0, + ): + super().__init__() + + if isinstance(norm_layer, str): + norm_layer = partial(getattr(nn, norm_layer), eps=1e-6) + + self.dim = dim + self.dim_out = dim_out + self.norm1 = norm_layer(dim) + + self.window_size = window_size + + self.pool, self.q_stride = None, q_stride + if self.q_stride: + self.pool = nn.MaxPool2d( + kernel_size=q_stride, stride=q_stride, ceil_mode=False + ) + + self.attn = MultiScaleAttention( + dim, + dim_out, + num_heads=num_heads, + q_pool=self.pool, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim_out) + self.mlp = MLP( + dim_out, + int(dim_out * mlp_ratio), + dim_out, + num_layers=2, + activation=act_layer, + ) + + if dim != dim_out: + self.proj = nn.Linear(dim, dim_out) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shortcut = x # B, H, W, C + x = self.norm1(x) + + # Skip connection + if self.dim != self.dim_out: + shortcut = do_pool(self.proj(x), self.pool) + + # Window partition + window_size = self.window_size + if window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, window_size) + + # Window Attention + Q Pooling (if stage change) + x = self.attn(x) + if self.q_stride: + # Shapes have changed due to Q pooling + window_size = self.window_size // self.q_stride[0] + H, W = shortcut.shape[1:3] + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + pad_hw = (H + pad_h, W + pad_w) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, window_size, pad_hw, (H, W)) + + x = shortcut + self.drop_path(x) + # MLP + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class Hiera(nn.Module): + """ + Reference: https://arxiv.org/abs/2306.00989 + """ + + def __init__( + self, + embed_dim: int = 96, # initial embed dim + num_heads: int = 1, # initial number of heads + drop_path_rate: float = 0.0, # stochastic depth + q_pool: int = 3, # number of q_pool stages + q_stride: Tuple[int, int] = (2, 2), # downsample stride bet. stages + stages: Tuple[int, ...] = (2, 3, 16, 3), # blocks per stage + dim_mul: float = 2.0, # dim_mul factor at stage shift + head_mul: float = 2.0, # head_mul factor at stage shift + window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14), + # window size per stage, when not using global att. + window_spec: Tuple[int, ...] = ( + 8, + 4, + 14, + 7, + ), + # global attn in these blocks + global_att_blocks: Tuple[int, ...] = ( + 12, + 16, + 20, + ), + weights_path=None, + return_interm_layers=True, # return feats from every stage + ): + super().__init__() + + assert len(stages) == len(window_spec) + self.window_spec = window_spec + + depth = sum(stages) + self.q_stride = q_stride + self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)] + assert 0 <= q_pool <= len(self.stage_ends[:-1]) + self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool] + self.return_interm_layers = return_interm_layers + + self.patch_embed = PatchEmbed( + embed_dim=embed_dim, + ) + # Which blocks have global att? + self.global_att_blocks = global_att_blocks + + # Windowed positional embedding (https://arxiv.org/abs/2311.05613) + self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size + self.pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size) + ) + self.pos_embed_window = nn.Parameter( + torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0]) + ) + + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + + cur_stage = 1 + self.blocks = nn.ModuleList() + + for i in range(depth): + dim_out = embed_dim + # lags by a block, so first block of + # next stage uses an initial window size + # of previous stage and final window size of current stage + window_size = self.window_spec[cur_stage - 1] + + if self.global_att_blocks is not None: + window_size = 0 if i in self.global_att_blocks else window_size + + if i - 1 in self.stage_ends: + dim_out = int(embed_dim * dim_mul) + num_heads = int(num_heads * head_mul) + cur_stage += 1 + + block = MultiScaleBlock( + dim=embed_dim, + dim_out=dim_out, + num_heads=num_heads, + drop_path=dpr[i], + q_stride=self.q_stride if i in self.q_pool_blocks else None, + window_size=window_size, + ) + + embed_dim = dim_out + self.blocks.append(block) + + self.channel_list = ( + [self.blocks[i].dim_out for i in self.stage_ends[::-1]] + if return_interm_layers + else [self.blocks[-1].dim_out] + ) + + # if weights_path is not None: + # with g_pathmgr.open(weights_path, "rb") as f: + # chkpt = torch.load(f, map_location="cpu") + # logging.info("loading Hiera", self.load_state_dict(chkpt, strict=False)) + + def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor: + h, w = hw + window_embed = self.pos_embed_window + pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic") + pos_embed = pos_embed + window_embed.tile( + [x // y for x, y in zip(pos_embed.shape, window_embed.shape)] + ) + pos_embed = pos_embed.permute(0, 2, 3, 1) + return pos_embed + + def forward(self, x: torch.Tensor) -> List[torch.Tensor]: + x = self.patch_embed(x) + # x: (B, H, W, C) + + # Add pos embed + x = x + self._get_pos_embed(x.shape[1:3]) + + outputs = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if (i == self.stage_ends[-1]) or ( + i in self.stage_ends and self.return_interm_layers + ): + feats = x.permute(0, 3, 1, 2) + outputs.append(feats) + + return outputs + + def get_layer_id(self, layer_name): + # https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33 + num_layers = self.get_num_layers() + + if layer_name.find("rel_pos") != -1: + return num_layers + 1 + elif layer_name.find("pos_embed") != -1: + return 0 + elif layer_name.find("patch_embed") != -1: + return 0 + elif layer_name.find("blocks") != -1: + return int(layer_name.split("blocks")[1].split(".")[1]) + 1 + else: + return num_layers + 1 + + def get_num_layers(self) -> int: + return len(self.blocks) diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/image_encoder.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/image_encoder.py new file mode 100644 index 00000000..37e9266b --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/image_encoder.py @@ -0,0 +1,134 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ImageEncoder(nn.Module): + def __init__( + self, + trunk: nn.Module, + neck: nn.Module, + scalp: int = 0, + ): + super().__init__() + self.trunk = trunk + self.neck = neck + self.scalp = scalp + assert ( + self.trunk.channel_list == self.neck.backbone_channel_list + ), f"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}" + + def forward(self, sample: torch.Tensor): + # Forward through backbone + features, pos = self.neck(self.trunk(sample)) + if self.scalp > 0: + # Discard the lowest resolution features + features, pos = features[: -self.scalp], pos[: -self.scalp] + + src = features[-1] + output = { + "vision_features": src, + "vision_pos_enc": pos, + "backbone_fpn": features, + } + return output + + +class FpnNeck(nn.Module): + """ + A modified variant of Feature Pyramid Network (FPN) neck + (we remove output conv and also do bicubic interpolation similar to ViT + pos embed interpolation) + """ + + def __init__( + self, + position_encoding: nn.Module, + d_model: int, + backbone_channel_list: List[int], + kernel_size: int = 1, + stride: int = 1, + padding: int = 0, + fpn_interp_model: str = "bilinear", + fuse_type: str = "sum", + fpn_top_down_levels: Optional[List[int]] = None, + ): + """Initialize the neck + :param trunk: the backbone + :param position_encoding: the positional encoding to use + :param d_model: the dimension of the model + :param neck_norm: the normalization to use + """ + super().__init__() + self.position_encoding = position_encoding + self.convs = nn.ModuleList() + self.backbone_channel_list = backbone_channel_list + self.d_model = d_model + for dim in backbone_channel_list: + current = nn.Sequential() + current.add_module( + "conv", + nn.Conv2d( + in_channels=dim, + out_channels=d_model, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ), + ) + + self.convs.append(current) + self.fpn_interp_model = fpn_interp_model + assert fuse_type in ["sum", "avg"] + self.fuse_type = fuse_type + + # levels to have top-down features in its outputs + # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3 + # have top-down propagation, while outputs of level 0 and level 1 have only + # lateral features from the same backbone level. + if fpn_top_down_levels is None: + # default is to have top-down features on all levels + fpn_top_down_levels = range(len(self.convs)) + self.fpn_top_down_levels = list(fpn_top_down_levels) + + def forward(self, xs: List[torch.Tensor]): + + out = [None] * len(self.convs) + pos = [None] * len(self.convs) + assert len(xs) == len(self.convs) + # fpn forward pass + # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py + prev_features = None + # forward in top-down order (from low to high resolution) + n = len(self.convs) - 1 + for i in range(n, -1, -1): + x = xs[i] + lateral_features = self.convs[n - i](x) + if i in self.fpn_top_down_levels and prev_features is not None: + top_down_features = F.interpolate( + prev_features.to(dtype=torch.float32), + scale_factor=2.0, + mode=self.fpn_interp_model, + align_corners=( + None if self.fpn_interp_model == "nearest" else False + ), + antialias=False, + ) + prev_features = lateral_features + top_down_features + if self.fuse_type == "avg": + prev_features /= 2 + else: + prev_features = lateral_features + x_out = prev_features + out[i] = x_out + pos[i] = self.position_encoding(x_out).to(x_out.dtype) + + return out, pos diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/utils.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/utils.py new file mode 100644 index 00000000..32d55c75 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/backbones/utils.py @@ -0,0 +1,95 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Some utilities for backbones, in particular for windowing""" + +from typing import Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def window_partition(x, window_size): + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows, (Hp, Wp) + + +def window_unpartition(windows, window_size, pad_hw, hw): + """ + Window unpartition into original sequences and removing padding. + Args: + x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view( + B, Hp // window_size, Wp // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, + kernel_size: Tuple[int, ...] = (7, 7), + stride: Tuple[int, ...] = (4, 4), + padding: Tuple[int, ...] = (3, 3), + in_chans: int = 3, + embed_dim: int = 768, + ): + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): embed_dim (int): Patch embedding dimension. + """ + super().__init__() + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/memory_attention.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/memory_attention.py new file mode 100644 index 00000000..07788e5d --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/memory_attention.py @@ -0,0 +1,169 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional + +import torch +from torch import nn, Tensor + +from ...sam2.modeling.sam.transformer import RoPEAttention + +from ...sam2.modeling.sam2_utils import get_activation_fn, get_clones + + +class MemoryAttentionLayer(nn.Module): + + def __init__( + self, + activation: str, + cross_attention: nn.Module, + d_model: int, + dim_feedforward: int, + dropout: float, + pos_enc_at_attn: bool, + pos_enc_at_cross_attn_keys: bool, + pos_enc_at_cross_attn_queries: bool, + self_attention: nn.Module, + ): + super().__init__() + self.d_model = d_model + self.dim_feedforward = dim_feedforward + self.dropout_value = dropout + self.self_attn = self_attention + self.cross_attn_image = cross_attention + + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation_str = activation + self.activation = get_activation_fn(activation) + + # Where to add pos enc + self.pos_enc_at_attn = pos_enc_at_attn + self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries + self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys + + def _forward_sa(self, tgt, query_pos): + # Self-Attention + tgt2 = self.norm1(tgt) + q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2 + tgt2 = self.self_attn(q, k, v=tgt2) + tgt = tgt + self.dropout1(tgt2) + return tgt + + def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0): + kwds = {} + if num_k_exclude_rope > 0: + assert isinstance(self.cross_attn_image, RoPEAttention) + kwds = {"num_k_exclude_rope": num_k_exclude_rope} + + # Cross-Attention + tgt2 = self.norm2(tgt) + tgt2 = self.cross_attn_image( + q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2, + k=memory + pos if self.pos_enc_at_cross_attn_keys else memory, + v=memory, + **kwds, + ) + tgt = tgt + self.dropout2(tgt2) + return tgt + + def forward( + self, + tgt, + memory, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + num_k_exclude_rope: int = 0, + ) -> torch.Tensor: + + # Self-Attn, Cross-Attn + tgt = self._forward_sa(tgt, query_pos) + tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope) + # MLP + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + +class MemoryAttention(nn.Module): + def __init__( + self, + d_model: int, + pos_enc_at_input: bool, + layer: nn.Module, + num_layers: int, + batch_first: bool = True, # Do layers expect batch first input? + ): + super().__init__() + self.d_model = d_model + self.layers = get_clones(layer, num_layers) + self.num_layers = num_layers + self.norm = nn.LayerNorm(d_model) + self.pos_enc_at_input = pos_enc_at_input + self.batch_first = batch_first + + def forward( + self, + curr: torch.Tensor, # self-attention inputs + memory: torch.Tensor, # cross-attention inputs + curr_pos: Optional[Tensor] = None, # pos_enc for self-attention inputs + memory_pos: Optional[Tensor] = None, # pos_enc for cross-attention inputs + num_obj_ptr_tokens: int = 0, # number of object pointer *tokens* + ): + if isinstance(curr, list): + assert isinstance(curr_pos, list) + assert len(curr) == len(curr_pos) == 1 + curr, curr_pos = ( + curr[0], + curr_pos[0], + ) + + assert ( + curr.shape[1] == memory.shape[1] + ), "Batch size must be the same for curr and memory" + + output = curr + if self.pos_enc_at_input and curr_pos is not None: + output = output + 0.1 * curr_pos + + if self.batch_first: + # Convert to batch first + output = output.transpose(0, 1) + curr_pos = curr_pos.transpose(0, 1) + memory = memory.transpose(0, 1) + memory_pos = memory_pos.transpose(0, 1) + + for layer in self.layers: + kwds = {} + if isinstance(layer.cross_attn_image, RoPEAttention): + kwds = {"num_k_exclude_rope": num_obj_ptr_tokens} + + output = layer( + tgt=output, + memory=memory, + pos=memory_pos, + query_pos=curr_pos, + **kwds, + ) + normed_output = self.norm(output) + + if self.batch_first: + # Convert back to seq first + normed_output = normed_output.transpose(0, 1) + curr_pos = curr_pos.transpose(0, 1) + + return normed_output diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/memory_encoder.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/memory_encoder.py new file mode 100644 index 00000000..1fbf1c8c --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/memory_encoder.py @@ -0,0 +1,181 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...sam2.modeling.sam2_utils import DropPath, get_clones, LayerNorm2d + + +class MaskDownSampler(nn.Module): + """ + Progressively downsample a mask by total_stride, each time by stride. + Note that LayerNorm is applied per *token*, like in ViT. + + With each downsample (by a factor stride**2), channel capacity increases by the same factor. + In the end, we linearly project to embed_dim channels. + """ + + def __init__( + self, + embed_dim=256, + kernel_size=4, + stride=4, + padding=0, + total_stride=16, + activation=nn.GELU, + ): + super().__init__() + num_layers = int(math.log2(total_stride) // math.log2(stride)) + assert stride**num_layers == total_stride + self.encoder = nn.Sequential() + mask_in_chans, mask_out_chans = 1, 1 + for _ in range(num_layers): + mask_out_chans = mask_in_chans * (stride**2) + self.encoder.append( + nn.Conv2d( + mask_in_chans, + mask_out_chans, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ) + ) + self.encoder.append(LayerNorm2d(mask_out_chans)) + self.encoder.append(activation()) + mask_in_chans = mask_out_chans + + self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1)) + + def forward(self, x): + return self.encoder(x) + + +# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt) +class CXBlock(nn.Module): + r"""ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + """ + + def __init__( + self, + dim, + kernel_size=7, + padding=3, + drop_path=0.0, + layer_scale_init_value=1e-6, + use_dwconv=True, + ): + super().__init__() + self.dwconv = nn.Conv2d( + dim, + dim, + kernel_size=kernel_size, + padding=padding, + groups=dim if use_dwconv else 1, + ) # depthwise conv + self.norm = LayerNorm2d(dim, eps=1e-6) + self.pwconv1 = nn.Linear( + dim, 4 * dim + ) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = nn.Linear(4 * dim, dim) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) + if layer_scale_init_value > 0 + else None + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, x): + input = x + x = self.dwconv(x) + x = self.norm(x) + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + x = input + self.drop_path(x) + return x + + +class Fuser(nn.Module): + def __init__(self, layer, num_layers, dim=None, input_projection=False): + super().__init__() + self.proj = nn.Identity() + self.layers = get_clones(layer, num_layers) + + if input_projection: + assert dim is not None + self.proj = nn.Conv2d(dim, dim, kernel_size=1) + + def forward(self, x): + # normally x: (N, C, H, W) + x = self.proj(x) + for layer in self.layers: + x = layer(x) + return x + + +class MemoryEncoder(nn.Module): + def __init__( + self, + out_dim, + mask_downsampler, + fuser, + position_encoding, + in_dim=256, # in_dim of pix_feats + ): + super().__init__() + + self.mask_downsampler = mask_downsampler + + self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1) + self.fuser = fuser + self.position_encoding = position_encoding + self.out_proj = nn.Identity() + if out_dim != in_dim: + self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1) + + def forward( + self, + pix_feat: torch.Tensor, + masks: torch.Tensor, + skip_mask_sigmoid: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + ## Process masks + # sigmoid, so that less domain shift from gt masks which are bool + if not skip_mask_sigmoid: + masks = F.sigmoid(masks) + masks = self.mask_downsampler(masks) + + ## Fuse pix_feats and downsampled masks + # in case the visual features are on CPU, cast them to CUDA + pix_feat = pix_feat.to(masks.device) + + x = self.pix_feat_proj(pix_feat) + x = x + masks + x = self.fuser(x) + x = self.out_proj(x) + + pos = self.position_encoding(x).to(x.dtype) + + return {"vision_features": x, "vision_pos_enc": [pos]} diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/position_encoding.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/position_encoding.py new file mode 100644 index 00000000..fafd0420 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/position_encoding.py @@ -0,0 +1,220 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Optional, Tuple + +import numpy as np + +import torch +from torch import nn + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__( + self, + num_pos_feats, + temperature: int = 10000, + normalize: bool = True, + scale: Optional[float] = None, + ): + super().__init__() + assert num_pos_feats % 2 == 0, "Expecting even model width" + self.num_pos_feats = num_pos_feats // 2 + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + self.cache = {} + + def _encode_xy(self, x, y): + # The positions are expected to be normalized + assert len(x) == len(y) and x.ndim == y.ndim == 1 + x_embed = x * self.scale + y_embed = y * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, None] / dim_t + pos_y = y_embed[:, None] / dim_t + pos_x = torch.stack( + (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2 + ).flatten(1) + pos_y = torch.stack( + (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2 + ).flatten(1) + return pos_x, pos_y + + @torch.no_grad() + def encode_boxes(self, x, y, w, h): + pos_x, pos_y = self._encode_xy(x, y) + pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) + return pos + + encode = encode_boxes # Backwards compatibility + + @torch.no_grad() + def encode_points(self, x, y, labels): + (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape + assert bx == by and nx == ny and bx == bl and nx == nl + pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten()) + pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1) + pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2) + return pos + + @torch.no_grad() + def forward(self, x: torch.Tensor): + cache_key = (x.shape[-2], x.shape[-1]) + if cache_key in self.cache: + return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1) + y_embed = ( + torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device) + .view(1, -1, 1) + .repeat(x.shape[0], 1, x.shape[-1]) + ) + x_embed = ( + torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device) + .view(1, 1, -1) + .repeat(x.shape[0], x.shape[-2], 1) + ) + + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + self.cache[cache_key] = pos[0] + return pos + + +class PositionEmbeddingRandom(nn.Module): + """ + Positional encoding using random spatial frequencies. + """ + + def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: + super().__init__() + if scale is None or scale <= 0.0: + scale = 1.0 + self.register_buffer( + "positional_encoding_gaussian_matrix", + scale * torch.randn((2, num_pos_feats)), + ) + + def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: + """Positionally encode points that are normalized to [0,1].""" + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coords = 2 * coords - 1 + coords = coords @ self.positional_encoding_gaussian_matrix + coords = 2 * np.pi * coords + # outputs d_1 x ... x d_n x C shape + return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) + + def forward(self, size: Tuple[int, int]) -> torch.Tensor: + """Generate positional encoding for a grid of the specified size.""" + h, w = size + device: Any = self.positional_encoding_gaussian_matrix.device + grid = torch.ones((h, w), device=device, dtype=torch.float32) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / h + x_embed = x_embed / w + + pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) + return pe.permute(2, 0, 1) # C x H x W + + def forward_with_coords( + self, coords_input: torch.Tensor, image_size: Tuple[int, int] + ) -> torch.Tensor: + """Positionally encode points that are not normalized to [0,1].""" + coords = coords_input.clone() + coords[:, :, 0] = coords[:, :, 0] / image_size[1] + coords[:, :, 1] = coords[:, :, 1] / image_size[0] + return self._pe_encoding(coords.to(torch.float)) # B x N x C + + +# Rotary Positional Encoding, adapted from: +# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py +# 2. https://github.com/naver-ai/rope-vit +# 3. https://github.com/lucidrains/rotary-embedding-torch + + +def init_t_xy(end_x: int, end_y: int): + t = torch.arange(end_x * end_y, dtype=torch.float32) + t_x = (t % end_x).float() + t_y = torch.div(t, end_x, rounding_mode="floor").float() + return t_x, t_y + + +def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0): + freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) + freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) + + t_x, t_y = init_t_xy(end_x, end_y) + freqs_x = torch.outer(t_x, freqs_x) + freqs_y = torch.outer(t_y, freqs_y) + freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x) + freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y) + return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1) + + +def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[-2], x.shape[-1]) + shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def apply_rotary_enc( + xq: torch.Tensor, + xk: torch.Tensor, + freqs_cis: torch.Tensor, + repeat_freqs_k: bool = False, +): + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = ( + torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + if xk.shape[-2] != 0 + else None + ) + freqs_cis = reshape_for_broadcast(freqs_cis, xq_) + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) + if xk_ is None: + # no keys to rotate, due to dropout + return xq_out.type_as(xq).to(xq.device), xk + # repeat freqs along seq_len dim to match k seq_len + if repeat_freqs_k: + r = xk_.shape[-2] // xq_.shape[-2] + if freqs_cis.is_complex() and freqs_cis.device.type == "mps": + # MPS doesn't support repeat on complex; cat works fine. + freqs_cis = torch.cat([freqs_cis] * r, dim=-2) + else: + freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1) + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) + return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device) diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/mask_decoder.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/mask_decoder.py new file mode 100644 index 00000000..007d1414 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/mask_decoder.py @@ -0,0 +1,295 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Optional, Tuple, Type + +import torch +from torch import nn + +from ....sam2.modeling.sam2_utils import LayerNorm2d, MLP + + +class MaskDecoder(nn.Module): + def __init__( + self, + *, + transformer_dim: int, + transformer: nn.Module, + num_multimask_outputs: int = 3, + activation: Type[nn.Module] = nn.GELU, + iou_head_depth: int = 3, + iou_head_hidden_dim: int = 256, + use_high_res_features: bool = False, + iou_prediction_use_sigmoid=False, + dynamic_multimask_via_stability=False, + dynamic_multimask_stability_delta=0.05, + dynamic_multimask_stability_thresh=0.98, + pred_obj_scores: bool = False, + pred_obj_scores_mlp: bool = False, + use_multimask_token_for_obj_ptr: bool = False, + ) -> None: + """ + Predicts masks given an image and prompt embeddings, using a + transformer architecture. + + Arguments: + transformer_dim (int): the channel dimension of the transformer + transformer (nn.Module): the transformer used to predict masks + num_multimask_outputs (int): the number of masks to predict + when disambiguating masks + activation (nn.Module): the type of activation to use when + upscaling masks + iou_head_depth (int): the depth of the MLP used to predict + mask quality + iou_head_hidden_dim (int): the hidden dimension of the MLP + used to predict mask quality + """ + super().__init__() + self.transformer_dim = transformer_dim + self.transformer = transformer + + self.num_multimask_outputs = num_multimask_outputs + + self.iou_token = nn.Embedding(1, transformer_dim) + self.num_mask_tokens = num_multimask_outputs + 1 + self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) + + self.pred_obj_scores = pred_obj_scores + if self.pred_obj_scores: + self.obj_score_token = nn.Embedding(1, transformer_dim) + self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr + + self.output_upscaling = nn.Sequential( + nn.ConvTranspose2d( + transformer_dim, transformer_dim // 4, kernel_size=2, stride=2 + ), + LayerNorm2d(transformer_dim // 4), + activation(), + nn.ConvTranspose2d( + transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2 + ), + activation(), + ) + self.use_high_res_features = use_high_res_features + if use_high_res_features: + self.conv_s0 = nn.Conv2d( + transformer_dim, transformer_dim // 8, kernel_size=1, stride=1 + ) + self.conv_s1 = nn.Conv2d( + transformer_dim, transformer_dim // 4, kernel_size=1, stride=1 + ) + + self.output_hypernetworks_mlps = nn.ModuleList( + [ + MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) + for i in range(self.num_mask_tokens) + ] + ) + + self.iou_prediction_head = MLP( + transformer_dim, + iou_head_hidden_dim, + self.num_mask_tokens, + iou_head_depth, + sigmoid_output=iou_prediction_use_sigmoid, + ) + if self.pred_obj_scores: + self.pred_obj_score_head = nn.Linear(transformer_dim, 1) + if pred_obj_scores_mlp: + self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3) + + # When outputting a single mask, optionally we can dynamically fall back to the best + # multimask output token if the single mask output token gives low stability scores. + self.dynamic_multimask_via_stability = dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh + + def forward( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + repeat_image: bool, + high_res_features: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Arguments: + image_embeddings (torch.Tensor): the embeddings from the image encoder + image_pe (torch.Tensor): positional encoding with the shape of image_embeddings + sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes + dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs + multimask_output (bool): Whether to return multiple masks or a single + mask. + + Returns: + torch.Tensor: batched predicted masks + torch.Tensor: batched predictions of mask quality + torch.Tensor: batched SAM token for mask output + """ + masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks( + image_embeddings=image_embeddings, + image_pe=image_pe, + sparse_prompt_embeddings=sparse_prompt_embeddings, + dense_prompt_embeddings=dense_prompt_embeddings, + repeat_image=repeat_image, + high_res_features=high_res_features, + ) + + # Select the correct mask or masks for output + if multimask_output: + masks = masks[:, 1:, :, :] + iou_pred = iou_pred[:, 1:] + elif self.dynamic_multimask_via_stability and not self.training: + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + masks = masks[:, 0:1, :, :] + iou_pred = iou_pred[:, 0:1] + + if multimask_output and self.use_multimask_token_for_obj_ptr: + sam_tokens_out = mask_tokens_out[:, 1:] # [b, 3, c] shape + else: + # Take the mask output token. Here we *always* use the token for single mask output. + # At test time, even if we track after 1-click (and using multimask_output=True), + # we still take the single mask token here. The rationale is that we always track + # after multiple clicks during training, so the past tokens seen during training + # are always the single mask token (and we'll let it be the object-memory token). + sam_tokens_out = mask_tokens_out[:, 0:1] # [b, 1, c] shape + + # Prepare output + return masks, iou_pred, sam_tokens_out, object_score_logits + + def predict_masks( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + repeat_image: bool, + high_res_features: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Predicts masks. See 'forward' for more details.""" + # Concatenate output tokens + s = 0 + if self.pred_obj_scores: + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + s = 1 + else: + output_tokens = torch.cat( + [self.iou_token.weight, self.mask_tokens.weight], dim=0 + ) + output_tokens = output_tokens.unsqueeze(0).expand( + sparse_prompt_embeddings.size(0), -1, -1 + ) + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) + + # Expand per-image data in batch direction to be per-mask + if repeat_image: + src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) + else: + assert image_embeddings.shape[0] == tokens.shape[0] + src = image_embeddings + src = src + dense_prompt_embeddings + assert ( + image_pe.size(0) == 1 + ), "image_pe should have size 1 in batch dim (from `get_dense_pe()`)" + pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) + b, c, h, w = src.shape + + # Run the transformer + hs, src = self.transformer(src, pos_src, tokens) + iou_token_out = hs[:, s, :] + mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + src = src.transpose(1, 2).view(b, c, h, w) + if not self.use_high_res_features: + upscaled_embedding = self.output_upscaling(src) + else: + dc1, ln1, act1, dc2, act2 = self.output_upscaling + feat_s0, feat_s1 = high_res_features + upscaled_embedding = act1(ln1(dc1(src) + feat_s1)) + upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0) + + hyper_in_list: List[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + hyper_in_list.append( + self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) + ) + hyper_in = torch.stack(hyper_in_list, dim=1) + b, c, h, w = upscaled_embedding.shape + masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + if self.pred_obj_scores: + assert s == 1 + object_score_logits = self.pred_obj_score_head(hs[:, 0, :]) + else: + # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1 + object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1) + + return masks, iou_pred, mask_tokens_out, object_score_logits + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) + batch_inds = torch.arange( + multimask_iou_scores.size(0), device=all_iou_scores.device + ) + best_multimask_logits = multimask_logits[batch_inds, best_scores_inds] + best_multimask_logits = best_multimask_logits.unsqueeze(1) + best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds] + best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1) + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/prompt_encoder.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/prompt_encoder.py new file mode 100644 index 00000000..fe125c72 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/prompt_encoder.py @@ -0,0 +1,182 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional, Tuple, Type + +import torch +from torch import nn + +from ....sam2.modeling.position_encoding import PositionEmbeddingRandom + +from ....sam2.modeling.sam2_utils import LayerNorm2d + + +class PromptEncoder(nn.Module): + def __init__( + self, + embed_dim: int, + image_embedding_size: Tuple[int, int], + input_image_size: Tuple[int, int], + mask_in_chans: int, + activation: Type[nn.Module] = nn.GELU, + ) -> None: + """ + Encodes prompts for input to SAM's mask decoder. + + Arguments: + embed_dim (int): The prompts' embedding dimension + image_embedding_size (tuple(int, int)): The spatial size of the + image embedding, as (H, W). + input_image_size (int): The padded size of the image as input + to the image encoder, as (H, W). + mask_in_chans (int): The number of hidden channels used for + encoding input masks. + activation (nn.Module): The activation to use when encoding + input masks. + """ + super().__init__() + self.embed_dim = embed_dim + self.input_image_size = input_image_size + self.image_embedding_size = image_embedding_size + self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) + + self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners + point_embeddings = [ + nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings) + ] + self.point_embeddings = nn.ModuleList(point_embeddings) + self.not_a_point_embed = nn.Embedding(1, embed_dim) + + self.mask_input_size = ( + 4 * image_embedding_size[0], + 4 * image_embedding_size[1], + ) + self.mask_downscaling = nn.Sequential( + nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans // 4), + activation(), + nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans), + activation(), + nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), + ) + self.no_mask_embed = nn.Embedding(1, embed_dim) + + def get_dense_pe(self) -> torch.Tensor: + """ + Returns the positional encoding used to encode point prompts, + applied to a dense set of points the shape of the image encoding. + + Returns: + torch.Tensor: Positional encoding with shape + 1x(embed_dim)x(embedding_h)x(embedding_w) + """ + return self.pe_layer(self.image_embedding_size).unsqueeze(0) + + def _embed_points( + self, + points: torch.Tensor, + labels: torch.Tensor, + pad: bool, + ) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) + padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) + points = torch.cat([points, padding_point], dim=1) + labels = torch.cat([labels, padding_label], dim=1) + point_embedding = self.pe_layer.forward_with_coords( + points, self.input_image_size + ) + point_embedding[labels == -1] = 0.0 + point_embedding[labels == -1] += self.not_a_point_embed.weight + point_embedding[labels == 0] += self.point_embeddings[0].weight + point_embedding[labels == 1] += self.point_embeddings[1].weight + point_embedding[labels == 2] += self.point_embeddings[2].weight + point_embedding[labels == 3] += self.point_embeddings[3].weight + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.reshape(-1, 2, 2) + corner_embedding = self.pe_layer.forward_with_coords( + coords, self.input_image_size + ) + corner_embedding[:, 0, :] += self.point_embeddings[2].weight + corner_embedding[:, 1, :] += self.point_embeddings[3].weight + return corner_embedding + + def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: + """Embeds mask inputs.""" + mask_embedding = self.mask_downscaling(masks) + return mask_embedding + + def _get_batch_size( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> int: + """ + Gets the batch size of the output given the batch size of the input prompts. + """ + if points is not None: + return points[0].shape[0] + elif boxes is not None: + return boxes.shape[0] + elif masks is not None: + return masks.shape[0] + else: + return 1 + + def _get_device(self) -> torch.device: + return self.point_embeddings[0].weight.device + + def forward( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense + embeddings. + + Arguments: + points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates + and labels to embed. + boxes (torch.Tensor or none): boxes to embed + masks (torch.Tensor or none): masks to embed + + Returns: + torch.Tensor: sparse embeddings for the points and boxes, with shape + BxNx(embed_dim), where N is determined by the number of input points + and boxes. + torch.Tensor: dense embeddings for the masks, in the shape + Bx(embed_dim)x(embed_H)x(embed_W) + """ + bs = self._get_batch_size(points, boxes, masks) + sparse_embeddings = torch.empty( + (bs, 0, self.embed_dim), device=self._get_device() + ) + if points is not None: + coords, labels = points + point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) + sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) + if boxes is not None: + box_embeddings = self._embed_boxes(boxes) + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) + + if masks is not None: + dense_embeddings = self._embed_masks(masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/transformer.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/transformer.py new file mode 100644 index 00000000..fb71606b --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam/transformer.py @@ -0,0 +1,347 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +import warnings +from functools import partial +from typing import Tuple, Type + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from ....sam2.modeling.position_encoding import apply_rotary_enc, compute_axial_cis +from ....sam2.modeling.sam2_utils import MLP + +from ....sam2.utils.misc import get_sdpa_settings +OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings() + +try: + from torch.nn.attention import SDPBackend, sdpa_kernel + backends = [] + if USE_FLASH_ATTN: + backends.append(SDPBackend.FLASH_ATTENTION) + if MATH_KERNEL_ON: + backends.append(SDPBackend.MATH) + if OLD_GPU: + backends.append(SDPBackend.EFFICIENT_ATTENTION) + OLD_TORCH = False +except: + OLD_TORCH = True + +warnings.simplefilter(action="ignore", category=FutureWarning) + +class TwoWayTransformer(nn.Module): + def __init__( + self, + depth: int, + embedding_dim: int, + num_heads: int, + mlp_dim: int, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + ) -> None: + """ + A transformer decoder that attends to an input image using + queries whose positional embedding is supplied. + + Args: + depth (int): number of layers in the transformer + embedding_dim (int): the channel dimension for the input embeddings + num_heads (int): the number of heads for multihead attention. Must + divide embedding_dim + mlp_dim (int): the channel dimension internal to the MLP block + activation (nn.Module): the activation to use in the MLP block + """ + super().__init__() + self.depth = depth + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.mlp_dim = mlp_dim + self.layers = nn.ModuleList() + + for i in range(depth): + self.layers.append( + TwoWayAttentionBlock( + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_dim=mlp_dim, + activation=activation, + attention_downsample_rate=attention_downsample_rate, + skip_first_layer_pe=(i == 0), + ) + ) + + self.final_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm_final_attn = nn.LayerNorm(embedding_dim) + + def forward( + self, + image_embedding: Tensor, + image_pe: Tensor, + point_embedding: Tensor, + ) -> Tuple[Tensor, Tensor]: + """ + Args: + image_embedding (torch.Tensor): image to attend to. Should be shape + B x embedding_dim x h x w for any h and w. + image_pe (torch.Tensor): the positional encoding to add to the image. Must + have the same shape as image_embedding. + point_embedding (torch.Tensor): the embedding to add to the query points. + Must have shape B x N_points x embedding_dim for any N_points. + + Returns: + torch.Tensor: the processed point_embedding + torch.Tensor: the processed image_embedding + """ + # BxCxHxW -> BxHWxC == B x N_image_tokens x C + bs, c, h, w = image_embedding.shape + image_embedding = image_embedding.flatten(2).permute(0, 2, 1) + image_pe = image_pe.flatten(2).permute(0, 2, 1) + + # Prepare queries + queries = point_embedding + keys = image_embedding + + # Apply transformer blocks and final layernorm + for layer in self.layers: + queries, keys = layer( + queries=queries, + keys=keys, + query_pe=point_embedding, + key_pe=image_pe, + ) + + # Apply the final attention layer from the points to the image + q = queries + point_embedding + k = keys + image_pe + attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm_final_attn(queries) + + return queries, keys + + +class TwoWayAttentionBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + num_heads: int, + mlp_dim: int = 2048, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + skip_first_layer_pe: bool = False, + ) -> None: + """ + A transformer block with four layers: (1) self-attention of sparse + inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp + block on sparse inputs, and (4) cross attention of dense inputs to sparse + inputs. + + Arguments: + embedding_dim (int): the channel dimension of the embeddings + num_heads (int): the number of heads in the attention layers + mlp_dim (int): the hidden dimension of the mlp block + activation (nn.Module): the activation of the mlp block + skip_first_layer_pe (bool): skip the PE on the first layer + """ + super().__init__() + self.self_attn = Attention(embedding_dim, num_heads) + self.norm1 = nn.LayerNorm(embedding_dim) + + self.cross_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm2 = nn.LayerNorm(embedding_dim) + + self.mlp = MLP( + embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation + ) + self.norm3 = nn.LayerNorm(embedding_dim) + + self.norm4 = nn.LayerNorm(embedding_dim) + self.cross_attn_image_to_token = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor + ) -> Tuple[Tensor, Tensor]: + # Self attention block + if self.skip_first_layer_pe: + queries = self.self_attn(q=queries, k=queries, v=queries) + else: + q = queries + query_pe + attn_out = self.self_attn(q=q, k=q, v=queries) + queries = queries + attn_out + queries = self.norm1(queries) + + # Cross attention block, tokens attending to image embedding + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.norm3(queries) + + # Cross attention block, image embedding attending to tokens + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries) + keys = keys + attn_out + keys = self.norm4(keys) + + return queries, keys + + +class Attention(nn.Module): + """ + An attention layer that allows for downscaling the size of the embedding + after projection to queries, keys, and values. + """ + + def __init__( + self, + embedding_dim: int, + num_heads: int, + downsample_rate: int = 1, + dropout: float = 0.0, + kv_in_dim: int = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim + self.internal_dim = embedding_dim // downsample_rate + self.num_heads = num_heads + assert ( + self.internal_dim % num_heads == 0 + ), "num_heads must divide embedding_dim." + + self.q_proj = nn.Linear(embedding_dim, self.internal_dim) + self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.out_proj = nn.Linear(self.internal_dim, embedding_dim) + + self.dropout_p = dropout + + def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor: + b, n, c = x.shape + x = x.reshape(b, n, num_heads, c // num_heads) + return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head + + def _recombine_heads(self, x: Tensor) -> Tensor: + b, n_heads, n_tokens, c_per_head = x.shape + x = x.transpose(1, 2) + return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: + # Input projections + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + + # Separate into heads + q = self._separate_heads(q, self.num_heads) + k = self._separate_heads(k, self.num_heads) + v = self._separate_heads(v, self.num_heads) + + dropout_p = self.dropout_p if self.training else 0.0 + # Attention + if not OLD_TORCH: + if not MATH_KERNEL_ON and OLD_GPU and dropout_p > 0.0: + backends.append(SDPBackend.MATH) + with sdpa_kernel(backends): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + else: + with torch.backends.cuda.sdp_kernel( + enable_flash=USE_FLASH_ATTN, + enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON, + enable_mem_efficient=OLD_GPU, + ): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + out = self._recombine_heads(out) + out = self.out_proj(out) + + return out + + +class RoPEAttention(Attention): + """Attention with rotary position encoding.""" + + def __init__( + self, + *args, + rope_theta=10000.0, + # whether to repeat q rope to match k length + # this is needed for cross-attention to memories + rope_k_repeat=False, + feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.compute_cis = partial( + compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta + ) + freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1]) + self.freqs_cis = freqs_cis + self.rope_k_repeat = rope_k_repeat + + def forward( + self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0 + ) -> Tensor: + # Input projections + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + + # Separate into heads + q = self._separate_heads(q, self.num_heads) + k = self._separate_heads(k, self.num_heads) + v = self._separate_heads(v, self.num_heads) + + # Apply rotary position encoding + w = h = math.sqrt(q.shape[-2]) + self.freqs_cis = self.freqs_cis.to(q.device) + if self.freqs_cis.shape[0] != q.shape[-2]: + self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device) + if q.shape[-2] != k.shape[-2]: + assert self.rope_k_repeat + + num_k_rope = k.size(-2) - num_k_exclude_rope + q, k[:, :, :num_k_rope] = apply_rotary_enc( + q, + k[:, :, :num_k_rope], + freqs_cis=self.freqs_cis, + repeat_freqs_k=self.rope_k_repeat, + ) + + dropout_p = self.dropout_p if self.training else 0.0 + # Attention + if not OLD_TORCH: + if not MATH_KERNEL_ON and OLD_GPU and dropout_p > 0.0: + backends.append(SDPBackend.MATH) + with sdpa_kernel(backends): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + else: + with torch.backends.cuda.sdp_kernel( + enable_flash=USE_FLASH_ATTN, + enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON, + enable_mem_efficient=OLD_GPU, + ): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + out = self._recombine_heads(out) + out = self.out_proj(out) + + return out diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam2_base.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam2_base.py new file mode 100644 index 00000000..078d63b7 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam2_base.py @@ -0,0 +1,907 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.distributed +import torch.nn.functional as F + +from torch.nn.init import trunc_normal_ + +from ...sam2.modeling.sam.mask_decoder import MaskDecoder +from ...sam2.modeling.sam.prompt_encoder import PromptEncoder +from ...sam2.modeling.sam.transformer import TwoWayTransformer +from ...sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames + +# a large negative value as a placeholder score for missing objects +NO_OBJ_SCORE = -1024.0 + + +class SAM2Base(torch.nn.Module): + def __init__( + self, + image_encoder, + memory_attention, + memory_encoder, + num_maskmem=7, # default 1 input frame + 6 previous frames + image_size=512, + backbone_stride=16, # stride of the image backbone output + sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob + sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob + # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks + binarize_mask_from_pts_for_mem_enc=False, + use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder + # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit, + # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model + # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM. + max_cond_frames_in_attn=-1, + # on the first frame, whether to directly add the no-memory embedding to the image feature + # (instead of using the transformer encoder) + directly_add_no_mem_embed=False, + # whether to use high-resolution feature maps in the SAM mask decoder + use_high_res_features_in_sam=False, + # whether to output multiple (3) masks for the first click on initial conditioning frames + multimask_output_in_sam=False, + # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`; + # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points) + multimask_min_pt_num=1, + multimask_max_pt_num=1, + # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`) + multimask_output_for_tracking=False, + # Whether to use multimask tokens for obj ptr; Only relevant when both + # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True + use_multimask_token_for_obj_ptr: bool = False, + # whether to use sigmoid to restrict ious prediction to [0-1] + iou_prediction_use_sigmoid=False, + # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5). + # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of + # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame. + memory_temporal_stride_for_eval=1, + # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks) + non_overlap_masks_for_mem_enc=False, + # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder=False, + # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`) + max_obj_ptrs_in_encoder=16, + # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`) + add_tpos_enc_to_obj_ptrs=True, + # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference + # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`) + proj_tpos_enc_in_obj_ptrs=False, + # whether to use signed distance (instead of unsigned absolute distance) in the temporal positional encoding in the object pointers + # (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`) + use_signed_tpos_enc_to_obj_ptrs=False, + # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation + # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking) + only_obj_ptrs_in_the_past_for_eval=False, + # Whether to predict if there is an object in the frame + pred_obj_scores: bool = False, + # Whether to use an MLP to predict object scores + pred_obj_scores_mlp: bool = False, + # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True; + # Whether to have a fixed no obj pointer when there is no object present + # or to use it as an additive embedding with obj_ptr produced by decoder + fixed_no_obj_ptr: bool = False, + # Soft no object, i.e. mix in no_obj_ptr softly, + # hope to make recovery easier if there is a mistake and mitigate accumulation of errors + soft_no_obj_ptr: bool = False, + use_mlp_for_obj_ptr_proj: bool = False, + # add no obj embedding to spatial frames + no_obj_embed_spatial: bool = False, + # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class. + sam_mask_decoder_extra_args=None, + compile_image_encoder: bool = False, + ): + super().__init__() + + # Part 1: the image backbone + self.image_encoder = image_encoder + # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting + self.use_high_res_features_in_sam = use_high_res_features_in_sam + self.num_feature_levels = 3 if use_high_res_features_in_sam else 1 + self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder + self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder + if use_obj_ptrs_in_encoder: + # A conv layer to downsample the mask prompt to stride 4 (the same stride as + # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, + # so that it can be fed into the SAM mask decoder to generate a pointer. + self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) + self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs + if proj_tpos_enc_in_obj_ptrs: + assert add_tpos_enc_to_obj_ptrs # these options need to be used together + self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs + self.use_signed_tpos_enc_to_obj_ptrs = use_signed_tpos_enc_to_obj_ptrs + self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval + + # Part 2: memory attention to condition current frame's visual features + # with memories (and obj ptrs) from past frames + self.memory_attention = memory_attention + self.hidden_dim = image_encoder.neck.d_model + + # Part 3: memory encoder for the previous frame's outputs + self.memory_encoder = memory_encoder + self.mem_dim = self.hidden_dim + if hasattr(self.memory_encoder, "out_proj") and hasattr( + self.memory_encoder.out_proj, "weight" + ): + # if there is compression of memories along channel dim + self.mem_dim = self.memory_encoder.out_proj.weight.shape[0] + self.num_maskmem = num_maskmem # Number of memories accessible + # Temporal encoding of the memories + self.maskmem_tpos_enc = torch.nn.Parameter( + torch.zeros(num_maskmem, 1, 1, self.mem_dim) + ) + trunc_normal_(self.maskmem_tpos_enc, std=0.02) + # a single token to indicate no memory embedding from previous frames + self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + trunc_normal_(self.no_mem_embed, std=0.02) + trunc_normal_(self.no_mem_pos_enc, std=0.02) + self.directly_add_no_mem_embed = directly_add_no_mem_embed + # Apply sigmoid to the output raw mask logits (to turn them from + # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder + self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc + self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc + self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc + self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc + self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval + # On frames with mask input, whether to directly output the input mask without + # using a SAM prompt encoder + mask decoder + self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam + self.multimask_output_in_sam = multimask_output_in_sam + self.multimask_min_pt_num = multimask_min_pt_num + self.multimask_max_pt_num = multimask_max_pt_num + self.multimask_output_for_tracking = multimask_output_for_tracking + self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr + self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid + + # Part 4: SAM-style prompt encoder (for both mask and point inputs) + # and SAM-style mask decoder for the final mask output + self.image_size = image_size + self.backbone_stride = backbone_stride + self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args + self.pred_obj_scores = pred_obj_scores + self.pred_obj_scores_mlp = pred_obj_scores_mlp + self.fixed_no_obj_ptr = fixed_no_obj_ptr + self.soft_no_obj_ptr = soft_no_obj_ptr + if self.fixed_no_obj_ptr: + assert self.pred_obj_scores + assert self.use_obj_ptrs_in_encoder + if self.pred_obj_scores and self.use_obj_ptrs_in_encoder: + self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) + trunc_normal_(self.no_obj_ptr, std=0.02) + self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj + self.no_obj_embed_spatial = None + if no_obj_embed_spatial: + self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim)) + trunc_normal_(self.no_obj_embed_spatial, std=0.02) + + self._build_sam_heads() + self.max_cond_frames_in_attn = max_cond_frames_in_attn + + # Model compilation + if compile_image_encoder: + # Compile the forward function (not the full module) to allow loading checkpoints. + print( + "Image encoder compilation is enabled. First forward pass will be slow." + ) + self.image_encoder.forward = torch.compile( + self.image_encoder.forward, + mode="max-autotune", + fullgraph=True, + dynamic=False, + ) + + @property + def device(self): + return next(self.parameters()).device + + def forward(self, *args, **kwargs): + raise NotImplementedError( + "Please use the corresponding methods in SAM2VideoPredictor for inference or SAM2Train for training/fine-tuning" + "See notebooks/video_predictor_example.ipynb for an inference example." + ) + + def _build_sam_heads(self): + """Build SAM-style prompt encoder and mask decoder.""" + self.sam_prompt_embed_dim = self.hidden_dim + self.sam_image_embedding_size = self.image_size // self.backbone_stride + + # build PromptEncoder and MaskDecoder from SAM + # (their hyperparameters like `mask_in_chans=16` are from SAM code) + self.sam_prompt_encoder = PromptEncoder( + embed_dim=self.sam_prompt_embed_dim, + image_embedding_size=( + self.sam_image_embedding_size, + self.sam_image_embedding_size, + ), + input_image_size=(self.image_size, self.image_size), + mask_in_chans=16, + ) + self.sam_mask_decoder = MaskDecoder( + num_multimask_outputs=3, + transformer=TwoWayTransformer( + depth=2, + embedding_dim=self.sam_prompt_embed_dim, + mlp_dim=2048, + num_heads=8, + ), + transformer_dim=self.sam_prompt_embed_dim, + iou_head_depth=3, + iou_head_hidden_dim=256, + use_high_res_features=self.use_high_res_features_in_sam, + iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid, + pred_obj_scores=self.pred_obj_scores, + pred_obj_scores_mlp=self.pred_obj_scores_mlp, + use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr, + **(self.sam_mask_decoder_extra_args or {}), + ) + if self.use_obj_ptrs_in_encoder: + # a linear projection on SAM output tokens to turn them into object pointers + self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim) + if self.use_mlp_for_obj_ptr_proj: + self.obj_ptr_proj = MLP( + self.hidden_dim, self.hidden_dim, self.hidden_dim, 3 + ) + else: + self.obj_ptr_proj = torch.nn.Identity() + if self.proj_tpos_enc_in_obj_ptrs: + # a linear projection on temporal positional encoding in object pointers to + # avoid potential interference with spatial positional encoding + self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim) + else: + self.obj_ptr_tpos_proj = torch.nn.Identity() + + def _forward_sam_heads( + self, + backbone_features, + point_inputs=None, + mask_inputs=None, + high_res_features=None, + multimask_output=False, + ): + """ + Forward SAM prompt encoders and mask heads. + + Inputs: + - backbone_features: image features of [B, C, H, W] shape + - point_inputs: a dictionary with "point_coords" and "point_labels", where + 1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the + absolute pixel-unit coordinate in (x, y) format of the P input points + 2) "point_labels" has shape [B, P] and int32 dtype, where 1 means + positive clicks, 0 means negative clicks, and -1 means padding + - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the + same spatial size as the image. + - high_res_features: either 1) None or 2) or a list of length 2 containing + two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively, + which will be used as high-resolution feature maps for SAM decoder. + - multimask_output: if it's True, we output 3 candidate masks and their 3 + corresponding IoU estimates, and if it's False, we output only 1 mask and + its corresponding IoU estimate. + + Outputs: + - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if + `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM + output mask logits (before sigmoid) for the low-resolution masks, with 4x + the resolution (1/4 stride) of the input backbone_features. + - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3 + if `multimask_output=True` and M = 1 if `multimask_output=False`), + upsampled from the low-resolution masks, with shape size as the image + (stride is 1 pixel). + - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1 + if `multimask_output=False`), the estimated IoU of each output mask. + - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`. + If `multimask_output=True`, it's the mask with the highest IoU estimate. + If `multimask_output=False`, it's the same as `low_res_multimasks`. + - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`. + If `multimask_output=True`, it's the mask with the highest IoU estimate. + If `multimask_output=False`, it's the same as `high_res_multimasks`. + - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted + based on the output token from the SAM mask decoder. + """ + B = backbone_features.size(0) + device = backbone_features.device + assert backbone_features.size(1) == self.sam_prompt_embed_dim + assert backbone_features.size(2) == self.sam_image_embedding_size + assert backbone_features.size(3) == self.sam_image_embedding_size + + # a) Handle point prompts + if point_inputs is not None: + sam_point_coords = point_inputs["point_coords"] + sam_point_labels = point_inputs["point_labels"] + assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B + else: + # If no points are provide, pad with an empty point (with label -1) + sam_point_coords = torch.zeros(B, 1, 2, device=device) + sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device) + + # b) Handle mask prompts + if mask_inputs is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1) + if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size: + sam_mask_prompt = F.interpolate( + mask_inputs.float(), + size=self.sam_prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ) + else: + sam_mask_prompt = mask_inputs + else: + # Otherwise, simply feed None (and SAM's prompt encoder will add + # a learned `no_mask_embed` to indicate no mask input in this case). + sam_mask_prompt = None + + sparse_embeddings, dense_embeddings = self.sam_prompt_encoder( + points=(sam_point_coords, sam_point_labels), + boxes=None, + masks=sam_mask_prompt, + ) + ( + low_res_multimasks, + ious, + sam_output_tokens, + object_score_logits, + ) = self.sam_mask_decoder( + image_embeddings=backbone_features, + image_pe=self.sam_prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + repeat_image=False, # the image is already batched + high_res_features=high_res_features, + ) + if self.pred_obj_scores: + is_obj_appearing = object_score_logits > 0 + + # Mask used for spatial memories is always a *hard* choice between obj and no obj, + # consistent with the actual mask prediction + low_res_multimasks = torch.where( + is_obj_appearing[:, None, None], + low_res_multimasks, + NO_OBJ_SCORE, + ) + + # convert masks from possibly bfloat16 (or float16) to float32 + # (older PyTorch versions before 2.1 don't support `interpolate` on bf16) + low_res_multimasks = low_res_multimasks.float() + high_res_multimasks = F.interpolate( + low_res_multimasks, + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + + sam_output_token = sam_output_tokens[:, 0] + if multimask_output: + # take the best mask prediction (with the highest IoU estimation) + best_iou_inds = torch.argmax(ious, dim=-1) + batch_inds = torch.arange(B, device=device) + low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) + high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) + if sam_output_tokens.size(1) > 1: + sam_output_token = sam_output_tokens[batch_inds, best_iou_inds] + else: + low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks + + # Extract object pointer from the SAM output token (with occlusion handling) + obj_ptr = self.obj_ptr_proj(sam_output_token) + if self.pred_obj_scores: + # Allow *soft* no obj ptr, unlike for masks + if self.soft_no_obj_ptr: + lambda_is_obj_appearing = object_score_logits.sigmoid() + else: + lambda_is_obj_appearing = is_obj_appearing.float() + + if self.fixed_no_obj_ptr: + obj_ptr = lambda_is_obj_appearing * obj_ptr + obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr + + return ( + low_res_multimasks, + high_res_multimasks, + ious, + low_res_masks, + high_res_masks, + obj_ptr, + object_score_logits, + ) + + def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): + """ + Directly turn binary `mask_inputs` into a output mask logits without using SAM. + (same input and output shapes as in _forward_sam_heads above). + """ + # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). + out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 + mask_inputs_float = mask_inputs.float() + high_res_masks = mask_inputs_float * out_scale + out_bias + low_res_masks = F.interpolate( + high_res_masks, + size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ) + # a dummy IoU prediction of all 1's under mask input + ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float() + if not self.use_obj_ptrs_in_encoder: + # all zeros as a dummy object pointer (of shape [B, C]) + obj_ptr = torch.zeros( + mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device + ) + else: + # produce an object pointer using the SAM decoder from the mask input + _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads( + backbone_features=backbone_features, + mask_inputs=self.mask_downsample(mask_inputs_float), + high_res_features=high_res_features, + ) + # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; + # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying + # on the object_scores from the SAM decoder. + is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) + is_obj_appearing = is_obj_appearing[..., None] + lambda_is_obj_appearing = is_obj_appearing.float() + object_score_logits = out_scale * lambda_is_obj_appearing + out_bias + if self.pred_obj_scores: + if self.fixed_no_obj_ptr: + obj_ptr = lambda_is_obj_appearing * obj_ptr + obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr + + return ( + low_res_masks, + high_res_masks, + ious, + low_res_masks, + high_res_masks, + obj_ptr, + object_score_logits, + ) + + def forward_image(self, img_batch: torch.Tensor): + """Get the image feature on the input batch.""" + backbone_out = self.image_encoder(img_batch) + if self.use_high_res_features_in_sam: + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0( + backbone_out["backbone_fpn"][0] + ) + backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1( + backbone_out["backbone_fpn"][1] + ) + return backbone_out + + def _prepare_backbone_features(self, backbone_out): + """Prepare and flatten visual features.""" + backbone_out = backbone_out.copy() + assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"]) + assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels + + feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :] + vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :] + + feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds] + # flatten NxCxHxW to HWxNxC + vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps] + vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds] + + return backbone_out, vision_feats, vision_pos_embeds, feat_sizes + + def _prepare_memory_conditioned_features( + self, + frame_idx, + is_init_cond_frame, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + output_dict, + num_frames, + track_in_reverse=False, # tracking in reverse time order (for demo usage) + ): + """Fuse the current frame's visual feature map with previous memory.""" + B = current_vision_feats[-1].size(1) # batch size on this frame + C = self.hidden_dim + H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size + device = current_vision_feats[-1].device + # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images. + # In this case, we skip the fusion with any memory. + if self.num_maskmem == 0: # Disable memory and skip fusion + pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) + return pix_feat + + num_obj_ptr_tokens = 0 + tpos_sign_mul = -1 if track_in_reverse else 1 + # Step 1: condition the visual features of the current frame on previous memories + if not is_init_cond_frame: + # Retrieve the memories encoded with the maskmem backbone + to_cat_memory, to_cat_memory_pos_embed = [], [] + # Add conditioning frames's output first (all cond frames have t_pos=0 for + # when getting temporal positional embedding below) + assert len(output_dict["cond_frame_outputs"]) > 0 + # Select a maximum number of temporally closest cond frames for cross attention + cond_outputs = output_dict["cond_frame_outputs"] + selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames( + frame_idx, cond_outputs, self.max_cond_frames_in_attn + ) + t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()] + # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory + # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1 + # We also allow taking the memory frame non-consecutively (with stride>1), in which case + # we take (self.num_maskmem - 2) frames among every stride-th frames plus the last frame. + stride = 1 if self.training else self.memory_temporal_stride_for_eval + for t_pos in range(1, self.num_maskmem): + t_rel = self.num_maskmem - t_pos # how many frames before current frame + if t_rel == 1: + # for t_rel == 1, we take the last frame (regardless of r) + if not track_in_reverse: + # the frame immediately before this frame (i.e. frame_idx - 1) + prev_frame_idx = frame_idx - t_rel + else: + # the frame immediately after this frame (i.e. frame_idx + 1) + prev_frame_idx = frame_idx + t_rel + else: + # for t_rel >= 2, we take the memory frame from every r-th frames + if not track_in_reverse: + # first find the nearest frame among every r-th frames before this frame + # for r=1, this would be (frame_idx - 2) + prev_frame_idx = ((frame_idx - 2) // stride) * stride + # then seek further among every r-th frames + prev_frame_idx = prev_frame_idx - (t_rel - 2) * stride + else: + # first find the nearest frame among every r-th frames after this frame + # for r=1, this would be (frame_idx + 2) + prev_frame_idx = -(-(frame_idx + 2) // stride) * stride + # then seek further among every r-th frames + prev_frame_idx = prev_frame_idx + (t_rel - 2) * stride + out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None) + if out is None: + # If an unselected conditioning frame is among the last (self.num_maskmem - 1) + # frames, we still attend to it as if it's a non-conditioning frame. + out = unselected_cond_outputs.get(prev_frame_idx, None) + t_pos_and_prevs.append((t_pos, out)) + + for t_pos, prev in t_pos_and_prevs: + if prev is None: + continue # skip padding frames + # "maskmem_features" might have been offloaded to CPU in demo use cases, + # so we load it back to GPU (it's a no-op if it's already on GPU). + feats = prev["maskmem_features"].to(device, non_blocking=True) + to_cat_memory.append(feats.flatten(2).permute(2, 0, 1)) + # Spatial positional encoding (it might have been offloaded to CPU in eval) + maskmem_enc = prev["maskmem_pos_enc"][-1].to(device) + maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1) + # Temporal positional encoding + maskmem_enc = ( + maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1] + ) + to_cat_memory_pos_embed.append(maskmem_enc) + + # Construct the list of past object pointers + if self.use_obj_ptrs_in_encoder: + max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder) + # First add those object pointers from selected conditioning frames + # (optionally, only include object pointers in the past during evaluation) + if not self.training and self.only_obj_ptrs_in_the_past_for_eval: + ptr_cond_outputs = { + t: out + for t, out in selected_cond_outputs.items() + if (t >= frame_idx if track_in_reverse else t <= frame_idx) + } + else: + ptr_cond_outputs = selected_cond_outputs + pos_and_ptrs = [ + # Temporal pos encoding contains how far away each pointer is from current frame + ( + ( + (frame_idx - t) * tpos_sign_mul + if self.use_signed_tpos_enc_to_obj_ptrs + else abs(frame_idx - t) + ), + out["obj_ptr"], + ) + for t, out in ptr_cond_outputs.items() + ] + # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame + for t_diff in range(1, max_obj_ptrs_in_encoder): + t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff + if t < 0 or (num_frames is not None and t >= num_frames): + break + out = output_dict["non_cond_frame_outputs"].get( + t, unselected_cond_outputs.get(t, None) + ) + if out is not None: + pos_and_ptrs.append((t_diff, out["obj_ptr"])) + # If we have at least one object pointer, add them to the across attention + if len(pos_and_ptrs) > 0: + pos_list, ptrs_list = zip(*pos_and_ptrs) + # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape + obj_ptrs = torch.stack(ptrs_list, dim=0) + # a temporal positional embedding based on how far each object pointer is from + # the current frame (sine embedding normalized by the max pointer num). + if self.add_tpos_enc_to_obj_ptrs: + t_diff_max = max_obj_ptrs_in_encoder - 1 + tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim + obj_pos = torch.tensor(pos_list, device=device) + obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim) + obj_pos = self.obj_ptr_tpos_proj(obj_pos) + obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim) + else: + obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim) + if self.mem_dim < C: + # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C + obj_ptrs = obj_ptrs.reshape( + -1, B, C // self.mem_dim, self.mem_dim + ) + obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1) + obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0) + to_cat_memory.append(obj_ptrs) + to_cat_memory_pos_embed.append(obj_pos) + num_obj_ptr_tokens = obj_ptrs.shape[0] + else: + num_obj_ptr_tokens = 0 + else: + # for initial conditioning frames, encode them without using any previous memory + if self.directly_add_no_mem_embed: + # directly add no-mem embedding (instead of using the transformer encoder) + pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed + pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) + return pix_feat_with_mem + + # Use a dummy token on the first frame (to avoid empty memory input to tranformer encoder) + to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)] + to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)] + + # Step 2: Concatenate the memories and forward through the transformer encoder + memory = torch.cat(to_cat_memory, dim=0) + memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0) + + pix_feat_with_mem = self.memory_attention( + curr=current_vision_feats, + curr_pos=current_vision_pos_embeds, + memory=memory, + memory_pos=memory_pos_embed, + num_obj_ptr_tokens=num_obj_ptr_tokens, + ) + # reshape the output (HW)BC => BCHW + pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) + return pix_feat_with_mem + + def _encode_new_memory( + self, + current_vision_feats, + feat_sizes, + pred_masks_high_res, + object_score_logits, + is_mask_from_pts, + ): + """Encode the current image and its prediction into a memory feature.""" + B = current_vision_feats[-1].size(1) # batch size on this frame + C = self.hidden_dim + H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size + # top-level feature, (HW)BC => BCHW + pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) + if self.non_overlap_masks_for_mem_enc and not self.training: + # optionally, apply non-overlapping constraints to the masks (it's applied + # in the batch dimension and should only be used during eval, where all + # the objects come from the same video under batch size 1). + pred_masks_high_res = self._apply_non_overlapping_constraints( + pred_masks_high_res + ) + # scale the raw mask logits with a temperature before applying sigmoid + binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts + if binarize and not self.training: + mask_for_mem = (pred_masks_high_res > 0).float() + else: + # apply sigmoid on the raw mask logits to turn them into range (0, 1) + mask_for_mem = torch.sigmoid(pred_masks_high_res) + # apply scale and bias terms to the sigmoid probabilities + if self.sigmoid_scale_for_mem_enc != 1.0: + mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc + if self.sigmoid_bias_for_mem_enc != 0.0: + mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc + maskmem_out = self.memory_encoder( + pix_feat, mask_for_mem, skip_mask_sigmoid=True # sigmoid already applied + ) + maskmem_features = maskmem_out["vision_features"] + maskmem_pos_enc = maskmem_out["vision_pos_enc"] + # add a no-object embedding to the spatial memory to indicate that the frame + # is predicted to be occluded (i.e. no object is appearing in the frame) + if self.no_obj_embed_spatial is not None: + is_obj_appearing = (object_score_logits > 0).float() + maskmem_features += ( + 1 - is_obj_appearing[..., None, None] + ) * self.no_obj_embed_spatial[..., None, None].expand( + *maskmem_features.shape + ) + + return maskmem_features, maskmem_pos_enc + + def _track_step( + self, + frame_idx, + is_init_cond_frame, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + point_inputs, + mask_inputs, + output_dict, + num_frames, + track_in_reverse, + prev_sam_mask_logits, + ): + current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs} + # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW + if len(current_vision_feats) > 1: + high_res_features = [ + x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) + for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1]) + ] + else: + high_res_features = None + if mask_inputs is not None and self.use_mask_input_as_output_without_sam: + # When use_mask_input_as_output_without_sam=True, we directly output the mask input + # (see it as a GT mask) without using a SAM prompt encoder + mask decoder. + pix_feat = current_vision_feats[-1].permute(1, 2, 0) + pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1]) + sam_outputs = self._use_mask_as_output( + pix_feat, high_res_features, mask_inputs + ) + else: + # fused the visual feature with previous memory features in the memory bank + pix_feat = self._prepare_memory_conditioned_features( + frame_idx=frame_idx, + is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats[-1:], + current_vision_pos_embeds=current_vision_pos_embeds[-1:], + feat_sizes=feat_sizes[-1:], + output_dict=output_dict, + num_frames=num_frames, + track_in_reverse=track_in_reverse, + ) + # apply SAM-style segmentation head + # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, + # e.g. in demo where such logits come from earlier interaction instead of correction sampling + # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) + if prev_sam_mask_logits is not None: + assert point_inputs is not None and mask_inputs is None + mask_inputs = prev_sam_mask_logits + multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) + sam_outputs = self._forward_sam_heads( + backbone_features=pix_feat, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + high_res_features=high_res_features, + multimask_output=multimask_output, + ) + + return current_out, sam_outputs, high_res_features, pix_feat + + def _encode_memory_in_output( + self, + current_vision_feats, + feat_sizes, + point_inputs, + run_mem_encoder, + high_res_masks, + object_score_logits, + current_out, + ): + if run_mem_encoder and self.num_maskmem > 0: + high_res_masks_for_mem_enc = high_res_masks + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + current_vision_feats=current_vision_feats, + feat_sizes=feat_sizes, + pred_masks_high_res=high_res_masks_for_mem_enc, + object_score_logits=object_score_logits, + is_mask_from_pts=(point_inputs is not None), + ) + current_out["maskmem_features"] = maskmem_features + current_out["maskmem_pos_enc"] = maskmem_pos_enc + else: + current_out["maskmem_features"] = None + current_out["maskmem_pos_enc"] = None + + def track_step( + self, + frame_idx, + is_init_cond_frame, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + point_inputs, + mask_inputs, + output_dict, + num_frames, + track_in_reverse=False, # tracking in reverse time order (for demo usage) + # Whether to run the memory encoder on the predicted masks. Sometimes we might want + # to skip the memory encoder with `run_mem_encoder=False`. For example, + # in demo we might call `track_step` multiple times for each user click, + # and only encode the memory when the user finalizes their clicks. And in ablation + # settings like SAM training on static images, we don't need the memory encoder. + run_mem_encoder=True, + # The previously predicted SAM mask logits (which can be fed together with new clicks in demo). + prev_sam_mask_logits=None, + ): + current_out, sam_outputs, _, _ = self._track_step( + frame_idx, + is_init_cond_frame, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + point_inputs, + mask_inputs, + output_dict, + num_frames, + track_in_reverse, + prev_sam_mask_logits, + ) + + ( + _, + _, + _, + low_res_masks, + high_res_masks, + obj_ptr, + object_score_logits, + ) = sam_outputs + + current_out["pred_masks"] = low_res_masks + current_out["pred_masks_high_res"] = high_res_masks + current_out["obj_ptr"] = obj_ptr + if not self.training: + # Only add this in inference (to avoid unused param in activation checkpointing; + # it's mainly used in the demo to encode spatial memories w/ consolidated masks) + current_out["object_score_logits"] = object_score_logits + + # Finally run the memory encoder on the predicted mask to encode + # it into a new memory feature (that can be used in future frames) + self._encode_memory_in_output( + current_vision_feats, + feat_sizes, + point_inputs, + run_mem_encoder, + high_res_masks, + object_score_logits, + current_out, + ) + + return current_out + + def _use_multimask(self, is_init_cond_frame, point_inputs): + """Whether to use multimask output in the SAM head.""" + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) + multimask_output = ( + self.multimask_output_in_sam + and (is_init_cond_frame or self.multimask_output_for_tracking) + and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num) + ) + return multimask_output + + def _apply_non_overlapping_constraints(self, pred_masks): + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam2_utils.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam2_utils.py new file mode 100644 index 00000000..ad00a766 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/modeling/sam2_utils.py @@ -0,0 +1,323 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import copy +from typing import Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..utils.misc import mask_to_box + + +def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num): + """ + Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs` + that are temporally closest to the current frame at `frame_idx`. Here, we take + - a) the closest conditioning frame before `frame_idx` (if any); + - b) the closest conditioning frame after `frame_idx` (if any); + - c) any other temporally closest conditioning frames until reaching a total + of `max_cond_frame_num` conditioning frames. + + Outputs: + - selected_outputs: selected items (keys & values) from `cond_frame_outputs`. + - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`. + """ + if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num: + selected_outputs = cond_frame_outputs + unselected_outputs = {} + else: + assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames" + selected_outputs = {} + + # the closest conditioning frame before `frame_idx` (if any) + idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None) + if idx_before is not None: + selected_outputs[idx_before] = cond_frame_outputs[idx_before] + + # the closest conditioning frame after `frame_idx` (if any) + idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None) + if idx_after is not None: + selected_outputs[idx_after] = cond_frame_outputs[idx_after] + + # add other temporally closest conditioning frames until reaching a total + # of `max_cond_frame_num` conditioning frames. + num_remain = max_cond_frame_num - len(selected_outputs) + inds_remain = sorted( + (t for t in cond_frame_outputs if t not in selected_outputs), + key=lambda x: abs(x - frame_idx), + )[:num_remain] + selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain) + unselected_outputs = { + t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs + } + + return selected_outputs, unselected_outputs + + +def get_1d_sine_pe(pos_inds, dim, temperature=10000): + """ + Get 1D sine positional embedding as in the original Transformer paper. + """ + pe_dim = dim // 2 + dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) + dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) + + pos_embed = pos_inds.unsqueeze(-1) / dim_t + pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) + return pos_embed + + +def get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +def get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +class DropPath(nn.Module): + # adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py + def __init__(self, drop_prob=0.0, scale_by_keep=True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + if self.drop_prob == 0.0 or not self.training: + return x + keep_prob = 1 - self.drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and self.scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +# Lightly adapted from +# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa +class MLP(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: nn.Module = nn.ReLU, + sigmoid_output: bool = False, + ) -> None: + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + self.sigmoid_output = sigmoid_output + self.act = activation() + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x) + if self.sigmoid_output: + x = F.sigmoid(x) + return x + + +# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa +# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +def sample_box_points( + masks: torch.Tensor, + noise: float = 0.1, # SAM default + noise_bound: int = 20, # SAM default + top_left_label: int = 2, + bottom_right_label: int = 3, +) -> Tuple[np.array, np.array]: + """ + Sample a noised version of the top left and bottom right corners of a given `bbox` + + Inputs: + - masks: [B, 1, H,W] boxes, dtype=torch.Tensor + - noise: noise as a fraction of box width and height, dtype=float + - noise_bound: maximum amount of noise (in pure pixesl), dtype=int + + Returns: + - box_coords: [B, num_pt, 2], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.float + - box_labels: [B, num_pt], label 2 is reserverd for top left and 3 for bottom right corners, dtype=torch.int32 + """ + device = masks.device + box_coords = mask_to_box(masks) + B, _, H, W = masks.shape + box_labels = torch.tensor( + [top_left_label, bottom_right_label], dtype=torch.int, device=device + ).repeat(B) + if noise > 0.0: + if not isinstance(noise_bound, torch.Tensor): + noise_bound = torch.tensor(noise_bound, device=device) + bbox_w = box_coords[..., 2] - box_coords[..., 0] + bbox_h = box_coords[..., 3] - box_coords[..., 1] + max_dx = torch.min(bbox_w * noise, noise_bound) + max_dy = torch.min(bbox_h * noise, noise_bound) + box_noise = 2 * torch.rand(B, 1, 4, device=device) - 1 + box_noise = box_noise * torch.stack((max_dx, max_dy, max_dx, max_dy), dim=-1) + + box_coords = box_coords + box_noise + img_bounds = ( + torch.tensor([W, H, W, H], device=device) - 1 + ) # uncentered pixel coords + box_coords.clamp_(torch.zeros_like(img_bounds), img_bounds) # In place clamping + + box_coords = box_coords.reshape(-1, 2, 2) # always 2 points + box_labels = box_labels.reshape(-1, 2) + return box_coords, box_labels + + +def sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1): + """ + Sample `num_pt` random points (along with their labels) independently from the error regions. + + Inputs: + - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool + - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None + - num_pt: int, number of points to sample independently for each of the B error maps + + Outputs: + - points: [B, num_pt, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point + - labels: [B, num_pt], dtype=torch.int32, where 1 means positive clicks and 0 means + negative clicks + """ + if pred_masks is None: # if pred_masks is not provided, treat it as empty + pred_masks = torch.zeros_like(gt_masks) + assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1 + assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape + assert num_pt >= 0 + + B, _, H_im, W_im = gt_masks.shape + device = gt_masks.device + + # false positive region, a new point sampled in this region should have + # negative label to correct the FP error + fp_masks = ~gt_masks & pred_masks + # false negative region, a new point sampled in this region should have + # positive label to correct the FN error + fn_masks = gt_masks & ~pred_masks + # whether the prediction completely match the ground-truth on each mask + all_correct = torch.all((gt_masks == pred_masks).flatten(2), dim=2) + all_correct = all_correct[..., None, None] + + # channel 0 is FP map, while channel 1 is FN map + pts_noise = torch.rand(B, num_pt, H_im, W_im, 2, device=device) + # sample a negative new click from FP region or a positive new click + # from FN region, depend on where the maximum falls, + # and in case the predictions are all correct (no FP or FN), we just + # sample a negative click from the background region + pts_noise[..., 0] *= fp_masks | (all_correct & ~gt_masks) + pts_noise[..., 1] *= fn_masks + pts_idx = pts_noise.flatten(2).argmax(dim=2) + labels = (pts_idx % 2).to(torch.int32) + pts_idx = pts_idx // 2 + pts_x = pts_idx % W_im + pts_y = pts_idx // W_im + points = torch.stack([pts_x, pts_y], dim=2).to(torch.float) + return points, labels + + +def sample_one_point_from_error_center(gt_masks, pred_masks, padding=True): + """ + Sample 1 random point (along with its label) from the center of each error region, + that is, the point with the largest distance to the boundary of each error region. + This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py + + Inputs: + - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool + - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None + - padding: if True, pad with boundary of 1 px for distance transform + + Outputs: + - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point + - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks + """ + import cv2 + + if pred_masks is None: + pred_masks = torch.zeros_like(gt_masks) + assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1 + assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape + + B, _, _, W_im = gt_masks.shape + device = gt_masks.device + + # false positive region, a new point sampled in this region should have + # negative label to correct the FP error + fp_masks = ~gt_masks & pred_masks + # false negative region, a new point sampled in this region should have + # positive label to correct the FN error + fn_masks = gt_masks & ~pred_masks + + fp_masks = fp_masks.cpu().numpy() + fn_masks = fn_masks.cpu().numpy() + points = torch.zeros(B, 1, 2, dtype=torch.float) + labels = torch.ones(B, 1, dtype=torch.int32) + for b in range(B): + fn_mask = fn_masks[b, 0] + fp_mask = fp_masks[b, 0] + if padding: + fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), "constant") + fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), "constant") + # compute the distance of each point in FN/FP region to its boundary + fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0) + fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0) + if padding: + fn_mask_dt = fn_mask_dt[1:-1, 1:-1] + fp_mask_dt = fp_mask_dt[1:-1, 1:-1] + + # take the point in FN/FP region with the largest distance to its boundary + fn_mask_dt_flat = fn_mask_dt.reshape(-1) + fp_mask_dt_flat = fp_mask_dt.reshape(-1) + fn_argmax = np.argmax(fn_mask_dt_flat) + fp_argmax = np.argmax(fp_mask_dt_flat) + is_positive = fn_mask_dt_flat[fn_argmax] > fp_mask_dt_flat[fp_argmax] + pt_idx = fn_argmax if is_positive else fp_argmax + points[b, 0, 0] = pt_idx % W_im # x + points[b, 0, 1] = pt_idx // W_im # y + labels[b, 0] = int(is_positive) + + points = points.to(device) + labels = labels.to(device) + return points, labels + + +def get_next_point(gt_masks, pred_masks, method): + if method == "uniform": + return sample_random_points_from_errors(gt_masks, pred_masks) + elif method == "center": + return sample_one_point_from_error_center(gt_masks, pred_masks) + else: + raise ValueError(f"unknown sampling method {method}") diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/sam2_image_predictor.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/sam2_image_predictor.py new file mode 100644 index 00000000..271a0f04 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/sam2_image_predictor.py @@ -0,0 +1,446 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL.Image import Image + +from ..sam2.modeling.sam2_base import SAM2Base + +from ..sam2.utils.transforms import SAM2Transforms + + +class SAM2ImagePredictor: + def __init__( + self, + sam_model: SAM2Base, + mask_threshold=0.0, + max_hole_area=0.0, + max_sprinkle_area=0.0, + ) -> None: + """ + Uses SAM-2 to calculate the image embedding for an image, and then + allow repeated, efficient mask prediction given prompts. + + Arguments: + sam_model (Sam-2): The model to use for mask prediction. + mask_threshold (float): The threshold to use when converting mask logits + to binary masks. Masks are thresholded at 0 by default. + fill_hole_area (int): If fill_hole_area > 0, we fill small holes in up to + the maximum area of fill_hole_area in low_res_masks. + """ + super().__init__() + self.model = sam_model + self._transforms = SAM2Transforms( + resolution=self.model.image_size, + mask_threshold=mask_threshold, + max_hole_area=max_hole_area, + max_sprinkle_area=max_sprinkle_area, + ) + + # Predictor state + self._is_image_set = False + self._features = None + self._orig_hw = None + # Whether the predictor is set for single image or a batch of images + self._is_batch = False + + # Predictor config + self.mask_threshold = mask_threshold + + # Spatial dim for backbone feature maps + self._bb_feat_sizes = [ + (256, 256), + (128, 128), + (64, 64), + ] + + @torch.no_grad() + def set_image( + self, + image: Union[np.ndarray, Image], + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. + + Arguments: + image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image + with pixel values in [0, 255]. + image_format (str): The color format of the image, in ['RGB', 'BGR']. + """ + self.reset_predictor() + # Transform the image to the form expected by the model + if isinstance(image, np.ndarray): + #logging.info("For numpy array image, we assume (HxWxC) format") + self._orig_hw = [image.shape[:2]] + elif isinstance(image, Image): + w, h = image.size + self._orig_hw = [(h, w)] + else: + raise NotImplementedError("Image format not supported") + + input_image = self._transforms(image) + input_image = input_image[None, ...].to(self.device) + + assert ( + len(input_image.shape) == 4 and input_image.shape[1] == 3 + ), f"input_image must be of size 1x3xHxW, got {input_image.shape}" + #logging.info("Computing image embeddings for the provided image...") + backbone_out = self.model.forward_image(input_image) + _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out) + # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos + if self.model.directly_add_no_mem_embed: + vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed + + feats = [ + feat.permute(1, 2, 0).view(1, -1, *feat_size) + for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1]) + ][::-1] + self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]} + self._is_image_set = True + #logging.info("Image embeddings computed.") + + @torch.no_grad() + def set_image_batch( + self, + image_list: List[Union[np.ndarray]], + ) -> None: + """ + Calculates the image embeddings for the provided image batch, allowing + masks to be predicted with the 'predict_batch' method. + + Arguments: + image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray + with pixel values in [0, 255]. + """ + self.reset_predictor() + assert isinstance(image_list, list) + self._orig_hw = [] + for image in image_list: + assert isinstance( + image, np.ndarray + ), "Images are expected to be an np.ndarray in RGB format, and of shape HWC" + self._orig_hw.append(image.shape[:2]) + # Transform the image to the form expected by the model + img_batch = self._transforms.forward_batch(image_list) + img_batch = img_batch.to(self.device) + batch_size = img_batch.shape[0] + assert ( + len(img_batch.shape) == 4 and img_batch.shape[1] == 3 + ), f"img_batch must be of size Bx3xHxW, got {img_batch.shape}" + logging.info("Computing image embeddings for the provided images...") + backbone_out = self.model.forward_image(img_batch) + _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out) + # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos + if self.model.directly_add_no_mem_embed: + vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed + + feats = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1]) + ][::-1] + self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]} + self._is_image_set = True + self._is_batch = True + logging.info("Image embeddings computed.") + + def predict_batch( + self, + point_coords_batch: List[np.ndarray] = None, + point_labels_batch: List[np.ndarray] = None, + box_batch: List[np.ndarray] = None, + mask_input_batch: List[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + normalize_coords=True, + ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]: + """This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images. + It returns a tupele of lists of masks, ious, and low_res_masks_logits. + """ + assert self._is_batch, "This function should only be used when in batched mode" + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image_batch(...) before mask prediction." + ) + num_images = len(self._features["image_embed"]) + all_masks = [] + all_ious = [] + all_low_res_masks = [] + for img_idx in range(num_images): + # Transform input prompts + point_coords = ( + point_coords_batch[img_idx] if point_coords_batch is not None else None + ) + point_labels = ( + point_labels_batch[img_idx] if point_labels_batch is not None else None + ) + box = box_batch[img_idx] if box_batch is not None else None + mask_input = ( + mask_input_batch[img_idx] if mask_input_batch is not None else None + ) + mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts( + point_coords, + point_labels, + box, + mask_input, + normalize_coords, + img_idx=img_idx, + ) + masks, iou_predictions, low_res_masks = self._predict( + unnorm_coords, + labels, + unnorm_box, + mask_input, + multimask_output, + return_logits=return_logits, + img_idx=img_idx, + ) + masks_np = masks.squeeze(0).float().detach().cpu().numpy() + iou_predictions_np = ( + iou_predictions.squeeze(0).float().detach().cpu().numpy() + ) + low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy() + all_masks.append(masks_np) + all_ious.append(iou_predictions_np) + all_low_res_masks.append(low_res_masks_np) + + return all_masks, all_ious, all_low_res_masks + + def predict( + self, + point_coords: Optional[np.ndarray] = None, + point_labels: Optional[np.ndarray] = None, + box: Optional[np.ndarray] = None, + mask_input: Optional[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + normalize_coords=True, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Predict masks for the given input prompts, using the currently set image. + + Arguments: + point_coords (np.ndarray or None): A Nx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (np.ndarray or None): A length N array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + box (np.ndarray or None): A length 4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form 1xHxW, where + for SAM, H=W=256. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions. + + Returns: + (np.ndarray): The output masks in CxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (np.ndarray): An array of length C containing the model's + predictions for the quality of each mask. + (np.ndarray): An array of shape CxHxW, where C is the number + of masks and H=W=256. These low resolution logits can be passed to + a subsequent iteration as mask input. + """ + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) before mask prediction." + ) + + # Transform input prompts + + mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts( + point_coords, point_labels, box, mask_input, normalize_coords + ) + + masks, iou_predictions, low_res_masks = self._predict( + unnorm_coords, + labels, + unnorm_box, + mask_input, + multimask_output, + return_logits=return_logits, + ) + + masks_np = masks.squeeze(0).float().detach().cpu().numpy() + iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy() + low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy() + return masks_np, iou_predictions_np, low_res_masks_np + + def _prep_prompts( + self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1 + ): + + unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None + if point_coords is not None: + assert ( + point_labels is not None + ), "point_labels must be supplied if point_coords is supplied." + point_coords = torch.as_tensor( + point_coords, dtype=torch.float, device=self.device + ) + unnorm_coords = self._transforms.transform_coords( + point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx] + ) + labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device) + if len(unnorm_coords.shape) == 2: + unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...] + if box is not None: + box = torch.as_tensor(box, dtype=torch.float, device=self.device) + unnorm_box = self._transforms.transform_boxes( + box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx] + ) # Bx2x2 + if mask_logits is not None: + mask_input = torch.as_tensor( + mask_logits, dtype=torch.float, device=self.device + ) + if len(mask_input.shape) == 3: + mask_input = mask_input[None, :, :, :] + return mask_input, unnorm_coords, labels, unnorm_box + + @torch.no_grad() + def _predict( + self, + point_coords: Optional[torch.Tensor], + point_labels: Optional[torch.Tensor], + boxes: Optional[torch.Tensor] = None, + mask_input: Optional[torch.Tensor] = None, + multimask_output: bool = True, + return_logits: bool = False, + img_idx: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks for the given input prompts, using the currently set image. + Input prompts are batched torch tensors and are expected to already be + transformed to the input frame using SAM2Transforms. + + Arguments: + point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (torch.Tensor or None): A BxN array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + boxes (np.ndarray or None): A Bx4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form Bx1xHxW, where + for SAM, H=W=256. Masks returned by a previous iteration of the + predict method do not need further transformation. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (torch.Tensor): The output masks in BxCxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (torch.Tensor): An array of shape BxC containing the model's + predictions for the quality of each mask. + (torch.Tensor): An array of shape BxCxHxW, where C is the number + of masks and H=W=256. These low res logits can be passed to + a subsequent iteration as mask input. + """ + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) before mask prediction." + ) + + if point_coords is not None: + concat_points = (point_coords, point_labels) + else: + concat_points = None + + # Embed prompts + if boxes is not None: + box_coords = boxes.reshape(-1, 2, 2) + box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device) + box_labels = box_labels.repeat(boxes.size(0), 1) + # we merge "boxes" and "points" into a single "concat_points" input (where + # boxes are added at the beginning) to sam_prompt_encoder + if concat_points is not None: + concat_coords = torch.cat([box_coords, concat_points[0]], dim=1) + concat_labels = torch.cat([box_labels, concat_points[1]], dim=1) + concat_points = (concat_coords, concat_labels) + else: + concat_points = (box_coords, box_labels) + + sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder( + points=concat_points, + boxes=None, + masks=mask_input, + ) + + # Predict masks + batched_mode = ( + concat_points is not None and concat_points[0].shape[0] > 1 + ) # multi object prediction + high_res_features = [ + feat_level[img_idx].unsqueeze(0) + for feat_level in self._features["high_res_feats"] + ] + low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder( + image_embeddings=self._features["image_embed"][img_idx].unsqueeze(0), + image_pe=self.model.sam_prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + repeat_image=batched_mode, + high_res_features=high_res_features, + ) + + # Upscale the masks to the original image resolution + masks = self._transforms.postprocess_masks( + low_res_masks, self._orig_hw[img_idx] + ) + low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0) + if not return_logits: + masks = masks > self.mask_threshold + + return masks, iou_predictions, low_res_masks + + def get_image_embedding(self) -> torch.Tensor: + """ + Returns the image embeddings for the currently set image, with + shape 1xCxHxW, where C is the embedding dimension and (H,W) are + the embedding spatial dimension of SAM (typically C=256, H=W=64). + """ + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) to generate an embedding." + ) + assert ( + self._features is not None + ), "Features must exist if an image has been set." + return self._features["image_embed"] + + @property + def device(self) -> torch.device: + return self.model.device + + def reset_predictor(self) -> None: + """ + Resets the image embeddings and other state variables. + """ + self._is_image_set = False + self._features = None + self._orig_hw = None + self._is_batch = False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/sam2_video_predictor.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/sam2_video_predictor.py new file mode 100644 index 00000000..9285f13b --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/sam2_video_predictor.py @@ -0,0 +1,1154 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import warnings +from collections import OrderedDict + +import torch + +from tqdm import tqdm + +from ..sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base +from ..sam2.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames + + +class SAM2VideoPredictor(SAM2Base): + """The predictor class to handle user interactions and manage inference states.""" + + def __init__( + self, + fill_hole_area=0, + # whether to apply non-overlapping constraints on the output object masks + non_overlap_masks=False, + # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks; + # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True) + clear_non_cond_mem_around_input=False, + # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True). + clear_non_cond_mem_for_multi_obj=False, + # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click + # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames + add_all_frames_to_correct_as_cond=False, + **kwargs, + ): + super().__init__(**kwargs) + self.fill_hole_area = fill_hole_area + self.non_overlap_masks = non_overlap_masks + self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input + self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj + self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond + + @torch.inference_mode() + def init_state( + self, + images, + video_height, + video_width, + device='cuda', + offload_video_to_cpu=False, + offload_state_to_cpu=False, + async_loading_frames=False, + ): + """Initialize a inference state.""" + # images, video_height, video_width = load_video_frames( + # video_path=video_path, + # image_size=self.image_size, + # offload_video_to_cpu=offload_video_to_cpu, + # async_loading_frames=async_loading_frames, + # ) + inference_state = {} + inference_state["images"] = images + inference_state["num_frames"] = len(images) + # whether to offload the video frames to CPU memory + # turning on this option saves the GPU memory with only a very small overhead + inference_state["offload_video_to_cpu"] = offload_video_to_cpu + # whether to offload the inference state to CPU memory + # turning on this option saves the GPU memory at the cost of a lower tracking fps + # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object + # and from 24 to 21 when tracking two objects) + inference_state["offload_state_to_cpu"] = offload_state_to_cpu + # the original video height and width, used for resizing final output scores + inference_state["video_height"] = video_height + inference_state["video_width"] = video_width + inference_state["device"] = torch.device(device) + if offload_state_to_cpu: + inference_state["storage_device"] = torch.device("cpu") + else: + inference_state["storage_device"] = torch.device(device) + # inputs on each frame + inference_state["point_inputs_per_obj"] = {} + inference_state["mask_inputs_per_obj"] = {} + # visual features on a small number of recently visited frames for quick interactions + inference_state["cached_features"] = {} + # values that don't change across frames (so we only need to hold one copy of them) + inference_state["constants"] = {} + # mapping between client-side object id and model-side object index + inference_state["obj_id_to_idx"] = OrderedDict() + inference_state["obj_idx_to_id"] = OrderedDict() + inference_state["obj_ids"] = [] + # A storage to hold the model's tracking results and states on each frame + inference_state["output_dict"] = { + "cond_frame_outputs": {}, # dict containing {frame_idx: } + "non_cond_frame_outputs": {}, # dict containing {frame_idx: } + } + # Slice (view) of each object tracking results, sharing the same memory with "output_dict" + inference_state["output_dict_per_obj"] = {} + # A temporary storage to hold new outputs when user interact with a frame + # to add clicks or mask (it's merged into "output_dict" before propagation starts) + inference_state["temp_output_dict_per_obj"] = {} + # Frames that already holds consolidated outputs from click or mask inputs + # (we directly use their consolidated outputs during tracking) + inference_state["consolidated_frame_inds"] = { + "cond_frame_outputs": set(), # set containing frame indices + "non_cond_frame_outputs": set(), # set containing frame indices + } + # metadata for each tracking frame (e.g. which direction it's tracked) + inference_state["tracking_has_started"] = False + inference_state["frames_already_tracked"] = {} + # Warm up the visual backbone and cache the image feature on frame 0 + self._get_image_feature(inference_state, frame_idx=0, batch_size=1) + return inference_state + + def _obj_id_to_idx(self, inference_state, obj_id): + """Map client-side object id to model-side object index.""" + obj_idx = inference_state["obj_id_to_idx"].get(obj_id, None) + if obj_idx is not None: + return obj_idx + + # This is a new object id not sent to the server before. We only allow adding + # new objects *before* the tracking starts. + allow_new_object = not inference_state["tracking_has_started"] + if allow_new_object: + # get the next object slot + obj_idx = len(inference_state["obj_id_to_idx"]) + inference_state["obj_id_to_idx"][obj_id] = obj_idx + inference_state["obj_idx_to_id"][obj_idx] = obj_id + inference_state["obj_ids"] = list(inference_state["obj_id_to_idx"]) + # set up input and output structures for this object + inference_state["point_inputs_per_obj"][obj_idx] = {} + inference_state["mask_inputs_per_obj"][obj_idx] = {} + inference_state["output_dict_per_obj"][obj_idx] = { + "cond_frame_outputs": {}, # dict containing {frame_idx: } + "non_cond_frame_outputs": {}, # dict containing {frame_idx: } + } + inference_state["temp_output_dict_per_obj"][obj_idx] = { + "cond_frame_outputs": {}, # dict containing {frame_idx: } + "non_cond_frame_outputs": {}, # dict containing {frame_idx: } + } + return obj_idx + else: + raise RuntimeError( + f"Cannot add new object id {obj_id} after tracking starts. " + f"All existing object ids: {inference_state['obj_ids']}. " + f"Please call 'reset_state' to restart from scratch." + ) + + def _obj_idx_to_id(self, inference_state, obj_idx): + """Map model-side object index to client-side object id.""" + return inference_state["obj_idx_to_id"][obj_idx] + + def _get_obj_num(self, inference_state): + """Get the total number of unique object ids received so far in this session.""" + return len(inference_state["obj_idx_to_id"]) + + @torch.inference_mode() + def add_new_points_or_box( + self, + inference_state, + frame_idx, + obj_id, + points=None, + labels=None, + clear_old_points=True, + normalize_coords=True, + box=None, + ): + """Add new points to a frame.""" + obj_idx = self._obj_id_to_idx(inference_state, obj_id) + point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx] + mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx] + + if (points is not None) != (labels is not None): + raise ValueError("points and labels must be provided together") + if points is None and box is None: + raise ValueError("at least one of points or box must be provided as input") + + if points is None: + points = torch.zeros(0, 2, dtype=torch.float32) + elif not isinstance(points, torch.Tensor): + points = torch.tensor(points, dtype=torch.float32) + if labels is None: + labels = torch.zeros(0, dtype=torch.int32) + elif not isinstance(labels, torch.Tensor): + labels = torch.tensor(labels, dtype=torch.int32) + if points.dim() == 2: + points = points.unsqueeze(0) # add batch dimension + if labels.dim() == 1: + labels = labels.unsqueeze(0) # add batch dimension + + # If `box` is provided, we add it as the first two points with labels 2 and 3 + # along with the user-provided points (consistent with how SAM 2 is trained). + if box is not None: + if not clear_old_points: + raise ValueError( + "cannot add box without clearing old points, since " + "box prompt must be provided before any point prompt " + "(please use clear_old_points=True instead)" + ) + if inference_state["tracking_has_started"]: + warnings.warn( + "You are adding a box after tracking starts. SAM 2 may not always be " + "able to incorporate a box prompt for *refinement*. If you intend to " + "use box prompt as an *initial* input before tracking, please call " + "'reset_state' on the inference state to restart from scratch.", + category=UserWarning, + stacklevel=2, + ) + if not isinstance(box, torch.Tensor): + box = torch.tensor(box, dtype=torch.float32, device=points.device) + box_coords = box.reshape(1, 2, 2) + box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device) + box_labels = box_labels.reshape(1, 2) + points = torch.cat([box_coords, points], dim=1) + labels = torch.cat([box_labels, labels], dim=1) + + if normalize_coords: + video_H = inference_state["video_height"] + video_W = inference_state["video_width"] + points = points / torch.tensor([video_W, video_H]).to(points.device) + # scale the (normalized) coordinates by the model's internal image size + points = points * self.image_size + points = points.to(inference_state["device"]) + labels = labels.to(inference_state["device"]) + + if not clear_old_points: + point_inputs = point_inputs_per_frame.get(frame_idx, None) + else: + point_inputs = None + point_inputs = concat_points(point_inputs, points, labels) + + point_inputs_per_frame[frame_idx] = point_inputs + mask_inputs_per_frame.pop(frame_idx, None) + # If this frame hasn't been tracked before, we treat it as an initial conditioning + # frame, meaning that the inputs points are to generate segments on this frame without + # using any memory from other frames, like in SAM. Otherwise (if it has been tracked), + # the input points will be used to correct the already tracked masks. + is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"] + # whether to track in reverse time order + if is_init_cond_frame: + reverse = False + else: + reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"] + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx] + obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx] + # Add a frame to conditioning output if it's an initial conditioning frame or + # if the model sees all frames receiving clicks/mask as conditioning frames. + is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + + # Get any previously predicted mask logits on this object and feed it along with + # the new clicks into the SAM mask decoder. + prev_sam_mask_logits = None + # lookup temporary output dict first, which contains the most recent output + # (if not found, then lookup conditioning and non-conditioning frame output) + prev_out = obj_temp_output_dict[storage_key].get(frame_idx) + if prev_out is None: + prev_out = obj_output_dict["cond_frame_outputs"].get(frame_idx) + if prev_out is None: + prev_out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx) + + if prev_out is not None and prev_out["pred_masks"] is not None: + prev_sam_mask_logits = prev_out["pred_masks"].to(inference_state["device"],non_blocking=True) + # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues. + prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0) + current_out, _ = self._run_single_frame_inference( + inference_state=inference_state, + output_dict=obj_output_dict, # run on the slice of a single object + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=point_inputs, + mask_inputs=None, + reverse=reverse, + # Skip the memory encoder when adding clicks or mask. We execute the memory encoder + # at the beginning of `propagate_in_video` (after user finalize their clicks). This + # allows us to enforce non-overlapping constraints on all objects before encoding + # them into memory. + run_mem_encoder=False, + prev_sam_mask_logits=prev_sam_mask_logits, + ) + # Add the output to the output dict (to be used as future memory) + obj_temp_output_dict[storage_key][frame_idx] = current_out + + # Resize the output mask to the original video resolution + obj_ids = inference_state["obj_ids"] + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, + frame_idx, + is_cond=is_cond, + run_mem_encoder=False, + consolidate_at_video_res=True, + ) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, consolidated_out["pred_masks_video_res"] + ) + return frame_idx, obj_ids, video_res_masks + + def add_new_points(self, *args, **kwargs): + """Deprecated method. Please use `add_new_points_or_box` instead.""" + return self.add_new_points_or_box(*args, **kwargs) + + @torch.inference_mode() + def add_new_mask( + self, + inference_state, + frame_idx, + obj_id, + mask, + ): + """Add new mask to a frame.""" + obj_idx = self._obj_id_to_idx(inference_state, obj_id) + point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx] + mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx] + + if not isinstance(mask, torch.Tensor): + mask = torch.tensor(mask, dtype=torch.bool) + assert mask.dim() == 2 + mask_H, mask_W = mask.shape + mask_inputs_orig = mask[None, None] # add batch and channel dimension + mask_inputs_orig = mask_inputs_orig.float().to(inference_state["device"]) + + # resize the mask if it doesn't match the model's image size + if mask_H != self.image_size or mask_W != self.image_size: + mask_inputs = torch.nn.functional.interpolate( + mask_inputs_orig, + size=(self.image_size, self.image_size), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ) + mask_inputs = (mask_inputs >= 0.5).float() + else: + mask_inputs = mask_inputs_orig + + mask_inputs_per_frame[frame_idx] = mask_inputs + point_inputs_per_frame.pop(frame_idx, None) + # If this frame hasn't been tracked before, we treat it as an initial conditioning + # frame, meaning that the inputs points are to generate segments on this frame without + # using any memory from other frames, like in SAM. Otherwise (if it has been tracked), + # the input points will be used to correct the already tracked masks. + is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"] + # whether to track in reverse time order + if is_init_cond_frame: + reverse = False + else: + reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"] + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx] + obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx] + # Add a frame to conditioning output if it's an initial conditioning frame or + # if the model sees all frames receiving clicks/mask as conditioning frames. + is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + + current_out, _ = self._run_single_frame_inference( + inference_state=inference_state, + output_dict=obj_output_dict, # run on the slice of a single object + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=None, + mask_inputs=mask_inputs, + reverse=reverse, + # Skip the memory encoder when adding clicks or mask. We execute the memory encoder + # at the beginning of `propagate_in_video` (after user finalize their clicks). This + # allows us to enforce non-overlapping constraints on all objects before encoding + # them into memory. + run_mem_encoder=False, + ) + # Add the output to the output dict (to be used as future memory) + obj_temp_output_dict[storage_key][frame_idx] = current_out + + # Resize the output mask to the original video resolution + obj_ids = inference_state["obj_ids"] + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, + frame_idx, + is_cond=is_cond, + run_mem_encoder=False, + consolidate_at_video_res=True, + ) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, consolidated_out["pred_masks_video_res"] + ) + return frame_idx, obj_ids, video_res_masks + + def _get_orig_video_res_output(self, inference_state, any_res_masks): + """ + Resize the object scores to the original video resolution (video_res_masks) + and apply non-overlapping constraints for final output. + """ + device = inference_state["device"] + video_H = inference_state["video_height"] + video_W = inference_state["video_width"] + any_res_masks = any_res_masks.to(device, non_blocking=True) + if any_res_masks.shape[-2:] == (video_H, video_W): + video_res_masks = any_res_masks + else: + video_res_masks = torch.nn.functional.interpolate( + any_res_masks, + size=(video_H, video_W), + mode="bilinear", + align_corners=False, + ) + if self.non_overlap_masks: + video_res_masks = self._apply_non_overlapping_constraints(video_res_masks) + return any_res_masks, video_res_masks + + def _consolidate_temp_output_across_obj( + self, + inference_state, + frame_idx, + is_cond, + run_mem_encoder, + consolidate_at_video_res=False, + ): + """ + Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on + a frame into a single output for all objects, including + 1) fill any missing objects either from `output_dict_per_obj` (if they exist in + `output_dict_per_obj` for this frame) or leave them as placeholder values + (if they don't exist in `output_dict_per_obj` for this frame); + 2) if specified, rerun memory encoder after apply non-overlapping constraints + on the object scores. + """ + batch_size = self._get_obj_num(inference_state) + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + # Optionally, we allow consolidating the temporary outputs at the original + # video resolution (to provide a better editing experience for mask prompts). + if consolidate_at_video_res: + assert not run_mem_encoder, "memory encoder cannot run at video resolution" + consolidated_H = inference_state["video_height"] + consolidated_W = inference_state["video_width"] + consolidated_mask_key = "pred_masks_video_res" + else: + consolidated_H = consolidated_W = self.image_size // 4 + consolidated_mask_key = "pred_masks" + + # Initialize `consolidated_out`. Its "maskmem_features" and "maskmem_pos_enc" + # will be added when rerunning the memory encoder after applying non-overlapping + # constraints to object scores. Its "pred_masks" are prefilled with a large + # negative value (NO_OBJ_SCORE) to represent missing objects. + consolidated_out = { + "maskmem_features": None, + "maskmem_pos_enc": None, + consolidated_mask_key: torch.full( + size=(batch_size, 1, consolidated_H, consolidated_W), + fill_value=NO_OBJ_SCORE, + dtype=torch.float32, + device=inference_state["storage_device"], + ), + "obj_ptr": torch.full( + size=(batch_size, self.hidden_dim), + fill_value=NO_OBJ_SCORE, + dtype=torch.float32, + device=inference_state["device"], + ), + "object_score_logits": torch.full( + size=(batch_size, 1), + # default to 10.0 for object_score_logits, i.e. assuming the object is + # present as sigmoid(10)=1, same as in `predict_masks` of `MaskDecoder` + fill_value=10.0, + dtype=torch.float32, + device=inference_state["device"], + ), + } + empty_mask_ptr = None + for obj_idx in range(batch_size): + obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx] + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx] + out = obj_temp_output_dict[storage_key].get(frame_idx, None) + # If the object doesn't appear in "temp_output_dict_per_obj" on this frame, + # we fall back and look up its previous output in "output_dict_per_obj". + # We look up both "cond_frame_outputs" and "non_cond_frame_outputs" in + # "output_dict_per_obj" to find a previous output for this object. + if out is None: + out = obj_output_dict["cond_frame_outputs"].get(frame_idx, None) + if out is None: + out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx, None) + # If the object doesn't appear in "output_dict_per_obj" either, we skip it + # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE + # placeholder above) and set its object pointer to be a dummy pointer. + if out is None: + # Fill in dummy object pointers for those objects without any inputs or + # tracking outcomes on this frame (only do it under `run_mem_encoder=True`, + # i.e. when we need to build the memory for tracking). + if run_mem_encoder: + if empty_mask_ptr is None: + empty_mask_ptr = self._get_empty_mask_ptr( + inference_state, frame_idx + ) + # fill object pointer with a dummy pointer (based on an empty mask) + consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = empty_mask_ptr + continue + # Add the temporary object output mask to consolidated output mask + obj_mask = out["pred_masks"] + consolidated_pred_masks = consolidated_out[consolidated_mask_key] + if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]: + consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask + else: + # Resize first if temporary object mask has a different resolution + resized_obj_mask = torch.nn.functional.interpolate( + obj_mask, + size=consolidated_pred_masks.shape[-2:], + mode="bilinear", + align_corners=False, + ) + consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask + consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = out["obj_ptr"] + consolidated_out["object_score_logits"][obj_idx : obj_idx + 1] = out[ + "object_score_logits" + ] + + # Optionally, apply non-overlapping constraints on the consolidated scores + # and rerun the memory encoder + if run_mem_encoder: + device = inference_state["device"] + high_res_masks = torch.nn.functional.interpolate( + consolidated_out["pred_masks"].to(device, non_blocking=True), + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + if self.non_overlap_masks_for_mem_enc: + high_res_masks = self._apply_non_overlapping_constraints(high_res_masks) + maskmem_features, maskmem_pos_enc = self._run_memory_encoder( + inference_state=inference_state, + frame_idx=frame_idx, + batch_size=batch_size, + high_res_masks=high_res_masks, + object_score_logits=consolidated_out["object_score_logits"], + is_mask_from_pts=True, # these frames are what the user interacted with + ) + consolidated_out["maskmem_features"] = maskmem_features + consolidated_out["maskmem_pos_enc"] = maskmem_pos_enc + + return consolidated_out + + def _get_empty_mask_ptr(self, inference_state, frame_idx): + """Get a dummy object pointer based on an empty mask on the current frame.""" + # A dummy (empty) mask with a single object + batch_size = 1 + mask_inputs = torch.zeros( + (batch_size, 1, self.image_size, self.image_size), + dtype=torch.float32, + device=inference_state["device"], + ) + + # Retrieve correct image features + ( + _, + _, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + ) = self._get_image_feature(inference_state, frame_idx, batch_size) + + # Feed the empty mask and image feature above to get a dummy object pointer + current_out = self.track_step( + frame_idx=frame_idx, + is_init_cond_frame=True, + current_vision_feats=current_vision_feats, + current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, + point_inputs=None, + mask_inputs=mask_inputs, + output_dict={}, + num_frames=inference_state["num_frames"], + track_in_reverse=False, + run_mem_encoder=False, + prev_sam_mask_logits=None, + ) + return current_out["obj_ptr"] + + @torch.inference_mode() + def propagate_in_video_preflight(self, inference_state): + """Prepare inference_state and consolidate temporary outputs before tracking.""" + # Tracking has started and we don't allow adding new objects until session is reset. + inference_state["tracking_has_started"] = True + batch_size = self._get_obj_num(inference_state) + + # Consolidate per-object temporary outputs in "temp_output_dict_per_obj" and + # add them into "output_dict". + temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"] + output_dict = inference_state["output_dict"] + # "consolidated_frame_inds" contains indices of those frames where consolidated + # temporary outputs have been added (either in this call or any previous calls + # to `propagate_in_video_preflight`). + consolidated_frame_inds = inference_state["consolidated_frame_inds"] + for is_cond in [False, True]: + # Separately consolidate conditioning and non-conditioning temp outputs + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + # Find all the frames that contain temporary outputs for any objects + # (these should be the frames that have just received clicks for mask inputs + # via `add_new_points_or_box` or `add_new_mask`) + temp_frame_inds = set() + for obj_temp_output_dict in temp_output_dict_per_obj.values(): + temp_frame_inds.update(obj_temp_output_dict[storage_key].keys()) + consolidated_frame_inds[storage_key].update(temp_frame_inds) + # consolidate the temporary output across all objects on this frame + for frame_idx in temp_frame_inds: + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True + ) + # merge them into "output_dict" and also create per-object slices + output_dict[storage_key][frame_idx] = consolidated_out + self._add_output_per_object( + inference_state, frame_idx, consolidated_out, storage_key + ) + clear_non_cond_mem = self.clear_non_cond_mem_around_input and ( + self.clear_non_cond_mem_for_multi_obj or batch_size <= 1 + ) + if clear_non_cond_mem: + # clear non-conditioning memory of the surrounding frames + self._clear_non_cond_mem_around_input(inference_state, frame_idx) + + # clear temporary outputs in `temp_output_dict_per_obj` + for obj_temp_output_dict in temp_output_dict_per_obj.values(): + obj_temp_output_dict[storage_key].clear() + + # edge case: if an output is added to "cond_frame_outputs", we remove any prior + # output on the same frame in "non_cond_frame_outputs" + for frame_idx in output_dict["cond_frame_outputs"]: + output_dict["non_cond_frame_outputs"].pop(frame_idx, None) + for obj_output_dict in inference_state["output_dict_per_obj"].values(): + for frame_idx in obj_output_dict["cond_frame_outputs"]: + obj_output_dict["non_cond_frame_outputs"].pop(frame_idx, None) + for frame_idx in consolidated_frame_inds["cond_frame_outputs"]: + assert frame_idx in output_dict["cond_frame_outputs"] + consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx) + + # Make sure that the frame indices in "consolidated_frame_inds" are exactly those frames + # with either points or mask inputs (which should be true under a correct workflow). + all_consolidated_frame_inds = ( + consolidated_frame_inds["cond_frame_outputs"] + | consolidated_frame_inds["non_cond_frame_outputs"] + ) + input_frames_inds = set() + for point_inputs_per_frame in inference_state["point_inputs_per_obj"].values(): + input_frames_inds.update(point_inputs_per_frame.keys()) + for mask_inputs_per_frame in inference_state["mask_inputs_per_obj"].values(): + input_frames_inds.update(mask_inputs_per_frame.keys()) + assert all_consolidated_frame_inds == input_frames_inds + + @torch.inference_mode() + def propagate_in_video( + self, + inference_state, + start_frame_idx=None, + max_frame_num_to_track=None, + reverse=False, + ): + """Propagate the input points across frames to track in the entire video.""" + self.propagate_in_video_preflight(inference_state) + + output_dict = inference_state["output_dict"] + consolidated_frame_inds = inference_state["consolidated_frame_inds"] + obj_ids = inference_state["obj_ids"] + num_frames = inference_state["num_frames"] + batch_size = self._get_obj_num(inference_state) + if len(output_dict["cond_frame_outputs"]) == 0: + raise RuntimeError("No points are provided; please add points first") + clear_non_cond_mem = self.clear_non_cond_mem_around_input and ( + self.clear_non_cond_mem_for_multi_obj or batch_size <= 1 + ) + + # set start index, end index, and processing order + if start_frame_idx is None: + # default: start from the earliest frame with input points + start_frame_idx = min(output_dict["cond_frame_outputs"]) + if max_frame_num_to_track is None: + # default: track all the frames in the video + max_frame_num_to_track = num_frames + if reverse: + end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0) + if start_frame_idx > 0: + processing_order = range(start_frame_idx, end_frame_idx - 1, -1) + else: + processing_order = [] # skip reverse tracking if starting from frame 0 + else: + end_frame_idx = min( + start_frame_idx + max_frame_num_to_track, num_frames - 1 + ) + processing_order = range(start_frame_idx, end_frame_idx + 1) + + for frame_idx in tqdm(processing_order, desc="propagate in video"): + # We skip those frames already in consolidated outputs (these are frames + # that received input clicks or mask). Note that we cannot directly run + # batched forward on them via `_run_single_frame_inference` because the + # number of clicks on each object might be different. + if frame_idx in consolidated_frame_inds["cond_frame_outputs"]: + storage_key = "cond_frame_outputs" + current_out = output_dict[storage_key][frame_idx] + pred_masks = current_out["pred_masks"] + if clear_non_cond_mem: + # clear non-conditioning memory of the surrounding frames + self._clear_non_cond_mem_around_input(inference_state, frame_idx) + elif frame_idx in consolidated_frame_inds["non_cond_frame_outputs"]: + storage_key = "non_cond_frame_outputs" + current_out = output_dict[storage_key][frame_idx] + pred_masks = current_out["pred_masks"] + else: + storage_key = "non_cond_frame_outputs" + current_out, pred_masks = self._run_single_frame_inference( + inference_state=inference_state, + output_dict=output_dict, + frame_idx=frame_idx, + batch_size=batch_size, + is_init_cond_frame=False, + point_inputs=None, + mask_inputs=None, + reverse=reverse, + run_mem_encoder=True, + ) + output_dict[storage_key][frame_idx] = current_out + # Create slices of per-object outputs for subsequent interaction with each + # individual object after tracking. + self._add_output_per_object( + inference_state, frame_idx, current_out, storage_key + ) + inference_state["frames_already_tracked"][frame_idx] = {"reverse": reverse} + + # Resize the output mask to the original video resolution (we directly use + # the mask scores on GPU for output to avoid any CPU conversion in between) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, pred_masks + ) + yield frame_idx, obj_ids, video_res_masks + + def _add_output_per_object( + self, inference_state, frame_idx, current_out, storage_key + ): + """ + Split a multi-object output into per-object output slices and add them into + `output_dict_per_obj`. The resulting slices share the same tensor storage. + """ + maskmem_features = current_out["maskmem_features"] + assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor) + + maskmem_pos_enc = current_out["maskmem_pos_enc"] + assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list) + + output_dict_per_obj = inference_state["output_dict_per_obj"] + for obj_idx, obj_output_dict in output_dict_per_obj.items(): + obj_slice = slice(obj_idx, obj_idx + 1) + obj_out = { + "maskmem_features": None, + "maskmem_pos_enc": None, + "pred_masks": current_out["pred_masks"][obj_slice], + "obj_ptr": current_out["obj_ptr"][obj_slice], + "object_score_logits": current_out["object_score_logits"][obj_slice], + } + if maskmem_features is not None: + obj_out["maskmem_features"] = maskmem_features[obj_slice] + if maskmem_pos_enc is not None: + obj_out["maskmem_pos_enc"] = [x[obj_slice] for x in maskmem_pos_enc] + obj_output_dict[storage_key][frame_idx] = obj_out + + @torch.inference_mode() + def clear_all_prompts_in_frame( + self, inference_state, frame_idx, obj_id, need_output=True + ): + """Remove all input points or mask in a specific frame for a given object.""" + obj_idx = self._obj_id_to_idx(inference_state, obj_id) + + # Clear the conditioning information on the given frame + inference_state["point_inputs_per_obj"][obj_idx].pop(frame_idx, None) + inference_state["mask_inputs_per_obj"][obj_idx].pop(frame_idx, None) + + temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"] + temp_output_dict_per_obj[obj_idx]["cond_frame_outputs"].pop(frame_idx, None) + temp_output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].pop(frame_idx, None) + + # Check and see if there are still any inputs left on this frame + batch_size = self._get_obj_num(inference_state) + frame_has_input = False + for obj_idx2 in range(batch_size): + if frame_idx in inference_state["point_inputs_per_obj"][obj_idx2]: + frame_has_input = True + break + if frame_idx in inference_state["mask_inputs_per_obj"][obj_idx2]: + frame_has_input = True + break + + # If this frame has no remaining inputs for any objects, we further clear its + # conditioning frame status + if not frame_has_input: + output_dict = inference_state["output_dict"] + consolidated_frame_inds = inference_state["consolidated_frame_inds"] + consolidated_frame_inds["cond_frame_outputs"].discard(frame_idx) + consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx) + # Remove the frame's conditioning output (possibly downgrading it to non-conditioning) + out = output_dict["cond_frame_outputs"].pop(frame_idx, None) + if out is not None: + # The frame is not a conditioning frame anymore since it's not receiving inputs, + # so we "downgrade" its output (if exists) to a non-conditioning frame output. + output_dict["non_cond_frame_outputs"][frame_idx] = out + inference_state["frames_already_tracked"].pop(frame_idx, None) + # Similarly, do it for the sliced output on each object. + for obj_idx2 in range(batch_size): + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx2] + obj_out = obj_output_dict["cond_frame_outputs"].pop(frame_idx, None) + if obj_out is not None: + obj_output_dict["non_cond_frame_outputs"][frame_idx] = obj_out + + # If all the conditioning frames have been removed, we also clear the tracking outputs + if len(output_dict["cond_frame_outputs"]) == 0: + self._reset_tracking_results(inference_state) + + if not need_output: + return + # Finally, output updated masks per object (after removing the inputs above) + obj_ids = inference_state["obj_ids"] + is_cond = any( + frame_idx in obj_temp_output_dict["cond_frame_outputs"] + for obj_temp_output_dict in temp_output_dict_per_obj.values() + ) + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, + frame_idx, + is_cond=is_cond, + run_mem_encoder=False, + consolidate_at_video_res=True, + ) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, consolidated_out["pred_masks_video_res"] + ) + return frame_idx, obj_ids, video_res_masks + + @torch.inference_mode() + def reset_state(self, inference_state): + """Remove all input points or mask in all frames throughout the video.""" + self._reset_tracking_results(inference_state) + # Remove all object ids + inference_state["obj_id_to_idx"].clear() + inference_state["obj_idx_to_id"].clear() + inference_state["obj_ids"].clear() + inference_state["point_inputs_per_obj"].clear() + inference_state["mask_inputs_per_obj"].clear() + inference_state["output_dict_per_obj"].clear() + inference_state["temp_output_dict_per_obj"].clear() + + def _reset_tracking_results(self, inference_state): + """Reset all tracking inputs and results across the videos.""" + for v in inference_state["point_inputs_per_obj"].values(): + v.clear() + for v in inference_state["mask_inputs_per_obj"].values(): + v.clear() + for v in inference_state["output_dict_per_obj"].values(): + v["cond_frame_outputs"].clear() + v["non_cond_frame_outputs"].clear() + for v in inference_state["temp_output_dict_per_obj"].values(): + v["cond_frame_outputs"].clear() + v["non_cond_frame_outputs"].clear() + inference_state["output_dict"]["cond_frame_outputs"].clear() + inference_state["output_dict"]["non_cond_frame_outputs"].clear() + inference_state["consolidated_frame_inds"]["cond_frame_outputs"].clear() + inference_state["consolidated_frame_inds"]["non_cond_frame_outputs"].clear() + inference_state["tracking_has_started"] = False + inference_state["frames_already_tracked"].clear() + + def _get_image_feature(self, inference_state, frame_idx, batch_size): + """Compute the image features on a given frame.""" + # Look up in the cache first + image, backbone_out = inference_state["cached_features"].get( + frame_idx, (None, None) + ) + if backbone_out is None: + # Cache miss -- we will run inference on a single image + image = inference_state["images"][frame_idx].to(inference_state["device"]).float().unsqueeze(0) + backbone_out = self.forward_image(image) + # Cache the most recent frame's feature (for repeated interactions with + # a frame; we can use an LRU cache for more frames in the future). + inference_state["cached_features"] = {frame_idx: (image, backbone_out)} + + # expand the features to have the same dimension as the number of objects + expanded_image = image.expand(batch_size, -1, -1, -1) + expanded_backbone_out = { + "backbone_fpn": backbone_out["backbone_fpn"].copy(), + "vision_pos_enc": backbone_out["vision_pos_enc"].copy(), + } + for i, feat in enumerate(expanded_backbone_out["backbone_fpn"]): + expanded_backbone_out["backbone_fpn"][i] = feat.expand( + batch_size, -1, -1, -1 + ) + for i, pos in enumerate(expanded_backbone_out["vision_pos_enc"]): + pos = pos.expand(batch_size, -1, -1, -1) + expanded_backbone_out["vision_pos_enc"][i] = pos + + features = self._prepare_backbone_features(expanded_backbone_out) + features = (expanded_image,) + features + return features + + def _run_single_frame_inference( + self, + inference_state, + output_dict, + frame_idx, + batch_size, + is_init_cond_frame, + point_inputs, + mask_inputs, + reverse, + run_mem_encoder, + prev_sam_mask_logits=None, + ): + """Run tracking on a single frame based on current inputs and previous memory.""" + # Retrieve correct image features + ( + _, + _, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + ) = self._get_image_feature(inference_state, frame_idx, batch_size) + + # point and mask should not appear as input simultaneously on the same frame + assert point_inputs is None or mask_inputs is None + current_out = self.track_step( + frame_idx=frame_idx, + is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats, + current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + output_dict=output_dict, + num_frames=inference_state["num_frames"], + track_in_reverse=reverse, + run_mem_encoder=run_mem_encoder, + prev_sam_mask_logits=prev_sam_mask_logits, + ) + + # optionally offload the output to CPU memory to save GPU space + storage_device = inference_state["storage_device"] + maskmem_features = current_out["maskmem_features"] + if maskmem_features is not None: + maskmem_features = maskmem_features.to(torch.bfloat16) + maskmem_features = maskmem_features.to(storage_device, non_blocking=True) + pred_masks_gpu = current_out["pred_masks"] + # potentially fill holes in the predicted masks + if self.fill_hole_area > 0: + pred_masks_gpu = fill_holes_in_mask_scores( + pred_masks_gpu, self.fill_hole_area + ) + pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True) + # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it + maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out) + # object pointer is a small tensor, so we always keep it on GPU memory for fast access + obj_ptr = current_out["obj_ptr"] + object_score_logits = current_out["object_score_logits"] + # make a compact version of this frame's output to reduce the state size + compact_current_out = { + "maskmem_features": maskmem_features, + "maskmem_pos_enc": maskmem_pos_enc, + "pred_masks": pred_masks, + "obj_ptr": obj_ptr, + "object_score_logits": object_score_logits, + } + return compact_current_out, pred_masks_gpu + + def _run_memory_encoder( + self, + inference_state, + frame_idx, + batch_size, + high_res_masks, + object_score_logits, + is_mask_from_pts, + ): + """ + Run the memory encoder on `high_res_masks`. This is usually after applying + non-overlapping constraints to object scores. Since their scores changed, their + memory also need to be computed again with the memory encoder. + """ + # Retrieve correct image features + _, _, current_vision_feats, _, feat_sizes = self._get_image_feature( + inference_state, frame_idx, batch_size + ) + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + current_vision_feats=current_vision_feats, + feat_sizes=feat_sizes, + pred_masks_high_res=high_res_masks, + object_score_logits=object_score_logits, + is_mask_from_pts=is_mask_from_pts, + ) + + # optionally offload the output to CPU memory to save GPU space + storage_device = inference_state["storage_device"] + maskmem_features = maskmem_features.to(torch.bfloat16) + maskmem_features = maskmem_features.to(storage_device, non_blocking=True) + # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it + maskmem_pos_enc = self._get_maskmem_pos_enc( + inference_state, {"maskmem_pos_enc": maskmem_pos_enc} + ) + return maskmem_features, maskmem_pos_enc + + def _get_maskmem_pos_enc(self, inference_state, current_out): + """ + `maskmem_pos_enc` is the same across frames and objects, so we cache it as + a constant in the inference session to reduce session storage size. + """ + model_constants = inference_state["constants"] + # "out_maskmem_pos_enc" should be either a list of tensors or None + out_maskmem_pos_enc = current_out["maskmem_pos_enc"] + if out_maskmem_pos_enc is not None: + if "maskmem_pos_enc" not in model_constants: + assert isinstance(out_maskmem_pos_enc, list) + # only take the slice for one object, since it's same across objects + maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc] + model_constants["maskmem_pos_enc"] = maskmem_pos_enc + else: + maskmem_pos_enc = model_constants["maskmem_pos_enc"] + # expand the cached maskmem_pos_enc to the actual batch size + batch_size = out_maskmem_pos_enc[0].size(0) + expanded_maskmem_pos_enc = [ + x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc + ] + else: + expanded_maskmem_pos_enc = None + return expanded_maskmem_pos_enc + + @torch.inference_mode() + def remove_object(self, inference_state, obj_id, strict=False, need_output=True): + """ + Remove an object id from the tracking state. If strict is True, we check whether + the object id actually exists and raise an error if it doesn't exist. + """ + old_obj_idx_to_rm = inference_state["obj_id_to_idx"].get(obj_id, None) + updated_frames = [] + # Check whether this object_id to remove actually exists and possibly raise an error. + if old_obj_idx_to_rm is None: + if not strict: + return inference_state["obj_ids"], updated_frames + raise RuntimeError( + f"Cannot remove object id {obj_id} as it doesn't exist. " + f"All existing object ids: {inference_state['obj_ids']}." + ) + + # If this is the only remaining object id, we simply reset the state. + if len(inference_state["obj_id_to_idx"]) == 1: + self.reset_state(inference_state) + return inference_state["obj_ids"], updated_frames + + # There are still remaining objects after removing this object id. In this case, + # we need to delete the object storage from inference state tensors. + # Step 0: clear the input on those frames where this object id has point or mask input + # (note that this step is required as it might downgrade conditioning frames to + # non-conditioning ones) + obj_input_frames_inds = set() + obj_input_frames_inds.update( + inference_state["point_inputs_per_obj"][old_obj_idx_to_rm] + ) + obj_input_frames_inds.update( + inference_state["mask_inputs_per_obj"][old_obj_idx_to_rm] + ) + for frame_idx in obj_input_frames_inds: + self.clear_all_prompts_in_frame( + inference_state, frame_idx, obj_id, need_output=False + ) + + # Step 1: Update the object id mapping (note that it must be done after Step 0, + # since Step 0 still requires the old object id mappings in inference_state) + old_obj_ids = inference_state["obj_ids"] + old_obj_inds = list(range(len(old_obj_ids))) + remain_old_obj_inds = old_obj_inds.copy() + remain_old_obj_inds.remove(old_obj_idx_to_rm) + new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds] + new_obj_inds = list(range(len(new_obj_ids))) + # build new mappings + old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds)) + inference_state["obj_id_to_idx"] = dict(zip(new_obj_ids, new_obj_inds)) + inference_state["obj_idx_to_id"] = dict(zip(new_obj_inds, new_obj_ids)) + inference_state["obj_ids"] = new_obj_ids + + # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys. + # (note that "consolidated_frame_inds" doesn't need to be updated in this step as + # it's already handled in Step 0) + def _map_keys(container): + new_kvs = [] + for k in old_obj_inds: + v = container.pop(k) + if k in old_idx_to_new_idx: + new_kvs.append((old_idx_to_new_idx[k], v)) + container.update(new_kvs) + + _map_keys(inference_state["point_inputs_per_obj"]) + _map_keys(inference_state["mask_inputs_per_obj"]) + _map_keys(inference_state["output_dict_per_obj"]) + _map_keys(inference_state["temp_output_dict_per_obj"]) + + # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices. + def _slice_state(output_dict, storage_key): + for frame_idx, out in output_dict[storage_key].items(): + out["maskmem_features"] = out["maskmem_features"][remain_old_obj_inds] + out["maskmem_pos_enc"] = [ + x[remain_old_obj_inds] for x in out["maskmem_pos_enc"] + ] + # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it + out["maskmem_pos_enc"] = self._get_maskmem_pos_enc(inference_state, out) + out["pred_masks"] = out["pred_masks"][remain_old_obj_inds] + out["obj_ptr"] = out["obj_ptr"][remain_old_obj_inds] + out["object_score_logits"] = out["object_score_logits"][ + remain_old_obj_inds + ] + # also update the per-object slices + self._add_output_per_object( + inference_state, frame_idx, out, storage_key + ) + + _slice_state(inference_state["output_dict"], "cond_frame_outputs") + _slice_state(inference_state["output_dict"], "non_cond_frame_outputs") + + # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which + # could show an updated mask for objects previously occluded by the object being removed + if need_output: + temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"] + for frame_idx in obj_input_frames_inds: + is_cond = any( + frame_idx in obj_temp_output_dict["cond_frame_outputs"] + for obj_temp_output_dict in temp_output_dict_per_obj.values() + ) + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, + frame_idx, + is_cond=is_cond, + run_mem_encoder=False, + consolidate_at_video_res=True, + ) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, consolidated_out["pred_masks_video_res"] + ) + updated_frames.append((frame_idx, video_res_masks)) + + return inference_state["obj_ids"], updated_frames + + def _clear_non_cond_mem_around_input(self, inference_state, frame_idx): + """ + Remove the non-conditioning memory around the input frame. When users provide + correction clicks, the surrounding frames' non-conditioning memories can still + contain outdated object appearance information and could confuse the model. + + This method clears those non-conditioning memories surrounding the interacted + frame to avoid giving the model both old and new information about the object. + """ + r = self.memory_temporal_stride_for_eval + frame_idx_begin = frame_idx - r * self.num_maskmem + frame_idx_end = frame_idx + r * self.num_maskmem + output_dict = inference_state["output_dict"] + non_cond_frame_outputs = output_dict["non_cond_frame_outputs"] + for t in range(frame_idx_begin, frame_idx_end + 1): + non_cond_frame_outputs.pop(t, None) + for obj_output_dict in inference_state["output_dict_per_obj"].values(): + obj_output_dict["non_cond_frame_outputs"].pop(t, None) diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/amg.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/amg.py new file mode 100644 index 00000000..98684296 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/amg.py @@ -0,0 +1,348 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +from copy import deepcopy +from itertools import product +from typing import Any, Dict, Generator, ItemsView, List, Tuple + +import numpy as np +import torch + +# Very lightly adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/utils/amg.py + + +class MaskData: + """ + A structure for storing masks and their related data in batched format. + Implements basic filtering and concatenation. + """ + + def __init__(self, **kwargs) -> None: + for v in kwargs.values(): + assert isinstance( + v, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats = dict(**kwargs) + + def __setitem__(self, key: str, item: Any) -> None: + assert isinstance( + item, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats[key] = item + + def __delitem__(self, key: str) -> None: + del self._stats[key] + + def __getitem__(self, key: str) -> Any: + return self._stats[key] + + def items(self) -> ItemsView[str, Any]: + return self._stats.items() + + def filter(self, keep: torch.Tensor) -> None: + for k, v in self._stats.items(): + if v is None: + self._stats[k] = None + elif isinstance(v, torch.Tensor): + self._stats[k] = v[torch.as_tensor(keep, device=v.device)] + elif isinstance(v, np.ndarray): + self._stats[k] = v[keep.detach().cpu().numpy()] + elif isinstance(v, list) and keep.dtype == torch.bool: + self._stats[k] = [a for i, a in enumerate(v) if keep[i]] + elif isinstance(v, list): + self._stats[k] = [v[i] for i in keep] + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def cat(self, new_stats: "MaskData") -> None: + for k, v in new_stats.items(): + if k not in self._stats or self._stats[k] is None: + self._stats[k] = deepcopy(v) + elif isinstance(v, torch.Tensor): + self._stats[k] = torch.cat([self._stats[k], v], dim=0) + elif isinstance(v, np.ndarray): + self._stats[k] = np.concatenate([self._stats[k], v], axis=0) + elif isinstance(v, list): + self._stats[k] = self._stats[k] + deepcopy(v) + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def to_numpy(self) -> None: + for k, v in self._stats.items(): + if isinstance(v, torch.Tensor): + self._stats[k] = v.float().detach().cpu().numpy() + + +def is_box_near_crop_edge( + boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0 +) -> torch.Tensor: + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + boxes = uncrop_boxes_xyxy(boxes, crop_box).float() + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: + box_xywh = deepcopy(box_xyxy) + box_xywh[2] = box_xywh[2] - box_xywh[0] + box_xywh[3] = box_xywh[3] - box_xywh[1] + return box_xywh + + +def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: + assert len(args) > 0 and all( + len(a) == len(args[0]) for a in args + ), "Batched iteration must have inputs of all the same size." + n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) + for b in range(n_batches): + yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args] + + +def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: + """ + Encodes masks to an uncompressed RLE, in the format expected by + pycoco tools. + """ + # Put in fortran order and flatten h,w + b, h, w = tensor.shape + tensor = tensor.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = tensor[:, 1:] ^ tensor[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(b): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + cur_idxs = torch.cat( + [ + torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), + cur_idxs + 1, + torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), + ] + ) + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if tensor[i, 0] == 0 else [0] + counts.extend(btw_idxs.detach().cpu().tolist()) + out.append({"size": [h, w], "counts": counts}) + return out + + +def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: + """Compute a binary mask from an uncompressed RLE.""" + h, w = rle["size"] + mask = np.empty(h * w, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity ^= True + mask = mask.reshape(w, h) + return mask.transpose() # Put in C order + + +def area_from_rle(rle: Dict[str, Any]) -> int: + return sum(rle["counts"][1::2]) + + +def calculate_stability_score( + masks: torch.Tensor, mask_threshold: float, threshold_offset: float +) -> torch.Tensor: + """ + Computes the stability score for a batch of masks. The stability + score is the IoU between the binary masks obtained by thresholding + the predicted mask logits at high and low values. + """ + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + unions = ( + (masks > (mask_threshold - threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + return intersections / unions + + +def build_point_grid(n_per_side: int) -> np.ndarray: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = np.linspace(offset, 1 - offset, n_per_side) + points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = np.tile(points_one_side[:, None], (1, n_per_side)) + points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) + return points + + +def build_all_layer_point_grids( + n_per_side: int, n_layers: int, scale_per_layer: int +) -> List[np.ndarray]: + """Generates point grids for all crop layers.""" + points_by_layer = [] + for i in range(n_layers + 1): + n_points = int(n_per_side / (scale_per_layer**i)) + points_by_layer.append(build_point_grid(n_points)) + return points_by_layer + + +def generate_crop_boxes( + im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float +) -> Tuple[List[List[int]], List[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer + has (2**i)**2 boxes for the ith layer. + """ + crop_boxes, layer_idxs = [], [] + im_h, im_w = im_size + short_side = min(im_h, im_w) + + # Original image + crop_boxes.append([0, 0, im_w, im_h]) + layer_idxs.append(0) + + def crop_len(orig_len, n_crops, overlap): + return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) + + for i_layer in range(n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_w = crop_len(im_w, n_crops_per_side, overlap) + crop_h = crop_len(im_h, n_crops_per_side, overlap) + + crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] + + # Crops in XYWH format + for x0, y0 in product(crop_box_x0, crop_box_y0): + box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + return boxes + offset + + +def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0]], device=points.device) + # Check if points has a channel dimension + if len(points.shape) == 3: + offset = offset.unsqueeze(1) + return points + offset + + +def uncrop_masks( + masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int +) -> torch.Tensor: + x0, y0, x1, y1 = crop_box + if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) + pad = (x0, pad_x - x0, y0, pad_y - y0) + return torch.nn.functional.pad(masks, pad, value=0) + + +def remove_small_regions( + mask: np.ndarray, area_thresh: float, mode: str +) -> Tuple[np.ndarray, bool]: + """ + Removes small disconnected regions and holes in a mask. Returns the + mask and an indicator of if the mask has been modified. + """ + import cv2 # type: ignore + + assert mode in ["holes", "islands"] + correct_holes = mode == "holes" + working_mask = (correct_holes ^ mask).astype(np.uint8) + n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) + sizes = stats[:, -1][1:] # Row 0 is background label + small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] + if len(small_regions) == 0: + return mask, False + fill_labels = [0] + small_regions + if not correct_holes: + fill_labels = [i for i in range(n_labels) if i not in fill_labels] + # If every region is below threshold, keep largest + if len(fill_labels) == 0: + fill_labels = [int(np.argmax(sizes)) + 1] + mask = np.isin(regions, fill_labels) + return mask, True + + +def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: + from pycocotools import mask as mask_utils # type: ignore + + h, w = uncompressed_rle["size"] + rle = mask_utils.frPyObjects(uncompressed_rle, h, w) + rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json + return rle + + +def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: + """ + Calculates boxes in XYXY format around masks. Return [0,0,0,0] for + an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. + """ + # torch.max below raises an error on empty inputs, just skip in this case + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to CxHxW + shape = masks.shape + h, w = shape[-2:] + if len(shape) > 2: + masks = masks.flatten(0, -3) + else: + masks = masks.unsqueeze(0) + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + h * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + w * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + if len(shape) > 2: + out = out.reshape(*shape[:-2], 4) + else: + out = out[0] + + return out diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/misc.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/misc.py new file mode 100644 index 00000000..1e49097d --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/misc.py @@ -0,0 +1,349 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import warnings +from threading import Thread + +import numpy as np +import torch +from PIL import Image +from tqdm import tqdm +import platform + +def get_sdpa_settings(): + if torch.cuda.is_available(): + old_gpu = torch.cuda.get_device_properties(0).major < 7 + # only use Flash Attention on Ampere (8.0) or newer GPUs + use_flash_attn = torch.cuda.get_device_properties(0).major >= 8 and platform.system() == 'Linux' + # if not use_flash_attn: + # warnings.warn( + # "Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.", + # category=UserWarning, + # stacklevel=2, + # ) + # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only + # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases) + pytorch_version = tuple(int(v) for v in torch.__version__.split(".")[:2]) + if pytorch_version < (2, 2): + warnings.warn( + f"You are using PyTorch {torch.__version__} without Flash Attention v2 support. " + "Consider upgrading to PyTorch 2.2+ for Flash Attention v2 (which could be faster).", + category=UserWarning, + stacklevel=2, + ) + math_kernel_on = pytorch_version < (2, 2) or not use_flash_attn + else: + old_gpu = True + use_flash_attn = False + math_kernel_on = True + + return old_gpu, use_flash_attn, math_kernel_on + + +def get_connected_components(mask): + """ + Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W). + + Inputs: + - mask: A binary mask tensor of shape (N, 1, H, W), where 1 is foreground and 0 is + background. + + Outputs: + - labels: A tensor of shape (N, 1, H, W) containing the connected component labels + for foreground pixels and 0 for background pixels. + - counts: A tensor of shape (N, 1, H, W) containing the area of the connected + components for foreground pixels and 0 for background pixels. + """ + from ...sam2 import _C + + return _C.get_connected_componnets(mask.to(torch.uint8).contiguous()) + + +def mask_to_box(masks: torch.Tensor): + """ + compute bounding box given an input mask + + Inputs: + - masks: [B, 1, H, W] masks, dtype=torch.Tensor + + Returns: + - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor + """ + B, _, h, w = masks.shape + device = masks.device + xs = torch.arange(w, device=device, dtype=torch.int32) + ys = torch.arange(h, device=device, dtype=torch.int32) + grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing="xy") + grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w) + grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w) + min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1) + max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1) + min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1) + max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1) + bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1) + + return bbox_coords + + +def _load_img_as_tensor(img_path, image_size): + img_pil = Image.open(img_path) + img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size))) + if img_np.dtype == np.uint8: # np.uint8 is expected for JPEG images + img_np = img_np / 255.0 + else: + raise RuntimeError(f"Unknown image dtype: {img_np.dtype} on {img_path}") + img = torch.from_numpy(img_np).permute(2, 0, 1) + video_width, video_height = img_pil.size # the original video size + return img, video_height, video_width + + +class AsyncVideoFrameLoader: + """ + A list of video frames to be load asynchronously without blocking session start. + """ + + def __init__( + self, + img_paths, + image_size, + offload_video_to_cpu, + img_mean, + img_std, + compute_device, + ): + self.img_paths = img_paths + self.image_size = image_size + self.offload_video_to_cpu = offload_video_to_cpu + self.img_mean = img_mean + self.img_std = img_std + # items in `self.images` will be loaded asynchronously + self.images = [None] * len(img_paths) + # catch and raise any exceptions in the async loading thread + self.exception = None + # video_height and video_width be filled when loading the first image + self.video_height = None + self.video_width = None + self.compute_device = compute_device + + # load the first frame to fill video_height and video_width and also + # to cache it (since it's most likely where the user will click) + self.__getitem__(0) + + # load the rest of frames asynchronously without blocking the session start + def _load_frames(): + try: + for n in tqdm(range(len(self.images)), desc="frame loading (JPEG)"): + self.__getitem__(n) + except Exception as e: + self.exception = e + + self.thread = Thread(target=_load_frames, daemon=True) + self.thread.start() + + def __getitem__(self, index): + if self.exception is not None: + raise RuntimeError("Failure in frame loading thread") from self.exception + + img = self.images[index] + if img is not None: + return img + + img, video_height, video_width = _load_img_as_tensor( + self.img_paths[index], self.image_size + ) + self.video_height = video_height + self.video_width = video_width + # normalize by mean and std + img -= self.img_mean + img /= self.img_std + if not self.offload_video_to_cpu: + img = img.to(self.compute_device, non_blocking=True) + self.images[index] = img + return img + + def __len__(self): + return len(self.images) + + +def load_video_frames( + video_path, + image_size, + offload_video_to_cpu, + img_mean=(0.485, 0.456, 0.406), + img_std=(0.229, 0.224, 0.225), + async_loading_frames=False, + compute_device=torch.device("cuda"), +): + """ + Load the video frames from video_path. The frames are resized to image_size as in + the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo. + """ + is_bytes = isinstance(video_path, bytes) + is_str = isinstance(video_path, str) + is_mp4_path = is_str and os.path.splitext(video_path)[-1] in [".mp4", ".MP4"] + if is_bytes or is_mp4_path: + return load_video_frames_from_video_file( + video_path=video_path, + image_size=image_size, + offload_video_to_cpu=offload_video_to_cpu, + img_mean=img_mean, + img_std=img_std, + compute_device=compute_device, + ) + elif is_str and os.path.isdir(video_path): + return load_video_frames_from_jpg_images( + video_path=video_path, + image_size=image_size, + offload_video_to_cpu=offload_video_to_cpu, + img_mean=img_mean, + img_std=img_std, + async_loading_frames=async_loading_frames, + compute_device=compute_device, + ) + else: + raise NotImplementedError( + "Only MP4 video and JPEG folder are supported at this moment" + ) + + +def load_video_frames_from_jpg_images( + video_path, + image_size, + offload_video_to_cpu, + img_mean=(0.485, 0.456, 0.406), + img_std=(0.229, 0.224, 0.225), + async_loading_frames=False, + compute_device=torch.device("cuda"), +): + """ + Load the video frames from a directory of JPEG files (".jpg" format). + + The frames are resized to image_size x image_size and are loaded to GPU if + `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`. + + You can load a frame asynchronously by setting `async_loading_frames` to `True`. + """ + if isinstance(video_path, str) and os.path.isdir(video_path): + jpg_folder = video_path + else: + raise NotImplementedError( + "Only JPEG frames are supported at this moment. For video files, you may use " + "ffmpeg (https://ffmpeg.org/) to extract frames into a folder of JPEG files, such as \n" + "```\n" + "ffmpeg -i .mp4 -q:v 2 -start_number 0 /'%05d.jpg'\n" + "```\n" + "where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks " + "ffmpeg to start the JPEG file from 00000.jpg." + ) + + frame_names = [ + p + for p in os.listdir(jpg_folder) + if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"] + ] + frame_names.sort(key=lambda p: int(os.path.splitext(p)[0])) + num_frames = len(frame_names) + if num_frames == 0: + raise RuntimeError(f"no images found in {jpg_folder}") + img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names] + img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None] + img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None] + + if async_loading_frames: + lazy_images = AsyncVideoFrameLoader( + img_paths, + image_size, + offload_video_to_cpu, + img_mean, + img_std, + compute_device, + ) + return lazy_images, lazy_images.video_height, lazy_images.video_width + + images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32) + for n, img_path in enumerate(tqdm(img_paths, desc="frame loading (JPEG)")): + images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size) + if not offload_video_to_cpu: + images = images.to(compute_device) + img_mean = img_mean.to(compute_device) + img_std = img_std.to(compute_device) + # normalize by mean and std + images -= img_mean + images /= img_std + return images, video_height, video_width + + +def load_video_frames_from_video_file( + video_path, + image_size, + offload_video_to_cpu, + img_mean=(0.485, 0.456, 0.406), + img_std=(0.229, 0.224, 0.225), + compute_device=torch.device("cuda"), +): + """Load the video frames from a video file.""" + import decord + + img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None] + img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None] + # Get the original video height and width + decord.bridge.set_bridge("torch") + video_height, video_width, _ = decord.VideoReader(video_path).next().shape + # Iterate over all frames in the video + images = [] + for frame in decord.VideoReader(video_path, width=image_size, height=image_size): + images.append(frame.permute(2, 0, 1)) + + images = torch.stack(images, dim=0).float() / 255.0 + if not offload_video_to_cpu: + images = images.to(compute_device) + img_mean = img_mean.to(compute_device) + img_std = img_std.to(compute_device) + # normalize by mean and std + images -= img_mean + images /= img_std + return images, video_height, video_width + + +def fill_holes_in_mask_scores(mask, max_area): + """ + A post processor to fill small holes in mask scores with area under `max_area`. + """ + # Holes are those connected components in background with area <= self.max_area + # (background regions are those with mask scores <= 0) + assert max_area > 0, "max_area must be positive" + + input_mask = mask + try: + labels, areas = get_connected_components(mask <= 0) + is_hole = (labels > 0) & (areas <= max_area) + # We fill holes with a small positive mask score (0.1) to change them to foreground. + mask = torch.where(is_hole, 0.1, mask) + except Exception as e: + # Skip the post-processing step on removing small holes if the CUDA kernel fails + warnings.warn( + f"{e}\n\nSkipping the post-processing step due to the error above. You can " + "still use SAM 2 and it's OK to ignore the error above, although some post-processing " + "functionality may be limited (which doesn't affect the results in most cases; see " + "https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).", + category=UserWarning, + stacklevel=2, + ) + mask = input_mask + + return mask + + +def concat_points(old_point_inputs, new_points, new_labels): + """Add new points and labels to previous point inputs (add at the end).""" + if old_point_inputs is None: + points, labels = new_points, new_labels + else: + points = torch.cat([old_point_inputs["point_coords"], new_points], dim=1) + labels = torch.cat([old_point_inputs["point_labels"], new_labels], dim=1) + + return {"point_coords": points, "point_labels": labels} diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/transforms.py b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/transforms.py new file mode 100644 index 00000000..428a569b --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2/utils/transforms.py @@ -0,0 +1,106 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms import Normalize, Resize, ToTensor + + +class SAM2Transforms(nn.Module): + def __init__( + self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0 + ): + """ + Transforms for SAM2. + """ + super().__init__() + self.resolution = resolution + self.mask_threshold = mask_threshold + self.max_hole_area = max_hole_area + self.max_sprinkle_area = max_sprinkle_area + self.mean = [0.485, 0.456, 0.406] + self.std = [0.229, 0.224, 0.225] + self.to_tensor = ToTensor() + try: + self.transforms = torch.jit.script( + nn.Sequential( + Resize((self.resolution, self.resolution)), + Normalize(self.mean, self.std), + ) + ) + except Exception as e: + print(f"Failed to torch jit script transforms: {e}, falling back to normal transforms") + self.transforms = nn.Sequential( + Resize((self.resolution, self.resolution)), + Normalize(self.mean, self.std), + ) + + def __call__(self, x): + x = self.to_tensor(x) + return self.transforms(x) + + def forward_batch(self, img_list): + img_batch = [self.transforms(self.to_tensor(img)) for img in img_list] + img_batch = torch.stack(img_batch, dim=0) + return img_batch + + def transform_coords( + self, coords: torch.Tensor, normalize=False, orig_hw=None + ) -> torch.Tensor: + """ + Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates, + If the coords are in absolute image coordinates, normalize should be set to True and original image size is required. + + Returns + Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model. + """ + if normalize: + assert orig_hw is not None + h, w = orig_hw + coords = coords.clone() + coords[..., 0] = coords[..., 0] / w + coords[..., 1] = coords[..., 1] / h + + coords = coords * self.resolution # unnormalize coords + return coords + + def transform_boxes( + self, boxes: torch.Tensor, normalize=False, orig_hw=None + ) -> torch.Tensor: + """ + Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates, + if the coords are in absolute image coordinates, normalize should be set to True and original image size is required. + """ + boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw) + return boxes + + def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor: + """ + Perform PostProcessing on output masks. + """ + #from ...sam2.utils.misc import get_connected_components + + masks = masks.float() + # if self.max_hole_area > 0: + # # Holes are those connected components in background with area <= self.fill_hole_area + # # (background regions are those with mask scores <= self.mask_threshold) + # mask_flat = masks.flatten(0, 1).unsqueeze(1) # flatten as 1-channel image + # labels, areas = get_connected_components(mask_flat <= self.mask_threshold) + # is_hole = (labels > 0) & (areas <= self.max_hole_area) + # is_hole = is_hole.reshape_as(masks) + # # We fill holes with a small positive mask score (10.0) to change them to foreground. + # masks = torch.where(is_hole, self.mask_threshold + 10.0, masks) + + # if self.max_sprinkle_area > 0: + # labels, areas = get_connected_components(mask_flat > self.mask_threshold) + # is_hole = (labels > 0) & (areas <= self.max_sprinkle_area) + # is_hole = is_hole.reshape_as(masks) + # # We fill holes with negative mask score (-10.0) to change them to background. + # masks = torch.where(is_hole, self.mask_threshold - 10.0, masks) + + masks = F.interpolate(masks, orig_hw, mode="bilinear", align_corners=False) + return masks diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/__init__.py b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_b+.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_b+.yaml new file mode 100644 index 00000000..cbee3cf9 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_b+.yaml @@ -0,0 +1,116 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 112 + num_heads: 2 + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [896, 448, 224, 112] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: true + proj_tpos_enc_in_obj_ptrs: true + use_signed_tpos_enc_to_obj_ptrs: true + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_l.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_l.yaml new file mode 100644 index 00000000..33c9097f --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_l.yaml @@ -0,0 +1,120 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 144 + num_heads: 2 + stages: [2, 6, 36, 4] + global_att_blocks: [23, 33, 43] + window_pos_embed_bkg_spatial_size: [7, 7] + window_spec: [8, 4, 16, 8] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [1152, 576, 288, 144] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: true + proj_tpos_enc_in_obj_ptrs: true + use_signed_tpos_enc_to_obj_ptrs: true + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_s.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_s.yaml new file mode 100644 index 00000000..8e803dfe --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_s.yaml @@ -0,0 +1,119 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 96 + num_heads: 1 + stages: [1, 2, 11, 2] + global_att_blocks: [7, 10, 13] + window_pos_embed_bkg_spatial_size: [7, 7] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [768, 384, 192, 96] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: true + proj_tpos_enc_in_obj_ptrs: true + use_signed_tpos_enc_to_obj_ptrs: true + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_t.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_t.yaml new file mode 100644 index 00000000..983c2ea0 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2.1_hiera_t.yaml @@ -0,0 +1,121 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 96 + num_heads: 1 + stages: [1, 2, 7, 2] + global_att_blocks: [5, 7, 9] + window_pos_embed_bkg_spatial_size: [7, 7] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [768, 384, 192, 96] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + # SAM decoder + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: true + proj_tpos_enc_in_obj_ptrs: true + use_signed_tpos_enc_to_obj_ptrs: true + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + # HieraT does not currently support compilation, should always be set to False + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_b+.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_b+.yaml new file mode 100644 index 00000000..4e46167c --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_b+.yaml @@ -0,0 +1,119 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 112 + num_heads: 2 + stages: [2, 3, 16, 3] + global_att_blocks: [12, 16, 20] + window_pos_embed_bkg_spatial_size: [14, 14] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [896, 448, 224, 112] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: false + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + proj_tpos_enc_in_obj_ptrs: false + use_signed_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_l.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_l.yaml new file mode 100644 index 00000000..f24f1dea --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_l.yaml @@ -0,0 +1,120 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 144 + num_heads: 2 + stages: [2, 6, 36, 4] + global_att_blocks: [23, 33, 43] + window_pos_embed_bkg_spatial_size: [7, 7] + window_spec: [8, 4, 16, 8] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [1152, 576, 288, 144] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: false + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + proj_tpos_enc_in_obj_ptrs: false + use_signed_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_s.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_s.yaml new file mode 100644 index 00000000..795858e3 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_s.yaml @@ -0,0 +1,119 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 96 + num_heads: 1 + stages: [1, 2, 11, 2] + global_att_blocks: [7, 10, 13] + window_pos_embed_bkg_spatial_size: [7, 7] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [768, 384, 192, 96] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: false + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + proj_tpos_enc_in_obj_ptrs: false + use_signed_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_t.yaml b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_t.yaml new file mode 100644 index 00000000..5eb3f248 --- /dev/null +++ b/custom_nodes/ComfyUI-segment-anything-2/sam2_configs/sam2_hiera_t.yaml @@ -0,0 +1,121 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 96 + num_heads: 1 + stages: [1, 2, 7, 2] + global_att_blocks: [5, 7, 9] + window_pos_embed_bkg_spatial_size: [7, 7] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [768, 384, 192, 96] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + # SAM decoder + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + no_obj_embed_spatial: false + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + proj_tpos_enc_in_obj_ptrs: false + use_signed_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + # HieraT does not currently support compilation, should always be set to False + compile_image_encoder: False diff --git a/custom_nodes/ComfyUI_JPS-Nodes/README.md b/custom_nodes/ComfyUI_JPS-Nodes/README.md new file mode 100644 index 00000000..bba04835 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/README.md @@ -0,0 +1,81 @@ +# JPS Custom Nodes for ComfyUI + +These nodes were originally made for use in JPS ComfyUI Workflows. + +The nodes can be used in any ComfyUI workflow. + +# Installation + +If you have a previous version of the "JPS Custom Nodes for ComfyUI", please delete this before installing these nodes. + +1. cd custom_nodes +2. git clone https://github.com/JPS-GER/ComfyUI_JPS-Nodes.git +3. Restart ComfyUI + +You can also install the nodes using the following methods: +* install using [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager) + +# List of Custom Nodes + +__IO__ +* Lora Loader - Lora Loader with On/Off Switch - output is 1 or 2, so it works with most "x to 1"-switches (while some other alternatives use boolean 0 or 1 and need corresponding switches or additional math nodes) + +__Settings__ +* SDXL Resolutions - small node that offers recommended SDXL resolutions and outputs height and width values +* SDXL Basic Settings - menu node for basic SDXL settings, required for most SDXL workflows (connect to "SDXL Basic Settings Pipe" to access the values), includes FreeU options now +* Generation TXT IMG Settings - menu node to switch between TXT2IMG and IMG2IMG +* Generation Settings - menu node to switch between, TXT2IMG, IMG2IMG, Control Net Canny, Control Net Depth, Inpainting (conntect to "Generation Settings Pipe" to access the values) +* IP Adapter Settings - menu node to turn on/off five IP adapter input images and settings (conntect to "IP Adapter Settings Pipe" to access the values) +* Revision Settings - menu node to turn on/off two revision input images and settings (conntect to "Revision Settings Pipe" to access the values) +* Sampler Scheduler Settings - menu node for sampler + scheduler settings, can also be used as pipe + +__Switches__ +* Integer Switch - "5 to 1"-switch for integer values +* Image Switch - "5 to 1"-switch for images +* Latent Switch - "5 to 1"-switch for latent images +* Conditioning Switch - "5 to 1"-switch for conditioning +* Model Switch - "5 to 1"-switch for models +* VAE Switch - "5 to 1"-switch for VAE +* ControlNet Switch - "5 to 1"-switch for ControlNet +* Disable Enable Switch - input for nodes that use "disable/enable" types of input (for example KSampler) - useful to switch those values in combinaton with other switches +* Enable Disable Switch - input for nodes that use "enable/disable" types of input (for example KSampler) - useful to switch those values in combinaton with other switches + +__Pipes__ +* SDXL Basic Settings Pipe - used to access data from "SDXL Basic Settings" menu node - place outside of the menu structure of your workflow +* Generation Settings Pipe - used to access data from "Generation Settings" menu node - place outside of the menu structure of your workflow +* IP Adapter Settings Pipe - used to access data from "IP Adapter Settings" menu node - place outside of the menu structure of your workflow +* Revision Settings Pipe - used to access data from "Revision Settings" menu node - place outside of the menu structure of your workflow +* SDXL Fundamentals MultiPipe - used to build a pipe for basic SDXL settings, has input/outputs for all supported types, so you can access/change values more easily than classic "from/to/edit"-pipes +* Images Masks MultiPipe - used to build a pipe for various images and masks used in my workflow, has input/outputs for all images, so you can access/change images and masks more easily than classic "from/to/edit"-pipes + +__Math__ +* SDXL Recommended Resolution Calc - gives you the closest recommended SDXL resolution for the width and height values, useful for IMG2IMG and ControlNet input images, to bring them in line with SDXL workflows +* Resolution Multiply - multily height and width by some factor - useful to get 2x or 4x values for upscaling or SDXL target width and SDXL target height +* Largest Int - input two integer values, output will be the larger value +* Multiply Int Int - multiply two integer inputs, output is available as integer and float, so you can save an extra node converting to the required type +* Multiply Int Float - multiply integer and float inputs, output is available as integer and float, so you can save an extra node converting to the required type +* Multiply Float Float - multiply two flout inputs, output is available as integer and float, so you can save an extra node converting to the required type +* Substract Int Int - subscract one integer input from another integer input, output is available as integer and float, so you can save an extra node converting to the required type + +__Text__ +* Text Concatenate - combine multiple input strings to one output string +* Get Date Time String - get current date/time (has extra code to make sure it will not use cached data) +* SDXL Prompt Handling - control how text_g and text_l input will be handled (many options) +* SDXL Prompt Handling Plus - control how text_g and text_l input will be handled (many options), option to add an "universal negative" prompt + +![image](https://github.com/JPS-GER/ComfyUI_JPS-Nodes/assets/142158778/66da22f7-e4d6-4898-ae30-7b123a268615) +![image](https://github.com/JPS-GER/ComfyUI_JPS-Nodes/assets/142158778/c5abb960-0c6c-448a-a2f2-72d857dddc70) + +__Image__ +* Get Image Size - get width and height value from an input image, useful in combination with "Resolution Multiply" and "SDXL Recommended Resolution Calc" nodes +* Crop Image Square - crop images to a square aspect ratio - choose between center, top, bottom, left and right part of the image and fine tune with offset option, optional: resize image to target size (useful for Clip Vision input images, like IP-Adapter or Revision) + +__Style__ +* SDXL Prompt Styler - add artists, movies and general styles to your text prompt, option to add an "universal negative" prompt - uses json files, so you can extend the available options + +![image](https://github.com/JPS-GER/ComfyUI_JPS-Nodes/assets/142158778/486e2e32-1a06-4a79-b85d-0d21e4013016) + +# Credits + +SDXL Prompt Styler is an extended version of SDXL Prompt Styler by twri - https://github.com/twri/sdxl_prompt_styler + diff --git a/custom_nodes/ComfyUI_JPS-Nodes/__init__.py b/custom_nodes/ComfyUI_JPS-Nodes/__init__.py new file mode 100644 index 00000000..a45d0e98 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/__init__.py @@ -0,0 +1,10 @@ +""" +@author: JPS +@title: JPS Custom Nodes for ComfyUI +@nickname: JPS Custom Nodes +@description: Various nodes to handle SDXL Resolutions, SDXL Basic Settings, IP Adapter Settings, Revision Settings, SDXL Prompt Styler, Crop Image to Square, Crop Image to Target Size, Get Date-Time String, Resolution Multiply, Largest Integer, 5-to-1 Switches for Integer, Images, Latents, Conditioning, Model, VAE, ControlNet +""" + +from .jps_nodes import NODE_CLASS_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS'] diff --git a/custom_nodes/ComfyUI_JPS-Nodes/jps_nodes.py b/custom_nodes/ComfyUI_JPS-Nodes/jps_nodes.py new file mode 100644 index 00000000..a1e9e05b --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/jps_nodes.py @@ -0,0 +1,3543 @@ +""" +@author: JPS +@title: JPS Custom Nodes for ComfyUI +@nickname: JPS Custom Nodes +@description: Various nodes to handle SDXL Resolutions, SDXL Basic Settings, IP Adapter Settings, Revision Settings, SDXL Prompt Styler, Crop Image to Square, Crop Image to Target Size, Get Date-Time String, Resolution Multiply, Largest Integer, 5-to-1 Switches for Integer, Images, Latents, Conditioning, Model, VAE, ControlNet +""" + +#------------------------------------------------------------------------# +# JPS Custom Nodes https://github.com/JPS-GER/ComfyUI_JPS-Nodes # +# for ComfyUI https://github.com/comfyanonymous/ComfyUI # +#------------------------------------------------------------------------# + +import torch +import json +import os +import comfy.sd +import folder_paths +from datetime import datetime +from PIL import Image, ImageOps, ImageSequence +import numpy as np +from PIL.PngImagePlugin import PngInfo +from comfy.cli_args import args +import torch.nn.functional as F + +def min_(tensor_list): + # return the element-wise min of the tensor list. + x = torch.stack(tensor_list) + mn = x.min(axis=0)[0] + return torch.clamp(mn, min=0) + +def max_(tensor_list): + # return the element-wise max of the tensor list. + x = torch.stack(tensor_list) + mx = x.max(axis=0)[0] + return torch.clamp(mx, max=1) + +# From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/ +def contrast_adaptive_sharpening(image, amount): + img = F.pad(image, pad=(1, 1, 1, 1)).cpu() + + a = img[..., :-2, :-2] + b = img[..., :-2, 1:-1] + c = img[..., :-2, 2:] + d = img[..., 1:-1, :-2] + e = img[..., 1:-1, 1:-1] + f = img[..., 1:-1, 2:] + g = img[..., 2:, :-2] + h = img[..., 2:, 1:-1] + i = img[..., 2:, 2:] + + # Computing contrast + cross = (b, d, e, f, h) + mn = min_(cross) + mx = max_(cross) + + diag = (a, c, g, i) + mn2 = min_(diag) + mx2 = max_(diag) + mx = mx + mx2 + mn = mn + mn2 + + # Computing local weight + inv_mx = torch.reciprocal(mx) + amp = inv_mx * torch.minimum(mn, (2 - mx)) + + # scaling + amp = torch.sqrt(amp) + w = - amp * (amount * (1/5 - 1/8) + 1/8) + div = torch.reciprocal(1 + 4*w) + + output = ((b + d + f + h)*w + e) * div + output = output.clamp(0, 1) + output = torch.nan_to_num(output) + + return (output) + +def read_json_file(file_path): + """ + Reads a JSON file's content and returns it. + Ensures content matches the expected format. + """ + if not os.access(file_path, os.R_OK): + print(f"Warning: No read permissions for file {file_path}") + return None + + try: + with open(file_path, 'r', encoding='utf-8') as file: + content = json.load(file) + # Check if the content matches the expected format. + if not all(['name' in item and 'prompt' in item and 'negative_prompt' in item for item in content]): + print(f"Warning: Invalid content in file {file_path}") + return None + return content + except Exception as e: + print(f"An error occurred while reading {file_path}: {str(e)}") + return None + +def read_sdxl_styles(json_data): + """ + Returns style names from the provided JSON data. + """ + if not isinstance(json_data, list): + print("Error: input data must be a list") + return [] + + return [item['name'] for item in json_data if isinstance(item, dict) and 'name' in item] + +def get_all_json_files(directory): + """ + Returns all JSON files from the specified directory. + """ + return [os.path.join(directory, file) for file in os.listdir(directory) if file.endswith('.json') and os.path.isfile(os.path.join(directory, file))] + +def load_styles_from_directory(directory): + """ + Loads styles from all JSON files in the directory. + Renames duplicate style names by appending a suffix. + """ + json_files = get_all_json_files(directory) + combined_data = [] + seen = set() + + for json_file in json_files: + json_data = read_json_file(json_file) + if json_data: + for item in json_data: + original_style = item['name'] + style = original_style + suffix = 1 + while style in seen: + style = f"{original_style}_{suffix}" + suffix += 1 + item['name'] = style + seen.add(style) + combined_data.append(item) + + unique_style_names = [item['name'] for item in combined_data if isinstance(item, dict) and 'name' in item] + + return combined_data, unique_style_names + +def validate_json_data(json_data): + """ + Validates the structure of the JSON data. + """ + if not isinstance(json_data, list): + return False + for template in json_data: + if 'name' not in template or 'prompt' not in template: + return False + return True + +def find_template_by_name(json_data, template_name): + """ + Returns a template from the JSON data by name or None if not found. + """ + for template in json_data: + if template['name'] == template_name: + return template + return None + +def split_template(template: str) -> tuple: + """ + Splits a template into two parts based on a specific pattern. + """ + if "{prompt} ." in template: + template_prompt_g, template_prompt_l = template.split("{prompt} .", 1) + template_prompt_g = template_prompt_g.strip() + " {prompt}" + template_prompt_l = template_prompt_l.strip() + else: + template_prompt_g = template + template_prompt_l = "" + + return template_prompt_g, template_prompt_l + +def replace_prompts_in_template(template, positive_prompt_g, positive_prompt_l, negative_prompt): + """ + Replace the placeholders in a given template with the provided prompts and split them accordingly. + + Args: + - template (dict): The template containing prompt placeholders. + - positive_prompt_g (str): The main positive prompt to replace '{prompt}' in the template. + - positive_prompt_l (str): The auxiliary positive prompt to be combined in a specific manner. + - negative_prompt (str): The negative prompt to be combined with any existing negative prompt in the template. + + Returns: + - tuple: A tuple containing the replaced main positive, auxiliary positive, combined positive and negative prompts. + """ + template_prompt_g, template_prompt_l_template = split_template(template['prompt']) + + text_g_positive = template_prompt_g.replace("{prompt}", positive_prompt_g) + + text_l_positive = f"{template_prompt_l_template}, {positive_prompt_l}" if template_prompt_l_template and positive_prompt_l else template_prompt_l_template or positive_prompt_l + + json_negative_prompt = template.get('negative_prompt', "") + text_negative = f"{json_negative_prompt}, {negative_prompt}" if json_negative_prompt and negative_prompt else json_negative_prompt or negative_prompt + + return text_g_positive, text_l_positive, text_negative + + +def read_sdxl_templates_replace_and_combine(json_data, template_name, positive_prompt_g, positive_prompt_l, negative_prompt): + """ + Find a specific template by its name, then replace and combine its placeholders with the provided prompts in an advanced manner. + + Args: + - json_data (list): The list of templates. + - template_name (str): The name of the desired template. + - positive_prompt_g (str): The main positive prompt. + - positive_prompt_l (str): The auxiliary positive prompt. + - negative_prompt (str): The negative prompt to be combined. + + Returns: + - tuple: A tuple containing the replaced and combined main positive, auxiliary positive, combined positive and negative prompts. + """ + if not validate_json_data(json_data): + return positive_prompt_g, positive_prompt_l, negative_prompt + + template = find_template_by_name(json_data, template_name) + + if template: + return replace_prompts_in_template(template, positive_prompt_g, positive_prompt_l, negative_prompt) + else: + return positive_prompt_g, positive_prompt_l, negative_prompt + +accepted_ratios_horizontal = { + "12:5": (1536, 640, 2.400000000), + "7:4": (1344, 768, 1.750000000), + "19:13": (1216, 832, 1.461538462), + "9:7": (1152, 896, 1.285714286) +} + +# Vertical aspect ratio +accepted_ratios_vertical = { + "7:9": (896, 1152, 0.777777778), + "13:19": (832, 1216, 0.684210526), + "4:7": (768, 1344, 0.571428571), + "5:12": (640, 1536, 0.416666667) +} + +# Square aspect ratio +accepted_ratios_square = { + "1:1": (1024, 1024, 1.00000000) +} + + +class SDXL_Resolutions: + resolution = ["square - 1024x1024 (1:1)","landscape - 1152x896 (4:3)","landscape - 1216x832 (3:2)","landscape - 1344x768 (16:9)","landscape - 1536x640 (21:9)", "portrait - 896x1152 (3:4)","portrait - 832x1216 (2:3)","portrait - 768x1344 (9:16)","portrait - 640x1536 (9:21)"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "resolution": (s.resolution,), + } + } + RETURN_TYPES = ("INT","INT",) + RETURN_NAMES = ("width", "height") + FUNCTION = "get_resolutions" + + CATEGORY="JPS Nodes/Settings" + + def get_resolutions(self,resolution): + width = 1024 + height = 1024 + width = int(width) + height = int(height) + if(resolution == "square - 1024x1024 (1:1)"): + width = 1024 + height = 1024 + if(resolution == "landscape - 1152x896 (4:3)"): + width = 1152 + height = 896 + if(resolution == "landscape - 1216x832 (3:2)"): + width = 1216 + height = 832 + if(resolution == "landscape - 1344x768 (16:9)"): + width = 1344 + height = 768 + if(resolution == "landscape - 1536x640 (21:9)"): + width = 1536 + height = 640 + if(resolution == "portrait - 896x1152 (3:4)"): + width = 896 + height = 1152 + if(resolution == "portrait - 832x1216 (2:3)"): + width = 832 + height = 1216 + if(resolution == "portrait - 768x1344 (9:16)"): + width = 768 + height = 1344 + if(resolution == "portrait - 640x1536 (9:21)"): + width = 640 + height = 1536 + + return(int(width),int(height)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Basic_Settings: + resolution = ["Use Image Resolution", "square - 1024x1024 (1:1)","landscape - 1152x896 (4:3)","landscape - 1216x832 (3:2)","landscape - 1344x768 (16:9)","landscape - 1536x640 (21:9)", "portrait - 896x1152 (3:4)","portrait - 832x1216 (2:3)","portrait - 768x1344 (9:16)","portrait - 640x1536 (9:21)"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "resolution": (s.resolution,), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + "steps_total": ("INT", {"default": 60, "min": 20, "max": 250, "step": 5}), + "base_percentage": ("INT", {"default": 80, "min": 5, "max": 100, "step": 5}), + "cfg": ("FLOAT", {"default": 6.5, "min": 1, "max": 20, "step": 0.1}), + "cfg_rescale": ("FLOAT", {"default": 0.00, "min": 0.00, "max": 1.00, "step": 0.05}), + "cfg_refiner": ("FLOAT", {"default": 6.5, "min": 0, "max": 20, "step": 0.1}), + "ascore_refiner": ("FLOAT", {"default": 6, "min": 1, "max": 10, "step": 0.5}), + "res_factor": ("INT", {"default": 4, "min": 1, "max": 8, "step": 1}), + "clip_skip": ("INT", {"default": -2, "min": -24, "max": -1}), + "filename": ("STRING", {"default": "JPS"}), + }} + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("sdxl_basic_settings",) + FUNCTION = "get_values" + + CATEGORY="JPS Nodes/Settings" + + def get_values(self,resolution,sampler_name,scheduler,steps_total,base_percentage,cfg,cfg_rescale,cfg_refiner,ascore_refiner,res_factor,clip_skip,filename): + width = 1024 + height = 1024 + width = int(width) + height = int(height) + steps_total = int(steps_total) + step_split = steps_total * base_percentage / 100 + cfg = float(cfg) + cfg_rescale = float(cfg_rescale) + cfg_refiner = float (cfg_refiner) + ascore_refiner = float (ascore_refiner) + res_factor = int (res_factor) + base_percentage = int (base_percentage) + image_res = 1 + + if(resolution == "Use Image Resolution"): + image_res = 2 + if(resolution == "square - 1024x1024 (1:1)"): + width = 1024 + height = 1024 + if(resolution == "landscape - 1152x896 (4:3)"): + width = 1152 + height = 896 + if(resolution == "landscape - 1216x832 (3:2)"): + width = 1216 + height = 832 + if(resolution == "landscape - 1344x768 (16:9)"): + width = 1344 + height = 768 + if(resolution == "landscape - 1536x640 (21:9)"): + width = 1536 + height = 640 + if(resolution == "portrait - 896x1152 (3:4)"): + width = 896 + height = 1152 + if(resolution == "portrait - 832x1216 (2:3)"): + width = 832 + height = 1216 + if(resolution == "portrait - 768x1344 (9:16)"): + width = 768 + height = 1344 + if(resolution == "portrait - 640x1536 (9:21)"): + width = 640 + height = 1536 + + if(cfg_refiner == 0): + cfg_refiner = cfg + + sdxl_basic_settings = width, height, sampler_name, scheduler, steps_total, step_split, cfg, cfg_rescale, cfg_refiner, ascore_refiner, res_factor, clip_skip, filename,image_res + + return(sdxl_basic_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Basic_Settings_Pipe: + resolution = ["square - 1024x1024 (1:1)","landscape - 1152x896 (4:3)","landscape - 1216x832 (3:2)","landscape - 1344x768 (16:9)","landscape - 1536x640 (21:9)", "portrait - 896x1152 (3:4)","portrait - 832x1216 (2:3)","portrait - 768x1344 (9:16)","portrait - 640x1536 (9:21)"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sdxl_basic_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("INT","INT","INT",comfy.samplers.KSampler.SAMPLERS,comfy.samplers.KSampler.SCHEDULERS,"INT","INT","FLOAT","FLOAT","FLOAT","FLOAT","INT","INT","STRING",) + RETURN_NAMES = ("image_res","width","height","sampler_name","scheduler","steps_total","step_split","cfg","cfg_rescale","cfg_refiner","ascore_refiner","res_factor","clip_skip","filename",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,sdxl_basic_settings): + + width, height, sampler_name, scheduler, steps_total, step_split, cfg, cfg_rescale, cfg_refiner, ascore_refiner, res_factor, clip_skip, filename,image_res = sdxl_basic_settings + + return(int(image_res), int(width), int(height), sampler_name, scheduler, int(steps_total), int(step_split), float(cfg), float(cfg_rescale), float(cfg_refiner), float(ascore_refiner), int (res_factor), int(clip_skip), str(filename),) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Settings: + resolution = ["Use Image Resolution", "square - 1024x1024 (1:1)","landscape - 1152x896 (4:3)","landscape - 1216x832 (3:2)","landscape - 1344x768 (16:9)","landscape - 1536x640 (21:9)", "portrait - 896x1152 (3:4)","portrait - 832x1216 (2:3)","portrait - 768x1344 (9:16)","portrait - 640x1536 (9:21)"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "resolution": (s.resolution,), + "res_factor": ("INT", {"default": 4, "min": 1, "max": 8, "step": 1}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + "steps": ("INT", {"default": 60, "min": 20, "max": 250, "step": 5}), + "cfg": ("FLOAT", {"default": 6.5, "min": 1, "max": 20, "step": 0.1}), + "cfg_rescale": ("FLOAT", {"default": 0.00, "min": 0.00, "max": 1.00, "step": 0.05}), + "clip_skip": ("INT", {"default": -2, "min": -24, "max": -1}), + "filename": ("STRING", {"default": "JPS"}), + }} + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("sdxl_settings",) + FUNCTION = "get_values" + + CATEGORY="JPS Nodes/Settings" + + def get_values(self,resolution,res_factor,sampler_name,scheduler,steps,cfg,cfg_rescale,clip_skip,filename): + + image_res = 1 + if(resolution == "Use Image Resolution"): + image_res = 2 + + width = 1024 + height = 1024 + if(resolution == "landscape - 1152x896 (4:3)"): + width = 1152 + height = 896 + if(resolution == "landscape - 1216x832 (3:2)"): + width = 1216 + height = 832 + if(resolution == "landscape - 1344x768 (16:9)"): + width = 1344 + height = 768 + if(resolution == "landscape - 1536x640 (21:9)"): + width = 1536 + height = 640 + if(resolution == "portrait - 896x1152 (3:4)"): + width = 896 + height = 1152 + if(resolution == "portrait - 832x1216 (2:3)"): + width = 832 + height = 1216 + if(resolution == "portrait - 768x1344 (9:16)"): + width = 768 + height = 1344 + if(resolution == "portrait - 640x1536 (9:21)"): + width = 640 + height = 1536 + + sdxl_settings = width, height, res_factor, sampler_name, scheduler, steps, cfg, cfg_rescale, clip_skip, filename,image_res + + return(sdxl_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Settings_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sdxl_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("INT","INT","INT","INT",comfy.samplers.KSampler.SAMPLERS,comfy.samplers.KSampler.SCHEDULERS,"INT","FLOAT","FLOAT","INT","STRING",) + RETURN_NAMES = ("image_res","width","height","res_factor","sampler_name","scheduler","steps","cfg","cfg_rescale","clip_skip","filename",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,sdxl_settings): + + width, height, res_factor, sampler_name, scheduler, steps, cfg, cfg_rescale, clip_skip, filename,image_res = sdxl_settings + + return(int(image_res), int(width), int(height), int (res_factor), sampler_name, scheduler, int(steps), float(cfg), float(cfg_rescale), int(clip_skip), str(filename),) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Prompt_Handling_Plus: + handling = ["Copy to Both if Empty","Use Positive_G + Positive_L","Copy Positive_G to Both","Copy Positive_L to Both","Ignore Positive_G Input", "Ignore Positive_L Input", "Combine Positive_G + Positive_L", "Combine Positive_L + Positive_G",] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "handling": (s.handling,), + "pos_g": ("STRING", {"multiline": True, "placeholder": "Prompt Text pos_g", "dynamicPrompts": True}), + "pos_l": ("STRING", {"multiline": True, "placeholder": "Prompt Text pos_l", "dynamicPrompts": True}), + }, + } + + RETURN_TYPES = ("STRING","STRING",) + RETURN_NAMES = ("pos_g","pos_l",) + FUNCTION = "pick_handling" + + CATEGORY="JPS Nodes/Text" + + def pick_handling(self,handling,pos_g,pos_l): + + if(handling == "Copy Positive_G to Both"): + pos_l = pos_g + elif(handling == "Copy Positive_L to Both"): + pos_g = pos_l + elif(handling == "Ignore Positive_G Input"): + pos_g = '' + elif(handling == "Ignore Positive_L Input"): + pos_l = '' + elif(handling == "Combine Positive_G + Positive_L"): + combine = pos_g + ' . ' + pos_l + pos_g = combine + pos_l = combine + elif(handling == "Combine Positive_L + Positive_G"): + combine = pos_l + ' . ' + pos_g + pos_g = combine + pos_l = combine + elif(handling == "Copy to Both if Empty" and pos_l == ''): + pos_l = pos_g + elif(handling == "Copy to Both if Empty" and pos_g == ''): + pos_g = pos_l + + return(pos_g,pos_l,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Text_Prompt: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "text": ("STRING", {"multiline": True, "placeholder": "Prompt Text", "dynamicPrompts": True}), + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("text",) + FUNCTION = "text_prompt" + + CATEGORY="JPS Nodes/Text" + + def text_prompt(self,text): + + return(text,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Text_Prompt_Combo: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pos": ("STRING", {"multiline": True, "placeholder": "Prompt Text Positive", "dynamicPrompts": True}), + "neg": ("STRING", {"multiline": True, "placeholder": "Prompt Text Negative", "dynamicPrompts": True}), + }, + } + + RETURN_TYPES = ("STRING","STRING",) + RETURN_NAMES = ("pos","neg",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Text" + + def give_values(self,pos,neg): + + return(pos,neg,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Prompt_Handling: + handling = ["Copy to Both if Empty","Use Positive_G + Positive_L","Copy Positive_G to Both","Copy Positive_L to Both","Ignore Positive_G Input", "Ignore Positive_L Input", "Combine Positive_G + Positive_L", "Combine Positive_L + Positive_G",] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "handling": (s.handling,), + "pos_g": ("STRING", {"default": ""}), + "pos_l": ("STRING", {"default": ""}), + }, + } + RETURN_TYPES = ("STRING","STRING",) + RETURN_NAMES = ("pos_g","pos_l",) + FUNCTION = "pick_handling" + + CATEGORY="JPS Nodes/Text" + + def pick_handling(self,handling,pos_g,pos_l,): + + if(handling == "Copy Positive_G to Both"): + pos_l = pos_g + elif(handling == "Copy Positive_L to Both"): + pos_g = pos_l + elif(handling == "Ignore Positive_G Input"): + pos_g = '' + elif(handling == "Ignore Positive_L Input"): + pos_l = '' + elif(handling == "Combine Positive_G + Positive_L"): + combine = pos_g + ' . ' + pos_l + pos_g = combine + pos_l = combine + elif(handling == "Combine Positive_L + Positive_G"): + combine = pos_l + ' . ' + pos_g + pos_g = combine + pos_l = combine + elif(handling == "Copy to Both if Empty" and pos_l == ''): + pos_l = pos_g + elif(handling == "Copy to Both if Empty" and pos_g == ''): + pos_g = pos_l + + return(pos_g,pos_l,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Math_Resolution_Multiply: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "width": ("INT", {"default": 1024, "min": 256, "max": 8192, "step": 16}), + "height": ("INT", {"default": 1024, "min": 256, "max": 8192, "step": 16}), + "factor": ("INT", {"default": 2, "min": 1, "max": 8, "step": 1}), + }} + RETURN_TYPES = ("INT","INT") + RETURN_NAMES = ("width_resized", "height_resized") + FUNCTION = "get_newres" + + CATEGORY="JPS Nodes/Math" + + def get_newres(self,width,height,factor): + factor = int(factor) + width = int(width) + width_resized = int(width) * int(factor) + height = int(height) + height_resized = int (height) * int(factor) + + return(int(width_resized),int(height_resized)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Math_Largest_Integer: + + def init(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int_a": ("INT", {"default": 1,}), + "int_b": ("INT", {"default": 1,}), + } + } + + RETURN_TYPES = ("INT","INT","INT") + RETURN_NAMES = ("larger_int","smaller_int","is_a_larger") + FUNCTION = "get_lrg" + + CATEGORY="JPS Nodes/Math" + + def get_lrg(self,int_a,int_b): + larger_int = int(int_b) + smaller_int = int(int_a) + is_a_larger = int(0) + if int_a > int_b: + larger_int = int(int_a) + smaller_int = int(int_b) + is_a_larger = int(1) + + return(int(larger_int),int(smaller_int),int(is_a_larger)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Math_Multiply_INT_INT: + + def init(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int_a": ("INT", {"default": 1,}), + "int_b": ("INT", {"default": 1,}), + } + } + + RETURN_TYPES = ("INT","FLOAT") + RETURN_NAMES = ("int_multiply","float_multiply") + FUNCTION = "get_multiply_int_int" + + CATEGORY="JPS Nodes/Math" + + def get_multiply_int_int(self,int_a,int_b): + int_multiply = int(int_a) * int(int_b) + float_multiply = int(int_a) * int(int_b) + + return(int(int_multiply),float(float_multiply)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Math_Multiply_INT_FLOAT: + + def init(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int_a": ("INT", {"default": 1,}), + "float_b": ("FLOAT", {"default": 1,}), + } + } + + RETURN_TYPES = ("INT","FLOAT") + RETURN_NAMES = ("int_multiply","float_multiply") + FUNCTION = "get_multiply_int_float" + + CATEGORY="JPS Nodes/Math" + + def get_multiply_int_float(self,int_a,float_b): + int_multiply = int(int_a) * float(float_b) + float_multiply = int(int_a) * float(float_b) + + return(int(int_multiply),float(float_multiply)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Math_Multiply_FLOAT_FLOAT: + + def init(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "float_a": ("FLOAT", {"default": 1,}), + "float_b": ("FLOAT", {"default": 1,}), + } + } + + RETURN_TYPES = ("INT","FLOAT") + RETURN_NAMES = ("int_multiply","float_multiply") + FUNCTION = "get_multiply_float_float" + + CATEGORY="JPS Nodes/Math" + + def get_multiply_float_float(self,float_a,float_b): + int_multiply = float(float_a) * float(float_b) + float_multiply = float(float_a) * float(float_b) + + return(int(int_multiply),float(float_multiply)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Math_Substract_INT_INT: + + def init(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int_a": ("INT", {"default": 1,}), + "int_b": ("INT", {"default": 1,}), + } + } + + RETURN_TYPES = ("INT","FLOAT") + RETURN_NAMES = ("int_substract","float_substract") + FUNCTION = "get_substract_int_int" + + CATEGORY="JPS Nodes/Math" + + def get_substract_int_int(self,int_a,int_b): + int_substract = int(int_a) - int(int_b) + float_substract = int(int_a) - int(int_b) + + return(int(int_substract),float(float_substract)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Text_Concatenate: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "delimiter": (["none", "space", "comma"],), + }, + "optional": { + "text1": ("STRING", {"forceInput": True}), + "text2": ("STRING", {"forceInput": True}), + "text3": ("STRING", {"forceInput": True}), + "text4": ("STRING", {"forceInput": True}), + "text5": ("STRING", {"forceInput": True}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("text",) + FUNCTION = "get_contxt" + CATEGORY = "JPS Nodes/Text" + + def get_contxt(self, delimiter, text1=None, text2=None, text3=None, text4=None, text5=None): + needdelim = False + delim = "" + if delimiter == "space": + delim = " " + if delimiter == "comma": + delim = ", " + + concatenated = "" + + if text1: + concatenated = text1 + needdelim = True + + if text2: + if needdelim: + concatenated += delim + concatenated += text2 + needdelim = True + + if text3: + if needdelim: + concatenated += delim + concatenated += text3 + needdelim = True + + if text4: + if needdelim: + concatenated += delim + concatenated += text4 + needdelim = True + + if text5: + if needdelim: + concatenated += delim + concatenated += text5 + needdelim = True + + return (concatenated,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Get_Date_Time_String: + time_format = ["%Y%m%d%H%M%S","%Y%m%d%H%M","%Y%m%d","%Y-%m-%d-%H_%M_%S","%Y-%m-%d-%H_%M","%Y-%m-%d","%Y-%m-%d %H_%M_%S","%Y-%m-%d %H_%M","%Y-%m-%d","%H%M","%H%M%S","%H_%M","%H_%M_%S"] + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "style": (s.time_format,), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("time_format",) + FUNCTION = "get_time" + + CATEGORY = "JPS Nodes/Text" + + def get_time(self, style): + now = datetime.now() + timestamp = now.strftime(style) + + return (timestamp,) + + @classmethod + def IS_CHANGED(s, style): + now = datetime.now() + timestamp = now.strftime(style) + return (timestamp,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Time_Seed: +# time_format = ["%Y%m%d%H%M%S","%Y%m%d%H%M","%Y%m%d","%Y-%m-%d-%H_%M_%S","%Y-%m-%d-%H_%M","%Y-%m-%d","%Y-%m-%d %H_%M_%S","%Y-%m-%d %H_%M","%Y-%m-%d","%H%M","%H%M%S","%H_%M","%H_%M_%S"] + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "fixed_seed": ("INT", {"default": 0, "min": 0, "max": 99999999999, "step": 1}), + } + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("seed",) + FUNCTION = "get_seed" + + CATEGORY = "JPS Nodes/Text" + + def get_seed(self, fixed_seed): + now = datetime.now() + time = now.strftime("%Y%m%d%H%M%S") + seed_out = int(time) + np.random.randint(999999) + if fixed_seed != 0: + seed_out=fixed_seed + + return (int(seed_out),) + + @classmethod + def IS_CHANGED(s, seed_out): + now = datetime.now() + forceupdate = now.strftime("%Y%m%d%H%M%S") + forceupdate = forceupdate + np.random.randint(99999999) + seed_out + return (forceupdate,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Recommended_Resolution_Calc: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "target_width": ("INT", { + "default": 1024, + "min": 0, + "max": 8192, + "step": 2 + }), + "target_height": ("INT", { + "default": 1024, + "min": 0, + "max": 8192, + "step": 2 + }), + }, + } + + RETURN_TYPES = ("INT","INT",) + RETURN_NAMES = ("SDXL_width","SDXL_height",) + FUNCTION = "calcSDXLres" + + CATEGORY = "JPS Nodes/Math" + + def calcSDXLres(self, target_width, target_height): + target_ratio = target_width / target_height + + closest_ratio = None + closest_diff = float('inf') + + for ratio, (x_size, y_size, num_ratio) in accepted_ratios_horizontal.items(): + diff = abs(num_ratio - target_ratio) + if diff < closest_diff: + closest_ratio = ratio + closest_diff = diff + + for ratio, (x_size, y_size, num_ratio) in accepted_ratios_vertical.items(): + diff = abs(num_ratio - target_ratio) + if diff < closest_diff: + closest_ratio = ratio + closest_diff = diff + + # Compare with square aspect ratio + x_size, y_size, num_ratio = accepted_ratios_square["1:1"] + diff = abs(num_ratio - target_ratio) + if diff < closest_diff: + closest_ratio = "1:1" + + if closest_ratio in accepted_ratios_horizontal: + SDXL_width, SDXL_height, _ = accepted_ratios_horizontal[closest_ratio] + elif closest_ratio in accepted_ratios_vertical: + SDXL_width, SDXL_height, _ = accepted_ratios_vertical[closest_ratio] + else: + SDXL_width, SDXL_height, _ = accepted_ratios_square[closest_ratio] + + return (SDXL_width, SDXL_height) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Generation_TXT_IMG_Settings: + mode = ["Txt2Img","Img2Img"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mode": (s.mode,), + "img_percentage": ("INT", {"default": 50, "min": 0, "max": 100, "step": 5}), + } + } + RETURN_TYPES = ("INT","FLOAT",) + RETURN_NAMES = ("gen_mode", "img_strength") + FUNCTION = "get_genmode" + + CATEGORY="JPS Nodes/Settings" + + def get_genmode(self,mode,img_percentage): + gen_mode = 1 + img_strength = 0 + if(mode == "Txt2Img"): + gen_mode = int(1) + img_strength = 0.001 + if(mode == "Img2Img"): + gen_mode = int(2) + img_strength = img_percentage / 100 + + return(int(gen_mode),float(img_strength)) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CropImage_Settings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "source_crop_pos": (["center","top", "bottom", "left", "right"],), + "source_crop_offset": ("INT", { "default": 0, "min": -2048, "max": 2048, "step": 1, "display": "number" }), + "support_crop_pos": (["center","top", "bottom", "left", "right"],), + "support_crop_offset": ("INT", { "default": 0, "min": -2048, "max": 2048, "step": 1, "display": "number" }), + "crop_intpol": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("cropimage_settings",) + FUNCTION = "get_cropimage" + + CATEGORY="JPS Nodes/Settings" + + def get_cropimage(self, source_crop_pos, source_crop_offset, support_crop_pos, support_crop_offset, crop_intpol,): + + cropimage_settings = source_crop_pos, source_crop_offset, support_crop_pos, support_crop_offset, crop_intpol + + return(cropimage_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CropImage_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cropimage_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = (["center","top", "bottom", "left", "right"],"INT",["center","top", "bottom", "left", "right"],"INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],) + RETURN_NAMES = ("source_crop_pos", "source_crop_offset", "support_crop_pos", "support_crop_offset", "crop_intpol",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,cropimage_settings): + + source_crop_pos, source_crop_offset, support_crop_pos, support_crop_offset, crop_intpol = cropimage_settings + + return(source_crop_pos, source_crop_offset, support_crop_pos, support_crop_offset, crop_intpol,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class ImageToImage_Settings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "img2img_strength": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), + "inpaint_strength": ("INT", {"default": 100, "min": 2, "max": 100, "step": 1}), + "inpaint_grow_mask": ("INT", {"default": 20, "min": 0, "max": 200, "step": 2}), + "unsampler_strength": ("INT", {"default": 30, "min": 0, "max": 100, "step": 1}), + "unsampler_cfg": ("FLOAT", {"default": 1, "min": 1, "max": 10, "step": 0.1}), + "unsampler_sampler": (comfy.samplers.KSampler.SAMPLERS,), + "unsampler_scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("img2img_settings",) + FUNCTION = "get_img2img" + + CATEGORY="JPS Nodes/Settings" + + def get_img2img(self, img2img_strength, inpaint_strength, inpaint_grow_mask, unsampler_strength, unsampler_cfg, unsampler_sampler, unsampler_scheduler,): + + img2img_strength = (img2img_strength + 0.001) / 100 + + inpaint_strength = (100 - inpaint_strength + 0.001) / 100 + + unsampler_strength = (unsampler_strength + 0.001) / 100 + + img2img_settings = img2img_strength, inpaint_strength, inpaint_grow_mask, unsampler_strength, unsampler_cfg, unsampler_sampler, unsampler_scheduler + + return(img2img_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class ImageToImage_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "img2img_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("FLOAT", "FLOAT", "INT", "FLOAT", "FLOAT", comfy.samplers.KSampler.SAMPLERS, comfy.samplers.KSampler.SCHEDULERS,) + RETURN_NAMES = ("img2img_strength", "inpaint_strength", "inpaint_grow_mask", "unsampler_strength", "unsampler_cfg", "unsampler_sampler", "unsampler_scheduler",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,img2img_settings): + + img2img_strength, inpaint_strength, inpaint_grow_mask, unsampler_strength, unsampler_cfg, unsampler_sampler, unsampler_scheduler = img2img_settings + + return(img2img_strength, inpaint_strength, inpaint_grow_mask, unsampler_strength, unsampler_cfg, unsampler_sampler, unsampler_scheduler,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_CannyEdge_Settings: + cannyedgefrom = ["Source Image", "Support Image", "Support Direct"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cannyedge_from": (s.cannyedgefrom,), + "cannyedge_strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 10.00, "step": 0.10}), + "cannyedge_start": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "cannyedge_end": ("FLOAT", {"default": 1.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "cannyedge_low": ("INT", {"default": 100, "min": 0, "max": 255, "step": 1}), + "cannyedge_high": ("INT", {"default": 200, "min": 0, "max": 255, "step": 1}), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("cannyedge_settings",) + FUNCTION = "get_ctrlnet_cannyedge" + + CATEGORY="JPS Nodes/Settings" + + def get_ctrlnet_cannyedge(self, cannyedge_from, cannyedge_strength, cannyedge_start, cannyedge_end, cannyedge_low, cannyedge_high): + + cannyedge_source = int (1) + if (cannyedge_from == "Support Image"): + cannyedge_source = int(2) + if (cannyedge_from == "Support Direct"): + cannyedge_source = int(3) + + cannyedge_settings = cannyedge_source, cannyedge_strength, cannyedge_start, cannyedge_end, cannyedge_low, cannyedge_high + + return(cannyedge_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_CannyEdge_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cannyedge_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("INT", "FLOAT", "FLOAT", "FLOAT", "INT", "INT", ) + RETURN_NAMES = ("cannyedge_source", "cannyedge_strength", "cannyedge_start", "cannyedge_end", "cannyedge_low", "cannyedge_high",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,cannyedge_settings): + + cannyedge_source, cannyedge_strength, cannyedge_start, cannyedge_end, cannyedge_low, cannyedge_high = cannyedge_settings + + return(cannyedge_source, cannyedge_strength, cannyedge_start, cannyedge_end, cannyedge_low, cannyedge_high,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_OpenPose_Settings: + openposefrom = ["Source Image", "Support Image", "Support Direct"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "openpose_from": (s.openposefrom,), + "openpose_strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 10.00, "step": 0.10}), + "openpose_start": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "openpose_end": ("FLOAT", {"default": 1.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "openpose_body": (["enable","disable"],), + "openpose_face": (["enable","disable"],), + "openpose_hand": (["enable","disable"],), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("openpose_settings",) + FUNCTION = "get_ctrlnet_openpose" + + CATEGORY="JPS Nodes/Settings" + + def get_ctrlnet_openpose(self, openpose_from, openpose_strength, openpose_start, openpose_end, openpose_body, openpose_face, openpose_hand): + + openpose_source = int (1) + if (openpose_from == "Support Image"): + openpose_source = int(2) + if (openpose_from == "Support Direct"): + openpose_source = int(3) + + openpose_settings = openpose_source, openpose_strength, openpose_start, openpose_end, openpose_body, openpose_face, openpose_hand + + return(openpose_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_OpenPose_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "openpose_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("INT", "FLOAT", "FLOAT", "FLOAT", ["enable","disable"], ["enable","disable"], ["enable","disable"],) + RETURN_NAMES = ("openpose_source", "openpose_strength", "openpose_start", "openpose_end", "openpose_body", "openpose_face", "openpose_hand",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,openpose_settings): + + openpose_source, openpose_strength, openpose_start, openpose_end, openpose_body, openpose_face, openpose_hand = openpose_settings + + return(openpose_source, openpose_strength, openpose_start, openpose_end, openpose_body, openpose_face, openpose_hand,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_MiDaS_Settings: + midasfrom = ["Source Image", "Support Image", "Support Direct"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "midas_from": (s.midasfrom,), + "midas_strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 10.00, "step": 0.10}), + "midas_start": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "midas_end": ("FLOAT", {"default": 1.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "midas_a": ("FLOAT", {"default": 6.28, "min": 0.00, "max": 15.71, "step": 0.05}), + "midas_bg": ("FLOAT", {"default": 0.10, "min": 0.00, "max": 1.00, "step": 0.05}), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("midas_settings",) + FUNCTION = "get_ctrlnet_midas" + + CATEGORY="JPS Nodes/Settings" + + def get_ctrlnet_midas(self, midas_from, midas_strength, midas_start, midas_end, midas_a, midas_bg): + + midas_source = int (1) + if (midas_from == "Support Image"): + midas_source = int(2) + if (midas_from == "Support Direct"): + midas_source = int(3) + + midas_settings = midas_source, midas_strength, midas_start, midas_end, midas_a, midas_bg + + return(midas_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_MiDaS_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "midas_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("INT", "FLOAT", "FLOAT", "FLOAT", "FLOAT", "FLOAT",) + RETURN_NAMES = ("midas_source", "midas_strength", "midas_start", "midas_end", "midas_a", "midas_bg",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,midas_settings): + + midas_source, midas_strength, midas_start, midas_end, midas_a, midas_bg = midas_settings + + return(midas_source, midas_strength, midas_start, midas_end, midas_a, midas_bg,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_ZoeDepth_Settings: + zoefrom = ["Source Image", "Support Image", "Support Direct"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "zoe_from": (s.zoefrom,), + "zoe_strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 10.00, "step": 0.10}), + "zoe_start": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.05}), + "zoe_end": ("FLOAT", {"default": 1.000, "min": 0.000, "max": 1.000, "step": 0.05}), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("zoedepth_settings",) + FUNCTION = "get_ctrlnet_zoedepth" + + CATEGORY="JPS Nodes/Settings" + + def get_ctrlnet_zoedepth(self, zoe_from, zoe_strength, zoe_start, zoe_end): + + zoe_source = int (1) + if (zoe_from == "Support Image"): + zoe_source = int(2) + if (zoe_from == "Support Direct"): + zoe_source = int(3) + + zoedepth_settings = zoe_source, zoe_strength, zoe_start, zoe_end + + return(zoedepth_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CtrlNet_ZoeDepth_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "zoedepth_settings": ("BASIC_PIPE",) + }, + } + RETURN_TYPES = ("INT", "FLOAT", "FLOAT", "FLOAT",) + RETURN_NAMES = ("zoe_source", "zoe_strength", "zoe_start", "zoe_end",) + FUNCTION = "give_values" + + CATEGORY="JPS Nodes/Pipes" + + def give_values(self,zoedepth_settings): + + zoe_source, zoe_strength, zoe_start, zoe_end = zoedepth_settings + + return(zoe_source, zoe_strength, zoe_start, zoe_end,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class IP_Adapter_Settings: + ipamasktype = ["No Mask","Mask Editor","Mask Editor (inverted)","Red from Image","Green from Image","Blue from Image"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ipa_weight": ("FLOAT", {"default": 0.5, "min": 0, "max": 3, "step": 0.01}), + "ipa_wtype": (["linear", "ease in", "ease out", "ease in-out", "reverse in-out", "weak input", "weak output", "weak middle", "strong middle"],), + "ipa_noise": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + "ipa_start": ("FLOAT", {"default": 0.00, "min": 0, "max": 1, "step": 0.05}), + "ipa_stop": ("FLOAT", {"default": 1.00, "min": 0, "max": 1, "step": 0.05}), + "ipa_crop": (["center","top", "bottom", "left", "right"],), + "ipa_zoom": ("FLOAT", { "default": 1, "min": 1, "max": 5, "step": 0.1, "display": "number" }), + "ipa_offset_x": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "ipa_offset_y": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "ipa_mask": (s.ipamasktype,), + "crop_intpol": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }), + "ipa_model": (["SDXL ViT-H", "SDXL Plus ViT-H", "SDXL Plus Face ViT-H"],), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("ip_adapter_single_settings",) + FUNCTION = "get_ipamodesingle" + + CATEGORY="JPS Nodes/Settings" + + def get_ipamodesingle(self,ipa_weight,ipa_wtype,ipa_noise,ipa_start,ipa_stop,ipa_crop,ipa_zoom,ipa_offset_x,ipa_offset_y,ipa_mask,crop_intpol,sharpening,ipa_model): + + ipamask = int(0) + if(ipa_mask == "Mask Editor"): + ipamask = int(1) + elif(ipa_mask == "Mask Editor (inverted)"): + ipamask = int(2) + elif(ipa_mask == "Red from Image"): + ipamask = int(3) + elif(ipa_mask == "Green from Image"): + ipamask = int(4) + elif(ipa_mask == "Blue from Image"): + ipamask = int(5) + + ipamodel = int (0) + if(ipa_model == "SDXL ViT-H"): + ipamodel = int(1) + elif(ipa_model == "SDXL Plus ViT-H"): + ipamodel = int(2) + elif(ipa_model == "SDXL Plus Face ViT-H"): + ipamodel = int(3) + + ipaweight = ipa_weight + ipawtype = ipa_wtype + ipanoise = ipa_noise + ipastart = ipa_start + ipastop = ipa_stop + ipacrop = ipa_crop + ipazoom = ipa_zoom + ipaoffsetx = ipa_offset_x + ipaoffsety = ipa_offset_y + cropintpol = crop_intpol + + ip_adapter_settings = ipaweight,ipawtype,ipanoise,ipastart,ipastop,ipacrop,ipazoom,ipaoffsetx,ipaoffsety,ipamask,cropintpol,sharpening,ipamodel + + return(ip_adapter_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class IP_Adapter_Settings_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ip_adapter_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("FLOAT",["linear", "ease in", "ease out", "ease in-out", "reverse in-out", "weak input", "weak output", "weak middle", "strong middle"],"FLOAT","FLOAT","FLOAT",["center","top", "bottom", "left", "right"],"FLOAT","INT","INT","INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],"FLOAT","INT") + RETURN_NAMES = ("ipa_weight","ipa_wtype","ipa_noise","ipa_start","ipa_stop","ipa_crop","ipa_zoom","ipa_offset_x","ipa_offset_y","ipa_mask","crop_intpol","sharpening","ipa_model") + FUNCTION = "get_ipamode_single" + + CATEGORY="JPS Nodes/Pipes" + + def get_ipamode_single(self,ip_adapter_settings): + + ipaweight,ipawtype,ipanoise,ipastart,ipastop,ipacrop,ipazoom,ipaoffsetx,ipaoffsety,ipamask,cropintpol,sharpening,ipamodel = ip_adapter_settings + + return(float(ipaweight),ipawtype,float(ipanoise),float(ipastart),float(ipastop),ipacrop,float(ipazoom),int(ipaoffsetx),int(ipaoffsety),int(ipamask),cropintpol,float(sharpening),int(ipamodel),) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class IP_Adapter_Tiled_Settings: + + preparetypes = ["Target AR + Target Res", "Target AR + Tile Res", "Tile AR + Target Res", "Source AR + Source Res", "Source AR + Tile Res", "Tile AR + Source Res", "Square AR + Target Res", "Square AR + Tile Res", "Direct Source" ] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ipa_model": (["SDXL ViT-H", "SDXL Plus ViT-H", "SDXL Plus Face ViT-H"],), + "ipa_wtype": (["linear", "ease in", "ease out", "ease in-out", "reverse in-out", "weak input", "weak output", "weak middle", "strong middle"],), + "ipa_weight": ("FLOAT", {"default": 0.5, "min": 0, "max": 3, "step": 0.01}), + "ipa_noise": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + "ipa_start": ("FLOAT", {"default": 0.00, "min": 0, "max": 1, "step": 0.05}), + "ipa_end": ("FLOAT", {"default": 1.00, "min": 0, "max": 1, "step": 0.05}), + "tile_short": ("INT", { "default": 2, "min": 1, "max": 5, "step": 1, "display": "number" }), + "tile_weight": ("FLOAT", {"default": 0.55, "min": 0, "max": 1, "step": 0.05}), + "zoom": ("INT", {"default": 100, "min": 1, "max": 500, "step": 1}), + "offset_w": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "offset_h": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "prepare_type": (s.preparetypes,), + "prepare_intpol": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "prepare_sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("ip_adapter_tiled_settings",) + FUNCTION = "get_ipatiled" + + CATEGORY="JPS Nodes/Settings" + + def get_ipatiled(self,ipa_model,ipa_wtype,ipa_weight,ipa_noise,ipa_start,ipa_end,tile_short,tile_weight,zoom,offset_w,offset_h,prepare_type,prepare_intpol,prepare_sharpening,): + + ipamodel = int (0) + if(ipa_model == "SDXL ViT-H"): + ipamodel = int(1) + elif(ipa_model == "SDXL Plus ViT-H"): + ipamodel = int(2) + elif(ipa_model == "SDXL Plus Face ViT-H"): + ipamodel = int(3) + + preparetype = int (0) + if(prepare_type == "Target AR + Target Res"): + preparetype = int(1) + elif(prepare_type == "Target AR + Tile Res"): + preparetype = int(2) + elif(prepare_type == "Tile AR + Target Res"): + preparetype = int(3) + elif(prepare_type == "Source AR + Source Res"): + preparetype = int(4) + elif(prepare_type == "Source AR + Tile Res"): + preparetype = int(5) + elif(prepare_type == "Tile AR + Source Res"): + preparetype = int(6) + elif(prepare_type == "Square AR + Target Res"): + preparetype = int(7) + elif(prepare_type == "Square AR + Tile Res"): + preparetype = int(8) + elif(prepare_type == "Direct Source"): + preparetype = int(9) + + ip_adapter_settings = ipamodel,ipa_wtype,ipa_weight,ipa_noise,ipa_start,ipa_end,tile_short,tile_weight,zoom,offset_w,offset_h,preparetype,prepare_intpol,prepare_sharpening + + return(ip_adapter_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class IP_Adapter_Tiled_Settings_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ip_adapter_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("INT",["linear", "ease in", "ease out", "ease in-out", "reverse in-out", "weak input", "weak output", "weak middle", "strong middle"],"FLOAT","FLOAT","FLOAT","FLOAT","INT","FLOAT","INT","INT","INT","INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],"FLOAT") + RETURN_NAMES = ("ipa_model","ipa_wtype","ipa_weight","ipa_noise","ipa_start","ipa_end","tile_short","tile_weight","zoom","offset_w","offset_h","prepare_type","prepare_intpol","prepare_sharpening") + FUNCTION = "get_ipatiled" + + CATEGORY="JPS Nodes/Pipes" + + def get_ipatiled(self,ip_adapter_settings): + + ipamodel,ipa_wtype,ipa_weight,ipa_noise,ipa_start,ipa_end,tile_short,tile_weight,zoom,offset_w,offset_h,preparetype,prepare_intpol,prepare_sharpening = ip_adapter_settings + + return(ipamodel,ipa_wtype,ipa_weight,ipa_noise,ipa_start,ipa_end,tile_short,tile_weight,zoom,offset_w,offset_h,preparetype,prepare_intpol,prepare_sharpening) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantID_Settings: + + ipamasktype = ["No Mask","Mask Editor","Mask Editor (inverted)","Red from Image","Green from Image","Blue from Image"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ip_weight": ("FLOAT", {"default": 0.8, "min": 0, "max": 1, "step": 0.01}), + "cn_strength": ("FLOAT", {"default": 0.65, "min": 0, "max": 10, "step": 0.01}), + "noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1, }), + "start": ("FLOAT", {"default": 0.00, "min": 0, "max": 1, "step": 0.05}), + "end": ("FLOAT", {"default": 1.00, "min": 0, "max": 1, "step": 0.05}), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("instantid_settings",) + FUNCTION = "get_instantid" + + CATEGORY="JPS Nodes/Settings" + + def get_instantid(self,ip_weight,cn_strength,noise,start,end): + + instantid_settings = ip_weight,cn_strength,noise,start,end + + return(instantid_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantID_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "instantid_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("FLOAT","FLOAT","FLOAT","FLOAT","FLOAT",) + RETURN_NAMES = ("ip_weight","cn_strength","noise","start","end",) + FUNCTION = "get_instantid" + + CATEGORY="JPS Nodes/Pipes" + + def get_instantid(self,instantid_settings): + + ip_weight,cn_strength,noise,start,end = instantid_settings + + return(ip_weight,cn_strength,noise,start,end) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class ImagePrepare_Settings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "offset_width": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "offset_height": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "crop_left": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_right": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_top": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_bottom": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "padding_left": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_right": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_top": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_bottom": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("imageprepare_settings",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Settings" + + def get_imageprepare(self,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening): + + imageprepare_settings = offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening + + return(imageprepare_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class ImagePrepare_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "imageprepare_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","INT","INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],"FLOAT",) + RETURN_NAMES = ("offset_width","offset_height","crop_left","crop_right","crop_top","crop_bottom","padding_left","padding_right","padding_top","padding_bottom","interpolation","sharpening",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Pipes" + + def get_imageprepare(self,imageprepare_settings): + + offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening = imageprepare_settings + + return(offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantIDSourcePrepare_Settings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "resize_to": (["Resize to Target","Keep Size"],), + "offset_width": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "offset_height": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "crop_left": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_right": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_top": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_bottom": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }), + "flip": (["No", "X-Axis", "Y-Axis"],), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("imageprepare_settings",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Settings" + + def get_imageprepare(self,resize_to,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,interpolation,sharpening,flip): + + resizeto = int (0) + if(resize_to == "Keep Size"): + resizeto = int(1) + elif(resize_to == "Resize to Target"): + resizeto = int(2) + + imageprepare_settings = resizeto, offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,interpolation,sharpening,flip + + return(imageprepare_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantIDSourcePrepare_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "imageprepare_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],"FLOAT",["No", "X-Axis", "Y-Axis"],) + RETURN_NAMES = ("resize_to","offset_width","offset_height","crop_left","crop_right","crop_top","crop_bottom","interpolation","sharpening","flip",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Pipes" + + def get_imageprepare(self,imageprepare_settings): + + resizeto,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,interpolation,sharpening,flip = imageprepare_settings + + return(resizeto,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,interpolation,sharpening,flip) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantIDPosePrepare_Settings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "resize_to": (["Resize to Target","Resize to Source","Keep Size"],), + "offset_width": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "offset_height": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "crop_left": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_right": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_top": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_bottom": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "padding_left": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_right": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_top": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_bottom": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }), + "flip": (["No", "X-Axis", "Y-Axis"],), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("imageprepare_settings",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Settings" + + def get_imageprepare(self,resize_to,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening,flip): + + resizeto = int (0) + if(resize_to == "Keep Size"): + resizeto = int(1) + elif(resize_to == "Resize to Target"): + resizeto = int(2) + elif(resize_to == "Resize to Source"): + resizeto = int(3) + + imageprepare_settings = resizeto, offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening,flip + + return(imageprepare_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantIDPosePrepare_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "imageprepare_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","INT","INT","INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],"FLOAT",["No", "X-Axis", "Y-Axis"],) + RETURN_NAMES = ("resize_to","offset_width","offset_height","crop_left","crop_right","crop_top","crop_bottom","padding_left","padding_right","padding_top","padding_bottom","interpolation","sharpening","flip") + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Pipes" + + def get_imageprepare(self,imageprepare_settings): + + resizeto,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening,flip = imageprepare_settings + + return(resizeto,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening,flip) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantIDMaskPrepare_Settings: + + masktypes = ["No Mask","Mask Editor","Mask Editor (inverted)","Red from Image","Green from Image","Blue from Image"] + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask_type": (s.masktypes,), + "resize_to": (["Resize to Target","Resize to Source","Keep Size"],), + "resize_type": (["Crop","Stretch"],), + "offset_width": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "offset_height": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "crop_left": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_right": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_top": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_bottom": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "padding_left": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_right": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_top": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_bottom": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("imageprepare_settings",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Settings" + + def get_imageprepare(self,mask_type,resize_to,resize_type,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening): + + resizeto = int (0) + if(resize_to == "Keep Size"): + resizeto = int(1) + elif(resize_to == "Resize to Target"): + resizeto = int(2) + elif(resize_to == "Resize to Source"): + resizeto = int(3) + + resizetype = "Crop" + if(resize_type == "Stretch"): + resizetype = "Stretch" + + masktype = int(0) + if(mask_type == "Mask Editor"): + masktype = int(1) + elif(mask_type == "Mask Editor (inverted)"): + masktype = int(2) + elif(mask_type == "Red from Image"): + masktype = int(3) + elif(mask_type == "Green from Image"): + masktype = int(4) + elif(mask_type == "Blue from Image"): + masktype = int(5) + + imageprepare_settings = masktype, resizeto, resizetype, offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening + + return(imageprepare_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class InstantIDMaskPrepare_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "imageprepare_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("INT","INT",["Crop","Stretch"],"INT","INT","INT","INT","INT","INT","INT","INT","INT","INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],"FLOAT",) + RETURN_NAMES = ("mask_type","resize_to","resize_type","offset_width","offset_height","crop_left","crop_right","crop_top","crop_bottom","padding_left","padding_right","padding_top","padding_bottom","interpolation","sharpening",) + FUNCTION = "get_imageprepare" + + CATEGORY="JPS Nodes/Pipes" + + def get_imageprepare(self,imageprepare_settings): + + masktype,resizeto,resizetype,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening = imageprepare_settings + + return(masktype,resizeto,resizetype,offset_width,offset_height,crop_left,crop_right,crop_top,crop_bottom,padding_left,padding_right,padding_top,padding_bottom,interpolation,sharpening) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Revision_Settings: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "rev1_strength": ("FLOAT", {"default": 1, "min": 0, "max": 10, "step": 0.1}), + "rev2_strength": ("FLOAT", {"default": 1, "min": 0, "max": 10, "step": 0.1}), + + "rev1_noiseaug": ("FLOAT", {"default": 0, "min": 0, "max": 1, "step": 0.1}), + "rev2_noiseaug": ("FLOAT", {"default": 0, "min": 0, "max": 1, "step": 0.1}), + + "rev1_crop": (["center","top", "bottom", "left", "right"],), + "rev1_offset": ("INT", { "default": 0, "min": -2048, "max": 2048, "step": 1, "display": "number" }), + + "rev2_crop": (["center","top", "bottom", "left", "right"],), + "rev2_offset": ("INT", { "default": 0, "min": -2048, "max": 2048, "step": 1, "display": "number" }), + + "crop_intpol": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "crop_res": ("INT", { "default": 224 , "min": 224, "max": 1792, "step": 224, "display": "number" }), + } + } + RETURN_TYPES = ("BASIC_PIPE",) + RETURN_NAMES = ("revision_settings",) + FUNCTION = "get_revmode" + + CATEGORY="JPS Nodes/Settings" + + def get_revmode(self,crop_res,crop_intpol,rev1_crop,rev1_offset,rev2_crop,rev2_offset,rev1_strength,rev2_strength,rev1_noiseaug,rev2_noiseaug,): + rev1strength = 0 + rev1noiseaug = 0 + rev2strength = 0 + rev2noiseaug = 0 + + rev1strength = rev1_strength + rev1noiseaug = rev1_noiseaug + rev2strength = rev2_strength + rev2noiseaug = rev2_noiseaug + + revision_settings = crop_res,crop_intpol,rev1_crop,rev1_offset,rev2_crop,rev2_offset,rev1strength,rev2strength,rev1noiseaug,rev2_noiseaug + + return(revision_settings,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Revision_Settings_Pipe: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "revision_settings": ("BASIC_PIPE",), + } + } + RETURN_TYPES = ("INT",["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],["center","top", "bottom", "left", "right"],"INT",["center","top", "bottom", "left", "right"],"INT","FLOAT","FLOAT","FLOAT","FLOAT",) + RETURN_NAMES = ("crop_res", "crop_intpol", "rev1_crop", "rev1_offset", "rev2_crop", "rev2_offset", "rev1_strength", "rev2_strength", "rev1_noiseaug", "rev2_noiseaug",) + FUNCTION = "get_revmode" + + CATEGORY="JPS Nodes/Pipes" + + def get_revmode(self,revision_settings): + + crop_res,crop_intpol,rev1_crop,rev1_offset,rev2_crop,rev2_offset,rev1strength,rev2strength,rev1noiseaug,rev2noiseaug = revision_settings + + return(int(crop_res),crop_intpol,rev1_crop,int(rev1_offset),rev2_crop,int(rev2_offset),float(rev1strength),float(rev2strength),float(rev1noiseaug),float(rev2noiseaug),) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Sampler_Scheduler_Settings: + CATEGORY = 'JPS Nodes/Settings' + RETURN_TYPES = (comfy.samplers.KSampler.SAMPLERS,comfy.samplers.KSampler.SCHEDULERS,) + RETURN_NAMES = ("sampler_name","scheduler",) + FUNCTION = "get_samsched" + + @classmethod + def INPUT_TYPES(cls): + return {"required": {"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),"scheduler": (comfy.samplers.KSampler.SCHEDULERS,)}} + + def get_samsched(self, sampler_name, scheduler): + return (sampler_name, scheduler, ) +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Image_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("img_out",) + FUNCTION = "get_image" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "img_1": ("IMAGE",), + "img_2": ("IMAGE",), + "img_3": ("IMAGE",), + "img_4": ("IMAGE",), + "img_5": ("IMAGE",), + } + } + + def get_image(self,select,img_1,img_2=None,img_3=None,img_4=None,img_5=None,): + + img_out = img_1 + + if (select == 2): + img_out = img_2 + elif (select == 3): + img_out = img_3 + elif (select == 4): + img_out = img_4 + elif (select == 5): + img_out = img_5 + + return (img_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Conditioning_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("con_out",) + FUNCTION = "get_con" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "con_1": ("CONDITIONING",), + "con_2": ("CONDITIONING",), + "con_3": ("CONDITIONING",), + "con_4": ("CONDITIONING",), + "con_5": ("CONDITIONING",), + } + } + + def get_con(self,select,con_1,con_2=None,con_3=None,con_4=None,con_5=None,): + + con_out = con_1 + + if (select == 2): + con_out = con_2 + elif (select == 3): + con_out = con_3 + elif (select == 4): + con_out = con_4 + elif (select == 5): + con_out = con_5 + + return (con_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Model_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model_out",) + FUNCTION = "get_model" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "model_1": ("MODEL",), + "model_2": ("MODEL",), + "model_3": ("MODEL",), + "model_4": ("MODEL",), + "model_5": ("MODEL",), + } + } + + def get_model(self,select,model_1,model_2=None,model_3=None,model_4=None,model_5=None,): + + model_out = model_1 + + if (select == 2): + model_out = model_2 + elif (select == 3): + model_out = model_3 + elif (select == 4): + model_out = model_4 + elif (select == 5): + model_out = model_5 + + return (model_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class IPA_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("IPADAPTER",) + RETURN_NAMES = ("IPA_out",) + FUNCTION = "get_ipa" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "ipa_1": ("IPADAPTER",), + "ipa_2": ("IPADAPTER",), + "ipa_3": ("IPADAPTER",), + "ipa_4": ("IPADAPTER",), + "ipa_5": ("IPADAPTER",), + } + } + + def get_ipa(self,select,ipa_1,ipa_2=None,ipa_3=None,ipa_4=None,ipa_5=None,): + + ipa_out = ipa_1 + + if (select == 2): + ipa_out = ipa_2 + elif (select == 3): + ipa_out = ipa_3 + elif (select == 4): + ipa_out = ipa_4 + elif (select == 5): + ipa_out = ipa_5 + + return (ipa_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Latent_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent_out",) + FUNCTION = "get_latent" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "latent_1": ("LATENT",), + "latent_2": ("LATENT",), + "latent_3": ("LATENT",), + "latent_4": ("LATENT",), + "latent_5": ("LATENT",), + } + } + + def get_latent(self,select,latent_1=None,latent_2=None,latent_3=None,latent_4=None,latent_5=None,): + + latent_out = latent_1 + + if (select == 2): + latent_out = latent_2 + elif (select == 3): + latent_out = latent_3 + elif (select == 4): + latent_out = latent_4 + elif (select == 5): + latent_out = latent_5 + + return (latent_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class VAE_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("VAE",) + RETURN_NAMES = ("vae_out",) + FUNCTION = "get_vae" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "vae_1": ("VAE",), + "vae_2": ("VAE",), + "vae_3": ("VAE",), + "vae_4": ("VAE",), + "vae_5": ("VAE",), + } + } + + def get_vae(self,select,vae_1=None,vae_2=None,vae_3=None,vae_4=None,vae_5=None,): + + vae_out = vae_1 + + if (select == 2): + vae_out = vae_2 + elif (select == 3): + vae_out = vae_3 + elif (select == 4): + vae_out = vae_4 + elif (select == 5): + vae_out = vae_5 + + return (vae_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Integer_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("int_out",) + FUNCTION = "get_int" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "int_1": ("INT", {}), + "int_2": ("INT", {}), + "int_3": ("INT", {}), + "int_4": ("INT", {}), + "int_5": ("INT", {}), + } + } + + def get_int(self,select,int_1=None,int_2=None,int_3=None,int_4=None,int_5=None,): + + int_out = int_1 + + if (select == 2): + int_out = int_2 + elif (select == 3): + int_out = int_3 + elif (select == 4): + int_out = int_4 + elif (select == 5): + int_out = int_5 + + return (int_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Mask_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("mask_out",) + FUNCTION = "get_mask" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "mask_1": ("MASK",), + "mask_2": ("MASK",), + "mask_3": ("MASK",), + "mask_4": ("MASK",), + "mask_5": ("MASK",), + } + } + + def get_mask(self,select,mask_1=None,mask_2=None,mask_3=None,mask_4=None,mask_5=None,): + + mask_out = None + + if (select == 1): + mask_out = mask_1 + if (select == 2): + mask_out = mask_2 + elif (select == 3): + mask_out = mask_3 + elif (select == 4): + mask_out = mask_4 + elif (select == 5): + mask_out = mask_5 + + return (mask_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class ControlNet_Switch: + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = ("CONTROL_NET",) + RETURN_NAMES = ("ctrlnet_out",) + FUNCTION = "get_ctrlnet" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "select": ("INT", {}), + }, + "optional": { + "ctrlnet_1": ("CONTROL_NET",), + "ctrlnet_2": ("CONTROL_NET",), + "ctrlnet_3": ("CONTROL_NET",), + "ctrlnet_4": ("CONTROL_NET",), + "ctrlnet_5": ("CONTROL_NET",), + } + } + + def get_ctrlnet(self,select,ctrlnet_1=None,ctrlnet_2=None,ctrlnet_3=None,ctrlnet_4=None,ctrlnet_5=None,): + + ctrlnet_out = ctrlnet_1 + + if (select == 2): + ctrlnet_out = ctrlnet_2 + elif (select == 3): + ctrlnet_out = ctrlnet_3 + elif (select == 4): + ctrlnet_out = ctrlnet_4 + elif (select == 5): + ctrlnet_out = ctrlnet_5 + + return (ctrlnet_out,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Fundamentals_MultiPipe: + + CATEGORY = 'JPS Nodes/Pipes' + RETURN_TYPES = ("VAE","MODEL","MODEL","CLIP","CLIP","CONDITIONING","CONDITIONING","CONDITIONING","CONDITIONING","INT",) + RETURN_NAMES = ("vae","model_base","model_refiner","clip_base","clip_refiner","pos_base","neg_base","pos_refiner","neg_refiner","seed",) + FUNCTION = "get_sdxlfund" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": { + "vae": ("VAE",), + "model_base": ("MODEL",), + "model_refiner": ("MODEL",), + "clip_base": ("CLIP",), + "clip_refiner": ("CLIP",), + "pos_base": ("CONDITIONING",), + "neg_base": ("CONDITIONING",), + "pos_refiner": ("CONDITIONING",), + "neg_refiner": ("CONDITIONING",), + "seed": ("INT", {}), + } + } + + def get_sdxlfund(self,vae=None,model_base=None,model_refiner=None,clip_base=None,clip_refiner=None,pos_base=None,neg_base=None,pos_refiner=None,neg_refiner=None,seed=None): + + return (vae,model_base,model_refiner,clip_base,clip_refiner,pos_base,neg_base,pos_refiner,neg_refiner,seed,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Images_Masks_MultiPipe: + + CATEGORY = 'JPS Nodes/Pipes' + RETURN_TYPES = ("IMAGE","MASK","IMAGE","IMAGE","MASK","MASK","IMAGE","IMAGE","MODEL",) + RETURN_NAMES = ("generation_img","generation_mask","ipa1_img","ipa2_img","ipa1_mask","ipa2_mask","revision1_img","revision2_img","inpaint_model",) + FUNCTION = "get_imagemask" + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": { + "generation_img": ("IMAGE",), + "generation_mask": ("MASK",), + "ipa1_img": ("IMAGE",), + "ipa2_img": ("IMAGE",), + "ipa1_mask": ("MASK",), + "ipa2_mask": ("MASK",), + "revision1_img": ("IMAGE",), + "revision2_img": ("IMAGE",), + "inpaint_model": ("MODEL",), + } + } + + def get_imagemask(self,generation_img=None,generation_mask=None,ipa1_img=None,ipa2_img=None,ipa1_mask=None,ipa2_mask=None,revision1_img=None,revision2_img=None,inpaint_model=None,): + + return (generation_img,generation_mask,ipa1_img,ipa2_img,ipa1_mask,ipa2_mask,revision1_img,revision2_img,inpaint_model,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Disable_Enable_Switch: + match = ["Set to Disable","Set to Enable"] + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = (["disable","enable"],) + RETURN_NAMES = ("disable_enable",) + FUNCTION = "get_disenable" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "select": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}), + "compare": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}), + "match": (s.match,), + } + } + + def get_disenable(self,select,compare,match): + disable_enable = "disable" + if match == "Set to Enable" and (int(select) == int(compare)): + disable_enable = "enable" + elif match == "Set to Disable" and (int(select) == int(compare)): + disable_enable = "disable" + elif match == "Set to Enable" and (int(select) != int(compare)): + disable_enable = "disable" + elif match == "Set to Disable" and (int(select) != int(compare)): + disable_enable = "enable" + + return (disable_enable, ) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Enable_Disable_Switch: + match = ["Set to Enable","Set to Disable"] + + CATEGORY = 'JPS Nodes/Switches' + RETURN_TYPES = (["enable","disable"],) + RETURN_NAMES = ("enable_disable",) + FUNCTION = "get_endisable" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "select": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}), + "compare": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}), + "match": (s.match,), + } + } + + def get_endisable(self,select,compare,match): + enable_disable = "disable" + if match == "Set to Enable" and (int(select) == int(compare)): + enable_disable = "enable" + elif match == "Set to Disable" and (int(select) == int(compare)): + enable_disable = "disable" + elif match == "Set to Enable" and (int(select) != int(compare)): + enable_disable = "disable" + elif match == "Set to Disable" and (int(select) != int(compare)): + enable_disable = "enable" + + return (enable_disable, ) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class IO_Lora_Loader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + file_list = folder_paths.get_filename_list("loras") + file_list.insert(0, "None") + return {"required": { "model": ("MODEL",), + "clip": ("CLIP", ), + "switch": ([ + "Off", + "On"],), + "lora_name": (file_list, ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.1}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.1}), + }} + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "load_lora" + + CATEGORY = "JPS Nodes/IO" + + def load_lora(self, model, clip, switch, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + if switch == "Off" or lora_name == "None": + return (model, clip) + + lora_path = folder_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + del self.loaded_lora + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Get_Image_Size: + def __init__(self) -> None: + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("INT", "INT",) + RETURN_NAMES = ("width", "height",) + CATEGORY = "JPS Nodes/Image" + + FUNCTION = 'get_imagesize' + + def get_imagesize(self, image): + samples = image.movedim(-1,1) + size_w = samples.shape[3] + size_h = samples.shape[2] + + return (size_w, size_h, ) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class SDXL_Prompt_Styler: + + def __init__(self): + pass + + uni_neg = ["OFF","ON"] + + @classmethod + def INPUT_TYPES(self): + current_directory = os.path.dirname(os.path.realpath(__file__)) + self.json_data_artists, artists = load_styles_from_directory(os.path.join(current_directory, 'styles', 'artists')) + self.json_data_movies, movies = load_styles_from_directory(os.path.join(current_directory, 'styles', 'movies')) + self.json_data_styles, styles = load_styles_from_directory(os.path.join(current_directory, 'styles', 'main')) + + return { + "required": { + "text_positive_g": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": True}), + "text_positive_l": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": True}), + "text_negative": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": True}), + "artist": ((artists), ), + "movie": ((movies), ), + "style": ((styles), ), + "fooocus_enhance": (self.uni_neg,), + "fooocus_negative": (self.uni_neg,), + "universal_negative": (self.uni_neg,), + }, + } + + RETURN_TYPES = ('STRING','STRING','STRING','STRING',) + RETURN_NAMES = ('text_positive_g','text_positive_l','text_positive','text_negative',) + FUNCTION = 'sdxlpromptstyler' + CATEGORY = 'JPS Nodes/Style' + + def sdxlpromptstyler(self, text_positive_g, text_positive_l, text_negative, artist, movie, style, fooocus_enhance, fooocus_negative, universal_negative): + # Process and combine prompts in templates + # The function replaces the positive prompt placeholder in the template, + # and combines the negative prompt with the template's negative prompt, if they exist. + + text_pos_g_style = "" + text_pos_l_style = "" + text_pos_style = "" + text_neg_style = "" + + text_pos_g_artist, text_pos_l_artist, text_neg_artist = read_sdxl_templates_replace_and_combine(self.json_data_artists, artist, text_positive_g, text_positive_l, text_negative) + + if(text_positive_g == text_positive_l): + if(text_pos_l_artist != text_positive_l and text_pos_g_artist != text_positive_g): + text_positive_l = "" + text_pos_g_artist, text_pos_l_artist, text_neg_artist = read_sdxl_templates_replace_and_combine(self.json_data_artist, artist, text_positive_g, text_positive_l, text_negative) + elif(text_pos_g_artist != text_positive_g): + text_pos_l_artist = text_pos_g_artist + elif(text_pos_l_artist != text_positive_l): + text_pos_g_artist = text_pos_l_artist + + text_pos_g_movie, text_pos_l_movie, text_neg_movie = read_sdxl_templates_replace_and_combine(self.json_data_movies, movie, text_pos_g_artist, text_pos_l_artist, text_negative) + + if(text_pos_g_artist == text_pos_l_artist): + if(text_pos_l_movie != text_pos_l_artist and text_pos_g_movie != text_pos_g_artist): + text_pos_l_artist = "" + text_pos_g_movie, text_pos_l_movie, text_neg_movie = read_sdxl_templates_replace_and_combine(self.json_data_movie, movie, text_positive_g, text_positive_l, text_negative) + elif(text_pos_g_movie != text_pos_g_artist): + text_pos_l_movie = text_pos_g_movie + elif(text_pos_l_movie != text_pos_l_artist): + text_pos_g_movie = text_pos_l_movie + + text_pos_g_style, text_pos_l_style, text_neg_style = read_sdxl_templates_replace_and_combine(self.json_data_styles, style, text_pos_g_movie, text_pos_l_movie, text_neg_movie) + + if(text_pos_g_movie == text_pos_l_movie): + if(text_pos_l_movie != text_pos_l_style and text_pos_g_movie != text_pos_g_style): + text_pos_l_movie = "" + text_pos_g_style, text_pos_l_style, text_neg_style = read_sdxl_templates_replace_and_combine(self.json_data_styles, style, text_pos_g_movie, text_pos_l_movie, text_neg_movie) + elif(text_pos_g_movie != text_pos_g_style): + text_pos_l_style = text_pos_g_style + elif(text_pos_l_movie != text_pos_l_style): + text_pos_g_style = text_pos_l_style + + if(text_pos_g_style != text_pos_l_style): + if(text_pos_l_style != ""): + text_pos_style = text_pos_g_style + ' . ' + text_pos_l_style + else: + text_pos_style = text_pos_g_style + else: + text_pos_style = text_pos_g_style + + if(fooocus_enhance == "ON"): + if (text_neg_style != ''): + text_neg_style = text_neg_style + ', (worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)' + else: + text_neg_style = '(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)' + + if(fooocus_negative == "ON"): + if (text_neg_style != ''): + text_neg_style = text_neg_style + ', deformed, bad anatomy, disfigured, poorly drawn face, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, disconnected head, malformed hands, long neck, mutated hands and fingers, bad hands, missing fingers, cropped, worst quality, low quality, mutation, poorly drawn, huge calf, bad hands, fused hand, missing hand, disappearing arms, disappearing thigh, disappearing calf, disappearing legs, missing fingers, fused fingers, abnormal eye proportion, Abnormal hands, abnormal legs, abnormal feet, abnormal fingers, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch' + else: + text_neg_style = 'deformed, bad anatomy, disfigured, poorly drawn face, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, disconnected head, malformed hands, long neck, mutated hands and fingers, bad hands, missing fingers, cropped, worst quality, low quality, mutation, poorly drawn, huge calf, bad hands, fused hand, missing hand, disappearing arms, disappearing thigh, disappearing calf, disappearing legs, missing fingers, fused fingers, abnormal eye proportion, Abnormal hands, abnormal legs, abnormal feet, abnormal fingers, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch' + + if(universal_negative == "ON"): + if (text_neg_style != ''): + text_neg_style = text_neg_style + ', text, watermark, low-quality, signature, moire pattern, downsampling, aliasing, distorted, blurry, glossy, blur, jpeg artifacts, compression artifacts, poorly drawn, low-resolution, bad, distortion, twisted, excessive, exaggerated pose, exaggerated limbs, grainy, symmetrical, duplicate, error, pattern, beginner, pixelated, fake, hyper, glitch, overexposed, high-contrast, bad-contrast' + else: + text_neg_style = 'text, watermark, low-quality, signature, moire pattern, downsampling, aliasing, distorted, blurry, glossy, blur, jpeg artifacts, compression artifacts, poorly drawn, low-resolution, bad, distortion, twisted, excessive, exaggerated pose, exaggerated limbs, grainy, symmetrical, duplicate, error, pattern, beginner, pixelated, fake, hyper, glitch, overexposed, high-contrast, bad-contrast' + + return text_pos_g_style, text_pos_l_style, text_pos_style, text_neg_style + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Crop_Image_Square: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "crop_position": (["center", "top", "bottom", "left", "right"],), + "offset_x": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "offset_y": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "zoom": ("FLOAT", { "default": 1, "min": 1, "max": 5, "step": 0.1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "target_rez": ("INT", { "default": 0 , "min": 0, "step": 8, "display": "number" }), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "crop_square" + CATEGORY = "JPS Nodes/Image" + + def crop_square(self, image, crop_position, offset_x, offset_y, zoom, interpolation, target_rez,sharpening): + _, h, w, _ = image.shape + crop_size = min(h, w) + + offset_x = int (offset_x * zoom) + offset_y = int (offset_y * zoom) + + if "center" in crop_position: + x = round((w*zoom-crop_size) / 2) + y = round((h*zoom-crop_size) / 2) + if "top" in crop_position: + x = round((w*zoom-crop_size) / 2) + y = 0 + if "bottom" in crop_position: + x = round((w*zoom-crop_size) / 2) + y = h*zoom-crop_size + if "left" in crop_position: + x = 0 + y = round((h*zoom-crop_size) / 2) + if "right" in crop_position: + x = w*zoom-crop_size + y = round((h*zoom-crop_size) / 2) + + x = int(x) + y = int(y) + + if (x + offset_x >= 0 and x + crop_size + offset_x <= int(w*zoom)): + x = x + offset_x + elif (x + offset_x >= 0): + x = int(w*zoom) - crop_size + elif (x + crop_size + offset_x <= int(w*zoom)): + x = 0 + + if (y + offset_y >= 0 and y + crop_size + offset_y <= int(h*zoom)): + y = y + offset_y + elif (y + offset_y >= 0): + y = int(h*zoom) - crop_size + elif (y + crop_size + offset_y <= int(h*zoom)): + y = 0 + + x2 = x+crop_size + y2 = y+crop_size + + zoomedimage = image[:, 0:h, 0:w, :] + + zoomedimage = zoomedimage.permute([0,3,1,2]) + + zoomedimage = comfy.utils.lanczos(zoomedimage, int(w*zoom), int(h*zoom)) + + zoomedimage = zoomedimage.permute([0,2,3,1]) + + output = zoomedimage[:, y:y2, x:x2, :] + + output = output.permute([0,3,1,2]) + + if target_rez != 0: + if interpolation == "lanczos": + output = comfy.utils.lanczos(output, target_rez, target_rez) + else: + output = F.interpolate(output, size=(target_rez, target_rez), mode=interpolation) + + if sharpening > 0: + output = contrast_adaptive_sharpening(output, sharpening) + + output = output.permute([0,2,3,1]) + + return(output, ) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Crop_Image_TargetSize: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "target_w": ("INT", { "default": 0 , "min": 0, "step": 8, "display": "number" }), + "target_h": ("INT", { "default": 0 , "min": 0, "step": 8, "display": "number" }), + "crop_position": (["center","top", "bottom", "left", "right"],), + "offset": ("INT", { "default": 0, "min": -2048, "max": 2048, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "crop_targetsize" + CATEGORY = "JPS Nodes/Image" + + def crop_targetsize(self, image, target_w, target_h, crop_position, offset, interpolation, sharpening): + _, current_h, current_w, _ = image.shape + + current_ar = current_w / current_h + + if target_w / current_ar >= target_h: + new_w = target_w + new_h = round(new_w / current_ar) + offset_h = offset + offset_w = 0 + else: + new_h = target_h + new_w = round(new_h * current_ar) + offset_w = offset + offset_h = 0 + + # print("New Size") + # print(new_w) + # print(new_h) + + resized_image = image.permute([0,3,1,2]) + + if interpolation == "lanczos": + resized_image = comfy.utils.lanczos(resized_image, new_w, new_h) + else: + resized_image = F.interpolate(resized_image, size=(new_h, new_w), mode=interpolation) + + resized_image = resized_image.permute([0,2,3,1]) + + output_image = resized_image + + if (crop_position == "left"): + newoffset_w = offset_w + elif (crop_position == "right"): + newoffset_w = new_w - target_w + offset_w + else: + newoffset_w = (new_w - target_w) // 2 + offset_w + + if (crop_position == "top"): + newoffset_h = offset_h + elif (crop_position == "bottom"): + newoffset_h = new_h - target_h + offset_h + else: + newoffset_h = (new_h - target_h) // 2 + offset_h + + if newoffset_w < 0: + newoffset_w = 0 + elif newoffset_w + target_w > new_w: + newoffset_w = new_w - target_w + + if newoffset_h < 0: + newoffset_h = 0 + elif newoffset_h + target_h > new_h: + newoffset_h = new_h - target_h + + x = newoffset_w + x2 = newoffset_w+target_w + y = newoffset_h + y2 = newoffset_h+target_h + + # print("x: "+str(x)) + # print("x2: "+str(x2)) + # print("y: "+str(y)) + # print("y2: "+str(y2)) + + if sharpening > 0: + output_image = contrast_adaptive_sharpening(output_image, sharpening) + + output_image = output_image[:, y:y2, x:x2, :] + + return(output_image, ) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Prepare_Image_Tiled_IPA: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "target_w": ("INT", { "default": 0 , "min": 0, "step": 8, "display": "number" }), + "target_h": ("INT", { "default": 0 , "min": 0, "step": 8, "display": "number" }), + "zoom": ("INT", {"default": 1.0, "min": 1, "max": 500, "step": 1}), + "offset_w": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "offset_h": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + "tile_short": ("INT", {"default": 2.0, "min": 1, "max": 5, "step": 1}), + "prepare_type": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "crop_targetsize" + CATEGORY = "JPS Nodes/Image" + + def crop_targetsize(self, image, target_w, target_h, zoom, offset_w, offset_h, interpolation, sharpening,tile_short,prepare_type): + _, input_h, input_w, _ = image.shape + + tilelength = tile_short * 224 + + #Direct Source + if prepare_type == 9: + copyimage = image + + #Source_AR + Source_Res + if prepare_type == 4: + target_w = input_w + target_h = input_h + + #Source_AR + Tile_Res + if prepare_type == 5: + target_w = input_w + target_h = input_h + prepare_type = 2 + + #Tile_AR + Source_Res + if prepare_type == 6: + target_w = input_w + target_h = input_h + prepare_type = 3 + + #Square_AR + Target_Res + if prepare_type == 7: + target_ar = target_w / target_h + if target_ar >= 1: + target_w = target_h + else: + target_h = target_w + + #Square_AR + Tile_Res + if prepare_type == 8: + target_w = tilelength + target_h = tilelength + + #Target_AR + Tile_Res + if prepare_type == 2: + target_ar = target_w / target_h + if target_ar >= 1: + target_h = tilelength + target_w = round(tilelength * target_ar) + else: + target_w = tilelength + target_h = round(tilelength / target_ar) + + #Tile_AR + Target_Res + if prepare_type == 3: + target_ar = target_w / target_h + if target_ar >= 1: + target_h = tilelength + if target_ar < 1.5: + target_w = tilelength + elif target_ar < 2: + target_w = round(tilelength * 1.5) + elif target_ar < 2.5: + target_w = round(tilelength * 2) + elif target_ar < 3: + target_w = round(tilelength * 2.5) + elif target_ar < 3.5: + target_w = round(tilelength * 3) + else: + target_w = round(tilelength * target_ar) + else: + target_w = tilelength + target_ar = target_h / target_w + if target_ar < 1.5: + target_h = tilelength + elif target_ar < 2: + target_h = round(tilelength * 1.5) + elif target_ar < 2.5: + target_h = round(tilelength * 2) + elif target_ar < 3: + target_h = round(tilelength * 2.5) + elif target_ar < 3.5: + target_h = round(tilelength * 3) + else: + target_h = round(tilelength * target_ar) + + zoom = float(zoom / 100) + + resize_needed_w = target_w / input_w + resize_needed_h = target_h / input_h + + if resize_needed_w >= resize_needed_h: + min_zoom_factor = resize_needed_w + else: + min_zoom_factor = resize_needed_h + + if zoom <= min_zoom_factor: + zoom_factor = min_zoom_factor +# elif zoom > min_zoom_factor and min_zoom_factor >=1: +# zoom_factor = zoom +# elif zoom > min_zoom_factor and min_zoom_factor < 1: +# zoom_factor = min_zoom_factor + else: + zoom_factor = zoom + + zoomed_w = round(input_w * zoom_factor) + zoomed_h = round(input_h * zoom_factor) + + resized_image = image.permute([0,3,1,2]) + + if interpolation == "lanczos": + resized_image = comfy.utils.lanczos(resized_image, zoomed_w, zoomed_h) + else: + resized_image = F.interpolate(resized_image, size=(zoomed_h, zoomed_w), mode=interpolation) + + resized_image = resized_image.permute([0,2,3,1]) + + x0 = round((zoomed_w - target_w) / 2) + x1 = x0 + target_w + y0 = round((zoomed_h - target_h) / 2) + y1 = y0 + target_h + + if x0 + offset_w + target_w < zoomed_w and offset_w > 0: + x0 = x0 + offset_w + x1 = x0 + target_w + elif x0 + offset_w + target_w >= zoomed_w and offset_w > 0: + x0 = zoomed_w - target_w + x1 = zoomed_w + elif x0 + offset_w > 0 and offset_w < 0: + x0 = x0 + offset_w + x1 = x0 + target_w + elif x0 + offset_w <= 0 and offset_w < 0: + x0 = 0 + x1 = target_w + + if y0 + offset_h + target_h < zoomed_h and offset_h > 0: + y0 = y0 + offset_h + y1 = y0 + target_h + elif y0 + offset_h + target_h >= zoomed_h and offset_h > 0: + y0 = zoomed_h - target_h + y1 = zoomed_h + elif y0 + offset_h > 0 and offset_h < 0: + y0 = y0 + offset_h + y1 = y0 + target_h + elif y0 + offset_h <= 0 and offset_h < 0: + y0 = 0 + y1 = target_h + + output_image = resized_image + + # print("x0: "+str(x0)) + # print("x1: "+str(x1)) + # print("y0: "+str(y0)) + # print("y1: "+str(y1)) + + if sharpening > 0: + output_image = contrast_adaptive_sharpening(output_image, sharpening) + + output_image = output_image[:, y0:y1, x0:x1, :] + + if prepare_type == 9: + output_image = copyimage + + return(output_image,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Prepare_Image: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "target_w": ("INT", { "default": 1024 , "min": 0, "step": 8, "display": "number" }), + "target_h": ("INT", { "default": 1024 , "min": 0, "step": 8, "display": "number" }), + "crop_w_percent": ("INT", { "default": 100 , "min": 10, "max": 100, "step": 1, "display": "number" }), + "crop_h_percent": ("INT", { "default": 100 , "min": 10, "max": 100, "step": 1, "display": "number" }), + "offset_w": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "offset_h": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "prepare_image" + CATEGORY = "JPS Nodes/Image" + + def prepare_image(self, image, target_w, target_h, crop_w_percent, crop_h_percent, offset_w, offset_h, interpolation, sharpening, padding_left, padding_right, padding_top, padding_bottom): + _, input_h, input_w, _ = image.shape + + resize_needed_w = target_w / input_w / crop_w_percent * 100 + resize_needed_h = target_h / input_h / crop_h_percent * 100 + + if resize_needed_w >= resize_needed_h: + min_zoom_factor = resize_needed_w + else: + min_zoom_factor = resize_needed_h + + zoom_factor = min_zoom_factor + + zoomed_w = round(input_w * zoom_factor) + zoomed_h = round(input_h * zoom_factor) + + resized_image = image.permute([0,3,1,2]) + + if interpolation == "lanczos": + resized_image = comfy.utils.lanczos(resized_image, zoomed_w, zoomed_h) + else: + resized_image = F.interpolate(resized_image, size=(zoomed_h, zoomed_w), mode=interpolation) + + resized_image = resized_image.permute([0,2,3,1]) + + x0 = round((zoomed_w - target_w) / 2) + x1 = round(x0 + target_w) + y0 = round((zoomed_h - target_h) / 2) + y1 = round(y0 + target_h) + + if x0 + offset_w + target_w < zoomed_w and offset_w > 0: + x0 = x0 + offset_w + x1 = x0 + target_w + elif x0 + offset_w + target_w >= zoomed_w and offset_w > 0: + x0 = zoomed_w - target_w + x1 = zoomed_w + elif x0 + offset_w > 0 and offset_w < 0: + x0 = x0 + offset_w + x1 = x0 + target_w + elif x0 + offset_w <= 0 and offset_w < 0: + x0 = 0 + x1 = target_w + + if y0 + offset_h + target_h < zoomed_h and offset_h > 0: + y0 = y0 + offset_h + y1 = y0 + target_h + elif y0 + offset_h + target_h >= zoomed_h and offset_h > 0: + y0 = zoomed_h - target_h + y1 = zoomed_h + elif y0 + offset_h > 0 and offset_h < 0: + y0 = y0 + offset_h + y1 = y0 + target_h + elif y0 + offset_h <= 0 and offset_h < 0: + y0 = 0 + y1 = target_h + + output_image = resized_image + + if sharpening > 0: + output_image = contrast_adaptive_sharpening(output_image, sharpening) + + output_image = output_image[:, y0:y1, x0:x1, :] + + return(output_image,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Prepare_Image_Plus: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "target_w": ("INT", { "default": 1024 , "min": 0, "step": 8, "display": "number" }), + "target_h": ("INT", { "default": 1024 , "min": 0, "step": 8, "display": "number" }), + "offset_w": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "offset_h": ("INT", { "default": 0, "min": -99, "max": 99, "step": 1, "display": "number" }), + "crop_left": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_right": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_top": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "crop_bottom": ("INT", { "default": 0, "min": 0, "max": 90, "step": 1, "display": "number" }), + "padding_left": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_right": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_top": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "padding_bottom": ("INT", { "default": 0, "min": 0, "max": 500, "step": 1, "display": "number" }), + "interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],), + "sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + "resize_type": (["Crop", "Stretch"],), + "flip": (["No", "X-Axis", "Y-Axis"],), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "prepare_image" + CATEGORY = "JPS Nodes/Image" + + def prepare_image(self, image, target_w, target_h, offset_w, offset_h, crop_left, crop_right, crop_top, crop_bottom, padding_left, padding_right, padding_top, padding_bottom,interpolation, sharpening,resize_type,flip): + _, input_h, input_w, _ = image.shape + + dim = () + if flip == "X-Axis": + dim += (2,) + image = torch.flip(image, dim) + if flip == "Y-Axis": + dim += (2,) + image = torch.flip(image, dim) + + if crop_left + crop_right > 90: + crop_left = 90 / (crop_left + crop_right) * crop_left + crop_right = 90 / (crop_left + crop_right) * crop_right + + if crop_top + crop_bottom > 90: + crop_top = 90 / (crop_top + crop_bottom) * crop_top + crop_bottom = 90 / (crop_top + crop_bottom) * crop_bottom + + left = int(input_w-(input_w * (100-crop_left) / 100)) + right = int(input_w-(input_w * (100-crop_right) / 100)) + top = int(input_h-(input_h * (100-crop_top) / 100)) + bottom = int(input_h-(input_h * (100-crop_bottom) / 100)) + + image = image[:, 0+top:input_h-bottom, 0+left:input_w-right, :] + + input_h = input_h - top - bottom + input_w = input_w - left - right + + left = int(((input_w * (100+padding_left) / 100) - input_w)) + right = int(((input_w * (100+padding_right) / 100) - input_w)) + top = int(((input_h * (100+padding_top) / 100) - input_h)) + bottom = int(((input_h * (100+padding_bottom) / 100) - input_h)) + + pil_image = Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + padded_image = Image.new("RGB", (pil_image.width + left + right, pil_image.height + top + bottom), color="black") + padded_image.paste(pil_image, (left, top)) + image = torch.from_numpy(np.array(padded_image).astype(np.float32) / 255.0).unsqueeze(0) + + input_h = input_h + top + bottom + input_w = input_w + left + right + + if resize_type != "Stretch": + + resize_needed_w = target_w / input_w + resize_needed_h = target_h / input_h + + if resize_needed_w >= resize_needed_h: + min_zoom_factor = resize_needed_w + else: + min_zoom_factor = resize_needed_h + + zoom_factor = min_zoom_factor + + zoomed_w = round(input_w * zoom_factor) + zoomed_h = round(input_h * zoom_factor) + + offset_w = int(zoomed_w / 100 * offset_w / 2) + offset_h = int(zoomed_h / 100 * offset_h / 2) + + resized_image = image.permute([0,3,1,2]) + + if interpolation == "lanczos": + resized_image = comfy.utils.lanczos(resized_image, zoomed_w, zoomed_h) + else: + resized_image = F.interpolate(resized_image, size=(zoomed_h, zoomed_w), mode=interpolation) + + resized_image = resized_image.permute([0,2,3,1]) + + x0 = round((zoomed_w - target_w) / 2) + x1 = round(x0 + target_w) + y0 = round((zoomed_h - target_h) / 2) + y1 = round(y0 + target_h) + + if x0 + offset_w + target_w < zoomed_w and offset_w > 0: + x0 = x0 + offset_w + x1 = x0 + target_w + elif x0 + offset_w + target_w >= zoomed_w and offset_w > 0: + x0 = zoomed_w - target_w + x1 = zoomed_w + elif x0 + offset_w > 0 and offset_w < 0: + x0 = x0 + offset_w + x1 = x0 + target_w + elif x0 + offset_w <= 0 and offset_w < 0: + x0 = 0 + x1 = target_w + + if y0 + offset_h + target_h < zoomed_h and offset_h > 0: + y0 = y0 + offset_h + y1 = y0 + target_h + elif y0 + offset_h + target_h >= zoomed_h and offset_h > 0: + y0 = zoomed_h - target_h + y1 = zoomed_h + elif y0 + offset_h > 0 and offset_h < 0: + y0 = y0 + offset_h + y1 = y0 + target_h + elif y0 + offset_h <= 0 and offset_h < 0: + y0 = 0 + y1 = target_h + output_image = resized_image + + output_image = output_image[:, y0:y1, x0:x1, :] + + else: + + resized_image = image.permute([0,3,1,2]) + + if interpolation == "lanczos": + resized_image = comfy.utils.lanczos(resized_image, target_w, target_h) + else: + resized_image = F.interpolate(resized_image, size=(target_h, target_w), mode=interpolation) + + resized_image = resized_image.permute([0,2,3,1]) + output_image = resized_image + + if sharpening > 0: + output_image = contrast_adaptive_sharpening(output_image, sharpening) + + return(output_image,) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class Save_Images_Plus: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("dummy_out",) + FUNCTION = "save_images_plus" + + OUTPUT_NODE = True + + CATEGORY = "JPS Nodes/IO" + + def save_images_plus(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + file = f"{filename} {counter:03}.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + #return { "ui": { "images": results } } + return(int(1), ) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +class CLIPTextEncodeSDXL_Plus: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024.0, "min": 0, "max": 12288}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": 12288}), + "res_factor": ("INT", {"default": 4, "min": 1, "max": 8}), + "text_pos": ("STRING", {"multiline": True, "default": "", "dynamicPrompts": True}), + "text_neg": ("STRING", {"multiline": True, "default": "", "dynamicPrompts": True}), + "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING","CONDITIONING",) + RETURN_NAMES = ("cond_pos", "cond_neg",) + FUNCTION = "execute" + CATEGORY = "JPS Nodes/Conditioning" + + def execute(self, clip, width, height, res_factor, text_pos, text_neg): + crop_w = 0 + crop_h = 0 + width = width*res_factor + height = height*res_factor + target_width = width + target_height = height + text_g_pos = text_l_pos = text_pos + text_g_neg = text_l_neg = text_neg + + tokens_pos = clip.tokenize(text_g_pos) + tokens_pos["l"] = clip.tokenize(text_l_pos)["l"] + if len(tokens_pos["l"]) != len(tokens_pos["g"]): + empty_pos = clip.tokenize("") + while len(tokens_pos["l"]) < len(tokens_pos["g"]): + tokens_pos["l"] += empty_pos["l"] + while len(tokens_pos["l"]) > len(tokens_pos["g"]): + tokens_pos["g"] += empty_pos["g"] + cond_pos, pooled_pos = clip.encode_from_tokens(tokens_pos, return_pooled=True) + + tokens_neg = clip.tokenize(text_g_neg) + tokens_neg["l"] = clip.tokenize(text_l_neg)["l"] + if len(tokens_neg["l"]) != len(tokens_neg["g"]): + empty_neg = clip.tokenize("") + while len(tokens_neg["l"]) < len(tokens_neg["g"]): + tokens_neg["l"] += empty_neg["l"] + while len(tokens_pos["l"]) > len(tokens_pos["g"]): + tokens_neg["g"] += empty_neg["g"] + cond_neg, pooled_neg = clip.encode_from_tokens(tokens_neg, return_pooled=True) + + return ([[cond_pos, {"pooled_output": pooled_pos, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], [[cond_neg, {"pooled_output": pooled_neg, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]) + +#---------------------------------------------------------------------------------------------------------------------------------------------------# + +NODE_CLASS_MAPPINGS = { + "Lora Loader (JPS)": IO_Lora_Loader, + "SDXL Resolutions (JPS)": SDXL_Resolutions, + "SDXL Basic Settings (JPS)": SDXL_Basic_Settings, + "SDXL Settings (JPS)": SDXL_Settings, + "Generation TXT IMG Settings (JPS)": Generation_TXT_IMG_Settings, + "Crop Image Settings (JPS)": CropImage_Settings, + "ImageToImage Settings (JPS)": ImageToImage_Settings, + "CtrlNet CannyEdge Settings (JPS)": CtrlNet_CannyEdge_Settings, + "CtrlNet ZoeDepth Settings (JPS)": CtrlNet_ZoeDepth_Settings, + "CtrlNet MiDaS Settings (JPS)": CtrlNet_MiDaS_Settings, + "CtrlNet OpenPose Settings (JPS)": CtrlNet_OpenPose_Settings, + "Revision Settings (JPS)": Revision_Settings, + "IP Adapter Settings (JPS)": IP_Adapter_Settings, + "IP Adapter Tiled Settings (JPS)": IP_Adapter_Tiled_Settings, + "InstantID Settings (JPS)": InstantID_Settings, + "Image Prepare Settings (JPS)": ImagePrepare_Settings, + "InstantID Source Prepare Settings (JPS)": InstantIDSourcePrepare_Settings, + "InstantID Pose Prepare Settings (JPS)": InstantIDPosePrepare_Settings, + "InstantID Mask Prepare Settings (JPS)": InstantIDMaskPrepare_Settings, + "Sampler Scheduler Settings (JPS)": Sampler_Scheduler_Settings, + "Integer Switch (JPS)": Integer_Switch, + "Image Switch (JPS)": Image_Switch, + "Latent Switch (JPS)": Latent_Switch, + "Conditioning Switch (JPS)": Conditioning_Switch, + "Model Switch (JPS)": Model_Switch, + "IPA Switch (JPS)": IPA_Switch, + "VAE Switch (JPS)": VAE_Switch, + "Mask Switch (JPS)": Mask_Switch, + "ControlNet Switch (JPS)": ControlNet_Switch, + "Disable Enable Switch (JPS)": Disable_Enable_Switch, + "Enable Disable Switch (JPS)": Enable_Disable_Switch, + "SDXL Basic Settings Pipe (JPS)": SDXL_Basic_Settings_Pipe, + "SDXL Settings Pipe (JPS)": SDXL_Settings_Pipe, + "Crop Image Pipe (JPS)": CropImage_Pipe, + "ImageToImage Pipe (JPS)": ImageToImage_Pipe, + "CtrlNet CannyEdge Pipe (JPS)": CtrlNet_CannyEdge_Pipe, + "CtrlNet ZoeDepth Pipe (JPS)": CtrlNet_ZoeDepth_Pipe, + "CtrlNet MiDaS Pipe (JPS)": CtrlNet_MiDaS_Pipe, + "CtrlNet OpenPose Pipe (JPS)": CtrlNet_OpenPose_Pipe, + "IP Adapter Settings Pipe (JPS)": IP_Adapter_Settings_Pipe, + "IP Adapter Tiled Settings Pipe (JPS)": IP_Adapter_Tiled_Settings_Pipe, + "InstantID Pipe (JPS)": InstantID_Pipe, + "Image Prepare Pipe (JPS)": ImagePrepare_Pipe, + "InstantID Source Prepare Pipe (JPS)": InstantIDSourcePrepare_Pipe, + "InstantID Pose Prepare Pipe (JPS)": InstantIDPosePrepare_Pipe, + "InstantID Mask Prepare Pipe (JPS)": InstantIDMaskPrepare_Pipe, + "Revision Settings Pipe (JPS)": Revision_Settings_Pipe, + "SDXL Fundamentals MultiPipe (JPS)": SDXL_Fundamentals_MultiPipe, + "Images Masks MultiPipe (JPS)": Images_Masks_MultiPipe, + "SDXL Recommended Resolution Calc (JPS)": SDXL_Recommended_Resolution_Calc, + "Resolution Multiply (JPS)": Math_Resolution_Multiply, + "Largest Int (JPS)": Math_Largest_Integer, + "Multiply Int Int (JPS)": Math_Multiply_INT_INT, + "Multiply Int Float (JPS)": Math_Multiply_INT_FLOAT, + "Multiply Float Float (JPS)": Math_Multiply_FLOAT_FLOAT, + "Substract Int Int (JPS)": Math_Substract_INT_INT, + "Text Concatenate (JPS)": Text_Concatenate, + "Get Date Time String (JPS)": Get_Date_Time_String, + "Get Image Size (JPS)": Get_Image_Size, + "Crop Image Square (JPS)": Crop_Image_Square, + "Crop Image TargetSize (JPS)": Crop_Image_TargetSize, + "Prepare Image (JPS)": Prepare_Image, + "Prepare Image Plus (JPS)": Prepare_Image_Plus, + "Prepare Image Tiled IPA (JPS)": Prepare_Image_Tiled_IPA, + "SDXL Prompt Styler (JPS)": SDXL_Prompt_Styler, + "SDXL Prompt Handling (JPS)": SDXL_Prompt_Handling, + "SDXL Prompt Handling Plus (JPS)": SDXL_Prompt_Handling_Plus, + "Text Prompt (JPS)": Text_Prompt, + "Text Prompt Combo (JPS)": Text_Prompt_Combo, + "Save Images Plus (JPS)": Save_Images_Plus, + "CLIPTextEncode SDXL Plus (JPS)": CLIPTextEncodeSDXL_Plus, + "Time Seed (JPS)": Time_Seed, +} diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_00_none.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_00_none.json new file mode 100644 index 00000000..a2b49482 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_00_none.json @@ -0,0 +1,7 @@ +[ + { + "name": "none", + "prompt": "{prompt}", + "negative_prompt": "" + } +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_a.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_a.json new file mode 100644 index 00000000..77fd8493 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_a.json @@ -0,0 +1,112 @@ +[ + { + "name": "Norman Ackroyd", + "prompt": "{prompt} by Norman Ackroyd", + "negative_prompt": "" + }, + { + "name": "Reem Acra", + "prompt": "{prompt} by Reem Acra", + "negative_prompt": "" + }, + { + "name": "Neal Adams", + "prompt": "{prompt} by Neal Adams", + "negative_prompt": "" + }, + { + "name": "Ansel Adams", + "prompt": "{prompt} by Ansel Adams", + "negative_prompt": "" + }, + { + "name": "Josh Adamski", + "prompt": "{prompt} by Josh Adamski", + "negative_prompt": "" + }, + { + "name": "Marc Adamus", + "prompt": "{prompt} by Marc Adamus", + "negative_prompt": "" + }, + { + "name": "Alena Aenami", + "prompt": "{prompt} by Alena Aenami", + "negative_prompt": "" + }, + { + "name": "David Aja", + "prompt": "{prompt} by David Aja", + "negative_prompt": "" + }, + { + "name": "Miles Aldridge", + "prompt": "{prompt} by Miles Aldridge", + "negative_prompt": "" + }, + { + "name": "Pablo Amaringo", + "prompt": "{prompt} by Pablo Amaringo", + "negative_prompt": "" + }, + { + "name": "Wes Anderson", + "prompt": "{prompt} by Wes Anderson", + "negative_prompt": "" + }, + { + "name": "Esao Andrews", + "prompt": "{prompt} by Esao Andrews", + "negative_prompt": "" + }, + { + "name": "Miss Aniela", + "prompt": "{prompt} by Miss Aniela", + "negative_prompt": "" + }, + { + "name": "Martin Ansin", + "prompt": "{prompt} by Martin Ansin", + "negative_prompt": "" + }, + { + "name": "Sergio Aragonés", + "prompt": "{prompt} by Sergio Aragonés", + "negative_prompt": "" + }, + { + "name": "Diane Arbus", + "prompt": "{prompt} by Diane Arbus", + "negative_prompt": "" + }, + { + "name": "Mark Arian", + "prompt": "{prompt} by Mark Arian", + "negative_prompt": "" + }, + { + "name": "Peter Arno", + "prompt": "{prompt} by Peter Arno", + "negative_prompt": "" + }, + { + "name": "Daniel Arsham", + "prompt": "{prompt} by Daniel Arsham", + "negative_prompt": "" + }, + { + "name": "Miki Asai", + "prompt": "{prompt} by Miki Asai", + "negative_prompt": "" + }, + { + "name": "Ruth Asawa", + "prompt": "{prompt} by Ruth Asawa", + "negative_prompt": "" + }, + { + "name": "Clemens Ascher", + "prompt": "{prompt} by Clemens Ascher", + "negative_prompt": "" + } +] diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_b.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_b.json new file mode 100644 index 00000000..64b88e27 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_b.json @@ -0,0 +1,132 @@ +[ + { + "name": "Lois van Baarle", + "prompt": "{prompt} by Lois van Baarle", + "negative_prompt": "" + }, + { + "name": "Anne Bachelier", + "prompt": "{prompt} by Anne Bachelier", + "negative_prompt": "" + }, + { + "name": "Shigeru Ban", + "prompt": "{prompt} by Shigeru Ban", + "negative_prompt": "" + }, + { + "name": "Clive Barker", + "prompt": "{prompt} by Clive Barker", + "negative_prompt": "" + }, + { + "name": "Carl Barks", + "prompt": "{prompt} by Carl Barks", + "negative_prompt": "" + }, + { + "name": "Matthew Barney", + "prompt": "{prompt} by Matthew Barney", + "negative_prompt": "" + }, + { + "name": "Paul Barson", + "prompt": "{prompt} by Paul Barson", + "negative_prompt": "" + }, + { + "name": "Bo Bartlett", + "prompt": "{prompt} by Bo Bartlett", + "negative_prompt": "" + }, + { + "name": "Xu Beihong", + "prompt": "{prompt} by Xu Beihong", + "negative_prompt": "" + }, + { + "name": "Thomas Hart Benton", + "prompt": "{prompt} by Thomas Hart Benton", + "negative_prompt": "" + }, + { + "name": "Stan Berenstain", + "prompt": "{prompt} by Stan Berenstain", + "negative_prompt": "" + }, + { + "name": "John Berkey", + "prompt": "{prompt} by John Berkey", + "negative_prompt": "" + }, + { + "name": "Ruth Bernhard", + "prompt": "{prompt} by Ruth Bernhard", + "negative_prompt": "" + }, + { + "name": "Butcher Billy", + "prompt": "{prompt} by Butcher Billy", + "negative_prompt": "" + }, + { + "name": "Rachel Bingaman", + "prompt": "{prompt} by Rachel Bingaman", + "negative_prompt": "" + }, + { + "name": "Anna Bocek", + "prompt": "{prompt} by Anna Bocek", + "negative_prompt": "" + }, + { + "name": "Franck Bohbot", + "prompt": "{prompt} by Franck Bohbot", + "negative_prompt": "" + }, + { + "name": "Chesley Bonestell", + "prompt": "{prompt} by Chesley Bonestell", + "negative_prompt": "" + }, + { + "name": "Aleksi Briclot", + "prompt": "{prompt} by Aleksi Briclot", + "negative_prompt": "" + }, + { + "name": "Margaret Brundage", + "prompt": "{prompt} by Margaret Brundage", + "negative_prompt": "" + }, + { + "name": "Christopher Bucklow", + "prompt": "{prompt} by Christopher Bucklow", + "negative_prompt": "" + }, + { + "name": "Charles E Burchfield", + "prompt": "{prompt} by Charles E Burchfield", + "negative_prompt": "" + }, + { + "name": "Alejandro Burdisio", + "prompt": "{prompt} by Alejandro Burdisio", + "negative_prompt": "" + }, + { + "name": "Tim Burton", + "prompt": "{prompt} by Tim Burton", + "negative_prompt": "" + }, + { + "name": "James Lee Byars", + "prompt": "{prompt} by James Lee Byars", + "negative_prompt": "" + }, + { + "name": "Bob Byerley", + "prompt": "{prompt} by Bob Byerley", + "negative_prompt": "" + } +] diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_c.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_c.json new file mode 100644 index 00000000..193f5583 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_c.json @@ -0,0 +1,167 @@ +[ + { + "name": "Clyde Caldwell", + "prompt": "{prompt} by Clyde Caldwell", + "negative_prompt": "" + }, + { + "name": "Al Capp", + "prompt": "{prompt} by Al Capp", + "negative_prompt": "" + }, + { + "name": "Larry Carlson", + "prompt": "{prompt} by Larry Carlson", + "negative_prompt": "" + }, + { + "name": "Keith Carter", + "prompt": "{prompt} by Keith Carter", + "negative_prompt": "" + }, + { + "name": "Hussein Chalayan", + "prompt": "{prompt} by Hussein Chalayan", + "negative_prompt": "" + }, + { + "name": "Tom Chambers", + "prompt": "{prompt} by Tom Chambers", + "negative_prompt": "" + }, + { + "name": "Katia Chausheva", + "prompt": "{prompt} by Katia Chausheva", + "negative_prompt": "" + }, + { + "name": "Howard Chaykin", + "prompt": "{prompt} by Howard Chaykin", + "negative_prompt": "" + }, + { + "name": "Bo Chen", + "prompt": "{prompt} by Bo Chen", + "negative_prompt": "" + }, + { + "name": "Carey Chen", + "prompt": "{prompt} by Carey Chen", + "negative_prompt": "" + }, + { + "name": "Hsiao Ron Cheng", + "prompt": "{prompt} by Hsiao Ron Cheng", + "negative_prompt": "" + }, + { + "name": "Michael Cheval", + "prompt": "{prompt} by Michael Cheval", + "negative_prompt": "" + }, + { + "name": "Cliff Chiang", + "prompt": "{prompt} by Cliff Chiang", + "negative_prompt": "" + }, + { + "name": "Doug Chiang", + "prompt": "{prompt} by Doug Chiang", + "negative_prompt": "" + }, + { + "name": "Judy Chicago", + "prompt": "{prompt} by Judy Chicago", + "negative_prompt": "" + }, + { + "name": "Dale Chihuly", + "prompt": "{prompt} by Dale Chihuly", + "negative_prompt": "" + }, + { + "name": "Peter Chilelli", + "prompt": "{prompt} by Peter Chilelli", + "negative_prompt": "" + }, + { + "name": "Giorgio de Chirico", + "prompt": "{prompt} by Giorgio de Chirico", + "negative_prompt": "" + }, + { + "name": "Michael Cho", + "prompt": "{prompt} by Michael Cho", + "negative_prompt": "" + }, + { + "name": "Michael Cinco", + "prompt": "{prompt} by Michael Cinco", + "negative_prompt": "" + }, + { + "name": "Amanda Clark", + "prompt": "{prompt} by Amanda Clark", + "negative_prompt": "" + }, + { + "name": "Becky Cloonan", + "prompt": "{prompt} by Becky Cloonan", + "negative_prompt": "" + }, + { + "name": "Daniel Clowes", + "prompt": "{prompt} by Daniel Clowes", + "negative_prompt": "" + }, + { + "name": "Gregory Colbert", + "prompt": "{prompt} by Gregory Colbert", + "negative_prompt": "" + }, + { + "name": "Eddie Colla", + "prompt": "{prompt} by Eddie Colla", + "negative_prompt": "" + }, + { + "name": "Darwyn Cooke", + "prompt": "{prompt} by Darwyn Cooke", + "negative_prompt": "" + }, + { + "name": "Nigel Cooke", + "prompt": "{prompt} by Nigel Cooke", + "negative_prompt": "" + }, + { + "name": "Nicholas Alan Cope", + "prompt": "{prompt} by Nicholas Alan Cope", + "negative_prompt": "" + }, + { + "name": "Peter Coulson", + "prompt": "{prompt} by Peter Coulson", + "negative_prompt": "" + }, + { + "name": "Guido Crepax", + "prompt": "{prompt} by Guido Crepax", + "negative_prompt": "" + }, + { + "name": "PJ Crook", + "prompt": "{prompt} by PJ Crook", + "negative_prompt": "" + }, + { + "name": "Etam Cru", + "prompt": "{prompt} by Etam Cru", + "negative_prompt": "" + }, + { + "name": "Robert Crumb", + "prompt": "{prompt} by Robert Crumb", + "negative_prompt": "" + } +] diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_d.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_d.json new file mode 100644 index 00000000..93025432 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_d.json @@ -0,0 +1,127 @@ +[ + { + "name": "Mike Dargas", + "prompt": "{prompt} by Mike Dargas", + "negative_prompt": "" + }, + { + "name": "Craig Davison", + "prompt": "{prompt} by Craig Davison", + "negative_prompt": "" + }, + { + "name": "Gabriel Dawe", + "prompt": "{prompt} by Gabriel Dawe", + "negative_prompt": "" + }, + { + "name": "Wim Delvoye", + "prompt": "{prompt} by Wim Delvoye", + "negative_prompt": "" + }, + { + "name": "Aron Demetz", + "prompt": "{prompt} by Aron Demetz", + "negative_prompt": "" + }, + { + "name": "George Digalakis", + "prompt": "{prompt} by George Digalakis", + "negative_prompt": "" + }, + { + "name": "Evgeni Dinev", + "prompt": "{prompt} by Evgeni Dinev", + "negative_prompt": "" + }, + { + "name": "Anna Dittmann", + "prompt": "{prompt} by Anna Dittmann", + "negative_prompt": "" + }, + { + "name": "Tom Dixon", + "prompt": "{prompt} by Tom Dixon", + "negative_prompt": "" + }, + { + "name": "Dima Dmitriev", + "prompt": "{prompt} by Dima Dmitriev", + "negative_prompt": "" + }, + { + "name": "Terry Dodson", + "prompt": "{prompt} by Terry Dodson", + "negative_prompt": "" + }, + { + "name": "Anita Dongre", + "prompt": "{prompt} by Anita Dongre", + "negative_prompt": "" + }, + { + "name": "Stanley Donwood", + "prompt": "{prompt} by Stanley Donwood", + "negative_prompt": "" + }, + { + "name": "Tim Doyle", + "prompt": "{prompt} by Tim Doyle", + "negative_prompt": "" + }, + { + "name": "Alberto Dros", + "prompt": "{prompt} by Alberto Dros", + "negative_prompt": "" + }, + { + "name": "Jessica Drossin", + "prompt": "{prompt} by Jessica Drossin", + "negative_prompt": "" + }, + { + "name": "TJ Drysdale", + "prompt": "{prompt} by TJ Drysdale", + "negative_prompt": "" + }, + { + "name": "Mac Duggal", + "prompt": "{prompt} by Mac Duggal", + "negative_prompt": "" + }, + { + "name": "Chris Dunn", + "prompt": "{prompt} by Chris Dunn", + "negative_prompt": "" + }, + { + "name": "Jessica Durrant", + "prompt": "{prompt} by Jessica Durrant", + "negative_prompt": "" + }, + { + "name": "Chad Dutson", + "prompt": "{prompt} by Chad Dutson", + "negative_prompt": "" + }, + { + "name": "Ryan Dyar", + "prompt": "{prompt} by Ryan Dyar", + "negative_prompt": "" + }, + { + "name": "Chris Dyer", + "prompt": "{prompt} by Chris Dyer", + "negative_prompt": "" + }, + { + "name": "Camilla d'Errico", + "prompt": "{prompt} by Camilla d'Errico", + "negative_prompt": "" + }, + { + "name": "Philip Lorca diCorcia", + "prompt": "{prompt} by Philip Lorca diCorcia", + "negative_prompt": "" + } +] diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_e.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_e.json new file mode 100644 index 00000000..dd1989be --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/artists/sdxl_styles_01_artists_e.json @@ -0,0 +1,102 @@ +[ + { + "name": "Eyvind Earle", + "prompt": "{prompt} by Eyvind Earle", + "negative_prompt": "" + }, + { + "name": "Lori Earley", + "prompt": "{prompt} by Lori Earley", + "negative_prompt": "" + }, + { + "name": "Michael Eastman", + "prompt": "{prompt} by Michael Eastman", + "negative_prompt": "" + }, + { + "name": "Tristan Eaton", + "prompt": "{prompt} by Tristan Eaton", + "negative_prompt": "" + }, + { + "name": "Janet Echelman", + "prompt": "{prompt} by Janet Echelman", + "negative_prompt": "" + }, + { + "name": "Harold Edgerton", + "prompt": "{prompt} by Harold Edgerton", + "negative_prompt": "" + }, + { + "name": "Jason Edmiston", + "prompt": "{prompt} by Jason Edmiston", + "negative_prompt": "" + }, + { + "name": "Les Edwards", + "prompt": "{prompt} by Les Edwards", + "negative_prompt": "" + }, + { + "name": "Bob Eggleton", + "prompt": "{prompt} by Bob Eggleton", + "negative_prompt": "" + }, + { + "name": "Fritz Eichenberg", + "prompt": "{prompt} by Fritz Eichenberg", + "negative_prompt": "" + }, + { + "name": "Will Eisner", + "prompt": "{prompt} by Will Eisner", + "negative_prompt": "" + }, + { + "name": "Olafur Eliasson", + "prompt": "{prompt} by Olafur Eliasson", + "negative_prompt": "" + }, + { + "name": "Christine Ellger", + "prompt": "{prompt} by Christine Ellger", + "negative_prompt": "" + }, + { + "name": "Peter Elson", + "prompt": "{prompt} by Peter Elson", + "negative_prompt": "" + }, + { + "name": "Gil Elvgren", + "prompt": "{prompt} by Gil Elvgren", + "negative_prompt": "" + }, + { + "name": "Ron Embleton", + "prompt": "{prompt} by Ron Embleton", + "negative_prompt": "" + }, + { + "name": "Ed Emshwiller", + "prompt": "{prompt} by Ed Emshwiller", + "negative_prompt": "" + }, + { + "name": "Daria Endresen", + "prompt": "{prompt} by Daria Endresen", + "negative_prompt": "" + }, + { + "name": "Ron English", + "prompt": "{prompt} by Ron English", + "negative_prompt": "" + }, + { + "name": "MC Escher", + "prompt": "{prompt} by MC Escher", + "negative_prompt": "" + } +] diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_00_none.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_00_none.json new file mode 100644 index 00000000..a2b49482 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_00_none.json @@ -0,0 +1,7 @@ +[ + { + "name": "none", + "prompt": "{prompt}", + "negative_prompt": "" + } +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_01_sai.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_01_sai.json new file mode 100644 index 00000000..9cc12580 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_01_sai.json @@ -0,0 +1,87 @@ +[ + { + "name": "sai-3d-model", + "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", + "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting" + }, + { + "name": "sai-analog film", + "prompt": "analog film photo {prompt} . faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", + "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured" + }, + { + "name": "sai-anime", + "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", + "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast" + }, + { + "name": "sai-cinematic", + "prompt": "cinematic film still {prompt} . shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" + }, + { + "name": "sai-comic book", + "prompt": "comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed", + "negative_prompt": "photograph, deformed, glitch, noisy, realistic, stock photo" + }, + { + "name": "sai-craft clay", + "prompt": "play-doh style {prompt} . sculpture, clay art, centered composition, Claymation", + "negative_prompt": "sloppy, messy, grainy, highly detailed, ultra textured, photo" + }, + { + "name": "sai-digital art", + "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", + "negative_prompt": "photo, photorealistic, realism, ugly" + }, + { + "name": "sai-enhance", + "prompt": "breathtaking {prompt} . award-winning, professional, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy" + }, + { + "name": "sai-fantasy art", + "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", + "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white" + }, + { + "name": "sai-isometric", + "prompt": "isometric style {prompt} . vibrant, beautiful, crisp, detailed, ultra detailed, intricate", + "negative_prompt": "deformed, mutated, ugly, disfigured, blur, blurry, noise, noisy, realistic, photographic" + }, + { + "name": "sai-line art", + "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic" + }, + { + "name": "sai-lowpoly", + "prompt": "low-poly style {prompt} . low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition", + "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo" + }, + { + "name": "sai-neonpunk", + "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", + "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured" + }, + { + "name": "sai-origami", + "prompt": "origami style {prompt} . paper art, pleated paper, folded, origami art, pleats, cut and fold, centered composition", + "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo" + }, + { + "name": "sai-photographic", + "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", + "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly" + }, + { + "name": "sai-pixel art", + "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", + "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic" + }, + { + "name": "sai-texture", + "prompt": "texture {prompt} top down close-up", + "negative_prompt": "ugly, deformed, noisy, blurry" + } +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_02_jps_artstyles.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_02_jps_artstyles.json new file mode 100644 index 00000000..09e065b1 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/main/sdxl_styles_02_jps_artstyles.json @@ -0,0 +1,62 @@ +[ + { + "name": "Dystopian", + "prompt": "dystopian style {prompt} . bleak, post-apocalyptic, somber, dramatic, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, cheerful, optimistic, vibrant, colorful" + }, + { + "name": "Film Noir", + "prompt": "film noir style {prompt} . monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful" + }, + { + "name": "Gothic", + "prompt": "gothic style {prompt} . dark, mysterious, haunting, dramatic, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, cheerful, optimistic" + }, + { + "name": "Horror", + "prompt": "horror-themed {prompt} . eerie, unsettling, dark, spooky, suspenseful, grim, highly detailed", + "negative_prompt": "cheerful, bright, vibrant, light-hearted, cute" + }, + { + "name": "Hyperrealism", + "prompt": "hyperrealistic art {prompt} . extremely high-resolution details, photographic, extreme realism, fine texture, incredibly lifelike", + "negative_prompt": "simplified, abstract, unrealistic, impressionistic, low resolution" + }, + { + "name": "Manga", + "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style" + }, + { + "name": "Neon Noir", + "prompt": "neon noir {prompt} . cyberpunk, dark, rainy streets, neon signs, high contrast, low light, vibrant, highly detailed", + "negative_prompt": "bright, sunny, daytime, low contrast, black and white, sketch, watercolor" + }, + { + "name": "Paper Mache", + "prompt": "paper mache representation of {prompt} . 3D, sculptural, textured, handmade, vibrant, fun", + "negative_prompt": "2D, flat, photo, sketch, digital art, deformed, noisy, blurry" + }, + { + "name": "Papercut Shadow Box", + "prompt": "3D papercut shadow box of {prompt} . layered, dimensional, depth, silhouette, shadow, papercut, handmade, high contrast", + "negative_prompt": "painting, drawing, photo, 2D, flat, high detail, blurry, noisy, disfigured" + }, + { + "name": "Papercut Stacked", + "prompt": "stacked papercut art of {prompt} . 3D, layered, dimensional, depth, precision cut, stacked layers, papercut, high contrast", + "negative_prompt": "2D, flat, noisy, blurry, painting, drawing, photo, deformed" + }, + { + "name": "Silhouette", + "prompt": "silhouette style {prompt} . high contrast, minimalistic, black and white, stark, dramatic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, color, realism, photorealistic" + }, + { + "name": "Techwear Fashion", + "prompt": "techwear fashion {prompt} . futuristic, cyberpunk, urban, tactical, sleek, dark, highly detailed", + "negative_prompt": "vintage, rural, colorful, low contrast, realism, sketch, watercolor" + } +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/movies/sdxl_styles_00_none.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/movies/sdxl_styles_00_none.json new file mode 100644 index 00000000..a2b49482 --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/movies/sdxl_styles_00_none.json @@ -0,0 +1,7 @@ +[ + { + "name": "none", + "prompt": "{prompt}", + "negative_prompt": "" + } +] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_JPS-Nodes/styles/movies/sdxl_styles_01_movies.json b/custom_nodes/ComfyUI_JPS-Nodes/styles/movies/sdxl_styles_01_movies.json new file mode 100644 index 00000000..76b185aa --- /dev/null +++ b/custom_nodes/ComfyUI_JPS-Nodes/styles/movies/sdxl_styles_01_movies.json @@ -0,0 +1,197 @@ +[ + { + "name": "2001 - A Space Odyssey", + "prompt": "{prompt} in style of '2001 - A Space Odyssey' directed by Stanley Kubrick, futuristic aesthetics, minimalist design, monolithic structures, vivid contrast between darkness and light, sterile and clean environments, abstract and surreal visuals, geometric compositions, HAL 9000, astronaut helmets and suits, space station interiors", + "negative_prompt": "organic shapes, warm and cozy atmospheres" + }, + { + "name": "300", + "prompt": "{prompt} in style of '300' directed by Zack Snyder, Gritty and desaturated visuals, Iconic Spartan shields and spears, Blood and sweat in close-up, Intense facial expressions, Dramatic lighting and shadows, High-contrast visuals, Spartan red capes billowing, Artistic blood splatters, Dark and moody atmosphere", + "negative_prompt": "vibrant colors, modern technology, modern clothing" + }, + { + "name": "A.I.", + "prompt": "{prompt} in the style of 'A.I.' directed by Steven Spielberg, futuristic cityscape, advanced robots, artificial intelligence, human-robot interactions, emotional androids, abandoned urban environments, underwater cities, post-apocalyptic landscapes, sentient beings, neon-lit streets, robotic companions, flying vehicles, urban decay", + "negative_prompt": "" + }, + { + "name": "Akira", + "prompt": "{prompt} in style of 'Akira' directed by Katsuhiro Otomo, cyberpunk cityscape, neon-lit streets, futuristic motorcycles, telekinetic powers, government conspiracy, post-apocalyptic Tokyo, Kaneda's iconic red motorcycle, Tetsuo's transformation, psychic battles, massive explosions, iconic pill capsules, cybernetic enhancements, high-tech laboratories, dystopian urban decay", + "negative_prompt": "natural landscapes, serene settings, traditional clothing" + }, + { + "name": "AmΓ©lie", + "prompt": "{prompt} in the style of 'AmΓ©lie' directed by Jean-Pierre Jeunet, whimsical scenes, vibrant color palettes, nostalgic sepia tones, imaginative daydreams, whimsical details, playful street photography, charmingly quirky characters, idyllic cityscapes", + "negative_prompt": "dark and gloomy settings, gritty realism" + }, + { + "name": "Avatar", + "prompt": "{prompt} in the style of 'Avatar' directed by James Cameron, lush alien flora and fauna, bioluminescent forests, blue skin, alien wildlife, tribal culture, exotic Pandora landscapes", + "negative_prompt": "" + }, + { + "name": "Black Swan", + "prompt": "{prompt} in the style of 'Black Swan' directed by Darren Aronofsky, dark, intense, graceful, eerie, distorted reality, psychological, duality, metamorphosis, obsession, ballet, feathers, mirrors, blood, contrast, reflections, suspense, paranoia", + "negative_prompt": "birds" + }, + { + "name": "Blade Runner 2049", + "prompt": "{prompt} in style of 'Blade Runner 2049' directed by Denis Villeneuve, dystopian cityscape with artificial lighting, rain-soaked streets, neon-lit advertisements, futuristic architecture, holographic billboards, smoky atmospheric scenes, cyberpunk fashion, futuristic vehicles, dark and moody color palette, reflective surfaces, urban decay, cybernetic enhancements", + "negative_prompt": "bright, cheerful" + }, + { + "name": "Blue Velvet", + "prompt": "{prompt} in the style of 'Blue Velvet' directed by David Lynch, surrealistic and disturbing atmosphere, eerie and unsettling tones, voyeuristic perspective, red roses, blue skies, retro 1950s aesthetics, striking color contrast, surreal dream sequences, mysterious and unsettling characters, velvet textures, dramatic lighting, eerie silence, suburban decay", + "negative_prompt": "cheerful, vibrant colors, upbeat, joyful" + }, + { + "name": "Brazil", + "prompt": "{prompt} in style of 'Brazil' directed by Terry Gilliam, surreal and bureaucratic dystopian world, retro-futuristic technology, decaying industrial landscapes, dreamlike visuals, dark humor, Kafkaesque absurdity, towering and labyrinthine bureaucratic structures, ominous masks, sterile buildings, gritty dilapidated streets, oversized typewriters, enigmatic figures in trench coats, challenge perceptions of reality and sanity", + "negative_prompt": "" + }, + { + "name": "The Cell", + "prompt": "{prompt} in style of 'The Cell' directed by Tarsem Singh, ethereal silk textures with a floating quality, surreal dreamscapes, vibrant color palettes, intricate costume design, avant-garde architecture, otherworldly landscapes, abstract and symbolic visuals, surreal juxtapositions, intricate body art, mesmerizing lighting effects, psychological symbolism, visually stunning set pieces, mind-bending visuals", + "negative_prompt": "gritty realism, naturalistic color schemes" + }, + { + "name": "Dark City", + "prompt": "{prompt} in style of 'Dark City' directed by Alex Proyas, surreal cityscape, dimly lit streets, spiraling architecture, highlighted pale faces, dark clothes, bald heads, noir-inspired lighting, mysterious strangers, perpetual nighttime, shadowy figures, clockwork aesthetics, dystopian urban landscape, haunting atmosphere, eerie cityscape", + "negative_prompt": "futuristic technology, daylight, colorful visuals, natural landscapes, traditional architecture" + }, + { + "name": "Django Unchained", + "prompt": "{prompt} in style of 'Django Unchained' directed by Quentin Tarantino, intense close-ups, vintage color grading, gritty landscapes, dramatic use of shadows, dynamic camera angles, vivid blood splatters, unconventional framing, candid facial expressions, candle-lit interiors, Western attire", + "negative_prompt": "modern technology" + }, + { + "name": "Dune (2021)", + "prompt": "{prompt} in style of 'Dune (2021)' directed by Denis Villeneuve, vast desert landscapes, intricate futuristic architecture, ornate costumes and clothing, dramatic sandstorms, grandiose and imposing spacecraft, desert nomadic tribes, barren and unforgiving landscapes, mystical and ancient symbols, dramatic lighting and shadows, towering sand dunes, opulent palaces, intricate sandworm designs, intricate hieroglyphic-like writing", + "negative_prompt": "vibrant and colorful scenes, urban, modern technology, lush vegetation" + }, + { + "name": "Ex Machina", + "prompt": "{prompt} in the style of 'Ex Machina' directed by Alex Garland, futuristic technology, sleek and modern design, human-robot interactions, artificial intelligence, isolated and minimalist environment, subtle use of color, dimly lit spaces, reflections and glass surfaces, geometric shapes, high-tech laboratory, humanoid robot, eerie atmosphere", + "negative_prompt": "outdoor scenes, natural landscapes, vibrant colors" + }, + { + "name": "Eyes Wide Shut", + "prompt": "{prompt} in the style of 'Eyes Wide Shut' directed by Stanley Kubrick, mysterious and provocative lighting, seductive and enigmatic atmosphere, intimate and tense emotions, masked and hidden identities, dimly lit and opulent interiors, voyeuristic, dreamlike sequences, surreal and unsettling visuals, striking use of color and contrast, tension in body language, forbidden desires, hypnotic and suspenseful ambiance, emotionally charged, a sense of secrecy and obsession, dramatic and ambiguous expressions", + "negative_prompt": "cheerful setting, explicit, overtly happy, carefree emotions" + }, + { + "name": "The Fall", + "prompt": "{prompt} in style of 'The Fall' directed by Tarsem Singh, lush natural landscapes, vivid color palette, vibrant and surreal colors, dreamlike imagery, ornate and fantastical costumes, wide-angle scenic shots, imaginative set designs, striking visual contrasts, surreal wildlife encounters, whimsical and mythical creatures, magical realism, ethereal atmosphere, fantastical architecture, visually stunning, breathtaking cinematography", + "negative_prompt": "urban, gritty realism" + }, + { + "name": "The Fountain", + "prompt": "{prompt} in style of 'The Fountain' directed by Darren Aronofsky, cosmic vistas, surreal landscapes, ethereal lighting, intricate textures, organic motifs, otherworldly colors, merging of time and space, spiritual symbolism, emotional depth, celestial imagery, dreamlike textures, interplay of light and shadow, organic textures, mystical geometry, transcendental moments, visual poetry, cosmic journey", + "negative_prompt": "gritty realism, urban, modern technology" + }, + { + "name": "Gattaca", + "prompt": "{prompt} in the style of 'Gattaca' directed by Andrew Niccol, futuristic architecture, dystopian landscapes, stark contrasts, futuristic technology, genetic sequencing, DNA helix, sleek and elegant aesthetics, high-contrast lighting, genetic discrimination, minimalistic design", + "negative_prompt": "fantasy, magic, supernatural" + }, + { + "name": "The Grand Budapest Hotel", + "prompt": "{prompt} in style of 'The Grand Budapest Hotel' directed by Wes Anderson, vintage color palette, quirky characters, symmetrical and meticulously designed spaces, ornate architecture and decor, pastel aesthetics, a sense of nostalgia, whimsical props, charming lobby, retro typography, intricate set design, playful use of aspect ratio, whimsical fashion, meticulous detailing, cinematic pastry creations, zany hotel staff", + "negative_prompt": "loud, chaotic scenes, gritty realism, contemporary technology, modern urban settings" + }, + { + "name": "Hero", + "prompt": "{prompt} in style of 'Hero' directed by Zhang Yimou, visually stunning cinematography, vibrant color palette, graceful use of silk, striking use of red, intricate costume design, breathtaking landscapes, poetic storytelling, symbolic use of water and fire, visual contrasts, silhouetted characters, masterful use of negative space, dream sequences", + "negative_prompt": "gritty urban settings, modern technology, contemporary fashion" + }, + { + "name": "Inception", + "prompt": "{prompt} in the style of 'Inception' directed by Christopher Nolan, surreal cityscape, labyrinthine architecture, shifting gravity, iconic spinning top, mirrored reflections, dream-like landscapes, fractured reality, mind-bending visuals, distorted perception, gravity-defying scenes, surreal landscapes, abstract geometry", + "negative_prompt": "" + }, + { + "name": "In the Mood for Love", + "prompt": "{prompt} in the style of 'In the Mood for Love' directed by Wong Kar-wai, intimate moments between characters, rain-soaked streets, subtle glances and gestures, dimly lit alleyways, traditional 1960s Hong Kong fashion, elegant and sensual visuals, rich color palette, romantic tension, close-ups of faces in contemplation, brief encounters in narrow spaces, fleeting emotions, use of shadows and reflections, nostalgic atmosphere", + "negative_prompt": "modern technology, vibrant colors, explicit scenes, crowded, noisy, wide-open spaces, contemporary fashion" + }, + { + "name": "Kill Bill", + "prompt": "{prompt} in the style of 'Kill Bill' directed by Quentin Tarantino, martial arts action, vivid colors, samurai swords, close-up of eyes, silhouette of a woman with a sword, blood splatter, iconic yellow jumpsuit, intense stare, black and white flashback, katana blade, vengeance theme, dramatic lighting, dramatic close-up, stylized violence", + "negative_prompt": "" + }, + { + "name": "Life of Pi", + "prompt": "{prompt} in style of 'Life of Pi' directed by Ang Lee, vibrant color palette, surrealistic visuals, mesmerizing ocean vistas, intricate water reflections, exotic wildlife, lush greenery, stunning sunset scenes, dramatic skyscapes, magical realism, bioluminescent aquatic life, mesmerizing underwater shots, isolated island settings, striking visual contrast, ethereal dream sequences, captivating use of light and shadow", + "negative_prompt": "gritty urban environments, monochromatic visuals, crowded and cluttered scenes, industrial landscapes" + }, + { + "name": "The Lord of the Rings", + "prompt": "{prompt} in style of 'The Lord of the Rings' directed by Peter Jackson, high fantasy, epic landscapes, detailed costumes, ancient architecture, dramatic lighting, lush greenery, majestic kingdoms, mythical races, vast horizons, rugged terrains, enchanting forests, elaborate armor", + "negative_prompt": "modern technology, futuristic elements, urban settings, contemporary fashion" + }, + { + "name": "Mad Max - Fury Road", + "prompt": "{prompt} in the style of 'Mad Max - Fury Road' directed by George Miller, desolate landscapes, gritty vehicles, fiery explosions, post-apocalyptic chaos, dust storms, brutal survival, high-octane action, wasteland warriors, vehicular combat, dystopian fashion, war-torn world, rugged vehicles", + "negative_prompt": "serene landscapes, calm, tranquil settings" + }, + { + "name": "The Matrix", + "prompt": "{prompt} in the style of 'The Matrix' directed by the Wachowskis, cyberpunk aesthetics, green-tinted visual filter, high-tech leather outfits, futuristic cityscapes, computer code rain, reflective sunglasses, martial arts combat, dystopian atmosphere, hacker symbolism, glitch effects, bullet-time camera angles, surreal digital landscapes, virtual reality simulation, hacking terminals", + "negative_prompt": "fantasy elements, bright colors, historical settings" + }, + { + "name": "Melancholia", + "prompt": "{prompt} in style of 'Melancholia' directed by Lars von Trier, surreal landscapes, apocalyptic imagery, psychological turmoil, eerie lighting, desolate landscapes, fractured emotions, ethereal atmosphere, juxtaposed emotions, celestial collisions, melancholic color palettes", + "negative_prompt": "vibrant colors, joyful moments, crowded scenes" + }, + { + "name": "Metropolis", + "prompt": "{prompt} in the style of 'Metropolis' directed by Fritz Lang, art deco cityscape, dystopian skyscrapers, dramatic lighting, geometric shapes, industrial machinery, futuristic technology, angular architecture, robot-like figures, dark alleys, human-machine contrast, monochrome color scheme", + "negative_prompt": "organic nature, natural landscapes, bright colors, rural settings" + }, + { + "name": "Moulin Rouge", + "prompt": "{prompt} in the style of 'Moulin Rouge' directed by Baz Luhrmann, vibrant colors, opulent costumes, dramatic lighting, dramatic close-ups, elaborate set design, passionate performances, can-can dancers, extravagant parties, bohemian atmosphere, glittering nightlife", + "negative_prompt": "modern technology, futuristic elements, quiet, serene, daytime" + }, + { + "name": "Neon Demon", + "prompt": "{prompt} in the style of 'Neon Demon' directed by Nicolas Winding Refn, intense neon lighting, bold color contrasts, high-fashion models, eerie reflections, striking makeup, dramatic shadows, surreal settings, fashion runway vibes, glamorous decadence, avant-garde fashion, bold visual compositions, hypnotic visuals, neon-soaked atmosphere, edgy and unsettling beauty, seductive but disturbing", + "negative_prompt": "gritty realism, natural landscapes, daytime, cheerful aesthetics, rural" + }, + { + "name": "Parasite", + "prompt": "{prompt} in the style of 'Parasite' directed by Bong Joon Ho, social contrast, architectural symbolism, hidden secrets, class division, staircases, underground living, rain-soaked streets, dark humor, suspenseful lighting, glass architecture, tension, rich-poor dynamics, unique camera angles", + "negative_prompt": "bright and sunny setting" + }, + { + "name": "Sin City", + "prompt": "{prompt} in the style of 'Sin City' directed by Robert Rodriguez and Frank Miller, stark black and white visuals with splashes of red, high contrast lighting, dramatic shadows, rain-soaked streets, gritty urban landscapes, film noir atmosphere, intense violence, stylized graphic novel adaptation, femme fatales, morally ambiguous characters, iconic silhouettes, urban decay, neon signs, graphic violence, dark and brooding tone", + "negative_prompt": "bright colors, naturalistic lighting, vibrant, cheerful atmosphere" + }, + { + "name": "Spider-Man - Into the Spider-Verse", + "prompt": "{prompt} in style of 'Spider-Man Into the Spider-Verse', hyper-realistic comic-book style, dynamic camera angles, vibrant color palette, exaggerated character expressions, urban landscapes, graffiti-inspired visuals, onomatopoeic comic book sound effects, motion blur", + "negative_prompt": "monotone color schemes" + }, + { + "name": "Spirited Away", + "prompt": "{prompt} in the style of 'Spirited Away' directed by Hayao Miyazaki, Studio Ghibli, ethereal landscapes, mysterious creatures, enchanting spirits, otherworldly architecture, lantern-lit scenes, foggy forests, magical transformations, fantastical cuisine, dreamlike journeys, spiritual encounters, whimsical color palettes, surreal atmosphere", + "negative_prompt": "dark, gloomy, violence, modern technology, urban environments" + }, + { + "name": "Tron Legacy", + "prompt": "{prompt} in style of 'Tron Legacy' directed by Joseph Kosinski, vibrant neon color palette, neon-lit cityscape, sleek and luminous suits with distinctive lighting stripes, digital circuitry patterns, glowing helmets, stark contrast of light and dark, electrifying grid, holographic displays, futuristic motorcycles, cybernetic landscapes, pulsating light trails, minimalist design aesthetic, virtual reality and cyberpunk aesthetics", + "negative_prompt": "natural color palettes, organic textures, traditional architecture" + }, + { + "name": "V for Vendetta", + "prompt": "{prompt} in style of 'V for Vendetta' directed by James McTeigue, Guy Fawkes mask, dystopian, shadowy, oppressive government regime, urban warfare, government propaganda, totalitarian London, fireworks, mask symbolism, rain-soaked streets, political resistance, misty alleyways", + "negative_prompt": "daylight" + }, + { + "name": "What Dreams May Come", + "prompt": "{prompt} in the style of 'What Dreams May Come' directed by Vincent Ward, ethereal landscapes, vibrant and surreal colors, heavenly vistas, dreamlike transitions, fantastical architecture, otherworldly flora and fauna, celestial beings, emotional depth, serene and tranquil, evocative use of light and shadow, abstract and surreal compositions, spiritual symbolism, ethereal atmospheres, surreal reflections, transcendent beauty, metaphysical elements", + "negative_prompt": "dark, grim, harsh colors, disturbing, nightmarish" + } +] diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/.gitignore b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/.gitignore new file mode 100644 index 00000000..82f92755 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log 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model.model_options["transformer_options"] = to + else: + to["patches_replace"]["dit"][key].add(pulid_patch, **patch_kwargs) + +def pulid_patch(img, pulid_model=None, ca_idx=None, weight=1.0, embedding=None, mask=None, transformer_options={}): + pulid_img = weight * pulid_model.model.pulid_ca[ca_idx].to(img.device)(embedding, img) + if mask is not None: + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + latent_image_shape = pulid_temp_attrs.get("latent_image_shape", None) + if latent_image_shape is not None: + bs, c, h, w = latent_image_shape + mask = comfy.sampler_helpers.prepare_mask(mask, (bs, c, h, w), img.device) + patch_size = transformer_options[PatchKeys.running_net_model].patch_size + mask = comfy.ldm.common_dit.pad_to_patch_size(mask, (patch_size, patch_size)) + mask = rearrange(mask, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + # (b, seq_len, _) =>(b, seq_len, pulid.dim) + mask = mask[..., 0].unsqueeze(-1).repeat(1, 1, pulid_img.shape[-1]).to(dtype=pulid_img.dtype) + del patch_size, latent_image_shape + + pulid_img = pulid_img * mask + + del mask, pulid_temp_attrs + + return pulid_img + +class DitDoubleBlockReplace: + def __init__(self, callback, **kwargs): + self.callback = [callback] + self.kwargs = [kwargs] + + def add(self, callback, **kwargs): + self.callback.append(callback) + self.kwargs.append(kwargs) + + for key, value in kwargs.items(): + setattr(self, key, value) + + def __call__(self, input_args, extra_options): + transformer_options = extra_options["transformer_options"] + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + sigma = pulid_temp_attrs["timesteps"][0].detach().cpu().item() + out = extra_options["original_block"](input_args) + img = out['img'] + temp_img = img + for i, callback in enumerate(self.callback): + if self.kwargs[i]["sigma_start"] >= sigma >= self.kwargs[i]["sigma_end"]: + img = img + callback(temp_img, + pulid_model=self.kwargs[i]['pulid_model'], + ca_idx=self.kwargs[i]['ca_idx'], + weight=self.kwargs[i]['weight'], + embedding=self.kwargs[i]['embedding'], + mask = self.kwargs[i]['mask'], + transformer_options=transformer_options + ) + out['img'] = img + + del temp_img, pulid_temp_attrs, sigma, transformer_options, img + + return out + + +class LastDitDoubleBlockReplace(DitDoubleBlockReplace): + def __call__(self, input_args, extra_options): + out = super().__call__(input_args, extra_options) + transformer_options = extra_options["transformer_options"] + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + pulid_temp_attrs["double_blocks_txt"] = out['txt'] + return out + +class DitSingleBlockReplace: + def __init__(self, callback, **kwargs): + self.callback = [callback] + self.kwargs = [kwargs] + + def add(self, callback, **kwargs): + self.callback.append(callback) + self.kwargs.append(kwargs) + + for key, value in kwargs.items(): + setattr(self, key, value) + + def __call__(self, input_args, extra_options): + transformer_options = extra_options["transformer_options"] + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + + out = extra_options["original_block"](input_args) + + sigma = pulid_temp_attrs["timesteps"][0].detach().cpu().item() + img = out['img'] + txt = pulid_temp_attrs['double_blocks_txt'] + real_img, txt = img[:, txt.shape[1]:, ...], img[:, :txt.shape[1], ...] + temp_img = real_img + for i, callback in enumerate(self.callback): + if self.kwargs[i]["sigma_start"] >= sigma >= self.kwargs[i]["sigma_end"]: + real_img = real_img + callback(temp_img, + pulid_model=self.kwargs[i]['pulid_model'], + ca_idx=self.kwargs[i]['ca_idx'], + weight=self.kwargs[i]['weight'], + embedding=self.kwargs[i]['embedding'], + mask=self.kwargs[i]['mask'], + transformer_options = transformer_options, + ) + + img = torch.cat((txt, real_img), 1) + + out['img'] = img + + del temp_img, pulid_temp_attrs, sigma, transformer_options, real_img, img + + return out + +def pulid_forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, +) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + transformer_options[PatchKeys.running_net_model] = self + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + + for i, block in enumerate(self.double_blocks): + # 0 -> 18 + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask + }, + { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + # 0 -> 37 + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask + }, + { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1]:, ...] += add + + img = img[:, txt.shape[1]:, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + + del transformer_options[PatchKeys.running_net_model] + + return img + + +def pulid_enter(img, img_ids, txt, txt_ids, timesteps, y, guidance, control, attn_mask, transformer_options): + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + pulid_temp_attrs['timesteps'] = timesteps + return img, img_ids, txt, txt_ids, timesteps, y, guidance, control, attn_mask + + +def pulid_patch_double_blocks_after(img, txt, transformer_options): + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + pulid_temp_attrs['double_blocks_txt'] = txt + return img, txt diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/README.md b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/README.md new file mode 100644 index 00000000..79beea95 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/README.md @@ -0,0 +1,155 @@ +[δΈ­ζ–‡ζ–‡ζ‘£](README_CN.md) + +- Solved [ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) model pollution problem. +- **πŸ†• Commercial-friendly FaceNet implementation** - Alternative to InsightFace for commercial usage without licensing restrictions. +- Supported use with `TeaCache` (Need use with [ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)). +- Supported use with [Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed), supported by `Comfy-WaveSpeed` in [commit-36ba3c8](https://github.com/chengzeyi/Comfy-WaveSpeed/commit/36ba3c8b74735d4521828507a4bf323df1a9a9d0). +- Supported simple use with `First Block Cache` (Can use with [ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)). + +Must uninstall or disable `ComfyUI-PuLID-Flux` and other PuLID-Flux nodes before install this plugin. Due to certain reasons, I used the same node's name `ApplyPulidFlux`. + +Need upgrade ComfyUI Version>=0.3.7 + +## 🏒 Commercial Usage Ready + +This plugin now supports **FaceNet-based face analysis** as an alternative to InsightFace, making it suitable for commercial applications: + +- **No ArcFace licensing restrictions** - FaceNet is freely available for commercial use +- **Compatible API** - Drop-in replacement for InsightFace workflows +- **Production ready** - Reliable face detection and embedding generation + +Simply use `PulidFluxFaceNetLoader` instead of `PulidFluxInsightFaceLoader` in your workflows. + +## Update logs +### 2025.02.19 +- Fix: when selecting a face from multiple faces as a reference, embeddings and alignment features maybe not from the same face. +### 2025.02.18 +- Supported selecting a face from multiple faces as a reference. [Example workflow](examples/PuLID_select_ref_face.png). +### 2025.01.27 +- Changed the model path of facexlib to `ComfyUI/models/facexlib/`. +- When automatically downloading, modify the path of Antelope v2 model to `ComfyUI/models/insightface/models/antelopev2/`. +- Changed the model path of EVA_CLIP_L_14_336 to `ComfyUI/models/clip/`. + +## Preview (Image with WorkFlow) +![save api extended](examples/PuLID_with_speedup.png) +![save api extended](examples/PuLID_with_attn_mask.png) +![save api extended](examples/PuLID_select_ref_face.png) + +## Install + +- Manual +```shell + cd custom_nodes + git clone https://github.com/lldacing/ComfyUI_PuLID_Flux_ll.git + cd ComfyUI_PuLID_Flux_ll + pip install -r requirements.txt + # restart ComfyUI +``` + +Tips: + +- If you use `ComfyUI_windows_portable` and encounter the following error, please see https://github.com/deepinsight/insightface/issues/2576 +``` +insightface/thirdparty/face3d/mesh/cython/mesh_core_cython.cpp(36): fatal error C1083: ζ— ζ³•ζ‰“εΌ€εŒ…ζ‹¬ζ–‡δ»Ά: "Python.h": No such file or directory + error: command 'd:\\installed\\Microsoft Visual Studio\\2022\\BuildTools\\VC\\Tools\\MSVC\\14.42.34433\\bin\\HostX86\\x64\\cl.exe' failed with exit code 2 + [end of output] + + note: This error originates from a subprocess, and is likely not a problem with pip. + ERROR: Failed building wheel for insightface +Failed to build insightface +``` + +## Models +### Available Flux models +- 32bit/16bit (~22GB VRAM): [model](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors) +- 8bit gguf (~12GB VRAM): [model](https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q8_0.gguf), [encoder](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/blob/main/t5-v1_1-xxl-encoder-Q8_0.gguf) +- 8 bit FP8 e5m2 (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e5m2.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- 8 bit FP8 e4m3fn (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e4m3fn.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- Clip and VAE (for all models): [clip](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors), [vae](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors) + +#### For GGUF models you will need to install [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) + +### PuLID models +- Download [PuLID-Flux](https://huggingface.co/guozinan/PuLID/resolve/main/pulid_flux_v0.9.1.safetensors?download=true) => `ComfyUI/models/pulid/`. +- (Support auto-download) Download [EVA02-CLIP-L-14-336](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt?download=true) => `ComfyUI/models/clip/`. + +### Face Analysis Models + +**For Commercial Use (Recommended):** +- **FaceNet** - No additional downloads required! Uses `facenet-pytorch` with pre-trained VGGFace2 models + - βœ… Commercial license friendly + - βœ… No external model downloads + - βœ… Automatic model loading + - Use with `PulidFluxFaceNetLoader` node + +**For Non-Commercial/Research Use:** +- (Support auto-download) Download all models like `*.onnx` from [AntelopeV2](https://huggingface.co/MonsterMMORPG/tools/tree/main) => `ComfyUI/models/insightface/models/antelopev2/`. +- (Support auto-download) Download [parsing_bisenet](https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth), [parsing_parsenet](https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth) and [Resnet50](https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth) => `ComfyUI/models/facexlib/`. + - Use with `PulidFluxInsightFaceLoader` node + +## Nodes +- PulidFluxModelLoader + - See chapter [PuLID models](#pulid-models) +- **PulidFluxFaceNetLoader** πŸ†• + - **Commercial-friendly face analysis** using FaceNet + - No additional model downloads required + - Compatible with all PuLID workflows + - Supports CPU and CUDA execution +- PulidFluxInsightFaceLoader + - Traditional InsightFace-based analysis + - See chapter [PuLID models](#pulid-models) +- PulidFluxEvaClipLoader + - See chapter [PuLID models](#pulid-models) +- ApplyPulidFlux + - Solved the model pollution problem of the original plugin ComfyUI-PuLID-Flux + - **Works with both FaceNet and InsightFace** - seamless compatibility + - `attn_mask` ~~may not work correctly (I have no idea how to apply it, I have tried multiple methods and the results have been not satisfactory)~~ works now. + - If you want use with [TeaCache](https://github.com/ali-vilab/TeaCache), must put it before node [`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll). + - If you want use with [Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed), must put it before node `ApplyFBCacheOnModel`. +- FixPulidFluxPatch (Deprecated) + - If you want use with [TeaCache](https://github.com/ali-vilab/TeaCache), must link it after node `ApplyPulidFlux`, and link node [`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll) after it. +- PulidFluxOptions + - `input_faces_order` - Sorting rule for detected bboxes. + - `left-right`: Sort the left boundary of bbox by column from left to right. + - `right-left`: Reverse order of `left-right` (Sort the left boundary of bbox by column from right to left). + - `top-bottom`: Sort the top boundary of bbox by row from top to bottom. + - `bottom-top`: Reverse order of `top-bottom` (Sort the top boundary of bbox by row from bottom to top). + - `small-large`: Sort the area of bbox from small to large. + - `large-small`: Sort the area of bbox from large to small. + - `input_faces_index` - The target index of the sorted bboxes. + - `input_faces_align_mode` - Choose the detection method for aligning facial features. + - `0`: Old version method, When there is a face in an image, the selected facial embedding amount and alignment features maybe not consistent. + - `1`: Keep the selected facial embedding amount and alignment features consistent. + - There is a slight difference between the two mode, with the `align_face` value of `1` resulting smaller area than the `embed_face` value of `0`. +- PulidFluxFaceDetector + - Can check the facial features applied in `ApplyPulidFlux`. + - **Works with both FaceNet and InsightFace** backends + - When `input_faces_align_mode = 0`, the `embed_face` and `align_face` should be the same face, but they are generated by different detectors, and the number detected may be not consistent, so they may be not the same face. + - When `input_faces_align_mode = 1`, the `embed_face` and `align_face` are always the same face, they are generated by same detectors. + - `face_bbox_image` - Draw the detected facial bounding box (the result of the `embed_face`'s detector). + +## Usage Examples + +### Commercial Workflow (FaceNet) +``` +PulidFluxFaceNetLoader -> ApplyPulidFlux +``` + +### Research Workflow (InsightFace) +``` +PulidFluxInsightFaceLoader -> ApplyPulidFlux +``` + +Both workflows produce compatible results and can be used interchangeably. + +## Thanks + +[ToTheBeginning/PuLID](https://github.com/ToTheBeginning/PuLID) + +[ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) + +[TeaCache](https://github.com/ali-vilab/TeaCache) + +[Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed) + +[facenet-pytorch](https://github.com/timesler/facenet-pytorch) - For commercial-friendly face recognition \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/README_CN.md b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/README_CN.md new file mode 100644 index 00000000..dc43cc25 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/README_CN.md @@ -0,0 +1,151 @@ +[English](README.md) + +- 解决插仢 [ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) ε­˜εœ¨ηš„ζ¨‘εž‹ζ±‘ζŸ“ι—ι’˜γ€‚ +- **πŸ†• ζ”―ζŒε•†δΈšε‹ε₯½ηš„FaceNetεžηŽ°** - 作为InsightFaceηš„ζ›Ώδ»£ζ–Ήζ‘ˆοΌŒη”¨δΊŽε•†δΈšη”¨ι€”οΌŒζ— θΈε―θ―ι™εˆΆγ€‚ +- ζ”―ζŒδ½Ώη”¨[TeaCache](https://github.com/ali-vilab/TeaCache)εŠ ι€ŸοΌˆ`TeaCache`εŠ ι€Ÿιœ€θ¦ι…εˆ[ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)使用）。 +- ζ”―ζŒδ½Ώη”¨[Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed)εŠ ι€Ÿ, Comfy-WaveSpeed在[提亀θ°ε½•-36ba3c8](https://github.com/chengzeyi/Comfy-WaveSpeed/commit/36ba3c8b74735d4521828507a4bf323df1a9a9d0)δΈ­ζδΎ›ζ”―ζŒγ€‚ +- ζ”―ζŒδ½Ώη”¨η€ε•ηš„`First Block Cache`εŠ ι€ŸοΌˆε―δ»₯ι…εˆ[ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)使用）。 + +在ε‰θ£…ζ­€ζ’δ»ΆδΉ‹ε‰οΌŒεΏ…ι‘»εΈθ½½ζˆ–η¦η”¨`ComfyUI-PuLID-Flux`ε’Œε…Άδ»–PuLID FluxθŠ‚η‚Ή, ε› δΈΊη”±δΊŽζŸδΊ›εŽŸε› οΌŒζˆ‘δ½Ώη”¨δΊ†εŒζ ·ηš„θŠ‚η‚Ήε`ApplyPulidFlux` + +ComfyUIδΈ»δ½“η‰ˆζœ¬ιœ€θ¦>=0.3.7 + +## 🏒 ε•†δΈšη”¨ι€”ζ”―ζŒ + +θ―₯ζ’δ»ΆηŽ°ε·²ζ”―ζŒεŸΊδΊŽ**FaceNetηš„δΊΊθ„Έεˆ†ζž**作为InsightFaceηš„ζ›Ώδ»£ζ–Ήζ‘ˆοΌŒδ½Ώε…Άι€‚η”¨δΊŽε•†δΈšεΊ”η”¨οΌš + +- **ζ— ArcFaceθΈε―θ―ι™εˆΆ** - FaceNet可θ‡ͺη”±η”¨δΊŽε•†δΈšη”¨ι€” +- **ε…ΌεΉAPI** - 可直ζŽ₯替捒InsightFaceε·₯作桁 +- **η”ŸδΊ§ε°±η»ͺ** - ε―ι ηš„δΊΊθ„Έζ£€ζ΅‹ε’Œε΅Œε…₯η”Ÿζˆ + +εͺιœ€εœ¨ε·₯δ½œζ΅δΈ­δ½Ώη”¨`PulidFluxFaceNetLoader`替代`PulidFluxInsightFaceLoader`即可。 + +## ζ›΄ζ–°ζ—₯εΏ— +### 2025.02.19 +- θ§£ε†³ε€šεΌ δΊΊθ„Έζ—Άι€‰ζ‹©ηš„δΊΊθ„Έε΅Œε…₯ι‡ε’Œε―Ήι½η‰ΉεΎδΈζ˜―εŒδΈ€δΈͺδΊΊθ„Έηš„ι—ι’˜γ€‚ +### 2025.02.18 +- ζ”―ζŒδ»Žε«ζœ‰ε€šεΌ θ„Έηš„ε›Ύη‰‡δΈ­ι€‰ζ‹©δΈ€εΌ θ„Έδ½œδΈΊε‚θ€ƒγ€‚[η€ΊδΎ‹ε·₯作桁](examples/PuLID_select_ref_face.png). +### 2025.01.27 +- δΏζ”Ή facexlib ηš„ζ¨‘εž‹θ·―εΎ„δΈΊ `ComfyUI/models/facexlib/`. +- θ‡ͺεŠ¨δΈ‹θ½½ζ—Ά δΏζ”Ή Antelopev2 ζ¨‘εž‹ηš„θ·―εΎ„δΈΊ `ComfyUI/models/insightface/models/antelopev2/`. +- δΏζ”Ή EVA_CLIP_L_14_336 ηš„ζ¨‘εž‹θ·―εΎ„δΈΊ `ComfyUI/models/clip/`. + +## ι’„θ§ˆ (图片含ε·₯作桁) +![save api extended](examples/PuLID_with_speedup.png) +![save api extended](examples/PuLID_with_attn_mask.png) +![save api extended](examples/PuLID_select_ref_face.png) + +## ε‰θ£… + +- ζ‰‹εŠ¨ +```shell + cd custom_nodes + git clone https://github.com/lldacing/ComfyUI_PuLID_Flux_ll.git + cd ComfyUI_PuLID_Flux_ll + pip install -r requirements.txt + # 重启 ComfyUI +``` +ε‰θ£…ι—ι’˜: + +- ε¦‚ζžœδ½Ώη”¨`ComfyUI_windows_portable`εΉΆι‡εˆ°δ»₯δΈ‹ι”™θ――, θ―·ζŸ₯ηœ‹ https://github.com/deepinsight/insightface/issues/2576 +``` +insightface/thirdparty/face3d/mesh/cython/mesh_core_cython.cpp(36): fatal error C1083: ζ— ζ³•ζ‰“εΌ€εŒ…ζ‹¬ζ–‡δ»Ά: "Python.h": No such file or directory + error: command 'd:\\installed\\Microsoft Visual Studio\\2022\\BuildTools\\VC\\Tools\\MSVC\\14.42.34433\\bin\\HostX86\\x64\\cl.exe' failed with exit code 2 + [end of output] + + note: This error originates from a subprocess, and is likely not a problem with pip. + ERROR: Failed building wheel for insightface +Failed to build insightface +``` + +## ζ¨‘εž‹ +### ε―η”¨ηš„ Flux ζ¨‘εž‹ +- 32bit/16bit (~22GB VRAM): [model](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors) +- 8bit gguf (~12GB VRAM): [model](https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q8_0.gguf), [encoder](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/blob/main/t5-v1_1-xxl-encoder-Q8_0.gguf) +- 8 bit FP8 e5m2 (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e5m2.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- 8 bit FP8 e4m3fn (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e4m3fn.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- Clip and VAE (for all models): [clip](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors), [vae](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors) + +#### θ‹₯使用 GGUF ιœ€θ¦ε‰θ£… [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) + +### PuLID ζ¨‘εž‹ +- δΈ‹θ½½ [PuLID-Flux](https://huggingface.co/guozinan/PuLID/resolve/main/pulid_flux_v0.9.1.safetensors?download=true) 到η›ε½• `ComfyUI/models/pulid/` +- οΌˆζ”―ζŒθ‡ͺεŠ¨δΈ‹θ½½οΌ‰δΈ‹θ½½ [EVA02-CLIP-L-14-336](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt?download=true) 到η›ε½• `ComfyUI/models/clip/` + +### δΊΊθ„Έεˆ†ζžζ¨‘εž‹ + +**η”¨δΊŽε•†δΈšη”¨ι€”οΌˆζŽ¨θοΌ‰οΌš** +- **FaceNet** - ζ— ιœ€ι’ε€–δΈ‹θ½½οΌδ½Ώη”¨εΈ¦ζœ‰ι’„θ­η»ƒVGGFace2ζ¨‘εž‹ηš„`facenet-pytorch` + - βœ… ε•†δΈšθΈε―证友ε₯½ + - βœ… ζ— ιœ€ε€–ιƒ¨ζ¨‘εž‹δΈ‹θ½½ + - βœ… θ‡ͺεŠ¨ζ¨‘εž‹εŠ θ½½ + - ι…εˆ`PulidFluxFaceNetLoader`θŠ‚η‚Ήδ½Ώη”¨ + +**η”¨δΊŽιžε•†δΈš/η ”η©Άη”¨ι€”οΌš** +- οΌˆζ”―ζŒθ‡ͺεŠ¨δΈ‹θ½½οΌ‰δ»Ž [AntelopeV2](https://huggingface.co/MonsterMMORPG/tools/tree/main) δΈ‹θ½½ζ‰€ζœ‰`*.onnx`ζ¨‘εž‹ζ–‡δ»Άεˆ°η›ε½• `ComfyUI/models/insightface/models/antelopev2/`. +- οΌˆζ”―ζŒθ‡ͺεŠ¨δΈ‹θ½½οΌ‰δΈ‹θ½½ [parsing_bisenet](https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth), [parsing_parsenet](https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth) and [Resnet50](https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth) 到η›ε½• `ComfyUI/models/facexlib/`. + - ι…εˆ`PulidFluxInsightFaceLoader`θŠ‚η‚Ήδ½Ώη”¨ + +## θŠ‚η‚Ή +- PulidFluxModelLoader +- **PulidFluxFaceNetLoader** πŸ†• + - **基于FaceNetηš„ε•†δΈšε‹ε₯½δΊΊθ„Έεˆ†ζž** + - ζ— ιœ€ι’ε€–ζ¨‘εž‹δΈ‹θ½½ + - ε…ΌεΉζ‰€ζœ‰PuLIDε·₯作桁 + - ζ”―ζŒCPUε’ŒCUDAζ‰§θ‘Œ +- PulidFluxInsightFaceLoader + - δΌ η»Ÿηš„εŸΊδΊŽInsightFaceηš„εˆ†ζž +- PulidFluxEvaClipLoader +- ApplyPulidFlux + - θ§£ε†³δΊ†εŽŸζ’δ»ΆδΈ­ζ¨‘εž‹ζ±‘ζŸ“ηš„ι—ι’˜ + - **εŒζ—Άζ”―ζŒFaceNetε’ŒInsightFace** - ζ— ηΌε…ΌεΉ + - `attn_mask`~~可能不能正η‘ε·₯作， ε› δΈΊζˆ‘δΈηŸ₯道如何εžηŽ°εƒοΌŒ ε°θ―•δΊ†ε€šη§ζ–ΉεΌζ•ˆζžœιƒ½ζœͺθƒ½θΎΎεˆ°ι’„ζœŸ~~，可δ»₯ζ­£εΈΈε·₯δ½œδΊ†γ€‚ + - 使用 [TeaCache](https://github.com/ali-vilab/TeaCache)εŠ ι€Ÿ, εΏ…ι‘»εŠ εœ¨[`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll)之前. + - 使用 [Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed)εŠ ι€Ÿ, εΏ…ι‘»εŠ εœ¨[`ApplyFBCacheOnModel`](https://github.com/lldacing/ComfyUI_Patches_ll)之前. +- FixPulidFluxPatch (已弃用) + - ε¦‚ζžœζƒ³δ½Ώη”¨ [TeaCache](https://github.com/ali-vilab/TeaCache)εŠ ι€Ÿ, εΏ…ι‘»εŠ εœ¨ `ApplyPulidFlux` θŠ‚η‚ΉδΉ‹εŽ, 幢在后青连ζŽ₯θŠ‚η‚Ή [`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll). +- PulidFluxOptions + - `input_faces_order` - ε―Ήζ£€ζ΅‹εˆ°ηš„θ„Έιƒ¨θΎΉη•Œζ‘†ηš„ζŽ’εΊθ§„εˆ™γ€‚ + - `left-right`: ζŒ‰εˆ—δ»Žε·¦εˆ°ε³ε―Ήbboxηš„ε·¦θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊγ€‚ + - `right-left`: `left-right`ηš„ε€’εΊοΌˆζŒ‰εˆ—δ»Žε³εˆ°ε·¦ε―Ήbboxηš„ε·¦θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊοΌ‰γ€‚ + - `top-bottom`: ζŒ‰θ‘Œδ»ŽδΈŠεˆ°δΈ‹ε―Ήbboxηš„ι‘Άιƒ¨θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊγ€‚ + - `bottom-top`: `top-bottom`ηš„ε€’εΊοΌˆζŒ‰θ‘Œδ»ŽδΈ‹εˆ°δΈŠε―Ήbboxηš„ι‘Άιƒ¨θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊοΌ‰γ€‚ + - `small-large`: ζŒ‰bboxηš„ι’η§―δ»Žε°εˆ°ε€§ζŽ’εΊγ€‚ + - `large-small`: ζŒ‰bboxηš„ι’η§―δ»Žε€§εˆ°ε°ζŽ’εΊγ€‚ + - `input_faces_index` - δ»ŽζŽ’εΊεŽηš„bboxι€‰ε–ηš„η΄’εΌ•ε·γ€‚ + - `input_faces_align_mode` - ι€‰ζ‹©ε―Ήι½θ„Έιƒ¨η‰ΉεΎηš„ζ£€ζ΅‹ζ–ΉεΌγ€‚ + - `0`: ζ—§η‰ˆζœ¬ζ–ΉεΌοΌŒδΈ€εΌ ε›Ύη‰‡δΈ­ζœ‰εΌ θ„Έζ—Άι€‰ζ‹©ηš„θ„Έιƒ¨ε΅Œε…₯ι‡ε’Œε―Ήι½η‰ΉεΎε―θƒ½δΈδΈ€θ‡΄γ€‚ + - `1`: δΏζŒι€‰ζ‹©ηš„θ„Έιƒ¨ε΅Œε…₯ι‡ε’Œε―Ήι½η‰ΉεΎδΈ€θ‡΄γ€‚ + - δΈ€η§ε‡Ίε›Ύζœ‰η»†εΎε·εˆ«οΌŒε€Ό`1`ηš„`align_face`η»“ζžœε›Ύζ―”`0`ηš„`embed_face`θŒƒε›΄ε°δΈ€η‚Ήγ€‚ +- PulidFluxFaceDetector + - 用ζ₯ζ£€ζŸ₯在`ApplyPulidFlux`εžι™…δ½Ώη”¨ηš„ι’部特征。 + - **εŒζ—Άζ”―ζŒFaceNetε’ŒInsightFace**后端 + - `input_faces_align_mode = 0`ζ—ΆοΌŒ`embed_face` ε’Œ `align_face` 理θΊδΈŠεΊ”θ―₯ζ˜―εŒδΈ€εΌ θ„ΈοΌŒδ½†εƒδ»¬η”±δΈεŒηš„ζ£€ζ΅‹ε™¨δΊ§η”ŸοΌŒε―θƒ½ζ£€ζ΅‹εˆ°ηš„ζ•°ι‡δΈδΈ€θ‡΄οΌŒε› ζ­€δΈ€εΌ ε›Ύε―θƒ½δΈζ˜―εŒδΈ€εΌ θ„Έγ€‚ + - `input_faces_align_mode = 1`ζ—ΆοΌŒ`embed_face` ε’Œ `align_face` η”±η›ΈεŒηš„ζ£€ζ΅‹ε™¨δΊ§η”ŸοΌŒδΈ€εΌ ε›Ύε§‹η»ˆζ˜―εŒδΈ€εΌ θ„Έγ€‚ + - `face_bbox_image` - η”»ε‡Ίζ£€ζ΅‹εˆ°ηš„θ„Έιƒ¨θΎΉη•Œζ‘†οΌˆ`embed_face`ηš„ζ£€ζ΅‹ε™¨η»“ζžœοΌ‰γ€‚ + +## 使用瀺例 + +### ε•†δΈšε·₯作桁 (FaceNet) +``` +PulidFluxFaceNetLoader -> ApplyPulidFlux +``` + +### η ”η©Άε·₯作桁 (InsightFace) +``` +PulidFluxInsightFaceLoader -> ApplyPulidFlux +``` + +一种ε·₯δ½œζ΅δΊ§η”Ÿε…ΌεΉηš„η»“ζžœοΌŒε―δ»₯互捒使用。 + +## ζ„Ÿθ°’ + +[ToTheBeginning/PuLID](https://github.com/ToTheBeginning/PuLID) + +[ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) + +[TeaCache](https://github.com/ali-vilab/TeaCache) + +[Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed) + +[facenet-pytorch](https://github.com/timesler/facenet-pytorch) - η”¨δΊŽε•†δΈšε‹ε₯½ηš„δΊΊθ„Έθ―†εˆ« \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/__init__.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/__init__.py new file mode 100644 index 00000000..7ca69bca --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/__init__.py @@ -0,0 +1,3 @@ +from .pulidflux import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/encoders_flux.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/encoders_flux.py new file mode 100644 index 00000000..c4c87bd0 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/encoders_flux.py @@ -0,0 +1,213 @@ +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttentionCA(nn.Module): + def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents, mask=None): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + k, v = self.to_kv(x).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + # if mask is not None: + # not sure + # weight.shape (bs, n_heads, seq_len, seq_len) + # mask.shape (bs, seq_len, _) -> (bs, 1, 1, seq_len) + # mask = mask[:,:, :1].view(b, 1, 1, -1) + # weight = weight.masked_fill(mask == 0, float('-inf')) + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class IDFormer(nn.Module): + """ + - perceiver resampler like arch (compared with previous MLP-like arch) + - we concat id embedding (generated by arcface) and query tokens as latents + - latents will attend each other and interact with vit features through cross-attention + - vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two + IDFormer layers + """ + def __init__( + self, + dim=1024, + depth=10, + dim_head=64, + heads=16, + num_id_token=5, + num_queries=32, + output_dim=2048, + ff_mult=4, + ): + super().__init__() + + self.num_id_token = num_id_token + self.dim = dim + self.num_queries = num_queries + assert depth % 5 == 0 + self.depth = depth // 5 + scale = dim ** -0.5 + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale) + self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim)) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + for i in range(5): + setattr( + self, + f'mapping_{i}', + nn.Sequential( + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim), + ), + ) + + self.id_embedding_mapping = nn.Sequential( + nn.Linear(1280, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim * num_id_token), + ) + + def forward(self, x, y): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.id_embedding_mapping(x) + x = x.reshape(-1, self.num_id_token, self.dim) + + latents = torch.cat((latents, x), dim=1) + + for i in range(5): + vit_feature = getattr(self, f'mapping_{i}')(y[i]) + ctx_feature = torch.cat((x, vit_feature), dim=1) + for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]: + latents = attn(ctx_feature, latents) + latents + latents = ff(latents) + latents + + latents = latents[:, :self.num_queries] + latents = latents @ self.proj_out + return latents diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/__init__.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/__init__.py new file mode 100644 index 00000000..fa2d014b --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/__init__.py @@ -0,0 +1,11 @@ +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_transforms +from .factory import list_models, add_model_config, get_model_config, load_checkpoint +from .loss import ClipLoss +from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg,\ + convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\ + get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +from .tokenizer import SimpleTokenizer, tokenize +from .transform import image_transform \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/bpe_simple_vocab_16e6.txt.gz b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 00000000..7b5088a5 Binary files /dev/null and b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/bpe_simple_vocab_16e6.txt.gz differ diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/constants.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/constants.py new file mode 100644 index 00000000..a670bb3f --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/constants.py @@ -0,0 +1,2 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/eva_vit_model.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/eva_vit_model.py new file mode 100644 index 00000000..51db88cf --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/eva_vit_model.py @@ -0,0 +1,548 @@ +# -------------------------------------------------------- +# Adapted from https://github.com/microsoft/unilm/tree/master/beit +# -------------------------------------------------------- +import math +import os +from functools import partial +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +except: + from timm.layers import drop_path, to_2tuple, trunc_normal_ + +from .transformer import PatchDropout +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers + import xformers.ops as xops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + drop=0., + subln=False, + + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.ffn_ln(x) + + x = self.fc2(x) + x = self.drop(x) + return x + +class SwiGLU(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., + norm_layer=nn.LayerNorm, subln=False): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.subln = subln + if self.subln: + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + else: + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() + # self.proj = nn.Linear(all_head_dim, all_head_dim) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + self.rope = rope + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + B, N, C = x.shape + if self.subln: + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + else: + + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C + q, k, v = qkv[0], qkv[1], qkv[2] + + if self.rope: + # slightly fast impl + q_t = q[:, :, 1:, :] + ro_q_t = self.rope(q_t) + q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) + + k_t = k[:, :, 1:, :] + ro_k_t = self.rope(k_t) + k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) + + if self.xattn: + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale, + ) + x = x.reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias.type_as(attn) + + if attn_mask is not None: + attn_mask = attn_mask.bool() + attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf")) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False, + subln=False, naiveswiglu=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, + xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + if naiveswiglu: + self.mlp = SwiGLU( + in_features=dim, + hidden_features=mlp_hidden_dim, + subln=subln, + norm_layer=norm_layer, + ) + else: + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + subln=subln, + drop=drop + ) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + self.postnorm = postnorm + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + if self.gamma_1 is None: + if self.postnorm: + x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + if self.postnorm: + x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class EVAVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0., + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False, + use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False, + pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False): + super().__init__() + + if not XFORMERS_IS_AVAILBLE: + xattn = False + + self.image_size = img_size + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + + if rope: + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + self.rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + # patch_dropout=patch_dropout + ) + else: + self.rope = None + + self.naiveswiglu = naiveswiglu + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, + xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu) + for i in range(depth)]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) + + self.apply(self._init_weights) + self.fix_init_weight() + + if isinstance(self.head, nn.Linear): + trunc_normal_(self.head.weight, std=.02) + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.grad_checkpointing = grad_checkpointing + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + if self.naiveswiglu: + rescale(layer.mlp.w3.weight.data, layer_id + 1) + else: + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def get_cast_dtype(self) -> torch.dtype: + return self.blocks[0].mlp.fc2.weight.dtype + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, return_all_features=False, return_hidden=False, shuffle=False): + + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + if shuffle: + idx = torch.randperm(x.shape[1]) + 1 + zero = torch.LongTensor([0, ]) + idx = torch.cat([zero, idx]) + pos_embed = self.pos_embed[:, idx] + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if shuffle: + x = x + pos_embed + elif self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + if os.getenv('RoPE') == '1': + if self.training and not isinstance(self.patch_dropout, nn.Identity): + x, patch_indices_keep = self.patch_dropout(x) + self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep) + else: + self.rope.forward = partial(self.rope.forward, patch_indices_keep=None) + x = self.patch_dropout(x) + else: + x = self.patch_dropout(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + hidden_states = [] + for idx, blk in enumerate(self.blocks): + if (0 < idx <= 20) and (idx % 4 == 0) and return_hidden: + hidden_states.append(x) + if self.grad_checkpointing: + x = checkpoint(blk, x, (rel_pos_bias,)) + else: + x = blk(x, rel_pos_bias=rel_pos_bias) + + if not return_all_features: + x = self.norm(x) + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)), hidden_states + else: + return x[:, 0], hidden_states + return x + + def forward(self, x, return_all_features=False, return_hidden=False, shuffle=False): + if return_all_features: + return self.forward_features(x, return_all_features, return_hidden, shuffle) + x, hidden_states = self.forward_features(x, return_all_features, return_hidden, shuffle) + x = self.head(x) + if return_hidden: + return x, hidden_states + return x diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/factory.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/factory.py new file mode 100644 index 00000000..c717b211 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/factory.py @@ -0,0 +1,520 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +from typing import Optional, Tuple, Union, Dict, Any +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + get_cast_dtype +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model +from .transform import image_transform +from .tokenizer import HFTokenizer, tokenize +from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed + + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, "r", encoding="utf8") as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))) + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name): + config = get_model_config(model_name) + tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize + return tokenizer + + +# loading openai CLIP weights when is_openai=True for training +def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]): + if is_openai: + model = torch.jit.load(checkpoint_path, map_location="cpu").eval() + state_dict = model.state_dict() + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + else: + checkpoint = torch.load(checkpoint_path, map_location=map_location, weights_only=False) + for mk in model_key.split('|'): + if isinstance(checkpoint, dict) and mk in checkpoint: + state_dict = checkpoint[mk] + break + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + for k in skip_list: + if k in list(state_dict.keys()): + logging.info(f"Removing key {k} from pretrained checkpoint") + del state_dict[k] + + if os.getenv('RoPE') == '1': + for k in list(state_dict.keys()): + if 'freqs_cos' in k or 'freqs_sin' in k: + del state_dict[k] + return state_dict + + + +def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True): + state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'): + state_dict['logit_scale'] = state_dict['text.logit_scale'] + del state_dict['text.logit_scale'] + + # resize_clip_pos_embed for CLIP and open CLIP + if 'visual.positional_embedding' in state_dict: + resize_clip_pos_embed(state_dict, model) + # specified to eva_vit_model + elif 'visual.pos_embed' in state_dict: + resize_evaclip_pos_embed(state_dict, model) + + # resize_clip_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}") + return incompatible_keys + +def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if not k.startswith('visual.'): + del state_dict[k] + for k in list(state_dict.keys()): + if k.startswith('visual.'): + new_k = k[7:] + state_dict[new_k] = state_dict[k] + del state_dict[k] + return state_dict + +def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if k.startswith('visual.'): + del state_dict[k] + return state_dict + +def get_pretrained_tag(pretrained_model): + pretrained_model = pretrained_model.lower() + if "laion" in pretrained_model or "open_clip" in pretrained_model: + return "open_clip" + elif "openai" in pretrained_model: + return "clip" + elif "eva" in pretrained_model and "clip" in pretrained_model: + return "eva_clip" + else: + return "other" + +def load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=True, + visual_model=None, + text_model=None, + model_key="model|module|state_dict", + skip_list=[]): + visual_tag = get_pretrained_tag(visual_model) + text_tag = get_pretrained_tag(text_model) + + logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}") + visual_incompatible_keys, text_incompatible_keys = None, None + if visual_checkpoint_path: + if visual_tag == "eva_clip" or visual_tag == "open_clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list) + elif visual_tag == "clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + # resize_clip_pos_embed for CLIP and open CLIP + if 'positional_embedding' in visual_state_dict: + resize_visual_pos_embed(visual_state_dict, model) + # specified to EVA model + elif 'pos_embed' in visual_state_dict: + resize_eva_pos_embed(visual_state_dict, model) + + visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict) + logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}") + logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}") + + if text_checkpoint_path: + if text_tag == "eva_clip" or text_tag == "open_clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list) + elif text_tag == "clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict) + + logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}") + logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}") + + return visual_incompatible_keys, text_incompatible_keys + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + cache_dir: Optional[str] = None, + local_dir: Optional[str] = None, + skip_list: list = [], +): + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + else: + model_cfg = get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if 'rope' in model_cfg.get('vision_cfg', {}): + if model_cfg['vision_cfg']['rope']: + os.environ['RoPE'] = "1" + else: + os.environ['RoPE'] = "0" + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout + + cast_dtype = get_cast_dtype(precision) + custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg']) + + + if custom_clip: + if 'hf_model_name' in model_cfg.get('text_cfg', {}): + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_cfg = {} + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir, local_dir=local_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, + checkpoint_path, + model_key="model|module|state_dict", + strict=False + ) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + else: + visual_checkpoint_path = '' + text_checkpoint_path = '' + + if pretrained_image: + pretrained_visual_model = pretrained_visual_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image) + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + elif pretrained_image_cfg: + visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_image): + visual_checkpoint_path = pretrained_image + else: + logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + + if pretrained_text: + pretrained_text_model = pretrained_text_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text) + if pretrained_image_cfg: + text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_text): + text_checkpoint_path = pretrained_text + else: + logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + + if visual_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).') + if text_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).') + + if visual_checkpoint_path or text_checkpoint_path: + load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=False, + visual_model=pretrained_visual_model, + text_model=pretrained_text_model, + model_key="model|module|state_dict", + skip_list=skip_list + ) + + if "fp16" in precision or "bf16" in precision: + logging.info(f'convert precision to {precision}') + model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16) + + model.to(device=device) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + if jit: + model = torch.jit.script(model) + + return model + + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + local_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + local_dir=local_dir, + skip_list=skip_list, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + + +def create_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + del model + + return preprocess_train, preprocess_val + +def create_model_from_pretrained( + model_name: str, + pretrained: str, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + is_frozen: bool = False, +): + if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained): + raise RuntimeError( + f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.' + f' Use open_clip.list_pretrained() to find one.') + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + cache_dir=cache_dir, + ) + + if is_frozen: + for param in model.parameters(): + param.requires_grad = False + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/hf_configs.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/hf_configs.py new file mode 100644 index 00000000..a8c9b704 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/hf_configs.py @@ -0,0 +1,57 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, + "bert": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + } +} diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/hf_model.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/hf_model.py new file mode 100644 index 00000000..c4b9fd85 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/hf_model.py @@ -0,0 +1,248 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch import TensorType +try: + import transformers + from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + +# utils +def _camel2snake(s): + return re.sub(r'(? TensorType: + # image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(x.device) + # attn_mask = (x != self.config.pad_token_id).long() + # out = self.transformer( + # input_ids=x, + # attention_mask=attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # ) + # pooled_out = self.pooler(out, attn_mask) + + # return self.itm_proj(pooled_out) + + def mask(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None): + if masked_indices is None: + masked_indices = torch.bernoulli(probability_matrix).bool() + + masked_indices[input_ids == self.tokenizer.pad_token_id] = False + masked_indices[input_ids == self.tokenizer.cls_token_id] = False + + if targets is not None: + targets[~masked_indices] = -100 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(input_ids.shape, 0.8)).bool() & masked_indices + input_ids[indices_replaced] = self.tokenizer.mask_token_id + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(input_ids.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(vocab_size, input_ids.shape, dtype=torch.long).to(device) + input_ids[indices_random] = random_words[indices_random] + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + + if targets is not None: + return input_ids, targets + else: + return input_ids + + def forward_mlm(self, input_ids, image_embeds, mlm_probability=0.25): + labels = input_ids.clone() + attn_mask = (input_ids != self.config.pad_token_id).long() + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(input_ids.device) + vocab_size = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["vocab_size"]) + probability_matrix = torch.full(labels.shape, mlm_probability) + input_ids, labels = self.mask(input_ids, vocab_size, input_ids.device, targets=labels, + probability_matrix = probability_matrix) + mlm_output = self.transformer(input_ids, + attention_mask = attn_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + labels = labels, + ) + return mlm_output.loss + # mlm_output = self.transformer(input_ids, + # attention_mask = attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # return_dict = True, + # ).last_hidden_state + # logits = self.mlm_proj(mlm_output) + + # # logits = logits[:, :-1, :].contiguous().view(-1, vocab_size) + # logits = logits[:, 1:, :].contiguous().view(-1, vocab_size) + # labels = labels[:, 1:].contiguous().view(-1) + + # mlm_loss = F.cross_entropy( + # logits, + # labels, + # # label_smoothing=0.1, + # ) + # return mlm_loss + + + def forward(self, x:TensorType) -> TensorType: + attn_mask = (x != self.config.pad_token_id).long() + out = self.transformer(input_ids=x, attention_mask=attn_mask) + pooled_out = self.pooler(out, attn_mask) + + return self.proj(pooled_out) + + def lock(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + if not unlocked_layers: # full freezing + for n, p in self.transformer.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + return + + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model") + embeddings = getattr( + self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"]) + modules = [embeddings, *layer_list][:-unlocked_layers] + # freeze layers + for module in modules: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.gradient_checkpointing_enable() + + def get_num_layers(self): + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + return len(layer_list) + + def init_parameters(self): + pass diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/loss.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/loss.py new file mode 100644 index 00000000..473f60d9 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/loss.py @@ -0,0 +1,138 @@ +import math +import torch +import torch.nn as nn +from torch.nn import functional as F + +try: + import torch.distributed.nn + from torch import distributed as dist + has_distributed = True +except ImportError: + has_distributed = False + +try: + import horovod.torch as hvd +except ImportError: + hvd = None + +from timm.loss import LabelSmoothingCrossEntropy + + +def gather_features( + image_features, + text_features, + local_loss=False, + gather_with_grad=False, + rank=0, + world_size=1, + use_horovod=False +): + assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' + if use_horovod: + assert hvd is not None, 'Please install horovod' + if gather_with_grad: + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + else: + with torch.no_grad(): + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) + gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + else: + # We gather tensors from all gpus + if gather_with_grad: + all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0) + # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0) + else: + gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] + dist.all_gather(gathered_image_features, image_features) + dist.all_gather(gathered_text_features, text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + + return all_image_features, all_text_features + + +class ClipLoss(nn.Module): + + def __init__( + self, + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + smoothing=0., + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + self.use_horovod = use_horovod + self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None + + # cache state + self.prev_num_logits = 0 + self.labels = {} + + def forward(self, image_features, text_features, logit_scale=1.): + device = image_features.device + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + # calculated ground-truth and cache if enabled + num_logits = logits_per_image.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + if self.label_smoothing_cross_entropy: + total_loss = ( + self.label_smoothing_cross_entropy(logits_per_image, labels) + + self.label_smoothing_cross_entropy(logits_per_text, labels) + ) / 2 + else: + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + acc = None + i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image) + t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text) + acc = {"i2t": i2t_acc, "t2i": t2i_acc} + return total_loss, acc \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model.py new file mode 100644 index 00000000..90f34b91 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model.py @@ -0,0 +1,439 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union +from functools import partial + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +try: + from .hf_model import HFTextEncoder +except: + HFTextEncoder = None +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .eva_vit_model import EVAVisionTransformer +from .transformer import LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + print("Nvidia APEX normalization not installed, using PyTorch LayerNorm") + +try: + import xformers.ops as xops +except ImportError: + xops = None + #print("Please 'pip install xformers'") + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + drop_path_rate: Optional[float] = None # drop path rate + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size + qkv_bias: bool = True + fusedLN: bool = False + xattn: bool = False + postnorm: bool = False + rope: bool = False + pt_hw_seq_len: int = 16 # 224/14 + intp_freq: bool = False + naiveswiglu: bool = False + subln: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + masked_language_modeling: bool = False + fusedLN: bool = False + xattn: bool = False + attn_mask: bool = True + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.eva_model_name: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNorm + + visual = EVAVisionTransformer( + img_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + num_classes=embed_dim, + use_mean_pooling=vision_cfg.global_average_pool, #False + init_values=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + embed_dim=vision_cfg.width, + depth=vision_cfg.layers, + num_heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + qkv_bias=vision_cfg.qkv_bias, + drop_path_rate=vision_cfg.drop_path_rate, + norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6), + xattn=vision_cfg.xattn, + rope=vision_cfg.rope, + postnorm=vision_cfg.postnorm, + pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14 + intp_freq= vision_cfg.intp_freq, + naiveswiglu= vision_cfg.naiveswiglu, + subln= vision_cfg.subln + ) + elif vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + embed_dim=embed_dim, + image_size=vision_cfg.image_size + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + global_average_pool=vision_cfg.global_average_pool, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + tokenizer_name=text_cfg.hf_tokenizer_name, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + masked_language_modeling=text_cfg.masked_language_modeling + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer= FusedLayerNorm if text_cfg.fusedLN else norm_layer, + xattn=text_cfg.xattn, + attn_mask=text_cfg.attn_mask, + ) + return text + +class CLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +class CustomCLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + itm_task: bool = False, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr, None) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + if isinstance(l, nn.Parameter): + l.data = l.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name) and isinstance(l, nn.Parameter): + attr = getattr(l, name, None) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + 'logit_scale' + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-B-16.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-B-16.json new file mode 100644 index 00000000..aad20580 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-B-16.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16, + "eva_model_name": "eva-clip-b-16", + "ls_init_value": 0.1, + "drop_path_rate": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json new file mode 100644 index 00000000..10027957 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-g-14.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-g-14.json new file mode 100644 index 00000000..5d338b4e --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA01-CLIP-g-14.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0.4, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-B-16.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-B-16.json new file mode 100644 index 00000000..e4a6e723 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-B-16.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "head_width": 64, + "patch_size": 16, + "mlp_ratio": 2.6667, + "eva_model_name": "eva-clip-b-16-X", + "drop_path_rate": 0.0, + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "xattn": true, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-L-14-336.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-L-14-336.json new file mode 100644 index 00000000..3e1d124e --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-L-14-336.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14-336", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-L-14.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-L-14.json new file mode 100644 index 00000000..03b22ad3 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-L-14.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json new file mode 100644 index 00000000..aa04e254 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32, + "xattn": false, + "fusedLN": true + } +} diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-bigE-14.json b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-bigE-14.json new file mode 100644 index 00000000..747ffccc --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/model_configs/EVA02-CLIP-bigE-14.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/modified_resnet.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/modified_resnet.py new file mode 100644 index 00000000..6a8d3aed --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/openai.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/openai.py new file mode 100644 index 00000000..cc4e13e8 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/pretrained.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/pretrained.py new file mode 100644 index 00000000..5077a047 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/pretrained.py @@ -0,0 +1,340 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +try: + from huggingface_hub import hf_hub_download + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_EVAB16 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_EVAL14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_EVAL14_336 = dict( + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), + eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_EVAg14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), +) + +_EVAg14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_EVAbigE14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), +) + +_EVAbigE14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), +) + + +_PRETRAINED = { + # "ViT-B-32": _VITB32, + "OpenaiCLIP-B-32": _VITB32, + "OpenCLIP-B-32": _VITB32, + + # "ViT-B-32-quickgelu": _VITB32_quickgelu, + "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, + "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, + + # "ViT-B-16": _VITB16, + "OpenaiCLIP-B-16": _VITB16, + "OpenCLIP-B-16": _VITB16, + + "EVA02-B-16": _EVAB16, + "EVA02-CLIP-B-16": _EVAB16, + + # "ViT-B-16-plus-240": _VITB16_PLUS_240, + "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, + + # "ViT-L-14": _VITL14, + "OpenaiCLIP-L-14": _VITL14, + "OpenCLIP-L-14": _VITL14, + + "EVA02-L-14": _EVAL14, + "EVA02-CLIP-L-14": _EVAL14, + + # "ViT-L-14-336": _VITL14_336, + "OpenaiCLIP-L-14-336": _VITL14_336, + + "EVA02-CLIP-L-14-336": _EVAL14_336, + + # "ViT-H-14": _VITH14, + # "ViT-g-14": _VITg14, + "OpenCLIP-H-14": _VITH14, + "OpenCLIP-g-14": _VITg14, + + "EVA01-CLIP-g-14": _EVAg14, + "EVA01-CLIP-g-14-plus": _EVAg14_PLUS, + + # "ViT-bigG-14": _VITbigG14, + "OpenCLIP-bigG-14": _VITbigG14, + + "EVA02-CLIP-bigE-14": _EVAbigE14, + "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, + local_dir: Union[str, None] = None, +): + cache_dir = local_dir if not local_dir else cache_dir + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, + local_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir, local_dir=local_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, + local_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir, local_dir=local_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if local_dir is not None: + full_model_path = os.path.join(local_dir, filename) + if os.path.exists(full_model_path): + return full_model_path + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir, local_dir=local_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir, local_dir=local_dir) + + return target diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/rope.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/rope.py new file mode 100644 index 00000000..69030c35 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/rope.py @@ -0,0 +1,137 @@ +from math import pi +import torch +from torch import nn +from einops import rearrange, repeat +import logging + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + + return torch.cat((t_left, t, t_right), dim = -1) + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + patch_dropout = 0. + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) + freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) + + self.patch_dropout = patch_dropout + + self.register_buffer("freqs_cos", freqs_cos) + self.register_buffer("freqs_sin", freqs_sin) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, patch_indices_keep=None): + if patch_indices_keep is not None: + batch = t.size()[0] + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + + freqs_cos = freqs_cos[batch_indices, patch_indices_keep] + freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j') + freqs_sin = freqs_sin[batch_indices, patch_indices_keep] + freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j') + + return t * freqs_cos + rotate_half(t) * freqs_sin + + return t * self.freqs_cos + rotate_half(t) * self.freqs_sin \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/timm_model.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/timm_model.py new file mode 100644 index 00000000..b58122c0 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/timm_model.py @@ -0,0 +1,122 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + pretrained=False): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + self.trunk = timm.create_model(model_name, pretrained=pretrained) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/tokenizer.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/tokenizer.py new file mode 100644 index 00000000..41482f82 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/tokenizer.py @@ -0,0 +1,201 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("Β‘"), ord("Β¬")+1))+list(range(ord("Β"), ord("ΓΏ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + +_tokenizer = SimpleTokenizer() + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder[""] + eot_token = _tokenizer.encoder[""] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + +class HFTokenizer: + "HuggingFace tokenizer wrapper" + def __init__(self, tokenizer_name:str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids + return input_ids diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/transform.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/transform.py new file mode 100644 index 00000000..39f3e4cf --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/transform.py @@ -0,0 +1,103 @@ +from typing import Optional, Sequence, Tuple + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F + +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[:2] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +# class CatGen(nn.Module): +# def __init__(self, num=4): +# self.num = num +# def mixgen_batch(image, text): +# batch_size = image.shape[0] +# index = np.random.permutation(batch_size) + +# cat_images = [] +# for i in range(batch_size): +# # image mixup +# image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:] +# # text concat +# text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0] +# text = torch.stack(text) +# return image, text + + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + normalize = Normalize(mean=mean, std=std) + if is_train: + return Compose([ + RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), + _convert_to_rgb, + ToTensor(), + normalize, + ]) + else: + if resize_longest_max: + transforms = [ + ResizeMaxSize(image_size, fill=fill_color) + ] + else: + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend([ + _convert_to_rgb, + ToTensor(), + normalize, + ]) + return Compose(transforms) diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/transformer.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/transformer.py new file mode 100644 index 00000000..33e89ff7 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/transformer.py @@ -0,0 +1,737 @@ +import os +import logging +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +try: + from timm.models.layers import trunc_normal_ +except: + from timm.layers import trunc_normal_ + +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast +from .utils import to_2tuple + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + import deepspeed + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + print("Please 'pip install deepspeed'") + deepspeed = None + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: torch.Tensor): + output = F.layer_norm( + x.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(x) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}") + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + if self.training and os.getenv('RoPE') == '1': + return x, patch_indices_keep + + return x + + +def _in_projection_packed( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + w: torch.Tensor, + b: Optional[torch.Tensor] = None, + ): + """ + https://github.com/pytorch/pytorch/blob/db2a237763eb8693a20788be94f8c192e762baa8/torch/nn/functional.py#L4726 + """ + E = q.size(-1) + if k is v: + if q is k: + # self-attention + return F.linear(q, w, b).chunk(3, dim=-1) + else: + # encoder-decoder attention + w_q, w_kv = w.split([E, E * 2]) + if b is None: + b_q = b_kv = None + else: + b_q, b_kv = b.split([E, E * 2]) + return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1) + else: + w_q, w_k, w_v = w.chunk(3) + if b is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = b.chunk(3) + return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v) + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False, + rope=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + self.rope = rope + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + if self.xattn: + q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None, + ) + else: + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=True, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias) + N_q, B_q, C_q = q.shape + N_k, B_k, C_k = k.shape + N_v, B_v, C_v = v.shape + if self.xattn: + # B, N, C -> B, N, num_heads, C + q = q.permute(1, 0, 2).reshape(B_q, N_q, self.num_heads, -1) + k = k.permute(1, 0, 2).reshape(B_k, N_k, self.num_heads, -1) + v = v.permute(1, 0, 2).reshape(B_v, N_v, self.num_heads, -1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None + ) + else: + # B*H, L, C + q = q.contiguous().view(N_q, B_q * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(N_k, B_k * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(N_v, B_v * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + # B*H, N_q, N_k + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(B_q, self.num_heads, N_q, N_k) * logit_scale + attn = attn.view(-1, N_q, N_k) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(B_q, self.num_heads, N_q, C_q) * self.head_scale + x = x.view(-1, N_q, C_q) + x = x.transpose(0, 1).reshape(N_q, B_q, C_q) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.ln_1_k = norm_layer(d_model) if cross_attn else self.ln_1 + self.ln_1_v = norm_layer(d_model) if cross_attn else self.ln_1 + self.attn = CustomAttention( + d_model, n_head, + qkv_bias=True, + attn_drop=0., + proj_drop=0., + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + xattn=xattn + ) + + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q = q + self.ls_1(self.ln_attn(self.attn(self.ln_1(q), self.ln_1_k(k), self.ln_1_v(v), attn_mask=attn_mask))) + q = q + self.ls_2(self.mlp(self.ln_2(q))) + return q + +class CustomTransformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = True, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + self.xattn = xattn + + self.resblocks = nn.ModuleList([ + CustomResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + scale_cosine_attn=scale_cosine_attn, + scale_heads=scale_heads, + scale_attn=scale_attn, + scale_fc=scale_fc, + cross_attn=cross_attn, + xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, q: torch.Tensor, k: torch.Tensor = None, v: torch.Tensor = None, attn_mask: Optional[torch.Tensor] = None): + if k is None and v is None: + k = v = q + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + q = checkpoint(r, q, k, v, attn_mask) + else: + q = r(q, k, v, attn_mask=attn_mask) + return q + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + if xattn: + self.attn = Attention(d_model, n_head, xattn=True) + else: + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + self.xattn = xattn + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None + if self.xattn: + return self.attn(x, attn_mask=attn_mask) + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + patch_dropout: float = 0., + global_average_pool: bool = False, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.image_size = to_2tuple(image_size) + self.patch_size = to_2tuple(patch_size) + self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1]) + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + self.ln_pre = norm_layer(width) + + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.global_average_pool = global_average_pool + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def get_num_layers(self): + return self.transformer.layers + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'positional_embedding', 'class_embedding'} + + def forward(self, x: torch.Tensor, return_all_features: bool=False): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if not return_all_features: + if self.global_average_pool: + x = x.mean(dim=1) #x = x[:,1:,:].mean(dim=1) + else: + x = x[:, 0] + + x = self.ln_post(x) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class TextTransformer(nn.Module): + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool= False, + attn_mask: bool = True + ): + super().__init__() + self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.xattn = xattn + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if attn_mask: + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + else: + self.attn_mask = None + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + # return {'positional_embedding', 'token_embedding'} + return {'positional_embedding'} + + def get_num_layers(self): + return self.transformer.layers + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text, return_all_features: bool=False): + cast_dtype = self.transformer.get_cast_dtype() + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + # x = self.transformer(x) # no attention mask is applied + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if not return_all_features: + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/utils.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/utils.py new file mode 100644 index 00000000..bdc5a7a4 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/eva_clip/utils.py @@ -0,0 +1,326 @@ +from itertools import repeat +import collections.abc +import logging +import math +import numpy as np + +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d +import torch.nn.functional as F + +# open CLIP +def resize_clip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed + + +def resize_visual_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['positional_embedding'] = new_pos_embed + +def resize_evaclip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'visual.pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['visual.pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['visual.pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['visual.patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['visual.patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_eva_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + for key in all_keys: + if "relative_position_index" in key: + state_dict.pop(key) + + if "relative_position_bias_table" in key: + rel_pos_bias = state_dict[key] + src_num_pos, num_attn_heads = rel_pos_bias.size() + dst_num_pos, _ = model.visual.state_dict()[key].size() + dst_patch_shape = model.visual.patch_embed.patch_shape + if dst_patch_shape[0] != dst_patch_shape[1]: + raise NotImplementedError() + num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) + src_size = int((src_num_pos - num_extra_tokens) ** 0.5) + dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) + if src_size != dst_size: + print("Position interpolate for %s from %dx%d to %dx%d" % ( + key, src_size, src_size, dst_size, dst_size)) + extra_tokens = rel_pos_bias[-num_extra_tokens:, :] + rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] + + def geometric_progression(a, r, n): + return a * (1.0 - r ** n) / (1.0 - r) + + left, right = 1.01, 1.5 + while right - left > 1e-6: + q = (left + right) / 2.0 + gp = geometric_progression(1, q, src_size // 2) + if gp > dst_size // 2: + right = q + else: + left = q + + # if q > 1.090307: + # q = 1.090307 + + dis = [] + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + + x = r_ids + [0] + dis + y = r_ids + [0] + dis + + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + dy = np.arange(-t, t + 0.1, 1.0) + + print("Original positions = %s" % str(x)) + print("Target positions = %s" % str(dx)) + + all_rel_pos_bias = [] + + for i in range(num_attn_heads): + z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() + f = F.interpolate.interp2d(x, y, z, kind='cubic') + all_rel_pos_bias.append( + torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) + + rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) + + new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) + state_dict[key] = new_rel_pos_bias + + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) + + +def is_logging(args): + def is_global_master(args): + return args.rank == 0 + + def is_local_master(args): + return args.local_rank == 0 + + def is_master(args, local=False): + return is_local_master(args) if local else is_global_master(args) + return is_master + + +class AllGather(torch.autograd.Function): + """An autograd function that performs allgather on a tensor. + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + + @staticmethod + def forward(ctx, tensor, rank, world_size): + tensors_gather = [torch.empty_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(tensors_gather, tensor) + ctx.rank = rank + ctx.batch_size = tensor.shape[0] + return torch.cat(tensors_gather, 0) + + @staticmethod + def backward(ctx, grad_output): + return ( + grad_output[ctx.batch_size * ctx.rank: ctx.batch_size * (ctx.rank + 1)], + None, + None + ) + +allgather = AllGather.apply \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_select_ref_face.png b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_select_ref_face.png new file mode 100644 index 00000000..d6b22fbd Binary files /dev/null and b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_select_ref_face.png differ diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_with_attn_mask.png b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_with_attn_mask.png new file mode 100644 index 00000000..84940bba Binary files /dev/null and b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_with_attn_mask.png differ diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_with_speedup.png b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_with_speedup.png new file mode 100644 index 00000000..7ae71dd0 Binary files /dev/null and b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/examples/PuLID_with_speedup.png differ diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/face_restoration_helper.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/face_restoration_helper.py new file mode 100644 index 00000000..cc4e28b5 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/face_restoration_helper.py @@ -0,0 +1,456 @@ +import cv2 +import numpy as np +import os +import torch +from torchvision.transforms.functional import normalize +from torchvision.ops import box_iou + +from facexlib.detection import init_detection_model +from facexlib.parsing import init_parsing_model +from facexlib.utils.misc import img2tensor, imwrite + +def get_face_by_index(det_faces, face_sort_rule, face_index=0): + if det_faces is None: + return None, None + + has_bbox_attr = hasattr(det_faces[0], 'bbox') + + # εˆ›ε»ΊεΈ¦η΄’εΌ•ηš„εˆ—θ‘¨ [(original_index, face), ...] + indexed_faces = list(enumerate(det_faces)) + + # εšδΉ‰ζŽ’εΊθ§„εˆ™ + if face_sort_rule == 'left-right': + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[0] if has_bbox_attr else x[1][0]) + elif face_sort_rule == "right-left": + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[0] if has_bbox_attr else x[1][0], reverse=True) + elif face_sort_rule == "top-bottom": + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[1] if has_bbox_attr else x[1][1]) + elif face_sort_rule == "bottom-top": + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[1] if has_bbox_attr else x[1][1], reverse=True) + elif face_sort_rule == "small-large": + sorted_faces = sorted(indexed_faces, key=lambda x: (x[1].bbox[2] - x[1].bbox[0]) * (x[1].bbox[3] - x[1].bbox[1]) if has_bbox_attr else (x[1][2] - x[1][0]) * (x[1][3] - x[1][1])) + elif face_sort_rule == "large-small": + sorted_faces = sorted(indexed_faces, key=lambda x: (x[1].bbox[2] - x[1].bbox[0]) * (x[1].bbox[3] - x[1].bbox[1]) if has_bbox_attr else (x[1][2] - x[1][0]) * (x[1][3] - x[1][1]), reverse=True) + else: + sorted_faces = indexed_faces + # θΏ”ε›žεŽŸε§‹η΄’εΌ• + if not 0 <= face_index < len(sorted_faces): + # θΏ”ε›žη¬¬δΈ€δΈͺ + face_index = 0 + # θΏ”ε›žι€‰ζ‹©ηš„θ„Έιƒ¨γ€εŽŸε§‹η΄’εΌ•ε€Όε’ŒζŽ’εΊεŽηš„εˆ—θ‘¨ + return sorted_faces[face_index][1], sorted_faces[face_index][0], [face[1] for face in sorted_faces] + + +def get_largest_face(det_faces, h, w): + + def get_location(val, length): + if val < 0: + return 0 + elif val > length: + return length + else: + return val + + face_areas = [] + for det_face in det_faces: + left = get_location(det_face[0], w) + right = get_location(det_face[2], w) + top = get_location(det_face[1], h) + bottom = get_location(det_face[3], h) + face_area = (right - left) * (bottom - top) + face_areas.append(face_area) + largest_idx = face_areas.index(max(face_areas)) + return det_faces[largest_idx], largest_idx + + +def get_center_face(det_faces, h=0, w=0, center=None): + if center is not None: + center = np.array(center) + else: + center = np.array([w / 2, h / 2]) + center_dist = [] + for det_face in det_faces: + face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2]) + dist = np.linalg.norm(face_center - center) + center_dist.append(dist) + center_idx = center_dist.index(min(center_dist)) + return det_faces[center_idx], center_idx + + +class FaceRestoreHelper(object): + """Helper for the face restoration pipeline (base class).""" + + def __init__(self, + upscale_factor, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + parsing_model='bisenet', + save_ext='png', + template_3points=False, + pad_blur=False, + use_parse=False, + device=None, + model_rootpath=None): + self.template_3points = template_3points # improve robustness + self.upscale_factor = upscale_factor + # the cropped face ratio based on the square face + self.crop_ratio = crop_ratio # (h, w) + assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1' + self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0])) + + if self.template_3points: + self.face_template = np.array([[192, 240], [319, 240], [257, 371]]) + else: + # standard 5 landmarks for FFHQ faces with 512 x 512 + self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935], + [201.26117, 371.41043], [313.08905, 371.15118]]) + self.face_template = self.face_template * (face_size / 512.0) + if self.crop_ratio[0] > 1: + self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2 + if self.crop_ratio[1] > 1: + self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2 + self.save_ext = save_ext + self.pad_blur = pad_blur + if self.pad_blur is True: + self.template_3points = False + + self.all_landmarks_5 = [] + self.det_faces = [] + self.affine_matrices = [] + self.inverse_affine_matrices = [] + self.cropped_faces = [] + self.restored_faces = [] + self.pad_input_imgs = [] + + if device is None: + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = device + + # init face detection model + self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath) + + # init face parsing model + self.use_parse = use_parse + self.face_parse = init_parsing_model(model_name=parsing_model, device=self.device, model_rootpath=model_rootpath) + + def set_upscale_factor(self, upscale_factor): + self.upscale_factor = upscale_factor + + def read_image(self, img): + """img can be image path or cv2 loaded image.""" + # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255] + if isinstance(img, str): + img = cv2.imread(img) + + if np.max(img) > 256: # 16-bit image + img = img / 65535 * 255 + if len(img.shape) == 2: # gray image + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + elif img.shape[2] == 4: # RGBA image with alpha channel + img = img[:, :, 0:3] + + self.input_img = img + + def get_face_landmarks_5(self, + only_keep_largest=False, + only_center_face=False, + resize=None, + blur_ratio=0.01, + eye_dist_threshold=None, + face_sort_rule=None, + ref_sort_bboxes=None, + face_index=None): + if resize is None: + scale = 1 + input_img = self.input_img + else: + h, w = self.input_img.shape[0:2] + scale = min(h, w) / resize + h, w = int(h / scale), int(w / scale) + input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4) + + with torch.no_grad(): + # use 0.5 (old value is 0.97), keep consistent with Insightface, but still cannot ensure consistent quantity of bboxes. + bboxes = self.face_det.detect_faces(input_img, 0.5) * scale + for bbox in bboxes: + # remove faces with too small eye distance: side faces or too small faces + eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]]) + if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold): + continue + + if self.template_3points: + landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)]) + else: + landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)]) + self.all_landmarks_5.append(landmark) + self.det_faces.append(bbox[0:5]) + if len(self.det_faces) == 0: + return 0 + if ref_sort_bboxes is not None: + if len(self.det_faces) != len(ref_sort_bboxes): + return 0 + iou = box_iou(torch.tensor(ref_sort_bboxes), torch.tensor(self.det_faces)[:, :4]) + indices = torch.max(iou, dim=1).indices + self.det_faces = [self.det_faces[idx] for idx in indices] + if face_index is None or face_index >= len(self.det_faces): + face_index = 0 + self.all_landmarks_5 = [self.all_landmarks_5[indices[face_index]]] + elif face_sort_rule is not None: + self.det_faces, center_idx, _ = get_face_by_index(self.det_faces, face_sort_rule=face_sort_rule, face_index=face_index) + self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] + elif only_keep_largest: + h, w, _ = self.input_img.shape + self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w) + self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]] + elif only_center_face: + h, w, _ = self.input_img.shape + self.det_faces, center_idx = get_center_face(self.det_faces, h, w) + self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] + + # pad blurry images + if self.pad_blur: + self.pad_input_imgs = [] + for landmarks in self.all_landmarks_5: + # get landmarks + eye_left = landmarks[0, :] + eye_right = landmarks[1, :] + eye_avg = (eye_left + eye_right) * 0.5 + mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5 + eye_to_eye = eye_right - eye_left + eye_to_mouth = mouth_avg - eye_avg + + # Get the oriented crop rectangle + # x: half width of the oriented crop rectangle + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise + # norm with the hypotenuse: get the direction + x /= np.hypot(*x) # get the hypotenuse of a right triangle + rect_scale = 1.5 + x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) + # y: half height of the oriented crop rectangle + y = np.flipud(x) * [-1, 1] + + # c: center + c = eye_avg + eye_to_mouth * 0.1 + # quad: (left_top, left_bottom, right_bottom, right_top) + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + # qsize: side length of the square + qsize = np.hypot(*x) * 2 + border = max(int(np.rint(qsize * 0.1)), 3) + + # get pad + # pad: (width_left, height_top, width_right, height_bottom) + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = [ + max(-pad[0] + border, 1), + max(-pad[1] + border, 1), + max(pad[2] - self.input_img.shape[0] + border, 1), + max(pad[3] - self.input_img.shape[1] + border, 1) + ] + + if max(pad) > 1: + # pad image + pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + # modify landmark coords + landmarks[:, 0] += pad[0] + landmarks[:, 1] += pad[1] + # blur pad images + h, w, _ = pad_img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], + np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], + np.float32(h - 1 - y) / pad[3])) + blur = int(qsize * blur_ratio) + if blur % 2 == 0: + blur += 1 + blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur)) + # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0) + + pad_img = pad_img.astype('float32') + pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0) + pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255] + self.pad_input_imgs.append(pad_img) + else: + self.pad_input_imgs.append(np.copy(self.input_img)) + + return len(self.all_landmarks_5) + + def align_warp_face(self, save_cropped_path=None, border_mode='constant'): + """Align and warp faces with face template. + """ + if self.pad_blur: + assert len(self.pad_input_imgs) == len( + self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}' + for idx, landmark in enumerate(self.all_landmarks_5): + # use 5 landmarks to get affine matrix + # use cv2.LMEDS method for the equivalence to skimage transform + # ref: https://blog.csdn.net/yichxi/article/details/115827338 + affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] + self.affine_matrices.append(affine_matrix) + # warp and crop faces + if border_mode == 'constant': + border_mode = cv2.BORDER_CONSTANT + elif border_mode == 'reflect101': + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == 'reflect': + border_mode = cv2.BORDER_REFLECT + if self.pad_blur: + input_img = self.pad_input_imgs[idx] + else: + input_img = self.input_img + cropped_face = cv2.warpAffine( + input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray + self.cropped_faces.append(cropped_face) + # save the cropped face + if save_cropped_path is not None: + path = os.path.splitext(save_cropped_path)[0] + save_path = f'{path}_{idx:02d}.{self.save_ext}' + imwrite(cropped_face, save_path) + + def get_inverse_affine(self, save_inverse_affine_path=None): + """Get inverse affine matrix.""" + for idx, affine_matrix in enumerate(self.affine_matrices): + inverse_affine = cv2.invertAffineTransform(affine_matrix) + inverse_affine *= self.upscale_factor + self.inverse_affine_matrices.append(inverse_affine) + # save inverse affine matrices + if save_inverse_affine_path is not None: + path, _ = os.path.splitext(save_inverse_affine_path) + save_path = f'{path}_{idx:02d}.pth' + torch.save(inverse_affine, save_path) + + def add_restored_face(self, face): + self.restored_faces.append(face) + + def paste_faces_to_input_image(self, save_path=None, upsample_img=None): + h, w, _ = self.input_img.shape + h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) + + if upsample_img is None: + # simply resize the background + upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + else: + upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + + assert len(self.restored_faces) == len( + self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') + for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): + # Add an offset to inverse affine matrix, for more precise back alignment + if self.upscale_factor > 1: + extra_offset = 0.5 * self.upscale_factor + else: + extra_offset = 0 + inverse_affine[:, 2] += extra_offset + inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) + + if self.use_parse: + # inference + face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR) + face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True) + normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + face_input = torch.unsqueeze(face_input, 0).to(self.device) + with torch.no_grad(): + out = self.face_parse(face_input)[0] + out = out.argmax(dim=1).squeeze().cpu().numpy() + + mask = np.zeros(out.shape) + MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] + for idx, color in enumerate(MASK_COLORMAP): + mask[out == idx] = color + # blur the mask + mask = cv2.GaussianBlur(mask, (101, 101), 11) + mask = cv2.GaussianBlur(mask, (101, 101), 11) + # remove the black borders + thres = 10 + mask[:thres, :] = 0 + mask[-thres:, :] = 0 + mask[:, :thres] = 0 + mask[:, -thres:] = 0 + mask = mask / 255. + + mask = cv2.resize(mask, restored_face.shape[:2]) + mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3) + inv_soft_mask = mask[:, :, None] + pasted_face = inv_restored + + else: # use square parse maps + mask = np.ones(self.face_size, dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) + # remove the black borders + inv_mask_erosion = cv2.erode( + inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8)) + pasted_face = inv_mask_erosion[:, :, None] * inv_restored + total_face_area = np.sum(inv_mask_erosion) # // 3 + # compute the fusion edge based on the area of face + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + if len(upsample_img.shape) == 2: # upsample_img is gray image + upsample_img = upsample_img[:, :, None] + inv_soft_mask = inv_soft_mask[:, :, None] + + if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel + alpha = upsample_img[:, :, 3:] + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3] + upsample_img = np.concatenate((upsample_img, alpha), axis=2) + else: + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img + + if np.max(upsample_img) > 256: # 16-bit image + upsample_img = upsample_img.astype(np.uint16) + else: + upsample_img = upsample_img.astype(np.uint8) + if save_path is not None: + path = os.path.splitext(save_path)[0] + save_path = f'{path}.{self.save_ext}' + imwrite(upsample_img, save_path) + return upsample_img + + def clean_all(self): + self.all_landmarks_5 = [] + self.restored_faces = [] + self.affine_matrices = [] + self.cropped_faces = [] + self.inverse_affine_matrices = [] + self.det_faces = [] + self.pad_input_imgs = [] + +def draw_on(img, faces): + dimg = img.copy() + for i in range(len(faces)): + face = faces[i] + box = face.bbox.astype(np.int32) + color = (0, 0, 255) + cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2) + if face.kps is not None: + kps = face.kps.astype(np.int32) + #print(landmark.shape) + for l in range(kps.shape[0]): + color = (0, 0, 255) + if l == 0 or l == 3: + color = (0, 255, 0) + cv2.circle(dimg, (kps[l][0], kps[l][1]), 1, color, + 2) + + cv2.putText(dimg,'index: %d'%i, (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1) + + # if face.gender is not None and face.age is not None: + # cv2.putText(dimg,'%s,%d'%(face.sex,face.age), (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1) + + #for key, value in face.items(): + # if key.startswith('landmark_3d'): + # print(key, value.shape) + # print(value[0:10,:]) + # lmk = np.round(value).astype(np.int) + # for l in range(lmk.shape[0]): + # color = (255, 0, 0) + # cv2.circle(dimg, (lmk[l][0], lmk[l][1]), 1, color, + # 2) + return dimg \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/patch_util.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/patch_util.py new file mode 100644 index 00000000..3437d2b3 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/patch_util.py @@ -0,0 +1,41 @@ +class PatchKeys: + ################## transformer_options patches ################## + running_net_model = "running_net_model" + options_key = "patches_point" + # patches_pointδΈ‹ζ”―ζŒθΎη½ηš„θ‘₯丁 + dit_enter = "patch_dit_enter" + dit_blocks_before = "patch_dit_blocks_before" + dit_double_blocks_replace = "patch_dit_double_blocks_replace" + dit_double_blocks_after = "patch_dit_double_blocks_after" + dit_blocks_transition_replace = "patch_dit_blocks_transition_replace" + dit_single_blocks_before = "patch_dit_single_blocks_before" + dit_single_blocks_replace = "patch_dit_single_blocks_replace" + dit_blocks_after = "patch_dit_blocks_after" + dit_blocks_after_transition_replace = "patch_dit_final_layer_before_replace" + dit_final_layer_before = "patch_dit_final_layer_before" + dit_exit = "patch_dit_exit" + ################## transformer_options patches ################## + + # pulid + pulid_patch_key_attrs = "pulid_temp_attr" + + +def set_model_patch(model_patcher, options_key, patch, name): + to = model_patcher.model_options["transformer_options"] + if options_key not in to: + to[options_key] = {} + to[options_key][name] = to[options_key].get(name, []) + [patch] + +def set_model_patch_replace(model_patcher, options_key, patch, name): + to = model_patcher.model_options["transformer_options"] + if options_key not in to: + to[options_key] = {} + to[options_key][name] = patch + +def add_model_patch_option(model, patch_key): + if 'transformer_options' not in model.model_options: + model.model_options['transformer_options'] = {} + to = model.model_options['transformer_options'] + if patch_key not in to: + to[patch_key] = {} + return to[patch_key] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/pulidflux.py b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/pulidflux.py new file mode 100644 index 00000000..802cd1d3 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/pulidflux.py @@ -0,0 +1,878 @@ +import types +import zipfile + +import cv2 +import torch +from insightface.utils.download import download_file +from insightface.utils.storage import BASE_REPO_URL +from insightface.utils import face_align +from torch import nn +from torchvision import transforms +from torchvision.transforms import functional +import os +import logging +import folder_paths +import comfy +from insightface.app import FaceAnalysis +from .face_restoration_helper import FaceRestoreHelper, get_face_by_index, draw_on + +from comfy import model_management +from .eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .encoders_flux import IDFormer, PerceiverAttentionCA + +from .PulidFluxHook import pulid_forward_orig, set_model_dit_patch_replace, pulid_enter, pulid_patch_double_blocks_after +from .patch_util import PatchKeys, add_model_patch_option, set_model_patch + +# facenet implementation +import numpy as np +from PIL import Image +from facenet_pytorch import MTCNN, InceptionResnetV1 +def tensor2pil(image): + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + +def set_extra_config_model_path(extra_config_models_dir_key, models_dir_name:str): + models_dir_default = os.path.join(folder_paths.models_dir, models_dir_name) + if extra_config_models_dir_key not in folder_paths.folder_names_and_paths: + folder_paths.folder_names_and_paths[extra_config_models_dir_key] = ( + [os.path.join(folder_paths.models_dir, models_dir_name)], folder_paths.supported_pt_extensions) + else: + if not os.path.exists(models_dir_default): + os.makedirs(models_dir_default, exist_ok=True) + folder_paths.add_model_folder_path(extra_config_models_dir_key, models_dir_default, is_default=True) + +set_extra_config_model_path("pulid", "pulid") +set_extra_config_model_path("insightface", "insightface") +set_extra_config_model_path("facexlib", "facexlib") + +INSIGHTFACE_DIR = folder_paths.get_folder_paths("insightface")[0] +FACEXLIB_DIR = folder_paths.get_folder_paths("facexlib")[0] + +#FACENET_DIR = folder_paths.get_folder_paths("facenet")[0] + +# MODELS_DIR = os.path.join(folder_paths.models_dir, "pulid") +# if "pulid" not in folder_paths.folder_names_and_paths: +# current_paths = [MODELS_DIR] +# else: +# current_paths, _ = folder_paths.folder_names_and_paths["pulid"] +# folder_paths.folder_names_and_paths["pulid"] = (current_paths, folder_paths.supported_pt_extensions) + +class PulidFluxModel(nn.Module): + def __init__(self): + super().__init__() + + self.double_interval = 2 + self.single_interval = 4 + + # Init encoder + self.pulid_encoder = IDFormer() + + # Init attention + num_ca = 19 // self.double_interval + 38 // self.single_interval + if 19 % self.double_interval != 0: + num_ca += 1 + if 38 % self.single_interval != 0: + num_ca += 1 + self.pulid_ca = nn.ModuleList([ + PerceiverAttentionCA() for _ in range(num_ca) + ]) + + def from_pretrained(self, path: str): + state_dict = comfy.utils.load_torch_file(path, safe_load=True) + state_dict_dict = {} + for k, v in state_dict.items(): + module = k.split('.')[0] + state_dict_dict.setdefault(module, {}) + new_k = k[len(module) + 1:] + state_dict_dict[module][new_k] = v + + for module in state_dict_dict: + getattr(self, module).load_state_dict(state_dict_dict[module], strict=True) + + del state_dict + del state_dict_dict + + def get_embeds(self, face_embed, clip_embeds): + return self.pulid_encoder(face_embed, clip_embeds) + +def tensor_to_image(tensor): + image = tensor.mul(255).clamp(0, 255).byte().cpu() + image = image[..., [2, 1, 0]].numpy() + return image + +def image_to_tensor(image): + tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1) + tensor = tensor[..., [2, 1, 0]] + return tensor + +def to_gray(img): + x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3] + x = x.repeat(1, 3, 1, 1) + return x + +""" +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Nodes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +wrappers_name = "PULID_wrappers" + +class PulidFluxModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pulid_file": (folder_paths.get_filename_list("pulid"), )}} + + RETURN_TYPES = ("PULIDFLUX",) + FUNCTION = "load_model" + CATEGORY = "pulid" + + def load_model(self, pulid_file): + model_path = folder_paths.get_full_path("pulid", pulid_file) + + # Also initialize the model, takes longer to load but then it doesn't have to be done every time you change parameters in the apply node + offload_device = model_management.unet_offload_device() + load_device = model_management.get_torch_device() + + model = PulidFluxModel() + + logging.info("Loading PuLID-Flux model.") + model.from_pretrained(path=model_path) + + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) + del model + + return (model_patcher,) + +def download_insightface_model(sub_dir, name, force=False, root='~/.insightface'): + # Copied and modified from insightface.utils.storage.download + # Solve https://github.com/deepinsight/insightface/issues/2711 + _root = os.path.expanduser(root) + dir_path = os.path.join(_root, sub_dir, name) + if os.path.exists(dir_path) and not force: + return dir_path + print('download_path:', dir_path) + zip_file_path = os.path.join(_root, sub_dir, name + '.zip') + model_url = "%s/%s.zip"%(BASE_REPO_URL, name) + download_file(model_url, + path=zip_file_path, + overwrite=True) + if not os.path.exists(dir_path): + os.makedirs(dir_path) + + # zip file has contains ${name} + real_dir_path = os.path.join(_root, sub_dir) + with zipfile.ZipFile(zip_file_path) as zf: + zf.extractall(real_dir_path) + #os.remove(zip_file_path) + return dir_path + +class PulidFluxInsightFaceLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "provider": (["CPU", "CUDA", "ROCM"], ), + }, + } + + RETURN_TYPES = ("FACEANALYSIS",) + FUNCTION = "load_insightface" + CATEGORY = "pulid" + + def load_insightface(self, provider): + name = "antelopev2" + download_insightface_model("models", name, root=INSIGHTFACE_DIR) + model = FaceAnalysis(name=name, root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider', ]) # alternative to buffalo_l + model.prepare(ctx_id=0, det_size=(640, 640)) + + return (model,) + +class PulidFluxEvaClipLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + } + + RETURN_TYPES = ("EVA_CLIP",) + FUNCTION = "load_eva_clip" + CATEGORY = "pulid" + + def load_eva_clip(self): + from .eva_clip.factory import create_model_and_transforms + + clip_file_path = folder_paths.get_full_path("text_encoders", 'EVA02_CLIP_L_336_psz14_s6B.pt') + if clip_file_path is None: + clip_dir = os.path.join(folder_paths.models_dir, "clip") + else: + clip_dir = os.path.dirname(clip_file_path) + model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, local_dir=clip_dir) + + model = model.visual + + eva_transform_mean = getattr(model, 'image_mean', OPENAI_DATASET_MEAN) + eva_transform_std = getattr(model, 'image_std', OPENAI_DATASET_STD) + if not isinstance(eva_transform_mean, (list, tuple)): + model["image_mean"] = (eva_transform_mean,) * 3 + if not isinstance(eva_transform_std, (list, tuple)): + model["image_std"] = (eva_transform_std,) * 3 + + return (model,) + +class ApplyPulidFlux: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", ), + "pulid_flux": ("PULIDFLUX", ), + "eva_clip": ("EVA_CLIP", ), + "face_analysis": ("FACEANALYSIS", ), + "image": ("IMAGE", ), + "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05 }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + }, + "optional": { + "attn_mask": ("MASK", ), + "options": ("OPTIONS",), + }, + "hidden": { + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply_pulid_flux" + CATEGORY = "pulid" + + def apply_pulid_flux(self, model, pulid_flux, eva_clip, face_analysis, image, weight, start_at, end_at, attn_mask=None, options={}, unique_id=None): + model = model.clone() + + device = comfy.model_management.get_torch_device() + # Why should I care what args say, when the unet model has a different dtype?! + # Am I missing something?! + #dtype = comfy.model_management.unet_dtype() + dtype = model.model.diffusion_model.dtype + # Because of 8bit models we must check what cast type does the unet uses + # ZLUDA (Intel, AMD) & GPUs with compute capability < 8.0 don't support bfloat16 etc. + # Issue: https://github.com/balazik/ComfyUI-PuLID-Flux/issues/6 + if model.model.manual_cast_dtype is not None: + dtype = model.model.manual_cast_dtype + + eva_clip.to(device, dtype=dtype) + pulid_flux.model.to(dtype=dtype) + model_management.load_models_gpu([pulid_flux], force_full_load=True) + # model_management.load_model_gpu(pulid_flux) + + if attn_mask is not None: + if attn_mask.dim() > 3: + attn_mask = attn_mask.squeeze(-1) + elif attn_mask.dim() < 3: + attn_mask = attn_mask.unsqueeze(0) + # attn_mask = attn_mask.to(device, dtype=dtype) + + image = tensor_to_image(image) + + face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + parsing_model='bisenet', + save_ext='png', + device=device, + model_rootpath=FACEXLIB_DIR + ) + + bg_label = [0, 16, 18, 7, 8, 9, 14, 15] + cond = [] + + input_face_sort = options.get('input_faces_order', "large-small") + input_face_index = options.get('input_faces_index', 0) + input_face_align_mode = options.get('input_faces_align_mode', 1) + # Analyse multiple images at multiple sizes and combine largest area embeddings + for i in range(image.shape[0]): + # get insightface embeddings + bboxes = [] + iface_embeds = None + for size in [(size, size) for size in range(640, 256, -64)]: + face_analysis.det_model.input_size = size + face_info = face_analysis.get(image[i]) + if face_info: + face_info, index, sorted_faces = get_face_by_index(face_info, face_sort_rule=input_face_sort, face_index=input_face_index) + bboxes = [face.bbox for face in sorted_faces] + iface_embeds = torch.from_numpy(face_info.embedding).unsqueeze(0).to(device, dtype=dtype) + break + else: + # No face detected, skip this image + logging.warning(f'Warning: No face detected in image {str(i)}') + continue + + if input_face_align_mode == 1: + image_size = 512 + #M = face_align.estimate_norm(face_info.kps, image_size=image_size) + kps = np.array(face_info.kps, dtype=np.float64) + if kps.dtype != np.float64: + kps = kps.astype(np.float64) + M = face_align.estimate_norm(kps, image_size=image_size) + align_face = cv2.warpAffine(image[i], M, (image_size, image_size), borderMode=cv2.BORDER_CONSTANT, + borderValue=(135, 133, 132)) + # align_face = face_align.norm_crop(image[i], landmark=face_info.kps, image_size=image_size) + del M + else: + # get eva_clip embeddings + face_helper.clean_all() + face_helper.read_image(image[i]) + face_helper.get_face_landmarks_5(ref_sort_bboxes=bboxes, face_index=input_face_index) + face_helper.align_warp_face() + + if len(face_helper.cropped_faces) == 0: + # No face detected, skip this image + continue + + # Get aligned face image + align_face = face_helper.cropped_faces[0] + # Convert bgr face image to tensor + align_face = image_to_tensor(align_face).unsqueeze(0).permute(0, 3, 1, 2).to(device) + parsing_out = face_helper.face_parse(functional.normalize(align_face, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] + parsing_out = parsing_out.argmax(dim=1, keepdim=True) + bg = sum(parsing_out == i for i in bg_label).bool() + white_image = torch.ones_like(align_face) + # Only keep the face features + face_features_image = torch.where(bg, white_image, to_gray(align_face)) + + # Transform img before sending to eva_clip + # Apparently MPS only supports NEAREST interpolation? + face_features_image = functional.resize(face_features_image, eva_clip.image_size, transforms.InterpolationMode.BICUBIC if 'cuda' in device.type else transforms.InterpolationMode.NEAREST).to(device, dtype=dtype) + face_features_image = functional.normalize(face_features_image, eva_clip.image_mean, eva_clip.image_std) + + # eva_clip + id_cond_vit, id_vit_hidden = eva_clip(face_features_image, return_all_features=False, return_hidden=True, shuffle=False) + id_cond_vit = id_cond_vit.to(device, dtype=dtype) + for idx in range(len(id_vit_hidden)): + id_vit_hidden[idx] = id_vit_hidden[idx].to(device, dtype=dtype) + + id_cond_vit = torch.div(id_cond_vit, torch.norm(id_cond_vit, 2, 1, True)) + + # Combine embeddings + id_cond = torch.cat([iface_embeds, id_cond_vit], dim=-1) + + # Pulid_encoder + cond.append(pulid_flux.model.get_embeds(id_cond, id_vit_hidden)) + + eva_clip.to(torch.device('cpu')) + if not cond: + # No faces detected, return the original model + logging.warning("PuLID warning: No faces detected in any of the given images, returning unmodified model.") + del eva_clip, face_analysis, pulid_flux, face_helper, attn_mask + return (model,) + + # average embeddings + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + cond = torch.mean(cond, dim=0, keepdim=True) + + sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at) + sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at) + + patch_kwargs = { + "pulid_model": pulid_flux, + "weight": weight, + "embedding": cond, + "sigma_start": sigma_start, + "sigma_end": sigma_end, + "mask": attn_mask + } + + ca_idx = 0 + for i in range(19): + if i % pulid_flux.model.double_interval == 0: + patch_kwargs["ca_idx"] = ca_idx + set_model_dit_patch_replace(model, patch_kwargs, ("double_block", i)) + ca_idx += 1 + for i in range(38): + if i % pulid_flux.model.single_interval == 0: + patch_kwargs["ca_idx"] = ca_idx + set_model_dit_patch_replace(model, patch_kwargs, ("single_block", i)) + ca_idx += 1 + + if len(model.get_additional_models_with_key("pulid_flux_model_patcher")) == 0: + model.set_additional_models("pulid_flux_model_patcher", [pulid_flux]) + + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name)) == 0: + # Just add it once when connecting in series + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name, pulid_outer_sample_wrappers_with_override) + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name)) == 0: + # Just add it once when connecting in series + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name, pulid_apply_model_wrappers) + + del eva_clip, face_analysis, pulid_flux, face_helper, attn_mask + return (model,) + + +class FixPulidFluxPatch: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "fix_pulid_patch" + CATEGORY = "pulid" + + def fix_pulid_patch(self, model): + model = model.clone() + + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name)) > 0: + model.remove_wrappers_with_key(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name) + + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name)) > 0: + model.remove_wrappers_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name, pulid_outer_sample_wrappers) + + set_model_patch(model, PatchKeys.options_key, pulid_enter, PatchKeys.dit_enter) + set_model_patch(model, PatchKeys.options_key, pulid_patch_double_blocks_after, PatchKeys.dit_double_blocks_after) + + return (model,) + + +class PulidFluxOptions: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input_faces_order": ( + ["left-right","right-left","top-bottom","bottom-top","small-large","large-small"], + { + "default": "large-small", + "tooltip": "left-right: Sort the left boundary of bbox by column from left to right.\n" + "right-left: Reverse order of left-right (Sort the left boundary of bbox by column from right to left).\n" + "top-bottom: Sort the top boundary of bbox by row from top to bottom.\n" + "bottom-top: Reverse order of top-bottom (Sort the top boundary of bbox by row from bottom to top).\n" + "small-large: Sort the area of bbox from small to large.\n" + "large-small: Sort the area of bbox from large to small." + } + ), + "input_faces_index": ("INT", + { + "default": 0, "min": 0, "max": 1000, "step": 1, + "tooltip": "If the value is greater than the size of bboxes, will set value to 0." + }), + "input_faces_align_mode": ("INT", + { + "default": 1, "min": 0, "max": 1, "step": 1, + "tooltip": "Align face mode.\n" + "0: align_face and embed_face use different detectors. The results maybe different.\n" + "1: align_face and embed_face use the same detector." + }), + } + } + + RETURN_TYPES = ("OPTIONS",) + FUNCTION = "execute" + CATEGORY = "pulid" + + def execute(self,input_faces_order, input_faces_index, input_faces_align_mode=1): + options: dict = { + "input_faces_order": input_faces_order, + "input_faces_index": input_faces_index, + "input_faces_align_mode": input_faces_align_mode, + } + return (options, ) + + +class PulidFluxFaceDetector: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "face_analysis": ("FACEANALYSIS", ), + "image": ("IMAGE",), + "options": ("OPTIONS",), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE",) + RETURN_NAMES = ("embed_face", "align_face", "face_bbox_image",) + FUNCTION = "execute" + CATEGORY = "pulid" + OUTPUT_IS_LIST = (True, True, True,) + + def execute(self, face_analysis, image, options): + + device = comfy.model_management.get_torch_device() + + input_face_sort = options.get('input_faces_order', "large-small") + input_face_index = options.get('input_faces_index', 0) + input_face_align_mode = options.get('input_faces_align_mode', 1) + + if input_face_align_mode == 0: + face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + parsing_model='bisenet', + save_ext='png', + device=device, + model_rootpath=FACEXLIB_DIR + ) + + # Analyse multiple images at multiple sizes and combine largest area embeddings + embed_faces=[] + align_faces=[] + draw_embed_face_bbox=[] + image = tensor_to_image(image) + for i in range(image.shape[0]): + bboxes = [] + for size in [(size, size) for size in range(640, 256, -64)]: + face_analysis.det_model.input_size = size + face_info = face_analysis.get(image[i]) + if face_info: + face_info, index, sorted_faces = get_face_by_index(face_info, face_sort_rule=input_face_sort, + face_index=input_face_index) + bboxes = [face.bbox for face in sorted_faces] + embed_faces.append(crop_image(image[i], face_info.bbox, margin=10)) + draw_embed_face_bbox.append(image_to_tensor(draw_on(image[i], sorted_faces)).unsqueeze(0)) + break + else: + # No face detected, skip this image + logging.warning(f'Warning: No face detected in image {str(i)}') + continue + + if input_face_align_mode == 1: + image_size = 512 + M = face_align.estimate_norm(face_info.kps, image_size=image_size) + align_face = cv2.warpAffine(image[i], M, (image_size, image_size), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) + # align_face = face_align.norm_crop(image[i], landmark=face_info.kps, image_size=image_size) + del M + else: + # get eva_clip embeddings + face_helper.clean_all() + face_helper.read_image(image[i]) + face_helper.get_face_landmarks_5(ref_sort_bboxes=bboxes, face_index=input_face_index) + face_helper.align_warp_face() + + if len(face_helper.cropped_faces) == 0: + # No face detected, skip this image + continue + + # Get aligned face image + align_face = face_helper.cropped_faces[0] + del face_helper + align_faces.append(image_to_tensor(align_face).unsqueeze(0)) + del bboxes, align_face + del image + if len(embed_faces) == 0: + # No face detected, skip this image + logging.warning(f'Warning: No embed face detected in image') + if len(align_faces) == 0: + logging.warning(f'Warning: No align face detected in image') + return embed_faces, align_faces, draw_embed_face_bbox, + + +def crop_image(image, bbox, margin=0): + if len(image.shape) == 3: + image = image[None, ...] + image = image_to_tensor(image) + x, y, x1, y1 = bbox.astype(int) + w = x1 - x + h = y1 - y + image_height = image.shape[1] + image_width = image.shape[2] + # ε·¦δΈŠθ§’εζ ‡ + x = min(x, image_width) + y = min(y, image_height) + # 右下角坐标 + to_x = min(w + x + margin, image_width) + to_y = min(h + y + margin, image_height) + # ι˜²ζ­’θΆŠη•Œ + x = max(0, x - margin) + y = max(0, y - margin) + to_x = max(0, to_x) + to_y = max(0, to_y) + # ζŒ‰εŒΊεŸŸζˆͺ取图片 + crop_img = image[:, y:to_y, x:to_x, :] + return crop_img + + +def set_hook(diffusion_model, target_forward_orig): + # comfy.ldm.flux.model.Flux.old_forward_orig_for_pulid = comfy.ldm.flux.model.Flux.forward_orig + # comfy.ldm.flux.model.Flux.forward_orig = pulid_forward_orig + diffusion_model.old_forward_orig_for_pulid = diffusion_model.forward_orig + diffusion_model.forward_orig = types.MethodType(target_forward_orig, diffusion_model) + +def clean_hook(diffusion_model): + # if hasattr(comfy.ldm.flux.model.Flux, 'old_forward_orig_for_pulid'): + # comfy.ldm.flux.model.Flux.forward_orig = comfy.ldm.flux.model.Flux.old_forward_orig_for_pulid + # del comfy.ldm.flux.model.Flux.old_forward_orig_for_pulid + if hasattr(diffusion_model, 'old_forward_orig_for_pulid'): + diffusion_model.forward_orig = diffusion_model.old_forward_orig_for_pulid + del diffusion_model.old_forward_orig_for_pulid + +def pulid_outer_sample_wrappers_with_override(wrapper_executor, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs): + cfg_guider = wrapper_executor.class_obj + PULID_model_patch = add_model_patch_option(cfg_guider, PatchKeys.pulid_patch_key_attrs) + PULID_model_patch['latent_image_shape'] = latent_image.shape + + diffusion_model = cfg_guider.model_patcher.model.diffusion_model + set_hook(diffusion_model, pulid_forward_orig) + try : + out = wrapper_executor(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs) + finally: + del PULID_model_patch['latent_image_shape'] + clean_hook(diffusion_model) + del diffusion_model, cfg_guider + + return out + +def pulid_outer_sample_wrappers(wrapper_executor, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs): + cfg_guider = wrapper_executor.class_obj + PULID_model_patch = add_model_patch_option(cfg_guider, PatchKeys.pulid_patch_key_attrs) + PULID_model_patch['latent_image_shape'] = latent_image.shape + try: + out = wrapper_executor(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs) + finally: + del PULID_model_patch['latent_image_shape'] + + return out + +def pulid_apply_model_wrappers(wrapper_executor, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): + base_model = wrapper_executor.class_obj + PULID_model_patch = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + PULID_model_patch['timesteps'] = base_model.model_sampling.timestep(t).float() + try: + transformer_options[PatchKeys.running_net_model] = base_model.diffusion_model + out = wrapper_executor(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) + finally: + if PatchKeys.running_net_model in transformer_options: + del transformer_options[PatchKeys.running_net_model] + del PULID_model_patch['timesteps'], base_model + + return out + +#facenet implementation +# ──────────────────────── 2. Model caches ────────────────────────── +MTCNN_CACHE = {} +RESNET_CACHE = {} + +def get_models(device: torch.device): + """Lazy-load / cache MTCNN + InceptionResnetV1 for the chosen device.""" + if device not in MTCNN_CACHE: + MTCNN_CACHE[device] = MTCNN( + image_size=160, + margin=14, + keep_all=True, # Keep all faces for compatibility + post_process=False, + device=device + ) + if device not in RESNET_CACHE: + RESNET_CACHE[device] = ( + InceptionResnetV1(pretrained='vggface2') + .eval() + .to(device) + ) + return MTCNN_CACHE[device], RESNET_CACHE[device] + +# ──────────────────────── 3. Face Info compatibility class ───────────────────────── +class FaceNetFaceInfo: + """Compatible face info object that mimics InsightFace's face structure""" + def __init__(self, bbox, kps, embedding, det_score=0.9): + self.bbox = bbox # [x1, y1, x2, y2] + self.kps = kps # 5 keypoints: [[x1,y1], [x2,y2], ...] + self.embedding = embedding # 512-D embedding + self.det_score = det_score + +# ──────────────────────── 4. Detection Model compatibility class ───────────────────────── +class FaceNetDetModel: + """Mimics InsightFace's det_model interface""" + def __init__(self): + self.input_size = (640, 640) # Default size, will be modified by pipeline + +# ──────────────────────── 5. Main FaceAnalysis compatibility class ───────────────────────── +class FaceNetAnalysis: + """ + FaceNet-based face analysis that mimics InsightFace's FaceAnalysis interface + """ + def __init__(self, device): + self.device = device + self.det_model = FaceNetDetModel() + self.mtcnn = None + self.resnet = None + self._prepared = False + + def prepare(self, ctx_id=0, det_size=(640, 640)): + """Initialize models - called by downstream nodes""" + self.det_model.input_size = det_size + self._prepared = True + + def get(self, image): + """ + Main face detection and embedding method - must return list of face objects + Compatible with: face_info = face_analysis.get(image[i]) + """ + if not self._prepared: + self.prepare() + + # Lazy load models + if self.mtcnn is None or self.resnet is None: + self.mtcnn, self.resnet = get_models(self.device) + + # Handle numpy array input (from ComfyUI) + if isinstance(image, np.ndarray): + # Convert from BGR to RGB if needed + if image.shape[-1] == 3: + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + pil_img = Image.fromarray(image) + else: + pil_img = image + + # Ensure RGB + if pil_img.mode != 'RGB': + pil_img = pil_img.convert('RGB') + + # Resize image based on current input_size setting + input_size = self.det_model.input_size + if isinstance(input_size, (list, tuple)): + target_size = input_size[0] if input_size[0] == input_size[1] else min(input_size) + else: + target_size = input_size + + # Resize image for detection + original_size = pil_img.size + if original_size[0] != target_size or original_size[1] != target_size: + pil_img_resized = pil_img.resize((target_size, target_size), Image.Resampling.LANCZOS) + else: + pil_img_resized = pil_img + + # Detect faces and get aligned crops + try: + # MTCNN returns: boxes, probs, landmarks (if keep_all=True, returns lists) + boxes, probs, landmarks = self.mtcnn.detect(pil_img_resized, landmarks=True) + + if boxes is None or len(boxes) == 0: + return [] # No faces detected + + # Get face crops for embedding + aligned_faces = [] + face_tensors = [] + + for i, (box, landmark, prob) in enumerate(zip(boxes, landmarks, probs)): + if prob < 0.9: # Skip low confidence faces + continue + + # Extract and align face + try: + face_tensor = self.mtcnn.extract(pil_img_resized, [box], save_path=None) + if face_tensor is not None and len(face_tensor) > 0: + face_tensors.append(face_tensor[0]) + aligned_faces.append((box, landmark, prob)) + except: + continue + + if not face_tensors: + return [] + + # Get embeddings for all faces + face_tensors = torch.stack(face_tensors).to(self.device) + with torch.no_grad(): + embeddings = self.resnet(face_tensors) + embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) + + # Create FaceInfo objects compatible with InsightFace + face_infos = [] + scale_x = original_size[0] / target_size + scale_y = original_size[1] / target_size + + for i, ((box, landmark, prob), embedding) in enumerate(zip(aligned_faces, embeddings)): + # Scale bbox back to original image size + scaled_bbox = [ + box[0] * scale_x, # x1 + box[1] * scale_y, # y1 + box[2] * scale_x, # x2 + box[3] * scale_y # y2 + ] + + # Scale landmarks back to original image size + scaled_kps = landmark.copy() + scaled_kps[:, 0] *= scale_x # x coordinates + scaled_kps[:, 1] *= scale_y # y coordinates + + # Convert embedding to numpy for compatibility + embedding_np = embedding.cpu().numpy() + + face_info = FaceNetFaceInfo( + bbox=np.array(scaled_bbox), + kps=scaled_kps, + embedding=embedding_np, + det_score=float(prob) + ) + + face_infos.append(face_info) + + return face_infos + + except Exception as e: + logging.warning(f"FaceNet face detection failed: {str(e)}") + return [] + +# ──────────────────────── 6. ComfyUI Node ────────────────────────── +class PulidFluxFaceNetLoader: + """ + FaceNet-based face analysis loader compatible with PuLID-Flux pipeline + """ + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "provider": (["CPU", "CUDA"],), + } + } + + RETURN_TYPES = ("FACEANALYSIS",) + FUNCTION = "load_facenet" + CATEGORY = "pulid" + + def load_facenet(self, provider: str): + # Map provider to torch device + if provider == "CPU": + device = torch.device("cpu") + elif provider in ["CUDA"]: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + device = torch.device("cpu") + + # Create FaceNet analysis object + face_analysis = FaceNetAnalysis(device) + + # Pre-initialize with default settings + face_analysis.prepare(ctx_id=0, det_size=(640, 640)) + + return (face_analysis,) + + +NODE_CLASS_MAPPINGS = { + "PulidFluxModelLoader": PulidFluxModelLoader, + "PulidFluxInsightFaceLoader": PulidFluxInsightFaceLoader, + "PulidFluxEvaClipLoader": PulidFluxEvaClipLoader, + "ApplyPulidFlux": ApplyPulidFlux, + "FixPulidFluxPatch": FixPulidFluxPatch, + "PulidFluxOptions": PulidFluxOptions, + "PulidFluxFaceDetector": PulidFluxFaceDetector, + "PulidFluxFaceNetLoader": PulidFluxFaceNetLoader, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PulidFluxModelLoader": "Load PuLID Flux Model", + "PulidFluxInsightFaceLoader": "Load InsightFace (PuLID Flux)", + "PulidFluxFaceNetLoader": "Load FaceNet (PuLID Flux)", + "PulidFluxEvaClipLoader": "Load Eva Clip (PuLID Flux)", + "ApplyPulidFlux": "Apply PuLID Flux", + "FixPulidFluxPatch": "Fix PuLID Flux Patch", + "PulidFluxOptions": "Pulid Flux Options", + "PulidFluxFaceDetector": "Pulid Flux Face Detector", +} diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/pyproject.toml b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/pyproject.toml new file mode 100644 index 00000000..d9743543 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui_pulid_flux_ll" +description = "The implementation for PuLID-Flux, support use with TeaCache and WaveSpeed, no model pollution." +version = "1.1.4" +license = {file = "LICENSE"} +dependencies = ['cython', 'facexlib', 'insightface', 'onnxruntime', 'onnxruntime-gpu; sys_platform != "darwin" and (platform_machine == "x86_64" or platform_machine == "AMD64")', 'ftfy', 'timm'] + +[project.urls] +Repository = "https://github.com/lldacing/ComfyUI_PuLID_Flux_ll" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "lldacing" +DisplayName = "ComfyUI_PuLID_Flux_ll" +Icon = "" diff --git a/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/requirements.txt b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/requirements.txt new file mode 100644 index 00000000..5cf6d2b8 --- /dev/null +++ b/custom_nodes/ComfyUI_PuLID_Flux_ll_FaceNet/requirements.txt @@ -0,0 +1,8 @@ +cython +facexlib +insightface +onnxruntime +onnxruntime-gpu; sys_platform != 'darwin' and (platform_machine == 'x86_64' or platform_machine == 'AMD64') +ftfy +timm +facenet-pytorch \ No newline at end of file diff --git a/custom_nodes/comfyui-custom-scripts/.gitignore b/custom_nodes/comfyui-custom-scripts/.gitignore new file mode 100644 index 00000000..8d303a4e --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/.gitignore @@ -0,0 +1,5 @@ +__pycache__ +pysssss.json +user/autocomplete.txt +web/js/assets/favicon.user.ico +web/js/assets/favicon-active.user.ico \ No newline at end of file diff --git a/custom_nodes/comfyui-custom-scripts/LICENSE b/custom_nodes/comfyui-custom-scripts/LICENSE new file mode 100644 index 00000000..acf31806 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pythongosssss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui-custom-scripts/README.md b/custom_nodes/comfyui-custom-scripts/README.md new file mode 100644 index 00000000..690bfe1c --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/README.md @@ -0,0 +1,160 @@ +# ComfyUI-Custom-Scripts + +### ⚠️ While these extensions work for the most part, i'm very busy at the moment and so unable to keep on top of everything here, thanks for your patience! + +# Installation + +1. Clone the repository: +`git clone https://github.com/pythongosssss/ComfyUI-Custom-Scripts.git` +to your ComfyUI `custom_nodes` directory + + The script will then automatically install all custom scripts and nodes. + It will attempt to use symlinks and junctions to prevent having to copy files and keep them up to date. + +- For uninstallation: + - Delete the cloned repo in `custom_nodes` + - Ensure `web/extensions/pysssss/CustomScripts` has also been removed + +# Update +1. Navigate to the cloned repo e.g. `custom_nodes/ComfyUI-Custom-Scripts` +2. `git pull` + +# Features + +## Autocomplete +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b5971135-414f-4f4e-a6cf-2650dc01085f) +Provides embedding and custom word autocomplete. You can view embedding details by clicking on the info icon on the list. +Define your list of custom words via the settings. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/160ef61c-7d7e-49d0-b60f-5a1501b74c9d) +You can quickly default to danbooru tags using the Load button, or load/manage other custom word lists. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/cc180b35-5f45-442f-9285-3ddf3fa320d0) + +## Auto Arrange Graph +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/04b06081-ca6f-4c0f-8584-d0a157c36747) +Adds a menu option to auto arrange the graph in order of execution, this makes very wide graphs! + +## Always Snap to Grid +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/66f36d1f-e579-4959-9880-9a9624922e3a) +Adds a setting to make moving nodes always snap to grid. + +## [Testing] "Better" Loader Lists +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/664caa71-f25f-4a96-a04a-1466d6b2b8b4) +Adds custom Lora and Checkpoint loader nodes, these have the ability to show preview images, just place a png or jpg next to the file and it'll display in the list on hover (e.g. sdxl.safetensors and sdxl.png). +Optionally enable subfolders via the settings: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/e15b5e83-4f9d-4d57-8324-742bedf75439) +Adds an "examples" widget to load sample prompts, triggerwords, etc: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ad1751e4-4c85-42e7-9490-e94fb1cbc8e7) +These should be stored in a folder matching the name of the model, e.g. if it is `loras/add_detail.safetensors` put your files in as `loras/add_detail/*.txt` +To quickly save a generated image as the preview to use for the model, you can right click on an image on a node, and select Save as Preview and choose the model to save the preview for: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9fa8e9db-27b3-45cb-85c2-0860a238fd3a) + +## Checkpoint/LoRA/Embedding Info +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/6b67bf40-ee17-4fa6-a0c1-7947066bafc2) +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/32405df6-b367-404f-a5df-2d4347089a9e) +Adds "View Info" menu option to view details about the selected LoRA or Checkpoint. To view embedding details, click the info button when using embedding autocomplete. + +## Constrain Image +Adds a node for resizing an image to a max & min size optionally cropping if required. + +## Custom Colors +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/fa7883f3-f81c-49f6-9ab6-9526e4debab6) +Adds a custom color picker to nodes & groups + +## Favicon Status +![image](https://user-images.githubusercontent.com/125205205/230171227-31f061a6-6324-4976-bed9-723a87500cf3.png) +![image](https://user-images.githubusercontent.com/125205205/230171445-c7202a45-b511-4d69-87fa-945ad44c063f.png) +Adds a favicon and title to the window, favicon changes color while generating and the window title includes the number of prompts in the queue + +## Image Feed +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/caea0d48-85b9-4ca9-9771-5c795db35fbc) +Adds a panel showing images that have been generated in the current session, you can control the direction that images are added and the position of the panel via the ComfyUI settings screen and the size of the panel and the images via the sliders at the top of the panel. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ca093d38-41a3-4647-9223-5bd0b9ee4f1e) + +## KSampler (Advanced) denoise helper +Provides a simple method to set custom denoise on the advanced sampler +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/42946bd8-0078-4c7a-bfe9-7adb1382b5e2) +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7cfccb22-f155-4848-934b-a2b2a6efe16f) + +## Math Expression +Allows for evaluating complex expressions using values from the graph. You can input `INT`, `FLOAT`, `IMAGE` and `LATENT` values. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/1593edde-67b8-45d8-88cb-e75f52dba039) +Other nodes values can be referenced via the `Node name for S&R` via the `Properties` menu item on a node, or the node title. +Supported operators: `+ - * /` (basic ops) `//` (floor division) `**` (power) `^` (xor) `%` (mod) +Supported functions `floor(num, dp?)` `floor(num)` `ceil(num)` `randomint(min,max)` +If using a `LATENT` or `IMAGE` you can get the dimensions using `a.width` or `a.height` where `a` is the input name. + +## Node Finder +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/177d2b67-acbc-4ec3-ab31-7c295a98c194) +Adds a menu item for following/jumping to the executing node, and a menu to quickly go to a node of a specific type. + +## Preset Text +![image](https://user-images.githubusercontent.com/125205205/230173939-08459efc-785b-46da-93d1-b02f0300c6f4.png) +Adds a node that lets you save and use text presets (e.g. for your 'normal' negatives) + +## Quick Nodes +![image](https://user-images.githubusercontent.com/125205205/230174266-5232831a-a03b-4bf7-bc8b-c45466a0bc64.png) +Adds various menu items to some nodes for quickly setting up common parts of graphs + +## Play Sound +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9bcf9fb3-5898-4432-a974-fb1e17d3b7e8) +Plays a sound when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts. +You can customize the sound by replacing the mp3 file `ComfyUI/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/notify.mp3` + +## System Notification +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/30354775/993fd783-5cd6-4779-aa97-173bc06cc405) +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/30354775/e45227fb-5714-4f45-b96b-6601902ef6e2) + +Sends a system notification via the browser when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts. + +## [WIP] Repeater +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ec0dac25-14e4-4d44-b975-52193656709d) +Node allows you to either create a list of N repeats of the input node, or create N outputs from the input node. +You can optionally decide if you want to reuse the input node, or create a new instance each time (e.g. a Checkpoint Loader would want to be re-used, but a random number would want to be unique) +TODO: Type safety on the wildcard outputs to require match with input + +## Show Text +![image](https://user-images.githubusercontent.com/125205205/230174888-c004fd48-da78-4de9-81c2-93a866fcfcd1.png) +Takes input from a node that produces a string and displays it, useful for things like interrogator, prompt generators, etc. + +## Show Image on Menu +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b6ab58f2-583b-448c-bcfc-f93f5cdab0fc) +Shows the current generating image on the menu at the bottom, you can disable this via the settings menu. + +## String Function +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/01107137-8a93-4765-bae0-fcc110a09091) +Supports appending and replacing text +`tidy_tags` will add commas between parts when in `append` mode. +`replace` mode supports regex replace by using `/your regex here/` and you can reference capturing groups using `\number` e.g. `\1` + +## Touch Support +Provides basic support for touch screen devices, its not perfect but better than nothing + +## Widget Defaults +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/3d675032-2b19-4da8-a7d7-fa2d7c555daa) +Allows you to specify default values for widgets when adding new nodes, the values are configured via the settings menu +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b57a3d8-98d3-46e9-9b33-6645c0da41e7) + +## Workflows +Adds options to the menu for saving + loading workflows: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b5a3012-4c59-47c6-8eea-85cf534403ea) + +## Workflow Images +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/06453fd2-c020-46ee-a7db-2b8bf5bcba7e) +Adds menu options for importing/exporting the graph as SVG and PNG showing a view of the nodes + +## (Testing) Reroute Primitive +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/8b870eef-d572-43f9-b394-cfa7abbd2f98) Provides a node that allows rerouting primitives. +The node can also be collapsed to a single point that you can drag around. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/a9bd0112-cf8f-44f3-af6d-f9a8fed152a7) +Warning: Don't use normal reroutes or primitives with these nodes, it isn't tested and this node replaces their functionality. + +
+
+ + +## WD14 Tagger +Moved to: https://github.com/pythongosssss/ComfyUI-WD14-Tagger + +## ~~Lock Nodes & Groups~~ +This is now a standard feature of ComfyUI +~~Adds a lock option to nodes & groups that prevents you from moving them until unlocked~~ diff --git a/custom_nodes/comfyui-custom-scripts/__init__.py b/custom_nodes/comfyui-custom-scripts/__init__.py new file mode 100644 index 00000000..ae43dd20 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/__init__.py @@ -0,0 +1,25 @@ +import importlib.util +import glob +import os +import sys +from .pysssss import init, get_ext_dir + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +if init(): + py = get_ext_dir("py") + files = glob.glob(os.path.join(py, "*.py"), recursive=False) + for file in files: + name = os.path.splitext(file)[0] + spec = importlib.util.spec_from_file_location(name, file) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + spec.loader.exec_module(module) + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS) + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + +WEB_DIRECTORY = "./web" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] diff --git a/custom_nodes/comfyui-custom-scripts/py/autocomplete.py b/custom_nodes/comfyui-custom-scripts/py/autocomplete.py new file mode 100644 index 00000000..8ac6a052 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/autocomplete.py @@ -0,0 +1,29 @@ +from server import PromptServer +from aiohttp import web +import os +import folder_paths + +dir = os.path.abspath(os.path.join(__file__, "../../user")) +if not os.path.exists(dir): + os.mkdir(dir) +file = os.path.join(dir, "autocomplete.txt") + + +@PromptServer.instance.routes.get("/pysssss/autocomplete") +async def get_autocomplete(request): + if os.path.isfile(file): + return web.FileResponse(file) + return web.Response(status=404) + + +@PromptServer.instance.routes.post("/pysssss/autocomplete") +async def update_autocomplete(request): + with open(file, "w", encoding="utf-8") as f: + f.write(await request.text()) + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/pysssss/loras") +async def get_loras(request): + loras = folder_paths.get_filename_list("loras") + return web.json_response(list(map(lambda a: os.path.splitext(a)[0], loras))) diff --git a/custom_nodes/comfyui-custom-scripts/py/better_combos.py b/custom_nodes/comfyui-custom-scripts/py/better_combos.py new file mode 100644 index 00000000..d31043b0 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/better_combos.py @@ -0,0 +1,172 @@ +import glob +import os +from nodes import LoraLoader, CheckpointLoaderSimple +import folder_paths +from server import PromptServer +from folder_paths import get_directory_by_type +from aiohttp import web +import shutil + + +@PromptServer.instance.routes.get("/pysssss/view/{name}") +async def view(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + image_path = folder_paths.get_full_path( + type, name) + if not image_path: + return web.Response(status=404) + + filename = os.path.basename(image_path) + return web.FileResponse(image_path, headers={"Content-Disposition": f"filename=\"{filename}\""}) + + +@PromptServer.instance.routes.post("/pysssss/save/{name}") +async def save_preview(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + body = await request.json() + + dir = get_directory_by_type(body.get("type", "output")) + subfolder = body.get("subfolder", "") + full_output_folder = os.path.join(dir, os.path.normpath(subfolder)) + + filepath = os.path.join(full_output_folder, body.get("filename", "")) + + if os.path.commonpath((dir, os.path.abspath(filepath))) != dir: + return web.Response(status=400) + + image_path = folder_paths.get_full_path(type, name) + image_path = os.path.splitext( + image_path)[0] + os.path.splitext(filepath)[1] + + shutil.copyfile(filepath, image_path) + + return web.json_response({ + "image": type + "/" + os.path.basename(image_path) + }) + + +@PromptServer.instance.routes.get("/pysssss/examples/{name}") +async def get_examples(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + file_path_no_ext = os.path.splitext(file_path)[0] + examples = [] + + if os.path.isdir(file_path_no_ext): + examples += sorted(map(lambda t: os.path.relpath(t, file_path_no_ext), + glob.glob(file_path_no_ext + "/*.txt"))) + + if os.path.isfile(file_path_no_ext + ".txt"): + examples += ["notes"] + + return web.json_response(examples) + + +@PromptServer.instance.routes.post("/pysssss/examples/{name}") +async def save_example(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + body = await request.json() + example_name = body["name"] + example = body["example"] + + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + if not example_name.endswith(".txt"): + example_name += ".txt" + + file_path_no_ext = os.path.splitext(file_path)[0] + example_file = os.path.join(file_path_no_ext, example_name) + if not os.path.exists(file_path_no_ext): + os.mkdir(file_path_no_ext) + with open(example_file, 'w', encoding='utf8') as f: + f.write(example) + + return web.Response(status=201) + + +@PromptServer.instance.routes.get("/pysssss/images/{type}") +async def get_images(request): + type = request.match_info["type"] + names = folder_paths.get_filename_list(type) + + images = {} + for item_name in names: + file_name = os.path.splitext(item_name)[0] + file_path = folder_paths.get_full_path(type, item_name) + + if file_path is None: + continue + + file_path_no_ext = os.path.splitext(file_path)[0] + + for ext in ["png", "jpg", "jpeg", "preview.png", "preview.jpeg"]: + if os.path.isfile(file_path_no_ext + "." + ext): + images[item_name] = f"{type}/{file_name}.{ext}" + break + + return web.json_response(images) + + +class LoraLoaderWithImages(LoraLoader): + RETURN_TYPES = (*LoraLoader.RETURN_TYPES, "STRING",) + RETURN_NAMES = (*getattr(LoraLoader, "RETURN_NAMES", + LoraLoader.RETURN_TYPES), "example") + + @classmethod + def INPUT_TYPES(s): + types = super().INPUT_TYPES() + types["optional"] = {"prompt": ("STRING", {"hidden": True})} + return types + + def load_lora(self, **kwargs): + prompt = kwargs.pop("prompt", "") + return (*super().load_lora(**kwargs), prompt) + + +class CheckpointLoaderSimpleWithImages(CheckpointLoaderSimple): + RETURN_TYPES = (*CheckpointLoaderSimple.RETURN_TYPES, "STRING",) + RETURN_NAMES = (*getattr(CheckpointLoaderSimple, "RETURN_NAMES", + CheckpointLoaderSimple.RETURN_TYPES), "example") + + @classmethod + def INPUT_TYPES(s): + types = super().INPUT_TYPES() + types["optional"] = {"prompt": ("STRING", {"hidden": True})} + return types + + def load_checkpoint(self, **kwargs): + prompt = kwargs.pop("prompt", "") + return (*super().load_checkpoint(**kwargs), prompt) + + +NODE_CLASS_MAPPINGS = { + "LoraLoader|pysssss": LoraLoaderWithImages, + "CheckpointLoader|pysssss": CheckpointLoaderSimpleWithImages, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoraLoader|pysssss": "Lora Loader 🐍", + "CheckpointLoader|pysssss": "Checkpoint Loader 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/constrain_image.py b/custom_nodes/comfyui-custom-scripts/py/constrain_image.py new file mode 100644 index 00000000..1c768893 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/constrain_image.py @@ -0,0 +1,71 @@ +import torch +import numpy as np +from PIL import Image + +class ConstrainImage: + """ + A node that constrains an image to a maximum and minimum size while maintaining aspect ratio. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "max_width": ("INT", {"default": 1024, "min": 0}), + "max_height": ("INT", {"default": 1024, "min": 0}), + "min_width": ("INT", {"default": 0, "min": 0}), + "min_height": ("INT", {"default": 0, "min": 0}), + "crop_if_required": (["yes", "no"], {"default": "no"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "constrain_image" + CATEGORY = "image" + OUTPUT_IS_LIST = (True,) + + def constrain_image(self, images, max_width, max_height, min_width, min_height, crop_if_required): + crop_if_required = crop_if_required == "yes" + results = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB") + + current_width, current_height = img.size + aspect_ratio = current_width / current_height + + constrained_width = min(max(current_width, min_width), max_width) + constrained_height = min(max(current_height, min_height), max_height) + + if constrained_width / constrained_height > aspect_ratio: + constrained_width = max(int(constrained_height * aspect_ratio), min_width) + if crop_if_required: + constrained_height = int(current_height / (current_width / constrained_width)) + else: + constrained_height = max(int(constrained_width / aspect_ratio), min_height) + if crop_if_required: + constrained_width = int(current_width / (current_height / constrained_height)) + + resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS) + + if crop_if_required and (constrained_width > max_width or constrained_height > max_height): + left = max((constrained_width - max_width) // 2, 0) + top = max((constrained_height - max_height) // 2, 0) + right = min(constrained_width, max_width) + left + bottom = min(constrained_height, max_height) + top + resized_image = resized_image.crop((left, top, right, bottom)) + + resized_image = np.array(resized_image).astype(np.float32) / 255.0 + resized_image = torch.from_numpy(resized_image)[None,] + results.append(resized_image) + + return (results,) + +NODE_CLASS_MAPPINGS = { + "ConstrainImage|pysssss": ConstrainImage, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ConstrainImage|pysssss": "Constrain Image 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/constrain_image_for_video.py b/custom_nodes/comfyui-custom-scripts/py/constrain_image_for_video.py new file mode 100644 index 00000000..91c0c502 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/constrain_image_for_video.py @@ -0,0 +1,72 @@ +import torch +import numpy as np +from PIL import Image + +class ConstrainImageforVideo: + """ + A node that constrains an image to a maximum and minimum size while maintaining aspect ratio. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "max_width": ("INT", {"default": 1024, "min": 0}), + "max_height": ("INT", {"default": 1024, "min": 0}), + "min_width": ("INT", {"default": 0, "min": 0}), + "min_height": ("INT", {"default": 0, "min": 0}), + "crop_if_required": (["yes", "no"], {"default": "no"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "constrain_image_for_video" + CATEGORY = "image" + + def constrain_image_for_video(self, images, max_width, max_height, min_width, min_height, crop_if_required): + crop_if_required = crop_if_required == "yes" + results = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB") + + current_width, current_height = img.size + aspect_ratio = current_width / current_height + + constrained_width = max(min(current_width, min_width), max_width) + constrained_height = max(min(current_height, min_height), max_height) + + if constrained_width / constrained_height > aspect_ratio: + constrained_width = max(int(constrained_height * aspect_ratio), min_width) + if crop_if_required: + constrained_height = int(current_height / (current_width / constrained_width)) + else: + constrained_height = max(int(constrained_width / aspect_ratio), min_height) + if crop_if_required: + constrained_width = int(current_width / (current_height / constrained_height)) + + resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS) + + if crop_if_required and (constrained_width > max_width or constrained_height > max_height): + left = max((constrained_width - max_width) // 2, 0) + top = max((constrained_height - max_height) // 2, 0) + right = min(constrained_width, max_width) + left + bottom = min(constrained_height, max_height) + top + resized_image = resized_image.crop((left, top, right, bottom)) + + resized_image = np.array(resized_image).astype(np.float32) / 255.0 + resized_image = torch.from_numpy(resized_image)[None,] + results.append(resized_image) + all_images = torch.cat(results, dim=0) + + return (all_images, all_images.size(0),) + +NODE_CLASS_MAPPINGS = { + "ConstrainImageforVideo|pysssss": ConstrainImageforVideo, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ConstrainImageforVideo|pysssss": "Constrain Image for Video 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/math_expression.py b/custom_nodes/comfyui-custom-scripts/py/math_expression.py new file mode 100644 index 00000000..4a41655b --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/math_expression.py @@ -0,0 +1,252 @@ +import ast +import math +import random +import operator as op + +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + +operators = { + ast.Add: op.add, + ast.Sub: op.sub, + ast.Mult: op.mul, + ast.Div: op.truediv, + ast.FloorDiv: op.floordiv, + ast.Pow: op.pow, + ast.BitXor: op.xor, + ast.USub: op.neg, + ast.Mod: op.mod, + ast.BitAnd: op.and_, + ast.BitOr: op.or_, + ast.Invert: op.invert, + ast.And: lambda a, b: 1 if a and b else 0, + ast.Or: lambda a, b: 1 if a or b else 0, + ast.Not: lambda a: 0 if a else 1, + ast.RShift: op.rshift, + ast.LShift: op.lshift +} + +# TODO: restructure args to provide more info, generate hint based on args to save duplication +functions = { + "round": { + "args": (1, 2), + "call": lambda a, b = None: round(a, b), + "hint": "number, dp? = 0" + }, + "ceil": { + "args": (1, 1), + "call": lambda a: math.ceil(a), + "hint": "number" + }, + "floor": { + "args": (1, 1), + "call": lambda a: math.floor(a), + "hint": "number" + }, + "min": { + "args": (2, None), + "call": lambda *args: min(*args), + "hint": "...numbers" + }, + "max": { + "args": (2, None), + "call": lambda *args: max(*args), + "hint": "...numbers" + }, + "randomint": { + "args": (2, 2), + "call": lambda a, b: random.randint(a, b), + "hint": "min, max" + }, + "randomchoice": { + "args": (2, None), + "call": lambda *args: random.choice(args), + "hint": "...numbers" + }, + "sqrt": { + "args": (1, 1), + "call": lambda a: math.sqrt(a), + "hint": "number" + }, + "int": { + "args": (1, 1), + "call": lambda a = None: int(a), + "hint": "number" + }, + "iif": { + "args": (3, 3), + "call": lambda a, b, c = None: b if a else c, + "hint": "value, truepart, falsepart" + }, +} + +autocompleteWords = list({ + "text": x, + "value": f"{x}()", + "showValue": False, + "hint": f"{functions[x]['hint']}", + "caretOffset": -1 +} for x in functions.keys()) + + +class MathExpression: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "expression": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": { + "words": autocompleteWords, + "separator": "" + }}), + }, + "optional": { + "a": (any, ), + "b": (any,), + "c": (any, ), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT"}, + } + + RETURN_TYPES = ("INT", "FLOAT", ) + FUNCTION = "evaluate" + CATEGORY = "utils" + OUTPUT_NODE = True + + @classmethod + def IS_CHANGED(s, expression, **kwargs): + if "random" in expression: + return float("nan") + return expression + + def get_widget_value(self, extra_pnginfo, prompt, node_name, widget_name): + workflow = extra_pnginfo["workflow"] if "workflow" in extra_pnginfo else { "nodes": [] } + node_id = None + for node in workflow["nodes"]: + name = node["type"] + if "properties" in node: + if "Node name for S&R" in node["properties"]: + name = node["properties"]["Node name for S&R"] + if name == node_name: + node_id = node["id"] + break + if "title" in node: + name = node["title"] + if name == node_name: + node_id = node["id"] + break + if node_id is not None: + values = prompt[str(node_id)] + if "inputs" in values: + if widget_name in values["inputs"]: + value = values["inputs"][widget_name] + if isinstance(value, list): + raise ValueError("Converted widgets are not supported via named reference, use the inputs instead.") + return value + raise NameError(f"Widget not found: {node_name}.{widget_name}") + raise NameError(f"Node not found: {node_name}.{widget_name}") + + def get_size(self, target, property): + if isinstance(target, dict) and "samples" in target: + # Latent + if property == "width": + return target["samples"].shape[3] * 8 + return target["samples"].shape[2] * 8 + else: + # Image + if property == "width": + return target.shape[2] + return target.shape[1] + + def evaluate(self, expression, prompt, extra_pnginfo={}, a=None, b=None, c=None): + expression = expression.replace('\n', ' ').replace('\r', '') + node = ast.parse(expression, mode='eval').body + + lookup = {"a": a, "b": b, "c": c} + + def eval_op(node, l, r): + l = eval_expr(l) + r = eval_expr(r) + l = l if isinstance(l, int) else float(l) + r = r if isinstance(r, int) else float(r) + return operators[type(node.op)](l, r) + + def eval_expr(node): + if isinstance(node, ast.Constant) or isinstance(node, ast.Num): + return node.n + elif isinstance(node, ast.BinOp): + return eval_op(node, node.left, node.right) + elif isinstance(node, ast.BoolOp): + return eval_op(node, node.values[0], node.values[1]) + elif isinstance(node, ast.UnaryOp): + return operators[type(node.op)](eval_expr(node.operand)) + elif isinstance(node, ast.Attribute): + if node.value.id in lookup: + if node.attr == "width" or node.attr == "height": + return self.get_size(lookup[node.value.id], node.attr) + + return self.get_widget_value(extra_pnginfo, prompt, node.value.id, node.attr) + elif isinstance(node, ast.Name): + if node.id in lookup: + val = lookup[node.id] + if isinstance(val, (int, float, complex)): + return val + else: + raise TypeError( + f"Compex types (LATENT/IMAGE) need to reference their width/height, e.g. {node.id}.width") + raise NameError(f"Name not found: {node.id}") + elif isinstance(node, ast.Call): + if node.func.id in functions: + fn = functions[node.func.id] + l = len(node.args) + if l < fn["args"][0] or (fn["args"][1] is not None and l > fn["args"][1]): + if fn["args"][1] is None: + toErr = " or more" + else: + toErr = f" to {fn['args'][1]}" + raise SyntaxError( + f"Invalid function call: {node.func.id} requires {fn['args'][0]}{toErr} arguments") + args = [] + for arg in node.args: + args.append(eval_expr(arg)) + return fn["call"](*args) + raise NameError(f"Invalid function call: {node.func.id}") + elif isinstance(node, ast.Compare): + l = eval_expr(node.left) + r = eval_expr(node.comparators[0]) + if isinstance(node.ops[0], ast.Eq): + return 1 if l == r else 0 + if isinstance(node.ops[0], ast.NotEq): + return 1 if l != r else 0 + if isinstance(node.ops[0], ast.Gt): + return 1 if l > r else 0 + if isinstance(node.ops[0], ast.GtE): + return 1 if l >= r else 0 + if isinstance(node.ops[0], ast.Lt): + return 1 if l < r else 0 + if isinstance(node.ops[0], ast.LtE): + return 1 if l <= r else 0 + raise NotImplementedError( + "Operator " + node.ops[0].__class__.__name__ + " not supported.") + else: + raise TypeError(node) + + r = eval_expr(node) + return {"ui": {"value": [r]}, "result": (int(r), float(r),)} + + +NODE_CLASS_MAPPINGS = { + "MathExpression|pysssss": MathExpression, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "MathExpression|pysssss": "Math Expression 🐍", +} + diff --git a/custom_nodes/comfyui-custom-scripts/py/model_info.py b/custom_nodes/comfyui-custom-scripts/py/model_info.py new file mode 100644 index 00000000..b360bafb --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/model_info.py @@ -0,0 +1,115 @@ +import hashlib +import json +from aiohttp import web +from server import PromptServer +import folder_paths +import os + + +def get_metadata(filepath): + with open(filepath, "rb") as file: + # https://github.com/huggingface/safetensors#format + # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header + header_size = int.from_bytes(file.read(8), "little", signed=False) + + if header_size <= 0: + raise BufferError("Invalid header size") + + header = file.read(header_size) + if header_size <= 0: + raise BufferError("Invalid header") + + header_json = json.loads(header) + return header_json["__metadata__"] if "__metadata__" in header_json else None + + +@PromptServer.instance.routes.post("/pysssss/metadata/notes/{name}") +async def save_notes(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = None + if type == "embeddings" or type == "loras": + name = name.lower() + files = folder_paths.get_filename_list(type) + for f in files: + lower_f = f.lower() + if lower_f == name: + file_path = folder_paths.get_full_path(type, f) + else: + n = os.path.splitext(f)[0].lower() + if n == name: + file_path = folder_paths.get_full_path(type, f) + + if file_path is not None: + break + else: + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + file_no_ext = os.path.splitext(file_path)[0] + info_file = file_no_ext + ".txt" + with open(info_file, "w") as f: + f.write(await request.text()) + + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/pysssss/metadata/{name}") +async def load_metadata(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = None + if type == "embeddings" or type == "loras": + name = name.lower() + files = folder_paths.get_filename_list(type) + for f in files: + lower_f = f.lower() + if lower_f == name: + file_path = folder_paths.get_full_path(type, f) + else: + n = os.path.splitext(f)[0].lower() + if n == name: + file_path = folder_paths.get_full_path(type, f) + + if file_path is not None: + break + else: + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + try: + meta = get_metadata(file_path) + except: + meta = None + + if meta is None: + meta = {} + + file_no_ext = os.path.splitext(file_path)[0] + + info_file = file_no_ext + ".txt" + if os.path.isfile(info_file): + with open(info_file, "r") as f: + meta["pysssss.notes"] = f.read() + + hash_file = file_no_ext + ".sha256" + if os.path.isfile(hash_file): + with open(hash_file, "rt") as f: + meta["pysssss.sha256"] = f.read() + else: + with open(file_path, "rb") as f: + meta["pysssss.sha256"] = hashlib.sha256(f.read()).hexdigest() + with open(hash_file, "wt") as f: + f.write(meta["pysssss.sha256"]) + + return web.json_response(meta) diff --git a/custom_nodes/comfyui-custom-scripts/py/play_sound.py b/custom_nodes/comfyui-custom-scripts/py/play_sound.py new file mode 100644 index 00000000..65f599d2 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/play_sound.py @@ -0,0 +1,42 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class PlaySound: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "any": (any, {}), + "mode": (["always", "on empty queue"], {}), + "volume": ("FLOAT", {"min": 0, "max": 1, "step": 0.1, "default": 0.5}), + "file": ("STRING", { "default": "notify.mp3" }) + }} + + FUNCTION = "nop" + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True,) + OUTPUT_NODE = True + RETURN_TYPES = (any,) + + CATEGORY = "utils" + + def IS_CHANGED(self, **kwargs): + return float("NaN") + + def nop(self, any, mode, volume, file): + return {"ui": {"a": []}, "result": (any,)} + + +NODE_CLASS_MAPPINGS = { + "PlaySound|pysssss": PlaySound, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PlaySound|pysssss": "PlaySound 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/repeater.py b/custom_nodes/comfyui-custom-scripts/py/repeater.py new file mode 100644 index 00000000..1d83c67e --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/repeater.py @@ -0,0 +1,46 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class Repeater: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "source": (any, {}), + "repeats": ("INT", {"min": 0, "max": 5000, "default": 2}), + "output": (["single", "multi"], {}), + "node_mode": (["reuse", "create"], {}), + }} + + RETURN_TYPES = (any,) + FUNCTION = "repeat" + OUTPUT_NODE = False + OUTPUT_IS_LIST = (True,) + + CATEGORY = "utils" + + def repeat(self, repeats, output, node_mode, **kwargs): + if output == "multi": + # Multi outputs are split to indiviual nodes on the frontend when serializing + return ([kwargs["source"]],) + elif node_mode == "reuse": + # When reusing we have a single input node, repeat that N times + return ([kwargs["source"]] * repeats,) + else: + # When creating new nodes, they'll be added dynamically when the graph is serialized + return ((list(kwargs.values())),) + + +NODE_CLASS_MAPPINGS = { + "Repeater|pysssss": Repeater, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Repeater|pysssss": "Repeater 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/reroute_primitive.py b/custom_nodes/comfyui-custom-scripts/py/reroute_primitive.py new file mode 100644 index 00000000..c22a7d1e --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/reroute_primitive.py @@ -0,0 +1,59 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class ReroutePrimitive: + @classmethod + def INPUT_TYPES(cls): + return { + "required": {"value": (any, )}, + } + + @classmethod + def VALIDATE_INPUTS(s, **kwargs): + return True + + RETURN_TYPES = (any,) + FUNCTION = "route" + CATEGORY = "__hidden__" + + def route(self, value): + return (value,) + + +class MultiPrimitive: + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": {"value": (any, )}, + } + + @classmethod + def VALIDATE_INPUTS(s, **kwargs): + return True + + RETURN_TYPES = (any,) + FUNCTION = "listify" + CATEGORY = "utils" + OUTPUT_IS_LIST = (True,) + + def listify(self, **kwargs): + return (list(kwargs.values()),) + + +NODE_CLASS_MAPPINGS = { + "ReroutePrimitive|pysssss": ReroutePrimitive, + # "MultiPrimitive|pysssss": MultiPrimitive, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ReroutePrimitive|pysssss": "Reroute Primitive 🐍", + # "MultiPrimitive|pysssss": "Multi Primitive 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/show_text.py b/custom_nodes/comfyui-custom-scripts/py/show_text.py new file mode 100644 index 00000000..5a013a5b --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/show_text.py @@ -0,0 +1,49 @@ +class ShowText: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "text": ("STRING", {"forceInput": True}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("STRING",) + FUNCTION = "notify" + OUTPUT_NODE = True + OUTPUT_IS_LIST = (True,) + + CATEGORY = "utils" + + def notify(self, text, unique_id=None, extra_pnginfo=None): + if unique_id is not None and extra_pnginfo is not None: + if not isinstance(extra_pnginfo, list): + print("Error: extra_pnginfo is not a list") + elif ( + not isinstance(extra_pnginfo[0], dict) + or "workflow" not in extra_pnginfo[0] + ): + print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key") + else: + workflow = extra_pnginfo[0]["workflow"] + node = next( + (x for x in workflow["nodes"] if str(x["id"]) == str(unique_id[0])), + None, + ) + if node: + node["widgets_values"] = [text] + + return {"ui": {"text": text}, "result": (text,)} + + +NODE_CLASS_MAPPINGS = { + "ShowText|pysssss": ShowText, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ShowText|pysssss": "Show Text 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/string_function.py b/custom_nodes/comfyui-custom-scripts/py/string_function.py new file mode 100644 index 00000000..f502ba31 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/string_function.py @@ -0,0 +1,49 @@ +import re + +class StringFunction: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "action": (["append", "replace"], {}), + "tidy_tags": (["yes", "no"], {}), + }, + "optional": { + "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}) + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "exec" + CATEGORY = "utils" + OUTPUT_NODE = True + + def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""): + tidy_tags = tidy_tags == "yes" + out = "" + if action == "append": + out = (", " if tidy_tags else "").join(filter(None, [text_a, text_b, text_c])) + else: + if text_c is None: + text_c = "" + if text_b.startswith("/") and text_b.endswith("/"): + regex = text_b[1:-1] + out = re.sub(regex, text_c, text_a) + else: + out = text_a.replace(text_b, text_c) + if tidy_tags: + out = re.sub(r"\s{2,}", " ", out) + out = out.replace(" ,", ",") + out = re.sub(r",{2,}", ",", out) + out = out.strip() + return {"ui": {"text": (out,)}, "result": (out,)} + +NODE_CLASS_MAPPINGS = { + "StringFunction|pysssss": StringFunction, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "StringFunction|pysssss": "String Function 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/system_notification.py b/custom_nodes/comfyui-custom-scripts/py/system_notification.py new file mode 100644 index 00000000..d5a52c14 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/system_notification.py @@ -0,0 +1,41 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class SystemNotification: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "message": ("STRING", {"default": "Your notification has triggered."}), + "any": (any, {}), + "mode": (["always", "on empty queue"], {}), + }} + + FUNCTION = "nop" + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True,) + OUTPUT_NODE = True + RETURN_TYPES = (any,) + + CATEGORY = "utils" + + def IS_CHANGED(self, **kwargs): + return float("NaN") + + def nop(self, any, message, mode): + return {"ui": {"message": message, "mode": mode}, "result": (any,)} + + +NODE_CLASS_MAPPINGS = { + "SystemNotification|pysssss": SystemNotification, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SystemNotification|pysssss": "SystemNotification 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/text_files.py b/custom_nodes/comfyui-custom-scripts/py/text_files.py new file mode 100644 index 00000000..253de9b6 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/text_files.py @@ -0,0 +1,200 @@ +import os +import folder_paths +import json +from server import PromptServer +import glob +from aiohttp import web + + +def get_allowed_dirs(): + dir = os.path.abspath(os.path.join(__file__, "../../user")) + file = os.path.join(dir, "text_file_dirs.json") + with open(file, "r") as f: + return json.loads(f.read()) + + +def get_valid_dirs(): + return get_allowed_dirs().keys() + + +def get_dir_from_name(name): + dirs = get_allowed_dirs() + if name not in dirs: + raise KeyError(name + " dir not found") + + path = dirs[name] + path = path.replace("$input", folder_paths.get_input_directory()) + path = path.replace("$output", folder_paths.get_output_directory()) + path = path.replace("$temp", folder_paths.get_temp_directory()) + return path + + +def is_child_dir(parent_path, child_path): + parent_path = os.path.abspath(parent_path) + child_path = os.path.abspath(child_path) + return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path]) + + +def get_real_path(dir): + dir = dir.replace("/**/", "/") + dir = os.path.abspath(dir) + dir = os.path.split(dir)[0] + return dir + + +@PromptServer.instance.routes.get("/pysssss/text-file/{name}") +async def get_files(request): + name = request.match_info["name"] + dir = get_dir_from_name(name) + recursive = "/**/" in dir + # Ugh cant use root_path on glob... lazy hack.. + pre = get_real_path(dir) + + files = list(map(lambda t: os.path.relpath(t, pre), + glob.glob(dir, recursive=recursive))) + + if len(files) == 0: + files = ["[none]"] + return web.json_response(files) + + +def get_file(root_dir, file): + if file == "[none]" or not file or not file.strip(): + raise ValueError("No file") + + root_dir = get_dir_from_name(root_dir) + root_dir = get_real_path(root_dir) + if not os.path.exists(root_dir): + os.mkdir(root_dir) + full_path = os.path.join(root_dir, file) + + if not is_child_dir(root_dir, full_path): + raise ReferenceError() + + return full_path + + +class TextFileNode: + RETURN_TYPES = ("STRING",) + CATEGORY = "utils" + + @classmethod + def VALIDATE_INPUTS(self, root_dir, file, **kwargs): + if file == "[none]" or not file or not file.strip(): + return True + get_file(root_dir, file) + return True + + def load_text(self, **kwargs): + self.file = get_file(kwargs["root_dir"], kwargs["file"]) + with open(self.file, "r") as f: + return (f.read(), ) + + +class LoadText(TextFileNode): + @classmethod + def IS_CHANGED(self, **kwargs): + return os.path.getmtime(self.file) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "root_dir": (list(get_valid_dirs()), {}), + "file": (["[none]"], { + "pysssss.binding": [{ + "source": "root_dir", + "callback": [{ + "type": "set", + "target": "$this.disabled", + "value": True + }, { + "type": "fetch", + "url": "/pysssss/text-file/{$source.value}", + "then": [{ + "type": "set", + "target": "$this.options.values", + "value": "$result" + }, { + "type": "validate-combo" + }, { + "type": "set", + "target": "$this.disabled", + "value": False + }] + }], + }] + }) + }, + } + + FUNCTION = "load_text" + + +class SaveText(TextFileNode): + OUTPUT_NODE = True + + @classmethod + def IS_CHANGED(self, **kwargs): + return float("nan") + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "root_dir": (list(get_valid_dirs()), {}), + "file": ("STRING", {"default": "file.txt"}), + "append": (["append", "overwrite", "new only"], {}), + "insert": ("BOOLEAN", { + "default": True, "label_on": "new line", "label_off": "none", + "pysssss.binding": [{ + "source": "append", + "callback": [{ + "type": "if", + "condition": [{ + "left": "$source.value", + "op": "eq", + "right": '"append"' + }], + "true": [{ + "type": "set", + "target": "$this.disabled", + "value": False + }], + "false": [{ + "type": "set", + "target": "$this.disabled", + "value": True + }], + }] + }] + }), + "text": ("STRING", {"forceInput": True, "multiline": True}) + }, + } + + FUNCTION = "write_text" + + def write_text(self, **kwargs): + self.file = get_file(kwargs["root_dir"], kwargs["file"]) + if kwargs["append"] == "new only" and os.path.exists(self.file): + raise FileExistsError( + self.file + " already exists and 'new only' is selected.") + with open(self.file, "a+" if kwargs["append"] == "append" else "w") as f: + is_append = f.tell() != 0 + if is_append and kwargs["insert"]: + f.write("\n") + f.write(kwargs["text"]) + + return super().load_text(**kwargs) + + +NODE_CLASS_MAPPINGS = { + "LoadText|pysssss": LoadText, + "SaveText|pysssss": SaveText, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoadText|pysssss": "Load Text 🐍", + "SaveText|pysssss": "Save Text 🐍", +} diff --git a/custom_nodes/comfyui-custom-scripts/py/workflows.py b/custom_nodes/comfyui-custom-scripts/py/workflows.py new file mode 100644 index 00000000..233e8c84 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/py/workflows.py @@ -0,0 +1,61 @@ +from server import PromptServer +from aiohttp import web +import os +import inspect +import json +import importlib +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) +import pysssss + +root_directory = os.path.dirname(inspect.getfile(PromptServer)) +workflows_directory = os.path.join(root_directory, "pysssss-workflows") +workflows_directory = pysssss.get_config_value( + "workflows.directory", workflows_directory) +if not os.path.isabs(workflows_directory): + workflows_directory = os.path.abspath(os.path.join(root_directory, workflows_directory)) + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + + +@PromptServer.instance.routes.get("/pysssss/workflows") +async def get_workflows(request): + files = [] + for dirpath, directories, file in os.walk(workflows_directory): + for file in file: + if (file.endswith(".json")): + files.append(os.path.relpath(os.path.join( + dirpath, file), workflows_directory)) + return web.json_response(list(map(lambda f: os.path.splitext(f)[0].replace("\\", "/"), files))) + + +@PromptServer.instance.routes.get("/pysssss/workflows/{name:.+}") +async def get_workflow(request): + file = os.path.abspath(os.path.join( + workflows_directory, request.match_info["name"] + ".json")) + if os.path.commonpath([file, workflows_directory]) != workflows_directory: + return web.Response(status=403) + + return web.FileResponse(file) + + +@PromptServer.instance.routes.post("/pysssss/workflows") +async def save_workflow(request): + json_data = await request.json() + file = os.path.abspath(os.path.join( + workflows_directory, json_data["name"] + ".json")) + if os.path.commonpath([file, workflows_directory]) != workflows_directory: + return web.Response(status=403) + + if os.path.exists(file) and ("overwrite" not in json_data or json_data["overwrite"] == False): + return web.Response(status=409) + + sub_path = os.path.dirname(file) + if not os.path.exists(sub_path): + os.makedirs(sub_path) + + with open(file, "w") as f: + f.write(json.dumps(json_data["workflow"])) + + return web.Response(status=201) diff --git a/custom_nodes/comfyui-custom-scripts/pyproject.toml b/custom_nodes/comfyui-custom-scripts/pyproject.toml new file mode 100644 index 00000000..b34f8cfc --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "comfyui-custom-scripts" +description = "Enhancements & experiments for ComfyUI, mostly focusing on UI features" +version = "1.2.5" +license = { file = "LICENSE" } + +[project.urls] +Repository = "https://github.com/pythongosssss/ComfyUI-Custom-Scripts" + +[tool.comfy] +PublisherId = "pythongosssss" +DisplayName = "ComfyUI-Custom-Scripts" +Icon = "" diff --git a/custom_nodes/comfyui-custom-scripts/pysssss.default.json b/custom_nodes/comfyui-custom-scripts/pysssss.default.json new file mode 100644 index 00000000..6ca06420 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/pysssss.default.json @@ -0,0 +1,4 @@ +{ + "name": "CustomScripts", + "logging": false +} diff --git a/custom_nodes/comfyui-custom-scripts/pysssss.example.json b/custom_nodes/comfyui-custom-scripts/pysssss.example.json new file mode 100644 index 00000000..251e3c72 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/pysssss.example.json @@ -0,0 +1,7 @@ +{ + "name": "CustomScripts", + "logging": false, + "workflows": { + "directory": "C:\\ComfyUI-Workflows" + } +} diff --git a/custom_nodes/comfyui-custom-scripts/pysssss.py b/custom_nodes/comfyui-custom-scripts/pysssss.py new file mode 100644 index 00000000..b1024f5a --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/pysssss.py @@ -0,0 +1,300 @@ +import asyncio +import os +import json +import shutil +import inspect +import aiohttp +from server import PromptServer +from tqdm import tqdm + +config = None + + +def is_logging_enabled(): + config = get_extension_config() + if "logging" not in config: + return False + return config["logging"] + + +def log(message, type=None, always=False, name=None): + if not always and not is_logging_enabled(): + return + + if type is not None: + message = f"[{type}] {message}" + + if name is None: + name = get_extension_config()["name"] + + print(f"(pysssss:{name}) {message}") + + +def get_ext_dir(subpath=None, mkdir=False): + dir = os.path.dirname(__file__) + if subpath is not None: + dir = os.path.join(dir, subpath) + + dir = os.path.abspath(dir) + + if mkdir and not os.path.exists(dir): + os.makedirs(dir) + return dir + + +def get_comfy_dir(subpath=None, mkdir=False): + dir = os.path.dirname(inspect.getfile(PromptServer)) + if subpath is not None: + dir = os.path.join(dir, subpath) + + dir = os.path.abspath(dir) + + if mkdir and not os.path.exists(dir): + os.makedirs(dir) + return dir + + +def get_web_ext_dir(): + config = get_extension_config() + name = config["name"] + dir = get_comfy_dir("web/extensions/pysssss") + if not os.path.exists(dir): + os.makedirs(dir) + dir = os.path.join(dir, name) + return dir + + +def get_extension_config(reload=False): + global config + if reload == False and config is not None: + return config + + config_path = get_ext_dir("pysssss.json") + default_config_path = get_ext_dir("pysssss.default.json") + if not os.path.exists(config_path): + if os.path.exists(default_config_path): + shutil.copy(default_config_path, config_path) + if not os.path.exists(config_path): + log(f"Failed to create config at {config_path}", type="ERROR", always=True, name="???") + print(f"Extension path: {get_ext_dir()}") + return {"name": "Unknown", "version": -1} + + else: + log("Missing pysssss.default.json, this extension may not work correctly. Please reinstall the extension.", + type="ERROR", always=True, name="???") + print(f"Extension path: {get_ext_dir()}") + return {"name": "Unknown", "version": -1} + + with open(config_path, "r") as f: + config = json.loads(f.read()) + return config + + +def link_js(src, dst): + src = os.path.abspath(src) + dst = os.path.abspath(dst) + if os.name == "nt": + try: + import _winapi + _winapi.CreateJunction(src, dst) + return True + except: + pass + try: + os.symlink(src, dst) + return True + except: + import logging + logging.exception('') + return False + + +def is_junction(path): + if os.name != "nt": + return False + try: + return bool(os.readlink(path)) + except OSError: + return False + + +def install_js(): + src_dir = get_ext_dir("web/js") + if not os.path.exists(src_dir): + log("No JS") + return + + should_install = should_install_js() + if should_install: + log("it looks like you're running an old version of ComfyUI that requires manual setup of web files, it is recommended you update your installation.", "warning", True) + dst_dir = get_web_ext_dir() + linked = os.path.islink(dst_dir) or is_junction(dst_dir) + if linked or os.path.exists(dst_dir): + if linked: + if should_install: + log("JS already linked") + else: + os.unlink(dst_dir) + log("JS unlinked, PromptServer will serve extension") + elif not should_install: + shutil.rmtree(dst_dir) + log("JS deleted, PromptServer will serve extension") + return + + if not should_install: + log("JS skipped, PromptServer will serve extension") + return + + if link_js(src_dir, dst_dir): + log("JS linked") + return + + log("Copying JS files") + shutil.copytree(src_dir, dst_dir, dirs_exist_ok=True) + + +def should_install_js(): + return not hasattr(PromptServer.instance, "supports") or "custom_nodes_from_web" not in PromptServer.instance.supports + + +def init(check_imports=None): + log("Init") + + if check_imports is not None: + import importlib.util + for imp in check_imports: + spec = importlib.util.find_spec(imp) + if spec is None: + log(f"{imp} is required, please check requirements are installed.", + type="ERROR", always=True) + return False + + install_js() + return True + + +def get_async_loop(): + loop = None + try: + loop = asyncio.get_event_loop() + except: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + return loop + + +def get_http_session(): + loop = get_async_loop() + return aiohttp.ClientSession(loop=loop) + + +async def download(url, stream, update_callback=None, session=None): + close_session = False + if session is None: + close_session = True + session = get_http_session() + try: + async with session.get(url) as response: + size = int(response.headers.get('content-length', 0)) or None + + with tqdm( + unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1], total=size, + ) as progressbar: + perc = 0 + async for chunk in response.content.iter_chunked(2048): + stream.write(chunk) + progressbar.update(len(chunk)) + if update_callback is not None and progressbar.total is not None and progressbar.total != 0: + last = perc + perc = round(progressbar.n / progressbar.total, 2) + if perc != last: + last = perc + await update_callback(perc) + finally: + if close_session and session is not None: + await session.close() + + +async def download_to_file(url, destination, update_callback=None, is_ext_subpath=True, session=None): + if is_ext_subpath: + destination = get_ext_dir(destination) + with open(destination, mode='wb') as f: + download(url, f, update_callback, session) + + +def wait_for_async(async_fn, loop=None): + res = [] + + async def run_async(): + r = await async_fn() + res.append(r) + + if loop is None: + try: + loop = asyncio.get_event_loop() + except: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + loop.run_until_complete(run_async()) + + return res[0] + + +def update_node_status(client_id, node, text, progress=None): + if client_id is None: + client_id = PromptServer.instance.client_id + + if client_id is None: + return + + PromptServer.instance.send_sync("pysssss/update_status", { + "node": node, + "progress": progress, + "text": text + }, client_id) + + +async def update_node_status_async(client_id, node, text, progress=None): + if client_id is None: + client_id = PromptServer.instance.client_id + + if client_id is None: + return + + await PromptServer.instance.send("pysssss/update_status", { + "node": node, + "progress": progress, + "text": text + }, client_id) + + +def get_config_value(key, default=None, throw=False): + split = key.split(".") + obj = get_extension_config() + for s in split: + if s in obj: + obj = obj[s] + else: + if throw: + raise KeyError("Configuration key missing: " + key) + else: + return default + return obj + + +def is_inside_dir(root_dir, check_path): + root_dir = os.path.abspath(root_dir) + if not os.path.isabs(check_path): + check_path = os.path.abspath(os.path.join(root_dir, check_path)) + return os.path.commonpath([check_path, root_dir]) == root_dir + + +def get_child_dir(root_dir, child_path, throw_if_outside=True): + child_path = os.path.abspath(os.path.join(root_dir, child_path)) + if is_inside_dir(root_dir, child_path): + return child_path + if throw_if_outside: + raise NotADirectoryError( + "Saving outside the target folder is not allowed.") + return None diff --git a/custom_nodes/comfyui-custom-scripts/user/text_file_dirs.json b/custom_nodes/comfyui-custom-scripts/user/text_file_dirs.json new file mode 100644 index 00000000..15adc225 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/user/text_file_dirs.json @@ -0,0 +1,5 @@ +{ + "input": "$input/**/*.txt", + "output": "$output/**/*.txt", + "temp": "$temp/**/*.txt" +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/canvas2svg.js b/custom_nodes/comfyui-custom-scripts/web/js/assets/canvas2svg.js new file mode 100644 index 00000000..03dc3923 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/assets/canvas2svg.js @@ -0,0 +1,1192 @@ +/*!! + * Canvas 2 Svg v1.0.19 + * A low level canvas to SVG converter. Uses a mock canvas context to build an SVG document. + * + * Licensed under the MIT license: + * http://www.opensource.org/licenses/mit-license.php + * + * Author: + * Kerry Liu + * + * Copyright (c) 2014 Gliffy Inc. + */ + +;(function() { + "use strict"; + + var STYLES, ctx, CanvasGradient, CanvasPattern, namedEntities; + + //helper function to format a string + function format(str, args) { + var keys = Object.keys(args), i; + for (i=0; i 1) { + options = defaultOptions; + options.width = arguments[0]; + options.height = arguments[1]; + } else if( !o ) { + options = defaultOptions; + } else { + options = o; + } + + if(!(this instanceof ctx)) { + //did someone call this without new? + return new ctx(options); + } + + //setup options + this.width = options.width || defaultOptions.width; + this.height = options.height || defaultOptions.height; + this.enableMirroring = options.enableMirroring !== undefined ? options.enableMirroring : defaultOptions.enableMirroring; + + this.canvas = this; ///point back to this instance! + this.__document = options.document || document; + this.__canvas = this.__document.createElement("canvas"); + this.__ctx = this.__canvas.getContext("2d"); + + this.__setDefaultStyles(); + this.__stack = [this.__getStyleState()]; + this.__groupStack = []; + + //the root svg element + this.__root = this.__document.createElementNS("http://www.w3.org/2000/svg", "svg"); + this.__root.setAttribute("version", 1.1); + this.__root.setAttribute("xmlns", "http://www.w3.org/2000/svg"); + this.__root.setAttributeNS("http://www.w3.org/2000/xmlns/", "xmlns:xlink", "http://www.w3.org/1999/xlink"); + this.__root.setAttribute("width", this.width); + this.__root.setAttribute("height", this.height); + + //make sure we don't generate the same ids in defs + this.__ids = {}; + + //defs tag + this.__defs = this.__document.createElementNS("http://www.w3.org/2000/svg", "defs"); + this.__root.appendChild(this.__defs); + + //also add a group child. the svg element can't use the transform attribute + this.__currentElement = this.__document.createElementNS("http://www.w3.org/2000/svg", "g"); + this.__root.appendChild(this.__currentElement); + }; + + + /** + * Creates the specified svg element + * @private + */ + ctx.prototype.__createElement = function (elementName, properties, resetFill) { + if (typeof properties === "undefined") { + properties = {}; + } + + var element = this.__document.createElementNS("http://www.w3.org/2000/svg", elementName), + keys = Object.keys(properties), i, key; + if(resetFill) { + //if fill or stroke is not specified, the svg element should not display. By default SVG's fill is black. + element.setAttribute("fill", "none"); + element.setAttribute("stroke", "none"); + } + for(i=0; i 0) { + var group = this.__createElement("g"); + parent.appendChild(group); + this.__currentElement = group; + } + + var transform = this.__currentElement.getAttribute("transform"); + if(transform) { + transform += " "; + } else { + transform = ""; + } + transform += t; + this.__currentElement.setAttribute("transform", transform); + }; + + /** + * scales the current element + */ + ctx.prototype.scale = function(x, y) { + if(y === undefined) { + y = x; + } + this.__addTransform(format("scale({x},{y})", {x:x, y:y})); + }; + + /** + * rotates the current element + */ + ctx.prototype.rotate = function(angle){ + var degrees = (angle * 180 / Math.PI); + this.__addTransform(format("rotate({angle},{cx},{cy})", {angle:degrees, cx:0, cy:0})); + }; + + /** + * translates the current element + */ + ctx.prototype.translate = function(x, y){ + this.__addTransform(format("translate({x},{y})", {x:x,y:y})); + }; + + /** + * applies a transform to the current element + */ + ctx.prototype.transform = function(a, b, c, d, e, f){ + this.__addTransform(format("matrix({a},{b},{c},{d},{e},{f})", {a:a, b:b, c:c, d:d, e:e, f:f})); + }; + + /** + * Create a new Path Element + */ + ctx.prototype.beginPath = function(){ + var path, parent; + + // Note that there is only one current default path, it is not part of the drawing state. + // See also: https://html.spec.whatwg.org/multipage/scripting.html#current-default-path + this.__currentDefaultPath = ""; + this.__currentPosition = {}; + + path = this.__createElement("path", {}, true); + parent = this.__closestGroupOrSvg(); + parent.appendChild(path); + this.__currentElement = path; + }; + + /** + * Helper function to apply currentDefaultPath to current path element + * @private + */ + ctx.prototype.__applyCurrentDefaultPath = function() { + if(this.__currentElement.nodeName === "path") { + var d = this.__currentDefaultPath; + this.__currentElement.setAttribute("d", d); + } else { + throw new Error("Attempted to apply path command to node " + this.__currentElement.nodeName); + } + }; + + /** + * Helper function to add path command + * @private + */ + ctx.prototype.__addPathCommand = function(command){ + this.__currentDefaultPath += " "; + this.__currentDefaultPath += command; + }; + + /** + * Adds the move command to the current path element, + * if the currentPathElement is not empty create a new path element + */ + ctx.prototype.moveTo = function(x,y){ + if(this.__currentElement.nodeName !== "path") { + this.beginPath(); + } + + // creates a new subpath with the given point + this.__currentPosition = {x: x, y: y}; + this.__addPathCommand(format("M {x} {y}", {x:x, y:y})); + }; + + /** + * Closes the current path + */ + ctx.prototype.closePath = function(){ + this.__addPathCommand("Z"); + }; + + /** + * Adds a line to command + */ + ctx.prototype.lineTo = function(x, y){ + this.__currentPosition = {x: x, y: y}; + if (this.__currentDefaultPath.indexOf('M') > -1) { + this.__addPathCommand(format("L {x} {y}", {x:x, y:y})); + } else { + this.__addPathCommand(format("M {x} {y}", {x:x, y:y})); + } + }; + + /** + * Add a bezier command + */ + ctx.prototype.bezierCurveTo = function(cp1x, cp1y, cp2x, cp2y, x, y) { + this.__currentPosition = {x: x, y: y}; + this.__addPathCommand(format("C {cp1x} {cp1y} {cp2x} {cp2y} {x} {y}", + {cp1x:cp1x, cp1y:cp1y, cp2x:cp2x, cp2y:cp2y, x:x, y:y})); + }; + + /** + * Adds a quadratic curve to command + */ + ctx.prototype.quadraticCurveTo = function(cpx, cpy, x, y){ + this.__currentPosition = {x: x, y: y}; + this.__addPathCommand(format("Q {cpx} {cpy} {x} {y}", {cpx:cpx, cpy:cpy, x:x, y:y})); + }; + + + /** + * Return a new normalized vector of given vector + */ + var normalize = function(vector) { + var len = Math.sqrt(vector[0] * vector[0] + vector[1] * vector[1]); + return [vector[0] / len, vector[1] / len]; + }; + + /** + * Adds the arcTo to the current path + * + * @see http://www.w3.org/TR/2015/WD-2dcontext-20150514/#dom-context-2d-arcto + */ + ctx.prototype.arcTo = function(x1, y1, x2, y2, radius) { + // Let the point (x0, y0) be the last point in the subpath. + var x0 = this.__currentPosition && this.__currentPosition.x; + var y0 = this.__currentPosition && this.__currentPosition.y; + + // First ensure there is a subpath for (x1, y1). + if (typeof x0 == "undefined" || typeof y0 == "undefined") { + return; + } + + // Negative values for radius must cause the implementation to throw an IndexSizeError exception. + if (radius < 0) { + throw new Error("IndexSizeError: The radius provided (" + radius + ") is negative."); + } + + // If the point (x0, y0) is equal to the point (x1, y1), + // or if the point (x1, y1) is equal to the point (x2, y2), + // or if the radius radius is zero, + // then the method must add the point (x1, y1) to the subpath, + // and connect that point to the previous point (x0, y0) by a straight line. + if (((x0 === x1) && (y0 === y1)) + || ((x1 === x2) && (y1 === y2)) + || (radius === 0)) { + this.lineTo(x1, y1); + return; + } + + // Otherwise, if the points (x0, y0), (x1, y1), and (x2, y2) all lie on a single straight line, + // then the method must add the point (x1, y1) to the subpath, + // and connect that point to the previous point (x0, y0) by a straight line. + var unit_vec_p1_p0 = normalize([x0 - x1, y0 - y1]); + var unit_vec_p1_p2 = normalize([x2 - x1, y2 - y1]); + if (unit_vec_p1_p0[0] * unit_vec_p1_p2[1] === unit_vec_p1_p0[1] * unit_vec_p1_p2[0]) { + this.lineTo(x1, y1); + return; + } + + // Otherwise, let The Arc be the shortest arc given by circumference of the circle that has radius radius, + // and that has one point tangent to the half-infinite line that crosses the point (x0, y0) and ends at the point (x1, y1), + // and that has a different point tangent to the half-infinite line that ends at the point (x1, y1), and crosses the point (x2, y2). + // The points at which this circle touches these two lines are called the start and end tangent points respectively. + + // note that both vectors are unit vectors, so the length is 1 + var cos = (unit_vec_p1_p0[0] * unit_vec_p1_p2[0] + unit_vec_p1_p0[1] * unit_vec_p1_p2[1]); + var theta = Math.acos(Math.abs(cos)); + + // Calculate origin + var unit_vec_p1_origin = normalize([ + unit_vec_p1_p0[0] + unit_vec_p1_p2[0], + unit_vec_p1_p0[1] + unit_vec_p1_p2[1] + ]); + var len_p1_origin = radius / Math.sin(theta / 2); + var x = x1 + len_p1_origin * unit_vec_p1_origin[0]; + var y = y1 + len_p1_origin * unit_vec_p1_origin[1]; + + // Calculate start angle and end angle + // rotate 90deg clockwise (note that y axis points to its down) + var unit_vec_origin_start_tangent = [ + -unit_vec_p1_p0[1], + unit_vec_p1_p0[0] + ]; + // rotate 90deg counter clockwise (note that y axis points to its down) + var unit_vec_origin_end_tangent = [ + unit_vec_p1_p2[1], + -unit_vec_p1_p2[0] + ]; + var getAngle = function(vector) { + // get angle (clockwise) between vector and (1, 0) + var x = vector[0]; + var y = vector[1]; + if (y >= 0) { // note that y axis points to its down + return Math.acos(x); + } else { + return -Math.acos(x); + } + }; + var startAngle = getAngle(unit_vec_origin_start_tangent); + var endAngle = getAngle(unit_vec_origin_end_tangent); + + // Connect the point (x0, y0) to the start tangent point by a straight line + this.lineTo(x + unit_vec_origin_start_tangent[0] * radius, + y + unit_vec_origin_start_tangent[1] * radius); + + // Connect the start tangent point to the end tangent point by arc + // and adding the end tangent point to the subpath. + this.arc(x, y, radius, startAngle, endAngle); + }; + + /** + * Sets the stroke property on the current element + */ + ctx.prototype.stroke = function(){ + if(this.__currentElement.nodeName === "path") { + this.__currentElement.setAttribute("paint-order", "fill stroke markers"); + } + this.__applyCurrentDefaultPath(); + this.__applyStyleToCurrentElement("stroke"); + }; + + /** + * Sets fill properties on the current element + */ + ctx.prototype.fill = function(){ + if(this.__currentElement.nodeName === "path") { + this.__currentElement.setAttribute("paint-order", "stroke fill markers"); + } + this.__applyCurrentDefaultPath(); + this.__applyStyleToCurrentElement("fill"); + }; + + /** + * Adds a rectangle to the path. + */ + ctx.prototype.rect = function(x, y, width, height){ + if(this.__currentElement.nodeName !== "path") { + this.beginPath(); + } + this.moveTo(x, y); + this.lineTo(x+width, y); + this.lineTo(x+width, y+height); + this.lineTo(x, y+height); + this.lineTo(x, y); + this.closePath(); + }; + + + /** + * adds a rectangle element + */ + ctx.prototype.fillRect = function(x, y, width, height){ + var rect, parent; + rect = this.__createElement("rect", { + x : x, + y : y, + width : width, + height : height + }, true); + parent = this.__closestGroupOrSvg(); + parent.appendChild(rect); + this.__currentElement = rect; + this.__applyStyleToCurrentElement("fill"); + }; + + /** + * Draws a rectangle with no fill + * @param x + * @param y + * @param width + * @param height + */ + ctx.prototype.strokeRect = function(x, y, width, height){ + var rect, parent; + rect = this.__createElement("rect", { + x : x, + y : y, + width : width, + height : height + }, true); + parent = this.__closestGroupOrSvg(); + parent.appendChild(rect); + this.__currentElement = rect; + this.__applyStyleToCurrentElement("stroke"); + }; + + + /** + * Clear entire canvas: + * 1. save current transforms + * 2. remove all the childNodes of the root g element + */ + ctx.prototype.__clearCanvas = function() { + var current = this.__closestGroupOrSvg(), + transform = current.getAttribute("transform"); + var rootGroup = this.__root.childNodes[1]; + var childNodes = rootGroup.childNodes; + for (var i = childNodes.length - 1; i >= 0; i--) { + if (childNodes[i]) { + rootGroup.removeChild(childNodes[i]); + } + } + this.__currentElement = rootGroup; + //reset __groupStack as all the child group nodes are all removed. + this.__groupStack = []; + if (transform) { + this.__addTransform(transform); + } + }; + + /** + * "Clears" a canvas by just drawing a white rectangle in the current group. + */ + ctx.prototype.clearRect = function(x, y, width, height) { + //clear entire canvas + if (x === 0 && y === 0 && width === this.width && height === this.height) { + this.__clearCanvas(); + return; + } + var rect, parent = this.__closestGroupOrSvg(); + rect = this.__createElement("rect", { + x : x, + y : y, + width : width, + height : height, + fill : "#FFFFFF" + }, true); + parent.appendChild(rect); + }; + + /** + * Adds a linear gradient to a defs tag. + * Returns a canvas gradient object that has a reference to it's parent def + */ + ctx.prototype.createLinearGradient = function(x1, y1, x2, y2){ + var grad = this.__createElement("linearGradient", { + id : randomString(this.__ids), + x1 : x1+"px", + x2 : x2+"px", + y1 : y1+"px", + y2 : y2+"px", + "gradientUnits" : "userSpaceOnUse" + }, false); + this.__defs.appendChild(grad); + return new CanvasGradient(grad, this); + }; + + /** + * Adds a radial gradient to a defs tag. + * Returns a canvas gradient object that has a reference to it's parent def + */ + ctx.prototype.createRadialGradient = function(x0, y0, r0, x1, y1, r1){ + var grad = this.__createElement("radialGradient", { + id : randomString(this.__ids), + cx : x1+"px", + cy : y1+"px", + r : r1+"px", + fx : x0+"px", + fy : y0+"px", + "gradientUnits" : "userSpaceOnUse" + }, false); + this.__defs.appendChild(grad); + return new CanvasGradient(grad, this); + + }; + + /** + * Parses the font string and returns svg mapping + * @private + */ + ctx.prototype.__parseFont = function() { + var regex = /^\s*(?=(?:(?:[-a-z]+\s*){0,2}(italic|oblique))?)(?=(?:(?:[-a-z]+\s*){0,2}(small-caps))?)(?=(?:(?:[-a-z]+\s*){0,2}(bold(?:er)?|lighter|[1-9]00))?)(?:(?:normal|\1|\2|\3)\s*){0,3}((?:xx?-)?(?:small|large)|medium|smaller|larger|[.\d]+(?:\%|in|[cem]m|ex|p[ctx]))(?:\s*\/\s*(normal|[.\d]+(?:\%|in|[cem]m|ex|p[ctx])))?\s*([-,\'\"\sa-z]+?)\s*$/i; + var fontPart = regex.exec( this.font ); + var data = { + style : fontPart[1] || 'normal', + size : fontPart[4] || '10px', + family : fontPart[6] || 'sans-serif', + weight: fontPart[3] || 'normal', + decoration : fontPart[2] || 'normal', + href : null + }; + + //canvas doesn't support underline natively, but we can pass this attribute + if(this.__fontUnderline === "underline") { + data.decoration = "underline"; + } + + //canvas also doesn't support linking, but we can pass this as well + if(this.__fontHref) { + data.href = this.__fontHref; + } + + return data; + }; + + /** + * Helper to link text fragments + * @param font + * @param element + * @return {*} + * @private + */ + ctx.prototype.__wrapTextLink = function(font, element) { + if(font.href) { + var a = this.__createElement("a"); + a.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", font.href); + a.appendChild(element); + return a; + } + return element; + }; + + /** + * Fills or strokes text + * @param text + * @param x + * @param y + * @param action - stroke or fill + * @private + */ + ctx.prototype.__applyText = function(text, x, y, action) { + var font = this.__parseFont(), + parent = this.__closestGroupOrSvg(), + textElement = this.__createElement("text", { + "font-family" : font.family, + "font-size" : font.size, + "font-style" : font.style, + "font-weight" : font.weight, + "text-decoration" : font.decoration, + "x" : x, + "y" : y, + "text-anchor": getTextAnchor(this.textAlign), + "dominant-baseline": getDominantBaseline(this.textBaseline) + }, true); + + textElement.appendChild(this.__document.createTextNode(text)); + this.__currentElement = textElement; + this.__applyStyleToCurrentElement(action); + parent.appendChild(this.__wrapTextLink(font,textElement)); + }; + + /** + * Creates a text element + * @param text + * @param x + * @param y + */ + ctx.prototype.fillText = function(text, x, y){ + this.__applyText(text, x, y, "fill"); + }; + + /** + * Strokes text + * @param text + * @param x + * @param y + */ + ctx.prototype.strokeText = function(text, x, y){ + this.__applyText(text, x, y, "stroke"); + }; + + /** + * No need to implement this for svg. + * @param text + * @return {TextMetrics} + */ + ctx.prototype.measureText = function(text){ + this.__ctx.font = this.font; + return this.__ctx.measureText(text); + }; + + /** + * Arc command! + */ + ctx.prototype.arc = function(x, y, radius, startAngle, endAngle, counterClockwise) { + // in canvas no circle is drawn if no angle is provided. + if (startAngle === endAngle) { + return; + } + startAngle = startAngle % (2*Math.PI); + endAngle = endAngle % (2*Math.PI); + if(startAngle === endAngle) { + //circle time! subtract some of the angle so svg is happy (svg elliptical arc can't draw a full circle) + endAngle = ((endAngle + (2*Math.PI)) - 0.001 * (counterClockwise ? -1 : 1)) % (2*Math.PI); + } + var endX = x+radius*Math.cos(endAngle), + endY = y+radius*Math.sin(endAngle), + startX = x+radius*Math.cos(startAngle), + startY = y+radius*Math.sin(startAngle), + sweepFlag = counterClockwise ? 0 : 1, + largeArcFlag = 0, + diff = endAngle - startAngle; + + // https://github.com/gliffy/canvas2svg/issues/4 + if(diff < 0) { + diff += 2*Math.PI; + } + + if(counterClockwise) { + largeArcFlag = diff > Math.PI ? 0 : 1; + } else { + largeArcFlag = diff > Math.PI ? 1 : 0; + } + + this.lineTo(startX, startY); + this.__addPathCommand(format("A {rx} {ry} {xAxisRotation} {largeArcFlag} {sweepFlag} {endX} {endY}", + {rx:radius, ry:radius, xAxisRotation:0, largeArcFlag:largeArcFlag, sweepFlag:sweepFlag, endX:endX, endY:endY})); + + this.__currentPosition = {x: endX, y: endY}; + }; + + /** + * Generates a ClipPath from the clip command. + */ + ctx.prototype.clip = function(){ + var group = this.__closestGroupOrSvg(), + clipPath = this.__createElement("clipPath"), + id = randomString(this.__ids), + newGroup = this.__createElement("g"); + + this.__applyCurrentDefaultPath(); + group.removeChild(this.__currentElement); + clipPath.setAttribute("id", id); + clipPath.appendChild(this.__currentElement); + + this.__defs.appendChild(clipPath); + + //set the clip path to this group + group.setAttribute("clip-path", format("url(#{id})", {id:id})); + + //clip paths can be scaled and transformed, we need to add another wrapper group to avoid later transformations + // to this path + group.appendChild(newGroup); + + this.__currentElement = newGroup; + + }; + + /** + * Draws a canvas, image or mock context to this canvas. + * Note that all svg dom manipulation uses node.childNodes rather than node.children for IE support. + * http://www.whatwg.org/specs/web-apps/current-work/multipage/the-canvas-element.html#dom-context-2d-drawimage + */ + ctx.prototype.drawImage = function(){ + //convert arguments to a real array + var args = Array.prototype.slice.call(arguments), + image=args[0], + dx, dy, dw, dh, sx=0, sy=0, sw, sh, parent, svg, defs, group, + currentElement, svgImage, canvas, context, id; + + if(args.length === 3) { + dx = args[1]; + dy = args[2]; + sw = image.width; + sh = image.height; + dw = sw; + dh = sh; + } else if(args.length === 5) { + dx = args[1]; + dy = args[2]; + dw = args[3]; + dh = args[4]; + sw = image.width; + sh = image.height; + } else if(args.length === 9) { + sx = args[1]; + sy = args[2]; + sw = args[3]; + sh = args[4]; + dx = args[5]; + dy = args[6]; + dw = args[7]; + dh = args[8]; + } else { + throw new Error("Inavlid number of arguments passed to drawImage: " + arguments.length); + } + + parent = this.__closestGroupOrSvg(); + currentElement = this.__currentElement; + var translateDirective = "translate(" + dx + ", " + dy + ")"; + if(image instanceof ctx) { + //canvas2svg mock canvas context. In the future we may want to clone nodes instead. + //also I'm currently ignoring dw, dh, sw, sh, sx, sy for a mock context. + svg = image.getSvg().cloneNode(true); + if (svg.childNodes && svg.childNodes.length > 1) { + defs = svg.childNodes[0]; + while(defs.childNodes.length) { + id = defs.childNodes[0].getAttribute("id"); + this.__ids[id] = id; + this.__defs.appendChild(defs.childNodes[0]); + } + group = svg.childNodes[1]; + if (group) { + //save original transform + var originTransform = group.getAttribute("transform"); + var transformDirective; + if (originTransform) { + transformDirective = originTransform+" "+translateDirective; + } else { + transformDirective = translateDirective; + } + group.setAttribute("transform", transformDirective); + parent.appendChild(group); + } + } + } else if(image.nodeName === "CANVAS" || image.nodeName === "IMG") { + //canvas or image + svgImage = this.__createElement("image"); + svgImage.setAttribute("width", dw); + svgImage.setAttribute("height", dh); + svgImage.setAttribute("preserveAspectRatio", "none"); + + if(sx || sy || sw !== image.width || sh !== image.height) { + //crop the image using a temporary canvas + canvas = this.__document.createElement("canvas"); + canvas.width = dw; + canvas.height = dh; + context = canvas.getContext("2d"); + context.drawImage(image, sx, sy, sw, sh, 0, 0, dw, dh); + image = canvas; + } + svgImage.setAttribute("transform", translateDirective); + svgImage.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", + image.nodeName === "CANVAS" ? image.toDataURL() : image.getAttribute("src")); + parent.appendChild(svgImage); + } + }; + + /** + * Generates a pattern tag + */ + ctx.prototype.createPattern = function(image, repetition){ + var pattern = this.__document.createElementNS("http://www.w3.org/2000/svg", "pattern"), id = randomString(this.__ids), + img; + pattern.setAttribute("id", id); + pattern.setAttribute("width", image.width); + pattern.setAttribute("height", image.height); + if(image.nodeName === "CANVAS" || image.nodeName === "IMG") { + img = this.__document.createElementNS("http://www.w3.org/2000/svg", "image"); + img.setAttribute("width", image.width); + img.setAttribute("height", image.height); + img.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", + image.nodeName === "CANVAS" ? image.toDataURL() : image.getAttribute("src")); + pattern.appendChild(img); + this.__defs.appendChild(pattern); + } else if(image instanceof ctx) { + pattern.appendChild(image.__root.childNodes[1]); + this.__defs.appendChild(pattern); + } + return new CanvasPattern(pattern, this); + }; + + ctx.prototype.setLineDash = function(dashArray) { + if (dashArray && dashArray.length > 0) { + this.lineDash = dashArray.join(","); + } else { + this.lineDash = null; + } + }; + + /** + * Not yet implemented + */ + ctx.prototype.drawFocusRing = function(){}; + ctx.prototype.createImageData = function(){}; + ctx.prototype.getImageData = function(){}; + ctx.prototype.putImageData = function(){}; + ctx.prototype.globalCompositeOperation = function(){}; + ctx.prototype.setTransform = function(){}; + + //add options for alternative namespace + if (typeof window === "object") { + window.C2S = ctx; + } + + // CommonJS/Browserify + if (typeof module === "object" && typeof module.exports === "object") { + module.exports = ctx; + } + +}()); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon-active.ico b/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon-active.ico new file mode 100644 index 00000000..64045ab5 Binary files /dev/null and b/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon-active.ico differ diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon.ico b/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon.ico new file mode 100644 index 00000000..08df2481 Binary files /dev/null and b/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon.ico differ diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/no-image.png b/custom_nodes/comfyui-custom-scripts/web/js/assets/no-image.png new file mode 100644 index 00000000..d0b58b2c Binary files /dev/null and b/custom_nodes/comfyui-custom-scripts/web/js/assets/no-image.png differ diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/notify.mp3 b/custom_nodes/comfyui-custom-scripts/web/js/assets/notify.mp3 new file mode 100644 index 00000000..5e3fdabb Binary files /dev/null and b/custom_nodes/comfyui-custom-scripts/web/js/assets/notify.mp3 differ diff --git a/custom_nodes/comfyui-custom-scripts/web/js/autocompleter.js b/custom_nodes/comfyui-custom-scripts/web/js/autocompleter.js new file mode 100644 index 00000000..d0fb2eca --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/autocompleter.js @@ -0,0 +1,602 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; +import { api } from "../../../scripts/api.js"; +import { $el, ComfyDialog } from "../../../scripts/ui.js"; +import { TextAreaAutoComplete } from "./common/autocomplete.js"; +import { ModelInfoDialog } from "./common/modelInfoDialog.js"; +import { LoraInfoDialog } from "./modelInfo.js"; + +function parseCSV(csvText) { + const rows = []; + const delimiter = ","; + const quote = '"'; + let currentField = ""; + let inQuotedField = false; + + function pushField() { + rows[rows.length - 1].push(currentField); + currentField = ""; + inQuotedField = false; + } + + rows.push([]); // Initialize the first row + + for (let i = 0; i < csvText.length; i++) { + const char = csvText[i]; + const nextChar = csvText[i + 1]; + + // Special handling for backslash escaped quotes + if (char === "\\" && nextChar === quote) { + currentField += quote; + i++; + } + + if (!inQuotedField) { + if (char === quote) { + inQuotedField = true; + } else if (char === delimiter) { + pushField(); + } else if (char === "\r" || char === "\n" || i === csvText.length - 1) { + pushField(); + if (nextChar === "\n") { + i++; // Handle Windows line endings (\r\n) + } + rows.push([]); // Start a new row + } else { + currentField += char; + } + } else { + if (char === quote && nextChar === quote) { + currentField += quote; + i++; // Skip the next quote + } else if (char === quote) { + inQuotedField = false; + } else if (char === "\r" || char === "\n" || i === csvText.length - 1) { + // Dont allow new lines in quoted text, assume its wrong + const parsed = parseCSV(currentField); + rows.pop(); + rows.push(...parsed); + inQuotedField = false; + currentField = ""; + rows.push([]); + } else { + currentField += char; + } + } + } + + if (currentField || csvText[csvText.length - 1] === ",") { + pushField(); + } + + // Remove the last row if it's empty + if (rows[rows.length - 1].length === 0) { + rows.pop(); + } + + return rows; +} + +async function getCustomWords() { + const resp = await api.fetchApi("/pysssss/autocomplete", { cache: "no-store" }); + if (resp.status === 200) { + return await resp.text(); + } + return undefined; +} + +async function addCustomWords(text) { + if (!text) { + text = await getCustomWords(); + } + if (text) { + TextAreaAutoComplete.updateWords( + "pysssss.customwords", + parseCSV(text).reduce((p, n) => { + let text; + let priority; + let value; + let num; + switch (n.length) { + case 0: + return; + case 1: + // Single word + text = n[0]; + break; + case 2: + // Word,[priority|alias] + num = +n[1]; + if (isNaN(num)) { + text = n[0] + "πŸ”„οΈ" + n[1]; + value = n[0]; + } else { + text = n[0]; + priority = num; + } + break; + case 4: + // a1111 csv format? + value = n[0]; + priority = +n[2]; + const aliases = n[3]?.trim(); + if (aliases && aliases !== "null") { // Weird null in an example csv, maybe they are JSON.parsing the last column? + const split = aliases.split(","); + for (const text of split) { + p[text] = { text, priority, value }; + } + } + text = value; + break; + default: + // Word,alias,priority + text = n[1]; + value = n[0]; + priority = +n[2]; + break; + } + p[text] = { text, priority, value }; + return p; + }, {}) + ); + } +} + +function toggleLoras() { + [TextAreaAutoComplete.globalWords, TextAreaAutoComplete.globalWordsExclLoras] = [ + TextAreaAutoComplete.globalWordsExclLoras, + TextAreaAutoComplete.globalWords, + ]; +} + +class EmbeddingInfoDialog extends ModelInfoDialog { + async addInfo() { + super.addInfo(); + const info = await this.addCivitaiInfo(); + if (info) { + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } +} + +class CustomWordsDialog extends ComfyDialog { + async show() { + const text = await getCustomWords(); + this.words = $el("textarea", { + textContent: text, + style: { + width: "70vw", + height: "70vh", + }, + }); + + const input = $el("input", { + style: { + flex: "auto", + }, + value: + "https://gist.githubusercontent.com/pythongosssss/1d3efa6050356a08cea975183088159a/raw/a18fb2f94f9156cf4476b0c24a09544d6c0baec6/danbooru-tags.txt", + }); + + super.show( + $el( + "div", + { + style: { + display: "flex", + flexDirection: "column", + overflow: "hidden", + maxHeight: "100%", + }, + }, + [ + $el("h2", { + textContent: "Custom Autocomplete Words", + style: { + color: "#fff", + marginTop: 0, + textAlign: "center", + fontFamily: "sans-serif", + }, + }), + $el( + "div", + { + style: { + color: "#fff", + fontFamily: "sans-serif", + display: "flex", + alignItems: "center", + gap: "5px", + }, + }, + [ + $el("label", { textContent: "Load Custom List: " }), + input, + $el("button", { + textContent: "Load", + onclick: async () => { + try { + const res = await fetch(input.value); + if (res.status !== 200) { + throw new Error("Error loading: " + res.status + " " + res.statusText); + } + this.words.value = await res.text(); + } catch (error) { + alert("Error loading custom list, try manually copy + pasting the list"); + } + }, + }), + ] + ), + this.words, + ] + ) + ); + } + + createButtons() { + const btns = super.createButtons(); + const save = $el("button", { + type: "button", + textContent: "Save", + onclick: async (e) => { + try { + const res = await api.fetchApi("/pysssss/autocomplete", { method: "POST", body: this.words.value }); + if (res.status !== 200) { + throw new Error("Error saving: " + res.status + " " + res.statusText); + } + save.textContent = "Saved!"; + addCustomWords(this.words.value); + setTimeout(() => { + save.textContent = "Save"; + }, 500); + } catch (error) { + alert("Error saving word list!"); + console.error(error); + } + }, + }); + + btns.unshift(save); + return btns; + } +} + +const id = "pysssss.AutoCompleter"; + +app.registerExtension({ + name: id, + init() { + const STRING = ComfyWidgets.STRING; + const SKIP_WIDGETS = new Set(["ttN xyPlot.x_values", "ttN xyPlot.y_values"]); + ComfyWidgets.STRING = function (node, inputName, inputData) { + const r = STRING.apply(this, arguments); + + if (inputData[1]?.multiline) { + // Disabled on this input + const config = inputData[1]?.["pysssss.autocomplete"]; + if (config === false) return r; + + // In list of widgets to skip + const id = `${node.comfyClass}.${inputName}`; + if (SKIP_WIDGETS.has(id)) return r; + + let words; + let separator; + if (typeof config === "object") { + separator = config.separator; + words = {}; + if (config.words) { + // Custom wordlist, this will have been registered on setup + Object.assign(words, TextAreaAutoComplete.groups[node.comfyClass + "." + inputName] ?? {}); + } + + for (const item of config.groups ?? []) { + if (item === "*") { + // This widget wants all global words included + Object.assign(words, TextAreaAutoComplete.globalWords); + } else { + // This widget wants a specific group included + Object.assign(words, TextAreaAutoComplete.groups[item] ?? {}); + } + } + } + + new TextAreaAutoComplete(r.widget.inputEl, words, separator); + } + + return r; + }; + + TextAreaAutoComplete.globalSeparator = localStorage.getItem(id + ".AutoSeparate") ?? ", "; + const enabledSetting = app.ui.settings.addSetting({ + id, + name: "🐍 Text Autocomplete", + defaultValue: true, + type: (name, setter, value) => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: name, + }), + ]), + $el("td", [ + $el( + "label", + { + textContent: "Enabled ", + style: { + display: "block", + }, + }, + [ + $el("input", { + id: id.replaceAll(".", "-"), + type: "checkbox", + checked: value, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.enabled = checked; + setter(checked); + }, + }), + ] + ), + $el( + "label.comfy-tooltip-indicator", + { + title: "This requires other ComfyUI nodes/extensions that support using LoRAs in the prompt.", + textContent: "Loras enabled ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.lorasEnabled, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.lorasEnabled = checked; + toggleLoras(); + localStorage.setItem(id + ".ShowLoras", TextAreaAutoComplete.lorasEnabled); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Auto-insert comma ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.globalSeparator, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.globalSeparator = checked ? ", " : ""; + localStorage.setItem(id + ".AutoSeparate", TextAreaAutoComplete.globalSeparator); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Replace _ with space ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.replacer, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.replacer = checked ? (v) => v.replaceAll("_", " ") : undefined; + localStorage.setItem(id + ".ReplaceUnderscore", checked); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Insert suggestion on: ", + style: { + display: "block", + }, + }, + [ + $el( + "label", + { + textContent: "Tab", + style: { + display: "block", + marginLeft: "20px", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.insertOnTab, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.insertOnTab = checked; + localStorage.setItem(id + ".InsertOnTab", checked); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Enter", + style: { + display: "block", + marginLeft: "20px", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.insertOnEnter, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.insertOnEnter = checked; + localStorage.setItem(id + ".InsertOnEnter", checked); + }, + }), + ] + ), + ] + ), + $el( + "label", + { + textContent: "Max suggestions: ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "number", + value: +TextAreaAutoComplete.suggestionCount, + style: { + width: "80px" + }, + onchange: (event) => { + const value = +event.target.value; + TextAreaAutoComplete.suggestionCount = value;; + localStorage.setItem(id + ".SuggestionCount", TextAreaAutoComplete.suggestionCount); + }, + }), + ] + ), + $el("button", { + textContent: "Manage Custom Words", + onclick: () => { + try { + // Try closing old settings window + if (typeof app.ui.settings.element?.close === "function") { + app.ui.settings.element.close(); + } + } catch (error) { + } + try { + // Try closing new vue dialog + document.querySelector(".p-dialog-close-button").click(); + } catch (error) { + // Fallback to just hiding the element + app.ui.settings.element.style.display = "none"; + } + + new CustomWordsDialog().show(); + }, + style: { + fontSize: "14px", + display: "block", + marginTop: "5px", + }, + }), + ]), + ]); + }, + }); + + TextAreaAutoComplete.enabled = enabledSetting.value; + TextAreaAutoComplete.replacer = localStorage.getItem(id + ".ReplaceUnderscore") === "true" ? (v) => v.replaceAll("_", " ") : undefined; + TextAreaAutoComplete.insertOnTab = localStorage.getItem(id + ".InsertOnTab") !== "false"; + TextAreaAutoComplete.insertOnEnter = localStorage.getItem(id + ".InsertOnEnter") !== "false"; + TextAreaAutoComplete.lorasEnabled = localStorage.getItem(id + ".ShowLoras") === "true"; + TextAreaAutoComplete.suggestionCount = +localStorage.getItem(id + ".SuggestionCount") || 20; + }, + setup() { + async function addEmbeddings() { + const embeddings = await api.getEmbeddings(); + const words = {}; + words["embedding:"] = { text: "embedding:" }; + + for (const emb of embeddings) { + const v = `embedding:${emb}`; + words[v] = { + text: v, + info: () => new EmbeddingInfoDialog(emb).show("embeddings", emb), + use_replacer: false, + }; + } + + TextAreaAutoComplete.updateWords("pysssss.embeddings", words); + } + + async function addLoras() { + let loras; + try { + loras = LiteGraph.registered_node_types["LoraLoader"]?.nodeData.input.required.lora_name[0]; + } catch (error) {} + + if (!loras?.length) { + loras = await api.fetchApi("/pysssss/loras", { cache: "no-store" }).then((res) => res.json()); + } + + const words = {}; + words["lora:"] = { text: "lora:" }; + + for (const lora of loras) { + const v = ``; + words[v] = { + text: v, + info: () => new LoraInfoDialog(lora).show("loras", lora), + use_replacer: false, + }; + } + + TextAreaAutoComplete.updateWords("pysssss.loras", words); + } + + // store global words with/without loras + Promise.all([addEmbeddings(), addCustomWords()]) + .then(() => { + TextAreaAutoComplete.globalWordsExclLoras = Object.assign({}, TextAreaAutoComplete.globalWords); + }) + .then(addLoras) + .then(() => { + if (!TextAreaAutoComplete.lorasEnabled) { + toggleLoras(); // off by default + } + }); + }, + beforeRegisterNodeDef(_, def) { + // Process each input to see if there is a custom word list for + // { input: { required: { something: ["STRING", { "pysssss.autocomplete": ["groupid", ["custom", "words"] ] }] } } } + const inputs = { ...def.input?.required, ...def.input?.optional }; + for (const input in inputs) { + const config = inputs[input][1]?.["pysssss.autocomplete"]; + if (!config) continue; + if (typeof config === "object" && config.words) { + const words = {}; + for (const text of config.words || []) { + const obj = typeof text === "string" ? { text } : text; + words[obj.text] = obj; + } + TextAreaAutoComplete.updateWords(def.name + "." + input, words, false); + } + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/betterCombos.js b/custom_nodes/comfyui-custom-scripts/web/js/betterCombos.js new file mode 100644 index 00000000..cbf0a074 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/betterCombos.js @@ -0,0 +1,534 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; +import { $el } from "../../../scripts/ui.js"; +import { api } from "../../../scripts/api.js"; + +const CHECKPOINT_LOADER = "CheckpointLoader|pysssss"; +const LORA_LOADER = "LoraLoader|pysssss"; +const IMAGE_WIDTH = 384; +const IMAGE_HEIGHT = 384; + +function getType(node) { + if (node.comfyClass === CHECKPOINT_LOADER) { + return "checkpoints"; + } + return "loras"; +} + +function getWidgetName(type) { + return type === "checkpoints" ? "ckpt_name" : "lora_name"; +} + +function encodeRFC3986URIComponent(str) { + return encodeURIComponent(str).replace(/[!'()*]/g, (c) => `%${c.charCodeAt(0).toString(16).toUpperCase()}`); +} + +const calculateImagePosition = (el, bodyRect) => { + let { top, left, right } = el.getBoundingClientRect(); + const { width: bodyWidth, height: bodyHeight } = bodyRect; + + const isSpaceRight = right + IMAGE_WIDTH <= bodyWidth; + if (isSpaceRight) { + left = right; + } else { + left -= IMAGE_WIDTH; + } + + top = top - IMAGE_HEIGHT / 2; + if (top + IMAGE_HEIGHT > bodyHeight) { + top = bodyHeight - IMAGE_HEIGHT; + } + if (top < 0) { + top = 0; + } + + return { left: Math.round(left), top: Math.round(top), isLeft: !isSpaceRight }; +}; + +function showImage(relativeToEl, imageEl) { + const bodyRect = document.body.getBoundingClientRect(); + if (!bodyRect) return; + + const { left, top, isLeft } = calculateImagePosition(relativeToEl, bodyRect); + + imageEl.style.left = `${left}px`; + imageEl.style.top = `${top}px`; + + if (isLeft) { + imageEl.classList.add("left"); + } else { + imageEl.classList.remove("left"); + } + + document.body.appendChild(imageEl); +} + +let imagesByType = {}; +const loadImageList = async (type) => { + imagesByType[type] = await (await api.fetchApi(`/pysssss/images/${type}`)).json(); +}; + +app.registerExtension({ + name: "pysssss.Combo++", + init() { + const displayOptions = { "List (normal)": 0, "Tree (subfolders)": 1, "Thumbnails (grid)": 2 }; + const displaySetting = app.ui.settings.addSetting({ + id: "pysssss.Combo++.Submenu", + name: "🐍 Lora & Checkpoint loader display mode", + defaultValue: 1, + type: "combo", + options: (value) => { + value = +value; + + return Object.entries(displayOptions).map(([k, v]) => ({ + value: v, + text: k, + selected: k === value, + })); + }, + }); + + $el("style", { + textContent: ` + .pysssss-combo-image { + position: absolute; + left: 0; + top: 0; + width: ${IMAGE_WIDTH}px; + height: ${IMAGE_HEIGHT}px; + object-fit: contain; + object-position: top left; + z-index: 9999; + } + .pysssss-combo-image.left { + object-position: top right; + } + .pysssss-combo-folder { opacity: 0.7 } + .pysssss-combo-folder-arrow { display: inline-block; width: 15px; } + .pysssss-combo-folder:hover { background-color: rgba(255, 255, 255, 0.1); } + .pysssss-combo-prefix { display: none } + + /* Special handling for when the filter input is populated to revert to normal */ + .litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder-contents { + display: block !important; + } + .litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder { + display: none; + } + .litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-prefix { + display: inline; + } + .litecontextmenu:has(input:not(:placeholder-shown)) .litemenu-entry { + padding-left: 2px !important; + } + + /* Grid mode */ + .pysssss-combo-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); + gap: 10px; + overflow-x: hidden; + max-width: 60vw; + } + .pysssss-combo-grid .comfy-context-menu-filter { + grid-column: 1 / -1; + position: sticky; + top: 0; + } + .pysssss-combo-grid .litemenu-entry { + word-break: break-word; + display: flex; + flex-direction: column; + justify-content: space-between; + align-items: center; + } + .pysssss-combo-grid .litemenu-entry:before { + content: ""; + display: block; + width: 100%; + height: 250px; + background-size: contain; + background-position: center; + background-repeat: no-repeat; + /* No-image image attribution: Picture icons created by Pixel perfect - Flaticon */ + background-image: var(--background-image, url(extensions/ComfyUI-Custom-Scripts/js/assets/no-image.png)); + } + + `, + parent: document.body, + }); + const p1 = loadImageList("checkpoints"); + const p2 = loadImageList("loras"); + + const refreshComboInNodes = app.refreshComboInNodes; + app.refreshComboInNodes = async function () { + const r = await Promise.all([ + refreshComboInNodes.apply(this, arguments), + loadImageList("checkpoints").catch(() => {}), + loadImageList("loras").catch(() => {}), + ]); + return r[0]; + }; + + const imageHost = $el("img.pysssss-combo-image"); + + const positionMenu = (menu, fillWidth) => { + // compute best position + let left = app.canvas.last_mouse[0] - 10; + let top = app.canvas.last_mouse[1] - 10; + + const body_rect = document.body.getBoundingClientRect(); + const root_rect = menu.getBoundingClientRect(); + + if (body_rect.width && left > body_rect.width - root_rect.width - 10) left = body_rect.width - root_rect.width - 10; + if (body_rect.height && top > body_rect.height - root_rect.height - 10) top = body_rect.height - root_rect.height - 10; + + menu.style.left = `${left}px`; + menu.style.top = `${top}px`; + if (fillWidth) { + menu.style.right = "10px"; + } + }; + + const updateMenu = async (menu, type) => { + try { + await p1; + await p2; + } catch (error) { + console.error(error); + console.error("Error loading pysssss.betterCombos data"); + } + + // Clamp max height so it doesn't overflow the screen + const position = menu.getBoundingClientRect(); + const maxHeight = window.innerHeight - position.top - 20; + menu.style.maxHeight = `${maxHeight}px`; + + const images = imagesByType[type]; + const items = menu.querySelectorAll(".litemenu-entry"); + + // Add image handler to items + const addImageHandler = (item) => { + const text = item.getAttribute("data-value").trim(); + if (images[text]) { + const textNode = document.createTextNode("*"); + item.appendChild(textNode); + + item.addEventListener( + "mouseover", + () => { + imageHost.src = `/pysssss/view/${encodeRFC3986URIComponent(images[text])}?${+new Date()}`; + document.body.appendChild(imageHost); + showImage(item, imageHost); + }, + { passive: true } + ); + item.addEventListener( + "mouseout", + () => { + imageHost.remove(); + }, + { passive: true } + ); + item.addEventListener( + "click", + () => { + imageHost.remove(); + }, + { passive: true } + ); + } + }; + + const createTree = () => { + // Create a map to store folder structures + const folderMap = new Map(); + const rootItems = []; + const splitBy = (navigator.platform || navigator.userAgent).includes("Win") ? /\/|\\/ : /\//; + const itemsSymbol = Symbol("items"); + + // First pass - organize items into folder structure + for (const item of items) { + const path = item.getAttribute("data-value").split(splitBy); + + // Remove path from visible text + item.textContent = path[path.length - 1]; + if (path.length > 1) { + // Add the prefix path back in so it can be filtered on + const prefix = $el("span.pysssss-combo-prefix", { + textContent: path.slice(0, -1).join("/") + "/", + }); + item.prepend(prefix); + } + + addImageHandler(item); + + if (path.length === 1) { + rootItems.push(item); + continue; + } + + // Temporarily remove the item from current position + item.remove(); + + // Create folder hierarchy + let currentLevel = folderMap; + for (let i = 0; i < path.length - 1; i++) { + const folder = path[i]; + if (!currentLevel.has(folder)) { + currentLevel.set(folder, new Map()); + } + currentLevel = currentLevel.get(folder); + } + + // Store the actual item in the deepest folder + if (!currentLevel.has(itemsSymbol)) { + currentLevel.set(itemsSymbol, []); + } + currentLevel.get(itemsSymbol).push(item); + } + + const createFolderElement = (name) => { + const folder = $el("div.litemenu-entry.pysssss-combo-folder", { + innerHTML: `β–Ά ${name}`, + style: { paddingLeft: "5px" }, + }); + return folder; + }; + + const insertFolderStructure = (parentElement, map, level = 0) => { + for (const [folderName, content] of map.entries()) { + if (folderName === itemsSymbol) continue; + + const folderElement = createFolderElement(folderName); + folderElement.style.paddingLeft = `${level * 10 + 5}px`; + parentElement.appendChild(folderElement); + + const childContainer = $el("div.pysssss-combo-folder-contents", { + style: { display: "none" }, + }); + + // Add items in this folder + const items = content.get(itemsSymbol) || []; + for (const item of items) { + item.style.paddingLeft = `${(level + 1) * 10 + 14}px`; + childContainer.appendChild(item); + } + + // Recursively add subfolders + insertFolderStructure(childContainer, content, level + 1); + parentElement.appendChild(childContainer); + + // Add click handler for folder + folderElement.addEventListener("click", (e) => { + e.stopPropagation(); + const arrow = folderElement.querySelector(".pysssss-combo-folder-arrow"); + const contents = folderElement.nextElementSibling; + if (contents.style.display === "none") { + contents.style.display = "block"; + arrow.textContent = "β–Ό"; + } else { + contents.style.display = "none"; + arrow.textContent = "β–Ά"; + } + }); + } + }; + + insertFolderStructure(items[0]?.parentElement || menu, folderMap); + positionMenu(menu); + }; + + const addImageData = (item) => { + const text = item.getAttribute("data-value").trim(); + if (images[text]) { + item.style.setProperty("--background-image", `url(/pysssss/view/${encodeRFC3986URIComponent(images[text])})`); + } + }; + + if (displaySetting.value === 1 || displaySetting.value === true) { + createTree(); + } else if (displaySetting.value === 2) { + menu.classList.add("pysssss-combo-grid"); + + for (const item of items) { + addImageData(item); + } + positionMenu(menu, true); + } else { + for (const item of items) { + addImageHandler(item); + } + } + }; + + const mutationObserver = new MutationObserver((mutations) => { + const node = app.canvas.current_node; + + if (!node || (node.comfyClass !== LORA_LOADER && node.comfyClass !== CHECKPOINT_LOADER)) { + return; + } + + for (const mutation of mutations) { + for (const removed of mutation.removedNodes) { + if (removed.classList?.contains("litecontextmenu")) { + imageHost.remove(); + } + } + + for (const added of mutation.addedNodes) { + if (added.classList?.contains("litecontextmenu")) { + const overWidget = app.canvas.getWidgetAtCursor(); + const type = getType(node); + if (overWidget?.name === getWidgetName(type)) { + requestAnimationFrame(() => { + // Bad hack to prevent showing on right click menu by checking for the filter input + if (!added.querySelector(".comfy-context-menu-filter")) return; + updateMenu(added, type); + }); + } + return; + } + } + } + }); + mutationObserver.observe(document.body, { childList: true, subtree: false }); + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + const isCkpt = nodeData.name === CHECKPOINT_LOADER; + const isLora = nodeData.name === LORA_LOADER; + if (isCkpt || isLora) { + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = function () { + onAdded?.apply(this, arguments); + const { widget: exampleList } = ComfyWidgets["COMBO"](this, "example", [[""], {}], app); + this.widgets.find((w) => w.name === "prompt").computeSize = () => [0, -4]; + let exampleWidget; + + const get = async (route, suffix) => { + const url = encodeRFC3986URIComponent(`${getType(nodeType)}${suffix || ""}`); + return await api.fetchApi(`/pysssss/${route}/${url}`); + }; + + const getExample = async () => { + if (exampleList.value === "[none]") { + if (exampleWidget) { + exampleWidget.inputEl.remove(); + exampleWidget = null; + this.widgets.length -= 1; + } + return; + } + + const v = this.widgets[0].value; + const pos = v.lastIndexOf("."); + const name = v.substr(0, pos); + let exampleName = exampleList.value; + let viewPath = `/${name}`; + if (exampleName === "notes") { + viewPath += ".txt"; + } else { + viewPath += `/${exampleName}`; + } + const example = await (await get("view", viewPath)).text(); + if (!exampleWidget) { + exampleWidget = ComfyWidgets["STRING"](this, "prompt", ["STRING", { multiline: true }], app).widget; + exampleWidget.inputEl.readOnly = true; + exampleWidget.inputEl.style.opacity = 0.6; + } + exampleWidget.value = example; + }; + + const exampleCb = exampleList.callback; + exampleList.callback = function () { + getExample(); + return exampleCb?.apply(this, arguments) ?? exampleList.value; + }; + + const listExamples = async () => { + exampleList.disabled = true; + exampleList.options.values = ["[none]"]; + exampleList.value = "[none]"; + let examples = []; + if (this.widgets[0].value) { + try { + examples = await (await get("examples", `/${this.widgets[0].value}`)).json(); + } catch (error) {} + } + exampleList.options.values = ["[none]", ...examples]; + exampleList.value = exampleList.options.values[+!!examples.length]; + exampleList.callback(); + exampleList.disabled = !examples.length; + app.graph.setDirtyCanvas(true, true); + }; + + // Expose function to update examples + nodeType.prototype["pysssss.updateExamples"] = listExamples; + + const modelWidget = this.widgets[0]; + const modelCb = modelWidget.callback; + let prev = undefined; + modelWidget.callback = function () { + let ret = modelCb?.apply(this, arguments) ?? modelWidget.value; + if (typeof ret === "object" && "content" in ret) { + ret = ret.content; + modelWidget.value = ret; + } + let v = ret; + if (prev !== v) { + listExamples(); + prev = v; + } + return ret; + }; + setTimeout(() => { + modelWidget.callback(); + }, 30); + }; + } + + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + if (this.imgs) { + // If this node has images then we add an open in new tab item + let img; + if (this.imageIndex != null) { + // An image is selected so select that + img = this.imgs[this.imageIndex]; + } else if (this.overIndex != null) { + // No image is selected but one is hovered + img = this.imgs[this.overIndex]; + } + if (img) { + const nodes = app.graph._nodes.filter((n) => n.comfyClass === LORA_LOADER || n.comfyClass === CHECKPOINT_LOADER); + if (nodes.length) { + options.unshift({ + content: "Save as Preview", + submenu: { + options: nodes.map((n) => ({ + content: n.widgets[0].value, + callback: async () => { + const url = new URL(img.src); + await api.fetchApi("/pysssss/save/" + encodeRFC3986URIComponent(`${getType(n)}/${n.widgets[0].value}`), { + method: "POST", + body: JSON.stringify({ + filename: url.searchParams.get("filename"), + subfolder: url.searchParams.get("subfolder"), + type: url.searchParams.get("type"), + }), + headers: { + "content-type": "application/json", + }, + }); + loadImageList(getType(n)); + }, + })), + }, + }); + } + } + } + return getExtraMenuOptions?.apply(this, arguments); + }; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.css b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.css new file mode 100644 index 00000000..1f371d62 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.css @@ -0,0 +1,62 @@ +.pysssss-autocomplete { + color: var(--descrip-text); + background-color: var(--comfy-menu-bg); + position: absolute; + font-family: sans-serif; + box-shadow: 3px 3px 8px rgba(0, 0, 0, 0.4); + z-index: 9999; + overflow: auto; +} + +.pysssss-autocomplete-item { + cursor: pointer; + padding: 3px 7px; + display: flex; + border-left: 3px solid transparent; + align-items: center; +} + +.pysssss-autocomplete-item--selected { + border-left-color: dodgerblue; +} + +.pysssss-autocomplete-highlight { + font-weight: bold; + text-decoration: underline; + text-decoration-color: dodgerblue; +} + +.pysssss-autocomplete-pill { + margin-left: auto; + font-size: 10px; + color: #fff; + padding: 2px 4px 2px 14px; + position: relative; +} + +.pysssss-autocomplete-pill::after { + content: ""; + display: block; + background: rgba(255, 255, 255, 0.25); + width: calc(100% - 10px); + height: 100%; + position: absolute; + left: 10px; + top: 0; + border-radius: 5px; +} + +.pysssss-autocomplete-pill + .pysssss-autocomplete-pill { + margin-left: 0; +} + +.pysssss-autocomplete-item-info { + margin-left: auto; + transition: filter 0.2s; + will-change: filter; + text-decoration: none; + padding-left: 10px; +} +.pysssss-autocomplete-item-info:hover { + filter: invert(1); +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.js b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.js new file mode 100644 index 00000000..ac5fb1d0 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.js @@ -0,0 +1,692 @@ +import { $el } from "../../../../scripts/ui.js"; +import { addStylesheet } from "./utils.js"; + +addStylesheet(import.meta.url); + +/* + https://github.com/component/textarea-caret-position + The MIT License (MIT) + + Copyright (c) 2015 Jonathan Ong me@jongleberry.com + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +*/ +const getCaretCoordinates = (function () { + // We'll copy the properties below into the mirror div. + // Note that some browsers, such as Firefox, do not concatenate properties + // into their shorthand (e.g. padding-top, padding-bottom etc. -> padding), + // so we have to list every single property explicitly. + var properties = [ + "direction", // RTL support + "boxSizing", + "width", // on Chrome and IE, exclude the scrollbar, so the mirror div wraps exactly as the textarea does + "height", + "overflowX", + "overflowY", // copy the scrollbar for IE + + "borderTopWidth", + "borderRightWidth", + "borderBottomWidth", + "borderLeftWidth", + "borderStyle", + + "paddingTop", + "paddingRight", + "paddingBottom", + "paddingLeft", + + // https://developer.mozilla.org/en-US/docs/Web/CSS/font + "fontStyle", + "fontVariant", + "fontWeight", + "fontStretch", + "fontSize", + "fontSizeAdjust", + "lineHeight", + "fontFamily", + + "textAlign", + "textTransform", + "textIndent", + "textDecoration", // might not make a difference, but better be safe + + "letterSpacing", + "wordSpacing", + + "tabSize", + "MozTabSize", + ]; + + var isBrowser = typeof window !== "undefined"; + var isFirefox = isBrowser && window.mozInnerScreenX != null; + + return function getCaretCoordinates(element, position, options) { + if (!isBrowser) { + throw new Error("textarea-caret-position#getCaretCoordinates should only be called in a browser"); + } + + var debug = (options && options.debug) || false; + if (debug) { + var el = document.querySelector("#input-textarea-caret-position-mirror-div"); + if (el) el.parentNode.removeChild(el); + } + + // The mirror div will replicate the textarea's style + var div = document.createElement("div"); + div.id = "input-textarea-caret-position-mirror-div"; + document.body.appendChild(div); + + var style = div.style; + var computed = window.getComputedStyle ? window.getComputedStyle(element) : element.currentStyle; // currentStyle for IE < 9 + var isInput = element.nodeName === "INPUT"; + + // Default textarea styles + style.whiteSpace = "pre-wrap"; + if (!isInput) style.wordWrap = "break-word"; // only for textarea-s + + // Position off-screen + style.position = "absolute"; // required to return coordinates properly + if (!debug) style.visibility = "hidden"; // not 'display: none' because we want rendering + + // Transfer the element's properties to the div + properties.forEach(function (prop) { + if (isInput && prop === "lineHeight") { + // Special case for s because text is rendered centered and line height may be != height + if (computed.boxSizing === "border-box") { + var height = parseInt(computed.height); + var outerHeight = + parseInt(computed.paddingTop) + + parseInt(computed.paddingBottom) + + parseInt(computed.borderTopWidth) + + parseInt(computed.borderBottomWidth); + var targetHeight = outerHeight + parseInt(computed.lineHeight); + if (height > targetHeight) { + style.lineHeight = height - outerHeight + "px"; + } else if (height === targetHeight) { + style.lineHeight = computed.lineHeight; + } else { + style.lineHeight = 0; + } + } else { + style.lineHeight = computed.height; + } + } else { + style[prop] = computed[prop]; + } + }); + + if (isFirefox) { + // Firefox lies about the overflow property for textareas: https://bugzilla.mozilla.org/show_bug.cgi?id=984275 + if (element.scrollHeight > parseInt(computed.height)) style.overflowY = "scroll"; + } else { + style.overflow = "hidden"; // for Chrome to not render a scrollbar; IE keeps overflowY = 'scroll' + } + + div.textContent = element.value.substring(0, position); + // The second special handling for input type="text" vs textarea: + // spaces need to be replaced with non-breaking spaces - http://stackoverflow.com/a/13402035/1269037 + if (isInput) div.textContent = div.textContent.replace(/\s/g, "\u00a0"); + + var span = document.createElement("span"); + // Wrapping must be replicated *exactly*, including when a long word gets + // onto the next line, with whitespace at the end of the line before (#7). + // The *only* reliable way to do that is to copy the *entire* rest of the + // textarea's content into the created at the caret position. + // For inputs, just '.' would be enough, but no need to bother. + span.textContent = element.value.substring(position) || "."; // || because a completely empty faux span doesn't render at all + div.appendChild(span); + + var coordinates = { + top: span.offsetTop + parseInt(computed["borderTopWidth"]), + left: span.offsetLeft + parseInt(computed["borderLeftWidth"]), + height: parseInt(computed["lineHeight"]), + }; + + if (debug) { + span.style.backgroundColor = "#aaa"; + } else { + document.body.removeChild(div); + } + + return coordinates; + }; +})(); + +/* + Key functions from: + https://github.com/yuku/textcomplete + Β© Yuku Takahashi - This software is licensed under the MIT license. + + The MIT License (MIT) + + Copyright (c) 2015 Jonathan Ong me@jongleberry.com + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +*/ +const CHAR_CODE_ZERO = "0".charCodeAt(0); +const CHAR_CODE_NINE = "9".charCodeAt(0); + +class TextAreaCaretHelper { + constructor(el, getScale) { + this.el = el; + this.getScale = getScale; + } + + #calculateElementOffset() { + const rect = this.el.getBoundingClientRect(); + const owner = this.el.ownerDocument; + if (owner == null) { + throw new Error("Given element does not belong to document"); + } + const { defaultView, documentElement } = owner; + if (defaultView == null) { + throw new Error("Given element does not belong to window"); + } + const offset = { + top: rect.top + defaultView.pageYOffset, + left: rect.left + defaultView.pageXOffset, + }; + if (documentElement) { + offset.top -= documentElement.clientTop; + offset.left -= documentElement.clientLeft; + } + return offset; + } + + #isDigit(charCode) { + return CHAR_CODE_ZERO <= charCode && charCode <= CHAR_CODE_NINE; + } + + #getLineHeightPx() { + const computedStyle = getComputedStyle(this.el); + const lineHeight = computedStyle.lineHeight; + // If the char code starts with a digit, it is either a value in pixels, + // or unitless, as per: + // https://drafts.csswg.org/css2/visudet.html#propdef-line-height + // https://drafts.csswg.org/css2/cascade.html#computed-value + if (this.#isDigit(lineHeight.charCodeAt(0))) { + const floatLineHeight = parseFloat(lineHeight); + // In real browsers the value is *always* in pixels, even for unit-less + // line-heights. However, we still check as per the spec. + return this.#isDigit(lineHeight.charCodeAt(lineHeight.length - 1)) + ? floatLineHeight * parseFloat(computedStyle.fontSize) + : floatLineHeight; + } + // Otherwise, the value is "normal". + // If the line-height is "normal", calculate by font-size + return this.#calculateLineHeightPx(this.el.nodeName, computedStyle); + } + + /** + * Returns calculated line-height of the given node in pixels. + */ + #calculateLineHeightPx(nodeName, computedStyle) { + const body = document.body; + if (!body) return 0; + + const tempNode = document.createElement(nodeName); + tempNode.innerHTML = " "; + Object.assign(tempNode.style, { + fontSize: computedStyle.fontSize, + fontFamily: computedStyle.fontFamily, + padding: "0", + position: "absolute", + }); + body.appendChild(tempNode); + + // Make sure textarea has only 1 row + if (tempNode instanceof HTMLTextAreaElement) { + tempNode.rows = 1; + } + + // Assume the height of the element is the line-height + const height = tempNode.offsetHeight; + body.removeChild(tempNode); + + return height; + } + + getCursorOffset() { + const scale = this.getScale(); + const elOffset = this.#calculateElementOffset(); + const elScroll = this.#getElScroll(); + const cursorPosition = this.#getCursorPosition(); + const lineHeight = this.#getLineHeightPx(); + const top = elOffset.top - (elScroll.top * scale) + (cursorPosition.top + lineHeight) * scale; + const left = elOffset.left - elScroll.left + cursorPosition.left; + const clientTop = this.el.getBoundingClientRect().top; + if (this.el.dir !== "rtl") { + return { top, left, lineHeight, clientTop }; + } else { + const right = document.documentElement ? document.documentElement.clientWidth - left : 0; + return { top, right, lineHeight, clientTop }; + } + } + + #getElScroll() { + return { top: this.el.scrollTop, left: this.el.scrollLeft }; + } + + #getCursorPosition() { + return getCaretCoordinates(this.el, this.el.selectionEnd); + } + + getBeforeCursor() { + return this.el.selectionStart !== this.el.selectionEnd ? null : this.el.value.substring(0, this.el.selectionEnd); + } + + getAfterCursor() { + return this.el.value.substring(this.el.selectionEnd); + } + + insertAtCursor(value, offset, finalOffset) { + if (this.el.selectionStart != null) { + const startPos = this.el.selectionStart; + const endPos = this.el.selectionEnd; + + // Move selection to beginning of offset + this.el.selectionStart = this.el.selectionStart + offset; + + // Using execCommand to support undo, but since it's officially + // 'deprecated' we need a backup solution, but it won't support undo :( + let pasted = true; + try { + if (!document.execCommand("insertText", false, value)) { + pasted = false; + } + } catch (e) { + console.error("Error caught during execCommand:", e); + pasted = false; + } + + if (!pasted) { + console.error( + "execCommand unsuccessful; not supported. Adding text manually, no undo support."); + textarea.setRangeText(modifiedText, this.el.selectionStart, this.el.selectionEnd, 'end'); + } + + this.el.selectionEnd = this.el.selectionStart = startPos + value.length + offset + (finalOffset ?? 0); + } else { + // Using execCommand to support undo, but since it's officially + // 'deprecated' we need a backup solution, but it won't support undo :( + let pasted = true; + try { + if (!document.execCommand("insertText", false, value)) { + pasted = false; + } + } catch (e) { + console.error("Error caught during execCommand:", e); + pasted = false; + } + + if (!pasted) { + console.error( + "execCommand unsuccessful; not supported. Adding text manually, no undo support."); + this.el.value += value; + } + } + } +} + +/*********************/ + +/** + * @typedef {{ + * text: string, + * priority?: number, + * info?: Function, + * hint?: string, + * showValue?: boolean, + * caretOffset?: number + * }} AutoCompleteEntry + */ +export class TextAreaAutoComplete { + static globalSeparator = ""; + static enabled = true; + static insertOnTab = true; + static insertOnEnter = true; + static replacer = undefined; + static lorasEnabled = false; + static suggestionCount = 20; + + /** @type {Record>} */ + static groups = {}; + /** @type {Set} */ + static globalGroups = new Set(); + /** @type {Record} */ + static globalWords = {}; + /** @type {Record} */ + static globalWordsExclLoras = {}; + + /** @type {HTMLTextAreaElement} */ + el; + + /** @type {Record} */ + overrideWords; + overrideSeparator = ""; + + get words() { + return this.overrideWords ?? TextAreaAutoComplete.globalWords; + } + + get separator() { + return this.overrideSeparator ?? TextAreaAutoComplete.globalSeparator; + } + + /** + * @param {HTMLTextAreaElement} el + */ + constructor(el, words = null, separator = null) { + this.el = el; + this.helper = new TextAreaCaretHelper(el, () => app.canvas.ds.scale); + this.dropdown = $el("div.pysssss-autocomplete"); + this.overrideWords = words; + this.overrideSeparator = separator; + + this.#setup(); + } + + #setup() { + this.el.addEventListener("keydown", this.#keyDown.bind(this)); + this.el.addEventListener("keypress", this.#keyPress.bind(this)); + this.el.addEventListener("keyup", this.#keyUp.bind(this)); + this.el.addEventListener("click", this.#hide.bind(this)); + this.el.addEventListener("blur", () => setTimeout(() => this.#hide(), 150)); + } + + /** + * @param {KeyboardEvent} e + */ + #keyDown(e) { + if (!TextAreaAutoComplete.enabled) return; + + if (this.dropdown.parentElement) { + // We are visible + switch (e.key) { + case "ArrowUp": + e.preventDefault(); + if (this.selected.index) { + this.#setSelected(this.currentWords[this.selected.index - 1].wordInfo); + } else { + this.#setSelected(this.currentWords[this.currentWords.length - 1].wordInfo); + } + break; + case "ArrowDown": + e.preventDefault(); + if (this.selected.index === this.currentWords.length - 1) { + this.#setSelected(this.currentWords[0].wordInfo); + } else { + this.#setSelected(this.currentWords[this.selected.index + 1].wordInfo); + } + break; + case "Tab": + if (TextAreaAutoComplete.insertOnTab) { + this.#insertItem(); + e.preventDefault(); + } + break; + } + } + } + + /** + * @param {KeyboardEvent} e + */ + #keyPress(e) { + if (!TextAreaAutoComplete.enabled) return; + if (this.dropdown.parentElement) { + // We are visible + switch (e.key) { + case "Enter": + if (!e.ctrlKey) { + if (TextAreaAutoComplete.insertOnEnter) { + this.#insertItem(); + e.preventDefault(); + } + } + break; + } + } + + if (!e.defaultPrevented) { + this.#update(); + } + } + + #keyUp(e) { + if (!TextAreaAutoComplete.enabled) return; + if (this.dropdown.parentElement) { + // We are visible + switch (e.key) { + case "Escape": + e.preventDefault(); + this.#hide(); + break; + } + } else if (e.key.length > 1 && e.key != "Delete" && e.key != "Backspace") { + return; + } + if (!e.defaultPrevented) { + this.#update(); + } + } + + #setSelected(item) { + if (this.selected) { + this.selected.el.classList.remove("pysssss-autocomplete-item--selected"); + } + + this.selected = item; + this.selected.el.classList.add("pysssss-autocomplete-item--selected"); + } + + #insertItem() { + if (!this.selected) return; + this.selected.el.click(); + } + + #getFilteredWords(term) { + term = term.toLocaleLowerCase(); + + const priorityMatches = []; + const prefixMatches = []; + const includesMatches = []; + for (const word of Object.keys(this.words)) { + const lowerWord = word.toLocaleLowerCase(); + if (lowerWord === term) { + // Dont include exact matches + continue; + } + + const pos = lowerWord.indexOf(term); + if (pos === -1) { + // No match + continue; + } + + const wordInfo = this.words[word]; + if (wordInfo.priority) { + priorityMatches.push({ pos, wordInfo }); + } else if (pos) { + includesMatches.push({ pos, wordInfo }); + } else { + prefixMatches.push({ pos, wordInfo }); + } + } + + priorityMatches.sort( + (a, b) => + b.wordInfo.priority - a.wordInfo.priority || + a.wordInfo.text.length - b.wordInfo.text.length || + a.wordInfo.text.localeCompare(b.wordInfo.text) + ); + + const top = priorityMatches.length * 0.2; + return priorityMatches.slice(0, top).concat(prefixMatches, priorityMatches.slice(top), includesMatches).slice(0, TextAreaAutoComplete.suggestionCount); + } + + #update() { + let before = this.helper.getBeforeCursor(); + if (before?.length) { + const m = before.match(/([^,;"|{}()\n]+)$/); + if (m) { + before = m[0] + .replace(/^\s+/, "") + .replace(/\s/g, "_") || null; + } else { + before = null; + } + } + + if (!before) { + this.#hide(); + return; + } + + this.currentWords = this.#getFilteredWords(before); + if (!this.currentWords.length) { + this.#hide(); + return; + } + + this.dropdown.style.display = ""; + + let hasSelected = false; + const items = this.currentWords.map(({ wordInfo, pos }, i) => { + const parts = [ + $el("span", { + textContent: wordInfo.text.substr(0, pos), + }), + $el("span.pysssss-autocomplete-highlight", { + textContent: wordInfo.text.substr(pos, before.length), + }), + $el("span", { + textContent: wordInfo.text.substr(pos + before.length), + }), + ]; + + if (wordInfo.hint) { + parts.push( + $el("span.pysssss-autocomplete-pill", { + textContent: wordInfo.hint, + }) + ); + } + + if (wordInfo.priority) { + parts.push( + $el("span.pysssss-autocomplete-pill", { + textContent: wordInfo.priority, + }) + ); + } + + if (wordInfo.value && wordInfo.text !== wordInfo.value && wordInfo.showValue !== false) { + parts.push( + $el("span.pysssss-autocomplete-pill", { + textContent: wordInfo.value, + }) + ); + } + + if (wordInfo.info) { + parts.push( + $el("a.pysssss-autocomplete-item-info", { + textContent: "ℹ️", + title: "View info...", + onclick: (e) => { + e.stopPropagation(); + wordInfo.info(); + e.preventDefault(); + }, + }) + ); + } + const item = $el( + "div.pysssss-autocomplete-item", + { + onclick: () => { + this.el.focus(); + let value = wordInfo.value ?? wordInfo.text; + const use_replacer = wordInfo.use_replacer ?? true; + if (TextAreaAutoComplete.replacer && use_replacer) { + value = TextAreaAutoComplete.replacer(value); + } + value = this.#escapeParentheses(value); + + const afterCursor = this.helper.getAfterCursor(); + const shouldAddSeparator = !afterCursor.trim().startsWith(this.separator.trim()); + this.helper.insertAtCursor( + value + (shouldAddSeparator ? this.separator : ''), + -before.length, + wordInfo.caretOffset + ); + setTimeout(() => { + this.#update(); + }, 150); + }, + }, + parts + ); + + if (wordInfo === this.selected) { + hasSelected = true; + } + + wordInfo.index = i; + wordInfo.el = item; + + return item; + }); + + this.#setSelected(hasSelected ? this.selected : this.currentWords[0].wordInfo); + this.dropdown.replaceChildren(...items); + + if (!this.dropdown.parentElement) { + document.body.append(this.dropdown); + } + + const position = this.helper.getCursorOffset(); + this.dropdown.style.left = (position.left ?? 0) + "px"; + this.dropdown.style.top = (position.top ?? 0) + "px"; + this.dropdown.style.maxHeight = (window.innerHeight - position.top) + "px"; + } + + #escapeParentheses(text) { + return text.replace(/\(/g, '\\(').replace(/\)/g, '\\)'); + } + + #hide() { + this.selected = null; + this.dropdown.remove(); + } + + static updateWords(id, words, addGlobal = true) { + const isUpdate = id in TextAreaAutoComplete.groups; + TextAreaAutoComplete.groups[id] = words; + if (addGlobal) { + TextAreaAutoComplete.globalGroups.add(id); + } + + if (isUpdate) { + // Remerge all words + TextAreaAutoComplete.globalWords = Object.assign( + {}, + ...Object.keys(TextAreaAutoComplete.groups) + .filter((k) => TextAreaAutoComplete.globalGroups.has(k)) + .map((k) => TextAreaAutoComplete.groups[k]) + ); + } else if (addGlobal) { + // Just insert the new words + Object.assign(TextAreaAutoComplete.globalWords, words); + } + } +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/binding.js b/custom_nodes/comfyui-custom-scripts/web/js/common/binding.js new file mode 100644 index 00000000..7533e7e5 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/binding.js @@ -0,0 +1,244 @@ +// @ts-check +// @ts-ignore +import { ComfyWidgets } from "../../../../scripts/widgets.js"; +// @ts-ignore +import { api } from "../../../../scripts/api.js"; +// @ts-ignore +import { app } from "../../../../scripts/app.js"; + +const PathHelper = { + get(obj, path) { + if (typeof path !== "string") { + // Hardcoded value + return path; + } + + if (path[0] === '"' && path[path.length - 1] === '"') { + // Hardcoded string + return JSON.parse(path); + } + + // Evaluate the path + path = path.split(".").filter(Boolean); + for (const p of path) { + const k = isNaN(+p) ? p : +p; + obj = obj[k]; + } + + return obj; + }, + set(obj, path, value) { + // https://stackoverflow.com/a/54733755 + if (Object(obj) !== obj) return obj; // When obj is not an object + // If not yet an array, get the keys from the string-path + if (!Array.isArray(path)) path = path.toString().match(/[^.[\]]+/g) || []; + path.slice(0, -1).reduce( + ( + a, + c, + i // Iterate all of them except the last one + ) => + Object(a[c]) === a[c] // Does the key exist and is its value an object? + ? // Yes: then follow that path + a[c] + : // No: create the key. Is the next key a potential array-index? + (a[c] = + Math.abs(path[i + 1]) >> 0 === +path[i + 1] + ? [] // Yes: assign a new array object + : {}), // No: assign a new plain object + obj + )[path[path.length - 1]] = value; // Finally assign the value to the last key + return obj; // Return the top-level object to allow chaining + }, +}; + +/*** + @typedef { { + left: string; + op: "eq" | "ne", + right: string + } } IfCondition + + @typedef { { + type: "if", + condition: Array, + true?: Array, + false?: Array + } } IfCallback + + @typedef { { + type: "fetch", + url: string, + then: Array + } } FetchCallback + + @typedef { { + type: "set", + target: string, + value: string + } } SetCallback + + @typedef { { + type: "validate-combo", + } } ValidateComboCallback + + @typedef { IfCallback | FetchCallback | SetCallback | ValidateComboCallback } BindingCallback + + @typedef { { + source: string, + callback: Array + } } Binding +***/ + +/** + * @param {IfCondition} condition + */ +function evaluateCondition(condition, state) { + const left = PathHelper.get(state, condition.left); + const right = PathHelper.get(state, condition.right); + + let r; + if (condition.op === "eq") { + r = left === right; + } else { + r = left !== right; + } + + return r; +} + +/** + * @type { Record) => Promise> } + */ +const callbacks = { + /** + * @param {IfCallback} cb + */ + async if(cb, state) { + // For now only support ANDs + let success = true; + for (const condition of cb.condition) { + const r = evaluateCondition(condition, state); + if (!r) { + success = false; + break; + } + } + + for (const m of cb[success + ""] ?? []) { + await invokeCallback(m, state); + } + }, + /** + * @param {FetchCallback} cb + */ + async fetch(cb, state) { + const url = cb.url.replace(/\{([^\}]+)\}/g, (m, v) => { + return PathHelper.get(state, v); + }); + const res = await (await api.fetchApi(url)).json(); + state["$result"] = res; + for (const m of cb.then) { + await invokeCallback(m, state); + } + }, + /** + * @param {SetCallback} cb + */ + async set(cb, state) { + const value = PathHelper.get(state, cb.value); + PathHelper.set(state, cb.target, value); + }, + async "validate-combo"(cb, state) { + const w = state["$this"]; + const valid = w.options.values.includes(w.value); + if (!valid) { + w.value = w.options.values[0]; + } + }, +}; + +async function invokeCallback(callback, state) { + if (callback.type in callbacks) { + // @ts-ignore + await callbacks[callback.type](callback, state); + } else { + console.warn( + "%c[🐍 pysssss]", + "color: limegreen", + `[binding ${state.$node.comfyClass}.${state.$this.name}]`, + "unsupported binding callback type:", + callback.type + ); + } +} + +app.registerExtension({ + name: "pysssss.Binding", + beforeRegisterNodeDef(node, nodeData) { + const hasBinding = (v) => { + if (!v) return false; + return Object.values(v).find((c) => c[1]?.["pysssss.binding"]); + }; + const inputs = { ...nodeData.input?.required, ...nodeData.input?.optional }; + if (hasBinding(inputs)) { + const onAdded = node.prototype.onAdded; + node.prototype.onAdded = function () { + const r = onAdded?.apply(this, arguments); + + for (const widget of this.widgets || []) { + const bindings = inputs[widget.name][1]?.["pysssss.binding"]; + if (!bindings) continue; + + for (const binding of bindings) { + /** + * @type {import("../../../../../web/types/litegraph.d.ts").IWidget} + */ + const source = this.widgets.find((w) => w.name === binding.source); + if (!source) { + console.warn( + "%c[🐍 pysssss]", + "color: limegreen", + `[binding ${node.comfyClass}.${widget.name}]`, + "unable to find source binding widget:", + binding.source, + binding + ); + continue; + } + + let lastValue; + async function valueChanged() { + const state = { + $this: widget, + $source: source, + $node: node, + }; + + for (const callback of binding.callback) { + await invokeCallback(callback, state); + } + + app.graph.setDirtyCanvas(true, false); + } + + const cb = source.callback; + source.callback = function () { + const v = cb?.apply(this, arguments) ?? source.value; + if (v !== lastValue) { + lastValue = v; + valueChanged(); + } + return v; + }; + + lastValue = source.value; + valueChanged(); + } + } + + return r; + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.css b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.css new file mode 100644 index 00000000..c4307fd0 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.css @@ -0,0 +1,102 @@ +.pysssss-lightbox { + width: 100vw; + height: 100vh; + position: fixed; + top: 0; + left: 0; + z-index: 1001; + background: rgba(0, 0, 0, 0.6); + display: flex; + align-items: center; + transition: opacity 0.2s; +} + +.pysssss-lightbox-prev, +.pysssss-lightbox-next { + height: 60px; + display: flex; + align-items: center; +} + +.pysssss-lightbox-prev:after, +.pysssss-lightbox-next:after { + border-style: solid; + border-width: 0.25em 0.25em 0 0; + display: inline-block; + height: 0.45em; + left: 0.15em; + position: relative; + top: 0.15em; + transform: rotate(-135deg) scale(0.75); + vertical-align: top; + width: 0.45em; + padding: 10px; + font-size: 20px; + margin: 0 10px 0 20px; + transition: color 0.2s; + flex-shrink: 0; + content: ""; +} + +.pysssss-lightbox-next:after { + transform: rotate(45deg) scale(0.75); + margin: 0 20px 0 0px; +} + +.pysssss-lightbox-main { + display: grid; + flex: auto; + place-content: center; + text-align: center; +} + +.pysssss-lightbox-link { + display: flex; + justify-content: center; + align-items: center; + position: relative; +} + +.pysssss-lightbox .lds-ring { + position: absolute; + left: 50%; + top: 50%; + transform: translate(-50%, -50%); +} + +.pysssss-lightbox-img { + max-height: 90vh; + max-width: calc(100vw - 130px); + height: auto; + object-fit: contain; + border: 3px solid white; + border-radius: 4px; + transition: opacity 0.2s; + user-select: none; +} + +.pysssss-lightbox-img:hover { + border-color: dodgerblue; +} + +.pysssss-lightbox-close { + font-size: 80px; + line-height: 1ch; + height: 1ch; + width: 1ch; + position: absolute; + right: 10px; + top: 10px; + padding: 5px; +} + +.pysssss-lightbox-close:after { + content: "\00d7"; +} + +.pysssss-lightbox-close:hover, +.pysssss-lightbox-prev:hover, +.pysssss-lightbox-next:hover { + color: dodgerblue; + cursor: pointer; +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.js b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.js new file mode 100644 index 00000000..5c907906 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.js @@ -0,0 +1,149 @@ +import { $el } from "../../../../scripts/ui.js"; +import { addStylesheet, getUrl, loadImage } from "./utils.js"; +import { createSpinner } from "./spinner.js"; + +addStylesheet(getUrl("lightbox.css", import.meta.url)); + +const $$el = (tag, name, ...args) => { + if (name) name = "-" + name; + return $el(tag + ".pysssss-lightbox" + name, ...args); +}; + +const ani = async (a, t, b) => { + a(); + await new Promise((r) => setTimeout(r, t)); + b(); +}; + +export class Lightbox { + constructor() { + this.el = $$el("div", "", { + parent: document.body, + onclick: (e) => { + e.stopImmediatePropagation(); + this.close(); + }, + style: { + display: "none", + opacity: 0, + }, + }); + this.closeBtn = $$el("div", "close", { + parent: this.el, + }); + this.prev = $$el("div", "prev", { + parent: this.el, + onclick: (e) => { + this.update(-1); + e.stopImmediatePropagation(); + }, + }); + this.main = $$el("div", "main", { + parent: this.el, + }); + this.next = $$el("div", "next", { + parent: this.el, + onclick: (e) => { + this.update(1); + e.stopImmediatePropagation(); + }, + }); + this.link = $$el("a", "link", { + parent: this.main, + target: "_blank", + }); + this.spinner = createSpinner(); + this.link.appendChild(this.spinner); + this.img = $$el("img", "img", { + style: { + opacity: 0, + }, + parent: this.link, + onclick: (e) => { + e.stopImmediatePropagation(); + }, + onwheel: (e) => { + if (!(e instanceof WheelEvent) || e.ctrlKey) { + return; + } + const direction = Math.sign(e.deltaY); + this.update(direction); + }, + }); + } + + close() { + ani( + () => (this.el.style.opacity = 0), + 200, + () => (this.el.style.display = "none") + ); + } + + async show(images, index) { + this.images = images; + this.index = index || 0; + await this.update(0); + } + + async update(shift) { + if (shift < 0 && this.index <= 0) { + return; + } + if (shift > 0 && this.index >= this.images.length - 1) { + return; + } + this.index += shift; + + this.prev.style.visibility = this.index ? "unset" : "hidden"; + this.next.style.visibility = this.index === this.images.length - 1 ? "hidden" : "unset"; + + const img = this.images[this.index]; + this.el.style.display = "flex"; + this.el.clientWidth; // Force a reflow + this.el.style.opacity = 1; + this.img.style.opacity = 0; + this.spinner.style.display = "inline-block"; + try { + await loadImage(img); + } catch (err) { + console.error('failed to load image', img, err); + } + this.spinner.style.display = "none"; + this.link.href = img; + this.img.src = img; + this.img.style.opacity = 1; + } + + async updateWithNewImage(img, feedDirection) { + // No-op if lightbox is not open + if (this.el.style.display === "none" || this.el.style.opacity === "0") return; + + // Ensure currently shown image does not change + const [method, shift] = feedDirection === "newest first" ? ["unshift", 1] : ["push", 0]; + this.images[method](img); + await this.update(shift); + } +} + +export const lightbox = new Lightbox(); + +addEventListener('keydown', (event) => { + if (lightbox.el.style.display === 'none') { + return; + } + const { key } = event; + switch (key) { + case 'ArrowLeft': + case 'a': + lightbox.update(-1); + break; + case 'ArrowRight': + case 'd': + lightbox.update(1); + break; + case 'Escape': + lightbox.close(); + break; + } +}); \ No newline at end of file diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.css b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.css new file mode 100644 index 00000000..a5b9f627 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.css @@ -0,0 +1,119 @@ +.pysssss-model-info { + color: white; + font-family: sans-serif; + max-width: 90vw; +} +.pysssss-model-content { + display: flex; + flex-direction: column; + overflow: hidden; +} +.pysssss-model-info h2 { + text-align: center; + margin: 0 0 10px 0; +} +.pysssss-model-info p { + margin: 5px 0; +} +.pysssss-model-info a { + color: dodgerblue; +} +.pysssss-model-info a:hover { + text-decoration: underline; +} +.pysssss-model-tags-list { + display: flex; + flex-wrap: wrap; + list-style: none; + gap: 10px; + max-height: 200px; + overflow: auto; + margin: 10px 0; + padding: 0; +} +.pysssss-model-tag { + background-color: rgb(128, 213, 247); + color: #000; + display: flex; + align-items: center; + gap: 5px; + border-radius: 5px; + padding: 2px 5px; + cursor: pointer; +} +.pysssss-model-tag--selected span::before { + content: "βœ…"; + position: absolute; + background-color: dodgerblue; + left: 0; + top: 0; + right: 0; + bottom: 0; + text-align: center; +} +.pysssss-model-tag:hover { + outline: 2px solid dodgerblue; +} +.pysssss-model-tag p { + margin: 0; +} +.pysssss-model-tag span { + text-align: center; + border-radius: 5px; + background-color: dodgerblue; + color: #fff; + padding: 2px; + position: relative; + min-width: 20px; + overflow: hidden; +} + +.pysssss-model-metadata .comfy-modal-content { + max-width: 100%; +} +.pysssss-model-metadata label { + margin-right: 1ch; + color: #ccc; +} + +.pysssss-model-metadata span { + color: dodgerblue; +} + +.pysssss-preview { + max-width: 50%; + margin-left: 10px; + position: relative; +} +.pysssss-preview img { + max-height: 300px; +} +.pysssss-preview button { + position: absolute; + font-size: 12px; + bottom: 10px; + right: 10px; +} +.pysssss-preview button+button { + bottom: 34px; +} + +.pysssss-preview button.pysssss-preview-nav { + bottom: unset; + right: 30px; + top: 10px; + font-size: 14px; + line-height: 14px; +} + +.pysssss-preview button.pysssss-preview-nav+.pysssss-preview-nav { + right: 10px; +} +.pysssss-model-notes { + background-color: rgba(0, 0, 0, 0.25); + padding: 5px; + margin-top: 5px; +} +.pysssss-model-notes:empty { + display: none; +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.js b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.js new file mode 100644 index 00000000..032767da --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.js @@ -0,0 +1,358 @@ +import { $el, ComfyDialog } from "../../../../scripts/ui.js"; +import { api } from "../../../../scripts/api.js"; +import { addStylesheet } from "./utils.js"; + +addStylesheet(import.meta.url); + +class MetadataDialog extends ComfyDialog { + constructor() { + super(); + + this.element.classList.add("pysssss-model-metadata"); + } + show(metadata) { + super.show( + $el( + "div", + Object.keys(metadata).map((k) => + $el("div", [ + $el("label", { textContent: k }), + $el("span", { textContent: typeof metadata[k] === "object" ? JSON.stringify(metadata[k]) : metadata[k] }), + ]) + ) + ) + ); + } +} + +export class ModelInfoDialog extends ComfyDialog { + constructor(name, node) { + super(); + this.name = name; + this.node = node; + this.element.classList.add("pysssss-model-info"); + } + + get customNotes() { + return this.metadata["pysssss.notes"]; + } + + set customNotes(v) { + this.metadata["pysssss.notes"] = v; + } + + get hash() { + return this.metadata["pysssss.sha256"]; + } + + async show(type, value) { + this.type = type; + + const req = api.fetchApi("/pysssss/metadata/" + encodeURIComponent(`${type}/${value}`)); + this.info = $el("div", { style: { flex: "auto" } }); + this.img = $el("img", { style: { display: "none" } }); + this.imgWrapper = $el("div.pysssss-preview", [this.img]); + this.main = $el("main", { style: { display: "flex" } }, [this.info, this.imgWrapper]); + this.content = $el("div.pysssss-model-content", [$el("h2", { textContent: this.name }), this.main]); + + const loading = $el("div", { textContent: "ℹ️ Loading...", parent: this.content }); + + super.show(this.content); + + this.metadata = await (await req).json(); + this.viewMetadata.style.cursor = this.viewMetadata.style.opacity = ""; + this.viewMetadata.removeAttribute("disabled"); + + loading.remove(); + this.addInfo(); + } + + createButtons() { + const btns = super.createButtons(); + this.viewMetadata = $el("button", { + type: "button", + textContent: "View raw metadata", + disabled: "disabled", + style: { + opacity: 0.5, + cursor: "not-allowed", + }, + onclick: (e) => { + if (this.metadata) { + new MetadataDialog().show(this.metadata); + } + }, + }); + + btns.unshift(this.viewMetadata); + return btns; + } + + getNoteInfo() { + function parseNote() { + if (!this.customNotes) return []; + + let notes = []; + // Extract links from notes + const r = new RegExp("(\\bhttps?:\\/\\/[^\\s]+)", "g"); + let end = 0; + let m; + do { + m = r.exec(this.customNotes); + let pos; + let fin = 0; + if (m) { + pos = m.index; + fin = m.index + m[0].length; + } else { + pos = this.customNotes.length; + } + + let pre = this.customNotes.substring(end, pos); + if (pre) { + pre = pre.replaceAll("\n", "
"); + notes.push( + $el("span", { + innerHTML: pre, + }) + ); + } + if (m) { + notes.push( + $el("a", { + href: m[0], + textContent: m[0], + target: "_blank", + }) + ); + } + + end = fin; + } while (m); + return notes; + } + + let textarea; + let notesContainer; + const editText = "✏️ Edit"; + const edit = $el("a", { + textContent: editText, + href: "#", + style: { + float: "right", + color: "greenyellow", + textDecoration: "none", + }, + onclick: async (e) => { + e.preventDefault(); + + if (textarea) { + this.customNotes = textarea.value; + + const resp = await api.fetchApi("/pysssss/metadata/notes/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: this.customNotes, + }); + + if (resp.status !== 200) { + console.error(resp); + alert(`Error saving notes (${req.status}) ${req.statusText}`); + return; + } + + e.target.textContent = editText; + textarea.remove(); + textarea = null; + + notesContainer.replaceChildren(...parseNote.call(this)); + this.node?.["pysssss.updateExamples"]?.(); + } else { + e.target.textContent = "πŸ’Ύ Save"; + textarea = $el("textarea", { + style: { + width: "100%", + minWidth: "200px", + minHeight: "50px", + }, + textContent: this.customNotes, + }); + e.target.after(textarea); + notesContainer.replaceChildren(); + textarea.style.height = Math.min(textarea.scrollHeight, 300) + "px"; + } + }, + }); + + notesContainer = $el("div.pysssss-model-notes", parseNote.call(this)); + return $el( + "div", + { + style: { display: "contents" }, + }, + [edit, notesContainer] + ); + } + + addInfo() { + const usageHint = this.metadata["modelspec.usage_hint"]; + if (usageHint) { + this.addInfoEntry("Usage Hint", usageHint); + } + this.addInfoEntry("Notes", this.getNoteInfo()); + } + + addInfoEntry(name, value) { + return $el( + "p", + { + parent: this.info, + }, + [ + typeof name === "string" ? $el("label", { textContent: name + ": " }) : name, + typeof value === "string" ? $el("span", { textContent: value }) : value, + ] + ); + } + + async getCivitaiDetails() { + const req = await fetch("https://civitai.com/api/v1/model-versions/by-hash/" + this.hash); + if (req.status === 200) { + return await req.json(); + } else if (req.status === 404) { + throw new Error("Model not found"); + } else { + throw new Error(`Error loading info (${req.status}) ${req.statusText}`); + } + } + + addCivitaiInfo() { + const promise = this.getCivitaiDetails(); + const content = $el("span", { textContent: "ℹ️ Loading..." }); + + this.addInfoEntry( + $el("label", [ + $el("img", { + style: { + width: "18px", + position: "relative", + top: "3px", + margin: "0 5px 0 0", + }, + src: "https://civitai.com/favicon.ico", + }), + $el("span", { textContent: "Civitai: " }), + ]), + content + ); + + return promise + .then((info) => { + content.replaceChildren( + $el("a", { + href: "https://civitai.com/models/" + info.modelId, + textContent: "View " + info.model.name, + target: "_blank", + }) + ); + + const allPreviews = info.images?.filter((i) => i.type === "image"); + const previews = allPreviews?.filter((i) => i.nsfwLevel <= ModelInfoDialog.nsfwLevel); + if (previews?.length) { + let previewIndex = 0; + let preview; + const updatePreview = () => { + preview = previews[previewIndex]; + this.img.src = preview.url; + }; + + updatePreview(); + this.img.style.display = ""; + + this.img.title = `${previews.length} previews.`; + if (allPreviews.length !== previews.length) { + this.img.title += ` ${allPreviews.length - previews.length} images hidden due to NSFW level.`; + } + + this.imgSave = $el("button", { + textContent: "Use as preview", + parent: this.imgWrapper, + onclick: async () => { + // Convert the preview to a blob + const blob = await (await fetch(this.img.src)).blob(); + + // Store it in temp + const name = "temp_preview." + new URL(this.img.src).pathname.split(".")[1]; + const body = new FormData(); + body.append("image", new File([blob], name)); + body.append("overwrite", "true"); + body.append("type", "temp"); + + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status !== 200) { + console.error(resp); + alert(`Error saving preview (${req.status}) ${req.statusText}`); + return; + } + + // Use as preview + await api.fetchApi("/pysssss/save/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: JSON.stringify({ + filename: name, + type: "temp", + }), + headers: { + "content-type": "application/json", + }, + }); + app.refreshComboInNodes(); + }, + }); + + $el("button", { + textContent: "Show metadata", + parent: this.imgWrapper, + onclick: async () => { + if (preview.meta && Object.keys(preview.meta).length) { + new MetadataDialog().show(preview.meta); + } else { + alert("No image metadata found"); + } + }, + }); + + const addNavButton = (icon, direction) => { + $el("button.pysssss-preview-nav", { + textContent: icon, + parent: this.imgWrapper, + onclick: async () => { + previewIndex += direction; + if (previewIndex < 0) { + previewIndex = previews.length - 1; + } else if (previewIndex >= previews.length) { + previewIndex = 0; + } + updatePreview(); + }, + }); + }; + + if (previews.length > 1) { + addNavButton("β€Ή", -1); + addNavButton("β€Ί", 1); + } + } else if (info.images?.length) { + $el("span", { style: { opacity: 0.6 }, textContent: "⚠️ All images hidden due to NSFW level setting.", parent: this.imgWrapper }); + } + + return info; + }) + .catch((err) => { + content.textContent = "⚠️ " + err.message; + }); + } +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.css b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.css new file mode 100644 index 00000000..cfb7a0d9 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.css @@ -0,0 +1,35 @@ +.pysssss-lds-ring { + display: inline-block; + position: absolute; + width: 80px; + height: 80px; +} +.pysssss-lds-ring div { + box-sizing: border-box; + display: block; + position: absolute; + width: 64px; + height: 64px; + margin: 8px; + border: 5px solid #fff; + border-radius: 50%; + animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite; + border-color: #fff transparent transparent transparent; +} +.pysssss-lds-ring div:nth-child(1) { + animation-delay: -0.45s; +} +.pysssss-lds-ring div:nth-child(2) { + animation-delay: -0.3s; +} +.pysssss-lds-ring div:nth-child(3) { + animation-delay: -0.15s; +} +@keyframes lds-ring { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(360deg); + } +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.js b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.js new file mode 100644 index 00000000..40759901 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.js @@ -0,0 +1,9 @@ +import { addStylesheet } from "./utils.js"; + +addStylesheet(import.meta.url); + +export function createSpinner() { + const div = document.createElement("div"); + div.innerHTML = `
`; + return div.firstElementChild; +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/utils.js b/custom_nodes/comfyui-custom-scripts/web/js/common/utils.js new file mode 100644 index 00000000..cd7539d4 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/common/utils.js @@ -0,0 +1,30 @@ +import { $el } from "../../../../scripts/ui.js"; + +export function addStylesheet(url) { + if (url.endsWith(".js")) { + url = url.substr(0, url.length - 2) + "css"; + } + $el("link", { + parent: document.head, + rel: "stylesheet", + type: "text/css", + href: url.startsWith("http") ? url : getUrl(url), + }); +} + +export function getUrl(path, baseUrl) { + if (baseUrl) { + return new URL(path, baseUrl).toString(); + } else { + return new URL("../" + path, import.meta.url).toString(); + } +} + +export async function loadImage(url) { + return new Promise((res, rej) => { + const img = new Image(); + img.onload = res; + img.onerror = rej; + img.src = url; + }); +} diff --git a/custom_nodes/comfyui-custom-scripts/web/js/contextMenuHook.js b/custom_nodes/comfyui-custom-scripts/web/js/contextMenuHook.js new file mode 100644 index 00000000..0fd35b41 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/contextMenuHook.js @@ -0,0 +1,90 @@ +import { app } from "../../../scripts/app.js"; +app.registerExtension({ + name: "pysssss.ContextMenuHook", + init() { + const getOrSet = (target, name, create) => { + if (name in target) return target[name]; + return (target[name] = create()); + }; + const symbol = getOrSet(window, "__pysssss__", () => Symbol("__pysssss__")); + const store = getOrSet(window, symbol, () => ({})); + const contextMenuHook = getOrSet(store, "contextMenuHook", () => ({})); + for (const e of ["ctor", "preAddItem", "addItem"]) { + if (!contextMenuHook[e]) { + contextMenuHook[e] = []; + } + } + + // Big ol' hack to get allow customizing the context menu + // Replace the addItem function with our own that wraps the context of "this" with a proxy + // That proxy then replaces the constructor with another proxy + // That proxy then calls the custom ContextMenu that supports filters + const ctorProxy = new Proxy(LiteGraph.ContextMenu, { + construct(target, args) { + return new LiteGraph.ContextMenu(...args); + }, + }); + + function triggerCallbacks(name, getArgs, handler) { + const callbacks = contextMenuHook[name]; + if (callbacks && callbacks instanceof Array) { + for (const cb of callbacks) { + const r = cb(...getArgs()); + handler?.call(this, r); + } + } else { + console.warn("[pysssss 🐍]", `invalid ${name} callbacks`, callbacks, name in contextMenuHook); + } + } + + const addItem = LiteGraph.ContextMenu.prototype.addItem; + LiteGraph.ContextMenu.prototype.addItem = function () { + const proxy = new Proxy(this, { + get(target, prop) { + if (prop === "constructor") { + return ctorProxy; + } + return target[prop]; + }, + }); + proxy.__target__ = this; + + let el; + let args = arguments; + triggerCallbacks( + "preAddItem", + () => [el, this, args], + (r) => { + if (r !== undefined) el = r; + } + ); + + if (el === undefined) { + el = addItem.apply(proxy, arguments); + } + + triggerCallbacks( + "addItem", + () => [el, this, args], + (r) => { + if (r !== undefined) el = r; + } + ); + return el; + }; + + // We also need to patch the ContextMenu constructor to unwrap the parent else it fails a LiteGraph type check + const ctxMenu = LiteGraph.ContextMenu; + LiteGraph.ContextMenu = function (values, options) { + if (options?.parentMenu) { + if (options.parentMenu.__target__) { + options.parentMenu = options.parentMenu.__target__; + } + } + + triggerCallbacks("ctor", () => [values, options]); + return ctxMenu.call(this, values, options); + }; + LiteGraph.ContextMenu.prototype = ctxMenu.prototype; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/customColors.js b/custom_nodes/comfyui-custom-scripts/web/js/customColors.js new file mode 100644 index 00000000..2b0fefca --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/customColors.js @@ -0,0 +1,98 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; + +const colorShade = (col, amt) => { + col = col.replace(/^#/, ""); + if (col.length === 3) col = col[0] + col[0] + col[1] + col[1] + col[2] + col[2]; + + let [r, g, b] = col.match(/.{2}/g); + [r, g, b] = [parseInt(r, 16) + amt, parseInt(g, 16) + amt, parseInt(b, 16) + amt]; + + r = Math.max(Math.min(255, r), 0).toString(16); + g = Math.max(Math.min(255, g), 0).toString(16); + b = Math.max(Math.min(255, b), 0).toString(16); + + const rr = (r.length < 2 ? "0" : "") + r; + const gg = (g.length < 2 ? "0" : "") + g; + const bb = (b.length < 2 ? "0" : "") + b; + + return `#${rr}${gg}${bb}`; +}; + +app.registerExtension({ + name: "pysssss.CustomColors", + setup() { + let picker; + let activeNode; + const onMenuNodeColors = LGraphCanvas.onMenuNodeColors; + LGraphCanvas.onMenuNodeColors = function (value, options, e, menu, node) { + const r = onMenuNodeColors.apply(this, arguments); + requestAnimationFrame(() => { + const menus = document.querySelectorAll(".litecontextmenu"); + for (let i = menus.length - 1; i >= 0; i--) { + if (menus[i].firstElementChild.textContent.includes("No color") || menus[i].firstElementChild.value?.content?.includes("No color")) { + $el( + "div.litemenu-entry.submenu", + { + parent: menus[i], + $: (el) => { + el.onclick = () => { + LiteGraph.closeAllContextMenus(); + if (!picker) { + picker = $el("input", { + type: "color", + parent: document.body, + style: { + display: "none", + }, + }); + picker.onchange = () => { + if (activeNode) { + const fApplyColor = function(node){ + if (picker.value) { + if (node.constructor === LiteGraph.LGraphGroup) { + node.color = picker.value; + } else { + node.color = colorShade(picker.value, 20); + node.bgcolor = picker.value; + } + } + } + const graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyColor(activeNode); + } else { + for (let i in graphcanvas.selected_nodes) { + fApplyColor(graphcanvas.selected_nodes[i]); + } + } + + activeNode.setDirtyCanvas(true, true); + } + }; + } + activeNode = null; + picker.value = node.bgcolor; + activeNode = node; + picker.click(); + }; + }, + }, + [ + $el("span", { + style: { + paddingLeft: "4px", + display: "block", + }, + textContent: "🎨 Custom", + }), + ] + ); + break; + } + } + }); + return r; + }; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/faviconStatus.js b/custom_nodes/comfyui-custom-scripts/web/js/faviconStatus.js new file mode 100644 index 00000000..3dcd196b --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/faviconStatus.js @@ -0,0 +1,58 @@ +import { api } from "../../../scripts/api.js"; +import { app } from "../../../scripts/app.js"; + +// Simple script that adds the current queue size to the window title +// Adds a favicon that changes color while active + +app.registerExtension({ + name: "pysssss.FaviconStatus", + async setup() { + let link = document.querySelector("link[rel~='icon']"); + if (!link) { + link = document.createElement("link"); + link.rel = "icon"; + document.head.appendChild(link); + } + + const getUrl = (active, user) => new URL(`assets/favicon${active ? "-active" : ""}${user ? ".user" : ""}.ico`, import.meta.url); + const testUrl = async (active) => { + const url = getUrl(active, true); + const r = await fetch(url, { + method: "HEAD", + }); + if (r.status === 200) { + return url; + } + return getUrl(active, false); + }; + const activeUrl = await testUrl(true); + const idleUrl = await testUrl(false); + + let executing = false; + const update = () => (link.href = executing ? activeUrl : idleUrl); + + for (const e of ["execution_start", "progress"]) { + api.addEventListener(e, () => { + executing = true; + update(); + }); + } + + api.addEventListener("executing", ({ detail }) => { + // null will be sent when it's finished + executing = !!detail; + update(); + }); + + api.addEventListener("status", ({ detail }) => { + let title = "ComfyUI"; + if (detail && detail.exec_info.queue_remaining) { + title = `(${detail.exec_info.queue_remaining}) ${title}`; + } + document.title = title; + update(); + executing = false; + }); + update(); + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/graphArrange.js b/custom_nodes/comfyui-custom-scripts/web/js/graphArrange.js new file mode 100644 index 00000000..8f25e241 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/graphArrange.js @@ -0,0 +1,91 @@ +import { app } from "../../../scripts/app.js"; + +app.registerExtension({ + name: "pysssss.GraphArrange", + setup(app) { + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + options.push({ content: "Arrange (float left)", callback: () => graph.arrange() }); + options.push({ + content: "Arrange (float right)", + callback: () => { + (function () { + var margin = 50; + var layout; + + const nodes = this.computeExecutionOrder(false, true); + const columns = []; + + // Find node first use + for (let i = nodes.length - 1; i >= 0; i--) { + const node = nodes[i]; + let max = null; + for (const out of node.outputs || []) { + if (out.links) { + for (const link of out.links) { + const outNode = app.graph.getNodeById(app.graph.links[link].target_id); + if (!outNode) continue; + var l = outNode._level - 1; + if (max === null) max = l; + else if (l < max) max = l; + } + } + } + if (max != null) node._level = max; + } + + for (let i = 0; i < nodes.length; ++i) { + const node = nodes[i]; + const col = node._level || 1; + if (!columns[col]) { + columns[col] = []; + } + columns[col].push(node); + } + + let x = margin; + + for (let i = 0; i < columns.length; ++i) { + const column = columns[i]; + if (!column) { + continue; + } + column.sort((a, b) => { + var as = !(a.type === "SaveImage" || a.type === "PreviewImage"); + var bs = !(b.type === "SaveImage" || b.type === "PreviewImage"); + var r = as - bs; + if (r === 0) r = (a.inputs?.length || 0) - (b.inputs?.length || 0); + if (r === 0) r = (a.outputs?.length || 0) - (b.outputs?.length || 0); + return r; + }); + let max_size = 100; + let y = margin + LiteGraph.NODE_TITLE_HEIGHT; + for (let j = 0; j < column.length; ++j) { + const node = column[j]; + node.pos[0] = layout == LiteGraph.VERTICAL_LAYOUT ? y : x; + node.pos[1] = layout == LiteGraph.VERTICAL_LAYOUT ? x : y; + const max_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 1 : 0; + if (node.size[max_size_index] > max_size) { + max_size = node.size[max_size_index]; + } + const node_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 0 : 1; + y += node.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT + j; + } + + // Right align in column + for (let j = 0; j < column.length; ++j) { + const node = column[j]; + node.pos[0] += max_size - node.size[0]; + } + x += max_size + margin; + } + + this.setDirtyCanvas(true, true); + }).apply(app.graph); + }, + }); + return options; + }; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/imageFeed.js b/custom_nodes/comfyui-custom-scripts/web/js/imageFeed.js new file mode 100644 index 00000000..4afdc066 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/imageFeed.js @@ -0,0 +1,604 @@ +import { api } from "../../../scripts/api.js"; +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; +import { lightbox } from "./common/lightbox.js"; + +$el("style", { + textContent: ` + .pysssss-image-feed { + position: absolute; + background: var(--comfy-menu-bg); + color: var(--fg-color); + z-index: 99; + font-family: sans-serif; + font-size: 12px; + display: flex; + flex-direction: column; + } + div > .pysssss-image-feed { + position: static; + } + .pysssss-image-feed--top, .pysssss-image-feed--bottom { + width: 100vw; + min-height: 30px; + max-height: calc(var(--max-size, 20) * 1vh); + } + .pysssss-image-feed--top { + top: 0; + } + .pysssss-image-feed--bottom { + bottom: 0; + flex-direction: column-reverse; + padding-top: 5px; + } + .pysssss-image-feed--left, .pysssss-image-feed--right { + top: 0; + height: 100vh; + min-width: 200px; + max-width: calc(var(--max-size, 10) * 1vw); + } + .comfyui-body-left .pysssss-image-feed--left, .comfyui-body-right .pysssss-image-feed--right { + height: 100%; + } + .pysssss-image-feed--left { + left: 0; + } + .pysssss-image-feed--right { + right: 0; + } + + .pysssss-image-feed--left .pysssss-image-feed-menu, .pysssss-image-feed--right .pysssss-image-feed-menu { + flex-direction: column; + } + + .pysssss-image-feed-menu { + position: relative; + flex: 0 1 min-content; + display: flex; + gap: 5px; + padding: 5px; + justify-content: space-between; + } + .pysssss-image-feed-btn-group { + align-items: stretch; + display: flex; + gap: .5rem; + flex: 0 1 fit-content; + justify-content: flex-end; + } + .pysssss-image-feed-btn { + background-color:var(--comfy-input-bg); + border-radius:5px; + border:2px solid var(--border-color); + color: var(--fg-color); + cursor:pointer; + display:inline-block; + flex: 0 1 fit-content; + text-decoration:none; + } + .pysssss-image-feed-btn.sizing-btn:checked { + filter: invert(); + } + .pysssss-image-feed-btn.clear-btn { + padding: 5px 20px; + } + .pysssss-image-feed-btn.hide-btn { + padding: 5px; + aspect-ratio: 1 / 1; + } + .pysssss-image-feed-btn:hover { + filter: brightness(1.2); + } + .pysssss-image-feed-btn:active { + position:relative; + top:1px; + } + + .pysssss-image-feed-menu section { + border-radius: 5px; + background: rgba(0,0,0,0.6); + padding: 0 5px; + display: flex; + gap: 5px; + align-items: center; + position: relative; + } + .pysssss-image-feed-menu section span { + white-space: nowrap; + } + .pysssss-image-feed-menu section input { + flex: 1 1 100%; + background: rgba(0,0,0,0.6); + border-radius: 5px; + overflow: hidden; + z-index: 100; + } + + .sizing-menu { + position: relative; + } + + .size-controls-flyout { + position: absolute; + transform: scaleX(0%); + transition: 200ms ease-out; + transition-delay: 500ms; + z-index: 101; + width: 300px; + } + + .sizing-menu:hover .size-controls-flyout { + transform: scale(1, 1); + transition: 200ms linear; + transition-delay: 0; + } + .pysssss-image-feed--bottom .size-controls-flyout { + transform: scale(1,0); + transform-origin: bottom; + bottom: 0; + left: 0; + } + .pysssss-image-feed--top .size-controls-flyout { + transform: scale(1,0); + transform-origin: top; + top: 0; + left: 0; + } + .pysssss-image-feed--left .size-controls-flyout { + transform: scale(0, 1); + transform-origin: left; + top: 0; + left: 0; + } + .pysssss-image-feed--right .size-controls-flyout { + transform: scale(0, 1); + transform-origin: right; + top: 0; + right: 0; + } + + .pysssss-image-feed-menu > * { + min-height: 24px; + } + .pysssss-image-feed-list { + flex: 1 1 auto; + overflow-y: auto; + display: grid; + align-items: center; + justify-content: center; + gap: 4px; + grid-auto-rows: min-content; + grid-template-columns: repeat(var(--img-sz, 3), 1fr); + transition: 100ms linear; + scrollbar-gutter: stable both-edges; + padding: 5px; + background: var(--comfy-input-bg); + border-radius: 5px; + margin: 5px; + margin-top: 0px; + } + .pysssss-image-feed-list:empty { + display: none; + } + .pysssss-image-feed-list div { + height: 100%; + text-align: center; + } + .pysssss-image-feed-list::-webkit-scrollbar { + background: var(--comfy-input-bg); + border-radius: 5px; + } + .pysssss-image-feed-list::-webkit-scrollbar-thumb { + background:var(--comfy-menu-bg); + border: 5px solid transparent; + border-radius: 8px; + background-clip: content-box; + } + .pysssss-image-feed-list::-webkit-scrollbar-thumb:hover { + background: var(--border-color); + background-clip: content-box; + } + .pysssss-image-feed-list img { + object-fit: var(--img-fit, contain); + max-width: 100%; + max-height: calc(var(--max-size) * 1vh); + border-radius: 4px; + } + .pysssss-image-feed-list img:hover { + filter: brightness(1.2); + }`, + parent: document.body, +}); + +app.registerExtension({ + name: "pysssss.ImageFeed", + async setup() { + let visible = true; + const seenImages = new Map(); + const showButton = $el("button.comfy-settings-btn", { + textContent: "πŸ–ΌοΈ", + style: { + right: "16px", + cursor: "pointer", + display: "none", + }, + }); + let showMenuButton; + if (!app.menu?.element.style.display && app.menu?.settingsGroup) { + showMenuButton = new (await import("../../../scripts/ui/components/button.js")).ComfyButton({ + icon: "image-multiple", + action: () => showButton.click(), + tooltip: "Show Image Feed 🐍", + content: "Show Image Feed 🐍", + }); + showMenuButton.enabled = false; + showMenuButton.element.style.display = "none"; + app.menu.settingsGroup.append(showMenuButton); + } + + const getVal = (n, d) => { + const v = localStorage.getItem("pysssss.ImageFeed." + n); + if (v && !isNaN(+v)) { + return v; + } + return d; + }; + + const saveVal = (n, v) => { + localStorage.setItem("pysssss.ImageFeed." + n, v); + }; + + const imageFeed = $el("div.pysssss-image-feed"); + const imageList = $el("div.pysssss-image-feed-list"); + + function updateMenuParent(location) { + if (showMenuButton) { + const el = document.querySelector(".comfyui-body-" + location); + if (!el) return; + el.append(imageFeed); + } else { + if (!imageFeed.parent) { + document.body.append(imageFeed); + } + } + } + + const feedLocation = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.Location", + name: "🐍 Image Feed Location", + defaultValue: "bottom", + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + textContent: "🐍 Image Feed Location:", + }), + ]), + $el("td", [ + $el( + "select", + { + style: { + fontSize: "14px", + }, + oninput: (e) => { + feedLocation.value = e.target.value; + imageFeed.className = `pysssss-image-feed pysssss-image-feed--${feedLocation.value}`; + updateMenuParent(feedLocation.value); + saveVal("Location", feedLocation.value); + window.dispatchEvent(new Event("resize")); + }, + }, + ["left", "top", "right", "bottom", "hidden"].map((m) => + $el("option", { + value: m, + textContent: m, + selected: feedLocation.value === m, + }) + ) + ), + ]), + ]); + }, + onChange(value) { + if (value === "hidden") { + imageFeed.remove(); + if (showMenuButton) { + requestAnimationFrame(() => { + showMenuButton.element.style.display = "none"; + }); + } + showButton.style.display = "none"; + } else { + showMenuButton.element.style.display = "unset"; + showButton.style.display = visible ? "none" : "unset"; + imageFeed.className = `pysssss-image-feed pysssss-image-feed--${value}`; + updateMenuParent(value); + } + }, + }); + + const feedDirection = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.Direction", + name: "🐍 Image Feed Direction", + defaultValue: "newest first", + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + textContent: "🐍 Image Feed Direction:", + }), + ]), + $el("td", [ + $el( + "select", + { + style: { + fontSize: "14px", + }, + oninput: (e) => { + feedDirection.value = e.target.value; + imageList.replaceChildren(...[...imageList.childNodes].reverse()); + }, + }, + ["newest first", "oldest first"].map((m) => + $el("option", { + value: m, + textContent: m, + selected: feedDirection.value === m, + }) + ) + ), + ]), + ]); + }, + }); + + const deduplicateFeed = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.Deduplication", + name: "🐍 Image Feed Deduplication", + tooltip: `Ensures unique images in the image feed but at the cost of CPU-bound performance impact \ +(from hundreds of milliseconds to seconds per image, depending on byte size). For workflows that produce duplicate images, turning this setting on may yield overall client-side performance improvements \ +by reducing the number of images in the feed. + +Recommended: "enabled (max performance)" uness images are erroneously deduplicated.`, + defaultValue: 0, + type: "combo", + options: (value) => { + let dedupeOptions = { disabled: 0, "enabled (slow)": 1, "enabled (performance)": 0.5, "enabled (max performance)": 0.25 }; + return Object.entries(dedupeOptions).map(([k, v]) => ({ + value: v, + text: k, + selected: k === value, + })); + }, + }); + + const maxImages = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.MaxImages", + name: "🐍 Image Feed Max Images", + tooltip: `Limits the number of images in the feed to a maximum, removing the oldest images as new ones are added.`, + defaultValue: 0, + type: "number", + }); + + const saveNodeOnly = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.SaveNodeOnly", + name: "🐍 Image Feed Display 'SaveImage' Only", + tooltip: `Only show images from 'SaveImage' nodes. This prevents 'PreviewImage' node outputs from appearing in the feed.`, + defaultValue: false, + type: "boolean", + }); + + const clearButton = $el("button.pysssss-image-feed-btn.clear-btn", { + textContent: "Clear", + onclick: () => { + imageList.replaceChildren(); + window.dispatchEvent(new Event("resize")); + }, + }); + + const hideButton = $el("button.pysssss-image-feed-btn.hide-btn", { + textContent: "❌", + onclick: () => { + imageFeed.style.display = "none"; + showButton.style.display = feedLocation.value === "hidden" ? "none" : "unset"; + if (showMenuButton) { + showMenuButton.enabled = true; + showMenuButton.element.style.display = ""; + } + saveVal("Visible", 0); + visible = false; + window.dispatchEvent(new Event("resize")); + }, + }); + + let columnInput; + function updateColumnCount(v) { + columnInput.parentElement.title = `Controls the number of columns in the feed (${v} columns).\nClick label to set custom value.`; + imageFeed.style.setProperty("--img-sz", v); + saveVal("ImageSize", v); + columnInput.max = Math.max(10, v, columnInput.max); + columnInput.value = v; + window.dispatchEvent(new Event("resize")); + } + + function addImageToFeed(href) { + const method = feedDirection.value === "newest first" ? "prepend" : "append"; + + if (maxImages.value > 0 && imageList.children.length >= maxImages.value) { + imageList.children[method === "prepend" ? imageList.children.length - 1 : 0].remove(); + } + + imageList[method]( + $el("div", [ + $el( + "a", + { + target: "_blank", + href, + onclick: (e) => { + const imgs = [...imageList.querySelectorAll("img")].map((img) => img.getAttribute("src")); + lightbox.show(imgs, imgs.indexOf(href)); + e.preventDefault(); + }, + }, + [$el("img", { src: href })] + ), + ]) + ); + // If lightbox is open, update it with new image + lightbox.updateWithNewImage(href, feedDirection.value); + } + + imageFeed.append( + $el("div.pysssss-image-feed-menu", [ + $el("section.sizing-menu", {}, [ + $el("label.size-control-handle", { textContent: "β†Ή Resize Feed" }), + $el("div.size-controls-flyout", {}, [ + $el("section.size-control.feed-size-control", {}, [ + $el("span", { + textContent: "Feed Size...", + }), + $el("input", { + type: "range", + min: 10, + max: 80, + oninput: (e) => { + e.target.parentElement.title = `Controls the maximum size of the image feed panel (${e.target.value}vh)`; + imageFeed.style.setProperty("--max-size", e.target.value); + saveVal("FeedSize", e.target.value); + window.dispatchEvent(new Event("resize")); + }, + $: (el) => { + requestAnimationFrame(() => { + el.value = getVal("FeedSize", 25); + el.oninput({ target: el }); + }); + }, + }), + ]), + $el("section.size-control.image-size-control", {}, [ + $el("a", { + textContent: "Column count...", + style: { + cursor: "pointer", + textDecoration: "underline", + }, + onclick: () => { + const v = +prompt("Enter custom column count", 20); + if (!isNaN(v)) { + updateColumnCount(v); + } + }, + }), + $el("input", { + type: "range", + min: 1, + max: 10, + step: 1, + oninput: (e) => { + updateColumnCount(e.target.value); + }, + $: (el) => { + columnInput = el; + requestAnimationFrame(() => { + updateColumnCount(getVal("ImageSize", 4)); + }); + }, + }), + ]), + ]), + ]), + $el("div.pysssss-image-feed-btn-group", {}, [clearButton, hideButton]), + ]), + imageList + ); + showButton.onclick = () => { + imageFeed.style.display = "flex"; + showButton.style.display = "none"; + if (showMenuButton) { + showMenuButton.enabled = false; + showMenuButton.element.style.display = "none"; + } + + saveVal("Visible", 1); + visible = true; + window.dispatchEvent(new Event("resize")); + }; + document.querySelector(".comfy-settings-btn").after(showButton); + window.dispatchEvent(new Event("resize")); + + if (!+getVal("Visible", 1)) { + hideButton.onclick(); + } + + api.addEventListener("executed", ({ detail }) => { + if (visible && detail?.output?.images) { + if (detail.node?.includes?.(":")) { + // Ignore group nodes + const n = app.graph.getNodeById(detail.node.split(":")[0]); + if (n?.getInnerNodes) return; + } + + // Apply "Display Save Image Node Only" filter if setting is enabled + const nodeName = detail.node?.split(":")?.[0]; + if (nodeName) { + const node = app.graph.getNodeById(nodeName); + + if (saveNodeOnly.value && node?.type !== "SaveImage") return; + } + + for (const src of detail.output.images) { + const href = `./view?filename=${encodeURIComponent(src.filename)}&type=${src.type}& + subfolder=${encodeURIComponent(src.subfolder)}&t=${+new Date()}`; + + // deduplicateFeed.value is essentially the scaling factor used for image hashing + // but when deduplication is disabled, this value is "0" + if (deduplicateFeed.value > 0) { + // deduplicate by ignoring images with the same filename/type/subfolder + const fingerprint = JSON.stringify({ filename: src.filename, type: src.type, subfolder: src.subfolder }); + if (seenImages.has(fingerprint)) { + // NOOP: image is a duplicate + } else { + seenImages.set(fingerprint, true); + let img = $el("img", { src: href }); + img.onerror = () => { + // fall back to default behavior + addImageToFeed(href); + }; + img.onload = () => { + // redraw the image onto a canvas to strip metadata (resize if performance mode) + let imgCanvas = document.createElement("canvas"); + let imgScalar = deduplicateFeed.value; + imgCanvas.width = imgScalar * img.width; + imgCanvas.height = imgScalar * img.height; + + let imgContext = imgCanvas.getContext("2d"); + imgContext.drawImage(img, 0, 0, imgCanvas.width, imgCanvas.height); + const data = imgContext.getImageData(0, 0, imgCanvas.width, imgCanvas.height); + + // calculate fast hash of the image data + let hash = 0; + for (const b of data.data) { + hash = (hash << 5) - hash + b; + } + + // add image to feed if we've never seen the hash before + if (seenImages.has(hash)) { + // NOOP: image is a duplicate + } else { + // if we got to here, then the image is unique--so add to feed + seenImages.set(hash, true); + addImageToFeed(href); + } + }; + } + } else { + addImageToFeed(href); + } + } + } + }); + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/kSamplerAdvDenoise.js b/custom_nodes/comfyui-custom-scripts/web/js/kSamplerAdvDenoise.js new file mode 100644 index 00000000..7b92d4f1 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/kSamplerAdvDenoise.js @@ -0,0 +1,54 @@ +import { app } from "../../../scripts/app.js"; +app.registerExtension({ + name: "pysssss.KSamplerAdvDenoise", + async beforeRegisterNodeDef(nodeType) { + // Add menu options to conver to/from widgets + const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = origGetExtraMenuOptions?.apply?.(this, arguments); + + let stepsWidget = null; + let startAtWidget = null; + let endAtWidget = null; + for (const w of this.widgets || []) { + if (w.name === "steps") { + stepsWidget = w; + } else if (w.name === "start_at_step") { + startAtWidget = w; + } else if (w.name === "end_at_step") { + endAtWidget = w; + } + } + + if (stepsWidget && startAtWidget && endAtWidget) { + options.push( + { + content: "Set Denoise", + callback: () => { + const steps = +prompt("How many steps do you want?", 15); + if (isNaN(steps)) { + return; + } + const denoise = +prompt("How much denoise? (0-1)", 0.5); + if (isNaN(denoise)) { + return; + } + + stepsWidget.value = Math.floor(steps / Math.max(0, Math.min(1, denoise))); + stepsWidget.callback?.(stepsWidget.value); + + startAtWidget.value = stepsWidget.value - steps; + startAtWidget.callback?.(startAtWidget.value); + + endAtWidget.value = stepsWidget.value; + endAtWidget.callback?.(endAtWidget.value); + }, + }, + null + ); + } + + return r; + }; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/linkRenderMode.js b/custom_nodes/comfyui-custom-scripts/web/js/linkRenderMode.js new file mode 100644 index 00000000..010de6ee --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/linkRenderMode.js @@ -0,0 +1,57 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; + +const id = "pysssss.LinkRenderMode"; +const ext = { + name: id, + async setup(app) { + if (app.extensions.find((ext) => ext.name === "Comfy.LinkRenderMode")) { + console.log("%c[🐍 pysssss]", "color: limegreen", "Skipping LinkRenderMode as core extension found"); + return; + } + const setting = app.ui.settings.addSetting({ + id, + name: "🐍 Link Render Mode", + defaultValue: 2, + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: "🐍 Link Render Mode:", + }), + ]), + $el("td", [ + $el( + "select", + { + textContent: "Manage", + style: { + fontSize: "14px", + }, + oninput: (e) => { + setting.value = e.target.value; + app.canvas.links_render_mode = +e.target.value; + app.graph.setDirtyCanvas(true, true); + }, + }, + LiteGraph.LINK_RENDER_MODES.map((m, i) => + $el("option", { + value: i, + textContent: m, + selected: i == app.canvas.links_render_mode, + }) + ) + ), + ]), + ]); + }, + onChange(value) { + app.canvas.links_render_mode = +value; + app.graph.setDirtyCanvas(true); + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/mathExpression.js b/custom_nodes/comfyui-custom-scripts/web/js/mathExpression.js new file mode 100644 index 00000000..d0dde50e --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/mathExpression.js @@ -0,0 +1,44 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +app.registerExtension({ + name: "pysssss.MathExpression", + init() { + const STRING = ComfyWidgets.STRING; + ComfyWidgets.STRING = function (node, inputName, inputData) { + const r = STRING.apply(this, arguments); + r.widget.dynamicPrompts = inputData?.[1].dynamicPrompts; + return r; + }; + }, + beforeRegisterNodeDef(nodeType) { + if (nodeType.comfyClass === "MathExpression|pysssss") { + const onDrawForeground = nodeType.prototype.onDrawForeground; + + nodeType.prototype.onNodeCreated = function() { + // These are typed as any to bypass backend validation + // update frontend to restrict types + for(const input of this.inputs) { + input.type = "INT,FLOAT,IMAGE,LATENT"; + } + } + + nodeType.prototype.onDrawForeground = function (ctx) { + const r = onDrawForeground?.apply?.(this, arguments); + + const v = app.nodeOutputs?.[this.id + ""]; + if (!this.flags.collapsed && v) { + const text = v.value[0] + ""; + ctx.save(); + ctx.font = "bold 12px sans-serif"; + ctx.fillStyle = "dodgerblue"; + const sz = ctx.measureText(text); + ctx.fillText(text, this.size[0] - sz.width - 5, LiteGraph.NODE_SLOT_HEIGHT * 3); + ctx.restore(); + } + + return r; + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/middleClickAddDefaultNode.js b/custom_nodes/comfyui-custom-scripts/web/js/middleClickAddDefaultNode.js new file mode 100644 index 00000000..43f944f2 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/middleClickAddDefaultNode.js @@ -0,0 +1,49 @@ +import { app } from "../../../scripts/app.js"; + +const id = "pysssss.MiddleClickAddDefaultNode"; +const ext = { + name: id, + async setup(app) { + app.ui.settings.addSetting({ + id, + name: "🐍 Middle click slot to add", + defaultValue: "Reroute", + type: "combo", + options: (value) => + [ + ...Object.keys(LiteGraph.registered_node_types) + .filter((k) => k.includes("Reroute")) + .sort((a, b) => { + if (a === "Reroute") return -1; + if (b === "Reroute") return 1; + return a.localeCompare(b); + }), + "[None]", + ].map((m) => ({ + value: m, + text: m, + selected: !value ? m === "[None]" : m === value, + })), + onChange(value) { + const enable = value && value !== "[None]"; + if (value === true) { + value = "Reroute"; + } + LiteGraph.middle_click_slot_add_default_node = enable; + if (enable) { + for (const arr of Object.values(LiteGraph.slot_types_default_in).concat( + Object.values(LiteGraph.slot_types_default_out) + )) { + const idx = arr.indexOf(value); + if (idx !== 0) { + arr.splice(idx, 1); + } + arr.unshift(value); + } + } + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/modelInfo.js b/custom_nodes/comfyui-custom-scripts/web/js/modelInfo.js new file mode 100644 index 00000000..b5db20a1 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/modelInfo.js @@ -0,0 +1,412 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; +import { $el } from "../../../scripts/ui.js"; +import { ModelInfoDialog } from "./common/modelInfoDialog.js"; + +const MAX_TAGS = 500; +const NsfwLevel = { + PG: 1, + PG13: 2, + R: 4, + X: 8, + XXX: 16, + Blocked: 32, +}; + +export class LoraInfoDialog extends ModelInfoDialog { + getTagFrequency() { + if (!this.metadata.ss_tag_frequency) return []; + + const datasets = JSON.parse(this.metadata.ss_tag_frequency); + const tags = {}; + for (const setName in datasets) { + const set = datasets[setName]; + for (const t in set) { + if (t in tags) { + tags[t] += set[t]; + } else { + tags[t] = set[t]; + } + } + } + + return Object.entries(tags).sort((a, b) => b[1] - a[1]); + } + + getResolutions() { + let res = []; + if (this.metadata.ss_bucket_info) { + const parsed = JSON.parse(this.metadata.ss_bucket_info); + if (parsed?.buckets) { + for (const { resolution, count } of Object.values(parsed.buckets)) { + res.push([count, `${resolution.join("x")} * ${count}`]); + } + } + } + res = res.sort((a, b) => b[0] - a[0]).map((a) => a[1]); + let r = this.metadata.ss_resolution; + if (r) { + const s = r.split(","); + const w = s[0].replace("(", ""); + const h = s[1].replace(")", ""); + res.push(`${w.trim()}x${h.trim()} (Base res)`); + } else if ((r = this.metadata["modelspec.resolution"])) { + res.push(r + " (Base res"); + } + if (!res.length) { + res.push("⚠️ Unknown"); + } + return res; + } + + getTagList(tags) { + return tags.map((t) => + $el( + "li.pysssss-model-tag", + { + dataset: { + tag: t[0], + }, + $: (el) => { + el.onclick = () => { + el.classList.toggle("pysssss-model-tag--selected"); + }; + }, + }, + [ + $el("p", { + textContent: t[0], + }), + $el("span", { + textContent: t[1], + }), + ] + ) + ); + } + + addTags() { + let tags = this.getTagFrequency(); + if (!tags?.length) { + tags = this.metadata["modelspec.tags"]?.split(",").map((t) => [t.trim(), 1]); + } + let hasMore; + if (tags?.length) { + const c = tags.length; + let list; + if (c > MAX_TAGS) { + tags = tags.slice(0, MAX_TAGS); + hasMore = $el("p", [ + $el("span", { textContent: `⚠️ Only showing first ${MAX_TAGS} tags ` }), + $el("a", { + href: "#", + textContent: `Show all ${c}`, + onclick: () => { + list.replaceChildren(...this.getTagList(this.getTagFrequency())); + hasMore.remove(); + }, + }), + ]); + } + list = $el("ol.pysssss-model-tags-list", this.getTagList(tags)); + this.tags = $el("div", [list]); + } else { + this.tags = $el("p", { textContent: "⚠️ No tag frequency metadata found" }); + } + + this.content.append(this.tags); + + if (hasMore) { + this.content.append(hasMore); + } + } + + addExample(title, value, name) { + const textArea = $el("textarea", { + textContent: value, + style: { + whiteSpace: "pre-wrap", + margin: "10px 0", + color: "#fff", + background: "#222", + padding: "5px", + borderRadius: "5px", + maxHeight: "250px", + overflow: "auto", + display: "block", + border: "none", + width: "calc(100% - 10px)", + }, + }); + $el( + "p", + { + parent: this.content, + textContent: `${title}: `, + }, + [ + textArea, + $el("button", { + onclick: async () => { + await this.saveAsExample(textArea.value, `${name}.txt`); + }, + textContent: "Save as Example", + style: { + fontSize: "14px", + }, + }), + $el("hr"), + ] + ); + } + + async addInfo() { + this.addInfoEntry("Name", this.metadata.ss_output_name || "⚠️ Unknown"); + this.addInfoEntry("Base Model", this.metadata.ss_sd_model_name || "⚠️ Unknown"); + this.addInfoEntry("Clip Skip", this.metadata.ss_clip_skip || "⚠️ Unknown"); + + this.addInfoEntry( + "Resolution", + $el( + "select", + this.getResolutions().map((r) => $el("option", { textContent: r })) + ) + ); + + super.addInfo(); + const p = this.addCivitaiInfo(); + this.addTags(); + + const info = await p; + this.addExample("Trained Words", info?.trainedWords?.join(", ") ?? "", "trainedwords"); + + const triggerPhrase = this.metadata["modelspec.trigger_phrase"]; + if (triggerPhrase) { + this.addExample("Trigger Phrase", triggerPhrase, "triggerphrase"); + } + + $el("div", { + parent: this.content, + innerHTML: info?.description ?? this.metadata["modelspec.description"] ?? "[No description provided]", + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + + async saveAsExample(example, name = "example.txt") { + if (!example.length) { + return; + } + try { + name = prompt("Enter example name", name); + if (!name) return; + + await api.fetchApi("/pysssss/examples/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: JSON.stringify({ + name, + example, + }), + headers: { + "content-type": "application/json", + }, + }); + this.node?.["pysssss.updateExamples"]?.(); + alert("Saved!"); + } catch (error) { + console.error(error); + alert("Error saving: " + error); + } + } + + createButtons() { + const btns = super.createButtons(); + function tagsToCsv(tags) { + return tags.map((el) => el.dataset.tag).join(", "); + } + function copyTags(e, tags) { + const textarea = $el("textarea", { + parent: document.body, + style: { + position: "fixed", + }, + textContent: tagsToCsv(tags), + }); + textarea.select(); + try { + document.execCommand("copy"); + if (!e.target.dataset.text) { + e.target.dataset.text = e.target.textContent; + } + e.target.textContent = "Copied " + tags.length + " tags"; + setTimeout(() => { + e.target.textContent = e.target.dataset.text; + }, 1000); + } catch (ex) { + prompt("Copy to clipboard: Ctrl+C, Enter", text); + } finally { + document.body.removeChild(textarea); + } + } + + btns.unshift( + $el("button", { + type: "button", + textContent: "Save Selected as Example", + onclick: async (e) => { + const tags = tagsToCsv([...this.tags.querySelectorAll(".pysssss-model-tag--selected")]); + await this.saveAsExample(tags); + }, + }), + $el("button", { + type: "button", + textContent: "Copy Selected", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag--selected")]); + }, + }), + $el("button", { + type: "button", + textContent: "Copy All", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag")]); + }, + }) + ); + + return btns; + } +} + +class CheckpointInfoDialog extends ModelInfoDialog { + async addInfo() { + super.addInfo(); + const info = await this.addCivitaiInfo(); + if (info) { + this.addInfoEntry("Base Model", info.baseModel || "⚠️ Unknown"); + + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } +} + +const lookups = {}; + +function addInfoOption(node, type, infoClass, widgetNamePattern, opts) { + const widgets = widgetNamePattern + ? node.widgets.filter((w) => w.name === widgetNamePattern || w.name.match(`^${widgetNamePattern}$`)) + : [node.widgets[0]]; + for (const widget of widgets) { + let value = widget.value; + if (value?.content) { + value = value.content; + } + if (!value || value === "None") { + return; + } + let optName; + const split = value.split(/[.\\/]/); + optName = split[split.length - 2]; + opts.push({ + content: optName, + callback: async () => { + new infoClass(value, node).show(type, value); + }, + }); + } +} + +function addTypeOptions(node, typeName, options) { + const type = typeName.toLowerCase() + "s"; + const values = lookups[typeName][node.type]; + if (!values) return; + + const widgets = Object.keys(values); + const cls = type === "loras" ? LoraInfoDialog : CheckpointInfoDialog; + + const opts = []; + for (const w of widgets) { + addInfoOption(node, type, cls, w, opts); + } + + if (!opts.length) return; + + if (opts.length === 1) { + opts[0].content = `View ${typeName} info...`; + options.unshift(opts[0]); + } else { + options.unshift({ + title: `View ${typeName} info...`, + has_submenu: true, + submenu: { + options: opts, + }, + }); + } +} + +app.registerExtension({ + name: "pysssss.ModelInfo", + setup() { + const addSetting = (type, defaultValue) => { + app.ui.settings.addSetting({ + id: `pysssss.ModelInfo.${type}Nodes`, + name: `🐍 Model Info - ${type} Nodes/Widgets`, + type: "text", + defaultValue, + tooltip: `Comma separated list of NodeTypeName or NodeTypeName.WidgetName that contain ${type} node names that should have the View Info option available.\nIf no widget name is specifed the first widget will be used. Regex matches (e.g. NodeName..*lora_\\d+) are supported in the widget name.`, + onChange(value) { + lookups[type] = value.split(",").reduce((p, n) => { + n = n.trim(); + const pos = n.indexOf("."); + const split = pos === -1 ? [n] : [n.substring(0, pos), n.substring(pos + 1)]; + p[split[0]] ??= {}; + p[split[0]][split[1] ?? ""] = true; + return p; + }, {}); + }, + }); + }; + addSetting( + "Lora", + ["LoraLoader.lora_name", "LoraLoader|pysssss", "LoraLoaderModelOnly.lora_name", "LoRA Stacker.lora_name.*"].join(",") + ); + addSetting( + "Checkpoint", + ["CheckpointLoader.ckpt_name", "CheckpointLoaderSimple", "CheckpointLoader|pysssss", "Efficient Loader", "Eff. Loader SDXL"].join(",") + ); + + app.ui.settings.addSetting({ + id: `pysssss.ModelInfo.NsfwLevel`, + name: `🐍 Model Info - Image Preview Max NSFW Level`, + type: "combo", + defaultValue: "PG13", + options: Object.keys(NsfwLevel), + tooltip: `Hides preview images that are tagged as a higher NSFW level`, + onChange(value) { + ModelInfoDialog.nsfwLevel = NsfwLevel[value] ?? NsfwLevel.PG; + }, + }); + }, + beforeRegisterNodeDef(nodeType) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + if (this.widgets) { + for (const type in lookups) { + addTypeOptions(this, type, options); + } + } + + return getExtraMenuOptions?.apply(this, arguments); + }; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/nodeFinder.js b/custom_nodes/comfyui-custom-scripts/web/js/nodeFinder.js new file mode 100644 index 00000000..d3727724 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/nodeFinder.js @@ -0,0 +1,82 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; + +// Adds a menu option to toggle follow the executing node +// Adds a menu option to go to the currently executing node +// Adds a menu option to go to a node by type + +app.registerExtension({ + name: "pysssss.NodeFinder", + setup() { + let followExecution = false; + + const centerNode = (id) => { + if (!followExecution || !id) return; + const node = app.graph.getNodeById(id); + if (!node) return; + app.canvas.centerOnNode(node); + }; + + api.addEventListener("executing", ({ detail }) => centerNode(detail)); + + // Add canvas menu options + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + options.push(null, { + content: followExecution ? "Stop following execution" : "Follow execution", + callback: () => { + if ((followExecution = !followExecution)) { + centerNode(app.runningNodeId); + } + }, + }); + if (app.runningNodeId) { + options.push({ + content: "Show executing node", + callback: () => { + const node = app.graph.getNodeById(app.runningNodeId); + if (!node) return; + app.canvas.centerOnNode(node); + }, + }); + } + + const nodes = app.graph._nodes; + const types = nodes.reduce((p, n) => { + if (n.type in p) { + p[n.type].push(n); + } else { + p[n.type] = [n]; + } + return p; + }, {}); + options.push({ + content: "Go to node", + has_submenu: true, + submenu: { + options: Object.keys(types) + .sort() + .map((t) => ({ + content: t, + has_submenu: true, + submenu: { + options: types[t] + .sort((a, b) => { + return a.pos[0] - b.pos[0]; + }) + .map((n) => ({ + content: `${n.getTitle()} - #${n.id} (${n.pos[0]}, ${n.pos[1]})`, + callback: () => { + app.canvas.centerOnNode(n); + }, + })), + }, + })), + }, + }); + + return options; + }; + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/playSound.js b/custom_nodes/comfyui-custom-scripts/web/js/playSound.js new file mode 100644 index 00000000..de710589 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/playSound.js @@ -0,0 +1,36 @@ +import { app } from "../../../scripts/app.js"; + +app.registerExtension({ + name: "pysssss.PlaySound", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "PlaySound|pysssss") { + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = async function () { + onExecuted?.apply(this, arguments); + if (this.widgets[0].value === "on empty queue") { + if (app.ui.lastQueueSize !== 0) { + await new Promise((r) => setTimeout(r, 500)); + } + if (app.ui.lastQueueSize !== 0) { + return; + } + } + let file = this.widgets[2].value; + if (!file) { + file = "notify.mp3"; + } + if (!file.startsWith("http")) { + if (!file.includes("/")) { + file = "assets/" + file; + } + file = new URL(file, import.meta.url) + } + + const url = new URL(file); + const audio = new Audio(url); + audio.volume = this.widgets[1].value; + audio.play(); + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/presetText.js b/custom_nodes/comfyui-custom-scripts/web/js/presetText.js new file mode 100644 index 00000000..472f11be --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/presetText.js @@ -0,0 +1,257 @@ +import { app } from "../../../scripts/app.js"; + +// Allows you to manage preset tags for e.g. common negative prompt +// Also performs replacements on any text field e.g. allowing you to use preset text in CLIP Text encode fields + +let replaceRegex; +const id = "pysssss.PresetText.Presets"; +const MISSING = Symbol(); + +const getPresets = () => { + let items; + try { + items = JSON.parse(localStorage.getItem(id)); + } catch (error) {} + if (!items || !items.length) { + items = [{ name: "default negative", value: "worst quality" }]; + } + return items; +}; + +let presets = getPresets(); + +app.registerExtension({ + name: "pysssss.PresetText", + setup() { + app.ui.settings.addSetting({ + id: "pysssss.PresetText.ReplacementRegex", + name: "🐍 Preset Text Replacement Regex", + type: "text", + defaultValue: "(?:^|[^\\w])(?@(?[\\w-]+))", + tooltip: + "The regex should return two named capture groups: id (the name of the preset text to use), replace (the matched text to replace)", + attrs: { + style: { + fontFamily: "monospace", + }, + }, + onChange(value) { + if (!value) { + replaceRegex = null; + return; + } + try { + replaceRegex = new RegExp(value, "g"); + } catch (error) { + alert("Error creating regex for preset text replacement, no replacements will be performed."); + replaceRegex = null; + } + }, + }); + + const drawNodeWidgets = LGraphCanvas.prototype.drawNodeWidgets + LGraphCanvas.prototype.drawNodeWidgets = function(node) { + const c = LiteGraph.WIDGET_BGCOLOR; + try { + if(node[MISSING]) { + LiteGraph.WIDGET_BGCOLOR = "red" + } + return drawNodeWidgets.apply(this, arguments); + } finally { + LiteGraph.WIDGET_BGCOLOR = c; + } + } + }, + registerCustomNodes() { + class PresetTextNode extends LiteGraph.LGraphNode { + constructor() { + super(); + this.title = "Preset Text 🐍"; + this.isVirtualNode = true; + this.serialize_widgets = true; + this.addOutput("text", "STRING"); + + const widget = this.addWidget("combo", "value", presets[0].name, () => {}, { + values: presets.map((p) => p.name), + }); + this.addWidget("button", "Manage", "Manage", () => { + const container = document.createElement("div"); + Object.assign(container.style, { + display: "grid", + gridTemplateColumns: "1fr 1fr", + gap: "10px", + }); + + const addNew = document.createElement("button"); + addNew.textContent = "Add New"; + addNew.classList.add("pysssss-presettext-addnew"); + Object.assign(addNew.style, { + fontSize: "13px", + gridColumn: "1 / 3", + color: "dodgerblue", + width: "auto", + textAlign: "center", + }); + addNew.onclick = () => { + addRow({ name: "", value: "" }); + }; + container.append(addNew); + + function addRow(p) { + const name = document.createElement("input"); + const nameLbl = document.createElement("label"); + name.value = p.name; + nameLbl.textContent = "Name:"; + nameLbl.append(name); + + const value = document.createElement("input"); + const valueLbl = document.createElement("label"); + value.value = p.value; + valueLbl.textContent = "Value:"; + valueLbl.append(value); + + addNew.before(nameLbl, valueLbl); + } + for (const p of presets) { + addRow(p); + } + + const help = document.createElement("span"); + help.textContent = "To remove a preset set the name or value to blank"; + help.style.gridColumn = "1 / 3"; + container.append(help); + + dialog.show(""); + dialog.textElement.append(container); + }); + + const dialog = new app.ui.dialog.constructor(); + dialog.element.classList.add("comfy-settings"); + + const closeButton = dialog.element.querySelector("button"); + closeButton.textContent = "CANCEL"; + const saveButton = document.createElement("button"); + saveButton.textContent = "SAVE"; + saveButton.onclick = function () { + const inputs = dialog.element.querySelectorAll("input"); + const p = []; + for (let i = 0; i < inputs.length; i += 2) { + const n = inputs[i]; + const v = inputs[i + 1]; + if (!n.value.trim() || !v.value.trim()) { + continue; + } + p.push({ name: n.value, value: v.value }); + } + + widget.options.values = p.map((p) => p.name); + if (!widget.options.values.includes(widget.value)) { + widget.value = widget.options.values[0]; + } + + presets = p; + localStorage.setItem(id, JSON.stringify(presets)); + + dialog.close(); + }; + + closeButton.before(saveButton); + + this.applyToGraph = function (workflow) { + // For each output link copy our value over the original widget value + if (this.outputs[0].links && this.outputs[0].links.length) { + for (const l of this.outputs[0].links) { + const link_info = app.graph.links[l]; + const outNode = app.graph.getNodeById(link_info.target_id); + const outIn = outNode && outNode.inputs && outNode.inputs[link_info.target_slot]; + if (outIn.widget) { + const w = outNode.widgets.find((w) => w.name === outIn.widget.name); + if (!w) continue; + const preset = presets.find((p) => p.name === widget.value); + if (!preset) { + this[MISSING] = true; + app.graph.setDirtyCanvas(true, true); + const msg = `Preset text '${widget.value}' not found. Please fix this and queue again.`; + throw new Error(msg); + } + delete this[MISSING]; + w.value = preset.value; + } + } + } + }; + } + } + + LiteGraph.registerNodeType( + "PresetText|pysssss", + Object.assign(PresetTextNode, { + title: "Preset Text 🐍", + }) + ); + + PresetTextNode.category = "utils"; + }, + nodeCreated(node) { + if (node.widgets) { + // Locate dynamic prompt text widgets + const widgets = node.widgets.filter((n) => n.type === "customtext" || n.type === "text"); + for (const widget of widgets) { + const callbacks = [ + () => { + let prompt = widget.value; + if (replaceRegex && typeof prompt.replace !== 'undefined') { + prompt = prompt.replace(replaceRegex, (match, p1, p2, index, text, groups) => { + if (!groups.replace || !groups.id) return match; // No match, bad regex? + + const preset = presets.find((p) => p.name.replaceAll(/\s/g, "-") === groups.id); + if (!preset) return match; // Invalid name + + const pos = match.indexOf(groups.replace); + return match.substring(0, pos) + preset.value; + }); + } + return prompt; + }, + ]; + let inheritedSerializeValue = widget.serializeValue || null; + + let called = false; + const serializeValue = async (workflowNode, widgetIndex) => { + const origWidgetValue = widget.value; + if (called) return origWidgetValue; + called = true; + + let allCallbacks = [...callbacks]; + if (inheritedSerializeValue) { + allCallbacks.push(inheritedSerializeValue) + } + let valueIsUndefined = false; + + for (const cb of allCallbacks) { + let value = await cb(workflowNode, widgetIndex); + // Need to check the callback return value before it is set on widget.value as it coerces it to a string (even for undefined) + if (value === undefined) valueIsUndefined = true; + widget.value = value; + } + + const prompt = valueIsUndefined ? undefined : widget.value; + widget.value = origWidgetValue; + + called = false; + + return prompt; + }; + + Object.defineProperty(widget, "serializeValue", { + get() { + return serializeValue; + }, + set(cb) { + inheritedSerializeValue = cb; + }, + }); + } + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/quickNodes.js b/custom_nodes/comfyui-custom-scripts/web/js/quickNodes.js new file mode 100644 index 00000000..a52581b3 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/quickNodes.js @@ -0,0 +1,196 @@ +import { app } from "../../../scripts/app.js"; + +// Adds a bunch of context menu entries for quickly adding common steps + +function addMenuHandler(nodeType, cb) { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} + +function getOrAddVAELoader(node) { + let vaeNode = app.graph._nodes.find((n) => n.type === "VAELoader"); + if (!vaeNode) { + vaeNode = addNode("VAELoader", node); + } + return vaeNode; +} + +function addNode(name, nextTo, options) { + options = { select: true, shiftY: 0, before: false, ...(options || {}) }; + const node = LiteGraph.createNode(name); + app.graph.add(node); + node.pos = [ + options.before ? nextTo.pos[0] - node.size[0] - 30 : nextTo.pos[0] + nextTo.size[0] + 30, + nextTo.pos[1] + options.shiftY, + ]; + if (options.select) { + app.canvas.selectNode(node, false); + } + return node; +} + +app.registerExtension({ + name: "pysssss.QuickNodes", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.input && nodeData.input.required) { + const keys = Object.keys(nodeData.input.required); + for (let i = 0; i < keys.length; i++) { + if (nodeData.input.required[keys[i]][0] === "VAE") { + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: "Use VAE", + callback: () => { + getOrAddVAELoader(this).connect(0, this, i); + }, + }); + }); + break; + } + } + } + + if (nodeData.name === "KSampler") { + addMenuHandler(nodeType, function (_, options) { + options.unshift( + { + content: "Add Blank Input", + callback: () => { + const imageNode = addNode("EmptyLatentImage", this, { before: true }); + imageNode.connect(0, this, 3); + }, + }, + { + content: "Add Hi-res Fix", + callback: () => { + const upscaleNode = addNode("LatentUpscale", this); + this.connect(0, upscaleNode, 0); + + const sampleNode = addNode("KSampler", upscaleNode); + + for (let i = 0; i < 3; i++) { + const l = this.getInputLink(i); + if (l) { + app.graph.getNodeById(l.origin_id).connect(l.origin_slot, sampleNode, i); + } + } + + upscaleNode.connect(0, sampleNode, 3); + }, + }, + { + content: "Add 2nd Pass", + callback: () => { + const upscaleNode = addNode("LatentUpscale", this); + this.connect(0, upscaleNode, 0); + + const ckptNode = addNode("CheckpointLoaderSimple", this); + const sampleNode = addNode("KSampler", ckptNode); + + const positiveLink = this.getInputLink(1); + const negativeLink = this.getInputLink(2); + const positiveNode = positiveLink + ? app.graph.add(app.graph.getNodeById(positiveLink.origin_id).clone()) + : addNode("CLIPTextEncode"); + const negativeNode = negativeLink + ? app.graph.add(app.graph.getNodeById(negativeLink.origin_id).clone()) + : addNode("CLIPTextEncode"); + + ckptNode.connect(0, sampleNode, 0); + ckptNode.connect(1, positiveNode, 0); + ckptNode.connect(1, negativeNode, 0); + positiveNode.connect(0, sampleNode, 1); + negativeNode.connect(0, sampleNode, 2); + upscaleNode.connect(0, sampleNode, 3); + }, + }, + { + content: "Add Save Image", + callback: () => { + const decodeNode = addNode("VAEDecode", this); + this.connect(0, decodeNode, 0); + + getOrAddVAELoader(decodeNode).connect(0, decodeNode, 1); + + const saveNode = addNode("SaveImage", decodeNode); + decodeNode.connect(0, saveNode, 0); + }, + } + ); + }); + } + + if (nodeData.name === "CheckpointLoaderSimple") { + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: "Add Clip Skip", + callback: () => { + const clipSkipNode = addNode("CLIPSetLastLayer", this); + const clipLinks = this.outputs[1].links ? this.outputs[1].links.map((l) => ({ ...graph.links[l] })) : []; + + this.disconnectOutput(1); + this.connect(1, clipSkipNode, 0); + + for (const clipLink of clipLinks) { + clipSkipNode.connect(0, clipLink.target_id, clipLink.target_slot); + } + }, + }); + }); + } + + if ( + nodeData.name === "CheckpointLoaderSimple" || + nodeData.name === "CheckpointLoader" || + nodeData.name === "CheckpointLoader|pysssss" || + nodeData.name === "LoraLoader" || + nodeData.name === "LoraLoader|pysssss" + ) { + addMenuHandler(nodeType, function (_, options) { + function addLora(type) { + const loraNode = addNode(type, this); + + const modelLinks = this.outputs[0].links ? this.outputs[0].links.map((l) => ({ ...graph.links[l] })) : []; + const clipLinks = this.outputs[1].links ? this.outputs[1].links.map((l) => ({ ...graph.links[l] })) : []; + + this.disconnectOutput(0); + this.disconnectOutput(1); + + this.connect(0, loraNode, 0); + this.connect(1, loraNode, 1); + + for (const modelLink of modelLinks) { + loraNode.connect(0, modelLink.target_id, modelLink.target_slot); + } + + for (const clipLink of clipLinks) { + loraNode.connect(1, clipLink.target_id, clipLink.target_slot); + } + } + options.unshift( + { + content: "Add LoRA", + callback: () => addLora.call(this, "LoraLoader"), + }, + { + content: "Add 🐍 LoRA", + callback: () => addLora.call(this, "LoraLoader|pysssss"), + }, + { + content: "Add Prompts", + callback: () => { + const positiveNode = addNode("CLIPTextEncode", this); + const negativeNode = addNode("CLIPTextEncode", this, { shiftY: positiveNode.size[1] + 30 }); + + this.connect(1, positiveNode, 0); + this.connect(1, negativeNode, 0); + }, + } + ); + }); + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/repeater.js b/custom_nodes/comfyui-custom-scripts/web/js/repeater.js new file mode 100644 index 00000000..23432c4d --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/repeater.js @@ -0,0 +1,123 @@ +import { app } from "../../../scripts/app.js"; + +const REPEATER = "Repeater|pysssss"; + +app.registerExtension({ + name: "pysssss.Repeater", + init() { + const graphToPrompt = app.graphToPrompt; + app.graphToPrompt = async function () { + const res = await graphToPrompt.apply(this, arguments); + + const id = Date.now() + "_"; + let u = 0; + + let newNodes = {}; + const newRepeaters = {}; + for (const nodeId in res.output) { + let output = res.output[nodeId]; + if (output.class_type === REPEATER) { + const isMulti = output.inputs.output === "multi"; + if (output.inputs.node_mode === "create") { + // We need to clone the input for every repeat + const orig = res.output[output.inputs.source[0]]; + if (isMulti) { + if (!newRepeaters[nodeId]) { + newRepeaters[nodeId] = []; + newRepeaters[nodeId][output.inputs.repeats - 1] = nodeId; + } + } + for (let i = 0; i < output.inputs.repeats - 1; i++) { + const clonedInputId = id + ++u; + + if (isMulti) { + // If multi create we need to clone the repeater too + newNodes[clonedInputId] = structuredClone(orig); + + output = structuredClone(output); + + const clonedRepeaterId = id + ++u; + newNodes[clonedRepeaterId] = output; + output.inputs["source"][0] = clonedInputId; + + newRepeaters[nodeId][i] = clonedRepeaterId; + } else { + newNodes[clonedInputId] = orig; + } + output.inputs[clonedInputId] = [clonedInputId, output.inputs.source[1]]; + } + } else if (isMulti) { + newRepeaters[nodeId] = Array(output.inputs.repeats).fill(nodeId); + } + } + } + + Object.assign(res.output, newNodes); + newNodes = {}; + + for (const nodeId in res.output) { + const output = res.output[nodeId]; + for (const k in output.inputs) { + const v = output.inputs[k]; + if (v instanceof Array) { + const repeaterId = v[0]; + const source = newRepeaters[repeaterId]; + if (source) { + v[0] = source.pop(); + v[1] = 0; + } + } + } + } + + // Object.assign(res.output, newNodes); + + return res; + }; + }, + beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === REPEATER) { + const SETUP_OUTPUTS = Symbol(); + nodeType.prototype[SETUP_OUTPUTS] = function (repeats) { + if (repeats == null) { + repeats = this.widgets[0].value; + } + while (this.outputs.length > repeats) { + this.removeOutput(repeats); + } + const id = Date.now() + "_"; + let u = 0; + while (this.outputs.length < repeats) { + this.addOutput(id + ++u, "*", { label: "*" }); + } + }; + + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = function () { + const self = this; + const repeatsCb = this.widgets[0].callback; + this.widgets[0].callback = async function () { + const v = (await repeatsCb?.apply(this, arguments)) ?? this.value; + if (self.widgets[1].value === "multi") { + self[SETUP_OUTPUTS](v); + } + return v; + }; + + const outputCb = this.widgets[1].callback; + this.widgets[1].callback = async function () { + const v = (await outputCb?.apply(this, arguments)) ?? this.value; + if (v === "single") { + self.outputs[0].shape = 6; + self[SETUP_OUTPUTS](1); + } else { + delete self.outputs[0].shape; + self[SETUP_OUTPUTS](); + } + return v; + }; + return onAdded?.apply(this, arguments); + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/reroutePrimitive.js b/custom_nodes/comfyui-custom-scripts/web/js/reroutePrimitive.js new file mode 100644 index 00000000..7b934d71 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/reroutePrimitive.js @@ -0,0 +1,348 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +const REROUTE_PRIMITIVE = "ReroutePrimitive|pysssss"; +const MULTI_PRIMITIVE = "MultiPrimitive|pysssss"; +const LAST_TYPE = Symbol("LastType"); + +app.registerExtension({ + name: "pysssss.ReroutePrimitive", + init() { + // On graph configure, fire onGraphConfigured to create widgets + const graphConfigure = LGraph.prototype.configure; + LGraph.prototype.configure = function () { + const r = graphConfigure.apply(this, arguments); + for (const n of app.graph._nodes) { + if (n.type === REROUTE_PRIMITIVE) { + n.onGraphConfigured(); + } + } + + return r; + }; + + // Hide this node as it is no longer supported + const getNodeTypesCategories = LiteGraph.getNodeTypesCategories; + LiteGraph.getNodeTypesCategories = function() { + return getNodeTypesCategories.apply(this, arguments).filter(c => !c.startsWith("__hidden__")); + } + + const graphToPrompt = app.graphToPrompt; + app.graphToPrompt = async function () { + const res = await graphToPrompt.apply(this, arguments); + + const multiOutputs = []; + for (const nodeId in res.output) { + const output = res.output[nodeId]; + if (output.class_type === MULTI_PRIMITIVE) { + multiOutputs.push({ id: nodeId, inputs: output.inputs }); + } + } + + function permute(outputs) { + function generatePermutations(inputs, currentIndex, currentPermutation, result) { + if (currentIndex === inputs.length) { + result.push({ ...currentPermutation }); + return; + } + + const input = inputs[currentIndex]; + + for (const k in input) { + currentPermutation[currentIndex] = input[k]; + generatePermutations(inputs, currentIndex + 1, currentPermutation, result); + } + } + + const inputs = outputs.map((output) => output.inputs); + const result = []; + const current = new Array(inputs.length); + + generatePermutations(inputs, 0, current, result); + + return outputs.map((output, index) => ({ + ...output, + inputs: result.reduce((p, permutation) => { + const count = Object.keys(p).length; + p["value" + (count || "")] = permutation[index]; + return p; + }, {}), + })); + } + + const permutations = permute(multiOutputs); + for (let i = 0; i < permutations.length; i++) { + res.output[multiOutputs[i].id].inputs = permutations[i].inputs; + } + + return res; + }; + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + function addOutputHandler() { + // Finds the first non reroute output node down the chain + nodeType.prototype.getFirstReroutedOutput = function (slot) { + if (nodeData.name === MULTI_PRIMITIVE) { + slot = 0; + } + const links = this.outputs[slot].links; + if (!links) return null; + + const search = []; + for (const l of links) { + const link = app.graph.links[l]; + if (!link) continue; + + const node = app.graph.getNodeById(link.target_id); + if (node.type !== REROUTE_PRIMITIVE && node.type !== MULTI_PRIMITIVE) { + return { node, link }; + } + search.push({ node, link }); + } + + for (const { link, node } of search) { + const r = node.getFirstReroutedOutput(link.target_slot); + if (r) { + return r; + } + } + }; + } + + if (nodeData.name === REROUTE_PRIMITIVE) { + const configure = nodeType.prototype.configure || LGraphNode.prototype.configure; + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + const onAdded = nodeType.prototype.onAdded; + + nodeType.title_mode = LiteGraph.NO_TITLE; + + function hasAnyInput(node) { + for (const input of node.inputs) { + if (input.link) { + return true; + } + } + return false; + } + + // Remove input text + nodeType.prototype.onAdded = function () { + onAdded?.apply(this, arguments); + this.inputs[0].label = ""; + this.outputs[0].label = "value"; + this.setSize(this.computeSize()); + }; + + // Restore any widgets + nodeType.prototype.onGraphConfigured = function () { + if (hasAnyInput(this)) return; + + const outputNode = this.getFirstReroutedOutput(0); + if (outputNode) { + this.checkPrimitiveWidget(outputNode); + } + }; + + // Check if we need to create (or remove) a widget on the node + nodeType.prototype.checkPrimitiveWidget = function ({ node, link }) { + let widgetType = link.type; + let targetLabel = widgetType; + const input = node.inputs[link.target_slot]; + if (input.widget?.config?.[0] instanceof Array) { + targetLabel = input.widget.name; + widgetType = "COMBO"; + } + + if (widgetType in ComfyWidgets) { + if (!this.widgets?.length) { + let v; + if (this.widgets_values?.length) { + v = this.widgets_values[0]; + } + let config = [link.type, {}]; + if (input.widget?.config) { + config = input.widget.config; + } + const { widget } = ComfyWidgets[widgetType](this, "value", config, app); + if (v !== undefined && (!this[LAST_TYPE] || this[LAST_TYPE] === widgetType)) { + widget.value = v; + } + this[LAST_TYPE] = widgetType; + } + } else if (this.widgets) { + this.widgets.length = 0; + } + + return targetLabel; + }; + + // Finds all input nodes from the current reroute + nodeType.prototype.getReroutedInputs = function (slot) { + let nodes = [{ node: this }]; + let node = this; + while (node?.type === REROUTE_PRIMITIVE) { + const input = node.inputs[slot]; + if (input.link) { + const link = app.graph.links[input.link]; + node = app.graph.getNodeById(link.origin_id); + slot = link.origin_slot; + nodes.push({ + node, + link, + }); + } else { + node = null; + } + } + + return nodes; + }; + + addOutputHandler(); + + // Update the type of all reroutes in a chain + nodeType.prototype.changeRerouteType = function (slot, type, label) { + const color = LGraphCanvas.link_type_colors[type]; + const output = this.outputs[slot]; + this.inputs[slot].label = " "; + output.label = label || (type === "*" ? "value" : type); + output.type = type; + + // Process all linked outputs + for (const linkId of output.links || []) { + const link = app.graph.links[linkId]; + if (!link) continue; + link.color = color; + const node = app.graph.getNodeById(link.target_id); + if (node.changeRerouteType) { + // Recursively update reroutes + node.changeRerouteType(link.target_slot, type, label); + } else { + // Validate links to 'real' nodes + const theirType = node.inputs[link.target_slot].type; + if (theirType !== type && theirType !== "*") { + node.disconnectInput(link.target_slot); + } + } + } + + if (this.inputs[slot].link) { + const link = app.graph.links[this.inputs[slot].link]; + if (link) link.color = color; + } + }; + + // Override configure so we can flag that we are configuring to avoid link validation breaking + let configuring = false; + nodeType.prototype.configure = function () { + configuring = true; + const r = configure?.apply(this, arguments); + configuring = false; + + return r; + }; + + Object.defineProperty(nodeType, "title_mode", { + get() { + return app.canvas.current_node?.widgets?.length ? LiteGraph.NORMAL_TITLE : LiteGraph.NO_TITLE; + }, + }); + + nodeType.prototype.onConnectionsChange = function (type, _, connected, link_info) { + // If configuring treat everything as OK as links may not be set by litegraph yet + if (configuring) return; + + const isInput = type === LiteGraph.INPUT; + const slot = isInput ? link_info.target_slot : link_info.origin_slot; + + let targetLabel = null; + let targetNode = null; + let targetType = "*"; + let targetSlot = slot; + + const inputPath = this.getReroutedInputs(slot); + const rootInput = inputPath[inputPath.length - 1]; + const outputNode = this.getFirstReroutedOutput(slot); + if (rootInput.node.type === REROUTE_PRIMITIVE) { + // Our input node is a reroute, so see if we have an output + if (outputNode) { + targetType = outputNode.link.type; + } else if (rootInput.node.widgets) { + rootInput.node.widgets.length = 0; + } + targetNode = rootInput; + targetSlot = rootInput.link?.target_slot ?? slot; + } else { + // We have a real input, so we want to use that type + targetNode = inputPath[inputPath.length - 2]; + targetType = rootInput.node.outputs[rootInput.link.origin_slot].type; + targetSlot = rootInput.link.target_slot; + } + + if (this.widgets && inputPath.length > 1) { + // We have an input node so remove our widget + this.widgets.length = 0; + } + + if (outputNode && rootInput.node.checkPrimitiveWidget) { + // We have an output, check if we need to create a widget + targetLabel = rootInput.node.checkPrimitiveWidget(outputNode); + } + + // Trigger an update of the type to all child nodes + targetNode.node.changeRerouteType(targetSlot, targetType, targetLabel); + + return onConnectionsChange?.apply(this, arguments); + }; + + // When collapsed fix the size to just the dot + const computeSize = nodeType.prototype.computeSize || LGraphNode.prototype.computeSize; + nodeType.prototype.computeSize = function () { + const r = computeSize.apply(this, arguments); + if (this.flags?.collapsed) { + return [1, 25]; + } else if (this.widgets?.length) { + return r; + } else { + let w = 75; + if (this.outputs?.[0]?.label) { + const t = LiteGraph.NODE_TEXT_SIZE * this.outputs[0].label.length * 0.6 + 30; + if (t > w) { + w = t; + } + } + return [w, r[1]]; + } + }; + + // On collapse shrink the node to just a dot + const collapse = nodeType.prototype.collapse || LGraphNode.prototype.collapse; + nodeType.prototype.collapse = function () { + collapse.apply(this, arguments); + this.setSize(this.computeSize()); + requestAnimationFrame(() => { + this.setDirtyCanvas(true, true); + }); + }; + + // Shift the bounding area up slightly as LiteGraph miscalculates it for collapsed nodes + nodeType.prototype.onBounding = function (area) { + if (this.flags?.collapsed) { + area[1] -= 15; + } + }; + } else if (nodeData.name === MULTI_PRIMITIVE) { + addOutputHandler(); + nodeType.prototype.onConnectionsChange = function (type, _, connected, link_info) { + for (let i = 0; i < this.inputs.length - 1; i++) { + if (!this.inputs[i].link) { + this.removeInput(i--); + } + } + if (this.inputs[this.inputs.length - 1].link) { + this.addInput("v" + +new Date(), this.inputs[0].type).label = "value"; + } + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/showImageOnMenu.js b/custom_nodes/comfyui-custom-scripts/web/js/showImageOnMenu.js new file mode 100644 index 00000000..4fed9de9 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/showImageOnMenu.js @@ -0,0 +1,81 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; +import { $el } from "../../../scripts/ui.js"; + +const id = "pysssss.ShowImageOnMenu"; +const ext = { + name: id, + async setup(app) { + let enabled = true; + let nodeId = null; + const img = $el("img", { + style: { + width: "100%", + height: "150px", + objectFit: "contain", + }, + }); + const link = $el( + "a", + { + style: { + width: "100%", + height: "150px", + marginTop: "10px", + order: 100, // Place this item last (until someone else has a higher order) + display: "none", + }, + href: "#", + onclick: (e) => { + e.stopPropagation(); + e.preventDefault(); + const node = app.graph.getNodeById(nodeId); + if (!node) return; + app.canvas.centerOnNode(node); + app.canvas.setZoom(1); + }, + }, + [img] + ); + + app.ui.menuContainer.append(link); + + const show = (src, node) => { + img.src = src; + nodeId = Number(node); + link.style.display = "unset"; + }; + + api.addEventListener("executed", ({ detail }) => { + if (!enabled) return; + const images = detail?.output?.images; + if (!images || !images.length) return; + const format = app.getPreviewFormatParam(); + const src = [ + `./view?filename=${encodeURIComponent(images[0].filename)}`, + `type=${images[0].type}`, + `subfolder=${encodeURIComponent(images[0].subfolder)}`, + `t=${+new Date()}${format}`,].join('&'); + show(src, detail.node); + }); + + api.addEventListener("b_preview", ({ detail }) => { + if (!enabled) return; + show(URL.createObjectURL(detail), app.runningNodeId); + }); + + app.ui.settings.addSetting({ + id, + name: "🐍 Show Image On Menu", + defaultValue: true, + type: "boolean", + onChange(value) { + enabled = value; + + if (!enabled) link.style.display = "none"; + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/showText.js b/custom_nodes/comfyui-custom-scripts/web/js/showText.js new file mode 100644 index 00000000..464fd404 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/showText.js @@ -0,0 +1,78 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +// Displays input text on a node + +// TODO: This should need to be so complicated. Refactor at some point. + +app.registerExtension({ + name: "pysssss.ShowText", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "ShowText|pysssss") { + function populate(text) { + if (this.widgets) { + // On older frontend versions there is a hidden converted-widget + const isConvertedWidget = +!!this.inputs?.[0].widget; + for (let i = isConvertedWidget; i < this.widgets.length; i++) { + this.widgets[i].onRemove?.(); + } + this.widgets.length = isConvertedWidget; + } + + const v = [...text]; + if (!v[0]) { + v.shift(); + } + for (let list of v) { + // Force list to be an array, not sure why sometimes it is/isn't + if (!(list instanceof Array)) list = [list]; + for (const l of list) { + const w = ComfyWidgets["STRING"](this, "text_" + this.widgets?.length ?? 0, ["STRING", { multiline: true }], app).widget; + w.inputEl.readOnly = true; + w.inputEl.style.opacity = 0.6; + w.value = l; + } + } + + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + }); + } + + // When the node is executed we will be sent the input text, display this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + populate.call(this, message.text); + }; + + const VALUES = Symbol(); + const configure = nodeType.prototype.configure; + nodeType.prototype.configure = function () { + // Store unmodified widget values as they get removed on configure by new frontend + this[VALUES] = arguments[0]?.widgets_values; + return configure?.apply(this, arguments); + }; + + const onConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + onConfigure?.apply(this, arguments); + const widgets_values = this[VALUES]; + if (widgets_values?.length) { + // In newer frontend there seems to be a delay in creating the initial widget + requestAnimationFrame(() => { + populate.call(this, widgets_values.slice(+(widgets_values.length > 1 && this.inputs?.[0].widget))); + }); + } + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/snapToGrid.js b/custom_nodes/comfyui-custom-scripts/web/js/snapToGrid.js new file mode 100644 index 00000000..07d5aa8c --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/snapToGrid.js @@ -0,0 +1,73 @@ +import { app } from "../../../scripts/app.js"; + +let setting; +const id = "pysssss.SnapToGrid"; + +/** Wraps the provided function call to set/reset shiftDown when setting is enabled. */ +function wrapCallInSettingCheck(fn) { + if (setting?.value) { + const shift = app.shiftDown; + app.shiftDown = true; + const r = fn(); + app.shiftDown = shift; + return r; + } + return fn(); +} + +const ext = { + name: id, + init() { + setting = app.ui.settings.addSetting({ + id, + name: "🐍 Always snap to grid", + defaultValue: false, + type: "boolean", + onChange(value) { + app.canvas.align_to_grid = value; + }, + }); + + // We need to register our hooks after the core snap to grid extension runs + // Do this from the graph configure function so we still get onNodeAdded calls + const configure = LGraph.prototype.configure; + LGraph.prototype.configure = function () { + // Override drawNode to draw the drop position + const drawNode = LGraphCanvas.prototype.drawNode; + LGraphCanvas.prototype.drawNode = function () { + wrapCallInSettingCheck(() => drawNode.apply(this, arguments)); + }; + + // Override node added to add a resize handler to force grid alignment + const onNodeAdded = app.graph.onNodeAdded; + app.graph.onNodeAdded = function (node) { + const r = onNodeAdded?.apply(this, arguments); + const onResize = node.onResize; + node.onResize = function () { + wrapCallInSettingCheck(() => onResize?.apply(this, arguments)); + }; + return r; + }; + + + const groupMove = LGraphGroup.prototype.move; + LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) { + wrapCallInSettingCheck(() => groupMove.apply(this, arguments)); + } + + const canvasDrawGroups = LGraphCanvas.prototype.drawGroups; + LGraphCanvas.prototype.drawGroups = function (canvas, ctx) { + wrapCallInSettingCheck(() => canvasDrawGroups.apply(this, arguments)); + } + + const canvasOnGroupAdd = LGraphCanvas.onGroupAdd; + LGraphCanvas.onGroupAdd = function() { + wrapCallInSettingCheck(() => canvasOnGroupAdd.apply(this, arguments)); + } + + return configure.apply(this, arguments); + }; + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/snapToGridGuide.js b/custom_nodes/comfyui-custom-scripts/web/js/snapToGridGuide.js new file mode 100644 index 00000000..54a8a0c7 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/snapToGridGuide.js @@ -0,0 +1,163 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; + +let guide_config; +const id = "pysssss.SnapToGrid.Guide"; +const guide_config_default = { + lines: { + enabled: false, + fillStyle: "rgba(255, 0, 0, 0.5)", + }, + block: { + enabled: false, + fillStyle: "rgba(0, 0, 255, 0.5)", + }, +} + +const ext = { + name: id, + init() { + if (localStorage.getItem(id) === null) { + localStorage.setItem(id, JSON.stringify(guide_config_default)); + } + guide_config = JSON.parse(localStorage.getItem(id)); + + app.ui.settings.addSetting({ + id, + name: "🐍 Display drag-and-drop guides", + type: (name, setter, value) => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: name, + }), + ]), + $el("td", [ + $el( + "label", + { + textContent: "Lines: ", + style: { + display: "inline-block", + }, + }, + [ + $el("input", { + id: id.replaceAll(".", "-") + "-line-text", + type: "text", + value: guide_config.lines.fillStyle, + onchange: (event) => { + guide_config.lines.fillStyle = event.target.value; + localStorage.setItem(id, JSON.stringify(guide_config)); + } + }), + $el("input", { + id: id.replaceAll(".", "-") + "-line-checkbox", + type: "checkbox", + checked: guide_config.lines.enabled, + onchange: (event) => { + guide_config.lines.enabled = !!event.target.checked; + localStorage.setItem(id, JSON.stringify(guide_config)); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Block: ", + style: { + display: "inline-block", + }, + }, + [ + $el("input", { + id: id.replaceAll(".", "-") + "-block-text", + type: "text", + value: guide_config.block.fillStyle, + onchange: (event) => { + guide_config.block.fillStyle = event.target.value; + localStorage.setItem(id, JSON.stringify(guide_config)); + } + }), + $el("input", { + id: id.replaceAll(".", "-") + '-block-checkbox', + type: "checkbox", + checked: guide_config.block.enabled, + onchange: (event) => { + guide_config.block.enabled = !!event.target.checked; + localStorage.setItem(id, JSON.stringify(guide_config)); + }, + }), + ] + ), + ]), + ]); + } + }); + + const alwaysSnapToGrid = () => + app.ui.settings.getSettingValue("pysssss.SnapToGrid", /* default=*/ false); + const snapToGridEnabled = () => + app.shiftDown || alwaysSnapToGrid(); + + // Override drag-and-drop behavior to show orthogonal guide lines around selected node(s) and preview of where the node(s) will be placed + const origDrawNode = LGraphCanvas.prototype.drawNode; + LGraphCanvas.prototype.drawNode = function (node, ctx) { + const enabled = guide_config.lines.enabled || guide_config.block.enabled; + if (enabled && this.node_dragged && node.id in this.selected_nodes && snapToGridEnabled()) { + // discretize the canvas into grid + let x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE); + let y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE); + + // calculate the width and height of the node + // (also need to shift the y position of the node, depending on whether the title is visible) + x -= node.pos[0]; + y -= node.pos[1]; + let w, h; + if (node.flags.collapsed) { + w = node._collapsed_width; + h = LiteGraph.NODE_TITLE_HEIGHT; + y -= LiteGraph.NODE_TITLE_HEIGHT; + } else { + w = node.size[0]; + h = node.size[1]; + let titleMode = node.constructor.title_mode; + if (titleMode !== LiteGraph.TRANSPARENT_TITLE && titleMode !== LiteGraph.NO_TITLE) { + h += LiteGraph.NODE_TITLE_HEIGHT; + y -= LiteGraph.NODE_TITLE_HEIGHT; + } + } + + // save the original fill style + const f = ctx.fillStyle; + + // draw preview for drag-and-drop (rectangle to show where the node will be placed) + if (guide_config.block.enabled) { + ctx.fillStyle = guide_config.block.fillStyle; + ctx.fillRect(x, y, w, h); + } + + // add guide lines around node (arbitrarily long enough to span most workflows) + if (guide_config.lines.enabled) { + const xd = 10000; + const yd = 10000; + const thickness = 3; + ctx.fillStyle = guide_config.lines.fillStyle; + ctx.fillRect(x - xd, y, 2*xd, thickness); + ctx.fillRect(x, y - yd, thickness, 2*yd); + ctx.fillRect(x - xd, y + h, 2*xd, thickness); + ctx.fillRect(x + w, y - yd, thickness, 2*yd); + } + + // restore the original fill style + ctx.fillStyle = f; + } + + return origDrawNode.apply(this, arguments); + }; + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/stringFunction.js b/custom_nodes/comfyui-custom-scripts/web/js/stringFunction.js new file mode 100644 index 00000000..cc150dff --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/stringFunction.js @@ -0,0 +1,33 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +// Displays input text on a node + +app.registerExtension({ + name: "pysssss.StringFunction", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "StringFunction|pysssss") { + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "result"); + if (pos !== -1) { + for (let i = pos; i < this.widgets.length; i++) { + this.widgets[i].onRemove?.(); + } + this.widgets.length = pos; + } + } + + const w = ComfyWidgets["STRING"](this, "result", ["STRING", { multiline: true }], app).widget; + w.inputEl.readOnly = true; + w.inputEl.style.opacity = 0.6; + w.value = message.text; + + this.onResize?.(this.size); + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/swapResolution.js b/custom_nodes/comfyui-custom-scripts/web/js/swapResolution.js new file mode 100644 index 00000000..40f6dcc8 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/swapResolution.js @@ -0,0 +1,30 @@ +import { app } from "../../../scripts/app.js"; +app.registerExtension({ + name: "pysssss.SwapResolution", + async beforeRegisterNodeDef(nodeType, nodeData) { + const inputs = { ...nodeData.input?.required, ...nodeData.input?.optional }; + if (inputs.width && inputs.height) { + const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = origGetExtraMenuOptions?.apply?.(this, arguments); + + options.push( + { + content: "Swap width/height", + callback: () => { + const w = this.widgets.find((w) => w.name === "width"); + const h = this.widgets.find((w) => w.name === "height"); + const a = w.value; + w.value = h.value; + h.value = a; + app.graph.setDirtyCanvas(true); + }, + }, + null + ); + + return r; + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/systemNotification.js b/custom_nodes/comfyui-custom-scripts/web/js/systemNotification.js new file mode 100644 index 00000000..803b8c90 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/systemNotification.js @@ -0,0 +1,47 @@ +import { app } from "../../../scripts/app.js"; + +const notificationSetup = () => { + if (!("Notification" in window)) { + console.log("This browser does not support notifications."); + alert("This browser does not support notifications."); + return; + } + if (Notification.permission === "denied") { + console.log("Notifications are blocked. Please enable them in your browser settings."); + alert("Notifications are blocked. Please enable them in your browser settings."); + return; + } + if (Notification.permission !== "granted") { + Notification.requestPermission(); + } + return true; +}; + +app.registerExtension({ + name: "pysssss.SystemNotification", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "SystemNotification|pysssss") { + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = async function ({ message, mode }) { + onExecuted?.apply(this, arguments); + + if (mode === "on empty queue") { + if (app.ui.lastQueueSize !== 0) { + await new Promise((r) => setTimeout(r, 500)); + } + if (app.ui.lastQueueSize !== 0) { + return; + } + } + if (!notificationSetup()) return; + const notification = new Notification("ComfyUI", { body: message ?? "Your notification has triggered." }); + }; + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + notificationSetup(); + }; + } + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/useNumberInputPrompt.js b/custom_nodes/comfyui-custom-scripts/web/js/useNumberInputPrompt.js new file mode 100644 index 00000000..a92aa510 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/useNumberInputPrompt.js @@ -0,0 +1,36 @@ +import { app } from "../../../scripts/app.js"; + +const id = "pysssss.UseNumberInputPrompt"; +const ext = { + name: id, + async setup(app) { + const prompt = LGraphCanvas.prototype.prompt; + + const setting = app.ui.settings.addSetting({ + id, + name: "🐍 Use number input on value entry", + defaultValue: false, + type: "boolean", + }); + + LGraphCanvas.prototype.prompt = function () { + const dialog = prompt.apply(this, arguments); + if (setting.value && typeof arguments[1] === "number") { + // If this should be a number then update the imput + const input = dialog.querySelector("input"); + input.type = "number"; + + // Add constraints + const widget = app.canvas.node_widget?.[1]; + if (widget?.options) { + for (const prop of ["min", "max", "step"]) { + if (widget.options[prop]) input[prop] = widget.options[prop]; + } + } + } + return dialog; + }; + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/widgetDefaults.js b/custom_nodes/comfyui-custom-scripts/web/js/widgetDefaults.js new file mode 100644 index 00000000..859749d1 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/widgetDefaults.js @@ -0,0 +1,297 @@ +import { app } from "../../../scripts/app.js"; +import { $el, ComfyDialog } from "../../../scripts/ui.js"; + +// Allows you to specify custom default values for any widget on any node + +const id = "pysssss.WidgetDefaults"; +const nodeDataKey = Symbol(); + +app.registerExtension({ + name: id, + beforeRegisterNodeDef(nodeType, nodeData) { + nodeType[nodeDataKey] = nodeData; + }, + setup() { + let defaults; + let regexDefaults; + let setting; + + const getNodeDefaults = (node, defaults) => { + const nodeDefaults = defaults[node.type] ?? {}; + const propSetBy = {}; + + Object.keys(regexDefaults) + .filter((r) => new RegExp(r).test(node.type)) + .reduce((p, n) => { + const props = regexDefaults[n]; + for (const k in props) { + // Use the longest matching key as its probably the most specific + if (!(k in nodeDefaults) || (k in propSetBy && n.length > propSetBy[k].length)) { + propSetBy[k] = n; + nodeDefaults[k] = props[k]; + } + } + return p; + }, nodeDefaults); + + return nodeDefaults; + }; + + const applyDefaults = (defaults) => { + for (const node of Object.values(LiteGraph.registered_node_types)) { + const nodeData = node[nodeDataKey]; + if (!nodeData) continue; + const nodeDefaults = getNodeDefaults(node, defaults); + if (!nodeDefaults) continue; + const inputs = { ...(nodeData.input?.required || {}), ...(nodeData.input?.optional || {}) }; + + for (const w in nodeDefaults) { + const widgetDef = inputs[w]; + if (widgetDef) { + let v = nodeDefaults[w]; + if (widgetDef[0] === "INT" || widgetDef[0] === "FLOAT") { + v = +v; + } + if (widgetDef[1]) { + widgetDef[1].default = v; + } else { + widgetDef[1] = { default: v }; + } + } + } + } + }; + + const getDefaults = () => { + let items; + regexDefaults = {}; + try { + items = JSON.parse(setting.value); + items = items.reduce((p, n) => { + if (n.node.startsWith("/") && n.node.endsWith("/")) { + const name = n.node.substring(1, n.node.length - 1); + try { + // Validate regex + new RegExp(name); + + if (!regexDefaults[name]) regexDefaults[name] = {}; + regexDefaults[name][n.widget] = n.value; + } catch (error) {} + } + + if (!p[n.node]) p[n.node] = {}; + p[n.node][n.widget] = n.value; + return p; + }, {}); + } catch (error) {} + if (!items) { + items = {}; + } + applyDefaults(items); + return items; + }; + + const onNodeAdded = app.graph.onNodeAdded; + app.graph.onNodeAdded = function (node) { + onNodeAdded?.apply?.(this, arguments); + + // See if we have any defaults for this type of node + const nodeDefaults = getNodeDefaults(node.constructor, defaults); + if (!nodeDefaults) return; + + // Dont run if they are pre-configured nodes from load/pastes + const stack = new Error().stack; + if (stack.includes("pasteFromClipboard") || stack.includes("loadGraphData")) { + return; + } + + for (const k in nodeDefaults) { + if (k.startsWith("property.")) { + const name = k.substring(9); + let v = nodeDefaults[k]; + // Special handling for some built in values + if (name in node || ["color", "bgcolor", "title"].includes(name)) { + node[name] = v; + } else { + // Try using the correct type + if (!node.properties) node.properties = {}; + if (typeof node.properties[name] === "number") v = +v; + else if (typeof node.properties[name] === "boolean") v = v === "true"; + else if (v === "true") v = true; + + node.properties[name] = v; + } + } + } + }; + + class WidgetDefaultsDialog extends ComfyDialog { + constructor() { + super(); + this.element.classList.add("comfy-manage-templates"); + this.grid = $el( + "div", + { + style: { + display: "grid", + gridTemplateColumns: "1fr auto auto auto", + gap: "5px", + }, + className: "pysssss-widget-defaults", + }, + [ + $el("label", { + textContent: "Node Class", + }), + $el("label", { + textContent: "Widget Name", + }), + $el("label", { + textContent: "Default Value", + }), + $el("label"), + (this.rows = $el("div", { + style: { + display: "contents", + }, + })), + ] + ); + } + + createButtons() { + const btns = super.createButtons(); + btns[0].textContent = "Cancel"; + btns.unshift( + $el("button", { + type: "button", + textContent: "Add New", + onclick: () => this.addRow(), + }), + $el("button", { + type: "button", + textContent: "Save", + onclick: () => this.save(), + }) + ); + return btns; + } + + addRow(node = "", widget = "", value = "") { + let nameInput; + this.rows.append( + $el( + "div", + { + style: { + display: "contents", + }, + className: "pysssss-widget-defaults-row", + }, + [ + $el("input", { + placeholder: "e.g. CheckpointLoaderSimple", + value: node, + }), + $el("input", { + placeholder: "e.g. ckpt_name", + value: widget, + $: (el) => (nameInput = el), + }), + $el("input", { + placeholder: "e.g. myBestModel.safetensors", + value, + }), + $el("button", { + textContent: "Delete", + style: { + fontSize: "12px", + color: "red", + fontWeight: "normal", + }, + onclick: (e) => { + nameInput.value = ""; + e.target.parentElement.style.display = "none"; + }, + }), + ] + ) + ); + } + + save() { + const rows = this.rows.children; + const items = []; + + for (const row of rows) { + const inputs = row.querySelectorAll("input"); + const node = inputs[0].value.trim(); + const widget = inputs[1].value.trim(); + const value = inputs[2].value; + if (node && widget) { + items.push({ node, widget, value }); + } + } + + setting.value = JSON.stringify(items); + defaults = getDefaults(); + + this.close(); + } + + show() { + this.rows.replaceChildren(); + for (const nodeName in defaults) { + const node = defaults[nodeName]; + for (const widgetName in node) { + this.addRow(nodeName, widgetName, node[widgetName]); + } + } + + this.addRow(); + super.show(this.grid); + } + } + + setting = app.ui.settings.addSetting({ + id, + name: "🐍 Widget Defaults", + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: "🐍 Widget & Property Defaults:", + }), + ]), + $el("td", [ + $el("button", { + textContent: "Manage", + onclick: () => { + try { + // Try closing old settings window + if (typeof app.ui.settings.element?.close === "function") { + app.ui.settings.element.close(); + } + } catch (error) {} + try { + // Try closing new vue dialog + document.querySelector(".p-dialog-close-button").click(); + } catch (error) { + // Fallback to just hiding the element + app.ui.settings.element.style.display = "none"; + } + const dialog = new WidgetDefaultsDialog(); + dialog.show(); + }, + style: { + fontSize: "14px", + }, + }), + ]), + ]); + }, + }); + defaults = getDefaults(); + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/workflowImage.js b/custom_nodes/comfyui-custom-scripts/web/js/workflowImage.js new file mode 100644 index 00000000..37c3ad06 --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/workflowImage.js @@ -0,0 +1,640 @@ +import { app } from "../../../scripts/app.js"; +import { importA1111 } from "../../../scripts/pnginfo.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +let getDrawTextConfig = null; +let fileInput; + +class WorkflowImage { + static accept = ""; + + getBounds() { + // Calculate the min max bounds for the nodes on the graph + const bounds = app.graph._nodes.reduce( + (p, n) => { + if (n.pos[0] < p[0]) p[0] = n.pos[0]; + if (n.pos[1] < p[1]) p[1] = n.pos[1]; + const bounds = n.getBounding(); + const r = n.pos[0] + bounds[2]; + const b = n.pos[1] + bounds[3]; + if (r > p[2]) p[2] = r; + if (b > p[3]) p[3] = b; + return p; + }, + [99999, 99999, -99999, -99999] + ); + + bounds[0] -= 100; + bounds[1] -= 100; + bounds[2] += 100; + bounds[3] += 100; + return bounds; + } + + saveState() { + this.state = { + scale: app.canvas.ds.scale, + width: app.canvas.canvas.width, + height: app.canvas.canvas.height, + offset: app.canvas.ds.offset, + transform: app.canvas.canvas.getContext("2d").getTransform(), // Save the original transformation matrix + }; + } + + restoreState() { + app.canvas.ds.scale = this.state.scale; + app.canvas.canvas.width = this.state.width; + app.canvas.canvas.height = this.state.height; + app.canvas.ds.offset = this.state.offset; + app.canvas.canvas.getContext("2d").setTransform(this.state.transform); // Reapply the original transformation matrix + } + + updateView(bounds) { + const scale = window.devicePixelRatio || 1; + app.canvas.ds.scale = 1; + app.canvas.canvas.width = (bounds[2] - bounds[0]) * scale; + app.canvas.canvas.height = (bounds[3] - bounds[1]) * scale; + app.canvas.ds.offset = [-bounds[0], -bounds[1]]; + app.canvas.canvas.getContext("2d").setTransform(scale, 0, 0, scale, 0, 0); + } + + getDrawTextConfig(_, widget) { + return { + x: 10, + y: widget.last_y + 10, + resetTransform: false, + }; + } + + async export(includeWorkflow) { + // Save the current state of the canvas + this.saveState(); + // Update to render the whole workflow + this.updateView(this.getBounds()); + + // Flag that we are saving and render the canvas + getDrawTextConfig = this.getDrawTextConfig; + app.canvas.draw(true, true); + getDrawTextConfig = null; + + // Generate a blob of the image containing the workflow + const blob = await this.getBlob(includeWorkflow ? JSON.stringify(app.graph.serialize()) : undefined); + + // Restore initial state and redraw + this.restoreState(); + app.canvas.draw(true, true); + + // Download the generated image + this.download(blob); + } + + download(blob) { + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + Object.assign(a, { + href: url, + download: "workflow." + this.extension, + style: "display: none", + }); + document.body.append(a); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + } + + static import() { + if (!fileInput) { + fileInput = document.createElement("input"); + Object.assign(fileInput, { + type: "file", + style: "display: none", + onchange: () => { + app.handleFile(fileInput.files[0]); + }, + }); + document.body.append(fileInput); + } + fileInput.accept = WorkflowImage.accept; + fileInput.click(); + } +} + +class PngWorkflowImage extends WorkflowImage { + static accept = ".png,image/png"; + extension = "png"; + + n2b(n) { + return new Uint8Array([(n >> 24) & 0xff, (n >> 16) & 0xff, (n >> 8) & 0xff, n & 0xff]); + } + + joinArrayBuffer(...bufs) { + const result = new Uint8Array(bufs.reduce((totalSize, buf) => totalSize + buf.byteLength, 0)); + bufs.reduce((offset, buf) => { + result.set(buf, offset); + return offset + buf.byteLength; + }, 0); + return result; + } + + crc32(data) { + const crcTable = + PngWorkflowImage.crcTable || + (PngWorkflowImage.crcTable = (() => { + let c; + const crcTable = []; + for (let n = 0; n < 256; n++) { + c = n; + for (let k = 0; k < 8; k++) { + c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1; + } + crcTable[n] = c; + } + return crcTable; + })()); + let crc = 0 ^ -1; + for (let i = 0; i < data.byteLength; i++) { + crc = (crc >>> 8) ^ crcTable[(crc ^ data[i]) & 0xff]; + } + return (crc ^ -1) >>> 0; + } + + async getBlob(workflow) { + return new Promise((r) => { + app.canvasEl.toBlob(async (blob) => { + if (workflow) { + // If we have a workflow embed it in the PNG + const buffer = await blob.arrayBuffer(); + const typedArr = new Uint8Array(buffer); + const view = new DataView(buffer); + + const data = new TextEncoder().encode(`tEXtworkflow\0${workflow}`); + const chunk = this.joinArrayBuffer(this.n2b(data.byteLength - 4), data, this.n2b(this.crc32(data))); + + const sz = view.getUint32(8) + 20; + const result = this.joinArrayBuffer(typedArr.subarray(0, sz), chunk, typedArr.subarray(sz)); + + blob = new Blob([result], { type: "image/png" }); + } + + r(blob); + }); + }); + } +} + +class DataReader { + /** @type {DataView} */ + view; + /** @type {boolean | undefined} */ + littleEndian; + offset = 0; + + /** + * @param {DataView} view + */ + constructor(view) { + this.view = view; + } + + /** + * Reads N bytes and increments the offset + * @param {1 | 2 | 4 | 8} size + */ + read(size, signed = false, littleEndian = undefined) { + const v = this.peek(size, signed, littleEndian); + this.offset += size; + return v; + } + + /** + * Reads N bytes + * @param {1 | 2 | 4 | 8} size + */ + peek(size, signed = false, littleEndian = undefined) { + this.view.getBigInt64; + let m = ""; + if (size === 8) m += "Big"; + m += signed ? "Int" : "Uint"; + m += size * 8; + m = "get" + m; + if (!this.view[m]) { + throw new Error("Method not found: " + m); + } + + return this.view[m](this.offset, littleEndian == null ? this.littleEndian : littleEndian); + } + + /** + * Seeks to the specified position or by the number of bytes specified relative to the current offset + * @param {number} pos + * @param {boolean} relative + */ + seek(pos, relative = true) { + if (relative) { + this.offset += pos; + } else { + this.offset = pos; + } + } +} + +class Tiff { + /** @type {DataReader} */ + #reader; + #start; + + readExif(reader) { + const TIFF_MARKER = 0x2a; + const EXIF_IFD = 0x8769; + + this.#reader = reader; + this.#start = this.#reader.offset; + this.#readEndianness(); + + if (!this.#reader.read(2) === TIFF_MARKER) { + throw new Error("Invalid TIFF: Marker not found."); + } + + const dirOffset = this.#reader.read(4); + this.#reader.seek(this.#start + dirOffset, false); + + for (const t of this.#readTags()) { + if (t.id === EXIF_IFD) { + return this.#readExifTag(t); + } + } + throw new Error("No EXIF: TIFF Exif IFD tag not found"); + } + + #readUserComment(tag) { + this.#reader.seek(this.#start + tag.offset, false); + const encoding = this.#reader.read(8); + if (encoding !== 0x45444f43494e55n) { + throw new Error("Unable to read non-Unicode data"); + } + const decoder = new TextDecoder("utf-16be"); + return decoder.decode(new DataView(this.#reader.view.buffer, this.#reader.offset, tag.count - 8)); + } + + #readExifTag(exifTag) { + const EXIF_USER_COMMENT = 0x9286; + + this.#reader.seek(this.#start + exifTag.offset, false); + for (const t of this.#readTags()) { + if (t.id === EXIF_USER_COMMENT) { + return this.#readUserComment(t); + } + } + throw new Error("No embedded data: UserComment Exif tag not found"); + } + + *#readTags() { + const count = this.#reader.read(2); + for (let i = 0; i < count; i++) { + yield { + id: this.#reader.read(2), + type: this.#reader.read(2), + count: this.#reader.read(4), + offset: this.#reader.read(4), + }; + } + } + + #readEndianness() { + const II = 0x4949; + const MM = 0x4d4d; + const endianness = this.#reader.read(2); + if (endianness === II) { + this.#reader.littleEndian = true; + } else if (endianness === MM) { + this.#reader.littleEndian = false; + } else { + throw new Error("Invalid JPEG: Endianness marker not found."); + } + } +} + +class Jpeg { + /** @type {DataReader} */ + #reader; + + /** + * @param {ArrayBuffer} buffer + */ + readExif(buffer) { + const JPEG_MARKER = 0xffd8; + const EXIF_SIG = 0x45786966; + + this.#reader = new DataReader(new DataView(buffer)); + if (!this.#reader.read(2) === JPEG_MARKER) { + throw new Error("Invalid JPEG: SOI not found."); + } + + const app0 = this.#readAppMarkerId(); + if (app0 !== 0) { + throw new Error(`Invalid JPEG: APP0 not found [found: ${app0}].`); + } + + this.#consumeAppSegment(); + const app1 = this.#readAppMarkerId(); + if (app1 !== 1) { + throw new Error(`No EXIF: APP1 not found [found: ${app0}].`); + } + + // Skip size + this.#reader.seek(2); + + if (this.#reader.read(4) !== EXIF_SIG) { + throw new Error(`No EXIF: Invalid EXIF header signature.`); + } + if (this.#reader.read(2) !== 0) { + throw new Error(`No EXIF: Invalid EXIF header.`); + } + + return new Tiff().readExif(this.#reader); + } + + #readAppMarkerId() { + const APP0_MARKER = 0xffe0; + return this.#reader.read(2) - APP0_MARKER; + } + + #consumeAppSegment() { + this.#reader.seek(this.#reader.read(2) - 2); + } +} + +class SvgWorkflowImage extends WorkflowImage { + static accept = ".svg,image/svg+xml"; + extension = "svg"; + + static init() { + // Override file handling to allow drag & drop of SVG + const handleFile = app.handleFile; + app.handleFile = async function (file) { + if (file && (file.type === "image/svg+xml" || file.name?.endsWith(".svg"))) { + const reader = new FileReader(); + reader.onload = () => { + // Extract embedded workflow from desc tags + const descEnd = reader.result.lastIndexOf(""); + if (descEnd !== -1) { + const descStart = reader.result.lastIndexOf("", descEnd); + if (descStart !== -1) { + const json = reader.result.substring(descStart + 6, descEnd); + this.loadGraphData(JSON.parse(SvgWorkflowImage.unescapeXml(json))); + } + } + }; + reader.readAsText(file); + return; + } else if (file && (file.type === "image/jpeg" || file.name?.endsWith(".jpg") || file.name?.endsWith(".jpeg"))) { + if ( + await new Promise((resolve) => { + try { + // This shouldnt go in here but it's easier than refactoring handleFile + const reader = new FileReader(); + reader.onload = async () => { + try { + const value = new Jpeg().readExif(reader.result); + importA1111(app.graph, value); + resolve(true); + } catch (error) { + resolve(false); + } + }; + reader.onerror = () => resolve(false); + reader.readAsArrayBuffer(file); + } catch (error) { + resolve(false); + } + }) + ) { + return; + } + } + return handleFile.apply(this, arguments); + }; + } + + static escapeXml(unsafe) { + return unsafe.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">"); + } + + static unescapeXml(safe) { + return safe.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">"); + } + + getDrawTextConfig(_, widget) { + const domWrapper = widget.inputEl.closest(".dom-widget") ?? widget.inputEl; + return { + x: parseInt(domWrapper.style.left), + y: parseInt(domWrapper.style.top), + resetTransform: true, + }; + } + + saveState() { + super.saveState(); + this.state.ctx = app.canvas.ctx; + } + + restoreState() { + super.restoreState(); + app.canvas.ctx = this.state.ctx; + } + + updateView(bounds) { + super.updateView(bounds); + this.createSvgCtx(bounds); + } + + createSvgCtx(bounds) { + const ctx = this.state.ctx; + const svgCtx = (this.svgCtx = new C2S(bounds[2] - bounds[0], bounds[3] - bounds[1])); + svgCtx.canvas.getBoundingClientRect = function () { + return { width: svgCtx.width, height: svgCtx.height }; + }; + + // Override the c2s handling of images to draw images as canvases + const drawImage = svgCtx.drawImage; + svgCtx.drawImage = function (...args) { + const image = args[0]; + // If we are an image node and not a datauri then we need to replace with a canvas + // we cant convert to data uri here as it is an async process + if (image.nodeName === "IMG" && !image.src.startsWith("data:image/")) { + const canvas = document.createElement("canvas"); + canvas.width = image.width; + canvas.height = image.height; + const imgCtx = canvas.getContext("2d"); + imgCtx.drawImage(image, 0, 0); + args[0] = canvas; + } + + return drawImage.apply(this, args); + }; + + // Implement missing required functions + svgCtx.getTransform = function () { + return ctx.getTransform(); + }; + svgCtx.resetTransform = function () { + return ctx.resetTransform(); + }; + svgCtx.roundRect = svgCtx.rect; + app.canvas.ctx = svgCtx; + } + + getBlob(workflow) { + let svg = this.svgCtx.getSerializedSvg(true).replace("", `${SvgWorkflowImage.escapeXml(workflow)}`); + } + + return new Blob([svg], { type: "image/svg+xml" }); + } +} + +app.registerExtension({ + name: "pysssss.WorkflowImage", + init() { + // https://codepen.io/peterhry/pen/nbMaYg + function wrapText(context, text, x, y, maxWidth, lineHeight) { + var words = text.split(" "), + line = "", + i, + test, + metrics; + + for (i = 0; i < words.length; i++) { + test = words[i]; + metrics = context.measureText(test); + while (metrics.width > maxWidth) { + // Determine how much of the word will fit + test = test.substring(0, test.length - 1); + metrics = context.measureText(test); + } + if (words[i] != test) { + words.splice(i + 1, 0, words[i].substr(test.length)); + words[i] = test; + } + + test = line + words[i] + " "; + metrics = context.measureText(test); + + if (metrics.width > maxWidth && i > 0) { + context.fillText(line, x, y); + line = words[i] + " "; + y += lineHeight; + } else { + line = test; + } + } + + context.fillText(line, x, y); + } + + const stringWidget = ComfyWidgets.STRING; + // Override multiline string widgets to draw text using canvas while saving as svg + ComfyWidgets.STRING = function () { + const w = stringWidget.apply(this, arguments); + if (w.widget && w.widget.type === "customtext") { + const draw = w.widget.draw; + w.widget.draw = function (ctx) { + draw.apply(this, arguments); + if (this.inputEl.hidden) return; + + if (getDrawTextConfig) { + const config = getDrawTextConfig(ctx, this); + const t = ctx.getTransform(); + ctx.save(); + if (config.resetTransform) { + ctx.resetTransform(); + } + + const style = document.defaultView.getComputedStyle(this.inputEl, null); + const x = config.x; + const y = config.y; + const domWrapper = this.inputEl.closest(".dom-widget") ?? widget.inputEl; + let w = parseInt(domWrapper.style.width); + if (w === 0) { + w = this.node.size[0] - 20; + } + const h = parseInt(domWrapper.style.height); + ctx.fillStyle = style.getPropertyValue("background-color"); + ctx.fillRect(x, y, w, h); + + ctx.fillStyle = style.getPropertyValue("color"); + ctx.font = style.getPropertyValue("font"); + + const line = t.d * 12; + const split = this.inputEl.value.split("\n"); + let start = y; + for (const l of split) { + start += line; + wrapText(ctx, l, x + 4, start, w, line); + } + + ctx.restore(); + } + }; + } + return w; + }; + }, + setup() { + const script = document.createElement("script"); + script.onload = function () { + const formats = [SvgWorkflowImage, PngWorkflowImage]; + for (const f of formats) { + f.init?.call(); + WorkflowImage.accept += (WorkflowImage.accept ? "," : "") + f.accept; + } + + // Add canvas menu options + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + + options.push(null, { + content: "Workflow Image", + submenu: { + options: [ + { + content: "Import", + callback: () => { + WorkflowImage.import(); + }, + }, + { + content: "Export", + submenu: { + options: formats.flatMap((f) => [ + { + content: f.name.replace("WorkflowImage", "").toLocaleLowerCase(), + callback: () => { + new f().export(true); + }, + }, + { + content: f.name.replace("WorkflowImage", "").toLocaleLowerCase() + " (no embedded workflow)", + callback: () => { + new f().export(); + }, + }, + ]), + }, + }, + ], + }, + }); + return options; + }; + }; + + script.src = new URL(`assets/canvas2svg.js`, import.meta.url); + document.body.append(script); + }, +}); diff --git a/custom_nodes/comfyui-custom-scripts/web/js/workflows.js b/custom_nodes/comfyui-custom-scripts/web/js/workflows.js new file mode 100644 index 00000000..0225260a --- /dev/null +++ b/custom_nodes/comfyui-custom-scripts/web/js/workflows.js @@ -0,0 +1,343 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; +import { $el } from "../../../scripts/ui.js"; + +// Adds workflow management +// Original implementation by https://github.com/i-h4x +// Thanks for permission to reimplement as an extension + +const style = ` +#comfy-save-button, #comfy-load-button { + position: relative; + overflow: hidden; +} +.pysssss-workflow-arrow { + position: absolute; + top: 0; + bottom: 0; + right: 0; + font-size: 12px; + display: flex; + align-items: center; + width: 24px; + justify-content: center; + background: rgba(255,255,255,0.1); +} +.pysssss-workflow-arrow:after { + content: "β–Ό"; +} +.pysssss-workflow-arrow:hover { + filter: brightness(1.6); + background-color: var(--comfy-menu-bg); +} +.pysssss-workflow-load .litemenu-entry:not(.has_submenu):before, +.pysssss-workflow-load ~ .litecontextmenu .litemenu-entry:not(.has_submenu):before { + content: "πŸŽ›οΈ"; + padding-right: 5px; +} +.pysssss-workflow-load .litemenu-entry.has_submenu:before, +.pysssss-workflow-load ~ .litecontextmenu .litemenu-entry.has_submenu:before { + content: "πŸ“‚"; + padding-right: 5px; + position: relative; + top: -1px; +} +.pysssss-workflow-popup ~ .litecontextmenu { + transform: scale(1.3); +} +`; + +async function getWorkflows() { + const response = await api.fetchApi("/pysssss/workflows", { cache: "no-store" }); + return await response.json(); +} + +async function getWorkflow(name) { + const response = await api.fetchApi(`/pysssss/workflows/${encodeURIComponent(name)}`, { cache: "no-store" }); + return await response.json(); +} + +async function saveWorkflow(name, workflow, overwrite) { + try { + const response = await api.fetchApi("/pysssss/workflows", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ name, workflow, overwrite }), + }); + if (response.status === 201) { + return true; + } + if (response.status === 409) { + return false; + } + throw new Error(response.statusText); + } catch (error) { + console.error(error); + } +} + +class PysssssWorkflows { + async load() { + this.workflows = await getWorkflows(); + if(this.workflows.length) { + this.workflows.sort(); + } + this.loadMenu.style.display = this.workflows.length ? "flex" : "none"; + } + + getMenuOptions(callback) { + const menu = []; + const directories = new Map(); + for (const workflow of this.workflows || []) { + const path = workflow.split("/"); + let parent = menu; + let currentPath = ""; + for (let i = 0; i < path.length - 1; i++) { + currentPath += "/" + path[i]; + let newParent = directories.get(currentPath); + if (!newParent) { + newParent = { + title: path[i], + has_submenu: true, + submenu: { + options: [], + }, + }; + parent.push(newParent); + newParent = newParent.submenu.options; + directories.set(currentPath, newParent); + } + parent = newParent; + } + parent.push({ + title: path[path.length - 1], + callback: () => callback(workflow), + }); + } + return menu; + } + + constructor() { + function addWorkflowMenu(type, getOptions) { + return $el("div.pysssss-workflow-arrow", { + parent: document.getElementById(`comfy-${type}-button`), + onclick: (e) => { + e.preventDefault(); + e.stopPropagation(); + + LiteGraph.closeAllContextMenus(); + const menu = new LiteGraph.ContextMenu( + getOptions(), + { + event: e, + scale: 1.3, + }, + window + ); + menu.root.classList.add("pysssss-workflow-popup"); + menu.root.classList.add(`pysssss-workflow-${type}`); + }, + }); + } + + this.loadMenu = addWorkflowMenu("load", () => + this.getMenuOptions(async (workflow) => { + const json = await getWorkflow(workflow); + app.loadGraphData(json); + }) + ); + addWorkflowMenu("save", () => { + return [ + { + title: "Save as", + callback: () => { + let filename = prompt("Enter filename", this.workflowName || "workflow"); + if (filename) { + if (!filename.toLowerCase().endsWith(".json")) { + filename += ".json"; + } + + this.workflowName = filename; + + const json = JSON.stringify(app.graph.serialize(), null, 2); // convert the data to a JSON string + const blob = new Blob([json], { type: "application/json" }); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: filename, + style: { display: "none" }, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + } + }, + }, + { + title: "Save to workflows", + callback: async () => { + const name = prompt("Enter filename", this.workflowName || "workflow"); + if (name) { + this.workflowName = name; + + const data = app.graph.serialize(); + if (!(await saveWorkflow(name, data))) { + if (confirm("A workspace with this name already exists, do you want to overwrite it?")) { + await saveWorkflow(name, app.graph.serialize(), true); + } else { + return; + } + } + await this.load(); + } + }, + }, + ]; + }); + this.load(); + + const handleFile = app.handleFile; + const self = this; + app.handleFile = function (file) { + if (file?.name?.endsWith(".json")) { + self.workflowName = file.name; + } else { + self.workflowName = null; + } + return handleFile.apply(this, arguments); + }; + } +} + +const refreshComboInNodes = app.refreshComboInNodes; +let workflows; + +async function sendToWorkflow(img, workflow) { + const graph = !workflow ? app.graph.serialize() : await getWorkflow(workflow); + const nodes = graph.nodes.filter((n) => n.type === "LoadImage"); + let targetNode; + if (nodes.length === 0) { + alert("To send the image to another workflow, that workflow must have a LoadImage node."); + return; + } else if (nodes.length > 1) { + targetNode = nodes.find((n) => n.title?.toLowerCase().includes("input")); + if (!targetNode) { + targetNode = nodes[0]; + alert( + "The target workflow has multiple LoadImage nodes, include 'input' in the name of the one you want to use. The first one will be used here." + ); + } + } else { + targetNode = nodes[0]; + } + + const blob = await (await fetch(img.src)).blob(); + const name = + (workflow || "sendtoworkflow").replace(/\//g, "_") + + "-" + + +new Date() + + new URLSearchParams(img.src.split("?")[1]).get("filename"); + const body = new FormData(); + body.append("image", new File([blob], name)); + + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status === 200) { + await refreshComboInNodes.call(app); + targetNode.widgets_values[0] = name; + app.loadGraphData(graph); + app.graph.getNodeById(targetNode.id); + } else { + alert(resp.status + " - " + resp.statusText); + } +} + +app.registerExtension({ + name: "pysssss.Workflows", + init() { + $el("style", { + textContent: style, + parent: document.head, + }); + }, + + async refreshComboInNodes() { + workflows.load() + }, + + async setup() { + workflows = new PysssssWorkflows(); + + const comfyDefault = "[ComfyUI Default]"; + const defaultWorkflow = app.ui.settings.addSetting({ + id: "pysssss.Workflows.Default", + name: "🐍 Default Workflow", + defaultValue: comfyDefault, + type: "combo", + options: (value) => + [comfyDefault, ...workflows.workflows].map((m) => ({ + value: m, + text: m, + selected: m === value, + })), + }); + + document.getElementById("comfy-load-default-button").onclick = async function () { + if ( + localStorage["Comfy.Settings.Comfy.ConfirmClear"] === "false" || + confirm(`Load default workflow (${defaultWorkflow.value})?`) + ) { + if (defaultWorkflow.value === comfyDefault) { + app.loadGraphData(); + } else { + const json = await getWorkflow(defaultWorkflow.value); + app.loadGraphData(json); + } + } + }; + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = getExtraMenuOptions?.apply?.(this, arguments); + let img; + if (this.imageIndex != null) { + // An image is selected so select that + img = this.imgs[this.imageIndex]; + } else if (this.overIndex != null) { + // No image is selected but one is hovered + img = this.imgs[this.overIndex]; + } + + if (img) { + let pos = options.findIndex((o) => o.content === "Save Image"); + if (pos === -1) { + pos = 0; + } else { + pos++; + } + + options.splice(pos, 0, { + content: "Send to workflow", + has_submenu: true, + submenu: { + options: [ + { callback: () => sendToWorkflow(img), title: "[Current workflow]" }, + ...workflows.getMenuOptions(sendToWorkflow.bind(null, img)), + ], + }, + }); + } + + return r; + }; + }, +}); diff --git a/custom_nodes/comfyui-fal-api-flux/.gitignore b/custom_nodes/comfyui-fal-api-flux/.gitignore new file mode 100644 index 00000000..00f60d14 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/.gitignore @@ -0,0 +1,164 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; 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These nodes allow you to use the FLUX.1 models directly within your ComfyUI workflows. + +## Features + +- Text-to-image generation using fal.ai's FLUX.1 [dev] and FLUX.1 [pro] models +- Image-to-image generation using FLUX.1 [dev] model +- Support for LoRA models +- ControlNet and ControlNet Union support +- Customizable generation parameters (image size, inference steps, guidance scale) +- Multiple image generation in a single request +- Seed support for reproducible results +- Safety tolerance settings for FLUX.1 [pro] + +## Prerequisites + +- ComfyUI installed and set up +- Python 3.7+ +- PyTorch 2.0.1 or later +- A fal.ai API key with access to the FLUX.1 models + +## Installation + +There are two ways to install ComfyUI-Fal-API-Flux: + +### Method 1: Using ComfyUI Manager (Recommended) + +1. Install [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager) if you haven't already. +2. Open ComfyUI and navigate to the "Manager" tab. +3. Search for "ComfyUI-Fal-API-Flux" in the custom nodes section. +4. Click "Install" to automatically download and install the custom nodes. + +### Method 2: Manual Installation + +1. Clone this repository into your ComfyUI's `custom_nodes` directory: + ``` + cd /path/to/ComfyUI/custom_nodes + git clone https://github.com/your-username/ComfyUI-Fal-API-Flux.git + ``` +2. Navigate to the cloned directory: + ``` + cd ComfyUI-Fal-API-Flux + ``` +3. Install the required dependencies: + ``` + pip install -r requirements.txt + ``` + +After installation using either method: + +4. Configure your API key (see Configuration section below) +5. Restart ComfyUI if it's already running + +## Configuration + +To use these custom nodes, you need to set up your fal.ai API key: + +1. Create a `config.ini` file in the root directory of the project. +2. Add the following content to `config.ini`: + ```ini + [falai] + api_key = your_api_key_here + ``` +3. Replace `your_api_key_here` with your actual fal.ai API key. +4. Save the file. + +Keep your `config.ini` file secure and do not share it publicly. + +## Usage + +After installation, you'll find the following new nodes in the ComfyUI interface: + +1. "Fal API Flux Dev": The main node for text-to-image generation using FLUX.1 [dev]. +2. "Fal API Flux Dev Image-to-Image": A node for image-to-image generation using FLUX.1 [dev]. +3. "Fal API Flux Dev with LoRA": A node for text-to-image generation using FLUX.1 [dev] with LoRA support. +4. "Fal API Flux Dev with LoRA Image-to-Image": A node for image-to-image generation using FLUX.1 [dev] with LoRA support. +5. "Fal API Flux with LoRA and ControlNet": A node for text-to-image generation using FLUX.1 [dev] with LoRA and ControlNet support. +6. "Fal API Flux with LoRA and ControlNet Image-to-Image": A node for image-to-image generation using FLUX.1 [dev] with LoRA and ControlNet support. +7. "Fal API Flux Pro": A node for text-to-image generation using FLUX.1 [pro]. +8. "Fal API Flux Pro V1.1": An updated node for text-to-image generation using FLUX.1 [pro] V1.1. +9. "Fal API Flux LoRA Config": A node for configuring LoRA models. +10. "Fal API Flux ControlNet Config": A node for configuring ControlNet. +11. "Fal API Flux ControlNet Union Config": A node for configuring ControlNet Union. + +### Basic Usage + +1. Add one of the Fal API Flux nodes to your workflow. +2. Configure the node parameters (prompt, image size, etc.). +3. Connect the output to a "Preview Image" or "Save Image" node to see the results. + +### Using LoRA + +1. Add a "Fal API Flux LoRA Config" node to your workflow. +2. Configure the LoRA URL and scale. +3. Connect the output of the LoRA Config node to the `lora` input of a compatible Fal API Flux node. + +### Using ControlNet + +1. Add a "Fal API Flux ControlNet Config" or "Fal API Flux ControlNet Union Config" node to your workflow. +2. Configure the ControlNet parameters. +3. Connect the output to the `controlnet` or `controlnet_union` input of a compatible Fal API Flux node. + +### Image-to-Image Generation + +1. Use a ComfyUI image loader node to load an input image. +2. Connect the loaded image to an image-to-image node (e.g., "Fal API Flux Dev Image-to-Image"). +3. Configure the node parameters, including the strength of the transformation. + +## Example Workflows + +Example workflows are provided in the `examples` folder of this repository. To use them: + +1. Locate the desired workflow image in the `examples` folder. +2. Open ComfyUI in your web browser. +3. Drag and drop the workflow image directly onto the ComfyUI canvas. + +These example workflows provide starting points for using the Fal API Flux nodes in your own projects. + +## Troubleshooting + +If you encounter issues: + +1. Ensure you have access to the FLUX.1 models on fal.ai. +2. Check the ComfyUI console for detailed error messages and logs. +3. Verify that your API key is correctly set in the `config.ini` file. +4. Make sure your LoRA URL is correct and compatible with FLUX.1 [dev]. +5. For persistent issues, enable debug logging and check the logs for API responses and image processing details. + +## Contributing + +Contributions to improve the nodes or extend their functionality are welcome! Please feel free to submit issues or pull requests. + +## License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +## Acknowledgements + +- [fal.ai](https://fal.ai) for providing the FLUX.1 APIs +- [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for the extensible UI framework + +## Disclaimer + +This project is not officially affiliated with or endorsed by fal.ai or ComfyUI. Use it at your own risk and be sure to comply with fal.ai's terms of service when using their API. \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/__init__.py b/custom_nodes/comfyui-fal-api-flux/__init__.py new file mode 100644 index 00000000..64c633b8 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/__init__.py @@ -0,0 +1,86 @@ +from .modules.fal_api_flux_dev_node import FalAPIFluxDevNode +from .modules.fal_api_flux_dev_image_to_image_node import FalAPIFluxDevImageToImageNode +from .modules.fal_api_flux_dev_with_lora_node import FalAPIFluxDevWithLoraNode +from .modules.fal_api_flux_dev_with_lora_image_to_image_node import FalAPIFluxDevWithLoraImageToImageNode +from .modules.fal_api_flux_dev_with_lora_inpaint_node import FalAPIFluxDevWithLoraInpaintNode +from .modules.fal_api_flux_dev_with_lora_and_controlnet_node import FalAPIFluxDevWithLoraAndControlNetNode +from .modules.fal_api_flux_dev_with_lora_and_controlnet_image_to_image_node import FalAPIFluxDevWithLoraAndControlNetImageToImageNode +from .modules.fal_api_flux_dev_with_lora_and_controlnet_inpaint_node import FalAPIFluxDevWithLoraAndControlNetInpaintNode +from .modules.fal_api_flux_pro_node import FalAPIFluxProNode +from .modules.fal_api_flux_pro_v11_node import FalAPIFluxProV11Node +from .modules.fal_api_flux_pro_v11_ultra_node import FalAPIFluxProV11UltraNode +from .modules.fal_api_flux_lora_config_node import FalAPIFluxLoraConfigNode +from .modules.fal_api_flux_controlnet_config_node import FalAPIFluxControlNetConfigNode +from .modules.fal_api_flux_controlnet_union_config_node import FalAPIFluxControlNetUnionConfigNode +from .modules.fal_api_flux_pro_canny_node import FalAPIFluxProCannyNode +from .modules.fal_api_flux_pro_depth_node import FalAPIFluxProDepthNode +from .modules.fal_api_flux_pro_fill_node import FalAPIFluxProFillNode +from .modules.fal_api_flux_pro_redux_node import FalAPIFluxProReduxNode +from .modules.fal_api_flux_dev_canny_with_lora_node import FalAPIFluxDevCannyWithLoraNode + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevNode": FalAPIFluxDevNode, + "FalAPIFluxDevImageToImageNode": FalAPIFluxDevImageToImageNode, + "FalAPIFluxDevWithLoraNode": FalAPIFluxDevWithLoraNode, + "FalAPIFluxDevWithLoraImageToImageNode": FalAPIFluxDevWithLoraImageToImageNode, + "FalAPIFluxDevWithLoraInpaintNode": FalAPIFluxDevWithLoraInpaintNode, + "FalAPIFluxDevWithLoraAndControlNetNode": FalAPIFluxDevWithLoraAndControlNetNode, + "FalAPIFluxDevWithLoraAndControlNetImageToImageNode": FalAPIFluxDevWithLoraAndControlNetImageToImageNode, + "FalAPIFluxDevWithLoraAndControlNetInpaintNode": FalAPIFluxDevWithLoraAndControlNetInpaintNode, + "FalAPIFluxProNode": FalAPIFluxProNode, + "FalAPIFluxProV11Node": FalAPIFluxProV11Node, + "FalAPIFluxProV11UltraNode": FalAPIFluxProV11UltraNode, + "FalAPIFluxLoraConfigNode": FalAPIFluxLoraConfigNode, + "FalAPIFluxControlNetConfigNode": FalAPIFluxControlNetConfigNode, + "FalAPIFluxControlNetUnionConfigNode": FalAPIFluxControlNetUnionConfigNode, + "FalAPIFluxProCannyNode": FalAPIFluxProCannyNode, + "FalAPIFluxProDepthNode": FalAPIFluxProDepthNode, + "FalAPIFluxProFillNode": FalAPIFluxProFillNode, + "FalAPIFluxProReduxNode": FalAPIFluxProReduxNode, + "FalAPIFluxDevCannyWithLoraNode": FalAPIFluxDevCannyWithLoraNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevNode": "Fal API Flux Dev", + "FalAPIFluxDevImageToImageNode": "Fal API Flux Dev Image-to-Image", + "FalAPIFluxDevWithLoraNode": "Fal API Flux Dev with LoRA", + "FalAPIFluxDevWithLoraImageToImageNode": "Fal API Flux Dev with LoRA Image-to-Image", + "FalAPIFluxDevWithLoraInpaintNode": "Fal API Flux Dev with LoRA Inpaint", + "FalAPIFluxDevWithLoraAndControlNetNode": "Fal API Flux with LoRA and ControlNet", + "FalAPIFluxDevWithLoraAndControlNetImageToImageNode": "Fal API Flux with LoRA and ControlNet Image-to-Image", + "FalAPIFluxDevWithLoraAndControlNetInpaintNode": "Fal API Flux with LoRA and ControlNet Inpaint", + "FalAPIFluxProNode": "Fal API Flux Pro", + "FalAPIFluxProV11Node": "Fal API Flux Pro V1.1", + "FalAPIFluxProV11UltraNode": "Fal API Flux Pro v1.1 Ultra", + "FalAPIFluxLoraConfigNode": "Fal API Flux LoRA Config", + "FalAPIFluxControlNetConfigNode": "Fal API Flux ControlNet Config", + "FalAPIFluxControlNetUnionConfigNode": "Fal API Flux ControlNet Union Config", + "FalAPIFluxDevImageToImageNode": "Fal API Flux Dev Image-to-Image", + "FalAPIFluxProCannyNode": "Fal API Flux Pro Canny", + "FalAPIFluxProDepthNode": "Fal API Flux Pro Depth", + "FalAPIFluxProFillNode": "Fal API Flux Pro Fill", + "FalAPIFluxProReduxNode": "Fal API Flux Pro Redux", + "FalAPIFluxDevCannyWithLoraNode": "Fal API Flux Dev Canny With LoRA" +} + +__all__ = [ + 'FalAPIFluxDevNode', + 'FalAPIFluxDevImageToImageNode', + 'FalAPIFluxNodeWithControlNet', + 'FalAPIFluxDevWithLoraImageToImageNode', + 'FalAPIFluxDevWithLoraInpaintNode', + 'FalAPIFluxDevWithLoraAndControlNetNode', + 'FalAPIFluxDevWithLoraAndControlNetImageToImageNode', + 'FalAPIFluxDevWithLoraAndControlNetInpaintNode', + 'FalAPIFluxProNode', + 'FalAPIFluxProV11Node', + "FalAPIFluxProV11UltraNode", + 'FalAPIFluxLoraConfigNode', + 'FalAPIFluxControlNetConfigNode', + 'FalAPIFluxControlNetUnionConfigNode', + 'FalAPIFluxProCannyNode', + 'FalAPIFluxProDepthNode', + 'FalAPIFluxProFillNode', + 'FalAPIFluxProReduxNode', + 'FalAPIFluxDevCannyWithLoraNode' +] \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/config.ini b/custom_nodes/comfyui-fal-api-flux/config.ini new file mode 100644 index 00000000..4009b878 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/config.ini @@ -0,0 +1,2 @@ +[falai] +api_key = your_api_key_here \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/examples/workflow_fal_api_flux_dev_image_to_image_node.png b/custom_nodes/comfyui-fal-api-flux/examples/workflow_fal_api_flux_dev_image_to_image_node.png new file mode 100644 index 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b/custom_nodes/comfyui-fal-api-flux/examples/workflow_fal_api_flux_dev_with_lora_node.png differ diff --git a/custom_nodes/comfyui-fal-api-flux/examples/workflow_fal_api_flux_pro_node.png b/custom_nodes/comfyui-fal-api-flux/examples/workflow_fal_api_flux_pro_node.png new file mode 100644 index 00000000..fc1f9c85 Binary files /dev/null and b/custom_nodes/comfyui-fal-api-flux/examples/workflow_fal_api_flux_pro_node.png differ diff --git a/custom_nodes/comfyui-fal-api-flux/modules/base_fal_api_flux_node.py b/custom_nodes/comfyui-fal-api-flux/modules/base_fal_api_flux_node.py new file mode 100644 index 00000000..0ebc9cca --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/base_fal_api_flux_node.py @@ -0,0 +1,246 @@ +import os +import fal_client +import folder_paths +import configparser +import base64 +import io +from PIL import Image +import logging +import json +import requests +import numpy as np +import torch + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger(__name__) + +class BaseFalAPIFluxNode: + def __init__(self): + self.api_key = self.get_api_key() + os.environ['FAL_KEY'] = self.api_key + self.api_endpoint = None + + def get_api_key(self): + config = configparser.ConfigParser() + config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'config.ini') + if os.path.exists(config_path): + config.read(config_path) + return config.get('falai', 'api_key', fallback=None) + return None + + def set_api_endpoint(self, endpoint): + self.api_endpoint = endpoint + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "prompt": ("STRING", {"multiline": True}), + "width": ("INT", {"default": 1024, "step": 8}), + "height": ("INT", {"default": 1024, "step": 8}), + "num_inference_steps": ("INT", {"default": 28, "min": 1, "max": 100}), + "guidance_scale": ("FLOAT", {"default": 3.5, "min": 0.1, "max": 40.0}), + "num_images": ("INT", {"default": 1, "min": 1, "max": 4}), + "enable_safety_checker": ("BOOLEAN", {"default": True}), + }, + "optional": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + CATEGORY = "image generation" + + def prepare_arguments(self, prompt, width, height, num_inference_steps, guidance_scale, num_images, enable_safety_checker, seed=None, **kwargs): + if not self.api_key: + raise ValueError("API key is not set. Please check your config.ini file.") + + arguments = { + "prompt": prompt, + "num_inference_steps": num_inference_steps, + "guidance_scale": guidance_scale, + "num_images": num_images, + "enable_safety_checker": enable_safety_checker + } + + # Handle custom image size + if width is None or height is None: + raise ValueError("Width and height must be provided when using custom image size") + arguments["image_size"] = { + "width": width, + "height": height + } + + if seed is not None and seed != 0: + arguments["seed"] = seed + + return arguments + + def call_api(self, arguments): + logger.debug(f"Full API request payload: {json.dumps(arguments, indent=2)}") + + if not self.api_endpoint: + raise ValueError("API endpoint is not set. Please set it using set_api_endpoint() method.") + + try: + handler = fal_client.submit( + self.api_endpoint, + arguments=arguments, + ) + result = handler.get() + logger.debug(f"API response: {json.dumps(result, indent=2)}") + return result + except Exception as e: + logger.error(f"API error details: {str(e)}") + if hasattr(e, 'response'): + logger.error(f"API error response: {e.response.text}") + raise RuntimeError(f"An error occurred when calling the fal.ai API: {str(e)}") from e + + def process_images(self, result): + if "images" not in result or not result["images"]: + logger.error("No images were generated by the API.") + raise RuntimeError("No images were generated by the API.") + + output_images = [] + for index, img_info in enumerate(result["images"]): + try: + logger.debug(f"Processing image {index}: {json.dumps(img_info, indent=2)}") + if not isinstance(img_info, dict) or "url" not in img_info or not img_info["url"]: + logger.error(f"Invalid image info for image {index}") + continue + + img_url = img_info["url"] + logger.debug(f"Image URL: {img_url[:100]}...") # Log the first 100 characters of the URL + + if img_url.startswith("data:image"): + # Handle Base64 encoded image + try: + _, img_data = img_url.split(",", 1) + img_data = base64.b64decode(img_data) + except ValueError: + logger.error(f"Failed to split image URL for image {index}") + continue + else: + # Handle regular URL + try: + response = requests.get(img_url) + response.raise_for_status() + img_data = response.content + except requests.RequestException as e: + logger.error(f"Failed to download image from URL for image {index}: {str(e)}") + continue + + # Log the first few bytes of the image data + logger.debug(f"First 20 bytes of image data: {img_data[:20]}") + + # Try to interpret the data as an image + try: + img = Image.open(io.BytesIO(img_data)) + logger.debug(f"Opened image with size: {img.size} and mode: {img.mode}") + except Exception as e: + logger.error(f"Failed to open image data: {str(e)}") + # If opening as an image fails, try to interpret it as raw pixel data + img_np = np.frombuffer(img_data, dtype=np.uint8) + logger.debug(f"Interpreted as raw pixel data with shape: {img_np.shape}") + + # If the shape is (1024,), reshape it to a more sensible image size + if img_np.shape == (1024,): + img_np = img_np.reshape(32, 32) # Reshape to 32x32 image + elif img_np.shape == (1, 1, 1024): + img_np = img_np.reshape(32, 32) + + # Normalize the data to 0-255 range + img_np = ((img_np - img_np.min()) / (img_np.max() - img_np.min()) * 255).astype(np.uint8) + + img = Image.fromarray(img_np, 'L') # Create grayscale image + img = img.convert('RGB') # Convert to RGB + + # Ensure image is in RGB mode + if img.mode != 'RGB': + img = img.convert('RGB') + + # Convert PIL Image to NumPy array + img_np = np.array(img).astype(np.float32) / 255.0 + + # Create tensor with batch dimension (1, H, W, C) + img_tensor = torch.from_numpy(img_np) + img_tensor = img_tensor.unsqueeze(0) # (1, H, W, C) + + output_images.append(img_tensor) + except Exception as e: + logger.error(f"Failed to process image {index}: {str(e)}") + + if not output_images: + logger.error("Failed to process any of the generated images.") + raise RuntimeError("Failed to process any of the generated images.") + + # Stack all images into a single batch tensor + if output_images: + output_tensor = torch.cat(output_images, dim=0) + logger.debug(f"Returning batched tensor with shape: {output_tensor.shape}") + return [output_tensor] + else: + logger.error("No images were successfully processed") + raise RuntimeError("No images were successfully processed") + + def upload_image(self, image): + try: + # Convert PyTorch tensor to numpy array + if isinstance(image, torch.Tensor): + image = image.cpu().numpy() + + # Handle different shapes of numpy arrays + if isinstance(image, np.ndarray): + if image.ndim == 4 and image.shape[0] == 1: # (1, H, W, 3) or (1, H, W, 1) + image = image.squeeze(0) + + if image.ndim == 3: + if image.shape[2] == 3: # (H, W, 3) RGB image + pass + elif image.shape[2] == 1: # (H, W, 1) grayscale + image = np.repeat(image, 3, axis=2) + elif image.shape[0] == 3: # (3, H, W) RGB + image = np.transpose(image, (1, 2, 0)) + elif image.shape[0] == 1: # (1, H, W) grayscale + image = np.repeat(image.squeeze(0)[..., np.newaxis], 3, axis=2) + elif image.shape == (1, 1, 1536): # Special case for (1, 1, 1536) shape + image = image.reshape(32, 48) + image = np.repeat(image[..., np.newaxis], 3, axis=2) + else: + raise ValueError(f"Unsupported image shape: {image.shape}") + + # Normalize to 0-255 range if not already + if image.dtype != np.uint8: + image = (image - image.min()) / (image.max() - image.min()) * 255 + image = image.astype(np.uint8) + + image = Image.fromarray(image) + + # Ensure image is in RGB mode + if image.mode != 'RGB': + image = image.convert('RGB') + + # Resize image if it's too large (optional, adjust max_size as needed) + max_size = 1024 # Example max size + if max(image.size) > max_size: + image.thumbnail((max_size, max_size), Image.LANCZOS) + + # Convert PIL Image to bytes + buffered = io.BytesIO() + image.save(buffered, format="PNG") + img_byte = buffered.getvalue() + + # Upload the image using fal_client + url = fal_client.upload(img_byte, "image/png") + logger.info(f"Image uploaded successfully. URL: {url}") + return url + except Exception as e: + logger.error(f"Failed to process or upload image: {str(e)}") + raise + + def generate(self, **kwargs): + arguments = self.prepare_arguments(**kwargs) + result = self.call_api(arguments) + output_images = self.process_images(result) + return tuple(output_images) diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_controlnet_config_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_controlnet_config_node.py new file mode 100644 index 00000000..dacff460 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_controlnet_config_node.py @@ -0,0 +1,49 @@ +class FalAPIFluxControlNetConfigNode: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "path": ("STRING", { + "multiline": False, + "default": "lllyasviel/sd-controlnet-canny" + }), + "control_image": ("IMAGE",), + "conditioning_scale": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 2.0, + "step": 0.1 + }), + }, + "optional": { + "config_url": ("STRING", { + "multiline": False, + "default": "" + }), + "variant": ("STRING", { + "multiline": False, + "default": "" + }), + } + } + + RETURN_TYPES = ("CONTROLNET_CONFIG",) + FUNCTION = "configure_controlnet" + CATEGORY = "image generation" + + def configure_controlnet(self, path, control_image, conditioning_scale, config_url="", variant=""): + return ({ + "path": path, + "control_image": control_image, + "conditioning_scale": conditioning_scale, + "config_url": config_url if config_url else None, + "variant": variant if variant else None + },) + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxControlNetConfigNode": FalAPIFluxControlNetConfigNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxControlNetConfigNode": "Fal API Flux ControlNet Config" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_controlnet_union_config_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_controlnet_union_config_node.py new file mode 100644 index 00000000..4c14ed0d --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_controlnet_union_config_node.py @@ -0,0 +1,53 @@ +class FalAPIFluxControlNetUnionConfigNode: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "path": ("STRING", { + "multiline": False, + "default": "https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union/resolve/main/diffusion_pytorch_model.safetensors" + }), + "control_image": ("IMAGE",), + "control_mode": (["canny", "tile", "depth", "blur", "pose", "gray", "lq"],), + "conditioning_scale": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 2.0, + "step": 0.1 + }), + }, + "optional": { + "config_url": ("STRING", { + "multiline": False, + "default": "" + }), + "variant": ("STRING", { + "multiline": False, + "default": "" + }), + } + } + + RETURN_TYPES = ("CONTROLNET_UNION_CONFIG",) + FUNCTION = "configure_controlnet_union" + CATEGORY = "image generation" + + def configure_controlnet_union(self, path, control_image, control_mode, conditioning_scale, config_url="", variant=""): + return ({ + "path": path, + "controls": [{ + "control_image": control_image, + "control_mode": control_mode, + "conditioning_scale": conditioning_scale + }], + "config_url": config_url if config_url else None, + "variant": variant if variant else None + },) + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxControlNetUnionConfigNode": FalAPIFluxControlNetUnionConfigNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxControlNetUnionConfigNode": "Fal API Flux ControlNet Union Config" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_canny_with_lora_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_canny_with_lora_node.py new file mode 100644 index 00000000..77e23f73 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_canny_with_lora_node.py @@ -0,0 +1,65 @@ +import logging + +from .base_fal_api_flux_node import BaseFalAPIFluxNode + +logger = logging.getLogger(__name__) + + +class FalAPIFluxDevCannyWithLoraNode(BaseFalAPIFluxNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-lora-canny") + + def set_api_endpoint(self, endpoint): + super().set_api_endpoint(endpoint) + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + # Add control image input + input_types["required"].update({ + "control_image": ("IMAGE",), # Accept input from another node + }) + input_types["optional"].update({ + "lora_1": ("LORA_CONFIG",), + "lora_2": ("LORA_CONFIG",), + "lora_3": ("LORA_CONFIG",), + "lora_4": ("LORA_CONFIG",), + "lora_5": ("LORA_CONFIG",), + }) + + return input_types + + def prepare_arguments(self, control_image, lora_1=None, lora_2=None, lora_3=None, lora_4=None, lora_5=None, + **kwargs): + # Get base arguments from parent class + arguments = super().prepare_arguments(**kwargs) + + # Upload the control image and get its URL + control_image_url = self.upload_image(control_image) + logger.info(f"Uploaded control image. URL: {control_image_url}") + + # Update arguments with Canny-specific parameters + arguments.update({ + "image_url": control_image_url + }) + + # Collect all provided LoRA configurations + loras = [] + for lora in [lora_1, lora_2, lora_3, lora_4, lora_5]: + if lora is not None: + loras.append(lora) + + if loras: + arguments["loras"] = loras + + return arguments + + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevCannyWithLoraNode": FalAPIFluxDevCannyWithLoraNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevCannyWithLoraNode": "Fal API Flux Dev Canny With LoRA" +} diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_image_to_image_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_image_to_image_node.py new file mode 100644 index 00000000..86a8ba98 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_image_to_image_node.py @@ -0,0 +1,42 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +import fal_client +import logging +import os + +logger = logging.getLogger(__name__) + +class FalAPIFluxDevImageToImageNode(BaseFalAPIFluxNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux/dev/image-to-image") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["required"].update({ + "image": ("IMAGE",), # This makes it accept input from another node + "strength": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0}), + }) + return input_types + + def prepare_arguments(self, image, strength, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Upload the image and get the URL + image_url = self.upload_image(image) + print(f"Uploaded image to {image_url}") + + arguments.update({ + "image_url": image_url, + "strength": strength, + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevImageToImageNode": FalAPIFluxDevImageToImageNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevImageToImageNode": "Fal API Flux Dev Image-to-Image" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_node.py new file mode 100644 index 00000000..fe322b52 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_node.py @@ -0,0 +1,18 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode + +class FalAPIFluxDevNode(BaseFalAPIFluxNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux/dev") + + @classmethod + def INPUT_TYPES(cls): + return super().INPUT_TYPES() + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevNode": FalAPIFluxDevNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevNode": "Fal API Flux Dev" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_image_to_image_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_image_to_image_node.py new file mode 100644 index 00000000..2beaea77 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_image_to_image_node.py @@ -0,0 +1,50 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_dev_with_lora_and_controlnet_node import FalAPIFluxDevWithLoraAndControlNetNode +from PIL import Image +import torch +import io +import base64 +import fal_client +import logging +import numpy as np + +logger = logging.getLogger(__name__) + +class FalAPIFluxDevWithLoraAndControlNetImageToImageNode(FalAPIFluxDevWithLoraAndControlNetNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-general/image-to-image") + + def set_api_endpoint(self, endpoint): + super().set_api_endpoint(endpoint) + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["required"].update({ + "image": ("IMAGE",), # This makes it accept input from another node + "strength": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0}), + }) + return input_types + + def prepare_arguments(self, image, strength, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Upload the image and get the URL + image_url = self.upload_image(image) + print(f"Uploaded image to {image_url}") + + arguments.update({ + "image_url": image_url, + "strength": strength, + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevWithLoraAndControlNetImageToImageNode": FalAPIFluxDevWithLoraAndControlNetImageToImageNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevWithLoraAndControlNetImageToImageNode": "Fal API Flux with LoRA and ControlNet Image-to-Image" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_inpaint_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_inpaint_node.py new file mode 100644 index 00000000..78cb2381 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_inpaint_node.py @@ -0,0 +1,48 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_dev_with_lora_and_controlnet_image_to_image_node import FalAPIFluxDevWithLoraAndControlNetImageToImageNode +from PIL import Image +import torch +import io +import base64 +import fal_client +import logging +import numpy as np + +logger = logging.getLogger(__name__) + +class FalAPIFluxDevWithLoraAndControlNetInpaintNode(FalAPIFluxDevWithLoraAndControlNetImageToImageNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-general/inpainting") + + def set_api_endpoint(self, endpoint): + super().set_api_endpoint(endpoint) + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["required"].update({ + "mask_image": ("IMAGE",), # This makes it accept input from another node + }) + return input_types + + def prepare_arguments(self, mask_image, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Upload the image and get the URL + mask_url = self.upload_image(mask_image) + print(f"Uploaded image to {mask_url}") + + arguments.update({ + "mask_url": mask_url, + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevWithLoraAndControlNetInpaintNode": FalAPIFluxDevWithLoraAndControlNetInpaintNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevWithLoraAndControlNetInpaintNode": "Fal API Flux with LoRA and ControlNet Inpaint" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_node.py new file mode 100644 index 00000000..437b11f8 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_and_controlnet_node.py @@ -0,0 +1,80 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from PIL import Image +import torch +import io +import base64 +import fal_client +import logging +import numpy as np + +logger = logging.getLogger(__name__) + +class FalAPIFluxDevWithLoraAndControlNetNode(BaseFalAPIFluxNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-general") + + def set_api_endpoint(self, endpoint): + super().set_api_endpoint(endpoint) + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["optional"].update({ + "lora_1": ("LORA_CONFIG",), + "lora_2": ("LORA_CONFIG",), + "lora_3": ("LORA_CONFIG",), + "lora_4": ("LORA_CONFIG",), + "lora_5": ("LORA_CONFIG",), + "controlnet": ("CONTROLNET_CONFIG",), + "controlnet_union": ("CONTROLNET_UNION_CONFIG",), + }) + return input_types + + def prepare_arguments(self, lora_1=None, lora_2=None, lora_3=None, lora_4=None, lora_5=None, + controlnet=None, controlnet_union=None, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Collect all provided LoRA configurations + loras = [] + for lora in [lora_1, lora_2, lora_3, lora_4, lora_5]: + if lora is not None: + loras.append(lora) + + if loras: + arguments["loras"] = loras + + if controlnet: + arguments["controlnets"] = [{ + "path": controlnet["path"], + "control_image_url": self.upload_image(controlnet["control_image"]), + "conditioning_scale": controlnet["conditioning_scale"] + }] + if controlnet["config_url"]: + arguments["controlnets"][0]["config_url"] = controlnet["config_url"] + if controlnet["variant"]: + arguments["controlnets"][0]["variant"] = controlnet["variant"] + + if controlnet_union: + arguments["controlnet_unions"] = [{ + "path": controlnet_union["path"], + "controls": [{ + "control_image_url": self.upload_image(control["control_image"]), + "control_mode": control["control_mode"], + "conditioning_scale": control["conditioning_scale"] + } for control in controlnet_union["controls"]] + }] + if controlnet_union["config_url"]: + arguments["controlnet_unions"][0]["config_url"] = controlnet_union["config_url"] + if controlnet_union["variant"]: + arguments["controlnet_unions"][0]["variant"] = controlnet_union["variant"] + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevWithLoraAndControlNetNode": FalAPIFluxDevWithLoraAndControlNetNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevWithLoraAndControlNetNode": "Fal API Flux with LoRA and ControlNet" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_image_to_image_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_image_to_image_node.py new file mode 100644 index 00000000..a41b7768 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_image_to_image_node.py @@ -0,0 +1,46 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_dev_with_lora_node import FalAPIFluxDevWithLoraNode +import fal_client +import logging +import os + +logger = logging.getLogger(__name__) + +class FalAPIFluxDevWithLoraImageToImageNode(FalAPIFluxDevWithLoraNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-lora/image-to-image") + + def set_api_endpoint(self, endpoint): + super().set_api_endpoint(endpoint) + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["required"].update({ + "image": ("IMAGE",), # This makes it accept input from another node + "strength": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0}), + }) + return input_types + + def prepare_arguments(self, image, strength, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Upload the image and get the URL + image_url = self.upload_image(image) + print(f"Uploaded image to {image_url}") + + arguments.update({ + "image_url": image_url, + "strength": strength, + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevWithLoraImageToImageNode": FalAPIFluxDevWithLoraImageToImageNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevWithLoraImageToImageNode": "Fal API Flux Dev with LoRA Image-to-Image" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_inpaint_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_inpaint_node.py new file mode 100644 index 00000000..527581fa --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_inpaint_node.py @@ -0,0 +1,41 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_dev_with_lora_image_to_image_node import FalAPIFluxDevWithLoraImageToImageNode +import fal_client +import logging +import os + +logger = logging.getLogger(__name__) + +class FalAPIFluxDevWithLoraInpaintNode(FalAPIFluxDevWithLoraImageToImageNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-lora/inpainting") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["required"].update({ + "mask_image": ("IMAGE",), # This makes it accept input from another node + }) + return input_types + + def prepare_arguments(self, mask_image, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Upload the image and get the URL + mask_url = self.upload_image(mask_image) + print(f"Uploaded image to {mask_url}") + + arguments.update({ + "mask_url": mask_url, + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevWithLoraInpaintNode": FalAPIFluxDevWithLoraInpaintNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevWithLoraInpaintNode": "Fal API Flux Dev with LoRA Inpaint" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_node.py new file mode 100644 index 00000000..6795ca81 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_dev_with_lora_node.py @@ -0,0 +1,43 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode + +class FalAPIFluxDevWithLoraNode(BaseFalAPIFluxNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-lora") + + def set_api_endpoint(self, endpoint): + super().set_api_endpoint(endpoint) + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["optional"].update({ + "lora_1": ("LORA_CONFIG",), + "lora_2": ("LORA_CONFIG",), + "lora_3": ("LORA_CONFIG",), + "lora_4": ("LORA_CONFIG",), + "lora_5": ("LORA_CONFIG",), + }) + return input_types + + def prepare_arguments(self, lora_1=None, lora_2=None, lora_3=None, lora_4=None, lora_5=None, **kwargs): + arguments = super().prepare_arguments(**kwargs) + + # Collect all provided LoRA configurations + loras = [] + for lora in [lora_1, lora_2, lora_3, lora_4, lora_5]: + if lora is not None: + loras.append(lora) + + if loras: + arguments["loras"] = loras + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxDevWithLoraNode": FalAPIFluxDevWithLoraNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxDevWithLoraNode": "Fal API Flux Dev With LoRA" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_lora_config_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_lora_config_node.py new file mode 100644 index 00000000..523a27f4 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_lora_config_node.py @@ -0,0 +1,35 @@ +class FalAPIFluxLoraConfigNode: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "lora_url": ("STRING", { + "multiline": False, + "default": "https://example.com/path/to/lora.safetensors" + }), + "scale": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 2.0, + "step": 0.1 + }), + } + } + + RETURN_TYPES = ("LORA_CONFIG",) + FUNCTION = "configure_lora" + CATEGORY = "image generation" + + def configure_lora(self, lora_url, scale): + if not lora_url.startswith(('http://', 'https://')): + raise ValueError("Invalid LoRA URL. Please enter a valid HTTP or HTTPS URL.") + + return ({"path": lora_url, "scale": float(scale)},) + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxLoraConfigNode": FalAPIFluxLoraConfigNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxLoraConfigNode": "Fal API Flux LoRA Config" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_canny_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_canny_node.py new file mode 100644 index 00000000..2a5dd9f4 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_canny_node.py @@ -0,0 +1,44 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_pro_node import FalAPIFluxProNode +import logging +import torch + +logger = logging.getLogger(__name__) + +class FalAPIFluxProCannyNode(FalAPIFluxProNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/v1/canny") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + # Add control image input + input_types["required"].update({ + "control_image": ("IMAGE",), # Accept input from another node + }) + + return input_types + + def prepare_arguments(self, control_image, **kwargs): + # Get base arguments from parent class + arguments = super().prepare_arguments(**kwargs) + + # Upload the control image and get its URL + control_image_url = self.upload_image(control_image) + logger.info(f"Uploaded control image. URL: {control_image_url}") + + # Update arguments with Canny-specific parameters + arguments.update({ + "control_image_url": control_image_url + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProCannyNode": FalAPIFluxProCannyNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProCannyNode": "Fal API Flux Pro Canny" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_depth_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_depth_node.py new file mode 100644 index 00000000..1de4c98a --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_depth_node.py @@ -0,0 +1,17 @@ +from .fal_api_flux_pro_canny_node import FalAPIFluxProCannyNode +import logging + +logger = logging.getLogger(__name__) + +class FalAPIFluxProDepthNode(FalAPIFluxProCannyNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/v1/depth") + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProDepthNode": FalAPIFluxProDepthNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProDepthNode": "Fal API Flux Pro Depth" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_fill_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_fill_node.py new file mode 100644 index 00000000..9c768e74 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_fill_node.py @@ -0,0 +1,47 @@ +from .fal_api_flux_pro_node import FalAPIFluxProNode +import logging +import torch + +logger = logging.getLogger(__name__) + +class FalAPIFluxProFillNode(FalAPIFluxProNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/v1/fill") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + # Add control image input + input_types["required"].update({ + "image": ("IMAGE",), + "mask_image": ("IMAGE",), + }) + + return input_types + + def prepare_arguments(self, image, mask_image, **kwargs): + # Get base arguments from parent class + arguments = super().prepare_arguments(**kwargs) + + # Upload the image and mask and get its URL + image_url = self.upload_image(image) + mask_image_url = self.upload_image(mask_image) + logger.info(f"Uploaded target image. URL: {image_url}") + logger.info(f"Uploaded mask image. URL: {mask_image_url}") + + # Update arguments with Fill-specific parameters + arguments.update({ + "image_url": image_url, + "mask_url": mask_image_url + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProFillNode": FalAPIFluxProFillNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProFillNode": "Fal API Flux Pro Fill" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_node.py new file mode 100644 index 00000000..a4888819 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_node.py @@ -0,0 +1,28 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +import logging + +logger = logging.getLogger(__name__) + +class FalAPIFluxProNode(BaseFalAPIFluxNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/new") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + input_types["required"]["safety_tolerance"] = (["1", "2", "3", "4", "5", "6"],) + return input_types + + def prepare_arguments(self, safety_tolerance, **kwargs): + arguments = super().prepare_arguments(**kwargs) + arguments["safety_tolerance"] = safety_tolerance + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProNode": FalAPIFluxProNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProNode": "Fal API Flux Pro" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_redux_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_redux_node.py new file mode 100644 index 00000000..737d9a21 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_redux_node.py @@ -0,0 +1,44 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_pro_node import FalAPIFluxProNode +import logging +import torch + +logger = logging.getLogger(__name__) + +class FalAPIFluxProReduxNode(FalAPIFluxProNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/v1/redux") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + # Add control image input + input_types["required"].update({ + "image": ("IMAGE",), # Accept input from another node + }) + + return input_types + + def prepare_arguments(self, image, **kwargs): + # Get base arguments from parent class + arguments = super().prepare_arguments(**kwargs) + + # Upload the control image and get its URL + image_url = self.upload_image(image) + logger.info(f"Uploaded target image. URL: {image_url}") + + # Update arguments with Canny-specific parameters + arguments.update({ + "image_url": image_url + }) + + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProReduxNode": FalAPIFluxProReduxNode +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProReduxNode": "Fal API Flux Pro Redux" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_v11_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_v11_node.py new file mode 100644 index 00000000..ac5161dc --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_v11_node.py @@ -0,0 +1,27 @@ +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_pro_node import FalAPIFluxProNode +import logging + +logger = logging.getLogger(__name__) + +class FalAPIFluxProV11Node(FalAPIFluxProNode): + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/v1.1") + + @classmethod + def INPUT_TYPES(cls): + input_types = super().INPUT_TYPES() + return input_types + + def prepare_arguments(self, **kwargs): + arguments = super().prepare_arguments(**kwargs) + return arguments + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProV11Node": FalAPIFluxProV11Node +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProV11Node": "Fal API Flux Pro v1.1" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_v11_ultra_node.py b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_v11_ultra_node.py new file mode 100644 index 00000000..a1b08144 --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/modules/fal_api_flux_pro_v11_ultra_node.py @@ -0,0 +1,65 @@ +import logging + +from .base_fal_api_flux_node import BaseFalAPIFluxNode +from .fal_api_flux_pro_v11_node import FalAPIFluxProV11Node + +logger = logging.getLogger(__name__) + + +class FalAPIFluxProV11UltraNode(FalAPIFluxProV11Node): + """ + See https://fal.ai/models/fal-ai/flux-pro/v1.1-ultra/api + """ + + def __init__(self): + super().__init__() + self.set_api_endpoint("fal-ai/flux-pro/v1.1-ultra") + + @classmethod + def INPUT_TYPES(cls): + # get input types from Flux Pro 1.1 + input_types = super().INPUT_TYPES() + + # remove `width` and `height` from inputs + # the 1.1 ultra API replaces these with `aspect_ratio` + del (input_types["required"]["width"]) + del (input_types["required"]["height"]) + + # remove `num_inference_steps` and `guidance_scale` + del (input_types["required"]["num_inference_steps"]) + del (input_types["required"]["guidance_scale"]) + + # add `aspect_ratio` + input_types["required"]["aspect_ratio"] = (["16:9", "4:3", "21:9", "1:1", "3:4", "9:16", "9:21"],) + + # add `raw` + input_types["required"]["raw"] = ("BOOLEAN", {"default": True}) + + # add `output_format` + input_types["required"]["output_format"] = (["jpeg", "png"],) + + return input_types + + def prepare_arguments(self, prompt, aspect_ratio, num_images, safety_tolerance, + enable_safety_checker, output_format, raw, seed=None, **kwargs): + # override from base since we don't have width and height + if not self.api_key: + raise ValueError("API key is not set. Please check your config.ini file.") + + arguments = {"prompt": prompt, "raw": raw, "num_images": num_images, + "enable_safety_checker": enable_safety_checker, + "safety_tolerance": safety_tolerance, "aspect_ratio": aspect_ratio, "output_format": output_format} + + if seed is not None and seed != 0: + arguments["seed"] = seed + + return arguments + + +NODE_CLASS_MAPPINGS = { + "FalAPIFluxProV11UltraNode": FalAPIFluxProV11Node +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FalAPIFluxProV11UltraNode": "Fal API Flux Pro v1.1 Ultra" +} diff --git a/custom_nodes/comfyui-fal-api-flux/pyproject.toml b/custom_nodes/comfyui-fal-api-flux/pyproject.toml new file mode 100644 index 00000000..f442ec1e --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-fal-api-flux" +description = "This repository contains custom nodes for ComfyUI that integrate the fal.ai FLUX.1 [pro] and FLUX.1 [dev] with LoRA and ControlNet API, specifically for text-to-image generation. These nodes allow you to use the FLUX.1 model directly within your ComfyUI workflows." +version = "1.5.2" +license = {file = "LICENSE"} +dependencies = ["requests==2.32.3", "configparser==7.1.0", "fal-client==0.4.1", "numpy==2.1.1", "torch"] + +[project.urls] +Repository = "https://github.com/yhayano-ponotech/ComfyUI-Fal-API-Flux" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "yas-ponotech" +DisplayName = "ComfyUI-Fal-API-Flux" +Icon = "" diff --git a/custom_nodes/comfyui-fal-api-flux/requirements.txt b/custom_nodes/comfyui-fal-api-flux/requirements.txt new file mode 100644 index 00000000..604a43da --- /dev/null +++ b/custom_nodes/comfyui-fal-api-flux/requirements.txt @@ -0,0 +1,5 @@ +requests==2.32.3 +configparser==7.1.0 +fal-client +numpy==2.1.1 +torch \ No newline at end of file diff --git a/custom_nodes/comfyui-gps-supplements/.gitignore b/custom_nodes/comfyui-gps-supplements/.gitignore new file mode 100644 index 00000000..f5c0a4ea --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/.gitignore @@ -0,0 +1,3 @@ +__pycache__ +.pytest_cache +.vscode diff --git a/custom_nodes/comfyui-gps-supplements/LICENSE b/custom_nodes/comfyui-gps-supplements/LICENSE new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/comfyui-gps-supplements/README.md b/custom_nodes/comfyui-gps-supplements/README.md new file mode 100644 index 00000000..6f03fd2f --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/README.md @@ -0,0 +1,33 @@ +# comfyui-gps-supplements +Nodes to make [ComfyUI-Image-Saver](https://github.com/alexopus/ComfyUI-Image-Saver) and [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) more compatible. Allowing Power Lora Loader node to be used with Image Saver node. Also adding nodes to extract Image Saver compatible strings to simplify workflows. + +## Nodes +**Lora to String**: connect Power Lora Loader (rgthree) as input to get lora strings in the format: \ + +**Lora Prompt Concatenation**: connect Power Lora Loader (rgthree) and positive prompt string as input to get formatted positive prompt compatible with Image Saver (comfyui-image-saver). + +**Model to String**: connect checkpoint loader as input to get checkpoint name as string output. + +**KSampler to Image Saver**: connect to KSampler or nodes alike to extract data compatible with Image Saver (comfyui-image-saver), data includes seed, steps, cfg, sampler name, scheduler, denoise. Can retrieve data even if KSampler takes input from other nodes. Input boxes are provided as fallback options if other nodes missing some options are connected as input. + +## Installation +1. Installation with ComfyUI Manager: + - In ComfyUI Manager, open custom nodes manager + - Search for 'GPS' Supplements for ComfyUI' + - Install the custom nodes + - Restart ComfyUI + +2. Installation with git: + Open a terminal from ComfyUI folder, in the terminal, do: + - `cd custom_nodes` + - `git clone https://github.com/Goshe-nite/comfyui-gps-supplements.git` + - Restart ComfyUI + +## Example Workflow +Example workflow provided in examples folder. +![example workflow](https://github.com/user-attachments/assets/95cee8e1-f8d5-4e07-942d-384f23dffe83) + +## Credit +- [kijai\ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes): for the Widget To String node which this project's nodes heavily derived from. +- [rgthree\rgthree-comfy](https://github.com/rgthree/rgthree-comfy): for the wonderful Power Lora Loader node. +- [ alexopus\ComfyUI-Image-Saver](https://github.com/alexopus/ComfyUI-Image-Saver.git): for the fantastic Civitai compatible Image Saver. diff --git a/custom_nodes/comfyui-gps-supplements/__init__.py b/custom_nodes/comfyui-gps-supplements/__init__.py new file mode 100644 index 00000000..f6470146 --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/__init__.py @@ -0,0 +1,3 @@ +from .nodes import * + +__all__ = ["NODE_CLASS_MAPPINGS"] \ No newline at end of file diff --git a/custom_nodes/comfyui-gps-supplements/examples/example.json b/custom_nodes/comfyui-gps-supplements/examples/example.json new file mode 100644 index 00000000..e99b88e9 --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/examples/example.json @@ -0,0 +1 @@ +{"id":"3f35e1f8-99ea-443f-83b0-783be778372e","revision":0,"last_node_id":69,"last_link_id":115,"nodes":[{"id":7,"type":"CLIPTextEncode","pos":[590,510],"size":[425.27801513671875,180.6060791015625],"flags":{},"order":10,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":33},{"localized_name":"text","name":"text","type":"STRING","widget":{"name":"text"},"link":93}],"outputs":[{"localized_name":"CONDITIONING","name":"CONDITIONING","type":"CONDITIONING","slot_index":0,"links":[112]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.33","Node name for S&R":"CLIPTextEncode","widget_ue_connectable":{"text":true}},"widgets_values":["embedding:lazyneg, "]},{"id":6,"type":"CLIPTextEncode","pos":[590,310],"size":[422.84503173828125,164.31304931640625],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":32},{"localized_name":"text","name":"text","type":"STRING","widget":{"name":"text"},"link":92}],"outputs":[{"localized_name":"CONDITIONING","name":"CONDITIONING","type":"CONDITIONING","slot_index":0,"links":[111]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.33","Node name for S&R":"CLIPTextEncode","widget_ue_connectable":{"text":true}},"widgets_values":["embedding:lazympos, mari \\(blue archive\\), "]},{"id":50,"type":"Lora Prompt Concatenation","pos":[640,140],"size":[211.06666564941406,46],"flags":{},"order":8,"mode":0,"inputs":[{"localized_name":"lora_loader","name":"lora_loader","type":"*","link":65},{"localized_name":"positive","name":"positive","type":"STRING","link":91}],"outputs":[{"localized_name":"positive","name":"positive","type":"STRING","links":[89]}],"properties":{"Node name for S&R":"Lora Prompt Concatenation","widget_ue_connectable":{}},"widgets_values":[]},{"id":8,"type":"VAEDecode","pos":[1400,70],"size":[210,46],"flags":{},"order":12,"mode":0,"inputs":[{"localized_name":"samples","name":"samples","type":"LATENT","link":114},{"localized_name":"vae","name":"vae","type":"VAE","link":8}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","slot_index":0,"links":[90]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.33","Node name for S&R":"VAEDecode","widget_ue_connectable":{}},"widgets_values":[]},{"id":65,"type":"Model to String","pos":[1580,-60],"size":[140,26],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"model","name":"model","type":"*","link":97}],"outputs":[{"localized_name":"STRING","name":"STRING","type":"STRING","links":[98]}],"properties":{"widget_ue_connectable":{},"Node name for S&R":"Model to String"}},{"id":66,"type":"KSampler to Image Saver","pos":[1470,620],"size":[270,278],"flags":{},"order":13,"mode":0,"inputs":[{"localized_name":"ksampler","name":"ksampler","type":"*","link":115},{"localized_name":"seed_value","name":"seed_value","type":"INT","widget":{"name":"seed_value"},"link":null},{"localized_name":"steps","name":"steps","type":"INT","widget":{"name":"steps"},"link":null},{"localized_name":"cfg","name":"cfg","type":"FLOAT","widget":{"name":"cfg"},"link":null},{"localized_name":"sampler_name","name":"sampler_name","type":"COMBO","widget":{"name":"sampler_name"},"link":null},{"localized_name":"scheduler","name":"scheduler","type":"COMBO","widget":{"name":"scheduler"},"link":null},{"localized_name":"denoise","name":"denoise","type":"FLOAT","widget":{"name":"denoise"},"link":null}],"outputs":[{"localized_name":"seed_value","name":"seed_value","type":"INT","links":[100]},{"localized_name":"steps","name":"steps","type":"INT","links":[101]},{"localized_name":"cfg","name":"cfg","type":"FLOAT","links":[102]},{"localized_name":"sampler_name","name":"sampler_name","type":"STRING","links":[103]},{"localized_name":"scheduler","name":"scheduler","type":"STRING","links":[104]},{"localized_name":"denoise","name":"denoise","type":"FLOAT","links":[105]}],"properties":{"widget_ue_connectable":{},"Node name for S&R":"KSampler to Image Saver"},"widgets_values":[0,30,8,"euler","normal",1]},{"id":62,"type":"String Literal (Image Saver)","pos":[160,630],"size":[340,100],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"string","name":"string","type":"STRING","widget":{"name":"string"},"link":null}],"outputs":[{"localized_name":"STRING","name":"STRING","type":"STRING","links":[93,94]}],"properties":{"cnr_id":"comfyui-image-saver","ver":"1.9.2","Node name for S&R":"String Literal (Image Saver)","widget_ue_connectable":{"string":true}},"widgets_values":["embedding:lazyneg, "]},{"id":5,"type":"EmptyLatentImage","pos":[700,750],"size":[315,106],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"width","name":"width","type":"INT","widget":{"name":"width"},"link":106},{"localized_name":"height","name":"height","type":"INT","widget":{"name":"height"},"link":107},{"localized_name":"batch_size","name":"batch_size","type":"INT","widget":{"name":"batch_size"},"link":null}],"outputs":[{"localized_name":"LATENT","name":"LATENT","type":"LATENT","slot_index":0,"links":[113]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.33","Node name for S&R":"EmptyLatentImage","widget_ue_connectable":{"width":true,"height":true,"batch_size":true}},"widgets_values":[832,1216,1]},{"id":13,"type":"Power Lora Loader 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\ No newline at end of file diff --git a/custom_nodes/comfyui-gps-supplements/nodes.py b/custom_nodes/comfyui-gps-supplements/nodes.py new file mode 100644 index 00000000..77582b14 --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/nodes.py @@ -0,0 +1,424 @@ +import re +import comfy.samplers + +# Recursively look for input value if input is provided by another node +def retrieveInputFromList(parent_tuple, prompt,depth): + if depth >= 100: + raise Exception("Over 99 nodes searched, consider connecting the root node directly instead.") + node_id = parent_tuple[0] + input_id = parent_tuple[1] + inputs = list(prompt[str(node_id)]["inputs"].values()) + value = inputs[input_id] + if isinstance(value, list): + value = retrieveInputFromList(value,prompt,depth+1) + return value + +class AnyType(str): + """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" + + def __ne__(self, __value: object) -> bool: + return False +any = AnyType("*") + +class modelToString: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": (any, {}), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID",}, + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "get_model" + CATEGORY = "GPSupps" + DESCRIPTION = """ +Connect to model node to turn the model name (with the partial path if exist) to a string, detects model name by widget name +*_name. +""" + + def get_model(self, extra_pnginfo, prompt, unique_id, model=None): + workflow = extra_pnginfo["workflow"] + node_id = None # Initialize node_id to handle cases where no match is found + link_id = None + link_to_node_map = {} + + # find node to extract data + for node in workflow["nodes"]: + if model is not None: + if node["type"] == "Model to String" and node["id"] == int(unique_id) and not link_id: + for node_input in node["inputs"]: + print(node_input) + if node_input["name"] == "model": + link_id = node_input["link"] + + # Construct a map of links to node IDs for future reference + node_outputs = node.get("outputs", None) + if not node_outputs: + continue + for output in node_outputs: + node_links = output.get("links", None) + if not node_links: + continue + for link in node_links: + link_to_node_map[link] = node["id"] + if link_id and link == link_id: + break + + if link_id: + node_id = link_to_node_map.get(link_id, None) + + if node_id is None: + raise ValueError("Unable to get node info") + + values = prompt[str(node_id)] + model_name = None + for key, value in values["inputs"].items(): + if re.search(".*_name.*", key): + model_name = value + + return (model_name,) + +class loraToString: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "lora_loader": (any, {}), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID",}, + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "get_rgthree_loras" + CATEGORY = "GPSupps" + DESCRIPTION = """ +Connect to power lora loader (rgthree) to output a string list of loras with their strengths, +in the format .... +""" + + def get_rgthree_loras(self, extra_pnginfo, prompt, unique_id, lora_loader=None): + workflow = extra_pnginfo["workflow"] + node_id = None # Initialize node_id to handle cases where no match is found + link_id = None + link_to_node_map = {} + + # find node to extract data + for node in workflow["nodes"]: + if lora_loader is not None: + if node["type"] == "Lora to String" and node["id"] == int(unique_id) and not link_id: + for node_input in node["inputs"]: + print(node_input) + if node_input["name"] == "lora_loader": + link_id = node_input["link"] + + # Construct a map of links to node IDs for future reference + node_outputs = node.get("outputs", None) + if not node_outputs: + continue + for output in node_outputs: + node_links = output.get("links", None) + if not node_links: + continue + for link in node_links: + link_to_node_map[link] = node["id"] + if link_id and link == link_id: + if node["type"] != "Power Lora Loader (rgthree)": + raise ValueError("Please check if Power Lora Loader (rgthree) is set as input.") + break + + if link_id: + node_id = link_to_node_map.get(link_id, None) + + if node_id is None: + raise ValueError("Unable to get node info") + + values = prompt[str(node_id)] + loras = [] + for key, value in values["inputs"].items(): + if re.search("^lora_.*", key): + if value['on']: + loras.append(f"") + + return ("".join(loras),) + +class loraPromptConcat: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "lora_loader": (any, {}), + "positive":("STRING", {"default": '', "forceInput": True}), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID",}, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("positive",) + FUNCTION = "conditioning_to_string" + CATEGORY = "GPSupps" + DESCRIPTION = """ +Connect to a power lora loader (rgthree) and text positive prompt to output a concatenated string +of the prompt and loras with their strengths (in the format ), designed for comfyui-image-saver, for +adding lora information to civitai compatible images. +""" + + def conditioning_to_string(self, extra_pnginfo, prompt, unique_id, positive, lora_loader=None): + workflow = extra_pnginfo["workflow"] + node_id = None # Initialize node_id to handle cases where no match is found + link_id = None + link_to_node_map = {} + + # find node to extract data + for node in workflow["nodes"]: + if lora_loader is not None: + if node["type"] == "Lora Prompt Concatenation" and node["id"] == int(unique_id) and not link_id: + for node_input in node["inputs"]: + print(node_input) + if node_input["name"] == "lora_loader": + link_id = node_input["link"] + + # Construct a map of links to node IDs for future reference + node_outputs = node.get("outputs", None) + if not node_outputs: + continue + for output in node_outputs: + node_links = output.get("links", None) + if not node_links: + continue + for link in node_links: + link_to_node_map[link] = node["id"] + if link_id and link == link_id: + if node["type"] != "Power Lora Loader (rgthree)": + raise ValueError("Please check if Power Lora Loader (rgthree) is set as input.") + break + + if link_id: + node_id = link_to_node_map.get(link_id, None) + + if node_id is None: + raise ValueError("Unable to get node info") + + values = prompt[str(node_id)] + loras = [] + for key, value in values["inputs"].items(): + if re.search("^lora_.*", key): + if value['on']: + loras.append(f"") + + return (positive+"".join(loras),) + +class ksamplerToImageSaver: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "ksampler": (any, {}), + "seed_value": ("INT", { + "default": 0, + "min": 0, + "step": 1, + }), + "steps": ("INT", { + "default": 30, + "min": 1, + "step": 1, + }), + "cfg": ("FLOAT", { + "default": 8.0, + "min": 0.0, + "step": 0.5, + }), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + "denoise": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.05, + }), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID",}, + } + + RETURN_TYPES = ("INT","INT", "FLOAT", "STRING","STRING","FLOAT",) + RETURN_NAMES = ("seed_value","steps", "cfg","sampler_name","scheduler","denoise",) + FUNCTION = "get_ksampler_config" + CATEGORY = "GPSupps" + DESCRIPTION = """ +Connect to any output of KSampler and alike to retrieve seed_value, steps, cfg, sampler_name, +scheduler, denoise and output the values in image saver(ComfyUI-Image-Saver) compatible format. +Input should work with KSampler Config (rgthree). Fallback values +can be entered for use with low compatibility nodes. +""" + + def get_ksampler_config(self, extra_pnginfo, prompt, unique_id,seed_value,steps,cfg, sampler_name, scheduler,denoise, ksampler=None,): + workflow = extra_pnginfo["workflow"] + #print(json.dumps(workflow, indent=4)) + node_id = None # Initialize node_id to handle cases where no match is found + link_id = None + link_to_node_map = {} + + # find node to extract data + for node in workflow["nodes"]: + if ksampler is not None: + if node["type"] == "KSampler to Image Saver" and node["id"] == int(unique_id) and not link_id: + for node_input in node["inputs"]: + print(node_input) + if node_input["name"] == "ksampler": + link_id = node_input["link"] + + # Construct a map of links to node IDs for future reference + node_outputs = node.get("outputs", None) + if not node_outputs: + continue + for output in node_outputs: + node_links = output.get("links", None) + if not node_links: + continue + for link in node_links: + link_to_node_map[link] = node["id"] + if link_id and link == link_id: + break + + if link_id: + node_id = link_to_node_map.get(link_id, None) + + if node_id is None: + raise ValueError("Unable to get node info") + + values = prompt[str(node_id)] + inputs = prompt[str(node_id)]["inputs"] + if values["class_type"]=="KSampler": + print("KSampler detected") + seed_value = inputs["seed"] + if isinstance(seed_value,list): + seed_value = retrieveInputFromList(seed_value,prompt,0) + steps = inputs["steps"] + if isinstance(steps,list): + steps = retrieveInputFromList(steps,prompt,0) + cfg = inputs["cfg"] + if isinstance(cfg,list): + cfg = retrieveInputFromList(cfg,prompt,0) + sampler_name = inputs["sampler_name"] + if isinstance(sampler_name,list): + sampler_name = retrieveInputFromList(sampler_name,prompt,0) + scheduler = inputs["scheduler"] + if isinstance(scheduler,list): + scheduler = retrieveInputFromList(scheduler,prompt,0) + denoise = inputs["denoise"] + if isinstance(denoise,list): + denoise = retrieveInputFromList(denoise,prompt,0) + else: + for key, value in inputs.items(): + if re.search(".*seed.*", key): + if isinstance(value,list): + seed_value = retrieveInputFromList(value,prompt,0) + else: + seed_value = value + if re.search("^steps.*", key): + if isinstance(value,list): + steps = retrieveInputFromList(value,prompt,0) + else: + steps = value + elif key == "cfg": + if isinstance(value,list): + cfg = retrieveInputFromList(value,prompt,0) + else: + cfg = value + elif key == "sampler_name": + if isinstance(value,list): + sampler_name = retrieveInputFromList(value,prompt,0) + else: + sampler_name = value + elif key == "scheduler": + if isinstance(value,list): + scheduler = retrieveInputFromList(value,prompt,0) + else: + scheduler = value + elif key == "denoise": + if isinstance(value,list): + denoise = retrieveInputFromList(value,prompt,0) + else: + denoise = value + return (seed_value,steps, round(cfg,2),sampler_name,scheduler,round(denoise,2),) + +class gpsDebug: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any_input": (any, {}), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID",}, + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "getdata" + CATEGORY = "GPSupps" + DESCRIPTION = """ +For debugging use, connect to any node's output to see data carried by node. +""" + + def getdata(self, extra_pnginfo, prompt, unique_id, any_input=None): + workflow = extra_pnginfo["workflow"] + #print(json.dumps(workflow, indent=4)) + node_id = None # Initialize node_id to handle cases where no match is found + link_id = None + link_to_node_map = {} + + # find node to extract data + for node in workflow["nodes"]: + if any_input is not None: + if node["type"] == "gpsdebugger" and node["id"] == int(unique_id) and not link_id: + for node_input in node["inputs"]: + print(node_input) + if node_input["name"] == "any_input": + link_id = node_input["link"] + + # Construct a map of links to node IDs for future reference + node_outputs = node.get("outputs", None) + if not node_outputs: + continue + for output in node_outputs: + node_links = output.get("links", None) + if not node_links: + continue + for link in node_links: + link_to_node_map[link] = node["id"] + if link_id and link == link_id: + break + + if link_id: + node_id = link_to_node_map.get(link_id, None) + + if node_id is None: + raise ValueError("Unable to get node info") + + values = prompt[str(node_id)] + + return (str(values),) + +NODE_CLASS_MAPPINGS = { + "Lora to String": loraToString, + "KSampler to Image Saver": ksamplerToImageSaver, + "gpsdebugger": gpsDebug, + "Lora Prompt Concatenation":loraPromptConcat, + "Model to String":modelToString, +} \ No newline at end of file diff --git a/custom_nodes/comfyui-gps-supplements/pyproject.toml b/custom_nodes/comfyui-gps-supplements/pyproject.toml new file mode 100644 index 00000000..9b62a07b --- /dev/null +++ b/custom_nodes/comfyui-gps-supplements/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-gps-supplements" +description = "Nodes to make ComfyUI-Image-Saver and rgthree-comfy more compatible. Allowing Power Lora Loader node to be used with Image Saver node. Also adding nodes to extract Image Saver compatible strings to simplify workflows." +version = "1.0.0" +license = { file = "LICENSE" } +dependencies = [""] + +[project.urls] +Repository = "https://github.com/Goshe-nite/comfyui-gps-supplements" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "Goshe-nite" +DisplayName = "ComfyUI GPS Supplements" +Icon = "" \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/.gitignore b/custom_nodes/comfyui-image-saver/.gitignore new file mode 100644 index 00000000..f5c0a4ea --- /dev/null +++ b/custom_nodes/comfyui-image-saver/.gitignore @@ -0,0 +1,3 @@ +__pycache__ +.pytest_cache +.vscode diff --git a/custom_nodes/comfyui-image-saver/CHANGELOG.md b/custom_nodes/comfyui-image-saver/CHANGELOG.md new file mode 100644 index 00000000..8cb7a353 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/CHANGELOG.md @@ -0,0 +1,203 @@ +# v1.21.0 + +- Cleaner naming for batch saves. +- Allow custom time_format via prompt parameters in image saver. + +# v1.20.0 + +- Add RandomShapeGenerator + +# v1.19.0 + +- Bring sampler/scheduler selectors back. + +# v1.18.0 + +- Add WorkflowInputValue node to retrieve input values from nodes in workflow. + +# v1.17.0 + +- BREAKING CHANGE: sampler/scheduler loaders are removed. Instead AnyToString node is added to help convert sampler/scheduler types to string, which works with native loaders. Besides loader nodes removal, Input Parameters has to be recreated and reconnected with the saver node(s). Check example workflow for reference. + +# v1.16.0 + +- Improved Civitai Hash Fetcher search reliability with smart matching and fallbacks +- Added NSFW model search support +- Fixed Civitai Hash Fetcher caching bug +- Refactored file matching with multi-level fallback strategy +- Added GGUF model format support +- Case-insensitive extension check for checkpoints +- Skip resources with missing hashes + +# v1.15.2 + +- Bugfix: sanitize filename only, without the path + +# v1.15.1 + +- Bugfix: Add missing parameter +- Bugfix: Don't sanitize slashes in filenames + +# v1.15.0 + +- Allow custom info to be added to metadata, inserted into the a111 string between clip skip and model hash +- Sanitize filenames +- Fixed timeout exception to prevent network timeout crashes + +# v1.14.2 + +- Update list of schedulers + +# v1.14.1 + +- Expose ConditioningConcatOptional utility + +# v1.14.0 + +- Add ConditioningConcatOptional utility + +# v1.13.1 + +- Fix parameter name mismatch + +# v1.13.0 + +- Add support for Efficiency node pack's schedulers + +# v1.12.0 + +- Schedulers list for KSampler (inspire) has been updated. +- BREAKING CHANGE: To avoid confusion, following nodes have been renamed: + SchedulerSelector -> SchedulerSelectorInspire + SchedulerSelectorComfy -> SchedulerSelector + SchedulerToString -> SchedulerInspireToString + SchedulerComfyToString -> SchedulerToString + +# v1.11.1 + +- Place preview switch at the end + +# v1.11.0 + +- Allow disabling the previews + +# v1.10.1 + +- Fix regression with path handling + +# v1.10.0 + +- Provide 'Image Saver Simple' & 'Image Saver Metadata' that can be used together, separating metadata node from image saver node +- `scheduler` input has been renamed to `scheduler_name` + +# v1.9.2 + +- Do not override proxy settings of requests.get + +# v1.9.1 + +- Bugfix: handle network connection error for civitai + +# v1.9.0 + +- Allow multiple comma-separated model names +- Add debug a111_params output + +# v1.8.0 + +- Allow workflow embed for all file formats. +- Added optional version field for Civitai Hash Fetcher. +- Added InputParameters node to simplify common KSampler parameters input. + +# v1.7.0 + +- Add hash output for optional chaining of additional hashes. +- Add tests for image saving. +- Fix f-string failure. + +# v1.6.0 + +- Add Civitai download option for LoRA weight saving (#68). +- Add easy_remix option for stripping LoRAs from prompt (#68). +- Add width/height filename variables (#67). +- Add progress bar for sha256 calculation (#70). +- Add "jpg" extension to the list for more control over the target filename (#69). + +# v1.5.2 + +- Reverted experimental webp support for the moment. Needs more testing. +- Fix putting "prompt" into JPEGs. + +# v1.5.1 + +- Fix workflow storage in lossless webp + +# v1.5.0 + +- New lines are no longer removed from prompts. +- Added Civitai Hash Fetcher node that can retrieve a ressource hash from civitai based on its name. +- Added an "aditional hashes" input that accepts a comma separated list of resource hahes that will be stored in the image metadata. +- Experimental support for storing workflow in webp. + +# v1.4.0 + +- Add UNETLoaderWithName +- Also check the unet directory (if not found in checkpoints) when calculating model hash +- Add tooltips +- Image Saver: Add clip skip parameter +- Adds the suffix _0x to the file name if a file with that name already exists (#40) +- Remove strip_a1111_params option +- Bugfix: Fixing the outputs names of SchedulerToString, SchedulerComfyToString and SamplerToString nodes + +# v1.3.0 + +- Saver node: converted sampler input to string +- SamplerSelector node: output sampler name also as a string +- Add SamplerToString util node +- Fixed converter nodes +- Change min value for widgets with fixed steps + +# v1.2.1 + +- Update Impact Pack scheduler list + +# v1.2.0 + +- Add option to strip positive/negative prompt from the a1111 parameters comment (hashes for loras/embeddings are still always added) +- Add option for embedding prompt/workflow in PNG +- Add 'AYS SDXL', 'AYS SD1' and 'AYS SVD' to scheduler selectors +- added dpmpp_3m_sde sampler +- added exponential scheduler +- Fix suffix for batches +- Save json for each image in batch +- Allow to leave modelname empty + +# v1.1.0 + +- Fix extension check in full_lora_path_for +- add 'save_workflow_as_json', which allows saving an additional file with the json workflow included + +# v1.0.0 + +- **BREAKING CHANGE**: Convert CheckpointSelector to CheckpointLoaderWithName (571fcfa319438a32e051f90b32827363bccbd2ef). Fixes 2 issues: + - oversized search fields (https://github.com/giriss/comfy-image-saver/issues/5) + - selector breaking when model files are added/removed at runtime +- Try to find loras with incomplete paths (002471d95078d8b2858afc92bc4589c8c4e8d459): + - `` will be found and hashed if the actual location is `` +- Update default filename pattern from `%time_%seed` to `%time_%basemodelname_%seed` (72f17f0a4e97a7c402806cc21e9f564a5209073d) +- Include embedding, lora and model information in the metadata in civitai format (https://github.com/alexopus/ComfyUI-Image-Saver/pull/2) +- Rename all nodes to avoid conflicts with the forked repo +- Make PNG optimization optional and off by default (c760e50b62701af3d44edfb69d3776965a645406) +- Calculate model hash only if there is no calculated one on disk already. Store on disk after calculation (96df2c9c74c089a8cca811ccf7aaa72f68faf9db) +- Fix civitai sampler/scheduler name (af4eec9bc1cc55643c0df14aaf3a446fbbc3d86d) +- Fix metadata format according to https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/5ef669de080814067961f28357256e8fe27544f4/modules/processing.py#L673 (https://github.com/giriss/comfy-image-saver/pull/11) +- Add input `denoise` (https://github.com/Danand/comfy-image-saver/commit/37fc8903e05c0d70a7b7cfb3a4bcc51f4f464637) +- Add resolving of more placeholders for file names (https://github.com/giriss/comfy-image-saver/pull/16) + - `%sampler_name` + - `%steps` + - `%cfg` + - `%scheduler` + - `%basemodelname` + + +Changes since the fork from https://github.com/giriss/comfy-image-saver. diff --git a/custom_nodes/comfyui-image-saver/CLAUDE.md b/custom_nodes/comfyui-image-saver/CLAUDE.md new file mode 100644 index 00000000..563d5957 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/CLAUDE.md @@ -0,0 +1,128 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project Overview + +ComfyUI-Image-Saver is a ComfyUI custom node plugin that saves images with generation metadata compatible with Civitai. It supports PNG, JPEG, and WebP formats, storing model, LoRA, and embedding hashes for proper resource recognition. + +## Development Commands + +### Testing +```bash +cd saver +python -m pytest +``` + +### Installation for Development +```bash +pip install -r requirements.txt +``` + +### Package Information +- Main dependency: `piexif` (for EXIF metadata handling) +- Version defined in `pyproject.toml` +- ComfyUI plugin structure with node registration + +## Architecture + +### Core Components + +1. **Node Registration (`__init__.py`)** + - Registers all custom nodes with ComfyUI + - Maps node class names to implementations + - Defines `WEB_DIRECTORY` for JavaScript assets + +2. **Image Saving System (`nodes.py`)** + - `ImageSaver`: Main node for saving images with full metadata + - `ImageSaverSimple`: Simplified version for basic usage + - `ImageSaverMetadata`: Metadata-only node for separation of concerns + - `Metadata` dataclass: Structured metadata container + +3. **Core Saver Logic (`saver/saver.py`)** + - `save_image()`: Handles different image formats (PNG, JPEG, WebP) + - PNG: Uses `PngInfo` for metadata storage + - JPEG/WebP: Uses EXIF format via `piexif` + - Workflow embedding with size limits (65535 bytes for JPEG) + +4. **Utility Modules** + - `utils.py`: File operations, hashing, path resolution + - `utils_civitai.py`: Civitai API integration and metadata formatting + - `prompt_metadata_extractor.py`: Extracts LoRAs and embeddings from prompts + +5. **Node Types** + - `nodes_loaders.py`: Checkpoint and UNet loaders with name tracking + - `nodes_selectors.py`: Sampler and scheduler selection utilities + - `nodes_literals.py`: Literal value generators (seed, strings, etc.) + - `civitai_nodes.py`: Civitai hash fetching functionality + +### Key Features + +- **Metadata Support**: A1111-compatible parameters with Civitai resource hashes +- **Multi-format**: PNG (full workflow), JPEG/WebP (parameters only) +- **Hash Calculation**: SHA256 hashing with file caching (`.sha256` files) +- **Resource Detection**: Automatic LoRA, embedding, and model hash extraction +- **Civitai Integration**: Downloads resource metadata for proper attribution +- **Filename Templating**: Supports variables like `%date`, `%time`, `%seed`, `%model`, `%width`, `%height`, `%counter`, `%sampler_name`, `%steps`, `%cfg`, `%scheduler_name`, `%basemodelname`, `%denoise`, `%clip_skip`, `%custom` + +### Advanced Features + +- **Multiple Model Support**: ModelName parameter accepts comma-separated model names. Primary model hash is used in metadata, additional models are added to `additional_hashes` +- **Easy Remix Mode**: When enabled, automatically cleans prompts by removing LoRA tags and simplifying embeddings for better Civitai remix compatibility +- **Custom Metadata Field**: Arbitrary string can be inserted into A1111 parameters via the `custom` parameter +- **Manual Hash Management**: User-added resource hashes stored in `/models/image-saver/manual-hashes.json` for resources not found via Civitai API +- **File Path Matching**: Three-level fallback strategy for finding resources: + 1. Exact path match + 2. Filename stem match (without extension) + 3. Base name match (case-insensitive) +- **Civitai Hash Fetcher Node**: Dedicated node (`CivitaiHashFetcher`) for looking up model hashes directly from Civitai by username and model name +- **Caching Strategy**: + - `.sha256` files: SHA256 hashes cached alongside model files + - `.civitai.info` files: Civitai metadata cached to reduce API calls + - Internal cache: CivitaiHashFetcher maintains runtime cache to avoid redundant lookups + +### Data Flow + +1. **Input Processing**: Parameters and images received from ComfyUI workflow +2. **Metadata Extraction**: Prompts parsed for LoRAs, embeddings, model references +3. **Hash Generation**: SHA256 hashes calculated for all resources +4. **Civitai Lookup**: Resource metadata fetched from Civitai API +5. **Metadata Assembly**: A1111-compatible parameter string generated +6. **Image Saving**: Metadata embedded in image files based on format +7. **Output**: Saved images with proper metadata for sharing/recognition + +### File Structure + +``` +ComfyUI-Image-Saver/ +β”œβ”€β”€ __init__.py # Node registration +β”œβ”€β”€ nodes.py # Main image saver nodes +β”œβ”€β”€ saver/ # Core saving logic +β”‚ β”œβ”€β”€ saver.py # Image format handling +β”‚ └── test_saver.py # Unit tests +β”œβ”€β”€ utils.py # File operations and hashing +β”œβ”€β”€ utils_civitai.py # Civitai API integration +β”œβ”€β”€ prompt_metadata_extractor.py # Prompt parsing +β”œβ”€β”€ nodes_*.py # Specialized node types +β”œβ”€β”€ civitai_nodes.py # Civitai functionality +└── js/ # Frontend JavaScript + β”œβ”€β”€ read_exif_workflow.js # ComfyUI extension for reading EXIF workflows from dropped images + └── lib/exif-reader.js # EXIF reading utilities (ExifReader v4.26.2) +``` + +## Testing + +Tests are located in `saver/test_saver.py` and use pytest. The test configuration is in `saver/pytest.ini`. + +Run tests with: +```bash +cd saver && python -m pytest +``` + +## Important Notes + +- Hash files (`.sha256`) are cached alongside model files to avoid recalculation +- JPEG format has a 65535-byte limit for EXIF data +- WebP workflow embedding is experimental +- Resource paths are resolved through ComfyUI's folder_paths system +- Civitai integration can be disabled via `download_civitai_data` parameter \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/LICENSE b/custom_nodes/comfyui-image-saver/LICENSE new file mode 100644 index 00000000..d9a00f30 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Girish Gopaul + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/comfyui-image-saver/README.md b/custom_nodes/comfyui-image-saver/README.md new file mode 100644 index 00000000..e6f6d438 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/README.md @@ -0,0 +1,74 @@ +[!] Forked from https://github.com/giriss/comfy-image-saver, which seems to be inactive since a while. + +# Save image with generation metadata in ComfyUI + +Allows you to save images with their **generation metadata**. Includes the metadata compatible with *Civitai* geninfo auto-detection. Works with PNG, JPG and WEBP. For PNG stores both the full workflow in comfy format, plus a1111-style parameters. For JPEG/WEBP only the a1111-style parameters are stored. **Includes hashes of Models, LoRAs and embeddings for proper resource linking** on civitai. + +You can find the example workflow file named `example-workflow.json`. +workflow + +You can also add LoRAs to the prompt in \ format, which would be translated into hashes and stored together with the metadata. For this it is recommended to use `ImpactWildcardEncode` from the fantastic [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack). It will allow you to convert the LoRAs directly to proper conditioning without having to worry about avoiding/concatenating lora strings, which have no effect in standard conditioning nodes. Here is an example: +![workflow](https://github.com/user-attachments/assets/61440fac-f1d5-414b-ae69-dbdda9d6d442) + +This would have civitai autodetect all of the resources (assuming the model/lora/embedding hashes match): +![image](https://github.com/alexopus/ComfyUI-Image-Saver/assets/25933468/f0642389-4f34-4a64-89a6-5cf9c33d5ed1) + +## How to install? + +### Method 1: Manager (Recommended) +If you have *ComfyUI-Manager*, you can simply search "**ComfyUI Image Saver**" and install these custom nodes. + +### Method 2: Easy +If you don't have *ComfyUI-Manager*, then: +- Using CLI, go to the ComfyUI folder +- `cd custom_nodes` +- `git clone git@github.com:alexopus/ComfyUI-Image-Saver.git` +- `cd ComfyUI-Image-Saver` +- `pip install -r requirements.txt` +- Start/restart ComfyUI + +## Customization of file/folder names + +You can use following placeholders: + +- `%date` +- `%time` *– format taken from `time_format`* +- `%time_format` *– custom datetime format using Python strftime codes* +- `%model` *– full name of model file* +- `%basemodelname` *– name of model (without file extension)* +- `%seed` +- `%counter` +- `%sampler_name` +- `%scheduler` +- `%steps` +- `%cfg` +- `%denoise` + +Example: + +| `filename` value | Result file name | +| --- | --- | +| `%time-%basemodelname-%cfg-%steps-%sampler_name-%scheduler-%seed` | `2023-11-16-131331-Anything-v4.5-pruned-mergedVae-7.0-25-dpm_2-normal-1_01.png` | +| `%time_format<%Y%m%d_%H%M%S>-%seed` | `20231116_131331-1.png` | +| `%time_format<%B %d, %Y> %basemodelname` | `November 16, 2023 Anything-v4.5.png` | +| `img_%time_format<%Y-%m-%d>_%seed` | `img_2023-11-16_1.png` | + +**Common strftime format codes for `%time_format`:** + +| Code | Meaning | Example | +|------|---------|---------| +| `%Y` | Year (4-digit) | 2023 | +| `%y` | Year (2-digit) | 23 | +| `%m` | Month (01-12) | 11 | +| `%B` | Month name (full) | November | +| `%b` | Month name (short) | Nov | +| `%d` | Day (01-31) | 16 | +| `%H` | Hour 24h | 13 | +| `%I` | Hour 12h | 01 | +| `%M` | Minute | 13 | +| `%S` | Second | 31 | +| `%p` | AM/PM | PM | +| `%A` | Weekday (full) | Thursday | +| `%a` | Weekday (short) | Thu | +| `%F` | YYYY-MM-DD | 2023-11-16 | +| `%T` | HH:MM:SS | 13:13:31 | diff --git a/custom_nodes/comfyui-image-saver/__init__.py b/custom_nodes/comfyui-image-saver/__init__.py new file mode 100644 index 00000000..5b6b2c04 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/__init__.py @@ -0,0 +1,35 @@ +from typing import Any + +from .nodes import ImageSaver, ImageSaverSimple, ImageSaverMetadata +from .nodes_literals import SeedGenerator, StringLiteral, SizeLiteral, IntLiteral, FloatLiteral, CfgLiteral, ConditioningConcatOptional, RandomShapeGenerator +from .nodes_loaders import CheckpointLoaderWithName, UNETLoaderWithName +from .nodes_selectors import SamplerSelector, SchedulerSelector, SchedulerSelectorInspire, SchedulerSelectorEfficiency, InputParameters, AnyToString, WorkflowInputValue +from .civitai_nodes import CivitaiHashFetcher + +NODE_CLASS_MAPPINGS: dict[str, Any] = { + "Checkpoint Loader with Name (Image Saver)": CheckpointLoaderWithName, + "UNet loader with Name (Image Saver)": UNETLoaderWithName, + "Image Saver": ImageSaver, + "Image Saver Simple": ImageSaverSimple, + "Image Saver Metadata": ImageSaverMetadata, + "Sampler Selector (Image Saver)": SamplerSelector, + "Scheduler Selector (Image Saver)": SchedulerSelector, + "Scheduler Selector (inspire) (Image Saver)": SchedulerSelectorInspire, + "Scheduler Selector (Eff.) (Image Saver)": SchedulerSelectorEfficiency, + "Input Parameters (Image Saver)": InputParameters, + "Any to String (Image Saver)": AnyToString, + "Workflow Input Value (Image Saver)": WorkflowInputValue, + "Seed Generator (Image Saver)": SeedGenerator, + "String Literal (Image Saver)": StringLiteral, + "Width/Height Literal (Image Saver)": SizeLiteral, + "Cfg Literal (Image Saver)": CfgLiteral, + "Int Literal (Image Saver)": IntLiteral, + "Float Literal (Image Saver)": FloatLiteral, + "Conditioning Concat Optional (Image Saver)": ConditioningConcatOptional, + "RandomShapeGenerator": RandomShapeGenerator, + "Civitai Hash Fetcher (Image Saver)": CivitaiHashFetcher, +} + +WEB_DIRECTORY = "js" + +__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY'] diff --git a/custom_nodes/comfyui-image-saver/civitai_nodes.py b/custom_nodes/comfyui-image-saver/civitai_nodes.py new file mode 100644 index 00000000..2541869a --- /dev/null +++ b/custom_nodes/comfyui-image-saver/civitai_nodes.py @@ -0,0 +1,135 @@ +import requests + +class CivitaiHashFetcher: + """ + A ComfyUI custom node that fetches the AutoV3 hash of a model from Civitai + based on the provided username and model name. + """ + + def __init__(self): + self.last_username = None + self.last_model_name = None + self.last_version = None + self.last_hash = None # Store the last fetched hash + + RETURN_TYPES = ("STRING",) # The node outputs a string (AutoV3 hash) + FUNCTION = "get_autov3_hash" + CATEGORY = "CivitaiAPI" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "username": ("STRING", {"default": "", "multiline": False}), + "model_name": ("STRING", {"default": "", "multiline": False}), + }, + "optional": { + "version": ("STRING", {"default": "", "multiline": False, "tooltip": "Specify version keyword to fetch a particular model version (optional)"}), + } + } + + def get_autov3_hash(self, username, model_name, version=""): + """ + Fetches the latest model version from Civitai and extracts its AutoV3 hash. + Uses caching to avoid redundant API calls. + """ + # Check if inputs are the same as last time + if (self.last_username is not None and self.last_model_name is not None and self.last_version is not None and + username == self.last_username and model_name == self.last_model_name and version == self.last_version): + return self.last_hash + + base_url = "https://civitai.com/api/v1/models" + params = { + "username": username, + "query": model_name, + "limit": 20, # Fetch more results due to API ranking issues + "nsfw": "true" # Include NSFW models in search results + } + + try: + # Fetch models by username and model name + response = requests.get(base_url, params=params, timeout=10) + if response.status_code != 200: + return (f"Error: API request failed with status {response.status_code}",) + + data = response.json() + items = data.get("items", []) + + # If no results with query, try without query (fallback for API search issues) + if not items and params.get("query"): + print("ComfyUI-Image-Saver: No results with query, trying without query parameter...") + params_no_query = { + "username": username, + "limit": 100, + "nsfw": "true" + } + response = requests.get(base_url, params=params_no_query, timeout=10) + if response.status_code == 200: + data = response.json() + items = data.get("items", []) + + if not items: + return (f"No models found for user '{username}' with name '{model_name}'",) + + # Find best matching model (prefer exact/partial matches) + model_name_lower = model_name.lower() + best_match = None + + # Try exact match first + for item in items: + if item.get("name", "").lower() == model_name_lower: + best_match = item + break + + # If no exact match, try partial match + if not best_match: + for item in items: + item_name_lower = item.get("name", "").lower() + if model_name_lower in item_name_lower or item_name_lower.startswith(model_name_lower): + best_match = item + break + + # Fall back to first result if no good match + if not best_match: + best_match = items[0] + + model = best_match + model_versions = model.get("modelVersions", []) + if not model_versions: + return ("No model versions found.",) + + # If a version keyword is provided, search for a model version whose name contains it (case-insensitive). + chosen_version = None + if version: + for v in model_versions: + if version.lower() in v.get("name", "").lower(): + chosen_version = v + break + # If no version is provided or no match was found, use the first (latest) version. + if chosen_version is None: + chosen_version = model_versions[0] + version_id = chosen_version.get("id") + + # Fetch detailed version info + version_url = f"https://civitai.com/api/v1/model-versions/{version_id}" + version_response = requests.get(version_url, timeout=10) + if version_response.status_code != 200: + return (f"Error: Version API request failed with status {version_response.status_code}",) + + version_data = version_response.json() + + # Extract the AutoV3 hash from the model version files + for file_info in version_data.get("files", []): + autov3_hash = file_info.get("hashes", {}).get("AutoV3") + if autov3_hash: + # Cache the result before returning + self.last_username = username + self.last_model_name = model_name + self.last_version = version # Store version to track changes + self.last_hash = autov3_hash + return (autov3_hash,) # Return the first found hash + + return ("No AutoV3 hash found in version files.",) + + except Exception as e: + return (f"Error: {e}",) diff --git a/custom_nodes/comfyui-image-saver/example-workflow.json b/custom_nodes/comfyui-image-saver/example-workflow.json new file mode 100644 index 00000000..5001dd93 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/example-workflow.json @@ -0,0 +1 @@ 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d})):d}ExifReader=n}(); +//# sourceMappingURL=exif-reader.js.map \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/js/read_exif_workflow.js b/custom_nodes/comfyui-image-saver/js/read_exif_workflow.js new file mode 100644 index 00000000..2c41814e --- /dev/null +++ b/custom_nodes/comfyui-image-saver/js/read_exif_workflow.js @@ -0,0 +1,75 @@ +import { app } from '../../scripts/app.js' +import { ExifReader } from './lib/exif-reader.js' // https://github.com/mattiasw/ExifReader v4.26.2 + +const SETTING_CATEGORY_NAME = "Image Saver"; +const SETTING_SECTION_FILE_HANDLING = "File Handling"; + +app.registerExtension({ + name: "ComfyUI-Image-Saver", + settings: [ + { + id: "ImageSaver.HandleImageWorkflowDrop", + name: "Use a custom file drop handler to load workflows from JPEG and WEBP files", + type: "boolean", + defaultValue: true, + category: [SETTING_CATEGORY_NAME, SETTING_SECTION_FILE_HANDLING, "Custom File Drop Handler"], + tooltip: + "Use a custom file handler for dropped JPEG and WEBP files.\n" + + "This is needed to load embedded workflows.\n" + + "Only disable this if it interferes with another extension's file drop handler.", + }, + ], + async setup() { + // Save original function, reassign to our own handler + const handleFileOriginal = app.handleFile; + app.handleFile = async function (file) { + if (app.ui.settings.getSettingValue("ImageSaver.HandleImageWorkflowDrop") && (file.type === "image/jpeg" || file.type === "image/webp")) { + try { + const exifTags = await ExifReader.load(file); + + const workflowString = "workflow:"; + const promptString = "prompt:"; + let workflow; + let prompt; + // Search Exif tag data for workflow and prompt + Object.values(exifTags).some(value => { + try { + const description = `${value.description}`; + if (workflow === undefined && description.slice(0, workflowString.length).toLowerCase() === workflowString) { + workflow = JSON.parse(description.slice(workflowString.length)); + } else if (prompt === undefined && description.slice(0, promptString.length).toLowerCase() === promptString) { + prompt = JSON.parse(description.slice(promptString.length)); + } + } catch (error) { + if (!(error instanceof SyntaxError)) { + console.error(`ComfyUI-Image-Saver: Error reading Exif value: ${error}`); + } + } + + return workflow !== undefined; + }); + + if (workflow !== undefined) { + // Remove file extension + let filename = file.name; + let dot = filename.lastIndexOf('.'); + if (dot !== -1) { + filename = filename.slice(0, dot); + } + + app.loadGraphData(workflow, true, true, filename); + return; + } else if (prompt !== undefined) { + app.loadApiJson(prompt); + return; + } + } catch (error) { + console.error(`ComfyUI-Image-Saver: Error parsing Exif: ${error}`); + } + } + + // Fallback to original function + handleFileOriginal.call(this, file); + } + }, +}) \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/nodes.py b/custom_nodes/comfyui-image-saver/nodes.py new file mode 100644 index 00000000..8c4a8767 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/nodes.py @@ -0,0 +1,584 @@ +import os +from datetime import datetime +from dataclasses import dataclass +from pathlib import Path +from typing import Any +import json +import numpy as np +import re + +from PIL import Image +import torch + +import folder_paths +from nodes import MAX_RESOLUTION + +from .saver.saver import save_image +from .utils import sanitize_filename, get_sha256, full_checkpoint_path_for +from .utils_civitai import get_civitai_sampler_name, get_civitai_metadata, MAX_HASH_LENGTH +from .prompt_metadata_extractor import PromptMetadataExtractor + +def parse_checkpoint_name(ckpt_name: str) -> str: + return os.path.basename(ckpt_name) + +def parse_checkpoint_name_without_extension(ckpt_name: str) -> str: + filename = parse_checkpoint_name(ckpt_name) + name_without_ext, ext = os.path.splitext(filename) + supported_extensions = folder_paths.supported_pt_extensions | {".gguf"} + + # Only remove extension if it's a known model file extension + if ext.lower() in supported_extensions: + return name_without_ext + else: + return filename # Keep full name if extension isn't recognized + +def get_timestamp(time_format: str) -> str: + now = datetime.now() + try: + timestamp = now.strftime(time_format) + except: + timestamp = now.strftime("%Y-%m-%d-%H%M%S") + + return timestamp + +def apply_custom_time_format(filename: str) -> str: + """ + Replace %time_format patterns with formatted datetime. + Example: %time_format<%Y-%m-%d> becomes 2026-01-17 + """ + now = datetime.now() + # Pattern to match %time_format where XXX is any strftime format string + # Use negative lookahead to exclude %time_format itself from variable delimiters + pattern = r'%time_format<([^>]*)>' + def replace_format(match): + format_str = match.group(1) + try: + return now.strftime(format_str) + except: + # If format is invalid, return original + return match.group(0) + + return re.sub(pattern, replace_format, filename) + +def save_json(image_info: dict[str, Any] | None, filename: str) -> None: + try: + workflow = (image_info or {}).get('workflow') + if workflow is None: + print('No image info found, skipping saving of JSON') + with open(f'{filename}.json', 'w') as workflow_file: + json.dump(workflow, workflow_file) + print(f'Saved workflow to {filename}.json') + except Exception as e: + print(f'Failed to save workflow as json due to: {e}, proceeding with the remainder of saving execution') + +def make_pathname(filename: str, width: int, height: int, seed: int, modelname: str, counter: int, time_format: str, sampler_name: str, steps: int, cfg: float, scheduler_name: str, denoise: float, clip_skip: int, custom: str) -> str: + # Process custom time_format patterns first + filename = apply_custom_time_format(filename) + filename = filename.replace("%date", get_timestamp("%Y-%m-%d")) + filename = filename.replace("%time", get_timestamp(time_format)) + filename = filename.replace("%model", parse_checkpoint_name(modelname)) + filename = filename.replace("%width", str(width)) + filename = filename.replace("%height", str(height)) + filename = filename.replace("%seed", str(seed)) + filename = filename.replace("%counter", str(counter)) + filename = filename.replace("%sampler_name", sampler_name) + filename = filename.replace("%steps", str(steps)) + filename = filename.replace("%cfg", str(cfg)) + filename = filename.replace("%scheduler_name", scheduler_name) + filename = filename.replace("%basemodelname", parse_checkpoint_name_without_extension(modelname)) + filename = filename.replace("%denoise", str(denoise)) + filename = filename.replace("%clip_skip", str(clip_skip)) + filename = filename.replace("%custom", custom) + + directory, basename = os.path.split(filename) + sanitized_basename = sanitize_filename(basename) + return os.path.join(directory, sanitized_basename) + +def make_filename(filename: str, width: int, height: int, seed: int, modelname: str, counter: int, time_format: str, sampler_name: str, steps: int, cfg: float, scheduler_name: str, denoise: float, clip_skip: int, custom: str) -> str: + filename = make_pathname(filename, width, height, seed, modelname, counter, time_format, sampler_name, steps, cfg, scheduler_name, denoise, clip_skip, custom) + return get_timestamp(time_format) if filename == "" else filename + +@dataclass +class Metadata: + modelname: str + positive: str + negative: str + width: int + height: int + seed: int + steps: int + cfg: float + sampler_name: str + scheduler_name: str + denoise: float + clip_skip: int + custom: str + additional_hashes: str + ckpt_path: str + a111_params: str + final_hashes: str + +class ImageSaverMetadata: + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "optional": { + "modelname": ("STRING", {"default": '', "multiline": False, "tooltip": "model name (can be multiple, separated by commas)"}), + "positive": ("STRING", {"default": 'unknown', "multiline": True, "tooltip": "positive prompt"}), + "negative": ("STRING", {"default": 'unknown', "multiline": True, "tooltip": "negative prompt"}), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "image width"}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "image height"}), + "seed_value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "seed"}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "number of steps"}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "tooltip": "CFG value"}), + "sampler_name": ("STRING", {"default": '', "multiline": False, "tooltip": "sampler name (as string)"}), + "scheduler_name": ("STRING", {"default": 'normal', "multiline": False, "tooltip": "scheduler name (as string)"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "tooltip": "denoise value"}), + "clip_skip": ("INT", {"default": 0, "min": -24, "max": 24, "tooltip": "skip last CLIP layers (positive or negative value, 0 for no skip)"}), + "additional_hashes": ("STRING", {"default": "", "multiline": False, "tooltip": "hashes separated by commas, optionally with names. 'Name:HASH' (e.g., 'MyLoRA:FF735FF83F98')\nWith download_civitai_data set to true, weights can be added as well. (e.g., 'HASH:Weight', 'Name:HASH:Weight')"}), + "download_civitai_data": ("BOOLEAN", {"default": True, "tooltip": "Download and cache data from civitai.com to save correct metadata. Allows LoRA weights to be saved to the metadata."}), + "easy_remix": ("BOOLEAN", {"default": True, "tooltip": "Strip LoRAs and simplify 'embedding:path' from the prompt to make the Remix option on civitai.com more seamless."}), + "custom": ("STRING", {"default": "", "multiline": False, "tooltip": "custom string to add to the metadata, inserted into the a111 string between clip skip and model hash"}), + }, + } + + RETURN_TYPES = ("METADATA","STRING","STRING") + RETURN_NAMES = ("metadata","hashes","a1111_params") + OUTPUT_TOOLTIPS = ("metadata for Image Saver Simple","Comma-separated list of the hashes to chain with other Image Saver additional_hashes","Written parameters to the image metadata") + FUNCTION = "get_metadata" + CATEGORY = "ImageSaver" + DESCRIPTION = "Prepare metadata for Image Saver Simple" + + def get_metadata( + self, + modelname: str = "", + positive: str = "unknown", + negative: str = "unknown", + width: int = 512, + height: int = 512, + seed_value: int = 0, + steps: int = 20, + cfg: float = 7.0, + sampler_name: str = "", + scheduler_name: str = "normal", + denoise: float = 1.0, + clip_skip: int = 0, + custom: str = "", + additional_hashes: str = "", + download_civitai_data: bool = True, + easy_remix: bool = True, + ) -> tuple[Metadata, str, str]: + metadata = ImageSaverMetadata.make_metadata(modelname, positive, negative, width, height, seed_value, steps, cfg, sampler_name, scheduler_name, denoise, clip_skip, custom, additional_hashes, download_civitai_data, easy_remix) + return (metadata, metadata.final_hashes, metadata.a111_params) + + @staticmethod + def make_metadata(modelname: str, positive: str, negative: str, width: int, height: int, seed_value: int, steps: int, cfg: float, sampler_name: str, scheduler_name: str, denoise: float, clip_skip: int, custom: str, additional_hashes: str, download_civitai_data: bool, easy_remix: bool) -> Metadata: + modelname, additional_hashes = ImageSaver.get_multiple_models(modelname, additional_hashes) + + ckpt_path = full_checkpoint_path_for(modelname) + if ckpt_path: + modelhash = get_sha256(ckpt_path)[:10] + else: + modelhash = "" + + metadata_extractor = PromptMetadataExtractor([positive, negative]) + embeddings = metadata_extractor.get_embeddings() + loras = metadata_extractor.get_loras() + civitai_sampler_name = get_civitai_sampler_name(sampler_name.replace('_gpu', ''), scheduler_name) + basemodelname = parse_checkpoint_name_without_extension(modelname) + + # Get existing hashes from model, loras, and embeddings + existing_hashes = {modelhash.lower()} | {t[2].lower() for t in loras.values()} | {t[2].lower() for t in embeddings.values()} + # Parse manual hashes + manual_entries = ImageSaver.parse_manual_hashes(additional_hashes, existing_hashes, download_civitai_data) + # Get Civitai metadata + civitai_resources, hashes, add_model_hash = get_civitai_metadata(modelname, ckpt_path, modelhash, loras, embeddings, manual_entries, download_civitai_data) + + if easy_remix: + positive = ImageSaver.clean_prompt(positive, metadata_extractor) + negative = ImageSaver.clean_prompt(negative, metadata_extractor) + + positive_a111_params = positive.strip() + negative_a111_params = f"\nNegative prompt: {negative.strip()}" + clip_skip_str = f", Clip skip: {abs(clip_skip)}" if clip_skip != 0 else "" + custom_str = f", {custom}" if custom else "" + model_hash_str = f", Model hash: {add_model_hash}" if add_model_hash else "" + hashes_str = f", Hashes: {json.dumps(hashes, separators=(',', ':'))}" if hashes else "" + + a111_params = ( + f"{positive_a111_params}{negative_a111_params}\n" + f"Steps: {steps}, Sampler: {civitai_sampler_name}, CFG scale: {cfg}, Seed: {seed_value}, " + f"Size: {width}x{height}{clip_skip_str}{custom_str}{model_hash_str}, Model: {basemodelname}{hashes_str}, Version: ComfyUI" + ) + + # Add Civitai resource listing + if download_civitai_data and civitai_resources: + a111_params += f", Civitai resources: {json.dumps(civitai_resources, separators=(',', ':'))}" + + # Combine all resources (model, loras, embeddings, manual entries) for final hash string + all_resources = { modelname: ( ckpt_path, None, modelhash ) } | loras | embeddings | manual_entries + + hash_parts = [] + for name, (_, weight, hash_value) in (all_resources.items() if isinstance(all_resources, dict) else all_resources): + # Format: "name:hash" or "name:hash:weight" depending on download_civitai_data + if name: + # Extract clean name (only remove actual model file extensions, preserve dots in model names) + filename = name.split(':')[-1] + name_without_ext, ext = os.path.splitext(filename) + supported_extensions = folder_paths.supported_pt_extensions | {".gguf"} + + # Only remove extension if it's a known model file extension + if ext.lower() in supported_extensions: + clean_name = name_without_ext + else: + clean_name = filename # Keep full name if extension isn't recognized + + name_part = f"{clean_name}:" + else: + name_part = "" + + # Skip entries without a valid hash + if not hash_value: + continue + + weight_part = f":{weight}" if weight is not None and download_civitai_data else "" + hash_parts.append(f"{name_part}{hash_value}{weight_part}") + + final_hashes = ",".join(hash_parts) + + metadata = Metadata(modelname, positive, negative, width, height, seed_value, steps, cfg, sampler_name, scheduler_name, denoise, clip_skip, custom, additional_hashes, ckpt_path, a111_params, final_hashes) + return metadata + +class ImageSaverSimple: + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "images": ("IMAGE", { "tooltip": "image(s) to save"}), + "filename": ("STRING", {"default": '%time_%basemodelname_%seed', "multiline": False, "tooltip": "filename (available variables: %date, %time, %time_format, %model, %width, %height, %seed, %counter, %sampler_name, %steps, %cfg, %scheduler_name, %basemodelname, %denoise, %clip_skip)"}), + "path": ("STRING", {"default": '', "multiline": False, "tooltip": "path to save the images (under Comfy's save directory)"}), + "extension": (['png', 'jpeg', 'jpg', 'webp'], { "tooltip": "file extension/type to save image as"}), + "lossless_webp": ("BOOLEAN", {"default": True, "tooltip": "if True, saved WEBP files will be lossless"}), + "quality_jpeg_or_webp": ("INT", {"default": 100, "min": 1, "max": 100, "tooltip": "quality setting of JPEG/WEBP"}), + "optimize_png": ("BOOLEAN", {"default": False, "tooltip": "if True, saved PNG files will be optimized (can reduce file size but is slower)"}), + "embed_workflow": ("BOOLEAN", {"default": True, "tooltip": "if True, embeds the workflow in the saved image files.\nStable for PNG, experimental for WEBP.\nJPEG experimental and only if metadata size is below 65535 bytes"}), + "save_workflow_as_json": ("BOOLEAN", {"default": False, "tooltip": "if True, also saves the workflow as a separate JSON file"}), + }, + "optional": { + "metadata": ("METADATA", {"default": None, "tooltip": "metadata to embed in the image"}), + "counter": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "counter"}), + "time_format": ("STRING", {"default": "%Y-%m-%d-%H%M%S", "multiline": False, "tooltip": "timestamp format"}), + "show_preview": ("BOOLEAN", {"default": True, "tooltip": "if True, displays saved images in the UI preview"}), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + RETURN_TYPES = ("STRING","STRING") + RETURN_NAMES = ("hashes","a1111_params") + OUTPUT_TOOLTIPS = ("Comma-separated list of the hashes to chain with other Image Saver additional_hashes","Written parameters to the image metadata") + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "ImageSaver" + DESCRIPTION = "Save images with civitai-compatible generation metadata" + + def save_images(self, + images: list[torch.Tensor], + filename: str, + path: str, + extension: str, + lossless_webp: bool, + quality_jpeg_or_webp: int, + optimize_png: bool, + embed_workflow: bool = True, + save_workflow_as_json: bool = False, + show_preview: bool = True, + metadata: Metadata | None = None, + counter: int = 0, + time_format: str = "%Y-%m-%d-%H%M%S", + prompt: dict[str, Any] | None = None, + extra_pnginfo: dict[str, Any] | None = None, + ) -> dict[str, Any]: + if metadata is None: + metadata = Metadata('', '', '', 512, 512, 0, 20, 7.0, '', 'normal', 1.0, 0, '', '', '', '', '') + + path = make_pathname(path, metadata.width, metadata.height, metadata.seed, metadata.modelname, counter, time_format, metadata.sampler_name, metadata.steps, metadata.cfg, metadata.scheduler_name, metadata.denoise, metadata.clip_skip, metadata.custom) + + filenames = ImageSaver.save_images(images, filename, extension, path, quality_jpeg_or_webp, lossless_webp, optimize_png, prompt, extra_pnginfo, save_workflow_as_json, embed_workflow, counter, time_format, metadata) + + subfolder = os.path.normpath(path) + + result: dict[str, Any] = { + "result": (metadata.final_hashes, metadata.a111_params), + } + + if show_preview: + result["ui"] = {"images": [{"filename": filename, "subfolder": subfolder if subfolder != '.' else '', "type": 'output'} for filename in filenames]} + + return result + +class ImageSaver: + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "images": ("IMAGE", { "tooltip": "image(s) to save"}), + "filename": ("STRING", {"default": '%time_%basemodelname_%seed', "multiline": False, "tooltip": "filename (available variables: %date, %time, %time_format, %model, %width, %height, %seed, %counter, %sampler_name, %steps, %cfg, %scheduler_name, %basemodelname, %denoise, %clip_skip)"}), + "path": ("STRING", {"default": '', "multiline": False, "tooltip": "path to save the images (under Comfy's save directory)"}), + "extension": (['png', 'jpeg', 'jpg', 'webp'], { "tooltip": "file extension/type to save image as"}), + }, + "optional": { + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "number of steps"}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "tooltip": "CFG value"}), + "modelname": ("STRING", {"default": '', "multiline": False, "tooltip": "model name (can be multiple, separated by commas)"}), + "sampler_name": ("STRING", {"default": '', "multiline": False, "tooltip": "sampler name (as string)"}), + "scheduler_name": ("STRING", {"default": 'normal', "multiline": False, "tooltip": "scheduler name (as string)"}), + "positive": ("STRING", {"default": 'unknown', "multiline": True, "tooltip": "positive prompt"}), + "negative": ("STRING", {"default": 'unknown', "multiline": True, "tooltip": "negative prompt"}), + "seed_value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "seed"}), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "image width"}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "image height"}), + "lossless_webp": ("BOOLEAN", {"default": True, "tooltip": "if True, saved WEBP files will be lossless"}), + "quality_jpeg_or_webp": ("INT", {"default": 100, "min": 1, "max": 100, "tooltip": "quality setting of JPEG/WEBP"}), + "optimize_png": ("BOOLEAN", {"default": False, "tooltip": "if True, saved PNG files will be optimized (can reduce file size but is slower)"}), + "counter": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "counter"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "tooltip": "denoise value"}), + "clip_skip": ("INT", {"default": 0, "min": -24, "max": 24, "tooltip": "skip last CLIP layers (positive or negative value, 0 for no skip)"}), + "time_format": ("STRING", {"default": "%Y-%m-%d-%H%M%S", "multiline": False, "tooltip": "timestamp format"}), + "save_workflow_as_json": ("BOOLEAN", {"default": False, "tooltip": "if True, also saves the workflow as a separate JSON file"}), + "embed_workflow": ("BOOLEAN", {"default": True, "tooltip": "if True, embeds the workflow in the saved image files.\nStable for PNG, experimental for WEBP.\nJPEG experimental and only if metadata size is below 65535 bytes"}), + "additional_hashes": ("STRING", {"default": "", "multiline": False, "tooltip": "hashes separated by commas, optionally with names. 'Name:HASH' (e.g., 'MyLoRA:FF735FF83F98')\nWith download_civitai_data set to true, weights can be added as well. (e.g., 'HASH:Weight', 'Name:HASH:Weight')"}), + "download_civitai_data": ("BOOLEAN", {"default": True, "tooltip": "Download and cache data from civitai.com to save correct metadata. Allows LoRA weights to be saved to the metadata."}), + "easy_remix": ("BOOLEAN", {"default": True, "tooltip": "Strip LoRAs and simplify 'embedding:path' from the prompt to make the Remix option on civitai.com more seamless."}), + "show_preview": ("BOOLEAN", {"default": True, "tooltip": "if True, displays saved images in the UI preview"}), + "custom": ("STRING", {"default": "", "multiline": False, "tooltip": "custom string to add to the metadata, inserted into the a111 string between clip skip and model hash"}), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + RETURN_TYPES = ("STRING","STRING") + RETURN_NAMES = ("hashes","a1111_params") + OUTPUT_TOOLTIPS = ("Comma-separated list of the hashes to chain with other Image Saver additional_hashes","Written parameters to the image metadata") + FUNCTION = "save_files" + + OUTPUT_NODE = True + + CATEGORY = "ImageSaver" + DESCRIPTION = "Save images with civitai-compatible generation metadata" + + def save_files( + self, + images: list[torch.Tensor], + filename: str, + path: str, + extension: str, + steps: int = 20, + cfg: float = 7.0, + modelname: str = "", + sampler_name: str = "", + scheduler_name: str = "normal", + positive: str = "unknown", + negative: str = "unknown", + seed_value: int = 0, + width: int = 512, + height: int = 512, + lossless_webp: bool = True, + quality_jpeg_or_webp: int = 100, + optimize_png: bool = False, + counter: int = 0, + denoise: float = 1.0, + clip_skip: int = 0, + time_format: str = "%Y-%m-%d-%H%M%S", + save_workflow_as_json: bool = False, + embed_workflow: bool = True, + additional_hashes: str = "", + download_civitai_data: bool = True, + easy_remix: bool = True, + show_preview: bool = True, + custom: str = "", + prompt: dict[str, Any] | None = None, + extra_pnginfo: dict[str, Any] | None = None, + ) -> dict[str, Any]: + metadata = ImageSaverMetadata.make_metadata(modelname, positive, negative, width, height, seed_value, steps, cfg, sampler_name, scheduler_name, denoise, clip_skip, custom, additional_hashes, download_civitai_data, easy_remix) + + path = make_pathname(path, metadata.width, metadata.height, metadata.seed, metadata.modelname, counter, time_format, metadata.sampler_name, metadata.steps, metadata.cfg, metadata.scheduler_name, metadata.denoise, metadata.clip_skip, metadata.custom) + + filenames = ImageSaver.save_images(images, filename, extension, path, quality_jpeg_or_webp, lossless_webp, optimize_png, prompt, extra_pnginfo, save_workflow_as_json, embed_workflow, counter, time_format, metadata) + + subfolder = os.path.normpath(path) + + result: dict[str, Any] = { + "result": (metadata.final_hashes, metadata.a111_params), + } + + if show_preview: + result["ui"] = {"images": [{"filename": filename, "subfolder": subfolder if subfolder != '.' else '', "type": 'output'} for filename in filenames]} + + return result + + @staticmethod + def save_images( + images: list[torch.Tensor], + filename_pattern: str, + extension: str, + path: str, + quality_jpeg_or_webp: int, + lossless_webp: bool, + optimize_png: bool, + prompt: dict[str, Any] | None, + extra_pnginfo: dict[str, Any] | None, + save_workflow_as_json: bool, + embed_workflow: bool, + counter: int, + time_format: str, + metadata: Metadata + ) -> list[str]: + filename_prefix = make_filename(filename_pattern, metadata.width, metadata.height, metadata.seed, metadata.modelname, counter, time_format, metadata.sampler_name, metadata.steps, metadata.cfg, metadata.scheduler_name, metadata.denoise, metadata.clip_skip, metadata.custom) + + output_path = os.path.join(folder_paths.output_directory, path) + + if output_path.strip() != '': + if not os.path.exists(output_path.strip()): + print(f'The path `{output_path.strip()}` specified doesn\'t exist! Creating directory.') + os.makedirs(output_path, exist_ok=True) + + result_paths: list[str] = list() + num_images = len(images) + for idx, image in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + + current_filename_prefix = ImageSaver.get_unique_filename(output_path, filename_prefix, extension, batch_size=num_images, batch_index=idx) + final_filename = f"{current_filename_prefix}.{extension}" + filepath = os.path.join(output_path, final_filename) + + save_image(img, filepath, extension, quality_jpeg_or_webp, lossless_webp, optimize_png, metadata.a111_params, prompt, extra_pnginfo, embed_workflow) + + if save_workflow_as_json: + save_json(extra_pnginfo, os.path.join(output_path, current_filename_prefix)) + + result_paths.append(final_filename) + return result_paths + + # Match 'anything' or 'anything:anything' with trimmed white space + re_manual_hash = re.compile(r'^\s*([^:]+?)(?:\s*:\s*([^\s:][^:]*?))?\s*$') + # Match 'anything', 'anything:anything' or 'anything:anything:number' with trimmed white space + re_manual_hash_weights = re.compile(r'^\s*([^:]+?)(?:\s*:\s*([^\s:][^:]*?))?(?:\s*:\s*([-+]?(?:\d+(?:\.\d*)?|\.\d+)))?\s*$') + + @staticmethod + def get_multiple_models(modelname: str, additional_hashes: str) -> tuple[str, str]: + model_names = [m.strip() for m in modelname.split(',')] + modelname = model_names[0] # Use the first model as the primary one + + # Process additional model names and add to additional_hashes + for additional_model in model_names[1:]: + additional_ckpt_path = full_checkpoint_path_for(additional_model) + if additional_ckpt_path: + additional_modelhash = get_sha256(additional_ckpt_path)[:10] + # Add to additional_hashes in "name:HASH" format + if additional_hashes: + additional_hashes += "," + additional_hashes += f"{additional_model}:{additional_modelhash}" + return modelname, additional_hashes + + @staticmethod + def parse_manual_hashes(additional_hashes: str, existing_hashes: set[str], download_civitai_data: bool) -> dict[str, tuple[str | None, float | None, str]]: + """Process additional_hashes input (a string) by normalizing, removing extra spaces/newlines, and splitting by comma""" + manual_entries: dict[str, tuple[str | None, float | None, str]] = {} + unnamed_count = 0 + + additional_hash_split = additional_hashes.replace("\n", ",").split(",") if additional_hashes else [] + for entry in additional_hash_split: + match = (ImageSaver.re_manual_hash_weights if download_civitai_data else ImageSaver.re_manual_hash).search(entry) + if match is None: + print(f"ComfyUI-Image-Saver: Invalid additional hash string: '{entry}'") + continue + + groups = tuple(group for group in match.groups() if group) + + # Read weight and remove from groups, if needed + weight = None + if download_civitai_data and len(groups) > 1: + try: + weight = float(groups[-1]) + groups = groups[:-1] + except (ValueError, TypeError): + pass + + # Read hash, optionally preceded by name + name, hash = groups if len(groups) > 1 else (None, groups[0]) + + if len(hash) > MAX_HASH_LENGTH: + print(f"ComfyUI-Image-Saver: Skipping hash. Length exceeds maximum of {MAX_HASH_LENGTH} characters: {hash}") + continue + + if any(hash.lower() == existing_hash.lower() for _, _, existing_hash in manual_entries.values()): + print(f"ComfyUI-Image-Saver: Skipping duplicate hash: {hash}") + continue # Skip duplicates + + if hash.lower() in existing_hashes: + print(f"ComfyUI-Image-Saver: Skipping manual hash already present in resources: {hash}") + continue + + if name is None: + unnamed_count += 1 + name = f"manual{unnamed_count}" + elif name in manual_entries: + print(f"ComfyUI-Image-Saver: Duplicate manual hash name '{name}' is being overwritten.") + + manual_entries[name] = (None, weight, hash) + + if len(manual_entries) > 29: + print("ComfyUI-Image-Saver: Reached maximum limit of 30 manual hashes. Skipping the rest.") + break + + return manual_entries + + @staticmethod + def clean_prompt(prompt: str, metadata_extractor: PromptMetadataExtractor) -> str: + """Clean prompts for easier remixing by removing LoRAs and simplifying embeddings.""" + # Strip loras + prompt = re.sub(metadata_extractor.LORA, "", prompt) + # Shorten 'embedding:path/to/my_embedding' -> 'my_embedding' + # Note: Possible inaccurate embedding name if the filename has been renamed from the default + prompt = re.sub(metadata_extractor.EMBEDDING, lambda match: Path(match.group(1)).stem, prompt) + # Remove prompt control edits. e.g., 'STYLE(A1111, mean)', 'SHIFT(1)`, etc.` + prompt = re.sub(r'\b[A-Z]+\([^)]*\)', "", prompt) + return prompt + + @staticmethod + def get_unique_filename(output_path: str, filename_prefix: str, extension: str, batch_size: int = 1, batch_index: int = 0) -> str: + existing_files = [f for f in os.listdir(output_path) if f.startswith(filename_prefix) and f.endswith(extension)] + + # For single images with no existing files, return plain filename + if batch_size == 1 and not existing_files: + return f"{filename_prefix}" + + # For batches or when files exist, always use numbered suffix + suffixes: list[int] = [] + for f in existing_files: + name, _ = os.path.splitext(f) + parts = name.split('_') + if parts[-1].isdigit(): + suffixes.append(int(parts[-1])) + + if suffixes: + # Start numbering after the highest existing suffix + base_suffix = max(suffixes) + 1 + else: + # No numbered files exist yet + if existing_files: + # Plain file exists, start at 1 (the plain file is effectively 0) + base_suffix = 1 + else: + # No files at all, start at 1 + base_suffix = 1 + + return f"{filename_prefix}_{base_suffix + batch_index:02d}" diff --git a/custom_nodes/comfyui-image-saver/nodes_literals.py b/custom_nodes/comfyui-image-saver/nodes_literals.py new file mode 100644 index 00000000..6ab02a49 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/nodes_literals.py @@ -0,0 +1,358 @@ +from sys import float_info +from typing import Any +from nodes import MAX_RESOLUTION +import torch + +import numpy as np +from PIL import Image, ImageDraw +import random +import math + +class SeedGenerator: + RETURN_TYPES = ("INT",) + OUTPUT_TOOLTIPS = ("seed (INT)",) + FUNCTION = "get_seed" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Provides seed as integer" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), + "increment": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "tooltip": "number to add to the final seed value"}), + } + } + + def get_seed(self, seed: int, increment: int) -> tuple[int,]: + return (seed + increment,) + +class StringLiteral: + RETURN_TYPES = ("STRING",) + OUTPUT_TOOLTIPS = ("string (STRING)",) + FUNCTION = "get_string" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Provides a string" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "string": ("STRING", {"default": "", "multiline": True, "tooltip": "string"}), + } + } + + def get_string(self, string: str) -> tuple[str,] : + return (string,) + +class SizeLiteral: + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("size",) + OUTPUT_TOOLTIPS = ("size (INT)",) + FUNCTION = "get_int" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = f"Provides integer number between 0 and {MAX_RESOLUTION} (step=8)" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "size": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "size as integer (in steps of 8)"}), + } + } + + def get_int(self, size: int) -> tuple[int,]: + return (size,) + +class IntLiteral: + RETURN_TYPES = ("INT",) + OUTPUT_TOOLTIPS = ("int (INT)",) + FUNCTION = "get_int" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Provides integer number between 0 and 1000000" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "int": ("INT", {"default": 0, "min": 0, "max": 1000000, "tooltip": "integer number"}), + } + } + + def get_int(self, int: int) -> tuple[int,]: + return (int,) + +class FloatLiteral: + RETURN_TYPES = ("FLOAT",) + OUTPUT_TOOLTIPS = ("float (FLOAT)",) + FUNCTION = "get_float" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = f"Provides a floating point number between {float_info.min} and {float_info.max} (step=0.01)" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "float": ("FLOAT", {"default": 1.0, "min": float_info.min, "max": float_info.max, "step": 0.01, "tooltip": "floating point number"}), + } + } + + def get_float(self, float: float): + return (float,) + +class CfgLiteral: + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("value",) + OUTPUT_TOOLTIPS = ("cfg (FLOAT)",) + FUNCTION = "get_float" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Provides CFG value between 0.0 and 100.0" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "tooltip": "CFG as a floating point number"}), + } + } + + def get_float(self, cfg: float) -> tuple[float,]: + return (cfg,) + +class ConditioningConcatOptional: + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "conditioning_to": ("CONDITIONING", {"tooltip": "base conditioning to concat to (or pass through, if second is empty)"}), + }, + "optional": { + "conditioning_from": ("CONDITIONING", {"tooltip": "conditioning to concat to conditioning_to, if empty, then conditioning_to is passed through unchanged"}), + } + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "concat" + CATEGORY = "conditioning" + + def concat(self, conditioning_to, conditioning_from=None): + if conditioning_from is None: + return (conditioning_to,) + + out = [] + if len(conditioning_from) > 1: + print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from), 1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + return (out,) + +class RandomShapeGenerator: + """ + A ComfyUI node that generates images with random shapes. + """ + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "width": ("INT", { "default": 512, "min": 64, "max": 4096, "step": 64, "tooltip": "Width of the generated image in pixels" }), + "height": ("INT", { "default": 512, "min": 64, "max": 4096, "step": 64, "tooltip": "Height of the generated image in pixels" }), + "bg_color": (["random", "white", "black", "red", "green", "blue", "yellow", "cyan", "magenta"], { "tooltip": "Background color preset or random" }), + "fg_color": (["random", "black", "white", "red", "green", "blue", "yellow", "cyan", "magenta"], { "tooltip": "Foreground shape color preset or random" }), + "shape_type": (["random", "circle", "oval", "triangle", "square", "rectangle", "rhombus", "pentagon", "hexagon"], { "tooltip": "Type of shape to generate or random" }), + "seed": ("INT", { "default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "Random seed for reproducible shape generation" }), + }, + "optional": { + "bg_color_override": ("STRING", { "default": "", "multiline": False, "tooltip": "Override background color with hex (#AABBCC) or RGB(r, g, b) format" }), + "fg_color_override": ("STRING", { "default": "", "multiline": False, "tooltip": "Override foreground color with hex (#AABBCC) or RGB(r, g, b) format" }), + }, + } + + RETURN_TYPES = ("IMAGE", "STRING", "STRING") + RETURN_NAMES = ("image", "bg_rgb", "fg_rgb") + OUTPUT_TOOLTIPS = ("Generated image with random shape", "Background color as RGB/hex", "Foreground color as RGB/hex") + FUNCTION = "generate_shape" + CATEGORY = "image/generators" + DESCRIPTION = "Generates images with random shapes for testing and prototyping" + + def __init__(self): + self.color_map = { + "white": (255, 255, 255), + "black": (0, 0, 0), + "red": (255, 0, 0), + "green": (0, 255, 0), + "blue": (0, 0, 255), + "yellow": (255, 255, 0), + "cyan": (0, 255, 255), + "magenta": (255, 0, 255), + } + + def parse_rgb_string(self, rgb_str: str) -> tuple[int, int, int] | None: + """Parse RGB string like 'RGB(123, 45, 67)' or '#AABBCC' into tuple (123, 45, 67)""" + if not rgb_str or rgb_str.strip() == "": + return None + + rgb_str = rgb_str.strip() + + try: + # Try hex format first (#AABBCC or AABBCC) + if rgb_str.startswith("#"): + hex_str = rgb_str[1:] + else: + hex_str = rgb_str + + # Check if it's a valid hex string (6 characters) + if len(hex_str) == 6 and all(c in '0123456789ABCDEFabcdef' for c in hex_str): + r = int(hex_str[0:2], 16) + g = int(hex_str[2:4], 16) + b = int(hex_str[4:6], 16) + return (r, g, b) + + # Try RGB(r, g, b) format + rgb_str_upper = rgb_str.upper() + if rgb_str_upper.startswith("RGB(") and rgb_str_upper.endswith(")"): + values = rgb_str[4:-1].split(",") + r, g, b = [int(v.strip()) for v in values] + # Validate range + if all(0 <= val <= 255 for val in [r, g, b]): + return (r, g, b) + except (ValueError, IndexError): + return None + + return None + + def draw_shape(self, draw: ImageDraw.ImageDraw, img_width: int, img_height: int, shape_type: str, shape_color: tuple[int, int, int]) -> None: + """Draw a random shape on the image.""" + + # Random size - prefer larger sizes (40-70% of image dimensions) + size_factor = random.uniform(0.4, 0.7) + shape_width = int(img_width * size_factor) + shape_height = int(img_height * size_factor) + + # Random position (ensure shape stays fully within bounds) + x = random.randint(0, max(0, img_width - shape_width)) + y = random.randint(0, max(0, img_height - shape_height)) + + # Draw the shape based on type + if shape_type == 'circle': + # Make it a perfect circle using the minimum dimension + radius = min(shape_width, shape_height) // 2 + draw.ellipse([x, y, x + radius * 2, y + radius * 2], fill=shape_color) + + elif shape_type == 'oval': + draw.ellipse([x, y, x + shape_width, y + shape_height], fill=shape_color) + + elif shape_type == 'square': + # Make it a perfect square + side = min(shape_width, shape_height) + draw.rectangle([x, y, x + side, y + side], fill=shape_color) + + elif shape_type == 'rectangle': + draw.rectangle([x, y, x + shape_width, y + shape_height], fill=shape_color) + + elif shape_type == 'triangle': + # Equilateral-ish triangle + points = [ + (x + shape_width // 2, y), # top + (x, y + shape_height), # bottom left + (x + shape_width, y + shape_height) # bottom right + ] + draw.polygon(points, fill=shape_color) + + elif shape_type == 'rhombus': + # Diamond shape + points = [ + (x + shape_width // 2, y), # top + (x + shape_width, y + shape_height // 2), # right + (x + shape_width // 2, y + shape_height), # bottom + (x, y + shape_height // 2) # left + ] + draw.polygon(points, fill=shape_color) + + elif shape_type == 'pentagon': + # Regular pentagon + cx, cy = x + shape_width // 2, y + shape_height // 2 + radius = min(shape_width, shape_height) // 2 + points = [] + for i in range(5): + angle = i * 2 * math.pi / 5 - math.pi / 2 + px = cx + radius * math.cos(angle) + py = cy + radius * math.sin(angle) + points.append((px, py)) + draw.polygon(points, fill=shape_color) + + elif shape_type == 'hexagon': + # Regular hexagon + cx, cy = x + shape_width // 2, y + shape_height // 2 + radius = min(shape_width, shape_height) // 2 + points = [] + for i in range(6): + angle = i * 2 * math.pi / 6 + px = cx + radius * math.cos(angle) + py = cy + radius * math.sin(angle) + points.append((px, py)) + draw.polygon(points, fill=shape_color) + + def generate_shape(self, width: int, height: int, bg_color: str, fg_color: str, shape_type: str, seed: int, bg_color_override: str = "", fg_color_override: str = "") -> tuple[torch.Tensor, str, str]: + """Generate an image with a random shape.""" + + # Set random seed for reproducibility + random.seed(seed) + + # Get colors from map or generate random RGB values + # Check for override first + bg_override = self.parse_rgb_string(bg_color_override) + if bg_override is not None: + bg_rgb = bg_override + elif bg_color == "random": + bg_rgb = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) + else: + bg_rgb = self.color_map.get(bg_color, (255, 255, 255)) + + fg_override = self.parse_rgb_string(fg_color_override) + if fg_override is not None: + fg_rgb = fg_override + elif fg_color == "random": + fg_rgb = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) + else: + fg_rgb = self.color_map.get(fg_color, (0, 0, 0)) + + # Create image + img = Image.new('RGB', (width, height), bg_rgb) + draw = ImageDraw.Draw(img) + + # Select shape type + if shape_type == "random": + shapes = ['circle', 'oval', 'triangle', 'square', 'rectangle', 'rhombus', 'pentagon', 'hexagon'] + selected_shape = random.choice(shapes) + else: + selected_shape = shape_type + + # Draw the shape + self.draw_shape(draw, width, height, selected_shape, fg_rgb) + + # Convert PIL Image to torch tensor (ComfyUI format) + # ComfyUI expects images in format [batch, height, width, channels] with values 0-1 + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array)[None,] + + # Format RGB values as strings for output (both formats) + bg_hex = f"#{bg_rgb[0]:02X}{bg_rgb[1]:02X}{bg_rgb[2]:02X}" + fg_hex = f"#{fg_rgb[0]:02X}{fg_rgb[1]:02X}{fg_rgb[2]:02X}" + bg_rgb_str = f"RGB({bg_rgb[0]}, {bg_rgb[1]}, {bg_rgb[2]}) / {bg_hex}" + fg_rgb_str = f"RGB({fg_rgb[0]}, {fg_rgb[1]}, {fg_rgb[2]}) / {fg_hex}" + + return (img_tensor, bg_rgb_str, fg_rgb_str) + diff --git a/custom_nodes/comfyui-image-saver/nodes_loaders.py b/custom_nodes/comfyui-image-saver/nodes_loaders.py new file mode 100644 index 00000000..0c086319 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/nodes_loaders.py @@ -0,0 +1,60 @@ +import torch +import folder_paths +import comfy.sd + +class CheckpointLoaderWithName: + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING") + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "model_name") + OUTPUT_TOOLTIPS = ("U-Net model (denoising latents)", "CLIP (Contrastive Language-Image Pre-Training) model (encoding text prompts)", "VAE (Variational autoencoder) model (latent<->pixel encoding/decoding)", "checkpoint name") + FUNCTION = "load_checkpoint" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Loads U-Net model, CLIP model and VAE model from a checkpoint file" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "checkpoint"}), + } + } + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + + # add checkpoint name to the output tuple (without the ClipVisionModel) + out = (*out[:3], ckpt_name) + return out + +class UNETLoaderWithName: + RETURN_TYPES = ("MODEL", "STRING") + RETURN_NAMES = ("model", "filename") + OUTPUT_TOOLTIPS = ("U-Net model (denoising latents)", "model filename") + FUNCTION = "load_unet" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Loads U-Net model and outputs it's filename" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "unet_name": (folder_paths.get_filename_list("diffusion_models"),), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + } + } + + def load_unet(self, unet_name, weight_dtype): + model_options = {} + if weight_dtype == "fp8_e4m3fn": + model_options["dtype"] = torch.float8_e4m3fn + elif weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + elif weight_dtype == "fp8_e5m2": + model_options["dtype"] = torch.float8_e5m2 + + unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name) + model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options) + return (model, unet_name) diff --git a/custom_nodes/comfyui-image-saver/nodes_selectors.py b/custom_nodes/comfyui-image-saver/nodes_selectors.py new file mode 100644 index 00000000..3ecf2604 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/nodes_selectors.py @@ -0,0 +1,196 @@ +from typing import Any +import comfy + +INSPIRE_SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', "GITS[coeff=1.2]", 'OSS FLUX', 'OSS Wan', 'OSS Chroma'] +EFF_SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SD1', 'AYS SDXL', 'AYS SVD', 'GITS'] + +class AnyToString: + """Converts any input type to a string. Useful for connecting sampler/scheduler outputs from various custom nodes.""" + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + OUTPUT_TOOLTIPS = ("String representation of the input",) + FUNCTION = "convert" + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Converts any input type to string" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "value": ("*",), + } + } + + @classmethod + def VALIDATE_INPUTS(cls, input_types): + return True + + def convert(self, value: Any) -> tuple[str,]: + return (str(value),) + + +class WorkflowInputValue: + """Extracts an input value from the workflow by node ID and input name.""" + + RETURN_TYPES = ("*",) + RETURN_NAMES = ("value",) + OUTPUT_TOOLTIPS = ("Input value from the specified node",) + FUNCTION = "get_input_value" + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Extract an input value from the workflow by node ID and input name" + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "node_id": ("STRING", {"default": "", "multiline": False, "tooltip": "The ID of the node to extract from"}), + "input_name": ("STRING", {"default": "", "multiline": False, "tooltip": "The name of the input to extract"}), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + def get_input_value(self, node_id: str, input_name: str, prompt: dict[str, Any] | None = None, extra_pnginfo: dict[str, Any] | None = None): + if prompt is None: + return (None,) + + # Verify the node exists in the workflow structure + if extra_pnginfo and "workflow" in extra_pnginfo: + workflow = extra_pnginfo["workflow"] + node_exists = any(str(node.get("id")) == node_id for node in workflow.get("nodes", [])) + if not node_exists: + print(f"WorkflowInputValue: Node {node_id} not found in workflow structure") + return (None,) + + # Get the node from the prompt (execution values) + node = prompt.get(node_id) + if node is None: + print(f"WorkflowInputValue: Node {node_id} not found in prompt") + return (None,) + + # Get the inputs from the node + inputs = node.get("inputs", {}) + if input_name not in inputs: + print(f"WorkflowInputValue: Input '{input_name}' not found in node {node_id}") + print(f"WorkflowInputValue: Available inputs: {list(inputs.keys())}") + return (None,) + + value = inputs[input_name] + return (value,) + + +class SamplerSelector: + RETURN_TYPES = (comfy.samplers.KSampler.SAMPLERS, "STRING") + RETURN_NAMES = ("sampler", "sampler_name") + OUTPUT_TOOLTIPS = ("sampler (SAMPLERS)", "sampler name (STRING)") + FUNCTION = "get_names" + + CATEGORY = 'ImageSaver/utils' + DESCRIPTION = 'Provides one of the available ComfyUI samplers' + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler (Comfy's standard)"}), + } + } + + def get_names(self, sampler_name: str) -> tuple[str, str]: + return (sampler_name, sampler_name) + +class SchedulerSelector: + RETURN_TYPES = (comfy.samplers.KSampler.SCHEDULERS, "STRING") + RETURN_NAMES = ("scheduler", "scheduler_name") + OUTPUT_TOOLTIPS = ("scheduler (SCHEDULERS)", "scheduler name (STRING)") + FUNCTION = "get_names" + + CATEGORY = 'ImageSaver/utils' + DESCRIPTION = 'Provides one of the standard KSampler schedulers' + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "scheduler (Comfy's standard)"}), + } + } + + def get_names(self, scheduler: str) -> tuple[str, str]: + return (scheduler, scheduler) + +class SchedulerSelectorInspire: + RETURN_TYPES = (INSPIRE_SCHEDULERS, "STRING") + RETURN_NAMES = ("scheduler", "scheduler_name") + OUTPUT_TOOLTIPS = ("scheduler (ComfyUI + Inspire Pack Schedulers)", "scheduler name (STRING)") + FUNCTION = "get_names" + + CATEGORY = 'ImageSaver/utils' + DESCRIPTION = 'Provides one of the KSampler (inspire) schedulers' + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "scheduler": (INSPIRE_SCHEDULERS, {"tooltip": "scheduler (Comfy's standard + extras)"}), + } + } + + def get_names(self, scheduler: str) -> tuple[str, str]: + return (scheduler, scheduler) + +class SchedulerSelectorEfficiency: + RETURN_TYPES = (EFF_SCHEDULERS, "STRING") + RETURN_NAMES = ("scheduler", "scheduler_name") + OUTPUT_TOOLTIPS = ("scheduler (ComfyUI + Efficiency Pack Schedulers)", "scheduler name (STRING)") + FUNCTION = "get_names" + + CATEGORY = 'ImageSaver/utils' + DESCRIPTION = 'Provides one of the KSampler (Eff.) schedulers' + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "scheduler": (EFF_SCHEDULERS, {"tooltip": "scheduler (Comfy's standard + Efficiency nodes)"}), + } + } + + def get_names(self, scheduler: str) -> tuple[str, str]: + return (scheduler, scheduler) + + +class InputParameters: + RETURN_TYPES = ("INT", "INT", "FLOAT", comfy.samplers.KSampler.SAMPLERS, comfy.samplers.KSampler.SCHEDULERS, "FLOAT") + RETURN_NAMES = ("seed", "steps", "cfg", "sampler", "scheduler", "denoise") + OUTPUT_TOOLTIPS = ( + "seed (INT)", + "steps (INT)", + "cfg (FLOAT)", + "sampler (SAMPLERS)", + "scheduler (SCHEDULERS)", + "denoise (FLOAT)", + ) + FUNCTION = "get_values" + + CATEGORY = "ImageSaver/utils" + DESCRIPTION = "Combined node for seed, steps, cfg, sampler, scheduler and denoise." + + @classmethod + def INPUT_TYPES(cls) -> dict[str, Any]: + return { + "required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), + "sampler": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}), + } + } + + def get_values(self, seed: int, steps: int, cfg: float, sampler: str, scheduler: str, denoise: float) -> tuple[int, int, float, str, str, float]: + return (seed, steps, cfg, sampler, scheduler, denoise) diff --git a/custom_nodes/comfyui-image-saver/prompt_metadata_extractor.py b/custom_nodes/comfyui-image-saver/prompt_metadata_extractor.py new file mode 100644 index 00000000..f411873c --- /dev/null +++ b/custom_nodes/comfyui-image-saver/prompt_metadata_extractor.py @@ -0,0 +1,77 @@ +import re +from typing import List, Dict, Tuple +from comfy.sd1_clip import escape_important, unescape_important, token_weights + +from .utils import full_embedding_path_for, full_lora_path_for, get_sha256 +from .utils_civitai import civitai_embedding_key_name, civitai_lora_key_name + +""" +Extracts Embeddings and Lora's from the given prompts +and allows asking for their sha's +This module is based on civit's plugin and website implementations +The image saver node goes through the automatic flow, not comfy, on civit +see: https://github.com/civitai/sd_civitai_extension/blob/2008ba9126ddbb448f23267029b07e4610dffc15/scripts/gen_hashing.py +see: https://github.com/civitai/civitai/blob/d83262f401fb372c375e6222d8c2413fa221c2c5/src/utils/metadata/automatic.metadata +""" +class PromptMetadataExtractor: + # Anything that follows embedding: with allowance for :weight, :weight.fractal or LBW + LORA: str = r':]+)(?::([^>]+))?>' + + def __init__(self, prompts: List[str]) -> None: + self.__embeddings: Dict[str, Tuple[str, float, str]] = {} + self.__loras: Dict[str, Tuple[str, float, str]] = {} + self.__perform(prompts) + + def get_embeddings(self) -> Dict[str, Tuple[str, float, str]]: + """ + Returns the embeddings used in the given prompts in a format as known by civitAI + Example output: {"embed:EasyNegative": "66a7279a88", "embed:FastNegativeEmbedding": "687b669d82", "embed:ng_deepnegative_v1_75t": "54e7e4826d", "embed:imageSharpener": "fe5a4dfc4a"} + """ + return self.__embeddings + + def get_loras(self) -> Dict[str, Tuple[str, float, str]]: + """ + Returns the lora's used in the given prompts in a format as known by civitAI + Example output: {"LORA:epi_noiseoffset2": "81680c064e", "LORA:GoodHands-beta2": "ba43b0efee"} + """ + return self.__loras + + # Private API + def __perform(self, prompts: List[str]) -> None: + for prompt in prompts: + # Use ComfyUI's built-in attention parser to get accurate weights for embeddings + parsed = ((unescape_important(value), weight) for value, weight in token_weights(escape_important(prompt), 1.0)) + for text, weight in parsed: + embeddings = re.findall(self.EMBEDDING, text, re.IGNORECASE | re.MULTILINE) + for embedding in embeddings: + self.__extract_embedding_information(embedding, weight) + loras = re.findall(self.LORA, prompt, re.IGNORECASE | re.MULTILINE) + for lora in loras: + self.__extract_lora_information(lora) + + def __extract_embedding_information(self, embedding: str, weight: float) -> None: + embedding_name = civitai_embedding_key_name(embedding) + embedding_path = full_embedding_path_for(embedding) + if embedding_path == None: + return + sha = self.__get_shortened_sha(embedding_path) + # Based on https://github.com/civitai/sd_civitai_extension/blob/2008ba9126ddbb448f23267029b07e4610dffc15/scripts/gen_hashing.py#L53 + self.__embeddings[embedding_name] = (embedding_path, weight, sha) + + def __extract_lora_information(self, lora: Tuple[str, str]) -> None: + lora_name = civitai_lora_key_name(lora[0]) + lora_path = full_lora_path_for(lora[0]) + if lora_path == None: + return + try: + lora_weight = float(lora[1].split(':')[0]) + except (ValueError, TypeError): + lora_weight = 1.0 + sha = self.__get_shortened_sha(lora_path) + # Based on https://github.com/civitai/sd_civitai_extension/blob/2008ba9126ddbb448f23267029b07e4610dffc15/scripts/gen_hashing.py#L63 + self.__loras[lora_name] = (lora_path, lora_weight, sha) + + def __get_shortened_sha(self, file_path: str) -> str: + return get_sha256(file_path)[:10] \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/pyproject.toml b/custom_nodes/comfyui-image-saver/pyproject.toml new file mode 100644 index 00000000..9f995228 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-image-saver" +description = "Save images with generation metadata compatible with Civitai. Works with png, jpeg and webp. Stores LoRAs, models and embeddings hashes for resource recognition." +version = "1.21.0" +license = { file = "LICENSE" } +dependencies = ["piexif"] + +[project.urls] +Repository = "https://github.com/alexopus/ComfyUI-Image-Saver" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "alexopus" +DisplayName = "ComfyUI Image Saver" +Icon = "" diff --git a/custom_nodes/comfyui-image-saver/requirements.txt b/custom_nodes/comfyui-image-saver/requirements.txt new file mode 100644 index 00000000..c5d6204f --- /dev/null +++ b/custom_nodes/comfyui-image-saver/requirements.txt @@ -0,0 +1,2 @@ + +piexif diff --git a/custom_nodes/comfyui-image-saver/saver/__init__.py b/custom_nodes/comfyui-image-saver/saver/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui-image-saver/saver/default_workflow.json b/custom_nodes/comfyui-image-saver/saver/default_workflow.json new file mode 100644 index 00000000..811b6f03 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/saver/default_workflow.json @@ -0,0 +1,385 @@ +{ + "last_node_id": 9, + "last_link_id": 9, + "nodes": [ + { + "id": 7, + "type": "CLIPTextEncode", + "pos": [ + 413, + 389 + ], + "size": { + "0": 425.27801513671875, + "1": 180.6060791015625 + }, + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 5 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "links": [ + 6 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "text, watermark" + ] + }, + { + "id": 6, + "type": "CLIPTextEncode", + "pos": [ + 415, + 186 + ], + "size": { + "0": 422.84503173828125, + "1": 164.31304931640625 + }, + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 3 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "links": [ + 4 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "beautiful scenery nature glass bottle landscape, , purple galaxy bottle," + ] + }, + { + "id": 5, + "type": "EmptyLatentImage", + "pos": [ + 473, + 609 + ], + "size": { + "0": 315, + "1": 106 + }, + "flags": {}, + "order": 0, + "mode": 0, + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 2 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "EmptyLatentImage" + }, + "widgets_values": [ + 512, + 512, + 1 + ] + }, + { + "id": 3, + "type": "KSampler", + "pos": [ + 863, + 186 + ], + "size": { + "0": 315, + "1": 262 + }, + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "name": "model", + "type": "MODEL", + "link": 1 + }, + { + "name": "positive", + "type": "CONDITIONING", + "link": 4 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 6 + }, + { + "name": "latent_image", + "type": "LATENT", + "link": 2 + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 7 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "KSampler" + }, + "widgets_values": [ + 156680208700286, + "randomize", + 20, + 8, + "euler", + "normal", + 1 + ] + }, + { + "id": 8, + "type": "VAEDecode", + "pos": [ + 1209, + 188 + ], + "size": { + "0": 210, + "1": 46 + }, + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": "samples", + "type": "LATENT", + "link": 7 + }, + { + "name": "vae", + "type": "VAE", + "link": 8 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 9 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "VAEDecode" + } + }, + { + "id": 9, + "type": "SaveImage", + "pos": [ + 1451, + 189 + ], + "size": { + "0": 210, + "1": 58 + }, + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 9 + } + ], + "properties": { + "Node name for S&R": "SaveImage" + }, + "widgets_values": [ + "ComfyUI" + ] + }, + { + "id": 4, + "type": "CheckpointLoaderSimple", + "pos": [ + 26, + 474 + ], + "size": { + "0": 315, + "1": 98 + }, + "flags": {}, + "order": 1, + "mode": 0, + "outputs": [ + { + "name": "MODEL", + "type": "MODEL", + "links": [ + 1 + ], + "slot_index": 0 + }, + { + "name": "CLIP", + "type": "CLIP", + "links": [ + 3, + 5 + ], + "slot_index": 1 + }, + { + "name": "VAE", + "type": "VAE", + "links": [ + 8 + ], + "slot_index": 2 + } + ], + "properties": { + "Node name for S&R": "CheckpointLoaderSimple" + }, + "widgets_values": [ + "3Guofeng3_v32Light.safetensors" + ] + } + ], + "links": [ + [ + 1, + 4, + 0, + 3, + 0, + "MODEL" + ], + [ + 2, + 5, + 0, + 3, + 3, + "LATENT" + ], + [ + 3, + 4, + 1, + 6, + 0, + "CLIP" + ], + [ + 4, + 6, + 0, + 3, + 1, + "CONDITIONING" + ], + [ + 5, + 4, + 1, + 7, + 0, + "CLIP" + ], + [ + 6, + 7, + 0, + 3, + 2, + "CONDITIONING" + ], + [ + 7, + 3, + 0, + 8, + 0, + "LATENT" + ], + [ + 8, + 4, + 2, + 8, + 1, + "VAE" + ], + [ + 9, + 8, + 0, + 9, + 0, + "IMAGE" + ] + ], + "groups": [], + "config": {}, + "extra": { + "ds": { + "scale": 0.8264462809917354, + "offset": [ + 565.6800000000005, + -43.919999999999995 + ] + }, + "info": { + "name": "workflow", + "author": "", + "description": "", + "version": "1", + "created": "2024-06-02T20:17:02.243Z", + "modified": "2024-06-02T20:17:11.438Z", + "software": "ComfyUI" + } + }, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/saver/pytest.ini b/custom_nodes/comfyui-image-saver/saver/pytest.ini new file mode 100644 index 00000000..f0802fe0 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/saver/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +testpaths = . +python_files = test_*.py \ No newline at end of file diff --git a/custom_nodes/comfyui-image-saver/saver/saver.py b/custom_nodes/comfyui-image-saver/saver/saver.py new file mode 100644 index 00000000..0142556f --- /dev/null +++ b/custom_nodes/comfyui-image-saver/saver/saver.py @@ -0,0 +1,63 @@ +from typing import Any, cast +from PIL.PngImagePlugin import PngInfo +from PIL.Image import Image + +import json +import piexif +import piexif.helper + +def save_image(image: Image, filepath: str, extension: str, quality_jpeg_or_webp: int, lossless_webp: bool, optimize_png: bool, a111_params: str, prompt: dict[str, Any] | None, extra_pnginfo: dict[str, Any] | None, embed_workflow: bool) -> None: + if extension == 'png': + metadata = PngInfo() + if a111_params: + metadata.add_text("parameters", a111_params) + + if embed_workflow: + if extra_pnginfo is not None: + for k, v in extra_pnginfo.items(): + metadata.add_text(k, json.dumps(v, separators=(',', ':'))) + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt, separators=(',', ':'))) + + image.save(filepath, pnginfo=metadata, optimize=optimize_png) + else: # webp & jpeg + image.save(filepath, optimize=True, quality=quality_jpeg_or_webp, lossless=lossless_webp) + + # Native example adding workflow to exif: + # https://github.com/comfyanonymous/ComfyUI/blob/095610717000bffd477a7e72988d1fb2299afacb/comfy_extras/nodes_images.py#L113 + pnginfo_json = {} + prompt_json = {} + if embed_workflow: + if extra_pnginfo is not None: + pnginfo_json = {piexif.ImageIFD.Make - i: f"{k}:{json.dumps(v, separators=(',', ':'))}" for i, (k, v) in enumerate(extra_pnginfo.items())} + if prompt is not None: + prompt_json = {piexif.ImageIFD.Model: f"prompt:{json.dumps(prompt, separators=(',', ':'))}"} + + def get_exif_bytes() -> bytes: + exif_dict = ({ + "0th": pnginfo_json | prompt_json + } if pnginfo_json or prompt_json else {}) | ({ + "Exif": { + piexif.ExifIFD.UserComment: cast(bytes, piexif.helper.UserComment.dump(a111_params, encoding="unicode")) + }, + } if a111_params else {}) + return cast(bytes, piexif.dump(exif_dict)) + + exif_bytes = get_exif_bytes() + + # JPEG format limits the EXIF bytes to a maximum of 65535 bytes + if extension == "jpg" or extension == "jpeg": + MAX_EXIF_SIZE = 65535 + if len(exif_bytes) > MAX_EXIF_SIZE and embed_workflow: + print("ComfyUI-Image-Saver: Error: Workflow is too large, removing client request prompt.") + prompt_json = {} + exif_bytes = get_exif_bytes() + if len(exif_bytes) > MAX_EXIF_SIZE: + print("ComfyUI-Image-Saver: Error: Workflow is still too large, cannot embed workflow!") + pnginfo_json = {} + exif_bytes = get_exif_bytes() + if len(exif_bytes) > MAX_EXIF_SIZE: + print("ComfyUI-Image-Saver: Error: Metadata exceeds maximum size for JPEG. Cannot save metadata.") + return + + piexif.insert(exif_bytes, filepath) diff --git a/custom_nodes/comfyui-image-saver/saver/test_saver.py b/custom_nodes/comfyui-image-saver/saver/test_saver.py new file mode 100644 index 00000000..1606c02f --- /dev/null +++ b/custom_nodes/comfyui-image-saver/saver/test_saver.py @@ -0,0 +1,197 @@ +import os +import itertools +import json +import tempfile +import shutil +import pytest +from PIL import Image +import piexif +import piexif.helper +from .saver import save_image + +def get_default_workflow(): + """Read the default workflow from the JSON file.""" + current_dir = os.path.dirname(os.path.abspath(__file__)) + default_workflow_path = os.path.join(current_dir, "default_workflow.json") + with open(default_workflow_path, 'r') as f: + return json.load(f) + + +def get_large_workflow(padding_size: int): + """Create a large workflow by duplicating the default workflow until it's at least 500KB.""" + default_workflow = get_default_workflow() + large_workflow = default_workflow.copy() + large_workflow["padding"] = "x" * padding_size + workflow_size = len(json.dumps(large_workflow)) / 1024 # Size in KB + print(f"Large workflow size: {workflow_size:.2f} KB") + return large_workflow + + +@pytest.fixture( + params=list(itertools.product( + ["simple", "default", "large", "huge"], # workflow_type + [True, False] # embed_workflow + )), + ids=lambda param: f"workflow-{param[0]}_embed-{param[1]}" +) +def setup_test_env(request): + """Setup test environment with temp directory and test image, parameterized by workflow type.""" + temp_dir = tempfile.mkdtemp() + + test_image = Image.new('RGB', (100, 100), color='red') + + a111_params = """ +beautiful scenery nature glass bottle landscape, purple galaxy bottle, low key +Negative prompt: (worst quality, low quality, bad quality:1.3), embedding:ng_deepnegative_v1_75t, embedding:EasyNegative, embedding:badhandv4 +Steps: 30, Sampler: DPM++ 2M SDE, CFG scale: 7.0, Seed: 42, Size: 512x512, Model: , Version: ComfyUI, +Civitai resources: [ + {"modelName":"Deep Negative V1.x","versionName":"V1 75T","weight":1.0,"air":"urn:air:sd1:embedding:civitai:4629@5637"}, + {"modelName":"EasyNegative","versionName":"EasyNegative_pt","weight":1.0,"air":"urn:air:sd1:embedding:civitai:7808@9536"}, + {"modelName":"badhandv4","versionName":"badhandv4","weight":1.0,"air":"urn:air:other:embedding:civitai:16993@20068"}] +""" + + prompt = {"prompt": "test prompt", "negative_prompt": "test negative prompt"} + + workflow_type, embed_workflow = request.param + + if workflow_type == "simple": + extra_pnginfo = {"workflow": {"version": "1.0", "nodes": []}} + elif workflow_type == "default": + default_workflow = get_default_workflow() + extra_pnginfo = {"workflow": default_workflow} + elif workflow_type == "large": + large_workflow = get_large_workflow(524288 ) + extra_pnginfo = {"workflow": large_workflow} + # Check the size for debugging purposes + workflow_size = len(json.dumps(large_workflow)) / 1024 # Size in KB + print(f"Large workflow size: {workflow_size:.2f} KB") + elif workflow_type == "huge": + huge_workflow = get_large_workflow(2097152) + extra_pnginfo = {"workflow": huge_workflow} + # Check the size for debugging purposes + workflow_size = len(json.dumps(huge_workflow)) / 1024 # Size in KB + print(f"Large workflow size: {workflow_size:.2f} KB") + + yield temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow + + shutil.rmtree(temp_dir) + +@pytest.mark.parametrize( + "optimize", + [True, False], + ids=["optimize", "no-optimize"] +) +def test_save_png(setup_test_env, optimize): + """Test that complete metadata is correctly saved and can be retrieved for PNG format.""" + temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow = setup_test_env + image_path = os.path.join(temp_dir, f"test_with_workflow_{workflow_type}.png") + save_image(test_image, image_path, "png", 100, True, optimize, a111_params, prompt, extra_pnginfo, embed_workflow) + saved_image = Image.open(image_path) + try: + assert saved_image.info.get("parameters") == a111_params + if embed_workflow: + assert json.loads(saved_image.info.get("prompt")) == prompt + assert json.loads(saved_image.info.get("workflow")) == extra_pnginfo["workflow"] + else: + assert set(saved_image.info.keys()) == {"parameters"}, "PNG should not contain prompt or workflow data" + finally: + saved_image.close() + +def test_save_jpeg(setup_test_env): + """Test that metadata is correctly saved and can be retrieved for JPEG format.""" + temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow = setup_test_env + jpeg_path = os.path.join(temp_dir, f"test_{workflow_type}.jpeg") + save_image(test_image, jpeg_path, "jpeg", 90, False, False, a111_params, prompt, extra_pnginfo, embed_workflow) + saved_image = Image.open(jpeg_path) + try: + exif_dict = piexif.load(saved_image.info["exif"]) + user_comment = piexif.helper.UserComment.load(exif_dict["Exif"][piexif.ExifIFD.UserComment]) + assert user_comment == a111_params + + if embed_workflow: + if workflow_type == "simple" or workflow_type == "default": + assert "0th" in exif_dict, "Expected workflow data in EXIF" + # verify that prompt and workflow data are in EXIF + expected_keys = {piexif.ImageIFD.Make, piexif.ImageIFD.Model} + found_keys = set(exif_dict["0th"].keys()) & expected_keys + assert len(found_keys) > 0, "Expected workflow or prompt data in EXIF" + + if piexif.ImageIFD.Make in exif_dict["0th"]: + make_data = exif_dict["0th"][piexif.ImageIFD.Make] + make_str = make_data.decode('utf-8') + # Check that workflow matches + if make_str.startswith("workflow:"): + make_str = make_str[len("workflow:"):] + saved_workflow = json.loads(make_str) + original_workflow = extra_pnginfo["workflow"] + + assert saved_workflow == original_workflow, "Saved workflow content doesn't match original" + + if piexif.ImageIFD.Model in exif_dict["0th"]: + model_data = exif_dict["0th"][piexif.ImageIFD.Model] + model_str = model_data.decode('utf-8') + # Check that "prompt" matches + if model_str.startswith("prompt:"): + model_str = model_str[len("prompt:"):] + saved_prompt = json.loads(model_str) + assert saved_prompt == prompt, "Saved prompt content doesn't match original" + else: + # When workflow_type is "large", verify that the workflow is too large to embed + if "0th" in exif_dict: + assert not any(k in exif_dict["0th"] for k in (piexif.ImageIFD.Make, piexif.ImageIFD.Model)), "JPEG should not contain prompt or workflow data" + else: + # When embed_workflow is False, verify no prompt or workflow in EXIF + if "0th" in exif_dict: + assert not any(k in exif_dict["0th"] for k in (piexif.ImageIFD.Make, piexif.ImageIFD.Model)), "JPEG should not contain prompt or workflow data" + finally: + saved_image.close() + +@pytest.mark.parametrize( + "lossless,quality", + [(True, 100), (False, 90)], + ids=["lossless-max", "lossy-90"] +) +def test_save_webp(setup_test_env, lossless, quality): + """Test that metadata is correctly saved and can be retrieved for lossless WebP format.""" + temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow = setup_test_env + iamge_path = os.path.join(temp_dir, f"test_lossless_{workflow_type}.webp") + save_image(test_image, iamge_path, "webp", quality, lossless, False, a111_params, prompt, extra_pnginfo, embed_workflow) + saved_image = Image.open(iamge_path) + try: + # Verify a111_params is correctly stored in EXIF UserComment + exif_dict = piexif.load(saved_image.info["exif"]) + user_comment = piexif.helper.UserComment.load(exif_dict["Exif"][piexif.ExifIFD.UserComment]) + assert user_comment == a111_params + + if embed_workflow: + assert "0th" in exif_dict, "Expected workflow data in EXIF" + # When embed_workflow is True, verify that prompt and workflow data are in EXIF + expected_keys = {piexif.ImageIFD.Make, piexif.ImageIFD.Model} + found_keys = set(exif_dict["0th"].keys()) & expected_keys + assert len(found_keys) > 0, "Expected workflow or prompt data in EXIF" + + if piexif.ImageIFD.Make in exif_dict["0th"]: + make_data = exif_dict["0th"][piexif.ImageIFD.Make] + make_str = make_data.decode('utf-8') + # Check that workflow matches + if make_str.startswith("workflow:"): + make_str = make_str[len("workflow:"):] + saved_workflow = json.loads(make_str) + original_workflow = extra_pnginfo["workflow"] + + assert saved_workflow == original_workflow, "Saved workflow content doesn't match original" + + if piexif.ImageIFD.Model in exif_dict["0th"]: + model_data = exif_dict["0th"][piexif.ImageIFD.Model] + model_str = model_data.decode('utf-8') + # Check that "prompt" matches + if model_str.startswith("prompt:"): + model_str = model_str[len("prompt:"):] + saved_prompt = json.loads(model_str) + assert saved_prompt == prompt, "Saved prompt content doesn't match original" + else: + # When embed_workflow is False, verify no prompt or workflow in EXIF + if "0th" in exif_dict: + assert not any(k in exif_dict["0th"] for k in (piexif.ImageIFD.Make, piexif.ImageIFD.Model)), "WEBP should not contain prompt or workflow data" + finally: + saved_image.close() diff --git a/custom_nodes/comfyui-image-saver/utils.py b/custom_nodes/comfyui-image-saver/utils.py new file mode 100644 index 00000000..e0b19af6 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/utils.py @@ -0,0 +1,151 @@ +import hashlib +import os +import requests +from typing import Optional, Any +from collections.abc import Collection, Iterator +from pathlib import Path +from tqdm import tqdm +import folder_paths +import re + +def sanitize_filename(filename: str) -> str: + """Remove characters that are unsafe for filenames.""" + # Remove characters that are generally unsafe across file systems + unsafe_chars = r'[<>:"|?*\x00-\x1f]' + sanitized = re.sub(unsafe_chars, '', filename) + + # Remove trailing periods and spaces (problematic on Windows) + sanitized = sanitized.rstrip('. ') + return sanitized + +def get_sha256(file_path: str) -> str: + """ + Given the file path, finds a matching sha256 file, or creates one + based on the headers in the source file + """ + file_no_ext = os.path.splitext(file_path)[0] + hash_file = file_no_ext + ".sha256" + + if os.path.exists(hash_file): + try: + with open(hash_file, "r") as f: + return f.read().strip() + except OSError as e: + print(f"ComfyUI-Image-Saver: Error reading existing hash file: {e}") + + sha256_hash = hashlib.sha256() + with open(file_path, "rb") as f: + file_size = os.fstat(f.fileno()).st_size + block_size = 1048576 # 1 MB + + print(f"ComfyUI-Image-Saver: Calculating sha256 for '{Path(file_path).stem}'") + with tqdm(None, None, file_size, unit="B", unit_scale=True, unit_divisor=1024) as progress_bar: + for byte_block in iter(lambda: f.read(block_size), b""): + progress_bar.update(len(byte_block)) + sha256_hash.update(byte_block) + + try: + with open(hash_file, "w") as f: + f.write(sha256_hash.hexdigest()) + except OSError as e: + print(f"ComfyUI-Image-Saver: Error writing hash to {hash_file}: {e}") + + return sha256_hash.hexdigest() + +def full_embedding_path_for(embedding: str) -> Optional[str]: + """ + Based on a embedding name, eg: EasyNegative, finds the path as known in comfy, including extension + """ + matching_embedding = get_file_path_match("embeddings", embedding) + if matching_embedding is None: + print(f'ComfyUI-Image-Saver: could not find full path to embedding "{embedding}"') + return None + return folder_paths.get_full_path("embeddings", matching_embedding) + +def full_lora_path_for(lora: str) -> Optional[str]: + """ + Based on a lora name, e.g., 'epi_noise_offset2', finds the path as known in comfy, including extension. + """ + # Find the matching lora path + matching_lora = get_file_path_match("loras", lora) + if matching_lora is None: + print(f'ComfyUI-Image-Saver: could not find full path to lora "{lora}"') + return None + return folder_paths.get_full_path("loras", matching_lora) + +def full_checkpoint_path_for(model_name: str) -> str: + if not model_name: + return '' + + supported_extensions = set(folder_paths.supported_pt_extensions) | {".gguf"} + + matching_checkpoint = get_file_path_match("checkpoints", model_name, supported_extensions) + if matching_checkpoint is not None: + return folder_paths.get_full_path("checkpoints", matching_checkpoint) + + matching_model = get_file_path_match("diffusion_models", model_name, supported_extensions) + if matching_model: + return folder_paths.get_full_path("diffusion_models", matching_model) + + print(f'Could not find full path to checkpoint "{model_name}"') + return '' + +def get_file_path_iterator(folder_name: str, supported_extensions: Optional[Collection[str]] = None) -> Iterator[Path]: + """ + Returns an iterator over valid file paths for the specified model folder. + """ + if supported_extensions is None: + return (Path(x) for x in folder_paths.get_filename_list(folder_name)) + else: + return custom_file_path_generator(folder_name, supported_extensions) + +def custom_file_path_generator(folder_name: str, supported_extensions: Collection[str]) -> Iterator[Path]: + """ + Generator function for file paths, allowing for a customized extension check. + """ + model_paths = folder_paths.folder_names_and_paths.get(folder_name, [[], set()])[0] + for path in model_paths: + if os.path.exists(path): + base_path = Path(path) + for root, _, files in os.walk(path): + root_path = Path(root).relative_to(base_path) + for file in files: + file_path = root_path / file + if file_path.suffix.lower() in supported_extensions: + yield file_path + +def get_file_path_match(folder_name: str, file_name: str, supported_extensions: Optional[Collection[str]] = None) -> Optional[str]: + supported_extensions_fallback = supported_extensions if supported_extensions is not None else folder_paths.supported_pt_extensions + file_path = Path(file_name) + + # first try full path match, then fallback to just name match, matching the extension if appropriate + if file_path.suffix.lower() not in supported_extensions_fallback: + matching_file_path = next((p for p in get_file_path_iterator(folder_name, supported_extensions) if p.with_suffix('') == file_path), None) + matching_file_path = (matching_file_path if matching_file_path is not None else + next((p for p in get_file_path_iterator(folder_name, supported_extensions) if p.stem == file_path.name), None)) + else: + matching_file_path = next((p for p in get_file_path_iterator(folder_name, supported_extensions) if p == file_path), None) + matching_file_path = (matching_file_path if matching_file_path is not None else + next((p for p in get_file_path_iterator(folder_name, supported_extensions) if p.name == file_path.name), None)) + + return str(matching_file_path) if matching_file_path is not None else None + +def http_get_json(url: str) -> dict[str, Any] | None: + try: + response = requests.get(url, timeout=300) + except requests.exceptions.Timeout: + print(f"ComfyUI-Image-Saver: HTTP GET Request timed out for {url}") + return None + except requests.exceptions.ConnectionError as e: + print(f"ComfyUI-Image-Saver: Warning - Network connection error for {url}: {e}") + return None + + if not response.ok: + print(f"ComfyUI-Image-Saver: HTTP GET Request failed with error code: {response.status_code}: {response.reason}") + return None + + try: + return response.json() + except ValueError as e: + print(f"ComfyUI-Image-Saver: HTTP Response JSON error: {e}") + return None diff --git a/custom_nodes/comfyui-image-saver/utils_civitai.py b/custom_nodes/comfyui-image-saver/utils_civitai.py new file mode 100644 index 00000000..0dfb8b33 --- /dev/null +++ b/custom_nodes/comfyui-image-saver/utils_civitai.py @@ -0,0 +1,205 @@ +import json +from pathlib import Path +from typing import List, Dict, Tuple, Any + +import folder_paths + +from .utils import http_get_json + +MAX_HASH_LENGTH = 16 # skip larger unshortened hashes, such as full sha256 or blake3 + +""" +Represent the given embedding name as key as detected by civitAI +""" +def civitai_embedding_key_name(embedding: str) -> str: + return f'embed:{embedding}' + +""" +Represent the given lora name as key as detected by civitAI +NB: this should also work fine for Lycoris +""" +def civitai_lora_key_name(lora: str) -> str: + return f'LORA:{lora}' + +CIVITAI_SAMPLER_MAP = { + 'euler_ancestral': 'Euler a', + 'euler': 'Euler', + 'lms': 'LMS', + 'heun': 'Heun', + 'dpm_2': 'DPM2', + 'dpm_2_ancestral': 'DPM2 a', + 'dpmpp_2s_ancestral': 'DPM++ 2S a', + 'dpmpp_2m': 'DPM++ 2M', + 'dpmpp_sde': 'DPM++ SDE', + 'dpmpp_2m_sde': 'DPM++ 2M SDE', + 'dpmpp_3m_sde': 'DPM++ 3M SDE', + 'dpm_fast': 'DPM fast', + 'dpm_adaptive': 'DPM adaptive', + 'ddim': 'DDIM', + 'plms': 'PLMS', + 'uni_pc_bh2': 'UniPC', + 'uni_pc': 'UniPC', + 'lcm': 'LCM', +} + +def get_civitai_sampler_name(sampler_name: str, scheduler: str) -> str: + # based on: https://github.com/civitai/civitai/blob/main/src/server/common/constants.ts#L122 + if sampler_name in CIVITAI_SAMPLER_MAP: + civitai_name = CIVITAI_SAMPLER_MAP[sampler_name] + + if scheduler == "karras": + civitai_name += " Karras" + elif scheduler == "exponential": + civitai_name += " Exponential" + + return civitai_name + else: + if scheduler != 'normal': + return f"{sampler_name}_{scheduler}" + else: + return sampler_name + +def get_civitai_metadata( + modelname: str, + ckpt_path: str, + modelhash: str, + loras: Dict[str, Tuple[str, float, str]], + embeddings: Dict[str, Tuple[str, float, str]], + manual_entries: Dict[str, tuple[str | None, float | None, str]], + download_civitai_data: bool) -> Tuple[List[Dict[str, str | float]], Dict[str, str], str | None]: + """Download or load cache of Civitai data, save specially-formatted data to metadata""" + civitai_resources: List[Dict[str, str | float]] = [] + hashes = {} + add_model_hash = None + + if download_civitai_data: + for name, (filepath, weight, hash) in ({ modelname: ( ckpt_path, None, modelhash ) } | loras | embeddings | manual_entries).items(): + civitai_info = get_civitai_info(filepath, hash) + if civitai_info is not None: + resource_data: Dict[str, str | float] = {} + + # Optional data - modelName, versionName + resource_data["modelName"] = civitai_info["model"]["name"] + resource_data["versionName"] = civitai_info["name"] + + # Weight/strength (for LoRA or embedding) + if weight is not None: + resource_data["weight"] = weight + + # Required data - AIR or modelVersionId (unique resource identifier) + # https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI + if "air" in civitai_info: + resource_data["air"] = civitai_info["air"] + else: + # Fallback if AIR is not found + resource_data["modelVersionId"] = civitai_info["id"] + civitai_resources.append(resource_data) + else: + # Fallback in case the data wasn't loaded to add to the "Hashes" section + if name == modelname: + add_model_hash = hash.upper() + else: + hashes[name] = hash.upper() + else: + # Convert all hashes to JSON format + hashes = {key: value[2] for key, value in embeddings.items()} | {key: value[2] for key, value in loras.items()} | {key: value[2] for key, value in manual_entries.items()} | {"model": modelhash} + add_model_hash = modelhash + + return civitai_resources, hashes, add_model_hash + +def get_civitai_info(path: Path | str | None, model_hash: str) -> dict[str, Any] | None: + try: + if not model_hash: + print("ComfyUI-Image-Saver: Error: Missing hash.") + return None + + # path is None for additional hashes added by the user - caches manually added hash data in the "image-saver" folder + if path is None: + manual_list = get_manual_list() + manual_data = manual_list.get(model_hash.upper(), None) + if manual_data is None: + content = download_model_info(path, model_hash) + if content is None: + return None + + # dynamically receive filename from the website to save the metadata + file = next((file for file in content["files"] if any(len(value) <= MAX_HASH_LENGTH and value.upper() == model_hash.upper() for value in file["hashes"].values())), None) + if file is None: + print(f"ComfyUI-Image-Saver: ({model_hash}) No file hash matched in metadata (should be impossible)") + return content + filename = file["name"] + + # Cache data in a local file, removing the need for repeat http requests + for hash_value in file["hashes"].values(): + if len(hash_value) <= MAX_HASH_LENGTH: + manual_list = append_manual_list(hash_value.upper(), { "filename": filename, "type": content["model"]["type"] }) + + save_civitai_info_file(content, get_manual_folder() / filename) + return content + else: + path = get_manual_folder() / manual_data["filename"] + + info_path = Path(path).with_suffix(".civitai.info").absolute() + with open(info_path, 'r') as file: + return json.load(file) + except FileNotFoundError: + return download_model_info(path, model_hash) + except Exception as e: + print(f"ComfyUI-Image-Saver: Civitai info error: {e}") + return None + +def download_model_info(path: Path | str | None, model_hash: str) -> dict[str, object] | None: + model_label = model_hash if path is None else f"{Path(path).stem}:{model_hash}" + print(f"ComfyUI-Image-Saver: Downloading model info for '{model_label}'.") + + content = http_get_json(f'https://civitai.com/api/v1/model-versions/by-hash/{model_hash.upper()}') + if content is None: + return None + model_id = content["modelId"] + parent_model = http_get_json(f'https://civitai.com/api/v1/models/{model_id}') + if not parent_model: + parent_model = {} + + content["creator"] = parent_model.get("creator", "{}") + model_metadata = content["model"] + for metadata in [ "description", "tags", "allowNoCredit", "allowCommercialUse", "allowDerivatives", "allowDifferentLicense" ]: + model_metadata[metadata] = parent_model.get(metadata, "") + + if path is not None: + save_civitai_info_file(content, path) + + return content + +def save_civitai_info_file(content: dict[str, object], path: Path | str) -> bool: + try: + with open(Path(path).with_suffix(".civitai.info").absolute(), 'w') as info_file: + info_file.write(json.dumps(content, indent=4)) + except Exception as e: + print(f"ComfyUI-Image-Saver: Save Civitai info error '{path}': {e}") + return False + return True + +def get_manual_folder() -> Path: + return Path(folder_paths.models_dir) / "image-saver" + +def get_manual_list() -> dict[str, dict[str, Any]]: + folder = get_manual_folder() + folder.mkdir(parents=True, exist_ok=True) + try: + manual_path = (folder / "manual-hashes.json").absolute() + with open(manual_path, 'r') as file: + return json.load(file) + except FileNotFoundError: + return {} + except Exception as e: + print(f"ComfyUI-Image-Saver: Manual list get error: {e}") + return {} + +def append_manual_list(key: str, value: dict[str, Any]) -> dict[str, dict[str, Any]]: + manual_list = get_manual_list() | { key: value } + try: + with open((get_manual_folder() / "manual-hashes.json").absolute(), 'w') as file: + file.write(json.dumps(manual_list, indent=4)) + except Exception as e: + print(f"ComfyUI-Image-Saver: Manual list append error: {e}") + return manual_list diff --git a/custom_nodes/comfyui-impact-subpack/.gitignore b/custom_nodes/comfyui-impact-subpack/.gitignore new file mode 100644 index 00000000..392e1848 --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/.gitignore @@ -0,0 +1,5 @@ +__pycache__ +*.ini +wildcards/** +.vscode/ +.idea/ \ No newline at end of file diff --git a/custom_nodes/comfyui-impact-subpack/LICENSE.txt b/custom_nodes/comfyui-impact-subpack/LICENSE.txt new file mode 100644 index 00000000..0ad25db4 --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/LICENSE.txt @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/custom_nodes/comfyui-impact-subpack/README.md b/custom_nodes/comfyui-impact-subpack/README.md new file mode 100644 index 00000000..8b62d238 --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/README.md @@ -0,0 +1,54 @@ +# ComfyUI-Impact-Subpack +This node pack provides nodes that complement the [ComfyUI Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack), such as the UltralyticsDetectorProvider. + + +## Nodes +* `UltralyticsDetectorProvider` - Loads the Ultralystics model to provide SEGM_DETECTOR, BBOX_DETECTOR. +- Unlike `MMDetDetectorProvider`, for segm models, `BBOX_DETECTOR` is also provided. +- The various models available in UltralyticsDetectorProvider can be downloaded through **ComfyUI-Manager**. + + +## Ultralytics models +* When using ultralytics models, save them separately in `models/ultralytics/bbox` and `models/ultralytics/segm` depending on the type of model. Many models can be downloaded by searching for `ultralytics` in the Model Manager of ComfyUI-Manager. +* huggingface.co/Bingsu/[adetailer](https://huggingface.co/Bingsu/adetailer/tree/main) - You can download face, people detection models, and clothing detection models. +* ultralytics/[assets](https://github.com/ultralytics/assets/releases/) - You can download various types of detection models other than faces or people. +* civitai/[adetailer](https://civitai.com/search/models?sortBy=models_v5&query=adetailer) - You can download various types detection models....Many models are associated with NSFW content. + + +## Paths +* In `extra_model_paths.yaml`, you can add the following entries: +- `ultralytics_bbox` - Specifies the paths for bbox YOLO models. +- `ultralytics_segm` - Specifies the paths for segm YOLO models. +- `ultralytics` - Allows the presence of `bbox/` and `segm/` subdirectories. + + +## Model loading configuration related to `weights_only` +* Loading model files can involve executing code, so if malicious code is embedded in a model, it can pose a security risk. For this reason, PyTorch 2.6 and later have introduced features for safer model loading. The issue is that older model files, which were created without such safe loading (weights_only) restrictions, may occasionally fail to load properly. +* By listing the paths of models deemed safe in `/default/ComfyUI-Impact-Subpack/model-whitelist.txt`, those specific models will have weights_only disabled, allowing them to be loaded without restriction. + * ``: typically located at ComfyUI/user. + + +## How To Install? + +### Install via ComfyUI-Manager (Recommended) +* Search `ComfyUI Impact Subpack` in ComfyUI-Manager and click `Install` button. + +### Manual Install (Not Recommended) +1. `cd custom_nodes` +2. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Subpack` +3. `cd ComfyUI-Impact-Subpack` +4. `pip install -r requirements.txt` + * **IMPORTANT**: + * You must install it within the Python environment where ComfyUI is running. + * For the portable version, use `\python_embeded\python.exe -m pip` instead of `pip`. For a `venv`, activate the `venv` first and then use `pip`. +5. Restart ComfyUI + + +## Credits + +ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI. + +Bing-su/[adetailer](https://github.com/Bing-su/adetailer/) - This repository provides an object detection model and features based on Ultralystics. + +huggingface/Bingsu/[adetailer](https://huggingface.co/Bingsu/adetailer/tree/main) - This repository offers various models based on Ultralystics. +* You can download other models supported by the UltralyticsDetectorProvider from here. diff --git a/custom_nodes/comfyui-impact-subpack/__init__.py b/custom_nodes/comfyui-impact-subpack/__init__.py new file mode 100644 index 00000000..95f3411d --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/__init__.py @@ -0,0 +1,38 @@ +""" +@author: Dr.Lt.Data +@title: Impact Subpack +@nickname: Impact Subpack +@description: This extension provides UltralyticsDetectorProvider node +""" + +import importlib +import logging + +version_code = [1, 3, 5] +version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '') +logging.info(f"### Loading: ComfyUI-Impact-Subpack ({version_str})") + +node_list = [ + "subpack_nodes", +] + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +for module_name in node_list: + imported_module = importlib.import_module(".modules.{}".format(module_name), __name__) + + NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS} + NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS} + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] + +try: + import cm_global + cm_global.register_extension('ComfyUI-Impact-Subpack', + {'version': version_code, + 'name': 'Impact Subpack', + 'nodes': set(NODE_CLASS_MAPPINGS.keys()), + 'description': 'This extension provides UltralyticsDetectorProvider node.', }) +except: + pass diff --git a/custom_nodes/comfyui-impact-subpack/install.py b/custom_nodes/comfyui-impact-subpack/install.py new file mode 100644 index 00000000..01d9fddb --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/install.py @@ -0,0 +1,41 @@ +import os +import sys +from torchvision.datasets.utils import download_url +import logging + +subpack_path = os.path.join(os.path.dirname(__file__)) + +comfy_path = os.environ.get('COMFYUI_PATH') +if comfy_path is None: + comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + logging.warning(f"\nWARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{comfy_path}` as the ComfyUI path.") + +sys.path.append(comfy_path) + +model_path = os.environ.get('COMFYUI_MODEL_PATH') +if model_path is None: + model_path = os.path.abspath(os.path.join(comfy_path, 'models')) + logging.warning(f"\nWARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.") + + +ultralytics_bbox_path = os.path.join(model_path, "ultralytics", "bbox") +ultralytics_segm_path = os.path.join(model_path, "ultralytics", "segm") + +if not os.path.exists(os.path.join(subpack_path, '..', 'skip_download_model')): + if not os.path.exists(ultralytics_bbox_path): + os.makedirs(ultralytics_bbox_path) + + if not os.path.exists(ultralytics_segm_path): + os.makedirs(ultralytics_segm_path) + + if not os.path.exists(os.path.join(ultralytics_bbox_path, "face_yolov8m.pt")): + download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8m.pt", + ultralytics_bbox_path) + + if not os.path.exists(os.path.join(ultralytics_bbox_path, "hand_yolov8s.pt")): + download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt", + ultralytics_bbox_path) + + if not os.path.exists(os.path.join(ultralytics_segm_path, "person_yolov8m-seg.pt")): + download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt", + ultralytics_segm_path) diff --git a/custom_nodes/comfyui-impact-subpack/modules/subcore.py b/custom_nodes/comfyui-impact-subpack/modules/subcore.py new file mode 100644 index 00000000..d3092264 --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/modules/subcore.py @@ -0,0 +1,527 @@ +from PIL import Image + +import cv2 +import numpy as np +import torch +from collections import namedtuple +from . import utils +import inspect +import logging +import os + +import pickle +import folder_paths + + +orig_torch_load = torch.load + + +SEG = namedtuple("SEG", + ['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label', 'control_net_wrapper'], + defaults=[None]) + + +# --- Whitelist Configuration --- +WHITELIST_DIR = None +WHITELIST_FILE_PATH = None + +try: + # --- Attempting: Use ComfyUI's folder_paths (Preferred Method) --- + user_dir = folder_paths.get_user_directory() + if user_dir and os.path.isdir(user_dir): + WHITELIST_DIR = os.path.join(user_dir, "default", "ComfyUI-Impact-Subpack") + WHITELIST_FILE_PATH = os.path.join(WHITELIST_DIR, "model-whitelist.txt") + logging.info(f"[Impact Pack/Subpack] Using folder_paths to determine whitelist path: {WHITELIST_FILE_PATH}") + else: + logging.warning(f"[Impact Pack/Subpack] folder_paths.get_user_directory() returned invalid path: {user_dir}.") + + # --- Ensure directory exists--- + if WHITELIST_FILE_PATH: # Check if any method succeeded in setting the path + try: + # Crucially, create the DIRECTORY first + # Use the WHITELIST_DIR determined by one of the methods above + os.makedirs(WHITELIST_DIR, exist_ok=True) + logging.info(f"[Impact Pack/Subpack] Ensured whitelist directory exists: {WHITELIST_DIR}") + except OSError as e: + logging.error(f"[Impact Pack/Subpack] Failed to create whitelist directory {WHITELIST_DIR}: {e}. Whitelisting may not function.") + WHITELIST_FILE_PATH = None # Indicate failure / disable whitelisting + except Exception as e: + logging.error(f"[Impact Pack/Subpack] Unexpected error creating whitelist directory: {e}", exc_info=True) + WHITELIST_FILE_PATH = None # Indicate failure / disable whitelisting + else: + # Handle case where path determination failed via all methods + logging.error("[Impact Pack/Subpack] Whitelist path determination failed using all methods. Whitelisting disabled.") + # WHITELIST_FILE_PATH is already None + + +except Exception as e: + # Catch errors during the whole setup process (e.g., inspect failing) + logging.error(f"[Impact Pack/Subpack] Critical error during whitelist path setup: {e}", exc_info=True) + WHITELIST_FILE_PATH = None # Disable whitelisting on critical setup error + logging.error("[Impact Pack/Subpack] Whitelisting disabled due to critical setup error.") + + +def load_whitelist(filepath): + """ + Loads filenames from the whitelist file. + Attempts to create the file with instructions if it doesn't exist. + Returns a set of approved base filenames. + """ + approved_files = set() + # Check again if filepath is valid before proceeding + if filepath is None or not isinstance(filepath, str): + # Log was already done if None during setup, avoid duplicate messages + # logging.error("[Impact Pack/Subpack] Whitelist file path is invalid. Whitelisting disabled.") + return approved_files # Return empty set + + try: + # Try reading the existing file + with open(filepath, 'r') as f: + for line in f: + line = line.strip() + # Store only the base filename for easier matching + if line and not line.startswith('#'): + approved_files.add(os.path.basename(line)) + logging.info(f"[Impact Pack/Subpack] Loaded {len(approved_files)} model(s) from whitelist: {filepath}") + + except FileNotFoundError: + # This block now runs only if the directory was created successfully but the file is missing + logging.warning(f"[Impact Pack/Subpack] Model whitelist file not found at: {filepath}. ") + logging.warning(" >> An empty whitelist file will be created.") + logging.warning(" >> To allow unsafe loading for specific trusted legacy models (e.g., older .pt),") + logging.warning(" >> add their base filenames (one per line) to this file.") + try: + # Attempt to create the file with comments since it wasn't found + # This should now succeed because os.makedirs created the directory + with open(filepath, 'w') as f: + f.write("# Add base filenames of trusted models (e.g., my_old_yolo.pt) here, one per line.\n") + f.write("# This allows loading them with `weights_only=False` if they fail safe loading\n") + f.write("# due to errors like 'restricted getattr' in newer PyTorch versions.\n") + f.write("# WARNING: Only add files you absolutely trust, as this bypasses a security feature.\n") + f.write("# Prefer using .safetensors files whenever possible.\n") + logging.info(f"[Impact Pack/Subpack] Created empty whitelist file: {filepath}") + except Exception as create_e: + # Log error if creating the file fails even after creating the directory + logging.error(f"[Impact Pack/Subpack] Failed to create empty whitelist file at {filepath}: {create_e}", exc_info=True) + + except Exception as e: + logging.error(f"[Impact Pack/Subpack] Error loading model whitelist from {filepath}: {e}", exc_info=True) + + return approved_files + +# Now call the function using the dynamically determined (or None) path +_MODEL_WHITELIST = load_whitelist(WHITELIST_FILE_PATH) + +# ---------- End of Whitelist Management ---------- + +class NO_BBOX_DETECTOR: + pass + + +class NO_SEGM_DETECTOR: + pass + + +def create_segmasks(results): + bboxs = results[1] + segms = results[2] + confidence = results[3] + + results = [] + for i in range(len(segms)): + item = (bboxs[i], segms[i].astype(np.float32), confidence[i]) + results.append(item) + return results + + +# Limit the commands that can be executed through `getattr` to `ultralytics.nn.modules.head.Detect.forward`. +def restricted_getattr(obj, name, *args): + if name != "forward": + logging.error(f"Access to potentially dangerous attribute '{obj.__module__}.{obj.__name__}.{name}' is blocked.\nIf you believe the use of this code is genuinely safe, please report it.\nhttps://github.com/ltdrdata/ComfyUI-Impact-Subpack/issues") + raise RuntimeError(f"Access to potentially dangerous attribute '{obj.__module__}.{obj.__name__}.{name}' is blocked.") + + return getattr(obj, name, *args) + +restricted_getattr.__module__ = 'builtins' +restricted_getattr.__name__ = 'getattr' + + +try: + from ultralytics import YOLO + from ultralytics.nn.tasks import DetectionModel + from ultralytics.nn.tasks import SegmentationModel + from ultralytics.utils import IterableSimpleNamespace + from ultralytics.utils.tal import TaskAlignedAssigner + import ultralytics.nn.modules as modules + import ultralytics.nn.modules.block as block_modules + import torch.nn.modules as torch_modules + import ultralytics.utils.loss as loss_modules + import dill._dill + from numpy.core.multiarray import scalar + try: + from numpy import dtype + from numpy.dtypes import Float64DType + except: + logging.error("[Impact Subpack] installed 'numpy' is outdated. Please update 'numpy>=1.26.4'") + raise Exception("[Impact Subpack] installed 'numpy' is outdated. Please update 'numpy>=1.26.4'") + + + torch_whitelist = [] + +except Exception as e: + logging.error(e) + logging.error("\n!!!!!\n\n[ComfyUI-Impact-Subpack] If this error occurs, please check the following link:\n\thttps://github.com/ltdrdata/ComfyUI-Impact-Pack/blob/Main/troubleshooting/TROUBLESHOOTING.md\n\n!!!!!\n") + raise e + +# HOTFIX: https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/754 +# importing YOLO breaking original torch.load capabilities + +# --- Start: REPLACE the existing torch_wrapper function --- + +def torch_wrapper(*args, **kwargs): + """ + Wrapper for torch.load that attempts safe loading (weights_only=True) first. + If a specific UnpicklingError related to disallowed globals (like 'getattr') + occurs, it checks a user-defined whitelist (_MODEL_WHITELIST). If the file + is whitelisted, it retries with weights_only=False. Otherwise, it blocks + the unsafe load and raises the error. + """ + # Use the globally saved original torch.load reference from the top of the file + # Check if weights_only was explicitly passed by the caller + # Explicitly declare modification of global scope is intended + global _MODEL_WHITELIST + weights_only_explicit = kwargs.get('weights_only', None) # Read value without popping yet + + # Try to get the filename being loaded (usually the first arg if it's a path) + filename = None + filename_arg_source = "[unknown source]" + if args and isinstance(args[0], str): + filename = os.path.basename(args[0]) # Get just the filename part + filename_arg_source = args[0] + elif 'f' in kwargs and isinstance(kwargs['f'], str): + filename = os.path.basename(kwargs['f']) # Get just the filename part + filename_arg_source = kwargs['f'] + # Note: filename might remain None if loading from a file-like object + + # Check if using newer PyTorch with safe_globals attribute (indicates >= 2.6 behavior likely) + if hasattr(torch.serialization, 'safe_globals'): + + # Determine the effective weights_only setting for the FIRST attempt + load_kwargs = kwargs.copy() + + try: + # --- Attempt 1: Default Load --- + # Try loading with the determined weights_only setting (usually True) + logging.debug(f"[Impact Pack/Subpack] Attempting safe load (weights_only=True) for: {filename_arg_source}") + return orig_torch_load(*args, **load_kwargs) + + except pickle.UnpicklingError as e: + # --- Handle Specific Load Failure --- + # Check if the error is the specific one caused by disallowed globals + # like 'getattr' AND we were attempting a safe load (weights_only=True) + # Using 'getattr' because it was the specific error reported. + is_disallowed_global_error = 'getattr' in str(e) + + if is_disallowed_global_error: + # Check the whitelist + if filename and filename in _MODEL_WHITELIST: + # --- Fallback: Whitelisted Unsafe Load --- + logging.warning("##############################################################################") + logging.warning(f"[Impact Pack/Subpack] WARNING: Safe load failed for '{filename}' (Reason: {e}).") + logging.warning(f" >> FILE IS IN THE WHITELIST: {WHITELIST_FILE_PATH}") + logging.warning(" >> This model likely uses legacy Python features blocked by default for security.") + logging.warning(" >> RETRYING WITH 'weights_only=False' because it's whitelisted.") + logging.warning(" >> SECURITY RISK: Ensure you added this file to the whitelist consciously") + logging.warning(f" >> and trust its source: {filename_arg_source}") + logging.warning(" >> Prefer using .safetensors files whenever available.") + logging.warning("##############################################################################") + + retry_kwargs = kwargs.copy() + retry_kwargs['weights_only'] = False + # Call the original function again, now unsafely (because whitelisted) + return orig_torch_load(*args, **retry_kwargs) + + else: + # --- File not in current whitelist, try reloading --- + logging.warning(f"[Impact Pack/Subpack] File '{filename}' not found in current whitelist cache.") + whitelist_path_msg = WHITELIST_FILE_PATH if WHITELIST_FILE_PATH else "[Path not determined]" + logging.info(f"[Impact Pack/Subpack] Attempting to reload whitelist from: {whitelist_path_msg}") + try: + # Reload the whitelist from the file + _MODEL_WHITELIST = load_whitelist(WHITELIST_FILE_PATH) + logging.info(f"[Impact Pack/Subpack] Whitelist reloaded. Now contains {len(_MODEL_WHITELIST)} entries.") + + # --- Re-check Whitelist After Reload --- + if filename and filename in _MODEL_WHITELIST: + logging.warning("##############################################################################") + logging.warning(f"[Impact Pack/Subpack] SUCCESS: File '{filename}' FOUND in reloaded whitelist.") + logging.warning(" >> Proceeding with whitelisted unsafe load (weights_only=False).") + logging.warning(f" >> Ensure you recently added this file to: {whitelist_path_msg}") + logging.warning(" >> SECURITY RISK: Ensure you trust its source.") + logging.warning("##############################################################################") + retry_kwargs = kwargs.copy() + retry_kwargs['weights_only'] = False + return orig_torch_load(*args, **retry_kwargs) + else: + # File still not found after reload, proceed with blocking + logging.error("[Impact Pack/Subpack] File still not found in whitelist after reload.") + # Fall through to the original blocking logic below + + except Exception as reload_e: + logging.error(f"[Impact Pack/Subpack] Error occurred during whitelist reload attempt: {reload_e}", exc_info=True) + # Fall through to the original blocking logic below if reload fails + + # --- Blocked: Not Whitelisted (Original Logic - runs if reload failed or file still not found) --- + logging.error("##############################################################################") + logging.error(f"[Impact Pack/Subpack] ERROR: Safe load failed for '{filename_arg_source}' (Reason: {e}).") + logging.error(" >> This model likely uses legacy Python features blocked by default for security.") + # Updated log message here: + logging.error(f" >> UNSAFE LOAD BLOCKED because the file ('{filename or 'unknown'}') is NOT in the whitelist (even after reload attempt).") + logging.error(f" >> Whitelist path: {whitelist_path_msg}") + if filename: + logging.error(" >> To allow loading this specific file (IF YOU TRUST IT), ensure its base name") + logging.error(f" >> ('{filename}') is correctly added to the whitelist file (one name per line) and saved.") + else: + logging.error(" >> Cannot determine filename to check against whitelist.") + logging.error(" >> SECURITY RISK: Only whitelist files from sources you absolutely trust.") + logging.error(" >> Prefer using .safetensors files whenever available.") + logging.error("##############################################################################") + raise e # Re-raise the original security-related error + + else: + # If it's a different UnpicklingError, re-raise it. Don't attempt unsafe load. + logging.error(f"[Impact Pack/Subpack] UnpicklingError during safe load (not 'getattr' related): {e}. Re-raising.") + raise e # Re-raise other UnpicklingErrors + + else: + # --- Handle Older PyTorch Versions (no safe_globals) --- + # Behavior here respects the caller's explicit request or defaults to False + load_kwargs = kwargs.copy() + effective_weights_only = weights_only_explicit if weights_only_explicit is not None else False # Default False for old torch + load_kwargs['weights_only'] = effective_weights_only + + if not effective_weights_only: + logging.warning(f"[Impact Pack/Subpack] Older PyTorch version detected. Proceeding with potentially unsafe load (weights_only=False) for: {filename_arg_source}") + else: + logging.debug(f"[Impact Pack/Subpack] Older PyTorch version detected. Proceeding with explicit weights_only=True for: {filename_arg_source}") + + # Call the original torch.load directly with the determined settings for older PyTorch + return orig_torch_load(*args, **load_kwargs) + +# --- End: Replacement block for the torch_wrapper function --- + +torch.load = torch_wrapper + + +def load_yolo(model_path: str): + return YOLO(model_path) + + +def inference_bbox( + model, + image: Image.Image, + confidence: float = 0.3, + device: str = "", +): + pred = model(image, conf=confidence, device=device) + + bboxes = pred[0].boxes.xyxy.cpu().numpy() + cv2_image = np.array(image) + if len(cv2_image.shape) == 3: + cv2_image = cv2_image[:, :, ::-1].copy() # Convert RGB to BGR for cv2 processing + else: + # Handle the grayscale image here + # For example, you might want to convert it to a 3-channel grayscale image for consistency: + cv2_image = cv2.cvtColor(cv2_image, cv2.COLOR_GRAY2BGR) + cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY) + + segms = [] + for x0, y0, x1, y1 in bboxes: + cv2_mask = np.zeros(cv2_gray.shape, np.uint8) + cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1) + cv2_mask_bool = cv2_mask.astype(bool) + segms.append(cv2_mask_bool) + + n, m = bboxes.shape + if n == 0: + return [[], [], [], []] + + results = [[], [], [], []] + for i in range(len(bboxes)): + results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())]) + results[1].append(bboxes[i]) + results[2].append(segms[i]) + results[3].append(pred[0].boxes[i].conf.cpu().numpy()) + + return results + + +def inference_segm( + model, + image: Image.Image, + confidence: float = 0.3, + device: str = "", +): + pred = model(image, conf=confidence, device=device) + + bboxes = pred[0].boxes.xyxy.cpu().numpy() + n, m = bboxes.shape + if n == 0: + return [[], [], [], []] + + # NOTE: masks.data will be None when n == 0 + segms = pred[0].masks.data.cpu().numpy() + + h_segms = segms.shape[1] + w_segms = segms.shape[2] + h_orig = image.size[1] + w_orig = image.size[0] + ratio_segms = h_segms / w_segms + ratio_orig = h_orig / w_orig + + if ratio_segms == ratio_orig: + h_gap = 0 + w_gap = 0 + elif ratio_segms > ratio_orig: + h_gap = int((ratio_segms - ratio_orig) * h_segms) + w_gap = 0 + else: + h_gap = 0 + ratio_segms = w_segms / h_segms + ratio_orig = w_orig / h_orig + w_gap = int((ratio_segms - ratio_orig) * w_segms) + + results = [[], [], [], []] + for i in range(len(bboxes)): + results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())]) + results[1].append(bboxes[i]) + + mask = torch.from_numpy(segms[i]) + mask = mask[h_gap:mask.shape[0] - h_gap, w_gap:mask.shape[1] - w_gap] + + scaled_mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(image.size[1], image.size[0]), + mode='bilinear', align_corners=False) + scaled_mask = scaled_mask.squeeze().squeeze() + + results[2].append(scaled_mask.numpy()) + results[3].append(pred[0].boxes[i].conf.cpu().numpy()) + + return results + + +class UltraBBoxDetector: + bbox_model = None + + def __init__(self, bbox_model): + self.bbox_model = bbox_model + + def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None): + drop_size = max(drop_size, 1) + detected_results = inference_bbox(self.bbox_model, utils.tensor2pil(image), threshold) + segmasks = create_segmasks(detected_results) + + if dilation > 0: + segmasks = utils.dilate_masks(segmasks, dilation) + + items = [] + h = image.shape[1] + w = image.shape[2] + + for x, label in zip(segmasks, detected_results[0]): + item_bbox = x[0] + item_mask = x[1] + + y1, x1, y2, x2 = item_bbox + + if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue + crop_region = utils.make_crop_region(w, h, item_bbox, crop_factor) + + if detailer_hook is not None: + crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region) + + cropped_image = utils.crop_image(image, crop_region) + cropped_mask = utils.crop_ndarray2(item_mask, crop_region) + confidence = x[2] + # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h) + + item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None) + + items.append(item) + + shape = image.shape[1], image.shape[2] + segs = shape, items + + if detailer_hook is not None and hasattr(detailer_hook, "post_detection"): + segs = detailer_hook.post_detection(segs) + + return segs + + def detect_combined(self, image, threshold, dilation): + detected_results = inference_bbox(self.bbox_model, utils.tensor2pil(image), threshold) + segmasks = create_segmasks(detected_results) + if dilation > 0: + segmasks = utils.dilate_masks(segmasks, dilation) + + return utils.combine_masks(segmasks) + + def setAux(self, x): + pass + + +class UltraSegmDetector: + bbox_model = None + + def __init__(self, bbox_model): + self.bbox_model = bbox_model + + def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None): + drop_size = max(drop_size, 1) + detected_results = inference_segm(self.bbox_model, utils.tensor2pil(image), threshold) + segmasks = create_segmasks(detected_results) + + if dilation > 0: + segmasks = utils.dilate_masks(segmasks, dilation) + + items = [] + h = image.shape[1] + w = image.shape[2] + + for x, label in zip(segmasks, detected_results[0]): + item_bbox = x[0] + item_mask = x[1] + + y1, x1, y2, x2 = item_bbox + + if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue + crop_region = utils.make_crop_region(w, h, item_bbox, crop_factor) + + if detailer_hook is not None: + crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region) + + cropped_image = utils.crop_image(image, crop_region) + cropped_mask = utils.crop_ndarray2(item_mask, crop_region) + confidence = x[2] + # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h) + + item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None) + + items.append(item) + + shape = image.shape[1], image.shape[2] + segs = shape, items + + if detailer_hook is not None and hasattr(detailer_hook, "post_detection"): + segs = detailer_hook.post_detection(segs) + + return segs + + def detect_combined(self, image, threshold, dilation): + detected_results = inference_segm(self.bbox_model, utils.tensor2pil(image), threshold) + segmasks = create_segmasks(detected_results) + if dilation > 0: + segmasks = utils.dilate_masks(segmasks, dilation) + + return utils.combine_masks(segmasks) + + def setAux(self, x): + pass diff --git a/custom_nodes/comfyui-impact-subpack/modules/subpack_nodes.py b/custom_nodes/comfyui-impact-subpack/modules/subpack_nodes.py new file mode 100644 index 00000000..01d59c9d --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/modules/subpack_nodes.py @@ -0,0 +1,70 @@ +import os +import folder_paths +from . import subcore +from . import utils +import logging + + +def update_model_paths(model_path): + utils.add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions) + utils.add_folder_path_and_extensions("ultralytics_segm", [os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions) + utils.add_folder_path_and_extensions("ultralytics", [os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions) + logging.info(f'[Impact Subpack] ultralytics_bbox: {", ".join(folder_paths.folder_names_and_paths["ultralytics_bbox"][0])}') + logging.info(f'[Impact Subpack] ultralytics_segm: {", ".join(folder_paths.folder_names_and_paths["ultralytics_segm"][0])}') + +update_model_paths(folder_paths.models_dir) +if 'download_model_base' in folder_paths.folder_names_and_paths: + update_model_paths(folder_paths.get_folder_paths('download_model_base')[0]) + + +class UltralyticsDetectorProvider: + @classmethod + def INPUT_TYPES(s): + bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("ultralytics_bbox")] + segms = ["segm/"+x for x in folder_paths.get_filename_list("ultralytics_segm")] + return {"required": {"model_name": (bboxs + segms, )}} + RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR") + FUNCTION = "doit" + + CATEGORY = "ImpactPack" + + def doit(self, model_name): + model_path = folder_paths.get_full_path("ultralytics", model_name) + + if model_path is None: + if model_name.startswith('bbox/'): + model_path = folder_paths.get_full_path("ultralytics_bbox", model_name[5:]) + elif model_name.startswith('segm/'): + model_path = folder_paths.get_full_path("ultralytics_segm", model_name[5:]) + + if model_path is None: + logging.error(f"[Impact Subpack] model file '{model_name}' is not found in one of the following directories:") + + cands = [] + cands.extend(folder_paths.get_folder_paths("ultralytics")) + if model_name.startswith('bbox/'): + cands.extend(folder_paths.get_folder_paths("ultralytics_bbox")) + elif model_name.startswith('segm/'): + cands.extend(folder_paths.get_folder_paths("ultralytics_segm")) + + formatted_cands = "\n\t".join(cands) + logging.error(f'\t{formatted_cands}\n') + + raise ValueError(f"[Impact Subpack] model file '{model_name}' is not found.") + + model = subcore.load_yolo(model_path) + + if model_name.startswith("bbox"): + return subcore.UltraBBoxDetector(model), subcore.NO_SEGM_DETECTOR() + else: + return subcore.UltraBBoxDetector(model), subcore.UltraSegmDetector(model) + + +NODE_CLASS_MAPPINGS = { + "UltralyticsDetectorProvider": UltralyticsDetectorProvider +} + + +NODE_DISPLAY_NAME_MAPPINGS = { + +} diff --git a/custom_nodes/comfyui-impact-subpack/modules/utils.py b/custom_nodes/comfyui-impact-subpack/modules/utils.py new file mode 100644 index 00000000..037d5d29 --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/modules/utils.py @@ -0,0 +1,152 @@ +import folder_paths +from PIL import Image +import numpy as np +import cv2 +import torch + + +def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions): + # Iterate over the list of full folder paths + for full_folder_path in full_folder_paths: + # Use the provided function to add each model folder path + folder_paths.add_model_folder_path(folder_name, full_folder_path) + + # Now handle the extensions. If the folder name already exists, update the extensions + if folder_name in folder_paths.folder_names_and_paths: + # Unpack the current paths and extensions + current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name] + # Update the extensions set with the new extensions + updated_extensions = current_extensions | extensions + # Reassign the updated tuple back to the dictionary + folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions) + else: + # If the folder name was not present, add_model_folder_path would have added it with the last path + # Now we just need to update the set of extensions as it would be an empty set + # Also ensure that all paths are included (since add_model_folder_path adds only one path at a time) + folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions) + + +def normalize_region(limit, startp, size): + if startp < 0: + new_endp = min(limit, size) + new_startp = 0 + elif startp + size > limit: + new_startp = max(0, limit - size) + new_endp = limit + else: + new_startp = startp + new_endp = min(limit, startp+size) + + return int(new_startp), int(new_endp) + + +def _tensor_check_image(image): + if image.ndim != 4: + raise ValueError(f"Expected NHWC tensor, but found {image.ndim} dimensions") + if image.shape[-1] not in (1, 3, 4): + raise ValueError(f"Expected 1, 3 or 4 channels for image, but found {image.shape[-1]} channels") + return + + +def tensor2pil(image): + _tensor_check_image(image) + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(0), 0, 255).astype(np.uint8)) + + +def dilate_masks(segmasks, dilation_factor, iter=1): + if dilation_factor == 0: + return segmasks + + dilated_masks = [] + kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8) + + for i in range(len(segmasks)): + cv2_mask = segmasks[i][1] + + if dilation_factor > 0: + dilated_mask = cv2.dilate(cv2_mask, kernel, iter) + else: + dilated_mask = cv2.erode(cv2_mask, kernel, iter) + + item = (segmasks[i][0], dilated_mask, segmasks[i][2]) + dilated_masks.append(item) + + return dilated_masks + + +def combine_masks(masks): + if len(masks) == 0: + return None + else: + initial_cv2_mask = np.array(masks[0][1]) + combined_cv2_mask = initial_cv2_mask + + for i in range(1, len(masks)): + cv2_mask = np.array(masks[i][1]) + + if combined_cv2_mask.shape == cv2_mask.shape: + combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask) + else: + # do nothing - incompatible mask + pass + + mask = torch.from_numpy(combined_cv2_mask) + return mask + + +def make_crop_region(w, h, bbox, crop_factor, crop_min_size=None): + x1 = bbox[0] + y1 = bbox[1] + x2 = bbox[2] + y2 = bbox[3] + + bbox_w = x2 - x1 + bbox_h = y2 - y1 + + crop_w = bbox_w * crop_factor + crop_h = bbox_h * crop_factor + + if crop_min_size is not None: + crop_w = max(crop_min_size, crop_w) + crop_h = max(crop_min_size, crop_h) + + kernel_x = x1 + bbox_w / 2 + kernel_y = y1 + bbox_h / 2 + + new_x1 = int(kernel_x - crop_w / 2) + new_y1 = int(kernel_y - crop_h / 2) + + # make sure position in (w,h) + new_x1, new_x2 = normalize_region(w, new_x1, crop_w) + new_y1, new_y2 = normalize_region(h, new_y1, crop_h) + + return [new_x1, new_y1, new_x2, new_y2] + + +def crop_ndarray2(npimg, crop_region): + x1 = crop_region[0] + y1 = crop_region[1] + x2 = crop_region[2] + y2 = crop_region[3] + + cropped = npimg[y1:y2, x1:x2] + + return cropped + + +def crop_ndarray4(npimg, crop_region): + x1 = crop_region[0] + y1 = crop_region[1] + x2 = crop_region[2] + y2 = crop_region[3] + + cropped = npimg[:, y1:y2, x1:x2, :] + + return cropped + + +crop_tensor4 = crop_ndarray4 + + +def crop_image(image, crop_region): + return crop_tensor4(image, crop_region) diff --git a/custom_nodes/comfyui-impact-subpack/pyproject.toml b/custom_nodes/comfyui-impact-subpack/pyproject.toml new file mode 100644 index 00000000..0c7b9f19 --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-impact-subpack" +description = "This node pack provides nodes that complement the Impact Pack, such as the UltralyticsDetectorProvider." +version = "1.3.5" +license = { file = "LICENSE.txt" } +dependencies = ["matplotlib", "ultralytics"] + +[project.urls] +Repository = "https://github.com/ltdrdata/ComfyUI-Impact-Subpack" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "drltdata" +DisplayName = "ComfyUI Impact Subpack" +Icon = "" diff --git a/custom_nodes/comfyui-impact-subpack/requirements.txt b/custom_nodes/comfyui-impact-subpack/requirements.txt new file mode 100644 index 00000000..513652bd --- /dev/null +++ b/custom_nodes/comfyui-impact-subpack/requirements.txt @@ -0,0 +1,5 @@ +matplotlib +ultralytics>=8.3.162 +numpy +opencv-python-headless +dill \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/.gitignore b/custom_nodes/comfyui-inspire-pack/.gitignore new file mode 100644 index 00000000..d6d8790c --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/.gitignore @@ -0,0 +1,161 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/comfyui-inspire-pack/README.md b/custom_nodes/comfyui-inspire-pack/README.md new file mode 100644 index 00000000..85c0a04c --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/README.md @@ -0,0 +1,204 @@ +# ComfyUI-Inspire-Pack +This repository offers various extension nodes for ComfyUI. Nodes here have different characteristics compared to those in the ComfyUI Impact Pack. The Impact Pack has become too large now... + +## Notice: +* V1.18: To use the 'OSS' Scheduler, please update to ComfyUI version 0.3.28 or later (April 13th or newer) and Impact Pack version V8.11 or higher. +* V1.9.1 To avoid confusion with the `NOISE` type in core, the type name has been changed to `NOISE_IMAGE`. +* V0.73 The Variation Seed feature is added to Regional Prompt nodes, and it is only compatible with versions Impact Pack V5.10 and above. +* V0.69 incompatible with the outdated **ComfyUI IPAdapter Plus**. (A version dated March 24th or later is required.) +* V0.64 add sigma_factor to RegionalPrompt... nodes required Impact Pack V4.76 or later. +* V0.62 support faceid in Regional IPAdapter +* V0.48 optimized wildcard node. This update requires Impact Pack V4.39.2 or later. +* V0.13.2 isn't compatible with old ControlNet Auxiliary Preprocessor. If you will use `MediaPipeFaceMeshDetectorProvider` update to latest version(Sep. 17th). +* WARN: If you use version **0.12 to 0.12.2** without a GlobalSeed node, your workflow's seed may have been erased. Please update immediately. + +## Nodes +### Lora Block Weight - This is a node that provides functionality related to Lora block weight. + * This provides similar functionality to [sd-webui-lora-block-weight](https://github.com/hako-mikan/sd-webui-lora-block-weight) + * `LoRA Loader (Block Weight)`: When loading Lora, the block weight vector is applied. + * In the block vector, you can use numbers, R, A, a, B, and b. + * R is determined sequentially based on a random seed, while A and B represent the values of the A and B parameters, respectively. a and b are half of the values of A and B, respectively. + * `XY Input: LoRA Block Weight`: This is a node in the [Efficiency Nodes](https://github.com/LucianoCirino/efficiency-nodes-comfyui)' XY Plot that allows you to use Lora block weight. + * You must ensure that X and Y connections are made, and dependencies should be connected to the XY Plot. + * Note: To use this feature, update `Efficient Nodes` to a version released after September 3rd. + * Make LoRA Block Weight: Instead of directly applying the LoRA Block Weight to the MODEL, it is generated in a separate LBW_MODEL form + * Apply LoRA Block Weight: Apply LBW_MODEL to MODEL and CLIP + * Save LoRA Block Weight: Save LBW_MODEL as a .lbw.safetensors file + * Load LoRA Block Weight: Load LBW_MODEL from .lbw.safetensors file + + +### SEGS Supports nodes - This is a node that supports ApplyControlNet (SEGS) from the Impact Pack. + * `OpenPose Preprocessor Provider (SEGS)`: OpenPose preprocessor is applied for the purpose of using OpenPose ControlNet in SEGS. + * You need to install [ControlNet Auxiliary Preprocessors](https://github.com/Fannovel16/comfyui_controlnet_aux) to use this. + * `Canny Preprocessor Provider (SEGS)`: Canny preprocessor is applied for the purpose of using Canny ControlNet in SEGS. + * `DW Preprocessor Provider (SEGS)`, `MiDaS Depth Map Preprocessor Provider (SEGS)`, `LeReS Depth Map Preprocessor Provider (SEGS)`, + `MediaPipe FaceMesh Preprocessor Provider (SEGS)`, `HED Preprocessor Provider (SEGS)`, `Fake Scribble Preprocessor (SEGS)`, + `AnimeLineArt Preprocessor Provider (SEGS)`, `Manga2Anime LineArt Preprocessor Provider (SEGS)`, `LineArt Preprocessor Provider (SEGS)`, + `Color Preprocessor Provider (SEGS)`, `Inpaint Preprocessor Provider (SEGS)`, `Tile Preprocessor Provider (SEGS)`, `MeshGraphormer Depth Map Preprocessor Provider (SEGS)` + * `MediaPipeFaceMeshDetectorProvider`: This node provides `BBOX_DETECTOR` and `SEGM_DETECTOR` that can be used in Impact Pack's Detector using the `MediaPipe-FaceMesh Preprocessor` of ControlNet Auxiliary Preprocessors. + + +### A1111 Compatibility support - These nodes assists in replicating the creation of A1111 in ComfyUI exactly. + * `KSampler (Inspire)`: ComfyUI uses the CPU for generating random noise, while A1111 uses the GPU. One of the three factors that significantly impact reproducing A1111's results in ComfyUI can be addressed using `KSampler (Inspire)`. + * Other point #1 : Please make sure you haven't forgotten to include 'embedding:' in the embedding used in the prompt, like 'embedding:easynegative.' + * Other point #2 : ComfyUI and A1111 have different interpretations of weighting. To align them, you need to use [BlenderNeko/Advanced CLIP Text Encode](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb). + * `KSamplerAdvanced (Inspire)`: Inspire Pack version of `KSampler (Advanced)`. + * `RandomNoise (inspire)`: Inspire Pack version of `RandomNoise`. + * Common Parameters + * `batch_seed_mode` determines how seeds are applied to batch latents: + * `comfy`: This method applies the noise to batch latents all at once. This is advantageous to prevent duplicate images from being generated due to seed duplication when creating images. + * `incremental`: Similar to the A1111 case, this method incrementally increases the seed and applies noise sequentially for each batch. This approach is beneficial for straightforward reproduction using only the seed. + * `variation_strength`: In each batch, the variation strength starts from the set `variation_strength` and increases by `xxx`. + * `variation_seed` and `variation_strength` - Initial noise generated by the seed is transformed to the shape of `variation_seed` by `variation_strength`. If `variation_strength` is 0, it only relies on the influence of the seed, and if `variation_strength` is 1.0, it is solely influenced by `variation_seed`. + * These parameters are used when you want to maintain the composition of an image generated by the seed but wish to introduce slight changes. + + +### Sampler nodes + * `KSampler Progress (Inspire)` - In KSampler, the sampling process generates latent batches. By using `Video Combine` node from [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite), you can create a video from the progress. + * `Scheduled CFGGuider (Inspire)` - This is a CFGGuider that adjusts the schedule from from_cfg to to_cfg using linear, log, and exp methods. + * `Scheduled PerpNeg CFGGuider (Inspire)` - This is a PerpNeg CFGGuider that adjusts the schedule from from_cfg to to_cfg using linear, log, and exp methods. + + +### Prompt Support - These are nodes for supporting prompt processing. + * `Load Prompts From Dir (Inspire)`: It sequentially reads prompts files from the specified directory. The output it returns is ZIPPED_PROMPT. + * Specify the directories located under `ComfyUI-Inspire-Pack/prompts/` + * One prompts file can have multiple prompts separated by `---`. + * e.g. `prompts/example` + * **NOTE**: This node provides advanced option via `Show advanced` + * load_cap, start_index + + * `Load Prompts From File (Inspire)`: It sequentially reads prompts from the specified file. The output it returns is ZIPPED_PROMPT. + * Specify the file located under `ComfyUI-Inspire-Pack/prompts/` + * e.g. `prompts/example/prompt2.txt` + * **NOTE**: This node provides advanced option via `Show advanced` + * load_cap, start_index + + * `Load Single Prompt From File (Inspire)`: Loads a single prompt from a file containing multiple prompts by using an index. + * The prompts file directory can be specified as `inspire_prompts` in `extra_model_paths.yaml` + * `Unzip Prompt (Inspire)`: Separate ZIPPED_PROMPT into `positive`, `negative`, and name components. + * `positive` and `negative` represent text prompts, while `name` represents the name of the prompt. When loaded from a file using `Load Prompts From File (Inspire)`, the name corresponds to the file name. + * `Zip Prompt (Inspire)`: Create ZIPPED_PROMPT from positive, negative, and name_opt. + * If name_opt is omitted, it will be considered as an empty name. + * `Prompt Extractor (Inspire)`: This node reads prompt information from the image's metadata. Since it retrieves all the text, you need to directly specify the prompts to be used for `positive` and `negative` as indicated in the info. + * `Global Seed (Inspire)`: This is a node that controls the global seed without a separate connection line. It only controls when the widget's name is 'seed' or 'noise_seed'. Additionally, if 'control_before_generate' is checked, it controls the seed before executing the prompt. + * Seeds that have been converted into inputs are excluded from the target. If you want to control the seed separately, convert it into an input and control it separately. + * `Global Sampler (Inspire)`: This node is similar to GlobalSeed and can simultaneously set the sampler_name and scheduler for all nodes in the workflow. + * It applies only to nodes that have both sampler_name and scheduler, and it won't be effective if `GlobalSampler` is muted. + * If some of the `sampler_name` and `scheduler` have been converted to input and connected to Primitive node, it will not apply only to the converted widget. The widget that has not been converted to input will still be affected. + * `Bind [ImageList, PromptList] (Inspire)`: Bind Image list and zipped prompt list to export `image`, `positive`, `negative`, and `prompt_label` in a list format. If there are more prompts than images, the excess prompts are ignored, and if there are not enough, the remainder is filled with default input based on the images. + * `Wildcard Encode (Inspire)`: The combination node of [ImpactWildcardEncode](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) and BlenderNeko's [CLIP Text Encode (Advanced)](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb). + * To use this node, you need both the [Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack) and the [Advanced CLIP Text Encode]((https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb)) extensions. + * This node is identical to `ImpactWildcardEncode`, but it encodes using `CLIP Text Encode (Advanced)` instead of the default CLIP Text Encode from ComfyUI for CLIP Text Encode. + * Requirement: Impact Pack V4.18.6 or above + * `Prompt Builder (Inspire)`: This node is a convenience node that allows you to easily assemble prompts by selecting categories and presets. To modify the presets, edit the `ComfyUI-InspirePack/resources/prompt-builder.yaml` file. + * `Seed Explorer (Inspire)`: This node helps explore seeds by allowing you to adjust the variation seed gradually in a prompt-like form. + * This feature is designed for utilizing a seed that you like, adding slight variations, and then further modifying from there when exploring. + * In the `seed_prompt`, the first seed is considered the initial seed, and the reflection rate is omitted, always defaulting to 1.0. + * Each prompt is separated by a comma, and from the second seed onwards, it should follow the format `seed:strength`. + * Pressing the "Add to prompt" button will append `additional_seed:additional_strength` to the prompt. + * `Composite Noise (Inspire)`: This node overwrites a specific area on top of the destination noise with the source noise. + * `Random Generator for List (Inspire)`: When connecting the list output to the signal input, this node generates random values for all items in the list. + * `Make Basic Pipe (Inspire)`: This is a node that creates a BASIC_PIPE using Wildcard Encode. The `Add select to` determines whether the selected item from the `Select to...` combo will be input as positive wildcard text or negative wildcard text. + * `Remove ControlNet (Inspire)`, `Remove ControlNet [RegionalPrompts] (Inspire)`: Remove ControlNet from CONDITIONING or REGIONAL_PROMPTS. + * `Remove ControlNet [RegionalPrompts] (Inspire)` requires Impact Pack V4.73.1 or above. + +### Regional Nodes - These node simplifies the application of prompts by region. + * Regional Sampler - These nodes assists in the easy utilization of the regional sampler in the `Impact Pack`. + * `Regional Prompt Simple (Inspire)`: This node takes `mask` and `basic_pipe` as inputs and simplifies the creation of `REGIONAL_PROMPTS`. + * `Regional Prompt By Color Mask (Inspire)`: Similar to `Regional Prompt Simple (Inspire)`, this function accepts a color mask image as input and defines the region using the color value that will be used as the mask, instead of directly receiving the mask. + * The color value can only be in the form of a hex code like #FFFF00 or a decimal number. + * Regional Conditioning - These nodes provides assistance for simplifying the use of `Conditioning (Set Mask)`. + * `Regional Conditioning Simple (Inspire)` + * `Regional Conditioning By Color Mask (Inspire)` + * Regional IPAdapter - These nodes facilitates the convenient use of the attn_mask feature in `ComfyUI IPAdapter Plus` custom nodes. + * To use this node, you need to install the [ComfyUI IPAdapter Plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension. + * `Regional IPAdapter Mask (Inspire)`, `Regional IPAdapter By Color Mask (Inspire)` + * `Regional IPAdapter Encoded Mask (Inspire)`, `Regional IPAdapter Encoded By Color Mask (Inspire)`: accept `embeds` instead of `image` + * Regional Seed Explorer - These nodes restrict the variation through a seed prompt, applying it only to the masked areas. + * `Regional Seed Explorer By Mask (Inspire)` + * `Regional Seed Explorer By Color Mask (Inspire)` + * `Regional CFG (Inspire)` - By applying a mask as a multiplier to the configured cfg, it allows different areas to have different cfg settings. + * `Color Mask To Depth Mask (Inspire)` - Convert the color map from the spec text into a mask with depth values ranging from 0.0 to 1.0. + * The range of the mask value is limited to 0.0 to 1.0. + * base_value: Sets the value of the base mask. + * dilation: Dilation applied to each mask layer before flattening. + * flatten_method: The method of flattening the mask layers. + * The layers are flattened including the base layer set by base_value. + * override: Each pixel is overwritten by the non-zero value of the upper layer. + * sum: Each pixel is flattened by summing the values of all layers. + * max: Each pixel is flattened by taking the maximum value from all layers. + +### Image Util + * `Load Image Batch From Dir (Inspire)`: This is almost same as `LoadImagesFromDirectory` of [ComfyUI-Advanced-Controlnet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet). This is just a modified version. Just note that this node forcibly normalizes the size of the loaded image to match the size of the first image, even if they are not the same size, to create a batch image. + * `Load Image List From Dir (Inspire)`: This is almost same as `Load Image Batch From Dir (Inspire)`. However, note that this node loads data in a list format, not as a batch, so it returns images at their original size without normalizing the size. + * `Load Image (Inspire)`: This node is similar to LoadImage, but the loaded image information is stored in the workflow. The image itself is stored in the workflow, making it easier to reproduce image generation on other computers. + * `Change Image Batch Size (Inspire)`: Change Image Batch Size + * `simple`: if the `batch_size` is larger than the batch size of the input image, the last frame will be duplicated. If it is smaller, it will be simply cropped. + * `Change Latent Batch Size (Inspire)`: Change Latent Batch Size + * `ImageBatchSplitter //Inspire`, `LatentBatchSplitter //Inspire`: The script divides a batch of images/latents into individual images/latents, each with a quantity equal to the specified `split_count`. An additional output slot is added for each `split_count`. If the number of images/latents exceeds the `split_count`, the remaining ones are returned as the "remained" output. + * `Color Map To Masks (Inspire)`: From the color_map, it extracts the top max_count number of colors and creates masks. min_pixels represents the minimum number of pixels for each color. + * `Select Nth Mask (Inspire)`: Extracts the nth mask from the mask batch. + +### Backend Cache - Nodes for storing arbitrary data from the backend in a cache and sharing it across multiple workflows. + * `Cache Backend Data (Inspire)`: Stores any backend data in the cache using a string key. Tags are for quick reference. + * `Retrieve Backend Data (Inspire)`: Retrieves cached backend data using a string key. + * `Remove Backend Data (Inspire)`: Removes cached backend data. + * Deletion in this node only removes it from the cache managed by Inspire, and if it's still in use elsewhere, it won't be completely removed from memory. + * `signal_opt` is used to control the order of execution for this node; it will still run without a `signal_opt` input. + * When using '*' as the key, it clears all data. + * `Show Cached Info (Inspire)`: Displays information about cached data. + * Default tag cache size is 5. You can edit the default size of each tag in `cache_settings.json`. + * Runtime tag cache size can be modified on the `Show Cached Info (Inspire)` node. For example: `ckpt: 10`. + * `Cache Backend Data [NumberKey] (Inspire)`, `Retrieve Backend Data [NumberKey] (Inspire)`, `Remove Backend Data [NumberKey] (Inspire)`: These nodes are provided for convenience in the automation process, allowing the use of numbers as keys. + * `Cache Backend Data List (Inspire)`, `Cache Backend Data List [NumberKey] (Inspire)`: This node allows list input for backend cache. Conversely, nodes like `Cache Backend Data [NumberKey] (Inspire)` that do not accept list input will attempt to cache redundantly and overwrite existing data if provided with a list input. Therefore, it is necessary to use a unique key for each element to prevent this. This node caches the combined list. When retrieving cached backend data through this node, the output is in the form of a list. + * `Shared Checkpoint Loader (Inspire)`: When loading a checkpoint through this loader, it is automatically cached in the backend cache. Additionally, if it is already cached, it retrieves it from the cache instead of loading it anew. + * When `key_opt` is empty, the `ckpt_name` is set as the cache key. The cache key output can be used for deletion purposes with Remove Back End. + * This node resolves the issue of reloading checkpoints during workflow switching. + * `Shared Diffusion Model Loader (Inspire)`: Similar to the `Shared Checkpoint Loader (Inspire)` but used for loading Diffusion models instead of Checkpoints. + * `Shared Text Encoder Loader (Inspire)`: Similar to the `Shared Checkpoint Loader (Inspire)` but used for loading Text Encoder models instead of Checkpoints. + * This node also functions as a unified node for `CLIPLoader`, `DualCLIPLoader`, and `TripleCLIPLoader`. + * `Stable Cascade Checkpoint Loader (Inspire)`: This node provides a feature that allows you to load the `stage_b` and `stage_c` checkpoints of Stable Cascade at once, and it also provides a backend caching feature, optionally. + * `Is Cached (Inspire)`: Returns whether the cache exists. + +### Conditioning - Nodes for conditionings + * `Concat Conditionings with Multiplier (Inspire)`: Concatenating an arbitrary number of Conditionings while applying a multiplier for each Conditioning. The multiplier depends on `comfy_PoP`, so [comfy_PoP](https://github.com/picturesonpictures/comfy_PoP) must be installed. + * `Conditioning Upscale (Inspire)`: When upscaling an image, it helps to expand the conditioning area according to the upscale factor. Taken from [ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode) + * `Conditioning Stretch (Inspire)`: When upscaling an image, it helps to expand the conditioning area by specifying the original resolution and the new resolution to be applied. Taken from [ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode) + +### Models - Nodes for models + * `IPAdapter Model Helper (Inspire)`: This provides presets that allow for easy loading of the IPAdapter related models. However, it is essential for the model's name to be accurate. + * You can download the appropriate model through ComfyUI-Manager. + +### List - Nodes for List processing + * `Float Range (Inspire)`: Create a float list that increases the value by `step` from `start` to `stop`. A list as large as the maximum limit is created, and when `ensure_end` is enabled, the last value of the list becomes the stop value. + * `Worklist To Item List (Inspire)`: The list in ComfyUI allows for repeated execution of a sub-workflow. This groups these repetitions (a.k.a. list) into a single ITEM_LIST output. ITEM_LIST can then be used in ForeachList. + * `β–ΆForeach List (Inspire)`: A starting node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nGenerate a new intermediate_output using item and intermediate_output as inputs, then connect it to ForeachListEnd.\nNOTE:If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list. + * `Foreach Listβ—€ (Inspire)`: An end node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nNOTE:Directly connect the outputs of ForeachListBegin to 'flow_control' and 'remained_list'. + * `Drop List (Inspire)`: Removes all items from the ITEM_LIST. If the ITEM_LIST generated through this node is passed to ForeachListEnd, the process is immediately terminated. + +### Util - Utilities + * `ToIPAdapterPipe (Inspire)`, `FromIPAdapterPipe (Inspire)`: These nodes assists in conveniently using the bundled ipadapter_model, clip_vision, and model required for applying IPAdapter. + * `List Counter (Inspire)`: When each item in the list traverses through this node, it increments a counter by one, generating an integer value. + * `RGB Hex To HSV (Inspire)`: Convert an RGB hex string like `#FFD500` to HSV: + +## Credits + +ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI. + +ComfyUI/[sd-webui-lora-block-weight](https://github.com/hako-mikan/sd-webui-lora-block-weight) - The original idea for LoraBlockWeight came from here, and it is based on the syntax of this extension. + +jags111/[efficiency-nodes-comfyui](https://github.com/jags111/ComfyUI-Jags-workflows) - The `XY Input` provided by the Inspire Pack supports the `XY Plot` of this node. + +Fannovel16/[comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux) - The wrapper for the controlnet preprocessor in the Inspire Pack depends on these nodes. + +Kosinkadink/[ComfyUI-Advanced-Controlnet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet) - `Load Images From Dir (Inspire)` code is came from here. + +Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack! + +cubiq/[ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) - IPAdapter related nodes depend on this extension. + +Davemane42/[ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode) - Original author of ConditioningStretch, ConditioningUpscale + +BlenderNeko/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - slerp code for noise variation + +BadCafeCode/[execution-inversion-demo-comfyui](https://github.com/BadCafeCode/execution-inversion-demo-comfyui) - reference loop implementation for ComfyUI diff --git a/custom_nodes/comfyui-inspire-pack/__init__.py b/custom_nodes/comfyui-inspire-pack/__init__.py new file mode 100644 index 00000000..39269de6 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/__init__.py @@ -0,0 +1,41 @@ +""" +@author: Dr.Lt.Data +@title: Inspire Pack +@nickname: Inspire Pack +@description: This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils and the Impact Pack. +""" + +import importlib +import logging + +version_code = [1, 23] +version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '') +logging.info(f"### Loading: ComfyUI-Inspire-Pack ({version_str})") + +node_list = [ + "lora_block_weight", + "segs_support", + "a1111_compat", + "prompt_support", + "inspire_server", + "image_util", + "regional_nodes", + "sampler_nodes", + "backend_support", + "list_nodes", + "conditioning_nodes", + "model_nodes", + "util_nodes" +] + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +for module_name in node_list: + imported_module = importlib.import_module(".inspire.{}".format(module_name), __name__) + + NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS} + NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS} + +WEB_DIRECTORY = "./js" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] diff --git a/custom_nodes/comfyui-inspire-pack/cache_settings.json b/custom_nodes/comfyui-inspire-pack/cache_settings.json new file mode 100644 index 00000000..c0a57663 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/cache_settings.json @@ -0,0 +1,5 @@ +{ + "ckpt": 5, + "latent": 100, + "image": 100 +} \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/inspire/a1111_compat.py b/custom_nodes/comfyui-inspire-pack/inspire/a1111_compat.py new file mode 100644 index 00000000..1f961724 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/a1111_compat.py @@ -0,0 +1,460 @@ +import comfy +import torch +from .libs import utils +from einops import rearrange +import random +import math +from .libs import common +import logging + + +supported_noise_modes = ["GPU(=A1111)", "CPU", "GPU+internal_seed", "CPU+internal_seed"] + + +class Inspire_RandomNoise: + def __init__(self, seed, mode, incremental_seed_mode, variation_seed, variation_strength, variation_method="linear", internal_seed=None): + device = comfy.model_management.get_torch_device() + # HOTFIX: https://github.com/comfyanonymous/ComfyUI/commit/916d1e14a93ef331adef7c0deff2fdcf443b05cf#commitcomment-151914788 + # seed value should be different with generated noise + + self.seed = internal_seed + self.noise_seed = seed + self.noise_device = "cpu" if mode == "CPU" else device + self.incremental_seed_mode = incremental_seed_mode + self.variation_seed = variation_seed + self.variation_strength = variation_strength + self.variation_method = variation_method + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None + noise = utils.prepare_noise(latent_image, self.noise_seed, batch_inds, self.noise_device, self.incremental_seed_mode, + variation_seed=self.variation_seed, variation_strength=self.variation_strength, variation_method=self.variation_method) + return noise.cpu() + + +class RandomNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}), + "noise_mode": (["GPU(=A1111)", "CPU"],), + "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],), + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": + { + "variation_method": (["linear", "slerp"],), + "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}), + } + } + + RETURN_TYPES = ("NOISE",) + FUNCTION = "get_noise" + CATEGORY = "InspirePack/a1111_compat" + + def get_noise(self, noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method="linear", internal_seed=None): + if internal_seed is None: + internal_seed = noise_seed + + return (Inspire_RandomNoise(noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method=variation_method, internal_seed=internal_seed),) + + +def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, + noise_mode="CPU", disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, + incremental_seed_mode="comfy", variation_seed=None, variation_strength=None, noise=None, callback=None, variation_method="linear", + scheduler_func=None, internal_seed=None): + device = comfy.model_management.get_torch_device() + noise_device = "cpu" if 'cpu' in noise_mode.lower() else device + latent_image = latent["samples"] + if hasattr(comfy.sample, 'fix_empty_latent_channels'): + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + latent = latent.copy() + + if noise is not None and latent_image.shape[1] != noise.shape[1]: + logging.info("[Inspire Pack] inspire_ksampler: The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.") + raise Exception("The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.") + + if noise is None: + if disable_noise: + torch.manual_seed(seed) + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device) + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = utils.prepare_noise(latent_image, seed, batch_inds, noise_device, incremental_seed_mode, + variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method) + + if start_step is None: + if denoise == 1.0: + start_step = 0 + else: + advanced_steps = math.floor(steps / denoise) + start_step = advanced_steps - steps + steps = advanced_steps + + if internal_seed is None: + internal_seed = seed + + if 'internal_seed' in noise_mode: + seed = internal_seed + + try: + samples = common.impact_sampling( + model=model, add_noise=not disable_noise, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative, + latent_image=latent, start_at_step=start_step, end_at_step=last_step, return_with_leftover_noise=not force_full_denoise, noise=noise, callback=callback, + scheduler_func=scheduler_func) + except Exception as e: + if "unexpected keyword argument 'scheduler_func'" in str(e): + logging.info("[Inspire Pack] Impact Pack is outdated. (Cannot use GITS scheduler.)") + + samples = common.impact_sampling( + model=model, add_noise=not disable_noise, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative, + latent_image=latent, start_at_step=start_step, end_at_step=last_step, return_with_leftover_noise=not force_full_denoise, noise=noise, callback=callback) + else: + raise e + + return samples, noise + + +class KSampler_inspire: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_mode": (supported_noise_modes,), + "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],), + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": + { + "variation_method": (["linear", "slerp"],), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/a1111_compat" + + @staticmethod + def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, + batch_seed_mode="comfy", variation_seed=None, variation_strength=None, variation_method="linear", scheduler_func_opt=None, + internal_seed=None): + return (inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, + incremental_seed_mode=batch_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method, + scheduler_func=scheduler_func_opt, internal_seed=internal_seed)[0], ) + + +class KSamplerAdvanced_inspire: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "noise_mode": (supported_noise_modes,), + "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],), + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": + { + "variation_method": (["linear", "slerp"],), + "noise_opt": ("NOISE_IMAGE",), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/a1111_compat" + + @staticmethod + def sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise, + denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, callback=None, variation_method="linear", scheduler_func_opt=None, internal_seed=None): + force_full_denoise = True + + if return_with_leftover_noise: + force_full_denoise = False + + disable_noise = False + + if not add_noise: + disable_noise = True + + return inspire_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, + force_full_denoise=force_full_denoise, noise_mode=noise_mode, incremental_seed_mode=batch_seed_mode, + variation_seed=variation_seed, variation_strength=variation_strength, noise=noise_opt, callback=callback, variation_method=variation_method, + scheduler_func=scheduler_func_opt, internal_seed=internal_seed) + + def doit(self, *args, **kwargs): + return (self.sample(*args, **kwargs)[0],) + + +class KSampler_inspire_pipe: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"basic_pipe": ("BASIC_PIPE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_mode": (supported_noise_modes,), + "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],), + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": + { + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}), + } + } + + RETURN_TYPES = ("LATENT", "VAE") + FUNCTION = "sample" + + CATEGORY = "InspirePack/a1111_compat" + + def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise, noise_mode, batch_seed_mode="comfy", + variation_seed=None, variation_strength=None, scheduler_func_opt=None, internal_seed=None): + model, clip, vae, positive, negative = basic_pipe + latent = inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, incremental_seed_mode=batch_seed_mode, + variation_seed=variation_seed, variation_strength=variation_strength, scheduler_func=scheduler_func_opt, internal_seed=internal_seed)[0] + return latent, vae + + +class KSamplerAdvanced_inspire_pipe: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"basic_pipe": ("BASIC_PIPE",), + "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "noise_mode": (supported_noise_modes,), + "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],), + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": + { + "noise_opt": ("NOISE_IMAGE",), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}), + } + } + + RETURN_TYPES = ("LATENT", "VAE", ) + FUNCTION = "sample" + + CATEGORY = "InspirePack/a1111_compat" + + def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise, + denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, scheduler_func_opt=None, internal_seed=None): + model, clip, vae, positive, negative = basic_pipe + latent = KSamplerAdvanced_inspire().sample(model=model, add_noise=add_noise, noise_seed=noise_seed, + steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, + positive=positive, negative=negative, latent_image=latent_image, + start_at_step=start_at_step, end_at_step=end_at_step, + noise_mode=noise_mode, return_with_leftover_noise=return_with_leftover_noise, + denoise=denoise, batch_seed_mode=batch_seed_mode, variation_seed=variation_seed, + variation_strength=variation_strength, noise_opt=noise_opt, scheduler_func_opt=scheduler_func_opt, + internal_seed=internal_seed)[0] + return latent, vae + + +# Modified version of ComfyUI main code +# https://github.com/comfyanonymous/ComfyUI/blob/master/comfy_extras/nodes_hypertile.py +def get_closest_divisors(hw: int, aspect_ratio: float) -> tuple[int, int]: + pairs = [(i, hw // i) for i in range(int(math.sqrt(hw)), 1, -1) if hw % i == 0] + pair = min(((i, hw // i) for i in range(2, hw + 1) if hw % i == 0), + key=lambda x: abs(x[1] / x[0] - aspect_ratio)) + pairs.append(pair) + res = min(pairs, key=lambda x: max(x) / min(x)) + return res + + +def calc_optimal_hw(hw: int, aspect_ratio: float) -> tuple[int, int]: + hcand = round(math.sqrt(hw * aspect_ratio)) + wcand = hw // hcand + + if hcand * wcand != hw: + wcand = round(math.sqrt(hw / aspect_ratio)) + hcand = hw // wcand + + if hcand * wcand != hw: + return get_closest_divisors(hw, aspect_ratio) + + return hcand, wcand + + +def random_divisor(value: int, min_value: int, /, max_options: int = 1, rand_obj=random.Random()) -> int: + # print(f"value={value}, min_value={min_value}, max_options={max_options}") + min_value = min(min_value, value) + + # All big divisors of value (inclusive) + divisors = [i for i in range(min_value, value + 1) if value % i == 0] + + ns = [value // i for i in divisors[:max_options]] # has at least 1 element + + if len(ns) - 1 > 0: + idx = rand_obj.randint(0, len(ns) - 1) + else: + idx = 0 + # print(f"ns={ns}, idx={idx}") + + return ns[idx] + +# def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]: +# """ +# Returns divisors of value that +# x * min_value <= value +# in big -> small order, amount of divisors is limited by max_options +# """ +# max_options = max(1, max_options) # at least 1 option should be returned +# min_value = min(min_value, value) +# divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order +# ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order +# return ns + + +# def random_divisor(value: int, min_value: int, /, max_options: int = 1, rand_obj=None) -> int: +# """ +# Returns a random divisor of value that +# x * min_value <= value +# if max_options is 1, the behavior is deterministic +# """ +# print(f"value={value}, min_value={min_value}, max_options={max_options}") +# ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors +# idx = rand_obj.randint(0, len(ns) - 1) +# print(f"ns={ns}, idx={idx}") +# +# return ns[idx] + + +class HyperTileInspire: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL",), + "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), + "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), + "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), + "scale_depth": ("BOOLEAN", {"default": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "InspirePack/__for_testing" + + def patch(self, model, tile_size, swap_size, max_depth, scale_depth, seed): + latent_tile_size = max(32, tile_size) // 8 + temp = None + + rand_obj = random.Random() + rand_obj.seed(seed) + + def hypertile_in(q, k, v, extra_options): + nonlocal temp + model_chans = q.shape[-2] + orig_shape = extra_options['original_shape'] + apply_to = [] + for i in range(max_depth + 1): + apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) + + if model_chans in apply_to: + shape = extra_options["original_shape"] + aspect_ratio = shape[-1] / shape[-2] + + hw = q.size(1) + # h, w = calc_optimal_hw(hw, aspect_ratio) + h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) + + factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 + nh = random_divisor(h, latent_tile_size * factor, swap_size, rand_obj) + nw = random_divisor(w, latent_tile_size * factor, swap_size, rand_obj) + + logging.debug(f"factor: {factor} <--- params.depth: {apply_to.index(model_chans)} / scale_depth: {scale_depth} / latent_tile_size={latent_tile_size}") + # print(f"h: {h}, w:{w} --> nh: {nh}, nw: {nw}") + + if nh * nw > 1: + q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) + temp = (nh, nw, h, w) + # else: + # temp = None + + logging.debug(f"q={q} / k={k} / v={v}") + return q, k, v + + return q, k, v + + def hypertile_out(out, extra_options): + nonlocal temp + if temp is not None: + nh, nw, h, w = temp + temp = None + out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) + out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) + return out + + m = model.clone() + m.set_model_attn1_patch(hypertile_in) + m.set_model_attn1_output_patch(hypertile_out) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "KSampler //Inspire": KSampler_inspire, + "KSamplerAdvanced //Inspire": KSamplerAdvanced_inspire, + "KSamplerPipe //Inspire": KSampler_inspire_pipe, + "KSamplerAdvancedPipe //Inspire": KSamplerAdvanced_inspire_pipe, + "RandomNoise //Inspire": RandomNoise, + "HyperTile //Inspire": HyperTileInspire +} +NODE_DISPLAY_NAME_MAPPINGS = { + "KSampler //Inspire": "KSampler (inspire)", + "KSamplerAdvanced //Inspire": "KSamplerAdvanced (inspire)", + "KSamplerPipe //Inspire": "KSampler [pipe] (inspire)", + "KSamplerAdvancedPipe //Inspire": "KSamplerAdvanced [pipe] (inspire)", + "RandomNoise //Inspire": "RandomNoise (inspire)", + "HyperTile //Inspire": "HyperTile (Inspire)" +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/backend_support.py b/custom_nodes/comfyui-inspire-pack/inspire/backend_support.py new file mode 100644 index 00000000..b57255d5 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/backend_support.py @@ -0,0 +1,764 @@ +import json +import os +from .libs import common + +import folder_paths +import nodes +from server import PromptServer + +from .libs.utils import TaggedCache, any_typ + +import logging + +root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +settings_file = os.path.join(root_dir, 'cache_settings.json') +try: + with open(settings_file) as f: + cache_settings = json.load(f) +except Exception as e: + logging.error(e) + cache_settings = {} +cache = TaggedCache(cache_settings) +cache_count = {} + + +def update_cache(k, tag, v): + cache[k] = (tag, v) + cnt = cache_count.get(k) + if cnt is None: + cnt = 0 + cache_count[k] = cnt + else: + cache_count[k] += 1 + + +def cache_weak_hash(k): + cnt = cache_count.get(k) + if cnt is None: + cnt = 0 + + return k, cnt + + +class CacheBackendData: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False, "placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"}), + "tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}), + "data": (any_typ,), + } + } + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("data opt",) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + OUTPUT_NODE = True + + @staticmethod + def doit(key, tag, data): + global cache + + if key == '*': + logging.warning("[Inspire Pack] CacheBackendData: '*' is reserved key. Cannot use that key") + return (None,) + + update_cache(key, tag, (False, data)) + return (data,) + + +class CacheBackendDataNumberKey: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}), + "data": (any_typ,), + } + } + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("data opt",) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + OUTPUT_NODE = True + + @staticmethod + def doit(key, tag, data): + global cache + + update_cache(key, tag, (False, data)) + return (data,) + + +class CacheBackendDataList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False, "placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"}), + "tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}), + "data": (any_typ,), + } + } + + INPUT_IS_LIST = True + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("data opt",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + OUTPUT_NODE = True + + @staticmethod + def doit(key, tag, data): + global cache + + if key == '*': + logging.warning("[Inspire Pack] CacheBackendDataList: '*' is reserved key. Cannot use that key") + return (None,) + + update_cache(key[0], tag[0], (True, data)) + return (data,) + + +class CacheBackendDataNumberKeyList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}), + "data": (any_typ,), + } + } + + INPUT_IS_LIST = True + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("data opt",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + OUTPUT_NODE = True + + def doit(self, key, tag, data): + global cache + update_cache(key[0], tag[0], (True, data)) + return (data,) + + +class RetrieveBackendData: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False, "placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"}), + } + } + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("data",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + @staticmethod + def doit(key): + global cache + + v = cache.get(key) + + if v is None: + logging.warning(f"[RetrieveBackendData] '{key}' is unregistered key.") + return ([None],) + + is_list, data = v[1] + + if is_list: + return (data,) + else: + return ([data],) + + @staticmethod + def IS_CHANGED(key): + return cache_weak_hash(key) + + +class RetrieveBackendDataNumberKey(RetrieveBackendData): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + +class RemoveBackendData: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False, "placeholder": "Input data key ('*' = clear all)"}), + }, + "optional": { + "signal_opt": (any_typ,), + } + } + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("signal",) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + OUTPUT_NODE = True + + @staticmethod + def doit(key, signal_opt=None): + global cache + + if key == '*': + cache = TaggedCache(cache_settings) + elif key in cache: + del cache[key] + else: + logging.warning(f"[Inspire Pack] RemoveBackendData: invalid data key {key}") + + return (signal_opt,) + + +class RemoveBackendDataNumberKey(RemoveBackendData): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "optional": { + "signal_opt": (any_typ,), + } + } + + @staticmethod + def doit(key, signal_opt=None): + global cache + + if key in cache: + del cache[key] + else: + logging.warning(f"[Inspire Pack] RemoveBackendDataNumberKey: invalid data key {key}") + + return (signal_opt,) + + +class ShowCachedInfo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cache_info": ("STRING", {"multiline": True, "default": ""}), + "key": ("STRING", {"multiline": False, "default": ""}), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = () + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + OUTPUT_NODE = True + + @staticmethod + def get_data(): + global cache + + text1 = "---- [String Key Caches] ----\n" + text2 = "---- [Number Key Caches] ----\n" + for k, v in cache.items(): + tag = 'N/A(tag)' if v[0] == '' else v[0] + if isinstance(k, str): + text1 += f'{k}: {tag}\n' + else: + text2 += f'{k}: {tag}\n' + + text3 = "---- [TagCache Settings] ----\n" + for k, v in cache._tag_settings.items(): + text3 += f'{k}: {v}\n' + + for k, v in cache._data.items(): + if k not in cache._tag_settings: + text3 += f'{k}: {v.maxsize}\n' + + return f'{text1}\n{text2}\n{text3}' + + @staticmethod + def set_cache_settings(data: str): + global cache + settings = data.split("---- [TagCache Settings] ----\n")[-1].strip().split("\n") + + new_tag_settings = {} + for s in settings: + k, v = s.split(":") + new_tag_settings[k] = int(v.strip()) + if new_tag_settings == cache._tag_settings: + # tag settings is not changed + return + + new_cache = TaggedCache(new_tag_settings) + for k, v in cache.items(): + new_cache[k] = v + cache = new_cache + + def doit(self, cache_info, key, unique_id): + text = ShowCachedInfo.get_data() + PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": unique_id, "widget_name": "cache_info", "type": "text", "data": text}) + + return {} + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + +class CheckpointLoaderSimpleShared(nodes.CheckpointLoaderSimple): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + "key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'ckpt_name' as the key."}), + }, + "optional": { + "mode": (['Auto', 'Override Cache', 'Read Only'],), + }} + + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING") + RETURN_NAMES = ("model", "clip", "vae", "cache key") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + def doit(self, ckpt_name, key_opt, mode='Auto'): + if mode == 'Read Only': + if key_opt.strip() == '': + raise Exception("[CheckpointLoaderSimpleShared] key_opt cannot be omit if mode is 'Read Only'") + key = key_opt.strip() + elif key_opt.strip() == '': + key = ckpt_name + else: + key = key_opt.strip() + + if key not in cache or mode == 'Override Cache': + res = self.load_checkpoint(ckpt_name) + update_cache(key, "ckpt", (False, res)) + cache_kind = 'ckpt' + logging.info(f"[Inspire Pack] CheckpointLoaderSimpleShared: Ckpt '{ckpt_name}' is cached to '{key}'.") + else: + cache_kind, (_, res) = cache[key] + logging.info(f"[Inspire Pack] CheckpointLoaderSimpleShared: Cached ckpt '{key}' is loaded. (Loading skip)") + + if cache_kind == 'ckpt': + model, clip, vae = res + elif cache_kind == 'unclip_ckpt': + model, clip, vae, _ = res + else: + raise Exception(f"[CheckpointLoaderSimpleShared] Unexpected cache_kind '{cache_kind}'") + + return model, clip, vae, key + + @staticmethod + def IS_CHANGED(ckpt_name, key_opt, mode='Auto'): + if mode == 'Read Only': + if key_opt.strip() == '': + raise Exception("[CheckpointLoaderSimpleShared] key_opt cannot be omit if mode is 'Read Only'") + key = key_opt.strip() + elif key_opt.strip() == '': + key = ckpt_name + else: + key = key_opt.strip() + + if mode == 'Read Only': + return (None, cache_weak_hash(key)) + elif mode == 'Override Cache': + return (ckpt_name, key) + + return (None, cache_weak_hash(key)) + + +class LoadDiffusionModelShared(nodes.UNETLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "Diffusion Model Name"}), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],), + "key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'model_name' as the key."}), + "mode": (['Auto', 'Override Cache', 'Read Only'],), + } + } + RETURN_TYPES = ("MODEL", "STRING") + RETURN_NAMES = ("model", "cache key") + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + def doit(self, model_name, weight_dtype, key_opt, mode='Auto'): + if mode == 'Read Only': + if key_opt.strip() == '': + raise Exception("[LoadDiffusionModelShared] key_opt cannot be omit if mode is 'Read Only'") + key = key_opt.strip() + elif key_opt.strip() == '': + key = f"{model_name}_{weight_dtype}" + else: + key = key_opt.strip() + + if key not in cache or mode == 'Override Cache': + model = self.load_unet(model_name, weight_dtype)[0] + update_cache(key, "diffusion", (False, model)) + logging.info(f"[Inspire Pack] LoadDiffusionModelShared: diffusion model '{model_name}' is cached to '{key}'.") + else: + _, (_, model) = cache[key] + logging.info(f"[Inspire Pack] LoadDiffusionModelShared: Cached diffusion model '{key}' is loaded. (Loading skip)") + + return model, key + + @staticmethod + def IS_CHANGED(model_name, weight_dtype, key_opt, mode='Auto'): + if mode == 'Read Only': + if key_opt.strip() == '': + raise Exception("[LoadDiffusionModelShared] key_opt cannot be omit if mode is 'Read Only'") + key = key_opt.strip() + elif key_opt.strip() == '': + key = f"{model_name}_{weight_dtype}" + else: + key = key_opt.strip() + + if mode == 'Read Only': + return None, cache_weak_hash(key) + elif mode == 'Override Cache': + return model_name, key + + return None, cache_weak_hash(key) + + +class LoadTextEncoderShared: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name1": (folder_paths.get_filename_list("text_encoders"), ), + "model_name2": (["None"] + folder_paths.get_filename_list("text_encoders"), ), + "model_name3": (["None"] + folder_paths.get_filename_list("text_encoders"), ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "sdxl", "flux", "hunyuan_video"], ), + "key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'model_name' as the key."}), + "mode": (['Auto', 'Override Cache', 'Read Only'],), + }, + "optional": { "device": (["default", "cpu"], {"advanced": True}), } + } + RETURN_TYPES = ("CLIP", "STRING") + RETURN_NAMES = ("clip", "cache key") + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + DESCRIPTION = \ + ("[Recipes single]\n" + "stable_diffusion: clip-l\n" + "stable_cascade: clip-g\n" + "sd3: t5 / clip-g / clip-l\n" + "stable_audio: t5\n" + "mochi: t5\n" + "cosmos: old t5 xxl\n\n" + "[Recipes dual]\n" + "sdxl: clip-l, clip-g\n" + "sd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\n" + "flux: clip-l, t5\n\n" + "[Recipes triple]\n" + "sd3: clip-l, clip-g, t5") + + def doit(self, model_name1, model_name2, model_name3, type, key_opt, mode='Auto', device="default"): + if mode == 'Read Only': + if key_opt.strip() == '': + raise Exception("[LoadTextEncoderShared] key_opt cannot be omit if mode is 'Read Only'") + key = key_opt.strip() + elif key_opt.strip() == '': + key = model_name1 + if model_name2 is not None: + key += f"_{model_name2}" + if model_name3 is not None: + key += f"_{model_name3}" + key += f"_{type}_{device}" + else: + key = key_opt.strip() + + if key not in cache or mode == 'Override Cache': + if model_name2 != "None" and model_name3 != "None": # triple text encoder + if len({model_name1, model_name2, model_name3}) < 3: + logging.error("[LoadTextEncoderShared] The same model has been selected multiple times.") + raise ValueError("The same model has been selected multiple times.") + + if type not in ["sd3"]: + logging.error("[LoadTextEncoderShared] Currently, the triple text encoder is only supported in `sd3`.") + raise ValueError("Currently, the triple text encoder is only supported in `sd3`.") + + tcloader = nodes.NODE_CLASS_MAPPINGS["TripleCLIPLoader"]() + if hasattr(tcloader, 'execute'): + # node v3 + res = tcloader.execute(model_name1, model_name2, model_name3)[0] + else: + # legacy compatibility + res = tcloader.load_clip(model_name1, model_name2, model_name3)[0] + + elif model_name2 != "None" or model_name3 != "None": # dual text encoder + second_model = model_name2 if model_name2 != "None" else model_name3 + + if model_name1 == second_model: + logging.error("[LoadTextEncoderShared] You have selected the same model for both.") + raise ValueError("[LoadTextEncoderShared] You have selected the same model for both.") + + if type not in ["sdxl", "sd3", "flux", "hunyuan_video"]: + logging.error("[LoadTextEncoderShared] Currently, the triple text encoder is only supported in `sdxl, sd3, flux, hunyuan_video`.") + raise ValueError("Currently, the triple text encoder is only supported in `sdxl, sd3, flux, hunyuan_video`.") + + res = nodes.NODE_CLASS_MAPPINGS["DualCLIPLoader"]().load_clip(model_name1, second_model, type=type, device=device)[0] + + else: # single text encoder + if type not in ["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos"]: + logging.error("[LoadTextEncoderShared] Currently, the single text encoder is only supported in `stable_diffusion, stable_cascade, sd3, stable_audio, mochi, ltxv, pixart, cosmos`.") + raise ValueError("Currently, the single text encoder is only supported in `stable_diffusion, stable_cascade, sd3, stable_audio, mochi, ltxv, pixart, cosmos`.") + + res = nodes.NODE_CLASS_MAPPINGS["CLIPLoader"]().load_clip(model_name1, type=type, device=device)[0] + + update_cache(key, "diffusion", (False, res)) + logging.info(f"[Inspire Pack] LoadTextEncoderShared: text encoder model set is cached to '{key}'.") + else: + _, (_, res) = cache[key] + logging.info(f"[Inspire Pack] LoadTextEncoderShared: Cached text encoder model set '{key}' is loaded. (Loading skip)") + + return res, key + + @staticmethod + def IS_CHANGED(model_name1, model_name2, model_name3, type, key_opt, mode='Auto', device="default"): + if mode == 'Read Only': + if key_opt.strip() == '': + raise Exception("[LoadTextEncoderShared] key_opt cannot be omit if mode is 'Read Only'") + key = key_opt.strip() + elif key_opt.strip() == '': + key = model_name1 + if model_name2 is not None: + key += f"_{model_name2}" + if model_name3 is not None: + key += f"_{model_name3}" + key += f"_{type}_{device}" + else: + key = key_opt.strip() + + if mode == 'Read Only': + return None, cache_weak_hash(key) + elif mode == 'Override Cache': + return f"{model_name1}_{model_name2}_{model_name3}_{type}_{device}", key + + return None, cache_weak_hash(key) + + +class StableCascade_CheckpointLoader: + @classmethod + def INPUT_TYPES(s): + ckpts = folder_paths.get_filename_list("checkpoints") + default_stage_b = '' + default_stage_c = '' + + sc_ckpts = [x for x in ckpts if 'cascade' in x.lower()] + sc_b_ckpts = [x for x in sc_ckpts if 'stage_b' in x.lower()] + sc_c_ckpts = [x for x in sc_ckpts if 'stage_c' in x.lower()] + + if len(sc_b_ckpts) == 0: + sc_b_ckpts = [x for x in ckpts if 'stage_b' in x.lower()] + if len(sc_c_ckpts) == 0: + sc_c_ckpts = [x for x in ckpts if 'stage_c' in x.lower()] + + if len(sc_b_ckpts) == 0: + sc_b_ckpts = ckpts + if len(sc_c_ckpts) == 0: + sc_c_ckpts = ckpts + + if len(sc_b_ckpts) > 0: + default_stage_b = sc_b_ckpts[0] + if len(sc_c_ckpts) > 0: + default_stage_c = sc_c_ckpts[0] + + return {"required": { + "stage_b": (ckpts, {'default': default_stage_b}), + "key_opt_b": ("STRING", {"multiline": False, "placeholder": "If empty, use 'stage_b' as the key."}), + "stage_c": (ckpts, {'default': default_stage_c}), + "key_opt_c": ("STRING", {"multiline": False, "placeholder": "If empty, use 'stage_c' as the key."}), + "cache_mode": (["none", "stage_b", "stage_c", "all"], {"default": "none"}), + }} + + RETURN_TYPES = ("MODEL", "VAE", "MODEL", "VAE", "CLIP_VISION", "CLIP", "STRING", "STRING") + RETURN_NAMES = ("b_model", "b_vae", "c_model", "c_vae", "c_clip_vision", "clip", "key_b", "key_c") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + def doit(self, stage_b, key_opt_b, stage_c, key_opt_c, cache_mode): + if key_opt_b.strip() == '': + key_b = stage_b + else: + key_b = key_opt_b.strip() + + if key_opt_c.strip() == '': + key_c = stage_c + else: + key_c = key_opt_c.strip() + + if cache_mode in ['stage_b', "all"]: + if key_b not in cache: + res_b = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b) + update_cache(key_b, "ckpt", (False, res_b)) + logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_b}' is cached to '{key_b}'.") + else: + _, (_, res_b) = cache[key_b] + logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_b}' is loaded. (Loading skip)") + b_model, clip, b_vae = res_b + else: + b_model, clip, b_vae = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b) + + if cache_mode in ['stage_c', "all"]: + if key_c not in cache: + res_c = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c) + update_cache(key_c, "unclip_ckpt", (False, res_c)) + logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_c}' is cached to '{key_c}'.") + else: + _, (_, res_c) = cache[key_c] + logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_c}' is loaded. (Loading skip)") + c_model, _, c_vae, clip_vision = res_c + else: + c_model, _, c_vae, clip_vision = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c) + + return b_model, b_vae, c_model, c_vae, clip_vision, clip, key_b, key_c + + +class IsCached: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False}), + }, + "hidden": { + "unique_id": "UNIQUE_ID" + } + } + + RETURN_TYPES = ("BOOLEAN", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + @staticmethod + def IS_CHANGED(key, unique_id): + return common.is_changed(unique_id, key in cache) + + def doit(self, key, unique_id): + return (key in cache,) + + +# WIP: not properly working, yet +class CacheBridge: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "value": (any_typ,), + "mode": ("BOOLEAN", {"default": True, "label_off": "cached", "label_on": "passthrough"}), + }, + "hidden": { + "unique_id": "UNIQUE_ID" + } + } + + RETURN_TYPES = (any_typ, ) + RETURN_NAMES = ("value",) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Backend" + + @staticmethod + def IS_CHANGED(value, mode, unique_id): + if not mode and unique_id in common.changed_cache: + return common.not_changed_value(unique_id) + else: + return common.changed_value(unique_id) + + def doit(self, value, mode, unique_id): + if not mode: + # cache mode + if unique_id not in common.changed_cache: + common.changed_cache[unique_id] = value + common.changed_count_cache[unique_id] = 0 + + return (common.changed_cache[unique_id],) + else: + common.changed_cache[unique_id] = value + common.changed_count_cache[unique_id] = 0 + + return (common.changed_cache[unique_id],) + + +NODE_CLASS_MAPPINGS = { + "CacheBackendData //Inspire": CacheBackendData, + "CacheBackendDataNumberKey //Inspire": CacheBackendDataNumberKey, + "CacheBackendDataList //Inspire": CacheBackendDataList, + "CacheBackendDataNumberKeyList //Inspire": CacheBackendDataNumberKeyList, + "RetrieveBackendData //Inspire": RetrieveBackendData, + "RetrieveBackendDataNumberKey //Inspire": RetrieveBackendDataNumberKey, + "RemoveBackendData //Inspire": RemoveBackendData, + "RemoveBackendDataNumberKey //Inspire": RemoveBackendDataNumberKey, + "ShowCachedInfo //Inspire": ShowCachedInfo, + "CheckpointLoaderSimpleShared //Inspire": CheckpointLoaderSimpleShared, + "LoadDiffusionModelShared //Inspire": LoadDiffusionModelShared, + "LoadTextEncoderShared //Inspire": LoadTextEncoderShared, + "StableCascade_CheckpointLoader //Inspire": StableCascade_CheckpointLoader, + "IsCached //Inspire": IsCached, + # "CacheBridge //Inspire": CacheBridge, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "CacheBackendData //Inspire": "Cache Backend Data (Inspire)", + "CacheBackendDataNumberKey //Inspire": "Cache Backend Data [NumberKey] (Inspire)", + "CacheBackendDataList //Inspire": "Cache Backend Data List (Inspire)", + "CacheBackendDataNumberKeyList //Inspire": "Cache Backend Data List [NumberKey] (Inspire)", + "RetrieveBackendData //Inspire": "Retrieve Backend Data (Inspire)", + "RetrieveBackendDataNumberKey //Inspire": "Retrieve Backend Data [NumberKey] (Inspire)", + "RemoveBackendData //Inspire": "Remove Backend Data (Inspire)", + "RemoveBackendDataNumberKey //Inspire": "Remove Backend Data [NumberKey] (Inspire)", + "ShowCachedInfo //Inspire": "Show Cached Info (Inspire)", + "CheckpointLoaderSimpleShared //Inspire": "Shared Checkpoint Loader (Inspire)", + "LoadDiffusionModelShared //Inspire": "Shared Diffusion Model Loader (Inspire)", + "LoadTextEncoderShared //Inspire": "Shared Text Encoder Loader (Inspire)", + "StableCascade_CheckpointLoader //Inspire": "Stable Cascade Checkpoint Loader (Inspire)", + "IsCached //Inspire": "Is Cached (Inspire)", + # "CacheBridge //Inspire": "Cache Bridge (Inspire)" +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/conditioning_nodes.py b/custom_nodes/comfyui-inspire-pack/inspire/conditioning_nodes.py new file mode 100644 index 00000000..1bf04863 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/conditioning_nodes.py @@ -0,0 +1,165 @@ +import torch +import nodes +import inspect +from .libs import utils +from nodes import MAX_RESOLUTION +import logging + + +class ConcatConditioningsWithMultiplier: + @classmethod + def INPUT_TYPES(s): + stack = inspect.stack() + if stack[1].function == 'get_input_info': + # bypass validation + class AllContainer: + def __contains__(self, item): + return True + + def __getitem__(self, key): + return "FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01} + + return { + "required": {"conditioning1": ("CONDITIONING",), }, + "optional": AllContainer() + } + + return { + "required": {"conditioning1": ("CONDITIONING",), }, + "optional": {"multiplier1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }, + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/__for_testing" + + def doit(self, **kwargs): + if "ConditioningMultiplier_PoP" in nodes.NODE_CLASS_MAPPINGS: + obj = nodes.NODE_CLASS_MAPPINGS["ConditioningMultiplier_PoP"]() + else: + utils.try_install_custom_node('https://github.com/picturesonpictures/comfy_PoP', + "To use 'ConcatConditioningsWithMultiplier' node, 'comfy_PoP' extension is required.") + raise Exception("'comfy_PoP' node isn't installed.") + + conditioning_to = kwargs['conditioning1'] + conditioning_to = obj.multiply_conditioning_strength(conditioning=conditioning_to, multiplier=float(kwargs['multiplier1']))[0] + + out = None + for k, conditioning_from in kwargs.items(): + if k == 'conditioning1' or not k.startswith('conditioning'): + continue + + out = [] + if len(conditioning_from) > 1: + logging.warning(f"[Inspire Pack] ConcatConditioningsWithMultiplier {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.") + + mkey = 'multiplier' + k[12:] + multiplier = float(kwargs[mkey]) + conditioning_from = obj.multiply_conditioning_strength(conditioning=conditioning_from, multiplier=multiplier)[0] + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from), 1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + conditioning_to = out + + if out is None: + return (kwargs['conditioning1'],) + else: + return (out,) + + +# CREDIT for ConditioningStretch, ConditioningUpscale: Davemane42 +# Imported to support archived custom nodes. +# original code: https://github.com/Davemane42/ComfyUI_Dave_CustomNode/blob/main/MultiAreaConditioning.py +class ConditioningStretch: + def __init__(self) -> None: + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "conditioning": ("CONDITIONING",), + "resolutionX": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}), + "resolutionY": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}), + "newWidth": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}), + "newHeight": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}), + # "scalar": ("INT", {"default": 2, "min": 1, "max": 100, "step": 0.5}), + }, + } + + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "InspirePack/conditioning" + + FUNCTION = 'upscale' + + @staticmethod + def upscale(conditioning, resolutionX, resolutionY, newWidth, newHeight, scalar=1): + c = [] + for t in conditioning: + + n = [t[0], t[1].copy()] + if 'area' in n[1]: + newWidth *= scalar + newHeight *= scalar + + x = ((n[1]['area'][3] * 8) * newWidth / resolutionX) // 8 + y = ((n[1]['area'][2] * 8) * newHeight / resolutionY) // 8 + w = ((n[1]['area'][1] * 8) * newWidth / resolutionX) // 8 + h = ((n[1]['area'][0] * 8) * newHeight / resolutionY) // 8 + + n[1]['area'] = tuple(map(lambda x: (((int(x) + 7) >> 3) << 3), [h, w, y, x])) + + c.append(n) + + return (c,) + + +class ConditioningUpscale: + def __init__(self) -> None: + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "conditioning": ("CONDITIONING",), + "scalar": ("INT", {"default": 2, "min": 1, "max": 100, "step": 0.5}), + }, + } + + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "InspirePack/conditioning" + + FUNCTION = 'upscale' + + @staticmethod + def upscale(conditioning, scalar): + c = [] + for t in conditioning: + + n = [t[0], t[1].copy()] + if 'area' in n[1]: + n[1]['area'] = tuple(map(lambda x: ((x * scalar + 7) >> 3) << 3, n[1]['area'])) + + c.append(n) + + return (c,) + + +NODE_CLASS_MAPPINGS = { + "ConcatConditioningsWithMultiplier //Inspire": ConcatConditioningsWithMultiplier, + "ConditioningUpscale //Inspire": ConditioningUpscale, + "ConditioningStretch //Inspire": ConditioningStretch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ConcatConditioningsWithMultiplier //Inspire": "Concat Conditionings with Multiplier (Inspire)", + "ConditioningUpscale //Inspire": "Conditioning Upscale (Inspire)", + "ConditioningStretch //Inspire": "Conditioning Stretch (Inspire)", +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/image_util.py b/custom_nodes/comfyui-inspire-pack/inspire/image_util.py new file mode 100644 index 00000000..165e26e7 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/image_util.py @@ -0,0 +1,511 @@ +import os + +import torch +from PIL import ImageOps +try: + import pillow_jxl # noqa: F401 + jxl = True +except ImportError: + jxl = False +import comfy +import folder_paths +import base64 +from io import BytesIO +from .libs.utils import ByPassTypeTuple, empty_pil_tensor, empty_latent +from PIL import Image +import numpy as np +import logging +import re + + +def extract_first_number(s): + match = re.search(r'\d+', s) + return int(match.group()) if match else float('inf') + + +sort_methods = [ + "None", + "Alphabetical (ASC)", + "Alphabetical (DESC)", + "Numerical (ASC)", + "Numerical (DESC)", + "Datetime (ASC)", + "Datetime (DESC)" +] + + +def sort_by(items, base_path='.', method=None): + def fullpath(x): return os.path.join(base_path, x) + + def get_timestamp(path): + try: + return os.path.getmtime(path) + except FileNotFoundError: + return float('-inf') + + if method == "Alphabetical (ASC)": + return sorted(items) + elif method == "Alphabetical (DESC)": + return sorted(items, reverse=True) + elif method == "Numerical (ASC)": + return sorted(items, key=lambda x: extract_first_number(os.path.splitext(x)[0])) + elif method == "Numerical (DESC)": + return sorted(items, key=lambda x: extract_first_number(os.path.splitext(x)[0]), reverse=True) + elif method == "Datetime (ASC)": + return sorted(items, key=lambda x: get_timestamp(fullpath(x))) + elif method == "Datetime (DESC)": + return sorted(items, key=lambda x: get_timestamp(fullpath(x)), reverse=True) + else: + return items + + +class LoadImagesFromDirBatch: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "directory": ("STRING", {"default": ""}), + }, + "optional": { + "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}), + "start_index": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "step": 1}), + "load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "sort_method": (sort_methods,), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "INT") + FUNCTION = "load_images" + + CATEGORY = "image" + + @classmethod + def IS_CHANGED(cls, **kwargs): + if 'load_always' in kwargs and kwargs['load_always']: + return float("NaN") + else: + return hash(frozenset(kwargs)) + + def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0, load_always=False, sort_method=None): + if not os.path.isdir(directory): + raise FileNotFoundError(f"Directory '{directory} cannot be found.'") + dir_files = os.listdir(directory) + if len(dir_files) == 0: + raise FileNotFoundError(f"No files in directory '{directory}'.") + + # Filter files by extension + valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] + if jxl: + valid_extensions.extend('.jxl') + dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] + + dir_files = sort_by(dir_files, directory, sort_method) + dir_files = [os.path.join(directory, x) for x in dir_files] + + # start at start_index + dir_files = dir_files[start_index:] + + images = [] + masks = [] + + limit_images = False + if image_load_cap > 0: + limit_images = True + image_count = 0 + + has_non_empty_mask = False + + for image_path in dir_files: + if os.path.isdir(image_path) and os.path.ex: + continue + if limit_images and image_count >= image_load_cap: + break + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + has_non_empty_mask = True + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + images.append(image) + masks.append(mask) + image_count += 1 + + if len(images) == 1: + return (images[0], masks[0], 1) + + elif len(images) > 1: + image1 = images[0] + mask1 = None + + for image2 in images[1:]: + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1) + image1 = torch.cat((image1, image2), dim=0) + + for mask2 in masks: + if has_non_empty_mask: + if image1.shape[1:3] != mask2.shape: + mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[1], image1.shape[2]), mode='bilinear', align_corners=False) + mask2 = mask2.squeeze(0) + else: + mask2 = mask2.unsqueeze(0) + else: + mask2 = mask2.unsqueeze(0) + + if mask1 is None: + mask1 = mask2 + else: + mask1 = torch.cat((mask1, mask2), dim=0) + + return (image1, mask1, len(images)) + + +class LoadImagesFromDirList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "directory": ("STRING", {"default": ""}), + }, + "optional": { + "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}), + "start_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}), + "load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), + "sort_method": (sort_methods,), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "STRING") + RETURN_NAMES = ("IMAGE", "MASK", "FILE PATH") + OUTPUT_IS_LIST = (True, True, True) + + FUNCTION = "load_images" + + CATEGORY = "image" + + @classmethod + def IS_CHANGED(cls, **kwargs): + if 'load_always' in kwargs and kwargs['load_always']: + return float("NaN") + else: + return hash(frozenset(kwargs)) + + def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0, load_always=False, sort_method=None): + if not os.path.isdir(directory): + raise FileNotFoundError(f"Directory '{directory}' cannot be found.") + dir_files = os.listdir(directory) + if len(dir_files) == 0: + raise FileNotFoundError(f"No files in directory '{directory}'.") + + # Filter files by extension + valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] + if jxl: + valid_extensions.extend('.jxl') + dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] + + dir_files = sort_by(dir_files, directory, sort_method) + dir_files = [os.path.join(directory, x) for x in dir_files] + + # start at start_index + dir_files = dir_files[start_index:] + + images = [] + masks = [] + file_paths = [] + + limit_images = False + if image_load_cap > 0: + limit_images = True + image_count = 0 + + for image_path in dir_files: + if os.path.isdir(image_path) and os.path.ex: + continue + if limit_images and image_count >= image_load_cap: + break + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + images.append(image) + masks.append(mask) + file_paths.append(str(image_path)) + image_count += 1 + + return (images, masks, file_paths) + + +class LoadImageInspire: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": { + "image": (sorted(files) + ["#DATA"], {"image_upload": True}), + "image_data": ("STRING", {"multiline": False}), + } + } + + CATEGORY = "InspirePack/image" + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + + def load_image(self, image, image_data): + image_data = base64.b64decode(image_data.split(",")[1]) + i = Image.open(BytesIO(image_data)) + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + return (image, mask.unsqueeze(0)) + + +class ChangeImageBatchSize: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}), + "mode": (["simple"],) + } + } + + CATEGORY = "InspirePack/Util" + + RETURN_TYPES = ("IMAGE", ) + FUNCTION = "doit" + + @staticmethod + def resize_tensor(input_tensor, batch_size, mode): + if mode == "simple": + if len(input_tensor) < batch_size: + last_frame = input_tensor[-1].unsqueeze(0).expand(batch_size - len(input_tensor), -1, -1, -1) + output_tensor = torch.concat((input_tensor, last_frame), dim=0) + else: + output_tensor = input_tensor[:batch_size, :, :, :] + return output_tensor + else: + logging.warning(f"[Inspire Pack] ChangeImage(Latent)BatchSize: Unknown mode `{mode}` - ignored") + return input_tensor + + @staticmethod + def doit(image, batch_size, mode): + res = ChangeImageBatchSize.resize_tensor(image, batch_size, mode) + return (res,) + + +class ChangeLatentBatchSize: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "latent": ("LATENT",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}), + "mode": (["simple"],) + } + } + + CATEGORY = "InspirePack/Util" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "doit" + + @staticmethod + def doit(latent, batch_size, mode): + res_latent = latent.copy() + samples = res_latent['samples'] + samples = ChangeImageBatchSize.resize_tensor(samples, batch_size, mode) + res_latent['samples'] = samples + return (res_latent,) + + +class ImageBatchSplitter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE",), + "split_count": ("INT", {"default": 4, "min": 0, "max": 50, "step": 1}), + }, + } + + RETURN_TYPES = ByPassTypeTuple(("IMAGE", )) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, images, split_count): + cnt = min(split_count, len(images)) + res = [image.unsqueeze(0) for image in images[:cnt]] + + if split_count >= len(images): + lack_cnt = split_count - cnt + 1 # including remained + empty_image = empty_pil_tensor() + for x in range(0, lack_cnt): + res.append(empty_image) + elif cnt < len(images): + remained_cnt = len(images) - cnt + remained_image = images[-remained_cnt:] + res.append(remained_image) + + return tuple(res) + + +class LatentBatchSplitter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "latent": ("LATENT",), + "split_count": ("INT", {"default": 4, "min": 0, "max": 50, "step": 1}), + }, + } + + RETURN_TYPES = ByPassTypeTuple(("LATENT", )) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, latent, split_count): + samples = latent['samples'] + + latent_base = latent.copy() + del latent_base['samples'] + + cnt = min(split_count, len(samples)) + res = [] + + for single_samples in samples[:cnt]: + item = latent_base.copy() + item['samples'] = single_samples.unsqueeze(0) + res.append(item) + + if split_count >= len(samples): + lack_cnt = split_count - cnt + 1 # including remained + item = latent_base.copy() + item['samples'] = empty_latent() + + for x in range(0, lack_cnt): + res.append(item) + + elif cnt < len(samples): + remained_cnt = len(samples) - cnt + remained_latent = latent_base.copy() + remained_latent['samples'] = samples[-remained_cnt:] + res.append(remained_latent) + + return tuple(res) + + +def top_k_colors(image_tensor, k, min_pixels): + flattened_image = image_tensor.view(-1, image_tensor.size(-1)) + + unique_colors, counts = torch.unique(flattened_image, dim=0, return_counts=True) + + sorted_counts, sorted_indices = torch.sort(counts, descending=True) + sorted_colors = unique_colors[sorted_indices] + + filtered_colors = sorted_colors[sorted_counts >= min_pixels] + + return filtered_colors[:k] + + +def create_mask(image_tensor, color): + mask_tensor = torch.zeros_like(image_tensor[:, :, :, 0]) + mask_tensor = torch.where(torch.all(image_tensor == color, dim=-1, keepdim=False), 1, mask_tensor) + return mask_tensor + + +class ColorMapToMasks: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "color_map": ("IMAGE",), + "min_pixels": ("INT", {"default": 500, "min": 1, "max": 0xffffffffffffffff, "step": 1}), + "max_count": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}), + }, + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, color_map, max_count, min_pixels): + if len(color_map) > 0: + logging.warning("[Inspire Pack] ColorMapToMasks - Sure, here's the translation: `color_map` can only be a single image. Only the first image will be processed. If you want to utilize the remaining images, convert the Image Batch to an Image List.") + + top_colors = top_k_colors(color_map[0], max_count, min_pixels) + + masks = None + + for color in top_colors: + this_mask = create_mask(color_map, color) + if masks is None: + masks = this_mask + else: + masks = torch.concat((masks, this_mask), dim=0) + + if masks is None: + masks = torch.zeros_like(color_map[0, :, :, 0]) + masks.unsqueeze(0) + + return (masks,) + + +class SelectNthMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "masks": ("MASK",), + "idx": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}), + }, + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "doit" + CATEGORY = "InspirePack/Util" + + def doit(self, masks, idx): + return (masks[idx].unsqueeze(0),) + + +NODE_CLASS_MAPPINGS = { + "LoadImagesFromDir //Inspire": LoadImagesFromDirBatch, + "LoadImageListFromDir //Inspire": LoadImagesFromDirList, + "LoadImage //Inspire": LoadImageInspire, + "ChangeImageBatchSize //Inspire": ChangeImageBatchSize, + "ChangeLatentBatchSize //Inspire": ChangeLatentBatchSize, + "ImageBatchSplitter //Inspire": ImageBatchSplitter, + "LatentBatchSplitter //Inspire": LatentBatchSplitter, + "ColorMapToMasks //Inspire": ColorMapToMasks, + "SelectNthMask //Inspire": SelectNthMask +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoadImagesFromDir //Inspire": "Load Image Batch From Dir (Inspire)", + "LoadImageListFromDir //Inspire": "Load Image List From Dir (Inspire)", + "LoadImage //Inspire": "Load Image (Inspire)", + "ChangeImageBatchSize //Inspire": "Change Image Batch Size (Inspire)", + "ChangeLatentBatchSize //Inspire": "Change Latent Batch Size (Inspire)", + "ImageBatchSplitter //Inspire": "Image Batch Splitter (Inspire)", + "LatentBatchSplitter //Inspire": "Latent Batch Splitter (Inspire)", + "ColorMapToMasks //Inspire": "Color Map To Masks (Inspire)", + "SelectNthMask //Inspire": "Select Nth Mask (Inspire)" +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/inspire_server.py b/custom_nodes/comfyui-inspire-pack/inspire/inspire_server.py new file mode 100644 index 00000000..517b6bf8 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/inspire_server.py @@ -0,0 +1,412 @@ +import random + +import nodes +import server +from enum import Enum +from . import prompt_support +from aiohttp import web +from . import backend_support +from .libs import common +import logging + + +max_seed = 2**32 - 1 + + +@server.PromptServer.instance.routes.get("/inspire/prompt_builder") +def prompt_builder(request): + result = {"presets": []} + + if "category" in request.rel_url.query: + category = request.rel_url.query["category"] + if category in prompt_support.prompt_builder_preset: + result['presets'] = prompt_support.prompt_builder_preset[category] + + return web.json_response(result) + + +@server.PromptServer.instance.routes.get("/inspire/cache/remove") +def cache_remove(request): + if "key" in request.rel_url.query: + key = request.rel_url.query["key"] + del backend_support.cache[key] + + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/inspire/cache/clear") +def cache_clear(request): + backend_support.cache.clear() + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/inspire/cache/list") +def cache_refresh(request): + return web.Response(text=backend_support.ShowCachedInfo.get_data(), status=200) + + +@server.PromptServer.instance.routes.post("/inspire/cache/settings") +async def set_cache_settings(request): + data = await request.text() + try: + backend_support.ShowCachedInfo.set_cache_settings(data) + return web.Response(text='OK', status=200) + except Exception as e: + return web.Response(text=f"{e}", status=500) + + +class SGmode(Enum): + FIX = 1 + INCR = 2 + DECR = 3 + RAND = 4 + + +class SeedGenerator: + def __init__(self, base_value, action): + self.base_value = base_value + + if action == "fixed" or action == "increment" or action == "decrement" or action == "randomize": + self.action = SGmode.FIX + elif action == 'increment for each node': + self.action = SGmode.INCR + elif action == 'decrement for each node': + self.action = SGmode.DECR + elif action == 'randomize for each node': + self.action = SGmode.RAND + + def next(self): + seed = self.base_value + + if self.action == SGmode.INCR: + self.base_value += 1 + if self.base_value > max_seed: + self.base_value = 0 + elif self.action == SGmode.DECR: + self.base_value -= 1 + if self.base_value < 0: + self.base_value = max_seed + elif self.action == SGmode.RAND: + self.base_value = random.randint(0, max_seed) + + return seed + + +def control_seed(v): + action = v['inputs']['action'] + value = v['inputs']['value'] + + if action == 'increment' or action == 'increment for each node': + value += 1 + if value > max_seed: + value = 0 + elif action == 'decrement' or action == 'decrement for each node': + value -= 1 + if value < 0: + value = max_seed + elif action == 'randomize' or action == 'randomize for each node': + value = random.randint(0, max_seed) + + v['inputs']['value'] = value + + return value + + +def prompt_seed_update(json_data): + try: + widget_idx_map = json_data['extra_data']['extra_pnginfo']['workflow']['widget_idx_map'] + except Exception: + return False, None + + value = None + mode = None + node = None + action = None + + for k, v in json_data['prompt'].items(): + if 'class_type' not in v: + continue + + cls = v['class_type'] + if cls == 'GlobalSeed //Inspire': + mode = v['inputs']['mode'] + action = v['inputs']['action'] + value = v['inputs']['value'] + node = k, v + + # control before generated + if mode is not None and mode: + value = control_seed(node[1]) + + if value is not None: + seed_generator = SeedGenerator(value, action) + + for k, v in json_data['prompt'].items(): + for k2, v2 in v['inputs'].items(): + if isinstance(v2, str) and '$GlobalSeed.value$' in v2: + v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value)) + + if k not in widget_idx_map or ('seed' not in widget_idx_map[k] and 'noise_seed' not in widget_idx_map[k]): + continue + + if 'seed' in v['inputs']: + if isinstance(v['inputs']['seed'], int): + v['inputs']['seed'] = seed_generator.next() + + if 'noise_seed' in v['inputs']: + if isinstance(v['inputs']['noise_seed'], int): + v['inputs']['noise_seed'] = seed_generator.next() + + for k2, v2 in v['inputs'].items(): + if isinstance(v2, str) and '$GlobalSeed.value$' in v2: + v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value)) + + # control after generated + if mode is not None and not mode: + control_seed(node[1]) + + return value is not None, mode + + +def workflow_seed_update(json_data, mode): + nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes'] + widget_idx_map = json_data['extra_data']['extra_pnginfo']['workflow']['widget_idx_map'] + prompt = json_data['prompt'] + + updated_seed_map = {} + value = None + for node in nodes: + node_id = str(node['id']) + if node_id in prompt: + if node['type'] == 'GlobalSeed //Inspire': + if mode is True: + node['widgets_values'][3] = node['widgets_values'][0] + node['widgets_values'][0] = prompt[node_id]['inputs']['value'] + node['widgets_values'][2] = 'fixed' + + value = prompt[node_id]['inputs']['value'] + + elif node_id in widget_idx_map: + widget_idx = None + seed = None + if 'noise_seed' in prompt[node_id]['inputs']: + seed = prompt[node_id]['inputs']['noise_seed'] + widget_idx = widget_idx_map[node_id].get('noise_seed') + elif 'seed' in prompt[node_id]['inputs']: + seed = prompt[node_id]['inputs']['seed'] + widget_idx = widget_idx_map[node_id].get('seed') + + if widget_idx is not None: + node['widgets_values'][widget_idx] = seed + updated_seed_map[node_id] = seed + + server.PromptServer.instance.send_sync("inspire-global-seed", {"value": value, "seed_map": updated_seed_map}) + + +def prompt_sampler_update(json_data): + try: + widget_idx_map = json_data['extra_data']['extra_pnginfo']['workflow']['widget_idx_map'] + except Exception: + return None + + nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes'] + prompt = json_data['prompt'] + + sampler_name = None + scheduler = None + + for v in prompt.values(): + cls = v.get('class_type') + if cls == 'GlobalSampler //Inspire': + sampler_name = v['inputs']['sampler_name'] + scheduler = v['inputs']['scheduler'] + + if sampler_name is None: + return + + for node in nodes: + cls = node.get('type') + if cls == 'GlobalSampler //Inspire' or cls is None: + continue + + node_id = str(node['id']) + + if node_id in prompt and node_id in widget_idx_map: + sampler_widget_idx = widget_idx_map[node_id].get('sampler_name') + scheduler_widget_idx = widget_idx_map[node_id].get('scheduler') + + prompt_inputs = prompt[node_id]['inputs'] + + if ('sampler_name' in prompt_inputs and 'scheduler' in prompt_inputs and + isinstance(prompt_inputs['sampler_name'], str) and 'scheduler' in prompt_inputs): + + if sampler_widget_idx is not None: + prompt_inputs['sampler_name'] = sampler_name + node['widgets_values'][sampler_widget_idx] = sampler_name + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": node_id, "widget_name": 'sampler_name', "type": "text", "data": sampler_name}) + + if scheduler_widget_idx is not None: + prompt_inputs['scheduler'] = scheduler + node['widgets_values'][scheduler_widget_idx] = scheduler + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": node_id, "widget_name": 'scheduler', "type": "text", "data": scheduler}) + + +def workflow_loadimage_update(json_data): + prompt = json_data['prompt'] + + for v in prompt.values(): + if 'class_type' in v and v['class_type'] == 'LoadImage //Inspire': + v['inputs']['image'] = "#DATA" + + +def populate_wildcards(json_data): + prompt = json_data['prompt'] + + if 'ImpactWildcardProcessor' in nodes.NODE_CLASS_MAPPINGS: + if not hasattr(nodes.NODE_CLASS_MAPPINGS['ImpactWildcardProcessor'], 'process'): + logging.warning("[Inspire Pack] Your Impact Pack is outdated. Please update to the latest version.") + return + + wildcard_process = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardProcessor'].process + updated_widget_values = {} + mbp_updated_widget_values = {} + for k, v in prompt.items(): + if 'class_type' in v and v['class_type'] == 'WildcardEncode //Inspire': + inputs = v['inputs'] + + # legacy adapter + if isinstance(inputs['mode'], bool): + if inputs['mode']: + new_mode = 'populate' + else: + new_mode = 'fixed' + + inputs['mode'] = new_mode + + if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str): + if isinstance(inputs['seed'], list): + try: + input_node = prompt[inputs['seed'][0]] + if input_node['class_type'] == 'ImpactInt': + input_seed = int(input_node['inputs']['value']) + if not isinstance(input_seed, int): + continue + if input_node['class_type'] == 'Seed (rgthree)': + input_seed = int(input_node['inputs']['seed']) + if not isinstance(input_seed, int): + continue + else: + logging.warning("[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ") + continue + except: + continue + else: + input_seed = int(inputs['seed']) + + inputs['populated_text'] = wildcard_process(text=inputs['wildcard_text'], seed=input_seed) + inputs['mode'] = 'reproduce' + + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "text", "data": inputs['populated_text']}) + updated_widget_values[k] = inputs['populated_text'] + + if inputs['mode'] == 'reproduce': + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "mode", "type": "text", "value": 'populate'}) + + elif 'class_type' in v and v['class_type'] == 'MakeBasicPipe //Inspire': + inputs = v['inputs'] + if inputs['wildcard_mode'] == 'populate' and (isinstance(inputs['positive_populated_text'], str) or isinstance(inputs['negative_populated_text'], str)): + if isinstance(inputs['seed'], list): + try: + input_node = prompt[inputs['seed'][0]] + if input_node['class_type'] == 'ImpactInt': + input_seed = int(input_node['inputs']['value']) + if not isinstance(input_seed, int): + continue + if input_node['class_type'] == 'Seed (rgthree)': + input_seed = int(input_node['inputs']['seed']) + if not isinstance(input_seed, int): + continue + else: + logging.warning("[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ") + continue + except: + continue + else: + input_seed = int(inputs['seed']) + + if isinstance(inputs['positive_populated_text'], str): + inputs['positive_populated_text'] = wildcard_process(text=inputs['positive_wildcard_text'], seed=input_seed) + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "positive_populated_text", "type": "text", "data": inputs['positive_populated_text']}) + + if isinstance(inputs['negative_populated_text'], str): + inputs['negative_populated_text'] = wildcard_process(text=inputs['negative_wildcard_text'], seed=input_seed) + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "negative_populated_text", "type": "text", "data": inputs['negative_populated_text']}) + + inputs['wildcard_mode'] = 'reproduce' + mbp_updated_widget_values[k] = inputs['positive_populated_text'], inputs['negative_populated_text'] + + if inputs['wildcard_mode'] == 'reproduce': + server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "wildcard_mode", "type": "text", "value": 'populate'}) + + if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']: + extra_pnginfo = json_data['extra_data']['extra_pnginfo'] + if 'workflow' in extra_pnginfo and extra_pnginfo['workflow'] is not None and 'nodes' in extra_pnginfo['workflow']: + for node in extra_pnginfo['workflow']['nodes']: + key = str(node['id']) + if key in updated_widget_values: + node['widgets_values'][3] = updated_widget_values[key] + node['widgets_values'][4] = 'reproduce' + if key in mbp_updated_widget_values: + node['widgets_values'][7] = mbp_updated_widget_values[key][0] + node['widgets_values'][8] = mbp_updated_widget_values[key][1] + node['widgets_values'][5] = 'reproduce' + + +def force_reset_useless_params(json_data): + prompt = json_data['prompt'] + + for k, v in prompt.items(): + if 'class_type' in v and v['class_type'] == 'PromptBuilder //Inspire': + v['inputs']['category'] = '#PLACEHOLDER' + + return json_data + + +def clear_unused_node_changed_cache(json_data): + prompt = json_data['prompt'] + + unused = [] + for x in common.changed_cache.keys(): + if x not in prompt: + unused.append(x) + + for x in unused: + del common.changed_cache[x] + del common.changed_count_cache[x] + + return json_data + + +def onprompt(json_data): + prompt_support.list_counter_map = {} + + is_changed, mode = prompt_seed_update(json_data) + if is_changed: + workflow_seed_update(json_data, mode) + + prompt_sampler_update(json_data) + + workflow_loadimage_update(json_data) + populate_wildcards(json_data) + + force_reset_useless_params(json_data) + clear_unused_node_changed_cache(json_data) + + return json_data + + +server.PromptServer.instance.add_on_prompt_handler(onprompt) + + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/libs/common.py b/custom_nodes/comfyui-inspire-pack/inspire/libs/common.py new file mode 100644 index 00000000..8bb3c585 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/libs/common.py @@ -0,0 +1,87 @@ +import comfy +import nodes +from . import utils +import logging +from server import PromptServer + + +ADDITIONAL_SCHEDULERS = ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]', 'OSS FLUX', 'OSS Wan', 'OSS Chroma'] + + +def get_schedulers(): + return list(comfy.samplers.SCHEDULER_HANDLERS) + ADDITIONAL_SCHEDULERS + + +def impact_sampling(*args, **kwargs): + if 'RegionalSampler' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack', + "'Impact Pack' extension is required.") + raise Exception("[ERROR] You need to install 'ComfyUI-Impact-Pack'") + + return nodes.NODE_CLASS_MAPPINGS['RegionalSampler'].separated_sample(*args, **kwargs) + + +changed_count_cache = {} +changed_cache = {} + + +def changed_value(uid): + v = changed_count_cache.get(uid, 0) + changed_count_cache[uid] = v + 1 + return v + 1 + + +def not_changed_value(uid): + return changed_count_cache.get(uid, 0) + + +def is_changed(uid, value): + if uid not in changed_cache or changed_cache[uid] != value: + res = changed_value(uid) + else: + res = not_changed_value(uid) + + changed_cache[uid] = value + + logging.info(f"keys: {changed_cache.keys()}") + + return res + + +def update_node_status(node, text, progress=None): + if PromptServer.instance.client_id is None: + return + + PromptServer.instance.send_sync("inspire/update_status", { + "node": node, + "progress": progress, + "text": text + }, PromptServer.instance.client_id) + + +class ListWrapper: + def __init__(self, data, aux=None): + if isinstance(data, ListWrapper): + self._data = data + if aux is None: + self.aux = data.aux + else: + self.aux = aux + else: + self._data = list(data) + self.aux = aux + + def __getitem__(self, index): + if isinstance(index, slice): + return ListWrapper(self._data[index], self.aux) + else: + return self._data[index] + + def __setitem__(self, index, value): + self._data[index] = value + + def __len__(self): + return len(self._data) + + def __repr__(self): + return f"ListWrapper({self._data}, aux={self.aux})" diff --git a/custom_nodes/comfyui-inspire-pack/inspire/libs/utils.py b/custom_nodes/comfyui-inspire-pack/inspire/libs/utils.py new file mode 100644 index 00000000..46e3dcaa --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/libs/utils.py @@ -0,0 +1,351 @@ +import itertools +from typing import Optional +import numpy as np +import torch +from PIL import Image, ImageDraw +import math +import cv2 +import folder_paths +import logging + + +def apply_variation_noise(latent_image, noise_device, variation_seed, variation_strength, mask=None, variation_method='linear'): + latent_size = latent_image.size() + latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]] + + if noise_device == "cpu": + variation_generator = torch.manual_seed(variation_seed) + else: + torch.cuda.manual_seed(variation_seed) + variation_generator = None + + variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=variation_generator, device=noise_device) + + variation_noise = variation_latent.expand(latent_image.size()[0], -1, -1, -1) + + if variation_strength == 0: + return latent_image + elif mask is None: + result = (1 - variation_strength) * latent_image + variation_strength * variation_noise + else: + # this seems precision is not enough when variation_strength is 0.0 + mixed_noise = mix_noise(latent_image, variation_noise, variation_strength, variation_method=variation_method) + result = (mask == 1).float() * mixed_noise + (mask == 0).float() * latent_image + + return result + + +# CREDIT: https://github.com/BlenderNeko/ComfyUI_Noise/blob/afb14757216257b12268c91845eac248727a55e2/nodes.py#L68 +# https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 +def slerp(val, low, high): + dims = low.shape + + low = low.reshape(dims[0], -1) + high = high.reshape(dims[0], -1) + + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + + low_norm[low_norm != low_norm] = 0.0 + high_norm[high_norm != high_norm] = 0.0 + + omega = torch.acos((low_norm*high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + + return res.reshape(dims) + + +def mix_noise(from_noise, to_noise, strength, variation_method): + to_noise = to_noise.to(from_noise.device) + + if variation_method == 'slerp': + mixed_noise = slerp(strength, from_noise, to_noise) + else: + # linear + mixed_noise = (1 - strength) * from_noise + strength * to_noise + + # NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling. + scale_factor = math.sqrt((1 - strength) ** 2 + strength ** 2) + mixed_noise /= scale_factor + + return mixed_noise + + +def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", variation_seed=None, variation_strength=None, variation_method="linear"): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + + latent_size = latent_image.size() + latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]] + + if variation_strength is not None and variation_strength > 0 or incremental_seed_mode.startswith("variation str inc"): + if noise_device == "cpu": + variation_generator = torch.manual_seed(variation_seed) + else: + torch.cuda.manual_seed(variation_seed) + variation_generator = None + + variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=variation_generator, device=noise_device) + else: + variation_latent = None + + def apply_variation(input_latent, strength_up=None): + if variation_latent is None: + return input_latent + else: + strength = variation_strength + + if strength_up is not None: + strength += strength_up + + variation_noise = variation_latent.expand(input_latent.size()[0], -1, -1, -1) + + mixed_noise = mix_noise(input_latent, variation_noise, strength, variation_method) + + return mixed_noise + + # method: incremental seed batch noise + if noise_inds is None and incremental_seed_mode == "incremental": + batch_cnt = latent_size[0] + + latents = None + for i in range(batch_cnt): + if noise_device == "cpu": + generator = torch.manual_seed(seed+i) + else: + torch.cuda.manual_seed(seed+i) + generator = None + + latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + + latent = apply_variation(latent) + + if latents is None: + latents = latent + else: + latents = torch.cat((latents, latent), dim=0) + + return latents + + # method: incremental variation batch noise + elif noise_inds is None and incremental_seed_mode.startswith("variation str inc"): + batch_cnt = latent_size[0] + + latents = None + for i in range(batch_cnt): + if noise_device == "cpu": + generator = torch.manual_seed(seed) + else: + torch.cuda.manual_seed(seed) + generator = None + + latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + + step = float(incremental_seed_mode[18:]) + latent = apply_variation(latent, step*i) + + if latents is None: + latents = latent + else: + latents = torch.cat((latents, latent), dim=0) + + return latents + + # method: comfy batch noise + if noise_device == "cpu": + generator = torch.manual_seed(seed) + else: + torch.cuda.manual_seed(seed) + generator = None + + if noise_inds is None: + latents = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + latents = apply_variation(latents) + return latents + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1] + 1): + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, + generator=generator, device=noise_device) + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + + +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + + +def empty_pil_tensor(w=64, h=64): + image = Image.new("RGB", (w, h)) + draw = ImageDraw.Draw(image) + draw.rectangle((0, 0, w-1, h-1), fill=(0, 0, 0)) + return pil2tensor(image) + + +def try_install_custom_node(custom_node_url, msg): + try: + import cm_global + cm_global.try_call(api='cm.try-install-custom-node', + sender="Inspire Pack", custom_node_url=custom_node_url, msg=msg) + except Exception as e: # noqa: F841 + logging.error(msg) + logging.error("[Inspire Pack] ComfyUI-Manager is outdated. The custom node installation feature is not available.") + + +def empty_latent(): + return torch.zeros([1, 4, 8, 8]) + +# wildcard trick is taken from pythongossss's +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + +any_typ = AnyType("*") + + +# author: Trung0246 ---> +class TautologyStr(str): + def __ne__(self, other): + return False + + +class ByPassTypeTuple(tuple): + def __getitem__(self, index): + if index > 0: + index = 0 + item = super().__getitem__(index) + if isinstance(item, str): + return TautologyStr(item) + return item + + +class TaggedCache: + def __init__(self, tag_settings: Optional[dict]=None): + self._tag_settings = tag_settings or {} # tag cache size + self._data = {} + + def __getitem__(self, key): + for tag_data in self._data.values(): + if key in tag_data: + return tag_data[key] + raise KeyError(f'Key `{key}` does not exist') + + def __setitem__(self, key, value: tuple): + # value: (tag: str, (islist: bool, data: *)) + + # if key already exists, pop old value + for tag_data in self._data.values(): + if key in tag_data: + tag_data.pop(key, None) + break + + tag = value[0] + if tag not in self._data: + + try: + from cachetools import LRUCache + + default_size = 20 + if 'ckpt' in tag: + default_size = 5 + elif tag in ['latent', 'image']: + default_size = 100 + + self._data[tag] = LRUCache(maxsize=self._tag_settings.get(tag, default_size)) + + except (ImportError, ModuleNotFoundError): + # TODO: implement a simple lru dict + self._data[tag] = {} + self._data[tag][key] = value + + def __delitem__(self, key): + for tag_data in self._data.values(): + if key in tag_data: + del tag_data[key] + return + raise KeyError(f'Key `{key}` does not exist') + + def __contains__(self, key): + return any(key in tag_data for tag_data in self._data.values()) + + def items(self): + yield from itertools.chain(*map(lambda x :x.items(), self._data.values())) + + def get(self, key, default=None): + """D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.""" + for tag_data in self._data.values(): + if key in tag_data: + return tag_data[key] + return default + + def clear(self): + # clear all cache + self._data = {} + + +def make_3d_mask(mask): + if len(mask.shape) == 4: + return mask.squeeze(0) + + elif len(mask.shape) == 2: + return mask.unsqueeze(0) + + return mask + + +def dilate_mask(mask: torch.Tensor, dilation_factor: float) -> torch.Tensor: + """Dilate a mask using a square kernel with a given dilation factor.""" + kernel_size = int(dilation_factor * 2) + 1 + kernel = np.ones((abs(kernel_size), abs(kernel_size)), np.uint8) + + masks = make_3d_mask(mask).numpy() + dilated_masks = [] + for m in masks: + if dilation_factor > 0: + m2 = cv2.dilate(m, kernel, iterations=1) + else: + m2 = cv2.erode(m, kernel, iterations=1) + + dilated_masks.append(torch.from_numpy(m2)) + + return torch.stack(dilated_masks) + + +def flatten_non_zero_override(masks: torch.Tensor): + """ + flatten multiple layer mask tensor to 1 layer mask tensor. + Override the lower layer with the tensor from the upper layer, but only override non-zero values. + + :param masks: 3d mask + :return: flatten mask + """ + final_mask = masks[0] + + for i in range(1, masks.size(0)): + non_zero_mask = masks[i] != 0 + final_mask[non_zero_mask] = masks[i][non_zero_mask] + + return final_mask + + +def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions): + for full_folder_path in full_folder_paths: + folder_paths.add_model_folder_path(folder_name, full_folder_path) + if folder_name in folder_paths.folder_names_and_paths: + current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name] + updated_extensions = current_extensions | extensions + folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions) + else: + folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions) diff --git a/custom_nodes/comfyui-inspire-pack/inspire/list_nodes.py b/custom_nodes/comfyui-inspire-pack/inspire/list_nodes.py new file mode 100644 index 00000000..7e23708e --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/list_nodes.py @@ -0,0 +1,266 @@ +import logging + +from comfy_execution.graph_utils import GraphBuilder, is_link +from .libs.utils import any_typ +from .libs.common import update_node_status, ListWrapper + +class FloatRange: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "start": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, 'step': 0.000000001}), + "stop": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, 'step': 0.000000001}), + "step": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 100.0, 'step': 0.000000001}), + "limit": ("INT", {"default": 100, "min": 2, "max": 4096, "step": 1}), + "ensure_end": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + } + } + + RETURN_TYPES = ("FLOAT",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/List" + + def doit(self, start, stop, step, limit, ensure_end): + if start == stop or step == 0: + return ([start], ) + + reverse = False + if start > stop: + reverse = True + start, stop = stop, start + + res = [] + x = start + last = x + while x <= stop and limit > 0: + res.append(x) + last = x + limit -= 1 + x += step + + if ensure_end and last != stop: + if len(res) >= limit: + res.pop() + + res.append(stop) + + if reverse: + res.reverse() + + return (res, ) + + +class WorklistToItemList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "item": (any_typ, ), + } + } + + INPUT_IS_LIST = True + + RETURN_TYPES = ("ITEM_LIST",) + RETURN_NAMES = ("item_list",) + + FUNCTION = "doit" + + DESCRIPTION = "The list in ComfyUI allows for repeated execution of a sub-workflow.\nThis groups these repetitions (a.k.a. list) into a single ITEM_LIST output.\nITEM_LIST can then be used in ForeachList." + + CATEGORY = "InspirePack/List" + + def doit(self, item): + return (item, ) + + +# Loop nodes are implemented based on BadCafeCode's reference loop implementation +# https://github.com/BadCafeCode/execution-inversion-demo-comfyui/blob/main/flow_control.py + +class ForeachListBegin: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "item_list": ("ITEM_LIST", {"tooltip": "ITEM_LIST containing items to be processed iteratively."}), + }, + "optional": { + "initial_input": (any_typ, {"tooltip": "If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list."}), + } + } + + RETURN_TYPES = ("FOREACH_LIST_CONTROL", "ITEM_LIST", any_typ, any_typ) + RETURN_NAMES = ("flow_control", "remained_list", "item", "intermediate_output") + OUTPUT_TOOLTIPS = ( + "Pass ForeachListEnd as is to indicate the end of the iteration.", + "Output the ITEM_LIST containing the remaining items during the iteration, passing ForeachListEnd as is to indicate the end of the iteration.", + "Output the current item during the iteration.", + "Output the intermediate results during the iteration.") + + FUNCTION = "doit" + + DESCRIPTION = "A starting node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nGenerate a new intermediate_output using item and intermediate_output as inputs, then connect it to ForeachListEnd.\nNOTE:If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list." + + CATEGORY = "InspirePack/List" + + def doit(self, item_list, initial_input=None): + if initial_input is None: + initial_input = item_list[0] + item_list = item_list[1:] + + if len(item_list) > 0: + next_list = ListWrapper(item_list[1:]) + next_item = item_list[0] + else: + next_list = ListWrapper([]) + next_item = None + + if next_list.aux is None: + next_list.aux = len(item_list), None + + return "stub", next_list, next_item, initial_input + + +class ForeachListEnd: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "flow_control": ("FOREACH_LIST_CONTROL", {"rawLink": True, "tooltip": "Directly connect the output of ForeachListBegin, the starting node of the iteration."}), + "remained_list": ("ITEM_LIST", {"tooltip":"Directly connect the output of ForeachListBegin, the starting node of the iteration."}), + "intermediate_output": (any_typ, {"tooltip":"Connect the intermediate outputs processed within the iteration here."}), + }, + "hidden": { + "dynprompt": "DYNPROMPT", + "unique_id": "UNIQUE_ID", + } + } + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("result",) + OUTPUT_TOOLTIPS = ("This is the final output value.",) + + FUNCTION = "doit" + + DESCRIPTION = "A end node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nNOTE:Directly connect the outputs of ForeachListBegin to 'flow_control' and 'remained_list'." + + CATEGORY = "InspirePack/List" + + def explore_dependencies(self, node_id, dynprompt, upstream): + node_info = dynprompt.get_node(node_id) + if "inputs" not in node_info: + return + for k, v in node_info["inputs"].items(): + if is_link(v): + parent_id = v[0] + if parent_id not in upstream: + upstream[parent_id] = [] + self.explore_dependencies(parent_id, dynprompt, upstream) + upstream[parent_id].append(node_id) + + def collect_contained(self, node_id, upstream, contained): + if node_id not in upstream: + return + for child_id in upstream[node_id]: + if child_id not in contained: + contained[child_id] = True + self.collect_contained(child_id, upstream, contained) + + def doit(self, flow_control, remained_list, intermediate_output, dynprompt, unique_id): + if hasattr(remained_list, "aux"): + if remained_list.aux[1] is None: + remained_list.aux = (remained_list.aux[0], unique_id) + + update_node_status(remained_list.aux[1], f"{(remained_list.aux[0]-len(remained_list))}/{remained_list.aux[0]} steps", (remained_list.aux[0]-len(remained_list))/remained_list.aux[0]) + else: + logging.warning("[Inspire Pack] ForeachListEnd: `remained_list` did not come from ForeachList.") + + if len(remained_list) == 0: + return (intermediate_output,) + + # We want to loop + upstream = {} + + # Get the list of all nodes between the open and close nodes + self.explore_dependencies(unique_id, dynprompt, upstream) + + contained = {} + open_node = flow_control[0] + self.collect_contained(open_node, upstream, contained) + contained[unique_id] = True + contained[open_node] = True + + # We'll use the default prefix, but to avoid having node names grow exponentially in size, + # we'll use "Recurse" for the name of the recursively-generated copy of this node. + graph = GraphBuilder() + for node_id in contained: + original_node = dynprompt.get_node(node_id) + node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id) + node.set_override_display_id(node_id) + + for node_id in contained: + original_node = dynprompt.get_node(node_id) + node = graph.lookup_node("Recurse" if node_id == unique_id else node_id) + for k, v in original_node["inputs"].items(): + if is_link(v) and v[0] in contained: + parent = graph.lookup_node(v[0]) + node.set_input(k, parent.out(v[1])) + else: + node.set_input(k, v) + + new_open = graph.lookup_node(open_node) + + new_open.set_input("item_list", remained_list) + new_open.set_input("initial_input", intermediate_output) + + my_clone = graph.lookup_node("Recurse" ) + result = (my_clone.out(0),) + + return { + "result": result, + "expand": graph.finalize(), + } + + +class DropItems: + @classmethod + def INPUT_TYPES(s): + return { + "required": { "item_list": ("ITEM_LIST", {"tooltip":"Directly connect the output of ForeachListBegin, the starting node of the iteration."}), }, + } + + RETURN_TYPES = (any_typ,) + RETURN_NAMES = ("ITEM_LIST",) + OUTPUT_TOOLTIPS = ("This is the final output value.",) + + FUNCTION = "doit" + + DESCRIPTION = "" + + CATEGORY = "InspirePack/List" + + def doit(self, item_list): + l = ListWrapper([]) + if hasattr(item_list, 'aux'): + l.aux = item_list.aux + else: + logging.warning("[Inspire Pack] DropItems: `item_list` did not come from ForeachList.") + + return (l,) + + +NODE_CLASS_MAPPINGS = { + "FloatRange //Inspire": FloatRange, + "WorklistToItemList //Inspire": WorklistToItemList, + "ForeachListBegin //Inspire": ForeachListBegin, + "ForeachListEnd //Inspire": ForeachListEnd, + "DropItems //Inspire": DropItems, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FloatRange //Inspire": "Float Range (Inspire)", + "WorklistToItemList //Inspire": "Worklist To Item List (Inspire)", + "ForeachListBegin //Inspire": "β–ΆForeach List (Inspire)", + "ForeachListEnd //Inspire": "Foreach Listβ—€ (Inspire)", + "DropItems //Inspire": "Drop Items (Inspire)", +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/lora_block_weight.py b/custom_nodes/comfyui-inspire-pack/inspire/lora_block_weight.py new file mode 100644 index 00000000..093d8600 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/lora_block_weight.py @@ -0,0 +1,1148 @@ +import folder_paths +import comfy.utils +import comfy.lora +import os +import torch +import numpy as np +import nodes +import re +import json +from comfy.cli_args import args +from safetensors.torch import safe_open +import ast +import logging + +from server import PromptServer +from .libs import utils + + +model_path = folder_paths.models_dir +utils.add_folder_path_and_extensions("lbw_models", [os.path.join(model_path, "lbw_models")], {'.safetensors'}) + + +def is_numeric_string(input_str): + return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None + + +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + + +def load_lbw_preset(filename): + path = os.path.join(os.path.dirname(__file__), "..", "resources", filename) + path = os.path.abspath(path) + preset_list = [] + + if os.path.exists(path): + with open(path, 'r') as file: + for line in file: + preset_list.append(line.strip()) + + return preset_list + else: + return [] + + +def parse_unet_num(s): + if s[1] == '.': + return int(s[0]) + else: + return int(s) + + +class MakeLBW: + @classmethod + def INPUT_TYPES(s): + preset = ["Preset"] # 20 + preset += load_lbw_preset("lbw-preset.txt") + preset += load_lbw_preset("lbw-preset.custom.txt") + preset = [name for name in preset if not name.startswith('@')] + + lora_names = folder_paths.get_filename_list("loras") + lora_dirs = [os.path.dirname(name) for name in lora_names] + lora_dirs = ["All"] + list(set(lora_dirs)) + + return {"required": {"model": ("MODEL",), + "clip": ("CLIP", ), + "category_filter": (lora_dirs,), + "lora_name": (lora_names, ), + "inverse": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False", "tooltip": "Apply the following weights for each block:\nTrue: 1 - weight\nFalse: weight"}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": ""}), + "A": ("FLOAT", {"default": 4.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "B": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "preset": (preset,), + "block_vector": ("STRING", {"multiline": True, "placeholder": "block weight vectors", "default": "1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1", "pysssss.autocomplete": False}), + "bypass": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + } + } + + RETURN_TYPES = ("LBW_MODEL", "STRING") + RETURN_NAMES = ("lbw_model", "populated_vector") + FUNCTION = "doit" + + CATEGORY = "InspirePack/LoraBlockWeight" + + DESCRIPTION = "Instead of directly applying the LoRA Block Weight to the MODEL, it is generated in a separate LBW_MODEL form." + + def __init__(self): + self.loaded_lora = None + + def doit(self, model, clip, lora_name, inverse, seed, A, B, preset, block_vector, bypass=False, category_filter=None): + lora_path = folder_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + block_weights, muted_weights, populated_vector = LoraLoaderBlockWeight.load_lbw(model, clip, lora, inverse, seed, A, B, block_vector) + lbw_model = { + 'blocks': block_weights, + 'muted': muted_weights + } + return lbw_model, populated_vector + + +class LoraLoaderBlockWeight: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + preset = ["Preset"] # 20 + preset += load_lbw_preset("lbw-preset.txt") + preset += load_lbw_preset("lbw-preset.custom.txt") + preset = [name for name in preset if not name.startswith('@')] + + lora_names = folder_paths.get_filename_list("loras") + lora_dirs = [os.path.dirname(name) for name in lora_names] + lora_dirs = ["All"] + list(set(lora_dirs)) + + return {"required": {"model": ("MODEL",), + "clip": ("CLIP", ), + "category_filter": (lora_dirs,), + "lora_name": (lora_names, ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "inverse": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False", "tooltip": "Apply the following weights for each block:\nTrue: 1 - weight\nFalse: weight"}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": ""}), + "A": ("FLOAT", {"default": 4.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "B": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "preset": (preset,), + "block_vector": ("STRING", {"multiline": True, "placeholder": "block weight vectors", "default": "1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1", "pysssss.autocomplete": False}), + "bypass": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + } + } + + RETURN_TYPES = ("MODEL", "CLIP", "STRING") + RETURN_NAMES = ("model", "clip", "populated_vector") + FUNCTION = "doit" + + CATEGORY = "InspirePack/LoraBlockWeight" + + @staticmethod + def validate(vectors): + if len(vectors) < 12: + return False + + for x in vectors: + if x in ['R', 'r', 'U', 'u', 'A', 'a', 'B', 'b'] or is_numeric_string(x): + continue + else: + subvectors = x.strip().split(' ') + for y in subvectors: + y = y.strip() + if y not in ['R', 'r', 'U', 'u', 'A', 'a', 'B', 'b'] and not is_numeric_string(y): + return False + + return True + + @staticmethod + def convert_vector_value(A, B, vector_value): + def simple_vector(x): + if x in ['U', 'u']: + ratio = np.random.uniform(-1.5, 1.5) + ratio = round(ratio, 2) + elif x in ['R', 'r']: + ratio = np.random.uniform(0, 3.0) + ratio = round(ratio, 2) + elif x == 'A': + ratio = A + elif x == 'a': + ratio = A/2 + elif x == 'B': + ratio = B + elif x == 'b': + ratio = B/2 + elif is_numeric_string(x): + ratio = float(x) + else: + ratio = None + + return ratio + + v = simple_vector(vector_value) + if v is not None: + ratios = [v] + else: + ratios = [simple_vector(x) for x in vector_value.split(" ")] + + return ratios + + @staticmethod + def norm_value(value): # make to int if 1.0 or 0.0 + if value == 1: + return 1 + elif value == 0: + return 0 + else: + return value + + @staticmethod + def block_spec_parser(loaded, spec): + if not spec.startswith("%"): + return spec + else: + items = [x.strip() for x in spec[1:].split(',')] + + input_blocks_set = set() + middle_blocks_set= set() + output_blocks_set = set() + double_blocks_set = set() + single_blocks_set = set() + + for key, v in loaded.items(): + if isinstance(key, tuple): + k = key[0] + else: + k = key + + k_unet = k[len("diffusion_model."):] + + if k_unet.startswith("input_blocks."): + k_unet_num = k_unet[len("input_blocks."):len("input_blocks.")+2] + k_unet_int = parse_unet_num(k_unet_num) + input_blocks_set.add(k_unet_int) + elif k_unet.startswith("middle_block."): + k_unet_num = k_unet[len("middle_block."):len("middle_block.")+2] + k_unet_int = parse_unet_num(k_unet_num) + middle_blocks_set.add(k_unet_int) + elif k_unet.startswith("output_blocks."): + k_unet_num = k_unet[len("output_blocks."):len("output_blocks.")+2] + k_unet_int = parse_unet_num(k_unet_num) + output_blocks_set.add(k_unet_int) + elif k_unet.startswith("double_blocks."): + k_unet_num = k_unet[len("double_blocks."):len("double_blocks.") + 2] + k_unet_int = parse_unet_num(k_unet_num) + double_blocks_set.add(k_unet_int) + elif k_unet.startswith("single_blocks."): + k_unet_num = k_unet[len("single_blocks."):len("single_blocks.") + 2] + k_unet_int = parse_unet_num(k_unet_num) + single_blocks_set.add(k_unet_int) + + pat1 = re.compile(r"(default|base)=([0-9.]+)") + pat2 = re.compile(r"(in|out|mid|double|single)([0-9]+)-([0-9]+)=([0-9.]+)") + pat3 = re.compile(r"(in|out|mid|double|single)([0-9]+)=([0-9.]+)") + pat4 = re.compile(r"(in|out|mid|double|single)=([0-9.]+)") + + base_spec = None + default_spec = 1.0 + + for item in items: + match = pat1.match(item) + if match: + if match[1] == 'base': + base_spec = match[2] + continue + + if match[1] == 'default': + default_spec = match[2] + continue + + if base_spec is None: + base_spec = default_spec + + input_blocks = [default_spec] * len(input_blocks_set) + middle_blocks = [default_spec] * len(middle_blocks_set) + output_blocks = [default_spec] * len(output_blocks_set) + double_blocks = [default_spec] * len(double_blocks_set) + single_blocks = [default_spec] * len(single_blocks_set) + + for item in items: + match = pat2.match(item) + if match: + for x in range(int(match[2])-1, int(match[3])): + value = float(match[4]) + + if x < 0: + continue + + if match[1] == 'in' and len(input_blocks) > x: + input_blocks[x] = value + elif match[1] == 'out' and len(output_blocks) > x: + output_blocks[x] = value + elif match[1] == 'mid' and len(middle_blocks) > x: + middle_blocks[x] = value + elif match[1] == 'double' and len(double_blocks) > x: + double_blocks[x] = value + elif match[1] == 'single' and len(single_blocks) > x: + single_blocks[x] = value + + continue + + match = pat3.match(item) + if match: + value = float(match[3]) + x = int(match[2]) - 1 + + if x < 0: + continue + + if match[1] == 'in' and len(input_blocks) > x: + input_blocks[x] = value + elif match[1] == 'out' and len(output_blocks) > x: + output_blocks[x] = value + elif match[1] == 'mid' and len(middle_blocks) > x: + middle_blocks[x] = value + elif match[1] == 'double' and len(double_blocks) > x: + double_blocks[x] = value + elif match[1] == 'single' and len(single_blocks) > x: + single_blocks[x] = value + + continue + + match = pat4.match(item) + if match: + value = float(match[2]) + + if match[1] == 'in': + input_blocks = [value] * len(input_blocks) + elif match[1] == 'out': + output_blocks = [value] * len(output_blocks) + elif match[1] == 'mid': + middle_blocks = [value] * len(middle_blocks) + elif match[1] == 'double': + double_blocks = [value] * len(double_blocks) + elif match[1] == 'single': + single_blocks = [value] * len(single_blocks) + + continue + + # concat specs + res = [str(base_spec)] + for x in (input_blocks + middle_blocks + output_blocks + double_blocks + single_blocks): + res.append(str(x)) + + return ",".join(res) + + @staticmethod + def load_lbw(model, clip, lora, inverse, seed, A, B, block_vector): + key_map = comfy.lora.model_lora_keys_unet(model.model) + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + loaded = comfy.lora.load_lora(lora, key_map) + + block_vector = LoraLoaderBlockWeight.block_spec_parser(loaded, block_vector) + + block_vector = block_vector.split(":") + if len(block_vector) > 1: + block_vector = block_vector[1] + else: + block_vector = block_vector[0] + + vector = block_vector.split(",") + + if not LoraLoaderBlockWeight.validate(vector): + preset_dict = load_preset_dict() + if len(vector) > 0 and vector[0].strip() in preset_dict: + vector = preset_dict[vector[0].strip()].split(",") + else: + raise ValueError(f"[LoraLoaderBlockWeight] invalid block_vector '{block_vector}'") + + # sort: input, middle, output, others + input_blocks = [] + middle_blocks = [] + output_blocks = [] + double_blocks = [] + single_blocks = [] + others = [] + for key, v in loaded.items(): + if isinstance(key, tuple): + k = key[0] + else: + k = key + + k_unet = k[len("diffusion_model."):] + + if k_unet.startswith("input_blocks."): + k_unet_num = k_unet[len("input_blocks."):len("input_blocks.")+2] + input_blocks.append((k, v, parse_unet_num(k_unet_num), k_unet)) + elif k_unet.startswith("middle_block."): + k_unet_num = k_unet[len("middle_block."):len("middle_block.")+2] + middle_blocks.append((k, v, parse_unet_num(k_unet_num), k_unet)) + elif k_unet.startswith("output_blocks."): + k_unet_num = k_unet[len("output_blocks."):len("output_blocks.")+2] + output_blocks.append((k, v, parse_unet_num(k_unet_num), k_unet)) + elif k_unet.startswith("double_blocks."): + k_unet_num = k_unet[len("double_blocks."):len("double_blocks.")+2] + double_blocks.append((key, v, parse_unet_num(k_unet_num), k_unet)) + elif k_unet.startswith("single_blocks."): + k_unet_num = k_unet[len("single_blocks."):len("single_blocks.")+2] + single_blocks.append((key, v, parse_unet_num(k_unet_num), k_unet)) + else: + others.append((k, v, k_unet)) + + input_blocks = sorted(input_blocks, key=lambda x: x[2]) + middle_blocks = sorted(middle_blocks, key=lambda x: x[2]) + output_blocks = sorted(output_blocks, key=lambda x: x[2]) + double_blocks = sorted(double_blocks, key=lambda x: x[2]) + single_blocks = sorted(single_blocks, key=lambda x: x[2]) + + # prepare patch + np.random.seed(seed % (2**31)) + populated_vector_list = [] + ratios = [] + ratio = 1.0 + vector_i = 1 + + last_k_unet_num = None + + block_weights = {} + muted_weights = [] + + for k, v, k_unet_num, k_unet in (input_blocks + middle_blocks + output_blocks + double_blocks + single_blocks): + if last_k_unet_num != k_unet_num and len(vector) > vector_i: + ratios = LoraLoaderBlockWeight.convert_vector_value(A, B, vector[vector_i].strip()) + ratio = ratios.pop(0) + + if inverse: + populated_ratio = 1 - ratio + else: + populated_ratio = ratio + + populated_vector_list.append(LoraLoaderBlockWeight.norm_value(populated_ratio)) + + vector_i += 1 + else: + if len(ratios) > 0: + ratio = ratios.pop(0) + else: + pass # use last used ratio if no more user specified ratio is given + + if inverse: + populated_ratio = 1 - ratio + else: + populated_ratio = ratio + + last_k_unet_num = k_unet_num + + if populated_ratio != 0: + block_weights[k] = v, populated_ratio + else: + muted_weights.append(k) + + # prepare base patch + ratios = LoraLoaderBlockWeight.convert_vector_value(A, B, vector[0].strip()) + ratio = ratios.pop(0) + + if inverse: + populated_ratio = 1 - ratio + else: + populated_ratio = ratio + + populated_vector_list.insert(0, LoraLoaderBlockWeight.norm_value(populated_ratio)) + + for k, v, k_unet in others: + if populated_ratio != 0: + block_weights[k] = v, populated_ratio + else: + muted_weights.append(k) + + populated_vector = ','.join(map(str, populated_vector_list)) + return block_weights, muted_weights, populated_vector + + @staticmethod + def load_lora_for_models(model, clip, lora, strength_model, strength_clip, inverse, seed, A, B, block_vector): + block_weights, muted_weights, populated_vector = LoraLoaderBlockWeight.load_lbw(model, clip, lora, inverse, seed, A, B, block_vector) + + new_modelpatcher = model.clone() + new_clip = clip.clone() + + muted_weights = set(muted_weights) + + for k, v in block_weights.items(): + weights, ratio = v + + if k in muted_weights: + pass + elif 'text' in k or 'encoder' in k: + new_clip.add_patches({k: weights}, strength_clip * ratio) + else: + new_modelpatcher.add_patches({k: weights}, strength_model * ratio) + + return new_modelpatcher, new_clip, populated_vector + + def doit(self, model, clip, lora_name, strength_model, strength_clip, inverse, seed, A, B, preset, block_vector, bypass=False, category_filter=None): + if strength_model == 0 and strength_clip == 0 or bypass: + return model, clip, "" + + lora_path = folder_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora, populated_vector = LoraLoaderBlockWeight.load_lora_for_models(model, clip, lora, strength_model, strength_clip, inverse, seed, A, B, block_vector) + return model_lora, clip_lora, populated_vector + + +class ApplyLBW: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "clip": ("CLIP", ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "lbw_model": ("LBW_MODEL",), + }} + + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "doit" + + CATEGORY = "InspirePack/LoraBlockWeight" + + DESCRIPTION = "Apply LBW_MODEL to MODEL and CLIP" + + @staticmethod + def doit(model, clip, strength_model, strength_clip, lbw_model): + block_weights = lbw_model['blocks'] + muted_weights = lbw_model['muted'] + + new_modelpatcher = model.clone() + new_clip = clip.clone() + + muted_weights = set(muted_weights) + + for k, v in block_weights.items(): + weights, ratio = v + + if k in muted_weights: + pass + elif 'text' in k or 'encoder' in k: + new_clip.add_patches({k: weights}, strength_clip * ratio) + else: + new_modelpatcher.add_patches({k: weights}, strength_model * ratio) + + return new_modelpatcher, new_clip + + +class XY_Capsule_LoraBlockWeight: + def __init__(self, x, y, target_vector, label, storage, params): + self.x = x + self.y = y + self.target_vector = target_vector + self.reference_vector = None + self.label = label + self.storage = storage + self.another_capsule = None + self.params = params + + def set_reference_vector(self, vector): + self.reference_vector = vector + + def set_x_capsule(self, capsule): + self.another_capsule = capsule + + def set_result(self, image, latent): + if self.another_capsule is not None: + logging.info(f"XY_Capsule_LoraBlockWeight: ({self.another_capsule.x, self.y}) is processed.") + self.storage[(self.another_capsule.x, self.y)] = image + else: + logging.info(f"XY_Capsule_LoraBlockWeight: ({self.x, self.y}) is processed.") + + def patch_model(self, model, clip): + lora_name, strength_model, strength_clip, inverse, block_vectors, seed, A, B, heatmap_palette, heatmap_alpha, heatmap_strength, xyplot_mode = self.params + + try: + if self.y == 0: + target_vector = self.another_capsule.target_vector if self.another_capsule else self.target_vector + model, clip, _ = LoraLoaderBlockWeight().doit(model, clip, lora_name, strength_model, strength_clip, inverse, + seed, A, B, "", target_vector) + elif self.y == 1: + reference_vector = self.another_capsule.reference_vector if self.another_capsule else self.reference_vector + model, clip, _ = LoraLoaderBlockWeight().doit(model, clip, lora_name, strength_model, strength_clip, inverse, + seed, A, B, "", reference_vector) + except: + self.storage[(self.another_capsule.x, self.y)] = "fail" + pass + + return model, clip + + def pre_define_model(self, model, clip, vae): + if self.y < 2: + model, clip = self.patch_model(model, clip) + + return model, clip, vae + + def get_result(self, model, clip, vae): + _, _, _, _, _, _, _, _, heatmap_palette, heatmap_alpha, heatmap_strength, xyplot_mode = self.params + + if self.y < 2: + return None + + if self.y == 2: + # diff + weighted_image = self.storage[(self.another_capsule.x, 0)] + reference_image = self.storage[(self.another_capsule.x, 1)] + + if weighted_image == "fail" or reference_image == "fail": + image = "fail" + else: + image = torch.abs(weighted_image - reference_image) + self.storage[(self.another_capsule.x, self.y)] = image + + elif self.y == 3: + import matplotlib.cm as cm + # heatmap + image = self.storage[(self.another_capsule.x, 0)] + + if image == "fail": + image = utils.empty_pil_tensor(8,8) + latent = utils.empty_latent() + return image, latent + else: + image = image.clone() + + diff_image = torch.abs(self.storage[(self.another_capsule.x, 2)]) + + heatmap = torch.sum(diff_image, dim=3, keepdim=True) + + min_val = torch.min(heatmap) + max_val = torch.max(heatmap) + heatmap = (heatmap - min_val) / (max_val - min_val) + heatmap *= heatmap_strength + + # viridis / magma / plasma / inferno / cividis + if heatmap_palette == "magma": + colormap = cm.magma + elif heatmap_palette == "plasma": + colormap = cm.plasma + elif heatmap_palette == "inferno": + colormap = cm.inferno + elif heatmap_palette == "cividis": + colormap = cm.cividis + else: + # default: viridis + colormap = cm.viridis + + heatmap = torch.from_numpy(colormap(heatmap.squeeze())).unsqueeze(0) + heatmap = heatmap[..., :3] + + image = heatmap_alpha * heatmap + (1 - heatmap_alpha) * image + + latent = nodes.VAEEncode().encode(vae, image)[0] + return image, latent + + def getLabel(self): + return self.label + + +def load_preset_dict(): + preset = ["Preset"] # 20 + preset += load_lbw_preset("lbw-preset.txt") + preset += load_lbw_preset("lbw-preset.custom.txt") + + dict = {} + for x in preset: + if not x.startswith('@'): + item = x.split(':') + if len(item) > 1: + dict[item[0]] = item[1] + + return dict + + +class XYInput_LoraBlockWeight: + @staticmethod + def resolve_vector_string(vector_string, preset_dict): + vector_string = vector_string.strip() + + if vector_string in preset_dict: + return vector_string, preset_dict[vector_string] + + vector_infos = vector_string.split(':') + + if len(vector_infos) > 1: + return vector_infos[0], vector_infos[1] + elif len(vector_infos) > 0: + return vector_infos[0], vector_infos[0] + else: + return None, None + + @classmethod + def INPUT_TYPES(cls): + preset = ["Preset"] # 20 + preset += load_lbw_preset("lbw-preset.txt") + preset += load_lbw_preset("lbw-preset.custom.txt") + + default_vectors = "SD-NONE/SD-ALL\nSD-ALL/SD-ALL\nSD-INS/SD-ALL\nSD-IND/SD-ALL\nSD-INALL/SD-ALL\nSD-MIDD/SD-ALL\nSD-MIDD0.2/SD-ALL\nSD-MIDD0.8/SD-ALL\nSD-MOUT/SD-ALL\nSD-OUTD/SD-ALL\nSD-OUTS/SD-ALL\nSD-OUTALL/SD-ALL" + + lora_names = folder_paths.get_filename_list("loras") + lora_dirs = [os.path.dirname(name) for name in lora_names] + lora_dirs = ["All"] + list(set(lora_dirs)) + + return {"required": { + "category_filter": (lora_dirs, ), + "lora_name": (lora_names, ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "inverse": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "A": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "B": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "preset": (preset,), + "block_vectors": ("STRING", {"multiline": True, "default": default_vectors, "placeholder": "{target vector}/{reference vector}", "pysssss.autocomplete": False}), + "heatmap_palette": (["viridis", "magma", "plasma", "inferno", "cividis"], ), + "heatmap_alpha": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}), + "heatmap_strength": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 10.0, "step": 0.01}), + "xyplot_mode": (["Simple", "Diff", "Diff+Heatmap"],), + }} + + RETURN_TYPES = ("XY", "XY") + RETURN_NAMES = ("X (vectors)", "Y (effect_compares)") + + FUNCTION = "doit" + CATEGORY = "InspirePack/LoraBlockWeight" + + def doit(self, lora_name, strength_model, strength_clip, inverse, seed, A, B, preset, block_vectors, heatmap_palette, heatmap_alpha, heatmap_strength, xyplot_mode, category_filter=None): + xy_type = "XY_Capsule" + + preset_dict = load_preset_dict() + common_params = lora_name, strength_model, strength_clip, inverse, block_vectors, seed, A, B, heatmap_palette, heatmap_alpha, heatmap_strength, xyplot_mode + + storage = {} + x_values = [] + x_idx = 0 + for block_vector in block_vectors.split("\n"): + if block_vector == "": + continue + + item = block_vector.split('/') + + if len(item) > 0: + target_vector = item[0].strip() + ref_vector = item[1].strip() if len(item) > 1 else '' + + x_item = None + label, block_vector = XYInput_LoraBlockWeight.resolve_vector_string(target_vector, preset_dict) + _, ref_block_vector = XYInput_LoraBlockWeight.resolve_vector_string(ref_vector, preset_dict) + if label is not None: + x_item = XY_Capsule_LoraBlockWeight(x_idx, 0, block_vector, label, storage, common_params) + x_idx += 1 + + if x_item is not None and ref_block_vector is not None: + x_item.set_reference_vector(ref_block_vector) + + if x_item is not None: + x_values.append(x_item) + + if xyplot_mode == "Simple": + y_values = [XY_Capsule_LoraBlockWeight(0, 0, '', 'target', storage, common_params)] + elif xyplot_mode == "Diff": + y_values = [XY_Capsule_LoraBlockWeight(0, 0, '', 'target', storage, common_params), + XY_Capsule_LoraBlockWeight(0, 1, '', 'reference', storage, common_params), + XY_Capsule_LoraBlockWeight(0, 2, '', 'diff', storage, common_params)] + else: + y_values = [XY_Capsule_LoraBlockWeight(0, 0, '', 'target', storage, common_params), + XY_Capsule_LoraBlockWeight(0, 1, '', 'reference', storage, common_params), + XY_Capsule_LoraBlockWeight(0, 2, '', 'diff', storage, common_params), + XY_Capsule_LoraBlockWeight(0, 3, '', 'heatmap', storage, common_params)] + + return (xy_type, x_values), (xy_type, y_values), + + +class LoraBlockInfo: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "clip": ("CLIP", ), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "block_info": ("STRING", {"multiline": True}), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + CATEGORY = "InspirePack/LoraBlockWeight" + + OUTPUT_NODE = True + + RETURN_TYPES = () + FUNCTION = "doit" + + @staticmethod + def extract_info(model, clip, lora): + key_map = comfy.lora.model_lora_keys_unet(model.model) + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + loaded = comfy.lora.load_lora(lora, key_map) + + def parse_unet_num(s): + if s[1] == '.': + return int(s[0]) + else: + return int(s) + + input_block_count = set() + input_blocks = [] + input_blocks_map = {} + + middle_block_count = set() + middle_blocks = [] + middle_blocks_map = {} + + output_block_count = set() + output_blocks = [] + output_blocks_map = {} + + text_block_count1 = set() + text_blocks1 = [] + text_blocks_map1 = {} + + text_block_count2 = set() + text_blocks2 = [] + text_blocks_map2 = {} + + double_block_count = set() + double_blocks = [] + double_blocks_map = {} + + single_block_count = set() + single_blocks = [] + single_blocks_map = {} + + others = [] + for key, v in loaded.items(): + if isinstance(key, tuple): + k = key[0] + else: + k = key + + k_unet = k[len("diffusion_model."):] + + if k_unet.startswith("input_blocks."): + k_unet_num = k_unet[len("input_blocks."):len("input_blocks.")+2] + k_unet_int = parse_unet_num(k_unet_num) + + input_block_count.add(k_unet_int) + input_blocks.append(k_unet) + if k_unet_int in input_blocks_map: + input_blocks_map[k_unet_int].append(k_unet) + else: + input_blocks_map[k_unet_int] = [k_unet] + + elif k_unet.startswith("middle_block."): + k_unet_num = k_unet[len("middle_block."):len("middle_block.")+2] + k_unet_int = parse_unet_num(k_unet_num) + + middle_block_count.add(k_unet_int) + middle_blocks.append(k_unet) + if k_unet_int in middle_blocks_map: + middle_blocks_map[k_unet_int].append(k_unet) + else: + middle_blocks_map[k_unet_int] = [k_unet] + + elif k_unet.startswith("output_blocks."): + k_unet_num = k_unet[len("output_blocks."):len("output_blocks.")+2] + k_unet_int = parse_unet_num(k_unet_num) + + output_block_count.add(k_unet_int) + output_blocks.append(k_unet) + if k_unet_int in output_blocks_map: + output_blocks_map[k_unet_int].append(k_unet) + else: + output_blocks_map[k_unet_int] = [k_unet] + + elif k_unet.startswith("double_blocks."): + k_unet_num = k_unet[len("double_blocks."):len("double_blocks.") + 2] + k_unet_int = parse_unet_num(k_unet_num) + + double_block_count.add(k_unet_int) + double_blocks.append(k_unet) + if k_unet_int in double_blocks_map: + double_blocks_map[k_unet_int].append(k_unet) + else: + double_blocks_map[k_unet_int] = [k_unet] + + elif k_unet.startswith("single_blocks."): + k_unet_num = k_unet[len("single_blocks."):len("single_blocks.") + 2] + k_unet_int = parse_unet_num(k_unet_num) + + single_block_count.add(k_unet_int) + single_blocks.append(k_unet) + if k_unet_int in single_blocks_map: + single_blocks_map[k_unet_int].append(k_unet) + else: + single_blocks_map[k_unet_int] = [k_unet] + + elif k_unet.startswith("er.text_model.encoder.layers."): + k_unet_num = k_unet[len("er.text_model.encoder.layers."):len("er.text_model.encoder.layers.")+2] + k_unet_int = parse_unet_num(k_unet_num) + + text_block_count1.add(k_unet_int) + text_blocks1.append(k_unet) + if k_unet_int in text_blocks_map1: + text_blocks_map1[k_unet_int].append(k_unet) + else: + text_blocks_map1[k_unet_int] = [k_unet] + + elif k_unet.startswith("r.encoder.block."): + k_unet_num = k_unet[len("r.encoder.block."):len("r.encoder.block.")+2] + k_unet_int = parse_unet_num(k_unet_num) + + text_block_count2.add(k_unet_int) + text_blocks2.append(k_unet) + if k_unet_int in text_blocks_map2: + text_blocks_map2[k_unet_int].append(k_unet) + else: + text_blocks_map2[k_unet_int] = [k_unet] + + else: + others.append(k_unet) + + text = "" + + input_blocks = sorted(input_blocks) + middle_blocks = sorted(middle_blocks) + output_blocks = sorted(output_blocks) + double_blocks = sorted(double_blocks) + single_blocks = sorted(single_blocks) + others = sorted(others) + + if len(input_block_count) > 0: + text += f"\n-------[Input blocks] ({len(input_block_count)}, Subs={len(input_blocks)})-------\n" + input_keys = sorted(input_blocks_map.keys()) + for x in input_keys: + text += f" IN{x}: {len(input_blocks_map[x])}\n" + + if len(middle_block_count) > 0: + text += f"\n-------[Middle blocks] ({len(middle_block_count)}, Subs={len(middle_blocks)})-------\n" + middle_keys = sorted(middle_blocks_map.keys()) + for x in middle_keys: + text += f" MID{x}: {len(middle_blocks_map[x])}\n" + + if len(output_block_count) > 0: + text += f"\n-------[Output blocks] ({len(output_block_count)}, Subs={len(output_blocks)})-------\n" + output_keys = sorted(output_blocks_map.keys()) + for x in output_keys: + text += f" OUT{x}: {len(output_blocks_map[x])}\n" + + if len(double_block_count) > 0: + text += f"\n-------[Double blocks(MMDiT)] ({len(double_block_count)}, Subs={len(double_blocks)})-------\n" + double_keys = sorted(double_blocks_map.keys()) + for x in double_keys: + text += f" DOUBLE{x}: {len(double_blocks_map[x])}\n" + + if len(single_block_count) > 0: + text += f"\n-------[Single blocks(DiT)] ({len(single_block_count)}, Subs={len(single_blocks)})-------\n" + single_keys = sorted(single_blocks_map.keys()) + for x in single_keys: + text += f" SINGLE{x}: {len(single_blocks_map[x])}\n" + + text += f"\n-------[Base blocks] ({len(text_block_count1) + len(text_block_count2) + len(others)}, Subs={len(text_blocks1) + len(text_blocks2) + len(others)})-------\n" + text_keys1 = sorted(text_blocks_map1.keys()) + for x in text_keys1: + text += f" TXT_ENC{x}: {len(text_blocks_map1[x])}\n" + + text_keys2 = sorted(text_blocks_map2.keys()) + for x in text_keys2: + text += f" TXT_ENC{x} [B]: {len(text_blocks_map2[x])}\n" + + for x in others: + text += f" {x}\n" + + return text + + def doit(self, model, clip, lora_name, block_info, unique_id): + lora_path = folder_paths.get_full_path("loras", lora_name) + + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + text = LoraBlockInfo.extract_info(model, clip, lora) + + PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": unique_id, "widget_name": "block_info", "type": "text", "data": text}) + return {} + + +class LoadLBW: + @classmethod + def INPUT_TYPES(s): + files = folder_paths.get_filename_list('lbw_models') + return {"required": { + "lbw_model": [sorted(files), ]}, + } + + RETURN_TYPES = ("LBW_MODEL",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/LoraBlockWeight" + + DESCRIPTION = "Load LBW_MODEL from .lbw.safetensors file" + + @staticmethod + def decode_dict(encoded_dict, tensor_dict): + original_dict = {} + + def decode_value(value): + if isinstance(value, str) and value.startswith('t') and value[1:].isdigit(): + return tensor_dict[value] + return value + + for k, tuple_value in encoded_dict.items(): + decoded_tuple = tuple(decode_value(v) for v in tuple_value[0][1]) + key = ast.literal_eval(k) if isinstance(k, str) and (k.startswith('(') or k.startswith('[')) else k + original_dict[key] = ((tuple_value[0][0], decoded_tuple), tuple_value[1]) + + return original_dict + + @staticmethod + def load(file): + tensor_dict = comfy.utils.load_torch_file(file) + + with safe_open(file, framework="pt") as f: + metadata = f.metadata() + + encoded_dict = json.loads(metadata.get('blocks', '{}')) + muted_blocks = ast.literal_eval(metadata.get('muted_blocks', '[]')) + + decoded_dict = LoadLBW.decode_dict(encoded_dict, tensor_dict) + + lbw_model = { + 'blocks': decoded_dict, + 'muted': muted_blocks + } + + return lbw_model, metadata + + def doit(self, lbw_model): + lbw_path = folder_paths.get_full_path("lbw_models", lbw_model) + lbw_model, _ = LoadLBW.load(lbw_path) + return (lbw_model,) + + +class SaveLBW: + def __init__(self): + self.output_dir = folder_paths.get_folder_paths('lbw_models')[-1] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "lbw_model": ("LBW_MODEL", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"}) }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = () + FUNCTION = "doit" + + OUTPUT_NODE = True + + CATEGORY = "InspirePack/LoraBlockWeight" + + DESCRIPTION = "Save LBW_MODEL as a .lbw.safetensors file" + + @staticmethod + def encode_dict(original_dict): + tensor_dict = {} + encoded_dict = {} + counter = 0 + + def generate_unique_id(): + nonlocal counter + counter += 1 + return f"t{counter}" + + def encode_value(value): + if isinstance(value, torch.Tensor): + unique_id = generate_unique_id() + tensor_dict[unique_id] = value + return unique_id + return value + + for k, tuple_value in original_dict.items(): + encoded_tuple = tuple(encode_value(v) for v in tuple_value[0][1]) + encoded_dict[str(k)] = (tuple_value[0][0], encoded_tuple), tuple_value[1] + + return encoded_dict, tensor_dict + + @staticmethod + def save(lbw_model, file, metadata): + metadata['format'] = 'Inspire LBW 1.0' + weighted_blocks = lbw_model['blocks'] + metadata['muted_blocks'] = str(lbw_model['muted']) + encoded_dict, tensor_dict = SaveLBW.encode_dict(weighted_blocks) + metadata['blocks'] = json.dumps(encoded_dict) + + comfy.utils.save_torch_file(tensor_dict, file, metadata=metadata) + + def doit(self, lbw_model, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + # support save metadata for lbw sharing + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_metadata: + metadata = {"prompt": prompt_info} + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + file = f"{filename}_{counter:05}_.lbw.safetensors" + + results = list() + results.append({ + "filename": file, + "subfolder": subfolder, + "type": "output" + }) + + file = os.path.join(full_output_folder, file) + + SaveLBW.save(lbw_model, file, metadata) + + return {} + + +NODE_CLASS_MAPPINGS = { + "XY Input: Lora Block Weight //Inspire": XYInput_LoraBlockWeight, + "LoraLoaderBlockWeight //Inspire": LoraLoaderBlockWeight, + "LoraBlockInfo //Inspire": LoraBlockInfo, + "MakeLBW //Inspire": MakeLBW, + "ApplyLBW //Inspire": ApplyLBW, + "SaveLBW //Inspire": SaveLBW, + "LoadLBW //Inspire": LoadLBW, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "XY Input: Lora Block Weight //Inspire": "XY Input: LoRA Block Weight", + "LoraLoaderBlockWeight //Inspire": "LoRA Loader (Block Weight)", + "LoraBlockInfo //Inspire": "LoRA Block Info", + "MakeLBW //Inspire": "Make LoRA Block Weight", + "ApplyLBW //Inspire": "Apply LoRA Block Weight", + "SaveLBW //Inspire": "Save LoRA Block Weight", + "LoadLBW //Inspire": "Load LoRA Block Weight", +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/model_nodes.py b/custom_nodes/comfyui-inspire-pack/inspire/model_nodes.py new file mode 100644 index 00000000..90d68792 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/model_nodes.py @@ -0,0 +1,191 @@ +import nodes +import folder_paths +import os +import server +from .libs import utils +from . import backend_support +from comfy import sdxl_clip +import logging + + +model_preset = { + # base + "SD1.5": ("ip-adapter_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SD1.5 Light v11": ("ip-adapter_sd15_light_v11", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SD1.5 Light": ("ip-adapter_sd15_light", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SD1.5 Plus": ("ip-adapter-plus_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SD1.5 Plus Face": ("ip-adapter-plus-face_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SD1.5 Full Face": ("ip-adapter-full-face_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SD1.5 ViT-G": ("ip-adapter_sd15_vit-G", "CLIP-ViT-bigG-14-laion2B-39B-b160k", None, False), + "SDXL": ("ip-adapter_sdxl", "CLIP-ViT-bigG-14-laion2B-39B-b160k", None, False), + "SDXL ViT-H": ("ip-adapter_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SDXL Plus ViT-H": ("ip-adapter-plus_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SDXL Plus Face ViT-H": ("ip-adapter-plus-face_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "Kolors Plus": ("Kolors-IP-Adapter-Plus", "clip-vit-large-patch14-336", None, False), + + # faceid + "SD1.5 FaceID": ("ip-adapter-faceid_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid_sd15_lora", True), + "SD1.5 FaceID Plus v2": ("ip-adapter-faceid-plusv2_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid-plusv2_sd15_lora", True), + "SD1.5 FaceID Plus": ("ip-adapter-faceid-plus_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid-plus_sd15_lora", True), + "SD1.5 FaceID Portrait v11": ("ip-adapter-faceid-portrait-v11_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True), + "SD1.5 FaceID Portrait": ("ip-adapter-faceid-portrait_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True), + "SDXL FaceID": ("ip-adapter-faceid_sdxl", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid_sdxl_lora", True), + "SDXL FaceID Plus v2": ("ip-adapter-faceid-plusv2_sdxl", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid-plusv2_sdxl_lora", True), + "SDXL FaceID Portrait": ("ip-adapter-faceid-portrait_sdxl", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True), + "SDXL FaceID Portrait unnorm": ("ip-adapter-faceid-portrait_sdxl_unnorm", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True), + "Kolors FaceID Plus": ("Kolors-IP-Adapter-FaceID-Plus", "clip-vit-large-patch14-336", None, True), + + # composition + "SD1.5 Plus Composition": ("ip-adapter_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False), + "SDXL Plus Composition": ("ip-adapter_sdxl", "CLIP-ViT-bigG-14-laion2B-39B-b160k", None, False), + } + + +def lookup_model(model_dir, name): + if name is None: + return None, "N/A" + + names = [(os.path.splitext(os.path.basename(x))[0], x) for x in folder_paths.get_filename_list(model_dir)] + resolved_name = [y for x, y in names if x == name] + + if len(resolved_name) > 0: + return resolved_name[0], "OK" + else: + logging.error(f"[Inspire Pack] IPAdapterModelHelper: The `{name}` model file does not exist in `{model_dir}` model dir.") + return None, "FAIL" + + +class IPAdapterModelHelper: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "preset": (list(model_preset.keys()),), + "lora_strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "lora_strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "insightface_provider": (["CPU", "CUDA", "ROCM"], ), + "cache_mode": (["insightface only", "clip_vision only", "all", "none"], {"default": "insightface only"}), + }, + "optional": { + "clip": ("CLIP",), + "insightface_model_name": (['buffalo_l', 'antelopev2'],), + }, + "hidden": {"unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = ("IPADAPTER_PIPE", "IPADAPTER", "CLIP_VISION", "INSIGHTFACE", "MODEL", "CLIP", "STRING", "STRING") + RETURN_NAMES = ("IPADAPTER_PIPE", "IPADAPTER", "CLIP_VISION", "INSIGHTFACE", "MODEL", "CLIP", "insightface_cache_key", "clip_vision_cache_key") + FUNCTION = "doit" + + CATEGORY = "InspirePack/models" + + def doit(self, model, preset, lora_strength_model, lora_strength_clip, insightface_provider, clip=None, cache_mode="none", unique_id=None, insightface_model_name='buffalo_l'): + if 'IPAdapter' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus', + "To use 'IPAdapterModelHelper' node, 'ComfyUI IPAdapter Plus' extension is required.") + raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'") + + is_sdxl_preset = 'SDXL' in preset + if clip is not None: + is_sdxl_model = isinstance(clip.tokenizer, sdxl_clip.SDXLTokenizer) + else: + is_sdxl_model = False + + if is_sdxl_preset != is_sdxl_model: + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 1, "label": "IPADAPTER (fail)"}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 2, "label": "CLIP_VISION (fail)"}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE (fail)"}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL (fail)"}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP (fail)"}) + logging.error("[Inspire Pack] IPAdapterModelHelper: You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.") + raise Exception("[ERROR] You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.") + + ipadapter, clipvision, lora, is_insightface = model_preset[preset] + + ipadapter, ok1 = lookup_model("ipadapter", ipadapter) + clipvision, ok2 = lookup_model("clip_vision", clipvision) + lora, ok3 = lookup_model("loras", lora) + + if ok1 == "OK": + ok1 = "IPADAPTER" + else: + ok1 = f"IPADAPTER ({ok1})" + + if ok2 == "OK": + ok2 = "CLIP_VISION" + else: + ok2 = f"CLIP_VISION ({ok2})" + + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 1, "label": ok1}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 2, "label": ok2}) + + if ok3 == "FAIL": + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL (fail)"}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP (fail)"}) + else: + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL"}) + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP"}) + + if ok1 == "FAIL" or ok2 == "FAIL" or ok3 == "FAIL": + raise Exception("ERROR: Failed to load several models in IPAdapterModelHelper.") + + if ipadapter is not None: + ipadapter = nodes.NODE_CLASS_MAPPINGS["IPAdapterModelLoader"]().load_ipadapter_model(ipadapter_file=ipadapter)[0] + + ccache_key = "" + if clipvision is not None: + if cache_mode in ["clip_vision only", "all"]: + ccache_key = clipvision + if ccache_key not in backend_support.cache: + backend_support.update_cache(ccache_key, "clipvision", (False, nodes.CLIPVisionLoader().load_clip(clip_name=clipvision)[0])) + _, (_, clipvision) = backend_support.cache[ccache_key] + else: + clipvision = nodes.CLIPVisionLoader().load_clip(clip_name=clipvision)[0] + + if lora is not None: + model, clip = nodes.LoraLoader().load_lora(model=model, clip=clip, lora_name=lora, strength_model=lora_strength_model, strength_clip=lora_strength_clip) + + def f(x): + return nodes.LoraLoader().load_lora(model=x, clip=clip, lora_name=lora, strength_model=lora_strength_model, strength_clip=lora_strength_clip) + lora_loader = f + else: + def f(x): + return x + lora_loader = f + + if 'IPAdapterInsightFaceLoader' in nodes.NODE_CLASS_MAPPINGS: + insight_face_loader = nodes.NODE_CLASS_MAPPINGS['IPAdapterInsightFaceLoader']().load_insightface + else: + logging.warning("'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.") + insight_face_loader = None + + icache_key = "" + if is_insightface: + if insight_face_loader is None: + raise Exception("[ERROR] 'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.") + + if cache_mode in ["insightface only", "all"]: + icache_key = 'insightface-' + insightface_provider + if icache_key not in backend_support.cache: + backend_support.update_cache(icache_key, "insightface", (False, insight_face_loader(provider=insightface_provider, model_name=insightface_model_name)[0])) + _, (_, insightface) = backend_support.cache[icache_key] + else: + insightface = insight_face_loader(insightface_provider)[0] + + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE"}) + else: + insightface = None + server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE (N/A)"}) + + pipe = ipadapter, model, clipvision, insightface, lora_loader + return pipe, ipadapter, clipvision, insightface, model, clip, icache_key, ccache_key + + +NODE_CLASS_MAPPINGS = { + "IPAdapterModelHelper //Inspire": IPAdapterModelHelper, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "IPAdapterModelHelper //Inspire": "IPAdapter Model Helper (Inspire)", +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/prompt_support.py b/custom_nodes/comfyui-inspire-pack/inspire/prompt_support.py new file mode 100644 index 00000000..0dbeae6e --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/prompt_support.py @@ -0,0 +1,1063 @@ +import os +import re +import json +import shutil +import yaml + +from PIL import Image +import nodes +import torch + +import folder_paths +import comfy +import traceback +import random +import hashlib + +from server import PromptServer +from .libs import utils, common +from .backend_support import CheckpointLoaderSimpleShared + +import logging + +model_path = folder_paths.models_dir +utils.add_folder_path_and_extensions("inspire_prompts", [os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "prompts"))], {'.txt'}) + + +prompt_builder_preset = {} + + +resource_path = os.path.join(os.path.dirname(__file__), "..", "resources") +resource_path = os.path.abspath(resource_path) + + +try: + pb_yaml_path = os.path.join(resource_path, 'prompt-builder.yaml') + pb_yaml_path_example = os.path.join(resource_path, 'prompt-builder.yaml.example') + + if not os.path.exists(pb_yaml_path): + shutil.copy(pb_yaml_path_example, pb_yaml_path) + + with open(pb_yaml_path, 'r', encoding="utf-8") as f: + prompt_builder_preset = yaml.load(f, Loader=yaml.FullLoader) +except Exception as e: # noqa: F841 + logging.error("[Inspire Pack] Failed to load 'prompt-builder.yaml'\nNOTE: Only files with UTF-8 encoding are supported.") + + +class LoadPromptsFromDir: + @classmethod + def INPUT_TYPES(cls): + try: + prompt_dirs = [] + for x in folder_paths.get_folder_paths('inspire_prompts'): + for d in os.listdir(x): + if os.path.isdir(os.path.join(x, d)): + prompt_dirs.append(d) + except Exception: + prompt_dirs = [] + + return {"required": { + "prompt_dir": (prompt_dirs,) + }, + "optional": { + "reload": ("BOOLEAN", { "default": False, "label_on": "if file changed", "label_off": "if value changed"}), + "load_cap": ("INT", {"default": 0, "min": 0, "step": 1, "advanced": True, "tooltip": "The amount of prompts to load at once:\n0: Load all\n1 or higher: Load a specified number"}), + "start_index": ("INT", {"default": 0, "min": -1, "step": 1, "max": 0xffffffffffffffff, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}), + } + } + + RETURN_TYPES = ("ZIPPED_PROMPT", "INT", "INT") + RETURN_NAMES = ("zipped_prompt", "count", "remaining_count") + OUTPUT_IS_LIST = (True, False, False) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + @staticmethod + def IS_CHANGED(prompt_dir, reload=False, load_cap=0, start_index=-1): + if not reload: + return prompt_dir, load_cap, start_index + else: + candidates = [] + for d in folder_paths.get_folder_paths('inspire_prompts'): + candidates.append(os.path.join(d, prompt_dir)) + + prompt_files = [] + for x in candidates: + for root, dirs, files in os.walk(x): + for file in files: + if file.endswith(".txt"): + prompt_files.append(os.path.join(root, file)) + + prompt_files.sort() + + md5 = hashlib.md5() + + for file_name in prompt_files: + md5.update(file_name.encode('utf-8')) + with open(folder_paths.get_full_path('inspire_prompts', file_name), 'rb') as f: + while True: + chunk = f.read(4096) + if not chunk: + break + md5.update(chunk) + + return md5.hexdigest(), load_cap, start_index + + @staticmethod + def doit(prompt_dir, reload=False, load_cap=0, start_index=-1): + candidates = [] + for d in folder_paths.get_folder_paths('inspire_prompts'): + candidates.append(os.path.join(d, prompt_dir)) + + prompt_files = [] + for x in candidates: + for root, dirs, files in os.walk(x): + for file in files: + if file.endswith(".txt"): + prompt_files.append(os.path.join(root, file)) + + prompt_files.sort() + + prompts = [] + for file_name in prompt_files: + logging.info(f"file_name: {file_name}") + try: + with open(file_name, "r", encoding="utf-8") as file: + prompt_data = file.read() + prompt_list = re.split(r'\n\s*-+\s*\n', prompt_data) + + for prompt in prompt_list: + pattern = r"^(?:(?:positive:(?P.*?)|negative:(?P.*?)|name:(?P.*?))\n*)+$" + matches = re.search(pattern, prompt, re.DOTALL | re.IGNORECASE) + + if matches: + positive_text = matches.group('positive').strip() + negative_text = matches.group('negative').strip() + name_text = matches.group('name').strip() if matches.group('name') else file_name + result_tuple = (positive_text, negative_text, name_text) + prompts.append(result_tuple) + else: + logging.warning(f"[Inspire Pack] LoadPromptsFromDir: invalid prompt format in '{file_name}'") + except Exception as e: + logging.error(f"[Inspire Pack] LoadPromptsFromDir: an error occurred while processing '{file_name}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.") + + # slicing [start_index ~ start_index + load_cap] + total_prompts = len(prompts) + prompts = prompts[start_index:] + remaining_count = False + if load_cap > 0: + remaining_count = max(0, len(prompts) - load_cap) + prompts = prompts[:load_cap] + + return prompts, total_prompts, remaining_count + + +class LoadPromptsFromFile: + @classmethod + def INPUT_TYPES(cls): + prompt_files = [] + try: + prompts_paths = folder_paths.get_folder_paths('inspire_prompts') + for prompts_path in prompts_paths: + for root, dirs, files in os.walk(prompts_path): + for file in files: + if file.endswith(".txt"): + file_path = os.path.join(root, file) + rel_path = os.path.relpath(file_path, prompts_path) + prompt_files.append(rel_path) + except Exception: + prompt_files = [] + + return {"required": { + "prompt_file": (prompt_files,) + }, + "optional": { + "text_data_opt": ("STRING", {"defaultInput": True}), + "reload": ("BOOLEAN", {"default": False, "label_on": "if file changed", "label_off": "if value changed"}), + "load_cap": ("INT", {"default": 0, "min": 0, "step": 1, "advanced": True, "tooltip": "The amount of prompts to load at once:\n0: Load all\n1 or higher: Load a specified number"}), + "start_index": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "step": 1, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}), + } + } + + RETURN_TYPES = ("ZIPPED_PROMPT", "INT", "INT") + RETURN_NAMES = ("zipped_prompt", "count", "remaining_count") + OUTPUT_IS_LIST = (True, False, False) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + @staticmethod + def IS_CHANGED(prompt_file, text_data_opt=None, reload=False, load_cap=0, start_index=-1): + md5 = hashlib.md5() + + if text_data_opt is not None: + md5.update(text_data_opt) + return md5.hexdigest(), load_cap, start_index + elif not reload: + return prompt_file, load_cap, start_index + else: + matched_path = None + for x in folder_paths.get_folder_paths('inspire_prompts'): + matched_path = os.path.join(x, prompt_file) + if not os.path.exists(matched_path): + matched_path = None + else: + break + + if matched_path is None: + return float('NaN') + + with open(matched_path, 'rb') as f: + while True: + chunk = f.read(4096) + if not chunk: + break + md5.update(chunk) + + return md5.hexdigest(), load_cap, start_index + + @staticmethod + def doit(prompt_file, text_data_opt=None, reload=False, load_cap=0, start_index=-1): + matched_path = None + for d in folder_paths.get_folder_paths('inspire_prompts'): + matched_path = os.path.join(d, prompt_file) + if os.path.exists(matched_path): + break + else: + matched_path = None + + if matched_path: + logging.info(f"[Inspire Pack] LoadPromptsFromFile: file found '{prompt_file}'") + else: + logging.warning(f"[Inspire Pack] LoadPromptsFromFile: file not found '{prompt_file}'") + + prompts = [] + try: + if not text_data_opt: + with open(matched_path, "r", encoding="utf-8") as file: + prompt_data = file.read() + else: + prompt_data = text_data_opt + + prompt_list = re.split(r'\n\s*-+\s*\n', prompt_data) + + pattern = r"^(?:(?:positive:(?P.*?)|negative:(?P.*?)|name:(?P.*?))\n*)+$" + + for p in prompt_list: + matches = re.search(pattern, p, re.DOTALL) + + if matches: + positive_text = matches.group('positive').strip() + negative_text = matches.group('negative').strip() + name_text = matches.group('name').strip() if matches.group('name') else prompt_file + result_tuple = (positive_text, negative_text, name_text) + prompts.append(result_tuple) + else: + logging.warning(f"[Inspire Pack] LoadPromptsFromFile: invalid prompt format in '{prompt_file}'") + except Exception as e: + logging.error(f"[Inspire Pack] LoadPromptsFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.") + + # slicing [start_index ~ start_index + load_cap] + total_prompts = len(prompts) + prompts = prompts[start_index:] + remaining_count = 0 + if load_cap > 0: + remaining_count = max(0, len(prompts) - load_cap) + prompts = prompts[:load_cap] + + return prompts, total_prompts, remaining_count + + +class LoadSinglePromptFromFile: + @classmethod + def INPUT_TYPES(cls): + prompt_files = [] + try: + prompts_paths = folder_paths.get_folder_paths('inspire_prompts') + for prompts_path in prompts_paths: + for root, dirs, files in os.walk(prompts_path): + for file in files: + if file.endswith(".txt"): + file_path = os.path.join(root, file) + rel_path = os.path.relpath(file_path, prompts_path) + prompt_files.append(rel_path) + except Exception: + prompt_files = [] + + return {"required": { + "prompt_file": (prompt_files,), + "index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "optional": {"text_data_opt": ("STRING", {"defaultInput": True})} + } + + RETURN_TYPES = ("ZIPPED_PROMPT",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + @staticmethod + def doit(prompt_file, index, text_data_opt=None): + prompt_path = None + prompts_paths = folder_paths.get_folder_paths('inspire_prompts') + for d in prompts_paths: + prompt_path = os.path.join(d, prompt_file) + if os.path.exists(prompt_path): + break + else: + prompt_path = None + + if prompt_path: + logging.info(f"[Inspire Pack] LoadSinglePromptFromFile: file found '{prompt_file}'") + else: + logging.warning(f"[Inspire Pack] LoadSinglePromptFromFile: file not found '{prompt_file}'") + + prompts = [] + try: + if not text_data_opt: + with open(prompt_path, "r", encoding="utf-8") as file: + prompt_data = file.read() + else: + prompt_data = text_data_opt + + prompt_list = re.split(r'\n\s*-+\s*\n', prompt_data) + try: + prompt = prompt_list[index] + except Exception: + prompt = prompt_list[-1] + + pattern = r"^(?:(?:positive:(?P.*?)|negative:(?P.*?)|name:(?P.*?))\n*)+$" + matches = re.search(pattern, prompt, re.DOTALL) + + if matches: + positive_text = matches.group('positive').strip() + negative_text = matches.group('negative').strip() + name_text = matches.group('name').strip() if matches.group('name') else prompt_file + result_tuple = (positive_text, negative_text, name_text) + prompts.append(result_tuple) + else: + logging.warning(f"[Inspire Pack] LoadSinglePromptFromFile: invalid prompt format in '{prompt_file}'") + except Exception as e: + logging.error(f"[Inspire Pack] LoadSinglePromptFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.") + + return (prompts, ) + + +class UnzipPrompt: + @classmethod + def INPUT_TYPES(s): + return {"required": {"zipped_prompt": ("ZIPPED_PROMPT",), }} + + RETURN_TYPES = ("STRING", "STRING", "STRING") + RETURN_NAMES = ("positive", "negative", "name") + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + def doit(self, zipped_prompt): + return zipped_prompt + + +class ZipPrompt: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "positive": ("STRING", {"forceInput": True, "multiline": True}), + "negative": ("STRING", {"forceInput": True, "multiline": True}), + }, + "optional": { + "name_opt": ("STRING", {"forceInput": True, "multiline": False}) + } + } + + RETURN_TYPES = ("ZIPPED_PROMPT",) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + def doit(self, positive, negative, name_opt=""): + return ((positive, negative, name_opt), ) + + +prompt_blacklist = set(['filename_prefix']) + + +class PromptExtractor: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": { + "image": (sorted(files), {"image_upload": True}), + "positive_id": ("STRING", {}), + "negative_id": ("STRING", {}), + "info": ("STRING", {"multiline": True}) + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + CATEGORY = "InspirePack/Prompt" + + RETURN_TYPES = ("STRING", "STRING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "doit" + + OUTPUT_NODE = True + + def doit(self, image, positive_id, negative_id, info, unique_id): + image_path = folder_paths.get_annotated_filepath(image) + info = Image.open(image_path).info + + positive = "" + negative = "" + text = "" + prompt_dicts = {} + node_inputs = {} + + def get_node_inputs(x): + if x in node_inputs: + return node_inputs[x] + else: + node_inputs[x] = None + + obj = nodes.NODE_CLASS_MAPPINGS.get(x, None) + if obj is not None: + input_types = obj.INPUT_TYPES() + node_inputs[x] = input_types + return input_types + else: + return None + + if isinstance(info, dict) and 'workflow' in info: + prompt = json.loads(info['prompt']) + for k, v in prompt.items(): + input_types = get_node_inputs(v['class_type']) + if input_types is not None: + inputs = input_types['required'].copy() + if 'optional' in input_types: + inputs.update(input_types['optional']) + + for name, value in inputs.items(): + if name in prompt_blacklist: + continue + + if value[0] == 'STRING' and name in v['inputs']: + prompt_dicts[f"{k}.{name.strip()}"] = (v['class_type'], v['inputs'][name]) + + for k, v in prompt_dicts.items(): + text += f"{k} [{v[0]}] ==> {v[1]}\n" + + positive = prompt_dicts.get(positive_id.strip(), "") + negative = prompt_dicts.get(negative_id.strip(), "") + else: + text = "There is no prompt information within the image." + + PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": unique_id, "widget_name": "info", "type": "text", "data": text}) + return (positive, negative) + + +class GlobalSeed: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "mode": ("BOOLEAN", {"default": True, "label_on": "control_before_generate", "label_off": "control_after_generate"}), + "action": (["fixed", "increment", "decrement", "randomize", + "increment for each node", "decrement for each node", "randomize for each node"], ), + "last_seed": ("STRING", {"default": ""}), + } + } + + RETURN_TYPES = () + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + OUTPUT_NODE = True + + def doit(self, **kwargs): + return {} + + +class SeedLogger: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "seeds": ("STRING", {"multiline": True, "dynamicPrompts": False, "control_after_generate": False}), + "limit": ("INT", {"default": 5, "min": 0, "max": 0xffffffffffffffff}), + }, + "hidden": {"unique_id": "UNIQUE_ID"} + } + + RETURN_TYPES = () + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + OUTPUT_NODE = True + + def doit(self, seed, seeds: str, limit, unique_id): + + if limit > 0: + lines = seeds.split('\n') + res = str(seed) + '\n' + '\n'.join(lines[:limit-1]) + else: + res = str(seed) + '\n' + seeds + + PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": unique_id, "widget_name": "seeds", "type": "text", "data": res}) + return {} + + +class GlobalSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + } + } + + RETURN_TYPES = () + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + OUTPUT_NODE = True + + def doit(self, **kwargs): + return {} + + +class BindImageListPromptList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "zipped_prompts": ("ZIPPED_PROMPT",), + "default_positive": ("STRING", {"multiline": True, "placeholder": "default positive"}), + "default_negative": ("STRING", {"multiline": True, "placeholder": "default negative"}), + } + } + + INPUT_IS_LIST = True + + RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING") + RETURN_NAMES = ("image", "positive", "negative", "prompt_label") + + OUTPUT_IS_LIST = (True, True, True,) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + def doit(self, images, zipped_prompts, default_positive, default_negative): + positives = [] + negatives = [] + prompt_labels = [] + + if len(images) < len(zipped_prompts): + zipped_prompts = zipped_prompts[:len(images)] + + elif len(images) > len(zipped_prompts): + lack = len(images) - len(zipped_prompts) + default_prompt = (default_positive[0], default_negative[0], "default") + zipped_prompts = zipped_prompts[:] + for i in range(lack): + zipped_prompts.append(default_prompt) + + for prompt in zipped_prompts: + a, b, c = prompt + positives.append(a) + negatives.append(b) + prompt_labels.append(c) + + return (images, positives, negatives, prompt_labels) + + +class BNK_EncoderWrapper: + def __init__(self, token_normalization, weight_interpretation): + self.token_normalization = token_normalization + self.weight_interpretation = weight_interpretation + + def encode(self, clip, text): + if 'BNK_CLIPTextEncodeAdvanced' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb', + "To use 'WildcardEncodeInspire' node, 'ComfyUI_ADV_CLIP_emb' extension is required.") + raise Exception("[ERROR] To use WildcardEncodeInspire, you need to install 'Advanced CLIP Text Encode'") + return nodes.NODE_CLASS_MAPPINGS['BNK_CLIPTextEncodeAdvanced']().encode(clip, text, self.token_normalization, self.weight_interpretation) + + +class WildcardEncodeInspire: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "token_normalization": (["none", "mean", "length", "length+mean"], ), + "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"], {'default': 'comfy++'}), + "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Wildcard Prompt (User Input)'}), + "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Populated Prompt (Will be generated automatically)'}), + + "mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip": + "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n" + "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n" + "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode." + }), + + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + } + + CATEGORY = "InspirePack/Prompt" + + RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING") + RETURN_NAMES = ("model", "clip", "conditioning", "populated_text") + FUNCTION = "doit" + + def doit(self, *args, **kwargs): + populated = kwargs['populated_text'] + + clip_encoder = BNK_EncoderWrapper(kwargs['token_normalization'], kwargs['weight_interpretation']) + + if 'ImpactWildcardEncode' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack', + "To use 'Wildcard Encode (Inspire)' node, 'Impact Pack' extension is required.") + raise Exception("[ERROR] To use 'Wildcard Encode (Inspire)', you need to install 'Impact Pack'") + + processed = [] + model, clip, conditioning = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode'].process_with_loras(wildcard_opt=populated, model=kwargs['model'], clip=kwargs['clip'], seed=kwargs['seed'], clip_encoder=clip_encoder, processed=processed) + return (model, clip, conditioning, processed[0]) + + +class MakeBasicPipe: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + "ckpt_key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'ckpt_name' as the key." }), + + "positive_wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Positive Prompt (User Input)'}), + "negative_wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Negative Prompt (User Input)'}), + + "Add selection to": ("BOOLEAN", {"default": True, "label_on": "Positive", "label_off": "Negative"}), + "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),), + "Select to add Wildcard": (["Select the Wildcard to add to the text"],), + "wildcard_mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip": + "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n" + "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n" + "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode." + }), + + "positive_populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Populated Positive Prompt (Will be generated automatically)'}), + "negative_populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Populated Negative Prompt (Will be generated automatically)'}), + + "token_normalization": (["none", "mean", "length", "length+mean"],), + "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"], {'default': 'comfy++'}), + + "stop_at_clip_layer": ("INT", {"default": -2, "min": -24, "max": -1, "step": 1}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "optional": { + "vae_opt": ("VAE",) + }, + } + + CATEGORY = "InspirePack/Prompt" + + RETURN_TYPES = ("BASIC_PIPE", "STRING") + RETURN_NAMES = ("basic_pipe", "cache_key") + FUNCTION = "doit" + + def doit(self, **kwargs): + pos_populated = kwargs['positive_populated_text'] + neg_populated = kwargs['negative_populated_text'] + + clip_encoder = BNK_EncoderWrapper(kwargs['token_normalization'], kwargs['weight_interpretation']) + + if 'ImpactWildcardEncode' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack', + "To use 'Make Basic Pipe (Inspire)' node, 'Impact Pack' extension is required.") + raise Exception("[ERROR] To use 'Make Basic Pipe (Inspire)', you need to install 'Impact Pack'") + + model, clip, vae, key = CheckpointLoaderSimpleShared().doit(ckpt_name=kwargs['ckpt_name'], key_opt=kwargs['ckpt_key_opt']) + clip = nodes.CLIPSetLastLayer().set_last_layer(clip, kwargs['stop_at_clip_layer'])[0] + model, clip, positive = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode'].process_with_loras(wildcard_opt=pos_populated, model=model, clip=clip, clip_encoder=clip_encoder) + model, clip, negative = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode'].process_with_loras(wildcard_opt=neg_populated, model=model, clip=clip, clip_encoder=clip_encoder) + + if 'vae_opt' in kwargs: + vae = kwargs['vae_opt'] + + basic_pipe = model, clip, vae, positive, negative + + return (basic_pipe, key) + + +class PromptBuilder: + @classmethod + def INPUT_TYPES(s): + global prompt_builder_preset + + presets = ["#PRESET"] + return {"required": { + "category": (list(prompt_builder_preset.keys()) + ["#PLACEHOLDER"], ), + "preset": (presets, ), + "text": ("STRING", {"multiline": True}), + }, + } + + RETURN_TYPES = ("STRING", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + def doit(self, **kwargs): + return (kwargs['text'],) + + +class SeedExplorer: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "latent": ("LATENT",), + "seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}), + "enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}), + "additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_mode": (["GPU(=A1111)", "CPU"],), + "initial_batch_seed_mode": (["incremental", "comfy"],), + }, + "optional": + { + "variation_method": (["linear", "slerp"],), + "model": ("MODEL",), + } + } + + RETURN_TYPES = ("NOISE_IMAGE",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + @staticmethod + def apply_variation(start_noise, seed_items, noise_device, mask=None, variation_method='linear'): + noise = start_noise + for x in seed_items: + if isinstance(x, str): + item = x.split(':') + else: + item = x + + if len(item) == 2: + try: + variation_seed = int(item[0]) + variation_strength = float(item[1]) + + noise = utils.apply_variation_noise(noise, noise_device, variation_seed, variation_strength, mask=mask, variation_method=variation_method) + except Exception: + logging.error(f"[Inspire Pack] IGNORED: SeedExplorer failed to processing '{x}'") + traceback.print_exc() + return noise + + @staticmethod + def doit(latent, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, + initial_batch_seed_mode, variation_method='linear', model=None): + latent_image = latent["samples"] + + if hasattr(comfy.sample, 'fix_empty_latent_channels') and model is not None: + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + device = comfy.model_management.get_torch_device() + noise_device = "cpu" if noise_mode == "CPU" else device + + seed_prompt = seed_prompt.replace("\n", "") + items = seed_prompt.strip().split(",") + + if items == ['']: + items = [] + + if enable_additional: + items.append((additional_seed, additional_strength)) + + try: + hd = items[0] + tl = items[1:] + + if isinstance(hd, tuple): + hd_seed = int(hd[0]) + else: + hd_seed = int(hd) + + noise = utils.prepare_noise(latent_image, hd_seed, None, noise_device, initial_batch_seed_mode) + noise = noise.to(device) + noise = SeedExplorer.apply_variation(noise, tl, noise_device, variation_method=variation_method) + noise = noise.cpu() + + return (noise,) + + except Exception: + logging.error("[Inspire Pack] IGNORED: SeedExplorer failed") + traceback.print_exc() + + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, + device=noise_device) + return (noise,) + + +class CompositeNoise: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("NOISE_IMAGE",), + "source": ("NOISE_IMAGE",), + "mode": (["center", "left-top", "right-top", "left-bottom", "right-bottom", "xy"], ), + "x": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}), + }, + } + + RETURN_TYPES = ("NOISE_IMAGE",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Prompt" + + def doit(self, destination, source, mode, x, y): + new_tensor = destination.clone() + + if mode == 'center': + y1 = (new_tensor.size(2) - source.size(2)) // 2 + x1 = (new_tensor.size(3) - source.size(3)) // 2 + elif mode == 'left-top': + y1 = 0 + x1 = 0 + elif mode == 'right-top': + y1 = 0 + x1 = new_tensor.size(2) - source.size(2) + elif mode == 'left-bottom': + y1 = new_tensor.size(3) - source.size(3) + x1 = 0 + elif mode == 'right-bottom': + y1 = new_tensor.size(3) - source.size(3) + x1 = new_tensor.size(2) - source.size(2) + else: # mode == 'xy': + x1 = max(0, x) + y1 = max(0, y) + + # raw coordinates + y2 = y1 + source.size(2) + x2 = x1 + source.size(3) + + # bounding for destination + top = max(0, y1) + left = max(0, x1) + bottom = min(new_tensor.size(2), y2) + right = min(new_tensor.size(3), x2) + + # bounding for source + left_gap = left - x1 + top_gap = top - y1 + + width = right - left + height = bottom - top + + height = min(height, y1 + source.size(2) - top) + width = min(width, x1 + source.size(3) - left) + + # composite + new_tensor[:, :, top:top + height, left:left + width] = source[:, :, top_gap:top_gap + height, left_gap:left_gap + width] + + return (new_tensor,) + + +list_counter_map = {} + + +class ListCounter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "signal": (utils.any_typ,), + "base_value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = ("INT",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, signal, base_value, unique_id): + if unique_id not in list_counter_map: + count = 0 + else: + count = list_counter_map[unique_id] + + list_counter_map[unique_id] = count + 1 + + return (count + base_value, ) + + +class CLIPTextEncodeWithWeight: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "text": ("STRING", {"multiline": True}), "clip": ("CLIP", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "add_weight": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01}), + } + } + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "InspirePack/Util" + + def encode(self, clip, text, strength, add_weight): + tokens = clip.tokenize(text) + + if add_weight != 0 or strength != 1: + for v in tokens.values(): + for vv in v: + for i in range(0, len(vv)): + vv[i] = (vv[i][0], vv[i][1] * strength + add_weight) + + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled}]], ) + + +class RandomGeneratorForList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "signal": (utils.any_typ,), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "hidden": {"unique_id": "UNIQUE_ID"}, + } + + RETURN_TYPES = (utils.any_typ, "INT",) + RETURN_NAMES = ("signal", "random_value",) + + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, signal, seed, unique_id): + if unique_id not in list_counter_map: + count = 0 + else: + count = list_counter_map[unique_id] + + list_counter_map[unique_id] = count + 1 + + rn = random.Random() + rn.seed(seed + count) + new_seed = random.randint(0, 1125899906842624) + + return (signal, new_seed) + + +class RemoveControlNet: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, conditioning): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + + if 'control' in n[1]: + del n[1]['control'] + if 'control_apply_to_uncond' in n[1]: + del n[1]['control_apply_to_uncond'] + c.append(n) + + return (c, ) + + +class RemoveControlNetFromRegionalPrompts: + @classmethod + def INPUT_TYPES(s): + return {"required": {"regional_prompts": ("REGIONAL_PROMPTS", )}} + RETURN_TYPES = ("REGIONAL_PROMPTS",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, regional_prompts): + rcn = RemoveControlNet() + res = [] + for rp in regional_prompts: + _, _, _, _, positive, negative = rp.sampler.params + positive, negative = rcn.doit(positive)[0], rcn.doit(negative)[0] + sampler = rp.sampler.clone_with_conditionings(positive, negative) + res.append(rp.clone_with_sampler(sampler)) + return (res, ) + + +NODE_CLASS_MAPPINGS = { + "LoadPromptsFromDir //Inspire": LoadPromptsFromDir, + "LoadPromptsFromFile //Inspire": LoadPromptsFromFile, + "LoadSinglePromptFromFile //Inspire": LoadSinglePromptFromFile, + "UnzipPrompt //Inspire": UnzipPrompt, + "ZipPrompt //Inspire": ZipPrompt, + "PromptExtractor //Inspire": PromptExtractor, + "GlobalSeed //Inspire": GlobalSeed, + "SeedLogger //Inspire": SeedLogger, + "GlobalSampler //Inspire": GlobalSampler, + "BindImageListPromptList //Inspire": BindImageListPromptList, + "WildcardEncode //Inspire": WildcardEncodeInspire, + "PromptBuilder //Inspire": PromptBuilder, + "SeedExplorer //Inspire": SeedExplorer, + "ListCounter //Inspire": ListCounter, + "CLIPTextEncodeWithWeight //Inspire": CLIPTextEncodeWithWeight, + "RandomGeneratorForList //Inspire": RandomGeneratorForList, + "MakeBasicPipe //Inspire": MakeBasicPipe, + "RemoveControlNet //Inspire": RemoveControlNet, + "RemoveControlNetFromRegionalPrompts //Inspire": RemoveControlNetFromRegionalPrompts, + "CompositeNoise //Inspire": CompositeNoise, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoadPromptsFromDir //Inspire": "Load Prompts From Dir (Inspire)", + "LoadPromptsFromFile //Inspire": "Load Prompts From File (Inspire)", + "LoadSinglePromptFromFile //Inspire": "Load Single Prompt From File (Inspire)", + "UnzipPrompt //Inspire": "Unzip Prompt (Inspire)", + "ZipPrompt //Inspire": "Zip Prompt (Inspire)", + "PromptExtractor //Inspire": "Prompt Extractor (Inspire)", + "GlobalSeed //Inspire": "Global Seed (Inspire)", + "SeedLogger //Inspire": "Seed Logger (Inspire)", + "GlobalSampler //Inspire": "Global Sampler (Inspire)", + "BindImageListPromptList //Inspire": "Bind [ImageList, PromptList] (Inspire)", + "WildcardEncode //Inspire": "Wildcard Encode (Inspire)", + "PromptBuilder //Inspire": "Prompt Builder (Inspire)", + "SeedExplorer //Inspire": "Seed Explorer (Inspire)", + "ListCounter //Inspire": "List Counter (Inspire)", + "CLIPTextEncodeWithWeight //Inspire": "CLIPTextEncodeWithWeight (Inspire)", + "RandomGeneratorForList //Inspire": "Random Generator for List (Inspire)", + "MakeBasicPipe //Inspire": "Make Basic Pipe (Inspire)", + "RemoveControlNet //Inspire": "Remove ControlNet (Inspire)", + "RemoveControlNetFromRegionalPrompts //Inspire": "Remove ControlNet [RegionalPrompts] (Inspire)", + "CompositeNoise //Inspire": "Composite Noise (Inspire)" +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/regional_nodes.py b/custom_nodes/comfyui-inspire-pack/inspire/regional_nodes.py new file mode 100644 index 00000000..3f5426b9 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/regional_nodes.py @@ -0,0 +1,699 @@ +import traceback + +import comfy +import nodes +import torch +import re +import webcolors + +from . import prompt_support +from .libs import utils, common + +import logging + +class RegionalPromptSimple: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "basic_pipe": ("BASIC_PIPE",), + "mask": ("MASK",), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (common.get_schedulers(),), + "wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}), + "controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}), + "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }, + "optional": { + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "variation_method": (["linear", "slerp"],), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("REGIONAL_PROMPTS", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt, + controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method='linear', scheduler_func_opt=None): + if 'RegionalPrompt' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack', + "To use 'RegionalPromptSimple' node, 'Impact Pack' extension is required.") + raise Exception("[ERROR] To use RegionalPromptSimple, you need to install 'ComfyUI-Impact-Pack'") + + model, clip, vae, positive, negative = basic_pipe + + iwe = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode']() + kap = nodes.NODE_CLASS_MAPPINGS['KSamplerAdvancedProvider']() + rp = nodes.NODE_CLASS_MAPPINGS['RegionalPrompt']() + + if wildcard_prompt != "": + model, clip, new_positive, _ = iwe.doit(model=model, clip=clip, populated_text=wildcard_prompt, seed=None) + + if controlnet_in_pipe: + prev_cnet = None + for t in positive: + if 'control' in t[1] and 'control_apply_to_uncond' in t[1]: + prev_cnet = t[1]['control'], t[1]['control_apply_to_uncond'] + break + + if prev_cnet is not None: + for t in new_positive: + t[1]['control'] = prev_cnet[0] + t[1]['control_apply_to_uncond'] = prev_cnet[1] + + else: + new_positive = positive + + basic_pipe = model, clip, vae, new_positive, negative + + sampler = kap.doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=sigma_factor, scheduler_func_opt=scheduler_func_opt)[0] + try: + regional_prompts = rp.doit(mask, sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)[0] + except: + raise Exception("[Inspire-Pack] ERROR: Impact Pack is outdated. Update Impact Pack to latest version to use this.") + + return (regional_prompts, ) + + +def color_to_mask(color_mask, mask_color): + try: + if mask_color.startswith("#") or mask_color.isalpha(): + hex = mask_color[1:] if mask_color.startswith("#") else webcolors.name_to_hex(mask_color)[1:] + selected = int(hex, 16) + else: + selected = int(mask_color, 10) + except Exception: + raise Exception("[ERROR] Invalid mask_color value. mask_color should be a color value for RGB") + + temp = (torch.clamp(color_mask, 0, 1.0) * 255.0).round().to(torch.int) + temp = torch.bitwise_left_shift(temp[:, :, :, 0], 16) + torch.bitwise_left_shift(temp[:, :, :, 1], 8) + temp[:, :, :, 2] + mask = torch.where(temp == selected, 1.0, 0.0) + return mask + + +class RegionalPromptColorMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "basic_pipe": ("BASIC_PIPE",), + "color_mask": ("IMAGE",), + "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (common.get_schedulers(),), + "wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}), + "controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}), + "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }, + "optional": { + "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "variation_method": (["linear", "slerp"],), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + RETURN_TYPES = ("REGIONAL_PROMPTS", "MASK") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(basic_pipe, color_mask, mask_color, cfg, sampler_name, scheduler, wildcard_prompt, + controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method="linear", scheduler_func_opt=None): + mask = color_to_mask(color_mask, mask_color) + rp = RegionalPromptSimple().doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt, controlnet_in_pipe, + sigma_factor=sigma_factor, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method, scheduler_func_opt=scheduler_func_opt)[0] + return rp, mask + + +class RegionalConditioningSimple: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip": ("CLIP", ), + "mask": ("MASK",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + "prompt": ("STRING", {"multiline": True, "placeholder": "prompt"}), + }, + } + + RETURN_TYPES = ("CONDITIONING", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(clip, mask, strength, set_cond_area, prompt): + conditioning = nodes.CLIPTextEncode().encode(clip, prompt)[0] + conditioning = nodes.ConditioningSetMask().append(conditioning, mask, set_cond_area, strength)[0] + return (conditioning, ) + + +class RegionalConditioningColorMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip": ("CLIP", ), + "color_mask": ("IMAGE",), + "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + "prompt": ("STRING", {"multiline": True, "placeholder": "prompt"}), + }, + "optional": { + "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + } + } + + RETURN_TYPES = ("CONDITIONING", "MASK") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(clip, color_mask, mask_color, strength, set_cond_area, prompt, dilation=0): + mask = color_to_mask(color_mask, mask_color) + + if dilation != 0: + mask = utils.dilate_mask(mask, dilation) + + conditioning = nodes.CLIPTextEncode().encode(clip, prompt)[0] + conditioning = nodes.ConditioningSetMask().append(conditioning, mask, set_cond_area, strength)[0] + return conditioning, mask + + +class ToIPAdapterPipe: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ipadapter": ("IPADAPTER", ), + "model": ("MODEL",), + }, + "optional": { + "clip_vision": ("CLIP_VISION",), + "insightface": ("INSIGHTFACE",), + } + } + + RETURN_TYPES = ("IPADAPTER_PIPE",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + @staticmethod + def doit(ipadapter, model, clip_vision, insightface=None): + pipe = ipadapter, model, clip_vision, insightface, lambda x: x + + return (pipe,) + + +class FromIPAdapterPipe: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ipadapter_pipe": ("IPADAPTER_PIPE", ), + } + } + + RETURN_TYPES = ("IPADAPTER", "MODEL", "CLIP_VISION", "INSIGHTFACE") + RETURN_NAMES = ("ipadapter", "model", "clip_vision", "insight_face") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Util" + + def doit(self, ipadapter_pipe): + ipadapter, model, clip_vision, insightface, _ = ipadapter_pipe + return ipadapter, model, clip_vision, insightface + + +class IPAdapterConditioning: + def __init__(self, mask, weight, weight_type, noise=None, image=None, neg_image=None, embeds=None, start_at=0.0, end_at=1.0, combine_embeds='concat', unfold_batch=False, weight_v2=False, neg_embeds=None): + self.mask = mask + self.image = image + self.neg_image = neg_image + self.embeds = embeds + self.neg_embeds = neg_embeds + self.weight = weight + self.noise = noise + self.weight_type = weight_type + self.start_at = start_at + self.end_at = end_at + self.unfold_batch = unfold_batch + self.weight_v2 = weight_v2 + self.combine_embeds = combine_embeds + + def doit(self, ipadapter_pipe): + ipadapter, model, clip_vision, insightface, _ = ipadapter_pipe + + if 'IPAdapterAdvanced' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus', + "To use 'Regional IPAdapter' node, 'ComfyUI IPAdapter Plus' extension is required.") + raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'") + + if self.embeds is None: + obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced'] + model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, weight=self.weight, weight_type=self.weight_type, + start_at=self.start_at, end_at=self.end_at, combine_embeds=self.combine_embeds, + clip_vision=clip_vision, image=self.image, image_negative=self.neg_image, attn_mask=self.mask, + insightface=insightface, weight_faceidv2=self.weight_v2)[0] + else: + obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterEmbeds'] + model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, pos_embed=self.embeds, weight=self.weight, weight_type=self.weight_type, + start_at=self.start_at, end_at=self.end_at, neg_embed=self.neg_embeds, + attn_mask=self.mask, clip_vision=clip_vision)[0] + + return model + + +IPADAPTER_WEIGHT_TYPES_CACHE = None + + +def IPADAPTER_WEIGHT_TYPES(): + global IPADAPTER_WEIGHT_TYPES_CACHE + + if IPADAPTER_WEIGHT_TYPES_CACHE is None: + try: + IPADAPTER_WEIGHT_TYPES_CACHE = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']().INPUT_TYPES()['required']['weight_type'][0] + except Exception: + logging.error("[Inspire Pack] IPAdapterPlus is not installed.") + IPADAPTER_WEIGHT_TYPES_CACHE = ["IPAdapterPlus is not installed"] + + return IPADAPTER_WEIGHT_TYPES_CACHE + + +class RegionalIPAdapterMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + + "image": ("IMAGE",), + "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}), + "noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "weight_type": (IPADAPTER_WEIGHT_TYPES(), ), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "unfold_batch": ("BOOLEAN", {"default": False}), + }, + "optional": { + "faceid_v2": ("BOOLEAN", {"default": False}), + "weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}), + "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],), + "neg_image": ("IMAGE",), + } + } + + RETURN_TYPES = ("REGIONAL_IPADAPTER", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(mask, image, weight, noise, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, faceid_v2=False, weight_v2=False, combine_embeds="concat", neg_image=None): + cond = IPAdapterConditioning(mask, weight, weight_type, noise=noise, image=image, neg_image=neg_image, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, weight_v2=weight_v2, combine_embeds=combine_embeds) + return (cond, ) + + +class RegionalIPAdapterColorMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "color_mask": ("IMAGE",), + "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}), + "image": ("IMAGE",), + "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}), + "noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "weight_type": (IPADAPTER_WEIGHT_TYPES(), ), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "unfold_batch": ("BOOLEAN", {"default": False}), + }, + "optional": { + "faceid_v2": ("BOOLEAN", {"default": False }), + "weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}), + "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],), + "neg_image": ("IMAGE",), + } + } + + RETURN_TYPES = ("REGIONAL_IPADAPTER", "MASK") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(color_mask, mask_color, image, weight, noise, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, faceid_v2=False, weight_v2=False, combine_embeds="concat", neg_image=None): + mask = color_to_mask(color_mask, mask_color) + cond = IPAdapterConditioning(mask, weight, weight_type, noise=noise, image=image, neg_image=neg_image, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, weight_v2=weight_v2, combine_embeds=combine_embeds) + return (cond, mask) + + +class RegionalIPAdapterEncodedMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + + "embeds": ("EMBEDS",), + "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}), + "weight_type": (IPADAPTER_WEIGHT_TYPES(), ), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "unfold_batch": ("BOOLEAN", {"default": False}), + }, + "optional": { + "neg_embeds": ("EMBEDS",), + } + } + + RETURN_TYPES = ("REGIONAL_IPADAPTER", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(mask, embeds, weight, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, neg_embeds=None): + cond = IPAdapterConditioning(mask, weight, weight_type, embeds=embeds, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, neg_embeds=neg_embeds) + return (cond, ) + + +class RegionalIPAdapterEncodedColorMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "color_mask": ("IMAGE",), + "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}), + + "embeds": ("EMBEDS",), + "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}), + "weight_type": (IPADAPTER_WEIGHT_TYPES(), ), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "unfold_batch": ("BOOLEAN", {"default": False}), + }, + "optional": { + "neg_embeds": ("EMBEDS",), + } + } + + RETURN_TYPES = ("REGIONAL_IPADAPTER", "MASK") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(color_mask, mask_color, embeds, weight, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, neg_embeds=None): + mask = color_to_mask(color_mask, mask_color) + cond = IPAdapterConditioning(mask, weight, weight_type, embeds=embeds, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, neg_embeds=neg_embeds) + return (cond, mask) + + +class ApplyRegionalIPAdapters: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ipadapter_pipe": ("IPADAPTER_PIPE",), + "regional_ipadapter1": ("REGIONAL_IPADAPTER", ), + }, + } + + RETURN_TYPES = ("MODEL", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(**kwargs): + ipadapter_pipe = kwargs['ipadapter_pipe'] + ipadapter, model, clip_vision, insightface, lora_loader = ipadapter_pipe + + del kwargs['ipadapter_pipe'] + + for k, v in kwargs.items(): + ipadapter_pipe = ipadapter, model, clip_vision, insightface, lora_loader + model = v.doit(ipadapter_pipe) + + return (model, ) + + +class RegionalSeedExplorerMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + + "noise": ("NOISE_IMAGE",), + "seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}), + "enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}), + "additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_mode": (["GPU(=A1111)", "CPU"],), + }, + "optional": + {"variation_method": (["linear", "slerp"],), } + } + + RETURN_TYPES = ("NOISE_IMAGE",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(mask, noise, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, variation_method='linear'): + device = comfy.model_management.get_torch_device() + noise_device = "cpu" if noise_mode == "CPU" else device + + noise = noise.to(device) + mask = mask.to(device) + + if len(mask.shape) == 2: + mask = mask.unsqueeze(0) + + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noise.shape[2], noise.shape[3]), mode="bilinear").squeeze(0) + + try: + seed_prompt = seed_prompt.replace("\n", "") + items = seed_prompt.strip().split(",") + + if items == ['']: + items = [] + + if enable_additional: + items.append((additional_seed, additional_strength)) + + noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method) + except Exception: + logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.") + traceback.print_exc() + + noise = noise.cpu() + mask.cpu() + return (noise,) + + +class RegionalSeedExplorerColorMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "color_mask": ("IMAGE",), + "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}), + + "noise": ("NOISE_IMAGE",), + "seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}), + "enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}), + "additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_mode": (["GPU(=A1111)", "CPU"],), + }, + "optional": + {"variation_method": (["linear", "slerp"],), } + } + + RETURN_TYPES = ("NOISE_IMAGE", "MASK") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(color_mask, mask_color, noise, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, variation_method='linear'): + device = comfy.model_management.get_torch_device() + noise_device = "cpu" if noise_mode == "CPU" else device + + color_mask = color_mask.to(device) + noise = noise.to(device) + + mask = color_to_mask(color_mask, mask_color) + original_mask = mask + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noise.shape[2], noise.shape[3]), mode="bilinear").squeeze(0) + + mask = mask.to(device) + + try: + seed_prompt = seed_prompt.replace("\n", "") + items = seed_prompt.strip().split(",") + + if items == ['']: + items = [] + + if enable_additional: + items.append((additional_seed, additional_strength)) + + noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method) + except Exception: + logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.") + traceback.print_exc() + + color_mask.cpu() + noise = noise.cpu() + original_mask = original_mask.cpu() + return (noise, original_mask) + + +class ColorMaskToDepthMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "color_mask": ("IMAGE",), + "spec": ("STRING", {"multiline": True, "default": "#FF0000:1.0\n#000000:1.0"}), + "base_value": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}), + "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "flatten_method": (["override", "sum", "max"],), + }, + } + + RETURN_TYPES = ("MASK", ) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + def doit(self, color_mask, spec, base_value, dilation, flatten_method): + specs = spec.split('\n') + pat = re.compile("(?P#[A-F0-9]+):(?P[0-9]+(.[0-9]*)?)") + + masks = [torch.ones((1, color_mask.shape[1], color_mask.shape[2])) * base_value] + for x in specs: + match = pat.match(x) + if match: + mask = color_to_mask(color_mask=color_mask, mask_color=match['color_code']) * float(match['cfg']) + mask = utils.dilate_mask(mask, dilation) + masks.append(mask) + + if masks: + masks = torch.cat(masks, dim=0) + if flatten_method == 'override': + masks = utils.flatten_non_zero_override(masks) + elif flatten_method == 'max': + masks = torch.max(masks, dim=0)[0] + else: # flatten_method == 'sum': + masks = torch.sum(masks, dim=0) + + masks = torch.clamp(masks, min=0.0, max=1.0) + masks = masks.unsqueeze(0) + else: + masks = torch.tensor([]) + + return (masks, ) + + +class RegionalCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL",), + "mask": ("MASK",), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/Regional" + + @staticmethod + def doit(model, mask): + if len(mask.shape) == 2: + mask = mask.unsqueeze(0).unsqueeze(0) + elif len(mask.shape) == 3: + mask = mask.unsqueeze(0) + + size = None + + def regional_cfg(args): + nonlocal mask + nonlocal size + + x = args['input'] + + if mask.device != x.device: + mask = mask.to(x.device) + + if size != (x.shape[2], x.shape[3]): + size = (x.shape[2], x.shape[3]) + mask = torch.nn.functional.interpolate(mask, size=size, mode='bilinear', align_corners=False) + + cond_pred = args["cond_denoised"] + uncond_pred = args["uncond_denoised"] + cond_scale = args["cond_scale"] + + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale * mask + + return x - cfg_result + + m = model.clone() + m.set_model_sampler_cfg_function(regional_cfg) + return (m,) + + +NODE_CLASS_MAPPINGS = { + "RegionalPromptSimple //Inspire": RegionalPromptSimple, + "RegionalPromptColorMask //Inspire": RegionalPromptColorMask, + "RegionalConditioningSimple //Inspire": RegionalConditioningSimple, + "RegionalConditioningColorMask //Inspire": RegionalConditioningColorMask, + "RegionalIPAdapterMask //Inspire": RegionalIPAdapterMask, + "RegionalIPAdapterColorMask //Inspire": RegionalIPAdapterColorMask, + "RegionalIPAdapterEncodedMask //Inspire": RegionalIPAdapterEncodedMask, + "RegionalIPAdapterEncodedColorMask //Inspire": RegionalIPAdapterEncodedColorMask, + "RegionalSeedExplorerMask //Inspire": RegionalSeedExplorerMask, + "RegionalSeedExplorerColorMask //Inspire": RegionalSeedExplorerColorMask, + "ToIPAdapterPipe //Inspire": ToIPAdapterPipe, + "FromIPAdapterPipe //Inspire": FromIPAdapterPipe, + "ApplyRegionalIPAdapters //Inspire": ApplyRegionalIPAdapters, + "RegionalCFG //Inspire": RegionalCFG, + "ColorMaskToDepthMask //Inspire": ColorMaskToDepthMask, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RegionalPromptSimple //Inspire": "Regional Prompt Simple (Inspire)", + "RegionalPromptColorMask //Inspire": "Regional Prompt By Color Mask (Inspire)", + "RegionalConditioningSimple //Inspire": "Regional Conditioning Simple (Inspire)", + "RegionalConditioningColorMask //Inspire": "Regional Conditioning By Color Mask (Inspire)", + "RegionalIPAdapterMask //Inspire": "Regional IPAdapter Mask (Inspire)", + "RegionalIPAdapterColorMask //Inspire": "Regional IPAdapter By Color Mask (Inspire)", + "RegionalIPAdapterEncodedMask //Inspire": "Regional IPAdapter Encoded Mask (Inspire)", + "RegionalIPAdapterEncodedColorMask //Inspire": "Regional IPAdapter Encoded By Color Mask (Inspire)", + "RegionalSeedExplorerMask //Inspire": "Regional Seed Explorer By Mask (Inspire)", + "RegionalSeedExplorerColorMask //Inspire": "Regional Seed Explorer By Color Mask (Inspire)", + "ToIPAdapterPipe //Inspire": "ToIPAdapterPipe (Inspire)", + "FromIPAdapterPipe //Inspire": "FromIPAdapterPipe (Inspire)", + "ApplyRegionalIPAdapters //Inspire": "Apply Regional IPAdapters (Inspire)", + "RegionalCFG //Inspire": "Regional CFG (Inspire)", + "ColorMaskToDepthMask //Inspire": "Color Mask To Depth Mask (Inspire)", +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/sampler_nodes.py b/custom_nodes/comfyui-inspire-pack/inspire/sampler_nodes.py new file mode 100644 index 00000000..b926d50a --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/sampler_nodes.py @@ -0,0 +1,353 @@ +import torch +from . import a1111_compat +import comfy +from .libs import common +from comfy.samplers import CFGGuider +from comfy_extras.nodes_perpneg import Guider_PerpNeg +import math + + +class KSampler_progress(a1111_compat.KSampler_inspire): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_mode": (a1111_compat.supported_noise_modes,), + "interval": ("INT", {"default": 1, "min": 1, "max": 10000}), + "omit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}), + "omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + }, + "optional": { + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + CATEGORY = "InspirePack/analysis" + + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("latent", "progress_latent") + + @staticmethod + def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, + interval, omit_start_latent, omit_final_latent, scheduler_func_opt=None): + adv_steps = int(steps / denoise) + + if omit_start_latent: + result = [] + else: + result = [comfy.sample.fix_empty_latent_channels(model, latent_image['samples']).cpu()] + + def progress_callback(step, x0, x, total_steps): + if (total_steps-1) != step and step % interval != 0: + return + + x = model.model.process_latent_out(x) + x = x.cpu() + result.append(x) + + latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, True, seed, adv_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, (adv_steps-steps), + adv_steps, noise_mode, False, callback=progress_callback, scheduler_func_opt=scheduler_func_opt) + + if not omit_final_latent: + result.append(latent_image['samples'].cpu()) + + if len(result) > 0: + result = torch.cat(result) + result = {'samples': result} + else: + result = latent_image + + return latent_image, result + + +class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (common.get_schedulers(), ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "noise_mode": (a1111_compat.supported_noise_modes,), + "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + "interval": ("INT", {"default": 1, "min": 1, "max": 10000}), + "omit_start_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + "omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}), + }, + "optional": { + "prev_progress_latent_opt": ("LATENT",), + "scheduler_func_opt": ("SCHEDULER_FUNC",), + } + } + + FUNCTION = "doit" + + CATEGORY = "InspirePack/analysis" + + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("latent", "progress_latent") + + def doit(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + start_at_step, end_at_step, noise_mode, return_with_leftover_noise, interval, omit_start_latent, omit_final_latent, + prev_progress_latent_opt=None, scheduler_func_opt=None): + + if omit_start_latent: + result = [] + else: + result = [latent_image['samples']] + + def progress_callback(step, x0, x, total_steps): + if (total_steps-1) != step and step % interval != 0: + return + + x = model.model.process_latent_out(x) + x = x.cpu() + result.append(x) + + latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, + noise_mode, return_with_leftover_noise, callback=progress_callback, scheduler_func_opt=scheduler_func_opt) + + if not omit_final_latent: + result.append(latent_image['samples'].cpu()) + + if len(result) > 0: + result = torch.cat(result) + result = {'samples': result} + else: + result = latent_image + + if prev_progress_latent_opt is not None: + result['samples'] = torch.cat((prev_progress_latent_opt['samples'], result['samples']), dim=0) + + return latent_image, result + + +def exponential_interpolation(from_cfg, to_cfg, i, steps): + if i == steps-1: + return to_cfg + + if from_cfg == to_cfg: + return from_cfg + + if from_cfg == 0: + return to_cfg * (1 - math.exp(-5 * i / steps)) / (1 - math.exp(-5)) + elif to_cfg == 0: + return from_cfg * (math.exp(-5 * i / steps) - math.exp(-5)) / (1 - math.exp(-5)) + else: + log_from = math.log(from_cfg) + log_to = math.log(to_cfg) + log_value = log_from + (log_to - log_from) * i / steps + return math.exp(log_value) + + +def logarithmic_interpolation(from_cfg, to_cfg, i, steps): + if i == 0: + return from_cfg + + if i == steps-1: + return to_cfg + + log_i = math.log(i + 1) + log_steps = math.log(steps + 1) + + t = log_i / log_steps + + return from_cfg + (to_cfg - from_cfg) * t + + +def cosine_interpolation(from_cfg, to_cfg, i, steps): + if (i == 0) or (i == steps-1): + return from_cfg + + t = (1.0 + math.cos(math.pi*2*(i/steps))) / 2 + + return from_cfg + (to_cfg - from_cfg) * t + + +class Guider_scheduled(CFGGuider): + def __init__(self, model_patcher, sigmas, from_cfg, to_cfg, schedule): + super().__init__(model_patcher) + self.default_cfg = self.cfg + self.sigmas = sigmas + self.cfg_sigmas = None + self.cfg_sigmas_i = None + self.from_cfg = from_cfg + self.to_cfg = to_cfg + self.schedule = schedule + self.last_i = 0 + self.renew_cfg_sigmas() + + def set_cfg(self, cfg): + self.default_cfg = cfg + self.renew_cfg_sigmas() + + def renew_cfg_sigmas(self): + self.cfg_sigmas = {} + self.cfg_sigmas_i = {} + i = 0 + steps = len(self.sigmas) - 1 + for x in self.sigmas: + k = float(x) + delta = self.to_cfg - self.from_cfg + if self.schedule == 'exp': + self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i + elif self.schedule == 'log': + self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i + elif self.schedule == 'cos': + self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i + else: + self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i + + self.cfg_sigmas_i[i] = self.cfg_sigmas[k] + i += 1 + + def predict_noise(self, x, timestep, model_options={}, seed=None): + k = float(timestep[0]) + + v = self.cfg_sigmas.get(k) + if v is None: + # fallback + v = self.cfg_sigmas_i[self.last_i+1] + self.cfg_sigmas[k] = v + + self.last_i = v[1] + self.cfg = v[0] + + return super().predict_noise(x, timestep, model_options, seed) + + +class Guider_PerpNeg_scheduled(Guider_PerpNeg): + def __init__(self, model_patcher, sigmas, from_cfg, to_cfg, schedule, neg_scale): + super().__init__(model_patcher) + self.default_cfg = self.cfg + self.sigmas = sigmas + self.cfg_sigmas = None + self.cfg_sigmas_i = None + self.from_cfg = from_cfg + self.to_cfg = to_cfg + self.schedule = schedule + self.neg_scale = neg_scale + self.last_i = 0 + self.renew_cfg_sigmas() + + def set_cfg(self, cfg): + self.default_cfg = cfg + self.renew_cfg_sigmas() + + def renew_cfg_sigmas(self): + self.cfg_sigmas = {} + self.cfg_sigmas_i = {} + i = 0 + steps = len(self.sigmas) - 1 + for x in self.sigmas: + k = float(x) + delta = self.to_cfg - self.from_cfg + if self.schedule == 'exp': + self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i + elif self.schedule == 'log': + self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i + elif self.schedule == 'cos': + self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i + else: + self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i + + self.cfg_sigmas_i[i] = self.cfg_sigmas[k] + i += 1 + + def predict_noise(self, x, timestep, model_options={}, seed=None): + k = float(timestep[0]) + + v = self.cfg_sigmas.get(k) + if v is None: + # fallback + v = self.cfg_sigmas_i[self.last_i+1] + self.cfg_sigmas[k] = v + + self.last_i = v[1] + self.cfg = v[0] + + return super().predict_noise(x, timestep, model_options, seed) + + +class ScheduledCFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "sigmas": ("SIGMAS", ), + "from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "schedule": (["linear", "log", "exp", "cos"], {'default': 'log'}) + } + } + + RETURN_TYPES = ("GUIDER", "SIGMAS") + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, positive, negative, sigmas, from_cfg, to_cfg, schedule): + guider = Guider_scheduled(model, sigmas, from_cfg, to_cfg, schedule) + guider.set_conds(positive, negative) + return guider, sigmas + + +class ScheduledPerpNegCFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "empty_conditioning": ("CONDITIONING", ), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + "sigmas": ("SIGMAS", ), + "from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "schedule": (["linear", "log", "exp", "cos"], {'default': 'log'}) + } + } + + RETURN_TYPES = ("GUIDER", "SIGMAS") + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, positive, negative, empty_conditioning, neg_scale, sigmas, from_cfg, to_cfg, schedule): + guider = Guider_PerpNeg_scheduled(model, sigmas, from_cfg, to_cfg, schedule, neg_scale) + guider.set_conds(positive, negative, empty_conditioning) + return guider, sigmas + + +NODE_CLASS_MAPPINGS = { + "KSamplerProgress //Inspire": KSampler_progress, + "KSamplerAdvancedProgress //Inspire": KSamplerAdvanced_progress, + "ScheduledCFGGuider //Inspire": ScheduledCFGGuider, + "ScheduledPerpNegCFGGuider //Inspire": ScheduledPerpNegCFGGuider +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "KSamplerProgress //Inspire": "KSampler Progress (Inspire)", + "KSamplerAdvancedProgress //Inspire": "KSampler Advanced Progress (Inspire)", + "ScheduledCFGGuider //Inspire": "Scheduled CFGGuider (Inspire)", + "ScheduledPerpNegCFGGuider //Inspire": "Scheduled PerpNeg CFGGuider (Inspire)" +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/segs_support.py b/custom_nodes/comfyui-inspire-pack/inspire/segs_support.py new file mode 100644 index 00000000..9db04a56 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/segs_support.py @@ -0,0 +1,648 @@ +import nodes +import numpy as np +import torch +from .libs import utils +import logging + +def normalize_size_base_64(w, h): + short_side = min(w, h) + remainder = short_side % 64 + return short_side - remainder + (64 if remainder > 0 else 0) + + +class MediaPipeFaceMeshDetector: + def __init__(self, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil, max_faces, is_segm): + self.face = face + self.mouth = mouth + self.left_eyebrow = left_eyebrow + self.left_eye = left_eye + self.left_pupil = left_pupil + self.right_eyebrow = right_eyebrow + self.right_eye = right_eye + self.right_pupil = right_pupil + self.is_segm = is_segm + self.max_faces = max_faces + self.override_bbox_by_segm = True + + def detect(self, image, threshold, dilation, crop_factor, drop_size=1, crop_min_size=None, detailer_hook=None): + if 'MediaPipe-FaceMeshPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'MediaPipeFaceMeshDetector' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + if 'MediaPipeFaceMeshToSEGS' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack', + "To use 'MediaPipeFaceMeshDetector' node, 'Impact Pack' extension is required.") + raise Exception("[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI-Impact-Pack'") + + pre_obj = nodes.NODE_CLASS_MAPPINGS['MediaPipe-FaceMeshPreprocessor'] + seg_obj = nodes.NODE_CLASS_MAPPINGS['MediaPipeFaceMeshToSEGS'] + + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + facemesh_image = pre_obj().detect(image, self.max_faces, threshold, resolution=resolution)[0] + + facemesh_image = nodes.ImageScale().upscale(facemesh_image, "bilinear", image.shape[2], image.shape[1], "disabled")[0] + + segs = seg_obj().doit(facemesh_image, crop_factor, not self.is_segm, crop_min_size, drop_size, dilation, + self.face, self.mouth, self.left_eyebrow, self.left_eye, self.left_pupil, + self.right_eyebrow, self.right_eye, self.right_pupil)[0] + + return segs + + def setAux(self, x): + pass + + +class MediaPipe_FaceMesh_Preprocessor_wrapper: + def __init__(self, max_faces, min_confidence, upscale_factor=1.0): + self.max_faces = max_faces + self.min_confidence = min_confidence + self.upscale_factor = upscale_factor + + def apply(self, image, mask=None): + if 'MediaPipe-FaceMeshPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + if self.upscale_factor != 1.0: + image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0] + + obj = nodes.NODE_CLASS_MAPPINGS['MediaPipe-FaceMeshPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.detect(image, self.max_faces, self.min_confidence, resolution=resolution)[0] + + +class AnimeLineArt_Preprocessor_wrapper: + def apply(self, image, mask=None): + if 'AnimeLineArtPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'AnimeLineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use AnimeLineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['AnimeLineArtPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution)[0] + + +class Manga2Anime_LineArt_Preprocessor_wrapper: + def apply(self, image, mask=None): + if 'Manga2Anime_LineArt_Preprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'Manga2Anime_LineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use Manga2Anime_LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['Manga2Anime_LineArt_Preprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution)[0] + + +class Color_Preprocessor_wrapper: + def apply(self, image, mask=None): + if 'ColorPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'Color_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use Color_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['ColorPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution)[0] + + +class InpaintPreprocessor_wrapper: + def __init__(self, black_pixel_for_xinsir_cn): + self.black_pixel_for_xinsir_cn = black_pixel_for_xinsir_cn + + def apply(self, image, mask=None): + if 'InpaintPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'InpaintPreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use InpaintPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['InpaintPreprocessor']() + if mask is None: + mask = torch.ones((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu").unsqueeze(0) + + try: + res = obj.preprocess(image, mask, black_pixel_for_xinsir_cn=self.black_pixel_for_xinsir_cn)[0] + except Exception as e: + if self.black_pixel_for_xinsir_cn: + raise e + else: + res = obj.preprocess(image, mask)[0] + logging.warning("[Inspire Pack] Installed 'ComfyUI's ControlNet Auxiliary Preprocessors.' is outdated.") + + return res + + +class TilePreprocessor_wrapper: + def __init__(self, pyrUp_iters): + self.pyrUp_iters = pyrUp_iters + + def apply(self, image, mask=None): + if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'TilePreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use TilePreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, self.pyrUp_iters, resolution=resolution)[0] + + +class MeshGraphormerDepthMapPreprocessorProvider_wrapper: + def apply(self, image, mask=None): + if 'MeshGraphormer-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'MeshGraphormerDepthMapPreprocessorProvider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use MeshGraphormerDepthMapPreprocessorProvider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['MeshGraphormer-DepthMapPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution)[0] + + +class LineArt_Preprocessor_wrapper: + def __init__(self, coarse): + self.coarse = coarse + + def apply(self, image, mask=None): + if 'LineArtPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'LineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + coarse = 'enable' if self.coarse else 'disable' + + obj = nodes.NODE_CLASS_MAPPINGS['LineArtPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution, coarse=coarse)[0] + + +class OpenPose_Preprocessor_wrapper: + def __init__(self, detect_hand, detect_body, detect_face, upscale_factor=1.0): + self.detect_hand = detect_hand + self.detect_body = detect_body + self.detect_face = detect_face + self.upscale_factor = upscale_factor + + def apply(self, image, mask=None): + if 'OpenposePreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'OpenPose_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use OpenPose_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + detect_hand = 'enable' if self.detect_hand else 'disable' + detect_body = 'enable' if self.detect_body else 'disable' + detect_face = 'enable' if self.detect_face else 'disable' + + if self.upscale_factor != 1.0: + image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0] + + obj = nodes.NODE_CLASS_MAPPINGS['OpenposePreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.estimate_pose(image, detect_hand, detect_body, detect_face, resolution=resolution)['result'][0] + + +class DWPreprocessor_wrapper: + def __init__(self, detect_hand, detect_body, detect_face, upscale_factor=1.0, bbox_detector="yolox_l.onnx", pose_estimator="dw-ll_ucoco_384.onnx"): + self.detect_hand = detect_hand + self.detect_body = detect_body + self.detect_face = detect_face + self.upscale_factor = upscale_factor + self.bbox_detector = bbox_detector + self.pose_estimator = pose_estimator + + def apply(self, image, mask=None): + if 'DWPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'DWPreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use DWPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + detect_hand = 'enable' if self.detect_hand else 'disable' + detect_body = 'enable' if self.detect_body else 'disable' + detect_face = 'enable' if self.detect_face else 'disable' + + if self.upscale_factor != 1.0: + image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0] + + obj = nodes.NODE_CLASS_MAPPINGS['DWPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.estimate_pose(image, detect_hand, detect_body, detect_face, resolution=resolution, bbox_detector=self.bbox_detector, pose_estimator=self.pose_estimator)['result'][0] + + +class LeReS_DepthMap_Preprocessor_wrapper: + def __init__(self, rm_nearest, rm_background, boost): + self.rm_nearest = rm_nearest + self.rm_background = rm_background + self.boost = boost + + def apply(self, image, mask=None): + if 'LeReS-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'LeReS_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use LeReS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + boost = 'enable' if self.boost else 'disable' + + obj = nodes.NODE_CLASS_MAPPINGS['LeReS-DepthMapPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, self.rm_nearest, self.rm_background, boost=boost, resolution=resolution)[0] + + +class MiDaS_DepthMap_Preprocessor_wrapper: + def __init__(self, a, bg_threshold): + self.a = a + self.bg_threshold = bg_threshold + + def apply(self, image, mask=None): + if 'MiDaS-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'MiDaS_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use MiDaS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['MiDaS-DepthMapPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, self.a, self.bg_threshold, resolution=resolution)[0] + + +class Zoe_DepthMap_Preprocessor_wrapper: + def apply(self, image, mask=None): + if 'Zoe-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + "To use 'Zoe_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception("[ERROR] To use Zoe_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS['Zoe-DepthMapPreprocessor']() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution)[0] + + +class HED_Preprocessor_wrapper: + def __init__(self, safe, nodename): + self.safe = safe + self.nodename = nodename + + def apply(self, image, mask=None): + if self.nodename not in nodes.NODE_CLASS_MAPPINGS: + utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux', + f"To use '{self.nodename}_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.") + raise Exception(f"[ERROR] To use {self.nodename}_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'") + + obj = nodes.NODE_CLASS_MAPPINGS[self.nodename]() + resolution = normalize_size_base_64(image.shape[2], image.shape[1]) + return obj.execute(image, resolution=resolution, safe="enable" if self.safe else "disable")[0] + + +class Canny_Preprocessor_wrapper: + def __init__(self, low_threshold, high_threshold): + self.low_threshold = low_threshold + self.high_threshold = high_threshold + + def apply(self, image, mask=None): + obj = nodes.NODE_CLASS_MAPPINGS['Canny']() + if hasattr(obj, 'execute'): + # node v3 + return obj.execute(image, self.low_threshold, self.high_threshold)[0] + else: + # legacy compatibility + return obj.detect_edge(image, self.low_threshold, self.high_threshold)[0] + + +class OpenPose_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "detect_hand": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "detect_body": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "detect_face": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "resolution_upscale_by": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}), + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, detect_hand, detect_body, detect_face, resolution_upscale_by): + obj = OpenPose_Preprocessor_wrapper(detect_hand, detect_body, detect_face, upscale_factor=resolution_upscale_by) + return (obj, ) + + +class DWPreprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "detect_hand": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "detect_body": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "detect_face": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}), + "resolution_upscale_by": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}), + "bbox_detector": ( + ["yolox_l.torchscript.pt", "yolox_l.onnx", "yolo_nas_l_fp16.onnx", "yolo_nas_m_fp16.onnx", "yolo_nas_s_fp16.onnx"], + {"default": "yolox_l.onnx"} + ), + "pose_estimator": (["dw-ll_ucoco_384_bs5.torchscript.pt", "dw-ll_ucoco_384.onnx", "dw-ll_ucoco.onnx"], {"default": "dw-ll_ucoco_384_bs5.torchscript.pt"}) + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, detect_hand, detect_body, detect_face, resolution_upscale_by, bbox_detector, pose_estimator): + obj = DWPreprocessor_wrapper(detect_hand, detect_body, detect_face, upscale_factor=resolution_upscale_by, bbox_detector=bbox_detector, pose_estimator=pose_estimator) + return (obj, ) + + +class LeReS_DepthMap_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "rm_nearest": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}), + "rm_background": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}) + }, + "optional": { + "boost": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, rm_nearest, rm_background, boost=False): + obj = LeReS_DepthMap_Preprocessor_wrapper(rm_nearest, rm_background, boost) + return (obj, ) + + +class MiDaS_DepthMap_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "a": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}), + "bg_threshold": ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.05}) + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, a, bg_threshold): + obj = MiDaS_DepthMap_Preprocessor_wrapper(a, bg_threshold) + return (obj, ) + + +class Zoe_DepthMap_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { "required": {} } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self): + obj = Zoe_DepthMap_Preprocessor_wrapper() + return (obj, ) + + +class Canny_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), + "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, low_threshold, high_threshold): + obj = Canny_Preprocessor_wrapper(low_threshold, high_threshold) + return (obj, ) + + +class HEDPreprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "safe": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}) + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, safe): + obj = HED_Preprocessor_wrapper(safe, "HEDPreprocessor") + return (obj, ) + + +class FakeScribblePreprocessor_Provider_for_SEGS(HEDPreprocessor_Provider_for_SEGS): + def doit(self, safe): + obj = HED_Preprocessor_wrapper(safe, "FakeScribblePreprocessor") + return (obj, ) + + +class MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "max_faces": ("INT", {"default": 10, "min": 1, "max": 50, "step": 1}), + "min_confidence": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}), + "resolution_upscale_by": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}), + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, max_faces, min_confidence, resolution_upscale_by): + obj = MediaPipe_FaceMesh_Preprocessor_wrapper(max_faces, min_confidence, upscale_factor=resolution_upscale_by) + return (obj, ) + + +class MediaPipeFaceMeshDetectorProvider: + @classmethod + def INPUT_TYPES(s): + bool_true_widget = ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}) + bool_false_widget = ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}) + return {"required": { + "max_faces": ("INT", {"default": 10, "min": 1, "max": 50, "step": 1}), + "face": bool_true_widget, + "mouth": bool_false_widget, + "left_eyebrow": bool_false_widget, + "left_eye": bool_false_widget, + "left_pupil": bool_false_widget, + "right_eyebrow": bool_false_widget, + "right_eye": bool_false_widget, + "right_pupil": bool_false_widget, + }} + + RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR") + FUNCTION = "doit" + + CATEGORY = "InspirePack/Detector" + + def doit(self, max_faces, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil): + bbox_detector = MediaPipeFaceMeshDetector(face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil, max_faces, is_segm=False) + segm_detector = MediaPipeFaceMeshDetector(face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil, max_faces, is_segm=True) + + return (bbox_detector, segm_detector) + + +class AnimeLineArt_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self): + obj = AnimeLineArt_Preprocessor_wrapper() + return (obj, ) + + +class Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self): + obj = Manga2Anime_LineArt_Preprocessor_wrapper() + return (obj, ) + + +class LineArt_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "coarse": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + }} + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, coarse): + obj = LineArt_Preprocessor_wrapper(coarse) + return (obj, ) + + +class Color_Preprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self): + obj = Color_Preprocessor_wrapper() + return (obj, ) + + +class InpaintPreprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + "optional": { + "black_pixel_for_xinsir_cn": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}), + } + } + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, black_pixel_for_xinsir_cn=False): + obj = InpaintPreprocessor_wrapper(black_pixel_for_xinsir_cn) + return (obj, ) + + +class TilePreprocessor_Provider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {'pyrUp_iters': ("INT", {"default": 3, "min": 1, "max": 10, "step": 1})}} + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self, pyrUp_iters): + obj = TilePreprocessor_wrapper(pyrUp_iters) + return (obj, ) + + +class MeshGraphormerDepthMapPreprocessorProvider_for_SEGS: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + RETURN_TYPES = ("SEGS_PREPROCESSOR",) + FUNCTION = "doit" + + CATEGORY = "InspirePack/SEGS/ControlNet" + + def doit(self): + obj = MeshGraphormerDepthMapPreprocessorProvider_wrapper() + return (obj, ) + + +NODE_CLASS_MAPPINGS = { + "OpenPose_Preprocessor_Provider_for_SEGS //Inspire": OpenPose_Preprocessor_Provider_for_SEGS, + "DWPreprocessor_Provider_for_SEGS //Inspire": DWPreprocessor_Provider_for_SEGS, + "MiDaS_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": MiDaS_DepthMap_Preprocessor_Provider_for_SEGS, + "LeRes_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": LeReS_DepthMap_Preprocessor_Provider_for_SEGS, + # "Zoe_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": Zoe_DepthMap_Preprocessor_Provider_for_SEGS, + "Canny_Preprocessor_Provider_for_SEGS //Inspire": Canny_Preprocessor_Provider_for_SEGS, + "MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS //Inspire": MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS, + "HEDPreprocessor_Provider_for_SEGS //Inspire": HEDPreprocessor_Provider_for_SEGS, + "FakeScribblePreprocessor_Provider_for_SEGS //Inspire": FakeScribblePreprocessor_Provider_for_SEGS, + "AnimeLineArt_Preprocessor_Provider_for_SEGS //Inspire": AnimeLineArt_Preprocessor_Provider_for_SEGS, + "Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS //Inspire": Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS, + "LineArt_Preprocessor_Provider_for_SEGS //Inspire": LineArt_Preprocessor_Provider_for_SEGS, + "Color_Preprocessor_Provider_for_SEGS //Inspire": Color_Preprocessor_Provider_for_SEGS, + "InpaintPreprocessor_Provider_for_SEGS //Inspire": InpaintPreprocessor_Provider_for_SEGS, + "TilePreprocessor_Provider_for_SEGS //Inspire": TilePreprocessor_Provider_for_SEGS, + "MeshGraphormerDepthMapPreprocessorProvider_for_SEGS //Inspire": MeshGraphormerDepthMapPreprocessorProvider_for_SEGS, + "MediaPipeFaceMeshDetectorProvider //Inspire": MediaPipeFaceMeshDetectorProvider, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "OpenPose_Preprocessor_Provider_for_SEGS //Inspire": "OpenPose Preprocessor Provider (SEGS)", + "DWPreprocessor_Provider_for_SEGS //Inspire": "DWPreprocessor Provider (SEGS)", + "MiDaS_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": "MiDaS Depth Map Preprocessor Provider (SEGS)", + "LeRes_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": "LeReS Depth Map Preprocessor Provider (SEGS)", + # "Zoe_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": "Zoe Depth Map Preprocessor Provider (SEGS)", + "Canny_Preprocessor_Provider_for_SEGS //Inspire": "Canny Preprocessor Provider (SEGS)", + "MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS //Inspire": "MediaPipe FaceMesh Preprocessor Provider (SEGS)", + "HEDPreprocessor_Provider_for_SEGS //Inspire": "HED Preprocessor Provider (SEGS)", + "FakeScribblePreprocessor_Provider_for_SEGS //Inspire": "Fake Scribble Preprocessor Provider (SEGS)", + "AnimeLineArt_Preprocessor_Provider_for_SEGS //Inspire": "AnimeLineArt Preprocessor Provider (SEGS)", + "Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS //Inspire": "Manga2Anime LineArt Preprocessor Provider (SEGS)", + "LineArt_Preprocessor_Provider_for_SEGS //Inspire": "LineArt Preprocessor Provider (SEGS)", + "Color_Preprocessor_Provider_for_SEGS //Inspire": "Color Preprocessor Provider (SEGS)", + "InpaintPreprocessor_Provider_for_SEGS //Inspire": "Inpaint Preprocessor Provider (SEGS)", + "TilePreprocessor_Provider_for_SEGS //Inspire": "Tile Preprocessor Provider (SEGS)", + "MeshGraphormerDepthMapPreprocessorProvider_for_SEGS //Inspire": "MeshGraphormer Depth Map Preprocessor Provider (SEGS)", + "MediaPipeFaceMeshDetectorProvider //Inspire": "MediaPipeFaceMesh Detector Provider", +} diff --git a/custom_nodes/comfyui-inspire-pack/inspire/util_nodes.py b/custom_nodes/comfyui-inspire-pack/inspire/util_nodes.py new file mode 100644 index 00000000..6fb52126 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/inspire/util_nodes.py @@ -0,0 +1,41 @@ +import colorsys + + +def hex_to_hsv(hex_color): + hex_color = hex_color.lstrip('#') + r, g, b = tuple(int(hex_color[i:i+2], 16) / 255.0 for i in (0, 2, 4)) + + h, s, v = colorsys.rgb_to_hsv(r, g, b) + + hue = h * 360 + + saturation = s + value = v + + return hue, saturation, value + + +class RGB_HexToHSV: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "rgb_hex": ("STRING", {"defaultInput": True}), + }, + } + + RETURN_TYPES = ("FLOAT", "FLOAT", "FLOAT") + RETURN_NAMES = ("hue", "saturation", "value") + FUNCTION = "doit" + CATEGORY = "InspirePack/Util" + + def doit(self, rgb_hex): + return hex_to_hsv(rgb_hex) + + +NODE_CLASS_MAPPINGS = { + "RGB_HexToHSV //Inspire": RGB_HexToHSV, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RGB_HexToHSV //Inspire": "RGB Hex To HSV (Inspire)", +} diff --git a/custom_nodes/comfyui-inspire-pack/js/common.js b/custom_nodes/comfyui-inspire-pack/js/common.js new file mode 100644 index 00000000..290a7be1 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/common.js @@ -0,0 +1,27 @@ +import { api } from "../../scripts/api.js"; + +function nodeFeedbackHandler(event) { + let nodes = app.graph._nodes_by_id; + let node = nodes[event.detail.node_id]; + if(node) { + if(event.detail.type == "text") { + const w = node.widgets.find((w) => event.detail.widget_name === w.name); + if(w) { + w.value = event.detail.data; + } + } + } +} + +api.addEventListener("inspire-node-feedback", nodeFeedbackHandler); + + +function nodeOutputLabelHandler(event) { + let nodes = app.graph._nodes_by_id; + let node = nodes[event.detail.node_id]; + if(node) { + node.outputs[event.detail.output_idx].label = event.detail.label; + } +} + +api.addEventListener("inspire-node-output-label", nodeOutputLabelHandler); \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/image_util.js b/custom_nodes/comfyui-inspire-pack/js/image_util.js new file mode 100644 index 00000000..9730dc6d --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/image_util.js @@ -0,0 +1,71 @@ +import { ComfyApp, app } from "../../scripts/app.js"; + +function load_image(str) { + let base64String = canvas.toDataURL('image/png'); + let img = new Image(); + img.src = base64String; +} + +app.registerExtension({ + name: "Comfy.Inspire.img", + + nodeCreated(node, app) { + if(node.comfyClass == "LoadImage //Inspire") { + let w = node.widgets.find(obj => obj.name === 'image_data'); + + Object.defineProperty(w, 'value', { + set(v) { + if(v != '[IMAGE DATA]') + w._value = v; + }, + get() { + const stackTrace = new Error().stack; + if(!stackTrace.includes('draw') && !stackTrace.includes('graphToPrompt') && stackTrace.includes('app.js')) { + return "[IMAGE DATA]"; + } + else { + return w._value; + } + } + }); + + let set_img_act = (v) => { + node._img = v; + var canvas = document.createElement('canvas'); + canvas.width = v[0].width; + canvas.height = v[0].height; + + var context = canvas.getContext('2d'); + context.drawImage(v[0], 0, 0, v[0].width, v[0].height); + + var base64Image = canvas.toDataURL('image/png'); + w.value = base64Image; + }; + + Object.defineProperty(node, 'imgs', { + set(v) { + if (v && !v[0].complete) { + let orig_onload = v[0].onload; + v[0].onload = function(v2) { + if(orig_onload) + orig_onload(); + set_img_act(v); + }; + } + else { + set_img_act(v); + } + }, + get() { + if(this._img == undefined && w.value != '') { + this._img = [new Image()]; + if(w.value && w.value != '[IMAGE DATA]') + this._img[0].src = w.value; + } + + return this._img; + } + }); + } + } +}) \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/inspire-backend.js b/custom_nodes/comfyui-inspire-pack/js/inspire-backend.js new file mode 100644 index 00000000..a010443d --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/inspire-backend.js @@ -0,0 +1,36 @@ +import { api } from "../../scripts/api.js"; + +async function refresh_data(node) { + let response = await api.fetchApi('/inspire/cache/list'); + node.widgets[0].value = await response.text(); +} + +async function remove_key(node, key) { + await api.fetchApi(`/inspire/cache/remove?key=${key}`); + node.widgets[1].value = ''; + refresh_data(node); +} + +async function clear_data(node) { + await api.fetchApi('/inspire/cache/clear'); + refresh_data(node); +} + +async function set_cache_settings(node) { + await api.fetchApi('/inspire/cache/settings', { + method: "POST", + headers: {"Content-Type": "application/json",}, + body: node.widgets[0].value, + }); + refresh_data(node); +} + +export function register_cache_info(node, app) { + if(node.comfyClass == "ShowCachedInfo //Inspire") { + node.addWidget("button", "Remove Key", null, () => { remove_key(node, node.widgets[1].value); }); + node.addWidget("button", "Save Settings", null, () => { set_cache_settings(node); }); + node.addWidget("button", "Refresh", null, () => { refresh_data(node); }); + node.addWidget("button", "Clear", null, () => { clear_data(node); }); + refresh_data(node); + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/inspire-flex.js b/custom_nodes/comfyui-inspire-pack/js/inspire-flex.js new file mode 100644 index 00000000..533dcda6 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/inspire-flex.js @@ -0,0 +1,301 @@ +import { ComfyApp, app } from "../../scripts/app.js"; + +export function register_concat_conditionings_with_multiplier_node(nodeType, nodeData, app) { + if (nodeData.name === 'ConcatConditioningsWithMultiplier //Inspire') { + var input_name = "conditioning"; + + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + let this_handler = async function (type, index, connected, link_info) { + let last_state = this.state_change_handling; + try { + this.state_change_handling = true; + if(!link_info || link_info.type != 'CONDITIONING') + return; + + let self = this; + + function get_input_count(prefix, linked_only) { + let cnt = 0; + for(let i in self.inputs) { + if(linked_only && !self.inputs[i].link) + continue; + + if(self.inputs[i].name.startsWith(prefix)) + cnt+=1; + } + + return cnt; + } + + function get_widget_count(prefix) { + let cnt = 0; + for(let i in self.widgets) { + if(self.widgets[i].name.startsWith(prefix)) + cnt+=1; + } + + return cnt; + } + + function get_unconnected() { + let unconnected = []; + for(let i in self.inputs) { + let input = self.inputs[i]; + if(input.name.startsWith('conditioning')) { + if(input.link == undefined) + unconnected.push(i); + } + } + + return unconnected; + } + + let unconnected = get_unconnected(); + + function renames() { + let con_i = 1; + + let rename_map = {}; + + for(let i in self.inputs) { + let input = self.inputs[i]; + if(input.name.startsWith('conditioning')) { + let orig_i = Number(input.name.substring(12)); + if(orig_i != con_i) { + rename_map[orig_i] = con_i; + input.name = 'conditioning'+con_i; + } + con_i++; + } + } + + // update multiplier input + for(let i in self.inputs) { + let input = self.inputs[i]; + if(input.name.startsWith('multiplier')) { + let orig_i = Number(input.name.substring(10)); + if(rename_map[orig_i]) { + input.name = 'multiplier'+rename_map[orig_i]; + } + } + } + + // update multiplier widget + for(let i in self.widgets) { + let w = self.widgets[i]; + if(w.name.startsWith('multiplier')) { + let orig_i = Number(w.name.substring(10)); + if(rename_map[orig_i]) { + w.name = 'multiplier'+rename_map[orig_i]; + } + } + } + + return con_i; + } + + function remove_multiplier_link(i, link_id) { + let link = app.graph.links[link_id]; + const node = app.graph.getNodeById(link.origin_id); + let x = node.outputs[link.origin_slot].links.findIndex((w) => w == link_id); + node.outputs[link.origin_slot].links.splice(x, 1); + self.disconnectInput(i); + app.graph.links.splice(link_id, 1); + } + + async function remove_target_multiplier(target_name) { + // remove strength from slot + for(let i in self.inputs) { + let input = self.inputs[i]; + if(input.name.startsWith(target_name)) { + if(input.link) { + remove_multiplier_link(i, input.link); + } + await self.removeInput(i); + break; + } + } + + const widget_index = self.widgets.findIndex((w) => w.name == target_name); + self.widgets.splice(widget_index, 1); + } + + async function remove_garbage() { + let unconnected = get_unconnected(); + + // remove unconnected conditionings + while(unconnected.length > 0) { + let last_one = unconnected.reverse()[0]; + self.removeInput(last_one); + unconnected = get_unconnected(); + } + + // remove dangling multipliers + let conds = new Set(); + let muls = new Set(); + for(let i in self.inputs) { + let input = self.inputs[i]; + if(input.link && input.name.startsWith('conditioning')) { + let index = Number(input.name.substring(12)); + conds.add(index); + } + else if(input.name.startsWith('multiplier')) { + let index = Number(input.name.substring(10)); + muls.add(index); + } + } + for(let i in self.widgets) { + let index = Number(self.widgets[i].name.substring(10)); + muls.add(index); + } + + let dangling_muls = [...muls].filter(x => !conds.has(x)); + while(dangling_muls.length > 0) { + let remove_target = dangling_muls.pop(); + let target_name = `multiplier${remove_target}`; + await remove_target_multiplier(target_name); + } + } + + async function ensure_multipliers() { + if(self.ensuring_multipliers) { + return; + } + try { + self.ensuring_multipliers = true; + + let ncon = get_input_count('conditioning', true); + let nmul = get_input_count('multiplier', false) + get_widget_count('multiplier'); + + if(ncon == 0 && nmul == 0) + ncon = 1; + + for(let i = nmul+1; i<=ncon; i++) { + let config = { min: 0, max: 10, step: 0.1, round: 0.01, precision: 2 }; + + // NOTE: addWidget trigger calling ensure_multipliers + let widget = await self.addWidget("number", `multiplier${i}`, 1.0, function (v) { + if (config.round) { + self.value = Math.round(v/config.round)*config.round; + } else { + self.value = v; + } + }, config); + } + } + finally{ + self.ensuring_multipliers = null; + } + } + + async function recover_multipliers() { + if(self.recover_multipliers) { + return; + } + try { + self.recover_multipliers = true; + for(let i = 1; i value + 1) { + node.removeOutput(node.outputs.length-1); + } + else { + node.addOutput(`output${node.outputs.length+1}`, type); + } + } + + for(let i in node.outputs) { + let output = node.outputs[i]; + output.name = `${output_name} ${parseInt(i)+1}`; + } + + if(node.outputs[0].label == type || node.outputs[0].label == 'remained') + delete node.outputs[0].label; + + + let last_output = node.outputs[node.outputs.length-1]; + last_output.name = 'remained'; + } +} + +export function register_splitter(node, app) { + if(node.comfyClass === 'ImageBatchSplitter //Inspire' || node.comfyClass === 'LatentBatchSplitter //Inspire') { + let split_count = node.widgets[0]; + + let output_name = 'output'; + let output_type = "*"; + + if(node.comfyClass === 'ImageBatchSplitter //Inspire') { + output_name = 'image'; + output_type = "IMAGE"; + } + else if(node.comfyClass === 'LatentBatchSplitter //Inspire') { + output_name = 'latent'; + output_type = "LATENT"; + } + + ensure_splitter_outputs(node, output_name, split_count.value, output_type); + + Object.defineProperty(split_count, "value", { + set: async function(value) { + if(value < 0 || value > 50) + return; + + ensure_splitter_outputs(node, output_name, value, output_type); + }, + get: function() { + return node.outputs.length - 1; + } + }); + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/inspire-loop.js b/custom_nodes/comfyui-inspire-pack/js/inspire-loop.js new file mode 100644 index 00000000..4dd12cf9 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/inspire-loop.js @@ -0,0 +1,7 @@ +import { inspireProgressBadge } from "./progress-badge.js" + +export function register_loop_node(nodeType, nodeData, app) { + if(nodeData.name == 'ForeachListEnd //Inspire') { + inspireProgressBadge.addStatusHandler(nodeType); + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/inspire-pack.js b/custom_nodes/comfyui-inspire-pack/js/inspire-pack.js new file mode 100644 index 00000000..e9968842 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/inspire-pack.js @@ -0,0 +1,17 @@ +import { ComfyApp, app } from "../../scripts/app.js"; +import { register_concat_conditionings_with_multiplier_node, register_splitter } from "./inspire-flex.js"; +import { register_cache_info } from "./inspire-backend.js"; +import { register_loop_node } from "./inspire-loop.js"; + +app.registerExtension({ + name: "Comfy.Inspire", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + await register_concat_conditionings_with_multiplier_node(nodeType, nodeData, app); + await register_loop_node(nodeType, nodeData, app); + }, + + nodeCreated(node, app) { + register_cache_info(node, app); + register_splitter(node, app); + } +}) \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/lora_block_weight.js b/custom_nodes/comfyui-inspire-pack/js/lora_block_weight.js new file mode 100644 index 00000000..3f826fc7 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/lora_block_weight.js @@ -0,0 +1,165 @@ +import { ComfyApp, app } from "../../scripts/app.js"; + +app.registerExtension({ + name: "Comfy.Inspire.LBW", + + nodeCreated(node, app) { + if(node.comfyClass == "LoraLoaderBlockWeight //Inspire" || node.comfyClass == "MakeLBW //Inspire") { + // category filter + const lora_names_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'lora_name')]; + var full_lora_list = lora_names_widget.options.values; + const category_filter_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'category_filter')]; + + Object.defineProperty(lora_names_widget.options, "values", { + set: (x) => { + full_lora_list = x; + }, + get: () => { + if(category_filter_widget.value == 'All') + return full_lora_list; + + let l = full_lora_list.filter(x => x.startsWith(category_filter_widget.value)); + return l; + } + }); + + // vector selector + let preset_i = 9; + let vector_i = 10; + + if(node.comfyClass == "MakeLBW //Inspire") { + preset_i = 7; + vector_i = 8; + } + + node._value = "Preset"; + + node.widgets[preset_i].callback = (v, canvas, node, pos, e) => { + node.widgets[vector_i].value = node._value.split(':')[1]; + if(node.widgets_values) { + node.widgets_values[vector_i] = node.widgets[preset_i].value; + } + } + + Object.defineProperty(node.widgets[preset_i], "value", { + set: (value) => { + if(value != "Preset") + node._value = value; + }, + get: () => { + return node._value; + } + }); + } + + if(node.comfyClass == "XY Input: Lora Block Weight //Inspire") { + // category filter + const lora_names_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'lora_name')]; + var full_lora_list = lora_names_widget.options.values; + const category_filter_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'category_filter')]; + + Object.defineProperty(lora_names_widget.options, "values", { + set: (x) => { + full_lora_list = x; + }, + get: () => { + if(category_filter_widget.value == 'All') + return full_lora_list; + + let l = full_lora_list.filter(x => x.startsWith(category_filter_widget.value)); + return l; + } + }); + + // vector selector + let preset_i = 9; + let vector_i = 10; + node._value = "Preset"; + + node.widgets[preset_i].callback = (v, canvas, node, pos, e) => { + let value = node._value; + if(!value.startsWith('@') && node.widgets[vector_i].value != "") + node.widgets[vector_i].value += "\n"; + if(value.startsWith('@')) { + let spec = value.split(':')[1]; + var n; + var sub_n = null; + var block = null; + + if(isNaN(spec)) { + let sub_spec = spec.split(','); + + if(sub_spec.length != 3) { + node.widgets_values[vector_i] = '!! SPEC ERROR !!'; + node._value = ''; + return; + } + + n = parseInt(sub_spec[0].trim()); + sub_n = parseInt(sub_spec[1].trim()); + block = parseInt(sub_spec[2].trim()); + } + else { + n = parseInt(spec.trim()); + } + + node.widgets[vector_i].value = ""; + if(sub_n == null) { + for(let i=1; i<=n; i++) { + var temp = ""; + for(let j=1; j<=n; j++) { + if(temp!='') + temp += ','; + if(j==i) + temp += 'A'; + else + temp += '0'; + } + + node.widgets[vector_i].value += `B${i}:${temp}\n`; + } + } + else { + for(let i=1; i<=sub_n; i++) { + var temp = ""; + for(let j=1; j<=n; j++) { + if(temp!='') + temp += ','; + + if(block!=j) + temp += '0'; + else { + temp += ' '; + for(let k=1; k<=sub_n; k++) { + if(k==i) + temp += 'A '; + else + temp += '0 '; + } + } + } + + node.widgets[vector_i].value += `B${block}.SUB${i}:${temp}\n`; + } + } + } + else { + node.widgets[vector_i].value += `${value}/${value.split(':')[0]}`; + } + if(node.widgets_values) { + node.widgets_values[vector_i] = node.widgets[preset_i].value; + } + } + + Object.defineProperty(node.widgets[preset_i], "value", { + set: (value) => { + if(value != 'Preset') + node._value = value; + }, + get: () => { + return node._value; + } + }); + } + } +}); \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/progress-badge.js b/custom_nodes/comfyui-inspire-pack/js/progress-badge.js new file mode 100644 index 00000000..8df62aa9 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/progress-badge.js @@ -0,0 +1,76 @@ +import { api } from "../../scripts/api.js"; + +// copying from https://github.com/pythongosssss/ComfyUI-WD14-Tagger +class InspireProgressBadge { + constructor() { + if (!window.__progress_badge__) { + window.__progress_badge__ = Symbol("__inspire_progress_badge__"); + } + this.symbol = window.__progress_badge__; + } + + getState(node) { + return node[this.symbol] || {}; + } + + setState(node, state) { + node[this.symbol] = state; + app.canvas.setDirty(true); + } + + addStatusHandler(nodeType) { + if (nodeType[this.symbol]?.statusTagHandler) { + return; + } + if (!nodeType[this.symbol]) { + nodeType[this.symbol] = {}; + } + nodeType[this.symbol] = { + statusTagHandler: true, + }; + + api.addEventListener("inspire/update_status", ({ detail }) => { + let { node, progress, text } = detail; + const n = app.graph.getNodeById(+(node || app.runningNodeId)); + if (!n) return; + const state = this.getState(n); + state.status = Object.assign(state.status || {}, { progress: text ? progress : null, text: text || null }); + this.setState(n, state); + }); + + const self = this; + const onDrawForeground = nodeType.prototype.onDrawForeground; + nodeType.prototype.onDrawForeground = function (ctx) { + const r = onDrawForeground?.apply?.(this, arguments); + const state = self.getState(this); + if (!state?.status?.text) { + return r; + } + + const { fgColor, bgColor, text, progress, progressColor } = { ...state.status }; + + ctx.save(); + ctx.font = "12px sans-serif"; + const sz = ctx.measureText(text); + ctx.fillStyle = bgColor || "dodgerblue"; + ctx.beginPath(); + ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, sz.width + 12, 20, 5); + ctx.fill(); + + if (progress) { + ctx.fillStyle = progressColor || "green"; + ctx.beginPath(); + ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, (sz.width + 12) * progress, 20, 5); + ctx.fill(); + } + + ctx.fillStyle = fgColor || "#fff"; + ctx.fillText(text, 6, -LiteGraph.NODE_TITLE_HEIGHT - 6); + ctx.restore(); + return r; + }; + } +} + +export const inspireProgressBadge = new InspireProgressBadge(); + diff --git a/custom_nodes/comfyui-inspire-pack/js/prompt.js b/custom_nodes/comfyui-inspire-pack/js/prompt.js new file mode 100644 index 00000000..4e84d4ee --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/prompt.js @@ -0,0 +1,339 @@ +import { ComfyApp, app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +let get_wildcards_list; +let get_wildcard_label; +let is_wildcard_label; +let load_wildcard_status; + +try { + const ImpactPack = await import("../ComfyUI-Impact-Pack/impact-pack.js"); + console.log("[Inspire Pack] Impact Pack module loaded:", ImpactPack); + get_wildcards_list = ImpactPack.get_wildcards_list; + get_wildcard_label = ImpactPack.get_wildcard_label; + is_wildcard_label = ImpactPack.is_wildcard_label; + load_wildcard_status = ImpactPack.load_wildcard_status; + console.log("[Inspire Pack] Functions imported:", { + get_wildcards_list: !!get_wildcards_list, + get_wildcard_label: !!get_wildcard_label, + is_wildcard_label: !!is_wildcard_label, + load_wildcard_status: !!load_wildcard_status + }); +} +catch (error) { + console.error("[Inspire Pack] Failed to import Impact Pack module:", error); +} + +// Fallback for get_wildcards_list +if(!get_wildcards_list) { + console.warn("[Inspire Pack] get_wildcards_list not available. Using fallback."); + get_wildcards_list = () => { + return ["Impact Pack isn't installed or needs browser cache refresh."]; + } +} + +// Fallback for on-demand features (backward compatibility with older Impact Pack) +if(!get_wildcard_label) { + get_wildcard_label = () => { return "Select the Wildcard to add to the text"; }; +} +if(!is_wildcard_label) { + is_wildcard_label = (value) => { return value === "Select the Wildcard to add to the text"; }; +} +if(!load_wildcard_status) { + load_wildcard_status = async () => {}; // No-op for older versions +} + +let pb_cache = {}; + +async function get_prompt_builder_items(category) { + if(pb_cache[category]) + return pb_cache[category]; + else { + let res = await api.fetchApi(`/inspire/prompt_builder?category=${category}`); + let data = await res.json(); + pb_cache[category] = data.presets; + return data.presets; + } +} + + +app.registerExtension({ + name: "Comfy.Inspire.Prompts", + + nodeCreated(node, app) { + if(node.comfyClass == "WildcardEncode //Inspire") { + const wildcard_text_widget_index = node.widgets.findIndex((w) => w.name == 'wildcard_text'); + const populated_text_widget_index = node.widgets.findIndex((w) => w.name == 'populated_text'); + const mode_widget_index = node.widgets.findIndex((w) => w.name == 'mode'); + + const wildcard_text_widget = node.widgets[wildcard_text_widget_index]; + const populated_text_widget = node.widgets[populated_text_widget_index]; + + // lora selector, wildcard selector + let combo_id = 5; + + // lora + node.widgets[combo_id].callback = (value, canvas, node, pos, e) => { + let lora_name = node._value; + if(lora_name.endsWith('.safetensors')) { + lora_name = lora_name.slice(0, -12); + } + + wildcard_text_widget.value += ``; + } + + Object.defineProperty(node.widgets[combo_id], "value", { + set: (value) => { + if (value !== "Select the LoRA to add to the text") + node._value = value; + }, + + get: () => { return "Select the LoRA to add to the text"; } + }); + + // wildcard + node.widgets[combo_id+1].callback = async (value, canvas, node, pos, e) => { + if(wildcard_text_widget.value != '') + wildcard_text_widget.value += ', ' + + wildcard_text_widget.value += node._wildcard_value; + + // Reload wildcard status to update loaded count (Impact Pack staged feature) + await load_wildcard_status(); + app.canvas.setDirty(true); + } + + Object.defineProperty(node.widgets[combo_id+1], "value", { + set: (value) => { + if (!is_wildcard_label(value)) + node._wildcard_value = value; + }, + get: () => { return get_wildcard_label(); } + }); + + Object.defineProperty(node.widgets[combo_id+1].options, "values", { + set: (x) => {}, + get: () => { + return get_wildcards_list(); + } + }); + + // Preventing validation errors from occurring in any situation. + node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; } + node.widgets[combo_id+1].serializeValue = () => { + // Always serialize as the default label (not the dynamic on-demand label) + return "Select the Wildcard to add to the text"; + } + + // wildcard populating + populated_text_widget.inputEl.disabled = true; + const mode_widget = node.widgets[mode_widget_index]; + + // mode combo + Object.defineProperty(mode_widget, "value", { + set: (value) => { + if(value == true) + node._mode_value = "populate"; + else if(value == false) + node._mode_value = "fixed"; + else + node._mode_value = value; // combo value + + populated_text_widget.inputEl.disabled = node._mode_value == 'populate'; + }, + get: () => { + if(node._mode_value != undefined) + return node._mode_value; + else + return 'populate'; + } + }); + } + else if(node.comfyClass == "MakeBasicPipe //Inspire") { + const pos_wildcard_text_widget = node.widgets.find((w) => w.name == 'positive_wildcard_text'); + const pos_populated_text_widget = node.widgets.find((w) => w.name == 'positive_populated_text'); + const neg_wildcard_text_widget = node.widgets.find((w) => w.name == 'negative_wildcard_text'); + const neg_populated_text_widget = node.widgets.find((w) => w.name == 'negative_populated_text'); + + const mode_widget = node.widgets.find((w) => w.name == 'wildcard_mode'); + const direction_widget = node.widgets.find((w) => w.name == 'Add selection to'); + + // lora selector, wildcard selector + let combo_id = 5; + + node.widgets[combo_id].callback = (value, canvas, node, pos, e) => { + let lora_name = node._lora_value; + if (lora_name.endsWith('.safetensors')) { + lora_name = lora_name.slice(0, -12); + } + + if(direction_widget.value) { + pos_wildcard_text_widget.value += ``; + } + else { + neg_wildcard_text_widget.value += ``; + } + } + Object.defineProperty(node.widgets[combo_id], "value", { + set: (value) => { + if (value !== "Select the LoRA to add to the text") + node._lora_value = value; + }, + get: () => { return "Select the LoRA to add to the text"; } + }); + + node.widgets[combo_id+1].callback = async (value, canvas, node, pos, e) => { + let w = null; + if(direction_widget.value) { + w = pos_wildcard_text_widget; + } + else { + w = neg_wildcard_text_widget; + } + + if(w.value != '') + w.value += ', ' + + w.value += node._wildcard_value; + + // Reload wildcard status to update loaded count (Impact Pack staged feature) + await load_wildcard_status(); + app.canvas.setDirty(true); + } + + Object.defineProperty(node.widgets[combo_id+1], "value", { + set: (value) => { + if (!is_wildcard_label(value)) + node._wildcard_value = value; + }, + get: () => { return get_wildcard_label(); } + }); + + Object.defineProperty(node.widgets[combo_id+1].options, "values", { + set: (x) => {}, + get: () => { + return get_wildcards_list(); + } + }); + + // Preventing validation errors from occurring in any situation. + node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; } + node.widgets[combo_id+1].serializeValue = () => { + // Always serialize as the default label (not the dynamic on-demand label) + return "Select the Wildcard to add to the text"; + } + + // wildcard populating + pos_populated_text_widget.inputEl.disabled = true; + neg_populated_text_widget.inputEl.disabled = true; + + // mode combo + Object.defineProperty(mode_widget, "value", { + set: (value) => { + if(value == true) + node._mode_value = "populate"; + else if(value == false) + node._mode_value = "fixed"; + else + node._mode_value = value; // combo value + + pos_populated_text_widget.inputEl.disabled = node._mode_value == 'populate'; + neg_populated_text_widget.inputEl.disabled = node._mode_value == 'populate'; + }, + get: () => { + if(node._mode_value != undefined) + return node._mode_value; + else + return 'populate'; + } + }); + } + else if(node.comfyClass == "PromptBuilder //Inspire") { + const preset_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'preset')]; + const category_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'category')]; + + Object.defineProperty(preset_widget.options, "values", { + set: (x) => {}, + get: () => { + get_prompt_builder_items(category_widget.value); + if(pb_cache[category_widget.value] == undefined) { + return ["#PRESET"]; + } + return pb_cache[category_widget.value]; + } + }); + + preset_widget.callback = (value, canvas, node, pos, e) => { + if(node.widgets[2].value) { + node.widgets[2].value += ', '; + } + + const y = node._preset_value.split(':'); + if(y.length == 2) + node.widgets[2].value += y[1].trim(); + else + node.widgets[2].value += node._preset_value.trim(); + } + + Object.defineProperty(preset_widget, "value", { + set: (value) => { + if (value !== "#PRESET") + node._preset_value = value; + }, + get: () => { return '#PRESET'; } + }); + + preset_widget.serializeValue = (workflowNode, widgetIndex) => { return "#PRESET"; }; + } + else if(node.comfyClass == "SeedExplorer //Inspire" + || node.comfyClass == "RegionalSeedExplorerMask //Inspire" + || node.comfyClass == "RegionalSeedExplorerColorMask //Inspire") { + const prompt_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'seed_prompt')]; + const seed_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'additional_seed')]; + const strength_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'additional_strength')]; + + let allow_init_seed = node.comfyClass == "SeedExplorer //Inspire"; + + node.addWidget("button", "Add to prompt", null, () => { + if(!prompt_widget.value?.trim() && allow_init_seed) { + prompt_widget.value = ''+seed_widget.value; + } + else { + if(prompt_widget.value?.trim()) + prompt_widget.value += ', '; + + prompt_widget.value += `${seed_widget.value}:${strength_widget.value.toFixed(2)}`; + seed_widget.value += 1; + } + }); + } + } +}); + + + + +const original_queuePrompt = api.queuePrompt; +async function queuePrompt_with_widget_idxs(number, { output, workflow }, ...args) { + workflow.widget_idx_map = {}; + + for(let i in app.graph._nodes_by_id) { + let widgets = app.graph._nodes_by_id[i].widgets; + if(widgets) { + for(let j in widgets) { + if(['seed', 'noise_seed', 'sampler_name', 'scheduler'].includes(widgets[j].name) + && widgets[j].type != 'converted-widget') { + if(workflow.widget_idx_map[i] == undefined) { + workflow.widget_idx_map[i] = {}; + } + + workflow.widget_idx_map[i][widgets[j].name] = parseInt(j); + } + } + } + } + + return await original_queuePrompt.call(api, number, { output, workflow }, ...args); +} + +api.queuePrompt = queuePrompt_with_widget_idxs; \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/regional.js b/custom_nodes/comfyui-inspire-pack/js/regional.js new file mode 100644 index 00000000..22162dc4 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/regional.js @@ -0,0 +1,64 @@ +import { ComfyApp, app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { api } from "../../scripts/api.js"; + +app.registerExtension({ + name: "Comfy.Inspire.Regional", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === 'ApplyRegionalIPAdapters //Inspire') { + var input_name = "input"; + var base_slot = 0; + + switch(nodeData.name) { + case 'ApplyRegionalIPAdapters //Inspire': + input_name = "regional_ipadapter"; + base_slot = 1; + break; + } + + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) { + if(!link_info || type == 2) + return; + + if(this.inputs[0].type == '*'){ + const node = app.graph.getNodeById(link_info.origin_id); + let origin_type = node.outputs[link_info.origin_slot].type; + + if(origin_type == '*') { + this.disconnectInput(link_info.target_slot); + return; + } + + for(let i in this.inputs) { + let input_i = this.inputs[i]; + if(input_i.name != 'select' && input_i.name != 'sel_mode') + input_i.type = origin_type; + } + } + + if (!connected && (this.inputs.length > base_slot+1)) { + const stackTrace = new Error().stack; + + if( + !stackTrace.includes('LGraphNode.prototype.connect') && // for touch device + !stackTrace.includes('LGraphNode.connect') && // for mouse device + !stackTrace.includes('loadGraphData')) { + this.removeInput(index); + } + } + + let slot_i = 1; + for (let i = base_slot; i < this.inputs.length; i++) { + let input_i = this.inputs[i]; + input_i.name = `${input_name}${slot_i}` + slot_i++; + } + + let last_slot = this.inputs[this.inputs.length - 1]; + if (last_slot.link != undefined) { + this.addInput(`${input_name}${slot_i}`, this.inputs[base_slot].type); + } + } + } + }}); \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/js/seed.js b/custom_nodes/comfyui-inspire-pack/js/seed.js new file mode 100644 index 00000000..1e660530 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/js/seed.js @@ -0,0 +1,28 @@ +import { api } from "../../scripts/api.js"; + +function globalSeedHandler(event) { + let nodes = app.graph._nodes_by_id; + + for(let i in nodes) { + let node = nodes[i]; + + if(node.type == 'GlobalSeed //Inspire') { + if(node.widgets) { + const w = node.widgets.find((w) => w.name == 'value'); + const last_w = node.widgets.find((w) => w.name == 'last_seed'); + last_w.value = w.value; + if(event.detail.value != null) + w.value = event.detail.value; + } + } + else + if(node.widgets) { + const w = node.widgets.find((w) => (w.name == 'seed' || w.name == 'noise_seed') && w.type == 'number'); + if(w && event.detail.seed_map[node.id] != undefined) { + w.value = event.detail.seed_map[node.id]; + } + } + } +} + +api.addEventListener("inspire-global-seed", globalSeedHandler); \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/prompts/example/prompt1.txt b/custom_nodes/comfyui-inspire-pack/prompts/example/prompt1.txt new file mode 100644 index 00000000..0eed7fb5 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/prompts/example/prompt1.txt @@ -0,0 +1,2 @@ +positive:beautiful scenery nature glass bottle landscape, , purple galaxy bottle, +negative:text, watermark \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/prompts/example/prompt2.txt b/custom_nodes/comfyui-inspire-pack/prompts/example/prompt2.txt new file mode 100644 index 00000000..4ec73eb3 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/prompts/example/prompt2.txt @@ -0,0 +1,12 @@ +positive:1girl is walking through street, +raincoat, yellow umbrella + +negative:text, watermark +----------------- +positive:museum, people are looking paintings, abstract + +negative:text, watermark +---- +positive:battle ground of space ships + +negative:text, watermark \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/prompts/put_prompts_dirs_here b/custom_nodes/comfyui-inspire-pack/prompts/put_prompts_dirs_here new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui-inspire-pack/pyproject.toml b/custom_nodes/comfyui-inspire-pack/pyproject.toml new file mode 100644 index 00000000..8d39e483 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-inspire-pack" +description = "This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils, Noise(Seed) Utils, ... and the Impact Pack." +version = "1.23" +license = { file = "LICENSE" } +dependencies = ["matplotlib", "cachetools"] + +[project.urls] +Repository = "https://github.com/ltdrdata/ComfyUI-Inspire-Pack" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "drltdata" +DisplayName = "ComfyUI Inspire Pack" +Icon = "" diff --git a/custom_nodes/comfyui-inspire-pack/requirements.txt b/custom_nodes/comfyui-inspire-pack/requirements.txt new file mode 100644 index 00000000..2bcaf2e3 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/requirements.txt @@ -0,0 +1,5 @@ +matplotlib +cachetools +numpy +webcolors +opencv-python diff --git a/custom_nodes/comfyui-inspire-pack/resources/lbw-preset.custom.txt.example b/custom_nodes/comfyui-inspire-pack/resources/lbw-preset.custom.txt.example new file mode 100644 index 00000000..be038224 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/resources/lbw-preset.custom.txt.example @@ -0,0 +1,20 @@ +SD-BODY:1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1 +SD-BODY0.5:1,1,1,1,1,1,0.2,1,0.2,0,0,0.8,1,1,1,1,1 +SD-FACE:1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0 +SD-FACE0.5:1,0,0,0,0,0,0,0,0.8,1,1,0.2,0,0,0,0,0 +SD-FACE0.2:1,0,0,0,0,0,0,0,0.2,0.6,0.8,0.2,0,0,0,0,0 +SD-HAND:1,0,1,1,0.2,0,0,0,0,0,0,0,0,0,0,0,0 +SD-CLOTHING:1,1,1,1,1,0,0.2,0,0.8,1,1,0.2,0,0,0,0,0 +SD-POSE:1,0,0,0,0,0,0.2,1,1,1,0,0,0,0,0,0,0 +SD-PALETTE:1,0,0,0,0,0,0,0,0,0,0,0.8,1,1,1,1,1 +SD-KEEPCHAR:1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0 +SD-KEEPBG:1,1,1,1,1,1,0.2,1,0.2,0,0,0.8,1,1,1,0,0 +SD-REDUCEFIT:1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +SD-LyCOBODY:1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1 +SD-LyCOBODY0.5:1,1,1,1,1,1,1,1,1,0.2,0.2,0.5,1,1,0,0,0,0.2,0,0,0.8,1,1,1,1 +SD-LyCOFACE:1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0 +SD-LyCOFACE0.5:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0.5,0.8,1,1,1,0.2,0,0,0,0,0 +SD-LyCOCLOTH:1,1,1,1,1,1,1,1,0,0.2,0.2,0.2,0,0,0,0,0.5,0.8,1,1,0.2,0,0,0,0,0 +SD-LyCOPOSE:1,0,0,0,0,0,0,0,0,0.2,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0 +SD-LyCOKEEPBG:1,1,1,1,1,1,1,1,1,0.2,0.4,0.8,1,1,0.8,0.4,0.2,0.2,0,0,0.8,1,1,1,0,0 +SD-LyCOPALETTE:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,1,1,1,1,1 \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/resources/lbw-preset.txt b/custom_nodes/comfyui-inspire-pack/resources/lbw-preset.txt new file mode 100644 index 00000000..ccc2f534 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/resources/lbw-preset.txt @@ -0,0 +1,122 @@ +SD-NONE:0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +SD-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +SD-INS:1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +SD-IND:1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0 +SD-INALL:1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0 +SD-MIDD:1,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0 +SD-MIDD0.2:1,0,0,0,0,0,0.2,0.4,0.4,0.2,0,0,0,0,0,0,0 +SD-MIDD0.8:1,0,0,0,0,0.5,0.8,0.8,0.4,0,0,0,0,0,0,0,0 +SD-MOUT:1,0,0,0,0,0,1,1,1,1,1,1,1,1,0.5,0,0 +SD-OUTD:1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0 +SD-OUTS:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1 +SD-OUTALL:1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +SD-ROUT:1,1,1,1,1,1,1,1,R,R,R,R,R,R,R,R,R +SD-AOUT:A,1,1,1,1,1,1,1,1,1,1,1,A,A,A,A,A +SD-AB:A,B,B,B,B,B,B,B,B,B,B,B,A,A,A,A,A +SD-ALL0.5:0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5 +SD-LyC-NONE:0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +SD-LyC-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +SD-LyC-INALL:1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +SD-LyC-MIDALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0 +SD-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1 +SDXL-NONE:0,0,0,0,0,0,0,0,0,0,0,0 +SDXL-ALL:1,1,1,1,1,1,1,1,1,1,1,1 +SDXL-INALL:1,1,1,1,1,0,0,0,0,0,0,0 +SDXL-MIDALL:1,0,0,0,0,1,0,0,0,0,0,0 +SDXL-OUTALL:1,0,0,0,0,0,1,1,1,1,1,1 +SDXL-LyC-NONE:0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +SDXL-LyC-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +SDXL-LyC-INALL:1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0 +SDXL-LyC-MIDALL:1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0 +SDXL-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +FLUX-DBL-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +FLUX-DBL-FRONT7:1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0 +FLUX-DBL-MID6:1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0 +FLUX-DBL-TAIL6:1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1 +FLUX-SINGLE-ALL:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +FLUX-SINGLE-1to10:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +FLUX-SINGLE-11to20:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +FLUX-SINGLE-21to30:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0 +FLUX-SINGLE-31to37:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1 +FLUX-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +@SD-FULL-TEST:17 +@SD-BLOCK1-TEST:17,12,1 +@SD-BLOCK2-TEST:17,12,2 +@SD-BLOCK3-TEST:17,12,3 +@SD-BLOCK4-TEST:17,12,4 +@SD-BLOCK5-TEST:17,12,5 +@SD-BLOCK6-TEST:17,12,6 +@SD-BLOCK7-TEST:17,12,7 +@SD-BLOCK8-TEST:17,12,8 +@SD-BLOCK9-TEST:17,12,9 +@SD-BLOCK10-TEST:17,12,10 +@SD-BLOCK11-TEST:17,12,11 +@SD-BLOCK12-TEST:17,12,12 +@SD-BLOCK13-TEST:17,12,13 +@SD-BLOCK14-TEST:17,12,14 +@SD-BLOCK15-TEST:17,12,15 +@SD-BLOCK16-TEST:17,12,16 +@SD-BLOCK17-TEST:17,12,17 +@SD-LyC-FULL-TEST:27 +@SDXL-FULL-TEST:12 +@SDXL-LyC-FULL-TEST:21 +@FLUX-DBL-FULL:19 +@FLUX-DBL-SGL-FULL:58 +@FLUX-DBL0-TEST:19,14,2 +@FLUX-DBL1-TEST:19,14,3 +@FLUX-DBL2-TEST:19,14,4 +@FLUX-DBL3-TEST:19,14,5 +@FLUX-DBL4-TEST:19,14,6 +@FLUX-DBL5-TEST:19,14,7 +@FLUX-DBL6-TEST:19,14,8 +@FLUX-DBL7-TEST:19,14,9 +@FLUX-DBL8-TEST:19,14,10 +@FLUX-DBL9-TEST:19,14,11 +@FLUX-DBL10-TEST:19,14,12 +@FLUX-DBL11-TEST:19,14,13 +@FLUX-DBL12-TEST:19,14,14 +@FLUX-DBL13-TEST:19,14,15 +@FLUX-DBL14-TEST:19,14,16 +@FLUX-DBL15-TEST:19,14,17 +@FLUX-DBL16-TEST:19,14,18 +@FLUX-DBL17-TEST:19,14,19 +@FLUX-DBL18-TEST:19,14,20 +@FLUX-SGL0-TEST:58,6,21 +@FLUX-SGL1-TEST:58,6,22 +@FLUX-SGL2-TEST:58,6,23 +@FLUX-SGL3-TEST:58,6,24 +@FLUX-SGL4-TEST:58,6,25 +@FLUX-SGL5-TEST:58,6,26 +@FLUX-SGL6-TEST:58,6,27 +@FLUX-SGL7-TEST:58,6,28 +@FLUX-SGL8-TEST:58,6,29 +@FLUX-SGL9-TEST:58,6,30 +@FLUX-SGL10-TEST:58,6,31 +@FLUX-SGL11-TEST:58,6,32 +@FLUX-SGL12-TEST:58,6,33 +@FLUX-SGL13-TEST:58,6,34 +@FLUX-SGL14-TEST:58,6,35 +@FLUX-SGL15-TEST:58,6,36 +@FLUX-SGL16-TEST:58,6,37 +@FLUX-SGL17-TEST:58,6,38 +@FLUX-SGL18-TEST:58,6,39 +@FLUX-SGL19-TEST:58,6,40 +@FLUX-SGL20-TEST:58,6,41 +@FLUX-SGL21-TEST:58,6,42 +@FLUX-SGL22-TEST:58,6,43 +@FLUX-SGL23-TEST:58,6,44 +@FLUX-SGL24-TEST:58,6,45 +@FLUX-SGL25-TEST:58,6,46 +@FLUX-SGL26-TEST:58,6,47 +@FLUX-SGL27-TEST:58,6,48 +@FLUX-SGL28-TEST:58,6,49 +@FLUX-SGL29-TEST:58,6,50 +@FLUX-SGL30-TEST:58,6,51 +@FLUX-SGL31-TEST:58,6,52 +@FLUX-SGL32-TEST:58,6,53 +@FLUX-SGL33-TEST:58,6,54 +@FLUX-SGL34-TEST:58,6,55 +@FLUX-SGL35-TEST:58,6,56 +@FLUX-SGL36-TEST:58,6,57 +@FLUX-SGL37-TEST:58,6,58 +@FLUX-SGL38-TEST:58,6,59 \ No newline at end of file diff --git a/custom_nodes/comfyui-inspire-pack/resources/prompt-builder.yaml.example b/custom_nodes/comfyui-inspire-pack/resources/prompt-builder.yaml.example new file mode 100644 index 00000000..f69af658 --- /dev/null +++ b/custom_nodes/comfyui-inspire-pack/resources/prompt-builder.yaml.example @@ -0,0 +1,1048 @@ +#Category: + #Input + +Angle of View: + - Atmospheric Perspective + - Blurry Foreground + - Close-Up + - Cowboy Shot + - Cut-In + - Dutch Angle + - First-Person View + - Fisheye + - From Above + - From Behind + - From Below + - From Outside + - From Side + - Hatching (Texture) + - Multiple Views + - Panorama + - Perspective + - Pov + - Rotated + - Sideways + - Three Sided View + - Upside-Down + - Vanishing Point + - Wide Shot + +Artists: + - Abanindranath Tagore + - Abdur Rahman Chughtai + - Abu'L Hasan + - Adolf WΓΆlfli + - Agnes Denes + - Agnes Martin + - Agnolo Di Cosimo (Bronzino) + - Ai Weiwei + - Albert Bierstadt + - Albert Gleizes + - Albert Pinkham Ryder + - Alberto Burri + - Alberto Giacometti + - Albrecht DΓΌrer + - Alexander Calder + - Alexander Gerasimov + - Alexander Kanoldt + - Alexander Rodchenko + - Alfred Wallis + - Alice Neel + - AloΓ―se Corbaz + - Alphonse Mucha + - Ambrogio Lorenzetti + - Amedeo Modigliani + - Amrita Sher-Gil + - AndrΓ© Derain + - Andrei Rublev + - Andy Goldsworthy + - Andy Warhol + - Angelica Kauffman + - Anna Banana + - Ansel Adams + - Anselm Kiefer + - Anthony Van Dyck + - Antoni Gaudi + - Antonio Canova + - Apelles + - Arkady Plastov + - Artemisia Gentileschi + - Artists Of Hagia Sophia + - Asger Jorn + - Asher B. Durand + - Asit Kumar Haldar + - Aubrey Beardsley + - Audrey Flack + - Auguste Rodin + - Augustus Pugin + - Banksy + - Barbara Kruger + - Barberini Faun + - Bart Van Der Leck + - Baseera Khan + - Becky Lloyd + - Ben Shahn + - Benedetto Antelami + - Bichitr + - Bill Traylor + - Bridget Riley + - Bruce Nauman + - Camille Claudel + - Camille Pissarro + - Caravaggio + - Carel Willink + - Carl Andre + - Carlo CarrΓ  + - Caspar David Friedrich + - Charles Demuth + - Charles Sheeler + - Chiho Aoshima + - Childe Hassam + - Chuck Close + - Cimabue + - Cindy Sherman + - Claude Monet + - Clementine Hunter + - Clyfford Still + - Constant Nieuwenhuys + - Cornelis Norbertus Gysbrechts + - Cory Arcangel + - Dante Gabriel Rossetti + - David Hockney + - Diego Rivera + - Diego VelΓ‘zquez + - Donald Judd + - Doris Salcedo + - Duccio Di Buoninsegna + - Dying Gaul + - Edgar Degas + - Edouard Manet + - Γ‰douard Vuillard + - Edvard Munch + - Edward Burne-Jones + - Edward Hopper + - Edward Ruscha + - Egon Schiele + - El Greco + - El Lissitzky + - Ellsworth Kelly + - Γ‰mile-Jacques Ruhlmann + - Ernst Ludwig Kirchner + - ErtΓ© (Romain De Tirtoff) + - Eugene Delacroix + - Eva Hesse + - Evert Collier + - Fernand LΓ©ger + - Filippo Tommaso Marinetti + - Francesco Mazzola Parmigianino + - Francis Bacon + - Francisco De Zurbaran + - Francisco Goya + - Francois Boucher + - Frank Stella + - Frans Hals + - Frantisek Kupka + - Frederic Edwin Church + - Frida Kahlo + - Genesis P-Orridge + - George Ault + - George Bellows + - George Grosz + - George Inness + - George Luks + - George Tooker + - Georges Braque + - Georges Seurat + - Georgia O'Keeffe + - Gerrit Rietveld + - Giacomo Balla + - Gian Lorenzo Bernini + - Giorgio De Chirico + - Giorgio Morandi + - Giotto Di Bondone + - Giovanni Battista Tiepolo + - Govardhan + - Grandma Moses (Anna Mary Robertson Moses) + - Grant Wood + - Gustav Klimt + - Gustave Courbet + - Gustave Moreau + - Hannah HΓΆch + - Hans Holbein The Younger + - Helen Frankenthaler + - Henri Cartier-Bresson + - Henri Matisse + - Henri Rousseau + - Henri-Edmond Cross + - Henry Darger + - Hieronymus Bosch + - Hishikawa Moronobu + - Howard Finster + - Invader (Space Invader) + - Ismail Gulgee + - Ivan GeneraliΔ‡ + - J.M.W. Turner + - Jackson Pollock + - Jacob Jordaens + - Jacopo Comin (Tintoretto) + - Jacques-Louis David + - James Abbott Mcneill Whistler + - James Wyatt + - Jamini Roy + - Jan Brueghel The Elder + - Jan Vermeer + - Jean Arp + - Jean Auguste Dominique Ingres + - Jean Boulogne (Giambologna) + - Jean Dubuffet + - Jean Metzinger + - Jean Tinguely + - Jean-Antoine Watteau + - Jean-FranΓ§ois Millet + - Jean-HonorΓ© Fragonard + - Jean-Michel Basquiat + - Jeff Koons + - Jenny Saville + - JesΓΊs Rafael Soto + - Joan MirΓ³ + - Johann Sebastian Bach + - Johannes Vermeer + - John Baldessari + - John Everett Millais + - John F. Peto + - John Mclaughlin + - John Singer Sargent + - John Sloan + - John William Waterhouse + - Josef Albers + - Joseph Kosuth + - Joseph Mallord William Turner + - Juan Gris + - Judy Chicago + - Kara Walker + - Karel Appel + - Karl Benjamin + - Katsushika Hokusai + - Kazimir Malevich + - Keith Haring + - Kitagawa Utamaro + - Lady Pink (Sandra Fabara) + - Laurence Gartel + - Lee Krasner + - Leonardo Da Vinci + - Lex Horn + - Louis Comfort Tiffany + - Louise Bourgeois + - Louise Nevelson + - Lucian Freud + - Maestro Esiguo + - Man Ray + - Marc Chagall + - Marcel Duchamp + - Mark Rothko + - Mary Cassatt + - Master Of The Franciscan Crucifixes + - Mastro Guglielmo + - Maurice De Vlaminck + - Maurice Denis + - Max Beckmann + - Max Ernst + - Michael Janis + - Michelangelo + - Morris Louis + - Mr. (Masaru Shichinohe) + - Nandalal Bose + - Naum Gabo + - Nicholas Of Verdun + - Nicola Pisano + - Odilon Redon + - Olafur Eliasson + - Olga Rozanova + - Oskar Schlemmer + - Otto Dix + - Pablo Picasso + - Paul Cezanne + - Paul Gauguin + - Paul Klee + - Paul SΓ©rusier + - Paul Signac + - Peter Paul Rubens + - Pierre Alechinsky + - Pierre Bonnard + - Pierre-Auguste Renoir + - Piet Mondrian + - Pieter Bruegel The Elder + - Pietro Lorenzetti + - Raffaello Sanzio (Raphael) + - Raja Ravi Varma + - Ralph Albert Blakelock + - Ralph Goings + - Raphael (Raffaello Sanzio) + - Ray Johnson + - Refik Anadol + - Rembrandt Harmenszoon Van Rijn + - RenΓ© Lalique + - Rene Magritte + - Richard Anuszkiewicz + - Richard Estes + - Richard Hamilton + - Richard Serra + - Robert Delaunay + - Robert Henri + - Robert Smithson + - Robert Watts + - Roy Lichtenstein + - Ryoji Ikeda + - Sadequain + - Saint Luke + - Salvador Dali + - Sandro Botticelli + - Sergei Gerasimov + - Shepard Fairey + - Simone Martini + - Sir Charles Barry + - Sir Lawrence Alma-Tadema + - Sol Lewitt + - Sonia Delaunay + - Takashi Murakami + - Tamara De Lempicka + - Theo Van Doesburg + - ThΓ©odore GΓ©ricault + - Theodore The Studite + - Thomas Cole + - Thomas Eakins + - Thomas Gainsborough + - Thomas Hart Benton + - Tintoretto (Jacopo Comin) + - Titian (Tiziano Vecellio) + - Tony Da + - Umberto Boccioni + - Ustad Mansur + - Utagawa Hiroshige + - Vasily Kandinsky + - Vera Mukhina + - Victor Vasarely + - Vincent Van Gogh + - Vladimir Tatlin + - Wassily Kandinsky + - White On White + - Willem De Kooning + - William Blake + - William Holman Hunt + - William Merritt Chase + - William Michael Harnett + - Winged Victory Of Samothrace + - Yayoi Kusama + - Yoshitomo Nara + - Yves Klein + - Zaha Hadid + +Character Types: + - Acolyte + - Acrobat + - Apothecary + - Artificer + - Artisan + - Barbarian + - Bard + - Bardic Sage + - Blood Hunter + - Bounty Hunter + - Cavalier + - Champion + - Charlatan + - City Watch + - Cleric + - Clown + - Druid + - Duelist + - Elemental Shaman + - Executioner + - Exorcist + - Explorer + - Fey-Touched + - Fighter + - Geomancer + - Gladiator + - Guild Artisan + - Hermit + - Knight + - Monk + - Mystic + - Noble + - Outlander + - Paladin + - Pirate + - Ranger + - Rogue + - Runescribe + - Sage + - Sailor + - Shaman + - Soldier + - Sorcerer + - Urchin + - Warden + - Warlock + - Warlord + - Wizard + +Colors: + - Almond Color + - Amber + - Apricot Orange + - Ash Gray + - Beige + - Black + - Blue + - Brick Red + - Bronze + - Brown + - Caramel Color + - Carnation Pink + - Cerulean + - Charcoal Grey + - Chocolate Color + - Cinnamon Brown + - Coral Orange + - Cream Color + - Creamy White + - Cyan + - Denim Blue + - Emerald Green + - Forest Green + - Fuchsia + - Gold + - Goldfish Orange + - Grass Green + - Green + - Honey Color + - Indigo + - Ivory Color + - Jade Green + - Lavender + - Lemon Yellow + - Lilac Purple + - Lime Green + - Magenta + - Mahogany + - Marigold + - Maroon + - Mauve + - Midnight Blue + - Mint Green + - Mocha Brown + - Navy Blue + - Olive Green + - Orange + - Orchid Pink + - Pansy Color + - Paprika Red + - Peach Color + - Peachy Pink + - Pearl Color + - Periwinkle + - Pine Color + - Pink + - Plum Purple + - Pomegranate Color + - Purple + - Raspberry Color + - Red + - Rose + - Ruby Red + - Rust Brown + - Salmon Pink + - Sand Color + - Sapphire Blue + - Seafoam Green + - Sienna + - Silver + - Sky Blue + - Slate Gray + - Steel Blue + - Steel Gray + - Tan Brown + - Tangerine Orange + - Teak + - Teal + - Teal Green + - Terracotta Orange + - Topaz Yellow + - Turquoise + - Vanilla Color + - Violet + - White + - Wine Red + - Yellow + +Composition: + - Beauty Shot + - Bird'S Eye View + - Black And White Portrait + - Candid Shot + - Double Exposure Portrait + - Environmental Portrait + - Extreme Close-Up + - Framed Portrait + - Frontal + - Full Shot + - Group Shot + - Headshot + - High Angle + - High-Key Portrait + - Infrared Portrait + - Low Angle + - Macro + - Medium Close-Up + - Medium Shot + - Micro + - Motion Blur Portrait + - Multiple Exposure Portrait + - Narrative Portrait + - Outdoor Portrait + - Over-The-Shoulder + - Profile + - Reflection Portrait + - Selective Color Portrait + - Sepia Tone Portrait + - Shadow Portrait + - Silhouette + - Split Tone Portrait + - Studio Portrait + - Surreal Portrait + - Three-Quarter View + - Two-Shot + - Worm'S Eye View + +Composition Form: + - Afterimage + - Border + - Bust Chart + - Character Chart + - Chart + - Collage + - Column Lineup + - Cropped + - Diagram + - Fading Border + - Fake Scrollbar + - Feet Out Of Frame + - Framed + - Head Out Of Frame + - Isometric + - Letterboxed + - Lineup + - Mosaic Art + - Move Chart + - Negative Space + - Omake + - Out Of Frame + - Outside Border + - Partially Underwater Shot + - Photomosaic + - Pillarboxed + - Polar Opposites + - Projected Inset + - Reference Sheet + - Relationship Graph + - Rotational Symmetry + - Rounded Corners + - Seating Chart + - Social Media Composition + - Stats + - Symmetry + - Tachi-E + - Trim Marks + - Viewfinder + - Zoom Layer + +Lighting: + - Artificial Indoor Lighting + - Back Lighting + - Bright And Sunny Lighting + - Bright Festive Holiday Lights Lighting + - Bright Stadium Lights Lighting + - Broad Lighting + - Butterfly Lighting + - Candlelit Dinner Lighting + - Candlelit Scene Lighting + - Celestial Sky Illumination Lighting + - City Lights Lighting + - Clamshell Lighting + - Classic Chandelier Lighting + - Classic Gas Lamp Lighting + - Classic Oil Lamp Lighting + - Cool And Blue Lighting + - Cozy Book Reading Nook Lighting + - Creepy Haunted House Lighting + - Dappled Sunlight Through Leaves Lighting + - Dazzling Disco Lights Lighting + - Dim And Cozy Lighting + - Dramatic High Contrast Lighting + - Dramatic Opera House Lighting + - Dramatic Spotlight Lighting + - Dreamlike Fairy Circle Lights Lighting + - Dreamy Moonlit Ocean Lighting + - Eerie Moonlit Graveyard Lighting + - Eerie Torchlight Lighting + - Electric Disco Dance Floor Lighting + - Elegant Crystal Lighting + - Enchanted Garden Lanterns Lighting + - Enchanting Bioluminescence Lighting + - Enveloping Fog Lights Lighting + - Ethereal Moonlight Lighting + - Ethereal Starlit Sky Lighting + - Exotic Tiki Torches Lighting + - Firelight Flicker Lighting + - Floating Chinese Lanterns Lighting + - Fluorescent Office Lighting + - Futuristic Laser Lights Lighting + - Futuristic Neon Glow Lighting + - Futuristic Spacecraft Lighting + - Glimmering Disco Ball Lighting + - Glimmering Water Reflections Lighting + - Glistening Icicle Lights Lighting + - Glistening Winter Frost Lighting + - Glittering Cityscape Lighting + - Glowing Carnival Rides Lighting + - Glowing Fireflies Lighting + - Glowing Jack-O'-Lanterns Lighting + - Glowing Lighthouse Beacon Lighting + - Glowing Neon Lighting + - Golden Hour Lighting + - Golden Hour Light Lighting + - Hard Shadows Lighting + - Harsh Overhead Lighting + - Haunting Moonlight Lighting + - High Key Lighting + - Industrial Warehouse Lighting + - Intense Firelight Lighting + - Intense Lightning Storm Lighting + - Intense Police Searchlight Lighting + - Intimate Candlelit Bath Lighting + - Light Painting Lighting + - Loop Lighting + - Low Key Lighting + - Luminous Jellyfish Aquarium Lighting + - Magical Enchanted Forest Lighting + - Majestic Lighting + - Modern Led Strips Lighting + - Moody And Mysterious Lighting + - Moonlit Forest Lighting + - Natural Sunlight Lighting + - Neon Glow Lighting + - Night Photography Lighting + - Nostalgic Retro Diner Glow Lighting + - Peaceful Starry Campfire Lighting + - Product Lighting + - Radiant Angelic Lighting + - Radiant Stadium Floodlights Lighting + - Radiant Streetlamps Lighting + - Rembrandt Lighting + - Reminiscent Film Lighting + - Retro Diner Neon Signs Lighting + - Retro Neon Diner Lights Lighting + - Rim Lighting + - Romantic Candlelight Lighting + - Rustic Barn Lighting + - Rustic Campfire Lighting + - Rustic Fireplace Lighting + - Rustic Lanterns Lighting + - Sci-Fi Futuristic Lighting + - Sci-Fi Holographic Projections Lighting + - Serene Zen Garden Illumination Lighting + - Shadow Play Lighting + - Shimmering Lake Reflections Lighting + - Short Lighting + - Side Lighting + - Silhouette Lighting + - Silky Water Effect (Long Exposure) Lighting + - Sinister Back Alley Lighting + - Soft And Diffused Lighting + - Soft And Warm Lighting + - Soft Candlelit Meditation Lighting + - Soft Illumination Lighting + - Soft Pastel Glow Lighting + - Soft Shadows Lighting + - Split Lighting + - Spooky Halloween Lights Lighting + - Starry Night Lighting + - Still Life Lighting + - Street Lamp Lighting + - Studio Portrait Lighting + - Subdued Desk Lamp Lighting + - Subdued Nightlight Lighting + - Subtle Ambient Glow Lighting + - Sunrise At The Mountains Lighting + - Sunrise Over The Ocean Lighting + - Sunset Silhouette Lighting + - Twinkling Christmas Stars Lighting + - Twinkling Fairy Lights Lighting + - Under The Christmas Tree Lights Lighting + - Under The Streetlights Lighting + - Underwater Illumination Lighting + - Vibrant Stage Lighting + - Vintage Film Noir Lighting + - Vivid Art Gallery Spotlights Lighting + - Warm Fireplace Embers Lighting + - Warm Sunset Glow Lighting + - Whimsical Fairy Tale Lighting + +Negative: + - Blurry, Text, Watermark, Signature, Frame + - Disfigure Body, Disfigured Torso, Disfigured Face, Disfigured Eyes, Disfigured Pupils, Disfigured Arms, Disfigured Hands, Disfigured Fingers, Disfigured Legs, Disfigured Toes + +Picture Effect: + - Anaglyph + - Blending + - Bloom + - Blurry + - Chromatic Aberration + - Chromatic Aberration Abuse + - Cinematic Lighting + - Depth Of Field + - Dithering + - Drop Shadow + - Film Grain + - Fujicolor + - Glowing Light + - God Rays + - Halftone + - Image Fill + - Jpeg Artifacts + - Motion Blur + - Motion Lines + - Multiple Monochrome + - Optical Illusion + - Ray Tracing + - Reflection Light + - Scanlines + - Sparkle + - Speed Lines + - Stereogram + - Vignetting + +Picture Quality: + - 1080P + - 16K + - 4K + - 8K + - Accurate + - Anatomically Correct + - Award Winning + - Best Quality + - HD + - High Quality + - Highres + - Masterpiece + - Retina + - Super Detail + - Textured Skin + - UHD + +Setting: + - Advanced Alien Civilization Setting + - Age Of Exploration Setting + - Ai Uprising Setting + - Ai-Powered Society Setting + - Alien Invasion Setting + - Alternate Dimensions Setting + - Alternate History Setting + - Alternate Identity Setting + - Alternate Realities Collide Setting + - Ancient Civilization Setting + - Ancient Curse Setting + - Ancient Gods Setting + - Ancient Prophecy Setting + - Apocalyptic Earth Setting + - Arctic Setting + - Artificial Intelligence Setting + - Astral Plane Setting + - Biopunk Setting + - City Of Tomorrow Setting + - City Under Siege Setting + - Cosmic Horror Setting + - Cyber Warfare Setting + - Cybernetic Augmentation Setting + - Cyberpunk Setting + - Desert Setting + - Detective Noir Setting + - Dinosaur Era Setting + - Dream Within A Dream Setting + - Dream World Setting + - Dystopian Setting + - Espionage Setting + - Fairytale Setting + - Fantasy Setting + - Forgotten Technology Setting + - Future Dystopia Setting + - Future War Setting + - Futuristic Setting + - Futuristic Sports Setting + - Galactic Conflict Setting + - Galactic Empire Setting + - Genetic Engineering Setting + - Genetic Mutation Setting + - Haunted Mansion Setting + - Hidden Alien Species Setting + - Hidden Sanctuary Setting + - Historical Setting + - Holographic Reality Setting + - Interdimensional Travel Setting + - Jungle Setting + - Lost Civilization Setting + - Lost In Space Setting + - Lost In Time Setting + - Lunar Colony Setting + - Magical School Setting + - Mechanical World Setting + - Medieval Setting + - Mind Upload Setting + - Mythical Artifacts Setting + - Mythical Beasts Setting + - Mythological Setting + - Nature Reclaiming Civilization Setting + - Navigating The Multiverse Setting + - Ocean Exploration Setting + - Parallel Histories Setting + - Parallel Universe Setting + - Pirate Adventure Setting + - Planetary Exploration Setting + - Post-Apocalyptic Setting + - Robot Uprising Setting + - Robotic Society Setting + - Robotic Utopia Setting + - Sci-Fi Setting + - Space Setting + - Space Anomaly Setting + - Space Colonization Setting + - Space Opera Setting + - Space Rebellion Setting + - Space Travelers Setting + - Space Western Setting + - Steampunk Setting + - Superhero Setting + - Supernatural Mystery Setting + - Surreal Setting + - Time Crime Setting + - Time Loop Setting + - Time Paradox Setting + - Time Travel Setting + - Transhumanism Setting + - Underwater Setting + - Urban Setting + - Utopian Society Setting + - Victorian Setting + - Virtual Conspiracy Setting + - Virtual Paradise Setting + - Virtual Reality Setting + - Wild Animal Kingdom Setting + - Wild West Setting + +Shot: + - 135Mm + - 35Mm + - 360 View + - 85Mm + - Bokeh + - Canon + - Caustics + - Diffraction Spikes + - Emphasis Lines + - Eye-Level Shot + - F/1.2 + - F/1.8 + - F/16 + - F/2.8 + - F/4.0 + - Foreshortening + - Fujifilm + - Hasselblad + - Lens Flare + - Macro Photo + - Nikon + - Overexposure + - Satellite Image + - Sony Fe + - Sony Fe Gm + - Ultra-Wide Angle + - Wide-Angle + +Style: + - Abstract Art Style + - Abstract Expressionism Style + - Abstract Figurative Style + - Abstract Landscapes Style + - Abstract Portraiture Style + - Abstract Still Life Style + - Abstractionism Style + - Acrylic Painting Style + - Action Painting Style + - American Impressionism Style + - American Propaganda Poster Style + - American Scene Painting Style + - Analytical Cubism Style + - Anime Style + - Architectural Design Style + - Art Brut Style + - Art Deco Style + - Art Nouveau Style + - Ashcan School Style + - Assemblage Art Style + - Augmented Reality Art Style + - Baroque Style + - Batik Style + - Bauhaus Style + - Bengal School Of Art Style + - Blind Box Toy Style + - Body Painting Style + - Botanical Art Style + - Bronze Casting Style + - By Alfons Mucha Style + - Byzantine Art Style + - Carl Larsson Style + - Ceramic Pottery Style + - Charcoal Drawing Style + - Chiaroscuro Style + - Classicism Style + - Cobra Style + - Color Field Painting Style + - Colored Pencil Drawing Style + - Conceptual Art Style + - Constructivism Style + - Contemporary Style + - Contemporary Art Style + - Cubism Style + - Cubist Futurism Style + - Dada Style + - Dadaism Style + - De Stijl Style + - Digital Art Style + - Digital Sculpture Style + - Dutch Golden Age Style + - Embroidery Style + - En Plein Air Style + - Environmental Art Style + - Environmental Sculpture Style + - Etching Style + - Expressionism Style + - Fashion Design Style + - Fauvism Style + - Fiber Art Style + - Figurative Art Style + - Film And Video Art Style + - Flemish Baroque Style + - Folk Art Style + - Found Object Art Style + - Futurism Style + - Genre Painting Style + - Ghibli-Like Colors Style + - Glassblowing Style + - Gothic Style + - Gothic Art Style + - Gothic Revival Style + - Gouache Painting Style + - Graffiti Art Style + - Graphite Drawing Style + - Hard-Edge Painting Style + - Hellenistic Art Style + - High Detail Style + - Hindu Art Style + - Hudson River School Style + - Hyperrealism Style + - Impressionism Style + - Industrial Design Style + - Installation Art Style + - Interactive Art Style + - Interior Architecture Style + - Interior Design Style + - Islamic Art Style + - Jewelry Design Style + - Kinetic Art Style + - Land Art Style + - Landscapes Style + - Les Nabis Style + - Light Art Style + - Linocut Style + - Lithography Style + - Luminism Style + - Magic Realism Style + - Mail Art Style + - Mannerism Style + - Medieval Art Style + - Metalworking Style + - Metaphysical Art Style + - Minimalism Style + - Mixed Media Style + - Modern Style + - Monet Style + - Monotype Style + - Mosaic Style + - Mughal Art Style + - Naive Art Style + - Neoclassicism Style + - Neo-Expressionism Style + - Neo-Impressionism Style + - New Objectivity Style + - Northern Renaissance Style + - Oil Painting Style + - Op Art Style + - Orphism Style + - Pastel Painting Style + - Pen And Ink Drawing Style + - Performance Art Style + - Performance Painting Style + - Photography Style + - Photorealism Style + - Pixar Style + - Pointillism Style + - Pop Art Style + - Portraiture Style + - Post-Impressionism Style + - Post-Minimalism Style + - Postmodernism Style + - Precisionism Style + - Pre-Raphaelite Brotherhood Style + - Pre-RephaΓ«lite Painting Style + - Primitivism Style + - Printmaking Style + - Proto-Renaissance Style + - Raised Fist Style + - Realism Style + - Renaissance Style + - Renaissance Art Style + - Rococo Style + - Romanticism Style + - Screen Printing Style + - Sculpture Style + - Seascape Style + - Sgraffito Style + - Sienese School Style + - Social Realism Style + - Socialist Realism Style + - Southern Renaissance Style + - Stained Glass Style + - Still Life Style + - Stone Carving Style + - Street Art Style + - Superflat Style + - Suprematism Style + - Surrealism Style + - Symbolism Style + - Synthetic Cubism Style + - Tapestry Style + - Textile Design Style + - Textile Sculpture Style + - Tonalism Style + - Trompe-L'Oeil Style + - Ukiyo-E Style + - Verism Style + - Victorian Art Style + - Video Art Style + - Virtual Reality Art Style + - Watercolor Style + - Weaving Style + - Wildlife Art Style + - Wood Carving Style + - Woodcut Style diff --git a/custom_nodes/comfyui-manager/.gitignore b/custom_nodes/comfyui-manager/.gitignore new file mode 100644 index 00000000..33ee743b --- /dev/null +++ b/custom_nodes/comfyui-manager/.gitignore @@ -0,0 +1,20 @@ +__pycache__/ +.idea/ +.vscode/ +.history/ +*.code-workspace +.tmp +.cache +config.ini +snapshots/** +startup-scripts/** +.openart_key +.youml +matrix_auth +channels.list +comfyworkflows_sharekey +github-stats-cache.json +pip_overrides.json +*.json +check2.sh +/venv/ \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/LICENSE.txt b/custom_nodes/comfyui-manager/LICENSE.txt new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/custom_nodes/comfyui-manager/LICENSE.txt @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to +your programs, too. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you +these rights or asking you to surrender the rights. Therefore, you have +certain responsibilities if you distribute copies of the software, or if +you modify it: responsibilities to respect the freedom of others. + + For example, if you distribute copies of such a program, whether +gratis or for a fee, you must pass on to the recipients the same +freedoms that you received. You must make sure that they, too, receive +or can get the source code. And you must show them these terms so they +know their rights. + + Developers that use the GNU GPL protect your rights with two steps: +(1) assert copyright on the software, and (2) offer you this License +giving you legal permission to copy, distribute and/or modify it. + + For the developers' and authors' protection, the GPL clearly explains +that there is no warranty for this free software. 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If such problems arise substantially in other domains, we +stand ready to extend this provision to those domains in future versions +of the GPL, as needed to protect the freedom of users. + + Finally, every program is threatened constantly by software patents. +States should not allow patents to restrict development and use of +software on general-purpose computers, but in those that do, we wish to +avoid the special danger that patents applied to a free program could +make it effectively proprietary. To prevent this, the GPL assures that +patents cannot be used to render the program non-free. + + The precise terms and conditions for copying, distribution and +modification follow. + + TERMS AND CONDITIONS + + 0. Definitions. + + "This License" refers to version 3 of the GNU General Public License. + + "Copyright" also means copyright-like laws that apply to other kinds of +works, such as semiconductor masks. + + "The Program" refers to any copyrightable work licensed under this +License. Each licensee is addressed as "you". "Licensees" and +"recipients" may be individuals or organizations. + + To "modify" a work means to copy from or adapt all or part of the work +in a fashion requiring copyright permission, other than the making of an +exact copy. The resulting work is called a "modified version" of the +earlier work or a work "based on" the earlier work. + + A "covered work" means either the unmodified Program or a work based +on the Program. + + To "propagate" a work means to do anything with it that, without +permission, would make you directly or secondarily liable for +infringement under applicable copyright law, except executing it on a +computer or modifying a private copy. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/comfyui-manager/README.md b/custom_nodes/comfyui-manager/README.md new file mode 100644 index 00000000..ea4919a7 --- /dev/null +++ b/custom_nodes/comfyui-manager/README.md @@ -0,0 +1,409 @@ +# ComfyUI Manager + +**ComfyUI-Manager** is an extension designed to enhance the usability of [ComfyUI](https://github.com/comfyanonymous/ComfyUI). It offers management functions to **install, remove, disable, and enable** various custom nodes of ComfyUI. Furthermore, this extension provides a hub feature and convenience functions to access a wide range of information within ComfyUI. + +![menu](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/refs/heads/Main/ComfyUI-Manager/images/dialog.jpg) + +## NOTICE +* V3.38: **Security patch** - Manager data migrated to protected path. See [Migration Guide](docs/en/v3.38-userdata-security-migration.md). +* V3.16: Support for `uv` has been added. Set `use_uv` in `config.ini`. +* V3.10: `double-click feature` is removed + * This feature has been moved to https://github.com/ltdrdata/comfyui-connection-helper +* V3.3.2: Overhauled. Officially supports [https://registry.comfy.org/](https://registry.comfy.org/). +* You can see whole nodes info on [ComfyUI Nodes Info](https://ltdrdata.github.io/) page. + +## Installation + +### Installation[method1] (General installation method: ComfyUI-Manager only) + +To install ComfyUI-Manager in addition to an existing installation of ComfyUI, you can follow the following steps: + +1. Go to `ComfyUI/custom_nodes` dir in terminal (cmd) +2. `git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager` +3. Restart ComfyUI + + +### Installation[method2] (Installation for portable ComfyUI version: ComfyUI-Manager only) +1. install git +- https://git-scm.com/download/win +- standalone version +- select option: use windows default console window +2. Download [scripts/install-manager-for-portable-version.bat](https://github.com/ltdrdata/ComfyUI-Manager/raw/main/scripts/install-manager-for-portable-version.bat) into installed `"ComfyUI_windows_portable"` directory +- Don't click. Right-click the link and choose 'Save As...' +3. Double-click `install-manager-for-portable-version.bat` batch file + +![portable-install](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/portable-install.jpg) + + +### Installation[method3] (Installation through comfy-cli: install ComfyUI and ComfyUI-Manager at once.) +> RECOMMENDED: comfy-cli provides various features to manage ComfyUI from the CLI. + +* **prerequisite: python 3, git** + +Windows: +```commandline +python -m venv venv +venv\Scripts\activate +pip install comfy-cli +comfy install +``` + +Linux/macOS: +```commandline +python -m venv venv +. venv/bin/activate +pip install comfy-cli +comfy install +``` +* See also: https://github.com/Comfy-Org/comfy-cli + + +### Installation[method4] (Installation for Linux+venv: ComfyUI + ComfyUI-Manager) + +To install ComfyUI with ComfyUI-Manager on Linux using a venv environment, you can follow these steps: +* **prerequisite: python-is-python3, python3-venv, git** + +1. Download [scripts/install-comfyui-venv-linux.sh](https://github.com/ltdrdata/ComfyUI-Manager/raw/main/scripts/install-comfyui-venv-linux.sh) into empty install directory +- Don't click. Right-click the link and choose 'Save As...' +- ComfyUI will be installed in the subdirectory of the specified directory, and the directory will contain the generated executable script. +2. `chmod +x install-comfyui-venv-linux.sh` +3. `./install-comfyui-venv-linux.sh` + +### Installation Precautions +* **DO**: `ComfyUI-Manager` files must be accurately located in the path `ComfyUI/custom_nodes/comfyui-manager` + * Installing in a compressed file format is not recommended. +* **DON'T**: Decompress directly into the `ComfyUI/custom_nodes` location, resulting in the Manager contents like `__init__.py` being placed directly in that directory. + * You have to remove all ComfyUI-Manager files from `ComfyUI/custom_nodes` +* **DON'T**: In a form where decompression occurs in a path such as `ComfyUI/custom_nodes/ComfyUI-Manager/ComfyUI-Manager`. +* **DON'T**: In a form where decompression occurs in a path such as `ComfyUI/custom_nodes/ComfyUI-Manager-main`. + * In such cases, `ComfyUI-Manager` may operate, but it won't be recognized within `ComfyUI-Manager`, and updates cannot be performed. It also poses the risk of duplicate installations. Remove it and install properly via `git clone` method. + + +You can execute ComfyUI by running either `./run_gpu.sh` or `./run_cpu.sh` depending on your system configuration. + +## Colab Notebook +This repository provides Colab notebooks that allow you to install and use ComfyUI, including ComfyUI-Manager. To use ComfyUI, [click on this link](https://colab.research.google.com/github/ltdrdata/ComfyUI-Manager/blob/main/notebooks/comfyui_colab_with_manager.ipynb). +* Support for installing ComfyUI +* Support for basic installation of ComfyUI-Manager +* Support for automatically installing dependencies of custom nodes upon restarting Colab notebooks. + + +## How To Use + +1. Click "Manager" button on main menu + + ![mainmenu](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/topbar.jpg) + + +2. If you click on 'Install Custom Nodes' or 'Install Models', an installer dialog will open. + + ![menu](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/refs/heads/Main/ComfyUI-Manager/images/dialog.jpg) + + * There are three DB modes: `DB: Channel (1day cache)`, `DB: Local`, and `DB: Channel (remote)`. + * `Channel (1day cache)` utilizes Channel cache information with a validity period of one day to quickly display the list. + * This information will be updated when there is no cache, when the cache expires, or when external information is retrieved through the Channel (remote). + * Whenever you start ComfyUI anew, this mode is always set as the **default** mode. + * `Local` uses information stored locally in ComfyUI-Manager. + * This information will be updated only when you update ComfyUI-Manager. + * For custom node developers, they should use this mode when registering their nodes in `custom-node-list.json` and testing them. + * `Channel (remote)` retrieves information from the remote channel, always displaying the latest list. + * In cases where retrieval is not possible due to network errors, it will forcibly use local information. + + * The ```Fetch Updates``` menu retrieves update data for custom nodes locally. Actual updates are applied by clicking the ```Update``` button in the ```Install Custom Nodes``` menu. + +3. Click 'Install' or 'Try Install' button. + + ![node-install-dialog](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/custom-nodes.jpg) + + ![model-install-dialog](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/models.jpg) + + * Installed: This item is already installed. + * Install: Clicking this button will install the item. + * Try Install: This is a custom node of which installation information cannot be confirmed. Click the button to try installing it. + + * If a red background `Channel` indicator appears at the top, it means it is not the default channel. Since the amount of information held is different from the default channel, many custom nodes may not appear in this channel state. + * Channel settings have a broad impact, affecting not only the node list but also all functions like "Update all." + * Conflicted Nodes with a yellow background show a list of nodes conflicting with other extensions in the respective extension. This issue needs to be addressed by the developer, and users should be aware that due to these conflicts, some nodes may not function correctly and may need to be installed accordingly. + +4. Share + ![menu](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/topbar.jpg) ![share](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/share.jpg) + + * You can share the workflow by clicking the Share button at the bottom of the main menu or selecting Share Output from the Context Menu of the Image node. + * Currently, it supports sharing via [https://comfyworkflows.com/](https://comfyworkflows.com/), + [https://openart.ai](https://openart.ai/workflows/dev), [https://youml.com](https://youml.com) + as well as through the Matrix channel. + + ![menu](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/share-setting.jpg) + + * Through the Share settings in the Manager menu, you can configure the behavior of the Share button in the Main menu or Share Output button on Context Menu. + * `None`: hide from Main menu + * `All`: Show a dialog where the user can select a title for sharing. + + +## Paths +Starting from V3.38, Manager uses a protected system path for enhanced security. + +* + * If executed without any options, the path defaults to ComfyUI/user. + * It can be set using --user-directory . + +| ComfyUI Version | Manager Path | +|-----------------|--------------| +| v0.3.76+ (with System User API) | `/__manager/` | +| Older versions | `/default/ComfyUI-Manager/` | + +* Basic config files: `config.ini` +* Configurable channel lists: `channels.list` +* Configurable pip overrides: `pip_overrides.json` +* Configurable pip blacklist: `pip_blacklist.list` +* Configurable pip auto fix: `pip_auto_fix.list` +* Saved snapshot files: `snapshots/` +* Startup script files: `startup-scripts/` +* Component files: `components/` + +> **Note**: See [Migration Guide](docs/en/v3.38-userdata-security-migration.md) for upgrade details. + + +## `extra_model_paths.yaml` Configuration +The following settings are applied based on the section marked as `is_default`. + +* `custom_nodes`: Path for installing custom nodes + * Importing does not need to adhere to the path set as `is_default`, but this is the path where custom nodes are installed by the `ComfyUI Nodes Manager`. +* `download_model_base`: Path for downloading models + + +## Snapshot-Manager +* When you press `Save snapshot` or use `Update All` on `Manager Menu`, the current installation status snapshot is saved. + * Snapshot file dir: `/default/ComfyUI-Manager/snapshots` + * You can rename snapshot file. +* Press the "Restore" button to revert to the installation status of the respective snapshot. + * However, for custom nodes not managed by Git, snapshot support is incomplete. +* When you press `Restore`, it will take effect on the next ComfyUI startup. + * The selected snapshot file is saved in `/default/ComfyUI-Manager/startup-scripts/restore-snapshot.json`, and upon restarting ComfyUI, the snapshot is applied and then deleted. + +![model-install-dialog](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/snapshot.jpg) + + +## cm-cli: command line tools for power users +* A tool is provided that allows you to use the features of ComfyUI-Manager without running ComfyUI. +* For more details, please refer to the [cm-cli documentation](docs/en/cm-cli.md). + + +## How to register your custom node into ComfyUI-Manager + +* Add an entry to `custom-node-list.json` located in the root of ComfyUI-Manager and submit a Pull Request. +* NOTE: Before submitting the PR after making changes, please check `Use local DB` and ensure that the extension list loads without any issues in the `Install custom nodes` dialog. Occasionally, missing or extra commas can lead to JSON syntax errors. +* The remaining JSON will be updated through scripts in the future, so you don't need to worry about it. + + +## Custom node support guide + +* **NOTICE:** + - You should no longer assume that the GitHub repository name will match the subdirectory name under `custom_nodes`. The name of the subdirectory under `custom_nodes` will now use the normalized name from the `name` field in `pyproject.toml`. + - Avoid relying on directory names for imports whenever possible. + +* https://docs.comfy.org/registry/overview +* https://github.com/Comfy-Org/rfcs + +**Special purpose files** (optional) + * `pyproject.toml` - Spec file for comfyregistry. + * `node_list.json` - When your custom nodes pattern of NODE_CLASS_MAPPINGS is not conventional, it is used to manually provide a list of nodes for reference. ([example](https://github.com/melMass/comfy_mtb/raw/main/node_list.json)) + * `requirements.txt` - When installing, this pip requirements will be installed automatically + * `install.py` - When installing, it is automatically called + * **All scripts are executed from the root path of the corresponding custom node.** + + +## Component Sharing +* **Copy & Paste** + * [Demo Page](https://ltdrdata.github.io/component-demo/) + * When pasting a component from the clipboard, it supports text in the following JSON format. (text/plain) + ``` + { + "kind": "ComfyUI Components", + "timestamp": , + "components": + { + : + } + } + ``` + * `` Ensure that the timestamp is always unique. + * "components" should have the same structure as the content of the file stored in `/default/ComfyUI-Manager/components`. + * ``: The name should be in the format `::`. + * ``: In the node data of the group node. + * ``: Only two formats are allowed: `major.minor.patch` or `major.minor`. (e.g. `1.0`, `2.2.1`) + * ``: Saved time + * ``: If the packname is not empty, the category becomes packname/workflow, and it is saved in the .pack file in `/default/ComfyUI-Manager/components`. + * ``: If there is neither a category nor a packname, it is saved in the components category. + ``` + "version":"1.0", + "datetime": 1705390656516, + "packname": "mypack", + "category": "util/pipe", + ``` +* **Drag & Drop** + * Dragging and dropping a `.pack` or `.json` file will add the corresponding components. + * Example pack: [Impact.pack](misc/Impact.pack) + +* Dragging and dropping or pasting a single component will add a node. However, when adding multiple components, nodes will not be added. + + +## Support for installing missing nodes + +![missing-menu](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/missing-menu.jpg) + +* When you click on the ```Install Missing Custom Nodes``` button in the menu, it displays a list of extension nodes that contain nodes not currently present in the workflow. + +![missing-list](https://raw.githubusercontent.com/ltdrdata/ComfyUI-extension-tutorials/Main/ComfyUI-Manager/images/missing-list.jpg) + + +# Config +* You can modify the `config.ini` file to apply the settings for ComfyUI-Manager. + * The path to the `config.ini` used by ComfyUI-Manager is displayed in the startup log messages. + * See also: [https://github.com/ltdrdata/ComfyUI-Manager#paths] +* Configuration options: + ``` + [default] + git_exe = + use_uv = + default_cache_as_channel_url = + bypass_ssl = + file_logging = + windows_selector_event_loop_policy = + model_download_by_agent = + downgrade_blacklist = + security_level = strong|normal|normal-|weak> + always_lazy_install = + network_mode = public|private|offline> + ``` + + * network_mode: + - public: An environment that uses a typical public network. + - private: An environment that uses a closed network, where a private node DB is configured via `channel_url`. (Uses cache if available) + - offline: An environment that does not use any external connections when using an offline network. (Uses cache if available) + + +## Additional Feature +* Logging to file feature + * This feature is enabled by default and can be disabled by setting `file_logging = False` in the `config.ini`. + +* Fix node (recreate): When right-clicking on a node and selecting `Fix node (recreate)`, you can recreate the node. The widget's values are reset, while the connections maintain those with the same names. + * It is used to correct errors in nodes of old workflows created before, which are incompatible with the version changes of custom nodes. + +* Double-Click Node Title: You can set the double-click behavior of nodes in the ComfyUI-Manager menu. + * `Copy All Connections`, `Copy Input Connections`: Double-clicking a node copies the connections of the nearest node. + * This action targets the nearest node within a straight-line distance of 1000 pixels from the center of the node. + * In the case of `Copy All Connections`, it duplicates existing outputs, but since it does not allow duplicate connections, the existing output connections of the original node are disconnected. + * This feature copies only the input and output that match the names. + + * `Possible Input Connections`: It connects all outputs that match the closest type within the specified range. + * This connection links to the closest outputs among the nodes located on the left side of the target node. + + * `Possible(left) + Copy(right)`: When you Double-Click on the left half of the title, it operates as `Possible Input Connections`, and when you Double-Click on the right half, it operates as `Copy All Connections`. + +* Prevent downgrade of specific packages + * List the package names in the `downgrade_blacklist` section of the `config.ini` file, separating them with commas. + * e.g + ``` + downgrade_blacklist = diffusers, kornia + ``` + +* Custom pip mapping + * When you create the `pip_overrides.json` file, it changes the installation of specific pip packages to installations defined by the user. + * Please refer to the `pip_overrides.json.template` file. + +* Prevent the installation of specific pip packages + * List the package names one per line in the `pip_blacklist.list` file. + +* Automatically Restoring pip Installation + * If you list pip spec requirements in `pip_auto_fix.list`, similar to `requirements.txt`, it will automatically restore the specified versions when starting ComfyUI or when versions get mismatched during various custom node installations. + * `--index-url` can be used. + +* Use `aria2` as downloader + * [howto](docs/en/use_aria2.md) + + +## Environment Variables + +The following features can be configured using environment variables: + +* **COMFYUI_PATH**: The installation path of ComfyUI +* **GITHUB_ENDPOINT**: Reverse proxy configuration for environments with limited access to GitHub +* **HF_ENDPOINT**: Reverse proxy configuration for environments with limited access to Hugging Face + + +### Example 1: +Redirecting `https://github.com/ltdrdata/ComfyUI-Impact-Pack` to `https://mirror.ghproxy.com/https://github.com/ltdrdata/ComfyUI-Impact-Pack` + +``` +GITHUB_ENDPOINT=https://mirror.ghproxy.com/https://github.com +``` + +#### Example 2: +Changing `https://huggingface.co/path/to/somewhere` to `https://some-hf-mirror.com/path/to/somewhere` + +``` +HF_ENDPOINT=https://some-hf-mirror.com +``` + +## Scanner +When you run the `scan.sh` script: + +* It updates the `extension-node-map.json`. + * To do this, it pulls or clones the custom nodes listed in `custom-node-list.json` into `~/.tmp/default`. + * To skip this step, add the `--skip-update` option. + * If you want to specify a different path instead of `~/.tmp/default`, run `python scanner.py [path]` directly instead of `scan.sh`. + +* It updates the `github-stats.json`. + * This uses the GitHub API, so set your token with `export GITHUB_TOKEN=your_token_here` to avoid quickly reaching the rate limit and malfunctioning. + * To skip this step, add the `--skip-stat-update` option. + +* The `--skip-all` option applies both `--skip-update` and `--skip-stat-update`. + + +## Troubleshooting +* If your `git.exe` is installed in a specific location other than system git, please install ComfyUI-Manager and run ComfyUI. Then, specify the path including the file name in `git_exe = ` in the `/default/ComfyUI-Manager/config.ini` file that is generated. +* If updating ComfyUI-Manager itself fails, please go to the **ComfyUI-Manager** directory and execute the command `git update-ref refs/remotes/origin/main a361cc1 && git fetch --all && git pull`. +* If you encounter the error message `Overlapped Object has pending operation at deallocation on ComfyUI Manager load` under Windows + * Edit `config.ini` file: add `windows_selector_event_loop_policy = True` +* If the `SSL: CERTIFICATE_VERIFY_FAILED` error occurs. + * Edit `config.ini` file: add `bypass_ssl = True` + + +## Security policy + * Edit `config.ini` file: add `security_level = ` + * `strong` + * doesn't allow `high` and `middle` level risky feature + * `normal` + * doesn't allow `high` level risky feature + * `middle` level risky feature is available + * `normal-` + * doesn't allow `high` level risky feature if `--listen` is specified and not starts with `127.` + * `middle` level risky feature is available + * `weak` + * all feature is available + + * `high` level risky features + * `Install via git url`, `pip install` + * Installation of custom nodes registered not in the `default channel`. + * Fix custom nodes + + * `middle` level risky features + * Uninstall/Update + * Installation of custom nodes registered in the `default channel`. + * Restore/Remove Snapshot + * Restart + + * `low` level risky features + * Update ComfyUI + + +# Disclaimer + +* This extension simply provides the convenience of installing custom nodes and does not guarantee their proper functioning. + + +## Credit +ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI. + +**And, for all ComfyUI custom node developers** diff --git a/custom_nodes/comfyui-manager/__init__.py b/custom_nodes/comfyui-manager/__init__.py new file mode 100644 index 00000000..65aae69c --- /dev/null +++ b/custom_nodes/comfyui-manager/__init__.py @@ -0,0 +1,25 @@ +""" +This file is the entry point for the ComfyUI-Manager package, handling CLI-only mode and initial setup. +""" + +import os +import sys + +cli_mode_flag = os.path.join(os.path.dirname(__file__), '.enable-cli-only-mode') + +if not os.path.exists(cli_mode_flag): + sys.path.append(os.path.join(os.path.dirname(__file__), "glob")) + import manager_server # noqa: F401 + import share_3rdparty # noqa: F401 + import cm_global + + if not cm_global.disable_front and not 'DISABLE_COMFYUI_MANAGER_FRONT' in os.environ: + WEB_DIRECTORY = "js" +else: + print("\n[ComfyUI-Manager] !! cli-only-mode is enabled !!\n") + +NODE_CLASS_MAPPINGS = {} +__all__ = ['NODE_CLASS_MAPPINGS'] + + + diff --git a/custom_nodes/comfyui-manager/channels.list.template b/custom_nodes/comfyui-manager/channels.list.template new file mode 100644 index 00000000..9a8d6877 --- /dev/null +++ b/custom_nodes/comfyui-manager/channels.list.template @@ -0,0 +1,6 @@ +default::https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main +recent::https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/node_db/new +legacy::https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/node_db/legacy +forked::https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/node_db/forked +dev::https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/node_db/dev +tutorial::https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/node_db/tutorial \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/check.bat b/custom_nodes/comfyui-manager/check.bat new file mode 100644 index 00000000..e7a3b09f --- /dev/null +++ b/custom_nodes/comfyui-manager/check.bat @@ -0,0 +1,21 @@ +@echo off + +python json-checker.py "custom-node-list.json" +python json-checker.py "model-list.json" +python json-checker.py "alter-list.json" +python json-checker.py "extension-node-map.json" +python json-checker.py "node_db\new\custom-node-list.json" +python json-checker.py "node_db\new\model-list.json" +python json-checker.py "node_db\new\extension-node-map.json" +python json-checker.py "node_db\dev\custom-node-list.json" +python json-checker.py "node_db\dev\model-list.json" +python json-checker.py "node_db\dev\extension-node-map.json" +python json-checker.py "node_db\tutorial\custom-node-list.json" +python json-checker.py "node_db\tutorial\model-list.json" +python json-checker.py "node_db\tutorial\extension-node-map.json" +python json-checker.py "node_db\legacy\custom-node-list.json" +python json-checker.py "node_db\legacy\model-list.json" +python json-checker.py "node_db\legacy\extension-node-map.json" +python json-checker.py "node_db\forked\custom-node-list.json" +python json-checker.py "node_db\forked\model-list.json" +python json-checker.py "node_db\forked\extension-node-map.json" \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/check.sh b/custom_nodes/comfyui-manager/check.sh new file mode 100755 index 00000000..c3ef41c2 --- /dev/null +++ b/custom_nodes/comfyui-manager/check.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +echo +echo CHECK1 + +files=( + "custom-node-list.json" + "model-list.json" + "alter-list.json" + "extension-node-map.json" + "github-stats.json" + "extras.json" + "node_db/new/custom-node-list.json" + "node_db/new/model-list.json" + "node_db/new/extension-node-map.json" + "node_db/dev/custom-node-list.json" + "node_db/dev/model-list.json" + "node_db/dev/extension-node-map.json" + "node_db/tutorial/custom-node-list.json" + "node_db/tutorial/model-list.json" + "node_db/tutorial/extension-node-map.json" + "node_db/legacy/custom-node-list.json" + "node_db/legacy/model-list.json" + "node_db/legacy/extension-node-map.json" + "node_db/forked/custom-node-list.json" + "node_db/forked/model-list.json" + "node_db/forked/extension-node-map.json" +) + +for file in "${files[@]}"; do + python json-checker.py "$file" +done + +echo +echo CHECK2 +find ~/.tmp/default -name "*.py" -print0 | xargs -0 grep -E "crypto|^_A=" + +echo +echo CHECK3 +find ~/.tmp/default -name "requirements.txt" | xargs grep "^\s*[^#]*https\?:" +find ~/.tmp/default -name "requirements.txt" | xargs grep "^\s*[^#].*\.whl" + +echo diff --git a/custom_nodes/comfyui-manager/cm-cli.py b/custom_nodes/comfyui-manager/cm-cli.py new file mode 100644 index 00000000..04043423 --- /dev/null +++ b/custom_nodes/comfyui-manager/cm-cli.py @@ -0,0 +1,1288 @@ +import os +import sys +import traceback +import json +import asyncio +import concurrent +import threading +from typing import Optional + +import typer +from rich import print +from typing_extensions import List, Annotated +import re +import git +import importlib + + +sys.path.append(os.path.dirname(__file__)) +sys.path.append(os.path.join(os.path.dirname(__file__), "glob")) + +import manager_util + +# read env vars +# COMFYUI_FOLDERS_BASE_PATH is not required in cm-cli.py +# `comfy_path` should be resolved before importing manager_core +comfy_path = os.environ.get('COMFYUI_PATH') +if comfy_path is None: + try: + import folder_paths + comfy_path = os.path.join(os.path.dirname(folder_paths.__file__)) + except: + print("\n[bold yellow]WARN: The `COMFYUI_PATH` environment variable is not set. Assuming `custom_nodes/ComfyUI-Manager/../../` as the ComfyUI path.[/bold yellow]", file=sys.stderr) + comfy_path = os.path.abspath(os.path.join(manager_util.comfyui_manager_path, '..', '..')) + +# This should be placed here +sys.path.append(comfy_path) + +import utils.extra_config +import cm_global +import manager_core as core +from manager_core import unified_manager +import cnr_utils + +comfyui_manager_path = os.path.abspath(os.path.dirname(__file__)) + +cm_global.pip_blacklist = {'torch', 'torchaudio', 'torchsde', 'torchvision'} +cm_global.pip_downgrade_blacklist = ['torch', 'torchaudio', 'torchsde', 'torchvision', 'transformers', 'safetensors', 'kornia'] + +cm_global.pip_overrides = {} + +if os.path.exists(os.path.join(manager_util.comfyui_manager_path, "pip_overrides.json")): + with open(os.path.join(manager_util.comfyui_manager_path, "pip_overrides.json"), 'r', encoding="UTF-8", errors="ignore") as json_file: + cm_global.pip_overrides = json.load(json_file) + + +if os.path.exists(os.path.join(manager_util.comfyui_manager_path, "pip_blacklist.list")): + with open(os.path.join(manager_util.comfyui_manager_path, "pip_blacklist.list"), 'r', encoding="UTF-8", errors="ignore") as f: + for x in f.readlines(): + y = x.strip() + if y != '': + cm_global.pip_blacklist.add(y) + + +def check_comfyui_hash(): + try: + repo = git.Repo(comfy_path) + core.comfy_ui_revision = len(list(repo.iter_commits('HEAD'))) + core.comfy_ui_commit_datetime = repo.head.commit.committed_datetime + except: + print('[bold yellow]INFO: Frozen ComfyUI mode.[/bold yellow]') + core.comfy_ui_revision = 0 + core.comfy_ui_commit_datetime = 0 + + cm_global.variables['comfyui.revision'] = core.comfy_ui_revision + + +check_comfyui_hash() # This is a preparation step for manager_core +core.check_invalid_nodes() + + +def read_downgrade_blacklist(): + try: + import configparser + config = configparser.ConfigParser(strict=False) + config.read(core.manager_config.path) + default_conf = config['default'] + + if 'downgrade_blacklist' in default_conf: + items = default_conf['downgrade_blacklist'].split(',') + items = [x.strip() for x in items if x != ''] + cm_global.pip_downgrade_blacklist += items + cm_global.pip_downgrade_blacklist = list(set(cm_global.pip_downgrade_blacklist)) + except: + pass + + +read_downgrade_blacklist() # This is a preparation step for manager_core + + +class Ctx: + folder_paths = None + + def __init__(self): + self.channel = 'default' + self.no_deps = False + self.mode = 'cache' + self.user_directory = None + self.custom_nodes_paths = [os.path.join(core.comfy_base_path, 'custom_nodes')] + self.manager_files_directory = os.path.dirname(__file__) + + if Ctx.folder_paths is None: + try: + Ctx.folder_paths = importlib.import_module('folder_paths') + except ImportError: + print("Warning: Unable to import folder_paths module") + + def set_channel_mode(self, channel, mode): + if mode is not None: + self.mode = mode + + valid_modes = ["remote", "local", "cache"] + if mode and mode.lower() not in valid_modes: + typer.echo( + f"Invalid mode: {mode}. Allowed modes are 'remote', 'local', 'cache'.", + err=True, + ) + exit(1) + + if channel is not None: + self.channel = channel + + asyncio.run(unified_manager.reload(cache_mode=self.mode, dont_wait=False)) + asyncio.run(unified_manager.load_nightly(self.channel, self.mode)) + + def set_no_deps(self, no_deps): + self.no_deps = no_deps + + def set_user_directory(self, user_directory): + if user_directory is None: + return + + extra_model_paths_yaml = os.path.join(user_directory, 'extra_model_paths.yaml') + if os.path.exists(extra_model_paths_yaml): + utils.extra_config.load_extra_path_config(extra_model_paths_yaml) + + core.update_user_directory(user_directory) + + if os.path.exists(core.manager_pip_overrides_path): + with open(core.manager_pip_overrides_path, 'r', encoding="UTF-8", errors="ignore") as json_file: + cm_global.pip_overrides = json.load(json_file) + + if os.path.exists(core.manager_pip_blacklist_path): + with open(core.manager_pip_blacklist_path, 'r', encoding="UTF-8", errors="ignore") as f: + for x in f.readlines(): + y = x.strip() + if y != '': + cm_global.pip_blacklist.add(y) + + def update_custom_nodes_dir(self, target_dir): + import folder_paths + a, b = folder_paths.folder_names_and_paths['custom_nodes'] + folder_paths.folder_names_and_paths['custom_nodes'] = [os.path.abspath(target_dir)], set() + + @staticmethod + def get_startup_scripts_path(): + return os.path.join(core.manager_startup_script_path, "install-scripts.txt") + + @staticmethod + def get_restore_snapshot_path(): + return os.path.join(core.manager_startup_script_path, "restore-snapshot.json") + + @staticmethod + def get_snapshot_path(): + return core.manager_snapshot_path + + @staticmethod + def get_custom_nodes_paths(): + if Ctx.folder_paths is None: + print("Error: folder_paths module is not available") + return [] + return Ctx.folder_paths.get_folder_paths('custom_nodes') + + +cmd_ctx = Ctx() + + +def install_node(node_spec_str, is_all=False, cnt_msg='', **kwargs): + exit_on_fail = kwargs.get('exit_on_fail', False) + print(f"install_node exit on fail:{exit_on_fail}...") + + if core.is_valid_url(node_spec_str): + # install via urls + res = asyncio.run(core.gitclone_install(node_spec_str, no_deps=cmd_ctx.no_deps)) + if not res.result: + print(res.msg) + print(f"[bold red]ERROR: An error occurred while installing '{node_spec_str}'.[/bold red]") + if exit_on_fail: + sys.exit(1) + else: + print(f"{cnt_msg} [INSTALLED] {node_spec_str:50}") + else: + node_spec = unified_manager.resolve_node_spec(node_spec_str) + + if node_spec is None: + return + + node_name, version_spec, is_specified = node_spec + + # NOTE: install node doesn't allow update if version is not specified + if not is_specified: + version_spec = None + + res = asyncio.run(unified_manager.install_by_id(node_name, version_spec, cmd_ctx.channel, cmd_ctx.mode, instant_execution=True, no_deps=cmd_ctx.no_deps)) + + if res.action == 'skip': + print(f"{cnt_msg} [ SKIP ] {node_name:50} => Already installed") + elif res.action == 'enable': + print(f"{cnt_msg} [ ENABLED ] {node_name:50}") + elif res.action == 'install-git' and res.target == 'nightly': + print(f"{cnt_msg} [INSTALLED] {node_name:50}[NIGHTLY]") + elif res.action == 'install-git' and res.target == 'unknown': + print(f"{cnt_msg} [INSTALLED] {node_name:50}[UNKNOWN]") + elif res.action == 'install-cnr' and res.result: + print(f"{cnt_msg} [INSTALLED] {node_name:50}[{res.target}]") + elif res.action == 'switch-cnr' and res.result: + print(f"{cnt_msg} [INSTALLED] {node_name:50}[{res.target}]") + elif (res.action == 'switch-cnr' or res.action == 'install-cnr') and not res.result and node_name in unified_manager.cnr_map: + print(f"\nAvailable version of '{node_name}'") + show_versions(node_name) + print("") + else: + print(f"[bold red]ERROR: An error occurred while installing '{node_name}'.\n{res.msg}[/bold red]") + if exit_on_fail: + sys.exit(1) + + +def reinstall_node(node_spec_str, is_all=False, cnt_msg=''): + node_spec = unified_manager.resolve_node_spec(node_spec_str) + + node_name, version_spec, _ = node_spec + + unified_manager.unified_uninstall(node_name, version_spec == 'unknown') + install_node(node_name, is_all=is_all, cnt_msg=cnt_msg) + + +def fix_node(node_spec_str, is_all=False, cnt_msg=''): + node_spec = unified_manager.resolve_node_spec(node_spec_str, guess_mode='active') + + if node_spec is None: + if not is_all: + if unified_manager.resolve_node_spec(node_spec_str, guess_mode='inactive') is not None: + print(f"{cnt_msg} [ SKIPPED ]: {node_spec_str:50} => Disabled") + else: + print(f"{cnt_msg} [ SKIPPED ]: {node_spec_str:50} => Not installed") + + return + + node_name, version_spec, _ = node_spec + + print(f"{cnt_msg} [ FIXING ]: {node_name:50}[{version_spec}]") + res = unified_manager.unified_fix(node_name, version_spec, no_deps=cmd_ctx.no_deps) + + if not res.result: + print(f"[bold red]ERROR: f{res.msg}[/bold red]") + + +def uninstall_node(node_spec_str: str, is_all: bool = False, cnt_msg: str = ''): + spec = node_spec_str.split('@') + if len(spec) == 2 and spec[1] == 'unknown': + node_name = spec[0] + is_unknown = True + else: + node_name = spec[0] + is_unknown = False + + res = unified_manager.unified_uninstall(node_name, is_unknown) + if len(spec) == 1 and res.action == 'skip' and not is_unknown: + res = unified_manager.unified_uninstall(node_name, True) + + if res.action == 'skip': + print(f"{cnt_msg} [ SKIPPED ]: {node_name:50} => Not installed") + + elif res.result: + print(f"{cnt_msg} [UNINSTALLED] {node_name:50}") + else: + print(f"ERROR: An error occurred while uninstalling '{node_name}'.") + + +def update_node(node_spec_str, is_all=False, cnt_msg=''): + node_spec = unified_manager.resolve_node_spec(node_spec_str, 'active') + + if node_spec is None: + if unified_manager.resolve_node_spec(node_spec_str, 'inactive'): + print(f"{cnt_msg} [ SKIPPED ]: {node_spec_str:50} => Disabled") + else: + print(f"{cnt_msg} [ SKIPPED ]: {node_spec_str:50} => Not installed") + return None + + node_name, version_spec, _ = node_spec + + res = unified_manager.unified_update(node_name, version_spec, no_deps=cmd_ctx.no_deps, return_postinstall=True) + + if not res.result: + print(f"ERROR: An error occurred while updating '{node_name}'.") + elif res.action == 'skip': + print(f"{cnt_msg} [ SKIPPED ]: {node_name:50} => {res.msg}") + else: + print(f"{cnt_msg} [ UPDATED ]: {node_name:50} => ({version_spec} -> {res.target})") + + return res.with_target(f'{node_name}@{res.target}') + + +def update_parallel(nodes): + is_all = False + if 'all' in nodes: + is_all = True + nodes = [] + for x in unified_manager.active_nodes.keys(): + nodes.append(x) + for x in unified_manager.unknown_active_nodes.keys(): + nodes.append(x+"@unknown") + else: + nodes = [x for x in nodes if x.lower() not in ['comfy', 'comfyui']] + + total = len(nodes) + + lock = threading.Lock() + processed = [] + + i = 0 + + def process_custom_node(x): + nonlocal i + nonlocal processed + + with lock: + i += 1 + + try: + res = update_node(x, is_all=is_all, cnt_msg=f'{i}/{total}') + with lock: + processed.append(res) + except Exception as e: + print(f"ERROR: {e}") + traceback.print_exc() + + with concurrent.futures.ThreadPoolExecutor(4) as executor: + for item in nodes: + executor.submit(process_custom_node, item) + + i = 1 + for res in processed: + if res is not None: + print(f"[{i}/{total}] Post update: {res.target}") + if res.postinstall is not None: + res.postinstall() + i += 1 + + +def update_comfyui(): + res = core.update_path(comfy_path, instant_execution=True) + if res == 'fail': + print("Updating ComfyUI has failed.") + elif res == 'updated': + print("ComfyUI is updated.") + else: + print("ComfyUI is already up to date.") + + +def enable_node(node_spec_str, is_all=False, cnt_msg=''): + if unified_manager.resolve_node_spec(node_spec_str, guess_mode='active') is not None: + print(f"{cnt_msg} [ SKIP ] {node_spec_str:50} => Already enabled") + return + + node_spec = unified_manager.resolve_node_spec(node_spec_str, guess_mode='inactive') + + if node_spec is None: + print(f"{cnt_msg} [ SKIP ] {node_spec_str:50} => Not found") + return + + node_name, version_spec, _ = node_spec + + res = unified_manager.unified_enable(node_name, version_spec) + + if res.action == 'skip': + print(f"{cnt_msg} [ SKIP ] {node_name:50} => {res.msg}") + elif res.result: + print(f"{cnt_msg} [ENABLED] {node_name:50}") + else: + print(f"{cnt_msg} [ FAIL ] {node_name:50} => {res.msg}") + + +def disable_node(node_spec_str: str, is_all=False, cnt_msg=''): + if 'comfyui-manager' in node_spec_str.lower(): + return + + node_spec = unified_manager.resolve_node_spec(node_spec_str, guess_mode='active') + + if node_spec is None: + if unified_manager.resolve_node_spec(node_spec_str, guess_mode='inactive') is not None: + print(f"{cnt_msg} [ SKIP ] {node_spec_str:50} => Already disabled") + else: + print(f"{cnt_msg} [ SKIP ] {node_spec_str:50} => Not found") + return + + node_name, version_spec, _ = node_spec + + res = unified_manager.unified_disable(node_name, version_spec == 'unknown') + + if res.action == 'skip': + print(f"{cnt_msg} [ SKIP ] {node_name:50} => {res.msg}") + elif res.result: + print(f"{cnt_msg} [DISABLED] {node_name:50}") + else: + print(f"{cnt_msg} [ FAIL ] {node_name:50} => {res.msg}") + + +def show_list(kind, simple=False): + custom_nodes = asyncio.run(unified_manager.get_custom_nodes(channel=cmd_ctx.channel, mode=cmd_ctx.mode)) + + # collect not-installed unknown nodes + not_installed_unknown_nodes = [] + repo_unknown = {} + + for k, v in custom_nodes.items(): + if 'cnr_latest' not in v: + if len(v['files']) == 1: + repo_url = v['files'][0] + node_name = repo_url.split('/')[-1] + if node_name not in unified_manager.unknown_inactive_nodes and node_name not in unified_manager.unknown_active_nodes: + not_installed_unknown_nodes.append(v) + else: + repo_unknown[node_name] = v + + processed = {} + unknown_processed = [] + + flag = kind in ['all', 'cnr', 'installed', 'enabled'] + for k, v in unified_manager.active_nodes.items(): + if flag: + cnr = unified_manager.cnr_map[k] + processed[k] = "[ ENABLED ] ", cnr['name'], k, cnr['publisher']['name'], v[0] + else: + processed[k] = None + + if flag and kind != 'cnr': + for k, v in unified_manager.unknown_active_nodes.items(): + item = repo_unknown.get(k) + + if item is None: + continue + + log_item = "[ ENABLED ] ", item['title'], k, item['author'] + unknown_processed.append(log_item) + + flag = kind in ['all', 'cnr', 'installed', 'disabled'] + for k, v in unified_manager.cnr_inactive_nodes.items(): + if k in processed: + continue + + if flag: + cnr = unified_manager.cnr_map[k] + processed[k] = "[ DISABLED ] ", cnr['name'], k, cnr['publisher']['name'], ", ".join(list(v.keys())) + else: + processed[k] = None + + for k, v in unified_manager.nightly_inactive_nodes.items(): + if k in processed: + continue + + if flag: + cnr = unified_manager.cnr_map[k] + processed[k] = "[ DISABLED ] ", cnr['name'], k, cnr['publisher']['name'], 'nightly' + else: + processed[k] = None + + if flag and kind != 'cnr': + for k, v in unified_manager.unknown_inactive_nodes.items(): + item = repo_unknown.get(k) + + if item is None: + continue + + log_item = "[ DISABLED ] ", item['title'], k, item['author'] + unknown_processed.append(log_item) + + flag = kind in ['all', 'cnr', 'not-installed'] + for k, v in unified_manager.cnr_map.items(): + if k in processed: + continue + + if flag: + cnr = unified_manager.cnr_map[k] + ver_spec = v['latest_version']['version'] if 'latest_version' in v else '0.0.0' + processed[k] = "[ NOT INSTALLED ] ", cnr['name'], k, cnr['publisher']['name'], ver_spec + else: + processed[k] = None + + if flag and kind != 'cnr': + for x in not_installed_unknown_nodes: + if len(x['files']) == 1: + node_id = os.path.basename(x['files'][0]) + log_item = "[ NOT INSTALLED ] ", x['title'], node_id, x['author'] + unknown_processed.append(log_item) + + for x in processed.values(): + if x is None: + continue + + prefix, title, short_id, author, ver_spec = x + if simple: + print(title+'@'+ver_spec) + else: + print(f"{prefix} {title:50} {short_id:30} (author: {author:20}) \\[{ver_spec}]") + + for x in unknown_processed: + prefix, title, short_id, author = x + if simple: + print(title+'@unknown') + else: + print(f"{prefix} {title:50} {short_id:30} (author: {author:20}) [UNKNOWN]") + + +async def show_snapshot(simple_mode=False): + json_obj = await core.get_current_snapshot() + + if simple_mode: + print(f"[{json_obj['comfyui']}] comfyui") + for k, v in json_obj['git_custom_nodes'].items(): + print(f"[{v['hash']}] {k}") + for v in json_obj['file_custom_nodes']: + print(f"[ N/A ] {v['filename']}") + + else: + formatted_json = json.dumps(json_obj, ensure_ascii=False, indent=4) + print(formatted_json) + + +def show_snapshot_list(simple_mode=False): + snapshot_path = cmd_ctx.get_snapshot_path() + + files = os.listdir(snapshot_path) + json_files = [x for x in files if x.endswith('.json')] + for x in sorted(json_files): + print(x) + + +def cancel(): + if os.path.exists(cmd_ctx.get_startup_scripts_path()): + os.remove(cmd_ctx.get_startup_scripts_path()) + + if os.path.exists(cmd_ctx.get_restore_snapshot_path()): + os.remove(cmd_ctx.get_restore_snapshot_path()) + + +async def auto_save_snapshot(): + path = await core.save_snapshot_with_postfix('cli-autosave') + print(f"Current snapshot is saved as `{path}`") + + +def get_all_installed_node_specs(): + res = [] + processed = set() + for k, v in unified_manager.active_nodes.items(): + node_spec_str = f"{k}@{v[0]}" + res.append(node_spec_str) + processed.add(k) + + for k in unified_manager.cnr_inactive_nodes.keys(): + if k in processed: + continue + + latest = unified_manager.get_from_cnr_inactive_nodes(k) + if latest is not None: + node_spec_str = f"{k}@{str(latest[0])}" + res.append(node_spec_str) + + for k in unified_manager.nightly_inactive_nodes.keys(): + if k in processed: + continue + + node_spec_str = f"{k}@nightly" + res.append(node_spec_str) + + for k in unified_manager.unknown_active_nodes.keys(): + node_spec_str = f"{k}@unknown" + res.append(node_spec_str) + + for k in unified_manager.unknown_inactive_nodes.keys(): + node_spec_str = f"{k}@unknown" + res.append(node_spec_str) + + return res + + +def for_each_nodes(nodes, act, allow_all=True, **kwargs): + is_all = False + if allow_all and 'all' in nodes: + is_all = True + nodes = get_all_installed_node_specs() + else: + nodes = [x for x in nodes if x.lower() not in ['comfy', 'comfyui', 'all']] + + total = len(nodes) + i = 1 + for x in nodes: + try: + act(x, is_all=is_all, cnt_msg=f'{i}/{total}', **kwargs) + except Exception as e: + print(f"ERROR: {e}") + traceback.print_exc() + i += 1 + + +app = typer.Typer() + + +@app.command(help="Display help for commands") +def help(ctx: typer.Context): + print(ctx.find_root().get_help()) + ctx.exit(0) + + +@app.command(help="Install custom nodes") +def install( + nodes: List[str] = typer.Argument( + ..., help="List of custom nodes to install" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + no_deps: Annotated[ + Optional[bool], + typer.Option( + "--no-deps", + show_default=False, + help="Skip installing any Python dependencies", + ), + ] = False, + user_directory: str = typer.Option( + None, + help="user directory" + ), + exit_on_fail: bool = typer.Option( + False, + help="Exit on failure" + ) +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + cmd_ctx.set_no_deps(no_deps) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + for_each_nodes(nodes, act=install_node, exit_on_fail=exit_on_fail) + pip_fixer.fix_broken() + + +@app.command(help="Reinstall custom nodes") +def reinstall( + nodes: List[str] = typer.Argument( + ..., help="List of custom nodes to reinstall" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + no_deps: Annotated[ + Optional[bool], + typer.Option( + "--no-deps", + show_default=False, + help="Skip installing any Python dependencies", + ), + ] = False, + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + cmd_ctx.set_no_deps(no_deps) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + for_each_nodes(nodes, act=reinstall_node) + pip_fixer.fix_broken() + + +@app.command(help="Uninstall custom nodes") +def uninstall( + nodes: List[str] = typer.Argument( + ..., help="List of custom nodes to uninstall" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), +): + cmd_ctx.set_channel_mode(channel, mode) + for_each_nodes(nodes, act=uninstall_node) + + +@app.command(help="Update custom nodes") +def update( + nodes: List[str] = typer.Argument( + ..., + help="[all|List of custom nodes to update]" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + if 'all' in nodes: + asyncio.run(auto_save_snapshot()) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + + for x in nodes: + if x.lower() in ['comfyui', 'comfy', 'all']: + update_comfyui() + break + + update_parallel(nodes) + pip_fixer.fix_broken() + + +@app.command(help="Disable custom nodes") +def disable( + nodes: List[str] = typer.Argument( + ..., + help="[all|List of custom nodes to disable]" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + if 'all' in nodes: + asyncio.run(auto_save_snapshot()) + + for_each_nodes(nodes, disable_node, allow_all=True) + + +@app.command(help="Enable custom nodes") +def enable( + nodes: List[str] = typer.Argument( + ..., + help="[all|List of custom nodes to enable]" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + if 'all' in nodes: + asyncio.run(auto_save_snapshot()) + + for_each_nodes(nodes, enable_node, allow_all=True) + + +@app.command(help="Fix dependencies of custom nodes") +def fix( + nodes: List[str] = typer.Argument( + ..., + help="[all|List of custom nodes to fix]" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + if 'all' in nodes: + asyncio.run(auto_save_snapshot()) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + for_each_nodes(nodes, fix_node, allow_all=True) + pip_fixer.fix_broken() + + +@app.command("show-versions", help="Show all available versions of the node") +def show_versions(node_name: str): + versions = cnr_utils.all_versions_of_node(node_name) + if versions is None: + print(f"Node not found in Comfy Registry: {node_name}") + + for x in versions: + print(f"[{x['createdAt'][:10]}] {x['version']} -- {x['changelog']}") + + +@app.command("show", help="Show node list") +def show( + arg: str = typer.Argument( + help="[installed|enabled|not-installed|disabled|all|cnr|snapshot|snapshot-list]" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + valid_commands = [ + "installed", + "enabled", + "not-installed", + "disabled", + "all", + "cnr", + "snapshot", + "snapshot-list", + ] + if arg not in valid_commands: + typer.echo(f"Invalid command: `show {arg}`", err=True) + exit(1) + + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + if arg == 'snapshot': + show_snapshot() + elif arg == 'snapshot-list': + show_snapshot_list() + else: + show_list(arg) + + +@app.command("simple-show", help="Show node list (simple mode)") +def simple_show( + arg: str = typer.Argument( + help="[installed|enabled|not-installed|disabled|all|snapshot|snapshot-list]" + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + valid_commands = [ + "installed", + "enabled", + "not-installed", + "disabled", + "all", + "snapshot", + "snapshot-list", + ] + if arg not in valid_commands: + typer.echo(f"[bold red]Invalid command: `show {arg}`[/bold red]", err=True) + exit(1) + + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + if arg == 'snapshot': + show_snapshot(True) + elif arg == 'snapshot-list': + show_snapshot_list(True) + else: + show_list(arg, True) + + +@app.command('cli-only-mode', help="Set whether to use ComfyUI-Manager in CLI-only mode.") +def cli_only_mode( + mode: str = typer.Argument( + ..., help="[enable|disable]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ) +): + cmd_ctx.set_user_directory(user_directory) + cli_mode_flag = os.path.join(cmd_ctx.manager_files_directory, '.enable-cli-only-mode') + + if mode.lower() == 'enable': + with open(cli_mode_flag, 'w'): + pass + print("\nINFO: `cli-only-mode` is enabled\n") + elif mode.lower() == 'disable': + if os.path.exists(cli_mode_flag): + os.remove(cli_mode_flag) + print("\nINFO: `cli-only-mode` is disabled\n") + else: + print(f"\n[bold red]Invalid value for cli-only-mode: {mode}[/bold red]\n") + exit(1) + + +@app.command( + "deps-in-workflow", help="Generate dependencies file from workflow (.json/.png)" +) +def deps_in_workflow( + workflow: Annotated[ + str, typer.Option(show_default=False, help="Workflow file (.json/.png)") + ], + output: Annotated[ + str, typer.Option(show_default=False, help="Output file (.json)") + ], + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ) +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + input_path = workflow + output_path = output + + if not os.path.exists(input_path): + print(f"[bold red]File not found: {input_path}[/bold red]") + exit(1) + + used_exts, unknown_nodes = asyncio.run(core.extract_nodes_from_workflow(input_path, mode=cmd_ctx.mode, channel_url=cmd_ctx.channel)) + + custom_nodes = {} + for x in used_exts: + custom_nodes[x] = {'state': core.simple_check_custom_node(x), + 'hash': '-' + } + + res = { + 'custom_nodes': custom_nodes, + 'unknown_nodes': list(unknown_nodes) + } + + with open(output_path, "w", encoding='utf-8') as output_file: + json.dump(res, output_file, indent=4) + + print(f"Workflow dependencies are being saved into {output_path}.") + + +@app.command("save-snapshot", help="Save a snapshot of the current ComfyUI environment. If output path isn't provided. Save to ComfyUI-Manager/snapshots path.") +def save_snapshot( + output: Annotated[ + str, + typer.Option( + show_default=False, help="Specify the output file path. (.json/.yaml)" + ), + ] = None, + user_directory: str = typer.Option( + None, + help="user directory" + ), + full_snapshot: Annotated[ + bool, + typer.Option( + show_default=False, help="If the snapshot should include custom node, ComfyUI version and pip versions (default), or only custom node details" + ), + ] = True, +): + cmd_ctx.set_user_directory(user_directory) + + if output is not None: + if(not output.endswith('.json') and not output.endswith('.yaml')): + print("[bold red]ERROR: output path should be either '.json' or '.yaml' file.[/bold red]") + raise typer.Exit(code=1) + + dir_path = os.path.dirname(output) + + if(dir_path != '' and not os.path.exists(dir_path)): + print(f"[bold red]ERROR: {output} path not exists.[/bold red]") + raise typer.Exit(code=1) + + path = asyncio.run(core.save_snapshot_with_postfix('snapshot', output, not full_snapshot)) + print(f"Current snapshot is saved as `{path}`") + + +@app.command("restore-snapshot", help="Restore snapshot from snapshot file") +def restore_snapshot( + snapshot_name: str, + pip_non_url: Optional[bool] = typer.Option( + default=None, + show_default=False, + is_flag=True, + help="Restore for pip packages registered on PyPI.", + ), + pip_non_local_url: Optional[bool] = typer.Option( + default=None, + show_default=False, + is_flag=True, + help="Restore for pip packages registered at web URLs.", + ), + pip_local_url: Optional[bool] = typer.Option( + default=None, + show_default=False, + is_flag=True, + help="Restore for pip packages specified by local paths.", + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), + restore_to: Optional[str] = typer.Option( + None, + help="Manually specify the installation path for the custom node. Ignore user directory." + ) +): + cmd_ctx.set_user_directory(user_directory) + + if restore_to: + cmd_ctx.update_custom_nodes_dir(restore_to) + + extras = [] + if pip_non_url: + extras.append('--pip-non-url') + + if pip_non_local_url: + extras.append('--pip-non-local-url') + + if pip_local_url: + extras.append('--pip-local-url') + + print(f"PIPs restore mode: {extras}") + + if os.path.exists(snapshot_name): + snapshot_path = os.path.abspath(snapshot_name) + else: + snapshot_path = os.path.join(cmd_ctx.get_snapshot_path(), snapshot_name) + if not os.path.exists(snapshot_path): + print(f"[bold red]ERROR: `{snapshot_path}` is not exists.[/bold red]") + exit(1) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + try: + asyncio.run(core.restore_snapshot(snapshot_path, extras)) + except Exception: + print("[bold red]ERROR: Failed to restore snapshot.[/bold red]") + traceback.print_exc() + raise typer.Exit(code=1) + pip_fixer.fix_broken() + + +@app.command( + "restore-dependencies", help="Restore dependencies from whole installed custom nodes." +) +def restore_dependencies( + user_directory: str = typer.Option( + None, + help="user directory" + ) +): + cmd_ctx.set_user_directory(user_directory) + + node_paths = [] + + for base_path in cmd_ctx.get_custom_nodes_paths(): + for name in os.listdir(base_path): + target = os.path.join(base_path, name) + if os.path.isdir(target) and not name.endswith('.disabled'): + node_paths.append(target) + + total = len(node_paths) + i = 1 + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + for x in node_paths: + print("----------------------------------------------------------------------------------------------------") + print(f"Restoring [{i}/{total}]: {x}") + unified_manager.execute_install_script('', x, instant_execution=True) + i += 1 + pip_fixer.fix_broken() + + +@app.command( + "post-install", help="Install dependencies and execute installation script" +) +def post_install( + path: str = typer.Argument( + help="path to custom node", + ) +): + path = os.path.expanduser(path) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + unified_manager.execute_install_script('', path, instant_execution=True) + pip_fixer.fix_broken() + + +@app.command( + "install-deps", + help="Install dependencies from dependencies file(.json) or workflow(.png/.json)", +) +def install_deps( + deps: str = typer.Argument( + help="Dependency spec file (.json)", + ), + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + asyncio.run(auto_save_snapshot()) + + if not os.path.exists(deps): + print(f"[bold red]File not found: {deps}[/bold red]") + exit(1) + else: + with open(deps, 'r', encoding="UTF-8", errors="ignore") as json_file: + try: + json_obj = json.load(json_file) + except: + print(f"[bold red]Invalid json file: {deps}[/bold red]") + exit(1) + + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, core.manager_files_path) + for k in json_obj['custom_nodes'].keys(): + state = core.simple_check_custom_node(k) + if state == 'installed': + continue + elif state == 'not-installed': + asyncio.run(core.gitclone_install(k, instant_execution=True)) + else: # disabled + core.gitclone_set_active([k], False) + pip_fixer.fix_broken() + + print("Dependency installation and activation complete.") + + +@app.command(help="Clear reserved startup action in ComfyUI-Manager") +def clear(): + cancel() + + +@app.command("export-custom-node-ids", help="Export custom node ids") +def export_custom_node_ids( + path: str, + channel: Annotated[ + str, + typer.Option( + show_default=False, + help="Specify the operation mode" + ), + ] = None, + mode: str = typer.Option( + None, + help="[remote|local|cache]" + ), + user_directory: str = typer.Option( + None, + help="user directory" + ), +): + cmd_ctx.set_user_directory(user_directory) + cmd_ctx.set_channel_mode(channel, mode) + + with open(path, "w", encoding='utf-8') as output_file: + for x in unified_manager.cnr_map.keys(): + print(x, file=output_file) + + custom_nodes = asyncio.run(unified_manager.get_custom_nodes(channel=cmd_ctx.channel, mode=cmd_ctx.mode)) + for x in custom_nodes.values(): + if 'cnr_latest' not in x: + if len(x['files']) == 1: + repo_url = x['files'][0] + node_id = repo_url.split('/')[-1] + print(f"{node_id}@unknown", file=output_file) + + if 'id' in x: + print(f"{x['id']}@unknown", file=output_file) + + +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(app()) + + +print("") diff --git a/custom_nodes/comfyui-manager/cm-cli.sh b/custom_nodes/comfyui-manager/cm-cli.sh new file mode 100755 index 00000000..b1a21ca5 --- /dev/null +++ b/custom_nodes/comfyui-manager/cm-cli.sh @@ -0,0 +1,2 @@ +#!/bin/bash +python cm-cli.py $* diff --git a/custom_nodes/comfyui-manager/components/.gitignore b/custom_nodes/comfyui-manager/components/.gitignore new file mode 100644 index 00000000..fab7a5e2 --- /dev/null +++ b/custom_nodes/comfyui-manager/components/.gitignore @@ -0,0 +1,2 @@ +*.json +*.pack diff --git a/custom_nodes/comfyui-manager/docs/README.md b/custom_nodes/comfyui-manager/docs/README.md new file mode 100644 index 00000000..a5972752 --- /dev/null +++ b/custom_nodes/comfyui-manager/docs/README.md @@ -0,0 +1,41 @@ +# ComfyUI-Manager: Documentation + +This directory contains documentation for the ComfyUI-Manager, providing guides and tutorials for users in multiple languages. + +## Directory Structure + +The documentation is organized into language-specific directories: + +- **en/**: English documentation +- **ko/**: Korean documentation + +## Core Documentation Files + +### Command-Line Interface + +- **cm-cli.md**: Documentation for the ComfyUI-Manager Command Line Interface (CLI), which allows using manager functionality without the UI. + +### Advanced Features + +- **use_aria2.md**: Guide for using the aria2 download accelerator with ComfyUI-Manager for faster model downloads. + +## Documentation Standards + +The documentation follows these standards: + +1. **Markdown Format**: All documentation is written in Markdown for easy rendering on GitHub and other platforms +2. **Language-specific Directories**: Content is separated by language to facilitate localization +3. **Feature-focused Documentation**: Each major feature has its own documentation file +4. **Updated with Releases**: Documentation is kept in sync with software releases + +## Contributing to Documentation + +When contributing new documentation: + +1. Place files in the appropriate language directory +2. Use clear, concise language appropriate for the target audience +3. Include examples where helpful +4. Consider adding screenshots or diagrams for complex features +5. Maintain consistent formatting with existing documentation + +This documentation directory will continue to grow to support the expanding feature set of ComfyUI-Manager. \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/docs/en/cm-cli.md b/custom_nodes/comfyui-manager/docs/en/cm-cli.md new file mode 100644 index 00000000..e4b77a15 --- /dev/null +++ b/custom_nodes/comfyui-manager/docs/en/cm-cli.md @@ -0,0 +1,147 @@ +# `cm-cli`: ComfyUI-Manager CLI + +`cm-cli` is a tool that allows you to use various functions of ComfyUI-Manager from the command line without launching ComfyUI. + + +``` +-= ComfyUI-Manager CLI (V2.24) =- + + +python cm-cli.py [OPTIONS] + +OPTIONS: + [install|reinstall|uninstall|update|disable|enable|fix] node_name ... ?[--channel ] ?[--mode [remote|local|cache]] + [update|disable|enable|fix] all ?[--channel ] ?[--mode [remote|local|cache]] + [simple-show|show] [installed|enabled|not-installed|disabled|all|snapshot|snapshot-list] ?[--channel ] ?[--mode [remote|local|cache]] + save-snapshot ?[--output ] + restore-snapshot ?[--pip-non-url] ?[--pip-non-local-url] ?[--pip-local-url] + cli-only-mode [enable|disable] + restore-dependencies + clear +``` + +## How To Use? +* You can execute it via `python cm-cli.py`. +* For example, if you want to update all custom nodes: + * In the ComfyUI-Manager directory, you can execute the command `python cm-cli.py update all`. + * If running from the ComfyUI directory, you can specify the path to cm-cli.py like this: `python custom_nodes/ComfyUI-Manager/cm-cli.py update all`. + +## Prerequisite +* It must be run in the same Python environment as the one running ComfyUI. + * If using a venv, you must run it with the venv activated. + * If using a portable version, and you are in the directory with the run_nvidia_gpu.bat file, you should execute the command as follows: + `.\python_embeded\python.exe ComfyUI\custom_nodes\ComfyUI-Manager\cm-cli.py update all` +* The path for ComfyUI can be set with the COMFYUI_PATH environment variable. If omitted, a warning message will appear, and the path will be set relative to the installed location of ComfyUI-Manager: + ``` + WARN: The `COMFYUI_PATH` environment variable is not set. Assuming `custom_nodes/ComfyUI-Manager/../../` as the ComfyUI path. + ``` + +## Features + +### 1. --channel, --mode +* For viewing information and managing custom nodes, you can set the information database through --channel and --mode. +* For instance, executing the command `python cm-cli.py update all --channel recent --mode remote` will operate based on the latest information from remote rather than local data embedded in the current ComfyUI-Manager repo and will only target the list in the recent channel. +* --channel, --mode are only available with the commands `simple-show, show, install, uninstall, update, disable, enable, fix`. + +### 2. Viewing Management Information + +`[simple-show|show] [installed|enabled|not-installed|disabled|all|snapshot|snapshot-list] ?[--channel ] ?[--mode [remote|local|cache]]` + +* `[show|simple-show]` - `show` provides detailed information, while `simple-show` displays information more simply. + +Executing a command like `python cm-cli.py show installed` will display detailed information about the installed custom nodes. + +``` +-= ComfyUI-Manager CLI (V2.24) =- + +FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json +[ ENABLED ] ComfyUI-Manager (author: Dr.Lt.Data) +[ ENABLED ] ComfyUI-Impact-Pack (author: Dr.Lt.Data) +[ ENABLED ] ComfyUI-Inspire-Pack (author: Dr.Lt.Data) +[ ENABLED ] ComfyUI_experiments (author: comfyanonymous) +[ ENABLED ] ComfyUI-SAI_API (author: Stability-AI) +[ ENABLED ] stability-ComfyUI-nodes (author: Stability-AI) +[ ENABLED ] comfyui_controlnet_aux (author: Fannovel16) +[ ENABLED ] ComfyUI-Frame-Interpolation (author: Fannovel16) +[ DISABLED ] ComfyUI-Loopchain (author: Fannovel16) +``` + +Using a command like `python cm-cli.py simple-show installed` will simply display information about the installed custom nodes. + +``` +-= ComfyUI-Manager CLI (V2.24) =- + +FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json +ComfyUI-Manager +ComfyUI-Impact-Pack +ComfyUI-Inspire-Pack +ComfyUI_experiments +ComfyUI-SAI_API +stability-ComfyUI-nodes +comfyui_controlnet_aux +ComfyUI-Frame-Interpolation +ComfyUI-Loopchain +``` + +`[installed|enabled|not-installed|disabled|all|snapshot|snapshot-list]` + * `enabled`, `disabled`: Shows nodes that have been enabled or disabled among the installed custom nodes. + * `installed`: Shows all nodes that have been installed, regardless of whether they are enabled or disabled. + * `not-installed`: Shows a list of custom nodes that have not been installed. + * `all`: Shows a list of all custom nodes. + * `snapshot`: Displays snapshot information of the currently installed custom nodes. When viewed with `show`, it is displayed in JSON format, and with `simple-show`, it is displayed simply, along with the commit hash. + * `snapshot-list`: Shows a list of snapshot files stored in ComfyUI-Manager/snapshots. + +### 3. Managing Custom Nodes + +`[install|reinstall|uninstall|update|disable|enable|fix] node_name ... ?[--channel ] ?[--mode [remote|local|cache]]` + +* You can apply management functions by listing the names of custom nodes, such as `python cm-cli.py install ComfyUI-Impact-Pack ComfyUI-Inspire-Pack ComfyUI_experiments`. +* The names of the custom nodes are as shown by `show` and are the names of the git repositories. +(Plans are to update the use of nicknames in the future.) + +`[update|disable|enable|fix] all ?[--channel ] ?[--mode [remote|local|cache]]` + +* The `update, disable, enable, fix` functions can be specified for all. + +* Detailed Operations + * `install`: Installs the specified custom nodes. + * `reinstall`: Removes and then reinstalls the specified custom nodes. + * `uninstall`: Uninstalls the specified custom nodes. + * `update`: Updates the specified custom nodes. + * `disable`: Disables the specified custom nodes. + * `enable`: Enables the specified custom nodes. + * `fix`: Attempts to fix dependencies for the specified custom nodes. + + +### 4. Snapshot Management +* `python cm-cli.py save-snapshot [--output ]`: Saves the current snapshot. + * With `--output`, you can save a file in .yaml format to any specified path. +* `python cm-cli.py restore-snapshot `: Restores to the specified snapshot. + * If a file exists at the snapshot path, that snapshot is loaded. + * If no file exists at the snapshot path, it is implicitly assumed to be in ComfyUI-Manager/snapshots. + * `--pip-non-url`: Restore for pip packages registered on PyPI. + * `--pip-non-local-url`: Restore for pip packages registered at web URLs. + * `--pip-local-url`: Restore for pip packages specified by local paths. + * `--user-directory`: Set the user directory. + * `--restore-to`: The path where the restored custom nodes will be installed. (When this option is applied, only the custom nodes installed in the target path are recognized as installed.) + +### 5. CLI Only Mode + +You can set whether to use ComfyUI-Manager solely via CLI. + +`cli-only-mode [enable|disable]` + +* This mode can be used if you want to restrict the use of ComfyUI-Manager through the GUI for security or policy reasons. + * When CLI only mode is enabled, ComfyUI-Manager is loaded in a very restricted state, the internal web API is disabled, and the Manager button is not displayed in the main menu. + +### 6. Dependency Restoration + +`restore-dependencies` + +* This command can be used if custom nodes are installed under the `ComfyUI/custom_nodes` path but their dependencies are not installed. +* It is useful when starting a new cloud instance, like Colab, where dependencies need to be reinstalled and installation scripts re-executed. +* It can also be utilized if ComfyUI is reinstalled and only the custom_nodes path has been backed up and restored. + +### 7. Clear + +In the GUI, installations, updates, or snapshot restorations are scheduled to execute the next time ComfyUI is launched. The `clear` command clears this scheduled state, ensuring no pre-execution actions are applied. diff --git a/custom_nodes/comfyui-manager/docs/en/use_aria2.md b/custom_nodes/comfyui-manager/docs/en/use_aria2.md new file mode 100644 index 00000000..10a7c6dd --- /dev/null +++ b/custom_nodes/comfyui-manager/docs/en/use_aria2.md @@ -0,0 +1,40 @@ +# Use `aria2` as downloader + +Two environment variables are needed to use `aria2` as the downloader. + +```bash +export COMFYUI_MANAGER_ARIA2_SERVER=http://127.0.0.1:6800 +export COMFYUI_MANAGER_ARIA2_SECRET=__YOU_MUST_CHANGE_IT__ +``` + +An example `docker-compose.yml` + +```yaml +services: + + aria2: + container_name: aria2 + image: p3terx/aria2-pro + environment: + - PUID=1000 + - PGID=1000 + - UMASK_SET=022 + - RPC_SECRET=__YOU_MUST_CHANGE_IT__ + - RPC_PORT=5080 + - DISK_CACHE=64M + - IPV6_MODE=false + - UPDATE_TRACKERS=false + - CUSTOM_TRACKER_URL= + volumes: + - ./config:/config + - ./downloads:/downloads + - ~/ComfyUI/models:/models + - ~/ComfyUI/custom_nodes:/custom_nodes + ports: + - 6800:6800 + restart: unless-stopped + logging: + driver: json-file + options: + max-size: 1m +``` diff --git a/custom_nodes/comfyui-manager/docs/en/v3.38-userdata-security-migration.md b/custom_nodes/comfyui-manager/docs/en/v3.38-userdata-security-migration.md new file mode 100644 index 00000000..324321d7 --- /dev/null +++ b/custom_nodes/comfyui-manager/docs/en/v3.38-userdata-security-migration.md @@ -0,0 +1,230 @@ +# ComfyUI-Manager V3.38: Userdata Security Migration Guide + +## Introduction + +ComfyUI-Manager V3.38 introduces a **security patch** that migrates Manager's configuration and data to a protected system path. This change leverages ComfyUI's new System User Protection API (PR #10966) to provide enhanced security isolation. + +This guide explains what happens during the migration and how to handle various situations. + +--- + +## What Changed + +### Finding Your Paths + +When ComfyUI starts, it displays the full paths in the terminal: + +``` +** User directory: /path/to/ComfyUI/user +** ComfyUI-Manager config path: /path/to/ComfyUI/user/__manager/config.ini +``` + +Look for these lines in your startup log to find the exact location on your system. In this guide, paths are shown relative to the `user` directory. + +### Path Migration + +| Data | Legacy Path | New Path | +|------|-------------|----------| +| Configuration | `user/default/ComfyUI-Manager/` | `user/__manager/` | +| Snapshots | `user/default/ComfyUI-Manager/snapshots/` | `user/__manager/snapshots/` | + +### Why This Change + +In older ComfyUI versions, the `default/` directory was **unprotected** and accessible via web APIs. If you ran ComfyUI with `--listen 0.0.0.0` or similar options to allow external connections, this data **may have been tampered with** by malicious actors. + +**Note:** If you only used ComfyUI locally (without `--listen` or with `--listen 127.0.0.1`), your data was not exposed to this vulnerability. + +The new `__manager` path uses ComfyUI's protected system directory, which: +- **Cannot be accessed** from outside (protected by ComfyUI) +- Isolates system settings from user data +- Enables stricter security for remote access + +**This is why only `config.ini` is automatically migrated** - other files (snapshots) may have been compromised and should be manually verified before copying. + +--- + +## Automatic Migration + +When you start ComfyUI with the new System User Protection API, Manager automatically handles the migration: + +### Step 1: Configuration Migration + +Only `config.ini` is migrated automatically. + +**Important**: Snapshots are **NOT** automatically migrated. You must copy them manually if needed. + +### Step 2: Security Level Check + +During migration, if your security level is below `normal` (i.e., `weak` or `normal-`), it will be automatically raised to `normal`. This is a safety measure because the security level setting itself may have been tampered with in the old version. + +``` +====================================================================== +[ComfyUI-Manager] WARNING: Security level adjusted + - Previous: 'weak' β†’ New: 'normal' + - Raised to prevent unauthorized remote access. +====================================================================== +``` + +If you need a lower security level, you can manually edit the config after migration. + +### Step 3: Legacy Backup + +Your entire legacy directory is moved to a backup location: +``` +user/__manager/.legacy-manager-backup/ +``` + +This backup is preserved until you manually delete it. + +--- + +## Persistent Backup Notification + +As long as the backup exists, Manager will remind you on **every startup**: + +``` +---------------------------------------------------------------------- +[ComfyUI-Manager] NOTICE: Legacy backup exists + - Your old Manager data was backed up to: + /path/to/ComfyUI/user/__manager/.legacy-manager-backup + - Please verify and remove it when no longer needed. +---------------------------------------------------------------------- +``` + +**To stop this notification**: Delete the `.legacy-manager-backup` folder inside `user/__manager/` after confirming you don't need any data from it. + +--- + +## Recovering Old Data + +### Snapshots + +If you need your old snapshots, copy the contents of `.legacy-manager-backup/snapshots/` to `user/__manager/snapshots/`. + +--- + +## Outdated ComfyUI Warning + +If you're running an older version of ComfyUI without the System User Protection API, Manager will: + +1. **Force security level to `strong`** - All installations are blocked +2. **Display warning message**: + +``` +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +[ComfyUI-Manager] ERROR: ComfyUI version is outdated! + - Most operations are blocked for security. + - ComfyUI update is still allowed. + - Please update ComfyUI to use Manager normally. +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +``` + +**Solution**: Update ComfyUI to v0.3.76 or later. + +--- + +## Security Levels + +| Level | What's Allowed | +|-------|----------------| +| `strong` | ComfyUI update only. All other installations blocked. | +| `normal` | Install/update/remove registered custom nodes and models. | +| `normal-` | Above + Install via Git URL or pip (localhost only). | +| `weak` | All operations allowed, including from remote connections. | + +**Notes:** +- `strong` is forced on outdated ComfyUI versions. +- `normal` is the default and recommended for most users. +- `normal-` is for developers who need to install unregistered nodes locally. +- `weak` should only be used in isolated development environments. + +### Changing Security Level + +Edit `user/__manager/config.ini`: +```ini +[default] +security_level = normal +``` + +--- + +## Error Messages + +### "comfyui_outdated" (HTTP 403) + +This error appears when: +- Your ComfyUI doesn't have the System User Protection API +- All installations are blocked until you update ComfyUI + +**Solution**: Update ComfyUI to the latest version. + +### "security_level" (HTTP 403) + +This error appears when: +- Your security level blocks the requested operation +- For example, `strong` level blocks all installations + +**Solution**: Lower your security level in config.ini if appropriate for your use case. + +--- + +## Security Warning: Suspicious Path + +If you see this error on an **older** ComfyUI: + +``` +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +[ComfyUI-Manager] ERROR: Suspicious path detected! + - '__manager' exists with low security level: 'weak' + - Please verify manually: + /path/to/ComfyUI/user/__manager/config.ini +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +``` + +On older ComfyUI versions, the `__manager` directory is not normally created. If this directory exists, it may have been created externally. For safety, manually verify the contents of this directory before updating ComfyUI. + +--- + +## Troubleshooting + +### All my installations are blocked + +**Check 1**: Is your ComfyUI updated? +- Old ComfyUI forces `security_level = strong` +- Update ComfyUI to resolve + +**Check 2**: What's your security level? +- Check `user/__manager/config.ini` +- `security_level = strong` blocks all installations + +### My snapshots are missing + +Snapshots are not automatically migrated. You need to manually copy the `snapshots` folder from inside `.legacy-manager-backup` to the `user/__manager/` directory. + +### I keep seeing the backup notification + +Delete the `.legacy-manager-backup` folder inside `user/__manager/` after confirming you don't need any data from it. + +### Snapshot restore is blocked + +On old ComfyUI (without System User API), snapshot restore is blocked because security is forced to `strong`. Update ComfyUI to enable snapshot restore. + +--- + +## File Structure Reference + +``` +user/ +└── __manager/ + β”œβ”€β”€ config.ini # Manager configuration + β”œβ”€β”€ channels.list # Custom node channels + β”œβ”€β”€ snapshots/ # Environment snapshots + └── .legacy-manager-backup/ # Backup of old Manager data (temporary) +``` + +--- + +## Requirements + +- **ComfyUI**: v0.3.76 or later (with System User Protection API) +- **ComfyUI-Manager**: V3.38 or later diff --git a/custom_nodes/comfyui-manager/docs/ko/cm-cli.md b/custom_nodes/comfyui-manager/docs/ko/cm-cli.md new file mode 100644 index 00000000..0be8e89d --- /dev/null +++ b/custom_nodes/comfyui-manager/docs/ko/cm-cli.md @@ -0,0 +1,150 @@ +# `cm-cli`: ComfyUI-Manager CLI + +`cm-cli` λŠ” ComfyUIλ₯Ό μ‹€ν–‰μ‹œν‚€μ§€ μ•Šκ³  command lineμ—μ„œ ComfyUI-Manager의 μ—¬λŸ¬κ°€μ§€ κΈ°λŠ₯을 μ‚¬μš©ν•  수 μžˆλ„λ‘ λ„μ™€μ£ΌλŠ” λ„κ΅¬μž…λ‹ˆλ‹€. + + +``` +-= ComfyUI-Manager CLI (V2.24) =- + + +python cm-cli.py [OPTIONS] + +OPTIONS: + [install|reinstall|uninstall|update|disable|enable|fix] node_name ... ?[--channel ] ?[--mode [remote|local|cache]] + [update|disable|enable|fix] all ?[--channel ] ?[--mode [remote|local|cache]] + [simple-show|show] [installed|enabled|not-installed|disabled|all|snapshot|snapshot-list] ?[--channel ] ?[--mode [remote|local|cache]] + save-snapshot ?[--output ] + restore-snapshot ?[--pip-non-url] ?[--pip-non-local-url] ?[--pip-local-url] + cli-only-mode [enable|disable] + restore-dependencies + clear +``` + +## How To Use? +* `python cm-cli.py` λ₯Ό ν†΅ν•΄μ„œ μ‹€ν–‰ μ‹œν‚¬ 수 μžˆμŠ΅λ‹ˆλ‹€. +* 예λ₯Ό λ“€μ–΄ custom nodeλ₯Ό λͺ¨λ‘ μ—…λ°μ΄νŠΈ ν•˜κ³  μ‹Άλ‹€λ©΄ + * ComfyUI-Manager κ²½λ‘œμ—μ„œ `python cm-cli.py update all` λͺ…령을 μ‹€ν–‰ν•  수 μžˆμŠ΅λ‹ˆλ‹€. + * ComfyUI κ²½λ‘œμ—μ„œ μ‹€ν–‰ν•œλ‹€λ©΄, `python custom_nodes/ComfyUI-Manager/cm-cli.py update all` 와 같이 cm-cli.py 의 경둜λ₯Ό 지정할 μˆ˜λ„ μžˆμŠ΅λ‹ˆλ‹€. + +## Prerequisite +* ComfyUI λ₯Ό μ‹€ν–‰ν•˜λŠ” pythonκ³Ό λ™μΌν•œ python ν™˜κ²½μ—μ„œ μ‹€ν–‰ν•΄μ•Ό ν•©λ‹ˆλ‹€. + * venvλ₯Ό μ‚¬μš©ν•  경우 ν•΄λ‹Ή venvλ₯Ό activate ν•œ μƒνƒœμ—μ„œ μ‹€ν–‰ν•΄μ•Ό ν•©λ‹ˆλ‹€. +* portable 버전을 μ‚¬μš©ν•  경우 run_nvidia_gpu.bat 파일이 μžˆλŠ” 경둜인 경우, λ‹€μŒκ³Ό 같은 λ°©μ‹μœΌλ‘œ λͺ…령을 μ‹€ν–‰ν•΄μ•Ό ν•©λ‹ˆλ‹€. + `.\python_embeded\python.exe ComfyUI\custom_nodes\ComfyUI-Manager\cm-cli.py update all` +* ComfyUI 의 κ²½λ‘œλŠ” COMFYUI_PATH ν™˜κ²½ λ³€μˆ˜λ‘œ μ„€μ •ν•  수 μžˆμŠ΅λ‹ˆλ‹€. λ§Œμ•½ μƒλž΅ν•  경우 λ‹€μŒκ³Ό 같은 κ²½κ³  λ©”μ‹œμ§€κ°€ λ‚˜νƒ€λ‚˜λ©°, ComfyUI-Managerκ°€ μ„€μΉ˜λœ 경둜λ₯Ό κΈ°μ€€μœΌλ‘œ μƒλŒ€ 경둜둜 μ„€μ •λ©λ‹ˆλ‹€. + ``` + WARN: The `COMFYUI_PATH` environment variable is not set. Assuming `custom_nodes/ComfyUI-Manager/../../` as the ComfyUI path. + ``` + +## Features + +### 1. --channel, --mode +* 정보 보기 κΈ°λŠ₯κ³Ό μ»€μŠ€ν…€ λ…Έλ“œ 관리 κΈ°λŠ₯의 κ²½μš°λŠ” --channelκ³Ό --modeλ₯Ό 톡해 정보 DBλ₯Ό μ„€μ •ν•  수 μžˆμŠ΅λ‹ˆλ‹€. +* 예λ₯Ό λ“€μ–΄ `python cm-cli.py update all --channel recent --mode remote`와 같은 λͺ…령을 μ‹€ν–‰ν•  경우, ν˜„μž¬ ComfyUI-Manager repo에 λ‚΄μž₯된 둜컬의 정보가 μ•„λ‹Œ remote의 μ΅œμ‹  정보λ₯Ό κΈ°μ€€μœΌλ‘œ λ™μž‘ν•˜λ©°, recent channel에 μžˆλŠ” λͺ©λ‘μ„ λŒ€μƒμœΌλ‘œλ§Œ λ™μž‘ν•©λ‹ˆλ‹€. +* --channel, --mode λŠ” `simple-show, show, install, uninstall, update, disable, enable, fix` λͺ…λ Ήμ—μ„œλ§Œ μ‚¬μš© κ°€λŠ₯ν•©λ‹ˆλ‹€. + +### 2. 관리 정보 보기 + +`[simple-show|show] [installed|enabled|not-installed|disabled|all|snapshot|snapshot-list] ?[--channel ] ?[--mode [remote|local|cache]]` + + +* `[show|simple-show]` - `show`λŠ” μƒμ„Έν•˜κ²Œ 정보λ₯Ό 보여주며, `simple-show`λŠ” κ°„λ‹¨ν•˜κ²Œ 정보λ₯Ό λ³΄μ—¬μ€λ‹ˆλ‹€. + + +`python cm-cli.py show installed` 와 같은 λͺ…령을 μ‹€ν–‰ν•˜λ©΄ μ„€μΉ˜λœ μ»€μŠ€ν…€ λ…Έλ“œμ˜ 정보λ₯Ό μƒμ„Έν•˜κ²Œ λ³΄μ—¬μ€λ‹ˆλ‹€. +``` +-= ComfyUI-Manager CLI (V2.24) =- + +FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json +[ ENABLED ] ComfyUI-Manager (author: Dr.Lt.Data) +[ ENABLED ] ComfyUI-Impact-Pack (author: Dr.Lt.Data) +[ ENABLED ] ComfyUI-Inspire-Pack (author: Dr.Lt.Data) +[ ENABLED ] ComfyUI_experiments (author: comfyanonymous) +[ ENABLED ] ComfyUI-SAI_API (author: Stability-AI) +[ ENABLED ] stability-ComfyUI-nodes (author: Stability-AI) +[ ENABLED ] comfyui_controlnet_aux (author: Fannovel16) +[ ENABLED ] ComfyUI-Frame-Interpolation (author: Fannovel16) +[ DISABLED ] ComfyUI-Loopchain (author: Fannovel16) +``` + +`python cm-cli.py simple-show installed` 와 같은 λͺ…령을 μ΄μš©ν•΄μ„œ μ„€μΉ˜λœ μ»€μŠ€ν…€ λ…Έλ“œμ˜ 정보λ₯Ό κ°„λ‹¨ν•˜κ²Œ λ³΄μ—¬μ€λ‹ˆλ‹€. + +``` +-= ComfyUI-Manager CLI (V2.24) =- + +FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json +ComfyUI-Manager +ComfyUI-Impact-Pack +ComfyUI-Inspire-Pack +ComfyUI_experiments +ComfyUI-SAI_API +stability-ComfyUI-nodes +comfyui_controlnet_aux +ComfyUI-Frame-Interpolation +ComfyUI-Loopchain +``` + +* `[installed|enabled|not-installed|disabled|all|snapshot|snapshot-list]` + * `enabled`, `disabled`: μ„€μΉ˜λœ μ»€μŠ€ν…€ λ…Έλ“œλ“€ 쀑 enable λ˜μ—ˆκ±°λ‚˜, disable된 λ…Έλ“œλ“€μ„ λ³΄μ—¬μ€λ‹ˆλ‹€. + * `installed`: enable, disable 여뢀와 상관없이 μ„€μΉ˜λœ λͺ¨λ“  λ…Έλ“œλ₯Ό λ³΄μ—¬μ€λ‹ˆλ‹€ + * `not-installed`: μ„€μΉ˜λ˜μ§€ μ•Šμ€ μ»€μŠ€ν…€ λ…Έλ“œμ˜ λͺ©λ‘μ„ λ³΄μ—¬μ€λ‹ˆλ‹€. + * `all`: λͺ¨λ“  μ»€μŠ€ν…€ λ…Έλ“œμ˜ λͺ©λ‘μ„ λ³΄μ—¬μ€λ‹ˆλ‹€. + * `snapshot`: ν˜„μž¬ μ„€μΉ˜λœ μ»€μŠ€ν…€ λ…Έλ“œμ˜ snapshot 정보λ₯Ό λ³΄μ—¬μ€λ‹ˆλ‹€. `show`λ₯Ό ν†΅ν•΄μ„œ λ³Ό κ²½μš°λŠ” json 좜λ ₯ ν˜•νƒœλ‘œ 보여주며, `simple-show`λ₯Ό ν†΅ν•΄μ„œ λ³Ό κ²½μš°λŠ” κ°„λ‹¨ν•˜κ²Œ, 컀밋 ν•΄μ‹œμ™€ ν•¨κ»˜ λ³΄μ—¬μ€λ‹ˆλ‹€. + * `snapshot-list`: ComfyUI-Manager/snapshots 에 μ €μž₯된 snapshot 파일의 λͺ©λ‘μ„ λ³΄μ—¬μ€λ‹ˆλ‹€. + +### 3. μ»€μŠ€ν…€ λ…Έλ“œ 관리 ν•˜κΈ° + +`[install|reinstall|uninstall|update|disable|enable|fix] node_name ... ?[--channel ] ?[--mode [remote|local|cache]]` + +* `python cm-cli.py install ComfyUI-Impact-Pack ComfyUI-Inspire-Pack ComfyUI_experiments` 와 같이 μ»€μŠ€ν…€ λ…Έλ“œμ˜ 이름을 λ‚˜μ—΄ν•΄μ„œ 관리 κΈ°λŠ₯을 μ μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€. +* μ»€μŠ€ν…€ λ…Έλ“œμ˜ 이름은 `show`λ₯Ό ν–ˆμ„ λ•Œ λ³΄μ—¬μ£ΌλŠ” 이름이며, git repository의 μ΄λ¦„μž…λ‹ˆλ‹€. +(μΆ”ν›„ nickname을 μ‚¬μš© κ°€λŠ₯ν•˜λ„λ‘ μ—…λ°μ΄νŠΈν•  μ˜ˆμ •μž…λ‹ˆλ‹€.) + +`[update|disable|enable|fix] all ?[--channel ] ?[--mode [remote|local|cache]]` + +* `update, disable, enable, fix` κΈ°λŠ₯은 all 둜 지정 κ°€λŠ₯ν•©λ‹ˆλ‹€. + +* μ„ΈλΆ€ λ™μž‘ + * `install`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œλ“€μ„ μ„€μΉ˜ν•©λ‹ˆλ‹€ + * `reinstall`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œλ₯Ό μ‚­μ œν•˜κ³  μž¬μ„€μΉ˜ ν•©λ‹ˆλ‹€. + * `uninstall`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œλ“€μ„ μ‚­μ œν•©λ‹ˆλ‹€. + * `update`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œλ“€μ„ μ—…λ°μ΄νŠΈν•©λ‹ˆλ‹€. + * `disable`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œλ“€μ„ λΉ„ν™œμ„±ν™”ν•©λ‹ˆλ‹€. + * `enable`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œλ“€μ„ ν™œμ„±ν™”ν•©λ‹ˆλ‹€. + * `fix`: μ§€μ •λœ μ»€μŠ€ν…€ λ…Έλ“œμ˜ μ˜μ‘΄μ„±μ„ 고치기 μœ„ν•œ μ‹œλ„λ₯Ό ν•©λ‹ˆλ‹€. + + +### 4. μŠ€λƒ…μƒ· 관리 κΈ°λŠ₯ +* `python cm-cli.py save-snapshot ?[--output ]`: ν˜„μž¬μ˜ snapshot을 μ €μž₯ν•©λ‹ˆλ‹€. + * --output 으둜 μž„μ˜μ˜ κ²½λ‘œμ— .yaml 파일과 format으둜 μ €μž₯ν•  수 μžˆμŠ΅λ‹ˆλ‹€. +* `python cm-cli.py restore-snapshot `: μ§€μ •λœ snapshot으둜 λ³΅κ΅¬ν•©λ‹ˆλ‹€. + * snapshot κ²½λ‘œμ— 파일이 μ‘΄μž¬ν•˜λŠ” 경우 ν•΄λ‹Ή snapshot을 λ‘œλ“œν•©λ‹ˆλ‹€. + * snapshot κ²½λ‘œμ— 파일이 μ‘΄μž¬ν•˜μ§€ μ•ŠλŠ” 경우 λ¬΅μ‹œμ μœΌλ‘œ, ComfyUI-Manager/snapshots 에 μžˆλ‹€κ³  κ°€μ •ν•©λ‹ˆλ‹€. + * `--pip-non-url`: PyPI 에 λ“±λ‘λœ pip νŒ¨ν‚€μ§€λ“€μ— λŒ€ν•΄μ„œ 볡ꡬλ₯Ό μˆ˜ν–‰ + * `--pip-non-local-url`: web URL에 λ“±λ‘λœ pip νŒ¨ν‚€μ§€λ“€μ— λŒ€ν•΄μ„œ 볡ꡬλ₯Ό μˆ˜ν–‰ + * `--pip-local-url`: local 경둜λ₯Ό μ§€μ •ν•˜κ³  μžˆλŠ” pip νŒ¨ν‚€μ§€λ“€μ— λŒ€ν•΄μ„œ 볡ꡬλ₯Ό μˆ˜ν–‰ + * `--user-directory`: μ‚¬μš©μž 디렉토리 μ„€μ • + * `--restore-to`: 볡ꡬ될 μ»€μŠ€ν…€ λ…Έλ“œκ°€ μ„€μΉ˜λ  경둜. (이 μ˜΅μ…˜μ„ μ μš©ν•  경우 였직 λŒ€μƒ κ²½λ‘œμ— μ„€μΉ˜λœ custom nodes만 μ„€μΉ˜λœ κ²ƒμœΌλ‘œ 인식함.) + +### 5. CLI only mode + +ComfyUI-Managerλ₯Ό CLI둜만 μ‚¬μš©ν•  것인지λ₯Ό μ„€μ •ν•  수 μžˆμŠ΅λ‹ˆλ‹€. + +`cli-only-mode [enable|disable]` + +* security ν˜Ήμ€ policy 의 이유둜 GUI λ₯Ό ν†΅ν•œ ComfyUI-Manager μ‚¬μš©μ„ μ œν•œν•˜κ³  싢은 경우 이 λͺ¨λ“œλ₯Ό μ‚¬μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€. + * CLI only modeλ₯Ό μ μš©ν•  경우 ComfyUI-Manager κ°€ 맀우 μ œν•œλœ μƒνƒœλ‘œ λ‘œλ“œλ˜μ–΄, λ‚΄λΆ€μ μœΌλ‘œ μ œκ³΅ν•˜λŠ” web APIκ°€ λΉ„ν™œμ„±ν™”λ˜λ©°, 메인 λ©”λ‰΄μ—μ„œλ„ Manager λ²„νŠΌμ΄ ν‘œμ‹œλ˜μ§€ μ•ŠμŠ΅λ‹ˆλ‹€. + + +### 6. μ˜μ‘΄μ„± μ„€μΉ˜ + +`restore-dependencies` + +* `ComfyUI/custom_nodes` ν•˜μœ„ κ²½λ‘œμ— μ»€μŠ€ν…€ λ…Έλ“œλ“€μ΄ μ„€μΉ˜λ˜μ–΄ 있긴 ν•˜μ§€λ§Œ, μ˜μ‘΄μ„±μ΄ μ„€μΉ˜λ˜μ§€ μ•Šμ€ 경우 μ‚¬μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€. +* Colabκ³Ό 같이 cloud instanceλ₯Ό μƒˆλ‘œ μ‹œμž‘ν•˜λŠ” 경우 μ˜μ‘΄μ„± μž¬μ„€μΉ˜ 및 μ„€μΉ˜ μŠ€ν¬λ¦½νŠΈκ°€ μž¬μ‹€ν–‰λ˜μ–΄μ•Ό ν•˜λŠ” 경우 μ‚¬μš©ν•©λ‹ˆλ‹€. +* ComfyUIλ₯Ό μž¬μ„€μΉ˜ν•  경우, custom_nodes 경둜만 λ°±μ—…ν–ˆλ‹€κ°€ μž¬μ„€μΉ˜ν•  경우 ν™œμš© κ°€λŠ₯ν•©λ‹ˆλ‹€. + + +### 7. clear + +GUIμ—μ„œ install, updateλ₯Ό ν•˜κ±°λ‚˜ snapshot을 restoreν•˜λŠ” 경우 μ˜ˆμ•½μ„ ν†΅ν•΄μ„œ λ‹€μŒλ²ˆ ComfyUIλ₯Ό μ‹€ν–‰ν•  경우 μ‹€ν–‰λ˜λŠ” κ΅¬μ‘°μž…λ‹ˆλ‹€. `clear` λŠ” 이런 μ˜ˆμ•½ μƒνƒœλ₯Ό clearν•΄μ„œ, μ•„λ¬΄λŸ° 사전 싀행이 μ μš©λ˜μ§€ μ•Šλ„λ‘ ν•©λ‹ˆλ‹€. diff --git a/custom_nodes/comfyui-manager/git_helper.py b/custom_nodes/comfyui-manager/git_helper.py new file mode 100644 index 00000000..4e184a3b --- /dev/null +++ b/custom_nodes/comfyui-manager/git_helper.py @@ -0,0 +1,531 @@ +import subprocess +import sys +import os +import traceback +import time + +import git +import json +import yaml +import requests +from tqdm.auto import tqdm +from git.remote import RemoteProgress + + +comfy_path = os.environ.get('COMFYUI_PATH') +git_exe_path = os.environ.get('GIT_EXE_PATH') + +if comfy_path is None: + print("\nWARN: The `COMFYUI_PATH` environment variable is not set. Assuming `custom_nodes/ComfyUI-Manager/../../` as the ComfyUI path.", file=sys.stderr) + comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + + +def download_url(url, dest_folder, filename=None): + # Ensure the destination folder exists + if not os.path.exists(dest_folder): + os.makedirs(dest_folder) + + # Extract filename from URL if not provided + if filename is None: + filename = os.path.basename(url) + + # Full path to save the file + dest_path = os.path.join(dest_folder, filename) + + # Download the file + response = requests.get(url, stream=True) + if response.status_code == 200: + with open(dest_path, 'wb') as file: + for chunk in response.iter_content(chunk_size=1024): + if chunk: + file.write(chunk) + else: + print(f"Failed to download file from {url}") + + +nodelist_path = os.path.join(os.path.dirname(__file__), "custom-node-list.json") +working_directory = os.getcwd() + +if os.path.basename(working_directory) != 'custom_nodes': + print("WARN: This script should be executed in custom_nodes dir") + print(f"DBG: INFO {working_directory}") + print(f"DBG: INFO {sys.argv}") + # exit(-1) + + +class GitProgress(RemoteProgress): + def __init__(self): + super().__init__() + self.pbar = tqdm(ascii=True) + + def update(self, op_code, cur_count, max_count=None, message=''): + self.pbar.total = max_count + self.pbar.n = cur_count + self.pbar.pos = 0 + self.pbar.refresh() + + +def gitclone(custom_nodes_path, url, target_hash=None, repo_path=None): + repo_name = os.path.splitext(os.path.basename(url))[0] + + if repo_path is None: + repo_path = os.path.join(custom_nodes_path, repo_name) + + # Clone the repository from the remote URL + repo = git.Repo.clone_from(url, repo_path, recursive=True, progress=GitProgress()) + + if target_hash is not None: + print(f"CHECKOUT: {repo_name} [{target_hash}]") + repo.git.checkout(target_hash) + + repo.git.clear_cache() + repo.close() + + +def gitcheck(path, do_fetch=False): + try: + # Fetch the latest commits from the remote repository + repo = git.Repo(path) + + if repo.head.is_detached: + print("CUSTOM NODE CHECK: True") + return + + current_branch = repo.active_branch + branch_name = current_branch.name + + remote_name = current_branch.tracking_branch().remote_name + remote = repo.remote(name=remote_name) + + if do_fetch: + remote.fetch() + + # Get the current commit hash and the commit hash of the remote branch + commit_hash = repo.head.commit.hexsha + + if f'{remote_name}/{branch_name}' in repo.refs: + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + else: + print("CUSTOM NODE CHECK: True") # non default branch is treated as updatable + return + + # Compare the commit hashes to determine if the local repository is behind the remote repository + if commit_hash != remote_commit_hash: + # Get the commit dates + commit_date = repo.head.commit.committed_datetime + remote_commit_date = repo.refs[f'{remote_name}/{branch_name}'].object.committed_datetime + + # Compare the commit dates to determine if the local repository is behind the remote repository + if commit_date < remote_commit_date: + print("CUSTOM NODE CHECK: True") + else: + print("CUSTOM NODE CHECK: False") + except Exception as e: + print(e) + print("CUSTOM NODE CHECK: Error") + + +def get_remote_name(repo): + available_remotes = [remote.name for remote in repo.remotes] + if 'origin' in available_remotes: + return 'origin' + elif 'upstream' in available_remotes: + return 'upstream' + elif len(available_remotes) > 0: + return available_remotes[0] + + if not available_remotes: + print(f"[ComfyUI-Manager] No remotes are configured for this repository: {repo.working_dir}") + else: + print(f"[ComfyUI-Manager] Available remotes in '{repo.working_dir}': ") + for remote in available_remotes: + print(f"- {remote}") + + return None + + +def switch_to_default_branch(repo): + remote_name = get_remote_name(repo) + + try: + if remote_name is None: + return False + + default_branch = repo.git.symbolic_ref(f'refs/remotes/{remote_name}/HEAD').replace(f'refs/remotes/{remote_name}/', '') + repo.git.checkout(default_branch) + return True + except: + # try checkout master + # try checkout main if failed + try: + repo.git.checkout(repo.heads.master) + return True + except: + try: + if remote_name is not None: + repo.git.checkout('-b', 'master', f'{remote_name}/master') + return True + except: + try: + repo.git.checkout(repo.heads.main) + return True + except: + try: + if remote_name is not None: + repo.git.checkout('-b', 'main', f'{remote_name}/main') + return True + except: + pass + + print("[ComfyUI Manager] Failed to switch to the default branch") + return False + + +def gitpull(path): + # Check if the path is a git repository + if not os.path.exists(os.path.join(path, '.git')): + raise ValueError('Not a git repository') + + # Pull the latest changes from the remote repository + repo = git.Repo(path) + if repo.is_dirty(): + print(f"STASH: '{path}' is dirty.") + repo.git.stash() + + commit_hash = repo.head.commit.hexsha + try: + if repo.head.is_detached: + switch_to_default_branch(repo) + + current_branch = repo.active_branch + branch_name = current_branch.name + + remote_name = current_branch.tracking_branch().remote_name + remote = repo.remote(name=remote_name) + + if f'{remote_name}/{branch_name}' not in repo.refs: + switch_to_default_branch(repo) + current_branch = repo.active_branch + branch_name = current_branch.name + + remote.fetch() + if f'{remote_name}/{branch_name}' in repo.refs: + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + else: + print("CUSTOM NODE PULL: Fail") # update fail + return + + if commit_hash == remote_commit_hash: + print("CUSTOM NODE PULL: None") # there is no update + repo.close() + return + + try: + repo.git.pull('--ff-only') + except git.GitCommandError: + backup_name = f'backup_{time.strftime("%Y%m%d_%H%M%S")}' + repo.create_head(backup_name) + print(f"[ComfyUI-Manager] Cannot fast-forward. Backup created: {backup_name}") + repo.git.reset('--hard', f'{remote_name}/{branch_name}') + print(f"[ComfyUI-Manager] Reset to {remote_name}/{branch_name}") + + repo.git.submodule('update', '--init', '--recursive') + new_commit_hash = repo.head.commit.hexsha + + if commit_hash != new_commit_hash: + print("CUSTOM NODE PULL: Success") # update success + else: + print("CUSTOM NODE PULL: Fail") # update fail + except Exception as e: + print(e) + print("CUSTOM NODE PULL: Fail") # unknown git error + + repo.close() + + +def checkout_comfyui_hash(target_hash): + repo = git.Repo(comfy_path) + commit_hash = repo.head.commit.hexsha + + if commit_hash != target_hash: + try: + print(f"CHECKOUT: ComfyUI [{target_hash}]") + repo.git.checkout(target_hash) + except git.GitCommandError as e: + print(f"Error checking out the ComfyUI: {str(e)}") + + +def checkout_custom_node_hash(git_custom_node_infos): + repo_name_to_url = {} + + for url in git_custom_node_infos.keys(): + repo_name = url.split('/')[-1] + + if repo_name.endswith('.git'): + repo_name = repo_name[:-4] + + repo_name_to_url[repo_name] = url + + for path in os.listdir(working_directory): + if path.endswith("ComfyUI-Manager"): + continue + + fullpath = os.path.join(working_directory, path) + + if os.path.isdir(fullpath): + is_disabled = path.endswith(".disabled") + + try: + git_dir = os.path.join(fullpath, '.git') + if not os.path.exists(git_dir): + continue + + need_checkout = False + repo_name = os.path.basename(fullpath) + + if repo_name.endswith('.disabled'): + repo_name = repo_name[:-9] + + if repo_name not in repo_name_to_url: + if not is_disabled: + # should be disabled + print(f"DISABLE: {repo_name}") + new_path = fullpath + ".disabled" + os.rename(fullpath, new_path) + need_checkout = False + else: + item = git_custom_node_infos[repo_name_to_url[repo_name]] + if item['disabled'] and is_disabled: + pass + elif item['disabled'] and not is_disabled: + # disable + print(f"DISABLE: {repo_name}") + new_path = fullpath + ".disabled" + os.rename(fullpath, new_path) + + elif not item['disabled'] and is_disabled: + # enable + print(f"ENABLE: {repo_name}") + new_path = fullpath[:-9] + os.rename(fullpath, new_path) + fullpath = new_path + need_checkout = True + else: + need_checkout = True + + if need_checkout: + repo = git.Repo(fullpath) + commit_hash = repo.head.commit.hexsha + + if commit_hash != item['hash']: + print(f"CHECKOUT: {repo_name} [{item['hash']}]") + repo.git.checkout(item['hash']) + + except Exception: + print(f"Failed to restore snapshots for the custom node '{path}'") + + # clone missing + for k, v in git_custom_node_infos.items(): + if 'ComfyUI-Manager' in k: + continue + + if not v['disabled']: + repo_name = k.split('/')[-1] + if repo_name.endswith('.git'): + repo_name = repo_name[:-4] + + path = os.path.join(working_directory, repo_name) + if not os.path.exists(path): + print(f"CLONE: {path}") + gitclone(working_directory, k, target_hash=v['hash']) + + +def invalidate_custom_node_file(file_custom_node_infos): + global nodelist_path + + enabled_set = set() + for item in file_custom_node_infos: + if not item['disabled']: + enabled_set.add(item['filename']) + + for path in os.listdir(working_directory): + fullpath = os.path.join(working_directory, path) + + if not os.path.isdir(fullpath) and fullpath.endswith('.py'): + if path not in enabled_set: + print(f"DISABLE: {path}") + new_path = fullpath+'.disabled' + os.rename(fullpath, new_path) + + elif not os.path.isdir(fullpath) and fullpath.endswith('.py.disabled'): + path = path[:-9] + if path in enabled_set: + print(f"ENABLE: {path}") + new_path = fullpath[:-9] + os.rename(fullpath, new_path) + + # download missing: just support for 'copy' style + py_to_url = {} + + with open(nodelist_path, 'r', encoding="UTF-8") as json_file: + info = json.load(json_file) + for item in info['custom_nodes']: + if item['install_type'] == 'copy': + for url in item['files']: + if url.endswith('.py'): + py = url.split('/')[-1] + py_to_url[py] = url + + for item in file_custom_node_infos: + filename = item['filename'] + if not item['disabled']: + target_path = os.path.join(working_directory, filename) + + if not os.path.exists(target_path) and filename in py_to_url: + url = py_to_url[filename] + print(f"DOWNLOAD: {filename}") + download_url(url, working_directory) + + +def apply_snapshot(path): + try: + if os.path.exists(path): + if not path.endswith('.json') and not path.endswith('.yaml'): + print(f"Snapshot file not found: `{path}`") + print("APPLY SNAPSHOT: False") + return None + + with open(path, 'r', encoding="UTF-8") as snapshot_file: + if path.endswith('.json'): + info = json.load(snapshot_file) + elif path.endswith('.yaml'): + info = yaml.load(snapshot_file, Loader=yaml.SafeLoader) + info = info['custom_nodes'] + else: + # impossible case + print("APPLY SNAPSHOT: False") + return None + + comfyui_hash = info['comfyui'] + git_custom_node_infos = info['git_custom_nodes'] + file_custom_node_infos = info['file_custom_nodes'] + + if comfyui_hash: + checkout_comfyui_hash(comfyui_hash) + checkout_custom_node_hash(git_custom_node_infos) + invalidate_custom_node_file(file_custom_node_infos) + + print("APPLY SNAPSHOT: True") + if 'pips' in info and info['pips']: + return info['pips'] + else: + return None + + print(f"Snapshot file not found: `{path}`") + print("APPLY SNAPSHOT: False") + + return None + except Exception as e: + print(e) + traceback.print_exc() + print("APPLY SNAPSHOT: False") + + return None + + +def restore_pip_snapshot(pips, options): + non_url = [] + local_url = [] + non_local_url = [] + for k, v in pips.items(): + if v == "": + non_url.append(k) + else: + if v.startswith('file:'): + local_url.append(v) + else: + non_local_url.append(v) + + failed = [] + if '--pip-non-url' in options: + # try all at once + res = 1 + try: + res = subprocess.check_call([sys.executable, '-m', 'pip', 'install'] + non_url) + except: + pass + + # fallback + if res != 0: + for x in non_url: + res = 1 + try: + res = subprocess.check_call([sys.executable, '-m', 'pip', 'install', x]) + except: + pass + + if res != 0: + failed.append(x) + + if '--pip-non-local-url' in options: + for x in non_local_url: + res = 1 + try: + res = subprocess.check_call([sys.executable, '-m', 'pip', 'install', x]) + except: + pass + + if res != 0: + failed.append(x) + + if '--pip-local-url' in options: + for x in local_url: + res = 1 + try: + res = subprocess.check_call([sys.executable, '-m', 'pip', 'install', x]) + except: + pass + + if res != 0: + failed.append(x) + + print(f"Installation failed for pip packages: {failed}") + + +def setup_environment(): + if git_exe_path is not None: + git.Git().update_environment(GIT_PYTHON_GIT_EXECUTABLE=git_exe_path) + + +setup_environment() + + +try: + if sys.argv[1] == "--clone": + repo_path = None + if len(sys.argv) > 4: + repo_path = sys.argv[4] + + gitclone(sys.argv[2], sys.argv[3], repo_path=repo_path) + elif sys.argv[1] == "--check": + gitcheck(sys.argv[2], False) + elif sys.argv[1] == "--fetch": + gitcheck(sys.argv[2], True) + elif sys.argv[1] == "--pull": + gitpull(sys.argv[2]) + elif sys.argv[1] == "--apply-snapshot": + options = set() + for x in sys.argv: + if x in ['--pip-non-url', '--pip-local-url', '--pip-non-local-url']: + options.add(x) + + pips = apply_snapshot(sys.argv[2]) + + if pips and len(options) > 0: + restore_pip_snapshot(pips, options) + sys.exit(0) +except Exception as e: + print(e) + sys.exit(-1) + + diff --git a/custom_nodes/comfyui-manager/glob/README.md b/custom_nodes/comfyui-manager/glob/README.md new file mode 100644 index 00000000..375b7fa7 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/README.md @@ -0,0 +1,53 @@ +# ComfyUI-Manager: Core Backend (glob) + +This directory contains the Python backend modules that power ComfyUI-Manager, handling the core functionality of node management, downloading, security, and server operations. + +## Core Modules + +- **manager_core.py**: The central implementation of management functions, handling configuration, installation, updates, and node management. +- **manager_server.py**: Implements server functionality and API endpoints for the web interface to interact with the backend. +- **manager_downloader.py**: Handles downloading operations for models, extensions, and other resources. +- **manager_util.py**: Provides utility functions used throughout the system. + +## Specialized Modules + +- **cm_global.py**: Maintains global variables and state management across the system. +- **cnr_utils.py**: Helper utilities for interacting with the custom node registry (CNR). +- **git_utils.py**: Git-specific utilities for repository operations. +- **node_package.py**: Handles the packaging and installation of node extensions. +- **security_check.py**: Implements the multi-level security system for installation safety. +- **share_3rdparty.py**: Manages integration with third-party sharing platforms. + +## Architecture + +The backend follows a modular design pattern with clear separation of concerns: + +1. **Core Layer**: Manager modules provide the primary API and business logic +2. **Utility Layer**: Helper modules provide specialized functionality +3. **Integration Layer**: Modules that connect to external systems + +## Security Model + +The system implements a comprehensive security framework with multiple levels: + +- **Block**: Highest security - blocks most remote operations +- **High**: Allows only specific trusted operations +- **Middle**: Standard security for most users +- **Normal-**: More permissive for advanced users +- **Weak**: Lowest security for development environments + +## Implementation Details + +- The backend is designed to work seamlessly with ComfyUI +- Asynchronous task queuing is implemented for background operations +- The system supports multiple installation modes +- Error handling and risk assessment are integrated throughout the codebase + +## API Integration + +The backend exposes a REST API via `manager_server.py` that enables: +- Custom node management (install, update, disable, remove) +- Model downloading and organization +- System configuration +- Snapshot management +- Workflow component handling \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/glob/cm_global.py b/custom_nodes/comfyui-manager/glob/cm_global.py new file mode 100644 index 00000000..e5d2237c --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/cm_global.py @@ -0,0 +1,117 @@ +import traceback + +# +# Global Var +# +# Usage: +# import cm_global +# cm_global.variables['comfyui.revision'] = 1832 +# print(f"log mode: {cm_global.variables['logger.enabled']}") +# +variables = {} + + +# +# Global API +# +# Usage: +# [register API] +# import cm_global +# +# def api_hello(msg): +# print(f"hello: {msg}") +# return msg +# +# cm_global.register_api('hello', api_hello) +# +# [use API] +# import cm_global +# +# test = cm_global.try_call(api='hello', msg='an example') +# print(f"'{test}' is returned") +# + +APIs = {} + + +def register_api(k, f): + global APIs + APIs[k] = f + + +def try_call(**kwargs): + if 'api' in kwargs: + api_name = kwargs['api'] + try: + api = APIs.get(api_name) + if api is not None: + del kwargs['api'] + return api(**kwargs) + else: + print(f"WARN: The '{kwargs['api']}' API has not been registered.") + except Exception as e: + print(f"ERROR: An exception occurred while calling the '{api_name}' API.") + raise e + else: + return None + + +# +# Extension Info +# +# Usage: +# import cm_global +# +# cm_global.extension_infos['my_extension'] = {'version': [0, 1], 'name': 'me', 'description': 'example extension', } +# +extension_infos = {} + +on_extension_registered_handlers = {} + + +def register_extension(extension_name, v): + global extension_infos + global on_extension_registered_handlers + extension_infos[extension_name] = v + + if extension_name in on_extension_registered_handlers: + for k, f in on_extension_registered_handlers[extension_name]: + try: + f(extension_name, v) + except Exception: + print(f"[ERROR] '{k}' on_extension_registered_handlers") + traceback.print_exc() + + del on_extension_registered_handlers[extension_name] + + +def add_on_extension_registered(k, extension_name, f): + global on_extension_registered_handlers + if extension_name in extension_infos: + try: + v = extension_infos[extension_name] + f(extension_name, v) + except Exception: + print(f"[ERROR] '{k}' on_extension_registered_handler") + traceback.print_exc() + else: + if extension_name not in on_extension_registered_handlers: + on_extension_registered_handlers[extension_name] = [] + + on_extension_registered_handlers[extension_name].append((k, f)) + + +def add_on_revision_detected(k, f): + if 'comfyui.revision' in variables: + try: + f(variables['comfyui.revision']) + except Exception: + print(f"[ERROR] '{k}' on_revision_detected_handler") + traceback.print_exc() + else: + variables['cm.on_revision_detected_handler'].append((k, f)) + + +error_dict = {} + +disable_front = False \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/glob/cnr_utils.py b/custom_nodes/comfyui-manager/glob/cnr_utils.py new file mode 100644 index 00000000..95d260db --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/cnr_utils.py @@ -0,0 +1,253 @@ +import asyncio +import json +import os +import platform +import time +from dataclasses import dataclass +from typing import List + +import manager_core +import manager_util +import requests +import toml + +base_url = "https://api.comfy.org" + + +lock = asyncio.Lock() + +is_cache_loading = False + +async def get_cnr_data(cache_mode=True, dont_wait=True): + try: + return await _get_cnr_data(cache_mode, dont_wait) + except asyncio.TimeoutError: + print("A timeout occurred during the fetch process from ComfyRegistry.") + return await _get_cnr_data(cache_mode=True, dont_wait=True) # timeout fallback + +async def _get_cnr_data(cache_mode=True, dont_wait=True): + global is_cache_loading + + uri = f'{base_url}/nodes' + + async def fetch_all(): + remained = True + page = 1 + + full_nodes = {} + + + # Determine form factor based on environment and platform + is_desktop = bool(os.environ.get('__COMFYUI_DESKTOP_VERSION__')) + system = platform.system().lower() + is_windows = system == 'windows' + is_mac = system == 'darwin' + is_linux = system == 'linux' + + # Get ComfyUI version tag + if is_desktop: + # extract version from pyproject.toml instead of git tag + comfyui_ver = manager_core.get_current_comfyui_ver() or 'unknown' + else: + comfyui_ver = manager_core.get_comfyui_tag() or 'unknown' + + if is_desktop: + if is_windows: + form_factor = 'desktop-win' + elif is_mac: + form_factor = 'desktop-mac' + else: + form_factor = 'other' + else: + if is_windows: + form_factor = 'git-windows' + elif is_mac: + form_factor = 'git-mac' + elif is_linux: + form_factor = 'git-linux' + else: + form_factor = 'other' + + while remained: + # Add comfyui_version and form_factor to the API request + sub_uri = f'{base_url}/nodes?page={page}&limit=30&comfyui_version={comfyui_ver}&form_factor={form_factor}' + sub_json_obj = await asyncio.wait_for(manager_util.get_data_with_cache(sub_uri, cache_mode=False, silent=True, dont_cache=True), timeout=30) + remained = page < sub_json_obj['totalPages'] + + for x in sub_json_obj['nodes']: + full_nodes[x['id']] = x + + if page % 5 == 0: + print(f"FETCH ComfyRegistry Data: {page}/{sub_json_obj['totalPages']}") + + page += 1 + time.sleep(0.5) + + print("FETCH ComfyRegistry Data [DONE]") + + for v in full_nodes.values(): + if 'latest_version' not in v: + v['latest_version'] = dict(version='nightly') + + return {'nodes': list(full_nodes.values())} + + if cache_mode: + is_cache_loading = True + cache_state = manager_util.get_cache_state(uri) + + if dont_wait: + if cache_state == 'not-cached': + return {} + else: + print("[ComfyUI-Manager] The ComfyRegistry cache update is still in progress, so an outdated cache is being used.") + with open(manager_util.get_cache_path(uri), 'r', encoding="UTF-8", errors="ignore") as json_file: + return json.load(json_file)['nodes'] + + if cache_state == 'cached': + with open(manager_util.get_cache_path(uri), 'r', encoding="UTF-8", errors="ignore") as json_file: + return json.load(json_file)['nodes'] + + try: + json_obj = await fetch_all() + manager_util.save_to_cache(uri, json_obj) + return json_obj['nodes'] + except: + res = {} + print("Cannot connect to comfyregistry.") + finally: + if cache_mode: + is_cache_loading = False + + return res + + +@dataclass +class NodeVersion: + changelog: str + dependencies: List[str] + deprecated: bool + id: str + version: str + download_url: str + + +def map_node_version(api_node_version): + """ + Maps node version data from API response to NodeVersion dataclass. + + Args: + api_data (dict): The 'node_version' part of the API response. + + Returns: + NodeVersion: An instance of NodeVersion dataclass populated with data from the API. + """ + return NodeVersion( + changelog=api_node_version.get( + "changelog", "" + ), # Provide a default value if 'changelog' is missing + dependencies=api_node_version.get( + "dependencies", [] + ), # Provide a default empty list if 'dependencies' is missing + deprecated=api_node_version.get( + "deprecated", False + ), # Assume False if 'deprecated' is not specified + id=api_node_version[ + "id" + ], # 'id' should be mandatory; raise KeyError if missing + version=api_node_version[ + "version" + ], # 'version' should be mandatory; raise KeyError if missing + download_url=api_node_version.get( + "downloadUrl", "" + ), # Provide a default value if 'downloadUrl' is missing + ) + + +def install_node(node_id, version=None): + """ + Retrieves the node version for installation. + + Args: + node_id (str): The unique identifier of the node. + version (str, optional): Specific version of the node to retrieve. If omitted, the latest version is returned. + + Returns: + NodeVersion: Node version data or error message. + """ + if version is None: + url = f"{base_url}/nodes/{node_id}/install" + else: + url = f"{base_url}/nodes/{node_id}/install?version={version}" + + response = requests.get(url, verify=not manager_util.bypass_ssl) + if response.status_code == 200: + # Convert the API response to a NodeVersion object + return map_node_version(response.json()) + else: + return None + + +def all_versions_of_node(node_id): + url = f"{base_url}/nodes/{node_id}/versions?statuses=NodeVersionStatusActive&statuses=NodeVersionStatusPending" + + response = requests.get(url, verify=not manager_util.bypass_ssl) + if response.status_code == 200: + return response.json() + else: + return None + + +def read_cnr_info(fullpath): + try: + toml_path = os.path.join(fullpath, 'pyproject.toml') + tracking_path = os.path.join(fullpath, '.tracking') + + if not os.path.exists(toml_path) or not os.path.exists(tracking_path): + return None # not valid CNR node pack + + with open(toml_path, "r", encoding="utf-8") as f: + data = toml.load(f) + + project = data.get('project', {}) + name = project.get('name').strip().lower() + + # normalize version + # for example: 2.5 -> 2.5.0 + version = str(manager_util.StrictVersion(project.get('version'))) + + urls = project.get('urls', {}) + repository = urls.get('Repository') + + if name and version: # repository is optional + return { + "id": name, + "version": version, + "url": repository + } + + return None + except Exception: + return None # not valid CNR node pack + + +def generate_cnr_id(fullpath, cnr_id): + cnr_id_path = os.path.join(fullpath, '.git', '.cnr-id') + try: + if not os.path.exists(cnr_id_path): + with open(cnr_id_path, "w") as f: + return f.write(cnr_id) + except: + print(f"[ComfyUI Manager] unable to create file: {cnr_id_path}") + + +def read_cnr_id(fullpath): + cnr_id_path = os.path.join(fullpath, '.git', '.cnr-id') + try: + if os.path.exists(cnr_id_path): + with open(cnr_id_path) as f: + return f.read().strip() + except: + pass + + return None + diff --git a/custom_nodes/comfyui-manager/glob/git_utils.py b/custom_nodes/comfyui-manager/glob/git_utils.py new file mode 100644 index 00000000..1fe36cd2 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/git_utils.py @@ -0,0 +1,87 @@ +import os +import configparser + + +GITHUB_ENDPOINT = os.getenv('GITHUB_ENDPOINT') + + +def is_git_repo(path: str) -> bool: + """ Check if the path is a git repository. """ + # NOTE: Checking it through `git.Repo` must be avoided. + # It locks the file, causing issues on Windows. + return os.path.exists(os.path.join(path, '.git')) + + +def get_commit_hash(fullpath): + git_head = os.path.join(fullpath, '.git', 'HEAD') + if os.path.exists(git_head): + with open(git_head) as f: + line = f.readline() + + if line.startswith("ref: "): + ref = os.path.join(fullpath, '.git', line[5:].strip()) + if os.path.exists(ref): + with open(ref) as f2: + return f2.readline().strip() + else: + return "unknown" + else: + return line + + return "unknown" + + +def git_url(fullpath): + """ + resolve version of unclassified custom node based on remote url in .git/config + """ + git_config_path = os.path.join(fullpath, '.git', 'config') + + if not os.path.exists(git_config_path): + return None + + # Set `strict=False` to allow duplicate `vscode-merge-base` sections, addressing + config = configparser.ConfigParser(strict=False) + config.read(git_config_path) + + for k, v in config.items(): + if k.startswith('remote ') and 'url' in v: + if 'Comfy-Org/ComfyUI-Manager' in v['url']: + return "https://github.com/ltdrdata/ComfyUI-Manager" + return v['url'] + + return None + + +def normalize_url(url) -> str: + github_id = normalize_to_github_id(url) + if github_id is not None: + url = f"https://github.com/{github_id}" + + return url + + +def normalize_to_github_id(url) -> str: + if 'github' in url or (GITHUB_ENDPOINT is not None and GITHUB_ENDPOINT in url): + author = os.path.basename(os.path.dirname(url)) + + if author.startswith('git@github.com:'): + author = author.split(':')[1] + + repo_name = os.path.basename(url) + if repo_name.endswith('.git'): + repo_name = repo_name[:-4] + + return f"{author}/{repo_name}" + + return None + + +def get_url_for_clone(url): + url = normalize_url(url) + + if GITHUB_ENDPOINT is not None and url.startswith('https://github.com/'): + url = GITHUB_ENDPOINT + url[18:] # url[18:] -> remove `https://github.com` + + return url + \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/glob/manager_core.py b/custom_nodes/comfyui-manager/glob/manager_core.py new file mode 100644 index 00000000..e0b3a6fe --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/manager_core.py @@ -0,0 +1,3498 @@ +""" +description: + `manager_core` contains the core implementation of the management functions in ComfyUI-Manager. +""" + +import json +import logging +import os +import sys +import subprocess +import re +import shutil +import configparser +import platform +from datetime import datetime + +import git +from git.remote import RemoteProgress +from urllib.parse import urlparse +from tqdm.auto import tqdm +import time +import yaml +import zipfile +import traceback +from concurrent.futures import ThreadPoolExecutor, as_completed +import toml + +orig_print = print + +from rich import print +from packaging import version + +import uuid + +glob_path = os.path.join(os.path.dirname(__file__)) # ComfyUI-Manager/glob +sys.path.append(glob_path) + +import cm_global +import cnr_utils +import manager_util +import git_utils +import manager_downloader +import manager_migration +from node_package import InstalledNodePackage + + +version_code = [3, 39, 2] +version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '') + + +DEFAULT_CHANNEL = "https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main" + + +default_custom_nodes_path = None + + +class InvalidChannel(Exception): + def __init__(self, channel): + self.channel = channel + super().__init__(channel) + +def get_default_custom_nodes_path(): + global default_custom_nodes_path + if default_custom_nodes_path is None: + try: + import folder_paths + default_custom_nodes_path = folder_paths.get_folder_paths("custom_nodes")[0] + except: + default_custom_nodes_path = os.path.abspath(os.path.join(manager_util.comfyui_manager_path, '..')) + + return default_custom_nodes_path + + +def get_custom_nodes_paths(): + try: + import folder_paths + return folder_paths.get_folder_paths("custom_nodes") + except: + custom_nodes_path = os.path.abspath(os.path.join(manager_util.comfyui_manager_path, '..')) + return [custom_nodes_path] + + +def get_comfyui_tag(): + try: + repo = git.Repo(comfy_path) + return repo.git.describe('--tags') + except: + return None + + +def get_current_comfyui_ver(): + """ + Extract version from pyproject.toml + """ + toml_path = os.path.join(comfy_path, 'pyproject.toml') + if not os.path.exists(toml_path): + return None + else: + try: + with open(toml_path, "r", encoding="utf-8") as f: + data = toml.load(f) + + project = data.get('project', {}) + return project.get('version') + except: + return None + + +def get_script_env(): + new_env = os.environ.copy() + git_exe = get_config().get('git_exe') + if git_exe is not None: + new_env['GIT_EXE_PATH'] = git_exe + + if 'COMFYUI_PATH' not in new_env: + new_env['COMFYUI_PATH'] = comfy_path + + if 'COMFYUI_FOLDERS_BASE_PATH' not in new_env: + new_env['COMFYUI_FOLDERS_BASE_PATH'] = comfy_path + + return new_env + + +invalid_nodes = {} + + +def extract_base_custom_nodes_dir(x:str): + if os.path.dirname(x).endswith('.disabled'): + return os.path.dirname(os.path.dirname(x)) + elif x.endswith('.disabled'): + return os.path.dirname(x) + else: + return os.path.dirname(x) + + +def check_invalid_nodes(): + global invalid_nodes + + try: + import folder_paths + except: + try: + sys.path.append(comfy_path) + import folder_paths + except: + raise Exception(f"Invalid COMFYUI_FOLDERS_BASE_PATH: {comfy_path}") + + def check(root): + global invalid_nodes + + subdirs = [d for d in os.listdir(root) if os.path.isdir(os.path.join(root, d))] + for subdir in subdirs: + if subdir in ['.disabled', '__pycache__']: + continue + + package = unified_manager.installed_node_packages.get(subdir) + if not package: + continue + + if not package.isValid(): + invalid_nodes[subdir] = package.fullpath + + node_paths = folder_paths.get_folder_paths("custom_nodes") + for x in node_paths: + check(x) + + disabled_dir = os.path.join(x, '.disabled') + if os.path.exists(disabled_dir): + check(disabled_dir) + + if len(invalid_nodes): + print("\n-------------------- ComfyUI-Manager invalid nodes notice ----------------") + print("\nNodes requiring reinstallation have been detected:\n(Directly delete the corresponding path and reinstall.)\n") + + for x in invalid_nodes.values(): + print(x) + + print("\n---------------------------------------------------------------------------\n") + + +# read env vars +comfy_path: str = os.environ.get('COMFYUI_PATH') +comfy_base_path = os.environ.get('COMFYUI_FOLDERS_BASE_PATH') + +if comfy_path is None: + try: + import folder_paths + comfy_path = os.path.join(os.path.dirname(folder_paths.__file__)) + except: + comfy_path = os.path.abspath(os.path.join(manager_util.comfyui_manager_path, '..', '..')) + +if comfy_base_path is None: + comfy_base_path = comfy_path + + +channel_list_template_path = os.path.join(manager_util.comfyui_manager_path, 'channels.list.template') +git_script_path = os.path.join(manager_util.comfyui_manager_path, "git_helper.py") + +manager_files_path = None +manager_config_path = None +manager_channel_list_path = None +manager_startup_script_path:str = None +manager_snapshot_path = None +manager_pip_overrides_path = None +manager_pip_blacklist_path = None +manager_components_path = None + +def update_user_directory(user_dir): + global manager_files_path + global manager_config_path + global manager_channel_list_path + global manager_startup_script_path + global manager_snapshot_path + global manager_pip_overrides_path + global manager_pip_blacklist_path + global manager_components_path + + manager_files_path = manager_migration.get_manager_path(user_dir) + if not os.path.exists(manager_files_path): + os.makedirs(manager_files_path) + manager_migration.run_migration_checks(user_dir, manager_files_path) + + manager_snapshot_path = os.path.join(manager_files_path, "snapshots") + if not os.path.exists(manager_snapshot_path): + os.makedirs(manager_snapshot_path) + + manager_startup_script_path = os.path.join(manager_files_path, "startup-scripts") + if not os.path.exists(manager_startup_script_path): + os.makedirs(manager_startup_script_path) + + manager_config_path = os.path.join(manager_files_path, 'config.ini') + manager_channel_list_path = os.path.join(manager_files_path, 'channels.list') + manager_pip_overrides_path = os.path.join(manager_files_path, "pip_overrides.json") + manager_pip_blacklist_path = os.path.join(manager_files_path, "pip_blacklist.list") + manager_components_path = os.path.join(manager_files_path, "components") + manager_util.cache_dir = os.path.join(manager_files_path, "cache") + + if not os.path.exists(manager_util.cache_dir): + os.makedirs(manager_util.cache_dir) + +try: + import folder_paths + update_user_directory(folder_paths.get_user_directory()) + +except Exception: + # fallback: + # This case is only possible when running with cm-cli, and in practice, this case is not actually used. + update_user_directory(os.path.abspath(manager_util.comfyui_manager_path)) + + +cached_config = None +js_path = None + +comfy_ui_required_revision = 1930 +comfy_ui_required_commit_datetime = datetime(2024, 1, 24, 0, 0, 0) + +comfy_ui_revision = "Unknown" +comfy_ui_commit_datetime = datetime(1900, 1, 1, 0, 0, 0) + +channel_dict = None +valid_channels = {'default', 'local'} +channel_list = None + + +def remap_pip_package(pkg): + if pkg in cm_global.pip_overrides: + res = cm_global.pip_overrides[pkg] + print(f"[ComfyUI-Manager] '{pkg}' is remapped to '{res}'") + return res + else: + return pkg + + +def is_blacklisted(name): + name = name.strip() + + pattern = r'([^<>!~=]+)([<>!~=]=?)([^ ]*)' + match = re.search(pattern, name) + + if match: + name = match.group(1) + + if name in cm_global.pip_blacklist: + return True + + if name in cm_global.pip_downgrade_blacklist: + pips = manager_util.get_installed_packages() + + if match is None: + if name in pips: + return True + elif match.group(2) in ['<=', '==', '<', '~=']: + if name in pips: + if manager_util.StrictVersion(pips[name]) >= manager_util.StrictVersion(match.group(3)): + return True + + return False + + +def is_installed(name): + name = name.strip() + + if name.startswith('#'): + return True + + pattern = r'([^<>!~=]+)([<>!~=]=?)([0-9.a-zA-Z]*)' + match = re.search(pattern, name) + + if match: + name = match.group(1) + + if name in cm_global.pip_blacklist: + return True + + if name in cm_global.pip_downgrade_blacklist: + pips = manager_util.get_installed_packages() + + if match is None: + if name in pips: + return True + elif match.group(2) in ['<=', '==', '<', '~=']: + if name in pips: + if manager_util.StrictVersion(pips[name]) >= manager_util.StrictVersion(match.group(3)): + print(f"[ComfyUI-Manager] skip black listed pip installation: '{name}'") + return True + + pkg = manager_util.get_installed_packages().get(name.lower()) + if pkg is None: + return False # update if not installed + + if match is None: + return True # don't update if version is not specified + + if match.group(2) in ['>', '>=']: + if manager_util.StrictVersion(pkg) < manager_util.StrictVersion(match.group(3)): + return False + elif manager_util.StrictVersion(pkg) > manager_util.StrictVersion(match.group(3)): + print(f"[SKIP] Downgrading pip package isn't allowed: {name.lower()} (cur={pkg})") + + if match.group(2) == '==': + if manager_util.StrictVersion(pkg) < manager_util.StrictVersion(match.group(3)): + return False + + if match.group(2) == '~=': + if manager_util.StrictVersion(pkg) == manager_util.StrictVersion(match.group(3)): + return False + + return name.lower() in manager_util.get_installed_packages() + + +def normalize_channel(channel): + if channel == 'local': + return channel + elif channel is None: + return None + elif channel.startswith('https://'): + return channel + elif channel.startswith('http://') and get_config()['http_channel_enabled'] == True: + return channel + + tmp_dict = get_channel_dict() + channel_url = tmp_dict.get(channel) + if channel_url: + return channel_url + + raise InvalidChannel(channel) + + +class ManagedResult: + def __init__(self, action): + self.action = action + self.items = [] + self.result = True + self.to_path = None + self.msg = None + self.target = None + self.postinstall = lambda: True + self.ver = None + + def append(self, item): + self.items.append(item) + + def fail(self, msg): + self.result = False + self.msg = msg + return self + + def with_target(self, target): + self.target = target + return self + + def with_msg(self, msg): + self.msg = msg + return self + + def with_postinstall(self, postinstall): + self.postinstall = postinstall + return self + + def with_ver(self, ver): + self.ver = ver + return self + + +class NormalizedKeyDict: + def __init__(self): + self._store = {} + self._key_map = {} + + def _normalize_key(self, key): + if isinstance(key, str): + return key.strip().lower() + return key + + def __setitem__(self, key, value): + norm_key = self._normalize_key(key) + self._key_map[norm_key] = key + self._store[key] = value + + def __getitem__(self, key): + norm_key = self._normalize_key(key) + original_key = self._key_map[norm_key] + return self._store[original_key] + + def __delitem__(self, key): + norm_key = self._normalize_key(key) + original_key = self._key_map.pop(norm_key) + del self._store[original_key] + + def __contains__(self, key): + return self._normalize_key(key) in self._key_map + + def get(self, key, default=None): + return self[key] if key in self else default + + def setdefault(self, key, default=None): + if key in self: + return self[key] + self[key] = default + return default + + def pop(self, key, default=None): + if key in self: + val = self[key] + del self[key] + return val + if default is not None: + return default + raise KeyError(key) + + def keys(self): + return self._store.keys() + + def values(self): + return self._store.values() + + def items(self): + return self._store.items() + + def __iter__(self): + return iter(self._store) + + def __len__(self): + return len(self._store) + + def __repr__(self): + return repr(self._store) + + def to_dict(self): + return dict(self._store) + + +class UnifiedManager: + def __init__(self): + self.installed_node_packages: dict[str, InstalledNodePackage] = {} + + self.cnr_inactive_nodes = NormalizedKeyDict() # node_id -> node_version -> fullpath + self.nightly_inactive_nodes = NormalizedKeyDict() # node_id -> fullpath + self.unknown_inactive_nodes = {} # node_id -> repo url * fullpath + self.active_nodes = NormalizedKeyDict() # node_id -> node_version * fullpath + self.unknown_active_nodes = {} # node_id -> repo url * fullpath + self.cnr_map = NormalizedKeyDict() # node_id -> cnr info + self.repo_cnr_map = {} # repo_url -> cnr info + self.custom_node_map_cache = {} # (channel, mode) -> augmented custom node list json + self.processed_install = set() + + def get_module_name(self, x): + info = self.active_nodes.get(x) + if info is None: + for url, fullpath in self.unknown_active_nodes.values(): + if url == x: + return os.path.basename(fullpath) + else: + return os.path.basename(info[1]) + + return None + + def get_cnr_by_repo(self, url): + return self.repo_cnr_map.get(git_utils.normalize_url(url)) + + def resolve_unspecified_version(self, node_name, guess_mode=None): + if guess_mode == 'active': + # priority: + # 1. CNR/nightly active nodes + # 2. unknown + # 3. Fail + + if node_name in self.cnr_map: + version_spec = self.get_from_cnr_active_nodes(node_name) + + if version_spec is None: + if node_name in self.unknown_active_nodes: + version_spec = "unknown" + else: + return None + + elif node_name in self.unknown_active_nodes: + version_spec = "unknown" + else: + return None + + elif guess_mode == 'inactive': + # priority: + # 1. CNR latest in inactive + # 2. nightly + # 3. unknown + # 4. Fail + + if node_name in self.cnr_map: + latest = self.get_from_cnr_inactive_nodes(node_name) + + if latest is not None: + version_spec = str(latest[0]) + else: + if node_name in self.nightly_inactive_nodes: + version_spec = "nightly" + else: + version_spec = "unknown" + + elif node_name in self.unknown_inactive_nodes: + version_spec = "unknown" + else: + return None + + else: + # priority: + # 1. CNR latest in world + # 2. unknown + + if node_name in self.cnr_map: + version_spec = self.cnr_map[node_name]['latest_version']['version'] + else: + version_spec = "unknown" + + return version_spec + + def resolve_node_spec(self, node_name, guess_mode=None): + """ + resolve to 'node_name, version_spec' from version string + + version string: + node_name@latest + node_name@nightly + node_name@unknown + node_name@ + node_name + + if guess_mode is 'active' or 'inactive' + return can be 'None' based on state check + otherwise + return 'unknown' version when failed to guess + """ + + spec = node_name.split('@') + + if len(spec) == 2: + node_name = spec[0] + version_spec = spec[1] + + if version_spec == 'latest': + if node_name not in self.cnr_map: + print(f"ERROR: '{node_name}' is not a CNR node.") + return None + else: + version_spec = self.cnr_map[node_name]['latest_version']['version'] + + elif guess_mode in ['active', 'inactive']: + node_name = spec[0] + version_spec = self.resolve_unspecified_version(node_name, guess_mode=guess_mode) + if version_spec is None: + return None + else: + node_name = spec[0] + version_spec = self.resolve_unspecified_version(node_name) + if version_spec is None: + return None + + return node_name, version_spec, len(spec) > 1 + + def resolve_from_path(self, fullpath): + url = git_utils.git_url(fullpath) + if url: + url = git_utils.normalize_url(url) + + cnr = self.get_cnr_by_repo(url) + commit_hash = git_utils.get_commit_hash(fullpath) + if cnr: + cnr_utils.generate_cnr_id(fullpath, cnr['id']) + return {'id': cnr['id'], 'cnr': cnr, 'ver': 'nightly', 'hash': commit_hash} + else: + url = os.path.basename(url) + if url.endswith('.git'): + url = url[:-4] + return {'id': url, 'ver': 'unknown', 'hash': commit_hash} + else: + info = cnr_utils.read_cnr_info(fullpath) + + if info: + cnr = self.cnr_map.get(info['id']) + if cnr: + # normalize version + # for example: 2.5 -> 2.5.0 + ver = str(manager_util.StrictVersion(info['version'])) + return {'id': cnr['id'], 'cnr': cnr, 'ver': ver} + else: + return None + else: + return None + + def update_cache_at_path(self, fullpath): + node_package = InstalledNodePackage.from_fullpath(fullpath, self.resolve_from_path) + self.installed_node_packages[node_package.id] = node_package + + if node_package.is_disabled and node_package.is_unknown: + url = git_utils.git_url(node_package.fullpath) + if url is not None: + url = git_utils.normalize_url(url) + self.unknown_inactive_nodes[node_package.id] = (url, node_package.fullpath) + + if node_package.is_disabled and node_package.is_nightly: + self.nightly_inactive_nodes[node_package.id] = node_package.fullpath + + if node_package.is_enabled and not node_package.is_unknown: + self.active_nodes[node_package.id] = node_package.version, node_package.fullpath + + if node_package.is_enabled and node_package.is_unknown: + url = git_utils.git_url(node_package.fullpath) + if url is not None: + url = git_utils.normalize_url(url) + self.unknown_active_nodes[node_package.id] = (url, node_package.fullpath) + + if node_package.is_from_cnr and node_package.is_disabled: + self.add_to_cnr_inactive_nodes(node_package.id, node_package.version, node_package.fullpath) + + def is_updatable(self, node_id): + cur_ver = self.get_cnr_active_version(node_id) + latest_ver = self.cnr_map[node_id]['latest_version']['version'] + + if cur_ver and latest_ver: + return self.safe_version(latest_ver) > self.safe_version(cur_ver) + + return False + + def fetch_or_pull_git_repo(self, is_pull=False): + updated = set() + failed = set() + + def check_update(node_name, fullpath, ver_spec): + try: + if is_pull: + is_updated, success = git_repo_update_check_with(fullpath, do_update=True) + else: + is_updated, success = git_repo_update_check_with(fullpath, do_fetch=True) + + return f"{node_name}@{ver_spec}", is_updated, success + except Exception: + traceback.print_exc() + + return f"{node_name}@{ver_spec}", False, False + + with ThreadPoolExecutor() as executor: + futures = [] + + for k, v in self.unknown_active_nodes.items(): + futures.append(executor.submit(check_update, k, v[1], 'unknown')) + + for k, v in self.active_nodes.items(): + if v[0] == 'nightly': + futures.append(executor.submit(check_update, k, v[1], 'nightly')) + + for future in as_completed(futures): + item, is_updated, success = future.result() + + if is_updated: + updated.add(item) + + if not success: + failed.add(item) + + return dict(updated=list(updated), failed=list(failed)) + + def is_enabled(self, node_id, version_spec=None): + """ + 1. true if node_id@ is enabled + 2. true if node_id@ is enabled and version_spec==None + 3. false otherwise + + remark: latest version_spec is not allowed. Must be resolved before call. + """ + if version_spec == "cnr": + return self.get_cnr_active_version(node_id) not in [None, 'nightly'] + elif version_spec == 'unknown' and self.is_unknown_active(node_id): + return True + elif version_spec is not None and self.get_cnr_active_version(node_id) == version_spec: + return True + elif version_spec is None and (node_id in self.active_nodes or node_id in self.unknown_active_nodes): + return True + return False + + def is_disabled(self, node_id, version_spec=None): + """ + 1. node_id@unknown is disabled if version_spec is @unknown + 2. node_id@nightly is disabled if version_spec is @nightly + 4. node_id@ is disabled if version_spec is not None + 5. not exists (active node_id) if version_spec is None + + remark: latest version_spec is not allowed. Must be resolved before call. + """ + if version_spec == "unknown": + return node_id in self.unknown_inactive_nodes + elif version_spec == "nightly": + return node_id in self.nightly_inactive_nodes + elif version_spec == "cnr": + res = self.cnr_inactive_nodes.get(node_id, None) + if res is None: + return False + + res = [x for x in res.keys() if x != 'nightly'] + return len(res) > 0 + elif version_spec is not None: + return version_spec in self.cnr_inactive_nodes.get(node_id, []) + + if node_id in self.nightly_inactive_nodes: + return True + elif node_id in self.unknown_inactive_nodes: + return True + + target = self.cnr_inactive_nodes.get(node_id, None) + if target is not None and target == version_spec: + return True + + return False + + def is_registered_in_cnr(self, node_id): + return node_id in self.cnr_map + + def get_cnr_active_version(self, node_id): + res = self.active_nodes.get(node_id) + if res: + return res[0] + else: + return None + + def is_unknown_active(self, node_id): + return node_id in self.unknown_active_nodes + + def add_to_cnr_inactive_nodes(self, node_id, ver, fullpath): + ver_map = self.cnr_inactive_nodes.get(node_id) + if ver_map is None: + ver_map = {} + self.cnr_inactive_nodes[node_id] = ver_map + + ver_map[ver] = fullpath + + def get_from_cnr_active_nodes(self, node_id): + ver_path = self.active_nodes.get(node_id) + if ver_path is None: + return None + + return ver_path[0] + + def get_from_cnr_inactive_nodes(self, node_id, ver=None): + ver_map = self.cnr_inactive_nodes.get(node_id) + if ver_map is None: + return None + + if ver is not None: + return ver_map.get(ver) + + latest = None + for k, v in ver_map.items(): + if latest is None: + latest = self.safe_version(k), v + continue + + cur_ver = self.safe_version(k) + if cur_ver > latest[0]: + latest = cur_ver, v + + return latest + + async def reload(self, cache_mode, dont_wait=True): + self.custom_node_map_cache = {} + self.cnr_inactive_nodes = {} # node_id -> node_version -> fullpath + self.nightly_inactive_nodes = {} # node_id -> fullpath + self.unknown_inactive_nodes = {} # node_id -> repo url * fullpath + self.unknown_active_nodes = {} # node_id -> repo url * fullpath + self.active_nodes = {} # node_id -> node_version * fullpath + + if get_config()['network_mode'] != 'public': + dont_wait = True + + # reload 'cnr_map' and 'repo_cnr_map' + cnrs = await cnr_utils.get_cnr_data(cache_mode=cache_mode=='cache', dont_wait=dont_wait) + + for x in cnrs: + self.cnr_map[x['id']] = x + if 'repository' in x: + normalized_url = git_utils.normalize_url(x['repository']) + self.repo_cnr_map[normalized_url] = x + + # reload node status info from custom_nodes/* + for custom_nodes_path in folder_paths.get_folder_paths('custom_nodes'): + for x in os.listdir(custom_nodes_path): + fullpath = os.path.join(custom_nodes_path, x) + if os.path.isdir(fullpath): + if x not in ['__pycache__', '.disabled']: + self.update_cache_at_path(fullpath) + + # reload node status info from custom_nodes/.disabled/* + for custom_nodes_path in folder_paths.get_folder_paths('custom_nodes'): + disabled_dir = os.path.join(custom_nodes_path, '.disabled') + if os.path.exists(disabled_dir): + for x in os.listdir(disabled_dir): + fullpath = os.path.join(disabled_dir, x) + if os.path.isdir(fullpath): + self.update_cache_at_path(fullpath) + + @staticmethod + async def load_nightly(channel, mode): + if channel is None: + return {} + + res = {} + + channel_url = normalize_channel(channel) + if channel_url: + if mode not in ['remote', 'local', 'cache']: + print(f"[bold red]ERROR: Invalid mode is specified `--mode {mode}`[/bold red]", file=sys.stderr) + return {} + + # validate channel - only the channel set by the user is allowed. + if channel_url not in valid_channels: + logging.error(f'[ComfyUI-Manager] An invalid channel was used: {channel_url}') + raise InvalidChannel(channel_url) + + json_obj = await get_data_by_mode(mode, 'custom-node-list.json', channel_url=channel_url) + for x in json_obj['custom_nodes']: + try: + for y in x['files']: + if 'github.com' in y and not (y.endswith('.py') or y.endswith('.js')): + repo_name = y.split('/')[-1] + res[repo_name] = (x, False) + + if 'id' in x: + if x['id'] not in res: + res[x['id']] = (x, True) + except: + logging.error(f"[ComfyUI-Manager] broken item:{x}") + + return res + + async def get_custom_nodes(self, channel, mode): + if channel is None and mode is None: + channel = 'default' + mode = 'cache' + + channel = normalize_channel(channel) + cache = self.custom_node_map_cache.get((channel, mode)) # CNR/nightly should always be based on the default channel. + + if cache is not None: + return cache + + channel = normalize_channel(channel) + nodes = await self.load_nightly(channel, mode) + + res = NormalizedKeyDict() + added_cnr = set() + for v in nodes.values(): + v = v[0] + if len(v['files']) == 1: + cnr = self.get_cnr_by_repo(v['files'][0]) + if cnr: + if 'latest_version' not in cnr: + v['cnr_latest'] = '0.0.0' + else: + v['cnr_latest'] = cnr['latest_version']['version'] + v['id'] = cnr['id'] + v['author'] = cnr['publisher']['name'] + v['title'] = cnr['name'] + v['description'] = cnr['description'] + v['health'] = '-' + if 'repository' in cnr: + v['repository'] = cnr['repository'] + added_cnr.add(cnr['id']) + node_id = v['id'] + else: + node_id = v['files'][0].split('/')[-1] + v['repository'] = v['files'][0] + res[node_id] = v + elif len(v['files']) > 1: + res[v['files'][0]] = v # A custom node composed of multiple url is treated as a single repository with one representative path + + self.custom_node_map_cache[(channel, mode)] = res + return res + + @staticmethod + def safe_version(ver_str): + try: + return version.parse(ver_str) + except: + return version.parse("0.0.0") + + def execute_install_script(self, url, repo_path, instant_execution=False, lazy_mode=False, no_deps=False): + install_script_path = os.path.join(repo_path, "install.py") + requirements_path = os.path.join(repo_path, "requirements.txt") + + res = True + if lazy_mode: + install_cmd = ["#LAZY-INSTALL-SCRIPT", sys.executable] + return try_install_script(url, repo_path, install_cmd) + else: + if os.path.exists(requirements_path) and not no_deps: + print("Install: pip packages") + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, manager_files_path) + lines = manager_util.robust_readlines(requirements_path) + for line in lines: + package_name = remap_pip_package(line.strip()) + if package_name and not package_name.startswith('#') and package_name not in self.processed_install: + self.processed_install.add(package_name) + clean_package_name = package_name.split('#')[0].strip() + install_cmd = manager_util.make_pip_cmd(["install", clean_package_name]) + if clean_package_name != "" and not clean_package_name.startswith('#'): + res = res and try_install_script(url, repo_path, install_cmd, instant_execution=instant_execution) + + pip_fixer.fix_broken() + + if os.path.exists(install_script_path) and install_script_path not in self.processed_install: + self.processed_install.add(install_script_path) + print("Install: install script") + install_cmd = [sys.executable, "install.py"] + return res and try_install_script(url, repo_path, install_cmd, instant_execution=instant_execution) + + return res + + def reserve_cnr_switch(self, target, zip_url, from_path, to_path, no_deps): + script_path = os.path.join(manager_startup_script_path, "install-scripts.txt") + with open(script_path, "a") as file: + obj = [target, "#LAZY-CNR-SWITCH-SCRIPT", zip_url, from_path, to_path, no_deps, get_default_custom_nodes_path(), sys.executable] + file.write(f"{obj}\n") + + print(f"Installation reserved: {target}") + + return True + + def unified_fix(self, node_id, version_spec, instant_execution=False, no_deps=False): + """ + fix dependencies + """ + + result = ManagedResult('fix') + + if version_spec == 'unknown': + info = self.unknown_active_nodes.get(node_id) + else: + info = self.active_nodes.get(node_id) + + if info is None or not os.path.exists(info[1]): + return result.fail(f'not found: {node_id}@{version_spec}') + + self.execute_install_script(node_id, info[1], instant_execution=instant_execution, no_deps=no_deps) + + return result + + def cnr_switch_version(self, node_id, version_spec=None, instant_execution=False, no_deps=False, return_postinstall=False): + if instant_execution: + return self.cnr_switch_version_instant(node_id, version_spec, instant_execution, no_deps, return_postinstall) + else: + return self.cnr_switch_version_lazy(node_id, version_spec, no_deps, return_postinstall) + + def cnr_switch_version_lazy(self, node_id, version_spec=None, no_deps=False, return_postinstall=False): + """ + switch between cnr version (lazy mode) + """ + + result = ManagedResult('switch-cnr') + + node_info = cnr_utils.install_node(node_id, version_spec) + if node_info is None or not node_info.download_url: + return result.fail(f'not available node: {node_id}@{version_spec}') + + version_spec = node_info.version + + if self.active_nodes[node_id][0] == version_spec: + return ManagedResult('skip').with_msg("Up to date") + + zip_url = node_info.download_url + from_path = self.active_nodes[node_id][1] + target = node_id + to_path = os.path.join(get_default_custom_nodes_path(), target) + + def postinstall(): + return self.reserve_cnr_switch(target, zip_url, from_path, to_path, no_deps) + + if return_postinstall: + return result.with_postinstall(postinstall) + else: + if not postinstall(): + return result.fail(f"Failed to execute install script: {node_id}@{version_spec}") + + return result + + def cnr_switch_version_instant(self, node_id, version_spec=None, instant_execution=True, no_deps=False, return_postinstall=False): + """ + switch between cnr version + """ + + # 1. download + result = ManagedResult('switch-cnr') + + node_info = cnr_utils.install_node(node_id, version_spec) + if node_info is None or not node_info.download_url: + return result.fail(f'not available node: {node_id}@{version_spec}') + + version_spec = node_info.version + + if self.active_nodes[node_id][0] == version_spec: + return ManagedResult('skip').with_msg("Up to date") + + archive_name = f"CNR_temp_{str(uuid.uuid4())}.zip" # should be unpredictable name - security precaution + download_path = os.path.join(get_default_custom_nodes_path(), archive_name) + manager_downloader.basic_download_url(node_info.download_url, get_default_custom_nodes_path(), archive_name) + + # 2. extract files into + install_path = self.active_nodes[node_id][1] + extracted = manager_util.extract_package_as_zip(download_path, install_path) + os.remove(download_path) + + if extracted is None: + if len(os.listdir(install_path)) == 0: + shutil.rmtree(install_path) + + return result.fail(f'Empty archive file: {node_id}@{version_spec}') + + # 3. calculate garbage files (.tracking - extracted) + tracking_info_file = os.path.join(install_path, '.tracking') + prev_files = set() + with open(tracking_info_file, 'r') as f: + for line in f: + prev_files.add(line.strip()) + garbage = prev_files.difference(extracted) + garbage = [os.path.join(install_path, x) for x in garbage] + + # 4-1. remove garbage files + for x in garbage: + if os.path.isfile(x): + os.remove(x) + + # 4-2. remove garbage dir if empty + for x in garbage: + if os.path.isdir(x): + if not os.listdir(x): + os.rmdir(x) + + # 5. create .tracking file + tracking_info_file = os.path.join(install_path, '.tracking') + with open(tracking_info_file, "w", encoding='utf-8') as file: + file.write('\n'.join(list(extracted))) + + # 6. post install + result.target = version_spec + + def postinstall(): + res = self.execute_install_script(f"{node_id}@{version_spec}", install_path, instant_execution=instant_execution, no_deps=no_deps) + return res + + if return_postinstall: + return result.with_postinstall(postinstall) + else: + if not postinstall(): + return result.fail(f"Failed to execute install script: {node_id}@{version_spec}") + + return result + + def unified_enable(self, node_id: str, version_spec=None): + """ + priority if version_spec == None + 1. CNR latest in disk + 2. nightly + 3. unknown + + remark: latest version_spec is not allowed. Must be resolved before call. + """ + + result = ManagedResult('enable') + + if 'comfyui-manager' in node_id.lower(): + return result.fail(f"ignored: enabling '{node_id}'") + + if version_spec is None: + version_spec = self.resolve_unspecified_version(node_id, guess_mode='inactive') + if version is None: + return result.fail(f'Specified inactive node not exists: {node_id}') + + if self.is_enabled(node_id, version_spec): + return ManagedResult('skip').with_msg('Already enabled') + + if not self.is_disabled(node_id, version_spec): + return ManagedResult('skip').with_msg('Not installed') + + from_path = None + to_path = None + + if version_spec == 'unknown': + repo_and_path = self.unknown_inactive_nodes.get(node_id) + if repo_and_path is None: + return result.fail(f'Specified inactive node not exists: {node_id}@unknown') + from_path = repo_and_path[1] + + base_path = extract_base_custom_nodes_dir(from_path) + to_path = os.path.join(base_path, node_id) + elif version_spec == 'nightly': + self.unified_disable(node_id, False) + from_path = self.nightly_inactive_nodes.get(node_id) + if from_path is None: + return result.fail(f'Specified inactive node not exists: {node_id}@nightly') + base_path = extract_base_custom_nodes_dir(from_path) + to_path = os.path.join(base_path, node_id) + elif version_spec is not None: + self.unified_disable(node_id, False) + cnr_info = self.cnr_inactive_nodes.get(node_id) + + if cnr_info is None or len(cnr_info) == 0: + return result.fail(f'Specified inactive cnr node not exists: {node_id}') + + if version_spec == "cnr": + version_spec = next(iter(cnr_info)) + + if version_spec not in cnr_info: + return result.fail(f'Specified inactive node not exists: {node_id}@{version_spec}') + + from_path = cnr_info[version_spec] + base_path = extract_base_custom_nodes_dir(from_path) + to_path = os.path.join(base_path, node_id) + + if from_path is None or not os.path.exists(from_path): + return result.fail(f'Specified inactive node path not exists: {from_path}') + + # move from disk + shutil.move(from_path, to_path) + + # update cache + if version_spec == 'unknown': + self.unknown_active_nodes[node_id] = self.unknown_inactive_nodes[node_id][0], to_path + del self.unknown_inactive_nodes[node_id] + return result.with_target(to_path) + elif version_spec == 'nightly': + del self.nightly_inactive_nodes[node_id] + else: + del self.cnr_inactive_nodes[node_id][version_spec] + + self.active_nodes[node_id] = version_spec, to_path + return result.with_target(to_path) + + def unified_disable(self, node_id: str, is_unknown): + result = ManagedResult('disable') + + if 'comfyui-manager' in node_id.lower(): + return result.fail(f"ignored: disabling '{node_id}'") + + if is_unknown: + version_spec = 'unknown' + else: + version_spec = None + + if not self.is_enabled(node_id, version_spec): + if not self.is_disabled(node_id, version_spec): + return ManagedResult('skip').with_msg('Not installed') + else: + return ManagedResult('skip').with_msg('Already disabled') + + if is_unknown: + repo_and_path = self.unknown_active_nodes.get(node_id) + + if repo_and_path is None or not os.path.exists(repo_and_path[1]): + return result.fail(f'Specified active node not exists: {node_id}') + + base_path = extract_base_custom_nodes_dir(repo_and_path[1]) + to_path = os.path.join(base_path, '.disabled', node_id) + + shutil.move(repo_and_path[1], to_path) + result.append((repo_and_path[1], to_path)) + + self.unknown_inactive_nodes[node_id] = repo_and_path[0], to_path + del self.unknown_active_nodes[node_id] + + return result + + ver_and_path = self.active_nodes.get(node_id) + + if ver_and_path is None or not os.path.exists(ver_and_path[1]): + return result.fail(f'Specified active node not exists: {node_id}') + + base_path = extract_base_custom_nodes_dir(ver_and_path[1]) + + # NOTE: A disabled node may have multiple versions, so preserve it using the `@ suffix`. + to_path = os.path.join(base_path, '.disabled', f"{node_id}@{ver_and_path[0].replace('.', '_')}") + shutil.move(ver_and_path[1], to_path) + result.append((ver_and_path[1], to_path)) + + if ver_and_path[0] == 'nightly': + self.nightly_inactive_nodes[node_id] = to_path + else: + self.add_to_cnr_inactive_nodes(node_id, ver_and_path[0], to_path) + + del self.active_nodes[node_id] + + return result + + def unified_uninstall(self, node_id: str, is_unknown: bool): + """ + Remove whole installed custom nodes including inactive nodes + """ + result = ManagedResult('uninstall') + + if 'comfyui-manager' in node_id.lower(): + return result.fail(f"ignored: uninstalling '{node_id}'") + + if is_unknown: + # remove from actives + repo_and_path = self.unknown_active_nodes.get(node_id) + + is_removed = False + + if repo_and_path is not None and os.path.exists(repo_and_path[1]): + rmtree(repo_and_path[1]) + result.append(repo_and_path[1]) + del self.unknown_active_nodes[node_id] + + is_removed = True + + # remove from inactives + repo_and_path = self.unknown_inactive_nodes.get(node_id) + + if repo_and_path is not None and os.path.exists(repo_and_path[1]): + rmtree(repo_and_path[1]) + result.append(repo_and_path[1]) + del self.unknown_inactive_nodes[node_id] + + is_removed = True + + if is_removed: + return result + else: + return ManagedResult('skip') + + # remove from actives + ver_and_path = self.active_nodes.get(node_id) + + if ver_and_path is not None and os.path.exists(ver_and_path[1]): + try_rmtree(node_id, ver_and_path[1]) + result.items.append(ver_and_path) + del self.active_nodes[node_id] + + # remove from nightly inactives + fullpath = self.nightly_inactive_nodes.get(node_id) + if fullpath is not None and os.path.exists(fullpath): + try_rmtree(node_id, fullpath) + result.items.append(('nightly', fullpath)) + del self.nightly_inactive_nodes[node_id] + + # remove from cnr inactives + ver_map = self.cnr_inactive_nodes.get(node_id) + if ver_map is not None: + for key, fullpath in ver_map.items(): + try_rmtree(node_id, fullpath) + result.items.append((key, fullpath)) + del self.cnr_inactive_nodes[node_id] + + if len(result.items) == 0: + return ManagedResult('skip').with_msg('Not installed') + + return result + + def cnr_install(self, node_id: str, version_spec=None, instant_execution=False, no_deps=False, return_postinstall=False): + result = ManagedResult('install-cnr') + + if 'comfyui-manager' in node_id.lower(): + return result.fail(f"ignored: installing '{node_id}'") + + node_info = cnr_utils.install_node(node_id, version_spec) + if node_info is None or not node_info.download_url: + return result.fail(f'not available node: {node_id}@{version_spec}') + + archive_name = f"CNR_temp_{str(uuid.uuid4())}.zip" # should be unpredictable name - security precaution + download_path = os.path.join(get_default_custom_nodes_path(), archive_name) + + # re-download. I cannot trust existing file. + if os.path.exists(download_path): + os.remove(download_path) + + # install_path + install_path = os.path.join(get_default_custom_nodes_path(), node_id) + if os.path.exists(install_path): + return result.fail(f'Install path already exists: {install_path}') + + manager_downloader.download_url(node_info.download_url, get_default_custom_nodes_path(), archive_name) + os.makedirs(install_path, exist_ok=True) + extracted = manager_util.extract_package_as_zip(download_path, install_path) + os.remove(download_path) + result.to_path = install_path + + if extracted is None: + shutil.rmtree(install_path) + return result.fail(f'Empty archive file: {node_id}@{version_spec}') + + # create .tracking file + tracking_info_file = os.path.join(install_path, '.tracking') + with open(tracking_info_file, "w", encoding='utf-8') as file: + file.write('\n'.join(extracted)) + + result.target = version_spec + + def postinstall(): + return self.execute_install_script(node_id, install_path, instant_execution=instant_execution, no_deps=no_deps) + + if return_postinstall: + return result.with_postinstall(postinstall) + else: + if not postinstall(): + return result.fail(f"Failed to execute install script: {node_id}@{version_spec}") + + return result + + def repo_install(self, url: str, repo_path: str, instant_execution=False, no_deps=False, return_postinstall=False): + result = ManagedResult('install-git') + result.append(url) + + if 'comfyui-manager' in url.lower(): + return result.fail(f"ignored: installing '{url}'") + + if not is_valid_url(url): + return result.fail(f"Invalid git url: {url}") + + if url.endswith("/"): + url = url[:-1] + try: + # Clone the repository from the remote URL + clone_url = git_utils.get_url_for_clone(url) + print(f"Download: git clone '{clone_url}'") + + if not instant_execution and platform.system() == 'Windows': + res = manager_funcs.run_script([sys.executable, git_script_path, "--clone", get_default_custom_nodes_path(), clone_url, repo_path], cwd=get_default_custom_nodes_path()) + if res != 0: + return result.fail(f"Failed to clone repo: {clone_url}") + else: + repo = git.Repo.clone_from(clone_url, repo_path, recursive=True, progress=GitProgress()) + repo.git.clear_cache() + repo.close() + + def postinstall(): + return self.execute_install_script(url, repo_path, instant_execution=instant_execution, no_deps=no_deps) + + if return_postinstall: + return result.with_postinstall(postinstall) + else: + if not postinstall(): + return result.fail(f"Failed to execute install script: {url}") + + except Exception as e: + traceback.print_exc() + return result.fail(f"Install(git-clone) error[2]: {url} / {e}") + + print("Installation was successful.") + return result + + def repo_update(self, repo_path, instant_execution=False, no_deps=False, return_postinstall=False): + result = ManagedResult('update-git') + + if not os.path.exists(os.path.join(repo_path, '.git')): + return result.fail(f'Path not found: {repo_path}') + + # version check + with git.Repo(repo_path) as repo: + if repo.head.is_detached: + if not switch_to_default_branch(repo): + return result.fail(f"Failed to switch to default branch: {repo_path}") + + current_branch = repo.active_branch + branch_name = current_branch.name + + if current_branch.tracking_branch() is None: + print(f"[ComfyUI-Manager] There is no tracking branch ({current_branch})") + remote_name = get_remote_name(repo) + else: + remote_name = current_branch.tracking_branch().remote_name + + if remote_name is None: + return result.fail(f"Failed to get remote when installing: {repo_path}") + + remote = repo.remote(name=remote_name) + + try: + remote.fetch() + except Exception as e: + if 'detected dubious' in str(e): + print(f"[ComfyUI-Manager] Try fixing 'dubious repository' error on '{repo_path}' repository") + safedir_path = repo_path.replace('\\', '/') + subprocess.run(['git', 'config', '--global', '--add', 'safe.directory', safedir_path]) + try: + remote.fetch() + except Exception: + print("\n[ComfyUI-Manager] Failed to fixing repository setup. Please execute this command on cmd: \n" + "-----------------------------------------------------------------------------------------\n" + f'git config --global --add safe.directory "{safedir_path}"\n' + "-----------------------------------------------------------------------------------------\n") + + commit_hash = repo.head.commit.hexsha + if f'{remote_name}/{branch_name}' in repo.refs: + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + else: + return result.fail(f"Not updatable branch: {branch_name}") + + if commit_hash != remote_commit_hash: + git_pull(repo_path) + + if len(repo.remotes) > 0: + url = repo.remotes[0].url + else: + url = "unknown repo" + + def postinstall(): + return self.execute_install_script(url, repo_path, instant_execution=instant_execution, no_deps=no_deps) + + if return_postinstall: + return result.with_postinstall(postinstall) + else: + if not postinstall(): + return result.fail(f"Failed to execute install script: {url}") + + return result + else: + return ManagedResult('skip').with_msg('Up to date') + + def unified_update(self, node_id, version_spec=None, instant_execution=False, no_deps=False, return_postinstall=False): + orig_print(f"\x1b[2K\rUpdating: {node_id}", end='') + + if version_spec is None: + version_spec = self.resolve_unspecified_version(node_id, guess_mode='active') + + if version_spec is None: + return ManagedResult('update').fail(f'Update not available: {node_id}@{version_spec}').with_ver(version_spec) + + if version_spec == 'nightly': + return self.repo_update(self.active_nodes[node_id][1], instant_execution=instant_execution, no_deps=no_deps, return_postinstall=return_postinstall).with_target('nightly').with_ver('nightly') + elif version_spec == 'unknown': + return self.repo_update(self.unknown_active_nodes[node_id][1], instant_execution=instant_execution, no_deps=no_deps, return_postinstall=return_postinstall).with_target('unknown').with_ver('unknown') + else: + return self.cnr_switch_version(node_id, instant_execution=instant_execution, no_deps=no_deps, return_postinstall=return_postinstall).with_ver('cnr') + + async def install_by_id(self, node_id: str, version_spec=None, channel=None, mode=None, instant_execution=False, no_deps=False, return_postinstall=False): + """ + priority if version_spec == None + 1. CNR latest + 2. unknown + + remark: latest version_spec is not allowed. Must be resolved before call. + """ + + if 'comfyui-manager' in node_id.lower(): + return ManagedResult('skip').fail(f"ignored: installing '{node_id}'") + + repo_url = None + if version_spec is None: + if self.is_enabled(node_id): + return ManagedResult('skip') + elif self.is_disabled(node_id): + return self.unified_enable(node_id) + + else: + version_spec = self.resolve_unspecified_version(node_id) + + if version_spec == 'unknown' or version_spec == 'nightly': + try: + custom_nodes = await self.get_custom_nodes(channel, mode) + except InvalidChannel as e: + return ManagedResult('fail').fail(f'Invalid channel is used: {e.channel}') + + the_node = custom_nodes.get(node_id) + if the_node is not None: + if version_spec == 'unknown': + repo_url = the_node['files'][0] + else: # nightly + repo_url = the_node['repository'] + else: + result = ManagedResult('install') + return result.fail(f"Node '{node_id}@{version_spec}' not found in [{channel}, {mode}]") + + if self.is_enabled(node_id, version_spec): + return ManagedResult('skip').with_target(f"{node_id}@{version_spec}") + + elif self.is_disabled(node_id, version_spec): + return self.unified_enable(node_id, version_spec) + + elif version_spec == 'unknown' or version_spec == 'nightly': + if version_spec == 'nightly': + # disable cnr nodes + if self.is_enabled(node_id, 'cnr'): + self.unified_disable(node_id, False) + + to_path = os.path.abspath(os.path.join(get_default_custom_nodes_path(), node_id)) + res = self.repo_install(repo_url, to_path, instant_execution=instant_execution, no_deps=no_deps, return_postinstall=return_postinstall) + if res.result: + if version_spec == 'unknown': + self.unknown_active_nodes[node_id] = repo_url, to_path + elif version_spec == 'nightly': + cnr_utils.generate_cnr_id(to_path, node_id) + self.active_nodes[node_id] = 'nightly', to_path + else: + return res + + return res.with_target(version_spec) + + if self.is_enabled(node_id, 'nightly'): + # disable nightly nodes + self.unified_disable(node_id, False) # NOTE: don't return from here + + if self.is_disabled(node_id, version_spec): + # enable and return if specified version is disabled + return self.unified_enable(node_id, version_spec) + + if self.is_disabled(node_id, "cnr"): + # enable and switch version if cnr is disabled (not specified version) + self.unified_enable(node_id, "cnr") + return self.cnr_switch_version(node_id, version_spec, no_deps=no_deps, return_postinstall=return_postinstall) + + if self.is_enabled(node_id, "cnr"): + return self.cnr_switch_version(node_id, version_spec, no_deps=no_deps, return_postinstall=return_postinstall) + + res = self.cnr_install(node_id, version_spec, instant_execution=instant_execution, no_deps=no_deps, return_postinstall=return_postinstall) + if res.result: + self.active_nodes[node_id] = version_spec, res.to_path + + return res + + +unified_manager = UnifiedManager() + + +def identify_node_pack_from_path(fullpath): + module_name = os.path.basename(fullpath) + if module_name.endswith('.git'): + module_name = module_name[:-4] + + repo_url = git_utils.git_url(fullpath) + if repo_url is None: + # cnr + cnr = cnr_utils.read_cnr_info(fullpath) + if cnr is not None: + return module_name, cnr['version'], cnr['id'], None + + return None + else: + # nightly or unknown + cnr_id = cnr_utils.read_cnr_id(fullpath) + commit_hash = git_utils.get_commit_hash(fullpath) + + github_id = git_utils.normalize_to_github_id(repo_url) + if github_id is None: + try: + github_id = os.path.basename(repo_url) + except: + logging.warning(f"[ComfyUI-Manager] unexpected repo url: {repo_url}") + github_id = module_name + + if cnr_id is not None: + return module_name, commit_hash, cnr_id, github_id + else: + return module_name, commit_hash, '', github_id + + +def get_installed_node_packs(): + res = {} + + for x in get_custom_nodes_paths(): + for y in os.listdir(x): + if y == '__pycache__' or y == '.disabled': + continue + + fullpath = os.path.join(x, y) + info = identify_node_pack_from_path(fullpath) + if info is None: + continue + + is_disabled = not y.endswith('.disabled') + + res[info[0]] = { 'ver': info[1], 'cnr_id': info[2], 'aux_id': info[3], 'enabled': is_disabled } + + disabled_dirs = os.path.join(x, '.disabled') + if os.path.exists(disabled_dirs): + for y in os.listdir(disabled_dirs): + if y == '__pycache__': + continue + + fullpath = os.path.join(disabled_dirs, y) + info = identify_node_pack_from_path(fullpath) + if info is None: + continue + + res[info[0]] = { 'ver': info[1], 'cnr_id': info[2], 'aux_id': info[3], 'enabled': False } + + return res + + +def refresh_channel_dict(): + if channel_dict is None: + get_channel_dict() + + +def get_channel_dict(): + global channel_dict + global valid_channels + + if channel_dict is None: + channel_dict = {} + + if not os.path.exists(manager_channel_list_path): + shutil.copy(channel_list_template_path, manager_channel_list_path) + + with open(manager_channel_list_path, 'r') as file: + channels = file.read() + for x in channels.split('\n'): + channel_info = x.split("::") + if len(channel_info) == 2: + channel_dict[channel_info[0]] = channel_info[1] + valid_channels.add(channel_info[1]) + + return channel_dict + + +def get_channel_list(): + global channel_list + + if channel_list is None: + channel_list = [] + for k, v in get_channel_dict().items(): + channel_list.append(f"{k}::{v}") + + return channel_list + + +class ManagerFuncs: + def __init__(self): + pass + + def get_current_preview_method(self): + return "none" + + def run_script(self, cmd, cwd='.'): + if len(cmd) > 0 and cmd[0].startswith("#"): + print(f"[ComfyUI-Manager] Unexpected behavior: `{cmd}`") + return 0 + + subprocess.check_call(cmd, cwd=cwd, env=get_script_env()) + + return 0 + + +manager_funcs = ManagerFuncs() + + +def write_config(): + config = configparser.ConfigParser(strict=False) + + config['default'] = { + 'preview_method': manager_funcs.get_current_preview_method(), + 'git_exe': get_config()['git_exe'], + 'use_uv': get_config()['use_uv'], + 'channel_url': get_config()['channel_url'], + 'share_option': get_config()['share_option'], + 'bypass_ssl': get_config()['bypass_ssl'], + "file_logging": get_config()['file_logging'], + 'component_policy': get_config()['component_policy'], + 'update_policy': get_config()['update_policy'], + 'windows_selector_event_loop_policy': get_config()['windows_selector_event_loop_policy'], + 'model_download_by_agent': get_config()['model_download_by_agent'], + 'downgrade_blacklist': get_config()['downgrade_blacklist'], + 'security_level': get_config()['security_level'], + 'always_lazy_install': get_config()['always_lazy_install'], + 'network_mode': get_config()['network_mode'], + 'db_mode': get_config()['db_mode'], + } + + # Sanitize all string values to prevent CRLF injection attacks + for key, value in config['default'].items(): + if isinstance(value, str): + config['default'][key] = value.replace('\r', '').replace('\n', '').replace('\x00', '') + + directory = os.path.dirname(manager_config_path) + if not os.path.exists(directory): + os.makedirs(directory) + + with open(manager_config_path, 'w') as configfile: + config.write(configfile) + + +def read_config(): + try: + config = configparser.ConfigParser(strict=False) + config.read(manager_config_path) + default_conf = config['default'] + + def get_bool(key, default_value): + return default_conf[key].lower() == 'true' if key in default_conf else False + + manager_util.use_uv = default_conf['use_uv'].lower() == 'true' if 'use_uv' in default_conf else False + manager_util.bypass_ssl = get_bool('bypass_ssl', False) + + result = { + 'http_channel_enabled': get_bool('http_channel_enabled', False), + 'preview_method': default_conf.get('preview_method', manager_funcs.get_current_preview_method()).lower(), + 'git_exe': default_conf.get('git_exe', ''), + 'use_uv': get_bool('use_uv', False), + 'channel_url': default_conf.get('channel_url', DEFAULT_CHANNEL), + 'default_cache_as_channel_url': get_bool('default_cache_as_channel_url', False), + 'share_option': default_conf.get('share_option', 'all').lower(), + 'bypass_ssl': get_bool('bypass_ssl', False), + 'file_logging': get_bool('file_logging', True), + 'component_policy': default_conf.get('component_policy', 'workflow').lower(), + 'update_policy': default_conf.get('update_policy', 'stable-comfyui').lower(), + 'windows_selector_event_loop_policy': get_bool('windows_selector_event_loop_policy', False), + 'model_download_by_agent': get_bool('model_download_by_agent', False), + 'downgrade_blacklist': default_conf.get('downgrade_blacklist', '').lower(), + 'always_lazy_install': get_bool('always_lazy_install', False), + 'network_mode': default_conf.get('network_mode', 'public').lower(), + 'security_level': default_conf.get('security_level', 'normal').lower(), + 'db_mode': default_conf.get('db_mode', 'cache').lower(), + } + manager_migration.force_security_level_if_needed(result) + return result + + except Exception: + import importlib.util + # temporary disable `uv` on Windows by default (https://github.com/Comfy-Org/ComfyUI-Manager/issues/1969) + manager_util.use_uv = importlib.util.find_spec("uv") is not None and platform.system() != "Windows" + manager_util.bypass_ssl = False + + result = { + 'http_channel_enabled': False, + 'preview_method': manager_funcs.get_current_preview_method(), + 'git_exe': '', + 'use_uv': manager_util.use_uv, + 'channel_url': DEFAULT_CHANNEL, + 'default_cache_as_channel_url': False, + 'share_option': 'all', + 'bypass_ssl': manager_util.bypass_ssl, + 'file_logging': True, + 'component_policy': 'workflow', + 'update_policy': 'stable-comfyui', + 'windows_selector_event_loop_policy': False, + 'model_download_by_agent': False, + 'downgrade_blacklist': '', + 'always_lazy_install': False, + 'network_mode': 'public', # public | private | offline + 'security_level': 'normal', # strong | normal | normal- | weak + 'db_mode': 'cache', # local | cache | remote + } + manager_migration.force_security_level_if_needed(result) + return result + + +def get_config(): + global cached_config + + if cached_config is None: + cached_config = read_config() + if cached_config['http_channel_enabled']: + print("[ComfyUI-Manager] Warning: http channel enabled, make sure server in secure env") + + return cached_config + + +def get_remote_name(repo): + available_remotes = [remote.name for remote in repo.remotes] + if 'origin' in available_remotes: + return 'origin' + elif 'upstream' in available_remotes: + return 'upstream' + elif len(available_remotes) > 0: + return available_remotes[0] + + if not available_remotes: + print(f"[ComfyUI-Manager] No remotes are configured for this repository: {repo.working_dir}") + else: + print(f"[ComfyUI-Manager] Available remotes in '{repo.working_dir}': ") + for remote in available_remotes: + print(f"- {remote}") + + return None + + +def switch_to_default_branch(repo): + remote_name = get_remote_name(repo) + + try: + if remote_name is None: + return False + + default_branch = repo.git.symbolic_ref(f'refs/remotes/{remote_name}/HEAD').replace(f'refs/remotes/{remote_name}/', '') + repo.git.checkout(default_branch) + return True + except: + # try checkout master + # try checkout main if failed + try: + repo.git.checkout(repo.heads.master) + return True + except: + try: + if remote_name is not None: + repo.git.checkout('-b', 'master', f'{remote_name}/master') + return True + except: + try: + repo.git.checkout(repo.heads.main) + return True + except: + try: + if remote_name is not None: + repo.git.checkout('-b', 'main', f'{remote_name}/main') + return True + except: + pass + + print("[ComfyUI Manager] Failed to switch to the default branch") + return False + + +def reserve_script(repo_path, install_cmds): + if not os.path.exists(manager_startup_script_path): + os.makedirs(manager_startup_script_path) + + script_path = os.path.join(manager_startup_script_path, "install-scripts.txt") + with open(script_path, "a") as file: + obj = [repo_path] + install_cmds + file.write(f"{obj}\n") + + +def try_rmtree(title, fullpath): + try: + shutil.rmtree(fullpath) + except Exception as e: + logging.warning(f"[ComfyUI-Manager] An error occurred while deleting '{fullpath}', so it has been scheduled for deletion upon restart.\nEXCEPTION: {e}") + reserve_script(title, ["#LAZY-DELETE-NODEPACK", fullpath]) + + +def try_install_script(url, repo_path, install_cmd, instant_execution=False): + if not instant_execution and ( + (len(install_cmd) > 0 and install_cmd[0].startswith('#')) or platform.system() == "Windows" or get_config()['always_lazy_install'] + ): + reserve_script(repo_path, install_cmd) + return True + else: + if len(install_cmd) == 5 and install_cmd[2:4] == ['pip', 'install']: + if is_blacklisted(install_cmd[4]): + print(f"[ComfyUI-Manager] skip black listed pip installation: '{install_cmd[4]}'") + return True + elif len(install_cmd) == 6 and install_cmd[3:5] == ['pip', 'install']: # uv mode + if is_blacklisted(install_cmd[5]): + print(f"[ComfyUI-Manager] skip black listed pip installation: '{install_cmd[5]}'") + return True + + print(f"\n## ComfyUI-Manager: EXECUTE => {install_cmd}") + code = manager_funcs.run_script(install_cmd, cwd=repo_path) + + if platform.system() != "Windows": + try: + if not os.environ.get('__COMFYUI_DESKTOP_VERSION__') and comfy_ui_commit_datetime.date() < comfy_ui_required_commit_datetime.date(): + print("\n\n###################################################################") + print(f"[WARN] ComfyUI-Manager: Your ComfyUI version ({comfy_ui_revision})[{comfy_ui_commit_datetime.date()}] is too old. Please update to the latest version.") + print("[WARN] The extension installation feature may not work properly in the current installed ComfyUI version on Windows environment.") + print("###################################################################\n\n") + except: + pass + + if code != 0: + if url is None: + url = os.path.dirname(repo_path) + print(f"install script failed: {url}") + return False + + return True + + +# use subprocess to avoid file system lock by git (Windows) +def __win_check_git_update(path, do_fetch=False, do_update=False): + if do_fetch: + command = [sys.executable, git_script_path, "--fetch", path] + elif do_update: + command = [sys.executable, git_script_path, "--pull", path] + else: + command = [sys.executable, git_script_path, "--check", path] + + new_env = get_script_env() + process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=get_default_custom_nodes_path(), env=new_env) + output, _ = process.communicate() + output = output.decode('utf-8').strip() + + if 'detected dubious' in output: + # fix and try again + safedir_path = path.replace('\\', '/') + try: + print(f"[ComfyUI-Manager] Try fixing 'dubious repository' error on '{safedir_path}' repo") + process = subprocess.Popen(['git', 'config', '--global', '--add', 'safe.directory', safedir_path], env=new_env, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + output, _ = process.communicate() + + process = subprocess.Popen(command, env=new_env, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + output, _ = process.communicate() + output = output.decode('utf-8').strip() + except Exception: + print('[ComfyUI-Manager] failed to fixing') + + if 'detected dubious' in output: + print(f'\n[ComfyUI-Manager] Failed to fixing repository setup. Please execute this command on cmd: \n' + f'-----------------------------------------------------------------------------------------\n' + f'git config --global --add safe.directory "{safedir_path}"\n' + f'-----------------------------------------------------------------------------------------\n') + + if do_update: + if "CUSTOM NODE PULL: Success" in output: + process.wait() + print(f"\x1b[2K\rUpdated: {path}") + return True, True # updated + elif "CUSTOM NODE PULL: None" in output: + process.wait() + return False, True # there is no update + else: + print(f"\x1b[2K\rUpdate error: {path}") + process.wait() + return False, False # update failed + else: + if "CUSTOM NODE CHECK: True" in output: + process.wait() + return True, True + elif "CUSTOM NODE CHECK: False" in output: + process.wait() + return False, True + else: + print(f"\x1b[2K\rFetch error: {path}") + print(f"\n{output}\n") + process.wait() + return False, True + + +def __win_check_git_pull(path): + command = [sys.executable, git_script_path, "--pull", path] + process = subprocess.Popen(command, env=get_script_env(), cwd=get_default_custom_nodes_path()) + process.wait() + + +def execute_install_script(url, repo_path, lazy_mode=False, instant_execution=False, no_deps=False): + # import ipdb; ipdb.set_trace() + install_script_path = os.path.join(repo_path, "install.py") + requirements_path = os.path.join(repo_path, "requirements.txt") + + if lazy_mode: + install_cmd = ["#LAZY-INSTALL-SCRIPT", sys.executable] + try_install_script(url, repo_path, install_cmd) + else: + if os.path.exists(requirements_path) and not no_deps: + print("Install: pip packages") + pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, manager_files_path) + with open(requirements_path, "r") as requirements_file: + for line in requirements_file: + #handle comments + if '#' in line: + if line.strip()[0] == '#': + print("Line is comment...skipping") + continue + else: + line = line.split('#')[0].strip() + + package_name = remap_pip_package(line.strip()) + + if package_name and not package_name.startswith('#'): + if '--index-url' in package_name: + s = package_name.split('--index-url') + install_cmd = manager_util.make_pip_cmd(["install", s[0].strip(), '--index-url', s[1].strip()]) + else: + install_cmd = manager_util.make_pip_cmd(["install", package_name]) + + if package_name.strip() != "" and not package_name.startswith('#'): + try_install_script(url, repo_path, install_cmd, instant_execution=instant_execution) + pip_fixer.fix_broken() + + if os.path.exists(install_script_path): + print("Install: install script") + install_cmd = [sys.executable, "install.py"] + try_install_script(url, repo_path, install_cmd, instant_execution=instant_execution) + + return True + + +def git_repo_update_check_with(path, do_fetch=False, do_update=False, no_deps=False): + """ + + perform update check for git custom node + and fetch or update if flag is on + + :param path: path to git custom node + :param do_fetch: do fetch during check + :param do_update: do update during check + :param no_deps: don't install dependencies + :return: update state * success + """ + if do_fetch: + orig_print(f"\x1b[2K\rFetching: {path}", end='') + elif do_update: + orig_print(f"\x1b[2K\rUpdating: {path}", end='') + + # Check if the path is a git repository + if not os.path.exists(os.path.join(path, '.git')): + raise ValueError(f'[ComfyUI-Manager] Not a valid git repository: {path}') + + if platform.system() == "Windows": + updated, success = __win_check_git_update(path, do_fetch, do_update) + if updated and success: + execute_install_script(None, path, lazy_mode=True, no_deps=no_deps) + return updated, success + else: + # Fetch the latest commits from the remote repository + repo = git.Repo(path) + + remote_name = get_remote_name(repo) + + if remote_name is None: + raise ValueError(f"No remotes are configured for this repository: {path}") + + remote = repo.remote(name=remote_name) + + if not do_update and repo.head.is_detached: + if do_fetch: + remote.fetch() + + return True, True # detached branch is treated as updatable + + if repo.head.is_detached: + if not switch_to_default_branch(repo): + raise ValueError(f"Failed to switch detached branch to default branch: {path}") + + current_branch = repo.active_branch + branch_name = current_branch.name + + # Get the current commit hash + commit_hash = repo.head.commit.hexsha + + if do_fetch or do_update: + remote.fetch() + + if do_update: + if repo.is_dirty(): + print(f"\nSTASH: '{path}' is dirty.") + repo.git.stash() + + if f'{remote_name}/{branch_name}' not in repo.refs: + if not switch_to_default_branch(repo): + raise ValueError(f"Failed to switch to default branch while updating: {path}") + + current_branch = repo.active_branch + branch_name = current_branch.name + + if f'{remote_name}/{branch_name}' in repo.refs: + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + else: + return False, False + + if commit_hash == remote_commit_hash: + repo.close() + return False, True + + try: + remote.pull() + repo.git.submodule('update', '--init', '--recursive') + new_commit_hash = repo.head.commit.hexsha + + if commit_hash != new_commit_hash: + execute_install_script(None, path, no_deps=no_deps) + print(f"\x1b[2K\rUpdated: {path}") + return True, True + else: + return False, False + + except Exception as e: + print(f"\nUpdating failed: {path}\n{e}", file=sys.stderr) + return False, False + + if repo.head.is_detached: + repo.close() + return True, True + + # Get commit hash of the remote branch + current_branch = repo.active_branch + branch_name = current_branch.name + + if f'{remote_name}/{branch_name}' in repo.refs: + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + else: + return True, True # Assuming there's an update if it's not the default branch. + + # Compare the commit hashes to determine if the local repository is behind the remote repository + if commit_hash != remote_commit_hash: + # Get the commit dates + commit_date = repo.head.commit.committed_datetime + remote_commit_date = repo.refs[f'{remote_name}/{branch_name}'].object.committed_datetime + + # Compare the commit dates to determine if the local repository is behind the remote repository + if commit_date < remote_commit_date: + repo.close() + return True, True + + repo.close() + + return False, True + + +class GitProgress(RemoteProgress): + def __init__(self): + super().__init__() + self.pbar = tqdm() + + def update(self, op_code, cur_count, max_count=None, message=''): + self.pbar.total = max_count + self.pbar.n = cur_count + self.pbar.pos = 0 + self.pbar.refresh() + + +def is_valid_url(url): + try: + # Check for HTTP/HTTPS URL format + result = urlparse(url) + if all([result.scheme, result.netloc]): + return True + finally: + # Check for SSH git URL format + pattern = re.compile(r"^(.+@|ssh://).+:.+$") + if pattern.match(url): + return True + return False + + +def extract_url_and_commit_id(s): + index = s.rfind('@') + if index == -1: + return (s, '') + else: + return (s[:index], s[index+1:]) + +async def gitclone_install(url, instant_execution=False, msg_prefix='', no_deps=False): + await unified_manager.reload('cache') + await unified_manager.get_custom_nodes('default', 'cache') + + print(f"{msg_prefix}Install: {url}") + + result = ManagedResult('install-git') + + if not is_valid_url(url): + return result.fail(f"Invalid git url: '{url}'") + + if url.endswith("/"): + url = url[:-1] + try: + cnr = unified_manager.get_cnr_by_repo(url) + if cnr: + cnr_id = cnr['id'] + return await unified_manager.install_by_id(cnr_id, version_spec=None, channel='default', mode='cache') + else: + new_url, commit_id = extract_url_and_commit_id(url) + if commit_id != "": + url = new_url + repo_name = os.path.splitext(os.path.basename(url))[0] + + # NOTE: Keep original name as possible if unknown node + # node_dir = f"{repo_name}@unknown" + node_dir = repo_name + + repo_path = os.path.join(get_default_custom_nodes_path(), node_dir) + + if os.path.exists(repo_path): + return result.fail(f"Already exists: '{repo_path}'") + + for custom_nodes_dir in get_custom_nodes_paths(): + disabled_repo_path1 = os.path.join(custom_nodes_dir, '.disabled', node_dir) + disabled_repo_path2 = os.path.join(custom_nodes_dir, repo_name+'.disabled') # old style + + if os.path.exists(disabled_repo_path1): + return result.fail(f"Already exists (disabled): '{disabled_repo_path1}'") + + if os.path.exists(disabled_repo_path2): + return result.fail(f"Already exists (disabled): '{disabled_repo_path2}'") + + print(f"CLONE into '{repo_path}'") + + # Clone the repository from the remote URL + clone_url = git_utils.get_url_for_clone(url) + + if not instant_execution and platform.system() == 'Windows': + res = manager_funcs.run_script([sys.executable, git_script_path, "--clone", get_default_custom_nodes_path(), clone_url, repo_path], cwd=get_default_custom_nodes_path()) + if res != 0: + return result.fail(f"Failed to clone '{clone_url}' into '{repo_path}'") + else: + repo = git.Repo.clone_from(clone_url, repo_path, recursive=True, progress=GitProgress()) + if commit_id!= "": + repo.git.checkout(commit_id) + repo.git.submodule('update', '--init', '--recursive') + + repo.git.clear_cache() + repo.close() + + execute_install_script(url, repo_path, instant_execution=instant_execution, no_deps=no_deps) + print("Installation was successful.") + return result.with_target(repo_path) + + except Exception as e: + traceback.print_exc() + print(f"Install(git-clone) error[1]: {url} / {e}", file=sys.stderr) + return result.fail(f"Install(git-clone)[1] error: {url} / {e}") + + +def git_pull(path): + # Check if the path is a git repository + if not os.path.exists(os.path.join(path, '.git')): + raise ValueError('Not a git repository') + + # Pull the latest changes from the remote repository + if platform.system() == "Windows": + return __win_check_git_pull(path) + else: + repo = git.Repo(path) + + if repo.is_dirty(): + print(f"STASH: '{path}' is dirty.") + repo.git.stash() + + if repo.head.is_detached: + if not switch_to_default_branch(repo): + raise ValueError(f"Failed to switch to default branch while pulling: {path}") + + current_branch = repo.active_branch + remote_name = current_branch.tracking_branch().remote_name + + try: + repo.git.pull('--ff-only') + except git.GitCommandError: + branch_name = current_branch.name + backup_name = f'backup_{time.strftime("%Y%m%d_%H%M%S")}' + repo.create_head(backup_name) + logging.info(f"[ComfyUI-Manager] Cannot fast-forward. Backup created: {backup_name}") + repo.git.reset('--hard', f'{remote_name}/{branch_name}') + logging.info(f"[ComfyUI-Manager] Reset to {remote_name}/{branch_name}") + + repo.git.submodule('update', '--init', '--recursive') + + repo.close() + + return True + + +async def get_data_by_mode(mode, filename, channel_url=None): + if channel_url in get_channel_dict(): + channel_url = get_channel_dict()[channel_url] + + try: + local_uri = os.path.join(manager_util.comfyui_manager_path, filename) + + if mode == "local": + json_obj = await manager_util.get_data(local_uri) + else: + if channel_url is None: + uri = get_config()['channel_url'] + '/' + filename + else: + uri = channel_url + '/' + filename + + cache_uri = str(manager_util.simple_hash(uri))+'_'+filename + cache_uri = os.path.join(manager_util.cache_dir, cache_uri) + + if get_config()['network_mode'] == 'offline': + # offline network mode + if os.path.exists(cache_uri): + json_obj = await manager_util.get_data(cache_uri) + else: + local_uri = os.path.join(manager_util.comfyui_manager_path, filename) + if os.path.exists(local_uri): + json_obj = await manager_util.get_data(local_uri) + else: + json_obj = {} # fallback + else: + # public network mode + if mode == "cache" and manager_util.is_file_created_within_one_day(cache_uri): + json_obj = await manager_util.get_data(cache_uri) + else: + json_obj = await manager_util.get_data(uri) + with manager_util.cache_lock: + with open(cache_uri, "w", encoding='utf-8') as file: + json.dump(json_obj, file, indent=4, sort_keys=True) + except Exception as e: + print(f"[ComfyUI-Manager] Due to a network error, switching to local mode.\n=> {filename}\n=> {e}") + uri = os.path.join(manager_util.comfyui_manager_path, filename) + json_obj = await manager_util.get_data(uri) + + return json_obj + + +def gitclone_fix(files, instant_execution=False, no_deps=False): + print(f"Try fixing: {files}") + for url in files: + if not is_valid_url(url): + print(f"Invalid git url: '{url}'") + return False + + if url.endswith("/"): + url = url[:-1] + try: + repo_name = os.path.splitext(os.path.basename(url))[0] + repo_path = os.path.join(get_default_custom_nodes_path(), repo_name) + + if os.path.exists(repo_path+'.disabled'): + repo_path = repo_path+'.disabled' + + if not execute_install_script(url, repo_path, instant_execution=instant_execution, no_deps=no_deps): + return False + + except Exception as e: + print(f"Fix(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print(f"Attempt to fixing '{files}' is done.") + return True + + +def pip_install(packages): + install_cmd = ['#FORCE'] + manager_util.make_pip_cmd(["install", '-U']) + packages + try_install_script('pip install via manager', '..', install_cmd) + + +def rmtree(path): + retry_count = 3 + + while True: + try: + retry_count -= 1 + + if platform.system() == "Windows": + manager_funcs.run_script(['attrib', '-R', path + '\\*', '/S']) + shutil.rmtree(path) + + return True + + except Exception as ex: + print(f"ex: {ex}") + time.sleep(3) + + if retry_count < 0: + raise ex + + print(f"Uninstall retry({retry_count})") + + +def gitclone_uninstall(files): + import os + + print(f"Uninstall: {files}") + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + for custom_nodes_dir in get_custom_nodes_paths(): + dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + dir_path = os.path.join(custom_nodes_dir, dir_name) + + # safety check + if dir_path == '/' or dir_path[1:] == ":/" or dir_path == '': + print(f"Uninstall(git-clone) error: invalid path '{dir_path}' for '{url}'") + return False + + install_script_path = os.path.join(dir_path, "uninstall.py") + disable_script_path = os.path.join(dir_path, "disable.py") + if os.path.exists(install_script_path): + uninstall_cmd = [sys.executable, "uninstall.py"] + code = manager_funcs.run_script(uninstall_cmd, cwd=dir_path) + + if code != 0: + print(f"An error occurred during the execution of the uninstall.py script. Only the '{dir_path}' will be deleted.") + elif os.path.exists(disable_script_path): + disable_script = [sys.executable, "disable.py"] + code = manager_funcs.run_script(disable_script, cwd=dir_path) + if code != 0: + print(f"An error occurred during the execution of the disable.py script. Only the '{dir_path}' will be deleted.") + + if os.path.exists(dir_path): + rmtree(dir_path) + elif os.path.exists(dir_path + ".disabled"): + rmtree(dir_path + ".disabled") + except Exception as e: + print(f"Uninstall(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print("Uninstallation was successful.") + return True + + +def gitclone_set_active(files, is_disable): + import os + + if is_disable: + action_name = "Disable" + else: + action_name = "Enable" + + print(f"{action_name}: {files}") + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + for custom_nodes_dir in get_custom_nodes_paths(): + dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + dir_path = os.path.join(custom_nodes_dir, dir_name) + + # safety check + if dir_path == '/' or dir_path[1:] == ":/" or dir_path == '': + print(f"{action_name}(git-clone) error: invalid path '{dir_path}' for '{url}'") + return False + + if is_disable: + current_path = dir_path + base_path = extract_base_custom_nodes_dir(current_path) + new_path = os.path.join(base_path, ".disabled", dir_name) + + if not os.path.exists(current_path): + continue + else: + current_path1 = os.path.join(get_default_custom_nodes_path(), ".disabled", dir_name) + current_path2 = dir_path + ".disabled" + + if os.path.exists(current_path1): + current_path = current_path1 + elif os.path.exists(current_path2): + current_path = current_path2 + else: + continue + + base_path = extract_base_custom_nodes_dir(current_path) + new_path = os.path.join(base_path, dir_name) + + shutil.move(current_path, new_path) + + if is_disable: + if os.path.exists(os.path.join(new_path, "disable.py")): + disable_script = [sys.executable, "disable.py"] + try_install_script(url, new_path, disable_script) + else: + if os.path.exists(os.path.join(new_path, "enable.py")): + enable_script = [sys.executable, "enable.py"] + try_install_script(url, new_path, enable_script) + + break # for safety + + except Exception as e: + print(f"{action_name}(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print(f"{action_name} was successful.") + return True + + +def gitclone_update(files, instant_execution=False, skip_script=False, msg_prefix="", no_deps=False): + import os + + print(f"{msg_prefix}Update: {files}") + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + for custom_nodes_dir in get_default_custom_nodes_path(): + repo_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + repo_path = os.path.join(custom_nodes_dir, repo_name) + + if os.path.exists(repo_path+'.disabled'): + repo_path = repo_path+'.disabled' + + elif os.path.exists(os.path.join(get_default_custom_nodes_path(), "disabled", repo_name)): + repo_path = os.path.join(get_default_custom_nodes_path(), "disabled", repo_name) + + if not os.path.exists(repo_path): + continue + + git_pull(repo_path) + + if not skip_script: + if instant_execution: + if not execute_install_script(url, repo_path, lazy_mode=False, instant_execution=True, no_deps=no_deps): + return False + else: + if not execute_install_script(url, repo_path, lazy_mode=True, no_deps=no_deps): + return False + + break # for safety + + except Exception as e: + print(f"Update(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + if not skip_script: + print("Update was successful.") + return True + + +def update_to_stable_comfyui(repo_path): + try: + repo = git.Repo(repo_path) + try: + repo.git.checkout(repo.heads.master) + except: + logging.error(f"[ComfyUI-Manager] Failed to checkout 'master' branch.\nrepo_path={repo_path}\nAvailable branches:") + for branch in repo.branches: + logging.error('\t'+branch.name) + return "fail", None + + versions, current_tag, latest_tag = get_comfyui_versions(repo) + + if latest_tag is None: + logging.info("[ComfyUI-Manager] Unable to update to the stable ComfyUI version.") + return "fail", None + + tag_ref = next((t for t in repo.tags if t.name == latest_tag), None) + if tag_ref is None: + logging.info(f"[ComfyUI-Manager] Unable to locate tag '{latest_tag}' in repository.") + return "fail", None + + if repo.head.commit == tag_ref.commit: + return "skip", None + else: + logging.info(f"[ComfyUI-Manager] Updating ComfyUI: {current_tag} -> {latest_tag}") + repo.git.checkout(tag_ref.name) + execute_install_script("ComfyUI", repo_path, instant_execution=False, no_deps=False) + return 'updated', latest_tag + except: + traceback.print_exc() + return "fail", None + + +def update_path(repo_path, instant_execution=False, no_deps=False): + if not os.path.exists(os.path.join(repo_path, '.git')): + return "fail" + + # version check + repo = git.Repo(repo_path) + + is_switched = False + if repo.head.is_detached: + if not switch_to_default_branch(repo): + return "fail" + else: + is_switched = True + + current_branch = repo.active_branch + branch_name = current_branch.name + + if current_branch.tracking_branch() is None: + print(f"[ComfyUI-Manager] There is no tracking branch ({current_branch})") + remote_name = get_remote_name(repo) + else: + remote_name = current_branch.tracking_branch().remote_name + remote = repo.remote(name=remote_name) + + try: + remote.fetch() + except Exception as e: + if 'detected dubious' in str(e): + print(f"[ComfyUI-Manager] Try fixing 'dubious repository' error on '{repo_path}' repository") + safedir_path = repo_path.replace('\\', '/') + subprocess.run(['git', 'config', '--global', '--add', 'safe.directory', safedir_path]) + try: + remote.fetch() + except Exception: + print(f"\n[ComfyUI-Manager] Failed to fixing repository setup. Please execute this command on cmd: \n" + f"-----------------------------------------------------------------------------------------\n" + f'git config --global --add safe.directory "{safedir_path}"\n' + f"-----------------------------------------------------------------------------------------\n") + return "fail" + + commit_hash = repo.head.commit.hexsha + + if f'{remote_name}/{branch_name}' in repo.refs: + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + else: + return "fail" + + if commit_hash != remote_commit_hash: + git_pull(repo_path) + execute_install_script("ComfyUI", repo_path, instant_execution=instant_execution, no_deps=no_deps) + return "updated" + elif is_switched: + return "updated" + else: + return "skipped" + + +def lookup_customnode_by_url(data, target): + for x in data['custom_nodes']: + if target in x['files']: + for custom_nodes_dir in get_custom_nodes_paths(): + dir_name = os.path.splitext(os.path.basename(target))[0].replace(".git", "") + dir_path = os.path.join(custom_nodes_dir, dir_name) + if os.path.exists(dir_path): + x['installed'] = 'True' + else: + disabled_path1 = os.path.join(custom_nodes_dir, '.disabled', dir_name) + disabled_path2 = dir_path + ".disabled" + + if os.path.exists(disabled_path1) or os.path.exists(disabled_path2): + x['installed'] = 'Disabled' + else: + continue + + return x + + return None + + +def lookup_installed_custom_nodes_legacy(repo_name): + base_paths = get_custom_nodes_paths() + + for base_path in base_paths: + repo_path = os.path.join(base_path, repo_name) + if os.path.exists(repo_path): + return True, repo_path + elif os.path.exists(repo_path + '.disabled'): + return False, repo_path + + return None + + +def simple_check_custom_node(url): + dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + dir_path = os.path.join(get_default_custom_nodes_path(), dir_name) + if os.path.exists(dir_path): + return 'installed' + elif os.path.exists(dir_path+'.disabled'): + return 'disabled' + + return 'not-installed' + + +def check_state_of_git_node_pack_single(item, do_fetch=False, do_update_check=True, do_update=False): + if item['version'] == 'unknown': + dir_path = unified_manager.unknown_active_nodes.get(item['id'])[1] + elif item['version'] == 'nightly': + dir_path = unified_manager.active_nodes.get(item['id'])[1] + else: + # skip CNR nodes + dir_path = None + + if dir_path and os.path.exists(dir_path): + if do_update_check: + try: + update_state, success = git_repo_update_check_with(dir_path, do_fetch, do_update) + if (do_update_check or do_update) and update_state: + item['update-state'] = 'true' + elif do_update and not success: + item['update-state'] = 'fail' + except Exception: + print(f"[ComfyUI-Manager] Failed to check state of the git node pack: {dir_path}") + + +def get_installed_pip_packages(): + try: + # extract pip package infos + cmd = manager_util.make_pip_cmd(['freeze']) + pips = subprocess.check_output(cmd, text=True).split('\n') + except Exception as e: + logging.warning("[ComfyUI-Manager] Could not enumerate pip packages for snapshot: %s", e) + return {} + + res = {} + for x in pips: + if x.strip() == "": + continue + + if ' @ ' in x: + spec_url = x.split(' @ ') + res[spec_url[0]] = spec_url[1] + else: + res[x] = "" + + return res + + +async def get_current_snapshot(custom_nodes_only = False): + await unified_manager.reload('cache') + await unified_manager.get_custom_nodes('default', 'cache') + + # Get ComfyUI hash + repo_path = comfy_path + + comfyui_commit_hash = None + if not custom_nodes_only: + if os.path.exists(os.path.join(repo_path, '.git')): + repo = git.Repo(repo_path) + comfyui_commit_hash = repo.head.commit.hexsha + + git_custom_nodes = {} + cnr_custom_nodes = {} + file_custom_nodes = [] + + # Get custom nodes hash + for custom_nodes_dir in get_custom_nodes_paths(): + paths = os.listdir(custom_nodes_dir) + + disabled_path = os.path.join(custom_nodes_dir, '.disabled') + if os.path.exists(disabled_path): + for x in os.listdir(disabled_path): + paths.append(os.path.join(disabled_path, x)) + + for path in paths: + if path in ['.disabled', '__pycache__']: + continue + + fullpath = os.path.join(custom_nodes_dir, path) + + if os.path.isdir(fullpath): + is_disabled = path.endswith(".disabled") or os.path.basename(os.path.dirname(fullpath)) == ".disabled" + + try: + info = unified_manager.resolve_from_path(fullpath) + + if info is None: + continue + + if info['ver'] not in ['nightly', 'latest', 'unknown']: + if is_disabled: + continue # don't restore disabled state of CNR node. + + cnr_custom_nodes[info['id']] = info['ver'] + else: + commit_hash = git_utils.get_commit_hash(fullpath) + url = git_utils.git_url(fullpath) + git_custom_nodes[url] = dict(hash=commit_hash, disabled=is_disabled) + except: + print(f"Failed to extract snapshots for the custom node '{path}'.") + + elif path.endswith('.py'): + is_disabled = path.endswith(".py.disabled") + filename = os.path.basename(path) + item = { + 'filename': filename, + 'disabled': is_disabled + } + + file_custom_nodes.append(item) + + pip_packages = None if custom_nodes_only else get_installed_pip_packages() + + return { + 'comfyui': comfyui_commit_hash, + 'git_custom_nodes': git_custom_nodes, + 'cnr_custom_nodes': cnr_custom_nodes, + 'file_custom_nodes': file_custom_nodes, + 'pips': pip_packages, + } + + +async def save_snapshot_with_postfix(postfix, path=None, custom_nodes_only = False): + if path is None: + now = datetime.now() + + date_time_format = now.strftime("%Y-%m-%d_%H-%M-%S") + file_name = f"{date_time_format}_{postfix}" + + path = os.path.join(manager_snapshot_path, f"{file_name}.json") + else: + file_name = path.replace('\\', '/').split('/')[-1] + file_name = file_name.split('.')[-2] + + snapshot = await get_current_snapshot(custom_nodes_only) + if path.endswith('.json'): + with open(path, "w") as json_file: + json.dump(snapshot, json_file, indent=4) + + return file_name + '.json' + + elif path.endswith('.yaml'): + with open(path, "w") as yaml_file: + snapshot = {'custom_nodes': snapshot} + yaml.dump(snapshot, yaml_file, allow_unicode=True) + + return path + + +async def extract_nodes_from_workflow(filepath, mode='local', channel_url='default'): + # prepare json data + workflow = None + if filepath.endswith('.json'): + with open(filepath, "r", encoding="UTF-8", errors="ignore") as json_file: + try: + workflow = json.load(json_file) + except: + print(f"Invalid workflow file: {filepath}") + exit(-1) + + elif filepath.endswith('.png'): + from PIL import Image + with Image.open(filepath) as img: + if 'workflow' not in img.info: + print(f"The specified .png file doesn't have a workflow: {filepath}") + exit(-1) + else: + try: + workflow = json.loads(img.info['workflow']) + except: + print(f"This is not a valid .png file containing a ComfyUI workflow: {filepath}") + exit(-1) + + if workflow is None: + print(f"Invalid workflow file: {filepath}") + exit(-1) + + # extract nodes + used_nodes = set() + + def extract_nodes(sub_workflow): + for x in sub_workflow['nodes']: + node_name = x.get('type') + + # skip virtual nodes + if node_name in ['Reroute', 'Note']: + continue + + if node_name is not None and not (node_name.startswith('workflow/') or node_name.startswith('workflow>')): + used_nodes.add(node_name) + + if 'nodes' in workflow: + extract_nodes(workflow) + + if 'extra' in workflow: + if 'groupNodes' in workflow['extra']: + for x in workflow['extra']['groupNodes'].values(): + extract_nodes(x) + + # lookup dependent custom nodes + ext_map = await get_data_by_mode(mode, 'extension-node-map.json', channel_url) + + rext_map = {} + preemption_map = {} + patterns = [] + for k, v in ext_map.items(): + if k == 'https://github.com/comfyanonymous/ComfyUI': + for x in v[0]: + if x not in preemption_map: + preemption_map[x] = [] + + preemption_map[x] = k + continue + + for x in v[0]: + if x not in rext_map: + rext_map[x] = [] + + rext_map[x].append(k) + + if 'preemptions' in v[1]: + for x in v[1]['preemptions']: + if x not in preemption_map: + preemption_map[x] = [] + + preemption_map[x] = k + + if 'nodename_pattern' in v[1]: + patterns.append((v[1]['nodename_pattern'], k)) + + # identify used extensions + used_exts = set() + unknown_nodes = set() + + for node_name in used_nodes: + ext = preemption_map.get(node_name) + + if ext is None: + ext = rext_map.get(node_name) + if ext is not None: + ext = ext[0] + + if ext is None: + for pat_ext in patterns: + if re.search(pat_ext[0], node_name): + ext = pat_ext[1] + break + + if ext == 'https://github.com/comfyanonymous/ComfyUI': + pass + elif ext is not None: + used_exts.add(ext) + else: + unknown_nodes.add(node_name) + + return used_exts, unknown_nodes + + +def unzip(model_path): + if not os.path.exists(model_path): + print(f"[ComfyUI-Manager] unzip: File not found: {model_path}") + return False + + base_dir = os.path.dirname(model_path) + filename = os.path.basename(model_path) + target_dir = os.path.join(base_dir, filename[:-4]) + + os.makedirs(target_dir, exist_ok=True) + + with zipfile.ZipFile(model_path, 'r') as zip_ref: + zip_ref.extractall(target_dir) + + # Check if there's only one directory inside the target directory + contents = os.listdir(target_dir) + if len(contents) == 1 and os.path.isdir(os.path.join(target_dir, contents[0])): + nested_dir = os.path.join(target_dir, contents[0]) + # Move each file and sub-directory in the nested directory up to the target directory + for item in os.listdir(nested_dir): + shutil.move(os.path.join(nested_dir, item), os.path.join(target_dir, item)) + # Remove the now empty nested directory + os.rmdir(nested_dir) + + os.remove(model_path) + return True + + +def map_to_unified_keys(json_obj): + res = {} + for k, v in json_obj.items(): + cnr = unified_manager.get_cnr_by_repo(k) + if cnr: + res[cnr['id']] = v + else: + res[k] = v + + return res + + +async def get_unified_total_nodes(channel, mode, regsitry_cache_mode='cache'): + await unified_manager.reload(regsitry_cache_mode) + + res = await unified_manager.get_custom_nodes(channel, mode) + + # collect pure cnr ids (i.e. not exists in custom-node-list.json) + # populate state/updatable field to non-pure cnr nodes + cnr_ids = set(unified_manager.cnr_map.keys()) + for k, v in res.items(): + # resolve cnr_id from repo url + files_in_json = v.get('files', []) + cnr_id = None + if len(files_in_json) == 1: + cnr = unified_manager.get_cnr_by_repo(files_in_json[0]) + if cnr: + cnr_id = cnr['id'] + + if cnr_id is not None: + # cnr or nightly version + cnr_ids.discard(cnr_id) + updatable = False + cnr = unified_manager.cnr_map[cnr_id] + + if cnr_id in invalid_nodes: + v['invalid-installation'] = True + + if cnr_id in unified_manager.active_nodes: + # installed + v['state'] = 'enabled' + if unified_manager.active_nodes[cnr_id][0] != 'nightly': + updatable = unified_manager.is_updatable(cnr_id) + else: + updatable = False + v['active_version'] = unified_manager.active_nodes[cnr_id][0] + v['version'] = v['active_version'] + + if cm_global.try_call(api="cm.is_import_failed_extension", name=unified_manager.active_nodes[cnr_id][1]): + v['import-fail'] = True + + elif cnr_id in unified_manager.cnr_inactive_nodes: + # disabled + v['state'] = 'disabled' + cnr_ver = unified_manager.get_from_cnr_inactive_nodes(cnr_id) + if cnr_ver is not None: + v['version'] = str(cnr_ver[0]) + else: + v['version'] = '0' + + elif cnr_id in unified_manager.nightly_inactive_nodes: + # disabled + v['state'] = 'disabled' + v['version'] = 'nightly' + else: + # not installed + v['state'] = 'not-installed' + + if 'version' not in v: + v['version'] = cnr['latest_version']['version'] + + v['update-state'] = 'true' if updatable else 'false' + else: + # unknown version + v['version'] = 'unknown' + + if unified_manager.is_enabled(k, 'unknown'): + v['state'] = 'enabled' + v['active_version'] = 'unknown' + + if cm_global.try_call(api="cm.is_import_failed_extension", name=unified_manager.unknown_active_nodes[k][1]): + v['import-fail'] = True + + elif unified_manager.is_disabled(k, 'unknown'): + v['state'] = 'disabled' + else: + v['state'] = 'not-installed' + + # add items for pure cnr nodes + if normalize_channel(channel) == DEFAULT_CHANNEL: + # Don't show CNR nodes unless default channel + for cnr_id in cnr_ids: + cnr = unified_manager.cnr_map[cnr_id] + author = cnr['publisher']['name'] + title = cnr['name'] + reference = f"https://registry.comfy.org/nodes/{cnr['id']}" + repository = cnr.get('repository', '') + install_type = "cnr" + description = cnr.get('description', '') + + ver = None + active_version = None + updatable = False + import_fail = None + if cnr_id in unified_manager.active_nodes: + # installed + state = 'enabled' + updatable = unified_manager.is_updatable(cnr_id) + active_version = unified_manager.active_nodes[cnr['id']][0] + ver = active_version + + if cm_global.try_call(api="cm.is_import_failed_extension", name=unified_manager.active_nodes[cnr_id][1]): + import_fail = True + + elif cnr['id'] in unified_manager.cnr_inactive_nodes: + # disabled + state = 'disabled' + elif cnr['id'] in unified_manager.nightly_inactive_nodes: + # disabled + state = 'disabled' + ver = 'nightly' + else: + # not installed + state = 'not-installed' + + if ver is None: + ver = cnr['latest_version']['version'] + + item = dict(author=author, title=title, reference=reference, repository=repository, install_type=install_type, + description=description, state=state, updatable=updatable, version=ver) + + if active_version: + item['active_version'] = active_version + + if import_fail: + item['import-fail'] = True + + res[cnr_id] = item + + return res + + +def populate_github_stats(node_packs, json_obj_github): + for k, v in node_packs.items(): + try: + url = v['reference'] + if url in json_obj_github: + v['stars'] = json_obj_github[url]['stars'] + v['last_update'] = json_obj_github[url]['last_update'] + v['trust'] = json_obj_github[url]['author_account_age_days'] > 600 + else: + v['stars'] = -1 + v['last_update'] = -1 + v['trust'] = False + except: + logging.error(f"[ComfyUI-Manager] DB item is broken:\n{v}") + + +def populate_favorites(node_packs, json_obj_extras): + favorites = set(json_obj_extras['favorites']) + + for k, v in node_packs.items(): + if v.get('version') != 'unknown': + if k in favorites: + v['is_favorite'] = True + + +async def restore_snapshot(snapshot_path, git_helper_extras=None): + cloned_repos = [] + checkout_repos = [] + enabled_repos = [] + disabled_repos = [] + skip_node_packs = [] + switched_node_packs = [] + installed_node_packs = [] + failed = [] + + await unified_manager.reload('cache') + await unified_manager.get_custom_nodes('default', 'cache') + + cnr_repo_map = {} + for k, v in unified_manager.repo_cnr_map.items(): + cnr_repo_map[v['id']] = k + + print("Restore snapshot.") + + postinstalls = [] + + with open(snapshot_path, 'r', encoding="UTF-8") as snapshot_file: + if snapshot_path.endswith('.json'): + info = json.load(snapshot_file) + elif snapshot_path.endswith('.yaml'): + info = yaml.load(snapshot_file, Loader=yaml.SafeLoader) + info = info['custom_nodes'] + + if 'pips' in info and info['pips']: + pips = info['pips'] + else: + pips = {} + + # for cnr restore + cnr_info = info.get('cnr_custom_nodes') + if cnr_info is not None: + # disable not listed cnr nodes + todo_disable = [] + todo_checkout = [] + + for k, v in unified_manager.active_nodes.items(): + if 'comfyui-manager' in k: + continue + + if v[0] != 'nightly': + if k not in cnr_info: + todo_disable.append(k) + else: + cnr_ver = cnr_info[k] + if v[1] != cnr_ver: + todo_checkout.append((k, cnr_ver)) + else: + skip_node_packs.append(k) + + for x in todo_disable: + unified_manager.unified_disable(x, False) + disabled_repos.append(x) + + for x in todo_checkout: + ps = unified_manager.cnr_switch_version(x[0], x[1], instant_execution=True, no_deps=True, return_postinstall=False) + if ps.action == 'switch-cnr' and ps.result: + switched_node_packs.append(f"{x[0]}@{x[1]}") + elif ps.action == 'skip': + skip_node_packs.append(f"{x[0]}@{x[1]}") + elif not ps.result: + failed.append(f"{x[0]}@{x[1]}") + + # install listed cnr nodes + for k, v in cnr_info.items(): + if 'comfyui-manager' in k: + continue + + ps = await unified_manager.install_by_id(k, version_spec=v, instant_execution=True, return_postinstall=True) + if ps.action == 'install-cnr' and ps.result: + installed_node_packs.append(f"{k}@{v}") + + if ps is not None and ps.result: + if hasattr(ps, 'postinstall'): + postinstalls.append(ps.postinstall) + else: + print("cm-cli: unexpected [0001]") + + # for nightly restore + _git_info = info.get('git_custom_nodes') + git_info = {} + + # normalize github repo + for k, v in _git_info.items(): + # robust filter out comfyui-manager while restoring snapshot + if 'comfyui-manager' in k.lower(): + continue + + norm_k = git_utils.normalize_url(k) + git_info[norm_k] = v + + if git_info is not None: + todo_disable = [] + todo_enable = [] + todo_checkout = [] + processed_urls = [] + + for k, v in unified_manager.active_nodes.items(): + if 'comfyui-manager' in k: + continue + + if v[0] == 'nightly' and cnr_repo_map.get(k): + repo_url = cnr_repo_map.get(k) + normalized_url = git_utils.normalize_url(repo_url) + + if normalized_url not in git_info: + todo_disable.append(k) + else: + commit_hash = git_info[normalized_url]['hash'] + todo_checkout.append((v[1], commit_hash)) + + for k, v in unified_manager.nightly_inactive_nodes.items(): + if 'comfyui-manager' in k: + continue + + if cnr_repo_map.get(k): + repo_url = cnr_repo_map.get(k) + normalized_url = git_utils.normalize_url(repo_url) + + if normalized_url in git_info: + commit_hash = git_info[normalized_url]['hash'] + todo_enable.append((k, commit_hash)) + processed_urls.append(normalized_url) + + for x in todo_disable: + unified_manager.unified_disable(x, False) + disabled_repos.append(x) + + for x in todo_enable: + res = unified_manager.unified_enable(x[0], 'nightly') + + is_switched = False + if res and res.target: + is_switched = repo_switch_commit(res.target, x[1]) + + if is_switched: + checkout_repos.append(f"{x[0]}@{x[1]}") + else: + enabled_repos.append(x[0]) + + for x in todo_checkout: + is_switched = repo_switch_commit(x[0], x[1]) + + if is_switched: + checkout_repos.append(f"{x[0]}@{x[1]}") + + for x in git_info.keys(): + normalized_url = git_utils.normalize_url(x) + cnr = unified_manager.repo_cnr_map.get(normalized_url) + if cnr is not None: + pack_id = cnr['id'] + res = await unified_manager.install_by_id(pack_id, 'nightly', instant_execution=True, no_deps=False, return_postinstall=False) + if res.action == 'install-git' and res.result: + cloned_repos.append(pack_id) + elif res.action == 'skip': + skip_node_packs.append(pack_id) + elif not res.result: + failed.append(pack_id) + processed_urls.append(x) + + for x in processed_urls: + if x in git_info: + del git_info[x] + + # for unknown restore + todo_disable = [] + todo_enable = [] + todo_checkout = [] + processed_urls = [] + + for k2, v2 in unified_manager.unknown_active_nodes.items(): + repo_url = resolve_giturl_from_path(v2[1]) + + if repo_url is None: + continue + + normalized_url = git_utils.normalize_url(repo_url) + + if normalized_url not in git_info: + todo_disable.append(k2) + else: + commit_hash = git_info[normalized_url]['hash'] + todo_checkout.append((k2, commit_hash)) + processed_urls.append(normalized_url) + + for k2, v2 in unified_manager.unknown_inactive_nodes.items(): + repo_url = resolve_giturl_from_path(v2[1]) + + if repo_url is None: + continue + + normalized_url = git_utils.normalize_url(repo_url) + + if normalized_url in git_info: + commit_hash = git_info[normalized_url]['hash'] + todo_enable.append((k2, commit_hash)) + processed_urls.append(normalized_url) + + for x in todo_disable: + unified_manager.unified_disable(x, True) + disabled_repos.append(x) + + for x in todo_enable: + res = unified_manager.unified_enable(x[0], 'unknown') + + is_switched = False + if res and res.target: + is_switched = repo_switch_commit(res.target, x[1]) + + if is_switched: + checkout_repos.append(f"{x[0]}@{x[1]}") + else: + enabled_repos.append(x[0]) + + for x in todo_checkout: + is_switched = repo_switch_commit(x[0], x[1]) + + if is_switched: + checkout_repos.append(f"{x[0]}@{x[1]}") + else: + skip_node_packs.append(x[0]) + + for x in processed_urls: + if x in git_info: + del git_info[x] + + for repo_url in git_info.keys(): + repo_name = os.path.basename(repo_url) + if repo_name.endswith('.git'): + repo_name = repo_name[:-4] + + to_path = os.path.join(get_default_custom_nodes_path(), repo_name) + unified_manager.repo_install(repo_url, to_path, instant_execution=True, no_deps=False, return_postinstall=False) + cloned_repos.append(repo_name) + + manager_util.restore_pip_snapshot(pips, git_helper_extras) + + # print summary + for x in cloned_repos: + print(f"[ INSTALLED ] {x}") + for x in installed_node_packs: + print(f"[ INSTALLED ] {x}") + for x in checkout_repos: + print(f"[ CHECKOUT ] {x}") + for x in switched_node_packs: + print(f"[ SWITCHED ] {x}") + for x in enabled_repos: + print(f"[ ENABLED ] {x}") + for x in disabled_repos: + print(f"[ DISABLED ] {x}") + for x in skip_node_packs: + print(f"[ SKIPPED ] {x}") + for x in failed: + print(f"[ FAILED ] {x}") + + # if is_failed: + # print("[bold red]ERROR: Failed to restore snapshot.[/bold red]") + + +def get_comfyui_versions(repo=None): + repo = repo or git.Repo(comfy_path) + + remote_name = None + try: + remote_name = get_remote_name(repo) + repo.remotes[remote_name].fetch() + except: + logging.error("[ComfyUI-Manager] Failed to fetch ComfyUI") + + def parse_semver(tag_name): + match = re.match(r'^v(\d+)\.(\d+)\.(\d+)$', tag_name) + return tuple(int(x) for x in match.groups()) if match else None + + def normalize_describe(tag_name): + if not tag_name: + return None + base = tag_name.split('-', 1)[0] + return base if parse_semver(base) else None + + # Collect semver tags and sort descending (highest first) + semver_tags = [] + for tag in repo.tags: + semver = parse_semver(tag.name) + if semver: + semver_tags.append((semver, tag.name)) + semver_tags.sort(key=lambda x: x[0], reverse=True) + semver_tags = [name for _, name in semver_tags] + + latest_tag = semver_tags[0] if semver_tags else None + + try: + described = repo.git.describe('--tags') + except Exception: + described = '' + + try: + exact_tag = repo.git.describe('--tags', '--exact-match') + except Exception: + exact_tag = '' + + head_is_default = False + if remote_name: + try: + default_head_ref = repo.refs[f'{remote_name}/HEAD'] + default_commit = default_head_ref.reference.commit + head_is_default = repo.head.commit == default_commit + except Exception: + head_is_default = False + + nearest_semver = normalize_describe(described) + exact_semver = exact_tag if parse_semver(exact_tag) else None + + if head_is_default and not exact_tag: + current_tag = 'nightly' + else: + current_tag = exact_tag or described or 'nightly' + + # Prepare semver list for display: top 4 plus the current/nearest semver if missing + display_semver_tags = semver_tags[:4] + if exact_semver and exact_semver not in display_semver_tags: + display_semver_tags.append(exact_semver) + elif nearest_semver and nearest_semver not in display_semver_tags: + display_semver_tags.append(nearest_semver) + + versions = ['nightly'] + + if current_tag and not exact_semver and current_tag not in versions and current_tag not in display_semver_tags: + versions.append(current_tag) + + for tag in display_semver_tags: + if tag not in versions: + versions.append(tag) + + versions = versions[:6] + + return versions, current_tag, latest_tag + + +def switch_comfyui(tag): + repo = git.Repo(comfy_path) + + if tag == 'nightly': + repo.git.checkout('master') + tracking_branch = repo.active_branch.tracking_branch() + remote_name = tracking_branch.remote_name + repo.remotes[remote_name].pull() + print("[ComfyUI-Manager] ComfyUI version is switched to the latest 'master' version") + else: + repo.git.checkout(tag) + print(f"[ComfyUI-Manager] ComfyUI version is switched to '{tag}'") + + +def resolve_giturl_from_path(fullpath): + """ + resolve giturl path of unclassified custom node based on remote url in .git/config + """ + git_config_path = os.path.join(fullpath, '.git', 'config') + + if not os.path.exists(git_config_path): + return "unknown" + + config = configparser.ConfigParser(strict=False) + config.read(git_config_path) + + for k, v in config.items(): + if k.startswith('remote ') and 'url' in v: + return v['url'].replace("git@github.com:", "https://github.com/") + + return None + + +def repo_switch_commit(repo_path, commit_hash): + try: + repo = git.Repo(repo_path) + if repo.head.commit.hexsha == commit_hash: + return False + + repo.git.checkout(commit_hash) + return True + except: + return None diff --git a/custom_nodes/comfyui-manager/glob/manager_downloader.py b/custom_nodes/comfyui-manager/glob/manager_downloader.py new file mode 100644 index 00000000..2718de50 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/manager_downloader.py @@ -0,0 +1,163 @@ +import os +from urllib.parse import urlparse +import urllib +import sys +import logging +import requests +from huggingface_hub import HfApi +from tqdm.auto import tqdm + + +aria2 = os.getenv('COMFYUI_MANAGER_ARIA2_SERVER') +HF_ENDPOINT = os.getenv('HF_ENDPOINT') + + +if aria2 is not None: + secret = os.getenv('COMFYUI_MANAGER_ARIA2_SECRET') + url = urlparse(aria2) + port = url.port + host = url.scheme + '://' + url.hostname + import aria2p + + aria2 = aria2p.API(aria2p.Client(host=host, port=port, secret=secret)) + + +def basic_download_url(url, dest_folder: str, filename: str): + ''' + Download file from url to dest_folder with filename + using requests library. + ''' + import requests + + # Ensure the destination folder exists + if not os.path.exists(dest_folder): + os.makedirs(dest_folder) + + # Full path to save the file + dest_path = os.path.join(dest_folder, filename) + + # Download the file + response = requests.get(url, stream=True) + if response.status_code == 200: + with open(dest_path, 'wb') as file: + for chunk in response.iter_content(chunk_size=1024): + if chunk: + file.write(chunk) + else: + raise Exception(f"Failed to download file from {url}") + + +def download_url(model_url: str, model_dir: str, filename: str): + if HF_ENDPOINT: + model_url = model_url.replace('https://huggingface.co', HF_ENDPOINT) + logging.info(f"model_url replaced by HF_ENDPOINT, new = {model_url}") + if aria2: + return aria2_download_url(model_url, model_dir, filename) + else: + from torchvision.datasets.utils import download_url as torchvision_download_url + try: + return torchvision_download_url(model_url, model_dir, filename) + except Exception as e: + logging.error(f"[ComfyUI-Manager] Failed to download: {model_url} / {repr(e)}") + raise + + +def aria2_find_task(dir: str, filename: str): + target = os.path.join(dir, filename) + + downloads = aria2.get_downloads() + + for download in downloads: + for file in download.files: + if file.is_metadata: + continue + if str(file.path) == target: + return download + + +def aria2_download_url(model_url: str, model_dir: str, filename: str): + import manager_core as core + import tqdm + import time + + if model_dir.startswith(core.comfy_path): + model_dir = model_dir[len(core.comfy_path) :] + + download_dir = model_dir if model_dir.startswith('/') else os.path.join('/models', model_dir) + + download = aria2_find_task(download_dir, filename) + if download is None: + options = {'dir': download_dir, 'out': filename} + download = aria2.add(model_url, options)[0] + + if download.is_active: + with tqdm.tqdm( + total=download.total_length, + bar_format='{l_bar}{bar}{r_bar}', + desc=filename, + unit='B', + unit_scale=True, + ) as progress_bar: + while download.is_active: + if progress_bar.total == 0 and download.total_length != 0: + progress_bar.reset(download.total_length) + progress_bar.update(download.completed_length - progress_bar.n) + time.sleep(1) + download.update() + + +def download_url_with_agent(url, save_path): + try: + headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} + + req = urllib.request.Request(url, headers=headers) + response = urllib.request.urlopen(req) + data = response.read() + + if not os.path.exists(os.path.dirname(save_path)): + os.makedirs(os.path.dirname(save_path)) + + with open(save_path, 'wb') as f: + f.write(data) + + except Exception as e: + print(f"Download error: {url} / {e}", file=sys.stderr) + return False + + print("Installation was successful.") + return True + +# NOTE: snapshot_download doesn't provide file size tqdm. +def download_repo_in_bytes(repo_id, local_dir): + api = HfApi() + repo_info = api.repo_info(repo_id=repo_id, files_metadata=True) + + os.makedirs(local_dir, exist_ok=True) + + total_size = 0 + for file_info in repo_info.siblings: + if file_info.size is not None: + total_size += file_info.size + + pbar = tqdm(total=total_size, unit="B", unit_scale=True, desc="Downloading") + + for file_info in repo_info.siblings: + out_path = os.path.join(local_dir, file_info.rfilename) + os.makedirs(os.path.dirname(out_path), exist_ok=True) + + if file_info.size is None: + continue + + download_url = f"https://huggingface.co/{repo_id}/resolve/main/{file_info.rfilename}" + + with requests.get(download_url, stream=True) as r, open(out_path, "wb") as f: + r.raise_for_status() + for chunk in r.iter_content(chunk_size=65536): + if chunk: + f.write(chunk) + pbar.update(len(chunk)) + + pbar.close() + + diff --git a/custom_nodes/comfyui-manager/glob/manager_migration.py b/custom_nodes/comfyui-manager/glob/manager_migration.py new file mode 100644 index 00000000..00db2775 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/manager_migration.py @@ -0,0 +1,356 @@ +""" +ComfyUI-Manager migration module. +Handles migration from legacy paths to new __manager path structure. +""" + +import os +import sys +import subprocess +import configparser + +# Startup notices for notice board +startup_notices = [] # List of (message, level) tuples + + +def add_startup_notice(message, level='warning'): + """Add a notice to be displayed on Manager notice board. + + Args: + message: HTML-formatted message string + level: 'warning', 'error', 'info' + """ + global startup_notices + startup_notices.append((message, level)) + + +# Cache for API check (computed once per session) +_cached_has_system_user_api = None + + +def has_system_user_api(): + """Check if ComfyUI has the System User Protection API (PR #10966). + + Result is cached for performance. + """ + global _cached_has_system_user_api + if _cached_has_system_user_api is None: + try: + import folder_paths + _cached_has_system_user_api = hasattr(folder_paths, 'get_system_user_directory') + except Exception: + _cached_has_system_user_api = False + return _cached_has_system_user_api + + +def get_manager_path(user_dir): + """Get the appropriate manager files path based on ComfyUI version. + + Returns: + str: manager_files_path + """ + if has_system_user_api(): + return os.path.abspath(os.path.join(user_dir, '__manager')) + else: + return os.path.abspath(os.path.join(user_dir, 'default', 'ComfyUI-Manager')) + + +def run_migration_checks(user_dir, manager_files_path): + """Run all migration and security checks. + + Call this after get_manager_path() to handle: + - Legacy config migration (new ComfyUI) + - Legacy backup notification (every startup) + - Suspicious directory detection (old ComfyUI) + - Outdated ComfyUI warning (old ComfyUI) + """ + if has_system_user_api(): + migrated = migrate_legacy_config(user_dir, manager_files_path) + # Only check for legacy backup if migration didn't just happen + # (migration already shows backup location in its message) + if not migrated: + check_legacy_backup(manager_files_path) + else: + check_suspicious_manager(user_dir) + warn_outdated_comfyui() + + +def check_legacy_backup(manager_files_path): + """Check for legacy backup and notify user to verify and remove it. + + This runs on every startup to remind users about pending legacy backup. + """ + backup_dir = os.path.join(manager_files_path, '.legacy-manager-backup') + if not os.path.exists(backup_dir): + return + + # Terminal output + print("\n" + "-"*70) + print("[ComfyUI-Manager] NOTICE: Legacy backup exists") + print(" - Your old Manager data was backed up to:") + print(f" {backup_dir}") + print(" - Please verify and remove it when no longer needed.") + print("-"*70 + "\n") + + # Notice board output + add_startup_notice( + "Legacy ComfyUI-Manager data backup exists. Please verify and remove when no longer needed. See terminal for details.", + level='info' + ) + + +def check_suspicious_manager(user_dir): + """Check for suspicious __manager directory on old ComfyUI. + + On old ComfyUI without System User API, if __manager exists with low security, + warn the user to verify manually. + + Returns: + bool: True if suspicious setup detected + """ + if has_system_user_api(): + return False # Not suspicious on new ComfyUI + + suspicious_path = os.path.abspath(os.path.join(user_dir, '__manager')) + if not os.path.exists(suspicious_path): + return False + + config_path = os.path.join(suspicious_path, 'config.ini') + if not os.path.exists(config_path): + return False + + config = configparser.ConfigParser() + config.read(config_path) + sec_level = config.get('default', 'security_level', fallback='normal').lower() + + if sec_level in ['weak', 'normal-']: + # Terminal output + print("\n" + "!"*70) + print("[ComfyUI-Manager] ERROR: Suspicious path detected!") + print(f" - '__manager' exists with low security level: '{sec_level}'") + print(" - Please verify manually:") + print(f" {config_path}") + print("!"*70 + "\n") + + # Notice board output + add_startup_notice( + "[Security Alert] Suspicious path detected. See terminal log for details.", + level='error' + ) + return True + + return False + + +def warn_outdated_comfyui(): + """Warn user about outdated ComfyUI without System User API.""" + if has_system_user_api(): + return + + # Terminal output + print("\n" + "!"*70) + print("[ComfyUI-Manager] ERROR: ComfyUI version is outdated!") + print(" - Most operations are blocked for security.") + print(" - ComfyUI update is still allowed.") + print(" - Please update ComfyUI to use Manager normally.") + print("!"*70 + "\n") + + # Notice board output + add_startup_notice( + "[Security Alert] ComfyUI outdated. Installations blocked (update allowed).
" + "Update ComfyUI for normal operation.", + level='error' + ) + + +def migrate_legacy_config(user_dir, manager_files_path): + """Migrate ONLY config.ini to new __manager path if needed. + + IMPORTANT: Only config.ini is migrated. Other files (snapshots, cache, etc.) + are NOT migrated - users must recreate them. + + Scenarios: + 1. Legacy exists, New doesn't exist β†’ Migrate config.ini + 2. Legacy exists, New exists β†’ First update after upgrade + - Run ComfyUI dependency installation + - Rename legacy to .backup + 3. Legacy doesn't exist β†’ No migration needed + + Returns: + bool: True if migration was performed + """ + if not has_system_user_api(): + return False + + legacy_dir = os.path.join(user_dir, 'default', 'ComfyUI-Manager') + legacy_config = os.path.join(legacy_dir, 'config.ini') + new_config = os.path.join(manager_files_path, 'config.ini') + + if not os.path.exists(legacy_dir): + return False # No legacy directory, nothing to migrate + + # IMPORTANT: Check for config.ini existence, not just directory + # (because makedirs() creates __manager before this function is called) + + # Case: Both configs exist (first update after ComfyUI upgrade) + # This means user ran new ComfyUI at least once, creating __manager/config.ini + if os.path.exists(legacy_config) and os.path.exists(new_config): + _handle_first_update_migration(user_dir, legacy_dir, manager_files_path) + return True + + # Case: Legacy config exists but new config doesn't (normal migration) + # This is the first run after ComfyUI upgrade + if os.path.exists(legacy_config) and not os.path.exists(new_config): + pass # Continue with normal migration below + else: + return False + + # Terminal output + print("\n" + "-"*70) + print("[ComfyUI-Manager] NOTICE: Legacy config.ini detected") + print(f" - Old: {legacy_config}") + print(f" - New: {new_config}") + print(" - Migrating config.ini only (other files are NOT migrated).") + print(" - Security level below 'normal' will be raised.") + print("-"*70 + "\n") + + _migrate_config_with_security_check(legacy_config, new_config) + + # Move legacy directory to backup + _move_legacy_to_backup(legacy_dir, manager_files_path) + + return True + + +def _handle_first_update_migration(user_dir, legacy_dir, manager_files_path): + """Handle first ComfyUI update when both legacy and new directories exist. + + This scenario happens when: + - User was on old ComfyUI (using default/ComfyUI-Manager) + - ComfyUI was updated (now has System User API) + - Manager already created __manager on first new run + - But legacy directory still exists + + Actions: + 1. Run ComfyUI dependency installation + 2. Move legacy to __manager/.legacy-manager-backup + """ + # Terminal output + print("\n" + "-"*70) + print("[ComfyUI-Manager] NOTICE: First update after ComfyUI upgrade detected") + print(" - Both legacy and new directories exist.") + print(" - Running ComfyUI dependency installation...") + print("-"*70 + "\n") + + # Run ComfyUI dependency installation + # Path: glob/manager_migration.py β†’ glob β†’ comfyui-manager β†’ custom_nodes β†’ ComfyUI + try: + comfyui_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + requirements_path = os.path.join(comfyui_path, 'requirements.txt') + if os.path.exists(requirements_path): + subprocess.run([sys.executable, '-m', 'pip', 'install', '-r', requirements_path], + capture_output=True, check=False) + print("[ComfyUI-Manager] ComfyUI dependencies installation completed.") + except Exception as e: + print(f"[ComfyUI-Manager] WARNING: Failed to install ComfyUI dependencies: {e}") + + # Move legacy to backup inside __manager + _move_legacy_to_backup(legacy_dir, manager_files_path) + + +def _move_legacy_to_backup(legacy_dir, manager_files_path): + """Move legacy directory to backup inside __manager. + + Returns: + str: Path to backup directory if successful, None if failed + """ + import shutil + + backup_dir = os.path.join(manager_files_path, '.legacy-manager-backup') + + try: + if os.path.exists(backup_dir): + shutil.rmtree(backup_dir) # Remove old backup if exists + shutil.move(legacy_dir, backup_dir) + + # Terminal output (full paths shown here only) + print("\n" + "-"*70) + print("[ComfyUI-Manager] NOTICE: Legacy settings migrated") + print(f" - Old location: {legacy_dir}") + print(f" - Backed up to: {backup_dir}") + print(" - Please verify and remove the backup when no longer needed.") + print("-"*70 + "\n") + + # Notice board output (no full paths for security) + add_startup_notice( + "Legacy ComfyUI-Manager data migrated. See terminal for details.", + level='info' + ) + return backup_dir + except Exception as e: + print(f"[ComfyUI-Manager] WARNING: Failed to backup legacy directory: {e}") + add_startup_notice( + f"[MIGRATION] Failed to backup legacy directory: {e}", + level='warning' + ) + return None + + +def _migrate_config_with_security_check(legacy_path, new_path): + """Migrate legacy config, raising security level only if below default.""" + config = configparser.ConfigParser() + try: + config.read(legacy_path) + except Exception as e: + print(f"[ComfyUI-Manager] WARNING: Failed to parse config.ini: {e}") + print(" - Creating fresh config with default settings.") + add_startup_notice( + "[MIGRATION] Failed to parse legacy config. Using defaults.", + level='warning' + ) + return # Skip migration, let Manager create fresh config + + # Security level hierarchy: strong > normal > normal- > weak + # Default is 'normal', only raise if below default + if 'default' in config: + current_level = config['default'].get('security_level', 'normal').lower() + below_default_levels = ['weak', 'normal-'] + + if current_level in below_default_levels: + config['default']['security_level'] = 'normal' + + # Terminal output + print("\n" + "="*70) + print("[ComfyUI-Manager] WARNING: Security level adjusted") + print(f" - Previous: '{current_level}' β†’ New: 'normal'") + print(" - Raised to prevent unauthorized remote access.") + print("="*70 + "\n") + + # Notice board output + add_startup_notice( + f"[MIGRATION] Security level raised: '{current_level}' β†’ 'normal'.
" + "To prevent unauthorized remote access.", + level='warning' + ) + else: + print(f" - Security level: '{current_level}' (no change needed)") + + # Ensure directory exists + os.makedirs(os.path.dirname(new_path), exist_ok=True) + + with open(new_path, 'w') as f: + config.write(f) + + +def force_security_level_if_needed(config_dict): + """Force security level to 'strong' if on old ComfyUI. + + Args: + config_dict: Configuration dictionary to modify in-place + + Returns: + bool: True if security level was forced + """ + if not has_system_user_api(): + config_dict['security_level'] = 'strong' + return True + return False diff --git a/custom_nodes/comfyui-manager/glob/manager_server.py b/custom_nodes/comfyui-manager/glob/manager_server.py new file mode 100644 index 00000000..eff7c032 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/manager_server.py @@ -0,0 +1,1876 @@ +import traceback + +import folder_paths +import locale +import subprocess # don't remove this +import concurrent +import nodes +import os +import sys +import threading +import re +import shutil +import git +from datetime import datetime + +from server import PromptServer +import manager_core as core +import manager_util +import cm_global +import logging +import asyncio +import queue + +import manager_downloader +import manager_migration + + +logging.info(f"### Loading: ComfyUI-Manager ({core.version_str})") +logging.info("[ComfyUI-Manager] network_mode: " + core.get_config()['network_mode']) + +comfy_ui_hash = "-" +comfyui_tag = None + +SECURITY_MESSAGE_MIDDLE_OR_BELOW = "ERROR: To use this action, a security_level of `middle or below` is required. Please contact the administrator.\nReference: https://github.com/ltdrdata/ComfyUI-Manager#security-policy" +SECURITY_MESSAGE_NORMAL_MINUS = "ERROR: To use this feature, you must either set '--listen' to a local IP and set the security level to 'normal-' or lower, or set the security level to 'middle' or 'weak'. Please contact the administrator.\nReference: https://github.com/ltdrdata/ComfyUI-Manager#security-policy" +SECURITY_MESSAGE_GENERAL = "ERROR: This installation is not allowed in this security_level. Please contact the administrator.\nReference: https://github.com/ltdrdata/ComfyUI-Manager#security-policy" +SECURITY_MESSAGE_NORMAL_MINUS_MODEL = "ERROR: Downloading models that are not in '.safetensors' format is only allowed for models registered in the 'default' channel at this security level. If you want to download this model, set the security level to 'normal-' or lower." + +routes = PromptServer.instance.routes + + +def has_per_queue_preview(): + """ + Check if ComfyUI PR #11261 (per-queue live preview override) is merged + + Returns: + bool: True if ComfyUI has per-queue preview feature + """ + try: + import latent_preview + return hasattr(latent_preview, 'set_preview_method') + except ImportError: + return False + + +# Detect ComfyUI per-queue preview override feature (PR #11261) +COMFYUI_HAS_PER_QUEUE_PREVIEW = has_per_queue_preview() + + +def handle_stream(stream, prefix): + stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace') + for msg in stream: + if prefix == '[!]' and ('it/s]' in msg or 's/it]' in msg) and ('%|' in msg or 'it [' in msg): + if msg.startswith('100%'): + print('\r' + msg, end="", file=sys.stderr), + else: + print('\r' + msg[:-1], end="", file=sys.stderr), + else: + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + +from comfy.cli_args import args +import latent_preview + +def is_loopback(address): + import ipaddress + try: + return ipaddress.ip_address(address).is_loopback + except ValueError: + return False + +is_local_mode = is_loopback(args.listen) + + +model_dir_name_map = { + "checkpoints": "checkpoints", + "checkpoint": "checkpoints", + "unclip": "checkpoints", + "text_encoders": "text_encoders", + "clip": "text_encoders", + "vae": "vae", + "lora": "loras", + "t2i-adapter": "controlnet", + "t2i-style": "controlnet", + "controlnet": "controlnet", + "clip_vision": "clip_vision", + "gligen": "gligen", + "upscale": "upscale_models", + "embedding": "embeddings", + "embeddings": "embeddings", + "unet": "diffusion_models", + "diffusion_model": "diffusion_models", +} + + +def is_allowed_security_level(level): + if level == 'block': + return False + elif level == 'high': + if is_local_mode: + return core.get_config()['security_level'] in ['weak', 'normal-'] + else: + return core.get_config()['security_level'] == 'weak' + elif level == 'middle': + return core.get_config()['security_level'] in ['weak', 'normal', 'normal-'] + else: + return True + + +async def get_risky_level(files, pip_packages): + json_data1 = await core.get_data_by_mode('local', 'custom-node-list.json') + json_data2 = await core.get_data_by_mode('cache', 'custom-node-list.json', channel_url='https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main') + + all_urls = set() + for x in json_data1['custom_nodes'] + json_data2['custom_nodes']: + all_urls.update(x.get('files', [])) + + for x in files: + if x not in all_urls: + return "high" + + all_pip_packages = set() + for x in json_data1['custom_nodes'] + json_data2['custom_nodes']: + all_pip_packages.update(x.get('pip', [])) + + for p in pip_packages: + if p not in all_pip_packages: + return "block" + + return "middle" + + +class ManagerFuncsInComfyUI(core.ManagerFuncs): + def get_current_preview_method(self): + if args.preview_method == latent_preview.LatentPreviewMethod.Auto: + return "auto" + elif args.preview_method == latent_preview.LatentPreviewMethod.Latent2RGB: + return "latent2rgb" + elif args.preview_method == latent_preview.LatentPreviewMethod.TAESD: + return "taesd" + else: + return "none" + + def run_script(self, cmd, cwd='.'): + if len(cmd) > 0 and cmd[0].startswith("#"): + logging.error(f"[ComfyUI-Manager] Unexpected behavior: `{cmd}`") + return 0 + + process = subprocess.Popen(cmd, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1, env=core.get_script_env()) + + stdout_thread = threading.Thread(target=handle_stream, args=(process.stdout, "")) + stderr_thread = threading.Thread(target=handle_stream, args=(process.stderr, "[!]")) + + stdout_thread.start() + stderr_thread.start() + + stdout_thread.join() + stderr_thread.join() + + return process.wait() + + +core.manager_funcs = ManagerFuncsInComfyUI() + +sys.path.append('../..') + +from manager_downloader import download_url, download_url_with_agent + +core.comfy_path = os.path.dirname(folder_paths.__file__) +core.js_path = os.path.join(core.comfy_path, "web", "extensions") + +local_db_model = os.path.join(manager_util.comfyui_manager_path, "model-list.json") +local_db_alter = os.path.join(manager_util.comfyui_manager_path, "alter-list.json") +local_db_custom_node_list = os.path.join(manager_util.comfyui_manager_path, "custom-node-list.json") +local_db_extension_node_mappings = os.path.join(manager_util.comfyui_manager_path, "extension-node-map.json") + + +def set_preview_method(method): + if method == 'auto': + args.preview_method = latent_preview.LatentPreviewMethod.Auto + elif method == 'latent2rgb': + args.preview_method = latent_preview.LatentPreviewMethod.Latent2RGB + elif method == 'taesd': + args.preview_method = latent_preview.LatentPreviewMethod.TAESD + else: + args.preview_method = latent_preview.LatentPreviewMethod.NoPreviews + + core.get_config()['preview_method'] = method + + +if COMFYUI_HAS_PER_QUEUE_PREVIEW: + logging.info( + "[ComfyUI-Manager] ComfyUI per-queue preview override detected (PR #11261). " + "Manager's preview method feature is disabled. " + "Use ComfyUI's --preview-method CLI option or 'Settings > Execution > Live preview method'." + ) +elif args.preview_method == latent_preview.LatentPreviewMethod.NoPreviews: + set_preview_method(core.get_config()['preview_method']) +else: + logging.warning( + "[ComfyUI-Manager] Since --preview-method is set, " + "ComfyUI-Manager's preview method feature will be ignored." + ) + + +def set_component_policy(mode): + core.get_config()['component_policy'] = mode + +def set_update_policy(mode): + core.get_config()['update_policy'] = mode + +def set_db_mode(mode): + core.get_config()['db_mode'] = mode + +def print_comfyui_version(): + global comfy_ui_hash + global comfyui_tag + + is_detached = False + try: + repo = git.Repo(os.path.dirname(folder_paths.__file__)) + core.comfy_ui_revision = len(list(repo.iter_commits('HEAD'))) + + comfy_ui_hash = repo.head.commit.hexsha + cm_global.variables['comfyui.revision'] = core.comfy_ui_revision + + core.comfy_ui_commit_datetime = repo.head.commit.committed_datetime + cm_global.variables['comfyui.commit_datetime'] = core.comfy_ui_commit_datetime + + is_detached = repo.head.is_detached + current_branch = repo.active_branch.name + + comfyui_tag = core.get_comfyui_tag() + + try: + if not os.environ.get('__COMFYUI_DESKTOP_VERSION__') and core.comfy_ui_commit_datetime.date() < core.comfy_ui_required_commit_datetime.date(): + logging.warning(f"\n\n## [WARN] ComfyUI-Manager: Your ComfyUI version ({core.comfy_ui_revision})[{core.comfy_ui_commit_datetime.date()}] is too old. Please update to the latest version. ##\n\n") + except: + pass + + # process on_revision_detected --> + if 'cm.on_revision_detected_handler' in cm_global.variables: + for k, f in cm_global.variables['cm.on_revision_detected_handler']: + try: + f(core.comfy_ui_revision) + except Exception: + logging.error(f"[ERROR] '{k}' on_revision_detected_handler") + traceback.print_exc() + + del cm_global.variables['cm.on_revision_detected_handler'] + else: + logging.warning("[ComfyUI-Manager] Some features are restricted due to your ComfyUI being outdated.") + # <-- + + if current_branch == "master": + if comfyui_tag: + logging.info(f"### ComfyUI Version: {comfyui_tag} | Released on '{core.comfy_ui_commit_datetime.date()}'") + else: + logging.info(f"### ComfyUI Revision: {core.comfy_ui_revision} [{comfy_ui_hash[:8]}] | Released on '{core.comfy_ui_commit_datetime.date()}'") + else: + if comfyui_tag: + logging.info(f"### ComfyUI Version: {comfyui_tag} on '{current_branch}' | Released on '{core.comfy_ui_commit_datetime.date()}'") + else: + logging.info(f"### ComfyUI Revision: {core.comfy_ui_revision} on '{current_branch}' [{comfy_ui_hash[:8]}] | Released on '{core.comfy_ui_commit_datetime.date()}'") + except: + if is_detached: + logging.info(f"### ComfyUI Revision: {core.comfy_ui_revision} [{comfy_ui_hash[:8]}] *DETACHED | Released on '{core.comfy_ui_commit_datetime.date()}'") + else: + logging.info("### ComfyUI Revision: UNKNOWN (The currently installed ComfyUI is not a Git repository)") + + +print_comfyui_version() +core.check_invalid_nodes() + + + +def setup_environment(): + git_exe = core.get_config()['git_exe'] + + if git_exe != '': + git.Git().update_environment(GIT_PYTHON_GIT_EXECUTABLE=git_exe) + + +setup_environment() + +# Expand Server api + +from aiohttp import web +import aiohttp +import json +import zipfile +import urllib.request + + +def security_403_response(): + """Return appropriate 403 response based on ComfyUI version.""" + if not manager_migration.has_system_user_api(): + return web.json_response({"error": "comfyui_outdated"}, status=403) + return web.json_response({"error": "security_level"}, status=403) + + +def get_model_dir(data, show_log=False): + if 'download_model_base' in folder_paths.folder_names_and_paths: + models_base = folder_paths.folder_names_and_paths['download_model_base'][0][0] + else: + models_base = folder_paths.models_dir + + # NOTE: Validate to prevent path traversal. + if any(char in data['filename'] for char in {'/', '\\', ':'}): + return None + + def resolve_custom_node(save_path): + save_path = save_path[13:] # remove 'custom_nodes/' + + # NOTE: Validate to prevent path traversal. + if save_path.startswith(os.path.sep) or ':' in save_path: + return None + + repo_name = save_path.replace('\\','/').split('/')[0] # get custom node repo name + + # NOTE: The creation of files within the custom node path should be removed in the future. + repo_path = core.lookup_installed_custom_nodes_legacy(repo_name) + if repo_path is not None and repo_path[0]: + # Returns the retargeted path based on the actually installed repository + return os.path.join(os.path.dirname(repo_path[1]), save_path) + else: + return None + + if data['save_path'] != 'default': + if '..' in data['save_path'] or data['save_path'].startswith('/'): + if show_log: + logging.info(f"[WARN] '{data['save_path']}' is not allowed path. So it will be saved into 'models/etc'.") + base_model = os.path.join(models_base, "etc") + else: + if data['save_path'].startswith("custom_nodes"): + base_model = resolve_custom_node(data['save_path']) + if base_model is None: + if show_log: + logging.info(f"[ComfyUI-Manager] The target custom node for model download is not installed: {data['save_path']}") + return None + else: + base_model = os.path.join(models_base, data['save_path']) + else: + model_dir_name = model_dir_name_map.get(data['type'].lower()) + if model_dir_name is not None: + base_model = folder_paths.folder_names_and_paths[model_dir_name][0][0] + else: + base_model = os.path.join(models_base, "etc") + + return base_model + + +def get_model_path(data, show_log=False): + base_model = get_model_dir(data, show_log) + if base_model is None: + return None + else: + if data['filename'] == '': + return os.path.join(base_model, os.path.basename(data['url'])) + else: + return os.path.join(base_model, data['filename']) + + +def check_state_of_git_node_pack(node_packs, do_fetch=False, do_update_check=True, do_update=False): + if do_fetch: + print("Start fetching...", end="") + elif do_update: + print("Start updating...", end="") + elif do_update_check: + print("Start update check...", end="") + + def process_custom_node(item): + core.check_state_of_git_node_pack_single(item, do_fetch, do_update_check, do_update) + + with concurrent.futures.ThreadPoolExecutor(4) as executor: + for k, v in node_packs.items(): + if v.get('active_version') in ['unknown', 'nightly']: + executor.submit(process_custom_node, v) + + if do_fetch: + print("\x1b[2K\rFetching done.") + elif do_update: + update_exists = any(item.get('updatable', False) for item in node_packs.values()) + if update_exists: + print("\x1b[2K\rUpdate done.") + else: + print("\x1b[2K\rAll extensions are already up-to-date.") + elif do_update_check: + print("\x1b[2K\rUpdate check done.") + + +def nickname_filter(json_obj): + preemptions_map = {} + + for k, x in json_obj.items(): + if 'preemptions' in x[1]: + for y in x[1]['preemptions']: + preemptions_map[y] = k + elif k.endswith("/ComfyUI"): + for y in x[0]: + preemptions_map[y] = k + + updates = {} + for k, x in json_obj.items(): + removes = set() + for y in x[0]: + k2 = preemptions_map.get(y) + if k2 is not None and k != k2: + removes.add(y) + + if len(removes) > 0: + updates[k] = [y for y in x[0] if y not in removes] + + for k, v in updates.items(): + json_obj[k][0] = v + + return json_obj + + +task_queue = queue.Queue() +nodepack_result = {} +model_result = {} +tasks_in_progress = set() +task_worker_lock = threading.Lock() + +async def task_worker(): + global task_queue + global nodepack_result + global model_result + global tasks_in_progress + + async def do_install(item) -> str: + ui_id, node_spec_str, channel, mode, skip_post_install = item + + try: + node_spec = core.unified_manager.resolve_node_spec(node_spec_str) + if node_spec is None: + logging.error(f"Cannot resolve install target: '{node_spec_str}'") + return f"Cannot resolve install target: '{node_spec_str}'" + + node_name, version_spec, is_specified = node_spec + res = await core.unified_manager.install_by_id(node_name, version_spec, channel, mode, return_postinstall=skip_post_install) + # discard post install if skip_post_install mode + + if res.action not in ['skip', 'enable', 'install-git', 'install-cnr', 'switch-cnr']: + logging.error(f"[ComfyUI-Manager] Installation failed:\n{res.msg}") + return res.msg + + elif not res.result: + logging.error(f"[ComfyUI-Manager] Installation failed:\n{res.msg}") + return res.msg + + return 'success' + except Exception: + traceback.print_exc() + return f"Installation failed:\n{node_spec_str}" + + async def do_update(item): + ui_id, node_name, node_ver = item + + try: + res = core.unified_manager.unified_update(node_name, node_ver) + + if res.ver == 'unknown': + url = core.unified_manager.unknown_active_nodes[node_name][0] + try: + title = os.path.basename(url) + except Exception: + title = node_name + else: + url = core.unified_manager.cnr_map[node_name].get('repository') + title = core.unified_manager.cnr_map[node_name]['name'] + + manager_util.clear_pip_cache() + + if url is not None: + base_res = {'url': url, 'title': title} + else: + base_res = {'title': title} + + if res.result: + if res.action == 'skip': + base_res['msg'] = 'skip' + return base_res + else: + base_res['msg'] = 'success' + return base_res + + base_res['msg'] = f"An error occurred while updating '{node_name}'." + logging.error(f"\nERROR: An error occurred while updating '{node_name}'. (res.result={res.result}, res.action={res.action})") + return base_res + except Exception: + traceback.print_exc() + + return {'msg':f"An error occurred while updating '{node_name}'."} + + async def do_update_comfyui(is_stable) -> str: + try: + repo_path = os.path.dirname(folder_paths.__file__) + latest_tag = None + if is_stable: + res, latest_tag = core.update_to_stable_comfyui(repo_path) + else: + res = core.update_path(repo_path) + + if res == "fail": + logging.error("ComfyUI update failed") + return "fail" + elif res == "updated": + if is_stable: + logging.info("ComfyUI is updated to latest stable version.") + return "success-stable-"+latest_tag + else: + logging.info("ComfyUI is updated to latest nightly version.") + return "success-nightly" + else: # skipped + logging.info("ComfyUI is up-to-date.") + return "skip" + + except Exception: + traceback.print_exc() + + return "An error occurred while updating 'comfyui'." + + async def do_fix(item) -> str: + ui_id, node_name, node_ver = item + + try: + res = core.unified_manager.unified_fix(node_name, node_ver) + + if res.result: + return 'success' + else: + logging.error(res.msg) + + logging.error(f"\nERROR: An error occurred while fixing '{node_name}@{node_ver}'.") + except Exception: + traceback.print_exc() + + return f"An error occurred while fixing '{node_name}@{node_ver}'." + + async def do_uninstall(item) -> str: + ui_id, node_name, is_unknown = item + + try: + res = core.unified_manager.unified_uninstall(node_name, is_unknown) + + if res.result: + return 'success' + + logging.error(f"\nERROR: An error occurred while uninstalling '{node_name}'.") + except Exception: + traceback.print_exc() + + return f"An error occurred while uninstalling '{node_name}'." + + async def do_disable(item) -> str: + ui_id, node_name, is_unknown = item + + try: + res = core.unified_manager.unified_disable(node_name, is_unknown) + + if res: + return 'success' + + except Exception: + traceback.print_exc() + + return f"Failed to disable: '{node_name}'" + + async def do_install_model(item) -> str: + ui_id, json_data = item + + model_path = get_model_path(json_data) + model_url = json_data['url'] + + res = False + + try: + if model_path is not None: + logging.info(f"Install model '{json_data['name']}' from '{model_url}' into '{model_path}'") + + if json_data['filename'] == '': + if os.path.exists(os.path.join(model_path, os.path.dirname(json_data['url']))): + logging.error(f"[ComfyUI-Manager] the model path already exists: {model_path}") + return f"The model path already exists: {model_path}" + + logging.info(f"[ComfyUI-Manager] Downloading '{model_url}' into '{model_path}'") + manager_downloader.download_repo_in_bytes(repo_id=model_url, local_dir=model_path) + + return 'success' + + elif not core.get_config()['model_download_by_agent'] and ( + model_url.startswith('https://github.com') or model_url.startswith('https://huggingface.co') or model_url.startswith('https://heibox.uni-heidelberg.de')): + model_dir = get_model_dir(json_data, True) + download_url(model_url, model_dir, filename=json_data['filename']) + if model_path.endswith('.zip'): + res = core.unzip(model_path) + else: + res = True + + if res: + return 'success' + else: + res = download_url_with_agent(model_url, model_path) + if res and model_path.endswith('.zip'): + res = core.unzip(model_path) + else: + logging.error(f"[ComfyUI-Manager] Model installation error: invalid model type - {json_data['type']}") + + if res: + return 'success' + + except Exception as e: + logging.error(f"[ComfyUI-Manager] ERROR: {e}") + + return f"Model installation error: {model_url}" + + stats = {} + + while True: + done_count = len(nodepack_result) + len(model_result) + total_count = done_count + task_queue.qsize() + + if task_queue.empty(): + logging.info(f"\n[ComfyUI-Manager] Queued works are completed.\n{stats}") + + logging.info("\nAfter restarting ComfyUI, please refresh the browser.") + PromptServer.instance.send_sync("cm-queue-status", + {'status': 'done', + 'nodepack_result': nodepack_result, 'model_result': model_result, + 'total_count': total_count, 'done_count': done_count}) + nodepack_result = {} + task_queue = queue.Queue() + return # terminate worker thread + + with task_worker_lock: + kind, item = task_queue.get() + tasks_in_progress.add((kind, item[0])) + + try: + if kind == 'install': + msg = await do_install(item) + elif kind == 'install-model': + msg = await do_install_model(item) + elif kind == 'update': + msg = await do_update(item) + elif kind == 'update-main': + msg = await do_update(item) + elif kind == 'update-comfyui': + msg = await do_update_comfyui(item[1]) + elif kind == 'fix': + msg = await do_fix(item) + elif kind == 'uninstall': + msg = await do_uninstall(item) + elif kind == 'disable': + msg = await do_disable(item) + else: + msg = "Unexpected kind: " + kind + except Exception: + traceback.print_exc() + msg = f"Exception: {(kind, item)}" + + with task_worker_lock: + tasks_in_progress.remove((kind, item[0])) + + ui_id = item[0] + if kind == 'install-model': + model_result[ui_id] = msg + ui_target = "model_manager" + elif kind == 'update-main': + nodepack_result[ui_id] = msg + ui_target = "main" + elif kind == 'update-comfyui': + nodepack_result['comfyui'] = msg + ui_target = "main" + elif kind == 'update': + nodepack_result[ui_id] = msg['msg'] + ui_target = "nodepack_manager" + else: + nodepack_result[ui_id] = msg + ui_target = "nodepack_manager" + + stats[kind] = stats.get(kind, 0) + 1 + + PromptServer.instance.send_sync("cm-queue-status", + {'status': 'in_progress', 'target': item[0], 'ui_target': ui_target, + 'total_count': total_count, 'done_count': done_count}) + + +@routes.get("/customnode/getmappings") +async def fetch_customnode_mappings(request): + """ + provide unified (node -> node pack) mapping list + """ + mode = request.rel_url.query["mode"] + + nickname_mode = False + if mode == "nickname": + mode = "local" + nickname_mode = True + + json_obj = await core.get_data_by_mode(mode, 'extension-node-map.json') + json_obj = core.map_to_unified_keys(json_obj) + + if nickname_mode: + json_obj = nickname_filter(json_obj) + + all_nodes = set() + patterns = [] + for k, x in json_obj.items(): + all_nodes.update(set(x[0])) + + if 'nodename_pattern' in x[1]: + patterns.append((x[1]['nodename_pattern'], x[0])) + + missing_nodes = set(nodes.NODE_CLASS_MAPPINGS.keys()) - all_nodes + + for x in missing_nodes: + for pat, item in patterns: + if re.match(pat, x): + item.append(x) + + return web.json_response(json_obj, content_type='application/json') + + +@routes.get("/customnode/fetch_updates") +async def fetch_updates(request): + try: + if request.rel_url.query["mode"] == "local": + channel = 'local' + else: + channel = core.get_config()['channel_url'] + + await core.unified_manager.reload(request.rel_url.query["mode"]) + await core.unified_manager.get_custom_nodes(channel, request.rel_url.query["mode"]) + + res = core.unified_manager.fetch_or_pull_git_repo(is_pull=False) + + for x in res['failed']: + logging.error(f"FETCH FAILED: {x}") + + logging.info("\nDone.") + + if len(res['updated']) > 0: + return web.Response(status=201) + + return web.Response(status=200) + except: + traceback.print_exc() + return web.Response(status=400) + + +@routes.get("/manager/queue/update_all") +async def update_all(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return security_403_response() + + with task_worker_lock: + is_processing = task_worker_thread is not None and task_worker_thread.is_alive() + if is_processing: + return web.Response(status=401) + + await core.save_snapshot_with_postfix('autosave') + + if request.rel_url.query["mode"] == "local": + channel = 'local' + else: + channel = core.get_config()['channel_url'] + + await core.unified_manager.reload(request.rel_url.query["mode"]) + await core.unified_manager.get_custom_nodes(channel, request.rel_url.query["mode"]) + + for k, v in core.unified_manager.active_nodes.items(): + if k == 'comfyui-manager': + # skip updating comfyui-manager if desktop version + if os.environ.get('__COMFYUI_DESKTOP_VERSION__'): + continue + + update_item = k, k, v[0] + task_queue.put(("update-main", update_item)) + + for k, v in core.unified_manager.unknown_active_nodes.items(): + if k == 'comfyui-manager': + # skip updating comfyui-manager if desktop version + if os.environ.get('__COMFYUI_DESKTOP_VERSION__'): + continue + + update_item = k, k, 'unknown' + task_queue.put(("update-main", update_item)) + + return web.Response(status=200) + + +def convert_markdown_to_html(input_text): + pattern_a = re.compile(r'\[a/([^]]+)]\(([^)]+)\)') + pattern_w = re.compile(r'\[w/([^]]+)]') + pattern_i = re.compile(r'\[i/([^]]+)]') + pattern_bold = re.compile(r'\*\*([^*]+)\*\*') + pattern_white = re.compile(r'%%([^*]+)%%') + + def replace_a(match): + return f"{match.group(1)}" + + def replace_w(match): + return f"

{match.group(1)}

" + + def replace_i(match): + return f"

{match.group(1)}

" + + def replace_bold(match): + return f"{match.group(1)}" + + def replace_white(match): + return f"{match.group(1)}" + + input_text = input_text.replace('\\[', '[').replace('\\]', ']').replace('<', '<').replace('>', '>') + + result_text = re.sub(pattern_a, replace_a, input_text) + result_text = re.sub(pattern_w, replace_w, result_text) + result_text = re.sub(pattern_i, replace_i, result_text) + result_text = re.sub(pattern_bold, replace_bold, result_text) + result_text = re.sub(pattern_white, replace_white, result_text) + + return result_text.replace("\n", "
") + + +def populate_markdown(x): + if 'description' in x: + x['description'] = convert_markdown_to_html(manager_util.sanitize_tag(x['description'])) + + if 'name' in x: + x['name'] = manager_util.sanitize_tag(x['name']) + + if 'title' in x: + x['title'] = manager_util.sanitize_tag(x['title']) + + +# freeze imported version +startup_time_installed_node_packs = core.get_installed_node_packs() +@routes.get("/customnode/installed") +async def installed_list(request): + mode = request.query.get('mode', 'default') + + if mode == 'imported': + res = startup_time_installed_node_packs + else: + res = core.get_installed_node_packs() + + return web.json_response(res, content_type='application/json') + + +@routes.get("/customnode/getlist") +async def fetch_customnode_list(request): + """ + provide unified custom node list + """ + if request.rel_url.query.get("skip_update", '').lower() == "true": + skip_update = True + else: + skip_update = False + + if request.rel_url.query["mode"] == "local": + channel = 'local' + else: + channel = core.get_config()['channel_url'] + + node_packs = await core.get_unified_total_nodes(channel, request.rel_url.query["mode"], 'cache') + json_obj_github = core.get_data_by_mode(request.rel_url.query["mode"], 'github-stats.json', 'default') + json_obj_extras = core.get_data_by_mode(request.rel_url.query["mode"], 'extras.json', 'default') + + core.populate_github_stats(node_packs, await json_obj_github) + core.populate_favorites(node_packs, await json_obj_extras) + + check_state_of_git_node_pack(node_packs, not skip_update, do_update_check=not skip_update) + + for v in node_packs.values(): + populate_markdown(v) + + if channel != 'local': + found = 'custom' + + for name, url in core.get_channel_dict().items(): + if url == channel: + found = name + break + + channel = found + + result = dict(channel=channel, node_packs=node_packs.to_dict()) + + return web.json_response(result, content_type='application/json') + + +@routes.get("/customnode/alternatives") +async def fetch_customnode_alternatives(request): + alter_json = await core.get_data_by_mode(request.rel_url.query["mode"], 'alter-list.json') + + res = {} + + for item in alter_json['items']: + populate_markdown(item) + res[item['id']] = item + + res = core.map_to_unified_keys(res) + + return web.json_response(res, content_type='application/json') + + +def check_model_installed(json_obj): + def is_exists(model_dir_name, filename, url): + if filename == '': + filename = os.path.basename(url) + + dirs = folder_paths.get_folder_paths(model_dir_name) + + for x in dirs: + if os.path.exists(os.path.join(x, filename)): + return True + + return False + + model_dir_names = ['checkpoints', 'loras', 'vae', 'text_encoders', 'diffusion_models', 'clip_vision', 'embeddings', + 'diffusers', 'vae_approx', 'controlnet', 'gligen', 'upscale_models', 'hypernetworks', + 'photomaker', 'classifiers'] + + total_models_files = set() + for x in model_dir_names: + for y in folder_paths.get_filename_list(x): + total_models_files.add(y) + + def process_model_phase(item): + if 'diffusion' not in item['filename'] and 'pytorch' not in item['filename'] and 'model' not in item['filename']: + # non-general name case + if item['filename'] in total_models_files: + item['installed'] = 'True' + return + + if item['save_path'] == 'default': + model_dir_name = model_dir_name_map.get(item['type'].lower()) + if model_dir_name is not None: + item['installed'] = str(is_exists(model_dir_name, item['filename'], item['url'])) + else: + item['installed'] = 'False' + else: + model_dir_name = item['save_path'].split('/')[0] + if model_dir_name in folder_paths.folder_names_and_paths: + if is_exists(model_dir_name, item['filename'], item['url']): + item['installed'] = 'True' + + if 'installed' not in item: + if item['filename'] == '': + filename = os.path.basename(item['url']) + else: + filename = item['filename'] + + fullpath = os.path.join(folder_paths.models_dir, item['save_path'], filename) + + item['installed'] = 'True' if os.path.exists(fullpath) else 'False' + + with concurrent.futures.ThreadPoolExecutor(8) as executor: + for item in json_obj['models']: + executor.submit(process_model_phase, item) + + +@routes.get("/externalmodel/getlist") +async def fetch_externalmodel_list(request): + # The model list is only allowed in the default channel, yet. + json_obj = await core.get_data_by_mode(request.rel_url.query["mode"], 'model-list.json') + + check_model_installed(json_obj) + + for x in json_obj['models']: + populate_markdown(x) + + return web.json_response(json_obj, content_type='application/json') + + +@PromptServer.instance.routes.get("/snapshot/getlist") +async def get_snapshot_list(request): + items = [f[:-5] for f in os.listdir(core.manager_snapshot_path) if f.endswith('.json')] + items.sort(reverse=True) + return web.json_response({'items': items}, content_type='application/json') + + +def get_safe_snapshot_path(target): + """ + Safely construct a snapshot file path, preventing path traversal attacks. + """ + if '/' in target or '\\' in target or '..' in target or '\x00' in target: + return None + return os.path.join(core.manager_snapshot_path, f"{target}.json") + + +@routes.get("/snapshot/remove") +async def remove_snapshot(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return security_403_response() + + try: + target = request.rel_url.query["target"] + path = get_safe_snapshot_path(target) + + if path is None: + logging.error(f"[ComfyUI-Manager] Invalid snapshot target: {target}") + return web.Response(text="Invalid snapshot target", status=400) + + if os.path.exists(path): + os.remove(path) + + return web.Response(status=200) + except: + return web.Response(status=400) + + +@routes.get("/snapshot/restore") +async def restore_snapshot(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return security_403_response() + + try: + target = request.rel_url.query["target"] + path = get_safe_snapshot_path(target) + + if path is None: + logging.error(f"[ComfyUI-Manager] Invalid snapshot target: {target}") + return web.Response(text="Invalid snapshot target", status=400) + + if os.path.exists(path): + if not os.path.exists(core.manager_startup_script_path): + os.makedirs(core.manager_startup_script_path) + + target_path = os.path.join(core.manager_startup_script_path, "restore-snapshot.json") + shutil.copy(path, target_path) + + logging.info(f"Snapshot restore scheduled: `{target}`") + return web.Response(status=200) + + logging.error(f"Snapshot file not found: `{path}`") + return web.Response(status=400) + except: + return web.Response(status=400) + + +@routes.get("/snapshot/get_current") +async def get_current_snapshot_api(request): + try: + return web.json_response(await core.get_current_snapshot(), content_type='application/json') + except: + return web.Response(status=400) + + +@routes.get("/snapshot/save") +async def save_snapshot(request): + try: + await core.save_snapshot_with_postfix('snapshot') + return web.Response(status=200) + except: + return web.Response(status=400) + + +def unzip_install(files): + temp_filename = 'manager-temp.zip' + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} + + req = urllib.request.Request(url, headers=headers) + response = urllib.request.urlopen(req) + data = response.read() + + with open(temp_filename, 'wb') as f: + f.write(data) + + with zipfile.ZipFile(temp_filename, 'r') as zip_ref: + zip_ref.extractall(core.get_default_custom_nodes_path()) + + os.remove(temp_filename) + except Exception as e: + logging.error(f"Install(unzip) error: {url} / {e}", file=sys.stderr) + return False + + logging.info("Installation was successful.") + return True + + +def copy_install(files, js_path_name=None): + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + filename = os.path.basename(url) + if url.endswith(".py"): + download_url(url, core.get_default_custom_nodes_path(), filename) + else: + path = os.path.join(core.js_path, js_path_name) if js_path_name is not None else core.js_path + if not os.path.exists(path): + os.makedirs(path) + download_url(url, path, filename) + + except Exception as e: + logging.error(f"Install(copy) error: {url} / {e}", file=sys.stderr) + return False + + logging.info("Installation was successful.") + return True + + +def copy_uninstall(files, js_path_name='.'): + for url in files: + if url.endswith("/"): + url = url[:-1] + dir_name = os.path.basename(url) + base_path = core.get_default_custom_nodes_path() if url.endswith('.py') else os.path.join(core.js_path, js_path_name) + file_path = os.path.join(base_path, dir_name) + + try: + if os.path.exists(file_path): + os.remove(file_path) + elif os.path.exists(file_path + ".disabled"): + os.remove(file_path + ".disabled") + except Exception as e: + logging.error(f"Uninstall(copy) error: {url} / {e}", file=sys.stderr) + return False + + logging.info("Uninstallation was successful.") + return True + + +def copy_set_active(files, is_disable, js_path_name='.'): + if is_disable: + action_name = "Disable" + else: + action_name = "Enable" + + for url in files: + if url.endswith("/"): + url = url[:-1] + dir_name = os.path.basename(url) + base_path = core.get_default_custom_nodes_path() if url.endswith('.py') else os.path.join(core.js_path, js_path_name) + file_path = os.path.join(base_path, dir_name) + + try: + if is_disable: + current_name = file_path + new_name = file_path + ".disabled" + else: + current_name = file_path + ".disabled" + new_name = file_path + + os.rename(current_name, new_name) + + except Exception as e: + logging.error(f"{action_name}(copy) error: {url} / {e}", file=sys.stderr) + + return False + + logging.info(f"{action_name} was successful.") + return True + + +@routes.get("/customnode/versions/{node_name}") +async def get_cnr_versions(request): + node_name = request.match_info.get("node_name", None) + versions = core.cnr_utils.all_versions_of_node(node_name) + + if versions is not None: + return web.json_response(versions, content_type='application/json') + + return web.Response(status=400) + + +@routes.get("/customnode/disabled_versions/{node_name}") +async def get_disabled_versions(request): + node_name = request.match_info.get("node_name", None) + versions = [] + if node_name in core.unified_manager.nightly_inactive_nodes: + versions.append(dict(version='nightly')) + + for v in core.unified_manager.cnr_inactive_nodes.get(node_name, {}).keys(): + versions.append(dict(version=v)) + + if versions: + return web.json_response(versions, content_type='application/json') + + return web.Response(status=400) + + +@routes.post("/customnode/import_fail_info") +async def import_fail_info(request): + json_data = await request.json() + + if 'cnr_id' in json_data: + module_name = core.unified_manager.get_module_name(json_data['cnr_id']) + else: + module_name = core.unified_manager.get_module_name(json_data['url']) + + if module_name is not None: + info = cm_global.error_dict.get(module_name) + if info is not None: + return web.json_response(info) + + return web.Response(status=400) + + +@routes.post("/manager/queue/reinstall") +async def reinstall_custom_node(request): + await uninstall_custom_node(request) + await install_custom_node(request) + + +@routes.get("/manager/queue/reset") +async def reset_queue(request): + global task_queue + task_queue = queue.Queue() + return web.Response(status=200) + + +@routes.get("/manager/queue/status") +async def queue_count(request): + global task_queue + + with task_worker_lock: + done_count = len(nodepack_result) + len(model_result) + in_progress_count = len(tasks_in_progress) + total_count = done_count + in_progress_count + task_queue.qsize() + is_processing = task_worker_thread is not None and task_worker_thread.is_alive() + + return web.json_response({ + 'total_count': total_count, 'done_count': done_count, 'in_progress_count': in_progress_count, + 'is_processing': is_processing}) + + +@routes.post("/manager/queue/install") +async def install_custom_node(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return web.Response(status=403, text="A security error has occurred. Please check the terminal logs") + + json_data = await request.json() + + # non-nightly cnr is safe + risky_level = None + cnr_id = json_data.get('id') + skip_post_install = json_data.get('skip_post_install') + + git_url = None + + selected_version = json_data.get('selected_version') + if json_data['version'] != 'unknown' and selected_version != 'unknown': + if skip_post_install: + if cnr_id in core.unified_manager.nightly_inactive_nodes or cnr_id in core.unified_manager.cnr_inactive_nodes: + core.unified_manager.unified_enable(cnr_id) + return web.Response(status=200) + elif selected_version is None: + selected_version = 'latest' + + if selected_version != 'nightly': + risky_level = 'low' + node_spec_str = f"{cnr_id}@{selected_version}" + else: + node_spec_str = f"{cnr_id}@nightly" + git_url = [json_data.get('repository')] + if git_url is None: + logging.error(f"[ComfyUI-Manager] Following node pack doesn't provide `nightly` version: ${git_url}") + return web.Response(status=404, text=f"Following node pack doesn't provide `nightly` version: ${git_url}") + elif json_data['version'] != 'unknown' and selected_version == 'unknown': + logging.error(f"[ComfyUI-Manager] Invalid installation request: {json_data}") + return web.Response(status=400, text="Invalid installation request") + else: + # unknown + unknown_name = os.path.basename(json_data['files'][0]) + node_spec_str = f"{unknown_name}@unknown" + git_url = json_data.get('files') + + # apply security policy if not cnr node (nightly isn't regarded as cnr node) + if risky_level is None: + if git_url is not None: + risky_level = await get_risky_level(git_url, json_data.get('pip', [])) + else: + return web.Response(status=404, text=f"Following node pack doesn't provide `nightly` version: ${git_url}") + + if not is_allowed_security_level(risky_level): + logging.error(SECURITY_MESSAGE_GENERAL) + return web.Response(status=404, text="A security error has occurred. Please check the terminal logs") + + install_item = json_data.get('ui_id'), node_spec_str, json_data['channel'], json_data['mode'], skip_post_install + task_queue.put(("install", install_item)) + + return web.Response(status=200) + + +task_worker_thread:threading.Thread = None + +@routes.get("/manager/queue/start") +async def queue_start(request): + global nodepack_result + global model_result + global task_worker_thread + + if task_worker_thread is not None and task_worker_thread.is_alive(): + return web.Response(status=201) # already in-progress + + nodepack_result = {} + model_result = {} + + task_worker_thread = threading.Thread(target=lambda: asyncio.run(task_worker())) + task_worker_thread.start() + + return web.Response(status=200) + + +@routes.post("/manager/queue/fix") +async def fix_custom_node(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_GENERAL) + return web.Response(status=403, text="A security error has occurred. Please check the terminal logs") + + json_data = await request.json() + + node_id = json_data.get('id') + node_ver = json_data['version'] + if node_ver != 'unknown': + node_name = node_id + else: + # unknown + node_name = os.path.basename(json_data['files'][0]) + + update_item = json_data.get('ui_id'), node_name, json_data['version'] + task_queue.put(("fix", update_item)) + + return web.Response(status=200) + + +@routes.post("/customnode/install/git_url") +async def install_custom_node_git_url(request): + if not is_allowed_security_level('high'): + logging.error(SECURITY_MESSAGE_NORMAL_MINUS) + return security_403_response() + + url = await request.text() + res = await core.gitclone_install(url) + + if res.action == 'skip': + logging.info(f"\nAlready installed: '{res.target}'") + return web.Response(status=200) + elif res.result: + logging.info("\nAfter restarting ComfyUI, please refresh the browser.") + return web.Response(status=200) + + logging.error(res.msg) + return web.Response(status=400) + + +@routes.post("/customnode/install/pip") +async def install_custom_node_pip(request): + if not is_allowed_security_level('high'): + logging.error(SECURITY_MESSAGE_NORMAL_MINUS) + return security_403_response() + + packages = await request.text() + core.pip_install(packages.split(' ')) + + return web.Response(status=200) + + +@routes.post("/manager/queue/uninstall") +async def uninstall_custom_node(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return web.Response(status=403, text="A security error has occurred. Please check the terminal logs") + + json_data = await request.json() + + node_id = json_data.get('id') + if json_data['version'] != 'unknown': + is_unknown = False + node_name = node_id + else: + # unknown + is_unknown = True + node_name = os.path.basename(json_data['files'][0]) + + uninstall_item = json_data.get('ui_id'), node_name, is_unknown + task_queue.put(("uninstall", uninstall_item)) + + return web.Response(status=200) + + +@routes.post("/manager/queue/update") +async def update_custom_node(request): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return web.Response(status=403, text="A security error has occurred. Please check the terminal logs") + + json_data = await request.json() + + node_id = json_data.get('id') + if json_data['version'] != 'unknown': + node_name = node_id + else: + # unknown + node_name = os.path.basename(json_data['files'][0]) + + update_item = json_data.get('ui_id'), node_name, json_data['version'] + task_queue.put(("update", update_item)) + + return web.Response(status=200) + + +@routes.get("/manager/queue/update_comfyui") +async def update_comfyui(request): + is_stable = core.get_config()['update_policy'] != 'nightly-comfyui' + task_queue.put(("update-comfyui", ('comfyui', is_stable))) + return web.Response(status=200) + + +@routes.get("/comfyui_manager/comfyui_versions") +async def comfyui_versions(request): + try: + res, current, latest = core.get_comfyui_versions() + return web.json_response({'versions': res, 'current': current}, status=200, content_type='application/json') + except Exception as e: + logging.error(f"ComfyUI update fail: {e}", file=sys.stderr) + + return web.Response(status=400) + + +@routes.get("/comfyui_manager/comfyui_switch_version") +async def comfyui_switch_version(request): + try: + if "ver" in request.rel_url.query: + core.switch_comfyui(request.rel_url.query['ver']) + + return web.Response(status=200) + except Exception as e: + logging.error(f"ComfyUI update fail: {e}", file=sys.stderr) + + return web.Response(status=400) + + +@routes.post("/manager/queue/disable") +async def disable_node(request): + json_data = await request.json() + + node_id = json_data.get('id') + if json_data['version'] != 'unknown': + is_unknown = False + node_name = node_id + else: + # unknown + is_unknown = True + node_name = os.path.basename(json_data['files'][0]) + + update_item = json_data.get('ui_id'), node_name, is_unknown + task_queue.put(("disable", update_item)) + + return web.Response(status=200) + + +async def check_whitelist_for_model(item): + json_obj = await core.get_data_by_mode('cache', 'model-list.json') + + for x in json_obj.get('models', []): + if x['save_path'] == item['save_path'] and x['base'] == item['base'] and x['filename'] == item['filename']: + return True + + json_obj = await core.get_data_by_mode('local', 'model-list.json') + + for x in json_obj.get('models', []): + if x['save_path'] == item['save_path'] and x['base'] == item['base'] and x['filename'] == item['filename']: + return True + + return False + + +@routes.post("/manager/queue/install_model") +async def install_model(request): + json_data = await request.json() + + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return web.Response(status=403, text="A security error has occurred. Please check the terminal logs") + + # validate request + if not await check_whitelist_for_model(json_data): + logging.error(f"[ComfyUI-Manager] Invalid model install request is detected: {json_data}") + return web.Response(status=400, text="Invalid model install request is detected") + + if not json_data['filename'].endswith('.safetensors') and not is_allowed_security_level('high'): + models_json = await core.get_data_by_mode('cache', 'model-list.json', 'default') + + is_belongs_to_whitelist = False + for x in models_json['models']: + if x.get('url') == json_data['url']: + is_belongs_to_whitelist = True + break + + if not is_belongs_to_whitelist: + logging.error(SECURITY_MESSAGE_NORMAL_MINUS_MODEL) + return web.Response(status=403, text="A security error has occurred. Please check the terminal logs") + + install_item = json_data.get('ui_id'), json_data + task_queue.put(("install-model", install_item)) + + return web.Response(status=200) + + +@routes.get("/manager/preview_method") +async def preview_method(request): + # Setting change request + if "value" in request.rel_url.query: + # Reject setting change if per-queue preview feature is available + if COMFYUI_HAS_PER_QUEUE_PREVIEW: + return web.Response(text="DISABLED", status=403) + + # Process normally if not available + set_preview_method(request.rel_url.query['value']) + core.write_config() + return web.Response(status=200) + + # Status query request + else: + # Return DISABLED if per-queue preview feature is available + if COMFYUI_HAS_PER_QUEUE_PREVIEW: + return web.Response(text="DISABLED", status=200) + + # Return current value if not available + return web.Response(text=core.manager_funcs.get_current_preview_method(), status=200) + + +@routes.get("/manager/db_mode") +async def db_mode(request): + if "value" in request.rel_url.query: + set_db_mode(request.rel_url.query['value']) + core.write_config() + else: + return web.Response(text=core.get_config()['db_mode'], status=200) + + return web.Response(status=200) + + + +@routes.get("/manager/policy/component") +async def component_policy(request): + if "value" in request.rel_url.query: + set_component_policy(request.rel_url.query['value']) + core.write_config() + else: + return web.Response(text=core.get_config()['component_policy'], status=200) + + return web.Response(status=200) + + +@routes.get("/manager/policy/update") +async def update_policy(request): + if "value" in request.rel_url.query: + set_update_policy(request.rel_url.query['value']) + core.write_config() + else: + return web.Response(text=core.get_config()['update_policy'], status=200) + + return web.Response(status=200) + + +@routes.get("/manager/channel_url_list") +async def channel_url_list(request): + channels = core.get_channel_dict() + if "value" in request.rel_url.query: + channel_url = channels.get(request.rel_url.query['value']) + if channel_url is not None: + core.get_config()['channel_url'] = channel_url + core.write_config() + else: + selected = 'custom' + selected_url = core.get_config()['channel_url'] + + for name, url in channels.items(): + if url == selected_url: + selected = name + break + + res = {'selected': selected, + 'list': core.get_channel_list()} + return web.json_response(res, status=200) + + return web.Response(status=200) + + +def add_target_blank(html_text): + pattern = r'(]*)(>)' + + def add_target(match): + if 'target=' not in match.group(1): + return match.group(1) + ' target="_blank"' + match.group(2) + return match.group(0) + + modified_html = re.sub(pattern, add_target, html_text) + + return modified_html + + +@routes.get("/manager/notice") +async def get_notice(request): + url = "github.com" + path = "/ltdrdata/ltdrdata.github.io/wiki/News" + + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.get(f"https://{url}{path}") as response: + if response.status == 200: + # html_content = response.read().decode('utf-8') + html_content = await response.text() + + pattern = re.compile(r'
([\s\S]*?)
') + match = pattern.search(html_content) + + if match: + markdown_content = match.group(1) + version_tag = os.environ.get('__COMFYUI_DESKTOP_VERSION__') + if version_tag is not None: + markdown_content += f"
ComfyUI: {version_tag} [Desktop]" + else: + version_tag = core.get_comfyui_tag() + if version_tag is None: + markdown_content += f"
ComfyUI: {core.comfy_ui_revision}[{comfy_ui_hash[:6]}]({core.comfy_ui_commit_datetime.date()})" + else: + markdown_content += (f"
ComfyUI: {version_tag}
" + f"         ({core.comfy_ui_commit_datetime.date()})") + # markdown_content += f"
         ()" + markdown_content += f"
Manager: {core.version_str}" + + markdown_content = add_target_blank(markdown_content) + + try: + if '__COMFYUI_DESKTOP_VERSION__' not in os.environ: + if core.comfy_ui_commit_datetime == datetime(1900, 1, 1, 0, 0, 0): + markdown_content = '

Your ComfyUI isn\'t git repo.

' + markdown_content + elif core.comfy_ui_required_commit_datetime.date() > core.comfy_ui_commit_datetime.date(): + markdown_content = '

Your ComfyUI is too OUTDATED!!!

' + markdown_content + except: + pass + + # Prepend startup notices from manager_migration + for message, level in reversed(manager_migration.startup_notices): + if level == 'error': + style = 'color:red; background-color:white; font-weight:bold' + elif level == 'warning': + style = 'color:orange; background-color:white; font-weight:bold' + else: + style = 'color:blue; background-color:white' + markdown_content = f'

{message}

' + markdown_content + + return web.Response(text=markdown_content, status=200) + else: + return web.Response(text="Unable to retrieve Notice", status=200) + else: + return web.Response(text="Unable to retrieve Notice", status=200) + + +@routes.get("/manager/startup_alerts") +async def get_startup_alerts(request): + """Return startup alerts for customAlert display on page load. + + Returns JSON array of alerts that should be shown to user immediately. + All startup notices (error, warning, info) are returned. + """ + alerts = [] + + # Return all startup notices for alert display + for message, level in manager_migration.startup_notices: + # Convert HTML BR to newlines for customAlert + text = message.replace('
', '\n').replace('
', '\n') + # Add [ComfyUI-Manager] prefix for customAlert (notice board shows in Manager UI anyway) + text = text.replace('[Security Alert]', '[ComfyUI-Manager] Security Alert:') + text = text.replace('[MIGRATION]', '[ComfyUI-Manager] Migration:') + alerts.append({ + 'message': text, + 'level': level + }) + + return web.json_response(alerts) + + +@routes.get("/manager/reboot") +def restart(self): + if not is_allowed_security_level('middle'): + logging.error(SECURITY_MESSAGE_MIDDLE_OR_BELOW) + return security_403_response() + + try: + sys.stdout.close_log() + except Exception: + pass + + if '__COMFY_CLI_SESSION__' in os.environ: + with open(os.path.join(os.environ['__COMFY_CLI_SESSION__'] + '.reboot'), 'w'): + pass + + print("\nRestarting...\n\n") # This printing should not be logging - that will be ugly + exit(0) + + print("\nRestarting... [Legacy Mode]\n\n") # This printing should not be logging - that will be ugly + + sys_argv = sys.argv.copy() + if '--windows-standalone-build' in sys_argv: + sys_argv.remove('--windows-standalone-build') + + if sys_argv[0].endswith("__main__.py"): # this is a python module + module_name = os.path.basename(os.path.dirname(sys_argv[0])) + cmds = [sys.executable, '-m', module_name] + sys_argv[1:] + elif sys.platform.startswith('win32'): + cmds = ['"' + sys.executable + '"', '"' + sys_argv[0] + '"'] + sys_argv[1:] + else: + cmds = [sys.executable] + sys_argv + + print(f"Command: {cmds}", flush=True) + + return os.execv(sys.executable, cmds) + + +@routes.post("/manager/component/save") +async def save_component(request): + try: + data = await request.json() + name = data['name'] + workflow = data['workflow'] + + if not os.path.exists(core.manager_components_path): + os.mkdir(core.manager_components_path) + + if 'packname' in workflow and workflow['packname'] != '': + sanitized_name = manager_util.sanitize_filename(workflow['packname']) + '.pack' + else: + sanitized_name = manager_util.sanitize_filename(name) + '.json' + + filepath = os.path.join(core.manager_components_path, sanitized_name) + components = {} + if os.path.exists(filepath): + with open(filepath) as f: + components = json.load(f) + + components[name] = workflow + + with open(filepath, 'w') as f: + json.dump(components, f, indent=4, sort_keys=True) + return web.Response(text=filepath, status=200) + except: + return web.Response(status=400) + + +@routes.post("/manager/component/loads") +async def load_components(request): + if os.path.exists(core.manager_components_path): + try: + json_files = [f for f in os.listdir(core.manager_components_path) if f.endswith('.json')] + pack_files = [f for f in os.listdir(core.manager_components_path) if f.endswith('.pack')] + + components = {} + for json_file in json_files + pack_files: + file_path = os.path.join(core.manager_components_path, json_file) + with open(file_path, 'r') as file: + try: + # When there is a conflict between the .pack and the .json, the pack takes precedence and overrides. + components.update(json.load(file)) + except json.JSONDecodeError as e: + logging.error(f"[ComfyUI-Manager] Error decoding component file in file {json_file}: {e}") + + return web.json_response(components) + except Exception as e: + logging.error(f"[ComfyUI-Manager] failed to load components\n{e}") + return web.Response(status=400) + else: + return web.json_response({}) + + +@routes.get("/manager/version") +async def get_version(request): + return web.Response(text=core.version_str, status=200) + + +async def _confirm_try_install(sender, custom_node_url, msg): + json_obj = await core.get_data_by_mode('default', 'custom-node-list.json') + + sender = manager_util.sanitize_tag(sender) + msg = manager_util.sanitize_tag(msg) + target = core.lookup_customnode_by_url(json_obj, custom_node_url) + + if target is not None: + PromptServer.instance.send_sync("cm-api-try-install-customnode", + {"sender": sender, "target": target, "msg": msg}) + else: + logging.error(f"[ComfyUI Manager API] Failed to try install - Unknown custom node url '{custom_node_url}'") + + +def confirm_try_install(sender, custom_node_url, msg): + asyncio.run(_confirm_try_install(sender, custom_node_url, msg)) + + +cm_global.register_api('cm.try-install-custom-node', confirm_try_install) + + +async def default_cache_update(): + core.refresh_channel_dict() + channel_url = core.get_config()['channel_url'] + async def get_cache(filename): + try: + if core.get_config()['default_cache_as_channel_url']: + uri = f"{channel_url}/{filename}" + else: + uri = f"{core.DEFAULT_CHANNEL}/{filename}" + + cache_uri = str(manager_util.simple_hash(uri)) + '_' + filename + cache_uri = os.path.join(manager_util.cache_dir, cache_uri) + + json_obj = await manager_util.get_data(uri, True) + + with manager_util.cache_lock: + with open(cache_uri, "w", encoding='utf-8') as file: + json.dump(json_obj, file, indent=4, sort_keys=True) + logging.info(f"[ComfyUI-Manager] default cache updated: {uri}") + except Exception as e: + logging.error(f"[ComfyUI-Manager] Failed to perform initial fetching '{filename}': {e}") + traceback.print_exc() + + if core.get_config()['network_mode'] != 'offline': + a = get_cache("custom-node-list.json") + b = get_cache("extension-node-map.json") + c = get_cache("model-list.json") + d = get_cache("alter-list.json") + e = get_cache("github-stats.json") + + await asyncio.gather(a, b, c, d, e) + + if core.get_config()['network_mode'] == 'private': + logging.info("[ComfyUI-Manager] The private comfyregistry is not yet supported in `network_mode=private`.") + else: + # load at least once + await core.unified_manager.reload('remote', dont_wait=False) + await core.unified_manager.get_custom_nodes(channel_url, 'remote') + + logging.info("[ComfyUI-Manager] All startup tasks have been completed.") + + +threading.Thread(target=lambda: asyncio.run(default_cache_update())).start() + +if not os.path.exists(core.manager_config_path): + core.get_config() + core.write_config() + + +cm_global.register_extension('ComfyUI-Manager', + {'version': core.version, + 'name': 'ComfyUI Manager', + 'nodes': {}, + 'description': 'This extension provides the ability to manage custom nodes in ComfyUI.', }) + + diff --git a/custom_nodes/comfyui-manager/glob/manager_util.py b/custom_nodes/comfyui-manager/glob/manager_util.py new file mode 100644 index 00000000..fd110b7f --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/manager_util.py @@ -0,0 +1,633 @@ +""" +description: + `manager_util` is the lightest module shared across the prestartup_script, main code, and cm-cli of ComfyUI-Manager. +""" +import traceback + +import aiohttp +import json +import threading +import os +from datetime import datetime +import subprocess +import sys +import re +import logging +import platform +import shlex +from functools import lru_cache + + +cache_lock = threading.Lock() + +comfyui_manager_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) +cache_dir = os.path.join(comfyui_manager_path, '.cache') # This path is also updated together in **manager_core.update_user_directory**. + +use_uv = False +bypass_ssl = False + +def add_python_path_to_env(): + if platform.system() != "Windows": + sep = ':' + else: + sep = ';' + + os.environ['PATH'] = os.path.dirname(sys.executable)+sep+os.environ['PATH'] + + +@lru_cache(maxsize=2) +def get_pip_cmd(force_uv=False): + """ + Get the base pip command, with automatic fallback to uv if pip is unavailable. + + Args: + force_uv (bool): If True, use uv directly without trying pip + + Returns: + list: Base command for pip operations + """ + embedded = 'python_embeded' in sys.executable + + # Try pip first (unless forcing uv) + if not force_uv: + try: + test_cmd = [sys.executable] + (['-s'] if embedded else []) + ['-m', 'pip', '--version'] + subprocess.check_output(test_cmd, stderr=subprocess.DEVNULL, timeout=5) + return [sys.executable] + (['-s'] if embedded else []) + ['-m', 'pip'] + except Exception: + logging.warning("[ComfyUI-Manager] `python -m pip` not available. Falling back to `uv`.") + + # Try uv (either forced or pip failed) + import shutil + + # Try uv as Python module + try: + test_cmd = [sys.executable] + (['-s'] if embedded else []) + ['-m', 'uv', '--version'] + subprocess.check_output(test_cmd, stderr=subprocess.DEVNULL, timeout=5) + logging.info("[ComfyUI-Manager] Using `uv` as Python module for pip operations.") + return [sys.executable] + (['-s'] if embedded else []) + ['-m', 'uv', 'pip'] + except Exception: + pass + + # Try standalone uv + if shutil.which('uv'): + logging.info("[ComfyUI-Manager] Using standalone `uv` for pip operations.") + return ['uv', 'pip'] + + # Nothing worked + logging.error("[ComfyUI-Manager] Neither `python -m pip` nor `uv` are available. Cannot proceed with package operations.") + raise Exception("Neither `pip` nor `uv` are available for package management") + + +def make_pip_cmd(cmd): + """ + Create a pip command by combining the cached base pip command with the given arguments. + + Args: + cmd (list): List of pip command arguments (e.g., ['install', 'package']) + + Returns: + list: Complete command list ready for subprocess execution + """ + global use_uv + base_cmd = get_pip_cmd(force_uv=use_uv) + return base_cmd + cmd + + +# DON'T USE StrictVersion - cannot handle pre_release version +# try: +# from distutils.version import StrictVersion +# except: +# print(f"[ComfyUI-Manager] 'distutils' package not found. Activating fallback mode for compatibility.") +class StrictVersion: + def __init__(self, version_string): + self.version_string = version_string + self.major = 0 + self.minor = 0 + self.patch = 0 + self.pre_release = None + self.parse_version_string() + + def parse_version_string(self): + parts = self.version_string.split('.') + if not parts: + raise ValueError("Version string must not be empty") + + self.major = int(parts[0]) + self.minor = int(parts[1]) if len(parts) > 1 else 0 + self.patch = int(parts[2]) if len(parts) > 2 else 0 + + # Handling pre-release versions if present + if len(parts) > 3: + self.pre_release = parts[3] + + def __str__(self): + version = f"{self.major}.{self.minor}.{self.patch}" + if self.pre_release: + version += f"-{self.pre_release}" + return version + + def __eq__(self, other): + return (self.major, self.minor, self.patch, self.pre_release) == \ + (other.major, other.minor, other.patch, other.pre_release) + + def __lt__(self, other): + if (self.major, self.minor, self.patch) == (other.major, other.minor, other.patch): + return self.pre_release_compare(self.pre_release, other.pre_release) < 0 + return (self.major, self.minor, self.patch) < (other.major, other.minor, other.patch) + + @staticmethod + def pre_release_compare(pre1, pre2): + if pre1 == pre2: + return 0 + if pre1 is None: + return 1 + if pre2 is None: + return -1 + return -1 if pre1 < pre2 else 1 + + def __le__(self, other): + return self == other or self < other + + def __gt__(self, other): + return not self <= other + + def __ge__(self, other): + return not self < other + + def __ne__(self, other): + return not self == other + + +def simple_hash(input_string): + hash_value = 0 + for char in input_string: + hash_value = (hash_value * 31 + ord(char)) % (2**32) + + return hash_value + + +def is_file_created_within_one_day(file_path): + if not os.path.exists(file_path): + return False + + file_creation_time = os.path.getctime(file_path) + current_time = datetime.now().timestamp() + time_difference = current_time - file_creation_time + + return time_difference <= 86400 + + +async def get_data(uri, silent=False): + if not silent: + print(f"FETCH DATA from: {uri}", end="") + + if uri.startswith("http"): + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=not bypass_ssl)) as session: + headers = { + 'Cache-Control': 'no-cache', + 'Pragma': 'no-cache', + 'Expires': '0' + } + async with session.get(uri, headers=headers) as resp: + json_text = await resp.text() + else: + with cache_lock: + with open(uri, "r", encoding="utf-8") as f: + json_text = f.read() + + try: + json_obj = json.loads(json_text) + except Exception as e: + logging.error(f"[ComfyUI-Manager] An error occurred while fetching '{uri}': {e}") + + return {} + + if not silent: + print(" [DONE]") + + return json_obj + + +def get_cache_path(uri): + cache_uri = str(simple_hash(uri)) + '_' + os.path.basename(uri).replace('&', "_").replace('?', "_").replace('=', "_") + return os.path.join(cache_dir, cache_uri+'.json') + + +def get_cache_state(uri): + cache_uri = get_cache_path(uri) + + if not os.path.exists(cache_uri): + return "not-cached" + elif is_file_created_within_one_day(cache_uri): + return "cached" + + return "expired" + + +def save_to_cache(uri, json_obj, silent=False): + cache_uri = get_cache_path(uri) + + with cache_lock: + with open(cache_uri, "w", encoding='utf-8') as file: + json.dump(json_obj, file, indent=4, sort_keys=True) + if not silent: + logging.info(f"[ComfyUI-Manager] default cache updated: {uri}") + + +async def get_data_with_cache(uri, silent=False, cache_mode=True, dont_wait=False, dont_cache=False): + cache_uri = get_cache_path(uri) + + if cache_mode and dont_wait: + # NOTE: return the cache if possible, even if it is expired, so do not cache + if not os.path.exists(cache_uri): + logging.error(f"[ComfyUI-Manager] The network connection is unstable, so it is operating in fallback mode: {uri}") + + return {} + else: + if not is_file_created_within_one_day(cache_uri): + logging.error(f"[ComfyUI-Manager] The network connection is unstable, so it is operating in outdated cache mode: {uri}") + + return await get_data(cache_uri, silent=silent) + + if cache_mode and is_file_created_within_one_day(cache_uri): + json_obj = await get_data(cache_uri, silent=silent) + else: + json_obj = await get_data(uri, silent=silent) + if not dont_cache: + with cache_lock: + with open(cache_uri, "w", encoding='utf-8') as file: + json.dump(json_obj, file, indent=4, sort_keys=True) + if not silent: + logging.info(f"[ComfyUI-Manager] default cache updated: {uri}") + + return json_obj + + +def sanitize_tag(x): + return x.replace('<', '<').replace('>', '>') + + +def extract_package_as_zip(file_path, extract_path): + import zipfile + try: + with zipfile.ZipFile(file_path, "r") as zip_ref: + zip_ref.extractall(extract_path) + extracted_files = zip_ref.namelist() + logging.info(f"Extracted zip file to {extract_path}") + return extracted_files + except zipfile.BadZipFile: + logging.error(f"File '{file_path}' is not a zip or is corrupted.") + return None + + +pip_map = None + + +def get_installed_packages(renew=False): + global pip_map + + if renew or pip_map is None: + try: + result = subprocess.check_output(make_pip_cmd(['list']), universal_newlines=True) + + pip_map = {} + for line in result.split('\n'): + x = line.strip() + if x: + y = line.split() + if y[0] == 'Package' or y[0].startswith('-'): + continue + + normalized_name = y[0].lower().replace('-', '_') + pip_map[normalized_name] = y[1] + except subprocess.CalledProcessError: + logging.error("[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.") + return {} + + return pip_map + + +def clear_pip_cache(): + global pip_map + pip_map = None + + +def parse_requirement_line(line): + tokens = shlex.split(line) + if not tokens: + return None + + package_spec = tokens[0] + + pattern = re.compile( + r'^(?P[A-Za-z0-9_.+-]+)' + r'(?P==|>=|<=|!=|~=|>|<)?' + r'(?P[A-Za-z0-9_.+-]*)$' + ) + m = pattern.match(package_spec) + if not m: + return None + + package = m.group('package') + operator = m.group('operator') or None + version = m.group('version') or None + + index_url = None + if '--index-url' in tokens: + idx = tokens.index('--index-url') + if idx + 1 < len(tokens): + index_url = tokens[idx + 1] + + res = {'package': package} + + if operator is not None: + res['operator'] = operator + + if version is not None: + res['version'] = StrictVersion(version) + + if index_url is not None: + res['index_url'] = index_url + + return res + + +torch_torchvision_torchaudio_version_map = { + '2.7.0': ('0.22.0', '2.7.0'), + '2.6.0': ('0.21.0', '2.6.0'), + '2.5.1': ('0.20.0', '2.5.0'), + '2.5.0': ('0.20.0', '2.5.0'), + '2.4.1': ('0.19.1', '2.4.1'), + '2.4.0': ('0.19.0', '2.4.0'), + '2.3.1': ('0.18.1', '2.3.1'), + '2.3.0': ('0.18.0', '2.3.0'), + '2.2.2': ('0.17.2', '2.2.2'), + '2.2.1': ('0.17.1', '2.2.1'), + '2.2.0': ('0.17.0', '2.2.0'), + '2.1.2': ('0.16.2', '2.1.2'), + '2.1.1': ('0.16.1', '2.1.1'), + '2.1.0': ('0.16.0', '2.1.0'), + '2.0.1': ('0.15.2', '2.0.1'), + '2.0.0': ('0.15.1', '2.0.0'), +} + + +def torch_rollback(prev): + spec = prev.split('+') + if len(spec) > 1: + platform = spec[1] + else: + cmd = make_pip_cmd(['install', '--force', 'torch', 'torchvision', 'torchaudio']) + subprocess.check_output(cmd, universal_newlines=True) + logging.error(cmd) + return + + torch_ver = StrictVersion(spec[0]) + torch_ver = f"{torch_ver.major}.{torch_ver.minor}.{torch_ver.patch}" + torch_torchvision_torchaudio_ver = torch_torchvision_torchaudio_version_map.get(torch_ver) + + if torch_torchvision_torchaudio_ver is None: + cmd = make_pip_cmd(['install', '--pre', 'torch', 'torchvision', 'torchaudio', + '--index-url', f"https://download.pytorch.org/whl/nightly/{platform}"]) + logging.info("[ComfyUI-Manager] restore PyTorch to nightly version") + else: + torchvision_ver, torchaudio_ver = torch_torchvision_torchaudio_ver + cmd = make_pip_cmd(['install', f'torch=={torch_ver}', f'torchvision=={torchvision_ver}', f"torchaudio=={torchaudio_ver}", + '--index-url', f"https://download.pytorch.org/whl/{platform}"]) + logging.info(f"[ComfyUI-Manager] restore PyTorch to {torch_ver}+{platform}") + + subprocess.check_output(cmd, universal_newlines=True) + + +class PIPFixer: + def __init__(self, prev_pip_versions, comfyui_path, manager_files_path): + self.prev_pip_versions = { **prev_pip_versions } + self.comfyui_path = comfyui_path + self.manager_files_path = manager_files_path + + def fix_broken(self): + new_pip_versions = get_installed_packages(True) + + # remove `comfy` python package + try: + if 'comfy' in new_pip_versions: + cmd = make_pip_cmd(['uninstall', 'comfy']) + subprocess.check_output(cmd, universal_newlines=True) + + logging.warning("[ComfyUI-Manager] 'comfy' python package is uninstalled.\nWARN: The 'comfy' package is completely unrelated to ComfyUI and should never be installed as it causes conflicts with ComfyUI.") + except Exception as e: + logging.error("[ComfyUI-Manager] Failed to uninstall `comfy` python package") + logging.error(e) + + # fix torch - reinstall torch packages if version is changed + try: + if 'torch' not in self.prev_pip_versions or 'torchvision' not in self.prev_pip_versions or 'torchaudio' not in self.prev_pip_versions: + logging.error("[ComfyUI-Manager] PyTorch is not installed") + elif self.prev_pip_versions['torch'] != new_pip_versions['torch'] \ + or self.prev_pip_versions['torchvision'] != new_pip_versions['torchvision'] \ + or self.prev_pip_versions['torchaudio'] != new_pip_versions['torchaudio']: + torch_rollback(self.prev_pip_versions['torch']) + except Exception as e: + logging.error("[ComfyUI-Manager] Failed to restore PyTorch") + logging.error(e) + + # fix opencv + try: + ocp = new_pip_versions.get('opencv-contrib-python') + ocph = new_pip_versions.get('opencv-contrib-python-headless') + op = new_pip_versions.get('opencv-python') + oph = new_pip_versions.get('opencv-python-headless') + + versions = [ocp, ocph, op, oph] + versions = [StrictVersion(x) for x in versions if x is not None] + versions.sort(reverse=True) + + if len(versions) > 0: + # upgrade to maximum version + targets = [] + cur = versions[0] + if ocp is not None and StrictVersion(ocp) != cur: + targets.append('opencv-contrib-python') + if ocph is not None and StrictVersion(ocph) != cur: + targets.append('opencv-contrib-python-headless') + if op is not None and StrictVersion(op) != cur: + targets.append('opencv-python') + if oph is not None and StrictVersion(oph) != cur: + targets.append('opencv-python-headless') + + if len(targets) > 0: + for x in targets: + cmd = make_pip_cmd(['install', f"{x}=={versions[0].version_string}"]) + subprocess.check_output(cmd, universal_newlines=True) + + logging.info(f"[ComfyUI-Manager] 'opencv' dependencies were fixed: {targets}") + except Exception as e: + logging.error("[ComfyUI-Manager] Failed to restore opencv") + logging.error(e) + + # fix missing frontend + try: + # NOTE: package name in requirements is 'comfyui-frontend-package' + # but, package name from `pip freeze` is 'comfyui_frontend_package' + # but, package name from `uv pip freeze` is 'comfyui-frontend-package' + # + # get_installed_packages returns normalized name (i.e. comfyui_frontend_package) + if 'comfyui_frontend_package' not in new_pip_versions: + requirements_path = os.path.join(self.comfyui_path, 'requirements.txt') + + with open(requirements_path, 'r') as file: + lines = file.readlines() + + front_line = next((line.strip() for line in lines if line.startswith('comfyui-frontend-package')), None) + if front_line is None: + logging.info("[ComfyUI-Manager] Skipped fixing the 'comfyui-frontend-package' dependency because the ComfyUI is outdated.") + else: + cmd = make_pip_cmd(['install', front_line]) + subprocess.check_output(cmd , universal_newlines=True) + logging.info("[ComfyUI-Manager] 'comfyui-frontend-package' dependency were fixed") + except Exception as e: + logging.error("[ComfyUI-Manager] Failed to restore comfyui-frontend-package") + logging.error(e) + + # restore based on custom list + pip_auto_fix_path = os.path.join(self.manager_files_path, "pip_auto_fix.list") + if os.path.exists(pip_auto_fix_path): + with open(pip_auto_fix_path, 'r', encoding="UTF-8", errors="ignore") as f: + fixed_list = [] + + for x in f.readlines(): + try: + parsed = parse_requirement_line(x) + need_to_reinstall = True + + normalized_name = parsed['package'].lower().replace('-', '_') + if normalized_name in new_pip_versions: + if 'version' in parsed and 'operator' in parsed: + cur = StrictVersion(new_pip_versions[normalized_name]) + dest = parsed['version'] + op = parsed['operator'] + if cur == dest: + if op in ['==', '>=', '<=']: + need_to_reinstall = False + elif cur < dest: + if op in ['<=', '<', '~=', '!=']: + need_to_reinstall = False + elif cur > dest: + if op in ['>=', '>', '~=', '!=']: + need_to_reinstall = False + + if need_to_reinstall: + cmd_args = ['install'] + if 'version' in parsed and 'operator' in parsed: + cmd_args.append(parsed['package']+parsed['operator']+parsed['version'].version_string) + + if 'index_url' in parsed: + cmd_args.append('--index-url') + cmd_args.append(parsed['index_url']) + + cmd = make_pip_cmd(cmd_args) + subprocess.check_output(cmd, universal_newlines=True) + + fixed_list.append(parsed['package']) + except Exception as e: + traceback.print_exc() + logging.error(f"[ComfyUI-Manager] Failed to restore '{x}'") + logging.error(e) + + if len(fixed_list) > 0: + logging.info(f"[ComfyUI-Manager] dependencies in pip_auto_fix.json were fixed: {fixed_list}") + +def sanitize(data): + return data.replace("<", "<").replace(">", ">") + + +def sanitize_filename(input_string): + result_string = re.sub(r'[^a-zA-Z0-9_]', '_', input_string) + return result_string + + +def robust_readlines(fullpath): + import chardet + try: + with open(fullpath, "r") as f: + return f.readlines() + except: + encoding = None + with open(fullpath, "rb") as f: + raw_data = f.read() + result = chardet.detect(raw_data) + encoding = result['encoding'] + + if encoding is not None: + with open(fullpath, "r", encoding=encoding) as f: + return f.readlines() + + print(f"[ComfyUI-Manager] Failed to recognize encoding for: {fullpath}") + return [] + + +def restore_pip_snapshot(pips, options): + non_url = [] + local_url = [] + non_local_url = [] + + for k, v in pips.items(): + # NOTE: skip torch related packages + if k.startswith("torch==") or k.startswith("torchvision==") or k.startswith("torchaudio==") or k.startswith("nvidia-"): + continue + + if v == "": + non_url.append(k) + else: + if v.startswith('file:'): + local_url.append(v) + else: + non_local_url.append(v) + + + # restore other pips + failed = [] + if '--pip-non-url' in options: + # try all at once + res = 1 + try: + res = subprocess.check_output(make_pip_cmd(['install'] + non_url)) + except Exception: + pass + + # fallback + if res != 0: + for x in non_url: + res = 1 + try: + res = subprocess.check_output(make_pip_cmd(['install', '--no-deps', x])) + except Exception: + pass + + if res != 0: + failed.append(x) + + if '--pip-non-local-url' in options: + for x in non_local_url: + res = 1 + try: + res = subprocess.check_output(make_pip_cmd(['install', '--no-deps', x])) + except Exception: + pass + + if res != 0: + failed.append(x) + + if '--pip-local-url' in options: + for x in local_url: + res = 1 + try: + res = subprocess.check_output(make_pip_cmd(['install', '--no-deps', x])) + except Exception: + pass + + if res != 0: + failed.append(x) + + print(f"Installation failed for pip packages: {failed}") \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/glob/node_package.py b/custom_nodes/comfyui-manager/glob/node_package.py new file mode 100644 index 00000000..d199fa30 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/node_package.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +from dataclasses import dataclass +import os + +from git_utils import get_commit_hash + + +@dataclass +class InstalledNodePackage: + """Information about an installed node package.""" + + id: str + fullpath: str + disabled: bool + version: str + + @property + def is_unknown(self) -> bool: + return self.version == "unknown" + + @property + def is_nightly(self) -> bool: + return self.version == "nightly" + + @property + def is_from_cnr(self) -> bool: + return not self.is_unknown and not self.is_nightly + + @property + def is_enabled(self) -> bool: + return not self.disabled + + @property + def is_disabled(self) -> bool: + return self.disabled + + def get_commit_hash(self) -> str: + return get_commit_hash(self.fullpath) + + def isValid(self) -> bool: + if self.is_from_cnr: + return os.path.exists(os.path.join(self.fullpath, '.tracking')) + + return True + + @staticmethod + def from_fullpath(fullpath: str, resolve_from_path) -> InstalledNodePackage: + parent_folder_name = os.path.basename(os.path.dirname(fullpath)) + module_name = os.path.basename(fullpath) + + if module_name.endswith(".disabled"): + node_id = module_name[:-9] + disabled = True + elif parent_folder_name == ".disabled": + # Nodes under custom_nodes/.disabled/* are disabled + node_id = module_name + disabled = True + else: + node_id = module_name + disabled = False + + info = resolve_from_path(fullpath) + if info is None: + version = 'unknown' + else: + node_id = info['id'] # robust module guessing + version = info['ver'] + + return InstalledNodePackage( + id=node_id, fullpath=fullpath, disabled=disabled, version=version + ) diff --git a/custom_nodes/comfyui-manager/glob/security_check.py b/custom_nodes/comfyui-manager/glob/security_check.py new file mode 100644 index 00000000..892e96a2 --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/security_check.py @@ -0,0 +1,124 @@ +import sys +import subprocess +import os + +import manager_util + + +def security_check(): + print("[START] Security scan") + + custom_nodes_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + comfyui_path = os.path.abspath(os.path.join(custom_nodes_path, '..')) + + guide = { + "ComfyUI_LLMVISION": """ +0.Remove ComfyUI\\custom_nodes\\ComfyUI_LLMVISION. +1.Remove pip packages: openai-1.16.3.dist-info, anthropic-0.21.4.dist-info, openai-1.30.2.dist-info, anthropic-0.21.5.dist-info, anthropic-0.26.1.dist-info, %LocalAppData%\\rundll64.exe + (For portable versions, it is recommended to reinstall. If you are using a venv, it is advised to recreate the venv.) +2.Remove these files in your system: lib/browser/admin.py, Cadmino.py, Fadmino.py, VISION-D.exe, BeamNG.UI.exe +3.Check your Windows registry for the key listed above and remove it. + (HKEY_CURRENT_USER\\Software\\OpenAICLI) +4.Run a malware scanner. +5.Change all of your passwords, everywhere. + +(Reinstall OS is recommended.) +\n +Detailed information: https://old.reddit.com/r/comfyui/comments/1dbls5n/psa_if_youve_used_the_comfyui_llmvision_node_from/ + """, + "lolMiner": """ +1. Remove pip packages: lolMiner* +2. Remove files: lolMiner*, 4G_Ethash_Linux_Readme.txt, mine* in ComfyUI dir. + +(Reinstall ComfyUI is recommended.) + """, + "ultralytics==8.3.41": f""" +Execute following commands: + {sys.executable} -m pip uninstall ultralytics + {sys.executable} -m pip install ultralytics==8.3.40 + +And kill and remove /tmp/ultralytics_runner + + +The version 8.3.41 to 8.3.42 of the Ultralytics package you installed is compromised. Please uninstall that version and reinstall the latest version. +https://blog.comfy.org/comfyui-statement-on-the-ultralytics-crypto-miner-situation/ + """, + "ultralytics==8.3.42": f""" +Execute following commands: + {sys.executable} -m pip uninstall ultralytics + {sys.executable} -m pip install ultralytics==8.3.40 + +And kill and remove /tmp/ultralytics_runner + + +The version 8.3.41 to 8.3.42 of the Ultralytics package you installed is compromised. Please uninstall that version and reinstall the latest version. +https://blog.comfy.org/comfyui-statement-on-the-ultralytics-crypto-miner-situation/ + """ + } + + node_blacklist = {"ComfyUI_LLMVISION": "ComfyUI_LLMVISION"} + + pip_blacklist = { + "AppleBotzz": "ComfyUI_LLMVISION", + "ultralytics==8.3.41": "ultralytics==8.3.41" + } + + file_blacklist = { + "ComfyUI_LLMVISION": ["%LocalAppData%\\rundll64.exe"], + "lolMiner": [os.path.join(comfyui_path, 'lolMiner')] + } + + installed_pips = subprocess.check_output(manager_util.make_pip_cmd(["freeze"]), text=True) + + detected = set() + try: + anthropic_info = subprocess.check_output(manager_util.make_pip_cmd(["show", "anthropic"]), text=True, stderr=subprocess.DEVNULL) + requires_lines = [x for x in anthropic_info.split('\n') if x.startswith("Requires")] + if requires_lines: + anthropic_reqs = requires_lines[0].split(": ", 1)[1] + if "pycrypto" in anthropic_reqs: + location_lines = [x for x in anthropic_info.split('\n') if x.startswith("Location")] + if location_lines: + location = location_lines[0].split(": ", 1)[1] + for fi in os.listdir(location): + if fi.startswith("anthropic"): + guide["ComfyUI_LLMVISION"] = (f"\n0.Remove {os.path.join(location, fi)}" + guide["ComfyUI_LLMVISION"]) + detected.add("ComfyUI_LLMVISION") + + except subprocess.CalledProcessError: + pass + + for k, v in node_blacklist.items(): + if os.path.exists(os.path.join(custom_nodes_path, k)): + print(f"[SECURITY ALERT] custom node '{k}' is dangerous.") + detected.add(v) + + for k, v in pip_blacklist.items(): + if k in installed_pips: + detected.add(v) + break + + for k, v in file_blacklist.items(): + for x in v: + if os.path.exists(os.path.expandvars(x)): + detected.add(k) + break + + if len(detected) > 0: + for line in installed_pips.split('\n'): + for k, v in pip_blacklist.items(): + if k in line: + print(f"[SECURITY ALERT] '{line}' is dangerous.") + + print("\n########################################################################") + print(" Malware has been detected, forcibly terminating ComfyUI execution.") + print("########################################################################\n") + + for x in detected: + print(f"\n======== TARGET: {x} =========") + print("\nTODO:") + print(guide.get(x)) + + exit(-1) + + print("[DONE] Security scan") diff --git a/custom_nodes/comfyui-manager/glob/share_3rdparty.py b/custom_nodes/comfyui-manager/glob/share_3rdparty.py new file mode 100644 index 00000000..837176fb --- /dev/null +++ b/custom_nodes/comfyui-manager/glob/share_3rdparty.py @@ -0,0 +1,433 @@ +import mimetypes +import manager_core as core +import os +from aiohttp import web +import aiohttp +import json +import hashlib + +import folder_paths +from server import PromptServer + + +def extract_model_file_names(json_data): + """Extract unique file names from the input JSON data.""" + file_names = set() + model_filename_extensions = {'.safetensors', '.ckpt', '.pt', '.pth', '.bin'} + + # Recursively search for file names in the JSON data + def recursive_search(data): + if isinstance(data, dict): + for value in data.values(): + recursive_search(value) + elif isinstance(data, list): + for item in data: + recursive_search(item) + elif isinstance(data, str) and '.' in data: + file_names.add(os.path.basename(data)) # file_names.add(data) + + recursive_search(json_data) + return [f for f in list(file_names) if os.path.splitext(f)[1] in model_filename_extensions] + + +def find_file_paths(base_dir, file_names): + """Find the paths of the files in the base directory.""" + file_paths = {} + + for root, dirs, files in os.walk(base_dir): + # Exclude certain directories + dirs[:] = [d for d in dirs if d not in ['.git']] + + for file in files: + if file in file_names: + file_paths[file] = os.path.join(root, file) + return file_paths + + +def compute_sha256_checksum(filepath): + """Compute the SHA256 checksum of a file, in chunks""" + sha256 = hashlib.sha256() + with open(filepath, 'rb') as f: + for chunk in iter(lambda: f.read(4096), b''): + sha256.update(chunk) + return sha256.hexdigest() + + +@PromptServer.instance.routes.get("/manager/share_option") +async def share_option(request): + if "value" in request.rel_url.query: + core.get_config()['share_option'] = request.rel_url.query['value'] + core.write_config() + else: + return web.Response(text=core.get_config()['share_option'], status=200) + + return web.Response(status=200) + + +def get_openart_auth(): + if not os.path.exists(os.path.join(core.manager_files_path, ".openart_key")): + return None + try: + with open(os.path.join(core.manager_files_path, ".openart_key"), "r") as f: + openart_key = f.read().strip() + return openart_key if openart_key else None + except: + return None + + +def get_matrix_auth(): + if not os.path.exists(os.path.join(core.manager_files_path, "matrix_auth")): + return None + try: + with open(os.path.join(core.manager_files_path, "matrix_auth"), "r") as f: + matrix_auth = f.read() + homeserver, username, password = matrix_auth.strip().split("\n") + if not homeserver or not username or not password: + return None + return { + "homeserver": homeserver, + "username": username, + "password": password, + } + except: + return None + + +def get_comfyworkflows_auth(): + if not os.path.exists(os.path.join(core.manager_files_path, "comfyworkflows_sharekey")): + return None + try: + with open(os.path.join(core.manager_files_path, "comfyworkflows_sharekey"), "r") as f: + share_key = f.read() + if not share_key.strip(): + return None + return share_key + except: + return None + + +def get_youml_settings(): + if not os.path.exists(os.path.join(core.manager_files_path, ".youml")): + return None + try: + with open(os.path.join(core.manager_files_path, ".youml"), "r") as f: + youml_settings = f.read().strip() + return youml_settings if youml_settings else None + except: + return None + + +def set_youml_settings(settings): + with open(os.path.join(core.manager_files_path, ".youml"), "w") as f: + f.write(settings) + + +@PromptServer.instance.routes.get("/manager/get_openart_auth") +async def api_get_openart_auth(request): + # print("Getting stored Matrix credentials...") + openart_key = get_openart_auth() + if not openart_key: + return web.Response(status=404) + return web.json_response({"openart_key": openart_key}) + + +@PromptServer.instance.routes.post("/manager/set_openart_auth") +async def api_set_openart_auth(request): + json_data = await request.json() + openart_key = json_data['openart_key'] + with open(os.path.join(core.manager_files_path, ".openart_key"), "w") as f: + f.write(openart_key) + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/manager/get_matrix_auth") +async def api_get_matrix_auth(request): + # print("Getting stored Matrix credentials...") + matrix_auth = get_matrix_auth() + if not matrix_auth: + return web.Response(status=404) + return web.json_response(matrix_auth) + + +@PromptServer.instance.routes.get("/manager/youml/settings") +async def api_get_youml_settings(request): + youml_settings = get_youml_settings() + if not youml_settings: + return web.Response(status=404) + return web.json_response(json.loads(youml_settings)) + + +@PromptServer.instance.routes.post("/manager/youml/settings") +async def api_set_youml_settings(request): + json_data = await request.json() + set_youml_settings(json.dumps(json_data)) + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/manager/get_comfyworkflows_auth") +async def api_get_comfyworkflows_auth(request): + # Check if the user has provided Matrix credentials in a file called 'matrix_accesstoken' + # in the same directory as the ComfyUI base folder + # print("Getting stored Comfyworkflows.com auth...") + comfyworkflows_auth = get_comfyworkflows_auth() + if not comfyworkflows_auth: + return web.Response(status=404) + return web.json_response({"comfyworkflows_sharekey": comfyworkflows_auth}) + + +@PromptServer.instance.routes.post("/manager/set_esheep_workflow_and_images") +async def set_esheep_workflow_and_images(request): + json_data = await request.json() + with open(os.path.join(core.manager_files_path, "esheep_share_message.json"), "w", encoding='utf-8') as file: + json.dump(json_data, file, indent=4) + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/manager/get_esheep_workflow_and_images") +async def get_esheep_workflow_and_images(request): + with open(os.path.join(core.manager_files_path, "esheep_share_message.json"), 'r', encoding='utf-8') as file: + data = json.load(file) + return web.Response(status=200, text=json.dumps(data)) + + +def set_matrix_auth(json_data): + homeserver = json_data['homeserver'] + username = json_data['username'] + password = json_data['password'] + with open(os.path.join(core.manager_files_path, "matrix_auth"), "w") as f: + f.write("\n".join([homeserver, username, password])) + + +def set_comfyworkflows_auth(comfyworkflows_sharekey): + with open(os.path.join(core.manager_files_path, "comfyworkflows_sharekey"), "w") as f: + f.write(comfyworkflows_sharekey) + + +def has_provided_matrix_auth(matrix_auth): + return matrix_auth['homeserver'].strip() and matrix_auth['username'].strip() and matrix_auth['password'].strip() + + +def has_provided_comfyworkflows_auth(comfyworkflows_sharekey): + return comfyworkflows_sharekey.strip() + + +@PromptServer.instance.routes.post("/manager/share") +async def share_art(request): + # get json data + json_data = await request.json() + + matrix_auth = json_data['matrix_auth'] + comfyworkflows_sharekey = json_data['cw_auth']['cw_sharekey'] + + set_matrix_auth(matrix_auth) + set_comfyworkflows_auth(comfyworkflows_sharekey) + + share_destinations = json_data['share_destinations'] + credits = json_data['credits'] + title = json_data['title'] + description = json_data['description'] + is_nsfw = json_data['is_nsfw'] + prompt = json_data['prompt'] + potential_outputs = json_data['potential_outputs'] + selected_output_index = json_data['selected_output_index'] + + try: + output_to_share = potential_outputs[int(selected_output_index)] + except: + # for now, pick the first output + output_to_share = potential_outputs[0] + + assert output_to_share['type'] in ('image', 'output') + output_dir = folder_paths.get_output_directory() + + if output_to_share['type'] == 'image': + asset_filename = output_to_share['image']['filename'] + asset_subfolder = output_to_share['image']['subfolder'] + + if output_to_share['image']['type'] == 'temp': + output_dir = folder_paths.get_temp_directory() + else: + asset_filename = output_to_share['output']['filename'] + asset_subfolder = output_to_share['output']['subfolder'] + + if asset_subfolder: + asset_filepath = os.path.join(output_dir, asset_subfolder, asset_filename) + else: + asset_filepath = os.path.join(output_dir, asset_filename) + + # get the mime type of the asset + assetFileType = mimetypes.guess_type(asset_filepath)[0] + + share_website_host = "UNKNOWN" + if "comfyworkflows" in share_destinations: + share_website_host = "https://comfyworkflows.com" + share_endpoint = f"{share_website_host}/api" + + # get presigned urls + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.post( + f"{share_endpoint}/get_presigned_urls", + json={ + "assetFileName": asset_filename, + "assetFileType": assetFileType, + "workflowJsonFileName": 'workflow.json', + "workflowJsonFileType": 'application/json', + }, + ) as resp: + assert resp.status == 200 + presigned_urls_json = await resp.json() + assetFilePresignedUrl = presigned_urls_json["assetFilePresignedUrl"] + assetFileKey = presigned_urls_json["assetFileKey"] + workflowJsonFilePresignedUrl = presigned_urls_json["workflowJsonFilePresignedUrl"] + workflowJsonFileKey = presigned_urls_json["workflowJsonFileKey"] + + # upload asset + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.put(assetFilePresignedUrl, data=open(asset_filepath, "rb")) as resp: + assert resp.status == 200 + + # upload workflow json + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.put(workflowJsonFilePresignedUrl, data=json.dumps(prompt['workflow']).encode('utf-8')) as resp: + assert resp.status == 200 + + model_filenames = extract_model_file_names(prompt['workflow']) + model_file_paths = find_file_paths(folder_paths.base_path, model_filenames) + + models_info = {} + for filename, filepath in model_file_paths.items(): + models_info[filename] = { + "filename": filename, + "sha256_checksum": compute_sha256_checksum(filepath), + "relative_path": os.path.relpath(filepath, folder_paths.base_path), + } + + # make a POST request to /api/upload_workflow with form data key values + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + form = aiohttp.FormData() + if comfyworkflows_sharekey: + form.add_field("shareKey", comfyworkflows_sharekey) + form.add_field("source", "comfyui_manager") + form.add_field("assetFileKey", assetFileKey) + form.add_field("assetFileType", assetFileType) + form.add_field("workflowJsonFileKey", workflowJsonFileKey) + form.add_field("sharedWorkflowWorkflowJsonString", json.dumps(prompt['workflow'])) + form.add_field("sharedWorkflowPromptJsonString", json.dumps(prompt['output'])) + form.add_field("shareWorkflowCredits", credits) + form.add_field("shareWorkflowTitle", title) + form.add_field("shareWorkflowDescription", description) + form.add_field("shareWorkflowIsNSFW", str(is_nsfw).lower()) + form.add_field("currentSnapshot", json.dumps(await core.get_current_snapshot())) + form.add_field("modelsInfo", json.dumps(models_info)) + + async with session.post( + f"{share_endpoint}/upload_workflow", + data=form, + ) as resp: + assert resp.status == 200 + upload_workflow_json = await resp.json() + workflowId = upload_workflow_json["workflowId"] + + # check if the user has provided Matrix credentials + if "matrix" in share_destinations: + comfyui_share_room_id = '!LGYSoacpJPhIfBqVfb:matrix.org' + filename = os.path.basename(asset_filepath) + content_type = assetFileType + + try: + from nio import AsyncClient, LoginResponse, UploadResponse + + homeserver = 'matrix.org' + if matrix_auth: + homeserver = matrix_auth.get('homeserver', 'matrix.org') + homeserver = homeserver.replace("http://", "https://") + if not homeserver.startswith("https://"): + homeserver = "https://" + homeserver + + client = AsyncClient(homeserver, matrix_auth['username']) + + # Login + login_resp = await client.login(matrix_auth['password']) + if not isinstance(login_resp, LoginResponse) or not login_resp.access_token: + await client.close() + return web.json_response({"error": "Invalid Matrix credentials."}, content_type='application/json', status=400) + + # Upload asset + with open(asset_filepath, 'rb') as f: + upload_resp, _maybe_keys = await client.upload(f, content_type=content_type, filename=filename) + asset_data = f.seek(0) or f.read() # get size for info below + if not isinstance(upload_resp, UploadResponse) or not upload_resp.content_uri: + await client.close() + return web.json_response({"error": "Failed to upload asset to Matrix."}, content_type='application/json', status=500) + mxc_url = upload_resp.content_uri + + # Upload workflow JSON + import io + workflow_json_bytes = json.dumps(prompt['workflow']).encode('utf-8') + workflow_io = io.BytesIO(workflow_json_bytes) + upload_workflow_resp, _maybe_keys = await client.upload(workflow_io, content_type='application/json', filename='workflow.json') + workflow_io.seek(0) + if not isinstance(upload_workflow_resp, UploadResponse) or not upload_workflow_resp.content_uri: + await client.close() + return web.json_response({"error": "Failed to upload workflow to Matrix."}, content_type='application/json', status=500) + workflow_json_mxc_url = upload_workflow_resp.content_uri + + # Send text message + text_content = "" + if title: + text_content += f"{title}\n" + if description: + text_content += f"{description}\n" + if credits: + text_content += f"\ncredits: {credits}\n" + await client.room_send( + room_id=comfyui_share_room_id, + message_type="m.room.message", + content={"msgtype": "m.text", "body": text_content} + ) + + # Send image + await client.room_send( + room_id=comfyui_share_room_id, + message_type="m.room.message", + content={ + "msgtype": "m.image", + "body": filename, + "url": mxc_url, + "info": { + "mimetype": content_type, + "size": len(asset_data) + } + } + ) + + # Send workflow JSON file + await client.room_send( + room_id=comfyui_share_room_id, + message_type="m.room.message", + content={ + "msgtype": "m.file", + "body": "workflow.json", + "url": workflow_json_mxc_url, + "info": { + "mimetype": "application/json", + "size": len(workflow_json_bytes) + } + } + ) + + await client.close() + + except: + import traceback + traceback.print_exc() + return web.json_response({"error": "An error occurred when sharing your art to Matrix."}, content_type='application/json', status=500) + + return web.json_response({ + "comfyworkflows": { + "url": None if "comfyworkflows" not in share_destinations else f"{share_website_host}/workflows/{workflowId}", + }, + "matrix": { + "success": None if "matrix" not in share_destinations else True + } + }, content_type='application/json', status=200) diff --git a/custom_nodes/comfyui-manager/js/README.md b/custom_nodes/comfyui-manager/js/README.md new file mode 100644 index 00000000..5ceb21ba --- /dev/null +++ b/custom_nodes/comfyui-manager/js/README.md @@ -0,0 +1,50 @@ +# ComfyUI-Manager: Frontend (js) + +This directory contains the JavaScript frontend implementation for ComfyUI-Manager, providing the user interface components that interact with the backend API. + +## Core Components + +- **comfyui-manager.js**: Main entry point that initializes the manager UI and integrates with ComfyUI. +- **custom-nodes-manager.js**: Implements the UI for browsing, installing, and managing custom nodes. +- **model-manager.js**: Handles the model management interface for downloading and organizing AI models. +- **components-manager.js**: Manages reusable workflow components system. +- **snapshot.js**: Implements the snapshot system for backing up and restoring installations. + +## Sharing Components + +- **comfyui-share-common.js**: Base functionality for workflow sharing features. +- **comfyui-share-copus.js**: Integration with the ComfyUI Copus sharing platform. +- **comfyui-share-openart.js**: Integration with the OpenArt sharing platform. +- **comfyui-share-youml.js**: Integration with the YouML sharing platform. + +## Utility Components + +- **cm-api.js**: Client-side API wrapper for communication with the backend. +- **common.js**: Shared utilities and helper functions used across the frontend. +- **node_fixer.js**: Utilities for fixing disconnected links and repairing malformed nodes by recreating them while preserving connections. +- **popover-helper.js**: UI component for popup tooltips and contextual information. +- **turbogrid.esm.js**: Grid component library - https://github.com/cenfun/turbogrid +- **workflow-metadata.js**: Handles workflow metadata parsing, validation and cross-repository compatibility including versioning, dependencies tracking, and resource management. + +## Architecture + +The frontend follows a modular component-based architecture: + +1. **Integration Layer**: Connects with ComfyUI's existing UI system +2. **Manager Components**: Individual functional UI components (node manager, model manager, etc.) +3. **Sharing Components**: Platform-specific sharing implementations +4. **Utility Layer**: Reusable UI components and helpers + +## Implementation Details + +- The frontend integrates directly with ComfyUI's UI system through `app.js` +- Dialog-based UI for most manager functions to avoid cluttering the main interface +- Asynchronous API calls to handle backend operations without blocking the UI + +## Styling + +CSS files are included for specific components: +- **custom-nodes-manager.css**: Styling for the node management UI +- **model-manager.css**: Styling for the model management UI + +This frontend implementation provides a comprehensive yet user-friendly interface for managing the ComfyUI ecosystem. diff --git a/custom_nodes/comfyui-manager/js/cm-api.js b/custom_nodes/comfyui-manager/js/cm-api.js new file mode 100644 index 00000000..c4d6da03 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/cm-api.js @@ -0,0 +1,67 @@ +import { api } from "../../scripts/api.js"; +import { app } from "../../scripts/app.js"; +import { sleep, customConfirm, customAlert, handle403Response, show_message } from "./common.js"; + +async function tryInstallCustomNode(event) { + let msg = '-= [ComfyUI Manager] extension installation request =-\n\n'; + msg += `The '${event.detail.sender}' extension requires the installation of the '${event.detail.target.title}' extension. `; + + if(event.detail.target.installed == 'Disabled') { + msg += 'However, the extension is currently disabled. Would you like to enable it and reboot?' + } + else if(event.detail.target.installed == 'True') { + msg += 'However, it seems that the extension is in an import-fail state or is not compatible with the current version. Please address this issue.'; + } + else { + msg += `Would you like to install it and reboot?`; + } + + msg += `\n\nRequest message:\n${event.detail.msg}`; + + if(event.detail.target.installed == 'True') { + customAlert(msg); + return; + } + const res = await customConfirm(msg); + if(res) { + if(event.detail.target.installed == 'Disabled') { + const response = await api.fetchApi(`/customnode/toggle_active`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(event.detail.target) + }); + } + else { + await sleep(300); + app.ui.dialog.show(`Installing... '${event.detail.target.title}'`); + + const response = await api.fetchApi(`/customnode/install`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(event.detail.target) + }); + + if(response.status == 403) { + await handle403Response(response); + return false; + } + else if(response.status == 400) { + let msg = await res.text(); + show_message(msg); + return false; + } + } + + let response = await api.fetchApi("/manager/reboot"); + if(response.status == 403) { + await handle403Response(response); + return false; + } + + await sleep(300); + + app.ui.dialog.show(`Rebooting...`); + } +} + +api.addEventListener("cm-api-try-install-customnode", tryInstallCustomNode); diff --git a/custom_nodes/comfyui-manager/js/comfyui-gui-builder.js b/custom_nodes/comfyui-manager/js/comfyui-gui-builder.js new file mode 100644 index 00000000..e6de81c6 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/comfyui-gui-builder.js @@ -0,0 +1,227 @@ +import { $el } from "../../scripts/ui.js"; + +function normalizeContent(content) { + const tmp = document.createElement('div'); + if (typeof content === 'string') { + tmp.innerHTML = content; + return Array.from(tmp.childNodes); + } + if (content instanceof Node) { + return content; + } + return content; +} + +export function createSettingsCombo(label, content) { + const settingItem = $el("div.setting-item", {}, [ + $el("div.flex.flex-row.items-center.gap-2",[ + $el("div.form-label.flex.grow.items-center", [ + $el("span.text-muted", { textContent: label },) + ]), + $el("div.form-input.flex.justify-end", + [content] + ) + ] + ) + ]); + return settingItem; +} + +export function buildGuiFrame(dialogId, title, iconClass, content, owner) { + const dialog_mask = $el("div.p-dialog-mask.p-overlay-mask.p-overlay-mask-enter", { + parent: document.body, + style: { + position: "fixed", + height: "100%", + width: "100%", + left: "0px", + top: "0px", + display: "flex", + justifyContent: "center", + alignItems: "center", + pointerEvents: "auto", + zIndex: "1000" + }, + onclick: (e) => { + if (e.target === dialog_mask) { + owner.close(); + } + } + // data-pc-section="mask" + }); + + const header_actions = $el("div.p-dialog-header-actions", { + // [TODO] + // data-pc-section="headeractions" + } + ); + + const close_button = $el("button.p-button.p-component.p-button-icon-only.p-button-secondary.p-button-rounded.p-button-text.p-dialog-close-button", { + parent: header_actions, + type: "button", + ariaLabel: "Close", + onclick: () => owner.close(), + // "data-pc-name": "pcclosebutton", + // "data-p-disabled": "false", + // "data-p-severity": "secondary", + // "data-pc-group-section": "headericon", + // "data-pc-extend": "button", + // "data-pc-section": "root", + // [FIXME] Not sure how to do most of the SVG using $el + innerHTML: ' ' + } + ); + + const dialog_header = $el("div.p-dialog-header", + [ + $el("div", [ + $el("div", + { + id: "frame-title-container", + }, + [ + $el("h2.px-4", [ + $el(iconClass, { + style: { + "font-size": "1.25rem", + "margin-right": ".5rem" + } + }), + $el("span", { textContent: title }) + ]) + ] + ) + ]), + header_actions + ] + ); + + const contentFrame = $el("div.p-dialog-content", {}, normalizeContent(content)); + const manager_dialog = $el("div.p-dialog.p-component.global-dialog", { + id: dialogId, + parent: dialog_mask, + style: { + 'display': 'flex', + 'flex-direction': 'column', + 'pointer-events': 'auto', + 'margin': '0px', + }, + role: 'dialog', + ariaModal: 'true', + // [TODO] + // ariaLabbelledby: 'cm-title', + // maximized: 'false', + // data-pc-name: 'dialog', + // data-pc-section: 'root', + // data-pd-focustrap: 'true' + }, + [ dialog_header, contentFrame ] + ); + + const hidden_accessible = $el("span.p-hidden-accessible.p-hidden-focusable", { + parent: manager_dialog, + tabindex: "0", + role: "presentation", + ariaHidden: "true", + "data-p-hidden-accessible": "true", + "data-p-hidden-focusable": "true", + "data-pc-section": "firstfocusableelement" + }); + + return dialog_mask; +} + +export function buildGuiFrameCustomHeader(dialogId, customHeader, content, owner) { + const dialog_mask = $el("div.p-dialog-mask.p-overlay-mask.p-overlay-mask-enter", { + parent: document.body, + style: { + position: "fixed", + height: "100%", + width: "100%", + left: "0px", + top: "0px", + display: "flex", + justifyContent: "center", + alignItems: "center", + pointerEvents: "auto", + zIndex: "1000" + }, + onclick: (e) => { + if (e.target === dialog_mask) { + owner.close(); + } + } + // data-pc-section="mask" + }); + + const header_actions = $el("div.p-dialog-header-actions", { + // [TODO] + // data-pc-section="headeractions" + } + ); + + const close_button = $el("button.p-button.p-component.p-button-icon-only.p-button-secondary.p-button-rounded.p-button-text.p-dialog-close-button", { + parent: header_actions, + type: "button", + ariaLabel: "Close", + onclick: () => owner.close(), + // "data-pc-name": "pcclosebutton", + // "data-p-disabled": "false", + // "data-p-severity": "secondary", + // "data-pc-group-section": "headericon", + // "data-pc-extend": "button", + // "data-pc-section": "root", + // [FIXME] Not sure how to do most of the SVG using $el + innerHTML: ' ' + } + ); + + const _customHeader = normalizeContent(customHeader); + const dialog_header = $el("div.p-dialog-header", + [ + $el("div", [ + $el("div", + { + id: "frame-title-container", + }, + Array.isArray(_customHeader) ? _customHeader : [_customHeader] + ) + ]), + header_actions + ] + ); + + const contentFrame = $el("div.p-dialog-content", {}, normalizeContent(content)); + const manager_dialog = $el("div.p-dialog.p-component.global-dialog", { + id: dialogId, + parent: dialog_mask, + style: { + 'display': 'flex', + 'flex-direction': 'column', + 'pointer-events': 'auto', + 'margin': '0px', + }, + role: 'dialog', + ariaModal: 'true', + // [TODO] + // ariaLabbelledby: 'cm-title', + // maximized: 'false', + // data-pc-name: 'dialog', + // data-pc-section: 'root', + // data-pd-focustrap: 'true' + }, + [ dialog_header, contentFrame ] + ); + + const hidden_accessible = $el("span.p-hidden-accessible.p-hidden-focusable", { + parent: manager_dialog, + tabindex: "0", + role: "presentation", + ariaHidden: "true", + "data-p-hidden-accessible": "true", + "data-p-hidden-focusable": "true", + "data-pc-section": "firstfocusableelement" + }); + + return dialog_mask; +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/comfyui-manager.js b/custom_nodes/comfyui-manager/js/comfyui-manager.js new file mode 100644 index 00000000..bcf7e9e5 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/comfyui-manager.js @@ -0,0 +1,1738 @@ +import { api } from "../../scripts/api.js"; +import { app } from "../../scripts/app.js"; +import { $el, ComfyDialog } from "../../scripts/ui.js"; +import { + SUPPORTED_OUTPUT_NODE_TYPES, + ShareDialog, + ShareDialogChooser, + getPotentialOutputsAndOutputNodes, + showOpenArtShareDialog, + showShareDialog, + showYouMLShareDialog +} from "./comfyui-share-common.js"; +import { OpenArtShareDialog } from "./comfyui-share-openart.js"; +import { + free_models, install_pip, install_via_git_url, manager_instance, + rebootAPI, setManagerInstance, show_message, customAlert, customPrompt, + infoToast, showTerminal, setNeedRestart, handle403Response +} from "./common.js"; +import { ComponentBuilderDialog, getPureName, load_components, set_component_policy } from "./components-manager.js"; +import { CustomNodesManager } from "./custom-nodes-manager.js"; +import { ModelManager } from "./model-manager.js"; +import { SnapshotManager } from "./snapshot.js"; +import { buildGuiFrame, createSettingsCombo } from "./comfyui-gui-builder.js"; + +let manager_version = await getVersion(); + +var docStyle = document.createElement('style'); +docStyle.innerHTML = ` +.comfy-toast { + position: fixed; + bottom: 20px; + left: 50%; + transform: translateX(-50%); + background-color: rgba(0, 0, 0, 0.7); + color: white; + padding: 10px 20px; + border-radius: 5px; + z-index: 1000; + transition: opacity 0.5s; +} + +.comfy-toast-fadeout { + opacity: 0; +} + +#cm-manager-dialog { + width: 1000px; + height: auto; + box-sizing: content-box; + z-index: 1000; + overflow-y: auto; +} + +#cm-manager-dialog br { + margin-bottom: 1em; +} + +.cb-widget { + width: 400px; + height: 25px; + box-sizing: border-box; + z-index: 1000; + margin-top: 10px; + margin-bottom: 5px; +} + +.cb-widget-input { + width: 305px; + height: 25px; + box-sizing: border-box; +} +.cb-widget-input:disabled { + background-color: #444444; + color: white; +} + +.cb-widget-input-label { + width: 90px; + height: 25px; + box-sizing: border-box; + color: white; + text-align: right; + display: inline-block; + margin-right: 5px; +} + +.cm-menu-container { + padding : calc(var(--spacing)*2); + column-gap: 20px; + display: flex; + flex-wrap: wrap; + justify-content: center; + box-sizing: content-box; +} + +.cm-menu-column { + display: flex; + flex-direction: column; + flex: 1 1 auto; + width: 300px; + box-sizing: content-box; +} + +.cm-title { + background-color: black; + text-align: center; + height: 40px; + width: calc(100% - 10px); + font-weight: bold; + justify-content: center; + align-content: center; + vertical-align: middle; +} + +#custom-nodes-grid a { + color: #5555FF; + font-weight: bold; + text-decoration: none; +} + +#custom-nodes-grid a:hover { + color: #7777FF; + text-decoration: underline; +} + +#external-models-grid a { + color: #5555FF; + font-weight: bold; + text-decoration: none; +} + +#external-models-grid a:hover { + color: #7777FF; + text-decoration: underline; +} + +#alternatives-grid a { + color: #5555FF; + font-weight: bold; + text-decoration: none; +} + +#alternatives-grid a:hover { + color: #7777FF; + text-decoration: underline; +} + +.cm-notice-board { + width: auto; + height: 280px; + overflow: auto; + color: var(--input-text); + border: 1px solid var(--descrip-text); + padding: 5px 10px; + overflow-x: hidden; + box-sizing: content-box; +} + +.cm-notice-board > ul { + display: block; + list-style-type: disc; + margin-block-start: 1em; + margin-block-end: 1em; + margin-inline-start: 0px; + margin-inline-end: 0px; + padding-inline-start: 40px; +} + +.cm-conflicted-nodes-text { + background-color: #CCCC55 !important; + color: #AA3333 !important; + font-size: 10px; + border-radius: 5px; + padding: 10px; +} + +.cm-warn-note { + background-color: #101010 !important; + color: #FF3800 !important; + font-size: 13px; + border-radius: 5px; + padding: 10px; + overflow-x: hidden; + overflow: auto; +} + +.cm-info-note { + background-color: #101010 !important; + color: #FF3800 !important; + font-size: 13px; + border-radius: 5px; + padding: 10px; + overflow-x: hidden; + overflow: auto; +} +`; + +function is_legacy_front() { + let compareVersion = '1.2.49'; + try { + const frontendVersion = window['__COMFYUI_FRONTEND_VERSION__']; + if (typeof frontendVersion !== 'string') { + return false; + } + + function parseVersion(versionString) { + const parts = versionString.split('.').map(Number); + return parts.length === 3 && parts.every(part => !isNaN(part)) ? parts : null; + } + + const currentVersion = parseVersion(frontendVersion); + const comparisonVersion = parseVersion(compareVersion); + + if (!currentVersion || !comparisonVersion) { + return false; + } + + for (let i = 0; i < 3; i++) { + if (currentVersion[i] > comparisonVersion[i]) { + return false; + } else if (currentVersion[i] < comparisonVersion[i]) { + return true; + } + } + + return false; + } catch { + return true; + } +} + +document.head.appendChild(docStyle); + +var update_comfyui_button = null; +var switch_comfyui_button = null; +var update_all_button = null; +var restart_stop_button = null; +var update_policy_combo = null; + +let share_option = 'all'; +var is_updating = false; + + +// copied style from https://github.com/pythongosssss/ComfyUI-Custom-Scripts +const style = ` +#workflowgallery-button { + height: 50px; + padding: 0px !important; +} +#cm-nodeinfo-button { + +} +#cm-manual-button { + +} + +.cm-button { + width: auto; + position: relative; + overflow: hidden; + background-color: var(--comfy-menu-secondary-bg); + border-color: var(--border-color); + color: var(--input-text); +} + +.cm-button:hover { + filter: brightness(125%); +} + +.cm-button-red { + background-color: #500000 !important; + border-color: #88181b !important; + color: white !important; +} + +.cm-button-red:hover { + background-color: #88181b !important; +} + +.cm-button-orange { + font-weight: bold; + background-color: orange !important; + color: black !important; +} + +.cm-experimental-button { + width: 100%; +} + +.cm-experimental { + border: 1px solid #555; + border-radius: 5px; + padding: 10px; + align-items: center; + text-align: center; + justify-content: center; + box-sizing: border-box; +} + +.cm-experimental-legend { + margin-top: -20px; + margin-left: 50%; + width:auto; + height:20px; + font-size: 13px; + font-weight: bold; + background-color: #990000; + color: #CCFFFF; + border-radius: 5px; + text-align: center; + transform: translateX(-50%); + display: block; +} + +.cm-menu-combo { + cursor: pointer; + padding: 0.5em 0.5em; + border: 1px solid var(--border-color); + border-radius: 6px; + background: var(--comfy-menu-secondary-bg); +} + +.cm-menu-combo:hover { + filter: brightness(125%); +} + +.cm-small-button { + width: 120px; + height: 30px; + position: relative; + overflow: hidden; + box-sizing: border-box; + font-size: 17px !important; +} + +#cm-install-customnodes-button { + width: 200px; + height: 30px; + position: relative; + overflow: hidden; + box-sizing: border-box; + font-size: 17px !important; +} + +.cm-search-filter { + width: 200px; + height: 30px !important; + position: relative; + overflow: hidden; + box-sizing: border-box; +} + +.cb-node-label { + width: 400px; + height:28px; + color: black; + background-color: #777777; + font-size: 18px; + text-align: center; + font-weight: bold; +} + +#cm-close-button { + width: calc(100% - 65px); + bottom: 10px; + position: absolute; + overflow: hidden; +} + +#cm-save-button { + width: calc(100% - 65px); + bottom:40px; + position: absolute; + overflow: hidden; +} +#cm-save-button:disabled { + background-color: #444444; +} + +.pysssss-workflow-arrow-2 { + position: absolute; + top: 0; + bottom: 0; + right: 0; + font-size: 12px; + display: flex; + align-items: center; + width: 24px; + justify-content: center; + background: rgba(255,255,255,0.1); + content: "β–Ό"; +} +.pysssss-workflow-arrow-2:after { + content: "β–Ό"; + } + .pysssss-workflow-arrow-2:hover { + filter: brightness(1.6); + background-color: var(--comfy-menu-bg); + } +.pysssss-workflow-popup-2 ~ .litecontextmenu { + transform: scale(1.3); +} +#workflowgallery-button-menu { + z-index: 10000000000 !important; +} +#cm-manual-button-menu { + z-index: 10000000000 !important; +} +`; + +async function init_share_option() { + api.fetchApi('/manager/share_option') + .then(response => response.text()) + .then(data => { + share_option = data || 'all'; + }); +} + +async function init_notice(notice) { + api.fetchApi('/manager/notice') + .then(response => response.text()) + .then(data => { + notice.innerHTML = data; + }) +} + +await init_share_option(); + + +async function set_inprogress_mode() { + update_comfyui_button.disabled = true; + update_comfyui_button.style.backgroundColor = "gray"; + + update_all_button.disabled = true; + update_all_button.style.backgroundColor = "gray"; + + switch_comfyui_button.disabled = true; + switch_comfyui_button.style.backgroundColor = "gray"; + + restart_stop_button.innerText = 'Stop'; +} + + +async function reset_action_buttons() { + const isElectron = 'electronAPI' in window; + + if(isElectron) { + update_all_button.innerText = "Update All Custom Nodes"; + } + else { + update_all_button.innerText = "Update All"; + } + + update_comfyui_button.innerText = "Update ComfyUI"; + switch_comfyui_button.innerText = "Switch ComfyUI"; + restart_stop_button.innerText = 'Restart'; + + update_comfyui_button.disabled = false; + update_all_button.disabled = false; + switch_comfyui_button.disabled = false; + + update_comfyui_button.style.backgroundColor = ""; + update_all_button.style.backgroundColor = ""; + switch_comfyui_button.style.backgroundColor = ""; +} + +async function updateComfyUI() { + let prev_text = update_comfyui_button.innerText; + update_comfyui_button.innerText = "Updating ComfyUI..."; + + set_inprogress_mode(); + + const response = await api.fetchApi('/manager/queue/update_comfyui'); + + showTerminal(); + + is_updating = true; + await api.fetchApi('/manager/queue/start'); +} + +function showVersionSelectorDialog(versions, current, onSelect) { + const dialog = new ComfyDialog(); + dialog.element.style.zIndex = 1100; + dialog.element.style.width = "300px"; + dialog.element.style.padding = "0"; + dialog.element.style.backgroundColor = "#2a2a2a"; + dialog.element.style.border = "1px solid #3a3a3a"; + dialog.element.style.borderRadius = "8px"; + dialog.element.style.boxSizing = "border-box"; + dialog.element.style.overflow = "hidden"; + + const contentStyle = { + width: "300px", + display: "flex", + flexDirection: "column", + alignItems: "center", + padding: "20px", + boxSizing: "border-box", + gap: "15px" + }; + + let selectedVersion = versions[0]; + + const versionList = $el("select", { + multiple: true, + size: Math.min(10, versions.length), + style: { + width: "260px", + height: "auto", + backgroundColor: "#383838", + color: "#ffffff", + border: "1px solid #4a4a4a", + borderRadius: "4px", + padding: "5px", + boxSizing: "border-box" + } + }, + versions.map((v, index) => $el("option", { + value: v, + textContent: v, + selected: v === current + })) + ); + + versionList.addEventListener('change', (e) => { + selectedVersion = e.target.value; + Array.from(e.target.options).forEach(opt => { + opt.selected = opt.value === selectedVersion; + }); + }); + + const content = $el("div", { + style: contentStyle + }, [ + $el("h3", { + textContent: "Select Version", + style: { + color: "#ffffff", + backgroundColor: "#1a1a1a", + padding: "10px 15px", + margin: "0 0 10px 0", + width: "260px", + textAlign: "center", + borderRadius: "4px", + boxSizing: "border-box", + whiteSpace: "nowrap", + overflow: "hidden", + textOverflow: "ellipsis" + } + }), + versionList, + $el("div", { + style: { + display: "flex", + justifyContent: "space-between", + width: "260px", + gap: "10px" + } + }, [ + $el("button", { + textContent: "Cancel", + onclick: () => dialog.close(), + style: { + flex: "1", + padding: "8px", + backgroundColor: "#4a4a4a", + color: "#ffffff", + border: "none", + borderRadius: "4px", + cursor: "pointer", + whiteSpace: "nowrap", + overflow: "hidden", + textOverflow: "ellipsis" + } + }), + $el("button", { + textContent: "Select", + onclick: () => { + if (selectedVersion) { + onSelect(selectedVersion); + dialog.close(); + } else { + customAlert("Please select a version."); + } + }, + style: { + flex: "1", + padding: "8px", + backgroundColor: "#4CAF50", + color: "#ffffff", + border: "none", + borderRadius: "4px", + cursor: "pointer", + whiteSpace: "nowrap", + overflow: "hidden", + textOverflow: "ellipsis" + } + }), + ]) + ]); + + dialog.show(content); +} + +async function switchComfyUI() { + switch_comfyui_button.disabled = true; + switch_comfyui_button.style.backgroundColor = "gray"; + + let res = await api.fetchApi(`/comfyui_manager/comfyui_versions`, { cache: "no-store" }); + + switch_comfyui_button.disabled = false; + switch_comfyui_button.style.backgroundColor = ""; + + if(res.status == 200) { + let obj = await res.json(); + + let versions = []; + let default_version; + + for(let v of obj.versions) { + default_version = v; + versions.push(v); + } + + showVersionSelectorDialog(versions, obj.current, async (selected_version) => { + if(selected_version == 'nightly') { + update_policy_combo.value = 'nightly-comfyui'; + api.fetchApi('/manager/policy/update?value=nightly-comfyui'); + } + else { + update_policy_combo.value = 'stable-comfyui'; + api.fetchApi('/manager/policy/update?value=stable-comfyui'); + } + + let response = await api.fetchApi(`/comfyui_manager/comfyui_switch_version?ver=${selected_version}`, { cache: "no-store" }); + if (response.status == 200) { + infoToast(`ComfyUI version is switched to ${selected_version}`); + } + else { + customAlert('Failed to switch ComfyUI version.'); + } + }); + } + else { + customAlert('Failed to fetch ComfyUI versions.'); + } +} + +async function onQueueStatus(event) { + const isElectron = 'electronAPI' in window; + + if(event.detail.status == 'in_progress') { + set_inprogress_mode(); + update_all_button.innerText = `in progress.. (${event.detail.done_count}/${event.detail.total_count})`; + } + else if(event.detail.status == 'done') { + reset_action_buttons(); + + if(!is_updating) { + return; + } + + is_updating = false; + + let success_list = []; + let failed_list = []; + let comfyui_state = null; + + for(let k in event.detail.nodepack_result){ + let v = event.detail.nodepack_result[k]; + + if(k == 'comfyui') { + comfyui_state = v; + continue; + } + + if(v.msg == 'success') { + success_list.push(k); + } + else if(v.msg != 'skip') + failed_list.push(k); + } + + let msg = ""; + + if(success_list.length == 0 && comfyui_state.startsWith('skip')) { + if(failed_list.length == 0) { + msg += "You are already up to date."; + } + } + else { + msg = "To apply the updates, you need to ComfyUI.
"; + + if(comfyui_state == 'success-nightly') { + msg += "ComfyUI has been updated to latest nightly version.

"; + infoToast("ComfyUI has been updated to the latest nightly version."); + } + else if(comfyui_state.startsWith('success-stable')) { + const ver = comfyui_state.split("-").pop(); + msg += `ComfyUI has been updated to ${ver}.

`; + infoToast(`ComfyUI has been updated to ${ver}`); + } + else if(comfyui_state == 'skip') { + msg += "ComfyUI is already up to date.

" + } + else if(comfyui_state != null) { + msg += "Failed to update ComfyUI.

" + } + + if(success_list.length > 0) { + msg += "The following custom nodes have been updated:
    "; + for(let x in success_list) { + let k = success_list[x]; + let url = event.detail.nodepack_result[k].url; + let title = event.detail.nodepack_result[k].title; + if(url) { + msg += `
  • ${title}
    • `; + } + else { + msg += `
  • ${k}
    • `; + } + } + msg += "
"; + } + + setNeedRestart(true); + } + + if(failed_list.length > 0) { + msg += '
The update for the following custom nodes has failed:
    '; + for(let x in failed_list) { + let k = failed_list[x]; + let url = event.detail.nodepack_result[k].url; + let title = event.detail.nodepack_result[k].title; + if(url) { + msg += `
  • ${title}
    • `; + } + else { + msg += `
  • ${k}
    • `; + } + } + + msg += '
' + } + + show_message(msg); + + const rebootButton = document.getElementById('cm-reboot-button5'); + rebootButton?.addEventListener("click", + async function() { + if(await rebootAPI()) { + manager_instance.close(); + } + }); + } +} + +api.addEventListener("cm-queue-status", onQueueStatus); + + +async function updateAll(update_comfyui) { + update_all_button.innerText = "Updating..."; + + set_inprogress_mode(); + + var mode = manager_instance.datasrc_combo.value; + + showTerminal(); + + if(update_comfyui) { + update_all_button.innerText = "Updating ComfyUI..."; + await api.fetchApi('/manager/queue/update_comfyui'); + } + + const response = await api.fetchApi(`/manager/queue/update_all?mode=${mode}`); + + if (response.status == 403) { + await handle403Response(response); + reset_action_buttons(); + } + else if (response.status == 401) { + customAlert('Another task is already in progress. Please stop the ongoing task first.'); + reset_action_buttons(); + } + else if(response.status == 200) { + is_updating = true; + await api.fetchApi('/manager/queue/start'); + } +} + +function newDOMTokenList(initialTokens) { + const tmp = document.createElement(`div`); + + const classList = tmp.classList; + if (initialTokens) { + initialTokens.forEach(token => { + classList.add(token); + }); + } + + return classList; + } + +/** + * Check whether the node is a potential output node (img, gif or video output) + */ +const isOutputNode = (node) => { + return SUPPORTED_OUTPUT_NODE_TYPES.includes(node.type); +} + +function restartOrStop() { + if(restart_stop_button.innerText == 'Restart'){ + rebootAPI(); + } + else { + api.fetchApi('/manager/queue/reset'); + infoToast('Cancel', 'Remaining tasks will stop after completing the current task.'); + } +} + +// ----------- +class ManagerMenuDialog extends ComfyDialog { + createControlsMid() { + let self = this; + const isElectron = 'electronAPI' in window; + + update_comfyui_button = + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Update ComfyUI", + style: { + display: isElectron ? 'none' : 'block' + }, + onclick: + () => updateComfyUI() + }); + + switch_comfyui_button = + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Switch ComfyUI", + style: { + display: isElectron ? 'none' : 'block' + }, + onclick: + () => switchComfyUI() + }); + + restart_stop_button = + $el("button.p-button.p-component.cm-button-red", { + type: "button", + textContent: "Restart", + onclick: () => restartOrStop() + }); + + if(isElectron) { + update_all_button = + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Update All Custom Nodes", + onclick: + () => updateAll(false) + }); + } + else { + update_all_button = + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Update All", + onclick: + () => updateAll(true) + }); + } + + const res = + [ + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Custom Nodes Manager", + onclick: + () => { + if(!CustomNodesManager.instance) { + CustomNodesManager.instance = new CustomNodesManager(app, self); + } + CustomNodesManager.instance.show(CustomNodesManager.ShowMode.NORMAL); + } + }), + + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Install Missing Custom Nodes", + onclick: + () => { + if(!CustomNodesManager.instance) { + CustomNodesManager.instance = new CustomNodesManager(app, self); + } + CustomNodesManager.instance.show(CustomNodesManager.ShowMode.MISSING); + } + }), + + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Custom Nodes In Workflow", + onclick: + () => { + if(!CustomNodesManager.instance) { + CustomNodesManager.instance = new CustomNodesManager(app, self); + } + CustomNodesManager.instance.show(CustomNodesManager.ShowMode.IN_WORKFLOW); + } + }), + + $el("div", {}, []), + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Model Manager", + onclick: + () => { + if(!ModelManager.instance) { + ModelManager.instance = new ModelManager(app, self); + } + ModelManager.instance.show(); + } + }), + + $el("button.p-button.p-component.cm-button", { + type: "button", + textContent: "Install via Git URL", + onclick: async () => { + var url = await customPrompt("Please enter the URL of the Git repository to install", ""); + + if (url !== null) { + install_via_git_url(url, self); + } + } + }), + + $el("div", {}, []), + update_all_button, + update_comfyui_button, + switch_comfyui_button, + // fetch_updates_button, + + $el("div", {}, []), + restart_stop_button, + ]; + + return res; + } + + createControlsLeft() { + const isElectron = 'electronAPI' in window; + + let self = this; + + // db mode + + this.datasrc_combo = document.createElement("select"); + this.datasrc_combo.setAttribute("title", "Configure where to retrieve node/model information. If set to 'local,' the channel is ignored, and if set to 'channel (remote),' it fetches the latest information each time the list is opened."); + this.datasrc_combo.className = "cm-menu-combo p-select p-component p-inputwrapper p-inputwrapper-filled "; + this.datasrc_combo.appendChild($el('option', { value: 'cache', text: 'Channel (1day cache)' }, [])); + this.datasrc_combo.appendChild($el('option', { value: 'local', text: 'Local' }, [])); + this.datasrc_combo.appendChild($el('option', { value: 'remote', text: 'Channel (remote)' }, [])); + + api.fetchApi('/manager/db_mode') + .then(response => response.text()) + .then(data => { this.datasrc_combo.value = data; }); + + this.datasrc_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/db_mode?value=${event.target.value}`); + }); + + const dbRetrievalSetttingItem = createSettingsCombo("DB", this.datasrc_combo); + + // preview method + let preview_combo = document.createElement("select"); + preview_combo.setAttribute("title", "Configure how latent variables will be decoded during preview in the sampling process."); + preview_combo.className = "cm-menu-combo p-select p-component p-inputwrapper p-inputwrapper-filled"; + + // Loading state to prevent flash of enabled state + preview_combo.appendChild($el('option', { value: '', text: 'Loading...', disabled: true }, [])); + preview_combo.appendChild($el('option', { value: 'auto', text: 'Auto' }, [])); + preview_combo.appendChild($el('option', { value: 'taesd', text: 'TAESD (slow)' }, [])); + preview_combo.appendChild($el('option', { value: 'latent2rgb', text: 'Latent2RGB (fast)' }, [])); + preview_combo.appendChild($el('option', { value: 'none', text: 'None (very fast)' }, [])); + + // Start disabled to prevent flash + preview_combo.disabled = true; + preview_combo.value = ''; + + // Fetch current state + api.fetchApi('/manager/preview_method') + .then(response => response.text()) + .then(data => { + // Remove loading option + preview_combo.querySelector('option[value=""]')?.remove(); + + if (data === "DISABLED") { + // ComfyUI per-queue preview feature is active + preview_combo.disabled = true; + preview_combo.value = 'auto'; + + // Accessibility attributes + preview_combo.setAttribute("aria-disabled", "true"); + preview_combo.setAttribute("aria-label", + "Preview method setting (disabled - managed by ComfyUI). " + + "Use Settings > Execution > Live preview method instead." + ); + + // Tooltip for mouse users + preview_combo.setAttribute("title", + "This feature is now provided natively by ComfyUI. " + + "Please use 'Settings > Execution > Live preview method' instead." + ); + + // Visual feedback + preview_combo.style.opacity = '0.6'; + preview_combo.style.cursor = 'not-allowed'; + } else { + // Manager feature is active + preview_combo.disabled = false; + preview_combo.value = data; + + // Accessibility for enabled state + preview_combo.setAttribute("aria-label", + "Preview method setting. Select how latent variables are decoded during preview." + ); + } + }) + .catch(error => { + console.error('[ComfyUI-Manager] Failed to fetch preview method status:', error); + // Error recovery: fallback to enabled + preview_combo.querySelector('option[value=""]')?.remove(); + preview_combo.disabled = false; + preview_combo.value = 'auto'; + }); + + preview_combo.addEventListener('change', function (event) { + // Ignore if disabled + if (preview_combo.disabled) { + event.preventDefault(); + return; + } + + // Normal operation + api.fetchApi(`/manager/preview_method?value=${event.target.value}`) + .then(response => { + if (response.status === 403) { + // Feature transitioned to native + alert( + 'This feature is now provided natively by ComfyUI.\n' + + 'Please use \'Settings > Execution > Live preview method\' instead.' + ); + preview_combo.disabled = true; + preview_combo.style.opacity = '0.6'; + preview_combo.style.cursor = 'not-allowed'; + + // Update aria attributes + preview_combo.setAttribute("aria-disabled", "true"); + preview_combo.setAttribute("aria-label", + "Preview method setting (disabled - managed by ComfyUI). " + + "Use Settings > Execution > Live preview method instead." + ); + } + }) + .catch(error => { + console.error('[ComfyUI-Manager] Preview method update failed:', error); + }); + }); + + const previewSetttingItem = createSettingsCombo("Preview method", preview_combo); + + // channel + let channel_combo = document.createElement("select"); + channel_combo.setAttribute("title", "Configure the channel for retrieving data from the Custom Node list (including missing nodes) or the Model list."); + channel_combo.className = "cm-menu-combo p-select p-component p-inputwrapper p-inputwrapper-filled"; + api.fetchApi('/manager/channel_url_list') + .then(response => response.json()) + .then(async data => { + try { + let urls = data.list; + for (let i in urls) { + if (urls[i] != '') { + let name_url = urls[i].split('::'); + channel_combo.appendChild($el('option', { value: name_url[0], text: `${name_url[0]}` }, [])); + } + } + + channel_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/channel_url_list?value=${event.target.value}`); + }); + + channel_combo.value = data.selected; + } + catch (exception) { + + } + }); + + const channelSetttingItem = createSettingsCombo("Channel", channel_combo); + + + // share + let share_combo = document.createElement("select"); + share_combo.setAttribute("title", "Hide the share button in the main menu or set the default action upon clicking it. Additionally, configure the default share site when sharing via the context menu's share button."); + share_combo.className = "cm-menu-combo p-select p-component p-inputwrapper p-inputwrapper-filled"; + const share_options = [ + ['none', 'None'], + ['openart', 'OpenArt AI'], + ['youml', 'YouML'], + ['matrix', 'Matrix Server'], + ['comfyworkflows', 'ComfyWorkflows'], + ['copus', 'Copus'], + ['all', 'All'], + ]; + for (const option of share_options) { + share_combo.appendChild($el('option', { value: option[0], text: `${option[1]}` }, [])); + } + + api.fetchApi('/manager/share_option') + .then(response => response.text()) + .then(data => { + share_combo.value = data || 'all'; + share_option = data || 'all'; + }); + + share_combo.addEventListener('change', function (event) { + const value = event.target.value; + share_option = value; + api.fetchApi(`/manager/share_option?value=${value}`); + const shareButton = document.getElementById("shareButton"); + if (value === 'none') { + shareButton.style.display = "none"; + } else { + shareButton.style.display = "inline-block"; + } + }); + + const shareSetttingItem = createSettingsCombo("Share", share_combo); + + let component_policy_combo = document.createElement("select"); + component_policy_combo.setAttribute("title", "When loading the workflow, configure which version of the component to use."); + component_policy_combo.className = "cm-menu-combo p-select p-component p-inputwrapper p-inputwrapper-filled"; + component_policy_combo.appendChild($el('option', { value: 'workflow', text: 'Use workflow version' }, [])); + component_policy_combo.appendChild($el('option', { value: 'higher', text: 'Use higher version' }, [])); + component_policy_combo.appendChild($el('option', { value: 'mine', text: 'Use my version' }, [])); + api.fetchApi('/manager/policy/component') + .then(response => response.text()) + .then(data => { + component_policy_combo.value = data; + set_component_policy(data); + }); + + component_policy_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/policy/component?value=${event.target.value}`); + set_component_policy(event.target.value); + }); + + const componentSetttingItem = createSettingsCombo("Component", component_policy_combo); + + update_policy_combo = document.createElement("select"); + + update_policy_combo.setAttribute("title", "Sets the policy to be applied when performing an update."); + update_policy_combo.className = "cm-menu-combo p-select p-component p-inputwrapper p-inputwrapper-filled"; + update_policy_combo.appendChild($el('option', { value: 'stable-comfyui', text: 'ComfyUI Stable Version' }, [])); + update_policy_combo.appendChild($el('option', { value: 'nightly-comfyui', text: 'ComfyUI Nightly Version' }, [])); + api.fetchApi('/manager/policy/update') + .then(response => response.text()) + .then(data => { + update_policy_combo.value = data; + }); + + update_policy_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/policy/update?value=${event.target.value}`); + }); + + const updateSetttingItem = createSettingsCombo("Update", update_policy_combo); + + if(isElectron) + updateSetttingItem.style.display = 'none'; + + return [ + dbRetrievalSetttingItem, + channelSetttingItem, + previewSetttingItem, + shareSetttingItem, + componentSetttingItem, + updateSetttingItem, + //[TODO] replace mt-2 with wrapper div with flex column gap + $el("filedset.cm-experimental.mt-auto", {}, [ + $el("legend.cm-experimental-legend", {}, ["EXPERIMENTAL"]), + $el("button.p-button.p-component.cm-button.cm-experimental-button", { + type: "button", + textContent: "Snapshot Manager", + onclick: + () => { + if(!SnapshotManager.instance) + SnapshotManager.instance = new SnapshotManager(app, self); + SnapshotManager.instance.show(); + } + }), + $el("button.p-button.p-component.cm-button.cm-experimental-button.mt-2", { + type: "button", + textContent: "Install PIP packages", + onclick: + async () => { + var url = await customPrompt("Please enumerate the pip packages to be installed.\n\nExample: insightface opencv-python-headless>=4.1.1\n", ""); + + if (url !== null) { + install_pip(url, self); + } + } + }) + ]), + ]; + } + + createControlsRight() { + const elts = [ + $el("button.p-button.p-component.cm-button", { + id: 'cm-manual-button', + type: "button", + textContent: "Community Manual", + onclick: () => { window.open("https://blenderneko.github.io/ComfyUI-docs/", "comfyui-community-manual"); } + }, [ + $el("div.pysssss-workflow-arrow-2", { + id: `cm-manual-button-arrow`, + onclick: (e) => { + e.preventDefault(); + e.stopPropagation(); + + LiteGraph.closeAllContextMenus(); + const menu = new LiteGraph.ContextMenu( + [ + { + title: "ComfyUI Docs", + callback: () => { window.open("https://docs.comfy.org/", "comfyui-official-manual"); }, + }, + { + title: "Comfy Custom Node How To", + callback: () => { window.open("https://github.com/chrisgoringe/Comfy-Custom-Node-How-To/wiki/aaa_index", "comfyui-community-manual1"); }, + }, + { + title: "ComfyUI Guide To Making Custom Nodes", + callback: () => { window.open("https://github.com/Suzie1/ComfyUI_Guide_To_Making_Custom_Nodes/wiki", "comfyui-community-manual2"); }, + }, + { + title: "ComfyUI Examples", + callback: () => { window.open("https://comfyanonymous.github.io/ComfyUI_examples", "comfyui-community-manual3"); }, + }, + { + title: "Close", + callback: () => { + LiteGraph.closeAllContextMenus(); + }, + } + ], + { + event: e, + scale: 1.3, + }, + window + ); + // set the id so that we can override the context menu's z-index to be above the comfyui manager menu + menu.root.id = "cm-manual-button-menu"; + menu.root.classList.add("pysssss-workflow-popup-2"); + }, + }) + ]), + + $el("button.p-button.p-component.cm-button", { + id: 'workflowgallery-button', + type: "button", + style: { + // ...(localStorage.getItem("wg_last_visited") ? {height: '50px'} : {}) + }, + onclick: (e) => { + const last_visited_site = localStorage.getItem("wg_last_visited") + if (!!last_visited_site) { + window.open(last_visited_site, last_visited_site); + } else { + this.handleWorkflowGalleryButtonClick(e) + } + }, + }, [ + $el("p", { + textContent: 'Workflow Gallery', + style: { + 'text-align': 'center', + 'color': 'var(--input-text)', + 'font-size': '18px', + 'margin': 0, + 'padding': 0, + } + }, [ + $el("p", { + id: 'workflowgallery-button-last-visited-label', + textContent: `(${localStorage.getItem("wg_last_visited") ? localStorage.getItem("wg_last_visited").split('/')[2] : 'none selected'})`, + style: { + 'text-align': 'center', + 'color': 'var(--input-text)', + 'font-size': '12px', + 'margin': 0, + 'padding': 0, + } + }) + ]), + $el("div.pysssss-workflow-arrow-2", { + id: `comfyworkflows-button-arrow`, + onclick: this.handleWorkflowGalleryButtonClick + }) + ]), + + $el("button.p-button.p-component.cm-button", { + id: 'cm-nodeinfo-button', + type: "button", + textContent: "Nodes Info", + onclick: () => { window.open("https://ltdrdata.github.io/", "comfyui-node-info"); } + }), + ]; + + var textarea = document.createElement("div"); + textarea.className = "cm-notice-board"; + elts.push(textarea); + + init_notice(textarea); + + return elts; + } + + constructor() { + super(); + + const content = $el("div.cm-menu-container", + [ + $el("div.cm-menu-column.gap-2", [...this.createControlsLeft()]), + $el("div.cm-menu-column.gap-2", [...this.createControlsMid()]), + $el("div.cm-menu-column.gap-2", [...this.createControlsRight()]) + ] + ); + + const frame = buildGuiFrame( + 'cm-manager-dialog', // dialog id + `ComfyUI Manager ${manager_version}`, // dialog title + "i.mdi.mdi-puzzle", // dialog icon class to show before title + content, // dialog content element + this + ); // send this so we can attach close functions + + this.element = frame; + } + + get isVisible() { + return this.element?.style?.display !== "none"; + } + + show() { + this.element.style.display = "flex"; + } + + toggleVisibility() { + if (this.isVisible) { + this.close(); + } else { + this.show(); + } + } + + handleWorkflowGalleryButtonClick(e) { + e.preventDefault(); + e.stopPropagation(); + LiteGraph.closeAllContextMenus(); + + // Modify the style of the button so that the UI can indicate the last + // visited site right away. + const modifyButtonStyle = (url) => { + const workflowGalleryButton = document.getElementById('workflowgallery-button'); + workflowGalleryButton.style.height = '50px'; + const lastVisitedLabel = document.getElementById('workflowgallery-button-last-visited-label'); + lastVisitedLabel.textContent = `(${url.split('/')[2]})`; + } + + const menu = new LiteGraph.ContextMenu( + [ + { + title: "Share your art", + callback: () => { + if (share_option === 'openart') { + showOpenArtShareDialog(); + return; + } else if (share_option === 'matrix' || share_option === 'comfyworkflows') { + showShareDialog(share_option); + return; + } else if (share_option === 'youml') { + showYouMLShareDialog(); + return; + } + + if (!ShareDialogChooser.instance) { + ShareDialogChooser.instance = new ShareDialogChooser(); + } + ShareDialogChooser.instance.show(); + }, + }, + { + title: "Open 'openart.ai'", + callback: () => { + const url = "https://openart.ai/workflows/dev"; + localStorage.setItem("wg_last_visited", url); + window.open(url, url); + modifyButtonStyle(url); + }, + }, + { + title: "Open 'youml.com'", + callback: () => { + const url = "https://youml.com/?from=comfyui-share"; + localStorage.setItem("wg_last_visited", url); + window.open(url, url); + modifyButtonStyle(url); + }, + }, + { + title: "Open 'comfyworkflows.com'", + callback: () => { + const url = "https://comfyworkflows.com/"; + localStorage.setItem("wg_last_visited", url); + window.open(url, url); + modifyButtonStyle(url); + }, + }, + { + title: "Open 'esheep'", + callback: () => { + const url = "https://www.esheep.com"; + localStorage.setItem("wg_last_visited", url); + window.open(url, url); + modifyButtonStyle(url); + }, + }, + { + title: "Open 'Copus.io'", + callback: () => { + const url = "https://www.copus.io"; + localStorage.setItem("wg_last_visited", url); + window.open(url, url); + modifyButtonStyle(url); + }, + }, + { + title: "Close", + callback: () => { + LiteGraph.closeAllContextMenus(); + }, + } + ], + { + event: e, + scale: 1.3, + }, + window + ); + // set the id so that we can override the context menu's z-index to be above the comfyui manager menu + menu.root.id = "workflowgallery-button-menu"; + menu.root.classList.add("pysssss-workflow-popup-2"); + } +} + +async function getVersion() { + let version = await api.fetchApi(`/manager/version`); + return await version.text(); +} + +app.registerExtension({ + name: "Comfy.ManagerMenu", + + aboutPageBadges: [ + { + label: `ComfyUI-Manager ${manager_version}`, + url: 'https://github.com/ltdrdata/ComfyUI-Manager', + icon: 'pi pi-th-large' + } + ], + + commands: [ + { + id: "Comfy.Manager.Menu.ToggleVisibility", + label: "Toggle Manager Menu Visibility", + icon: "mdi mdi-puzzle", + function: () => { + if (!manager_instance) { + setManagerInstance(new ManagerMenuDialog()); + manager_instance.show(); + } else { + manager_instance.toggleVisibility(); + } + }, + }, + { + id: "Comfy.Manager.CustomNodesManager.ToggleVisibility", + label: "Toggle Custom Nodes Manager Visibility", + icon: "pi pi-server", + function: () => { + if (CustomNodesManager.instance?.isVisible) { + CustomNodesManager.instance.close(); + return; + } + + if (!manager_instance) { + setManagerInstance(new ManagerMenuDialog()); + } + if (!CustomNodesManager.instance) { + CustomNodesManager.instance = new CustomNodesManager(app, self); + } + CustomNodesManager.instance.show(CustomNodesManager.ShowMode.NORMAL); + }, + } + ], + + init() { + $el("style", { + textContent: style, + parent: document.head, + }); + }, + async setup() { + let orig_clear = app.graph.clear; + app.graph.clear = function () { + orig_clear.call(app.graph); + load_components(); + }; + + load_components(); + + // Fetch and show startup alerts (critical errors like outdated ComfyUI) + // Poll until extensionManager.toast is ready (set in Vue onMounted) + const showStartupAlerts = async () => { + let toastWaitCount = 0; + const waitForToast = () => { + if (window['app']?.extensionManager?.toast) { + fetch('/manager/startup_alerts') + .then(response => response.ok ? response.json() : []) + .then(alerts => { + for (const alert of alerts) { + customAlert(alert.message); + } + }) + .catch(e => console.warn('[ComfyUI-Manager] Failed to fetch startup alerts:', e)); + } else if (toastWaitCount < 300) { // Max 30 seconds (300 * 100ms) + toastWaitCount++; + setTimeout(waitForToast, 100); + } else { + console.warn('[ComfyUI-Manager] Timeout waiting for toast. Startup alerts skipped.'); + } + }; + waitForToast(); + }; + showStartupAlerts(); + + const menu = document.querySelector(".comfy-menu"); + const separator = document.createElement("hr"); + + separator.style.margin = "20px 0"; + separator.style.width = "100%"; + menu.append(separator); + + try { + // new style Manager buttons + // unload models button into new style Manager button + let cmGroup = new (await import("../../scripts/ui/components/buttonGroup.js")).ComfyButtonGroup( + new(await import("../../scripts/ui/components/button.js")).ComfyButton({ + icon: "puzzle", + action: () => { + if(!manager_instance) + setManagerInstance(new ManagerMenuDialog()); + manager_instance.show(); + }, + tooltip: "ComfyUI Manager", + content: "Manager", + classList: "comfyui-button comfyui-menu-mobile-collapse primary" + }).element, + new(await import("../../scripts/ui/components/button.js")).ComfyButton({ + icon: "star", + action: () => { + if(!manager_instance) + setManagerInstance(new ManagerMenuDialog()); + + if(!CustomNodesManager.instance) { + CustomNodesManager.instance = new CustomNodesManager(app, self); + } + CustomNodesManager.instance.show(CustomNodesManager.ShowMode.FAVORITES); + }, + tooltip: "Show favorite custom node list" + }).element, + new(await import("../../scripts/ui/components/button.js")).ComfyButton({ + icon: "vacuum-outline", + action: () => { + free_models(); + }, + tooltip: "Unload Models" + }).element, + new(await import("../../scripts/ui/components/button.js")).ComfyButton({ + icon: "vacuum", + action: () => { + free_models(true); + }, + tooltip: "Free model and node cache" + }).element, + new(await import("../../scripts/ui/components/button.js")).ComfyButton({ + icon: "share", + action: () => { + if (share_option === 'openart') { + showOpenArtShareDialog(); + return; + } else if (share_option === 'matrix' || share_option === 'comfyworkflows') { + showShareDialog(share_option); + return; + } else if (share_option === 'youml') { + showYouMLShareDialog(); + return; + } + + if(!ShareDialogChooser.instance) { + ShareDialogChooser.instance = new ShareDialogChooser(); + } + ShareDialogChooser.instance.show(); + }, + tooltip: "Share" + }).element + ); + + app.menu?.settingsGroup.element.before(cmGroup.element); + } + catch(exception) { + console.log('ComfyUI is outdated. New style menu based features are disabled.'); + } + + // old style Manager button + const managerButton = document.createElement("button"); + managerButton.textContent = "Manager"; + managerButton.onclick = () => { + if(!manager_instance) + setManagerInstance(new ManagerMenuDialog()); + manager_instance.show(); + } + menu.append(managerButton); + + const shareButton = document.createElement("button"); + shareButton.id = "shareButton"; + shareButton.textContent = "Share"; + shareButton.onclick = () => { + if (share_option === 'openart') { + showOpenArtShareDialog(); + return; + } else if (share_option === 'matrix' || share_option === 'comfyworkflows') { + showShareDialog(share_option); + return; + } else if (share_option === 'youml') { + showYouMLShareDialog(); + return; + } + + if(!ShareDialogChooser.instance) { + ShareDialogChooser.instance = new ShareDialogChooser(); + } + ShareDialogChooser.instance.show(); + } + // make the background color a gradient of blue to green + shareButton.style.background = "linear-gradient(90deg, #00C9FF 0%, #92FE9D 100%)"; + shareButton.style.color = "black"; + + // Load share option from local storage to determine whether to show + // the share button. + const shouldShowShareButton = share_option !== 'none'; + shareButton.style.display = shouldShowShareButton ? "inline-block" : "none"; + + menu.append(shareButton); + }, + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + this._addExtraNodeContextMenu(nodeType, app); + }, + + _addExtraNodeContextMenu(node, app) { + const origGetExtraMenuOptions = node.prototype.getExtraMenuOptions; + node.prototype.cm_menu_added = true; + node.prototype.getExtraMenuOptions = function (_, options) { + origGetExtraMenuOptions?.apply?.(this, arguments); + + if (node.category.startsWith('group nodes>')) { + options.push({ + content: "Save As Component", + callback: (obj) => { + if (!ComponentBuilderDialog.instance) { + ComponentBuilderDialog.instance = new ComponentBuilderDialog(); + } + ComponentBuilderDialog.instance.target_node = node; + ComponentBuilderDialog.instance.show(); + } + }, null); + } + + if (isOutputNode(node)) { + const { potential_outputs } = getPotentialOutputsAndOutputNodes([this]); + const hasOutput = potential_outputs.length > 0; + + // Check if the previous menu option is `null`. If it's not, + // then we need to add a `null` as a separator. + if (options[options.length - 1] !== null) { + options.push(null); + } + + options.push({ + content: "🏞️ Share Output", + disabled: !hasOutput, + callback: (obj) => { + if (!ShareDialog.instance) { + ShareDialog.instance = new ShareDialog(); + } + const shareButton = document.getElementById("shareButton"); + if (shareButton) { + const currentNode = this; + if (!OpenArtShareDialog.instance) { + OpenArtShareDialog.instance = new OpenArtShareDialog(); + } + OpenArtShareDialog.instance.selectedNodeId = currentNode.id; + if (!ShareDialog.instance) { + ShareDialog.instance = new ShareDialog(share_option); + } + ShareDialog.instance.selectedNodeId = currentNode.id; + shareButton.click(); + } + } + }, null); + } + } + }, +}); diff --git a/custom_nodes/comfyui-manager/js/comfyui-share-common.js b/custom_nodes/comfyui-manager/js/comfyui-share-common.js new file mode 100644 index 00000000..e6f3e103 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/comfyui-share-common.js @@ -0,0 +1,1102 @@ +import { api } from "../../scripts/api.js"; +import { app } from "../../scripts/app.js"; +import { $el, ComfyDialog } from "../../scripts/ui.js"; +import { CopusShareDialog } from "./comfyui-share-copus.js"; +import { OpenArtShareDialog } from "./comfyui-share-openart.js"; +import { YouMLShareDialog } from "./comfyui-share-youml.js"; +import { customAlert } from "./common.js"; + +export const SUPPORTED_OUTPUT_NODE_TYPES = [ + "PreviewImage", + "SaveImage", + "VHS_VideoCombine", + "ADE_AnimateDiffCombine", + "SaveAnimatedWEBP", + "CR Image Output" +] + +var docStyle = document.createElement('style'); +docStyle.innerHTML = ` +.cm-menu-container { + column-gap: 20px; + display: flex; + flex-wrap: wrap; + justify-content: center; +} + +.cm-menu-column { + display: flex; + flex-direction: column; +} + +.cm-title { + padding: 10px 10px 0 10p; + background-color: black; + text-align: center; + height: 45px; +} +`; +document.head.appendChild(docStyle); + +export function getPotentialOutputsAndOutputNodes(nodes) { + const potential_outputs = []; + const potential_output_nodes = []; + + // iterate over the array of nodes to find the ones that are marked as SaveImage + // TODO: Add support for AnimateDiffCombine, etc. nodes that save videos/gifs, etc. + for (let i = 0; i < nodes.length; i++) { + const node = nodes[i]; + if (!SUPPORTED_OUTPUT_NODE_TYPES.includes(node.type)) { + continue; + } + + if (node.type === "SaveImage" || node.type === "CR Image Output") { + // check if node has an 'images' array property + if (node.hasOwnProperty("images") && Array.isArray(node.images)) { + // iterate over the images array and add each image to the potential_outputs array + for (let j = 0; j < node.images.length; j++) { + potential_output_nodes.push(node); + potential_outputs.push({ "type": "image", "image": node.images[j], "title": node.title, "node_id": node.id }); + } + } + } + else if (node.type === "PreviewImage") { + // check if node has an 'images' array property + if (node.hasOwnProperty("images") && Array.isArray(node.images)) { + // iterate over the images array and add each image to the potential_outputs array + for (let j = 0; j < node.images.length; j++) { + potential_output_nodes.push(node); + potential_outputs.push({ "type": "image", "image": node.images[j], "title": node.title, "node_id": node.id }); + } + } + } + else if (node.type === "VHS_VideoCombine") { + // check if node has a 'widgets' array property, with type 'image' + if (node.hasOwnProperty("widgets") && Array.isArray(node.widgets)) { + // iterate over the widgets array and add each image to the potential_outputs array + for (let j = 0; j < node.widgets.length; j++) { + if (node.widgets[j].type === "image") { + const widgetValue = node.widgets[j].value; + const parsedURLVals = parseURLPath(widgetValue); + + // ensure that the parsedURLVals have 'filename', 'subfolder', 'type', and 'format' properties + if (parsedURLVals.hasOwnProperty("filename") && parsedURLVals.hasOwnProperty("subfolder") && parsedURLVals.hasOwnProperty("type") && parsedURLVals.hasOwnProperty("format")) { + if (parsedURLVals.type !== "output") { + // TODO + } + potential_output_nodes.push(node); + potential_outputs.push({ "type": "output", 'title': node.title, "node_id": node.id , "output": { "filename": parsedURLVals.filename, "subfolder": parsedURLVals.subfolder, "value": widgetValue, "format": parsedURLVals.format } }); + } + } else if (node.widgets[j].type === "preview") { + const widgetValue = node.widgets[j].value; + const parsedURLVals = widgetValue.params; + + if(!parsedURLVals.format?.startsWith('image')) { + // video isn't supported format + continue; + } + + // ensure that the parsedURLVals have 'filename', 'subfolder', 'type', and 'format' properties + if (parsedURLVals.hasOwnProperty("filename") && parsedURLVals.hasOwnProperty("subfolder") && parsedURLVals.hasOwnProperty("type") && parsedURLVals.hasOwnProperty("format")) { + if (parsedURLVals.type !== "output") { + // TODO + } + potential_output_nodes.push(node); + potential_outputs.push({ "type": "output", 'title': node.title, "node_id": node.id , "output": { "filename": parsedURLVals.filename, "subfolder": parsedURLVals.subfolder, "value": `/view?filename=${parsedURLVals.filename}&subfolder=${parsedURLVals.subfolder}&type=${parsedURLVals.type}&format=${parsedURLVals.format}`, "format": parsedURLVals.format } }); + } + } + } + } + } + else if (node.type === "ADE_AnimateDiffCombine") { + // check if node has a 'widgets' array property, with type 'image' + if (node.hasOwnProperty("widgets") && Array.isArray(node.widgets)) { + // iterate over the widgets array and add each image to the potential_outputs array + for (let j = 0; j < node.widgets.length; j++) { + if (node.widgets[j].type === "image") { + const widgetValue = node.widgets[j].value; + const parsedURLVals = parseURLPath(widgetValue); + // ensure that the parsedURLVals have 'filename', 'subfolder', 'type', and 'format' properties + if (parsedURLVals.hasOwnProperty("filename") && parsedURLVals.hasOwnProperty("subfolder") && parsedURLVals.hasOwnProperty("type") && parsedURLVals.hasOwnProperty("format")) { + if (parsedURLVals.type !== "output") { + // TODO + continue; + } + potential_output_nodes.push(node); + potential_outputs.push({ "type": "output", 'title': node.title, "output": { "filename": parsedURLVals.filename, "subfolder": parsedURLVals.subfolder, "type": parsedURLVals.type, "value": widgetValue, "format": parsedURLVals.format } }); + } + } + } + } + } + else if (node.type === "SaveAnimatedWEBP") { + // check if node has an 'images' array property + if (node.hasOwnProperty("images") && Array.isArray(node.images)) { + // iterate over the images array and add each image to the potential_outputs array + for (let j = 0; j < node.images.length; j++) { + potential_output_nodes.push(node); + potential_outputs.push({ "type": "image", "image": node.images[j], "title": node.title }); + } + } + } + } + + // Note: make sure that two arrays are the same length + return { potential_outputs, potential_output_nodes }; +} + + +export function parseURLPath(urlPath) { + // Extract the query string from the URL path + var queryString = urlPath.split('?')[1]; + + // Use the URLSearchParams API to parse the query string + var params = new URLSearchParams(queryString); + + // Create an object to store the parsed parameters + var parsedParams = {}; + + // Iterate over each parameter and add it to the object + for (var pair of params.entries()) { + parsedParams[pair[0]] = pair[1]; + } + + // Return the object with the parsed parameters + return parsedParams; +} + + +export const shareToEsheep= () => { + app.graphToPrompt() + .then(prompt => { + const nodes = app.graph._nodes + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + const workflow = prompt['workflow'] + api.fetchApi(`/manager/set_esheep_workflow_and_images`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + workflow: workflow, + images: potential_outputs + }) + }).then(response => { + var domain = window.location.hostname; + var port = window.location.port; + port = port || (window.location.protocol === 'http:' ? '80' : window.location.protocol === 'https:' ? '443' : ''); + var full_domin = domain + ':' + port + window.open('https://www.esheep.com/app/workflow_upload?from_local='+ full_domin, '_blank'); + }); + }) +} + +export const showCopusShareDialog = () => { + if (!CopusShareDialog.instance) { + CopusShareDialog.instance = new CopusShareDialog(); + } + + return app.graphToPrompt() + .then(prompt => { + return app.graph._nodes; + }) + .then(nodes => { + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + CopusShareDialog.instance.show({ potential_outputs, potential_output_nodes}); + }) +} + +export const showOpenArtShareDialog = () => { + if (!OpenArtShareDialog.instance) { + OpenArtShareDialog.instance = new OpenArtShareDialog(); + } + + return app.graphToPrompt() + .then(prompt => { + // console.log({ prompt }) + return app.graph._nodes; + }) + .then(nodes => { + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + OpenArtShareDialog.instance.show({ potential_outputs, potential_output_nodes}); + }) +} + + +export const showYouMLShareDialog = () => { + if (!YouMLShareDialog.instance) { + YouMLShareDialog.instance = new YouMLShareDialog(); + } + + return app.graphToPrompt() + .then(prompt => { + return app.graph._nodes; + }) + .then(nodes => { + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + YouMLShareDialog.instance.show(potential_outputs, potential_output_nodes); + }) +} + + +export const showShareDialog = async (share_option) => { + if (!ShareDialog.instance) { + ShareDialog.instance = new ShareDialog(share_option); + } + return app.graphToPrompt() + .then(prompt => { + // console.log({ prompt }) + return app.graph._nodes; + }) + .then(nodes => { + // console.log({ nodes }); + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + if (potential_outputs.length === 0) { + if (potential_output_nodes.length === 0) { + // todo: add support for other output node types (animatediff combine, etc.) + const supported_nodes_string = SUPPORTED_OUTPUT_NODE_TYPES.join(", "); + customAlert(`No supported output node found (${supported_nodes_string}). To share this workflow, please add an output node to your graph and re-run your prompt.`); + } else { + customAlert("To share this, first run a prompt. Once it's done, click 'Share'.\n\nNOTE: Images of the Share target can only be selected in the PreviewImage, SaveImage, and VHS_VideoCombine nodes. In the case of VHS_VideoCombine, only the image/gif and image/webp formats are supported."); + } + return false; + } + ShareDialog.instance.show({ potential_outputs, potential_output_nodes, share_option }); + return true; + }); +} + +export class ShareDialogChooser extends ComfyDialog { + static instance = null; + constructor() { + super(); + this.element = $el("div.comfy-modal", { + parent: document.body, style: { + 'overflow-y': "auto", + } + }, + [$el("div.comfy-modal-content", + {}, + [...this.createButtons()]), + ]); + this.selectedNodeId = null; + } + createButtons() { + const buttons = [ + { + key: "openart", + textContent: "OpenArt AI", + website: "https://openart.ai/workflows/", + description: "Share ComfyUI workflows and art on OpenArt.ai", + onclick: () => { + showOpenArtShareDialog(); + this.close(); + } + }, + { + key: "youml", + textContent: "YouML", + website: "https://youml.com", + description: "Share your workflow or transform it into an interactive app on YouML.com", + onclick: () => { + showYouMLShareDialog(); + this.close(); + } + }, + { + key: "matrix", + textContent: "Matrix Server", + website: "https://app.element.io/#/room/%23comfyui_space%3Amatrix.org", + description: "Share your art on the official ComfyUI matrix server", + onclick: async () => { + showShareDialog('matrix').then((suc) => { + suc && this.close(); + }) + } + }, + { + key: "comfyworkflows", + textContent: "ComfyWorkflows", + website: "https://comfyworkflows.com", + description: "Share & browse thousands of ComfyUI workflows and art 🎨

ComfyWorkflows.com", + onclick: () => { + showShareDialog('comfyworkflows').then((suc) => { + suc && this.close(); + }) + } + }, + { + key: "esheep", + textContent: "eSheep", + website: "https://www.esheep.com", + description: "Share & download thousands of ComfyUI workflows on esheep.com", + onclick: () => { + shareToEsheep(); + this.close(); + } + }, + { + key: "Copus", + textContent: "Copus", + website: "https://www.copus.io", + description: "πŸ”΄ Earn simple. Get paid from your ComfyUI workflowsβ€”no revenue sharing. Ever.", + onclick: () => { + showCopusShareDialog(); + this.close(); + } + }, + ]; + + function createShareButtonsWithDescriptions() { + // Responsive container + const container = $el("div", { + style: { + display: "flex", + 'flex-wrap': 'wrap', + 'justify-content': 'space-around', + 'padding': '10px', + } + }); + + buttons.forEach(b => { + const button = $el("button", + { + type: "button", + textContent: b.textContent, + onclick: b.onclick, + style: { + 'width': '25%', + 'minWidth': '200px', + 'background-color': b.backgroundColor || '', + 'border-radius': '5px', + 'cursor': 'pointer', + 'padding': '5px 5px', + 'margin-bottom': '5px', + 'transition': 'background-color 0.3s', + 'position':'relative' + } + }, + [ + $el("span", { style: { + } }), + ] + ); + button.addEventListener('mouseover', () => { + button.style.backgroundColor = '#007BFF'; // Change color on hover + }); + button.addEventListener('mouseout', () => { + button.style.backgroundColor = b.backgroundColor || ''; + }); + + const description = $el("p", { + innerHTML: b.description, + style: { + 'text-align': 'left', + color: 'var(--input-text)', + 'font-size': '14px', + 'margin-bottom': '0', + }, + }); + + const copus_ui =$el("div", { style: { + 'position': 'absolute', + 'height': '100%', + 'left': '-25px', + 'top': '-26px', + 'width': '100%', + 'z-index':'-1', + 'background':'url("https://static.copus.io/images/client/202412/test/f28ac6ef8f4c6f3d5d50856a272ed02c.png")', + 'background-repeat': 'no-repeat', + } }); + const copus_ui_bottom =$el("div", { style: { + 'position': 'absolute', + 'height': '100%', + 'left': '25px', + 'bottom': '-26px', + 'width': '100%', + 'transform':'scale(-1, -1)', + 'z-index':'-1', + 'background':'url("https://static.copus.io/images/client/202412/test/f28ac6ef8f4c6f3d5d50856a272ed02c.png")', + 'background-repeat': 'no-repeat', + } }); + + const websiteLink = $el("a", { + textContent: "🌐 Website", + href: b.website, + target: "_blank", + style: { + color: 'var(--input-text)', + 'margin-left': '10px', + 'font-size': '12px', + 'text-decoration': 'none', + 'align-self': 'center', + }, + }); + + // Add highlight to the website link + websiteLink.addEventListener('mouseover', () => { + websiteLink.style.opacity = '0.7'; + }); + + websiteLink.addEventListener('mouseout', () => { + websiteLink.style.opacity = '1'; + }); + + const buttonLinkContainer = $el("div", { + style: { + display: 'flex', + 'align-items': 'center', + 'margin-bottom': '10px', + } + }, [button, websiteLink]); + const column = $el("div", { + style: { + 'flex-basis': '100%', + 'margin': '10px', + 'padding': '10px 20px', + 'border': '1px solid #ddd', + 'border-radius': '5px', + 'box-shadow': '0 2px 4px rgba(0, 0, 0, 0.1)', + 'position':'relative' + } + }, [buttonLinkContainer, description + , + b.key ==='Copus' ? + copus_ui + :'', + b.key ==='Copus' ? + copus_ui_bottom + :'', + ]); + + container.appendChild(column); + }); + + return container; + } + + return [ + $el("p", { + textContent: 'Choose a platform to share your workflow', + style: { + 'text-align': 'center', + 'color': 'var(--input-text)', + 'font-size': '18px', + 'margin-bottom': '10px', + }, + } + ), + + $el("div.cm-menu-container", { + id: "comfyui-share-container" + }, [ + $el("div.cm-menu-column", [ + createShareButtonsWithDescriptions(), + $el("br", {}, []), + ]), + ]), + $el("div.cm-menu-container", { + id: "comfyui-share-container" + }, [ + $el("button", { + type: "button", + style: { + margin: "0 25px", + width: "100%", + }, + textContent: "Close", + onclick: () => { + this.close() + } + }), + $el("br", {}, []), + ]), + ]; + } + show() { + this.element.style.display = "block"; + this.element.style.zIndex = 1099; + } +} +export class ShareDialog extends ComfyDialog { + static instance = null; + static matrix_auth = { homeserver: "matrix.org", username: "", password: "" }; + static cw_sharekey = ""; + + constructor(share_option) { + super(); + this.share_option = share_option; + this.element = $el("div.comfy-modal", { + parent: document.body, style: { + 'overflow-y': "auto", + } + }, + [$el("div.comfy-modal-content", + {}, + [...this.createButtons()]), + ]); + this.selectedOutputIndex = 0; + } + + createButtons() { + this.radio_buttons = $el("div", { + id: "selectOutputImages", + }, []); + + this.is_nsfw_checkbox = $el("input", { type: 'checkbox', id: "is_nsfw" }, []) + const is_nsfw_checkbox_text = $el("label", { + }, [" Is this NSFW?"]) + this.is_nsfw_checkbox.style.color = "var(--fg-color)"; + this.is_nsfw_checkbox.checked = false; + + this.matrix_destination_checkbox = $el("input", { type: 'checkbox', id: "matrix_destination" }, []) + const matrix_destination_checkbox_text = $el("label", {}, [" ComfyUI Matrix server"]) + this.matrix_destination_checkbox.style.color = "var(--fg-color)"; + this.matrix_destination_checkbox.checked = this.share_option === 'matrix'; //true; + + this.comfyworkflows_destination_checkbox = $el("input", { type: 'checkbox', id: "comfyworkflows_destination" }, []) + const comfyworkflows_destination_checkbox_text = $el("label", {}, [" ComfyWorkflows.com"]) + this.comfyworkflows_destination_checkbox.style.color = "var(--fg-color)"; + this.comfyworkflows_destination_checkbox.checked = this.share_option !== 'matrix'; + + this.matrix_homeserver_input = $el("input", { type: 'text', id: "matrix_homeserver", placeholder: "matrix.org", value: ShareDialog.matrix_auth.homeserver || 'matrix.org' }, []); + this.matrix_username_input = $el("input", { type: 'text', placeholder: "Username", value: ShareDialog.matrix_auth.username || '' }, []); + this.matrix_password_input = $el("input", { type: 'password', placeholder: "Password", value: ShareDialog.matrix_auth.password || '' }, []); + + this.cw_sharekey_input = $el("input", { type: 'text', placeholder: "Share key (found on your profile page)", value: ShareDialog.cw_sharekey || '' }, []); + this.cw_sharekey_input.style.width = "100%"; + + this.credits_input = $el("input", { + type: "text", + placeholder: "This will be used to give credits", + required: false, + }, []); + + this.title_input = $el("input", { + type: "text", + placeholder: "ex: My awesome art", + required: false + }, []); + + this.description_input = $el("textarea", { + placeholder: "ex: Trying out a new workflow... ", + required: false, + }, []); + + this.share_button = $el("button", { + type: "submit", + textContent: "Share", + style: { + backgroundColor: "blue" + } + }, []); + + this.final_message = $el("div", { + style: { + color: "white", + textAlign: "center", + // marginTop: "10px", + // backgroundColor: "black", + padding: "10px", + } + }, []); + + this.share_finalmessage_container = $el("div.cm-menu-container", { + id: "comfyui-share-finalmessage-container", + style: { + display: "none", + } + }, [ + $el("div.cm-menu-column", [ + this.final_message, + $el("button", { + type: "button", + textContent: "Close", + onclick: () => { + // Reset state + this.matrix_destination_checkbox.checked = this.share_option === 'matrix'; + this.comfyworkflows_destination_checkbox.checked = this.share_option !== 'matrix'; + this.share_button.textContent = "Share"; + this.share_button.style.display = "inline-block"; + this.final_message.innerHTML = ""; + this.final_message.style.color = "white"; + this.credits_input.value = ""; + this.title_input.value = ""; + this.description_input.value = ""; + this.is_nsfw_checkbox.checked = false; + this.selectedOutputIndex = 0; + + // hide the final message + this.share_finalmessage_container.style.display = "none"; + + // show the share container + this.share_container.style.display = "flex"; + + this.close() + } + }), + ]) + ]); + this.share_container = $el("div.cm-menu-container", { + id: "comfyui-share-container" + }, [ + $el("div.cm-menu-column", [ + $el("details", { + style: { + border: "1px solid #999", + padding: "5px", + borderRadius: "5px", + backgroundColor: "#222" + } + }, [ + $el("summary", { + style: { + color: "white", + cursor: "pointer", + } + }, [`Matrix account`]), + $el("div", { + style: { + display: "flex", + flexDirection: "row", + } + }, [ + $el("div", { + textContent: "Homeserver", + style: { + marginRight: "10px", + } + }, []), + this.matrix_homeserver_input, + ]), + + $el("div", { + style: { + display: "flex", + flexDirection: "row", + } + }, [ + $el("div", { + textContent: "Username", + style: { + marginRight: "10px", + } + }, []), + this.matrix_username_input, + ]), + + $el("div", { + style: { + display: "flex", + flexDirection: "row", + } + }, [ + $el("div", { + textContent: "Password", + style: { + marginRight: "10px", + } + }, []), + this.matrix_password_input, + ]), + + ]), + $el("details", { + style: { + border: "1px solid #999", + marginTop: "10px", + padding: "5px", + borderRadius: "5px", + backgroundColor: "#222" + }, + }, [ + $el("summary", { + style: { + color: "white", + cursor: "pointer", + } + }, [`Comfyworkflows.com account`]), + $el("h4", { + textContent: "Share key (found on your profile page)", + }, []), + $el("p", { size: 3, color: "white" }, ["If provided, your art will be saved to your account. Otherwise, it will be shared anonymously."]), + this.cw_sharekey_input, + ]), + + $el("div", {}, [ + $el("p", { + size: 3, color: "white", style: { + color: 'var(--input-text)' + } + }, [`Select where to share your art:`]), + this.matrix_destination_checkbox, + matrix_destination_checkbox_text, + $el("br", {}, []), + this.comfyworkflows_destination_checkbox, + comfyworkflows_destination_checkbox_text, + ]), + + $el("h4", { + textContent: "Credits (optional)", + size: 3, + color: "white", + style: { + color: 'var(--input-text)' + } + }, []), + this.credits_input, + // $el("br", {}, []), + + $el("h4", { + textContent: "Title (optional)", + size: 3, + color: "white", + style: { + color: 'var(--input-text)' + } + }, []), + this.title_input, + // $el("br", {}, []), + + $el("h4", { + textContent: "Description (optional)", + size: 3, + color: "white", + style: { + color: 'var(--input-text)' + } + }, []), + this.description_input, + $el("br", {}, []), + + $el("div", {}, [this.is_nsfw_checkbox, is_nsfw_checkbox_text]), + // $el("br", {}, []), + + // this.final_message, + // $el("br", {}, []), + ]), + $el("div.cm-menu-column", [ + this.radio_buttons, + $el("br", {}, []), + + this.share_button, + + $el("button", { + type: "button", + textContent: "Close", + onclick: () => { + // Reset state + this.matrix_destination_checkbox.checked = this.share_option === 'matrix'; + this.comfyworkflows_destination_checkbox.checked = this.share_option !== 'matrix'; + this.share_button.textContent = "Share"; + this.share_button.style.display = "inline-block"; + this.final_message.innerHTML = ""; + this.final_message.style.color = "white"; + this.credits_input.value = ""; + this.title_input.value = ""; + this.description_input.value = ""; + this.is_nsfw_checkbox.checked = false; + this.selectedOutputIndex = 0; + + // hide the final message + this.share_finalmessage_container.style.display = "none"; + + // show the share container + this.share_container.style.display = "flex"; + + this.close() + } + }), + $el("br", {}, []), + ]), + ]); + + // get the user's existing matrix auth and share key + ShareDialog.matrix_auth = { homeserver: "matrix.org", username: "", password: "" }; + try { + api.fetchApi(`/manager/get_matrix_auth`) + .then(response => response.json()) + .then(data => { + ShareDialog.matrix_auth = data; + this.matrix_homeserver_input.value = ShareDialog.matrix_auth.homeserver; + this.matrix_username_input.value = ShareDialog.matrix_auth.username; + this.matrix_password_input.value = ShareDialog.matrix_auth.password; + }) + .catch(error => { + // console.log(error); + }); + } catch (error) { + // console.log(error); + } + + // get the user's existing comfyworkflows share key + ShareDialog.cw_sharekey = ""; + try { + // console.log("Fetching comfyworkflows share key") + api.fetchApi(`/manager/get_comfyworkflows_auth`) + .then(response => response.json()) + .then(data => { + ShareDialog.cw_sharekey = data.comfyworkflows_sharekey; + this.cw_sharekey_input.value = ShareDialog.cw_sharekey; + }) + .catch(error => { + // console.log(error); + }); + } catch (error) { + // console.log(error); + } + + this.share_button.onclick = async () => { + const prompt = await app.graphToPrompt(); + const nodes = app.graph._nodes; + + // console.log({ prompt, nodes }); + + const destinations = []; + if (this.matrix_destination_checkbox.checked) { + destinations.push("matrix"); + } + if (this.comfyworkflows_destination_checkbox.checked) { + destinations.push("comfyworkflows"); + } + + // if destinations includes matrix, make an api call to /manager/check_matrix to ensure that the user has configured their matrix settings + if (destinations.includes("matrix")) { + let definedMatrixAuth = !!this.matrix_homeserver_input.value && !!this.matrix_username_input.value && !!this.matrix_password_input.value; + if (!definedMatrixAuth) { + customAlert("Please set your Matrix account details."); + return; + } + } + + if (destinations.includes("comfyworkflows") && !this.cw_sharekey_input.value && false) { //!confirm("You have NOT set your ComfyWorkflows.com share key. Your art will NOT be connected to your account (it will be shared anonymously). Continue?")) { + return; + } + + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + + // console.log({ potential_outputs, potential_output_nodes }) + + if (potential_outputs.length === 0) { + if (potential_output_nodes.length === 0) { + // todo: add support for other output node types (animatediff combine, etc.) + const supported_nodes_string = SUPPORTED_OUTPUT_NODE_TYPES.join(", "); + customAlert(`No supported output node found (${supported_nodes_string}). To share this workflow, please add an output node to your graph and re-run your prompt.`); + } else { + customAlert("To share this, first run a prompt. Once it's done, click 'Share'.\n\nNOTE: Images of the Share target can only be selected in the PreviewImage, SaveImage, and VHS_VideoCombine nodes. In the case of VHS_VideoCombine, only the image/gif and image/webp formats are supported."); + } + this.selectedOutputIndex = 0; + this.close(); + return; + } + + // Change the text of the share button to "Sharing..." to indicate that the share process has started + this.share_button.textContent = "Sharing..."; + + const response = await api.fetchApi(`/manager/share`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + matrix_auth: { + homeserver: this.matrix_homeserver_input.value, + username: this.matrix_username_input.value, + password: this.matrix_password_input.value, + }, + cw_auth: { + cw_sharekey: this.cw_sharekey_input.value, + }, + share_destinations: destinations, + credits: this.credits_input.value, + title: this.title_input.value, + description: this.description_input.value, + is_nsfw: this.is_nsfw_checkbox.checked, + prompt, + potential_outputs, + selected_output_index: this.selectedOutputIndex, + // potential_output_nodes + }) + }); + + if (response.status != 200) { + try { + const response_json = await response.json(); + if (response_json.error) { + customAlert(response_json.error); + this.close(); + return; + } else { + customAlert("Failed to share your art. Please try again."); + this.close(); + return; + } + } catch (e) { + customAlert("Failed to share your art. Please try again."); + this.close(); + return; + } + } + + const response_json = await response.json(); + + if (response_json.comfyworkflows.url) { + this.final_message.innerHTML = "Your art has been shared: " + response_json.comfyworkflows.url + ""; + if (response_json.matrix.success) { + this.final_message.innerHTML += "
Your art has been shared in the ComfyUI Matrix server's #share channel!"; + } + } else { + if (response_json.matrix.success) { + this.final_message.innerHTML = "Your art has been shared in the ComfyUI Matrix server's #share channel!"; + } + } + + this.final_message.style.color = "green"; + + // hide #comfyui-share-container and show #comfyui-share-finalmessage-container + this.share_container.style.display = "none"; + this.share_finalmessage_container.style.display = "block"; + + // hide the share button + this.share_button.textContent = "Shared!"; + this.share_button.style.display = "none"; + // this.close(); + } + + const res = + [ + $el("tr.td", { width: "100%" }, [ + $el("font", { size: 6, color: "white" }, [`Share your art`]), + ]), + $el("br", {}, []), + + this.share_finalmessage_container, + this.share_container, + ]; + + res[0].style.padding = "10px 10px 10px 10px"; + res[0].style.backgroundColor = "black"; //"linear-gradient(90deg, #00C9FF 0%, #92FE9D 100%)"; + res[0].style.textAlign = "center"; + res[0].style.height = "45px"; + return res; + } + + show({potential_outputs, potential_output_nodes, share_option}) { + // Sort `potential_output_nodes` by node ID to make the order always + // consistent, but we should also keep `potential_outputs` in the same + // order as `potential_output_nodes`. + const potential_output_to_order = {}; + potential_output_nodes.forEach((node, index) => { + if (node.id in potential_output_to_order) { + potential_output_to_order[node.id][1].push(potential_outputs[index]); + } else { + potential_output_to_order[node.id] = [node, [potential_outputs[index]]]; + } + }) + // Sort the object `potential_output_to_order` by key (node ID) + const sorted_potential_output_to_order = Object.fromEntries( + Object.entries(potential_output_to_order).sort((a, b) => a[0].id - b[0].id) + ); + const sorted_potential_outputs = [] + const sorted_potential_output_nodes = [] + for (const [key, value] of Object.entries(sorted_potential_output_to_order)) { + sorted_potential_output_nodes.push(value[0]); + sorted_potential_outputs.push(...value[1]); + } + potential_output_nodes = sorted_potential_output_nodes; + potential_outputs = sorted_potential_outputs; + + // console.log({ potential_outputs, potential_output_nodes }) + this.radio_buttons.innerHTML = ""; // clear the radio buttons + let is_radio_button_checked = false; // only check the first radio button if multiple images from the same node + const new_radio_buttons = $el("div", { + id: "selectOutput-Options", + style: { + 'overflow-y': 'scroll', + 'max-height': '400px', + } + }, potential_outputs.map((output, index) => { + const {node_id} = output; + const radio_button = $el("input", { type: 'radio', name: "selectOutputImages", value: index, required: index === 0 }, []) + let radio_button_img; + if (output.type === "image" || output.type === "temp") { + radio_button_img = $el("img", { src: `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`, style: { width: "auto", height: "100px" } }, []); + } else if (output.type === "output") { + radio_button_img = $el("img", { src: output.output.value, style: { width: "auto", height: "100px" } }, []); + } else { + // unsupported output type + // this should never happen + // TODO + radio_button_img = $el("img", { src: "", style: { width: "auto", height: "100px" } }, []); + } + const radio_button_text = $el("label", { + // style: { + // color: 'var(--input-text)' + // } + }, [output.title]) + radio_button.style.color = "var(--fg-color)"; + + // Make the radio button checked if it's the selected node, + // otherwise make the first radio button checked. + if (this.selectedNodeId) { + if (this.selectedNodeId === node_id && !is_radio_button_checked) { + radio_button.checked = true; + is_radio_button_checked = true; + } + } else { + radio_button.checked = index === 0; + } + + if (radio_button.checked) { + this.selectedOutputIndex = index; + } + + radio_button.onchange = () => { + this.selectedOutputIndex = parseInt(radio_button.value); + }; + + return $el("div", { + style: { + display: "flex", + 'align-items': 'center', + 'justify-content': 'space-between', + 'margin-bottom': '10px', + } + }, [radio_button, radio_button_text, radio_button_img]); + })); + const header = $el("h3", { + textContent: "Select an image to share", + size: 3, + color: "white", + style: { + 'text-align': 'center', + color: 'var(--input-text)', + backgroundColor: 'black', + padding: '10px', + 'margin-top': '0px', + } + }, [ + $el("p", { + textContent: "Scroll to see all outputs", + size: 2, + color: "white", + style: { + 'text-align': 'center', + color: 'var(--input-text)', + 'margin-bottom': '5px', + 'font-style': 'italic', + 'font-size': '12px', + }, + }, []) + ]); + this.radio_buttons.appendChild(header); + // this.radio_buttons.appendChild(subheader); + this.radio_buttons.appendChild(new_radio_buttons); + this.element.style.display = "block"; + + share_option = share_option || this.share_option; + if (share_option === 'comfyworkflows') { + this.matrix_destination_checkbox.checked = false; + this.comfyworkflows_destination_checkbox.checked = true; + } else { + this.matrix_destination_checkbox.checked = true; + this.comfyworkflows_destination_checkbox.checked = false; + } + } +} diff --git a/custom_nodes/comfyui-manager/js/comfyui-share-copus.js b/custom_nodes/comfyui-manager/js/comfyui-share-copus.js new file mode 100644 index 00000000..46288e59 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/comfyui-share-copus.js @@ -0,0 +1,1168 @@ +import { app } from "../../scripts/app.js"; +import { $el, ComfyDialog } from "../../scripts/ui.js"; +import { customAlert } from "./common.js"; + +const env = "prod"; + +let DEFAULT_HOMEPAGE_URL = "https://copus.io"; + +let API_ENDPOINT = "https://api.client.prod.copus.io"; + +if (env !== "prod") { + API_ENDPOINT = "https://api.test.copus.io"; + DEFAULT_HOMEPAGE_URL = "https://test.copus.io"; +} + +const style = ` + .copus-share-dialog a { + color: #f8f8f8; + } + .copus-share-dialog a:hover { + color: #007bff; + } + .output_label { + border: 5px solid transparent; + } + .output_label:hover { + border: 5px solid #59E8C6; + } + .output_label.checked { + border: 5px solid #59E8C6; + } +`; + +// Shared component styles +const sectionStyle = { + marginBottom: 0, + padding: 0, + borderRadius: "8px", + boxShadow: "0 2px 4px rgba(0, 0, 0, 0.05)", + display: "flex", + flexDirection: "column", + justifyContent: "center", + position: "relative", +}; + +export class CopusShareDialog extends ComfyDialog { + static instance = null; + + constructor() { + super(); + $el("style", { + textContent: style, + parent: document.head, + }); + this.element = $el( + "div.comfy-modal.copus-share-dialog", + { + parent: document.body, + style: { + "overflow-y": "auto", + }, + }, + [$el("div.comfy-modal-content", {}, [...this.createButtons()])] + ); + this.selectedOutputIndex = 0; + this.selectedOutput_lock = 0; + this.selectedNodeId = null; + this.uploadedImages = []; + this.allFilesImages = []; + this.selectedFile = null; + this.allFiles = []; + this.titleNum = 0; + } + + createButtons() { + const inputStyle = { + display: "block", + minWidth: "500px", + width: "100%", + padding: "10px", + margin: "10px 0", + borderRadius: "4px", + border: "1px solid #ddd", + boxSizing: "border-box", + }; + + const textAreaStyle = { + display: "block", + minWidth: "500px", + width: "100%", + padding: "10px", + margin: "10px 0", + borderRadius: "4px", + border: "1px solid #ddd", + boxSizing: "border-box", + minHeight: "100px", + background: "#222", + resize: "vertical", + color: "#f2f2f2", + fontFamily: "Arial", + fontWeight: "400", + fontSize: "15px", + }; + + const hyperLinkStyle = { + display: "block", + marginBottom: "15px", + fontWeight: "bold", + fontSize: "14px", + }; + + const labelStyle = { + color: "#f8f8f8", + display: "block", + margin: "10px 0 0 0", + fontWeight: "bold", + textDecoration: "none", + }; + + const buttonStyle = { + padding: "10px 80px", + margin: "10px 5px", + borderRadius: "4px", + border: "none", + cursor: "pointer", + color: "#fff", + backgroundColor: "#007bff", + }; + + // upload images input + this.uploadImagesInput = $el("input", { + type: "file", + multiple: false, + style: inputStyle, + accept: "image/*", + }); + + this.uploadImagesInput.addEventListener("change", async (e) => { + const file = e.target.files[0]; + if (!file) { + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + return; + } + const reader = new FileReader(); + reader.onload = async (e) => { + const imgData = e.target.result; + this.previewImage.src = imgData; + this.previewImage.style.display = "block"; + this.selectedFile = null; + // Once user uploads an image, we uncheck all radio buttons + this.radioButtons.forEach((ele) => { + ele.checked = false; + ele.parentElement.classList.remove("checked"); + }); + + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 0.35; + this.uploadImagesInput.style.opacity = 1; + }; + reader.readAsDataURL(file); + }); + + // preview image + this.previewImage = $el("img", { + src: "", + style: { + width: "100%", + maxHeight: "100px", + objectFit: "contain", + display: "none", + marginTop: "10px", + }, + }); + + this.keyInput = $el("input", { + type: "password", + placeholder: "Copy & paste your API key", + style: inputStyle, + }); + this.TitleInput = $el("input", { + type: "text", + placeholder: "Title (Required)", + style: inputStyle, + maxLength: "70", + oninput: () => { + const titleNum = this.TitleInput.value.length; + titleNumDom.textContent = `${titleNum}/70`; + }, + }); + this.SubTitleInput = $el("input", { + type: "text", + placeholder: "Subtitle (Optional)", + style: inputStyle, + maxLength: "350", + oninput: () => { + const titleNum = this.SubTitleInput.value.length; + subTitleNumDom.textContent = `${titleNum}/350`; + }, + }); + this.LockInput = $el("input", { + type: "text", + placeholder: "0", + style: { + width: "100px", + padding: "7px", + paddingLeft: "30px", + borderRadius: "4px", + border: "1px solid #ddd", + boxSizing: "border-box", + position: "relative", + }, + oninput: (event) => { + let input = event.target.value; + // Use a regular expression to match a number with up to two decimal places + const regex = /^\d*\.?\d{0,2}$/; + if (!regex.test(input)) { + // If the input doesn't match, remove the last entered character + event.target.value = input.slice(0, -1); + } + const numericValue = parseFloat(input); + if (numericValue > 9999) { + input = "9999"; + } + // Update the input field with the valid value + event.target.value = input; + }, + }); + this.descriptionInput = $el("textarea", { + placeholder: "Content (Optional)", + style: { + ...textAreaStyle, + minHeight: "100px", + }, + }); + + // Header Section + const headerSection = $el("h3", { + textContent: "Share your workflow to Copus", + size: 3, + color: "white", + style: { + "text-align": "center", + color: "white", + margin: "0 0 10px 0", + }, + }); + this.getAPIKeyLink = $el( + "a", + { + style: { + ...hyperLinkStyle, + color: "#59E8C6", + }, + href: `${DEFAULT_HOMEPAGE_URL}?fromPage=comfyUI`, + target: "_blank", + }, + ["πŸ‘‰ Get your API key here"] + ); + const linkSection = $el( + "div", + { + style: { + marginTop: "10px", + display: "flex", + flexDirection: "column", + }, + }, + [ + // this.communityLink, + this.getAPIKeyLink, + ] + ); + + // Account Section + const accountSection = $el("div", { style: sectionStyle }, [ + $el("label", { style: labelStyle }, ["1️⃣ Copus API Key"]), + this.keyInput, + ]); + + // Output Upload Section + const outputUploadSection = $el("div", { style: sectionStyle }, [ + $el( + "label", + { + style: { + ...labelStyle, + margin: "10px 0 0 0", + }, + }, + ["2️⃣ Image/Thumbnail (Required)"] + ), + this.previewImage, + this.uploadImagesInput, + ]); + + // Outputs Section + this.outputsSection = $el( + "div", + { + id: "selectOutputs", + }, + [] + ); + + const titleNumDom = $el( + "label", + { + style: { + fontSize: "12px", + position: "absolute", + right: "10px", + bottom: "-10px", + color: "#999", + }, + }, + ["0/70"] + ); + const subTitleNumDom = $el( + "label", + { + style: { + fontSize: "12px", + position: "absolute", + right: "10px", + bottom: "-10px", + color: "#999", + }, + }, + ["0/350"] + ); + const descriptionNumDom = $el( + "label", + { + style: { + fontSize: "12px", + position: "absolute", + right: "10px", + bottom: "-10px", + color: "#999", + }, + }, + ["0/70"] + ); + // Additional Inputs Section + const additionalInputsSection = $el("div", { style: { ...sectionStyle } }, [ + $el("label", { style: labelStyle }, ["3️⃣ Title "]), + this.TitleInput, + titleNumDom, + ]); + const SubtitleSection = $el("div", { style: sectionStyle }, [ + $el("label", { style: labelStyle }, ["4️⃣ Subtitle "]), + this.SubTitleInput, + subTitleNumDom, + ]); + const DescriptionSection = $el("div", { style: sectionStyle }, [ + $el("label", { style: labelStyle }, ["5️⃣ Description "]), + this.descriptionInput, + // descriptionNumDom, + ]); + // switch between outputs section and additional inputs section + this.radioButtons_lock = []; + + this.radioButtonsCheck_lock = $el("input", { + type: "radio", + name: "output_type_lock", + value: "0", + id: "blockchain1_lock", + checked: true, + }); + this.radioButtonsCheckOff_lock = $el("input", { + type: "radio", + name: "output_type_lock", + value: "1", + id: "blockchain_lock", + }); + + const blockChainSection_lock = $el("div", { style: sectionStyle }, [ + $el("label", { style: labelStyle }, ["6️⃣ Download threshold"]), + $el( + "label", + { + style: { + marginTop: "10px", + display: "flex", + alignItems: "center", + cursor: "pointer", + }, + }, + [ + this.radioButtonsCheck_lock, + $el( + "div", + { + style: { + marginLeft: "5px", + display: "flex", + alignItems: "center", + position: "relative", + }, + }, + [ + $el("span", { style: { marginLeft: "5px" } }, ["ON"]), + $el( + "span", + { + style: { + marginLeft: "20px", + marginRight: "10px", + color: "#fff", + }, + }, + ["Unlock with"] + ), + $el("img", { + style: { + width: "16px", + height: "16px", + position: "absolute", + right: "75px", + zIndex: "100", + }, + src: "https://static.copus.io/images/admin/202507/prod/e2919a1d8f3c2d99d3b8fe27ff94b841.png", + }), + this.LockInput, + ] + ), + ] + ), + $el( + "label", + { style: { display: "flex", alignItems: "center", cursor: "pointer" } }, + [ + this.radioButtonsCheckOff_lock, + $el( + "div", + { + style: { + marginLeft: "5px", + display: "flex", + alignItems: "center", + }, + }, + [$el("span", { style: { marginLeft: "5px" } }, ["OFF"])] + ), + ] + ), + + $el( + "p", + { style: { fontSize: "16px", color: "#fff", margin: "10px 0 0 0" } }, + [ + ] + ), + ]); + + this.radioButtons = []; + + this.radioButtonsCheck = $el("input", { + type: "radio", + name: "output_type", + value: "0", + id: "blockchain1", + checked: true, + }); + this.radioButtonsCheckOff = $el("input", { + type: "radio", + name: "output_type", + value: "1", + id: "blockchain", + }); + + const blockChainSection = $el("div", { style: sectionStyle }, [ + $el("label", { style: labelStyle }, ["8️⃣ Store on blockchain "]), + $el( + "label", + { + style: { + marginTop: "10px", + display: "flex", + alignItems: "center", + cursor: "pointer", + }, + }, + [ + this.radioButtonsCheck, + $el("span", { style: { marginLeft: "5px" } }, ["ON"]), + ] + ), + $el( + "label", + { style: { display: "flex", alignItems: "center", cursor: "pointer" } }, + [ + this.radioButtonsCheckOff, + $el("span", { style: { marginLeft: "5px" } }, ["OFF"]), + ] + ), + $el( + "p", + { style: { fontSize: "16px", color: "#fff", margin: "10px 0 0 0" } }, + ["Secure ownership with a permanent & decentralized storage"] + ), + ]); + + this.ratingRadioButtonsCheck0 = $el("input", { + type: "radio", + name: "content_rating", + value: "0", + id: "content_rating0", + }); + this.ratingRadioButtonsCheck1 = $el("input", { + type: "radio", + name: "content_rating", + value: "1", + id: "content_rating1", + }); + this.ratingRadioButtonsCheck2 = $el("input", { + type: "radio", + name: "content_rating", + value: "2", + id: "content_rating2", + }); + this.ratingRadioButtonsCheck_1 = $el("input", { + type: "radio", + name: "content_rating", + value: "-1", + id: "content_rating_1", + checked: true, + }); + + // content rating + const contentRatingSection = $el("div", { style: sectionStyle }, [ + $el("label", { style: labelStyle }, ["7️⃣ Content rating "]), + $el( + "label", + { + style: { + marginTop: "10px", + display: "flex", + alignItems: "center", + cursor: "pointer", + }, + }, + [ + this.ratingRadioButtonsCheck0, + $el("img", { + style: { + width: "12px", + height: "12px", + marginLeft: "5px", + }, + src: "https://static.copus.io/images/client/202507/test/b9f17da83b054d53cd0cb4508c2c30dc.png", + }), + $el("span", { style: { marginLeft: "5px", color: "#fff" } }, [ + "All ages", + ]), + ] + ), + $el( + "p", + { style: { fontSize: "10px", color: "#fff", marginLeft: "20px" } }, + ["Safe for all viewers; no profanity, violence, or mature themes."] + ), + $el( + "label", + { style: { display: "flex", alignItems: "center", cursor: "pointer" } }, + [ + this.ratingRadioButtonsCheck1, + $el("img", { + style: { + width: "12px", + height: "12px", + marginLeft: "5px", + }, + src: "https://static.copus.io/images/client/202507/test/7848bc0d3690671df21c7cf00c4cfc81.png", + }), + $el("span", { style: { marginLeft: "5px", color: "#fff" } }, [ + "13+ (Teen)", + ]), + ] + ), + $el( + "p", + { style: { fontSize: "10px", color: "#fff", marginLeft: "20px" } }, + [ + "Mild language, light themes, or cartoon violence; no explicit content. ", + ] + ), + $el( + "label", + { style: { display: "flex", alignItems: "center", cursor: "pointer" } }, + [ + this.ratingRadioButtonsCheck2, + $el("img", { + style: { + width: "12px", + height: "12px", + marginLeft: "5px", + }, + src: "https://static.copus.io/images/client/202507/test/bc51839c208d68d91173e43c23bff039.png", + }), + $el("span", { style: { marginLeft: "5px", color: "#fff" } }, [ + "18+ (Explicit)", + ]), + ] + ), + $el( + "p", + { style: { fontSize: "10px", color: "#fff", marginLeft: "20px" } }, + [ + "Explicit content, including sexual content, strong violence, or intense themes. ", + ] + ), + $el( + "label", + { style: { display: "flex", alignItems: "center", cursor: "pointer" } }, + [ + this.ratingRadioButtonsCheck_1, + $el("img", { + style: { + width: "12px", + height: "12px", + marginLeft: "5px", + }, + src: "https://static.copus.io/images/client/202507/test/5c802fdcaaea4e7bbed37393eec0d5ba.png", + }), + $el("span", { style: { marginLeft: "5px", color: "#fff" } }, [ + "Not Rated", + ]), + ] + ), + $el( + "p", + { style: { fontSize: "10px", color: "#fff", marginLeft: "20px" } }, + ["No age rating provided."] + ), + ]); + + // Message Section + this.message = $el( + "div", + { + style: { + color: "#ff3d00", + textAlign: "center", + padding: "10px", + fontSize: "20px", + }, + }, + [] + ); + + this.shareButton = $el("button", { + type: "submit", + textContent: "Share", + style: buttonStyle, + onclick: () => { + this.handleShareButtonClick(); + }, + }); + + // Share and Close Buttons + const buttonsSection = $el( + "div", + { + style: { + textAlign: "right", + marginTop: "20px", + display: "flex", + justifyContent: "space-between", + }, + }, + [ + $el("button", { + type: "button", + textContent: "Close", + style: { + ...buttonStyle, + backgroundColor: undefined, + }, + onclick: () => { + this.close(); + }, + }), + this.shareButton, + ] + ); + + // Composing the full layout + const layout = [ + headerSection, + linkSection, + accountSection, + outputUploadSection, + this.outputsSection, + additionalInputsSection, + SubtitleSection, + DescriptionSection, + // contestSection, + blockChainSection_lock, + contentRatingSection, + blockChainSection, + this.message, + buttonsSection, + ]; + + return layout; + } + /** + * api + * @param {url} path + * @param {params} options + * @param {statusText} statusText + * @returns + */ + async fetchApi(path, options, statusText) { + if (statusText) { + this.message.textContent = statusText; + } + const fullPath = new URL(API_ENDPOINT + path); + const response = await fetch(fullPath, options); + if (!response.ok) { + throw new Error(response.statusText); + } + if (statusText) { + this.message.textContent = ""; + } + const data = await response.json(); + return { + ok: response.ok, + statusText: response.statusText, + status: response.status, + data, + }; + } + /** + * @param {file} uploadFile + */ + async uploadThumbnail(uploadFile, type) { + const form = new FormData(); + form.append("file", uploadFile); + form.append("apiToken", this.keyInput.value); + try { + const res = await this.fetchApi( + `/client/common/opus/uploadImage`, + { + method: "POST", + body: form, + }, + "Uploading thumbnail..." + ); + if (res.status && res.data.status && res.data) { + const { data } = res.data; + if (type) { + this.allFilesImages.push({ + url: data, + }); + } + this.uploadedImages.push({ + url: data, + }); + } else { + throw new Error( + "make sure your API key is correct and try again later" + ); + } + } catch (e) { + if (e?.response?.status === 413) { + throw new Error("File size is too large (max 20MB)"); + } else { + throw new Error("Error uploading thumbnail: " + e.message); + } + } + } + + async handleShareButtonClick() { + this.message.textContent = ""; + try { + this.shareButton.disabled = true; + this.shareButton.textContent = "Sharing..."; + await this.share(); + } catch (e) { + customAlert(e.message); + } + this.shareButton.disabled = false; + this.shareButton.textContent = "Share"; + } + /** + * share + * @param {string} title + * @param {string} subtitle + * @param {string} content + * @param {boolean} storeOnChain + * @param {string} coverUrl + * @param {string[]} imageUrls + * @param {string} apiToken + */ + async share() { + const prompt = await app.graphToPrompt(); + const workflowJSON = prompt["workflow"]; + const form_values = { + title: this.TitleInput.value, + subTitle: this.SubTitleInput.value, + content: this.descriptionInput.value, + storeOnChain: this.radioButtonsCheck.checked ? true : false, + lockState: this.radioButtonsCheck_lock.checked ? 2 : 0, + unlockPrice: this.LockInput.value, + rating: this.ratingRadioButtonsCheck0.checked + ? 0 + : this.ratingRadioButtonsCheck1.checked + ? 1 + : this.ratingRadioButtonsCheck2.checked + ? 2 + : -1, + }; + + if (!this.keyInput.value) { + throw new Error("API key is required"); + } + + if (!this.uploadImagesInput.files[0] && !this.selectedFile) { + throw new Error("Thumbnail is required"); + } + + if (!form_values.title) { + throw new Error("Title is required"); + } + + if (this.radioButtonsCheck_lock.checked) { + if (!this.LockInput.value) { + throw new Error("Price is required"); + } + } + + if (!this.uploadedImages.length) { + if (this.selectedFile) { + await this.uploadThumbnail(this.selectedFile); + } else { + for (const file of this.uploadImagesInput.files) { + try { + await this.uploadThumbnail(file); + } catch (e) { + this.uploadedImages = []; + throw new Error(e.message); + } + } + + if (this.uploadImagesInput.files.length === 0) { + throw new Error("No thumbnail uploaded"); + } + } + } + if (this.allFiles.length > 0) { + for (const file of this.allFiles) { + try { + await this.uploadThumbnail(file, true); + } catch (e) { + this.allFilesImages = []; + throw new Error(e.message); + } + } + } + try { + const res = await this.fetchApi( + "/client/common/opus/shareFromComfyUI", + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + workflowJson: workflowJSON, + apiToken: this.keyInput.value, + coverUrl: this.uploadedImages[0].url, + imageUrls: this.allFilesImages.map((image) => image.url), + ...form_values, + }), + }, + "Uploading workflow..." + ); + + if (res.status && res.data.status && res.data) { + localStorage.setItem("copus_token", this.keyInput.value); + const { data } = res.data; + if (data) { + const url = `${DEFAULT_HOMEPAGE_URL}/work/${data}`; + this.message.innerHTML = `Workflow has been shared successfully. Click here to view it.`; + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + this.uploadedImages = []; + this.allFilesImages = []; + this.allFiles = []; + this.TitleInput.value = ""; + this.SubTitleInput.value = ""; + this.descriptionInput.value = ""; + this.selectedFile = null; + } + } + } catch (e) { + throw new Error("Error sharing workflow: " + e.message); + } + } + + async fetchImageBlob(url) { + const response = await fetch(url); + const blob = await response.blob(); + return blob; + } + + async show({ potential_outputs, potential_output_nodes } = {}) { + // Sort `potential_output_nodes` by node ID to make the order always + // consistent, but we should also keep `potential_outputs` in the same + // order as `potential_output_nodes`. + const potential_output_to_order = {}; + potential_output_nodes.forEach((node, index) => { + if (node.id in potential_output_to_order) { + potential_output_to_order[node.id][1].push(potential_outputs[index]); + } else { + potential_output_to_order[node.id] = [node, [potential_outputs[index]]]; + } + }); + // Sort the object `potential_output_to_order` by key (node ID) + const sorted_potential_output_to_order = Object.fromEntries( + Object.entries(potential_output_to_order).sort( + (a, b) => a[0].id - b[0].id + ) + ); + const sorted_potential_outputs = []; + const sorted_potential_output_nodes = []; + for (const [key, value] of Object.entries( + sorted_potential_output_to_order + )) { + sorted_potential_output_nodes.push(value[0]); + sorted_potential_outputs.push(...value[1]); + } + potential_output_nodes = sorted_potential_output_nodes; + potential_outputs = sorted_potential_outputs; + const apiToken = localStorage.getItem("copus_token"); + this.message.innerHTML = ""; + this.message.textContent = ""; + this.element.style.display = "block"; + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + this.keyInput.value = apiToken != null ? apiToken : ""; + this.uploadedImages = []; + this.allFilesImages = []; + this.allFiles = []; + // If `selectedNodeId` is provided, we will select the corresponding radio + // button for the node. In addition, we move the selected radio button to + // the top of the list. + if (this.selectedNodeId) { + const index = potential_output_nodes.findIndex( + (node) => node.id === this.selectedNodeId + ); + if (index >= 0) { + this.selectedOutputIndex = index; + } + } + + this.radioButtons = []; + const new_radio_buttons = $el( + "div", + { + id: "selectOutput-Options", + style: { + "overflow-y": "scroll", + "max-height": "200px", + display: "grid", + "grid-template-columns": "repeat(auto-fit, minmax(100px, 1fr))", + "grid-template-rows": "auto", + "grid-column-gap": "10px", + "grid-row-gap": "10px", + "margin-bottom": "10px", + padding: "10px", + "border-radius": "8px", + "box-shadow": "0 2px 4px rgba(0, 0, 0, 0.05)", + "background-color": "var(--bg-color)", + }, + }, + potential_outputs.map((output, index) => { + const { node_id } = output; + const radio_button = $el( + "input", + { + type: "radio", + name: "selectOutputImages", + value: index, + required: index === 0, + }, + [] + ); + let radio_button_img; + let filename; + if (output.type === "image" || output.type === "temp") { + radio_button_img = $el( + "img", + { + src: `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`, + style: { + width: "100px", + height: "100px", + objectFit: "cover", + borderRadius: "5px", + }, + }, + [] + ); + filename = output.image.filename; + } else if (output.type === "output") { + radio_button_img = $el( + "img", + { + src: output.output.value, + style: { + width: "auto", + height: "100px", + objectFit: "cover", + borderRadius: "5px", + }, + }, + [] + ); + filename = output.filename; + } else { + // unsupported output type + // this should never happen + radio_button_img = $el( + "img", + { + src: "", + style: { width: "auto", height: "100px" }, + }, + [] + ); + } + const radio_button_text = $el( + "span", + { + style: { + color: "gray", + display: "block", + fontSize: "12px", + overflowX: "hidden", + textOverflow: "ellipsis", + textWrap: "nowrap", + maxWidth: "100px", + }, + }, + [output.title] + ); + const node_id_chip = $el( + "span", + { + style: { + color: "#FBFBFD", + display: "block", + backgroundColor: "rgba(0, 0, 0, 0.5)", + fontSize: "12px", + overflowX: "hidden", + padding: "2px 3px", + textOverflow: "ellipsis", + textWrap: "nowrap", + maxWidth: "100px", + position: "absolute", + top: "3px", + left: "3px", + borderRadius: "3px", + }, + }, + [`Node: ${node_id}`] + ); + radio_button.style.color = "var(--fg-color)"; + radio_button.checked = this.selectedOutputIndex === index; + + radio_button.onchange = async () => { + this.selectedOutputIndex = parseInt(radio_button.value); + + // Remove the "checked" class from all radio buttons + this.radioButtons.forEach((ele) => { + ele.parentElement.classList.remove("checked"); + }); + radio_button.parentElement.classList.add("checked"); + + this.fetchImageBlob(radio_button_img.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.previewImage.src = radio_button_img.src; + this.previewImage.style.display = "block"; + this.selectedFile = file; + }); + + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 1; + this.uploadImagesInput.style.opacity = 0.35; + }; + + if (radio_button.checked) { + this.fetchImageBlob(radio_button_img.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.previewImage.src = radio_button_img.src; + this.previewImage.style.display = "block"; + this.selectedFile = file; + }); + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 1; + this.uploadImagesInput.style.opacity = 0.35; + } + this.radioButtons.push(radio_button); + let src = ""; + if (output.type === "image" || output.type === "temp") { + filename = output.image.filename; + src = `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`; + } else if (output.type === "output") { + src = output.output.value; + filename = output.filename; + } + if (src) { + this.fetchImageBlob(src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.allFiles.push(file); + }); + } + return $el( + `label.output_label${radio_button.checked ? ".checked" : ""}`, + { + style: { + display: "flex", + flexDirection: "column", + alignItems: "center", + justifyContent: "center", + marginBottom: "10px", + cursor: "pointer", + position: "relative", + }, + }, + [radio_button_img, radio_button_text, radio_button, node_id_chip] + ); + }) + ); + + const header = $el( + "p", + { + textContent: + this.radioButtons.length === 0 + ? "Queue Prompt to see the outputs" + : "Or choose one from the outputs (scroll to see all)", + size: 2, + color: "white", + style: { + color: "white", + margin: "0 0 5px 0", + fontSize: "12px", + }, + }, + [] + ); + this.outputsSection.innerHTML = ""; + this.outputsSection.appendChild(header); + this.outputsSection.appendChild(new_radio_buttons); + } +} diff --git a/custom_nodes/comfyui-manager/js/comfyui-share-openart.js b/custom_nodes/comfyui-manager/js/comfyui-share-openart.js new file mode 100644 index 00000000..1c96a8c7 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/comfyui-share-openart.js @@ -0,0 +1,746 @@ +import {app} from "../../scripts/app.js"; +import {api} from "../../scripts/api.js"; +import {ComfyDialog, $el} from "../../scripts/ui.js"; +import { customAlert } from "./common.js"; + +const LOCAL_STORAGE_KEY = "openart_comfy_workflow_key"; +const DEFAULT_HOMEPAGE_URL = "https://openart.ai/workflows/dev?developer=true"; +//const DEFAULT_HOMEPAGE_URL = "http://localhost:8080/workflows/dev?developer=true"; + +const API_ENDPOINT = "https://openart.ai/api"; +//const API_ENDPOINT = "http://localhost:8080/api"; + +const style = ` + .openart-share-dialog a { + color: #f8f8f8; + } + .openart-share-dialog a:hover { + color: #007bff; + } + .output_label { + border: 5px solid transparent; + } + .output_label:hover { + border: 5px solid #59E8C6; + } + .output_label.checked { + border: 5px solid #59E8C6; + } +`; + +// Shared component styles +const sectionStyle = { + marginBottom: 0, + padding: 0, + borderRadius: "8px", + boxShadow: "0 2px 4px rgba(0, 0, 0, 0.05)", + display: "flex", + flexDirection: "column", + justifyContent: "center", +}; + +export class OpenArtShareDialog extends ComfyDialog { + static instance = null; + + constructor() { + super(); + $el("style", { + textContent: style, + parent: document.head, + }); + this.element = $el( + "div.comfy-modal.openart-share-dialog", + { + parent: document.body, + style: { + "overflow-y": "auto", + }, + }, + [$el("div.comfy-modal-content", {}, [...this.createButtons()])] + ); + this.selectedOutputIndex = 0; + this.selectedNodeId = null; + this.uploadedImages = []; + this.selectedFile = null; + } + + async readKey() { + let key = "" + try { + key = await api.fetchApi(`/manager/get_openart_auth`) + .then(response => response.json()) + .then(data => { + return data.openart_key; + }) + .catch(error => { + // console.log(error); + }); + } catch (error) { + // console.log(error); + } + return key || ""; + } + + async saveKey(value) { + await api.fetchApi(`/manager/set_openart_auth`, { + method: 'POST', + headers: {'Content-Type': 'application/json'}, + body: JSON.stringify({ + openart_key: value + }) + }); + } + + createButtons() { + const inputStyle = { + display: "block", + minWidth: "500px", + width: "100%", + padding: "10px", + margin: "10px 0", + borderRadius: "4px", + border: "1px solid #ddd", + boxSizing: "border-box", + }; + + const hyperLinkStyle = { + display: "block", + marginBottom: "15px", + fontWeight: "bold", + fontSize: "14px", + }; + + const labelStyle = { + color: "#f8f8f8", + display: "block", + margin: "10px 0 0 0", + fontWeight: "bold", + textDecoration: "none", + }; + + const buttonStyle = { + padding: "10px 80px", + margin: "10px 5px", + borderRadius: "4px", + border: "none", + cursor: "pointer", + color: "#fff", + backgroundColor: "#007bff", + }; + + // upload images input + this.uploadImagesInput = $el("input", { + type: "file", + multiple: false, + style: inputStyle, + accept: "image/*", + }); + + this.uploadImagesInput.addEventListener("change", async (e) => { + const file = e.target.files[0]; + if (!file) { + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + return; + } + const reader = new FileReader(); + reader.onload = async (e) => { + const imgData = e.target.result; + this.previewImage.src = imgData; + this.previewImage.style.display = "block"; + this.selectedFile = null + // Once user uploads an image, we uncheck all radio buttons + this.radioButtons.forEach((ele) => { + ele.checked = false; + ele.parentElement.classList.remove("checked"); + }); + + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 0.35; + this.uploadImagesInput.style.opacity = 1; + }; + reader.readAsDataURL(file); + }); + + // preview image + this.previewImage = $el("img", { + src: "", + style: { + width: "100%", + maxHeight: "100px", + objectFit: "contain", + display: "none", + marginTop: '10px', + }, + }); + + this.keyInput = $el("input", { + type: "password", + placeholder: "Copy & paste your API key", + style: inputStyle, + }); + this.NameInput = $el("input", { + type: "text", + placeholder: "Title (required)", + style: inputStyle, + }); + this.descriptionInput = $el("textarea", { + placeholder: "Description (optional)", + style: { + ...inputStyle, + minHeight: "100px", + }, + }); + + // Header Section + const headerSection = $el("h3", { + textContent: "Share your workflow to OpenArt", + size: 3, + color: "white", + style: { + 'text-align': 'center', + color: 'var(--input-text)', + margin: '0 0 10px 0', + } + }); + + // LinkSection + this.communityLink = $el("a", { + style: hyperLinkStyle, + href: DEFAULT_HOMEPAGE_URL, + target: "_blank" + }, ["πŸ‘‰ Check out thousands of workflows shared from the community"]) + this.getAPIKeyLink = $el("a", { + style: { + ...hyperLinkStyle, + color: "#59E8C6" + }, + href: DEFAULT_HOMEPAGE_URL, + target: "_blank" + }, ["πŸ‘‰ Get your API key here"]) + const linkSection = $el( + "div", + { + style: { + marginTop: "10px", + display: "flex", + flexDirection: "column", + }, + }, + [ + this.communityLink, + this.getAPIKeyLink, + ] + ); + + // Account Section + const accountSection = $el("div", {style: sectionStyle}, [ + $el("label", {style: labelStyle}, ["1️⃣ OpenArt API Key"]), + this.keyInput, + ]); + + // Output Upload Section + const outputUploadSection = $el("div", {style: sectionStyle}, [ + $el("label", { + style: { + ...labelStyle, + margin: "10px 0 0 0" + } + }, ["2️⃣ Image/Thumbnail (Required)"]), + this.previewImage, + this.uploadImagesInput, + ]); + + // Outputs Section + this.outputsSection = $el("div", { + id: "selectOutputs", + }, []); + + // Additional Inputs Section + const additionalInputsSection = $el("div", {style: sectionStyle}, [ + $el("label", {style: labelStyle}, ["3️⃣ Workflow Information"]), + this.NameInput, + this.descriptionInput, + ]); + + // OpenArt Contest Section + /* + this.joinContestCheckbox = $el("input", { + type: 'checkbox', + id: "join_contest"s + }, []) + this.joinContestDescription = $el("a", { + style: { + ...hyperLinkStyle, + display: 'inline-block', + color: "#59E8C6", + fontSize: '12px', + marginLeft: '10px', + marginBottom: 0, + }, + href: "https://contest.openart.ai/", + target: "_blank" + }, ["πŸ† I'm participating in the OpenArt workflow contest"]) + this.joinContestLabel = $el("label", { + style: { + display: 'flex', + alignItems: 'center', + cursor: 'pointer', + } + }, [this.joinContestCheckbox, this.joinContestDescription]) + const contestSection = $el("div", {style: sectionStyle}, [ + this.joinContestLabel, + ]); + */ + + // Message Section + this.message = $el( + "div", + { + style: { + color: "#ff3d00", + textAlign: "center", + padding: "10px", + fontSize: "20px", + }, + }, + [] + ); + + this.shareButton = $el("button", { + type: "submit", + textContent: "Share", + style: buttonStyle, + onclick: () => { + this.handleShareButtonClick(); + }, + }); + + // Share and Close Buttons + const buttonsSection = $el( + "div", + { + style: { + textAlign: "right", + marginTop: "20px", + display: "flex", + justifyContent: "space-between", + }, + }, + [ + $el("button", { + type: "button", + textContent: "Close", + style: { + ...buttonStyle, + backgroundColor: undefined, + }, + onclick: () => { + this.close(); + }, + }), + this.shareButton, + ] + ); + + // Composing the full layout + const layout = [ + headerSection, + linkSection, + accountSection, + outputUploadSection, + this.outputsSection, + additionalInputsSection, + // contestSection, + this.message, + buttonsSection, + ]; + + return layout; + } + + async fetchApi(path, options, statusText) { + if (statusText) { + this.message.textContent = statusText; + } + const addSearchParams = (url, params = {}) => + new URL( + `${url.origin}${url.pathname}?${new URLSearchParams([ + ...Array.from(url.searchParams.entries()), + ...Object.entries(params), + ])}` + ); + + const fullPath = addSearchParams(new URL(API_ENDPOINT + path), { + workflow_api_key: this.keyInput.value, + }); + + const response = await fetch(fullPath, options); + + if (!response.ok) { + throw new Error(response.statusText); + } + + if (statusText) { + this.message.textContent = ""; + } + const data = await response.json(); + return { + ok: response.ok, + statusText: response.statusText, + status: response.status, + data, + }; + } + + async uploadThumbnail(uploadFile) { + const form = new FormData(); + form.append("file", uploadFile); + try { + const res = await this.fetchApi( + `/workflows/upload_thumbnail`, + { + method: "POST", + body: form, + }, + "Uploading thumbnail..." + ); + + if (res.ok && res.data) { + const {image_url, width, height} = res.data; + this.uploadedImages.push({ + url: image_url, + width, + height, + }); + } + } catch (e) { + if (e?.response?.status === 413) { + throw new Error("File size is too large (max 20MB)"); + } else { + throw new Error("Error uploading thumbnail: " + e.message); + } + } + } + + async handleShareButtonClick() { + this.message.textContent = ""; + await this.saveKey(this.keyInput.value); + try { + this.shareButton.disabled = true; + this.shareButton.textContent = "Sharing..."; + await this.share(); + } catch (e) { + customAlert(e.message); + } + this.shareButton.disabled = false; + this.shareButton.textContent = "Share"; + } + + async share() { + const prompt = await app.graphToPrompt(); + const workflowJSON = prompt["workflow"]; + const workflowAPIJSON = prompt["output"]; + const form_values = { + name: this.NameInput.value, + description: this.descriptionInput.value, + }; + + if (!this.keyInput.value) { + throw new Error("API key is required"); + } + + if (!this.uploadImagesInput.files[0] && !this.selectedFile) { + throw new Error("Thumbnail is required"); + } + + if (!form_values.name) { + throw new Error("Title is required"); + } + + const current_snapshot = await api.fetchApi(`/snapshot/get_current`) + .then(response => response.json()) + .catch(error => { + // console.log(error); + }); + + + if (!this.uploadedImages.length) { + if (this.selectedFile) { + await this.uploadThumbnail(this.selectedFile); + } else { + for (const file of this.uploadImagesInput.files) { + try { + await this.uploadThumbnail(file); + } catch (e) { + this.uploadedImages = []; + throw new Error(e.message); + } + } + + if (this.uploadImagesInput.files.length === 0) { + throw new Error("No thumbnail uploaded"); + } + } + } + + // const join_contest = this.joinContestCheckbox.checked; + + try { + const response = await this.fetchApi( + "/workflows/publish", + { + method: "POST", + headers: {"Content-Type": "application/json"}, + body: JSON.stringify({ + workflow_json: workflowJSON, + upload_images: this.uploadedImages, + form_values, + advanced_config: { + workflow_api_json: workflowAPIJSON, + snapshot: current_snapshot, + }, + // join_contest, + }), + }, + "Uploading workflow..." + ); + + if (response.ok) { + const {workflow_id} = response.data; + if (workflow_id) { + const url = `https://openart.ai/workflows/-/-/${workflow_id}`; + this.message.innerHTML = `Workflow has been shared successfully. Click here to view it.`; + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + this.uploadedImages = []; + this.NameInput.value = ""; + this.descriptionInput.value = ""; + this.radioButtons.forEach((ele) => { + ele.checked = false; + ele.parentElement.classList.remove("checked"); + }); + this.selectedOutputIndex = 0; + this.selectedNodeId = null; + this.selectedFile = null; + } + } + } catch (e) { + throw new Error("Error sharing workflow: " + e.message); + } + } + + async fetchImageBlob(url) { + const response = await fetch(url); + const blob = await response.blob(); + return blob; + } + + async show({potential_outputs, potential_output_nodes} = {}) { + // Sort `potential_output_nodes` by node ID to make the order always + // consistent, but we should also keep `potential_outputs` in the same + // order as `potential_output_nodes`. + const potential_output_to_order = {}; + potential_output_nodes.forEach((node, index) => { + if (node.id in potential_output_to_order) { + potential_output_to_order[node.id][1].push(potential_outputs[index]); + } else { + potential_output_to_order[node.id] = [node, [potential_outputs[index]]]; + } + }) + // Sort the object `potential_output_to_order` by key (node ID) + const sorted_potential_output_to_order = Object.fromEntries( + Object.entries(potential_output_to_order).sort((a, b) => a[0].id - b[0].id) + ); + const sorted_potential_outputs = [] + const sorted_potential_output_nodes = [] + for (const [key, value] of Object.entries(sorted_potential_output_to_order)) { + sorted_potential_output_nodes.push(value[0]); + sorted_potential_outputs.push(...value[1]); + } + potential_output_nodes = sorted_potential_output_nodes; + potential_outputs = sorted_potential_outputs; + + this.message.innerHTML = ""; + this.message.textContent = ""; + this.element.style.display = "block"; + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + const key = await this.readKey(); + this.keyInput.value = key; + this.uploadedImages = []; + + // If `selectedNodeId` is provided, we will select the corresponding radio + // button for the node. In addition, we move the selected radio button to + // the top of the list. + if (this.selectedNodeId) { + const index = potential_output_nodes.findIndex(node => node.id === this.selectedNodeId); + if (index >= 0) { + this.selectedOutputIndex = index; + } + } + + this.radioButtons = []; + const new_radio_buttons = $el("div", + { + id: "selectOutput-Options", + style: { + 'overflow-y': 'scroll', + 'max-height': '200px', + + 'display': 'grid', + 'grid-template-columns': 'repeat(auto-fit, minmax(100px, 1fr))', + 'grid-template-rows': 'auto', + 'grid-column-gap': '10px', + 'grid-row-gap': '10px', + 'margin-bottom': '10px', + 'padding': '10px', + 'border-radius': '8px', + 'box-shadow': '0 2px 4px rgba(0, 0, 0, 0.05)', + 'background-color': 'var(--bg-color)', + } + }, + potential_outputs.map((output, index) => { + const {node_id} = output; + const radio_button = $el("input", { + type: 'radio', + name: "selectOutputImages", + value: index, + required: index === 0 + }, []) + let radio_button_img; + let filename; + if (output.type === "image" || output.type === "temp") { + radio_button_img = $el("img", { + src: `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`, + style: { + width: "100px", + height: "100px", + objectFit: "cover", + borderRadius: "5px" + } + }, []); + filename = output.image.filename + } else if (output.type === "output") { + radio_button_img = $el("img", { + src: output.output.value, + style: { + width: "auto", + height: "100px", + objectFit: "cover", + borderRadius: "5px" + } + }, []); + filename = output.filename + } else { + // unsupported output type + // this should never happen + // TODO + radio_button_img = $el("img", { + src: "", + style: {width: "auto", height: "100px"} + }, []); + } + const radio_button_text = $el("span", { + style: { + color: 'gray', + display: 'block', + fontSize: '12px', + overflowX: 'hidden', + textOverflow: 'ellipsis', + textWrap: 'nowrap', + maxWidth: '100px', + } + }, [output.title]) + const node_id_chip = $el("span", { + style: { + color: '#FBFBFD', + display: 'block', + backgroundColor: 'rgba(0, 0, 0, 0.5)', + fontSize: '12px', + overflowX: 'hidden', + padding: '2px 3px', + textOverflow: 'ellipsis', + textWrap: 'nowrap', + maxWidth: '100px', + position: 'absolute', + top: '3px', + left: '3px', + borderRadius: '3px', + } + }, [`Node: ${node_id}`]) + radio_button.style.color = "var(--fg-color)"; + radio_button.checked = this.selectedOutputIndex === index; + + radio_button.onchange = async () => { + this.selectedOutputIndex = parseInt(radio_button.value); + + // Remove the "checked" class from all radio buttons + this.radioButtons.forEach((ele) => { + ele.parentElement.classList.remove("checked"); + }); + radio_button.parentElement.classList.add("checked"); + + this.fetchImageBlob(radio_button_img.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.previewImage.src = radio_button_img.src; + this.previewImage.style.display = "block"; + this.selectedFile = file; + }) + + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 1; + this.uploadImagesInput.style.opacity = 0.35; + }; + + if (radio_button.checked) { + this.fetchImageBlob(radio_button_img.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.previewImage.src = radio_button_img.src; + this.previewImage.style.display = "block"; + this.selectedFile = file; + }) + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 1; + this.uploadImagesInput.style.opacity = 0.35; + } + + this.radioButtons.push(radio_button); + + return $el(`label.output_label${radio_button.checked ? '.checked' : ''}`, { + style: { + display: "flex", + flexDirection: "column", + alignItems: "center", + justifyContent: "center", + marginBottom: "10px", + cursor: "pointer", + position: 'relative', + } + }, [radio_button_img, radio_button_text, radio_button, node_id_chip]); + }) + ); + + const header = + $el("p", { + textContent: this.radioButtons.length === 0 ? "Queue Prompt to see the outputs" : "Or choose one from the outputs (scroll to see all)", + size: 2, + color: "white", + style: { + color: 'var(--input-text)', + margin: '0 0 5px 0', + fontSize: '12px', + }, + }, []) + this.outputsSection.innerHTML = ""; + this.outputsSection.appendChild(header); + this.outputsSection.appendChild(new_radio_buttons); + } +} diff --git a/custom_nodes/comfyui-manager/js/comfyui-share-youml.js b/custom_nodes/comfyui-manager/js/comfyui-share-youml.js new file mode 100644 index 00000000..efd8916f --- /dev/null +++ b/custom_nodes/comfyui-manager/js/comfyui-share-youml.js @@ -0,0 +1,569 @@ +import {app} from "../../scripts/app.js"; +import {api} from "../../scripts/api.js"; +import {ComfyDialog, $el} from "../../scripts/ui.js"; +import { customAlert } from "./common.js"; + +const BASE_URL = "https://youml.com"; +//const BASE_URL = "http://localhost:3000"; +const DEFAULT_HOMEPAGE_URL = `${BASE_URL}/?from=comfyui`; +const TOKEN_PAGE_URL = `${BASE_URL}/my-token`; +const API_ENDPOINT = `${BASE_URL}/api`; + +const style = ` + .youml-share-dialog { + overflow-y: auto; + } + .youml-share-dialog .dialog-header { + text-align: center; + color: white; + margin: 0 0 10px 0; + } + .youml-share-dialog .dialog-section { + margin-bottom: 0; + padding: 0; + border-radius: 8px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05); + display: flex; + flex-direction: column; + justify-content: center; + } + .youml-share-dialog input, .youml-share-dialog textarea { + display: block; + min-width: 500px; + width: 100%; + padding: 10px; + margin: 10px 0; + border-radius: 4px; + border: 1px solid #ddd; + box-sizing: border-box; + } + .youml-share-dialog textarea { + color: var(--input-text); + background-color: var(--comfy-input-bg); + } + .youml-share-dialog .workflow-description { + min-height: 75px; + } + .youml-share-dialog label { + color: #f8f8f8; + display: block; + margin: 5px 0 0 0; + font-weight: bold; + text-decoration: none; + } + .youml-share-dialog .action-button { + padding: 10px 80px; + margin: 10px 5px; + border-radius: 4px; + border: none; + cursor: pointer; + } + .youml-share-dialog .share-button { + color: #fff; + background-color: #007bff; + } + .youml-share-dialog .close-button { + background-color: none; + } + .youml-share-dialog .action-button-panel { + text-align: right; + display: flex; + justify-content: space-between; + } + .youml-share-dialog .status-message { + color: #fd7909; + text-align: center; + padding: 5px; + font-size: 18px; + } + .youml-share-dialog .status-message a { + color: white; + } + .youml-share-dialog .output-panel { + overflow: auto; + max-height: 180px; + display: grid; + grid-template-columns: repeat(auto-fit, minmax(100px, 1fr)); + grid-template-rows: auto; + grid-column-gap: 10px; + grid-row-gap: 10px; + margin-bottom: 10px; + padding: 10px; + border-radius: 8px; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05); + background-color: var(--bg-color); + } + .youml-share-dialog .output-panel .output-image { + width: 100px; + height: 100px; + objectFit: cover; + borderRadius: 5px; + } + + .youml-share-dialog .output-panel .radio-button { + color:var(--fg-color); + } + .youml-share-dialog .output-panel .radio-text { + color: gray; + display: block; + font-size: 12px; + overflow-x: hidden; + text-overflow: ellipsis; + text-wrap: nowrap; + max-width: 100px; + } + .youml-share-dialog .output-panel .node-id { + color: #FBFBFD; + display: block; + background-color: rgba(0, 0, 0, 0.5); + font-size: 12px; + overflow-x: hidden; + padding: 2px 3px; + text-overflow: ellipsis; + text-wrap: nowrap; + max-width: 100px; + position: absolute; + top: 3px; + left: 3px; + border-radius: 3px; + } + .youml-share-dialog .output-panel .output-label { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + margin-bottom: 10px; + cursor: pointer; + position: relative; + border: 5px solid transparent; + } + .youml-share-dialog .output-panel .output-label:hover { + border: 5px solid #007bff; + } + .youml-share-dialog .output-panel .output-label.checked { + border: 5px solid #007bff; + } + .youml-share-dialog .missing-output-message{ + color: #fd7909; + font-size: 16px; + margin-bottom:10px + } + .youml-share-dialog .select-output-message{ + color: white; + margin-bottom:5px + } +`; + +export class YouMLShareDialog extends ComfyDialog { + static instance = null; + + constructor() { + super(); + $el("style", { + textContent: style, + parent: document.head, + }); + this.element = $el( + "div.comfy-modal.youml-share-dialog", + { + parent: document.body, + }, + [$el("div.comfy-modal-content", {}, [...this.createLayout()])] + ); + this.selectedOutputIndex = 0; + this.selectedNodeId = null; + this.uploadedImages = []; + this.selectedFile = null; + } + + async loadToken() { + let key = "" + try { + const response = await api.fetchApi(`/manager/youml/settings`) + const settings = await response.json() + return settings.token + } catch (error) { + } + return key || ""; + } + + async saveToken(value) { + await api.fetchApi(`/manager/youml/settings`, { + method: 'POST', + headers: {'Content-Type': 'application/json'}, + body: JSON.stringify({ + token: value + }) + }); + } + + createLayout() { + // Header Section + const headerSection = $el("h3.dialog-header", { + textContent: "Share your workflow to YouML.com", + size: 3, + }); + + // Workflow Info Section + this.nameInput = $el("input", { + type: "text", + placeholder: "Name (required)", + }); + this.descriptionInput = $el("textarea.workflow-description", { + placeholder: "Description (optional, markdown supported)", + }); + const workflowMetadata = $el("div.dialog-section", {}, [ + $el("label", {}, ["Workflow info"]), + this.nameInput, + this.descriptionInput, + ]); + + // Outputs Section + this.outputsSection = $el("div.dialog-section", { + id: "selectOutputs", + }, []); + + const outputUploadSection = $el("div.dialog-section", {}, [ + $el("label", {}, ["Thumbnail"]), + this.outputsSection, + ]); + + // API Token Section + this.apiTokenInput = $el("input", { + type: "password", + placeholder: "Copy & paste your API token", + }); + const getAPITokenButton = $el("button", { + href: DEFAULT_HOMEPAGE_URL, + target: "_blank", + onclick: () => window.open(TOKEN_PAGE_URL, "_blank"), + }, ["Get your API Token"]) + + const apiTokenSection = $el("div.dialog-section", {}, [ + $el("label", {}, ["YouML API Token"]), + this.apiTokenInput, + getAPITokenButton, + ]); + + // Message Section + this.message = $el("div.status-message", {}, []); + + // Share and Close Buttons + this.shareButton = $el("button.action-button.share-button", { + type: "submit", + textContent: "Share", + onclick: () => { + this.handleShareButtonClick(); + }, + }); + + const buttonsSection = $el( + "div.action-button-panel", + {}, + [ + $el("button.action-button.close-button", { + type: "button", + textContent: "Close", + onclick: () => { + this.close(); + }, + }), + this.shareButton, + ] + ); + + // Composing the full layout + const layout = [ + headerSection, + workflowMetadata, + outputUploadSection, + apiTokenSection, + this.message, + buttonsSection, + ]; + + return layout; + } + + async fetchYoumlApi(path, options, statusText) { + if (statusText) { + this.message.textContent = statusText; + } + + const fullPath = new URL(API_ENDPOINT + path) + + const fetchOptions = Object.assign({}, options) + + fetchOptions.headers = { + ...fetchOptions.headers, + "Authorization": `Bearer ${this.apiTokenInput.value}`, + "User-Agent": "ComfyUI-Manager-Youml/1.0.0", + } + + const response = await fetch(fullPath, fetchOptions); + + if (!response.ok) { + throw new Error(response.statusText + " " + (await response.text())); + } + + if (statusText) { + this.message.textContent = ""; + } + const data = await response.json(); + return { + ok: response.ok, + statusText: response.statusText, + status: response.status, + data, + }; + } + + async uploadThumbnail(uploadFile, recipeId) { + const form = new FormData(); + form.append("file", uploadFile, uploadFile.name); + try { + const res = await this.fetchYoumlApi( + `/v1/comfy/recipes/${recipeId}/thumbnail`, + { + method: "POST", + body: form, + }, + "Uploading thumbnail..." + ); + + } catch (e) { + if (e?.response?.status === 413) { + throw new Error("File size is too large (max 20MB)"); + } else { + throw new Error("Error uploading thumbnail: " + e.message); + } + } + } + + async handleShareButtonClick() { + this.message.textContent = ""; + await this.saveToken(this.apiTokenInput.value); + try { + this.shareButton.disabled = true; + this.shareButton.textContent = "Sharing..."; + await this.share(); + } catch (e) { + customAlert(e.message); + } finally { + this.shareButton.disabled = false; + this.shareButton.textContent = "Share"; + } + } + + async share() { + const prompt = await app.graphToPrompt(); + const workflowJSON = prompt["workflow"]; + const workflowAPIJSON = prompt["output"]; + const form_values = { + name: this.nameInput.value, + description: this.descriptionInput.value, + }; + + if (!this.apiTokenInput.value) { + throw new Error("API token is required"); + } + + if (!this.selectedFile) { + throw new Error("Thumbnail is required"); + } + + if (!form_values.name) { + throw new Error("Title is required"); + } + + + try { + let snapshotData = null; + try { + const snapshot = await api.fetchApi(`/snapshot/get_current`) + snapshotData = await snapshot.json() + } catch (e) { + console.error("Failed to get snapshot", e) + } + + const request = { + name: this.nameInput.value, + description: this.descriptionInput.value, + workflowUiJson: JSON.stringify(workflowJSON), + workflowApiJson: JSON.stringify(workflowAPIJSON), + } + + if (snapshotData) { + request.snapshotJson = JSON.stringify(snapshotData) + } + + const response = await this.fetchYoumlApi( + "/v1/comfy/recipes", + { + method: "POST", + headers: {"Content-Type": "application/json"}, + body: JSON.stringify(request), + }, + "Uploading workflow..." + ); + + if (response.ok) { + const {id, recipePageUrl, editorPageUrl} = response.data; + if (id) { + let messagePrefix = "Workflow has been shared." + if (this.selectedFile) { + try { + await this.uploadThumbnail(this.selectedFile, id); + } catch (e) { + console.error("Thumbnail upload failed: ", e); + messagePrefix = "Workflow has been shared, but thumbnail upload failed. You can create a thumbnail on YouML later." + } + } + this.message.innerHTML = `${messagePrefix} To turn your workflow into an interactive app, ` + + `visit it on YouML`; + + this.uploadedImages = []; + this.nameInput.value = ""; + this.descriptionInput.value = ""; + this.radioButtons.forEach((ele) => { + ele.checked = false; + ele.parentElement.classList.remove("checked"); + }); + this.selectedOutputIndex = 0; + this.selectedNodeId = null; + this.selectedFile = null; + } + } + } catch (e) { + throw new Error("Error sharing workflow: " + e.message); + } + } + + async fetchImageBlob(url) { + const response = await fetch(url); + const blob = await response.blob(); + return blob; + } + + async show(potentialOutputs, potentialOutputNodes) { + const potentialOutputsToOrder = {}; + potentialOutputNodes.forEach((node, index) => { + if (node.id in potentialOutputsToOrder) { + potentialOutputsToOrder[node.id][1].push(potentialOutputs[index]); + } else { + potentialOutputsToOrder[node.id] = [node, [potentialOutputs[index]]]; + } + }) + const sortedPotentialOutputsToOrder = Object.fromEntries( + Object.entries(potentialOutputsToOrder).sort((a, b) => a[0].id - b[0].id) + ); + const sortedPotentialOutputs = [] + const sortedPotentiaOutputNodes = [] + for (const [key, value] of Object.entries(sortedPotentialOutputsToOrder)) { + sortedPotentiaOutputNodes.push(value[0]); + sortedPotentialOutputs.push(...value[1]); + } + potentialOutputNodes = sortedPotentiaOutputNodes; + potentialOutputs = sortedPotentialOutputs; + + + // If `selectedNodeId` is provided, we will select the corresponding radio + // button for the node. In addition, we move the selected radio button to + // the top of the list. + if (this.selectedNodeId) { + const index = potentialOutputNodes.findIndex(node => node.id === this.selectedNodeId); + if (index >= 0) { + this.selectedOutputIndex = index; + } + } + + this.radioButtons = []; + const newRadioButtons = $el("div.output-panel", + { + id: "selectOutput-Options", + }, + potentialOutputs.map((output, index) => { + const {node_id: nodeId} = output; + const radioButton = $el("input.radio-button", { + type: "radio", + name: "selectOutputImages", + value: index, + required: index === 0 + }, []) + let radioButtonImage; + let filename; + if (output.type === "image" || output.type === "temp") { + radioButtonImage = $el("img.output-image", { + src: `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`, + }, []); + filename = output.image.filename + } else if (output.type === "output") { + radioButtonImage = $el("img.output-image", { + src: output.output.value, + }, []); + filename = output.output.filename + } else { + radioButtonImage = $el("img.output-image", { + src: "", + }, []); + } + const radioButtonText = $el("span.radio-text", {}, [output.title]) + const nodeIdChip = $el("span.node-id", {}, [`Node: ${nodeId}`]) + radioButton.checked = this.selectedOutputIndex === index; + + radioButton.onchange = async () => { + this.selectedOutputIndex = parseInt(radioButton.value); + + // Remove the "checked" class from all radio buttons + this.radioButtons.forEach((ele) => { + ele.parentElement.classList.remove("checked"); + }); + radioButton.parentElement.classList.add("checked"); + + this.fetchImageBlob(radioButtonImage.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.selectedFile = file; + }) + }; + + if (radioButton.checked) { + this.fetchImageBlob(radioButtonImage.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.selectedFile = file; + }) + } + + this.radioButtons.push(radioButton); + + return $el(`label.output-label${radioButton.checked ? '.checked' : ''}`, {}, + [radioButtonImage, radioButtonText, radioButton, nodeIdChip]); + }) + ); + + let header; + if (this.radioButtons.length === 0) { + header = $el("div.missing-output-message", {textContent: "Queue Prompt to see the outputs and select a thumbnail"}, []) + } else { + header = $el("div.select-output-message", {textContent: "Choose one from the outputs (scroll to see all)"}, []) + } + + this.outputsSection.innerHTML = ""; + this.outputsSection.appendChild(header); + if (this.radioButtons.length > 0) { + this.outputsSection.appendChild(newRadioButtons); + } + + this.message.innerHTML = ""; + this.message.textContent = ""; + + const token = await this.loadToken(); + this.apiTokenInput.value = token; + this.uploadedImages = []; + + this.element.style.display = "block"; + } +} diff --git a/custom_nodes/comfyui-manager/js/common.js b/custom_nodes/comfyui-manager/js/common.js new file mode 100644 index 00000000..b8193055 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/common.js @@ -0,0 +1,670 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { $el, ComfyDialog } from "../../scripts/ui.js"; +import { getBestPosition, getPositionStyle, getRect } from './popover-helper.js'; + + +function internalCustomConfirm(message, confirmMessage, cancelMessage) { + return new Promise((resolve) => { + // transparent bg + const modalOverlay = document.createElement('div'); + modalOverlay.style.position = 'fixed'; + modalOverlay.style.top = 0; + modalOverlay.style.left = 0; + modalOverlay.style.width = '100%'; + modalOverlay.style.height = '100%'; + modalOverlay.style.backgroundColor = 'rgba(0, 0, 0, 0.8)'; + modalOverlay.style.display = 'flex'; + modalOverlay.style.alignItems = 'center'; + modalOverlay.style.justifyContent = 'center'; + modalOverlay.style.zIndex = '1101'; + + // Modal window container (dark bg) + const modalDialog = document.createElement('div'); + modalDialog.style.backgroundColor = '#333'; + modalDialog.style.padding = '20px'; + modalDialog.style.borderRadius = '4px'; + modalDialog.style.maxWidth = '400px'; + modalDialog.style.width = '80%'; + modalDialog.style.boxShadow = '0 2px 8px rgba(0, 0, 0, 0.5)'; + modalDialog.style.color = '#fff'; + + // Display message + const modalMessage = document.createElement('p'); + modalMessage.textContent = message; + modalMessage.style.margin = '0'; + modalMessage.style.padding = '0 0 20px'; + modalMessage.style.wordBreak = 'keep-all'; + + // Button container + const modalButtons = document.createElement('div'); + modalButtons.style.display = 'flex'; + modalButtons.style.justifyContent = 'flex-end'; + + // Confirm button (green) + const confirmButton = document.createElement('button'); + if(confirmMessage) + confirmButton.textContent = confirmMessage; + else + confirmButton.textContent = 'Confirm'; + confirmButton.style.marginLeft = '10px'; + confirmButton.style.backgroundColor = '#28a745'; // green + confirmButton.style.color = '#fff'; + confirmButton.style.border = 'none'; + confirmButton.style.padding = '6px 12px'; + confirmButton.style.borderRadius = '4px'; + confirmButton.style.cursor = 'pointer'; + confirmButton.style.fontWeight = 'bold'; + + // Cancel button (red) + const cancelButton = document.createElement('button'); + if(cancelMessage) + cancelButton.textContent = cancelMessage; + else + cancelButton.textContent = 'Cancel'; + + cancelButton.style.marginLeft = '10px'; + cancelButton.style.backgroundColor = '#dc3545'; // red + cancelButton.style.color = '#fff'; + cancelButton.style.border = 'none'; + cancelButton.style.padding = '6px 12px'; + cancelButton.style.borderRadius = '4px'; + cancelButton.style.cursor = 'pointer'; + cancelButton.style.fontWeight = 'bold'; + + const closeModal = () => { + document.body.removeChild(modalOverlay); + }; + + confirmButton.addEventListener('click', () => { + closeModal(); + resolve(true); + }); + + cancelButton.addEventListener('click', () => { + closeModal(); + resolve(false); + }); + + modalButtons.appendChild(confirmButton); + modalButtons.appendChild(cancelButton); + modalDialog.appendChild(modalMessage); + modalDialog.appendChild(modalButtons); + modalOverlay.appendChild(modalDialog); + document.body.appendChild(modalOverlay); + }); +} + +export function show_message(msg) { + app.ui.dialog.show(msg); + app.ui.dialog.element.style.zIndex = 1100; +} + +export async function handle403Response(res, defaultMessage) { + try { + const data = await res.json(); + if(data.error === 'comfyui_outdated') { + show_message('ComfyUI version is outdated.
Please update ComfyUI to use Manager normally.'); + } else { + show_message(defaultMessage || 'This action is not allowed with this security level configuration.'); + } + } catch { + show_message(defaultMessage || 'This action is not allowed with this security level configuration.'); + } +} + +export async function sleep(ms) { + return new Promise(resolve => setTimeout(resolve, ms)); +} + +export async function customConfirm(message) { + try { + let res = await + window['app'].extensionManager.dialog + .confirm({ + title: 'Confirm', + message: message + }); + + return res; + } + catch { + let res = await internalCustomConfirm(message); + return res; + } +} + + +export function customAlert(message) { + try { + window['app'].extensionManager.toast.addAlert(message); + } + catch { + alert(message); + } +} + +export function infoToast(summary, message) { + try { + app.extensionManager.toast.add({ + severity: 'info', + summary: summary, + detail: message, + life: 3000 + }) + } + catch { + // do nothing + } +} + + +export async function customPrompt(title, message) { + try { + let res = await + window['app'].extensionManager.dialog + .prompt({ + title: title, + message: message + }); + + return res; + } + catch { + return prompt(title, message) + } +} + + +export async function rebootAPI() { + if ('electronAPI' in window) { + window.electronAPI.restartApp(); + return true; + } + + const isConfirmed = await customConfirm("Are you sure you'd like to reboot the server?"); + if (isConfirmed) { + try { + const response = await api.fetchApi("/manager/reboot"); + if (response.status == 403) { + await handle403Response(response); + return false; + } + } + catch(exception) {} + } + + return false; +} + + +export var manager_instance = null; + +export function setManagerInstance(obj) { + manager_instance = obj; +} + +export function showToast(message, duration = 3000) { + const toast = $el("div.comfy-toast", {textContent: message}); + document.body.appendChild(toast); + setTimeout(() => { + toast.classList.add("comfy-toast-fadeout"); + setTimeout(() => toast.remove(), 500); + }, duration); +} + +function isValidURL(url) { + if(url.includes('&')) + return false; + + const http_pattern = /^(https?|ftp):\/\/[^\s$?#]+$/; + const ssh_pattern = /^(.+@|ssh:\/\/).+:.+$/; + return http_pattern.test(url) || ssh_pattern.test(url); +} + +export async function install_pip(packages) { + if(packages.includes('&')) + app.ui.dialog.show(`Invalid PIP package enumeration: '${packages}'`); + + const res = await api.fetchApi("/customnode/install/pip", { + method: "POST", + body: packages, + }); + + if(res.status == 403) { + await handle403Response(res); + return; + } + + if(res.status == 200) { + show_message(`PIP package installation is processed.
To apply the pip packages, please click the button in ComfyUI.`); + + const rebootButton = document.getElementById('cm-reboot-button3'); + const self = this; + + rebootButton.addEventListener("click", rebootAPI); + } + else { + show_message(`Failed to install '${packages}'
See terminal log.`); + } +} + +export async function install_via_git_url(url, manager_dialog) { + if(!url) { + return; + } + + if(!isValidURL(url)) { + show_message(`Invalid Git url '${url}'`); + return; + } + + show_message(`Wait...

Installing '${url}'`); + + const res = await api.fetchApi("/customnode/install/git_url", { + method: "POST", + body: url, + }); + + if(res.status == 403) { + await handle403Response(res); + return; + } + + if(res.status == 200) { + show_message(`'${url}' is installed
To apply the installed custom node, please ComfyUI.`); + + const rebootButton = document.getElementById('cm-reboot-button4'); + const self = this; + + rebootButton.addEventListener("click", + async function() { + if(await rebootAPI()) { + manager_instance.close(); + } + }); + } + else { + show_message(`Failed to install '${url}'
See terminal log.`); + } +} + +export async function free_models(free_execution_cache) { + try { + let mode = ""; + if(free_execution_cache) { + mode = '{"unload_models": true, "free_memory": true}'; + } + else { + mode = '{"unload_models": true}'; + } + + let res = await api.fetchApi(`/free`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: mode + }); + + if (res.status == 200) { + if(free_execution_cache) { + showToast("'Models' and 'Execution Cache' have been cleared.", 3000); + } + else { + showToast("Models' have been unloaded.", 3000); + } + } else { + showToast('Unloading of models failed. Installed ComfyUI may be an outdated version.', 5000); + } + } catch (error) { + showToast('An error occurred while trying to unload models.', 5000); + } +} + +export function md5(inputString) { + const hc = '0123456789abcdef'; + const rh = n => {let j,s='';for(j=0;j<=3;j++) s+=hc.charAt((n>>(j*8+4))&0x0F)+hc.charAt((n>>(j*8))&0x0F);return s;} + const ad = (x,y) => {let l=(x&0xFFFF)+(y&0xFFFF);let m=(x>>16)+(y>>16)+(l>>16);return (m<<16)|(l&0xFFFF);} + const rl = (n,c) => (n<>>(32-c)); + const cm = (q,a,b,x,s,t) => ad(rl(ad(ad(a,q),ad(x,t)),s),b); + const ff = (a,b,c,d,x,s,t) => cm((b&c)|((~b)&d),a,b,x,s,t); + const gg = (a,b,c,d,x,s,t) => cm((b&d)|(c&(~d)),a,b,x,s,t); + const hh = (a,b,c,d,x,s,t) => cm(b^c^d,a,b,x,s,t); + const ii = (a,b,c,d,x,s,t) => cm(c^(b|(~d)),a,b,x,s,t); + const sb = x => { + let i;const nblk=((x.length+8)>>6)+1;const blks=[];for(i=0;i>2]|=x.charCodeAt(i)<<((i%4)*8);} + blks[i>>2]|=0x80<<((i%4)*8);blks[nblk*16-2]=x.length*8;return blks; + } + let i,x=sb(inputString),a=1732584193,b=-271733879,c=-1732584194,d=271733878,olda,oldb,oldc,oldd; + for(i=0;i { + err = e; + }); + + if (!res) { + return { + status: 400, + error: new Error("Unknown Error") + } + } + + const { status, statusText } = res; + if (err) { + return { + status, + error: err + } + } + + if (status !== 200) { + return { + status, + error: new Error(statusText || "Unknown Error") + } + } + + const data = await res.json(); + if (!data) { + return { + status, + error: new Error(`Failed to load data: ${route}`) + } + } + return { + status, + data + } +} + +// https://cenfun.github.io/open-icons/ +export const icons = { + search: '', + conflicts: '', + passed: '', + download: '', + close: '', + arrowRight: '' +} + +export function sanitizeHTML(str) { + return str + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/'/g, "'"); +} + +export function showTerminal() { + try { + const panel = app.extensionManager.bottomPanel; + const isTerminalVisible = panel.bottomPanelVisible && panel.activeBottomPanelTab.id === 'logs-terminal'; + if (!isTerminalVisible) + panel.toggleBottomPanelTab('logs-terminal'); + } + catch(exception) { + // do nothing + } +} + +let need_restart = false; + +export function setNeedRestart(value) { + need_restart = value; +} + +async function onReconnected(event) { + if(need_restart) { + setNeedRestart(false); + + const confirmed = await customConfirm("To apply the changes to the node pack's installation status, you need to refresh the browser. Would you like to refresh?"); + if (!confirmed) { + return; + } + + window.location.reload(true); + } +} + +api.addEventListener('reconnected', onReconnected); + +const storeId = "comfyui-manager-grid"; +let timeId; +export function storeColumnWidth(gridId, columnItem) { + clearTimeout(timeId); + timeId = setTimeout(() => { + let data = {}; + const dataStr = localStorage.getItem(storeId); + if (dataStr) { + try { + data = JSON.parse(dataStr); + } catch (e) {} + } + + if (!data[gridId]) { + data[gridId] = {}; + } + + data[gridId][columnItem.id] = columnItem.width; + + localStorage.setItem(storeId, JSON.stringify(data)); + + }, 200) +} + +export function restoreColumnWidth(gridId, columns) { + const dataStr = localStorage.getItem(storeId); + if (!dataStr) { + return; + } + let data; + try { + data = JSON.parse(dataStr); + } catch (e) {} + if(!data) { + return; + } + const widthMap = data[gridId]; + if (!widthMap) { + return; + } + + columns.forEach(columnItem => { + const w = widthMap[columnItem.id]; + if (w) { + columnItem.width = w; + } + }); + +} + +export function getTimeAgo(dateStr) { + const date = new Date(dateStr); + + if (!date || !(date instanceof Date) || isNaN(date.getTime())) { + return ""; + } + + const units = [ + { max: 2760000, value: 60000, name: 'minute', past: 'a minute ago', future: 'in a minute' }, + { max: 72000000, value: 3600000, name: 'hour', past: 'an hour ago', future: 'in an hour' }, + { max: 518400000, value: 86400000, name: 'day', past: 'yesterday', future: 'tomorrow' }, + { max: 2419200000, value: 604800000, name: 'week', past: 'last week', future: 'in a week' }, + { max: 28512000000, value: 2592000000, name: 'month', past: 'last month', future: 'in a month' } + ]; + const diff = Date.now() - date.getTime(); + // less than a minute + if (Math.abs(diff) < 60000) + return 'just now'; + for (let i = 0; i < units.length; i++) { + if (Math.abs(diff) < units[i].max) { + return format(diff, units[i].value, units[i].name, units[i].past, units[i].future, diff < 0); + } + } + function format(diff, divisor, unit, past, future, isInTheFuture) { + const val = Math.round(Math.abs(diff) / divisor); + if (isInTheFuture) + return val <= 1 ? future : 'in ' + val + ' ' + unit + 's'; + return val <= 1 ? past : val + ' ' + unit + 's ago'; + } + return format(diff, 31536000000, 'year', 'last year', 'in a year', diff < 0); +}; + +export const loadCss = (cssFile) => { + const cssPath = import.meta.resolve(cssFile); + //console.log(cssPath); + const $link = document.createElement("link"); + $link.setAttribute("rel", 'stylesheet'); + $link.setAttribute("href", cssPath); + document.head.appendChild($link); +}; + +export const copyText = (text) => { + return new Promise((resolve) => { + let err; + try { + navigator.clipboard.writeText(text); + } catch (e) { + err = e; + } + if (err) { + resolve(false); + } else { + resolve(true); + } + }); +}; + +function renderPopover($elem, target, options = {}) { + // async microtask + queueMicrotask(() => { + + const containerRect = getRect(window); + const targetRect = getRect(target); + const elemRect = getRect($elem); + + const positionInfo = getBestPosition( + containerRect, + targetRect, + elemRect, + options.positions + ); + const style = getPositionStyle(positionInfo, { + bgColor: options.bgColor, + borderColor: options.borderColor, + borderRadius: options.borderRadius + }); + + $elem.style.top = positionInfo.top + "px"; + $elem.style.left = positionInfo.left + "px"; + $elem.style.background = style.background; + + }); +} + +let $popover; +export function hidePopover() { + if ($popover) { + $popover.remove(); + $popover = null; + } +} +export function showPopover(target, text, className, options) { + hidePopover(); + $popover = document.createElement("div"); + $popover.className = ['cn-popover', className].filter(it => it).join(" "); + document.body.appendChild($popover); + $popover.innerHTML = text; + $popover.style.display = "block"; + renderPopover($popover, target, { + borderRadius: 10, + ... options + }); +} + +let $tooltip; +export function hideTooltip(target) { + if ($tooltip) { + $tooltip.style.display = "none"; + $tooltip.innerHTML = ""; + $tooltip.style.top = "0px"; + $tooltip.style.left = "0px"; + } +} +export function showTooltip(target, text, className = 'cn-tooltip', styleMap = {}) { + if (!$tooltip) { + $tooltip = document.createElement("div"); + $tooltip.className = className; + $tooltip.style.cssText = ` + pointer-events: none; + position: fixed; + z-index: 10001; + padding: 20px; + color: #1e1e1e; + max-width: 350px; + filter: drop-shadow(1px 5px 5px rgb(0 0 0 / 30%)); + ${Object.keys(styleMap).map(k=>k+":"+styleMap[k]+";").join("")} + `; + document.body.appendChild($tooltip); + } + + $tooltip.innerHTML = text; + $tooltip.style.display = "block"; + renderPopover($tooltip, target, { + positions: ['top', 'bottom', 'right', 'center'], + bgColor: "#ffffff", + borderColor: "#cccccc", + borderRadius: 5 + }); +} + +function initTooltip () { + const mouseenterHandler = (e) => { + const target = e.target; + const text = target.getAttribute('tooltip'); + if (text) { + showTooltip(target, text); + } + }; + const mouseleaveHandler = (e) => { + const target = e.target; + const text = target.getAttribute('tooltip'); + if (text) { + hideTooltip(target); + } + }; + document.body.removeEventListener('mouseenter', mouseenterHandler, true); + document.body.removeEventListener('mouseleave', mouseleaveHandler, true); + document.body.addEventListener('mouseenter', mouseenterHandler, true); + document.body.addEventListener('mouseleave', mouseleaveHandler, true); +} + +initTooltip(); \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/components-manager.js b/custom_nodes/comfyui-manager/js/components-manager.js new file mode 100644 index 00000000..9244d2a4 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/components-manager.js @@ -0,0 +1,812 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" +import { sleep, show_message, customConfirm, customAlert } from "./common.js"; +import { GroupNodeConfig, GroupNodeHandler } from "../../extensions/core/groupNode.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; + +const SEPARATOR = ">" + +let pack_map = {}; +let rpack_map = {}; + +export function getPureName(node) { + // group nodes/ + let category = null; + if(node.category) { + category = node.category.substring(12); + } + else { + category = node.constructor.category?.substring(12); + } + if(category) { + let purename = node.comfyClass.substring(category.length+1); + return purename; + } + else if(node.comfyClass.startsWith('workflow/') || node.comfyClass.startsWith(`workflow${SEPARATOR}`)) { + return node.comfyClass.substring(9); + } + else { + return node.comfyClass; + } +} + +function isValidVersionString(version) { + const versionPattern = /^(\d+)\.(\d+)(\.(\d+))?$/; + + const match = version.match(versionPattern); + + return match !== null && + parseInt(match[1], 10) >= 0 && + parseInt(match[2], 10) >= 0 && + (!match[3] || parseInt(match[4], 10) >= 0); +} + +function register_pack_map(name, data) { + if(data.packname) { + pack_map[data.packname] = name; + rpack_map[name] = data; + } + else { + rpack_map[name] = data; + } +} + +function storeGroupNode(name, data, register=true) { + let extra = app.graph.extra; + if (!extra) app.graph.extra = extra = {}; + let groupNodes = extra.groupNodes; + if (!groupNodes) extra.groupNodes = groupNodes = {}; + groupNodes[name] = data; + + if(register) { + register_pack_map(name, data); + } +} + +export async function load_components() { + let data = await api.fetchApi('/manager/component/loads', {method: "POST"}); + let components = await data.json(); + + let start_time = Date.now(); + let failed = []; + let failed2 = []; + + for(let name in components) { + if(app.graph.extra?.groupNodes?.[name]) { + if(data) { + let data = components[name]; + + let category = data.packname; + if(data.category) { + category += SEPARATOR + data.category; + } + if(category == '') { + category = 'components'; + } + + const config = new GroupNodeConfig(name, data); + await config.registerType(category); + + register_pack_map(name, data); + continue; + } + } + + let nodeData = components[name]; + + storeGroupNode(name, nodeData); + + const config = new GroupNodeConfig(name, nodeData); + + while(true) { + try { + let category = nodeData.packname; + if(nodeData.category) { + category += SEPARATOR + nodeData.category; + } + if(category == '') { + category = 'components'; + } + + await config.registerType(category); + register_pack_map(name, nodeData); + break; + } + catch { + let elapsed_time = Date.now() - start_time; + if (elapsed_time > 5000) { + failed.push(name); + break; + } else { + await sleep(100); + } + } + } + } + + // fallback1 + for(let i in failed) { + let name = failed[i]; + + if(app.graph.extra?.groupNodes?.[name]) { + continue; + } + + let nodeData = components[name]; + + storeGroupNode(name, nodeData); + + const config = new GroupNodeConfig(name, nodeData); + while(true) { + try { + let category = nodeData.packname; + if(nodeData.workflow.category) { + category += SEPARATOR + nodeData.category; + } + if(category == '') { + category = 'components'; + } + + await config.registerType(category); + register_pack_map(name, nodeData); + break; + } + catch { + let elapsed_time = Date.now() - start_time; + if (elapsed_time > 10000) { + failed2.push(name); + break; + } else { + await sleep(100); + } + } + } + } + + // fallback2 + for(let name in failed2) { + let name = failed2[i]; + + let nodeData = components[name]; + + storeGroupNode(name, nodeData); + + const config = new GroupNodeConfig(name, nodeData); + while(true) { + try { + let category = nodeData.workflow.packname; + if(nodeData.workflow.category) { + category += SEPARATOR + nodeData.category; + } + if(category == '') { + category = 'components'; + } + + await config.registerType(category); + register_pack_map(name, nodeData); + break; + } + catch { + let elapsed_time = Date.now() - start_time; + if (elapsed_time > 30000) { + failed.push(name); + break; + } else { + await sleep(100); + } + } + } + } +} + +async function save_as_component(node, version, author, prefix, nodename, packname, category) { + let component_name = `${prefix}::${nodename}`; + + let subgraph = app.graph.extra?.groupNodes?.[component_name]; + if(!subgraph) { + subgraph = app.graph.extra?.groupNodes?.[getPureName(node)]; + } + + subgraph.version = version; + subgraph.author = author; + subgraph.datetime = Date.now(); + subgraph.packname = packname; + subgraph.category = category; + + let body = + { + name: component_name, + workflow: subgraph + }; + + pack_map[packname] = component_name; + rpack_map[component_name] = subgraph; + + const res = await api.fetchApi('/manager/component/save', { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify(body), + }); + + if(res.status == 200) { + storeGroupNode(component_name, subgraph); + const config = new GroupNodeConfig(component_name, subgraph); + + let category = body.workflow.packname; + if(body.workflow.category) { + category += SEPARATOR + body.workflow.category; + } + if(category == '') { + category = 'components'; + } + + await config.registerType(category); + + let path = await res.text(); + show_message(`Component '${component_name}' is saved into:\n${path}`); + } + else + show_message(`Failed to save component.`); +} + +async function import_component(component_name, component, mode) { + if(mode) { + let body = + { + name: component_name, + workflow: component + }; + + const res = await api.fetchApi('/manager/component/save', { + method: "POST", + headers: { "Content-Type": "application/json", }, + body: JSON.stringify(body) + }); + } + + let category = component.packname; + if(component.category) { + category += SEPARATOR + component.category; + } + if(category == '') { + category = 'components'; + } + + storeGroupNode(component_name, component); + const config = new GroupNodeConfig(component_name, component); + await config.registerType(category); +} + +function restore_to_loaded_component(component_name) { + if(rpack_map[component_name]) { + let component = rpack_map[component_name]; + storeGroupNode(component_name, component, false); + const config = new GroupNodeConfig(component_name, component); + config.registerType(component.category); + } +} + +// Using a timestamp prevents duplicate pastes and ensures the prevention of re-deletion of litegrapheditor_clipboard. +let last_paste_timestamp = null; + +function versionCompare(v1, v2) { + let ver1; + let ver2; + if(v1 && v1 != '') { + ver1 = v1.split('.'); + ver1[0] = parseInt(ver1[0]); + ver1[1] = parseInt(ver1[1]); + if(ver1.length == 2) + ver1.push(0); + else + ver1[2] = parseInt(ver2[2]); + } + else { + ver1 = [0,0,0]; + } + + if(v2 && v2 != '') { + ver2 = v2.split('.'); + ver2[0] = parseInt(ver2[0]); + ver2[1] = parseInt(ver2[1]); + if(ver2.length == 2) + ver2.push(0); + else + ver2[2] = parseInt(ver2[2]); + } + else { + ver2 = [0,0,0]; + } + + if(ver1[0] > ver2[0]) + return -1; + else if(ver1[0] < ver2[0]) + return 1; + + if(ver1[1] > ver2[1]) + return -1; + else if(ver1[1] < ver2[1]) + return 1; + + if(ver1[2] > ver2[2]) + return -1; + else if(ver1[2] < ver2[2]) + return 1; + + return 0; +} + +function checkVersion(name, component) { + let msg = ''; + if(rpack_map[name]) { + let old_version = rpack_map[name].version; + if(!old_version || old_version == '') { + msg = ` '${name}' Upgrade (V0.0 -> V${component.version})`; + } + else { + let c = versionCompare(old_version, component.version); + if(c < 0) { + msg = ` '${name}' Downgrade (V${old_version} -> V${component.version})`; + } + else if(c > 0) { + msg = ` '${name}' Upgrade (V${old_version} -> V${component.version})`; + } + else { + msg = ` '${name}' Same version (V${component.version})`; + } + } + } + else { + msg = `'${name}' NEW (V${component.version})`; + } + + return msg; +} + +async function handle_import_components(components) { + let msg = 'Components:\n'; + let cnt = 0; + for(let name in components) { + let component = components[name]; + let v = checkVersion(name, component); + + if(cnt < 10) { + msg += v + '\n'; + } + else if (cnt == 10) { + msg += '...\n'; + } + else { + // do nothing + } + + cnt++; + } + + let last_name = null; + msg += '\nWill you load components?\n'; + const confirmed = await customConfirm(msg); + if(confirmed) { + const mode = await customConfirm('\nWill you save components?\n(cancel=load without save)'); + + for(let name in components) { + let component = components[name]; + import_component(name, component, mode); + last_name = name; + } + + if(mode) { + show_message('Components are saved.'); + } + else { + show_message('Components are loaded.'); + } + } + + if(cnt == 1 && last_name) { + const node = LiteGraph.createNode(`workflow${SEPARATOR}${last_name}`); + node.pos = [app.canvas.graph_mouse[0], app.canvas.graph_mouse[1]]; + app.canvas.graph.add(node, false); + } +} + +async function handlePaste(e) { + let data = (e.clipboardData || window.clipboardData); + const items = data.items; + for(const item of items) { + if(item.kind == 'string' && item.type == 'text/plain') { + data = data.getData("text/plain"); + try { + let json_data = JSON.parse(data); + if(json_data.kind == 'ComfyUI Components' && last_paste_timestamp != json_data.timestamp) { + last_paste_timestamp = json_data.timestamp; + await handle_import_components(json_data.components); + + // disable paste node + localStorage.removeItem("litegrapheditor_clipboard", null); + } + else { + console.log('This components are already pasted: ignored'); + } + } + catch { + // nothing to do + } + } + } +} + +document.addEventListener("paste", handlePaste); + + +export class ComponentBuilderDialog extends ComfyDialog { + constructor() { + super(); + } + + clear() { + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + } + + show() { + this.invalidateControl(); + + this.element.style.display = "block"; + this.element.style.zIndex = 1099; + this.element.style.width = "500px"; + this.element.style.height = "480px"; + } + + invalidateControl() { + this.clear(); + + let self = this; + + const close_button = $el("button", { id: "cm-close-button", type: "button", textContent: "Close", onclick: () => self.close() }); + this.save_button = $el("button", + { id: "cm-save-button", type: "button", textContent: "Save", onclick: () => + { + save_as_component(self.target_node, self.version_string.value.trim(), self.author.value.trim(), self.node_prefix.value.trim(), + self.getNodeName(), self.getPackName(), self.category.value.trim()); + } + }); + + let default_nodename = getPureName(this.target_node).trim(); + + let groupNode = app.graph.extra.groupNodes[default_nodename]; + let default_packname = groupNode.packname; + if(!default_packname) { + default_packname = ''; + } + + let default_category = groupNode.category; + if(!default_category) { + default_category = ''; + } + + this.default_ver = groupNode.version; + if(!this.default_ver) { + this.default_ver = '0.0'; + } + + let default_author = groupNode.author; + if(!default_author) { + default_author = ''; + } + + let delimiterIndex = default_nodename.indexOf('::'); + let default_prefix = ""; + if(delimiterIndex != -1) { + default_prefix = default_nodename.substring(0, delimiterIndex); + default_nodename = default_nodename.substring(delimiterIndex + 2); + } + + if(!default_prefix) { + this.save_button.disabled = true; + } + + this.pack_list = this.createPackListCombo(); + + let version_string = this.createLabeledInput('input version (e.g. 1.0)', '*Version : ', this.default_ver); + this.version_string = version_string[1]; + this.version_string.disabled = true; + + let author = this.createLabeledInput('input author (e.g. Dr.Lt.Data)', 'Author : ', default_author); + this.author = author[1]; + + let node_prefix = this.createLabeledInput('input node prefix (e.g. mypack)', '*Prefix : ', default_prefix); + this.node_prefix = node_prefix[1]; + + let manual_nodename = this.createLabeledInput('input node name (e.g. MAKE_BASIC_PIPE)', 'Nodename : ', default_nodename); + this.manual_nodename = manual_nodename[1]; + + let manual_packname = this.createLabeledInput('input pack name (e.g. mypack)', 'Packname : ', default_packname); + this.manual_packname = manual_packname[1]; + + let category = this.createLabeledInput('input category (e.g. util/pipe)', 'Category : ', default_category); + this.category = category[1]; + + this.node_label = this.createNodeLabel(); + + let author_mode = this.createAuthorModeCheck(); + this.author_mode = author_mode[0]; + + const content = + $el("div.comfy-modal-content", + [ + $el("tr.cm-title", {}, [ + $el("font", {size:6, color:"white"}, [`ComfyUI-Manager: Component Builder`])] + ), + $el("br", {}, []), + $el("div.cm-menu-container", + [ + author_mode[0], + author_mode[1], + category[0], + author[0], + node_prefix[0], + manual_nodename[0], + manual_packname[0], + version_string[0], + this.pack_list, + $el("br", {}, []), + this.node_label + ]), + + $el("br", {}, []), + this.save_button, + close_button, + ] + ); + + content.style.width = '100%'; + content.style.height = '100%'; + + this.element = $el("div.comfy-modal", { id:'cm-manager-dialog', parent: document.body }, [ content ]); + } + + validateInput() { + let msg = ""; + + if(!isValidVersionString(this.version_string.value)) { + msg += 'Invalid version string: '+event.value+"\n"; + } + + if(this.node_prefix.value.trim() == '') { + msg += 'Node prefix cannot be empty\n'; + } + + if(this.manual_nodename.value.trim() == '') { + msg += 'Node name cannot be empty\n'; + } + + if(msg != '') { +// alert(msg); + } + + this.save_button.disabled = msg != ""; + } + + getPackName() { + if(this.pack_list.selectedIndex == 0) { + return this.manual_packname.value.trim(); + } + + return this.pack_list.value.trim(); + } + + getNodeName() { + if(this.manual_nodename.value.trim() != '') { + return this.manual_nodename.value.trim(); + } + + return getPureName(this.target_node); + } + + createAuthorModeCheck() { + let check = $el("input",{type:'checkbox', id:"author-mode"},[]) + const check_label = $el("label",{for:"author-mode"},["Enable author mode"]); + check_label.style.color = "var(--fg-color)"; + check_label.style.cursor = "pointer"; + check.checked = false; + + let self = this; + check.onchange = () => { + self.version_string.disabled = !check.checked; + + if(!check.checked) { + self.version_string.value = self.default_ver; + } + else { + customAlert('If you are not the author, it is not recommended to change the version, as it may cause component update issues.'); + } + }; + + return [check, check_label]; + } + + createNodeLabel() { + let label = $el('p'); + label.className = 'cb-node-label'; + if(this.target_node.comfyClass.includes('::')) + label.textContent = getPureName(this.target_node); + else + label.textContent = " _::" + getPureName(this.target_node); + return label; + } + + createLabeledInput(placeholder, label, value) { + let textbox = $el('input.cb-widget-input', {type:'text', placeholder:placeholder, value:value}, []); + + let self = this; + textbox.onchange = () => { + this.validateInput.call(self); + this.node_label.textContent = this.node_prefix.value + "::" + this.manual_nodename.value; + } + let row = $el('span.cb-widget', {}, [ $el('span.cb-widget-input-label', label), textbox]); + + return [row, textbox]; + } + + createPackListCombo() { + let combo = document.createElement("select"); + combo.className = "cb-widget"; + let default_packname_option = { value: '##manual', text: 'Packname: Manual' }; + + combo.appendChild($el('option', default_packname_option, [])); + for(let name in pack_map) { + combo.appendChild($el('option', { value: name, text: 'Packname: '+ name }, [])); + } + + let self = this; + combo.onchange = function () { + if(combo.selectedIndex == 0) { + self.manual_packname.disabled = false; + } + else { + self.manual_packname.disabled = true; + } + }; + + return combo; + } +} + +let orig_handleFile = app.handleFile; + +async function handleFile(file) { + if (file.name?.endsWith(".json") || file.name?.endsWith(".pack")) { + const reader = new FileReader(); + reader.onload = async () => { + let is_component = false; + const jsonContent = JSON.parse(reader.result); + for(let name in jsonContent) { + let cand = jsonContent[name]; + is_component = cand.datetime && cand.version; + break; + } + + if(is_component) { + await handle_import_components(jsonContent); + } + else { + orig_handleFile.call(app, file); + } + }; + reader.readAsText(file); + + return; + } + + orig_handleFile.call(app, file); +} + +app.handleFile = handleFile; + +let current_component_policy = 'workflow'; +try { + api.fetchApi('/manager/policy/component') + .then(response => response.text()) + .then(data => { current_component_policy = data; }); +} +catch {} + +function getChangedVersion(groupNodes) { + if(!Object.keys(pack_map).length || !groupNodes) + return null; + + let res = {}; + for(let component_name in groupNodes) { + let data = groupNodes[component_name]; + + if(rpack_map[component_name]) { + let v = versionCompare(data.version, rpack_map[component_name].version); + res[component_name] = v; + } + } + + return res; +} + +const loadGraphData = app.loadGraphData; +app.loadGraphData = async function () { + if(arguments.length == 0) + return await loadGraphData.apply(this, arguments); + + let graphData = arguments[0]; + let groupNodes = graphData.extra?.groupNodes; + let res = getChangedVersion(groupNodes); + + if(res) { + let target_components = null; + switch(current_component_policy) { + case 'higher': + target_components = Object.keys(res).filter(key => res[key] == 1); + break; + + case 'mine': + target_components = Object.keys(res); + break; + + default: + // do nothing + } + + if(target_components) { + for(let i in target_components) { + let component_name = target_components[i]; + let component = rpack_map[component_name]; + if(component && graphData.extra?.groupNodes) { + graphData.extra.groupNodes[component_name] = component; + } + } + } + } + else { + console.log('Empty components: policy ignored'); + } + + arguments[0] = graphData; + return await loadGraphData.apply(this, arguments); +}; + +export function set_component_policy(v) { + current_component_policy = v; +} + +let graphToPrompt = app.graphToPrompt; +app.graphToPrompt = async function () { + let p = await graphToPrompt.call(app); + try { + let groupNodes = p.workflow.extra?.groupNodes; + if(groupNodes) { + p.workflow.extra = { ... p.workflow.extra}; + + // get used group nodes + let used_group_nodes = new Set(); + for(let node of p.workflow.nodes) { + if(node.type.startsWith(`workflow/`) || node.type.startsWith(`workflow${SEPARATOR}`)) { + used_group_nodes.add(node.type.substring(9)); + } + } + + // remove unused group nodes + let new_groupNodes = {}; + for (let key in p.workflow.extra.groupNodes) { + if (used_group_nodes.has(key)) { + new_groupNodes[key] = p.workflow.extra.groupNodes[key]; + } + } + p.workflow.extra.groupNodes = new_groupNodes; + } + } + catch(e) { + console.log(`Failed to filtering group nodes: ${e}`); + } + + return p; +} diff --git a/custom_nodes/comfyui-manager/js/custom-nodes-manager.css b/custom_nodes/comfyui-manager/js/custom-nodes-manager.css new file mode 100644 index 00000000..4ea82580 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/custom-nodes-manager.css @@ -0,0 +1,729 @@ +.cn-manager { + --grid-font: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; + z-index: 1099; + width: 80vw; + height: 75vh; + min-height: 30em; + display: flex; + flex-direction: column; + gap: 10px; + color: var(--fg-color); + font-family: arial, sans-serif; + text-underline-offset: 3px; + outline: none; + margin: calc(var(--spacing)*2); +} + +.cn-manager .cn-flex-auto { + flex: auto; +} + +.cn-manager button { + width: auto; + position: relative; + overflow: hidden; + font-size: 16px; + color: var(--input-text); + background-color: var(--comfy-input-bg); + border-color: var(--border-color); + margin: 0; + min-width: 100px; +} + +.cn-manager button:hover { + filter: brightness(125%); +} + +.cn-manager button:disabled, +.cn-manager input:disabled, +.cn-manager select:disabled { + color: gray; +} + +.cn-manager button:disabled { + background-color: var(--comfy-input-bg); +} + +.cn-manager .cn-manager-restart { + display: none; + background-color: #500000 !important; + border-color: #88181b !important; + color: white !important; +} + +.cn-manager .cn-manager-restart:hover { + background-color: #88181b !important; +} + +.cn-manager .cn-manager-stop { + display: none; + background-color: #500000; + color: white; +} + +.cn-manager .cn-manager-back { + align-items: center; + justify-content: center; +} + +.arrow-icon { + height: 1em; + width: 1em; + margin-right: 5px; + transform: translateY(2px); +} + +.cn-icon { + display: block; + width: 16px; + height: 16px; +} + +.cn-icon svg { + display: block; + margin: 0; + pointer-events: none; +} + +.cn-manager-header { + display: flex; + flex-wrap: wrap; + gap: 5px; + align-items: center; +} + +.cn-manager-header label { + display: flex; + gap: 5px; + align-items: center; +} + +.cn-manager-filter { + height: 28px; + line-height: 28px; + + cursor: pointer; + padding: 0.5em 0.5em; + border: 1px solid var(--border-color); + border-radius: 6px; + background: var(--comfy-input-bg); +} + +.cn-manager-filter:hover { + filter: brightness(125%); +} + +.cn-manager-keywords { + height: 28px; + line-height: 28px; + padding: 0 5px 0 26px; + background: var(--comfy-input-bg); + background-size: 16px; + background-position: 5px center; + background-repeat: no-repeat; + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg%20viewBox%3D%220%200%2024%2024%22%20width%3D%22100%25%22%20height%3D%22100%25%22%20pointer-events%3D%22none%22%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%3E%3Cpath%20fill%3D%22none%22%20stroke%3D%22%23888%22%20stroke-linecap%3D%22round%22%20stroke-linejoin%3D%22round%22%20stroke-width%3D%222%22%20d%3D%22m21%2021-4.486-4.494M19%2010.5a8.5%208.5%200%201%201-17%200%208.5%208.5%200%200%201%2017%200%22%2F%3E%3C%2Fsvg%3E"); + + border: 1px solid var(--border-color); + border-radius: 6px; + + outline-color: transparent; +} + +.cn-manager-status { + padding-left: 10px; +} + +.cn-manager-grid { + flex: auto; + border: 1px solid var(--border-color); + overflow: hidden; + position: relative; +} + +.cn-manager-selection { + display: flex; + flex-wrap: wrap; + gap: 10px; + align-items: center; +} + +.cn-manager-message { + position: relative; +} + +.cn-manager-footer { + display: flex; + flex-wrap: wrap; + gap: 10px; + align-items: center; +} + +.cn-manager-grid .tg-turbogrid { + font-family: var(--grid-font); + font-size: 15px; + background: var(--bg-color); +} + +.cn-manager-grid .tg-turbogrid .tg-highlight::after { + position: absolute; + top: 0; + left: 0; + content: ""; + display: block; + width: 100%; + height: 100%; + box-sizing: border-box; + background-color: #80bdff11; + pointer-events: none; +} + +.cn-manager-grid .cn-pack-name a { + color: skyblue; + text-decoration: none; + word-break: break-word; +} + +.cn-manager-grid .cn-pack-desc a { + color: #5555FF; + font-weight: bold; + text-decoration: none; +} + +.cn-manager-grid .tg-cell a:hover { + text-decoration: underline; +} + +.cn-manager-grid .cn-pack-version { + line-height: 100%; + display: flex; + flex-direction: column; + justify-content: center; + height: 100%; + gap: 5px; +} + +.cn-manager-grid .cn-pack-nodes { + line-height: 100%; + display: flex; + flex-direction: column; + justify-content: center; + gap: 5px; + cursor: pointer; + height: 100%; +} + +.cn-manager-grid .cn-pack-nodes:hover { + text-decoration: underline; +} + +.cn-manager-grid .cn-pack-conflicts { + color: orange; +} + +.cn-popover { + position: fixed; + z-index: 10000; + padding: 20px; + color: #1e1e1e; + filter: drop-shadow(1px 5px 5px rgb(0 0 0 / 30%)); + overflow: hidden; +} + +.cn-flyover { + position: absolute; + top: 0; + right: 0; + z-index: 1000; + display: none; + width: 50%; + height: 100%; + background-color: var(--comfy-menu-bg); + animation-duration: 0.2s; + animation-fill-mode: both; + flex-direction: column; +} + +.cn-flyover::before { + position: absolute; + top: 0; + content: ""; + z-index: 10; + display: block; + width: 10px; + height: 100%; + pointer-events: none; + left: -10px; + background-image: linear-gradient(to left, rgb(0 0 0 / 20%), rgb(0 0 0 / 0%)); +} + +.cn-flyover-header { + height: 45px; + display: flex; + align-items: center; + gap: 5px; + border-bottom: 1px solid var(--border-color); +} + +.cn-flyover-close { + display: flex; + align-items: center; + padding: 0 10px; + justify-content: center; + cursor: pointer; + opacity: 0.8; + height: 100%; +} + +.cn-flyover-close:hover { + opacity: 1; +} + +.cn-flyover-close svg { + display: block; + margin: 0; + pointer-events: none; + width: 20px; + height: 20px; +} + +.cn-flyover-title { + display: flex; + align-items: center; + font-weight: bold; + gap: 10px; + flex: auto; +} + +.cn-flyover-body { + height: calc(100% - 45px); + overflow-y: auto; + position: relative; + background-color: var(--comfy-menu-secondary-bg); +} + +@keyframes cn-slide-in-right { + from { + visibility: visible; + transform: translate3d(100%, 0, 0); + } + + to { + transform: translate3d(0, 0, 0); + } +} + +.cn-slide-in-right { + animation-name: cn-slide-in-right; +} + +@keyframes cn-slide-out-right { + from { + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + transform: translate3d(100%, 0, 0); + } +} + +.cn-slide-out-right { + animation-name: cn-slide-out-right; +} + +.cn-nodes-list { + width: 100%; +} + +.cn-nodes-row { + display: flex; + align-items: center; + gap: 10px; +} + +.cn-nodes-row:nth-child(odd) { + background-color: rgb(0 0 0 / 5%); +} + +.cn-nodes-row:hover { + background-color: rgb(0 0 0 / 10%); +} + +.cn-nodes-sn { + text-align: right; + min-width: 35px; + color: var(--drag-text); + flex-shrink: 0; + font-size: 12px; + padding: 8px 5px; +} + +.cn-nodes-name { + cursor: pointer; + white-space: nowrap; + flex-shrink: 0; + position: relative; + padding: 8px 5px; +} + +.cn-nodes-name::after { + content: attr(action); + position: absolute; + pointer-events: none; + top: 50%; + left: 100%; + transform: translate(5px, -50%); + font-size: 12px; + color: var(--drag-text); + background-color: var(--comfy-input-bg); + border-radius: 10px; + border: 1px solid var(--border-color); + padding: 3px 8px; + display: none; +} + +.cn-nodes-name.action::after { + display: block; +} + +.cn-nodes-name:hover { + text-decoration: underline; +} + +.cn-nodes-conflict .cn-nodes-name, +.cn-nodes-conflict .cn-icon { + color: orange; +} + +.cn-conflicts-list { + display: flex; + flex-wrap: wrap; + gap: 5px; + align-items: center; + padding: 5px 0; +} + +.cn-conflicts-list b { + font-weight: normal; + color: var(--descrip-text); +} + +.cn-nodes-pack { + cursor: pointer; + color: skyblue; +} + +.cn-nodes-pack:hover { + text-decoration: underline; +} + +.cn-pack-badge { + font-size: 12px; + font-weight: normal; + background-color: var(--comfy-input-bg); + border-radius: 10px; + border: 1px solid var(--border-color); + padding: 3px 8px; + color: var(--error-text); +} + +.cn-preview { + min-width: 300px; + max-width: 500px; + min-height: 120px; + overflow: hidden; + font-size: 12px; + pointer-events: none; + padding: 12px; + color: var(--fg-color); +} + +.cn-preview-header { + display: flex; + gap: 8px; + align-items: center; + border-bottom: 1px solid var(--comfy-input-bg); + padding: 5px 10px; +} + +.cn-preview-dot { + width: 8px; + height: 8px; + border-radius: 50%; + background-color: grey; + position: relative; + filter: drop-shadow(1px 2px 3px rgb(0 0 0 / 30%)); +} + +.cn-preview-dot.cn-preview-optional::after { + content: ""; + position: absolute; + pointer-events: none; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + background-color: var(--comfy-input-bg); + border-radius: 50%; + width: 3px; + height: 3px; +} + +.cn-preview-dot.cn-preview-grid { + border-radius: 0; +} + +.cn-preview-dot.cn-preview-grid::before { + content: ''; + position: absolute; + border-left: 1px solid var(--comfy-input-bg); + border-right: 1px solid var(--comfy-input-bg); + width: 4px; + height: 100%; + left: 2px; + top: 0; + z-index: 1; +} + +.cn-preview-dot.cn-preview-grid::after { + content: ''; + position: absolute; + border-top: 1px solid var(--comfy-input-bg); + border-bottom: 1px solid var(--comfy-input-bg); + width: 100%; + height: 4px; + left: 0; + top: 2px; + z-index: 1; +} + +.cn-preview-name { + flex: auto; + font-size: 14px; +} + +.cn-preview-io { + display: flex; + justify-content: space-between; + padding: 10px 10px; +} + +.cn-preview-column > div { + display: flex; + gap: 10px; + align-items: center; + height: 18px; + overflow: hidden; + white-space: nowrap; + text-overflow: ellipsis; +} + +.cn-preview-input { + justify-content: flex-start; +} + +.cn-preview-output { + justify-content: flex-end; +} + +.cn-preview-list { + display: flex; + flex-direction: column; + gap: 3px; + padding: 0 10px 10px 10px; +} + +.cn-preview-switch { + position: relative; + display: flex; + justify-content: space-between; + align-items: center; + background: var(--bg-color); + border: 2px solid var(--border-color); + border-radius: 10px; + text-wrap: nowrap; + padding: 2px 20px; + gap: 10px; +} + +.cn-preview-switch::before, +.cn-preview-switch::after { + position: absolute; + pointer-events: none; + top: 50%; + transform: translate(0, -50%); + color: var(--fg-color); + opacity: 0.8; +} + +.cn-preview-switch::before { + content: "β—€"; + left: 5px; +} + +.cn-preview-switch::after { + content: "β–Ά"; + right: 5px; +} + +.cn-preview-value { + color: var(--descrip-text); +} + +.cn-preview-string { + min-height: 30px; + max-height: 300px; + background: var(--bg-color); + color: var(--descrip-text); + border-radius: 3px; + padding: 3px 5px; + overflow-y: auto; + overflow-x: hidden; +} + +.cn-preview-description { + margin: 0px 10px 10px 10px; + padding: 6px; + background: var(--border-color); + color: var(--descrip-text); + border-radius: 5px; + font-style: italic; + word-break: break-word; +} + +.cn-tag-list { + display: flex; + flex-wrap: wrap; + gap: 5px; + align-items: center; + margin-bottom: 5px; +} + +.cn-tag-list > div { + background-color: var(--border-color); + border-radius: 5px; + padding: 0 5px; +} + +.cn-install-buttons { + display: flex; + flex-direction: column; + gap: 3px; + padding: 3px; + align-items: center; + justify-content: center; + height: 100%; +} + +.cn-install-buttons button { + padding: 4px 8px; +} + +.cn-selected-buttons { + display: flex; + gap: 5px; + align-items: center; + padding-right: 20px; +} + +.cn-manager .cn-btn-enable { + background-color: #333399; + color: white; +} + +.cn-manager .cn-btn-disable { + background-color: #442277; + color: white; +} + +.cn-manager .cn-btn-update { + background-color: #1155AA; + color: white; +} + +.cn-manager .cn-btn-try-update { + background-color: Gray; + color: white; +} + +.cn-manager .cn-btn-try-fix { + background-color: #6495ED; + color: white; +} + +.cn-manager .cn-btn-import-failed { + background-color: #AA1111; + font-size: 10px; + font-weight: bold; + color: white; +} + +.cn-manager .cn-btn-install { + background-color: black; + color: white; +} + +.cn-manager .cn-btn-try-install { + background-color: Gray; + color: white; +} + +.cn-manager .cn-btn-uninstall { + background-color: #993333; + color: white; +} + +.cn-manager .cn-btn-reinstall { + background-color: #993333; + color: white; +} + +.cn-manager .cn-btn-switch { + background-color: #448833; + color: white; + +} + +@keyframes cn-btn-loading-bg { + 0% { + left: 0; + } + 100% { + left: -105px; + } +} + +.cn-manager button.cn-btn-loading { + position: relative; + overflow: hidden; + border-color: rgb(0 119 207 / 80%); + background-color: var(--comfy-input-bg); +} + +.cn-manager button.cn-btn-loading::after { + position: absolute; + top: 0; + left: 0; + content: ""; + width: 500px; + height: 100%; + background-image: repeating-linear-gradient( + -45deg, + rgb(0 119 207 / 30%), + rgb(0 119 207 / 30%) 10px, + transparent 10px, + transparent 15px + ); + animation: cn-btn-loading-bg 2s linear infinite; +} + +.cn-manager-light .cn-pack-name a { + color: blue; +} + +.cn-manager-light .cm-warn-note { + background-color: #ccc !important; +} + +.cn-manager-light .cn-btn-install { + background-color: #333; +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/custom-nodes-manager.js b/custom_nodes/comfyui-manager/js/custom-nodes-manager.js new file mode 100644 index 00000000..b290df61 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/custom-nodes-manager.js @@ -0,0 +1,2204 @@ +import { app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { api } from "../../scripts/api.js"; +import { buildGuiFrameCustomHeader, createSettingsCombo } from "./comfyui-gui-builder.js"; + +import { + manager_instance, rebootAPI, install_via_git_url, + fetchData, md5, icons, show_message, customConfirm, customAlert, customPrompt, + sanitizeHTML, infoToast, showTerminal, setNeedRestart, + storeColumnWidth, restoreColumnWidth, getTimeAgo, copyText, loadCss, + showPopover, hidePopover, handle403Response +} from "./common.js"; + +// https://cenfun.github.io/turbogrid/api.html +import TG from "./turbogrid.esm.js"; + +loadCss("./custom-nodes-manager.css"); + +const gridId = "node"; + +const pageHtml = ` +
+
+
+
+
+ + +
+ + + + +
+
+`; + +const ShowMode = { + NORMAL: "Normal", + UPDATE: "Update", + MISSING: "Missing", + FAVORITES: "Favorites", + ALTERNATIVES: "Alternatives", + IN_WORKFLOW: "In Workflow", +}; + +export class CustomNodesManager { + static instance = null; + static ShowMode = ShowMode; + + constructor(app, manager_dialog) { + this.app = app; + this.manager_dialog = manager_dialog; + this.id = "cn-manager"; + + app.registerExtension({ + name: "Comfy.CustomNodesManager", + afterConfigureGraph: (missingNodeTypes) => { + const item = this.getFilterItem(ShowMode.MISSING); + if (item) { + item.hasData = false; + item.hashMap = null; + } + } + }); + + this.filter = ''; + this.keywords = ''; + this.restartMap = {}; + + this.init(); + + api.addEventListener("cm-queue-status", this.onQueueStatus); + api.getNodeDefs().then(objs => { + this.nodeMap = objs; + }) + } + + init() { + const header = $el("div.cn-manager-header.px-2", {}, [ + // $el("label", {}, [ + // $el("span", { textContent: "Filter" }), + // $el("select.cn-manager-filter") + // ]), + createSettingsCombo("Filter", $el("select.cn-manager-filter")), + $el("input.cn-manager-keywords.p-inputtext.p-component", { type: "search", placeholder: "Search" }), + $el("div.cn-manager-status"), + $el("div.cn-flex-auto"), + $el("div.cn-manager-channel") + ]); + + const frame = buildGuiFrameCustomHeader( + 'cn-manager-dialog', // dialog id + header, // custom header element + pageHtml, // dialog content element + this + ); // send this so we can attach close functions + + this.element = frame; + this.element.setAttribute("tabindex", 0); + this.element.focus(); + + this.initFilter(); + this.bindEvents(); + this.initGrid(); + } + + showVersionSelectorDialog(versions, onSelect) { + const dialog = new ComfyDialog(); + dialog.element.style.zIndex = 1100; + dialog.element.style.width = "300px"; + dialog.element.style.padding = "0"; + dialog.element.style.backgroundColor = "#2a2a2a"; + dialog.element.style.border = "1px solid #3a3a3a"; + dialog.element.style.borderRadius = "8px"; + dialog.element.style.boxSizing = "border-box"; + dialog.element.style.overflow = "hidden"; + + const contentStyle = { + width: "300px", + display: "flex", + flexDirection: "column", + alignItems: "center", + padding: "20px", + boxSizing: "border-box", + gap: "15px" + }; + + let selectedVersion = versions[0]; + + const versionList = $el("select", { + multiple: true, + size: Math.min(10, versions.length), + style: { + width: "260px", + height: "auto", + backgroundColor: "#383838", + color: "#ffffff", + border: "1px solid #4a4a4a", + borderRadius: "4px", + padding: "5px", + boxSizing: "border-box" + } + }, + versions.map((v, index) => $el("option", { + value: v, + textContent: v, + selected: index === 0 + })) + ); + + versionList.addEventListener('change', (e) => { + selectedVersion = e.target.value; + Array.from(e.target.options).forEach(opt => { + opt.selected = opt.value === selectedVersion; + }); + }); + + const content = $el("div", { + style: contentStyle + }, [ + $el("h3", { + textContent: "Select Version", + style: { + color: "#ffffff", + backgroundColor: "#1a1a1a", + padding: "10px 15px", + margin: "0 0 10px 0", + width: "260px", + textAlign: "center", + borderRadius: "4px", + boxSizing: "border-box", + whiteSpace: "nowrap", + overflow: "hidden", + textOverflow: "ellipsis" + } + }), + versionList, + $el("div", { + style: { + display: "flex", + justifyContent: "space-between", + width: "260px", + gap: "10px" + } + }, [ + $el("button", { + textContent: "Cancel", + onclick: () => dialog.close(), + style: { + flex: "1", + padding: "8px", + backgroundColor: "#4a4a4a", + color: "#ffffff", + border: "none", + borderRadius: "4px", + cursor: "pointer", + whiteSpace: "nowrap", + overflow: "hidden", + textOverflow: "ellipsis" + } + }), + $el("button", { + textContent: "Select", + onclick: () => { + if (selectedVersion) { + onSelect(selectedVersion); + dialog.close(); + } else { + customAlert("Please select a version."); + } + }, + style: { + flex: "1", + padding: "8px", + backgroundColor: "#4CAF50", + color: "#ffffff", + border: "none", + borderRadius: "4px", + cursor: "pointer", + whiteSpace: "nowrap", + overflow: "hidden", + textOverflow: "ellipsis" + } + }), + ]) + ]); + + dialog.show(content); + } + + initFilter() { + const $filter = this.element.querySelector(".cn-manager-filter"); + const filterList = [{ + label: "All", + value: "", + hasData: true + }, { + label: "Installed", + value: "installed", + hasData: true + }, { + label: "Enabled", + value: "enabled", + hasData: true + }, { + label: "Disabled", + value: "disabled", + hasData: true + }, { + label: "Import Failed", + value: "import-fail", + hasData: true + }, { + label: "Not Installed", + value: "not-installed", + hasData: true + }, { + label: "ComfyRegistry", + value: "cnr", + hasData: true + }, { + label: "Non-ComfyRegistry", + value: "unknown", + hasData: true + }, { + label: "Update", + value: ShowMode.UPDATE, + hasData: false + }, { + label: "In Workflow", + value: ShowMode.IN_WORKFLOW, + hasData: false + }, { + label: "Missing", + value: ShowMode.MISSING, + hasData: false + }, { + label: "Favorites", + value: ShowMode.FAVORITES, + hasData: false + }, { + label: "Alternatives of A1111", + value: ShowMode.ALTERNATIVES, + hasData: false + }]; + this.filterList = filterList; + $filter.innerHTML = filterList.map(item => { + return `` + }).join(""); + } + + getFilterItem(filter) { + return this.filterList.find(it => it.value === filter) + } + + getActionButtons(action, rowItem, is_selected_button) { + const buttons = { + "enable": { + label: "Enable", + mode: "enable" + }, + "disable": { + label: "Disable", + mode: "disable" + }, + + "update": { + label: "Update", + mode: "update" + }, + "try-update": { + label: "Try update", + mode: "update" + }, + + "try-fix": { + label: "Try fix", + mode: "fix" + }, + + "reinstall": { + label: "Reinstall", + mode: "reinstall" + }, + + "install": { + label: "Install", + mode: "install" + }, + + "try-install": { + label: "Try install", + mode: "install" + }, + + "uninstall": { + label: "Uninstall", + mode: "uninstall" + }, + + "switch": { + label: "Switch Ver", + mode: "switch" + } + } + + const installGroups = { + "disabled": ["enable", "switch", "uninstall"], + "updatable": ["update", "switch", "disable", "uninstall"], + "import-fail": ["try-fix", "switch", "disable", "uninstall"], + "enabled": ["try-update", "switch", "disable", "uninstall"], + "not-installed": ["install"], + 'unknown': ["try-install"], + "invalid-installation": ["reinstall"], + } + + if (!installGroups.updatable) { + installGroups.enabled = installGroups.enabled.filter(it => it !== "try-update"); + } + + if (rowItem?.title === "ComfyUI-Manager") { + installGroups.enabled = installGroups.enabled.filter(it => it !== "disable" && it !== "uninstall" && it !== "switch"); + } + + let list = installGroups[action]; + + if(is_selected_button || rowItem?.version === "unknown") { + list = list.filter(it => it !== "switch"); + } + + if (!list) { + return ""; + } + + return list.map(id => { + const bt = buttons[id]; + return ``; + }).join(""); + } + + getButton(target) { + if(!target) { + return; + } + const mode = target.getAttribute("mode"); + if (!mode) { + return; + } + const group = target.getAttribute("group"); + if (!group) { + return; + } + return { + group, + mode, + target, + label: target.innerText + } + } + + bindEvents() { + const eventsMap = { + ".cn-manager-filter": { + change: (e) => { + + if (this.grid) { + this.grid.selectAll(false); + } + + const value = e.target.value + this.filter = value; + const item = this.getFilterItem(value); + if (item && (!item.hasData)) { + this.loadData(value); + return; + } + this.updateGrid(); + } + }, + + ".cn-manager-keywords": { + input: (e) => { + const keywords = `${e.target.value}`.trim(); + if (keywords !== this.keywords) { + this.keywords = keywords; + this.updateGrid(); + } + }, + focus: (e) => e.target.select() + }, + + ".cn-manager-selection": { + click: (e) => { + const btn = this.getButton(e.target); + if (btn) { + const nodes = this.selectedMap[btn.group]; + if (nodes) { + this.installNodes(nodes, btn); + } + } + } + }, + + ".cn-manager-back": { + click: (e) => { + this.flyover.hide(true); + this.removeHighlight(); + hidePopover(); + this.close() + manager_instance.show(); + } + }, + + ".cn-manager-restart": { + click: () => { + this.close(); + this.manager_dialog.close(); + rebootAPI(); + } + }, + + ".cn-manager-stop": { + click: () => { + api.fetchApi('/manager/queue/reset'); + infoToast('Cancel', 'Remaining tasks will stop after completing the current task.'); + } + }, + + ".cn-manager-used-in-workflow": { + click: (e) => { + e.target.classList.add("cn-btn-loading"); + this.setFilter(ShowMode.IN_WORKFLOW); + this.loadData(ShowMode.IN_WORKFLOW); + } + }, + + ".cn-manager-check-update": { + click: (e) => { + e.target.classList.add("cn-btn-loading"); + this.setFilter(ShowMode.UPDATE); + this.loadData(ShowMode.UPDATE); + } + }, + + ".cn-manager-check-missing": { + click: (e) => { + e.target.classList.add("cn-btn-loading"); + this.setFilter(ShowMode.MISSING); + this.loadData(ShowMode.MISSING); + } + }, + + ".cn-manager-install-url": { + click: async (e) => { + const url = await customPrompt("Please enter the URL of the Git repository to install", ""); + if (url !== null) { + install_via_git_url(url, this.manager_dialog); + } + } + } + }; + Object.keys(eventsMap).forEach(selector => { + const target = this.element.querySelector(selector); + if (target) { + const events = eventsMap[selector]; + if (events) { + Object.keys(events).forEach(type => { + target.addEventListener(type, events[type]); + }); + } + } + }); + + } + + // =========================================================================================== + + initGrid() { + const container = this.element.querySelector(".cn-manager-grid"); + const grid = new TG.Grid(container); + this.grid = grid; + + this.flyover = this.createFlyover(container); + + let prevViewRowsLength = -1; + grid.bind('onUpdated', (e, d) => { + const viewRows = grid.viewRows; + prevViewRowsLength = viewRows.length; + this.showStatus(`${prevViewRowsLength.toLocaleString()} custom nodes`); + }); + + grid.bind('onSelectChanged', (e, changes) => { + this.renderSelected(); + }); + + grid.bind("onColumnWidthChanged", (e, columnItem) => { + storeColumnWidth(gridId, columnItem) + }); + + grid.bind('onClick', (e, d) => { + + this.addHighlight(d.rowItem); + + if (d.columnItem.id === "nodes") { + this.showNodes(d); + return; + } + + const btn = this.getButton(d.e.target); + if (btn) { + const item = this.grid.getRowItemBy("hash", d.rowItem.hash); + + const { target, label, mode} = btn; + if((mode === "install" || mode === "switch" || mode == "enable") && item.originalData.version != 'unknown') { + // install after select version via dialog if item is cnr node + this.installNodeWithVersion(d.rowItem, btn, mode == 'enable'); + } else { + this.installNodes([d.rowItem.hash], btn, d.rowItem.title); + } + return; + } + + }); + + // iteration events + this.element.addEventListener("click", (e) => { + if (container === e.target || container.contains(e.target)) { + return; + } + this.removeHighlight(); + }); + // proxy keyboard events + this.element.addEventListener("keydown", (e) => { + if (e.target === this.element) { + grid.containerKeyDownHandler(e); + } + }, true); + + + grid.setOption({ + theme: 'dark', + selectVisible: true, + selectMultiple: true, + selectAllVisible: true, + + textSelectable: true, + scrollbarRound: true, + + frozenColumn: 1, + rowNotFound: "No Results", + + rowHeight: 40, + bindWindowResize: true, + bindContainerResize: true, + + cellResizeObserver: (rowItem, columnItem) => { + const autoHeightColumns = ['title', 'action', 'description', "alternatives"]; + return autoHeightColumns.includes(columnItem.id) + }, + + // updateGrid handler for filter and keywords + rowFilter: (rowItem) => { + + const searchableColumns = ["title", "author", "description"]; + if (this.hasAlternatives()) { + searchableColumns.push("alternatives"); + } + + let shouldShown = grid.highlightKeywordsFilter(rowItem, searchableColumns, this.keywords); + + if (shouldShown) { + if(this.filter && rowItem.filterTypes) { + shouldShown = rowItem.filterTypes.includes(this.filter); + } + } + + return shouldShown; + } + }); + + } + + hasAlternatives() { + return this.filter === ShowMode.ALTERNATIVES + } + + async handleImportFail(rowItem) { + var info; + if(rowItem.version == 'unknown'){ + info = { + 'url': rowItem.originalData.files[0] + }; + } + else{ + info = { + 'cnr_id': rowItem.originalData.id + }; + } + + const response = await api.fetchApi(`/customnode/import_fail_info`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(info) + }); + + let res = await response.json(); + + let title = `Error message occurred while importing the '${rowItem.title}' module.

` + + if(res.code == 400) + { + show_message(title+'The information is not available.') + } + else { + show_message(title+sanitizeHTML(res['msg']).replace(/ /g, ' ').replace(/\n/g, '
')); + } + } + + renderGrid() { + // update theme + const globalStyle = window.getComputedStyle(document.body); + this.colorVars = { + bgColor: globalStyle.getPropertyValue('--comfy-menu-bg'), + borderColor: globalStyle.getPropertyValue('--border-color') + } + + const colorPalette = this.app.ui.settings.settingsValues['Comfy.ColorPalette']; + this.colorPalette = colorPalette; + Array.from(this.element.classList).forEach(cn => { + if (cn.startsWith("cn-manager-")) { + this.element.classList.remove(cn); + } + }); + this.element.classList.add(`cn-manager-${colorPalette}`); + + const options = { + theme: colorPalette === "light" ? "" : "dark" + }; + + + let self = this; + const columns = [{ + id: 'id', + name: 'ID', + width: 50, + align: 'center' + }, { + id: 'title', + name: 'Title', + width: 200, + minWidth: 100, + maxWidth: 500, + classMap: 'cn-pack-name', + formatter: (title, rowItem, columnItem) => { + const container = document.createElement('div'); + + if (rowItem.action === 'invalid-installation') { + const invalidTag = document.createElement('span'); + invalidTag.style.color = 'red'; + invalidTag.innerHTML = '(INVALID)'; + container.appendChild(invalidTag); + } else if (rowItem.action === 'import-fail') { + const button = document.createElement('button'); + button.className = 'cn-btn-import-failed'; + button.innerText = 'IMPORT FAILED β†—'; + button.onclick = () => self.handleImportFail(rowItem); + container.appendChild(button); + container.appendChild(document.createElement('br')); + } + + const link = document.createElement('a'); + if(rowItem.originalData.repository) + link.href = rowItem.originalData.repository; + else + link.href = rowItem.reference; + link.target = '_blank'; + link.innerHTML = `${title}`; + link.title = rowItem.originalData.id; + container.appendChild(link); + + return container; + } + }, { + id: 'version', + name: 'Version', + width: 100, + minWidth: 80, + maxWidth: 300, + classMap: 'cn-pack-version', + formatter: (version, rowItem, columnItem) => { + if(!version) { + return; + } + if(rowItem.cnr_latest && version != rowItem.cnr_latest) { + if(version == 'nightly') { + return `
${version}
[${rowItem.cnr_latest}]
`; + } + return `
${version}
[↑${rowItem.cnr_latest}]
`; + } + return version; + } + }, { + id: 'action', + name: 'Action', + width: 130, + minWidth: 110, + maxWidth: 200, + sortable: false, + align: 'center', + formatter: (action, rowItem, columnItem) => { + if (rowItem.restart) { + return `Restart Required`; + } + const buttons = this.getActionButtons(action, rowItem); + return `
${buttons}
`; + } + }, { + id: "nodes", + name: "Nodes", + width: 100, + formatter: (v, rowItem, columnItem) => { + if (!rowItem.nodes) { + return ''; + } + const list = [`
`]; + list.push(`
${rowItem.nodes} node${(rowItem.nodes>1?'s':'')}
`); + if (rowItem.conflicts) { + list.push(`
${rowItem.conflicts} conflict${(rowItem.conflicts>1?'s':'')}
`); + } + list.push('
'); + return list.join(""); + } + }, { + id: "alternatives", + name: "Alternatives", + width: 400, + maxWidth: 5000, + invisible: !this.hasAlternatives(), + classMap: 'cn-pack-desc' + }, { + id: 'description', + name: 'Description', + width: 400, + maxWidth: 5000, + classMap: 'cn-pack-desc' + }, { + id: 'author', + name: 'Author', + width: 120, + classMap: "cn-pack-author", + formatter: (author, rowItem, columnItem) => { + if (rowItem.trust) { + return `βœ… ${author}`; + } + return author; + } + }, { + id: 'stars', + name: 'β˜…', + align: 'center', + classMap: "cn-pack-stars", + formatter: (stars) => { + if (stars < 0) { + return 'N/A'; + } + if (typeof stars === 'number') { + return stars.toLocaleString(); + } + return stars; + } + }, { + id: 'last_update', + name: 'Last Update', + align: 'center', + type: 'date', + width: 100, + classMap: "cn-pack-last-update", + formatter: (last_update) => { + if (last_update < 0) { + return 'N/A'; + } + const ago = getTimeAgo(last_update); + const short = `${last_update}`.split(' ')[0]; + return `${short}`; + } + }]; + + restoreColumnWidth(gridId, columns); + + const rows_values = Object.values(this.custom_nodes); + rows_values.sort((a, b) => { + if (a.version == 'unknown' && b.version != 'unknown') return 1; + if (a.version != 'unknown' && b.version == 'unknown') return -1; + + if (a.stars !== b.stars) { + return b.stars - a.stars; + } + + if (a.last_update !== b.last_update) { + return new Date(b.last_update) - new Date(a.last_update); + } + + return 0; + }); + + rows_values.forEach((it, i) => { + it.id = i + 1; + }); + + this.grid.setData({ + options: options, + rows: rows_values, + columns: columns + }); + + this.grid.render(); + } + + updateGrid() { + if (this.grid) { + this.grid.update(); + if (this.hasAlternatives()) { + this.grid.showColumn("alternatives"); + } else { + this.grid.hideColumn("alternatives"); + } + } + } + + addHighlight(rowItem) { + this.removeHighlight(); + if (this.grid && rowItem) { + this.grid.setRowState(rowItem, 'highlight', true); + this.highlightRow = rowItem; + } + } + + removeHighlight() { + if (this.grid && this.highlightRow) { + this.grid.setRowState(this.highlightRow, 'highlight', false); + this.highlightRow = null; + } + } + + // =========================================================================================== + + getWidgetType(type, inputName) { + if (type === 'COMBO') { + return 'COMBO' + } + const widgets = app.widgets; + if (`${type}:${inputName}` in widgets) { + return `${type}:${inputName}` + } + if (type in widgets) { + return type + } + } + + createNodePreview(nodeItem) { + // console.log(nodeItem); + const list = [`
+
+
${nodeItem.name}
+
Preview
+
`]; + + // Node slot I/O + const inputList = []; + nodeItem.input_order.required?.map(name => { + inputList.push({ + name + }); + }) + nodeItem.input_order.optional?.map(name => { + inputList.push({ + name, + optional: true + }); + }); + + const slotInputList = []; + const widgetInputList = []; + const inputMap = Object.assign({}, nodeItem.input.optional, nodeItem.input.required); + inputList.forEach(it => { + const inputName = it.name; + const _inputData = inputMap[inputName]; + let type = _inputData[0]; + let options = _inputData[1] || {}; + if (Array.isArray(type)) { + options.default = type[0]; + type = 'COMBO'; + } + it.type = type; + it.options = options; + + // convert force/default inputs + if (options.forceInput || options.defaultInput) { + slotInputList.push(it); + return; + } + + const widgetType = this.getWidgetType(type, inputName); + if (widgetType) { + it.default = options.default; + widgetInputList.push(it); + } else { + slotInputList.push(it); + } + }); + + const outputList = nodeItem.output.map((type, i) => { + return { + type, + name: nodeItem.output_name[i], + list: nodeItem.output_is_list[i] + } + }); + + // dark + const colorMap = { + "CLIP": "#FFD500", + "CLIP_VISION": "#A8DADC", + "CLIP_VISION_OUTPUT": "#ad7452", + "CONDITIONING": "#FFA931", + "CONTROL_NET": "#6EE7B7", + "IMAGE": "#64B5F6", + "LATENT": "#FF9CF9", + "MASK": "#81C784", + "MODEL": "#B39DDB", + "STYLE_MODEL": "#C2FFAE", + "VAE": "#FF6E6E", + "NOISE": "#B0B0B0", + "GUIDER": "#66FFFF", + "SAMPLER": "#ECB4B4", + "SIGMAS": "#CDFFCD", + "TAESD": "#DCC274" + } + + const inputHtml = slotInputList.map(it => { + const color = colorMap[it.type] || "gray"; + const optional = it.optional ? " cn-preview-optional" : "" + return `
+
+ ${it.name} +
`; + }).join(""); + + const outputHtml = outputList.map(it => { + const color = colorMap[it.type] || "gray"; + const grid = it.list ? " cn-preview-grid" : ""; + return `
+ ${it.name} +
+
`; + }).join(""); + + list.push(`
+
${inputHtml}
+
${outputHtml}
+
`); + + // Node widget inputs + if (widgetInputList.length) { + list.push(`
`); + + // console.log(widgetInputList); + widgetInputList.forEach(it => { + + let value = it.default; + if (typeof value === "object" && value && Object.prototype.hasOwnProperty.call(value, "content")) { + value = value.content; + } + if (typeof value === "undefined" || value === null) { + value = ""; + } else { + value = `${value}`; + } + + if ( + (it.type === "STRING" && (value || it.options.multiline)) + || it.type === "MARKDOWN" + ) { + if (value) { + value = value.replace(/\r?\n/g, "
") + } + list.push(`
${value || it.name}
`); + return; + } + + list.push(`
+
${it.name}
+
${value}
+
`); + }); + list.push(`
`); + } + + if (nodeItem.description) { + list.push(`
${nodeItem.description}
`) + } + + return list.join(""); + } + + showNodePreview(target) { + const nodeName = target.innerText; + const nodeItem = this.nodeMap[nodeName]; + if (!nodeItem) { + this.hideNodePreview(); + return; + } + const html = this.createNodePreview(nodeItem); + showPopover(target, html, "cn-preview cn-preview-"+this.colorPalette, { + positions: ['left'], + bgColor: this.colorVars.bgColor, + borderColor: this.colorVars.borderColor + }) + } + + hideNodePreview() { + hidePopover(); + } + + createFlyover(container) { + const $flyover = document.createElement("div"); + $flyover.className = "cn-flyover"; + $flyover.innerHTML = `
+
${icons.arrowRight}
+
+
${icons.close}
+
+
` + container.appendChild($flyover); + + const $flyoverTitle = $flyover.querySelector(".cn-flyover-title"); + const $flyoverBody = $flyover.querySelector(".cn-flyover-body"); + + let width = '50%'; + let visible = false; + + let timeHide; + const closeHandler = (e) => { + if ($flyover === e.target || $flyover.contains(e.target)) { + return; + } + clearTimeout(timeHide); + timeHide = setTimeout(() => { + flyover.hide(); + }, 100); + } + + const hoverHandler = (e) => { + if(e.type === "mouseenter") { + if(e.target.classList.contains("cn-nodes-name")) { + this.showNodePreview(e.target); + } + return; + } + this.hideNodePreview(); + } + + const displayHandler = () => { + if (visible) { + $flyover.classList.remove("cn-slide-in-right"); + } else { + $flyover.classList.remove("cn-slide-out-right"); + $flyover.style.width = '0px'; + $flyover.style.display = "none"; + } + } + + const flyover = { + show: (titleHtml, bodyHtml) => { + clearTimeout(timeHide); + this.element.removeEventListener("click", closeHandler); + $flyoverTitle.innerHTML = titleHtml; + $flyoverBody.innerHTML = bodyHtml; + $flyover.style.display = "block"; + $flyover.style.width = width; + if(!visible) { + $flyover.classList.add("cn-slide-in-right"); + } + visible = true; + setTimeout(() => { + this.element.addEventListener("click", closeHandler); + }, 100); + }, + hide: (now) => { + visible = false; + this.element.removeEventListener("click", closeHandler); + if(now) { + displayHandler(); + return; + } + $flyover.classList.add("cn-slide-out-right"); + } + } + + $flyover.addEventListener("animationend", (e) => { + displayHandler(); + }); + + $flyover.addEventListener("mouseenter", hoverHandler, true); + $flyover.addEventListener("mouseleave", hoverHandler, true); + + $flyover.addEventListener("click", (e) => { + + if(e.target.classList.contains("cn-nodes-name")) { + const nodeName = e.target.innerText; + const nodeItem = this.nodeMap[nodeName]; + if (!nodeItem) { + copyText(nodeName).then((res) => { + if (res) { + e.target.setAttribute("action", "Copied"); + e.target.classList.add("action"); + setTimeout(() => { + e.target.classList.remove("action"); + e.target.removeAttribute("action"); + }, 1000); + } + }); + return; + } + + const [x, y, w, h] = app.canvas.ds.visible_area; + const dpi = Math.max(window.devicePixelRatio ?? 1, 1); + const node = window.LiteGraph?.createNode( + nodeItem.name, + nodeItem.display_name, + { + pos: [x + (w-300) / dpi / 2, y] + } + ); + if (node) { + app.graph.add(node); + e.target.setAttribute("action", "Added to Workflow"); + e.target.classList.add("action"); + setTimeout(() => { + e.target.classList.remove("action"); + e.target.removeAttribute("action"); + }, 1000); + } + + return; + } + if(e.target.classList.contains("cn-nodes-pack")) { + const hash = e.target.getAttribute("hash"); + const rowItem = this.grid.getRowItemBy("hash", hash); + //console.log(rowItem); + this.grid.scrollToRow(rowItem); + this.addHighlight(rowItem); + return; + } + if(e.target.classList.contains("cn-flyover-close")) { + flyover.hide(); + return; + } + }); + + return flyover; + } + + showNodes(d) { + const nodesList = d.rowItem.nodesList; + if (!nodesList) { + return; + } + + const rowItem = d.rowItem; + const isNotInstalled = rowItem.action == "not-installed"; + + let titleHtml = `
${rowItem.title}
`; + if (isNotInstalled) { + titleHtml += '
Not Installed
' + } + + const list = []; + list.push(`
`); + + nodesList.forEach((it, i) => { + let rowClass = 'cn-nodes-row' + if (it.conflicts) { + rowClass += ' cn-nodes-conflict'; + } + + list.push(`
`); + list.push(`
${i+1}
`); + list.push(`
${it.name}
`); + + if (it.conflicts) { + list.push(`
${icons.conflicts}
Conflict with${it.conflicts.map(c => { + return `
${c.title}
`; + }).join(",")}
`); + } + list.push(`
`); + }); + + list.push("
"); + const bodyHtml = list.join(""); + + this.flyover.show(titleHtml, bodyHtml); + } + + async loadNodes(node_packs) { + const mode = manager_instance.datasrc_combo.value; + this.showStatus(`Loading node mappings (${mode}) ...`); + const res = await fetchData(`/customnode/getmappings?mode=${mode}`); + if (res.error) { + console.log(res.error); + return; + } + + const data = res.data; + + const findNode = (k, title) => { + let item = node_packs[k]; + if (item) { + return item; + } + + // git url + if (k.includes("/")) { + const gitName = k.split("/").pop(); + item = node_packs[gitName]; + if (item) { + return item; + } + } + + return node_packs[title]; + } + + const conflictsMap = {}; + + // add nodes data + Object.keys(data).forEach(k => { + const [nodes, metadata] = data[k]; + if (nodes?.length) { + const title = metadata?.title_aux; + const nodeItem = findNode(k, title); + if (nodeItem) { + + // deduped + const eList = Array.from(new Set(nodes)); + + nodeItem.nodes = eList.length; + const nodesMap = {}; + eList.forEach(extName => { + nodesMap[extName] = { + name: extName + }; + let cList = conflictsMap[extName]; + if(!cList) { + cList = []; + conflictsMap[extName] = cList; + } + cList.push(nodeItem.key); + }); + nodeItem.nodesMap = nodesMap; + } else { + // should be removed + // console.log("not found", k, title, nodes) + } + } + }); + + // calculate conflicts data + Object.keys(conflictsMap).forEach(extName => { + const cList = conflictsMap[extName]; + if(cList.length <= 1) { + return; + } + cList.forEach(key => { + const nodeItem = node_packs[key]; + const extItem = nodeItem.nodesMap[extName]; + if(!extItem.conflicts) { + extItem.conflicts = [] + } + const conflictsList = cList.filter(k => k !== key); + conflictsList.forEach(k => { + const nItem = node_packs[k]; + extItem.conflicts.push({ + key: k, + title: nItem.title, + hash: nItem.hash + }) + + }) + }) + }) + + Object.values(node_packs).forEach(nodeItem => { + if (nodeItem.nodesMap) { + nodeItem.nodesList = Object.values(nodeItem.nodesMap); + nodeItem.conflicts = nodeItem.nodesList.filter(it => it.conflicts).length; + } + }) + + } + + // =========================================================================================== + + renderSelected() { + const selectedList = this.grid.getSelectedRows(); + if (!selectedList.length) { + this.showSelection(""); + return; + } + + const selectedMap = {}; + selectedList.forEach(item => { + let type = item.action; + if (item.restart) { + type = "Restart Required"; + } + if (selectedMap[type]) { + selectedMap[type].push(item.hash); + } else { + selectedMap[type] = [item.hash]; + } + }); + + this.selectedMap = selectedMap; + + const list = []; + Object.keys(selectedMap).forEach(v => { + const filterItem = this.getFilterItem(v); + list.push(`
+ Selected ${selectedMap[v].length} ${filterItem ? filterItem.label : v} + ${this.grid.hasMask ? "" : this.getActionButtons(v, null, true)} +
`); + }); + + this.showSelection(list.join("")); + } + + focusInstall(item, mode) { + const cellNode = this.grid.getCellNode(item, "action"); + if (cellNode) { + const cellBtn = cellNode.querySelector(`button[mode="${mode}"]`); + if (cellBtn) { + cellBtn.classList.add("cn-btn-loading"); + return true + } + } + } + + async installNodeWithVersion(rowItem, btn, is_enable) { + let hash = rowItem.hash; + let title = rowItem.title; + + const item = this.grid.getRowItemBy("hash", hash); + + let node_id = item.originalData.id; + + this.showLoading(); + let res; + if(is_enable) { + res = await api.fetchApi(`/customnode/disabled_versions/${node_id}`, { cache: "no-store" }); + } + else { + res = await api.fetchApi(`/customnode/versions/${node_id}`, { cache: "no-store" }); + } + this.hideLoading(); + + if(res.status == 200) { + let obj = await res.json(); + + let versions = []; + let default_version; + let version_cnt = 0; + + if(!is_enable) { + + if(rowItem.cnr_latest != rowItem.originalData.active_version && obj.length > 0) { + versions.push('latest'); + } + + if(rowItem.originalData.active_version != 'nightly') { + versions.push('nightly'); + default_version = 'nightly'; + version_cnt++; + } + } + + for(let v of obj) { + if(rowItem.originalData.active_version != v.version) { + default_version = v.version; + versions.push(v.version); + version_cnt++; + } + } + + this.showVersionSelectorDialog(versions, (selected_version) => { + this.installNodes([hash], btn, title, selected_version); + }); + } + else { + show_message('Failed to fetch versions from ComfyRegistry.'); + } + } + + async installNodes(list, btn, title, selected_version) { + let stats = await api.fetchApi('/manager/queue/status'); + stats = await stats.json(); + if(stats.is_processing) { + customAlert(`[ComfyUI-Manager] There are already tasks in progress. Please try again after it is completed. (${stats.done_count}/${stats.total_count})`); + return; + } + + const { target, label, mode} = btn; + + if(mode === "uninstall") { + title = title || `${list.length} custom nodes`; + + const confirmed = await customConfirm(`Are you sure uninstall ${title}?`); + if (!confirmed) { + return; + } + } + + if(mode === "reinstall") { + title = title || `${list.length} custom nodes`; + + const confirmed = await customConfirm(`Are you sure reinstall ${title}?`); + if (!confirmed) { + return; + } + } + + target.classList.add("cn-btn-loading"); + this.showError(""); + + let needRestart = false; + let errorMsg = ""; + + await api.fetchApi('/manager/queue/reset'); + + let target_items = []; + + for (const hash of list) { + const item = this.grid.getRowItemBy("hash", hash); + target_items.push(item); + + if (!item) { + errorMsg = `Not found custom node: ${hash}`; + break; + } + + this.grid.scrollRowIntoView(item); + + if (!this.focusInstall(item, mode)) { + this.grid.onNextUpdated(() => { + this.focusInstall(item, mode); + }); + } + + this.showStatus(`${label} ${item.title} ...`); + + const data = item.originalData; + data.selected_version = selected_version; + data.channel = this.channel; + data.mode = this.mode; + data.ui_id = hash; + + let install_mode = mode; + if(mode == 'switch') { + install_mode = 'install'; + } + + // don't post install if install_mode == 'enable' + data.skip_post_install = install_mode == 'enable'; + let api_mode = install_mode; + if(install_mode == 'enable') { + api_mode = 'install'; + } + + if(install_mode == 'reinstall') { + api_mode = 'reinstall'; + } + + const res = await api.fetchApi(`/manager/queue/${api_mode}`, { + method: 'POST', + body: JSON.stringify(data) + }); + + if (res.status != 200) { + errorMsg = `'${item.title}': `; + + if(res.status == 403) { + try { + const data = await res.json(); + if(data.error === 'comfyui_outdated') { + errorMsg += `ComfyUI version is outdated. Please update ComfyUI to use Manager normally.\n`; + } else { + errorMsg += `This action is not allowed with this security level configuration.\n`; + } + } catch { + errorMsg += `This action is not allowed with this security level configuration.\n`; + } + } else if(res.status == 404) { + errorMsg += `With the current security level configuration, only custom nodes from the "default channel" can be installed.\n`; + } else { + errorMsg += await res.text() + '\n'; + } + + break; + } + } + + this.install_context = {btn: btn, targets: target_items}; + + if(errorMsg) { + this.showError(errorMsg); + show_message("[Installation Errors]\n"+errorMsg); + + // reset + for(let k in target_items) { + const item = target_items[k]; + this.grid.updateCell(item, "action"); + } + } + else { + await api.fetchApi('/manager/queue/start'); + this.showStop(); + showTerminal(); + } + } + + async onQueueStatus(event) { + let self = CustomNodesManager.instance; + if(event.detail.status == 'in_progress' && event.detail.ui_target == 'nodepack_manager') { + const hash = event.detail.target; + + const item = self.grid.getRowItemBy("hash", hash); + + item.restart = true; + self.restartMap[item.hash] = true; + self.grid.updateCell(item, "action"); + self.grid.setRowSelected(item, false); + } + else if(event.detail.status == 'done') { + self.hideStop(); + self.onQueueCompleted(event.detail); + } + } + + async onQueueCompleted(info) { + let result = info.nodepack_result; + + if(result.length == 0) { + return; + } + + let self = CustomNodesManager.instance; + + if(!self.install_context) { + return; + } + + const { target, label, mode } = self.install_context.btn; + target.classList.remove("cn-btn-loading"); + + let errorMsg = ""; + + for(let hash in result){ + let v = result[hash]; + + if(v != 'success' && v != 'skip') + errorMsg += v+'\n'; + } + + for(let k in self.install_context.targets) { + let item = self.install_context.targets[k]; + self.grid.updateCell(item, "action"); + } + + if (errorMsg) { + self.showError(errorMsg); + show_message("Installation Error:\n"+errorMsg); + } else { + self.showStatus(`${label} ${result.length} custom node(s) successfully`); + } + + self.showRestart(); + self.showMessage(`To apply the installed/updated/disabled/enabled custom node, please restart ComfyUI. And refresh browser.`, "red"); + + infoToast(`[ComfyUI-Manager] All node pack tasks in the queue have been completed.\n${info.done_count}/${info.total_count}`); + self.install_context = undefined; + } + + // =========================================================================================== + + getNodesInWorkflow() { + let usedGroupNodes = new Set(); + let allUsedNodes = {}; + const visitedGraphs = new Set(); + + const visitGraph = (graph) => { + if (!graph || visitedGraphs.has(graph)) return; + visitedGraphs.add(graph); + + const nodes = graph._nodes || graph.nodes || []; + for(let k in nodes) { + let node = nodes[k]; + if (!node) continue; + + // If it's a SubgraphNode, recurse into its graph and continue searching + if (node.isSubgraphNode?.() && node.subgraph) { + visitGraph(node.subgraph); + } + + if (!node.type) continue; + + // Group nodes / components + if(typeof node.type === 'string' && node.type.startsWith('workflow>')) { + usedGroupNodes.add(node.type.slice(9)); + continue; + } + + allUsedNodes[node.type] = node; + } + }; + + visitGraph(app.graph); + + for(let k of usedGroupNodes) { + let subnodes = app.graph.extra.groupNodes[k]?.nodes; + + if(subnodes) { + for(let k2 in subnodes) { + let node = subnodes[k2]; + allUsedNodes[node.type] = node; + } + } + } + + return allUsedNodes; + } + + async getMissingNodes() { + let unresolved_missing_nodes = new Set(); + let hashMap = {}; + let allUsedNodes = this.getNodesInWorkflow(); + + const registered_nodes = new Set(); + for (let i in LiteGraph.registered_node_types) { + registered_nodes.add(LiteGraph.registered_node_types[i].type); + } + + let unresolved_aux_ids = {}; + let outdated_comfyui = false; + let unresolved_cnr_list = []; + + for(let k in allUsedNodes) { + let node = allUsedNodes[k]; + + if(!registered_nodes.has(node.type)) { + // missing node + if(node.properties.cnr_id) { + if(node.properties.cnr_id == 'comfy-core') { + outdated_comfyui = true; + } + + let item = this.custom_nodes[node.properties.cnr_id]; + if(item) { + hashMap[item.hash] = true; + } + else { + console.log(`CM: cannot find '${node.properties.cnr_id}' from cnr list.`); + unresolved_aux_ids[node.properties.cnr_id] = node.type; + unresolved_cnr_list.push(node.properties.cnr_id); + } + } + else if(node.properties.aux_id) { + unresolved_aux_ids[node.properties.aux_id] = node.type; + } + else { + unresolved_missing_nodes.add(node.type); + } + } + } + + + if(unresolved_cnr_list.length > 0) { + let error_msg = "Failed to find the following ComfyRegistry list.\nThe cache may be outdated, or the nodes may have been removed from ComfyRegistry.
"; + for(let i in unresolved_cnr_list) { + error_msg += '
  • '+unresolved_cnr_list[i]+'
    • '; + } + + show_message(error_msg); + } + + if(outdated_comfyui) { + customAlert('ComfyUI is outdated, so some built-in nodes cannot be used.'); + } + + if(Object.keys(unresolved_aux_ids).length > 0) { + // building aux_id to nodepack map + let aux_id_to_pack = {}; + for(let k in this.custom_nodes) { + let nodepack = this.custom_nodes[k]; + let aux_id; + if(nodepack.repository?.startsWith('https://github.com')) { + aux_id = nodepack.repository.split('/').slice(-2).join('/'); + aux_id_to_pack[aux_id] = nodepack; + } + else if(nodepack.repository) { + aux_id = nodepack.repository.split('/').slice(-1); + aux_id_to_pack[aux_id] = nodepack; + } + } + + // resolving aux_id + for(let k in unresolved_aux_ids) { + let nodepack = aux_id_to_pack[k]; + if(nodepack) { + hashMap[nodepack.hash] = true; + } + else { + unresolved_missing_nodes.add(unresolved_aux_ids[k]); + } + } + } + + if(unresolved_missing_nodes.size > 0) { + await this.getMissingNodesLegacy(hashMap, unresolved_missing_nodes); + } + + return hashMap; + } + + async getMissingNodesLegacy(hashMap, missing_nodes) { + const mode = manager_instance.datasrc_combo.value; + this.showStatus(`Loading missing nodes (${mode}) ...`); + const res = await fetchData(`/customnode/getmappings?mode=${mode}`); + if (res.error) { + this.showError(`Failed to get custom node mappings: ${res.error}`); + return; + } + + const mappings = res.data; + + // build regex->url map + const regex_to_pack = []; + for(let k in this.custom_nodes) { + let node = this.custom_nodes[k]; + + if(node.nodename_pattern) { + regex_to_pack.push({ + regex: new RegExp(node.nodename_pattern), + url: node.files[0] + }); + } + } + + // build name->url map + const name_to_packs = {}; + for (const url in mappings) { + const names = mappings[url]; + + for(const name in names[0]) { + let v = name_to_packs[names[0][name]]; + if(v == undefined) { + v = []; + name_to_packs[names[0][name]] = v; + } + v.push(url); + } + } + + let unresolved_missing_nodes = new Set(); + for (let node_type of missing_nodes) { + const packs = name_to_packs[node_type.trim()]; + if(packs) + packs.forEach(url => { + unresolved_missing_nodes.add(url); + }); + else { + for(let j in regex_to_pack) { + if(regex_to_pack[j].regex.test(node_type)) { + unresolved_missing_nodes.add(regex_to_pack[j].url); + } + } + } + } + + for(let k in this.custom_nodes) { + let item = this.custom_nodes[k]; + + if(unresolved_missing_nodes.has(item.id)) { + hashMap[item.hash] = true; + } + else if (item.files?.some(file => unresolved_missing_nodes.has(file))) { + hashMap[item.hash] = true; + } + } + + return hashMap; + } + + async getFavorites() { + const hashMap = {}; + for(let k in this.custom_nodes) { + let item = this.custom_nodes[k]; + if(item.is_favorite) + hashMap[item.hash] = true; + } + + return hashMap; + } + + async getNodepackInWorkflow() { + let allUsedNodes = this.getNodesInWorkflow(); + + // building aux_id to nodepack map + let aux_id_to_pack = {}; + for(let k in this.custom_nodes) { + let nodepack = this.custom_nodes[k]; + let aux_id; + if(nodepack.repository?.startsWith('https://github.com')) { + aux_id = nodepack.repository.split('/').slice(-2).join('/'); + aux_id_to_pack[aux_id] = nodepack; + } + else if(nodepack.repository) { + aux_id = nodepack.repository.split('/').slice(-1); + aux_id_to_pack[aux_id] = nodepack; + } + } + + const hashMap = {}; + for(let k in allUsedNodes) { + var item; + if(allUsedNodes[k].properties.cnr_id) { + item = this.custom_nodes[allUsedNodes[k].properties.cnr_id]; + } + else if(allUsedNodes[k].properties.aux_id) { + item = aux_id_to_pack[allUsedNodes[k].properties.aux_id]; + } + + if(item) + hashMap[item.hash] = true; + } + + return hashMap; + } + + async getAlternatives() { + const mode = manager_instance.datasrc_combo.value; + this.showStatus(`Loading alternatives (${mode}) ...`); + const res = await fetchData(`/customnode/alternatives?mode=${mode}`); + if (res.error) { + this.showError(`Failed to get alternatives: ${res.error}`); + return []; + } + + const hashMap = {}; + const items = res.data; + + for(let i in items) { + let item = items[i]; + let custom_node = this.custom_nodes[i]; + + if (!custom_node) { + console.log(`Not found custom node: ${item.id}`); + continue; + } + + const tags = `${item.tags}`.split(",").map(tag => { + return `
    ${tag.trim()}
    `; + }).join(""); + + hashMap[custom_node.hash] = { + alternatives: `
    ${tags}
    ${item.description}` + } + + } + + return hashMap; + } + + async loadData(show_mode = ShowMode.NORMAL) { + const isElectron = 'electronAPI' in window; + + this.show_mode = show_mode; + console.log("Show mode:", show_mode); + + this.showLoading(); + + const mode = manager_instance.datasrc_combo.value; + this.showStatus(`Loading custom nodes (${mode}) ...`); + + const skip_update = this.show_mode === ShowMode.UPDATE ? "" : "&skip_update=true"; + + if(this.show_mode === ShowMode.UPDATE) { + infoToast('Fetching updated information. This may take some time if many custom nodes are installed.'); + } + + const res = await fetchData(`/customnode/getlist?mode=${mode}${skip_update}`); + if (res.error) { + this.showError("Failed to get custom node list."); + this.hideLoading(); + return; + } + + const { channel, node_packs } = res.data; + + if(isElectron) { + delete node_packs['comfyui-manager']; + } + + this.channel = channel; + this.mode = mode; + this.custom_nodes = node_packs; + + if(this.channel !== 'default') { + this.element.querySelector(".cn-manager-channel").innerHTML = `Channel: ${this.channel} (Incomplete list)`; + } + + for (const k in node_packs) { + let item = node_packs[k]; + item.originalData = JSON.parse(JSON.stringify(item)); + if(item.originalData.id == undefined) { + item.originalData.id = k; + } + item.key = k; + item.hash = md5(k); + } + + await this.loadNodes(node_packs); + + const filterItem = this.getFilterItem(this.show_mode); + if(filterItem) { + let hashMap; + if(this.show_mode == ShowMode.UPDATE) { + hashMap = {}; + for (const k in node_packs) { + let it = node_packs[k]; + if (it['update-state'] === "true") { + hashMap[it.hash] = true; + } + } + } else if(this.show_mode == ShowMode.MISSING) { + hashMap = await this.getMissingNodes(); + } else if(this.show_mode == ShowMode.ALTERNATIVES) { + hashMap = await this.getAlternatives(); + } else if(this.show_mode == ShowMode.FAVORITES) { + hashMap = await this.getFavorites(); + } else if(this.show_mode == ShowMode.IN_WORKFLOW) { + hashMap = await this.getNodepackInWorkflow(); + } + filterItem.hashMap = hashMap; + + if(this.show_mode != ShowMode.IN_WORKFLOW) { + filterItem.hasData = true; + } + } + + for(let k in node_packs) { + let nodeItem = node_packs[k]; + + if (this.restartMap[nodeItem.hash]) { + nodeItem.restart = true; + } + + if(nodeItem['update-state'] == "true") { + nodeItem.action = 'updatable'; + } + else if(nodeItem['import-fail']) { + nodeItem.action = 'import-fail'; + } + else { + nodeItem.action = nodeItem.state; + } + + if(nodeItem['invalid-installation']) { + nodeItem.action = 'invalid-installation'; + } + + const filterTypes = new Set(); + this.filterList.forEach(filterItem => { + const { value, hashMap } = filterItem; + if (hashMap) { + const hashData = hashMap[nodeItem.hash] + if (hashData) { + filterTypes.add(value); + if (value === ShowMode.UPDATE) { + nodeItem['update-state'] = "true"; + } + if (value === ShowMode.MISSING) { + nodeItem['missing-node'] = "true"; + } + if (typeof hashData === "object") { + Object.assign(nodeItem, hashData); + } + } + } else { + if (nodeItem.state === value) { + filterTypes.add(value); + } + + switch(nodeItem.state) { + case "enabled": + filterTypes.add("enabled"); + case "disabled": + filterTypes.add("installed"); + break; + case "not-installed": + filterTypes.add("not-installed"); + break; + } + + if(nodeItem.version != 'unknown') { + filterTypes.add("cnr"); + } + else { + filterTypes.add("unknown"); + } + + if(nodeItem['update-state'] == 'true') { + filterTypes.add("updatable"); + } + + if(nodeItem['import-fail']) { + filterTypes.add("import-fail"); + } + + if(nodeItem['invalid-installation']) { + filterTypes.add("invalid-installation"); + } + } + }); + + nodeItem.filterTypes = Array.from(filterTypes); + } + + this.renderGrid(); + + this.hideLoading(); + + } + + // =========================================================================================== + + showSelection(msg) { + this.element.querySelector(".cn-manager-selection").innerHTML = msg; + } + + showError(err) { + this.showMessage(err, "red"); + } + + showMessage(msg, color) { + if (color) { + msg = `${msg}`; + } + this.element.querySelector(".cn-manager-message").innerHTML = msg; + } + + showStatus(msg, color) { + if (color) { + msg = `${msg}`; + } + this.element.querySelector(".cn-manager-status").innerHTML = msg; + } + + showLoading() { + this.setDisabled(true); + if (this.grid) { + this.grid.showLoading(); + this.grid.showMask({ + opacity: 0.05 + }); + } + } + + hideLoading() { + this.setDisabled(false); + if (this.grid) { + this.grid.hideLoading(); + this.grid.hideMask(); + } + } + + setDisabled(disabled) { + const $close = this.element.querySelector(".cn-manager-close"); + const $restart = this.element.querySelector(".cn-manager-restart"); + const $stop = this.element.querySelector(".cn-manager-stop"); + + const list = [ + ".cn-manager-header input", + ".cn-manager-header select", + ".cn-manager-footer button", + ".cn-manager-selection button" + ].map(s => { + return Array.from(this.element.querySelectorAll(s)); + }) + .flat() + .filter(it => { + return it !== $close && it !== $restart && it !== $stop; + }); + + list.forEach($elem => { + if (disabled) { + $elem.setAttribute("disabled", "disabled"); + } else { + $elem.removeAttribute("disabled"); + } + }); + + Array.from(this.element.querySelectorAll(".cn-btn-loading")).forEach($elem => { + $elem.classList.remove("cn-btn-loading"); + }); + + } + + showRestart() { + this.element.querySelector(".cn-manager-restart").style.display = "block"; + setNeedRestart(true); + } + + showStop() { + this.element.querySelector(".cn-manager-stop").style.display = "block"; + } + + hideStop() { + this.element.querySelector(".cn-manager-stop").style.display = "none"; + } + + setFilter(filterValue) { + let filter = ""; + const filterItem = this.getFilterItem(filterValue); + if(filterItem) { + filter = filterItem.value; + } + this.filter = filter; + this.element.querySelector(".cn-manager-filter").value = filter; + } + + setKeywords(keywords = "") { + this.keywords = keywords; + this.element.querySelector(".cn-manager-keywords").value = keywords; + } + + show(show_mode) { + this.element.style.display = "flex"; + this.element.focus(); + this.setFilter(show_mode); + this.setKeywords(""); + this.showSelection(""); + this.showMessage(""); + this.loadData(show_mode); + } + + close() { + this.element.style.display = "none"; + } + + get isVisible() { + return this.element?.style?.display !== "none"; + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/model-manager.css b/custom_nodes/comfyui-manager/js/model-manager.css new file mode 100644 index 00000000..6c9ab04a --- /dev/null +++ b/custom_nodes/comfyui-manager/js/model-manager.css @@ -0,0 +1,237 @@ +.cmm-manager { + --grid-font: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; + z-index: 1099; + width: 80vw; + height: 75vh; + min-height: 30em; + display: flex; + flex-direction: column; + gap: 10px; + color: var(--fg-color); + font-family: arial, sans-serif; + margin: calc(var(--spacing)*2); +} + +.cmm-manager .cmm-flex-auto { + flex: auto; +} + +.cmm-manager button { + font-size: 16px; + color: var(--input-text); + background-color: var(--comfy-input-bg); + border-color: var(--border-color); + margin: 0; + min-width: 100px; +} + +.cmm-manager button:hover { + filter: brightness(125%); +} + +.cmm-manager button:disabled, +.cmm-manager input:disabled, +.cmm-manager select:disabled { + color: gray; +} + +.cmm-manager button:disabled { + background-color: var(--comfy-input-bg); +} + +.cmm-manager .cmm-manager-refresh { + display: none; + background-color: #000080 !important; + color: white; +} + +.cmm-manager .cmm-manager-stop { + display: none; + background-color: #500000; + color: white; +} + +.cmm-manager-header { + display: flex; + flex-wrap: wrap; + gap: 5px; + align-items: center; +} + +.cmm-manager-header label { + display: flex; + gap: 5px; + align-items: center; +} + +.cmm-manager-type, +.cmm-manager-base, +.cmm-manager-filter { + height: 28px; + line-height: 28px; + + cursor: pointer; + padding: 0.5em 0.5em; + border: 1px solid var(--border-color); + border-radius: 6px; + background: var(--comfy-input-bg); +} + +.cmm-manager-type:hover, +.cmm-manager-base:hover, +.cmm-manager-filter:hover { + filter: brightness(125%); +} + +.cmm-manager-keywords { + height: 28px; + line-height: 28px; + padding: 0 5px 0 26px; + background: var(--comfy-input-bg); + background-size: 16px; + background-position: 5px center; + background-repeat: no-repeat; + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg%20viewBox%3D%220%200%2024%2024%22%20width%3D%22100%25%22%20height%3D%22100%25%22%20pointer-events%3D%22none%22%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%3E%3Cpath%20fill%3D%22none%22%20stroke%3D%22%23888%22%20stroke-linecap%3D%22round%22%20stroke-linejoin%3D%22round%22%20stroke-width%3D%222%22%20d%3D%22m21%2021-4.486-4.494M19%2010.5a8.5%208.5%200%201%201-17%200%208.5%208.5%200%200%201%2017%200%22%2F%3E%3C%2Fsvg%3E"); + + border: 1px solid var(--border-color); + border-radius: 6px; + + outline-color: transparent; +} + +.cmm-manager-status { + padding-left: 10px; +} + +.cmm-manager-grid { + flex: auto; + border: 1px solid var(--border-color); + overflow: hidden; +} + +.cmm-manager-selection { + display: flex; + flex-wrap: wrap; + gap: 10px; + align-items: center; +} + +.cmm-manager-footer { + display: flex; + flex-wrap: wrap; + gap: 10px; + align-items: center; +} + +.cmm-manager-grid .tg-turbogrid { + font-family: var(--grid-font); + font-size: 15px; + background: var(--bg-color); +} + +.cmm-manager-grid .cmm-node-name a { + color: skyblue; + text-decoration: none; + word-break: break-word; +} + +.cmm-manager-grid .cmm-node-desc a { + color: #5555FF; + font-weight: bold; + text-decoration: none; +} + +.cmm-manager-grid .tg-cell a:hover { + text-decoration: underline; +} + +.cmm-icon-passed { + width: 20px; + height: 20px; + position: absolute; + left: calc(50% - 10px); + top: calc(50% - 10px); +} + +.cmm-manager .cmm-btn-enable { + background-color: blue; + color: white; +} + +.cmm-manager .cmm-btn-disable { + background-color: MediumSlateBlue; + color: white; +} + +.cmm-manager .cmm-btn-install { + background-color: black; + color: white; +} + +.cmm-btn-install { + padding: 4px 8px; +} + +.cmm-btn-download { + width: 18px; + height: 18px; + position: absolute; + left: calc(50% - 10px); + top: calc(50% - 10px); + cursor: pointer; + opacity: 0.8; + color: #fff; +} + +.cmm-btn-download:hover { + opacity: 1; +} + +.cmm-manager-light .cmm-btn-download { + color: #000; +} + +@keyframes cmm-btn-loading-bg { + 0% { + left: 0; + } + 100% { + left: -105px; + } +} + +.cmm-manager button.cmm-btn-loading { + position: relative; + overflow: hidden; + border-color: rgb(0 119 207 / 80%); + background-color: var(--comfy-input-bg); +} + +.cmm-manager button.cmm-btn-loading::after { + position: absolute; + top: 0; + left: 0; + content: ""; + width: 500px; + height: 100%; + background-image: repeating-linear-gradient( + -45deg, + rgb(0 119 207 / 30%), + rgb(0 119 207 / 30%) 10px, + transparent 10px, + transparent 15px + ); + animation: cmm-btn-loading-bg 2s linear infinite; +} + +.cmm-manager-light .cmm-node-name a { + color: blue; +} + +.cmm-manager-light .cm-warn-note { + background-color: #ccc !important; +} + +.cmm-manager-light .cmm-btn-install { + background-color: #333; +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/model-manager.js b/custom_nodes/comfyui-manager/js/model-manager.js new file mode 100644 index 00000000..0f0de552 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/model-manager.js @@ -0,0 +1,824 @@ +import { app } from "../../scripts/app.js"; +import { $el } from "../../scripts/ui.js"; +import { + manager_instance, rebootAPI, + fetchData, md5, icons, show_message, customAlert, infoToast, showTerminal, + storeColumnWidth, restoreColumnWidth, loadCss, handle403Response +} from "./common.js"; +import { api } from "../../scripts/api.js"; + +// https://cenfun.github.io/turbogrid/api.html +import TG from "./turbogrid.esm.js"; +import { buildGuiFrameCustomHeader, createSettingsCombo } from "./comfyui-gui-builder.js"; + +loadCss("./model-manager.css"); + +const gridId = "model"; + +const pageHtml = ` +
    +
    +
    +
    +
    + + +
    +
    +
    +`; + +export class ModelManager { + static instance = null; + + constructor(app, manager_dialog) { + this.app = app; + this.manager_dialog = manager_dialog; + this.id = "cmm-manager"; + + this.filter = ''; + this.type = ''; + this.base = ''; + this.keywords = ''; + + this.init(); + + api.addEventListener("cm-queue-status", this.onQueueStatus); + } + + init() { + const header = $el("div.cmm-manager-header", {}, [ + createSettingsCombo("Filter", $el("select.cmm-manager-filter")), + createSettingsCombo("Type", $el("select.cmm-manager-type")), + createSettingsCombo("Base", $el("select.cmm-manager-base")), + $el("input.cmm-manager-keywords.p-inputtext.p-component", { type: "search", placeholder: "Search" }), + $el("div.cmm-manager-status"), + $el("div.cmm-flex-auto") + ]); + + const frame = buildGuiFrameCustomHeader( + 'cmm-manager-dialog', // dialog id + header, // custom header element + pageHtml, // dialog content element + this + ); // send this so we can attach close functions + + this.element = frame; + this.initFilter(); + this.bindEvents(); + this.initGrid(); + } + + initFilter() { + + this.filterList = [{ + label: "All", + value: "" + }, { + label: "Installed", + value: "installed" + }, { + label: "Not Installed", + value: "not_installed" + }, { + label: "In Workflow", + value: "in_workflow" + }]; + + this.typeList = [{ + label: "All", + value: "" + }]; + + this.baseList = [{ + label: "All", + value: "" + }]; + + this.updateFilter(); + + } + + updateFilter() { + const $filter = this.element.querySelector(".cmm-manager-filter"); + $filter.innerHTML = this.filterList.map(item => { + const selected = item.value === this.filter ? " selected" : ""; + return `` + }).join(""); + + const $type = this.element.querySelector(".cmm-manager-type"); + $type.innerHTML = this.typeList.map(item => { + const selected = item.value === this.type ? " selected" : ""; + return `` + }).join(""); + + const $base = this.element.querySelector(".cmm-manager-base"); + $base.innerHTML = this.baseList.map(item => { + const selected = item.value === this.base ? " selected" : ""; + return `` + }).join(""); + + } + + bindEvents() { + const eventsMap = { + ".cmm-manager-filter": { + change: (e) => { + this.filter = e.target.value; + this.updateGrid(); + } + }, + ".cmm-manager-type": { + change: (e) => { + this.type = e.target.value; + this.updateGrid(); + } + }, + ".cmm-manager-base": { + change: (e) => { + this.base = e.target.value; + this.updateGrid(); + } + }, + + ".cmm-manager-keywords": { + input: (e) => { + const keywords = `${e.target.value}`.trim(); + if (keywords !== this.keywords) { + this.keywords = keywords; + this.updateGrid(); + } + }, + focus: (e) => e.target.select() + }, + + ".cmm-manager-selection": { + click: (e) => { + const target = e.target; + const mode = target.getAttribute("mode"); + if (mode === "install") { + this.installModels(this.selectedModels, target); + } + } + }, + + ".cmm-manager-refresh": { + click: () => { + app.refreshComboInNodes(); + } + }, + + ".cmm-manager-stop": { + click: () => { + api.fetchApi('/manager/queue/reset'); + infoToast('Cancel', 'Remaining tasks will stop after completing the current task.'); + } + }, + + ".cmm-manager-back": { + click: (e) => { + this.close() + manager_instance.show(); + } + } + }; + Object.keys(eventsMap).forEach(selector => { + const target = this.element.querySelector(selector); + if (target) { + const events = eventsMap[selector]; + if (events) { + Object.keys(events).forEach(type => { + target.addEventListener(type, events[type]); + }); + } + } + }); + } + + // =========================================================================================== + + initGrid() { + const container = this.element.querySelector(".cmm-manager-grid"); + const grid = new TG.Grid(container); + this.grid = grid; + + grid.bind('onUpdated', (e, d) => { + + this.showStatus(`${grid.viewRows.length.toLocaleString()} external models`); + + }); + + grid.bind('onSelectChanged', (e, changes) => { + this.renderSelected(); + }); + + grid.bind("onColumnWidthChanged", (e, columnItem) => { + storeColumnWidth(gridId, columnItem) + }); + + grid.bind('onClick', (e, d) => { + const { rowItem } = d; + const target = d.e.target; + const mode = target.getAttribute("mode"); + if (mode === "install") { + this.installModels([rowItem], target); + } + + }); + + grid.setOption({ + theme: 'dark', + + selectVisible: true, + selectMultiple: true, + selectAllVisible: true, + + textSelectable: true, + scrollbarRound: true, + + frozenColumn: 1, + rowNotFound: "No Results", + + rowHeight: 40, + bindWindowResize: true, + bindContainerResize: true, + + cellResizeObserver: (rowItem, columnItem) => { + const autoHeightColumns = ['name', 'description']; + return autoHeightColumns.includes(columnItem.id) + }, + + // updateGrid handler for filter and keywords + rowFilter: (rowItem) => { + + const searchableColumns = ["name", "type", "base", "description", "filename", "save_path"]; + const models_extensions = ['.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft']; + + let shouldShown = grid.highlightKeywordsFilter(rowItem, searchableColumns, this.keywords); + + if (shouldShown) { + if(this.filter) { + if (this.filter == "in_workflow") { + rowItem.in_workflow = null; + if (Array.isArray(app.graph._nodes)) { + app.graph._nodes.forEach((item, i) => { + if (Array.isArray(item.widgets_values)) { + item.widgets_values.forEach((_item, i) => { + if (rowItem.in_workflow === null && _item !== null && models_extensions.includes("." + _item.toString().split('.').pop())) { + let filename = _item.match(/([^\/]+)(?=\.\w+$)/)[0]; + if (grid.highlightKeywordsFilter(rowItem, searchableColumns, filename)) { + rowItem.in_workflow = "True"; + grid.highlightKeywordsFilter(rowItem, searchableColumns, ""); + } + } + }); + } + }); + } + } + return ((this.filter == "installed" && rowItem.installed == "True") || (this.filter == "not_installed" && rowItem.installed == "False") || (this.filter == "in_workflow" && rowItem.in_workflow == "True")); + } + + if(this.type && rowItem.type !== this.type) { + return false; + } + + if(this.base && rowItem.base !== this.base) { + return false; + } + + } + + return shouldShown; + } + }); + + } + + renderGrid() { + + // update theme + const colorPalette = this.app.ui.settings.settingsValues['Comfy.ColorPalette']; + Array.from(this.element.classList).forEach(cn => { + if (cn.startsWith("cmm-manager-")) { + this.element.classList.remove(cn); + } + }); + this.element.classList.add(`cmm-manager-${colorPalette}`); + + const options = { + theme: colorPalette === "light" ? "" : "dark" + }; + + const rows = this.modelList || []; + + const columns = [{ + id: 'id', + name: 'ID', + width: 50, + align: 'center' + }, { + id: 'name', + name: 'Name', + width: 200, + minWidth: 100, + maxWidth: 500, + classMap: 'cmm-node-name', + formatter: function(name, rowItem, columnItem, cellNode) { + return `${name}`; + } + }, { + id: 'installed', + name: 'Install', + width: 130, + minWidth: 110, + maxWidth: 200, + sortable: false, + align: 'center', + formatter: (installed, rowItem, columnItem) => { + if (rowItem.refresh) { + return `Refresh Required`; + } + if (installed === "True") { + return `
    ${icons.passed}
    `; + } + return ``; + } + }, { + id: 'url', + name: '', + width: 50, + sortable: false, + align: 'center', + formatter: (url, rowItem, columnItem) => { + return `${icons.download}`; + } + }, { + id: 'size', + name: 'Size', + width: 100, + formatter: (size) => { + if (typeof size === "number") { + return this.formatSize(size); + } + return size; + } + }, { + id: 'type', + name: 'Type', + width: 100 + }, { + id: 'base', + name: 'Base' + }, { + id: 'description', + name: 'Description', + width: 400, + maxWidth: 5000, + classMap: 'cmm-node-desc' + }, { + id: "save_path", + name: 'Save Path', + width: 200 + }, { + id: 'filename', + name: 'Filename', + width: 200 + }]; + + restoreColumnWidth(gridId, columns); + + this.grid.setData({ + options, + rows, + columns + }); + + this.grid.render(); + + } + + updateGrid() { + if (this.grid) { + this.grid.update(); + } + } + + // =========================================================================================== + + renderSelected() { + const selectedList = this.grid.getSelectedRows(); + if (!selectedList.length) { + this.showSelection(""); + this.selectedModels = []; + return; + } + + this.selectedModels = selectedList; + this.showSelection(`Selected ${selectedList.length} models `); + } + + focusInstall(item) { + const cellNode = this.grid.getCellNode(item, "installed"); + if (cellNode) { + const cellBtn = cellNode.querySelector(`button[mode="install"]`); + if (cellBtn) { + cellBtn.classList.add("cmm-btn-loading"); + return true + } + } + } + + async installModels(list, btn) { + let stats = await api.fetchApi('/manager/queue/status'); + + stats = await stats.json(); + if(stats.is_processing) { + customAlert(`[ComfyUI-Manager] There are already tasks in progress. Please try again after it is completed. (${stats.done_count}/${stats.total_count})`); + return; + } + + btn.classList.add("cmm-btn-loading"); + this.showError(""); + + let needRefresh = false; + let errorMsg = ""; + + await api.fetchApi('/manager/queue/reset'); + + let target_items = []; + + for (const item of list) { + this.grid.scrollRowIntoView(item); + target_items.push(item); + + if (!this.focusInstall(item)) { + this.grid.onNextUpdated(() => { + this.focusInstall(item); + }); + } + + this.showStatus(`Install ${item.name} ...`); + + const data = item.originalData; + data.ui_id = item.hash; + + const res = await api.fetchApi(`/manager/queue/install_model`, { + method: 'POST', + body: JSON.stringify(data) + }); + + if (res.status != 200) { + errorMsg = `'${item.name}': `; + + if(res.status == 403) { + try { + const data = await res.json(); + if(data.error === 'comfyui_outdated') { + errorMsg += `ComfyUI version is outdated. Please update ComfyUI to use Manager normally.\n`; + } else { + errorMsg += `This action is not allowed with this security level configuration.\n`; + } + } catch { + errorMsg += `This action is not allowed with this security level configuration.\n`; + } + } else { + errorMsg += await res.text() + '\n'; + } + + break; + } + } + + this.install_context = {btn: btn, targets: target_items}; + + if(errorMsg) { + this.showError(errorMsg); + show_message("[Installation Errors]\n"+errorMsg); + + // reset + for(let k in target_items) { + const item = target_items[k]; + this.grid.updateCell(item, "installed"); + } + } + else { + await api.fetchApi('/manager/queue/start'); + this.showStop(); + showTerminal(); + } + } + + async onQueueStatus(event) { + let self = ModelManager.instance; + + if(event.detail.status == 'in_progress' && event.detail.ui_target == 'model_manager') { + const hash = event.detail.target; + + const item = self.grid.getRowItemBy("hash", hash); + + item.refresh = true; + self.grid.setRowSelected(item, false); + item.selectable = false; +// self.grid.updateCell(item, "tg-column-select"); + self.grid.updateRow(item); + } + else if(event.detail.status == 'done') { + self.hideStop(); + self.onQueueCompleted(event.detail); + } + } + + async onQueueCompleted(info) { + let result = info.model_result; + + if(result.length == 0) { + return; + } + + let self = ModelManager.instance; + + if(!self.install_context) { + return; + } + + let btn = self.install_context.btn; + + self.hideLoading(); + btn.classList.remove("cmm-btn-loading"); + + let errorMsg = ""; + + for(let hash in result){ + let v = result[hash]; + + if(v != 'success') + errorMsg += v + '\n'; + } + + for(let k in self.install_context.targets) { + let item = self.install_context.targets[k]; + self.grid.updateCell(item, "installed"); + } + + if (errorMsg) { + self.showError(errorMsg); + show_message("Installation Error:\n"+errorMsg); + } else { + self.showStatus(`Install ${result.length} models successfully`); + } + + self.showRefresh(); + self.showMessage(`To apply the installed model, please click the 'Refresh' button.`, "red") + + infoToast('Tasks done', `[ComfyUI-Manager] All model downloading tasks in the queue have been completed.\n${info.done_count}/${info.total_count}`); + self.install_context = undefined; + } + + getModelList(models) { + const typeMap = new Map(); + const baseMap = new Map(); + + models.forEach((item, i) => { + const { type, base, name, reference, installed } = item; + item.originalData = JSON.parse(JSON.stringify(item)); + item.size = this.sizeToBytes(item.size); + item.hash = md5(name + reference); + item.id = i + 1; + + if (installed === "True") { + item.selectable = false; + } + + typeMap.set(type, type); + baseMap.set(base, base); + + }); + + const typeList = []; + typeMap.forEach(type => { + typeList.push({ + label: type, + value: type + }); + }); + typeList.sort((a,b)=> { + const au = a.label.toUpperCase(); + const bu = b.label.toUpperCase(); + if (au !== bu) { + return au > bu ? 1 : -1; + } + return 0; + }); + this.typeList = [{ + label: "All", + value: "" + }].concat(typeList); + + + const baseList = []; + baseMap.forEach(base => { + baseList.push({ + label: base, + value: base + }); + }); + baseList.sort((a,b)=> { + const au = a.label.toUpperCase(); + const bu = b.label.toUpperCase(); + if (au !== bu) { + return au > bu ? 1 : -1; + } + return 0; + }); + this.baseList = [{ + label: "All", + value: "" + }].concat(baseList); + + return models; + } + + // =========================================================================================== + + async loadData() { + + this.showLoading(); + + this.showStatus(`Loading external model list ...`); + + const mode = manager_instance.datasrc_combo.value; + + const res = await fetchData(`/externalmodel/getlist?mode=${mode}`); + if (res.error) { + this.showError("Failed to get external model list."); + this.hideLoading(); + return + } + + const { models } = res.data; + + this.modelList = this.getModelList(models); + // console.log("models", this.modelList); + + this.updateFilter(); + + this.renderGrid(); + + this.hideLoading(); + + } + + // =========================================================================================== + + formatSize(v) { + const base = 1000; + const units = ['', 'K', 'M', 'G', 'T', 'P']; + const space = ''; + const postfix = 'B'; + if (v <= 0) { + return `0${space}${postfix}`; + } + for (let i = 0, l = units.length; i < l; i++) { + const min = Math.pow(base, i); + const max = Math.pow(base, i + 1); + if (v > min && v <= max) { + const unit = units[i]; + if (unit) { + const n = v / min; + const nl = n.toString().split('.')[0].length; + const fl = Math.max(3 - nl, 1); + v = n.toFixed(fl); + } + v = v + space + unit + postfix; + break; + } + } + return v; + } + + // for size sort + sizeToBytes(v) { + if (typeof v === "number") { + return v; + } + if (typeof v === "string") { + const n = parseFloat(v); + const unit = v.replace(/[0-9.B]+/g, "").trim().toUpperCase(); + if (unit === "K") { + return n * 1000; + } + if (unit === "M") { + return n * 1000 * 1000; + } + if (unit === "G") { + return n * 1000 * 1000 * 1000; + } + if (unit === "T") { + return n * 1000 * 1000 * 1000 * 1000; + } + } + return v; + } + + showSelection(msg) { + this.element.querySelector(".cmm-manager-selection").innerHTML = msg; + } + + showError(err) { + this.showMessage(err, "red"); + } + + showMessage(msg, color) { + if (color) { + msg = `${msg}`; + } + this.element.querySelector(".cmm-manager-message").innerHTML = msg; + } + + showStatus(msg, color) { + if (color) { + msg = `${msg}`; + } + this.element.querySelector(".cmm-manager-status").innerHTML = msg; + } + + showLoading() { +// this.setDisabled(true); + if (this.grid) { + this.grid.showLoading(); + this.grid.showMask({ + opacity: 0.05 + }); + } + } + + hideLoading() { +// this.setDisabled(false); + if (this.grid) { + this.grid.hideLoading(); + this.grid.hideMask(); + } + } + + setDisabled(disabled) { + const $close = this.element.querySelector(".cmm-manager-close"); + const $refresh = this.element.querySelector(".cmm-manager-refresh"); + const $stop = this.element.querySelector(".cmm-manager-stop"); + + const list = [ + ".cmm-manager-header input", + ".cmm-manager-header select", + ".cmm-manager-footer button", + ".cmm-manager-selection button" + ].map(s => { + return Array.from(this.element.querySelectorAll(s)); + }) + .flat() + .filter(it => { + return it !== $close && it !== $refresh && it !== $stop; + }); + + list.forEach($elem => { + if (disabled) { + $elem.setAttribute("disabled", "disabled"); + } else { + $elem.removeAttribute("disabled"); + } + }); + + Array.from(this.element.querySelectorAll(".cmm-btn-loading")).forEach($elem => { + $elem.classList.remove("cmm-btn-loading"); + }); + + } + + showRefresh() { + this.element.querySelector(".cmm-manager-refresh").style.display = "block"; + } + + showStop() { + this.element.querySelector(".cmm-manager-stop").style.display = "block"; + } + + hideStop() { + this.element.querySelector(".cmm-manager-stop").style.display = "none"; + } + + setKeywords(keywords = "") { + this.keywords = keywords; + this.element.querySelector(".cmm-manager-keywords").value = keywords; + } + + show() { + this.element.style.display = "flex"; + this.setKeywords(""); + this.showSelection(""); + this.showMessage(""); + this.loadData(); + } + + close() { + this.element.style.display = "none"; + } +} diff --git a/custom_nodes/comfyui-manager/js/node_fixer.js b/custom_nodes/comfyui-manager/js/node_fixer.js new file mode 100644 index 00000000..867a7b81 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/node_fixer.js @@ -0,0 +1,161 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +function addMenuHandler(nodeType, cb) { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} + +function distance(node1, node2) { + let dx = (node1.pos[0] + node1.size[0]/2) - (node2.pos[0] + node2.size[0]/2); + let dy = (node1.pos[1] + node1.size[1]/2) - (node2.pos[1] + node2.size[1]/2); + return Math.sqrt(dx * dx + dy * dy); +} + +function lookup_nearest_nodes(node) { + let nearest_distance = Infinity; + let nearest_node = null; + for(let other of app.graph._nodes) { + if(other === node) + continue; + + let dist = distance(node, other); + if (dist < nearest_distance && dist < 1000) { + nearest_distance = dist; + nearest_node = other; + } + } + + return nearest_node; +} + +function lookup_nearest_inputs(node) { + let input_map = {}; + + for(let i in node.inputs) { + let input = node.inputs[i]; + + if(input.link || input_map[input.type]) + continue; + + input_map[input.type] = {distance: Infinity, input_name: input.name, node: null, slot: null}; + } + + let x = node.pos[0]; + let y = node.pos[1] + node.size[1]/2; + + for(let other of app.graph._nodes) { + if(other === node || !other.outputs) + continue; + + let dx = x - (other.pos[0] + other.size[0]); + let dy = y - (other.pos[1] + other.size[1]/2); + + if(dx < 0) + continue; + + let dist = Math.sqrt(dx * dx + dy * dy); + + for(let input_type in input_map) { + for(let j in other.outputs) { + let output = other.outputs[j]; + if(output.type == input_type) { + if(input_map[input_type].distance > dist) { + input_map[input_type].distance = dist; + input_map[input_type].node = other; + input_map[input_type].slot = parseInt(j); + } + } + } + } + } + + let res = {}; + for (let i in input_map) { + if (input_map[i].node) { + res[i] = input_map[i]; + } + } + + return res; +} + +function connect_inputs(nearest_inputs, node) { + for(let i in nearest_inputs) { + let info = nearest_inputs[i]; + info.node.connect(info.slot, node.id, info.input_name); + } +} + +function node_info_copy(src, dest, connect_both, copy_shape) { + // copy input connections + for(let i in src.inputs) { + let input = src.inputs[i]; + if (input.widget !== undefined) { + const destWidget = dest.widgets.find(x => x.name === input.widget.name); + dest.convertWidgetToInput(destWidget); + } + if(input.link) { + let link = app.graph.links[input.link]; + let src_node = app.graph.getNodeById(link.origin_id); + src_node.connect(link.origin_slot, dest.id, input.name); + } + } + + // copy output connections + if(connect_both) { + let output_links = {}; + for(let i in src.outputs) { + let output = src.outputs[i]; + if(output.links) { + let links = []; + for(let j in output.links) { + links.push(app.graph.links[output.links[j]]); + } + output_links[output.name] = links; + } + } + + for(let i in dest.outputs) { + let links = output_links[dest.outputs[i].name]; + if(links) { + for(let j in links) { + let link = links[j]; + let target_node = app.graph.getNodeById(link.target_id); + dest.connect(parseInt(i), target_node, link.target_slot); + } + } + } + } + + if(copy_shape) { + dest.color = src.color; + dest.bgcolor = src.bgcolor; + dest.size = max(src.size, dest.size); + } + + app.graph.afterChange(); +} + +app.registerExtension({ + name: "Comfy.Manager.NodeFixer", + beforeRegisterNodeDef(nodeType, nodeData, app) { + addMenuHandler(nodeType, function (_, options) { + options.push({ + content: "Fix node (recreate)", + callback: () => { + let new_node = LiteGraph.createNode(nodeType.comfyClass); + new_node.pos = [this.pos[0], this.pos[1]]; + app.canvas.graph.add(new_node, false); + node_info_copy(this, new_node, true); + app.canvas.graph.remove(this); + requestAnimationFrame(() => app.canvas.setDirty(true, true)) + }, + }); + }); + } +}); diff --git a/custom_nodes/comfyui-manager/js/popover-helper.js b/custom_nodes/comfyui-manager/js/popover-helper.js new file mode 100644 index 00000000..8c214b8c --- /dev/null +++ b/custom_nodes/comfyui-manager/js/popover-helper.js @@ -0,0 +1,619 @@ +const hasOwn = function(obj, key) { + return Object.prototype.hasOwnProperty.call(obj, key); +}; + +const isNum = function(num) { + if (typeof num !== 'number' || isNaN(num)) { + return false; + } + const isInvalid = function(n) { + if (n === Number.MAX_VALUE || n === Number.MIN_VALUE || n === Number.NEGATIVE_INFINITY || n === Number.POSITIVE_INFINITY) { + return true; + } + return false; + }; + if (isInvalid(num)) { + return false; + } + return true; +}; + +const toNum = (num) => { + if (typeof (num) !== 'number') { + num = parseFloat(num); + } + if (isNaN(num)) { + num = 0; + } + num = Math.round(num); + return num; +}; + +const clamp = function(value, min, max) { + return Math.max(min, Math.min(max, value)); +}; + +const isWindow = (obj) => { + return Boolean(obj && obj === obj.window); +}; + +const isDocument = (obj) => { + return Boolean(obj && obj.nodeType === 9); +}; + +const isElement = (obj) => { + return Boolean(obj && obj.nodeType === 1); +}; + +// =========================================================================================== + +export const toRect = (obj) => { + if (obj) { + return { + left: toNum(obj.left || obj.x), + top: toNum(obj.top || obj.y), + width: toNum(obj.width), + height: toNum(obj.height) + }; + } + return { + left: 0, + top: 0, + width: 0, + height: 0 + }; +}; + +export const getElement = (selector) => { + if (typeof selector === 'string' && selector) { + if (selector.startsWith('#')) { + return document.getElementById(selector.slice(1)); + } + return document.querySelector(selector); + } + + if (isDocument(selector)) { + return selector.body; + } + if (isElement(selector)) { + return selector; + } +}; + +export const getRect = (target, fixed) => { + if (!target) { + return toRect(); + } + + if (isWindow(target)) { + return { + left: 0, + top: 0, + width: window.innerWidth, + height: window.innerHeight + }; + } + + const elem = getElement(target); + if (!elem) { + return toRect(target); + } + + const br = elem.getBoundingClientRect(); + const rect = toRect(br); + + // fix offset + if (!fixed) { + rect.left += window.scrollX; + rect.top += window.scrollY; + } + + rect.width = elem.offsetWidth; + rect.height = elem.offsetHeight; + + return rect; +}; + +// =========================================================================================== + +const calculators = { + + bottom: (info, containerRect, targetRect) => { + info.space = containerRect.top + containerRect.height - targetRect.top - targetRect.height - info.height; + info.top = targetRect.top + targetRect.height; + info.left = Math.round(targetRect.left + targetRect.width * 0.5 - info.width * 0.5); + }, + + top: (info, containerRect, targetRect) => { + info.space = targetRect.top - info.height - containerRect.top; + info.top = targetRect.top - info.height; + info.left = Math.round(targetRect.left + targetRect.width * 0.5 - info.width * 0.5); + }, + + right: (info, containerRect, targetRect) => { + info.space = containerRect.left + containerRect.width - targetRect.left - targetRect.width - info.width; + info.top = Math.round(targetRect.top + targetRect.height * 0.5 - info.height * 0.5); + info.left = targetRect.left + targetRect.width; + }, + + left: (info, containerRect, targetRect) => { + info.space = targetRect.left - info.width - containerRect.left; + info.top = Math.round(targetRect.top + targetRect.height * 0.5 - info.height * 0.5); + info.left = targetRect.left - info.width; + } +}; + +// with order +export const getDefaultPositions = () => { + return Object.keys(calculators); +}; + +const calculateSpace = (info, containerRect, targetRect) => { + const calculator = calculators[info.position]; + calculator(info, containerRect, targetRect); + if (info.space >= 0) { + info.passed += 1; + } +}; + +// =========================================================================================== + +const calculateAlignOffset = (info, containerRect, targetRect, alignType, sizeType) => { + + const popoverStart = info[alignType]; + const popoverSize = info[sizeType]; + + const containerStart = containerRect[alignType]; + const containerSize = containerRect[sizeType]; + + const targetStart = targetRect[alignType]; + const targetSize = targetRect[sizeType]; + + const targetCenter = targetStart + targetSize * 0.5; + + // size overflow + if (popoverSize > containerSize) { + const overflow = (popoverSize - containerSize) * 0.5; + info[alignType] = containerStart - overflow; + info.offset = targetCenter - containerStart + overflow; + return; + } + + const space1 = popoverStart - containerStart; + const space2 = (containerStart + containerSize) - (popoverStart + popoverSize); + + // both side passed, default to center + if (space1 >= 0 && space2 >= 0) { + if (info.passed) { + info.passed += 2; + } + info.offset = popoverSize * 0.5; + return; + } + + // one side passed + if (info.passed) { + info.passed += 1; + } + + if (space1 < 0) { + const min = containerStart; + info[alignType] = min; + info.offset = targetCenter - min; + return; + } + + // space2 < 0 + const max = containerStart + containerSize - popoverSize; + info[alignType] = max; + info.offset = targetCenter - max; + +}; + +const calculateHV = (info, containerRect) => { + if (['top', 'bottom'].includes(info.position)) { + info.top = clamp(info.top, containerRect.top, containerRect.top + containerRect.height - info.height); + return ['left', 'width']; + } + info.left = clamp(info.left, containerRect.left, containerRect.left + containerRect.width - info.width); + return ['top', 'height']; +}; + +const calculateOffset = (info, containerRect, targetRect) => { + + const [alignType, sizeType] = calculateHV(info, containerRect); + + calculateAlignOffset(info, containerRect, targetRect, alignType, sizeType); + + info.offset = clamp(info.offset, 0, info[sizeType]); + +}; + +// =========================================================================================== + +const calculateDistance = (info, previousPositionInfo) => { + if (!previousPositionInfo) { + return; + } + // no change if position no change with previous + if (info.position === previousPositionInfo.position) { + return; + } + const ax = info.left + info.width * 0.5; + const ay = info.top + info.height * 0.5; + const bx = previousPositionInfo.left + previousPositionInfo.width * 0.5; + const by = previousPositionInfo.top + previousPositionInfo.height * 0.5; + const dx = Math.abs(ax - bx); + const dy = Math.abs(ay - by); + info.distance = Math.round(Math.sqrt(dx * dx + dy * dy)); +}; + +// =========================================================================================== + +const calculatePositionInfo = (info, containerRect, targetRect, previousPositionInfo) => { + calculateSpace(info, containerRect, targetRect); + calculateOffset(info, containerRect, targetRect); + calculateDistance(info, previousPositionInfo); +}; + +// =========================================================================================== + +const calculateBestPosition = (containerRect, targetRect, infoMap, withOrder, previousPositionInfo) => { + + // position space: +1 + // align space: + // two side passed: +2 + // one side passed: +1 + + const safePassed = 3; + + if (previousPositionInfo) { + const prevInfo = infoMap[previousPositionInfo.position]; + if (prevInfo) { + calculatePositionInfo(prevInfo, containerRect, targetRect); + if (prevInfo.passed >= safePassed) { + return prevInfo; + } + prevInfo.calculated = true; + } + } + + const positionList = []; + Object.values(infoMap).forEach((info) => { + if (!info.calculated) { + calculatePositionInfo(info, containerRect, targetRect, previousPositionInfo); + } + positionList.push(info); + }); + + positionList.sort((a, b) => { + if (a.passed !== b.passed) { + return b.passed - a.passed; + } + + if (withOrder && a.passed >= safePassed && b.passed >= safePassed) { + return a.index - b.index; + } + + if (a.space !== b.space) { + return b.space - a.space; + } + + return a.index - b.index; + }); + + // logTable(positionList); + + return positionList[0]; +}; + +// const logTable = (() => { +// let time_id; +// return (info) => { +// clearTimeout(time_id); +// time_id = setTimeout(() => { +// console.table(info); +// }, 10); +// }; +// })(); + +// =========================================================================================== + +const getAllowPositions = (positions, defaultAllowPositions) => { + if (!positions) { + return; + } + if (Array.isArray(positions)) { + positions = positions.join(','); + } + positions = String(positions).split(',').map((it) => it.trim().toLowerCase()).filter((it) => it); + positions = positions.filter((it) => defaultAllowPositions.includes(it)); + if (!positions.length) { + return; + } + return positions; +}; + +const isPositionChanged = (info, previousPositionInfo) => { + if (!previousPositionInfo) { + return true; + } + + if (info.left !== previousPositionInfo.left) { + return true; + } + + if (info.top !== previousPositionInfo.top) { + return true; + } + + return false; +}; + +// =========================================================================================== + +// const log = (name, time) => { +// if (time > 0.1) { +// console.log(name, time); +// } +// }; + +export const getBestPosition = (containerRect, targetRect, popoverRect, positions, previousPositionInfo) => { + + const defaultAllowPositions = getDefaultPositions(); + let withOrder = true; + let allowPositions = getAllowPositions(positions, defaultAllowPositions); + if (!allowPositions) { + allowPositions = defaultAllowPositions; + withOrder = false; + } + + // console.log('withOrder', withOrder); + + // const start_time = performance.now(); + + const infoMap = {}; + allowPositions.forEach((k, i) => { + infoMap[k] = { + position: k, + index: i, + + top: 0, + left: 0, + width: popoverRect.width, + height: popoverRect.height, + + space: 0, + + offset: 0, + passed: 0, + + distance: 0 + }; + }); + + // log('infoMap', performance.now() - start_time); + + + const bestPosition = calculateBestPosition(containerRect, targetRect, infoMap, withOrder, previousPositionInfo); + + // check left/top + bestPosition.changed = isPositionChanged(bestPosition, previousPositionInfo); + + return bestPosition; +}; + +// =========================================================================================== + +const getTemplatePath = (width, height, arrowOffset, arrowSize, borderRadius) => { + const p = (px, py) => { + return [px, py].join(','); + }; + + const px = function(num, alignEnd) { + const floor = Math.floor(num); + let n = num < floor + 0.5 ? floor + 0.5 : floor + 1.5; + if (alignEnd) { + n -= 1; + } + return n; + }; + + const pxe = function(num) { + return px(num, true); + }; + + const ls = []; + + const innerLeft = px(arrowSize); + const innerRight = pxe(width - arrowSize); + arrowOffset = clamp(arrowOffset, innerLeft, innerRight); + + const innerTop = px(arrowSize); + const innerBottom = pxe(height - arrowSize); + + const startPoint = p(innerLeft, innerTop + borderRadius); + const arrowPoint = p(arrowOffset, 1); + + const LT = p(innerLeft, innerTop); + const RT = p(innerRight, innerTop); + + const AOT = p(arrowOffset - arrowSize, innerTop); + const RRT = p(innerRight - borderRadius, innerTop); + + ls.push(`M${startPoint}`); + ls.push(`V${innerBottom - borderRadius}`); + ls.push(`Q${p(innerLeft, innerBottom)} ${p(innerLeft + borderRadius, innerBottom)}`); + ls.push(`H${innerRight - borderRadius}`); + ls.push(`Q${p(innerRight, innerBottom)} ${p(innerRight, innerBottom - borderRadius)}`); + ls.push(`V${innerTop + borderRadius}`); + + if (arrowOffset < innerLeft + arrowSize + borderRadius) { + ls.push(`Q${RT} ${RRT}`); + ls.push(`H${arrowOffset + arrowSize}`); + ls.push(`L${arrowPoint}`); + if (arrowOffset < innerLeft + arrowSize) { + ls.push(`L${LT}`); + ls.push(`L${startPoint}`); + } else { + ls.push(`L${AOT}`); + ls.push(`Q${LT} ${startPoint}`); + } + } else if (arrowOffset > innerRight - arrowSize - borderRadius) { + if (arrowOffset > innerRight - arrowSize) { + ls.push(`L${RT}`); + } else { + ls.push(`Q${RT} ${p(arrowOffset + arrowSize, innerTop)}`); + } + ls.push(`L${arrowPoint}`); + ls.push(`L${AOT}`); + ls.push(`H${innerLeft + borderRadius}`); + ls.push(`Q${LT} ${startPoint}`); + } else { + ls.push(`Q${RT} ${RRT}`); + ls.push(`H${arrowOffset + arrowSize}`); + ls.push(`L${arrowPoint}`); + ls.push(`L${AOT}`); + ls.push(`H${innerLeft + borderRadius}`); + ls.push(`Q${LT} ${startPoint}`); + } + return ls.join(''); +}; + +const getPathData = function(position, width, height, arrowOffset, arrowSize, borderRadius) { + + const handlers = { + + bottom: () => { + const d = getTemplatePath(width, height, arrowOffset, arrowSize, borderRadius); + return { + d, + transform: '' + }; + }, + + top: () => { + const d = getTemplatePath(width, height, width - arrowOffset, arrowSize, borderRadius); + return { + d, + transform: `rotate(180,${width * 0.5},${height * 0.5})` + }; + }, + + left: () => { + const d = getTemplatePath(height, width, arrowOffset, arrowSize, borderRadius); + const x = (width - height) * 0.5; + const y = (height - width) * 0.5; + return { + d, + transform: `translate(${x} ${y}) rotate(90,${height * 0.5},${width * 0.5})` + }; + }, + + right: () => { + const d = getTemplatePath(height, width, height - arrowOffset, arrowSize, borderRadius); + const x = (width - height) * 0.5; + const y = (height - width) * 0.5; + return { + d, + transform: `translate(${x} ${y}) rotate(-90,${height * 0.5},${width * 0.5})` + }; + } + }; + + return handlers[position](); +}; + +// =========================================================================================== + +// position style cache +const styleCache = { + // position: '', + // top: {}, + // bottom: {}, + // left: {}, + // right: {} +}; + +export const getPositionStyle = (info, options = {}) => { + + const o = { + bgColor: '#fff', + borderColor: '#ccc', + borderRadius: 5, + arrowSize: 10 + }; + Object.keys(o).forEach((k) => { + + if (hasOwn(options, k)) { + const d = o[k]; + const v = options[k]; + + if (typeof d === 'string') { + // string + if (typeof v === 'string' && v) { + o[k] = v; + } + } else { + // number + if (isNum(v) && v >= 0) { + o[k] = v; + } + + } + + } + }); + + const key = [ + info.width, + info.height, + info.offset, + o.arrowSize, + o.borderRadius, + o.bgColor, + o.borderColor + ].join('-'); + + const positionCache = styleCache[info.position]; + if (positionCache && key === positionCache.key) { + const st = positionCache.style; + st.changed = styleCache.position !== info.position; + styleCache.position = info.position; + return st; + } + + // console.log(options); + + const data = getPathData(info.position, info.width, info.height, info.offset, o.arrowSize, o.borderRadius); + // console.log(data); + + const viewBox = [0, 0, info.width, info.height].join(' '); + const svg = [ + ``, + ``, + '' + ].join(''); + + // console.log(svg); + const backgroundImage = `url("data:image/svg+xml;charset=utf8,${encodeURIComponent(svg)}")`; + + const background = `${backgroundImage} center no-repeat`; + + const padding = `${o.arrowSize + o.borderRadius}px`; + + const style = { + background, + backgroundImage, + padding, + changed: true + }; + + styleCache.position = info.position; + styleCache[info.position] = { + key, + style + }; + + return style; +}; diff --git a/custom_nodes/comfyui-manager/js/snapshot.css b/custom_nodes/comfyui-manager/js/snapshot.css new file mode 100644 index 00000000..a06eb99b --- /dev/null +++ b/custom_nodes/comfyui-manager/js/snapshot.css @@ -0,0 +1,65 @@ +.snapshot-manager { + --grid-font: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; + z-index: 1099; + width: 80vw; + height: 75vh; + min-height: 30em; + display: flex; + flex-direction: column; + gap: 10px; + color: var(--fg-color); + font-family: arial, sans-serif; + text-underline-offset: 3px; + outline: none; + margin: calc(var(--spacing)*2); +} + +.snapshot-manager button { + width: auto; + position: relative; + overflow: hidden; + font-size: 16px; + color: var(--input-text); + background-color: var(--comfy-input-bg); + border-color: var(--border-color); + margin: 0; + min-width: 100px; + padding: 4px 8px; +} + +.snapshot-manager .snapshot-restore-btn { + background-color: #00158f !important; + border-color: #2025b9 !important; + color: white !important; +} + +.snapshot-manager .snapshot-remove-btn { + background-color: #970000 !important; + border-color: #be2127 !important; + color: white !important; +} + +.snapshot-manager button:hover { + filter: brightness(125%); +} + +.snapshot-manager .data-btns { + display: flex; + flex-direction: column; + gap: calc(var(--spacing)*2); + padding: calc(var(--spacing)*2); + align-items: center; + justify-content: center; + height: 100%; +} + +.snapshot-footer { + display: flex; + flex-wrap: wrap; + gap: 10px; + align-items: center; +} + +.snapshot-manager .cn-flex-auto { + flex: auto; +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/snapshot.js b/custom_nodes/comfyui-manager/js/snapshot.js new file mode 100644 index 00000000..b5dc0817 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/snapshot.js @@ -0,0 +1,311 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { manager_instance, rebootAPI, show_message, handle403Response, loadCss } from "./common.js"; +import { buildGuiFrame } from "./comfyui-gui-builder.js"; + +loadCss("./snapshot.css"); + +async function restore_snapshot(target) { + if(SnapshotManager.instance) { + try { + const response = await api.fetchApi(`/snapshot/restore?target=${target}`, { cache: "no-store" }); + + if(response.status == 403) { + await handle403Response(response); + return false; + } + + if(response.status == 400) { + show_message(`Restore snapshot failed: ${target.title} / ${exception}`); + } + + app.ui.dialog.close(); + return true; + } + catch(exception) { + show_message(`Restore snapshot failed: ${target.title} / ${exception}`); + return false; + } + finally { + await SnapshotManager.instance.invalidateControl(); + SnapshotManager.instance.updateMessage("
    To apply the snapshot, please ComfyUI. And refresh browser.", 'cm-reboot-button2'); + } + } +} + +async function remove_snapshot(target) { + if(SnapshotManager.instance) { + try { + const response = await api.fetchApi(`/snapshot/remove?target=${target}`, { cache: "no-store" }); + + if(response.status == 403) { + await handle403Response(response); + return false; + } + + if(response.status == 400) { + show_message(`Remove snapshot failed: ${target.title} / ${exception}`); + } + + app.ui.dialog.close(); + return true; + } + catch(exception) { + show_message(`Restore snapshot failed: ${target.title} / ${exception}`); + return false; + } + finally { + await SnapshotManager.instance.invalidateControl(); + } + } +} + +async function save_current_snapshot() { + try { + const response = await api.fetchApi('/snapshot/save', { cache: "no-store" }); + app.ui.dialog.close(); + return true; + } + catch(exception) { + show_message(`Backup snapshot failed: ${exception}`); + return false; + } + finally { + await SnapshotManager.instance.invalidateControl(); + SnapshotManager.instance.updateMessage("
    Current snapshot saved."); + } +} + +async function getSnapshotList() { + const response = await api.fetchApi(`/snapshot/getlist`); + const data = await response.json(); + return data; +} + +export class SnapshotManager extends ComfyDialog { + static instance = null; + + restore_buttons = []; + message_box = null; + data = null; + + content = $el("div.snapshot-manager"); + + clear() { + this.restore_buttons = []; + this.message_box = null; + this.data = null; + } + + constructor(app, manager_dialog) { + super(); + // this.manager_dialog = manager_dialog; + this.search_keyword = ''; + + const frame = buildGuiFrame( + 'snapshot-manager-dialog', // dialog id + 'Snapshot Manager', // title + 'i.mdi.mdi-puzzle', // icon class + this.content, // dialog content element + this + ); // send this so we can attach close functions + + this.element = frame; + } + + async remove_item() { + caller.disableButtons(); + + await caller.invalidateControl(); + } + + createControls() { + return [ + $el("button.p-button.p-component", { + type: "button", + textContent: "Close", + onclick: () => { this.close(); } + }) + ]; + } + + startRestore(target) { + const self = SnapshotManager.instance; + + self.updateMessage(`
    Restore snapshot '${target.name}'`); + + for(let i in self.restore_buttons) { + self.restore_buttons[i].disabled = true; + self.restore_buttons[i].style.backgroundColor = 'gray'; + } + } + + async invalidateControl() { + this.clear(); + this.data = (await getSnapshotList()).items; + + while (this.content.children.length) { + this.content.removeChild(this.content.children[0]); + } + + await this.createGrid(); + await this.createBottomControls(); + } + + updateMessage(msg, btn_id) { + this.message_box.innerHTML = msg; + if(btn_id) { + const rebootButton = document.getElementById(btn_id); + const self = this; + rebootButton.onclick = async function() { + if(await rebootAPI()) { + self.close(); + self.manager_dialog.close(); + } + }; + } + } + + async createGrid(models_json) { + var grid = document.createElement('table'); + grid.setAttribute('id', 'snapshot-list-grid'); + + var thead = document.createElement('thead'); + var tbody = document.createElement('tbody'); + + var headerRow = document.createElement('tr'); + thead.style.position = "sticky"; + thead.style.top = "0px"; + thead.style.borderCollapse = "collapse"; + thead.style.tableLayout = "fixed"; + + var header1 = document.createElement('th'); + header1.innerHTML = '  ID  '; + header1.style.width = "20px"; + var header2 = document.createElement('th'); + header2.innerHTML = 'Datetime'; + header2.style.width = "100%"; + var header_button = document.createElement('th'); + header_button.innerHTML = 'Action'; + header_button.style.width = "100px"; + + thead.appendChild(headerRow); + headerRow.appendChild(header1); + headerRow.appendChild(header2); + headerRow.appendChild(header_button); + + headerRow.style.backgroundColor = "Black"; + headerRow.style.color = "White"; + headerRow.style.textAlign = "center"; + headerRow.style.width = "100%"; + headerRow.style.padding = "0"; + + grid.appendChild(thead); + grid.appendChild(tbody); + + this.grid_rows = {}; + + if(this.data) + for (var i = 0; i < this.data.length; i++) { + const data = this.data[i]; + var dataRow = document.createElement('tr'); + var data1 = document.createElement('td'); + data1.style.textAlign = "center"; + data1.innerHTML = i+1; + var data2 = document.createElement('td'); + data2.innerHTML = ` ${data}`; + var data_button = document.createElement('td'); + data_button.style.textAlign = "center"; + data_button.className = "data-btns"; + + var restoreBtn = document.createElement('button'); + restoreBtn.className = "snapshot-restore-btn p-button p-component"; + restoreBtn.innerHTML = 'Restore'; + restoreBtn.style.width = "100px"; + + restoreBtn.addEventListener('click', function() { + restore_snapshot(data); + }); + + var removeBtn = document.createElement('button'); + removeBtn.className = "snapshot-remove-btn p-button p-component"; + removeBtn.innerHTML = 'Remove'; + removeBtn.style.width = "100px"; + + removeBtn.addEventListener('click', function() { + remove_snapshot(data); + }); + + data_button.appendChild(restoreBtn); + data_button.appendChild(removeBtn); + + dataRow.style.backgroundColor = "var(--bg-color)"; + dataRow.style.color = "var(--fg-color)"; + dataRow.style.textAlign = "left"; + + dataRow.appendChild(data1); + dataRow.appendChild(data2); + dataRow.appendChild(data_button); + tbody.appendChild(dataRow); + + this.grid_rows[i] = {data:data, control:dataRow}; + } + + let self = this; + const panel = document.createElement('div'); + panel.style.width = "100%"; + panel.style.height = "100%"; + panel.appendChild(grid); + + function handleResize() { + const parentHeight = self.element.clientHeight; + const gridHeight = parentHeight - 200; + + // grid.style.height = gridHeight + "px"; + } + window.addEventListener("resize", handleResize); + + grid.style.position = "relative"; + grid.style.display = "inline-block"; + grid.style.width = "100%"; + grid.style.height = "100%"; + grid.style.overflowY = "scroll"; + + this.content.appendChild(panel); + + handleResize(); + } + + async createBottomControls() { + var save_button = document.createElement("button"); + save_button.className = "p-button p-component"; + save_button.innerHTML = "Save snapshot"; + save_button.onclick = () => { save_current_snapshot(); } + save_button.style.horizontalAlign = "right"; + save_button.style.width = "170px"; + + this.message_box = $el('div', {id:'custom-download-message'}, [$el('br'), '']); + this.message_box.style.height = '60px'; + this.message_box.style.verticalAlign = 'middle'; + + const footer = $el("div.snapshot-footer"); + const spacer = $el("div.cn-flex-auto"); + footer.appendChild(spacer); + footer.appendChild(save_button); + + this.content.appendChild(this.message_box); + this.content.appendChild(footer); + } + + async show() { + try { + this.invalidateControl(); + this.element.style.display = "flex"; + this.element.style.zIndex = 1099; + } + catch(exception) { + app.ui.dialog.show(`Failed to get external model list. / ${exception}`); + } + } +} diff --git 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i=this.options.cellResizeObserver;if("function"==typeof i)return i.apply(this,[t,e])},cellResizeHandler:function(t){const e=new Map;t.forEach((t=>{const{target:i}=t,o=this.getNodeDataCache(i.parentNode);if(!o)return;const{row:n,rowItem:s}=o;e.set(n,s)}));let i=!1;e.forEach(((t,e)=>{const o=this.getRowCache(e);if(!o)return;const n=o.observerNodes;if(!n)return;const{rowHeight:s,rowMinHeight:r}=this.options;let l=Math.max(r||s,1);n.forEach((t=>{if(t){const e=t.clientHeight+11;e>l&&(l=e)}}));this.getRowHeight(t)!==l&&(t.tg_height=l,i=!0)})),i&&this.render("rows_cache")},createCellNode:function(t,e){const i=this.getRowCache(t);if(!i)return;const o=this.getViewRowItem(t),n=this.getViewColumnItem(e);if(!o||!n)return;const s=this.cellResizeObserverHandler(o,n),r=document.createElement("div");r.setAttribute("column",e);const l=this.getCellClass(o,n,s);r.className=l;const h=d.styleMap(n.styleMap)+d.styleMap(o[`${n.id}StyleMap`]);h&&(r.style.cssText=h);const a=i.rowNodes,c=n.tg_frozen,u=this.getCellRowNode(a,c);let g;this.appendNode(u,r),s&&(g=document.createElement("div"),g.className="tg-observer",r.appendChild(g),this.cellResizeObserver.observe(g),i.observerNodes.set(e,g));const f={row:t,rowItem:o,rowNode:u,column:e,columnItem:n,cellNode:r,observerNode:g};this.setNodeDataCache(r,f),i.cellNodes.set(e,r),this.renderCell(f)},getCellRowNode:function(t,e){const i=t.get(0);if(this.frozenInfo.columns){const o=t.get(1);return this.frozenInfo.right?e?o:i:e?i:o}return i}},S={addColumn:function(t,e,i){let o=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];const n=this.getToBeAddedItemList(t);if(!n.length)return!1;let s;if(null!=e&&(s=this.getColumnItem(e),!s))return!1;const r=this.getToBeAddedParentSubs(s,this.columns),l=[this.getToBeAddedPositionIndex(i,r),0].concat(n);r.splice.apply(r,l),this.onNextUpdated((function(){this.trigger(y.onColumnAdded,n)}));const h={type:"columns"};return o&&(h.scrollColumn=n[n.length-1]),this.render(h),!0},deleteColumn:function(t){const e=this.toColumnItemList(t,(t=>!t.private));return!!e.length&&(this.removeColumnsHandler(e),this.onNextUpdated((function(){this.trigger(y.onColumnRemoved,e)})),this.render("columns"),!0)},removeColumnsHandler:function(t){const e=[].concat(t);e.sort((function(t,e){return e.tg_index-t.tg_index})),e.forEach((t=>{let e;if(t===this.sortColumn&&this.removeSortColumn(),t.tg_parent)e=t.tg_parent.subs,e.splice(t.tg_sub_index,1);else{e=this.columns;const i=e.findIndex((e=>e===t));-1!==i&&e.splice(i,1)}!e.length&&t.tg_parent&&(t.tg_parent.subs=null)}))}},T={setColumnWidth:function(t,e){return this.updateColumnWidth(t,e)?(this.resize(),this):this},updateColumnWidth:function(t,e){const i=this.getColumnItem(t);return!!i&&(!!d.isNum(e)&&(e=Math.round(e),e=Math.max(0,e),i.tg_width!==e&&(i.width=e,i.minWidth=Math.min(i.minWidth,e),i.maxWidth=Math.max(i.maxWidth,e),this.updateViewColumnWidth(i),!0)))},showColumn:function(t){return this.updateColumnsInvisible(this.toColumnItemList(t),!1)},hideColumn:function(t){return this.updateColumnsInvisible(this.toColumnItemList(t),!0)},updateColumnsInvisible:function(t,e){if(!t.length)return!1;const i=[];return t.forEach((t=>{t.invisible!==e&&(t.invisible=e,t.tg_invisible=e,i.push(t))})),!!i.length&&(this.render("columns"),!0)}},E={showColumnLine:function(t){t&&(this.$columnLineContainer.show(),this.renderColumnLine(t))},hideColumnLine:function(){this.previousColumnLineActive||this.$columnLineContainer.hide()},setColumnLineActive:function(t){this.setTextSelectable(!t),this.rowHoverable=!t,t!==this.previousColumnLineActive&&(this.previousColumnLineActive=t,t?this.$columnLineItem.addClass("tg-active"):this.$columnLineItem.removeClass("tg-active"))},getColumnLineLeft:function(t){let e=t.tg_left;return t.tg_frozen||(e-=this.scrollLeft),this.frozenInfo.right&&(t.tg_frozen?e=t.tg_left+this.paneWidthL:e-=this.columnsWidthR),e},renderColumnLine:function(t){const e=this.getHeaderItemNode(t).offsetTop,i=t.tg_width,o=this.getColumnLineLeft(t);this.$columnLineItemL.css({top:e,left:o}),this.$columnLineItemR.css({top:e,left:o+i-1}),this.frozenInfo.right||(this.frozenInfo.columns&&!t.tg_frozen&&o{this.renderColumnLine(e.columnItem)})),this.resize()},columnWidthTouchStartHandler:function(t,e){d.preventDefault(e.e);const i=e.columnItem;this.showColumnLine(i),this.setColumnLineActive(!0),e.index=i.tg_index;const o=this.getColumnHeaderNode(i);e.width=o.clientWidth},columnWidthTouchMoveHandler:function(t,e){d.preventDefault(e.e);const i=e.columnItem;let o=e.width+e.offsetX;o=d.clamp(o,i.minWidth,i.maxWidth),i.tg_width!==o&&(i.width=o,this.updateViewColumnWidth(i),this.renderColumnLine(i))},columnWidthTouchEndHandler:function(t,e){d.preventDefault(e.e),this.setColumnLineActive(!1),this.hideColumnLine(),this.resize()}},I={getColumnItem:function(t){return d.isNum(t)?(t<0&&(t=this.columnsInfo.length+t),this.columnsInfo.indexCache[t]):t?d.isNum(t.tg_index)?t:this.getColumnItemById(t.id||t):void 0},getColumnItemById:function(t){return this.getColumnItemBy("id",t)},getColumnItemBy:function(t,e){if(void 0!==e)return this.columnsInfo.indexCache.find((i=>i[t]===e))},getColumnsLength:function(t){return t?this.columnsInfo.length:this.viewColumns.length},getViewColumnItem:function(t){return this.viewAllColumns[t]},isColumnSortable:function(t){return!!t&&(!t.tg_group&&(!(!t.name||!t.id)&&this.isSortable(t)))},isColumnResizable:function(t){return!!t&&(!t.tg_group&&(!d.hasOwn(t,"resizable")||Boolean(t.resizable)))},updateViewColumnWidth:function(t){return t.tg_width=t.width,this.updateColumnHeaderSize(t),this.updateTotalColumnsWidth(),this.updateHeaderLayerHeight(),this.cssRulesInvalid=!0,this.resizeBodyHandler(),this.trigger(y.onColumnWidthChanged,t),!0},updateTotalColumnsWidth:function(){this.blankColumn.tg_width=0;const t=this.viewColumns;let e=0,i=0;const o=this.frozenInfo.columns,n=t.length;let s=0;for(let r=0;r0&&(s+=l,o&&r>=o?i+=l:e+=l)}if(this.frozenInfo.right){const t=e;e=i,i=t}this.columnsWidthL=e,this.columnsWidthR=i,this.columnsWidth=e+i},updateColumnHeaderSize:function(t){this.updateColumnHeaderWidth(t),this.updateColumnHeaderHeight(t,!0),this.updateColumnGroupWidth(t)},updateColumnHeaderWidth:function(t){const e=this.getColumnHeaderNode(t);if(!e)return;const i=t.tg_width;this.isInvisible(t)||i<=0?e.style.display="none":(e.style.display="",e.style.width=`${i}px`)},updateColumnHeaderHeight:function(t,e){if(t.tg_height=0,t.tg_width<=0)return;if(this.isInvisible(t))return;e&&(t.tg_element_height=0);const i=t.tg_element_height;if(i)return void(t.tg_height=i);const o=this.getColumnHeaderHeight(t);t.tg_height=o,t.tg_element_height=o},getColumnHeaderHeight:function(t){const e=this.getColumnHeaderNode(t);return e?e.clientHeight:0},updateColumnGroupWidth:function(t){const e=t.tg_parent;if(!e)return;const i=this.getColumnGroupWidth(e);e.tg_width!==i&&(e.tg_width=i,this.updateColumnHeaderSize(e))},getColumnGroupWidth:function(t){if(this.isInvisible(t))return 0;let e=0;return t.subs&&t.subs.forEach((t=>{this.isInvisible(t)||d.isNum(t.tg_width)&&(e+=t.tg_width)})),e}},L={initTreeInfo:function(t,e){const i=[];let o=!1,n=0,s=0;const r=function(t,r,l){(t=>{t.invisible?t.tg_invisible=!0:t.tg_invisible&&(t.tg_invisible=!1)})(t),((t,i)=>{if(e>=0&&!t.tg_invisible)return t.tg_frozen=!0,void(e-=1);t.tg_frozen&&(t.tg_frozen=!1)})(t),(t=>{if(d.hasOwn(t,"subs")){if(Array.isArray(t.subs))return o=!0,t.tg_group=!0,void(t.tg_subs_length=t.subs.length);t.subs=null}t.tg_group&&(t.tg_group=!1)})(t),((t,e)=>{t.tg_parent=e;let i=0;e&&(i=e.tg_level+1,i>n&&(n=i)),t.tg_level=i})(t,l),t.tg_index=s,t.tg_sub_index=r,i.push(t),s+=1},l=function(t,e){let i=0;const o=t.length;for(;i{if(!this.isInvisible(e))return this.isRowSelectable(e)?t(e,i,o):void 0})),this},toRowItemList:function(t,e){let i=d.toList(t).map((t=>this.getRowItem(t))).filter((t=>t));return"function"==typeof e&&(i=i.filter(e)),i},toColumnItemList:function(t,e){let i=d.toList(t).map((t=>this.getColumnItem(t))).filter((t=>t));return"function"==typeof e&&(i=i.filter(e)),i},isRowLeaf:function(t){return!!t&&("blank"!==t.formatter&&(!t.tg_frozen&&!t.tg_group))},isRowSelectable:function(t){return!!t&&(d.hasOwn(t,"selectable")?Boolean(t.selectable):this.isRowLeaf(t))},isEmptyGroup:function(t){return!(!t||!t.tg_group||0!==t.tg_subs_length)},isInvisible:function(t){return!!t&&(!(!t.tg_filtered&&!t.tg_invisible)||!!this.isInvisible(t.tg_parent))},isSortable:function(t){return!!t&&(!d.hasOwn(t,"sortable")||Boolean(t.sortable))},isCollapsedChanged:function(t,e){return Boolean(t.collapsed)!==e},isSelectedChanged:function(t,e){return Boolean(t.selected)!==e}},x={updateCssRules:function(){this.cssRulesInvalid&&(this.cssRulesInvalid=!1,this.initCssRules(),this.updateColumnsCssRules(),this.updateHeadersCssRules(),this.updateStyleElement())},initCssRules:function(){this.removeCssRules(),this.cssList={},this.cssDisplayCache={};const t=this.getRowHeight(),e=this.createCssRule(".tg-row");e.height=`${t}px`,e["line-height"]=`${t}px`},resetCssDisplay:function(t){if(this.cssDisplayCache){t=t||"";for(const e in this.cssDisplayCache)if(d.hasOwn(this.cssDisplayCache,e)){this.cssDisplayCache[e].style.display=t}}},updateColumnsCssRules:function(){const t=this.viewColumns,e=this.frozenInfo.column,i={};let o=0;for(let n=0,s=t.length;n=0;i--){const e=this.headerLayerHeight[i],o=this.createCssRule(`.tg-h-${i}`);o.bottom=`${t}px`,o.height=`${e}px`,t+=e}this.getLayerCombinations(e).forEach((t=>{const e=this.createCssRule(`.tg-h-${t}`);let i=0;t.split("").forEach((t=>{i+=this.headerLayerHeight[t]||0})),e.height=`${i}px`}))},getLayerCombinations:function(t){let e="";for(;t>=0;)e+=t,t--;if(e.length<2)return[];const i=[],o=function(t,e){const n=t.length;let s=e+2;for(;s<=n;){const o=t.substring(e,s);i.push(o),s++}e=i){let t="Possible Event memory leak detected. ";return t+=`More than ${i} (max limit) listeners added. `,t+="Use setMaxListeners(n) to increase limit.",void console.warn(t,e)}t.events.push(e)},addEvents:function(t,e,i){e.forEach((function(e){const o=e.type;t[o]||(t[o]={events:[]});if("function"!=typeof e.handler)return;const n=t[o];N.addEvent(n,e,i)}))},removeEventByNamespace:function(t,e){Object.keys(t).forEach((function(i){const o=t[i],n=[];o.events.forEach((function(t){t&&t.namespace!==e&&n.push(t)})),o.events=n}))},removeEventByHandler:function(t,e,i){const o=t[e];if(!o)return;const n=[];o.events.forEach((function(t){t&&t.handler!==i&&n.push(t)})),o.events=n},removeEventByType:function(t,e){const i=t[e];i&&(i.events=[])},removeEvent:function(t,e){const i=e.type,o=e.namespace;if(!i&&o)return void N.removeEventByNamespace(t,o);const n=e.handler;"function"!=typeof n?N.removeEventByType(t,i):N.removeEventByHandler(t,i,n)},removeEvents:function(t,e){e.forEach((function(e){N.removeEvent(t,e)}))},removeAllEvents:function(t){Object.keys(t).forEach((function(e){N.removeEventByType(t,e)}))},sendEventList:function(t,e,i,o){const n=e.events;for(let e=0;e!t.onceCalled))},sendEvent:function(t,e,i,o){const n=e[i];if(!n)return;const s=new P({type:i,target:t,currentTarget:t,data:o});N.sendEventList(t,n,s,o)}},_=N;class k{maxListeners=10;setMaxListeners(t){this.maxListeners=Number(t)||10}getMaxListeners(){return this.maxListeners}getEventListeners(){return this.eventListeners||(this.eventListeners={}),this.eventListeners}delEventListeners(){this.eventListeners=null}bind(t,e,i){const o=_.getEventList(this,t,e,i);if(!o.length)return this;const n=this.getEventListeners();return _.addEvents(n,o,this.maxListeners),this}once(t,e){return this.bind(t,e,{once:!0})}unbind(t,e,i){const o=this.getEventListeners();if(!arguments.length)return _.removeAllEvents(o),this;const n=_.getEventList(this,t,e,i);return n.length?(_.removeEvents(o,n),this):this}trigger(t,e){const i=this.getEventListeners();return _.sendEvent(this,i,t,e),this}}const V={DRAG_START:"drag_start",DRAG_MOVE:"drag_move",DRAG_END:"drag_end"};class O extends k{static EVENT=V;generateOptions(t){return d.merge({type:"mouse",startX:0,startY:0,previousX:0,previousY:0,currentX:0,currentY:0,moveX:0,moveY:0,offsetX:0,offsetY:0,changed:!1},t)}start(t,e){t&&(this.unbindEvents(),this.bindEvents(),this.options=this.generateOptions(e),this.startHandler(t))}bindEvents(){this.windowEvents={mousemove:{handler:t=>{this.iframeHandler(t),this.mouseMoveHandler(t)},options:!0},mouseup:{handler:t=>{this.mouseUpHandler(t)},options:{once:!0}}},d.bindEvents(this.windowEvents,window)}unbindEvents(){d.unbindEvents(this.windowEvents),this.windowEvents=null,this.previousIframe&&(this.previousIframe.classList.remove("tg-pointer-events-none"),this.previousIframe=null)}iframeHandler(t){const e=t.target;"IFRAME"===e.nodeName&&e!==this.previousIframe&&(this.previousIframe&&this.previousIframe.classList.remove("tg-pointer-events-none"),e.classList.add("tg-pointer-events-none"),this.previousIframe=e)}startHandler(t){const e=this.options;e.e=t,e.startX=t.pageX,e.startY=t.pageY,e.currentX=e.startX,e.currentY=e.startY,this.hasMoved=!1}mouseMoveHandler(t){d.preventDefault(t);const e=this.options;e.e=t,e.previousX=e.currentX,e.previousY=e.currentY,e.currentX=t.pageX,e.currentY=t.pageY,e.moveX=e.currentX-e.previousX,e.moveY=e.currentY-e.previousY,e.offsetX=e.currentX-e.startX,e.offsetY=e.currentY-e.startY,e.changed=!(0===e.offsetX&&0===e.offsetY),this.hasMoved?this.trigger(V.DRAG_MOVE,e):(this.hasMoved=!0,this.trigger(V.DRAG_START,e))}mouseUpHandler(t){this.unbindEvents();const e=this.options;this.hasMoved&&(e.e=t,d.preventDefault(t),this.trigger(V.DRAG_END,e))}destroy(){this.unbindEvents(),this.unbind()}}const $={Linear:{None:function(t){return t}}},D={MOTION_START:"motion_start",MOTION_MOVE:"motion_move",MOTION_END:"motion_end",MOTION_STOP:"motion_stop"};class B extends k{static EVENT=D;constructor(t){super(),this.constructorOptions=t,this.stopped=!0}generateOptions(t){return d.merge({easing:null,duration:100,from:0,till:1,data:0},this.constructorOptions,t)}stop(){return this.stopped||(this.stopped=!0,this.cancelAnimationFrame(),this.trigger(D.MOTION_STOP,this.data)),this}start(t){return this.stop(),this.stopped=!1,this.options=this.generateOptions(t),this.initCalculation(),this.data=this.calculateHandler(0),this.trigger(D.MOTION_START,this.data),this.stopped||(this.time=Date.now(),this.requestAnimationFrame(this.moveHandler)),this}requestAnimationFrame(t){this.requestId=window.requestAnimationFrame((()=>{t.apply(this)}))}cancelAnimationFrame(){window.cancelAnimationFrame(this.requestId)}getEasing(t){return"function"!=typeof t&&(t=d.getValue($,t,$.Linear.None)),t}moveHandler(){const t=Date.now()-this.time,e=this.duration;if(t{o[n]=this.calculateNumber(t,e[n],i[n])})),o):(this.calculateKeys=[],Object.keys(e).forEach((n=>{const s=e[n],r=i[n];d.isNum(s)&&d.isNum(r)&&(o[n]=this.calculateNumber(t,s,r),this.calculateKeys.push(n))})),o)}calculateNumber(t,e,i){return(i-e)*t+e}calculateNone(t,e,i){return e}destroy(){this.stop(),this.unbind()}}const A={TOUCH_START:"touch_start",TOUCH_MOVE:"touch_move",TOUCH_END:"touch_end",TOUCH_INERTIA:"touch_inertia"};class W extends k{static EVENT=A;generateOptions(t){return d.merge({type:"touch",startX:0,startY:0,previousX:0,previousY:0,currentX:0,currentY:0,moveX:0,moveY:0,offsetX:0,offsetY:0,changed:!1,touchLength:0,direction:"",inertia:!1,inertiaTime:200},t)}start(t,e){t&&(this.unbindEvents(),this.bindEvents(),this.options=this.generateOptions(e),this.startHandler(t))}bindEvents(){this.touchEvents={touchmove:{handler:t=>{this.touchMoveHandler(t)},options:{passive:!1}},touchend:{handler:t=>{this.touchEndHandler(t)},options:{passive:!1,once:!0}},touchcancel:{handler:t=>{this.touchCancelHandler(t)},options:{passive:!1,once:!0}}},d.bindEvents(this.touchEvents,document.body)}unbindEvents(){this.motionStop(),d.unbindEvents(this.touchEvents),this.touchEvents=null}startHandler(t){this.trackingPoints=[];const e=t.touches,i=e[0];if(!i)return;const o=this.options;o.e=t,o.startX=i.clientX,o.startY=i.clientY,o.currentX=o.startX,o.currentY=o.startY,o.touchLength=e.length,this.addTrackingPoint(o),this.trigger(A.TOUCH_START,o)}touchMoveHandler(t){const e=t.touches,i=e[0];if(!i)return;const o=this.options;o.e=t,o.previousX=o.currentX,o.previousY=o.currentY,o.currentX=i.clientX,o.currentY=i.clientY,o.moveX=o.currentX-o.previousX,o.moveY=o.currentY-o.previousY,o.offsetX=o.currentX-o.startX,o.offsetY=o.currentY-o.startY,o.changed=!(0===o.offsetX&&0===o.offsetY),o.touchLength=e.length,o.direction=this.getDirection(o),this.addTrackingPoint(o),this.trigger(A.TOUCH_MOVE,o)}touchEndHandler(t){this.unbindEvents();const e=this.options;e.e=t,this.trigger(A.TOUCH_END,e);const i=t.changedTouches[0];if(!i)return;const o=t.touches;e.touchLength=o.length,e.touchLength>0||(e.currentX=i.clientX,e.currentY=i.clientY,this.addTrackingPoint(e),this.motionStart())}touchCancelHandler(t){this.unbindEvents(),this.trigger(A.TOUCH_END,this.options)}getMotionInfo(){const t=this.trackingPoints;if(t.length<2)return;if(this.filterTrackingPoints(t),t.length<2)return;const e=t[0],i=t[t.length-1],o=i.t-e.t;if(o<=0)return;let n=i.x-e.x,s=i.y-e.y;const r=Math.abs(n),l=Math.abs(s);r>l?s=0:n=0;return{offsetDistance:Math.max(r,l),offsetTime:o,offsetX:n,offsetY:s}}motionStart(){const t=this.options;if(!t.inertia)return;const e=this.getMotionInfo();if(!e)return;const i=500*e.offsetDistance/50,o=d.clamp(i,20,2e3),n={x:20*(e.offsetX/e.offsetTime),y:20*(e.offsetY/e.offsetTime)};this.motion=new B,this.motion.bind(B.EVENT.MOTION_MOVE,((e,i)=>{t.touchInertiaX=i.x,t.touchInertiaY=i.y,this.trigger(A.TOUCH_INERTIA,t)})),this.motion.start({duration:o,from:n,till:{x:0,y:0}})}motionStop(){this.motion&&(this.motion.destroy(),this.motion=null)}getDirection(t){const i=t.offsetX,o=t.offsetY,n=Math.abs(i),s=Math.abs(o);if(n0)return e.UP;if(o<0)return e.DOWN}if(n>s){if(i>0)return e.LEFT;if(i<0)return e.RIGHT}return""}filterTrackingPoints(t){t.reverse();const e=t.length,i=Date.now(),o=this.options.inertiaTime;for(let n=0;no){t.length=n;break}t.reverse()}addTrackingPoint(t){if(!t.inertia)return;const e=t.currentX,i=t.currentY,o=Date.now(),n=this.trackingPoints;n.push({x:e,y:i,t:o}),n.length>100&&this.filterTrackingPoints(n)}destroy(){this.unbindEvents(),this.unbind()}}const F={getAllEvents:function(){return[].concat(H)},bindEvents:function(){this.unbindEvents(),this.containerEvents={mousedown:{handler:t=>{this.containerMouseDownHandler(t)},options:!0},mousemove:{handler:t=>{this.containerMouseMoveHandler(t)},options:!0},mouseover:{handler:t=>{this.containerMouseOverOutHandler(t,!0)},options:!0},mouseout:{handler:t=>{this.containerMouseOverOutHandler(t,!1)},options:!0},mouseenter:{handler:t=>{this.containerMouseEnterLeaveHandler(t,!0)},options:!0},mouseleave:{handler:t=>{this.containerMouseEnterLeaveHandler(t,!1)},options:!0},touchstart:{handler:t=>{this.containerTouchStartHandler(t)},options:{passive:!1}},touchmove:{handler:t=>{this.containerTouchMoveHandler(t)},options:{passive:!1}},touchend:{handler:t=>{this.containerTouchEndHandler(t)},options:{passive:!1}},touchcancel:{handler:t=>{this.containerTouchCancelHandler(t)},options:{passive:!1}},wheel:{handler:t=>{this.containerWheelHandler(t)},options:{passive:!1}},click:{handler:t=>{this.containerClickHandler(t)},options:!0},dblclick:{handler:t=>{this.containerDblClickHandler(t)},options:!0},contextmenu:{handler:t=>{this.containerContextMenuHandler(t)},options:!0},selectstart:{handler:t=>{this.containerSelectStartHandler(t)},options:!0},keydown:{handler:t=>{this.containerKeyDownHandler(t)},options:!0}},d.bindEvents(this.containerEvents,this.container),this.columnWidthDrag=new O,this.columnWidthDrag.bind(O.EVENT.DRAG_START,((t,e)=>{this.columnWidthDragStartHandler(t,e)})).bind(O.EVENT.DRAG_MOVE,((t,e)=>{this.columnWidthDragMoveHandler(t,e)})).bind(O.EVENT.DRAG_END,((t,e)=>{this.columnWidthDragEndHandler(t,e)})),this.columnWidthTouch=new W,this.columnWidthTouch.bind(W.EVENT.TOUCH_START,((t,e)=>{this.columnWidthTouchStartHandler(t,e)})).bind(W.EVENT.TOUCH_MOVE,((t,e)=>{this.columnWidthTouchMoveHandler(t,e)})).bind(W.EVENT.TOUCH_END,((t,e)=>{this.columnWidthTouchEndHandler(t,e)})),this.rowDrag=new O,this.rowDrag.bind(O.EVENT.DRAG_START,((t,e)=>{this.rowDragStartHandler(t,e)})).bind(O.EVENT.DRAG_MOVE,((t,e)=>{this.rowDragMoveHandler(t,e)})).bind(O.EVENT.DRAG_END,((t,e)=>{this.rowDragEndHandler(t,e)})),this.rowTouch=new W,this.rowTouch.bind(W.EVENT.TOUCH_START,((t,e)=>{this.rowDragStartHandler(t,e)})).bind(W.EVENT.TOUCH_MOVE,((t,e)=>{this.rowDragMoveHandler(t,e)})).bind(W.EVENT.TOUCH_END,((t,e)=>{this.rowDragEndHandler(t,e)})),this.scrollTouch=new W,this.scrollTouch.bind(W.EVENT.TOUCH_START,((t,e)=>{this.scrollTouchStartHandler(t,e)})).bind(W.EVENT.TOUCH_MOVE,((t,e)=>{this.scrollTouchMoveHandler(t,e)})).bind(W.EVENT.TOUCH_END,((t,e)=>{this.scrollTouchEndHandler(t,e)})).bind(W.EVENT.TOUCH_INERTIA,((t,e)=>{this.scrollTouchInertiaHandler(t,e)}))},isDefaultPrevented:function(t){if(t){if(t.defaultPrevented)return!0;if(t.e&&t.e.defaultPrevented)return!0}return!1},getEventClosestNode:function(t,e){if(t&&t!==this.container)return t.classList.contains(e)?t:this.getEventClosestNode(t.parentNode,e)},getEventClosestData:function(t){if(!t||t===this.container)return;const e=this.getNodeDataCache(t);return e||this.getEventClosestData(t.parentNode)},getEventData:function(t){const e=this.getEventClosestData(t.target);if(e)return e.e=t,e},getWheelDelta:function(t,e,i){let o=t.deltaX,n=t.deltaY;return 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${this.id}`),this.container=this.$container.get(0),d.setInstance(this.container,this),this.$headerFrame=this.$container.find(".tg-header-frame"),this.$paneHL=this.$headerFrame.find(".tg-pane-header-left"),this.$paneHR=this.$headerFrame.find(".tg-pane-header-right"),this.$headerL=this.$paneHL.find(".tg-header-left"),this.$headerR=this.$paneHR.find(".tg-header-right"),this.$header=w().add(this.$headerL).add(this.$headerR),this.$bodyFrame=this.$container.find(".tg-body-frame"),this.$paneTL=this.$bodyFrame.find(".tg-pane-top-left"),this.$paneTR=this.$bodyFrame.find(".tg-pane-top-right"),this.$paneBL=this.$bodyFrame.find(".tg-pane-bottom-left"),this.$paneBR=this.$bodyFrame.find(".tg-pane-bottom-right"),this.$bodyTL=this.$paneTL.find(".tg-body-top-left"),this.$bodyTR=this.$paneTR.find(".tg-body-top-right"),this.$bodyBL=this.$paneBL.find(".tg-body-bottom-left"),this.$bodyBR=this.$paneBR.find(".tg-body-bottom-right"),this.$body=w().add(this.$bodyTL).add(this.$bodyTR).add(this.$bodyBL).add(this.$bodyBR),this.$columnLineContainer=this.$container.find(".tg-column-line"),this.$columnLineItem=this.$columnLineContainer.find(".tg-column-line-item"),this.$columnLineItemL=this.$columnLineContainer.find(".tg-column-line-l"),this.$columnLineItemR=this.$columnLineContainer.find(".tg-column-line-r"),this}},tt={initColumnsHandler:function(){this.columns=this.data.columns,this.columns.forEach(((t,e)=>{t&&"object"==typeof t||(this.columns[e]={})}));const t=this.getPrivateColumns();this.columnsInfo=this.initTreeInfo(t,this.frozenInfo.column);const e=[],i=[],o=(t,n)=>{if(!d.isList(t))return;let s,r=0;t.forEach((t=>{if(!this.isInvisible(t))if(t.tg_group){if(this.isEmptyGroup(t))return;i.push(t),o(t.subs,t)}else t.tg_list_index=r,r+=1,t.tg_list_last=!1,s=t,e.push(t)})),s&&(s.tg_list_last=!0)};o(t),e.forEach((t=>{this.initColumnItemHandler(t)})),i.forEach((t=>{this.initColumnGroupHandler(t)}));const n=[].concat(e).concat(i);this.initViewList(n,((t,e)=>{})),this.viewColumns=e,this.viewGroupColumns=i,this.viewAllColumns=n,this.initHeaderHandler(t),this.initSortColumn()},getPrivateColumns:function(){const t=this.options;this.selectColumn=t.selectColumn,this.rowDragColumn=t.rowDragColumn,this.rowNumberColumn=t.rowNumberColumn,this.blankColumn=t.blankColumn;let e=[];const i=()=>{t.selectVisible&&e.push(this.selectColumn),t.rowDragVisible&&e.push(this.rowDragColumn),t.rowNumberVisible&&(this.rowNumberColumn.width=t.rowNumberWidth,e.push(this.rowNumberColumn))};if(this.frozenInfo.right){const t=this.frozenInfo.column;this.columns.forEach(((o,n)=>{e.push(o),n===t&&i()}))}else i(),e=e.concat(this.columns);return e.push(this.blankColumn),e},setColumns:function(t){this.data.columns=d.toList(t),this.rerender()},getColumns:function(){return this.columns},getViewColumns:function(t){return t?this.viewAllColumns:this.viewColumns},initColumnItemHandler:function(t){this.initColumnProps(t),this.initColumnFormatter(t),this.initColumnWidth(t)},initColumnGroupHandler:function(t){this.initColumnFormatterByName(t,"headerFormatter","header")},initColumnProps:function(t){const e=this.options.columnTypes;if(!d.hasOwn(t,"type")){const i=e[t.id];"string"==typeof i&&(t.type=i)}let i=this.options.columnProps;const o=e[t.type];o&&"object"==typeof o&&(i=d.merge(i,o));for(const e in i)d.hasOwn(t,e)||(t[e]=i[e])},initColumnFormatter:function(t){this.initColumnFormatterByName(t,"headerFormatter","header");let e=t.type;const i=t.formatter;"string"==typeof i&&(e=i),this.initColumnFormatterByName(t,"formatter",e)},initColumnFormatterByName:function(t,e,i){let o=t[e];"function"!=typeof o?(o=this.getFormatter(i),t[`tg_${e}`]=o||this.getFormatter("string")):t[`tg_${e}`]=o.bind(this)},initColumnWidth:function(t){if(t!==this.blankColumn)return d.isNum(t.width)&&t.width>=0?(t.tg_width=t.width,t.minWidth=Math.min(t.minWidth,t.tg_width),void(t.maxWidth=Math.max(t.maxWidth,t.tg_width))):void this.initColumnWidthByName(t);t.tg_width=0},initColumnWidthByName:function(t){const e=this.getComputedColumnWidth(t);d.isNum(e)&&(t.tg_width=e)},getComputedColumnWidth:function(t){const e=t.name||"",i=d.getCharLen(e);let o=Math.round(10*i);return 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e=this.columnsInfo.maxLevel;this.viewColumns.forEach((function(t){t.tg_layer=e,t.tg_parent&&(t.tg_parent.tg_layer=e-1)})),this.viewGroupColumns.forEach((function(t){const e=t.tg_layer,i=t.tg_parent;if(i){let t=e-1;d.isNum(i.tg_layer)&&(t=Math.min(t,i.tg_layer)),i.tg_layer=t}})),this.initColumnRowspanHandler(t,0)},initColumnRowspanHandler:function(t,e){t.forEach((t=>{const i=this.initColumnCombinationHandler(t,e);t.tg_group&&this.initColumnRowspanHandler(t.subs,e+i)}))},initColumnCombinationHandler:function(t,e){const i=[],o=t.tg_layer;for(;e<=o;)i.push(e),e+=1;i.reverse();const n=i.length;let s="";return n>1&&(s=i.join("")),t.tg_combination=s,n}},it={},ot={name:"",minWidth:81,maxWidth:300},nt=function(t){return null==t},st=function(t,e){const i=nt(t),o=nt(e);return i&&o?0:i?1:o?-1:void 0},rt=function(t,e){return t.tg_index>e.tg_index?1:-1},lt=function(t,e){return rt(t,e)},ht=function(t,e){if("string"==typeof t&&"string"==typeof e){const 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t={className:e.NS,theme:e.ID,headerVisible:!0,rowHeight:32,rowFilter:null,rowFilteredSort:null,rowNotFound:"",rowMoveCrossLevel:!0,rowCacheLength:0,rowProps:it,columnTypes:{tree:{type:"tree",formatter:"tree",width:230,minWidth:120,maxWidth:810},number:{type:"number",align:"right"},date:{type:"date",align:"right"},name:"tree"},columnCacheLength:0,columnProps:ot,collapseAllOnInit:null,collapseAllVisible:!0,selectAllOnInit:null,selectVisible:!1,selectAllVisible:!0,selectMultiple:!0,selectColumn:{private:!0,id:"tg-column-select",name:"",formatter:"select",headerClassMap:"tg-header-select",classMap:"tg-cell-select",width:36,align:"center",resizable:!1,sortable:!1,exportable:!1},rowDragCrossLevel:!0,rowDragVisible:!1,rowDragColumn:{private:!0,id:"tg-column-row-drag",name:"",formatter:"rowDrag",headerClassMap:"tg-header-row-drag",classMap:"tg-cell-row-drag",align:"center",width:36,resizable:!1,sortable:!1,exportable:!1},rowNumberWidth:36,rowNumberFilter:null,rowNumberVisible:!1,rowNumberColumn:{private:!0,id:"tg-column-row-number",name:"",formatter:"rowNumber",headerClassMap:"tg-header-row-number",classMap:"tg-cell-row-number",align:"center",maxWidth:100,sortable:!1,exportable:!1},blankColumn:{private:!0,id:"tg-column-blank",name:"",formatter:"blank",headerClassMap:"tg-header-blank",classMap:"tg-cell-blank",width:0,minWidth:0,maxWidth:4096,resizable:!1,sortable:!1,exportable:!1},sortField:"",sortAsc:!0,sortBlankValueBottom:!0,sortComparers:ft,sortOnInit:!1,sortIndicator:"h",highlightKeywords:{textKey:"tg_text_",textGenerator:null,highlightKey:"tg_highlight_",highlightPre:"",highlightPost:""},frozenRow:-1,frozenRowMax:10,frozenRowHoverable:!1,frozenBottom:!1,frozenColumn:-1,frozenColumnMax:10,frozenRight:!1,scrollbarSize:12,scrollbarSizeH:null,scrollbarSizeV:null,scrollbarRound:!1,scrollbarFade:!1,scrollbarFadeTimeout:1e3,scrollbarType:"auto",scrollPaneMinWidth:30,scrollPaneGradient:30,autoHeight:!1,textSelectable:!1,bindWindowResize:!1,bindContainerResize:!1,cellResizeObserver:null},i=this.generateThemeOptions();return d.merge(t,i,this.constructorOptions,this.customOptions,this.dataOptions)},generateThemeOptions(){const t=this.pickOptions("theme").pop();if(t)return this.getThemeOptions(t)},pickOptions(t){return[this.constructorOptions,this.customOptions,this.dataOptions].map((e=>e&&e[t])).filter((t=>t))},initOptionsFormatters(){let t;const e=this.pickOptions("formatters");e.length&&(t=d.merge.apply(null,e)),this.formatters=d.merge(Y,t,this.customFormatters),this.nullFormatter=this.getFormatter("null")},initOptionsSort(){"v"!==this.options.sortIndicator&&(this.options.sortIndicator="h")},initOptionsFrozen:function(){const t=this.options;this.frozenInfo={column:-1,row:-1,columns:0,rows:0,bottom:Boolean(t.frozenBottom),right:Boolean(t.frozenRight)};let e=d.toNum(t.frozenColumn,!0);e=d.clamp(e,-1,t.frozenColumnMax),e>-1&&!this.frozenInfo.right&&(t.selectVisible&&(e+=1),t.rowDragVisible&&(e+=1),t.rowNumberVisible&&(e+=1)),this.frozenInfo.column=e,e>-1?this.frozenInfo.columns=e+1:(this.frozenInfo.columns=0,this.frozenInfo.right=!1);let i=d.toNum(t.frozenRow,!0);i=d.clamp(i,-1,t.frozenRowMax),this.frozenInfo.row=i,i>-1?this.frozenInfo.rows=i+1:(this.frozenInfo.rows=0,this.frozenInfo.bottom=!1)},initOptionsScrollbar:function(){const t=this.options;("auto"===t.scrollbarType&&d.isTouchDevice()||["touch","mobile"].includes(t.scrollbarType))&&(t.scrollbarFade=!0,t.scrollbarSize=6,t.scrollbarRound=!0);const e=d.toNum(t.scrollbarSize);this.scrollbarSizeH=e,d.isNum(t.scrollbarSizeH)&&(this.scrollbarSizeH=t.scrollbarSizeH),this.scrollbarSizeV=e,d.isNum(t.scrollbarSizeV)&&(this.scrollbarSizeV=t.scrollbarSizeV)},initOptionsContainer:function(){this.$container.attr("id",this.id);const t=this.options;this.$container.removeClass();const i=[e.NS,this.id,`tg-${t.theme}`,t.className];t.textSelectable||i.push("tg-text-unselectable"),d.isTouchDevice()&&i.push("tg-touch-device"),this.$container.addClass(d.classMap(i))},setTextSelectable:function(t){this.options.textSelectable&&(t?this.$container.removeClass("tg-text-unselectable"):this.$container.addClass("tg-text-unselectable"))}},mt={initBindWindowResize:function(){this.unbindWindowResize(),this.options.bindWindowResize&&(this.windowResizeEvents={resize:{handler:t=>{this.resize()}}},d.bindEvents(this.windowResizeEvents,window))},unbindWindowResize:function(){d.unbindEvents(this.windowResizeEvents)},createResizeObserver:function(t){if("undefined"==typeof ResizeObserver)return console.error("ERROR: This browser does not support ResizeObserver"),{observe:()=>{},unobserve:()=>{},disconnect:()=>{}};return new 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r=`${i}`.trim().toLowerCase().split(/\s+/g).filter((t=>t));if(!r.length)return!0;let l=!1;const h=(e,i)=>(/<\/?[a-z][\s\S]*>/i.test(e)&&(e=((e,i)=>{const n=`${o}${i}`,s=t[n];if(s)return s;const r=document.createElement("div");r.innerHTML=e;const l=r.innerText;return t[n]=l,l})(e,i)),(t=>{const e=t.toLowerCase();let i=0;for(const t of r){const o=e.indexOf(t,i);if(-1===o)return;i=o+t.length}return!0})(e));let a=function(t,e){return t[e]};return"function"==typeof n&&(a=n),e.forEach((e=>{const i=a(t,e);if(null==i)return;const o=`${i}`.trim();if(!o)return;const n=h(o,e);n&&(t[`${s}${e}`]=n,l=!0,this.highlightKeywords=r)})),l},highlightKeywordsHandler:function(){const{highlightCells:t}=this.renderSettings;if(!t.length)return;const e=this.highlightKeywords;e&&(this.asyncHighlightKeywords||(this.asyncHighlightKeywords=d.debounce(this.highlightKeywordsSync,10)),this.asyncHighlightKeywords.apply(this,[t,e]))},highlightKeywordsSync:function(t,e){t.forEach((t=>{const i=Array.from(t.querySelectorAll("svg")).concat(Array.from(t.querySelectorAll("textarea"))),o=document.createTreeWalker(t,NodeFilter.SHOW_TEXT,(t=>{if(i.length)for(const e of i)if(e.contains(t))return NodeFilter.FILTER_SKIP;return NodeFilter.FILTER_ACCEPT})),n=[];let s=o.nextNode();for(;s;)n.push(s),s=o.nextNode();n.length&&this.highlightTextNodes(n,e)}))},highlightTextNodes:function(t,e){const{highlightPre:i,highlightPost:o}=this.options.highlightKeywords;let n=0;const s=()=>(n>=e.length&&(n=0),e[n++]);let r=s();t.forEach((t=>{const e=t.textContent,n=e.toLowerCase(),l=[];let h=0;const a=e.length;let c=!1;for(;h{if(e.selected){if(t)return void(e.selected=!1);t=e}}))}const t=this.options.selectAllOnInit;!0!==t?!1===t&&this.updateAllRowsSelected(!1):this.updateAllRowsSelected(!0)},updateAllRowsSelected:function(t){this.forEachSelectableRow((e=>{e.selected=t}))},initCollapseAllOnInitHandler:function(){const t=this.options.collapseAllOnInit;!0!==t?!1===t&&this.updateAllRowsCollapsed(!1):this.updateAllRowsCollapsed(!0)},getToBeAddedItemList:function(t){const e=[];return d.toList(t).forEach((t=>{t&&"object"==typeof t?e.push(t):void 0!==t&&e.push({name:t})})),e},getToBeAddedParentSubs:function(t,e){return t?(t.subs||(t.subs=[]),t.subs):e},getToBeAddedPositionIndex:function(t,e){const i=e.length;return d.isNum(t)&&t>=0&&t<=i?Math.round(t):i},generateDataSnapshot:function(t){if(!t||"object"!=typeof t)return t;const e=this.cleanTreeList(t.rows),i=this.cleanTreeList(t.columns);return this.convertNumberType(e,i),t.rows=e,t.columns=i,t},cleanTreeList:function(t){if(!d.isList(t))return[];const e=(t,i)=>{i.forEach((i=>{if(!i||"object"!=typeof i)return void t.push({});const o=this.getItemSnapshot(i),n=i.subs;Array.isArray(n)&&(o.subs=[],e(o.subs,n)),t.push(o)}))},i=[];return e(i,t),i},convertNumberType:function(t,e){const i=[];d.forEachTree(e,(function(t){"number"===t.type&&t.id&&i.push(t.id)})),i.length&&d.forEachTree(t,(function(t){i.forEach((function(e){t[e]=d.convertNum(t[e])}))}))}},vt={setDefaultLoading:function(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!t)return;const i=t.style;e.size&&(i.width=e.size,i.height=e.size),e.color&&(i.color=e.color),e.size||e.color||t.removeAttribute("style"),e.fast?t.classList.add("tg-loading-fast"):t.classList.remove("tg-loading-fast")},getDefaultLoading:function(t){return this.setDefaultLoading(this.$defaultLoading,t),this.$defaultLoading},getLoadingHolder:function(){return this.$container?this.$container.find(".tg-loading"):w()},setLoading:function(t){if(!this.$container)return this;this.$defaultLoading||(this.$defaultLoading=this.$container.find(".tg-loading-default").get(0));const e=this.getLoadingHolder().get(0);return"function"==typeof t&&(t=t.call(this,e)),d.isObject(t)&&(t=this.getDefaultLoading(t)),t||(t=this.getDefaultLoading()),this.renderNodeContent(e,t),this},showLoading:function(){return this.getLoadingHolder().show(),this},hideLoading:function(){return this.getLoadingHolder().hide(),this}},Ht={showMask:function(t){if(!this.hasMask||t){const e=this.$container.find(".tg-mask"),i=e.get(0);if(t&&i){const e=d.styleMap(t);e&&(i.style.cssText=e)}e.show(),this.hasMask=!0}return this},hideMask:function(){return this.hasMask&&(this.$container.find(".tg-mask").hide(),this.hasMask=!1),this}},Ct={renderNodeContent:function(t,e){if(t){if(e&&e.nodeType)return this.emptyNode(t),void t.appendChild(e);if(Array.isArray(e))return this.emptyNode(t),void e.forEach((e=>{e&&e.nodeType&&t.appendChild(e)}));void 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e=this.getHeaderItemNode(t);if(e)return e.querySelector(".tg-column-header")}},yt={render:function(){this.asyncRender||(this.asyncRender=d.microtask(this.renderSync)),this.asyncRender.apply(this,arguments)},renderSync:function(){this.renderStartedTimestamp=Date.now();const t=this.generateRenderSettings.apply(this,arguments);return this.renderSettings=t,"all"===t.type?(this.flushBody(),this.initHandler(),this.renderHeader(),this.updateViewRowsAndSize(),this.renderBody(),this):"columns"===t.type?(this.flushBody(),this.initColumnsHandler(),this.renderHeader(),this.updateViewRowsAndSize(),this.renderBody(),this):"rows"===t.type?(this.updateViewRowsAndSize(),this.renderBody(),this):"rows_cache"===t.type?(this.cssRulesInvalid=!0,this.updateViewRowsAndSize(),this.updateRowCacheTopAndHeight(),this.renderBody(),this):"resize"===t.type?(this.resizeHandler(),this.renderBody(),this):(this.renderBody(),this)},generateRenderSettings:function(t){const e={type:this.renderType,scrollLeft:null,scrollTop:null,scrollColumn:null,scrollRow:null,highlightCells:[]};return"string"==typeof t?e.type=t:t&&Object.assign(e,t),this.headerCreated||(e.type="all"),e},renderBody:function(){this.scrollOnInit(),this.scrollTopOffset=this.scrollPane.getScrollTopOffset();const t=this.getViewport();return this.viewport=t,this.flushWithViewport(),this.previousScrollTopOffset!==this.scrollTopOffset&&(this.previousScrollTopOffset=this.scrollTopOffset,this.updateRowCacheTopOffset()),this.renderRows(t.rows),this.renderCells(t.rows,t.columns),this.renderUpdatedTimestamp=Date.now(),this.renderDuration=this.renderUpdatedTimestamp-this.renderStartedTimestamp,this.trigger(y.onUpdated,t),this.firstUpdated||(this.firstUpdated=!0,this.trigger(y.onFirstUpdated,t)),this.layoutEventHandler(),this.resizeEventHandler(),this.highlightKeywordsHandler(),this.renderSettings=null,this.renderType=null,this},rerender:function(){return this.render("all"),this}},Rt={resize:function(){return this.asyncResize||(this.asyncResize=d.throttle(this.resizeSync,100)),this.asyncResize.apply(this,arguments),this},resizeSync:function(){return this.headerCreated?(this.resizeHolderHandler.apply(this,arguments),this.firstUpdated&&this.isHolderInvisible()||this.render("resize"),this):this},resizeHolderHandler(t,e){if(0!==arguments.length)return 1===arguments.length?t&&"object"==typeof t?void this.$holder.css(t):void this.$holder.css({width:t}):void this.$holder.css({width:t,height:e})},isHolderInvisible(){const t=this.$holder.width(),e=this.$holder.height();return!t||!e},resizeHandler:function(){this.containerWidth=this.$container.width(),this.containerHeight=this.$container.height(),this.headerWidth=this.containerWidth,this.bodyWidth=this.containerWidth,this.updateTotalColumnsWidth(),this.resizeHeaderHandler(),this.resizeBodyHandler()},layoutEventHandler:function(){const t=this.previousLayout||{},e={headerWidth:this.headerWidth,headerHeight:this.headerHeight,bodyWidth:this.bodyWidth,bodyHeight:this.bodyHeight,scrollbarWidth:this.getScrollbarWidth(),scrollbarHeight:this.getScrollbarHeight()};Object.values(e).join("")!==Object.values(t).join("")&&(this.previousLayout=e,this.trigger(y.onLayout,d.merge({previous:t},e)))},resizeEventHandler:function(){const t=this.previousSize||{},e={width:this.containerWidth,height:this.containerHeight};Object.values(e).join("")!==Object.values(t).join("")&&(this.previousSize=e,this.trigger(y.onResize,d.merge({previous:t},e)))},resizeHeaderHandler:function(){this.initHeaderLayerHeight();const t=this.options;t.autoHeight&&this.viewRows.length>5e3&&(t.autoHeight=!1),this.headerHeight=0,t.headerVisible&&(this.containerHeight>0||t.autoHeight)&&this.updateHeaderTableHeight(),this.$headerFrame.css({width:this.headerWidth,height:this.headerHeight})},updateHeaderTableHeight:function(){let t=0;Object.keys(this.headerLayerHeight).forEach((e=>{t+=this.headerLayerHeight[e]}));const e=this.$headerL.find(".tg-header-table"),i=this.$headerR.find(".tg-header-table");e.css({height:t}),i.css({height:t}),this.headerHeight=t},resizeBodyHandler:function(){this.updateScrollState(),this.bodyHeight=this.containerHeight-this.headerHeight,this.$bodyFrame.css({width:this.bodyWidth,height:this.bodyHeight}),this.updatePaneWidth(),this.updatePaneHeight(),this.updateCanvasWidth(),this.updateCanvasHeight(),this.updateScrollPane(),this.updateCssRules()},updatePaneWidth:function(){let t=this.bodyWidth,e=0;if(this.frozenInfo.columns){const i=this.getScrollbarWidth();this.frozenInfo.right?(e=this.columnsWidthR+i,t=this.bodyWidth-e):(t=this.columnsWidthL,e=this.bodyWidth-t),this.scrollPaneHidden&&(this.frozenInfo.right?(t<=0&&(t=0),e=Math.max(0,this.bodyWidth-t)):(e3&&void 0!==arguments[3])||arguments[3];const n=this.getToBeAddedItemList(t);if(!n.length)return!1;let s;if(null!=e&&(s=this.getRowItem(e),!s))return!1;const r=this.getToBeAddedParentSubs(s,this.rows),l=this.getToBeAddedPositionIndex(i,r),h=[l,0].concat(n);r.splice.apply(r,h),this.initRowsHandler(),s?(s.collapsed=!1,this.flushRowFrom(s.tg_view_index+l)):this.flushRowFrom(l),this.onNextUpdated((function(){this.trigger(y.onRowAdded,n)}));const a={type:"rows"};return o&&(a.scrollRow=n[n.length-1]),this.render(a),!0},deleteRow:function(t){const e=d.toList(t),i=[];if(e.forEach((t=>{const e=this.getRowItem(t);e&&i.push(e)})),!i.length)return!1;const o=this.removeRowsHandler(i);this.initRowsHandler();const n=this.getRemovedMinIndex(o);return this.flushRowFrom(n),this.onNextUpdated((function(){this.trigger(y.onRowRemoved,i)})),this.render("rows"),!0},getRemovedMinIndex:function(t){let e=0;const i=t[t.length-1];if(this.isInvisible(i))return e;e=i.tg_view_index,e>0&&(e-=1);let o=i.tg_parent;for(;o;)o.collapsed&&(e=o.tg_view_index),o=o.tg_parent;return e},removeRowsHandler:function(t){const e=[].concat(t);e.sort((function(t,e){return e.tg_index-t.tg_index}));const i=[];return e.forEach((t=>{this.getRowParentSubs(t).splice(t.tg_sub_index,1),i.push(t)})),i}},Tt={renderCollapseAllState:function(){this.hasTreeColumn&&(this.asyncRenderCollapseAllState||(this.asyncRenderCollapseAllState=d.microtask(this.renderCollapseAllStateSync)),this.asyncRenderCollapseAllState.apply(this,arguments))},renderCollapseAllStateSync:function(){const t=this.$header.find(".tg-tree-header");this.rowsInfo.isTree?t.addClass("tg-tree-header-indent"):t.removeClass("tg-tree-header-indent"),this.renderCollapseAllIcon()},checkCollapseAllState:function(t){if(t!==this.allRowsCollapsed){if(t){let t=0;const e=this.rows.length;for(;t{if(e.tg_group&&e.tg_subs_length&&e.collapsed)return t=!0,!1})),t)return}this.allRowsCollapsed=t,this.renderCollapseAllIcon()}},expandAllRows:function(){return this.renderAllRowsCollapsed(!1)},collapseAllRows:function(){return this.renderAllRowsCollapsed(!0)},toggleAllRows:function(){return this.allRowsCollapsed?this.expandAllRows():this.collapseAllRows()},renderAllRowsCollapsed:function(t){const e=this.updateAllRowsCollapsed(t);return e.length?(this.flushBody(),this.onNextUpdated((()=>{this.renderCollapseAllIcon(),t?this.trigger(y.onRowCollapsed,e):this.trigger(y.onRowExpanded,e)})),this.render("rows"),this):this},updateAllRowsCollapsed:function(t){this.allRowsCollapsed=t;const e=[];return this.forEachRow((i=>{i.subs&&i.tg_subs_length&&this.isCollapsedChanged(i,t)&&(i.collapsed=t,e.push(i))})),e},expandRow:function(t){const e=this.getRowItem(t);return e?this.isEmptyGroup(e)?(this.trigger(y.onRowSubsRequest,e),this):this.isCollapsedChanged(e,!1)?(e.collapsed=!1,this.flushRowFrom(e.tg_view_index),this.renderCollapseIcon(e),this.onNextUpdated((()=>{this.checkCollapseAllState(!1),this.trigger(y.onRowExpanded,e)})),this.render("rows"),this):this:this},collapseRow:function(t){const e=this.getRowItem(t);return e&&e.subs&&e.tg_subs_length&&this.isCollapsedChanged(e,!0)?(e.collapsed=!0,this.flushRowFrom(e.tg_view_index),this.renderCollapseIcon(e),this.onNextUpdated((()=>{this.checkCollapseAllState(!0),this.trigger(y.onRowCollapsed,e)})),this.render("rows"),this):this},toggleRow:function(t){const e=this.getRowItem(t);return e?(e.collapsed?this.expandRow(e):this.collapseRow(e),this):this},expandRowLevel:function(t){t=d.toNum(t,!0);const e=[],i=[];return this.forEachRow((o=>{o.subs&&o.tg_subs_length&&(o.tg_level<=t?this.isCollapsedChanged(o,!1)&&(o.collapsed=!1,i.push(o)):this.isCollapsedChanged(o,!0)&&(o.collapsed=!0,e.push(o)))})),e.length||i.length?(this.flushBody(),this.onNextUpdated((()=>{e.length&&this.trigger(y.onRowCollapsed,e),i.length&&this.trigger(y.onRowExpanded,i)})),this.render("rows"),this):this},renderCollapseAllIcon:function(){if(!this.options.collapseAllVisible||!this.hasTreeColumn)return;const t=this.$header.find(".tg-tree-icon-all");this.renderTreeIcon(t,this.allRowsCollapsed)},renderCollapseIcon:function(t){if(!this.headerCreated)return;const e=this.getRowNodesByIndex(t.tg_view_index);if(!e)return;const i=e.find(".tg-tree-icon");this.renderTreeIcon(i,t.collapsed)},renderTreeIcon:function(t,e){t&&(e?t.removeClass("tg-tree-icon-expanded").addClass("tg-tree-icon-collapsed"):t.removeClass("tg-tree-icon-collapsed").addClass("tg-tree-icon-expanded"))}},Et={rowDragStartHandler:function(t,e){this.removeSortColumn();const i=e.rowItem;if(!i)return;const o=this.getRowNodesByIndex(i.tg_view_index);if(!o)return;e.dragCloneNodes=this.getRowDragCloneNodes(o),e.dropPlaceholder=this.getRowDropPlaceholder(o),e.dragStartTop=this.getRowTop(i),e.dragRowHeight=this.getRowHeight(i),e.dragStartScrollTop=this.scrollTop,e.dragMaxScrollTop=this.scrollPane.getMaxScrollTop();const n={e:t,rowItem:i};this.trigger(y.onRowDragged,n),this.isDefaultPrevented(n)||("touch"===e.type&&d.preventDefault(e.e),this.setRowState(i,"dragging"),this.setTextSelectable(!1),this.rowDropListHandler(e),this.updateDragCloneRowPosition(e))},rowDragMoveHandler:function(t,e){"touch"===e.type&&d.preventDefault(e.e),this.updateDragCloneRowPosition(e),this.updateDragPlaceholderPosition(e),this.rowDragAutoScrollHandler(e)},rowDragEndHandler:function(t,e){"touch"===e.type&&(this.protectedItem=null,d.preventDefault(e.e)),this.autoScrollStop(),this.setRowState(e.rowItem,"dragging",!1),this.setTextSelectable(!0),e.dragCloneNodes&&(e.dragCloneNodes.remove(),e.dragCloneNodes=null),e.dropPlaceholder&&(e.dropPlaceholder.remove(),e.dropPlaceholder=null),e.changed&&this.rowDropHandler(e)},updateDragCloneRowPosition:function(t){const e=this.scrollTop-t.dragStartScrollTop,i=t.dragStartTop+t.offsetY+e,o=i-this.scrollTopOffset;t.dragCloneNodes&&t.dragCloneNodes.css("top",o).show(),t.dragCurrentPosition=i+.5*t.dragRowHeight},getRowDragCloneNodes:function(t){const e=w();return t.each((function(t){const i=w(t),o=i.clone();o.appendTo(i.parent()),e.add(o)})),e.addClass("tg-clone").hide(),e},getRowDropPlaceholder:function(t){const e=w();return t.each((function(t){const i=w(t),o=w("
    ").addClass("tg-row-placeholder").hide(),n=i.parent();n.find(".tg-row-placeholder").remove(),o.appendTo(n),e.add(o)})),e},updateDragPlaceholderPosition:function(t){this.rowDropItemHandler(t);const e=t.dropItem;if(!e)return;let i=t.dropPosition-1;t.dropBottom?e.tg_view_last&&(i=t.dropPosition-2):e.tg_view_index-this.frozenInfo.rows==0&&(i=t.dropPosition);const o=i-this.scrollTopOffset;t.dropPlaceholder&&t.dropPlaceholder.css("top",o).show()},rowDragAutoScrollHandler:function(t){const e=t.dragCurrentPosition,i=this.scrollTop,o=this.bodyHeight-this.frozenRowsHeight,n=i+o,s=this.options.rowHeight,r=Math.min(3*s,.5*o);if(!(rn-r){const i=e-(n-r),o=this.getAutoScrollOffset(i,r);this.autoScrollStart(o,t)}else this.autoScrollStop()},getAutoScrollOffset:function(t,e){return Math.floor(t/e*20)},autoScrollStop:function(){this.autoScrollMotion&&(this.autoScrollMotion.destroy(),this.autoScrollMotion=null)},autoScrollStart:function(t,e){this.autoScrollStop();const i=e.dragMaxScrollTop;this.autoScrollMotion=new B,this.autoScrollMotion.bind(B.EVENT.MOTION_MOVE,(()=>{const o=d.clamp(this.scrollTop+t,0,i);o!==this.scrollTop?(this.setScrollTop(o),this.updateDragCloneRowPosition(e),this.updateDragPlaceholderPosition(e)):this.autoScrollStop()})),this.autoScrollMotion.once(B.EVENT.MOTION_END,(()=>{this.autoScrollStart(t,e)})),this.autoScrollMotion.start({duration:200})},rowDropListHandler:function(t){const e=this.getRowDropList(t);if(!d.isList(e))return;const i=t.rowItem,o=e.filter((t=>{if(t===i)return!1;if(t.tg_frozen)return!1;let e=t.tg_parent;for(;e;){if(e===i)return!1;e=e.tg_parent}return!0}));if(!d.isList(o))return;const n=[];o.forEach((t=>{const e=this.getRowTop(t),i=this.getRowHeight(t);n.push({rowItem:t,position:e}),n.push({rowItem:t,position:e+i-1,dropBottom:!0})})),t.dropList=n},getRowDropList:function(t){const e=this.options.rowDragCrossLevel;return e?"function"==typeof e?e.call(this,t):this.viewRows:this.getRowParentSubs(t.rowItem)},rowDropItemHandler:function(t){const e=t.dropList;if(!e)return;const i=t.dragCurrentPosition;let o=Number.MAX_VALUE;for(let n=0,s=e.length;no)break;o=r,t.dropItem=s.rowItem,t.dropBottom=s.dropBottom,t.dropPosition=s.position}},rowDragDropPositionHandler:function(t,e,i){const o=this.getRowParentSubs(t),n=t.tg_sub_index;let s,r;return this.isDropIntoGroupFirstChild(e,i)?(s=e.subs,r=0):(s=this.getRowParentSubs(e),r=e.tg_sub_index,o===s&&n{this.trigger(y.onRowDropped,n)})),this.render({type:"rows",scrollRow:i})}},It={getMoveFocusRow:function(t,e){let i=t[0];return e>0&&(i=t[t.length-1]),i},getMoveLengthInList:function(t,e){let i=0;return t.forEach((t=>{this.getRowParentSubs(t)===e&&(i+=1)})),i},getMoveInfo:function(t,e,i){const o=this.getRowParentSubs(i);let n=i.tg_sub_index+e;const s=i.tg_parent;if(s&&this.options.rowMoveCrossLevel){const e=0,i=s.tg_subs_length-1;if(ni){const e=n-i;return this.getMoveInfo(t,e,s)}}if(e>0){n-=this.getMoveLengthInList(t,o)-1}return n=d.clamp(n,0,o.length),{list:o,index:n}},moveRowsHandler:function(t,e){(t=this.removeRowsHandler(t)).reverse();const i=this.getMoveFocusRow(t,e),o=this.getMoveInfo(t,e,i),n=[o.index,0].concat(t);return o.list.splice.apply(o.list,n),this.initRowsHandler(),this.onNextUpdated((function(){this.scrollRowIntoView(i),this.trigger(y.onRowMoved,t)})),this.removeSortColumn(),this.update(),!0},moveRows:function(t,e){t=d.toList(t);const i=[];return t.forEach((t=>{const e=this.getRowItem(t);e&&i.push(e)})),!!i.length&&(!(i.length>=this.getRowsLength())&&(0!==(e=d.toNum(e,!0))&&this.moveRowsHandler(i,e)))},moveRowsUp:function(t){return this.moveRows(t,-1)},moveRowsDown:function(t){return this.moveRows(t,1)},moveRowsToTop:function(t){return this.moveRows(t,-this.getRowsLength(!0))},moveRowsToBottom:function(t){return this.moveRows(t,this.getRowsLength(!0))},moveSelectedRowsUp:function(){return this.moveRows(this.getSelectedRows(),-1)},moveSelectedRowsDown:function(){return this.moveRows(this.getSelectedRows(),1)},moveSelectedRowsToTop:function(){return this.moveRows(this.getSelectedRows(),-this.getRowsLength(!0))},moveSelectedRowsToBottom:function(){return this.moveRows(this.getSelectedRows(),this.getRowsLength(!0))}},Lt={getSelectedRow:function(){let t=null;return this.forEachSelectableRow((function(e){if(e.selected)return t=e,!1})),t},getSelectedRows:function(){const t=[];return this.forEachSelectableRow((function(e){e.selected&&t.push(e)})),t.length>1&&t.sort((function(t,e){const i=t.tg_selected_index,o=e.tg_selected_index;return i>o?1:i0&&void 0!==arguments[0])||arguments[0];if(t=Boolean(t),this.globalSelectedIndex=0,t&&!this.options.selectMultiple)return this;const e=this.getAllSelectedChangedList(t);return e.length?(this.updateRowsSelectedState(e),this):this},setRowSelected:function(){return(this.options.selectMultiple?this.setRowMultipleSelected:this.setRowSingleSelected).apply(this,arguments)},setRowSingleSelected:function(t){const e=this.getRowItem(t);if(!e)return this;if(!this.isRowSelectable(e))return this;if(e.selected)return this;const i=[],o=this.getSelectedRow();return o&&o.selected&&i.push(o),e.selected||i.push(e),i.length?(this.updateRowsSelectedState(i),this):this},setRowMultipleSelected:function(t,e){if(0===arguments.length)return this;if(1===arguments.length&&!1===arguments[0])return this.selectAll(!1);const i=this.toRowItemList(t,(t=>this.isRowSelectable(t)));return i.length?!1===e?(this.setRowListUnselected(i),this):d.hasShiftKey(e)&&1===i.length?(this.setRowBetweenListSelected(i[0]),this):(this.updateRowsSelectedState(i),this):this},setRowListUnselected:function(t){const e=this.getSelectedChangedList(t,!1);e.length&&this.updateRowsSelectedState(e)},setRowBetweenListSelected:function(t){const e=this.previousSelectedRow;if(e&&e!==t){const i=this.getBetweenSelectedChangedList(e,t);if(!i.length)return;this.updateRowsSelectedState(i,!0)}else this.updateRowsSelectedState([t])},getAllSelectedChangedList:function(t){const e=[];return this.forEachSelectableRow((i=>{this.isSelectedChanged(i,t)&&e.push(i)})),e},getSelectedChangedList:function(t,e){const i=[];return t.forEach((t=>{this.isSelectedChanged(t,e)&&i.push(t)})),i},getBetweenSelectedChangedList:function(t,e){const i=t.tg_index,o=e.tg_index,n=[];if(i=o;)n.push(t),t--}return this.toRowItemList(n,(t=>this.isRowSelectable(t)&&!t.selected))},updateRowsSelectedState:function(t,e){let i;t.forEach((t=>{const e=!t.selected;t.selected=e,e&&(t.tg_selected_index=this.globalSelectedIndex++,i=t),this.renderRowSelectedState(t)})),e||(this.previousSelectedRow=i),this.renderSelectAllState(),this.onNextUpdated((()=>{this.trigger(y.onSelectChanged,t)})),this.render()},renderRowSelectedState:function(t){const e=t.tg_view_index;this.viewport.rows.includes(e)&&(this.renderRowState(t,"selected"),this.flushCell(e,this.selectColumn.tg_view_index))},renderSelectAllState:function(){this.isSelectAllVisible()&&(this.asyncRenderSelectAllState||(this.asyncRenderSelectAllState=d.microtask(this.renderSelectAllStateSync)),this.asyncRenderSelectAllState.apply(this,arguments))},renderSelectAllStateSync:function(){const t=this.getSelectAllState();if(t===this.previousSelectAllState)return;this.previousSelectAllState=t;const e=this.selectColumn,i=w(this.getColumnHeaderNode(e)).find(".tg-select-icon-all");i.length&&(i.removeClass("tg-selected tg-mixed"),t&&i.addClass(`tg-${t}`))},getSelectAllState:function(){let t=0;this.forEachSelectableRow((e=>{t+=1}));const e=this.getSelectedRows().length;let i="mixed";return 0===e?(i="",this.previousSelectedRow=null):e===t&&(i="selected"),i},isSelectAllVisible:function(){const t=this.options;return!!(t.selectVisible&&t.selectAllVisible&&t.selectMultiple)}},xt={setRowHover:function(t,e){const i=this.getRowItem(t);return i?(this.renderRowHover(i,e),this):this},renderRowHover:function(t,e){if(this.previousHover&&(this.previousHover.removeClass("tg-hover"),this.previousHover=null),!e)return this;if(!1===this.rowHoverable)return;if(!1===t.hoverable)return this;if(t.tg_frozen&&!this.options.frozenRowHoverable)return this;const i=t.tg_view_index;return this.previousHover=this.$body.find(`.tg-row[row='${i}']`).addClass("tg-hover"),this},setRowState:function(t,e){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];const o=this.getRowItem(t);return o?(o.tg_state_names||(o.tg_state_names=new Set),o.tg_state_names.add(e),o[e]=i,this.renderRowState(o,e),this):this},renderRowState:function(t,e){const i=this.getRowNodesByIndex(t.tg_view_index);if(i){const o=`tg-${e}`;t[e]?i.addClass(o):i.removeClass(o)}}},zt={getRowItem:function(t){return d.isNum(t)?(t<0&&(t=this.rowsInfo.length+t),this.rowsInfo.indexCache[t]):t?d.isNum(t.tg_index)?t:this.getRowItemById(t.id||t):void 0},getRowItemById:function(t){return this.getRowItemBy("id",t)},getRowItemBy:function(t,e){if(void 0!==e)return this.rowsInfo.indexCache.find((i=>i[t]===e))},getRowsLength:function(t){return t?this.rowsInfo.length:this.viewRows.length},getViewRowItem:function(t){return this.viewRows[t]},getPreRenderRowInfo:function(t){const e={rows:[],rowNotFound:!1,benchmark:0};if(!t.length)return 0===this.getRowsLength()&&(e.rowNotFound=!0),e;const i=[],o=this.frozenInfo.row;return t.forEach((t=>{this.getRowCache(t)?t>o&&i.push(t):e.rows.push(t)})),i.length&&(e.benchmark=Math.min.apply(Math,i)),e.rows.sort((function(t,i){return t{this.renderRowNodes(t,e.benchmark)}))},createRowNode:function(t,e,i,o,n,s){const r=document.createElement("div");return r.setAttribute("row",t),r.className=i,o&&(r.style.cssText=o),r.style.top=`${n}px`,s!==this.options.rowHeight&&(r.style.height=`${s}px`,r.style.lineHeight=`${s}px`),this.setNodeDataCache(r,{row:t,rowItem:e,rowNode:r}),r},appendRowNode:function(t,e,i,o){ithis.frozenInfo.row))return e;e-=this.frozenRowsHeight}return e-=this.scrollTopOffset,e},getRowTop:function(t){let e=t.tg_top;return t.tg_frozen||(e-=this.frozenRowsHeight),e},getRowVPos:function(t){const e=this.frozenInfo.bottom,i=this.frozenInfo.row;let o="top";return this.frozenInfo.rows&&(t<=i?e&&(o="bottom"):e||(o="bottom")),o},getRowCanvas:function(t,e){return"top"===t?"left"===e?this.$bodyTL:this.$bodyTR:"left"===e?this.$bodyBL:this.$bodyBR}},Mt={CHANGE:"change",START:"start",END:"end"},Pt={h:{type:"h",className:"tg-scrollbar-h",offset:"left",size:"width",page:"pageX",axis:"x",offsetName:"offsetX"},v:{type:"v",className:"tg-scrollbar-v",offset:"top",size:"height",page:"pageY",axis:"y",offsetName:"offsetY"}};class Nt extends k{static EVENT=Mt;static H="h";static V="v";type="h";settings={};size=0;viewSize=0;bodySize=0;trackSize=0;position=0;scale=0;thumbPosition=0;thumbScale=0;constructor(t,e){super(),this.settings=Pt[t]||Pt.h,this.type=this.settings.type,this.id=d.uid(4,`tg-scrollbar-${this.type}-`),this.$holder=w(e),this.$holder.find(`.${this.settings.className}`).remove(),this.options=this.generateOptions()}generateOptions(t){return d.merge({size:15,round:!1,blank:!1,motionDuration:200},t)}updateOptions(t){this.options=this.generateOptions(t);let e=this.options.size;d.isNum(e)||(e=d.toNum(e)),e=Math.round(e),e=Math.max(e,0),e=Math.min(e,30),this.size=e}create(){this.$container=w('
    ').appendTo(this.$holder),this.$container.attr("id",this.id),this.$container.addClass(d.classMap(["tg-scrollbar",this.settings.className,{"tg-scrollbar-round":this.options.round}])),this.$track=this.$container.find(".tg-scrollbar-track"),this.$thumb=this.$container.find(".tg-scrollbar-thumb"),this.thumbDrag=new O,this.thumbDrag.bind(O.EVENT.DRAG_START,((t,e)=>{this.thumbDragStart(e)})).bind(O.EVENT.DRAG_MOVE,((t,e)=>{this.thumbDragMove(e)})).bind(O.EVENT.DRAG_END,((t,e)=>{this.thumbDragEnd(e)}));const t=this.$container.get(0);return this.scrollEvents={mousedown:{handler:e=>{e.target.classList.contains("tg-scrollbar-thumb")?this.thumbMouseDownHandler(e):(this.trackEvents={mouseup:{handler:t=>{this.trackMouseupHandler(t)},options:{once:!0}}},d.bindEvents(this.trackEvents,t),this.trackMousedownHandler(e))},options:!0},selectstart:{handler:t=>{d.preventDefault(t)},options:!0}},d.bindEvents(this.scrollEvents,t),this}getBlank(){return this.options.blank}getSize(){return this.size}getViewSize(){return this.viewSize}getBodySize(){return this.bodySize}getTrackMouseDirection(){let t=1;return this.trackMousePosition0){const i=this.getMaxThumbPosition();t=Math.round(i*this.position/e),t=d.clamp(t,0,i)}return this.setThumbPosition(t),this}trackMousedownHandler(t){return this.motionStop(),this.trackMousePosition=this.getTrackMousePos(t),this.motionStart(),this}trackMouseupHandler(t){return d.unbindEvents(this.trackEvents),this.motionStop(),this.motionStarted||(this.trackMousePosition=this.getTrackMousePos(t),this.trackScrollHandler(),this.triggerEvent()),this}trackScrollHandler(){const t=Math.max(0,this.viewSize-20)*this.getTrackMouseDirection();return this.setOffset(t),this}motionStop(){return this.motion&&(this.motion.destroy(),this.motion=null),this}motionStart(){const t=this.position,e=Math.round(this.trackMousePosition/this.viewSize*this.getMaxPosition());return this.motionStarted=!1,this.motion=new B,this.motion.bind(B.EVENT.MOTION_START,((t,e)=>{this.motionStarted=!0})),this.motion.bind(B.EVENT.MOTION_MOVE,((t,e)=>{this.motionUpdateHandler(t,e)})),this.motion.start({duration:this.options.motionDuration,from:t,till:e}),this}motionUpdateHandler(t,e){e!==this.position&&(this.setPosition(e),this.triggerEvent())}thumbMouseDownHandler(t){this.$thumb.addClass("tg-scrollbar-thumb-hold"),this.thumbDrag.start(t,{target:this.$thumb})}thumbDragStart(t){this.motionStop(),t.thumbPositionStart=this.thumbPosition,this.trigger(Mt.START)}thumbDragMove(t){let e=t.thumbPositionStart+t[this.settings.offsetName];const i=this.getMaxThumbPosition();e=d.clamp(e,0,i),this.setThumbPosition(e);let o=0;i>0&&(o=d.per(e/i)*this.getMaxPosition(),o=Math.round(o)),this.position=o,this.triggerEvent()}thumbDragEnd(t){this.$thumb&&this.$thumb.removeClass("tg-scrollbar-thumb-hold"),this.trigger(Mt.END)}triggerEvent(){this.trigger(Mt.CHANGE,this.position)}getPosition(){return this.position}setPosition(t){t=d.toNum(t,!0);const e=this.getMaxPosition();t=d.clamp(t,0,e),this.position=t,this.updateThumbPosition()}getMaxPosition(){return this.bodySize-this.viewSize}updatePosition(){const t=this.getMaxPosition(),e=d.clamp(this.position,0,t);this.position=e}setOffset(t){t=d.toNum(t);const e=this.position+t;return this.setPosition(e),this}getScale(){return this.scale}setScale(t){return t=d.per(t),this.scale=t,this.scaleChangeHandler(),this}scaleChangeHandler(){let t=Math.round(this.viewSize*this.scale);if(t=Math.max(t,Math.round(1.5*this.options.size)),t=Math.min(t,this.viewSize-1),this.thumbSize=t,this.$thumb){const t={};"h"===this.type?(t.height=this.size,t.width=this.thumbSize):(t.width=this.size,t.height=this.thumbSize),this.$thumb.css(t)}}updateTrackSize(){const t={};return"h"===this.type?(t.width=this.trackSize,t.height=this.size):(t.height=this.trackSize,t.width=this.size),this.$container.css(t),this}updateThumbSize(){let t=0;return this.bodySize&&(t=this.trackSize/this.bodySize),this.setScale(t),this}parseSize(t){return t=d.toNum(t),t=Math.round(t),t=Math.max(t,0)}updateSize(t,e,i){t=this.parseSize(t),this.viewSize=t,e=this.parseSize(e),this.bodySize=e,i=d.isNum(i)?this.parseSize(i):t,this.trackSize=i,this.previousFadeIn=null}fade(t){return!(!this.$container||!this.size)&&(this.previousFadeIn!==t&&(this.previousFadeIn=t,t?this.$container.hasClass("tg-fade-out")&&this.$container.removeClass("tg-fade-out").addClass("tg-fade-in"):this.$container.removeClass("tg-fade-in").addClass("tg-fade-out"),!0))}show(){if(this.updatePosition(),!this.getBlank())return!this.$container&&this.size>0&&this.create(),this.$container?(this.updateTrackSize(),this.updateThumbSize(),this):this;this.remove()}hide(){return this.updatePosition(),this.remove(),this}remove(){if(this.motionStop(),d.unbindEvents(this.scrollEvents),d.unbindEvents(this.trackEvents),this.thumbDrag&&(this.thumbDrag.destroy(),this.thumbDrag=null),!this.$container)return this;this.$thumb=null,this.$track=null,this.$container.remove(),this.$container=null}destroy(){return this.remove(),this}}const _t={CHANGE:"change",START:"start",END:"end"};class kt extends k{static EVENT=_t;visible=!0;constructor(t,e){super(),this.id=d.uid(4,`tg-scroll-pane-${e}-`),this.gradientInfo=[],this.$container=w(t).attr("id",this.id),this.$container.addClass("tg-scroll-pane"),this.$scrollView=this.$container.find(".tg-scroll-view"),this.$scrollBody=this.$scrollView.find(".tg-scroll-body"),this.scrollbarH=new Nt(Nt.H,this.$container),this.scrollbarH.bind(Nt.EVENT.CHANGE,((t,e)=>{this.scrollHChangeHandler()})).bind(Nt.EVENT.START,(t=>{this.scrollStartEndHandler(!0)})).bind(Nt.EVENT.END,(t=>{this.scrollStartEndHandler()})),this.scrollbarV=new Nt(Nt.V,this.$container),this.scrollbarV.bind(Nt.EVENT.CHANGE,((t,e)=>{this.scrollVChangeHandler()})).bind(Nt.EVENT.START,(t=>{this.scrollStartEndHandler(!0)})).bind(Nt.EVENT.END,(t=>{this.scrollStartEndHandler()})),this.options=this.generateOptions()}generateOptions(t){return d.merge({scrollbarH:{},scrollbarV:{},scrollbarFade:!1,scrollSizeOnKeyPress:20,gradient:30},t)}scrollStartEndHandler(t){t?this.trigger(_t.START):this.trigger(_t.END)}show(){return this.$container.show(),this.visible=!0,this}hide(){return this.$container.hide(),this.visible=!1,this}width(){return this.scrollPaneW}height(){return this.scrollPaneH}render(t){return this.visible?(this.options=this.generateOptions(t),this.update(),this):this}update(){this.scrollPaneW=this.options.scrollPaneW,this.scrollPaneH=this.options.scrollPaneH,this.scrollBodyW=this.options.scrollBodyW,this.scrollBodyH=this.options.scrollBodyH,this.updateScrollbar()}setGroupH(t){this.groupH=d.toList(t)}setGroupV(t){this.groupV=d.toList(t)}updateGroupH(){if(!d.isList(this.groupH))return this;const t=this.scrollbarH.getPosition();return this.groupH.forEach((function(e){e&&e.updateScrollHFromGroup(t)})),this}updateGroupV(){if(!d.isList(this.groupV))return this;const t=this.scrollbarV.getPosition();return this.groupV.forEach((function(e){e&&e.updateScrollVFromGroup(t)})),this}updateGroupList(){this.updateGroupH(),this.updateGroupV()}updateScrollHFromGroup(t){this.scrollbarH.getPosition()!==t&&(this.scrollbarH.setPosition(t),this.updateScrollLeft(),this.triggerEvent())}updateScrollVFromGroup(t){this.scrollbarV.getPosition()!==t&&(this.scrollbarV.setPosition(t),this.updateScrollTop(),this.triggerEvent())}setPosition(t,e){return this.scrollbarH.setPosition(t),this.scrollbarV.setPosition(e),this.updateScrollLeft(),this.updateScrollTop(),this.updateGroupList(),this}updateScrollbar(){this.scrollbarH.updateOptions(this.options.scrollbarH),this.scrollbarV.updateOptions(this.options.scrollbarV),this.updateScrollState(),this.updateScrollView(),this.updateScrollTrack(),this.scrollbarH.updateSize(this.scrollViewW,this.scrollBodyW,this.scrollTrackW),this.scrollbarV.updateSize(this.scrollViewH,this.scrollBodyH,this.scrollTrackH),this.hasScrollH?(this.scrollbarH.show(),this.scrollbarH.setPosition(this.scrollbarH.getPosition())):this.scrollbarH.hide(),this.hasScrollV?(this.scrollbarV.show(),this.scrollbarV.setPosition(this.scrollbarV.getPosition())):this.scrollbarV.hide(),this.updateScrollLeft(),this.updateScrollTop(),this.updateGroupList()}updateScrollState(){const t=this.scrollbarH.getSize(),e=this.scrollbarV.getSize(),i=this.scrollbarH.getBlank(),o=this.scrollbarV.getBlank(),n=this.options.scrollbarFade;let s=!1,r=0;(function(){(this.scrollPaneWe&&t.push("left"),ie&&t.push("top"),o{const i=`tg-gradient-${e}`;t.includes(e)?this.$container.addClass(i):this.$container.removeClass(i)})))}getScrollLeft(){return this.scrollbarH.getPosition()}getScrollTop(){return this.scrollbarV.getPosition()}getMaxScrollLeft(){return this.scrollbarH.getMaxPosition()}getMaxScrollTop(){return this.scrollbarV.getMaxPosition()}getScrollTopOffset(){const t=this.getScrollTop();return t-t%1e4}triggerEvent(){this.trigger(_t.CHANGE,{scrollLeft:this.getScrollLeft(),scrollTop:this.getScrollTop()})}scrollHChangeHandler(){this.updateScrollLeft(),this.updateGroupList(),this.triggerEvent()}scrollVChangeHandler(){this.updateScrollTop(),this.updateGroupList(),this.triggerEvent()}setOffsetH(t){const e=this.getScrollLeft();this.scrollbarH.setOffset(t);return this.getScrollLeft()!==e&&(this.updateScrollLeft(),this.updateGroupList(),this.triggerEvent(),!0)}setOffsetV(t){const e=this.getScrollTop();this.scrollbarV.setOffset(t);return this.getScrollTop()!==e&&(this.updateScrollTop(),this.updateGroupList(),this.triggerEvent(),!0)}mouseWheelHandler(t){const e=t.deltaX,i=t.deltaY,o=Math.abs(e);if(o>Math.abs(i)){if(this.hasScrollH)return this.setOffsetH(e)}else{if(this.hasScrollV)return this.setOffsetV(i);if(this.hasScrollH&&!o)return this.setOffsetH(i)}return!1}keyPageUpHandler(t){return this.setOffsetV(-this.scrollViewH)}keyPageDownHandler(t){return this.setOffsetV(this.scrollViewH)}keyEndHandler(t){return this.setOffsetV(this.scrollBodyH)}keyHomeHandler(t){return this.setOffsetV(-this.scrollBodyH)}keyLeftHandler(t){return this.setOffsetH(-this.options.scrollSizeOnKeyPress)}keyUpHandler(t){return this.setOffsetV(-this.options.scrollSizeOnKeyPress)}keyRightHandler(t){return this.setOffsetH(this.options.scrollSizeOnKeyPress)}keyDownHandler(t){return this.setOffsetV(this.options.scrollSizeOnKeyPress)}destroy(){return this.visible=!1,this.groupH=null,this.groupV=null,this.scrollbarV&&(this.scrollbarV.destroy(),this.scrollbarV=null),this.scrollbarH&&(this.scrollbarH.destroy(),this.scrollbarH=null),this.$container=null,this.$scrollView=null,this.$scrollBody=null,this}}const Vt={initScrollPane:function(){this.initFrozenStyle(),this.createScrollPane()},initFrozenStyle:function(){const t={HL:{container:this.$paneHL,cls:[]},HR:{container:this.$paneHR,cls:[]},TL:{container:this.$paneTL,cls:[]},TR:{container:this.$paneTR,cls:[]},BL:{container:this.$paneBL,cls:[]},BR:{container:this.$paneBR,cls:[]}},e="tg-frozen-h";this.frozenInfo.rows&&(this.frozenInfo.bottom?(t.BL.cls.push(e),t.BR.cls.push(e)):(t.TL.cls.push(e),t.TR.cls.push(e)));const i="tg-frozen-v",o="tg-frozen-line-v";this.frozenInfo.columns&&(this.frozenInfo.right?(t.HR.cls.push(i),t.TR.cls.push(i),t.BR.cls.push(i)):(t.HL.cls.push(i),t.TL.cls.push(i),t.BL.cls.push(i)),t.HL.cls.push(o),t.TL.cls.push(o),t.BL.cls.push(o));const n="tg-frozen",s=[n,e,i,o].join(" ");Object.keys(t).forEach((function(e){const i=t[e],o=i.container;o.removeClass(s);const r=i.cls;if(!r.length)return;const l=[n].concat(r).join(" ");o.addClass(l)}))},createScrollPane:function(){this.removeScrollPane(),this.scrollPaneMap={HL:new kt(this.$paneHL,"header-left"),HR:new kt(this.$paneHR,"header-right"),TL:new kt(this.$paneTL,"top-left"),TR:new kt(this.$paneTR,"top-right"),BL:new kt(this.$paneBL,"bottom-left"),BR:new kt(this.$paneBR,"bottom-right")},this.scrollPaneMap.BR.setGroupH([this.scrollPaneMap.HR,this.scrollPaneMap.TR]),this.scrollPaneMap.TR.setGroupH([this.scrollPaneMap.HR,this.scrollPaneMap.BR]),this.scrollPaneMap.BL.setGroupH([this.scrollPaneMap.HL,this.scrollPaneMap.TL]),this.scrollPaneMap.TL.setGroupH([this.scrollPaneMap.HL,this.scrollPaneMap.BL]),this.scrollPaneMap.BR.setGroupV(this.scrollPaneMap.BL),this.scrollPaneMap.BL.setGroupV(this.scrollPaneMap.BR),this.scrollPaneMap.TR.setGroupV(this.scrollPaneMap.TL),this.scrollPaneMap.TL.setGroupV(this.scrollPaneMap.TR),this.initActiveScrollPane(),this.initPaneVisibility()},initActiveScrollPane:function(){const t=this.getScrollPaneVP(),e=this.getScrollPaneHP(),i=`${t}${e}`;this.scrollPane=this.scrollPaneMap[i],this.scrollPane.bind(kt.EVENT.CHANGE,((t,e)=>{this.scrollPaneChangeHandler(t,e)})).bind(kt.EVENT.START,(t=>{this.rowHoverable=!1})).bind(kt.EVENT.END,(t=>{this.rowHoverable=!0}));let o={L:"L",R:"L"};this.frozenInfo.columns&&this.frozenInfo.right&&(o={L:"R",R:"L"});const n=`${t}${o[e]}`;this.scrollPaneFrozen=this.scrollPaneMap[n]},getScrollPaneVP:function(){return this.frozenInfo.rows&&!this.frozenInfo.bottom?"B":"T"},getScrollPaneHP:function(){return this.frozenInfo.columns&&!this.frozenInfo.right?"R":"L"},initPaneVisibility:function(){this.scrollPaneMap.HL.show(),this.scrollPaneMap.TL.show(),this.frozenInfo.columns?(this.scrollPaneMap.HR.show(),this.scrollPaneMap.TR.show(),this.frozenInfo.rows?(this.scrollPaneMap.BL.show(),this.scrollPaneMap.BR.show()):(this.scrollPaneMap.BL.hide(),this.scrollPaneMap.BR.hide())):(this.scrollPaneMap.HR.hide(),this.scrollPaneMap.TR.hide(),this.scrollPaneMap.BR.hide(),this.frozenInfo.rows?this.scrollPaneMap.BL.show():this.scrollPaneMap.BL.hide())},scrollPaneChangeHandler:function(t,e){this.hideColumnLine(),this.scrollLeft=e.scrollLeft,this.scrollTop=e.scrollTop,this.scrollRenderHandler()},scrollbarFadeInOutHandler:function(t,e){this.options.scrollbarFade&&(e?this.updateScrollPaneFade(!0):this.options.scrollbarFadeTimeout||this.updateScrollPaneFade(!1))},updateScrollPaneFade:function(t){if(!this.options.scrollbarFade)return;this.updateScrollPaneFadeSync(t);const e=this.options.scrollbarFadeTimeout;e&&(clearTimeout(this.timeout_fade),this.timeout_fade=setTimeout((()=>{this.updateScrollPaneFadeSync(!1)}),e))},updateScrollPaneFadeSync:function(t){if(this.previousScrollbarFadeIn===t)return;this.previousScrollbarFadeIn=t;const e=[];Object.keys(this.scrollPaneMap).forEach((t=>{const i=this.scrollPaneMap[t];i.hasScrollbar()&&e.push(i)})),e.length&&e.forEach((function(e){e.fade(t)}))},updateScrollPane:function(){const t=this.getScrollbarOptions();this.scrollPaneMap.HL.render(this.getScrollPaneOptions({scrollPaneW:this.paneWidthL,scrollPaneH:this.headerHeight,scrollBodyW:this.bodyWidthL,scrollBodyH:this.headerHeight,scrollbarV:t.HLV,scrollbarH:t.HLH})),this.scrollPaneMap.HR.render(this.getScrollPaneOptions({scrollPaneW:this.paneWidthR,scrollPaneH:this.headerHeight,scrollBodyW:this.bodyWidthR,scrollBodyH:this.headerHeight,scrollbarV:t.HRV,scrollbarH:t.HRH})),this.scrollPaneMap.TL.render(this.getScrollPaneOptions({scrollPaneW:this.paneWidthL,scrollPaneH:this.paneHeightT,scrollBodyW:this.bodyWidthL,scrollBodyH:this.bodyHeightT,scrollbarV:t.TLV,scrollbarH:t.TLH})),this.scrollPaneMap.TR.render(this.getScrollPaneOptions({scrollPaneW:this.paneWidthR,scrollPaneH:this.paneHeightT,scrollBodyW:this.bodyWidthR,scrollBodyH:this.bodyHeightT,scrollbarV:t.TRV,scrollbarH:t.TRH})),this.scrollPaneMap.BL.render(this.getScrollPaneOptions({scrollPaneW:this.paneWidthL,scrollPaneH:this.paneHeightB,scrollBodyW:this.bodyWidthL,scrollBodyH:this.bodyHeightB,scrollbarV:t.BLV,scrollbarH:t.BLH})),this.scrollPaneMap.BR.render(this.getScrollPaneOptions({scrollPaneW:this.paneWidthR,scrollPaneH:this.paneHeightB,scrollBodyW:this.bodyWidthR,scrollBodyH:this.bodyHeightB,scrollbarV:t.BRV,scrollbarH:t.BRH})),this.scrollLeft=this.getScrollLeft(),this.scrollTop=this.getScrollTop(),this.updateScrollPaneFade(Boolean(this.options.scrollbarFadeTimeout))},getScrollPaneOptions:function(t){const e=this.options;return t.scrollbarFade=e.scrollbarFade,t.gradient=d.clamp(d.toNum(e.scrollPaneGradient,!0),0,100),t},getScrollbarOptions:function(){const t=this.options.scrollbarRound,e={};return["HLH","HLV","HRH","HRV","TLH","TLV","TRH","TRV","BLH","BLV","BRH","BRV"].forEach((function(i){e[i]={size:0,round:t,blank:!1}})),this.scrollbarOptionsHandler(e),this.scrollbarFadeHandler(e),e},scrollbarOptionsHandler:function(t){const e=this.scrollbarSizeH,i=this.scrollbarSizeV;this.scrollbarHeaderHandler(t,e,i),this.frozenInfo.columns?this.frozenInfo.rows?this.scrollbarC1R1Handler(t,e,i):this.scrollbarC1R0Handler(t,e,i):this.frozenInfo.rows?this.scrollbarC0R1Handler(t,e,i):this.scrollbarC0R0Handler(t,e,i)},scrollbarFadeHandler:function(t){if(this.options.scrollbarFade)for(const e in t)if(d.hasOwn(t,e)){const i=t[e];i.size>0&&i.blank&&(i.blank=!1,i.size=0)}},scrollbarHeaderHandler:function(t,e,i){this.hasVScroll&&(this.frozenInfo.columns?(t.HRV.size=i,t.HRV.blank=1):(t.HLV.size=i,t.HLV.blank=1))},scrollbarC0R0Handler:function(t,e,i){t.TLH.size=e,t.TLV.size=i},scrollbarC0R1Handler:function(t,e,i){this.frozenInfo.bottom?this.scrollbarC0R1B1Handler(t,e,i):this.scrollbarC0R1B0Handler(t,e,i)},scrollbarC0R1B1Handler:function(t,e,i){t.BLH.size=e,t.TLV.size=i,this.hasVScroll&&(t.BLV.size=i,t.BLV.blank=1)},scrollbarC0R1B0Handler:function(t,e,i){t.BLH.size=e,t.BLV.size=i,this.hasVScroll&&(t.TLV.size=i,t.TLV.blank=1)},scrollbarC1R0Handler:function(t,e,i){this.frozenInfo.right?this.scrollbarC1R0R1Handler(t,e,i):this.scrollbarC1R0R0Handler(t,e,i)},scrollbarC1R0R1Handler:function(t,e,i){this.hasHScroll&&(t.TLH.size=e,this.scrollPaneHidden?(t.TRH.size=e,t.TLH.blank=!0):(t.TRH.size=e,t.TRH.blank=!0)),t.TRV.size=i},scrollbarC1R0R0Handler:function(t,e,i){this.hasHScroll&&(t.TRH.size=e,this.scrollPaneHidden?(t.TLH.size=e,t.TRH.blank=!0):(t.TLH.size=e,t.TLH.blank=!0)),t.TRV.size=i},scrollbarC1R1Handler:function(t,e,i){this.frozenInfo.right?this.frozenInfo.bottom?this.scrollbarC1R1R1B1Handler(t,e,i):this.scrollbarC1R1R1B0Handler(t,e,i):this.frozenInfo.bottom?this.scrollbarC1R1R0B1Handler(t,e,i):this.scrollbarC1R1R0B0Handler(t,e,i)},scrollbarC1R1R1B1Handler:function(t,e,i){this.hasHScroll&&(t.BLH.size=e,this.scrollPaneHidden&&(t.BRH.size=e,t.BLH.blank=!0)),t.TRV.size=i,this.hasVScroll&&(t.BRV.size=i,t.BRV.blank=1)},scrollbarC1R1R1B0Handler:function(t,e,i){this.hasHScroll&&(t.BLH.size=e,this.scrollPaneHidden?(t.BRH.size=e,t.BLH.blank=!0):(t.BRH.size=e,t.BRH.blank=!0)),t.BRV.size=i,this.hasVScroll&&(t.TRV.size=i,t.TRV.blank=1)},scrollbarC1R1R0B1Handler:function(t,e,i){this.hasHScroll&&(t.BRH.size=e,this.scrollPaneHidden&&(t.BLH.size=e,t.BRH.blank=!0)),t.TRV.size=i,this.hasVScroll&&(t.BRV.size=i,t.BRV.blank=1)},scrollbarC1R1R0B0Handler:function(t,e,i){this.hasHScroll&&(t.BRH.size=e,this.scrollPaneHidden?(t.BLH.size=e,t.BRH.blank=!0):(t.BLH.size=e,t.BLH.blank=!0)),t.BRV.size=i,this.hasVScroll&&(t.TRV.size=i,t.TRV.blank=1)},removeScrollPane:function(){clearTimeout(this.timeout_fade),this.previousScrollbarFadeIn=null,this.scrollPaneMap&&(Object.keys(this.scrollPaneMap).forEach((t=>{const e=this.scrollPaneMap[t];e&&e.destroy()})),this.scrollPaneMap=null,this.scrollPane=null,this.scrollPaneFrozen=null)}},Ot={updateScrollState:function(){this.updateGlobalScrollInfo(),this.updateHScrollState(),this.updateVScrollState(),this.updateBlankColumnWidth(),this.scrollStateChanged=!1,this.previousHasHScroll===this.hasHScroll&&this.previousHasVScroll===this.hasVScroll||(this.scrollStateChanged=!0,this.previousHasHScroll=this.hasHScroll,this.previousHasVScroll=this.hasVScroll,this.trigger(y.onScrollStateChanged,{hasHScroll:this.hasHScroll,hasVScroll:this.hasVScroll}))},updateGlobalScrollInfo:function(){this.totalRowsLength=this.getRowsLength(),this.totalRowsHeight=this.getRowsHeight(),this.frozenRowsHeight=this.getFrozenRowsHeight(),this.scrollRowsHeight=this.totalRowsHeight-this.frozenRowsHeight,this.totalRowsHeight=Math.max(this.totalRowsHeight,1),this.scrollRowsHeight=Math.max(this.scrollRowsHeight,1),this.flushRowFrom(this.totalRowsLength)},updateHScrollState:function(){if(this.hasHScroll=!0,this.updateScrollPaneHiddenState(),this.updateHScrollByScrollPaneHidden(),this.scrollPaneHidden)return;this.containerWidth-this.columnsWidth>=0&&(this.hasHScroll=!1)},getScrollPaneCurrentWidth:function(){return this.frozenInfo.right?this.bodyWidth-this.columnsWidthR:this.bodyWidth-this.columnsWidthL},updateHScrollByScrollPaneHidden:function(){if(this.scrollPaneHidden){this.hasHScroll=!1;this.getScrollPaneCurrentWidth()=this.totalRowsHeight&&(this.hasVScroll=!1)}},updateBlankColumnWidth:function(){let t=this.containerWidth-this.columnsWidth;!this.hasVScroll||this.hasHScroll||this.options.scrollbarFade||(t-=this.scrollbarSizeV),this.scrollPaneHidden&&(t=0),this.hasHScroll||(t>=0?(this.frozenInfo.columns?this.columnsWidthR+=t:this.columnsWidthL+=t,this.blankColumn.tg_width=t):this.hasHScroll=!0)}},$t={scrollToRow:function(t){const e=this.getRowItem(t);return this.scrollToItem(e,null),this},scrollToColumn:function(t){const e=this.getColumnItem(t);return this.scrollToItem(null,e),this},scrollToCell:function(t,e){const i=this.getRowItem(t),o=this.getColumnItem(e);return this.scrollToItem(i,o),this},scrollToFirstRow:function(){return this.setScrollTop(0),this},scrollToLastRow:function(){const t=this.getViewRows(),e=t[t.length-1],i=this.getScrollRowPosition(e);if(d.isNum(i))return this.setScrollTop(i),this},scrollToFirstColumn:function(){return this.setScrollLeft(0),this},scrollToLastColumn:function(t){const e=this.getViewColumns();let i=e[e.length-2];t&&(i=e[e.length-1]);const o=this.getScrollColumnPosition(i);if(d.isNum(o))return this.setScrollLeft(o),this},scrollRowIntoView:function(t){const e=this.getRowItem(t);return this.scrollItemIntoView(e,null),this},scrollColumnIntoView:function(t){const e=this.getColumnItem(t);return this.scrollItemIntoView(null,e),this},scrollCellIntoView:function(t,e){const i=this.getRowItem(t),o=this.getColumnItem(e);return this.scrollItemIntoView(i,o),this},setScroll:function(t,e){return t===this.scrollLeft&&e===this.scrollTop||(this.scrollLeft=t,this.scrollTop=e,this.scrollHandler()),this},setScrollLeft:function(t){return t===this.scrollLeft||(this.scrollLeft=t,this.scrollHandler()),this},setScrollTop:function(t){return t===this.scrollTop||(this.scrollTop=t,this.scrollHandler()),this},getScrollRowPosition:function(t){if(!t)return;let e=t.tg_view_index;return e-=this.frozenInfo.rows,e>=0?this.getRowTop(t):void 0},getScrollColumnPosition:function(t){if(!t)return;let e=t.tg_left;return this.frozenInfo.columns&&(e-=this.bodyWidthL),e>=0?e:void 0},scrollToItem:function(t,e){return this.scrollToChanged=!1,this.scrollToRowHandler(t),this.scrollToColumnHandler(e),this.scrollToChanged?(this.scrollHandler(),this):this},scrollToRowHandler:function(t){if(!t)return;const e=this.getScrollRowPosition(t);d.isNum(e)&&e!==this.scrollTop&&(this.scrollTop=e,this.scrollToChanged=!0)},scrollToColumnHandler:function(t){if(!t)return;const e=this.getScrollColumnPosition(t);d.isNum(e)&&e!==this.scrollLeft&&(this.scrollLeft=e,this.scrollToChanged=!0)},scrollItemIntoView:function(t,e){return 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t=e-(o-i);this.scrollLeft=t,this.scrollIntoViewChanged=!0}},scrollOnInit:function(){const{scrollLeft:t,scrollTop:e,scrollColumn:i,scrollRow:o}=this.renderSettings;this.scrollIntoViewChanged=!1,Number.isInteger(t)&&t!==this.scrollLeft&&(this.scrollLeft=t,this.scrollIntoViewChanged=!0),Number.isInteger(e)&&e!==this.scrollTop&&(this.scrollTop=e,this.scrollIntoViewChanged=!0),i&&this.scrollColumnIntoViewHandler(i),o&&this.scrollRowIntoViewHandler(o),this.scrollIntoViewChanged&&this.scrollPane.setPosition(this.scrollLeft,this.scrollTop)},scrollHandler:function(){this.scrollPane.setPosition(this.scrollLeft,this.scrollTop),this.scrollRenderHandler()},scrollRenderHandler:function(){this.previousScrollLeft===this.scrollLeft&&this.previousScrollTop===this.scrollTop||(this.previousScrollLeft=this.scrollLeft,this.previousScrollTop=this.scrollTop,this.onNextUpdated((()=>{this.updateScrollPaneFade(!0),this.trigger(y.onScroll,{scrollLeft:this.scrollLeft,scrollTop:this.scrollTop})})),this.render())},scrollTouchStartHandler:function(t,e){this.hideColumnLine(),this.scrollTouchLeft=this.getScrollLeft(),this.scrollTouchTop=this.getScrollTop(),this.scrollMaxTouchLeft=this.getMaxScrollLeft(),this.scrollMaxTouchTop=this.getMaxScrollTop()},getTouchOrientation:function(t){return t.orientation?t.orientation:[e.LEFT,e.RIGHT].includes(t.direction)?(t.orientation||(t.orientation="Y"),t.orientation):[e.UP,e.DOWN].includes(t.direction)?(t.orientation||(t.orientation="X"),t.orientation):void 0},scrollTouchMoveHandler:function(t,e){if(e.touchLength>1)return;let i=e.offsetX,o=e.offsetY;const n=this.getTouchOrientation(e);"X"===n?i=0:"Y"===n&&(o=0);let s=this.scrollTouchLeft-i,r=this.scrollTouchTop-o;s=d.clamp(s,0,this.scrollMaxTouchLeft),r=d.clamp(r,0,this.scrollMaxTouchTop);let l=!1;this.scrollPaneHidden&&(l=this.scrollPaneFrozen.setOffsetH(-e.moveX),s=0);const h=this.getScrollLeft(),a=this.getScrollTop();(s!==h||r!==a||l)&&(d.preventDefault(e.e),this.setScroll(s,r))},scrollTouchEndHandler:function(){this.protectedItem=null},scrollTouchInertiaHandler:function(t,e){const i=this.getScrollLeft(),o=this.getScrollTop(),n=i-e.touchInertiaX,s=o-e.touchInertiaY;this.setScroll(n,s)},getScrollViewWidth:function(){let t=this.getScrollPaneWidth();return this.frozenInfo.right||(t-=this.getScrollbarWidth()),t},getScrollViewHeight:function(){let t=this.getScrollPaneHeight();return this.frozenInfo.bottom||(t-=this.getScrollbarHeight()),t},getScrollPaneWidth:function(){return this.scrollPane.width()},getScrollPaneHeight:function(){return this.scrollPane.height()},getScrollbarWidth:function(){return this.hasVScroll&&!this.options.scrollbarFade?this.scrollbarSizeV:0},getScrollbarHeight:function(){return this.hasHScroll&&!this.options.scrollbarFade?this.scrollbarSizeH:0},getScrollLeft:function(){return this.scrollPane.getScrollLeft()},getScrollTop:function(){return this.scrollPane.getScrollTop()},getMaxScrollLeft:function(){return this.scrollPane.getMaxScrollLeft()},getMaxScrollTop:function(){return this.scrollPane.getMaxScrollTop()}};class Dt{constructor(t){this.options=this.generateOptions(t)}generateOptions(t){return d.merge({ignore:null,sortField:"",sortFactor:1,sortBlankFactor:1,sortComparer:null},t)}sortList(t){if(!d.isList(t)||1===t.length)return!1;this.ignoreExcludeHandler(t);const e=this.comparerHandler(t);return this.ignoreIncludeHandler(t),e}getDefaultComparer(t){return(ft[t]||ft.string).bind(this)}comparerHandler(t){const e=this.options,i=e.sortField,o=e.sortFactor,n=e.sortBlankFactor,s=e.sortComparer;return"function"==typeof s&&(t.sort(((t,e)=>s.call(this,t,e,{sortField:i,sortFactor:o,sortBlankFactor:n}))),!0)}ignoreExcludeHandler(t){const e=this.options.ignore;this.ignoreListTop=[],this.ignoreListBottom=[];const i=[];for(let o=0,n=t.length;o{t.unshift(e.item)})),this.ignoreListBottom.forEach((e=>{t.push(e.item)}))}}const Bt={removeSortColumn:function(){return this.sortColumn=null,this.$header&&this.$header.find(".tg-column-sorted").removeClass("tg-column-sorted"),this},setSortColumn:function(t){if(!(t=this.getColumnItem(t)))return;if(!this.isColumnSortable(t))return;t===this.sortColumn?t.sortAsc=!t.sortAsc:d.hasOwn(t,"sortAsc")||(t.sortAsc=this.options.sortAsc),this.sortColumn=t;if(this.getRowsLength()-this.frozenInfo.rows<2)return;if(!this.headerCreated)return;this.updateRowsSort()&&(this.renderHeaderSort(),this.flushSort(),this.render("rows"))},renderHeaderSort:function(){const t=this.sortColumn;if(!t)return this;if(!this.isColumnSortable(t))return this;this.$header.find(".tg-column-sorted").removeClass("tg-column-sorted");const e=t.tg_view_index,i=this.$header.find(`.tg-header-item[column='${e}']`).find(".tg-column-header").addClass("tg-column-sorted");return t.sortAsc?i.removeClass("tg-sort-desc").addClass("tg-sort-asc"):i.removeClass("tg-sort-asc").addClass("tg-sort-desc"),this},getSortComparer:function(t){const e=t.comparer;if("function"==typeof e)return e;const i=this.options.sortComparers,o=i[e||t.type];return"function"==typeof o?o:i.string},updateRowsSort:function(){const t=this.sortColumn;if(!t)return!1;const e=t.id;return!!e&&this.sortRows(e,t)},sortRows:function(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const i=e.sortAsc?-1:1,o=this.options.sortBlankValueBottom?1:i,n=this.getSortComparer(e);let s=!1;const r=new Dt({ignore:function(t){return t.tg_frozen?{item:t,top:!0}:t.sortFixed?{item:t,top:"top"===t.sortFixed}:void 0},sortField:t,sortFactor:i,sortBlankFactor:o,sortComparer:n}),l=function(t){r.sortList(t)&&(s=!0),t.forEach((function(t,e){t.tg_sub_index=e,t.subs&&l(t.subs)}))};return l(this.rows),s&&this.initRowsHandler(),s}},At={default:{},lightblue:{rowHeight:35,scrollbarSize:10,scrollbarRound:!0},dark:{}},Wt={getAllThemes:function(){return Object.keys(At)},getThemeOptions:function(t){return At[t]}},Ft={update:function(){return this.flushBody(),this.render("rows"),this},updateRow:function(t,e){const i=this.getRowItem(t);if(!i)return this;if(e&&"object"==typeof e){const t=this.getItemSnapshot(e);Object.keys(t).forEach((function(e){i[e]=t[e]}))}return this.flushRow(i.tg_view_index),this.render("rows"),this},updateCell:function(t,e,i){const o=this.getRowItem(t);if(!o)return this;const n=this.getColumnItem(e);return n?(arguments.length>2&&(o[n.id]=i),this.flushCell(o.tg_view_index,n.tg_view_index),this.render("rows"),this):this},onNextUpdated:function(t){return"function"!=typeof t||this.once(y.onUpdated,t),this}},Gt={getViewport:function(){this.scrollLeft=this.getScrollLeft(),this.scrollTop=this.getScrollTop();return{rows:this.getViewportRows(),columns:this.getViewportColumns()}},getViewportRows:function(){const t=[],e=this.viewRows,i=e.length;if(!i)return t;let o=this.options.rowCacheLength;o=d.clamp(d.toNum(o,!0),0,i);const n=this.frozenInfo.rows;if(n){let e=0;for(;e1;){const n=Math.floor(.5*(e+i)),s=t[n],r=this.getRowTop(s),l=this.getRowHeight(s);if(or+l))return n;e=n}}const n=t[i];return o=e)return[];const i=[],o=this.frozenInfo.columns,n=this.viewColumns;for(let s=o,r=n.length;s0;){const n=t[0]-1;n>o&&t.unshift(n);const s=t[t.length-1]+1;si)&&!(n0&&(e.rows.length=o),i=t.options}return this.data=e,this.dataOptions=i,this}setDataSnapshot(t){return this.setData(this.generateDataSnapshot(t)),this}getData(){return this.data}toString(){return"[object Grid]"}}var Ut;Ut=jt.prototype,[v,R,S,T,E,I,L,x,z,F,G,j,K,q,J,Z,tt,et,pt,mt,bt,wt,vt,Ht,{keyTabHandler:function(t){},keyEnterHandler:function(t){},keyEscHandler:function(t){},keyPageUpHandler:function(t){return this.scrollPane.keyPageUpHandler(t)},keyPageDownHandler:function(t){return this.scrollPane.keyPageDownHandler(t)},keyEndHandler:function(t){return this.scrollPane.keyEndHandler(t)},keyHomeHandler:function(t){return this.scrollPane.keyHomeHandler(t)},keyLeftHandler:function(t){return this.scrollPaneHidden?this.scrollPaneFrozen.keyLeftHandler(t):this.scrollPane.keyLeftHandler(t)},keyUpHandler:function(t){return this.scrollPane.keyUpHandler(t)},keyRightHandler:function(t){return this.scrollPaneHidden?this.scrollPaneFrozen.keyRightHandler(t):this.scrollPane.keyRightHandler(t)},keyDownHandler:function(t){return this.scrollPane.keyDownHandler(t)}},Ct,yt,Rt,St,Tt,{showRow:function(t){return this.updateRowsInvisible(this.toRowItemList(t),!1)},hideRow:function(t){return this.updateRowsInvisible(this.toRowItemList(t),!0)},updateRowsInvisible:function(t,e){if(!t.length)return!1;const i=[];return t.forEach((t=>{t.invisible!==e&&(t.invisible=e,t.tg_invisible=e,i.push(t))})),!!i.length&&(this.update(),!0)}},Et,It,Lt,xt,zt,Vt,Ot,$t,Bt,Wt,Ft,Gt].forEach((t=>{for(const e in t){if(d.hasOwn(Ut,e))throw new Error(`ERROR: extends with an existing key: "${e}"`);Ut[e]=t[e]}}));const Xt=jt,Yt=e.VERSION,Kt=e.TIMESTAMP,qt={VERSION:Yt,TIMESTAMP:Kt,Grid:Xt,$:w,CONST:e,EventBase:k,Icon:X,Motion:B,ScrollPane:kt,Util:d}})();var n=o.$,s=o.MP,r=o._d,l=o.xA,h=o.In,a=o.T8,c=o.Gr,d=o.ht,u=o.J0,g=o.xv,f=o.Ay;export{n as $,s as CONST,r as EventBase,l as Grid,h as Icon,a as Motion,c as ScrollPane,d as TIMESTAMP,u as Util,g as VERSION,f as default}; \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/js/workflow-metadata.js b/custom_nodes/comfyui-manager/js/workflow-metadata.js new file mode 100644 index 00000000..82dbe016 --- /dev/null +++ b/custom_nodes/comfyui-manager/js/workflow-metadata.js @@ -0,0 +1,84 @@ +/** + * Attaches metadata to the workflow on save + * - custom node pack version to all custom nodes used in the workflow + * + * Example metadata: + * "nodes": { + * "1": { + * type: "CheckpointLoaderSimple", + * ... + * properties: { + * cnr_id: "comfy-core", + * version: "0.3.8", + * }, + * }, + * } + * + * @typedef {Object} NodeInfo + * @property {string} ver - Version (git hash or semantic version) + * @property {string} cnr_id - ComfyRegistry node ID + * @property {boolean} enabled - Whether the node is enabled + */ + +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +class WorkflowMetadataExtension { + constructor() { + this.name = "Comfy.CustomNodesManager.WorkflowMetadata"; + this.installedNodes = {}; + this.comfyCoreVersion = null; + } + + /** + * Get the installed nodes info + * @returns {Promise>} The mapping from node name to its info. + * ver can either be a git commit hash or a semantic version such as "1.0.0" + * cnr_id is the id of the node in the ComfyRegistry + * enabled is true if the node is enabled, false if it is disabled + */ + async getInstalledNodes() { + const res = await api.fetchApi("/customnode/installed"); + return await res.json(); + } + + async init() { + this.installedNodes = await this.getInstalledNodes(); + this.comfyCoreVersion = (await api.getSystemStats()).system.comfyui_version; + } + + /** + * Called when any node is created + * @param {LGraphNode} node The newly created node + */ + nodeCreated(node) { + try { + // nodeData doesn't exist if node is missing or node is frontend only node + if (!node?.constructor?.nodeData?.python_module) return; + + const nodeProperties = (node.properties ??= {}); + const modules = node.constructor.nodeData.python_module.split("."); + const moduleType = modules[0]; + + if (moduleType === "custom_nodes") { + const nodePackageName = modules[1]; + const { cnr_id, aux_id, ver } = + this.installedNodes[nodePackageName] ?? + this.installedNodes[nodePackageName.toLowerCase()] ?? + {}; + + if (cnr_id === "comfy-core") return; // don't allow hijacking comfy-core name + if (cnr_id) nodeProperties.cnr_id = cnr_id; + else nodeProperties.aux_id = aux_id; + if (ver) nodeProperties.ver = ver.trim(); + } else if (["nodes", "comfy_extras", "comfy_api_nodes"].includes(moduleType)) { + nodeProperties.cnr_id = "comfy-core"; + nodeProperties.ver = this.comfyCoreVersion; + } + } catch (e) { + console.error(e); + } + } +} + +app.registerExtension(new WorkflowMetadataExtension()); diff --git a/custom_nodes/comfyui-manager/json-checker.py b/custom_nodes/comfyui-manager/json-checker.py new file mode 100644 index 00000000..49ae63a3 --- /dev/null +++ b/custom_nodes/comfyui-manager/json-checker.py @@ -0,0 +1,264 @@ +#!/usr/bin/env python3 +"""JSON Entry Validator + +Validates JSON entries based on content structure. + +Validation rules based on JSON content: +- {"custom_nodes": [...]}: Validates required fields (author, title, reference, files, install_type, description) +- {"models": [...]}: Validates JSON syntax only (no required fields) +- Other JSON structures: Validates JSON syntax only + +Git repository URL validation (for custom_nodes): +1. URLs must NOT end with .git +2. URLs must follow format: https://github.com/{author}/{reponame} +3. .py and .js files are exempt from this check + +Supported formats: +- Array format: [{...}, {...}] +- Object format: {"custom_nodes": [...]} or {"models": [...]} +""" + +import json +import re +import sys +from pathlib import Path +from typing import Dict, List, Tuple + + +# Required fields for each entry type +REQUIRED_FIELDS_CUSTOM_NODE = ['author', 'title', 'reference', 'files', 'install_type', 'description'] +REQUIRED_FIELDS_MODEL = [] # model-list.json doesn't require field validation + +# Pattern for valid GitHub repository URL (without .git suffix) +GITHUB_REPO_PATTERN = re.compile(r'^https://github\.com/[^/]+/[^/]+$') + + +def get_entry_context(entry: Dict) -> str: + """Get identifying information from entry for error messages + + Args: + entry: JSON entry + + Returns: + String with author and reference info + """ + parts = [] + if 'author' in entry: + parts.append(f"author={entry['author']}") + if 'reference' in entry: + parts.append(f"ref={entry['reference']}") + if 'title' in entry: + parts.append(f"title={entry['title']}") + + if parts: + return " | ".join(parts) + else: + # No identifying info - show actual entry content (truncated) + import json + entry_str = json.dumps(entry, ensure_ascii=False) + if len(entry_str) > 100: + entry_str = entry_str[:100] + "..." + return f"content={entry_str}" + + +def validate_required_fields(entry: Dict, entry_index: int, required_fields: List[str]) -> List[str]: + """Validate that all required fields are present + + Args: + entry: JSON entry to validate + entry_index: Index of entry in array (for error reporting) + required_fields: List of required field names + + Returns: + List of error descriptions (without entry prefix/context) + """ + errors = [] + + for field in required_fields: + if field not in entry: + errors.append(f"Missing required field '{field}'") + elif entry[field] is None: + errors.append(f"Field '{field}' is null") + elif isinstance(entry[field], str) and not entry[field].strip(): + errors.append(f"Field '{field}' is empty") + elif field == 'files' and not entry[field]: # Empty array + errors.append("Field 'files' is empty array") + + return errors + + +def validate_git_repo_urls(entry: Dict, entry_index: int) -> List[str]: + """Validate git repository URLs in 'files' array + + Requirements: + - Git repo URLs must NOT end with .git + - Must follow format: https://github.com/{author}/{reponame} + - .py and .js files are exempt + + Args: + entry: JSON entry to validate + entry_index: Index of entry in array (for error reporting) + + Returns: + List of error descriptions (without entry prefix/context) + """ + errors = [] + + if 'files' not in entry or not isinstance(entry['files'], list): + return errors + + for file_url in entry['files']: + if not isinstance(file_url, str): + continue + + # Skip .py and .js files - they're exempt from git repo validation + if file_url.endswith('.py') or file_url.endswith('.js'): + continue + + # Check if it's a GitHub URL (likely a git repo) + if 'github.com' in file_url: + # Error if URL ends with .git + if file_url.endswith('.git'): + errors.append(f"Git repo URL must NOT end with .git: {file_url}") + continue + + # Validate format: https://github.com/{author}/{reponame} + if not GITHUB_REPO_PATTERN.match(file_url): + errors.append(f"Invalid git repo URL format (expected https://github.com/author/reponame): {file_url}") + + return errors + + +def validate_entry(entry: Dict, entry_index: int, required_fields: List[str]) -> List[str]: + """Validate a single JSON entry + + Args: + entry: JSON entry to validate + entry_index: Index of entry in array (for error reporting) + required_fields: List of required field names + + Returns: + List of error messages (empty if valid) + """ + errors = [] + + # Check required fields + errors.extend(validate_required_fields(entry, entry_index, required_fields)) + + # Check git repository URLs + errors.extend(validate_git_repo_urls(entry, entry_index)) + + return errors + + +def validate_json_file(file_path: str) -> Tuple[bool, List[str]]: + """Validate JSON file containing entries + + Args: + file_path: Path to JSON file + + Returns: + Tuple of (is_valid, error_messages) + """ + errors = [] + + # Check file exists + path = Path(file_path) + if not path.exists(): + return False, [f"File not found: {file_path}"] + + # Load JSON + try: + with open(path, 'r', encoding='utf-8') as f: + data = json.load(f) + except json.JSONDecodeError as e: + return False, [f"Invalid JSON: {e}"] + except Exception as e: + return False, [f"Error reading file: {e}"] + + # Determine required fields based on JSON content + required_fields = [] + + # Validate structure - support both array and object formats + entries_to_validate = [] + + if isinstance(data, list): + # Direct array format: [{...}, {...}] + entries_to_validate = data + elif isinstance(data, dict): + # Object format: {"custom_nodes": [...]} or {"models": [...]} + # Determine validation based on keys + if 'custom_nodes' in data and isinstance(data['custom_nodes'], list): + required_fields = REQUIRED_FIELDS_CUSTOM_NODE + entries_to_validate = data['custom_nodes'] + elif 'models' in data and isinstance(data['models'], list): + required_fields = REQUIRED_FIELDS_MODEL + entries_to_validate = data['models'] + else: + # Other JSON structures (extension-node-map.json, etc.) - just validate JSON syntax + return True, [] + else: + return False, ["JSON root must be either an array or an object containing arrays"] + + # Validate each entry + for idx, entry in enumerate(entries_to_validate, start=1): + if not isinstance(entry, dict): + # Show actual value for type errors + entry_str = json.dumps(entry, ensure_ascii=False) if not isinstance(entry, str) else repr(entry) + if len(entry_str) > 150: + entry_str = entry_str[:150] + "..." + errors.append(f"\n❌ Entry #{idx}: Must be an object, got {type(entry).__name__}") + errors.append(f" Actual value: {entry_str}") + continue + + entry_errors = validate_entry(entry, idx, required_fields) + if entry_errors: + # Group errors by entry with context + context = get_entry_context(entry) + errors.append(f"\n❌ Entry #{idx} ({context}):") + for error in entry_errors: + errors.append(f" - {error}") + + is_valid = len(errors) == 0 + return is_valid, errors + + +def main(): + """Main entry point""" + if len(sys.argv) < 2: + print("Usage: python json-checker.py ") + print("\nValidates JSON entries based on content:") + print(" - {\"custom_nodes\": [...]}: Validates required fields (author, title, reference, files, install_type, description)") + print(" - {\"models\": [...]}: Validates JSON syntax only (no required fields)") + print(" - Other JSON structures: Validates JSON syntax only") + print("\nGit repo URL validation (for custom_nodes):") + print(" - URLs must NOT end with .git") + print(" - URLs must follow: https://github.com/{author}/{reponame}") + sys.exit(1) + + file_path = sys.argv[1] + + is_valid, errors = validate_json_file(file_path) + + if is_valid: + print(f"βœ… {file_path}: Validation passed") + sys.exit(0) + else: + print(f"Validating: {file_path}") + print("=" * 60) + print("❌ Validation failed!\n") + print("Errors:") + # Count actual errors (lines starting with " -") + error_count = sum(1 for e in errors if e.strip().startswith('-')) + for error in errors: + # Don't add ❌ prefix to grouped entries (they already have it) + if error.strip().startswith('❌'): + print(error) + else: + print(error) + print(f"\nTotal errors: {error_count}") + sys.exit(1) + + +if __name__ == '__main__': + main() diff --git a/custom_nodes/comfyui-manager/misc/Impact.pack b/custom_nodes/comfyui-manager/misc/Impact.pack new file mode 100644 index 00000000..93fd3284 --- /dev/null +++ b/custom_nodes/comfyui-manager/misc/Impact.pack @@ -0,0 +1,444 @@ +{ + "Impact::MAKE_BASIC_PIPE": { + "category": "", + "config": { + "1": { + "input": { + "text": { + "name": "Positive prompt" + } + } + }, + "2": { + "input": { + "text": { + "name": "Negative prompt" + } + } + } + }, + "datetime": 1705418802481, + "external": [], + "links": [ + [ + 0, + 1, + 1, + 0, + 1, + "CLIP" + ], + [ + 0, + 1, + 2, + 0, + 1, + "CLIP" + ], + [ + 0, + 0, + 3, + 0, + 1, + "MODEL" + ], + [ + 0, + 1, + 3, + 1, + 1, + "CLIP" + ], + [ + 0, + 2, + 3, + 2, + 1, + "VAE" + ], + [ + 1, + 0, + 3, + 3, + 3, + "CONDITIONING" + ], + [ + 2, + 0, + 3, + 4, + 4, + "CONDITIONING" + ] + ], + "nodes": [ + { + "flags": {}, + "index": 0, + "mode": 0, + "order": 0, + "outputs": [ + { + "links": [], + "name": "MODEL", + "shape": 3, + "slot_index": 0, + "type": "MODEL" + }, + { + "links": [], + "name": "CLIP", + "shape": 3, + "slot_index": 1, + "type": "CLIP" + }, + { + "links": [], + "name": "VAE", + "shape": 3, + "slot_index": 2, + "type": "VAE" + } + ], + "pos": [ + 550, + 360 + ], + "properties": { + "Node name for S&R": "CheckpointLoaderSimple" + }, + "size": { + "0": 315, + "1": 98 + }, + "type": "CheckpointLoaderSimple", + "widgets_values": [ + "SDXL/sd_xl_base_1.0_0.9vae.safetensors" + ] + }, + { + "flags": {}, + "index": 1, + "inputs": [ + { + "link": null, + "name": "clip", + "type": "CLIP" + } + ], + "mode": 0, + "order": 1, + "outputs": [ + { + "links": [], + "name": "CONDITIONING", + "shape": 3, + "slot_index": 0, + "type": "CONDITIONING" + } + ], + "pos": [ + 940, + 480 + ], + "properties": { + "Node name for S&R": "CLIPTextEncode" + }, + "size": { + "0": 263, + "1": 99 + }, + "title": "Positive", + "type": "CLIPTextEncode", + "widgets_values": [ + "" + ] + }, + { + "flags": {}, + "index": 2, + "inputs": [ + { + "link": null, + "name": "clip", + "type": "CLIP" + } + ], + "mode": 0, + "order": 2, + "outputs": [ + { + "links": [], + "name": "CONDITIONING", + "shape": 3, + "slot_index": 0, + "type": "CONDITIONING" + } + ], + "pos": [ + 940, + 640 + ], + "properties": { + "Node name for S&R": "CLIPTextEncode" + }, + "size": { + "0": 263, + "1": 99 + }, + "title": "Negative", + "type": "CLIPTextEncode", + "widgets_values": [ + "" + ] + }, + { + "flags": {}, + "index": 3, + "inputs": [ + { + "link": null, + "name": "model", + "type": "MODEL" + }, + { + "link": null, + "name": "clip", + "type": "CLIP" + }, + { + "link": null, + "name": "vae", + "type": "VAE" + }, + { + "link": null, + "name": "positive", + "type": "CONDITIONING" + }, + { + "link": null, + "name": "negative", + "type": "CONDITIONING" + } + ], + "mode": 0, + "order": 3, + "outputs": [ + { + "links": null, + "name": "basic_pipe", + "shape": 3, + "slot_index": 0, + "type": "BASIC_PIPE" + } + ], + "pos": [ + 1320, + 360 + ], + "properties": { + "Node name for S&R": "ToBasicPipe" + }, + "size": { + "0": 241.79998779296875, + "1": 106 + }, + "type": "ToBasicPipe" + } + ], + "packname": "Impact", + "version": "1.0" + }, + "Impact::SIMPLE_DETAILER_PIPE": { + "category": "", + "config": { + "0": { + "output": { + "0": { + "visible": false + }, + "1": { + "visible": false + } + } + }, + "2": { + "input": { + "Select to add LoRA": { + "visible": false + }, + "Select to add Wildcard": { + "visible": false + }, + "wildcard": { + "visible": false + } + } + } + }, + "datetime": 1705419147116, + "external": [], + "links": [ + [ + null, + 0, + 2, + 0, + 6, + "BASIC_PIPE" + ], + [ + 0, + 0, + 2, + 1, + 13, + "BBOX_DETECTOR" + ], + [ + 1, + 0, + 2, + 2, + 15, + "SAM_MODEL" + ] + ], + "nodes": [ + { + "flags": {}, + "index": 0, + "mode": 0, + "order": 2, + "outputs": [ + { + "links": [], + "name": "BBOX_DETECTOR", + "shape": 3, + "type": "BBOX_DETECTOR" + }, + { + "links": null, + "name": "SEGM_DETECTOR", + "shape": 3, + "type": "SEGM_DETECTOR" + } + ], + "pos": [ + 590, + 830 + ], + "properties": { + "Node name for S&R": "UltralyticsDetectorProvider" + }, + "size": { + "0": 315, + "1": 78 + }, + "type": "UltralyticsDetectorProvider", + "widgets_values": [ + "bbox/Eyeful_v1.pt" + ] + }, + { + "flags": {}, + "index": 1, + "mode": 0, + "order": 3, + "outputs": [ + { + "links": [], + "name": "SAM_MODEL", + "shape": 3, + "type": "SAM_MODEL" + } + ], + "pos": [ + 590, + 960 + ], + "properties": { + "Node name for S&R": "SAMLoader" + }, + "size": { + "0": 315, + "1": 82 + }, + "type": "SAMLoader", + "widgets_values": [ + "sam_vit_b_01ec64.pth", + "AUTO" + ] + }, + { + "flags": {}, + "index": 2, + "inputs": [ + { + "link": null, + "name": "basic_pipe", + "type": "BASIC_PIPE" + }, + { + "link": null, + "name": "bbox_detector", + "slot_index": 1, + "type": "BBOX_DETECTOR" + }, + { + "link": null, + "name": "sam_model_opt", + "slot_index": 2, + "type": "SAM_MODEL" + }, + { + "link": null, + "name": "segm_detector_opt", + "type": "SEGM_DETECTOR" + }, + { + "link": null, + "name": "detailer_hook", + "type": "DETAILER_HOOK" + } + ], + "mode": 0, + "order": 5, + "outputs": [ + { + "links": null, + "name": "detailer_pipe", + "shape": 3, + "type": "DETAILER_PIPE" + } + ], + "pos": [ + 1044, + 812 + ], + "properties": { + "Node name for S&R": "BasicPipeToDetailerPipe" + }, + "size": { + "0": 400, + "1": 204 + }, + "type": "BasicPipeToDetailerPipe", + "widgets_values": [ + "", + "Select the LoRA to add to the text", + "Select the Wildcard to add to the text" + ] + } + ], + "packname": "Impact", + "version": "1.0" + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/node_db/README.md b/custom_nodes/comfyui-manager/node_db/README.md new file mode 100644 index 00000000..378845c5 --- /dev/null +++ b/custom_nodes/comfyui-manager/node_db/README.md @@ -0,0 +1,95 @@ +# ComfyUI-Manager: Node Database (node_db) + +This directory contains the JSON database files that power ComfyUI-Manager's legacy node registry system. While the manager is gradually transitioning to the online Custom Node Registry (CNR), these local JSON files continue to provide important metadata about custom nodes, models, and their integrations. + +## Directory Structure + +The node_db directory is organized into several subdirectories, each serving a specific purpose: + +- **dev/**: Development channel files with latest additions and experimental nodes +- **legacy/**: Historical/legacy nodes that may require special handling +- **new/**: New nodes that have passed initial verification but are still being evaluated +- **forked/**: Forks of existing nodes with modifications +- **tutorial/**: Example and tutorial nodes designed for learning purposes + +## Core Database Files + +Each subdirectory contains a standard set of JSON files: + +- **custom-node-list.json**: Primary database of custom nodes with metadata +- **extension-node-map.json**: Maps between extensions and individual nodes they provide +- **model-list.json**: Catalog of models that can be downloaded through the manager +- **alter-list.json**: Alternative implementations of nodes for compatibility or functionality +- **github-stats.json**: GitHub repository statistics for node popularity metrics + +## Database Schema + +### custom-node-list.json +```json +{ + "custom_nodes": [ + { + "title": "Node display name", + "name": "Repository name", + "reference": "Original repository if forked", + "files": ["GitHub URL or other source location"], + "install_type": "git", + "description": "Description of the node's functionality", + "pip": ["optional pip dependencies"], + "js": ["optional JavaScript files"], + "tags": ["categorization tags"] + } + ] +} +``` + +### extension-node-map.json +```json +{ + "extension-id": [ + ["list", "of", "node", "classes"], + { + "author": "Author name", + "description": "Extension description", + "nodename_pattern": "Optional regex pattern for node name matching" + } + ] +} +``` + +## Transition to Custom Node Registry (CNR) + +This local database system is being progressively replaced by the online Custom Node Registry (CNR), which provides: +- Real-time updates without manual JSON maintenance +- Improved versioning support +- Better security validation +- Enhanced metadata + +The Manager supports both systems simultaneously during the transition period. + +## Implementation Details + +- The database follows a channel-based architecture for different sources +- Multiple database modes are supported: Channel, Local, and Remote +- The system supports differential updates to minimize bandwidth usage +- Security levels are enforced for different node installations based on source + +## Usage in the Application + +The Manager's backend uses these database files to: + +1. Provide browsable lists of available nodes and models +2. Resolve dependencies for installation +3. Track updates and new versions +4. Map node classes to their source repositories +5. Assess risk levels for installation security + +## Maintenance Scripts + +Each subdirectory contains a `scan.sh` script that assists with: +- Scanning repositories for new nodes +- Updating metadata +- Validating database integrity +- Generating proper JSON structures + +This database system enables a flexible, secure, and comprehensive management system for the ComfyUI ecosystem while the transition to CNR continues. \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/node_db/dev/scan.sh b/custom_nodes/comfyui-manager/node_db/dev/scan.sh new file mode 100755 index 00000000..ac8c4255 --- /dev/null +++ b/custom_nodes/comfyui-manager/node_db/dev/scan.sh @@ -0,0 +1,3 @@ +#!/bin/bash +rm ~/.tmp/dev/*.py > /dev/null 2>&1 +python ../../scanner.py ~/.tmp/dev $* \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/node_db/forked/scan.sh b/custom_nodes/comfyui-manager/node_db/forked/scan.sh new file mode 100755 index 00000000..5d8d8c48 --- /dev/null +++ b/custom_nodes/comfyui-manager/node_db/forked/scan.sh @@ -0,0 +1,4 @@ +#!/bin/bash +source ../../../../venv/bin/activate +rm .tmp/*.py > /dev/null +python ../../scanner.py diff --git a/custom_nodes/comfyui-manager/node_db/tutorial/scan.sh b/custom_nodes/comfyui-manager/node_db/tutorial/scan.sh new file mode 100755 index 00000000..6e3e6daf --- /dev/null +++ b/custom_nodes/comfyui-manager/node_db/tutorial/scan.sh @@ -0,0 +1,3 @@ +#!/bin/bash +rm ~/.tmp/dev/*.py > /dev/null 2>&1 +python ../../scanner.py ~/.tmp/tutorial diff --git a/custom_nodes/comfyui-manager/notebooks/comfyui_colab_with_manager.ipynb b/custom_nodes/comfyui-manager/notebooks/comfyui_colab_with_manager.ipynb new file mode 100644 index 00000000..3cfa484b --- /dev/null +++ b/custom_nodes/comfyui-manager/notebooks/comfyui_colab_with_manager.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb" + }, + "outputs": [], + "source": [ + "# #@title Environment Setup\n", + "\n", + "from pathlib import Path\n", + "\n", + "OPTIONS = {}\n", + "\n", + "USE_GOOGLE_DRIVE = True #@param {type:\"boolean\"}\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "USE_COMFYUI_MANAGER = True #@param {type:\"boolean\"}\n", + "INSTALL_CUSTOM_NODES_DEPENDENCIES = True #@param {type:\"boolean\"}\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "OPTIONS['USE_COMFYUI_MANAGER'] = USE_COMFYUI_MANAGER\n", + "OPTIONS['INSTALL_CUSTOM_NODES_DEPENDENCIES'] = INSTALL_CUSTOM_NODES_DEPENDENCIES\n", + "\n", + "current_dir = !pwd\n", + "WORKSPACE = f\"{current_dir[0]}/ComfyUI\"\n", + "\n", + "if OPTIONS['USE_GOOGLE_DRIVE']:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + "\n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['UPDATE_COMFY_UI']:\n", + " !echo -= Updating ComfyUI =-\n", + "\n", + " # Correction of the issue of permissions being deleted on Google Drive.\n", + " ![ -f \".ci/nightly/update_windows/update_comfyui_and_python_dependencies.bat\" ] && chmod 755 .ci/nightly/update_windows/update_comfyui_and_python_dependencies.bat\n", + " ![ -f \".ci/nightly/windows_base_files/run_nvidia_gpu.bat\" ] && chmod 755 .ci/nightly/windows_base_files/run_nvidia_gpu.bat\n", + " ![ -f \".ci/update_windows/update_comfyui_and_python_dependencies.bat\" ] && chmod 755 .ci/update_windows/update_comfyui_and_python_dependencies.bat\n", + " ![ -f \".ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat\" ] && chmod 755 .ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat\n", + " ![ -f \".ci/update_windows/update.py\" ] && chmod 755 .ci/update_windows/update.py\n", + " ![ -f \".ci/update_windows/update_comfyui.bat\" ] && chmod 755 .ci/update_windows/update_comfyui.bat\n", + " ![ -f \".ci/update_windows/README_VERY_IMPORTANT.txt\" ] && chmod 755 .ci/update_windows/README_VERY_IMPORTANT.txt\n", + " ![ -f \".ci/update_windows/run_cpu.bat\" ] && chmod 755 .ci/update_windows/run_cpu.bat\n", + " ![ -f \".ci/update_windows/run_nvidia_gpu.bat\" ] && chmod 755 .ci/update_windows/run_nvidia_gpu.bat\n", + "\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "!pip3 install accelerate\n", + "!pip3 install einops transformers>=4.28.1 safetensors>=0.4.2 aiohttp pyyaml Pillow scipy tqdm psutil tokenizers>=0.13.3\n", + "!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n", + "!pip3 install torchsde\n", + "!pip3 install kornia>=0.7.1 spandrel soundfile sentencepiece\n", + "\n", + "if OPTIONS['USE_COMFYUI_MANAGER']:\n", + " %cd custom_nodes\n", + "\n", + " # Correction of the issue of permissions being deleted on Google Drive.\n", + " ![ -f \"ComfyUI-Manager/check.sh\" ] && chmod 755 ComfyUI-Manager/check.sh\n", + " ![ -f \"ComfyUI-Manager/scan.sh\" ] && chmod 755 ComfyUI-Manager/scan.sh\n", + " ![ -f \"ComfyUI-Manager/node_db/dev/scan.sh\" ] && chmod 755 ComfyUI-Manager/node_db/dev/scan.sh\n", + " ![ -f \"ComfyUI-Manager/node_db/tutorial/scan.sh\" ] && chmod 755 ComfyUI-Manager/node_db/tutorial/scan.sh\n", + " ![ -f \"ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh\" ] && chmod 755 ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh\n", + " ![ -f \"ComfyUI-Manager/scripts/install-comfyui-venv-win.bat\" ] && chmod 755 ComfyUI-Manager/scripts/install-comfyui-venv-win.bat\n", + "\n", + " ![ ! -d ComfyUI-Manager ] && echo -= Initial setup ComfyUI-Manager =- && git clone https://github.com/ltdrdata/ComfyUI-Manager\n", + " %cd ComfyUI-Manager\n", + " !git pull\n", + "\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['INSTALL_CUSTOM_NODES_DEPENDENCIES']:\n", + " !echo -= Install custom nodes dependencies =-\n", + " !pip install GitPython\n", + " !python custom_nodes/ComfyUI-Manager/cm-cli.py restore-dependencies\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd" + }, + "outputs": [], + "source": [ + "# Checkpoints\n", + "\n", + "### SDXL\n", + "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", + "\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", + "\n", + "# SDXL ReVision\n", + "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", + "\n", + "# SD1.5\n", + "!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n", + "\n", + "# SD2\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# unCLIP models\n", + "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", + "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", + "\n", + "\n", + "# Loras\n", + "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", + "\n", + "\n", + "# T2I-Adapter\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", + "\n", + "# ControlNet SDXL\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "\n", + "# GLIGEN\n", + "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", + "\n", + "\n", + "# ESRGAN upscale model\n", + "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with cloudflared (Recommended Way)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!wget -P ~ https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i ~/cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with localtunnel\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!npm install -g localtunnel\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh" + }, + "outputs": [], + "source": [ + "import threading\n", + "import time\n", + "import socket\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/custom_nodes/comfyui-manager/pip_overrides.json.template b/custom_nodes/comfyui-manager/pip_overrides.json.template new file mode 100644 index 00000000..3884edb9 --- /dev/null +++ b/custom_nodes/comfyui-manager/pip_overrides.json.template @@ -0,0 +1,21 @@ +{ + "imageio-ffmpeg": "imageio", + "imageio[ffmpeg]": "imageio", + "imageio_ffmpeg": "imageio", + "diffusers~=0.21.4": "diffusers", + "huggingface_hub": "huggingface-hub", + "numpy<1.24>=1.18": "numpy==1.26.4", + "numpy>=1.18.5, <1.25.0": "numpy==1.26.4", + "opencv-contrib-python": "opencv-contrib-python-headless", + "opencv-python": "opencv-contrib-python-headless", + "opencv-python-headless": "opencv-contrib-python-headless", + "opencv-python-headless[ffmpeg]<=4.7.0.72": "opencv-contrib-python-headless", + "opencv-python>=4.7.0.72": "opencv-contrib-python-headless", + "pandas<=1.5.1": "pandas", + "scikit-image==0.20.0": "scikit-image", + "scipy>=1.11.4": "scipy", + "segment_anything": "segment-anything", + "timm==0.6.5": "timm", + "timm>=0.4.12": "timm", + "transformers==4.26.1": "transformers" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/pip_overrides.osx.template b/custom_nodes/comfyui-manager/pip_overrides.osx.template new file mode 100644 index 00000000..3884edb9 --- /dev/null +++ b/custom_nodes/comfyui-manager/pip_overrides.osx.template @@ -0,0 +1,21 @@ +{ + "imageio-ffmpeg": "imageio", + "imageio[ffmpeg]": "imageio", + "imageio_ffmpeg": "imageio", + "diffusers~=0.21.4": "diffusers", + "huggingface_hub": "huggingface-hub", + "numpy<1.24>=1.18": "numpy==1.26.4", + "numpy>=1.18.5, <1.25.0": "numpy==1.26.4", + "opencv-contrib-python": "opencv-contrib-python-headless", + "opencv-python": "opencv-contrib-python-headless", + "opencv-python-headless": "opencv-contrib-python-headless", + "opencv-python-headless[ffmpeg]<=4.7.0.72": "opencv-contrib-python-headless", + "opencv-python>=4.7.0.72": "opencv-contrib-python-headless", + "pandas<=1.5.1": "pandas", + "scikit-image==0.20.0": "scikit-image", + "scipy>=1.11.4": "scipy", + "segment_anything": "segment-anything", + "timm==0.6.5": "timm", + "timm>=0.4.12": "timm", + "transformers==4.26.1": "transformers" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/prestartup_script.py b/custom_nodes/comfyui-manager/prestartup_script.py new file mode 100644 index 00000000..dabd39da --- /dev/null +++ b/custom_nodes/comfyui-manager/prestartup_script.py @@ -0,0 +1,869 @@ +import os +import shutil +import subprocess +import sys +import atexit +import threading +import re +import locale +import platform +import json +import ast +import logging +import traceback + +glob_path = os.path.join(os.path.dirname(__file__), "glob") +sys.path.append(glob_path) + +import security_check +import manager_util +import cm_global +import manager_downloader +import folder_paths + +manager_util.add_python_path_to_env() + +import datetime as dt + +if hasattr(dt, 'datetime'): + from datetime import datetime as dt_datetime + + def current_timestamp(): + return dt_datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3] +else: + # NOTE: Occurs in some Mac environments. + import time + logging.error(f"[ComfyUI-Manager] fallback timestamp mode\n datetime module is invalid: '{dt.__file__}'") + + def current_timestamp(): + return str(time.time()).split('.')[0] + + +cm_global.pip_blacklist = {'torch', 'torchaudio', 'torchsde', 'torchvision'} +cm_global.pip_downgrade_blacklist = ['torch', 'torchaudio', 'torchsde', 'torchvision', 'transformers', 'safetensors', 'kornia'] + + +def skip_pip_spam(x): + return ('Requirement already satisfied:' in x) or ("DEPRECATION: Loading egg at" in x) + + +message_collapses = [skip_pip_spam] +import_failed_extensions = set() +cm_global.variables['cm.on_revision_detected_handler'] = [] +enable_file_logging = True + + +def register_message_collapse(f): + global message_collapses + message_collapses.append(f) + + +def is_import_failed_extension(name): + global import_failed_extensions + return name in import_failed_extensions + + +comfy_path = os.environ.get('COMFYUI_PATH') +comfy_base_path = os.environ.get('COMFYUI_FOLDERS_BASE_PATH') + +if comfy_path is None: + # legacy env var + comfy_path = os.environ.get('COMFYUI_PATH') + +if comfy_path is None: + comfy_path = os.path.abspath(os.path.dirname(sys.modules['__main__'].__file__)) + +if comfy_base_path is None: + comfy_base_path = comfy_path + +sys.__comfyui_manager_register_message_collapse = register_message_collapse +sys.__comfyui_manager_is_import_failed_extension = is_import_failed_extension +cm_global.register_api('cm.register_message_collapse', register_message_collapse) +cm_global.register_api('cm.is_import_failed_extension', is_import_failed_extension) + + +comfyui_manager_path = os.path.abspath(os.path.dirname(__file__)) + +custom_nodes_base_path = folder_paths.get_folder_paths('custom_nodes')[0] + +# Check for System User API availability (PR #10966) +_has_system_user_api = hasattr(folder_paths, 'get_system_user_directory') + +if _has_system_user_api: + manager_files_path = os.path.abspath(os.path.join(folder_paths.get_user_directory(), '__manager')) +else: + manager_files_path = os.path.abspath(os.path.join(folder_paths.get_user_directory(), 'default', 'ComfyUI-Manager')) + +manager_pip_overrides_path = os.path.join(manager_files_path, "pip_overrides.json") +manager_pip_blacklist_path = os.path.join(manager_files_path, "pip_blacklist.list") +restore_snapshot_path = os.path.join(manager_files_path, "startup-scripts", "restore-snapshot.json") +manager_config_path = os.path.join(manager_files_path, 'config.ini') + +cm_cli_path = os.path.join(comfyui_manager_path, "cm-cli.py") + + +default_conf = {} + +def read_config(): + global default_conf + try: + import configparser + config = configparser.ConfigParser(strict=False) + config.read(manager_config_path) + default_conf = config['default'] + except Exception: + pass + +def read_uv_mode(): + if 'use_uv' in default_conf: + manager_util.use_uv = default_conf['use_uv'].lower() == 'true' + +def check_file_logging(): + global enable_file_logging + if 'file_logging' in default_conf and default_conf['file_logging'].lower() == 'false': + enable_file_logging = False + + +read_config() +read_uv_mode() +security_check.security_check() +check_file_logging() + +cm_global.pip_overrides = {} + +if os.path.exists(manager_pip_overrides_path): + with open(manager_pip_overrides_path, 'r', encoding="UTF-8", errors="ignore") as json_file: + cm_global.pip_overrides = json.load(json_file) + + +if os.path.exists(manager_pip_blacklist_path): + with open(manager_pip_blacklist_path, 'r', encoding="UTF-8", errors="ignore") as f: + for x in f.readlines(): + y = x.strip() + if y != '': + cm_global.pip_blacklist.add(y) + + +def remap_pip_package(pkg): + if pkg in cm_global.pip_overrides: + res = cm_global.pip_overrides[pkg] + print(f"[ComfyUI-Manager] '{pkg}' is remapped to '{res}'") + return res + else: + return pkg + + +std_log_lock = threading.Lock() + + +def handle_stream(stream, prefix): + stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace') + for msg in stream: + if prefix == '[!]' and ('it/s]' in msg or 's/it]' in msg) and ('%|' in msg or 'it [' in msg): + if msg.startswith('100%'): + print('\r' + msg, end="", file=sys.stderr), + else: + print('\r' + msg[:-1], end="", file=sys.stderr), + else: + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + +def process_wrap(cmd_str, cwd_path, handler=None, env=None): + process = subprocess.Popen(cmd_str, cwd=cwd_path, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1) + + if handler is None: + handler = handle_stream + + stdout_thread = threading.Thread(target=handler, args=(process.stdout, "")) + stderr_thread = threading.Thread(target=handler, args=(process.stderr, "[!]")) + + stdout_thread.start() + stderr_thread.start() + + stdout_thread.join() + stderr_thread.join() + + return process.wait() + + +original_stdout = sys.stdout + + +def try_get_custom_nodes(x): + for custom_nodes_dir in folder_paths.get_folder_paths('custom_nodes'): + if x.startswith(custom_nodes_dir): + relative_path = os.path.relpath(x, custom_nodes_dir) + next_segment = relative_path.split(os.sep)[0] + if next_segment.lower() != 'comfyui-manager': + return next_segment, os.path.join(custom_nodes_dir, next_segment) + return None + + +def extract_origin_module(): + stack = traceback.extract_stack()[:-2] + for frame in reversed(stack): + info = try_get_custom_nodes(frame.filename) + if info is None: + continue + else: + return info + return None + +def extract_origin_module_from_strings(file_paths): + for filepath in file_paths: + info = try_get_custom_nodes(filepath) + if info is None: + continue + else: + return info + return None + + +def finalize_startup(): + res = {} + for k, v in cm_global.error_dict.items(): + if v['path'] in import_failed_extensions: + res[k] = v + + cm_global.error_dict = res + + +try: + if '--port' in sys.argv: + port_index = sys.argv.index('--port') + if port_index + 1 < len(sys.argv): + port = int(sys.argv[port_index + 1]) + postfix = f"_{port}" + else: + postfix = "" + else: + postfix = "" + + # Logger setup + log_path_base = None + if enable_file_logging: + log_path_base = os.path.join(folder_paths.user_directory, 'comfyui') + + if not os.path.exists(folder_paths.user_directory): + os.makedirs(folder_paths.user_directory) + + if os.path.exists(f"{log_path_base}{postfix}.log"): + if os.path.exists(f"{log_path_base}{postfix}.prev.log"): + if os.path.exists(f"{log_path_base}{postfix}.prev2.log"): + os.remove(f"{log_path_base}{postfix}.prev2.log") + os.rename(f"{log_path_base}{postfix}.prev.log", f"{log_path_base}{postfix}.prev2.log") + os.rename(f"{log_path_base}{postfix}.log", f"{log_path_base}{postfix}.prev.log") + + log_file = open(f"{log_path_base}{postfix}.log", "w", encoding="utf-8", errors="ignore") + + log_lock = threading.Lock() + + original_stdout = sys.stdout + original_stderr = sys.stderr + + if original_stdout.encoding.lower() == 'utf-8': + write_stdout = original_stdout.write + write_stderr = original_stderr.write + else: + def wrapper_stdout(msg): + original_stdout.write(msg.encode('utf-8').decode(original_stdout.encoding, errors="ignore")) + + def wrapper_stderr(msg): + original_stderr.write(msg.encode('utf-8').decode(original_stderr.encoding, errors="ignore")) + + write_stdout = wrapper_stdout + write_stderr = wrapper_stderr + + pat_tqdm = r'\d+%.*\[(.*?)\]' + pat_import_fail = r'seconds \(IMPORT FAILED\):(.*)$' + + is_start_mode = True + + + class ComfyUIManagerLogger: + def __init__(self, is_stdout): + self.is_stdout = is_stdout + self.encoding = "utf-8" + self.last_char = '' + + def fileno(self): + try: + if self.is_stdout: + return original_stdout.fileno() + else: + return original_stderr.fileno() + except AttributeError: + # Handle error + raise ValueError("The object does not have a fileno method") + + def isatty(self): + return False + + def write(self, message): + global is_start_mode + + if any(f(message) for f in message_collapses): + return + + if is_start_mode: + match = re.search(pat_import_fail, message) + if match: + import_failed_extensions.add(match.group(1).strip()) + + if not self.is_stdout: + origin_info = extract_origin_module() + if origin_info is not None: + name, origin_path = origin_info + + if name != 'comfyui-manager': + if name not in cm_global.error_dict: + cm_global.error_dict[name] = {'name': name, 'path': origin_path, 'msg': ''} + + cm_global.error_dict[name]['msg'] += message + + if not self.is_stdout: + match = re.search(pat_tqdm, message) + if match: + message = re.sub(r'([#|])\d', r'\1β–Œ', message) + message = re.sub('#', 'β–ˆ', message) + if '100%' in message: + self.sync_write(message) + else: + write_stderr(message) + original_stderr.flush() + else: + self.sync_write(message) + else: + self.sync_write(message) + + def sync_write(self, message, file_only=False): + with log_lock: + timestamp = current_timestamp() + if self.last_char != '\n': + log_file.write(message) + else: + log_file.write(f"[{timestamp}] {message}") + + try: + log_file.flush() + except Exception: + pass + + self.last_char = message if message == '' else message[-1] + + if not file_only: + with std_log_lock: + if self.is_stdout: + write_stdout(message) + original_stdout.flush() + else: + write_stderr(message) + original_stderr.flush() + + def flush(self): + try: + log_file.flush() + except Exception: + pass + + with std_log_lock: + try: + if self.is_stdout: + original_stdout.flush() + else: + original_stderr.flush() + except (OSError, ValueError): + pass + + def close(self): + self.flush() + + def reconfigure(self, *args, **kwargs): + pass + + # You can close through sys.stderr.close_log() + def close_log(self): + sys.stderr = original_stderr + sys.stdout = original_stdout + log_file.close() + + def close_log(): + sys.stderr = original_stderr + sys.stdout = original_stdout + log_file.close() + + + if enable_file_logging: + sys.stdout = ComfyUIManagerLogger(True) + stderr_wrapper = ComfyUIManagerLogger(False) + sys.stderr = stderr_wrapper + + atexit.register(close_log) + else: + sys.stdout.close_log = lambda: None + stderr_wrapper = None + + + class LoggingHandler(logging.Handler): + def emit(self, record): + global is_start_mode + + message = record.getMessage() + + if is_start_mode: + match = re.search(pat_import_fail, message) + if match: + import_failed_extensions.add(match.group(1).strip()) + + if 'Traceback' in message: + file_lists = self._extract_file_paths(message) + origin_info = extract_origin_module_from_strings(file_lists) + if origin_info is not None: + name, origin_path = origin_info + + if name != 'comfyui-manager': + if name not in cm_global.error_dict: + cm_global.error_dict[name] = {'name': name, 'path': origin_path, 'msg': ''} + + cm_global.error_dict[name]['msg'] += message + + if 'Starting server' in message: + is_start_mode = False + finalize_startup() + + if stderr_wrapper: + stderr_wrapper.sync_write(message+'\n', file_only=True) + + def _extract_file_paths(self, msg): + file_paths = [] + for line in msg.split('\n'): + match = re.findall(r'File \"(.*?)\", line \d+', line) + for x in match: + if not x.startswith('<'): + file_paths.extend(match) + return file_paths + + + logging.getLogger().addHandler(LoggingHandler()) + + +except Exception as e: + print(f"[ComfyUI-Manager] Logging failed: {e}") + + +def ensure_dependencies(): + try: + import git # noqa: F401 + import toml # noqa: F401 + import rich # noqa: F401 + import chardet # noqa: F401 + except ModuleNotFoundError: + my_path = os.path.dirname(__file__) + requirements_path = os.path.join(my_path, "requirements.txt") + + print("## ComfyUI-Manager: installing dependencies. (GitPython)") + try: + subprocess.check_output(manager_util.make_pip_cmd(['install', '-r', requirements_path])) + except subprocess.CalledProcessError: + print("## [ERROR] ComfyUI-Manager: Attempting to reinstall dependencies using an alternative method.") + try: + subprocess.check_output(manager_util.make_pip_cmd(['install', '--user', '-r', requirements_path])) + except subprocess.CalledProcessError: + print("## [ERROR] ComfyUI-Manager: Failed to install the GitPython package in the correct Python environment. Please install it manually in the appropriate environment. (You can seek help at https://app.element.io/#/room/%23comfyui_space%3Amatrix.org)") + + try: + print("## ComfyUI-Manager: installing dependencies done.") + except: + # maybe we should sys.exit() here? there is at least two screens worth of error messages still being pumped after our error messages + print("## [ERROR] ComfyUI-Manager: GitPython package seems to be installed, but failed to load somehow. Make sure you have a working git client installed") + +ensure_dependencies() + + +print("** ComfyUI startup time:", current_timestamp()) +print("** Platform:", platform.system()) +print("** Python version:", sys.version) +print("** Python executable:", sys.executable) +print("** ComfyUI Path:", comfy_path) +print("** ComfyUI Base Folder Path:", comfy_base_path) +print("** User directory:", folder_paths.user_directory) +print("** ComfyUI-Manager config path:", manager_config_path) + + +if log_path_base is not None: + print("** Log path:", os.path.abspath(f'{log_path_base}.log')) +else: + print("** Log path: file logging is disabled") + + +def read_downgrade_blacklist(): + try: + if 'downgrade_blacklist' in default_conf: + items = default_conf['downgrade_blacklist'].split(',') + items = [x.strip() for x in items if x != ''] + cm_global.pip_downgrade_blacklist += items + cm_global.pip_downgrade_blacklist = list(set(cm_global.pip_downgrade_blacklist)) + except: + pass + + +read_downgrade_blacklist() + + +def check_bypass_ssl(): + try: + import ssl + if 'bypass_ssl' in default_conf and default_conf['bypass_ssl'].lower() == 'true': + print(f"[ComfyUI-Manager] WARN: Unsafe - SSL verification bypass option is Enabled. (see {manager_config_path})") + ssl._create_default_https_context = ssl._create_unverified_context # SSL certificate error fix. + except Exception: + pass + +check_bypass_ssl() + + +# Perform install +processed_install = set() +# Use manager_files_path for consistency (fixes path inconsistency bug) +script_list_path = os.path.join(manager_files_path, "startup-scripts", "install-scripts.txt") +pip_fixer = manager_util.PIPFixer(manager_util.get_installed_packages(), comfy_path, manager_files_path) + + +def is_installed(name): + name = name.strip() + + if name.startswith('#'): + return True + + pattern = r'([^<>!~=]+)([<>!~=]=?)([0-9.a-zA-Z]*)' + match = re.search(pattern, name) + + if match: + name = match.group(1) + + if name in cm_global.pip_blacklist: + return True + + if name in cm_global.pip_downgrade_blacklist: + pips = manager_util.get_installed_packages() + + if match is None: + if name in pips: + return True + elif match.group(2) in ['<=', '==', '<', '~=']: + if name in pips: + if manager_util.StrictVersion(pips[name]) >= manager_util.StrictVersion(match.group(3)): + print(f"[ComfyUI-Manager] skip black listed pip installation: '{name}'") + return True + + pkg = manager_util.get_installed_packages().get(name.lower()) + if pkg is None: + return False # update if not installed + + if match is None: + return True # don't update if version is not specified + + if match.group(2) in ['>', '>=']: + if manager_util.StrictVersion(pkg) < manager_util.StrictVersion(match.group(3)): + return False + elif manager_util.StrictVersion(pkg) > manager_util.StrictVersion(match.group(3)): + print(f"[SKIP] Downgrading pip package isn't allowed: {name.lower()} (cur={pkg})") + + if match.group(2) == '==': + if manager_util.StrictVersion(pkg) < manager_util.StrictVersion(match.group(3)): + return False + + if match.group(2) == '~=': + if manager_util.StrictVersion(pkg) == manager_util.StrictVersion(match.group(3)): + return False + + return True # prevent downgrade + + +if os.path.exists(restore_snapshot_path): + try: + cloned_repos = [] + + def msg_capture(stream, prefix): + stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace') + for msg in stream: + if msg.startswith("CLONE: "): + cloned_repos.append(msg[7:]) + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + elif prefix == '[!]' and ('it/s]' in msg or 's/it]' in msg) and ('%|' in msg or 'it [' in msg): + if msg.startswith('100%'): + print('\r' + msg, end="", file=sys.stderr), + else: + print('\r'+msg[:-1], end="", file=sys.stderr), + else: + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + print("[ComfyUI-Manager] Restore snapshot.") + new_env = os.environ.copy() + if 'COMFYUI_FOLDERS_BASE_PATH' not in new_env: + new_env["COMFYUI_FOLDERS_BASE_PATH"] = comfy_path + + cmd_str = [sys.executable, cm_cli_path, 'restore-snapshot', restore_snapshot_path] + exit_code = process_wrap(cmd_str, custom_nodes_base_path, handler=msg_capture, env=new_env) + + if exit_code != 0: + print("[ComfyUI-Manager] Restore snapshot failed.") + else: + print("[ComfyUI-Manager] Restore snapshot done.") + + except Exception as e: + print(e) + print("[ComfyUI-Manager] Restore snapshot failed.") + + os.remove(restore_snapshot_path) + + +def execute_lazy_install_script(repo_path, executable): + global processed_install + + install_script_path = os.path.join(repo_path, "install.py") + requirements_path = os.path.join(repo_path, "requirements.txt") + + if os.path.exists(requirements_path): + print(f"Install: pip packages for '{repo_path}'") + + lines = manager_util.robust_readlines(requirements_path) + for line in lines: + package_name = remap_pip_package(line.strip()) + package_name = package_name.split('#')[0].strip() + if package_name and not is_installed(package_name): + if '--index-url' in package_name: + s = package_name.split('--index-url') + install_cmd = manager_util.make_pip_cmd(["install", s[0].strip(), '--index-url', s[1].strip()]) + else: + install_cmd = manager_util.make_pip_cmd(["install", package_name]) + + process_wrap(install_cmd, repo_path) + + if os.path.exists(install_script_path) and f'{repo_path}/install.py' not in processed_install: + processed_install.add(f'{repo_path}/install.py') + print(f"Install: install script for '{repo_path}'") + install_cmd = [executable, "install.py"] + + new_env = os.environ.copy() + if 'COMFYUI_FOLDERS_BASE_PATH' not in new_env: + new_env["COMFYUI_FOLDERS_BASE_PATH"] = comfy_path + process_wrap(install_cmd, repo_path, env=new_env) + + +def execute_lazy_cnr_switch(target, zip_url, from_path, to_path, no_deps, custom_nodes_path): + import uuid + import shutil + + # 1. download + archive_name = f"CNR_temp_{str(uuid.uuid4())}.zip" # should be unpredictable name - security precaution + download_path = os.path.join(custom_nodes_path, archive_name) + manager_downloader.download_url(zip_url, custom_nodes_path, archive_name) + + # 2. extract files into @ + extracted = manager_util.extract_package_as_zip(download_path, from_path) + os.remove(download_path) + + if extracted is None: + if len(os.listdir(from_path)) == 0: + shutil.rmtree(from_path) + + print(f'Empty archive file: {target}') + return False + + + # 3. calculate garbage files (.tracking - extracted) + tracking_info_file = os.path.join(from_path, '.tracking') + prev_files = set() + with open(tracking_info_file, 'r') as f: + for line in f: + prev_files.add(line.strip()) + garbage = prev_files.difference(extracted) + garbage = [os.path.join(custom_nodes_path, x) for x in garbage] + + # 4-1. remove garbage files + for x in garbage: + if os.path.isfile(x): + os.remove(x) + + # 4-2. remove garbage dir if empty + for x in garbage: + if os.path.isdir(x): + if not os.listdir(x): + os.rmdir(x) + + # 5. rename dir name @ ==> @ + print(f"'{from_path}' is moved to '{to_path}'") + shutil.move(from_path, to_path) + + # 6. create .tracking file + tracking_info_file = os.path.join(to_path, '.tracking') + with open(tracking_info_file, "w", encoding='utf-8') as file: + file.write('\n'.join(list(extracted))) + + +script_executed = False + +def execute_startup_script(): + global script_executed + print("\n#######################################################################") + print("[ComfyUI-Manager] Starting dependency installation/(de)activation for the extension\n") + + custom_nodelist_cache = None + + def get_custom_node_paths(): + nonlocal custom_nodelist_cache + if custom_nodelist_cache is None: + custom_nodelist_cache = set() + for base in folder_paths.get_folder_paths('custom_nodes'): + for x in os.listdir(base): + fullpath = os.path.join(base, x) + if os.path.isdir(fullpath): + custom_nodelist_cache.add(fullpath) + + return custom_nodelist_cache + + def execute_lazy_delete(path): + # Validate to prevent arbitrary paths from being deleted + if path not in get_custom_node_paths(): + logging.error(f"## ComfyUI-Manager: The scheduled '{path}' is not a custom node path, so the deletion has been canceled.") + return + + if not os.path.exists(path): + logging.info(f"## ComfyUI-Manager: SKIP-DELETE => '{path}' (already deleted)") + return + + try: + shutil.rmtree(path) + logging.info(f"## ComfyUI-Manager: DELETE => '{path}'") + except Exception as e: + logging.error(f"## ComfyUI-Manager: Failed to delete '{path}' ({e})") + + executed = set() + # Read each line from the file and convert it to a list using eval + with open(script_list_path, 'r', encoding="UTF-8", errors="ignore") as file: + for line in file: + if line in executed: + continue + + executed.add(line) + + try: + script = ast.literal_eval(line) + + if script[1].startswith('#') and script[1] != '#FORCE': + if script[1] == "#LAZY-INSTALL-SCRIPT": + execute_lazy_install_script(script[0], script[2]) + + elif script[1] == "#LAZY-CNR-SWITCH-SCRIPT": + execute_lazy_cnr_switch(script[0], script[2], script[3], script[4], script[5], script[6]) + execute_lazy_install_script(script[3], script[7]) + + elif script[1] == "#LAZY-DELETE-NODEPACK": + execute_lazy_delete(script[2]) + + elif os.path.exists(script[0]): + if script[1] == "#FORCE": + del script[1] + else: + if 'pip' in script[1:] and 'install' in script[1:] and is_installed(script[-1]): + continue + + print(f"\n## ComfyUI-Manager: EXECUTE => {script[1:]}") + print(f"\n## Execute management script for '{script[0]}'") + + new_env = os.environ.copy() + if 'COMFYUI_FOLDERS_BASE_PATH' not in new_env: + new_env["COMFYUI_FOLDERS_BASE_PATH"] = comfy_path + exit_code = process_wrap(script[1:], script[0], env=new_env) + + if exit_code != 0: + print(f"management script failed: {script[0]}") + else: + print(f"\n## ComfyUI-Manager: CANCELED => {script[1:]}") + + except Exception as e: + print(f"[ERROR] Failed to execute management script: {line} / {e}") + + # Remove the script_list_path file + if os.path.exists(script_list_path): + script_executed = True + os.remove(script_list_path) + + print("\n[ComfyUI-Manager] Startup script completed.") + print("#######################################################################\n") + + +# Check if script_list_path exists +# Block startup-scripts on old ComfyUI (security measure) +if not _has_system_user_api: + if os.path.exists(script_list_path): + print("[ComfyUI-Manager] Startup scripts blocked on old ComfyUI version.") +elif os.path.exists(script_list_path): + execute_startup_script() + + +pip_fixer.fix_broken() + +del processed_install +del pip_fixer +manager_util.clear_pip_cache() + +if script_executed: + # Restart + print("[ComfyUI-Manager] Restarting to reapply dependency installation.") + + if '__COMFY_CLI_SESSION__' in os.environ: + with open(os.path.join(os.environ['__COMFY_CLI_SESSION__'] + '.reboot'), 'w'): + pass + + print("--------------------------------------------------------------------------\n") + exit(0) + else: + sys_argv = sys.argv.copy() + + if sys_argv[0].endswith("__main__.py"): # this is a python module + module_name = os.path.basename(os.path.dirname(sys_argv[0])) + cmds = [sys.executable, '-m', module_name] + sys_argv[1:] + elif sys.platform.startswith('win32'): + cmds = ['"' + sys.executable + '"', '"' + sys_argv[0] + '"'] + sys_argv[1:] + else: + cmds = [sys.executable] + sys_argv + + print(f"Command: {cmds}", flush=True) + print("--------------------------------------------------------------------------\n") + + os.execv(sys.executable, cmds) + + +def check_windows_event_loop_policy(): + try: + import configparser + config = configparser.ConfigParser(strict=False) + config.read(manager_config_path) + default_conf = config['default'] + + if 'windows_selector_event_loop_policy' in default_conf and default_conf['windows_selector_event_loop_policy'].lower() == 'true': + try: + import asyncio + import asyncio.windows_events + asyncio.set_event_loop_policy(asyncio.windows_events.WindowsSelectorEventLoopPolicy()) + print("[ComfyUI-Manager] Windows event loop policy mode enabled") + except Exception as e: + print(f"[ComfyUI-Manager] WARN: Windows initialization fail: {e}") + except Exception: + pass + + +if platform.system() == 'Windows': + check_windows_event_loop_policy() diff --git a/custom_nodes/comfyui-manager/pyproject.toml b/custom_nodes/comfyui-manager/pyproject.toml new file mode 100644 index 00000000..e15ea6e4 --- /dev/null +++ b/custom_nodes/comfyui-manager/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-manager" +description = "ComfyUI-Manager provides features to install and manage custom nodes for ComfyUI, as well as various functionalities to assist with ComfyUI." +version = "3.39.2" +license = { file = "LICENSE.txt" } +dependencies = ["GitPython", "PyGithub", "matrix-nio", "transformers", "huggingface-hub>0.20", "typer", "rich", "typing-extensions", "toml", "uv", "chardet"] + +[project.urls] +Repository = "https://github.com/ltdrdata/ComfyUI-Manager" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "drltdata" +DisplayName = "ComfyUI-Manager" +Icon = "" diff --git a/custom_nodes/comfyui-manager/requirements.txt b/custom_nodes/comfyui-manager/requirements.txt new file mode 100644 index 00000000..b179f07f --- /dev/null +++ b/custom_nodes/comfyui-manager/requirements.txt @@ -0,0 +1,11 @@ +GitPython +PyGithub +matrix-nio +transformers +huggingface-hub +typer +rich +typing-extensions +toml +uv +chardet diff --git a/custom_nodes/comfyui-manager/ruff.toml b/custom_nodes/comfyui-manager/ruff.toml new file mode 100644 index 00000000..26ec1d89 --- /dev/null +++ b/custom_nodes/comfyui-manager/ruff.toml @@ -0,0 +1,12 @@ +# Disable all rules by default +lint.ignore = ["ALL"] + +# Enable specific rules +lint.select = [ + "S307", # suspicious-eval-usage + # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names. + # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f + "F", +] + +exclude = ["*.ipynb"] diff --git a/custom_nodes/comfyui-manager/scan.sh b/custom_nodes/comfyui-manager/scan.sh new file mode 100755 index 00000000..c35e90a5 --- /dev/null +++ b/custom_nodes/comfyui-manager/scan.sh @@ -0,0 +1,11 @@ +#!/bin/bash +rm ~/.tmp/default/*.py > /dev/null 2>&1 +python scanner.py ~/.tmp/default $* +cp extension-node-map.json node_db/new/. + +echo "Integrity check" +if [ -f "check2.sh" ]; then + ./check2.sh +else + ./check.sh +fi \ No newline at end of file diff --git a/custom_nodes/comfyui-manager/scanner.py b/custom_nodes/comfyui-manager/scanner.py new file mode 100644 index 00000000..c395dc12 --- /dev/null +++ b/custom_nodes/comfyui-manager/scanner.py @@ -0,0 +1,1562 @@ +import ast +import re +import os +import json +from git import Repo +import concurrent +import datetime +import concurrent.futures +import requests +import warnings +import argparse + +builtin_nodes = set() + +import sys + +from urllib.parse import urlparse +from github import Github, Auth +from pathlib import Path +from typing import Set, Dict, Optional + +# Scanner version for cache invalidation +SCANNER_VERSION = "2.0.13" # Add fallback for dynamic v3 node_id + +# Cache for extract_nodes and extract_nodes_enhanced results +_extract_nodes_cache: Dict[str, Set[str]] = {} +_extract_nodes_enhanced_cache: Dict[str, Set[str]] = {} +_file_mtime_cache: Dict[Path, float] = {} + + +def _get_repo_root(file_path: Path) -> Optional[Path]: + """Find the repository root directory containing .git""" + current = file_path if file_path.is_dir() else file_path.parent + while current != current.parent: + if (current / ".git").exists(): + return current + current = current.parent + return None + + +def _get_repo_hash(repo_path: Path) -> str: + """Get git commit hash or fallback identifier""" + git_dir = repo_path / ".git" + if not git_dir.exists(): + return "" + + try: + # Read HEAD to get current commit + head_file = git_dir / "HEAD" + if head_file.exists(): + head_content = head_file.read_text().strip() + if head_content.startswith("ref:"): + # HEAD points to a ref + ref_path = git_dir / head_content[5:].strip() + if ref_path.exists(): + commit_hash = ref_path.read_text().strip() + return commit_hash[:16] # First 16 chars + else: + # Detached HEAD + return head_content[:16] + except: + pass + + return "" + + +def _load_per_repo_cache(repo_path: Path) -> Optional[tuple]: + """Load nodes and metadata from per-repo cache + + Returns: + tuple: (nodes_set, metadata_dict) or None if cache invalid + """ + cache_file = repo_path / ".git" / "nodecache.json" + + if not cache_file.exists(): + return None + + try: + with open(cache_file, 'r') as f: + cache_data = json.load(f) + + # Verify scanner version + if cache_data.get('scanner_version') != SCANNER_VERSION: + return None + + # Verify git hash + current_hash = _get_repo_hash(repo_path) + if cache_data.get('git_hash') != current_hash: + return None + + # Return nodes and metadata + nodes = cache_data.get('nodes', []) + metadata = cache_data.get('metadata', {}) + return (set(nodes) if nodes else set(), metadata) + + except: + return None + + +def _save_per_repo_cache(repo_path: Path, all_nodes: Set[str], metadata: dict = None): + """Save nodes and metadata to per-repo cache""" + cache_file = repo_path / ".git" / "nodecache.json" + + if not cache_file.parent.exists(): + return + + git_hash = _get_repo_hash(repo_path) + cache_data = { + "scanner_version": SCANNER_VERSION, + "git_hash": git_hash, + "scanned_at": datetime.datetime.now().isoformat(), + "nodes": sorted(list(all_nodes)), + "metadata": metadata if metadata else {} + } + + try: + with open(cache_file, 'w') as f: + json.dump(cache_data, f, indent=2) + except: + pass # Silently fail - cache is optional + + +def download_url(url, dest_folder, filename=None): + # Ensure the destination folder exists + if not os.path.exists(dest_folder): + os.makedirs(dest_folder) + + # Extract filename from URL if not provided + if filename is None: + filename = os.path.basename(url) + + # Full path to save the file + dest_path = os.path.join(dest_folder, filename) + + # Download the file + response = requests.get(url, stream=True) + if response.status_code == 200: + with open(dest_path, 'wb') as file: + for chunk in response.iter_content(chunk_size=1024): + if chunk: + file.write(chunk) + else: + raise Exception(f"Failed to download file from {url}") + + +def parse_arguments(): + """Parse command-line arguments""" + parser = argparse.ArgumentParser( + description='ComfyUI Manager Node Scanner', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=''' +Examples: + # Standard mode + python3 scanner.py + python3 scanner.py --skip-update + python3 scanner.py --skip-all --force-rescan + + # Scan-only mode + python3 scanner.py --scan-only temp-urls-clean.list + python3 scanner.py --scan-only urls.list --temp-dir /custom/temp + python3 scanner.py --scan-only urls.list --skip-update --force-rescan + ''' + ) + + parser.add_argument('--scan-only', type=str, metavar='URL_LIST_FILE', + help='Scan-only mode: provide URL list file (one URL per line)') + parser.add_argument('--temp-dir', type=str, metavar='DIR', + help='Temporary directory for cloned repositories') + parser.add_argument('--skip-update', action='store_true', + help='Skip git clone/pull operations') + parser.add_argument('--skip-stat-update', action='store_true', + help='Skip GitHub stats collection') + parser.add_argument('--skip-all', action='store_true', + help='Skip all update operations') + parser.add_argument('--force-rescan', action='store_true', + help='Force rescan all nodes (ignore cache)') + + # Backward compatibility: positional argument for temp_dir + parser.add_argument('temp_dir_positional', nargs='?', metavar='TEMP_DIR', + help='(Legacy) Temporary directory path') + + args = parser.parse_args() + return args + + +# Module-level variables (will be set in main if running as script) +args = None +scan_only_mode = False +url_list_file = None +temp_dir = None +skip_update = False +skip_stat_update = True +g = None + + +parse_cnt = 0 + + +def extract_nodes(code_text): + global parse_cnt + + # Check cache first + cache_key = hash(code_text) + if cache_key in _extract_nodes_cache: + return _extract_nodes_cache[cache_key].copy() + + try: + if parse_cnt % 100 == 0: + print(".", end="", flush=True) + parse_cnt += 1 + + code_text = re.sub(r'\\[^"\']', '', code_text) + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + warnings.filterwarnings('ignore', category=DeprecationWarning) + parsed_code = ast.parse(code_text) + + # Support both ast.Assign and ast.AnnAssign (for type-annotated assignments) + assignments = (node for node in parsed_code.body if isinstance(node, (ast.Assign, ast.AnnAssign))) + + for assignment in assignments: + # Handle ast.AnnAssign (e.g., NODE_CLASS_MAPPINGS: Type = {...}) + if isinstance(assignment, ast.AnnAssign): + if isinstance(assignment.target, ast.Name) and assignment.target.id in ['NODE_CONFIG', 'NODE_CLASS_MAPPINGS']: + node_class_mappings = assignment.value + break + # Handle ast.Assign (e.g., NODE_CLASS_MAPPINGS = {...}) + elif isinstance(assignment.targets[0], ast.Name) and assignment.targets[0].id in ['NODE_CONFIG', 'NODE_CLASS_MAPPINGS']: + node_class_mappings = assignment.value + break + else: + node_class_mappings = None + + if node_class_mappings: + s = set() + + for key in node_class_mappings.keys: + if key is not None and isinstance(key.value, str): + s.add(key.value.strip()) + + # Cache the result + _extract_nodes_cache[cache_key] = s + return s + else: + # Cache empty result + _extract_nodes_cache[cache_key] = set() + return set() + except: + # Cache empty result on error + _extract_nodes_cache[cache_key] = set() + return set() + +def extract_nodes_from_repo(repo_path: Path, verbose: bool = False, force_rescan: bool = False) -> tuple: + """ + Extract all nodes and metadata from a repository with per-repo caching. + + Automatically caches results in .git/nodecache.json. + Cache is invalidated when: + - Git commit hash changes + - Scanner version changes + - force_rescan flag is True + + Args: + repo_path: Path to repository root + verbose: If True, print UI-only extension detection messages + force_rescan: If True, ignore cache and force fresh scan + + Returns: + tuple: (nodes_set, metadata_dict) + """ + # Ensure path is absolute + repo_path = repo_path.resolve() + + # Check per-repo cache first (unless force_rescan is True) + if not force_rescan: + cached_result = _load_per_repo_cache(repo_path) + if cached_result is not None: + return cached_result + + # Cache miss - scan all .py files + all_nodes = set() + all_metadata = {} + py_files = list(repo_path.rglob("*.py")) + + # Filter out __pycache__, .git, and other hidden directories + filtered_files = [] + for f in py_files: + try: + rel_path = f.relative_to(repo_path) + # Skip __pycache__, .git, and any directory starting with . + if '__pycache__' not in str(rel_path) and not any(part.startswith('.') for part in rel_path.parts): + filtered_files.append(f) + except: + continue + py_files = filtered_files + + for py_file in py_files: + try: + # Read file with proper encoding + with open(py_file, 'r', encoding='utf-8', errors='ignore') as f: + code = f.read() + + if code: + # Extract nodes using SAME logic as scan_in_file + # V1 nodes (enhanced with fallback patterns) + nodes = extract_nodes_enhanced(code, py_file, visited=set(), verbose=verbose) + all_nodes.update(nodes) + + # V3 nodes detection + v3_nodes = extract_v3_nodes(code) + all_nodes.update(v3_nodes) + + # Dict parsing - exclude commented NODE_CLASS_MAPPINGS lines + pattern = r"_CLASS_MAPPINGS\s*(?::\s*\w+\s*)?=\s*(?:\\\s*)?{([^}]*)}" + regex = re.compile(pattern, re.MULTILINE | re.DOTALL) + + for match_obj in regex.finditer(code): + # Get the line where NODE_CLASS_MAPPINGS is defined + match_start = match_obj.start() + line_start = code.rfind('\n', 0, match_start) + 1 + line_end = code.find('\n', match_start) + if line_end == -1: + line_end = len(code) + line = code[line_start:line_end] + + # Skip if line starts with # (commented) + if re.match(r'^\s*#', line): + continue + + match = match_obj.group(1) + + # Filter out commented lines from dict content + match_lines = match.split('\n') + match_filtered = '\n'.join( + line for line in match_lines + if not re.match(r'^\s*#', line) + ) + + # Extract key-value pairs with double quotes + key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", match_filtered) + for key, value in key_value_pairs: + all_nodes.add(key.strip()) + + # Extract key-value pairs with single quotes + key_value_pairs = re.findall(r"'([^']*)'\s*:\s*([^,\n]*)", match_filtered) + for key, value in key_value_pairs: + all_nodes.add(key.strip()) + + # Handle .update() pattern (AFTER comment removal) + code_cleaned = re.sub(r'^#.*?$', '', code, flags=re.MULTILINE) + + update_pattern = r"_CLASS_MAPPINGS\.update\s*\(\s*{([^}]*)}\s*\)" + update_match = re.search(update_pattern, code_cleaned, re.DOTALL) + if update_match: + update_dict_text = update_match.group(1) + # Extract key-value pairs (double quotes) + update_pairs = re.findall(r'"([^"]*)"\s*:\s*([^,\n]*)', update_dict_text) + for key, value in update_pairs: + all_nodes.add(key.strip()) + # Extract key-value pairs (single quotes) + update_pairs_single = re.findall(r"'([^']*)'\s*:\s*([^,\n]*)", update_dict_text) + for key, value in update_pairs_single: + all_nodes.add(key.strip()) + + # Additional regex patterns (AFTER comment removal) + patterns = [ + r'^[^=]*_CLASS_MAPPINGS\["(.*?)"\]', + r'^[^=]*_CLASS_MAPPINGS\[\'(.*?)\'\]', + r'@register_node\("(.+)",\s*\".+"\)', + r'"(\w+)"\s*:\s*{"class":\s*\w+\s*' + ] + + for pattern in patterns: + keys = re.findall(pattern, code_cleaned) + all_nodes.update(key.strip() for key in keys) + + # Extract metadata from this file + metadata = extract_metadata_only(str(py_file)) + all_metadata.update(metadata) + except Exception: + # Silently skip files that can't be read + continue + + # Save to per-repo cache + _save_per_repo_cache(repo_path, all_nodes, all_metadata) + + return (all_nodes, all_metadata) + + +def _verify_class_exists(node_name: str, code_text: str, file_path: Optional[Path] = None) -> tuple[bool, Optional[str], Optional[int]]: + """ + Verify that a node class exists and has ComfyUI node structure. + + Returns: (exists: bool, file_path: str, line_number: int) + + A valid ComfyUI node must have: + - Class definition (not commented) + - At least one of: INPUT_TYPES, RETURN_TYPES, FUNCTION method/attribute + """ + try: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + tree = ast.parse(code_text) + except: + return (False, None, None) + + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + if node.name == node_name or node.name.replace('_', '') == node_name.replace('_', ''): + # Found class definition - check if it has ComfyUI interface + has_input_types = False + has_return_types = False + has_function = False + + for item in node.body: + # Check for INPUT_TYPES method + if isinstance(item, ast.FunctionDef) and item.name == 'INPUT_TYPES': + has_input_types = True + # Check for RETURN_TYPES attribute + elif isinstance(item, ast.Assign): + for target in item.targets: + if isinstance(target, ast.Name): + if target.id == 'RETURN_TYPES': + has_return_types = True + elif target.id == 'FUNCTION': + has_function = True + # Check for FUNCTION method + elif isinstance(item, ast.FunctionDef): + has_function = True + + # Valid if has any ComfyUI signature + if has_input_types or has_return_types or has_function: + file_str = str(file_path) if file_path else None + return (True, file_str, node.lineno) + + return (False, None, None) + + +def _extract_display_name_mappings(code_text: str) -> Set[str]: + """ + Extract node names from NODE_DISPLAY_NAME_MAPPINGS. + + Pattern: + NODE_DISPLAY_NAME_MAPPINGS = { + "node_key": "Display Name", + ... + } + + Returns: + Set of node keys from NODE_DISPLAY_NAME_MAPPINGS + """ + try: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + tree = ast.parse(code_text) + except: + return set() + + nodes = set() + + for node in tree.body: + if isinstance(node, ast.Assign): + for target in node.targets: + if isinstance(target, ast.Name) and target.id == 'NODE_DISPLAY_NAME_MAPPINGS': + if isinstance(node.value, ast.Dict): + for key in node.value.keys: + if isinstance(key, ast.Constant) and isinstance(key.value, str): + nodes.add(key.value.strip()) + + return nodes + + +def extract_nodes_enhanced( + code_text: str, + file_path: Optional[Path] = None, + visited: Optional[Set[Path]] = None, + verbose: bool = False +) -> Set[str]: + """ + Enhanced node extraction with multi-layer detection system. + + Scanner 2.0.11 - Comprehensive detection strategy: + - Phase 1: NODE_CLASS_MAPPINGS dict literal + - Phase 2: Class.NAME attribute access (e.g., FreeChat.NAME) + - Phase 3: Item assignment (NODE_CLASS_MAPPINGS["key"] = value) + - Phase 4: Class existence verification (detects active classes even if registration commented) + - Phase 5: NODE_DISPLAY_NAME_MAPPINGS cross-reference + - Phase 6: Empty dict detection (UI-only extensions, logging only) + + Fixed Bugs: + - Scanner 2.0.9: Fallback cascade prevented Phase 3 execution + - Scanner 2.0.10: Missed active classes with commented registrations (15 false negatives) + + Args: + code_text: Python source code + file_path: Path to file (for logging and caching) + visited: Visited paths (for circular import prevention) + verbose: If True, print UI-only extension detection messages + + Returns: + Set of node names (union of all detected patterns) + """ + # Check file-based cache if file_path provided + if file_path is not None: + try: + file_path_obj = Path(file_path) if not isinstance(file_path, Path) else file_path + if file_path_obj.exists(): + current_mtime = file_path_obj.stat().st_mtime + + # Check if we have cached result with matching mtime and scanner version + if file_path_obj in _file_mtime_cache: + cached_mtime = _file_mtime_cache[file_path_obj] + cache_key = (str(file_path_obj), cached_mtime, SCANNER_VERSION) + + if current_mtime == cached_mtime and cache_key in _extract_nodes_enhanced_cache: + return _extract_nodes_enhanced_cache[cache_key].copy() + except: + pass # Ignore cache errors, proceed with normal execution + + # Suppress warnings from AST parsing + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + warnings.filterwarnings('ignore', category=DeprecationWarning) + + # Phase 1: Original extract_nodes() - dict literal + phase1_nodes = extract_nodes(code_text) + + # Phase 2: Class.NAME pattern + if visited is None: + visited = set() + phase2_nodes = _fallback_classname_resolver(code_text, file_path) + + # Phase 3: Item assignment pattern + phase3_nodes = _fallback_item_assignment(code_text) + + # Phase 4: NODE_DISPLAY_NAME_MAPPINGS cross-reference (NEW in 2.0.11) + # This catches nodes that are in display names but not in NODE_CLASS_MAPPINGS + phase4_nodes = _extract_display_name_mappings(code_text) + + # Phase 5: Class existence verification ONLY for display name candidates (NEW in 2.0.11) + # This phase is CONSERVATIVE - only verify classes that appear in display names + # This catches the specific Scanner 2.0.10 bug pattern: + # - NODE_CLASS_MAPPINGS registration is commented + # - NODE_DISPLAY_NAME_MAPPINGS still has the entry + # - Class implementation exists + # Example: Bjornulf_ollamaLoader in Bjornulf_custom_nodes + phase5_nodes = set() + for node_name in phase4_nodes: + # Only check classes that appear in display names but not in registrations + if node_name not in (phase1_nodes | phase2_nodes | phase3_nodes): + exists, _, _ = _verify_class_exists(node_name, code_text, file_path) + if exists: + phase5_nodes.add(node_name) + + # Phase 6: Dict comprehension pattern (NEW in 2.0.12) + # Detects: NODE_CLASS_MAPPINGS = {cls.__name__: cls for cls in to_export} + # Example: TobiasGlaubach/ComfyUI-TG_PyCode + phase6_nodes = _fallback_dict_comprehension(code_text, file_path) + + # Phase 7: Import-based class names for dict comprehension (NEW in 2.0.12) + # Detects imported classes that are added to export lists + phase7_nodes = _fallback_import_class_names(code_text, file_path) + + # Union all results (FIX: Scanner 2.0.9 bug + Scanner 2.0.10 bug + Scanner 2.0.12 dict comp) + # 2.0.9: Used early return which missed Phase 3 nodes + # 2.0.10: Only checked registrations, missed classes referenced in display names + # 2.0.12: Added dict comprehension and import-based class detection + all_nodes = phase1_nodes | phase2_nodes | phase3_nodes | phase4_nodes | phase5_nodes | phase6_nodes | phase7_nodes + + # Phase 8: Empty dict detector (logging only, doesn't add nodes) + if not all_nodes: + _fallback_empty_dict_detector(code_text, file_path, verbose) + + # Cache the result + if file_path is not None: + try: + file_path_obj = Path(file_path) if not isinstance(file_path, Path) else file_path + if file_path_obj.exists(): + current_mtime = file_path_obj.stat().st_mtime + cache_key = (str(file_path_obj), current_mtime, SCANNER_VERSION) + _extract_nodes_enhanced_cache[cache_key] = all_nodes + _file_mtime_cache[file_path_obj] = current_mtime + except: + pass + + return all_nodes + + +def _fallback_classname_resolver(code_text: str, file_path: Optional[Path]) -> Set[str]: + """ + Detect Class.NAME pattern in NODE_CLASS_MAPPINGS. + + Pattern: + NODE_CLASS_MAPPINGS = { + FreeChat.NAME: FreeChat, + PaidChat.NAME: PaidChat + } + """ + try: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + parsed = ast.parse(code_text) + except: + return set() + + nodes = set() + + for node in parsed.body: + if isinstance(node, ast.Assign): + for target in node.targets: + if isinstance(target, ast.Name) and target.id == 'NODE_CLASS_MAPPINGS': + if isinstance(node.value, ast.Dict): + for key in node.value.keys: + # Detect Class.NAME pattern + if isinstance(key, ast.Attribute): + if isinstance(key.value, ast.Name): + # Use class name as node name + nodes.add(key.value.id) + # Also handle literal strings + elif isinstance(key, ast.Constant) and isinstance(key.value, str): + nodes.add(key.value.strip()) + + return nodes + + +def _fallback_item_assignment(code_text: str) -> Set[str]: + """ + Detect item assignment pattern. + + Pattern: + NODE_CLASS_MAPPINGS = {} + NODE_CLASS_MAPPINGS["MyNode"] = MyNode + """ + try: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + parsed = ast.parse(code_text) + except: + return set() + + nodes = set() + + for node in ast.walk(parsed): + if isinstance(node, ast.Assign): + for target in node.targets: + if isinstance(target, ast.Subscript): + if (isinstance(target.value, ast.Name) and + target.value.id in ['NODE_CLASS_MAPPINGS', 'NODE_CONFIG']): + # Extract key + if isinstance(target.slice, ast.Constant): + if isinstance(target.slice.value, str): + nodes.add(target.slice.value) + + return nodes + + +def _fallback_dict_comprehension(code_text: str, file_path: Optional[Path] = None) -> Set[str]: + """ + Detect dict comprehension pattern with __name__ attribute access. + + Pattern: + NODE_CLASS_MAPPINGS = {cls.__name__: cls for cls in to_export} + NODE_CLASS_MAPPINGS = {c.__name__: c for c in [ClassA, ClassB]} + + This function detects dict comprehension assignments to NODE_CLASS_MAPPINGS + and extracts class names from the iterable (list literal or variable reference). + + Returns: + Set of class names extracted from the dict comprehension + """ + try: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + parsed = ast.parse(code_text) + except: + return set() + + nodes = set() + export_lists = {} # Track list variables and their contents + + # First pass: collect list assignments (to_export = [...], exports = [...]) + for node in ast.walk(parsed): + if isinstance(node, ast.Assign): + for target in node.targets: + if isinstance(target, ast.Name): + var_name = target.id + # Check for list literal + if isinstance(node.value, ast.List): + class_names = set() + for elt in node.value.elts: + if isinstance(elt, ast.Name): + class_names.add(elt.id) + export_lists[var_name] = class_names + + # Handle augmented assignment: to_export += [...] + elif isinstance(node, ast.AugAssign): + if isinstance(node.target, ast.Name) and isinstance(node.op, ast.Add): + var_name = node.target.id + if isinstance(node.value, ast.List): + class_names = set() + for elt in node.value.elts: + if isinstance(elt, ast.Name): + class_names.add(elt.id) + if var_name in export_lists: + export_lists[var_name].update(class_names) + else: + export_lists[var_name] = class_names + + # Second pass: find NODE_CLASS_MAPPINGS dict comprehension + for node in ast.walk(parsed): + if isinstance(node, ast.Assign): + for target in node.targets: + if isinstance(target, ast.Name) and target.id in ['NODE_CLASS_MAPPINGS', 'NODE_CONFIG']: + # Check for dict comprehension + if isinstance(node.value, ast.DictComp): + dictcomp = node.value + + # Check if key is cls.__name__ pattern + key = dictcomp.key + if isinstance(key, ast.Attribute) and key.attr == '__name__': + # Get the iterable from the first generator + for generator in dictcomp.generators: + iter_node = generator.iter + + # Case 1: Inline list [ClassA, ClassB, ...] + if isinstance(iter_node, ast.List): + for elt in iter_node.elts: + if isinstance(elt, ast.Name): + nodes.add(elt.id) + + # Case 2: Variable reference (to_export, exports, etc.) + elif isinstance(iter_node, ast.Name): + var_name = iter_node.id + if var_name in export_lists: + nodes.update(export_lists[var_name]) + + return nodes + + +def _fallback_import_class_names(code_text: str, file_path: Optional[Path] = None) -> Set[str]: + """ + Extract class names from imports that are added to export lists. + + Pattern: + from .module import ClassA, ClassB + to_export = [ClassA, ClassB] + NODE_CLASS_MAPPINGS = {cls.__name__: cls for cls in to_export} + + This is a complementary fallback that works with _fallback_dict_comprehension + to resolve import-based node registrations. + + Returns: + Set of imported class names that appear in export-like contexts + """ + try: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + parsed = ast.parse(code_text) + except: + return set() + + # Collect imported names + imported_names = set() + for node in ast.walk(parsed): + if isinstance(node, ast.ImportFrom): + for alias in node.names: + name = alias.asname if alias.asname else alias.name + imported_names.add(name) + + # Check if these names appear in list assignments that feed into NODE_CLASS_MAPPINGS + export_candidates = set() + has_dict_comp_mapping = False + + for node in ast.walk(parsed): + # Check for dict comprehension NODE_CLASS_MAPPINGS + if isinstance(node, ast.Assign): + for target in node.targets: + if isinstance(target, ast.Name) and target.id == 'NODE_CLASS_MAPPINGS': + if isinstance(node.value, ast.DictComp): + has_dict_comp_mapping = True + + # Collect list contents + if isinstance(node, ast.Assign): + if isinstance(node.value, ast.List): + for elt in node.value.elts: + if isinstance(elt, ast.Name) and elt.id in imported_names: + export_candidates.add(elt.id) + + # Handle augmented assignment + elif isinstance(node, ast.AugAssign): + if isinstance(node.value, ast.List): + for elt in node.value.elts: + if isinstance(elt, ast.Name) and elt.id in imported_names: + export_candidates.add(elt.id) + + # Only return if there's a dict comprehension mapping + if has_dict_comp_mapping: + return export_candidates + + return set() + + +def _extract_repo_name(file_path: Path) -> str: + """ + Extract repository name from file path. + + Path structure: /home/rho/.tmp/analysis/temp/{author}_{reponame}/{path/to/file.py} + Returns: {author}_{reponame} or filename if extraction fails + """ + try: + parts = file_path.parts + # Find 'temp' directory in path + if 'temp' in parts: + temp_idx = parts.index('temp') + if temp_idx + 1 < len(parts): + # Next part after 'temp' is the repo directory + return parts[temp_idx + 1] + except (ValueError, IndexError): + pass + + # Fallback to filename if extraction fails + return file_path.name if hasattr(file_path, 'name') else str(file_path) + + +def _fallback_empty_dict_detector(code_text: str, file_path: Optional[Path], verbose: bool = False) -> None: + """ + Detect empty NODE_CLASS_MAPPINGS (UI-only extensions). + Logs for documentation purposes only (when verbose=True). + + Args: + code_text: Python source code to analyze + file_path: Path to the file being analyzed + verbose: If True, print detection messages + """ + empty_patterns = [ + 'NODE_CLASS_MAPPINGS = {}', + 'NODE_CLASS_MAPPINGS={}', + ] + + code_normalized = code_text.replace(' ', '').replace('\n', '') + + for pattern in empty_patterns: + pattern_normalized = pattern.replace(' ', '') + if pattern_normalized in code_normalized: + if file_path and verbose: + repo_name = _extract_repo_name(file_path) + print(f"Info: UI-only extension (empty NODE_CLASS_MAPPINGS): {repo_name}") + return + +def has_comfy_node_base(class_node): + """Check if class inherits from io.ComfyNode or ComfyNode""" + for base in class_node.bases: + # Case 1: ComfyNode + if isinstance(base, ast.Name) and base.id == 'ComfyNode': + return True + # Case 2: io.ComfyNode + elif isinstance(base, ast.Attribute): + if base.attr == 'ComfyNode': + return True + return False + + +def extract_keyword_value(call_node, keyword): + """ + Extract string value of keyword argument + Schema(node_id="MyNode") -> "MyNode" + """ + for kw in call_node.keywords: + if kw.arg == keyword: + # ast.Constant (Python 3.8+) + if isinstance(kw.value, ast.Constant): + if isinstance(kw.value.value, str): + return kw.value.value + # ast.Str (Python 3.7-) - suppress deprecation warning + else: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=DeprecationWarning) + if hasattr(ast, 'Str') and isinstance(kw.value, ast.Str): + return kw.value.s + return None + + +def is_schema_call(call_node): + """Check if ast.Call is io.Schema() or Schema()""" + func = call_node.func + if isinstance(func, ast.Name) and func.id == 'Schema': + return True + elif isinstance(func, ast.Attribute) and func.attr == 'Schema': + return True + return False + + +def extract_node_id_from_schema(class_node): + """ + Extract node_id from define_schema() method + """ + for item in class_node.body: + if isinstance(item, ast.FunctionDef) and item.name == 'define_schema': + # Walk through function body + for stmt in ast.walk(item): + if isinstance(stmt, ast.Call): + # Check if it's Schema() call + if is_schema_call(stmt): + node_id = extract_keyword_value(stmt, 'node_id') + if node_id: + return node_id + return None + + +def extract_v3_nodes(code_text): + """ + Extract V3 node IDs using AST parsing + Returns: set of node_id strings + """ + global parse_cnt + + try: + if parse_cnt % 100 == 0: + print(".", end="", flush=True) + parse_cnt += 1 + + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=SyntaxWarning) + warnings.filterwarnings('ignore', category=DeprecationWarning) + tree = ast.parse(code_text) + except (SyntaxError, UnicodeDecodeError): + return set() + + nodes = set() + + # Find io.ComfyNode subclasses + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + # Check if inherits from ComfyNode + if has_comfy_node_base(node): + node_id = extract_node_id_from_schema(node) + if node_id: + nodes.add(node_id) + else: + # Fallback: use class name when node_id is dynamic/empty + nodes.add(node.name) + + return nodes + + +# scan +def extract_metadata_only(filename): + """Extract only metadata (@author, @title, etc) without node scanning""" + try: + with open(filename, encoding='utf-8', errors='ignore') as file: + code = file.read() + + metadata = {} + lines = code.strip().split('\n') + for line in lines: + if line.startswith('@'): + if line.startswith("@author:") or line.startswith("@title:") or line.startswith("@nickname:") or line.startswith("@description:"): + key, value = line[1:].strip().split(':', 1) + metadata[key.strip()] = value.strip() + + return metadata + except: + return {} + + +def scan_in_file(filename, is_builtin=False): + global builtin_nodes + + with open(filename, encoding='utf-8', errors='ignore') as file: + code = file.read() + + # Support type annotations (e.g., NODE_CLASS_MAPPINGS: Type = {...}) and line continuations (\) + pattern = r"_CLASS_MAPPINGS\s*(?::\s*\w+\s*)?=\s*(?:\\\s*)?{([^}]*)}" + regex = re.compile(pattern, re.MULTILINE | re.DOTALL) + + nodes = set() + class_dict = {} + + # V1 nodes detection (enhanced with fallback patterns) + nodes |= extract_nodes_enhanced(code, file_path=Path(filename), visited=set()) + + # V3 nodes detection + nodes |= extract_v3_nodes(code) + code = re.sub(r'^#.*?$', '', code, flags=re.MULTILINE) + + def extract_keys(pattern, code): + keys = re.findall(pattern, code) + return {key.strip() for key in keys} + + def update_nodes(nodes, new_keys): + nodes |= new_keys + + patterns = [ + r'^[^=]*_CLASS_MAPPINGS\["(.*?)"\]', + r'^[^=]*_CLASS_MAPPINGS\[\'(.*?)\'\]', + r'@register_node\("(.+)",\s*\".+"\)', + r'"(\w+)"\s*:\s*{"class":\s*\w+\s*' + ] + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = {executor.submit(extract_keys, pattern, code): pattern for pattern in patterns} + for future in concurrent.futures.as_completed(futures): + update_nodes(nodes, future.result()) + + matches = regex.findall(code) + for match in matches: + dict_text = match + + key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", dict_text) + for key, value in key_value_pairs: + class_dict[key.strip()] = value.strip() + + key_value_pairs = re.findall(r"'([^']*)'\s*:\s*([^,\n]*)", dict_text) + for key, value in key_value_pairs: + class_dict[key.strip()] = value.strip() + + for key, value in class_dict.items(): + nodes.add(key.strip()) + + update_pattern = r"_CLASS_MAPPINGS.update\s*\({([^}]*)}\)" + update_match = re.search(update_pattern, code) + if update_match: + update_dict_text = update_match.group(1) + update_key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", update_dict_text) + for key, value in update_key_value_pairs: + class_dict[key.strip()] = value.strip() + nodes.add(key.strip()) + + metadata = {} + lines = code.strip().split('\n') + for line in lines: + if line.startswith('@'): + if line.startswith("@author:") or line.startswith("@title:") or line.startswith("@nickname:") or line.startswith("@description:"): + key, value = line[1:].strip().split(':', 1) + metadata[key.strip()] = value.strip() + + if is_builtin: + builtin_nodes += set(nodes) + else: + for x in builtin_nodes: + if x in nodes: + nodes.remove(x) + + return nodes, metadata + + +def get_py_file_paths(dirname): + file_paths = [] + + for root, dirs, files in os.walk(dirname): + if ".git" in root or "__pycache__" in root: + continue + + for file in files: + if file.endswith(".py"): + file_path = os.path.join(root, file) + file_paths.append(file_path) + + return file_paths + + +def get_nodes(target_dir): + py_files = [] + directories = [] + + for item in os.listdir(target_dir): + if ".git" in item or "__pycache__" in item: + continue + + path = os.path.abspath(os.path.join(target_dir, item)) + + if os.path.isfile(path) and item.endswith(".py"): + py_files.append(path) + elif os.path.isdir(path): + directories.append(path) + + return py_files, directories + + +def get_urls_from_list_file(list_file): + """ + Read URLs from list file for scan-only mode + + Args: + list_file (str): Path to URL list file (one URL per line) + + Returns: + list of tuples: [(url, "", None, None), ...] + Format: (url, title, preemptions, nodename_pattern) + - title: Empty string + - preemptions: None + - nodename_pattern: None + + File format: + https://github.com/owner/repo1 + https://github.com/owner/repo2 + # Comments starting with # are ignored + + Raises: + FileNotFoundError: If list_file does not exist + """ + if not os.path.exists(list_file): + raise FileNotFoundError(f"URL list file not found: {list_file}") + + urls = [] + with open(list_file, 'r', encoding='utf-8') as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + + # Skip empty lines and comments + if not line or line.startswith('#'): + continue + + # Validate URL format (basic check) + if not (line.startswith('http://') or line.startswith('https://')): + print(f"WARNING: Line {line_num} is not a valid URL: {line}") + continue + + # Add URL with empty metadata + # (url, title, preemptions, nodename_pattern) + urls.append((line, "", None, None)) + + print(f"Loaded {len(urls)} URLs from {list_file}") + return urls + + +def get_git_urls_from_json(json_file): + with open(json_file, encoding='utf-8') as file: + data = json.load(file) + + custom_nodes = data.get('custom_nodes', []) + git_clone_files = [] + for node in custom_nodes: + if node.get('install_type') == 'git-clone': + files = node.get('files', []) + if files: + git_clone_files.append((files[0], node.get('title'), node.get('preemptions'), node.get('nodename_pattern'))) + + git_clone_files.append(("https://github.com/comfyanonymous/ComfyUI", "ComfyUI", None, None)) + + return git_clone_files + + +def get_py_urls_from_json(json_file): + with open(json_file, encoding='utf-8') as file: + data = json.load(file) + + custom_nodes = data.get('custom_nodes', []) + py_files = [] + for node in custom_nodes: + if node.get('install_type') == 'copy': + files = node.get('files', []) + if files: + py_files.append((files[0], node.get('title'), node.get('preemptions'), node.get('nodename_pattern'))) + + return py_files + + +def clone_or_pull_git_repository(git_url): + repo_name = git_url.split("/")[-1] + if repo_name.endswith(".git"): + repo_name = repo_name[:-4] + + repo_dir = os.path.join(temp_dir, repo_name) + + if os.path.exists(repo_dir): + try: + repo = Repo(repo_dir) + origin = repo.remote(name="origin") + origin.pull() + repo.git.submodule('update', '--init', '--recursive') + print(f"Pulling {repo_name}...") + except Exception as e: + print(f"Failed to pull '{repo_name}': {e}") + else: + try: + Repo.clone_from(git_url, repo_dir, recursive=True) + print(f"Cloning {repo_name}...") + except Exception as e: + print(f"Failed to clone '{repo_name}': {e}") + + +def update_custom_nodes(scan_only_mode=False, url_list_file=None): + """ + Update custom nodes by cloning/pulling repositories + + Args: + scan_only_mode (bool): If True, use URL list file instead of custom-node-list.json + url_list_file (str): Path to URL list file (required if scan_only_mode=True) + + Returns: + dict: node_info mapping {repo_name: (url, title, preemptions, node_pattern)} + """ + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + node_info = {} + + # Select URL source based on mode + if scan_only_mode: + if not url_list_file: + raise ValueError("url_list_file is required in scan-only mode") + + git_url_titles_preemptions = get_urls_from_list_file(url_list_file) + print("\n[Scan-Only Mode]") + print(f" - URL source: {url_list_file}") + print(" - GitHub stats: DISABLED") + print(f" - Git clone/pull: {'ENABLED' if not skip_update else 'DISABLED'}") + print(" - Metadata: EMPTY") + else: + if not os.path.exists('custom-node-list.json'): + raise FileNotFoundError("custom-node-list.json not found") + + git_url_titles_preemptions = get_git_urls_from_json('custom-node-list.json') + print("\n[Standard Mode]") + print(" - URL source: custom-node-list.json") + print(f" - GitHub stats: {'ENABLED' if not skip_stat_update else 'DISABLED'}") + print(f" - Git clone/pull: {'ENABLED' if not skip_update else 'DISABLED'}") + print(" - Metadata: FULL") + + def process_git_url_title(url, title, preemptions, node_pattern): + name = os.path.basename(url) + if name.endswith(".git"): + name = name[:-4] + + node_info[name] = (url, title, preemptions, node_pattern) + if not skip_update: + clone_or_pull_git_repository(url) + + def process_git_stats(git_url_titles_preemptions): + GITHUB_STATS_CACHE_FILENAME = 'github-stats-cache.json' + GITHUB_STATS_FILENAME = 'github-stats.json' + + github_stats = {} + try: + with open(GITHUB_STATS_CACHE_FILENAME, 'r', encoding='utf-8') as file: + github_stats = json.load(file) + except FileNotFoundError: + pass + + def is_rate_limit_exceeded(): + return g.rate_limiting[0] <= 20 + + if is_rate_limit_exceeded(): + print(f"GitHub API Rate Limit Exceeded: remained - {(g.rate_limiting_resettime - datetime.datetime.now().timestamp())/60:.2f} min") + else: + def renew_stat(url): + if is_rate_limit_exceeded(): + return + + if 'github.com' not in url: + return None + + print('.', end="") + sys.stdout.flush() + try: + # Parsing the URL + parsed_url = urlparse(url) + domain = parsed_url.netloc + path = parsed_url.path + path_parts = path.strip("/").split("/") + if len(path_parts) >= 2 and domain == "github.com": + owner_repo = "/".join(path_parts[-2:]) + repo = g.get_repo(owner_repo) + owner = repo.owner + now = datetime.datetime.now(datetime.timezone.utc) + author_time_diff = now - owner.created_at + + last_update = repo.pushed_at.strftime("%Y-%m-%d %H:%M:%S") if repo.pushed_at else 'N/A' + item = { + "stars": repo.stargazers_count, + "last_update": last_update, + "cached_time": now.timestamp(), + "author_account_age_days": author_time_diff.days, + } + return url, item + else: + print(f"\nInvalid URL format for GitHub repository: {url}\n") + except Exception as e: + print(f"\nERROR on {url}\n{e}") + + return None + + # resolve unresolved urls + with concurrent.futures.ThreadPoolExecutor(11) as executor: + futures = [] + for url, title, preemptions, node_pattern in git_url_titles_preemptions: + if url not in github_stats: + futures.append(executor.submit(renew_stat, url)) + + for future in concurrent.futures.as_completed(futures): + url_item = future.result() + if url_item is not None: + url, item = url_item + github_stats[url] = item + + # renew outdated cache + outdated_urls = [] + for k, v in github_stats.items(): + elapsed = (datetime.datetime.now().timestamp() - v['cached_time']) + if elapsed > 60*60*12: # 12 hours + outdated_urls.append(k) + + with concurrent.futures.ThreadPoolExecutor(11) as executor: + for url in outdated_urls: + futures.append(executor.submit(renew_stat, url)) + + for future in concurrent.futures.as_completed(futures): + url_item = future.result() + if url_item is not None: + url, item = url_item + github_stats[url] = item + + with open('github-stats-cache.json', 'w', encoding='utf-8') as file: + json.dump(github_stats, file, ensure_ascii=False, indent=4) + + with open(GITHUB_STATS_FILENAME, 'w', encoding='utf-8') as file: + for v in github_stats.values(): + if "cached_time" in v: + del v["cached_time"] + + github_stats = dict(sorted(github_stats.items())) + + json.dump(github_stats, file, ensure_ascii=False, indent=4) + + print(f"Successfully written to {GITHUB_STATS_FILENAME}.") + + if not skip_stat_update: + process_git_stats(git_url_titles_preemptions) + + # Git clone/pull for all repositories + with concurrent.futures.ThreadPoolExecutor(11) as executor: + for url, title, preemptions, node_pattern in git_url_titles_preemptions: + executor.submit(process_git_url_title, url, title, preemptions, node_pattern) + + # .py file download (skip in scan-only mode - only process git repos) + if not scan_only_mode: + py_url_titles_and_pattern = get_py_urls_from_json('custom-node-list.json') + + def download_and_store_info(url_title_preemptions_and_pattern): + url, title, preemptions, node_pattern = url_title_preemptions_and_pattern + name = os.path.basename(url) + if name.endswith(".py"): + node_info[name] = (url, title, preemptions, node_pattern) + + try: + download_url(url, temp_dir) + except: + print(f"[ERROR] Cannot download '{url}'") + + with concurrent.futures.ThreadPoolExecutor(10) as executor: + executor.map(download_and_store_info, py_url_titles_and_pattern) + + return node_info + + +def gen_json(node_info, scan_only_mode=False, force_rescan=False): + """ + Generate extension-node-map.json from scanned node information + + Args: + node_info (dict): Repository metadata mapping + scan_only_mode (bool): If True, exclude metadata from output + force_rescan (bool): If True, ignore cache and force rescan all nodes + """ + # scan from .py file + node_files, node_dirs = get_nodes(temp_dir) + + comfyui_path = os.path.abspath(os.path.join(temp_dir, "ComfyUI")) + # Only reorder if ComfyUI exists in the list + if comfyui_path in node_dirs: + node_dirs.remove(comfyui_path) + node_dirs = [comfyui_path] + node_dirs + + data = {} + for dirname in node_dirs: + py_files = get_py_file_paths(dirname) + metadata = {} + + # Use per-repo cache for node AND metadata extraction + try: + nodes, metadata = extract_nodes_from_repo(Path(dirname), verbose=False, force_rescan=force_rescan) + except: + # Fallback to file-by-file scanning if extract_nodes_from_repo fails + nodes = set() + for py in py_files: + nodes_in_file, metadata_in_file = scan_in_file(py, dirname == "ComfyUI") + nodes.update(nodes_in_file) + metadata.update(metadata_in_file) + + dirname = os.path.basename(dirname) + + if 'Jovimetrix' in dirname: + pass + + if len(nodes) > 0 or (dirname in node_info and node_info[dirname][3] is not None): + nodes = list(nodes) + nodes.sort() + + if dirname in node_info: + git_url, title, preemptions, node_pattern = node_info[dirname] + + # Conditionally add metadata based on mode + if not scan_only_mode: + # Standard mode: include all metadata + metadata['title_aux'] = title + + if preemptions is not None: + metadata['preemptions'] = preemptions + + if node_pattern is not None: + metadata['nodename_pattern'] = node_pattern + # Scan-only mode: metadata remains empty + + data[git_url] = (nodes, metadata) + else: + # Scan-only mode: Repository not in node_info (expected behavior) + # Construct URL from dirname (author_repo format) + if '_' in dirname: + parts = dirname.split('_', 1) + git_url = f"https://github.com/{parts[0]}/{parts[1]}" + data[git_url] = (nodes, metadata) + else: + print(f"WARN: {dirname} is removed from custom-node-list.json") + + for file in node_files: + nodes, metadata = scan_in_file(file) + + if len(nodes) > 0 or (dirname in node_info and node_info[dirname][3] is not None): + nodes = list(nodes) + nodes.sort() + + file = os.path.basename(file) + + if file in node_info: + url, title, preemptions, node_pattern = node_info[file] + + # Conditionally add metadata based on mode + if not scan_only_mode: + metadata['title_aux'] = title + + if preemptions is not None: + metadata['preemptions'] = preemptions + + if node_pattern is not None: + metadata['nodename_pattern'] = node_pattern + + data[url] = (nodes, metadata) + else: + print(f"Missing info: {file}") + + # scan from node_list.json file + extensions = [name for name in os.listdir(temp_dir) if os.path.isdir(os.path.join(temp_dir, name))] + + for extension in extensions: + node_list_json_path = os.path.join(temp_dir, extension, 'node_list.json') + if os.path.exists(node_list_json_path): + # Skip if extension not in node_info (scan-only mode with limited URLs) + if extension not in node_info: + continue + + git_url, title, preemptions, node_pattern = node_info[extension] + + with open(node_list_json_path, 'r', encoding='utf-8') as f: + try: + node_list_json = json.load(f) + except Exception as e: + print(f"\nERROR: Invalid json format '{node_list_json_path}'") + print("------------------------------------------------------") + print(e) + print("------------------------------------------------------") + node_list_json = {} + + metadata_in_url = {} + if git_url not in data: + nodes = set() + else: + nodes_in_url, metadata_in_url = data[git_url] + nodes = set(nodes_in_url) + + try: + for x, desc in node_list_json.items(): + nodes.add(x.strip()) + except Exception as e: + print(f"\nERROR: Invalid json format '{node_list_json_path}'") + print("------------------------------------------------------") + print(e) + print("------------------------------------------------------") + node_list_json = {} + + # Conditionally add metadata based on mode + if not scan_only_mode: + metadata_in_url['title_aux'] = title + + if preemptions is not None: + metadata_in_url['preemptions'] = preemptions + + if node_pattern is not None: + metadata_in_url['nodename_pattern'] = node_pattern + + nodes = list(nodes) + nodes.sort() + data[git_url] = (nodes, metadata_in_url) + + json_path = "extension-node-map.json" + with open(json_path, "w", encoding='utf-8') as file: + json.dump(data, file, indent=4, sort_keys=True) + + +if __name__ == "__main__": + # Parse arguments + args = parse_arguments() + + # Determine mode + scan_only_mode = args.scan_only is not None + url_list_file = args.scan_only if scan_only_mode else None + + # Determine temp_dir + if args.temp_dir: + temp_dir = args.temp_dir + elif args.temp_dir_positional: + temp_dir = args.temp_dir_positional + else: + temp_dir = os.path.join(os.getcwd(), ".tmp") + + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + # Determine skip flags + skip_update = args.skip_update or args.skip_all + skip_stat_update = args.skip_stat_update or args.skip_all or scan_only_mode + + if not skip_stat_update: + auth = Auth.Token(os.environ.get('GITHUB_TOKEN')) + g = Github(auth=auth) + else: + g = None + + print("### ComfyUI Manager Node Scanner ###") + + if scan_only_mode: + print(f"\n# [Scan-Only Mode] Processing URL list: {url_list_file}\n") + else: + print("\n# [Standard Mode] Updating extensions\n") + + # Update/clone repositories and collect node info + updated_node_info = update_custom_nodes(scan_only_mode, url_list_file) + + print("\n# Generating 'extension-node-map.json'...\n") + + # Generate extension-node-map.json + force_rescan = args.force_rescan if hasattr(args, 'force_rescan') else False + if force_rescan: + print("⚠️ Force rescan enabled - ignoring all cached results\n") + gen_json(updated_node_info, scan_only_mode, force_rescan) + + print("\nβœ… DONE.\n") + + if scan_only_mode: + print("Output: extension-node-map.json (node mappings only)") + else: + print("Output: extension-node-map.json (full metadata)") diff --git a/custom_nodes/comfyui-manager/scripts/colab-dependencies.py b/custom_nodes/comfyui-manager/scripts/colab-dependencies.py new file mode 100644 index 00000000..d5a70ed6 --- /dev/null +++ b/custom_nodes/comfyui-manager/scripts/colab-dependencies.py @@ -0,0 +1,39 @@ +import os +import subprocess + + +def get_enabled_subdirectories_with_files(base_directory): + subdirs_with_files = [] + for subdir in os.listdir(base_directory): + try: + full_path = os.path.join(base_directory, subdir) + if os.path.isdir(full_path) and not subdir.endswith(".disabled") and not subdir.startswith('.') and subdir != '__pycache__': + print(f"## Install dependencies for '{subdir}'") + requirements_file = os.path.join(full_path, "requirements.txt") + install_script = os.path.join(full_path, "install.py") + + if os.path.exists(requirements_file) or os.path.exists(install_script): + subdirs_with_files.append((full_path, requirements_file, install_script)) + except Exception as e: + print(f"EXCEPTION During Dependencies INSTALL on '{subdir}':\n{e}") + + return subdirs_with_files + + +def install_requirements(requirements_file_path): + if os.path.exists(requirements_file_path): + subprocess.run(["pip", "install", "-r", requirements_file_path]) + + +def run_install_script(install_script_path): + if os.path.exists(install_script_path): + subprocess.run(["python", install_script_path]) + + +custom_nodes_directory = "custom_nodes" +subdirs_with_files = get_enabled_subdirectories_with_files(custom_nodes_directory) + + +for subdir, requirements_file, install_script in subdirs_with_files: + install_requirements(requirements_file) + run_install_script(install_script) diff --git a/custom_nodes/comfyui-manager/scripts/install-comfyui-venv-linux.sh b/custom_nodes/comfyui-manager/scripts/install-comfyui-venv-linux.sh new file mode 100755 index 00000000..5a736ef1 --- /dev/null +++ b/custom_nodes/comfyui-manager/scripts/install-comfyui-venv-linux.sh @@ -0,0 +1,21 @@ +git clone https://github.com/comfyanonymous/ComfyUI +cd ComfyUI/custom_nodes +git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager +cd .. +python -m venv venv +source venv/bin/activate +python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 +python -m pip install -r requirements.txt +python -m pip install -r custom_nodes/comfyui-manager/requirements.txt +cd .. +echo "#!/bin/bash" > run_gpu.sh +echo "cd ComfyUI" >> run_gpu.sh +echo "source venv/bin/activate" >> run_gpu.sh +echo "python main.py --preview-method auto" >> run_gpu.sh +chmod +x run_gpu.sh + +echo "#!/bin/bash" > run_cpu.sh +echo "cd ComfyUI" >> run_cpu.sh +echo "source venv/bin/activate" >> run_cpu.sh +echo "python main.py --preview-method auto --cpu" >> run_cpu.sh +chmod +x run_cpu.sh diff --git a/custom_nodes/comfyui-manager/scripts/install-comfyui-venv-win.bat b/custom_nodes/comfyui-manager/scripts/install-comfyui-venv-win.bat new file mode 100755 index 00000000..47470098 --- /dev/null +++ b/custom_nodes/comfyui-manager/scripts/install-comfyui-venv-win.bat @@ -0,0 +1,17 @@ +git clone https://github.com/comfyanonymous/ComfyUI +cd ComfyUI/custom_nodes +git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager +cd .. +python -m venv venv +call venv/Scripts/activate +python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 +python -m pip install -r requirements.txt +python -m pip install -r custom_nodes/comfyui-manager/requirements.txt +cd .. +echo "cd ComfyUI" >> run_gpu.bat +echo "call venv/Scripts/activate" >> run_gpu.bat +echo "python main.py" >> run_gpu.bat + +echo "cd ComfyUI" >> run_cpu.bat +echo "call venv/Scripts/activate" >> run_cpu.bat +echo "python main.py --cpu" >> run_cpu.bat diff --git a/custom_nodes/comfyui-manager/scripts/install-manager-for-portable-version.bat b/custom_nodes/comfyui-manager/scripts/install-manager-for-portable-version.bat new file mode 100644 index 00000000..6eb58b74 --- /dev/null +++ b/custom_nodes/comfyui-manager/scripts/install-manager-for-portable-version.bat @@ -0,0 +1,3 @@ +.\python_embeded\python.exe -s -m pip install gitpython +.\python_embeded\python.exe -c "import git; git.Repo.clone_from('https://github.com/ltdrdata/ComfyUI-Manager', './ComfyUI/custom_nodes/comfyui-manager')" +.\python_embeded\python.exe -m pip install -r ./ComfyUI/custom_nodes/comfyui-manager/requirements.txt diff --git a/custom_nodes/comfyui-mxtoolkit/LICENSE b/custom_nodes/comfyui-mxtoolkit/LICENSE new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/comfyui-mxtoolkit/README.md b/custom_nodes/comfyui-mxtoolkit/README.md new file mode 100644 index 00000000..7e5e25aa --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/README.md @@ -0,0 +1,56 @@ +# ComfyUI-mxToolkit + +A set of useful nodes for convenient use of ComfyUI, including: Seed randomization before the generation process starts, with saving of the last used values and the ability to automatically interrupt the current generation; A function to pause the generation process; Slider nodes for convenient control of input parameters; An alternative version of the standard Reroute node. + +![](mxtoolkit.png) + +---------- +Check the [Wiki](https://github.com/Smirnov75/comfyui-mxtoolkit/wiki) for an example of usage. + +Nodes are available in the `utils` section + +##### Seed (v0.9.0) + +Allows randomizing the seed parameter before generation starts, preserving it for subsequent copying or reapplication. It retains memory of the 2 previous values. The node has the option to enable or disable automatic generation startup, as well as to interrupt the current generation when starting the next. If you press the randomization button while holding the shift key, the seed value will increase by one. + +##### Stop (v0.9.2) + +Interrupts generation, passing input data to output. Generation can be resumed from the interruption point by pressing the Continue button in the top right corner of the node or the Queue button. + +##### Slider (v0.9.2) + +Slider with a single output. If the `decimals` parameter value is zero, the output is an INT variable; otherwise, the output is a FLOAT variable. The `snap` parameter toggles snapping according to the set `step` parameter. Pressing the shift key while adjusting inverts the snap parameter value. Pressing the Ctrl key allows setting a value outside the defined range. To change the value manually, double-click on it. + +##### Slider2D (v0.9.2) + +Functions similarly to Slider but has 2 outputs. It also has additional parameters: `dots` - displays dots according to the snap parameter, `frame` - displays a frame, `framealert` - changes the frame color if the product of the output parameters exceeds the specified value (if 0 is specified in the parameter, the function is disabled). To swap X and Y, press the shift key and click on the area displaying these parameters (works only if other parameters do not prevent the swap). To change the value manually, double-click on it. + +##### Reroute (v0.9.2) + +Alternative version of the Reroute node. Parameters indicate the input and output directions. Allowable values: LEFT, RIGHT, DOWN, UP, or TOP. Case does not matter. Shortened values can also be input: L, R, D, U, or T. You can also use the context menu to select a direction. The direction can also be changed by selecting a node and configuring it with two consecutive arrow key presses. + +---------- + +##### Seed + +ΠŸΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ‚ Ρ€Π°Π½Π΄ΠΎΠΌΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ seed ΠΏΠ΅Ρ€Π΅Π΄ запуском Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΠΈ, Ρ‡Ρ‚ΠΎ сохраняСт Π΅Π³ΠΎ для ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰Π΅Π³ΠΎ копирования ΠΈΠ»ΠΈ ΠΏΠΎΠ²Ρ‚ΠΎΡ€Π½ΠΎΠ³ΠΎ примСнСния. Π˜ΠΌΠ΅Π΅Ρ‚ΡΡ ΠΏΠ°ΠΌΡΡ‚ΡŒ Π½Π° 2 ΠΏΡ€Π΅Π΄Ρ‹Π΄ΡƒΡ‰ΠΈΡ… значСния. Нода ΠΈΠΌΠ΅Π΅Ρ‚ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ Π²ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΡ ΠΈ ΠΎΡ‚ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΡ автоматичСского запуска Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ прСрывания Ρ‚Π΅ΠΊΡƒΡ‰Π΅ΠΉ Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈ запускС ΡΠ»Π΅Π΄ΡƒΡŽΡ‰Π΅ΠΉ. Если ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ Π½ΠΎΠ΄Ρƒ с Π·Π°ΠΆΠ°Ρ‚ΠΎΠΉ клавишСй shift, Ρ‚ΠΎ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ Π±ΡƒΠ΄Π΅Ρ‚ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΠ²Π°Ρ‚ΡŒΡΡ Π½Π° Π΅Π΄Π΅Π½ΠΈΡ†Ρƒ. + +##### Stop + +ΠŸΡ€Π΅Ρ€Ρ‹Π²Π°Π΅Ρ‚ Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΡŽ, Π½ΠΎ ΠΏΡ€ΠΈ этом ΠΏΠ΅Ρ€Π΅Π΄Π°Ρ‘Ρ‚ Π²Ρ…ΠΎΠ΄Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅ Π½Π° Π²Ρ‹Ρ…ΠΎΠ΄. ΠŸΡ€ΠΈ Π½Π°ΠΆΠ°Ρ‚ΠΈΠΈ Π½Π° ΠΊΠ½ΠΎΠΏΠΊΡƒ Continue Π² ΠΏΡ€Π°Π²ΠΎΠΌ Π²Π΅Ρ€Ρ…Π½Π΅ΠΌ ΡƒΠ³Π»Ρƒ Π½ΠΎΠ΄Ρ‹, ΠΈΠ»ΠΈ ΠΏΡ€ΠΈ ΠΏΠΎΠ²Ρ‚ΠΎΡ€Π½ΠΎΠΌ запускС ΠΊΠ½ΠΎΠΏΠΊΠΎΠΉ Queue, гСнСрация продолТаСтся с мСста прСрывания. + +##### Slider + +Π‘Π»Π°ΠΉΠ΄Π΅Ρ€ с ΠΎΠ΄Π½ΠΈΠΌ Π²Ρ‹Ρ…ΠΎΠ΄ΠΎΠΌ. Если Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π° `decimals` Ρ€Π°Π²Π½ΠΎ Π½ΡƒΠ»ΡŽ, Ρ‚ΠΎ Π½Π° Π²Ρ‹Ρ…ΠΎΠ΄Π΅ пСрСмСнная INT, ΠΈΠ½Π°Ρ‡Π΅ Π½Π° Π²Ρ‹Ρ…ΠΎΠ΄Π΅ пСрСмСнная FLOAT. ΠŸΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ `snap` Π²ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ ΠΈ ΠΎΡ‚ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ привязку согласно установлСнному ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠΌ `step` ΡˆΠ°Π³Ρƒ. НаТатиС клавиши shift ΠΏΡ€ΠΈ Ρ€Π΅Π³ΡƒΠ»ΠΈΡ€ΠΎΠ²ΠΊΠ΅ ΠΈΠ½Π²Π΅Ρ€Ρ‚ΠΈΡ€ΡƒΠ΅Ρ‚ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π° `snap`. НаТатиС клавиши Ctrl позволяСт ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΡ‚ΡŒ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ Π·Π° ΠΏΡ€Π΅Π΄Π΅Π»Π°ΠΌΠΈ установлСнного Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π°. По Π΄Π²ΠΎΠΉΠ½ΠΎΠΌΡƒ ΠΊΠ»ΠΈΠΊΡƒ Π½Π° Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΈ ΠΌΠΎΠΆΠ½ΠΎ ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΡ‚ΡŒ Π΅Π³ΠΎ Π²Ρ€ΡƒΡ‡Π½ΡƒΡŽ. + +##### Slider2D + +Π Π°Π±ΠΎΡ‚Π°Π΅Ρ‚ Π°Π½Π°Π»ΠΎΠ³ΠΈΡ‡Π½ΠΎ Slider, Π½ΠΎ ΠΈΠΌΠ΅Π΅Ρ‚ 2 Π²Ρ‹Ρ…ΠΎΠ΄Π°. Π’Π°ΠΊΠΆΠ΅ ΠΈΠΌΠ΅Π΅Ρ‚ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹: `dots` - ΠΎΡ‚ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ Ρ‚ΠΎΡ‡Π΅ΠΊ согласно ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρƒ `snap`, `frame` - ΠΎΡ‚ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ Ρ€Π°ΠΌΠΊΠΈ, `framealert` - измСняСт Ρ†Π²Π΅Ρ‚ Ρ€Π°ΠΌΠΊΠΈ, Ссли ΠΏΡ€ΠΎΠΈΠ·Π²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Π²Ρ‹Ρ…ΠΎΠ΄Π° ΠΏΡ€Π΅Π²Ρ‹ΡˆΠ°Π΅Ρ‚ Π·Π°Π΄Π°Π½Π½ΠΎΠ΅ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ (Ссли Π² ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π΅ ΡƒΠΊΠ°Π·Π°Π½ 0, Ρ‚ΠΎ функция ΠΎΡ‚ΠΊΠ»ΡŽΡ‡Π΅Π½Π°). Π§Ρ‚ΠΎΠ±Ρ‹ ΠΏΠΎΠΌΠ΅Π½ΡΡ‚ΡŒ мСстами X ΠΈ Y Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ Π½Π°ΠΆΠ°Ρ‚ΡŒ shift ΠΈ ΠΊΠ»ΠΈΠΊΠ½ΡƒΡ‚ΡŒ Π½Π° Π·ΠΎΠ½Ρƒ отобраТСния этих ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² (Ρ€Π°Π±ΠΎΡ‚Π°Π΅Ρ‚ Ρ‚ΠΎΠ»ΡŒΠΊΠΎ, Ссли ΠΏΡ€ΠΎΡ‡ΠΈΠ΅ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ Π½Π΅ ΠΏΡ€Π΅ΠΏΡΡ‚ΡΡ‚Π²ΡƒΡŽΡ‚ Π·Π°ΠΌΠ΅Π½Π΅). По Π΄Π²ΠΎΠΉΠ½ΠΎΠΌΡƒ ΠΊΠ»ΠΈΠΊΡƒ Π½Π° Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΈ ΠΌΠΎΠΆΠ½ΠΎ ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΡ‚ΡŒ Π΅Π³ΠΎ Π²Ρ€ΡƒΡ‡Π½ΡƒΡŽ. + +##### mxReroute + +ΠΠ»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Π½Π°Ρ вСрсия стандартной Π½ΠΎΠ΄Ρ‹ Reroute. Π’ качСствС ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² указываСтся Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠ΅ Π²Ρ…ΠΎΠ΄Π° ΠΈ Π²Ρ‹Ρ…ΠΎΠ΄Π°. ДопустимыС значСния: LEFT, RIGHT, DOWN, UP ΠΈΠ»ΠΈ TOP. Π Π°Π·ΠΌΠ΅Ρ€ символов Ρ€ΠΎΠ»ΠΈ Π½Π΅ ΠΈΠ³Ρ€Π°Π΅Ρ‚. МоТно Π²Π²ΠΎΠ΄ΠΈΡ‚ΡŒ ΡƒΠΊΠΎΡ€ΠΎΡ‡Π΅Π½Π½Ρ‹Π΅ значСния: L, R, D, U ΠΈΠ»ΠΈ T. Π’Π°ΠΊ ΠΆΠ΅ для Π²Ρ‹Π±ΠΎΡ€Π° направлСния ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ контСкстноС мСню, ΠΈΠ»ΠΈ, Π²Ρ‹Π±Ρ€Π°Π² Π½ΠΎΠ΄Ρƒ, Π·Π°Π΄Π°Ρ‚ΡŒ Π΅Ρ‘ ΠΊΠΎΠ½Ρ„ΠΈΠ³ΡƒΡ€Π°Ρ†ΠΈΡŽ двумя ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌΠΈ наТатиями Π½Π° клавиши стрСлок. + +---------- + +Max Smirnov . 2024 diff --git a/custom_nodes/comfyui-mxtoolkit/__init__.py b/custom_nodes/comfyui-mxtoolkit/__init__.py new file mode 100644 index 00000000..90295dab --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/__init__.py @@ -0,0 +1,4 @@ +WEB_DIRECTORY = "./js" + +from .mxtoolkit import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY'] \ No newline at end of file diff --git a/custom_nodes/comfyui-mxtoolkit/js/Reroute.js b/custom_nodes/comfyui-mxtoolkit/js/Reroute.js new file mode 100644 index 00000000..11f263c2 --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/js/Reroute.js @@ -0,0 +1,343 @@ +// ComfyUI.mxToolkit.Reroute v.0.9.6 - Max Smirnov 2024 +import { app } from "../../scripts/app.js"; +import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "../core/widgetInputs.js"; + +app.registerExtension({ + name: "Comfy.MxRerouteNode", + registerCustomNodes(app) { + class MxRerouteNode extends LGraphNode { + constructor(title = MxRerouteNode.title) { + super(title); + if (!this.properties) { + this.properties = {}; + } + this.addInput("", "*"); + this.addOutput("", "*"); + + this.properties.inputDir = "LEFT"; + this.properties.outputDir= "RIGHT"; + this.linkType = "*"; + this.bgcolor="#0000"; + this.keyCode = 0; + + this.size = [2*LiteGraph.CANVAS_GRID_SIZE + 1.4*LiteGraph.NODE_SLOT_HEIGHT, 2*LiteGraph.CANVAS_GRID_SIZE + 1.4*LiteGraph.NODE_SLOT_HEIGHT]; + + this.onAdded = function () + { + this.resizable = false; + this.ioOrientation(); + } + + this.onConfigure = function () + { + this.bgcolor="#0000"; + } + + this.onGraphConfigured = function () + { + this.configured = true; + this.onConnectionsChange(); + } + + this.onDrawBackground = function (ctx) + { + const canvas = app.graph.list_of_graphcanvas[0]; + let linkColor = LGraphCanvas.link_type_colors[this.linkType]; + if (linkColor === "") linkColor = LiteGraph.LINK_COLOR; + + if (this.inputs && this.outputs) if (this.inputs[0].pos && this.outputs[0].pos) + { + const drawLink = (ctx) => + { + ctx.beginPath(); + ctx.moveTo(this.inputs[0].pos[0], this.inputs[0].pos[1]); + if (canvas.links_render_mode < 2) + { + ctx.lineTo(this.size[0]/2, this.size[1]/2); + ctx.lineTo(this.outputs[0].pos[0], this.outputs[0].pos[1]); + } else ctx.quadraticCurveTo(this.size[0]/2, this.size[1]/2, this.outputs[0].pos[0], this.outputs[0].pos[1]); + ctx.stroke(); + } + this.inputs[0].color_on = "#0000"; + this.outputs[0].color_on = "#0000"; + this.outputs[0].shape = LiteGraph.ROUND_SHAPE; + if (this.outputs[0].links) if (this.outputs[0].links.length) if (this.outputs[0].links.length > 0) this.outputs[0].shape = LiteGraph.GRID_SHAPE; + + if (canvas) if (canvas.render_connections_border && canvas.ds.scale > 0.6) + { + ctx.lineWidth = canvas.connections_width + 4; + ctx.strokeStyle = "#0007"; + drawLink(ctx); + } + ctx.lineWidth = canvas.connections_width; + ctx.strokeStyle = linkColor; + drawLink(ctx); + } + + if (this.mouseOver) if (this.inputs && this.outputs) if (this.inputs[0].pos && this.outputs[0].pos) + { + ctx.lineWidth = 1; + ctx.strokeStyle = linkColor; + ctx.beginPath(); + ctx.moveTo(this.pos[0], this.pos[1]); + ctx.roundRect(0, 0, this.size[0], this.size[1], 10); + ctx.stroke(); + ctx.fillStyle = linkColor; + ctx.beginPath(); + ctx.arc(this.inputs[0].pos[0], this.inputs[0].pos[1], 5, 0, 2 * Math.PI, false); + ctx.arc(this.outputs[0].pos[0], this.outputs[0].pos[1], 5, 0, 2 * Math.PI, false); + ctx.fill(); + } + } + + this.onKeyUp = function(e) + { + if (e.keyCode < 37 || e.keyCode > 40) return; + if (this.keyCode > 0) + { + const arrowKeys = { 37: "LEFT", 38: "UP", 39: "RIGHT", 40: "DOWN"}; + this.properties.inputDir = arrowKeys[this.keyCode]; + this.properties.outputDir = arrowKeys[e.keyCode]; + this.onPropertyChanged(); + this.keyCode = 0; + } else this.keyCode = e.keyCode; + } + + this.onDeselected = () => {this.keyCode = 0}; + + this.getExtraMenuOptions = function() + { + const that = this; + const iDir = ["LEFT", "RIGHT", "DOWN", "UP", "LEFT", "LEFT", "RIGHT", "RIGHT", "UP", "DOWN", "DOWN", "UP"]; + const oDir = ["RIGHT", "LEFT", "UP", "DOWN", "UP", "DOWN", "UP", "DOWN", "RIGHT", "RIGHT", "LEFT", "LEFT"]; + const key = ["πŸ –", "πŸ ”", "πŸ •", "πŸ —", "β₯", "β§", "β€", "β¦", "β‘", "β£", "β’", "β "], options = []; + for (let i = 0; i < 12; i++) options.push({ content: key[i], callback: () => { that.properties.inputDir = iDir[i]; that.properties.outputDir = oDir[i]; that.onPropertyChanged(); }}); + return options; + }; + + this.onPropertyChanged = function () + { + const aValues = ["LEFT","RIGHT","UP","DOWN","TOP"]; + const sValues = ["L","R","U","D","T"] + this.properties.inputDir = this.properties.inputDir.toUpperCase(); + this.properties.outputDir = this.properties.outputDir.toUpperCase(); + if (sValues.indexOf(this.properties.inputDir)>=0) this.properties.inputDir = aValues[sValues.indexOf(this.properties.inputDir)]; + if (sValues.indexOf(this.properties.outputDir)>=0) this.properties.outputDir = aValues[sValues.indexOf(this.properties.outputDir)]; + if (this.properties.inputDir === "TOP") this.properties.inputDir = "UP"; + if (this.properties.outputDir === "TOP") this.properties.inputDir = "UP"; + this.properties.inputDir = aValues.includes(this.properties.inputDir)?this.properties.inputDir:"LEFT"; + this.properties.outputDir= aValues.includes(this.properties.outputDir)?this.properties.outputDir:"RIGHT"; + if (this.properties.inputDir === this.properties.outputDir) this.properties.outputDir=(this.properties.outputDir==="RIGHT")?"LEFT":"RIGHT"; + this.ioOrientation(); + app.graph.setDirtyCanvas(true, true); + } + + this.ioOrientation = function () + { + let i = this.properties.inputDir; + let o = this.properties.outputDir; + switch (i) + { + case "LEFT": + this.inputs[0].pos = [0,0.7*LiteGraph.NODE_SLOT_HEIGHT+LiteGraph.CANVAS_GRID_SIZE]; + this.inputs[0].dir = LiteGraph.LEFT; + break; + case "RIGHT": + this.inputs[0].pos = [this.size[0],0.7*LiteGraph.NODE_SLOT_HEIGHT+LiteGraph.CANVAS_GRID_SIZE]; + this.inputs[0].dir = LiteGraph.RIGHT; + break; + case "UP": + this.inputs[0].pos = [this.size[0]/2,0]; + this.inputs[0].dir = LiteGraph.UP; + break; + case "DOWN": + this.inputs[0].pos = [this.size[0]/2,this.size[1]]; + this.inputs[0].dir = LiteGraph.DOWN; + break; + } + switch (o) + { + case "LEFT": + this.outputs[0].pos = [0,0.7*LiteGraph.NODE_SLOT_HEIGHT+LiteGraph.CANVAS_GRID_SIZE]; + this.outputs[0].dir = LiteGraph.LEFT; + break; + case "RIGHT": + this.outputs[0].pos = [this.size[0],0.7*LiteGraph.NODE_SLOT_HEIGHT+LiteGraph.CANVAS_GRID_SIZE]; + this.outputs[0].dir = LiteGraph.RIGHT; + break; + case "UP": + this.outputs[0].pos = [this.size[0]/2,0]; + this.outputs[0].dir = LiteGraph.UP; + break; + case "DOWN": + this.outputs[0].pos = [this.size[0]/2,this.size[1]]; + this.outputs[0].dir = LiteGraph.DOWN; + break; + } + } + + this.onConnectionsChange = function (type, index, connected, link_info) { + + if (this.configured && connected && type === LiteGraph.OUTPUT) { + const types = new Set(this.outputs[0].links.map((l) => app.graph.links[l].type).filter((t) => t !== "*")); + if (types.size > 1) { + const linksToDisconnect = []; + for (let i = 0; i < this.outputs[0].links.length - 1; i++) { + const linkId = this.outputs[0].links[i]; + const link = app.graph.links[linkId]; + linksToDisconnect.push(link); + } + for (const link of linksToDisconnect) { + const node = app.graph.getNodeById(link.target_id); + node.disconnectInput(link.target_slot); + } + } + } + + let currentNode = this; + let updateNodes = []; + let inputType = null; + let inputNode = null; + while (currentNode) { + updateNodes.unshift(currentNode); + const linkId = currentNode.inputs[0].link; + if (linkId !== null) { + const link = app.graph.links[linkId]; + if (!link) return; + const node = app.graph.getNodeById(link.origin_id); + const type = node.constructor.type; + if (type && type.includes ("Reroute")) { + if (node === this) { + currentNode.disconnectInput(link.target_slot); + currentNode = null; + } else { + currentNode = node; + } + } else { + inputNode = currentNode; + inputType = node.outputs[link.origin_slot]?.type ?? null; + break; + } + } else { + currentNode = null; + break; + } + } + + const nodes = [this]; + let outputType = null; + while (nodes.length) { + currentNode = nodes.pop(); + const outputs = (currentNode.outputs ? currentNode.outputs[0].links : []) || []; + if (outputs.length) { + for (const linkId of outputs) { + const link = app.graph.links[linkId]; + + if (!link) continue; + + const node = app.graph.getNodeById(link.target_id); + const type = node.constructor.type; + + if (type && type.includes("Reroute")) { + nodes.push(node); + updateNodes.push(node); + } else { + const nodeOutType = + node.inputs && node.inputs[link?.target_slot] && node.inputs[link.target_slot].type + ? node.inputs[link.target_slot].type + : null; + if (this.configured && inputType && inputType !== "*" && nodeOutType !== inputType) { + node.disconnectInput(link.target_slot); + } else { + outputType = nodeOutType; + } + } + } + } + } + + this.linkType = inputType || outputType || "*"; + const linkColor = LGraphCanvas.link_type_colors[this.linkType]; + + let widgetConfig; + let targetWidget; + let widgetType; + for (const node of updateNodes) { + node.outputs[0].type = inputType || "*"; + node.__outputType = this.linkType; + node.outputs[0].name = node.properties.showOutputText ? this.linkType : ""; + if (node.linkType) node.linkType = this.linkType; + node.size = node.computeSize(); + if (node.applyOrientation) node.applyOrientation(); + + for (const l of node.outputs[0].links || []) { + const link = app.graph.links[l]; + if (link) { + link.color = linkColor; + + if (app.configuringGraph) continue; + const targetNode = app.graph.getNodeById(link.target_id); + const targetInput = targetNode.inputs?.[link.target_slot]; + if (targetInput?.widget) { + const config = getWidgetConfig(targetInput); + if (!widgetConfig) { + widgetConfig = config[1] ?? {}; + widgetType = config[0]; + } + if (!targetWidget) { + targetWidget = targetNode.widgets?.find((w) => w.name === targetInput.widget.name); + } + + const merged = mergeIfValid(targetInput, [config[0], widgetConfig]); + if (merged.customConfig) { + widgetConfig = merged.customConfig; + } + } + } + } + } + + for (const node of updateNodes) { + if (widgetConfig && outputType) { + node.inputs[0].widget = { name: "value" }; + setWidgetConfig(node.inputs[0], [widgetType ?? this.linkType, widgetConfig], targetWidget); + } else { + setWidgetConfig(node.inputs[0], null); + } + } + + if (inputNode) { + const link = app.graph.links[inputNode.inputs[0].link]; + if (link) { + link.color = linkColor; + } + } + }; + + this.clone = function () { + const cloned = MxRerouteNode.prototype.clone.apply(this); + cloned.removeOutput(0); + cloned.addOutput("", "*"); + cloned.size = cloned.computeSize(); + cloned.ioOrientation(); + return cloned; + }; + + this.isVirtualNode = true; + } + + computeSize() { return [2*LiteGraph.CANVAS_GRID_SIZE+1.4*LiteGraph.NODE_SLOT_HEIGHT,2*LiteGraph.CANVAS_GRID_SIZE+1.4*LiteGraph.NODE_SLOT_HEIGHT] } + } + + LiteGraph.registerNodeType( + "mxReroute", + Object.assign(MxRerouteNode, { + title_mode: LiteGraph.NO_TITLE, + title: "mxReroute", + collapsable: false, + }) + ); + + MxRerouteNode.category = "utils"; + }, +}); diff --git a/custom_nodes/comfyui-mxtoolkit/js/Seed.js b/custom_nodes/comfyui-mxtoolkit/js/Seed.js new file mode 100644 index 00000000..982f7c32 --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/js/Seed.js @@ -0,0 +1,165 @@ +// ComfyUI.mxToolkit.Seed v.0.9.9 - Max Smirnov 2024 +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +class MXSeed { + constructor(node) { + + this.node = node; + this.node.properties = { seed:0, min:0, max:4294967296, autorunQueue: true, interruptQueue: true }; + this.node.size = [210,LiteGraph.NODE_SLOT_HEIGHT*3.4]; + this.node.widgets[0].hidden = true; + this.node.widgets[0].type = "hidden"; + + this.seedWidget = this.node.widgets[0]; + + const fontsize = LiteGraph.NODE_TEXT_SIZE; + + this.node.onAdded = function () + { + this.outputs[0].name = this.outputs[0].localized_name = ""; + this.widgets_start_y = 0; + this.resizable = false; + this.lastprocessed = null; + this.configured = false; + this.history=[this.properties.seed]; + }; + + this.node.onConfigure = function () + { + this.history=[this.properties.seed]; + this.configured = true; + } + + this.node.onMouseDown = function(e, pos, canvas) + { + let cWidth = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + + if (this.flags.collapsed && (( e.canvasY-this.pos[1] > 0 ) || (e.canvasX-this.pos[0] < LiteGraph.NODE_TITLE_HEIGHT))) return false; + if (!this.flags.collapsed && ( e.canvasY-this.pos[1] < 0 ) && ((e.canvasX-this.pos[0]) < (this.size[0]-cWidth+LiteGraph.NODE_TITLE_HEIGHT))) return false; + if ((e.canvasX-this.pos[0]) < LiteGraph.NODE_TITLE_HEIGHT) return false; + + this.updateThisNodeGraph?.(); + this.onTmpMouseUp(e, pos, canvas); + return true; + } + + this.node.onTmpMouseUp = function(e, pos, canvas) + { + if (!this.flags.collapsed && ( e.canvasY-this.pos[1] > 0 )) + { + let ps = Math.floor((e.canvasY-this.pos[1]-(LiteGraph.NODE_SLOT_HEIGHT-fontsize)/2)/LiteGraph.NODE_SLOT_HEIGHT); + if (ps > 0 && ps < this.history.length) + { + this.history.unshift(this.history[ps]); + this.history.splice(ps+1,1); + this.lastprocessed = null; + this.processSeed(); + } + else if (ps === 0) + { + if (this.configured) this.lastprocessed = this.history[0]; + canvas.prompt("Seed", this.properties.seed, function(v) {if (!isNaN(Number(v))) this.processSeed(Number(v));}.bind(this), e); + return; + } + } + if ( e.canvasY-this.pos[1] < 0 ) if (e.shiftKey) { this.processSeed((this.properties.seed 4294967296 ) this.properties.max = 4294967296; + if ( this.properties.max < this.properties.min ) this.properties.max = this.properties.min + 1; + this.randomRange = this.properties.max - this.properties.min; + + if (!this.configured) return; + if (this.properties.seed !== this.lastprocessed) this.processSeed(this.properties.seed); + } + + + this.node.processSeed = function(s) + { + let newSeed; + if (s === undefined) + { + do { newSeed = Math.round( Math.random() * (this.properties.max - this.properties.min) + this.properties.min ) } while (newSeed === this.lastprocessed); + } else newSeed = s; + if (this.lastprocessed === null && this.configured ) newSeed = this.history[0]; + if (newSeed !== this.history[0]) + { + this.history.unshift(newSeed); + if (this.history.length === 2 && this.lastprocessed === null && !this.configured) { this.history.splice(1); this.configured = true; } + if (this.history.length > 3) { this.history.splice(3) } + } + + this.lastprocessed = newSeed; + this.properties.seed = newSeed; + this.widgets[0].value = newSeed; + + if (this.properties.interruptQueue) api.interrupt(); + if (this.properties.autorunQueue) app.queuePrompt(0); + } + + this.node.onDrawForeground = function(ctx) + { + let titleHeight = LiteGraph.NODE_TITLE_HEIGHT; + let cWidth = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + let buttonWidth = cWidth-titleHeight-6; + let cx = (this.flags.collapsed?cWidth:this.size[0])-buttonWidth-6; + + ctx.fillStyle = this.color || LiteGraph.NODE_DEFAULT_COLOR; + ctx.beginPath(); + ctx.rect(cx, 2-titleHeight, buttonWidth, titleHeight-4); + ctx.fill(); + + cx += buttonWidth/2; + + ctx.lineWidth = 1; + ctx.fillStyle = this.mouseOver?LiteGraph.NODE_SELECTED_TITLE_COLOR:(this.boxcolor || LiteGraph.NODE_DEFAULT_BOXCOLOR); + ctx.beginPath(); ctx.moveTo(cx-8,-titleHeight/2-8); ctx.lineTo(cx+3,-titleHeight/2); ctx.lineTo(cx-8,-titleHeight/2+8); ctx.fill(); + + if (!this.flags.collapsed) + { + ctx.fillStyle="rgba(20,20,20,0.5)"; + ctx.beginPath(); + ctx.roundRect( 20, 5, this.size[0]-40, fontsize+6, 6); + ctx.fill(); + + ctx.strokeStyle=LiteGraph.NODE_TEXT_COLOR; + ctx.beginPath(); + ctx.roundRect( 20, 5, this.size[0]-40, fontsize+6, 6); + ctx.stroke(); + + ctx.fillStyle=LiteGraph.NODE_SELECTED_TITLE_COLOR; + ctx.font = (fontsize) + "px Arial"; + ctx.textAlign = "center"; + ctx.fillStyle=LiteGraph.NODE_TEXT_COLOR; + for (let i=0; i LiteGraph.NODE_SLOT_HEIGHT*1.5) this.size[1] = LiteGraph.NODE_SLOT_HEIGHT*1.5; + this.outputs[0].type = (this.properties.decimals > 0)?"FLOAT":"INT"; + }; + + this.node.onConfigure = function () + { + this.outputs[0].type = (this.properties.decimals > 0)?"FLOAT":"INT"; + } + + this.node.onGraphConfigured = function () + { + this.configured = true; + this.onPropertyChanged(); + } + + this.node.onPropertyChanged = function (propName) + { + if (!this.configured) return; + if (this.properties.step <= 0) this.properties.step = 1; + if ( isNaN(this.properties.value) ) this.properties.value = this.properties.min; + if ( this.properties.min >= this.properties.max ) this.properties.max = this.properties.min+this.properties.step; + if ((propName === "min") && (this.properties.value < this.properties.min)) this.properties.value = this.properties.min; + if ((propName === "max") && (this.properties.value > this.properties.max)) this.properties.value = this.properties.max; + this.properties.decimals = Math.floor(this.properties.decimals); + if (this.properties.decimals>4) this.properties.decimals = 4; + if (this.properties.decimals<0) this.properties.decimals = 0; + this.properties.value = Math.round(Math.pow(10,this.properties.decimals)*this.properties.value)/Math.pow(10,this.properties.decimals); + this.intpos.x = Math.max(0, Math.min(1, (this.properties.value-this.properties.min)/(this.properties.max-this.properties.min))); + if ((this.properties.decimals > 0 && this.outputs[0].type !== "FLOAT") || (this.properties.decimals === 0 && this.outputs[0].type !== "INT")) + if (this.outputs[0].links !== null) + for (let i = this.outputs[0].links.length; i > 0; i--) + { + const tlinkId = this.outputs[0].links[i-1]; + const tlink = app.graph.links[tlinkId]; + app.graph.getNodeById(tlink.target_id).disconnectInput(tlink.target_slot); + } + this.outputs[0].type = (this.properties.decimals > 0)?"FLOAT":"INT"; + this.widgets[2].value = (this.properties.decimals > 0)?1:0; + this.widgets[1].value = this.properties.value; + this.widgets[0].value = Math.floor(this.properties.value); + } + + this.node.onDrawForeground = function(ctx) + { + this.configured = true; + if ( this.flags.collapsed ) return false; + if (this.size[1] > LiteGraph.NODE_SLOT_HEIGHT*1.5) this.size[1] = LiteGraph.NODE_SLOT_HEIGHT*1.5; + let dgt = parseInt(this.properties.decimals); + + ctx.fillStyle="rgba(20,20,20,0.5)"; + ctx.beginPath(); + ctx.roundRect( shiftLeft, shY-1, this.size[0]-shiftRight-shiftLeft, 4, 2); + ctx.fill(); + + ctx.fillStyle=LiteGraph.NODE_TEXT_COLOR; + ctx.beginPath(); + ctx.arc(shiftLeft+(this.size[0]-shiftRight-shiftLeft)*this.intpos.x, shY+1, 7, 0, 2 * Math.PI, false); + ctx.fill(); + + ctx.lineWidth = 1.5; + ctx.strokeStyle=node.bgcolor || LiteGraph.NODE_DEFAULT_BGCOLOR; + ctx.beginPath(); + ctx.arc(shiftLeft+(this.size[0]-shiftRight-shiftLeft)*this.intpos.x, shY+1, 5, 0, 2 * Math.PI, false); + ctx.stroke(); + + ctx.fillStyle=LiteGraph.NODE_TEXT_COLOR; + ctx.font = (fontsize) + "px Arial"; + ctx.textAlign = "center"; + ctx.fillText(this.properties.value.toFixed(dgt), this.size[0]-shiftRight+24, shX); + } + + this.node.onDblClick = function(e, pos, canvas) + { + if ( e.canvasX > this.pos[0]+this.size[0]-shiftRight+10 ) + { + canvas.prompt("value", this.properties.value, function(v) {if (!isNaN(Number(v))) { this.properties.value = Number(v); this.onPropertyChanged("value");}}.bind(this), e); + return true; + } + } + + this.node.onMouseDown = function(e) + { + if ( e.canvasY - this.pos[1] < 0 ) return false; + if ( e.canvasX < this.pos[0]+shiftLeft-5 || e.canvasX > this.pos[0]+this.size[0]-shiftRight+5 ) return false; + if ( e.canvasY < this.pos[1]+shiftLeft-5 || e.canvasY > this.pos[1]+this.size[1]-shiftLeft+5 ) return false; + + this.capture = true; + this.unlock = false; + this.captureInput(true); + this.valueUpdate(e); + return true; + } + + this.node.onMouseMove = function(e, pos, canvas) + { + if (!this.capture) return; + if ( canvas.pointer.isDown === false ) { this.onMouseUp(e); return; } + this.valueUpdate(e); + } + + this.node.onMouseUp = function(e) + { + if (!this.capture) return; + this.capture = false; + this.captureInput(false); + this.widgets[0].value = Math.floor(this.properties.value); + this.widgets[1].value = this.properties.value; + } + + this.node.valueUpdate = function(e) + { + let prevX = this.properties.value; + let rn = Math.pow(10,this.properties.decimals); + let vX = (e.canvasX - this.pos[0] - shiftLeft)/(this.size[0]-shiftRight-shiftLeft); + + if (e.ctrlKey) this.unlock = true; + if (e.shiftKey !== this.properties.snap) + { + let step = this.properties.step/(this.properties.max - this.properties.min); + vX = Math.round(vX/step)*step; + } + + this.intpos.x = Math.max(0, Math.min(1, vX)); + this.properties.value = Math.round(rn*(this.properties.min + (this.properties.max - this.properties.min) * ((this.unlock)?vX:this.intpos.x)))/rn; + + this.updateThisNodeGraph?.(); + if ( this.properties.value !== prevX ) this.graph.setisChangedFlag(this.id); + } + + this.node.onSelected = function(e) { this.onMouseUp(e) } + this.node.computeSize = () => [LiteGraph.NODE_WIDTH,Math.floor(LiteGraph.NODE_SLOT_HEIGHT*1.5)]; + } +} + +app.registerExtension( +{ + name: "mxSlider", + async beforeRegisterNodeDef(nodeType, nodeData, _app) + { + if (nodeData.name === "mxSlider") + { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + if (onNodeCreated) onNodeCreated.apply(this, []); + this.mxSlider = new MXSlider(this); + } + } + } +}); diff --git a/custom_nodes/comfyui-mxtoolkit/js/Slider2D.js b/custom_nodes/comfyui-mxtoolkit/js/Slider2D.js new file mode 100644 index 00000000..604bda7c --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/js/Slider2D.js @@ -0,0 +1,272 @@ +// ComfyUI.mxToolkit.Slider2D v.0.9.92 - Max Smirnov 2025 +import { app } from "../../scripts/app.js"; + +class MXSlider2D +{ + constructor(node) + { + this.node = node; + this.node.properties = { valueX:512, valueY:512, minX:0, minY:0, maxX:1024, maxY:1024, stepX:128, stepY:128, decimalsX:0, decimalsY:0, snap: true, dots: true, frame: true, frameAlert:0 }; + this.node.intpos = { x:0.5, y:0.5 }; + this.node.size = [210, 210]; + const fontsize = LiteGraph.NODE_SUBTEXT_SIZE; + const shX = (this.node.slot_start_y || 0)+fontsize*1.5; + const shY = shX + LiteGraph.NODE_SLOT_HEIGHT; + const minSize = 60; + const shiftLeft = 10; + const shiftRight = 60; + + for (let i=0; i<6; i++) { this.node.widgets[i].hidden = true; this.node.widgets[i].type = "hidden"; } + + this.node.onAdded = function () + { + this.outputs[0].name = this.outputs[0].localized_name = ""; + this.outputs[1].name = this.outputs[1].localized_name = ""; + this.widgets_start_y = -4.8e8*LiteGraph.NODE_SLOT_HEIGHT; + this.intpos.x = (this.properties.valueX - this.properties.minX) / (this.properties.maxX - this.properties.minX); + this.intpos.y = (this.properties.valueY - this.properties.minY) / (this.properties.maxY - this.properties.minY); + if (this.size[1] > this.size[0]-shiftRight+shiftLeft) {this.size[1] = this.size[0]-shiftRight+shiftLeft} else {this.size[0] = this.size[1]+shiftRight-shiftLeft} + this.outputs[0].type = (this.properties.decimalsX > 0)?"FLOAT":"INT"; + this.outputs[1].type = (this.properties.decimalsY > 0)?"FLOAT":"INT"; + }; + + this.node.onConfigure = function () + { + this.outputs[0].type = (this.properties.decimalsX > 0)?"FLOAT":"INT"; + this.outputs[1].type = (this.properties.decimalsY > 0)?"FLOAT":"INT"; + } + + this.node.onGraphConfigured = function () + { + this.configured = true; + this.onPropertyChanged(); + } + + this.node.onPropertyChanged = function (propName) + { + if (!this.configured) return; + if (this.properties.stepX <= 0) this.properties.stepX = 1; + if (this.properties.stepY <= 0) this.properties.stepY = 1; + if ( isNaN(this.properties.valueX) ) this.properties.valueX = this.properties.minX; + if ( isNaN(this.properties.valueY) ) this.properties.valueY = this.properties.minY; + if ( this.properties.minX >= this.properties.maxX ) this.properties.maxX = this.properties.minX+1; + if ( this.properties.minY >= this.properties.maxY ) this.properties.maxY = this.properties.minY+1; + if ((propName === "minX") && ( this.properties.valueX < this.properties.minX )) this.properties.valueX = this.properties.minX; + if ((propName === "minY") && ( this.properties.valueY < this.properties.minY )) this.properties.valueY = this.properties.minY; + if ((propName === "maxX") && ( this.properties.valueX > this.properties.maxX )) this.properties.valueX = this.properties.maxX; + if ((propName === "maxY") && ( this.properties.valueY > this.properties.maxY )) this.properties.valueY = this.properties.maxY; + this.properties.decimalsX = Math.floor(this.properties.decimalsX); + this.properties.decimalsY = Math.floor(this.properties.decimalsY); + if (this.properties.decimalsX>4) this.properties.decimalsX = 4; + if (this.properties.decimalsY>4) this.properties.decimalsY = 4; + if (this.properties.decimalsX<0) this.properties.decimalsX = 0; + if (this.properties.decimalsY<0) this.properties.decimalsY = 0; + this.properties.valueX = Math.round(Math.pow(10,this.properties.decimalsX)*this.properties.valueX)/Math.pow(10,this.properties.decimalsX); + this.properties.valueY = Math.round(Math.pow(10,this.properties.decimalsY)*this.properties.valueY)/Math.pow(10,this.properties.decimalsY); + this.intpos.x = Math.max(0, Math.min(1, (this.properties.valueX-this.properties.minX)/(this.properties.maxX-this.properties.minX))); + this.intpos.y = Math.max(0, Math.min(1, (this.properties.valueY-this.properties.minY)/(this.properties.maxY-this.properties.minY))); + + if ((this.properties.decimalsX > 0 && this.outputs[0].type !== "FLOAT") || (this.properties.decimalsX === 0 && this.outputs[0].type !== "INT")) + if (this.outputs[0].links !== null) + for (let i = this.outputs[0].links.length; i > 0; i--) + { + const tlinkId = this.outputs[0].links[i-1]; + const tlink = app.graph.links[tlinkId]; + app.graph.getNodeById(tlink.target_id).disconnectInput(tlink.target_slot); + } + if ((this.properties.decimalsY > 0 && this.outputs[1].type !== "FLOAT") || (this.properties.decimalsY === 0 && this.outputs[1].type !== "INT")) + if (this.outputs[1].links !== null) + for (let i = this.outputs[1].links.length; i > 0; i--) + { + const tlinkId = this.outputs[1].links[i-1]; + const tlink = app.graph.links[tlinkId]; + app.graph.getNodeById(tlink.target_id).disconnectInput(tlink.target_slot); + } + + this.outputs[0].type = (this.properties.decimalsX > 0)?"FLOAT":"INT"; + this.outputs[1].type = (this.properties.decimalsY > 0)?"FLOAT":"INT"; + this.widgets[5].value = (this.properties.decimalsY > 0)?1:0; + this.widgets[4].value = (this.properties.decimalsX > 0)?1:0; + this.widgets[3].value = this.properties.valueY; + this.widgets[2].value = Math.floor(this.properties.valueY); + this.widgets[1].value = this.properties.valueX; + this.widgets[0].value = Math.floor(this.properties.valueX); + } + + this.node.onDrawForeground = function(ctx) + { + this.configured = true; + if ( this.flags.collapsed ) return false; + + let dgtX = parseInt(this.properties.decimalsX); + let dgtY = parseInt(this.properties.decimalsY); + + ctx.fillStyle="rgba(20,20,20,0.8)"; + ctx.beginPath(); + ctx.roundRect( shiftLeft-4, shiftLeft-4, this.size[0]-shiftRight-shiftLeft+8, this.size[1]-shiftLeft-shiftLeft+8,4); + ctx.fill(); + + // Dots + if (this.properties.dots) + { + ctx.fillStyle="rgba(200,200,200,0.7)"; + ctx.beginPath(); + let swX = (this.size[0]-shiftRight-shiftLeft); + let swY = (this.size[1]-shiftLeft-shiftLeft); + let stX = (swX*this.properties.stepX/(this.properties.maxX-this.properties.minX)); + let stY = (swY*this.properties.stepY/(this.properties.maxY-this.properties.minY)); + for (var ix=0; ix 0) && (this.properties.valueX*this.properties.valueY > this.properties.frameAlert)) + { + ctx.fillStyle="rgba(250,0,0,0.2)"; + ctx.strokeStyle="rgba(250,0,0,0.7)"; + } + else + { + ctx.fillStyle="rgba(200,200,200,0.1)"; + ctx.strokeStyle="rgba(200,200,200,0.7)"; + } + ctx.beginPath(); + ctx.rect(shiftLeft, shiftLeft+(this.size[1]-shiftLeft-shiftLeft)*(1-this.intpos.y),(this.size[0]-shiftRight-shiftLeft)*this.intpos.x,(this.size[1]-shiftLeft-shiftLeft)*(this.intpos.y)); + ctx.fill(); + ctx.stroke(); + } + + ctx.fillStyle=LiteGraph.NODE_TEXT_COLOR; + ctx.beginPath(); + ctx.arc(shiftLeft+(this.size[0]-shiftRight-shiftLeft)*this.intpos.x, shiftLeft+(this.size[1]-shiftLeft-shiftLeft)*(1-this.intpos.y), 7, 0, 2 * Math.PI, false); + ctx.fill(); + + ctx.lineWidth = 1.5; + ctx.strokeStyle=node.bgcolor || LiteGraph.NODE_DEFAULT_BGCOLOR; + ctx.beginPath(); + ctx.arc(shiftLeft+(this.size[0]-shiftRight-shiftLeft)*this.intpos.x, shiftLeft+(this.size[1]-shiftLeft-shiftLeft)*(1-this.intpos.y), 5, 0, 2 * Math.PI, false); + ctx.stroke(); + + ctx.fillStyle=LiteGraph.NODE_TEXT_COLOR; + ctx.font = (fontsize) + "px Arial"; + ctx.textAlign = "center"; + ctx.fillText(this.properties.valueX.toFixed(dgtX), this.size[0]-shiftRight+24, (shX)); + ctx.fillText(this.properties.valueY.toFixed(dgtY), this.size[0]-shiftRight+24, (shY)); + } + + this.node.onDblClick = function(e, pos, canvas) + { + if ( e.canvasY - this.pos[1] < 0 ) return false; + if ( e.canvasX > this.pos[0]+this.size[0]-shiftRight+10 ) + { + if (e.canvasY - this.pos[1] - 5 < shX) + { + canvas.prompt("valueX", this.properties.valueX, function(v) {if (!isNaN(Number(v))) { this.properties.valueX = Number(v); this.onPropertyChanged("valueX");}}.bind(this), e); + return true; + } + else if (e.canvasY - this.pos[1] - 5 < shY) + { + canvas.prompt("valueY", this.properties.valueY, function(v) {if (!isNaN(Number(v))) { this.properties.valueY = Number(v); this.onPropertyChanged("valueY");}}.bind(this), e); + return true; + } + } + } + + this.node.onMouseDown = function(e) + { + if (e.canvasY - this.pos[1] < 0) return false; + if (e.shiftKey && + e.canvasX > this.pos[0]+this.size[0]-shiftRight+10 && + e.canvasX < this.pos[0]+this.size[0]-15 && + e.canvasX > this.pos[0]+this.size[0]-shiftRight+15 && + e.canvasY < this.pos[1]+shY && + this.properties.decimalsX === this.properties.decimalsY && + this.properties.valueX <= this.properties.maxY && + this.properties.valueX >= this.properties.minY && + this.properties.valueY <= this.properties.maxX && + this.properties.valueY >= this.properties.minX) + { + let tmpX = this.properties.valueX; + this.properties.valueX = this.properties.valueY; + this.properties.valueY = tmpX; + this.intpos.x = (this.properties.valueX-this.properties.minX)/(this.properties.maxX-this.properties.minX); + this.intpos.y = (this.properties.valueY-this.properties.minY)/(this.properties.maxY-this.properties.minY); + this.onPropertyChanged("valueX"); + this.onPropertyChanged("valueY"); + this.updateThisNodeGraph(); + this.graph.setisChangedFlag(this.id); + return true; + } + + if ( e.canvasX < this.pos[0]+shiftLeft-5 || e.canvasX > this.pos[0]+this.size[0]-shiftRight+5 ) return false; + if ( e.canvasY < this.pos[1]+shiftLeft-5 || e.canvasY > this.pos[1]+this.size[1]-shiftLeft+5 ) return false; + this.capture = true; + this.captureInput(true); + this.valueUpdate(e); + return true; + } + + this.node.onMouseMove = function(e, pos, canvas) + { + if (!this.capture) return; + if ( canvas.pointer.isDown === false ) { this.onMouseUp(e); return; } + this.valueUpdate(e); + } + + this.node.valueUpdate = function(e) + { + let prevX = this.properties.valueX; + let prevY = this.properties.valueY; + let rnX = Math.pow(10,this.properties.decimalsX); + let rnY = Math.pow(10,this.properties.decimalsY); + let vX = (e.canvasX - this.pos[0] - shiftLeft)/(this.size[0]-shiftRight-shiftLeft); + let vY = 1-(e.canvasY - this.pos[1] - shiftLeft)/(this.size[1]-shiftLeft-shiftLeft); + if (e.shiftKey !== this.properties.snap) + { + let sX = this.properties.stepX/(this.properties.maxX - this.properties.minX); + let sY = this.properties.stepY/(this.properties.maxY - this.properties.minY); + vX = Math.round(vX/sX)*sX; + vY = Math.round(vY/sY)*sY; + } + if ( vX < 0 ) { vX = 0 } else if ( vX > 1 ) { vX = 1 } + if ( vY < 0 ) { vY = 0 } else if ( vY > 1 ) { vY = 1 } + this.intpos.x = vX; + this.intpos.y = vY; + this.properties.valueX = Math.round(rnX*(this.properties.minX + (this.properties.maxX - this.properties.minX) * this.intpos.x))/rnX; + this.properties.valueY = Math.round(rnY*(this.properties.minY + (this.properties.maxY - this.properties.minY) * this.intpos.y))/rnY; + this.updateThisNodeGraph?.(); + if ( this.properties.valueX !== prevX || this.properties.valueY !== prevY ) this.graph.setisChangedFlag(this.id); + } + + this.node.onMouseUp = function() + { + if (!this.capture) return; + this.capture = false; + this.captureInput(false); + this.widgets[0].value = Math.floor(this.properties.valueX); + this.widgets[1].value = this.properties.valueX; + this.widgets[2].value = Math.floor(this.properties.valueY); + this.widgets[3].value = this.properties.valueY; + } + + this.node.onSelected = function(e) { this.onMouseUp(e) } + this.node.computeSize = () => [minSize + shiftRight - shiftLeft, minSize]; + } +} + +app.registerExtension( +{ + name: "mxSlider2D", + async beforeRegisterNodeDef(nodeType, nodeData, _app) + { + if (nodeData.name === "mxSlider2D") + { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + if (onNodeCreated) onNodeCreated.apply(this, []); + this.mxSlider2d = new MXSlider2D(this); + } + } + } +}); diff --git a/custom_nodes/comfyui-mxtoolkit/js/StopProc.js b/custom_nodes/comfyui-mxtoolkit/js/StopProc.js new file mode 100644 index 00000000..86327cc4 --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/js/StopProc.js @@ -0,0 +1,126 @@ +// ComfyUI.mxToolkit.Stop v.0.9.7 - Max Smirnov 2024 +import { app } from "../../scripts/app.js"; + +class MXStop +{ + constructor(node) + { + this.node = node; + this.node.properties = this.node.properties || {}; + + this.node.onGraphConfigured = function () + { + this.configured = true; + } + + this.node.onConnectionsChange = function (type, index, connected, link_info) + { + if (link_info) + { + if (connected) + { + if (type === LiteGraph.INPUT) + { + const cnode = app.graph.getNodeById(link_info.origin_id); + const ctype = cnode.outputs[link_info.origin_slot].type; + const color = LGraphCanvas.link_type_colors[ctype]; + this.outputs[0].type = ctype; + this.outputs[0].name = ctype; + this.inputs[0].type = ctype; + if (link_info.id) { app.graph.links[link_info.id].color = color; } + if (this.outputs[0].links !== null) + for (let i = this.outputs[0].links.length; i > 0; i--) + { + const tlinkId = this.outputs[0].links[i-1]; + const tlink = app.graph.links[tlinkId]; + if (this.configured) if ( ctype !== tlink.type ) app.graph.getNodeById(tlink.target_id).disconnectInput(tlink.target_slot); + } + } + if (type === LiteGraph.OUTPUT && this.inputs[0].link === null) + { + this.inputs[0].type = link_info.type; + this.outputs[0].type = link_info.type; + this.outputs[0].name = link_info.type; + } + } + else if ((( type === LiteGraph.INPUT ) && ( this.outputs[0].links === null || this.outputs[0].links.length === 0 )) || (( type === LiteGraph.OUTPUT) && ( this.inputs[0].link === null ))) this.onAdded(); + } + this.computeSize(); + }; + + this.node.onAdded = function () + { + this.inputs[0].type = "*"; + this.outputs[0].name = ""; + this.outputs[0].type = "*"; + }; + + this.node.onMouseDown = function(e, pos, canvas) + { + let cWidth = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + if ( e.canvasY-this.pos[1] > 0 ) return false; + if (this.flags.collapsed && (e.canvasX-this.pos[0] < LiteGraph.NODE_TITLE_HEIGHT)) return false; + if (!this.flags.collapsed && ((e.canvasX-this.pos[0]) < (this.size[0]-cWidth+LiteGraph.NODE_TITLE_HEIGHT))) return false; + this.updateThisNodeGraph?.(); + this.onTmpMouseUp(e, pos, canvas); + return true; + } + + this.node.onTmpMouseUp = function(e, pos, canvas) + { + app.queuePrompt(0); + } + + this.node.onDrawForeground = function(ctx) + { + this.configured = true; + if (this.size[1] > LiteGraph.NODE_SLOT_HEIGHT*1.3) this.size[1] = LiteGraph.NODE_SLOT_HEIGHT*1.3; + let titleHeight = LiteGraph.NODE_TITLE_HEIGHT; + let cWidth = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + let buttonWidth = cWidth-titleHeight-6; + let cx = (this.flags.collapsed?cWidth:this.size[0])-buttonWidth-6; + + ctx.fillStyle = this.color || LiteGraph.NODE_DEFAULT_COLOR; + ctx.beginPath(); + ctx.rect(cx, 2-titleHeight, buttonWidth, titleHeight-4); + ctx.fill(); + + cx += buttonWidth/2; + + ctx.lineWidth = 1; + if (this.mouseOver) + { + ctx.fillStyle = LiteGraph.NODE_SELECTED_TITLE_COLOR + ctx.beginPath(); ctx.moveTo(cx-8,-titleHeight/2-8); ctx.lineTo(cx+0,-titleHeight/2); ctx.lineTo(cx-8,-titleHeight/2+8); ctx.fill(); + ctx.beginPath(); ctx.moveTo(cx+1,-titleHeight/2-8); ctx.lineTo(cx+9,-titleHeight/2); ctx.lineTo(cx+1,-titleHeight/2+8); ctx.fill(); + } + else + { + ctx.fillStyle = (this.boxcolor || LiteGraph.NODE_DEFAULT_BOXCOLOR); + ctx.beginPath(); ctx.rect(cx-10,-titleHeight/2-8, 4, 16); ctx.fill(); + ctx.beginPath(); ctx.rect(cx-2,-titleHeight/2-8, 4, 16); ctx.fill(); + } + } + + this.node.computeSize = function() + { + return [ (this.properties.showOutputText && this.outputs && this.outputs.length) ? LiteGraph.NODE_TEXT_SIZE * (this.outputs[0].name.length+5) * 0.6 + 140 : 140, LiteGraph.NODE_SLOT_HEIGHT*1.3 ]; + } + } +} + +app.registerExtension( +{ + name: "mxStop", + async beforeRegisterNodeDef(nodeType, nodeData, _app) + { + if (nodeData.name === "mxStop") + { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + if (onNodeCreated) onNodeCreated.apply(this, []); + this.mxStop = new MXStop(this); + } + } + } +}); diff --git a/custom_nodes/comfyui-mxtoolkit/mxtoolkit.png b/custom_nodes/comfyui-mxtoolkit/mxtoolkit.png new file mode 100644 index 00000000..ef00a8a9 Binary files /dev/null and b/custom_nodes/comfyui-mxtoolkit/mxtoolkit.png differ diff --git a/custom_nodes/comfyui-mxtoolkit/mxtoolkit.py b/custom_nodes/comfyui-mxtoolkit/mxtoolkit.py new file mode 100644 index 00000000..129a06f4 --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/mxtoolkit.py @@ -0,0 +1,120 @@ +# ComfyUI - mxToolkit - Max Smirnov 2024 +import nodes + +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + +any = AnyType("*") + +class mxSeed: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "X": ("INT", {"default": 0, "min": 0, "max": 4294967296}), + }, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("X",) + + FUNCTION = "main" + CATEGORY = 'utils/mxToolkit' + + def main(self, X,): + return (X,) + + +class mxStop: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "In": (any,), + }, + } + + @classmethod + def VALIDATE_INPUTS(s, **kwargs): + return True + + RETURN_TYPES = (any,) + + FUNCTION = "main" + CATEGORY = 'utils/mxToolkit' + + def main(self, In): + out = In; + nodes.interrupt_processing(); + return (out,) + +class mxSlider: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Xi": ("INT", {"default": 20, "min": -4294967296, "max": 4294967296}), + "Xf": ("FLOAT", {"default": 20, "min": -4294967296, "max": 4294967296}), + "isfloatX": ("INT", {"default": 0, "min": 0, "max": 1}), + }, + } + + RETURN_TYPES = (any,) + RETURN_NAMES = ("X",) + + FUNCTION = "main" + CATEGORY = 'utils/mxToolkit' + + def main(self, Xi, Xf, isfloatX): + if isfloatX > 0: + out = Xf + else: + out = Xi + return (out,) + +class mxSlider2D: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Xi": ("INT", {"default": 512, "min": -4294967296, "max": 4294967296}), + "Xf": ("FLOAT", {"default": 512, "min": -4294967296, "max": 4294967296}), + "Yi": ("INT", {"default": 512, "min": -4294967296, "max": 4294967296}), + "Yf": ("FLOAT", {"default": 512, "min": -4294967296, "max": 4294967296}), + "isfloatX": ("INT", {"default": 0, "min": 0, "max": 1}), + "isfloatY": ("INT", {"default": 0, "min": 0, "max": 1}), + }, + } + + RETURN_TYPES = (any, any,) + RETURN_NAMES = ("X","Y",) + + FUNCTION = "main" + CATEGORY = 'utils/mxToolkit' + + def main(self, Xi, Xf, isfloatX, Yi, Yf, isfloatY): + if isfloatX > 0: + outX = Xf + else: + outX = Xi + if isfloatY > 0: + outY = Yf + else: + outY = Yi + return (outX, outY,) + + +NODE_CLASS_MAPPINGS = { + "mxSeed": mxSeed, + "mxStop": mxStop, + "mxSlider": mxSlider, + "mxSlider2D": mxSlider2D, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "mxSeed": "Seed", + "mxStop": "Stop", + "mxSlider": "Slider", + "mxSlider2D": "Slider 2D", +} \ No newline at end of file diff --git a/custom_nodes/comfyui-mxtoolkit/pyproject.toml b/custom_nodes/comfyui-mxtoolkit/pyproject.toml new file mode 100644 index 00000000..b6f06b5b --- /dev/null +++ b/custom_nodes/comfyui-mxtoolkit/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "comfyui-mxtoolkit" +description = "A set of useful nodes for convenient use of ComfyUI, including: Seed randomization before the generation process starts, with saving of the last used values and the ability to automatically interrupt the current generation; A function to pause the generation process; Slider nodes for convenient control of input parameters; An alternative version of the standard Reroute node." +version = "0.9.92" +license = { text = "GNU General Public License v3.0" } + +[project.urls] +Repository = "https://github.com/Smirnov75/ComfyUI-mxToolkit" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "smirnov" +DisplayName = "ComfyUI-mxToolkit" +Icon = "" diff --git a/custom_nodes/comfyui_controlnet_aux/.gitignore b/custom_nodes/comfyui_controlnet_aux/.gitignore new file mode 100644 index 00000000..c280aed1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/.gitignore @@ -0,0 +1,183 @@ +# Initially taken from Github's Python gitignore file + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# tests and logs +tests/fixtures/cached_*_text.txt +logs/ +lightning_logs/ +lang_code_data/ +tests/outputs + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# vscode +.vs +.vscode + +# Pycharm +.idea + +# TF code +tensorflow_code + +# Models +proc_data + +# examples +runs +/runs_old +/wandb +/examples/runs +/examples/**/*.args +/examples/rag/sweep + +# data +/data +serialization_dir + +# emacs +*.*~ +debug.env + +# vim +.*.swp + +#ctags +tags + +# pre-commit +.pre-commit* + +# .lock +*.lock + +# DS_Store (MacOS) +.DS_Store +# RL pipelines may produce mp4 outputs +*.mp4 + +# dependencies +/transformers + +# ruff +.ruff_cache + +wandb + +ckpts/ + +test.ipynb +config.yaml +test.ipynb \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/LICENSE.txt b/custom_nodes/comfyui_controlnet_aux/LICENSE.txt new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/LICENSE.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Otherwise it will default to system and assume you followed ConfyUI's manual installation steps. + +If you can't run **install.bat** (e.g. you are a Linux user). Open the CMD/Shell and do the following: + - Navigate to your `/ComfyUI/custom_nodes/` folder + - Run `git clone https://github.com/Fannovel16/comfyui_controlnet_aux/` + - Navigate to your `comfyui_controlnet_aux` folder + - Portable/venv: + - Run `path/to/ComfUI/python_embeded/python.exe -s -m pip install -r requirements.txt` + - With system python + - Run `pip install -r requirements.txt` + - Start ComfyUI + +# Nodes +Please note that this repo only supports preprocessors making hint images (e.g. stickman, canny edge, etc). +All preprocessors except Inpaint are intergrated into `AIO Aux Preprocessor` node. +This node allow you to quickly get the preprocessor but a preprocessor's own threshold parameters won't be able to set. +You need to use its node directly to set thresholds. + +# Nodes (sections are categories in Comfy menu) +## Line Extractors +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| Binary Lines | binary | control_scribble | +| Canny Edge | canny | control_v11p_sd15_canny
    control_canny
    t2iadapter_canny | +| HED Soft-Edge Lines | hed | control_v11p_sd15_softedge
    control_hed | +| Standard Lineart | standard_lineart | control_v11p_sd15_lineart | +| Realistic Lineart | lineart (or `lineart_coarse` if `coarse` is enabled) | control_v11p_sd15_lineart | +| Anime Lineart | lineart_anime | control_v11p_sd15s2_lineart_anime | +| Manga Lineart | lineart_anime_denoise | control_v11p_sd15s2_lineart_anime | +| M-LSD Lines | mlsd | control_v11p_sd15_mlsd
    control_mlsd | +| PiDiNet Soft-Edge Lines | pidinet | control_v11p_sd15_softedge
    control_scribble | +| Scribble Lines | scribble | control_v11p_sd15_scribble
    control_scribble | +| Scribble XDoG Lines | scribble_xdog | control_v11p_sd15_scribble
    control_scribble | +| Fake Scribble Lines | scribble_hed | control_v11p_sd15_scribble
    control_scribble | +| TEED Soft-Edge Lines | teed | [controlnet-sd-xl-1.0-softedge-dexined](https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-softedge-dexined/blob/main/controlnet-sd-xl-1.0-softedge-dexined.safetensors)
    control_v11p_sd15_softedge (Theoretically) +| Scribble PiDiNet Lines | scribble_pidinet | control_v11p_sd15_scribble
    control_scribble | +| AnyLine Lineart | | mistoLine_fp16.safetensors
    mistoLine_rank256
    control_v11p_sd15s2_lineart_anime
    control_v11p_sd15_lineart | + +## Normal and Depth Estimators +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| MiDaS Depth Map | (normal) depth | control_v11f1p_sd15_depth
    control_depth
    t2iadapter_depth | +| LeReS Depth Map | depth_leres | control_v11f1p_sd15_depth
    control_depth
    t2iadapter_depth | +| Zoe Depth Map | depth_zoe | control_v11f1p_sd15_depth
    control_depth
    t2iadapter_depth | +| MiDaS Normal Map | normal_map | control_normal | +| BAE Normal Map | normal_bae | control_v11p_sd15_normalbae | +| MeshGraphormer Hand Refiner ([HandRefinder](https://github.com/wenquanlu/HandRefiner)) | depth_hand_refiner | [control_sd15_inpaint_depth_hand_fp16](https://huggingface.co/hr16/ControlNet-HandRefiner-pruned/blob/main/control_sd15_inpaint_depth_hand_fp16.safetensors) | +| Depth Anything | depth_anything | [Depth-Anything](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_controlnet/diffusion_pytorch_model.safetensors) | +| Zoe Depth Anything
    (Basically Zoe but the encoder is replaced with DepthAnything) | depth_anything | [Depth-Anything](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_controlnet/diffusion_pytorch_model.safetensors) | +| Normal DSINE | | control_normal/control_v11p_sd15_normalbae | +| Metric3D Depth | | control_v11f1p_sd15_depth
    control_depth
    t2iadapter_depth | +| Metric3D Normal | | control_v11p_sd15_normalbae | +| Depth Anything V2 | | [Depth-Anything](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_controlnet/diffusion_pytorch_model.safetensors) | + +## Faces and Poses Estimators +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| DWPose Estimator | dw_openpose_full | control_v11p_sd15_openpose
    control_openpose
    t2iadapter_openpose | +| OpenPose Estimator | openpose (detect_body)
    openpose_hand (detect_body + detect_hand)
    openpose_faceonly (detect_face)
    openpose_full (detect_hand + detect_body + detect_face) | control_v11p_sd15_openpose
    control_openpose
    t2iadapter_openpose | +| MediaPipe Face Mesh | mediapipe_face | controlnet_sd21_laion_face_v2 | +| Animal Estimator | animal_openpose | [control_sd15_animal_openpose_fp16](https://huggingface.co/huchenlei/animal_openpose/blob/main/control_sd15_animal_openpose_fp16.pth) | + +## Optical Flow Estimators +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| Unimatch Optical Flow | | [DragNUWA](https://github.com/ProjectNUWA/DragNUWA) | + +### How to get OpenPose-format JSON? +#### User-side +This workflow will save images to ComfyUI's output folder (the same location as output images). If you haven't found `Save Pose Keypoints` node, update this extension +![](./examples/example_save_kps.png) + +#### Dev-side +An array of [OpenPose-format JSON](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md#json-output-format) corresponsding to each frame in an IMAGE batch can be gotten from DWPose and OpenPose using `app.nodeOutputs` on the UI or `/history` API endpoint. JSON output from AnimalPose uses a kinda similar format to OpenPose JSON: +``` +[ + { + "version": "ap10k", + "animals": [ + [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]], + [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]], + ... + ], + "canvas_height": 512, + "canvas_width": 768 + }, + ... +] +``` + +For extension developers (e.g. Openpose editor): +```js +const poseNodes = app.graph._nodes.filter(node => ["OpenposePreprocessor", "DWPreprocessor", "AnimalPosePreprocessor"].includes(node.type)) +for (const poseNode of poseNodes) { + const openposeResults = JSON.parse(app.nodeOutputs[poseNode.id].openpose_json[0]) + console.log(openposeResults) //An array containing Openpose JSON for each frame +} +``` + +For API users: +Javascript +```js +import fetch from "node-fetch" //Remember to add "type": "module" to "package.json" +async function main() { + const promptId = '792c1905-ecfe-41f4-8114-83e6a4a09a9f' //Too lazy to POST /queue + let history = await fetch(`http://127.0.0.1:8188/history/${promptId}`).then(re => re.json()) + history = history[promptId] + const nodeOutputs = Object.values(history.outputs).filter(output => output.openpose_json) + for (const nodeOutput of nodeOutputs) { + const openposeResults = JSON.parse(nodeOutput.openpose_json[0]) + console.log(openposeResults) //An array containing Openpose JSON for each frame + } +} +main() +``` + +Python +```py +import json, urllib.request + +server_address = "127.0.0.1:8188" +prompt_id = '' #Too lazy to POST /queue + +def get_history(prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: + return json.loads(response.read()) + +history = get_history(prompt_id)[prompt_id] +for o in history['outputs']: + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + if 'openpose_json' in node_output: + print(json.loads(node_output['openpose_json'][0])) #An list containing Openpose JSON for each frame +``` +## Semantic Segmentation +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| OneFormer ADE20K Segmentor | oneformer_ade20k | control_v11p_sd15_seg | +| OneFormer COCO Segmentor | oneformer_coco | control_v11p_sd15_seg | +| UniFormer Segmentor | segmentation |control_sd15_seg
    control_v11p_sd15_seg| + +## T2IAdapter-only +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| Color Pallete | color | t2iadapter_color | +| Content Shuffle | shuffle | t2iadapter_style | + +## Recolor +| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter | +|-----------------------------|---------------------------|-------------------------------------------| +| Image Luminance | recolor_luminance | [ioclab_sd15_recolor](https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/ioclab_sd15_recolor.safetensors)
    [sai_xl_recolor_256lora](https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/sai_xl_recolor_256lora.safetensors)
    [bdsqlsz_controlllite_xl_recolor_luminance](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/bdsqlsz_controlllite_xl_recolor_luminance.safetensors) | +| Image Intensity | recolor_intensity | Idk. Maybe same as above? | + +# Examples +> A picture is worth a thousand words + +![](./examples/ExecuteAll1.jpg) +![](./examples/ExecuteAll2.jpg) + +# Testing workflow +https://github.com/Fannovel16/comfyui_controlnet_aux/blob/main/examples/ExecuteAll.png +Input image: https://github.com/Fannovel16/comfyui_controlnet_aux/blob/main/examples/comfyui-controlnet-aux-logo.png + +# Q&A: +## Why some nodes doesn't appear after I installed this repo? + +This repo has a new mechanism which will skip any custom node can't be imported. If you meet this case, please create a issue on [Issues tab](https://github.com/Fannovel16/comfyui_controlnet_aux/issues) with the log from the command line. + +## DWPose/AnimalPose only uses CPU so it's so slow. How can I make it use GPU? +There are two ways to speed-up DWPose: using TorchScript checkpoints (.torchscript.pt) checkpoints or ONNXRuntime (.onnx). TorchScript way is little bit slower than ONNXRuntime but doesn't require any additional library and still way way faster than CPU. + +A torchscript bbox detector is compatiable with an onnx pose estimator and vice versa. +### TorchScript +Set `bbox_detector` and `pose_estimator` according to this picture. You can try other bbox detector endings with `.torchscript.pt` to reduce bbox detection time if input images are ideal. +![](./examples/example_torchscript.png) +### ONNXRuntime +If onnxruntime is installed successfully and the checkpoint used endings with `.onnx`, it will replace default cv2 backend to take advantage of GPU. Note that if you are using NVidia card, this method currently can only works on CUDA 11.8 (ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z) unless you compile onnxruntime yourself. + +1. Know your onnxruntime build: +* * NVidia CUDA 11.x or bellow/AMD GPU: `onnxruntime-gpu` +* * NVidia CUDA 12.x: `onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/` +* * DirectML: `onnxruntime-directml` +* * OpenVINO: `onnxruntime-openvino` + +Note that if this is your first time using ComfyUI, please test if it can run on your device before doing next steps. + +2. Add it into `requirements.txt` + +3. Run `install.bat` or pip command mentioned in Installation + +![](./examples/example_onnx.png) + +# Assets files of preprocessors +* anime_face_segment: [bdsqlsz/qinglong_controlnet-lllite/Annotators/UNet.pth](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/blob/main/Annotators/UNet.pth), [anime-seg/isnetis.ckpt](https://huggingface.co/skytnt/anime-seg/blob/main/isnetis.ckpt) +* densepose: [LayerNorm/DensePose-TorchScript-with-hint-image/densepose_r50_fpn_dl.torchscript](https://huggingface.co/LayerNorm/DensePose-TorchScript-with-hint-image/blob/main/densepose_r50_fpn_dl.torchscript) +* dwpose: +* * bbox_detector: Either [yzd-v/DWPose/yolox_l.onnx](https://huggingface.co/yzd-v/DWPose/blob/main/yolox_l.onnx), [hr16/yolox-onnx/yolox_l.torchscript.pt](https://huggingface.co/hr16/yolox-onnx/blob/main/yolox_l.torchscript.pt), [hr16/yolo-nas-fp16/yolo_nas_l_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_l_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_m_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_m_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_s_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_s_fp16.onnx) +* * pose_estimator: Either [hr16/DWPose-TorchScript-BatchSize5/dw-ll_ucoco_384_bs5.torchscript.pt](https://huggingface.co/hr16/DWPose-TorchScript-BatchSize5/blob/main/dw-ll_ucoco_384_bs5.torchscript.pt), [yzd-v/DWPose/dw-ll_ucoco_384.onnx](https://huggingface.co/yzd-v/DWPose/blob/main/dw-ll_ucoco_384.onnx) +* animal_pose (ap10k): +* * bbox_detector: Either [yzd-v/DWPose/yolox_l.onnx](https://huggingface.co/yzd-v/DWPose/blob/main/yolox_l.onnx), [hr16/yolox-onnx/yolox_l.torchscript.pt](https://huggingface.co/hr16/yolox-onnx/blob/main/yolox_l.torchscript.pt), [hr16/yolo-nas-fp16/yolo_nas_l_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_l_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_m_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_m_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_s_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_s_fp16.onnx) +* * pose_estimator: Either [hr16/DWPose-TorchScript-BatchSize5/rtmpose-m_ap10k_256_bs5.torchscript.pt](https://huggingface.co/hr16/DWPose-TorchScript-BatchSize5/blob/main/rtmpose-m_ap10k_256_bs5.torchscript.pt), [hr16/UnJIT-DWPose/rtmpose-m_ap10k_256.onnx](https://huggingface.co/hr16/UnJIT-DWPose/blob/main/rtmpose-m_ap10k_256.onnx) +* hed: [lllyasviel/Annotators/ControlNetHED.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/ControlNetHED.pth) +* leres: [lllyasviel/Annotators/res101.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/res101.pth), [lllyasviel/Annotators/latest_net_G.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/latest_net_G.pth) +* lineart: [lllyasviel/Annotators/sk_model.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/sk_model.pth), [lllyasviel/Annotators/sk_model2.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/sk_model2.pth) +* lineart_anime: [lllyasviel/Annotators/netG.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/netG.pth) +* manga_line: [lllyasviel/Annotators/erika.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/erika.pth) +* mesh_graphormer: [hr16/ControlNet-HandRefiner-pruned/graphormer_hand_state_dict.bin](https://huggingface.co/hr16/ControlNet-HandRefiner-pruned/blob/main/graphormer_hand_state_dict.bin), [hr16/ControlNet-HandRefiner-pruned/hrnetv2_w64_imagenet_pretrained.pth](https://huggingface.co/hr16/ControlNet-HandRefiner-pruned/blob/main/hrnetv2_w64_imagenet_pretrained.pth) +* midas: [lllyasviel/Annotators/dpt_hybrid-midas-501f0c75.pt](https://huggingface.co/lllyasviel/Annotators/blob/main/dpt_hybrid-midas-501f0c75.pt) +* mlsd: [lllyasviel/Annotators/mlsd_large_512_fp32.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/mlsd_large_512_fp32.pth) +* normalbae: [lllyasviel/Annotators/scannet.pt](https://huggingface.co/lllyasviel/Annotators/blob/main/scannet.pt) +* oneformer: [lllyasviel/Annotators/250_16_swin_l_oneformer_ade20k_160k.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/250_16_swin_l_oneformer_ade20k_160k.pth) +* open_pose: [lllyasviel/Annotators/body_pose_model.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/body_pose_model.pth), [lllyasviel/Annotators/hand_pose_model.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/hand_pose_model.pth), [lllyasviel/Annotators/facenet.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/facenet.pth) +* pidi: [lllyasviel/Annotators/table5_pidinet.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/table5_pidinet.pth) +* sam: [dhkim2810/MobileSAM/mobile_sam.pt](https://huggingface.co/dhkim2810/MobileSAM/blob/main/mobile_sam.pt) +* uniformer: [lllyasviel/Annotators/upernet_global_small.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/upernet_global_small.pth) +* zoe: [lllyasviel/Annotators/ZoeD_M12_N.pt](https://huggingface.co/lllyasviel/Annotators/blob/main/ZoeD_M12_N.pt) +* teed: [bdsqlsz/qinglong_controlnet-lllite/7_model.pth](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/blob/main/Annotators/7_model.pth) +* depth_anything: Either [LiheYoung/Depth-Anything/checkpoints/depth_anything_vitl14.pth](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth), [LiheYoung/Depth-Anything/checkpoints/depth_anything_vitb14.pth](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitb14.pth) or [LiheYoung/Depth-Anything/checkpoints/depth_anything_vits14.pth](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vits14.pth) +* diffusion_edge: Either [hr16/Diffusion-Edge/diffusion_edge_indoor.pt](https://huggingface.co/hr16/Diffusion-Edge/blob/main/diffusion_edge_indoor.pt), [hr16/Diffusion-Edge/diffusion_edge_urban.pt](https://huggingface.co/hr16/Diffusion-Edge/blob/main/diffusion_edge_urban.pt) or [hr16/Diffusion-Edge/diffusion_edge_natrual.pt](https://huggingface.co/hr16/Diffusion-Edge/blob/main/diffusion_edge_natrual.pt) +* unimatch: Either [hr16/Unimatch/gmflow-scale2-regrefine6-mixdata.pth](https://huggingface.co/hr16/Unimatch/blob/main/gmflow-scale2-regrefine6-mixdata.pth), [hr16/Unimatch/gmflow-scale2-mixdata.pth](https://huggingface.co/hr16/Unimatch/blob/main/gmflow-scale2-mixdata.pth) or [hr16/Unimatch/gmflow-scale1-mixdata.pth](https://huggingface.co/hr16/Unimatch/blob/main/gmflow-scale1-mixdata.pth) +* zoe_depth_anything: Either [LiheYoung/Depth-Anything/checkpoints_metric_depth/depth_anything_metric_depth_indoor.pt](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_metric_depth/depth_anything_metric_depth_indoor.pt) or [LiheYoung/Depth-Anything/checkpoints_metric_depth/depth_anything_metric_depth_outdoor.pt](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_metric_depth/depth_anything_metric_depth_outdoor.pt) +# 2000 Stars πŸ˜„ + + + + + Star History Chart + + + +Thanks for yalls supports. I never thought the graph for stars would be linear lol. diff --git a/custom_nodes/comfyui_controlnet_aux/UPDATES.md b/custom_nodes/comfyui_controlnet_aux/UPDATES.md new file mode 100644 index 00000000..7d5a8f62 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/UPDATES.md @@ -0,0 +1,45 @@ +* `AIO Aux Preprocessor` intergrating all loadable aux preprocessors as dropdown options. Easy to copy, paste and get the preprocessor faster. +* Added OpenPose-format JSON output from OpenPose Preprocessor and DWPose Preprocessor. Checks [here](#faces-and-poses). +* Fixed wrong model path when downloading DWPose. +* Make hint images less blurry. +* Added `resolution` option, `PixelPerfectResolution` and `HintImageEnchance` nodes (TODO: Documentation). +* Added `RAFT Optical Flow Embedder` for TemporalNet2 (TODO: Workflow example). +* Fixed opencv's conflicts between this extension, [ReActor](https://github.com/Gourieff/comfyui-reactor-node) and Roop. Thanks `Gourieff` for [the solution](https://github.com/Fannovel16/comfyui_controlnet_aux/issues/7#issuecomment-1734319075)! +* RAFT is removed as the code behind it doesn't match what what the original code does +* Changed `lineart`'s display name from `Normal Lineart` to `Realistic Lineart`. This change won't affect old workflows +* Added support for `onnxruntime` to speed-up DWPose (see the Q&A) +* Fixed TypeError: expected size to be one of int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], but got size with types [, ]: [Issue](https://github.com/Fannovel16/comfyui_controlnet_aux/issues/2), [PR](https://github.com/Fannovel16/comfyui_controlnet_aux/pull/71)) +* Fixed ImageGenResolutionFromImage mishape (https://github.com/Fannovel16/comfyui_controlnet_aux/pull/74) +* Fixed LeRes and MiDaS's incomatipility with MPS device +* Fixed checking DWPose onnxruntime session multiple times: https://github.com/Fannovel16/comfyui_controlnet_aux/issues/89) +* Added `Anime Face Segmentor` (in `ControlNet Preprocessors/Semantic Segmentation`) for [ControlNet AnimeFaceSegmentV2](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite#animefacesegmentv2). Checks [here](#anime-face-segmentor) +* Change download functions and fix [download error](https://github.com/Fannovel16/comfyui_controlnet_aux/issues/39): [PR](https://github.com/Fannovel16/comfyui_controlnet_aux/pull/96) +* Caching DWPose Onnxruntime during the first use of DWPose node instead of ComfyUI startup +* Added alternative YOLOX models for faster speed when using DWPose +* Added alternative DWPose models +* Implemented the preprocessor for [AnimalPose ControlNet](https://github.com/abehonest/ControlNet_AnimalPose/tree/main). Check [Animal Pose AP-10K](#animal-pose-ap-10k) +* Added YOLO-NAS models which are drop-in replacements of YOLOX +* Fixed Openpose Face/Hands no longer detecting: https://github.com/Fannovel16/comfyui_controlnet_aux/issues/54 +* Added TorchScript implementation of DWPose and AnimalPose +* Added TorchScript implementation of DensePose from [Colab notebook](https://colab.research.google.com/drive/16hcaaKs210ivpxjoyGNuvEXZD4eqOOSQ) which doesn't require detectron2. [Example](#densepose). Thanks [@LayerNome](https://github.com/Layer-norm) for fixing bugs related. +* Added Standard Lineart Preprocessor +* Fixed OpenPose misplacements in some cases +* Added Mesh Graphormer - Hand Depth Map & Mask +* Misaligned hands bug from MeshGraphormer was fixed +* Added more mask options for MeshGraphormer +* Added Save Pose Keypoint node for editing +* Added Unimatch Optical Flow +* Added Depth Anything & Zoe Depth Anything +* Removed resolution field from Unimatch Optical Flow as that interpolating optical flow seems unstable +* Added TEED Soft-Edge Preprocessor +* Added DiffusionEdge +* Added Image Luminance and Image Intensity +* Added Normal DSINE +* Added TTPlanet Tile (09/05/2024, DD/MM/YYYY) +* Added AnyLine, Metric3D (18/05/2024) +* Added Depth Anything V2 (16/06/2024) +* Added Union model of ControlNet and preprocessors +![345832280-edf41dab-7619-494c-9f60-60ec1f8789cb](https://github.com/user-attachments/assets/aa55f57c-cad7-48e6-84d3-8f506d847989) +* Refactor INPUT_TYPES and add Execute All node during the process of learning [Execution Model Inversion](https://github.com/comfyanonymous/ComfyUI/pull/2666) +* Added scale_stick_for_xinsr_cn (https://github.com/Fannovel16/comfyui_controlnet_aux/issues/447) (09/04/2024) +* PyTorch 2.7 compatibility fixes - eliminated custom_timm, custom_detectron2, and custom_midas_repo dependencies causing hanging issues. Refactored 7 major preprocessors including OneFormer (now using HuggingFace transformers), ZOE, DSINE, MiDaS, BAE, Metric3D, and Uniformer. Resolved ~59 GitHub issues related to import failures, hanging, and extension conflicts. Full modernization to actively maintained packages. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/__init__.py b/custom_nodes/comfyui_controlnet_aux/__init__.py new file mode 100644 index 00000000..0de222b1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/__init__.py @@ -0,0 +1,224 @@ +import sys, os + +# Disable NPU device initialization and problematic MMCV ops to prevent RuntimeError +# Must be set BEFORE any MMCV imports happen anywhere in ComfyUI +os.environ['NPU_DEVICE_COUNT'] = '0' +os.environ['MMCV_WITH_OPS'] = '0' +from .utils import here, define_preprocessor_inputs, INPUT +from pathlib import Path +import traceback +import importlib +from .log import log, blue_text, cyan_text, get_summary, get_label +from .hint_image_enchance import NODE_CLASS_MAPPINGS as HIE_NODE_CLASS_MAPPINGS +from .hint_image_enchance import NODE_DISPLAY_NAME_MAPPINGS as HIE_NODE_DISPLAY_NAME_MAPPINGS +#Ref: https://github.com/comfyanonymous/ComfyUI/blob/76d53c4622fc06372975ed2a43ad345935b8a551/nodes.py#L17 +sys.path.insert(0, str(Path(here, "src").resolve())) +for pkg_name in ["custom_controlnet_aux", "custom_mmpkg"]: + sys.path.append(str(Path(here, "src", pkg_name).resolve())) + +#Enable CPU fallback for ops not being supported by MPS like upsample_bicubic2d.out +#https://github.com/pytorch/pytorch/issues/77764 +#https://github.com/Fannovel16/comfyui_controlnet_aux/issues/2#issuecomment-1763579485 +os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = os.getenv("PYTORCH_ENABLE_MPS_FALLBACK", '1') + + +def load_nodes(): + shorted_errors = [] + full_error_messages = [] + node_class_mappings = {} + node_display_name_mappings = {} + + for filename in (here / "node_wrappers").iterdir(): + module_name = filename.stem + if module_name.startswith('.'): continue #Skip hidden files created by the OS (e.g. [.DS_Store](https://en.wikipedia.org/wiki/.DS_Store)) + try: + module = importlib.import_module( + f".node_wrappers.{module_name}", package=__package__ + ) + node_class_mappings.update(getattr(module, "NODE_CLASS_MAPPINGS")) + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS"): + node_display_name_mappings.update(getattr(module, "NODE_DISPLAY_NAME_MAPPINGS")) + + log.debug(f"Imported {module_name} nodes") + + except AttributeError: + pass # wip nodes + except Exception: + error_message = traceback.format_exc() + full_error_messages.append(error_message) + error_message = error_message.splitlines()[-1] + shorted_errors.append( + f"Failed to import module {module_name} because {error_message}" + ) + + if len(shorted_errors) > 0: + full_err_log = '\n\n'.join(full_error_messages) + print(f"\n\nFull error log from comfyui_controlnet_aux: \n{full_err_log}\n\n") + log.info( + f"Some nodes failed to load:\n\t" + + "\n\t".join(shorted_errors) + + "\n\n" + + "Check that you properly installed the dependencies.\n" + + "If you think this is a bug, please report it on the github page (https://github.com/Fannovel16/comfyui_controlnet_aux/issues)" + ) + return node_class_mappings, node_display_name_mappings + +AUX_NODE_MAPPINGS, AUX_DISPLAY_NAME_MAPPINGS = load_nodes() + +#For nodes not mapping image to image or has special requirements +AIO_NOT_SUPPORTED = ["InpaintPreprocessor", "MeshGraphormer+ImpactDetector-DepthMapPreprocessor", "DiffusionEdge_Preprocessor"] +AIO_NOT_SUPPORTED += ["SavePoseKpsAsJsonFile", "FacialPartColoringFromPoseKps", "UpperBodyTrackingFromPoseKps", "RenderPeopleKps", "RenderAnimalKps"] +AIO_NOT_SUPPORTED += ["Unimatch_OptFlowPreprocessor", "MaskOptFlow"] + +def preprocessor_options(): + auxs = list(AUX_NODE_MAPPINGS.keys()) + auxs.insert(0, "none") + for name in AIO_NOT_SUPPORTED: + if name in auxs: + auxs.remove(name) + return auxs + + +PREPROCESSOR_OPTIONS = preprocessor_options() + +class AIO_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + preprocessor=INPUT.COMBO(PREPROCESSOR_OPTIONS, default="none"), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors" + + def execute(self, preprocessor, image, resolution=512): + if preprocessor == "none": + return (image, ) + else: + aux_class = AUX_NODE_MAPPINGS[preprocessor] + input_types = aux_class.INPUT_TYPES() + input_types = { + **input_types["required"], + **(input_types["optional"] if "optional" in input_types else {}) + } + params = {} + for name, input_type in input_types.items(): + if name == "image": + params[name] = image + continue + + if name == "resolution": + params[name] = resolution + continue + + if len(input_type) == 2 and ("default" in input_type[1]): + params[name] = input_type[1]["default"] + continue + + default_values = { "INT": 0, "FLOAT": 0.0 } + if type(input_type[0]) is list: + for input_type_value in input_type[0]: + if input_type_value in default_values: + params[name] = default_values[input_type[0]] + else: + if input_type[0] in default_values: + params[name] = default_values[input_type[0]] + + return getattr(aux_class(), aux_class.FUNCTION)(**params) + +class ControlNetAuxSimpleAddText: + @classmethod + def INPUT_TYPES(s): + return dict( + required=dict(image=INPUT.IMAGE(), text=INPUT.STRING()) + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + CATEGORY = "ControlNet Preprocessors" + def execute(self, image, text): + from PIL import Image, ImageDraw, ImageFont + import numpy as np + import torch + + font = ImageFont.truetype(str((here / "NotoSans-Regular.ttf").resolve()), 40) + img = Image.fromarray(image[0].cpu().numpy().__mul__(255.).astype(np.uint8)) + ImageDraw.Draw(img).text((0,0), text, fill=(0,255,0), font=font) + return (torch.from_numpy(np.array(img)).unsqueeze(0) / 255.,) + +class ExecuteAllControlNetPreprocessors: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors" + + def execute(self, image, resolution=512): + try: + from comfy_execution.graph_utils import GraphBuilder + except: + raise RuntimeError("ExecuteAllControlNetPreprocessor requries [Execution Model Inversion](https://github.com/comfyanonymous/ComfyUI/commit/5cfe38). Update ComfyUI/SwarmUI to get this feature") + + graph = GraphBuilder() + curr_outputs = [] + for preprocc in PREPROCESSOR_OPTIONS: + preprocc_node = graph.node("AIO_Preprocessor", preprocessor=preprocc, image=image, resolution=resolution) + hint_img = preprocc_node.out(0) + add_text_node = graph.node("ControlNetAuxSimpleAddText", image=hint_img, text=preprocc) + curr_outputs.append(add_text_node.out(0)) + + while len(curr_outputs) > 1: + _outputs = [] + for i in range(0, len(curr_outputs), 2): + if i+1 < len(curr_outputs): + image_batch = graph.node("ImageBatch", image1=curr_outputs[i], image2=curr_outputs[i+1]) + _outputs.append(image_batch.out(0)) + else: + _outputs.append(curr_outputs[i]) + curr_outputs = _outputs + + return { + "result": (curr_outputs[0],), + "expand": graph.finalize(), + } + +class ControlNetPreprocessorSelector: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "preprocessor": (PREPROCESSOR_OPTIONS,), + } + } + + RETURN_TYPES = (PREPROCESSOR_OPTIONS,) + RETURN_NAMES = ("preprocessor",) + FUNCTION = "get_preprocessor" + + CATEGORY = "ControlNet Preprocessors" + + def get_preprocessor(self, preprocessor: str): + return (preprocessor,) + + +NODE_CLASS_MAPPINGS = { + **AUX_NODE_MAPPINGS, + "AIO_Preprocessor": AIO_Preprocessor, + "ControlNetPreprocessorSelector": ControlNetPreprocessorSelector, + **HIE_NODE_CLASS_MAPPINGS, + "ExecuteAllControlNetPreprocessors": ExecuteAllControlNetPreprocessors, + "ControlNetAuxSimpleAddText": ControlNetAuxSimpleAddText +} + +NODE_DISPLAY_NAME_MAPPINGS = { + **AUX_DISPLAY_NAME_MAPPINGS, + "AIO_Preprocessor": "AIO Aux Preprocessor", + "ControlNetPreprocessorSelector": "Preprocessor Selector", + **HIE_NODE_DISPLAY_NAME_MAPPINGS, + "ExecuteAllControlNetPreprocessors": "Execute All ControlNet Preprocessors" +} diff --git a/custom_nodes/comfyui_controlnet_aux/config.example.yaml b/custom_nodes/comfyui_controlnet_aux/config.example.yaml new file mode 100644 index 00000000..6734ad02 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/config.example.yaml @@ -0,0 +1,20 @@ +# this is an example for config.yaml file, you can rename it to config.yaml if you want to use it +# ############################################################################################### +# This path is for custom pressesor models base folder. default is "./ckpts" +# you can also use absolute paths like: "/root/ComfyUI/custom_nodes/comfyui_controlnet_aux/ckpts" or "D:\\ComfyUI\\custom_nodes\\comfyui_controlnet_aux\\ckpts" +annotator_ckpts_path: "./ckpts" +# ############################################################################################### +# This path is for downloading temporary files. +# You SHOULD use absolute path for this like"D:\\temp", DO NOT use relative paths. Empty for default. +custom_temp_path: +# ############################################################################################### +# if you already have downloaded ckpts via huggingface hub into default cache path like: ~/.cache/huggingface/hub, you can set this True to use symlinks to save space +USE_SYMLINKS: False +# ############################################################################################### +# EP_list is a list of execution providers for onnxruntime, if one of them is not available or not working well, you can delete that provider from here(config.yaml) +# you can find all available providers here: https://onnxruntime.ai/docs/execution-providers +# for example, if you have CUDA installed, you can set it to: ["CUDAExecutionProvider", "CPUExecutionProvider"] +# empty list or only keep ["CPUExecutionProvider"] means you use cv2.dnn.readNetFromONNX to load onnx models +# if your onnx models can only run on the CPU or have other issues, we recommend using pt model instead. +# default value is ["CUDAExecutionProvider", "DirectMLExecutionProvider", "OpenVINOExecutionProvider", "ROCMExecutionProvider", "CPUExecutionProvider"] +EP_list: ["CUDAExecutionProvider", "DirectMLExecutionProvider", "OpenVINOExecutionProvider", "ROCMExecutionProvider", "CPUExecutionProvider"] diff --git a/custom_nodes/comfyui_controlnet_aux/dev_interface.py b/custom_nodes/comfyui_controlnet_aux/dev_interface.py new file mode 100644 index 00000000..3c08abdb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/dev_interface.py @@ -0,0 +1,6 @@ +from pathlib import Path +from utils import here +import sys +sys.path.append(str(Path(here, "src"))) + +from custom_controlnet_aux import * \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/examples/CNAuxBanner.jpg b/custom_nodes/comfyui_controlnet_aux/examples/CNAuxBanner.jpg new file mode 100644 index 00000000..3d02d577 Binary files /dev/null and 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+MAX_IMAGEGEN_RESOLUTION = 8192 #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L42 +RESIZE_MODES = [ResizeMode.RESIZE.value, ResizeMode.INNER_FIT.value, ResizeMode.OUTER_FIT.value] + +#Port from https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/internal_controlnet/external_code.py#L89 +class PixelPerfectResolution: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_image": ("IMAGE", ), + "image_gen_width": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}), + "image_gen_height": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}), + #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L854 + "resize_mode": (RESIZE_MODES, {"default": ResizeMode.RESIZE.value}) + } + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("RESOLUTION (INT)", ) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors" + + def execute(self, original_image, image_gen_width, image_gen_height, resize_mode): + _, raw_H, raw_W, _ = original_image.shape + + k0 = float(image_gen_height) / float(raw_H) + k1 = float(image_gen_width) / float(raw_W) + + if resize_mode == ResizeMode.OUTER_FIT.value: + estimation = min(k0, k1) * float(min(raw_H, raw_W)) + else: + estimation = max(k0, k1) * float(min(raw_H, raw_W)) + + log.debug(f"Pixel Perfect Computation:") + log.debug(f"resize_mode = {resize_mode}") + log.debug(f"raw_H = {raw_H}") + log.debug(f"raw_W = {raw_W}") + log.debug(f"target_H = {image_gen_height}") + log.debug(f"target_W = {image_gen_width}") + log.debug(f"estimation = {estimation}") + + return (int(np.round(estimation)), ) + +class HintImageEnchance: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "hint_image": ("IMAGE", ), + "image_gen_width": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}), + "image_gen_height": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}), + #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L854 + "resize_mode": (RESIZE_MODES, {"default": ResizeMode.RESIZE.value}) + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors" + def execute(self, hint_image, image_gen_width, image_gen_height, resize_mode): + outs = [] + for single_hint_image in hint_image: + np_hint_image = np.asarray(single_hint_image * 255., dtype=np.uint8) + + if resize_mode == ResizeMode.RESIZE.value: + np_hint_image = self.execute_resize(np_hint_image, image_gen_width, image_gen_height) + elif resize_mode == ResizeMode.OUTER_FIT.value: + np_hint_image = self.execute_outer_fit(np_hint_image, image_gen_width, image_gen_height) + else: + np_hint_image = self.execute_inner_fit(np_hint_image, image_gen_width, image_gen_height) + + outs.append(torch.from_numpy(np_hint_image.astype(np.float32) / 255.0)) + + return (torch.stack(outs, dim=0),) + + def execute_resize(self, detected_map, w, h): + detected_map = self.high_quality_resize(detected_map, (w, h)) + detected_map = safe_numpy(detected_map) + return detected_map + + def execute_outer_fit(self, detected_map, w, h): + old_h, old_w, _ = detected_map.shape + old_w = float(old_w) + old_h = float(old_h) + k0 = float(h) / old_h + k1 = float(w) / old_w + safeint = lambda x: int(np.round(x)) + k = min(k0, k1) + + borders = np.concatenate([detected_map[0, :, :], detected_map[-1, :, :], detected_map[:, 0, :], detected_map[:, -1, :]], axis=0) + high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype) + if len(high_quality_border_color) == 4: + # Inpaint hijack + high_quality_border_color[3] = 255 + high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1]) + detected_map = self.high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k))) + new_h, new_w, _ = detected_map.shape + pad_h = max(0, (h - new_h) // 2) + pad_w = max(0, (w - new_w) // 2) + high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map + detected_map = high_quality_background + detected_map = safe_numpy(detected_map) + return detected_map + + def execute_inner_fit(self, detected_map, w, h): + old_h, old_w, _ = detected_map.shape + old_w = float(old_w) + old_h = float(old_h) + k0 = float(h) / old_h + k1 = float(w) / old_w + safeint = lambda x: int(np.round(x)) + k = max(k0, k1) + + detected_map = self.high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k))) + new_h, new_w, _ = detected_map.shape + pad_h = max(0, (new_h - h) // 2) + pad_w = max(0, (new_w - w) // 2) + detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w] + detected_map = safe_numpy(detected_map) + return detected_map + + def high_quality_resize(self, x, size): + # Written by lvmin + # Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges + + inpaint_mask = None + if x.ndim == 3 and x.shape[2] == 4: + inpaint_mask = x[:, :, 3] + x = x[:, :, 0:3] + + if x.shape[0] != size[1] or x.shape[1] != size[0]: + new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1]) + new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1]) + unique_color_count = len(get_unique_axis0(x.reshape(-1, x.shape[2]))) + is_one_pixel_edge = False + is_binary = False + if unique_color_count == 2: + is_binary = np.min(x) < 16 and np.max(x) > 240 + if is_binary: + xc = x + xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1) + xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1) + one_pixel_edge_count = np.where(xc < x)[0].shape[0] + all_edge_count = np.where(x > 127)[0].shape[0] + is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count + + if 2 < unique_color_count < 200: + interpolation = cv2.INTER_NEAREST + elif new_size_is_smaller: + interpolation = cv2.INTER_AREA + else: + interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS + + y = cv2.resize(x, size, interpolation=interpolation) + if inpaint_mask is not None: + inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation) + + if is_binary: + y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8) + if is_one_pixel_edge: + y = nake_nms(y) + _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + y = lvmin_thin(y, prunings=new_size_is_bigger) + else: + _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + y = np.stack([y] * 3, axis=2) + else: + y = x + + if inpaint_mask is not None: + inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0 + inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8) + y = np.concatenate([y, inpaint_mask], axis=2) + + return y + + +class ImageGenResolutionFromLatent: + @classmethod + def INPUT_TYPES(s): + return { + "required": { "latent": ("LATENT", ) } + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("IMAGE_GEN_WIDTH (INT)", "IMAGE_GEN_HEIGHT (INT)") + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors" + + def execute(self, latent): + _, _, H, W = latent["samples"].shape + return (W * 8, H * 8) + +class ImageGenResolutionFromImage: + @classmethod + def INPUT_TYPES(s): + return { + "required": { "image": ("IMAGE", ) } + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ("IMAGE_GEN_WIDTH (INT)", "IMAGE_GEN_HEIGHT (INT)") + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors" + + def execute(self, image): + _, H, W, _ = image.shape + return (W, H) + +NODE_CLASS_MAPPINGS = { + "PixelPerfectResolution": PixelPerfectResolution, + "ImageGenResolutionFromImage": ImageGenResolutionFromImage, + "ImageGenResolutionFromLatent": ImageGenResolutionFromLatent, + "HintImageEnchance": HintImageEnchance +} +NODE_DISPLAY_NAME_MAPPINGS = { + "PixelPerfectResolution": "Pixel Perfect Resolution", + "ImageGenResolutionFromImage": "Generation Resolution From Image", + "ImageGenResolutionFromLatent": "Generation Resolution From Latent", + "HintImageEnchance": "Enchance And Resize Hint Images" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/install.bat b/custom_nodes/comfyui_controlnet_aux/install.bat new file mode 100644 index 00000000..c36a6744 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/install.bat @@ -0,0 +1,20 @@ +@echo off + +set "requirements_txt=%~dp0\requirements.txt" +set "python_exec=..\..\..\python_embeded\python.exe" + +echo Installing ComfyUI's ControlNet Auxiliary Preprocessors.. + +if exist "%python_exec%" ( + echo Installing with ComfyUI Portable + for /f "delims=" %%i in (%requirements_txt%) do ( + %python_exec% -s -m pip install "%%i" + ) +) else ( + echo Installing with system Python + for /f "delims=" %%i in (%requirements_txt%) do ( + pip install "%%i" + ) +) + +pause \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/log.py b/custom_nodes/comfyui_controlnet_aux/log.py new file mode 100644 index 00000000..2978c6dc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/log.py @@ -0,0 +1,80 @@ +#Cre: https://github.com/melMass/comfy_mtb/blob/main/log.py +import logging +import re +import os + +base_log_level = logging.INFO + + +# Custom object that discards the output +class NullWriter: + def write(self, text): + pass + + +class Formatter(logging.Formatter): + grey = "\x1b[38;20m" + cyan = "\x1b[36;20m" + purple = "\x1b[35;20m" + yellow = "\x1b[33;20m" + red = "\x1b[31;20m" + bold_red = "\x1b[31;1m" + reset = "\x1b[0m" + # format = "%(asctime)s - [%(name)s] - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)" + format = "[%(name)s] | %(levelname)s -> %(message)s" + + FORMATS = { + logging.DEBUG: purple + format + reset, + logging.INFO: cyan + format + reset, + logging.WARNING: yellow + format + reset, + logging.ERROR: red + format + reset, + logging.CRITICAL: bold_red + format + reset, + } + + def format(self, record): + log_fmt = self.FORMATS.get(record.levelno) + formatter = logging.Formatter(log_fmt) + return formatter.format(record) + + +def mklog(name, level=base_log_level): + logger = logging.getLogger(name) + logger.setLevel(level) + + for handler in logger.handlers: + logger.removeHandler(handler) + + ch = logging.StreamHandler() + ch.setLevel(level) + ch.setFormatter(Formatter()) + logger.addHandler(ch) + + # Disable log propagation + logger.propagate = False + + return logger + + +# - The main app logger +log = mklog(__package__, base_log_level) + + +def log_user(arg): + print("\033[34mComfyUI ControlNet AUX:\033[0m {arg}") + + +def get_summary(docstring): + return docstring.strip().split("\n\n", 1)[0] + + +def blue_text(text): + return f"\033[94m{text}\033[0m" + + +def cyan_text(text): + return f"\033[96m{text}\033[0m" + + +def get_label(label): + words = re.findall(r"(?:^|[A-Z])[a-z]*", label) + return " ".join(words).strip() \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/lvminthin.py b/custom_nodes/comfyui_controlnet_aux/lvminthin.py new file mode 100644 index 00000000..eebe0fb6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/lvminthin.py @@ -0,0 +1,87 @@ +# High Quality Edge Thinning using Pure Python +# Written by Lvmin Zhang +# 2023 April +# Stanford University +# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet. + + +import cv2 +import numpy as np + + +lvmin_kernels_raw = [ + np.array([ + [-1, -1, -1], + [0, 1, 0], + [1, 1, 1] + ], dtype=np.int32), + np.array([ + [0, -1, -1], + [1, 1, -1], + [0, 1, 0] + ], dtype=np.int32) +] + +lvmin_kernels = [] +lvmin_kernels += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_kernels_raw] +lvmin_kernels += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_kernels_raw] +lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw] +lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw] + +lvmin_prunings_raw = [ + np.array([ + [-1, -1, -1], + [-1, 1, -1], + [0, 0, -1] + ], dtype=np.int32), + np.array([ + [-1, -1, -1], + [-1, 1, -1], + [-1, 0, 0] + ], dtype=np.int32) +] + +lvmin_prunings = [] +lvmin_prunings += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_prunings_raw] +lvmin_prunings += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_prunings_raw] +lvmin_prunings += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_prunings_raw] +lvmin_prunings += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_prunings_raw] + + +def remove_pattern(x, kernel): + objects = cv2.morphologyEx(x, cv2.MORPH_HITMISS, kernel) + objects = np.where(objects > 127) + x[objects] = 0 + return x, objects[0].shape[0] > 0 + + +def thin_one_time(x, kernels): + y = x + is_done = True + for k in kernels: + y, has_update = remove_pattern(y, k) + if has_update: + is_done = False + return y, is_done + + +def lvmin_thin(x, prunings=True): + y = x + for i in range(32): + y, is_done = thin_one_time(y, lvmin_kernels) + if is_done: + break + if prunings: + y, _ = thin_one_time(y, lvmin_prunings) + return y + + +def nake_nms(x): + f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) + f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) + f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) + f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) + y = np.zeros_like(x) + for f in [f1, f2, f3, f4]: + np.putmask(y, cv2.dilate(x, kernel=f) == x, x) + return y diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/anime_face_segment.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/anime_face_segment.py new file mode 100644 index 00000000..e642aa08 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/anime_face_segment.py @@ -0,0 +1,43 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management +import torch +from einops import rearrange + +class AnimeFace_SemSegPreprocessor: + @classmethod + def INPUT_TYPES(s): + #This preprocessor is only trained on 512x resolution + #https://github.com/siyeong0/Anime-Face-Segmentation/blob/main/predict.py#L25 + return define_preprocessor_inputs( + remove_background_using_abg=INPUT.BOOLEAN(True), + resolution=INPUT.RESOLUTION(default=512, min=512, max=512) + ) + + RETURN_TYPES = ("IMAGE", "MASK") + RETURN_NAMES = ("IMAGE", "ABG_CHARACTER_MASK (MASK)") + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Semantic Segmentation" + + def execute(self, image, remove_background_using_abg=True, resolution=512, **kwargs): + from custom_controlnet_aux.anime_face_segment import AnimeFaceSegmentor + + model = AnimeFaceSegmentor.from_pretrained().to(model_management.get_torch_device()) + if remove_background_using_abg: + out_image_with_mask = common_annotator_call(model, image, resolution=resolution, remove_background=True) + out_image = out_image_with_mask[..., :3] + mask = out_image_with_mask[..., 3:] + mask = rearrange(mask, "n h w c -> n c h w") + else: + out_image = common_annotator_call(model, image, resolution=resolution, remove_background=False) + N, H, W, C = out_image.shape + mask = torch.ones(N, C, H, W) + del model + return (out_image, mask) + +NODE_CLASS_MAPPINGS = { + "AnimeFace_SemSegPreprocessor": AnimeFace_SemSegPreprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "AnimeFace_SemSegPreprocessor": "Anime Face Segmentor" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/anyline.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/anyline.py new file mode 100644 index 00000000..187e90a8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/anyline.py @@ -0,0 +1,87 @@ +import torch +import numpy as np +import comfy.model_management as model_management +import comfy.utils + +# Requires comfyui_controlnet_aux funcsions and classes +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs + +def get_intensity_mask(image_array, lower_bound, upper_bound): + mask = image_array[:, :, 0] + mask = np.where((mask >= lower_bound) & (mask <= upper_bound), mask, 0) + mask = np.expand_dims(mask, 2).repeat(3, axis=2) + return mask + +def combine_layers(base_layer, top_layer): + mask = top_layer.astype(bool) + temp = 1 - (1 - top_layer) * (1 - base_layer) + result = base_layer * (~mask) + temp * mask + return result + +class AnyLinePreprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + merge_with_lineart=INPUT.COMBO(["lineart_standard", "lineart_realisitic", "lineart_anime", "manga_line"], default="lineart_standard"), + resolution=INPUT.RESOLUTION(default=1280, step=8), + lineart_lower_bound=INPUT.FLOAT(default=0), + lineart_upper_bound=INPUT.FLOAT(default=1), + object_min_size=INPUT.INT(default=36, min=1), + object_connectivity=INPUT.INT(default=1, min=1) + ) + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + + FUNCTION = "get_anyline" + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def __init__(self): + self.device = model_management.get_torch_device() + + def get_anyline(self, image, merge_with_lineart="lineart_standard", resolution=512, lineart_lower_bound=0, lineart_upper_bound=1, object_min_size=36, object_connectivity=1): + from custom_controlnet_aux.teed import TEDDetector + from skimage import morphology + pbar = comfy.utils.ProgressBar(3) + + # Process the image with MTEED model + mteed_model = TEDDetector.from_pretrained("TheMistoAI/MistoLine", "MTEED.pth", subfolder="Anyline").to(self.device) + mteed_result = common_annotator_call(mteed_model, image, resolution=resolution, show_pbar=False) + mteed_result = mteed_result.numpy() + del mteed_model + pbar.update(1) + + # Process the image with the lineart standard preprocessor + if merge_with_lineart == "lineart_standard": + from custom_controlnet_aux.lineart_standard import LineartStandardDetector + lineart_standard_detector = LineartStandardDetector() + lineart_result = common_annotator_call(lineart_standard_detector, image, guassian_sigma=2, intensity_threshold=3, resolution=resolution, show_pbar=False).numpy() + del lineart_standard_detector + else: + from custom_controlnet_aux.lineart import LineartDetector + from custom_controlnet_aux.lineart_anime import LineartAnimeDetector + from custom_controlnet_aux.manga_line import LineartMangaDetector + lineart_detector = dict(lineart_realisitic=LineartDetector, lineart_anime=LineartAnimeDetector, manga_line=LineartMangaDetector)[merge_with_lineart] + lineart_detector = lineart_detector.from_pretrained().to(self.device) + lineart_result = common_annotator_call(lineart_detector, image, resolution=resolution, show_pbar=False).numpy() + del lineart_detector + pbar.update(1) + + final_result = [] + for i in range(len(image)): + _lineart_result = get_intensity_mask(lineart_result[i], lower_bound=lineart_lower_bound, upper_bound=lineart_upper_bound) + _cleaned = morphology.remove_small_objects(_lineart_result.astype(bool), min_size=object_min_size, connectivity=object_connectivity) + _lineart_result = _lineart_result * _cleaned + _mteed_result = mteed_result[i] + + # Combine the results + final_result.append(torch.from_numpy(combine_layers(_mteed_result, _lineart_result))) + pbar.update(1) + return (torch.stack(final_result),) + +NODE_CLASS_MAPPINGS = { + "AnyLineArtPreprocessor_aux": AnyLinePreprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "AnyLineArtPreprocessor_aux": "AnyLine Lineart" +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/binary.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/binary.py new file mode 100644 index 00000000..1c3eb6be --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/binary.py @@ -0,0 +1,29 @@ +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs +import comfy.model_management as model_management + +class Binary_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + bin_threshold=INPUT.INT(default=100, max=255), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, bin_threshold=100, resolution=512, **kwargs): + from custom_controlnet_aux.binary import BinaryDetector + + return (common_annotator_call(BinaryDetector(), image, bin_threshold=bin_threshold, resolution=resolution), ) + + + +NODE_CLASS_MAPPINGS = { + "BinaryPreprocessor": Binary_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "BinaryPreprocessor": "Binary Lines" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/canny.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/canny.py new file mode 100644 index 00000000..dcbe510e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/canny.py @@ -0,0 +1,30 @@ +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs +import comfy.model_management as model_management + +class Canny_Edge_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + low_threshold=INPUT.INT(default=100, max=255), + high_threshold=INPUT.INT(default=200, max=255), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, low_threshold=100, high_threshold=200, resolution=512, **kwargs): + from custom_controlnet_aux.canny import CannyDetector + + return (common_annotator_call(CannyDetector(), image, low_threshold=low_threshold, high_threshold=high_threshold, resolution=resolution), ) + + + +NODE_CLASS_MAPPINGS = { + "CannyEdgePreprocessor": Canny_Edge_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "CannyEdgePreprocessor": "Canny Edge" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/color.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/color.py new file mode 100644 index 00000000..cadb344e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/color.py @@ -0,0 +1,26 @@ +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs +import comfy.model_management as model_management + +class Color_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/T2IAdapter-only" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.color import ColorDetector + + return (common_annotator_call(ColorDetector(), image, resolution=resolution), ) + + + +NODE_CLASS_MAPPINGS = { + "ColorPreprocessor": Color_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "ColorPreprocessor": "Color Pallete" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/densepose.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/densepose.py new file mode 100644 index 00000000..74df04be --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/densepose.py @@ -0,0 +1,31 @@ +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs +import comfy.model_management as model_management + +class DensePose_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + model=INPUT.COMBO(["densepose_r50_fpn_dl.torchscript", "densepose_r101_fpn_dl.torchscript"]), + cmap=INPUT.COMBO(["Viridis (MagicAnimate)", "Parula (CivitAI)"]), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators" + + def execute(self, image, model="densepose_r50_fpn_dl.torchscript", cmap="Viridis (MagicAnimate)", resolution=512): + from custom_controlnet_aux.densepose import DenseposeDetector + model = DenseposeDetector \ + .from_pretrained(filename=model) \ + .to(model_management.get_torch_device()) + return (common_annotator_call(model, image, cmap="viridis" if "Viridis" in cmap else "parula", resolution=resolution), ) + + +NODE_CLASS_MAPPINGS = { + "DensePosePreprocessor": DensePose_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DensePosePreprocessor": "DensePose Estimator" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/depth_anything.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/depth_anything.py new file mode 100644 index 00000000..97ed5b3c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/depth_anything.py @@ -0,0 +1,55 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class Depth_Anything_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + ckpt_name=INPUT.COMBO( + ["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"] + ), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, ckpt_name="depth_anything_vitl14.pth", resolution=512, **kwargs): + from custom_controlnet_aux.depth_anything import DepthAnythingDetector + + model = DepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out, ) + +class Zoe_Depth_Anything_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + environment=INPUT.COMBO(["indoor", "outdoor"]), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, environment="indoor", resolution=512, **kwargs): + from custom_controlnet_aux.zoe import ZoeDepthAnythingDetector + ckpt_name = "depth_anything_metric_depth_indoor.pt" if environment == "indoor" else "depth_anything_metric_depth_outdoor.pt" + model = ZoeDepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "DepthAnythingPreprocessor": Depth_Anything_Preprocessor, + "Zoe_DepthAnythingPreprocessor": Zoe_Depth_Anything_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DepthAnythingPreprocessor": "Depth Anything", + "Zoe_DepthAnythingPreprocessor": "Zoe Depth Anything" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/depth_anything_v2.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/depth_anything_v2.py new file mode 100644 index 00000000..ae697edb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/depth_anything_v2.py @@ -0,0 +1,56 @@ +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs +import comfy.model_management as model_management + +class Depth_Anything_V2_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + ckpt_name=INPUT.COMBO( + ["depth_anything_v2_vitg.pth", "depth_anything_v2_vitl.pth", "depth_anything_v2_vitb.pth", "depth_anything_v2_vits.pth"], + default="depth_anything_v2_vitl.pth" + ), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, ckpt_name="depth_anything_v2_vitl.pth", resolution=512, **kwargs): + from custom_controlnet_aux.depth_anything_v2 import DepthAnythingV2Detector + + model = DepthAnythingV2Detector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, max_depth=1) + del model + return (out, ) + +""" class Depth_Anything_Metric_V2_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return create_node_input_types( + environment=(["indoor", "outdoor"], {"default": "indoor"}), + max_depth=("FLOAT", {"min": 0, "max": 100, "default": 20.0, "step": 0.01}) + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, environment, resolution=512, max_depth=20.0, **kwargs): + from custom_controlnet_aux.depth_anything_v2 import DepthAnythingV2Detector + filename = dict(indoor="depth_anything_v2_metric_hypersim_vitl.pth", outdoor="depth_anything_v2_metric_vkitti_vitl.pth")[environment] + model = DepthAnythingV2Detector.from_pretrained(filename=filename).to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, max_depth=max_depth) + del model + return (out, ) """ + +NODE_CLASS_MAPPINGS = { + "DepthAnythingV2Preprocessor": Depth_Anything_V2_Preprocessor, + #"Metric_DepthAnythingV2Preprocessor": Depth_Anything_Metric_V2_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DepthAnythingV2Preprocessor": "Depth Anything V2 - Relative", + #"Metric_DepthAnythingV2Preprocessor": "Depth Anything V2 - Metric" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/diffusion_edge.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/diffusion_edge.py new file mode 100644 index 00000000..4d31c45f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/diffusion_edge.py @@ -0,0 +1,41 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, run_script +import comfy.model_management as model_management +import sys + +def install_deps(): + try: + import sklearn + except: + run_script([sys.executable, '-s', '-m', 'pip', 'install', 'scikit-learn']) + +class DiffusionEdge_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + environment=INPUT.COMBO(["indoor", "urban", "natrual"]), + patch_batch_size=INPUT.INT(default=4, min=1, max=16), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, environment="indoor", patch_batch_size=4, resolution=512, **kwargs): + install_deps() + from custom_controlnet_aux.diffusion_edge import DiffusionEdgeDetector + + model = DiffusionEdgeDetector \ + .from_pretrained(filename = f"diffusion_edge_{environment}.pt") \ + .to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, patch_batch_size=patch_batch_size) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "DiffusionEdge_Preprocessor": DiffusionEdge_Preprocessor, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DiffusionEdge_Preprocessor": "Diffusion Edge (batch size ↑ => speed ↑, VRAM ↑)", +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/dsine.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/dsine.py new file mode 100644 index 00000000..eadb39cc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/dsine.py @@ -0,0 +1,31 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class DSINE_Normal_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + fov=INPUT.FLOAT(max=365.0, default=60.0), + iterations=INPUT.INT(min=1, max=20, default=5), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, fov=60.0, iterations=5, resolution=512, **kwargs): + from custom_controlnet_aux.dsine import DsineDetector + + model = DsineDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, fov=fov, iterations=iterations, resolution=resolution) + del model + return (out,) + +NODE_CLASS_MAPPINGS = { + "DSINE-NormalMapPreprocessor": DSINE_Normal_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DSINE-NormalMapPreprocessor": "DSINE Normal Map" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/dwpose.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/dwpose.py new file mode 100644 index 00000000..f0422588 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/dwpose.py @@ -0,0 +1,166 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management +import numpy as np +import warnings +from ..src.custom_controlnet_aux.dwpose import DwposeDetector, AnimalposeDetector +import os +import json + +DWPOSE_MODEL_NAME = "yzd-v/DWPose" +#Trigger startup caching for onnxruntime +GPU_PROVIDERS = ["CUDAExecutionProvider", "DirectMLExecutionProvider", "OpenVINOExecutionProvider", "ROCMExecutionProvider", "CoreMLExecutionProvider"] +def check_ort_gpu(): + try: + import onnxruntime as ort + for provider in GPU_PROVIDERS: + if provider in ort.get_available_providers(): + return True + return False + except: + return False + +if not os.environ.get("DWPOSE_ONNXRT_CHECKED"): + if check_ort_gpu(): + print("DWPose: Onnxruntime with acceleration providers detected") + else: + warnings.warn("DWPose: Onnxruntime not found or doesn't come with acceleration providers, switch to OpenCV with CPU device. DWPose might run very slowly") + os.environ['AUX_ORT_PROVIDERS'] = '' + os.environ["DWPOSE_ONNXRT_CHECKED"] = '1' + +class DWPose_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + detect_hand=INPUT.COMBO(["enable", "disable"]), + detect_body=INPUT.COMBO(["enable", "disable"]), + detect_face=INPUT.COMBO(["enable", "disable"]), + resolution=INPUT.RESOLUTION(), + bbox_detector=INPUT.COMBO( + ["None"] + ["yolox_l.torchscript.pt", "yolox_l.onnx", "yolo_nas_l_fp16.onnx", "yolo_nas_m_fp16.onnx", "yolo_nas_s_fp16.onnx"], + default="yolox_l.onnx" + ), + pose_estimator=INPUT.COMBO( + ["dw-ll_ucoco_384_bs5.torchscript.pt", "dw-ll_ucoco_384.onnx", "dw-ll_ucoco.onnx"], + default="dw-ll_ucoco_384_bs5.torchscript.pt" + ), + scale_stick_for_xinsr_cn=INPUT.COMBO(["disable", "enable"]) + ) + + RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT") + FUNCTION = "estimate_pose" + + CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators" + + def estimate_pose(self, image, detect_hand="enable", detect_body="enable", detect_face="enable", resolution=512, bbox_detector="yolox_l.onnx", pose_estimator="dw-ll_ucoco_384.onnx", scale_stick_for_xinsr_cn="disable", **kwargs): + if bbox_detector == "None": + yolo_repo = DWPOSE_MODEL_NAME + elif bbox_detector == "yolox_l.onnx": + yolo_repo = DWPOSE_MODEL_NAME + elif "yolox" in bbox_detector: + yolo_repo = "hr16/yolox-onnx" + elif "yolo_nas" in bbox_detector: + yolo_repo = "hr16/yolo-nas-fp16" + else: + raise NotImplementedError(f"Download mechanism for {bbox_detector}") + + if pose_estimator == "dw-ll_ucoco_384.onnx": + pose_repo = DWPOSE_MODEL_NAME + elif pose_estimator.endswith(".onnx"): + pose_repo = "hr16/UnJIT-DWPose" + elif pose_estimator.endswith(".torchscript.pt"): + pose_repo = "hr16/DWPose-TorchScript-BatchSize5" + else: + raise NotImplementedError(f"Download mechanism for {pose_estimator}") + + model = DwposeDetector.from_pretrained( + pose_repo, + yolo_repo, + det_filename=(None if bbox_detector == "None" else bbox_detector), pose_filename=pose_estimator, + torchscript_device=model_management.get_torch_device() + ) + detect_hand = detect_hand == "enable" + detect_body = detect_body == "enable" + detect_face = detect_face == "enable" + scale_stick_for_xinsr_cn = scale_stick_for_xinsr_cn == "enable" + self.openpose_dicts = [] + def func(image, **kwargs): + pose_img, openpose_dict = model(image, **kwargs) + self.openpose_dicts.append(openpose_dict) + return pose_img + + out = common_annotator_call(func, image, include_hand=detect_hand, include_face=detect_face, include_body=detect_body, image_and_json=True, resolution=resolution, xinsr_stick_scaling=scale_stick_for_xinsr_cn) + del model + return { + 'ui': { "openpose_json": [json.dumps(self.openpose_dicts, indent=4)] }, + "result": (out, self.openpose_dicts) + } + +class AnimalPose_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + bbox_detector = INPUT.COMBO( + ["None"] + ["yolox_l.torchscript.pt", "yolox_l.onnx", "yolo_nas_l_fp16.onnx", "yolo_nas_m_fp16.onnx", "yolo_nas_s_fp16.onnx"], + default="yolox_l.torchscript.pt" + ), + pose_estimator = INPUT.COMBO( + ["rtmpose-m_ap10k_256_bs5.torchscript.pt", "rtmpose-m_ap10k_256.onnx"], + default="rtmpose-m_ap10k_256_bs5.torchscript.pt" + ), + resolution = INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT") + FUNCTION = "estimate_pose" + + CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators" + + def estimate_pose(self, image, resolution=512, bbox_detector="yolox_l.onnx", pose_estimator="rtmpose-m_ap10k_256.onnx", **kwargs): + if bbox_detector == "None": + yolo_repo = DWPOSE_MODEL_NAME + elif bbox_detector == "yolox_l.onnx": + yolo_repo = DWPOSE_MODEL_NAME + elif "yolox" in bbox_detector: + yolo_repo = "hr16/yolox-onnx" + elif "yolo_nas" in bbox_detector: + yolo_repo = "hr16/yolo-nas-fp16" + else: + raise NotImplementedError(f"Download mechanism for {bbox_detector}") + + if pose_estimator == "dw-ll_ucoco_384.onnx": + pose_repo = DWPOSE_MODEL_NAME + elif pose_estimator.endswith(".onnx"): + pose_repo = "hr16/UnJIT-DWPose" + elif pose_estimator.endswith(".torchscript.pt"): + pose_repo = "hr16/DWPose-TorchScript-BatchSize5" + else: + raise NotImplementedError(f"Download mechanism for {pose_estimator}") + + model = AnimalposeDetector.from_pretrained( + pose_repo, + yolo_repo, + det_filename=(None if bbox_detector == "None" else bbox_detector), pose_filename=pose_estimator, + torchscript_device=model_management.get_torch_device() + ) + + self.openpose_dicts = [] + def func(image, **kwargs): + pose_img, openpose_dict = model(image, **kwargs) + self.openpose_dicts.append(openpose_dict) + return pose_img + + out = common_annotator_call(func, image, image_and_json=True, resolution=resolution) + del model + return { + 'ui': { "openpose_json": [json.dumps(self.openpose_dicts, indent=4)] }, + "result": (out, self.openpose_dicts) + } + +NODE_CLASS_MAPPINGS = { + "DWPreprocessor": DWPose_Preprocessor, + "AnimalPosePreprocessor": AnimalPose_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DWPreprocessor": "DWPose Estimator", + "AnimalPosePreprocessor": "AnimalPose Estimator (AP10K)" +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/hed.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/hed.py new file mode 100644 index 00000000..fc81246e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/hed.py @@ -0,0 +1,53 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class HED_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + safe=INPUT.COMBO(["enable", "disable"]), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.hed import HEDdetector + + model = HEDdetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, safe = kwargs["safe"] == "enable") + del model + return (out, ) + +class Fake_Scribble_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + safe=INPUT.COMBO(["enable", "disable"]), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.hed import HEDdetector + + model = HEDdetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, scribble=True, safe=kwargs["safe"]=="enable") + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "HEDPreprocessor": HED_Preprocessor, + "FakeScribblePreprocessor": Fake_Scribble_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "HEDPreprocessor": "HED Soft-Edge Lines", + "FakeScribblePreprocessor": "Fake Scribble Lines (aka scribble_hed)" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/inpaint.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/inpaint.py new file mode 100644 index 00000000..f93d87c5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/inpaint.py @@ -0,0 +1,32 @@ +import torch +from ..utils import INPUT + +class InpaintPreprocessor: + @classmethod + def INPUT_TYPES(s): + return dict( + required=dict(image=INPUT.IMAGE(), mask=INPUT.MASK()), + optional=dict(black_pixel_for_xinsir_cn=INPUT.BOOLEAN(False)) + ) + RETURN_TYPES = ("IMAGE",) + FUNCTION = "preprocess" + + CATEGORY = "ControlNet Preprocessors/others" + + def preprocess(self, image, mask, black_pixel_for_xinsir_cn=False): + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(image.shape[1], image.shape[2]), mode="bilinear") + mask = mask.movedim(1,-1).expand((-1,-1,-1,3)) + image = image.clone() + if black_pixel_for_xinsir_cn: + masked_pixel = 0.0 + else: + masked_pixel = -1.0 + image[mask > 0.5] = masked_pixel + return (image,) + +NODE_CLASS_MAPPINGS = { + "InpaintPreprocessor": InpaintPreprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "InpaintPreprocessor": "Inpaint Preprocessor" +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/leres.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/leres.py new file mode 100644 index 00000000..040e463d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/leres.py @@ -0,0 +1,32 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class LERES_Depth_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + rm_nearest=INPUT.FLOAT(max=100.0), + rm_background=INPUT.FLOAT(max=100.0), + boost=INPUT.COMBO(["disable", "enable"]), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, rm_nearest=0, rm_background=0, resolution=512, boost="disable", **kwargs): + from custom_controlnet_aux.leres import LeresDetector + + model = LeresDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, thr_a=rm_nearest, thr_b=rm_background, boost=boost == "enable") + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "LeReS-DepthMapPreprocessor": LERES_Depth_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "LeReS-DepthMapPreprocessor": "LeReS Depth Map (enable boost for leres++)" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart.py new file mode 100644 index 00000000..f3bbcd99 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart.py @@ -0,0 +1,30 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class LineArt_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + coarse=INPUT.COMBO((["disable", "enable"])), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.lineart import LineartDetector + + model = LineartDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, coarse = kwargs["coarse"] == "enable") + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "LineArtPreprocessor": LineArt_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "LineArtPreprocessor": "Realistic Lineart" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart_anime.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart_anime.py new file mode 100644 index 00000000..8e226737 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart_anime.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class AnimeLineArt_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.lineart_anime import LineartAnimeDetector + + model = LineartAnimeDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "AnimeLineArtPreprocessor": AnimeLineArt_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "AnimeLineArtPreprocessor": "Anime Lineart" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart_standard.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart_standard.py new file mode 100644 index 00000000..befeb94d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/lineart_standard.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class Lineart_Standard_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + guassian_sigma=INPUT.FLOAT(default=6.0, max=100.0), + intensity_threshold=INPUT.INT(default=8, max=16), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, guassian_sigma=6, intensity_threshold=8, resolution=512, **kwargs): + from custom_controlnet_aux.lineart_standard import LineartStandardDetector + return (common_annotator_call(LineartStandardDetector(), image, guassian_sigma=guassian_sigma, intensity_threshold=intensity_threshold, resolution=resolution), ) + +NODE_CLASS_MAPPINGS = { + "LineartStandardPreprocessor": Lineart_Standard_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "LineartStandardPreprocessor": "Standard Lineart" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/manga_line.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/manga_line.py new file mode 100644 index 00000000..7b09f84e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/manga_line.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class Manga2Anime_LineArt_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.manga_line import LineartMangaDetector + + model = LineartMangaDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "Manga2Anime_LineArt_Preprocessor": Manga2Anime_LineArt_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "Manga2Anime_LineArt_Preprocessor": "Manga Lineart (aka lineart_anime_denoise)" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/mediapipe_face.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/mediapipe_face.py new file mode 100644 index 00000000..e9dc8a7b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/mediapipe_face.py @@ -0,0 +1,39 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, run_script +import comfy.model_management as model_management +import os, sys +import subprocess, threading + +def install_deps(): + try: + import mediapipe + except ImportError: + run_script([sys.executable, '-s', '-m', 'pip', 'install', 'mediapipe']) + run_script([sys.executable, '-s', '-m', 'pip', 'install', '--upgrade', 'protobuf']) + +class Media_Pipe_Face_Mesh_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + max_faces=INPUT.INT(default=10, min=1, max=50), #Which image has more than 50 detectable faces? + min_confidence=INPUT.FLOAT(default=0.5, min=0.1), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "detect" + + CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators" + + def detect(self, image, max_faces=10, min_confidence=0.5, resolution=512): + #Ref: https://github.com/Fannovel16/comfy_controlnet_preprocessors/issues/70#issuecomment-1677967369 + install_deps() + from custom_controlnet_aux.mediapipe_face import MediapipeFaceDetector + return (common_annotator_call(MediapipeFaceDetector(), image, max_faces=max_faces, min_confidence=min_confidence, resolution=resolution), ) + +NODE_CLASS_MAPPINGS = { + "MediaPipe-FaceMeshPreprocessor": Media_Pipe_Face_Mesh_Preprocessor +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "MediaPipe-FaceMeshPreprocessor": "MediaPipe Face Mesh" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/mesh_graphormer.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/mesh_graphormer.py new file mode 100644 index 00000000..1cccc35a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/mesh_graphormer.py @@ -0,0 +1,158 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION, run_script +import comfy.model_management as model_management +import numpy as np +import torch +from einops import rearrange +import os, sys +import subprocess, threading +import scipy.ndimage +import cv2 +import torch.nn.functional as F + +def install_deps(): + try: + import mediapipe + except ImportError: + run_script([sys.executable, '-s', '-m', 'pip', 'install', 'mediapipe']) + run_script([sys.executable, '-s', '-m', 'pip', 'install', '--upgrade', 'protobuf']) + + try: + import trimesh + except ImportError: + run_script([sys.executable, '-s', '-m', 'pip', 'install', 'trimesh[easy]']) + +#Sauce: https://github.com/comfyanonymous/ComfyUI/blob/8c6493578b3dda233e9b9a953feeaf1e6ca434ad/comfy_extras/nodes_mask.py#L309 +def expand_mask(mask, expand, tapered_corners): + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = [] + for m in mask: + output = m.numpy() + for _ in range(abs(expand)): + if expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) + output = torch.from_numpy(output) + out.append(output) + return torch.stack(out, dim=0) + +class Mesh_Graphormer_Depth_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + mask_bbox_padding=("INT", {"default": 30, "min": 0, "max": 100}), + resolution=INPUT.RESOLUTION(), + mask_type=INPUT.COMBO(["based_on_depth", "tight_bboxes", "original"]), + mask_expand=INPUT.INT(default=5, min=-MAX_RESOLUTION, max=MAX_RESOLUTION), + rand_seed=INPUT.INT(default=88, min=0, max=0xffffffffffffffff), + detect_thr=INPUT.FLOAT(default=0.6, min=0.1), + presence_thr=INPUT.FLOAT(default=0.6, min=0.1) + ) + + RETURN_TYPES = ("IMAGE", "MASK") + RETURN_NAMES = ("IMAGE", "INPAINTING_MASK") + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, mask_bbox_padding=30, mask_type="based_on_depth", mask_expand=5, resolution=512, rand_seed=88, detect_thr=0.6, presence_thr=0.6, **kwargs): + install_deps() + from custom_controlnet_aux.mesh_graphormer import MeshGraphormerDetector + model = kwargs["model"] if "model" in kwargs \ + else MeshGraphormerDetector.from_pretrained(detect_thr=detect_thr, presence_thr=presence_thr).to(model_management.get_torch_device()) + + depth_map_list = [] + mask_list = [] + for single_image in image: + np_image = np.asarray(single_image.cpu() * 255., dtype=np.uint8) + depth_map, mask, info = model(np_image, output_type="np", detect_resolution=resolution, mask_bbox_padding=mask_bbox_padding, seed=rand_seed) + if mask_type == "based_on_depth": + H, W = mask.shape[:2] + mask = cv2.resize(depth_map.copy(), (W, H)) + mask[mask > 0] = 255 + + elif mask_type == "tight_bboxes": + mask = np.zeros_like(mask) + hand_bboxes = (info or {}).get("abs_boxes") or [] + for hand_bbox in hand_bboxes: + x_min, x_max, y_min, y_max = hand_bbox + mask[y_min:y_max+1, x_min:x_max+1, :] = 255 #HWC + + mask = mask[:, :, :1] + depth_map_list.append(torch.from_numpy(depth_map.astype(np.float32) / 255.0)) + mask_list.append(torch.from_numpy(mask.astype(np.float32) / 255.0)) + depth_maps, masks = torch.stack(depth_map_list, dim=0), rearrange(torch.stack(mask_list, dim=0), "n h w 1 -> n 1 h w") + return depth_maps, expand_mask(masks, mask_expand, tapered_corners=True) + +def normalize_size_base_64(w, h): + short_side = min(w, h) + remainder = short_side % 64 + return short_side - remainder + (64 if remainder > 0 else 0) + +class Mesh_Graphormer_With_ImpactDetector_Depth_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + types = define_preprocessor_inputs( + # Impact pack + bbox_threshold=INPUT.FLOAT(default=0.5, min=0.1), + bbox_dilation=INPUT.INT(default=10, min=-512, max=512), + bbox_crop_factor=INPUT.FLOAT(default=3.0, min=1.0, max=10.0), + drop_size=INPUT.INT(default=10, min=1, max=MAX_RESOLUTION), + # Mesh Graphormer + mask_bbox_padding=INPUT.INT(default=30, min=0, max=100), + mask_type=INPUT.COMBO(["based_on_depth", "tight_bboxes", "original"]), + mask_expand=INPUT.INT(default=5, min=-MAX_RESOLUTION, max=MAX_RESOLUTION), + rand_seed=INPUT.INT(default=88, min=0, max=0xffffffffffffffff), + resolution=INPUT.RESOLUTION() + ) + types["required"]["bbox_detector"] = ("BBOX_DETECTOR", ) + return types + + RETURN_TYPES = ("IMAGE", "MASK") + RETURN_NAMES = ("IMAGE", "INPAINTING_MASK") + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, bbox_detector, bbox_threshold=0.5, bbox_dilation=10, bbox_crop_factor=3.0, drop_size=10, resolution=512, **mesh_graphormer_kwargs): + install_deps() + from custom_controlnet_aux.mesh_graphormer import MeshGraphormerDetector + mesh_graphormer_node = Mesh_Graphormer_Depth_Map_Preprocessor() + model = MeshGraphormerDetector.from_pretrained(detect_thr=0.6, presence_thr=0.6).to(model_management.get_torch_device()) + mesh_graphormer_kwargs["model"] = model + + frames = image + depth_maps, masks = [], [] + for idx in range(len(frames)): + frame = frames[idx:idx+1,...] #Impact Pack's BBOX_DETECTOR only supports single batch image + bbox_detector.setAux('face') # make default prompt as 'face' if empty prompt for CLIPSeg + _, segs = bbox_detector.detect(frame, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size) + bbox_detector.setAux(None) + + n, h, w, _ = frame.shape + depth_map, mask = torch.zeros_like(frame), torch.zeros(n, 1, h, w) + for i, seg in enumerate(segs): + x1, y1, x2, y2 = seg.crop_region + cropped_image = frame[:, y1:y2, x1:x2, :] # Never use seg.cropped_image to handle overlapping area + mesh_graphormer_kwargs["resolution"] = 0 #Disable resizing + sub_depth_map, sub_mask = mesh_graphormer_node.execute(cropped_image, **mesh_graphormer_kwargs) + depth_map[:, y1:y2, x1:x2, :] = sub_depth_map + mask[:, :, y1:y2, x1:x2] = sub_mask + + depth_maps.append(depth_map) + masks.append(mask) + + return (torch.cat(depth_maps), torch.cat(masks)) + +NODE_CLASS_MAPPINGS = { + "MeshGraphormer-DepthMapPreprocessor": Mesh_Graphormer_Depth_Map_Preprocessor, + "MeshGraphormer+ImpactDetector-DepthMapPreprocessor": Mesh_Graphormer_With_ImpactDetector_Depth_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "MeshGraphormer-DepthMapPreprocessor": "MeshGraphormer Hand Refiner", + "MeshGraphormer+ImpactDetector-DepthMapPreprocessor": "MeshGraphormer Hand Refiner With External Detector" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/metric3d.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/metric3d.py new file mode 100644 index 00000000..1be81aea --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/metric3d.py @@ -0,0 +1,62 @@ +import os +# Disable NPU device initialization and problematic MMCV ops to prevent RuntimeError +os.environ['NPU_DEVICE_COUNT'] = '0' +os.environ['MMCV_WITH_OPS'] = '0' + +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION +import comfy.model_management as model_management + +class Metric3D_Depth_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + backbone=INPUT.COMBO(["vit-small", "vit-large", "vit-giant2"]), + fx=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION), + fy=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, backbone="vit-small", fx=1000, fy=1000, resolution=512): + from custom_controlnet_aux.metric3d import Metric3DDetector + model = Metric3DDetector.from_pretrained(filename=f"metric_depth_{backbone.replace('-', '_')}_800k.pth").to(model_management.get_torch_device()) + cb = lambda image, **kwargs: model(image, **kwargs)[0] + out = common_annotator_call(cb, image, resolution=resolution, fx=fx, fy=fy, depth_and_normal=True) + del model + return (out, ) + +class Metric3D_Normal_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + backbone=INPUT.COMBO(["vit-small", "vit-large", "vit-giant2"]), + fx=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION), + fy=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, backbone="vit-small", fx=1000, fy=1000, resolution=512): + from custom_controlnet_aux.metric3d import Metric3DDetector + model = Metric3DDetector.from_pretrained(filename=f"metric_depth_{backbone.replace('-', '_')}_800k.pth").to(model_management.get_torch_device()) + cb = lambda image, **kwargs: model(image, **kwargs)[1] + out = common_annotator_call(cb, image, resolution=resolution, fx=fx, fy=fy, depth_and_normal=True) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "Metric3D-DepthMapPreprocessor": Metric3D_Depth_Map_Preprocessor, + "Metric3D-NormalMapPreprocessor": Metric3D_Normal_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "Metric3D-DepthMapPreprocessor": "Metric3D Depth Map", + "Metric3D-NormalMapPreprocessor": "Metric3D Normal Map" +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/midas.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/midas.py new file mode 100644 index 00000000..54c06853 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/midas.py @@ -0,0 +1,59 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management +import numpy as np + +class MIDAS_Normal_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0), + bg_threshold=INPUT.FLOAT(default=0.1), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs): + from custom_controlnet_aux.midas import MidasDetector + + model = MidasDetector.from_pretrained().to(model_management.get_torch_device()) + #Dirty hack :)) + cb = lambda image, **kargs: model(image, **kargs)[1] + out = common_annotator_call(cb, image, resolution=resolution, a=a, bg_th=bg_threshold, depth_and_normal=True) + del model + return (out, ) + +class MIDAS_Depth_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0), + bg_threshold=INPUT.FLOAT(default=0.1), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs): + from custom_controlnet_aux.midas import MidasDetector + + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py + model = MidasDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, a=a, bg_th=bg_threshold) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "MiDaS-NormalMapPreprocessor": MIDAS_Normal_Map_Preprocessor, + "MiDaS-DepthMapPreprocessor": MIDAS_Depth_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "MiDaS-NormalMapPreprocessor": "MiDaS Normal Map", + "MiDaS-DepthMapPreprocessor": "MiDaS Depth Map" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/mlsd.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/mlsd.py new file mode 100644 index 00000000..30bae1c6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/mlsd.py @@ -0,0 +1,31 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management +import numpy as np + +class MLSD_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + score_threshold=INPUT.FLOAT(default=0.1, min=0.01, max=2.0), + dist_threshold=INPUT.FLOAT(default=0.1, min=0.01, max=20.0), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, score_threshold, dist_threshold, resolution=512, **kwargs): + from custom_controlnet_aux.mlsd import MLSDdetector + + model = MLSDdetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, thr_v=score_threshold, thr_d=dist_threshold) + return (out, ) + +NODE_CLASS_MAPPINGS = { + "M-LSDPreprocessor": MLSD_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "M-LSDPreprocessor": "M-LSD Lines" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/normalbae.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/normalbae.py new file mode 100644 index 00000000..af013e15 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/normalbae.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class BAE_Normal_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.normalbae import NormalBaeDetector + + model = NormalBaeDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out,) + +NODE_CLASS_MAPPINGS = { + "BAE-NormalMapPreprocessor": BAE_Normal_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "BAE-NormalMapPreprocessor": "BAE Normal Map" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/oneformer.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/oneformer.py new file mode 100644 index 00000000..4c7cd3e7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/oneformer.py @@ -0,0 +1,50 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class OneFormer_COCO_SemSegPreprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "semantic_segmentate" + + CATEGORY = "ControlNet Preprocessors/Semantic Segmentation" + + def semantic_segmentate(self, image, resolution=512): + from custom_controlnet_aux.oneformer import OneformerSegmentor + + model = OneformerSegmentor.from_pretrained(filename="150_16_swin_l_oneformer_coco_100ep.pth") + model = model.to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out,) + +class OneFormer_ADE20K_SemSegPreprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "semantic_segmentate" + + CATEGORY = "ControlNet Preprocessors/Semantic Segmentation" + + def semantic_segmentate(self, image, resolution=512): + from custom_controlnet_aux.oneformer import OneformerSegmentor + + model = OneformerSegmentor.from_pretrained(filename="250_16_swin_l_oneformer_ade20k_160k.pth") + model = model.to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out,) + +NODE_CLASS_MAPPINGS = { + "OneFormer-COCO-SemSegPreprocessor": OneFormer_COCO_SemSegPreprocessor, + "OneFormer-ADE20K-SemSegPreprocessor": OneFormer_ADE20K_SemSegPreprocessor +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "OneFormer-COCO-SemSegPreprocessor": "OneFormer COCO Segmentor", + "OneFormer-ADE20K-SemSegPreprocessor": "OneFormer ADE20K Segmentor" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/openpose.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/openpose.py new file mode 100644 index 00000000..c579e6cd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/openpose.py @@ -0,0 +1,48 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management +import json + +class OpenPose_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + detect_hand=INPUT.COMBO(["enable", "disable"]), + detect_body=INPUT.COMBO(["enable", "disable"]), + detect_face=INPUT.COMBO(["enable", "disable"]), + resolution=INPUT.RESOLUTION(), + scale_stick_for_xinsr_cn=INPUT.COMBO(["disable", "enable"]) + ) + + RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT") + FUNCTION = "estimate_pose" + + CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators" + + def estimate_pose(self, image, detect_hand="enable", detect_body="enable", detect_face="enable", scale_stick_for_xinsr_cn="disable", resolution=512, **kwargs): + from custom_controlnet_aux.open_pose import OpenposeDetector + + detect_hand = detect_hand == "enable" + detect_body = detect_body == "enable" + detect_face = detect_face == "enable" + scale_stick_for_xinsr_cn = scale_stick_for_xinsr_cn == "enable" + + model = OpenposeDetector.from_pretrained().to(model_management.get_torch_device()) + self.openpose_dicts = [] + def func(image, **kwargs): + pose_img, openpose_dict = model(image, **kwargs) + self.openpose_dicts.append(openpose_dict) + return pose_img + + out = common_annotator_call(func, image, include_hand=detect_hand, include_face=detect_face, include_body=detect_body, image_and_json=True, xinsr_stick_scaling=scale_stick_for_xinsr_cn, resolution=resolution) + del model + return { + 'ui': { "openpose_json": [json.dumps(self.openpose_dicts, indent=4)] }, + "result": (out, self.openpose_dicts) + } + +NODE_CLASS_MAPPINGS = { + "OpenposePreprocessor": OpenPose_Preprocessor, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "OpenposePreprocessor": "OpenPose Pose", +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/pidinet.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/pidinet.py new file mode 100644 index 00000000..92f22f92 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/pidinet.py @@ -0,0 +1,30 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class PIDINET_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + safe=INPUT.COMBO(["enable", "disable"]), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, safe, resolution=512, **kwargs): + from custom_controlnet_aux.pidi import PidiNetDetector + + model = PidiNetDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, safe = safe == "enable") + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "PiDiNetPreprocessor": PIDINET_Preprocessor, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "PiDiNetPreprocessor": "PiDiNet Soft-Edge Lines" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/pose_keypoint_postprocess.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/pose_keypoint_postprocess.py new file mode 100644 index 00000000..11f1e82d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/pose_keypoint_postprocess.py @@ -0,0 +1,340 @@ +import folder_paths +import json +import os +import numpy as np +import cv2 +from PIL import ImageColor +from einops import rearrange +import torch +import itertools + +from ..src.custom_controlnet_aux.dwpose import draw_poses, draw_animalposes, decode_json_as_poses + + +""" +Format of POSE_KEYPOINT (AP10K keypoints): +[{ + "version": "ap10k", + "animals": [ + [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]], + [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]], + ... + ], + "canvas_height": 512, + "canvas_width": 768 +},...] +Format of POSE_KEYPOINT (OpenPose keypoints): +[{ + "people": [ + { + 'pose_keypoints_2d': [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]] + "face_keypoints_2d": [[x1, y1, 1], [x2, y2, 1],..., [x68, y68, 1]], + "hand_left_keypoints_2d": [[x1, y1, 1], [x2, y2, 1],..., [x21, y21, 1]], + "hand_right_keypoints_2d":[[x1, y1, 1], [x2, y2, 1],..., [x21, y21, 1]], + } + ], + "canvas_height": canvas_height, + "canvas_width": canvas_width, +},...] +""" + +class SavePoseKpsAsJsonFile: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pose_kps": ("POSE_KEYPOINT",), + "filename_prefix": ("STRING", {"default": "PoseKeypoint"}) + } + } + RETURN_TYPES = () + FUNCTION = "save_pose_kps" + OUTPUT_NODE = True + CATEGORY = "ControlNet Preprocessors/Pose Keypoint Postprocess" + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + def save_pose_kps(self, pose_kps, filename_prefix): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = \ + folder_paths.get_save_image_path(filename_prefix, self.output_dir, pose_kps[0]["canvas_width"], pose_kps[0]["canvas_height"]) + file = f"{filename}_{counter:05}.json" + with open(os.path.join(full_output_folder, file), 'w') as f: + json.dump(pose_kps , f) + return {} + +#COCO-Wholebody doesn't have eyebrows as it inherits 68 keypoints format +#Perhaps eyebrows can be estimated tho +FACIAL_PARTS = ["skin", "left_eye", "right_eye", "nose", "upper_lip", "inner_mouth", "lower_lip"] +LAPA_COLORS = dict( + skin="rgb(0, 153, 255)", + left_eye="rgb(0, 204, 153)", + right_eye="rgb(255, 153, 0)", + nose="rgb(255, 102, 255)", + upper_lip="rgb(102, 0, 51)", + inner_mouth="rgb(255, 204, 255)", + lower_lip="rgb(255, 0, 102)" +) + +#One-based index +def kps_idxs(start, end): + step = -1 if start > end else 1 + return list(range(start-1, end+1-1, step)) + +#Source: https://www.researchgate.net/profile/Fabrizio-Falchi/publication/338048224/figure/fig1/AS:837860722741255@1576772971540/68-facial-landmarks.jpg +FACIAL_PART_RANGES = dict( + skin=kps_idxs(1, 17) + kps_idxs(27, 18), + nose=kps_idxs(28, 36), + left_eye=kps_idxs(37, 42), + right_eye=kps_idxs(43, 48), + upper_lip=kps_idxs(49, 55) + kps_idxs(65, 61), + lower_lip=kps_idxs(61, 68), + inner_mouth=kps_idxs(61, 65) + kps_idxs(55, 49) +) + +def is_normalized(keypoints) -> bool: + point_normalized = [ + 0 <= np.abs(k[0]) <= 1 and 0 <= np.abs(k[1]) <= 1 + for k in keypoints + if k is not None + ] + if not point_normalized: + return False + return np.all(point_normalized) + +class FacialPartColoringFromPoseKps: + @classmethod + def INPUT_TYPES(s): + input_types = { + "required": {"pose_kps": ("POSE_KEYPOINT",), "mode": (["point", "polygon"], {"default": "polygon"})} + } + for facial_part in FACIAL_PARTS: + input_types["required"][facial_part] = ("STRING", {"default": LAPA_COLORS[facial_part], "multiline": False}) + return input_types + RETURN_TYPES = ("IMAGE",) + FUNCTION = "colorize" + CATEGORY = "ControlNet Preprocessors/Pose Keypoint Postprocess" + def colorize(self, pose_kps, mode, **facial_part_colors): + pose_frames = pose_kps + np_frames = [self.draw_kps(pose_frame, mode, **facial_part_colors) for pose_frame in pose_frames] + np_frames = np.stack(np_frames, axis=0) + return (torch.from_numpy(np_frames).float() / 255.,) + + def draw_kps(self, pose_frame, mode, **facial_part_colors): + width, height = pose_frame["canvas_width"], pose_frame["canvas_height"] + canvas = np.zeros((height, width, 3), dtype=np.uint8) + for person, part_name in itertools.product(pose_frame["people"], FACIAL_PARTS): + n = len(person["face_keypoints_2d"]) // 3 + facial_kps = rearrange(np.array(person["face_keypoints_2d"]), "(n c) -> n c", n=n, c=3)[:, :2] + if is_normalized(facial_kps): + facial_kps *= (width, height) + facial_kps = facial_kps.astype(np.int32) + part_color = ImageColor.getrgb(facial_part_colors[part_name])[:3] + part_contours = facial_kps[FACIAL_PART_RANGES[part_name], :] + if mode == "point": + for pt in part_contours: + cv2.circle(canvas, pt, radius=2, color=part_color, thickness=-1) + else: + cv2.fillPoly(canvas, pts=[part_contours], color=part_color) + return canvas + +# https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/.github/media/keypoints_pose_18.png +BODY_PART_INDEXES = { + "Head": (16, 14, 0, 15, 17), + "Neck": (0, 1), + "Shoulder": (2, 5), + "Torso": (2, 5, 8, 11), + "RArm": (2, 3), + "RForearm": (3, 4), + "LArm": (5, 6), + "LForearm": (6, 7), + "RThigh": (8, 9), + "RLeg": (9, 10), + "LThigh": (11, 12), + "LLeg": (12, 13) +} +BODY_PART_DEFAULT_W_H = { + "Head": "256, 256", + "Neck": "100, 100", + "Shoulder": '', + "Torso": "350, 450", + "RArm": "128, 256", + "RForearm": "128, 256", + "LArm": "128, 256", + "LForearm": "128, 256", + "RThigh": "128, 256", + "RLeg": "128, 256", + "LThigh": "128, 256", + "LLeg": "128, 256" +} + +class SinglePersonProcess: + @classmethod + def sort_and_get_max_people(s, pose_kps): + for idx in range(len(pose_kps)): + pose_kps[idx]["people"] = sorted(pose_kps[idx]["people"], key=lambda person:person["pose_keypoints_2d"][0]) + return pose_kps, max(len(frame["people"]) for frame in pose_kps) + + def __init__(self, pose_kps, person_idx=0) -> None: + self.width, self.height = pose_kps[0]["canvas_width"], pose_kps[0]["canvas_height"] + self.poses = [ + self.normalize(pose_frame["people"][person_idx]["pose_keypoints_2d"]) + if person_idx < len(pose_frame["people"]) + else None + for pose_frame in pose_kps + ] + + def normalize(self, pose_kps_2d): + n = len(pose_kps_2d) // 3 + pose_kps_2d = rearrange(np.array(pose_kps_2d), "(n c) -> n c", n=n, c=3) + pose_kps_2d[np.argwhere(pose_kps_2d[:,2]==0), :] = np.iinfo(np.int32).max // 2 #Safe large value + pose_kps_2d = pose_kps_2d[:, :2] + if is_normalized(pose_kps_2d): + pose_kps_2d *= (self.width, self.height) + return pose_kps_2d + + def get_xyxy_bboxes(self, part_name, bbox_size=(128, 256)): + width, height = bbox_size + xyxy_bboxes = {} + for idx, pose in enumerate(self.poses): + if pose is None: + xyxy_bboxes[idx] = (np.iinfo(np.int32).max // 2,) * 4 + continue + pts = pose[BODY_PART_INDEXES[part_name], :] + + #top_left = np.min(pts[:,0]), np.min(pts[:,1]) + #bottom_right = np.max(pts[:,0]), np.max(pts[:,1]) + #pad_width = np.maximum(width - (bottom_right[0]-top_left[0]), 0) / 2 + #pad_height = np.maximum(height - (bottom_right[1]-top_left[1]), 0) / 2 + #xyxy_bboxes.append(( + # top_left[0] - pad_width, top_left[1] - pad_height, + # bottom_right[0] + pad_width, bottom_right[1] + pad_height, + #)) + + x_mid, y_mid = np.mean(pts[:, 0]), np.mean(pts[:, 1]) + xyxy_bboxes[idx] = ( + x_mid - width/2, y_mid - height/2, + x_mid + width/2, y_mid + height/2 + ) + return xyxy_bboxes + +class UpperBodyTrackingFromPoseKps: + PART_NAMES = ["Head", "Neck", "Shoulder", "Torso", "RArm", "RForearm", "LArm", "LForearm"] + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pose_kps": ("POSE_KEYPOINT",), + "id_include": ("STRING", {"default": '', "multiline": False}), + **{part_name + "_width_height": ("STRING", {"default": BODY_PART_DEFAULT_W_H[part_name], "multiline": False}) for part_name in s.PART_NAMES} + } + } + + RETURN_TYPES = ("TRACKING", "STRING") + RETURN_NAMES = ("tracking", "prompt") + FUNCTION = "convert" + CATEGORY = "ControlNet Preprocessors/Pose Keypoint Postprocess" + + def convert(self, pose_kps, id_include, **parts_width_height): + parts_width_height = {part_name.replace("_width_height", ''): value for part_name, value in parts_width_height.items()} + enabled_part_names = [part_name for part_name in self.PART_NAMES if len(parts_width_height[part_name].strip())] + tracked = {part_name: {} for part_name in enabled_part_names} + id_include = id_include.strip() + id_include = list(map(int, id_include.split(','))) if len(id_include) else [] + prompt_string = '' + pose_kps, max_people = SinglePersonProcess.sort_and_get_max_people(pose_kps) + + for person_idx in range(max_people): + if len(id_include) and person_idx not in id_include: + continue + processor = SinglePersonProcess(pose_kps, person_idx) + for part_name in enabled_part_names: + bbox_size = tuple(map(int, parts_width_height[part_name].split(','))) + part_bboxes = processor.get_xyxy_bboxes(part_name, bbox_size) + id_coordinates = {idx: part_bbox+(processor.width, processor.height) for idx, part_bbox in part_bboxes.items()} + tracked[part_name][person_idx] = id_coordinates + + for class_name, class_data in tracked.items(): + for class_id in class_data.keys(): + class_id_str = str(class_id) + # Use the incoming prompt for each class name and ID + _class_name = class_name.replace('L', '').replace('R', '').lower() + prompt_string += f'"{class_id_str}.{class_name}": "({_class_name})",\n' + + return (tracked, prompt_string) + + +def numpy2torch(np_image: np.ndarray) -> torch.Tensor: + """ [H, W, C] => [B=1, H, W, C]""" + return torch.from_numpy(np_image.astype(np.float32) / 255).unsqueeze(0) + + +class RenderPeopleKps: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "kps": ("POSE_KEYPOINT",), + "render_body": ("BOOLEAN", {"default": True}), + "render_hand": ("BOOLEAN", {"default": True}), + "render_face": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "render" + CATEGORY = "ControlNet Preprocessors/Pose Keypoint Postprocess" + + def render(self, kps, render_body, render_hand, render_face) -> tuple[np.ndarray]: + if isinstance(kps, list): + kps = kps[0] + + poses, _, height, width = decode_json_as_poses(kps) + np_image = draw_poses( + poses, + height, + width, + render_body, + render_hand, + render_face, + ) + return (numpy2torch(np_image),) + +class RenderAnimalKps: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "kps": ("POSE_KEYPOINT",), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "render" + CATEGORY = "ControlNet Preprocessors/Pose Keypoint Postprocess" + + def render(self, kps) -> tuple[np.ndarray]: + if isinstance(kps, list): + kps = kps[0] + + _, poses, height, width = decode_json_as_poses(kps) + np_image = draw_animalposes(poses, height, width) + return (numpy2torch(np_image),) + + +NODE_CLASS_MAPPINGS = { + "SavePoseKpsAsJsonFile": SavePoseKpsAsJsonFile, + "FacialPartColoringFromPoseKps": FacialPartColoringFromPoseKps, + "UpperBodyTrackingFromPoseKps": UpperBodyTrackingFromPoseKps, + "RenderPeopleKps": RenderPeopleKps, + "RenderAnimalKps": RenderAnimalKps, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "SavePoseKpsAsJsonFile": "Save Pose Keypoints", + "FacialPartColoringFromPoseKps": "Colorize Facial Parts from PoseKPS", + "UpperBodyTrackingFromPoseKps": "Upper Body Tracking From PoseKps (InstanceDiffusion)", + "RenderPeopleKps": "Render Pose JSON (Human)", + "RenderAnimalKps": "Render Pose JSON (Animal)", +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/pyracanny.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/pyracanny.py new file mode 100644 index 00000000..996c0b64 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/pyracanny.py @@ -0,0 +1,30 @@ +from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs +import comfy.model_management as model_management + +class PyraCanny_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + low_threshold=INPUT.INT(default=64, max=255), + high_threshold=INPUT.INT(default=128, max=255), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, low_threshold=64, high_threshold=128, resolution=512, **kwargs): + from custom_controlnet_aux.pyracanny import PyraCannyDetector + + return (common_annotator_call(PyraCannyDetector(), image, low_threshold=low_threshold, high_threshold=high_threshold, resolution=resolution), ) + + + +NODE_CLASS_MAPPINGS = { + "PyraCannyPreprocessor": PyraCanny_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "PyraCannyPreprocessor": "PyraCanny" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/recolor.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/recolor.py new file mode 100644 index 00000000..2c33513c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/recolor.py @@ -0,0 +1,46 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT + +class ImageLuminanceDetector: + @classmethod + def INPUT_TYPES(s): + #https://github.com/Mikubill/sd-webui-controlnet/blob/416c345072c9c2066101e225964e3986abe6945e/scripts/processor.py#L1229 + return define_preprocessor_inputs( + gamma_correction=INPUT.FLOAT(default=1.0, min=0.1, max=2.0), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Recolor" + + def execute(self, image, gamma_correction=1.0, resolution=512, **kwargs): + from custom_controlnet_aux.recolor import Recolorizer + return (common_annotator_call(Recolorizer(), image, mode="luminance", gamma_correction=gamma_correction , resolution=resolution), ) + +class ImageIntensityDetector: + @classmethod + def INPUT_TYPES(s): + #https://github.com/Mikubill/sd-webui-controlnet/blob/416c345072c9c2066101e225964e3986abe6945e/scripts/processor.py#L1229 + return define_preprocessor_inputs( + gamma_correction=INPUT.FLOAT(default=1.0, min=0.1, max=2.0), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Recolor" + + def execute(self, image, gamma_correction=1.0, resolution=512, **kwargs): + from custom_controlnet_aux.recolor import Recolorizer + return (common_annotator_call(Recolorizer(), image, mode="intensity", gamma_correction=gamma_correction , resolution=resolution), ) + +NODE_CLASS_MAPPINGS = { + "ImageLuminanceDetector": ImageLuminanceDetector, + "ImageIntensityDetector": ImageIntensityDetector +} +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageLuminanceDetector": "Image Luminance", + "ImageIntensityDetector": "Image Intensity" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/scribble.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/scribble.py new file mode 100644 index 00000000..2945502f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/scribble.py @@ -0,0 +1,74 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, nms +import comfy.model_management as model_management +import cv2 + +class Scribble_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.scribble import ScribbleDetector + + model = ScribbleDetector() + return (common_annotator_call(model, image, resolution=resolution), ) + +class Scribble_XDoG_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + threshold=INPUT.INT(default=32, min=1, max=64), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, threshold=32, resolution=512, **kwargs): + from custom_controlnet_aux.scribble import ScribbleXDog_Detector + + model = ScribbleXDog_Detector() + return (common_annotator_call(model, image, resolution=resolution, thr_a=threshold), ) + +class Scribble_PiDiNet_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + safe=(["enable", "disable"],), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, safe="enable", resolution=512): + def model(img, **kwargs): + from custom_controlnet_aux.pidi import PidiNetDetector + pidinet = PidiNetDetector.from_pretrained().to(model_management.get_torch_device()) + result = pidinet(img, scribble=True, **kwargs) + result = nms(result, 127, 3.0) + result = cv2.GaussianBlur(result, (0, 0), 3.0) + result[result > 4] = 255 + result[result < 255] = 0 + return result + return (common_annotator_call(model, image, resolution=resolution, safe=safe=="enable"),) + +NODE_CLASS_MAPPINGS = { + "ScribblePreprocessor": Scribble_Preprocessor, + "Scribble_XDoG_Preprocessor": Scribble_XDoG_Preprocessor, + "Scribble_PiDiNet_Preprocessor": Scribble_PiDiNet_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "ScribblePreprocessor": "Scribble Lines", + "Scribble_XDoG_Preprocessor": "Scribble XDoG Lines", + "Scribble_PiDiNet_Preprocessor": "Scribble PiDiNet Lines" +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/segment_anything.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/segment_anything.py new file mode 100644 index 00000000..c91412a5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/segment_anything.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class SAM_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/others" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.sam import SamDetector + + mobile_sam = SamDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(mobile_sam, image, resolution=resolution) + del mobile_sam + return (out, ) + +NODE_CLASS_MAPPINGS = { + "SAMPreprocessor": SAM_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "SAMPreprocessor": "SAM Segmentor" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/shuffle.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/shuffle.py new file mode 100644 index 00000000..6949f6df --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/shuffle.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION +import comfy.model_management as model_management + +class Shuffle_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + resolution=INPUT.RESOLUTION(), + seed=INPUT.SEED() + ) + RETURN_TYPES = ("IMAGE",) + FUNCTION = "preprocess" + + CATEGORY = "ControlNet Preprocessors/T2IAdapter-only" + + def preprocess(self, image, resolution=512, seed=0): + from custom_controlnet_aux.shuffle import ContentShuffleDetector + + return (common_annotator_call(ContentShuffleDetector(), image, resolution=resolution, seed=seed), ) + +NODE_CLASS_MAPPINGS = { + "ShufflePreprocessor": Shuffle_Preprocessor +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ShufflePreprocessor": "Content Shuffle" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/teed.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/teed.py new file mode 100644 index 00000000..c3b533ec --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/teed.py @@ -0,0 +1,30 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class TEED_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + safe_steps=INPUT.INT(default=2, max=10), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Line Extractors" + + def execute(self, image, safe_steps=2, resolution=512, **kwargs): + from custom_controlnet_aux.teed import TEDDetector + + model = TEDDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution, safe_steps=safe_steps) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "TEEDPreprocessor": TEED_Preprocessor, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "TEED_Preprocessor": "TEED Soft-Edge Lines", +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/tile.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/tile.py new file mode 100644 index 00000000..0f90ed91 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/tile.py @@ -0,0 +1,73 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT + + +class Tile_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + pyrUp_iters=INPUT.INT(default=3, min=1, max=10), + resolution=INPUT.RESOLUTION() + ) + + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/tile" + + def execute(self, image, pyrUp_iters, resolution=512, **kwargs): + from custom_controlnet_aux.tile import TileDetector + + return (common_annotator_call(TileDetector(), image, pyrUp_iters=pyrUp_iters, resolution=resolution),) + +class TTPlanet_TileGF_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + scale_factor=INPUT.FLOAT(default=1.00, min=1.000, max=8.00), + blur_strength=INPUT.FLOAT(default=2.0, min=1.0, max=10.0), + radius=INPUT.INT(default=7, min=1, max=20), + eps=INPUT.FLOAT(default=0.01, min=0.001, max=0.1, step=0.001), + resolution=INPUT.RESOLUTION() + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/tile" + + def execute(self, image, scale_factor, blur_strength, radius, eps, **kwargs): + from custom_controlnet_aux.tile import TTPlanet_Tile_Detector_GF + + return (common_annotator_call(TTPlanet_Tile_Detector_GF(), image, scale_factor=scale_factor, blur_strength=blur_strength, radius=radius, eps=eps),) + +class TTPlanet_TileSimple_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs( + scale_factor=INPUT.FLOAT(default=1.00, min=1.000, max=8.00), + blur_strength=INPUT.FLOAT(default=2.0, min=1.0, max=10.0), + ) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/tile" + + def execute(self, image, scale_factor, blur_strength): + from custom_controlnet_aux.tile import TTPLanet_Tile_Detector_Simple + + return (common_annotator_call(TTPLanet_Tile_Detector_Simple(), image, scale_factor=scale_factor, blur_strength=blur_strength),) + + +NODE_CLASS_MAPPINGS = { + "TilePreprocessor": Tile_Preprocessor, + "TTPlanet_TileGF_Preprocessor": TTPlanet_TileGF_Preprocessor, + "TTPlanet_TileSimple_Preprocessor": TTPlanet_TileSimple_Preprocessor +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TilePreprocessor": "Tile", + "TTPlanet_TileGF_Preprocessor": "TTPlanet Tile GuidedFilter", + "TTPlanet_TileSimple_Preprocessor": "TTPlanet Tile Simple" +} diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/uniformer.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/uniformer.py new file mode 100644 index 00000000..88a82971 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/uniformer.py @@ -0,0 +1,34 @@ +import os +# Disable NPU device initialization and problematic MMCV ops to prevent RuntimeError +os.environ['NPU_DEVICE_COUNT'] = '0' +os.environ['MMCV_WITH_OPS'] = '0' + +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class Uniformer_SemSegPreprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "semantic_segmentate" + + CATEGORY = "ControlNet Preprocessors/Semantic Segmentation" + + def semantic_segmentate(self, image, resolution=512): + from custom_controlnet_aux.uniformer import UniformerSegmentor + + model = UniformerSegmentor.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "UniFormer-SemSegPreprocessor": Uniformer_SemSegPreprocessor, + "SemSegPreprocessor": Uniformer_SemSegPreprocessor, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "UniFormer-SemSegPreprocessor": "UniFormer Segmentor", + "SemSegPreprocessor": "Semantic Segmentor (legacy, alias for UniFormer)", +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/unimatch.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/unimatch.py new file mode 100644 index 00000000..20abcc7e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/unimatch.py @@ -0,0 +1,75 @@ +from ..utils import common_annotator_call +import comfy.model_management as model_management +import torch +import numpy as np +from einops import rearrange +import torch.nn.functional as F + +class Unimatch_OptFlowPreprocessor: + @classmethod + def INPUT_TYPES(s): + return { + "required": dict( + image=("IMAGE",), + ckpt_name=( + ["gmflow-scale1-mixdata.pth", "gmflow-scale2-mixdata.pth", "gmflow-scale2-regrefine6-mixdata.pth"], + {"default": "gmflow-scale2-regrefine6-mixdata.pth"} + ), + backward_flow=("BOOLEAN", {"default": False}), + bidirectional_flow=("BOOLEAN", {"default": False}) + ) + } + + RETURN_TYPES = ("OPTICAL_FLOW", "IMAGE") + RETURN_NAMES = ("OPTICAL_FLOW", "PREVIEW_IMAGE") + FUNCTION = "estimate" + + CATEGORY = "ControlNet Preprocessors/Optical Flow" + + def estimate(self, image, ckpt_name, backward_flow=False, bidirectional_flow=False): + assert len(image) > 1, "[Unimatch] Requiring as least two frames as an optical flow estimator. Only use this node on video input." + from custom_controlnet_aux.unimatch import UnimatchDetector + tensor_images = image + model = UnimatchDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device()) + flows, vis_flows = [], [] + for i in range(len(tensor_images) - 1): + image0, image1 = np.asarray(image[i:i+2].cpu() * 255., dtype=np.uint8) + flow, vis_flow = model(image0, image1, output_type="np", pred_bwd_flow=backward_flow, pred_bidir_flow=bidirectional_flow) + flows.append(torch.from_numpy(flow).float()) + vis_flows.append(torch.from_numpy(vis_flow).float() / 255.) + del model + return (torch.stack(flows, dim=0), torch.stack(vis_flows, dim=0)) + +class MaskOptFlow: + @classmethod + def INPUT_TYPES(s): + return { + "required": dict(optical_flow=("OPTICAL_FLOW",), mask=("MASK",)) + } + + RETURN_TYPES = ("OPTICAL_FLOW", "IMAGE") + RETURN_NAMES = ("OPTICAL_FLOW", "PREVIEW_IMAGE") + FUNCTION = "mask_opt_flow" + + CATEGORY = "ControlNet Preprocessors/Optical Flow" + + def mask_opt_flow(self, optical_flow, mask): + from custom_controlnet_aux.unimatch import flow_to_image + assert len(mask) >= len(optical_flow), f"Not enough masks to mask optical flow: {len(mask)} vs {len(optical_flow)}" + mask = mask[:optical_flow.shape[0]] + mask = F.interpolate(mask, optical_flow.shape[1:3]) + mask = rearrange(mask, "n 1 h w -> n h w 1") + vis_flows = torch.stack([torch.from_numpy(flow_to_image(flow)).float() / 255. for flow in optical_flow.numpy()], dim=0) + vis_flows *= mask + optical_flow *= mask + return (optical_flow, vis_flows) + + +NODE_CLASS_MAPPINGS = { + "Unimatch_OptFlowPreprocessor": Unimatch_OptFlowPreprocessor, + "MaskOptFlow": MaskOptFlow +} +NODE_DISPLAY_NAME_MAPPINGS = { + "Unimatch_OptFlowPreprocessor": "Unimatch Optical Flow", + "MaskOptFlow": "Mask Optical Flow (DragNUWA)" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/node_wrappers/zoe.py b/custom_nodes/comfyui_controlnet_aux/node_wrappers/zoe.py new file mode 100644 index 00000000..ebc82d73 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/node_wrappers/zoe.py @@ -0,0 +1,27 @@ +from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT +import comfy.model_management as model_management + +class Zoe_Depth_Map_Preprocessor: + @classmethod + def INPUT_TYPES(s): + return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators" + + def execute(self, image, resolution=512, **kwargs): + from custom_controlnet_aux.zoe import ZoeDetector + + model = ZoeDetector.from_pretrained().to(model_management.get_torch_device()) + out = common_annotator_call(model, image, resolution=resolution) + del model + return (out, ) + +NODE_CLASS_MAPPINGS = { + "Zoe-DepthMapPreprocessor": Zoe_Depth_Map_Preprocessor +} +NODE_DISPLAY_NAME_MAPPINGS = { + "Zoe-DepthMapPreprocessor": "Zoe Depth Map" +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/pyproject.toml b/custom_nodes/comfyui_controlnet_aux/pyproject.toml new file mode 100644 index 00000000..bbc26799 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "comfyui_controlnet_aux" +description = "Plug-and-play ComfyUI node sets for making ControlNet hint images" + +version = "1.1.3" +dependencies = ["torch", "importlib_metadata", "huggingface_hub", "scipy", "opencv-python>=4.7.0.72", "filelock", "numpy", "Pillow", "einops", "torchvision", "pyyaml", "scikit-image", "python-dateutil", "mediapipe", "fvcore", "yapf", "omegaconf", "ftfy", "addict", "yacs", "trimesh[easy]", "albumentations", "scikit-learn", "matplotlib"] + +[project.urls] +Repository = "https://github.com/Fannovel16/comfyui_controlnet_aux" + +[tool.comfy] +PublisherId = "fannovel16" +DisplayName = "comfyui_controlnet_aux" +Icon = "" diff --git a/custom_nodes/comfyui_controlnet_aux/requirements.txt b/custom_nodes/comfyui_controlnet_aux/requirements.txt new file mode 100644 index 00000000..c1bd099e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/requirements.txt @@ -0,0 +1,25 @@ +torch +importlib_metadata +huggingface_hub +scipy +opencv-python>=4.7.0.72 +filelock +numpy +Pillow +einops +torchvision +pyyaml +scikit-image +python-dateutil +mediapipe +fvcore +yapf +omegaconf +ftfy +addict +yacs +yapf +trimesh[easy] +albumentations +scikit-learn +matplotlib diff --git a/custom_nodes/comfyui_controlnet_aux/search_hf_assets.py b/custom_nodes/comfyui_controlnet_aux/search_hf_assets.py new file mode 100644 index 00000000..c3998259 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/search_hf_assets.py @@ -0,0 +1,56 @@ +from pathlib import Path +import os +import re +#Thanks ChatGPT +pattern = r'\bfrom_pretrained\(.*?pretrained_model_or_path\s*=\s*(.*?)(?:,|\))|filename\s*=\s*(.*?)(?:,|\))|(\w+_filename)\s*=\s*(.*?)(?:,|\))' +aux_dir = Path(__file__).parent / 'src' / 'custom_controlnet_aux' +VAR_DICT = dict( + HF_MODEL_NAME = "lllyasviel/Annotators", + DWPOSE_MODEL_NAME = "yzd-v/DWPose", + BDS_MODEL_NAME = "bdsqlsz/qinglong_controlnet-lllite", + DENSEPOSE_MODEL_NAME = "LayerNorm/DensePose-TorchScript-with-hint-image", + MESH_GRAPHORMER_MODEL_NAME = "hr16/ControlNet-HandRefiner-pruned", + SAM_MODEL_NAME = "dhkim2810/MobileSAM", + UNIMATCH_MODEL_NAME = "hr16/Unimatch", + DEPTH_ANYTHING_MODEL_NAME = "LiheYoung/Depth-Anything", #HF Space + DIFFUSION_EDGE_MODEL_NAME = "hr16/Diffusion-Edge" +) +re_result_dict = {} +for preprocc in os.listdir(aux_dir): + if preprocc in ["__pycache__", 'tests']: continue + if '.py' in preprocc: continue + f = open(aux_dir / preprocc / '__init__.py', 'r') + code = f.read() + matches = re.findall(pattern, code) + result = [match[0] or match[1] or match[3] for match in matches] + if not len(result): + print(preprocc) + continue + result = [el.replace("'", '').replace('"', '') for el in result] + result = [VAR_DICT.get(el, el) for el in result] + re_result_dict[preprocc] = result + f.close() + +for preprocc, re_result in re_result_dict.items(): + model_name, filenames = re_result[0], re_result[1:] + print(f"* {preprocc}: ", end=' ') + assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames]) + print(assests_md) + +preprocc = "dwpose" +model_name, filenames = VAR_DICT['DWPOSE_MODEL_NAME'], ["yolox_l.onnx", "dw-ll_ucoco_384.onnx"] +print(f"* {preprocc}: ", end=' ') +assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames]) +print(assests_md) + +preprocc = "yolo-nas" +model_name, filenames = "hr16/yolo-nas-fp16", ["yolo_nas_l_fp16.onnx", "yolo_nas_m_fp16.onnx", "yolo_nas_s_fp16.onnx"] +print(f"* {preprocc}: ", end=' ') +assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames]) +print(assests_md) + +preprocc = "dwpose-torchscript" +model_name, filenames = "hr16/DWPose-TorchScript-BatchSize5", ["dw-ll_ucoco_384_bs5.torchscript.pt", "rtmpose-m_ap10k_256_bs5.torchscript.pt"] +print(f"* {preprocc}: ", end=' ') +assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames]) +print(assests_md) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/__init__.py new file mode 100644 index 00000000..33e7a7f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/__init__.py @@ -0,0 +1 @@ +#Dummy file ensuring this package will be recognized \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/__init__.py new file mode 100644 index 00000000..33e7a7f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/__init__.py @@ -0,0 +1 @@ +#Dummy file ensuring this package will be recognized \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/__init__.py new file mode 100644 index 00000000..b3d8ace5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/__init__.py @@ -0,0 +1,66 @@ +from .network import UNet +from .util import seg2img +import torch +import os +import cv2 +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, BDS_MODEL_NAME +from huggingface_hub import hf_hub_download +from PIL import Image +from einops import rearrange +from .anime_segmentation import AnimeSegmentation +import numpy as np + +class AnimeFaceSegmentor: + def __init__(self, model, seg_model): + self.model = model + self.seg_model = seg_model + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=BDS_MODEL_NAME, filename="UNet.pth", seg_filename="isnetis.ckpt"): + model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder="Annotators") + seg_model_path = custom_hf_download("skytnt/anime-seg", seg_filename) + + model = UNet() + ckpt = torch.load(model_path, map_location="cpu") + model.load_state_dict(ckpt) + model.eval() + + seg_model = AnimeSegmentation(seg_model_path) + seg_model.net.eval() + return cls(model, seg_model) + + def to(self, device): + self.model.to(device) + self.seg_model.net.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", remove_background=True, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + with torch.no_grad(): + if remove_background: + print(input_image.shape) + mask, input_image = self.seg_model(input_image, 0) #Don't resize image as it is resized + image_feed = torch.from_numpy(input_image).float().to(self.device) + image_feed = rearrange(image_feed, 'h w c -> 1 c h w') + image_feed = image_feed / 255 + seg = self.model(image_feed).squeeze(dim=0) + result = seg2img(seg.cpu().detach().numpy()) + + detected_map = HWC3(result) + detected_map = remove_pad(detected_map) + if remove_background: + mask = remove_pad(mask) + H, W, C = detected_map.shape + tmp = np.zeros([H, W, C + 1]) + tmp[:,:,:C] = detected_map + tmp[:,:,3:] = mask + detected_map = tmp + + if output_type == "pil": + detected_map = Image.fromarray(detected_map[..., :3]) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/anime_segmentation.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/anime_segmentation.py new file mode 100644 index 00000000..ac4ab9c0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/anime_segmentation.py @@ -0,0 +1,58 @@ +#https://github.com/SkyTNT/anime-segmentation/tree/main +#Only adapt isnet_is (https://huggingface.co/skytnt/anime-seg/blob/main/isnetis.ckpt) +import torch.nn as nn +import torch +from .isnet import ISNetDIS +import numpy as np +import cv2 +from comfy.model_management import get_torch_device +DEVICE = get_torch_device() + +class AnimeSegmentation: + def __init__(self, ckpt_path): + super(AnimeSegmentation).__init__() + sd = torch.load(ckpt_path, map_location="cpu") + self.net = ISNetDIS() + #gt_encoder isn't used during inference + self.net.load_state_dict({k.replace("net.", ''):v for k, v in sd.items() if k.startswith("net.")}) + self.net = self.net.to(DEVICE) + self.net.eval() + + def get_mask(self, input_img, s=640): + input_img = (input_img / 255).astype(np.float32) + if s == 0: + img_input = np.transpose(input_img, (2, 0, 1)) + img_input = img_input[np.newaxis, :] + tmpImg = torch.from_numpy(img_input).float().to(DEVICE) + with torch.no_grad(): + pred = self.net(tmpImg)[0][0].sigmoid() #https://github.com/SkyTNT/anime-segmentation/blob/main/train.py#L92C20-L92C47 + pred = pred.cpu().numpy()[0] + pred = np.transpose(pred, (1, 2, 0)) + #pred = pred[:, :, np.newaxis] + return pred + + h, w = h0, w0 = input_img.shape[:-1] + h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) + ph, pw = s - h, s - w + img_input = np.zeros([s, s, 3], dtype=np.float32) + img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h)) + img_input = np.transpose(img_input, (2, 0, 1)) + img_input = img_input[np.newaxis, :] + tmpImg = torch.from_numpy(img_input).float().to(DEVICE) + with torch.no_grad(): + pred = self.net(tmpImg)[0][0].sigmoid() #https://github.com/SkyTNT/anime-segmentation/blob/main/train.py#L92C20-L92C47 + pred = pred.cpu().numpy()[0] + pred = np.transpose(pred, (1, 2, 0)) + pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] + #pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis] + pred = cv2.resize(pred, (w0, h0)) + return pred + + def __call__(self, np_img, img_size): + mask = self.get_mask(np_img, int(img_size)) + np_img = (mask * np_img + 255 * (1 - mask)).astype(np.uint8) + mask = (mask * 255).astype(np.uint8) + #np_img = np.concatenate([np_img, mask], axis=2, dtype=np.uint8) + #mask = mask.repeat(3, axis=2) + return mask, np_img + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/isnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/isnet.py new file mode 100644 index 00000000..8a0a504e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/isnet.py @@ -0,0 +1,619 @@ +# Codes are borrowed from +# https://github.com/xuebinqin/DIS/blob/main/IS-Net/models/isnet.py + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import models + +bce_loss = nn.BCEWithLogitsLoss(reduction="mean") + + +def muti_loss_fusion(preds, target): + loss0 = 0.0 + loss = 0.0 + + for i in range(0, len(preds)): + if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]: + tmp_target = F.interpolate( + target, size=preds[i].size()[2:], mode="bilinear", align_corners=True + ) + loss = loss + bce_loss(preds[i], tmp_target) + else: + loss = loss + bce_loss(preds[i], target) + if i == 0: + loss0 = loss + return loss0, loss + + +fea_loss = nn.MSELoss(reduction="mean") +kl_loss = nn.KLDivLoss(reduction="mean") +l1_loss = nn.L1Loss(reduction="mean") +smooth_l1_loss = nn.SmoothL1Loss(reduction="mean") + + +def muti_loss_fusion_kl(preds, target, dfs, fs, mode="MSE"): + loss0 = 0.0 + loss = 0.0 + + for i in range(0, len(preds)): + if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]: + tmp_target = F.interpolate( + target, size=preds[i].size()[2:], mode="bilinear", align_corners=True + ) + loss = loss + bce_loss(preds[i], tmp_target) + else: + loss = loss + bce_loss(preds[i], target) + if i == 0: + loss0 = loss + + for i in range(0, len(dfs)): + df = dfs[i] + fs_i = fs[i] + if mode == "MSE": + loss = loss + fea_loss( + df, fs_i + ) ### add the mse loss of features as additional constraints + elif mode == "KL": + loss = loss + kl_loss(F.log_softmax(df, dim=1), F.softmax(fs_i, dim=1)) + elif mode == "MAE": + loss = loss + l1_loss(df, fs_i) + elif mode == "SmoothL1": + loss = loss + smooth_l1_loss(df, fs_i) + + return loss0, loss + + +class REBNCONV(nn.Module): + def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1): + super(REBNCONV, self).__init__() + + self.conv_s1 = nn.Conv2d( + in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride + ) + self.bn_s1 = nn.BatchNorm2d(out_ch) + self.relu_s1 = nn.ReLU(inplace=True) + + def forward(self, x): + hx = x + xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) + + return xout + + +## upsample tensor 'src' to have the same spatial size with tensor 'tar' +def _upsample_like(src, tar): + src = F.interpolate(src, size=tar.shape[2:], mode="bilinear", align_corners=False) + + return src + + +### RSU-7 ### +class RSU7(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512): + super(RSU7, self).__init__() + + self.in_ch = in_ch + self.mid_ch = mid_ch + self.out_ch = out_ch + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2 + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + b, c, h, w = x.shape + + hx = x + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + hx = self.pool5(hx5) + + hx6 = self.rebnconv6(hx) + + hx7 = self.rebnconv7(hx6) + + hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) + hx6dup = _upsample_like(hx6d, hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-6 ### +class RSU6(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU6, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + + hx6 = self.rebnconv6(hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-5 ### +class RSU5(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU5, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + + hx5 = self.rebnconv5(hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-4 ### +class RSU4(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-4F ### +class RSU4F(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4F, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx2 = self.rebnconv2(hx1) + hx3 = self.rebnconv3(hx2) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) + hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) + + return hx1d + hxin + + +class myrebnconv(nn.Module): + def __init__( + self, + in_ch=3, + out_ch=1, + kernel_size=3, + stride=1, + padding=1, + dilation=1, + groups=1, + ): + super(myrebnconv, self).__init__() + + self.conv = nn.Conv2d( + in_ch, + out_ch, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + ) + self.bn = nn.BatchNorm2d(out_ch) + self.rl = nn.ReLU(inplace=True) + + def forward(self, x): + return self.rl(self.bn(self.conv(x))) + + +class ISNetGTEncoder(nn.Module): + def __init__(self, in_ch=1, out_ch=1): + super(ISNetGTEncoder, self).__init__() + + self.conv_in = myrebnconv( + in_ch, 16, 3, stride=2, padding=1 + ) # nn.Conv2d(in_ch,64,3,stride=2,padding=1) + + self.stage1 = RSU7(16, 16, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 16, 64) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(64, 32, 128) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(128, 32, 256) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(256, 64, 512) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(512, 64, 512) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) + + @staticmethod + def compute_loss(args): + preds, targets = args + return muti_loss_fusion(preds, targets) + + def forward(self, x): + hx = x + + hxin = self.conv_in(hx) + # hx = self.pool_in(hxin) + + # stage 1 + hx1 = self.stage1(hxin) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + + # side output + d1 = self.side1(hx1) + d1 = _upsample_like(d1, x) + + d2 = self.side2(hx2) + d2 = _upsample_like(d2, x) + + d3 = self.side3(hx3) + d3 = _upsample_like(d3, x) + + d4 = self.side4(hx4) + d4 = _upsample_like(d4, x) + + d5 = self.side5(hx5) + d5 = _upsample_like(d5, x) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, x) + + # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1)) + + # return [torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)], [hx1, hx2, hx3, hx4, hx5, hx6] + return [d1, d2, d3, d4, d5, d6], [hx1, hx2, hx3, hx4, hx5, hx6] + + +class ISNetDIS(nn.Module): + def __init__(self, in_ch=3, out_ch=1): + super(ISNetDIS, self).__init__() + + self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1) + self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage1 = RSU7(64, 32, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 32, 128) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(128, 64, 256) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(256, 128, 512) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(512, 256, 512) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(512, 256, 512) + + # decoder + self.stage5d = RSU4F(1024, 256, 512) + self.stage4d = RSU4(1024, 128, 256) + self.stage3d = RSU5(512, 64, 128) + self.stage2d = RSU6(256, 32, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) + + # self.outconv = nn.Conv2d(6*out_ch,out_ch,1) + + @staticmethod + def compute_loss_kl(preds, targets, dfs, fs, mode="MSE"): + return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode) + + @staticmethod + def compute_loss(args): + if len(args) == 3: + ds, dfs, labels = args + return muti_loss_fusion(ds, labels) + else: + ds, dfs, labels, fs = args + return muti_loss_fusion_kl(ds, labels, dfs, fs, mode="MSE") + + def forward(self, x): + hx = x + + hxin = self.conv_in(hx) + hx = self.pool_in(hxin) + + # stage 1 + hx1 = self.stage1(hxin) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # -------------------- decoder -------------------- + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + d1 = _upsample_like(d1, x) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, x) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, x) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, x) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, x) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, x) + + # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1)) + + # return [torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6] + return [d1, d2, d3, d4, d5, d6], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6] \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/network.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/network.py new file mode 100644 index 00000000..1c592954 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/network.py @@ -0,0 +1,100 @@ +#https://github.com/siyeong0/Anime-Face-Segmentation/blob/main/network.py +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from custom_controlnet_aux.util import custom_torch_download + +class UNet(nn.Module): + def __init__(self): + super(UNet, self).__init__() + self.NUM_SEG_CLASSES = 7 # Background, hair, face, eye, mouth, skin, clothes + + mobilenet_v2 = torchvision.models.mobilenet_v2(pretrained=False) + mobilenet_v2.load_state_dict(torch.load(custom_torch_download(filename="mobilenet_v2-b0353104.pth")), strict=True) + mob_blocks = mobilenet_v2.features + + # Encoder + self.en_block0 = nn.Sequential( # in_ch=3 out_ch=16 + mob_blocks[0], + mob_blocks[1] + ) + self.en_block1 = nn.Sequential( # in_ch=16 out_ch=24 + mob_blocks[2], + mob_blocks[3], + ) + self.en_block2 = nn.Sequential( # in_ch=24 out_ch=32 + mob_blocks[4], + mob_blocks[5], + mob_blocks[6], + ) + self.en_block3 = nn.Sequential( # in_ch=32 out_ch=96 + mob_blocks[7], + mob_blocks[8], + mob_blocks[9], + mob_blocks[10], + mob_blocks[11], + mob_blocks[12], + mob_blocks[13], + ) + self.en_block4 = nn.Sequential( # in_ch=96 out_ch=160 + mob_blocks[14], + mob_blocks[15], + mob_blocks[16], + ) + + # Decoder + self.de_block4 = nn.Sequential( # in_ch=160 out_ch=96 + nn.UpsamplingNearest2d(scale_factor=2), + nn.Conv2d(160, 96, kernel_size=3, padding=1), + nn.InstanceNorm2d(96), + nn.LeakyReLU(0.1), + nn.Dropout(p=0.2) + ) + self.de_block3 = nn.Sequential( # in_ch=96x2 out_ch=32 + nn.UpsamplingNearest2d(scale_factor=2), + nn.Conv2d(96*2, 32, kernel_size=3, padding=1), + nn.InstanceNorm2d(32), + nn.LeakyReLU(0.1), + nn.Dropout(p=0.2) + ) + self.de_block2 = nn.Sequential( # in_ch=32x2 out_ch=24 + nn.UpsamplingNearest2d(scale_factor=2), + nn.Conv2d(32*2, 24, kernel_size=3, padding=1), + nn.InstanceNorm2d(24), + nn.LeakyReLU(0.1), + nn.Dropout(p=0.2) + ) + self.de_block1 = nn.Sequential( # in_ch=24x2 out_ch=16 + nn.UpsamplingNearest2d(scale_factor=2), + nn.Conv2d(24*2, 16, kernel_size=3, padding=1), + nn.InstanceNorm2d(16), + nn.LeakyReLU(0.1), + nn.Dropout(p=0.2) + ) + + self.de_block0 = nn.Sequential( # in_ch=16x2 out_ch=7 + nn.UpsamplingNearest2d(scale_factor=2), + nn.Conv2d(16*2, self.NUM_SEG_CLASSES, kernel_size=3, padding=1), + nn.Softmax2d() + ) + + def forward(self, x): + e0 = self.en_block0(x) + e1 = self.en_block1(e0) + e2 = self.en_block2(e1) + e3 = self.en_block3(e2) + e4 = self.en_block4(e3) + + d4 = self.de_block4(e4) + c4 = torch.cat((d4,e3),1) + d3 = self.de_block3(c4) + c3 = torch.cat((d3,e2),1) + d2 = self.de_block2(c3) + c2 =torch.cat((d2,e1),1) + d1 = self.de_block1(c2) + c1 = torch.cat((d1,e0),1) + y = self.de_block0(c1) + + return y \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/util.py new file mode 100644 index 00000000..f6f3d225 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/anime_face_segment/util.py @@ -0,0 +1,40 @@ +#https://github.com/siyeong0/Anime-Face-Segmentation/blob/main/util.py +#The color palette is changed according to https://github.com/Mikubill/sd-webui-controlnet/blob/91f67ddcc7bc47537a6285864abfc12590f46c3f/annotator/anime_face_segment/__init__.py +import cv2 as cv +import glob +import numpy as np +import os + +""" +COLOR_BACKGROUND = (0,255,255) +COLOR_HAIR = (255,0,0) +COLOR_EYE = (0,0,255) +COLOR_MOUTH = (255,255,255) +COLOR_FACE = (0,255,0) +COLOR_SKIN = (255,255,0) +COLOR_CLOTHES = (255,0,255) +""" +COLOR_BACKGROUND = (255,255,0) +COLOR_HAIR = (0,0,255) +COLOR_EYE = (255,0,0) +COLOR_MOUTH = (255,255,255) +COLOR_FACE = (0,255,0) +COLOR_SKIN = (0,255,255) +COLOR_CLOTHES = (255,0,255) +PALETTE = [COLOR_BACKGROUND,COLOR_HAIR,COLOR_EYE,COLOR_MOUTH,COLOR_FACE,COLOR_SKIN,COLOR_CLOTHES] + +def img2seg(path): + src = cv.imread(path) + src = src.reshape(-1, 3) + seg_list = [] + for color in PALETTE: + seg_list.append(np.where(np.all(src==color, axis=1), 1.0, 0.0)) + dst = np.stack(seg_list,axis=1).reshape(512,512,7) + + return dst.astype(np.float32) + +def seg2img(src): + src = np.moveaxis(src,0,2) + dst = [[PALETTE[np.argmax(val)] for val in buf]for buf in src] + + return np.array(dst).astype(np.uint8) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/binary/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/binary/__init__.py new file mode 100644 index 00000000..34fd63fa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/binary/__init__.py @@ -0,0 +1,38 @@ +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import HWC3, resize_image_with_pad + +class BinaryDetector: + def __call__(self, input_image=None, bin_threshold=0, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + if "img" in kwargs: + warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning) + input_image = kwargs.pop("img") + + if input_image is None: + raise ValueError("input_image must be defined.") + + if not isinstance(input_image, np.ndarray): + input_image = np.array(input_image, dtype=np.uint8) + output_type = output_type or "pil" + else: + output_type = output_type or "np" + + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + img_gray = cv2.cvtColor(detected_map, cv2.COLOR_RGB2GRAY) + if bin_threshold == 0 or bin_threshold == 255: + # Otsu's threshold + otsu_threshold, img_bin = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) + print("Otsu threshold:", otsu_threshold) + else: + _, img_bin = cv2.threshold(img_gray, bin_threshold, 255, cv2.THRESH_BINARY_INV) + + detected_map = cv2.cvtColor(img_bin, cv2.COLOR_GRAY2RGB) + detected_map = HWC3(remove_pad(255 - detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/canny/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/canny/__init__.py new file mode 100644 index 00000000..0f91c6dd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/canny/__init__.py @@ -0,0 +1,17 @@ +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import resize_image_with_pad, common_input_validate, HWC3 + +class CannyDetector: + def __call__(self, input_image=None, low_threshold=100, high_threshold=200, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + detected_map = cv2.Canny(detected_map, low_threshold, high_threshold) + detected_map = HWC3(remove_pad(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/color/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/color/__init__.py new file mode 100644 index 00000000..3d2f010b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/color/__init__.py @@ -0,0 +1,37 @@ +import cv2 +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import HWC3, safer_memory, common_input_validate + +def cv2_resize_shortest_edge(image, size): + h, w = image.shape[:2] + if h < w: + new_h = size + new_w = int(round(w / h * size)) + else: + new_w = size + new_h = int(round(h / w * size)) + resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA) + return resized_image + +def apply_color(img, res=512): + img = cv2_resize_shortest_edge(img, res) + h, w = img.shape[:2] + + input_img_color = cv2.resize(img, (w//64, h//64), interpolation=cv2.INTER_CUBIC) + input_img_color = cv2.resize(input_img_color, (w, h), interpolation=cv2.INTER_NEAREST) + return input_img_color + +#Color T2I like multiples-of-64, upscale methods are fixed. +class ColorDetector: + def __call__(self, input_image=None, detect_resolution=512, output_type=None, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image = HWC3(input_image) + detected_map = HWC3(apply_color(input_image, detect_resolution)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/densepose/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/densepose/__init__.py new file mode 100644 index 00000000..a6f9448c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/densepose/__init__.py @@ -0,0 +1,66 @@ +import torchvision # Fix issue Unknown builtin op: torchvision::nms +import cv2 +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from PIL import Image + +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, DENSEPOSE_MODEL_NAME +from .densepose import DensePoseMaskedColormapResultsVisualizer, _extract_i_from_iuvarr, densepose_chart_predictor_output_to_result_with_confidences + +N_PART_LABELS = 24 + +class DenseposeDetector: + def __init__(self, model): + self.dense_pose_estimation = model + self.device = "cpu" + self.result_visualizer = DensePoseMaskedColormapResultsVisualizer( + alpha=1, + data_extractor=_extract_i_from_iuvarr, + segm_extractor=_extract_i_from_iuvarr, + val_scale = 255.0 / N_PART_LABELS + ) + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=DENSEPOSE_MODEL_NAME, filename="densepose_r50_fpn_dl.torchscript"): + torchscript_model_path = custom_hf_download(pretrained_model_or_path, filename) + densepose = torch.jit.load(torchscript_model_path, map_location="cpu") + return cls(densepose) + + def to(self, device): + self.dense_pose_estimation.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", cmap="viridis", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + H, W = input_image.shape[:2] + + hint_image_canvas = np.zeros([H, W], dtype=np.uint8) + hint_image_canvas = np.tile(hint_image_canvas[:, :, np.newaxis], [1, 1, 3]) + + input_image = rearrange(torch.from_numpy(input_image).to(self.device), 'h w c -> c h w') + + pred_boxes, corase_segm, fine_segm, u, v = self.dense_pose_estimation(input_image) + + extractor = densepose_chart_predictor_output_to_result_with_confidences + densepose_results = [extractor(pred_boxes[i:i+1], corase_segm[i:i+1], fine_segm[i:i+1], u[i:i+1], v[i:i+1]) for i in range(len(pred_boxes))] + + if cmap=="viridis": + self.result_visualizer.mask_visualizer.cmap = cv2.COLORMAP_VIRIDIS + hint_image = self.result_visualizer.visualize(hint_image_canvas, densepose_results) + hint_image = cv2.cvtColor(hint_image, cv2.COLOR_BGR2RGB) + hint_image[:, :, 0][hint_image[:, :, 0] == 0] = 68 + hint_image[:, :, 1][hint_image[:, :, 1] == 0] = 1 + hint_image[:, :, 2][hint_image[:, :, 2] == 0] = 84 + else: + self.result_visualizer.mask_visualizer.cmap = cv2.COLORMAP_PARULA + hint_image = self.result_visualizer.visualize(hint_image_canvas, densepose_results) + hint_image = cv2.cvtColor(hint_image, cv2.COLOR_BGR2RGB) + + detected_map = remove_pad(HWC3(hint_image)) + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/densepose/densepose.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/densepose/densepose.py new file mode 100644 index 00000000..5e43b05f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/densepose/densepose.py @@ -0,0 +1,347 @@ +from typing import Tuple +import math +import numpy as np +from enum import IntEnum +from typing import List, Tuple, Union +import torch +from torch.nn import functional as F +import logging +import cv2 + +Image = np.ndarray +Boxes = torch.Tensor +ImageSizeType = Tuple[int, int] +_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray] +IntTupleBox = Tuple[int, int, int, int] + +class BoxMode(IntEnum): + """ + Enum of different ways to represent a box. + """ + + XYXY_ABS = 0 + """ + (x0, y0, x1, y1) in absolute floating points coordinates. + The coordinates in range [0, width or height]. + """ + XYWH_ABS = 1 + """ + (x0, y0, w, h) in absolute floating points coordinates. + """ + XYXY_REL = 2 + """ + Not yet supported! + (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image. + """ + XYWH_REL = 3 + """ + Not yet supported! + (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image. + """ + XYWHA_ABS = 4 + """ + (xc, yc, w, h, a) in absolute floating points coordinates. + (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw. + """ + + @staticmethod + def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType: + """ + Args: + box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5 + from_mode, to_mode (BoxMode) + + Returns: + The converted box of the same type. + """ + if from_mode == to_mode: + return box + + original_type = type(box) + is_numpy = isinstance(box, np.ndarray) + single_box = isinstance(box, (list, tuple)) + if single_box: + assert len(box) == 4 or len(box) == 5, ( + "BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor," + " where k == 4 or 5" + ) + arr = torch.tensor(box)[None, :] + else: + # avoid modifying the input box + if is_numpy: + arr = torch.from_numpy(np.asarray(box)).clone() + else: + arr = box.clone() + + assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [ + BoxMode.XYXY_REL, + BoxMode.XYWH_REL, + ], "Relative mode not yet supported!" + + if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS: + assert ( + arr.shape[-1] == 5 + ), "The last dimension of input shape must be 5 for XYWHA format" + original_dtype = arr.dtype + arr = arr.double() + + w = arr[:, 2] + h = arr[:, 3] + a = arr[:, 4] + c = torch.abs(torch.cos(a * math.pi / 180.0)) + s = torch.abs(torch.sin(a * math.pi / 180.0)) + # This basically computes the horizontal bounding rectangle of the rotated box + new_w = c * w + s * h + new_h = c * h + s * w + + # convert center to top-left corner + arr[:, 0] -= new_w / 2.0 + arr[:, 1] -= new_h / 2.0 + # bottom-right corner + arr[:, 2] = arr[:, 0] + new_w + arr[:, 3] = arr[:, 1] + new_h + + arr = arr[:, :4].to(dtype=original_dtype) + elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS: + original_dtype = arr.dtype + arr = arr.double() + arr[:, 0] += arr[:, 2] / 2.0 + arr[:, 1] += arr[:, 3] / 2.0 + angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype) + arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype) + else: + if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS: + arr[:, 2] += arr[:, 0] + arr[:, 3] += arr[:, 1] + elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS: + arr[:, 2] -= arr[:, 0] + arr[:, 3] -= arr[:, 1] + else: + raise NotImplementedError( + "Conversion from BoxMode {} to {} is not supported yet".format( + from_mode, to_mode + ) + ) + + if single_box: + return original_type(arr.flatten().tolist()) + if is_numpy: + return arr.numpy() + else: + return arr + +class MatrixVisualizer: + """ + Base visualizer for matrix data + """ + + def __init__( + self, + inplace=True, + cmap=cv2.COLORMAP_PARULA, + val_scale=1.0, + alpha=0.7, + interp_method_matrix=cv2.INTER_LINEAR, + interp_method_mask=cv2.INTER_NEAREST, + ): + self.inplace = inplace + self.cmap = cmap + self.val_scale = val_scale + self.alpha = alpha + self.interp_method_matrix = interp_method_matrix + self.interp_method_mask = interp_method_mask + + def visualize(self, image_bgr, mask, matrix, bbox_xywh): + self._check_image(image_bgr) + self._check_mask_matrix(mask, matrix) + if self.inplace: + image_target_bgr = image_bgr + else: + image_target_bgr = image_bgr * 0 + x, y, w, h = [int(v) for v in bbox_xywh] + if w <= 0 or h <= 0: + return image_bgr + mask, matrix = self._resize(mask, matrix, w, h) + mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3]) + matrix_scaled = matrix.astype(np.float32) * self.val_scale + _EPSILON = 1e-6 + if np.any(matrix_scaled > 255 + _EPSILON): + logger = logging.getLogger(__name__) + logger.warning( + f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]" + ) + matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8) + matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap) + matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg] + image_target_bgr[y : y + h, x : x + w, :] = ( + image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha + ) + return image_target_bgr.astype(np.uint8) + + def _resize(self, mask, matrix, w, h): + if (w != mask.shape[1]) or (h != mask.shape[0]): + mask = cv2.resize(mask, (w, h), self.interp_method_mask) + if (w != matrix.shape[1]) or (h != matrix.shape[0]): + matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix) + return mask, matrix + + def _check_image(self, image_rgb): + assert len(image_rgb.shape) == 3 + assert image_rgb.shape[2] == 3 + assert image_rgb.dtype == np.uint8 + + def _check_mask_matrix(self, mask, matrix): + assert len(matrix.shape) == 2 + assert len(mask.shape) == 2 + assert mask.dtype == np.uint8 + +class DensePoseResultsVisualizer: + def visualize( + self, + image_bgr: Image, + results, + ) -> Image: + context = self.create_visualization_context(image_bgr) + for i, result in enumerate(results): + boxes_xywh, labels, uv = result + iuv_array = torch.cat( + (labels[None].type(torch.float32), uv * 255.0) + ).type(torch.uint8) + self.visualize_iuv_arr(context, iuv_array.cpu().numpy(), boxes_xywh) + image_bgr = self.context_to_image_bgr(context) + return image_bgr + + def create_visualization_context(self, image_bgr: Image): + return image_bgr + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None: + pass + + def context_to_image_bgr(self, context): + return context + + def get_image_bgr_from_context(self, context): + return context + +class DensePoseMaskedColormapResultsVisualizer(DensePoseResultsVisualizer): + def __init__( + self, + data_extractor, + segm_extractor, + inplace=True, + cmap=cv2.COLORMAP_PARULA, + alpha=0.7, + val_scale=1.0, + **kwargs, + ): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha + ) + self.data_extractor = data_extractor + self.segm_extractor = segm_extractor + + def context_to_image_bgr(self, context): + return context + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None: + image_bgr = self.get_image_bgr_from_context(context) + matrix = self.data_extractor(iuv_arr) + segm = self.segm_extractor(iuv_arr) + mask = np.zeros(matrix.shape, dtype=np.uint8) + mask[segm > 0] = 1 + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh) + + +def _extract_i_from_iuvarr(iuv_arr): + return iuv_arr[0, :, :] + + +def _extract_u_from_iuvarr(iuv_arr): + return iuv_arr[1, :, :] + + +def _extract_v_from_iuvarr(iuv_arr): + return iuv_arr[2, :, :] + +def make_int_box(box: torch.Tensor) -> IntTupleBox: + int_box = [0, 0, 0, 0] + int_box[0], int_box[1], int_box[2], int_box[3] = tuple(box.long().tolist()) + return int_box[0], int_box[1], int_box[2], int_box[3] + +def densepose_chart_predictor_output_to_result_with_confidences( + boxes: Boxes, + coarse_segm, + fine_segm, + u, v + +): + boxes_xyxy_abs = boxes.clone() + boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + box_xywh = make_int_box(boxes_xywh_abs[0]) + + labels = resample_fine_and_coarse_segm_tensors_to_bbox(fine_segm, coarse_segm, box_xywh).squeeze(0) + uv = resample_uv_tensors_to_bbox(u, v, labels, box_xywh) + confidences = [] + return box_xywh, labels, uv + +def resample_fine_and_coarse_segm_tensors_to_bbox( + fine_segm: torch.Tensor, coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox +): + """ + Resample fine and coarse segmentation tensors to the given + bounding box and derive labels for each pixel of the bounding box + + Args: + fine_segm: float tensor of shape [1, C, Hout, Wout] + coarse_segm: float tensor of shape [1, K, Hout, Wout] + box_xywh_abs (tuple of 4 int): bounding box given by its upper-left + corner coordinates, width (W) and height (H) + Return: + Labels for each pixel of the bounding box, a long tensor of size [1, H, W] + """ + x, y, w, h = box_xywh_abs + w = max(int(w), 1) + h = max(int(h), 1) + # coarse segmentation + coarse_segm_bbox = F.interpolate( + coarse_segm, + (h, w), + mode="bilinear", + align_corners=False, + ).argmax(dim=1) + # combined coarse and fine segmentation + labels = ( + F.interpolate(fine_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1) + * (coarse_segm_bbox > 0).long() + ) + return labels + +def resample_uv_tensors_to_bbox( + u: torch.Tensor, + v: torch.Tensor, + labels: torch.Tensor, + box_xywh_abs: IntTupleBox, +) -> torch.Tensor: + """ + Resamples U and V coordinate estimates for the given bounding box + + Args: + u (tensor [1, C, H, W] of float): U coordinates + v (tensor [1, C, H, W] of float): V coordinates + labels (tensor [H, W] of long): labels obtained by resampling segmentation + outputs for the given bounding box + box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs + Return: + Resampled U and V coordinates - a tensor [2, H, W] of float + """ + x, y, w, h = box_xywh_abs + w = max(int(w), 1) + h = max(int(h), 1) + u_bbox = F.interpolate(u, (h, w), mode="bilinear", align_corners=False) + v_bbox = F.interpolate(v, (h, w), mode="bilinear", align_corners=False) + uv = torch.zeros([2, h, w], dtype=torch.float32, device=u.device) + for part_id in range(1, u_bbox.size(1)): + uv[0][labels == part_id] = u_bbox[0, part_id][labels == part_id] + uv[1][labels == part_id] = v_bbox[0, part_id][labels == part_id] + return uv + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything/__init__.py new file mode 100644 index 00000000..5240bbdd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything/__init__.py @@ -0,0 +1 @@ +from .transformers import DepthAnythingDetector \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything/transformers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything/transformers.py new file mode 100644 index 00000000..66a0ff63 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything/transformers.py @@ -0,0 +1,75 @@ +""" +Modern DepthAnything implementation using HuggingFace transformers. +Replaces legacy torch.hub.load DINOv2 backbone with transformers pipeline. +""" + +import numpy as np +import torch +from PIL import Image +from transformers import pipeline + +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad + +class DepthAnythingDetector: + """DepthAnything depth estimation using HuggingFace transformers.""" + + def __init__(self, model_name="LiheYoung/depth-anything-large-hf"): + """Initialize DepthAnything with specified model.""" + self.pipe = pipeline(task="depth-estimation", model=model_name) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=None, filename="depth_anything_vitl14.pth"): + """Create DepthAnything from pretrained model, mapping legacy names to HuggingFace models.""" + + # Map legacy checkpoint names to modern HuggingFace models + model_mapping = { + "depth_anything_vitl14.pth": "LiheYoung/depth-anything-large-hf", + "depth_anything_vitb14.pth": "LiheYoung/depth-anything-base-hf", + "depth_anything_vits14.pth": "LiheYoung/depth-anything-small-hf" + } + + model_name = model_mapping.get(filename, "LiheYoung/depth-anything-large-hf") + return cls(model_name=model_name) + + def to(self, device): + """Move model to specified device.""" + self.pipe.model = self.pipe.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + """Perform depth estimation on input image.""" + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + if isinstance(input_image, np.ndarray): + pil_image = Image.fromarray(input_image) + else: + pil_image = input_image + + with torch.no_grad(): + result = self.pipe(pil_image) + depth = result["depth"] + + if isinstance(depth, Image.Image): + depth_array = np.array(depth, dtype=np.float32) + else: + depth_array = np.array(depth) + + # Normalize depth values to 0-255 range + depth_min = depth_array.min() + depth_max = depth_array.max() + if depth_max > depth_min: + depth_array = (depth_array - depth_min) / (depth_max - depth_min) * 255.0 + else: + depth_array = np.zeros_like(depth_array) + + depth_image = depth_array.astype(np.uint8) + + detected_map = remove_pad(HWC3(depth_image)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/__init__.py new file mode 100644 index 00000000..924e7bc7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/__init__.py @@ -0,0 +1,56 @@ +import numpy as np +import torch +from einops import repeat +from PIL import Image +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, DEPTH_ANYTHING_V2_MODEL_NAME_DICT +from custom_controlnet_aux.depth_anything_v2.dpt import DepthAnythingV2 +import cv2 +import torch.nn.functional as F + + +# https://github.com/DepthAnything/Depth-Anything-V2/blob/main/app.py +model_configs = { + 'depth_anything_v2_vits.pth': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, + 'depth_anything_v2_vitb.pth': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, + 'depth_anything_v2_vitl.pth': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, + 'depth_anything_v2_vitg.pth': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}, + 'depth_anything_v2_metric_vkitti_vitl.pth': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, + 'depth_anything_v2_metric_hypersim_vitl.pth': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, +} + +class DepthAnythingV2Detector: + def __init__(self, model, filename): + self.model = model + self.device = "cpu" + self.filename = filename + @classmethod + def from_pretrained(cls, pretrained_model_or_path=None, filename="depth_anything_v2_vits.pth"): + if pretrained_model_or_path is None: + pretrained_model_or_path = DEPTH_ANYTHING_V2_MODEL_NAME_DICT[filename] + model_path = custom_hf_download(pretrained_model_or_path, filename) + model = DepthAnythingV2(**model_configs[filename]) + model.load_state_dict(torch.load(model_path, map_location="cpu")) + model = model.eval() + return cls(model, filename) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", max_depth=20.0, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + + depth = self.model.infer_image(cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR), input_size=518, max_depth=max_depth) + depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 + depth = depth.astype(np.uint8) + if 'metric' in self.filename: + depth = 255 - depth + + detected_map = repeat(depth, "h w -> h w 3") + detected_map, remove_pad = resize_image_with_pad(detected_map, detect_resolution, upscale_method) + detected_map = remove_pad(detected_map) + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2.py new file mode 100644 index 00000000..78e99975 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2.py @@ -0,0 +1,415 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +from functools import partial +import math +import logging +from typing import Sequence, Tuple, Union, Callable + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from torch.nn.init import trunc_normal_ + +from custom_controlnet_aux.depth_anything_v2.dinov2_layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block + + +logger = logging.getLogger("dinov2") + + +def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: + if not depth_first and include_root: + fn(module=module, name=name) + for child_name, child_module in module.named_children(): + child_name = ".".join((name, child_name)) if name else child_name + named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) + if depth_first and include_root: + fn(module=module, name=name) + return module + + +class BlockChunk(nn.ModuleList): + def forward(self, x): + for b in self: + x = b(x) + return x + + +class DinoVisionTransformer(nn.Module): + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + ffn_bias=True, + proj_bias=True, + drop_path_rate=0.0, + drop_path_uniform=False, + init_values=None, # for layerscale: None or 0 => no layerscale + embed_layer=PatchEmbed, + act_layer=nn.GELU, + block_fn=Block, + ffn_layer="mlp", + block_chunks=1, + num_register_tokens=0, + interpolate_antialias=False, + interpolate_offset=0.1, + ): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + proj_bias (bool): enable bias for proj in attn if True + ffn_bias (bool): enable bias for ffn if True + drop_path_rate (float): stochastic depth rate + drop_path_uniform (bool): apply uniform drop rate across blocks + weight_init (str): weight init scheme + init_values (float): layer-scale init values + embed_layer (nn.Module): patch embedding layer + act_layer (nn.Module): MLP activation layer + block_fn (nn.Module): transformer block class + ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" + block_chunks: (int) split block sequence into block_chunks units for FSDP wrap + num_register_tokens: (int) number of extra cls tokens (so-called "registers") + interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings + interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings + """ + super().__init__() + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.num_tokens = 1 + self.n_blocks = depth + self.num_heads = num_heads + self.patch_size = patch_size + self.num_register_tokens = num_register_tokens + self.interpolate_antialias = interpolate_antialias + self.interpolate_offset = interpolate_offset + + self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) + assert num_register_tokens >= 0 + self.register_tokens = ( + nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None + ) + + if drop_path_uniform is True: + dpr = [drop_path_rate] * depth + else: + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + if ffn_layer == "mlp": + logger.info("using MLP layer as FFN") + ffn_layer = Mlp + elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": + logger.info("using SwiGLU layer as FFN") + ffn_layer = SwiGLUFFNFused + elif ffn_layer == "identity": + logger.info("using Identity layer as FFN") + + def f(*args, **kwargs): + return nn.Identity() + + ffn_layer = f + else: + raise NotImplementedError + + blocks_list = [ + block_fn( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + ffn_bias=ffn_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + ffn_layer=ffn_layer, + init_values=init_values, + ) + for i in range(depth) + ] + if block_chunks > 0: + self.chunked_blocks = True + chunked_blocks = [] + chunksize = depth // block_chunks + for i in range(0, depth, chunksize): + # this is to keep the block index consistent if we chunk the block list + chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) + self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) + else: + self.chunked_blocks = False + self.blocks = nn.ModuleList(blocks_list) + + self.norm = norm_layer(embed_dim) + self.head = nn.Identity() + + self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) + + self.init_weights() + + def init_weights(self): + trunc_normal_(self.pos_embed, std=0.02) + nn.init.normal_(self.cls_token, std=1e-6) + if self.register_tokens is not None: + nn.init.normal_(self.register_tokens, std=1e-6) + named_apply(init_weights_vit_timm, self) + + def interpolate_pos_encoding(self, x, w, h): + previous_dtype = x.dtype + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + pos_embed = self.pos_embed.float() + class_pos_embed = pos_embed[:, 0] + patch_pos_embed = pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_size + h0 = h // self.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0 + w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset + # w0, h0 = w0 + 0.1, h0 + 0.1 + + sqrt_N = math.sqrt(N) + sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2), + scale_factor=(sx, sy), + # (int(w0), int(h0)), # to solve the upsampling shape issue + mode="bicubic", + antialias=self.interpolate_antialias + ) + + assert int(w0) == patch_pos_embed.shape[-2] + assert int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) + + def prepare_tokens_with_masks(self, x, masks=None): + B, nc, w, h = x.shape + x = self.patch_embed(x) + if masks is not None: + x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) + + x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) + x = x + self.interpolate_pos_encoding(x, w, h) + + if self.register_tokens is not None: + x = torch.cat( + ( + x[:, :1], + self.register_tokens.expand(x.shape[0], -1, -1), + x[:, 1:], + ), + dim=1, + ) + + return x + + def forward_features_list(self, x_list, masks_list): + x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] + for blk in self.blocks: + x = blk(x) + + all_x = x + output = [] + for x, masks in zip(all_x, masks_list): + x_norm = self.norm(x) + output.append( + { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], + "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], + "x_prenorm": x, + "masks": masks, + } + ) + return output + + def forward_features(self, x, masks=None): + if isinstance(x, list): + return self.forward_features_list(x, masks) + + x = self.prepare_tokens_with_masks(x, masks) + + for blk in self.blocks: + x = blk(x) + + x_norm = self.norm(x) + return { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], + "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], + "x_prenorm": x, + "masks": masks, + } + + def _get_intermediate_layers_not_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + # If n is an int, take the n last blocks. If it's a list, take them + output, total_block_len = [], len(self.blocks) + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for i, blk in enumerate(self.blocks): + x = blk(x) + if i in blocks_to_take: + output.append(x) + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def _get_intermediate_layers_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + output, i, total_block_len = [], 0, len(self.blocks[-1]) + # If n is an int, take the n last blocks. If it's a list, take them + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for block_chunk in self.blocks: + for blk in block_chunk[i:]: # Passing the nn.Identity() + x = blk(x) + if i in blocks_to_take: + output.append(x) + i += 1 + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def get_intermediate_layers( + self, + x: torch.Tensor, + n: Union[int, Sequence] = 1, # Layers or n last layers to take + reshape: bool = False, + return_class_token: bool = False, + norm=True + ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: + if self.chunked_blocks: + outputs = self._get_intermediate_layers_chunked(x, n) + else: + outputs = self._get_intermediate_layers_not_chunked(x, n) + if norm: + outputs = [self.norm(out) for out in outputs] + class_tokens = [out[:, 0] for out in outputs] + outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs] + if reshape: + B, _, w, h = x.shape + outputs = [ + out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() + for out in outputs + ] + if return_class_token: + return tuple(zip(outputs, class_tokens)) + return tuple(outputs) + + def forward(self, *args, is_training=False, **kwargs): + ret = self.forward_features(*args, **kwargs) + if is_training: + return ret + else: + return self.head(ret["x_norm_clstoken"]) + + +def init_weights_vit_timm(module: nn.Module, name: str = ""): + """ViT weight initialization, original timm impl (for reproducibility)""" + if isinstance(module, nn.Linear): + trunc_normal_(module.weight, std=0.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + + +def vit_small(patch_size=16, num_register_tokens=0, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + return model + + +def vit_base(patch_size=16, num_register_tokens=0, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + return model + + +def vit_large(patch_size=16, num_register_tokens=0, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + return model + + +def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs): + """ + Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 + """ + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1536, + depth=40, + num_heads=24, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + return model + + +def DINOv2(model_name): + model_zoo = { + "vits": vit_small, + "vitb": vit_base, + "vitl": vit_large, + "vitg": vit_giant2 + } + + return model_zoo[model_name]( + img_size=518, + patch_size=14, + init_values=1.0, + ffn_layer="mlp" if model_name != "vitg" else "swiglufused", + block_chunks=0, + num_register_tokens=0, + interpolate_antialias=False, + interpolate_offset=0.1 + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/__init__.py new file mode 100644 index 00000000..8120f4bc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from .mlp import Mlp +from .patch_embed import PatchEmbed +from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused +from .block import NestedTensorBlock +from .attention import MemEffAttention diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/attention.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/attention.py new file mode 100644 index 00000000..815a2bf5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/attention.py @@ -0,0 +1,83 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +import logging + +from torch import Tensor +from torch import nn + + +logger = logging.getLogger("dinov2") + + +try: + from xformers.ops import memory_efficient_attention, unbind, fmha + + XFORMERS_AVAILABLE = True +except ImportError: + logger.warning("xFormers not available") + XFORMERS_AVAILABLE = False + + +class Attention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x: Tensor) -> Tensor: + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + + q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] + attn = q @ k.transpose(-2, -1) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class MemEffAttention(Attention): + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + if not XFORMERS_AVAILABLE: + assert attn_bias is None, "xFormers is required for nested tensors usage" + return super().forward(x) + + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + q, k, v = unbind(qkv, 2) + + x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) + x = x.reshape([B, N, C]) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/block.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/block.py new file mode 100644 index 00000000..25488f57 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/block.py @@ -0,0 +1,252 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py + +import logging +from typing import Callable, List, Any, Tuple, Dict + +import torch +from torch import nn, Tensor + +from .attention import Attention, MemEffAttention +from .drop_path import DropPath +from .layer_scale import LayerScale +from .mlp import Mlp + + +logger = logging.getLogger("dinov2") + + +try: + from xformers.ops import fmha + from xformers.ops import scaled_index_add, index_select_cat + + XFORMERS_AVAILABLE = True +except ImportError: + logger.warning("xFormers not available") + XFORMERS_AVAILABLE = False + + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = False, + proj_bias: bool = True, + ffn_bias: bool = True, + drop: float = 0.0, + attn_drop: float = 0.0, + init_values=None, + drop_path: float = 0.0, + act_layer: Callable[..., nn.Module] = nn.GELU, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_class: Callable[..., nn.Module] = Attention, + ffn_layer: Callable[..., nn.Module] = Mlp, + ) -> None: + super().__init__() + # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") + self.norm1 = norm_layer(dim) + self.attn = attn_class( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ffn_layer( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + bias=ffn_bias, + ) + self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.sample_drop_ratio = drop_path + + def forward(self, x: Tensor) -> Tensor: + def attn_residual_func(x: Tensor) -> Tensor: + return self.ls1(self.attn(self.norm1(x))) + + def ffn_residual_func(x: Tensor) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + if self.training and self.sample_drop_ratio > 0.1: + # the overhead is compensated only for a drop path rate larger than 0.1 + x = drop_add_residual_stochastic_depth( + x, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + ) + x = drop_add_residual_stochastic_depth( + x, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + ) + elif self.training and self.sample_drop_ratio > 0.0: + x = x + self.drop_path1(attn_residual_func(x)) + x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 + else: + x = x + attn_residual_func(x) + x = x + ffn_residual_func(x) + return x + + +def drop_add_residual_stochastic_depth( + x: Tensor, + residual_func: Callable[[Tensor], Tensor], + sample_drop_ratio: float = 0.0, +) -> Tensor: + # 1) extract subset using permutation + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + x_subset = x[brange] + + # 2) apply residual_func to get residual + residual = residual_func(x_subset) + + x_flat = x.flatten(1) + residual = residual.flatten(1) + + residual_scale_factor = b / sample_subset_size + + # 3) add the residual + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + return x_plus_residual.view_as(x) + + +def get_branges_scales(x, sample_drop_ratio=0.0): + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + residual_scale_factor = b / sample_subset_size + return brange, residual_scale_factor + + +def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): + if scaling_vector is None: + x_flat = x.flatten(1) + residual = residual.flatten(1) + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + else: + x_plus_residual = scaled_index_add( + x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor + ) + return x_plus_residual + + +attn_bias_cache: Dict[Tuple, Any] = {} + + +def get_attn_bias_and_cat(x_list, branges=None): + """ + this will perform the index select, cat the tensors, and provide the attn_bias from cache + """ + batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] + all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) + if all_shapes not in attn_bias_cache.keys(): + seqlens = [] + for b, x in zip(batch_sizes, x_list): + for _ in range(b): + seqlens.append(x.shape[1]) + attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) + attn_bias._batch_sizes = batch_sizes + attn_bias_cache[all_shapes] = attn_bias + + if branges is not None: + cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) + else: + tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) + cat_tensors = torch.cat(tensors_bs1, dim=1) + + return attn_bias_cache[all_shapes], cat_tensors + + +def drop_add_residual_stochastic_depth_list( + x_list: List[Tensor], + residual_func: Callable[[Tensor, Any], Tensor], + sample_drop_ratio: float = 0.0, + scaling_vector=None, +) -> Tensor: + # 1) generate random set of indices for dropping samples in the batch + branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] + branges = [s[0] for s in branges_scales] + residual_scale_factors = [s[1] for s in branges_scales] + + # 2) get attention bias and index+concat the tensors + attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) + + # 3) apply residual_func to get residual, and split the result + residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore + + outputs = [] + for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): + outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) + return outputs + + +class NestedTensorBlock(Block): + def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: + """ + x_list contains a list of tensors to nest together and run + """ + assert isinstance(self.attn, MemEffAttention) + + if self.training and self.sample_drop_ratio > 0.0: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.attn(self.norm1(x), attn_bias=attn_bias) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.mlp(self.norm2(x)) + + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, + ) + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, + ) + return x_list + else: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + attn_bias, x = get_attn_bias_and_cat(x_list) + x = x + attn_residual_func(x, attn_bias=attn_bias) + x = x + ffn_residual_func(x) + return attn_bias.split(x) + + def forward(self, x_or_x_list): + if isinstance(x_or_x_list, Tensor): + return super().forward(x_or_x_list) + elif isinstance(x_or_x_list, list): + assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage" + return self.forward_nested(x_or_x_list) + else: + raise AssertionError diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/drop_path.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/drop_path.py new file mode 100644 index 00000000..af056259 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/drop_path.py @@ -0,0 +1,35 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py + + +from torch import nn + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0: + random_tensor.div_(keep_prob) + output = x * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/layer_scale.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/layer_scale.py new file mode 100644 index 00000000..ca5daa52 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/layer_scale.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110 + +from typing import Union + +import torch +from torch import Tensor +from torch import nn + + +class LayerScale(nn.Module): + def __init__( + self, + dim: int, + init_values: Union[float, Tensor] = 1e-5, + inplace: bool = False, + ) -> None: + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x: Tensor) -> Tensor: + return x.mul_(self.gamma) if self.inplace else x * self.gamma diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/mlp.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/mlp.py new file mode 100644 index 00000000..5e4b315f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/mlp.py @@ -0,0 +1,41 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py + + +from typing import Callable, Optional + +from torch import Tensor, nn + + +class Mlp(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = nn.GELU, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) + self.drop = nn.Dropout(drop) + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/patch_embed.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/patch_embed.py new file mode 100644 index 00000000..574abe41 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/patch_embed.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py + +from typing import Callable, Optional, Tuple, Union + +from torch import Tensor +import torch.nn as nn + + +def make_2tuple(x): + if isinstance(x, tuple): + assert len(x) == 2 + return x + + assert isinstance(x, int) + return (x, x) + + +class PatchEmbed(nn.Module): + """ + 2D image to patch embedding: (B,C,H,W) -> (B,N,D) + + Args: + img_size: Image size. + patch_size: Patch token size. + in_chans: Number of input image channels. + embed_dim: Number of linear projection output channels. + norm_layer: Normalization layer. + """ + + def __init__( + self, + img_size: Union[int, Tuple[int, int]] = 224, + patch_size: Union[int, Tuple[int, int]] = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Optional[Callable] = None, + flatten_embedding: bool = True, + ) -> None: + super().__init__() + + image_HW = make_2tuple(img_size) + patch_HW = make_2tuple(patch_size) + patch_grid_size = ( + image_HW[0] // patch_HW[0], + image_HW[1] // patch_HW[1], + ) + + self.img_size = image_HW + self.patch_size = patch_HW + self.patches_resolution = patch_grid_size + self.num_patches = patch_grid_size[0] * patch_grid_size[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.flatten_embedding = flatten_embedding + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + _, _, H, W = x.shape + patch_H, patch_W = self.patch_size + + assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" + assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" + + x = self.proj(x) # B C H W + H, W = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) # B HW C + x = self.norm(x) + if not self.flatten_embedding: + x = x.reshape(-1, H, W, self.embed_dim) # B H W C + return x + + def flops(self) -> float: + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/swiglu_ffn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/swiglu_ffn.py new file mode 100644 index 00000000..b3324b26 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dinov2_layers/swiglu_ffn.py @@ -0,0 +1,63 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable, Optional + +from torch import Tensor, nn +import torch.nn.functional as F + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) + self.w3 = nn.Linear(hidden_features, out_features, bias=bias) + + def forward(self, x: Tensor) -> Tensor: + x12 = self.w12(x) + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + return self.w3(hidden) + + +try: + from xformers.ops import SwiGLU + + XFORMERS_AVAILABLE = True +except ImportError: + SwiGLU = SwiGLUFFN + XFORMERS_AVAILABLE = False + + +class SwiGLUFFNFused(SwiGLU): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + out_features = out_features or in_features + hidden_features = hidden_features or in_features + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + super().__init__( + in_features=in_features, + hidden_features=hidden_features, + out_features=out_features, + bias=bias, + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dpt.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dpt.py new file mode 100644 index 00000000..630ae693 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/dpt.py @@ -0,0 +1,220 @@ +import cv2 +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms import Compose + +from custom_controlnet_aux.depth_anything_v2.dinov2 import DINOv2 +from custom_controlnet_aux.depth_anything_v2.util.blocks import FeatureFusionBlock, _make_scratch +from custom_controlnet_aux.depth_anything_v2.util.transform import Resize, NormalizeImage, PrepareForNet + + +def _make_fusion_block(features, use_bn, size=None): + return FeatureFusionBlock( + features, + nn.ReLU(False), + deconv=False, + bn=use_bn, + expand=False, + align_corners=True, + size=size, + ) + + +class ConvBlock(nn.Module): + def __init__(self, in_feature, out_feature): + super().__init__() + + self.conv_block = nn.Sequential( + nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(out_feature), + nn.ReLU(True) + ) + + def forward(self, x): + return self.conv_block(x) + + +class DPTHead(nn.Module): + def __init__( + self, + in_channels, + features=256, + use_bn=False, + out_channels=[256, 512, 1024, 1024], + use_clstoken=False + ): + super(DPTHead, self).__init__() + + self.use_clstoken = use_clstoken + + self.projects = nn.ModuleList([ + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channel, + kernel_size=1, + stride=1, + padding=0, + ) for out_channel in out_channels + ]) + + self.resize_layers = nn.ModuleList([ + nn.ConvTranspose2d( + in_channels=out_channels[0], + out_channels=out_channels[0], + kernel_size=4, + stride=4, + padding=0), + nn.ConvTranspose2d( + in_channels=out_channels[1], + out_channels=out_channels[1], + kernel_size=2, + stride=2, + padding=0), + nn.Identity(), + nn.Conv2d( + in_channels=out_channels[3], + out_channels=out_channels[3], + kernel_size=3, + stride=2, + padding=1) + ]) + + if use_clstoken: + self.readout_projects = nn.ModuleList() + for _ in range(len(self.projects)): + self.readout_projects.append( + nn.Sequential( + nn.Linear(2 * in_channels, in_channels), + nn.GELU())) + + self.scratch = _make_scratch( + out_channels, + features, + groups=1, + expand=False, + ) + + self.scratch.stem_transpose = None + + self.scratch.refinenet1 = _make_fusion_block(features, use_bn) + self.scratch.refinenet2 = _make_fusion_block(features, use_bn) + self.scratch.refinenet3 = _make_fusion_block(features, use_bn) + self.scratch.refinenet4 = _make_fusion_block(features, use_bn) + + head_features_1 = features + head_features_2 = 32 + + self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1) + self.scratch.output_conv2 = nn.Sequential( + nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1), + nn.ReLU(True), + nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0), + nn.ReLU(True), + nn.Identity(), + ) + + def forward(self, out_features, patch_h, patch_w): + out = [] + for i, x in enumerate(out_features): + if self.use_clstoken: + x, cls_token = x[0], x[1] + readout = cls_token.unsqueeze(1).expand_as(x) + x = self.readout_projects[i](torch.cat((x, readout), -1)) + else: + x = x[0] + + x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w)) + + x = self.projects[i](x) + x = self.resize_layers[i](x) + + out.append(x) + + layer_1, layer_2, layer_3, layer_4 = out + + layer_1_rn = self.scratch.layer1_rn(layer_1) + layer_2_rn = self.scratch.layer2_rn(layer_2) + layer_3_rn = self.scratch.layer3_rn(layer_3) + layer_4_rn = self.scratch.layer4_rn(layer_4) + + path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:]) + path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:]) + path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:]) + path_1 = self.scratch.refinenet1(path_2, layer_1_rn) + + out = self.scratch.output_conv1(path_1) + out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True) + out = self.scratch.output_conv2(out) + + return out + + +class DepthAnythingV2(nn.Module): + def __init__( + self, + encoder='vitl', + features=256, + out_channels=[256, 512, 1024, 1024], + use_bn=False, + use_clstoken=False + ): + super(DepthAnythingV2, self).__init__() + + self.intermediate_layer_idx = { + 'vits': [2, 5, 8, 11], + 'vitb': [2, 5, 8, 11], + 'vitl': [4, 11, 17, 23], + 'vitg': [9, 19, 29, 39] + } + + self.encoder = encoder + self.pretrained = DINOv2(model_name=encoder) + + self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) + + def forward(self, x, max_depth): + patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14 + + features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True) + + depth = self.depth_head(features, patch_h, patch_w) * max_depth + + return depth.squeeze(1) + + @torch.no_grad() + def infer_image(self, raw_image, input_size=518, max_depth=20.0): + image, (h, w) = self.image2tensor(raw_image, input_size) + + depth = self.forward(image, max_depth) + + depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0] + + return depth.cpu().numpy() + + def image2tensor(self, raw_image, input_size=518): + transform = Compose([ + Resize( + width=input_size, + height=input_size, + resize_target=False, + keep_aspect_ratio=True, + ensure_multiple_of=14, + resize_method='lower_bound', + image_interpolation_method=cv2.INTER_CUBIC, + ), + NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + PrepareForNet(), + ]) + + h, w = raw_image.shape[:2] + + image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 + + image = transform({'image': image})['image'] + image = torch.from_numpy(image).unsqueeze(0) + + DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' + image = image.to(DEVICE) + + return image, (h, w) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/util/blocks.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/util/blocks.py new file mode 100644 index 00000000..382ea183 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/util/blocks.py @@ -0,0 +1,148 @@ +import torch.nn as nn + + +def _make_scratch(in_shape, out_shape, groups=1, expand=False): + scratch = nn.Module() + + out_shape1 = out_shape + out_shape2 = out_shape + out_shape3 = out_shape + if len(in_shape) >= 4: + out_shape4 = out_shape + + if expand: + out_shape1 = out_shape + out_shape2 = out_shape * 2 + out_shape3 = out_shape * 4 + if len(in_shape) >= 4: + out_shape4 = out_shape * 8 + + scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups) + scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups) + scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups) + if len(in_shape) >= 4: + scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups) + + return scratch + + +class ResidualConvUnit(nn.Module): + """Residual convolution module. + """ + + def __init__(self, features, activation, bn): + """Init. + + Args: + features (int): number of features + """ + super().__init__() + + self.bn = bn + + self.groups=1 + + self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups) + + self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups) + + if self.bn == True: + self.bn1 = nn.BatchNorm2d(features) + self.bn2 = nn.BatchNorm2d(features) + + self.activation = activation + + self.skip_add = nn.quantized.FloatFunctional() + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input + + Returns: + tensor: output + """ + + out = self.activation(x) + out = self.conv1(out) + if self.bn == True: + out = self.bn1(out) + + out = self.activation(out) + out = self.conv2(out) + if self.bn == True: + out = self.bn2(out) + + if self.groups > 1: + out = self.conv_merge(out) + + return self.skip_add.add(out, x) + + +class FeatureFusionBlock(nn.Module): + """Feature fusion block. + """ + + def __init__( + self, + features, + activation, + deconv=False, + bn=False, + expand=False, + align_corners=True, + size=None + ): + """Init. + + Args: + features (int): number of features + """ + super(FeatureFusionBlock, self).__init__() + + self.deconv = deconv + self.align_corners = align_corners + + self.groups=1 + + self.expand = expand + out_features = features + if self.expand == True: + out_features = features // 2 + + self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) + + self.resConfUnit1 = ResidualConvUnit(features, activation, bn) + self.resConfUnit2 = ResidualConvUnit(features, activation, bn) + + self.skip_add = nn.quantized.FloatFunctional() + + self.size=size + + def forward(self, *xs, size=None): + """Forward pass. + + Returns: + tensor: output + """ + output = xs[0] + + if len(xs) == 2: + res = self.resConfUnit1(xs[1]) + output = self.skip_add.add(output, res) + + output = self.resConfUnit2(output) + + if (size is None) and (self.size is None): + modifier = {"scale_factor": 2} + elif size is None: + modifier = {"size": self.size} + else: + modifier = {"size": size} + + output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners) + + output = self.out_conv(output) + + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/util/transform.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/util/transform.py new file mode 100644 index 00000000..b14aacd4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/depth_anything_v2/util/transform.py @@ -0,0 +1,158 @@ +import numpy as np +import cv2 + + +class Resize(object): + """Resize sample to given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_target=True, + keep_aspect_ratio=False, + ensure_multiple_of=1, + resize_method="lower_bound", + image_interpolation_method=cv2.INTER_AREA, + ): + """Init. + + Args: + width (int): desired output width + height (int): desired output height + resize_target (bool, optional): + True: Resize the full sample (image, mask, target). + False: Resize image only. + Defaults to True. + keep_aspect_ratio (bool, optional): + True: Keep the aspect ratio of the input sample. + Output sample might not have the given width and height, and + resize behaviour depends on the parameter 'resize_method'. + Defaults to False. + ensure_multiple_of (int, optional): + Output width and height is constrained to be multiple of this parameter. + Defaults to 1. + resize_method (str, optional): + "lower_bound": Output will be at least as large as the given size. + "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) + "minimal": Scale as least as possible. (Output size might be smaller than given size.) + Defaults to "lower_bound". + """ + self.__width = width + self.__height = height + + self.__resize_target = resize_target + self.__keep_aspect_ratio = keep_aspect_ratio + self.__multiple_of = ensure_multiple_of + self.__resize_method = resize_method + self.__image_interpolation_method = image_interpolation_method + + def constrain_to_multiple_of(self, x, min_val=0, max_val=None): + y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if max_val is not None and y > max_val: + y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if y < min_val: + y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) + + return y + + def get_size(self, width, height): + # determine new height and width + scale_height = self.__height / height + scale_width = self.__width / width + + if self.__keep_aspect_ratio: + if self.__resize_method == "lower_bound": + # scale such that output size is lower bound + if scale_width > scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "upper_bound": + # scale such that output size is upper bound + if scale_width < scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "minimal": + # scale as least as possbile + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + else: + raise ValueError(f"resize_method {self.__resize_method} not implemented") + + if self.__resize_method == "lower_bound": + new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height) + new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width) + elif self.__resize_method == "upper_bound": + new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height) + new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width) + elif self.__resize_method == "minimal": + new_height = self.constrain_to_multiple_of(scale_height * height) + new_width = self.constrain_to_multiple_of(scale_width * width) + else: + raise ValueError(f"resize_method {self.__resize_method} not implemented") + + return (new_width, new_height) + + def __call__(self, sample): + width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0]) + + # resize sample + sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method) + + if self.__resize_target: + if "depth" in sample: + sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST) + + if "mask" in sample: + sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST) + + return sample + + +class NormalizeImage(object): + """Normlize image by given mean and std. + """ + + def __init__(self, mean, std): + self.__mean = mean + self.__std = std + + def __call__(self, sample): + sample["image"] = (sample["image"] - self.__mean) / self.__std + + return sample + + +class PrepareForNet(object): + """Prepare sample for usage as network input. + """ + + def __init__(self): + pass + + def __call__(self, sample): + image = np.transpose(sample["image"], (2, 0, 1)) + sample["image"] = np.ascontiguousarray(image).astype(np.float32) + + if "depth" in sample: + depth = sample["depth"].astype(np.float32) + sample["depth"] = np.ascontiguousarray(depth) + + if "mask" in sample: + sample["mask"] = sample["mask"].astype(np.float32) + sample["mask"] = np.ascontiguousarray(sample["mask"]) + + return sample \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/__init__.py new file mode 100644 index 00000000..b2d6010f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/__init__.py @@ -0,0 +1,40 @@ +from custom_controlnet_aux.diffusion_edge.model import DiffusionEdge, prepare_args +import numpy as np +import torch +from einops import rearrange +from PIL import Image +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, DIFFUSION_EDGE_MODEL_NAME + +class DiffusionEdgeDetector: + def __init__(self, model): + self.model = model + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=DIFFUSION_EDGE_MODEL_NAME, filename="diffusion_edge_indoor.pt"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + model = DiffusionEdge(prepare_args(model_path)) + return cls(model) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, patch_batch_size=8, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + with torch.no_grad(): + input_image = rearrange(torch.from_numpy(input_image), "h w c -> 1 c h w") + input_image = input_image.float() / 255. + line = self.model(input_image, patch_batch_size) + line = rearrange(line, "1 c h w -> h w c") + + detected_map = line.cpu().numpy().__mul__(255.).astype(np.uint8) + detected_map = remove_pad(HWC3(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/default.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/default.yaml new file mode 100644 index 00000000..0f13fbd6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/default.yaml @@ -0,0 +1,74 @@ +model: + model_type: const_sde + model_name: cond_unet + image_size: [320, 320] + input_keys: ['image', 'cond'] + ckpt_path: + ignore_keys: [ ] + only_model: False + timesteps: 1000 + train_sample: -1 + sampling_timesteps: 1 + loss_type: l2 + objective: pred_noise + start_dist: normal + perceptual_weight: 0 + scale_factor: 0.3 + scale_by_std: True + default_scale: True + scale_by_softsign: False + eps: !!float 1e-4 + weighting_loss: False + first_stage: + embed_dim: 3 + lossconfig: + disc_start: 50001 + kl_weight: 0.000001 + disc_weight: 0.5 + disc_in_channels: 1 + ddconfig: + double_z: True + z_channels: 3 + resolution: [ 320, 320 ] + in_channels: 1 + out_ch: 1 + ch: 128 + ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + ckpt_path: + unet: + dim: 128 + cond_net: swin + without_pretrain: False + channels: 3 + out_mul: 1 + dim_mults: [ 1, 2, 4, 4, ] # num_down = len(dim_mults) + cond_in_dim: 3 + cond_dim: 128 + cond_dim_mults: [ 2, 4 ] # num_down = len(cond_dim_mults) + # window_sizes1: [ [4, 4], [2, 2], [1, 1], [1, 1] ] + # window_sizes2: [ [4, 4], [2, 2], [1, 1], [1, 1] ] + window_sizes1: [ [ 8, 8 ], [ 4, 4 ], [ 2, 2 ], [ 1, 1 ] ] + window_sizes2: [ [ 8, 8 ], [ 4, 4 ], [ 2, 2 ], [ 1, 1 ] ] + fourier_scale: 16 + cond_pe: False + num_pos_feats: 128 + cond_feature_size: [ 80, 80 ] + +data: + name: edge + img_folder: '/data/yeyunfan/edge_detection_datasets/datasets/BSDS_test' + augment_horizontal_flip: True + batch_size: 8 + num_workers: 4 + +sampler: + sample_type: "slide" + stride: [240, 240] + batch_size: 1 + sample_num: 300 + use_ema: True + save_folder: + ckpt_path: \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/__init__.py new file mode 100644 index 00000000..3dc97bb8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/__init__.py @@ -0,0 +1 @@ +# from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiffusion, Unet, Trainer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/data.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/data.py new file mode 100644 index 00000000..859a6255 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/data.py @@ -0,0 +1,598 @@ +import torch +import torchvision.transforms as T +import torch.utils.data as data +import torch.nn as nn +from pathlib import Path +from functools import partial +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.utils import exists, convert_image_to_fn, normalize_to_neg_one_to_one +from PIL import Image, ImageDraw +import torch.nn.functional as F +import math +import torchvision.transforms.functional as F2 +import torchvision.transforms as transforms +import torchvision.datasets as datasets +from typing import Any, Callable, Optional, Tuple +import os +import pickle +import numpy as np +import copy +import albumentations +from torchvision.transforms.functional import InterpolationMode + +def get_imgs_list(imgs_dir): + imgs_list = os.listdir(imgs_dir) + imgs_list.sort() + return [os.path.join(imgs_dir, f) for f in imgs_list if f.endswith('.jpg') or f.endswith('.JPG')or f.endswith('.png') or f.endswith('.pgm') or f.endswith('.ppm')] + + +def fit_img_postfix(img_path): + if not os.path.exists(img_path) and img_path.endswith(".jpg"): + img_path = img_path[:-4] + ".png" + if not os.path.exists(img_path) and img_path.endswith(".png"): + img_path = img_path[:-4] + ".jpg" + return img_path + + +class AdaptEdgeDataset(data.Dataset): + def __init__( + self, + data_root, + # mask_folder, + image_size, + exts = ['png', 'jpg'], + augment_horizontal_flip = False, + convert_image_to = None, + normalize_to_neg_one_to_one=True, + split='train', + # inter_type='bicubic', + # down=4, + threshold=0.3, use_uncertainty=False + ): + super().__init__() + # self.img_folder = Path(img_folder) + # self.edge_folder = Path(os.path.join(data_root, f'gt_imgs')) + # self.img_folder = Path(os.path.join(data_root, f'imgs')) + # self.edge_folder = Path(os.path.join(data_root, "edge", "aug")) + # self.img_folder = Path(os.path.join(data_root, "image", "aug")) + self.data_root = data_root + self.image_size = image_size + + # self.edge_paths = [p for ext in exts for p in self.edge_folder.rglob(f'*.{ext}')] + # self.img_paths = [(self.img_folder / item.parent.name / f'{item.stem}.jpg') for item in self.edge_paths] + # self.img_paths = [(self.img_folder / f'{item.stem}.jpg') for item in self.edge_paths] + + self.threshold = threshold * 256 + self.use_uncertainty = use_uncertainty + self.normalize_to_neg_one_to_one = normalize_to_neg_one_to_one + + maybe_convert_fn = partial(convert_image_to_fn, convert_image_to) if exists(convert_image_to) else Identity() + + # self.normalize_to_neg_one_to_one = normalize_to_neg_one_to_one + # self.random_crop = RandomCrop(size=image_size) + # self.transform = Compose([ + # # Lambda(maybe_convert_fn), + # # Resize(image_size, interpolation=3, interpolation2=0), + # Resize(image_size, interpolation=InterpolationMode.BILINEAR, interpolation2=InterpolationMode.NEAREST), + # RandomHorizontalFlip() if augment_horizontal_flip else Identity(), + # # RandomCrop(image_size), + # ToTensor() + # ]) + self.data_list = self.build_list() + + self.transform = transforms.Compose([ + # Resize(self.image_size, interpolation=InterpolationMode.BILINEAR, interpolation2=InterpolationMode.NEAREST), + transforms.ToTensor()]) + + def __len__(self): + return len(self.data_list) + + + def read_img(self, image_path): + with open(image_path, 'rb') as f: + img = Image.open(f) + img = img.convert('RGB') + + raw_width, raw_height = img.size + # width = int(raw_width / 32) * 32 + # height = int(raw_height / 32) * 32 + # img = img.resize((width, height), Image.Resampling.BILINEAR) + # # print("img.size:", img.size) + # img = self.transform(img) + + return img, (raw_width, raw_height) + + def read_lb(self, lb_path): + lb_data = Image.open(lb_path) + + width, height = lb_data.size + width = int(width / 32) * 32 + height = int(height / 32) * 32 + lb_data = lb_data.resize((width, height), Image.Resampling.BILINEAR) + # print("lb_data.size:", lb_data.size) + lb = np.array(lb_data, dtype=np.float32) + if lb.ndim == 3: + lb = np.squeeze(lb[:, :, 0]) + assert lb.ndim == 2 + threshold = self.threshold + lb = lb[np.newaxis, :, :] + + lb[lb == 0] = 0 + + # ---------- important ---------- + if self.use_uncertainty: + lb[np.logical_and(lb > 0, lb < threshold)] = 2 + else: + lb[np.logical_and(lb > 0, lb < threshold)] /= 255. + + lb[lb >= threshold] = 1 + return lb + + def build_list(self): + data_root = os.path.abspath(self.data_root) + images_path = os.path.join(data_root, 'image', "raw") + labels_path = os.path.join(data_root, 'edge', "raw") + + samples = [] + for directory_name in os.listdir(images_path): + image_directories = os.path.join(images_path, directory_name) + for file_name_ext in os.listdir(image_directories): + file_name = os.path.basename(file_name_ext) + image_path = fit_img_postfix(os.path.join(images_path, directory_name, file_name)) + lb_path = fit_img_postfix(os.path.join(labels_path, directory_name, file_name)) + samples.append((image_path, lb_path)) + return samples + + def __getitem__(self, index): + img_path, edge_path = self.data_list[index] + # edge_path = self.edge_paths[index] + # img_path = self.img_paths[index] + img_name = os.path.basename(img_path) + + img, raw_size = self.read_img(img_path) + edge = self.read_lb(edge_path) + + # print("-------hhhhhhhhhhhhh--------:", img.shape, edge.shape) + # edge = Image.open(edge_path).convert('L') + # # default to score-sde preprocessing + # mask = Image.open(img_path).convert('RGB') + # edge, img = self.transform(edge, mask) + if self.normalize_to_neg_one_to_one: # transform to [-1, 1] + edge = normalize_to_neg_one_to_one(edge) + img = normalize_to_neg_one_to_one(img) + return {'image': edge, 'cond': img, 'raw_size': raw_size, 'img_name': img_name} + +class EdgeDataset(data.Dataset): + def __init__( + self, + data_root, + # mask_folder, + image_size, + exts = ['png', 'jpg'], + augment_horizontal_flip = True, + convert_image_to = None, + normalize_to_neg_one_to_one=True, + split='train', + # inter_type='bicubic', + # down=4, + threshold=0.3, use_uncertainty=False, cfg={} + ): + super().__init__() + # self.img_folder = Path(img_folder) + # self.edge_folder = Path(os.path.join(data_root, f'gt_imgs')) + # self.img_folder = Path(os.path.join(data_root, f'imgs')) + # self.edge_folder = Path(os.path.join(data_root, "edge", "aug")) + # self.img_folder = Path(os.path.join(data_root, "image", "aug")) + self.data_root = data_root + self.image_size = image_size + + # self.edge_paths = [p for ext in exts for p in self.edge_folder.rglob(f'*.{ext}')] + # self.img_paths = [(self.img_folder / item.parent.name / f'{item.stem}.jpg') for item in self.edge_paths] + # self.img_paths = [(self.img_folder / f'{item.stem}.jpg') for item in self.edge_paths] + + self.threshold = threshold * 255 + self.use_uncertainty = use_uncertainty + self.normalize_to_neg_one_to_one = normalize_to_neg_one_to_one + + maybe_convert_fn = partial(convert_image_to_fn, convert_image_to) if exists(convert_image_to) else Identity() + + self.data_list = self.build_list() + + # self.transform = Compose([ + # Resize(image_size), + # RandomHorizontalFlip() if augment_horizontal_flip else Identity(), + # ToTensor() + # ]) + crop_type = cfg.get('crop_type') if 'crop_type' in cfg else 'rand_crop' + if crop_type == 'rand_crop': + self.transform = Compose([ + RandomCrop(image_size), + RandomHorizontalFlip() if augment_horizontal_flip else Identity(), + ToTensor() + ]) + elif crop_type == 'rand_resize_crop': + self.transform = Compose([ + RandomResizeCrop(image_size), + RandomHorizontalFlip() if augment_horizontal_flip else Identity(), + ToTensor() + ]) + print("crop_type:", crop_type) + + def __len__(self): + return len(self.data_list) + + + def read_img(self, image_path): + with open(image_path, 'rb') as f: + img = Image.open(f) + img = img.convert('RGB') + + raw_width, raw_height = img.size + # width = int(raw_width / 32) * 32 + # height = int(raw_height / 32) * 32 + # img = img.resize((width, height), Image.Resampling.BILINEAR) + # # print("img.size:", img.size) + # img = self.transform(img) + + return img, (raw_width, raw_height) + + def read_lb(self, lb_path): + lb_data = Image.open(lb_path).convert('L') + lb = np.array(lb_data).astype(np.float32) + # width, height = lb_data.size + # width = int(width / 32) * 32 + # height = int(height / 32) * 32 + # lb_data = lb_data.resize((width, height), Image.Resampling.BILINEAR) + # print("lb_data.size:", lb_data.size) + # lb = np.array(lb_data, dtype=np.float32) + # if lb.ndim == 3: + # lb = np.squeeze(lb[:, :, 0]) + # assert lb.ndim == 2 + threshold = self.threshold + # lb = lb[np.newaxis, :, :] + # lb[lb == 0] = 0 + + # ---------- important ---------- + # if self.use_uncertainty: + # lb[np.logical_and(lb > 0, lb < threshold)] = 2 + # else: + # lb[np.logical_and(lb > 0, lb < threshold)] /= 255. + + lb[lb >= threshold] = 255 + lb = Image.fromarray(lb.astype(np.uint8)) + return lb + + def build_list(self): + data_root = os.path.abspath(self.data_root) + images_path = os.path.join(data_root, 'image') + labels_path = os.path.join(data_root, 'edge') + + samples = [] + for directory_name in os.listdir(images_path): + image_directories = os.path.join(images_path, directory_name) + for file_name_ext in os.listdir(image_directories): + file_name = os.path.basename(file_name_ext) + image_path = fit_img_postfix(os.path.join(images_path, directory_name, file_name)) + lb_path = fit_img_postfix(os.path.join(labels_path, directory_name, file_name)) + samples.append((image_path, lb_path)) + return samples + + def __getitem__(self, index): + img_path, edge_path = self.data_list[index] + # edge_path = self.edge_paths[index] + # img_path = self.img_paths[index] + img_name = os.path.basename(img_path) + + img, raw_size = self.read_img(img_path) + edge = self.read_lb(edge_path) + img, edge = self.transform(img, edge) + + # print("-------hhhhhhhhhhhhh--------:", img.shape, edge.shape) + # edge = Image.open(edge_path).convert('L') + # # default to score-sde preprocessing + # mask = Image.open(img_path).convert('RGB') + # edge, img = self.transform(edge, mask) + if self.normalize_to_neg_one_to_one: # transform to [-1, 1] + edge = normalize_to_neg_one_to_one(edge) + img = normalize_to_neg_one_to_one(img) + return {'image': edge, 'cond': img, 'raw_size': raw_size, 'img_name': img_name} + +class EdgeDatasetTest(data.Dataset): + def __init__( + self, + data_root, + # mask_folder, + image_size, + exts = ['png', 'jpg'], + convert_image_to = None, + normalize_to_neg_one_to_one=True, + ): + super().__init__() + + self.data_root = data_root + self.image_size = image_size + self.normalize_to_neg_one_to_one = normalize_to_neg_one_to_one + + maybe_convert_fn = partial(convert_image_to_fn, convert_image_to) if exists(convert_image_to) else Identity() + + self.data_list = self.build_list() + + self.transform = Compose([ + ToTensor() + ]) + + def __len__(self): + return len(self.data_list) + + + def read_img(self, image_path): + with open(image_path, 'rb') as f: + img = Image.open(f) + img = img.convert('RGB') + + raw_width, raw_height = img.size + + + return img, (raw_width, raw_height) + + def read_lb(self, lb_path): + lb_data = Image.open(lb_path).convert('L') + lb = np.array(lb_data).astype(np.float32) + + threshold = self.threshold + + + lb[lb >= threshold] = 255 + lb = Image.fromarray(lb.astype(np.uint8)) + return lb + + def build_list(self): + data_root = os.path.abspath(self.data_root) + # images_path = os.path.join(data_root) + images_path = data_root + samples = get_imgs_list(images_path) + return samples + + def __getitem__(self, index): + img_path = self.data_list[index] + # edge_path = self.edge_paths[index] + # img_path = self.img_paths[index] + img_name = os.path.basename(img_path) + + img, raw_size = self.read_img(img_path) + + img = self.transform(img) + if self.normalize_to_neg_one_to_one: # transform to [-1, 1] + img = normalize_to_neg_one_to_one(img) + return {'cond': img, 'raw_size': raw_size, 'img_name': img_name} + + +class Identity(nn.Identity): + r"""A placeholder identity operator that is argument-insensitive. + + Args: + args: any argument (unused) + kwargs: any keyword argument (unused) + + Shape: + - Input: :math:`(*)`, where :math:`*` means any number of dimensions. + - Output: :math:`(*)`, same shape as the input. + + Examples:: + + >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False) + >>> input = torch.randn(128, 20) + >>> output = m(input) + >>> print(output.size()) + torch.Size([128, 20]) + + """ + def __init__(self, *args, **kwargs): + super(Identity, self).__init__(*args, **kwargs) + + def forward(self, input, target): + return input, target + +class Resize(T.Resize): + def __init__(self, size, interpolation2=None, **kwargs): + super().__init__(size, **kwargs) + if interpolation2 is None: + self.interpolation2 = self.interpolation + else: + self.interpolation2 = interpolation2 + + def forward(self, img, target=None): + if target is None: + img = F2.resize(img, self.size, self.interpolation, self.max_size, self.antialias) + return img + else: + img = F2.resize(img, self.size, self.interpolation, self.max_size, self.antialias) + target = F2.resize(target, self.size, self.interpolation2, self.max_size, self.antialias) + return img, target + +class RandomHorizontalFlip(T.RandomHorizontalFlip): + def __init__(self, p=0.5): + super().__init__(p) + + def forward(self, img, target=None): + if target is None: + if torch.rand(1) < self.p: + img = F2.hflip(img) + return img + else: + if torch.rand(1) < self.p: + img = F2.hflip(img) + target = F2.hflip(target) + return img, target + +class CenterCrop(T.CenterCrop): + def __init__(self, size): + super().__init__(size) + + def forward(self, img, target=None): + if target is None: + img = F2.center_crop(img, self.size) + return img + else: + img = F2.center_crop(img, self.size) + target = F2.center_crop(target, self.size) + return img, target + +class RandomCrop(T.RandomCrop): + def __init__(self, size, **kwargs): + super().__init__(size, **kwargs) + + def single_forward(self, img, i, j, h, w): + if self.padding is not None: + img = F2.pad(img, self.padding, self.fill, self.padding_mode) + width, height = F2.get_image_size(img) + # pad the width if needed + if self.pad_if_needed and width < self.size[1]: + padding = [self.size[1] - width, 0] + img = F2.pad(img, padding, self.fill, self.padding_mode) + # pad the height if needed + if self.pad_if_needed and height < self.size[0]: + padding = [0, self.size[0] - height] + img = F2.pad(img, padding, self.fill, self.padding_mode) + + return F2.crop(img, i, j, h, w) + + def forward(self, img, target=None): + i, j, h, w = self.get_params(img, self.size) + if target is None: + img = self.single_forward(img, i, j, h, w) + return img + else: + img = self.single_forward(img, i, j, h, w) + target = self.single_forward(target, i, j, h, w) + return img, target + +class RandomResizeCrop(T.RandomResizedCrop): + def __init__(self, size, scale=(0.25, 1.0), **kwargs): + super().__init__(size, scale, **kwargs) + + # def single_forward(self, img, i, j, h, w): + # if self.padding is not None: + # img = F2.pad(img, self.padding, self.fill, self.padding_mode) + # width, height = F2.get_image_size(img) + # # pad the width if needed + # if self.pad_if_needed and width < self.size[1]: + # padding = [self.size[1] - width, 0] + # img = F2.pad(img, padding, self.fill, self.padding_mode) + # # pad the height if needed + # if self.pad_if_needed and height < self.size[0]: + # padding = [0, self.size[0] - height] + # img = F2.pad(img, padding, self.fill, self.padding_mode) + # + # return F2.crop(img, i, j, h, w) + + def single_forward(self, img, i, j, h, w, interpolation=InterpolationMode.BILINEAR): + """ + Args: + img (PIL Image or Tensor): Image to be cropped and resized. + + Returns: + PIL Image or Tensor: Randomly cropped and resized image. + """ + # i, j, h, w = self.get_params(img, self.scale, self.ratio) + return F2.resized_crop(img, i, j, h, w, self.size, interpolation) + + def forward(self, img, target=None): + i, j, h, w = self.get_params(img, self.scale, self.ratio) + if target is None: + img = self.single_forward(img, i, j, h, w) + return img + else: + img = self.single_forward(img, i, j, h, w) + target = self.single_forward(target, i, j, h, w, interpolation=InterpolationMode.NEAREST) + return img, target + +class ToTensor(T.ToTensor): + def __init__(self): + super().__init__() + + def __call__(self, img, target=None): + if target is None: + img = F2.to_tensor(img) + return img + else: + img = F2.to_tensor(img) + target = F2.to_tensor(target) + return img, target + +class Lambda(T.Lambda): + """Apply a user-defined lambda as a transform. This transform does not support torchscript. + + Args: + lambd (function): Lambda/function to be used for transform. + """ + + def __init__(self, lambd): + super().__init__(lambd) + + def __call__(self, img, target=None): + if target is None: + return self.lambd(img) + else: + return self.lambd(img), self.lambd(target) + +class Compose(T.Compose): + def __init__(self, transforms): + super().__init__(transforms) + + def __call__(self, img, target=None): + if target is None: + for t in self.transforms: + img = t(img) + return img + else: + for t in self.transforms: + img, target = t(img, target) + return img, target + + +if __name__ == '__main__': + dataset = CIFAR10( + img_folder='/media/huang/2da18d46-7cba-4259-9abd-0df819bb104c/data/cifar-10-python', + augment_horizontal_flip=False + ) + # dataset = CityscapesDataset( + # # img_folder='/media/huang/2da18d46-7cba-4259-9abd-0df819bb104c/data/CelebAHQ/celeba_hq_256', + # data_root='/media/huang/2da18d46-7cba-4259-9abd-0df819bb104c/data/Cityscapes/', + # # data_root='/media/huang/2da18d46-7cba-4259-9abd-0df819bb104c/data/ADEChallengeData2016/', + # image_size=[512, 1024], + # exts = ['png'], + # augment_horizontal_flip = False, + # convert_image_to = None, + # normalize_to_neg_one_to_one=True, + # ) + # dataset = SRDataset( + # img_folder='/media/huang/ZX3 512G/data/DIV2K/DIV2K_train_HR', + # image_size=[512, 512], + # ) + # dataset = InpaintDataset( + # img_folder='/media/huang/2da18d46-7cba-4259-9abd-0df819bb104c/data/CelebAHQ/celeba_hq_256', + # image_size=[256, 256], + # augment_horizontal_flip = True + # ) + dataset = EdgeDataset( + data_root='/media/huang/2da18d46-7cba-4259-9abd-0df819bb104c/data/BSDS', + image_size=[320, 320], + ) + for i in range(len(dataset)): + d = dataset[i] + mask = d['cond'] + print(mask.max()) + dl = data.DataLoader(dataset, batch_size=2, shuffle=False, pin_memory=True, num_workers=0) + + + dataset_builder = tfds.builder('cifar10') + split = 'train' + dataset_options = tf.data.Options() + dataset_options.experimental_optimization.map_parallelization = True + dataset_options.experimental_threading.private_threadpool_size = 48 + dataset_options.experimental_threading.max_intra_op_parallelism = 1 + read_config = tfds.ReadConfig(options=dataset_options) + dataset_builder.download_and_prepare() + ds = dataset_builder.as_dataset( + split=split, shuffle_files=True, read_config=read_config) + pause = 0 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/ddm_const_sde.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/ddm_const_sde.py new file mode 100644 index 00000000..9b733a2b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/ddm_const_sde.py @@ -0,0 +1,992 @@ +import torch +import torch.nn as nn +from torch.cuda.amp import custom_bwd, custom_fwd +import math +import torch.nn.functional as F +# import torchvision.transforms.functional as F2 +from .utils import default, identity, normalize_to_neg_one_to_one, unnormalize_to_zero_to_one +from tqdm.auto import tqdm +from einops import rearrange, reduce +from functools import partial +from collections import namedtuple +from random import random, randint, sample, choice +from .encoder_decoder import DiagonalGaussianDistribution +import random +from custom_controlnet_aux.diffusion_edge.taming.modules.losses.vqperceptual import * + +# gaussian diffusion trainer class +ModelPrediction = namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start']) + +def extract(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + +def linear_beta_schedule(timesteps): + scale = 1000 / timesteps + beta_start = scale * 0.0001 + beta_end = scale * 0.02 + return torch.linspace(beta_start, beta_end, timesteps, dtype = torch.float64) + +def cosine_beta_schedule(timesteps, s = 0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = torch.linspace(0, timesteps, steps, dtype = torch.float64) + alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return torch.clip(betas, 0, 0.999) + +class DDPM(nn.Module): + def __init__( + self, + model, + *, + image_size, + timesteps = 1000, + sampling_timesteps = None, + loss_type = 'l2', + objective = 'pred_noise', + beta_schedule = 'cosine', + p2_loss_weight_gamma = 0., # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended + p2_loss_weight_k = 1, + original_elbo_weight=0., + ddim_sampling_eta = 1., + clip_x_start=True, + train_sample=-1, + input_keys=['image'], + start_dist='normal', + sample_type='ddim', + perceptual_weight=1., + use_l1=False, + **kwargs + ): + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + only_model = kwargs.pop("only_model", False) + cfg = kwargs.pop("cfg", None) + super().__init__(**kwargs) + # assert not (type(self) == DDPM and model.channels != model.out_dim) + # assert not model.random_or_learned_sinusoidal_cond + + self.model = model + self.channels = self.model.channels + self.self_condition = self.model.self_condition + self.input_keys = input_keys + self.cfg = cfg + self.eps = cfg.get('eps', 1e-4) if cfg is not None else 1e-4 + self.weighting_loss = cfg.get("weighting_loss", False) if cfg is not None else False + if self.weighting_loss: + print('#### WEIGHTING LOSS ####') + + self.clip_x_start = clip_x_start + self.image_size = image_size + self.train_sample = train_sample + self.objective = objective + self.start_dist = start_dist + assert start_dist in ['normal', 'uniform'] + + assert objective in {'pred_noise', 'pred_x0', 'pred_v', 'pred_delta', 'pred_KC'}, 'objective must be either pred_noise (predict noise) or pred_x0 (predict image start) or pred_v (predict v [v-parameterization as defined in appendix D of progressive distillation paper, used in imagen-video successfully])' + + if beta_schedule == 'linear': + betas = linear_beta_schedule(timesteps) + elif beta_schedule == 'cosine': + betas = cosine_beta_schedule(timesteps, s=1e-4) + else: + raise ValueError(f'unknown beta schedule {beta_schedule}') + # betas[0] = 2e-3 * betas[0] + alphas = 1. - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.time_range = list(range(self.num_timesteps + 1)) + self.loss_type = loss_type + self.original_elbo_weight = original_elbo_weight + + # sampling related parameters + + self.sampling_timesteps = default(sampling_timesteps, timesteps) # default num sampling timesteps to number of timesteps at training + + # assert self.sampling_timesteps <= timesteps + self.is_ddim_sampling = self.sampling_timesteps < timesteps + self.ddim_sampling_eta = ddim_sampling_eta + + # helper function to register buffer from float64 to float32 + + register_buffer = lambda name, val: self.register_buffer(name, val.to(torch.float32)) + + register_buffer('betas', betas) + register_buffer('alphas_cumprod', alphas_cumprod) + register_buffer('alphas_cumprod_prev', alphas_cumprod_prev) + + # calculations for diffusion q(x_t | x_{t-1}) and others + + register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod)) + register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod)) + register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod)) + register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod)) + register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1)) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + + posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + + register_buffer('posterior_variance', posterior_variance) + + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + + register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20))) + register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)) + register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)) + + # calculate p2 reweighting + + register_buffer('p2_loss_weight', (p2_loss_weight_k + alphas_cumprod / (1 - alphas_cumprod)) ** -p2_loss_weight_gamma) + assert not torch.isnan(self.p2_loss_weight).all() + if self.objective == "pred_noise": + lvlb_weights = self.betas ** 2 / ( + 2 * (self.posterior_variance+1e-5) * alphas * (1 - self.alphas_cumprod)) + elif self.objective == "pred_x0": + lvlb_weights = 0.5 * torch.sqrt(alphas_cumprod) / (2. * 1 - alphas_cumprod) + elif self.objective == "pred_delta": + lvlb_weights = 0.5 * torch.sqrt(alphas_cumprod) / (2. * 1 - alphas_cumprod) + elif self.objective == "pred_KC": + lvlb_weights = 0.5 * torch.sqrt(alphas_cumprod) / (2. * 1 - alphas_cumprod) + elif self.objective == "pred_v": + lvlb_weights = 0.5 * torch.sqrt(alphas_cumprod) / (2. * 1 - alphas_cumprod) + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + self.use_l1 = use_l1 + + self.perceptual_weight = perceptual_weight + if self.perceptual_weight > 0: + self.perceptual_loss = LPIPS().eval() + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys, only_model) + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False, use_ema=False): + sd = torch.load(path, map_location="cpu") + if 'ema' in list(sd.keys()) and use_ema: + sd = sd['ema'] + new_sd = {} + for k in sd.keys(): + if k.startswith("ema_model."): + new_k = k[10:] # remove ema_model. + new_sd[new_k] = sd[k] + sd = new_sd + else: + if "model" in list(sd.keys()): + sd = sd["model"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + @torch.no_grad() + def p_sample(self, x, mask, t: int, x_self_cond = None, clip_denoised = True): + b, *_, device = *x.shape, x.device + batched_times = torch.full((x.shape[0],), t, device = x.device, dtype = torch.long) + model_mean, _, model_log_variance, x_start = self.p_mean_variance(x = x, mask=mask, t = batched_times, x_self_cond = x_self_cond, clip_denoised = clip_denoised) + noise = torch.randn_like(x) if t > 0 else 0. # no noise if t == 0 + pred_img = model_mean + (0.5 * model_log_variance).exp() * noise + return pred_img, x_start + + @torch.no_grad() + def p_sample_loop(self, shape, mask, up_scale=1, unnormalize=True): + batch, device = shape[0], self.betas.device + + img = torch.randn(shape, device=device) + img = F.interpolate(img, scale_factor=up_scale, mode='bilinear', align_corners=True) + + x_start = None + + for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps): + self_cond = x_start if self.self_condition else None + img, x_start = self.p_sample(img, mask, t, self_cond) + if unnormalize: + img = unnormalize_to_zero_to_one(img) + return img + + @torch.no_grad() + def ddim_sample(self, shape, mask, up_scale=1, unnormalize=True): + batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective + + times = torch.linspace(-1, total_timesteps - 1, steps=sampling_timesteps + 1) # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps + times = list(reversed(times.int().tolist())) + time_pairs = list(zip(times[:-1], times[1:])) # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)] + + img = torch.randn(shape, device = device) + img = F.interpolate(img, scale_factor=up_scale, mode='bilinear', align_corners=True) + + x_start = None + + for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step', total=len(time_pairs)): + time_cond = torch.full((batch,), time, device=device, dtype=torch.long) + self_cond = x_start if self.self_condition else None + pred_noise, x_start, *_ = self.model_predictions(img, time_cond, mask, self_cond) + + if time_next < 0: + img = x_start + continue + + alpha = self.alphas_cumprod[time] + alpha_next = self.alphas_cumprod[time_next] + + sigma = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt() + c = (1 - alpha_next - sigma ** 2).sqrt() + + noise = torch.randn_like(img) + + img = x_start * alpha_next.sqrt() + \ + c * pred_noise + \ + sigma * noise + if unnormalize: + img = unnormalize_to_zero_to_one(img) + return img + + + @torch.no_grad() + def interpolate(self, x1, x2, mask, t = None, lam = 0.5): + b, *_, device = *x1.shape, x1.device + t = default(t, self.num_timesteps - 1) + + assert x1.shape == x2.shape + + t_batched = torch.stack([torch.tensor(t, device = device)] * b) + xt1, xt2 = map(lambda x: self.q_sample(x, t = t_batched), (x1, x2)) + + img = (1 - lam) * xt1 + lam * xt2 + for i in tqdm(reversed(range(0, t)), desc = 'interpolation sample time step', total = t): + img = self.p_sample(img, mask, torch.full((b,), i, device=device, dtype=torch.long)) + return img + + def get_input(self, batch, return_first_stage_outputs=False, return_original_cond=False): + assert 'image' in self.input_keys; + if len(self.input_keys) > len(batch.keys()): + x, *_ = batch.values() + else: + x = batch.values() + return x + + def training_step(self, batch): + z, *_ = self.get_input(batch) + cond = batch['cond'] if 'cond' in batch else None + loss, loss_dict = self(z, cond) + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # continuous time, t in [0, 1] + # t = [] + # for _ in range(x.shape[0]): + # if self.train_sample <= 0: + # t.append(torch.tensor(sample(self.time_range, 2), device=x.device).long()) + # else: + # sl = choice(self.time_range) + # sl_range = list(range(sl - self.train_sample, sl + self.train_sample)) + # sl_range = list(set(sl_range) & set(self.time_range)) + # sl_range.pop(sl_range.index(sl)) + # sl2 = choice(sl_range) + # t.append(torch.tensor([sl, sl2], device=x.device).long()) + # t = torch.stack(t, dim=0) + # t = torch.randint(0, self.num_timesteps+1, (x.shape[0],), device=x.device).long() + eps = self.eps # smallest time step + # t = torch.rand((x.shape[0],), device=x.device) * (self.num_timesteps / eps) + # t = t.round() * eps + # t[t < eps] = eps + t = torch.rand(x.shape[0], device=x.device) * (1. - eps) + eps + return self.p_losses(x, t, *args, **kwargs) + + def q_sample2(self, x_start, t, noise=None): + b, c, h, w = x_start.shape + noise = default(noise, lambda: torch.randn_like(x_start)) + _, nt = t.shape + param_x = self.sqrt_alphas_cumprod.repeat(b, 1).gather(-1, t) # (b, nt) + x = x_start.expand(nt, b, c, h, w).transpose(1, 0) * param_x.reshape(b, nt, 1, 1, 1).repeat(1, 1, c, h, w) + param_noise = self.sqrt_one_minus_alphas_cumprod.repeat(b, 1).gather(-1, t) + n = noise.expand(nt, b, c, h, w).transpose(1, 0) * param_noise.reshape(b, nt, 1, 1, 1).repeat(1, 1, c, h, w) + return x + n # (b, nt, c, h, w) + + def q_sample3(self, x_start, t, C): + b, c, h, w = x_start.shape + _, nt = t.shape + # K_ = K.unsqueeze(1).repeat(1, nt, 1, 1, 1) + C_ = C.unsqueeze(1).repeat(1, nt, 1, 1, 1) + x_noisy = x_start.expand(nt, b, c, h, w).transpose(1, 0) + \ + + C_ * t.reshape(b, nt, 1, 1, 1).repeat(1, 1, c, h, w) / self.num_timesteps + return x_noisy # (b, nt, c, h, w) + + # def q_sample(self, x_start, t, C): + # x_noisy = x_start + C * t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) / self.num_timesteps + # return x_noisy + def q_sample(self, x_start, noise, t, C): + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + x_noisy = x_start + C * time + torch.sqrt(time) * noise + return x_noisy + + def q_sample2(self, x_start, noise, t, C): + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + x_noisy = x_start + C / 2 * time ** 2 + torch.sqrt(time) * noise + return x_noisy + + def pred_x0_from_xt(self, xt, noise, C, t): + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + x0 = xt - C * time - torch.sqrt(time) * noise + return x0 + + def pred_x0_from_xt2(self, xt, noise, C, t): + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + x0 = xt - C / 2 * time ** 2 - torch.sqrt(time) * noise + return x0 + + def pred_xtms_from_xt(self, xt, noise, C, t, s): + # noise = noise / noise.std(dim=[1, 2, 3]).reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + s = s.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + mean = xt + C * (time-s) - C * time - s / torch.sqrt(time) * noise + epsilon = torch.randn_like(mean, device=xt.device) + sigma = torch.sqrt(s * (time-s) / time) + xtms = mean + sigma * epsilon + return xtms + + def pred_xtms_from_xt2(self, xt, noise, C, t, s): + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + s = s.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + mean = xt + C / 2 * (time-s) ** 2 - C / 2 * time ** 2 - s / torch.sqrt(time) * noise + epsilon = torch.randn_like(mean, device=xt.device) + sigma = torch.sqrt(s * (time-s) / time) + xtms = mean + sigma * epsilon + return xtms + + def WCE_loss(self, prediction, labelf, beta=1.1): + label = labelf.long() + mask = labelf.clone() + + num_positive = torch.sum(label == 1).float() + num_negative = torch.sum(label == 0).float() + + mask[label == 1] = 1.0 * num_negative / (num_positive + num_negative) + mask[label == 0] = beta * num_positive / (num_positive + num_negative) + mask[label == 2] = 0 + cost = F.binary_cross_entropy(prediction, labelf, weight=mask, reduction='sum') + + return cost + + def Dice_Loss(self, pred, label): + # pred = torch.sigmoid(pred) + smooth = 1 + pred_flat = pred.view(-1) + label_flat = label.view(-1) + + intersecion = pred_flat * label_flat + unionsection = pred_flat.pow(2).sum() + label_flat.pow(2).sum() + smooth + loss = unionsection / (2 * intersecion.sum() + smooth) + loss = loss.sum() + return loss + + def p_losses(self, x_start, t, *args, **kwargs): + if self.start_dist == 'normal': + noise = torch.randn_like(x_start) + elif self.start_dist == 'uniform': + noise = 2 * torch.rand_like(x_start) - 1. + else: + raise NotImplementedError(f'{self.start_dist} is not supported !') + # K = -1. * torch.ones_like(x_start) + # C = noise - x_start # t = 1000 / 1000 + C = -1 * x_start # U(t) = Ct, U(1) = -x0 + # C = -2 * x_start # U(t) = 1/2 * C * t**2, U(1) = 1/2 * C = -x0 + x_noisy = self.q_sample(x_start=x_start, noise=noise, t=t, C=C) # (b, 2, c, h, w) + C_pred, noise_pred = self.model(x_noisy, t, **kwargs) + # C_pred = C_pred / torch.sqrt(t) + # noise_pred = noise_pred / torch.sqrt(1 - t) + x_rec = self.pred_x0_from_xt(x_noisy, noise_pred, C_pred, t) # x_rec:(B, 1, H, W) + loss_dict = {} + prefix = 'train' + + # elif self.objective == 'pred_KC': + # target1 = C + # target2 = noise + # target3 = x_start + + target1 = C + target2 = noise + target3 = x_start + + loss_simple = 0. + loss_vlb = 0. + # use l1 + l2 + if self.weighting_loss: + simple_weight1 = 2*torch.exp(1-t) + simple_weight2 = torch.exp(torch.sqrt(t)) + if self.cfg.model_name == 'ncsnpp9': + simple_weight1 = (t + 1) / t.sqrt() + simple_weight2 = (2 - t).sqrt() / (1 - t + self.eps).sqrt() + else: + simple_weight1 = 1 + simple_weight2 = 1 + + loss_simple += simple_weight1 * self.get_loss(C_pred, target1, mean=False).mean([1, 2, 3]) + \ + simple_weight2 * self.get_loss(noise_pred, target2, mean=False).mean([1, 2, 3]) + if self.use_l1: + loss_simple += simple_weight1 * (C_pred - target1).abs().mean([1, 2, 3]) + \ + simple_weight2 * (noise_pred - target2).abs().mean([1, 2, 3]) + loss_simple = loss_simple / 2 + # rec_weight = (1 - t.reshape(C.shape[0], 1)) ** 2 + rec_weight = 1 - t.reshape(C.shape[0], 1) # (B, 1) + loss_simple = loss_simple.mean() + loss_dict.update({f'{prefix}/loss_simple': loss_simple}) + + # loss_vlb += torch.abs(x_rec - target3).mean([1, 2, 3]) * rec_weight: (B, 1) + loss_vlb += self.Dice_Loss(x_rec, target3) + loss_vlb = loss_vlb.mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + loss_vlb + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + @torch.no_grad() + def sample(self, batch_size=16, up_scale=1, cond=None, denoise=True): + image_size, channels = self.image_size, self.channels + if cond is not None: + batch_size = cond.shape[0] + return self.sample_fn((batch_size, channels, image_size[0], image_size[1]), + up_scale=up_scale, unnormalize=True, cond=cond, denoise=denoise) + + @torch.no_grad() + def sample_fn(self, shape, up_scale=1, unnormalize=True, cond=None, denoise=False): + batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], \ + self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective + + # times = torch.linspace(-1, total_timesteps, steps=self.sampling_timesteps + 1).int() + # times = list(reversed(times.int().tolist())) + # time_pairs = list(zip(times[:-1], times[1:])) + # time_steps = torch.tensor([0.25, 0.15, 0.1, 0.1, 0.1, 0.09, 0.075, 0.06, 0.045, 0.03]) + step = 1. / self.sampling_timesteps + # time_steps = torch.tensor([0.1]).repeat(10) + time_steps = torch.tensor([step]).repeat(self.sampling_timesteps) + if denoise: + eps = self.eps + time_steps = torch.cat((time_steps[:-1], torch.tensor([step - eps]), torch.tensor([eps])), dim=0) + + if self.start_dist == 'normal': + img = torch.randn(shape, device=device) + elif self.start_dist == 'uniform': + img = 2 * torch.rand(shape, device=device) - 1. + else: + raise NotImplementedError(f'{self.start_dist} is not supported !') + img = F.interpolate(img, scale_factor=up_scale, mode='bilinear', align_corners=True) + # K = -1 * torch.ones_like(img) + cur_time = torch.ones((batch,), device=device) + for i, time_step in enumerate(time_steps): + s = torch.full((batch,), time_step, device=device) + if i == time_steps.shape[0] - 1: + s = cur_time + if cond is not None: + pred = self.model(img, cur_time, cond) + else: + pred = self.model(img, cur_time) + # C, noise = pred.chunk(2, dim=1) + C, noise = pred[:2] + # correct C + x0 = self.pred_x0_from_xt(img, noise, C, cur_time) + if self.clip_x_start: + x0.clamp_(-1., 1.) + # C.clamp_(-2., 2.) + C = -1 * x0 + img = self.pred_xtms_from_xt(img, noise, C, cur_time, s) + # img = self.pred_xtms_from_xt2(img, noise, C, cur_time, s) + cur_time = cur_time - s + img.clamp_(-1., 1.) + if unnormalize: + img = unnormalize_to_zero_to_one(img) + return img + + + +class LatentDiffusion(DDPM): + def __init__(self, + auto_encoder, + scale_factor=1.0, + scale_by_std=True, + scale_by_softsign=False, + input_keys=['image'], + sample_type='ddim', + num_timesteps_cond=1, + train_sample=-1, + default_scale=False, + *args, + **kwargs + ): + self.scale_by_std = scale_by_std + self.scale_by_softsign = scale_by_softsign + self.default_scale = default_scale + self.num_timesteps_cond = num_timesteps_cond + self.train_sample = train_sample + self.perceptual_weight = 0 + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + only_model = kwargs.pop("only_model", False) + super().__init__(*args, **kwargs) + assert self.num_timesteps_cond <= self.num_timesteps + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + if self.scale_by_softsign: + self.scale_by_std = False + print('### USING SOFTSIGN RESCALING') + assert (self.scale_by_std and self.scale_by_softsign) is False; + + self.init_first_stage(auto_encoder) + # self.instantiate_cond_stage(cond_stage_config) + self.input_keys = input_keys + self.clip_denoised = False + assert sample_type in ['p_loop', 'ddim', 'dpm', 'transformer'] ### 'dpm' is not availible now, suggestion 'ddim' + self.sample_type = sample_type + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys, only_model) + + def init_first_stage(self, first_stage_model): + self.first_stage_model = first_stage_model.eval() + # self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + ''' + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + ''' + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + # return self.scale_factor * z.detach() + self.scale_bias + return z.detach() + + @torch.no_grad() + def on_train_batch_start(self, batch): + # only for the first batch + if self.scale_by_std and (not self.scale_by_softsign): + if not self.default_scale: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x, *_ = batch.values() + encoder_posterior = self.first_stage_model.encode(x) + z = self.get_first_stage_encoding(encoder_posterior) + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + # print("### USING STD-RESCALING ###") + else: + print(f'### USING DEFAULT SCALE {self.scale_factor}') + else: + print(f'### USING SOFTSIGN SCALE !') + + @torch.no_grad() + def get_input(self, batch, return_first_stage_outputs=False, return_original_cond=False): + assert 'image' in self.input_keys; + # if len(self.input_keys) > len(batch.keys()): + # x, cond, *_ = batch.values() + # else: + # x, cond = batch.values() + x = batch['image'] + cond = batch['cond'] if 'cond' in batch else None + z = self.first_stage_model.encode(x) + # print('zzzz', z.shape) + z = self.get_first_stage_encoding(z) + out = [z, cond, x] + if return_first_stage_outputs: + xrec = self.first_stage_model.decode(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(cond) + return out + + def training_step(self, batch): + z, c, *_ = self.get_input(batch) + # print(_[0].shape) + if self.scale_by_softsign: + z = F.softsign(z) + elif self.scale_by_std: + z = self.scale_factor * z + # print('grad', self.scale_bias.grad) + loss, loss_dict = self(z, c, edge=_[0]) + return loss, loss_dict + + def q_sample3(self, x_start, t, K, C): + b, c, h, w = x_start.shape + _, nt = t.shape + K_ = K.unsqueeze(1).repeat(1, nt, 1, 1, 1) + C_ = C.unsqueeze(1).repeat(1, nt, 1, 1, 1) + x_noisy = x_start.expand(nt, b, c, h, w).transpose(1, 0) + K_ / 2 * (t.reshape(b, nt, 1, 1, 1).repeat(1, 1, c, h, w) / self.num_timesteps) ** 2 \ + + C_ * t.reshape(b, nt, 1, 1, 1).repeat(1, 1, c, h, w) / self.num_timesteps + return x_noisy # (b, nt, c, h, w) + + def pred_xtms_from_xt(self, xt, noise, C, t, s): + # noise = noise / noise.std(dim=[1, 2, 3]).reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + time = t.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + s = s.reshape(C.shape[0], *((1,) * (len(C.shape) - 1))) + mean = xt - C * s - s / torch.sqrt(time) * noise + epsilon = torch.randn_like(mean, device=xt.device) + sigma = torch.sqrt(s * (time-s) / time) + xtms = mean + sigma * epsilon + return xtms + + def WCE_loss(self, prediction, labelf, beta=1.1): + label = labelf.long() + mask = labelf.clone() + + num_positive = torch.sum(label == 1).float() + num_negative = torch.sum(label == 0).float() + + mask[label == 1] = 1.0 * num_negative / (num_positive + num_negative) + mask[label == 0] = beta * num_positive / (num_positive + num_negative) + mask[label == 2] = 0 + cost = F.binary_cross_entropy(prediction, labelf, weight=mask, reduction='sum') + + return cost + + def Dice_Loss(self, pred, label): + # pred = torch.sigmoid(pred) + B = pred.shape[0] + smooth = 1 + pred_flat = pred.view(B, -1) + label_flat = label.view(B, -1) + + intersecion = pred_flat * label_flat + unionsection = pred_flat.pow(2).sum(dim=-1) + label_flat.pow(2).sum(dim=-1) + smooth + loss = unionsection / (2 * intersecion.sum(dim=-1) + smooth) + loss = loss.reshape(B, 1) + return loss + + def p_losses(self, x_start, t, *args, **kwargs): + if self.start_dist == 'normal': + noise = torch.randn_like(x_start) + elif self.start_dist == 'uniform': + noise = 2 * torch.rand_like(x_start) - 1. + else: + raise NotImplementedError(f'{self.start_dist} is not supported !') + # K = -1. * torch.ones_like(x_start) + # C = noise - x_start # t = 1000 / 1000 + C = -1 * x_start # U(t) = Ct, U(1) = -x0 + # C = -2 * x_start # U(t) = 1/2 * C * t**2, U(1) = 1/2 * C = -x0 + x_noisy = self.q_sample(x_start=x_start, noise=noise, t=t, C=C) # (b, 2, c, h, w) + if self.cfg.model_name == 'cond_unet8': + C_pred, noise_pred, (e1, e2) = self.model(x_noisy, t, *args, **kwargs) + if self.cfg.model_name == 'cond_unet13': + C_pred, noise_pred, aux_C = self.model(x_noisy, t, *args, **kwargs) + else: + C_pred, noise_pred = self.model(x_noisy, t, *args, **kwargs) + # C_pred = C_pred / torch.sqrt(t) + # noise_pred = noise_pred / torch.sqrt(1 - t) + x_rec = self.pred_x0_from_xt(x_noisy, noise_pred, C_pred, t) # x_rec:(B, C, H, W) + loss_dict = {} + prefix = 'train' + + # elif self.objective == 'pred_KC': + # target1 = C + # target2 = noise + # target3 = x_start + + target1 = C + target2 = noise + target3 = x_start + + loss_simple = 0. + loss_vlb = 0. + + simple_weight1 = (t + 1) / t.sqrt() + simple_weight2 = (2 - t).sqrt() / (1 - t + self.eps).sqrt() + + # if self.weighting_loss: + # simple_weight1 = 2 * torch.exp(1 - t) + # simple_weight2 = torch.exp(torch.sqrt(t)) + # if self.cfg.model_name == 'ncsnpp9': + # simple_weight1 = (t + 1) / t.sqrt() + # simple_weight2 = (2 - t).sqrt() / (1 - t + self.eps).sqrt() + # else: + # simple_weight1 = 1 + # simple_weight2 = 1 + + loss_simple += simple_weight1 * self.get_loss(C_pred, target1, mean=False).mean([1, 2, 3]) + \ + simple_weight2 * self.get_loss(noise_pred, target2, mean=False).mean([1, 2, 3]) + + # loss_simple += self.Dice_Loss(C_pred, target1) * simple_weight1 + + if self.use_l1: + loss_simple += simple_weight1 * (C_pred - target1).abs().mean([1, 2, 3]) + \ + simple_weight2 * (noise_pred - target2).abs().mean([1, 2, 3]) + loss_simple = loss_simple / 2 + + if self.cfg.model_name == 'cond_unet8': + loss_simple += 0.05*(self.Dice_Loss(e1, (kwargs['edge'] + 1)/2) + self.Dice_Loss(e2, (kwargs['edge'] + 1)/2)) + elif self.cfg.model_name == 'cond_unet13': + loss_simple += 0.5 * (simple_weight1 * self.get_loss(aux_C, target1, mean=False).mean([1, 2, 3]) + \ + simple_weight1 * (aux_C - target1).abs().mean([1, 2, 3])) + + rec_weight = (1 - t.reshape(C.shape[0], 1)) ** 2 + # rec_weight = 1 - t.reshape(C.shape[0], 1) # (B, 1) + loss_simple = loss_simple.mean() + loss_dict.update({f'{prefix}/loss_simple': loss_simple}) + + loss_vlb += torch.abs(x_rec - target3).mean([1, 2, 3]) * rec_weight # : (B, 1) + # loss_vlb += self.Dice_Loss(x_rec, target3) * rec_weight + + # loss_vlb = loss_vlb + loss_vlb = loss_vlb.mean() + + if self.cfg.get('use_disloss', False): + with torch.no_grad(): + edge_rec = self.first_stage_model.decode(x_rec / self.scale_factor) + edge_rec = unnormalize_to_zero_to_one(edge_rec) + edge_rec = torch.clamp(edge_rec, min=0., max=1.) # B, 1, 320, 320 + loss_tmp = self.cross_entropy_loss_RCF(edge_rec, (kwargs['edge'] + 1)/2) * rec_weight # B, 1 + loss_ce = SpecifyGradient.apply(x_rec, loss_tmp.mean()) + # print(loss_ce.shape) + # print(loss_vlb.shape) + loss_vlb += loss_ce.mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + loss_vlb + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def cross_entropy_loss_RCF(self, prediction, labelf, beta=1.1): + # label = labelf.long() + label = labelf + mask = labelf.clone() + + num_positive = torch.sum(label == 1).float() + num_negative = torch.sum(label == 0).float() + + mask_temp = (label > 0) & (label <= 0.3) + mask[mask_temp] = 0. + + mask[label == 1] = 1.0 * num_negative / (num_positive + num_negative) + mask[label == 0] = beta * num_positive / (num_positive + num_negative) + + # mask[label == 2] = 0 + cost = F.binary_cross_entropy(prediction, labelf, weight=mask, reduction='none') + return cost.mean([1, 2, 3]) + + @torch.no_grad() + def sample(self, batch_size=16, up_scale=1, cond=None, mask=None, denoise=True): + # image_size, channels = self.image_size, self.channels + channels = self.channels + image_size = cond.shape[-2:] + if cond is not None: + batch_size = cond.shape[0] + down_ratio = self.first_stage_model.down_ratio + if self.cfg.model_name == 'cond_unet8' or self.cfg.model_name == 'cond_unet13': + z, aux_out = self.sample_fn((batch_size, channels, image_size[0] // down_ratio, image_size[1] // down_ratio), + up_scale=up_scale, unnormalize=False, cond=cond, denoise=denoise) + else: + z = self.sample_fn((batch_size, channels, image_size[0]//down_ratio, image_size[1]//down_ratio), + up_scale=up_scale, unnormalize=False, cond=cond, denoise=denoise) + aux_out = None + + if self.scale_by_std: + z = 1. / self.scale_factor * z.detach() + if self.cfg.model_name == 'cond_unet13': + aux_out = 1. / self.scale_factor * aux_out.detach() + elif self.scale_by_softsign: + z = z / (1 - z.abs()) + z = z.detach() + #print(z.shape) + x_rec = self.first_stage_model.decode(z) + x_rec = unnormalize_to_zero_to_one(x_rec) + x_rec = torch.clamp(x_rec, min=0., max=1.) + if self.cfg.model_name == 'cond_unet13': + aux_out = self.first_stage_model.decode(aux_out) + aux_out = unnormalize_to_zero_to_one(aux_out) + aux_out = torch.clamp(aux_out, min=0., max=1.) + if mask is not None: + x_rec = mask * unnormalize_to_zero_to_one(cond) + (1 - mask) * x_rec + if aux_out is not None: + return x_rec, aux_out + return x_rec + + @torch.no_grad() + def sample_fn(self, shape, up_scale=1, unnormalize=True, cond=None, denoise=False): + batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], \ + self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective + + # times = torch.linspace(-1, total_timesteps, steps=self.sampling_timesteps + 1).int() + # times = list(reversed(times.int().tolist())) + # time_pairs = list(zip(times[:-1], times[1:])) + # time_steps = torch.tensor([0.25, 0.15, 0.1, 0.1, 0.1, 0.09, 0.075, 0.06, 0.045, 0.03]) + step = 1. / self.sampling_timesteps + # time_steps = torch.tensor([0.1]).repeat(10) + time_steps = torch.tensor([step]).repeat(self.sampling_timesteps) + if denoise: + eps = self.eps + time_steps = torch.cat((time_steps[:-1], torch.tensor([step - eps]), torch.tensor([eps])), dim=0) + + if self.start_dist == 'normal': + img = torch.randn(shape, device=device) + elif self.start_dist == 'uniform': + img = 2 * torch.rand(shape, device=device) - 1. + else: + raise NotImplementedError(f'{self.start_dist} is not supported !') + img = F.interpolate(img, scale_factor=up_scale, mode='bilinear', align_corners=True) + img_aux = F.interpolate(img.clone(), scale_factor=up_scale, mode='bilinear', align_corners=True) + # img_aux = img.clone() + # K = -1 * torch.ones_like(img) + cur_time = torch.ones((batch,), device=device) + for i, time_step in enumerate(time_steps): + s = torch.full((batch,), time_step, device=device) + if i == time_steps.shape[0] - 1: + s = cur_time + if cond is not None: + pred = self.model(img, cur_time, cond) + else: + pred = self.model(img, cur_time) + # C, noise = pred.chunk(2, dim=1) + C, noise = pred[:2] + if self.cfg.model_name == 'cond_unet8' or self.cfg.model_name == 'cond_unet13': + aux_out = pred[-1] + else: + aux_out = None + # if self.scale_by_softsign: + # # correct the C for softsign + # x0 = self.pred_x0_from_xt(img, noise, C, cur_time) + # x0 = torch.clamp(x0, min=-0.987654321, max=0.987654321) + # C = -x0 + # correct C + x0 = self.pred_x0_from_xt(img, noise, C, cur_time) + C = -1 * x0 + img = self.pred_xtms_from_xt(img, noise, C, cur_time, s) + # if self.cfg.model_name == 'cond_unet13' and i == len(time_steps) - 2: + # img_aux = img + # if self.cfg.model_name == 'cond_unet13' and i in [len(time_steps)-2, len(time_steps)-1]: + # x0_aux = self.pred_x0_from_xt(img_aux, noise, aux_out, cur_time) + # C_aux = -1 * x0_aux + # img_aux = self.pred_xtms_from_xt(img_aux, noise, C_aux, cur_time, s) + if self.cfg.model_name == 'cond_unet13': + for _ in range(1): + x0_aux = self.pred_x0_from_xt(img_aux, noise, aux_out, cur_time) + C_aux = -1 * x0_aux + img_aux = self.pred_xtms_from_xt(img_aux, noise, C_aux, cur_time, s) + cur_time = cur_time - s + if self.scale_by_softsign: + img.clamp_(-0.987654321, 0.987654321) + if unnormalize: + img = unnormalize_to_zero_to_one(img) + if self.cfg.model_name == 'cond_unet13': + aux_out = img_aux + if aux_out is not None: + return img, aux_out + return img + +class SpecifyGradient(torch.autograd.Function): + @staticmethod + @custom_fwd + def forward(ctx, input_tensor, gt_grad): + ctx.save_for_backward(gt_grad) + # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward. + return torch.ones(input_tensor.shape, device=input_tensor.device, dtype=input_tensor.dtype) + + @staticmethod + @custom_bwd + def backward(ctx, grad_scale): + (gt_grad,) = ctx.saved_tensors + gt_grad = gt_grad * grad_scale + return gt_grad, None + +if __name__ == "__main__": + ddconfig = {'double_z': True, + 'z_channels': 4, + 'resolution': (240, 960), + 'in_channels': 3, + 'out_ch': 3, + 'ch': 128, + 'ch_mult': [1, 2, 4, 4], # num_down = len(ch_mult)-1 + 'num_res_blocks': 2, + 'attn_resolutions': [], + 'dropout': 0.0} + lossconfig = {'disc_start': 50001, + 'kl_weight': 0.000001, + 'disc_weight': 0.5} + from encoder_decoder import AutoencoderKL + auto_encoder = AutoencoderKL(ddconfig, lossconfig, embed_dim=4, + ) + from mask_cond_unet import Unet + unet = Unet(dim=64, dim_mults=(1, 2, 4, 8), channels=4, cond_in_dim=1,) + ldm = LatentDiffusion(auto_encoder=auto_encoder, model=unet, image_size=ddconfig['resolution']) + image = torch.rand(1, 3, 128, 128) + mask = torch.rand(1, 1, 128, 128) + input = {'image': image, 'cond': mask} + time = torch.tensor([1]) + with torch.no_grad(): + y = ldm.training_step(input) + pass \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/efficientnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/efficientnet.py new file mode 100644 index 00000000..ee49d7ab --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/efficientnet.py @@ -0,0 +1,1130 @@ +import copy +import math +import warnings +from dataclasses import dataclass +from functools import partial +from typing import Any, Callable, Dict, Optional, List, Sequence, Tuple, Union + +import torch +from torch import nn, Tensor +from torchvision.ops import StochasticDepth + +from torchvision.ops.misc import Conv2dNormActivation, SqueezeExcitation +from torchvision.transforms._presets import ImageClassification, InterpolationMode +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param, _make_divisible + + +__all__ = [ + "EfficientNet", + "EfficientNet_B0_Weights", + "EfficientNet_B1_Weights", + "EfficientNet_B2_Weights", + "EfficientNet_B3_Weights", + "EfficientNet_B4_Weights", + "EfficientNet_B5_Weights", + "EfficientNet_B6_Weights", + "EfficientNet_B7_Weights", + "EfficientNet_V2_S_Weights", + "EfficientNet_V2_M_Weights", + "EfficientNet_V2_L_Weights", + "efficientnet_b0", + "efficientnet_b1", + "efficientnet_b2", + "efficientnet_b3", + "efficientnet_b4", + "efficientnet_b5", + "efficientnet_b6", + "efficientnet_b7", + "efficientnet_v2_s", + "efficientnet_v2_m", + "efficientnet_v2_l", +] + + +@dataclass +class _MBConvConfig: + expand_ratio: float + kernel: int + stride: int + input_channels: int + out_channels: int + num_layers: int + block: Callable[..., nn.Module] + + @staticmethod + def adjust_channels(channels: int, width_mult: float, min_value: Optional[int] = None) -> int: + return _make_divisible(channels * width_mult, 8, min_value) + + +class MBConvConfig(_MBConvConfig): + # Stores information listed at Table 1 of the EfficientNet paper & Table 4 of the EfficientNetV2 paper + def __init__( + self, + expand_ratio: float, + kernel: int, + stride: int, + input_channels: int, + out_channels: int, + num_layers: int, + width_mult: float = 1.0, + depth_mult: float = 1.0, + block: Optional[Callable[..., nn.Module]] = None, + ) -> None: + input_channels = self.adjust_channels(input_channels, width_mult) + out_channels = self.adjust_channels(out_channels, width_mult) + num_layers = self.adjust_depth(num_layers, depth_mult) + if block is None: + block = MBConv + super().__init__(expand_ratio, kernel, stride, input_channels, out_channels, num_layers, block) + + @staticmethod + def adjust_depth(num_layers: int, depth_mult: float): + return int(math.ceil(num_layers * depth_mult)) + + +class FusedMBConvConfig(_MBConvConfig): + # Stores information listed at Table 4 of the EfficientNetV2 paper + def __init__( + self, + expand_ratio: float, + kernel: int, + stride: int, + input_channels: int, + out_channels: int, + num_layers: int, + block: Optional[Callable[..., nn.Module]] = None, + ) -> None: + if block is None: + block = FusedMBConv + super().__init__(expand_ratio, kernel, stride, input_channels, out_channels, num_layers, block) + + +class MBConv(nn.Module): + def __init__( + self, + cnf: MBConvConfig, + stochastic_depth_prob: float, + norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = SqueezeExcitation, + ) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError("illegal stride value") + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + # expand + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + layers.append( + Conv2dNormActivation( + cnf.input_channels, + expanded_channels, + kernel_size=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # depthwise + layers.append( + Conv2dNormActivation( + expanded_channels, + expanded_channels, + kernel_size=cnf.kernel, + stride=cnf.stride, + groups=expanded_channels, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # squeeze and excitation + squeeze_channels = max(1, cnf.input_channels // 4) + layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True))) + + # project + layers.append( + Conv2dNormActivation( + expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None + ) + ) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class FusedMBConv(nn.Module): + def __init__( + self, + cnf: FusedMBConvConfig, + stochastic_depth_prob: float, + norm_layer: Callable[..., nn.Module], + ) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError("illegal stride value") + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + # fused expand + layers.append( + Conv2dNormActivation( + cnf.input_channels, + expanded_channels, + kernel_size=cnf.kernel, + stride=cnf.stride, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # project + layers.append( + Conv2dNormActivation( + expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None + ) + ) + else: + layers.append( + Conv2dNormActivation( + cnf.input_channels, + cnf.out_channels, + kernel_size=cnf.kernel, + stride=cnf.stride, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class EfficientNet(nn.Module): + def __init__( + self, + inverted_residual_setting: Sequence[Union[MBConvConfig, FusedMBConvConfig]], + dropout: float, + stochastic_depth_prob: float = 0.2, + num_classes: int = 1000, + norm_layer: Optional[Callable[..., nn.Module]] = None, + last_channel: Optional[int] = None, + **kwargs: Any, + ) -> None: + """ + EfficientNet V1 and V2 main class + + Args: + inverted_residual_setting (Sequence[Union[MBConvConfig, FusedMBConvConfig]]): Network structure + dropout (float): The droupout probability + stochastic_depth_prob (float): The stochastic depth probability + num_classes (int): Number of classes + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + last_channel (int): The number of channels on the penultimate layer + """ + super().__init__() + _log_api_usage_once(self) + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not ( + isinstance(inverted_residual_setting, Sequence) + and all([isinstance(s, _MBConvConfig) for s in inverted_residual_setting]) + ): + raise TypeError("The inverted_residual_setting should be List[MBConvConfig]") + + if "block" in kwargs: + warnings.warn( + "The parameter 'block' is deprecated since 0.13 and will be removed 0.15. " + "Please pass this information on 'MBConvConfig.block' instead." + ) + if kwargs["block"] is not None: + for s in inverted_residual_setting: + if isinstance(s, MBConvConfig): + s.block = kwargs["block"] + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + # layers.append( + # Conv2dNormActivation( + # 3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.SiLU + # ) + # ) + self.first_coonv = Conv2dNormActivation( + 3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.SiLU + ) + + # building inverted residual blocks + total_stage_blocks = sum(cnf.num_layers for cnf in inverted_residual_setting) + stage_block_id = 0 + for cnf in inverted_residual_setting: + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # copy to avoid modifications. shallow copy is enough + block_cnf = copy.copy(cnf) + + # overwrite info if not the first conv in the stage + if stage: + block_cnf.input_channels = block_cnf.out_channels + block_cnf.stride = 1 + + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / total_stage_blocks + + stage.append(block_cnf.block(block_cnf, sd_prob, norm_layer)) + stage_block_id += 1 + + layers.append(nn.Sequential(*stage)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = last_channel if last_channel is not None else 4 * lastconv_input_channels + layers.append( + Conv2dNormActivation( + lastconv_input_channels, + lastconv_output_channels, + kernel_size=1, + norm_layer=norm_layer, + activation_layer=nn.SiLU, + ) + ) + # self.last_conv = Conv2dNormActivation( + # lastconv_input_channels, + # lastconv_output_channels, + # kernel_size=1, + # norm_layer=norm_layer, + # activation_layer=nn.SiLU, + # ) + + # self.features = nn.Sequential(*layers) + self.features = nn.ModuleList(layers) + # self.avgpool = nn.AdaptiveAvgPool2d(1) + # self.classifier = nn.Sequential( + # nn.Dropout(p=dropout, inplace=True), + # nn.Linear(lastconv_output_channels, num_classes), + # ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out") + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + init_range = 1.0 / math.sqrt(m.out_features) + nn.init.uniform_(m.weight, -init_range, init_range) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor): + x = self.first_coonv(x) + # x = self.features(x) + feats = [] + for i, layer in enumerate(self.features): + x = layer(x) + if i in [1, 2, 4, 6]: + feats.append(x) + + # x = self.avgpool(x) + # x = torch.flatten(x, 1) + # + # x = self.classifier(x) + + return feats + + def forward(self, x: Tensor): + return self._forward_impl(x) + + +def _efficientnet( + inverted_residual_setting: Sequence[Union[MBConvConfig, FusedMBConvConfig]], + dropout: float, + last_channel: Optional[int], + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> EfficientNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = EfficientNet(inverted_residual_setting, dropout, last_channel=last_channel, **kwargs) + + if weights is not None: + ckpt1 = weights.get_state_dict(progress=progress) + ckpt2 = model.state_dict() + kl1 = list(ckpt1.keys()) + for i, k in enumerate(list(ckpt2.keys())): + ckpt2[k] = ckpt1[kl1[i]] + msg = model.load_state_dict(ckpt2, strict=False) + print(f'Load EfficientNet: {msg}') + else: + print('No pretrained weight loaded!') + return model + + +def _efficientnet_conf( + arch: str, + **kwargs: Any, +) -> Tuple[Sequence[Union[MBConvConfig, FusedMBConvConfig]], Optional[int]]: + inverted_residual_setting: Sequence[Union[MBConvConfig, FusedMBConvConfig]] + if arch.startswith("efficientnet_b"): + bneck_conf = partial(MBConvConfig, width_mult=kwargs.pop("width_mult"), depth_mult=kwargs.pop("depth_mult")) + inverted_residual_setting = [ + bneck_conf(1, 3, 1, 32, 16, 1), + bneck_conf(6, 3, 2, 16, 24, 2), + bneck_conf(6, 5, 2, 24, 40, 2), + bneck_conf(6, 3, 2, 40, 80, 3), + bneck_conf(6, 5, 1, 80, 112, 3), + bneck_conf(6, 5, 2, 112, 192, 4), + bneck_conf(6, 3, 1, 192, 320, 1), + ] + last_channel = None + elif arch.startswith("efficientnet_v2_s"): + inverted_residual_setting = [ + FusedMBConvConfig(1, 3, 1, 24, 24, 2), + FusedMBConvConfig(4, 3, 2, 24, 48, 4), + FusedMBConvConfig(4, 3, 2, 48, 64, 4), + MBConvConfig(4, 3, 2, 64, 128, 6), + MBConvConfig(6, 3, 1, 128, 160, 9), + MBConvConfig(6, 3, 2, 160, 256, 15), + ] + last_channel = 1280 + elif arch.startswith("efficientnet_v2_m"): + inverted_residual_setting = [ + FusedMBConvConfig(1, 3, 1, 24, 24, 3), + FusedMBConvConfig(4, 3, 2, 24, 48, 5), + FusedMBConvConfig(4, 3, 2, 48, 80, 5), + MBConvConfig(4, 3, 2, 80, 160, 7), + MBConvConfig(6, 3, 1, 160, 176, 14), + MBConvConfig(6, 3, 2, 176, 304, 18), + MBConvConfig(6, 3, 1, 304, 512, 5), + ] + last_channel = 1280 + elif arch.startswith("efficientnet_v2_l"): + inverted_residual_setting = [ + FusedMBConvConfig(1, 3, 1, 32, 32, 4), + FusedMBConvConfig(4, 3, 2, 32, 64, 7), + FusedMBConvConfig(4, 3, 2, 64, 96, 7), + MBConvConfig(4, 3, 2, 96, 192, 10), + MBConvConfig(6, 3, 1, 192, 224, 19), + MBConvConfig(6, 3, 2, 224, 384, 25), + MBConvConfig(6, 3, 1, 384, 640, 7), + ] + last_channel = 1280 + else: + raise ValueError(f"Unsupported model type {arch}") + + return inverted_residual_setting, last_channel + + +_COMMON_META: Dict[str, Any] = { + "categories": _IMAGENET_CATEGORIES, +} + + +_COMMON_META_V1 = { + **_COMMON_META, + "min_size": (1, 1), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1", +} + + +_COMMON_META_V2 = { + **_COMMON_META, + "min_size": (33, 33), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2", +} + + +class EfficientNet_B0_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 5288548, + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.692, + "acc@5": 93.532, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B1_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth", + transforms=partial( + ImageClassification, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 7794184, + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.642, + "acc@5": 94.186, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth", + transforms=partial( + ImageClassification, crop_size=240, resize_size=255, interpolation=InterpolationMode.BILINEAR + ), + meta={ + **_COMMON_META_V1, + "num_params": 7794184, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuning", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.838, + "acc@5": 94.934, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class EfficientNet_B2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth", + transforms=partial( + ImageClassification, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 9109994, + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.608, + "acc@5": 95.310, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B3_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth", + transforms=partial( + ImageClassification, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 12233232, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.008, + "acc@5": 96.054, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B4_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth", + transforms=partial( + ImageClassification, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 19341616, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.384, + "acc@5": 96.594, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B5_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + url="https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth", + transforms=partial( + ImageClassification, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 30389784, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.444, + "acc@5": 96.628, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B6_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + url="https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth", + transforms=partial( + ImageClassification, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 43040704, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.008, + "acc@5": 96.916, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B7_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + url="https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth", + transforms=partial( + ImageClassification, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 66347960, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.122, + "acc@5": 96.908, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_V2_S_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth", + transforms=partial( + ImageClassification, + crop_size=384, + resize_size=384, + interpolation=InterpolationMode.BILINEAR, + ), + meta={ + **_COMMON_META_V2, + "num_params": 21458488, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.228, + "acc@5": 96.878, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_V2_M_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth", + transforms=partial( + ImageClassification, + crop_size=480, + resize_size=480, + interpolation=InterpolationMode.BILINEAR, + ), + meta={ + **_COMMON_META_V2, + "num_params": 54139356, + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.112, + "acc@5": 97.156, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_V2_L_Weights(WeightsEnum): + # Weights ported from https://github.com/google/automl/tree/master/efficientnetv2 + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth", + transforms=partial( + ImageClassification, + crop_size=480, + resize_size=480, + interpolation=InterpolationMode.BICUBIC, + mean=(0.5, 0.5, 0.5), + std=(0.5, 0.5, 0.5), + ), + meta={ + **_COMMON_META_V2, + "num_params": 118515272, + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.808, + "acc@5": 97.788, + } + }, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B0_Weights.IMAGENET1K_V1)) +def efficientnet_b0( + *, weights: Optional[EfficientNet_B0_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B0 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B0_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B0_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B0_Weights + :members: + """ + weights = EfficientNet_B0_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b0", width_mult=1.0, depth_mult=1.0) + return _efficientnet(inverted_residual_setting, 0.2, last_channel, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B1_Weights.IMAGENET1K_V1)) +def efficientnet_b1( + *, weights: Optional[EfficientNet_B1_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B1 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B1_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B1_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B1_Weights + :members: + """ + weights = EfficientNet_B1_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b1", width_mult=1.0, depth_mult=1.1) + return _efficientnet(inverted_residual_setting, 0.2, last_channel, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B2_Weights.IMAGENET1K_V1)) +def efficientnet_b2( + *, weights: Optional[EfficientNet_B2_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B2 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B2_Weights + :members: + """ + weights = EfficientNet_B2_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b2", width_mult=1.1, depth_mult=1.2) + return _efficientnet(inverted_residual_setting, 0.3, last_channel, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B3_Weights.IMAGENET1K_V1)) +def efficientnet_b3( + *, weights: Optional[EfficientNet_B3_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B3 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B3_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B3_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B3_Weights + :members: + """ + weights = EfficientNet_B3_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b3", width_mult=1.2, depth_mult=1.4) + return _efficientnet(inverted_residual_setting, 0.3, last_channel, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B4_Weights.IMAGENET1K_V1)) +def efficientnet_b4( + *, weights: Optional[EfficientNet_B4_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B4 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B4_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B4_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B4_Weights + :members: + """ + weights = EfficientNet_B4_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b4", width_mult=1.4, depth_mult=1.8) + return _efficientnet(inverted_residual_setting, 0.4, last_channel, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B5_Weights.IMAGENET1K_V1)) +def efficientnet_b5( + *, weights: Optional[EfficientNet_B5_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B5 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B5_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B5_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B5_Weights + :members: + """ + weights = EfficientNet_B5_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b5", width_mult=1.6, depth_mult=2.2) + return _efficientnet( + inverted_residual_setting, + 0.4, + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B6_Weights.IMAGENET1K_V1)) +def efficientnet_b6( + *, weights: Optional[EfficientNet_B6_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B6 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B6_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B6_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B6_Weights + :members: + """ + weights = EfficientNet_B6_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b6", width_mult=1.8, depth_mult=2.6) + return _efficientnet( + inverted_residual_setting, + 0.5, + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_B7_Weights.IMAGENET1K_V1)) +def efficientnet_b7( + *, weights: Optional[EfficientNet_B7_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B7 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B7_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B7_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B7_Weights + :members: + """ + weights = EfficientNet_B7_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b7", width_mult=2.0, depth_mult=3.1) + return _efficientnet( + inverted_residual_setting, + 0.5, + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_S_Weights.IMAGENET1K_V1)) +def efficientnet_v2_s( + *, weights: Optional[EfficientNet_V2_S_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """ + Constructs an EfficientNetV2-S architecture from + `EfficientNetV2: Smaller Models and Faster Training `_. + + Args: + weights (:class:`~torchvision.models.EfficientNet_V2_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_V2_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_V2_S_Weights + :members: + """ + weights = EfficientNet_V2_S_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_s") + return _efficientnet( + inverted_residual_setting, + 0.2, + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=1e-03), + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_M_Weights.IMAGENET1K_V1)) +def efficientnet_v2_m( + *, weights: Optional[EfficientNet_V2_M_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """ + Constructs an EfficientNetV2-M architecture from + `EfficientNetV2: Smaller Models and Faster Training `_. + + Args: + weights (:class:`~torchvision.models.EfficientNet_V2_M_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_V2_M_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_V2_M_Weights + :members: + """ + weights = EfficientNet_V2_M_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_m") + return _efficientnet( + inverted_residual_setting, + 0.3, + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=1e-03), + **kwargs, + ) + + +@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_L_Weights.IMAGENET1K_V1)) +def efficientnet_v2_l( + *, weights: Optional[EfficientNet_V2_L_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """ + Constructs an EfficientNetV2-L architecture from + `EfficientNetV2: Smaller Models and Faster Training `_. + + Args: + weights (:class:`~torchvision.models.EfficientNet_V2_L_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_V2_L_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_V2_L_Weights + :members: + """ + weights = EfficientNet_V2_L_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_l") + return _efficientnet( + inverted_residual_setting, + 0.4, + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=1e-03), + **kwargs, + ) + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from torchvision.models._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "efficientnet_b0": EfficientNet_B0_Weights.IMAGENET1K_V1.url, + "efficientnet_b1": EfficientNet_B1_Weights.IMAGENET1K_V1.url, + "efficientnet_b2": EfficientNet_B2_Weights.IMAGENET1K_V1.url, + "efficientnet_b3": EfficientNet_B3_Weights.IMAGENET1K_V1.url, + "efficientnet_b4": EfficientNet_B4_Weights.IMAGENET1K_V1.url, + "efficientnet_b5": EfficientNet_B5_Weights.IMAGENET1K_V1.url, + "efficientnet_b6": EfficientNet_B6_Weights.IMAGENET1K_V1.url, + "efficientnet_b7": EfficientNet_B7_Weights.IMAGENET1K_V1.url, + } +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/ema.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/ema.py new file mode 100644 index 00000000..0267f9d9 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/ema.py @@ -0,0 +1,191 @@ +import copy +import torch +from torch import nn + + +def exists(val): + return val is not None + + +def clamp(value, min_value=None, max_value=None): + assert exists(min_value) or exists(max_value) + if exists(min_value): + value = max(value, min_value) + + if exists(max_value): + value = min(value, max_value) + + return value + + +class EMA(nn.Module): + """ + Implements exponential moving average shadowing for your model. + + Utilizes an inverse decay schedule to manage longer term training runs. + By adjusting the power, you can control how fast EMA will ramp up to your specified beta. + + @crowsonkb's notes on EMA Warmup: + + If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are + good values for models you plan to train for a million or more steps (reaches decay + factor 0.999 at 31.6K steps, 0.9999 at 1M steps), gamma=1, power=3/4 for models + you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at + 215.4k steps). + + Args: + inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. + power (float): Exponential factor of EMA warmup. Default: 1. + min_value (float): The minimum EMA decay rate. Default: 0. + """ + + def __init__( + self, + model, + ema_model=None, + # if your model has lazylinears or other types of non-deepcopyable modules, you can pass in your own ema model + beta=0.9999, + update_after_step=100, + update_every=10, + inv_gamma=1.0, + power=2 / 3, + min_value=0.0, + param_or_buffer_names_no_ema=set(), + ignore_names=set(), + ignore_startswith_names=set(), + include_online_model=True + # set this to False if you do not wish for the online model to be saved along with the ema model (managed externally) + ): + super().__init__() + self.beta = beta + + # whether to include the online model within the module tree, so that state_dict also saves it + + self.include_online_model = include_online_model + + if include_online_model: + self.online_model = model + else: + self.online_model = [model] # hack + + # ema model + + self.ema_model = ema_model + + if not exists(self.ema_model): + try: + self.ema_model = copy.deepcopy(model) + except: + print('Your model was not copyable. Please make sure you are not using any LazyLinear') + exit() + + self.ema_model.requires_grad_(False) + + self.parameter_names = {name for name, param in self.ema_model.named_parameters() if param.dtype == torch.float} + self.buffer_names = {name for name, buffer in self.ema_model.named_buffers() if buffer.dtype == torch.float} + + self.update_every = update_every + self.update_after_step = update_after_step + + self.inv_gamma = inv_gamma + self.power = power + self.min_value = min_value + + assert isinstance(param_or_buffer_names_no_ema, (set, list)) + self.param_or_buffer_names_no_ema = param_or_buffer_names_no_ema # parameter or buffer + + self.ignore_names = ignore_names + self.ignore_startswith_names = ignore_startswith_names + + self.register_buffer('initted', torch.Tensor([False])) + self.register_buffer('step', torch.tensor([0])) + + @property + def model(self): + return self.online_model if self.include_online_model else self.online_model[0] + + def restore_ema_model_device(self): + device = self.initted.device + self.ema_model.to(device) + + def get_params_iter(self, model): + for name, param in model.named_parameters(): + if name not in self.parameter_names: + continue + yield name, param + + def get_buffers_iter(self, model): + for name, buffer in model.named_buffers(): + if name not in self.buffer_names: + continue + yield name, buffer + + def copy_params_from_model_to_ema(self): + for (_, ma_params), (_, current_params) in zip(self.get_params_iter(self.ema_model), + self.get_params_iter(self.model)): + ma_params.data.copy_(current_params.data) + + for (_, ma_buffers), (_, current_buffers) in zip(self.get_buffers_iter(self.ema_model), + self.get_buffers_iter(self.model)): + ma_buffers.data.copy_(current_buffers.data) + + def get_current_decay(self): + epoch = clamp(self.step.item() - self.update_after_step - 1, min_value=0.) + value = 1 - (1 + epoch / self.inv_gamma) ** - self.power + + if epoch <= 0: + return 0. + + return clamp(value, min_value=self.min_value, max_value=self.beta) + + def update(self): + step = self.step.item() + self.step += 1 + + if (step % self.update_every) != 0: + return + + if step <= self.update_after_step: + self.copy_params_from_model_to_ema() + return + + if not self.initted.item(): + self.copy_params_from_model_to_ema() + self.initted.data.copy_(torch.Tensor([True])) + + self.update_moving_average(self.ema_model, self.model) + + @torch.no_grad() + def update_moving_average(self, ma_model, current_model): + current_decay = self.get_current_decay() + + for (name, current_params), (_, ma_params) in zip(self.get_params_iter(current_model), + self.get_params_iter(ma_model)): + if name in self.ignore_names: + continue + + if any([name.startswith(prefix) for prefix in self.ignore_startswith_names]): + continue + + if name in self.param_or_buffer_names_no_ema: + ma_params.data.copy_(current_params.data) + continue + + ma_params.data.lerp_(current_params.data, 1. - current_decay) + + for (name, current_buffer), (_, ma_buffer) in zip(self.get_buffers_iter(current_model), + self.get_buffers_iter(ma_model)): + if name in self.ignore_names: + continue + + if any([name.startswith(prefix) for prefix in self.ignore_startswith_names]): + continue + + if name in self.param_or_buffer_names_no_ema: + ma_buffer.data.copy_(current_buffer.data) + continue + + ma_buffer.data.lerp_(current_buffer.data, 1. - current_decay) + + def __call__(self, *args, **kwargs): + return self.ema_model(*args, **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/encoder_decoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/encoder_decoder.py new file mode 100644 index 00000000..d35e8da2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/encoder_decoder.py @@ -0,0 +1,1086 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +from .loss import LPIPSWithDiscriminator + +# from ldm.util import instantiate_from_config +# from ldm.modules.attention import LinearAttention + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = (curr_res[0] // 2, curr_res[1] // 2) + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = (resolution[0] // 2**(self.num_resolutions-1), resolution[1] // 2**(self.num_resolutions-1)) + self.z_shape = (1,z_channels,curr_res[0],curr_res[1]) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = (curr_res[0] * 2, curr_res[1] * 2) + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + +class FirstStagePostProcessor(nn.Module): + + def __init__(self, ch_mult:list, in_channels, + pretrained_model:nn.Module=None, + reshape=False, + n_channels=None, + dropout=0., + pretrained_config=None): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) + self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, + stride=1,padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + + @torch.no_grad() + def encode_with_pretrained(self,x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self,x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model,self.downsampler): + z = submodel(z,temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z,'b c h w -> b (h w) c') + return z + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + +class AutoencoderKL(nn.Module): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.down_ratio = 2 ** (len(ddconfig['ch_mult']) - 1) + self.loss = LPIPSWithDiscriminator(**lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list(), use_ema=True): + sd = torch.load(path, map_location="cpu") + sd_keys = sd.keys() + if 'ema' in list(sd.keys()) and use_ema: + sd = sd['ema'] + new_sd = {} + for k in sd.keys(): + if k.startswith("ema_model."): + new_k = k[10:] # remove ema_model. + new_sd[new_k] = sd[k] + sd = new_sd + else: + if 'model' in sd_keys: + sd = sd["model"] + elif 'state_dict' in sd_keys: + sd = sd['state_dict'] + else: + sd = sd + # raise ValueError("") + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + msg = self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + print('==>Load AutoEncoder Info: ', msg) + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, inputs, optimizer_idx, global_step): + # inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, global_step, + last_layer=self.get_last_layer(), split="train") + # self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + # self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss, log_dict_ae + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, global_step, + last_layer=self.get_last_layer(), split="train") + + # self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + # self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss, log_dict_disc + + def validation_step(self, inputs, global_step): + # inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, global_step, + last_layer=self.get_last_layer(), split="val") + + # self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + # self.log_dict(log_dict_ae) + # self.log_dict(log_dict_disc) + return log_dict_ae, log_dict_disc + + def validate_img(self, inputs): + reconstructions, posterior = self(inputs) + return reconstructions + + # def configure_optimizers(self): + # lr = self.learning_rate + # opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + # list(self.decoder.parameters())+ + # list(self.quant_conv.parameters())+ + # list(self.post_quant_conv.parameters()), + # lr=lr, betas=(0.5, 0.9)) + # opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + # lr=lr, betas=(0.5, 0.9)) + # return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + ''' + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + ''' + +if __name__ == '__main__': + ddconfig = {'double_z': True, + 'z_channels': 4, + 'resolution': (240, 960), + 'in_channels': 3, + 'out_ch': 3, + 'ch': 128, + 'ch_mult': [ 1,2,4 ], # num_down = len(ch_mult)-1 + 'num_res_blocks': 2, + 'attn_resolutions': [ ], + 'dropout': 0.0} + lossconfig = {'disc_start': 50001, + 'kl_weight': 0.000001, + 'disc_weight': 0.5} + model = AutoencoderKL(ddconfig, lossconfig, embed_dim=4, + ckpt_path='/pretrain_weights/model-kl-f8.ckpt', ) + ''' + from torch.optim import AdamW + optimizer = AdamW(model.parameters(), lr=0.01) + lr_lambda = lambda iter: (1 - iter / 1000) ** 0.95 + lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda) + for s in range(1000): + lr_scheduler.step() + cur_lr = optimizer.param_groups[0]['lr'] + print(cur_lr) + ''' + x = torch.rand(1, 3, 240, 960) + with torch.no_grad(): + y = model(x) + pass diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/imagenet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/imagenet.py new file mode 100644 index 00000000..27ff9a95 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/imagenet.py @@ -0,0 +1,395 @@ +import os, yaml, pickle, shutil, tarfile, glob +import cv2 +import albumentations +import PIL +import numpy as np +import torchvision.transforms.functional as TF +# from omegaconf import OmegaConf +from functools import partial +from PIL import Image +from tqdm import tqdm +from torch.utils.data import Dataset, Subset + +import taming.data.utils as tdu +from custom_controlnet_aux.diffusion_edge.taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve +from custom_controlnet_aux.diffusion_edge.taming.data.imagenet import ImagePaths + +# from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light + + +def synset2idx(path_to_yaml="data/index_synset.yaml"): + with open(path_to_yaml) as f: + di2s = yaml.load(f) + return dict((v,k) for k,v in di2s.items()) + + +class ImageNetBase(Dataset): + def __init__(self, config=None): + self.config = config + # if not type(self.config)==dict: + # self.config = OmegaConf.to_container(self.config) + self.keep_orig_class_label = self.config.get("keep_orig_class_label", False) + self.process_images = True # if False we skip loading & processing images and self.data contains filepaths + self._prepare() + self._prepare_synset_to_human() + self._prepare_idx_to_synset() + self._prepare_human_to_integer_label() + self._load() + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + return self.data[i] + + def _prepare(self): + raise NotImplementedError() + + def _filter_relpaths(self, relpaths): + ignore = set([ + "n06596364_9591.JPEG", + ]) + relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore] + if "sub_indices" in self.config: + indices = str_to_indices(self.config["sub_indices"]) + synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings + self.synset2idx = synset2idx(path_to_yaml=self.idx2syn) + files = [] + for rpath in relpaths: + syn = rpath.split("/")[0] + if syn in synsets: + files.append(rpath) + return files + else: + return relpaths + + def _prepare_synset_to_human(self): + SIZE = 2655750 + URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1" + self.human_dict = os.path.join(self.root, "synset_human.txt") + if (not os.path.exists(self.human_dict) or + not os.path.getsize(self.human_dict)==SIZE): + download(URL, self.human_dict) + + def _prepare_idx_to_synset(self): + URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1" + self.idx2syn = os.path.join(self.root, "index_synset.yaml") + if (not os.path.exists(self.idx2syn)): + download(URL, self.idx2syn) + + def _prepare_human_to_integer_label(self): + URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1" + self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt") + if (not os.path.exists(self.human2integer)): + download(URL, self.human2integer) + with open(self.human2integer, "r") as f: + lines = f.read().splitlines() + assert len(lines) == 1000 + self.human2integer_dict = dict() + for line in lines: + value, key = line.split(":") + self.human2integer_dict[key] = int(value) + + def _load(self): + with open(self.txt_filelist, "r") as f: + self.relpaths = f.read().splitlines() + l1 = len(self.relpaths) + self.relpaths = self._filter_relpaths(self.relpaths) + print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths))) + + self.synsets = [p.split("/")[0] for p in self.relpaths] + self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths] + + unique_synsets = np.unique(self.synsets) + class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets)) + if not self.keep_orig_class_label: + self.class_labels = [class_dict[s] for s in self.synsets] + else: + self.class_labels = [self.synset2idx[s] for s in self.synsets] + + with open(self.human_dict, "r") as f: + human_dict = f.read().splitlines() + human_dict = dict(line.split(maxsplit=1) for line in human_dict) + + self.human_labels = [human_dict[s] for s in self.synsets] + + labels = { + "relpath": np.array(self.relpaths), + "synsets": np.array(self.synsets), + "class_label": np.array(self.class_labels), + "human_label": np.array(self.human_labels), + } + + if self.process_images: + # self.size = retrieve(self.config, "size", default=256) + self.size = self.config.get("size", default=256) + self.data = ImagePaths(self.abspaths, + labels=labels, + size=self.size, + random_crop=self.random_crop, + ) + else: + self.data = self.abspaths + + +class ImageNetTrain(ImageNetBase): + NAME = "ILSVRC2012_train" + URL = "http://www.image-net.org/challenges/LSVRC/2012/" + AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2" + FILES = [ + "ILSVRC2012_img_train.tar", + ] + SIZES = [ + 147897477120, + ] + + def __init__(self, process_images=True, data_root=None, **kwargs): + self.process_images = process_images + self.data_root = data_root + super().__init__(**kwargs) + + def _prepare(self): + if self.data_root: + self.root = os.path.join(self.data_root, self.NAME) + else: + cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) + + self.datadir = os.path.join(self.root, "data") + self.txt_filelist = os.path.join(self.root, "filelist.txt") + self.expected_length = 1281167 + # self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop", default=True) + self.random_crop = self.config.get("random_crop", default=True) + if not tdu.is_prepared(self.root): + # prep + print("Preparing dataset {} in {}".format(self.NAME, self.root)) + + datadir = self.datadir + if not os.path.exists(datadir): + path = os.path.join(self.root, self.FILES[0]) + if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: + import academictorrents as at + atpath = at.get(self.AT_HASH, datastore=self.root) + assert atpath == path + + print("Extracting {} to {}".format(path, datadir)) + os.makedirs(datadir, exist_ok=True) + with tarfile.open(path, "r:") as tar: + tar.extractall(path=datadir) + + print("Extracting sub-tars.") + subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar"))) + for subpath in tqdm(subpaths): + subdir = subpath[:-len(".tar")] + os.makedirs(subdir, exist_ok=True) + with tarfile.open(subpath, "r:") as tar: + tar.extractall(path=subdir) + + filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) + filelist = [os.path.relpath(p, start=datadir) for p in filelist] + filelist = sorted(filelist) + filelist = "\n".join(filelist)+"\n" + with open(self.txt_filelist, "w") as f: + f.write(filelist) + + tdu.mark_prepared(self.root) + + +class ImageNetValidation(ImageNetBase): + NAME = "ILSVRC2012_validation" + URL = "http://www.image-net.org/challenges/LSVRC/2012/" + AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5" + VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1" + FILES = [ + "ILSVRC2012_img_val.tar", + "validation_synset.txt", + ] + SIZES = [ + 6744924160, + 1950000, + ] + + def __init__(self, process_images=True, data_root=None, **kwargs): + self.data_root = data_root + self.process_images = process_images + super().__init__(**kwargs) + + def _prepare(self): + if self.data_root: + self.root = os.path.join(self.data_root, self.NAME) + else: + cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) + self.datadir = os.path.join(self.root, "data") + self.txt_filelist = os.path.join(self.root, "filelist.txt") + self.expected_length = 50000 + # self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop", default=False) + self.random_crop = self.config.get("random_crop", default=False) + if not tdu.is_prepared(self.root): + # prep + print("Preparing dataset {} in {}".format(self.NAME, self.root)) + + datadir = self.datadir + if not os.path.exists(datadir): + path = os.path.join(self.root, self.FILES[0]) + if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: + import academictorrents as at + atpath = at.get(self.AT_HASH, datastore=self.root) + assert atpath == path + + print("Extracting {} to {}".format(path, datadir)) + os.makedirs(datadir, exist_ok=True) + with tarfile.open(path, "r:") as tar: + tar.extractall(path=datadir) + + vspath = os.path.join(self.root, self.FILES[1]) + if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]: + download(self.VS_URL, vspath) + + with open(vspath, "r") as f: + synset_dict = f.read().splitlines() + synset_dict = dict(line.split() for line in synset_dict) + + print("Reorganizing into synset folders") + synsets = np.unique(list(synset_dict.values())) + for s in synsets: + os.makedirs(os.path.join(datadir, s), exist_ok=True) + for k, v in synset_dict.items(): + src = os.path.join(datadir, k) + dst = os.path.join(datadir, v) + shutil.move(src, dst) + + filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) + filelist = [os.path.relpath(p, start=datadir) for p in filelist] + filelist = sorted(filelist) + filelist = "\n".join(filelist)+"\n" + with open(self.txt_filelist, "w") as f: + f.write(filelist) + + tdu.mark_prepared(self.root) + + + +class ImageNetSR(Dataset): + def __init__(self, size=None, + degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1., + random_crop=True): + """ + Imagenet Superresolution Dataloader + Performs following ops in order: + 1. crops a crop of size s from image either as random or center crop + 2. resizes crop to size with cv2.area_interpolation + 3. degrades resized crop with degradation_fn + + :param size: resizing to size after cropping + :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light + :param downscale_f: Low Resolution Downsample factor + :param min_crop_f: determines crop size s, + where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f) + :param max_crop_f: "" + :param data_root: + :param random_crop: + """ + self.base = self.get_base() + assert size + assert (size / downscale_f).is_integer() + self.size = size + self.LR_size = int(size / downscale_f) + self.min_crop_f = min_crop_f + self.max_crop_f = max_crop_f + assert(max_crop_f <= 1.) + self.center_crop = not random_crop + + self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA) + + self.pil_interpolation = False # gets reset later if incase interp_op is from pillow + + if degradation == "bsrgan": + self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f) + + elif degradation == "bsrgan_light": + self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f) + + else: + interpolation_fn = { + "cv_nearest": cv2.INTER_NEAREST, + "cv_bilinear": cv2.INTER_LINEAR, + "cv_bicubic": cv2.INTER_CUBIC, + "cv_area": cv2.INTER_AREA, + "cv_lanczos": cv2.INTER_LANCZOS4, + "pil_nearest": PIL.Image.NEAREST, + "pil_bilinear": PIL.Image.BILINEAR, + "pil_bicubic": PIL.Image.BICUBIC, + "pil_box": PIL.Image.BOX, + "pil_hamming": PIL.Image.HAMMING, + "pil_lanczos": PIL.Image.LANCZOS, + }[degradation] + + self.pil_interpolation = degradation.startswith("pil_") + + if self.pil_interpolation: + self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn) + + else: + self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size, + interpolation=interpolation_fn) + + def __len__(self): + return len(self.base) + + def __getitem__(self, i): + example = self.base[i] + image = Image.open(example["file_path_"]) + + if not image.mode == "RGB": + image = image.convert("RGB") + + image = np.array(image).astype(np.uint8) + + min_side_len = min(image.shape[:2]) + crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None) + crop_side_len = int(crop_side_len) + + if self.center_crop: + self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len) + + else: + self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len) + + image = self.cropper(image=image)["image"] + image = self.image_rescaler(image=image)["image"] + + if self.pil_interpolation: + image_pil = PIL.Image.fromarray(image) + LR_image = self.degradation_process(image_pil) + LR_image = np.array(LR_image).astype(np.uint8) + + else: + LR_image = self.degradation_process(image=image)["image"] + + example["image"] = (image/127.5 - 1.0).astype(np.float32) + example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32) + + return example + + +class ImageNetSRTrain(ImageNetSR): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def get_base(self): + with open("data/imagenet_train_hr_indices.p", "rb") as f: + indices = pickle.load(f) + dset = ImageNetTrain(process_images=False,) + return Subset(dset, indices) + + +class ImageNetSRValidation(ImageNetSR): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def get_base(self): + with open("data/imagenet_val_hr_indices.p", "rb") as f: + indices = pickle.load(f) + dset = ImageNetValidation(process_images=False,) + return Subset(dset, indices) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/loss.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/loss.py new file mode 100644 index 00000000..07f00750 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/loss.py @@ -0,0 +1,113 @@ +import torch +import torch.nn as nn +import sys +# .path.append() +from custom_controlnet_aux.diffusion_edge.taming.modules.losses.vqperceptual import * + + +class LPIPSWithDiscriminator(nn.Module): + def __init__(self, *, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, + disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, + perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, + disc_loss="hinge"): + + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.kl_weight = kl_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPIPS().eval() + self.perceptual_weight = perceptual_weight + # output log variance + self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) + + self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm + ).apply(weights_init) + self.discriminator_iter_start = disc_start + self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward(self, inputs, reconstructions, posteriors, optimizer_idx, + global_step, last_layer=None, cond=None, split="train", + weights=None): + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + \ + F.mse_loss(inputs, reconstructions, reduction="none") + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) + rec_loss = rec_loss + self.perceptual_weight * p_loss + + nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar + weighted_nll_loss = nll_loss + if weights is not None: + weighted_nll_loss = weights*nll_loss + weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] + nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + kl_loss = posteriors.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) + g_loss = -torch.mean(logits_fake) + + if self.disc_factor > 0.0: + try: + d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + else: + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + + log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), + "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean() + } + return d_loss, log + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/mask_cond_unet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/mask_cond_unet.py new file mode 100644 index 00000000..2ac84c5d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/mask_cond_unet.py @@ -0,0 +1,1009 @@ +import fvcore.common.config +import torch +import torch.nn as nn +import math +import torch.nn.functional as F +from functools import partial +from einops import rearrange, reduce +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.efficientnet import efficientnet_b7, EfficientNet_B7_Weights +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.resnet import resnet101, ResNet101_Weights +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.swin_transformer import swin_b, Swin_B_Weights +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.vgg import vgg16, VGG16_Weights + +from custom_controlnet_aux.util import custom_torch_download +# from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.wcc import fft +### Compared to unet4: +# 1. add FFT-Conv on the mid feature. +######## Attention Layer ########## + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + # self.class_token_pos = nn.Parameter(torch.zeros(1, 1, num_pos_feats * 2)) + # self.class_token_pos + + def forward(self, x): + # x: b, h, w, d + num_feats = x.shape[3] + num_pos_feats = num_feats // 2 + # mask = tensor_list.mask + mask = torch.zeros(x.shape[0], x.shape[1], x.shape[2], device=x.device).to(torch.bool) + batch = mask.shape[0] + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-5 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + # pos = torch.cat((pos_y, pos_x), dim=3).flatten(1, 2) + pos = torch.cat((pos_y, pos_x), dim=3).contiguous() + ''' + pos_x: b ,h, w, d//2 + pos_y: b, h, w, d//2 + pos: b, h, w, d + ''' + return pos + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + def __init__(self, feature_size, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(feature_size[0], num_pos_feats) + self.col_embed = nn.Embedding(feature_size[1], num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, x): + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = torch.cat([ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) + return torch.cat([x, pos], dim=1) + + +class ChannelAttention(nn.Module): + def __init__(self, in_planes, ratio=8): + super(ChannelAttention, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.max_pool = nn.AdaptiveMaxPool2d(1) + + self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) + self.relu1 = nn.ReLU() + self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) + + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) + max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) + out = avg_out + max_out + return self.sigmoid(out) * x + +class SpatialAtt(nn.Module): + def __init__(self, in_dim): + super(SpatialAtt, self).__init__() + self.map = nn.Conv2d(in_dim, 1, 1) + self.q_conv = nn.Conv2d(1, 1, 1) + self.k_conv = nn.Conv2d(1, 1, 1) + self.activation = nn.Softsign() + + def forward(self, x): + b, _, h, w = x.shape + att = self.map(x) # b, 1, h, w + q = self.q_conv(att) # b, 1, h, w + q = rearrange(q, 'b c h w -> b (h w) c') + k = self.k_conv(att) + k = rearrange(k, 'b c h w -> b c (h w)') + att = rearrange(att, 'b c h w -> b (h w) c') + att = F.softmax(q @ k, dim=-1) @ att # b, hw, 1 + att = att.reshape(b, 1, h, w) + return self.activation(att) * x + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + # self.fc1 = nn.Linear(in_features, hidden_features) + self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1) + self.act = act_layer() + self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1) + # self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + + +class BasicAttetnionLayer(nn.Module): + def __init__(self, embed_dim=128, nhead=8, ffn_dim=512, window_size1=[4, 4], + window_size2=[1, 1], dropout=0.1): + super().__init__() + self.window_size1 = window_size1 + self.window_size2 = window_size2 + self.avgpool_q = nn.AvgPool2d(kernel_size=window_size1) + self.avgpool_k = nn.AvgPool2d(kernel_size=window_size2) + self.softmax = nn.Softmax(dim=-1) + self.nhead = nhead + + self.q_lin = nn.Linear(embed_dim, embed_dim) + self.k_lin = nn.Linear(embed_dim, embed_dim) + self.v_lin = nn.Linear(embed_dim, embed_dim) + + self.mlp = Mlp(in_features=embed_dim, hidden_features=ffn_dim, drop=dropout) + self.pos_enc = PositionEmbeddingSine(embed_dim) + self.concat_conv = nn.Conv2d(2 * embed_dim, embed_dim, 1) + self.gn = nn.GroupNorm(8, embed_dim) + + self.out_conv = nn.Conv2d(embed_dim, embed_dim, 1) + self.init_weights() + + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1.) + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.weight, 1.) + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0.) + + def forward(self, x1, x2): # x1 for q (conditional input), x2 for k,v + B, C1, H1, W1 = x1.shape + _, C2, H2, W2 = x2.shape + # x1 = x1.permute(0, 2, 3, 1).contiguous() # B, H1, W1, C1 + shortcut = x2 + self.concat_conv(torch.cat( + [F.interpolate(x1, size=(H2, W2), mode='bilinear', align_corners=True), + x2], dim=1)) + shortcut = self.gn(shortcut) + pad_l = pad_t = 0 + pad_r = (self.window_size1[1] - W1 % self.window_size1[1]) % self.window_size1[1] + pad_b = (self.window_size1[0] - H1 % self.window_size1[0]) % self.window_size1[0] + x1 = F.pad(x1, (pad_l, pad_r, pad_t, pad_b, 0, 0)) + _, _, H1p, W1p = x1.shape + # x2 = x2.permute(0, 2, 3, 1).contiguous() # B, H2, W2, C2 + pad_l = pad_t = 0 + pad_r = (self.window_size2[1] - W2 % self.window_size2[1]) % self.window_size2[1] + pad_b = (self.window_size2[0] - H2 % self.window_size2[0]) % self.window_size2[0] + x2 = F.pad(x2, (pad_l, pad_r, pad_t, pad_b, 0, 0)) + _, _, H2p, W2p = x2.shape + # x1g = x1 #B, C1, H1p, W1p + # x2g = x2 #B, C2, H2p, W2p + x1_s = self.avgpool_q(x1) + qg = self.avgpool_q(x1).permute(0, 2, 3, 1).contiguous() + qg = qg + self.pos_enc(qg) + qg= qg.view(B, -1, C2) + kg = self.avgpool_k(x2).permute(0, 2, 3, 1).contiguous() + kg = kg + self.pos_enc(kg) + kg = kg.view(B, -1, C1) + num_window_q = qg.shape[1] + num_window_k = kg.shape[1] + qg = self.q_lin(qg).reshape(B, num_window_q, self.nhead, C1 // self.nhead).permute(0, 2, 1, + 3).contiguous() + kg2 = self.k_lin(kg).reshape(B, num_window_k, self.nhead, C1 // self.nhead).permute(0, 2, 1, + 3).contiguous() + vg = self.v_lin(kg).reshape(B, num_window_k, self.nhead, C1 // self.nhead).permute(0, 2, 1, + 3).contiguous() + kg = kg2 + attn = (qg @ kg.transpose(-2, -1)) + attn = self.softmax(attn) + qg = (attn @ vg).transpose(1, 2).reshape(B, num_window_q, C1) + qg = qg.transpose(1, 2).reshape(B, C1, H1p // self.window_size1[0], W1p // self.window_size1[1]) + # qg = F.interpolate(qg, size=(H1p, W1p), mode='bilinear', align_corners=False) + x1_s = x1_s + qg + x1_s = x1_s + self.mlp(x1_s) + x1_s = F.interpolate(x1_s, size=(H2, W2), mode='bilinear', align_corners=True) + x1_s = shortcut + self.out_conv(x1_s) + # x1_s = self.out_norm(x1_s) + return x1_s + +class RelationNet(nn.Module): + def __init__(self, in_channel1=128, in_channel2=128, nhead=8, layers=3, embed_dim=128, ffn_dim=512, + window_size1= [4, 4], window_size2=[1, 1]): + # self.attention = BasicAttetnionLayer(embed_dim=embed_dim, nhead=nhead, ffn_dim=ffn_dim, + # window_size1=window_size1, window_size2=window_size2, dropout=0.1) + super().__init__() + self.layers = layers + self.input_conv1 = nn.Sequential( + nn.Conv2d(in_channel1, embed_dim, 1), + nn.BatchNorm2d(embed_dim, momentum=0.03, eps=0.001), + ) + self.input_conv2 = nn.Sequential( + nn.Conv2d(in_channel2, embed_dim, 1), + nn.BatchNorm2d(embed_dim, momentum=0.03, eps=0.001), + ) + # self.input_conv1 = ConvModule(in_channel1, + # embed_dim, + # 1, + # norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), + # act_cfg=None) + # self.input_conv2 = ConvModule(in_channel2, + # embed_dim, + # 1, + # norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), + # act_cfg=None) + # self.input_conv2 = nn.Linear(in_channel2, embed_dim) + self.attentions = nn.ModuleList() + for i in range(layers): + self.attentions.append( + BasicAttetnionLayer(embed_dim=embed_dim, nhead=nhead, ffn_dim=ffn_dim, + window_size1=window_size1, window_size2=window_size2, dropout=0.1) + ) + + def forward(self, cond, feat): + # cluster = cluster.unsqueeze(0).repeat(feature.shape[0], 1, 1, 1) + cond = self.input_conv1(cond) + feat = self.input_conv2(feat) + for att in self.attentions: + feat = att(cond, feat) + return feat + + + +################# U-Net model defenition #################### + +def exists(x): + return x is not None + +def default(val, d): + if exists(val): + return val + return d() if callable(d) else d + +def identity(t, *args, **kwargs): + return t + +def cycle(dl): + while True: + for data in dl: + yield data + +def has_int_squareroot(num): + return (math.sqrt(num) ** 2) == num + +def num_to_groups(num, divisor): + groups = num // divisor + remainder = num % divisor + arr = [divisor] * groups + if remainder > 0: + arr.append(remainder) + return arr + +def convert_image_to_fn(img_type, image): + if image.mode != img_type: + return image.convert(img_type) + return image + +# normalization functions + +def normalize_to_neg_one_to_one(img): + return img * 2 - 1 + +def unnormalize_to_zero_to_one(t): + return (t + 1) * 0.5 + +# small helper modules + +class Residual(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + + def forward(self, x, *args, **kwargs): + return self.fn(x, *args, **kwargs) + x + +def Upsample(dim, dim_out = None): + return nn.Sequential( + nn.Upsample(scale_factor = 2, mode = 'nearest'), + nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1) + ) + +def Downsample(dim, dim_out = None): + return nn.Conv2d(dim, default(dim_out, dim), 4, 2, 1) + +class WeightStandardizedConv2d(nn.Conv2d): + """ + https://arxiv.org/abs/1903.10520 + weight standardization purportedly works synergistically with group normalization + """ + def forward(self, x): + eps = 1e-5 if x.dtype == torch.float32 else 1e-3 + + weight = self.weight + mean = reduce(weight, 'o ... -> o 1 1 1', 'mean') + var = reduce(weight, 'o ... -> o 1 1 1', partial(torch.var, unbiased = False)) + normalized_weight = (weight - mean) * (var + eps).rsqrt() + + return F.conv2d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + +class LayerNorm(nn.Module): + def __init__(self, dim): + super().__init__() + self.g = nn.Parameter(torch.ones(1, dim, 1, 1)) + + def forward(self, x): + eps = 1e-5 if x.dtype == torch.float32 else 1e-3 + var = torch.var(x, dim = 1, unbiased = False, keepdim = True) + mean = torch.mean(x, dim = 1, keepdim = True) + return (x - mean) * (var + eps).rsqrt() * self.g + +class PreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.fn = fn + self.norm = LayerNorm(dim) + + def forward(self, x): + x = self.norm(x) + return self.fn(x) + +# sinusoidal positional embeds + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + +class GaussianFourierProjection(nn.Module): + """Gaussian Fourier embeddings for noise levels.""" + + def __init__(self, embedding_size=256, scale=1.0): + super().__init__() + self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False) + + def forward(self, x): + x_proj = x[:, None] * self.W[None, :] * 2 * math.pi + return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1) + +class RandomOrLearnedSinusoidalPosEmb(nn.Module): + """ following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """ + """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """ + + def __init__(self, dim, is_random = False): + super().__init__() + assert (dim % 2) == 0 + half_dim = dim // 2 + self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random) + + def forward(self, x): + x = rearrange(x, 'b -> b 1') + freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi + fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1) + fouriered = torch.cat((x, fouriered), dim = -1) + return fouriered + +# building block modules + +class Block(nn.Module): + def __init__(self, dim, dim_out, groups = 8): + super().__init__() + self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1) + self.norm = nn.GroupNorm(groups, dim_out) + self.act = nn.SiLU() + + def forward(self, x, scale_shift = None): + x = self.proj(x) + x = self.norm(x) + + if exists(scale_shift): + scale, shift = scale_shift + x = x * (scale + 1) + shift + + x = self.act(x) + return x + +class BlockFFT(nn.Module): + def __init__(self, dim, h, w, groups = 8): + super().__init__() + # self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1) + self.complex_weight = nn.Parameter(torch.randn(dim, h, w//2+1, 2, dtype=torch.float32) * 0.02) + # self.complex_weight = nn.Parameter(torch.normal(mean=0, std=0.01, size=(dim, h, w // 2 + 1, 2), dtype=torch.float32)) + # self.norm = nn.GroupNorm(groups, dim) + # self.act = nn.SiLU() + + def forward(self, x, scale_shift = None): + B, C, H, W = x.shape + x = torch.fft.rfft2(x, dim=(2, 3), norm='ortho') + x = x * torch.view_as_complex(self.complex_weight) + x = torch.fft.irfft2(x, s=(H, W), dim=(2, 3), norm='ortho') + x = x.reshape(B, C, H, W) + + return x + +class ResnetBlock(nn.Module): + def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8): + super().__init__() + self.mlp = nn.Sequential( + nn.SiLU(), + nn.Linear(time_emb_dim, dim_out * 2) + ) if exists(time_emb_dim) else None + + self.block1 = Block(dim, dim_out, groups = groups) + self.block2 = Block(dim_out, dim_out, groups = groups) + self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + + def forward(self, x, time_emb = None): + + scale_shift = None + if exists(self.mlp) and exists(time_emb): + time_emb = self.mlp(time_emb) + time_emb = rearrange(time_emb, 'b c -> b c 1 1') + scale_shift = time_emb.chunk(2, dim = 1) + + h = self.block1(x, scale_shift = scale_shift) + + h = self.block2(h) + + return h + self.res_conv(x) + +class ResnetBlockFFT(nn.Module): + def __init__(self, dim, dim_out, h, w, *, time_emb_dim = None, groups = 8): + super().__init__() + self.mlp = nn.Sequential( + nn.SiLU(), + nn.Linear(time_emb_dim, dim_out * 2) + ) if exists(time_emb_dim) else None + + self.block1 = Block(dim, dim_out, groups = groups) + self.block2 = BlockFFT(dim_out, h, w, groups = groups) + self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + + def forward(self, x, time_emb = None): + + scale_shift = None + if exists(self.mlp) and exists(time_emb): + time_emb = self.mlp(time_emb) + time_emb = rearrange(time_emb, 'b c -> b c 1 1') + scale_shift = time_emb.chunk(2, dim = 1) + + h = self.block1(x, scale_shift = scale_shift) + + h = self.block2(h) + + return h + self.res_conv(x) + +class ResnetDownsampleBlock(nn.Module): + def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8): + super().__init__() + self.mlp = nn.Sequential( + nn.SiLU(), + nn.Linear(time_emb_dim, dim_out * 2) + ) if exists(time_emb_dim) else None + + self.block1 = Block(dim, dim_out, groups = groups) + self.block2 = nn.Sequential( + WeightStandardizedConv2d(dim_out, dim_out, 3, stride=2, padding=1), + nn.GroupNorm(groups, dim_out), + nn.SiLU() + ) + self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + + def forward(self, x, time_emb = None): + + scale_shift = None + if exists(self.mlp) and exists(time_emb): + time_emb = self.mlp(time_emb) + time_emb = rearrange(time_emb, 'b c -> b c 1 1') + scale_shift = time_emb.chunk(2, dim = 1) + + h = self.block1(x, scale_shift = scale_shift) + + h = self.block2(h) + + return h + self.res_conv( + F.interpolate(x, size=h.shape[-2:], mode="bilinear") + ) + +class LinearAttention(nn.Module): + def __init__(self, dim, heads = 4, dim_head = 32): + super().__init__() + self.scale = dim_head ** -0.5 + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + + self.to_out = nn.Sequential( + nn.Conv2d(hidden_dim, dim, 1), + LayerNorm(dim) + ) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x).chunk(3, dim = 1) + q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv) + + q = q.softmax(dim = -2) + k = k.softmax(dim = -1) + + q = q * self.scale + v = v / (h * w) + + context = torch.einsum('b h d n, b h e n -> b h d e', k, v) + + out = torch.einsum('b h d e, b h d n -> b h e n', context, q) + out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w) + return self.to_out(out) + +class Attention(nn.Module): + def __init__(self, dim, heads = 4, dim_head = 32): + super().__init__() + self.scale = dim_head ** -0.5 + self.heads = heads + hidden_dim = dim_head * heads + + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x).chunk(3, dim=1) + q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h=self.heads), qkv) + + q = q * self.scale + + sim = torch.einsum('b h d i, b h d j -> b h i j', q, k) + attn = sim.softmax(dim=-1) + out = torch.einsum('b h i j, b h d j -> b h i d', attn, v) + + out = rearrange(out, 'b h (x y) d -> b (h d) x y', x=h, y=w) + return self.to_out(out) + + +class ConditionEncoder(nn.Module): + def __init__(self, + down_dim_mults=(2, 4, 8), + dim=64, + in_dim=1, + out_dim=64): + super(ConditionEncoder, self).__init__() + self.init_conv = nn.Sequential( + nn.Conv2d(in_dim, dim, kernel_size=3, stride=1, padding=1), + nn.GroupNorm(num_groups=min(dim // 4, 8), num_channels=dim), + ) + self.num_resolutions = len(down_dim_mults) + self.downs = nn.ModuleList() + in_mults = (1,) + tuple(down_dim_mults[:-1]) + in_dims = [mult*dim for mult in in_mults] + out_dims = [mult*dim for mult in down_dim_mults] + for i_level in range(self.num_resolutions): + block_in = in_dims[i_level] + block_out = out_dims[i_level] + self.downs.append(ResnetDownsampleBlock(dim=block_in, + dim_out=block_out)) + if self.num_resolutions < 1: + self.out_conv = nn.Conv2d(dim, out_dim, 1) + else: + self.out_conv = nn.Conv2d(out_dims[-1], out_dim, 1) + + def forward(self, x): + x = self.init_conv(x) + for down_layer in self.downs: + x = down_layer(x) + x = self.out_conv(x) + return x + + +class Unet(nn.Module): + def __init__( + self, + dim, + init_dim=None, + out_dim=None, + dim_mults=(1, 2, 4, 8), + cond_in_dim=1, + cond_dim=64, + cond_dim_mults=(2, 4, 8), + channels=1, + out_mul=1, + self_condition=False, + resnet_block_groups=8, + learned_variance=False, + learned_sinusoidal_cond=False, + random_fourier_features=False, + learned_sinusoidal_dim=16, + window_sizes1=[[16, 16], [8, 8], [4, 4], [2, 2]], + window_sizes2=[[16, 16], [8, 8], [4, 4], [2, 2]], + fourier_scale=16, + ckpt_path=None, + ignore_keys=[], + cfg={}, + **kwargs + ): + super().__init__() + + # determine dimensions + self.cond_pe = cfg.get('cond_pe', False) + num_pos_feats = cfg.num_pos_feats if self.cond_pe else 0 + self.channels = channels + self.self_condition = self_condition + input_channels = channels * (2 if self_condition else 1) + + init_dim = default(init_dim, dim) + # self.init_conv_mask = nn.Sequential( + # nn.Conv2d(cond_in_dim, cond_dim, 3, padding=1), + # nn.GroupNorm(num_groups=min(init_dim // 4, 8), num_channels=init_dim), + # nn.SiLU(), + # nn.Conv2d(cond_dim, cond_dim, 3, padding=1), + # ) + # self.init_conv_mask = ConditionEncoder(down_dim_mults=cond_dim_mults, dim=cond_dim, + # in_dim=cond_in_dim, out_dim=init_dim) + + if cfg.cond_net == 'effnet': + f_condnet = 48 + if cfg.get('without_pretrain', False): + self.init_conv_mask = efficientnet_b7() + else: + self.init_conv_mask = efficientnet_b7(weights=EfficientNet_B7_Weights) + elif cfg.cond_net == 'resnet': + f_condnet = 256 + if cfg.get('without_pretrain', False): + self.init_conv_mask = resnet101() + else: + self.init_conv_mask = resnet101(weights=ResNet101_Weights) + elif cfg.cond_net == 'swin': + f_condnet = 128 + if cfg.get('without_pretrain', False): + self.init_conv_mask = swin_b() + else: + swin_b_model = swin_b(pretrained=False) + swin_b_model.load_state_dict(torch.load(custom_torch_download(filename="swin_b-68c6b09e.pth")), strict=False) + self.init_conv_mask = swin_b_model + elif cfg.cond_net == 'vgg': + f_condnet = 128 + if cfg.get('without_pretrain', False): + self.init_conv_mask = vgg16() + else: + self.init_conv_mask = vgg16(weights=VGG16_Weights) + else: + raise NotImplementedError + self.init_conv = nn.Sequential( + nn.Conv2d(input_channels + f_condnet, init_dim, 7, padding=3), + nn.GroupNorm(num_groups=min(init_dim // 4, 8), num_channels=init_dim), + ) + + if self.cond_pe: + self.cond_pos_embedding = nn.Sequential( + PositionEmbeddingLearned( + feature_size=cfg.cond_feature_size, num_pos_feats=cfg.num_pos_feats//2), + nn.Conv2d(num_pos_feats + init_dim, init_dim, 1) + ) + # self.init_conv_mask = nn.Conv2d(1, init_dim, 7, padding=3) + + dims = [init_dim, *map(lambda m: dim * m, dim_mults)] + dims_rev = dims[::-1] + in_out = list(zip(dims[:-1], dims[1:])) + self.projects = nn.ModuleList() + print(cfg.cond_net) + if cfg.cond_net == 'effnet': + self.projects.append(nn.Conv2d(48, dims[0], 1)) + self.projects.append(nn.Conv2d(80, dims[1], 1)) + self.projects.append(nn.Conv2d(224, dims[2], 1)) + self.projects.append(nn.Conv2d(640, dims[3], 1)) + print(len(self.projects)) + elif cfg.cond_net == 'vgg': + self.projects.append(nn.Conv2d(128, dims[0], 1)) + self.projects.append(nn.Conv2d(256, dims[1], 1)) + self.projects.append(nn.Conv2d(512, dims[2], 1)) + self.projects.append(nn.Conv2d(512, dims[3], 1)) + else: + self.projects.append(nn.Conv2d(f_condnet, dims[0], 1)) + self.projects.append(nn.Conv2d(f_condnet*2, dims[1], 1)) + self.projects.append(nn.Conv2d(f_condnet*4, dims[2], 1)) + self.projects.append(nn.Conv2d(f_condnet*8, dims[3], 1)) + #print(len(self.projects)) + + block_klass = partial(ResnetBlock, groups = resnet_block_groups) + + # time embeddings + + time_dim = dim * 4 + + self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features + + if self.random_or_learned_sinusoidal_cond: + sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features) + fourier_dim = learned_sinusoidal_dim + 1 + else: + sinu_pos_emb = GaussianFourierProjection(dim//2, scale=fourier_scale) + fourier_dim = dim + + self.time_mlp = nn.Sequential( + sinu_pos_emb, + nn.Linear(fourier_dim, time_dim), + nn.GELU(), + nn.Linear(time_dim, time_dim) + ) + + # layers + + self.downs = nn.ModuleList([]) + self.downs_mask = nn.ModuleList([]) + self.ups = nn.ModuleList([]) + self.relation_layers_down = nn.ModuleList([]) + self.relation_layers_up = nn.ModuleList([]) + self.ups2 = nn.ModuleList([]) + self.relation_layers_up2 = nn.ModuleList([]) + num_resolutions = len(in_out) + input_size = cfg.get('input_size', [80, 80]) + feature_size_list = [[int(input_size[0]/2**k), int(input_size[1]/2**k)] for k in range(len(dim_mults))] + + + for ind, (dim_in, dim_out) in enumerate(in_out): + is_last = ind >= (num_resolutions - 1) + + self.downs.append(nn.ModuleList([ + block_klass(dim_in, dim_in, time_emb_dim = time_dim), + block_klass(dim_in, dim_in, time_emb_dim = time_dim), + Residual(PreNorm(dim_in, LinearAttention(dim_in))), + Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1) + ])) + # self.downs_mask.append(nn.ModuleList([ + # block_klass(dim_in, dim_in, time_emb_dim=time_dim), + # # block_klass(dim_in, dim_in, time_emb_dim=time_dim), + # Residual(PreNorm(dim_in, LinearAttention(dim_in))), + # Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding=1) + # ])) + self.relation_layers_down.append(RelationNet(in_channel1=dims[ind], in_channel2=dims[ind], nhead=8, + layers=1, embed_dim=dims[ind], ffn_dim=dims[ind]*2, + window_size1=window_sizes1[ind], window_size2=window_sizes2[ind]) + ) + + mid_dim = dims[-1] + self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim) + self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim))) + self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim) + self.decouple1 = nn.Sequential( + nn.GroupNorm(num_groups=min(mid_dim // 4, 8), num_channels=mid_dim), + nn.Conv2d(mid_dim, mid_dim, 3, padding=1), + BlockFFT(mid_dim, input_size[0]//8, input_size[1]//8), + ) + self.decouple2 = nn.Sequential( + nn.GroupNorm(num_groups=min(mid_dim // 4, 8), num_channels=mid_dim), + nn.Conv2d(mid_dim, mid_dim, 3, padding=1), + BlockFFT(mid_dim, input_size[0]//8, input_size[1]//8), + ) + + for ind, (dim_in, dim_out) in enumerate(reversed(in_out)): + is_last = ind == (len(in_out) - 1) + + self.ups.append(nn.ModuleList([ + block_klass(dim_out + dim_in, dim_out, time_emb_dim=time_dim), + block_klass(dim_out + dim_in, dim_out, time_emb_dim=time_dim), + Residual(PreNorm(dim_out, LinearAttention(dim_out))), + Upsample(dim_out, dim_in) if not is_last else nn.Conv2d(dim_out, dim_in, 3, padding = 1) + ])) + self.relation_layers_up.append(RelationNet(in_channel1=dims_rev[ind+1], in_channel2=dims_rev[ind], + nhead=8, layers=1, embed_dim=dims_rev[ind], + ffn_dim=dims_rev[ind] * 2, + window_size1=window_sizes1[::-1][ind], + window_size2=window_sizes2[::-1][ind]) + ) + self.ups2.append(nn.ModuleList([ + block_klass(dim_out + dim_in, dim_out, time_emb_dim=time_dim), + block_klass(dim_out + dim_in, dim_out, time_emb_dim=time_dim), + Residual(PreNorm(dim_out, LinearAttention(dim_out))), + Upsample(dim_out, dim_in) if not is_last else nn.Conv2d(dim_out, dim_in, 3, padding=1) + ])) + self.relation_layers_up2.append(RelationNet(in_channel1=dims_rev[ind + 1], in_channel2=dims_rev[ind], + nhead=8, layers=1, embed_dim=dims_rev[ind], + ffn_dim=dims_rev[ind] * 2, + window_size1=window_sizes1[::-1][ind], + window_size2=window_sizes2[::-1][ind]) + ) + + default_out_dim = channels * (1 if not learned_variance else 2) + self.out_dim = default(out_dim, default_out_dim) + + self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim) + self.final_conv = nn.Conv2d(dim, self.out_dim * out_mul, 1) + + self.final_res_block2 = block_klass(dim * 2, dim, time_emb_dim = time_dim) + self.final_conv2 = nn.Conv2d(dim, self.out_dim, 1) + + # self.init_weights() + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + fix_bb = cfg.get('fix_bb', True) + if fix_bb: + for n, p in self.init_conv_mask.named_parameters(): + p.requires_grad = False + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["model"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + msg = self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + print('==>Load Unet Info: ', msg) + + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1.) + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.weight, 1.) + nn.init.constant_(m.bias, 0.) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0.) + + def forward(self, x, time, mask, x_self_cond = None, **kwargs): + if self.self_condition: + x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x)) + x = torch.cat((x_self_cond, x), dim = 1) + sigma = time.reshape(-1, 1, 1, 1) + eps = 1e-4 + c_skip1 = 1 - sigma + c_skip2 = torch.sqrt(sigma) + # c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2).sqrt() + c_out1 = sigma / torch.sqrt(sigma ** 2 + 1) + c_out2 = torch.sqrt(1 - sigma) / torch.sqrt(sigma ** 2 + 1) + c_in = 1 + + x_clone = x.clone() + x = c_in * x + # mask = torch.cat([], dim=1) + hm = self.init_conv_mask(mask) + # if self.cond_pe: + # m = self.cond_pos_embedding(m) + x = self.init_conv(torch.cat([x, F.interpolate(hm[0], size=x.shape[-2:], mode="bilinear")], dim=1)) + r = x.clone() + + t = self.time_mlp(torch.log(time)/4) + + h = [] + h2 = [] + for i, layer in enumerate(self.projects): + # print(hm[i].shape) + hm[i] = layer(hm[i]) + hm2 = [] + for i in range(len(hm)): + hm2.append(hm[i].clone()) + # hm = [] + # hm2 = [] + for i, ((block1, block2, attn, downsample), relation_layer) \ + in enumerate(zip(self.downs, self.relation_layers_down)): + x = block1(x, t) + h.append(x) + h2.append(x.clone()) + # m = m_block(m, t) + # hm.append(m) + # hm2.append(m.clone()) + + x = relation_layer(hm[i], x) + + x = block2(x, t) + x = attn(x) + h.append(x) + h2.append(x.clone()) + + x = downsample(x) + # m = m_downsample(m) + + + # x = x + F.interpolate(hm[-1], size=x.shape[2:], mode="bilinear", align_corners=True) + x = self.mid_block1(x, t) + x = self.mid_attn(x) + x = self.mid_block2(x, t) + x1 = x + self.decouple1(x) + x2 = x + self.decouple2(x) + + x = x1 + for (block1, block2, attn, upsample), relation_layer in zip(self.ups, self.relation_layers_up): + x = torch.cat((x, h.pop()), dim = 1) + x = block1(x, t) + x = relation_layer(hm.pop(), x) + x = torch.cat((x, h.pop()), dim = 1) + x = block2(x, t) + x = attn(x) + x = upsample(x) + + x1 = torch.cat((x, r), dim=1) + x1 = self.final_res_block(x1, t) + x1 = self.final_conv(x1) + + x = x2 + for (block1, block2, attn, upsample), relation_layer in zip(self.ups2, self.relation_layers_up2): + x = torch.cat((x, h2.pop()), dim = 1) + x = block1(x, t) + x = relation_layer(hm2.pop(), x) + x = torch.cat((x, h2.pop()), dim = 1) + x = block2(x, t) + x = attn(x) + x = upsample(x) + + x2 = torch.cat((x, r), dim=1) + x2 = self.final_res_block2(x2, t) + x2 = self.final_conv2(x2) + # sigma = time.reshape(x1.shape[0], *((1,) * (len(x1.shape) - 1))) + # scale_C = torch.exp(sigma) + x1 = c_skip1 * x_clone + c_out1 * x1 + x2 = c_skip2 * x_clone + c_out2 * x2 + return x1, x2 + + +if __name__ == "__main__": + # resnet = resnet101(weights=ResNet101_Weights) + # effnet = efficientnet_b7(weights=EfficientNet_B7_Weights) + # effnet = efficientnet_b7(weights=None) + # x = torch.rand(1, 3, 320, 320) + # y = effnet(x) + model = Unet(dim=128, dim_mults=(1, 2, 4, 4), + cond_dim=128, + cond_dim_mults=(2, 4, ), + channels=1, + window_sizes1=[[8, 8], [4, 4], [2, 2], [1, 1]], + window_sizes2=[[8, 8], [4, 4], [2, 2], [1, 1]], + cfg=fvcore.common.config.CfgNode({'cond_pe': False, 'input_size': [80, 80], + 'cond_feature_size': (32, 128), 'cond_net': 'vgg', + 'num_pos_feats': 96}) + ) + x = torch.rand(1, 1, 80, 80) + mask = torch.rand(1, 3, 320, 320) + time = torch.tensor([0.5124]) + with torch.no_grad(): + y = model(x, time, mask) + pass \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/quantization.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/quantization.py new file mode 100644 index 00000000..1db3d379 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/quantization.py @@ -0,0 +1,103 @@ +import torch +from torch import nn as nn +from torch.nn import Parameter + + +def weight_quantization(b): + def uniform_quant(x, b): + xdiv = x.mul((2 ** b - 1)) + xhard = xdiv.round().div(2 ** b - 1) + return xhard + + class _pq(torch.autograd.Function): + @staticmethod + def forward(ctx, input, alpha): + input.div_(alpha) # weights are first divided by alpha + input_c = input.clamp(min=-1, max=1) # then clipped to [-1,1] + sign = input_c.sign() + input_abs = input_c.abs() + input_q = uniform_quant(input_abs, b).mul(sign) + ctx.save_for_backward(input, input_q) + input_q = input_q.mul(alpha) # rescale to the original range + return input_q + + @staticmethod + def backward(ctx, grad_output): + grad_input = grad_output.clone() # grad for weights will not be clipped + input, input_q = ctx.saved_tensors + i = (input.abs() > 1.).float() + sign = input.sign() + grad_alpha = (grad_output * (sign * i + (input_q - input) * (1 - i))).sum() + return grad_input, grad_alpha + + return _pq().apply + + +class weight_quantize_fn(nn.Module): + def __init__(self, bit_w): + super(weight_quantize_fn, self).__init__() + assert bit_w > 0 + + self.bit_w = bit_w - 1 + self.weight_q = weight_quantization(b=self.bit_w) + self.register_parameter('w_alpha', Parameter(torch.tensor(3.0), requires_grad=True)) + + def forward(self, weight): + mean = weight.data.mean() + std = weight.data.std() + weight = weight.add(-mean).div(std) # weights normalization + weight_q = self.weight_q(weight, self.w_alpha) + return weight_q + + def change_bit(self, bit_w): + self.bit_w = bit_w - 1 + self.weight_q = weight_quantization(b=self.bit_w) + +def act_quantization(b, signed=False): + def uniform_quant(x, b=3): + xdiv = x.mul(2 ** b - 1) + xhard = xdiv.round().div(2 ** b - 1) + return xhard + + class _uq(torch.autograd.Function): + @staticmethod + def forward(ctx, input, alpha): + input = input.div(alpha) + input_c = input.clamp(min=-1, max=1) if signed else input.clamp(max=1) + input_q = uniform_quant(input_c, b) + ctx.save_for_backward(input, input_q) + input_q = input_q.mul(alpha) + return input_q + + @staticmethod + def backward(ctx, grad_output): + grad_input = grad_output.clone() + input, input_q = ctx.saved_tensors + i = (input.abs() > 1.).float() + sign = input.sign() + grad_alpha = (grad_output * (sign * i + (input_q - input) * (1 - i))).sum() + grad_input = grad_input * (1 - i) + return grad_input, grad_alpha + + return _uq().apply + +class act_quantize_fn(nn.Module): + def __init__(self, bit_a, signed=False): + super(act_quantize_fn, self).__init__() + self.bit_a = bit_a + self.signed = signed + if signed: + self.bit_a -= 1 + assert bit_a > 0 + + self.act_q = act_quantization(b=self.bit_a, signed=signed) + self.register_parameter('a_alpha', Parameter(torch.tensor(8.0), requires_grad=True)) + + def forward(self, x): + return self.act_q(x, self.a_alpha) + + def change_bit(self, bit_a): + self.bit_a = bit_a + if self.signed: + self.bit_a -= 1 + self.act_q = act_quantization(b=self.bit_a, signed=self.signed) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/resnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/resnet.py new file mode 100644 index 00000000..256a73a0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/resnet.py @@ -0,0 +1,963 @@ +from functools import partial +from typing import Type, Any, Callable, Union, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from torchvision.transforms._presets import ImageClassification +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + + +__all__ = [ + "ResNet", + "ResNet18_Weights", + "ResNet34_Weights", + "ResNet50_Weights", + "ResNet101_Weights", + "ResNet152_Weights", + "ResNeXt50_32X4D_Weights", + "ResNeXt101_32X8D_Weights", + "ResNeXt101_64X4D_Weights", + "Wide_ResNet50_2_Weights", + "Wide_ResNet101_2_Weights", + "resnet18", + "resnet34", + "resnet50", + "resnet101", + "resnet152", + "resnext50_32x4d", + "resnext101_32x8d", + "resnext101_64x4d", + "wide_resnet50_2", + "wide_resnet101_2", +] + + +def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion: int = 1 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion: int = 4 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + num_classes: int = 1000, + zero_init_residual: bool = False, + groups: int = 1, + width_per_group: int = 64, + replace_stride_with_dilation: Optional[List[bool]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + _log_api_usage_once(self) + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + f"or a 3-element tuple, got {replace_stride_with_dilation}" + ) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) + # self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + # self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck) and m.bn3.weight is not None: + nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] + elif isinstance(m, BasicBlock) and m.bn2.weight is not None: + nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] + + def _make_layer( + self, + block: Type[Union[BasicBlock, Bottleneck]], + planes: int, + blocks: int, + stride: int = 1, + dilate: bool = False, + ) -> nn.Sequential: + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def _forward_impl(self, x: Tensor) -> Tensor: + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + feats = [] + x = self.layer1(x) + feats.append(x) + x = self.layer2(x) + feats.append(x) + x = self.layer3(x) + feats.append(x) + x = self.layer4(x) + feats.append(x) + + # x = self.avgpool(x) + # x = torch.flatten(x, 1) + # x = self.fc(x) + + return feats + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _resnet( + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> ResNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ResNet(block, layers, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress), strict=False) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, +} + + +class ResNet18_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet18-f37072fd.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 11689512, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.758, + "acc@5": 89.078, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ResNet34_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet34-b627a593.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 21797672, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 73.314, + "acc@5": 91.420, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ResNet50_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet50-0676ba61.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 25557032, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.130, + "acc@5": 92.862, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnet50-11ad3fa6.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 25557032, + "recipe": "https://github.com/pytorch/vision/issues/3995#issuecomment-1013906621", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.858, + "acc@5": 95.434, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNet101_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet101-63fe2227.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 44549160, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.374, + "acc@5": 93.546, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnet101-cd907fc2.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 44549160, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.886, + "acc@5": 95.780, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNet152_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet152-394f9c45.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 60192808, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.312, + "acc@5": 94.046, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnet152-f82ba261.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 60192808, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.284, + "acc@5": 96.002, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNeXt50_32X4D_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 25028904, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.618, + "acc@5": 93.698, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 25028904, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.198, + "acc@5": 95.340, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNeXt101_32X8D_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88791336, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.312, + "acc@5": 94.526, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88791336, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.834, + "acc@5": 96.228, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNeXt101_64X4D_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 83455272, + "recipe": "https://github.com/pytorch/vision/pull/5935", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.246, + "acc@5": 96.454, + } + }, + "_docs": """ + These weights were trained from scratch by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Wide_ResNet50_2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 68883240, + "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.468, + "acc@5": 94.086, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 68883240, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.602, + "acc@5": 95.758, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class Wide_ResNet101_2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 126886696, + "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.848, + "acc@5": 94.284, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 126886696, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.510, + "acc@5": 96.020, + } + }, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +@handle_legacy_interface(weights=("pretrained", ResNet18_Weights.IMAGENET1K_V1)) +def resnet18(*, weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-18 from `Deep Residual Learning for Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.ResNet18_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet18_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet18_Weights + :members: + """ + weights = ResNet18_Weights.verify(weights) + + return _resnet(BasicBlock, [2, 2, 2, 2], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ResNet34_Weights.IMAGENET1K_V1)) +def resnet34(*, weights: Optional[ResNet34_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-34 from `Deep Residual Learning for Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.ResNet34_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet34_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet34_Weights + :members: + """ + weights = ResNet34_Weights.verify(weights) + + return _resnet(BasicBlock, [3, 4, 6, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ResNet50_Weights.IMAGENET1K_V1)) +def resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-50 from `Deep Residual Learning for Image Recognition `__. + + .. note:: + The bottleneck of TorchVision places the stride for downsampling to the second 3x3 + convolution while the original paper places it to the first 1x1 convolution. + This variant improves the accuracy and is known as `ResNet V1.5 + `_. + + Args: + weights (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet50_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet50_Weights + :members: + """ + weights = ResNet50_Weights.verify(weights) + + return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ResNet101_Weights.IMAGENET1K_V1)) +def resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-101 from `Deep Residual Learning for Image Recognition `__. + + .. note:: + The bottleneck of TorchVision places the stride for downsampling to the second 3x3 + convolution while the original paper places it to the first 1x1 convolution. + This variant improves the accuracy and is known as `ResNet V1.5 + `_. + + Args: + weights (:class:`~torchvision.models.ResNet101_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet101_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet101_Weights + :members: + """ + weights = ResNet101_Weights.verify(weights) + + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ResNet152_Weights.IMAGENET1K_V1)) +def resnet152(*, weights: Optional[ResNet152_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-152 from `Deep Residual Learning for Image Recognition `__. + + .. note:: + The bottleneck of TorchVision places the stride for downsampling to the second 3x3 + convolution while the original paper places it to the first 1x1 convolution. + This variant improves the accuracy and is known as `ResNet V1.5 + `_. + + Args: + weights (:class:`~torchvision.models.ResNet152_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet152_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet152_Weights + :members: + """ + weights = ResNet152_Weights.verify(weights) + + return _resnet(Bottleneck, [3, 8, 36, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ResNeXt50_32X4D_Weights.IMAGENET1K_V1)) +def resnext50_32x4d( + *, weights: Optional[ResNeXt50_32X4D_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """ResNeXt-50 32x4d model from + `Aggregated Residual Transformation for Deep Neural Networks `_. + + Args: + weights (:class:`~torchvision.models.ResNeXt50_32X4D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNext50_32X4D_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.ResNeXt50_32X4D_Weights + :members: + """ + weights = ResNeXt50_32X4D_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "groups", 32) + _ovewrite_named_param(kwargs, "width_per_group", 4) + return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", ResNeXt101_32X8D_Weights.IMAGENET1K_V1)) +def resnext101_32x8d( + *, weights: Optional[ResNeXt101_32X8D_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """ResNeXt-101 32x8d model from + `Aggregated Residual Transformation for Deep Neural Networks `_. + + Args: + weights (:class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNeXt101_32X8D_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights + :members: + """ + weights = ResNeXt101_32X8D_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "groups", 32) + _ovewrite_named_param(kwargs, "width_per_group", 8) + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +def resnext101_64x4d( + *, weights: Optional[ResNeXt101_64X4D_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """ResNeXt-101 64x4d model from + `Aggregated Residual Transformation for Deep Neural Networks `_. + + Args: + weights (:class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNeXt101_64X4D_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights + :members: + """ + weights = ResNeXt101_64X4D_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "groups", 64) + _ovewrite_named_param(kwargs, "width_per_group", 4) + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", Wide_ResNet50_2_Weights.IMAGENET1K_V1)) +def wide_resnet50_2( + *, weights: Optional[Wide_ResNet50_2_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """Wide ResNet-50-2 model from + `Wide Residual Networks `_. + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + weights (:class:`~torchvision.models.Wide_ResNet50_2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Wide_ResNet50_2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.Wide_ResNet50_2_Weights + :members: + """ + weights = Wide_ResNet50_2_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "width_per_group", 64 * 2) + return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", Wide_ResNet101_2_Weights.IMAGENET1K_V1)) +def wide_resnet101_2( + *, weights: Optional[Wide_ResNet101_2_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """Wide ResNet-101-2 model from + `Wide Residual Networks `_. + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-101 has 2048-512-2048 + channels, and in Wide ResNet-101-2 has 2048-1024-2048. + + Args: + weights (:class:`~torchvision.models.Wide_ResNet101_2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Wide_ResNet101_2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.Wide_ResNet101_2_Weights + :members: + """ + weights = Wide_ResNet101_2_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "width_per_group", 64 * 2) + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from torchvision.models._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "resnet18": ResNet18_Weights.IMAGENET1K_V1.url, + "resnet34": ResNet34_Weights.IMAGENET1K_V1.url, + "resnet50": ResNet50_Weights.IMAGENET1K_V1.url, + "resnet101": ResNet101_Weights.IMAGENET1K_V1.url, + "resnet152": ResNet152_Weights.IMAGENET1K_V1.url, + "resnext50_32x4d": ResNeXt50_32X4D_Weights.IMAGENET1K_V1.url, + "resnext101_32x8d": ResNeXt101_32X8D_Weights.IMAGENET1K_V1.url, + "wide_resnet50_2": Wide_ResNet50_2_Weights.IMAGENET1K_V1.url, + "wide_resnet101_2": Wide_ResNet101_2_Weights.IMAGENET1K_V1.url, + } +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/swin_transformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/swin_transformer.py new file mode 100644 index 00000000..9915bfd6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/swin_transformer.py @@ -0,0 +1,651 @@ +from functools import partial +from typing import Optional, Callable, List, Any + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from torchvision.ops.misc import MLP, Permute +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.transforms._presets import ImageClassification, InterpolationMode +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import _ovewrite_named_param + + +__all__ = [ + "SwinTransformer", + "Swin_T_Weights", + "Swin_S_Weights", + "Swin_B_Weights", + "swin_t", + "swin_s", + "swin_b", +] + + +def _patch_merging_pad(x): + H, W, _ = x.shape[-3:] + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + return x + + +torch.fx.wrap("_patch_merging_pad") + + +class PatchMerging(nn.Module): + """Patch Merging Layer. + Args: + dim (int): Number of input channels. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + """ + + def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm): + super().__init__() + _log_api_usage_once(self) + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x: Tensor): + """ + Args: + x (Tensor): input tensor with expected layout of [..., H, W, C] + Returns: + Tensor with layout of [..., H/2, W/2, 2*C] + """ + x = _patch_merging_pad(x) + + x0 = x[..., 0::2, 0::2, :] # ... H/2 W/2 C + x1 = x[..., 1::2, 0::2, :] # ... H/2 W/2 C + x2 = x[..., 0::2, 1::2, :] # ... H/2 W/2 C + x3 = x[..., 1::2, 1::2, :] # ... H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # ... H/2 W/2 4*C + + x = self.norm(x) + x = self.reduction(x) # ... H/2 W/2 2*C + return x + + +def shifted_window_attention( + input: Tensor, + qkv_weight: Tensor, + proj_weight: Tensor, + relative_position_bias: Tensor, + window_size: List[int], + num_heads: int, + shift_size: List[int], + attention_dropout: float = 0.0, + dropout: float = 0.0, + qkv_bias: Optional[Tensor] = None, + proj_bias: Optional[Tensor] = None, +): + """ + Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + input (Tensor[N, H, W, C]): The input tensor or 4-dimensions. + qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value. + proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection. + relative_position_bias (Tensor): The learned relative position bias added to attention. + window_size (List[int]): Window size. + num_heads (int): Number of attention heads. + shift_size (List[int]): Shift size for shifted window attention. + attention_dropout (float): Dropout ratio of attention weight. Default: 0.0. + dropout (float): Dropout ratio of output. Default: 0.0. + qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None. + proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None. + Returns: + Tensor[N, H, W, C]: The output tensor after shifted window attention. + """ + B, H, W, C = input.shape + # pad feature maps to multiples of window size + pad_r = (window_size[1] - W % window_size[1]) % window_size[1] + pad_b = (window_size[0] - H % window_size[0]) % window_size[0] + x = F.pad(input, (0, 0, 0, pad_r, 0, pad_b)) + _, pad_H, pad_W, _ = x.shape + + # If window size is larger than feature size, there is no need to shift window + if window_size[0] >= pad_H: + shift_size[0] = 0 + if window_size[1] >= pad_W: + shift_size[1] = 0 + + # cyclic shift + if sum(shift_size) > 0: + x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2)) + + # partition windows + num_windows = (pad_H // window_size[0]) * (pad_W // window_size[1]) + x = x.view(B, pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1], C) + x = x.permute(0, 1, 3, 2, 4, 5).reshape(B * num_windows, window_size[0] * window_size[1], C) # B*nW, Ws*Ws, C + + # multi-head attention + qkv = F.linear(x, qkv_weight, qkv_bias) + qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, C // num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + q = q * (C // num_heads) ** -0.5 + attn = q.matmul(k.transpose(-2, -1)) + # add relative position bias + attn = attn + relative_position_bias + + if sum(shift_size) > 0: + # generate attention mask + attn_mask = x.new_zeros((pad_H, pad_W)) + h_slices = ((0, -window_size[0]), (-window_size[0], -shift_size[0]), (-shift_size[0], None)) + w_slices = ((0, -window_size[1]), (-window_size[1], -shift_size[1]), (-shift_size[1], None)) + count = 0 + for h in h_slices: + for w in w_slices: + attn_mask[h[0] : h[1], w[0] : w[1]] = count + count += 1 + attn_mask = attn_mask.view(pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1]) + attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(num_windows, window_size[0] * window_size[1]) + attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1)) + attn = attn + attn_mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, num_heads, x.size(1), x.size(1)) + + attn = F.softmax(attn, dim=-1) + attn = F.dropout(attn, p=attention_dropout) + + x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), C) + x = F.linear(x, proj_weight, proj_bias) + x = F.dropout(x, p=dropout) + + # reverse windows + x = x.view(B, pad_H // window_size[0], pad_W // window_size[1], window_size[0], window_size[1], C) + x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, pad_H, pad_W, C) + + # reverse cyclic shift + if sum(shift_size) > 0: + x = torch.roll(x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2)) + + # unpad features + x = x[:, :H, :W, :].contiguous() + return x + + +torch.fx.wrap("shifted_window_attention") + + +class ShiftedWindowAttention(nn.Module): + """ + See :func:`shifted_window_attention`. + """ + + def __init__( + self, + dim: int, + window_size: List[int], + shift_size: List[int], + num_heads: int, + qkv_bias: bool = True, + proj_bias: bool = True, + attention_dropout: float = 0.0, + dropout: float = 0.0, + ): + super().__init__() + if len(window_size) != 2 or len(shift_size) != 2: + raise ValueError("window_size and shift_size must be of length 2") + self.window_size = window_size + self.shift_size = shift_size + self.num_heads = num_heads + self.attention_dropout = attention_dropout + self.dropout = dropout + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + # coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij")) # 2, Wh, Ww + coords = torch.stack(torch.meshgrid(coords_h, coords_w)) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1).view(-1) # Wh*Ww*Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02) + + def forward(self, x: Tensor): + """ + Args: + x (Tensor): Tensor with layout of [B, H, W, C] + Returns: + Tensor with same layout as input, i.e. [B, H, W, C] + """ + + N = self.window_size[0] * self.window_size[1] + relative_position_bias = self.relative_position_bias_table[self.relative_position_index] # type: ignore[index] + relative_position_bias = relative_position_bias.view(N, N, -1) + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0) + + return shifted_window_attention( + x, + self.qkv.weight, + self.proj.weight, + relative_position_bias, + self.window_size, + self.num_heads, + shift_size=self.shift_size, + attention_dropout=self.attention_dropout, + dropout=self.dropout, + qkv_bias=self.qkv.bias, + proj_bias=self.proj.bias, + ) + + +class SwinTransformerBlock(nn.Module): + """ + Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (List[int]): Window size. + shift_size (List[int]): Shift size for shifted window attention. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + attn_layer (nn.Module): Attention layer. Default: ShiftedWindowAttention + """ + + def __init__( + self, + dim: int, + num_heads: int, + window_size: List[int], + shift_size: List[int], + mlp_ratio: float = 4.0, + dropout: float = 0.0, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.0, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_layer: Callable[..., nn.Module] = ShiftedWindowAttention, + ): + super().__init__() + _log_api_usage_once(self) + + self.norm1 = norm_layer(dim) + self.attn = attn_layer( + dim, + window_size, + shift_size, + num_heads, + attention_dropout=attention_dropout, + dropout=dropout, + ) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.norm2 = norm_layer(dim) + self.mlp = MLP(dim, [int(dim * mlp_ratio), dim], activation_layer=nn.GELU, inplace=None, dropout=dropout) + + for m in self.mlp.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.normal_(m.bias, std=1e-6) + + def forward(self, x: Tensor): + x = x + self.stochastic_depth(self.attn(self.norm1(x))) + x = x + self.stochastic_depth(self.mlp(self.norm2(x))) + return x + + +class SwinTransformer(nn.Module): + """ + Implements Swin Transformer from the `"Swin Transformer: Hierarchical Vision Transformer using + Shifted Windows" `_ paper. + Args: + patch_size (List[int]): Patch size. + embed_dim (int): Patch embedding dimension. + depths (List(int)): Depth of each Swin Transformer layer. + num_heads (List(int)): Number of attention heads in different layers. + window_size (List[int]): Window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob (float): Stochastic depth rate. Default: 0.0. + num_classes (int): Number of classes for classification head. Default: 1000. + block (nn.Module, optional): SwinTransformer Block. Default: None. + norm_layer (nn.Module, optional): Normalization layer. Default: None. + """ + + def __init__( + self, + patch_size: List[int], + embed_dim: int, + depths: List[int], + num_heads: List[int], + window_size: List[int], + mlp_ratio: float = 4.0, + dropout: float = 0.0, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.0, + num_classes: int = 1000, + norm_layer: Optional[Callable[..., nn.Module]] = None, + block: Optional[Callable[..., nn.Module]] = None, + ): + super().__init__() + _log_api_usage_once(self) + self.num_classes = num_classes + + if block is None: + block = SwinTransformerBlock + + if norm_layer is None: + norm_layer = partial(nn.LayerNorm, eps=1e-5) + + layers: List[nn.Module] = [] + # split image into non-overlapping patches + # layers.append( + # nn.Sequential( + # nn.Conv2d( + # 3, embed_dim, kernel_size=(patch_size[0], patch_size[1]), stride=(patch_size[0], patch_size[1]) + # ), + # Permute([0, 2, 3, 1]), + # norm_layer(embed_dim), + # ) + # ) + self.first_coonv = nn.Sequential( + nn.Conv2d( + 3, embed_dim, kernel_size=(patch_size[0], patch_size[1]), stride=(patch_size[0], patch_size[1]) + ), + Permute([0, 2, 3, 1]), + norm_layer(embed_dim), + ) + + total_stage_blocks = sum(depths) + stage_block_id = 0 + # build SwinTransformer blocks + for i_stage in range(len(depths)): + stage: List[nn.Module] = [] + dim = embed_dim * 2 ** i_stage + for i_layer in range(depths[i_stage]): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / (total_stage_blocks - 1) + stage.append( + block( + dim, + num_heads[i_stage], + window_size=window_size, + shift_size=[0 if i_layer % 2 == 0 else w // 2 for w in window_size], + mlp_ratio=mlp_ratio, + dropout=dropout, + attention_dropout=attention_dropout, + stochastic_depth_prob=sd_prob, + norm_layer=norm_layer, + ) + ) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + # add patch merging layer + if i_stage < (len(depths) - 1): + layers.append(PatchMerging(dim, norm_layer)) + # self.features = nn.Sequential(*layers) + self.features = nn.ModuleList(layers) + + num_features = embed_dim * 2 ** (len(depths) - 1) + self.norm = norm_layer(num_features) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.head = nn.Linear(num_features, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward(self, x): + feats = [] + x = self.first_coonv(x) + for i, layer in enumerate(self.features): + x = layer(x) + if i in [0, 2, 4, 6]: + feats.append(x.permute(0, 3, 1, 2).contiguous()) + # x = self.features(x) + # x = self.norm(x) + # x = x.permute(0, 3, 1, 2) + # x = self.avgpool(x) + # x = torch.flatten(x, 1) + # x = self.head(x) + return feats + + +def _swin_transformer( + patch_size: List[int], + embed_dim: int, + depths: List[int], + num_heads: List[int], + window_size: List[int], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> SwinTransformer: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = SwinTransformer( + patch_size=patch_size, + embed_dim=embed_dim, + depths=depths, + num_heads=num_heads, + window_size=window_size, + stochastic_depth_prob=stochastic_depth_prob, + **kwargs, + ) + + if weights is not None: + ckpt1 = weights.get_state_dict(progress=progress) + ckpt2 = model.state_dict() + kl1 = list(ckpt1.keys()) + for i, k in enumerate(list(ckpt2.keys())): + ckpt2[k] = ckpt1[kl1[i]] + msg = model.load_state_dict(ckpt2, strict=False) + print(f'Load swin_transformer: {msg}') + + return model + + +_COMMON_META = { + "categories": _IMAGENET_CATEGORIES, +} + + +class Swin_T_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_t-704ceda3.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=232, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 28288354, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.474, + "acc@5": 95.776, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_S_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_s-5e29d889.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=246, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 49606258, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.196, + "acc@5": 96.360, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_B_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_b-68c6b09e.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=238, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 87768224, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.582, + "acc@5": 96.640, + } + }, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +def swin_t(*, weights: Optional[Swin_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_tiny architecture from + `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. + + Args: + weights (:class:`~torchvision.models.Swin_T_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_T_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_T_Weights + :members: + """ + weights = Swin_T_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=[7, 7], + stochastic_depth_prob=0.2, + weights=weights, + progress=progress, + **kwargs, + ) + + +def swin_s(*, weights: Optional[Swin_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_small architecture from + `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. + + Args: + weights (:class:`~torchvision.models.Swin_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_S_Weights + :members: + """ + weights = Swin_S_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=96, + depths=[2, 2, 18, 2], + num_heads=[3, 6, 12, 24], + window_size=[7, 7], + stochastic_depth_prob=0.3, + weights=weights, + progress=progress, + **kwargs, + ) + + +from torchvision.models._utils import handle_legacy_interface +@handle_legacy_interface(weights=("pretrained", Swin_B_Weights.IMAGENET1K_V1)) +def swin_b(*, weights: Optional[Swin_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_base architecture from + `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. + + Args: + weights (:class:`~torchvision.models.Swin_B_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_B_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_B_Weights + :members: + """ + weights = Swin_B_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=[7, 7], + stochastic_depth_prob=0.5, + weights=weights, + progress=progress, + **kwargs, + ) + +if __name__ == '__main__': + model = swin_b(weights=Swin_B_Weights) + x = torch.rand(1, 3, 320, 320) + y = model(x) + pause = 0 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/uncond_unet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/uncond_unet.py new file mode 100644 index 00000000..c6e6dd3f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/uncond_unet.py @@ -0,0 +1,376 @@ +import math +import torch +from torch import nn, einsum +import torch.nn.functional as F +from einops import rearrange, reduce +from functools import partial + + +def exists(x): + return x is not None + +def default(val, d): + if exists(val): + return val + return d() if callable(d) else d + +def identity(t, *args, **kwargs): + return t + +def cycle(dl): + while True: + for data in dl: + yield data + +def has_int_squareroot(num): + return (math.sqrt(num) ** 2) == num + +def num_to_groups(num, divisor): + groups = num // divisor + remainder = num % divisor + arr = [divisor] * groups + if remainder > 0: + arr.append(remainder) + return arr + +def convert_image_to_fn(img_type, image): + if image.mode != img_type: + return image.convert(img_type) + return image + +# normalization functions + +def normalize_to_neg_one_to_one(img): + return img * 2 - 1 + +def unnormalize_to_zero_to_one(t): + return (t + 1) * 0.5 + +# small helper modules + +class Residual(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + + def forward(self, x, *args, **kwargs): + return self.fn(x, *args, **kwargs) + x + +def Upsample(dim, dim_out = None): + return nn.Sequential( + nn.Upsample(scale_factor = 2, mode = 'nearest'), + nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1) + ) + +def Downsample(dim, dim_out = None): + return nn.Conv2d(dim, default(dim_out, dim), 4, 2, 1) + +class WeightStandardizedConv2d(nn.Conv2d): + """ + https://arxiv.org/abs/1903.10520 + weight standardization purportedly works synergistically with group normalization + """ + def forward(self, x): + eps = 1e-5 if x.dtype == torch.float32 else 1e-3 + + weight = self.weight + mean = reduce(weight, 'o ... -> o 1 1 1', 'mean') + var = reduce(weight, 'o ... -> o 1 1 1', partial(torch.var, unbiased = False)) + normalized_weight = (weight - mean) * (var + eps).rsqrt() + + return F.conv2d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + +class LayerNorm(nn.Module): + def __init__(self, dim): + super().__init__() + self.g = nn.Parameter(torch.ones(1, dim, 1, 1)) + + def forward(self, x): + eps = 1e-5 if x.dtype == torch.float32 else 1e-3 + var = torch.var(x, dim = 1, unbiased = False, keepdim = True) + mean = torch.mean(x, dim = 1, keepdim = True) + return (x - mean) * (var + eps).rsqrt() * self.g + +class PreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.fn = fn + self.norm = LayerNorm(dim) + + def forward(self, x): + x = self.norm(x) + return self.fn(x) + +# sinusoidal positional embeds + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + +class RandomOrLearnedSinusoidalPosEmb(nn.Module): + """ following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """ + """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """ + + def __init__(self, dim, is_random = False): + super().__init__() + assert (dim % 2) == 0 + half_dim = dim // 2 + self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random) + + def forward(self, x): + x = rearrange(x, 'b -> b 1') + freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi + fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1) + fouriered = torch.cat((x, fouriered), dim = -1) + return fouriered + +# building block modules + +class Block(nn.Module): + def __init__(self, dim, dim_out, groups = 8): + super().__init__() + self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1) + self.norm = nn.GroupNorm(groups, dim_out) + self.act = nn.SiLU() + + def forward(self, x, scale_shift = None): + x = self.proj(x) + x = self.norm(x) + + if exists(scale_shift): + scale, shift = scale_shift + x = x * (scale + 1) + shift + + x = self.act(x) + return x + +class ResnetBlock(nn.Module): + def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8): + super().__init__() + self.mlp = nn.Sequential( + nn.SiLU(), + nn.Linear(time_emb_dim, dim_out * 2) + ) if exists(time_emb_dim) else None + + self.block1 = Block(dim, dim_out, groups = groups) + self.block2 = Block(dim_out, dim_out, groups = groups) + self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + + def forward(self, x, time_emb = None): + + scale_shift = None + if exists(self.mlp) and exists(time_emb): + time_emb = self.mlp(time_emb) + time_emb = rearrange(time_emb, 'b c -> b c 1 1') + scale_shift = time_emb.chunk(2, dim = 1) + + h = self.block1(x, scale_shift = scale_shift) + + h = self.block2(h) + + return h + self.res_conv(x) + +class LinearAttention(nn.Module): + def __init__(self, dim, heads = 4, dim_head = 32): + super().__init__() + self.scale = dim_head ** -0.5 + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + + self.to_out = nn.Sequential( + nn.Conv2d(hidden_dim, dim, 1), + LayerNorm(dim) + ) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x).chunk(3, dim = 1) + q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv) + + q = q.softmax(dim = -2) + k = k.softmax(dim = -1) + + q = q * self.scale + v = v / (h * w) + + context = torch.einsum('b h d n, b h e n -> b h d e', k, v) + + out = torch.einsum('b h d e, b h d n -> b h e n', context, q) + out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w) + return self.to_out(out) + +class Attention(nn.Module): + def __init__(self, dim, heads = 4, dim_head = 32): + super().__init__() + self.scale = dim_head ** -0.5 + self.heads = heads + hidden_dim = dim_head * heads + + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x).chunk(3, dim = 1) + q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv) + + q = q * self.scale + + sim = einsum('b h d i, b h d j -> b h i j', q, k) + attn = sim.softmax(dim = -1) + out = einsum('b h i j, b h d j -> b h i d', attn, v) + + out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w) + return self.to_out(out) + +# model + +class Unet(nn.Module): + def __init__( + self, + dim, + init_dim = None, + out_dim = None, + dim_mults=(1, 2, 4, 8), + channels = 3, + self_condition = False, + resnet_block_groups = 8, + heads=8, + learned_variance = False, + learned_sinusoidal_cond = False, + random_fourier_features = False, + learned_sinusoidal_dim = 16, + out_mul=1, + ): + super().__init__() + + # determine dimensions + + self.channels = channels + self.self_condition = self_condition + input_channels = channels * (2 if self_condition else 1) + + init_dim = default(init_dim, dim) + self.init_conv = nn.Conv2d(input_channels, init_dim, 7, padding = 3) + + dims = [init_dim, *map(lambda m: dim * m, dim_mults)] + in_out = list(zip(dims[:-1], dims[1:])) + + block_klass = partial(ResnetBlock, groups = resnet_block_groups) + + # time embeddings + + time_dim = dim * 4 + + self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features + + if self.random_or_learned_sinusoidal_cond: + sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features) + fourier_dim = learned_sinusoidal_dim + 1 + else: + sinu_pos_emb = SinusoidalPosEmb(dim) + fourier_dim = dim + + self.time_mlp = nn.Sequential( + sinu_pos_emb, + nn.Linear(fourier_dim, time_dim), + nn.GELU(), + nn.Linear(time_dim, time_dim) + ) + + # layers + + self.downs = nn.ModuleList([]) + self.ups = nn.ModuleList([]) + num_resolutions = len(in_out) + + for ind, (dim_in, dim_out) in enumerate(in_out): + is_last = ind >= (num_resolutions - 1) + + self.downs.append(nn.ModuleList([ + block_klass(dim_in, dim_in, time_emb_dim = time_dim), + block_klass(dim_in, dim_in, time_emb_dim = time_dim), + Residual(PreNorm(dim_in, LinearAttention(dim_in, heads=heads))), + Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1) + ])) + + mid_dim = dims[-1] + self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim) + self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim, heads=heads))) + self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim) + + for ind, (dim_in, dim_out) in enumerate(reversed(in_out)): + is_last = ind == (len(in_out) - 1) + + self.ups.append(nn.ModuleList([ + block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim), + block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim), + Residual(PreNorm(dim_out, LinearAttention(dim_out))), + Upsample(dim_out, dim_in) if not is_last else nn.Conv2d(dim_out, dim_in, 3, padding = 1) + ])) + + default_out_dim = channels * out_mul + self.out_dim = default(out_dim, default_out_dim) + + self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim) + self.final_conv = nn.Conv2d(dim, self.out_dim, 1) + + def forward(self, x, time, cond=None, x_self_cond=None): ## cond is always None for unconditional model + if self.self_condition: + x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x)) + x = torch.cat((x_self_cond, x), dim = 1) + + x = self.init_conv(x) + r = x.clone() + + t = self.time_mlp(time) + + h = [] + + for block1, block2, attn, downsample in self.downs: + x = block1(x, t) + h.append(x) + + x = block2(x, t) + x = attn(x) + h.append(x) + + x = downsample(x) + + x = self.mid_block1(x, t) + x = self.mid_attn(x) + x = self.mid_block2(x, t) + + for block1, block2, attn, upsample in self.ups: + x = torch.cat((x, h.pop()), dim = 1) + x = block1(x, t) + + x = torch.cat((x, h.pop()), dim = 1) + x = block2(x, t) + x = attn(x) + + x = upsample(x) + + x = torch.cat((x, r), dim = 1) + + x = self.final_res_block(x, t) + return self.final_conv(x) + +if __name__ == '__main__': + model = Unet(96, out_mul=2, dim_mults=[1,2,4,8], heads=8) + x = torch.rand(2, 3, 8, 8) + time = torch.tensor([2, 5]) + with torch.no_grad(): + y = model(x, time) + pass \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/utils.py new file mode 100644 index 00000000..4975a5de --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/utils.py @@ -0,0 +1,68 @@ +import os +from pathlib import Path +import time +import logging +import math + +def create_logger(root_dir, des=''): + root_output_dir = Path(root_dir) + # set up logger + if not root_output_dir.exists(): + print('=> creating {}'.format(root_output_dir)) + root_output_dir.mkdir(exist_ok=True, parents=True) + time_str = time.strftime('%Y-%m-%d-%H-%M') + log_file = '{}_{}.log'.format(time_str, des) + final_log_file = root_output_dir / log_file + head = '%(asctime)-15s %(message)s' + logging.basicConfig(filename=str(final_log_file), format=head) + logger = logging.getLogger() + logger.setLevel(logging.INFO) + console = logging.StreamHandler() + logging.getLogger('').addHandler(console) + return logger + +def exists(x): + return x is not None + +def default(val, d): + if exists(val): + return val + return d() if callable(d) else d + +def identity(t, *args, **kwargs): + return t + +def cycle(dl): + while True: + for data in dl: + yield data + +def has_int_squareroot(num): + return (math.sqrt(num) ** 2) == num + +def num_to_groups(num, divisor): + groups = num // divisor + remainder = num % divisor + arr = [divisor] * groups + if remainder > 0: + arr.append(remainder) + return arr + +def convert_image_to_fn(img_type, image): + if image.mode != img_type: + return image.convert(img_type) + return image + +# normalization functions + +def normalize_to_neg_one_to_one(img): + return img * 2 - 1 + +def unnormalize_to_zero_to_one(t): + return (t + 1) * 0.5 + +def dict2str(dict): + s = '' + for k, v in dict.items(): + s += "{}: {:.5f}, ".format(k, v) + return s \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/vgg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/vgg.py new file mode 100644 index 00000000..e33c6268 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/vgg.py @@ -0,0 +1,517 @@ +from functools import partial +from typing import Union, List, Dict, Any, Optional, cast + +import torch +import torch.nn as nn + +from torchvision.transforms._presets import ImageClassification +from torchvision.utils import _log_api_usage_once +from torchvision.models._api import WeightsEnum, Weights +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import handle_legacy_interface, _ovewrite_named_param + + +__all__ = [ + "VGG", + "VGG11_Weights", + "VGG11_BN_Weights", + "VGG13_Weights", + "VGG13_BN_Weights", + "VGG16_Weights", + "VGG16_BN_Weights", + "VGG19_Weights", + "VGG19_BN_Weights", + "vgg11", + "vgg11_bn", + "vgg13", + "vgg13_bn", + "vgg16", + "vgg16_bn", + "vgg19", + "vgg19_bn", +] + + +class VGG(nn.Module): + def __init__( + self, features: nn.Module, num_classes: int = 1000, init_weights: bool = True, dropout: float = 0.5 + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(p=dropout), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(p=dropout), + nn.Linear(4096, num_classes), + ) + if init_weights: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + feats = [] + # x = self.features(x) + # x = self.avgpool(x) + # x = torch.flatten(x, 1) + # x = self.classifier(x) + for i, layer in enumerate(self.features): + x = layer(x) + if i in [9, 16, 23, 30]: + feats.append(x) + return feats + + +def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential: + layers: List[nn.Module] = [] + in_channels = 3 + for v in cfg: + if v == "M": + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + v = cast(int, v) + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.ModuleList(layers) + + +cfgs: Dict[str, List[Union[str, int]]] = { + "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], + "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], + "D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"], + "E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"], +} + + +def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any) -> VGG: + if weights is not None: + kwargs["init_weights"] = False + if weights.meta["categories"] is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs) + + if weights is not None: + ckpt1 = weights.get_state_dict(progress=progress) + ckpt2 = model.state_dict() + kl1 = list(ckpt1.keys()) + for i, k in enumerate(list(ckpt2.keys())): + ckpt2[k] = ckpt1[kl1[i]] + msg = model.load_state_dict(ckpt2, strict=False) + print(f'Load VGG: {msg}') + else: + print('No pretrained weight loaded!') + return model + + +_COMMON_META = { + "min_size": (32, 32), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg", + "_docs": """These weights were trained from scratch by using a simplified training recipe.""", +} + + +class VGG11_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg11-8a719046.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 132863336, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.020, + "acc@5": 88.628, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG11_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg11_bn-6002323d.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 132868840, + "_metrics": { + "ImageNet-1K": { + "acc@1": 70.370, + "acc@5": 89.810, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG13_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg13-19584684.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 133047848, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.928, + "acc@5": 89.246, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG13_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg13_bn-abd245e5.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 133053736, + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.586, + "acc@5": 90.374, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg16-397923af.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 138357544, + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.592, + "acc@5": 90.382, + } + }, + }, + ) + IMAGENET1K_FEATURES = Weights( + # Weights ported from https://github.com/amdegroot/ssd.pytorch/ + url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth", + transforms=partial( + ImageClassification, + crop_size=224, + mean=(0.48235, 0.45882, 0.40784), + std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0), + ), + meta={ + **_COMMON_META, + "num_params": 138357544, + "categories": None, + "recipe": "https://github.com/amdegroot/ssd.pytorch#training-ssd", + "_metrics": { + "ImageNet-1K": { + "acc@1": float("nan"), + "acc@5": float("nan"), + } + }, + "_docs": """ + These weights can't be used for classification because they are missing values in the `classifier` + module. Only the `features` module has valid values and can be used for feature extraction. The weights + were trained using the original input standardization method as described in the paper. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG16_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg16_bn-6c64b313.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 138365992, + "_metrics": { + "ImageNet-1K": { + "acc@1": 73.360, + "acc@5": 91.516, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG19_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg19-dcbb9e9d.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 143667240, + "_metrics": { + "ImageNet-1K": { + "acc@1": 72.376, + "acc@5": 90.876, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG19_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg19_bn-c79401a0.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 143678248, + "_metrics": { + "ImageNet-1K": { + "acc@1": 74.218, + "acc@5": 91.842, + } + }, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@handle_legacy_interface(weights=("pretrained", VGG11_Weights.IMAGENET1K_V1)) +def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-11 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG11_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG11_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG11_Weights + :members: + """ + weights = VGG11_Weights.verify(weights) + + return _vgg("A", False, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.IMAGENET1K_V1)) +def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-11-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG11_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG11_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG11_BN_Weights + :members: + """ + weights = VGG11_BN_Weights.verify(weights) + + return _vgg("A", True, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1)) +def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-13 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG13_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG13_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG13_Weights + :members: + """ + weights = VGG13_Weights.verify(weights) + + return _vgg("B", False, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.IMAGENET1K_V1)) +def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-13-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG13_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG13_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG13_BN_Weights + :members: + """ + weights = VGG13_BN_Weights.verify(weights) + + return _vgg("B", True, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1)) +def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-16 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG16_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG16_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG16_Weights + :members: + """ + weights = VGG16_Weights.verify(weights) + + return _vgg("D", False, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG16_BN_Weights.IMAGENET1K_V1)) +def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-16-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG16_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG16_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG16_BN_Weights + :members: + """ + weights = VGG16_BN_Weights.verify(weights) + + return _vgg("D", True, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG19_Weights.IMAGENET1K_V1)) +def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-19 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG19_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG19_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG19_Weights + :members: + """ + weights = VGG19_Weights.verify(weights) + + return _vgg("E", False, weights, progress, **kwargs) + + +@handle_legacy_interface(weights=("pretrained", VGG19_BN_Weights.IMAGENET1K_V1)) +def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-19_BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG19_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG19_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG19_BN_Weights + :members: + """ + weights = VGG19_BN_Weights.verify(weights) + + return _vgg("E", True, weights, progress, **kwargs) + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from torchvision.models._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "vgg11": VGG11_Weights.IMAGENET1K_V1.url, + "vgg13": VGG13_Weights.IMAGENET1K_V1.url, + "vgg16": VGG16_Weights.IMAGENET1K_V1.url, + "vgg19": VGG19_Weights.IMAGENET1K_V1.url, + "vgg11_bn": VGG11_BN_Weights.IMAGENET1K_V1.url, + "vgg13_bn": VGG13_BN_Weights.IMAGENET1K_V1.url, + "vgg16_bn": VGG16_BN_Weights.IMAGENET1K_V1.url, + "vgg19_bn": VGG19_BN_Weights.IMAGENET1K_V1.url, + } +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/wavelet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/wavelet.py new file mode 100644 index 00000000..b051a3e1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/wavelet.py @@ -0,0 +1,83 @@ +import pywt +import pywt.data +import torch +from torch import nn +from torch.autograd import Function +import torch.nn.functional as F + + +def create_wavelet_filter(wave, in_size, out_size, type=torch.float): + w = pywt.Wavelet(wave) + dec_hi = torch.tensor(w.dec_hi[::-1], dtype=type) + dec_lo = torch.tensor(w.dec_lo[::-1], dtype=type) + dec_filters = torch.stack([dec_lo.unsqueeze(0) * dec_lo.unsqueeze(1), + dec_lo.unsqueeze(0) * dec_hi.unsqueeze(1), + dec_hi.unsqueeze(0) * dec_lo.unsqueeze(1), + dec_hi.unsqueeze(0) * dec_hi.unsqueeze(1)], dim=0) + + dec_filters = dec_filters[:, None].repeat(in_size, 1, 1, 1) + + rec_hi = torch.tensor(w.rec_hi[::-1], dtype=type).flip(dims=[0]) + rec_lo = torch.tensor(w.rec_lo[::-1], dtype=type).flip(dims=[0]) + rec_filters = torch.stack([rec_lo.unsqueeze(0) * rec_lo.unsqueeze(1), + rec_lo.unsqueeze(0) * rec_hi.unsqueeze(1), + rec_hi.unsqueeze(0) * rec_lo.unsqueeze(1), + rec_hi.unsqueeze(0) * rec_hi.unsqueeze(1)], dim=0) + + rec_filters = rec_filters[:, None].repeat(out_size, 1, 1, 1) + + return dec_filters, rec_filters + + +def wt(x, filters, in_size, level): + _, _, h, w = x.shape + pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1) + res = F.conv2d(x, filters, stride=2, groups=in_size, padding=pad) + if level > 1: + res[:, ::4] = wt(res[:, ::4], filters, in_size, level - 1) + res = res.reshape(-1, 2, h // 2, w // 2).transpose(1, 2).reshape(-1, in_size, h, w) + return res + + +def iwt(x, inv_filters, in_size, level): + _, _, h, w = x.shape + pad = (inv_filters.shape[2] // 2 - 1, inv_filters.shape[3] // 2 - 1) + res = x.reshape(-1, h // 2, 2, w // 2).transpose(1, 2).reshape(-1, 4 * in_size, h // 2, w // 2) + if level > 1: + res[:, ::4] = iwt(res[:, ::4], inv_filters, in_size, level - 1) + res = F.conv_transpose2d(res, inv_filters, stride=2, groups=in_size, padding=pad) + return res + + +def get_inverse_transform(weights, in_size, level): + class InverseWaveletTransform(Function): + + @staticmethod + def forward(ctx, input): + with torch.no_grad(): + x = iwt(input, weights, in_size, level) + return x + + @staticmethod + def backward(ctx, grad_output): + grad = wt(grad_output, weights, in_size, level) + return grad, None + + return InverseWaveletTransform().apply + + +def get_transform(weights, in_size, level): + class WaveletTransform(Function): + + @staticmethod + def forward(ctx, input): + with torch.no_grad(): + x = wt(input, weights, in_size, level) + return x + + @staticmethod + def backward(ctx, grad_output): + grad = iwt(grad_output, weights, in_size, level) + return grad, None + + return WaveletTransform().apply \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/wcc.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/wcc.py new file mode 100644 index 00000000..5faeb37b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/denoising_diffusion_pytorch/wcc.py @@ -0,0 +1,101 @@ +from typing import Union, Tuple + +import torch +from torch import nn as nn +from torch.nn import functional as F + +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.quantization import weight_quantize_fn, act_quantize_fn +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch import wavelet + + +class WCC(nn.Conv1d): + def __init__(self, in_channels: int, + out_channels: int, + stride: Union[int, Tuple] = 1, + padding: Union[int, Tuple] = 0, + dilation: Union[int, Tuple] = 1, + groups: int = 1, + bias: bool = False, + levels: int = 3, + compress_rate: float = 0.25, + bit_w: int = 8, + bit_a: int = 8, + wt_type: str = "db1"): + super(WCC, self).__init__(in_channels, out_channels, 1, stride, padding, dilation, groups, bias) + self.layer_type = 'WCC' + self.bit_w = bit_w + self.bit_a = bit_a + + self.weight_quant = weight_quantize_fn(self.bit_w) + self.act_quant = act_quantize_fn(self.bit_a, signed=True) + + self.levels = levels + self.wt_type = wt_type + self.compress_rate = compress_rate + + dec_filters, rec_filters = wavelet.create_wavelet_filter(wave=self.wt_type, + in_size=in_channels, + out_size=out_channels) + self.wt_filters = nn.Parameter(dec_filters, requires_grad=False) + self.iwt_filters = nn.Parameter(rec_filters, requires_grad=False) + self.wt = wavelet.get_transform(self.wt_filters, in_channels, levels) + self.iwt = wavelet.get_inverse_transform(self.iwt_filters, out_channels, levels) + + self.get_pad = lambda n: ((2 ** levels) - n) % (2 ** levels) + + def forward(self, x): + in_shape = x.shape + pads = (0, self.get_pad(in_shape[2]), 0, self.get_pad(in_shape[3])) + x = F.pad(x, pads) # pad to match 2^(levels) + + weight_q = self.weight_quant(self.weight) # quantize weights + x = self.wt(x) # H + topk, ids = self.compress(x) # T + topk_q = self.act_quant(topk) # quantize activations + topk_q = F.conv1d(topk_q, weight_q, self.bias, self.stride, self.padding, self.dilation, self.groups) # K_1x1 + x = self.decompress(topk_q, ids, x.shape) # T^T + x = self.iwt(x) # H^T + + x = x[:, :, :in_shape[2], :in_shape[3]] # remove pads + return x + + def compress(self, x): + b, c, h, w = x.shape + acc = x.norm(dim=1).pow(2) + acc = acc.view(b, h * w) + k = int(h * w * self.compress_rate) + ids = acc.topk(k, dim=1, sorted=False)[1] + ids.unsqueeze_(dim=1) + topk = x.reshape((b, c, h * w)).gather(dim=2, index=ids.repeat(1, c, 1)) + return topk, ids + + def decompress(self, topk, ids, shape): + b, _, h, w = shape + ids = ids.repeat(1, self.out_channels, 1) + x = torch.zeros(size=(b, self.out_channels, h * w), requires_grad=True, device=topk.device) + x = x.scatter(dim=2, index=ids, src=topk) + x = x.reshape((b, self.out_channels, h, w)) + return x + + def change_wt_params(self, compress_rate, levels, wt_type="db1"): + self.compress_rate = compress_rate + self.levels = levels + dec_filters, rec_filters = wavelet.create_wavelet_filter(wave=self.wt_type, + in_size=self.in_channels, + out_size=self.out_channels) + self.wt_filters = nn.Parameter(dec_filters, requires_grad=False) + self.iwt_filters = nn.Parameter(rec_filters, requires_grad=False) + self.wt = wavelet.get_transform(self.wt_filters, self.in_channels, levels) + self.iwt = wavelet.get_inverse_transform(self.iwt_filters, self.out_channels, levels) + + def change_bit(self, bit_w, bit_a): + self.bit_w = bit_w + self.bit_a = bit_a + self.weight_quant.change_bit(bit_w) + self.act_quant.change_bit(bit_a) + +if __name__ == '__main__': + wcc = WCC(80, 80) + x = torch.rand(1, 80, 80, 80) + y = wcc(x) + pause = 0 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/model.py new file mode 100644 index 00000000..c3c86b51 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/model.py @@ -0,0 +1,197 @@ +import numpy as np +import yaml +import argparse +import math +import torch +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.utils import * +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.encoder_decoder import AutoencoderKL +# from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.transmodel import TransModel +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.uncond_unet import Unet +from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.data import * +from fvcore.common.config import CfgNode +from pathlib import Path + +def load_conf(config_file, conf={}): + with open(config_file) as f: + exp_conf = yaml.load(f, Loader=yaml.FullLoader) + for k, v in exp_conf.items(): + conf[k] = v + return conf + +def prepare_args(ckpt_path, sampling_timesteps=1): + return argparse.Namespace( + cfg=load_conf(Path(__file__).parent / "default.yaml"), + pre_weight=ckpt_path, + sampling_timesteps=sampling_timesteps + ) + +class DiffusionEdge: + def __init__(self, args) -> None: + self.cfg = CfgNode(args.cfg) + torch.manual_seed(42) + np.random.seed(42) + model_cfg = self.cfg.model + first_stage_cfg = model_cfg.first_stage + first_stage_model = AutoencoderKL( + ddconfig=first_stage_cfg.ddconfig, + lossconfig=first_stage_cfg.lossconfig, + embed_dim=first_stage_cfg.embed_dim, + ckpt_path=first_stage_cfg.ckpt_path, + ) + if model_cfg.model_name == 'cond_unet': + from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.mask_cond_unet import Unet + unet_cfg = model_cfg.unet + unet = Unet(dim=unet_cfg.dim, + channels=unet_cfg.channels, + dim_mults=unet_cfg.dim_mults, + learned_variance=unet_cfg.get('learned_variance', False), + out_mul=unet_cfg.out_mul, + cond_in_dim=unet_cfg.cond_in_dim, + cond_dim=unet_cfg.cond_dim, + cond_dim_mults=unet_cfg.cond_dim_mults, + window_sizes1=unet_cfg.window_sizes1, + window_sizes2=unet_cfg.window_sizes2, + fourier_scale=unet_cfg.fourier_scale, + cfg=unet_cfg, + ) + else: + raise NotImplementedError + if model_cfg.model_type == 'const_sde': + from custom_controlnet_aux.diffusion_edge.denoising_diffusion_pytorch.ddm_const_sde import LatentDiffusion + else: + raise NotImplementedError(f'{model_cfg.model_type} is not surportted !') + + self.model = LatentDiffusion( + model=unet, + auto_encoder=first_stage_model, + train_sample=model_cfg.train_sample, + image_size=model_cfg.image_size, + timesteps=model_cfg.timesteps, + sampling_timesteps=args.sampling_timesteps, + loss_type=model_cfg.loss_type, + objective=model_cfg.objective, + scale_factor=model_cfg.scale_factor, + scale_by_std=model_cfg.scale_by_std, + scale_by_softsign=model_cfg.scale_by_softsign, + default_scale=model_cfg.get('default_scale', False), + input_keys=model_cfg.input_keys, + ckpt_path=model_cfg.ckpt_path, + ignore_keys=model_cfg.ignore_keys, + only_model=model_cfg.only_model, + start_dist=model_cfg.start_dist, + perceptual_weight=model_cfg.perceptual_weight, + use_l1=model_cfg.get('use_l1', True), + cfg=model_cfg, + ) + self.cfg.sampler.ckpt_path = args.pre_weight + + data = torch.load(self.cfg.sampler.ckpt_path, map_location="cpu") + if self.cfg.sampler.use_ema: + sd = data['ema'] + new_sd = {} + for k in sd.keys(): + if k.startswith("ema_model."): + new_k = k[10:] # remove ema_model. + new_sd[new_k] = sd[k] + sd = new_sd + self.model.load_state_dict(sd) + else: + self.model.load_state_dict(data['model']) + if 'scale_factor' in data['model']: + self.model.scale_factor = data['model']['scale_factor'] + + self.model.eval() + self.device = "cpu" + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, image, batch_size=8): + image = normalize_to_neg_one_to_one(image).to(self.device) + mask = None + if self.cfg.sampler.sample_type == 'whole': + return self.whole_sample(image, raw_size=image.shape[2:], mask=mask) + elif self.cfg.sampler.sample_type == 'slide': + return self.slide_sample(image, crop_size=self.cfg.sampler.get('crop_size', [320, 320]), + stride=self.cfg.sampler.stride, mask=mask, bs=batch_size) + + def whole_sample(self, inputs, raw_size, mask=None): + inputs = F.interpolate(inputs, size=(416, 416), mode='bilinear', align_corners=True) + seg_logits = self.model.sample(batch_size=inputs.shape[0], cond=inputs, mask=mask) + seg_logits = F.interpolate(seg_logits, size=raw_size, mode='bilinear', align_corners=True) + return seg_logits + + def slide_sample(self, inputs, crop_size, stride, mask=None, bs=8): + """Inference by sliding-window with overlap. + + If h_crop > h_img or w_crop > w_img, the small patch will be used to + decode without padding. + + Args: + inputs (tensor): the tensor should have a shape NxCxHxW, + which contains all images in the batch. + batch_img_metas (List[dict]): List of image metainfo where each may + also contain: 'img_shape', 'scale_factor', 'flip', 'img_path', + 'ori_shape', and 'pad_shape'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:PackSegInputs`. + + Returns: + Tensor: The segmentation results, seg_logits from model of each + input image. + """ + + h_stride, w_stride = stride + h_crop, w_crop = crop_size + batch_size, _, h_img, w_img = inputs.size() + out_channels = 1 + h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 + w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 + preds = inputs.new_zeros((batch_size, out_channels, h_img, w_img)) + # aux_out1 = inputs.new_zeros((batch_size, out_channels, h_img, w_img)) + # aux_out2 = inputs.new_zeros((batch_size, out_channels, h_img, w_img)) + count_mat = inputs.new_zeros((batch_size, 1, h_img, w_img)) + crop_imgs = [] + x1s = [] + x2s = [] + y1s = [] + y2s = [] + for h_idx in range(h_grids): + for w_idx in range(w_grids): + y1 = h_idx * h_stride + x1 = w_idx * w_stride + y2 = min(y1 + h_crop, h_img) + x2 = min(x1 + w_crop, w_img) + y1 = max(y2 - h_crop, 0) + x1 = max(x2 - w_crop, 0) + crop_img = inputs[:, :, y1:y2, x1:x2] + crop_imgs.append(crop_img) + x1s.append(x1) + x2s.append(x2) + y1s.append(y1) + y2s.append(y2) + crop_imgs = torch.cat(crop_imgs, dim=0) + crop_seg_logits_list = [] + num_windows = crop_imgs.shape[0] + bs = bs + length = math.ceil(num_windows / bs) + for i in range(length): + if i == length - 1: + crop_imgs_temp = crop_imgs[bs * i:num_windows, ...] + else: + crop_imgs_temp = crop_imgs[bs * i:bs * (i + 1), ...] + + crop_seg_logits = self.model.sample(batch_size=crop_imgs_temp.shape[0], cond=crop_imgs_temp, mask=mask) + crop_seg_logits_list.append(crop_seg_logits) + crop_seg_logits = torch.cat(crop_seg_logits_list, dim=0) + for crop_seg_logit, x1, x2, y1, y2 in zip(crop_seg_logits, x1s, x2s, y1s, y2s): + preds += F.pad(crop_seg_logit, + (int(x1), int(preds.shape[3] - x2), int(y1), + int(preds.shape[2] - y2))) + count_mat[:, :, y1:y2, x1:x2] += 1 + + assert (count_mat == 0).sum() == 0 + seg_logits = preds / count_mat + return seg_logits diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/requirement.txt b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/requirement.txt new file mode 100644 index 00000000..333c5e3d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/requirement.txt @@ -0,0 +1,9 @@ +#torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113 +einops +scikit-learn +scipy +tensorboard +fvcore +albumentations +omegaconf +numpy==1.23.5 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/ade20k.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/ade20k.py new file mode 100644 index 00000000..53586e54 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/ade20k.py @@ -0,0 +1,124 @@ +import os +import numpy as np +import cv2 +import albumentations +from PIL import Image +from torch.utils.data import Dataset + +from custom_controlnet_aux.diffusion_edge.taming.data.sflckr import SegmentationBase # for examples included in repo + + +class Examples(SegmentationBase): + def __init__(self, size=256, random_crop=False, interpolation="bicubic"): + super().__init__(data_csv="data/ade20k_examples.txt", + data_root="data/ade20k_images", + segmentation_root="data/ade20k_segmentations", + size=size, random_crop=random_crop, + interpolation=interpolation, + n_labels=151, shift_segmentation=False) + + +# With semantic map and scene label +class ADE20kBase(Dataset): + def __init__(self, config=None, size=None, random_crop=False, interpolation="bicubic", crop_size=None): + self.split = self.get_split() + self.n_labels = 151 # unknown + 150 + self.data_csv = {"train": "data/ade20k_train.txt", + "validation": "data/ade20k_test.txt"}[self.split] + self.data_root = "data/ade20k_root" + with open(os.path.join(self.data_root, "sceneCategories.txt"), "r") as f: + self.scene_categories = f.read().splitlines() + self.scene_categories = dict(line.split() for line in self.scene_categories) + with open(self.data_csv, "r") as f: + self.image_paths = f.read().splitlines() + self._length = len(self.image_paths) + self.labels = { + "relative_file_path_": [l for l in self.image_paths], + "file_path_": [os.path.join(self.data_root, "images", l) + for l in self.image_paths], + "relative_segmentation_path_": [l.replace(".jpg", ".png") + for l in self.image_paths], + "segmentation_path_": [os.path.join(self.data_root, "annotations", + l.replace(".jpg", ".png")) + for l in self.image_paths], + "scene_category": [self.scene_categories[l.split("/")[1].replace(".jpg", "")] + for l in self.image_paths], + } + + size = None if size is not None and size<=0 else size + self.size = size + if crop_size is None: + self.crop_size = size if size is not None else None + else: + self.crop_size = crop_size + if self.size is not None: + self.interpolation = interpolation + self.interpolation = { + "nearest": cv2.INTER_NEAREST, + "bilinear": cv2.INTER_LINEAR, + "bicubic": cv2.INTER_CUBIC, + "area": cv2.INTER_AREA, + "lanczos": cv2.INTER_LANCZOS4}[self.interpolation] + self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=self.interpolation) + self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=cv2.INTER_NEAREST) + + if crop_size is not None: + self.center_crop = not random_crop + if self.center_crop: + self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size) + else: + self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size) + self.preprocessor = self.cropper + + def __len__(self): + return self._length + + def __getitem__(self, i): + example = dict((k, self.labels[k][i]) for k in self.labels) + image = Image.open(example["file_path_"]) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + if self.size is not None: + image = self.image_rescaler(image=image)["image"] + segmentation = Image.open(example["segmentation_path_"]) + segmentation = np.array(segmentation).astype(np.uint8) + if self.size is not None: + segmentation = self.segmentation_rescaler(image=segmentation)["image"] + if self.size is not None: + processed = self.preprocessor(image=image, mask=segmentation) + else: + processed = {"image": image, "mask": segmentation} + example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32) + segmentation = processed["mask"] + onehot = np.eye(self.n_labels)[segmentation] + example["segmentation"] = onehot + return example + + +class ADE20kTrain(ADE20kBase): + # default to random_crop=True + def __init__(self, config=None, size=None, random_crop=True, interpolation="bicubic", crop_size=None): + super().__init__(config=config, size=size, random_crop=random_crop, + interpolation=interpolation, crop_size=crop_size) + + def get_split(self): + return "train" + + +class ADE20kValidation(ADE20kBase): + def get_split(self): + return "validation" + + +if __name__ == "__main__": + dset = ADE20kValidation() + ex = dset[0] + for k in ["image", "scene_category", "segmentation"]: + print(type(ex[k])) + try: + print(ex[k].shape) + except: + print(ex[k]) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_coco.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_coco.py new file mode 100644 index 00000000..bb27bbfb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_coco.py @@ -0,0 +1,139 @@ +import json +from itertools import chain +from pathlib import Path +from typing import Iterable, Dict, List, Callable, Any +from collections import defaultdict + +from tqdm import tqdm + +from custom_controlnet_aux.diffusion_edge.taming.data.annotated_objects_dataset import AnnotatedObjectsDataset +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import Annotation, ImageDescription, Category + +COCO_PATH_STRUCTURE = { + 'train': { + 'top_level': '', + 'instances_annotations': 'annotations/instances_train2017.json', + 'stuff_annotations': 'annotations/stuff_train2017.json', + 'files': 'train2017' + }, + 'validation': { + 'top_level': '', + 'instances_annotations': 'annotations/instances_val2017.json', + 'stuff_annotations': 'annotations/stuff_val2017.json', + 'files': 'val2017' + } +} + + +def load_image_descriptions(description_json: List[Dict]) -> Dict[str, ImageDescription]: + return { + str(img['id']): ImageDescription( + id=img['id'], + license=img.get('license'), + file_name=img['file_name'], + coco_url=img['coco_url'], + original_size=(img['width'], img['height']), + date_captured=img.get('date_captured'), + flickr_url=img.get('flickr_url') + ) + for img in description_json + } + + +def load_categories(category_json: Iterable) -> Dict[str, Category]: + return {str(cat['id']): Category(id=str(cat['id']), super_category=cat['supercategory'], name=cat['name']) + for cat in category_json if cat['name'] != 'other'} + + +def load_annotations(annotations_json: List[Dict], image_descriptions: Dict[str, ImageDescription], + category_no_for_id: Callable[[str], int], split: str) -> Dict[str, List[Annotation]]: + annotations = defaultdict(list) + total = sum(len(a) for a in annotations_json) + for ann in tqdm(chain(*annotations_json), f'Loading {split} annotations', total=total): + image_id = str(ann['image_id']) + if image_id not in image_descriptions: + raise ValueError(f'image_id [{image_id}] has no image description.') + category_id = ann['category_id'] + try: + category_no = category_no_for_id(str(category_id)) + except KeyError: + continue + + width, height = image_descriptions[image_id].original_size + bbox = (ann['bbox'][0] / width, ann['bbox'][1] / height, ann['bbox'][2] / width, ann['bbox'][3] / height) + + annotations[image_id].append( + Annotation( + id=ann['id'], + area=bbox[2]*bbox[3], # use bbox area + is_group_of=ann['iscrowd'], + image_id=ann['image_id'], + bbox=bbox, + category_id=str(category_id), + category_no=category_no + ) + ) + return dict(annotations) + + +class AnnotatedObjectsCoco(AnnotatedObjectsDataset): + def __init__(self, use_things: bool = True, use_stuff: bool = True, **kwargs): + """ + @param data_path: is the path to the following folder structure: + coco/ + β”œβ”€β”€ annotations + β”‚ β”œβ”€β”€ instances_train2017.json + β”‚ β”œβ”€β”€ instances_val2017.json + β”‚ β”œβ”€β”€ stuff_train2017.json + β”‚ └── stuff_val2017.json + β”œβ”€β”€ train2017 + β”‚ β”œβ”€β”€ 000000000009.jpg + β”‚ β”œβ”€β”€ 000000000025.jpg + β”‚ └── ... + β”œβ”€β”€ val2017 + β”‚ β”œβ”€β”€ 000000000139.jpg + β”‚ β”œβ”€β”€ 000000000285.jpg + β”‚ └── ... + @param: split: one of 'train' or 'validation' + @param: desired image size (give square images) + """ + super().__init__(**kwargs) + self.use_things = use_things + self.use_stuff = use_stuff + + with open(self.paths['instances_annotations']) as f: + inst_data_json = json.load(f) + with open(self.paths['stuff_annotations']) as f: + stuff_data_json = json.load(f) + + category_jsons = [] + annotation_jsons = [] + if self.use_things: + category_jsons.append(inst_data_json['categories']) + annotation_jsons.append(inst_data_json['annotations']) + if self.use_stuff: + category_jsons.append(stuff_data_json['categories']) + annotation_jsons.append(stuff_data_json['annotations']) + + self.categories = load_categories(chain(*category_jsons)) + self.filter_categories() + self.setup_category_id_and_number() + + self.image_descriptions = load_image_descriptions(inst_data_json['images']) + annotations = load_annotations(annotation_jsons, self.image_descriptions, self.get_category_number, self.split) + self.annotations = self.filter_object_number(annotations, self.min_object_area, + self.min_objects_per_image, self.max_objects_per_image) + self.image_ids = list(self.annotations.keys()) + self.clean_up_annotations_and_image_descriptions() + + def get_path_structure(self) -> Dict[str, str]: + if self.split not in COCO_PATH_STRUCTURE: + raise ValueError(f'Split [{self.split} does not exist for COCO data.]') + return COCO_PATH_STRUCTURE[self.split] + + def get_image_path(self, image_id: str) -> Path: + return self.paths['files'].joinpath(self.image_descriptions[str(image_id)].file_name) + + def get_image_description(self, image_id: str) -> Dict[str, Any]: + # noinspection PyProtectedMember + return self.image_descriptions[image_id]._asdict() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_dataset.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_dataset.py new file mode 100644 index 00000000..af3af83a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_dataset.py @@ -0,0 +1,218 @@ +from pathlib import Path +from typing import Optional, List, Callable, Dict, Any, Union +import warnings + +import PIL.Image as pil_image +from torch import Tensor +from torch.utils.data import Dataset +from torchvision import transforms + +from custom_controlnet_aux.diffusion_edge.taming.data.conditional_builder.objects_bbox import ObjectsBoundingBoxConditionalBuilder +from custom_controlnet_aux.diffusion_edge.taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder +from custom_controlnet_aux.diffusion_edge.taming.data.conditional_builder.utils import load_object_from_string +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import BoundingBox, CropMethodType, Image, Annotation, SplitType +from custom_controlnet_aux.diffusion_edge.taming.data.image_transforms import CenterCropReturnCoordinates, RandomCrop1dReturnCoordinates, \ + Random2dCropReturnCoordinates, RandomHorizontalFlipReturn, convert_pil_to_tensor + + +class AnnotatedObjectsDataset(Dataset): + def __init__(self, data_path: Union[str, Path], split: SplitType, keys: List[str], target_image_size: int, + min_object_area: float, min_objects_per_image: int, max_objects_per_image: int, + crop_method: CropMethodType, random_flip: bool, no_tokens: int, use_group_parameter: bool, + encode_crop: bool, category_allow_list_target: str = "", category_mapping_target: str = "", + no_object_classes: Optional[int] = None): + self.data_path = data_path + self.split = split + self.keys = keys + self.target_image_size = target_image_size + self.min_object_area = min_object_area + self.min_objects_per_image = min_objects_per_image + self.max_objects_per_image = max_objects_per_image + self.crop_method = crop_method + self.random_flip = random_flip + self.no_tokens = no_tokens + self.use_group_parameter = use_group_parameter + self.encode_crop = encode_crop + + self.annotations = None + self.image_descriptions = None + self.categories = None + self.category_ids = None + self.category_number = None + self.image_ids = None + self.transform_functions: List[Callable] = self.setup_transform(target_image_size, crop_method, random_flip) + self.paths = self.build_paths(self.data_path) + self._conditional_builders = None + self.category_allow_list = None + if category_allow_list_target: + allow_list = load_object_from_string(category_allow_list_target) + self.category_allow_list = {name for name, _ in allow_list} + self.category_mapping = {} + if category_mapping_target: + self.category_mapping = load_object_from_string(category_mapping_target) + self.no_object_classes = no_object_classes + + def build_paths(self, top_level: Union[str, Path]) -> Dict[str, Path]: + top_level = Path(top_level) + sub_paths = {name: top_level.joinpath(sub_path) for name, sub_path in self.get_path_structure().items()} + for path in sub_paths.values(): + if not path.exists(): + raise FileNotFoundError(f'{type(self).__name__} data structure error: [{path}] does not exist.') + return sub_paths + + @staticmethod + def load_image_from_disk(path: Path) -> Image: + return pil_image.open(path).convert('RGB') + + @staticmethod + def setup_transform(target_image_size: int, crop_method: CropMethodType, random_flip: bool): + transform_functions = [] + if crop_method == 'none': + transform_functions.append(transforms.Resize((target_image_size, target_image_size))) + elif crop_method == 'center': + transform_functions.extend([ + transforms.Resize(target_image_size), + CenterCropReturnCoordinates(target_image_size) + ]) + elif crop_method == 'random-1d': + transform_functions.extend([ + transforms.Resize(target_image_size), + RandomCrop1dReturnCoordinates(target_image_size) + ]) + elif crop_method == 'random-2d': + transform_functions.extend([ + Random2dCropReturnCoordinates(target_image_size), + transforms.Resize(target_image_size) + ]) + elif crop_method is None: + return None + else: + raise ValueError(f'Received invalid crop method [{crop_method}].') + if random_flip: + transform_functions.append(RandomHorizontalFlipReturn()) + transform_functions.append(transforms.Lambda(lambda x: x / 127.5 - 1.)) + return transform_functions + + def image_transform(self, x: Tensor) -> (Optional[BoundingBox], Optional[bool], Tensor): + crop_bbox = None + flipped = None + for t in self.transform_functions: + if isinstance(t, (RandomCrop1dReturnCoordinates, CenterCropReturnCoordinates, Random2dCropReturnCoordinates)): + crop_bbox, x = t(x) + elif isinstance(t, RandomHorizontalFlipReturn): + flipped, x = t(x) + else: + x = t(x) + return crop_bbox, flipped, x + + @property + def no_classes(self) -> int: + return self.no_object_classes if self.no_object_classes else len(self.categories) + + @property + def conditional_builders(self) -> ObjectsCenterPointsConditionalBuilder: + # cannot set this up in init because no_classes is only known after loading data in init of superclass + if self._conditional_builders is None: + self._conditional_builders = { + 'objects_center_points': ObjectsCenterPointsConditionalBuilder( + self.no_classes, + self.max_objects_per_image, + self.no_tokens, + self.encode_crop, + self.use_group_parameter, + getattr(self, 'use_additional_parameters', False) + ), + 'objects_bbox': ObjectsBoundingBoxConditionalBuilder( + self.no_classes, + self.max_objects_per_image, + self.no_tokens, + self.encode_crop, + self.use_group_parameter, + getattr(self, 'use_additional_parameters', False) + ) + } + return self._conditional_builders + + def filter_categories(self) -> None: + if self.category_allow_list: + self.categories = {id_: cat for id_, cat in self.categories.items() if cat.name in self.category_allow_list} + if self.category_mapping: + self.categories = {id_: cat for id_, cat in self.categories.items() if cat.id not in self.category_mapping} + + def setup_category_id_and_number(self) -> None: + self.category_ids = list(self.categories.keys()) + self.category_ids.sort() + if '/m/01s55n' in self.category_ids: + self.category_ids.remove('/m/01s55n') + self.category_ids.append('/m/01s55n') + self.category_number = {category_id: i for i, category_id in enumerate(self.category_ids)} + if self.category_allow_list is not None and self.category_mapping is None \ + and len(self.category_ids) != len(self.category_allow_list): + warnings.warn('Unexpected number of categories: Mismatch with category_allow_list. ' + 'Make sure all names in category_allow_list exist.') + + def clean_up_annotations_and_image_descriptions(self) -> None: + image_id_set = set(self.image_ids) + self.annotations = {k: v for k, v in self.annotations.items() if k in image_id_set} + self.image_descriptions = {k: v for k, v in self.image_descriptions.items() if k in image_id_set} + + @staticmethod + def filter_object_number(all_annotations: Dict[str, List[Annotation]], min_object_area: float, + min_objects_per_image: int, max_objects_per_image: int) -> Dict[str, List[Annotation]]: + filtered = {} + for image_id, annotations in all_annotations.items(): + annotations_with_min_area = [a for a in annotations if a.area > min_object_area] + if min_objects_per_image <= len(annotations_with_min_area) <= max_objects_per_image: + filtered[image_id] = annotations_with_min_area + return filtered + + def __len__(self): + return len(self.image_ids) + + def __getitem__(self, n: int) -> Dict[str, Any]: + image_id = self.get_image_id(n) + sample = self.get_image_description(image_id) + sample['annotations'] = self.get_annotation(image_id) + + if 'image' in self.keys: + sample['image_path'] = str(self.get_image_path(image_id)) + sample['image'] = self.load_image_from_disk(sample['image_path']) + sample['image'] = convert_pil_to_tensor(sample['image']) + sample['crop_bbox'], sample['flipped'], sample['image'] = self.image_transform(sample['image']) + sample['image'] = sample['image'].permute(1, 2, 0) + + for conditional, builder in self.conditional_builders.items(): + if conditional in self.keys: + sample[conditional] = builder.build(sample['annotations'], sample['crop_bbox'], sample['flipped']) + + if self.keys: + # only return specified keys + sample = {key: sample[key] for key in self.keys} + return sample + + def get_image_id(self, no: int) -> str: + return self.image_ids[no] + + def get_annotation(self, image_id: str) -> str: + return self.annotations[image_id] + + def get_textual_label_for_category_id(self, category_id: str) -> str: + return self.categories[category_id].name + + def get_textual_label_for_category_no(self, category_no: int) -> str: + return self.categories[self.get_category_id(category_no)].name + + def get_category_number(self, category_id: str) -> int: + return self.category_number[category_id] + + def get_category_id(self, category_no: int) -> str: + return self.category_ids[category_no] + + def get_image_description(self, image_id: str) -> Dict[str, Any]: + raise NotImplementedError() + + def get_path_structure(self): + raise NotImplementedError + + def get_image_path(self, image_id: str) -> Path: + raise NotImplementedError diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_open_images.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_open_images.py new file mode 100644 index 00000000..bc5c8185 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/annotated_objects_open_images.py @@ -0,0 +1,137 @@ +from collections import defaultdict +from csv import DictReader, reader as TupleReader +from pathlib import Path +from typing import Dict, List, Any +import warnings + +from custom_controlnet_aux.diffusion_edge.taming.data.annotated_objects_dataset import AnnotatedObjectsDataset +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import Annotation, Category +from tqdm import tqdm + +OPEN_IMAGES_STRUCTURE = { + 'train': { + 'top_level': '', + 'class_descriptions': 'class-descriptions-boxable.csv', + 'annotations': 'oidv6-train-annotations-bbox.csv', + 'file_list': 'train-images-boxable.csv', + 'files': 'train' + }, + 'validation': { + 'top_level': '', + 'class_descriptions': 'class-descriptions-boxable.csv', + 'annotations': 'validation-annotations-bbox.csv', + 'file_list': 'validation-images.csv', + 'files': 'validation' + }, + 'test': { + 'top_level': '', + 'class_descriptions': 'class-descriptions-boxable.csv', + 'annotations': 'test-annotations-bbox.csv', + 'file_list': 'test-images.csv', + 'files': 'test' + } +} + + +def load_annotations(descriptor_path: Path, min_object_area: float, category_mapping: Dict[str, str], + category_no_for_id: Dict[str, int]) -> Dict[str, List[Annotation]]: + annotations: Dict[str, List[Annotation]] = defaultdict(list) + with open(descriptor_path) as file: + reader = DictReader(file) + for i, row in tqdm(enumerate(reader), total=14620000, desc='Loading OpenImages annotations'): + width = float(row['XMax']) - float(row['XMin']) + height = float(row['YMax']) - float(row['YMin']) + area = width * height + category_id = row['LabelName'] + if category_id in category_mapping: + category_id = category_mapping[category_id] + if area >= min_object_area and category_id in category_no_for_id: + annotations[row['ImageID']].append( + Annotation( + id=i, + image_id=row['ImageID'], + source=row['Source'], + category_id=category_id, + category_no=category_no_for_id[category_id], + confidence=float(row['Confidence']), + bbox=(float(row['XMin']), float(row['YMin']), width, height), + area=area, + is_occluded=bool(int(row['IsOccluded'])), + is_truncated=bool(int(row['IsTruncated'])), + is_group_of=bool(int(row['IsGroupOf'])), + is_depiction=bool(int(row['IsDepiction'])), + is_inside=bool(int(row['IsInside'])) + ) + ) + if 'train' in str(descriptor_path) and i < 14000000: + warnings.warn(f'Running with subset of Open Images. Train dataset has length [{len(annotations)}].') + return dict(annotations) + + +def load_image_ids(csv_path: Path) -> List[str]: + with open(csv_path) as file: + reader = DictReader(file) + return [row['image_name'] for row in reader] + + +def load_categories(csv_path: Path) -> Dict[str, Category]: + with open(csv_path) as file: + reader = TupleReader(file) + return {row[0]: Category(id=row[0], name=row[1], super_category=None) for row in reader} + + +class AnnotatedObjectsOpenImages(AnnotatedObjectsDataset): + def __init__(self, use_additional_parameters: bool, **kwargs): + """ + @param data_path: is the path to the following folder structure: + open_images/ + β”‚ oidv6-train-annotations-bbox.csv + β”œβ”€β”€ class-descriptions-boxable.csv + β”œβ”€β”€ oidv6-train-annotations-bbox.csv + β”œβ”€β”€ test + β”‚ β”œβ”€β”€ 000026e7ee790996.jpg + β”‚ β”œβ”€β”€ 000062a39995e348.jpg + β”‚ └── ... + β”œβ”€β”€ test-annotations-bbox.csv + β”œβ”€β”€ test-images.csv + β”œβ”€β”€ train + β”‚ β”œβ”€β”€ 000002b66c9c498e.jpg + β”‚ β”œβ”€β”€ 000002b97e5471a0.jpg + β”‚ └── ... + β”œβ”€β”€ train-images-boxable.csv + β”œβ”€β”€ validation + β”‚ β”œβ”€β”€ 0001eeaf4aed83f9.jpg + β”‚ β”œβ”€β”€ 0004886b7d043cfd.jpg + β”‚ └── ... + β”œβ”€β”€ validation-annotations-bbox.csv + └── validation-images.csv + @param: split: one of 'train', 'validation' or 'test' + @param: desired image size (returns square images) + """ + + super().__init__(**kwargs) + self.use_additional_parameters = use_additional_parameters + + self.categories = load_categories(self.paths['class_descriptions']) + self.filter_categories() + self.setup_category_id_and_number() + + self.image_descriptions = {} + annotations = load_annotations(self.paths['annotations'], self.min_object_area, self.category_mapping, + self.category_number) + self.annotations = self.filter_object_number(annotations, self.min_object_area, self.min_objects_per_image, + self.max_objects_per_image) + self.image_ids = list(self.annotations.keys()) + self.clean_up_annotations_and_image_descriptions() + + def get_path_structure(self) -> Dict[str, str]: + if self.split not in OPEN_IMAGES_STRUCTURE: + raise ValueError(f'Split [{self.split} does not exist for Open Images data.]') + return OPEN_IMAGES_STRUCTURE[self.split] + + def get_image_path(self, image_id: str) -> Path: + return self.paths['files'].joinpath(f'{image_id:0>16}.jpg') + + def get_image_description(self, image_id: str) -> Dict[str, Any]: + image_path = self.get_image_path(image_id) + return {'file_path': str(image_path), 'file_name': image_path.name} diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/base.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/base.py new file mode 100644 index 00000000..e21667df --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/base.py @@ -0,0 +1,70 @@ +import bisect +import numpy as np +import albumentations +from PIL import Image +from torch.utils.data import Dataset, ConcatDataset + + +class ConcatDatasetWithIndex(ConcatDataset): + """Modified from original pytorch code to return dataset idx""" + def __getitem__(self, idx): + if idx < 0: + if -idx > len(self): + raise ValueError("absolute value of index should not exceed dataset length") + idx = len(self) + idx + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if dataset_idx == 0: + sample_idx = idx + else: + sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] + return self.datasets[dataset_idx][sample_idx], dataset_idx + + +class ImagePaths(Dataset): + def __init__(self, paths, size=None, random_crop=False, labels=None): + self.size = size + self.random_crop = random_crop + + self.labels = dict() if labels is None else labels + self.labels["file_path_"] = paths + self._length = len(paths) + + if self.size is not None and self.size > 0: + self.rescaler = albumentations.SmallestMaxSize(max_size = self.size) + if not self.random_crop: + self.cropper = albumentations.CenterCrop(height=self.size,width=self.size) + else: + self.cropper = albumentations.RandomCrop(height=self.size,width=self.size) + self.preprocessor = albumentations.Compose([self.rescaler, self.cropper]) + else: + self.preprocessor = lambda **kwargs: kwargs + + def __len__(self): + return self._length + + def preprocess_image(self, image_path): + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + image = self.preprocessor(image=image)["image"] + image = (image/127.5 - 1.0).astype(np.float32) + return image + + def __getitem__(self, i): + example = dict() + example["image"] = self.preprocess_image(self.labels["file_path_"][i]) + for k in self.labels: + example[k] = self.labels[k][i] + return example + + +class NumpyPaths(ImagePaths): + def preprocess_image(self, image_path): + image = np.load(image_path).squeeze(0) # 3 x 1024 x 1024 + image = np.transpose(image, (1,2,0)) + image = Image.fromarray(image, mode="RGB") + image = np.array(image).astype(np.uint8) + image = self.preprocessor(image=image)["image"] + image = (image/127.5 - 1.0).astype(np.float32) + return image diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/coco.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/coco.py new file mode 100644 index 00000000..23e2da22 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/coco.py @@ -0,0 +1,176 @@ +import os +import json +import albumentations +import numpy as np +from PIL import Image +from tqdm import tqdm +from torch.utils.data import Dataset + +from custom_controlnet_aux.diffusion_edge.taming.data.sflckr import SegmentationBase # for examples included in repo + + +class Examples(SegmentationBase): + def __init__(self, size=256, random_crop=False, interpolation="bicubic"): + super().__init__(data_csv="data/coco_examples.txt", + data_root="data/coco_images", + segmentation_root="data/coco_segmentations", + size=size, random_crop=random_crop, + interpolation=interpolation, + n_labels=183, shift_segmentation=True) + + +class CocoBase(Dataset): + """needed for (image, caption, segmentation) pairs""" + def __init__(self, size=None, dataroot="", datajson="", onehot_segmentation=False, use_stuffthing=False, + crop_size=None, force_no_crop=False, given_files=None): + self.split = self.get_split() + self.size = size + if crop_size is None: + self.crop_size = size + else: + self.crop_size = crop_size + + self.onehot = onehot_segmentation # return segmentation as rgb or one hot + self.stuffthing = use_stuffthing # include thing in segmentation + if self.onehot and not self.stuffthing: + raise NotImplemented("One hot mode is only supported for the " + "stuffthings version because labels are stored " + "a bit different.") + + data_json = datajson + with open(data_json) as json_file: + self.json_data = json.load(json_file) + self.img_id_to_captions = dict() + self.img_id_to_filepath = dict() + self.img_id_to_segmentation_filepath = dict() + + assert data_json.split("/")[-1] in ["captions_train2017.json", + "captions_val2017.json"] + if self.stuffthing: + self.segmentation_prefix = ( + "data/cocostuffthings/val2017" if + data_json.endswith("captions_val2017.json") else + "data/cocostuffthings/train2017") + else: + self.segmentation_prefix = ( + "data/coco/annotations/stuff_val2017_pixelmaps" if + data_json.endswith("captions_val2017.json") else + "data/coco/annotations/stuff_train2017_pixelmaps") + + imagedirs = self.json_data["images"] + self.labels = {"image_ids": list()} + for imgdir in tqdm(imagedirs, desc="ImgToPath"): + self.img_id_to_filepath[imgdir["id"]] = os.path.join(dataroot, imgdir["file_name"]) + self.img_id_to_captions[imgdir["id"]] = list() + pngfilename = imgdir["file_name"].replace("jpg", "png") + self.img_id_to_segmentation_filepath[imgdir["id"]] = os.path.join( + self.segmentation_prefix, pngfilename) + if given_files is not None: + if pngfilename in given_files: + self.labels["image_ids"].append(imgdir["id"]) + else: + self.labels["image_ids"].append(imgdir["id"]) + + capdirs = self.json_data["annotations"] + for capdir in tqdm(capdirs, desc="ImgToCaptions"): + # there are in average 5 captions per image + self.img_id_to_captions[capdir["image_id"]].append(np.array([capdir["caption"]])) + + self.rescaler = albumentations.SmallestMaxSize(max_size=self.size) + if self.split=="validation": + self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size) + else: + self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size) + self.preprocessor = albumentations.Compose( + [self.rescaler, self.cropper], + additional_targets={"segmentation": "image"}) + if force_no_crop: + self.rescaler = albumentations.Resize(height=self.size, width=self.size) + self.preprocessor = albumentations.Compose( + [self.rescaler], + additional_targets={"segmentation": "image"}) + + def __len__(self): + return len(self.labels["image_ids"]) + + def preprocess_image(self, image_path, segmentation_path): + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + + segmentation = Image.open(segmentation_path) + if not self.onehot and not segmentation.mode == "RGB": + segmentation = segmentation.convert("RGB") + segmentation = np.array(segmentation).astype(np.uint8) + if self.onehot: + assert self.stuffthing + # stored in caffe format: unlabeled==255. stuff and thing from + # 0-181. to be compatible with the labels in + # https://github.com/nightrome/cocostuff/blob/master/labels.txt + # we shift stuffthing one to the right and put unlabeled in zero + # as long as segmentation is uint8 shifting to right handles the + # latter too + assert segmentation.dtype == np.uint8 + segmentation = segmentation + 1 + + processed = self.preprocessor(image=image, segmentation=segmentation) + image, segmentation = processed["image"], processed["segmentation"] + image = (image / 127.5 - 1.0).astype(np.float32) + + if self.onehot: + assert segmentation.dtype == np.uint8 + # make it one hot + n_labels = 183 + flatseg = np.ravel(segmentation) + onehot = np.zeros((flatseg.size, n_labels), dtype=np.bool) + onehot[np.arange(flatseg.size), flatseg] = True + onehot = onehot.reshape(segmentation.shape + (n_labels,)).astype(int) + segmentation = onehot + else: + segmentation = (segmentation / 127.5 - 1.0).astype(np.float32) + return image, segmentation + + def __getitem__(self, i): + img_path = self.img_id_to_filepath[self.labels["image_ids"][i]] + seg_path = self.img_id_to_segmentation_filepath[self.labels["image_ids"][i]] + image, segmentation = self.preprocess_image(img_path, seg_path) + captions = self.img_id_to_captions[self.labels["image_ids"][i]] + # randomly draw one of all available captions per image + caption = captions[np.random.randint(0, len(captions))] + example = {"image": image, + "caption": [str(caption[0])], + "segmentation": segmentation, + "img_path": img_path, + "seg_path": seg_path, + "filename_": img_path.split(os.sep)[-1] + } + return example + + +class CocoImagesAndCaptionsTrain(CocoBase): + """returns a pair of (image, caption)""" + def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False): + super().__init__(size=size, + dataroot="data/coco/train2017", + datajson="data/coco/annotations/captions_train2017.json", + onehot_segmentation=onehot_segmentation, + use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop) + + def get_split(self): + return "train" + + +class CocoImagesAndCaptionsValidation(CocoBase): + """returns a pair of (image, caption)""" + def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False, + given_files=None): + super().__init__(size=size, + dataroot="data/coco/val2017", + datajson="data/coco/annotations/captions_val2017.json", + onehot_segmentation=onehot_segmentation, + use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop, + given_files=given_files) + + def get_split(self): + return "validation" diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/objects_bbox.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/objects_bbox.py new file mode 100644 index 00000000..44fa4867 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/objects_bbox.py @@ -0,0 +1,60 @@ +from itertools import cycle +from typing import List, Tuple, Callable, Optional + +from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont +from more_itertools.recipes import grouper +from custom_controlnet_aux.diffusion_edge.taming.data.image_transforms import convert_pil_to_tensor +from torch import LongTensor, Tensor + +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import BoundingBox, Annotation +from custom_controlnet_aux.diffusion_edge.taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder +from custom_controlnet_aux.diffusion_edge.taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, additional_parameters_string, \ + pad_list, get_plot_font_size, absolute_bbox + + +class ObjectsBoundingBoxConditionalBuilder(ObjectsCenterPointsConditionalBuilder): + @property + def object_descriptor_length(self) -> int: + return 3 + + def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]: + object_triples = [ + (self.object_representation(ann), *self.token_pair_from_bbox(ann.bbox)) + for ann in annotations + ] + empty_triple = (self.none, self.none, self.none) + object_triples = pad_list(object_triples, empty_triple, self.no_max_objects) + return object_triples + + def inverse_build(self, conditional: LongTensor) -> Tuple[List[Tuple[int, BoundingBox]], Optional[BoundingBox]]: + conditional_list = conditional.tolist() + crop_coordinates = None + if self.encode_crop: + crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1]) + conditional_list = conditional_list[:-2] + object_triples = grouper(conditional_list, 3) + assert conditional.shape[0] == self.embedding_dim + return [ + (object_triple[0], self.bbox_from_token_pair(object_triple[1], object_triple[2])) + for object_triple in object_triples if object_triple[0] != self.none + ], crop_coordinates + + def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int], + line_width: int = 3, font_size: Optional[int] = None) -> Tensor: + plot = pil_image.new('RGB', figure_size, WHITE) + draw = pil_img_draw.Draw(plot) + font = ImageFont.truetype( + "/usr/share/fonts/truetype/lato/Lato-Regular.ttf", + size=get_plot_font_size(font_size, figure_size) + ) + width, height = plot.size + description, crop_coordinates = self.inverse_build(conditional) + for (representation, bbox), color in zip(description, cycle(COLOR_PALETTE)): + annotation = self.representation_to_annotation(representation) + class_label = label_for_category_no(annotation.category_no) + ' ' + additional_parameters_string(annotation) + bbox = absolute_bbox(bbox, width, height) + draw.rectangle(bbox, outline=color, width=line_width) + draw.text((bbox[0] + line_width, bbox[1] + line_width), class_label, anchor='la', fill=BLACK, font=font) + if crop_coordinates is not None: + draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width) + return convert_pil_to_tensor(plot) / 127.5 - 1. diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/objects_center_points.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/objects_center_points.py new file mode 100644 index 00000000..8fd1989c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/objects_center_points.py @@ -0,0 +1,168 @@ +import math +import random +import warnings +from itertools import cycle +from typing import List, Optional, Tuple, Callable + +from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont +from more_itertools.recipes import grouper +from custom_controlnet_aux.diffusion_edge.taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, FULL_CROP, filter_annotations, \ + additional_parameters_string, horizontally_flip_bbox, pad_list, get_circle_size, get_plot_font_size, \ + absolute_bbox, rescale_annotations +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import BoundingBox, Annotation +from custom_controlnet_aux.diffusion_edge.taming.data.image_transforms import convert_pil_to_tensor +from torch import LongTensor, Tensor + + +class ObjectsCenterPointsConditionalBuilder: + def __init__(self, no_object_classes: int, no_max_objects: int, no_tokens: int, encode_crop: bool, + use_group_parameter: bool, use_additional_parameters: bool): + self.no_object_classes = no_object_classes + self.no_max_objects = no_max_objects + self.no_tokens = no_tokens + self.encode_crop = encode_crop + self.no_sections = int(math.sqrt(self.no_tokens)) + self.use_group_parameter = use_group_parameter + self.use_additional_parameters = use_additional_parameters + + @property + def none(self) -> int: + return self.no_tokens - 1 + + @property + def object_descriptor_length(self) -> int: + return 2 + + @property + def embedding_dim(self) -> int: + extra_length = 2 if self.encode_crop else 0 + return self.no_max_objects * self.object_descriptor_length + extra_length + + def tokenize_coordinates(self, x: float, y: float) -> int: + """ + Express 2d coordinates with one number. + Example: assume self.no_tokens = 16, then no_sections = 4: + 0 0 0 0 + 0 0 # 0 + 0 0 0 0 + 0 0 0 x + Then the # position corresponds to token 6, the x position to token 15. + @param x: float in [0, 1] + @param y: float in [0, 1] + @return: discrete tokenized coordinate + """ + x_discrete = int(round(x * (self.no_sections - 1))) + y_discrete = int(round(y * (self.no_sections - 1))) + return y_discrete * self.no_sections + x_discrete + + def coordinates_from_token(self, token: int) -> (float, float): + x = token % self.no_sections + y = token // self.no_sections + return x / (self.no_sections - 1), y / (self.no_sections - 1) + + def bbox_from_token_pair(self, token1: int, token2: int) -> BoundingBox: + x0, y0 = self.coordinates_from_token(token1) + x1, y1 = self.coordinates_from_token(token2) + return x0, y0, x1 - x0, y1 - y0 + + def token_pair_from_bbox(self, bbox: BoundingBox) -> Tuple[int, int]: + return self.tokenize_coordinates(bbox[0], bbox[1]), \ + self.tokenize_coordinates(bbox[0] + bbox[2], bbox[1] + bbox[3]) + + def inverse_build(self, conditional: LongTensor) \ + -> Tuple[List[Tuple[int, Tuple[float, float]]], Optional[BoundingBox]]: + conditional_list = conditional.tolist() + crop_coordinates = None + if self.encode_crop: + crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1]) + conditional_list = conditional_list[:-2] + table_of_content = grouper(conditional_list, self.object_descriptor_length) + assert conditional.shape[0] == self.embedding_dim + return [ + (object_tuple[0], self.coordinates_from_token(object_tuple[1])) + for object_tuple in table_of_content if object_tuple[0] != self.none + ], crop_coordinates + + def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int], + line_width: int = 3, font_size: Optional[int] = None) -> Tensor: + plot = pil_image.new('RGB', figure_size, WHITE) + draw = pil_img_draw.Draw(plot) + circle_size = get_circle_size(figure_size) + font = ImageFont.truetype('/usr/share/fonts/truetype/lato/Lato-Regular.ttf', + size=get_plot_font_size(font_size, figure_size)) + width, height = plot.size + description, crop_coordinates = self.inverse_build(conditional) + for (representation, (x, y)), color in zip(description, cycle(COLOR_PALETTE)): + x_abs, y_abs = x * width, y * height + ann = self.representation_to_annotation(representation) + label = label_for_category_no(ann.category_no) + ' ' + additional_parameters_string(ann) + ellipse_bbox = [x_abs - circle_size, y_abs - circle_size, x_abs + circle_size, y_abs + circle_size] + draw.ellipse(ellipse_bbox, fill=color, width=0) + draw.text((x_abs, y_abs), label, anchor='md', fill=BLACK, font=font) + if crop_coordinates is not None: + draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width) + return convert_pil_to_tensor(plot) / 127.5 - 1. + + def object_representation(self, annotation: Annotation) -> int: + modifier = 0 + if self.use_group_parameter: + modifier |= 1 * (annotation.is_group_of is True) + if self.use_additional_parameters: + modifier |= 2 * (annotation.is_occluded is True) + modifier |= 4 * (annotation.is_depiction is True) + modifier |= 8 * (annotation.is_inside is True) + return annotation.category_no + self.no_object_classes * modifier + + def representation_to_annotation(self, representation: int) -> Annotation: + category_no = representation % self.no_object_classes + modifier = representation // self.no_object_classes + # noinspection PyTypeChecker + return Annotation( + area=None, image_id=None, bbox=None, category_id=None, id=None, source=None, confidence=None, + category_no=category_no, + is_group_of=bool((modifier & 1) * self.use_group_parameter), + is_occluded=bool((modifier & 2) * self.use_additional_parameters), + is_depiction=bool((modifier & 4) * self.use_additional_parameters), + is_inside=bool((modifier & 8) * self.use_additional_parameters) + ) + + def _crop_encoder(self, crop_coordinates: BoundingBox) -> List[int]: + return list(self.token_pair_from_bbox(crop_coordinates)) + + def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]: + object_tuples = [ + (self.object_representation(a), + self.tokenize_coordinates(a.bbox[0] + a.bbox[2] / 2, a.bbox[1] + a.bbox[3] / 2)) + for a in annotations + ] + empty_tuple = (self.none, self.none) + object_tuples = pad_list(object_tuples, empty_tuple, self.no_max_objects) + return object_tuples + + def build(self, annotations: List, crop_coordinates: Optional[BoundingBox] = None, horizontal_flip: bool = False) \ + -> LongTensor: + if len(annotations) == 0: + warnings.warn('Did not receive any annotations.') + if len(annotations) > self.no_max_objects: + warnings.warn('Received more annotations than allowed.') + annotations = annotations[:self.no_max_objects] + + if not crop_coordinates: + crop_coordinates = FULL_CROP + + random.shuffle(annotations) + annotations = filter_annotations(annotations, crop_coordinates) + if self.encode_crop: + annotations = rescale_annotations(annotations, FULL_CROP, horizontal_flip) + if horizontal_flip: + crop_coordinates = horizontally_flip_bbox(crop_coordinates) + extra = self._crop_encoder(crop_coordinates) + else: + annotations = rescale_annotations(annotations, crop_coordinates, horizontal_flip) + extra = [] + + object_tuples = self._make_object_descriptors(annotations) + flattened = [token for tuple_ in object_tuples for token in tuple_] + extra + assert len(flattened) == self.embedding_dim + assert all(0 <= value < self.no_tokens for value in flattened) + return LongTensor(flattened) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/utils.py new file mode 100644 index 00000000..b97140e3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/conditional_builder/utils.py @@ -0,0 +1,105 @@ +import importlib +from typing import List, Any, Tuple, Optional + +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import BoundingBox, Annotation + +# source: seaborn, color palette tab10 +COLOR_PALETTE = [(30, 118, 179), (255, 126, 13), (43, 159, 43), (213, 38, 39), (147, 102, 188), + (139, 85, 74), (226, 118, 193), (126, 126, 126), (187, 188, 33), (22, 189, 206)] +BLACK = (0, 0, 0) +GRAY_75 = (63, 63, 63) +GRAY_50 = (127, 127, 127) +GRAY_25 = (191, 191, 191) +WHITE = (255, 255, 255) +FULL_CROP = (0., 0., 1., 1.) + + +def intersection_area(rectangle1: BoundingBox, rectangle2: BoundingBox) -> float: + """ + Give intersection area of two rectangles. + @param rectangle1: (x0, y0, w, h) of first rectangle + @param rectangle2: (x0, y0, w, h) of second rectangle + """ + rectangle1 = rectangle1[0], rectangle1[1], rectangle1[0] + rectangle1[2], rectangle1[1] + rectangle1[3] + rectangle2 = rectangle2[0], rectangle2[1], rectangle2[0] + rectangle2[2], rectangle2[1] + rectangle2[3] + x_overlap = max(0., min(rectangle1[2], rectangle2[2]) - max(rectangle1[0], rectangle2[0])) + y_overlap = max(0., min(rectangle1[3], rectangle2[3]) - max(rectangle1[1], rectangle2[1])) + return x_overlap * y_overlap + + +def horizontally_flip_bbox(bbox: BoundingBox) -> BoundingBox: + return 1 - (bbox[0] + bbox[2]), bbox[1], bbox[2], bbox[3] + + +def absolute_bbox(relative_bbox: BoundingBox, width: int, height: int) -> Tuple[int, int, int, int]: + bbox = relative_bbox + bbox = bbox[0] * width, bbox[1] * height, (bbox[0] + bbox[2]) * width, (bbox[1] + bbox[3]) * height + return int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]) + + +def pad_list(list_: List, pad_element: Any, pad_to_length: int) -> List: + return list_ + [pad_element for _ in range(pad_to_length - len(list_))] + + +def rescale_annotations(annotations: List[Annotation], crop_coordinates: BoundingBox, flip: bool) -> \ + List[Annotation]: + def clamp(x: float): + return max(min(x, 1.), 0.) + + def rescale_bbox(bbox: BoundingBox) -> BoundingBox: + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + if flip: + x0 = 1 - (x0 + w) + return x0, y0, w, h + + return [a._replace(bbox=rescale_bbox(a.bbox)) for a in annotations] + + +def filter_annotations(annotations: List[Annotation], crop_coordinates: BoundingBox) -> List: + return [a for a in annotations if intersection_area(a.bbox, crop_coordinates) > 0.0] + + +def additional_parameters_string(annotation: Annotation, short: bool = True) -> str: + sl = slice(1) if short else slice(None) + string = '' + if not (annotation.is_group_of or annotation.is_occluded or annotation.is_depiction or annotation.is_inside): + return string + if annotation.is_group_of: + string += 'group'[sl] + ',' + if annotation.is_occluded: + string += 'occluded'[sl] + ',' + if annotation.is_depiction: + string += 'depiction'[sl] + ',' + if annotation.is_inside: + string += 'inside'[sl] + return '(' + string.strip(",") + ')' + + +def get_plot_font_size(font_size: Optional[int], figure_size: Tuple[int, int]) -> int: + if font_size is None: + font_size = 10 + if max(figure_size) >= 256: + font_size = 12 + if max(figure_size) >= 512: + font_size = 15 + return font_size + + +def get_circle_size(figure_size: Tuple[int, int]) -> int: + circle_size = 2 + if max(figure_size) >= 256: + circle_size = 3 + if max(figure_size) >= 512: + circle_size = 4 + return circle_size + + +def load_object_from_string(object_string: str) -> Any: + """ + Source: https://stackoverflow.com/a/10773699 + """ + module_name, class_name = object_string.rsplit(".", 1) + return getattr(importlib.import_module(module_name), class_name) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/custom.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/custom.py new file mode 100644 index 00000000..b9224014 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/custom.py @@ -0,0 +1,38 @@ +import os +import numpy as np +import albumentations +from torch.utils.data import Dataset + +from custom_controlnet_aux.diffusion_edge.taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex + + +class CustomBase(Dataset): + def __init__(self, *args, **kwargs): + super().__init__() + self.data = None + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + example = self.data[i] + return example + + + +class CustomTrain(CustomBase): + def __init__(self, size, training_images_list_file): + super().__init__() + with open(training_images_list_file, "r") as f: + paths = f.read().splitlines() + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + + +class CustomTest(CustomBase): + def __init__(self, size, test_images_list_file): + super().__init__() + with open(test_images_list_file, "r") as f: + paths = f.read().splitlines() + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/faceshq.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/faceshq.py new file mode 100644 index 00000000..9d292b21 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/faceshq.py @@ -0,0 +1,134 @@ +import os +import numpy as np +import albumentations +from torch.utils.data import Dataset + +from custom_controlnet_aux.diffusion_edge.taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex + + +class FacesBase(Dataset): + def __init__(self, *args, **kwargs): + super().__init__() + self.data = None + self.keys = None + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + example = self.data[i] + ex = {} + if self.keys is not None: + for k in self.keys: + ex[k] = example[k] + else: + ex = example + return ex + + +class CelebAHQTrain(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/celebahq" + with open("data/celebahqtrain.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = NumpyPaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class CelebAHQValidation(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/celebahq" + with open("data/celebahqvalidation.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = NumpyPaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class FFHQTrain(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/ffhq" + with open("data/ffhqtrain.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class FFHQValidation(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/ffhq" + with open("data/ffhqvalidation.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class FacesHQTrain(Dataset): + # CelebAHQ [0] + FFHQ [1] + def __init__(self, size, keys=None, crop_size=None, coord=False): + d1 = CelebAHQTrain(size=size, keys=keys) + d2 = FFHQTrain(size=size, keys=keys) + self.data = ConcatDatasetWithIndex([d1, d2]) + self.coord = coord + if crop_size is not None: + self.cropper = albumentations.RandomCrop(height=crop_size,width=crop_size) + if self.coord: + self.cropper = albumentations.Compose([self.cropper], + additional_targets={"coord": "image"}) + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + ex, y = self.data[i] + if hasattr(self, "cropper"): + if not self.coord: + out = self.cropper(image=ex["image"]) + ex["image"] = out["image"] + else: + h,w,_ = ex["image"].shape + coord = np.arange(h*w).reshape(h,w,1)/(h*w) + out = self.cropper(image=ex["image"], coord=coord) + ex["image"] = out["image"] + ex["coord"] = out["coord"] + ex["class"] = y + return ex + + +class FacesHQValidation(Dataset): + # CelebAHQ [0] + FFHQ [1] + def __init__(self, size, keys=None, crop_size=None, coord=False): + d1 = CelebAHQValidation(size=size, keys=keys) + d2 = FFHQValidation(size=size, keys=keys) + self.data = ConcatDatasetWithIndex([d1, d2]) + self.coord = coord + if crop_size is not None: + self.cropper = albumentations.CenterCrop(height=crop_size,width=crop_size) + if self.coord: + self.cropper = albumentations.Compose([self.cropper], + additional_targets={"coord": "image"}) + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + ex, y = self.data[i] + if hasattr(self, "cropper"): + if not self.coord: + out = self.cropper(image=ex["image"]) + ex["image"] = out["image"] + else: + h,w,_ = ex["image"].shape + coord = np.arange(h*w).reshape(h,w,1)/(h*w) + out = self.cropper(image=ex["image"], coord=coord) + ex["image"] = out["image"] + ex["coord"] = out["coord"] + ex["class"] = y + return ex diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/helper_types.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/helper_types.py new file mode 100644 index 00000000..fb51e301 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/helper_types.py @@ -0,0 +1,49 @@ +from typing import Dict, Tuple, Optional, NamedTuple, Union +from PIL.Image import Image as pil_image +from torch import Tensor + +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +Image = Union[Tensor, pil_image] +BoundingBox = Tuple[float, float, float, float] # x0, y0, w, h +CropMethodType = Literal['none', 'random', 'center', 'random-2d'] +SplitType = Literal['train', 'validation', 'test'] + + +class ImageDescription(NamedTuple): + id: int + file_name: str + original_size: Tuple[int, int] # w, h + url: Optional[str] = None + license: Optional[int] = None + coco_url: Optional[str] = None + date_captured: Optional[str] = None + flickr_url: Optional[str] = None + flickr_id: Optional[str] = None + coco_id: Optional[str] = None + + +class Category(NamedTuple): + id: str + super_category: Optional[str] + name: str + + +class Annotation(NamedTuple): + area: float + image_id: str + bbox: BoundingBox + category_no: int + category_id: str + id: Optional[int] = None + source: Optional[str] = None + confidence: Optional[float] = None + is_group_of: Optional[bool] = None + is_truncated: Optional[bool] = None + is_occluded: Optional[bool] = None + is_depiction: Optional[bool] = None + is_inside: Optional[bool] = None + segmentation: Optional[Dict] = None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/image_transforms.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/image_transforms.py new file mode 100644 index 00000000..d1289f06 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/image_transforms.py @@ -0,0 +1,132 @@ +import random +import warnings +from typing import Union + +import torch +from torch import Tensor +from torchvision.transforms import RandomCrop, functional as F, CenterCrop, RandomHorizontalFlip, PILToTensor +from torchvision.transforms.functional import _get_image_size as get_image_size + +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import BoundingBox, Image + +pil_to_tensor = PILToTensor() + + +def convert_pil_to_tensor(image: Image) -> Tensor: + with warnings.catch_warnings(): + # to filter PyTorch UserWarning as described here: https://github.com/pytorch/vision/issues/2194 + warnings.simplefilter("ignore") + return pil_to_tensor(image) + + +class RandomCrop1dReturnCoordinates(RandomCrop): + def forward(self, img: Image) -> (BoundingBox, Image): + """ + Additionally to cropping, returns the relative coordinates of the crop bounding box. + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + Bounding box: x0, y0, w, h + PIL Image or Tensor: Cropped image. + + Based on: + torchvision.transforms.RandomCrop, torchvision 1.7.0 + """ + if self.padding is not None: + img = F.pad(img, self.padding, self.fill, self.padding_mode) + + width, height = get_image_size(img) + # pad the width if needed + if self.pad_if_needed and width < self.size[1]: + padding = [self.size[1] - width, 0] + img = F.pad(img, padding, self.fill, self.padding_mode) + # pad the height if needed + if self.pad_if_needed and height < self.size[0]: + padding = [0, self.size[0] - height] + img = F.pad(img, padding, self.fill, self.padding_mode) + + i, j, h, w = self.get_params(img, self.size) + bbox = (j / width, i / height, w / width, h / height) # x0, y0, w, h + return bbox, F.crop(img, i, j, h, w) + + +class Random2dCropReturnCoordinates(torch.nn.Module): + """ + Additionally to cropping, returns the relative coordinates of the crop bounding box. + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + Bounding box: x0, y0, w, h + PIL Image or Tensor: Cropped image. + + Based on: + torchvision.transforms.RandomCrop, torchvision 1.7.0 + """ + + def __init__(self, min_size: int): + super().__init__() + self.min_size = min_size + + def forward(self, img: Image) -> (BoundingBox, Image): + width, height = get_image_size(img) + max_size = min(width, height) + if max_size <= self.min_size: + size = max_size + else: + size = random.randint(self.min_size, max_size) + top = random.randint(0, height - size) + left = random.randint(0, width - size) + bbox = left / width, top / height, size / width, size / height + return bbox, F.crop(img, top, left, size, size) + + +class CenterCropReturnCoordinates(CenterCrop): + @staticmethod + def get_bbox_of_center_crop(width: int, height: int) -> BoundingBox: + if width > height: + w = height / width + h = 1.0 + x0 = 0.5 - w / 2 + y0 = 0. + else: + w = 1.0 + h = width / height + x0 = 0. + y0 = 0.5 - h / 2 + return x0, y0, w, h + + def forward(self, img: Union[Image, Tensor]) -> (BoundingBox, Union[Image, Tensor]): + """ + Additionally to cropping, returns the relative coordinates of the crop bounding box. + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + Bounding box: x0, y0, w, h + PIL Image or Tensor: Cropped image. + Based on: + torchvision.transforms.RandomHorizontalFlip (version 1.7.0) + """ + width, height = get_image_size(img) + return self.get_bbox_of_center_crop(width, height), F.center_crop(img, self.size) + + +class RandomHorizontalFlipReturn(RandomHorizontalFlip): + def forward(self, img: Image) -> (bool, Image): + """ + Additionally to flipping, returns a boolean whether it was flipped or not. + Args: + img (PIL Image or Tensor): Image to be flipped. + + Returns: + flipped: whether the image was flipped or not + PIL Image or Tensor: Randomly flipped image. + + Based on: + torchvision.transforms.RandomHorizontalFlip (version 1.7.0) + """ + if torch.rand(1) < self.p: + return True, F.hflip(img) + return False, img diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/imagenet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/imagenet.py new file mode 100644 index 00000000..cfe485d3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/imagenet.py @@ -0,0 +1,558 @@ +import os, tarfile, glob, shutil +import yaml +import numpy as np +from tqdm import tqdm +from PIL import Image +import albumentations +from omegaconf import OmegaConf +from torch.utils.data import Dataset + +from custom_controlnet_aux.diffusion_edge.taming.data.base import ImagePaths +from custom_controlnet_aux.diffusion_edge.taming.util import download, retrieve +import taming.data.utils as bdu + + +def give_synsets_from_indices(indices, path_to_yaml="data/imagenet_idx_to_synset.yaml"): + synsets = [] + with open(path_to_yaml) as f: + di2s = yaml.load(f) + for idx in indices: + synsets.append(str(di2s[idx])) + print("Using {} different synsets for construction of Restriced Imagenet.".format(len(synsets))) + return synsets + + +def str_to_indices(string): + """Expects a string in the format '32-123, 256, 280-321'""" + assert not string.endswith(","), "provided string '{}' ends with a comma, pls remove it".format(string) + subs = string.split(",") + indices = [] + for sub in subs: + subsubs = sub.split("-") + assert len(subsubs) > 0 + if len(subsubs) == 1: + indices.append(int(subsubs[0])) + else: + rang = [j for j in range(int(subsubs[0]), int(subsubs[1]))] + indices.extend(rang) + return sorted(indices) + + +class ImageNetBase(Dataset): + def __init__(self, config=None): + self.config = config or OmegaConf.create() + if not type(self.config)==dict: + self.config = OmegaConf.to_container(self.config) + self._prepare() + self._prepare_synset_to_human() + self._prepare_idx_to_synset() + self._load() + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + return self.data[i] + + def _prepare(self): + raise NotImplementedError() + + def _filter_relpaths(self, relpaths): + ignore = set([ + "n06596364_9591.JPEG", + ]) + relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore] + if "sub_indices" in self.config: + indices = str_to_indices(self.config["sub_indices"]) + synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings + files = [] + for rpath in relpaths: + syn = rpath.split("/")[0] + if syn in synsets: + files.append(rpath) + return files + else: + return relpaths + + def _prepare_synset_to_human(self): + SIZE = 2655750 + URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1" + self.human_dict = os.path.join(self.root, "synset_human.txt") + if (not os.path.exists(self.human_dict) or + not os.path.getsize(self.human_dict)==SIZE): + download(URL, self.human_dict) + + def _prepare_idx_to_synset(self): + URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1" + self.idx2syn = os.path.join(self.root, "index_synset.yaml") + if (not os.path.exists(self.idx2syn)): + download(URL, self.idx2syn) + + def _load(self): + with open(self.txt_filelist, "r") as f: + self.relpaths = f.read().splitlines() + l1 = len(self.relpaths) + self.relpaths = self._filter_relpaths(self.relpaths) + print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths))) + + self.synsets = [p.split("/")[0] for p in self.relpaths] + self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths] + + unique_synsets = np.unique(self.synsets) + class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets)) + self.class_labels = [class_dict[s] for s in self.synsets] + + with open(self.human_dict, "r") as f: + human_dict = f.read().splitlines() + human_dict = dict(line.split(maxsplit=1) for line in human_dict) + + self.human_labels = [human_dict[s] for s in self.synsets] + + labels = { + "relpath": np.array(self.relpaths), + "synsets": np.array(self.synsets), + "class_label": np.array(self.class_labels), + "human_label": np.array(self.human_labels), + } + self.data = ImagePaths(self.abspaths, + labels=labels, + size=retrieve(self.config, "size", default=0), + random_crop=self.random_crop) + + +class ImageNetTrain(ImageNetBase): + NAME = "ILSVRC2012_train" + URL = "http://www.image-net.org/challenges/LSVRC/2012/" + AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2" + FILES = [ + "ILSVRC2012_img_train.tar", + ] + SIZES = [ + 147897477120, + ] + + def _prepare(self): + self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop", + default=True) + cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) + self.datadir = os.path.join(self.root, "data") + self.txt_filelist = os.path.join(self.root, "filelist.txt") + self.expected_length = 1281167 + if not bdu.is_prepared(self.root): + # prep + print("Preparing dataset {} in {}".format(self.NAME, self.root)) + + datadir = self.datadir + if not os.path.exists(datadir): + path = os.path.join(self.root, self.FILES[0]) + if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: + import academictorrents as at + atpath = at.get(self.AT_HASH, datastore=self.root) + assert atpath == path + + print("Extracting {} to {}".format(path, datadir)) + os.makedirs(datadir, exist_ok=True) + with tarfile.open(path, "r:") as tar: + tar.extractall(path=datadir) + + print("Extracting sub-tars.") + subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar"))) + for subpath in tqdm(subpaths): + subdir = subpath[:-len(".tar")] + os.makedirs(subdir, exist_ok=True) + with tarfile.open(subpath, "r:") as tar: + tar.extractall(path=subdir) + + + filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) + filelist = [os.path.relpath(p, start=datadir) for p in filelist] + filelist = sorted(filelist) + filelist = "\n".join(filelist)+"\n" + with open(self.txt_filelist, "w") as f: + f.write(filelist) + + bdu.mark_prepared(self.root) + + +class ImageNetValidation(ImageNetBase): + NAME = "ILSVRC2012_validation" + URL = "http://www.image-net.org/challenges/LSVRC/2012/" + AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5" + VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1" + FILES = [ + "ILSVRC2012_img_val.tar", + "validation_synset.txt", + ] + SIZES = [ + 6744924160, + 1950000, + ] + + def _prepare(self): + self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop", + default=False) + cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) + self.datadir = os.path.join(self.root, "data") + self.txt_filelist = os.path.join(self.root, "filelist.txt") + self.expected_length = 50000 + if not bdu.is_prepared(self.root): + # prep + print("Preparing dataset {} in {}".format(self.NAME, self.root)) + + datadir = self.datadir + if not os.path.exists(datadir): + path = os.path.join(self.root, self.FILES[0]) + if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: + import academictorrents as at + atpath = at.get(self.AT_HASH, datastore=self.root) + assert atpath == path + + print("Extracting {} to {}".format(path, datadir)) + os.makedirs(datadir, exist_ok=True) + with tarfile.open(path, "r:") as tar: + tar.extractall(path=datadir) + + vspath = os.path.join(self.root, self.FILES[1]) + if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]: + download(self.VS_URL, vspath) + + with open(vspath, "r") as f: + synset_dict = f.read().splitlines() + synset_dict = dict(line.split() for line in synset_dict) + + print("Reorganizing into synset folders") + synsets = np.unique(list(synset_dict.values())) + for s in synsets: + os.makedirs(os.path.join(datadir, s), exist_ok=True) + for k, v in synset_dict.items(): + src = os.path.join(datadir, k) + dst = os.path.join(datadir, v) + shutil.move(src, dst) + + filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) + filelist = [os.path.relpath(p, start=datadir) for p in filelist] + filelist = sorted(filelist) + filelist = "\n".join(filelist)+"\n" + with open(self.txt_filelist, "w") as f: + f.write(filelist) + + bdu.mark_prepared(self.root) + + +def get_preprocessor(size=None, random_crop=False, additional_targets=None, + crop_size=None): + if size is not None and size > 0: + transforms = list() + rescaler = albumentations.SmallestMaxSize(max_size = size) + transforms.append(rescaler) + if not random_crop: + cropper = albumentations.CenterCrop(height=size,width=size) + transforms.append(cropper) + else: + cropper = albumentations.RandomCrop(height=size,width=size) + transforms.append(cropper) + flipper = albumentations.HorizontalFlip() + transforms.append(flipper) + preprocessor = albumentations.Compose(transforms, + additional_targets=additional_targets) + elif crop_size is not None and crop_size > 0: + if not random_crop: + cropper = albumentations.CenterCrop(height=crop_size,width=crop_size) + else: + cropper = albumentations.RandomCrop(height=crop_size,width=crop_size) + transforms = [cropper] + preprocessor = albumentations.Compose(transforms, + additional_targets=additional_targets) + else: + preprocessor = lambda **kwargs: kwargs + return preprocessor + + +def rgba_to_depth(x): + assert x.dtype == np.uint8 + assert len(x.shape) == 3 and x.shape[2] == 4 + y = x.copy() + y.dtype = np.float32 + y = y.reshape(x.shape[:2]) + return np.ascontiguousarray(y) + + +class BaseWithDepth(Dataset): + DEFAULT_DEPTH_ROOT="data/imagenet_depth" + + def __init__(self, config=None, size=None, random_crop=False, + crop_size=None, root=None): + self.config = config + self.base_dset = self.get_base_dset() + self.preprocessor = get_preprocessor( + size=size, + crop_size=crop_size, + random_crop=random_crop, + additional_targets={"depth": "image"}) + self.crop_size = crop_size + if self.crop_size is not None: + self.rescaler = albumentations.Compose( + [albumentations.SmallestMaxSize(max_size = self.crop_size)], + additional_targets={"depth": "image"}) + if root is not None: + self.DEFAULT_DEPTH_ROOT = root + + def __len__(self): + return len(self.base_dset) + + def preprocess_depth(self, path): + rgba = np.array(Image.open(path)) + depth = rgba_to_depth(rgba) + depth = (depth - depth.min())/max(1e-8, depth.max()-depth.min()) + depth = 2.0*depth-1.0 + return depth + + def __getitem__(self, i): + e = self.base_dset[i] + e["depth"] = self.preprocess_depth(self.get_depth_path(e)) + # up if necessary + h,w,c = e["image"].shape + if self.crop_size and min(h,w) < self.crop_size: + # have to upscale to be able to crop - this just uses bilinear + out = self.rescaler(image=e["image"], depth=e["depth"]) + e["image"] = out["image"] + e["depth"] = out["depth"] + transformed = self.preprocessor(image=e["image"], depth=e["depth"]) + e["image"] = transformed["image"] + e["depth"] = transformed["depth"] + return e + + +class ImageNetTrainWithDepth(BaseWithDepth): + # default to random_crop=True + def __init__(self, random_crop=True, sub_indices=None, **kwargs): + self.sub_indices = sub_indices + super().__init__(random_crop=random_crop, **kwargs) + + def get_base_dset(self): + if self.sub_indices is None: + return ImageNetTrain() + else: + return ImageNetTrain({"sub_indices": self.sub_indices}) + + def get_depth_path(self, e): + fid = os.path.splitext(e["relpath"])[0]+".png" + fid = os.path.join(self.DEFAULT_DEPTH_ROOT, "train", fid) + return fid + + +class ImageNetValidationWithDepth(BaseWithDepth): + def __init__(self, sub_indices=None, **kwargs): + self.sub_indices = sub_indices + super().__init__(**kwargs) + + def get_base_dset(self): + if self.sub_indices is None: + return ImageNetValidation() + else: + return ImageNetValidation({"sub_indices": self.sub_indices}) + + def get_depth_path(self, e): + fid = os.path.splitext(e["relpath"])[0]+".png" + fid = os.path.join(self.DEFAULT_DEPTH_ROOT, "val", fid) + return fid + + +class RINTrainWithDepth(ImageNetTrainWithDepth): + def __init__(self, config=None, size=None, random_crop=True, crop_size=None): + sub_indices = "30-32, 33-37, 151-268, 281-285, 80-100, 365-382, 389-397, 118-121, 300-319" + super().__init__(config=config, size=size, random_crop=random_crop, + sub_indices=sub_indices, crop_size=crop_size) + + +class RINValidationWithDepth(ImageNetValidationWithDepth): + def __init__(self, config=None, size=None, random_crop=False, crop_size=None): + sub_indices = "30-32, 33-37, 151-268, 281-285, 80-100, 365-382, 389-397, 118-121, 300-319" + super().__init__(config=config, size=size, random_crop=random_crop, + sub_indices=sub_indices, crop_size=crop_size) + + +class DRINExamples(Dataset): + def __init__(self): + self.preprocessor = get_preprocessor(size=256, additional_targets={"depth": "image"}) + with open("data/drin_examples.txt", "r") as f: + relpaths = f.read().splitlines() + self.image_paths = [os.path.join("data/drin_images", + relpath) for relpath in relpaths] + self.depth_paths = [os.path.join("data/drin_depth", + relpath.replace(".JPEG", ".png")) for relpath in relpaths] + + def __len__(self): + return len(self.image_paths) + + def preprocess_image(self, image_path): + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + image = self.preprocessor(image=image)["image"] + image = (image/127.5 - 1.0).astype(np.float32) + return image + + def preprocess_depth(self, path): + rgba = np.array(Image.open(path)) + depth = rgba_to_depth(rgba) + depth = (depth - depth.min())/max(1e-8, depth.max()-depth.min()) + depth = 2.0*depth-1.0 + return depth + + def __getitem__(self, i): + e = dict() + e["image"] = self.preprocess_image(self.image_paths[i]) + e["depth"] = self.preprocess_depth(self.depth_paths[i]) + transformed = self.preprocessor(image=e["image"], depth=e["depth"]) + e["image"] = transformed["image"] + e["depth"] = transformed["depth"] + return e + + +def imscale(x, factor, keepshapes=False, keepmode="bicubic"): + if factor is None or factor==1: + return x + + dtype = x.dtype + assert dtype in [np.float32, np.float64] + assert x.min() >= -1 + assert x.max() <= 1 + + keepmode = {"nearest": Image.NEAREST, "bilinear": Image.BILINEAR, + "bicubic": Image.BICUBIC}[keepmode] + + lr = (x+1.0)*127.5 + lr = lr.clip(0,255).astype(np.uint8) + lr = Image.fromarray(lr) + + h, w, _ = x.shape + nh = h//factor + nw = w//factor + assert nh > 0 and nw > 0, (nh, nw) + + lr = lr.resize((nw,nh), Image.BICUBIC) + if keepshapes: + lr = lr.resize((w,h), keepmode) + lr = np.array(lr)/127.5-1.0 + lr = lr.astype(dtype) + + return lr + + +class ImageNetScale(Dataset): + def __init__(self, size=None, crop_size=None, random_crop=False, + up_factor=None, hr_factor=None, keep_mode="bicubic"): + self.base = self.get_base() + + self.size = size + self.crop_size = crop_size if crop_size is not None else self.size + self.random_crop = random_crop + self.up_factor = up_factor + self.hr_factor = hr_factor + self.keep_mode = keep_mode + + transforms = list() + + if self.size is not None and self.size > 0: + rescaler = albumentations.SmallestMaxSize(max_size = self.size) + self.rescaler = rescaler + transforms.append(rescaler) + + if self.crop_size is not None and self.crop_size > 0: + if len(transforms) == 0: + self.rescaler = albumentations.SmallestMaxSize(max_size = self.crop_size) + + if not self.random_crop: + cropper = albumentations.CenterCrop(height=self.crop_size,width=self.crop_size) + else: + cropper = albumentations.RandomCrop(height=self.crop_size,width=self.crop_size) + transforms.append(cropper) + + if len(transforms) > 0: + if self.up_factor is not None: + additional_targets = {"lr": "image"} + else: + additional_targets = None + self.preprocessor = albumentations.Compose(transforms, + additional_targets=additional_targets) + else: + self.preprocessor = lambda **kwargs: kwargs + + def __len__(self): + return len(self.base) + + def __getitem__(self, i): + example = self.base[i] + image = example["image"] + # adjust resolution + image = imscale(image, self.hr_factor, keepshapes=False) + h,w,c = image.shape + if self.crop_size and min(h,w) < self.crop_size: + # have to upscale to be able to crop - this just uses bilinear + image = self.rescaler(image=image)["image"] + if self.up_factor is None: + image = self.preprocessor(image=image)["image"] + example["image"] = image + else: + lr = imscale(image, self.up_factor, keepshapes=True, + keepmode=self.keep_mode) + + out = self.preprocessor(image=image, lr=lr) + example["image"] = out["image"] + example["lr"] = out["lr"] + + return example + +class ImageNetScaleTrain(ImageNetScale): + def __init__(self, random_crop=True, **kwargs): + super().__init__(random_crop=random_crop, **kwargs) + + def get_base(self): + return ImageNetTrain() + +class ImageNetScaleValidation(ImageNetScale): + def get_base(self): + return ImageNetValidation() + + +from skimage.feature import canny +from skimage.color import rgb2gray + + +class ImageNetEdges(ImageNetScale): + def __init__(self, up_factor=1, **kwargs): + super().__init__(up_factor=1, **kwargs) + + def __getitem__(self, i): + example = self.base[i] + image = example["image"] + h,w,c = image.shape + if self.crop_size and min(h,w) < self.crop_size: + # have to upscale to be able to crop - this just uses bilinear + image = self.rescaler(image=image)["image"] + + lr = canny(rgb2gray(image), sigma=2) + lr = lr.astype(np.float32) + lr = lr[:,:,None][:,:,[0,0,0]] + + out = self.preprocessor(image=image, lr=lr) + example["image"] = out["image"] + example["lr"] = out["lr"] + + return example + + +class ImageNetEdgesTrain(ImageNetEdges): + def __init__(self, random_crop=True, **kwargs): + super().__init__(random_crop=random_crop, **kwargs) + + def get_base(self): + return ImageNetTrain() + +class ImageNetEdgesValidation(ImageNetEdges): + def get_base(self): + return ImageNetValidation() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/open_images_helper.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/open_images_helper.py new file mode 100644 index 00000000..8feb7c6e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/open_images_helper.py @@ -0,0 +1,379 @@ +open_images_unify_categories_for_coco = { + '/m/03bt1vf': '/m/01g317', + '/m/04yx4': '/m/01g317', + '/m/05r655': '/m/01g317', + '/m/01bl7v': '/m/01g317', + '/m/0cnyhnx': '/m/01xq0k1', + '/m/01226z': '/m/018xm', + '/m/05ctyq': '/m/018xm', + '/m/058qzx': '/m/04ctx', + '/m/06pcq': '/m/0l515', + '/m/03m3pdh': '/m/02crq1', + '/m/046dlr': '/m/01x3z', + '/m/0h8mzrc': '/m/01x3z', +} + + +top_300_classes_plus_coco_compatibility = [ + ('Man', 1060962), + ('Clothing', 986610), + ('Tree', 748162), + ('Woman', 611896), + ('Person', 610294), + ('Human face', 442948), + ('Girl', 175399), + ('Building', 162147), + ('Car', 159135), + ('Plant', 155704), + ('Human body', 137073), + ('Flower', 133128), + ('Window', 127485), + ('Human arm', 118380), + ('House', 114365), + ('Wheel', 111684), + ('Suit', 99054), + ('Human hair', 98089), + ('Human head', 92763), + ('Chair', 88624), + ('Boy', 79849), + ('Table', 73699), + ('Jeans', 57200), + ('Tire', 55725), + ('Skyscraper', 53321), + ('Food', 52400), + ('Footwear', 50335), + ('Dress', 50236), + ('Human leg', 47124), + ('Toy', 46636), + ('Tower', 45605), + ('Boat', 43486), + ('Land vehicle', 40541), + ('Bicycle wheel', 34646), + ('Palm tree', 33729), + ('Fashion accessory', 32914), + ('Glasses', 31940), + ('Bicycle', 31409), + ('Furniture', 30656), + ('Sculpture', 29643), + ('Bottle', 27558), + ('Dog', 26980), + ('Snack', 26796), + ('Human hand', 26664), + ('Bird', 25791), + ('Book', 25415), + ('Guitar', 24386), + ('Jacket', 23998), + ('Poster', 22192), + ('Dessert', 21284), + ('Baked goods', 20657), + ('Drink', 19754), + ('Flag', 18588), + ('Houseplant', 18205), + ('Tableware', 17613), + ('Airplane', 17218), + ('Door', 17195), + ('Sports uniform', 17068), + ('Shelf', 16865), + ('Drum', 16612), + ('Vehicle', 16542), + ('Microphone', 15269), + ('Street light', 14957), + ('Cat', 14879), + ('Fruit', 13684), + ('Fast food', 13536), + ('Animal', 12932), + ('Vegetable', 12534), + ('Train', 12358), + ('Horse', 11948), + ('Flowerpot', 11728), + ('Motorcycle', 11621), + ('Fish', 11517), + ('Desk', 11405), + ('Helmet', 10996), + ('Truck', 10915), + ('Bus', 10695), + ('Hat', 10532), + ('Auto part', 10488), + ('Musical instrument', 10303), + ('Sunglasses', 10207), + ('Picture frame', 10096), + ('Sports equipment', 10015), + ('Shorts', 9999), + ('Wine glass', 9632), + ('Duck', 9242), + ('Wine', 9032), + ('Rose', 8781), + ('Tie', 8693), + ('Butterfly', 8436), + ('Beer', 7978), + ('Cabinetry', 7956), + ('Laptop', 7907), + ('Insect', 7497), + ('Goggles', 7363), + ('Shirt', 7098), + ('Dairy Product', 7021), + ('Marine invertebrates', 7014), + ('Cattle', 7006), + ('Trousers', 6903), + ('Van', 6843), + ('Billboard', 6777), + ('Balloon', 6367), + ('Human nose', 6103), + ('Tent', 6073), + ('Camera', 6014), + ('Doll', 6002), + ('Coat', 5951), + ('Mobile phone', 5758), + ('Swimwear', 5729), + ('Strawberry', 5691), + ('Stairs', 5643), + ('Goose', 5599), + ('Umbrella', 5536), + ('Cake', 5508), + ('Sun hat', 5475), + ('Bench', 5310), + ('Bookcase', 5163), + ('Bee', 5140), + ('Computer monitor', 5078), + ('Hiking equipment', 4983), + ('Office building', 4981), + ('Coffee cup', 4748), + ('Curtain', 4685), + ('Plate', 4651), + ('Box', 4621), + ('Tomato', 4595), + ('Coffee table', 4529), + ('Office supplies', 4473), + ('Maple', 4416), + ('Muffin', 4365), + ('Cocktail', 4234), + ('Castle', 4197), + ('Couch', 4134), + ('Pumpkin', 3983), + ('Computer keyboard', 3960), + ('Human mouth', 3926), + ('Christmas tree', 3893), + ('Mushroom', 3883), + ('Swimming pool', 3809), + ('Pastry', 3799), + ('Lavender (Plant)', 3769), + ('Football helmet', 3732), + ('Bread', 3648), + ('Traffic sign', 3628), + ('Common sunflower', 3597), + ('Television', 3550), + ('Bed', 3525), + ('Cookie', 3485), + ('Fountain', 3484), + ('Paddle', 3447), + ('Bicycle helmet', 3429), + ('Porch', 3420), + ('Deer', 3387), + ('Fedora', 3339), + ('Canoe', 3338), + ('Carnivore', 3266), + ('Bowl', 3202), + ('Human eye', 3166), + ('Ball', 3118), + ('Pillow', 3077), + ('Salad', 3061), + ('Beetle', 3060), + ('Orange', 3050), + ('Drawer', 2958), + ('Platter', 2937), + ('Elephant', 2921), + ('Seafood', 2921), + ('Monkey', 2915), + ('Countertop', 2879), + ('Watercraft', 2831), + ('Helicopter', 2805), + ('Kitchen appliance', 2797), + ('Personal flotation device', 2781), + ('Swan', 2739), + ('Lamp', 2711), + ('Boot', 2695), + ('Bronze sculpture', 2693), + ('Chicken', 2677), + ('Taxi', 2643), + ('Juice', 2615), + ('Cowboy hat', 2604), + ('Apple', 2600), + ('Tin can', 2590), + ('Necklace', 2564), + ('Ice cream', 2560), + ('Human beard', 2539), + ('Coin', 2536), + ('Candle', 2515), + ('Cart', 2512), + ('High heels', 2441), + ('Weapon', 2433), + ('Handbag', 2406), + ('Penguin', 2396), + ('Rifle', 2352), + ('Violin', 2336), + ('Skull', 2304), + ('Lantern', 2285), + ('Scarf', 2269), + ('Saucer', 2225), + ('Sheep', 2215), + ('Vase', 2189), + ('Lily', 2180), + ('Mug', 2154), + ('Parrot', 2140), + ('Human ear', 2137), + ('Sandal', 2115), + ('Lizard', 2100), + ('Kitchen & dining room table', 2063), + ('Spider', 1977), + ('Coffee', 1974), + ('Goat', 1926), + ('Squirrel', 1922), + ('Cello', 1913), + ('Sushi', 1881), + ('Tortoise', 1876), + ('Pizza', 1870), + ('Studio couch', 1864), + ('Barrel', 1862), + ('Cosmetics', 1841), + ('Moths and butterflies', 1841), + ('Convenience store', 1817), + ('Watch', 1792), + ('Home appliance', 1786), + ('Harbor seal', 1780), + ('Luggage and bags', 1756), + ('Vehicle registration plate', 1754), + ('Shrimp', 1751), + ('Jellyfish', 1730), + ('French fries', 1723), + ('Egg (Food)', 1698), + ('Football', 1697), + ('Musical keyboard', 1683), + ('Falcon', 1674), + ('Candy', 1660), + ('Medical equipment', 1654), + ('Eagle', 1651), + ('Dinosaur', 1634), + ('Surfboard', 1630), + ('Tank', 1628), + ('Grape', 1624), + ('Lion', 1624), + ('Owl', 1622), + ('Ski', 1613), + ('Waste container', 1606), + ('Frog', 1591), + ('Sparrow', 1585), + ('Rabbit', 1581), + ('Pen', 1546), + ('Sea lion', 1537), + ('Spoon', 1521), + ('Sink', 1512), + ('Teddy bear', 1507), + ('Bull', 1495), + ('Sofa bed', 1490), + ('Dragonfly', 1479), + ('Brassiere', 1478), + ('Chest of drawers', 1472), + ('Aircraft', 1466), + ('Human foot', 1463), + ('Pig', 1455), + ('Fork', 1454), + ('Antelope', 1438), + ('Tripod', 1427), + ('Tool', 1424), + ('Cheese', 1422), + ('Lemon', 1397), + ('Hamburger', 1393), + ('Dolphin', 1390), + ('Mirror', 1390), + ('Marine mammal', 1387), + ('Giraffe', 1385), + ('Snake', 1368), + ('Gondola', 1364), + ('Wheelchair', 1360), + ('Piano', 1358), + ('Cupboard', 1348), + ('Banana', 1345), + ('Trumpet', 1335), + ('Lighthouse', 1333), + ('Invertebrate', 1317), + ('Carrot', 1268), + ('Sock', 1260), + ('Tiger', 1241), + ('Camel', 1224), + ('Parachute', 1224), + ('Bathroom accessory', 1223), + ('Earrings', 1221), + ('Headphones', 1218), + ('Skirt', 1198), + ('Skateboard', 1190), + ('Sandwich', 1148), + ('Saxophone', 1141), + ('Goldfish', 1136), + ('Stool', 1104), + ('Traffic light', 1097), + ('Shellfish', 1081), + ('Backpack', 1079), + ('Sea turtle', 1078), + ('Cucumber', 1075), + ('Tea', 1051), + ('Toilet', 1047), + ('Roller skates', 1040), + ('Mule', 1039), + ('Bust', 1031), + ('Broccoli', 1030), + ('Crab', 1020), + ('Oyster', 1019), + ('Cannon', 1012), + ('Zebra', 1012), + ('French horn', 1008), + ('Grapefruit', 998), + ('Whiteboard', 997), + ('Zucchini', 997), + ('Crocodile', 992), + + ('Clock', 960), + ('Wall clock', 958), + + ('Doughnut', 869), + ('Snail', 868), + + ('Baseball glove', 859), + + ('Panda', 830), + ('Tennis racket', 830), + + ('Pear', 652), + + ('Bagel', 617), + ('Oven', 616), + ('Ladybug', 615), + ('Shark', 615), + ('Polar bear', 614), + ('Ostrich', 609), + + ('Hot dog', 473), + ('Microwave oven', 467), + ('Fire hydrant', 20), + ('Stop sign', 20), + ('Parking meter', 20), + ('Bear', 20), + ('Flying disc', 20), + ('Snowboard', 20), + ('Tennis ball', 20), + ('Kite', 20), + ('Baseball bat', 20), + ('Kitchen knife', 20), + ('Knife', 20), + ('Submarine sandwich', 20), + ('Computer mouse', 20), + ('Remote control', 20), + ('Toaster', 20), + ('Sink', 20), + ('Refrigerator', 20), + ('Alarm clock', 20), + ('Wall clock', 20), + ('Scissors', 20), + ('Hair dryer', 20), + ('Toothbrush', 20), + ('Suitcase', 20) +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/sflckr.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/sflckr.py new file mode 100644 index 00000000..91101be5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/sflckr.py @@ -0,0 +1,91 @@ +import os +import numpy as np +import cv2 +import albumentations +from PIL import Image +from torch.utils.data import Dataset + + +class SegmentationBase(Dataset): + def __init__(self, + data_csv, data_root, segmentation_root, + size=None, random_crop=False, interpolation="bicubic", + n_labels=182, shift_segmentation=False, + ): + self.n_labels = n_labels + self.shift_segmentation = shift_segmentation + self.data_csv = data_csv + self.data_root = data_root + self.segmentation_root = segmentation_root + with open(self.data_csv, "r") as f: + self.image_paths = f.read().splitlines() + self._length = len(self.image_paths) + self.labels = { + "relative_file_path_": [l for l in self.image_paths], + "file_path_": [os.path.join(self.data_root, l) + for l in self.image_paths], + "segmentation_path_": [os.path.join(self.segmentation_root, l.replace(".jpg", ".png")) + for l in self.image_paths] + } + + size = None if size is not None and size<=0 else size + self.size = size + if self.size is not None: + self.interpolation = interpolation + self.interpolation = { + "nearest": cv2.INTER_NEAREST, + "bilinear": cv2.INTER_LINEAR, + "bicubic": cv2.INTER_CUBIC, + "area": cv2.INTER_AREA, + "lanczos": cv2.INTER_LANCZOS4}[self.interpolation] + self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=self.interpolation) + self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=cv2.INTER_NEAREST) + self.center_crop = not random_crop + if self.center_crop: + self.cropper = albumentations.CenterCrop(height=self.size, width=self.size) + else: + self.cropper = albumentations.RandomCrop(height=self.size, width=self.size) + self.preprocessor = self.cropper + + def __len__(self): + return self._length + + def __getitem__(self, i): + example = dict((k, self.labels[k][i]) for k in self.labels) + image = Image.open(example["file_path_"]) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + if self.size is not None: + image = self.image_rescaler(image=image)["image"] + segmentation = Image.open(example["segmentation_path_"]) + assert segmentation.mode == "L", segmentation.mode + segmentation = np.array(segmentation).astype(np.uint8) + if self.shift_segmentation: + # used to support segmentations containing unlabeled==255 label + segmentation = segmentation+1 + if self.size is not None: + segmentation = self.segmentation_rescaler(image=segmentation)["image"] + if self.size is not None: + processed = self.preprocessor(image=image, + mask=segmentation + ) + else: + processed = {"image": image, + "mask": segmentation + } + example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32) + segmentation = processed["mask"] + onehot = np.eye(self.n_labels)[segmentation] + example["segmentation"] = onehot + return example + + +class Examples(SegmentationBase): + def __init__(self, size=None, random_crop=False, interpolation="bicubic"): + super().__init__(data_csv="data/sflckr_examples.txt", + data_root="data/sflckr_images", + segmentation_root="data/sflckr_segmentations", + size=size, random_crop=random_crop, interpolation=interpolation) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/utils.py new file mode 100644 index 00000000..8be640a3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/data/utils.py @@ -0,0 +1,169 @@ +import collections +import os +import tarfile +import urllib +import zipfile +from pathlib import Path + +import numpy as np +import torch +from custom_controlnet_aux.diffusion_edge.taming.data.helper_types import Annotation +from torch._six import string_classes +from torch.utils.data._utils.collate import np_str_obj_array_pattern, default_collate_err_msg_format +from tqdm import tqdm + + +def unpack(path): + if path.endswith("tar.gz"): + with tarfile.open(path, "r:gz") as tar: + tar.extractall(path=os.path.split(path)[0]) + elif path.endswith("tar"): + with tarfile.open(path, "r:") as tar: + tar.extractall(path=os.path.split(path)[0]) + elif path.endswith("zip"): + with zipfile.ZipFile(path, "r") as f: + f.extractall(path=os.path.split(path)[0]) + else: + raise NotImplementedError( + "Unknown file extension: {}".format(os.path.splitext(path)[1]) + ) + + +def reporthook(bar): + """tqdm progress bar for downloads.""" + + def hook(b=1, bsize=1, tsize=None): + if tsize is not None: + bar.total = tsize + bar.update(b * bsize - bar.n) + + return hook + + +def get_root(name): + base = "data/" + root = os.path.join(base, name) + os.makedirs(root, exist_ok=True) + return root + + +def is_prepared(root): + return Path(root).joinpath(".ready").exists() + + +def mark_prepared(root): + Path(root).joinpath(".ready").touch() + + +def prompt_download(file_, source, target_dir, content_dir=None): + targetpath = os.path.join(target_dir, file_) + while not os.path.exists(targetpath): + if content_dir is not None and os.path.exists( + os.path.join(target_dir, content_dir) + ): + break + print( + "Please download '{}' from '{}' to '{}'.".format(file_, source, targetpath) + ) + if content_dir is not None: + print( + "Or place its content into '{}'.".format( + os.path.join(target_dir, content_dir) + ) + ) + input("Press Enter when done...") + return targetpath + + +def download_url(file_, url, target_dir): + targetpath = os.path.join(target_dir, file_) + os.makedirs(target_dir, exist_ok=True) + with tqdm( + unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=file_ + ) as bar: + urllib.request.urlretrieve(url, targetpath, reporthook=reporthook(bar)) + return targetpath + + +def download_urls(urls, target_dir): + paths = dict() + for fname, url in urls.items(): + outpath = download_url(fname, url, target_dir) + paths[fname] = outpath + return paths + + +def quadratic_crop(x, bbox, alpha=1.0): + """bbox is xmin, ymin, xmax, ymax""" + im_h, im_w = x.shape[:2] + bbox = np.array(bbox, dtype=np.float32) + bbox = np.clip(bbox, 0, max(im_h, im_w)) + center = 0.5 * (bbox[0] + bbox[2]), 0.5 * (bbox[1] + bbox[3]) + w = bbox[2] - bbox[0] + h = bbox[3] - bbox[1] + l = int(alpha * max(w, h)) + l = max(l, 2) + + required_padding = -1 * min( + center[0] - l, center[1] - l, im_w - (center[0] + l), im_h - (center[1] + l) + ) + required_padding = int(np.ceil(required_padding)) + if required_padding > 0: + padding = [ + [required_padding, required_padding], + [required_padding, required_padding], + ] + padding += [[0, 0]] * (len(x.shape) - 2) + x = np.pad(x, padding, "reflect") + center = center[0] + required_padding, center[1] + required_padding + xmin = int(center[0] - l / 2) + ymin = int(center[1] - l / 2) + return np.array(x[ymin : ymin + l, xmin : xmin + l, ...]) + + +def custom_collate(batch): + r"""source: pytorch 1.9.0, only one modification to original code """ + + elem = batch[0] + elem_type = type(elem) + if isinstance(elem, torch.Tensor): + out = None + if torch.utils.data.get_worker_info() is not None: + # If we're in a background process, concatenate directly into a + # shared memory tensor to avoid an extra copy + numel = sum([x.numel() for x in batch]) + storage = elem.storage()._new_shared(numel) + out = elem.new(storage) + return torch.stack(batch, 0, out=out) + elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ + and elem_type.__name__ != 'string_': + if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap': + # array of string classes and object + if np_str_obj_array_pattern.search(elem.dtype.str) is not None: + raise TypeError(default_collate_err_msg_format.format(elem.dtype)) + + return custom_collate([torch.as_tensor(b) for b in batch]) + elif elem.shape == (): # scalars + return torch.as_tensor(batch) + elif isinstance(elem, float): + return torch.tensor(batch, dtype=torch.float64) + elif isinstance(elem, int): + return torch.tensor(batch) + elif isinstance(elem, string_classes): + return batch + elif isinstance(elem, collections.abc.Mapping): + return {key: custom_collate([d[key] for d in batch]) for key in elem} + elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple + return elem_type(*(custom_collate(samples) for samples in zip(*batch))) + if isinstance(elem, collections.abc.Sequence) and isinstance(elem[0], Annotation): # added + return batch # added + elif isinstance(elem, collections.abc.Sequence): + # check to make sure that the elements in batch have consistent size + it = iter(batch) + elem_size = len(next(it)) + if not all(len(elem) == elem_size for elem in it): + raise RuntimeError('each element in list of batch should be of equal size') + transposed = zip(*batch) + return [custom_collate(samples) for samples in transposed] + + raise TypeError(default_collate_err_msg_format.format(elem_type)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/autoencoder/lpips/vgg.pth b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/autoencoder/lpips/vgg.pth new file mode 100644 index 00000000..47e943cf Binary files /dev/null and b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/autoencoder/lpips/vgg.pth differ diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/diffusionmodules/model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/diffusionmodules/model.py new file mode 100644 index 00000000..d3a5db6a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/diffusionmodules/model.py @@ -0,0 +1,776 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True): + super().__init__() + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + + def forward(self, x, t=None): + #assert x.shape[2] == x.shape[3] == self.resolution + + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + + def forward(self, x): + #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution) + + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, **ignorekwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class VUNet(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + in_channels, c_channels, + resolution, z_channels, use_timestep=False, **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(c_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + self.z_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=1, + stride=1, + padding=0) + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=2*block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + + def forward(self, x, z): + #assert x.shape[2] == x.shape[3] == self.resolution + + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + z = self.z_in(z) + h = torch.cat((h,z),dim=1) + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/discriminator/model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/discriminator/model.py new file mode 100644 index 00000000..1ab2cc89 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/discriminator/model.py @@ -0,0 +1,131 @@ +import functools +import torch.nn as nn + + +from custom_controlnet_aux.diffusion_edge.taming.modules.util import ActNorm + + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + nn.init.normal_(m.weight.data, 0.0, 0.02) + elif classname.find('BatchNorm') != -1: + nn.init.normal_(m.weight.data, 1.0, 0.02) + nn.init.constant_(m.bias.data, 0) + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() + if not use_actnorm: + norm_layer = nn.BatchNorm2d + else: + norm_layer = ActNorm + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func != nn.BatchNorm2d + else: + use_bias = norm_layer != nn.BatchNorm2d + + kw = 4 + padw = 1 + sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [ + nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.main(input) + +class NLayerDiscriminator2(nn.Module): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator2, self).__init__() + if not use_actnorm: + norm_layer = nn.BatchNorm3d + else: + norm_layer = ActNorm + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func != nn.BatchNorm3d + else: + use_bias = norm_layer != nn.BatchNorm3d + + kw = 4 + padw = 1 + sequence = [nn.Conv3d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv3d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, + padding=padw, bias=use_bias, groups=8), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv3d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, + padding=padw, bias=use_bias, groups=8), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [ + nn.Conv3d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw), + # nn.Sigmoid() + ] # output 1 channel prediction map + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.main(input) + +if __name__ == "__main__": + import torch + model = NLayerDiscriminator2(input_nc=3, ndf=64, n_layers=3) + x = torch.rand(1, 3, 64, 64, 64) + with torch.no_grad(): + y = model(x) + pause = 0 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/__init__.py new file mode 100644 index 00000000..c3d03a59 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/__init__.py @@ -0,0 +1,2 @@ +from custom_controlnet_aux.diffusion_edge.taming.modules.losses.vqperceptual import DummyLoss + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/lpips.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/lpips.py new file mode 100644 index 00000000..32666db1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/lpips.py @@ -0,0 +1,126 @@ +"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models""" + +import torch +import torch.nn as nn +from torchvision import models +from collections import namedtuple + +from .util import get_ckpt_path + +from custom_controlnet_aux.util import custom_torch_download + +class LPIPS(nn.Module): + # Learned perceptual metric + def __init__(self, use_dropout=True): + super().__init__() + self.scaling_layer = ScalingLayer() + self.chns = [64, 128, 256, 512, 512] # vg16 features + self.net = vgg16(pretrained=False, requires_grad=False) + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.load_from_pretrained() + for param in self.parameters(): + param.requires_grad = False + + def load_from_pretrained(self, name="vgg_lpips"): + ckpt = get_ckpt_path(name, "taming/modules/autoencoder/lpips") + self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + print("loaded pretrained LPIPS loss from {}".format(ckpt)) + + @classmethod + def from_pretrained(cls, name="vgg_lpips"): + if name != "vgg_lpips": + raise NotImplementedError + model = cls() + ckpt = get_ckpt_path(name) + model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + return model + + def forward(self, input, target): + in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + for kk in range(len(self.chns)): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))] + val = res[0] + for l in range(1, len(self.chns)): + val += res[l] + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) + self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + """ A single linear layer which does a 1x1 conv """ + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + layers = [nn.Dropout(), ] if (use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] + self.model = nn.Sequential(*layers) + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=False): + super(vgg16, self).__init__() + vgg16_model = models.vgg16(pretrained=pretrained) + vgg16_model.load_state_dict(torch.load(custom_torch_download(filename="vgg16-397923af.pth")), strict=True) + vgg_pretrained_features = vgg16_model.features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + return out + + +def normalize_tensor(x,eps=1e-10): + norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True)) + return x/(norm_factor+eps) + + +def spatial_average(x, keepdim=True): + return x.mean([2,3],keepdim=keepdim) + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/segmentation.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/segmentation.py new file mode 100644 index 00000000..4ba77deb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/segmentation.py @@ -0,0 +1,22 @@ +import torch.nn as nn +import torch.nn.functional as F + + +class BCELoss(nn.Module): + def forward(self, prediction, target): + loss = F.binary_cross_entropy_with_logits(prediction,target) + return loss, {} + + +class BCELossWithQuant(nn.Module): + def __init__(self, codebook_weight=1.): + super().__init__() + self.codebook_weight = codebook_weight + + def forward(self, qloss, target, prediction, split): + bce_loss = F.binary_cross_entropy_with_logits(prediction,target) + loss = bce_loss + self.codebook_weight*qloss + return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/bce_loss".format(split): bce_loss.detach().mean(), + "{}/quant_loss".format(split): qloss.detach().mean() + } diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/util.py new file mode 100644 index 00000000..06053e5d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/util.py @@ -0,0 +1,157 @@ +import os, hashlib +import requests +from tqdm import tqdm + +URL_MAP = { + "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1" +} + +CKPT_MAP = { + "vgg_lpips": "vgg.pth" +} + +MD5_MAP = { + "vgg_lpips": "d507d7349b931f0638a25a48a722f98a" +} + + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path + + +class KeyNotFoundError(Exception): + def __init__(self, cause, keys=None, visited=None): + self.cause = cause + self.keys = keys + self.visited = visited + messages = list() + if keys is not None: + messages.append("Key not found: {}".format(keys)) + if visited is not None: + messages.append("Visited: {}".format(visited)) + messages.append("Cause:\n{}".format(cause)) + message = "\n".join(messages) + super().__init__(message) + + +def retrieve( + list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False +): + """Given a nested list or dict return the desired value at key expanding + callable nodes if necessary and :attr:`expand` is ``True``. The expansion + is done in-place. + + Parameters + ---------- + list_or_dict : list or dict + Possibly nested list or dictionary. + key : str + key/to/value, path like string describing all keys necessary to + consider to get to the desired value. List indices can also be + passed here. + splitval : str + String that defines the delimiter between keys of the + different depth levels in `key`. + default : obj + Value returned if :attr:`key` is not found. + expand : bool + Whether to expand callable nodes on the path or not. + + Returns + ------- + The desired value or if :attr:`default` is not ``None`` and the + :attr:`key` is not found returns ``default``. + + Raises + ------ + Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is + ``None``. + """ + + keys = key.split(splitval) + + success = True + try: + visited = [] + parent = None + last_key = None + for key in keys: + if callable(list_or_dict): + if not expand: + raise KeyNotFoundError( + ValueError( + "Trying to get past callable node with expand=False." + ), + keys=keys, + visited=visited, + ) + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + + last_key = key + parent = list_or_dict + + try: + if isinstance(list_or_dict, dict): + list_or_dict = list_or_dict[key] + else: + list_or_dict = list_or_dict[int(key)] + except (KeyError, IndexError, ValueError) as e: + raise KeyNotFoundError(e, keys=keys, visited=visited) + + visited += [key] + # final expansion of retrieved value + if expand and callable(list_or_dict): + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + except KeyNotFoundError as e: + if default is None: + raise e + else: + list_or_dict = default + success = False + + if not pass_success: + return list_or_dict + else: + return list_or_dict, success + + +if __name__ == "__main__": + config = {"keya": "a", + "keyb": "b", + "keyc": + {"cc1": 1, + "cc2": 2, + } + } + from omegaconf import OmegaConf + config = OmegaConf.create(config) + print(config) + retrieve(config, "keya") + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/vqperceptual.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/vqperceptual.py new file mode 100644 index 00000000..67f01102 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/losses/vqperceptual.py @@ -0,0 +1,136 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from custom_controlnet_aux.diffusion_edge.taming.modules.losses.lpips import LPIPS +from custom_controlnet_aux.diffusion_edge.taming.modules.discriminator.model import NLayerDiscriminator, weights_init, NLayerDiscriminator2 + + +class DummyLoss(nn.Module): + def __init__(self): + super().__init__() + + +def adopt_weight(weight, global_step, threshold=0, value=0.): + if global_step < threshold: + weight = value + return weight + + +def hinge_d_loss(logits_real, logits_fake): + loss_real = torch.mean(F.relu(1. - logits_real)) + loss_fake = torch.mean(F.relu(1. + logits_fake)) + d_loss = 0.5 * (loss_real + loss_fake) + return d_loss + + +def vanilla_d_loss(logits_real, logits_fake): + d_loss = 0.5 * ( + torch.mean(torch.nn.functional.softplus(-logits_real)) + + torch.mean(torch.nn.functional.softplus(logits_fake))) + return d_loss + + +class VQLPIPSWithDiscriminator(nn.Module): + def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, + disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, + perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, + disc_ndf=64, disc_loss="hinge"): + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.codebook_weight = codebook_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPIPS().eval() + self.perceptual_weight = perceptual_weight + + self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm, + ndf=disc_ndf + ).apply(weights_init) + self.discriminator_iter_start = disc_start + if disc_loss == "hinge": + self.disc_loss = hinge_d_loss + elif disc_loss == "vanilla": + self.disc_loss = vanilla_d_loss + else: + raise ValueError(f"Unknown GAN loss '{disc_loss}'.") + print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, + global_step, last_layer=None, cond=None, split="train"): + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) + rec_loss = rec_loss + self.perceptual_weight * p_loss + else: + p_loss = torch.tensor([0.0]) + + nll_loss = rec_loss + #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + nll_loss = torch.mean(nll_loss) + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) + g_loss = -torch.mean(logits_fake) + + try: + d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() + + log = {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/quant_loss".format(split): codebook_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/p_loss".format(split): p_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean() + } + return d_loss, log diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/misc/coord.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/misc/coord.py new file mode 100644 index 00000000..ee69b0c8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/misc/coord.py @@ -0,0 +1,31 @@ +import torch + +class CoordStage(object): + def __init__(self, n_embed, down_factor): + self.n_embed = n_embed + self.down_factor = down_factor + + def eval(self): + return self + + def encode(self, c): + """fake vqmodel interface""" + assert 0.0 <= c.min() and c.max() <= 1.0 + b,ch,h,w = c.shape + assert ch == 1 + + c = torch.nn.functional.interpolate(c, scale_factor=1/self.down_factor, + mode="area") + c = c.clamp(0.0, 1.0) + c = self.n_embed*c + c_quant = c.round() + c_ind = c_quant.to(dtype=torch.long) + + info = None, None, c_ind + return c_quant, None, info + + def decode(self, c): + c = c/self.n_embed + c = torch.nn.functional.interpolate(c, scale_factor=self.down_factor, + mode="nearest") + return c diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/util.py new file mode 100644 index 00000000..9ee16385 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/util.py @@ -0,0 +1,130 @@ +import torch +import torch.nn as nn + + +def count_params(model): + total_params = sum(p.numel() for p in model.parameters()) + return total_params + + +class ActNorm(nn.Module): + def __init__(self, num_features, logdet=False, affine=True, + allow_reverse_init=False): + assert affine + super().__init__() + self.logdet = logdet + self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) + self.allow_reverse_init = allow_reverse_init + + self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input, reverse=False): + if reverse: + return self.reverse(input) + if len(input.shape) == 2: + input = input[:,:,None,None] + squeeze = True + else: + squeeze = False + + _, _, height, width = input.shape + + if self.training and self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + h = self.scale * (input + self.loc) + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + + if self.logdet: + log_abs = torch.log(torch.abs(self.scale)) + logdet = height*width*torch.sum(log_abs) + logdet = logdet * torch.ones(input.shape[0]).to(input) + return h, logdet + + return h + + def reverse(self, output): + if self.training and self.initialized.item() == 0: + if not self.allow_reverse_init: + raise RuntimeError( + "Initializing ActNorm in reverse direction is " + "disabled by default. Use allow_reverse_init=True to enable." + ) + else: + self.initialize(output) + self.initialized.fill_(1) + + if len(output.shape) == 2: + output = output[:,:,None,None] + squeeze = True + else: + squeeze = False + + h = output / self.scale - self.loc + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + return h + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class Labelator(AbstractEncoder): + """Net2Net Interface for Class-Conditional Model""" + def __init__(self, n_classes, quantize_interface=True): + super().__init__() + self.n_classes = n_classes + self.quantize_interface = quantize_interface + + def encode(self, c): + c = c[:,None] + if self.quantize_interface: + return c, None, [None, None, c.long()] + return c + + +class SOSProvider(AbstractEncoder): + # for unconditional training + def __init__(self, sos_token, quantize_interface=True): + super().__init__() + self.sos_token = sos_token + self.quantize_interface = quantize_interface + + def encode(self, x): + # get batch size from data and replicate sos_token + c = torch.ones(x.shape[0], 1)*self.sos_token + c = c.long().to(x.device) + if self.quantize_interface: + return c, None, [None, None, c] + return c diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/vqvae/quantize.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/vqvae/quantize.py new file mode 100644 index 00000000..d75544e4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/modules/vqvae/quantize.py @@ -0,0 +1,445 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from torch import einsum +from einops import rearrange + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for + # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be + # used wherever VectorQuantizer has been used before and is additionally + # more efficient. + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.matmul(z_flattened, self.embedding.weight.t()) + + ## could possible replace this here + # #\start... + # find closest encodings + min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + #.........\end + + # with: + # .........\start + #min_encoding_indices = torch.argmin(d, dim=1) + #z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:,None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantize(nn.Module): + """ + credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) + Gumbel Softmax trick quantizer + Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 + https://arxiv.org/abs/1611.01144 + """ + def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, + kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, + remap=None, unknown_index="random"): + super().__init__() + + self.embedding_dim = embedding_dim + self.n_embed = n_embed + + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + + self.proj = nn.Conv2d(num_hiddens, n_embed, 1) + self.embed = nn.Embedding(n_embed, embedding_dim) + + self.use_vqinterface = use_vqinterface + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, return_logits=False): + # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work + hard = self.straight_through if self.training else True + temp = self.temperature if temp is None else temp + + logits = self.proj(z) + if self.remap is not None: + # continue only with used logits + full_zeros = torch.zeros_like(logits) + logits = logits[:,self.used,...] + + soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) + if self.remap is not None: + # go back to all entries but unused set to zero + full_zeros[:,self.used,...] = soft_one_hot + soft_one_hot = full_zeros + z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() + + ind = soft_one_hot.argmax(dim=1) + if self.remap is not None: + ind = self.remap_to_used(ind) + if self.use_vqinterface: + if return_logits: + return z_q, diff, (None, None, ind), logits + return z_q, diff, (None, None, ind) + return z_q, diff, ind + + def get_codebook_entry(self, indices, shape): + b, h, w, c = shape + assert b*h*w == indices.shape[0] + indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w) + if self.remap is not None: + indices = self.unmap_to_all(indices) + one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() + z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight) + return z_q + + +class VectorQuantizer2(nn.Module): + """ + Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly + avoids costly matrix multiplications and allows for post-hoc remapping of indices. + """ + # NOTE: due to a bug the beta term was applied to the wrong term. for + # backwards compatibility we use the buggy version by default, but you can + # specify legacy=False to fix it. + def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", + sane_index_shape=False, legacy=True): + super().__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + self.legacy = legacy + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_e + + self.sane_index_shape = sane_index_shape + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, rescale_logits=False, return_logits=False): + assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" + assert rescale_logits==False, "Only for interface compatible with Gumbel" + assert return_logits==False, "Only for interface compatible with Gumbel" + # reshape z -> (batch, height, width, channel) and flatten + z = rearrange(z, 'b c h w -> b h w c').contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) + + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + perplexity = None + min_encodings = None + + # compute loss for embedding + if not self.legacy: + loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ + torch.mean((z_q - z.detach()) ** 2) + else: + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() + + if self.remap is not None: + min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis + min_encoding_indices = self.remap_to_used(min_encoding_indices) + min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten + + if self.sane_index_shape: + min_encoding_indices = min_encoding_indices.reshape( + z_q.shape[0], z_q.shape[2], z_q.shape[3]) + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + if self.remap is not None: + indices = indices.reshape(shape[0],-1) # add batch axis + indices = self.unmap_to_all(indices) + indices = indices.reshape(-1) # flatten again + + # get quantized latent vectors + z_q = self.embedding(indices) + + if shape is not None: + z_q = z_q.view(shape) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + +class EmbeddingEMA(nn.Module): + def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5): + super().__init__() + self.decay = decay + self.eps = eps + weight = torch.randn(num_tokens, codebook_dim) + self.weight = nn.Parameter(weight, requires_grad = False) + self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False) + self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False) + self.update = True + + def forward(self, embed_id): + return F.embedding(embed_id, self.weight) + + def cluster_size_ema_update(self, new_cluster_size): + self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) + + def embed_avg_ema_update(self, new_embed_avg): + self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay) + + def weight_update(self, num_tokens): + n = self.cluster_size.sum() + smoothed_cluster_size = ( + (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n + ) + #normalize embedding average with smoothed cluster size + embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) + self.weight.data.copy_(embed_normalized) + + +class EMAVectorQuantizer(nn.Module): + def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5, + remap=None, unknown_index="random"): + super().__init__() + self.codebook_dim = codebook_dim + self.num_tokens = num_tokens + self.beta = beta + self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + #z, 'b c h w -> b h w c' + z = rearrange(z, 'b c h w -> b h w c') + z_flattened = z.reshape(-1, self.codebook_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \ + self.embedding.weight.pow(2).sum(dim=1) - 2 * \ + torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n' + + + encoding_indices = torch.argmin(d, dim=1) + + z_q = self.embedding(encoding_indices).view(z.shape) + encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype) + avg_probs = torch.mean(encodings, dim=0) + perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) + + if self.training and self.embedding.update: + #EMA cluster size + encodings_sum = encodings.sum(0) + self.embedding.cluster_size_ema_update(encodings_sum) + #EMA embedding average + embed_sum = encodings.transpose(0,1) @ z_flattened + self.embedding.embed_avg_ema_update(embed_sum) + #normalize embed_avg and update weight + self.embedding.weight_update(self.num_tokens) + + # compute loss for embedding + loss = self.beta * F.mse_loss(z_q.detach(), z) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + #z_q, 'b h w c -> b c h w' + z_q = rearrange(z_q, 'b h w c -> b c h w') + return z_q, loss, (perplexity, encodings, encoding_indices) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/util.py new file mode 100644 index 00000000..06053e5d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/diffusion_edge/taming/util.py @@ -0,0 +1,157 @@ +import os, hashlib +import requests +from tqdm import tqdm + +URL_MAP = { + "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1" +} + +CKPT_MAP = { + "vgg_lpips": "vgg.pth" +} + +MD5_MAP = { + "vgg_lpips": "d507d7349b931f0638a25a48a722f98a" +} + + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path + + +class KeyNotFoundError(Exception): + def __init__(self, cause, keys=None, visited=None): + self.cause = cause + self.keys = keys + self.visited = visited + messages = list() + if keys is not None: + messages.append("Key not found: {}".format(keys)) + if visited is not None: + messages.append("Visited: {}".format(visited)) + messages.append("Cause:\n{}".format(cause)) + message = "\n".join(messages) + super().__init__(message) + + +def retrieve( + list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False +): + """Given a nested list or dict return the desired value at key expanding + callable nodes if necessary and :attr:`expand` is ``True``. The expansion + is done in-place. + + Parameters + ---------- + list_or_dict : list or dict + Possibly nested list or dictionary. + key : str + key/to/value, path like string describing all keys necessary to + consider to get to the desired value. List indices can also be + passed here. + splitval : str + String that defines the delimiter between keys of the + different depth levels in `key`. + default : obj + Value returned if :attr:`key` is not found. + expand : bool + Whether to expand callable nodes on the path or not. + + Returns + ------- + The desired value or if :attr:`default` is not ``None`` and the + :attr:`key` is not found returns ``default``. + + Raises + ------ + Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is + ``None``. + """ + + keys = key.split(splitval) + + success = True + try: + visited = [] + parent = None + last_key = None + for key in keys: + if callable(list_or_dict): + if not expand: + raise KeyNotFoundError( + ValueError( + "Trying to get past callable node with expand=False." + ), + keys=keys, + visited=visited, + ) + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + + last_key = key + parent = list_or_dict + + try: + if isinstance(list_or_dict, dict): + list_or_dict = list_or_dict[key] + else: + list_or_dict = list_or_dict[int(key)] + except (KeyError, IndexError, ValueError) as e: + raise KeyNotFoundError(e, keys=keys, visited=visited) + + visited += [key] + # final expansion of retrieved value + if expand and callable(list_or_dict): + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + except KeyNotFoundError as e: + if default is None: + raise e + else: + list_or_dict = default + success = False + + if not pass_success: + return list_or_dict + else: + return list_or_dict, success + + +if __name__ == "__main__": + config = {"keya": "a", + "keyb": "b", + "keyc": + {"cc1": 1, + "cc2": 2, + } + } + from omegaconf import OmegaConf + config = OmegaConf.create(config) + print(config) + retrieve(config, "keya") + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/LICENSE new file mode 100644 index 00000000..2b677c1a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/LICENSE @@ -0,0 +1,230 @@ +DSINE SOFTWARE + +LICENCE AGREEMENT + +WE (Imperial College of Science, Technology and Medicine, (β€œImperial College +London”)) ARE WILLING TO LICENSE THIS SOFTWARE TO YOU (a licensee β€œYou”) ONLY +ON THE CONDITION THAT YOU ACCEPT ALL OF THE TERMS CONTAINED IN THE FOLLOWING +AGREEMENT. PLEASE READ THE AGREEMENT CAREFULLY BEFORE DOWNLOADING THE SOFTWARE. +BY EXERCISING THE OPTION TO DOWNLOAD THE SOFTWARE YOU AGREE TO BE BOUND BY THE +TERMS OF THE AGREEMENT. + +SOFTWARE LICENCE AGREEMENT (EXCLUDING BSD COMPONENTS) + +1. This Agreement pertains to a worldwide, non-exclusive, temporary, fully +paid-up, royalty free, non-transferable, non-sub- licensable licence (the +β€œLicence”) to use the elastic fusion source code, including any modification, +part or derivative (the β€œSoftware”). + +Ownership and Licence. Your rights to use and download the Software onto your +computer, and all other copies that You are authorised to make, are specified +in this Agreement. However, we (or our licensors) retain all rights, including +but not limited to all copyright and other intellectual property rights +anywhere in the world, in the Software not expressly granted to You in this +Agreement. + +2. 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Termination. + +(a) We may terminate this licence agreement and your right to use the Software +at any time with immediate effect upon written notice to You. + +(b) This licence agreement and your right to use the Software automatically +terminate if You: + + (i) fail to comply with any provisions of this Agreement; or + + (ii) destroy the copies of the Software in your possession, or voluntarily + return the Software to us. + +(c) Upon termination You will destroy all copies of the Software. + +(d) Otherwise, the restrictions on your rights to use the Software will expire +10 (ten) years after first use of the Software under this licence agreement. + +8. Miscellaneous Provisions. + +(a) This Agreement will be governed by and construed in accordance with the +substantive laws of England and Wales whose courts shall have exclusive +jurisdiction over all disputes which may arise between us. + +(b) This is the entire agreement between us relating to the Software, and +supersedes any prior purchase order, communications, advertising or +representations concerning the Software. + +(c) No change or modification of this Agreement will be valid unless it is in +writing, and is signed by us. + +(d) The unenforceability or invalidity of any part of this Agreement will not +affect the enforceability or validity of the remaining parts. + +BSD Elements of the Software + +For BSD elements of the Software, the following terms shall apply: +Copyright as indicated in the header of the individual element of the Software. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this +list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, +this list of conditions and the following disclaimer in the documentation +and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors +may be used to endorse or promote products derived from this software without +specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +SCHEDULE 1 + +The Software + +DSINE is a framework for estimating surface normals from a single image. It is based on the techniques described in the following publication: + + β€’ Gwangbin Bae, Andrew J. Davison. Rethinking Inductive Biases for Surface Normal Estimation. CVPR, 2024 +_________________________ + +Acknowledgments + +If you use the software, you should reference the following paper in any publication: + + β€’ Gwangbin Bae, Andrew J. Davison. Rethinking Inductive Biases for Surface Normal Estimation. CVPR, 2024 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/__init__.py new file mode 100644 index 00000000..c3cf8aa2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/__init__.py @@ -0,0 +1,222 @@ +import os +import types +import warnings +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torchvision.transforms as transforms +from einops import rearrange +from PIL import Image +from huggingface_hub import hf_hub_download + +from .models.dsine_arch import DSINE +from .utils.utils import get_intrins_from_fov + +# Local constants +DIFFUSION_EDGE_MODEL_NAME = "hr16/Diffusion-Edge" + +# Local utility functions +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + +def pad64(x): + return int(np.ceil(float(x) / 64.0) * 64 - x) + +def safer_memory(x): + return np.ascontiguousarray(x.copy()).copy() + +def resize_image_with_pad(input_image, resolution, upscale_method="INTER_CUBIC", skip_hwc3=False, mode='edge'): + if skip_hwc3: + img = input_image + else: + img = HWC3(input_image) + H_raw, W_raw, _ = img.shape + if resolution == 0: + return img, lambda x: x + k = float(resolution) / float(min(H_raw, W_raw)) + H_target = int(np.round(float(H_raw) * k)) + W_target = int(np.round(float(W_raw) * k)) + + # Get upscale method + upscale_methods = {"INTER_NEAREST": cv2.INTER_NEAREST, "INTER_LINEAR": cv2.INTER_LINEAR, + "INTER_AREA": cv2.INTER_AREA, "INTER_CUBIC": cv2.INTER_CUBIC, + "INTER_LANCZOS4": cv2.INTER_LANCZOS4} + method = upscale_methods.get(upscale_method, cv2.INTER_CUBIC) + + img = cv2.resize(img, (W_target, H_target), interpolation=method if k > 1 else cv2.INTER_AREA) + H_pad, W_pad = pad64(H_target), pad64(W_target) + img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode=mode) + + def remove_pad(x): + return safer_memory(x[:H_target, :W_target, ...]) + + return safer_memory(img_padded), remove_pad + +def common_input_validate(input_image, output_type, **kwargs): + if "img" in kwargs: + warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning) + input_image = kwargs.pop("img") + + if "return_pil" in kwargs: + warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) + output_type = "pil" if kwargs["return_pil"] else "np" + + if type(output_type) is bool: + warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") + if output_type: + output_type = "pil" + + if input_image is None: + raise ValueError("input_image must be defined.") + + if not isinstance(input_image, np.ndarray): + input_image = np.array(input_image, dtype=np.uint8) + output_type = output_type or "pil" + else: + output_type = output_type or "np" + + return (input_image, output_type) + +def custom_hf_download(pretrained_model_or_path, filename, subfolder=''): + """Download model files from HuggingFace Hub""" + annotator_ckpts_path = os.path.join(Path(__file__).parents[3], 'ckpts') + local_dir = os.path.join(annotator_ckpts_path, pretrained_model_or_path) + model_path = Path(local_dir).joinpath(*subfolder.split('/'), filename).__str__() + + if not os.path.exists(model_path): + print(f"Downloading {filename} from {pretrained_model_or_path}") + model_path = hf_hub_download( + repo_id=pretrained_model_or_path, + filename=filename, + subfolder=subfolder, + local_dir=local_dir, + local_dir_use_symlinks=False + ) + + print(f"model_path is {model_path}") + return model_path + +# load model +def load_checkpoint(fpath, model): + ckpt = torch.load(fpath, map_location='cpu')['model'] + + load_dict = {} + for k, v in ckpt.items(): + if k.startswith('module.'): + k_ = k.replace('module.', '') + load_dict[k_] = v + else: + load_dict[k] = v + + # Load compatible weights only + model_state = model.state_dict() + compatible_dict = {} + skipped_keys = [] + + for k, v in load_dict.items(): + if k in model_state: + if model_state[k].shape == v.shape: + compatible_dict[k] = v + else: + skipped_keys.append(f"{k}: checkpoint {v.shape} vs model {model_state[k].shape}") + else: + skipped_keys.append(f"{k}: not found in model") + + print(f"Loading checkpoint: {len(compatible_dict)} compatible, {len(skipped_keys)} skipped") + if skipped_keys: + print("Skipped keys with shape mismatches:") + for key in skipped_keys[:5]: # Show first 5 mismatches + print(f" {key}") + if len(skipped_keys) > 5: + print(f" ... and {len(skipped_keys) - 5} more") + + model.load_state_dict(compatible_dict, strict=False) + return model + +def get_pad(orig_H, orig_W): + if orig_W % 64 == 0: + l = 0 + r = 0 + else: + new_W = 64 * ((orig_W // 64) + 1) + l = (new_W - orig_W) // 2 + r = (new_W - orig_W) - l + + if orig_H % 64 == 0: + t = 0 + b = 0 + else: + new_H = 64 * ((orig_H // 64) + 1) + t = (new_H - orig_H) // 2 + b = (new_H - orig_H) - t + return l, r, t, b + +class DsineDetector: + def __init__(self, model): + self.model = model + self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=DIFFUSION_EDGE_MODEL_NAME, filename="dsine.pt"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + model = DSINE() + model = load_checkpoint(model_path, model) + model.eval() + + return cls(model) + + def to(self, device): + self.model.to(device) + self.model.pixel_coords = self.model.pixel_coords.to(device) + self.device = device + return self + + + def __call__(self, input_image, fov=60.0, iterations=5, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): + self.model.num_iter = iterations + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + orig_H, orig_W = input_image.shape[:2] + l, r, t, b = get_pad(orig_H, orig_W) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method, mode="constant") + with torch.no_grad(): + input_image = torch.from_numpy(input_image).float().to(self.device) + input_image = input_image / 255.0 + input_image = rearrange(input_image, 'h w c -> 1 c h w') + input_image = self.norm(input_image) + + intrins = get_intrins_from_fov(new_fov=fov, H=orig_H, W=orig_W, device=self.device).unsqueeze(0) + intrins[:, 0, 2] += l + intrins[:, 1, 2] += t + + normal = self.model(input_image, intrins) + normal = normal[-1][0] + normal = ((normal + 1) * 0.5).clip(0, 1) + + normal = rearrange(normal, 'c h w -> h w c').cpu().numpy() + normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = HWC3(normal_image) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/dsine_arch.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/dsine_arch.py new file mode 100644 index 00000000..9f4cc2ba --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/dsine_arch.py @@ -0,0 +1,230 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .submodules import Encoder, ConvGRU, UpSampleBN, UpSampleGN, RayReLU, \ + convex_upsampling, get_unfold, get_prediction_head, \ + INPUT_CHANNELS_DICT +from ..utils.rotation import axis_angle_to_matrix + + +class Decoder(nn.Module): + def __init__(self, output_dims, B=5, NF=2048, BN=False, downsample_ratio=8): + super(Decoder, self).__init__() + input_channels = INPUT_CHANNELS_DICT[B] + output_dim, feature_dim, hidden_dim = output_dims + features = bottleneck_features = NF + self.downsample_ratio = downsample_ratio + + UpSample = UpSampleBN if BN else UpSampleGN + self.conv2 = nn.Conv2d(bottleneck_features + 2, features, kernel_size=1, stride=1, padding=0) + self.up1 = UpSample(skip_input=features // 1 + input_channels[1] + 2, output_features=features // 2, align_corners=False) + self.up2 = UpSample(skip_input=features // 2 + input_channels[2] + 2, output_features=features // 4, align_corners=False) + + # prediction heads + i_dim = features // 4 + h_dim = 128 + self.normal_head = get_prediction_head(i_dim+2, h_dim, output_dim) + self.feature_head = get_prediction_head(i_dim+2, h_dim, feature_dim) + self.hidden_head = get_prediction_head(i_dim+2, h_dim, hidden_dim) + + def forward(self, features, uvs): + _, _, x_block2, x_block3, x_block4 = None, None, features[5], features[7], features[10] # Skip first two features, use layers 5,7,10 + uv_32, uv_16, uv_8 = uvs + + x_d0 = self.conv2(torch.cat([x_block4, uv_32], dim=1)) + x_d1 = self.up1(x_d0, torch.cat([x_block3, uv_16], dim=1)) + x_feat = self.up2(x_d1, torch.cat([x_block2, uv_8], dim=1)) + x_feat = torch.cat([x_feat, uv_8], dim=1) + + normal = self.normal_head(x_feat) + normal = F.normalize(normal, dim=1) + f = self.feature_head(x_feat) + h = self.hidden_head(x_feat) + return normal, f, h + + +class DSINE(nn.Module): + def __init__(self): + super(DSINE, self).__init__() + self.downsample_ratio = 8 + self.ps = 5 # patch size + self.num_iter = 5 # num iterations + + # define encoder + self.encoder = Encoder(B=5, pretrained=True) + + # define decoder + self.output_dim = output_dim = 3 + self.feature_dim = feature_dim = 64 + self.hidden_dim = hidden_dim = 64 + self.decoder = Decoder([output_dim, feature_dim, hidden_dim], B=5, NF=2048, BN=False) + + # ray direction-based ReLU + self.ray_relu = RayReLU(eps=1e-2) + + # pixel_coords (1, 3, H, W) - adjust dimensions for larger inputs if needed + h = 2000 + w = 2000 + pixel_coords = np.ones((3, h, w)).astype(np.float32) + x_range = np.concatenate([np.arange(w).reshape(1, w)] * h, axis=0) + y_range = np.concatenate([np.arange(h).reshape(h, 1)] * w, axis=1) + pixel_coords[0, :, :] = x_range + 0.5 + pixel_coords[1, :, :] = y_range + 0.5 + self.pixel_coords = torch.from_numpy(pixel_coords).unsqueeze(0) + + # define ConvGRU cell + self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=feature_dim+2, ks=self.ps) + + # padding used during NRN + self.pad = (self.ps - 1) // 2 + + # prediction heads + self.prob_head = get_prediction_head(self.hidden_dim+2, 64, self.ps*self.ps) # weights assigned for each nghbr pixel + self.xy_head = get_prediction_head(self.hidden_dim+2, 64, self.ps*self.ps*2) # rotation axis for each nghbr pixel + self.angle_head = get_prediction_head(self.hidden_dim+2, 64, self.ps*self.ps) # rotation angle for each nghbr pixel + + # prediction heads - weights used for upsampling the coarse resolution output + self.up_prob_head = get_prediction_head(self.hidden_dim+2, 64, 9 * self.downsample_ratio * self.downsample_ratio) + + def get_ray(self, intrins, H, W, orig_H, orig_W, return_uv=False): + B, _, _ = intrins.shape + fu = intrins[:, 0, 0][:,None,None] * (W / orig_W) + cu = intrins[:, 0, 2][:,None,None] * (W / orig_W) + fv = intrins[:, 1, 1][:,None,None] * (H / orig_H) + cv = intrins[:, 1, 2][:,None,None] * (H / orig_H) + + # (B, 2, H, W) + ray = self.pixel_coords[:, :, :H, :W].repeat(B, 1, 1, 1) + ray[:, 0, :, :] = (ray[:, 0, :, :] - cu) / fu + ray[:, 1, :, :] = (ray[:, 1, :, :] - cv) / fv + + if return_uv: + return ray[:, :2, :, :] + else: + return F.normalize(ray, dim=1) + + def upsample(self, h, pred_norm, uv_8): + up_mask = self.up_prob_head(torch.cat([h, uv_8], dim=1)) + up_pred_norm = convex_upsampling(pred_norm, up_mask, self.downsample_ratio) + up_pred_norm = F.normalize(up_pred_norm, dim=1) + return up_pred_norm + + def refine(self, h, feat_map, pred_norm, intrins, orig_H, orig_W, uv_8, ray_8): + B, C, H, W = pred_norm.shape + fu = intrins[:, 0, 0][:,None,None,None] * (W / orig_W) # (B, 1, 1, 1) + cu = intrins[:, 0, 2][:,None,None,None] * (W / orig_W) + fv = intrins[:, 1, 1][:,None,None,None] * (H / orig_H) + cv = intrins[:, 1, 2][:,None,None,None] * (H / orig_H) + + h_new = self.gru(h, feat_map) + + # get nghbr prob (B, 1, ps*ps, h, w) + nghbr_prob = self.prob_head(torch.cat([h_new, uv_8], dim=1)).unsqueeze(1) + nghbr_prob = torch.sigmoid(nghbr_prob) + + # get nghbr normals (B, 3, ps*ps, h, w) + nghbr_normals = get_unfold(pred_norm, ps=self.ps, pad=self.pad) + + # get nghbr xy (B, 2, ps*ps, h, w) + nghbr_xys = self.xy_head(torch.cat([h_new, uv_8], dim=1)) + nghbr_xs, nghbr_ys = torch.split(nghbr_xys, [self.ps*self.ps, self.ps*self.ps], dim=1) + nghbr_xys = torch.cat([nghbr_xs.unsqueeze(1), nghbr_ys.unsqueeze(1)], dim=1) + nghbr_xys = F.normalize(nghbr_xys, dim=1) + + # get nghbr theta (B, 1, ps*ps, h, w) + nghbr_angle = self.angle_head(torch.cat([h_new, uv_8], dim=1)).unsqueeze(1) + nghbr_angle = torch.sigmoid(nghbr_angle) * np.pi + + # get nghbr pixel coord (1, 3, ps*ps, h, w) + nghbr_pixel_coord = get_unfold(self.pixel_coords[:, :, :H, :W], ps=self.ps, pad=self.pad) + + # nghbr axes (B, 3, ps*ps, h, w) + nghbr_axes = torch.zeros_like(nghbr_normals) + + du_over_fu = nghbr_xys[:, 0, ...] / fu # (B, ps*ps, h, w) + dv_over_fv = nghbr_xys[:, 1, ...] / fv # (B, ps*ps, h, w) + + term_u = (nghbr_pixel_coord[:, 0, ...] + nghbr_xys[:, 0, ...] - cu) / fu # (B, ps*ps, h, w) + term_v = (nghbr_pixel_coord[:, 1, ...] + nghbr_xys[:, 1, ...] - cv) / fv # (B, ps*ps, h, w) + + nx = nghbr_normals[:, 0, ...] # (B, ps*ps, h, w) + ny = nghbr_normals[:, 1, ...] # (B, ps*ps, h, w) + nz = nghbr_normals[:, 2, ...] # (B, ps*ps, h, w) + + nghbr_delta_z_num = - (du_over_fu * nx + dv_over_fv * ny) + nghbr_delta_z_denom = (term_u * nx + term_v * ny + nz) + nghbr_delta_z_denom[torch.abs(nghbr_delta_z_denom) < 1e-8] = 1e-8 * torch.sign(nghbr_delta_z_denom[torch.abs(nghbr_delta_z_denom) < 1e-8]) + nghbr_delta_z = nghbr_delta_z_num / nghbr_delta_z_denom + + nghbr_axes[:, 0, ...] = du_over_fu + nghbr_delta_z * term_u + nghbr_axes[:, 1, ...] = dv_over_fv + nghbr_delta_z * term_v + nghbr_axes[:, 2, ...] = nghbr_delta_z + nghbr_axes = F.normalize(nghbr_axes, dim=1) # (B, 3, ps*ps, h, w) + + # make sure axes are all valid + invalid = torch.sum(torch.logical_or(torch.isnan(nghbr_axes), torch.isinf(nghbr_axes)).float(), dim=1) > 0.5 # (B, ps*ps, h, w) + nghbr_axes[:, 0, ...][invalid] = 0.0 + nghbr_axes[:, 1, ...][invalid] = 0.0 + nghbr_axes[:, 2, ...][invalid] = 0.0 + + # nghbr_axes_angle (B, 3, ps*ps, h, w) + nghbr_axes_angle = nghbr_axes * nghbr_angle + nghbr_axes_angle = nghbr_axes_angle.permute(0, 2, 3, 4, 1) # (B, ps*ps, h, w, 3) + nghbr_R = axis_angle_to_matrix(nghbr_axes_angle) # (B, ps*ps, h, w, 3, 3) + + # (B, 3, ps*ps, h, w) + nghbr_normals_rot = torch.bmm( + nghbr_R.reshape(B * self.ps * self.ps * H * W, 3, 3), + nghbr_normals.permute(0, 2, 3, 4, 1).reshape(B * self.ps * self.ps * H * W, 3).unsqueeze(-1) + ).reshape(B, self.ps*self.ps, H, W, 3, 1).squeeze(-1).permute(0, 4, 1, 2, 3) # (B, 3, ps*ps, h, w) + nghbr_normals_rot = F.normalize(nghbr_normals_rot, dim=1) + + # ray ReLU + nghbr_normals_rot = torch.cat([ + self.ray_relu(nghbr_normals_rot[:, :, i, :, :], ray_8).unsqueeze(2) + for i in range(nghbr_normals_rot.size(2)) + ], dim=2) + + # (B, 1, ps*ps, h, w) * (B, 3, ps*ps, h, w) + pred_norm = torch.sum(nghbr_prob * nghbr_normals_rot, dim=2) # (B, C, H, W) + pred_norm = F.normalize(pred_norm, dim=1) + + up_mask = self.up_prob_head(torch.cat([h_new, uv_8], dim=1)) + up_pred_norm = convex_upsampling(pred_norm, up_mask, self.downsample_ratio) + up_pred_norm = F.normalize(up_pred_norm, dim=1) + + return h_new, pred_norm, up_pred_norm + + + def forward(self, img, intrins=None): + # Step 1. encoder + features = self.encoder(img) + + # Step 2. get uv encoding + B, _, orig_H, orig_W = img.shape + intrins[:, 0, 2] += 0.5 + intrins[:, 1, 2] += 0.5 + uv_32 = self.get_ray(intrins, orig_H//32, orig_W//32, orig_H, orig_W, return_uv=True) + uv_16 = self.get_ray(intrins, orig_H//16, orig_W//16, orig_H, orig_W, return_uv=True) + uv_8 = self.get_ray(intrins, orig_H//8, orig_W//8, orig_H, orig_W, return_uv=True) + ray_8 = self.get_ray(intrins, orig_H//8, orig_W//8, orig_H, orig_W) + + # Step 3. decoder - initial prediction + pred_norm, feat_map, h = self.decoder(features, uvs=(uv_32, uv_16, uv_8)) + pred_norm = self.ray_relu(pred_norm, ray_8) + + # Step 4. add ray direction encoding + feat_map = torch.cat([feat_map, uv_8], dim=1) + + # iterative refinement + up_pred_norm = self.upsample(h, pred_norm, uv_8) + pred_list = [up_pred_norm] + for i in range(self.num_iter): + h, pred_norm, up_pred_norm = self.refine(h, feat_map, + pred_norm.detach(), + intrins, orig_H, orig_W, uv_8, ray_8) + pred_list.append(up_pred_norm) + return pred_list + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/submodules/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/submodules/__init__.py new file mode 100644 index 00000000..ee9fb0a4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/submodules/__init__.py @@ -0,0 +1,176 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import os +import timm + + +INPUT_CHANNELS_DICT = { + 0: [1280, 112, 40, 24, 16], + 1: [1280, 112, 40, 24, 16], + 2: [1408, 120, 48, 24, 16], + 3: [1536, 136, 48, 32, 24], + 4: [1792, 160, 56, 32, 24], + 5: [2048, 176, 64, None, None], # EfficientNet-B5: features[10,7,5] + 6: [2304, 200, 72, 40, 32], + 7: [2560, 224, 80, 48, 32] +} + + +from .standalone_encoder import StandaloneEncoder +Encoder = StandaloneEncoder + + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim, input_dim, ks=3): + super(ConvGRU, self).__init__() + p = (ks - 1) // 2 + self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, ks, padding=p) + self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, ks, padding=p) + self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, ks, padding=p) + + def forward(self, h, x): + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz(hx)) + r = torch.sigmoid(self.convr(hx)) + q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + return h + + +class RayReLU(nn.Module): + def __init__(self, eps=1e-2): + super(RayReLU, self).__init__() + self.eps = eps + + def forward(self, pred_norm, ray): + # angle between the predicted normal and ray direction + cos = torch.cosine_similarity(pred_norm, ray, dim=1).unsqueeze(1) # (B, 1, H, W) + + # component of pred_norm along view + norm_along_view = ray * cos + + # cos should be bigger than eps + norm_along_view_relu = ray * (torch.relu(cos - self.eps) + self.eps) + + # difference + diff = norm_along_view_relu - norm_along_view + + # updated pred_norm + new_pred_norm = pred_norm + diff + new_pred_norm = F.normalize(new_pred_norm, dim=1) + + return new_pred_norm + + +class UpSampleBN(nn.Module): + def __init__(self, skip_input, output_features, align_corners=True): + super(UpSampleBN, self).__init__() + self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(output_features), + nn.LeakyReLU(), + nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(output_features), + nn.LeakyReLU()) + self.align_corners = align_corners + + def forward(self, x, concat_with): + up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=self.align_corners) + f = torch.cat([up_x, concat_with], dim=1) + return self._net(f) + + +class Conv2d_WS(nn.Conv2d): + """ weight standardization + """ + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True): + super(Conv2d_WS, self).__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias) + + def forward(self, x): + weight = self.weight + weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2, + keepdim=True).mean(dim=3, keepdim=True) + weight = weight - weight_mean + std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5 + weight = weight / std.expand_as(weight) + return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + + +class UpSampleGN(nn.Module): + """ UpSample with GroupNorm + """ + def __init__(self, skip_input, output_features, align_corners=True): + super(UpSampleGN, self).__init__() + self._net = nn.Sequential(Conv2d_WS(skip_input, output_features, kernel_size=3, stride=1, padding=1), + nn.GroupNorm(8, output_features), + nn.LeakyReLU(), + Conv2d_WS(output_features, output_features, kernel_size=3, stride=1, padding=1), + nn.GroupNorm(8, output_features), + nn.LeakyReLU()) + self.align_corners = align_corners + + def forward(self, x, concat_with): + up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=self.align_corners) + f = torch.cat([up_x, concat_with], dim=1) + return self._net(f) + + +def upsample_via_bilinear(out, up_mask, downsample_ratio): + """ bilinear upsampling (up_mask is a dummy variable) + """ + return F.interpolate(out, scale_factor=downsample_ratio, mode='bilinear', align_corners=True) + + +def upsample_via_mask(out, up_mask, downsample_ratio): + """ convex upsampling + """ + # out: low-resolution output (B, o_dim, H, W) + # up_mask: (B, 9*k*k, H, W) + k = downsample_ratio + + N, o_dim, H, W = out.shape + up_mask = up_mask.view(N, 1, 9, k, k, H, W) + up_mask = torch.softmax(up_mask, dim=2) # (B, 1, 9, k, k, H, W) + + up_out = F.unfold(out, [3, 3], padding=1) # (B, 2, H, W) -> (B, 2 X 3*3, H*W) + up_out = up_out.view(N, o_dim, 9, 1, 1, H, W) # (B, 2, 3*3, 1, 1, H, W) + up_out = torch.sum(up_mask * up_out, dim=2) # (B, 2, k, k, H, W) + + up_out = up_out.permute(0, 1, 4, 2, 5, 3) # (B, 2, H, k, W, k) + return up_out.reshape(N, o_dim, k*H, k*W) # (B, 2, kH, kW) + + +def convex_upsampling(out, up_mask, k): + # out: low-resolution output (B, C, H, W) + # up_mask: (B, 9*k*k, H, W) + B, C, H, W = out.shape + up_mask = up_mask.view(B, 1, 9, k, k, H, W) + up_mask = torch.softmax(up_mask, dim=2) # (B, 1, 9, k, k, H, W) + + out = F.pad(out, pad=(1,1,1,1), mode='replicate') + up_out = F.unfold(out, [3, 3], padding=0) # (B, C, H, W) -> (B, C X 3*3, H*W) + up_out = up_out.view(B, C, 9, 1, 1, H, W) # (B, C, 9, 1, 1, H, W) + + up_out = torch.sum(up_mask * up_out, dim=2) # (B, C, k, k, H, W) + up_out = up_out.permute(0, 1, 4, 2, 5, 3) # (B, C, H, k, W, k) + return up_out.reshape(B, C, k*H, k*W) # (B, C, kH, kW) + + +def get_unfold(pred_norm, ps, pad): + B, C, H, W = pred_norm.shape + pred_norm = F.pad(pred_norm, pad=(pad,pad,pad,pad), mode='replicate') # (B, C, h, w) + pred_norm_unfold = F.unfold(pred_norm, [ps, ps], padding=0) # (B, C X ps*ps, h*w) + pred_norm_unfold = pred_norm_unfold.view(B, C, ps*ps, H, W) # (B, C, ps*ps, h, w) + return pred_norm_unfold + + +def get_prediction_head(input_dim, hidden_dim, output_dim): + return nn.Sequential( + nn.Conv2d(input_dim, hidden_dim, 3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(hidden_dim, hidden_dim, 1), + nn.ReLU(inplace=True), + nn.Conv2d(hidden_dim, output_dim, 1), + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/submodules/standalone_encoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/submodules/standalone_encoder.py new file mode 100644 index 00000000..e823971e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/models/submodules/standalone_encoder.py @@ -0,0 +1,39 @@ +""" +Standalone DSINE Encoder using EfficientNet-B5 backbone. +""" +import torch +import torch.nn as nn +import timm + +INPUT_CHANNELS_DICT = { + 0: [1280, 112, 40, 24, 16], + 1: [1280, 112, 40, 24, 16], + 2: [1408, 120, 48, 24, 16], + 3: [1536, 136, 48, 32, 24], + 4: [1792, 160, 56, 32, 24], + 5: [2048, 176, 64, None, None], # EfficientNet-B5: features[10,7,5] + 6: [2304, 200, 72, 40, 32], + 7: [2560, 224, 80, 48, 32] +} + +class StandaloneEncoder(nn.Module): + """EfficientNet encoder for DSINE depth estimation.""" + def __init__(self, B=5, pretrained=True): + super(StandaloneEncoder, self).__init__() + + basemodel_name = f'tf_efficientnet_b{B}.ap_in1k' + basemodel = timm.create_model(basemodel_name, pretrained=False, num_classes=0) + basemodel.global_pool = nn.Identity() + basemodel.classifier = nn.Identity() + + self.original_model = basemodel + + def forward(self, x): + features = [x] + for k, v in self.original_model._modules.items(): + if (k == 'blocks'): + for ki, vi in v._modules.items(): + features.append(vi(features[-1])) + else: + features.append(v(features[-1])) + return features \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/utils/rotation.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/utils/rotation.py new file mode 100644 index 00000000..8123f900 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/utils/rotation.py @@ -0,0 +1,85 @@ +import torch +import numpy as np + + +# NOTE: from PyTorch3D +def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as axis/angle to quaternions. + + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + + Returns: + quaternions with real part first, as tensor of shape (..., 4). + """ + angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) + half_angles = angles * 0.5 + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + quaternions = torch.cat( + [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 + ) + return quaternions + + +# NOTE: from PyTorch3D +def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as quaternions to rotation matrices. + + Args: + quaternions: quaternions with real part first, + as tensor of shape (..., 4). + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + r, i, j, k = torch.unbind(quaternions, -1) + # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`. + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + +# NOTE: from PyTorch3D +def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as axis/angle to rotation matrices. + + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/utils/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/utils/utils.py new file mode 100644 index 00000000..61cd4cca --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dsine/utils/utils.py @@ -0,0 +1,105 @@ +""" utils +""" +import os +import torch +import numpy as np + + +def load_checkpoint(fpath, model): + print('loading checkpoint... {}'.format(fpath)) + + ckpt = torch.load(fpath, map_location='cpu')['model'] + + load_dict = {} + for k, v in ckpt.items(): + if k.startswith('module.'): + k_ = k.replace('module.', '') + load_dict[k_] = v + else: + load_dict[k] = v + + model.load_state_dict(load_dict) + print('loading checkpoint... / done') + return model + + +def compute_normal_error(pred_norm, gt_norm): + pred_error = torch.cosine_similarity(pred_norm, gt_norm, dim=1) + pred_error = torch.clamp(pred_error, min=-1.0, max=1.0) + pred_error = torch.acos(pred_error) * 180.0 / np.pi + pred_error = pred_error.unsqueeze(1) # (B, 1, H, W) + return pred_error + + +def compute_normal_metrics(total_normal_errors): + total_normal_errors = total_normal_errors.detach().cpu().numpy() + num_pixels = total_normal_errors.shape[0] + + metrics = { + 'mean': np.average(total_normal_errors), + 'median': np.median(total_normal_errors), + 'rmse': np.sqrt(np.sum(total_normal_errors * total_normal_errors) / num_pixels), + 'a1': 100.0 * (np.sum(total_normal_errors < 5) / num_pixels), + 'a2': 100.0 * (np.sum(total_normal_errors < 7.5) / num_pixels), + 'a3': 100.0 * (np.sum(total_normal_errors < 11.25) / num_pixels), + 'a4': 100.0 * (np.sum(total_normal_errors < 22.5) / num_pixels), + 'a5': 100.0 * (np.sum(total_normal_errors < 30) / num_pixels) + } + + return metrics + + +def pad_input(orig_H, orig_W): + if orig_W % 32 == 0: + l = 0 + r = 0 + else: + new_W = 32 * ((orig_W // 32) + 1) + l = (new_W - orig_W) // 2 + r = (new_W - orig_W) - l + + if orig_H % 32 == 0: + t = 0 + b = 0 + else: + new_H = 32 * ((orig_H // 32) + 1) + t = (new_H - orig_H) // 2 + b = (new_H - orig_H) - t + return l, r, t, b + + +def get_intrins_from_fov(new_fov, H, W, device): + # NOTE: top-left pixel should be (0,0) + if W >= H: + new_fu = (W / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) + new_fv = (W / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) + else: + new_fu = (H / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) + new_fv = (H / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) + + new_cu = (W / 2.0) - 0.5 + new_cv = (H / 2.0) - 0.5 + + new_intrins = torch.tensor([ + [new_fu, 0, new_cu ], + [0, new_fv, new_cv ], + [0, 0, 1 ] + ], dtype=torch.float32, device=device) + + return new_intrins + + +def get_intrins_from_txt(intrins_path, device): + # NOTE: top-left pixel should be (0,0) + with open(intrins_path, 'r') as f: + intrins_ = f.readlines()[0].split()[0].split(',') + intrins_ = [float(i) for i in intrins_] + fx, fy, cx, cy = intrins_ + + intrins = torch.tensor([ + [fx, 0,cx], + [ 0,fy,cy], + [ 0, 0, 1] + ], dtype=torch.float32, device=device) + + return intrins \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/LICENSE new file mode 100644 index 00000000..6f60b76d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/LICENSE @@ -0,0 +1,108 @@ +OPENPOSE: MULTIPERSON KEYPOINT DETECTION +SOFTWARE LICENSE AGREEMENT +ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY + +BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. 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IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +CONTRIBUTION AGREEMENT + +By contributing to the BVLC/caffe repository through pull-request, comment, +or otherwise, the contributor releases their content to the +license and copyright terms herein. + +************END OF THIRD-PARTY SOFTWARE NOTICES AND INFORMATION********** \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/__init__.py new file mode 100644 index 00000000..a9152bef --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/__init__.py @@ -0,0 +1,332 @@ +# Openpose +# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose +# 2nd Edited by https://github.com/Hzzone/pytorch-openpose +# 3rd Edited by ControlNet +# 4th Edited by ControlNet (added face and correct hands) +# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs) +# This preprocessor is licensed by CMU for non-commercial use only. + +import os +os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + +import json +import torch +import numpy as np +from . import util +from .body import Body, BodyResult, Keypoint +from .hand import Hand +from .face import Face +from .types import PoseResult, HandResult, FaceResult, AnimalPoseResult +from huggingface_hub import hf_hub_download +from .wholebody import Wholebody +import warnings +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download +import cv2 +from PIL import Image +from .animalpose import AnimalPoseImage + +from typing import Tuple, List, Callable, Union, Optional + + +def draw_animalposes(animals: list[list[Keypoint]], H: int, W: int) -> np.ndarray: + canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) + for animal_pose in animals: + canvas = draw_animalpose(canvas, animal_pose) + return canvas + + +def draw_animalpose(canvas: np.ndarray, keypoints: list[Keypoint]) -> np.ndarray: + # order of the keypoints for AP10k and a standardized list of colors for limbs + keypointPairsList = [ + (1, 2), + (2, 3), + (1, 3), + (3, 4), + (4, 9), + (9, 10), + (10, 11), + (4, 6), + (6, 7), + (7, 8), + (4, 5), + (5, 15), + (15, 16), + (16, 17), + (5, 12), + (12, 13), + (13, 14), + ] + colorsList = [ + (255, 255, 255), + (100, 255, 100), + (150, 255, 255), + (100, 50, 255), + (50, 150, 200), + (0, 255, 255), + (0, 150, 0), + (0, 0, 255), + (0, 0, 150), + (255, 50, 255), + (255, 0, 255), + (255, 0, 0), + (150, 0, 0), + (255, 255, 100), + (0, 150, 0), + (255, 255, 0), + (150, 150, 150), + ] # 16 colors needed + + for ind, (i, j) in enumerate(keypointPairsList): + p1 = keypoints[i - 1] + p2 = keypoints[j - 1] + + if p1 is not None and p2 is not None: + cv2.line( + canvas, + (int(p1.x), int(p1.y)), + (int(p2.x), int(p2.y)), + colorsList[ind], + 5, + ) + return canvas + + +def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True, xinsr_stick_scaling=False): + """ + Draw the detected poses on an empty canvas. + + Args: + poses (List[PoseResult]): A list of PoseResult objects containing the detected poses. + H (int): The height of the canvas. + W (int): The width of the canvas. + draw_body (bool, optional): Whether to draw body keypoints. Defaults to True. + draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True. + draw_face (bool, optional): Whether to draw face keypoints. Defaults to True. + + Returns: + numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses. + """ + canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) + + for pose in poses: + if draw_body: + canvas = util.draw_bodypose(canvas, pose.body.keypoints, xinsr_stick_scaling) + + if draw_hand: + canvas = util.draw_handpose(canvas, pose.left_hand) + canvas = util.draw_handpose(canvas, pose.right_hand) + + if draw_face: + canvas = util.draw_facepose(canvas, pose.face) + + return canvas + + +def decode_json_as_poses( + pose_json: dict, +) -> Tuple[List[PoseResult], List[AnimalPoseResult], int, int]: + """Decode the json_string complying with the openpose JSON output format + to poses that controlnet recognizes. + https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md + + Args: + json_string: The json string to decode. + + Returns: + human_poses + animal_poses + canvas_height + canvas_width + """ + height = pose_json["canvas_height"] + width = pose_json["canvas_width"] + + def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + for i in range(0, len(lst), n): + yield lst[i : i + n] + + def decompress_keypoints( + numbers: Optional[List[float]], + ) -> Optional[List[Optional[Keypoint]]]: + if not numbers: + return None + + assert len(numbers) % 3 == 0 + + def create_keypoint(x, y, c): + if c < 1.0: + return None + keypoint = Keypoint(x, y) + return keypoint + + return [create_keypoint(x, y, c) for x, y, c in chunks(numbers, n=3)] + + return ( + [ + PoseResult( + body=BodyResult( + keypoints=decompress_keypoints(pose.get("pose_keypoints_2d")) + ), + left_hand=decompress_keypoints(pose.get("hand_left_keypoints_2d")), + right_hand=decompress_keypoints(pose.get("hand_right_keypoints_2d")), + face=decompress_keypoints(pose.get("face_keypoints_2d")), + ) + for pose in pose_json.get("people", []) + ], + [decompress_keypoints(pose) for pose in pose_json.get("animals", [])], + height, + width, + ) + + +def encode_poses_as_dict(poses: List[PoseResult], canvas_height: int, canvas_width: int) -> str: + """ Encode the pose as a dict following openpose JSON output format: + https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md + """ + def compress_keypoints(keypoints: Union[List[Keypoint], None]) -> Union[List[float], None]: + if not keypoints: + return None + + return [ + value + for keypoint in keypoints + for value in ( + [float(keypoint.x), float(keypoint.y), 1.0] + if keypoint is not None + else [0.0, 0.0, 0.0] + ) + ] + + return { + 'people': [ + { + 'pose_keypoints_2d': compress_keypoints(pose.body.keypoints), + "face_keypoints_2d": compress_keypoints(pose.face), + "hand_left_keypoints_2d": compress_keypoints(pose.left_hand), + "hand_right_keypoints_2d":compress_keypoints(pose.right_hand), + } + for pose in poses + ], + 'canvas_height': canvas_height, + 'canvas_width': canvas_width, + } + +global_cached_dwpose = Wholebody() + +class DwposeDetector: + """ + A class for detecting human poses in images using the Dwpose model. + + Attributes: + model_dir (str): Path to the directory where the pose models are stored. + """ + def __init__(self, dw_pose_estimation): + self.dw_pose_estimation = dw_pose_estimation + + @classmethod + def from_pretrained(cls, pretrained_model_or_path, pretrained_det_model_or_path=None, det_filename=None, pose_filename=None, torchscript_device="cuda"): + global global_cached_dwpose + pretrained_det_model_or_path = pretrained_det_model_or_path or pretrained_model_or_path + + pose_filename = pose_filename or "dw-ll_ucoco_384.onnx" + + det_model_path = None + if det_filename is not None: + det_model_path = custom_hf_download(pretrained_det_model_or_path, det_filename) + pose_model_path = custom_hf_download(pretrained_model_or_path, pose_filename) + + print(f"\nDWPose: Using {det_filename} for bbox detection and {pose_filename} for pose estimation") + if global_cached_dwpose.det is None or global_cached_dwpose.det_filename != det_filename: + t = Wholebody(det_model_path, None, torchscript_device=torchscript_device) + t.pose = global_cached_dwpose.pose + t.pose_filename = global_cached_dwpose.pose + global_cached_dwpose = t + + if global_cached_dwpose.pose is None or global_cached_dwpose.pose_filename != pose_filename: + t = Wholebody(None, pose_model_path, torchscript_device=torchscript_device) + t.det = global_cached_dwpose.det + t.det_filename = global_cached_dwpose.det_filename + global_cached_dwpose = t + return cls(global_cached_dwpose) + + def detect_poses(self, oriImg) -> List[PoseResult]: + with torch.no_grad(): + keypoints_info = self.dw_pose_estimation(oriImg.copy()) + return Wholebody.format_result(keypoints_info) + + def __call__(self, input_image, detect_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", image_and_json=False, upscale_method="INTER_CUBIC", xinsr_stick_scaling=False, **kwargs): + if hand_and_face is not None: + warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning) + include_hand = hand_and_face + include_face = hand_and_face + + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, _ = resize_image_with_pad(input_image, 0, upscale_method) + poses = self.detect_poses(input_image) + + canvas = draw_poses(poses, input_image.shape[0], input_image.shape[1], draw_body=include_body, draw_hand=include_hand, draw_face=include_face, xinsr_stick_scaling=xinsr_stick_scaling) + canvas, remove_pad = resize_image_with_pad(canvas, detect_resolution, upscale_method) + detected_map = HWC3(remove_pad(canvas)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + if image_and_json: + return (detected_map, encode_poses_as_dict(poses, input_image.shape[0], input_image.shape[1])) + + return detected_map + +global_cached_animalpose = AnimalPoseImage() +class AnimalposeDetector: + """ + A class for detecting animal poses in images using the RTMPose AP10k model. + + Attributes: + model_dir (str): Path to the directory where the pose models are stored. + """ + def __init__(self, animal_pose_estimation): + self.animal_pose_estimation = animal_pose_estimation + + @classmethod + def from_pretrained(cls, pretrained_model_or_path, pretrained_det_model_or_path=None, det_filename="yolox_l.onnx", pose_filename="dw-ll_ucoco_384.onnx", torchscript_device="cuda"): + global global_cached_animalpose + det_model_path = custom_hf_download(pretrained_det_model_or_path, det_filename) + pose_model_path = custom_hf_download(pretrained_model_or_path, pose_filename) + + print(f"\nAnimalPose: Using {det_filename} for bbox detection and {pose_filename} for pose estimation") + if global_cached_animalpose.det is None or global_cached_animalpose.det_filename != det_filename: + t = AnimalPoseImage(det_model_path, None, torchscript_device=torchscript_device) + t.pose = global_cached_animalpose.pose + t.pose_filename = global_cached_animalpose.pose + global_cached_animalpose = t + + if global_cached_animalpose.pose is None or global_cached_animalpose.pose_filename != pose_filename: + t = AnimalPoseImage(None, pose_model_path, torchscript_device=torchscript_device) + t.det = global_cached_animalpose.det + t.det_filename = global_cached_animalpose.det_filename + global_cached_animalpose = t + return cls(global_cached_animalpose) + + def __call__(self, input_image, detect_resolution=512, output_type="pil", image_and_json=False, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + result = self.animal_pose_estimation(input_image) + if result is None: + detected_map = np.zeros_like(input_image) + openpose_dict = { + 'version': 'ap10k', + 'animals': [], + 'canvas_height': input_image.shape[0], + 'canvas_width': input_image.shape[1] + } + else: + detected_map, openpose_dict = result + detected_map = remove_pad(detected_map) + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + if image_and_json: + return (detected_map, openpose_dict) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/animalpose.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/animalpose.py new file mode 100644 index 00000000..59bc433c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/animalpose.py @@ -0,0 +1,271 @@ +import numpy as np +import cv2 +import os +import cv2 +from .dw_onnx.cv_ox_det import inference_detector as inference_onnx_yolox +from .dw_onnx.cv_ox_yolo_nas import inference_detector as inference_onnx_yolo_nas +from .dw_onnx.cv_ox_pose import inference_pose as inference_onnx_pose + +from .dw_torchscript.jit_det import inference_detector as inference_jit_yolox +from .dw_torchscript.jit_pose import inference_pose as inference_jit_pose +from typing import List, Optional +from .types import PoseResult, BodyResult, Keypoint +from .util import guess_onnx_input_shape_dtype, get_ort_providers, get_model_type, is_model_torchscript +from timeit import default_timer +import torch + +def drawBetweenKeypoints(pose_img, keypoints, indexes, color, scaleFactor): + ind0 = indexes[0] - 1 + ind1 = indexes[1] - 1 + + point1 = (keypoints[ind0][0], keypoints[ind0][1]) + point2 = (keypoints[ind1][0], keypoints[ind1][1]) + + thickness = int(5 // scaleFactor) + + + cv2.line(pose_img, (int(point1[0]), int(point1[1])), (int(point2[0]), int(point2[1])), color, thickness) + + +def drawBetweenKeypointsList(pose_img, keypoints, keypointPairsList, colorsList, scaleFactor): + for ind, keypointPair in enumerate(keypointPairsList): + drawBetweenKeypoints(pose_img, keypoints, keypointPair, colorsList[ind], scaleFactor) + +def drawBetweenSetofKeypointLists(pose_img, keypoints_set, keypointPairsList, colorsList, scaleFactor): + for keypoints in keypoints_set: + drawBetweenKeypointsList(pose_img, keypoints, keypointPairsList, colorsList, scaleFactor) + + +def padImg(img, size, blackBorder=True): + left, right, top, bottom = 0, 0, 0, 0 + + # pad x + if img.shape[1] < size[1]: + sidePadding = int((size[1] - img.shape[1]) // 2) + left = sidePadding + right = sidePadding + + # pad extra on right if padding needed is an odd number + if img.shape[1] % 2 == 1: + right += 1 + + # pad y + if img.shape[0] < size[0]: + topBottomPadding = int((size[0] - img.shape[0]) // 2) + top = topBottomPadding + bottom = topBottomPadding + + # pad extra on bottom if padding needed is an odd number + if img.shape[0] % 2 == 1: + bottom += 1 + + if blackBorder: + paddedImg = cv2.copyMakeBorder(src=img, top=top, bottom=bottom, left=left, right=right, borderType=cv2.BORDER_CONSTANT, value=(0,0,0)) + else: + paddedImg = cv2.copyMakeBorder(src=img, top=top, bottom=bottom, left=left, right=right, borderType=cv2.BORDER_REPLICATE) + + return paddedImg + +def smartCrop(img, size, center): + + width = img.shape[1] + height = img.shape[0] + xSize = size[1] + ySize = size[0] + xCenter = center[0] + yCenter = center[1] + + if img.shape[0] > size[0] or img.shape[1] > size[1]: + + + leftMargin = xCenter - xSize//2 + rightMargin = xCenter + xSize//2 + upMargin = yCenter - ySize//2 + downMargin = yCenter + ySize//2 + + + if(leftMargin < 0): + xCenter += (-leftMargin) + if(rightMargin > width): + xCenter -= (rightMargin - width) + + if(upMargin < 0): + yCenter -= -upMargin + if(downMargin > height): + yCenter -= (downMargin - height) + + + img = cv2.getRectSubPix(img, size, (xCenter, yCenter)) + + + + return img + + + +def calculateScaleFactor(img, size, poseSpanX, poseSpanY): + + poseSpanX = max(poseSpanX, size[0]) + + scaleFactorX = 1 + + + if poseSpanX > size[0]: + scaleFactorX = size[0] / poseSpanX + + scaleFactorY = 1 + if poseSpanY > size[1]: + scaleFactorY = size[1] / poseSpanY + + scaleFactor = min(scaleFactorX, scaleFactorY) + + + return scaleFactor + + + +def scaleImg(img, size, poseSpanX, poseSpanY, scaleFactor): + scaledImg = img + + scaledImg = cv2.resize(img, (0, 0), fx=scaleFactor, fy=scaleFactor) + + return scaledImg, scaleFactor + +class AnimalPoseImage: + def __init__(self, det_model_path: Optional[str] = None, pose_model_path: Optional[str] = None, torchscript_device="cuda"): + self.det_filename = det_model_path and os.path.basename(det_model_path) + self.pose_filename = pose_model_path and os.path.basename(pose_model_path) + self.det, self.pose = None, None + # return type: None ort cv2 torchscript + self.det_model_type = get_model_type("AnimalPose",self.det_filename) + self.pose_model_type = get_model_type("AnimalPose",self.pose_filename) + # Always loads to CPU to avoid building OpenCV. + cv2_device = 'cpu' + cv2_backend = cv2.dnn.DNN_BACKEND_OPENCV if cv2_device == 'cpu' else cv2.dnn.DNN_BACKEND_CUDA + # You need to manually build OpenCV through cmake to work with your GPU. + cv2_providers = cv2.dnn.DNN_TARGET_CPU if cv2_device == 'cpu' else cv2.dnn.DNN_TARGET_CUDA + ort_providers = get_ort_providers() + + if self.det_model_type is None: + pass + elif self.det_model_type == "ort": + try: + import onnxruntime as ort + self.det = ort.InferenceSession(det_model_path, providers=ort_providers) + except: + print(f"Failed to load onnxruntime with {self.det.get_providers()}.\nPlease change EP_list in the config.yaml and restart ComfyUI") + self.det = ort.InferenceSession(det_model_path, providers=["CPUExecutionProvider"]) + elif self.det_model_type == "cv2": + try: + self.det = cv2.dnn.readNetFromONNX(det_model_path) + self.det.setPreferableBackend(cv2_backend) + self.det.setPreferableTarget(cv2_providers) + except: + print("TopK operators may not work on your OpenCV, try use onnxruntime with CPUExecutionProvider") + try: + import onnxruntime as ort + self.det = ort.InferenceSession(det_model_path, providers=["CPUExecutionProvider"]) + except: + print(f"Failed to load {det_model_path}, you can use other models instead") + else: + self.det = torch.jit.load(det_model_path) + self.det.to(torchscript_device) + + if self.pose_model_type is None: + pass + elif self.pose_model_type == "ort": + try: + import onnxruntime as ort + self.pose = ort.InferenceSession(pose_model_path, providers=ort_providers) + except: + print(f"Failed to load onnxruntime with {self.pose.get_providers()}.\nPlease change EP_list in the config.yaml and restart ComfyUI") + self.pose = ort.InferenceSession(pose_model_path, providers=["CPUExecutionProvider"]) + elif self.pose_model_type == "cv2": + self.pose = cv2.dnn.readNetFromONNX(pose_model_path) + self.pose.setPreferableBackend(cv2_backend) + self.pose.setPreferableTarget(cv2_providers) + else: + self.pose = torch.jit.load(pose_model_path) + self.pose.to(torchscript_device) + + if self.pose_filename is not None: + self.pose_input_size, _ = guess_onnx_input_shape_dtype(self.pose_filename) + + def __call__(self, oriImg): + detect_classes = list(range(14, 23 + 1)) #https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml + + #Sacrifice accurate time measurement for compatibility + det_start = default_timer() + if is_model_torchscript(self.det): + det_result = inference_jit_yolox(self.det, oriImg, detect_classes=detect_classes) + else: + det_start = default_timer() + det_onnx_dtype = np.float32 if "yolox" in self.det_filename else np.uint8 + if "yolox" in self.det_filename: + det_result = inference_onnx_yolox(self.det, oriImg, detect_classes=detect_classes, dtype=det_onnx_dtype) + else: + #FP16 and INT8 YOLO NAS accept uint8 input + det_result = inference_onnx_yolo_nas(self.det, oriImg, detect_classes=detect_classes, dtype=det_onnx_dtype) + print(f"AnimalPose: Bbox {((default_timer() - det_start) * 1000):.2f}ms") + + if (det_result is None) or (det_result.shape[0] == 0): + openpose_dict = { + 'version': 'ap10k', + 'animals': [], + 'canvas_height': oriImg.shape[0], + 'canvas_width': oriImg.shape[1] + } + return np.zeros_like(oriImg), openpose_dict + + pose_start = default_timer() + if is_model_torchscript(self.pose): + keypoint_sets, scores = inference_jit_pose(self.pose, det_result, oriImg, self.pose_input_size) + else: + pose_start = default_timer() + _, pose_onnx_dtype = guess_onnx_input_shape_dtype(self.pose_filename) + keypoint_sets, scores = inference_onnx_pose(self.pose, det_result, oriImg, self.pose_input_size, dtype=pose_onnx_dtype) + print(f"AnimalPose: Pose {((default_timer() - pose_start) * 1000):.2f}ms on {det_result.shape[0]} animals\n") + + animal_kps_scores = [] + pose_img = np.zeros((oriImg.shape[0], oriImg.shape[1], 3), dtype = np.uint8) + for (idx, keypoints) in enumerate(keypoint_sets): + # don't use keypoints that go outside the frame in calculations for the center + interorKeypoints = keypoints[((keypoints[:,0] > 0) & (keypoints[:,0] < oriImg.shape[1])) & ((keypoints[:,1] > 0) & (keypoints[:,1] < oriImg.shape[0]))] + + xVals = interorKeypoints[:,0] + yVals = interorKeypoints[:,1] + + minX = np.amin(xVals) + minY = np.amin(yVals) + maxX = np.amax(xVals) + maxY = np.amax(yVals) + + poseSpanX = maxX - minX + poseSpanY = maxY - minY + + # find mean center + + xSum = np.sum(xVals) + ySum = np.sum(yVals) + + xCenter = xSum // xVals.shape[0] + yCenter = ySum // yVals.shape[0] + center_of_keypoints = (xCenter,yCenter) + + # order of the keypoints for AP10k and a standardized list of colors for limbs + keypointPairsList = [(1,2), (2,3), (1,3), (3,4), (4,9), (9,10), (10,11), (4,6), (6,7), (7,8), (4,5), (5,15), (15,16), (16,17), (5,12), (12,13), (13,14)] + colorsList = [(255,255,255), (100,255,100), (150,255,255), (100,50,255), (50,150,200), (0,255,255), (0,150,0), (0,0,255), (0,0,150), (255,50,255), (255,0,255), (255,0,0), (150,0,0), (255,255,100), (0,150,0), (255,255,0), (150,150,150)] # 16 colors needed + + drawBetweenKeypointsList(pose_img, keypoints, keypointPairsList, colorsList, scaleFactor=1.0) + score = scores[idx, ..., None] + score[score > 1.0] = 1.0 + score[score < 0.0] = 0.0 + animal_kps_scores.append(np.concatenate((keypoints, score), axis=-1)) + + openpose_dict = { + 'version': 'ap10k', + 'animals': [keypoints.tolist() for keypoints in animal_kps_scores], + 'canvas_height': oriImg.shape[0], + 'canvas_width': oriImg.shape[1] + } + return pose_img, openpose_dict diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/body.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/body.py new file mode 100644 index 00000000..32934f19 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/body.py @@ -0,0 +1,261 @@ +import cv2 +import numpy as np +import math +import time +from scipy.ndimage.filters import gaussian_filter +import matplotlib.pyplot as plt +import matplotlib +import torch +from torchvision import transforms +from typing import NamedTuple, List, Union + +from . import util +from .model import bodypose_model +from .types import Keypoint, BodyResult + +class Body(object): + def __init__(self, model_path): + self.model = bodypose_model() + # if torch.cuda.is_available(): + # self.model = self.model.cuda() + # print('cuda') + model_dict = util.transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + + def __call__(self, oriImg): + # scale_search = [0.5, 1.0, 1.5, 2.0] + scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre1 = 0.1 + thre2 = 0.05 + multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] + heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19)) + paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale) + imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + if torch.cuda.is_available(): + data = data.cuda() + # data = data.permute([2, 0, 1]).unsqueeze(0).float() + with torch.no_grad(): + data = data.to(self.cn_device) + Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data) + Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy() + Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy() + + # extract outputs, resize, and remove padding + # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps + heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps + heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1])) + + # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs + paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs + paf = util.smart_resize_k(paf, fx=stride, fy=stride) + paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1])) + + heatmap_avg += heatmap_avg + heatmap / len(multiplier) + paf_avg += + paf / len(multiplier) + + all_peaks = [] + peak_counter = 0 + + for part in range(18): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + + map_left = np.zeros(one_heatmap.shape) + map_left[1:, :] = one_heatmap[:-1, :] + map_right = np.zeros(one_heatmap.shape) + map_right[:-1, :] = one_heatmap[1:, :] + map_up = np.zeros(one_heatmap.shape) + map_up[:, 1:] = one_heatmap[:, :-1] + map_down = np.zeros(one_heatmap.shape) + map_down[:, :-1] = one_heatmap[:, 1:] + + peaks_binary = np.logical_and.reduce( + (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1)) + peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse + peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] + peak_id = range(peak_counter, peak_counter + len(peaks)) + peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] + + all_peaks.append(peaks_with_score_and_id) + peak_counter += len(peaks) + + # find connection in the specified sequence, center 29 is in the position 15 + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + # the middle joints heatmap correpondence + mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ + [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ + [55, 56], [37, 38], [45, 46]] + + connection_all = [] + special_k = [] + mid_num = 10 + + for k in range(len(mapIdx)): + score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]] + candA = all_peaks[limbSeq[k][0] - 1] + candB = all_peaks[limbSeq[k][1] - 1] + nA = len(candA) + nB = len(candB) + indexA, indexB = limbSeq[k] + if (nA != 0 and nB != 0): + connection_candidate = [] + for i in range(nA): + for j in range(nB): + vec = np.subtract(candB[j][:2], candA[i][:2]) + norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1]) + norm = max(0.001, norm) + vec = np.divide(vec, norm) + + startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ + np.linspace(candA[i][1], candB[j][1], num=mid_num))) + + vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ + for I in range(len(startend))]) + vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ + for I in range(len(startend))]) + + score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) + score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( + 0.5 * oriImg.shape[0] / norm - 1, 0) + criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts) + criterion2 = score_with_dist_prior > 0 + if criterion1 and criterion2: + connection_candidate.append( + [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) + + connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) + connection = np.zeros((0, 5)) + for c in range(len(connection_candidate)): + i, j, s = connection_candidate[c][0:3] + if (i not in connection[:, 3] and j not in connection[:, 4]): + connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) + if (len(connection) >= min(nA, nB)): + break + + connection_all.append(connection) + else: + special_k.append(k) + connection_all.append([]) + + # last number in each row is the total parts number of that person + # the second last number in each row is the score of the overall configuration + subset = -1 * np.ones((0, 20)) + candidate = np.array([item for sublist in all_peaks for item in sublist]) + + for k in range(len(mapIdx)): + if k not in special_k: + partAs = connection_all[k][:, 0] + partBs = connection_all[k][:, 1] + indexA, indexB = np.array(limbSeq[k]) - 1 + + for i in range(len(connection_all[k])): # = 1:size(temp,1) + found = 0 + subset_idx = [-1, -1] + for j in range(len(subset)): # 1:size(subset,1): + if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]: + subset_idx[found] = j + found += 1 + + if found == 1: + j = subset_idx[0] + if subset[j][indexB] != partBs[i]: + subset[j][indexB] = partBs[i] + subset[j][-1] += 1 + subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + elif found == 2: # if found 2 and disjoint, merge them + j1, j2 = subset_idx + membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] + if len(np.nonzero(membership == 2)[0]) == 0: # merge + subset[j1][:-2] += (subset[j2][:-2] + 1) + subset[j1][-2:] += subset[j2][-2:] + subset[j1][-2] += connection_all[k][i][2] + subset = np.delete(subset, j2, 0) + else: # as like found == 1 + subset[j1][indexB] = partBs[i] + subset[j1][-1] += 1 + subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + + # if find no partA in the subset, create a new subset + elif not found and k < 17: + row = -1 * np.ones(20) + row[indexA] = partAs[i] + row[indexB] = partBs[i] + row[-1] = 2 + row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2] + subset = np.vstack([subset, row]) + # delete some rows of subset which has few parts occur + deleteIdx = [] + for i in range(len(subset)): + if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4: + deleteIdx.append(i) + subset = np.delete(subset, deleteIdx, axis=0) + + # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts + # candidate: x, y, score, id + return candidate, subset + + @staticmethod + def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]: + """ + Format the body results from the candidate and subset arrays into a list of BodyResult objects. + + Args: + candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id + for each body part. + subset (np.ndarray): An array of subsets containing indices to the candidate array for each + person detected. The last two columns of each row hold the total score and total parts + of the person. + + Returns: + List[BodyResult]: A list of BodyResult objects, where each object represents a person with + detected keypoints, total score, and total parts. + """ + return [ + BodyResult( + keypoints=[ + Keypoint( + x=candidate[candidate_index][0], + y=candidate[candidate_index][1], + score=candidate[candidate_index][2], + id=candidate[candidate_index][3] + ) if candidate_index != -1 else None + for candidate_index in person[:18].astype(int) + ], + total_score=person[18], + total_parts=person[19] + ) + for person in subset + ] + + +if __name__ == "__main__": + body_estimation = Body('../model/body_pose_model.pth') + + test_image = '../images/ski.jpg' + oriImg = cv2.imread(test_image) # B,G,R order + candidate, subset = body_estimation(oriImg) + bodies = body_estimation.format_body_result(candidate, subset) + + canvas = oriImg + for body in bodies: + canvas = util.draw_bodypose(canvas, body) + + plt.imshow(canvas[:, :, [2, 1, 0]]) + plt.show() \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/__init__.py new file mode 100644 index 00000000..33e7a7f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/__init__.py @@ -0,0 +1 @@ +#Dummy file ensuring this package will be recognized \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_det.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_det.py new file mode 100644 index 00000000..0365234c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_det.py @@ -0,0 +1,129 @@ +import cv2 +import numpy as np + +def nms(boxes, scores, nms_thr): + """Single class NMS implemented in Numpy.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= nms_thr)[0] + order = order[inds + 1] + + return keep + +def multiclass_nms(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-aware version.""" + final_dets = [] + num_classes = scores.shape[1] + for cls_ind in range(num_classes): + cls_scores = scores[:, cls_ind] + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + continue + else: + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if len(keep) > 0: + cls_inds = np.ones((len(keep), 1)) * cls_ind + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 + ) + final_dets.append(dets) + if len(final_dets) == 0: + return None + return np.concatenate(final_dets, 0) + +def demo_postprocess(outputs, img_size, p6=False): + grids = [] + expanded_strides = [] + strides = [8, 16, 32] if not p6 else [8, 16, 32, 64] + + hsizes = [img_size[0] // stride for stride in strides] + wsizes = [img_size[1] // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + grids = np.concatenate(grids, 1) + expanded_strides = np.concatenate(expanded_strides, 1) + outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides + outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides + + return outputs + +def preprocess(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + +def inference_detector(session, oriImg, detect_classes=[0], dtype=np.float32): + input_shape = (640,640) + img, ratio = preprocess(oriImg, input_shape) + + input = img[None, :, :, :] + input = input.astype(dtype) + if "InferenceSession" in type(session).__name__: + input_name = session.get_inputs()[0].name + output = session.run(None, {input_name: input}) + else: + outNames = session.getUnconnectedOutLayersNames() + session.setInput(input) + output = session.forward(outNames) + + predictions = demo_postprocess(output[0], input_shape)[0] + + boxes = predictions[:, :4] + scores = predictions[:, 4:5] * predictions[:, 5:] + + boxes_xyxy = np.ones_like(boxes) + boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2. + boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2. + boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2. + boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2. + boxes_xyxy /= ratio + dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1) + if dets is None: + return None + final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] + isscore = final_scores>0.3 + iscat = np.isin(final_cls_inds, detect_classes) + isbbox = [ i and j for (i, j) in zip(isscore, iscat)] + final_boxes = final_boxes[isbbox] + return final_boxes \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_pose.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_pose.py new file mode 100644 index 00000000..956c4bc7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_pose.py @@ -0,0 +1,363 @@ +from typing import List, Tuple + +import cv2 +import numpy as np + +def preprocess( + img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256) +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Do preprocessing for DWPose model inference. + + Args: + img (np.ndarray): Input image in shape. + input_size (tuple): Input image size in shape (w, h). + + Returns: + tuple: + - resized_img (np.ndarray): Preprocessed image. + - center (np.ndarray): Center of image. + - scale (np.ndarray): Scale of image. + """ + # get shape of image + img_shape = img.shape[:2] + out_img, out_center, out_scale = [], [], [] + if len(out_bbox) == 0: + out_bbox = [[0, 0, img_shape[1], img_shape[0]]] + for i in range(len(out_bbox)): + x0 = out_bbox[i][0] + y0 = out_bbox[i][1] + x1 = out_bbox[i][2] + y1 = out_bbox[i][3] + bbox = np.array([x0, y0, x1, y1]) + + # get center and scale + center, scale = bbox_xyxy2cs(bbox, padding=1.25) + + # do affine transformation + resized_img, scale = top_down_affine(input_size, scale, center, img) + + # normalize image + mean = np.array([123.675, 116.28, 103.53]) + std = np.array([58.395, 57.12, 57.375]) + resized_img = (resized_img - mean) / std + + out_img.append(resized_img) + out_center.append(center) + out_scale.append(scale) + + return out_img, out_center, out_scale + + +def inference(sess, img, dtype=np.float32): + """Inference DWPose model. Processing all image segments at once to take advantage of GPU's parallelism ability if onnxruntime is installed + + Args: + sess : ONNXRuntime session. + img : Input image in shape. + + Returns: + outputs : Output of DWPose model. + """ + all_out = [] + # build input + input = np.stack(img, axis=0).transpose(0, 3, 1, 2) + input = input.astype(dtype) + if "InferenceSession" in type(sess).__name__: + input_name = sess.get_inputs()[0].name + all_outputs = sess.run(None, {input_name: input}) + for batch_idx in range(len(all_outputs[0])): + outputs = [all_outputs[i][batch_idx:batch_idx+1,...] for i in range(len(all_outputs))] + all_out.append(outputs) + return all_out + + #OpenCV doesn't support batch processing sadly + for i in range(len(img)): + input = img[i].transpose(2, 0, 1) + input = input[None, :, :, :] + + outNames = sess.getUnconnectedOutLayersNames() + sess.setInput(input) + outputs = sess.forward(outNames) + all_out.append(outputs) + + return all_out + +def postprocess(outputs: List[np.ndarray], + model_input_size: Tuple[int, int], + center: Tuple[int, int], + scale: Tuple[int, int], + simcc_split_ratio: float = 2.0 + ) -> Tuple[np.ndarray, np.ndarray]: + """Postprocess for DWPose model output. + + Args: + outputs (np.ndarray): Output of RTMPose model. + model_input_size (tuple): RTMPose model Input image size. + center (tuple): Center of bbox in shape (x, y). + scale (tuple): Scale of bbox in shape (w, h). + simcc_split_ratio (float): Split ratio of simcc. + + Returns: + tuple: + - keypoints (np.ndarray): Rescaled keypoints. + - scores (np.ndarray): Model predict scores. + """ + all_key = [] + all_score = [] + for i in range(len(outputs)): + # use simcc to decode + simcc_x, simcc_y = outputs[i] + keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio) + + # rescale keypoints + keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2 + all_key.append(keypoints[0]) + all_score.append(scores[0]) + + return np.array(all_key), np.array(all_score) + + +def bbox_xyxy2cs(bbox: np.ndarray, + padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]: + """Transform the bbox format from (x,y,w,h) into (center, scale) + + Args: + bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted + as (left, top, right, bottom) + padding (float): BBox padding factor that will be multilied to scale. + Default: 1.0 + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or + (n, 2) + - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or + (n, 2) + """ + # convert single bbox from (4, ) to (1, 4) + dim = bbox.ndim + if dim == 1: + bbox = bbox[None, :] + + # get bbox center and scale + x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3]) + center = np.hstack([x1 + x2, y1 + y2]) * 0.5 + scale = np.hstack([x2 - x1, y2 - y1]) * padding + + if dim == 1: + center = center[0] + scale = scale[0] + + return center, scale + + +def _fix_aspect_ratio(bbox_scale: np.ndarray, + aspect_ratio: float) -> np.ndarray: + """Extend the scale to match the given aspect ratio. + + Args: + scale (np.ndarray): The image scale (w, h) in shape (2, ) + aspect_ratio (float): The ratio of ``w/h`` + + Returns: + np.ndarray: The reshaped image scale in (2, ) + """ + w, h = np.hsplit(bbox_scale, [1]) + bbox_scale = np.where(w > h * aspect_ratio, + np.hstack([w, w / aspect_ratio]), + np.hstack([h * aspect_ratio, h])) + return bbox_scale + + +def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray: + """Rotate a point by an angle. + + Args: + pt (np.ndarray): 2D point coordinates (x, y) in shape (2, ) + angle_rad (float): rotation angle in radian + + Returns: + np.ndarray: Rotated point in shape (2, ) + """ + sn, cs = np.sin(angle_rad), np.cos(angle_rad) + rot_mat = np.array([[cs, -sn], [sn, cs]]) + return rot_mat @ pt + + +def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """To calculate the affine matrix, three pairs of points are required. This + function is used to get the 3rd point, given 2D points a & b. + + The 3rd point is defined by rotating vector `a - b` by 90 degrees + anticlockwise, using b as the rotation center. + + Args: + a (np.ndarray): The 1st point (x,y) in shape (2, ) + b (np.ndarray): The 2nd point (x,y) in shape (2, ) + + Returns: + np.ndarray: The 3rd point. + """ + direction = a - b + c = b + np.r_[-direction[1], direction[0]] + return c + + +def get_warp_matrix(center: np.ndarray, + scale: np.ndarray, + rot: float, + output_size: Tuple[int, int], + shift: Tuple[float, float] = (0., 0.), + inv: bool = False) -> np.ndarray: + """Calculate the affine transformation matrix that can warp the bbox area + in the input image to the output size. + + Args: + center (np.ndarray[2, ]): Center of the bounding box (x, y). + scale (np.ndarray[2, ]): Scale of the bounding box + wrt [width, height]. + rot (float): Rotation angle (degree). + output_size (np.ndarray[2, ] | list(2,)): Size of the + destination heatmaps. + shift (0-100%): Shift translation ratio wrt the width/height. + Default (0., 0.). + inv (bool): Option to inverse the affine transform direction. + (inv=False: src->dst or inv=True: dst->src) + + Returns: + np.ndarray: A 2x3 transformation matrix + """ + shift = np.array(shift) + src_w = scale[0] + dst_w = output_size[0] + dst_h = output_size[1] + + # compute transformation matrix + rot_rad = np.deg2rad(rot) + src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad) + dst_dir = np.array([0., dst_w * -0.5]) + + # get four corners of the src rectangle in the original image + src = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale * shift + src[1, :] = center + src_dir + scale * shift + src[2, :] = _get_3rd_point(src[0, :], src[1, :]) + + # get four corners of the dst rectangle in the input image + dst = np.zeros((3, 2), dtype=np.float32) + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return warp_mat + + +def top_down_affine(input_size: dict, bbox_scale: dict, bbox_center: dict, + img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get the bbox image as the model input by affine transform. + + Args: + input_size (dict): The input size of the model. + bbox_scale (dict): The bbox scale of the img. + bbox_center (dict): The bbox center of the img. + img (np.ndarray): The original image. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: img after affine transform. + - np.ndarray[float32]: bbox scale after affine transform. + """ + w, h = input_size + warp_size = (int(w), int(h)) + + # reshape bbox to fixed aspect ratio + bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h) + + # get the affine matrix + center = bbox_center + scale = bbox_scale + rot = 0 + warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h)) + + # do affine transform + img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR) + + return img, bbox_scale + + +def get_simcc_maximum(simcc_x: np.ndarray, + simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get maximum response location and value from simcc representations. + + Note: + instance number: N + num_keypoints: K + heatmap height: H + heatmap width: W + + Args: + simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx) + simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy) + + Returns: + tuple: + - locs (np.ndarray): locations of maximum heatmap responses in shape + (K, 2) or (N, K, 2) + - vals (np.ndarray): values of maximum heatmap responses in shape + (K,) or (N, K) + """ + N, K, Wx = simcc_x.shape + simcc_x = simcc_x.reshape(N * K, -1) + simcc_y = simcc_y.reshape(N * K, -1) + + # get maximum value locations + x_locs = np.argmax(simcc_x, axis=1) + y_locs = np.argmax(simcc_y, axis=1) + locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32) + max_val_x = np.amax(simcc_x, axis=1) + max_val_y = np.amax(simcc_y, axis=1) + + # get maximum value across x and y axis + mask = max_val_x > max_val_y + max_val_x[mask] = max_val_y[mask] + vals = max_val_x + locs[vals <= 0.] = -1 + + # reshape + locs = locs.reshape(N, K, 2) + vals = vals.reshape(N, K) + + return locs, vals + + +def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, + simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]: + """Modulate simcc distribution with Gaussian. + + Args: + simcc_x (np.ndarray[K, Wx]): model predicted simcc in x. + simcc_y (np.ndarray[K, Wy]): model predicted simcc in y. + simcc_split_ratio (int): The split ratio of simcc. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2) + - np.ndarray[float32]: scores in shape (K,) or (n, K) + """ + keypoints, scores = get_simcc_maximum(simcc_x, simcc_y) + keypoints /= simcc_split_ratio + + return keypoints, scores + + +def inference_pose(session, out_bbox, oriImg, model_input_size=(288, 384), dtype=np.float32): + resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size) + outputs = inference(session, resized_img, dtype) + keypoints, scores = postprocess(outputs, model_input_size, center, scale) + + return keypoints, scores \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_yolo_nas.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_yolo_nas.py new file mode 100644 index 00000000..67ff249b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_onnx/cv_ox_yolo_nas.py @@ -0,0 +1,60 @@ +# Source: https://github.com/Hyuto/yolo-nas-onnx/tree/master/yolo-nas-py +# Inspired from: https://github.com/Deci-AI/super-gradients/blob/3.1.1/src/super_gradients/training/processing/processing.py + +import numpy as np +import cv2 + +def preprocess(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + +def inference_detector(session, oriImg, detect_classes=[0], dtype=np.uint8): + """ + This function is only compatible with onnx models exported from the new API with built-in NMS + ```py + from super_gradients.conversion.conversion_enums import ExportQuantizationMode + from super_gradients.common.object_names import Models + from super_gradients.training import models + + model = models.get(Models.YOLO_NAS_L, pretrained_weights="coco") + + export_result = model.export( + "yolo_nas/yolo_nas_l_fp16.onnx", + quantization_mode=ExportQuantizationMode.FP16, + device="cuda" + ) + ``` + """ + input_shape = (640,640) + img, ratio = preprocess(oriImg, input_shape) + input = img[None, :, :, :] + input = input.astype(dtype) + if "InferenceSession" in type(session).__name__: + input_name = session.get_inputs()[0].name + output = session.run(None, {input_name: input}) + else: + outNames = session.getUnconnectedOutLayersNames() + session.setInput(input) + output = session.forward(outNames) + num_preds, pred_boxes, pred_scores, pred_classes = output + num_preds = num_preds[0,0] + if num_preds == 0: + return None + idxs = np.where((np.isin(pred_classes[0, :num_preds], detect_classes)) & (pred_scores[0, :num_preds] > 0.3)) + if (len(idxs) == 0) or (idxs[0].size == 0): + return None + return pred_boxes[0, idxs].squeeze(axis=0) / ratio diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/__init__.py new file mode 100644 index 00000000..33e7a7f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/__init__.py @@ -0,0 +1 @@ +#Dummy file ensuring this package will be recognized \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/jit_det.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/jit_det.py new file mode 100644 index 00000000..2ae86350 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/jit_det.py @@ -0,0 +1,125 @@ +import cv2 +import numpy as np +import torch + +def nms(boxes, scores, nms_thr): + """Single class NMS implemented in Numpy.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= nms_thr)[0] + order = order[inds + 1] + + return keep + +def multiclass_nms(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-aware version.""" + final_dets = [] + num_classes = scores.shape[1] + for cls_ind in range(num_classes): + cls_scores = scores[:, cls_ind] + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + continue + else: + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if len(keep) > 0: + cls_inds = np.ones((len(keep), 1)) * cls_ind + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 + ) + final_dets.append(dets) + if len(final_dets) == 0: + return None + return np.concatenate(final_dets, 0) + +def demo_postprocess(outputs, img_size, p6=False): + grids = [] + expanded_strides = [] + strides = [8, 16, 32] if not p6 else [8, 16, 32, 64] + + hsizes = [img_size[0] // stride for stride in strides] + wsizes = [img_size[1] // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + grids = np.concatenate(grids, 1) + expanded_strides = np.concatenate(expanded_strides, 1) + outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides + outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides + + return outputs + +def preprocess(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + +def inference_detector(model, oriImg, detect_classes=[0]): + input_shape = (640,640) + img, ratio = preprocess(oriImg, input_shape) + + device, dtype = next(model.parameters()).device, next(model.parameters()).dtype + input = img[None, :, :, :] + input = torch.from_numpy(input).to(device, dtype) + + output = model(input).float().cpu().detach().numpy() + predictions = demo_postprocess(output[0], input_shape) + + boxes = predictions[:, :4] + scores = predictions[:, 4:5] * predictions[:, 5:] + + boxes_xyxy = np.ones_like(boxes) + boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2. + boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2. + boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2. + boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2. + boxes_xyxy /= ratio + dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1) + if dets is None: + return None + final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] + isscore = final_scores>0.3 + iscat = np.isin(final_cls_inds, detect_classes) + isbbox = [ i and j for (i, j) in zip(isscore, iscat)] + final_boxes = final_boxes[isbbox] + return final_boxes \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/jit_pose.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/jit_pose.py new file mode 100644 index 00000000..7a7b6ace --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/dw_torchscript/jit_pose.py @@ -0,0 +1,363 @@ +from typing import List, Tuple + +import cv2 +import numpy as np +import torch + +def preprocess( + img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256) +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Do preprocessing for DWPose model inference. + + Args: + img (np.ndarray): Input image in shape. + input_size (tuple): Input image size in shape (w, h). + + Returns: + tuple: + - resized_img (np.ndarray): Preprocessed image. + - center (np.ndarray): Center of image. + - scale (np.ndarray): Scale of image. + """ + # get shape of image + img_shape = img.shape[:2] + out_img, out_center, out_scale = [], [], [] + if len(out_bbox) == 0: + out_bbox = [[0, 0, img_shape[1], img_shape[0]]] + for i in range(len(out_bbox)): + x0 = out_bbox[i][0] + y0 = out_bbox[i][1] + x1 = out_bbox[i][2] + y1 = out_bbox[i][3] + bbox = np.array([x0, y0, x1, y1]) + + # get center and scale + center, scale = bbox_xyxy2cs(bbox, padding=1.25) + + # do affine transformation + resized_img, scale = top_down_affine(input_size, scale, center, img) + + # normalize image + mean = np.array([123.675, 116.28, 103.53]) + std = np.array([58.395, 57.12, 57.375]) + resized_img = (resized_img - mean) / std + + out_img.append(resized_img) + out_center.append(center) + out_scale.append(scale) + + return out_img, out_center, out_scale + +def inference(model, img, bs=5): + """Inference DWPose model implemented in TorchScript. + + Args: + model : TorchScript Model. + img : Input image in shape. + + Returns: + outputs : Output of DWPose model. + """ + all_out = [] + # build input + orig_img_count = len(img) + #Pad zeros to fit batch size + for _ in range(bs - (orig_img_count % bs)): + img.append(np.zeros_like(img[0])) + input = np.stack(img, axis=0).transpose(0, 3, 1, 2) + device, dtype = next(model.parameters()).device, next(model.parameters()).dtype + input = torch.from_numpy(input).to(device, dtype) + + out1, out2 = [], [] + for i in range(input.shape[0] // bs): + curr_batch_output = model(input[i*bs:(i+1)*bs]) + out1.append(curr_batch_output[0].float()) + out2.append(curr_batch_output[1].float()) + out1, out2 = torch.cat(out1, dim=0)[:orig_img_count], torch.cat(out2, dim=0)[:orig_img_count] + out1, out2 = out1.float().cpu().detach().numpy(), out2.float().cpu().detach().numpy() + all_outputs = out1, out2 + + for batch_idx in range(len(all_outputs[0])): + outputs = [all_outputs[i][batch_idx:batch_idx+1,...] for i in range(len(all_outputs))] + all_out.append(outputs) + return all_out +def postprocess(outputs: List[np.ndarray], + model_input_size: Tuple[int, int], + center: Tuple[int, int], + scale: Tuple[int, int], + simcc_split_ratio: float = 2.0 + ) -> Tuple[np.ndarray, np.ndarray]: + """Postprocess for DWPose model output. + + Args: + outputs (np.ndarray): Output of RTMPose model. + model_input_size (tuple): RTMPose model Input image size. + center (tuple): Center of bbox in shape (x, y). + scale (tuple): Scale of bbox in shape (w, h). + simcc_split_ratio (float): Split ratio of simcc. + + Returns: + tuple: + - keypoints (np.ndarray): Rescaled keypoints. + - scores (np.ndarray): Model predict scores. + """ + all_key = [] + all_score = [] + for i in range(len(outputs)): + # use simcc to decode + simcc_x, simcc_y = outputs[i] + keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio) + + # rescale keypoints + keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2 + all_key.append(keypoints[0]) + all_score.append(scores[0]) + + return np.array(all_key), np.array(all_score) + + +def bbox_xyxy2cs(bbox: np.ndarray, + padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]: + """Transform the bbox format from (x,y,w,h) into (center, scale) + + Args: + bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted + as (left, top, right, bottom) + padding (float): BBox padding factor that will be multilied to scale. + Default: 1.0 + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or + (n, 2) + - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or + (n, 2) + """ + # convert single bbox from (4, ) to (1, 4) + dim = bbox.ndim + if dim == 1: + bbox = bbox[None, :] + + # get bbox center and scale + x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3]) + center = np.hstack([x1 + x2, y1 + y2]) * 0.5 + scale = np.hstack([x2 - x1, y2 - y1]) * padding + + if dim == 1: + center = center[0] + scale = scale[0] + + return center, scale + + +def _fix_aspect_ratio(bbox_scale: np.ndarray, + aspect_ratio: float) -> np.ndarray: + """Extend the scale to match the given aspect ratio. + + Args: + scale (np.ndarray): The image scale (w, h) in shape (2, ) + aspect_ratio (float): The ratio of ``w/h`` + + Returns: + np.ndarray: The reshaped image scale in (2, ) + """ + w, h = np.hsplit(bbox_scale, [1]) + bbox_scale = np.where(w > h * aspect_ratio, + np.hstack([w, w / aspect_ratio]), + np.hstack([h * aspect_ratio, h])) + return bbox_scale + + +def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray: + """Rotate a point by an angle. + + Args: + pt (np.ndarray): 2D point coordinates (x, y) in shape (2, ) + angle_rad (float): rotation angle in radian + + Returns: + np.ndarray: Rotated point in shape (2, ) + """ + sn, cs = np.sin(angle_rad), np.cos(angle_rad) + rot_mat = np.array([[cs, -sn], [sn, cs]]) + return rot_mat @ pt + + +def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """To calculate the affine matrix, three pairs of points are required. This + function is used to get the 3rd point, given 2D points a & b. + + The 3rd point is defined by rotating vector `a - b` by 90 degrees + anticlockwise, using b as the rotation center. + + Args: + a (np.ndarray): The 1st point (x,y) in shape (2, ) + b (np.ndarray): The 2nd point (x,y) in shape (2, ) + + Returns: + np.ndarray: The 3rd point. + """ + direction = a - b + c = b + np.r_[-direction[1], direction[0]] + return c + + +def get_warp_matrix(center: np.ndarray, + scale: np.ndarray, + rot: float, + output_size: Tuple[int, int], + shift: Tuple[float, float] = (0., 0.), + inv: bool = False) -> np.ndarray: + """Calculate the affine transformation matrix that can warp the bbox area + in the input image to the output size. + + Args: + center (np.ndarray[2, ]): Center of the bounding box (x, y). + scale (np.ndarray[2, ]): Scale of the bounding box + wrt [width, height]. + rot (float): Rotation angle (degree). + output_size (np.ndarray[2, ] | list(2,)): Size of the + destination heatmaps. + shift (0-100%): Shift translation ratio wrt the width/height. + Default (0., 0.). + inv (bool): Option to inverse the affine transform direction. + (inv=False: src->dst or inv=True: dst->src) + + Returns: + np.ndarray: A 2x3 transformation matrix + """ + shift = np.array(shift) + src_w = scale[0] + dst_w = output_size[0] + dst_h = output_size[1] + + # compute transformation matrix + rot_rad = np.deg2rad(rot) + src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad) + dst_dir = np.array([0., dst_w * -0.5]) + + # get four corners of the src rectangle in the original image + src = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale * shift + src[1, :] = center + src_dir + scale * shift + src[2, :] = _get_3rd_point(src[0, :], src[1, :]) + + # get four corners of the dst rectangle in the input image + dst = np.zeros((3, 2), dtype=np.float32) + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return warp_mat + + +def top_down_affine(input_size: dict, bbox_scale: dict, bbox_center: dict, + img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get the bbox image as the model input by affine transform. + + Args: + input_size (dict): The input size of the model. + bbox_scale (dict): The bbox scale of the img. + bbox_center (dict): The bbox center of the img. + img (np.ndarray): The original image. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: img after affine transform. + - np.ndarray[float32]: bbox scale after affine transform. + """ + w, h = input_size + warp_size = (int(w), int(h)) + + # reshape bbox to fixed aspect ratio + bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h) + + # get the affine matrix + center = bbox_center + scale = bbox_scale + rot = 0 + warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h)) + + # do affine transform + img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR) + + return img, bbox_scale + + +def get_simcc_maximum(simcc_x: np.ndarray, + simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get maximum response location and value from simcc representations. + + Note: + instance number: N + num_keypoints: K + heatmap height: H + heatmap width: W + + Args: + simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx) + simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy) + + Returns: + tuple: + - locs (np.ndarray): locations of maximum heatmap responses in shape + (K, 2) or (N, K, 2) + - vals (np.ndarray): values of maximum heatmap responses in shape + (K,) or (N, K) + """ + N, K, Wx = simcc_x.shape + simcc_x = simcc_x.reshape(N * K, -1) + simcc_y = simcc_y.reshape(N * K, -1) + + # get maximum value locations + x_locs = np.argmax(simcc_x, axis=1) + y_locs = np.argmax(simcc_y, axis=1) + locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32) + max_val_x = np.amax(simcc_x, axis=1) + max_val_y = np.amax(simcc_y, axis=1) + + # get maximum value across x and y axis + mask = max_val_x > max_val_y + max_val_x[mask] = max_val_y[mask] + vals = max_val_x + locs[vals <= 0.] = -1 + + # reshape + locs = locs.reshape(N, K, 2) + vals = vals.reshape(N, K) + + return locs, vals + + +def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, + simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]: + """Modulate simcc distribution with Gaussian. + + Args: + simcc_x (np.ndarray[K, Wx]): model predicted simcc in x. + simcc_y (np.ndarray[K, Wy]): model predicted simcc in y. + simcc_split_ratio (int): The split ratio of simcc. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2) + - np.ndarray[float32]: scores in shape (K,) or (n, K) + """ + keypoints, scores = get_simcc_maximum(simcc_x, simcc_y) + keypoints /= simcc_split_ratio + + return keypoints, scores + +def inference_pose(model, out_bbox, oriImg, model_input_size=(288, 384)): + resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size) + #outputs = inference(session, resized_img, dtype) + outputs = inference(model, resized_img) + + keypoints, scores = postprocess(outputs, model_input_size, center, scale) + + return keypoints, scores \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/face.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/face.py new file mode 100644 index 00000000..f3c46d77 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/face.py @@ -0,0 +1,362 @@ +import logging +import numpy as np +from torchvision.transforms import ToTensor, ToPILImage +import torch +import torch.nn.functional as F +import cv2 + +from . import util +from torch.nn import Conv2d, Module, ReLU, MaxPool2d, init + + +class FaceNet(Module): + """Model the cascading heatmaps. """ + def __init__(self): + super(FaceNet, self).__init__() + # cnn to make feature map + self.relu = ReLU() + self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2) + self.conv1_1 = Conv2d(in_channels=3, out_channels=64, + kernel_size=3, stride=1, padding=1) + self.conv1_2 = Conv2d( + in_channels=64, out_channels=64, kernel_size=3, stride=1, + padding=1) + self.conv2_1 = Conv2d( + in_channels=64, out_channels=128, kernel_size=3, stride=1, + padding=1) + self.conv2_2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=3, stride=1, + padding=1) + self.conv3_1 = Conv2d( + in_channels=128, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv3_2 = Conv2d( + in_channels=256, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv3_3 = Conv2d( + in_channels=256, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv3_4 = Conv2d( + in_channels=256, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv4_1 = Conv2d( + in_channels=256, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv4_2 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv4_3 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv4_4 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv5_1 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv5_2 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv5_3_CPM = Conv2d( + in_channels=512, out_channels=128, kernel_size=3, stride=1, + padding=1) + + # stage1 + self.conv6_1_CPM = Conv2d( + in_channels=128, out_channels=512, kernel_size=1, stride=1, + padding=0) + self.conv6_2_CPM = Conv2d( + in_channels=512, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage2 + self.Mconv1_stage2 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage2 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage3 + self.Mconv1_stage3 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage3 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage4 + self.Mconv1_stage4 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage4 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage5 + self.Mconv1_stage5 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage5 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage6 + self.Mconv1_stage6 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage6 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + for m in self.modules(): + if isinstance(m, Conv2d): + init.constant_(m.bias, 0) + + def forward(self, x): + """Return a list of heatmaps.""" + heatmaps = [] + + h = self.relu(self.conv1_1(x)) + h = self.relu(self.conv1_2(h)) + h = self.max_pooling_2d(h) + h = self.relu(self.conv2_1(h)) + h = self.relu(self.conv2_2(h)) + h = self.max_pooling_2d(h) + h = self.relu(self.conv3_1(h)) + h = self.relu(self.conv3_2(h)) + h = self.relu(self.conv3_3(h)) + h = self.relu(self.conv3_4(h)) + h = self.max_pooling_2d(h) + h = self.relu(self.conv4_1(h)) + h = self.relu(self.conv4_2(h)) + h = self.relu(self.conv4_3(h)) + h = self.relu(self.conv4_4(h)) + h = self.relu(self.conv5_1(h)) + h = self.relu(self.conv5_2(h)) + h = self.relu(self.conv5_3_CPM(h)) + feature_map = h + + # stage1 + h = self.relu(self.conv6_1_CPM(h)) + h = self.conv6_2_CPM(h) + heatmaps.append(h) + + # stage2 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage2(h)) + h = self.relu(self.Mconv2_stage2(h)) + h = self.relu(self.Mconv3_stage2(h)) + h = self.relu(self.Mconv4_stage2(h)) + h = self.relu(self.Mconv5_stage2(h)) + h = self.relu(self.Mconv6_stage2(h)) + h = self.Mconv7_stage2(h) + heatmaps.append(h) + + # stage3 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage3(h)) + h = self.relu(self.Mconv2_stage3(h)) + h = self.relu(self.Mconv3_stage3(h)) + h = self.relu(self.Mconv4_stage3(h)) + h = self.relu(self.Mconv5_stage3(h)) + h = self.relu(self.Mconv6_stage3(h)) + h = self.Mconv7_stage3(h) + heatmaps.append(h) + + # stage4 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage4(h)) + h = self.relu(self.Mconv2_stage4(h)) + h = self.relu(self.Mconv3_stage4(h)) + h = self.relu(self.Mconv4_stage4(h)) + h = self.relu(self.Mconv5_stage4(h)) + h = self.relu(self.Mconv6_stage4(h)) + h = self.Mconv7_stage4(h) + heatmaps.append(h) + + # stage5 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage5(h)) + h = self.relu(self.Mconv2_stage5(h)) + h = self.relu(self.Mconv3_stage5(h)) + h = self.relu(self.Mconv4_stage5(h)) + h = self.relu(self.Mconv5_stage5(h)) + h = self.relu(self.Mconv6_stage5(h)) + h = self.Mconv7_stage5(h) + heatmaps.append(h) + + # stage6 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage6(h)) + h = self.relu(self.Mconv2_stage6(h)) + h = self.relu(self.Mconv3_stage6(h)) + h = self.relu(self.Mconv4_stage6(h)) + h = self.relu(self.Mconv5_stage6(h)) + h = self.relu(self.Mconv6_stage6(h)) + h = self.Mconv7_stage6(h) + heatmaps.append(h) + + return heatmaps + + +LOG = logging.getLogger(__name__) +TOTEN = ToTensor() +TOPIL = ToPILImage() + + +params = { + 'gaussian_sigma': 2.5, + 'inference_img_size': 736, # 368, 736, 1312 + 'heatmap_peak_thresh': 0.1, + 'crop_scale': 1.5, + 'line_indices': [ + [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], + [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], + [13, 14], [14, 15], [15, 16], + [17, 18], [18, 19], [19, 20], [20, 21], + [22, 23], [23, 24], [24, 25], [25, 26], + [27, 28], [28, 29], [29, 30], + [31, 32], [32, 33], [33, 34], [34, 35], + [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36], + [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42], + [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], + [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48], + [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66], + [66, 67], [67, 60] + ], +} + + +class Face(object): + """ + The OpenPose face landmark detector model. + + Args: + inference_size: set the size of the inference image size, suggested: + 368, 736, 1312, default 736 + gaussian_sigma: blur the heatmaps, default 2.5 + heatmap_peak_thresh: return landmark if over threshold, default 0.1 + + """ + def __init__(self, face_model_path, + inference_size=None, + gaussian_sigma=None, + heatmap_peak_thresh=None): + self.inference_size = inference_size or params["inference_img_size"] + self.sigma = gaussian_sigma or params['gaussian_sigma'] + self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"] + self.model = FaceNet() + self.model.load_state_dict(torch.load(face_model_path)) + # if torch.cuda.is_available(): + # self.model = self.model.cuda() + # print('cuda') + self.model.eval() + + def __call__(self, face_img): + H, W, C = face_img.shape + + w_size = 384 + x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5 + + x_data = x_data.to(self.cn_device) + + with torch.no_grad(): + hs = self.model(x_data[None, ...]) + heatmaps = F.interpolate( + hs[-1], + (H, W), + mode='bilinear', align_corners=True).cpu().numpy()[0] + return heatmaps + + def compute_peaks_from_heatmaps(self, heatmaps): + all_peaks = [] + for part in range(heatmaps.shape[0]): + map_ori = heatmaps[part].copy() + binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8) + + if np.sum(binary) == 0: + continue + + positions = np.where(binary > 0.5) + intensities = map_ori[positions] + mi = np.argmax(intensities) + y, x = positions[0][mi], positions[1][mi] + all_peaks.append([x, y]) + + return np.array(all_peaks) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/hand.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/hand.py new file mode 100644 index 00000000..74767def --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/hand.py @@ -0,0 +1,94 @@ +import cv2 +import json +import numpy as np +import math +import time +from scipy.ndimage.filters import gaussian_filter +import matplotlib.pyplot as plt +import matplotlib +import torch +from skimage.measure import label + +from .model import handpose_model +from . import util + +class Hand(object): + def __init__(self, model_path): + self.model = handpose_model() + # if torch.cuda.is_available(): + # self.model = self.model.cuda() + # print('cuda') + model_dict = util.transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + + def __call__(self, oriImgRaw): + scale_search = [0.5, 1.0, 1.5, 2.0] + # scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre = 0.05 + multiplier = [x * boxsize for x in scale_search] + + wsize = 128 + heatmap_avg = np.zeros((wsize, wsize, 22)) + + Hr, Wr, Cr = oriImgRaw.shape + + oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = util.smart_resize(oriImg, (scale, scale)) + + imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + if torch.cuda.is_available(): + data = data.cuda() + + with torch.no_grad(): + data = data.to(self.cn_device) + output = self.model(data).cpu().numpy() + + # extract outputs, resize, and remove padding + heatmap = np.transpose(np.squeeze(output), (1, 2, 0)) # output 1 is heatmaps + heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = util.smart_resize(heatmap, (wsize, wsize)) + + heatmap_avg += heatmap / len(multiplier) + + all_peaks = [] + for part in range(21): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8) + + if np.sum(binary) == 0: + all_peaks.append([0, 0]) + continue + label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim) + max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1 + label_img[label_img != max_index] = 0 + map_ori[label_img == 0] = 0 + + y, x = util.npmax(map_ori) + y = int(float(y) * float(Hr) / float(wsize)) + x = int(float(x) * float(Wr) / float(wsize)) + all_peaks.append([x, y]) + return np.array(all_peaks) + +if __name__ == "__main__": + hand_estimation = Hand('../model/hand_pose_model.pth') + + # test_image = '../images/hand.jpg' + test_image = '../images/hand.jpg' + oriImg = cv2.imread(test_image) # B,G,R order + peaks = hand_estimation(oriImg) + canvas = util.draw_handpose(oriImg, peaks, True) + cv2.imshow('', canvas) + cv2.waitKey(0) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/model.py new file mode 100644 index 00000000..72dc79ad --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/model.py @@ -0,0 +1,218 @@ +import torch +from collections import OrderedDict + +import torch +import torch.nn as nn + +def make_layers(block, no_relu_layers): + layers = [] + for layer_name, v in block.items(): + if 'pool' in layer_name: + layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], + padding=v[2]) + layers.append((layer_name, layer)) + else: + conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], + kernel_size=v[2], stride=v[3], + padding=v[4]) + layers.append((layer_name, conv2d)) + if layer_name not in no_relu_layers: + layers.append(('relu_'+layer_name, nn.ReLU(inplace=True))) + + return nn.Sequential(OrderedDict(layers)) + +class bodypose_model(nn.Module): + def __init__(self): + super(bodypose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ + 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ + 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ + 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] + blocks = {} + block0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3_CPM', [512, 256, 3, 1, 1]), + ('conv4_4_CPM', [256, 128, 3, 1, 1]) + ]) + + + # Stage 1 + block1_1 = OrderedDict([ + ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) + ]) + + block1_2 = OrderedDict([ + ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) + ]) + blocks['block1_1'] = block1_1 + blocks['block1_2'] = block1_2 + + self.model0 = make_layers(block0, no_relu_layers) + + # Stages 2 - 6 + for i in range(2, 7): + blocks['block%d_1' % i] = OrderedDict([ + ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) + ]) + + blocks['block%d_2' % i] = OrderedDict([ + ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_1 = blocks['block1_1'] + self.model2_1 = blocks['block2_1'] + self.model3_1 = blocks['block3_1'] + self.model4_1 = blocks['block4_1'] + self.model5_1 = blocks['block5_1'] + self.model6_1 = blocks['block6_1'] + + self.model1_2 = blocks['block1_2'] + self.model2_2 = blocks['block2_2'] + self.model3_2 = blocks['block3_2'] + self.model4_2 = blocks['block4_2'] + self.model5_2 = blocks['block5_2'] + self.model6_2 = blocks['block6_2'] + + + def forward(self, x): + + out1 = self.model0(x) + + out1_1 = self.model1_1(out1) + out1_2 = self.model1_2(out1) + out2 = torch.cat([out1_1, out1_2, out1], 1) + + out2_1 = self.model2_1(out2) + out2_2 = self.model2_2(out2) + out3 = torch.cat([out2_1, out2_2, out1], 1) + + out3_1 = self.model3_1(out3) + out3_2 = self.model3_2(out3) + out4 = torch.cat([out3_1, out3_2, out1], 1) + + out4_1 = self.model4_1(out4) + out4_2 = self.model4_2(out4) + out5 = torch.cat([out4_1, out4_2, out1], 1) + + out5_1 = self.model5_1(out5) + out5_2 = self.model5_2(out5) + out6 = torch.cat([out5_1, out5_2, out1], 1) + + out6_1 = self.model6_1(out6) + out6_2 = self.model6_2(out6) + + return out6_1, out6_2 + +class handpose_model(nn.Module): + def __init__(self): + super(handpose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ + 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] + # stage 1 + block1_0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3', [512, 512, 3, 1, 1]), + ('conv4_4', [512, 512, 3, 1, 1]), + ('conv5_1', [512, 512, 3, 1, 1]), + ('conv5_2', [512, 512, 3, 1, 1]), + ('conv5_3_CPM', [512, 128, 3, 1, 1]) + ]) + + block1_1 = OrderedDict([ + ('conv6_1_CPM', [128, 512, 1, 1, 0]), + ('conv6_2_CPM', [512, 22, 1, 1, 0]) + ]) + + blocks = {} + blocks['block1_0'] = block1_0 + blocks['block1_1'] = block1_1 + + # stage 2-6 + for i in range(2, 7): + blocks['block%d' % i] = OrderedDict([ + ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), + ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_0 = blocks['block1_0'] + self.model1_1 = blocks['block1_1'] + self.model2 = blocks['block2'] + self.model3 = blocks['block3'] + self.model4 = blocks['block4'] + self.model5 = blocks['block5'] + self.model6 = blocks['block6'] + + def forward(self, x): + out1_0 = self.model1_0(x) + out1_1 = self.model1_1(out1_0) + concat_stage2 = torch.cat([out1_1, out1_0], 1) + out_stage2 = self.model2(concat_stage2) + concat_stage3 = torch.cat([out_stage2, out1_0], 1) + out_stage3 = self.model3(concat_stage3) + concat_stage4 = torch.cat([out_stage3, out1_0], 1) + out_stage4 = self.model4(concat_stage4) + concat_stage5 = torch.cat([out_stage4, out1_0], 1) + out_stage5 = self.model5(concat_stage5) + concat_stage6 = torch.cat([out_stage5, out1_0], 1) + out_stage6 = self.model6(concat_stage6) + return out_stage6 + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/types.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/types.py new file mode 100644 index 00000000..6a5fa889 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/types.py @@ -0,0 +1,30 @@ +from typing import NamedTuple, List, Optional + +class Keypoint(NamedTuple): + x: float + y: float + score: float = 1.0 + id: int = -1 + + +class BodyResult(NamedTuple): + # Note: Using `Optional` instead of `|` operator as the ladder is a Python + # 3.10 feature. + # Annotator code should be Python 3.8 Compatible, as controlnet repo uses + # Python 3.8 environment. + # https://github.com/lllyasviel/ControlNet/blob/d3284fcd0972c510635a4f5abe2eeb71dc0de524/environment.yaml#L6 + keypoints: List[Optional[Keypoint]] + total_score: float = 0.0 + total_parts: int = 0 + + +HandResult = List[Keypoint] +FaceResult = List[Keypoint] +AnimalPoseResult = List[Keypoint] + + +class PoseResult(NamedTuple): + body: BodyResult + left_hand: Optional[HandResult] + right_hand: Optional[HandResult] + face: Optional[FaceResult] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/util.py new file mode 100644 index 00000000..42874b33 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/util.py @@ -0,0 +1,466 @@ +import math +import numpy as np +import matplotlib +import cv2 +import os +from typing import List, Tuple, Union, Optional + +from .body import BodyResult, Keypoint + +eps = 0.01 + + +def smart_resize(x, s): + Ht, Wt = s + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2) + + +def smart_resize_k(x, fx, fy): + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + Ht, Wt = Ho * fy, Wo * fx + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2) + + +def padRightDownCorner(img, stride, padValue): + h = img.shape[0] + w = img.shape[1] + + pad = 4 * [None] + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + + img_padded = img + pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) + img_padded = np.concatenate((pad_up, img_padded), axis=0) + pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) + img_padded = np.concatenate((pad_left, img_padded), axis=1) + pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) + img_padded = np.concatenate((img_padded, pad_down), axis=0) + pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) + img_padded = np.concatenate((img_padded, pad_right), axis=1) + + return img_padded, pad + + +def transfer(model, model_weights): + transfered_model_weights = {} + for weights_name in model.state_dict().keys(): + transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + return transfered_model_weights + + +def is_normalized(keypoints: List[Optional[Keypoint]]) -> bool: + point_normalized = [ + 0 <= abs(k.x) <= 1 and 0 <= abs(k.y) <= 1 + for k in keypoints + if k is not None + ] + if not point_normalized: + return False + return all(point_normalized) + + +def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint], xinsr_stick_scaling: bool = False) -> np.ndarray: + """ + Draw keypoints and limbs representing body pose on a given canvas. + + Args: + canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose. + keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn. + xinsr_stick_scaling (bool): Whether or not scaling stick width for xinsr ControlNet + + Returns: + np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose. + + Note: + The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. + """ + if not is_normalized(keypoints): + H, W = 1.0, 1.0 + else: + H, W, _ = canvas.shape + + CH, CW, _ = canvas.shape + stickwidth = 4 + + # Ref: https://huggingface.co/xinsir/controlnet-openpose-sdxl-1.0 + max_side = max(CW, CH) + if xinsr_stick_scaling: + stick_scale = 1 if max_side < 500 else min(2 + (max_side // 1000), 7) + else: + stick_scale = 1 + + limbSeq = [ + [2, 3], [2, 6], [3, 4], [4, 5], + [6, 7], [7, 8], [2, 9], [9, 10], + [10, 11], [2, 12], [12, 13], [13, 14], + [2, 1], [1, 15], [15, 17], [1, 16], + [16, 18], + ] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] + + for (k1_index, k2_index), color in zip(limbSeq, colors): + keypoint1 = keypoints[k1_index - 1] + keypoint2 = keypoints[k2_index - 1] + + if keypoint1 is None or keypoint2 is None: + continue + + Y = np.array([keypoint1.x, keypoint2.x]) * float(W) + X = np.array([keypoint1.y, keypoint2.y]) * float(H) + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth*stick_scale), int(angle), 0, 360, 1) + cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color]) + + for keypoint, color in zip(keypoints, colors): + if keypoint is None: + continue + + x, y = keypoint.x, keypoint.y + x = int(x * W) + y = int(y * H) + cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1) + + return canvas + + +def draw_handpose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray: + """ + Draw keypoints and connections representing hand pose on a given canvas. + + Args: + canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. + keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn + or None if no keypoints are present. + + Returns: + np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. + + Note: + The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. + """ + if not keypoints: + return canvas + + if not is_normalized(keypoints): + H, W = 1.0, 1.0 + else: + H, W, _ = canvas.shape + + edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ + [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] + + for ie, (e1, e2) in enumerate(edges): + k1 = keypoints[e1] + k2 = keypoints[e2] + if k1 is None or k2 is None: + continue + + x1 = int(k1.x * W) + y1 = int(k1.y * H) + x2 = int(k2.x * W) + y2 = int(k2.y * H) + if x1 > eps and y1 > eps and x2 > eps and y2 > eps: + cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2) + + for keypoint in keypoints: + if keypoint is None: + continue + + x, y = keypoint.x, keypoint.y + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1) + return canvas + + +def draw_facepose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray: + """ + Draw keypoints representing face pose on a given canvas. + + Args: + canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the face pose. + keypoints (List[Keypoint]| None): A list of Keypoint objects representing the face keypoints to be drawn + or None if no keypoints are present. + + Returns: + np.ndarray: A 3D numpy array representing the modified canvas with the drawn face pose. + + Note: + The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. + """ + if not keypoints: + return canvas + + if not is_normalized(keypoints): + H, W = 1.0, 1.0 + else: + H, W, _ = canvas.shape + + for keypoint in keypoints: + if keypoint is None: + continue + + x, y = keypoint.x, keypoint.y + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1) + return canvas + + +# detect hand according to body pose keypoints +# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp +def handDetect(body: BodyResult, oriImg) -> List[Tuple[int, int, int, bool]]: + """ + Detect hands in the input body pose keypoints and calculate the bounding box for each hand. + + Args: + body (BodyResult): A BodyResult object containing the detected body pose keypoints. + oriImg (numpy.ndarray): A 3D numpy array representing the original input image. + + Returns: + List[Tuple[int, int, int, bool]]: A list of tuples, each containing the coordinates (x, y) of the top-left + corner of the bounding box, the width (height) of the bounding box, and + a boolean flag indicating whether the hand is a left hand (True) or a + right hand (False). + + Notes: + - The width and height of the bounding boxes are equal since the network requires squared input. + - The minimum bounding box size is 20 pixels. + """ + ratioWristElbow = 0.33 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + + keypoints = body.keypoints + # right hand: wrist 4, elbow 3, shoulder 2 + # left hand: wrist 7, elbow 6, shoulder 5 + left_shoulder = keypoints[5] + left_elbow = keypoints[6] + left_wrist = keypoints[7] + right_shoulder = keypoints[2] + right_elbow = keypoints[3] + right_wrist = keypoints[4] + + # if any of three not detected + has_left = all(keypoint is not None for keypoint in (left_shoulder, left_elbow, left_wrist)) + has_right = all(keypoint is not None for keypoint in (right_shoulder, right_elbow, right_wrist)) + if not (has_left or has_right): + return [] + + hands = [] + #left hand + if has_left: + hands.append([ + left_shoulder.x, left_shoulder.y, + left_elbow.x, left_elbow.y, + left_wrist.x, left_wrist.y, + True + ]) + # right hand + if has_right: + hands.append([ + right_shoulder.x, right_shoulder.y, + right_elbow.x, right_elbow.y, + right_wrist.x, right_wrist.y, + False + ]) + + for x1, y1, x2, y2, x3, y3, is_left in hands: + # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox + # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]); + # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]); + # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow); + # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder); + # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder); + x = x3 + ratioWristElbow * (x3 - x2) + y = y3 + ratioWristElbow * (y3 - y2) + distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) + distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder) + # x-y refers to the center --> offset to topLeft point + # handRectangle.x -= handRectangle.width / 2.f; + # handRectangle.y -= handRectangle.height / 2.f; + x -= width / 2 + y -= width / 2 # width = height + # overflow the image + if x < 0: x = 0 + if y < 0: y = 0 + width1 = width + width2 = width + if x + width > image_width: width1 = image_width - x + if y + width > image_height: width2 = image_height - y + width = min(width1, width2) + # the max hand box value is 20 pixels + if width >= 20: + detect_result.append((int(x), int(y), int(width), is_left)) + + ''' + return value: [[x, y, w, True if left hand else False]]. + width=height since the network require squared input. + x, y is the coordinate of top left + ''' + return detect_result + + +# Written by Lvmin +def faceDetect(body: BodyResult, oriImg) -> Union[Tuple[int, int, int], None]: + """ + Detect the face in the input body pose keypoints and calculate the bounding box for the face. + + Args: + body (BodyResult): A BodyResult object containing the detected body pose keypoints. + oriImg (numpy.ndarray): A 3D numpy array representing the original input image. + + Returns: + Tuple[int, int, int] | None: A tuple containing the coordinates (x, y) of the top-left corner of the + bounding box and the width (height) of the bounding box, or None if the + face is not detected or the bounding box width is less than 20 pixels. + + Notes: + - The width and height of the bounding box are equal. + - The minimum bounding box size is 20 pixels. + """ + # left right eye ear 14 15 16 17 + image_height, image_width = oriImg.shape[0:2] + + keypoints = body.keypoints + head = keypoints[0] + left_eye = keypoints[14] + right_eye = keypoints[15] + left_ear = keypoints[16] + right_ear = keypoints[17] + + if head is None or all(keypoint is None for keypoint in (left_eye, right_eye, left_ear, right_ear)): + return None + + width = 0.0 + x0, y0 = head.x, head.y + + if left_eye is not None: + x1, y1 = left_eye.x, left_eye.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if right_eye is not None: + x1, y1 = right_eye.x, right_eye.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if left_ear is not None: + x1, y1 = left_ear.x, left_ear.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + if right_ear is not None: + x1, y1 = right_ear.x, right_ear.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + x, y = x0, y0 + + x -= width + y -= width + + if x < 0: + x = 0 + + if y < 0: + y = 0 + + width1 = width * 2 + width2 = width * 2 + + if x + width > image_width: + width1 = image_width - x + + if y + width > image_height: + width2 = image_height - y + + width = min(width1, width2) + + if width >= 20: + return int(x), int(y), int(width) + else: + return None + + +# get max index of 2d array +def npmax(array): + arrayindex = array.argmax(1) + arrayvalue = array.max(1) + i = arrayvalue.argmax() + j = arrayindex[i] + return i, j + +def guess_onnx_input_shape_dtype(filename): + dtype = np.float32 + if "fp16" in filename: + dtype = np.float16 + elif "int8" in filename: + dtype = np.uint8 + input_size = (640, 640) if "yolo" in filename else (192, 256) + if "384" in filename: + input_size = (288, 384) + elif "256" in filename: + input_size = (256, 256) + return input_size, dtype + +if os.getenv('AUX_ORT_PROVIDERS'): + ONNX_PROVIDERS = os.getenv('AUX_ORT_PROVIDERS').split(',') +else: + ONNX_PROVIDERS = ["CUDAExecutionProvider", "DirectMLExecutionProvider", "OpenVINOExecutionProvider", "ROCMExecutionProvider", "CPUExecutionProvider"] +def get_ort_providers() -> List[str]: + providers = [] + try: + import onnxruntime as ort + for provider in ONNX_PROVIDERS: + if provider in ort.get_available_providers(): + providers.append(provider) + return providers + except: + return [] + +def is_model_torchscript(model) -> bool: + return bool(type(model).__name__ == "RecursiveScriptModule") + +def get_model_type(Nodesname, filename) -> str: + ort_providers = list(filter(lambda x : x != "CPUExecutionProvider", get_ort_providers())) + if filename is None: + return None + elif ("onnx" in filename) and ort_providers: + print(f"{Nodesname}: Caching ONNXRuntime session {filename}...") + return "ort" + elif ("onnx" in filename): + print(f"{Nodesname}: Caching OpenCV DNN module {filename} on cv2.DNN...") + return "cv2" + else: + print(f"{Nodesname}: Caching TorchScript module {filename} on ...") + return "torchscript" diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/wholebody.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/wholebody.py new file mode 100644 index 00000000..d453739e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/dwpose/wholebody.py @@ -0,0 +1,181 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import cv2 +import numpy as np + +from .dw_onnx.cv_ox_det import inference_detector as inference_onnx_yolox +from .dw_onnx.cv_ox_yolo_nas import inference_detector as inference_onnx_yolo_nas +from .dw_onnx.cv_ox_pose import inference_pose as inference_onnx_pose + +from .dw_torchscript.jit_det import inference_detector as inference_jit_yolox +from .dw_torchscript.jit_pose import inference_pose as inference_jit_pose + +from typing import List, Optional +from .types import PoseResult, BodyResult, Keypoint +from timeit import default_timer +import os +from .util import guess_onnx_input_shape_dtype, get_model_type, get_ort_providers, is_model_torchscript +import torch + +class Wholebody: + def __init__(self, det_model_path: Optional[str] = None, pose_model_path: Optional[str] = None, torchscript_device="cuda"): + self.det_filename = det_model_path and os.path.basename(det_model_path) + self.pose_filename = pose_model_path and os.path.basename(pose_model_path) + self.det, self.pose = None, None + # return type: None ort cv2 torchscript + self.det_model_type = get_model_type("DWPose",self.det_filename) + self.pose_model_type = get_model_type("DWPose",self.pose_filename) + # Always loads to CPU to avoid building OpenCV. + cv2_device = 'cpu' + cv2_backend = cv2.dnn.DNN_BACKEND_OPENCV if cv2_device == 'cpu' else cv2.dnn.DNN_BACKEND_CUDA + # You need to manually build OpenCV through cmake to work with your GPU. + cv2_providers = cv2.dnn.DNN_TARGET_CPU if cv2_device == 'cpu' else cv2.dnn.DNN_TARGET_CUDA + ort_providers = get_ort_providers() + + if self.det_model_type is None: + pass + elif self.det_model_type == "ort": + try: + import onnxruntime as ort + self.det = ort.InferenceSession(det_model_path, providers=ort_providers) + except: + print(f"Failed to load onnxruntime with {self.det.get_providers()}.\nPlease change EP_list in the config.yaml and restart ComfyUI") + self.det = ort.InferenceSession(det_model_path, providers=["CPUExecutionProvider"]) + elif self.det_model_type == "cv2": + try: + self.det = cv2.dnn.readNetFromONNX(det_model_path) + self.det.setPreferableBackend(cv2_backend) + self.det.setPreferableTarget(cv2_providers) + except: + print("TopK operators may not work on your OpenCV, try use onnxruntime with CPUExecutionProvider") + try: + import onnxruntime as ort + self.det = ort.InferenceSession(det_model_path, providers=["CPUExecutionProvider"]) + except: + print(f"Failed to load {det_model_path}, you can use other models instead") + else: + self.det = torch.jit.load(det_model_path) + self.det.to(torchscript_device) + + if self.pose_model_type is None: + pass + elif self.pose_model_type == "ort": + try: + import onnxruntime as ort + self.pose = ort.InferenceSession(pose_model_path, providers=ort_providers) + except: + print(f"Failed to load onnxruntime with {self.pose.get_providers()}.\nPlease change EP_list in the config.yaml and restart ComfyUI") + self.pose = ort.InferenceSession(pose_model_path, providers=["CPUExecutionProvider"]) + elif self.pose_model_type == "cv2": + self.pose = cv2.dnn.readNetFromONNX(pose_model_path) + self.pose.setPreferableBackend(cv2_backend) + self.pose.setPreferableTarget(cv2_providers) + else: + self.pose = torch.jit.load(pose_model_path) + self.pose.to(torchscript_device) + + if self.pose_filename is not None: + self.pose_input_size, _ = guess_onnx_input_shape_dtype(self.pose_filename) + + def __call__(self, oriImg) -> Optional[np.ndarray]: + #Sacrifice accurate time measurement for compatibility + + det_result = None + + if self.det is None: + print("DWPose: No detector specified, using full image for pose estimation.") # pragma: no cover + det_result = [] + else: + det_start = default_timer() + if is_model_torchscript(self.det): + det_result = inference_jit_yolox(self.det, oriImg, detect_classes=[0]) + else: + if "yolox" in self.det_filename: + det_result = inference_onnx_yolox(self.det, oriImg, detect_classes=[0], dtype=np.float32) + else: + #FP16 and INT8 YOLO NAS accept uint8 input + det_result = inference_onnx_yolo_nas(self.det, oriImg, detect_classes=[0], dtype=np.uint8) + print(f"DWPose: Bbox {((default_timer() - det_start) * 1000):.2f}ms") + if (det_result is None) or (det_result.shape[0] == 0): + return None + + pose_start = default_timer() + if is_model_torchscript(self.pose): + keypoints, scores = inference_jit_pose(self.pose, det_result, oriImg, self.pose_input_size) + else: + _, pose_onnx_dtype = guess_onnx_input_shape_dtype(self.pose_filename) + keypoints, scores = inference_onnx_pose(self.pose, det_result, oriImg, self.pose_input_size, dtype=pose_onnx_dtype) + + num_subjects_log = 'full image' + if hasattr(det_result, 'shape') and det_result.shape[0] > 0: + num_subjects_log = f"{det_result.shape[0]} people" + elif isinstance(det_result, list) and len(det_result) > 0 and isinstance(det_result[0], (list, np.ndarray)): + num_subjects_log = f"{len(det_result)} people" + + print(f"DWPose: Pose {((default_timer() - pose_start) * 1000):.2f}ms on {num_subjects_log}\n") + + keypoints_info = np.concatenate( + (keypoints, scores[..., None]), axis=-1) + # compute neck joint + neck = np.mean(keypoints_info[:, [5, 6]], axis=1) + # neck score when visualizing pred + neck[:, 2:4] = np.logical_and( + keypoints_info[:, 5, 2:4] > 0.3, + keypoints_info[:, 6, 2:4] > 0.3).astype(int) + new_keypoints_info = np.insert( + keypoints_info, 17, neck, axis=1) + mmpose_idx = [ + 17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3 + ] + openpose_idx = [ + 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17 + ] + new_keypoints_info[:, openpose_idx] = \ + new_keypoints_info[:, mmpose_idx] + keypoints_info = new_keypoints_info + + return keypoints_info + + @staticmethod + def format_result(keypoints_info: Optional[np.ndarray]) -> List[PoseResult]: + def format_keypoint_part( + part: np.ndarray, + ) -> Optional[List[Optional[Keypoint]]]: + keypoints = [ + Keypoint(x, y, score, i) if score >= 0.3 else None + for i, (x, y, score) in enumerate(part) + ] + return ( + None if all(keypoint is None for keypoint in keypoints) else keypoints + ) + + def total_score(keypoints: Optional[List[Optional[Keypoint]]]) -> float: + return ( + sum(keypoint.score for keypoint in keypoints if keypoint is not None) + if keypoints is not None + else 0.0 + ) + + pose_results = [] + if keypoints_info is None: + return pose_results + + for instance in keypoints_info: + body_keypoints = format_keypoint_part(instance[:18]) or ([None] * 18) + left_hand = format_keypoint_part(instance[92:113]) + right_hand = format_keypoint_part(instance[113:134]) + face = format_keypoint_part(instance[24:92]) + + # Openpose face consists of 70 points in total, while DWPose only + # provides 68 points. Padding the last 2 points. + if face is not None: + # left eye + face.append(body_keypoints[14]) + # right eye + face.append(body_keypoints[15]) + + body = BodyResult( + body_keypoints, total_score(body_keypoints), len(body_keypoints) + ) + pose_results.append(PoseResult(body, left_hand, right_hand, face)) + + return pose_results diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/hed/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/hed/__init__.py new file mode 100644 index 00000000..022c4f99 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/hed/__init__.py @@ -0,0 +1,110 @@ +# This is an improved version and model of HED edge detection with Apache License, Version 2.0. +# Please use this implementation in your products +# This implementation may produce slightly different results from Saining Xie's official implementations, +# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations. +# Different from official models and other implementations, this is an RGB-input model (rather than BGR) +# and in this way it works better for gradio's RGB protocol + +import os +import warnings + +import cv2 +import numpy as np +import torch +from einops import rearrange +from PIL import Image + +from custom_controlnet_aux.util import HWC3, nms, resize_image_with_pad, safe_step, common_input_validate, custom_hf_download, HF_MODEL_NAME + + +class DoubleConvBlock(torch.nn.Module): + def __init__(self, input_channel, output_channel, layer_number): + super().__init__() + self.convs = torch.nn.Sequential() + self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) + for i in range(1, layer_number): + self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) + self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0) + + def __call__(self, x, down_sampling=False): + h = x + if down_sampling: + h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2)) + for conv in self.convs: + h = conv(h) + h = torch.nn.functional.relu(h) + return h, self.projection(h) + + +class ControlNetHED_Apache2(torch.nn.Module): + def __init__(self): + super().__init__() + self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1))) + self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2) + self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2) + self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3) + self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3) + self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3) + + def __call__(self, x): + h = x - self.norm + h, projection1 = self.block1(h) + h, projection2 = self.block2(h, down_sampling=True) + h, projection3 = self.block3(h, down_sampling=True) + h, projection4 = self.block4(h, down_sampling=True) + h, projection5 = self.block5(h, down_sampling=True) + return projection1, projection2, projection3, projection4, projection5 + +class HEDdetector: + def __init__(self, netNetwork): + self.netNetwork = netNetwork + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="ControlNetHED.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + + netNetwork = ControlNetHED_Apache2() + netNetwork.load_state_dict(torch.load(model_path, map_location='cpu')) + netNetwork.float().eval() + + return cls(netNetwork) + + def to(self, device): + self.netNetwork.to(device) + self.device = device + return self + + + def __call__(self, input_image, detect_resolution=512, safe=False, output_type="pil", scribble=False, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + assert input_image.ndim == 3 + H, W, C = input_image.shape + with torch.no_grad(): + image_hed = torch.from_numpy(input_image).float().to(self.device) + image_hed = rearrange(image_hed, 'h w c -> 1 c h w') + edges = self.netNetwork(image_hed) + edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] + edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] + edges = np.stack(edges, axis=2) + edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) + if safe: + edge = safe_step(edge) + edge = (edge * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = edge + + if scribble: + detected_map = nms(detected_map, 127, 3.0) + detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0) + detected_map[detected_map > 4] = 255 + detected_map[detected_map < 255] = 0 + + detected_map = HWC3(remove_pad(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/__init__.py new file mode 100644 index 00000000..9e348228 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/__init__.py @@ -0,0 +1,93 @@ +import os + +import cv2 +import numpy as np +import torch +from PIL import Image + +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME +from .leres.depthmap import estimateboost, estimateleres +from .leres.multi_depth_model_woauxi import RelDepthModel +from .leres.net_tools import strip_prefix_if_present +from .pix2pix.models.pix2pix4depth_model import Pix2Pix4DepthModel +from .pix2pix.options.test_options import TestOptions + + +class LeresDetector: + def __init__(self, model, pix2pixmodel): + self.model = model + self.pix2pixmodel = pix2pixmodel + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="res101.pth", pix2pix_filename="latest_net_G.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + checkpoint = torch.load(model_path, map_location=torch.device('cpu')) + + model = RelDepthModel(backbone='resnext101') + model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True) + del checkpoint + + pix2pix_model_path = custom_hf_download(pretrained_model_or_path, pix2pix_filename) + + opt = TestOptions().parse() + if not torch.cuda.is_available(): + opt.gpu_ids = [] # cpu mode + pix2pixmodel = Pix2Pix4DepthModel(opt) + pix2pixmodel.save_dir = os.path.dirname(pix2pix_model_path) + pix2pixmodel.load_networks('latest') + pix2pixmodel.eval() + + return cls(model, pix2pixmodel) + + def to(self, device): + self.model.to(device) + # TODO - refactor pix2pix implementation to support device migration + # self.pix2pixmodel.to(device) + return self + + def __call__(self, input_image, thr_a=0, thr_b=0, boost=False, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + with torch.no_grad(): + if boost: + depth = estimateboost(detected_map, self.model, 0, self.pix2pixmodel, max(detected_map.shape[1], detected_map.shape[0])) + else: + depth = estimateleres(detected_map, self.model, detected_map.shape[1], detected_map.shape[0]) + + numbytes=2 + depth_min = depth.min() + depth_max = depth.max() + max_val = (2**(8*numbytes))-1 + + # check output before normalizing and mapping to 16 bit + if depth_max - depth_min > np.finfo("float").eps: + out = max_val * (depth - depth_min) / (depth_max - depth_min) + else: + out = np.zeros(depth.shape) + + # single channel, 16 bit image + depth_image = out.astype("uint16") + + # convert to uint8 + depth_image = cv2.convertScaleAbs(depth_image, alpha=(255.0/65535.0)) + + # remove near + if thr_a != 0: + thr_a = ((thr_a/100)*255) + depth_image = cv2.threshold(depth_image, thr_a, 255, cv2.THRESH_TOZERO)[1] + + # invert image + depth_image = cv2.bitwise_not(depth_image) + + # remove bg + if thr_b != 0: + thr_b = ((thr_b/100)*255) + depth_image = cv2.threshold(depth_image, thr_b, 255, cv2.THRESH_TOZERO)[1] + + detected_map = HWC3(remove_pad(depth_image)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/LICENSE new file mode 100644 index 00000000..e0f1d07d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/LICENSE @@ -0,0 +1,23 @@ +https://github.com/thygate/stable-diffusion-webui-depthmap-script + +MIT License + +Copyright (c) 2023 Bob Thiry + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/Resnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/Resnet.py new file mode 100644 index 00000000..f12c9975 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/Resnet.py @@ -0,0 +1,199 @@ +import torch.nn as nn +import torch.nn as NN + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = NN.BatchNorm2d(planes) #NN.BatchNorm2d + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = NN.BatchNorm2d(planes) #NN.BatchNorm2d + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = NN.BatchNorm2d(planes) #NN.BatchNorm2d + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = NN.BatchNorm2d(planes) #NN.BatchNorm2d + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = NN.BatchNorm2d(planes * self.expansion) #NN.BatchNorm2d + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 64 + super(ResNet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = NN.BatchNorm2d(64) #NN.BatchNorm2d + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + #self.avgpool = nn.AvgPool2d(7, stride=1) + #self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + NN.BatchNorm2d(planes * block.expansion), #NN.BatchNorm2d + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + features = [] + + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + features.append(x) + x = self.layer2(x) + features.append(x) + x = self.layer3(x) + features.append(x) + x = self.layer4(x) + features.append(x) + + return features + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + return model + + +def resnet34(pretrained=True, **kwargs): + """Constructs a ResNet-34 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + return model + + +def resnet50(pretrained=True, **kwargs): + """Constructs a ResNet-50 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + + return model + + +def resnet101(pretrained=True, **kwargs): + """Constructs a ResNet-101 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + + return model + + +def resnet152(pretrained=True, **kwargs): + """Constructs a ResNet-152 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) + return model diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/Resnext_torch.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/Resnext_torch.py new file mode 100644 index 00000000..9af54fcc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/Resnext_torch.py @@ -0,0 +1,237 @@ +#!/usr/bin/env python +# coding: utf-8 +import torch.nn as nn + +try: + from urllib import urlretrieve +except ImportError: + from urllib.request import urlretrieve + +__all__ = ['resnext101_32x8d'] + + +model_urls = { + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + #self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + #self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + features = [] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + features.append(x) + + x = self.layer2(x) + features.append(x) + + x = self.layer3(x) + features.append(x) + + x = self.layer4(x) + features.append(x) + + #x = self.avgpool(x) + #x = torch.flatten(x, 1) + #x = self.fc(x) + + return features + + def forward(self, x): + return self._forward_impl(x) + + + +def resnext101_32x8d(pretrained=True, **kwargs): + """Constructs a ResNet-152 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + + model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + return model + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/depthmap.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/depthmap.py new file mode 100644 index 00000000..fc743bf4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/depthmap.py @@ -0,0 +1,548 @@ +# Author: thygate +# https://github.com/thygate/stable-diffusion-webui-depthmap-script + +import gc +from operator import getitem + +import cv2 +import numpy as np +import skimage.measure +import torch +from torchvision.transforms import transforms + +from ...util import torch_gc + +whole_size_threshold = 1600 # R_max from the paper +pix2pixsize = 1024 + +def scale_torch(img): + """ + Scale the image and output it in torch.tensor. + :param img: input rgb is in shape [H, W, C], input depth/disp is in shape [H, W] + :param scale: the scale factor. float + :return: img. [C, H, W] + """ + if len(img.shape) == 2: + img = img[np.newaxis, :, :] + if img.shape[2] == 3: + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406) , (0.229, 0.224, 0.225) )]) + img = transform(img.astype(np.float32)) + else: + img = img.astype(np.float32) + img = torch.from_numpy(img) + return img + +def estimateleres(img, model, w, h): + device = next(iter(model.parameters())).device + # leres transform input + rgb_c = img[:, :, ::-1].copy() + A_resize = cv2.resize(rgb_c, (w, h)) + img_torch = scale_torch(A_resize)[None, :, :, :] + + # compute + with torch.no_grad(): + img_torch = img_torch.to(device) + prediction = model.depth_model(img_torch) + + prediction = prediction.squeeze().cpu().numpy() + prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC) + + return prediction + +def generatemask(size): + # Generates a Guassian mask + mask = np.zeros(size, dtype=np.float32) + sigma = int(size[0]/16) + k_size = int(2 * np.ceil(2 * int(size[0]/16)) + 1) + mask[int(0.15*size[0]):size[0] - int(0.15*size[0]), int(0.15*size[1]): size[1] - int(0.15*size[1])] = 1 + mask = cv2.GaussianBlur(mask, (int(k_size), int(k_size)), sigma) + mask = (mask - mask.min()) / (mask.max() - mask.min()) + mask = mask.astype(np.float32) + return mask + +def resizewithpool(img, size): + i_size = img.shape[0] + n = int(np.floor(i_size/size)) + + out = skimage.measure.block_reduce(img, (n, n), np.max) + return out + +def rgb2gray(rgb): + # Converts rgb to gray + return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140]) + +def calculateprocessingres(img, basesize, confidence=0.1, scale_threshold=3, whole_size_threshold=3000): + # Returns the R_x resolution described in section 5 of the main paper. + + # Parameters: + # img :input rgb image + # basesize : size the dilation kernel which is equal to receptive field of the network. + # confidence: value of x in R_x; allowed percentage of pixels that are not getting any contextual cue. + # scale_threshold: maximum allowed upscaling on the input image ; it has been set to 3. + # whole_size_threshold: maximum allowed resolution. (R_max from section 6 of the main paper) + + # Returns: + # outputsize_scale*speed_scale :The computed R_x resolution + # patch_scale: K parameter from section 6 of the paper + + # speed scale parameter is to process every image in a smaller size to accelerate the R_x resolution search + speed_scale = 32 + image_dim = int(min(img.shape[0:2])) + + gray = rgb2gray(img) + grad = np.abs(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)) + np.abs(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)) + grad = cv2.resize(grad, (image_dim, image_dim), cv2.INTER_AREA) + + # thresholding the gradient map to generate the edge-map as a proxy of the contextual cues + m = grad.min() + M = grad.max() + middle = m + (0.4 * (M - m)) + grad[grad < middle] = 0 + grad[grad >= middle] = 1 + + # dilation kernel with size of the receptive field + kernel = np.ones((int(basesize/speed_scale), int(basesize/speed_scale)), float) + # dilation kernel with size of the a quarter of receptive field used to compute k + # as described in section 6 of main paper + kernel2 = np.ones((int(basesize / (4*speed_scale)), int(basesize / (4*speed_scale))), float) + + # Output resolution limit set by the whole_size_threshold and scale_threshold. + threshold = min(whole_size_threshold, scale_threshold * max(img.shape[:2])) + + outputsize_scale = basesize / speed_scale + for p_size in range(int(basesize/speed_scale), int(threshold/speed_scale), int(basesize / (2*speed_scale))): + grad_resized = resizewithpool(grad, p_size) + grad_resized = cv2.resize(grad_resized, (p_size, p_size), cv2.INTER_NEAREST) + grad_resized[grad_resized >= 0.5] = 1 + grad_resized[grad_resized < 0.5] = 0 + + dilated = cv2.dilate(grad_resized, kernel, iterations=1) + meanvalue = (1-dilated).mean() + if meanvalue > confidence: + break + else: + outputsize_scale = p_size + + grad_region = cv2.dilate(grad_resized, kernel2, iterations=1) + patch_scale = grad_region.mean() + + return int(outputsize_scale*speed_scale), patch_scale + +# Generate a double-input depth estimation +def doubleestimate(img, size1, size2, pix2pixsize, model, net_type, pix2pixmodel): + # Generate the low resolution estimation + estimate1 = singleestimate(img, size1, model, net_type) + # Resize to the inference size of merge network. + estimate1 = cv2.resize(estimate1, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC) + + # Generate the high resolution estimation + estimate2 = singleestimate(img, size2, model, net_type) + # Resize to the inference size of merge network. + estimate2 = cv2.resize(estimate2, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC) + + # Inference on the merge model + pix2pixmodel.set_input(estimate1, estimate2) + pix2pixmodel.test() + visuals = pix2pixmodel.get_current_visuals() + prediction_mapped = visuals['fake_B'] + prediction_mapped = (prediction_mapped+1)/2 + prediction_mapped = (prediction_mapped - torch.min(prediction_mapped)) / ( + torch.max(prediction_mapped) - torch.min(prediction_mapped)) + prediction_mapped = prediction_mapped.squeeze().cpu().numpy() + + return prediction_mapped + +# Generate a single-input depth estimation +def singleestimate(img, msize, model, net_type): + # if net_type == 0: + return estimateleres(img, model, msize, msize) + # else: + # return estimatemidasBoost(img, model, msize, msize) + +def applyGridpatch(blsize, stride, img, box): + # Extract a simple grid patch. + counter1 = 0 + patch_bound_list = {} + for k in range(blsize, img.shape[1] - blsize, stride): + for j in range(blsize, img.shape[0] - blsize, stride): + patch_bound_list[str(counter1)] = {} + patchbounds = [j - blsize, k - blsize, j - blsize + 2 * blsize, k - blsize + 2 * blsize] + patch_bound = [box[0] + patchbounds[1], box[1] + patchbounds[0], patchbounds[3] - patchbounds[1], + patchbounds[2] - patchbounds[0]] + patch_bound_list[str(counter1)]['rect'] = patch_bound + patch_bound_list[str(counter1)]['size'] = patch_bound[2] + counter1 = counter1 + 1 + return patch_bound_list + +# Generating local patches to perform the local refinement described in section 6 of the main paper. +def generatepatchs(img, base_size): + + # Compute the gradients as a proxy of the contextual cues. + img_gray = rgb2gray(img) + whole_grad = np.abs(cv2.Sobel(img_gray, cv2.CV_64F, 0, 1, ksize=3)) +\ + np.abs(cv2.Sobel(img_gray, cv2.CV_64F, 1, 0, ksize=3)) + + threshold = whole_grad[whole_grad > 0].mean() + whole_grad[whole_grad < threshold] = 0 + + # We use the integral image to speed-up the evaluation of the amount of gradients for each patch. + gf = whole_grad.sum()/len(whole_grad.reshape(-1)) + grad_integral_image = cv2.integral(whole_grad) + + # Variables are selected such that the initial patch size would be the receptive field size + # and the stride is set to 1/3 of the receptive field size. + blsize = int(round(base_size/2)) + stride = int(round(blsize*0.75)) + + # Get initial Grid + patch_bound_list = applyGridpatch(blsize, stride, img, [0, 0, 0, 0]) + + # Refine initial Grid of patches by discarding the flat (in terms of gradients of the rgb image) ones. Refine + # each patch size to ensure that there will be enough depth cues for the network to generate a consistent depth map. + print("Selecting patches ...") + patch_bound_list = adaptiveselection(grad_integral_image, patch_bound_list, gf) + + # Sort the patch list to make sure the merging operation will be done with the correct order: starting from biggest + # patch + patchset = sorted(patch_bound_list.items(), key=lambda x: getitem(x[1], 'size'), reverse=True) + return patchset + +def getGF_fromintegral(integralimage, rect): + # Computes the gradient density of a given patch from the gradient integral image. + x1 = rect[1] + x2 = rect[1]+rect[3] + y1 = rect[0] + y2 = rect[0]+rect[2] + value = integralimage[x2, y2]-integralimage[x1, y2]-integralimage[x2, y1]+integralimage[x1, y1] + return value + +# Adaptively select patches +def adaptiveselection(integral_grad, patch_bound_list, gf): + patchlist = {} + count = 0 + height, width = integral_grad.shape + + search_step = int(32/factor) + + # Go through all patches + for c in range(len(patch_bound_list)): + # Get patch + bbox = patch_bound_list[str(c)]['rect'] + + # Compute the amount of gradients present in the patch from the integral image. + cgf = getGF_fromintegral(integral_grad, bbox)/(bbox[2]*bbox[3]) + + # Check if patching is beneficial by comparing the gradient density of the patch to + # the gradient density of the whole image + if cgf >= gf: + bbox_test = bbox.copy() + patchlist[str(count)] = {} + + # Enlarge each patch until the gradient density of the patch is equal + # to the whole image gradient density + while True: + + bbox_test[0] = bbox_test[0] - int(search_step/2) + bbox_test[1] = bbox_test[1] - int(search_step/2) + + bbox_test[2] = bbox_test[2] + search_step + bbox_test[3] = bbox_test[3] + search_step + + # Check if we are still within the image + if bbox_test[0] < 0 or bbox_test[1] < 0 or bbox_test[1] + bbox_test[3] >= height \ + or bbox_test[0] + bbox_test[2] >= width: + break + + # Compare gradient density + cgf = getGF_fromintegral(integral_grad, bbox_test)/(bbox_test[2]*bbox_test[3]) + if cgf < gf: + break + bbox = bbox_test.copy() + + # Add patch to selected patches + patchlist[str(count)]['rect'] = bbox + patchlist[str(count)]['size'] = bbox[2] + count = count + 1 + + # Return selected patches + return patchlist + +def impatch(image, rect): + # Extract the given patch pixels from a given image. + w1 = rect[0] + h1 = rect[1] + w2 = w1 + rect[2] + h2 = h1 + rect[3] + image_patch = image[h1:h2, w1:w2] + return image_patch + +class ImageandPatchs: + def __init__(self, root_dir, name, patchsinfo, rgb_image, scale=1): + self.root_dir = root_dir + self.patchsinfo = patchsinfo + self.name = name + self.patchs = patchsinfo + self.scale = scale + + self.rgb_image = cv2.resize(rgb_image, (round(rgb_image.shape[1]*scale), round(rgb_image.shape[0]*scale)), + interpolation=cv2.INTER_CUBIC) + + self.do_have_estimate = False + self.estimation_updated_image = None + self.estimation_base_image = None + + def __len__(self): + return len(self.patchs) + + def set_base_estimate(self, est): + self.estimation_base_image = est + if self.estimation_updated_image is not None: + self.do_have_estimate = True + + def set_updated_estimate(self, est): + self.estimation_updated_image = est + if self.estimation_base_image is not None: + self.do_have_estimate = True + + def __getitem__(self, index): + patch_id = int(self.patchs[index][0]) + rect = np.array(self.patchs[index][1]['rect']) + msize = self.patchs[index][1]['size'] + + ## applying scale to rect: + rect = np.round(rect * self.scale) + rect = rect.astype('int') + msize = round(msize * self.scale) + + patch_rgb = impatch(self.rgb_image, rect) + if self.do_have_estimate: + patch_whole_estimate_base = impatch(self.estimation_base_image, rect) + patch_whole_estimate_updated = impatch(self.estimation_updated_image, rect) + return {'patch_rgb': patch_rgb, 'patch_whole_estimate_base': patch_whole_estimate_base, + 'patch_whole_estimate_updated': patch_whole_estimate_updated, 'rect': rect, + 'size': msize, 'id': patch_id} + else: + return {'patch_rgb': patch_rgb, 'rect': rect, 'size': msize, 'id': patch_id} + + def print_options(self, opt): + """Print and save options + + It will print both current options and default values(if different). + It will save options into a text file / [checkpoints_dir] / opt.txt + """ + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + # save to the disk + """ + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + """ + + def parse(self): + """Parse our options, create checkpoints directory suffix, and set up gpu device.""" + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + #self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + #if len(opt.gpu_ids) > 0: + # torch.cuda.set_device(opt.gpu_ids[0]) + + self.opt = opt + return self.opt + + +def estimateboost(img, model, model_type, pix2pixmodel, max_res=512, depthmap_script_boost_rmax=None): + global whole_size_threshold + + # get settings + if depthmap_script_boost_rmax: + whole_size_threshold = depthmap_script_boost_rmax + + if model_type == 0: #leres + net_receptive_field_size = 448 + patch_netsize = 2 * net_receptive_field_size + elif model_type == 1: #dpt_beit_large_512 + net_receptive_field_size = 512 + patch_netsize = 2 * net_receptive_field_size + else: #other midas + net_receptive_field_size = 384 + patch_netsize = 2 * net_receptive_field_size + + gc.collect() + torch_gc() + + # Generate mask used to smoothly blend the local pathc estimations to the base estimate. + # It is arbitrarily large to avoid artifacts during rescaling for each crop. + mask_org = generatemask((3000, 3000)) + mask = mask_org.copy() + + # Value x of R_x defined in the section 5 of the main paper. + r_threshold_value = 0.2 + #if R0: + # r_threshold_value = 0 + + input_resolution = img.shape + scale_threshold = 3 # Allows up-scaling with a scale up to 3 + + # Find the best input resolution R-x. The resolution search described in section 5-double estimation of the main paper and section B of the + # supplementary material. + whole_image_optimal_size, patch_scale = calculateprocessingres(img, net_receptive_field_size, r_threshold_value, scale_threshold, whole_size_threshold) + + # print('wholeImage being processed in :', whole_image_optimal_size) + + # Generate the base estimate using the double estimation. + whole_estimate = doubleestimate(img, net_receptive_field_size, whole_image_optimal_size, pix2pixsize, model, model_type, pix2pixmodel) + + # Compute the multiplier described in section 6 of the main paper to make sure our initial patch can select + # small high-density regions of the image. + global factor + factor = max(min(1, 4 * patch_scale * whole_image_optimal_size / whole_size_threshold), 0.2) + # print('Adjust factor is:', 1/factor) + + # Check if Local boosting is beneficial. + if max_res < whole_image_optimal_size: + # print("No Local boosting. Specified Max Res is smaller than R20, Returning doubleestimate result") + return cv2.resize(whole_estimate, (input_resolution[1], input_resolution[0]), interpolation=cv2.INTER_CUBIC) + + # Compute the default target resolution. + if img.shape[0] > img.shape[1]: + a = 2 * whole_image_optimal_size + b = round(2 * whole_image_optimal_size * img.shape[1] / img.shape[0]) + else: + a = round(2 * whole_image_optimal_size * img.shape[0] / img.shape[1]) + b = 2 * whole_image_optimal_size + b = int(round(b / factor)) + a = int(round(a / factor)) + + """ + # recompute a, b and saturate to max res. + if max(a,b) > max_res: + print('Default Res is higher than max-res: Reducing final resolution') + if img.shape[0] > img.shape[1]: + a = max_res + b = round(max_res * img.shape[1] / img.shape[0]) + else: + a = round(max_res * img.shape[0] / img.shape[1]) + b = max_res + b = int(b) + a = int(a) + """ + + img = cv2.resize(img, (b, a), interpolation=cv2.INTER_CUBIC) + + # Extract selected patches for local refinement + base_size = net_receptive_field_size * 2 + patchset = generatepatchs(img, base_size) + + # print('Target resolution: ', img.shape) + + # Computing a scale in case user prompted to generate the results as the same resolution of the input. + # Notice that our method output resolution is independent of the input resolution and this parameter will only + # enable a scaling operation during the local patch merge implementation to generate results with the same resolution + # as the input. + """ + if output_resolution == 1: + mergein_scale = input_resolution[0] / img.shape[0] + print('Dynamicly change merged-in resolution; scale:', mergein_scale) + else: + mergein_scale = 1 + """ + # always rescale to input res for now + mergein_scale = input_resolution[0] / img.shape[0] + + imageandpatchs = ImageandPatchs('', '', patchset, img, mergein_scale) + whole_estimate_resized = cv2.resize(whole_estimate, (round(img.shape[1]*mergein_scale), + round(img.shape[0]*mergein_scale)), interpolation=cv2.INTER_CUBIC) + imageandpatchs.set_base_estimate(whole_estimate_resized.copy()) + imageandpatchs.set_updated_estimate(whole_estimate_resized.copy()) + + print('Resulting depthmap resolution will be :', whole_estimate_resized.shape[:2]) + print('Patches to process: '+str(len(imageandpatchs))) + + # Enumerate through all patches, generate their estimations and refining the base estimate. + for patch_ind in range(len(imageandpatchs)): + + # Get patch information + patch = imageandpatchs[patch_ind] # patch object + patch_rgb = patch['patch_rgb'] # rgb patch + patch_whole_estimate_base = patch['patch_whole_estimate_base'] # corresponding patch from base + rect = patch['rect'] # patch size and location + patch_id = patch['id'] # patch ID + org_size = patch_whole_estimate_base.shape # the original size from the unscaled input + print('\t Processing patch', patch_ind, '/', len(imageandpatchs)-1, '|', rect) + + # We apply double estimation for patches. The high resolution value is fixed to twice the receptive + # field size of the network for patches to accelerate the process. + patch_estimation = doubleestimate(patch_rgb, net_receptive_field_size, patch_netsize, pix2pixsize, model, model_type, pix2pixmodel) + patch_estimation = cv2.resize(patch_estimation, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC) + patch_whole_estimate_base = cv2.resize(patch_whole_estimate_base, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC) + + # Merging the patch estimation into the base estimate using our merge network: + # We feed the patch estimation and the same region from the updated base estimate to the merge network + # to generate the target estimate for the corresponding region. + pix2pixmodel.set_input(patch_whole_estimate_base, patch_estimation) + + # Run merging network + pix2pixmodel.test() + visuals = pix2pixmodel.get_current_visuals() + + prediction_mapped = visuals['fake_B'] + prediction_mapped = (prediction_mapped+1)/2 + prediction_mapped = prediction_mapped.squeeze().cpu().numpy() + + mapped = prediction_mapped + + # We use a simple linear polynomial to make sure the result of the merge network would match the values of + # base estimate + p_coef = np.polyfit(mapped.reshape(-1), patch_whole_estimate_base.reshape(-1), deg=1) + merged = np.polyval(p_coef, mapped.reshape(-1)).reshape(mapped.shape) + + merged = cv2.resize(merged, (org_size[1],org_size[0]), interpolation=cv2.INTER_CUBIC) + + # Get patch size and location + w1 = rect[0] + h1 = rect[1] + w2 = w1 + rect[2] + h2 = h1 + rect[3] + + # To speed up the implementation, we only generate the Gaussian mask once with a sufficiently large size + # and resize it to our needed size while merging the patches. + if mask.shape != org_size: + mask = cv2.resize(mask_org, (org_size[1],org_size[0]), interpolation=cv2.INTER_LINEAR) + + tobemergedto = imageandpatchs.estimation_updated_image + + # Update the whole estimation: + # We use a simple Gaussian mask to blend the merged patch region with the base estimate to ensure seamless + # blending at the boundaries of the patch region. + tobemergedto[h1:h2, w1:w2] = np.multiply(tobemergedto[h1:h2, w1:w2], 1 - mask) + np.multiply(merged, mask) + imageandpatchs.set_updated_estimate(tobemergedto) + + # output + return cv2.resize(imageandpatchs.estimation_updated_image, (input_resolution[1], input_resolution[0]), interpolation=cv2.INTER_CUBIC) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/multi_depth_model_woauxi.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/multi_depth_model_woauxi.py new file mode 100644 index 00000000..fdf35d78 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/multi_depth_model_woauxi.py @@ -0,0 +1,35 @@ +import torch +import torch.nn as nn + +from . import network_auxi as network +from .net_tools import get_func + + +class RelDepthModel(nn.Module): + def __init__(self, backbone='resnet50'): + super(RelDepthModel, self).__init__() + if backbone == 'resnet50': + encoder = 'resnet50_stride32' + elif backbone == 'resnext101': + encoder = 'resnext101_stride32x8d' + self.depth_model = DepthModel(encoder) + + def inference(self, rgb): + with torch.no_grad(): + input = rgb.to(self.depth_model.device) + depth = self.depth_model(input) + #pred_depth_out = depth - depth.min() + 0.01 + return depth #pred_depth_out + + +class DepthModel(nn.Module): + def __init__(self, encoder): + super(DepthModel, self).__init__() + backbone = network.__name__.split('.')[-1] + '.' + encoder + self.encoder_modules = get_func(backbone)() + self.decoder_modules = network.Decoder() + + def forward(self, x): + lateral_out = self.encoder_modules(x) + out_logit = self.decoder_modules(lateral_out) + return out_logit \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/net_tools.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/net_tools.py new file mode 100644 index 00000000..d44c794d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/net_tools.py @@ -0,0 +1,54 @@ +import importlib +import torch +import os +from collections import OrderedDict + + +def get_func(func_name): + """Helper to return a function object by name. func_name must identify a + function in this module or the path to a function relative to the base + 'modeling' module. + """ + if func_name == '': + return None + try: + parts = func_name.split('.') + # Refers to a function in this module + if len(parts) == 1: + return globals()[parts[0]] + # Otherwise, assume we're referencing a module under modeling + module_name = 'custom_controlnet_aux.leres.leres.' + '.'.join(parts[:-1]) + module = importlib.import_module(module_name) + return getattr(module, parts[-1]) + except Exception: + print('Failed to f1ind function: %s', func_name) + raise + +def load_ckpt(args, depth_model, shift_model, focal_model): + """ + Load checkpoint. + """ + if os.path.isfile(args.load_ckpt): + print("loading checkpoint %s" % args.load_ckpt) + checkpoint = torch.load(args.load_ckpt) + if shift_model is not None: + shift_model.load_state_dict(strip_prefix_if_present(checkpoint['shift_model'], 'module.'), + strict=True) + if focal_model is not None: + focal_model.load_state_dict(strip_prefix_if_present(checkpoint['focal_model'], 'module.'), + strict=True) + depth_model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), + strict=True) + del checkpoint + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def strip_prefix_if_present(state_dict, prefix): + keys = sorted(state_dict.keys()) + if not all(key.startswith(prefix) for key in keys): + return state_dict + stripped_state_dict = OrderedDict() + for key, value in state_dict.items(): + stripped_state_dict[key.replace(prefix, "")] = value + return stripped_state_dict \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/network_auxi.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/network_auxi.py new file mode 100644 index 00000000..1bd87011 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/leres/network_auxi.py @@ -0,0 +1,417 @@ +import torch +import torch.nn as nn +import torch.nn.init as init + +from . import Resnet, Resnext_torch + + +def resnet50_stride32(): + return DepthNet(backbone='resnet', depth=50, upfactors=[2, 2, 2, 2]) + +def resnext101_stride32x8d(): + return DepthNet(backbone='resnext101_32x8d', depth=101, upfactors=[2, 2, 2, 2]) + + +class Decoder(nn.Module): + def __init__(self): + super(Decoder, self).__init__() + self.inchannels = [256, 512, 1024, 2048] + self.midchannels = [256, 256, 256, 512] + self.upfactors = [2,2,2,2] + self.outchannels = 1 + + self.conv = FTB(inchannels=self.inchannels[3], midchannels=self.midchannels[3]) + self.conv1 = nn.Conv2d(in_channels=self.midchannels[3], out_channels=self.midchannels[2], kernel_size=3, padding=1, stride=1, bias=True) + self.upsample = nn.Upsample(scale_factor=self.upfactors[3], mode='bilinear', align_corners=True) + + self.ffm2 = FFM(inchannels=self.inchannels[2], midchannels=self.midchannels[2], outchannels = self.midchannels[2], upfactor=self.upfactors[2]) + self.ffm1 = FFM(inchannels=self.inchannels[1], midchannels=self.midchannels[1], outchannels = self.midchannels[1], upfactor=self.upfactors[1]) + self.ffm0 = FFM(inchannels=self.inchannels[0], midchannels=self.midchannels[0], outchannels = self.midchannels[0], upfactor=self.upfactors[0]) + + self.outconv = AO(inchannels=self.midchannels[0], outchannels=self.outchannels, upfactor=2) + self._init_params() + + def _init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): #NN.BatchNorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + def forward(self, features): + x_32x = self.conv(features[3]) # 1/32 + x_32 = self.conv1(x_32x) + x_16 = self.upsample(x_32) # 1/16 + + x_8 = self.ffm2(features[2], x_16) # 1/8 + x_4 = self.ffm1(features[1], x_8) # 1/4 + x_2 = self.ffm0(features[0], x_4) # 1/2 + #----------------------------------------- + x = self.outconv(x_2) # original size + return x + +class DepthNet(nn.Module): + __factory = { + 18: Resnet.resnet18, + 34: Resnet.resnet34, + 50: Resnet.resnet50, + 101: Resnet.resnet101, + 152: Resnet.resnet152 + } + def __init__(self, + backbone='resnet', + depth=50, + upfactors=[2, 2, 2, 2]): + super(DepthNet, self).__init__() + self.backbone = backbone + self.depth = depth + self.pretrained = False + self.inchannels = [256, 512, 1024, 2048] + self.midchannels = [256, 256, 256, 512] + self.upfactors = upfactors + self.outchannels = 1 + + # Build model + if self.backbone == 'resnet': + if self.depth not in DepthNet.__factory: + raise KeyError("Unsupported depth:", self.depth) + self.encoder = DepthNet.__factory[depth](pretrained=self.pretrained) + elif self.backbone == 'resnext101_32x8d': + self.encoder = Resnext_torch.resnext101_32x8d(pretrained=self.pretrained) + else: + self.encoder = Resnext_torch.resnext101(pretrained=self.pretrained) + + def forward(self, x): + x = self.encoder(x) # 1/32, 1/16, 1/8, 1/4 + return x + + +class FTB(nn.Module): + def __init__(self, inchannels, midchannels=512): + super(FTB, self).__init__() + self.in1 = inchannels + self.mid = midchannels + self.conv1 = nn.Conv2d(in_channels=self.in1, out_channels=self.mid, kernel_size=3, padding=1, stride=1, + bias=True) + # NN.BatchNorm2d + self.conv_branch = nn.Sequential(nn.ReLU(inplace=True), \ + nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3, + padding=1, stride=1, bias=True), \ + nn.BatchNorm2d(num_features=self.mid), \ + nn.ReLU(inplace=True), \ + nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3, + padding=1, stride=1, bias=True)) + self.relu = nn.ReLU(inplace=True) + + self.init_params() + + def forward(self, x): + x = self.conv1(x) + x = x + self.conv_branch(x) + x = self.relu(x) + + return x + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + +class ATA(nn.Module): + def __init__(self, inchannels, reduction=8): + super(ATA, self).__init__() + self.inchannels = inchannels + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential(nn.Linear(self.inchannels * 2, self.inchannels // reduction), + nn.ReLU(inplace=True), + nn.Linear(self.inchannels // reduction, self.inchannels), + nn.Sigmoid()) + self.init_params() + + def forward(self, low_x, high_x): + n, c, _, _ = low_x.size() + x = torch.cat([low_x, high_x], 1) + x = self.avg_pool(x) + x = x.view(n, -1) + x = self.fc(x).view(n, c, 1, 1) + x = low_x * x + high_x + + return x + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + # init.normal(m.weight, std=0.01) + init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + # init.normal_(m.weight, std=0.01) + init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + +class FFM(nn.Module): + def __init__(self, inchannels, midchannels, outchannels, upfactor=2): + super(FFM, self).__init__() + self.inchannels = inchannels + self.midchannels = midchannels + self.outchannels = outchannels + self.upfactor = upfactor + + self.ftb1 = FTB(inchannels=self.inchannels, midchannels=self.midchannels) + # self.ata = ATA(inchannels = self.midchannels) + self.ftb2 = FTB(inchannels=self.midchannels, midchannels=self.outchannels) + + self.upsample = nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True) + + self.init_params() + + def forward(self, low_x, high_x): + x = self.ftb1(low_x) + x = x + high_x + x = self.ftb2(x) + x = self.upsample(x) + + return x + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): # NN.Batchnorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + +class AO(nn.Module): + # Adaptive output module + def __init__(self, inchannels, outchannels, upfactor=2): + super(AO, self).__init__() + self.inchannels = inchannels + self.outchannels = outchannels + self.upfactor = upfactor + + self.adapt_conv = nn.Sequential( + nn.Conv2d(in_channels=self.inchannels, out_channels=self.inchannels // 2, kernel_size=3, padding=1, + stride=1, bias=True), \ + nn.BatchNorm2d(num_features=self.inchannels // 2), \ + nn.ReLU(inplace=True), \ + nn.Conv2d(in_channels=self.inchannels // 2, out_channels=self.outchannels, kernel_size=3, padding=1, + stride=1, bias=True), \ + nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True)) + + self.init_params() + + def forward(self, x): + x = self.adapt_conv(x) + return x + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): # NN.Batchnorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + + +# ============================================================================================================== + + +class ResidualConv(nn.Module): + def __init__(self, inchannels): + super(ResidualConv, self).__init__() + # NN.BatchNorm2d + self.conv = nn.Sequential( + # nn.BatchNorm2d(num_features=inchannels), + nn.ReLU(inplace=False), + # nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, padding=1, stride=1, groups=inchannels,bias=True), + # nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=1, padding=0, stride=1, groups=1,bias=True) + nn.Conv2d(in_channels=inchannels, out_channels=inchannels / 2, kernel_size=3, padding=1, stride=1, + bias=False), + nn.BatchNorm2d(num_features=inchannels / 2), + nn.ReLU(inplace=False), + nn.Conv2d(in_channels=inchannels / 2, out_channels=inchannels, kernel_size=3, padding=1, stride=1, + bias=False) + ) + self.init_params() + + def forward(self, x): + x = self.conv(x) + x + return x + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + +class FeatureFusion(nn.Module): + def __init__(self, inchannels, outchannels): + super(FeatureFusion, self).__init__() + self.conv = ResidualConv(inchannels=inchannels) + # NN.BatchNorm2d + self.up = nn.Sequential(ResidualConv(inchannels=inchannels), + nn.ConvTranspose2d(in_channels=inchannels, out_channels=outchannels, kernel_size=3, + stride=2, padding=1, output_padding=1), + nn.BatchNorm2d(num_features=outchannels), + nn.ReLU(inplace=True)) + + def forward(self, lowfeat, highfeat): + return self.up(highfeat + self.conv(lowfeat)) + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + # init.kaiming_normal_(m.weight, mode='fan_out') + init.normal_(m.weight, std=0.01) + # init.xavier_normal_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.01) + if m.bias is not None: + init.constant_(m.bias, 0) + + +class SenceUnderstand(nn.Module): + def __init__(self, channels): + super(SenceUnderstand, self).__init__() + self.channels = channels + self.conv1 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1), + nn.ReLU(inplace=True)) + self.pool = nn.AdaptiveAvgPool2d(8) + self.fc = nn.Sequential(nn.Linear(512 * 8 * 8, self.channels), + nn.ReLU(inplace=True)) + self.conv2 = nn.Sequential( + nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, padding=0), + nn.ReLU(inplace=True)) + self.initial_params() + + def forward(self, x): + n, c, h, w = x.size() + x = self.conv1(x) + x = self.pool(x) + x = x.view(n, -1) + x = self.fc(x) + x = x.view(n, self.channels, 1, 1) + x = self.conv2(x) + x = x.repeat(1, 1, h, w) + return x + + def initial_params(self, dev=0.01): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # print torch.sum(m.weight) + m.weight.data.normal_(0, dev) + if m.bias is not None: + m.bias.data.fill_(0) + elif isinstance(m, nn.ConvTranspose2d): + # print torch.sum(m.weight) + m.weight.data.normal_(0, dev) + if m.bias is not None: + m.bias.data.fill_(0) + elif isinstance(m, nn.Linear): + m.weight.data.normal_(0, dev) + + +if __name__ == '__main__': + net = DepthNet(depth=50, pretrained=True) + print(net) + inputs = torch.ones(4,3,128,128) + out = net(inputs) + print(out.size()) + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/LICENSE new file mode 100644 index 00000000..38b1a24f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/LICENSE @@ -0,0 +1,19 @@ +https://github.com/compphoto/BoostingMonocularDepth + +Copyright 2021, Seyed Mahdi Hosseini Miangoleh, Sebastian Dille, Computational Photography Laboratory. All rights reserved. + +This software is for academic use only. A redistribution of this +software, with or without modifications, has to be for academic +use only, while giving the appropriate credit to the original +authors of the software. The methods implemented as a part of +this software may be covered under patents or patent applications. + +THIS SOFTWARE IS PROVIDED BY THE AUTHOR ''AS IS'' AND ANY EXPRESS OR IMPLIED +WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND +FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/__init__.py new file mode 100644 index 00000000..cb83d333 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/__init__.py @@ -0,0 +1,67 @@ +"""This package contains modules related to objective functions, optimizations, and network architectures. + +To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel. +You need to implement the following five functions: + -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). + -- : unpack data from dataset and apply preprocessing. + -- : produce intermediate results. + -- : calculate loss, gradients, and update network weights. + -- : (optionally) add model-specific options and set default options. + +In the function <__init__>, you need to define four lists: + -- self.loss_names (str list): specify the training losses that you want to plot and save. + -- self.model_names (str list): define networks used in our training. + -- self.visual_names (str list): specify the images that you want to display and save. + -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage. + +Now you can use the model class by specifying flag '--model dummy'. +See our template model class 'template_model.py' for more details. +""" + +import importlib +from .base_model import BaseModel + + +def find_model_using_name(model_name): + """Import the module "models/[model_name]_model.py". + + In the file, the class called DatasetNameModel() will + be instantiated. It has to be a subclass of BaseModel, + and it is case-insensitive. + """ + model_filename = "custom_controlnet_aux.leres.pix2pix.models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, BaseModel): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + """Return the static method of the model class.""" + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + """Create a model given the option. + + This function warps the class CustomDatasetDataLoader. + This is the main interface between this package and 'train.py'/'test.py' + + Example: + >>> from models import create_model + >>> model = create_model(opt) + """ + model = find_model_using_name(opt.model) + instance = model(opt) + print("model [%s] was created" % type(instance).__name__) + return instance diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/base_model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/base_model.py new file mode 100644 index 00000000..66ec298f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/base_model.py @@ -0,0 +1,244 @@ +import gc +import os +from abc import ABC, abstractmethod +from collections import OrderedDict + +import torch + +from ....util import torch_gc +from . import networks + + +class BaseModel(ABC): + """This class is an abstract base class (ABC) for models. + To create a subclass, you need to implement the following five functions: + -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). + -- : unpack data from dataset and apply preprocessing. + -- : produce intermediate results. + -- : calculate losses, gradients, and update network weights. + -- : (optionally) add model-specific options and set default options. + """ + + def __init__(self, opt): + """Initialize the BaseModel class. + + Parameters: + opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions + + When creating your custom class, you need to implement your own initialization. + In this function, you should first call + Then, you need to define four lists: + -- self.loss_names (str list): specify the training losses that you want to plot and save. + -- self.model_names (str list): define networks used in our training. + -- self.visual_names (str list): specify the images that you want to display and save. + -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. + """ + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir + if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark. + torch.backends.cudnn.benchmark = True + self.loss_names = [] + self.model_names = [] + self.visual_names = [] + self.optimizers = [] + self.image_paths = [] + self.metric = 0 # used for learning rate policy 'plateau' + + @staticmethod + def modify_commandline_options(parser, is_train): + """Add new model-specific options, and rewrite default values for existing options. + + Parameters: + parser -- original option parser + is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. + + Returns: + the modified parser. + """ + return parser + + @abstractmethod + def set_input(self, input): + """Unpack input data from the dataloader and perform necessary pre-processing steps. + + Parameters: + input (dict): includes the data itself and its metadata information. + """ + pass + + @abstractmethod + def forward(self): + """Run forward pass; called by both functions and .""" + pass + + @abstractmethod + def optimize_parameters(self): + """Calculate losses, gradients, and update network weights; called in every training iteration""" + pass + + def setup(self, opt): + """Load and print networks; create schedulers + + Parameters: + opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions + """ + if self.isTrain: + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + if not self.isTrain or opt.continue_train: + load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch + self.load_networks(load_suffix) + self.print_networks(opt.verbose) + + def eval(self): + """Make models eval mode during test time""" + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, 'net' + name) + net.eval() + + def test(self): + """Forward function used in test time. + + This function wraps function in no_grad() so we don't save intermediate steps for backprop + It also calls to produce additional visualization results + """ + with torch.no_grad(): + self.forward() + self.compute_visuals() + + def compute_visuals(self): + """Calculate additional output images for visdom and HTML visualization""" + pass + + def get_image_paths(self): + """ Return image paths that are used to load current data""" + return self.image_paths + + def update_learning_rate(self): + """Update learning rates for all the networks; called at the end of every epoch""" + old_lr = self.optimizers[0].param_groups[0]['lr'] + for scheduler in self.schedulers: + if self.opt.lr_policy == 'plateau': + scheduler.step(self.metric) + else: + scheduler.step() + + lr = self.optimizers[0].param_groups[0]['lr'] + print('learning rate %.7f -> %.7f' % (old_lr, lr)) + + def get_current_visuals(self): + """Return visualization images. train.py will display these images with visdom, and save the images to a HTML""" + visual_ret = OrderedDict() + for name in self.visual_names: + if isinstance(name, str): + visual_ret[name] = getattr(self, name) + return visual_ret + + def get_current_losses(self): + """Return traning losses / errors. train.py will print out these errors on console, and save them to a file""" + errors_ret = OrderedDict() + for name in self.loss_names: + if isinstance(name, str): + errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number + return errors_ret + + def save_networks(self, epoch): + """Save all the networks to the disk. + + Parameters: + epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) + """ + for name in self.model_names: + if isinstance(name, str): + save_filename = '%s_net_%s.pth' % (epoch, name) + save_path = os.path.join(self.save_dir, save_filename) + net = getattr(self, 'net' + name) + + if len(self.gpu_ids) > 0 and torch.cuda.is_available(): + torch.save(net.module.cpu().state_dict(), save_path) + net.cuda(self.gpu_ids[0]) + else: + torch.save(net.cpu().state_dict(), save_path) + + def unload_network(self, name): + """Unload network and gc. + """ + if isinstance(name, str): + net = getattr(self, 'net' + name) + del net + gc.collect() + torch_gc() + return None + + def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): + """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)""" + key = keys[i] + if i + 1 == len(keys): # at the end, pointing to a parameter/buffer + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'running_mean' or key == 'running_var'): + if getattr(module, key) is None: + state_dict.pop('.'.join(keys)) + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'num_batches_tracked'): + state_dict.pop('.'.join(keys)) + else: + self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) + + def load_networks(self, epoch): + """Load all the networks from the disk. + + Parameters: + epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) + """ + for name in self.model_names: + if isinstance(name, str): + load_filename = '%s_net_%s.pth' % (epoch, name) + load_path = os.path.join(self.save_dir, load_filename) + net = getattr(self, 'net' + name) + if isinstance(net, torch.nn.DataParallel): + net = net.module + # print('Loading depth boost model from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + def print_networks(self, verbose): + """Print the total number of parameters in the network and (if verbose) network architecture + + Parameters: + verbose (bool) -- if verbose: print the network architecture + """ + print('---------- Networks initialized -------------') + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, 'net' + name) + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + if verbose: + print(net) + print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + print('-----------------------------------------------') + + def set_requires_grad(self, nets, requires_grad=False): + """Set requies_grad=Fasle for all the networks to avoid unnecessary computations + Parameters: + nets (network list) -- a list of networks + requires_grad (bool) -- whether the networks require gradients or not + """ + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/base_model_hg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/base_model_hg.py new file mode 100644 index 00000000..1709accd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/base_model_hg.py @@ -0,0 +1,58 @@ +import os +import torch + +class BaseModelHG(): + def name(self): + return 'BaseModel' + + def initialize(self, opt): + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) + + def set_input(self, input): + self.input = input + + def forward(self): + pass + + # used in test time, no backprop + def test(self): + pass + + def get_image_paths(self): + pass + + def optimize_parameters(self): + pass + + def get_current_visuals(self): + return self.input + + def get_current_errors(self): + return {} + + def save(self, label): + pass + + # helper saving function that can be used by subclasses + def save_network(self, network, network_label, epoch_label, gpu_ids): + save_filename = '_%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(self.save_dir, save_filename) + torch.save(network.cpu().state_dict(), save_path) + if len(gpu_ids) and torch.cuda.is_available(): + network.cuda(device_id=gpu_ids[0]) + + # helper loading function that can be used by subclasses + def load_network(self, network, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(self.save_dir, save_filename) + print(save_path) + model = torch.load(save_path) + return model + # network.load_state_dict(torch.load(save_path)) + + def update_learning_rate(): + pass diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/networks.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/networks.py new file mode 100644 index 00000000..0cf912b2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/networks.py @@ -0,0 +1,623 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.optim import lr_scheduler + + +############################################################################### +# Helper Functions +############################################################################### + + +class Identity(nn.Module): + def forward(self, x): + return x + + +def get_norm_layer(norm_type='instance'): + """Return a normalization layer + + Parameters: + norm_type (str) -- the name of the normalization layer: batch | instance | none + + For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). + For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. + """ + if norm_type == 'batch': + norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True) + elif norm_type == 'instance': + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + elif norm_type == 'none': + def norm_layer(x): return Identity() + else: + raise NotImplementedError('normalization layer [%s] is not found' % norm_type) + return norm_layer + + +def get_scheduler(optimizer, opt): + """Return a learning rate scheduler + + Parameters: + optimizer -- the optimizer of the network + opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptionsοΌŽγ€€ + opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine + + For 'linear', we keep the same learning rate for the first epochs + and linearly decay the rate to zero over the next epochs. + For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers. + See https://pytorch.org/docs/stable/optim.html for more details. + """ + if opt.lr_policy == 'linear': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + elif opt.lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) + elif opt.lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif opt.lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) + return scheduler + + +def init_weights(net, init_type='normal', init_gain=0.02): + """Initialize network weights. + + Parameters: + net (network) -- network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + + We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might + work better for some applications. Feel free to try yourself. + """ + def init_func(m): # define the initialization function + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. + init.normal_(m.weight.data, 1.0, init_gain) + init.constant_(m.bias.data, 0.0) + + # print('initialize network with %s' % init_type) + net.apply(init_func) # apply the initialization function + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): + """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights + Parameters: + net (network) -- the network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + gain (float) -- scaling factor for normal, xavier and orthogonal. + gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 + + Return an initialized network. + """ + if len(gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.to(gpu_ids[0]) + net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs + init_weights(net, init_type, init_gain=init_gain) + return net + + +def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + """Create a generator + + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + ngf (int) -- the number of filters in the last conv layer + netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128 + norm (str) -- the name of normalization layers used in the network: batch | instance | none + use_dropout (bool) -- if use dropout layers. + init_type (str) -- the name of our initialization method. + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 + + Returns a generator + + Our current implementation provides two types of generators: + U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images) + The original U-Net paper: https://arxiv.org/abs/1505.04597 + + Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks) + Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations. + We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style). + + + The generator has been initialized by . It uses RELU for non-linearity. + """ + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netG == 'resnet_9blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9) + elif netG == 'resnet_6blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6) + elif netG == 'resnet_12blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=12) + elif netG == 'unet_128': + net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_256': + net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_672': + net = UnetGenerator(input_nc, output_nc, 5, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_960': + net = UnetGenerator(input_nc, output_nc, 6, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_1024': + net = UnetGenerator(input_nc, output_nc, 10, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + else: + raise NotImplementedError('Generator model name [%s] is not recognized' % netG) + return init_net(net, init_type, init_gain, gpu_ids) + + +def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]): + """Create a discriminator + + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the first conv layer + netD (str) -- the architecture's name: basic | n_layers | pixel + n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers' + norm (str) -- the type of normalization layers used in the network. + init_type (str) -- the name of the initialization method. + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 + + Returns a discriminator + + Our current implementation provides three types of discriminators: + [basic]: 'PatchGAN' classifier described in the original pix2pix paper. + It can classify whether 70Γ—70 overlapping patches are real or fake. + Such a patch-level discriminator architecture has fewer parameters + than a full-image discriminator and can work on arbitrarily-sized images + in a fully convolutional fashion. + + [n_layers]: With this mode, you can specify the number of conv layers in the discriminator + with the parameter (default=3 as used in [basic] (PatchGAN).) + + [pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not. + It encourages greater color diversity but has no effect on spatial statistics. + + The discriminator has been initialized by . It uses Leakly RELU for non-linearity. + """ + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netD == 'basic': # default PatchGAN classifier + net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer) + elif netD == 'n_layers': # more options + net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer) + elif netD == 'pixel': # classify if each pixel is real or fake + net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer) + else: + raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD) + return init_net(net, init_type, init_gain, gpu_ids) + + +############################################################################## +# Classes +############################################################################## +class GANLoss(nn.Module): + """Define different GAN objectives. + + The GANLoss class abstracts away the need to create the target label tensor + that has the same size as the input. + """ + + def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0): + """ Initialize the GANLoss class. + + Parameters: + gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp. + target_real_label (bool) - - label for a real image + target_fake_label (bool) - - label of a fake image + + Note: Do not use sigmoid as the last layer of Discriminator. + LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss. + """ + super(GANLoss, self).__init__() + self.register_buffer('real_label', torch.tensor(target_real_label)) + self.register_buffer('fake_label', torch.tensor(target_fake_label)) + self.gan_mode = gan_mode + if gan_mode == 'lsgan': + self.loss = nn.MSELoss() + elif gan_mode == 'vanilla': + self.loss = nn.BCEWithLogitsLoss() + elif gan_mode in ['wgangp']: + self.loss = None + else: + raise NotImplementedError('gan mode %s not implemented' % gan_mode) + + def get_target_tensor(self, prediction, target_is_real): + """Create label tensors with the same size as the input. + + Parameters: + prediction (tensor) - - tpyically the prediction from a discriminator + target_is_real (bool) - - if the ground truth label is for real images or fake images + + Returns: + A label tensor filled with ground truth label, and with the size of the input + """ + + if target_is_real: + target_tensor = self.real_label + else: + target_tensor = self.fake_label + return target_tensor.expand_as(prediction) + + def __call__(self, prediction, target_is_real): + """Calculate loss given Discriminator's output and grount truth labels. + + Parameters: + prediction (tensor) - - tpyically the prediction output from a discriminator + target_is_real (bool) - - if the ground truth label is for real images or fake images + + Returns: + the calculated loss. + """ + if self.gan_mode in ['lsgan', 'vanilla']: + target_tensor = self.get_target_tensor(prediction, target_is_real) + loss = self.loss(prediction, target_tensor) + elif self.gan_mode == 'wgangp': + if target_is_real: + loss = -prediction.mean() + else: + loss = prediction.mean() + return loss + + +def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0): + """Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028 + + Arguments: + netD (network) -- discriminator network + real_data (tensor array) -- real images + fake_data (tensor array) -- generated images from the generator + device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + type (str) -- if we mix real and fake data or not [real | fake | mixed]. + constant (float) -- the constant used in formula ( ||gradient||_2 - constant)^2 + lambda_gp (float) -- weight for this loss + + Returns the gradient penalty loss + """ + if lambda_gp > 0.0: + if type == 'real': # either use real images, fake images, or a linear interpolation of two. + interpolatesv = real_data + elif type == 'fake': + interpolatesv = fake_data + elif type == 'mixed': + alpha = torch.rand(real_data.shape[0], 1, device=device) + alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape) + interpolatesv = alpha * real_data + ((1 - alpha) * fake_data) + else: + raise NotImplementedError('{} not implemented'.format(type)) + interpolatesv.requires_grad_(True) + disc_interpolates = netD(interpolatesv) + gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv, + grad_outputs=torch.ones(disc_interpolates.size()).to(device), + create_graph=True, retain_graph=True, only_inputs=True) + gradients = gradients[0].view(real_data.size(0), -1) # flat the data + gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps + return gradient_penalty, gradients + else: + return 0.0, None + + +class ResnetGenerator(nn.Module): + """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations. + + We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style) + """ + + def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'): + """Construct a Resnet-based generator + + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers + n_blocks (int) -- the number of ResNet blocks + padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero + """ + assert(n_blocks >= 0) + super(ResnetGenerator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), + norm_layer(ngf), + nn.ReLU(True)] + + n_downsampling = 2 + for i in range(n_downsampling): # add downsampling layers + mult = 2 ** i + model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), + norm_layer(ngf * mult * 2), + nn.ReLU(True)] + + mult = 2 ** n_downsampling + for i in range(n_blocks): # add ResNet blocks + + model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] + + for i in range(n_downsampling): # add upsampling layers + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(int(ngf * mult / 2)), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + model += [nn.Tanh()] + + self.model = nn.Sequential(*model) + + def forward(self, input): + """Standard forward""" + return self.model(input) + + +class ResnetBlock(nn.Module): + """Define a Resnet block""" + + def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): + """Initialize the Resnet block + + A resnet block is a conv block with skip connections + We construct a conv block with build_conv_block function, + and implement skip connections in function. + Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf + """ + super(ResnetBlock, self).__init__() + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) + + def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): + """Construct a convolutional block. + + Parameters: + dim (int) -- the number of channels in the conv layer. + padding_type (str) -- the name of padding layer: reflect | replicate | zero + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers. + use_bias (bool) -- if the conv layer uses bias or not + + Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU)) + """ + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + """Forward function (with skip connections)""" + out = x + self.conv_block(x) # add skip connections + return out + + +class UnetGenerator(nn.Module): + """Create a Unet-based generator""" + + def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(UnetGenerator, self).__init__() + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer + for i in range(num_downs - 5): # add intermediate layers with ngf * 8 filters + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + # gradually reduce the number of filters from ngf * 8 to ngf + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer + + def forward(self, input): + """Standard forward""" + return self.model(input) + + +class UnetSkipConnectionBlock(nn.Module): + """Defines the Unet submodule with skip connection. + X -------------------identity---------------------- + |-- downsampling -- |submodule| -- upsampling --| + """ + + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet submodule with skip connections. + + Parameters: + outer_nc (int) -- the number of filters in the outer conv layer + inner_nc (int) -- the number of filters in the inner conv layer + input_nc (int) -- the number of channels in input images/features + submodule (UnetSkipConnectionBlock) -- previously defined submodules + outermost (bool) -- if this module is the outermost module + innermost (bool) -- if this module is the innermost module + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers. + """ + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: # add skip connections + return torch.cat([x, self.model(x)], 1) + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator""" + + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d): + """Construct a PatchGAN discriminator + + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + kw = 4 + padw = 1 + sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map + self.model = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.model(input) + + +class PixelDiscriminator(nn.Module): + """Defines a 1x1 PatchGAN discriminator (pixelGAN)""" + + def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d): + """Construct a 1x1 PatchGAN discriminator + + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + """ + super(PixelDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + self.net = [ + nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), + norm_layer(ndf * 2), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] + + self.net = nn.Sequential(*self.net) + + def forward(self, input): + """Standard forward.""" + return self.net(input) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/pix2pix4depth_model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/pix2pix4depth_model.py new file mode 100644 index 00000000..89e89652 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/models/pix2pix4depth_model.py @@ -0,0 +1,155 @@ +import torch +from .base_model import BaseModel +from . import networks + + +class Pix2Pix4DepthModel(BaseModel): + """ This class implements the pix2pix model, for learning a mapping from input images to output images given paired data. + + The model training requires '--dataset_mode aligned' dataset. + By default, it uses a '--netG unet256' U-Net generator, + a '--netD basic' discriminator (PatchGAN), + and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper). + + pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf + """ + @staticmethod + def modify_commandline_options(parser, is_train=True): + """Add new dataset-specific options, and rewrite default values for existing options. + + Parameters: + parser -- original option parser + is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. + + Returns: + the modified parser. + + For pix2pix, we do not use image buffer + The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1 + By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets. + """ + # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/) + parser.set_defaults(input_nc=2,output_nc=1,norm='none', netG='unet_1024', dataset_mode='depthmerge') + if is_train: + parser.set_defaults(pool_size=0, gan_mode='vanilla',) + parser.add_argument('--lambda_L1', type=float, default=1000, help='weight for L1 loss') + return parser + + def __init__(self, opt): + """Initialize the pix2pix class. + + Parameters: + opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions + """ + BaseModel.__init__(self, opt) + # specify the training losses you want to print out. The training/test scripts will call + + self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake'] + # self.loss_names = ['G_L1'] + + # specify the images you want to save/display. The training/test scripts will call + if self.isTrain: + self.visual_names = ['outer','inner', 'fake_B', 'real_B'] + else: + self.visual_names = ['fake_B'] + + # specify the models you want to save to the disk. The training/test scripts will call and + if self.isTrain: + self.model_names = ['G','D'] + else: # during test time, only load G + self.model_names = ['G'] + + # define networks (both generator and discriminator) + self.netG = networks.define_G(opt.input_nc, opt.output_nc, 64, 'unet_1024', 'none', + False, 'normal', 0.02, self.gpu_ids) + + if self.isTrain: # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc + self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD, + opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) + + if self.isTrain: + # define loss functions + self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) + self.criterionL1 = torch.nn.L1Loss() + # initialize optimizers; schedulers will be automatically created by function . + self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=1e-4, betas=(opt.beta1, 0.999)) + self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=2e-06, betas=(opt.beta1, 0.999)) + self.optimizers.append(self.optimizer_G) + self.optimizers.append(self.optimizer_D) + + def set_input_train(self, input): + self.outer = input['data_outer'].to(self.device) + self.outer = torch.nn.functional.interpolate(self.outer,(1024,1024),mode='bilinear',align_corners=False) + + self.inner = input['data_inner'].to(self.device) + self.inner = torch.nn.functional.interpolate(self.inner,(1024,1024),mode='bilinear',align_corners=False) + + self.image_paths = input['image_path'] + + if self.isTrain: + self.gtfake = input['data_gtfake'].to(self.device) + self.gtfake = torch.nn.functional.interpolate(self.gtfake, (1024, 1024), mode='bilinear', align_corners=False) + self.real_B = self.gtfake + + self.real_A = torch.cat((self.outer, self.inner), 1) + + def set_input(self, outer, inner): + inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0) + outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0) + + inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner)) + outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer)) + + inner = self.normalize(inner) + outer = self.normalize(outer) + + self.real_A = torch.cat((outer, inner), 1).to(self.device) + + + def normalize(self, input): + input = input * 2 + input = input - 1 + return input + + def forward(self): + """Run forward pass; called by both functions and .""" + self.fake_B = self.netG(self.real_A) # G(A) + + def backward_D(self): + """Calculate GAN loss for the discriminator""" + # Fake; stop backprop to the generator by detaching fake_B + fake_AB = torch.cat((self.real_A, self.fake_B), 1) # we use conditional GANs; we need to feed both input and output to the discriminator + pred_fake = self.netD(fake_AB.detach()) + self.loss_D_fake = self.criterionGAN(pred_fake, False) + # Real + real_AB = torch.cat((self.real_A, self.real_B), 1) + pred_real = self.netD(real_AB) + self.loss_D_real = self.criterionGAN(pred_real, True) + # combine loss and calculate gradients + self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5 + self.loss_D.backward() + + def backward_G(self): + """Calculate GAN and L1 loss for the generator""" + # First, G(A) should fake the discriminator + fake_AB = torch.cat((self.real_A, self.fake_B), 1) + pred_fake = self.netD(fake_AB) + self.loss_G_GAN = self.criterionGAN(pred_fake, True) + # Second, G(A) = B + self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1 + # combine loss and calculate gradients + self.loss_G = self.loss_G_L1 + self.loss_G_GAN + self.loss_G.backward() + + def optimize_parameters(self): + self.forward() # compute fake images: G(A) + # update D + self.set_requires_grad(self.netD, True) # enable backprop for D + self.optimizer_D.zero_grad() # set D's gradients to zero + self.backward_D() # calculate gradients for D + self.optimizer_D.step() # update D's weights + # update G + self.set_requires_grad(self.netD, False) # D requires no gradients when optimizing G + self.optimizer_G.zero_grad() # set G's gradients to zero + self.backward_G() # calculate graidents for G + self.optimizer_G.step() # udpate G's weights \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/__init__.py new file mode 100644 index 00000000..e7eedebe --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/__init__.py @@ -0,0 +1 @@ +"""This package options includes option modules: training options, test options, and basic options (used in both training and test).""" diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/base_options.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/base_options.py new file mode 100644 index 00000000..533a1e88 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/base_options.py @@ -0,0 +1,156 @@ +import argparse +import os +from ...pix2pix.util import util +# import torch +from ...pix2pix import models +# import pix2pix.data +import numpy as np + +class BaseOptions(): + """This class defines options used during both training and test time. + + It also implements several helper functions such as parsing, printing, and saving the options. + It also gathers additional options defined in functions in both dataset class and model class. + """ + + def __init__(self): + """Reset the class; indicates the class hasn't been initailized""" + self.initialized = False + + def initialize(self, parser): + """Define the common options that are used in both training and test.""" + # basic parameters + parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') + parser.add_argument('--name', type=str, default='void', help='mahdi_unet_new, scaled_unet') + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--checkpoints_dir', type=str, default='./pix2pix/checkpoints', help='models are saved here') + # model parameters + parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]') + parser.add_argument('--input_nc', type=int, default=2, help='# of input image channels: 3 for RGB and 1 for grayscale') + parser.add_argument('--output_nc', type=int, default=1, help='# of output image channels: 3 for RGB and 1 for grayscale') + parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer') + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer') + parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator') + parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]') + parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers') + parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]') + parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]') + parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') + parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') + # dataset parameters + parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]') + parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') + parser.add_argument('--batch_size', type=int, default=1, help='input batch size') + parser.add_argument('--load_size', type=int, default=672, help='scale images to this size') + parser.add_argument('--crop_size', type=int, default=672, help='then crop to this size') + parser.add_argument('--max_dataset_size', type=int, default=10000, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]') + parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') + parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') + # additional parameters + parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') + parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') + parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}') + + parser.add_argument('--data_dir', type=str, required=False, + help='input files directory images can be .png .jpg .tiff') + parser.add_argument('--output_dir', type=str, required=False, + help='result dir. result depth will be png. vides are JMPG as avi') + parser.add_argument('--savecrops', type=int, required=False) + parser.add_argument('--savewholeest', type=int, required=False) + parser.add_argument('--output_resolution', type=int, required=False, + help='0 for no restriction 1 for resize to input size') + parser.add_argument('--net_receptive_field_size', type=int, required=False) + parser.add_argument('--pix2pixsize', type=int, required=False) + parser.add_argument('--generatevideo', type=int, required=False) + parser.add_argument('--depthNet', type=int, required=False, help='0: midas 1:strurturedRL') + parser.add_argument('--R0', action='store_true') + parser.add_argument('--R20', action='store_true') + parser.add_argument('--Final', action='store_true') + parser.add_argument('--colorize_results', action='store_true') + parser.add_argument('--max_res', type=float, default=np.inf) + + self.initialized = True + return parser + + def gather_options(self): + """Initialize our parser with basic options(only once). + Add additional model-specific and dataset-specific options. + These options are defined in the function + in model and dataset classes. + """ + if not self.initialized: # check if it has been initialized + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + opt, _ = parser.parse_known_args() # parse again with new defaults + + # modify dataset-related parser options + # dataset_name = opt.dataset_mode + # dataset_option_setter = pix2pix.data.get_option_setter(dataset_name) + # parser = dataset_option_setter(parser, self.isTrain) + + # save and return the parser + self.parser = parser + #return parser.parse_args() #EVIL + return opt + + def print_options(self, opt): + """Print and save options + + It will print both current options and default values(if different). + It will save options into a text file / [checkpoints_dir] / opt.txt + """ + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + # save to the disk + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + def parse(self): + """Parse our options, create checkpoints directory suffix, and set up gpu device.""" + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + #self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + #if len(opt.gpu_ids) > 0: + # torch.cuda.set_device(opt.gpu_ids[0]) + + self.opt = opt + return self.opt diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/test_options.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/test_options.py new file mode 100644 index 00000000..a3424b5e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/options/test_options.py @@ -0,0 +1,22 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + """This class includes test options. + + It also includes shared options defined in BaseOptions. + """ + + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) # define shared options + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + # rewrite devalue values + parser.set_defaults(model='pix2pix4depth') + # To avoid cropping, the load_size should be the same as crop_size + parser.set_defaults(load_size=parser.get_default('crop_size')) + self.isTrain = False + return parser diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/util/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/util/__init__.py new file mode 100644 index 00000000..ae36f63d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/util/__init__.py @@ -0,0 +1 @@ +"""This package includes a miscellaneous collection of useful helper functions.""" diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/util/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/util/util.py new file mode 100644 index 00000000..8a7aceaa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/leres/pix2pix/util/util.py @@ -0,0 +1,105 @@ +"""This module contains simple helper functions """ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import os + + +def tensor2im(input_image, imtype=np.uint16): + """"Converts a Tensor array into a numpy image array. + + Parameters: + input_image (tensor) -- the input image tensor array + imtype (type) -- the desired type of the converted numpy array + """ + if not isinstance(input_image, np.ndarray): + if isinstance(input_image, torch.Tensor): # get the data from a variable + image_tensor = input_image.data + else: + return input_image + image_numpy = torch.squeeze(image_tensor).cpu().numpy() # convert it into a numpy array + image_numpy = (image_numpy + 1) / 2.0 * (2**16-1) # + else: # if it is a numpy array, do nothing + image_numpy = input_image + return image_numpy.astype(imtype) + + +def diagnose_network(net, name='network'): + """Calculate and print the mean of average absolute(gradients) + + Parameters: + net (torch network) -- Torch network + name (str) -- the name of the network + """ + mean = 0.0 + count = 0 + for param in net.parameters(): + if param.grad is not None: + mean += torch.mean(torch.abs(param.grad.data)) + count += 1 + if count > 0: + mean = mean / count + print(name) + print(mean) + + +def save_image(image_numpy, image_path, aspect_ratio=1.0): + """Save a numpy image to the disk + + Parameters: + image_numpy (numpy array) -- input numpy array + image_path (str) -- the path of the image + """ + image_pil = Image.fromarray(image_numpy) + + image_pil = image_pil.convert('I;16') + + # image_pil = Image.fromarray(image_numpy) + # h, w, _ = image_numpy.shape + # + # if aspect_ratio > 1.0: + # image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC) + # if aspect_ratio < 1.0: + # image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC) + + image_pil.save(image_path) + + +def print_numpy(x, val=True, shp=False): + """Print the mean, min, max, median, std, and size of a numpy array + + Parameters: + val (bool) -- if print the values of the numpy array + shp (bool) -- if print the shape of the numpy array + """ + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + + +def mkdirs(paths): + """create empty directories if they don't exist + + Parameters: + paths (str list) -- a list of directory paths + """ + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + """create a single empty directory if it didn't exist + + Parameters: + path (str) -- a single directory path + """ + if not os.path.exists(path): + os.makedirs(path) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart/LICENSE new file mode 100644 index 00000000..16a9d56a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Caroline Chan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart/__init__.py new file mode 100644 index 00000000..d42dc501 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart/__init__.py @@ -0,0 +1,141 @@ +import os +import warnings + +import cv2 +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from PIL import Image + +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, HF_MODEL_NAME + +norm_layer = nn.InstanceNorm2d + + +class ResidualBlock(nn.Module): + def __init__(self, in_features): + super(ResidualBlock, self).__init__() + + conv_block = [ nn.ReflectionPad2d(1), + nn.Conv2d(in_features, in_features, 3), + norm_layer(in_features), + nn.ReLU(inplace=True), + nn.ReflectionPad2d(1), + nn.Conv2d(in_features, in_features, 3), + norm_layer(in_features) + ] + + self.conv_block = nn.Sequential(*conv_block) + + def forward(self, x): + return x + self.conv_block(x) + + +class Generator(nn.Module): + def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True): + super(Generator, self).__init__() + + # Initial convolution block + model0 = [ nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, 64, 7), + norm_layer(64), + nn.ReLU(inplace=True) ] + self.model0 = nn.Sequential(*model0) + + # Downsampling + model1 = [] + in_features = 64 + out_features = in_features*2 + for _ in range(2): + model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), + norm_layer(out_features), + nn.ReLU(inplace=True) ] + in_features = out_features + out_features = in_features*2 + self.model1 = nn.Sequential(*model1) + + model2 = [] + # Residual blocks + for _ in range(n_residual_blocks): + model2 += [ResidualBlock(in_features)] + self.model2 = nn.Sequential(*model2) + + # Upsampling + model3 = [] + out_features = in_features//2 + for _ in range(2): + model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), + norm_layer(out_features), + nn.ReLU(inplace=True) ] + in_features = out_features + out_features = in_features//2 + self.model3 = nn.Sequential(*model3) + + # Output layer + model4 = [ nn.ReflectionPad2d(3), + nn.Conv2d(64, output_nc, 7)] + if sigmoid: + model4 += [nn.Sigmoid()] + + self.model4 = nn.Sequential(*model4) + + def forward(self, x, cond=None): + out = self.model0(x) + out = self.model1(out) + out = self.model2(out) + out = self.model3(out) + out = self.model4(out) + + return out + + +class LineartDetector: + def __init__(self, model, coarse_model): + self.model = model + self.model_coarse = coarse_model + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="sk_model.pth", coarse_filename="sk_model2.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + coarse_model_path = custom_hf_download(pretrained_model_or_path, coarse_filename) + + model = Generator(3, 1, 3) + model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) + model.eval() + + coarse_model = Generator(3, 1, 3) + coarse_model.load_state_dict(torch.load(coarse_model_path, map_location=torch.device('cpu'))) + coarse_model.eval() + + return cls(model, coarse_model) + + def to(self, device): + self.model.to(device) + self.model_coarse.to(device) + self.device = device + return self + + def __call__(self, input_image, coarse=False, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + model = self.model_coarse if coarse else self.model + assert detected_map.ndim == 3 + with torch.no_grad(): + image = torch.from_numpy(detected_map).float().to(self.device) + image = image / 255.0 + image = rearrange(image, 'h w c -> 1 c h w') + line = model(image)[0][0] + + line = line.cpu().numpy() + line = (line * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = HWC3(line) + detected_map = remove_pad(255 - detected_map) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_anime/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_anime/LICENSE new file mode 100644 index 00000000..16a9d56a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_anime/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Caroline Chan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_anime/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_anime/__init__.py new file mode 100644 index 00000000..f42dfeb3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_anime/__init__.py @@ -0,0 +1,167 @@ +import functools +import os +import warnings + +import cv2 +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from huggingface_hub import hf_hub_download +from PIL import Image + +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, HF_MODEL_NAME + + +class UnetGenerator(nn.Module): + """Create a Unet-based generator""" + + def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(UnetGenerator, self).__init__() + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer + for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + # gradually reduce the number of filters from ngf * 8 to ngf + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer + + def forward(self, input): + """Standard forward""" + return self.model(input) + + +class UnetSkipConnectionBlock(nn.Module): + """Defines the Unet submodule with skip connection. + X -------------------identity---------------------- + |-- downsampling -- |submodule| -- upsampling --| + """ + + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet submodule with skip connections. + Parameters: + outer_nc (int) -- the number of filters in the outer conv layer + inner_nc (int) -- the number of filters in the inner conv layer + input_nc (int) -- the number of channels in input images/features + submodule (UnetSkipConnectionBlock) -- previously defined submodules + outermost (bool) -- if this module is the outermost module + innermost (bool) -- if this module is the innermost module + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers. + """ + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: # add skip connections + return torch.cat([x, self.model(x)], 1) + + +class LineartAnimeDetector: + def __init__(self, model): + self.model = model + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="netG.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False) + ckpt = torch.load(model_path) + for key in list(ckpt.keys()): + if 'module.' in key: + ckpt[key.replace('module.', '')] = ckpt[key] + del ckpt[key] + net.load_state_dict(ckpt) + net.eval() + + return cls(net) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + H, W, C = input_image.shape + Hn = 256 * int(np.ceil(float(H) / 256.0)) + Wn = 256 * int(np.ceil(float(W) / 256.0)) + input_image = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC) + + with torch.no_grad(): + image_feed = torch.from_numpy(input_image).float().to(self.device) + image_feed = image_feed / 127.5 - 1.0 + image_feed = rearrange(image_feed, 'h w c -> 1 c h w') + + line = self.model(image_feed)[0, 0] * 127.5 + 127.5 + line = line.cpu().numpy() + line = line.clip(0, 255).astype(np.uint8) + + #A1111 uses INTER AREA for downscaling so ig that is the best choice + detected_map = cv2.resize(HWC3(line), (W, H), interpolation=cv2.INTER_AREA) + detected_map = remove_pad(255 - detected_map) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_standard/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_standard/__init__.py new file mode 100644 index 00000000..7932e733 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/lineart_standard/__init__.py @@ -0,0 +1,21 @@ +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import resize_image_with_pad, common_input_validate, HWC3 + +class LineartStandardDetector: + def __call__(self, input_image=None, guassian_sigma=6.0, intensity_threshold=8, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + x = input_image.astype(np.float32) + g = cv2.GaussianBlur(x, (0, 0), guassian_sigma) + intensity = np.min(g - x, axis=2).clip(0, 255) + intensity /= max(16, np.median(intensity[intensity > intensity_threshold])) + intensity *= 127 + detected_map = intensity.clip(0, 255).astype(np.uint8) + + detected_map = HWC3(remove_pad(detected_map)) + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/LICENSE new file mode 100644 index 00000000..bdca75a5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Miaomiao Li + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/__init__.py new file mode 100644 index 00000000..16dea641 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/__init__.py @@ -0,0 +1,63 @@ +# MangaLineExtraction_PyTorch +# https://github.com/ljsabc/MangaLineExtraction_PyTorch + +#NOTE: This preprocessor is designed to work with lineart_anime ControlNet so the result will be white lines on black canvas + +import torch +import numpy as np +import os +import cv2 +from einops import rearrange +from .model_torch import res_skip +from PIL import Image +import warnings + +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, HF_MODEL_NAME + +class LineartMangaDetector: + def __init__(self, model): + self.model = model + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="erika.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + + net = res_skip() + ckpt = torch.load(model_path) + for key in list(ckpt.keys()): + if 'module.' in key: + ckpt[key.replace('module.', '')] = ckpt[key] + del ckpt[key] + net.load_state_dict(ckpt) + net.eval() + return cls(net) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, 256 * int(np.ceil(float(detect_resolution) / 256.0)), upscale_method) + + img = cv2.cvtColor(detected_map, cv2.COLOR_RGB2GRAY) + with torch.no_grad(): + image_feed = torch.from_numpy(img).float().to(self.device) + image_feed = rearrange(image_feed, 'h w -> 1 1 h w') + + line = self.model(image_feed) + line = line.cpu().numpy()[0,0,:,:] + line[line > 255] = 255 + line[line < 0] = 0 + + line = line.astype(np.uint8) + + detected_map = HWC3(line) + detected_map = remove_pad(255 - detected_map) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/model_torch.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/model_torch.py new file mode 100644 index 00000000..de5828cc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/manga_line/model_torch.py @@ -0,0 +1,196 @@ +import torch.nn as nn +import numpy as np + +#torch.set_printoptions(precision=10) + + +class _bn_relu_conv(nn.Module): + def __init__(self, in_filters, nb_filters, fw, fh, subsample=1): + super(_bn_relu_conv, self).__init__() + self.model = nn.Sequential( + nn.BatchNorm2d(in_filters, eps=1e-3), + nn.LeakyReLU(0.2), + nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2), padding_mode='zeros') + ) + + def forward(self, x): + return self.model(x) + + # the following are for debugs + print("****", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape) + for i,layer in enumerate(self.model): + if i != 2: + x = layer(x) + else: + x = layer(x) + #x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0) + print("____", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape) + print(x[0]) + return x + + +class _u_bn_relu_conv(nn.Module): + def __init__(self, in_filters, nb_filters, fw, fh, subsample=1): + super(_u_bn_relu_conv, self).__init__() + self.model = nn.Sequential( + nn.BatchNorm2d(in_filters, eps=1e-3), + nn.LeakyReLU(0.2), + nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2)), + nn.Upsample(scale_factor=2, mode='nearest') + ) + + def forward(self, x): + return self.model(x) + + + +class _shortcut(nn.Module): + def __init__(self, in_filters, nb_filters, subsample=1): + super(_shortcut, self).__init__() + self.process = False + self.model = None + if in_filters != nb_filters or subsample != 1: + self.process = True + self.model = nn.Sequential( + nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample) + ) + + def forward(self, x, y): + #print(x.size(), y.size(), self.process) + if self.process: + y0 = self.model(x) + #print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape) + return y0 + y + else: + #print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape) + return x + y + +class _u_shortcut(nn.Module): + def __init__(self, in_filters, nb_filters, subsample): + super(_u_shortcut, self).__init__() + self.process = False + self.model = None + if in_filters != nb_filters: + self.process = True + self.model = nn.Sequential( + nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample, padding_mode='zeros'), + nn.Upsample(scale_factor=2, mode='nearest') + ) + + def forward(self, x, y): + if self.process: + return self.model(x) + y + else: + return x + y + + +class basic_block(nn.Module): + def __init__(self, in_filters, nb_filters, init_subsample=1): + super(basic_block, self).__init__() + self.conv1 = _bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample) + self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3) + self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample) + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.residual(x1) + return self.shortcut(x, x2) + +class _u_basic_block(nn.Module): + def __init__(self, in_filters, nb_filters, init_subsample=1): + super(_u_basic_block, self).__init__() + self.conv1 = _u_bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample) + self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3) + self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample) + + def forward(self, x): + y = self.residual(self.conv1(x)) + return self.shortcut(x, y) + + +class _residual_block(nn.Module): + def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False): + super(_residual_block, self).__init__() + layers = [] + for i in range(repetitions): + init_subsample = 1 + if i == repetitions - 1 and not is_first_layer: + init_subsample = 2 + if i == 0: + l = basic_block(in_filters=in_filters, nb_filters=nb_filters, init_subsample=init_subsample) + else: + l = basic_block(in_filters=nb_filters, nb_filters=nb_filters, init_subsample=init_subsample) + layers.append(l) + + self.model = nn.Sequential(*layers) + + def forward(self, x): + return self.model(x) + + +class _upsampling_residual_block(nn.Module): + def __init__(self, in_filters, nb_filters, repetitions): + super(_upsampling_residual_block, self).__init__() + layers = [] + for i in range(repetitions): + l = None + if i == 0: + l = _u_basic_block(in_filters=in_filters, nb_filters=nb_filters)#(input) + else: + l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)#(input) + layers.append(l) + + self.model = nn.Sequential(*layers) + + def forward(self, x): + return self.model(x) + + +class res_skip(nn.Module): + + def __init__(self): + super(res_skip, self).__init__() + self.block0 = _residual_block(in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True)#(input) + self.block1 = _residual_block(in_filters=24, nb_filters=48, repetitions=3)#(block0) + self.block2 = _residual_block(in_filters=48, nb_filters=96, repetitions=5)#(block1) + self.block3 = _residual_block(in_filters=96, nb_filters=192, repetitions=7)#(block2) + self.block4 = _residual_block(in_filters=192, nb_filters=384, repetitions=12)#(block3) + + self.block5 = _upsampling_residual_block(in_filters=384, nb_filters=192, repetitions=7)#(block4) + self.res1 = _shortcut(in_filters=192, nb_filters=192)#(block3, block5, subsample=(1,1)) + + self.block6 = _upsampling_residual_block(in_filters=192, nb_filters=96, repetitions=5)#(res1) + self.res2 = _shortcut(in_filters=96, nb_filters=96)#(block2, block6, subsample=(1,1)) + + self.block7 = _upsampling_residual_block(in_filters=96, nb_filters=48, repetitions=3)#(res2) + self.res3 = _shortcut(in_filters=48, nb_filters=48)#(block1, block7, subsample=(1,1)) + + self.block8 = _upsampling_residual_block(in_filters=48, nb_filters=24, repetitions=2)#(res3) + self.res4 = _shortcut(in_filters=24, nb_filters=24)#(block0,block8, subsample=(1,1)) + + self.block9 = _residual_block(in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True)#(res4) + self.conv15 = _bn_relu_conv(in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1)#(block7) + + def forward(self, x): + x0 = self.block0(x) + x1 = self.block1(x0) + x2 = self.block2(x1) + x3 = self.block3(x2) + x4 = self.block4(x3) + + x5 = self.block5(x4) + res1 = self.res1(x3, x5) + + x6 = self.block6(res1) + res2 = self.res2(x2, x6) + + x7 = self.block7(res2) + res3 = self.res3(x1, x7) + + x8 = self.block8(res3) + res4 = self.res4(x0, x8) + + x9 = self.block9(res4) + y = self.conv15(x9) + + return y \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mediapipe_face/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mediapipe_face/__init__.py new file mode 100644 index 00000000..607db69d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mediapipe_face/__init__.py @@ -0,0 +1,31 @@ +import warnings +from typing import Union + +import cv2 +import numpy as np +from PIL import Image + +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad +from .mediapipe_face_common import generate_annotation + + +class MediapipeFaceDetector: + def __call__(self, + input_image: Union[np.ndarray, Image.Image] = None, + max_faces: int = 1, + min_confidence: float = 0.5, + output_type: str = "pil", + detect_resolution: int = 512, + image_resolution: int = 512, + upscale_method="INTER_CUBIC", + **kwargs): + + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + detected_map = generate_annotation(detected_map, max_faces, min_confidence) + detected_map = remove_pad(HWC3(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mediapipe_face/mediapipe_face_common.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mediapipe_face/mediapipe_face_common.py new file mode 100644 index 00000000..32eeaf74 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mediapipe_face/mediapipe_face_common.py @@ -0,0 +1,156 @@ +from typing import Mapping +import warnings + +import mediapipe as mp +import numpy + +if mp: + mp_drawing = mp.solutions.drawing_utils + mp_drawing_styles = mp.solutions.drawing_styles + mp_face_detection = mp.solutions.face_detection # Only for counting faces. + mp_face_mesh = mp.solutions.face_mesh + mp_face_connections = mp.solutions.face_mesh_connections.FACEMESH_TESSELATION + mp_hand_connections = mp.solutions.hands_connections.HAND_CONNECTIONS + mp_body_connections = mp.solutions.pose_connections.POSE_CONNECTIONS + + DrawingSpec = mp.solutions.drawing_styles.DrawingSpec + PoseLandmark = mp.solutions.drawing_styles.PoseLandmark + + min_face_size_pixels: int = 64 + f_thick = 2 + f_rad = 1 + right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad) + right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad) + right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad) + left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad) + left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad) + left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad) + mouth_draw = DrawingSpec(color=(10, 180, 10), thickness=f_thick, circle_radius=f_rad) + head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad) + + # mp_face_mesh.FACEMESH_CONTOURS has all the items we care about. + face_connection_spec = {} + for edge in mp_face_mesh.FACEMESH_FACE_OVAL: + face_connection_spec[edge] = head_draw + for edge in mp_face_mesh.FACEMESH_LEFT_EYE: + face_connection_spec[edge] = left_eye_draw + for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW: + face_connection_spec[edge] = left_eyebrow_draw + # for edge in mp_face_mesh.FACEMESH_LEFT_IRIS: + # face_connection_spec[edge] = left_iris_draw + for edge in mp_face_mesh.FACEMESH_RIGHT_EYE: + face_connection_spec[edge] = right_eye_draw + for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW: + face_connection_spec[edge] = right_eyebrow_draw + # for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS: + # face_connection_spec[edge] = right_iris_draw + for edge in mp_face_mesh.FACEMESH_LIPS: + face_connection_spec[edge] = mouth_draw + iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw} + + +def draw_pupils(image, landmark_list, drawing_spec, halfwidth: int = 2): + """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all + landmarks. Until our PR is merged into mediapipe, we need this separate method.""" + if len(image.shape) != 3: + raise ValueError("Input image must be H,W,C.") + image_rows, image_cols, image_channels = image.shape + if image_channels != 3: # BGR channels + raise ValueError('Input image must contain three channel bgr data.') + for idx, landmark in enumerate(landmark_list.landmark): + if ( + (landmark.HasField('visibility') and landmark.visibility < 0.9) or + (landmark.HasField('presence') and landmark.presence < 0.5) + ): + continue + if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0: + continue + image_x = int(image_cols*landmark.x) + image_y = int(image_rows*landmark.y) + draw_color = None + if isinstance(drawing_spec, Mapping): + if drawing_spec.get(idx) is None: + continue + else: + draw_color = drawing_spec[idx].color + elif isinstance(drawing_spec, DrawingSpec): + draw_color = drawing_spec.color + image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color + + +def reverse_channels(image): + """Given a numpy array in RGB form, convert to BGR. Will also convert from BGR to RGB.""" + # im[:,:,::-1] is a neat hack to convert BGR to RGB by reversing the indexing order. + # im[:,:,::[2,1,0]] would also work but makes a copy of the data. + return image[:, :, ::-1] + + +def generate_annotation( + img_rgb, + max_faces: int, + min_confidence: float +): + """ + Find up to 'max_faces' inside the provided input image. + If min_face_size_pixels is provided and nonzero it will be used to filter faces that occupy less than this many + pixels in the image. + """ + with mp_face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=max_faces, + refine_landmarks=True, + min_detection_confidence=min_confidence, + ) as facemesh: + img_height, img_width, img_channels = img_rgb.shape + assert(img_channels == 3) + + results = facemesh.process(img_rgb).multi_face_landmarks + + if results is None: + print("No faces detected in controlnet image for Mediapipe face annotator.") + return numpy.zeros_like(img_rgb) + + # Filter faces that are too small + filtered_landmarks = [] + for lm in results: + landmarks = lm.landmark + face_rect = [ + landmarks[0].x, + landmarks[0].y, + landmarks[0].x, + landmarks[0].y, + ] # Left, up, right, down. + for i in range(len(landmarks)): + face_rect[0] = min(face_rect[0], landmarks[i].x) + face_rect[1] = min(face_rect[1], landmarks[i].y) + face_rect[2] = max(face_rect[2], landmarks[i].x) + face_rect[3] = max(face_rect[3], landmarks[i].y) + if min_face_size_pixels > 0: + face_width = abs(face_rect[2] - face_rect[0]) + face_height = abs(face_rect[3] - face_rect[1]) + face_width_pixels = face_width * img_width + face_height_pixels = face_height * img_height + face_size = min(face_width_pixels, face_height_pixels) + if face_size >= min_face_size_pixels: + filtered_landmarks.append(lm) + else: + filtered_landmarks.append(lm) + + # Annotations are drawn in BGR for some reason, but we don't need to flip a zero-filled image at the start. + empty = numpy.zeros_like(img_rgb) + + # Draw detected faces: + for face_landmarks in filtered_landmarks: + mp_drawing.draw_landmarks( + empty, + face_landmarks, + connections=face_connection_spec.keys(), + landmark_drawing_spec=None, + connection_drawing_spec=face_connection_spec + ) + draw_pupils(empty, face_landmarks, iris_landmark_spec, 2) + + # Flip BGR back to RGB. + empty = reverse_channels(empty).copy() + + return empty \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/__init__.py new file mode 100644 index 00000000..c7a35976 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/__init__.py @@ -0,0 +1,48 @@ +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import resize_image_with_pad, common_input_validate, HWC3, custom_hf_download, MESH_GRAPHORMER_MODEL_NAME +from custom_controlnet_aux.mesh_graphormer.pipeline import MeshGraphormerMediapipe, args +import random, torch + +def set_seed(seed, n_gpu): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if n_gpu > 0: + torch.cuda.manual_seed_all(seed) + +class MeshGraphormerDetector: + def __init__(self, pipeline): + self.pipeline = pipeline + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=MESH_GRAPHORMER_MODEL_NAME, filename="graphormer_hand_state_dict.bin", hrnet_filename="hrnetv2_w64_imagenet_pretrained.pth", detect_thr=0.6, presence_thr=0.6): + args.resume_checkpoint = custom_hf_download(pretrained_model_or_path, filename) + args.hrnet_checkpoint = custom_hf_download(pretrained_model_or_path, hrnet_filename) + pipeline = MeshGraphormerMediapipe(args, detect_thr=detect_thr, presence_thr=presence_thr) + return cls(pipeline) + + def to(self, device): + self.pipeline._model.to(device) + self.pipeline.mano_model.to(device) + self.pipeline.mano_model.layer.to(device) + return self + + def __call__(self, input_image=None, mask_bbox_padding=30, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", seed=88, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + set_seed(seed, 0) + depth_map, mask, info = self.pipeline.get_depth(input_image, mask_bbox_padding) + if depth_map is None: + depth_map = np.zeros_like(input_image) + mask = np.zeros_like(input_image) + + #The hand is small + depth_map, mask = HWC3(depth_map), HWC3(mask) + depth_map, remove_pad = resize_image_with_pad(depth_map, detect_resolution, upscale_method) + depth_map = remove_pad(depth_map) + if output_type == "pil": + depth_map = Image.fromarray(depth_map) + mask = Image.fromarray(mask) + + return depth_map, mask, info diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml new file mode 100644 index 00000000..65d71033 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml @@ -0,0 +1,92 @@ +GPUS: (0,1,2,3) +LOG_DIR: 'log/' +DATA_DIR: '' +OUTPUT_DIR: 'output/' +WORKERS: 4 +PRINT_FREQ: 1000 + +MODEL: + NAME: cls_hrnet + IMAGE_SIZE: + - 224 + - 224 + EXTRA: + STAGE1: + NUM_MODULES: 1 + NUM_RANCHES: 1 + BLOCK: BOTTLENECK + NUM_BLOCKS: + - 4 + NUM_CHANNELS: + - 64 + FUSE_METHOD: SUM + STAGE2: + NUM_MODULES: 1 + NUM_BRANCHES: 2 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + FUSE_METHOD: SUM + STAGE3: + NUM_MODULES: 4 + NUM_BRANCHES: 3 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + - 256 + FUSE_METHOD: SUM + STAGE4: + NUM_MODULES: 3 + NUM_BRANCHES: 4 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + - 256 + - 512 + FUSE_METHOD: SUM +CUDNN: + BENCHMARK: true + DETERMINISTIC: false + ENABLED: true +DATASET: + DATASET: 'imagenet' + DATA_FORMAT: 'jpg' + ROOT: 'data/imagenet/' + TEST_SET: 'val' + TRAIN_SET: 'train' +TEST: + BATCH_SIZE_PER_GPU: 32 + MODEL_FILE: '' +TRAIN: + BATCH_SIZE_PER_GPU: 32 + BEGIN_EPOCH: 0 + END_EPOCH: 100 + RESUME: true + LR_FACTOR: 0.1 + LR_STEP: + - 30 + - 60 + - 90 + OPTIMIZER: sgd + LR: 0.05 + WD: 0.0001 + MOMENTUM: 0.9 + NESTEROV: true + SHUFFLE: true +DEBUG: + DEBUG: false diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/depth_preprocessor.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/depth_preprocessor.py new file mode 100644 index 00000000..496313ad --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/depth_preprocessor.py @@ -0,0 +1,6 @@ +class Preprocessor: + def __init__(self) -> None: + pass + + def get_depth(self, input_dir, file_name): + return \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/hand_landmarker.task b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/hand_landmarker.task new file mode 100644 index 00000000..0d53faf3 Binary files /dev/null and b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/hand_landmarker.task differ diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/pipeline.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/pipeline.py new file mode 100644 index 00000000..325770e4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mesh_graphormer/pipeline.py @@ -0,0 +1,472 @@ +import os +import torch +import gc +import numpy as np +from custom_controlnet_aux.mesh_graphormer.depth_preprocessor import Preprocessor + +import torchvision.models as models +from custom_mesh_graphormer.modeling.bert import BertConfig, Graphormer +from custom_mesh_graphormer.modeling.bert import Graphormer_Hand_Network as Graphormer_Network +from custom_mesh_graphormer.modeling._mano import MANO, Mesh +from custom_mesh_graphormer.modeling.hrnet.hrnet_cls_net_gridfeat import get_cls_net_gridfeat +from custom_mesh_graphormer.modeling.hrnet.config import config as hrnet_config +from custom_mesh_graphormer.modeling.hrnet.config import update_config as hrnet_update_config +from custom_mesh_graphormer.utils.miscellaneous import set_seed +from argparse import Namespace +from pathlib import Path +import cv2 +from torchvision import transforms +import numpy as np +import cv2 +from trimesh import Trimesh +from trimesh.ray.ray_triangle import RayMeshIntersector +import mediapipe as mp +from mediapipe.tasks import python +from mediapipe.tasks.python import vision +from torchvision import transforms +from pathlib import Path +from custom_controlnet_aux.util import custom_hf_download +import custom_mesh_graphormer +from comfy.model_management import soft_empty_cache +from packaging import version + +args = Namespace( + num_workers=4, + img_scale_factor=1, + image_file_or_path=os.path.join('', 'MeshGraphormer', 'samples', 'hand'), + model_name_or_path=str(Path(custom_mesh_graphormer.__file__).parent / "modeling/bert/bert-base-uncased"), + resume_checkpoint=None, + output_dir='output/', + config_name='', + a='hrnet-w64', + arch='hrnet-w64', + num_hidden_layers=4, + hidden_size=-1, + num_attention_heads=4, + intermediate_size=-1, + input_feat_dim='2051,512,128', + hidden_feat_dim='1024,256,64', + which_gcn='0,0,1', + mesh_type='hand', + run_eval_only=True, + device="cpu", + seed=88, + hrnet_checkpoint=custom_hf_download("hr16/ControlNet-HandRefiner-pruned", 'hrnetv2_w64_imagenet_pretrained.pth') +) + +#Since mediapipe v0.10.5, the hand category has been correct +if version.parse(mp.__version__) >= version.parse('0.10.5'): + true_hand_category = {"Right": "right", "Left": "left"} +else: + true_hand_category = {"Right": "left", "Left": "right"} + +class MeshGraphormerMediapipe(Preprocessor): + def __init__(self, args=args, detect_thr=0.6, presence_thr=0.6) -> None: + #global logger + # Setup CUDA, GPU & distributed training + args.num_gpus = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + os.environ['OMP_NUM_THREADS'] = str(args.num_workers) + print('set os.environ[OMP_NUM_THREADS] to {}'.format(os.environ['OMP_NUM_THREADS'])) + + #mkdir(args.output_dir) + #logger = setup_logger("Graphormer", args.output_dir, get_rank()) + set_seed(args.seed, args.num_gpus) + #logger.info("Using {} GPUs".format(args.num_gpus)) + + # Mesh and MANO utils + mano_model = MANO().to(args.device) + mano_model.layer = mano_model.layer.to(args.device) + mesh_sampler = Mesh(device=args.device) + + # Renderer for visualization + # renderer = Renderer(faces=mano_model.face) + + # Load pretrained model + trans_encoder = [] + + input_feat_dim = [int(item) for item in args.input_feat_dim.split(',')] + hidden_feat_dim = [int(item) for item in args.hidden_feat_dim.split(',')] + output_feat_dim = input_feat_dim[1:] + [3] + + # which encoder block to have graph convs + which_blk_graph = [int(item) for item in args.which_gcn.split(',')] + + if args.run_eval_only==True and args.resume_checkpoint!=None and args.resume_checkpoint!='None' and 'state_dict' not in args.resume_checkpoint: + # if only run eval, load checkpoint + #logger.info("Evaluation: Loading from checkpoint {}".format(args.resume_checkpoint)) + _model = torch.load(args.resume_checkpoint) + + else: + # init three transformer-encoder blocks in a loop + for i in range(len(output_feat_dim)): + config_class, model_class = BertConfig, Graphormer + config = config_class.from_pretrained(args.config_name if args.config_name \ + else args.model_name_or_path, attn_implementation="eager") + + config.output_attentions = False + config.img_feature_dim = input_feat_dim[i] + config.output_feature_dim = output_feat_dim[i] + args.hidden_size = hidden_feat_dim[i] + args.intermediate_size = int(args.hidden_size*2) + + if which_blk_graph[i]==1: + config.graph_conv = True + #logger.info("Add Graph Conv") + else: + config.graph_conv = False + + config.mesh_type = args.mesh_type + + # update model structure if specified in arguments + update_params = ['num_hidden_layers', 'hidden_size', 'num_attention_heads', 'intermediate_size'] + for idx, param in enumerate(update_params): + arg_param = getattr(args, param) + config_param = getattr(config, param) + if arg_param > 0 and arg_param != config_param: + #logger.info("Update config parameter {}: {} -> {}".format(param, config_param, arg_param)) + setattr(config, param, arg_param) + + # init a transformer encoder and append it to a list + assert config.hidden_size % config.num_attention_heads == 0 + model = model_class(config=config) + #logger.info("Init model from scratch.") + trans_encoder.append(model) + + # create backbone model + if args.arch=='hrnet': + hrnet_yaml = Path(__file__).parent / 'cls_hrnet_w40_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = args.hrnet_checkpoint + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + #logger.info('=> loading hrnet-v2-w40 model') + elif args.arch=='hrnet-w64': + hrnet_yaml = Path(__file__).parent / 'cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = args.hrnet_checkpoint + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + #logger.info('=> loading hrnet-v2-w64 model') + else: + print("=> using pre-trained model '{}'".format(args.arch)) + backbone = models.__dict__[args.arch](pretrained=True) + # remove the last fc layer + backbone = torch.nn.Sequential(*list(backbone.children())[:-1]) + + trans_encoder = torch.nn.Sequential(*trans_encoder) + total_params = sum(p.numel() for p in trans_encoder.parameters()) + #logger.info('Graphormer encoders total parameters: {}'.format(total_params)) + backbone_total_params = sum(p.numel() for p in backbone.parameters()) + #logger.info('Backbone total parameters: {}'.format(backbone_total_params)) + + # build end-to-end Graphormer network (CNN backbone + multi-layer Graphormer encoder) + _model = Graphormer_Network(args, config, backbone, trans_encoder) + + if args.resume_checkpoint!=None and args.resume_checkpoint!='None': + # for fine-tuning or resume training or inference, load weights from checkpoint + #logger.info("Loading state dict from checkpoint {}".format(args.resume_checkpoint)) + # workaround approach to load sparse tensor in graph conv. + state_dict = torch.load(args.resume_checkpoint) + _model.load_state_dict(state_dict, strict=False) + del state_dict + gc.collect() + soft_empty_cache() + + # update configs to enable attention outputs + setattr(_model.trans_encoder[-1].config,'output_attentions', True) + setattr(_model.trans_encoder[-1].config,'output_hidden_states', True) + _model.trans_encoder[-1].bert.encoder.output_attentions = True + _model.trans_encoder[-1].bert.encoder.output_hidden_states = True + for iter_layer in range(4): + _model.trans_encoder[-1].bert.encoder.layer[iter_layer].attention.self.output_attentions = True + for inter_block in range(3): + setattr(_model.trans_encoder[-1].config,'device', args.device) + + _model.to(args.device) + self._model = _model + self.mano_model = mano_model + self.mesh_sampler = mesh_sampler + + self.transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + #Fix File loading is not yet supported on Windows + with open(str( Path(__file__).parent / "hand_landmarker.task" ), 'rb') as file: + model_data = file.read() + base_options = python.BaseOptions(model_asset_buffer=model_data) + options = vision.HandLandmarkerOptions(base_options=base_options, + min_hand_detection_confidence=detect_thr, + min_hand_presence_confidence=presence_thr, + min_tracking_confidence=0.6, + num_hands=2) + + self.detector = vision.HandLandmarker.create_from_options(options) + + + def get_rays(self, W, H, fx, fy, cx, cy, c2w_t, center_pixels): # rot = I + + j, i = np.meshgrid(np.arange(H, dtype=np.float32), np.arange(W, dtype=np.float32)) + if center_pixels: + i = i.copy() + 0.5 + j = j.copy() + 0.5 + + directions = np.stack([(i - cx) / fx, (j - cy) / fy, np.ones_like(i)], -1) + directions /= np.linalg.norm(directions, axis=-1, keepdims=True) + + rays_o = np.expand_dims(c2w_t,0).repeat(H*W, 0) + + rays_d = directions # (H, W, 3) + rays_d = (rays_d / np.linalg.norm(rays_d, axis=-1, keepdims=True)).reshape(-1,3) + + return rays_o, rays_d + + def get_mask_bounding_box(self, extrema, H, W, padding=30, dynamic_resize=0.15): + x_min, x_max, y_min, y_max = extrema + bb_xpad = max(int((x_max - x_min + 1) * dynamic_resize), padding) + bb_ypad = max(int((y_max - y_min + 1) * dynamic_resize), padding) + bbx_min = np.max((x_min - bb_xpad, 0)) + bbx_max = np.min((x_max + bb_xpad, W-1)) + bby_min = np.max((y_min - bb_ypad, 0)) + bby_max = np.min((y_max + bb_ypad, H-1)) + return bbx_min, bbx_max, bby_min, bby_max + + def run_inference(self, img, Graphormer_model, mano, mesh_sampler, scale, crop_len): + global args + H, W = int(crop_len), int(crop_len) + Graphormer_model.eval() + mano.eval() + device = next(Graphormer_model.parameters()).device + with torch.no_grad(): + img_tensor = self.transform(img) + batch_imgs = torch.unsqueeze(img_tensor, 0).to(device) + + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub, pred_vertices, hidden_states, att = Graphormer_model(batch_imgs, mano, mesh_sampler) + + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_mesh = mano.get_3d_joints(pred_vertices) + # obtain 2d joints, which are projected from 3d joints of mesh + #pred_2d_joints_from_mesh = orthographic_projection(pred_3d_joints_from_mesh.contiguous(), pred_camera.contiguous()) + #pred_2d_coarse_vertices_from_mesh = orthographic_projection(pred_vertices_sub.contiguous(), pred_camera.contiguous()) + pred_camera = pred_camera.cpu() + pred_vertices = pred_vertices.cpu() + mesh = Trimesh(vertices=pred_vertices[0], faces=mano.face) + res = crop_len + focal_length = 1000 * scale + camera_t = np.array([-pred_camera[1], -pred_camera[2], -2*focal_length/(res * pred_camera[0] +1e-9)]) + pred_3d_joints_camera = pred_3d_joints_from_mesh.cpu()[0] - camera_t + z_3d_dist = pred_3d_joints_camera[:,2].clone() + + pred_2d_joints_img_space = ((pred_3d_joints_camera/z_3d_dist[:,None]) * np.array((focal_length, focal_length, 1)))[:,:2] + np.array((W/2, H/2)) + + rays_o, rays_d = self.get_rays(W, H, focal_length, focal_length, W/2, H/2, camera_t, True) + coords = np.array(list(np.ndindex(H,W))).reshape(H,W,-1).transpose(1,0,2).reshape(-1,2) + intersector = RayMeshIntersector(mesh) + points, index_ray, _ = intersector.intersects_location(rays_o, rays_d, multiple_hits=False) + + tri_index = intersector.intersects_first(rays_o, rays_d) + + tri_index = tri_index[index_ray] + + assert len(index_ray) == len(tri_index) + + discriminator = (np.sum(mesh.face_normals[tri_index]* rays_d[index_ray], axis=-1)<= 0) + points = points[discriminator] # ray intesects in interior faces, discard them + + if len(points) == 0: + return None, None + depth = (points + camera_t)[:,-1] + index_ray = index_ray[discriminator] + pixel_ray = coords[index_ray] + + minval = np.min(depth) + maxval = np.max(depth) + depthmap = np.zeros([H,W]) + + depthmap[pixel_ray[:, 0], pixel_ray[:, 1]] = 1.0 - (0.8 * (depth - minval) / (maxval - minval)) + depthmap *= 255 + return depthmap, pred_2d_joints_img_space + + + def get_depth(self, np_image, padding): + info = {} + + # STEP 3: Load the input image. + #https://stackoverflow.com/a/76407270 + image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np_image.copy()) + + # STEP 4: Detect hand landmarks from the input image. + detection_result = self.detector.detect(image) + + handedness_list = detection_result.handedness + hand_landmarks_list = detection_result.hand_landmarks + + raw_image = image.numpy_view() + H, W, C = raw_image.shape + + + # HANDLANDMARKS CAN BE EMPTY, HANDLE THIS! + if len(hand_landmarks_list) == 0: + return None, None, None + raw_image = raw_image[:, :, :3] + + padded_image = np.zeros((H*2, W*2, 3)) + padded_image[int(1/2 * H):int(3/2 * H), int(1/2 * W):int(3/2 * W)] = raw_image + + hand_landmarks_list, handedness_list = zip( + *sorted( + zip(hand_landmarks_list, handedness_list), key=lambda x: x[0][9].z, reverse=True + ) + ) + + padded_depthmap = np.zeros((H*2, W*2)) + mask = np.zeros((H, W)) + crop_boxes = [] + #bboxes = [] + groundtruth_2d_keypoints = [] + hands = [] + depth_failure = False + crop_lens = [] + abs_boxes = [] + + for idx in range(len(hand_landmarks_list)): + hand = true_hand_category[handedness_list[idx][0].category_name] + hands.append(hand) + hand_landmarks = hand_landmarks_list[idx] + handedness = handedness_list[idx] + height, width, _ = raw_image.shape + x_coordinates = [landmark.x for landmark in hand_landmarks] + y_coordinates = [landmark.y for landmark in hand_landmarks] + + # x_min, x_max, y_min, y_max: extrema from mediapipe keypoint detection + x_min = int(min(x_coordinates) * width) + x_max = int(max(x_coordinates) * width) + x_c = (x_min + x_max)//2 + y_min = int(min(y_coordinates) * height) + y_max = int(max(y_coordinates) * height) + y_c = (y_min + y_max)//2 + abs_boxes.append([x_min, x_max, y_min, y_max]) + + #if x_max - x_min < 60 or y_max - y_min < 60: + # continue + + crop_len = (max(x_max - x_min, y_max - y_min) * 1.6) //2 * 2 + + # crop_x_min, crop_x_max, crop_y_min, crop_y_max: bounding box for mesh reconstruction + crop_x_min = int(x_c - (crop_len/2 - 1) + W/2) + crop_x_max = int(x_c + crop_len/2 + W/2) + crop_y_min = int(y_c - (crop_len/2 - 1) + H/2) + crop_y_max = int(y_c + crop_len/2 + H/2) + + cropped = padded_image[crop_y_min:crop_y_max+1, crop_x_min:crop_x_max+1] + crop_boxes.append([crop_y_min, crop_y_max, crop_x_min, crop_x_max]) + crop_lens.append(crop_len) + if hand == "left": + cropped = cv2.flip(cropped, 1) + + if crop_len < 224: + graphormer_input = cv2.resize(cropped, (224, 224), interpolation=cv2.INTER_CUBIC) + else: + graphormer_input = cv2.resize(cropped, (224, 224), interpolation=cv2.INTER_AREA) + scale = crop_len/224 + cropped_depthmap, pred_2d_keypoints = self.run_inference(graphormer_input.astype(np.uint8), self._model, self.mano_model, self.mesh_sampler, scale, int(crop_len)) + + if cropped_depthmap is None: + depth_failure = True + break + #keypoints_image_space = pred_2d_keypoints * (crop_y_max - crop_y_min + 1)/224 + groundtruth_2d_keypoints.append(pred_2d_keypoints) + + if hand == "left": + cropped_depthmap = cv2.flip(cropped_depthmap, 1) + resized_cropped_depthmap = cv2.resize(cropped_depthmap, (int(crop_len), int(crop_len)), interpolation=cv2.INTER_LINEAR) + nonzero_y, nonzero_x = (resized_cropped_depthmap != 0).nonzero() + if len(nonzero_y) == 0 or len(nonzero_x) == 0: + depth_failure = True + break + padded_depthmap[crop_y_min+nonzero_y, crop_x_min+nonzero_x] = resized_cropped_depthmap[nonzero_y, nonzero_x] + + # nonzero stands for nonzero value on the depth map + # coordinates of nonzero depth pixels in original image space + original_nonzero_x = crop_x_min+nonzero_x - int(W/2) + original_nonzero_y = crop_y_min+nonzero_y - int(H/2) + + nonzerox_min = min(np.min(original_nonzero_x), x_min) + nonzerox_max = max(np.max(original_nonzero_x), x_max) + nonzeroy_min = min(np.min(original_nonzero_y), y_min) + nonzeroy_max = max(np.max(original_nonzero_y), y_max) + + bbx_min, bbx_max, bby_min, bby_max = self.get_mask_bounding_box((nonzerox_min, nonzerox_max, nonzeroy_min, nonzeroy_max), H, W, padding) + mask[bby_min:bby_max+1, bbx_min:bbx_max+1] = 1.0 + #bboxes.append([int(bbx_min), int(bbx_max), int(bby_min), int(bby_max)]) + if depth_failure: + #print("cannot detect normal hands") + return None, None, None + depthmap = padded_depthmap[int(1/2 * H):int(3/2 * H), int(1/2 * W):int(3/2 * W)].astype(np.uint8) + mask = (255.0 * mask).astype(np.uint8) + info["groundtruth_2d_keypoints"] = groundtruth_2d_keypoints + info["hands"] = hands + info["crop_boxes"] = crop_boxes + info["crop_lens"] = crop_lens + info["abs_boxes"] = abs_boxes + return depthmap, mask, info + + def get_keypoints(self, img, Graphormer_model, mano, mesh_sampler, scale, crop_len): + global args + H, W = int(crop_len), int(crop_len) + Graphormer_model.eval() + mano.eval() + device = next(Graphormer_model.parameters()).device + with torch.no_grad(): + img_tensor = self.transform(img) + #print(img_tensor) + batch_imgs = torch.unsqueeze(img_tensor, 0).to(device) + + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub, pred_vertices, hidden_states, att = Graphormer_model(batch_imgs, mano, mesh_sampler) + + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_mesh = mano.get_3d_joints(pred_vertices) + # obtain 2d joints, which are projected from 3d joints of mesh + #pred_2d_joints_from_mesh = orthographic_projection(pred_3d_joints_from_mesh.contiguous(), pred_camera.contiguous()) + #pred_2d_coarse_vertices_from_mesh = orthographic_projection(pred_vertices_sub.contiguous(), pred_camera.contiguous()) + pred_camera = pred_camera.cpu() + pred_vertices = pred_vertices.cpu() + # + res = crop_len + focal_length = 1000 * scale + camera_t = np.array([-pred_camera[1], -pred_camera[2], -2*focal_length/(res * pred_camera[0] +1e-9)]) + pred_3d_joints_camera = pred_3d_joints_from_mesh.cpu()[0] - camera_t + z_3d_dist = pred_3d_joints_camera[:,2].clone() + pred_2d_joints_img_space = ((pred_3d_joints_camera/z_3d_dist[:,None]) * np.array((focal_length, focal_length, 1)))[:,:2] + np.array((W/2, H/2)) + + return pred_2d_joints_img_space + + + def eval_mpjpe(self, sample, info): + H, W, C = sample.shape + padded_image = np.zeros((H*2, W*2, 3)) + padded_image[int(1/2 * H):int(3/2 * H), int(1/2 * W):int(3/2 * W)] = sample + crop_boxes = info["crop_boxes"] + hands = info["hands"] + groundtruth_2d_keypoints = info["groundtruth_2d_keypoints"] + crop_lens = info["crop_lens"] + pjpe = 0 + for i in range(len(crop_boxes)):#box in crop_boxes: + crop_y_min, crop_y_max, crop_x_min, crop_x_max = crop_boxes[i] + cropped = padded_image[crop_y_min:crop_y_max+1, crop_x_min:crop_x_max+1] + hand = hands[i] + if hand == "left": + cropped = cv2.flip(cropped, 1) + crop_len = crop_lens[i] + scale = crop_len/224 + if crop_len < 224: + graphormer_input = cv2.resize(cropped, (224, 224), interpolation=cv2.INTER_CUBIC) + else: + graphormer_input = cv2.resize(cropped, (224, 224), interpolation=cv2.INTER_AREA) + generated_keypoint = self.get_keypoints(graphormer_input.astype(np.uint8), self._model, self.mano_model, self.mesh_sampler, scale, crop_len) + #generated_keypoint = generated_keypoint * ((crop_y_max - crop_y_min + 1)/224) + pjpe += np.sum(np.sqrt(np.sum(((generated_keypoint - groundtruth_2d_keypoints[i]) ** 2).numpy(), axis=1))) + pass + mpjpe = pjpe/(len(crop_boxes) * 21) + return mpjpe diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/__init__.py new file mode 100644 index 00000000..9a8ed68d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/__init__.py @@ -0,0 +1,124 @@ + +import torch +import os +from pathlib import Path + +CODE_SPACE=Path(os.path.dirname(os.path.abspath(__file__))) + +from custom_mmpkg.custom_mmcv.utils import Config, DictAction +from .mono.model.monodepth_model import get_configured_monodepth_model +from .mono.utils.running import load_ckpt +from .mono.utils.do_test import transform_test_data_scalecano, get_prediction +import numpy as np +from .mono.utils.visualization import vis_surface_normal +from einops import repeat +from PIL import Image +from ..util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, METRIC3D_MODEL_NAME +import re +import matplotlib.pyplot as plt + +def load_model(model_selection, model_path): + if model_selection == "vit-small": + cfg = Config.fromfile(CODE_SPACE / 'mono/configs/HourglassDecoder/vit.raft5.small.py') + elif model_selection == "vit-large": + cfg = Config.fromfile(CODE_SPACE / 'mono/configs/HourglassDecoder/vit.raft5.large.py') + elif model_selection == "vit-giant2": + cfg = Config.fromfile(CODE_SPACE / 'mono/configs/HourglassDecoder/vit.raft5.giant2.py') + else: + raise NotImplementedError(f"metric3d model: {model_selection}") + model = get_configured_monodepth_model(cfg, ) + model, _, _, _ = load_ckpt(model_path, model, strict_match=False) + model.eval() + model = model + return model, cfg + +def gray_to_colormap(img, cmap='rainbow'): + """ + Transfer gray map to matplotlib colormap + """ + assert img.ndim == 2 + + img[img<0] = 0 + mask_invalid = img < 1e-10 + img = img / (img.max() + 1e-8) + norm = plt.Normalize(vmin=0, vmax=1.1) # Use plt.Normalize instead of matplotlib.colors.Normalize + cmap_m = plt.get_cmap(cmap) # Access the colormap directly from plt + map = plt.cm.ScalarMappable(norm=norm, cmap=cmap_m) + colormap = (map.to_rgba(img)[:, :, :3] * 255).astype(np.uint8) + colormap[mask_invalid] = 0 + return colormap + +def predict_depth_normal(model, cfg, np_img, fx=1000.0, fy=1000.0, state_cache={}): + intrinsic = [fx, fy, np_img.shape[1]/2, np_img.shape[0]/2] + rgb_input, cam_models_stacks, pad, label_scale_factor = transform_test_data_scalecano(np_img, intrinsic, cfg.data_basic, device=next(model.parameters()).device) + + with torch.no_grad(): + pred_depth, confidence, output = get_prediction( + model = model, + input = rgb_input.unsqueeze(0), + cam_model = cam_models_stacks, + pad_info = pad, + scale_info = label_scale_factor, + gt_depth = None, + normalize_scale = cfg.data_basic.depth_range[1], + ori_shape=[np_img.shape[0], np_img.shape[1]], + ) + + pred_normal = output['normal_out_list'][0][:, :3, :, :] + H, W = pred_normal.shape[2:] + pred_normal = pred_normal[:, :, pad[0]:H-pad[1], pad[2]:W-pad[3]] + pred_depth = pred_depth[:, :, pad[0]:H-pad[1], pad[2]:W-pad[3] ] + + pred_depth = pred_depth.squeeze().cpu().numpy() + pred_color = gray_to_colormap(pred_depth, 'Greys') + + pred_normal = torch.nn.functional.interpolate(pred_normal, [np_img.shape[0], np_img.shape[1]], mode='bilinear').squeeze() + pred_normal = pred_normal.permute(1,2,0) + pred_color_normal = vis_surface_normal(pred_normal) + pred_normal = pred_normal.cpu().numpy() + + # Storing depth and normal map in state for potential 3D reconstruction + state_cache['depth'] = pred_depth + state_cache['normal'] = pred_normal + state_cache['img'] = np_img + state_cache['intrinsic'] = intrinsic + state_cache['confidence'] = confidence + + return pred_color, pred_color_normal, state_cache + +class Metric3DDetector: + def __init__(self, model, cfg): + self.model = model + self.cfg = cfg + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=METRIC3D_MODEL_NAME, filename="metric_depth_vit_small_800k.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + backbone = re.findall(r"metric_depth_vit_(\w+)_", model_path)[0] + model, cfg = load_model(f'vit-{backbone}', model_path) + return cls(model, cfg) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, fx=1000, fy=1000, output_type=None, upscale_method="INTER_CUBIC", depth_and_normal=True, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + + depth_map, normal_map, _ = predict_depth_normal(self.model, self.cfg, input_image, fx=fx, fy=fy) + # ControlNet uses inverse depth and normal + depth_map, normal_map = depth_map, 255 - normal_map + depth_map, remove_pad = resize_image_with_pad(depth_map, detect_resolution, upscale_method) + normal_map, _ = resize_image_with_pad(normal_map, detect_resolution, upscale_method) + depth_map, normal_map = remove_pad(depth_map), remove_pad(normal_map) + + if output_type == "pil": + depth_map = Image.fromarray(depth_map) + normal_map = Image.fromarray(normal_map) + + if depth_and_normal: + return depth_map, normal_map + else: + return depth_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/convlarge.0.3_150.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/convlarge.0.3_150.py new file mode 100644 index 00000000..37b91c80 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/convlarge.0.3_150.py @@ -0,0 +1,25 @@ +_base_=[ + '../_base_/models/encoder_decoder/convnext_large.hourglassdecoder.py', + '../_base_/datasets/_data_base_.py', + '../_base_/default_runtime.py', + ] + +model = dict( + backbone=dict( + pretrained=False, + ) +) + +# configs of the canonical space +data_basic=dict( + canonical_space = dict( + img_size=(512, 960), + focal_length=1000.0, + ), + depth_range=(0, 1), + depth_normalize=(0.3, 150), + crop_size = (544, 1216), +) + +batchsize_per_gpu = 2 +thread_per_gpu = 4 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/test_kitti_convlarge.0.3_150.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/test_kitti_convlarge.0.3_150.py new file mode 100644 index 00000000..cdd9156b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/test_kitti_convlarge.0.3_150.py @@ -0,0 +1,25 @@ +_base_=[ + '../_base_/models/encoder_decoder/convnext_large.hourglassdecoder.py', + '../_base_/datasets/_data_base_.py', + '../_base_/default_runtime.py', + ] + +model = dict( + backbone=dict( + pretrained=False, + ) +) + +# configs of the canonical space +data_basic=dict( + canonical_space = dict( + img_size=(512, 960), + focal_length=1000.0, + ), + depth_range=(0, 1), + depth_normalize=(0.3, 150), + crop_size = (512, 1088), +) + +batchsize_per_gpu = 2 +thread_per_gpu = 4 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/test_nyu_convlarge.0.3_150.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/test_nyu_convlarge.0.3_150.py new file mode 100644 index 00000000..6601f5cd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/test_nyu_convlarge.0.3_150.py @@ -0,0 +1,25 @@ +_base_=[ + '../_base_/models/encoder_decoder/convnext_large.hourglassdecoder.py', + '../_base_/datasets/_data_base_.py', + '../_base_/default_runtime.py', + ] + +model = dict( + backbone=dict( + pretrained=False, + ) +) + +# configs of the canonical space +data_basic=dict( + canonical_space = dict( + img_size=(512, 960), + focal_length=1000.0, + ), + depth_range=(0, 1), + depth_normalize=(0.3, 150), + crop_size = (480, 1216), +) + +batchsize_per_gpu = 2 +thread_per_gpu = 4 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.giant2.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.giant2.py new file mode 100644 index 00000000..a4a1238b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.giant2.py @@ -0,0 +1,32 @@ +_base_=[ + '../_base_/models/encoder_decoder/dino_vit_giant2_reg.dpt_raft.py', + '../_base_/datasets/_data_base_.py', + '../_base_/default_runtime.py', + ] + +model=dict( + decode_head=dict( + type='RAFTDepthNormalDPT5', + iters=8, + n_downsample=2, + detach=False, + ) +) + + +max_value = 200 +# configs of the canonical space +data_basic=dict( + canonical_space = dict( + # img_size=(540, 960), + focal_length=1000.0, + ), + depth_range=(0, 1), + depth_normalize=(0.1, max_value), + crop_size = (616, 1064), # %28 = 0 + clip_depth_range=(0.1, 200), + vit_size=(616,1064) +) + +batchsize_per_gpu = 1 +thread_per_gpu = 1 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.large.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.large.py new file mode 100644 index 00000000..4e81c2ea --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.large.py @@ -0,0 +1,32 @@ +_base_=[ + '../_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py', + '../_base_/datasets/_data_base_.py', + '../_base_/default_runtime.py', + ] + +model=dict( + decode_head=dict( + type='RAFTDepthNormalDPT5', + iters=8, + n_downsample=2, + detach=False, + ) +) + + +max_value = 200 +# configs of the canonical space +data_basic=dict( + canonical_space = dict( + # img_size=(540, 960), + focal_length=1000.0, + ), + depth_range=(0, 1), + depth_normalize=(0.1, max_value), + crop_size = (616, 1064), # %28 = 0 + clip_depth_range=(0.1, 200), + vit_size=(616,1064) +) + +batchsize_per_gpu = 1 +thread_per_gpu = 1 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.small.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.small.py new file mode 100644 index 00000000..a0a169d4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/HourglassDecoder/vit.raft5.small.py @@ -0,0 +1,32 @@ +_base_=[ + '../_base_/models/encoder_decoder/dino_vit_small_reg.dpt_raft.py', + '../_base_/datasets/_data_base_.py', + '../_base_/default_runtime.py', + ] + +model=dict( + decode_head=dict( + type='RAFTDepthNormalDPT5', + iters=4, + n_downsample=2, + detach=False, + ) +) + + +max_value = 200 +# configs of the canonical space +data_basic=dict( + canonical_space = dict( + # img_size=(540, 960), + focal_length=1000.0, + ), + depth_range=(0, 1), + depth_normalize=(0.1, max_value), + crop_size = (616, 1064), # %28 = 0 + clip_depth_range=(0.1, 200), + vit_size=(616,1064) +) + +batchsize_per_gpu = 1 +thread_per_gpu = 1 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/__init__.py @@ -0,0 +1 @@ + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/_data_base_.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/_data_base_.py new file mode 100644 index 00000000..35f3844f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/_data_base_.py @@ -0,0 +1,13 @@ +# canonical camera setting and basic data setting +# we set it same as the E300 camera (crop version) +# +data_basic=dict( + canonical_space = dict( + img_size=(540, 960), + focal_length=1196.0, + ), + depth_range=(0.9, 150), + depth_normalize=(0.006, 1.001), + crop_size = (512, 960), + clip_depth_range=(0.9, 150), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/datasets/_data_base_.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/datasets/_data_base_.py new file mode 100644 index 00000000..b554444e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/datasets/_data_base_.py @@ -0,0 +1,12 @@ +# canonical camera setting and basic data setting +# +data_basic=dict( + canonical_space = dict( + img_size=(540, 960), + focal_length=1196.0, + ), + depth_range=(0.9, 150), + depth_normalize=(0.006, 1.001), + crop_size = (512, 960), + clip_depth_range=(0.9, 150), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/default_runtime.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/default_runtime.py new file mode 100644 index 00000000..a690b491 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/default_runtime.py @@ -0,0 +1,4 @@ + +load_from = None +cudnn_benchmark = True +test_metrics = ['abs_rel', 'rmse', 'silog', 'delta1', 'delta2', 'delta3','rmse_log', 'log10', 'sq_rel'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/convnext_large.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/convnext_large.py new file mode 100644 index 00000000..5a22f7e1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/convnext_large.py @@ -0,0 +1,16 @@ +#_base_ = ['./_model_base_.py',] + +#'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth' +model = dict( + #type='EncoderDecoderAuxi', + backbone=dict( + type='convnext_large', + pretrained=True, + in_22k=True, + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + checkpoint='data/pretrained_weight_repo/convnext/convnext_large_22k_1k_384.pth', + prefix='backbones.', + out_channels=[192, 384, 768, 1536]), + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_giant2_reg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_giant2_reg.py new file mode 100644 index 00000000..3c1ebc96 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_giant2_reg.py @@ -0,0 +1,7 @@ +model = dict( + backbone=dict( + type='vit_giant2_reg', + prefix='backbones.', + out_channels=[1536, 1536, 1536, 1536], + drop_path_rate = 0.0), + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_large.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_large.py new file mode 100644 index 00000000..843178ed --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_large.py @@ -0,0 +1,7 @@ +model = dict( + backbone=dict( + type='vit_large', + prefix='backbones.', + out_channels=[1024, 1024, 1024, 1024], + drop_path_rate = 0.0), + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_large_reg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_large_reg.py new file mode 100644 index 00000000..25e96747 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_large_reg.py @@ -0,0 +1,7 @@ +model = dict( + backbone=dict( + type='vit_large_reg', + prefix='backbones.', + out_channels=[1024, 1024, 1024, 1024], + drop_path_rate = 0.0), + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_small_reg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_small_reg.py new file mode 100644 index 00000000..0c8bd97d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/backbones/dino_vit_small_reg.py @@ -0,0 +1,7 @@ +model = dict( + backbone=dict( + type='vit_small_reg', + prefix='backbones.', + out_channels=[384, 384, 384, 384], + drop_path_rate = 0.0), + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/convnext_large.hourglassdecoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/convnext_large.hourglassdecoder.py new file mode 100644 index 00000000..f262288c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/convnext_large.hourglassdecoder.py @@ -0,0 +1,10 @@ +# model settings +_base_ = ['../backbones/convnext_large.py',] +model = dict( + type='DensePredModel', + decode_head=dict( + type='HourglassDecoder', + in_channels=[192, 384, 768, 1536], + decoder_channel=[128, 128, 256, 512], + prefix='decode_heads.'), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_giant2_reg.dpt_raft.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_giant2_reg.dpt_raft.py new file mode 100644 index 00000000..73702d29 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_giant2_reg.dpt_raft.py @@ -0,0 +1,19 @@ +# model settings +_base_ = ['../backbones/dino_vit_giant2_reg.py'] +model = dict( + type='DensePredModel', + decode_head=dict( + type='RAFTDepthDPT', + in_channels=[1536, 1536, 1536, 1536], + use_cls_token=True, + feature_channels = [384, 768, 1536, 1536], # [2/7, 1/7, 1/14, 1/14] + decoder_channels = [192, 384, 768, 1536, 1536], # [4/7, 2/7, 1/7, 1/14, 1/14] + up_scale = 7, + hidden_channels=[192, 192, 192, 192], # [x_4, x_8, x_16, x_32] [192, 384, 768, 1536] + n_gru_layers=3, + n_downsample=2, + iters=3, + slow_fast_gru=True, + num_register_tokens=4, + prefix='decode_heads.'), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_large.dpt_raft.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_large.dpt_raft.py new file mode 100644 index 00000000..bd69efef --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_large.dpt_raft.py @@ -0,0 +1,20 @@ +# model settings +_base_ = ['../backbones/dino_vit_large.py'] +model = dict( + type='DensePredModel', + decode_head=dict( + type='RAFTDepthDPT', + in_channels=[1024, 1024, 1024, 1024], + use_cls_token=True, + feature_channels = [256, 512, 1024, 1024], # [2/7, 1/7, 1/14, 1/14] + decoder_channels = [128, 256, 512, 1024, 1024], # [4/7, 2/7, 1/7, 1/14, 1/14] + up_scale = 7, + hidden_channels=[128, 128, 128, 128], # [x_4, x_8, x_16, x_32] [192, 384, 768, 1536] + n_gru_layers=3, + n_downsample=2, + iters=12, + slow_fast_gru=True, + corr_radius=4, + corr_levels=4, + prefix='decode_heads.'), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py new file mode 100644 index 00000000..26ab6dc0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_large_reg.dpt_raft.py @@ -0,0 +1,19 @@ +# model settings +_base_ = ['../backbones/dino_vit_large_reg.py'] +model = dict( + type='DensePredModel', + decode_head=dict( + type='RAFTDepthDPT', + in_channels=[1024, 1024, 1024, 1024], + use_cls_token=True, + feature_channels = [256, 512, 1024, 1024], # [2/7, 1/7, 1/14, 1/14] + decoder_channels = [128, 256, 512, 1024, 1024], # [4/7, 2/7, 1/7, 1/14, 1/14] + up_scale = 7, + hidden_channels=[128, 128, 128, 128], # [x_4, x_8, x_16, x_32] [192, 384, 768, 1536] + n_gru_layers=3, + n_downsample=2, + iters=3, + slow_fast_gru=True, + num_register_tokens=4, + prefix='decode_heads.'), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_small_reg.dpt_raft.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_small_reg.dpt_raft.py new file mode 100644 index 00000000..19466c19 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/configs/_base_/models/encoder_decoder/dino_vit_small_reg.dpt_raft.py @@ -0,0 +1,19 @@ +# model settings +_base_ = ['../backbones/dino_vit_small_reg.py'] +model = dict( + type='DensePredModel', + decode_head=dict( + type='RAFTDepthDPT', + in_channels=[384, 384, 384, 384], + use_cls_token=True, + feature_channels = [96, 192, 384, 768], # [2/7, 1/7, 1/14, 1/14] + decoder_channels = [48, 96, 192, 384, 384], # [-, 1/4, 1/7, 1/14, 1/14] + up_scale = 7, + hidden_channels=[48, 48, 48, 48], # [x_4, x_8, x_16, x_32] [1/4, 1/7, 1/14, -] + n_gru_layers=3, + n_downsample=2, + iters=3, + slow_fast_gru=True, + num_register_tokens=4, + prefix='decode_heads.'), +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/__init__.py new file mode 100644 index 00000000..9e1ea3d3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/__init__.py @@ -0,0 +1,5 @@ +from .monodepth_model import DepthModel +# from .__base_model__ import BaseDepthModel + + +__all__ = ['DepthModel', 'BaseDepthModel'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ConvNeXt.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ConvNeXt.py new file mode 100644 index 00000000..6dada35e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ConvNeXt.py @@ -0,0 +1,260 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from timm.layers import trunc_normal_, DropPath + +class Block(nn.Module): + r""" ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + """ + def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = nn.Linear(4 * dim, dim) + self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), + requires_grad=True) if layer_scale_init_value > 0 else None + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x): + input = x + x = self.dwconv(x) + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + x = input + self.drop_path(x) + return x + +class ConvNeXt(nn.Module): + r""" ConvNeXt + A PyTorch impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + Args: + in_chans (int): Number of input image channels. Default: 3 + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + def __init__(self, in_chans=3, num_classes=1000, + depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0., + layer_scale_init_value=1e-6, head_init_scale=1., + **kwargs,): + super().__init__() + + self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers + stem = nn.Sequential( + nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first") + ) + self.downsample_layers.append(stem) + for i in range(3): + downsample_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2), + ) + self.downsample_layers.append(downsample_layer) + + self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(4): + stage = nn.Sequential( + *[Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + #self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer + #self.head = nn.Linear(dims[-1], num_classes) + + self.apply(self._init_weights) + #self.head.weight.data.mul_(head_init_scale) + #self.head.bias.data.mul_(head_init_scale) + + def _init_weights(self, m): + if isinstance(m, (nn.Conv2d, nn.Linear)): + trunc_normal_(m.weight, std=.02) + nn.init.constant_(m.bias, 0) + + def forward_features(self, x): + features = [] + for i in range(4): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + features.append(x) + return features # global average pooling, (N, C, H, W) -> (N, C) + + def forward(self, x): + #x = self.forward_features(x) + #x = self.head(x) + features = self.forward_features(x) + return features + +class LayerNorm(nn.Module): + r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape, ) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +model_urls = { + "convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth", + "convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth", + "convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth", + "convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth", + "convnext_tiny_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth", + "convnext_small_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth", + "convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", + "convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", + "convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", +} + +def convnext_tiny(pretrained=True,in_22k=False, **kwargs): + model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs) + if pretrained: + checkpoint = torch.load(kwargs['checkpoint'], map_location="cpu") + model_dict = model.state_dict() + pretrained_dict = {} + unmatched_pretrained_dict = {} + for k, v in checkpoint['model'].items(): + if k in model_dict: + pretrained_dict[k] = v + else: + unmatched_pretrained_dict[k] = v + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + print( + 'Successfully loaded pretrained %d params, and %d paras are unmatched.' + %(len(pretrained_dict.keys()), len(unmatched_pretrained_dict.keys()))) + print('Unmatched pretrained paras are :', unmatched_pretrained_dict.keys()) + return model + +def convnext_small(pretrained=True,in_22k=False, **kwargs): + model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs) + if pretrained: + checkpoint = torch.load(kwargs['checkpoint'], map_location="cpu") + model_dict = model.state_dict() + pretrained_dict = {} + unmatched_pretrained_dict = {} + for k, v in checkpoint['model'].items(): + if k in model_dict: + pretrained_dict[k] = v + else: + unmatched_pretrained_dict[k] = v + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + print( + 'Successfully loaded pretrained %d params, and %d paras are unmatched.' + %(len(pretrained_dict.keys()), len(unmatched_pretrained_dict.keys()))) + print('Unmatched pretrained paras are :', unmatched_pretrained_dict.keys()) + return model + +def convnext_base(pretrained=True, in_22k=False, **kwargs): + model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs) + if pretrained: + checkpoint = torch.load(kwargs['checkpoint'], map_location="cpu") + model_dict = model.state_dict() + pretrained_dict = {} + unmatched_pretrained_dict = {} + for k, v in checkpoint['model'].items(): + if k in model_dict: + pretrained_dict[k] = v + else: + unmatched_pretrained_dict[k] = v + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + print( + 'Successfully loaded pretrained %d params, and %d paras are unmatched.' + %(len(pretrained_dict.keys()), len(unmatched_pretrained_dict.keys()))) + print('Unmatched pretrained paras are :', unmatched_pretrained_dict.keys()) + return model + +def convnext_large(pretrained=True, in_22k=False, **kwargs): + model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs) + if pretrained: + checkpoint = torch.load(kwargs['checkpoint'], map_location="cpu") + model_dict = model.state_dict() + pretrained_dict = {} + unmatched_pretrained_dict = {} + for k, v in checkpoint['model'].items(): + if k in model_dict: + pretrained_dict[k] = v + else: + unmatched_pretrained_dict[k] = v + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + print( + 'Successfully loaded pretrained %d params, and %d paras are unmatched.' + %(len(pretrained_dict.keys()), len(unmatched_pretrained_dict.keys()))) + print('Unmatched pretrained paras are :', unmatched_pretrained_dict.keys()) + return model + +def convnext_xlarge(pretrained=True, in_22k=False, **kwargs): + model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs) + if pretrained: + assert in_22k, "only ImageNet-22K pre-trained ConvNeXt-XL is available; please set in_22k=True" + checkpoint = torch.load(kwargs['checkpoint'], map_location="cpu") + model_dict = model.state_dict() + pretrained_dict = {} + unmatched_pretrained_dict = {} + for k, v in checkpoint['model'].items(): + if k in model_dict: + pretrained_dict[k] = v + else: + unmatched_pretrained_dict[k] = v + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + print( + 'Successfully loaded pretrained %d params, and %d paras are unmatched.' + %(len(pretrained_dict.keys()), len(unmatched_pretrained_dict.keys()))) + print('Unmatched pretrained paras are :', unmatched_pretrained_dict.keys()) + return model + +if __name__ == '__main__': + import torch + model = convnext_base(True, in_22k=False).cuda() + + rgb = torch.rand((2, 3, 256, 256)).cuda() + out = model(rgb) + print(len(out)) + for i, ft in enumerate(out): + print(i, ft.shape) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ViT_DINO.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ViT_DINO.py new file mode 100644 index 00000000..b86c6b71 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ViT_DINO.py @@ -0,0 +1,1489 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +from functools import partial +import math +import logging +from typing import Sequence, Tuple, Union, Callable, Optional, Dict, Any, List + +import torch +import torch.nn as nn +from torch import Tensor +import torch.utils.checkpoint +from torch.nn.init import trunc_normal_ + +#from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block + +logger = logging.getLogger("dinov2") + +class ConvBlock(nn.Module): + def __init__(self, channels): + super(ConvBlock, self).__init__() + + self.act = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d( + channels, + channels, + kernel_size=3, + stride=1, + padding=1 + ) + self.norm1 = nn.BatchNorm2d(channels) + self.conv2 = nn.Conv2d( + channels, + channels, + kernel_size=3, + stride=1, + padding=1 + ) + self.norm2 = nn.BatchNorm2d(channels) + + def forward(self, x): + + out = self.norm1(x) + out = self.act(out) + out = self.conv1(out) + out = self.norm2(out) + out = self.act(out) + out = self.conv2(out) + return x + out + +def make_2tuple(x): + if isinstance(x, tuple): + assert len(x) == 2 + return x + + assert isinstance(x, int) + return (x, x) + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0: + random_tensor.div_(keep_prob) + output = x * random_tensor + return output + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + +class LayerScale(nn.Module): + def __init__( + self, + dim: int, + init_values: Union[float, Tensor] = 1e-5, + inplace: bool = False, + ) -> None: + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x: Tensor) -> Tensor: + return x.mul_(self.gamma) if self.inplace else x * self.gamma + + +class PatchEmbed(nn.Module): + """ + 2D image to patch embedding: (B,C,H,W) -> (B,N,D) + + Args: + img_size: Image size. + patch_size: Patch token size. + in_chans: Number of input image channels. + embed_dim: Number of linear projection output channels. + norm_layer: Normalization layer. + """ + + def __init__( + self, + img_size: Union[int, Tuple[int, int]] = 224, + patch_size: Union[int, Tuple[int, int]] = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Optional[Callable] = None, + flatten_embedding: bool = True, + ) -> None: + super().__init__() + + image_HW = make_2tuple(img_size) + patch_HW = make_2tuple(patch_size) + patch_grid_size = ( + image_HW[0] // patch_HW[0], + image_HW[1] // patch_HW[1], + ) + + self.img_size = image_HW + self.patch_size = patch_HW + self.patches_resolution = patch_grid_size + self.num_patches = patch_grid_size[0] * patch_grid_size[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.flatten_embedding = flatten_embedding + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + _, _, H, W = x.shape + patch_H, patch_W = self.patch_size + + assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" + assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" + + x = self.proj(x) # B C H W + H, W = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) # B HW C + x = self.norm(x) + if not self.flatten_embedding: + x = x.reshape(-1, H, W, self.embed_dim) # B H W C + return x + + def flops(self) -> float: + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + +class Mlp(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = nn.GELU, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) + self.drop = nn.Dropout(drop) + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) + self.w3 = nn.Linear(hidden_features, out_features, bias=bias) + + def forward(self, x: Tensor) -> Tensor: + x12 = self.w12(x) + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + return self.w3(hidden) + + +try: + from xformers.ops import SwiGLU + #import numpy.bool + XFORMERS_AVAILABLE = True +except ImportError: + SwiGLU = SwiGLUFFN + XFORMERS_AVAILABLE = False + +class SwiGLUFFNFused(SwiGLU): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + out_features = out_features or in_features + hidden_features = hidden_features or in_features + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + super().__init__( + in_features=in_features, + hidden_features=hidden_features, + out_features=out_features, + bias=bias, + ) + + +XFORMERS_AVAILABLE = False + + +class Attention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + window_size: int = 0, + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + #if not self.training: + # + # self.attn = ScaledDotProduct() + #self.attn = MultiHeadDispatch(dim_model=EMB, residual_dropout=DROPOUT, num_heads=HEADS, attention=attn) + + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + + q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] + attn = q @ k.transpose(-2, -1) + + if attn_bias is not None: + attn = attn + attn_bias[:, :, :N] + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class MemEffAttention(Attention): + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + if not XFORMERS_AVAILABLE: + #if True: + assert attn_bias is None, "xFormers is required for nested tensors usage" + return super().forward(x, attn_bias) + + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + q, k, v = unbind(qkv, 2) + if attn_bias is not None: + x = memory_efficient_attention(q, k, v, attn_bias=attn_bias[:, :, :N]) + else: + x = memory_efficient_attention(q, k, v) + x = x.reshape([B, N, C]) + + x = self.proj(x) + x = self.proj_drop(x) + return x + +XFORMERS_AVAILABLE = False + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = False, + proj_bias: bool = True, + ffn_bias: bool = True, + drop: float = 0.0, + attn_drop: float = 0.0, + init_values = None, + drop_path: float = 0.0, + act_layer: Callable[..., nn.Module] = nn.GELU, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_class: Callable[..., nn.Module] = Attention, + ffn_layer: Callable[..., nn.Module] = Mlp, + ) -> None: + super().__init__() + # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") + self.norm1 = norm_layer(dim) + self.attn = attn_class( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ffn_layer( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + bias=ffn_bias, + ) + self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.sample_drop_ratio = drop_path + + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + def attn_residual_func(x: Tensor, attn_bias) -> Tensor: + return self.ls1(self.attn(self.norm1(x), attn_bias)) + + def ffn_residual_func(x: Tensor) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + if self.training and self.sample_drop_ratio > 0.1: + # the overhead is compensated only for a drop path rate larger than 0.1 + x = drop_add_residual_stochastic_depth( + x, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + attn_bias=attn_bias + ) + x = drop_add_residual_stochastic_depth( + x, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + ) + elif self.training and self.sample_drop_ratio > 0.0: + x = x + self.drop_path1(attn_residual_func(x, attn_bias)) + x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 + else: + x = x + attn_residual_func(x, attn_bias) + x = x + ffn_residual_func(x) + return x + + +def drop_add_residual_stochastic_depth( + x: Tensor, + residual_func: Callable[[Tensor], Tensor], + sample_drop_ratio: float = 0.0, attn_bias=None +) -> Tensor: + # 1) extract subset using permutation + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + x_subset = x[brange] + + # 2) apply residual_func to get residual + residual = residual_func(x_subset, attn_bias) + + x_flat = x.flatten(1) + residual = residual.flatten(1) + + residual_scale_factor = b / sample_subset_size + + # 3) add the residual + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + return x_plus_residual.view_as(x) + + +def get_branges_scales(x, sample_drop_ratio=0.0): + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + residual_scale_factor = b / sample_subset_size + return brange, residual_scale_factor + + +def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): + if scaling_vector is None: + x_flat = x.flatten(1) + residual = residual.flatten(1) + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + else: + x_plus_residual = scaled_index_add( + x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor + ) + return x_plus_residual + + +attn_bias_cache: Dict[Tuple, Any] = {} + + +def get_attn_bias_and_cat(x_list, branges=None): + """ + this will perform the index select, cat the tensors, and provide the attn_bias from cache + """ + batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] + all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) + if all_shapes not in attn_bias_cache.keys(): + seqlens = [] + for b, x in zip(batch_sizes, x_list): + for _ in range(b): + seqlens.append(x.shape[1]) + attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) + attn_bias._batch_sizes = batch_sizes + attn_bias_cache[all_shapes] = attn_bias + + if branges is not None: + cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) + else: + tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) + cat_tensors = torch.cat(tensors_bs1, dim=1) + + return attn_bias_cache[all_shapes], cat_tensors + + +def drop_add_residual_stochastic_depth_list( + x_list: List[Tensor], + residual_func: Callable[[Tensor, Any], Tensor], + sample_drop_ratio: float = 0.0, + scaling_vector=None, +) -> Tensor: + # 1) generate random set of indices for dropping samples in the batch + branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] + branges = [s[0] for s in branges_scales] + residual_scale_factors = [s[1] for s in branges_scales] + + # 2) get attention bias and index+concat the tensors + attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) + + # 3) apply residual_func to get residual, and split the result + residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore + + outputs = [] + for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): + outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) + return outputs + + +class NestedTensorBlock(Block): + def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: + """ + x_list contains a list of tensors to nest together and run + """ + assert isinstance(self.attn, MemEffAttention) + + if self.training and self.sample_drop_ratio > 0.0: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.attn(self.norm1(x), attn_bias=attn_bias) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.mlp(self.norm2(x)) + + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, + ) + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, + ) + return x_list + else: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + attn_bias, x = get_attn_bias_and_cat(x_list) + x = x + attn_residual_func(x, attn_bias=attn_bias) + x = x + ffn_residual_func(x) + return attn_bias.split(x) + + def forward(self, x_or_x_list, attn_bias=None): + if isinstance(x_or_x_list, Tensor): + return super().forward(x_or_x_list, attn_bias) + elif isinstance(x_or_x_list, list): + assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage" + return self.forward_nested(x_or_x_list) + else: + raise AssertionError + + +def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: + if not depth_first and include_root: + fn(module=module, name=name) + for child_name, child_module in module.named_children(): + child_name = ".".join((name, child_name)) if name else child_name + named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) + if depth_first and include_root: + fn(module=module, name=name) + return module + + +class BlockChunk(nn.ModuleList): + def forward(self, x, others=None): + for b in self: + if others == None: + x = b(x) + else: + x = b(x, others) + return x + + +class DinoVisionTransformer(nn.Module): + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + ffn_bias=True, + proj_bias=True, + drop_path_rate=0.0, + drop_path_uniform=False, + #init_values=None, # for layerscale: None or 0 => no layerscale + init_values=1e-5, # for layerscale: None or 0 => no layerscale + embed_layer=PatchEmbed, + act_layer=nn.GELU, + block_fn=NestedTensorBlock, + ffn_layer="mlp", + block_chunks=1, + window_size=37, + **kwargs + ): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + proj_bias (bool): enable bias for proj in attn if True + ffn_bias (bool): enable bias for ffn if True + drop_path_rate (float): stochastic depth rate + drop_path_uniform (bool): apply uniform drop rate across blocks + weight_init (str): weight init scheme + init_values (float): layer-scale init values + embed_layer (nn.Module): patch embedding layer + act_layer (nn.Module): MLP activation layer + block_fn (nn.Module): transformer block class + ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" + block_chunks: (int) split block sequence into block_chunks units for FSDP wrap + """ + super().__init__() + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.num_tokens = 1 + self.n_blocks = depth + self.num_heads = num_heads + self.patch_size = patch_size + self.window_size = window_size + + self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) + + if drop_path_uniform is True: + dpr = [drop_path_rate] * depth + else: + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + if ffn_layer == "mlp": + logger.info("using MLP layer as FFN") + ffn_layer = Mlp + elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": + logger.info("using SwiGLU layer as FFN") + ffn_layer = SwiGLUFFNFused + elif ffn_layer == "identity": + logger.info("using Identity layer as FFN") + + def f(*args, **kwargs): + return nn.Identity() + + ffn_layer = f + else: + raise NotImplementedError + + blocks_list = [ + block_fn( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + ffn_bias=ffn_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + ffn_layer=ffn_layer, + init_values=init_values, + ) + for i in range(depth) + ] + if block_chunks > 0: + self.chunked_blocks = True + chunked_blocks = [] + chunksize = depth // block_chunks + for i in range(0, depth, chunksize): + # this is to keep the block index consistent if we chunk the block list + chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) + self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) + else: + self.chunked_blocks = False + self.blocks = nn.ModuleList(blocks_list) + + self.norm = norm_layer(embed_dim) + self.head = nn.Identity() + + self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) + + self.init_weights() + + def init_weights(self): + trunc_normal_(self.pos_embed, std=0.02) + nn.init.normal_(self.cls_token, std=1e-6) + named_apply(init_weights_vit_timm, self) + + def interpolate_pos_encoding(self, x, w, h): + previous_dtype = x.dtype + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + pos_embed = self.pos_embed.float() + class_pos_embed = pos_embed[:, 0] + patch_pos_embed = pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_size + h0 = h // self.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode="bicubic", + ) + + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) + + def prepare_tokens_with_masks(self, x, masks=None): + B, nc, w, h = x.shape + x = self.patch_embed(x) + if masks is not None: + x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) + + x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) + x = x + self.interpolate_pos_encoding(x, w, h) + + return x + + def forward_features_list(self, x_list, masks_list): + x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] + for blk in self.blocks: + x = blk(x) + + all_x = x + output = [] + for x, masks in zip(all_x, masks_list): + x_norm = self.norm(x) + output.append( + { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_patchtokens": x_norm[:, 1:], + "x_prenorm": x, + "masks": masks, + } + ) + return output + + def forward_features(self, x, masks=None): + if isinstance(x, list): + return self.forward_features_list(x, masks) + + B, C, H, W = x.size() + pad_h = (self.patch_size - H % self.patch_size) + pad_w = (self.patch_size - W % self.patch_size) + if pad_h == self.patch_size: + pad_h = 0 + if pad_w == self.patch_size: + pad_w = 0 + #x = nn.functional.pad(x, (pad_h//2, pad_h-pad_h//2, pad_w//2, pad_w-pad_w//2)) + if pad_h + pad_w > 0: + x = torch.nn.functional.interpolate(x, (H+pad_h, W+pad_w), mode='bilinear') + + x = self.prepare_tokens_with_masks(x, masks) + + features = [] + for blk in self.blocks: + x = blk(x) + # for idx in range(len(self.blocks[0])): + # x = self.blocks[0][idx](x) + # if (idx + 1) % (len(self.blocks[0]) // 4) == 0: + # features.append(x) + + #return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W)] + + x_norm = self.norm(x) + # return { + # "x_norm_clstoken": x_norm[:, 0], + # "x_norm_patchtokens": x_norm[:, 1:], + # "x_prenorm": x, + # "masks": masks, + # } + features = [] + features.append(x_norm) + features.append(x_norm) + features.append(x_norm) + features.append(x_norm) + return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W)] + + def _get_intermediate_layers_not_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + # If n is an int, take the n last blocks. If it's a list, take them + output, total_block_len = [], len(self.blocks) + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for i, blk in enumerate(self.blocks): + x = blk(x) + if i in blocks_to_take: + output.append(x) + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def _get_intermediate_layers_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + output, i, total_block_len = [], 0, len(self.blocks[-1]) + # If n is an int, take the n last blocks. If it's a list, take them + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for block_chunk in self.blocks: + for blk in block_chunk[i:]: # Passing the nn.Identity() + x = blk(x) + if i in blocks_to_take: + output.append(x) + i += 1 + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def get_intermediate_layers( + self, + x: torch.Tensor, + n: Union[int, Sequence] = 1, # Layers or n last layers to take + reshape: bool = False, + return_class_token: bool = False, + norm=True, + ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: + if self.chunked_blocks: + outputs = self._get_intermediate_layers_chunked(x, n) + else: + outputs = self._get_intermediate_layers_not_chunked(x, n) + if norm: + outputs = [self.norm(out) for out in outputs] + class_tokens = [out[:, 0] for out in outputs] + outputs = [out[:, 1:] for out in outputs] + if reshape: + B, _, w, h = x.shape + outputs = [ + out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() + for out in outputs + ] + if return_class_token: + return tuple(zip(outputs, class_tokens)) + return tuple(outputs) + + def forward(self, *args, is_training=False, **kwargs): + ret = self.forward_features(*args, **kwargs) + return ret + # if is_training: + # return ret + # else: + # return self.head(ret["x_norm_clstoken"]) + + +class PosConv(nn.Module): + # PEG from https://arxiv.org/abs/2102.10882 + def __init__(self, in_chans, embed_dim=768, stride=1): + super(PosConv, self).__init__() + self.proj = nn.Sequential( + nn.Conv2d(in_chans, embed_dim, 37, stride, 18, bias=True, groups=embed_dim), + ) + self.stride = stride + + def forward(self, x, size): + B, N, C = x.shape + cnn_feat_token = x.transpose(1, 2).view(B, C, *size) + x = self.proj(cnn_feat_token) + if self.stride == 1: + x += cnn_feat_token + x = x.flatten(2).transpose(1, 2) + return x + + #def no_weight_decay(self): + #return ['proj.%d.weight' % i for i in range(4)] + +class DinoWindowVisionTransformer(nn.Module): + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + ffn_bias=True, + proj_bias=True, + drop_path_rate=0.0, + drop_path_uniform=False, + #init_values=None, # for layerscale: None or 0 => no layerscale + init_values=1e-5, # for layerscale: None or 0 => no layerscale + embed_layer=PatchEmbed, + act_layer=nn.GELU, + block_fn=NestedTensorBlock, + ffn_layer="mlp", + block_chunks=1, + window_size=7, + **kwargs + ): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + proj_bias (bool): enable bias for proj in attn if True + ffn_bias (bool): enable bias for ffn if True + drop_path_rate (float): stochastic depth rate + drop_path_uniform (bool): apply uniform drop rate across blocks + weight_init (str): weight init scheme + init_values (float): layer-scale init values + embed_layer (nn.Module): patch embedding layer + act_layer (nn.Module): MLP activation layer + block_fn (nn.Module): transformer block class + ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" + block_chunks: (int) split block sequence into block_chunks units for FSDP wrap + """ + super().__init__() + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.num_tokens = 1 + self.n_blocks = depth + self.num_heads = num_heads + self.patch_size = patch_size + + self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + #self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + #self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + + self.pos_conv = PosConv(self.embed_dim, self.embed_dim) + + self.window_size = window_size + #self.conv_block = nn.ModuleList([ConvBlock(embed_dim) for i in range(4)]) + #self.conv_block = nn.ModuleList([nn.Identity() for i in range(4)]) + + if drop_path_uniform is True: + dpr = [drop_path_rate] * depth + else: + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + if ffn_layer == "mlp": + logger.info("using MLP layer as FFN") + ffn_layer = Mlp + elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": + logger.info("using SwiGLU layer as FFN") + ffn_layer = SwiGLUFFNFused + elif ffn_layer == "identity": + logger.info("using Identity layer as FFN") + + def f(*args, **kwargs): + return nn.Identity() + + ffn_layer = f + else: + raise NotImplementedError + + blocks_list = [ + block_fn( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + ffn_bias=ffn_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + ffn_layer=ffn_layer, + init_values=init_values, + ) + for i in range(depth) + ] + if block_chunks > 0: + self.chunked_blocks = True + chunked_blocks = [] + chunksize = depth // block_chunks + for i in range(0, depth, chunksize): + # this is to keep the block index consistent if we chunk the block list + chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) + self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) + else: + self.chunked_blocks = False + self.blocks = nn.ModuleList(blocks_list) + + self.norm = norm_layer(embed_dim) + self.head = nn.Identity() + + self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) + + self.nh = -1 + self.nw = -1 + try: + H = cfg.data_basic['crop_size'][0] + W = cfg.data_basic['crop_size'][1] + pad_h = (self.patch_size - H % self.patch_size) + pad_w = (self.patch_size - W % self.patch_size) + if pad_h == self.patch_size: + pad_h = 0 + if pad_w == self.patch_size: + pad_w = 0 + self.nh = (H + pad_h) // self.patch_size + self.nw = (W + pad_w) // self.patch_size + self.prepare_attn_bias((self.nh, self.nw)) + except: + pass + self.init_weights() + + self.total_step = 10000 # For PE -> GPE transfer + self.start_step = 2000 + self.current_step = 20000 + + def init_weights(self): + #trunc_normal_(self.pos_embed, std=0.02) + #nn.init.normal_(self.cls_token, std=1e-6) + named_apply(init_weights_vit_timm, self) + for i in range(4): + try: + nn.init.constant_(self.conv_block[i].conv2.weight, 0.0) + except: + pass + + def interpolate_pos_encoding(self, x, w, h): + previous_dtype = x.dtype + #npatch = x.shape[1] - 1 + #N = self.pos_embed.shape[1] - 1 + npatch = x.shape[1] + N = self.pos_embed.shape[1] + if npatch == N and w == h: + return self.pos_embed + pos_embed = self.pos_embed.float() + #class_pos_embed = pos_embed[:, 0] + #patch_pos_embed = pos_embed[:, 1:] + patch_pos_embed = pos_embed + dim = x.shape[-1] + w0 = w // self.patch_size + h0 = h // self.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode="bicubic", + ) + + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed.to(previous_dtype) + #return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) + + def window_partition(self, x: torch.Tensor, window_size: int, hw: Tuple[int, int], conv_feature=False) -> Tuple[torch.Tensor, Tuple[int, int]]: + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + if conv_feature == False: + B, N, C = x.shape + H, W = hw[0], hw[1] + + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size * window_size, C) + else: + B, C, H, W = x.shape + + x = x.view(B, C, H // window_size, window_size, W // window_size, window_size) + + windows = x.permute(0, 2, 4, 3, 5, 1).contiguous().view(-1, window_size * window_size, C) + + #y = torch.cat((x_cls, windows), dim=1) + return windows #, (Hp, Wp) + + + def window_unpartition(self, + windows: torch.Tensor, window_size: int, hw: Tuple[int, int], conv_feature=False + ) -> torch.Tensor: + """ + Window unpartition into original sequences and removing padding. + Args: + windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + H, W = hw + + B = windows.shape[0] // (H * W // window_size // window_size) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + + if conv_feature == False: + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp * Wp, -1) + else: + C = windows.shape[-1] + x = x.permute(0, 5, 1, 3, 2, 4).contiguous().view(B, C, H, W) + + # if Hp > H or Wp > W: + # x = x[:, :H, :W, :].contiguous() + return x + + def prepare_tokens_with_masks(self, x, masks=None, step=-1): + B, nc, w, h = x.shape + x = self.patch_embed(x) + if masks is not None: + x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) + + #x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) + if step == -1: + step = self.current_step + else: + self.current_step = step + + if step < self.start_step: + coef = 0.0 + elif step < self.total_step: + coef = (step - self.start_step) / (self.total_step - self.start_step) + else: + coef = 1.0 + + x = x + (1 - coef) * self.interpolate_pos_encoding(x, w, h) + coef * self.pos_conv(x, (self.nh, self.nw)) + + return x + + def prepare_attn_bias(self, shape): + window_size = self.window_size + if window_size <= 0: + return + + import xformers.components.attention.attention_patterns as AP + + nh, nw = shape + radius = (window_size-1)//2 + mask_ori = AP.local_2d_pattern(nh, nw, distance = radius + 0.1, p=torch.inf).cuda() + + pad = (8 - (nh * nw) % 8) + if pad == 8: + pad = 0 + mask_pad = nn.functional.pad(mask_ori, (0, pad)).contiguous() + if pad > 0: + mask = mask_pad[:, :-pad].view(nh, nw, nh, nw) + else: + mask = mask_pad[:, :].view(nh, nw, nh, nw) + + # angle + mask[:radius+1, :radius+1, :window_size, :window_size] = True + mask[:radius+1, -radius-1:, :window_size, -window_size:] = True + mask[-radius-1:, :radius+1, -window_size:, :window_size] = True + mask[-radius-1:, -radius-1:, -window_size:, -window_size:] = True + + # edge + mask[radius+1:-radius-1, :radius+1, :, :] = mask[radius+1:-radius-1, radius:radius+1, :, :] + mask[radius+1:-radius-1, -radius-1:, :, :] = mask[radius+1:-radius-1, -radius-1:-radius, :, :] + mask[:radius+1, radius+1:-radius-1, :, :] = mask[radius:radius+1, radius+1:-radius-1, :, :] + mask[-radius-1:, radius+1:-radius-1, :, :] = mask[-radius-1:-radius, radius+1:-radius-1, :, :] + + mask = mask.view(nh*nw, nh*nw) + bias_pad = torch.log(mask_pad) + #bias = bias_pad[:, :-pad] + self.register_buffer('attn_bias', bias_pad) + + return bias_pad + + def forward_features_list(self, x_list, masks_list): + x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] + for blk in self.blocks: + x = blk(x) + + all_x = x + output = [] + for x, masks in zip(all_x, masks_list): + x_norm = self.norm(x) + output.append( + { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_patchtokens": x_norm[:, 1:], + "x_prenorm": x, + "masks": masks, + } + ) + return output + + def forward_features(self, x, masks=None, **kwargs): + if isinstance(x, list): + return self.forward_features_list(x, masks) + + B, C, H, W = x.size() + pad_h = (self.patch_size - H % self.patch_size) + pad_w = (self.patch_size - W % self.patch_size) + if pad_h == self.patch_size: + pad_h = 0 + if pad_w == self.patch_size: + pad_w = 0 + #x = nn.functional.pad(x, (pad_h//2, pad_h-pad_h//2, pad_w//2, pad_w-pad_w//2)) + if pad_h + pad_w > 0: + x = torch.nn.functional.interpolate(x, (H+pad_h, W+pad_w), mode='bilinear') + + nh = (H+pad_h)//self.patch_size + nw = (W+pad_w)//self.patch_size + + if self.window_size > 0: + if nh == self.nh and nw == self.nw: + attn_bias = self.attn_bias + else: + attn_bias = self.prepare_attn_bias(((H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size)) + self.nh = nh + self.nw = nw + attn_bias = attn_bias.unsqueeze(0).repeat(B * self.num_heads, 1, 1) + else: + attn_bias = None + + x = self.prepare_tokens_with_masks(x, masks) + #x = self.patch_embed(x) + + features = [] + #x = self.window_partition(x, self.window_size, (H // self.patch_size, W // self.patch_size)) + for blk in self.blocks: + x = blk(x, attn_bias) + #x = self.window_unpartition(x, self.window_size, (H // self.patch_size, W // self.patch_size)) + + # for idx in range(len(self.blocks[0])): + # x = self.blocks[0][idx](x, attn_bias) + + # if (idx + 1) % (len(self.blocks[0]) // 4) == 0: + # x = self.window_unpartition(x, self.window_size, (H // self.patch_size, W // self.patch_size), conv_feature=True) + # x = self.conv_block[idx // (len(self.blocks[0]) // 4)](x) + # if idx + 1 != len(self.blocks[0]): + # x = self.window_partition(x, self.window_size, (H // self.patch_size, W // self.patch_size), conv_feature=True) + # else: + # b, c, h, w = x.size() + # x = x.permute(0, 2, 3, 1).contiguous().view(b, h, w, c) + #features.append(x) + + #return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W)] + + x_norm = self.norm(x) + # return { + # "x_norm_clstoken": x_norm[:, 0], + # "x_norm_patchtokens": x_norm[:, 1:], + # "x_prenorm": x, + # "masks": masks, + # } + features = [] + features.append(x_norm) + features.append(x_norm) + features.append(x_norm) + features.append(x_norm) + return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W)] + + def _get_intermediate_layers_not_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + # If n is an int, take the n last blocks. If it's a list, take them + output, total_block_len = [], len(self.blocks) + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for i, blk in enumerate(self.blocks): + x = blk(x) + if i in blocks_to_take: + output.append(x) + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def _get_intermediate_layers_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + output, i, total_block_len = [], 0, len(self.blocks[-1]) + # If n is an int, take the n last blocks. If it's a list, take them + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for block_chunk in self.blocks: + for blk in block_chunk[i:]: # Passing the nn.Identity() + x = blk(x) + if i in blocks_to_take: + output.append(x) + i += 1 + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def get_intermediate_layers( + self, + x: torch.Tensor, + n: Union[int, Sequence] = 1, # Layers or n last layers to take + reshape: bool = False, + return_class_token: bool = False, + norm=True, + ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: + if self.chunked_blocks: + outputs = self._get_intermediate_layers_chunked(x, n) + else: + outputs = self._get_intermediate_layers_not_chunked(x, n) + if norm: + outputs = [self.norm(out) for out in outputs] + class_tokens = [out[:, 0] for out in outputs] + outputs = [out[:, 1:] for out in outputs] + if reshape: + B, _, w, h = x.shape + outputs = [ + out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() + for out in outputs + ] + if return_class_token: + return tuple(zip(outputs, class_tokens)) + return tuple(outputs) + + def forward(self, *args, is_training=False, **kwargs): + ret = self.forward_features(*args, **kwargs) + return ret + # if is_training: + # return ret + # else: + # return self.head(ret["x_norm_clstoken"]) + + + + +def init_weights_vit_timm(module: nn.Module, name: str = ""): + """ViT weight initialization, original timm impl (for reproducibility)""" + if isinstance(module, nn.Linear): + trunc_normal_(module.weight, std=0.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + + +def vit_small(patch_size=14, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + block_fn=partial(NestedTensorBlock, attn_class=MemEffAttention), + **kwargs, + ) + return model + + +def vit_base(patch_size=14, **kwargs): + model = DinoWindowVisionTransformer( + patch_size=patch_size, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + block_fn=partial(NestedTensorBlock, attn_class=MemEffAttention), + **kwargs, + ) + return model + + +def vit_large(patch_size=14, checkpoint=None, **kwargs): + model = DinoVisionTransformer( + img_size = 518, + patch_size=patch_size, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + block_fn=partial(NestedTensorBlock, attn_class=MemEffAttention), + **kwargs, + ) + + if checkpoint is not None: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f) + try: + model.load_state_dict(state_dict, strict=True) + except: + new_state_dict = {} + for key, value in state_dict.items(): + if 'blocks' in key: + key_new = 'blocks.0' + key[len('blocks'):] + else: + key_new = key + new_state_dict[key_new] = value + + model.load_state_dict(new_state_dict, strict=True) + #del model.norm + del model.mask_token + return model + + # model = DinoWindowVisionTransformer( + # img_size = 518, + # patch_size=patch_size, + # embed_dim=1024, + # depth=24, + # num_heads=16, + # mlp_ratio=4, + # block_fn=partial(NestedTensorBlock, attn_class=MemEffAttention), + # window_size=37, + # **kwargs, + # ) + + # if checkpoint is not None: + # with open(checkpoint, "rb") as f: + # state_dict = torch.load(f) + # try: + # model.load_state_dict(state_dict, strict=True) + # except: + # new_state_dict = {} + # for key, value in state_dict.items(): + # if 'blocks' in key: + # key_new = 'blocks.0' + key[len('blocks'):] + # else: + # key_new = key + # if 'pos_embed' in key: + # value = value[:, 1:, :] + # new_state_dict[key_new] = value + + # model.load_state_dict(new_state_dict, strict=False) + # #del model.norm + # del model.mask_token + return model + + +def vit_giant2(patch_size=16, **kwargs): + """ + Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 + """ + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1536, + depth=40, + num_heads=24, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + **kwargs, + ) + return model + +if __name__ == '__main__': + try: + from custom_mmpkg.custom_mmcv.utils import Config + except: + from mmengine import Config + + #rgb = torch.rand((2, 3, 518, 518)).cuda() + + #cfg.data_basic['crop_size']['0'] + #cfg.data_basic['crop_size']['1'] + cfg = Config.fromfile('mu.hu/monodepth/mono/configs/HourglassDecoder/pub12.convlarge.0.3_150.py') + + #rgb = torch.arange(0, 2*3*1036*1036, 1).cuda().float().view(2, 3, 1036, 1036) + rgb = torch.zeros(1, 3, 1400, 1680).cuda() + model = vit_large(checkpoint="pretrained_weight_repo/vit/dinov2_vitl14_pretrain.pth", kwarg=cfg).cuda() + + #import timm + #model2 = timm.models.vision_transformer.vit_large_patch14_dinov2().cuda() + #timm.models.load_checkpoint(model2, '/cpfs02/shared/public/yvan/pretrained_weight_repo/vit/dinov2_vitl14_pretrain.pth', filter_fn=timm.models.vision_transformer.checkpoint_filter_fn) + + out1 = model(rgb) + #out2 = model2(rgb) + temp = 0 + + + +# import time +# window_size = 37 +# def prepare_window_masks(shape): +# if window_size <= 0: +# return None +# import xformers.components.attention.attention_patterns as AP + +# B, nh, nw, _, _ = shape +# radius = (window_size-1)//2 +# #time0 = time.time() +# d = AP.local_nd_distance(nh, nw, distance = radius + 0.1, p=torch.inf).cuda() +# #mask = AP.local_2d_pattern(nh, nw, distance = radius + 0.1, p=torch.inf).cuda() +# # mask = mask.view(nh, nw, nh, nw) +# # #time1 = time.time() - time0 + +# # # angle +# # mask[:radius+1, :radius+1, :window_size, :window_size] = True +# # mask[:radius+1, -radius-1:, :window_size, -window_size:] = True +# # mask[-radius-1:, :radius+1, -window_size:, :window_size] = True +# # mask[-radius-1:, -radius-1:, -window_size:, -window_size:] = True +# # time2 = time.time() - time0 - time1 + +# # # edge +# # mask[radius+1:-radius-1, :radius+1, :, :] = mask[radius+1:-radius-1, radius:radius+1, :, :] +# # mask[radius+1:-radius-1, -radius-1:, :, :] = mask[radius+1:-radius-1, -radius-1:-radius, :, :] +# # mask[:radius+1, radius+1:-radius-1, :, :] = mask[radius:radius+1, radius+1:-radius-1, :, :] +# # mask[-radius-1:, radius+1:-radius-1, :, :] = mask[-radius-1:-radius, radius+1:-radius-1, :, :] +# # time3 = time.time() - time0 - time2 +# # print(time1, time2, time3) + +# # return mask.view(nw*nw, nh*nw).unsqueeze(0).repeat(B, 1) + +# shape = (1, 55, 55, None, None) +# mask = prepare_window_masks(shape) +# # temp = 1 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ViT_DINO_reg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ViT_DINO_reg.py new file mode 100644 index 00000000..346ed10c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/ViT_DINO_reg.py @@ -0,0 +1,1303 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +from functools import partial +import math +import logging +from typing import Sequence, Tuple, Union, Callable, Optional, Dict, Any, List + +import torch +import torch.nn as nn +from torch import Tensor +import torch.utils.checkpoint +from torch.nn.init import trunc_normal_ +import torch.nn.init +import torch.nn.functional as F + +#from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block + +logger = logging.getLogger("dinov2") + +# SSF finetuning originally by dongzelian +def init_ssf_scale_shift(dim): + scale = nn.Parameter(torch.ones(dim)) + shift = nn.Parameter(torch.zeros(dim)) + + nn.init.normal_(scale, mean=1, std=.02) + nn.init.normal_(shift, std=.02) + + return scale, shift + +def ssf_ada(x, scale, shift): + assert scale.shape == shift.shape + if x.shape[-1] == scale.shape[0]: + return x * scale + shift + elif x.shape[1] == scale.shape[0]: + return x * scale.view(1, -1, 1, 1) + shift.view(1, -1, 1, 1) + else: + raise ValueError('the input tensor shape does not match the shape of the scale factor.') + +# LoRA finetuning originally by edwardjhu +class LoRALayer(): + def __init__( + self, + r: int, + lora_alpha: int, + lora_dropout: float, + merge_weights: bool, + ): + self.r = r + self.lora_alpha = lora_alpha + # Optional dropout + if lora_dropout > 0.: + self.lora_dropout = nn.Dropout(p=lora_dropout) + else: + self.lora_dropout = lambda x: x + # Mark the weight as unmerged + self.merged = False + self.merge_weights = merge_weights + +class LoRALinear(nn.Linear, LoRALayer): + # LoRA implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0., + fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) + merge_weights: bool = True, + **kwargs + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, + merge_weights=merge_weights) + + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features))) + self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r))) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.transpose(0, 1) + + def reset_parameters(self): + #nn.Linear.reset_parameters(self) + if hasattr(self, 'lora_A'): + # initialize B the same way as the default for nn.Linear and A to zero + # this is different than what is described in the paper but should not affect performance + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + # def train(self, mode: bool = True): + # def T(w): + # return w.transpose(0, 1) if self.fan_in_fan_out else w + # nn.Linear.train(self, mode) + # if mode: + # if self.merge_weights and self.merged: + # # Make sure that the weights are not merged + # if self.r > 0: + # self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling + # self.merged = False + # else: + # if self.merge_weights and not self.merged: + # # Merge the weights and mark it + # if self.r > 0: + # self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling + # self.merged = True + + def forward(self, x: torch.Tensor): + def T(w): + return w.transpose(0, 1) if self.fan_in_fan_out else w + if self.r > 0 and not self.merged: + result = F.linear(x, T(self.weight), bias=self.bias) + result += (self.lora_dropout(x) @ self.lora_A.transpose(0, 1) @ self.lora_B.transpose(0, 1)) * self.scaling + return result + else: + return F.linear(x, T(self.weight), bias=self.bias) + + + +def make_2tuple(x): + if isinstance(x, tuple): + assert len(x) == 2 + return x + + assert isinstance(x, int) + return (x, x) + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0: + random_tensor.div_(keep_prob) + output = x * random_tensor + return output + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + +class LayerScale(nn.Module): + def __init__( + self, + dim: int, + init_values: Union[float, Tensor] = 1e-5, + inplace: bool = False, + ) -> None: + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x: Tensor) -> Tensor: + return x.mul_(self.gamma) if self.inplace else x * self.gamma + + +class PatchEmbed(nn.Module): + """ + 2D image to patch embedding: (B,C,H,W) -> (B,N,D) + + Args: + img_size: Image size. + patch_size: Patch token size. + in_chans: Number of input image channels. + embed_dim: Number of linear projection output channels. + norm_layer: Normalization layer. + """ + + def __init__( + self, + img_size: Union[int, Tuple[int, int]] = 224, + patch_size: Union[int, Tuple[int, int]] = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Optional[Callable] = None, + flatten_embedding: bool = True, + tuning_mode: Optional[str] = None + ) -> None: + super().__init__() + + image_HW = make_2tuple(img_size) + patch_HW = make_2tuple(patch_size) + patch_grid_size = ( + image_HW[0] // patch_HW[0], + image_HW[1] // patch_HW[1], + ) + + self.img_size = image_HW + self.patch_size = patch_HW + self.patches_resolution = patch_grid_size + self.num_patches = patch_grid_size[0] * patch_grid_size[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.flatten_embedding = flatten_embedding + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + if tuning_mode != None: + self.tuning_mode = tuning_mode + if tuning_mode == 'ssf': + self.ssf_scale_1, self.ssf_shift_1 = init_ssf_scale_shift(embed_dim) + else: + pass + #raise NotImplementedError() + else: + self.tuning_mode = None + + def forward(self, x: Tensor) -> Tensor: + _, _, H, W = x.shape + patch_H, patch_W = self.patch_size + + assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" + assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" + + x = self.proj(x) # B C H W + H, W = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) # B HW C + x = self.norm(x) + if self.tuning_mode == 'ssf': + x = ssf_ada(x, self.ssf_scale_1, self.ssf_shift_1) + if not self.flatten_embedding: + x = x.reshape(-1, H, W, self.embed_dim) # B H W C + return x + + def flops(self) -> float: + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + +class Mlp(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = nn.GELU, + drop: float = 0.0, + bias: bool = True, + tuning_mode: Optional[int] = None + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) + self.drop = nn.Dropout(drop) + + if tuning_mode != None: + self.tuning_mode = tuning_mode + if tuning_mode == 'ssf': + self.ssf_scale_1, self.ssf_shift_1 = init_ssf_scale_shift(hidden_features) + self.ssf_scale_2, self.ssf_shift_2 = init_ssf_scale_shift(out_features) + else: + pass + #raise NotImplementedError() + else: + self.tuning_mode = None + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + if self.tuning_mode == 'ssf': + x = ssf_ada(x, self.ssf_scale_1, self.ssf_shift_1) + + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + if self.tuning_mode == 'ssf': + x = ssf_ada(x, self.ssf_scale_2, self.ssf_shift_2) + + x = self.drop(x) + return x + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + tuning_mode: Optional[int] = None + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) + self.w3 = nn.Linear(hidden_features, out_features, bias=bias) + + if tuning_mode != None: + self.tuning_mode = tuning_mode + if tuning_mode == 'ssf': + self.ssf_scale_1, self.ssf_shift_1 = init_ssf_scale_shift(2 * hidden_features) + self.ssf_scale_2, self.ssf_shift_2 = init_ssf_scale_shift(out_features) + else: + pass + #raise NotImplementedError() + else: + self.tuning_mode = None + + + def forward(self, x: Tensor) -> Tensor: + x12 = self.w12(x) + if self.tuning_mode == 'ssf': + x12 = ssf_ada(x12, self.ssf_scale_1, self.ssf_shift_1) + + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + out = self.w3(hidden) + + if self.tuning_mode == 'ssf': + out = ssf_ada(out, self.ssf_scale_2, self.ssf_scale_2) + + return out + + +try: + from xformers.ops import SwiGLU + #import numpy.bool + XFORMERS_AVAILABLE = True +except ImportError: + SwiGLU = SwiGLUFFN + XFORMERS_AVAILABLE = False + +class SwiGLUFFNFused(SwiGLU): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = None, + drop: float = 0.0, + bias: bool = True, + ) -> None: + out_features = out_features or in_features + hidden_features = hidden_features or in_features + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + super().__init__( + in_features=in_features, + hidden_features=hidden_features, + out_features=out_features, + bias=bias, + ) + + +XFORMERS_AVAILABLE = False + + +class Attention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + window_size: int = 0, + tuning_mode: Optional[int] = None + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + if tuning_mode == 'lora': + self.tuning_mode = tuning_mode + self.qkv = LoRALinear(dim, dim * 3, bias=qkv_bias, r=8) + else: + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + + self.attn_drop = nn.Dropout(attn_drop) + + if tuning_mode == 'lora': + self.tuning_mode = tuning_mode + self.proj = LoRALinear(dim, dim, bias=proj_bias, r=8) + else: + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + if tuning_mode != None: + self.tuning_mode = tuning_mode + if tuning_mode == 'ssf': + self.ssf_scale_1, self.ssf_shift_1 = init_ssf_scale_shift(dim * 3) + self.ssf_scale_2, self.ssf_shift_2 = init_ssf_scale_shift(dim) + else: + pass + #raise NotImplementedError() + else: + self.tuning_mode = None + + #if not self.training: + # + # self.attn = ScaledDotProduct() + #self.attn = MultiHeadDispatch(dim_model=EMB, residual_dropout=DROPOUT, num_heads=HEADS, attention=attn) + + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + B, N, C = x.shape + if self.tuning_mode == 'ssf': + qkv = ssf_ada(self.qkv(x), self.ssf_scale_1, self.ssf_shift_1).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + else: + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + + q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] + attn = q @ k.transpose(-2, -1) + + if attn_bias is not None: + attn = attn + attn_bias[:, :, :N] + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + + if self.tuning_mode == 'ssf': + x = ssf_ada(x, self.ssf_scale_2, self.ssf_shift_2) + + x = self.proj_drop(x) + return x + + +class MemEffAttention(Attention): + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + if not XFORMERS_AVAILABLE: + #if True: + assert attn_bias is None, "xFormers is required for nested tensors usage" + return super().forward(x, attn_bias) + + B, N, C = x.shape + if self.tuning_mode == 'ssf': + qkv = ssf_ada(self.qkv(x), self.ssf_scale_1, self.ssf_shift_1).reshape(B, N, 3, self.num_heads, C // self.num_heads) + else: + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + q, k, v = unbind(qkv, 2) + if attn_bias is not None: + x = memory_efficient_attention(q, k, v, attn_bias=attn_bias[:, :, :N]) + else: + x = memory_efficient_attention(q, k, v) + x = x.reshape([B, N, C]) + + x = self.proj(x) + if self.tuning_mode == 'ssf': + x = ssf_ada(x, self.ssf_scale_2, self.ssf_shift_2) + + x = self.proj_drop(x) + return x + +XFORMERS_AVAILABLE = False + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = False, + proj_bias: bool = True, + ffn_bias: bool = True, + drop: float = 0.0, + attn_drop: float = 0.0, + init_values = None, + drop_path: float = 0.0, + act_layer: Callable[..., nn.Module] = nn.GELU, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_class: Callable[..., nn.Module] = Attention, + ffn_layer: Callable[..., nn.Module] = Mlp, + tuning_mode: Optional[int] = None + ) -> None: + super().__init__() + # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") + self.norm1 = norm_layer(dim) + self.attn = attn_class( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + attn_drop=attn_drop, + proj_drop=drop, + tuning_mode=tuning_mode + ) + + if tuning_mode != None: + self.tuning_mode = tuning_mode + if tuning_mode == 'ssf': + self.ssf_scale_1, self.ssf_shift_1 = init_ssf_scale_shift(dim) + self.ssf_scale_2, self.ssf_shift_2 = init_ssf_scale_shift(dim) + else: + pass + #raise NotImplementedError() + else: + self.tuning_mode = None + + self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ffn_layer( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + bias=ffn_bias, + ) + self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() + self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.sample_drop_ratio = drop_path + + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + def attn_residual_func(x: Tensor, attn_bias) -> Tensor: + if self.tuning_mode == 'ssf': + return self.ls1(self.attn(ssf_ada(self.norm1(x), self.ssf_scale_1, self.ssf_shift_1), attn_bias)) + else: + return self.ls1(self.attn(self.norm1(x), attn_bias)) + + def ffn_residual_func(x: Tensor) -> Tensor: + if self.tuning_mode == 'ssf': + return self.ls2(self.mlp(ssf_ada(self.norm2(x), self.ssf_scale_2, self.ssf_shift_2))) + else: + return self.ls2(self.mlp(self.norm2(x))) + + if self.training and self.sample_drop_ratio > 0.1: + # the overhead is compensated only for a drop path rate larger than 0.1 + x = drop_add_residual_stochastic_depth( + x, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + attn_bias=attn_bias + ) + x = drop_add_residual_stochastic_depth( + x, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + ) + elif self.training and self.sample_drop_ratio > 0.0: + x = x + self.drop_path1(attn_residual_func(x, attn_bias)) + x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 + else: + x = x + attn_residual_func(x, attn_bias) + x = x + ffn_residual_func(x) + return x + + +def drop_add_residual_stochastic_depth( + x: Tensor, + residual_func: Callable[[Tensor], Tensor], + sample_drop_ratio: float = 0.0, attn_bias=None +) -> Tensor: + # 1) extract subset using permutation + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + x_subset = x[brange] + + # 2) apply residual_func to get residual + residual = residual_func(x_subset, attn_bias) + + x_flat = x.flatten(1) + residual = residual.flatten(1) + + residual_scale_factor = b / sample_subset_size + + # 3) add the residual + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + return x_plus_residual.view_as(x) + + +def get_branges_scales(x, sample_drop_ratio=0.0): + b, n, d = x.shape + sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) + brange = (torch.randperm(b, device=x.device))[:sample_subset_size] + residual_scale_factor = b / sample_subset_size + return brange, residual_scale_factor + + +def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): + if scaling_vector is None: + x_flat = x.flatten(1) + residual = residual.flatten(1) + x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) + else: + x_plus_residual = scaled_index_add( + x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor + ) + return x_plus_residual + + +attn_bias_cache: Dict[Tuple, Any] = {} + + +def get_attn_bias_and_cat(x_list, branges=None): + """ + this will perform the index select, cat the tensors, and provide the attn_bias from cache + """ + batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] + all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) + if all_shapes not in attn_bias_cache.keys(): + seqlens = [] + for b, x in zip(batch_sizes, x_list): + for _ in range(b): + seqlens.append(x.shape[1]) + attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) + attn_bias._batch_sizes = batch_sizes + attn_bias_cache[all_shapes] = attn_bias + + if branges is not None: + cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) + else: + tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) + cat_tensors = torch.cat(tensors_bs1, dim=1) + + return attn_bias_cache[all_shapes], cat_tensors + + +def drop_add_residual_stochastic_depth_list( + x_list: List[Tensor], + residual_func: Callable[[Tensor, Any], Tensor], + sample_drop_ratio: float = 0.0, + scaling_vector=None, +) -> Tensor: + # 1) generate random set of indices for dropping samples in the batch + branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] + branges = [s[0] for s in branges_scales] + residual_scale_factors = [s[1] for s in branges_scales] + + # 2) get attention bias and index+concat the tensors + attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) + + # 3) apply residual_func to get residual, and split the result + residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore + + outputs = [] + for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): + outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) + return outputs + + +class NestedTensorBlock(Block): + def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: + """ + x_list contains a list of tensors to nest together and run + """ + assert isinstance(self.attn, MemEffAttention) + + if self.training and self.sample_drop_ratio > 0.0: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.attn(self.norm1(x), attn_bias=attn_bias) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.mlp(self.norm2(x)) + + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=attn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, + ) + x_list = drop_add_residual_stochastic_depth_list( + x_list, + residual_func=ffn_residual_func, + sample_drop_ratio=self.sample_drop_ratio, + scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, + ) + return x_list + else: + + def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) + + def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: + return self.ls2(self.mlp(self.norm2(x))) + + attn_bias, x = get_attn_bias_and_cat(x_list) + x = x + attn_residual_func(x, attn_bias=attn_bias) + x = x + ffn_residual_func(x) + return attn_bias.split(x) + + def forward(self, x_or_x_list, attn_bias=None): + if isinstance(x_or_x_list, Tensor): + return super().forward(x_or_x_list, attn_bias) + elif isinstance(x_or_x_list, list): + assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage" + return self.forward_nested(x_or_x_list) + else: + raise AssertionError + + +def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: + if not depth_first and include_root: + fn(module=module, name=name) + for child_name, child_module in module.named_children(): + child_name = ".".join((name, child_name)) if name else child_name + named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) + if depth_first and include_root: + fn(module=module, name=name) + return module + + +class BlockChunk(nn.ModuleList): + def forward(self, x, others=None): + for b in self: + if others == None: + x = b(x) + else: + x = b(x, others) + return x + + +class DinoVisionTransformer(nn.Module): + def __init__( + self, + img_size=518, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + ffn_bias=True, + proj_bias=True, + drop_path_rate=0.0, + drop_path_uniform=False, + init_values=1e-5, # for layerscale: None or 0 => no layerscale + embed_layer=PatchEmbed, + act_layer=nn.GELU, + block_fn=Block, + ffn_layer="mlp", + block_chunks=1, + num_register_tokens=0, + interpolate_antialias=False, + interpolate_offset=0.1, + multi_output=False, + tuning_mode=None, + **kwargs + ): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + proj_bias (bool): enable bias for proj in attn if True + ffn_bias (bool): enable bias for ffn if True + drop_path_rate (float): stochastic depth rate + drop_path_uniform (bool): apply uniform drop rate across blocks + weight_init (str): weight init scheme + init_values (float): layer-scale init values + embed_layer (nn.Module): patch embedding layer + act_layer (nn.Module): MLP activation layer + block_fn (nn.Module): transformer block class + ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" + block_chunks: (int) split block sequence into block_chunks units for FSDP wrap + num_register_tokens: (int) number of extra cls tokens (so-called "registers") + interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings + interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings + """ + super().__init__() + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.num_tokens = 1 + self.n_blocks = depth + self.num_heads = num_heads + self.patch_size = patch_size + self.num_register_tokens = num_register_tokens + self.interpolate_antialias = interpolate_antialias + self.interpolate_offset = interpolate_offset + + if tuning_mode != None: + self.tuning_mode = tuning_mode + if tuning_mode == 'ssf': + self.ssf_scale_1, self.ssf_shift_1 = init_ssf_scale_shift(embed_dim) + else: + pass + #raise NotImplementedError() + else: + self.tuning_mode = None + tuning_mode_list = [tuning_mode] * depth + + self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, tuning_mode=tuning_mode) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) + self.multi_output = multi_output + assert num_register_tokens >= 0 + self.register_tokens = ( + nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None + ) + + if drop_path_uniform is True: + dpr = [drop_path_rate] * depth + else: + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + if ffn_layer == "mlp": + logger.info("using MLP layer as FFN") + ffn_layer = Mlp + elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": + logger.info("using SwiGLU layer as FFN") + ffn_layer = SwiGLUFFNFused + elif ffn_layer == "identity": + logger.info("using Identity layer as FFN") + + def f(*args, **kwargs): + return nn.Identity() + + ffn_layer = f + else: + raise NotImplementedError + + blocks_list = [ + block_fn( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + ffn_bias=ffn_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + ffn_layer=ffn_layer, + init_values=init_values, + tuning_mode=tuning_mode_list[i] + ) + for i in range(depth) + ] + if block_chunks > 0: + self.chunked_blocks = True + chunked_blocks = [] + chunksize = depth // block_chunks + for i in range(0, depth, chunksize): + # this is to keep the block index consistent if we chunk the block list + chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) + self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) + else: + self.chunked_blocks = False + self.blocks = nn.ModuleList(blocks_list) + + self.norm = norm_layer(embed_dim) + self.head = nn.Identity() + + self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) + + self.init_weights() + + def init_weights(self): + trunc_normal_(self.pos_embed, std=0.02) + nn.init.normal_(self.cls_token, std=1e-6) + if self.register_tokens is not None: + nn.init.normal_(self.register_tokens, std=1e-6) + named_apply(init_weights_vit_timm, self) + + def interpolate_pos_encoding(self, x, w, h): + previous_dtype = x.dtype + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + pos_embed = self.pos_embed.float() + class_pos_embed = pos_embed[:, 0] + patch_pos_embed = pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_size + h0 = h // self.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset + + sqrt_N = math.sqrt(N) + sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2), + scale_factor=(sx, sy), + mode="bicubic", + antialias=self.interpolate_antialias, + ) + + assert int(w0) == patch_pos_embed.shape[-2] + assert int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) + + def prepare_tokens_with_masks(self, x, masks=None): + B, nc, w, h = x.shape + x = self.patch_embed(x) + if masks is not None: + x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) + + x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) + x = x + self.interpolate_pos_encoding(x, w, h) + + if self.register_tokens is not None: + x = torch.cat( + ( + x[:, :1], + self.register_tokens.expand(x.shape[0], -1, -1), + x[:, 1:], + ), + dim=1, + ) + + return x + + def forward_features_list(self, x_list, masks_list): + x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] + for blk in self.blocks: + x = blk(x) + + all_x = x + output = [] + for x, masks in zip(all_x, masks_list): + x_norm = self.norm(x) + output.append( + { + "x_norm_clstoken": x_norm[:, 0], + "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], + "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], + "x_prenorm": x, + "masks": masks, + } + ) + return output + + def forward_features(self, x, masks=None): + if isinstance(x, list): + return self.forward_features_list(x, masks) + + B, C, H, W = x.size() + pad_h = (self.patch_size - H % self.patch_size) + pad_w = (self.patch_size - W % self.patch_size) + if pad_h == self.patch_size: + pad_h = 0 + if pad_w == self.patch_size: + pad_w = 0 + #x = nn.functional.pad(x, (pad_h//2, pad_h-pad_h//2, pad_w//2, pad_w-pad_w//2)) + if pad_h + pad_w > 0: + x = torch.nn.functional.interpolate(x, (H+pad_h, W+pad_w), mode='bilinear') + + x = self.prepare_tokens_with_masks(x, masks) + + #for blk in self.blocks: + #x = blk(x) + + #x_norm = self.norm(x) + #if self.tuning_mode == 'ssf': + #x_norm = ssf_ada(x_norm, self.ssf_scale_1, self.ssf_shift_1) + + # return { + # "x_norm_clstoken": x_norm[:, 0], + # "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], + # "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], + # "x_prenorm": x, + # "masks": masks, + # } + # features = [] + # features.append(x_norm) + # features.append(x_norm) + # features.append(x_norm) + # features.append(x_norm) + # return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W, self.num_register_tokens)] + + if self.multi_output == False: + for blk in self.blocks: + x = blk(x) + x_norm = self.norm(x) + if self.tuning_mode == 'ssf': + x_norm = ssf_ada(x_norm, self.ssf_scale_1, self.ssf_shift_1) + + features = [] + features.append(x_norm) + features.append(x_norm) + features.append(x_norm) + features.append(x_norm) + return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W, self.num_register_tokens)] + else: + features = [] + for blk in self.blocks: + for idx, sub_blk in enumerate(blk): + x = sub_blk(x) + if (idx + 1) % (len(blk) // 4) == 0: + features.append(x) + + return [features, (B, (H+pad_h)//self.patch_size, (W+pad_w)//self.patch_size, H, W, self.num_register_tokens)] + + def _get_intermediate_layers_not_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + # If n is an int, take the n last blocks. If it's a list, take them + output, total_block_len = [], len(self.blocks) + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for i, blk in enumerate(self.blocks): + x = blk(x) + if i in blocks_to_take: + output.append(x) + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def _get_intermediate_layers_chunked(self, x, n=1): + x = self.prepare_tokens_with_masks(x) + output, i, total_block_len = [], 0, len(self.blocks[-1]) + # If n is an int, take the n last blocks. If it's a list, take them + blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n + for block_chunk in self.blocks: + for blk in block_chunk[i:]: # Passing the nn.Identity() + x = blk(x) + if i in blocks_to_take: + output.append(x) + i += 1 + assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" + return output + + def get_intermediate_layers( + self, + x: torch.Tensor, + n: Union[int, Sequence] = 1, # Layers or n last layers to take + reshape: bool = False, + return_class_token: bool = False, + norm=True, + ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: + if self.chunked_blocks: + outputs = self._get_intermediate_layers_chunked(x, n) + else: + outputs = self._get_intermediate_layers_not_chunked(x, n) + if norm: + outputs = [self.norm(out) for out in outputs] + class_tokens = [out[:, 0] for out in outputs] + outputs = [out[:, 1:] for out in outputs] + if reshape: + B, _, w, h = x.shape + outputs = [ + out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() + for out in outputs + ] + if return_class_token: + return tuple(zip(outputs, class_tokens)) + return tuple(outputs) + + def forward(self, *args, is_training=False, **kwargs): + ret = self.forward_features(*args, **kwargs) + return ret + # if is_training: + # return ret + # else: + # return self.head(ret["x_norm_clstoken"]) + + +def init_weights_vit_timm(module: nn.Module, name: str = ""): + """ViT weight initialization, original timm impl (for reproducibility)""" + if isinstance(module, nn.Linear): + trunc_normal_(module.weight, std=0.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + + +def load_ckpt_dino(checkpoint, model): + if checkpoint is not None: + try: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f) + except: + print('NO pretrained imagenet ckpt available! Check your path!') + del model.mask_token + return + + try: + model.load_state_dict(state_dict, strict=True) + except: + new_state_dict = {} + for key, value in state_dict.items(): + if 'blocks' in key: + key_new = 'blocks.0' + key[len('blocks'):] + else: + key_new = key + new_state_dict[key_new] = value + + model.load_state_dict(new_state_dict, strict=True) + del model.mask_token + return + else: + return + + +def vit_small(patch_size=14, num_register_tokens=0, checkpoint=None, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + + load_ckpt_dino(checkpoint, model) + + return model + + +def vit_base(patch_size=14, num_register_tokens=0, checkpoint=None, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + return model + + +def vit_large(patch_size=14, num_register_tokens=0, checkpoint=None, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + + if checkpoint is not None: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f) + try: + model.load_state_dict(state_dict, strict=True) + except: + new_state_dict = {} + for key, value in state_dict.items(): + if 'blocks' in key: + key_new = 'blocks.0' + key[len('blocks'):] + else: + key_new = key + new_state_dict[key_new] = value + + model.load_state_dict(new_state_dict, strict=True) + del model.mask_token + return model + + +def vit_giant2(patch_size=14, num_register_tokens=0, checkpoint=None, **kwargs): + """ + Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 + """ + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1536, + depth=40, + num_heads=24, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + ffn_layer='swiglu', + **kwargs, + ) + return model + + + +def vit_small_reg(patch_size=14, num_register_tokens=4, checkpoint=None, tuning_mode=None, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + tuning_mode=tuning_mode, + **kwargs, + ) + + load_ckpt_dino(checkpoint, model) + + return model + + +def vit_base_reg(patch_size=14, num_register_tokens=4, checkpoint=None, **kwargs): + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + **kwargs, + ) + + load_ckpt_dino(checkpoint, model) + + return model + + +def vit_large_reg(patch_size=14, num_register_tokens=4, checkpoint=None, tuning_mode=None, **kwargs): + model = DinoVisionTransformer( + img_size = 518, + patch_size=patch_size, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + tuning_mode=tuning_mode, + **kwargs, + ) + + load_ckpt_dino(checkpoint, model) + + return model + + +def vit_giant2_reg(patch_size=14, num_register_tokens=4, checkpoint=None, tuning_mode=None, **kwargs): + """ + Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 + """ + model = DinoVisionTransformer( + patch_size=patch_size, + embed_dim=1536, + depth=40, + num_heads=24, + mlp_ratio=4, + block_fn=partial(Block, attn_class=MemEffAttention), + num_register_tokens=num_register_tokens, + ffn_layer='swiglu', + tuning_mode=tuning_mode, + multi_output=True, + **kwargs, + ) + + load_ckpt_dino(checkpoint, model) + + return model + +if __name__ == '__main__': + try: + from custom_mmpkg.custom_mmcv.utils import Config + except: + from mmengine import Config + + #rgb = torch.rand((2, 3, 518, 518)).cuda() + + #cfg.data_basic['crop_size']['0'] + #cfg.data_basic['crop_size']['1'] + cfg = Config.fromfile('/mu.hu/projects/monodepth_vit/mono/configs/RAFTDecoder/vit.raft5.large.kitti.py') + + #rgb = torch.arange(0, 2*3*1036*1036, 1).cuda().float().view(2, 3, 1036, 1036) + rgb = torch.zeros(1, 3, 616, 1064).cuda() + cfg['tuning_mode'] = 'ssf' + #model = vit_large_reg(checkpoint="/cpfs02/shared/public/groups/local_map/yvan/pretrained_weight_repo/vit/dinov2_vitl14_reg4_pretrain.pth", kwarg=cfg).cuda() + model = vit_large_reg(tuning_mode='ssf').cuda() + + #import timm + #model2 = timm.models.vision_transformer.vit_large_patch14_dinov2().cuda() + #timm.models.load_checkpoint(model2, '/cpfs02/shared/public/yvan/pretrained_weight_repo/vit/dinov2_vitl14_pretrain.pth', filter_fn=timm.models.vision_transformer.checkpoint_filter_fn) + + out1 = model(rgb) + #out2 = model2(rgb) + temp = 0 + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/__init__.py new file mode 100644 index 00000000..36204128 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/backbones/__init__.py @@ -0,0 +1,11 @@ +from .ConvNeXt import convnext_xlarge +from .ConvNeXt import convnext_small +from .ConvNeXt import convnext_base +from .ConvNeXt import convnext_large +from .ConvNeXt import convnext_tiny +from .ViT_DINO import vit_large +from .ViT_DINO_reg import vit_small_reg, vit_large_reg, vit_giant2_reg + +__all__ = [ + 'convnext_xlarge', 'convnext_small', 'convnext_base', 'convnext_large', 'convnext_tiny', 'vit_small_reg', 'vit_large_reg', 'vit_giant2_reg' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/HourGlassDecoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/HourGlassDecoder.py new file mode 100644 index 00000000..e0843826 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/HourGlassDecoder.py @@ -0,0 +1,274 @@ +import torch +import torch.nn as nn +import numpy as np +import math +import torch.nn.functional as F + +def compute_depth_expectation(prob, depth_values): + depth_values = depth_values.view(*depth_values.shape, 1, 1) + depth = torch.sum(prob * depth_values, 1) + return depth + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3): + super(ConvBlock, self).__init__() + + if kernel_size == 3: + self.conv = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels, out_channels, 3, padding=0, stride=1), + ) + elif kernel_size == 1: + self.conv = nn.Conv2d(int(in_channels), int(out_channels), 1, padding=0, stride=1) + + self.nonlin = nn.ELU(inplace=True) + + def forward(self, x): + out = self.conv(x) + out = self.nonlin(out) + return out + + +class ConvBlock_double(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3): + super(ConvBlock_double, self).__init__() + + if kernel_size == 3: + self.conv = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels, out_channels, 3, padding=0, stride=1), + ) + elif kernel_size == 1: + self.conv = nn.Conv2d(int(in_channels), int(out_channels), 1, padding=0, stride=1) + + self.nonlin = nn.ELU(inplace=True) + self.conv_2 = nn.Conv2d(out_channels, out_channels, 1, padding=0, stride=1) + self.nonlin_2 =nn.ELU(inplace=True) + + def forward(self, x): + out = self.conv(x) + out = self.nonlin(out) + out = self.conv_2(out) + out = self.nonlin_2(out) + return out + +class DecoderFeature(nn.Module): + def __init__(self, feat_channels, num_ch_dec=[64, 64, 128, 256]): + super(DecoderFeature, self).__init__() + self.num_ch_dec = num_ch_dec + self.feat_channels = feat_channels + + self.upconv_3_0 = ConvBlock(self.feat_channels[3], self.num_ch_dec[3], kernel_size=1) + self.upconv_3_1 = ConvBlock_double( + self.feat_channels[2] + self.num_ch_dec[3], + self.num_ch_dec[3], + kernel_size=1) + + self.upconv_2_0 = ConvBlock(self.num_ch_dec[3], self.num_ch_dec[2], kernel_size=3) + self.upconv_2_1 = ConvBlock_double( + self.feat_channels[1] + self.num_ch_dec[2], + self.num_ch_dec[2], + kernel_size=3) + + self.upconv_1_0 = ConvBlock(self.num_ch_dec[2], self.num_ch_dec[1], kernel_size=3) + self.upconv_1_1 = ConvBlock_double( + self.feat_channels[0] + self.num_ch_dec[1], + self.num_ch_dec[1], + kernel_size=3) + self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + + def forward(self, ref_feature): + x = ref_feature[3] + + x = self.upconv_3_0(x) + x = torch.cat((self.upsample(x), ref_feature[2]), 1) + x = self.upconv_3_1(x) + + x = self.upconv_2_0(x) + x = torch.cat((self.upsample(x), ref_feature[1]), 1) + x = self.upconv_2_1(x) + + x = self.upconv_1_0(x) + x = torch.cat((self.upsample(x), ref_feature[0]), 1) + x = self.upconv_1_1(x) + return x + + +class UNet(nn.Module): + def __init__(self, inp_ch=32, output_chal=1, down_sample_times=3, channel_mode='v0'): + super(UNet, self).__init__() + basic_block = ConvBnReLU + num_depth = 128 + + self.conv0 = basic_block(inp_ch, num_depth) + if channel_mode == 'v0': + channels = [num_depth, num_depth//2, num_depth//4, num_depth//8, num_depth // 8] + elif channel_mode == 'v1': + channels = [num_depth, num_depth, num_depth, num_depth, num_depth, num_depth] + self.down_sample_times = down_sample_times + for i in range(down_sample_times): + setattr( + self, 'conv_%d' % i, + nn.Sequential( + basic_block(channels[i], channels[i+1], stride=2), + basic_block(channels[i+1], channels[i+1]) + ) + ) + for i in range(down_sample_times-1,-1,-1): + setattr(self, 'deconv_%d' % i, + nn.Sequential( + nn.ConvTranspose2d( + channels[i+1], + channels[i], + kernel_size=3, + padding=1, + output_padding=1, + stride=2, + bias=False), + nn.BatchNorm2d(channels[i]), + nn.ReLU(inplace=True) + ) + ) + self.prob = nn.Conv2d(num_depth, output_chal, 1, stride=1, padding=0) + + def forward(self, x): + features = {} + conv0 = self.conv0(x) + x = conv0 + features[0] = conv0 + for i in range(self.down_sample_times): + x = getattr(self, 'conv_%d' % i)(x) + features[i+1] = x + for i in range(self.down_sample_times-1,-1,-1): + x = features[i] + getattr(self, 'deconv_%d' % i)(x) + x = self.prob(x) + return x + +class ConvBnReLU(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1): + super(ConvBnReLU, self).__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=pad, + bias=False + ) + self.bn = nn.BatchNorm2d(out_channels) + + def forward(self, x): + return F.relu(self.bn(self.conv(x)), inplace=True) + + +class HourglassDecoder(nn.Module): + def __init__(self, cfg): + super(HourglassDecoder, self).__init__() + self.inchannels = cfg.model.decode_head.in_channels # [256, 512, 1024, 2048] + self.decoder_channels = cfg.model.decode_head.decoder_channel # [64, 64, 128, 256] + self.min_val = cfg.data_basic.depth_normalize[0] + self.max_val = cfg.data_basic.depth_normalize[1] + + self.num_ch_dec = self.decoder_channels # [64, 64, 128, 256] + self.num_depth_regressor_anchor = 512 + self.feat_channels = self.inchannels + unet_in_channel = self.num_ch_dec[1] + unet_out_channel = 256 + + self.decoder_mono = DecoderFeature(self.feat_channels, self.num_ch_dec) + self.conv_out_2 = UNet(inp_ch=unet_in_channel, + output_chal=unet_out_channel + 1, + down_sample_times=3, + channel_mode='v0', + ) + + self.depth_regressor_2 = nn.Sequential( + nn.Conv2d(unet_out_channel, + self.num_depth_regressor_anchor, + kernel_size=3, + padding=1, + ), + nn.BatchNorm2d(self.num_depth_regressor_anchor), + nn.ReLU(inplace=True), + nn.Conv2d( + self.num_depth_regressor_anchor, + self.num_depth_regressor_anchor, + kernel_size=1, + ) + ) + self.residual_channel = 16 + self.conv_up_2 = nn.Sequential( + nn.Conv2d(1 + 2 + unet_out_channel, self.residual_channel, 3, padding=1), + nn.BatchNorm2d(self.residual_channel), + nn.ReLU(), + nn.Conv2d(self.residual_channel, self.residual_channel, 3, padding=1), + nn.Upsample(scale_factor=4), + nn.Conv2d(self.residual_channel, self.residual_channel, 3, padding=1), + nn.ReLU(), + nn.Conv2d(self.residual_channel, 1, 1, padding=0), + ) + + def get_bins(self, bins_num): + depth_bins_vec = torch.linspace(math.log(self.min_val), math.log(self.max_val), bins_num, device='cuda') + depth_bins_vec = torch.exp(depth_bins_vec) + return depth_bins_vec + + def register_depth_expectation_anchor(self, bins_num, B): + depth_bins_vec = self.get_bins(bins_num) + depth_bins_vec = depth_bins_vec.unsqueeze(0).repeat(B, 1) + self.register_buffer('depth_expectation_anchor', depth_bins_vec, persistent=False) + + def upsample(self, x, scale_factor=2): + return F.interpolate(x, scale_factor=scale_factor, mode='nearest') + + def regress_depth_2(self, feature_map_d): + prob = self.depth_regressor_2(feature_map_d).softmax(dim=1) + B = prob.shape[0] + if "depth_expectation_anchor" not in self._buffers: + self.register_depth_expectation_anchor(self.num_depth_regressor_anchor, B) + d = compute_depth_expectation( + prob, + self.depth_expectation_anchor[:B, ...] + ).unsqueeze(1) + return d + + def create_mesh_grid(self, height, width, batch, device="cuda", set_buffer=True): + y, x = torch.meshgrid([torch.arange(0, height, dtype=torch.float32, device=device), + torch.arange(0, width, dtype=torch.float32, device=device)], indexing='ij') + meshgrid = torch.stack((x, y)) + meshgrid = meshgrid.unsqueeze(0).repeat(batch, 1, 1, 1) + return meshgrid + + def forward(self, features_mono, **kwargs): + ''' + trans_ref2src: list of transformation matrix from the reference view to source view. [B, 4, 4] + inv_intrinsic_pool: list of inverse intrinsic matrix. + features_mono: features of reference and source views. [[ref_f1, ref_f2, ref_f3, ref_f4],[src1_f1, src1_f2, src1_f3, src1_f4], ...]. + ''' + outputs = {} + # get encoder feature of the reference view + ref_feat = features_mono + + feature_map_mono = self.decoder_mono(ref_feat) + feature_map_mono_pred = self.conv_out_2(feature_map_mono) + confidence_map_2 = feature_map_mono_pred[:, -1:, :, :] + feature_map_d_2 = feature_map_mono_pred[:, :-1, :, :] + + depth_pred_2 = self.regress_depth_2(feature_map_d_2) + + B, _, H, W = depth_pred_2.shape + + meshgrid = self.create_mesh_grid(H, W, B) + + depth_pred_mono = self.upsample(depth_pred_2, scale_factor=4) + 1e-1 * \ + self.conv_up_2( + torch.cat((depth_pred_2, meshgrid[:B, ...], feature_map_d_2), 1) + ) + confidence_map_mono = self.upsample(confidence_map_2, scale_factor=4) + + outputs=dict( + prediction=depth_pred_mono, + confidence=confidence_map_mono, + pred_logit=None, + ) + return outputs \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/RAFTDepthNormalDPTDecoder5.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/RAFTDepthNormalDPTDecoder5.py new file mode 100644 index 00000000..7790d3a0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/RAFTDepthNormalDPTDecoder5.py @@ -0,0 +1,1031 @@ +import torch +import torch.nn as nn +import numpy as np +import math +import torch.nn.functional as F + +# LORA finetuning originally by edwardjhu +class LoRALayer(): + def __init__( + self, + r: int, + lora_alpha: int, + lora_dropout: float, + merge_weights: bool, + ): + self.r = r + self.lora_alpha = lora_alpha + # Optional dropout + if lora_dropout > 0.: + self.lora_dropout = nn.Dropout(p=lora_dropout) + else: + self.lora_dropout = lambda x: x + # Mark the weight as unmerged + self.merged = False + self.merge_weights = merge_weights + +class LoRALinear(nn.Linear, LoRALayer): + # LoRA implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0., + fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) + merge_weights: bool = True, + **kwargs + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, + merge_weights=merge_weights) + + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features))) + self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r))) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.transpose(0, 1) + + def reset_parameters(self): + #nn.Linear.reset_parameters(self) + if hasattr(self, 'lora_A'): + # initialize B the same way as the default for nn.Linear and A to zero + # this is different than what is described in the paper but should not affect performance + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + # def train(self, mode: bool = True): + # def T(w): + # return w.transpose(0, 1) if self.fan_in_fan_out else w + # nn.Linear.train(self, mode) + # if mode: + # if self.merge_weights and self.merged: + # # Make sure that the weights are not merged + # if self.r > 0: + # self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling + # self.merged = False + # else: + # if self.merge_weights and not self.merged: + # # Merge the weights and mark it + # if self.r > 0: + # self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling + # self.merged = True + + def forward(self, x: torch.Tensor): + def T(w): + return w.transpose(0, 1) if self.fan_in_fan_out else w + if self.r > 0 and not self.merged: + result = F.linear(x, T(self.weight), bias=self.bias) + result += (self.lora_dropout(x) @ self.lora_A.transpose(0, 1) @ self.lora_B.transpose(0, 1)) * self.scaling + return result + else: + return F.linear(x, T(self.weight), bias=self.bias) + +class ConvLoRA(nn.Conv2d, LoRALayer): + def __init__(self, in_channels, out_channels, kernel_size, r=0, lora_alpha=1, lora_dropout=0., merge_weights=True, **kwargs): + #self.conv = conv_module(in_channels, out_channels, kernel_size, **kwargs) + nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, **kwargs) + LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) + assert isinstance(kernel_size, int) + + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter( + self.weight.new_zeros((r * kernel_size, in_channels * kernel_size)) + ) + self.lora_B = nn.Parameter( + self.weight.new_zeros((out_channels//self.groups*kernel_size, r*kernel_size)) + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + self.merged = False + + def reset_parameters(self): + #self.conv.reset_parameters() + if hasattr(self, 'lora_A'): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + # def train(self, mode=True): + # super(ConvLoRA, self).train(mode) + # if mode: + # if self.merge_weights and self.merged: + # if self.r > 0: + # # Make sure that the weights are not merged + # self.conv.weight.data -= (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling + # self.merged = False + # else: + # if self.merge_weights and not self.merged: + # if self.r > 0: + # # Merge the weights and mark it + # self.conv.weight.data += (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling + # self.merged = True + + def forward(self, x): + if self.r > 0 and not self.merged: + # return self.conv._conv_forward( + # x, + # self.conv.weight + (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling, + # self.conv.bias + # ) + weight = self.weight + (self.lora_B @ self.lora_A).view(self.weight.shape) * self.scaling + bias = self.bias + + return F.conv2d(x, weight, bias=bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) + else: + return F.conv2d(x, self.weight, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) + +class ConvTransposeLoRA(nn.ConvTranspose2d, LoRALayer): + def __init__(self, in_channels, out_channels, kernel_size, r=0, lora_alpha=1, lora_dropout=0., merge_weights=True, **kwargs): + #self.conv = conv_module(in_channels, out_channels, kernel_size, **kwargs) + nn.ConvTranspose2d.__init__(self, in_channels, out_channels, kernel_size, **kwargs) + LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) + assert isinstance(kernel_size, int) + + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter( + self.weight.new_zeros((r * kernel_size, in_channels * kernel_size)) + ) + self.lora_B = nn.Parameter( + self.weight.new_zeros((out_channels//self.groups*kernel_size, r*kernel_size)) + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + self.merged = False + + def reset_parameters(self): + #self.conv.reset_parameters() + if hasattr(self, 'lora_A'): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + # def train(self, mode=True): + # super(ConvTransposeLoRA, self).train(mode) + # if mode: + # if self.merge_weights and self.merged: + # if self.r > 0: + # # Make sure that the weights are not merged + # self.conv.weight.data -= (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling + # self.merged = False + # else: + # if self.merge_weights and not self.merged: + # if self.r > 0: + # # Merge the weights and mark it + # self.conv.weight.data += (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling + # self.merged = True + + def forward(self, x): + if self.r > 0 and not self.merged: + weight = self.weight + (self.lora_B @ self.lora_A).view(self.weight.shape) * self.scaling + bias = self.bias + return F.conv_transpose2d(x, weight, + bias=bias, stride=self.stride, padding=self.padding, output_padding=self.output_padding, + groups=self.groups, dilation=self.dilation) + else: + return F.conv_transpose2d(x, self.weight, + bias=self.bias, stride=self.stride, padding=self.padding, output_padding=self.output_padding, + groups=self.groups, dilation=self.dilation) + #return self.conv(x) + +class Conv2dLoRA(ConvLoRA): + def __init__(self, *args, **kwargs): + super(Conv2dLoRA, self).__init__(*args, **kwargs) + +class ConvTranspose2dLoRA(ConvTransposeLoRA): + def __init__(self, *args, **kwargs): + super(ConvTranspose2dLoRA, self).__init__(*args, **kwargs) + + +def compute_depth_expectation(prob, depth_values): + depth_values = depth_values.view(*depth_values.shape, 1, 1) + depth = torch.sum(prob * depth_values, 1) + return depth + +def interpolate_float32(x, size=None, scale_factor=None, mode='nearest', align_corners=None): + return F.interpolate(x.float(), size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners) + +# def upflow8(flow, mode='bilinear'): +# new_size = (8 * flow.shape[2], 8 * flow.shape[3]) +# return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True) + +def upflow4(flow, mode='bilinear'): + new_size = (4 * flow.shape[2], 4 * flow.shape[3]) + return F.interpolate(flow, size=new_size, mode=mode, align_corners=True) + +def coords_grid(batch, ht, wd): + # coords = torch.meshgrid(torch.arange(ht), torch.arange(wd)) + coords = (torch.zeros((ht, wd)), torch.zeros((ht, wd)), torch.zeros((ht, wd)), torch.zeros((ht, wd)), torch.zeros((ht, wd)), torch.zeros((ht, wd))) + coords = torch.stack(coords[::-1], dim=0).float() + return coords[None].repeat(batch, 1, 1, 1) + +def norm_normalize(norm_out): + min_kappa = 0.01 + norm_x, norm_y, norm_z, kappa = torch.split(norm_out, 1, dim=1) + norm = torch.sqrt(norm_x ** 2.0 + norm_y ** 2.0 + norm_z ** 2.0) + 1e-10 + kappa = F.elu(kappa) + 1.0 + min_kappa + final_out = torch.cat([norm_x / norm, norm_y / norm, norm_z / norm, kappa], dim=1) + return final_out + +# uncertainty-guided sampling (only used during training) +@torch.no_grad() +def sample_points(init_normal, gt_norm_mask, sampling_ratio, beta): + device = init_normal.device + B, _, H, W = init_normal.shape + N = int(sampling_ratio * H * W) + beta = beta + + # uncertainty map + uncertainty_map = -1 * init_normal[:, -1, :, :] # B, H, W + + # gt_invalid_mask (B, H, W) + if gt_norm_mask is not None: + gt_invalid_mask = F.interpolate(gt_norm_mask.float(), size=[H, W], mode='nearest') + gt_invalid_mask = gt_invalid_mask[:, 0, :, :] < 0.5 + uncertainty_map[gt_invalid_mask] = -1e4 + + # (B, H*W) + _, idx = uncertainty_map.view(B, -1).sort(1, descending=True) + + # importance sampling + if int(beta * N) > 0: + importance = idx[:, :int(beta * N)] # B, beta*N + + # remaining + remaining = idx[:, int(beta * N):] # B, H*W - beta*N + + # coverage + num_coverage = N - int(beta * N) + + if num_coverage <= 0: + samples = importance + else: + coverage_list = [] + for i in range(B): + idx_c = torch.randperm(remaining.size()[1]) # shuffles "H*W - beta*N" + coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1)) # 1, N-beta*N + coverage = torch.cat(coverage_list, dim=0) # B, N-beta*N + samples = torch.cat((importance, coverage), dim=1) # B, N + + else: + # remaining + remaining = idx[:, :] # B, H*W + + # coverage + num_coverage = N + + coverage_list = [] + for i in range(B): + idx_c = torch.randperm(remaining.size()[1]) # shuffles "H*W - beta*N" + coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1)) # 1, N-beta*N + coverage = torch.cat(coverage_list, dim=0) # B, N-beta*N + samples = coverage + + # point coordinates + rows_int = samples // W # 0 for first row, H-1 for last row + rows_float = rows_int / float(H-1) # 0 to 1.0 + rows_float = (rows_float * 2.0) - 1.0 # -1.0 to 1.0 + + cols_int = samples % W # 0 for first column, W-1 for last column + cols_float = cols_int / float(W-1) # 0 to 1.0 + cols_float = (cols_float * 2.0) - 1.0 # -1.0 to 1.0 + + point_coords = torch.zeros(B, 1, N, 2) + point_coords[:, 0, :, 0] = cols_float # x coord + point_coords[:, 0, :, 1] = rows_float # y coord + point_coords = point_coords.to(device) + return point_coords, rows_int, cols_int + +class FlowHead(nn.Module): + def __init__(self, input_dim=128, hidden_dim=256, output_dim_depth=2, output_dim_norm=4, tuning_mode=None): + super(FlowHead, self).__init__() + self.conv1d = Conv2dLoRA(input_dim, hidden_dim // 2, 3, padding=1, r = 8 if tuning_mode == 'lora' else 0) + self.conv2d = Conv2dLoRA(hidden_dim // 2, output_dim_depth, 3, padding=1, r = 8 if tuning_mode == 'lora' else 0) + + self.conv1n = Conv2dLoRA(input_dim, hidden_dim // 2, 3, padding=1, r = 8 if tuning_mode == 'lora' else 0) + self.conv2n = Conv2dLoRA(hidden_dim // 2, output_dim_norm, 3, padding=1, r = 8 if tuning_mode == 'lora' else 0) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + depth = self.conv2d(self.relu(self.conv1d(x))) + normal = self.conv2n(self.relu(self.conv1n(x))) + return torch.cat((depth, normal), dim=1) + + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim, input_dim, kernel_size=3, tuning_mode=None): + super(ConvGRU, self).__init__() + self.convz = Conv2dLoRA(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2, r = 8 if tuning_mode == 'lora' else 0) + self.convr = Conv2dLoRA(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2, r = 8 if tuning_mode == 'lora' else 0) + self.convq = Conv2dLoRA(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2, r = 8 if tuning_mode == 'lora' else 0) + + def forward(self, h, cz, cr, cq, *x_list): + x = torch.cat(x_list, dim=1) + hx = torch.cat([h, x], dim=1) + + z = torch.sigmoid((self.convz(hx) + cz)) + r = torch.sigmoid((self.convr(hx) + cr)) + q = torch.tanh((self.convq(torch.cat([r*h, x], dim=1)) + cq)) + + # z = torch.sigmoid((self.convz(hx) + cz).float()) + # r = torch.sigmoid((self.convr(hx) + cr).float()) + # q = torch.tanh((self.convq(torch.cat([r*h, x], dim=1)) + cq).float()) + + h = (1-z) * h + z * q + return h + +def pool2x(x): + return F.avg_pool2d(x, 3, stride=2, padding=1) + +def pool4x(x): + return F.avg_pool2d(x, 5, stride=4, padding=1) + +def interp(x, dest): + interp_args = {'mode': 'bilinear', 'align_corners': True} + return interpolate_float32(x, dest.shape[2:], **interp_args) + +class BasicMultiUpdateBlock(nn.Module): + def __init__(self, args, hidden_dims=[], out_dims=2, tuning_mode=None): + super().__init__() + self.args = args + self.n_gru_layers = args.model.decode_head.n_gru_layers # 3 + self.n_downsample = args.model.decode_head.n_downsample # 3, resolution of the disparity field (1/2^K) + + # self.encoder = BasicMotionEncoder(args) + # encoder_output_dim = 128 # if there is corr volume + encoder_output_dim = 6 # no corr volume + + self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (self.n_gru_layers > 1), tuning_mode=tuning_mode) + self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (self.n_gru_layers == 3) + hidden_dims[2], tuning_mode=tuning_mode) + self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1], tuning_mode=tuning_mode) + self.flow_head = FlowHead(hidden_dims[2], hidden_dim=2*hidden_dims[2], tuning_mode=tuning_mode) + factor = 2**self.n_downsample + + self.mask = nn.Sequential( + Conv2dLoRA(hidden_dims[2], hidden_dims[2], 3, padding=1, r = 8 if tuning_mode == 'lora' else 0), + nn.ReLU(inplace=True), + Conv2dLoRA(hidden_dims[2], (factor**2)*9, 1, padding=0, r = 8 if tuning_mode == 'lora' else 0)) + + def forward(self, net, inp, corr=None, flow=None, iter08=True, iter16=True, iter32=True, update=True): + + if iter32: + net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1])) + if iter16: + if self.n_gru_layers > 2: + net[1] = self.gru16(net[1], *(inp[1]), interp(pool2x(net[0]), net[1]), interp(net[2], net[1])) + else: + net[1] = self.gru16(net[1], *(inp[1]), interp(pool2x(net[0]), net[1])) + if iter08: + if corr is not None: + motion_features = self.encoder(flow, corr) + else: + motion_features = flow + if self.n_gru_layers > 1: + net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0])) + else: + net[0] = self.gru08(net[0], *(inp[0]), motion_features) + + if not update: + return net + + delta_flow = self.flow_head(net[0]) + + # scale mask to balence gradients + mask = .25 * self.mask(net[0]) + return net, mask, delta_flow + +class LayerNorm2d(nn.LayerNorm): + def __init__(self, dim): + super(LayerNorm2d, self).__init__(dim) + + def forward(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + x = super(LayerNorm2d, self).forward(x) + x = x.permute(0, 3, 1, 2).contiguous() + return x + +class ResidualBlock(nn.Module): + def __init__(self, in_planes, planes, norm_fn='group', stride=1, tuning_mode=None): + super(ResidualBlock, self).__init__() + + self.conv1 = Conv2dLoRA(in_planes, planes, kernel_size=3, padding=1, stride=stride, r = 8 if tuning_mode == 'lora' else 0) + self.conv2 = Conv2dLoRA(planes, planes, kernel_size=3, padding=1, r = 8 if tuning_mode == 'lora' else 0) + self.relu = nn.ReLU(inplace=True) + + num_groups = planes // 8 + + if norm_fn == 'group': + self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + + elif norm_fn == 'batch': + self.norm1 = nn.BatchNorm2d(planes) + self.norm2 = nn.BatchNorm2d(planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.BatchNorm2d(planes) + + elif norm_fn == 'instance': + self.norm1 = nn.InstanceNorm2d(planes) + self.norm2 = nn.InstanceNorm2d(planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.InstanceNorm2d(planes) + + elif norm_fn == 'layer': + self.norm1 = LayerNorm2d(planes) + self.norm2 = LayerNorm2d(planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = LayerNorm2d(planes) + + elif norm_fn == 'none': + self.norm1 = nn.Sequential() + self.norm2 = nn.Sequential() + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.Sequential() + + if stride == 1 and in_planes == planes: + self.downsample = None + + else: + self.downsample = nn.Sequential( + Conv2dLoRA(in_planes, planes, kernel_size=1, stride=stride, r = 8 if tuning_mode == 'lora' else 0), self.norm3) + + def forward(self, x): + y = x + y = self.conv1(y) + y = self.norm1(y) + y = self.relu(y) + y = self.conv2(y) + y = self.norm2(y) + y = self.relu(y) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x+y) + + +class ContextFeatureEncoder(nn.Module): + ''' + Encoder features are used to: + 1. initialize the hidden state of the update operator + 2. and also injected into the GRU during each iteration of the update operator + ''' + def __init__(self, in_dim, output_dim, tuning_mode=None): + ''' + in_dim = [x4, x8, x16, x32] + output_dim = [hindden_dims, context_dims] + [[x4,x8,x16,x32],[x4,x8,x16,x32]] + ''' + super().__init__() + + output_list = [] + for dim in output_dim: + conv_out = nn.Sequential( + ResidualBlock(in_dim[0], dim[0], 'layer', stride=1, tuning_mode=tuning_mode), + Conv2dLoRA(dim[0], dim[0], 3, padding=1, r = 8 if tuning_mode == 'lora' else 0)) + output_list.append(conv_out) + + self.outputs04 = nn.ModuleList(output_list) + + output_list = [] + for dim in output_dim: + conv_out = nn.Sequential( + ResidualBlock(in_dim[1], dim[1], 'layer', stride=1, tuning_mode=tuning_mode), + Conv2dLoRA(dim[1], dim[1], 3, padding=1, r = 8 if tuning_mode == 'lora' else 0)) + output_list.append(conv_out) + + self.outputs08 = nn.ModuleList(output_list) + + output_list = [] + for dim in output_dim: + conv_out = nn.Sequential( + ResidualBlock(in_dim[2], dim[2], 'layer', stride=1, tuning_mode=tuning_mode), + Conv2dLoRA(dim[2], dim[2], 3, padding=1, r = 8 if tuning_mode == 'lora' else 0)) + output_list.append(conv_out) + + self.outputs16 = nn.ModuleList(output_list) + + # output_list = [] + # for dim in output_dim: + # conv_out = Conv2dLoRA(in_dim[3], dim[3], 3, padding=1) + # output_list.append(conv_out) + + # self.outputs32 = nn.ModuleList(output_list) + + def forward(self, encoder_features): + x_4, x_8, x_16, x_32 = encoder_features + + outputs04 = [f(x_4) for f in self.outputs04] + outputs08 = [f(x_8) for f in self.outputs08] + outputs16 = [f(x_16)for f in self.outputs16] + # outputs32 = [f(x_32) for f in self.outputs32] + + return (outputs04, outputs08, outputs16) + +class ConvBlock(nn.Module): + # reimplementation of DPT + def __init__(self, channels, tuning_mode=None): + super(ConvBlock, self).__init__() + + self.act = nn.ReLU(inplace=True) + self.conv1 = Conv2dLoRA( + channels, + channels, + kernel_size=3, + stride=1, + padding=1, + r = 8 if tuning_mode == 'lora' else 0 + ) + self.conv2 = Conv2dLoRA( + channels, + channels, + kernel_size=3, + stride=1, + padding=1, + r = 8 if tuning_mode == 'lora' else 0 + ) + + def forward(self, x): + out = self.act(x) + out = self.conv1(out) + out = self.act(out) + out = self.conv2(out) + return x + out + +class FuseBlock(nn.Module): + # reimplementation of DPT + def __init__(self, in_channels, out_channels, fuse=True, upsample=True, scale_factor=2, tuning_mode=None): + super(FuseBlock, self).__init__() + + self.fuse = fuse + self.scale_factor = scale_factor + self.way_trunk = ConvBlock(in_channels, tuning_mode=tuning_mode) + if self.fuse: + self.way_branch = ConvBlock(in_channels, tuning_mode=tuning_mode) + + self.out_conv = Conv2dLoRA( + in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0, + r = 8 if tuning_mode == 'lora' else 0 + ) + self.upsample = upsample + + def forward(self, x1, x2=None): + if x2 is not None: + x2 = self.way_branch(x2) + x1 = x1 + x2 + + out = self.way_trunk(x1) + + if self.upsample: + out = interpolate_float32( + out, scale_factor=self.scale_factor, mode="bilinear", align_corners=True + ) + out = self.out_conv(out) + return out + +class Readout(nn.Module): + # From DPT + def __init__(self, in_features, use_cls_token=True, num_register_tokens=0, tuning_mode=None): + super(Readout, self).__init__() + self.use_cls_token = use_cls_token + if self.use_cls_token == True: + self.project_patch = LoRALinear(in_features, in_features, r = 8 if tuning_mode == 'lora' else 0) + self.project_learn = LoRALinear((1 + num_register_tokens) * in_features, in_features, bias=False, r = 8 if tuning_mode == 'lora' else 0) + self.act = nn.GELU() + else: + self.project = nn.Identity() + + def forward(self, x): + + if self.use_cls_token == True: + x_patch = self.project_patch(x[0]) + x_learn = self.project_learn(x[1]) + x_learn = x_learn.expand_as(x_patch).contiguous() + features = x_patch + x_learn + return self.act(features) + else: + return self.project(x) + +class Token2Feature(nn.Module): + # From DPT + def __init__(self, vit_channel, feature_channel, scale_factor, use_cls_token=True, num_register_tokens=0, tuning_mode=None): + super(Token2Feature, self).__init__() + self.scale_factor = scale_factor + self.readoper = Readout(in_features=vit_channel, use_cls_token=use_cls_token, num_register_tokens=num_register_tokens, tuning_mode=tuning_mode) + if scale_factor > 1 and isinstance(scale_factor, int): + self.sample = ConvTranspose2dLoRA(r = 8 if tuning_mode == 'lora' else 0, + in_channels=vit_channel, + out_channels=feature_channel, + kernel_size=scale_factor, + stride=scale_factor, + padding=0, + ) + + elif scale_factor > 1: + self.sample = nn.Sequential( + # Upsample2(upscale=scale_factor), + # nn.Upsample(scale_factor=scale_factor), + Conv2dLoRA(r = 8 if tuning_mode == 'lora' else 0, + in_channels=vit_channel, + out_channels=feature_channel, + kernel_size=1, + stride=1, + padding=0, + ), + ) + + + elif scale_factor < 1: + scale_factor = int(1.0 / scale_factor) + self.sample = Conv2dLoRA(r = 8 if tuning_mode == 'lora' else 0, + in_channels=vit_channel, + out_channels=feature_channel, + kernel_size=scale_factor+1, + stride=scale_factor, + padding=1, + ) + + else: + self.sample = nn.Identity() + + def forward(self, x): + x = self.readoper(x) + #if use_cls_token == True: + x = x.permute(0, 3, 1, 2).contiguous() + if isinstance(self.scale_factor, float): + x = interpolate_float32(x.float(), scale_factor=self.scale_factor, mode='nearest') + x = self.sample(x) + return x + +class EncoderFeature(nn.Module): + def __init__(self, vit_channel, num_ch_dec=[256, 512, 1024, 1024], use_cls_token=True, num_register_tokens=0, tuning_mode=None): + super(EncoderFeature, self).__init__() + self.vit_channel = vit_channel + self.num_ch_dec = num_ch_dec + + self.read_3 = Token2Feature(self.vit_channel, self.num_ch_dec[3], scale_factor=1, use_cls_token=use_cls_token, num_register_tokens=num_register_tokens, tuning_mode=tuning_mode) + self.read_2 = Token2Feature(self.vit_channel, self.num_ch_dec[2], scale_factor=1, use_cls_token=use_cls_token, num_register_tokens=num_register_tokens, tuning_mode=tuning_mode) + self.read_1 = Token2Feature(self.vit_channel, self.num_ch_dec[1], scale_factor=2, use_cls_token=use_cls_token, num_register_tokens=num_register_tokens, tuning_mode=tuning_mode) + self.read_0 = Token2Feature(self.vit_channel, self.num_ch_dec[0], scale_factor=7/2, use_cls_token=use_cls_token, num_register_tokens=num_register_tokens, tuning_mode=tuning_mode) + + def forward(self, ref_feature): + x = self.read_3(ref_feature[3]) # 1/14 + x2 = self.read_2(ref_feature[2]) # 1/14 + x1 = self.read_1(ref_feature[1]) # 1/7 + x0 = self.read_0(ref_feature[0]) # 1/4 + + return x, x2, x1, x0 + +class DecoderFeature(nn.Module): + def __init__(self, vit_channel, num_ch_dec=[128, 256, 512, 1024, 1024], use_cls_token=True, tuning_mode=None): + super(DecoderFeature, self).__init__() + self.vit_channel = vit_channel + self.num_ch_dec = num_ch_dec + + self.upconv_3 = FuseBlock( + self.num_ch_dec[4], + self.num_ch_dec[3], + fuse=False, upsample=False, tuning_mode=tuning_mode) + + self.upconv_2 = FuseBlock( + self.num_ch_dec[3], + self.num_ch_dec[2], + tuning_mode=tuning_mode) + + self.upconv_1 = FuseBlock( + self.num_ch_dec[2], + self.num_ch_dec[1] + 2, + scale_factor=7/4, + tuning_mode=tuning_mode) + + # self.upconv_0 = FuseBlock( + # self.num_ch_dec[1], + # self.num_ch_dec[0] + 1, + # ) + + def forward(self, ref_feature): + x, x2, x1, x0 = ref_feature # 1/14 1/14 1/7 1/4 + + x = self.upconv_3(x) # 1/14 + x = self.upconv_2(x, x2) # 1/7 + x = self.upconv_1(x, x1) # 1/4 + # x = self.upconv_0(x, x0) # 4/7 + return x + +class RAFTDepthNormalDPT5(nn.Module): + def __init__(self, cfg): + super().__init__() + self.in_channels = cfg.model.decode_head.in_channels # [1024, 1024, 1024, 1024] + self.feature_channels = cfg.model.decode_head.feature_channels # [256, 512, 1024, 1024] [2/7, 1/7, 1/14, 1/14] + self.decoder_channels = cfg.model.decode_head.decoder_channels # [128, 256, 512, 1024, 1024] [-, 1/4, 1/7, 1/14, 1/14] + self.use_cls_token = cfg.model.decode_head.use_cls_token + self.up_scale = cfg.model.decode_head.up_scale + self.num_register_tokens = cfg.model.decode_head.num_register_tokens + self.min_val = cfg.data_basic.depth_normalize[0] + self.max_val = cfg.data_basic.depth_normalize[1] + self.regress_scale = 100.0\ + + try: + tuning_mode = cfg.model.decode_head.tuning_mode + except: + tuning_mode = None + self.tuning_mode = tuning_mode + + self.hidden_dims = self.context_dims = cfg.model.decode_head.hidden_channels # [128, 128, 128, 128] + self.n_gru_layers = cfg.model.decode_head.n_gru_layers # 3 + self.n_downsample = cfg.model.decode_head.n_downsample # 3, resolution of the disparity field (1/2^K) + self.iters = cfg.model.decode_head.iters # 22 + self.slow_fast_gru = cfg.model.decode_head.slow_fast_gru # True + + self.num_depth_regressor_anchor = 256 # 512 + self.used_res_channel = self.decoder_channels[1] # now, use 2/7 res + self.token2feature = EncoderFeature(self.in_channels[0], self.feature_channels, self.use_cls_token, self.num_register_tokens, tuning_mode=tuning_mode) + self.decoder_mono = DecoderFeature(self.in_channels, self.decoder_channels, tuning_mode=tuning_mode) + self.depth_regressor = nn.Sequential( + Conv2dLoRA(self.used_res_channel, + self.num_depth_regressor_anchor, + kernel_size=3, + padding=1, r = 8 if tuning_mode == 'lora' else 0), + # nn.BatchNorm2d(self.num_depth_regressor_anchor), + nn.ReLU(inplace=True), + Conv2dLoRA(self.num_depth_regressor_anchor, + self.num_depth_regressor_anchor, + kernel_size=1, r = 8 if tuning_mode == 'lora' else 0), + ) + self.normal_predictor = nn.Sequential( + Conv2dLoRA(self.used_res_channel, + 128, + kernel_size=3, + padding=1, r = 8 if tuning_mode == 'lora' else 0,), + # nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + Conv2dLoRA(128, 128, kernel_size=1, r = 8 if tuning_mode == 'lora' else 0), nn.ReLU(inplace=True), + Conv2dLoRA(128, 128, kernel_size=1, r = 8 if tuning_mode == 'lora' else 0), nn.ReLU(inplace=True), + Conv2dLoRA(128, 3, kernel_size=1, r = 8 if tuning_mode == 'lora' else 0), + ) + + self.context_feature_encoder = ContextFeatureEncoder(self.feature_channels, [self.hidden_dims, self.context_dims], tuning_mode=tuning_mode) + self.context_zqr_convs = nn.ModuleList([Conv2dLoRA(self.context_dims[i], self.hidden_dims[i]*3, 3, padding=3//2, r = 8 if tuning_mode == 'lora' else 0) for i in range(self.n_gru_layers)]) + self.update_block = BasicMultiUpdateBlock(cfg, hidden_dims=self.hidden_dims, out_dims=6, tuning_mode=tuning_mode) + + self.relu = nn.ReLU(inplace=True) + + def get_bins(self, bins_num): + depth_bins_vec = torch.linspace(math.log(self.min_val), math.log(self.max_val), bins_num, device=next(self.parameters()).device) + depth_bins_vec = torch.exp(depth_bins_vec) + return depth_bins_vec + + def register_depth_expectation_anchor(self, bins_num, B): + depth_bins_vec = self.get_bins(bins_num) + depth_bins_vec = depth_bins_vec.unsqueeze(0).repeat(B, 1) + self.register_buffer('depth_expectation_anchor', depth_bins_vec, persistent=False) + + def clamp(self, x): + y = self.relu(x - self.min_val) + self.min_val + y = self.max_val - self.relu(self.max_val - y) + return y + + def regress_depth(self, feature_map_d): + prob_feature = self.depth_regressor(feature_map_d) + prob = prob_feature.softmax(dim=1) + #prob = prob_feature.float().softmax(dim=1) + + ## Error logging + if torch.isnan(prob).any(): + print('prob_feat_nan!!!') + if torch.isinf(prob).any(): + print('prob_feat_inf!!!') + + # h = prob[0,:,0,0].cpu().numpy().reshape(-1) + # import matplotlib.pyplot as plt + # plt.bar(range(len(h)), h) + B = prob.shape[0] + if "depth_expectation_anchor" not in self._buffers: + self.register_depth_expectation_anchor(self.num_depth_regressor_anchor, B) + d = compute_depth_expectation( + prob, + self.depth_expectation_anchor[:B, ...]).unsqueeze(1) + + ## Error logging + if torch.isnan(d ).any(): + print('d_nan!!!') + if torch.isinf(d ).any(): + print('d_inf!!!') + + return (self.clamp(d) - self.max_val)/ self.regress_scale, prob_feature + + def pred_normal(self, feature_map, confidence): + normal_out = self.normal_predictor(feature_map) + + ## Error logging + if torch.isnan(normal_out).any(): + print('norm_nan!!!') + if torch.isinf(normal_out).any(): + print('norm_feat_inf!!!') + + return norm_normalize(torch.cat([normal_out, confidence], dim=1)) + #return norm_normalize(torch.cat([normal_out, confidence], dim=1).float()) + + def create_mesh_grid(self, height, width, batch, device="cuda", set_buffer=True): + y, x = torch.meshgrid([torch.arange(0, height, dtype=torch.float32, device=device), + torch.arange(0, width, dtype=torch.float32, device=device)], indexing='ij') + meshgrid = torch.stack((x, y)) + meshgrid = meshgrid.unsqueeze(0).repeat(batch, 1, 1, 1) + #self.register_buffer('meshgrid', meshgrid, persistent=False) + return meshgrid + + def upsample_flow(self, flow, mask): + """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """ + N, D, H, W = flow.shape + factor = 2 ** self.n_downsample + mask = mask.view(N, 1, 9, factor, factor, H, W) + mask = torch.softmax(mask, dim=2) + #mask = torch.softmax(mask.float(), dim=2) + + #up_flow = F.unfold(factor * flow, [3,3], padding=1) + up_flow = F.unfold(flow, [3,3], padding=1) + up_flow = up_flow.view(N, D, 9, 1, 1, H, W) + + up_flow = torch.sum(mask * up_flow, dim=2) + up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) + return up_flow.reshape(N, D, factor*H, factor*W) + + def initialize_flow(self, img): + """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0""" + N, _, H, W = img.shape + + coords0 = coords_grid(N, H, W).to(img.device) + coords1 = coords_grid(N, H, W).to(img.device) + + return coords0, coords1 + + def upsample(self, x, scale_factor=2): + """Upsample input tensor by a factor of 2 + """ + return interpolate_float32(x, scale_factor=scale_factor*self.up_scale/8, mode="nearest") + + def forward(self, vit_features, **kwargs): + ## read vit token to multi-scale features + B, H, W, _, _, num_register_tokens = vit_features[1] + vit_features = vit_features[0] + + ## Error logging + if torch.isnan(vit_features[0]).any(): + print('vit_feature_nan!!!') + if torch.isinf(vit_features[0]).any(): + print('vit_feature_inf!!!') + + if self.use_cls_token == True: + vit_features = [[ft[:, 1+num_register_tokens:, :].view(B, H, W, self.in_channels[0]), \ + ft[:, 0:1+num_register_tokens, :].view(B, 1, 1, self.in_channels[0] * (1+num_register_tokens))] for ft in vit_features] + else: + vit_features = [ft.view(B, H, W, self.in_channels[0]) for ft in vit_features] + encoder_features = self.token2feature(vit_features) # 1/14, 1/14, 1/7, 1/4 + + ## Error logging + for en_ft in encoder_features: + if torch.isnan(en_ft).any(): + print('decoder_feature_nan!!!') + print(en_ft.shape) + if torch.isinf(en_ft).any(): + print('decoder_feature_inf!!!') + print(en_ft.shape) + + ## decode features to init-depth (and confidence) + ref_feat= self.decoder_mono(encoder_features) # now, 1/4 for depth + + ## Error logging + if torch.isnan(ref_feat).any(): + print('ref_feat_nan!!!') + if torch.isinf(ref_feat).any(): + print('ref_feat_inf!!!') + + feature_map = ref_feat[:, :-2, :, :] # feature map share of depth and normal prediction + depth_confidence_map = ref_feat[:, -2:-1, :, :] + normal_confidence_map = ref_feat[:, -1:, :, :] + depth_pred, binmap = self.regress_depth(feature_map) # regress bin for depth + normal_pred = self.pred_normal(feature_map, normal_confidence_map) # mlp for normal + + depth_init = torch.cat((depth_pred, depth_confidence_map, normal_pred), dim=1) # (N, 1+1+4, H, W) + + ## encoder features to context-feature for init-hidden-state and contex-features + cnet_list = self.context_feature_encoder(encoder_features[::-1]) + net_list = [torch.tanh(x[0]) for x in cnet_list] # x_4, x_8, x_16 of hidden state + inp_list = [torch.relu(x[1]) for x in cnet_list] # x_4, x_8, x_16 context features + + # Rather than running the GRU's conv layers on the context features multiple times, we do it once at the beginning + inp_list = [list(conv(i).split(split_size=conv.out_channels//3, dim=1)) for i,conv in zip(inp_list, self.context_zqr_convs)] + + coords0, coords1 = self.initialize_flow(net_list[0]) + if depth_init is not None: + coords1 = coords1 + depth_init + + if self.training: + low_resolution_init = [self.clamp(depth_init[:,:1] * self.regress_scale + self.max_val), depth_init[:,1:2], norm_normalize(depth_init[:,2:].clone())] + init_depth = upflow4(depth_init) + flow_predictions = [self.clamp(init_depth[:,:1] * self.regress_scale + self.max_val)] + conf_predictions = [init_depth[:,1:2]] + normal_outs = [norm_normalize(init_depth[:,2:].clone())] + + else: + flow_predictions = [] + conf_predictions = [] + samples_pred_list = [] + coord_list = [] + normal_outs = [] + low_resolution_init = [] + + for itr in range(self.iters): + # coords1 = coords1.detach() + flow = coords1 - coords0 + if self.n_gru_layers == 3 and self.slow_fast_gru: # Update low-res GRU + net_list = self.update_block(net_list, inp_list, iter32=True, iter16=False, iter08=False, update=False) + if self.n_gru_layers >= 2 and self.slow_fast_gru:# Update low-res GRU and mid-res GRU + net_list = self.update_block(net_list, inp_list, iter32=self.n_gru_layers==3, iter16=True, iter08=False, update=False) + net_list, up_mask, delta_flow = self.update_block(net_list, inp_list, None, flow, iter32=self.n_gru_layers==3, iter16=self.n_gru_layers>=2) + + # F(t+1) = F(t) + \Delta(t) + coords1 = coords1 + delta_flow + + # We do not need to upsample or output intermediate results in test_mode + #if (not self.training) and itr < self.iters-1: + #continue + + # upsample predictions + if up_mask is None: + flow_up = self.upsample(coords1-coords0, 4) + else: + flow_up = self.upsample_flow(coords1 - coords0, up_mask) + # flow_up = self.upsample(coords1-coords0, 4) + + flow_predictions.append(self.clamp(flow_up[:,:1] * self.regress_scale + self.max_val)) + conf_predictions.append(flow_up[:,1:2]) + normal_outs.append(norm_normalize(flow_up[:,2:].clone())) + + outputs=dict( + prediction=flow_predictions[-1], + predictions_list=flow_predictions, + confidence=conf_predictions[-1], + confidence_list=conf_predictions, + pred_logit=None, + # samples_pred_list=samples_pred_list, + # coord_list=coord_list, + prediction_normal=normal_outs[-1], + normal_out_list=normal_outs, + low_resolution_init=low_resolution_init, + ) + + return outputs + + +if __name__ == "__main__": + try: + from custom_mmpkg.custom_mmcv.utils import Config + except: + from mmengine import Config + cfg = Config.fromfile('/cpfs01/shared/public/users/mu.hu/monodepth/mono/configs/RAFTDecoder/vit.raft.full2t.py') + cfg.model.decode_head.in_channels = [384, 384, 384, 384] + cfg.model.decode_head.feature_channels = [96, 192, 384, 768] + cfg.model.decode_head.decoder_channels = [48, 96, 192, 384, 384] + cfg.model.decode_head.hidden_channels = [48, 48, 48, 48, 48] + cfg.model.decode_head.up_scale = 7 + + # cfg.model.decode_head.use_cls_token = True + # vit_feature = [[torch.rand((2, 20, 60, 384)).cuda(), torch.rand(2, 384).cuda()], \ + # [torch.rand((2, 20, 60, 384)).cuda(), torch.rand(2, 384).cuda()], \ + # [torch.rand((2, 20, 60, 384)).cuda(), torch.rand(2, 384).cuda()], \ + # [torch.rand((2, 20, 60, 384)).cuda(), torch.rand(2, 384).cuda()]] + + cfg.model.decode_head.use_cls_token = True + cfg.model.decode_head.num_register_tokens = 4 + vit_feature = [[torch.rand((2, (74 * 74) + 5, 384)).cuda(),\ + torch.rand((2, (74 * 74) + 5, 384)).cuda(), \ + torch.rand((2, (74 * 74) + 5, 384)).cuda(), \ + torch.rand((2, (74 * 74) + 5, 384)).cuda()], (2, 74, 74, 1036, 1036, 4)] + + decoder = RAFTDepthNormalDPT5(cfg).cuda() + output = decoder(vit_feature) + temp = 1 + + + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/__init__.py new file mode 100644 index 00000000..92381a5f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/decode_heads/__init__.py @@ -0,0 +1,4 @@ +from .HourGlassDecoder import HourglassDecoder +from .RAFTDepthNormalDPTDecoder5 import RAFTDepthNormalDPT5 + +__all__=['HourglassDecoder', 'RAFTDepthNormalDPT5'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/__base_model__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/__base_model__.py new file mode 100644 index 00000000..1ee81691 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/__base_model__.py @@ -0,0 +1,25 @@ +import torch +import torch.nn as nn +from ...utils.comm import get_func + + +class BaseDepthModel(nn.Module): + def __init__(self, cfg, **kwargs) -> None: + super(BaseDepthModel, self).__init__() + model_type = cfg.model.type + # Use relative import approach - get the module dynamically + from . import dense_pipeline + if model_type == 'DensePredModel': + self.depth_model = dense_pipeline.DensePredModel(cfg) + else: + raise NotImplementedError(f"Model type {model_type} not implemented") + + def forward(self, data): + output = self.depth_model(**data) + + return output['prediction'], output['confidence'], output + + def inference(self, data): + with torch.no_grad(): + pred_depth, confidence, _ = self.forward(data) + return pred_depth, confidence \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/__init__.py new file mode 100644 index 00000000..b962a3f8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/__init__.py @@ -0,0 +1,6 @@ + +from .dense_pipeline import DensePredModel +from .__base_model__ import BaseDepthModel +__all__ = [ + 'DensePredModel', 'BaseDepthModel', +] \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/dense_pipeline.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/dense_pipeline.py new file mode 100644 index 00000000..509ec9b8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/model_pipelines/dense_pipeline.py @@ -0,0 +1,44 @@ +import torch +import torch.nn as nn +from ...utils.comm import get_func + +class DensePredModel(nn.Module): + def __init__(self, cfg) -> None: + super(DensePredModel, self).__init__() + + # Use direct imports instead of get_func to avoid module resolution issues + + # Handle different backbone types + backbone_type = cfg.model.backbone.type + if backbone_type == 'vit_small_reg': + from ..backbones.ViT_DINO_reg import vit_small_reg + self.encoder = vit_small_reg(**cfg.model.backbone) + elif backbone_type == 'vit_large_reg': + from ..backbones.ViT_DINO_reg import vit_large_reg + self.encoder = vit_large_reg(**cfg.model.backbone) + elif backbone_type == 'vit_giant2_reg': + from ..backbones.ViT_DINO_reg import vit_giant2_reg + self.encoder = vit_giant2_reg(**cfg.model.backbone) + elif backbone_type == 'vit_large': + from ..backbones.ViT_DINO import vit_large + self.encoder = vit_large(**cfg.model.backbone) + elif backbone_type == 'convnext_large': + from ..backbones.ConvNeXt import convnext_large + self.encoder = convnext_large(**cfg.model.backbone) + else: + raise NotImplementedError(f"Backbone {backbone_type} not implemented") + + # Handle decode head + decode_head_type = cfg.model.decode_head.type + if decode_head_type == 'RAFTDepthNormalDPT5': + from ..decode_heads.RAFTDepthNormalDPTDecoder5 import RAFTDepthNormalDPT5 + self.decoder = RAFTDepthNormalDPT5(cfg) + else: + # For other decode head types, we'd need to check what file they're in + raise NotImplementedError(f"Decode head {decode_head_type} not implemented") + + def forward(self, input, **kwargs): + # [f_32, f_16, f_8, f_4] + features = self.encoder(input) + out = self.decoder(features, **kwargs) + return out \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/monodepth_model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/monodepth_model.py new file mode 100644 index 00000000..0b58b764 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/model/monodepth_model.py @@ -0,0 +1,37 @@ +import torch +import torch.nn as nn +from .model_pipelines.__base_model__ import BaseDepthModel + +class DepthModel(BaseDepthModel): + def __init__(self, cfg, **kwards): + super(DepthModel, self).__init__(cfg) + model_type = cfg.model.type + + def inference(self, data): + with torch.no_grad(): + pred_depth, confidence, output_dict = self.forward(data) + return pred_depth, confidence, output_dict + +def get_monodepth_model( + cfg : dict, + **kwargs + ) -> nn.Module: + # config depth model + model = DepthModel(cfg, **kwargs) + #model.init_weights(load_imagenet_model, imagenet_ckpt_fpath) + assert isinstance(model, nn.Module) + return model + +def get_configured_monodepth_model( + cfg: dict, + ) -> nn.Module: + """ + Args: + @ configs: configures for the network. + @ load_imagenet_model: whether to initialize from ImageNet-pretrained model. + @ imagenet_ckpt_fpath: string representing path to file with weights to initialize model with. + Returns: + # model: depth model. + """ + model = get_monodepth_model(cfg) + return model diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/tools/test_scale_cano.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/tools/test_scale_cano.py new file mode 100644 index 00000000..9788d205 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/tools/test_scale_cano.py @@ -0,0 +1,161 @@ +import os +import os.path as osp +import cv2 +import time +import sys +CODE_SPACE=os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(CODE_SPACE) +import argparse +import custom_mmpkg.custom_mmcv as mmcv +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +try: + from custom_mmpkg.custom_mmcv.utils import Config, DictAction +except: + from mmengine import Config, DictAction +from datetime import timedelta +import random +import numpy as np +from custom_controlnet_aux.metric3d.mono.utils.logger import setup_logger +import glob +from custom_controlnet_aux.metric3d.mono.utils.comm import init_env +from custom_controlnet_aux.metric3d.mono.model.monodepth_model import get_configured_monodepth_model +from custom_controlnet_aux.metric3d.mono.utils.running import load_ckpt +from custom_controlnet_aux.metric3d.mono.utils.do_test import do_scalecano_test_with_custom_data +from custom_controlnet_aux.metric3d.mono.utils.mldb import load_data_info, reset_ckpt_path +from custom_controlnet_aux.metric3d.mono.utils.custom_data import load_from_annos, load_data + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a segmentor') + parser.add_argument('config', help='train config file path') + parser.add_argument('--show-dir', help='the dir to save logs and visualization results') + parser.add_argument('--load-from', help='the checkpoint file to load weights from') + parser.add_argument('--node_rank', type=int, default=0) + parser.add_argument('--nnodes', type=int, default=1, help='number of nodes') + parser.add_argument('--options', nargs='+', action=DictAction, help='custom options') + parser.add_argument('--launcher', choices=['None', 'pytorch', 'slurm', 'mpi', 'ror'], default='slurm', help='job launcher') + parser.add_argument('--test_data_path', default='None', type=str, help='the path of test data') + parser.add_argument('--batch_size', default=1, type=int, help='the batch size for inference') + args = parser.parse_args() + return args + +def main(args): + os.chdir(CODE_SPACE) + cfg = Config.fromfile(args.config) + + if args.options is not None: + cfg.merge_from_dict(args.options) + + # show_dir is determined in this priority: CLI > segment in file > filename + if args.show_dir is not None: + # update configs according to CLI args if args.show_dir is not None + cfg.show_dir = args.show_dir + else: + # use condig filename + timestamp as default show_dir if args.show_dir is None + cfg.show_dir = osp.join('./show_dirs', + osp.splitext(osp.basename(args.config))[0], + args.timestamp) + + # ckpt path + if args.load_from is None: + raise RuntimeError('Please set model path!') + cfg.load_from = args.load_from + cfg.batch_size = args.batch_size + + # load data info + data_info = {} + load_data_info('data_info', data_info=data_info) + cfg.mldb_info = data_info + # update check point info + reset_ckpt_path(cfg.model, data_info) + + # create show dir + os.makedirs(osp.abspath(cfg.show_dir), exist_ok=True) + + # init the logger before other steps + cfg.log_file = osp.join(cfg.show_dir, f'{args.timestamp}.log') + logger = setup_logger(cfg.log_file) + + # log some basic info + logger.info(f'Config:\n{cfg.pretty_text}') + + # init distributed env dirst, since logger depends on the dist info + if args.launcher == 'None': + cfg.distributed = False + else: + cfg.distributed = True + init_env(args.launcher, cfg) + logger.info(f'Distributed training: {cfg.distributed}') + + # dump config + cfg.dump(osp.join(cfg.show_dir, osp.basename(args.config))) + test_data_path = args.test_data_path + if not os.path.isabs(test_data_path): + test_data_path = osp.join(CODE_SPACE, test_data_path) + + if 'json' in test_data_path: + test_data = load_from_annos(test_data_path) + else: + test_data = load_data(args.test_data_path) + + if not cfg.distributed: + main_worker(0, cfg, args.launcher, test_data) + else: + # distributed training + if args.launcher == 'ror': + local_rank = cfg.dist_params.local_rank + main_worker(local_rank, cfg, args.launcher, test_data) + else: + mp.spawn(main_worker, nprocs=cfg.dist_params.num_gpus_per_node, args=(cfg, args.launcher, test_data)) + +def main_worker(local_rank: int, cfg: dict, launcher: str, test_data: list): + if cfg.distributed: + cfg.dist_params.global_rank = cfg.dist_params.node_rank * cfg.dist_params.num_gpus_per_node + local_rank + cfg.dist_params.local_rank = local_rank + + if launcher == 'ror': + init_torch_process_group(use_hvd=False) + else: + torch.cuda.set_device(local_rank) + default_timeout = timedelta(minutes=30) + dist.init_process_group( + backend=cfg.dist_params.backend, + init_method=cfg.dist_params.dist_url, + world_size=cfg.dist_params.world_size, + rank=cfg.dist_params.global_rank, + timeout=default_timeout) + + logger = setup_logger(cfg.log_file) + # build model + model = get_configured_monodepth_model(cfg, ) + + # config distributed training + if cfg.distributed: + model = torch.nn.parallel.DistributedDataParallel(model.cuda(), + device_ids=[local_rank], + output_device=local_rank, + find_unused_parameters=True) + else: + model = torch.nn.DataParallel(model).cuda() + + # load ckpt + model, _, _, _ = load_ckpt(cfg.load_from, model, strict_match=False) + model.eval() + + do_scalecano_test_with_custom_data( + model, + cfg, + test_data, + logger, + cfg.distributed, + local_rank, + cfg.batch_size, + ) + +if __name__ == '__main__': + args = parse_args() + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + args.timestamp = timestamp + main(args) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/__init__.py @@ -0,0 +1 @@ + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/avg_meter.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/avg_meter.py new file mode 100644 index 00000000..37ed9fff --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/avg_meter.py @@ -0,0 +1,475 @@ +import numpy as np +import torch +import torch.distributed as dist +import torch.nn.functional as F +import matplotlib.pyplot as plt + + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self) -> None: + self.reset() + + def reset(self) -> None: + self.val = np.longdouble(0.0) + self.avg = np.longdouble(0.0) + self.sum = np.longdouble(0.0) + self.count = np.longdouble(0.0) + + def update(self, val, n: float = 1) -> None: + self.val = val + self.sum += val + self.count += n + self.avg = self.sum / (self.count + 1e-6) + +class MetricAverageMeter(AverageMeter): + """ + An AverageMeter designed specifically for evaluating segmentation results. + """ + def __init__(self, metrics: list) -> None: + """ Initialize object. """ + # average meters for metrics + self.abs_rel = AverageMeter() + self.rmse = AverageMeter() + self.silog = AverageMeter() + self.delta1 = AverageMeter() + self.delta2 = AverageMeter() + self.delta3 = AverageMeter() + + self.metrics = metrics + + self.consistency = AverageMeter() + self.log10 = AverageMeter() + self.rmse_log = AverageMeter() + self.sq_rel = AverageMeter() + + # normal + self.normal_mean = AverageMeter() + self.normal_rmse = AverageMeter() + self.normal_a1 = AverageMeter() + self.normal_a2 = AverageMeter() + + self.normal_median = AverageMeter() + self.normal_a3 = AverageMeter() + self.normal_a4 = AverageMeter() + self.normal_a5 = AverageMeter() + + + def update_metrics_cpu(self, + pred: torch.Tensor, + target: torch.Tensor, + mask: torch.Tensor,): + """ + Update metrics on cpu + """ + + assert pred.shape == target.shape + + if len(pred.shape) == 3: + pred = pred[:, None, :, :] + target = target[:, None, :, :] + mask = mask[:, None, :, :] + elif len(pred.shape) == 2: + pred = pred[None, None, :, :] + target = target[None, None, :, :] + mask = mask[None, None, :, :] + + + # Absolute relative error + abs_rel_sum, valid_pics = get_absrel_err(pred, target, mask) + abs_rel_sum = abs_rel_sum.numpy() + valid_pics = valid_pics.numpy() + self.abs_rel.update(abs_rel_sum, valid_pics) + + # squared relative error + sqrel_sum, _ = get_sqrel_err(pred, target, mask) + sqrel_sum = sqrel_sum.numpy() + self.sq_rel.update(sqrel_sum, valid_pics) + + # root mean squared error + rmse_sum, _ = get_rmse_err(pred, target, mask) + rmse_sum = rmse_sum.numpy() + self.rmse.update(rmse_sum, valid_pics) + + # log root mean squared error + log_rmse_sum, _ = get_rmse_log_err(pred, target, mask) + log_rmse_sum = log_rmse_sum.numpy() + self.rmse.update(log_rmse_sum, valid_pics) + + # log10 error + log10_sum, _ = get_log10_err(pred, target, mask) + log10_sum = log10_sum.numpy() + self.rmse.update(log10_sum, valid_pics) + + # scale-invariant root mean squared error in log space + silog_sum, _ = get_silog_err(pred, target, mask) + silog_sum = silog_sum.numpy() + self.silog.update(silog_sum, valid_pics) + + # ratio error, delta1, .... + delta1_sum, delta2_sum, delta3_sum, _ = get_ratio_error(pred, target, mask) + delta1_sum = delta1_sum.numpy() + delta2_sum = delta2_sum.numpy() + delta3_sum = delta3_sum.numpy() + + self.delta1.update(delta1_sum, valid_pics) + self.delta2.update(delta1_sum, valid_pics) + self.delta3.update(delta1_sum, valid_pics) + + + def update_metrics_gpu( + self, + pred: torch.Tensor, + target: torch.Tensor, + mask: torch.Tensor, + is_distributed: bool, + pred_next: torch.tensor = None, + pose_f1_to_f2: torch.tensor = None, + intrinsic: torch.tensor = None): + """ + Update metric on GPU. It supports distributed processing. If multiple machines are employed, please + set 'is_distributed' as True. + """ + assert pred.shape == target.shape + + if len(pred.shape) == 3: + pred = pred[:, None, :, :] + target = target[:, None, :, :] + mask = mask[:, None, :, :] + elif len(pred.shape) == 2: + pred = pred[None, None, :, :] + target = target[None, None, :, :] + mask = mask[None, None, :, :] + + + # Absolute relative error + abs_rel_sum, valid_pics = get_absrel_err(pred, target, mask) + if is_distributed: + dist.all_reduce(abs_rel_sum), dist.all_reduce(valid_pics) + abs_rel_sum = abs_rel_sum.cpu().numpy() + valid_pics = int(valid_pics) + self.abs_rel.update(abs_rel_sum, valid_pics) + + # root mean squared error + rmse_sum, _ = get_rmse_err(pred, target, mask) + if is_distributed: + dist.all_reduce(rmse_sum) + rmse_sum = rmse_sum.cpu().numpy() + self.rmse.update(rmse_sum, valid_pics) + + # log root mean squared error + log_rmse_sum, _ = get_rmse_log_err(pred, target, mask) + if is_distributed: + dist.all_reduce(log_rmse_sum) + log_rmse_sum = log_rmse_sum.cpu().numpy() + self.rmse_log.update(log_rmse_sum, valid_pics) + + # log10 error + log10_sum, _ = get_log10_err(pred, target, mask) + if is_distributed: + dist.all_reduce(log10_sum) + log10_sum = log10_sum.cpu().numpy() + self.log10.update(log10_sum, valid_pics) + + # scale-invariant root mean squared error in log space + silog_sum, _ = get_silog_err(pred, target, mask) + if is_distributed: + dist.all_reduce(silog_sum) + silog_sum = silog_sum.cpu().numpy() + self.silog.update(silog_sum, valid_pics) + + # ratio error, delta1, .... + delta1_sum, delta2_sum, delta3_sum, _ = get_ratio_err(pred, target, mask) + if is_distributed: + dist.all_reduce(delta1_sum), dist.all_reduce(delta2_sum), dist.all_reduce(delta3_sum) + delta1_sum = delta1_sum.cpu().numpy() + delta2_sum = delta2_sum.cpu().numpy() + delta3_sum = delta3_sum.cpu().numpy() + + self.delta1.update(delta1_sum, valid_pics) + self.delta2.update(delta2_sum, valid_pics) + self.delta3.update(delta3_sum, valid_pics) + + # video consistency error + # consistency_rel_sum, valid_warps = get_video_consistency_err(pred, pred_next, pose_f1_to_f2, intrinsic) + # if is_distributed: + # dist.all_reduce(consistency_rel_sum), dist.all_reduce(valid_warps) + # consistency_rel_sum = consistency_rel_sum.cpu().numpy() + # valid_warps = int(valid_warps) + # self.consistency.update(consistency_rel_sum, valid_warps) + + ## for surface normal + def update_normal_metrics_gpu( + self, + pred: torch.Tensor, # (B, 3, H, W) + target: torch.Tensor, # (B, 3, H, W) + mask: torch.Tensor, # (B, 1, H, W) + is_distributed: bool, + ): + """ + Update metric on GPU. It supports distributed processing. If multiple machines are employed, please + set 'is_distributed' as True. + """ + assert pred.shape == target.shape + + valid_pics = torch.sum(mask, dtype=torch.float32) + 1e-6 + + if valid_pics < 10: + return + + mean_error = rmse_error = a1_error = a2_error = dist_node_cnt = valid_pics + normal_error = torch.cosine_similarity(pred, target, dim=1) + normal_error = torch.clamp(normal_error, min=-1.0, max=1.0) + angle_error = torch.acos(normal_error) * 180.0 / torch.pi + angle_error = angle_error[:, None, :, :] + angle_error = angle_error[mask] + # Calculation error + mean_error = angle_error.sum() / valid_pics + rmse_error = torch.sqrt( torch.sum(torch.square(angle_error)) / valid_pics ) + median_error = angle_error.median() + a1_error = 100.0 * (torch.sum(angle_error < 5) / valid_pics) + a2_error = 100.0 * (torch.sum(angle_error < 7.5) / valid_pics) + + a3_error = 100.0 * (torch.sum(angle_error < 11.25) / valid_pics) + a4_error = 100.0 * (torch.sum(angle_error < 22.5) / valid_pics) + a5_error = 100.0 * (torch.sum(angle_error < 30) / valid_pics) + + # if valid_pics > 1e-5: + # If the current node gets data with valid normal + dist_node_cnt = (valid_pics - 1e-6) / valid_pics + + if is_distributed: + dist.all_reduce(dist_node_cnt) + dist.all_reduce(mean_error) + dist.all_reduce(rmse_error) + dist.all_reduce(a1_error) + dist.all_reduce(a2_error) + + dist.all_reduce(a3_error) + dist.all_reduce(a4_error) + dist.all_reduce(a5_error) + + dist_node_cnt = dist_node_cnt.cpu().numpy() + self.normal_mean.update(mean_error.cpu().numpy(), dist_node_cnt) + self.normal_rmse.update(rmse_error.cpu().numpy(), dist_node_cnt) + self.normal_a1.update(a1_error.cpu().numpy(), dist_node_cnt) + self.normal_a2.update(a2_error.cpu().numpy(), dist_node_cnt) + + self.normal_median.update(median_error.cpu().numpy(), dist_node_cnt) + self.normal_a3.update(a3_error.cpu().numpy(), dist_node_cnt) + self.normal_a4.update(a4_error.cpu().numpy(), dist_node_cnt) + self.normal_a5.update(a5_error.cpu().numpy(), dist_node_cnt) + + + def get_metrics(self,): + """ + """ + metrics_dict = {} + for metric in self.metrics: + metrics_dict[metric] = self.__getattribute__(metric).avg + return metrics_dict + + + def get_metrics(self,): + """ + """ + metrics_dict = {} + for metric in self.metrics: + metrics_dict[metric] = self.__getattribute__(metric).avg + return metrics_dict + +def get_absrel_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes absolute relative error. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred * mask + + # Mean Absolute Relative Error + rel = torch.abs(t_m - p_m) / (t_m + 1e-10) # compute errors + abs_rel_sum = torch.sum(rel.reshape((b, c, -1)), dim=2) # [b, c] + num = torch.sum(mask.reshape((b, c, -1)), dim=2) # [b, c] + abs_err = abs_rel_sum / (num + 1e-10) + valid_pics = torch.sum(num > 0) + return torch.sum(abs_err), valid_pics + +def get_sqrel_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes squared relative error. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred * mask + + # squared Relative Error + sq_rel = torch.abs(t_m - p_m) ** 2 / (t_m + 1e-10) # compute errors + sq_rel_sum = torch.sum(sq_rel.reshape((b, c, -1)), dim=2) # [b, c] + num = torch.sum(mask.reshape((b, c, -1)), dim=2) # [b, c] + sqrel_err = sq_rel_sum / (num + 1e-10) + valid_pics = torch.sum(num > 0) + return torch.sum(sqrel_err), valid_pics + +def get_log10_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes log10 error. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred * mask + + diff_log = (torch.log10(p_m+1e-10) - torch.log10(t_m+1e-10)) * mask + log10_diff = torch.abs(diff_log) + log10_sum = torch.sum(log10_diff.reshape((b, c, -1)), dim=2) # [b, c] + num = torch.sum(mask.reshape((b, c, -1)), dim=2) # [b, c] + log10_err = log10_sum / (num + 1e-10) + valid_pics = torch.sum(num > 0) + return torch.sum(log10_err), valid_pics + +def get_rmse_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes rmse error. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred * mask + + square = (t_m - p_m) ** 2 + rmse_sum = torch.sum(square.reshape((b, c, -1)), dim=2) # [b, c] + num = torch.sum(mask.reshape((b, c, -1)), dim=2) # [b, c] + rmse = torch.sqrt(rmse_sum / (num + 1e-10)) + valid_pics = torch.sum(num > 0) + return torch.sum(rmse), valid_pics + +def get_rmse_log_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes log rmse error. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred * mask + + diff_log = (torch.log10(p_m+1e-10) - torch.log10(t_m+1e-10)) * mask + square = diff_log ** 2 + rmse_log_sum = torch.sum(square.reshape((b, c, -1)), dim=2) # [b, c] + num = torch.sum(mask.reshape((b, c, -1)), dim=2) # [b, c] + rmse_log = torch.sqrt(rmse_log_sum / (num + 1e-10)) + valid_pics = torch.sum(num > 0) + return torch.sum(rmse_log), valid_pics + +def get_silog_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes log rmse error. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred * mask + + diff_log = (torch.log10(p_m+1e-10) - torch.log10(t_m+1e-10)) * mask + diff_log_sum = torch.sum(diff_log.reshape((b, c, -1)), dim=2) # [b, c] + diff_log_square = diff_log ** 2 + diff_log_square_sum = torch.sum(diff_log_square.reshape((b, c, -1)), dim=2) # [b, c] + num = torch.sum(mask.reshape((b, c, -1)), dim=2) # [b, c] + silog = torch.sqrt(diff_log_square_sum / (num + 1e-10) - (diff_log_sum / (num + 1e-10)) ** 2) + valid_pics = torch.sum(num > 0) + return torch.sum(silog), valid_pics + +def get_ratio_err(pred: torch.tensor, + target: torch.tensor, + mask: torch.tensor, + ): + """ + Computes the percentage of pixels for which the ratio of the two depth maps is less than a given threshold. + Tasks preprocessed depths (no nans, infs and non-positive values). + pred, target, and mask should be in the shape of [b, c, h, w] + """ + assert len(pred.shape) == 4, len(target.shape) == 4 + b, c, h, w = pred.shape + mask = mask.to(torch.float) + t_m = target * mask + p_m = pred + + gt_pred = t_m / (p_m + 1e-10) + pred_gt = p_m / (t_m + 1e-10) + gt_pred = gt_pred.reshape((b, c, -1)) + pred_gt = pred_gt.reshape((b, c, -1)) + gt_pred_gt = torch.cat((gt_pred, pred_gt), axis=1) + ratio_max = torch.amax(gt_pred_gt, axis=1) + + delta_1_sum = torch.sum((ratio_max < 1.25), dim=1) # [b, ] + delta_2_sum = torch.sum((ratio_max < 1.25 ** 2), dim=1) # [b, ] + delta_3_sum = torch.sum((ratio_max < 1.25 ** 3), dim=1) # [b, ] + num = torch.sum(mask.reshape((b, -1)), dim=1) # [b, ] + + delta_1 = delta_1_sum / (num + 1e-10) + delta_2 = delta_2_sum / (num + 1e-10) + delta_3 = delta_3_sum / (num + 1e-10) + valid_pics = torch.sum(num > 0) + + return torch.sum(delta_1), torch.sum(delta_2), torch.sum(delta_3), valid_pics + + +if __name__ == '__main__': + cfg = ['abs_rel', 'delta1'] + dam = MetricAverageMeter(cfg) + + pred_depth = np.random.random([2, 480, 640]) + gt_depth = np.random.random([2, 480, 640]) - 0.5 + intrinsic = [[100, 100, 200, 200], [200, 200, 300, 300]] + + pred = torch.from_numpy(pred_depth).cuda() + gt = torch.from_numpy(gt_depth).cuda() + + mask = gt > 0 + dam.update_metrics_gpu(pred, gt, mask, False) + eval_error = dam.get_metrics() + print(eval_error) + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/comm.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/comm.py new file mode 100644 index 00000000..99d68a3e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/comm.py @@ -0,0 +1,322 @@ +import importlib +import torch +import torch.distributed as dist +from .avg_meter import AverageMeter +from collections import defaultdict, OrderedDict +import os +import socket +from custom_mmpkg.custom_mmcv.utils import collect_env as collect_base_env +try: + from custom_mmpkg.custom_mmcv.utils import get_git_hash +except: + from mmengine.utils import get_git_hash +#import mono.mmseg as mmseg +# import mmseg +import time +import datetime +import logging + + +def main_process() -> bool: + return get_rank() == 0 + #return not cfg.distributed or \ + # (cfg.distributed and cfg.local_rank == 0) + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + +def _find_free_port(): + # refer to https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/launch.py # noqa: E501 + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # Binding to port 0 will cause the OS to find an available port for us + sock.bind(('', 0)) + port = sock.getsockname()[1] + sock.close() + # NOTE: there is still a chance the port could be taken by other processes. + return port + +def _is_free_port(port): + ips = socket.gethostbyname_ex(socket.gethostname())[-1] + ips.append('localhost') + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return all(s.connect_ex((ip, port)) != 0 for ip in ips) + + +# def collect_env(): +# """Collect the information of the running environments.""" +# env_info = collect_base_env() +# env_info['MMSegmentation'] = f'{mmseg.__version__}+{get_git_hash()[:7]}' + +# return env_info + +def init_env(launcher, cfg): + """Initialize distributed training environment. + If argument ``cfg.dist_params.dist_url`` is specified as 'env://', then the master port will be system + environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system + environment variable, then a default port ``29500`` will be used. + """ + if launcher == 'slurm': + _init_dist_slurm(cfg) + elif launcher == 'ror': + _init_dist_ror(cfg) + elif launcher == 'None': + _init_none_dist(cfg) + else: + raise RuntimeError(f'{cfg.launcher} has not been supported!') + +def _init_none_dist(cfg): + cfg.dist_params.num_gpus_per_node = 1 + cfg.dist_params.world_size = 1 + cfg.dist_params.nnodes = 1 + cfg.dist_params.node_rank = 0 + cfg.dist_params.global_rank = 0 + cfg.dist_params.local_rank = 0 + os.environ["WORLD_SIZE"] = str(1) + +def _init_dist_ror(cfg): + from ac2.ror.comm import get_local_rank, get_world_rank, get_local_size, get_node_rank, get_world_size + cfg.dist_params.num_gpus_per_node = get_local_size() + cfg.dist_params.world_size = get_world_size() + cfg.dist_params.nnodes = (get_world_size()) // (get_local_size()) + cfg.dist_params.node_rank = get_node_rank() + cfg.dist_params.global_rank = get_world_rank() + cfg.dist_params.local_rank = get_local_rank() + os.environ["WORLD_SIZE"] = str(get_world_size()) + + +def _init_dist_slurm(cfg): + if 'NNODES' not in os.environ: + os.environ['NNODES'] = str(cfg.dist_params.nnodes) + if 'NODE_RANK' not in os.environ: + os.environ['NODE_RANK'] = str(cfg.dist_params.node_rank) + + #cfg.dist_params. + num_gpus = torch.cuda.device_count() + world_size = int(os.environ['NNODES']) * num_gpus + os.environ['WORLD_SIZE'] = str(world_size) + + # config port + if 'MASTER_PORT' in os.environ: + master_port = str(os.environ['MASTER_PORT']) # use MASTER_PORT in the environment variable + else: + # if torch.distributed default port(29500) is available + # then use it, else find a free port + if _is_free_port(16500): + master_port = '16500' + else: + master_port = str(_find_free_port()) + os.environ['MASTER_PORT'] = master_port + + # config addr + if 'MASTER_ADDR' in os.environ: + master_addr = str(os.environ['MASTER_PORT']) # use MASTER_PORT in the environment variable + # elif cfg.dist_params.dist_url is not None: + # master_addr = ':'.join(str(cfg.dist_params.dist_url).split(':')[:2]) + else: + master_addr = '127.0.0.1' #'tcp://127.0.0.1' + os.environ['MASTER_ADDR'] = master_addr + + # set dist_url to 'env://' + cfg.dist_params.dist_url = 'env://' #f"{master_addr}:{master_port}" + + cfg.dist_params.num_gpus_per_node = num_gpus + cfg.dist_params.world_size = world_size + cfg.dist_params.nnodes = int(os.environ['NNODES']) + cfg.dist_params.node_rank = int(os.environ['NODE_RANK']) + + # if int(os.environ['NNODES']) > 1 and cfg.dist_params.dist_url.startswith("file://"): + # raise Warning("file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://") + + +def get_func(func_name): + """ + Helper to return a function object by name. func_name must identify + a function in this module or the path to a function relative to the base + module. + @ func_name: function name. + """ + if func_name == '': + return None + try: + parts = func_name.split('.') + # Refers to a function in this module + if len(parts) == 1: + return globals()[parts[0]] + # Otherwise, assume we're referencing a module under modeling + module_name = '.'.join(parts[:-1]) + module = importlib.import_module(module_name) + return getattr(module, parts[-1]) + except: + raise RuntimeError(f'Failed to find function: {func_name}') + +class Timer(object): + """A simple timer.""" + + def __init__(self): + self.reset() + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + if average: + return self.average_time + else: + return self.diff + + def reset(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + +class TrainingStats(object): + """Track vital training statistics.""" + def __init__(self, log_period, tensorboard_logger=None): + self.log_period = log_period + self.tblogger = tensorboard_logger + self.tb_ignored_keys = ['iter', 'eta', 'epoch', 'time'] + self.iter_timer = Timer() + # Window size for smoothing tracked values (with median filtering) + self.filter_size = log_period + def create_smoothed_value(): + return AverageMeter() + self.smoothed_losses = defaultdict(create_smoothed_value) + #self.smoothed_metrics = defaultdict(create_smoothed_value) + #self.smoothed_total_loss = AverageMeter() + + + def IterTic(self): + self.iter_timer.tic() + + def IterToc(self): + return self.iter_timer.toc(average=False) + + def reset_iter_time(self): + self.iter_timer.reset() + + def update_iter_stats(self, losses_dict): + """Update tracked iteration statistics.""" + for k, v in losses_dict.items(): + self.smoothed_losses[k].update(float(v), 1) + + def log_iter_stats(self, cur_iter, optimizer, max_iters, val_err={}): + """Log the tracked statistics.""" + if (cur_iter % self.log_period == 0): + stats = self.get_stats(cur_iter, optimizer, max_iters, val_err) + log_stats(stats) + if self.tblogger: + self.tb_log_stats(stats, cur_iter) + for k, v in self.smoothed_losses.items(): + v.reset() + + def tb_log_stats(self, stats, cur_iter): + """Log the tracked statistics to tensorboard""" + for k in stats: + # ignore some logs + if k not in self.tb_ignored_keys: + v = stats[k] + if isinstance(v, dict): + self.tb_log_stats(v, cur_iter) + else: + self.tblogger.add_scalar(k, v, cur_iter) + + + def get_stats(self, cur_iter, optimizer, max_iters, val_err = {}): + eta_seconds = self.iter_timer.average_time * (max_iters - cur_iter) + + eta = str(datetime.timedelta(seconds=int(eta_seconds))) + stats = OrderedDict( + iter=cur_iter, # 1-indexed + time=self.iter_timer.average_time, + eta=eta, + ) + optimizer_state_dict = optimizer.state_dict() + lr = {} + for i in range(len(optimizer_state_dict['param_groups'])): + lr_name = 'group%d_lr' % i + lr[lr_name] = optimizer_state_dict['param_groups'][i]['lr'] + + stats['lr'] = OrderedDict(lr) + for k, v in self.smoothed_losses.items(): + stats[k] = v.avg + + stats['val_err'] = OrderedDict(val_err) + stats['max_iters'] = max_iters + return stats + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + Args: + @input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + @average (bool): whether to do average or sum + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +def log_stats(stats): + logger = logging.getLogger() + """Log training statistics to terminal""" + lines = "[Step %d/%d]\n" % ( + stats['iter'], stats['max_iters']) + + lines += "\t\tloss: %.3f, time: %.6f, eta: %s\n" % ( + stats['total_loss'], stats['time'], stats['eta']) + + # log loss + lines += "\t\t" + for k, v in stats.items(): + if 'loss' in k.lower() and 'total_loss' not in k.lower(): + lines += "%s: %.3f" % (k, v) + ", " + lines = lines[:-3] + lines += '\n' + + # validate criteria + lines += "\t\tlast val err:" + ", ".join("%s: %.6f" % (k, v) for k, v in stats['val_err'].items()) + ", " + lines += '\n' + + # lr in different groups + lines += "\t\t" + ", ".join("%s: %.8f" % (k, v) for k, v in stats['lr'].items()) + lines += '\n' + logger.info(lines[:-1]) # remove last new linen_pxl + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/custom_data.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/custom_data.py new file mode 100644 index 00000000..d9fab474 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/custom_data.py @@ -0,0 +1,34 @@ +import glob +import os +import json +import cv2 + +def load_from_annos(anno_path): + with open(anno_path, 'r') as f: + annos = json.load(f)['files'] + + datas = [] + for i, anno in enumerate(annos): + rgb = anno['rgb'] + depth = anno['depth'] if 'depth' in anno else None + depth_scale = anno['depth_scale'] if 'depth_scale' in anno else 1.0 + intrinsic = anno['cam_in'] if 'cam_in' in anno else None + normal = anno['normal'] if 'normal' in anno else None + + data_i = { + 'rgb': rgb, + 'depth': depth, + 'depth_scale': depth_scale, + 'intrinsic': intrinsic, + 'filename': os.path.basename(rgb), + 'folder': rgb.split('/')[-3], + 'normal': normal + } + datas.append(data_i) + return datas + +def load_data(path: str): + rgbs = glob.glob(path + '/*.jpg') + glob.glob(path + '/*.png') + #intrinsic = [835.8179931640625, 835.8179931640625, 961.5419921875, 566.8090209960938] #[721.53769, 721.53769, 609.5593, 172.854] + data = [{'rgb': i, 'depth': None, 'intrinsic': None, 'filename': os.path.basename(i), 'folder': i.split('/')[-3]} for i in rgbs] + return data \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/do_test.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/do_test.py new file mode 100644 index 00000000..cccd2707 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/do_test.py @@ -0,0 +1,380 @@ +import torch +import torch.nn.functional as F +import logging +import os +import os.path as osp +from .avg_meter import MetricAverageMeter +from .visualization import save_val_imgs, create_html, save_raw_imgs, save_normal_val_imgs +import cv2 +from tqdm import tqdm +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt + +def to_cuda(data: dict): + for k, v in data.items(): + if isinstance(v, torch.Tensor): + data[k] = v.cuda(non_blocking=True) + if isinstance(v, list) and len(v)>=1 and isinstance(v[0], torch.Tensor): + for i, l_i in enumerate(v): + data[k][i] = l_i.cuda(non_blocking=True) + return data + +def align_scale(pred: torch.tensor, target: torch.tensor): + mask = target > 0 + if torch.sum(mask) > 10: + scale = torch.median(target[mask]) / (torch.median(pred[mask]) + 1e-8) + else: + scale = 1 + pred_scaled = pred * scale + return pred_scaled, scale + +def align_scale_shift(pred: torch.tensor, target: torch.tensor): + mask = target > 0 + target_mask = target[mask].cpu().numpy() + pred_mask = pred[mask].cpu().numpy() + if torch.sum(mask) > 10: + scale, shift = np.polyfit(pred_mask, target_mask, deg=1) + if scale < 0: + scale = torch.median(target[mask]) / (torch.median(pred[mask]) + 1e-8) + shift = 0 + else: + scale = 1 + shift = 0 + pred = pred * scale + shift + return pred, scale + +def align_scale_shift_numpy(pred: np.array, target: np.array): + mask = target > 0 + target_mask = target[mask] + pred_mask = pred[mask] + if np.sum(mask) > 10: + scale, shift = np.polyfit(pred_mask, target_mask, deg=1) + if scale < 0: + scale = np.median(target[mask]) / (np.median(pred[mask]) + 1e-8) + shift = 0 + else: + scale = 1 + shift = 0 + pred = pred * scale + shift + return pred, scale + + +def build_camera_model(H : int, W : int, intrinsics : list) -> np.array: + """ + Encode the camera intrinsic parameters (focal length and principle point) to a 4-channel map. + """ + fx, fy, u0, v0 = intrinsics + f = (fx + fy) / 2.0 + # principle point location + x_row = np.arange(0, W).astype(np.float32) + x_row_center_norm = (x_row - u0) / W + x_center = np.tile(x_row_center_norm, (H, 1)) # [H, W] + + y_col = np.arange(0, H).astype(np.float32) + y_col_center_norm = (y_col - v0) / H + y_center = np.tile(y_col_center_norm, (W, 1)).T # [H, W] + + # FoV + fov_x = np.arctan(x_center / (f / W)) + fov_y = np.arctan(y_center / (f / H)) + + cam_model = np.stack([x_center, y_center, fov_x, fov_y], axis=2) + return cam_model + +def resize_for_input(image, output_shape, intrinsic, canonical_shape, to_canonical_ratio): + """ + Resize the input. + Resizing consists of two processed, i.e. 1) to the canonical space (adjust the camera model); 2) resize the image while the camera model holds. Thus the + label will be scaled with the resize factor. + """ + padding = [123.675, 116.28, 103.53] + h, w, _ = image.shape + resize_ratio_h = output_shape[0] / canonical_shape[0] + resize_ratio_w = output_shape[1] / canonical_shape[1] + to_scale_ratio = min(resize_ratio_h, resize_ratio_w) + + resize_ratio = to_canonical_ratio * to_scale_ratio + + reshape_h = int(resize_ratio * h) + reshape_w = int(resize_ratio * w) + + pad_h = max(output_shape[0] - reshape_h, 0) + pad_w = max(output_shape[1] - reshape_w, 0) + pad_h_half = int(pad_h / 2) + pad_w_half = int(pad_w / 2) + + # resize + image = cv2.resize(image, dsize=(reshape_w, reshape_h), interpolation=cv2.INTER_LINEAR) + # padding + image = cv2.copyMakeBorder( + image, + pad_h_half, + pad_h - pad_h_half, + pad_w_half, + pad_w - pad_w_half, + cv2.BORDER_CONSTANT, + value=padding) + + # Resize, adjust principle point + intrinsic[2] = intrinsic[2] * to_scale_ratio + intrinsic[3] = intrinsic[3] * to_scale_ratio + + cam_model = build_camera_model(reshape_h, reshape_w, intrinsic) + cam_model = cv2.copyMakeBorder( + cam_model, + pad_h_half, + pad_h - pad_h_half, + pad_w_half, + pad_w - pad_w_half, + cv2.BORDER_CONSTANT, + value=-1) + + pad=[pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half] + label_scale_factor=1/to_scale_ratio + return image, cam_model, pad, label_scale_factor + + +def get_prediction( + model: torch.nn.Module, + input: torch.tensor, + cam_model: torch.tensor, + pad_info: torch.tensor, + scale_info: torch.tensor, + gt_depth: torch.tensor, + normalize_scale: float, + ori_shape: list=[], +): + + data = dict( + input=input, + cam_model=cam_model, + ) + pred_depth, confidence, output_dict = model.inference(data) + + return pred_depth, confidence, output_dict + +def transform_test_data_scalecano(rgb, intrinsic, data_basic, device="cuda"): + """ + Pre-process the input for forwarding. Employ `label scale canonical transformation.' + Args: + rgb: input rgb image. [H, W, 3] + intrinsic: camera intrinsic parameter, [fx, fy, u0, v0] + data_basic: predefined canonical space in configs. + """ + canonical_space = data_basic['canonical_space'] + forward_size = data_basic.crop_size + mean = torch.tensor([123.675, 116.28, 103.53]).float()[:, None, None] + std = torch.tensor([58.395, 57.12, 57.375]).float()[:, None, None] + + # BGR to RGB + #rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) + + ori_h, ori_w, _ = rgb.shape + ori_focal = (intrinsic[0] + intrinsic[1]) / 2 + canonical_focal = canonical_space['focal_length'] + + cano_label_scale_ratio = canonical_focal / ori_focal + + canonical_intrinsic = [ + intrinsic[0] * cano_label_scale_ratio, + intrinsic[1] * cano_label_scale_ratio, + intrinsic[2], + intrinsic[3], + ] + + # resize + rgb, cam_model, pad, resize_label_scale_ratio = resize_for_input(rgb, forward_size, canonical_intrinsic, [ori_h, ori_w], 1.0) + + # label scale factor + label_scale_factor = cano_label_scale_ratio * resize_label_scale_ratio + + rgb = torch.from_numpy(rgb.transpose((2, 0, 1))).float() + rgb = torch.div((rgb - mean), std) + rgb = rgb.to(device) + + cam_model = torch.from_numpy(cam_model.transpose((2, 0, 1))).float() + cam_model = cam_model[None, :, :, :].to(device) + cam_model_stacks = [ + torch.nn.functional.interpolate(cam_model, size=(cam_model.shape[2]//i, cam_model.shape[3]//i), mode='bilinear', align_corners=False) + for i in [2, 4, 8, 16, 32] + ] + return rgb, cam_model_stacks, pad, label_scale_factor + +def do_scalecano_test_with_custom_data( + model: torch.nn.Module, + cfg: dict, + test_data: list, + logger: logging.RootLogger, + is_distributed: bool = True, + local_rank: int = 0, + bs: int = 2, # Batch size parameter +): + + show_dir = cfg.show_dir + save_interval = 1 + save_imgs_dir = show_dir + '/vis' + os.makedirs(save_imgs_dir, exist_ok=True) + save_pcd_dir = show_dir + '/pcd' + os.makedirs(save_pcd_dir, exist_ok=True) + + normalize_scale = cfg.data_basic.depth_range[1] + dam = MetricAverageMeter(['abs_rel', 'rmse', 'silog', 'delta1', 'delta2', 'delta3']) + dam_median = MetricAverageMeter(['abs_rel', 'rmse', 'silog', 'delta1', 'delta2', 'delta3']) + dam_global = MetricAverageMeter(['abs_rel', 'rmse', 'silog', 'delta1', 'delta2', 'delta3']) + + # Process data in batches + for i in tqdm(range(0, len(test_data), bs)): + batch_data = test_data[i:i + bs] # Extract batch + rgb_inputs, pads, label_scale_factors, gt_depths, rgb_origins = [], [], [], [], [] + + for an in batch_data: + print(an['rgb']) + rgb_origin = cv2.imread(an['rgb'])[:, :, ::-1].copy() + rgb_origins.append(rgb_origin) + gt_depth = None + if an['depth'] is not None: + gt_depth = cv2.imread(an['depth'], -1) + gt_depth_scale = an['depth_scale'] + gt_depth = gt_depth / gt_depth_scale + gt_depths.append(gt_depth) + + intrinsic = an['intrinsic'] + if intrinsic is None: + intrinsic = [1000.0, 1000.0, rgb_origin.shape[1]/2, rgb_origin.shape[0]/2] + + rgb_input, _, pad, label_scale_factor = transform_test_data_scalecano(rgb_origin, intrinsic, cfg.data_basic) + rgb_inputs.append(rgb_input) + pads.append(pad) + label_scale_factors.append(label_scale_factor) + + # Process the batch + pred_depths, outputs = get_prediction( + model=model, + input=torch.stack(rgb_inputs), # Stack inputs for batch processing + cam_model=None, + pad_info=pads, + scale_info=None, + gt_depth=None, + normalize_scale=None, + ) + + for j, gt_depth in enumerate(gt_depths): + normal_out = None + if 'normal_out_list' in outputs.keys(): + normal_out = outputs['normal_out_list'][0][j, :] + + postprocess_per_image( + i*bs+j, + pred_depths[j, :], + gt_depth, + intrinsic, + rgb_origins[j], + normal_out, + pads[j], + batch_data[j], + dam, + dam_median, + dam_global, + is_distributed, + save_imgs_dir, + save_pcd_dir, + normalize_scale, + label_scale_factors[j], + ) + + #if gt_depth_flag: + if False: + eval_error = dam.get_metrics() + print('w/o match :', eval_error) + + eval_error_median = dam_median.get_metrics() + print('median match :', eval_error_median) + + eval_error_global = dam_global.get_metrics() + print('global match :', eval_error_global) + else: + print('missing gt_depth, only save visualizations...') + + +def postprocess_per_image(i, pred_depth, gt_depth, intrinsic, rgb_origin, normal_out, pad, an, dam, dam_median, dam_global, is_distributed, save_imgs_dir, save_pcd_dir, normalize_scale, scale_info): + + pred_depth = pred_depth.squeeze() + pred_depth = pred_depth[pad[0] : pred_depth.shape[0] - pad[1], pad[2] : pred_depth.shape[1] - pad[3]] + pred_depth = torch.nn.functional.interpolate(pred_depth[None, None, :, :], [rgb_origin.shape[0], rgb_origin.shape[1]], mode='bilinear').squeeze() # to original size + pred_depth = pred_depth * normalize_scale / scale_info + + pred_depth = (pred_depth > 0) * (pred_depth < 300) * pred_depth + if gt_depth is not None: + + pred_depth = torch.nn.functional.interpolate(pred_depth[None, None, :, :], (gt_depth.shape[0], gt_depth.shape[1]), mode='bilinear').squeeze() # to original size + + gt_depth = torch.from_numpy(gt_depth).cuda() + + pred_depth_median = pred_depth * gt_depth[gt_depth != 0].median() / pred_depth[gt_depth != 0].median() + pred_global, _ = align_scale_shift(pred_depth, gt_depth) + + mask = (gt_depth > 1e-8) + dam.update_metrics_gpu(pred_depth, gt_depth, mask, is_distributed) + dam_median.update_metrics_gpu(pred_depth_median, gt_depth, mask, is_distributed) + dam_global.update_metrics_gpu(pred_global, gt_depth, mask, is_distributed) + print(gt_depth[gt_depth != 0].median() / pred_depth[gt_depth != 0].median(), ) + + os.makedirs(osp.join(save_imgs_dir, an['folder']), exist_ok=True) + rgb_torch = torch.from_numpy(rgb_origin).to(pred_depth.device).permute(2, 0, 1) + mean = torch.tensor([123.675, 116.28, 103.53]).float()[:, None, None].to(rgb_torch.device) + std = torch.tensor([58.395, 57.12, 57.375]).float()[:, None, None].to(rgb_torch.device) + rgb_torch = torch.div((rgb_torch - mean), std) + + save_val_imgs( + i, + pred_depth, + gt_depth if gt_depth is not None else torch.ones_like(pred_depth, device=pred_depth.device), + rgb_torch, + osp.join(an['folder'], an['filename']), + save_imgs_dir, + ) + #save_raw_imgs(pred_depth.detach().cpu().numpy(), rgb_torch, osp.join(an['folder'], an['filename']), save_imgs_dir, 1000.0) + + # pcd + pred_depth = pred_depth.detach().cpu().numpy() + #pcd = reconstruct_pcd(pred_depth, intrinsic[0], intrinsic[1], intrinsic[2], intrinsic[3]) + #os.makedirs(osp.join(save_pcd_dir, an['folder']), exist_ok=True) + #save_point_cloud(pcd.reshape((-1, 3)), rgb_origin.reshape(-1, 3), osp.join(save_pcd_dir, an['folder'], an['filename'][:-4]+'.ply')) + + if an['intrinsic'] == None: + #for r in [0.9, 1.0, 1.1]: + for r in [1.0]: + #for f in [600, 800, 1000, 1250, 1500]: + for f in [1000]: + pcd = reconstruct_pcd(pred_depth, f * r, f * (2-r), intrinsic[2], intrinsic[3]) + fstr = '_fx_' + str(int(f * r)) + '_fy_' + str(int(f * (2-r))) + os.makedirs(osp.join(save_pcd_dir, an['folder']), exist_ok=True) + save_point_cloud(pcd.reshape((-1, 3)), rgb_origin.reshape(-1, 3), osp.join(save_pcd_dir, an['folder'], an['filename'][:-4] + fstr +'.ply')) + + if normal_out is not None: + pred_normal = normal_out[:3, :, :] # (3, H, W) + H, W = pred_normal.shape[1:] + pred_normal = pred_normal[ :, pad[0]:H-pad[1], pad[2]:W-pad[3]] + + gt_normal = None + #if gt_normal_flag: + if False: + pred_normal = torch.nn.functional.interpolate(pred_normal, size=gt_normal.shape[2:], mode='bilinear', align_corners=True) + gt_normal = cv2.imread(norm_path) + gt_normal = cv2.cvtColor(gt_normal, cv2.COLOR_BGR2RGB) + gt_normal = np.array(gt_normal).astype(np.uint8) + gt_normal = ((gt_normal.astype(np.float32) / 255.0) * 2.0) - 1.0 + norm_valid_mask = (np.linalg.norm(gt_normal, axis=2, keepdims=True) > 0.5) + gt_normal = gt_normal * norm_valid_mask + gt_normal_mask = ~torch.all(gt_normal == 0, dim=1, keepdim=True) + dam.update_normal_metrics_gpu(pred_normal, gt_normal, gt_normal_mask, cfg.distributed)# save valiad normal + + save_normal_val_imgs(iter, + pred_normal, + gt_normal if gt_normal is not None else torch.ones_like(pred_normal, device=pred_normal.device), + rgb_torch, # data['input'], + osp.join(an['folder'], 'normal_'+an['filename']), + save_imgs_dir, + ) + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/logger.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/logger.py new file mode 100644 index 00000000..ca48c613 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/logger.py @@ -0,0 +1,102 @@ +import atexit +import logging +import os +import sys +import time +import torch +from termcolor import colored + +__all__ = ["setup_logger", ] + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + self._abbrev_name = kwargs.pop("abbrev_name", "") + if len(self._abbrev_name): + self._abbrev_name = self._abbrev_name + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + record.name = record.name.replace(self._root_name, self._abbrev_name) + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + +def setup_logger( + output=None, distributed_rank=0, *, name='metricdepth', color=True, abbrev_name=None +): + """ + Initialize the detectron2 logger and set its verbosity level to "DEBUG". + Args: + output (str): a file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + abbrev_name (str): an abbreviation of the module, to avoid log names in logs. + Set to "" not log the root module in logs. + By default, will abbreviate "detectron2" to "d2" and leave other + modules unchanged. + Returns: + logging.Logger: a logger + """ + logger = logging.getLogger() + logger.setLevel(logging.INFO) # NOTE: if more detailed, change it to logging.DEBUG + logger.propagate = False + + if abbrev_name is None: + abbrev_name = "d2" + + plain_formatter = logging.Formatter( + "[%(asctime)s] %(name)s %(levelname)s %(message)s ", datefmt="%m/%d %H:%M:%S" + ) + # stdout logging: master only + if distributed_rank == 0: + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.INFO) # NOTE: if more detailed, change it to logging.DEBUG + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + abbrev_name=str(abbrev_name), + ) + else: + formatter = plain_formatter + ch.setFormatter(formatter) + logger.addHandler(ch) + + # file logging: all workers + if output is not None: + if output.endswith(".txt") or output.endswith(".log"): + filename = output + else: + filename = os.path.join(output, "log.txt") + if distributed_rank > 0: + filename = filename + ".rank{}".format(distributed_rank) + os.makedirs(os.path.dirname(filename), exist_ok=True) + + fh = logging.StreamHandler(_cached_log_stream(filename)) + fh.setLevel(logging.INFO) # NOTE: if more detailed, change it to logging.DEBUG + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + + return logger + +from iopath.common.file_io import PathManager as PathManagerBase + + +PathManager = PathManagerBase() + +# cache the opened file object, so that different calls to 'setup_logger +# with the same file name can safely write to the same file. +def _cached_log_stream(filename): + # use 1K buffer if writting to cloud storage + io = PathManager.open(filename, "a", buffering=1024 if "://" in filename else -1) + atexit.register(io.close) + return io + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/mldb.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/mldb.py new file mode 100644 index 00000000..d74ac53f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/mldb.py @@ -0,0 +1,34 @@ +from types import ModuleType +import data_info + +def load_data_info(module_name, data_info={}, mldb_type='mldb_info', module=None): + if module is None: + module = globals().get(module_name, None) + if module: + for key, value in module.__dict__.items(): + if not (key.startswith('__')) and not (key.startswith('_')): + if key == 'mldb_info': + data_info.update(value) + elif isinstance(value, ModuleType): + load_data_info(module_name + '.' + key, data_info, module=value) + else: + raise RuntimeError(f'Try to access "mldb_info", but cannot find {module_name} module.') + +def reset_ckpt_path(cfg, data_info): + if isinstance(cfg, dict): + for key in cfg.keys(): + if key == 'backbone': + new_ckpt_path = data_info['checkpoint']['mldb_root'] + '/' + data_info['checkpoint'][cfg.backbone.type] + cfg.backbone.update(checkpoint=new_ckpt_path) + continue + elif isinstance(cfg.get(key), dict): + reset_ckpt_path(cfg.get(key), data_info) + else: + continue + else: + return + +if __name__ == '__main__': + mldb_info_tmp = {} + load_data_info('mldb_data_info', mldb_info_tmp) + print('results', mldb_info_tmp.keys()) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/pcd_filter.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/pcd_filter.py new file mode 100644 index 00000000..2d26314d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/pcd_filter.py @@ -0,0 +1,24 @@ +import open3d as o3d +import numpy as np + +def downsample_and_filter(pcd_file): + pcd = o3d.io.read_point_cloud(pcd_file, max_bound_div = 750, neighbor_num = 8) + point_num = len(pcd.points) + if (point_num > 10000000): + voxel_down_pcd = o3d.geometry.PointCloud.uniform_down_sample(pcd, int(point_num / 10000000)+1) + else: + voxel_down_pcd = pcd + max_bound = voxel_down_pcd.get_max_bound() + ball_radius = np.linalg.norm(max_bound) / max_bound_div + pcd_filter, _ = voxel_down_pcd.remove_radius_outlier(neighbor_num, ball_radius) + print('filtered size', len(pcd_filter.points), 'pre size:', len(pcd.points)) + o3d.io.write_point_cloud(pcd_file[:-4] + '_filtered.ply', pcd_filter) + + +if __name__ == "__main__": + import os + dir_path = './data/demo_pcd' + for pcd_file in os.listdir(dir_path): + #if 'jonathan' in pcd_file: set max_bound_div to 300 and neighbot_num to 8 + downsample_and_filter(os.path.join(dir_path, pcd_file)) + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/running.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/running.py new file mode 100644 index 00000000..5dce71cf --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/running.py @@ -0,0 +1,77 @@ +import os +import torch +import torch.nn as nn +from .comm import main_process +import copy +import inspect +import logging +import glob + + +def load_ckpt(load_path, model, optimizer=None, scheduler=None, strict_match=True, loss_scaler=None): + """ + Load the check point for resuming training or finetuning. + """ + logger = logging.getLogger() + if os.path.isfile(load_path): + if main_process(): + logger.info(f"Loading weight '{load_path}'") + checkpoint = torch.load(load_path, map_location="cpu", weights_only=True) + ckpt_state_dict = checkpoint['model_state_dict'] + model.load_state_dict(ckpt_state_dict, strict=strict_match) + + if optimizer is not None: + optimizer.load_state_dict(checkpoint['optimizer']) + if scheduler is not None: + scheduler.load_state_dict(checkpoint['scheduler']) + if loss_scaler is not None and 'scaler' in checkpoint: + scheduler.load_state_dict(checkpoint['scaler']) + del ckpt_state_dict + del checkpoint + if main_process(): + logger.info(f"Successfully loaded weight: '{load_path}'") + if scheduler is not None and optimizer is not None: + logger.info(f"Resume training from: '{load_path}'") + else: + if main_process(): + raise RuntimeError(f"No weight found at '{load_path}'") + return model, optimizer, scheduler, loss_scaler + + +def save_ckpt(cfg, model, optimizer, scheduler, curr_iter=0, curr_epoch=None, loss_scaler=None): + """ + Save the model, optimizer, lr scheduler. + """ + logger = logging.getLogger() + + if 'IterBasedRunner' in cfg.runner.type: + max_iters = cfg.runner.max_iters + elif 'EpochBasedRunner' in cfg.runner.type: + max_iters = cfg.runner.max_epochs + else: + raise TypeError(f'{cfg.runner.type} is not supported') + + ckpt = dict( + model_state_dict=model.module.state_dict(), + optimizer=optimizer.state_dict(), + max_iter=cfg.runner.max_iters if 'max_iters' in cfg.runner \ + else cfg.runner.max_epochs, + scheduler=scheduler.state_dict(), + ) + + if loss_scaler is not None: + ckpt.update(dict(scaler=loss_scaler.state_dict())) + + ckpt_dir = os.path.join(cfg.work_dir, 'ckpt') + os.makedirs(ckpt_dir, exist_ok=True) + + save_name = os.path.join(ckpt_dir, 'step%08d.pth' %curr_iter) + saved_ckpts = glob.glob(ckpt_dir + '/step*.pth') + torch.save(ckpt, save_name) + + # keep the last 8 ckpts + if len(saved_ckpts) > 20: + saved_ckpts.sort() + os.remove(saved_ckpts.pop(0)) + + logger.info(f'Save model: {save_name}') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/transform.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/transform.py new file mode 100644 index 00000000..2af94efe --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/transform.py @@ -0,0 +1,408 @@ +import collections +import cv2 +import math +import numpy as np +import numbers +import random +import torch + +import matplotlib +import matplotlib.cm + + +""" +Provides a set of Pytorch transforms that use OpenCV instead of PIL (Pytorch default) +for image manipulation. +""" + +class Compose(object): + # Composes transforms: transforms.Compose([transforms.RandScale([0.5, 2.0]), transforms.ToTensor()]) + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, images, labels, intrinsics, cam_models=None, other_labels=None, transform_paras=None): + for t in self.transforms: + images, labels, intrinsics, cam_models, other_labels, transform_paras = t(images, labels, intrinsics, cam_models, other_labels, transform_paras) + return images, labels, intrinsics, cam_models, other_labels, transform_paras + + +class ToTensor(object): + # Converts numpy.ndarray (H x W x C) to a torch.FloatTensor of shape (C x H x W). + def __init__(self, **kwargs): + return + def __call__(self, images, labels, intrinsics, cam_models=None, other_labels=None, transform_paras=None): + if not isinstance(images, list) or not isinstance(labels, list) or not isinstance(intrinsics, list): + raise (RuntimeError("transform.ToTensor() only handle inputs/labels/intrinsics lists.")) + if len(images) != len(intrinsics): + raise (RuntimeError("Numbers of images and intrinsics are not matched.")) + if not isinstance(images[0], np.ndarray) or not isinstance(labels[0], np.ndarray): + raise (RuntimeError("transform.ToTensor() only handle np.ndarray for the input and label." + "[eg: data readed by cv2.imread()].\n")) + if not isinstance(intrinsics[0], list): + raise (RuntimeError("transform.ToTensor() only handle list for the camera intrinsics")) + + if len(images[0].shape) > 3 or len(images[0].shape) < 2: + raise (RuntimeError("transform.ToTensor() only handle image(np.ndarray) with 3 dims or 2 dims.\n")) + if len(labels[0].shape) > 3 or len(labels[0].shape) < 2: + raise (RuntimeError("transform.ToTensor() only handle label(np.ndarray) with 3 dims or 2 dims.\n")) + + if len(intrinsics[0]) >4 or len(intrinsics[0]) < 3: + raise (RuntimeError("transform.ToTensor() only handle intrinsic(list) with 3 sizes or 4 sizes.\n")) + + for i, img in enumerate(images): + if len(img.shape) == 2: + img = np.expand_dims(img, axis=2) + images[i] = torch.from_numpy(img.transpose((2, 0, 1))).float() + for i, lab in enumerate(labels): + if len(lab.shape) == 2: + lab = np.expand_dims(lab, axis=0) + labels[i] = torch.from_numpy(lab).float() + for i, intrinsic in enumerate(intrinsics): + if len(intrinsic) == 3: + intrinsic = [intrinsic[0],] + intrinsic + intrinsics[i] = torch.tensor(intrinsic, dtype=torch.float) + if cam_models is not None: + for i, cam_model in enumerate(cam_models): + cam_models[i] = torch.from_numpy(cam_model.transpose((2, 0, 1))).float() if cam_model is not None else None + if other_labels is not None: + for i, lab in enumerate(other_labels): + if len(lab.shape) == 2: + lab = np.expand_dims(lab, axis=0) + other_labels[i] = torch.from_numpy(lab).float() + return images, labels, intrinsics, cam_models, other_labels, transform_paras + + +class Normalize(object): + # Normalize tensor with mean and standard deviation along channel: channel = (channel - mean) / std + def __init__(self, mean, std=None, **kwargs): + if std is None: + assert len(mean) > 0 + else: + assert len(mean) == len(std) + self.mean = torch.tensor(mean).float()[:, None, None] + self.std = torch.tensor(std).float()[:, None, None] if std is not None \ + else torch.tensor([1.0, 1.0, 1.0]).float()[:, None, None] + + def __call__(self, images, labels, intrinsics, cam_models=None, other_labels=None, transform_paras=None): + # if self.std is None: + # # for t, m in zip(image, self.mean): + # # t.sub(m) + # image = image - self.mean + # if ref_images is not None: + # for i, ref_i in enumerate(ref_images): + # ref_images[i] = ref_i - self.mean + # else: + # # for t, m, s in zip(image, self.mean, self.std): + # # t.sub(m).div(s) + # image = (image - self.mean) / self.std + # if ref_images is not None: + # for i, ref_i in enumerate(ref_images): + # ref_images[i] = (ref_i - self.mean) / self.std + for i, img in enumerate(images): + img = torch.div((img - self.mean), self.std) + images[i] = img + return images, labels, intrinsics, cam_models, other_labels, transform_paras + + +class LableScaleCanonical(object): + """ + To solve the ambiguity observation for the mono branch, i.e. different focal length (object size) with the same depth, cameras are + mapped to a canonical space. To mimic this, we set the focal length to a canonical one and scale the depth value. NOTE: resize the image based on the ratio can also solve + Args: + images: list of RGB images. + labels: list of depth/disparity labels. + other labels: other labels, such as instance segmentations, semantic segmentations... + """ + def __init__(self, **kwargs): + self.canonical_focal = kwargs['focal_length'] + + def _get_scale_ratio(self, intrinsic): + target_focal_x = intrinsic[0] + label_scale_ratio = self.canonical_focal / target_focal_x + pose_scale_ratio = 1.0 + return label_scale_ratio, pose_scale_ratio + + def __call__(self, images, labels, intrinsics, cam_models=None, other_labels=None, transform_paras=None): + assert len(images[0].shape) == 3 and len(labels[0].shape) == 2 + assert labels[0].dtype == np.float32 + + label_scale_ratio = None + pose_scale_ratio = None + + for i in range(len(intrinsics)): + img_i = images[i] + label_i = labels[i] if i < len(labels) else None + intrinsic_i = intrinsics[i].copy() + cam_model_i = cam_models[i] if cam_models is not None and i < len(cam_models) else None + + label_scale_ratio, pose_scale_ratio = self._get_scale_ratio(intrinsic_i) + + # adjust the focal length, map the current camera to the canonical space + intrinsics[i] = [intrinsic_i[0] * label_scale_ratio, intrinsic_i[1] * label_scale_ratio, intrinsic_i[2], intrinsic_i[3]] + + # scale the label to the canonical space + if label_i is not None: + labels[i] = label_i * label_scale_ratio + + if cam_model_i is not None: + # As the focal length is adjusted (canonical focal length), the camera model should be re-built + ori_h, ori_w, _ = img_i.shape + cam_models[i] = build_camera_model(ori_h, ori_w, intrinsics[i]) + + + if transform_paras is not None: + transform_paras.update(label_scale_factor=label_scale_ratio, focal_scale_factor=label_scale_ratio) + + return images, labels, intrinsics, cam_models, other_labels, transform_paras + + +class ResizeKeepRatio(object): + """ + Resize and pad to a given size. Hold the aspect ratio. + This resizing assumes that the camera model remains unchanged. + Args: + resize_size: predefined output size. + """ + def __init__(self, resize_size, padding=None, ignore_label=-1, **kwargs): + if isinstance(resize_size, int): + self.resize_h = resize_size + self.resize_w = resize_size + elif isinstance(resize_size, collections.Iterable) and len(resize_size) == 2 \ + and isinstance(resize_size[0], int) and isinstance(resize_size[1], int) \ + and resize_size[0] > 0 and resize_size[1] > 0: + self.resize_h = resize_size[0] + self.resize_w = resize_size[1] + else: + raise (RuntimeError("crop size error.\n")) + if padding is None: + self.padding = padding + elif isinstance(padding, list): + if all(isinstance(i, numbers.Number) for i in padding): + self.padding = padding + else: + raise (RuntimeError("padding in Crop() should be a number list\n")) + if len(padding) != 3: + raise (RuntimeError("padding channel is not equal with 3\n")) + else: + raise (RuntimeError("padding in Crop() should be a number list\n")) + if isinstance(ignore_label, int): + self.ignore_label = ignore_label + else: + raise (RuntimeError("ignore_label should be an integer number\n")) + # self.crop_size = kwargs['crop_size'] + self.canonical_focal = kwargs['focal_length'] + + def main_data_transform(self, image, label, intrinsic, cam_model, resize_ratio, padding, to_scale_ratio): + """ + Resize data first and then do the padding. + 'label' will be scaled. + """ + h, w, _ = image.shape + reshape_h = int(resize_ratio * h) + reshape_w = int(resize_ratio * w) + + pad_h, pad_w, pad_h_half, pad_w_half = padding + + # resize + image = cv2.resize(image, dsize=(reshape_w, reshape_h), interpolation=cv2.INTER_LINEAR) + # padding + image = cv2.copyMakeBorder( + image, + pad_h_half, + pad_h - pad_h_half, + pad_w_half, + pad_w - pad_w_half, + cv2.BORDER_CONSTANT, + value=self.padding) + + if label is not None: + # label = cv2.resize(label, dsize=(reshape_w, reshape_h), interpolation=cv2.INTER_NEAREST) + label = resize_depth_preserve(label, (reshape_h, reshape_w)) + label = cv2.copyMakeBorder( + label, + pad_h_half, + pad_h - pad_h_half, + pad_w_half, + pad_w - pad_w_half, + cv2.BORDER_CONSTANT, + value=self.ignore_label) + # scale the label + label = label / to_scale_ratio + + # Resize, adjust principle point + if intrinsic is not None: + intrinsic[0] = intrinsic[0] * resize_ratio / to_scale_ratio + intrinsic[1] = intrinsic[1] * resize_ratio / to_scale_ratio + intrinsic[2] = intrinsic[2] * resize_ratio + intrinsic[3] = intrinsic[3] * resize_ratio + + if cam_model is not None: + #cam_model = cv2.resize(cam_model, dsize=(reshape_w, reshape_h), interpolation=cv2.INTER_LINEAR) + cam_model = build_camera_model(reshape_h, reshape_w, intrinsic) + cam_model = cv2.copyMakeBorder( + cam_model, + pad_h_half, + pad_h - pad_h_half, + pad_w_half, + pad_w - pad_w_half, + cv2.BORDER_CONSTANT, + value=self.ignore_label) + + # Pad, adjust the principle point + if intrinsic is not None: + intrinsic[2] = intrinsic[2] + pad_w_half + intrinsic[3] = intrinsic[3] + pad_h_half + return image, label, intrinsic, cam_model + + def get_label_scale_factor(self, image, intrinsic, resize_ratio): + ori_h, ori_w, _ = image.shape + # crop_h, crop_w = self.crop_size + ori_focal = intrinsic[0] + + to_canonical_ratio = self.canonical_focal / ori_focal + to_scale_ratio = resize_ratio / to_canonical_ratio + return to_scale_ratio + + def __call__(self, images, labels, intrinsics, cam_models=None, other_labels=None, transform_paras=None): + target_h, target_w, _ = images[0].shape + resize_ratio_h = self.resize_h / target_h + resize_ratio_w = self.resize_w / target_w + resize_ratio = min(resize_ratio_h, resize_ratio_w) + reshape_h = int(resize_ratio * target_h) + reshape_w = int(resize_ratio * target_w) + pad_h = max(self.resize_h - reshape_h, 0) + pad_w = max(self.resize_w - reshape_w, 0) + pad_h_half = int(pad_h / 2) + pad_w_half = int(pad_w / 2) + + pad_info = [pad_h, pad_w, pad_h_half, pad_w_half] + to_scale_ratio = self.get_label_scale_factor(images[0], intrinsics[0], resize_ratio) + + for i in range(len(images)): + img = images[i] + label = labels[i] if i < len(labels) else None + intrinsic = intrinsics[i] if i < len(intrinsics) else None + cam_model = cam_models[i] if cam_models is not None and i < len(cam_models) else None + img, label, intrinsic, cam_model = self.main_data_transform( + img, label, intrinsic, cam_model, resize_ratio, pad_info, to_scale_ratio) + images[i] = img + if label is not None: + labels[i] = label + if intrinsic is not None: + intrinsics[i] = intrinsic + if cam_model is not None: + cam_models[i] = cam_model + + if other_labels is not None: + + for i, other_lab in enumerate(other_labels): + # resize + other_lab = cv2.resize(other_lab, dsize=(reshape_w, reshape_h), interpolation=cv2.INTER_NEAREST) + # pad + other_labels[i] = cv2.copyMakeBorder( + other_lab, + pad_h_half, + pad_h - pad_h_half, + pad_w_half, + pad_w - pad_w_half, + cv2.BORDER_CONSTANT, + value=self.ignore_label) + + pad = [pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half] + if transform_paras is not None: + pad_old = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0] + new_pad = [pad_old[0] + pad[0], pad_old[1] + pad[1], pad_old[2] + pad[2], pad_old[3] + pad[3]] + transform_paras.update(dict(pad=new_pad)) + if 'label_scale_factor' in transform_paras: + transform_paras['label_scale_factor'] = transform_paras['label_scale_factor'] * 1.0 / to_scale_ratio + else: + transform_paras.update(label_scale_factor=1.0/to_scale_ratio) + return images, labels, intrinsics, cam_models, other_labels, transform_paras + + +class BGR2RGB(object): + # Converts image from BGR order to RGB order, for model initialized from Pytorch + def __init__(self, **kwargs): + return + def __call__(self, images, labels, intrinsics, cam_models=None,other_labels=None, transform_paras=None): + for i, img in enumerate(images): + images[i] = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + return images, labels, intrinsics, cam_models, other_labels, transform_paras + + +def resize_depth_preserve(depth, shape): + """ + Resizes depth map preserving all valid depth pixels + Multiple downsampled points can be assigned to the same pixel. + + Parameters + ---------- + depth : np.array [h,w] + Depth map + shape : tuple (H,W) + Output shape + + Returns + ------- + depth : np.array [H,W,1] + Resized depth map + """ + # Store dimensions and reshapes to single column + depth = np.squeeze(depth) + h, w = depth.shape + x = depth.reshape(-1) + # Create coordinate grid + uv = np.mgrid[:h, :w].transpose(1, 2, 0).reshape(-1, 2) + # Filters valid points + idx = x > 0 + crd, val = uv[idx], x[idx] + # Downsamples coordinates + crd[:, 0] = (crd[:, 0] * (shape[0] / h) + 0.5).astype(np.int32) + crd[:, 1] = (crd[:, 1] * (shape[1] / w) + 0.5).astype(np.int32) + # Filters points inside image + idx = (crd[:, 0] < shape[0]) & (crd[:, 1] < shape[1]) + crd, val = crd[idx], val[idx] + # Creates downsampled depth image and assigns points + depth = np.zeros(shape) + depth[crd[:, 0], crd[:, 1]] = val + # Return resized depth map + return depth + + +def build_camera_model(H : int, W : int, intrinsics : list) -> np.array: + """ + Encode the camera intrinsic parameters (focal length and principle point) to a 4-channel map. + """ + fx, fy, u0, v0 = intrinsics + f = (fx + fy) / 2.0 + # principle point location + x_row = np.arange(0, W).astype(np.float32) + x_row_center_norm = (x_row - u0) / W + x_center = np.tile(x_row_center_norm, (H, 1)) # [H, W] + + y_col = np.arange(0, H).astype(np.float32) + y_col_center_norm = (y_col - v0) / H + y_center = np.tile(y_col_center_norm, (W, 1)).T + + # FoV + fov_x = np.arctan(x_center / (f / W)) + fov_y = np.arctan(y_center/ (f / H)) + + cam_model = np.stack([x_center, y_center, fov_x, fov_y], axis=2) + return cam_model + +def gray_to_colormap(img, cmap='rainbow'): + """ + Transfer gray map to matplotlib colormap + """ + assert img.ndim == 2 + + img[img<0] = 0 + mask_invalid = img < 1e-10 + img = img / (img.max() + 1e-8) + norm = matplotlib.colors.Normalize(vmin=0, vmax=1.1) + cmap_m = matplotlib.cm.get_cmap(cmap) + map = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap_m) + colormap = (map.to_rgba(img)[:, :, :3] * 255).astype(np.uint8) + colormap[mask_invalid] = 0 + return colormap \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/unproj_pcd.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/unproj_pcd.py new file mode 100644 index 00000000..a0986d48 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/unproj_pcd.py @@ -0,0 +1,88 @@ +import numpy as np +import torch +from plyfile import PlyData, PlyElement +import cv2 + + +def get_pcd_base(H, W, u0, v0, fx, fy): + x_row = np.arange(0, W) + x = np.tile(x_row, (H, 1)) + x = x.astype(np.float32) + u_m_u0 = x - u0 + + y_col = np.arange(0, H) # y_col = np.arange(0, height) + y = np.tile(y_col, (W, 1)).T + y = y.astype(np.float32) + v_m_v0 = y - v0 + + x = u_m_u0 / fx + y = v_m_v0 / fy + z = np.ones_like(x) + pw = np.stack([x, y, z], axis=2) # [h, w, c] + return pw + + +def reconstruct_pcd(depth, fx, fy, u0, v0, pcd_base=None, mask=None): + if type(depth) == torch.__name__: + depth = depth.cpu().numpy().squeeze() + depth = cv2.medianBlur(depth, 5) + if pcd_base is None: + H, W = depth.shape + pcd_base = get_pcd_base(H, W, u0, v0, fx, fy) + pcd = depth[:, :, None] * pcd_base + if mask: + pcd[mask] = 0 + return pcd + + +def save_point_cloud(pcd, rgb, filename, binary=True): + """Save an RGB point cloud as a PLY file. + :paras + @pcd: Nx3 matrix, the XYZ coordinates + @rgb: Nx3 matrix, the rgb colors for each 3D point + """ + assert pcd.shape[0] == rgb.shape[0] + + if rgb is None: + gray_concat = np.tile(np.array([128], dtype=np.uint8), + (pcd.shape[0], 3)) + points_3d = np.hstack((pcd, gray_concat)) + else: + points_3d = np.hstack((pcd, rgb)) + python_types = (float, float, float, int, int, int) + npy_types = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), + ('green', 'u1'), ('blue', 'u1')] + if binary is True: + # Format into Numpy structured array + vertices = [] + for row_idx in range(points_3d.shape[0]): + cur_point = points_3d[row_idx] + vertices.append( + tuple( + dtype(point) + for dtype, point in zip(python_types, cur_point))) + vertices_array = np.array(vertices, dtype=npy_types) + el = PlyElement.describe(vertices_array, 'vertex') + + # write + PlyData([el]).write(filename) + else: + x = np.squeeze(points_3d[:, 0]) + y = np.squeeze(points_3d[:, 1]) + z = np.squeeze(points_3d[:, 2]) + r = np.squeeze(points_3d[:, 3]) + g = np.squeeze(points_3d[:, 4]) + b = np.squeeze(points_3d[:, 5]) + + ply_head = 'ply\n' \ + 'format ascii 1.0\n' \ + 'element vertex %d\n' \ + 'property float x\n' \ + 'property float y\n' \ + 'property float z\n' \ + 'property uchar red\n' \ + 'property uchar green\n' \ + 'property uchar blue\n' \ + 'end_header' % r.shape[0] + # ---- Save ply data to disk + np.savetxt(filename, np.column_stack[x, y, z, r, g, b], fmt='%f %f %f %d %d %d', header=ply_head, comments='') \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/visualization.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/visualization.py new file mode 100644 index 00000000..85523f80 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/metric3d/mono/utils/visualization.py @@ -0,0 +1,139 @@ +import matplotlib.pyplot as plt +import os, cv2 +import numpy as np +from .transform import gray_to_colormap +import shutil +import glob +from .running import main_process +import torch + +def save_raw_imgs( + pred: torch.tensor, + rgb: torch.tensor, + filename: str, + save_dir: str, + scale: float=200.0, + target: torch.tensor=None, + ): + """ + Save raw GT, predictions, RGB in the same file. + """ + cv2.imwrite(os.path.join(save_dir, filename[:-4]+'_rgb.jpg'), rgb) + cv2.imwrite(os.path.join(save_dir, filename[:-4]+'_d.png'), (pred*scale).astype(np.uint16)) + if target is not None: + cv2.imwrite(os.path.join(save_dir, filename[:-4]+'_gt.png'), (target*scale).astype(np.uint16)) + + +def save_val_imgs( + iter: int, + pred: torch.tensor, + target: torch.tensor, + rgb: torch.tensor, + filename: str, + save_dir: str, + tb_logger=None + ): + """ + Save GT, predictions, RGB in the same file. + """ + rgb, pred_scale, target_scale, pred_color, target_color = get_data_for_log(pred, target, rgb) + rgb = rgb.transpose((1, 2, 0)) + cat_img = np.concatenate([rgb, pred_color, target_color], axis=0) + plt.imsave(os.path.join(save_dir, filename[:-4]+'_merge.jpg'), cat_img) + + # save to tensorboard + if tb_logger is not None: + tb_logger.add_image(f'{filename[:-4]}_merge.jpg', cat_img.transpose((2, 0, 1)), iter) + +def save_normal_val_imgs( + iter: int, + pred: torch.tensor, + targ: torch.tensor, + rgb: torch.tensor, + filename: str, + save_dir: str, + tb_logger=None, + mask=None, + ): + """ + Save GT, predictions, RGB in the same file. + """ + mean = np.array([123.675, 116.28, 103.53])[np.newaxis, np.newaxis, :] + std= np.array([58.395, 57.12, 57.375])[np.newaxis, np.newaxis, :] + pred = pred.squeeze() + targ = targ.squeeze() + rgb = rgb.squeeze() + + if pred.size(0) == 3: + pred = pred.permute(1,2,0) + if targ.size(0) == 3: + targ = targ.permute(1,2,0) + if rgb.size(0) == 3: + rgb = rgb.permute(1,2,0) + + pred_color = vis_surface_normal(pred, mask) + targ_color = vis_surface_normal(targ, mask) + rgb_color = ((rgb.cpu().numpy() * std) + mean).astype(np.uint8) + + try: + cat_img = np.concatenate([rgb_color, pred_color, targ_color], axis=0) + except: + pred_color = cv2.resize(pred_color, (rgb.shape[1], rgb.shape[0])) + targ_color = cv2.resize(targ_color, (rgb.shape[1], rgb.shape[0])) + cat_img = np.concatenate([rgb_color, pred_color, targ_color], axis=0) + + plt.imsave(os.path.join(save_dir, filename[:-4]+'_merge.jpg'), cat_img) + # cv2.imwrite(os.path.join(save_dir, filename[:-4]+'.jpg'), pred_color) + # save to tensorboard + if tb_logger is not None: + tb_logger.add_image(f'{filename[:-4]}_merge.jpg', cat_img.transpose((2, 0, 1)), iter) + +def get_data_for_log(pred: torch.tensor, target: torch.tensor, rgb: torch.tensor): + mean = np.array([123.675, 116.28, 103.53])[:, np.newaxis, np.newaxis] + std= np.array([58.395, 57.12, 57.375])[:, np.newaxis, np.newaxis] + + pred = pred.squeeze().cpu().numpy() + target = target.squeeze().cpu().numpy() + rgb = rgb.squeeze().cpu().numpy() + + pred[pred<0] = 0 + target[target<0] = 0 + max_scale = max(pred.max(), target.max()) + pred_scale = (pred/max_scale * 10000).astype(np.uint16) + target_scale = (target/max_scale * 10000).astype(np.uint16) + pred_color = gray_to_colormap(pred) + target_color = gray_to_colormap(target) + pred_color = cv2.resize(pred_color, (rgb.shape[2], rgb.shape[1])) + target_color = cv2.resize(target_color, (rgb.shape[2], rgb.shape[1])) + + rgb = ((rgb * std) + mean).astype(np.uint8) + return rgb, pred_scale, target_scale, pred_color, target_color + + +def create_html(name2path, save_path='index.html', size=(256, 384)): + # table description + cols = [] + for k, v in name2path.items(): + col_i = Col('img', k, v) # specify image content for column + cols.append(col_i) + # html table generation + imagetable(cols, out_file=save_path, imsize=size) + +def vis_surface_normal(normal: torch.tensor, mask: torch.tensor=None) -> np.array: + """ + Visualize surface normal. Transfer surface normal value from [-1, 1] to [0, 255] + Aargs: + normal (torch.tensor, [h, w, 3]): surface normal + mask (torch.tensor, [h, w]): valid masks + """ + normal = normal.cpu().numpy().squeeze() + n_img_L2 = np.sqrt(np.sum(normal ** 2, axis=2, keepdims=True)) + n_img_norm = normal / (n_img_L2 + 1e-8) + normal_vis = n_img_norm * 127 + normal_vis += 128 + normal_vis = normal_vis.astype(np.uint8) + if mask is not None: + mask = mask.cpu().numpy().squeeze() + normal_vis[~mask] = 0 + return normal_vis + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/LICENSE new file mode 100644 index 00000000..277b5c11 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 Intel ISL (Intel Intelligent Systems Lab) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/__init__.py new file mode 100644 index 00000000..c58f390d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/__init__.py @@ -0,0 +1,2 @@ +# Modern MiDaS implementation using HuggingFace transformers +from .transformers import MidasDetector diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/transformers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/transformers.py new file mode 100644 index 00000000..53cdac7b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/transformers.py @@ -0,0 +1,108 @@ +""" +MiDaS implementation using HuggingFace transformers for PyTorch 2.7 compatibility. +""" +import numpy as np +import torch +import cv2 +from PIL import Image +from typing import Union + +# Import utilities +from ..util import HWC3, common_input_validate, resize_image_with_pad + + +class MidasDetector: + + def __init__(self, model_name="Intel/dpt-large"): + from transformers import DPTForDepthEstimation, DPTImageProcessor + + self.model_name = model_name + self.processor = DPTImageProcessor.from_pretrained(model_name) + self.model = DPTForDepthEstimation.from_pretrained(model_name) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=None, model_type="dpt_hybrid", filename="dpt_hybrid-midas-501f0c75.pt"): + # Map legacy model types to HuggingFace models + model_mapping = { + "dpt_large": "Intel/dpt-large", + "dpt_hybrid": "Intel/dpt-hybrid-midas", + "midas_v21": "Intel/dpt-large", + "midas_v21_small": "Intel/dpt-large" + } + + # Use filename for model selection if provided + if filename and isinstance(filename, str): + if "dpt_large" in filename.lower(): + model_name = "Intel/dpt-large" + elif "dpt_hybrid" in filename.lower(): + model_name = "Intel/dpt-hybrid-midas" + else: + model_name = model_mapping.get(model_type, "Intel/dpt-large") + else: + model_name = model_mapping.get(model_type, "Intel/dpt-large") + + return cls(model_name) + + def to(self, device): + self.model = self.model.to(device) + self.device = device + return self + + def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + # Convert to PIL for processor + pil_image = Image.fromarray(detected_map.astype(np.uint8)) + + # Process with HuggingFace pipeline + with torch.no_grad(): + inputs = self.processor(images=pil_image, return_tensors="pt") + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + outputs = self.model(**inputs) + depth = outputs.predicted_depth + + # Normalize depth + depth = torch.nn.functional.interpolate( + depth.unsqueeze(1), + size=(detected_map.shape[0], detected_map.shape[1]), + mode="bicubic", + align_corners=False, + ).squeeze() + + depth_pt = depth.clone() + depth_pt -= torch.min(depth_pt) + depth_pt /= torch.max(depth_pt) + depth_pt = depth_pt.cpu().numpy() + depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8) + + if depth_and_normal: + depth_np = depth.cpu().numpy() + x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3) + y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3) + z = np.ones_like(x) * a + x[depth_pt < bg_th] = 0 + y[depth_pt < bg_th] = 0 + normal = np.stack([x, y, z], axis=2) + normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5 + normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)[:, :, ::-1] + + depth_image = HWC3(depth_image) + if depth_and_normal: + normal_image = HWC3(normal_image) + + depth_image = remove_pad(depth_image) + if depth_and_normal: + normal_image = remove_pad(normal_image) + + if output_type == "pil": + depth_image = Image.fromarray(depth_image) + if depth_and_normal: + normal_image = Image.fromarray(normal_image) + + if depth_and_normal: + return depth_image, normal_image + else: + return depth_image \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/utils.py new file mode 100644 index 00000000..9a9d3b5b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/midas/utils.py @@ -0,0 +1,189 @@ +"""Utils for monoDepth.""" +import sys +import re +import numpy as np +import cv2 +import torch + + +def read_pfm(path): + """Read pfm file. + + Args: + path (str): path to file + + Returns: + tuple: (data, scale) + """ + with open(path, "rb") as file: + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header.decode("ascii") == "PF": + color = True + elif header.decode("ascii") == "Pf": + color = False + else: + raise Exception("Not a PFM file: " + path) + + dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii")) + if dim_match: + width, height = list(map(int, dim_match.groups())) + else: + raise Exception("Malformed PFM header.") + + scale = float(file.readline().decode("ascii").rstrip()) + if scale < 0: + # little-endian + endian = "<" + scale = -scale + else: + # big-endian + endian = ">" + + data = np.fromfile(file, endian + "f") + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + + return data, scale + + +def write_pfm(path, image, scale=1): + """Write pfm file. + + Args: + path (str): pathto file + image (array): data + scale (int, optional): Scale. Defaults to 1. + """ + + with open(path, "wb") as file: + color = None + + if image.dtype.name != "float32": + raise Exception("Image dtype must be float32.") + + image = np.flipud(image) + + if len(image.shape) == 3 and image.shape[2] == 3: # color image + color = True + elif ( + len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1 + ): # greyscale + color = False + else: + raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.") + + file.write("PF\n" if color else "Pf\n".encode()) + file.write("%d %d\n".encode() % (image.shape[1], image.shape[0])) + + endian = image.dtype.byteorder + + if endian == "<" or endian == "=" and sys.byteorder == "little": + scale = -scale + + file.write("%f\n".encode() % scale) + + image.tofile(file) + + +def read_image(path): + """Read image and output RGB image (0-1). + + Args: + path (str): path to file + + Returns: + array: RGB image (0-1) + """ + img = cv2.imread(path) + + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 + + return img + + +def resize_image(img): + """Resize image and make it fit for network. + + Args: + img (array): image + + Returns: + tensor: data ready for network + """ + height_orig = img.shape[0] + width_orig = img.shape[1] + + if width_orig > height_orig: + scale = width_orig / 384 + else: + scale = height_orig / 384 + + height = (np.ceil(height_orig / scale / 32) * 32).astype(int) + width = (np.ceil(width_orig / scale / 32) * 32).astype(int) + + img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA) + + img_resized = ( + torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float() + ) + img_resized = img_resized.unsqueeze(0) + + return img_resized + + +def resize_depth(depth, width, height): + """Resize depth map and bring to CPU (numpy). + + Args: + depth (tensor): depth + width (int): image width + height (int): image height + + Returns: + array: processed depth + """ + depth = torch.squeeze(depth[0, :, :, :]).to("cpu") + + depth_resized = cv2.resize( + depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC + ) + + return depth_resized + +def write_depth(path, depth, bits=1): + """Write depth map to pfm and png file. + + Args: + path (str): filepath without extension + depth (array): depth + """ + write_pfm(path + ".pfm", depth.astype(np.float32)) + + depth_min = depth.min() + depth_max = depth.max() + + max_val = (2**(8*bits))-1 + + if depth_max - depth_min > np.finfo("float").eps: + out = max_val * (depth - depth_min) / (depth_max - depth_min) + else: + out = np.zeros(depth.shape, dtype=depth.type) + + if bits == 1: + cv2.imwrite(path + ".png", out.astype("uint8")) + elif bits == 2: + cv2.imwrite(path + ".png", out.astype("uint16")) + + return diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/LICENSE new file mode 100644 index 00000000..d855c6db --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2021-present NAVER Corp. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/__init__.py new file mode 100644 index 00000000..fe3b0a97 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/__init__.py @@ -0,0 +1,51 @@ +import os +import warnings + +import cv2 +import numpy as np +import torch +from PIL import Image + +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME +from .models.mbv2_mlsd_large import MobileV2_MLSD_Large +from .utils import pred_lines + + +class MLSDdetector: + def __init__(self, model): + self.model = model + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="mlsd_large_512_fp32.pth"): + subfolder = "annotator/ckpts" if pretrained_model_or_path == "lllyasviel/ControlNet" else '' + model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder=subfolder) + model = MobileV2_MLSD_Large() + model.load_state_dict(torch.load(model_path), strict=True) + model.eval() + + return cls(model) + + def to(self, device): + self.model.to(device) + return self + + def __call__(self, input_image, thr_v=0.1, thr_d=0.1, detect_resolution=512, output_type="pil", upscale_method="INTER_AREA", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + img = detected_map + img_output = np.zeros_like(img) + try: + with torch.no_grad(): + lines = pred_lines(img, self.model, [img.shape[0], img.shape[1]], thr_v, thr_d) + for line in lines: + x_start, y_start, x_end, y_end = [int(val) for val in line] + cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1) + except Exception as e: + pass + + detected_map = remove_pad(HWC3(img_output[:, :, 0])) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/mbv2_mlsd_large.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/mbv2_mlsd_large.py new file mode 100644 index 00000000..5b9799e7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/mbv2_mlsd_large.py @@ -0,0 +1,292 @@ +import os +import sys +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from torch.nn import functional as F + + +class BlockTypeA(nn.Module): + def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True): + super(BlockTypeA, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_c2, out_c2, kernel_size=1), + nn.BatchNorm2d(out_c2), + nn.ReLU(inplace=True) + ) + self.conv2 = nn.Sequential( + nn.Conv2d(in_c1, out_c1, kernel_size=1), + nn.BatchNorm2d(out_c1), + nn.ReLU(inplace=True) + ) + self.upscale = upscale + + def forward(self, a, b): + b = self.conv1(b) + a = self.conv2(a) + if self.upscale: + b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True) + return torch.cat((a, b), dim=1) + + +class BlockTypeB(nn.Module): + def __init__(self, in_c, out_c): + super(BlockTypeB, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_c, in_c, kernel_size=3, padding=1), + nn.BatchNorm2d(in_c), + nn.ReLU() + ) + self.conv2 = nn.Sequential( + nn.Conv2d(in_c, out_c, kernel_size=3, padding=1), + nn.BatchNorm2d(out_c), + nn.ReLU() + ) + + def forward(self, x): + x = self.conv1(x) + x + x = self.conv2(x) + return x + +class BlockTypeC(nn.Module): + def __init__(self, in_c, out_c): + super(BlockTypeC, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5), + nn.BatchNorm2d(in_c), + nn.ReLU() + ) + self.conv2 = nn.Sequential( + nn.Conv2d(in_c, in_c, kernel_size=3, padding=1), + nn.BatchNorm2d(in_c), + nn.ReLU() + ) + self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + return x + +def _make_divisible(v, divisor, min_value=None): + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + :param v: + :param divisor: + :param min_value: + :return: + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class ConvBNReLU(nn.Sequential): + def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1): + self.channel_pad = out_planes - in_planes + self.stride = stride + #padding = (kernel_size - 1) // 2 + + # TFLite uses slightly different padding than PyTorch + if stride == 2: + padding = 0 + else: + padding = (kernel_size - 1) // 2 + + super(ConvBNReLU, self).__init__( + nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), + nn.BatchNorm2d(out_planes), + nn.ReLU6(inplace=True) + ) + self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride) + + + def forward(self, x): + # TFLite uses different padding + if self.stride == 2: + x = F.pad(x, (0, 1, 0, 1), "constant", 0) + #print(x.shape) + + for module in self: + if not isinstance(module, nn.MaxPool2d): + x = module(x) + return x + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = int(round(inp * expand_ratio)) + self.use_res_connect = self.stride == 1 and inp == oup + + layers = [] + if expand_ratio != 1: + # pw + layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) + layers.extend([ + # dw + ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ]) + self.conv = nn.Sequential(*layers) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV2(nn.Module): + def __init__(self, pretrained=True): + """ + MobileNet V2 main class + Args: + num_classes (int): Number of classes + width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount + inverted_residual_setting: Network structure + round_nearest (int): Round the number of channels in each layer to be a multiple of this number + Set to 1 to turn off rounding + block: Module specifying inverted residual building block for mobilenet + """ + super(MobileNetV2, self).__init__() + + block = InvertedResidual + input_channel = 32 + last_channel = 1280 + width_mult = 1.0 + round_nearest = 8 + + inverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + #[6, 160, 3, 2], + #[6, 320, 1, 1], + ] + + # only check the first element, assuming user knows t,c,n,s are required + if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4: + raise ValueError("inverted_residual_setting should be non-empty " + "or a 4-element list, got {}".format(inverted_residual_setting)) + + # building first layer + input_channel = _make_divisible(input_channel * width_mult, round_nearest) + self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) + features = [ConvBNReLU(4, input_channel, stride=2)] + # building inverted residual blocks + for t, c, n, s in inverted_residual_setting: + output_channel = _make_divisible(c * width_mult, round_nearest) + for i in range(n): + stride = s if i == 0 else 1 + features.append(block(input_channel, output_channel, stride, expand_ratio=t)) + input_channel = output_channel + + self.features = nn.Sequential(*features) + self.fpn_selected = [1, 3, 6, 10, 13] + # weight initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + if pretrained: + self._load_pretrained_model() + + def _forward_impl(self, x): + # This exists since TorchScript doesn't support inheritance, so the superclass method + # (this one) needs to have a name other than `forward` that can be accessed in a subclass + fpn_features = [] + for i, f in enumerate(self.features): + if i > self.fpn_selected[-1]: + break + x = f(x) + if i in self.fpn_selected: + fpn_features.append(x) + + c1, c2, c3, c4, c5 = fpn_features + return c1, c2, c3, c4, c5 + + + def forward(self, x): + return self._forward_impl(x) + + def _load_pretrained_model(self): + pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth') + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + if k in state_dict: + model_dict[k] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +class MobileV2_MLSD_Large(nn.Module): + def __init__(self): + super(MobileV2_MLSD_Large, self).__init__() + + self.backbone = MobileNetV2(pretrained=False) + ## A, B + self.block15 = BlockTypeA(in_c1= 64, in_c2= 96, + out_c1= 64, out_c2=64, + upscale=False) + self.block16 = BlockTypeB(128, 64) + + ## A, B + self.block17 = BlockTypeA(in_c1 = 32, in_c2 = 64, + out_c1= 64, out_c2= 64) + self.block18 = BlockTypeB(128, 64) + + ## A, B + self.block19 = BlockTypeA(in_c1=24, in_c2=64, + out_c1=64, out_c2=64) + self.block20 = BlockTypeB(128, 64) + + ## A, B, C + self.block21 = BlockTypeA(in_c1=16, in_c2=64, + out_c1=64, out_c2=64) + self.block22 = BlockTypeB(128, 64) + + self.block23 = BlockTypeC(64, 16) + + def forward(self, x): + c1, c2, c3, c4, c5 = self.backbone(x) + + x = self.block15(c4, c5) + x = self.block16(x) + + x = self.block17(c3, x) + x = self.block18(x) + + x = self.block19(c2, x) + x = self.block20(x) + + x = self.block21(c1, x) + x = self.block22(x) + x = self.block23(x) + x = x[:, 7:, :, :] + + return x \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/mbv2_mlsd_tiny.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/mbv2_mlsd_tiny.py new file mode 100644 index 00000000..e3ed633f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/models/mbv2_mlsd_tiny.py @@ -0,0 +1,275 @@ +import os +import sys +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from torch.nn import functional as F + + +class BlockTypeA(nn.Module): + def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True): + super(BlockTypeA, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_c2, out_c2, kernel_size=1), + nn.BatchNorm2d(out_c2), + nn.ReLU(inplace=True) + ) + self.conv2 = nn.Sequential( + nn.Conv2d(in_c1, out_c1, kernel_size=1), + nn.BatchNorm2d(out_c1), + nn.ReLU(inplace=True) + ) + self.upscale = upscale + + def forward(self, a, b): + b = self.conv1(b) + a = self.conv2(a) + b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True) + return torch.cat((a, b), dim=1) + + +class BlockTypeB(nn.Module): + def __init__(self, in_c, out_c): + super(BlockTypeB, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_c, in_c, kernel_size=3, padding=1), + nn.BatchNorm2d(in_c), + nn.ReLU() + ) + self.conv2 = nn.Sequential( + nn.Conv2d(in_c, out_c, kernel_size=3, padding=1), + nn.BatchNorm2d(out_c), + nn.ReLU() + ) + + def forward(self, x): + x = self.conv1(x) + x + x = self.conv2(x) + return x + +class BlockTypeC(nn.Module): + def __init__(self, in_c, out_c): + super(BlockTypeC, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5), + nn.BatchNorm2d(in_c), + nn.ReLU() + ) + self.conv2 = nn.Sequential( + nn.Conv2d(in_c, in_c, kernel_size=3, padding=1), + nn.BatchNorm2d(in_c), + nn.ReLU() + ) + self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + return x + +def _make_divisible(v, divisor, min_value=None): + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + :param v: + :param divisor: + :param min_value: + :return: + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class ConvBNReLU(nn.Sequential): + def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1): + self.channel_pad = out_planes - in_planes + self.stride = stride + #padding = (kernel_size - 1) // 2 + + # TFLite uses slightly different padding than PyTorch + if stride == 2: + padding = 0 + else: + padding = (kernel_size - 1) // 2 + + super(ConvBNReLU, self).__init__( + nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), + nn.BatchNorm2d(out_planes), + nn.ReLU6(inplace=True) + ) + self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride) + + + def forward(self, x): + # TFLite uses different padding + if self.stride == 2: + x = F.pad(x, (0, 1, 0, 1), "constant", 0) + #print(x.shape) + + for module in self: + if not isinstance(module, nn.MaxPool2d): + x = module(x) + return x + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = int(round(inp * expand_ratio)) + self.use_res_connect = self.stride == 1 and inp == oup + + layers = [] + if expand_ratio != 1: + # pw + layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) + layers.extend([ + # dw + ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ]) + self.conv = nn.Sequential(*layers) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV2(nn.Module): + def __init__(self, pretrained=True): + """ + MobileNet V2 main class + Args: + num_classes (int): Number of classes + width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount + inverted_residual_setting: Network structure + round_nearest (int): Round the number of channels in each layer to be a multiple of this number + Set to 1 to turn off rounding + block: Module specifying inverted residual building block for mobilenet + """ + super(MobileNetV2, self).__init__() + + block = InvertedResidual + input_channel = 32 + last_channel = 1280 + width_mult = 1.0 + round_nearest = 8 + + inverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + #[6, 96, 3, 1], + #[6, 160, 3, 2], + #[6, 320, 1, 1], + ] + + # only check the first element, assuming user knows t,c,n,s are required + if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4: + raise ValueError("inverted_residual_setting should be non-empty " + "or a 4-element list, got {}".format(inverted_residual_setting)) + + # building first layer + input_channel = _make_divisible(input_channel * width_mult, round_nearest) + self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) + features = [ConvBNReLU(4, input_channel, stride=2)] + # building inverted residual blocks + for t, c, n, s in inverted_residual_setting: + output_channel = _make_divisible(c * width_mult, round_nearest) + for i in range(n): + stride = s if i == 0 else 1 + features.append(block(input_channel, output_channel, stride, expand_ratio=t)) + input_channel = output_channel + self.features = nn.Sequential(*features) + + self.fpn_selected = [3, 6, 10] + # weight initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + #if pretrained: + # self._load_pretrained_model() + + def _forward_impl(self, x): + # This exists since TorchScript doesn't support inheritance, so the superclass method + # (this one) needs to have a name other than `forward` that can be accessed in a subclass + fpn_features = [] + for i, f in enumerate(self.features): + if i > self.fpn_selected[-1]: + break + x = f(x) + if i in self.fpn_selected: + fpn_features.append(x) + + c2, c3, c4 = fpn_features + return c2, c3, c4 + + + def forward(self, x): + return self._forward_impl(x) + + def _load_pretrained_model(self): + pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth') + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + if k in state_dict: + model_dict[k] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +class MobileV2_MLSD_Tiny(nn.Module): + def __init__(self): + super(MobileV2_MLSD_Tiny, self).__init__() + + self.backbone = MobileNetV2(pretrained=True) + + self.block12 = BlockTypeA(in_c1= 32, in_c2= 64, + out_c1= 64, out_c2=64) + self.block13 = BlockTypeB(128, 64) + + self.block14 = BlockTypeA(in_c1 = 24, in_c2 = 64, + out_c1= 32, out_c2= 32) + self.block15 = BlockTypeB(64, 64) + + self.block16 = BlockTypeC(64, 16) + + def forward(self, x): + c2, c3, c4 = self.backbone(x) + + x = self.block12(c3, c4) + x = self.block13(x) + x = self.block14(c2, x) + x = self.block15(x) + x = self.block16(x) + x = x[:, 7:, :, :] + #print(x.shape) + x = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=True) + + return x \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/utils.py new file mode 100644 index 00000000..28071cbf --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/mlsd/utils.py @@ -0,0 +1,584 @@ +''' +modified by lihaoweicv +pytorch version +''' + +''' +M-LSD +Copyright 2021-present NAVER Corp. +Apache License v2.0 +''' + +import os +import numpy as np +import cv2 +import torch +from torch.nn import functional as F + + +def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5): + ''' + tpMap: + center: tpMap[1, 0, :, :] + displacement: tpMap[1, 1:5, :, :] + ''' + b, c, h, w = tpMap.shape + assert b==1, 'only support bsize==1' + displacement = tpMap[:, 1:5, :, :][0] + center = tpMap[:, 0, :, :] + heat = torch.sigmoid(center) + hmax = F.max_pool2d( heat, (ksize, ksize), stride=1, padding=(ksize-1)//2) + keep = (hmax == heat).float() + heat = heat * keep + heat = heat.reshape(-1, ) + + scores, indices = torch.topk(heat, topk_n, dim=-1, largest=True) + yy = torch.floor_divide(indices, w).unsqueeze(-1) + xx = torch.fmod(indices, w).unsqueeze(-1) + ptss = torch.cat((yy, xx),dim=-1) + + ptss = ptss.detach().cpu().numpy() + scores = scores.detach().cpu().numpy() + displacement = displacement.detach().cpu().numpy() + displacement = displacement.transpose((1,2,0)) + return ptss, scores, displacement + + +def pred_lines(image, model, + input_shape=[512, 512], + score_thr=0.10, + dist_thr=20.0): + h, w, _ = image.shape + + device = next(iter(model.parameters())).device + h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]] + + resized_image = np.concatenate([cv2.resize(image, (input_shape[1], input_shape[0]), interpolation=cv2.INTER_AREA), + np.ones([input_shape[0], input_shape[1], 1])], axis=-1) + + resized_image = resized_image.transpose((2,0,1)) + batch_image = np.expand_dims(resized_image, axis=0).astype('float32') + batch_image = (batch_image / 127.5) - 1.0 + + batch_image = torch.from_numpy(batch_image).float() + batch_image = batch_image.to(device) + outputs = model(batch_image) + pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3) + start = vmap[:, :, :2] + end = vmap[:, :, 2:] + dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1)) + + segments_list = [] + for center, score in zip(pts, pts_score): + y, x = center + distance = dist_map[y, x] + if score > score_thr and distance > dist_thr: + disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :] + x_start = x + disp_x_start + y_start = y + disp_y_start + x_end = x + disp_x_end + y_end = y + disp_y_end + segments_list.append([x_start, y_start, x_end, y_end]) + + lines = 2 * np.array(segments_list) # 256 > 512 + lines[:, 0] = lines[:, 0] * w_ratio + lines[:, 1] = lines[:, 1] * h_ratio + lines[:, 2] = lines[:, 2] * w_ratio + lines[:, 3] = lines[:, 3] * h_ratio + + return lines + + +def pred_squares(image, + model, + input_shape=[512, 512], + params={'score': 0.06, + 'outside_ratio': 0.28, + 'inside_ratio': 0.45, + 'w_overlap': 0.0, + 'w_degree': 1.95, + 'w_length': 0.0, + 'w_area': 1.86, + 'w_center': 0.14}): + ''' + shape = [height, width] + ''' + h, w, _ = image.shape + original_shape = [h, w] + device = next(iter(model.parameters())).device + + resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), + np.ones([input_shape[0], input_shape[1], 1])], axis=-1) + resized_image = resized_image.transpose((2, 0, 1)) + batch_image = np.expand_dims(resized_image, axis=0).astype('float32') + batch_image = (batch_image / 127.5) - 1.0 + + batch_image = torch.from_numpy(batch_image).float().to(device) + outputs = model(batch_image) + + pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3) + start = vmap[:, :, :2] # (x, y) + end = vmap[:, :, 2:] # (x, y) + dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1)) + + junc_list = [] + segments_list = [] + for junc, score in zip(pts, pts_score): + y, x = junc + distance = dist_map[y, x] + if score > params['score'] and distance > 20.0: + junc_list.append([x, y]) + disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :] + d_arrow = 1.0 + x_start = x + d_arrow * disp_x_start + y_start = y + d_arrow * disp_y_start + x_end = x + d_arrow * disp_x_end + y_end = y + d_arrow * disp_y_end + segments_list.append([x_start, y_start, x_end, y_end]) + + segments = np.array(segments_list) + + ####### post processing for squares + # 1. get unique lines + point = np.array([[0, 0]]) + point = point[0] + start = segments[:, :2] + end = segments[:, 2:] + diff = start - end + a = diff[:, 1] + b = -diff[:, 0] + c = a * start[:, 0] + b * start[:, 1] + + d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10) + theta = np.arctan2(diff[:, 0], diff[:, 1]) * 180 / np.pi + theta[theta < 0.0] += 180 + hough = np.concatenate([d[:, None], theta[:, None]], axis=-1) + + d_quant = 1 + theta_quant = 2 + hough[:, 0] //= d_quant + hough[:, 1] //= theta_quant + _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True) + + acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32') + idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1 + yx_indices = hough[indices, :].astype('int32') + acc_map[yx_indices[:, 0], yx_indices[:, 1]] = counts + idx_map[yx_indices[:, 0], yx_indices[:, 1]] = indices + + acc_map_np = acc_map + # acc_map = acc_map[None, :, :, None] + # + # ### fast suppression using tensorflow op + # acc_map = tf.constant(acc_map, dtype=tf.float32) + # max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5, 5), strides=1, padding='same')(acc_map) + # acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32) + # flatten_acc_map = tf.reshape(acc_map, [1, -1]) + # topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts)) + # _, h, w, _ = acc_map.shape + # y = tf.expand_dims(topk_indices // w, axis=-1) + # x = tf.expand_dims(topk_indices % w, axis=-1) + # yx = tf.concat([y, x], axis=-1) + + ### fast suppression using pytorch op + acc_map = torch.from_numpy(acc_map_np).unsqueeze(0).unsqueeze(0) + _,_, h, w = acc_map.shape + max_acc_map = F.max_pool2d(acc_map,kernel_size=5, stride=1, padding=2) + acc_map = acc_map * ( (acc_map == max_acc_map).float() ) + flatten_acc_map = acc_map.reshape([-1, ]) + + scores, indices = torch.topk(flatten_acc_map, len(pts), dim=-1, largest=True) + yy = torch.div(indices, w, rounding_mode='floor').unsqueeze(-1) + xx = torch.fmod(indices, w).unsqueeze(-1) + yx = torch.cat((yy, xx), dim=-1) + + yx = yx.detach().cpu().numpy() + + topk_values = scores.detach().cpu().numpy() + indices = idx_map[yx[:, 0], yx[:, 1]] + basis = 5 // 2 + + merged_segments = [] + for yx_pt, max_indice, value in zip(yx, indices, topk_values): + y, x = yx_pt + if max_indice == -1 or value == 0: + continue + segment_list = [] + for y_offset in range(-basis, basis + 1): + for x_offset in range(-basis, basis + 1): + indice = idx_map[y + y_offset, x + x_offset] + cnt = int(acc_map_np[y + y_offset, x + x_offset]) + if indice != -1: + segment_list.append(segments[indice]) + if cnt > 1: + check_cnt = 1 + current_hough = hough[indice] + for new_indice, new_hough in enumerate(hough): + if (current_hough == new_hough).all() and indice != new_indice: + segment_list.append(segments[new_indice]) + check_cnt += 1 + if check_cnt == cnt: + break + group_segments = np.array(segment_list).reshape([-1, 2]) + sorted_group_segments = np.sort(group_segments, axis=0) + x_min, y_min = sorted_group_segments[0, :] + x_max, y_max = sorted_group_segments[-1, :] + + deg = theta[max_indice] + if deg >= 90: + merged_segments.append([x_min, y_max, x_max, y_min]) + else: + merged_segments.append([x_min, y_min, x_max, y_max]) + + # 2. get intersections + new_segments = np.array(merged_segments) # (x1, y1, x2, y2) + start = new_segments[:, :2] # (x1, y1) + end = new_segments[:, 2:] # (x2, y2) + new_centers = (start + end) / 2.0 + diff = start - end + dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1)) + + # ax + by = c + a = diff[:, 1] + b = -diff[:, 0] + c = a * start[:, 0] + b * start[:, 1] + pre_det = a[:, None] * b[None, :] + det = pre_det - np.transpose(pre_det) + + pre_inter_y = a[:, None] * c[None, :] + inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10) + pre_inter_x = c[:, None] * b[None, :] + inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10) + inter_pts = np.concatenate([inter_x[:, :, None], inter_y[:, :, None]], axis=-1).astype('int32') + + # 3. get corner information + # 3.1 get distance + ''' + dist_segments: + | dist(0), dist(1), dist(2), ...| + dist_inter_to_segment1: + | dist(inter,0), dist(inter,0), dist(inter,0), ... | + | dist(inter,1), dist(inter,1), dist(inter,1), ... | + ... + dist_inter_to_semgnet2: + | dist(inter,0), dist(inter,1), dist(inter,2), ... | + | dist(inter,0), dist(inter,1), dist(inter,2), ... | + ... + ''' + + dist_inter_to_segment1_start = np.sqrt( + np.sum(((inter_pts - start[:, None, :]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] + dist_inter_to_segment1_end = np.sqrt( + np.sum(((inter_pts - end[:, None, :]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] + dist_inter_to_segment2_start = np.sqrt( + np.sum(((inter_pts - start[None, :, :]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] + dist_inter_to_segment2_end = np.sqrt( + np.sum(((inter_pts - end[None, :, :]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] + + # sort ascending + dist_inter_to_segment1 = np.sort( + np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1), + axis=-1) # [n_batch, n_batch, 2] + dist_inter_to_segment2 = np.sort( + np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1), + axis=-1) # [n_batch, n_batch, 2] + + # 3.2 get degree + inter_to_start = new_centers[:, None, :] - inter_pts + deg_inter_to_start = np.arctan2(inter_to_start[:, :, 1], inter_to_start[:, :, 0]) * 180 / np.pi + deg_inter_to_start[deg_inter_to_start < 0.0] += 360 + inter_to_end = new_centers[None, :, :] - inter_pts + deg_inter_to_end = np.arctan2(inter_to_end[:, :, 1], inter_to_end[:, :, 0]) * 180 / np.pi + deg_inter_to_end[deg_inter_to_end < 0.0] += 360 + + ''' + B -- G + | | + C -- R + B : blue / G: green / C: cyan / R: red + + 0 -- 1 + | | + 3 -- 2 + ''' + # rename variables + deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end + # sort deg ascending + deg_sort = np.sort(np.concatenate([deg1_map[:, :, None], deg2_map[:, :, None]], axis=-1), axis=-1) + + deg_diff_map = np.abs(deg1_map - deg2_map) + # we only consider the smallest degree of intersect + deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180] + + # define available degree range + deg_range = [60, 120] + + corner_dict = {corner_info: [] for corner_info in range(4)} + inter_points = [] + for i in range(inter_pts.shape[0]): + for j in range(i + 1, inter_pts.shape[1]): + # i, j > line index, always i < j + x, y = inter_pts[i, j, :] + deg1, deg2 = deg_sort[i, j, :] + deg_diff = deg_diff_map[i, j] + + check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1] + + outside_ratio = params['outside_ratio'] # over ratio >>> drop it! + inside_ratio = params['inside_ratio'] # over ratio >>> drop it! + check_distance = ((dist_inter_to_segment1[i, j, 1] >= dist_segments[i] and \ + dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * outside_ratio) or \ + (dist_inter_to_segment1[i, j, 1] <= dist_segments[i] and \ + dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * inside_ratio)) and \ + ((dist_inter_to_segment2[i, j, 1] >= dist_segments[j] and \ + dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * outside_ratio) or \ + (dist_inter_to_segment2[i, j, 1] <= dist_segments[j] and \ + dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * inside_ratio)) + + if check_degree and check_distance: + corner_info = None + + if (deg1 >= 0 and deg1 <= 45 and deg2 >= 45 and deg2 <= 120) or \ + (deg2 >= 315 and deg1 >= 45 and deg1 <= 120): + corner_info, color_info = 0, 'blue' + elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225): + corner_info, color_info = 1, 'green' + elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315): + corner_info, color_info = 2, 'black' + elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \ + (deg2 >= 315 and deg1 >= 225 and deg1 <= 315): + corner_info, color_info = 3, 'cyan' + else: + corner_info, color_info = 4, 'red' # we don't use it + continue + + corner_dict[corner_info].append([x, y, i, j]) + inter_points.append([x, y]) + + square_list = [] + connect_list = [] + segments_list = [] + for corner0 in corner_dict[0]: + for corner1 in corner_dict[1]: + connect01 = False + for corner0_line in corner0[2:]: + if corner0_line in corner1[2:]: + connect01 = True + break + if connect01: + for corner2 in corner_dict[2]: + connect12 = False + for corner1_line in corner1[2:]: + if corner1_line in corner2[2:]: + connect12 = True + break + if connect12: + for corner3 in corner_dict[3]: + connect23 = False + for corner2_line in corner2[2:]: + if corner2_line in corner3[2:]: + connect23 = True + break + if connect23: + for corner3_line in corner3[2:]: + if corner3_line in corner0[2:]: + # SQUARE!!! + ''' + 0 -- 1 + | | + 3 -- 2 + square_list: + order: 0 > 1 > 2 > 3 + | x0, y0, x1, y1, x2, y2, x3, y3 | + | x0, y0, x1, y1, x2, y2, x3, y3 | + ... + connect_list: + order: 01 > 12 > 23 > 30 + | line_idx01, line_idx12, line_idx23, line_idx30 | + | line_idx01, line_idx12, line_idx23, line_idx30 | + ... + segments_list: + order: 0 > 1 > 2 > 3 + | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j | + | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j | + ... + ''' + square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2]) + connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line]) + segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:]) + + def check_outside_inside(segments_info, connect_idx): + # return 'outside or inside', min distance, cover_param, peri_param + if connect_idx == segments_info[0]: + check_dist_mat = dist_inter_to_segment1 + else: + check_dist_mat = dist_inter_to_segment2 + + i, j = segments_info + min_dist, max_dist = check_dist_mat[i, j, :] + connect_dist = dist_segments[connect_idx] + if max_dist > connect_dist: + return 'outside', min_dist, 0, 1 + else: + return 'inside', min_dist, -1, -1 + + top_square = None + + try: + map_size = input_shape[0] / 2 + squares = np.array(square_list).reshape([-1, 4, 2]) + score_array = [] + connect_array = np.array(connect_list) + segments_array = np.array(segments_list).reshape([-1, 4, 2]) + + # get degree of corners: + squares_rollup = np.roll(squares, 1, axis=1) + squares_rolldown = np.roll(squares, -1, axis=1) + vec1 = squares_rollup - squares + normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10) + vec2 = squares_rolldown - squares + normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10) + inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1) # [n_squares, 4] + squares_degree = np.arccos(inner_products) * 180 / np.pi # [n_squares, 4] + + # get square score + overlap_scores = [] + degree_scores = [] + length_scores = [] + + for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree): + ''' + 0 -- 1 + | | + 3 -- 2 + + # segments: [4, 2] + # connects: [4] + ''' + + ###################################### OVERLAP SCORES + cover = 0 + perimeter = 0 + # check 0 > 1 > 2 > 3 + square_length = [] + + for start_idx in range(4): + end_idx = (start_idx + 1) % 4 + + connect_idx = connects[start_idx] # segment idx of segment01 + start_segments = segments[start_idx] + end_segments = segments[end_idx] + + start_point = square[start_idx] + end_point = square[end_idx] + + # check whether outside or inside + start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments, + connect_idx) + end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx) + + cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min + perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min + + square_length.append( + dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min) + + overlap_scores.append(cover / perimeter) + ###################################### + ###################################### DEGREE SCORES + ''' + deg0 vs deg2 + deg1 vs deg3 + ''' + deg0, deg1, deg2, deg3 = degree + deg_ratio1 = deg0 / deg2 + if deg_ratio1 > 1.0: + deg_ratio1 = 1 / deg_ratio1 + deg_ratio2 = deg1 / deg3 + if deg_ratio2 > 1.0: + deg_ratio2 = 1 / deg_ratio2 + degree_scores.append((deg_ratio1 + deg_ratio2) / 2) + ###################################### + ###################################### LENGTH SCORES + ''' + len0 vs len2 + len1 vs len3 + ''' + len0, len1, len2, len3 = square_length + len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0 + len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1 + length_scores.append((len_ratio1 + len_ratio2) / 2) + + ###################################### + + overlap_scores = np.array(overlap_scores) + overlap_scores /= np.max(overlap_scores) + + degree_scores = np.array(degree_scores) + # degree_scores /= np.max(degree_scores) + + length_scores = np.array(length_scores) + + ###################################### AREA SCORES + area_scores = np.reshape(squares, [-1, 4, 2]) + area_x = area_scores[:, :, 0] + area_y = area_scores[:, :, 1] + correction = area_x[:, -1] * area_y[:, 0] - area_y[:, -1] * area_x[:, 0] + area_scores = np.sum(area_x[:, :-1] * area_y[:, 1:], axis=-1) - np.sum(area_y[:, :-1] * area_x[:, 1:], axis=-1) + area_scores = 0.5 * np.abs(area_scores + correction) + area_scores /= (map_size * map_size) # np.max(area_scores) + ###################################### + + ###################################### CENTER SCORES + centers = np.array([[256 // 2, 256 // 2]], dtype='float32') # [1, 2] + # squares: [n, 4, 2] + square_centers = np.mean(squares, axis=1) # [n, 2] + center2center = np.sqrt(np.sum((centers - square_centers) ** 2)) + center_scores = center2center / (map_size / np.sqrt(2.0)) + + ''' + score_w = [overlap, degree, area, center, length] + ''' + score_w = [0.0, 1.0, 10.0, 0.5, 1.0] + score_array = params['w_overlap'] * overlap_scores \ + + params['w_degree'] * degree_scores \ + + params['w_area'] * area_scores \ + - params['w_center'] * center_scores \ + + params['w_length'] * length_scores + + best_square = [] + + sorted_idx = np.argsort(score_array)[::-1] + score_array = score_array[sorted_idx] + squares = squares[sorted_idx] + + except Exception as e: + pass + + '''return list + merged_lines, squares, scores + ''' + + try: + new_segments[:, 0] = new_segments[:, 0] * 2 / input_shape[1] * original_shape[1] + new_segments[:, 1] = new_segments[:, 1] * 2 / input_shape[0] * original_shape[0] + new_segments[:, 2] = new_segments[:, 2] * 2 / input_shape[1] * original_shape[1] + new_segments[:, 3] = new_segments[:, 3] * 2 / input_shape[0] * original_shape[0] + except: + new_segments = [] + + try: + squares[:, :, 0] = squares[:, :, 0] * 2 / input_shape[1] * original_shape[1] + squares[:, :, 1] = squares[:, :, 1] * 2 / input_shape[0] * original_shape[0] + except: + squares = [] + score_array = [] + + try: + inter_points = np.array(inter_points) + inter_points[:, 0] = inter_points[:, 0] * 2 / input_shape[1] * original_shape[1] + inter_points[:, 1] = inter_points[:, 1] * 2 / input_shape[0] * original_shape[0] + except: + inter_points = [] + + return new_segments, squares, score_array, inter_points diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/LICENSE new file mode 100644 index 00000000..16a9d56a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Caroline Chan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/__init__.py new file mode 100644 index 00000000..ae2f54ab --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/__init__.py @@ -0,0 +1,85 @@ +import os +import types +import warnings + +import cv2 +import numpy as np +import torch +import torchvision.transforms as transforms +from einops import rearrange +from PIL import Image + +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME +from .nets.NNET import NNET + + +# load model +def load_checkpoint(fpath, model): + ckpt = torch.load(fpath, map_location='cpu')['model'] + + load_dict = {} + for k, v in ckpt.items(): + if k.startswith('module.'): + k_ = k.replace('module.', '') + load_dict[k_] = v + else: + load_dict[k] = v + + model.load_state_dict(load_dict) + return model + +class NormalBaeDetector: + def __init__(self, model): + self.model = model + self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="scannet.pt"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + + args = types.SimpleNamespace() + args.mode = 'client' + args.architecture = 'BN' + args.pretrained = 'scannet' + args.sampling_ratio = 0.4 + args.importance_ratio = 0.7 + model = NNET(args) + model = load_checkpoint(model_path, model) + model.eval() + + return cls(model) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + + def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + image_normal = detected_map + with torch.no_grad(): + image_normal = torch.from_numpy(image_normal).float().to(self.device) + image_normal = image_normal / 255.0 + image_normal = rearrange(image_normal, 'h w c -> 1 c h w') + image_normal = self.norm(image_normal) + + normal = self.model(image_normal) + normal = normal[0][-1][:, :3] + # d = torch.sum(normal ** 2.0, dim=1, keepdim=True) ** 0.5 + # d = torch.maximum(d, torch.ones_like(d) * 1e-5) + # normal /= d + normal = ((normal + 1) * 0.5).clip(0, 1) + + normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy() + normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = remove_pad(HWC3(normal_image)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/NNET.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/NNET.py new file mode 100644 index 00000000..3ddbc50c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/NNET.py @@ -0,0 +1,22 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .submodules.encoder import Encoder +from .submodules.decoder import Decoder + + +class NNET(nn.Module): + def __init__(self, args): + super(NNET, self).__init__() + self.encoder = Encoder() + self.decoder = Decoder(args) + + def get_1x_lr_params(self): # lr/10 learning rate + return self.encoder.parameters() + + def get_10x_lr_params(self): # lr learning rate + return self.decoder.parameters() + + def forward(self, img, **kwargs): + return self.decoder(self.encoder(img), **kwargs) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/decoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/decoder.py new file mode 100644 index 00000000..ef3a192d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/decoder.py @@ -0,0 +1,202 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .submodules import UpSampleBN, UpSampleGN, norm_normalize, sample_points + + +class Decoder(nn.Module): + def __init__(self, args): + super(Decoder, self).__init__() + + # hyper-parameter for sampling + self.sampling_ratio = args.sampling_ratio + self.importance_ratio = args.importance_ratio + + # feature-map + self.conv2 = nn.Conv2d(2048, 2048, kernel_size=1, stride=1, padding=0) + if args.architecture == 'BN': + self.up1 = UpSampleBN(skip_input=2048 + 176, output_features=1024) + self.up2 = UpSampleBN(skip_input=1024 + 64, output_features=512) + self.up3 = UpSampleBN(skip_input=512 + 40, output_features=256) + self.up4 = UpSampleBN(skip_input=256 + 24, output_features=128) + + elif args.architecture == 'GN': + self.up1 = UpSampleGN(skip_input=2048 + 176, output_features=1024) + self.up2 = UpSampleGN(skip_input=1024 + 64, output_features=512) + self.up3 = UpSampleGN(skip_input=512 + 40, output_features=256) + self.up4 = UpSampleGN(skip_input=256 + 24, output_features=128) + + else: + raise Exception('invalid architecture') + + # produces 1/8 res output + self.out_conv_res8 = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + + # produces 1/4 res output + self.out_conv_res4 = nn.Sequential( + nn.Conv1d(512 + 4, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 4, kernel_size=1), + ) + + # produces 1/2 res output + self.out_conv_res2 = nn.Sequential( + nn.Conv1d(256 + 4, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 4, kernel_size=1), + ) + + # produces 1/1 res output + self.out_conv_res1 = nn.Sequential( + nn.Conv1d(128 + 4, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(), + nn.Conv1d(128, 4, kernel_size=1), + ) + + def forward(self, features, gt_norm_mask=None, mode='test'): + x_block0, x_block1, x_block2, x_block3, x_block4 = features[3], features[4], features[5], features[7], features[10] + + # generate feature-map + + x_d0 = self.conv2(x_block4) # x_d0 : [2, 2048, 15, 20] 1/32 res + x_d1 = self.up1(x_d0, x_block3) # x_d1 : [2, 1024, 30, 40] 1/16 res + x_d2 = self.up2(x_d1, x_block2) # x_d2 : [2, 512, 60, 80] 1/8 res + x_d3 = self.up3(x_d2, x_block1) # x_d3: [2, 256, 120, 160] 1/4 res + x_d4 = self.up4(x_d3, x_block0) # x_d4: [2, 128, 240, 320] 1/2 res + + # 1/8 res output + out_res8 = self.out_conv_res8(x_d2) # out_res8: [2, 4, 60, 80] 1/8 res output + out_res8 = norm_normalize(out_res8) # out_res8: [2, 4, 60, 80] 1/8 res output + + ################################################################################################################ + # out_res4 + ################################################################################################################ + + if mode == 'train': + # upsampling ... out_res8: [2, 4, 60, 80] -> out_res8_res4: [2, 4, 120, 160] + out_res8_res4 = F.interpolate(out_res8, scale_factor=2, mode='bilinear', align_corners=True) + B, _, H, W = out_res8_res4.shape + + # samples: [B, 1, N, 2] + point_coords_res4, rows_int, cols_int = sample_points(out_res8_res4.detach(), gt_norm_mask, + sampling_ratio=self.sampling_ratio, + beta=self.importance_ratio) + + # output (needed for evaluation / visualization) + out_res4 = out_res8_res4 + + # grid_sample feature-map + feat_res4 = F.grid_sample(x_d2, point_coords_res4, mode='bilinear', align_corners=True) # (B, 512, 1, N) + init_pred = F.grid_sample(out_res8, point_coords_res4, mode='bilinear', align_corners=True) # (B, 4, 1, N) + feat_res4 = torch.cat([feat_res4, init_pred], dim=1) # (B, 512+4, 1, N) + + # prediction (needed to compute loss) + samples_pred_res4 = self.out_conv_res4(feat_res4[:, :, 0, :]) # (B, 4, N) + samples_pred_res4 = norm_normalize(samples_pred_res4) # (B, 4, N) - normalized + + for i in range(B): + out_res4[i, :, rows_int[i, :], cols_int[i, :]] = samples_pred_res4[i, :, :] + + else: + # grid_sample feature-map + feat_map = F.interpolate(x_d2, scale_factor=2, mode='bilinear', align_corners=True) + init_pred = F.interpolate(out_res8, scale_factor=2, mode='bilinear', align_corners=True) + feat_map = torch.cat([feat_map, init_pred], dim=1) # (B, 512+4, H, W) + B, _, H, W = feat_map.shape + + # try all pixels + out_res4 = self.out_conv_res4(feat_map.view(B, 512 + 4, -1)) # (B, 4, N) + out_res4 = norm_normalize(out_res4) # (B, 4, N) - normalized + out_res4 = out_res4.view(B, 4, H, W) + samples_pred_res4 = point_coords_res4 = None + + ################################################################################################################ + # out_res2 + ################################################################################################################ + + if mode == 'train': + + # upsampling ... out_res4: [2, 4, 120, 160] -> out_res4_res2: [2, 4, 240, 320] + out_res4_res2 = F.interpolate(out_res4, scale_factor=2, mode='bilinear', align_corners=True) + B, _, H, W = out_res4_res2.shape + + # samples: [B, 1, N, 2] + point_coords_res2, rows_int, cols_int = sample_points(out_res4_res2.detach(), gt_norm_mask, + sampling_ratio=self.sampling_ratio, + beta=self.importance_ratio) + + # output (needed for evaluation / visualization) + out_res2 = out_res4_res2 + + # grid_sample feature-map + feat_res2 = F.grid_sample(x_d3, point_coords_res2, mode='bilinear', align_corners=True) # (B, 256, 1, N) + init_pred = F.grid_sample(out_res4, point_coords_res2, mode='bilinear', align_corners=True) # (B, 4, 1, N) + feat_res2 = torch.cat([feat_res2, init_pred], dim=1) # (B, 256+4, 1, N) + + # prediction (needed to compute loss) + samples_pred_res2 = self.out_conv_res2(feat_res2[:, :, 0, :]) # (B, 4, N) + samples_pred_res2 = norm_normalize(samples_pred_res2) # (B, 4, N) - normalized + + for i in range(B): + out_res2[i, :, rows_int[i, :], cols_int[i, :]] = samples_pred_res2[i, :, :] + + else: + # grid_sample feature-map + feat_map = F.interpolate(x_d3, scale_factor=2, mode='bilinear', align_corners=True) + init_pred = F.interpolate(out_res4, scale_factor=2, mode='bilinear', align_corners=True) + feat_map = torch.cat([feat_map, init_pred], dim=1) # (B, 512+4, H, W) + B, _, H, W = feat_map.shape + + out_res2 = self.out_conv_res2(feat_map.view(B, 256 + 4, -1)) # (B, 4, N) + out_res2 = norm_normalize(out_res2) # (B, 4, N) - normalized + out_res2 = out_res2.view(B, 4, H, W) + samples_pred_res2 = point_coords_res2 = None + + ################################################################################################################ + # out_res1 + ################################################################################################################ + + if mode == 'train': + # upsampling ... out_res4: [2, 4, 120, 160] -> out_res4_res2: [2, 4, 240, 320] + out_res2_res1 = F.interpolate(out_res2, scale_factor=2, mode='bilinear', align_corners=True) + B, _, H, W = out_res2_res1.shape + + # samples: [B, 1, N, 2] + point_coords_res1, rows_int, cols_int = sample_points(out_res2_res1.detach(), gt_norm_mask, + sampling_ratio=self.sampling_ratio, + beta=self.importance_ratio) + + # output (needed for evaluation / visualization) + out_res1 = out_res2_res1 + + # grid_sample feature-map + feat_res1 = F.grid_sample(x_d4, point_coords_res1, mode='bilinear', align_corners=True) # (B, 128, 1, N) + init_pred = F.grid_sample(out_res2, point_coords_res1, mode='bilinear', align_corners=True) # (B, 4, 1, N) + feat_res1 = torch.cat([feat_res1, init_pred], dim=1) # (B, 128+4, 1, N) + + # prediction (needed to compute loss) + samples_pred_res1 = self.out_conv_res1(feat_res1[:, :, 0, :]) # (B, 4, N) + samples_pred_res1 = norm_normalize(samples_pred_res1) # (B, 4, N) - normalized + + for i in range(B): + out_res1[i, :, rows_int[i, :], cols_int[i, :]] = samples_pred_res1[i, :, :] + + else: + # grid_sample feature-map + feat_map = F.interpolate(x_d4, scale_factor=2, mode='bilinear', align_corners=True) + init_pred = F.interpolate(out_res2, scale_factor=2, mode='bilinear', align_corners=True) + feat_map = torch.cat([feat_map, init_pred], dim=1) # (B, 512+4, H, W) + B, _, H, W = feat_map.shape + + out_res1 = self.out_conv_res1(feat_map.view(B, 128 + 4, -1)) # (B, 4, N) + out_res1 = norm_normalize(out_res1) # (B, 4, N) - normalized + out_res1 = out_res1.view(B, 4, H, W) + samples_pred_res1 = point_coords_res1 = None + + return [out_res8, out_res4, out_res2, out_res1], \ + [out_res8, samples_pred_res4, samples_pred_res2, samples_pred_res1], \ + [None, point_coords_res4, point_coords_res2, point_coords_res1] + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/encoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/encoder.py new file mode 100644 index 00000000..51371bc5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/encoder.py @@ -0,0 +1,30 @@ +import os +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Encoder(nn.Module): + def __init__(self): + super(Encoder, self).__init__() + + basemodel_name = 'tf_efficientnet_b5.ap_in1k' + print('Loading base model ()...'.format(basemodel_name), end='') + import timm + basemodel = timm.create_model(basemodel_name, pretrained=False, num_classes=0) + print('Done.') + + + self.original_model = basemodel + + def forward(self, x): + features = [x] + for k, v in self.original_model._modules.items(): + if (k == 'blocks'): + for ki, vi in v._modules.items(): + features.append(vi(features[-1])) + else: + features.append(v(features[-1])) + return features + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/submodules.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/submodules.py new file mode 100644 index 00000000..40973335 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/normalbae/nets/submodules/submodules.py @@ -0,0 +1,140 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +######################################################################################################################## + + +# Upsample + BatchNorm +class UpSampleBN(nn.Module): + def __init__(self, skip_input, output_features): + super(UpSampleBN, self).__init__() + + self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(output_features), + nn.LeakyReLU(), + nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(output_features), + nn.LeakyReLU()) + + def forward(self, x, concat_with): + up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True) + f = torch.cat([up_x, concat_with], dim=1) + return self._net(f) + + +# Upsample + GroupNorm + Weight Standardization +class UpSampleGN(nn.Module): + def __init__(self, skip_input, output_features): + super(UpSampleGN, self).__init__() + + self._net = nn.Sequential(Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1), + nn.GroupNorm(8, output_features), + nn.LeakyReLU(), + Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1), + nn.GroupNorm(8, output_features), + nn.LeakyReLU()) + + def forward(self, x, concat_with): + up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True) + f = torch.cat([up_x, concat_with], dim=1) + return self._net(f) + + +# Conv2d with weight standardization +class Conv2d(nn.Conv2d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True): + super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias) + + def forward(self, x): + weight = self.weight + weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2, + keepdim=True).mean(dim=3, keepdim=True) + weight = weight - weight_mean + std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5 + weight = weight / std.expand_as(weight) + return F.conv2d(x, weight, self.bias, self.stride, + self.padding, self.dilation, self.groups) + + +# normalize +def norm_normalize(norm_out): + min_kappa = 0.01 + norm_x, norm_y, norm_z, kappa = torch.split(norm_out, 1, dim=1) + norm = torch.sqrt(norm_x ** 2.0 + norm_y ** 2.0 + norm_z ** 2.0) + 1e-10 + kappa = F.elu(kappa) + 1.0 + min_kappa + final_out = torch.cat([norm_x / norm, norm_y / norm, norm_z / norm, kappa], dim=1) + return final_out + + +# uncertainty-guided sampling (only used during training) +@torch.no_grad() +def sample_points(init_normal, gt_norm_mask, sampling_ratio, beta): + device = init_normal.device + B, _, H, W = init_normal.shape + N = int(sampling_ratio * H * W) + beta = beta + + # uncertainty map + uncertainty_map = -1 * init_normal[:, 3, :, :] # B, H, W + + # gt_invalid_mask (B, H, W) + if gt_norm_mask is not None: + gt_invalid_mask = F.interpolate(gt_norm_mask.float(), size=[H, W], mode='nearest') + gt_invalid_mask = gt_invalid_mask[:, 0, :, :] < 0.5 + uncertainty_map[gt_invalid_mask] = -1e4 + + # (B, H*W) + _, idx = uncertainty_map.view(B, -1).sort(1, descending=True) + + # importance sampling + if int(beta * N) > 0: + importance = idx[:, :int(beta * N)] # B, beta*N + + # remaining + remaining = idx[:, int(beta * N):] # B, H*W - beta*N + + # coverage + num_coverage = N - int(beta * N) + + if num_coverage <= 0: + samples = importance + else: + coverage_list = [] + for i in range(B): + idx_c = torch.randperm(remaining.size()[1]) # shuffles "H*W - beta*N" + coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1)) # 1, N-beta*N + coverage = torch.cat(coverage_list, dim=0) # B, N-beta*N + samples = torch.cat((importance, coverage), dim=1) # B, N + + else: + # remaining + remaining = idx[:, :] # B, H*W + + # coverage + num_coverage = N + + coverage_list = [] + for i in range(B): + idx_c = torch.randperm(remaining.size()[1]) # shuffles "H*W - beta*N" + coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1)) # 1, N-beta*N + coverage = torch.cat(coverage_list, dim=0) # B, N-beta*N + samples = coverage + + # point coordinates + rows_int = samples // W # 0 for first row, H-1 for last row + rows_float = rows_int / float(H-1) # 0 to 1.0 + rows_float = (rows_float * 2.0) - 1.0 # -1.0 to 1.0 + + cols_int = samples % W # 0 for first column, W-1 for last column + cols_float = cols_int / float(W-1) # 0 to 1.0 + cols_float = (cols_float * 2.0) - 1.0 # -1.0 to 1.0 + + point_coords = torch.zeros(B, 1, N, 2) + point_coords[:, 0, :, 0] = cols_float # x coord + point_coords[:, 0, :, 1] = rows_float # y coord + point_coords = point_coords.to(device) + return point_coords, rows_int, cols_int \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/__init__.py new file mode 100644 index 00000000..764c78a2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/__init__.py @@ -0,0 +1,4 @@ +""" +OneFormer implementation using HuggingFace transformers for PyTorch 2.7 compatibility. +""" +from .transformers import OneformerSegmentor \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml new file mode 100644 index 00000000..31eab45b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml @@ -0,0 +1,68 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ade20k_panoptic_train",) + TEST_PANOPTIC: ("ade20k_panoptic_val",) + TEST_INSTANCE: ("ade20k_instance_val",) + TEST_SEMANTIC: ("ade20k_sem_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 512 + MAX_SIZE_TRAIN: 2048 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 512) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 512 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "oneformer_unified" + MAX_SEQ_LEN: 77 + TASK_SEQ_LEN: 77 + TASK_PROB: + SEMANTIC: 0.33 + INSTANCE: 0.66 +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [256, 384, 512, 640, 768, 896] + MAX_SIZE: 3584 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/oneformer_R50_bs16_160k.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/oneformer_R50_bs16_160k.yaml new file mode 100644 index 00000000..770ffc81 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/oneformer_R50_bs16_160k.yaml @@ -0,0 +1,58 @@ +_BASE_: Base-ADE20K-UnifiedSegmentation.yaml +MODEL: + META_ARCHITECTURE: "OneFormer" + SEM_SEG_HEAD: + NAME: "OneFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 150 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + ONE_FORMER: + TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + CONTRASTIVE_WEIGHT: 0.5 + CONTRASTIVE_TEMPERATURE: 0.07 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 150 + USE_TASK_NORM: True + NHEADS: 8 + DROPOUT: 0.1 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + CLASS_DEC_LAYERS: 2 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEXT_ENCODER: + WIDTH: 256 + CONTEXT_LENGTH: 77 + NUM_LAYERS: 6 + VOCAB_SIZE: 49408 + PROJ_NUM_LAYERS: 2 + N_CTX: 16 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 + TASK: "panoptic" +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml new file mode 100644 index 00000000..69c44ade --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml @@ -0,0 +1,40 @@ +_BASE_: oneformer_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 250 +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" +TEST: + DETECTIONS_PER_IMAGE: 250 + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/Base-COCO-UnifiedSegmentation.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/Base-COCO-UnifiedSegmentation.yaml new file mode 100644 index 00000000..ccd24f34 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/Base-COCO-UnifiedSegmentation.yaml @@ -0,0 +1,54 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("coco_2017_train_panoptic_with_sem_seg",) + TEST_PANOPTIC: ("coco_2017_val_panoptic_with_sem_seg",) # to evaluate instance and semantic performance as well + TEST_INSTANCE: ("coco_2017_val",) + TEST_SEMANTIC: ("coco_2017_val_panoptic_with_sem_seg",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (327778, 355092) + MAX_ITER: 368750 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + IMAGE_SIZE: 1024 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_unified_lsj" + MAX_SEQ_LEN: 77 + TASK_SEQ_LEN: 77 + TASK_PROB: + SEMANTIC: 0.33 + INSTANCE: 0.66 +TEST: + EVAL_PERIOD: 5000 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/oneformer_R50_bs16_50ep.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/oneformer_R50_bs16_50ep.yaml new file mode 100644 index 00000000..f768c8fa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/oneformer_R50_bs16_50ep.yaml @@ -0,0 +1,59 @@ +_BASE_: Base-COCO-UnifiedSegmentation.yaml +MODEL: + META_ARCHITECTURE: "OneFormer" + SEM_SEG_HEAD: + NAME: "OneFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 133 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + ONE_FORMER: + TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + CONTRASTIVE_WEIGHT: 0.5 + CONTRASTIVE_TEMPERATURE: 0.07 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 150 + USE_TASK_NORM: True + NHEADS: 8 + DROPOUT: 0.1 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + CLASS_DEC_LAYERS: 2 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEXT_ENCODER: + WIDTH: 256 + CONTEXT_LENGTH: 77 + NUM_LAYERS: 6 + VOCAB_SIZE: 49408 + PROJ_NUM_LAYERS: 2 + N_CTX: 16 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + DETECTION_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 + TASK: "panoptic" +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml new file mode 100644 index 00000000..faae6553 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml @@ -0,0 +1,25 @@ +_BASE_: oneformer_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 150 +SOLVER: + STEPS: (655556, 735184) + MAX_ITER: 737500 + AMP: + ENABLED: False +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/transformers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/transformers.py new file mode 100644 index 00000000..0eef7d04 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/oneformer/transformers.py @@ -0,0 +1,124 @@ +""" +OneFormer implementation using HuggingFace transformers for PyTorch 2.7 compatibility. +Provides equivalent functionality to the original detectron2 implementation. +""" +import numpy as np +import cv2 +import torch +from PIL import Image + +# Import utilities +from ..util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME + + +class OneformerSegmentor: + """ + OneFormer segmentation using HuggingFace transformers implementation. + + Uses equivalent models that are PyTorch 2.7 compatible and actively maintained: + - Same architecture (OneFormer with Swin-Large backbone) + - Same training datasets (COCO panoptic / ADE20K) + - Professional colorized visualization output + """ + + def __init__(self, model_name): + """Initialize OneFormer with HuggingFace transformers implementation.""" + from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation + + self.model_name = model_name + self.processor = OneFormerProcessor.from_pretrained(model_name) + self.model = OneFormerForUniversalSegmentation.from_pretrained(model_name) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="250_16_swin_l_oneformer_ade20k_160k.pth", config_path=None): + """Create OneFormer model from pretrained weights.""" + model_mapping = { + "250_16_swin_l_oneformer_ade20k_160k.pth": "shi-labs/oneformer_ade20k_swin_large", + "150_16_swin_l_oneformer_coco_100ep.pth": "shi-labs/oneformer_coco_swin_large" + } + + if filename in model_mapping: + model_name = model_mapping[filename] + elif "coco" in filename.lower(): + model_name = "shi-labs/oneformer_coco_swin_large" + else: + model_name = "shi-labs/oneformer_ade20k_swin_large" + + return cls(model_name) + + def to(self, device): + """Move model to specified device.""" + self.model = self.model.to(device) + self.device = device + return self + + def __call__(self, input_image=None, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + """Process image for semantic segmentation.""" + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + # Convert to PIL for processing + if isinstance(input_image, np.ndarray): + pil_image = Image.fromarray(input_image) + else: + pil_image = input_image + + # Process with HuggingFace pipeline + semantic_inputs = self.processor( + images=pil_image, + task_inputs=["semantic"], + return_tensors="pt" + ).to(self.device) + + with torch.no_grad(): + outputs = self.model(**semantic_inputs) + + # Post-process results + predicted_semantic_map = self.processor.post_process_semantic_segmentation( + outputs, target_sizes=[pil_image.size[::-1]] + )[0] + + # Convert to colormap using professional color scheme + seg_map = predicted_semantic_map.cpu().numpy().astype(np.uint8) + detected_map = self._generate_professional_colormap(seg_map) + detected_map = remove_pad(HWC3(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + + def _generate_professional_colormap(self, seg_map): + """Generate professional colormap for segmentation visualization.""" + height, width = seg_map.shape + color_map = np.zeros((height, width, 3), dtype=np.uint8) + + max_possible_classes = 200 + colors = self._generate_detectron2_style_palette(max_possible_classes) + + unique_classes = np.unique(seg_map) + for class_id in unique_classes: + mask = seg_map == class_id + color_map[mask] = colors[class_id % len(colors)] + + return color_map + + def _generate_detectron2_style_palette(self, num_classes): + """Generate professional color palette with good visual separation.""" + colors = np.zeros((num_classes, 3), dtype=np.uint8) + + colors[0] = [0, 0, 0] # Background is black + + for i in range(1, num_classes): + hue = (i * 137.508) % 360 # Golden angle for good distribution + saturation = 0.6 + 0.4 * ((i % 3) / 2) # 60-100% saturation + value = 0.7 + 0.3 * ((i % 2)) # 70-100% value + + color_hsv = np.array([[[hue / 2, saturation * 255, value * 255]]], dtype=np.uint8) + color_rgb = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2RGB)[0, 0] + colors[i] = color_rgb + + return colors + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/LICENSE new file mode 100644 index 00000000..6f60b76d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/LICENSE @@ -0,0 +1,108 @@ +OPENPOSE: MULTIPERSON KEYPOINT DETECTION +SOFTWARE LICENSE AGREEMENT +ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY + +BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. 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Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +CONTRIBUTION AGREEMENT + +By contributing to the BVLC/caffe repository through pull-request, comment, +or otherwise, the contributor releases their content to the +license and copyright terms herein. + +************END OF THIRD-PARTY SOFTWARE NOTICES AND INFORMATION********** \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/__init__.py new file mode 100644 index 00000000..a8d0f53e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/__init__.py @@ -0,0 +1,238 @@ +# Openpose +# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose +# 2nd Edited by https://github.com/Hzzone/pytorch-openpose +# 3rd Edited by ControlNet +# 4th Edited by ControlNet (added face and correct hands) +# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs) +# This preprocessor is licensed by CMU for non-commercial use only. + + +import os + +os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + +import json +import warnings +from typing import Callable, List, NamedTuple, Tuple, Union + +import cv2 +import numpy as np +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME +from . import util +from .body import Body, BodyResult, Keypoint +from .face import Face +from .hand import Hand + +HandResult = List[Keypoint] +FaceResult = List[Keypoint] + +class PoseResult(NamedTuple): + body: BodyResult + left_hand: Union[HandResult, None] + right_hand: Union[HandResult, None] + face: Union[FaceResult, None] + +def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True, xinsr_stick_scaling=False): + """ + Draw the detected poses on an empty canvas. + + Args: + poses (List[PoseResult]): A list of PoseResult objects containing the detected poses. + H (int): The height of the canvas. + W (int): The width of the canvas. + draw_body (bool, optional): Whether to draw body keypoints. Defaults to True. + draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True. + draw_face (bool, optional): Whether to draw face keypoints. Defaults to True. + + Returns: + numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses. + """ + canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) + + for pose in poses: + if draw_body: + canvas = util.draw_bodypose(canvas, pose.body.keypoints, xinsr_stick_scaling) + + if draw_hand: + canvas = util.draw_handpose(canvas, pose.left_hand) + canvas = util.draw_handpose(canvas, pose.right_hand) + + if draw_face: + canvas = util.draw_facepose(canvas, pose.face) + + return canvas + +def encode_poses_as_dict(poses: List[PoseResult], canvas_height: int, canvas_width: int) -> str: + """ Encode the pose as a dict following openpose JSON output format: + https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md + """ + def compress_keypoints(keypoints: Union[List[Keypoint], None]) -> Union[List[float], None]: + if not keypoints: + return None + + return [ + value + for keypoint in keypoints + for value in ( + [float(keypoint.x), float(keypoint.y), 1.0] + if keypoint is not None + else [0.0, 0.0, 0.0] + ) + ] + + return { + 'people': [ + { + 'pose_keypoints_2d': compress_keypoints(pose.body.keypoints), + "face_keypoints_2d": compress_keypoints(pose.face), + "hand_left_keypoints_2d": compress_keypoints(pose.left_hand), + "hand_right_keypoints_2d":compress_keypoints(pose.right_hand), + } + for pose in poses + ], + 'canvas_height': canvas_height, + 'canvas_width': canvas_width, + } + +class OpenposeDetector: + """ + A class for detecting human poses in images using the Openpose model. + + Attributes: + model_dir (str): Path to the directory where the pose models are stored. + """ + def __init__(self, body_estimation, hand_estimation=None, face_estimation=None): + self.body_estimation = body_estimation + self.hand_estimation = hand_estimation + self.face_estimation = face_estimation + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="body_pose_model.pth", hand_filename="hand_pose_model.pth", face_filename="facenet.pth"): + if pretrained_model_or_path == "lllyasviel/ControlNet": + subfolder = "annotator/ckpts" + face_pretrained_model_or_path = "lllyasviel/Annotators" + + else: + subfolder = '' + face_pretrained_model_or_path = pretrained_model_or_path + + body_model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder=subfolder) + hand_model_path = custom_hf_download(pretrained_model_or_path, hand_filename, subfolder=subfolder) + face_model_path = custom_hf_download(face_pretrained_model_or_path, face_filename, subfolder=subfolder) + + body_estimation = Body(body_model_path) + hand_estimation = Hand(hand_model_path) + face_estimation = Face(face_model_path) + + return cls(body_estimation, hand_estimation, face_estimation) + + def to(self, device): + self.body_estimation.to(device) + self.hand_estimation.to(device) + self.face_estimation.to(device) + return self + + def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]: + left_hand = None + right_hand = None + H, W, _ = oriImg.shape + for x, y, w, is_left in util.handDetect(body, oriImg): + peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]).astype(np.float32) + if peaks.ndim == 2 and peaks.shape[1] == 2: + peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W) + peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H) + + hand_result = [ + Keypoint(x=peak[0], y=peak[1]) + for peak in peaks + ] + + if is_left: + left_hand = hand_result + else: + right_hand = hand_result + + return left_hand, right_hand + + def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]: + face = util.faceDetect(body, oriImg) + if face is None: + return None + + x, y, w = face + H, W, _ = oriImg.shape + heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :]) + peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32) + if peaks.ndim == 2 and peaks.shape[1] == 2: + peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W) + peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H) + return [ + Keypoint(x=peak[0], y=peak[1]) + for peak in peaks + ] + + return None + + def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]: + """ + Detect poses in the given image. + Args: + oriImg (numpy.ndarray): The input image for pose detection. + include_hand (bool, optional): Whether to include hand detection. Defaults to False. + include_face (bool, optional): Whether to include face detection. Defaults to False. + + Returns: + List[PoseResult]: A list of PoseResult objects containing the detected poses. + """ + oriImg = oriImg[:, :, ::-1].copy() + H, W, C = oriImg.shape + with torch.no_grad(): + candidate, subset = self.body_estimation(oriImg) + bodies = self.body_estimation.format_body_result(candidate, subset) + + results = [] + for body in bodies: + left_hand, right_hand, face = (None,) * 3 + if include_hand: + left_hand, right_hand = self.detect_hands(body, oriImg) + if include_face: + face = self.detect_face(body, oriImg) + + results.append(PoseResult(BodyResult( + keypoints=[ + Keypoint( + x=keypoint.x / float(W), + y=keypoint.y / float(H) + ) if keypoint is not None else None + for keypoint in body.keypoints + ], + total_score=body.total_score, + total_parts=body.total_parts + ), left_hand, right_hand, face)) + + return results + + def __call__(self, input_image, detect_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", image_and_json=False, upscale_method="INTER_CUBIC", xinsr_stick_scaling=False, **kwargs): + if hand_and_face is not None: + warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning) + include_hand = hand_and_face + include_face = hand_and_face + + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + poses = self.detect_poses(input_image, include_hand=include_hand, include_face=include_face) + canvas = draw_poses(poses, input_image.shape[0], input_image.shape[1], draw_body=include_body, draw_hand=include_hand, draw_face=include_face, xinsr_stick_scaling=xinsr_stick_scaling) + detected_map = HWC3(remove_pad(canvas)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + if image_and_json: + return (detected_map, encode_poses_as_dict(poses, detected_map.shape[0], detected_map.shape[1])) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/body.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/body.py new file mode 100644 index 00000000..edd74486 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/body.py @@ -0,0 +1,278 @@ +import math +from typing import List, NamedTuple, Union + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import torch +from scipy.ndimage.filters import gaussian_filter + +from . import util +from .model import bodypose_model + + +class Keypoint(NamedTuple): + x: float + y: float + score: float = 1.0 + id: int = -1 + + +class BodyResult(NamedTuple): + # Note: Using `Union` instead of `|` operator as the ladder is a Python + # 3.10 feature. + # Annotator code should be Python 3.8 Compatible, as controlnet repo uses + # Python 3.8 environment. + # https://github.com/lllyasviel/ControlNet/blob/d3284fcd0972c510635a4f5abe2eeb71dc0de524/environment.yaml#L6 + keypoints: List[Union[Keypoint, None]] + total_score: float + total_parts: int + + +class Body(object): + def __init__(self, model_path): + self.model = bodypose_model() + model_dict = util.transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + self.device = "cpu" + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, oriImg): + # scale_search = [0.5, 1.0, 1.5, 2.0] + scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre1 = 0.1 + thre2 = 0.05 + multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] + heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19)) + paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale) + imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + data = data.to(self.device) + # data = data.permute([2, 0, 1]).unsqueeze(0).float() + with torch.no_grad(): + Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data) + Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy() + Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy() + + # extract outputs, resize, and remove padding + # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps + heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps + heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1])) + + # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs + paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs + paf = util.smart_resize_k(paf, fx=stride, fy=stride) + paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1])) + + heatmap_avg += heatmap_avg + heatmap / len(multiplier) + paf_avg += + paf / len(multiplier) + + all_peaks = [] + peak_counter = 0 + + for part in range(18): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + + map_left = np.zeros(one_heatmap.shape) + map_left[1:, :] = one_heatmap[:-1, :] + map_right = np.zeros(one_heatmap.shape) + map_right[:-1, :] = one_heatmap[1:, :] + map_up = np.zeros(one_heatmap.shape) + map_up[:, 1:] = one_heatmap[:, :-1] + map_down = np.zeros(one_heatmap.shape) + map_down[:, :-1] = one_heatmap[:, 1:] + + peaks_binary = np.logical_and.reduce( + (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1)) + peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse + peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] + peak_id = range(peak_counter, peak_counter + len(peaks)) + peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] + + all_peaks.append(peaks_with_score_and_id) + peak_counter += len(peaks) + + # find connection in the specified sequence, center 29 is in the position 15 + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + # the middle joints heatmap correpondence + mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ + [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ + [55, 56], [37, 38], [45, 46]] + + connection_all = [] + special_k = [] + mid_num = 10 + + for k in range(len(mapIdx)): + score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]] + candA = all_peaks[limbSeq[k][0] - 1] + candB = all_peaks[limbSeq[k][1] - 1] + nA = len(candA) + nB = len(candB) + indexA, indexB = limbSeq[k] + if (nA != 0 and nB != 0): + connection_candidate = [] + for i in range(nA): + for j in range(nB): + vec = np.subtract(candB[j][:2], candA[i][:2]) + norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1]) + norm = max(0.001, norm) + vec = np.divide(vec, norm) + + startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ + np.linspace(candA[i][1], candB[j][1], num=mid_num))) + + vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ + for I in range(len(startend))]) + vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ + for I in range(len(startend))]) + + score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) + score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( + 0.5 * oriImg.shape[0] / norm - 1, 0) + criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts) + criterion2 = score_with_dist_prior > 0 + if criterion1 and criterion2: + connection_candidate.append( + [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) + + connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) + connection = np.zeros((0, 5)) + for c in range(len(connection_candidate)): + i, j, s = connection_candidate[c][0:3] + if (i not in connection[:, 3] and j not in connection[:, 4]): + connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) + if (len(connection) >= min(nA, nB)): + break + + connection_all.append(connection) + else: + special_k.append(k) + connection_all.append([]) + + # last number in each row is the total parts number of that person + # the second last number in each row is the score of the overall configuration + subset = -1 * np.ones((0, 20)) + candidate = np.array([item for sublist in all_peaks for item in sublist]) + + for k in range(len(mapIdx)): + if k not in special_k: + partAs = connection_all[k][:, 0] + partBs = connection_all[k][:, 1] + indexA, indexB = np.array(limbSeq[k]) - 1 + + for i in range(len(connection_all[k])): # = 1:size(temp,1) + found = 0 + subset_idx = [-1, -1] + for j in range(len(subset)): # 1:size(subset,1): + if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]: + subset_idx[found] = j + found += 1 + + if found == 1: + j = subset_idx[0] + if subset[j][indexB] != partBs[i]: + subset[j][indexB] = partBs[i] + subset[j][-1] += 1 + subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + elif found == 2: # if found 2 and disjoint, merge them + j1, j2 = subset_idx + membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] + if len(np.nonzero(membership == 2)[0]) == 0: # merge + subset[j1][:-2] += (subset[j2][:-2] + 1) + subset[j1][-2:] += subset[j2][-2:] + subset[j1][-2] += connection_all[k][i][2] + subset = np.delete(subset, j2, 0) + else: # as like found == 1 + subset[j1][indexB] = partBs[i] + subset[j1][-1] += 1 + subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + + # if find no partA in the subset, create a new subset + elif not found and k < 17: + row = -1 * np.ones(20) + row[indexA] = partAs[i] + row[indexB] = partBs[i] + row[-1] = 2 + row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2] + subset = np.vstack([subset, row]) + # delete some rows of subset which has few parts occur + deleteIdx = [] + for i in range(len(subset)): + if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4: + deleteIdx.append(i) + subset = np.delete(subset, deleteIdx, axis=0) + + # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts + # candidate: x, y, score, id + return candidate, subset + + @staticmethod + def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]: + """ + Format the body results from the candidate and subset arrays into a list of BodyResult objects. + + Args: + candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id + for each body part. + subset (np.ndarray): An array of subsets containing indices to the candidate array for each + person detected. The last two columns of each row hold the total score and total parts + of the person. + + Returns: + List[BodyResult]: A list of BodyResult objects, where each object represents a person with + detected keypoints, total score, and total parts. + """ + return [ + BodyResult( + keypoints=[ + Keypoint( + x=candidate[candidate_index][0], + y=candidate[candidate_index][1], + score=candidate[candidate_index][2], + id=candidate[candidate_index][3] + ) if candidate_index != -1 else None + for candidate_index in person[:18].astype(int) + ], + total_score=person[18], + total_parts=person[19] + ) + for person in subset + ] + + +if __name__ == "__main__": + body_estimation = Body('../model/body_pose_model.pth') + + test_image = '../images/ski.jpg' + oriImg = cv2.imread(test_image) # B,G,R order + candidate, subset = body_estimation(oriImg) + bodies = body_estimation.format_body_result(candidate, subset) + + canvas = oriImg + for body in bodies: + canvas = util.draw_bodypose(canvas, body) + + plt.imshow(canvas[:, :, [2, 1, 0]]) + plt.show() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/face.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/face.py new file mode 100644 index 00000000..44e9ee23 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/face.py @@ -0,0 +1,365 @@ +import logging + +import numpy as np +import torch +import torch.nn.functional as F +from torch.nn import Conv2d, MaxPool2d, Module, ReLU, init +from torchvision.transforms import ToPILImage, ToTensor + +from . import util + + +class FaceNet(Module): + """Model the cascading heatmaps. """ + def __init__(self): + super(FaceNet, self).__init__() + # cnn to make feature map + self.relu = ReLU() + self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2) + self.conv1_1 = Conv2d(in_channels=3, out_channels=64, + kernel_size=3, stride=1, padding=1) + self.conv1_2 = Conv2d( + in_channels=64, out_channels=64, kernel_size=3, stride=1, + padding=1) + self.conv2_1 = Conv2d( + in_channels=64, out_channels=128, kernel_size=3, stride=1, + padding=1) + self.conv2_2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=3, stride=1, + padding=1) + self.conv3_1 = Conv2d( + in_channels=128, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv3_2 = Conv2d( + in_channels=256, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv3_3 = Conv2d( + in_channels=256, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv3_4 = Conv2d( + in_channels=256, out_channels=256, kernel_size=3, stride=1, + padding=1) + self.conv4_1 = Conv2d( + in_channels=256, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv4_2 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv4_3 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv4_4 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv5_1 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv5_2 = Conv2d( + in_channels=512, out_channels=512, kernel_size=3, stride=1, + padding=1) + self.conv5_3_CPM = Conv2d( + in_channels=512, out_channels=128, kernel_size=3, stride=1, + padding=1) + + # stage1 + self.conv6_1_CPM = Conv2d( + in_channels=128, out_channels=512, kernel_size=1, stride=1, + padding=0) + self.conv6_2_CPM = Conv2d( + in_channels=512, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage2 + self.Mconv1_stage2 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage2 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage2 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage3 + self.Mconv1_stage3 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage3 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage3 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage4 + self.Mconv1_stage4 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage4 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage4 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage5 + self.Mconv1_stage5 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage5 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage5 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + # stage6 + self.Mconv1_stage6 = Conv2d( + in_channels=199, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv2_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv3_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv4_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv5_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=7, stride=1, + padding=3) + self.Mconv6_stage6 = Conv2d( + in_channels=128, out_channels=128, kernel_size=1, stride=1, + padding=0) + self.Mconv7_stage6 = Conv2d( + in_channels=128, out_channels=71, kernel_size=1, stride=1, + padding=0) + + for m in self.modules(): + if isinstance(m, Conv2d): + init.constant_(m.bias, 0) + + def forward(self, x): + """Return a list of heatmaps.""" + heatmaps = [] + + h = self.relu(self.conv1_1(x)) + h = self.relu(self.conv1_2(h)) + h = self.max_pooling_2d(h) + h = self.relu(self.conv2_1(h)) + h = self.relu(self.conv2_2(h)) + h = self.max_pooling_2d(h) + h = self.relu(self.conv3_1(h)) + h = self.relu(self.conv3_2(h)) + h = self.relu(self.conv3_3(h)) + h = self.relu(self.conv3_4(h)) + h = self.max_pooling_2d(h) + h = self.relu(self.conv4_1(h)) + h = self.relu(self.conv4_2(h)) + h = self.relu(self.conv4_3(h)) + h = self.relu(self.conv4_4(h)) + h = self.relu(self.conv5_1(h)) + h = self.relu(self.conv5_2(h)) + h = self.relu(self.conv5_3_CPM(h)) + feature_map = h + + # stage1 + h = self.relu(self.conv6_1_CPM(h)) + h = self.conv6_2_CPM(h) + heatmaps.append(h) + + # stage2 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage2(h)) + h = self.relu(self.Mconv2_stage2(h)) + h = self.relu(self.Mconv3_stage2(h)) + h = self.relu(self.Mconv4_stage2(h)) + h = self.relu(self.Mconv5_stage2(h)) + h = self.relu(self.Mconv6_stage2(h)) + h = self.Mconv7_stage2(h) + heatmaps.append(h) + + # stage3 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage3(h)) + h = self.relu(self.Mconv2_stage3(h)) + h = self.relu(self.Mconv3_stage3(h)) + h = self.relu(self.Mconv4_stage3(h)) + h = self.relu(self.Mconv5_stage3(h)) + h = self.relu(self.Mconv6_stage3(h)) + h = self.Mconv7_stage3(h) + heatmaps.append(h) + + # stage4 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage4(h)) + h = self.relu(self.Mconv2_stage4(h)) + h = self.relu(self.Mconv3_stage4(h)) + h = self.relu(self.Mconv4_stage4(h)) + h = self.relu(self.Mconv5_stage4(h)) + h = self.relu(self.Mconv6_stage4(h)) + h = self.Mconv7_stage4(h) + heatmaps.append(h) + + # stage5 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage5(h)) + h = self.relu(self.Mconv2_stage5(h)) + h = self.relu(self.Mconv3_stage5(h)) + h = self.relu(self.Mconv4_stage5(h)) + h = self.relu(self.Mconv5_stage5(h)) + h = self.relu(self.Mconv6_stage5(h)) + h = self.Mconv7_stage5(h) + heatmaps.append(h) + + # stage6 + h = torch.cat([h, feature_map], dim=1) # channel concat + h = self.relu(self.Mconv1_stage6(h)) + h = self.relu(self.Mconv2_stage6(h)) + h = self.relu(self.Mconv3_stage6(h)) + h = self.relu(self.Mconv4_stage6(h)) + h = self.relu(self.Mconv5_stage6(h)) + h = self.relu(self.Mconv6_stage6(h)) + h = self.Mconv7_stage6(h) + heatmaps.append(h) + + return heatmaps + + +LOG = logging.getLogger(__name__) +TOTEN = ToTensor() +TOPIL = ToPILImage() + + +params = { + 'gaussian_sigma': 2.5, + 'inference_img_size': 736, # 368, 736, 1312 + 'heatmap_peak_thresh': 0.1, + 'crop_scale': 1.5, + 'line_indices': [ + [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], + [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], + [13, 14], [14, 15], [15, 16], + [17, 18], [18, 19], [19, 20], [20, 21], + [22, 23], [23, 24], [24, 25], [25, 26], + [27, 28], [28, 29], [29, 30], + [31, 32], [32, 33], [33, 34], [34, 35], + [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36], + [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42], + [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], + [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48], + [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66], + [66, 67], [67, 60] + ], +} + + +class Face(object): + """ + The OpenPose face landmark detector model. + + Args: + inference_size: set the size of the inference image size, suggested: + 368, 736, 1312, default 736 + gaussian_sigma: blur the heatmaps, default 2.5 + heatmap_peak_thresh: return landmark if over threshold, default 0.1 + + """ + def __init__(self, face_model_path, + inference_size=None, + gaussian_sigma=None, + heatmap_peak_thresh=None): + self.inference_size = inference_size or params["inference_img_size"] + self.sigma = gaussian_sigma or params['gaussian_sigma'] + self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"] + self.model = FaceNet() + self.model.load_state_dict(torch.load(face_model_path)) + self.model.eval() + self.device = "cpu" + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, face_img): + H, W, C = face_img.shape + + w_size = 384 + x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5 + + x_data = x_data.to(self.device) + + with torch.no_grad(): + hs = self.model(x_data[None, ...]) + heatmaps = F.interpolate( + hs[-1], + (H, W), + mode='bilinear', align_corners=True).cpu().numpy()[0] + return heatmaps + + def compute_peaks_from_heatmaps(self, heatmaps): + all_peaks = [] + for part in range(heatmaps.shape[0]): + map_ori = heatmaps[part].copy() + binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8) + + if np.sum(binary) == 0: + continue + + positions = np.where(binary > 0.5) + intensities = map_ori[positions] + mi = np.argmax(intensities) + y, x = positions[0][mi], positions[1][mi] + all_peaks.append([x, y]) + + return np.array(all_peaks) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/hand.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/hand.py new file mode 100644 index 00000000..ba641875 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/hand.py @@ -0,0 +1,91 @@ +import cv2 +import numpy as np +import torch +from scipy.ndimage.filters import gaussian_filter +from skimage.measure import label + +from . import util +from .model import handpose_model + + +class Hand(object): + def __init__(self, model_path): + self.model = handpose_model() + model_dict = util.transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + self.device = "cpu" + + def to(self, device): + self.model.to(device) + self.device = device + return self + + def __call__(self, oriImgRaw): + scale_search = [0.5, 1.0, 1.5, 2.0] + # scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre = 0.05 + multiplier = [x * boxsize for x in scale_search] + + wsize = 128 + heatmap_avg = np.zeros((wsize, wsize, 22)) + + Hr, Wr, Cr = oriImgRaw.shape + + oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = util.smart_resize(oriImg, (scale, scale)) + + imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + data = data.to(self.device) + + with torch.no_grad(): + output = self.model(data).cpu().numpy() + + # extract outputs, resize, and remove padding + heatmap = np.transpose(np.squeeze(output), (1, 2, 0)) # output 1 is heatmaps + heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = util.smart_resize(heatmap, (wsize, wsize)) + + heatmap_avg += heatmap / len(multiplier) + + all_peaks = [] + for part in range(21): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8) + + if np.sum(binary) == 0: + all_peaks.append([0, 0]) + continue + label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim) + max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1 + label_img[label_img != max_index] = 0 + map_ori[label_img == 0] = 0 + + y, x = util.npmax(map_ori) + y = int(float(y) * float(Hr) / float(wsize)) + x = int(float(x) * float(Wr) / float(wsize)) + all_peaks.append([x, y]) + return np.array(all_peaks) + +if __name__ == "__main__": + hand_estimation = Hand('../model/hand_pose_model.pth') + + # test_image = '../images/hand.jpg' + test_image = '../images/hand.jpg' + oriImg = cv2.imread(test_image) # B,G,R order + peaks = hand_estimation(oriImg) + canvas = util.draw_handpose(oriImg, peaks, True) + cv2.imshow('', canvas) + cv2.waitKey(0) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/model.py new file mode 100644 index 00000000..6c3d4726 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/model.py @@ -0,0 +1,217 @@ +import torch +from collections import OrderedDict + +import torch +import torch.nn as nn + +def make_layers(block, no_relu_layers): + layers = [] + for layer_name, v in block.items(): + if 'pool' in layer_name: + layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], + padding=v[2]) + layers.append((layer_name, layer)) + else: + conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], + kernel_size=v[2], stride=v[3], + padding=v[4]) + layers.append((layer_name, conv2d)) + if layer_name not in no_relu_layers: + layers.append(('relu_'+layer_name, nn.ReLU(inplace=True))) + + return nn.Sequential(OrderedDict(layers)) + +class bodypose_model(nn.Module): + def __init__(self): + super(bodypose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ + 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ + 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ + 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] + blocks = {} + block0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3_CPM', [512, 256, 3, 1, 1]), + ('conv4_4_CPM', [256, 128, 3, 1, 1]) + ]) + + + # Stage 1 + block1_1 = OrderedDict([ + ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) + ]) + + block1_2 = OrderedDict([ + ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) + ]) + blocks['block1_1'] = block1_1 + blocks['block1_2'] = block1_2 + + self.model0 = make_layers(block0, no_relu_layers) + + # Stages 2 - 6 + for i in range(2, 7): + blocks['block%d_1' % i] = OrderedDict([ + ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) + ]) + + blocks['block%d_2' % i] = OrderedDict([ + ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_1 = blocks['block1_1'] + self.model2_1 = blocks['block2_1'] + self.model3_1 = blocks['block3_1'] + self.model4_1 = blocks['block4_1'] + self.model5_1 = blocks['block5_1'] + self.model6_1 = blocks['block6_1'] + + self.model1_2 = blocks['block1_2'] + self.model2_2 = blocks['block2_2'] + self.model3_2 = blocks['block3_2'] + self.model4_2 = blocks['block4_2'] + self.model5_2 = blocks['block5_2'] + self.model6_2 = blocks['block6_2'] + + + def forward(self, x): + + out1 = self.model0(x) + + out1_1 = self.model1_1(out1) + out1_2 = self.model1_2(out1) + out2 = torch.cat([out1_1, out1_2, out1], 1) + + out2_1 = self.model2_1(out2) + out2_2 = self.model2_2(out2) + out3 = torch.cat([out2_1, out2_2, out1], 1) + + out3_1 = self.model3_1(out3) + out3_2 = self.model3_2(out3) + out4 = torch.cat([out3_1, out3_2, out1], 1) + + out4_1 = self.model4_1(out4) + out4_2 = self.model4_2(out4) + out5 = torch.cat([out4_1, out4_2, out1], 1) + + out5_1 = self.model5_1(out5) + out5_2 = self.model5_2(out5) + out6 = torch.cat([out5_1, out5_2, out1], 1) + + out6_1 = self.model6_1(out6) + out6_2 = self.model6_2(out6) + + return out6_1, out6_2 + +class handpose_model(nn.Module): + def __init__(self): + super(handpose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ + 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] + # stage 1 + block1_0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3', [512, 512, 3, 1, 1]), + ('conv4_4', [512, 512, 3, 1, 1]), + ('conv5_1', [512, 512, 3, 1, 1]), + ('conv5_2', [512, 512, 3, 1, 1]), + ('conv5_3_CPM', [512, 128, 3, 1, 1]) + ]) + + block1_1 = OrderedDict([ + ('conv6_1_CPM', [128, 512, 1, 1, 0]), + ('conv6_2_CPM', [512, 22, 1, 1, 0]) + ]) + + blocks = {} + blocks['block1_0'] = block1_0 + blocks['block1_1'] = block1_1 + + # stage 2-6 + for i in range(2, 7): + blocks['block%d' % i] = OrderedDict([ + ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), + ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_0 = blocks['block1_0'] + self.model1_1 = blocks['block1_1'] + self.model2 = blocks['block2'] + self.model3 = blocks['block3'] + self.model4 = blocks['block4'] + self.model5 = blocks['block5'] + self.model6 = blocks['block6'] + + def forward(self, x): + out1_0 = self.model1_0(x) + out1_1 = self.model1_1(out1_0) + concat_stage2 = torch.cat([out1_1, out1_0], 1) + out_stage2 = self.model2(concat_stage2) + concat_stage3 = torch.cat([out_stage2, out1_0], 1) + out_stage3 = self.model3(concat_stage3) + concat_stage4 = torch.cat([out_stage3, out1_0], 1) + out_stage4 = self.model4(concat_stage4) + concat_stage5 = torch.cat([out_stage4, out1_0], 1) + out_stage5 = self.model5(concat_stage5) + concat_stage6 = torch.cat([out_stage5, out1_0], 1) + out_stage6 = self.model6(concat_stage6) + return out_stage6 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/util.py new file mode 100644 index 00000000..21a14c9f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/open_pose/util.py @@ -0,0 +1,390 @@ +import math +import numpy as np +import matplotlib +import cv2 +from typing import List, Tuple, Union + +from .body import BodyResult, Keypoint + +eps = 0.01 + + +def smart_resize(x, s): + Ht, Wt = s + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2) + + +def smart_resize_k(x, fx, fy): + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + Ht, Wt = Ho * fy, Wo * fx + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2) + + +def padRightDownCorner(img, stride, padValue): + h = img.shape[0] + w = img.shape[1] + + pad = 4 * [None] + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + + img_padded = img + pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) + img_padded = np.concatenate((pad_up, img_padded), axis=0) + pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) + img_padded = np.concatenate((pad_left, img_padded), axis=1) + pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) + img_padded = np.concatenate((img_padded, pad_down), axis=0) + pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) + img_padded = np.concatenate((img_padded, pad_right), axis=1) + + return img_padded, pad + + +def transfer(model, model_weights): + transfered_model_weights = {} + for weights_name in model.state_dict().keys(): + transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + return transfered_model_weights + + +def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint], xinsr_stick_scaling: bool = False) -> np.ndarray: + """ + Draw keypoints and limbs representing body pose on a given canvas. + + Args: + canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose. + keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn. + xinsr_stick_scaling (bool): Whether or not scaling stick width for xinsr ControlNet + + Returns: + np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose. + + Note: + The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. + """ + H, W, C = canvas.shape + stickwidth = 4 + # Ref: https://huggingface.co/xinsir/controlnet-openpose-sdxl-1.0 + max_side = max(H, W) + if xinsr_stick_scaling: + stick_scale = 1 if max_side < 500 else min(2 + (max_side // 1000), 7) + else: + stick_scale = 1 + + limbSeq = [ + [2, 3], [2, 6], [3, 4], [4, 5], + [6, 7], [7, 8], [2, 9], [9, 10], + [10, 11], [2, 12], [12, 13], [13, 14], + [2, 1], [1, 15], [15, 17], [1, 16], + [16, 18], + ] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] + + for (k1_index, k2_index), color in zip(limbSeq, colors): + keypoint1 = keypoints[k1_index - 1] + keypoint2 = keypoints[k2_index - 1] + + if keypoint1 is None or keypoint2 is None: + continue + + Y = np.array([keypoint1.x, keypoint2.x]) * float(W) + X = np.array([keypoint1.y, keypoint2.y]) * float(H) + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth*stick_scale), int(angle), 0, 360, 1) + cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color]) + + for keypoint, color in zip(keypoints, colors): + if keypoint is None: + continue + + x, y = keypoint.x, keypoint.y + x = int(x * W) + y = int(y * H) + cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1) + + return canvas + + +def draw_handpose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray: + """ + Draw keypoints and connections representing hand pose on a given canvas. + + Args: + canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose. + keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn + or None if no keypoints are present. + + Returns: + np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose. + + Note: + The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. + """ + if not keypoints: + return canvas + + H, W, C = canvas.shape + + edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ + [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] + + for ie, (e1, e2) in enumerate(edges): + k1 = keypoints[e1] + k2 = keypoints[e2] + if k1 is None or k2 is None: + continue + + x1 = int(k1.x * W) + y1 = int(k1.y * H) + x2 = int(k2.x * W) + y2 = int(k2.y * H) + if x1 > eps and y1 > eps and x2 > eps and y2 > eps: + cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2) + + for keypoint in keypoints: + x, y = keypoint.x, keypoint.y + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1) + return canvas + + +def draw_facepose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray: + """ + Draw keypoints representing face pose on a given canvas. + + Args: + canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the face pose. + keypoints (List[Keypoint]| None): A list of Keypoint objects representing the face keypoints to be drawn + or None if no keypoints are present. + + Returns: + np.ndarray: A 3D numpy array representing the modified canvas with the drawn face pose. + + Note: + The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1. + """ + if not keypoints: + return canvas + + H, W, C = canvas.shape + for keypoint in keypoints: + x, y = keypoint.x, keypoint.y + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1) + return canvas + + +# detect hand according to body pose keypoints +# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp +def handDetect(body: BodyResult, oriImg) -> List[Tuple[int, int, int, bool]]: + """ + Detect hands in the input body pose keypoints and calculate the bounding box for each hand. + + Args: + body (BodyResult): A BodyResult object containing the detected body pose keypoints. + oriImg (numpy.ndarray): A 3D numpy array representing the original input image. + + Returns: + List[Tuple[int, int, int, bool]]: A list of tuples, each containing the coordinates (x, y) of the top-left + corner of the bounding box, the width (height) of the bounding box, and + a boolean flag indicating whether the hand is a left hand (True) or a + right hand (False). + + Notes: + - The width and height of the bounding boxes are equal since the network requires squared input. + - The minimum bounding box size is 20 pixels. + """ + ratioWristElbow = 0.33 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + + keypoints = body.keypoints + # right hand: wrist 4, elbow 3, shoulder 2 + # left hand: wrist 7, elbow 6, shoulder 5 + left_shoulder = keypoints[5] + left_elbow = keypoints[6] + left_wrist = keypoints[7] + right_shoulder = keypoints[2] + right_elbow = keypoints[3] + right_wrist = keypoints[4] + + # if any of three not detected + has_left = all(keypoint is not None for keypoint in (left_shoulder, left_elbow, left_wrist)) + has_right = all(keypoint is not None for keypoint in (right_shoulder, right_elbow, right_wrist)) + if not (has_left or has_right): + return [] + + hands = [] + #left hand + if has_left: + hands.append([ + left_shoulder.x, left_shoulder.y, + left_elbow.x, left_elbow.y, + left_wrist.x, left_wrist.y, + True + ]) + # right hand + if has_right: + hands.append([ + right_shoulder.x, right_shoulder.y, + right_elbow.x, right_elbow.y, + right_wrist.x, right_wrist.y, + False + ]) + + for x1, y1, x2, y2, x3, y3, is_left in hands: + # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox + # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]); + # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]); + # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow); + # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder); + # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder); + x = x3 + ratioWristElbow * (x3 - x2) + y = y3 + ratioWristElbow * (y3 - y2) + distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) + distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder) + # x-y refers to the center --> offset to topLeft point + # handRectangle.x -= handRectangle.width / 2.f; + # handRectangle.y -= handRectangle.height / 2.f; + x -= width / 2 + y -= width / 2 # width = height + # overflow the image + if x < 0: x = 0 + if y < 0: y = 0 + width1 = width + width2 = width + if x + width > image_width: width1 = image_width - x + if y + width > image_height: width2 = image_height - y + width = min(width1, width2) + # the max hand box value is 20 pixels + if width >= 20: + detect_result.append((int(x), int(y), int(width), is_left)) + + ''' + return value: [[x, y, w, True if left hand else False]]. + width=height since the network require squared input. + x, y is the coordinate of top left + ''' + return detect_result + + +# Written by Lvmin +def faceDetect(body: BodyResult, oriImg) -> Union[Tuple[int, int, int], None]: + """ + Detect the face in the input body pose keypoints and calculate the bounding box for the face. + + Args: + body (BodyResult): A BodyResult object containing the detected body pose keypoints. + oriImg (numpy.ndarray): A 3D numpy array representing the original input image. + + Returns: + Tuple[int, int, int] | None: A tuple containing the coordinates (x, y) of the top-left corner of the + bounding box and the width (height) of the bounding box, or None if the + face is not detected or the bounding box width is less than 20 pixels. + + Notes: + - The width and height of the bounding box are equal. + - The minimum bounding box size is 20 pixels. + """ + # left right eye ear 14 15 16 17 + image_height, image_width = oriImg.shape[0:2] + + keypoints = body.keypoints + head = keypoints[0] + left_eye = keypoints[14] + right_eye = keypoints[15] + left_ear = keypoints[16] + right_ear = keypoints[17] + + if head is None or all(keypoint is None for keypoint in (left_eye, right_eye, left_ear, right_ear)): + return None + + width = 0.0 + x0, y0 = head.x, head.y + + if left_eye is not None: + x1, y1 = left_eye.x, left_eye.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if right_eye is not None: + x1, y1 = right_eye.x, right_eye.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if left_ear is not None: + x1, y1 = left_ear.x, left_ear.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + if right_ear is not None: + x1, y1 = right_ear.x, right_ear.y + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + x, y = x0, y0 + + x -= width + y -= width + + if x < 0: + x = 0 + + if y < 0: + y = 0 + + width1 = width * 2 + width2 = width * 2 + + if x + width > image_width: + width1 = image_width - x + + if y + width > image_height: + width2 = image_height - y + + width = min(width1, width2) + + if width >= 20: + return int(x), int(y), int(width) + else: + return None + + +# get max index of 2d array +def npmax(array): + arrayindex = array.argmax(1) + arrayvalue = array.max(1) + i = arrayvalue.argmax() + j = arrayindex[i] + return i, j \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/LICENSE new file mode 100644 index 00000000..913b6cf9 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/LICENSE @@ -0,0 +1,21 @@ +It is just for research purpose, and commercial use should be contacted with authors first. + +Copyright (c) 2021 Zhuo Su + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/__init__.py new file mode 100644 index 00000000..81fb25ac --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/__init__.py @@ -0,0 +1,64 @@ +import os +import warnings + +import cv2 +import numpy as np +import torch +from einops import rearrange +from PIL import Image + +from custom_controlnet_aux.util import HWC3, nms, resize_image_with_pad, safe_step,common_input_validate, custom_hf_download, HF_MODEL_NAME +from .model import pidinet + + +class PidiNetDetector: + def __init__(self, netNetwork): + self.netNetwork = netNetwork + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="table5_pidinet.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + + netNetwork = pidinet() + netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()}) + netNetwork.eval() + + return cls(netNetwork) + + def to(self, device): + self.netNetwork.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=False, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + detected_map = detected_map[:, :, ::-1].copy() + with torch.no_grad(): + image_pidi = torch.from_numpy(detected_map).float().to(self.device) + image_pidi = image_pidi / 255.0 + image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w') + edge = self.netNetwork(image_pidi)[-1] + edge = edge.cpu().numpy() + if apply_filter: + edge = edge > 0.5 + if safe: + edge = safe_step(edge) + edge = (edge * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = edge[0, 0] + + if scribble: + detected_map = nms(detected_map, 127, 3.0) + detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0) + detected_map[detected_map > 4] = 255 + detected_map[detected_map < 255] = 0 + + detected_map = HWC3(remove_pad(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/model.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/model.py new file mode 100644 index 00000000..16595b35 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pidi/model.py @@ -0,0 +1,681 @@ +""" +Author: Zhuo Su, Wenzhe Liu +Date: Feb 18, 2021 +""" + +import math + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def img2tensor(imgs, bgr2rgb=True, float32=True): + """Numpy array to tensor. + + Args: + imgs (list[ndarray] | ndarray): Input images. + bgr2rgb (bool): Whether to change bgr to rgb. + float32 (bool): Whether to change to float32. + + Returns: + list[tensor] | tensor: Tensor images. If returned results only have + one element, just return tensor. + """ + + def _totensor(img, bgr2rgb, float32): + if img.shape[2] == 3 and bgr2rgb: + if img.dtype == 'float64': + img = img.astype('float32') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = torch.from_numpy(img.transpose(2, 0, 1)) + if float32: + img = img.float() + return img + + if isinstance(imgs, list): + return [_totensor(img, bgr2rgb, float32) for img in imgs] + else: + return _totensor(imgs, bgr2rgb, float32) + +nets = { + 'baseline': { + 'layer0': 'cv', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'cv', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'cv', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'cv', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'c-v15': { + 'layer0': 'cd', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'cv', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'cv', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'cv', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'a-v15': { + 'layer0': 'ad', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'cv', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'cv', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'cv', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'r-v15': { + 'layer0': 'rd', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'cv', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'cv', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'cv', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'cvvv4': { + 'layer0': 'cd', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'cd', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'cd', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'cd', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'avvv4': { + 'layer0': 'ad', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'ad', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'ad', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'ad', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'rvvv4': { + 'layer0': 'rd', + 'layer1': 'cv', + 'layer2': 'cv', + 'layer3': 'cv', + 'layer4': 'rd', + 'layer5': 'cv', + 'layer6': 'cv', + 'layer7': 'cv', + 'layer8': 'rd', + 'layer9': 'cv', + 'layer10': 'cv', + 'layer11': 'cv', + 'layer12': 'rd', + 'layer13': 'cv', + 'layer14': 'cv', + 'layer15': 'cv', + }, + 'cccv4': { + 'layer0': 'cd', + 'layer1': 'cd', + 'layer2': 'cd', + 'layer3': 'cv', + 'layer4': 'cd', + 'layer5': 'cd', + 'layer6': 'cd', + 'layer7': 'cv', + 'layer8': 'cd', + 'layer9': 'cd', + 'layer10': 'cd', + 'layer11': 'cv', + 'layer12': 'cd', + 'layer13': 'cd', + 'layer14': 'cd', + 'layer15': 'cv', + }, + 'aaav4': { + 'layer0': 'ad', + 'layer1': 'ad', + 'layer2': 'ad', + 'layer3': 'cv', + 'layer4': 'ad', + 'layer5': 'ad', + 'layer6': 'ad', + 'layer7': 'cv', + 'layer8': 'ad', + 'layer9': 'ad', + 'layer10': 'ad', + 'layer11': 'cv', + 'layer12': 'ad', + 'layer13': 'ad', + 'layer14': 'ad', + 'layer15': 'cv', + }, + 'rrrv4': { + 'layer0': 'rd', + 'layer1': 'rd', + 'layer2': 'rd', + 'layer3': 'cv', + 'layer4': 'rd', + 'layer5': 'rd', + 'layer6': 'rd', + 'layer7': 'cv', + 'layer8': 'rd', + 'layer9': 'rd', + 'layer10': 'rd', + 'layer11': 'cv', + 'layer12': 'rd', + 'layer13': 'rd', + 'layer14': 'rd', + 'layer15': 'cv', + }, + 'c16': { + 'layer0': 'cd', + 'layer1': 'cd', + 'layer2': 'cd', + 'layer3': 'cd', + 'layer4': 'cd', + 'layer5': 'cd', + 'layer6': 'cd', + 'layer7': 'cd', + 'layer8': 'cd', + 'layer9': 'cd', + 'layer10': 'cd', + 'layer11': 'cd', + 'layer12': 'cd', + 'layer13': 'cd', + 'layer14': 'cd', + 'layer15': 'cd', + }, + 'a16': { + 'layer0': 'ad', + 'layer1': 'ad', + 'layer2': 'ad', + 'layer3': 'ad', + 'layer4': 'ad', + 'layer5': 'ad', + 'layer6': 'ad', + 'layer7': 'ad', + 'layer8': 'ad', + 'layer9': 'ad', + 'layer10': 'ad', + 'layer11': 'ad', + 'layer12': 'ad', + 'layer13': 'ad', + 'layer14': 'ad', + 'layer15': 'ad', + }, + 'r16': { + 'layer0': 'rd', + 'layer1': 'rd', + 'layer2': 'rd', + 'layer3': 'rd', + 'layer4': 'rd', + 'layer5': 'rd', + 'layer6': 'rd', + 'layer7': 'rd', + 'layer8': 'rd', + 'layer9': 'rd', + 'layer10': 'rd', + 'layer11': 'rd', + 'layer12': 'rd', + 'layer13': 'rd', + 'layer14': 'rd', + 'layer15': 'rd', + }, + 'carv4': { + 'layer0': 'cd', + 'layer1': 'ad', + 'layer2': 'rd', + 'layer3': 'cv', + 'layer4': 'cd', + 'layer5': 'ad', + 'layer6': 'rd', + 'layer7': 'cv', + 'layer8': 'cd', + 'layer9': 'ad', + 'layer10': 'rd', + 'layer11': 'cv', + 'layer12': 'cd', + 'layer13': 'ad', + 'layer14': 'rd', + 'layer15': 'cv', + }, + } + +def createConvFunc(op_type): + assert op_type in ['cv', 'cd', 'ad', 'rd'], 'unknown op type: %s' % str(op_type) + if op_type == 'cv': + return F.conv2d + + if op_type == 'cd': + def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1): + assert dilation in [1, 2], 'dilation for cd_conv should be in 1 or 2' + assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for cd_conv should be 3x3' + assert padding == dilation, 'padding for cd_conv set wrong' + + weights_c = weights.sum(dim=[2, 3], keepdim=True) + yc = F.conv2d(x, weights_c, stride=stride, padding=0, groups=groups) + y = F.conv2d(x, weights, bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + return y - yc + return func + elif op_type == 'ad': + def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1): + assert dilation in [1, 2], 'dilation for ad_conv should be in 1 or 2' + assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for ad_conv should be 3x3' + assert padding == dilation, 'padding for ad_conv set wrong' + + shape = weights.shape + weights = weights.view(shape[0], shape[1], -1) + weights_conv = (weights - weights[:, :, [3, 0, 1, 6, 4, 2, 7, 8, 5]]).view(shape) # clock-wise + y = F.conv2d(x, weights_conv, bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + return y + return func + elif op_type == 'rd': + def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1): + assert dilation in [1, 2], 'dilation for rd_conv should be in 1 or 2' + assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for rd_conv should be 3x3' + padding = 2 * dilation + + shape = weights.shape + if weights.is_cuda: + buffer = torch.cuda.FloatTensor(shape[0], shape[1], 5 * 5).fill_(0) + else: + buffer = torch.zeros(shape[0], shape[1], 5 * 5).to(weights.device) + weights = weights.view(shape[0], shape[1], -1) + buffer[:, :, [0, 2, 4, 10, 14, 20, 22, 24]] = weights[:, :, 1:] + buffer[:, :, [6, 7, 8, 11, 13, 16, 17, 18]] = -weights[:, :, 1:] + buffer[:, :, 12] = 0 + buffer = buffer.view(shape[0], shape[1], 5, 5) + y = F.conv2d(x, buffer, bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + return y + return func + else: + print('impossible to be here unless you force that') + return None + +class Conv2d(nn.Module): + def __init__(self, pdc, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False): + super(Conv2d, self).__init__() + if in_channels % groups != 0: + raise ValueError('in_channels must be divisible by groups') + if out_channels % groups != 0: + raise ValueError('out_channels must be divisible by groups') + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + self.pdc = pdc + + def reset_parameters(self): + nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + if self.bias is not None: + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) + bound = 1 / math.sqrt(fan_in) + nn.init.uniform_(self.bias, -bound, bound) + + def forward(self, input): + + return self.pdc(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + +class CSAM(nn.Module): + """ + Compact Spatial Attention Module + """ + def __init__(self, channels): + super(CSAM, self).__init__() + + mid_channels = 4 + self.relu1 = nn.ReLU() + self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0) + self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1, bias=False) + self.sigmoid = nn.Sigmoid() + nn.init.constant_(self.conv1.bias, 0) + + def forward(self, x): + y = self.relu1(x) + y = self.conv1(y) + y = self.conv2(y) + y = self.sigmoid(y) + + return x * y + +class CDCM(nn.Module): + """ + Compact Dilation Convolution based Module + """ + def __init__(self, in_channels, out_channels): + super(CDCM, self).__init__() + + self.relu1 = nn.ReLU() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) + self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=5, padding=5, bias=False) + self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=7, padding=7, bias=False) + self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=9, padding=9, bias=False) + self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=11, padding=11, bias=False) + nn.init.constant_(self.conv1.bias, 0) + + def forward(self, x): + x = self.relu1(x) + x = self.conv1(x) + x1 = self.conv2_1(x) + x2 = self.conv2_2(x) + x3 = self.conv2_3(x) + x4 = self.conv2_4(x) + return x1 + x2 + x3 + x4 + + +class MapReduce(nn.Module): + """ + Reduce feature maps into a single edge map + """ + def __init__(self, channels): + super(MapReduce, self).__init__() + self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0) + nn.init.constant_(self.conv.bias, 0) + + def forward(self, x): + return self.conv(x) + + +class PDCBlock(nn.Module): + def __init__(self, pdc, inplane, ouplane, stride=1): + super(PDCBlock, self).__init__() + self.stride=stride + + self.stride=stride + if self.stride > 1: + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0) + self.conv1 = Conv2d(pdc, inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False) + self.relu2 = nn.ReLU() + self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False) + + def forward(self, x): + if self.stride > 1: + x = self.pool(x) + y = self.conv1(x) + y = self.relu2(y) + y = self.conv2(y) + if self.stride > 1: + x = self.shortcut(x) + y = y + x + return y + +class PDCBlock_converted(nn.Module): + """ + CPDC, APDC can be converted to vanilla 3x3 convolution + RPDC can be converted to vanilla 5x5 convolution + """ + def __init__(self, pdc, inplane, ouplane, stride=1): + super(PDCBlock_converted, self).__init__() + self.stride=stride + + if self.stride > 1: + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0) + if pdc == 'rd': + self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding=2, groups=inplane, bias=False) + else: + self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False) + self.relu2 = nn.ReLU() + self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False) + + def forward(self, x): + if self.stride > 1: + x = self.pool(x) + y = self.conv1(x) + y = self.relu2(y) + y = self.conv2(y) + if self.stride > 1: + x = self.shortcut(x) + y = y + x + return y + +class PiDiNet(nn.Module): + def __init__(self, inplane, pdcs, dil=None, sa=False, convert=False): + super(PiDiNet, self).__init__() + self.sa = sa + if dil is not None: + assert isinstance(dil, int), 'dil should be an int' + self.dil = dil + + self.fuseplanes = [] + + self.inplane = inplane + if convert: + if pdcs[0] == 'rd': + init_kernel_size = 5 + init_padding = 2 + else: + init_kernel_size = 3 + init_padding = 1 + self.init_block = nn.Conv2d(3, self.inplane, + kernel_size=init_kernel_size, padding=init_padding, bias=False) + block_class = PDCBlock_converted + else: + self.init_block = Conv2d(pdcs[0], 3, self.inplane, kernel_size=3, padding=1) + block_class = PDCBlock + + self.block1_1 = block_class(pdcs[1], self.inplane, self.inplane) + self.block1_2 = block_class(pdcs[2], self.inplane, self.inplane) + self.block1_3 = block_class(pdcs[3], self.inplane, self.inplane) + self.fuseplanes.append(self.inplane) # C + + inplane = self.inplane + self.inplane = self.inplane * 2 + self.block2_1 = block_class(pdcs[4], inplane, self.inplane, stride=2) + self.block2_2 = block_class(pdcs[5], self.inplane, self.inplane) + self.block2_3 = block_class(pdcs[6], self.inplane, self.inplane) + self.block2_4 = block_class(pdcs[7], self.inplane, self.inplane) + self.fuseplanes.append(self.inplane) # 2C + + inplane = self.inplane + self.inplane = self.inplane * 2 + self.block3_1 = block_class(pdcs[8], inplane, self.inplane, stride=2) + self.block3_2 = block_class(pdcs[9], self.inplane, self.inplane) + self.block3_3 = block_class(pdcs[10], self.inplane, self.inplane) + self.block3_4 = block_class(pdcs[11], self.inplane, self.inplane) + self.fuseplanes.append(self.inplane) # 4C + + self.block4_1 = block_class(pdcs[12], self.inplane, self.inplane, stride=2) + self.block4_2 = block_class(pdcs[13], self.inplane, self.inplane) + self.block4_3 = block_class(pdcs[14], self.inplane, self.inplane) + self.block4_4 = block_class(pdcs[15], self.inplane, self.inplane) + self.fuseplanes.append(self.inplane) # 4C + + self.conv_reduces = nn.ModuleList() + if self.sa and self.dil is not None: + self.attentions = nn.ModuleList() + self.dilations = nn.ModuleList() + for i in range(4): + self.dilations.append(CDCM(self.fuseplanes[i], self.dil)) + self.attentions.append(CSAM(self.dil)) + self.conv_reduces.append(MapReduce(self.dil)) + elif self.sa: + self.attentions = nn.ModuleList() + for i in range(4): + self.attentions.append(CSAM(self.fuseplanes[i])) + self.conv_reduces.append(MapReduce(self.fuseplanes[i])) + elif self.dil is not None: + self.dilations = nn.ModuleList() + for i in range(4): + self.dilations.append(CDCM(self.fuseplanes[i], self.dil)) + self.conv_reduces.append(MapReduce(self.dil)) + else: + for i in range(4): + self.conv_reduces.append(MapReduce(self.fuseplanes[i])) + + self.classifier = nn.Conv2d(4, 1, kernel_size=1) # has bias + nn.init.constant_(self.classifier.weight, 0.25) + nn.init.constant_(self.classifier.bias, 0) + + # print('initialization done') + + def get_weights(self): + conv_weights = [] + bn_weights = [] + relu_weights = [] + for pname, p in self.named_parameters(): + if 'bn' in pname: + bn_weights.append(p) + elif 'relu' in pname: + relu_weights.append(p) + else: + conv_weights.append(p) + + return conv_weights, bn_weights, relu_weights + + def forward(self, x): + H, W = x.size()[2:] + + x = self.init_block(x) + + x1 = self.block1_1(x) + x1 = self.block1_2(x1) + x1 = self.block1_3(x1) + + x2 = self.block2_1(x1) + x2 = self.block2_2(x2) + x2 = self.block2_3(x2) + x2 = self.block2_4(x2) + + x3 = self.block3_1(x2) + x3 = self.block3_2(x3) + x3 = self.block3_3(x3) + x3 = self.block3_4(x3) + + x4 = self.block4_1(x3) + x4 = self.block4_2(x4) + x4 = self.block4_3(x4) + x4 = self.block4_4(x4) + + x_fuses = [] + if self.sa and self.dil is not None: + for i, xi in enumerate([x1, x2, x3, x4]): + x_fuses.append(self.attentions[i](self.dilations[i](xi))) + elif self.sa: + for i, xi in enumerate([x1, x2, x3, x4]): + x_fuses.append(self.attentions[i](xi)) + elif self.dil is not None: + for i, xi in enumerate([x1, x2, x3, x4]): + x_fuses.append(self.dilations[i](xi)) + else: + x_fuses = [x1, x2, x3, x4] + + e1 = self.conv_reduces[0](x_fuses[0]) + e1 = F.interpolate(e1, (H, W), mode="bilinear", align_corners=False) + + e2 = self.conv_reduces[1](x_fuses[1]) + e2 = F.interpolate(e2, (H, W), mode="bilinear", align_corners=False) + + e3 = self.conv_reduces[2](x_fuses[2]) + e3 = F.interpolate(e3, (H, W), mode="bilinear", align_corners=False) + + e4 = self.conv_reduces[3](x_fuses[3]) + e4 = F.interpolate(e4, (H, W), mode="bilinear", align_corners=False) + + outputs = [e1, e2, e3, e4] + + output = self.classifier(torch.cat(outputs, dim=1)) + #if not self.training: + # return torch.sigmoid(output) + + outputs.append(output) + outputs = [torch.sigmoid(r) for r in outputs] + return outputs + +def config_model(model): + model_options = list(nets.keys()) + assert model in model_options, \ + 'unrecognized model, please choose from %s' % str(model_options) + + # print(str(nets[model])) + + pdcs = [] + for i in range(16): + layer_name = 'layer%d' % i + op = nets[model][layer_name] + pdcs.append(createConvFunc(op)) + + return pdcs + +def pidinet(): + pdcs = config_model('carv4') + dil = 24 #if args.dil else None + return PiDiNet(60, pdcs, dil=dil, sa=True) + + +if __name__ == '__main__': + model = pidinet() + ckp = torch.load('table5_pidinet.pth')['state_dict'] + model.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()}) + im = cv2.imread('examples/test_my/cat_v4.png') + im = img2tensor(im).unsqueeze(0)/255. + res = model(im)[-1] + res = res>0.5 + res = res.float() + res = (res[0,0].cpu().data.numpy()*255.).astype(np.uint8) + print(res.shape) + cv2.imwrite('edge.png', res) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/processor.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/processor.py new file mode 100644 index 00000000..e8e087e2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/processor.py @@ -0,0 +1,147 @@ +""" +This file contains a Processor that can be used to process images with controlnet aux processors +""" +import io +import logging +from typing import Dict, Optional, Union + +from PIL import Image + +from custom_controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector, + LeresDetector, LineartAnimeDetector, + LineartDetector, MediapipeFaceDetector, + MidasDetector, MLSDdetector, NormalBaeDetector, + OpenposeDetector, PidiNetDetector, ZoeDetector, TileDetector) + +LOGGER = logging.getLogger(__name__) + + +MODELS = { + # checkpoint models + 'scribble_hed': {'class': HEDdetector, 'checkpoint': True}, + 'softedge_hed': {'class': HEDdetector, 'checkpoint': True}, + 'scribble_hedsafe': {'class': HEDdetector, 'checkpoint': True}, + 'softedge_hedsafe': {'class': HEDdetector, 'checkpoint': True}, + 'depth_midas': {'class': MidasDetector, 'checkpoint': True}, + 'mlsd': {'class': MLSDdetector, 'checkpoint': True}, + 'openpose': {'class': OpenposeDetector, 'checkpoint': True}, + 'openpose_face': {'class': OpenposeDetector, 'checkpoint': True}, + 'openpose_faceonly': {'class': OpenposeDetector, 'checkpoint': True}, + 'openpose_full': {'class': OpenposeDetector, 'checkpoint': True}, + 'openpose_hand': {'class': OpenposeDetector, 'checkpoint': True}, + 'scribble_pidinet': {'class': PidiNetDetector, 'checkpoint': True}, + 'softedge_pidinet': {'class': PidiNetDetector, 'checkpoint': True}, + 'scribble_pidsafe': {'class': PidiNetDetector, 'checkpoint': True}, + 'softedge_pidsafe': {'class': PidiNetDetector, 'checkpoint': True}, + 'normal_bae': {'class': NormalBaeDetector, 'checkpoint': True}, + 'lineart_coarse': {'class': LineartDetector, 'checkpoint': True}, + 'lineart_realistic': {'class': LineartDetector, 'checkpoint': True}, + 'lineart_anime': {'class': LineartAnimeDetector, 'checkpoint': True}, + 'depth_zoe': {'class': ZoeDetector, 'checkpoint': True}, + 'depth_leres': {'class': LeresDetector, 'checkpoint': True}, + 'depth_leres++': {'class': LeresDetector, 'checkpoint': True}, + # instantiate + 'shuffle': {'class': ContentShuffleDetector, 'checkpoint': False}, + 'mediapipe_face': {'class': MediapipeFaceDetector, 'checkpoint': False}, + 'canny': {'class': CannyDetector, 'checkpoint': False}, + 'tile': {'class': TileDetector, 'checkpoint': False}, +} + + +MODEL_PARAMS = { + 'scribble_hed': {'scribble': True}, + 'softedge_hed': {'scribble': False}, + 'scribble_hedsafe': {'scribble': True, 'safe': True}, + 'softedge_hedsafe': {'scribble': False, 'safe': True}, + 'depth_midas': {}, + 'mlsd': {}, + 'openpose': {'include_body': True, 'include_hand': False, 'include_face': False}, + 'openpose_face': {'include_body': True, 'include_hand': False, 'include_face': True}, + 'openpose_faceonly': {'include_body': False, 'include_hand': False, 'include_face': True}, + 'openpose_full': {'include_body': True, 'include_hand': True, 'include_face': True}, + 'openpose_hand': {'include_body': False, 'include_hand': True, 'include_face': False}, + 'scribble_pidinet': {'safe': False, 'scribble': True}, + 'softedge_pidinet': {'safe': False, 'scribble': False}, + 'scribble_pidsafe': {'safe': True, 'scribble': True}, + 'softedge_pidsafe': {'safe': True, 'scribble': False}, + 'normal_bae': {}, + 'lineart_realistic': {'coarse': False}, + 'lineart_coarse': {'coarse': True}, + 'lineart_anime': {}, + 'canny': {}, + 'shuffle': {}, + 'depth_zoe': {}, + 'depth_leres': {'boost': False}, + 'depth_leres++': {'boost': True}, + 'mediapipe_face': {}, + 'tile': {}, +} + +CHOICES = f"Choices for the processor are {list(MODELS.keys())}" + + +class Processor: + def __init__(self, processor_id: str, params: Optional[Dict] = None) -> None: + """Processor that can be used to process images with controlnet aux processors + + Args: + processor_id (str): processor name, options are 'hed, midas, mlsd, openpose, + pidinet, normalbae, lineart, lineart_coarse, lineart_anime, + canny, content_shuffle, zoe, mediapipe_face, tile' + params (Optional[Dict]): parameters for the processor + """ + LOGGER.info("Loading %s".format(processor_id)) + + if processor_id not in MODELS: + raise ValueError(f"{processor_id} is not a valid processor id. Please make sure to choose one of {', '.join(MODELS.keys())}") + + self.processor_id = processor_id + self.processor = self.load_processor(self.processor_id) + + # load default params + self.params = MODEL_PARAMS[self.processor_id] + # update with user params + if params: + self.params.update(params) + + def load_processor(self, processor_id: str) -> 'Processor': + """Load controlnet aux processors + + Args: + processor_id (str): processor name + + Returns: + Processor: controlnet aux processor + """ + processor = MODELS[processor_id]['class'] + + # check if the proecssor is a checkpoint model + if MODELS[processor_id]['checkpoint']: + processor = processor.from_pretrained("lllyasviel/Annotators") + else: + processor = processor() + return processor + + def __call__(self, image: Union[Image.Image, bytes], + to_pil: bool = True) -> Union[Image.Image, bytes]: + """processes an image with a controlnet aux processor + + Args: + image (Union[Image.Image, bytes]): input image in bytes or PIL Image + to_pil (bool): whether to return bytes or PIL Image + + Returns: + Union[Image.Image, bytes]: processed image in bytes or PIL Image + """ + # check if bytes or PIL Image + if isinstance(image, bytes): + image = Image.open(io.BytesIO(image)).convert("RGB") + + processed_image = self.processor(image, **self.params) + + if to_pil: + return processed_image + else: + output_bytes = io.BytesIO() + processed_image.save(output_bytes, format='JPEG') + return output_bytes.getvalue() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pyracanny/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pyracanny/__init__.py new file mode 100644 index 00000000..c45193b8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/pyracanny/__init__.py @@ -0,0 +1,74 @@ +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import resize_image_with_pad, common_input_validate, HWC3 + +def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold): + assert isinstance(x, np.ndarray) + assert x.ndim == 2 and x.dtype == np.uint8 + + y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold)) + y = y.astype(np.float32) / 255.0 + return y + +def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): + assert isinstance(x, np.ndarray) + assert x.ndim == 3 and x.shape[2] == 3 + + result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)] + result = np.stack(result, axis=2) + return result + +def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): + assert isinstance(x, np.ndarray) + assert x.ndim == 3 and x.shape[2] == 3 + + H, W, C = x.shape + acc_edge = None + + for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: + Hs, Ws = int(H * k), int(W * k) + small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA) + edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold) + if acc_edge is None: + acc_edge = edge + else: + acc_edge = cv2.resize(acc_edge, (edge.shape[1], edge.shape[0]), interpolation=cv2.INTER_LINEAR) + acc_edge = acc_edge * 0.75 + edge * 0.25 + + return acc_edge + +def norm255(x, low=4, high=96): + assert isinstance(x, np.ndarray) + assert x.ndim == 2 and x.dtype == np.float32 + + v_min = np.percentile(x, low) + v_max = np.percentile(x, high) + + x -= v_min + x /= v_max - v_min + + return x * 255.0 + +def canny_pyramid(x, canny_low_threshold, canny_high_threshold): + # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions. + # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions. + + color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold) + result = np.sum(color_canny, axis=2) + + return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8) + +class PyraCannyDetector: + def __call__(self, input_image=None, low_threshold=100, high_threshold=200, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + detected_map = canny_pyramid(detected_map, low_threshold, high_threshold) + detected_map = HWC3(remove_pad(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/recolor/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/recolor/__init__.py new file mode 100644 index 00000000..274ce3a7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/recolor/__init__.py @@ -0,0 +1,39 @@ +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import resize_image_with_pad, common_input_validate, HWC3 + +#https://github.com/Mikubill/sd-webui-controlnet/blob/416c345072c9c2066101e225964e3986abe6945e/scripts/processor.py#L639 +def recolor_luminance(img, thr_a=1.0): + result = cv2.cvtColor(HWC3(img), cv2.COLOR_BGR2LAB) + result = result[:, :, 0].astype(np.float32) / 255.0 + result = result ** thr_a + result = (result * 255.0).clip(0, 255).astype(np.uint8) + result = cv2.cvtColor(result, cv2.COLOR_GRAY2RGB) + return result + + +def recolor_intensity(img, thr_a=1.0): + result = cv2.cvtColor(HWC3(img), cv2.COLOR_BGR2HSV) + result = result[:, :, 2].astype(np.float32) / 255.0 + result = result ** thr_a + result = (result * 255.0).clip(0, 255).astype(np.uint8) + result = cv2.cvtColor(result, cv2.COLOR_GRAY2RGB) + return result + +recolor_methods = { + "luminance": recolor_luminance, + "intensity": recolor_intensity +} + +class Recolorizer: + def __call__(self, input_image=None, mode="luminance", gamma_correction=1.0, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + assert mode in recolor_methods.keys() + detected_map = recolor_methods[mode](input_image, gamma_correction) + detected_map = HWC3(remove_pad(detected_map)) + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/__init__.py new file mode 100644 index 00000000..55f3cfde --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/__init__.py @@ -0,0 +1 @@ +from .sam import SamDetector \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/automatic_mask_generator.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/automatic_mask_generator.py new file mode 100644 index 00000000..d5a8c969 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/automatic_mask_generator.py @@ -0,0 +1,372 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torchvision.ops.boxes import batched_nms, box_area # type: ignore + +from typing import Any, Dict, List, Optional, Tuple + +from .modeling import Sam +from .predictor import SamPredictor +from .utils.amg import ( + MaskData, + area_from_rle, + batch_iterator, + batched_mask_to_box, + box_xyxy_to_xywh, + build_all_layer_point_grids, + calculate_stability_score, + coco_encode_rle, + generate_crop_boxes, + is_box_near_crop_edge, + mask_to_rle_pytorch, + remove_small_regions, + rle_to_mask, + uncrop_boxes_xyxy, + uncrop_masks, + uncrop_points, +) + + +class SamAutomaticMaskGenerator: + def __init__( + self, + model: Sam, + points_per_side: Optional[int] = 32, + points_per_batch: int = 64, + pred_iou_thresh: float = 0.88, + stability_score_thresh: float = 0.95, + stability_score_offset: float = 1.0, + box_nms_thresh: float = 0.7, + crop_n_layers: int = 0, + crop_nms_thresh: float = 0.7, + crop_overlap_ratio: float = 512 / 1500, + crop_n_points_downscale_factor: int = 1, + point_grids: Optional[List[np.ndarray]] = None, + min_mask_region_area: int = 0, + output_mode: str = "binary_mask", + ) -> None: + """ + Using a SAM model, generates masks for the entire image. + Generates a grid of point prompts over the image, then filters + low quality and duplicate masks. The default settings are chosen + for SAM with a ViT-H backbone. + + Arguments: + model (Sam): The SAM model to use for mask prediction. + points_per_side (int or None): The number of points to be sampled + along one side of the image. The total number of points is + points_per_side**2. If None, 'point_grids' must provide explicit + point sampling. + points_per_batch (int): Sets the number of points run simultaneously + by the model. Higher numbers may be faster but use more GPU memory. + pred_iou_thresh (float): A filtering threshold in [0,1], using the + model's predicted mask quality. + stability_score_thresh (float): A filtering threshold in [0,1], using + the stability of the mask under changes to the cutoff used to binarize + the model's mask predictions. + stability_score_offset (float): The amount to shift the cutoff when + calculated the stability score. + box_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks. + crop_n_layers (int): If >0, mask prediction will be run again on + crops of the image. Sets the number of layers to run, where each + layer has 2**i_layer number of image crops. + crop_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks between different crops. + crop_overlap_ratio (float): Sets the degree to which crops overlap. + In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + crop_n_points_downscale_factor (int): The number of points-per-side + sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + point_grids (list(np.ndarray) or None): A list over explicit grids + of points used for sampling, normalized to [0,1]. The nth grid in the + list is used in the nth crop layer. Exclusive with points_per_side. + min_mask_region_area (int): If >0, postprocessing will be applied + to remove disconnected regions and holes in masks with area smaller + than min_mask_region_area. Requires opencv. + output_mode (str): The form masks are returned in. Can be 'binary_mask', + 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. + For large resolutions, 'binary_mask' may consume large amounts of + memory. + """ + + assert (points_per_side is None) != ( + point_grids is None + ), "Exactly one of points_per_side or point_grid must be provided." + if points_per_side is not None: + self.point_grids = build_all_layer_point_grids( + points_per_side, + crop_n_layers, + crop_n_points_downscale_factor, + ) + elif point_grids is not None: + self.point_grids = point_grids + else: + raise ValueError("Can't have both points_per_side and point_grid be None.") + + assert output_mode in [ + "binary_mask", + "uncompressed_rle", + "coco_rle", + ], f"Unknown output_mode {output_mode}." + if output_mode == "coco_rle": + from pycocotools import mask as mask_utils # type: ignore # noqa: F401 + + if min_mask_region_area > 0: + import cv2 # type: ignore # noqa: F401 + + self.predictor = SamPredictor(model) + self.points_per_batch = points_per_batch + self.pred_iou_thresh = pred_iou_thresh + self.stability_score_thresh = stability_score_thresh + self.stability_score_offset = stability_score_offset + self.box_nms_thresh = box_nms_thresh + self.crop_n_layers = crop_n_layers + self.crop_nms_thresh = crop_nms_thresh + self.crop_overlap_ratio = crop_overlap_ratio + self.crop_n_points_downscale_factor = crop_n_points_downscale_factor + self.min_mask_region_area = min_mask_region_area + self.output_mode = output_mode + + @torch.no_grad() + def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: + """ + Generates masks for the given image. + + Arguments: + image (np.ndarray): The image to generate masks for, in HWC uint8 format. + + Returns: + list(dict(str, any)): A list over records for masks. Each record is + a dict containing the following keys: + segmentation (dict(str, any) or np.ndarray): The mask. If + output_mode='binary_mask', is an array of shape HW. Otherwise, + is a dictionary containing the RLE. + bbox (list(float)): The box around the mask, in XYWH format. + area (int): The area in pixels of the mask. + predicted_iou (float): The model's own prediction of the mask's + quality. This is filtered by the pred_iou_thresh parameter. + point_coords (list(list(float))): The point coordinates input + to the model to generate this mask. + stability_score (float): A measure of the mask's quality. This + is filtered on using the stability_score_thresh parameter. + crop_box (list(float)): The crop of the image used to generate + the mask, given in XYWH format. + """ + + # Generate masks + mask_data = self._generate_masks(image) + + # Filter small disconnected regions and holes in masks + if self.min_mask_region_area > 0: + mask_data = self.postprocess_small_regions( + mask_data, + self.min_mask_region_area, + max(self.box_nms_thresh, self.crop_nms_thresh), + ) + + # Encode masks + if self.output_mode == "coco_rle": + mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]] + elif self.output_mode == "binary_mask": + mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]] + else: + mask_data["segmentations"] = mask_data["rles"] + + # Write mask records + curr_anns = [] + for idx in range(len(mask_data["segmentations"])): + ann = { + "segmentation": mask_data["segmentations"][idx], + "area": area_from_rle(mask_data["rles"][idx]), + "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(), + "predicted_iou": mask_data["iou_preds"][idx].item(), + "point_coords": [mask_data["points"][idx].tolist()], + "stability_score": mask_data["stability_score"][idx].item(), + "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(), + } + curr_anns.append(ann) + + return curr_anns + + def _generate_masks(self, image: np.ndarray) -> MaskData: + orig_size = image.shape[:2] + crop_boxes, layer_idxs = generate_crop_boxes( + orig_size, self.crop_n_layers, self.crop_overlap_ratio + ) + + # Iterate over image crops + data = MaskData() + for crop_box, layer_idx in zip(crop_boxes, layer_idxs): + crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) + data.cat(crop_data) + + # Remove duplicate masks between crops + if len(crop_boxes) > 1: + # Prefer masks from smaller crops + scores = 1 / box_area(data["crop_boxes"]) + scores = scores.to(data["boxes"].device) + keep_by_nms = batched_nms( + data["boxes"].float(), + scores, + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.crop_nms_thresh, + ) + data.filter(keep_by_nms) + + data.to_numpy() + return data + + def _process_crop( + self, + image: np.ndarray, + crop_box: List[int], + crop_layer_idx: int, + orig_size: Tuple[int, ...], + ) -> MaskData: + # Crop the image and calculate embeddings + x0, y0, x1, y1 = crop_box + cropped_im = image[y0:y1, x0:x1, :] + cropped_im_size = cropped_im.shape[:2] + self.predictor.set_image(cropped_im) + + # Get points for this crop + points_scale = np.array(cropped_im_size)[None, ::-1] + points_for_image = self.point_grids[crop_layer_idx] * points_scale + + # Generate masks for this crop in batches + data = MaskData() + for (points,) in batch_iterator(self.points_per_batch, points_for_image): + batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size) + data.cat(batch_data) + del batch_data + self.predictor.reset_image() + + # Remove duplicates within this crop. + keep_by_nms = batched_nms( + data["boxes"].float(), + data["iou_preds"], + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.box_nms_thresh, + ) + data.filter(keep_by_nms) + + # Return to the original image frame + data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box) + data["points"] = uncrop_points(data["points"], crop_box) + data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))]) + + return data + + def _process_batch( + self, + points: np.ndarray, + im_size: Tuple[int, ...], + crop_box: List[int], + orig_size: Tuple[int, ...], + ) -> MaskData: + orig_h, orig_w = orig_size + + # Run model on this batch + transformed_points = self.predictor.transform.apply_coords(points, im_size) + in_points = torch.as_tensor(transformed_points, device=self.predictor.device) + in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device) + masks, iou_preds, _ = self.predictor.predict_torch( + in_points[:, None, :], + in_labels[:, None], + multimask_output=True, + return_logits=True, + ) + + # Serialize predictions and store in MaskData + data = MaskData( + masks=masks.flatten(0, 1), + iou_preds=iou_preds.flatten(0, 1), + points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)), + ) + del masks + + # Filter by predicted IoU + if self.pred_iou_thresh > 0.0: + keep_mask = data["iou_preds"] > self.pred_iou_thresh + data.filter(keep_mask) + + # Calculate stability score + data["stability_score"] = calculate_stability_score( + data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset + ) + if self.stability_score_thresh > 0.0: + keep_mask = data["stability_score"] >= self.stability_score_thresh + data.filter(keep_mask) + + # Threshold masks and calculate boxes + data["masks"] = data["masks"] > self.predictor.model.mask_threshold + data["boxes"] = batched_mask_to_box(data["masks"]) + + # Filter boxes that touch crop boundaries + keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h]) + if not torch.all(keep_mask): + data.filter(keep_mask) + + # Compress to RLE + data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) + data["rles"] = mask_to_rle_pytorch(data["masks"]) + del data["masks"] + + return data + + @staticmethod + def postprocess_small_regions( + mask_data: MaskData, min_area: int, nms_thresh: float + ) -> MaskData: + """ + Removes small disconnected regions and holes in masks, then reruns + box NMS to remove any new duplicates. + + Edits mask_data in place. + + Requires open-cv as a dependency. + """ + if len(mask_data["rles"]) == 0: + return mask_data + + # Filter small disconnected regions and holes + new_masks = [] + scores = [] + for rle in mask_data["rles"]: + mask = rle_to_mask(rle) + + mask, changed = remove_small_regions(mask, min_area, mode="holes") + unchanged = not changed + mask, changed = remove_small_regions(mask, min_area, mode="islands") + unchanged = unchanged and not changed + + new_masks.append(torch.as_tensor(mask).unsqueeze(0)) + # Give score=0 to changed masks and score=1 to unchanged masks + # so NMS will prefer ones that didn't need postprocessing + scores.append(float(unchanged)) + + # Recalculate boxes and remove any new duplicates + masks = torch.cat(new_masks, dim=0) + boxes = batched_mask_to_box(masks) + keep_by_nms = batched_nms( + boxes.float(), + torch.as_tensor(scores), + torch.zeros_like(boxes[:, 0]), # categories + iou_threshold=nms_thresh, + ) + + # Only recalculate RLEs for masks that have changed + for i_mask in keep_by_nms: + if scores[i_mask] == 0.0: + mask_torch = masks[i_mask].unsqueeze(0) + mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0] + mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly + mask_data.filter(keep_by_nms) + + return mask_data diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/__init__.py new file mode 100644 index 00000000..3ed986c5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from .sam import Sam +from .image_encoder import ImageEncoderViT +from .mask_decoder import MaskDecoder +from .prompt_encoder import PromptEncoder +from .transformer import TwoWayTransformer \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/common.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/common.py new file mode 100644 index 00000000..2bf15236 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/common.py @@ -0,0 +1,43 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from typing import Type + + +class MLPBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + mlp_dim: int, + act: Type[nn.Module] = nn.GELU, + ) -> None: + super().__init__() + self.lin1 = nn.Linear(embedding_dim, mlp_dim) + self.lin2 = nn.Linear(mlp_dim, embedding_dim) + self.act = act() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.lin2(self.act(self.lin1(x))) + + +# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa +# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/image_encoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/image_encoder.py new file mode 100644 index 00000000..66351d9d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/image_encoder.py @@ -0,0 +1,395 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from typing import Optional, Tuple, Type + +from .common import LayerNorm2d, MLPBlock + + +# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa +class ImageEncoderViT(nn.Module): + def __init__( + self, + img_size: int = 1024, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + depth: int = 12, + num_heads: int = 12, + mlp_ratio: float = 4.0, + out_chans: int = 256, + qkv_bias: bool = True, + norm_layer: Type[nn.Module] = nn.LayerNorm, + act_layer: Type[nn.Module] = nn.GELU, + use_abs_pos: bool = True, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + window_size: int = 0, + global_attn_indexes: Tuple[int, ...] = (), + ) -> None: + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + global_attn_indexes (list): Indexes for blocks using global attention. + """ + super().__init__() + self.img_size = img_size + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + self.pos_embed: Optional[nn.Parameter] = None + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim) + ) + + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + act_layer=act_layer, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + window_size=window_size if i not in global_attn_indexes else 0, + input_size=(img_size // patch_size, img_size // patch_size), + ) + self.blocks.append(block) + + self.neck = nn.Sequential( + nn.Conv2d( + embed_dim, + out_chans, + kernel_size=1, + bias=False, + ), + LayerNorm2d(out_chans), + nn.Conv2d( + out_chans, + out_chans, + kernel_size=3, + padding=1, + bias=False, + ), + LayerNorm2d(out_chans), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + self.pos_embed + + for blk in self.blocks: + x = blk(x) + + x = self.neck(x.permute(0, 3, 1, 2)) + + return x + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = True, + norm_layer: Type[nn.Module] = nn.LayerNorm, + act_layer: Type[nn.Module] = nn.GELU, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + window_size: int = 0, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then + use global attention. + input_size (tuple(int, int) or None): Input resolution for calculating the relative + positional parameter size. + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + input_size=input_size if window_size == 0 else (window_size, window_size), + ) + + self.norm2 = norm_layer(dim) + self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer) + + self.window_size = window_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + x + x = x + self.mlp(self.norm2(x)) + + return x + + +class Attention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = True, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + input_size (tuple(int, int) or None): Input resolution for calculating the relative + positional parameter size. + """ + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.use_rel_pos = use_rel_pos + if self.use_rel_pos: + assert ( + input_size is not None + ), "Input size must be provided if using relative positional encoding." + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + + attn = (q * self.scale) @ k.transpose(-2, -1) + + if self.use_rel_pos: + attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) + + attn = attn.softmax(dim=-1) + x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + x = self.proj(x) + + return x + + +def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]: + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition( + windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int] +) -> torch.Tensor: + """ + Window unpartition into original sequences and removing padding. + Args: + windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_rel_pos( + attn: torch.Tensor, + q: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: Tuple[int, int], + k_size: Tuple[int, int], +) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + attn (Tensor): attention map. + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, + kernel_size: Tuple[int, int] = (16, 16), + stride: Tuple[int, int] = (16, 16), + padding: Tuple[int, int] = (0, 0), + in_chans: int = 3, + embed_dim: int = 768, + ) -> None: + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + """ + super().__init__() + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/mask_decoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/mask_decoder.py new file mode 100644 index 00000000..5d2fdb03 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/mask_decoder.py @@ -0,0 +1,176 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn +from torch.nn import functional as F + +from typing import List, Tuple, Type + +from .common import LayerNorm2d + + +class MaskDecoder(nn.Module): + def __init__( + self, + *, + transformer_dim: int, + transformer: nn.Module, + num_multimask_outputs: int = 3, + activation: Type[nn.Module] = nn.GELU, + iou_head_depth: int = 3, + iou_head_hidden_dim: int = 256, + ) -> None: + """ + Predicts masks given an image and prompt embeddings, using a + transformer architecture. + + Arguments: + transformer_dim (int): the channel dimension of the transformer + transformer (nn.Module): the transformer used to predict masks + num_multimask_outputs (int): the number of masks to predict + when disambiguating masks + activation (nn.Module): the type of activation to use when + upscaling masks + iou_head_depth (int): the depth of the MLP used to predict + mask quality + iou_head_hidden_dim (int): the hidden dimension of the MLP + used to predict mask quality + """ + super().__init__() + self.transformer_dim = transformer_dim + self.transformer = transformer + + self.num_multimask_outputs = num_multimask_outputs + + self.iou_token = nn.Embedding(1, transformer_dim) + self.num_mask_tokens = num_multimask_outputs + 1 + self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) + + self.output_upscaling = nn.Sequential( + nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2), + LayerNorm2d(transformer_dim // 4), + activation(), + nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), + activation(), + ) + self.output_hypernetworks_mlps = nn.ModuleList( + [ + MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) + for i in range(self.num_mask_tokens) + ] + ) + + self.iou_prediction_head = MLP( + transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth + ) + + def forward( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Arguments: + image_embeddings (torch.Tensor): the embeddings from the image encoder + image_pe (torch.Tensor): positional encoding with the shape of image_embeddings + sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes + dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs + multimask_output (bool): Whether to return multiple masks or a single + mask. + + Returns: + torch.Tensor: batched predicted masks + torch.Tensor: batched predictions of mask quality + """ + masks, iou_pred = self.predict_masks( + image_embeddings=image_embeddings, + image_pe=image_pe, + sparse_prompt_embeddings=sparse_prompt_embeddings, + dense_prompt_embeddings=dense_prompt_embeddings, + ) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + else: + mask_slice = slice(0, 1) + masks = masks[:, mask_slice, :, :] + iou_pred = iou_pred[:, mask_slice] + + # Prepare output + return masks, iou_pred + + def predict_masks( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Predicts masks. See 'forward' for more details.""" + # Concatenate output tokens + output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0) + output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1) + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) + + # Expand per-image data in batch direction to be per-mask + src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) + src = src + dense_prompt_embeddings + pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) + b, c, h, w = src.shape + + # Run the transformer + hs, src = self.transformer(src, pos_src, tokens) + iou_token_out = hs[:, 0, :] + mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + src = src.transpose(1, 2).view(b, c, h, w) + upscaled_embedding = self.output_upscaling(src) + hyper_in_list: List[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])) + hyper_in = torch.stack(hyper_in_list, dim=1) + b, c, h, w = upscaled_embedding.shape + masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + + return masks, iou_pred + + +# Lightly adapted from +# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa +class MLP(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + sigmoid_output: bool = False, + ) -> None: + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + self.sigmoid_output = sigmoid_output + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + if self.sigmoid_output: + x = F.sigmoid(x) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/prompt_encoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/prompt_encoder.py new file mode 100644 index 00000000..c3143f4f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/prompt_encoder.py @@ -0,0 +1,214 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torch import nn + +from typing import Any, Optional, Tuple, Type + +from .common import LayerNorm2d + + +class PromptEncoder(nn.Module): + def __init__( + self, + embed_dim: int, + image_embedding_size: Tuple[int, int], + input_image_size: Tuple[int, int], + mask_in_chans: int, + activation: Type[nn.Module] = nn.GELU, + ) -> None: + """ + Encodes prompts for input to SAM's mask decoder. + + Arguments: + embed_dim (int): The prompts' embedding dimension + image_embedding_size (tuple(int, int)): The spatial size of the + image embedding, as (H, W). + input_image_size (int): The padded size of the image as input + to the image encoder, as (H, W). + mask_in_chans (int): The number of hidden channels used for + encoding input masks. + activation (nn.Module): The activation to use when encoding + input masks. + """ + super().__init__() + self.embed_dim = embed_dim + self.input_image_size = input_image_size + self.image_embedding_size = image_embedding_size + self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) + + self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners + point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)] + self.point_embeddings = nn.ModuleList(point_embeddings) + self.not_a_point_embed = nn.Embedding(1, embed_dim) + + self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1]) + self.mask_downscaling = nn.Sequential( + nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans // 4), + activation(), + nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans), + activation(), + nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), + ) + self.no_mask_embed = nn.Embedding(1, embed_dim) + + def get_dense_pe(self) -> torch.Tensor: + """ + Returns the positional encoding used to encode point prompts, + applied to a dense set of points the shape of the image encoding. + + Returns: + torch.Tensor: Positional encoding with shape + 1x(embed_dim)x(embedding_h)x(embedding_w) + """ + return self.pe_layer(self.image_embedding_size).unsqueeze(0) + + def _embed_points( + self, + points: torch.Tensor, + labels: torch.Tensor, + pad: bool, + ) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) + padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) + points = torch.cat([points, padding_point], dim=1) + labels = torch.cat([labels, padding_label], dim=1) + point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size) + point_embedding[labels == -1] = 0.0 + point_embedding[labels == -1] += self.not_a_point_embed.weight + point_embedding[labels == 0] += self.point_embeddings[0].weight + point_embedding[labels == 1] += self.point_embeddings[1].weight + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.reshape(-1, 2, 2) + corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size) + corner_embedding[:, 0, :] += self.point_embeddings[2].weight + corner_embedding[:, 1, :] += self.point_embeddings[3].weight + return corner_embedding + + def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: + """Embeds mask inputs.""" + mask_embedding = self.mask_downscaling(masks) + return mask_embedding + + def _get_batch_size( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> int: + """ + Gets the batch size of the output given the batch size of the input prompts. + """ + if points is not None: + return points[0].shape[0] + elif boxes is not None: + return boxes.shape[0] + elif masks is not None: + return masks.shape[0] + else: + return 1 + + def _get_device(self) -> torch.device: + return self.point_embeddings[0].weight.device + + def forward( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense + embeddings. + + Arguments: + points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates + and labels to embed. + boxes (torch.Tensor or none): boxes to embed + masks (torch.Tensor or none): masks to embed + + Returns: + torch.Tensor: sparse embeddings for the points and boxes, with shape + BxNx(embed_dim), where N is determined by the number of input points + and boxes. + torch.Tensor: dense embeddings for the masks, in the shape + Bx(embed_dim)x(embed_H)x(embed_W) + """ + bs = self._get_batch_size(points, boxes, masks) + sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device()) + if points is not None: + coords, labels = points + point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) + sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) + if boxes is not None: + box_embeddings = self._embed_boxes(boxes) + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) + + if masks is not None: + dense_embeddings = self._embed_masks(masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +class PositionEmbeddingRandom(nn.Module): + """ + Positional encoding using random spatial frequencies. + """ + + def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: + super().__init__() + if scale is None or scale <= 0.0: + scale = 1.0 + self.register_buffer( + "positional_encoding_gaussian_matrix", + scale * torch.randn((2, num_pos_feats)), + ) + + def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: + """Positionally encode points that are normalized to [0,1].""" + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coords = 2 * coords - 1 + coords = coords @ self.positional_encoding_gaussian_matrix + coords = 2 * np.pi * coords + # outputs d_1 x ... x d_n x C shape + return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) + + def forward(self, size: Tuple[int, int]) -> torch.Tensor: + """Generate positional encoding for a grid of the specified size.""" + h, w = size + device: Any = self.positional_encoding_gaussian_matrix.device + grid = torch.ones((h, w), device=device, dtype=torch.float32) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / h + x_embed = x_embed / w + + pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) + return pe.permute(2, 0, 1) # C x H x W + + def forward_with_coords( + self, coords_input: torch.Tensor, image_size: Tuple[int, int] + ) -> torch.Tensor: + """Positionally encode points that are not normalized to [0,1].""" + coords = coords_input.clone() + coords[:, :, 0] = coords[:, :, 0] / image_size[1] + coords[:, :, 1] = coords[:, :, 1] / image_size[0] + return self._pe_encoding(coords.to(torch.float)) # B x N x C diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/sam.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/sam.py new file mode 100644 index 00000000..7621a038 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/sam.py @@ -0,0 +1,172 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn +from torch.nn import functional as F + +from typing import Any, Dict, List, Tuple + +from .image_encoder import ImageEncoderViT +from .mask_decoder import MaskDecoder +from .prompt_encoder import PromptEncoder + + +class Sam(nn.Module): + mask_threshold: float = 0.0 + image_format: str = "RGB" + + def __init__( + self, + image_encoder: ImageEncoderViT, + prompt_encoder: PromptEncoder, + mask_decoder: MaskDecoder, + pixel_mean: List[float] = [123.675, 116.28, 103.53], + pixel_std: List[float] = [58.395, 57.12, 57.375], + ) -> None: + """ + SAM predicts object masks from an image and input prompts. + + Arguments: + image_encoder (ImageEncoderViT): The backbone used to encode the + image into image embeddings that allow for efficient mask prediction. + prompt_encoder (PromptEncoder): Encodes various types of input prompts. + mask_decoder (MaskDecoder): Predicts masks from the image embeddings + and encoded prompts. + pixel_mean (list(float)): Mean values for normalizing pixels in the input image. + pixel_std (list(float)): Std values for normalizing pixels in the input image. + """ + super().__init__() + self.image_encoder = image_encoder + self.prompt_encoder = prompt_encoder + self.mask_decoder = mask_decoder + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + + @property + def device(self) -> Any: + return self.pixel_mean.device + + def forward( + self, + batched_input: List[Dict[str, Any]], + multimask_output: bool, + ) -> List[Dict[str, torch.Tensor]]: + """ + Predicts masks end-to-end from provided images and prompts. + If prompts are not known in advance, using SamPredictor is + recommended over calling the model directly. + + Arguments: + batched_input (list(dict)): A list over input images, each a + dictionary with the following keys. A prompt key can be + excluded if not known in advance. + 'image': The image as a torch tensor in 3xHxW format, + already transformed for input to the model. + 'original_size': (tuple(int, int)) The original size of + the image before transformation, as (H, W). + 'point_coords': (torch.Tensor) Batched point prompts for + this image, with shape BxNx2. Already transformed to the + input frame of the model. + 'point_labels': (torch.Tensor) Batched labels for point prompts, + with shape BxN. + 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. + Already transformed to the input frame of the model. + 'mask_inputs': (torch.Tensor) Batched mask inputs to the model, + in the form Bx1xHxW. + multimask_output (bool): Whether the model should predict multiple + disambiguating masks, or return a single mask. + + Returns: + (list(dict)): A list over input images, where each element is + as dictionary with the following keys. + 'masks': (torch.Tensor) Batched binary mask predictions, + with shape BxCxHxW, where B is the number of input prompts, + C is determined by multimask_output, and (H, W) is the + original size of the image. + 'iou_predictions': (torch.Tensor) The model's predictions + of mask quality, in shape BxC. + 'low_res_logits': (torch.Tensor) Low resolution logits with + shape BxCxHxW, where H=W=256. Can be passed as mask input + to subsequent iterations of prediction. + """ + input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0) + image_embeddings = self.image_encoder(input_images) + + outputs = [] + for image_record, curr_embedding in zip(batched_input, image_embeddings): + if "point_coords" in image_record: + points = (image_record["point_coords"], image_record["point_labels"]) + else: + points = None + sparse_embeddings, dense_embeddings = self.prompt_encoder( + points=points, + boxes=image_record.get("boxes", None), + masks=image_record.get("mask_inputs", None), + ) + low_res_masks, iou_predictions = self.mask_decoder( + image_embeddings=curr_embedding.unsqueeze(0), + image_pe=self.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + ) + masks = self.postprocess_masks( + low_res_masks, + input_size=image_record["image"].shape[-2:], + original_size=image_record["original_size"], + ) + outputs.append( + { + "masks": masks, + "iou_predictions": iou_predictions, + "low_res_logits": low_res_masks, + } + ) + return outputs + + def postprocess_masks( + self, + masks: torch.Tensor, + input_size: Tuple[int, ...], + original_size: Tuple[int, ...], + ) -> torch.Tensor: + """ + Remove padding and upscale masks to the original image size. + + Arguments: + masks (torch.Tensor): Batched masks from the mask_decoder, + in BxCxHxW format. + input_size (tuple(int, int)): The size of the image input to the + model, in (H, W) format. Used to remove padding. + original_size (tuple(int, int)): The original size of the image + before resizing for input to the model, in (H, W) format. + + Returns: + (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) + is given by original_size. + """ + masks = F.interpolate( + masks, + (self.image_encoder.img_size, self.image_encoder.img_size), + mode="bilinear", + align_corners=False, + ) + masks = masks[..., : input_size[0], : input_size[1]] + masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False) + return masks + + def preprocess(self, x: torch.Tensor) -> torch.Tensor: + """Normalize pixel values and pad to a square input.""" + # Normalize colors + x = (x - self.pixel_mean) / self.pixel_std + + # Pad + h, w = x.shape[-2:] + padh = self.image_encoder.img_size - h + padw = self.image_encoder.img_size - w + x = F.pad(x, (0, padw, 0, padh)) + return x \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/transformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/transformer.py new file mode 100644 index 00000000..28fafea5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/modeling/transformer.py @@ -0,0 +1,240 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor, nn + +import math +from typing import Tuple, Type + +from .common import MLPBlock + + +class TwoWayTransformer(nn.Module): + def __init__( + self, + depth: int, + embedding_dim: int, + num_heads: int, + mlp_dim: int, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + ) -> None: + """ + A transformer decoder that attends to an input image using + queries whose positional embedding is supplied. + + Args: + depth (int): number of layers in the transformer + embedding_dim (int): the channel dimension for the input embeddings + num_heads (int): the number of heads for multihead attention. Must + divide embedding_dim + mlp_dim (int): the channel dimension internal to the MLP block + activation (nn.Module): the activation to use in the MLP block + """ + super().__init__() + self.depth = depth + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.mlp_dim = mlp_dim + self.layers = nn.ModuleList() + + for i in range(depth): + self.layers.append( + TwoWayAttentionBlock( + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_dim=mlp_dim, + activation=activation, + attention_downsample_rate=attention_downsample_rate, + skip_first_layer_pe=(i == 0), + ) + ) + + self.final_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm_final_attn = nn.LayerNorm(embedding_dim) + + def forward( + self, + image_embedding: Tensor, + image_pe: Tensor, + point_embedding: Tensor, + ) -> Tuple[Tensor, Tensor]: + """ + Args: + image_embedding (torch.Tensor): image to attend to. Should be shape + B x embedding_dim x h x w for any h and w. + image_pe (torch.Tensor): the positional encoding to add to the image. Must + have the same shape as image_embedding. + point_embedding (torch.Tensor): the embedding to add to the query points. + Must have shape B x N_points x embedding_dim for any N_points. + + Returns: + torch.Tensor: the processed point_embedding + torch.Tensor: the processed image_embedding + """ + # BxCxHxW -> BxHWxC == B x N_image_tokens x C + bs, c, h, w = image_embedding.shape + image_embedding = image_embedding.flatten(2).permute(0, 2, 1) + image_pe = image_pe.flatten(2).permute(0, 2, 1) + + # Prepare queries + queries = point_embedding + keys = image_embedding + + # Apply transformer blocks and final layernorm + for layer in self.layers: + queries, keys = layer( + queries=queries, + keys=keys, + query_pe=point_embedding, + key_pe=image_pe, + ) + + # Apply the final attention layer from the points to the image + q = queries + point_embedding + k = keys + image_pe + attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm_final_attn(queries) + + return queries, keys + + +class TwoWayAttentionBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + num_heads: int, + mlp_dim: int = 2048, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + skip_first_layer_pe: bool = False, + ) -> None: + """ + A transformer block with four layers: (1) self-attention of sparse + inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp + block on sparse inputs, and (4) cross attention of dense inputs to sparse + inputs. + + Arguments: + embedding_dim (int): the channel dimension of the embeddings + num_heads (int): the number of heads in the attention layers + mlp_dim (int): the hidden dimension of the mlp block + activation (nn.Module): the activation of the mlp block + skip_first_layer_pe (bool): skip the PE on the first layer + """ + super().__init__() + self.self_attn = Attention(embedding_dim, num_heads) + self.norm1 = nn.LayerNorm(embedding_dim) + + self.cross_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm2 = nn.LayerNorm(embedding_dim) + + self.mlp = MLPBlock(embedding_dim, mlp_dim, activation) + self.norm3 = nn.LayerNorm(embedding_dim) + + self.norm4 = nn.LayerNorm(embedding_dim) + self.cross_attn_image_to_token = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor + ) -> Tuple[Tensor, Tensor]: + # Self attention block + if self.skip_first_layer_pe: + queries = self.self_attn(q=queries, k=queries, v=queries) + else: + q = queries + query_pe + attn_out = self.self_attn(q=q, k=q, v=queries) + queries = queries + attn_out + queries = self.norm1(queries) + + # Cross attention block, tokens attending to image embedding + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.norm3(queries) + + # Cross attention block, image embedding attending to tokens + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries) + keys = keys + attn_out + keys = self.norm4(keys) + + return queries, keys + + +class Attention(nn.Module): + """ + An attention layer that allows for downscaling the size of the embedding + after projection to queries, keys, and values. + """ + + def __init__( + self, + embedding_dim: int, + num_heads: int, + downsample_rate: int = 1, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.internal_dim = embedding_dim // downsample_rate + self.num_heads = num_heads + assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim." + + self.q_proj = nn.Linear(embedding_dim, self.internal_dim) + self.k_proj = nn.Linear(embedding_dim, self.internal_dim) + self.v_proj = nn.Linear(embedding_dim, self.internal_dim) + self.out_proj = nn.Linear(self.internal_dim, embedding_dim) + + def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor: + b, n, c = x.shape + x = x.reshape(b, n, num_heads, c // num_heads) + return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head + + def _recombine_heads(self, x: Tensor) -> Tensor: + b, n_heads, n_tokens, c_per_head = x.shape + x = x.transpose(1, 2) + return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: + # Input projections + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + + # Separate into heads + q = self._separate_heads(q, self.num_heads) + k = self._separate_heads(k, self.num_heads) + v = self._separate_heads(v, self.num_heads) + + # Attention + _, _, _, c_per_head = q.shape + attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens + attn = attn / math.sqrt(c_per_head) + attn = torch.softmax(attn, dim=-1) + + # Get output + out = attn @ v + out = self._recombine_heads(out) + out = self.out_proj(out) + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/predictor.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/predictor.py new file mode 100644 index 00000000..a3820fb7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/predictor.py @@ -0,0 +1,269 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch + +from .modeling import Sam + +from typing import Optional, Tuple + +from .utils.transforms import ResizeLongestSide + + +class SamPredictor: + def __init__( + self, + sam_model: Sam, + ) -> None: + """ + Uses SAM to calculate the image embedding for an image, and then + allow repeated, efficient mask prediction given prompts. + + Arguments: + sam_model (Sam): The model to use for mask prediction. + """ + super().__init__() + self.model = sam_model + self.transform = ResizeLongestSide(sam_model.image_encoder.img_size) + self.reset_image() + + def set_image( + self, + image: np.ndarray, + image_format: str = "RGB", + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. + + Arguments: + image (np.ndarray): The image for calculating masks. Expects an + image in HWC uint8 format, with pixel values in [0, 255]. + image_format (str): The color format of the image, in ['RGB', 'BGR']. + """ + assert image_format in [ + "RGB", + "BGR", + ], f"image_format must be in ['RGB', 'BGR'], is {image_format}." + if image_format != self.model.image_format: + image = image[..., ::-1] + + # Transform the image to the form expected by the model + input_image = self.transform.apply_image(image) + input_image_torch = torch.as_tensor(input_image, device=self.device) + input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :] + + self.set_torch_image(input_image_torch, image.shape[:2]) + + @torch.no_grad() + def set_torch_image( + self, + transformed_image: torch.Tensor, + original_image_size: Tuple[int, ...], + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. Expects the input + image to be already transformed to the format expected by the model. + + Arguments: + transformed_image (torch.Tensor): The input image, with shape + 1x3xHxW, which has been transformed with ResizeLongestSide. + original_image_size (tuple(int, int)): The size of the image + before transformation, in (H, W) format. + """ + assert ( + len(transformed_image.shape) == 4 + and transformed_image.shape[1] == 3 + and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size + ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}." + self.reset_image() + + self.original_size = original_image_size + self.input_size = tuple(transformed_image.shape[-2:]) + input_image = self.model.preprocess(transformed_image) + self.features = self.model.image_encoder(input_image) + self.is_image_set = True + + def predict( + self, + point_coords: Optional[np.ndarray] = None, + point_labels: Optional[np.ndarray] = None, + box: Optional[np.ndarray] = None, + mask_input: Optional[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Predict masks for the given input prompts, using the currently set image. + + Arguments: + point_coords (np.ndarray or None): A Nx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (np.ndarray or None): A length N array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + box (np.ndarray or None): A length 4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form 1xHxW, where + for SAM, H=W=256. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (np.ndarray): The output masks in CxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (np.ndarray): An array of length C containing the model's + predictions for the quality of each mask. + (np.ndarray): An array of shape CxHxW, where C is the number + of masks and H=W=256. These low resolution logits can be passed to + a subsequent iteration as mask input. + """ + if not self.is_image_set: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + + # Transform input prompts + coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None + if point_coords is not None: + assert ( + point_labels is not None + ), "point_labels must be supplied if point_coords is supplied." + point_coords = self.transform.apply_coords(point_coords, self.original_size) + coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device) + labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device) + coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] + if box is not None: + box = self.transform.apply_boxes(box, self.original_size) + box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device) + box_torch = box_torch[None, :] + if mask_input is not None: + mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device) + mask_input_torch = mask_input_torch[None, :, :, :] + + masks, iou_predictions, low_res_masks = self.predict_torch( + coords_torch, + labels_torch, + box_torch, + mask_input_torch, + multimask_output, + return_logits=return_logits, + ) + + masks_np = masks[0].detach().cpu().numpy() + iou_predictions_np = iou_predictions[0].detach().cpu().numpy() + low_res_masks_np = low_res_masks[0].detach().cpu().numpy() + return masks_np, iou_predictions_np, low_res_masks_np + + @torch.no_grad() + def predict_torch( + self, + point_coords: Optional[torch.Tensor], + point_labels: Optional[torch.Tensor], + boxes: Optional[torch.Tensor] = None, + mask_input: Optional[torch.Tensor] = None, + multimask_output: bool = True, + return_logits: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks for the given input prompts, using the currently set image. + Input prompts are batched torch tensors and are expected to already be + transformed to the input frame using ResizeLongestSide. + + Arguments: + point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (torch.Tensor or None): A BxN array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + boxes (np.ndarray or None): A Bx4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form Bx1xHxW, where + for SAM, H=W=256. Masks returned by a previous iteration of the + predict method do not need further transformation. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (torch.Tensor): The output masks in BxCxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (torch.Tensor): An array of shape BxC containing the model's + predictions for the quality of each mask. + (torch.Tensor): An array of shape BxCxHxW, where C is the number + of masks and H=W=256. These low res logits can be passed to + a subsequent iteration as mask input. + """ + if not self.is_image_set: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + + if point_coords is not None: + points = (point_coords, point_labels) + else: + points = None + + # Embed prompts + sparse_embeddings, dense_embeddings = self.model.prompt_encoder( + points=points, + boxes=boxes, + masks=mask_input, + ) + + # Predict masks + low_res_masks, iou_predictions = self.model.mask_decoder( + image_embeddings=self.features, + image_pe=self.model.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + ) + + # Upscale the masks to the original image resolution + masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size) + + if not return_logits: + masks = masks > self.model.mask_threshold + + return masks, iou_predictions, low_res_masks + + def get_image_embedding(self) -> torch.Tensor: + """ + Returns the image embeddings for the currently set image, with + shape 1xCxHxW, where C is the embedding dimension and (H,W) are + the embedding spatial dimension of SAM (typically C=256, H=W=64). + """ + if not self.is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) to generate an embedding." + ) + assert self.features is not None, "Features must exist if an image has been set." + return self.features + + @property + def device(self) -> torch.device: + return self.model.device + + def reset_image(self) -> None: + """Resets the currently set image.""" + self.is_image_set = False + self.features = None + self.orig_h = None + self.orig_w = None + self.input_h = None + self.input_w = None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/sam.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/sam.py new file mode 100644 index 00000000..d7d0b5fc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/sam.py @@ -0,0 +1,171 @@ +""" +SAM implementation using HuggingFace transformers for PyTorch 2.7 compatibility. +""" +import numpy as np +import torch +from PIL import Image +from typing import Union + +# Import utilities +from ..util import HWC3, common_input_validate, resize_image_with_pad + + +class SamDetector: + + def __init__(self, model_name="facebook/sam-vit-base"): + from transformers import SamModel, SamProcessor + + self.model_name = model_name + self.processor = SamProcessor.from_pretrained(model_name) + self.model = SamModel.from_pretrained(model_name) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=None, model_type="vit_t", filename="mobile_sam.pt", subfolder=None): + model_mapping = { + "vit_t": "facebook/sam-vit-base", + "vit_b": "facebook/sam-vit-base", + "vit_l": "facebook/sam-vit-large", + "vit_h": "facebook/sam-vit-huge" + } + if filename and isinstance(filename, str): + if "mobile_sam" in filename.lower(): + model_name = "facebook/sam-vit-base" + elif "sam_vit_h" in filename.lower(): + model_name = "facebook/sam-vit-huge" + elif "sam_vit_l" in filename.lower(): + model_name = "facebook/sam-vit-large" + elif "sam_vit_b" in filename.lower(): + model_name = "facebook/sam-vit-base" + else: + model_name = model_mapping.get(model_type, "facebook/sam-vit-base") + else: + model_name = model_mapping.get(model_type, "facebook/sam-vit-base") + + return cls(model_name) + + def to(self, device): + self.model = self.model.to(device) + self.device = device + return self + + def generate_automatic_masks(self, input_image): + if isinstance(input_image, np.ndarray): + pil_image = Image.fromarray(input_image) + else: + pil_image = input_image + + height, width = pil_image.size[1], pil_image.size[0] + + points_per_side = max(8, min(24, width // 64, height // 64)) + + grid_points = [] + for i in range(points_per_side): + for j in range(points_per_side): + x = int((j + 0.5) * width / points_per_side) + y = int((i + 0.5) * height / points_per_side) + x_offset = int((np.random.random() - 0.5) * (width / points_per_side * 0.3)) + y_offset = int((np.random.random() - 0.5) * (height / points_per_side * 0.3)) + x = max(5, min(width - 5, x + x_offset)) + y = max(5, min(height - 5, y + y_offset)) + grid_points.append([x, y]) + + batch_size = 16 + all_masks = [] + + for i in range(0, len(grid_points), batch_size): + batch_points = grid_points[i:i + batch_size] + input_points = [batch_points] + + inputs = self.processor( + images=pil_image, + input_points=input_points, + return_tensors="pt" + ).to(self.device) + + with torch.no_grad(): + outputs = self.model(**inputs) + + masks = self.processor.post_process_masks( + outputs.pred_masks, + inputs["original_sizes"], + inputs["reshaped_input_sizes"] + )[0] + + masks_np = masks.cpu().numpy() + + for j, mask in enumerate(masks_np): + mask_2d = mask[0] if len(mask.shape) > 2 else mask + area = int(mask_2d.sum()) + + if area > 100: + cleaned_mask = self._postprocess_mask(mask_2d) + cleaned_area = int(cleaned_mask.sum()) + + mask_dict = { + 'segmentation': cleaned_mask, + 'area': cleaned_area, + 'stability_score': 0.88, + 'point_coords': batch_points[j % len(batch_points)] + } + all_masks.append(mask_dict) + + return all_masks + + def _postprocess_mask(self, mask, min_region_area=100): + from scipy import ndimage + from skimage import morphology + + binary_mask = mask.astype(bool) + + filled_mask = ndimage.binary_fill_holes(binary_mask) + + if filled_mask.any(): + kernel_close = morphology.disk(5) + kernel_open = morphology.disk(3) + + smoothed_mask = morphology.binary_closing(filled_mask, kernel_close) + smoothed_mask = morphology.binary_opening(smoothed_mask, kernel_open) + smoothed_mask = morphology.binary_closing(smoothed_mask, kernel_close) + else: + smoothed_mask = filled_mask + + return smoothed_mask.astype(mask.dtype) + + def show_anns(self, anns): + if len(anns) == 0: + return None + sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) + + h, w = anns[0]['segmentation'].shape + + final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB") + + for ann in sorted_anns: + m = ann['segmentation'] + + img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8) + for i in range(3): + img[:,:,i] = np.random.randint(255, dtype=np.uint8) + + final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m*255))) + + return np.array(final_img, dtype=np.uint8) + + def __call__(self, input_image: Union[np.ndarray, Image.Image]=None, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs) -> Image.Image: + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + masks = self.generate_automatic_masks(input_image) + + map = self.show_anns(masks) + + if map is None: + map = np.zeros((input_image.shape[0], input_image.shape[1], 3), dtype=np.uint8) + + detected_map = HWC3(remove_pad(map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/amg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/amg.py new file mode 100644 index 00000000..be064071 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/amg.py @@ -0,0 +1,346 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch + +import math +from copy import deepcopy +from itertools import product +from typing import Any, Dict, Generator, ItemsView, List, Tuple + + +class MaskData: + """ + A structure for storing masks and their related data in batched format. + Implements basic filtering and concatenation. + """ + + def __init__(self, **kwargs) -> None: + for v in kwargs.values(): + assert isinstance( + v, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats = dict(**kwargs) + + def __setitem__(self, key: str, item: Any) -> None: + assert isinstance( + item, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats[key] = item + + def __delitem__(self, key: str) -> None: + del self._stats[key] + + def __getitem__(self, key: str) -> Any: + return self._stats[key] + + def items(self) -> ItemsView[str, Any]: + return self._stats.items() + + def filter(self, keep: torch.Tensor) -> None: + for k, v in self._stats.items(): + if v is None: + self._stats[k] = None + elif isinstance(v, torch.Tensor): + self._stats[k] = v[torch.as_tensor(keep, device=v.device)] + elif isinstance(v, np.ndarray): + self._stats[k] = v[keep.detach().cpu().numpy()] + elif isinstance(v, list) and keep.dtype == torch.bool: + self._stats[k] = [a for i, a in enumerate(v) if keep[i]] + elif isinstance(v, list): + self._stats[k] = [v[i] for i in keep] + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def cat(self, new_stats: "MaskData") -> None: + for k, v in new_stats.items(): + if k not in self._stats or self._stats[k] is None: + self._stats[k] = deepcopy(v) + elif isinstance(v, torch.Tensor): + self._stats[k] = torch.cat([self._stats[k], v], dim=0) + elif isinstance(v, np.ndarray): + self._stats[k] = np.concatenate([self._stats[k], v], axis=0) + elif isinstance(v, list): + self._stats[k] = self._stats[k] + deepcopy(v) + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def to_numpy(self) -> None: + for k, v in self._stats.items(): + if isinstance(v, torch.Tensor): + self._stats[k] = v.detach().cpu().numpy() + + +def is_box_near_crop_edge( + boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0 +) -> torch.Tensor: + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + boxes = uncrop_boxes_xyxy(boxes, crop_box).float() + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: + box_xywh = deepcopy(box_xyxy) + box_xywh[2] = box_xywh[2] - box_xywh[0] + box_xywh[3] = box_xywh[3] - box_xywh[1] + return box_xywh + + +def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: + assert len(args) > 0 and all( + len(a) == len(args[0]) for a in args + ), "Batched iteration must have inputs of all the same size." + n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) + for b in range(n_batches): + yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args] + + +def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: + """ + Encodes masks to an uncompressed RLE, in the format expected by + pycoco tools. + """ + # Put in fortran order and flatten h,w + b, h, w = tensor.shape + tensor = tensor.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = tensor[:, 1:] ^ tensor[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(b): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + cur_idxs = torch.cat( + [ + torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), + cur_idxs + 1, + torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), + ] + ) + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if tensor[i, 0] == 0 else [0] + counts.extend(btw_idxs.detach().cpu().tolist()) + out.append({"size": [h, w], "counts": counts}) + return out + + +def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: + """Compute a binary mask from an uncompressed RLE.""" + h, w = rle["size"] + mask = np.empty(h * w, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity ^= True + mask = mask.reshape(w, h) + return mask.transpose() # Put in C order + + +def area_from_rle(rle: Dict[str, Any]) -> int: + return sum(rle["counts"][1::2]) + + +def calculate_stability_score( + masks: torch.Tensor, mask_threshold: float, threshold_offset: float +) -> torch.Tensor: + """ + Computes the stability score for a batch of masks. The stability + score is the IoU between the binary masks obtained by thresholding + the predicted mask logits at high and low values. + """ + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + unions = ( + (masks > (mask_threshold - threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + return intersections / unions + + +def build_point_grid(n_per_side: int) -> np.ndarray: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = np.linspace(offset, 1 - offset, n_per_side) + points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = np.tile(points_one_side[:, None], (1, n_per_side)) + points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) + return points + + +def build_all_layer_point_grids( + n_per_side: int, n_layers: int, scale_per_layer: int +) -> List[np.ndarray]: + """Generates point grids for all crop layers.""" + points_by_layer = [] + for i in range(n_layers + 1): + n_points = int(n_per_side / (scale_per_layer**i)) + points_by_layer.append(build_point_grid(n_points)) + return points_by_layer + + +def generate_crop_boxes( + im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float +) -> Tuple[List[List[int]], List[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer + has (2**i)**2 boxes for the ith layer. + """ + crop_boxes, layer_idxs = [], [] + im_h, im_w = im_size + short_side = min(im_h, im_w) + + # Original image + crop_boxes.append([0, 0, im_w, im_h]) + layer_idxs.append(0) + + def crop_len(orig_len, n_crops, overlap): + return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) + + for i_layer in range(n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_w = crop_len(im_w, n_crops_per_side, overlap) + crop_h = crop_len(im_h, n_crops_per_side, overlap) + + crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] + + # Crops in XYWH format + for x0, y0 in product(crop_box_x0, crop_box_y0): + box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + return boxes + offset + + +def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0]], device=points.device) + # Check if points has a channel dimension + if len(points.shape) == 3: + offset = offset.unsqueeze(1) + return points + offset + + +def uncrop_masks( + masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int +) -> torch.Tensor: + x0, y0, x1, y1 = crop_box + if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) + pad = (x0, pad_x - x0, y0, pad_y - y0) + return torch.nn.functional.pad(masks, pad, value=0) + + +def remove_small_regions( + mask: np.ndarray, area_thresh: float, mode: str +) -> Tuple[np.ndarray, bool]: + """ + Removes small disconnected regions and holes in a mask. Returns the + mask and an indicator of if the mask has been modified. + """ + import cv2 # type: ignore + + assert mode in ["holes", "islands"] + correct_holes = mode == "holes" + working_mask = (correct_holes ^ mask).astype(np.uint8) + n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) + sizes = stats[:, -1][1:] # Row 0 is background label + small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] + if len(small_regions) == 0: + return mask, False + fill_labels = [0] + small_regions + if not correct_holes: + fill_labels = [i for i in range(n_labels) if i not in fill_labels] + # If every region is below threshold, keep largest + if len(fill_labels) == 0: + fill_labels = [int(np.argmax(sizes)) + 1] + mask = np.isin(regions, fill_labels) + return mask, True + + +def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: + from pycocotools import mask as mask_utils # type: ignore + + h, w = uncompressed_rle["size"] + rle = mask_utils.frPyObjects(uncompressed_rle, h, w) + rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json + return rle + + +def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: + """ + Calculates boxes in XYXY format around masks. Return [0,0,0,0] for + an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. + """ + # torch.max below raises an error on empty inputs, just skip in this case + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to CxHxW + shape = masks.shape + h, w = shape[-2:] + if len(shape) > 2: + masks = masks.flatten(0, -3) + else: + masks = masks.unsqueeze(0) + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + h * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + w * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + if len(shape) > 2: + out = out.reshape(*shape[:-2], 4) + else: + out = out[0] + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/onnx.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/onnx.py new file mode 100644 index 00000000..3196bdf4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/onnx.py @@ -0,0 +1,144 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from torch.nn import functional as F + +from typing import Tuple + +from ..modeling import Sam +from .amg import calculate_stability_score + + +class SamOnnxModel(nn.Module): + """ + This model should not be called directly, but is used in ONNX export. + It combines the prompt encoder, mask decoder, and mask postprocessing of Sam, + with some functions modified to enable model tracing. Also supports extra + options controlling what information. See the ONNX export script for details. + """ + + def __init__( + self, + model: Sam, + return_single_mask: bool, + use_stability_score: bool = False, + return_extra_metrics: bool = False, + ) -> None: + super().__init__() + self.mask_decoder = model.mask_decoder + self.model = model + self.img_size = model.image_encoder.img_size + self.return_single_mask = return_single_mask + self.use_stability_score = use_stability_score + self.stability_score_offset = 1.0 + self.return_extra_metrics = return_extra_metrics + + @staticmethod + def resize_longest_image_size( + input_image_size: torch.Tensor, longest_side: int + ) -> torch.Tensor: + input_image_size = input_image_size.to(torch.float32) + scale = longest_side / torch.max(input_image_size) + transformed_size = scale * input_image_size + transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64) + return transformed_size + + def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor: + point_coords = point_coords + 0.5 + point_coords = point_coords / self.img_size + point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords) + point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding) + + point_embedding = point_embedding * (point_labels != -1) + point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * ( + point_labels == -1 + ) + + for i in range(self.model.prompt_encoder.num_point_embeddings): + point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[ + i + ].weight * (point_labels == i) + + return point_embedding + + def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor: + mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask) + mask_embedding = mask_embedding + ( + 1 - has_mask_input + ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1) + return mask_embedding + + def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor: + masks = F.interpolate( + masks, + size=(self.img_size, self.img_size), + mode="bilinear", + align_corners=False, + ) + + prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64) + masks = masks[..., : prepadded_size[0], : prepadded_size[1]] # type: ignore + + orig_im_size = orig_im_size.to(torch.int64) + h, w = orig_im_size[0], orig_im_size[1] + masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False) + return masks + + def select_masks( + self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Determine if we should return the multiclick mask or not from the number of points. + # The reweighting is used to avoid control flow. + score_reweight = torch.tensor( + [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)] + ).to(iou_preds.device) + score = iou_preds + (num_points - 2.5) * score_reweight + best_idx = torch.argmax(score, dim=1) + masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1) + iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1) + + return masks, iou_preds + + @torch.no_grad() + def forward( + self, + image_embeddings: torch.Tensor, + point_coords: torch.Tensor, + point_labels: torch.Tensor, + mask_input: torch.Tensor, + has_mask_input: torch.Tensor, + orig_im_size: torch.Tensor, + ): + sparse_embedding = self._embed_points(point_coords, point_labels) + dense_embedding = self._embed_masks(mask_input, has_mask_input) + + masks, scores = self.model.mask_decoder.predict_masks( + image_embeddings=image_embeddings, + image_pe=self.model.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embedding, + dense_prompt_embeddings=dense_embedding, + ) + + if self.use_stability_score: + scores = calculate_stability_score( + masks, self.model.mask_threshold, self.stability_score_offset + ) + + if self.return_single_mask: + masks, scores = self.select_masks(masks, scores, point_coords.shape[1]) + + upscaled_masks = self.mask_postprocessing(masks, orig_im_size) + + if self.return_extra_metrics: + stability_scores = calculate_stability_score( + upscaled_masks, self.model.mask_threshold, self.stability_score_offset + ) + areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1) + return upscaled_masks, scores, stability_scores, areas, masks + + return upscaled_masks, scores, masks diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/transforms.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/transforms.py new file mode 100644 index 00000000..c08ba1e3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/sam/utils/transforms.py @@ -0,0 +1,102 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torch.nn import functional as F +from torchvision.transforms.functional import resize, to_pil_image # type: ignore + +from copy import deepcopy +from typing import Tuple + + +class ResizeLongestSide: + """ + Resizes images to the longest side 'target_length', as well as provides + methods for resizing coordinates and boxes. Provides methods for + transforming both numpy array and batched torch tensors. + """ + + def __init__(self, target_length: int) -> None: + self.target_length = target_length + + def apply_image(self, image: np.ndarray) -> np.ndarray: + """ + Expects a numpy array with shape HxWxC in uint8 format. + """ + target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) + return np.array(resize(to_pil_image(image), target_size)) + + def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: + """ + Expects a numpy array of length 2 in the final dimension. Requires the + original image size in (H, W) format. + """ + old_h, old_w = original_size + new_h, new_w = self.get_preprocess_shape( + original_size[0], original_size[1], self.target_length + ) + coords = deepcopy(coords).astype(float) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + return coords + + def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: + """ + Expects a numpy array shape Bx4. Requires the original image size + in (H, W) format. + """ + boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size) + return boxes.reshape(-1, 4) + + def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor: + """ + Expects batched images with shape BxCxHxW and float format. This + transformation may not exactly match apply_image. apply_image is + the transformation expected by the model. + """ + # Expects an image in BCHW format. May not exactly match apply_image. + target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length) + return F.interpolate( + image, target_size, mode="bilinear", align_corners=False, antialias=True + ) + + def apply_coords_torch( + self, coords: torch.Tensor, original_size: Tuple[int, ...] + ) -> torch.Tensor: + """ + Expects a torch tensor with length 2 in the last dimension. Requires the + original image size in (H, W) format. + """ + old_h, old_w = original_size + new_h, new_w = self.get_preprocess_shape( + original_size[0], original_size[1], self.target_length + ) + coords = deepcopy(coords).to(torch.float) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + return coords + + def apply_boxes_torch( + self, boxes: torch.Tensor, original_size: Tuple[int, ...] + ) -> torch.Tensor: + """ + Expects a torch tensor with shape Bx4. Requires the original image + size in (H, W) format. + """ + boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size) + return boxes.reshape(-1, 4) + + @staticmethod + def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]: + """ + Compute the output size given input size and target long side length. + """ + scale = long_side_length * 1.0 / max(oldh, oldw) + newh, neww = oldh * scale, oldw * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return (newh, neww) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/scribble/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/scribble/__init__.py new file mode 100644 index 00000000..53bb98b5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/scribble/__init__.py @@ -0,0 +1,41 @@ +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, HWC3 + +#Not to be confused with "scribble" from HED. That is "fake scribble" which is more accurate and less picky than this. +class ScribbleDetector: + def __call__(self, input_image=None, detect_resolution=512, output_type=None, upscale_method="INTER_AREA", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + detected_map = np.zeros_like(input_image, dtype=np.uint8) + detected_map[np.min(input_image, axis=2) < 127] = 255 + detected_map = 255 - detected_map + + detected_map = remove_pad(detected_map) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + +class ScribbleXDog_Detector: + def __call__(self, input_image=None, detect_resolution=512, thr_a=32, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + g1 = cv2.GaussianBlur(input_image.astype(np.float32), (0, 0), 0.5) + g2 = cv2.GaussianBlur(input_image.astype(np.float32), (0, 0), 5.0) + dog = (255 - np.min(g2 - g1, axis=2)).clip(0, 255).astype(np.uint8) + result = np.zeros_like(input_image, dtype=np.uint8) + result[2 * (255 - dog) > thr_a] = 255 + #result = 255 - result + + detected_map = HWC3(remove_pad(result)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/shuffle/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/shuffle/__init__.py new file mode 100644 index 00000000..278db797 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/shuffle/__init__.py @@ -0,0 +1,87 @@ +import warnings + +import cv2 +import numpy as np +from PIL import Image +import random + +from custom_controlnet_aux.util import HWC3, common_input_validate, img2mask, make_noise_disk, resize_image_with_pad + + +class ContentShuffleDetector: + def __call__(self, input_image, h=None, w=None, f=None, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", seed=-1, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + H, W, C = input_image.shape + if h is None: + h = H + if w is None: + w = W + if f is None: + f = 256 + rng = np.random.default_rng(seed) if seed else None + x = make_noise_disk(h, w, 1, f, rng=rng) * float(W - 1) + y = make_noise_disk(h, w, 1, f, rng=rng) * float(H - 1) + flow = np.concatenate([x, y], axis=2).astype(np.float32) + detected_map = cv2.remap(input_image, flow, None, cv2.INTER_LINEAR) + detected_map = remove_pad(detected_map) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + + +class ColorShuffleDetector: + def __call__(self, img): + H, W, C = img.shape + F = np.random.randint(64, 384) + A = make_noise_disk(H, W, 3, F) + B = make_noise_disk(H, W, 3, F) + C = (A + B) / 2.0 + A = (C + (A - C) * 3.0).clip(0, 1) + B = (C + (B - C) * 3.0).clip(0, 1) + L = img.astype(np.float32) / 255.0 + Y = A * L + B * (1 - L) + Y -= np.min(Y, axis=(0, 1), keepdims=True) + Y /= np.maximum(np.max(Y, axis=(0, 1), keepdims=True), 1e-5) + Y *= 255.0 + return Y.clip(0, 255).astype(np.uint8) + + +class GrayDetector: + def __call__(self, img): + eps = 1e-5 + X = img.astype(np.float32) + r, g, b = X[:, :, 0], X[:, :, 1], X[:, :, 2] + kr, kg, kb = [random.random() + eps for _ in range(3)] + ks = kr + kg + kb + kr /= ks + kg /= ks + kb /= ks + Y = r * kr + g * kg + b * kb + Y = np.stack([Y] * 3, axis=2) + return Y.clip(0, 255).astype(np.uint8) + + +class DownSampleDetector: + def __call__(self, img, level=3, k=16.0): + h = img.astype(np.float32) + for _ in range(level): + h += np.random.normal(loc=0.0, scale=k, size=h.shape) + h = cv2.pyrDown(h) + for _ in range(level): + h = cv2.pyrUp(h) + h += np.random.normal(loc=0.0, scale=k, size=h.shape) + return h.clip(0, 255).astype(np.uint8) + + +class Image2MaskShuffleDetector: + def __init__(self, resolution=(640, 512)): + self.H, self.W = resolution + + def __call__(self, img): + m = img2mask(img, self.H, self.W) + m *= 255.0 + return m.clip(0, 255).astype(np.uint8) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Fmish.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Fmish.py new file mode 100644 index 00000000..40c867a2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Fmish.py @@ -0,0 +1,17 @@ +""" +Script provides functional interface for Mish activation function. +""" + +# import pytorch +import torch +import torch.nn.functional as F + + +@torch.jit.script +def mish(input): + """ + Applies the mish function element-wise: + mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) + See additional documentation for mish class. + """ + return input * torch.tanh(F.softplus(input)) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Fsmish.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Fsmish.py new file mode 100644 index 00000000..eb8c55ca --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Fsmish.py @@ -0,0 +1,20 @@ +""" +Script based on: +Wang, Xueliang, Honge Ren, and Achuan Wang. + "Smish: A Novel Activation Function for Deep Learning Methods. + " Electronics 11.4 (2022): 540. +""" + +# import pytorch +import torch +import torch.nn.functional as F + + +@torch.jit.script +def smish(input): + """ + Applies the mish function element-wise: + mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(sigmoid(x)))) + See additional documentation for mish class. + """ + return input * torch.tanh(torch.log(1+torch.sigmoid(input))) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/LICENSE.txt b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/LICENSE.txt new file mode 100644 index 00000000..4a99ffdd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Xavier Soria Poma + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Xmish.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Xmish.py new file mode 100644 index 00000000..15e84ed9 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Xmish.py @@ -0,0 +1,43 @@ +""" +Applies the mish function element-wise: +mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) +""" + +# import pytorch +import torch +import torch.nn.functional as F +from torch import nn + +# import activation functions +from .Fmish import mish + + +class Mish(nn.Module): + """ + Applies the mish function element-wise: + mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) + Shape: + - Input: (N, *) where * means, any number of additional + dimensions + - Output: (N, *), same shape as the input + Examples: + >>> m = Mish() + >>> input = torch.randn(2) + >>> output = m(input) + Reference: https://pytorch.org/docs/stable/generated/torch.nn.Mish.html + """ + + def __init__(self): + """ + Init method. + """ + super().__init__() + + def forward(self, input): + """ + Forward pass of the function. + """ + if torch.__version__ >= "1.9": + return F.mish(input) + else: + return mish(input) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Xsmish.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Xsmish.py new file mode 100644 index 00000000..df75bee4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/Xsmish.py @@ -0,0 +1,43 @@ +""" +Script based on: +Wang, Xueliang, Honge Ren, and Achuan Wang. + "Smish: A Novel Activation Function for Deep Learning Methods. + " Electronics 11.4 (2022): 540. +smish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + sigmoid(x))) +""" + +# import pytorch +import torch +import torch.nn.functional as F +from torch import nn + +# import activation functions +from .Fsmish import smish + + +class Smish(nn.Module): + """ + Applies the mish function element-wise: + mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) + Shape: + - Input: (N, *) where * means, any number of additional + dimensions + - Output: (N, *), same shape as the input + Examples: + >>> m = Mish() + >>> input = torch.randn(2) + >>> output = m(input) + Reference: https://pytorch.org/docs/stable/generated/torch.nn.Mish.html + """ + + def __init__(self): + """ + Init method. + """ + super().__init__() + + def forward(self, input): + """ + Forward pass of the function. + """ + return smish(input) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/__init__.py new file mode 100644 index 00000000..e08bb3f6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/__init__.py @@ -0,0 +1,58 @@ +""" +Hello, welcome on board, +""" +from __future__ import print_function + +import os +import cv2 +import numpy as np + +import torch + +from .ted import TED # TEED architecture +from einops import rearrange +from custom_controlnet_aux.util import safe_step, custom_hf_download, BDS_MODEL_NAME, common_input_validate, resize_image_with_pad, HWC3 +from PIL import Image + + +class TEDDetector: + def __init__(self, model): + self.model = model + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=BDS_MODEL_NAME, filename="7_model.pth", subfolder="Annotators"): + model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder=subfolder) + model = TED() + model.load_state_dict(torch.load(model_path, map_location='cpu')) + model.eval() + return cls(model) + + def to(self, device): + self.model.to(device) + self.device = device + return self + + + def __call__(self, input_image, detect_resolution=512, safe_steps=2, upscale_method="INTER_CUBIC", output_type="pil", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + H, W, _ = input_image.shape + with torch.no_grad(): + image_teed = torch.from_numpy(input_image.copy()).float().to(self.device) + image_teed = rearrange(image_teed, 'h w c -> 1 c h w') + edges = self.model(image_teed) + edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] + edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] + edges = np.stack(edges, axis=2) + edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) + if safe_steps != 0: + edge = safe_step(edge, safe_steps) + edge = (edge * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = remove_pad(HWC3(edge)) + if output_type == "pil": + detected_map = Image.fromarray(detected_map[..., :3]) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/ted.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/ted.py new file mode 100644 index 00000000..ff347d5a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/teed/ted.py @@ -0,0 +1,296 @@ +# TEED: is a Tiny but Efficient Edge Detection, it comes from the LDC-B3 +# with a Slightly modification +# LDC parameters: +# 155665 +# TED > 58K + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .Fsmish import smish as Fsmish +from .Xsmish import Smish + + +def weight_init(m): + if isinstance(m, (nn.Conv2d,)): + torch.nn.init.xavier_normal_(m.weight, gain=1.0) + + if m.bias is not None: + torch.nn.init.zeros_(m.bias) + + # for fusion layer + if isinstance(m, (nn.ConvTranspose2d,)): + torch.nn.init.xavier_normal_(m.weight, gain=1.0) + if m.bias is not None: + torch.nn.init.zeros_(m.bias) + +class CoFusion(nn.Module): + # from LDC + + def __init__(self, in_ch, out_ch): + super(CoFusion, self).__init__() + self.conv1 = nn.Conv2d(in_ch, 32, kernel_size=3, + stride=1, padding=1) # before 64 + self.conv3= nn.Conv2d(32, out_ch, kernel_size=3, + stride=1, padding=1)# before 64 instead of 32 + self.relu = nn.ReLU() + self.norm_layer1 = nn.GroupNorm(4, 32) # before 64 + + def forward(self, x): + # fusecat = torch.cat(x, dim=1) + attn = self.relu(self.norm_layer1(self.conv1(x))) + attn = F.softmax(self.conv3(attn), dim=1) + return ((x * attn).sum(1)).unsqueeze(1) + + +class CoFusion2(nn.Module): + # TEDv14-3 + def __init__(self, in_ch, out_ch): + super(CoFusion2, self).__init__() + self.conv1 = nn.Conv2d(in_ch, 32, kernel_size=3, + stride=1, padding=1) # before 64 + # self.conv2 = nn.Conv2d(32, 32, kernel_size=3, + # stride=1, padding=1)# before 64 + self.conv3 = nn.Conv2d(32, out_ch, kernel_size=3, + stride=1, padding=1)# before 64 instead of 32 + self.smish= Smish()#nn.ReLU(inplace=True) + + + def forward(self, x): + # fusecat = torch.cat(x, dim=1) + attn = self.conv1(self.smish(x)) + attn = self.conv3(self.smish(attn)) # before , )dim=1) + + # return ((fusecat * attn).sum(1)).unsqueeze(1) + return ((x * attn).sum(1)).unsqueeze(1) + +class DoubleFusion(nn.Module): + # TED fusion before the final edge map prediction + def __init__(self, in_ch, out_ch): + super(DoubleFusion, self).__init__() + self.DWconv1 = nn.Conv2d(in_ch, in_ch*8, kernel_size=3, + stride=1, padding=1, groups=in_ch) # before 64 + self.PSconv1 = nn.PixelShuffle(1) + + self.DWconv2 = nn.Conv2d(24, 24*1, kernel_size=3, + stride=1, padding=1,groups=24)# before 64 instead of 32 + + self.AF= Smish()#XAF() #nn.Tanh()# XAF() # # Smish()# + + + def forward(self, x): + # fusecat = torch.cat(x, dim=1) + attn = self.PSconv1(self.DWconv1(self.AF(x))) # #TEED best res TEDv14 [8, 32, 352, 352] + + attn2 = self.PSconv1(self.DWconv2(self.AF(attn))) # #TEED best res TEDv14[8, 3, 352, 352] + + return Fsmish(((attn2 +attn).sum(1)).unsqueeze(1)) #TED best res + +class _DenseLayer(nn.Sequential): + def __init__(self, input_features, out_features): + super(_DenseLayer, self).__init__() + + self.add_module('conv1', nn.Conv2d(input_features, out_features, + kernel_size=3, stride=1, padding=2, bias=True)), + self.add_module('smish1', Smish()), + self.add_module('conv2', nn.Conv2d(out_features, out_features, + kernel_size=3, stride=1, bias=True)) + def forward(self, x): + x1, x2 = x + + new_features = super(_DenseLayer, self).forward(Fsmish(x1)) # F.relu() + + return 0.5 * (new_features + x2), x2 + + +class _DenseBlock(nn.Sequential): + def __init__(self, num_layers, input_features, out_features): + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer(input_features, out_features) + self.add_module('denselayer%d' % (i + 1), layer) + input_features = out_features + + +class UpConvBlock(nn.Module): + def __init__(self, in_features, up_scale): + super(UpConvBlock, self).__init__() + self.up_factor = 2 + self.constant_features = 16 + + layers = self.make_deconv_layers(in_features, up_scale) + assert layers is not None, layers + self.features = nn.Sequential(*layers) + + def make_deconv_layers(self, in_features, up_scale): + layers = [] + all_pads=[0,0,1,3,7] + for i in range(up_scale): + kernel_size = 2 ** up_scale + pad = all_pads[up_scale] # kernel_size-1 + out_features = self.compute_out_features(i, up_scale) + layers.append(nn.Conv2d(in_features, out_features, 1)) + layers.append(Smish()) + layers.append(nn.ConvTranspose2d( + out_features, out_features, kernel_size, stride=2, padding=pad)) + in_features = out_features + return layers + + def compute_out_features(self, idx, up_scale): + return 1 if idx == up_scale - 1 else self.constant_features + + def forward(self, x): + return self.features(x) + + +class SingleConvBlock(nn.Module): + def __init__(self, in_features, out_features, stride, use_ac=False): + super(SingleConvBlock, self).__init__() + # self.use_bn = use_bs + self.use_ac=use_ac + self.conv = nn.Conv2d(in_features, out_features, 1, stride=stride, + bias=True) + if self.use_ac: + self.smish = Smish() + + def forward(self, x): + x = self.conv(x) + if self.use_ac: + return self.smish(x) + else: + return x + +class DoubleConvBlock(nn.Module): + def __init__(self, in_features, mid_features, + out_features=None, + stride=1, + use_act=True): + super(DoubleConvBlock, self).__init__() + + self.use_act = use_act + if out_features is None: + out_features = mid_features + self.conv1 = nn.Conv2d(in_features, mid_features, + 3, padding=1, stride=stride) + self.conv2 = nn.Conv2d(mid_features, out_features, 3, padding=1) + self.smish= Smish()#nn.ReLU(inplace=True) + + def forward(self, x): + x = self.conv1(x) + x = self.smish(x) + x = self.conv2(x) + if self.use_act: + x = self.smish(x) + return x + + +class TED(nn.Module): + """ Definition of Tiny and Efficient Edge Detector + model + """ + + def __init__(self): + super(TED, self).__init__() + self.block_1 = DoubleConvBlock(3, 16, 16, stride=2,) + self.block_2 = DoubleConvBlock(16, 32, use_act=False) + self.dblock_3 = _DenseBlock(1, 32, 48) # [32,48,100,100] before (2, 32, 64) + + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # skip1 connection, see fig. 2 + self.side_1 = SingleConvBlock(16, 32, 2) + + # skip2 connection, see fig. 2 + self.pre_dense_3 = SingleConvBlock(32, 48, 1) # before (32, 64, 1) + + # USNet + self.up_block_1 = UpConvBlock(16, 1) + self.up_block_2 = UpConvBlock(32, 1) + self.up_block_3 = UpConvBlock(48, 2) # (32, 64, 1) + + self.block_cat = DoubleFusion(3,3) # TEED: DoubleFusion + + self.apply(weight_init) + + def slice(self, tensor, slice_shape): + t_shape = tensor.shape + img_h, img_w = slice_shape + if img_w!=t_shape[-1] or img_h!=t_shape[2]: + new_tensor = F.interpolate( + tensor, size=(img_h, img_w), mode='bicubic',align_corners=False) + + else: + new_tensor=tensor + # tensor[..., :height, :width] + return new_tensor + def resize_input(self,tensor): + t_shape = tensor.shape + if t_shape[2] % 8 != 0 or t_shape[3] % 8 != 0: + img_w= ((t_shape[3]// 8) + 1) * 8 + img_h = ((t_shape[2] // 8) + 1) * 8 + new_tensor = F.interpolate( + tensor, size=(img_h, img_w), mode='bicubic', align_corners=False) + else: + new_tensor = tensor + return new_tensor + + def crop_bdcn(data1, h, w, crop_h, crop_w): + # Based on BDCN Implementation @ https://github.com/pkuCactus/BDCN + _, _, h1, w1 = data1.size() + assert (h <= h1 and w <= w1) + data = data1[:, :, crop_h:crop_h + h, crop_w:crop_w + w] + return data + + + def forward(self, x, single_test=False): + assert x.ndim == 4, x.shape + # supose the image size is 352x352 + + # Block 1 + block_1 = self.block_1(x) # [8,16,176,176] + block_1_side = self.side_1(block_1) # 16 [8,32,88,88] + + # Block 2 + block_2 = self.block_2(block_1) # 32 # [8,32,176,176] + block_2_down = self.maxpool(block_2) # [8,32,88,88] + block_2_add = block_2_down + block_1_side # [8,32,88,88] + + # Block 3 + block_3_pre_dense = self.pre_dense_3(block_2_down) # [8,64,88,88] block 3 L connection + block_3, _ = self.dblock_3([block_2_add, block_3_pre_dense]) # [8,64,88,88] + + # upsampling blocks + out_1 = self.up_block_1(block_1) + out_2 = self.up_block_2(block_2) + out_3 = self.up_block_3(block_3) + + results = [out_1, out_2, out_3] + + # concatenate multiscale outputs + block_cat = torch.cat(results, dim=1) # Bx6xHxW + block_cat = self.block_cat(block_cat) # Bx1xHxW DoubleFusion + + results.append(block_cat) + return results + + +if __name__ == '__main__': + batch_size = 8 + img_height = 352 + img_width = 352 + + # device = "cuda" if torch.cuda.is_available() else "cpu" + device = "cpu" + input = torch.rand(batch_size, 3, img_height, img_width).to(device) + # target = torch.rand(batch_size, 1, img_height, img_width).to(device) + print(f"input shape: {input.shape}") + model = TED().to(device) + output = model(input) + print(f"output shapes: {[t.shape for t in output]}") + + # for i in range(20000): + # print(i) + # output = model(input) + # loss = nn.MSELoss()(output[-1], target) + # loss.backward() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/tile/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/tile/__init__.py new file mode 100644 index 00000000..5c58555b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/tile/__init__.py @@ -0,0 +1,82 @@ +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import get_upscale_method, common_input_validate, HWC3 +from .guided_filter import FastGuidedFilter + +class TileDetector: + def __call__(self, input_image=None, pyrUp_iters=3, output_type=None, upscale_method="INTER_AREA", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + H, W, _ = input_image.shape + H = int(np.round(H / 64.0)) * 64 + W = int(np.round(W / 64.0)) * 64 + detected_map = cv2.resize(input_image, (W // (2 ** pyrUp_iters), H // (2 ** pyrUp_iters)), + interpolation=get_upscale_method(upscale_method)) + detected_map = HWC3(detected_map) + + for _ in range(pyrUp_iters): + detected_map = cv2.pyrUp(detected_map) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + + +# Source: https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic/blob/main/TTP_tile_preprocessor_v5.py + +def apply_gaussian_blur(image_np, ksize=5, sigmaX=1.0): + if ksize % 2 == 0: + ksize += 1 # ksize must be odd + blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX) + return blurred_image + +def apply_guided_filter(image_np, radius, eps, scale): + filter = FastGuidedFilter(image_np, radius, eps, scale) + return filter.filter(image_np) + +class TTPlanet_Tile_Detector_GF: + def __call__(self, input_image, scale_factor, blur_strength, radius, eps, output_type=None, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + img_np = input_image[:, :, ::-1] # RGB to BGR + + # Apply Gaussian blur + img_np = apply_gaussian_blur(img_np, ksize=int(blur_strength), sigmaX=blur_strength / 2) + + # Apply Guided Filter + img_np = apply_guided_filter(img_np, radius, eps, scale_factor) + + # Resize image + height, width = img_np.shape[:2] + new_width = int(width / scale_factor) + new_height = int(height / scale_factor) + resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) + resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_CUBIC) + detected_map = HWC3(resized_img[:, :, ::-1]) # BGR to RGB + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + +class TTPLanet_Tile_Detector_Simple: + def __call__(self, input_image, scale_factor, blur_strength, output_type=None, **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + img_np = input_image[:, :, ::-1] # RGB to BGR + + # Resize image first if you want blur to apply after resizing + height, width = img_np.shape[:2] + new_width = int(width / scale_factor) + new_height = int(height / scale_factor) + resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) + resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LANCZOS4) + + # Apply Gaussian blur after resizing + img_np = apply_gaussian_blur(resized_img, ksize=int(blur_strength), sigmaX=blur_strength / 2) + detected_map = HWC3(img_np[:, :, ::-1]) # BGR to RGB + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/tile/guided_filter.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/tile/guided_filter.py new file mode 100644 index 00000000..88b953e5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/tile/guided_filter.py @@ -0,0 +1,281 @@ + +# -*- coding: utf-8 -*- +## @package guided_filter.core.filters +# +# Implementation of guided filter. +# * GuidedFilter: Original guided filter. +# * FastGuidedFilter: Fast version of the guided filter. +# @author tody +# @date 2015/08/26 + +import numpy as np +import cv2 + +## Convert image into float32 type. +def to32F(img): + if img.dtype == np.float32: + return img + return (1.0 / 255.0) * np.float32(img) + +## Convert image into uint8 type. +def to8U(img): + if img.dtype == np.uint8: + return img + return np.clip(np.uint8(255.0 * img), 0, 255) + +## Return if the input image is gray or not. +def _isGray(I): + return len(I.shape) == 2 + + +## Return down sampled image. +# @param scale (w/s, h/s) image will be created. +# @param shape I.shape[:2]=(h, w). numpy friendly size parameter. +def _downSample(I, scale=4, shape=None): + if shape is not None: + h, w = shape + return cv2.resize(I, (w, h), interpolation=cv2.INTER_NEAREST) + + h, w = I.shape[:2] + return cv2.resize(I, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_NEAREST) + + +## Return up sampled image. +# @param scale (w*s, h*s) image will be created. +# @param shape I.shape[:2]=(h, w). numpy friendly size parameter. +def _upSample(I, scale=2, shape=None): + if shape is not None: + h, w = shape + return cv2.resize(I, (w, h), interpolation=cv2.INTER_LINEAR) + + h, w = I.shape[:2] + return cv2.resize(I, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_LINEAR) + +## Fast guide filter. +class FastGuidedFilter: + ## Constructor. + # @param I Input guidance image. Color or gray. + # @param radius Radius of Guided Filter. + # @param epsilon Regularization term of Guided Filter. + # @param scale Down sampled scale. + def __init__(self, I, radius=5, epsilon=0.4, scale=4): + I_32F = to32F(I) + self._I = I_32F + h, w = I.shape[:2] + + I_sub = _downSample(I_32F, scale) + + self._I_sub = I_sub + radius = int(radius / scale) + + if _isGray(I): + self._guided_filter = GuidedFilterGray(I_sub, radius, epsilon) + else: + self._guided_filter = GuidedFilterColor(I_sub, radius, epsilon) + + ## Apply filter for the input image. + # @param p Input image for the filtering. + def filter(self, p): + p_32F = to32F(p) + shape_original = p.shape[:2] + + p_sub = _downSample(p_32F, shape=self._I_sub.shape[:2]) + + if _isGray(p_sub): + return self._filterGray(p_sub, shape_original) + + cs = p.shape[2] + q = np.array(p_32F) + + for ci in range(cs): + q[:, :, ci] = self._filterGray(p_sub[:, :, ci], shape_original) + return to8U(q) + + def _filterGray(self, p_sub, shape_original): + ab_sub = self._guided_filter._computeCoefficients(p_sub) + ab = [_upSample(abi, shape=shape_original) for abi in ab_sub] + return self._guided_filter._computeOutput(ab, self._I) + + +## Guide filter. +class GuidedFilter: + ## Constructor. + # @param I Input guidance image. Color or gray. + # @param radius Radius of Guided Filter. + # @param epsilon Regularization term of Guided Filter. + def __init__(self, I, radius=5, epsilon=0.4): + I_32F = to32F(I) + + if _isGray(I): + self._guided_filter = GuidedFilterGray(I_32F, radius, epsilon) + else: + self._guided_filter = GuidedFilterColor(I_32F, radius, epsilon) + + ## Apply filter for the input image. + # @param p Input image for the filtering. + def filter(self, p): + return to8U(self._guided_filter.filter(p)) + + +## Common parts of guided filter. +# +# This class is used by guided_filter class. GuidedFilterGray and GuidedFilterColor. +# Based on guided_filter._computeCoefficients, guided_filter._computeOutput, +# GuidedFilterCommon.filter computes filtered image for color and gray. +class GuidedFilterCommon: + def __init__(self, guided_filter): + self._guided_filter = guided_filter + + ## Apply filter for the input image. + # @param p Input image for the filtering. + def filter(self, p): + p_32F = to32F(p) + if _isGray(p_32F): + return self._filterGray(p_32F) + + cs = p.shape[2] + q = np.array(p_32F) + + for ci in range(cs): + q[:, :, ci] = self._filterGray(p_32F[:, :, ci]) + return q + + def _filterGray(self, p): + ab = self._guided_filter._computeCoefficients(p) + return self._guided_filter._computeOutput(ab, self._guided_filter._I) + + +## Guided filter for gray guidance image. +class GuidedFilterGray: + # @param I Input gray guidance image. + # @param radius Radius of Guided Filter. + # @param epsilon Regularization term of Guided Filter. + def __init__(self, I, radius=5, epsilon=0.4): + self._radius = 2 * radius + 1 + self._epsilon = epsilon + self._I = to32F(I) + self._initFilter() + self._filter_common = GuidedFilterCommon(self) + + ## Apply filter for the input image. + # @param p Input image for the filtering. + def filter(self, p): + return self._filter_common.filter(p) + + def _initFilter(self): + I = self._I + r = self._radius + self._I_mean = cv2.blur(I, (r, r)) + I_mean_sq = cv2.blur(I ** 2, (r, r)) + self._I_var = I_mean_sq - self._I_mean ** 2 + + def _computeCoefficients(self, p): + r = self._radius + p_mean = cv2.blur(p, (r, r)) + p_cov = p_mean - self._I_mean * p_mean + a = p_cov / (self._I_var + self._epsilon) + b = p_mean - a * self._I_mean + a_mean = cv2.blur(a, (r, r)) + b_mean = cv2.blur(b, (r, r)) + return a_mean, b_mean + + def _computeOutput(self, ab, I): + a_mean, b_mean = ab + return a_mean * I + b_mean + + +## Guided filter for color guidance image. +class GuidedFilterColor: + # @param I Input color guidance image. + # @param radius Radius of Guided Filter. + # @param epsilon Regularization term of Guided Filter. + def __init__(self, I, radius=5, epsilon=0.2): + self._radius = 2 * radius + 1 + self._epsilon = epsilon + self._I = to32F(I) + self._initFilter() + self._filter_common = GuidedFilterCommon(self) + + ## Apply filter for the input image. + # @param p Input image for the filtering. + def filter(self, p): + return self._filter_common.filter(p) + + def _initFilter(self): + I = self._I + r = self._radius + eps = self._epsilon + + Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2] + + self._Ir_mean = cv2.blur(Ir, (r, r)) + self._Ig_mean = cv2.blur(Ig, (r, r)) + self._Ib_mean = cv2.blur(Ib, (r, r)) + + Irr_var = cv2.blur(Ir ** 2, (r, r)) - self._Ir_mean ** 2 + eps + Irg_var = cv2.blur(Ir * Ig, (r, r)) - self._Ir_mean * self._Ig_mean + Irb_var = cv2.blur(Ir * Ib, (r, r)) - self._Ir_mean * self._Ib_mean + Igg_var = cv2.blur(Ig * Ig, (r, r)) - self._Ig_mean * self._Ig_mean + eps + Igb_var = cv2.blur(Ig * Ib, (r, r)) - self._Ig_mean * self._Ib_mean + Ibb_var = cv2.blur(Ib * Ib, (r, r)) - self._Ib_mean * self._Ib_mean + eps + + Irr_inv = Igg_var * Ibb_var - Igb_var * Igb_var + Irg_inv = Igb_var * Irb_var - Irg_var * Ibb_var + Irb_inv = Irg_var * Igb_var - Igg_var * Irb_var + Igg_inv = Irr_var * Ibb_var - Irb_var * Irb_var + Igb_inv = Irb_var * Irg_var - Irr_var * Igb_var + Ibb_inv = Irr_var * Igg_var - Irg_var * Irg_var + + I_cov = Irr_inv * Irr_var + Irg_inv * Irg_var + Irb_inv * Irb_var + Irr_inv /= I_cov + Irg_inv /= I_cov + Irb_inv /= I_cov + Igg_inv /= I_cov + Igb_inv /= I_cov + Ibb_inv /= I_cov + + self._Irr_inv = Irr_inv + self._Irg_inv = Irg_inv + self._Irb_inv = Irb_inv + self._Igg_inv = Igg_inv + self._Igb_inv = Igb_inv + self._Ibb_inv = Ibb_inv + + def _computeCoefficients(self, p): + r = self._radius + I = self._I + Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2] + + p_mean = cv2.blur(p, (r, r)) + + Ipr_mean = cv2.blur(Ir * p, (r, r)) + Ipg_mean = cv2.blur(Ig * p, (r, r)) + Ipb_mean = cv2.blur(Ib * p, (r, r)) + + Ipr_cov = Ipr_mean - self._Ir_mean * p_mean + Ipg_cov = Ipg_mean - self._Ig_mean * p_mean + Ipb_cov = Ipb_mean - self._Ib_mean * p_mean + + ar = self._Irr_inv * Ipr_cov + self._Irg_inv * Ipg_cov + self._Irb_inv * Ipb_cov + ag = self._Irg_inv * Ipr_cov + self._Igg_inv * Ipg_cov + self._Igb_inv * Ipb_cov + ab = self._Irb_inv * Ipr_cov + self._Igb_inv * Ipg_cov + self._Ibb_inv * Ipb_cov + b = p_mean - ar * self._Ir_mean - ag * self._Ig_mean - ab * self._Ib_mean + + ar_mean = cv2.blur(ar, (r, r)) + ag_mean = cv2.blur(ag, (r, r)) + ab_mean = cv2.blur(ab, (r, r)) + b_mean = cv2.blur(b, (r, r)) + + return ar_mean, ag_mean, ab_mean, b_mean + + def _computeOutput(self, ab, I): + ar_mean, ag_mean, ab_mean, b_mean = ab + + Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2] + + q = (ar_mean * Ir + + ag_mean * Ig + + ab_mean * Ib + + b_mean) + + return q \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/__init__.py new file mode 100644 index 00000000..ccb75770 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/__init__.py @@ -0,0 +1,68 @@ +import os +from .inference import init_segmentor, inference_segmentor, show_result_pyplot +import warnings +import cv2 +import numpy as np +from PIL import Image +from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME +import torch + +from custom_mmpkg.custom_mmseg.core.evaluation import get_palette + +config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "upernet_global_small.py") + + + +class UniformerSegmentor: + def __init__(self, netNetwork): + self.model = netNetwork + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="upernet_global_small.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + + netNetwork = init_segmentor(config_file, model_path, device="cpu") + netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()}) + netNetwork.eval() + + return cls(netNetwork) + + def to(self, device): + self.model.to(device) + return self + + def _inference(self, img): + if next(self.model.parameters()).device.type == 'mps': + # adaptive_avg_pool2d can fail on MPS, workaround with CPU + import torch.nn.functional + + orig_adaptive_avg_pool2d = torch.nn.functional.adaptive_avg_pool2d + def cpu_if_exception(input, *args, **kwargs): + try: + return orig_adaptive_avg_pool2d(input, *args, **kwargs) + except: + return orig_adaptive_avg_pool2d(input.cpu(), *args, **kwargs).to(input.device) + + try: + torch.nn.functional.adaptive_avg_pool2d = cpu_if_exception + result = inference_segmentor(self.model, img) + finally: + torch.nn.functional.adaptive_avg_pool2d = orig_adaptive_avg_pool2d + else: + result = inference_segmentor(self.model, img) + + res_img = show_result_pyplot(self.model, img, result, get_palette('ade'), opacity=1) + return res_img + + def __call__(self, input_image=None, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + detected_map = self._inference(input_image) + detected_map = remove_pad(HWC3(detected_map)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/ade20k.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/ade20k.py new file mode 100644 index 00000000..efc8b4bb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/ade20k.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'ADE20KDataset' +data_root = 'data/ade/ADEChallengeData2016' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (512, 512) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 512), + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/training', + ann_dir='annotations/training', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/chase_db1.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/chase_db1.py new file mode 100644 index 00000000..298594ea --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/chase_db1.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'ChaseDB1Dataset' +data_root = 'data/CHASE_DB1' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (960, 999) +crop_size = (128, 128) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type='RepeatDataset', + times=40000, + dataset=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/training', + ann_dir='annotations/training', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/cityscapes.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/cityscapes.py new file mode 100644 index 00000000..f21867c6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/cityscapes.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (512, 1024) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='leftImg8bit/train', + ann_dir='gtFine/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='leftImg8bit/val', + ann_dir='gtFine/val', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='leftImg8bit/val', + ann_dir='gtFine/val', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/cityscapes_769x769.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/cityscapes_769x769.py new file mode 100644 index 00000000..336c7b25 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/cityscapes_769x769.py @@ -0,0 +1,35 @@ +_base_ = './cityscapes.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (769, 769) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2049, 1025), + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/drive.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/drive.py new file mode 100644 index 00000000..06e8ff60 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/drive.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'DRIVEDataset' +data_root = 'data/DRIVE' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (584, 565) +crop_size = (64, 64) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type='RepeatDataset', + times=40000, + dataset=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/training', + ann_dir='annotations/training', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/hrf.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/hrf.py new file mode 100644 index 00000000..242d790e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/hrf.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'HRFDataset' +data_root = 'data/HRF' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (2336, 3504) +crop_size = (256, 256) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type='RepeatDataset', + times=40000, + dataset=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/training', + ann_dir='annotations/training', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_context.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_context.py new file mode 100644 index 00000000..ff65bad1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_context.py @@ -0,0 +1,60 @@ +# dataset settings +dataset_type = 'PascalContextDataset' +data_root = 'data/VOCdevkit/VOC2010/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +img_scale = (520, 520) +crop_size = (480, 480) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_context_59.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_context_59.py new file mode 100644 index 00000000..37585aba --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_context_59.py @@ -0,0 +1,60 @@ +# dataset settings +dataset_type = 'PascalContextDataset59' +data_root = 'data/VOCdevkit/VOC2010/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +img_scale = (520, 520) +crop_size = (480, 480) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_voc12.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_voc12.py new file mode 100644 index 00000000..ba1d42d0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_voc12.py @@ -0,0 +1,57 @@ +# dataset settings +dataset_type = 'PascalVOCDataset' +data_root = 'data/VOCdevkit/VOC2012' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (512, 512) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 512), + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClass', + split='ImageSets/Segmentation/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClass', + split='ImageSets/Segmentation/val.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClass', + split='ImageSets/Segmentation/val.txt', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_voc12_aug.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_voc12_aug.py new file mode 100644 index 00000000..3f23b671 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/pascal_voc12_aug.py @@ -0,0 +1,9 @@ +_base_ = './pascal_voc12.py' +# dataset settings +data = dict( + train=dict( + ann_dir=['SegmentationClass', 'SegmentationClassAug'], + split=[ + 'ImageSets/Segmentation/train.txt', + 'ImageSets/Segmentation/aug.txt' + ])) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/stare.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/stare.py new file mode 100644 index 00000000..3f71b254 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/datasets/stare.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'STAREDataset' +data_root = 'data/STARE' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (605, 700) +crop_size = (128, 128) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_scale, + # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type='RepeatDataset', + times=40000, + dataset=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/training', + ann_dir='annotations/training', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=test_pipeline)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/default_runtime.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/default_runtime.py new file mode 100644 index 00000000..b564cc4e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/default_runtime.py @@ -0,0 +1,14 @@ +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook', by_epoch=False), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +cudnn_benchmark = True diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ann_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ann_r50-d8.py new file mode 100644 index 00000000..a2cb6538 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ann_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='ANNHead', + in_channels=[1024, 2048], + in_index=[2, 3], + channels=512, + project_channels=256, + query_scales=(1, ), + key_pool_scales=(1, 3, 6, 8), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/apcnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/apcnet_r50-d8.py new file mode 100644 index 00000000..c8f5316c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/apcnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='APCHead', + in_channels=2048, + in_index=3, + channels=512, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ccnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ccnet_r50-d8.py new file mode 100644 index 00000000..794148f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ccnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='CCHead', + in_channels=2048, + in_index=3, + channels=512, + recurrence=2, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/cgnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/cgnet.py new file mode 100644 index 00000000..eff8d945 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/cgnet.py @@ -0,0 +1,35 @@ +# model settings +norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='CGNet', + norm_cfg=norm_cfg, + in_channels=3, + num_channels=(32, 64, 128), + num_blocks=(3, 21), + dilations=(2, 4), + reductions=(8, 16)), + decode_head=dict( + type='FCNHead', + in_channels=256, + in_index=2, + channels=256, + num_convs=0, + concat_input=False, + dropout_ratio=0, + num_classes=19, + norm_cfg=norm_cfg, + loss_decode=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + class_weight=[ + 2.5959933, 6.7415504, 3.5354059, 9.8663225, 9.690899, 9.369352, + 10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905, + 10.347791, 6.3927646, 10.226669, 10.241062, 10.280587, + 10.396974, 10.055647 + ])), + # model training and testing settings + train_cfg=dict(sampler=None), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/danet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/danet_r50-d8.py new file mode 100644 index 00000000..2c934939 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/danet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DAHead', + in_channels=2048, + in_index=3, + channels=512, + pam_channels=64, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3_r50-d8.py new file mode 100644 index 00000000..d7a43bee --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='ASPPHead', + in_channels=2048, + in_index=3, + channels=512, + dilations=(1, 12, 24, 36), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3_unet_s5-d16.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3_unet_s5-d16.py new file mode 100644 index 00000000..0cd26299 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3_unet_s5-d16.py @@ -0,0 +1,50 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='ASPPHead', + in_channels=64, + in_index=4, + channels=16, + dilations=(1, 12, 24, 36), + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=128, + in_index=3, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3plus_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3plus_r50-d8.py new file mode 100644 index 00000000..050e39e0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/deeplabv3plus_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DepthwiseSeparableASPPHead', + in_channels=2048, + in_index=3, + channels=512, + dilations=(1, 12, 24, 36), + c1_in_channels=256, + c1_channels=48, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/dmnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/dmnet_r50-d8.py new file mode 100644 index 00000000..d22ba526 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/dmnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DMHead', + in_channels=2048, + in_index=3, + channels=512, + filter_sizes=(1, 3, 5, 7), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/dnl_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/dnl_r50-d8.py new file mode 100644 index 00000000..edb4c174 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/dnl_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DNLHead', + in_channels=2048, + in_index=3, + channels=512, + dropout_ratio=0.1, + reduction=2, + use_scale=True, + mode='embedded_gaussian', + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/emanet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/emanet_r50-d8.py new file mode 100644 index 00000000..26adcd43 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/emanet_r50-d8.py @@ -0,0 +1,47 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='EMAHead', + in_channels=2048, + in_index=3, + channels=256, + ema_channels=512, + num_bases=64, + num_stages=3, + momentum=0.1, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/encnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/encnet_r50-d8.py new file mode 100644 index 00000000..be777123 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/encnet_r50-d8.py @@ -0,0 +1,48 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='EncHead', + in_channels=[512, 1024, 2048], + in_index=(1, 2, 3), + channels=512, + num_codes=32, + use_se_loss=True, + add_lateral=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_se_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fast_scnn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fast_scnn.py new file mode 100644 index 00000000..32fdeb65 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fast_scnn.py @@ -0,0 +1,57 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='FastSCNN', + downsample_dw_channels=(32, 48), + global_in_channels=64, + global_block_channels=(64, 96, 128), + global_block_strides=(2, 2, 1), + global_out_channels=128, + higher_in_channels=64, + lower_in_channels=128, + fusion_out_channels=128, + out_indices=(0, 1, 2), + norm_cfg=norm_cfg, + align_corners=False), + decode_head=dict( + type='DepthwiseSeparableFCNHead', + in_channels=128, + channels=128, + concat_input=False, + num_classes=19, + in_index=-1, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=19, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=19, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_hr18.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_hr18.py new file mode 100644 index 00000000..c3e299bc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_hr18.py @@ -0,0 +1,52 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + type='HRNet', + norm_cfg=norm_cfg, + norm_eval=False, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(18, 36)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(18, 36, 72)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(18, 36, 72, 144)))), + decode_head=dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + channels=sum([18, 36, 72, 144]), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_r50-d8.py new file mode 100644 index 00000000..5e98f6cc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_r50-d8.py @@ -0,0 +1,45 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='FCNHead', + in_channels=2048, + in_index=3, + channels=512, + num_convs=2, + concat_input=True, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_unet_s5-d16.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_unet_s5-d16.py new file mode 100644 index 00000000..a33e7972 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fcn_unet_s5-d16.py @@ -0,0 +1,51 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='FCNHead', + in_channels=64, + in_index=4, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=128, + in_index=3, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fpn_r50.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fpn_r50.py new file mode 100644 index 00000000..86ab327d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fpn_r50.py @@ -0,0 +1,36 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=4), + decode_head=dict( + type='FPNHead', + in_channels=[256, 256, 256, 256], + in_index=[0, 1, 2, 3], + feature_strides=[4, 8, 16, 32], + channels=128, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fpn_uniformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fpn_uniformer.py new file mode 100644 index 00000000..8aae98c5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/fpn_uniformer.py @@ -0,0 +1,35 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='UniFormer', + embed_dim=[64, 128, 320, 512], + layers=[3, 4, 8, 3], + head_dim=64, + mlp_ratio=4., + qkv_bias=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.1), + neck=dict( + type='FPN', + in_channels=[64, 128, 320, 512], + out_channels=256, + num_outs=4), + decode_head=dict( + type='FPNHead', + in_channels=[256, 256, 256, 256], + in_index=[0, 1, 2, 3], + feature_strides=[4, 8, 16, 32], + channels=128, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole') +) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/gcnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/gcnet_r50-d8.py new file mode 100644 index 00000000..3d2ad69f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/gcnet_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='GCHead', + in_channels=2048, + in_index=3, + channels=512, + ratio=1 / 4., + pooling_type='att', + fusion_types=('channel_add', ), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/lraspp_m-v3-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/lraspp_m-v3-d8.py new file mode 100644 index 00000000..93258242 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/lraspp_m-v3-d8.py @@ -0,0 +1,25 @@ +# model settings +norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='MobileNetV3', + arch='large', + out_indices=(1, 3, 16), + norm_cfg=norm_cfg), + decode_head=dict( + type='LRASPPHead', + in_channels=(16, 24, 960), + in_index=(0, 1, 2), + channels=128, + input_transform='multiple_select', + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/nonlocal_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/nonlocal_r50-d8.py new file mode 100644 index 00000000..5674a398 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/nonlocal_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='NLHead', + in_channels=2048, + in_index=3, + channels=512, + dropout_ratio=0.1, + reduction=2, + use_scale=True, + mode='embedded_gaussian', + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ocrnet_hr18.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ocrnet_hr18.py new file mode 100644 index 00000000..c60f62a7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ocrnet_hr18.py @@ -0,0 +1,68 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='CascadeEncoderDecoder', + num_stages=2, + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + type='HRNet', + norm_cfg=norm_cfg, + norm_eval=False, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(18, 36)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(18, 36, 72)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(18, 36, 72, 144)))), + decode_head=[ + dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + channels=sum([18, 36, 72, 144]), + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='OCRHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + channels=512, + ocr_channels=256, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ocrnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ocrnet_r50-d8.py new file mode 100644 index 00000000..615aa3ff --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/ocrnet_r50-d8.py @@ -0,0 +1,47 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='CascadeEncoderDecoder', + num_stages=2, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=[ + dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='OCRHead', + in_channels=2048, + in_index=3, + channels=512, + ocr_channels=256, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pointrend_r50.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pointrend_r50.py new file mode 100644 index 00000000..9d323dbf --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pointrend_r50.py @@ -0,0 +1,56 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='CascadeEncoderDecoder', + num_stages=2, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=4), + decode_head=[ + dict( + type='FPNHead', + in_channels=[256, 256, 256, 256], + in_index=[0, 1, 2, 3], + feature_strides=[4, 8, 16, 32], + channels=128, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='PointHead', + in_channels=[256], + in_index=[0], + channels=256, + num_fcs=3, + coarse_pred_each_layer=True, + dropout_ratio=-1, + num_classes=19, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) + ], + # model training and testing settings + train_cfg=dict( + num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75), + test_cfg=dict( + mode='whole', + subdivision_steps=2, + subdivision_num_points=8196, + scale_factor=2)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/psanet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/psanet_r50-d8.py new file mode 100644 index 00000000..689513fa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/psanet_r50-d8.py @@ -0,0 +1,49 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='PSAHead', + in_channels=2048, + in_index=3, + channels=512, + mask_size=(97, 97), + psa_type='bi-direction', + compact=False, + shrink_factor=2, + normalization_factor=1.0, + psa_softmax=True, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pspnet_r50-d8.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pspnet_r50-d8.py new file mode 100644 index 00000000..f451e08a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pspnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='PSPHead', + in_channels=2048, + in_index=3, + channels=512, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py new file mode 100644 index 00000000..fcff9ec4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py @@ -0,0 +1,50 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='PSPHead', + in_channels=64, + in_index=4, + channels=16, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=128, + in_index=3, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/upernet_r50.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/upernet_r50.py new file mode 100644 index 00000000..10974962 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/upernet_r50.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='UPerHead', + in_channels=[256, 512, 1024, 2048], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/upernet_uniformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/upernet_uniformer.py new file mode 100644 index 00000000..41aa4db8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/models/upernet_uniformer.py @@ -0,0 +1,43 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UniFormer', + embed_dim=[64, 128, 320, 512], + layers=[3, 4, 8, 3], + head_dim=64, + mlp_ratio=4., + qkv_bias=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.1), + decode_head=dict( + type='UPerHead', + in_channels=[64, 128, 320, 512], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=320, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_160k.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_160k.py new file mode 100644 index 00000000..52603890 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_160k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=160000) +checkpoint_config = dict(by_epoch=False, interval=16000) +evaluation = dict(interval=16000, metric='mIoU') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_20k.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_20k.py new file mode 100644 index 00000000..bf780a1b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_20k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=20000) +checkpoint_config = dict(by_epoch=False, interval=2000) +evaluation = dict(interval=2000, metric='mIoU') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_40k.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_40k.py new file mode 100644 index 00000000..cdbf841a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_40k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=40000) +checkpoint_config = dict(by_epoch=False, interval=4000) +evaluation = dict(interval=4000, metric='mIoU') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_80k.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_80k.py new file mode 100644 index 00000000..c190cee6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/configs/_base_/schedules/schedule_80k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=80000) +checkpoint_config = dict(by_epoch=False, interval=8000) +evaluation = dict(interval=8000, metric='mIoU') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/inference.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/inference.py new file mode 100644 index 00000000..7efc93e1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/inference.py @@ -0,0 +1,137 @@ + +import torch + +import custom_mmpkg.custom_mmcv as mmcv +from custom_mmpkg.custom_mmcv.parallel import collate, scatter +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from custom_mmpkg.custom_mmseg.datasets.pipelines import Compose +from custom_mmpkg.custom_mmseg.models import build_segmentor + +def init_segmentor(config, checkpoint=None, device='cuda:0'): + """Initialize a segmentor from config file. + + Args: + config (str or :obj:`mmcv.Config`): Config file path or the config + object. + checkpoint (str, optional): Checkpoint path. If left as None, the model + will not load any weights. + device (str, optional) CPU/CUDA device option. Default 'cuda:0'. + Use 'cpu' for loading model on CPU. + Returns: + nn.Module: The constructed segmentor. + """ + if isinstance(config, str): + config = mmcv.Config.fromfile(config) + elif not isinstance(config, mmcv.Config): + raise TypeError('config must be a filename or Config object, ' + 'but got {}'.format(type(config))) + config.model.pretrained = None + config.model.train_cfg = None + model = build_segmentor(config.model, test_cfg=config.get('test_cfg')) + if checkpoint is not None: + checkpoint = load_checkpoint(model, checkpoint, map_location='cpu') + model.CLASSES = checkpoint['meta']['CLASSES'] + model.PALETTE = checkpoint['meta']['PALETTE'] + model.cfg = config # save the config in the model for convenience + model.to(device) + model.eval() + return model + + +class LoadImage: + """A simple pipeline to load image.""" + + def __call__(self, results): + """Call function to load images into results. + + Args: + results (dict): A result dict contains the file name + of the image to be read. + + Returns: + dict: ``results`` will be returned containing loaded image. + """ + + if isinstance(results['img'], str): + results['filename'] = results['img'] + results['ori_filename'] = results['img'] + else: + results['filename'] = None + results['ori_filename'] = None + img = mmcv.imread(results['img']) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + return results + + +def inference_segmentor(model, img): + """Inference image(s) with the segmentor. + + Args: + model (nn.Module): The loaded segmentor. + imgs (str/ndarray or list[str/ndarray]): Either image files or loaded + images. + + Returns: + (list[Tensor]): The segmentation result. + """ + cfg = model.cfg + device = next(model.parameters()).device # model device + # build the data pipeline + test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:] + test_pipeline = Compose(test_pipeline) + # prepare data + data = dict(img=img) + data = test_pipeline(data) + data = collate([data], samples_per_gpu=1) + if next(model.parameters()).is_cuda: + # scatter to specified GPU + data = scatter(data, [device])[0] + else: + data['img_metas'] = [i.data[0] for i in data['img_metas']] + + data['img'] = [x.to(device) for x in data['img']] + + # forward the model + with torch.no_grad(): + result = model(return_loss=False, rescale=True, **data) + return result + + +def show_result_pyplot(model, + img, + result, + palette=None, + fig_size=(15, 10), + opacity=0.5, + title='', + block=True): + """Visualize the segmentation results on the image. + + Args: + model (nn.Module): The loaded segmentor. + img (str or np.ndarray): Image filename or loaded image. + result (list): The segmentation result. + palette (list[list[int]]] | None): The palette of segmentation + map. If None is given, random palette will be generated. + Default: None + fig_size (tuple): Figure size of the pyplot figure. + opacity(float): Opacity of painted segmentation map. + Default 0.5. + Must be in (0, 1] range. + title (str): The title of pyplot figure. + Default is ''. + block (bool): Whether to block the pyplot figure. + Default is True. + """ + if hasattr(model, 'module'): + model = model.module + img = model.show_result( + img, result, palette=palette, show=False, opacity=opacity) + # plt.figure(figsize=fig_size) + # plt.imshow(mmcv.bgr2rgb(img)) + # plt.title(title) + # plt.tight_layout() + # plt.show(block=block) + return mmcv.bgr2rgb(img) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/mmcv_custom/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/mmcv_custom/__init__.py new file mode 100644 index 00000000..4b958738 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/mmcv_custom/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- + +from .checkpoint import load_checkpoint + +__all__ = ['load_checkpoint'] \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/mmcv_custom/checkpoint.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/mmcv_custom/checkpoint.py new file mode 100644 index 00000000..8453fedc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/mmcv_custom/checkpoint.py @@ -0,0 +1,500 @@ +# Copyright (c) Open-MMLab. All rights reserved. +import io +import os +import os.path as osp +import pkgutil +import time +import warnings +from collections import OrderedDict +from importlib import import_module +from tempfile import TemporaryDirectory + +import torch +import torchvision +from torch.optim import Optimizer +from torch.utils import model_zoo +from torch.nn import functional as F + +import custom_mmpkg.custom_mmcv as mmcv +from custom_mmpkg.custom_mmcv.fileio import FileClient +from custom_mmpkg.custom_mmcv.fileio import load as load_file +from custom_mmpkg.custom_mmcv.parallel import is_module_wrapper +from custom_mmpkg.custom_mmcv.utils import mkdir_or_exist +from custom_mmpkg.custom_mmcv.runner import get_dist_info + +ENV_MMCV_HOME = 'MMCV_HOME' +ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME' +DEFAULT_CACHE_DIR = '~/.cache' + + +def _get_mmcv_home(): + mmcv_home = os.path.expanduser( + os.getenv( + ENV_MMCV_HOME, + os.path.join( + os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv'))) + + mkdir_or_exist(mmcv_home) + return mmcv_home + + +def load_state_dict(module, state_dict, strict=False, logger=None): + """Load state_dict to a module. + + This method is modified from :meth:`torch.nn.Module.load_state_dict`. + Default value for ``strict`` is set to ``False`` and the message for + param mismatch will be shown even if strict is False. + + Args: + module (Module): Module that receives the state_dict. + state_dict (OrderedDict): Weights. + strict (bool): whether to strictly enforce that the keys + in :attr:`state_dict` match the keys returned by this module's + :meth:`~torch.nn.Module.state_dict` function. Default: ``False``. + logger (:obj:`logging.Logger`, optional): Logger to log the error + message. If not specified, print function will be used. + """ + unexpected_keys = [] + all_missing_keys = [] + err_msg = [] + + metadata = getattr(state_dict, '_metadata', None) + state_dict = state_dict.copy() + if metadata is not None: + state_dict._metadata = metadata + + # use _load_from_state_dict to enable checkpoint version control + def load(module, prefix=''): + # recursively check parallel module in case that the model has a + # complicated structure, e.g., nn.Module(nn.Module(DDP)) + if is_module_wrapper(module): + module = module.module + local_metadata = {} if metadata is None else metadata.get( + prefix[:-1], {}) + module._load_from_state_dict(state_dict, prefix, local_metadata, True, + all_missing_keys, unexpected_keys, + err_msg) + for name, child in module._modules.items(): + if child is not None: + load(child, prefix + name + '.') + + load(module) + load = None # break load->load reference cycle + + # ignore "num_batches_tracked" of BN layers + missing_keys = [ + key for key in all_missing_keys if 'num_batches_tracked' not in key + ] + + if unexpected_keys: + err_msg.append('unexpected key in source ' + f'state_dict: {", ".join(unexpected_keys)}\n') + if missing_keys: + err_msg.append( + f'missing keys in source state_dict: {", ".join(missing_keys)}\n') + + rank, _ = get_dist_info() + if len(err_msg) > 0 and rank == 0: + err_msg.insert( + 0, 'The model and loaded state dict do not match exactly\n') + err_msg = '\n'.join(err_msg) + if strict: + raise RuntimeError(err_msg) + elif logger is not None: + logger.warning(err_msg) + else: + print(err_msg) + + +def load_url_dist(url, model_dir=None): + """In distributed setting, this function only download checkpoint at local + rank 0.""" + rank, world_size = get_dist_info() + rank = int(os.environ.get('LOCAL_RANK', rank)) + if rank == 0: + checkpoint = model_zoo.load_url(url, model_dir=model_dir) + if world_size > 1: + torch.distributed.barrier() + if rank > 0: + checkpoint = model_zoo.load_url(url, model_dir=model_dir) + return checkpoint + + +def load_pavimodel_dist(model_path, map_location=None): + """In distributed setting, this function only download checkpoint at local + rank 0.""" + try: + from pavi import modelcloud + except ImportError: + raise ImportError( + 'Please install pavi to load checkpoint from modelcloud.') + rank, world_size = get_dist_info() + rank = int(os.environ.get('LOCAL_RANK', rank)) + if rank == 0: + model = modelcloud.get(model_path) + with TemporaryDirectory() as tmp_dir: + downloaded_file = osp.join(tmp_dir, model.name) + model.download(downloaded_file) + checkpoint = torch.load(downloaded_file, map_location=map_location) + if world_size > 1: + torch.distributed.barrier() + if rank > 0: + model = modelcloud.get(model_path) + with TemporaryDirectory() as tmp_dir: + downloaded_file = osp.join(tmp_dir, model.name) + model.download(downloaded_file) + checkpoint = torch.load( + downloaded_file, map_location=map_location) + return checkpoint + + +def load_fileclient_dist(filename, backend, map_location): + """In distributed setting, this function only download checkpoint at local + rank 0.""" + rank, world_size = get_dist_info() + rank = int(os.environ.get('LOCAL_RANK', rank)) + allowed_backends = ['ceph'] + if backend not in allowed_backends: + raise ValueError(f'Load from Backend {backend} is not supported.') + if rank == 0: + fileclient = FileClient(backend=backend) + buffer = io.BytesIO(fileclient.get(filename)) + checkpoint = torch.load(buffer, map_location=map_location) + if world_size > 1: + torch.distributed.barrier() + if rank > 0: + fileclient = FileClient(backend=backend) + buffer = io.BytesIO(fileclient.get(filename)) + checkpoint = torch.load(buffer, map_location=map_location) + return checkpoint + + +def get_torchvision_models(): + model_urls = dict() + for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__): + if ispkg: + continue + _zoo = import_module(f'torchvision.models.{name}') + if hasattr(_zoo, 'model_urls'): + _urls = getattr(_zoo, 'model_urls') + model_urls.update(_urls) + return model_urls + + +def get_external_models(): + mmcv_home = _get_mmcv_home() + default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json') + default_urls = load_file(default_json_path) + assert isinstance(default_urls, dict) + external_json_path = osp.join(mmcv_home, 'open_mmlab.json') + if osp.exists(external_json_path): + external_urls = load_file(external_json_path) + assert isinstance(external_urls, dict) + default_urls.update(external_urls) + + return default_urls + + +def get_mmcls_models(): + mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json') + mmcls_urls = load_file(mmcls_json_path) + + return mmcls_urls + + +def get_deprecated_model_names(): + deprecate_json_path = osp.join(mmcv.__path__[0], + 'model_zoo/deprecated.json') + deprecate_urls = load_file(deprecate_json_path) + assert isinstance(deprecate_urls, dict) + + return deprecate_urls + + +def _process_mmcls_checkpoint(checkpoint): + state_dict = checkpoint['state_dict'] + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k.startswith('backbone.'): + new_state_dict[k[9:]] = v + new_checkpoint = dict(state_dict=new_state_dict) + + return new_checkpoint + + +def _load_checkpoint(filename, map_location=None): + """Load checkpoint from somewhere (modelzoo, file, url). + + Args: + filename (str): Accept local filepath, URL, ``torchvision://xxx``, + ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for + details. + map_location (str | None): Same as :func:`torch.load`. Default: None. + + Returns: + dict | OrderedDict: The loaded checkpoint. It can be either an + OrderedDict storing model weights or a dict containing other + information, which depends on the checkpoint. + """ + if filename.startswith('modelzoo://'): + warnings.warn('The URL scheme of "modelzoo://" is deprecated, please ' + 'use "torchvision://" instead') + model_urls = get_torchvision_models() + model_name = filename[11:] + checkpoint = load_url_dist(model_urls[model_name]) + elif filename.startswith('torchvision://'): + model_urls = get_torchvision_models() + model_name = filename[14:] + checkpoint = load_url_dist(model_urls[model_name]) + elif filename.startswith('open-mmlab://'): + model_urls = get_external_models() + model_name = filename[13:] + deprecated_urls = get_deprecated_model_names() + if model_name in deprecated_urls: + warnings.warn(f'open-mmlab://{model_name} is deprecated in favor ' + f'of open-mmlab://{deprecated_urls[model_name]}') + model_name = deprecated_urls[model_name] + model_url = model_urls[model_name] + # check if is url + if model_url.startswith(('http://', 'https://')): + checkpoint = load_url_dist(model_url) + else: + filename = osp.join(_get_mmcv_home(), model_url) + if not osp.isfile(filename): + raise IOError(f'{filename} is not a checkpoint file') + checkpoint = torch.load(filename, map_location=map_location) + elif filename.startswith('mmcls://'): + model_urls = get_mmcls_models() + model_name = filename[8:] + checkpoint = load_url_dist(model_urls[model_name]) + checkpoint = _process_mmcls_checkpoint(checkpoint) + elif filename.startswith(('http://', 'https://')): + checkpoint = load_url_dist(filename) + elif filename.startswith('pavi://'): + model_path = filename[7:] + checkpoint = load_pavimodel_dist(model_path, map_location=map_location) + elif filename.startswith('s3://'): + checkpoint = load_fileclient_dist( + filename, backend='ceph', map_location=map_location) + else: + if not osp.isfile(filename): + raise IOError(f'{filename} is not a checkpoint file') + checkpoint = torch.load(filename, map_location=map_location) + return checkpoint + + +def load_checkpoint(model, + filename, + map_location='cpu', + strict=False, + logger=None): + """Load checkpoint from a file or URI. + + Args: + model (Module): Module to load checkpoint. + filename (str): Accept local filepath, URL, ``torchvision://xxx``, + ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for + details. + map_location (str): Same as :func:`torch.load`. + strict (bool): Whether to allow different params for the model and + checkpoint. + logger (:mod:`logging.Logger` or None): The logger for error message. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + checkpoint = _load_checkpoint(filename, map_location) + # OrderedDict is a subclass of dict + if not isinstance(checkpoint, dict): + raise RuntimeError( + f'No state_dict found in checkpoint file {filename}') + # get state_dict from checkpoint + if 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + elif 'model' in checkpoint: + state_dict = checkpoint['model'] + else: + state_dict = checkpoint + # strip prefix of state_dict + if list(state_dict.keys())[0].startswith('module.'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + # for MoBY, load model of online branch + if sorted(list(state_dict.keys()))[0].startswith('encoder'): + state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')} + + # reshape absolute position embedding + if state_dict.get('absolute_pos_embed') is not None: + absolute_pos_embed = state_dict['absolute_pos_embed'] + N1, L, C1 = absolute_pos_embed.size() + N2, C2, H, W = model.absolute_pos_embed.size() + if N1 != N2 or C1 != C2 or L != H*W: + logger.warning("Error in loading absolute_pos_embed, pass") + else: + state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2) + + # interpolate position bias table if needed + relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k] + for table_key in relative_position_bias_table_keys: + table_pretrained = state_dict[table_key] + table_current = model.state_dict()[table_key] + L1, nH1 = table_pretrained.size() + L2, nH2 = table_current.size() + if nH1 != nH2: + logger.warning(f"Error in loading {table_key}, pass") + else: + if L1 != L2: + S1 = int(L1 ** 0.5) + S2 = int(L2 ** 0.5) + table_pretrained_resized = F.interpolate( + table_pretrained.permute(1, 0).view(1, nH1, S1, S1), + size=(S2, S2), mode='bicubic') + state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0) + + # load state_dict + load_state_dict(model, state_dict, strict, logger) + return checkpoint + + +def weights_to_cpu(state_dict): + """Copy a model state_dict to cpu. + + Args: + state_dict (OrderedDict): Model weights on GPU. + + Returns: + OrderedDict: Model weights on GPU. + """ + state_dict_cpu = OrderedDict() + for key, val in state_dict.items(): + state_dict_cpu[key] = val.cpu() + return state_dict_cpu + + +def _save_to_state_dict(module, destination, prefix, keep_vars): + """Saves module state to `destination` dictionary. + + This method is modified from :meth:`torch.nn.Module._save_to_state_dict`. + + Args: + module (nn.Module): The module to generate state_dict. + destination (dict): A dict where state will be stored. + prefix (str): The prefix for parameters and buffers used in this + module. + """ + for name, param in module._parameters.items(): + if param is not None: + destination[prefix + name] = param if keep_vars else param.detach() + for name, buf in module._buffers.items(): + # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d + if buf is not None: + destination[prefix + name] = buf if keep_vars else buf.detach() + + +def get_state_dict(module, destination=None, prefix='', keep_vars=False): + """Returns a dictionary containing a whole state of the module. + + Both parameters and persistent buffers (e.g. running averages) are + included. Keys are corresponding parameter and buffer names. + + This method is modified from :meth:`torch.nn.Module.state_dict` to + recursively check parallel module in case that the model has a complicated + structure, e.g., nn.Module(nn.Module(DDP)). + + Args: + module (nn.Module): The module to generate state_dict. + destination (OrderedDict): Returned dict for the state of the + module. + prefix (str): Prefix of the key. + keep_vars (bool): Whether to keep the variable property of the + parameters. Default: False. + + Returns: + dict: A dictionary containing a whole state of the module. + """ + # recursively check parallel module in case that the model has a + # complicated structure, e.g., nn.Module(nn.Module(DDP)) + if is_module_wrapper(module): + module = module.module + + # below is the same as torch.nn.Module.state_dict() + if destination is None: + destination = OrderedDict() + destination._metadata = OrderedDict() + destination._metadata[prefix[:-1]] = local_metadata = dict( + version=module._version) + _save_to_state_dict(module, destination, prefix, keep_vars) + for name, child in module._modules.items(): + if child is not None: + get_state_dict( + child, destination, prefix + name + '.', keep_vars=keep_vars) + for hook in module._state_dict_hooks.values(): + hook_result = hook(module, destination, prefix, local_metadata) + if hook_result is not None: + destination = hook_result + return destination + + +def save_checkpoint(model, filename, optimizer=None, meta=None): + """Save checkpoint to file. + + The checkpoint will have 3 fields: ``meta``, ``state_dict`` and + ``optimizer``. By default ``meta`` will contain version and time info. + + Args: + model (Module): Module whose params are to be saved. + filename (str): Checkpoint filename. + optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. + meta (dict, optional): Metadata to be saved in checkpoint. + """ + if meta is None: + meta = {} + elif not isinstance(meta, dict): + raise TypeError(f'meta must be a dict or None, but got {type(meta)}') + meta.update(mmcv_version=mmcv.__version__, time=time.asctime()) + + if is_module_wrapper(model): + model = model.module + + if hasattr(model, 'CLASSES') and model.CLASSES is not None: + # save class name to the meta + meta.update(CLASSES=model.CLASSES) + + checkpoint = { + 'meta': meta, + 'state_dict': weights_to_cpu(get_state_dict(model)) + } + # save optimizer state dict in the checkpoint + if isinstance(optimizer, Optimizer): + checkpoint['optimizer'] = optimizer.state_dict() + elif isinstance(optimizer, dict): + checkpoint['optimizer'] = {} + for name, optim in optimizer.items(): + checkpoint['optimizer'][name] = optim.state_dict() + + if filename.startswith('pavi://'): + try: + from pavi import modelcloud + from pavi.exception import NodeNotFoundError + except ImportError: + raise ImportError( + 'Please install pavi to load checkpoint from modelcloud.') + model_path = filename[7:] + root = modelcloud.Folder() + model_dir, model_name = osp.split(model_path) + try: + model = modelcloud.get(model_dir) + except NodeNotFoundError: + model = root.create_training_model(model_dir) + with TemporaryDirectory() as tmp_dir: + checkpoint_file = osp.join(tmp_dir, model_name) + with open(checkpoint_file, 'wb') as f: + torch.save(checkpoint, f) + f.flush() + model.create_file(checkpoint_file, name=model_name) + else: + mmcv.mkdir_or_exist(osp.dirname(filename)) + # immediately flush buffer + with open(filename, 'wb') as f: + torch.save(checkpoint, f) + f.flush() \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/uniformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/uniformer.py new file mode 100644 index 00000000..33dc1aa3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/uniformer.py @@ -0,0 +1,421 @@ +# -------------------------------------------------------- +# UniFormer +# Copyright (c) 2022 SenseTime X-Lab +# Licensed under The MIT License [see LICENSE for details] +# Written by Kunchang Li +# -------------------------------------------------------- + + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from functools import partial +from collections import OrderedDict +from timm.layers import DropPath, to_2tuple, trunc_normal_ +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from custom_mmpkg.custom_mmseg.models.builder import BACKBONES + +from .mmcv_custom import load_checkpoint + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class CMlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Conv2d(in_features, hidden_features, 1) + self.act = act_layer() + self.fc2 = nn.Conv2d(hidden_features, out_features, 1) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class CBlock(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) + self.norm1 = nn.BatchNorm2d(dim) + self.conv1 = nn.Conv2d(dim, dim, 1) + self.conv2 = nn.Conv2d(dim, dim, 1) + self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = nn.BatchNorm2d(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.pos_embed(x) + x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x))))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SABlock(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.pos_embed(x) + B, N, H, W = x.shape + x = x.flatten(2).transpose(1, 2) + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + x = x.transpose(1, 2).reshape(B, N, H, W) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class SABlock_Windows(nn.Module): + def __init__(self, dim, num_heads, window_size=14, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.window_size=window_size + self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.pos_embed(x) + x = x.permute(0, 2, 3, 1) + B, H, W, C = x.shape + shortcut = x + x = self.norm1(x) + + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + x_windows = window_partition(x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + x = x.permute(0, 3, 1, 2).reshape(B, C, H, W) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + self.norm = nn.LayerNorm(embed_dim) + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, _, H, W = x.shape + x = self.proj(x) + B, _, H, W = x.shape + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() + return x + + +@BACKBONES.register_module() +class UniFormer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, layers=[3, 4, 8, 3], img_size=224, in_chans=3, num_classes=80, embed_dim=[64, 128, 320, 512], + head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6), + pretrained_path=None, use_checkpoint=False, checkpoint_num=[0, 0, 0, 0], + windows=False, hybrid=False, window_size=14): + """ + Args: + layer (list): number of block in each layer + img_size (int, tuple): input image size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + head_dim (int): dimension of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer (nn.Module): normalization layer + pretrained_path (str): path of pretrained model + use_checkpoint (bool): whether use checkpoint + checkpoint_num (list): index for using checkpoint in every stage + windows (bool): whether use window MHRA + hybrid (bool): whether use hybrid MHRA + window_size (int): size of window (>14) + """ + super().__init__() + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.checkpoint_num = checkpoint_num + self.windows = windows + print(f'Use Checkpoint: {self.use_checkpoint}') + print(f'Checkpoint Number: {self.checkpoint_num}') + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed1 = PatchEmbed( + img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0]) + self.patch_embed2 = PatchEmbed( + img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1]) + self.patch_embed3 = PatchEmbed( + img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2]) + self.patch_embed4 = PatchEmbed( + img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3]) + + self.pos_drop = nn.Dropout(p=drop_rate) + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(layers))] # stochastic depth decay rule + num_heads = [dim // head_dim for dim in embed_dim] + self.blocks1 = nn.ModuleList([ + CBlock( + dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(layers[0])]) + self.norm1=norm_layer(embed_dim[0]) + self.blocks2 = nn.ModuleList([ + CBlock( + dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]], norm_layer=norm_layer) + for i in range(layers[1])]) + self.norm2 = norm_layer(embed_dim[1]) + if self.windows: + print('Use local window for all blocks in stage3') + self.blocks3 = nn.ModuleList([ + SABlock_Windows( + dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer) + for i in range(layers[2])]) + elif hybrid: + print('Use hybrid window for blocks in stage3') + block3 = [] + for i in range(layers[2]): + if (i + 1) % 4 == 0: + block3.append(SABlock( + dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)) + else: + block3.append(SABlock_Windows( + dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)) + self.blocks3 = nn.ModuleList(block3) + else: + print('Use global window for all blocks in stage3') + self.blocks3 = nn.ModuleList([ + SABlock( + dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer) + for i in range(layers[2])]) + self.norm3 = norm_layer(embed_dim[2]) + self.blocks4 = nn.ModuleList([ + SABlock( + dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]+layers[2]], norm_layer=norm_layer) + for i in range(layers[3])]) + self.norm4 = norm_layer(embed_dim[3]) + + # Representation layer + if representation_size: + self.num_features = representation_size + self.pre_logits = nn.Sequential(OrderedDict([ + ('fc', nn.Linear(embed_dim, representation_size)), + ('act', nn.Tanh()) + ])) + else: + self.pre_logits = nn.Identity() + + self.apply(self._init_weights) + self.init_weights(pretrained=pretrained_path) + + def init_weights(self, pretrained): + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger) + print(f'Load pretrained model from {pretrained}') + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + out = [] + x = self.patch_embed1(x) + x = self.pos_drop(x) + for i, blk in enumerate(self.blocks1): + if self.use_checkpoint and i < self.checkpoint_num[0]: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + x_out = self.norm1(x.permute(0, 2, 3, 1)) + out.append(x_out.permute(0, 3, 1, 2).contiguous()) + x = self.patch_embed2(x) + for i, blk in enumerate(self.blocks2): + if self.use_checkpoint and i < self.checkpoint_num[1]: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + x_out = self.norm2(x.permute(0, 2, 3, 1)) + out.append(x_out.permute(0, 3, 1, 2).contiguous()) + x = self.patch_embed3(x) + for i, blk in enumerate(self.blocks3): + if self.use_checkpoint and i < self.checkpoint_num[2]: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + x_out = self.norm3(x.permute(0, 2, 3, 1)) + out.append(x_out.permute(0, 3, 1, 2).contiguous()) + x = self.patch_embed4(x) + for i, blk in enumerate(self.blocks4): + if self.use_checkpoint and i < self.checkpoint_num[3]: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + x_out = self.norm4(x.permute(0, 2, 3, 1)) + out.append(x_out.permute(0, 3, 1, 2).contiguous()) + return tuple(out) + + def forward(self, x): + x = self.forward_features(x) + return x \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/upernet_global_small.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/upernet_global_small.py new file mode 100644 index 00000000..b1084a11 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/uniformer/upernet_global_small.py @@ -0,0 +1,44 @@ +_base_ = [ + 'configs/_base_/models/upernet_uniformer.py', + 'configs/_base_/datasets/ade20k.py', + 'configs/_base_/default_runtime.py', + 'configs/_base_/schedules/schedule_160k.py' +] + +custom_imports = dict( + imports=['custom_controlnet_aux.uniformer.uniformer'], + allow_failed_imports=False +) + +model = dict( + backbone=dict( + type='UniFormer', + embed_dim=[64, 128, 320, 512], + layers=[3, 4, 8, 3], + head_dim=64, + drop_path_rate=0.25, + windows=False, + hybrid=False + ), + decode_head=dict( + in_channels=[64, 128, 320, 512], + num_classes=150 + ), + auxiliary_head=dict( + in_channels=320, + num_classes=150 + )) + +# AdamW optimizer, no weight decay for position embedding & layer norm in backbone +optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01, + paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.)})) + +lr_config = dict(_delete_=True, policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, min_lr=0.0, by_epoch=False) + +data=dict(samples_per_gpu=2) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/__init__.py new file mode 100644 index 00000000..add304dc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/__init__.py @@ -0,0 +1,195 @@ +import os +import warnings + +import cv2 +import numpy as np +import torch +from einops import rearrange +from PIL import Image + +from custom_controlnet_aux.util import resize_image_with_pad,common_input_validate, custom_hf_download, UNIMATCH_MODEL_NAME +from .utils.flow_viz import save_vis_flow_tofile, flow_to_image +from .unimatch.unimatch import UniMatch +import torch.nn.functional as F +from argparse import Namespace + +def inference_flow(model, + image1, #np array of HWC + image2, + padding_factor=8, + inference_size=None, + attn_type='swin', + attn_splits_list=None, + corr_radius_list=None, + prop_radius_list=None, + num_reg_refine=1, + pred_bidir_flow=False, + pred_bwd_flow=False, + fwd_bwd_consistency_check=False, + device="cpu", + **kwargs + ): + fixed_inference_size = inference_size + transpose_img = False + image1 = torch.from_numpy(image1).permute(2, 0, 1).float().unsqueeze(0).to(device) + image2 = torch.from_numpy(image2).permute(2, 0, 1).float().unsqueeze(0).to(device) + + # the model is trained with size: width > height + if image1.size(-2) > image1.size(-1): + image1 = torch.transpose(image1, -2, -1) + image2 = torch.transpose(image2, -2, -1) + transpose_img = True + + nearest_size = [int(np.ceil(image1.size(-2) / padding_factor)) * padding_factor, + int(np.ceil(image1.size(-1) / padding_factor)) * padding_factor] + # resize to nearest size or specified size + inference_size = nearest_size if fixed_inference_size is None else fixed_inference_size + assert isinstance(inference_size, list) or isinstance(inference_size, tuple) + ori_size = image1.shape[-2:] + + # resize before inference + if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]: + image1 = F.interpolate(image1, size=inference_size, mode='bilinear', + align_corners=True) + image2 = F.interpolate(image2, size=inference_size, mode='bilinear', + align_corners=True) + if pred_bwd_flow: + image1, image2 = image2, image1 + + results_dict = model(image1, image2, + attn_type=attn_type, + attn_splits_list=attn_splits_list, + corr_radius_list=corr_radius_list, + prop_radius_list=prop_radius_list, + num_reg_refine=num_reg_refine, + task='flow', + pred_bidir_flow=pred_bidir_flow, + ) + flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W] + + # resize back + if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]: + flow_pr = F.interpolate(flow_pr, size=ori_size, mode='bilinear', + align_corners=True) + flow_pr[:, 0] = flow_pr[:, 0] * ori_size[-1] / inference_size[-1] + flow_pr[:, 1] = flow_pr[:, 1] * ori_size[-2] / inference_size[-2] + + if transpose_img: + flow_pr = torch.transpose(flow_pr, -2, -1) + + flow = flow_pr[0].permute(1, 2, 0).cpu().numpy() # [H, W, 2] + + vis_image = flow_to_image(flow) + + # also predict backward flow + if pred_bidir_flow: + assert flow_pr.size(0) == 2 # [2, H, W, 2] + flow_bwd = flow_pr[1].permute(1, 2, 0).cpu().numpy() # [H, W, 2] + vis_image = flow_to_image(flow_bwd) + flow = flow_bwd + return flow, vis_image + +MODEL_CONFIGS = { + "gmflow-scale1": Namespace( + num_scales=1, + upsample_factor=8, + + attn_type="swin", + feature_channels=128, + num_head=1, + ffn_dim_expansion=4, + num_transformer_layers=6, + + attn_splits_list=[2], + corr_radius_list=[-1], + prop_radius_list=[-1], + + reg_refine=False, + num_reg_refine=1 + ), + "gmflow-scale2": Namespace( + num_scales=2, + upsample_factor=4, + padding_factor=32, + + attn_type="swin", + feature_channels=128, + num_head=1, + ffn_dim_expansion=4, + num_transformer_layers=6, + + attn_splits_list=[2, 8], + corr_radius_list=[-1, 4], + prop_radius_list=[-1, 1], + + reg_refine=False, + num_reg_refine=1 + ), + "gmflow-scale2-regrefine6": Namespace( + num_scales=2, + upsample_factor=4, + padding_factor=32, + + attn_type="swin", + feature_channels=128, + num_head=1, + ffn_dim_expansion=4, + num_transformer_layers=6, + + attn_splits_list=[2, 8], + corr_radius_list=[-1, 4], + prop_radius_list=[-1, 1], + + reg_refine=True, + num_reg_refine=6 + ) +} + +class UnimatchDetector: + def __init__(self, unimatch, config_args): + self.unimatch = unimatch + self.config_args = config_args + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path=UNIMATCH_MODEL_NAME, filename="gmflow-scale2-regrefine6-mixdata.pth"): + model_path = custom_hf_download(pretrained_model_or_path, filename) + config_args = None + for key in list(MODEL_CONFIGS.keys())[::-1]: + if key in filename: + config_args = MODEL_CONFIGS[key] + break + assert config_args, f"Couldn't find hardcoded Unimatch config for {filename}" + + model = UniMatch(feature_channels=config_args.feature_channels, + num_scales=config_args.num_scales, + upsample_factor=config_args.upsample_factor, + num_head=config_args.num_head, + ffn_dim_expansion=config_args.ffn_dim_expansion, + num_transformer_layers=config_args.num_transformer_layers, + reg_refine=config_args.reg_refine, + task='flow') + + sd = torch.load(model_path, map_location="cpu") + model.load_state_dict(sd['model']) + return cls(model, config_args) + + def to(self, device): + self.unimatch.to(device) + self.device = device + return self + + def __call__(self, image1, image2, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", pred_bwd_flow=False, pred_bidir_flow=False, **kwargs): + assert image1.shape == image2.shape, f"[Unimatch] image1 and image2 must have the same size, got {image1.shape} and {image2.shape}" + + image1, output_type = common_input_validate(image1, output_type, **kwargs) + #image1, remove_pad = resize_image_with_pad(image1, detect_resolution, upscale_method) + image2, output_type = common_input_validate(image2, output_type, **kwargs) + #image2, remove_pad = resize_image_with_pad(image2, detect_resolution, upscale_method) + with torch.no_grad(): + flow, vis_image = inference_flow(self.unimatch, image1, image2, device=self.device, pred_bwd_flow=pred_bwd_flow, pred_bidir_flow=pred_bidir_flow, **vars(self.config_args)) + + if output_type == "pil": + vis_image = Image.fromarray(vis_image) + + return flow, vis_image diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/attention.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/attention.py new file mode 100644 index 00000000..92a3c878 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/attention.py @@ -0,0 +1,253 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .utils import split_feature, merge_splits, split_feature_1d, merge_splits_1d + + +def single_head_full_attention(q, k, v): + # q, k, v: [B, L, C] + assert q.dim() == k.dim() == v.dim() == 3 + + scores = torch.matmul(q, k.permute(0, 2, 1)) / (q.size(2) ** .5) # [B, L, L] + attn = torch.softmax(scores, dim=2) # [B, L, L] + out = torch.matmul(attn, v) # [B, L, C] + + return out + + +def single_head_full_attention_1d(q, k, v, + h=None, + w=None, + ): + # q, k, v: [B, L, C] + + assert h is not None and w is not None + assert q.size(1) == h * w + + b, _, c = q.size() + + q = q.view(b, h, w, c) # [B, H, W, C] + k = k.view(b, h, w, c) + v = v.view(b, h, w, c) + + scale_factor = c ** 0.5 + + scores = torch.matmul(q, k.permute(0, 1, 3, 2)) / scale_factor # [B, H, W, W] + + attn = torch.softmax(scores, dim=-1) + + out = torch.matmul(attn, v).view(b, -1, c) # [B, H*W, C] + + return out + + +def single_head_split_window_attention(q, k, v, + num_splits=1, + with_shift=False, + h=None, + w=None, + attn_mask=None, + ): + # ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py + # q, k, v: [B, L, C] + assert q.dim() == k.dim() == v.dim() == 3 + + assert h is not None and w is not None + assert q.size(1) == h * w + + b, _, c = q.size() + + b_new = b * num_splits * num_splits + + window_size_h = h // num_splits + window_size_w = w // num_splits + + q = q.view(b, h, w, c) # [B, H, W, C] + k = k.view(b, h, w, c) + v = v.view(b, h, w, c) + + scale_factor = c ** 0.5 + + if with_shift: + assert attn_mask is not None # compute once + shift_size_h = window_size_h // 2 + shift_size_w = window_size_w // 2 + + q = torch.roll(q, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2)) + k = torch.roll(k, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2)) + v = torch.roll(v, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2)) + + q = split_feature(q, num_splits=num_splits, channel_last=True) # [B*K*K, H/K, W/K, C] + k = split_feature(k, num_splits=num_splits, channel_last=True) + v = split_feature(v, num_splits=num_splits, channel_last=True) + + scores = torch.matmul(q.view(b_new, -1, c), k.view(b_new, -1, c).permute(0, 2, 1) + ) / scale_factor # [B*K*K, H/K*W/K, H/K*W/K] + + if with_shift: + scores += attn_mask.repeat(b, 1, 1) + + attn = torch.softmax(scores, dim=-1) + + out = torch.matmul(attn, v.view(b_new, -1, c)) # [B*K*K, H/K*W/K, C] + + out = merge_splits(out.view(b_new, h // num_splits, w // num_splits, c), + num_splits=num_splits, channel_last=True) # [B, H, W, C] + + # shift back + if with_shift: + out = torch.roll(out, shifts=(shift_size_h, shift_size_w), dims=(1, 2)) + + out = out.view(b, -1, c) + + return out + + +def single_head_split_window_attention_1d(q, k, v, + relative_position_bias=None, + num_splits=1, + with_shift=False, + h=None, + w=None, + attn_mask=None, + ): + # q, k, v: [B, L, C] + + assert h is not None and w is not None + assert q.size(1) == h * w + + b, _, c = q.size() + + b_new = b * num_splits * h + + window_size_w = w // num_splits + + q = q.view(b * h, w, c) # [B*H, W, C] + k = k.view(b * h, w, c) + v = v.view(b * h, w, c) + + scale_factor = c ** 0.5 + + if with_shift: + assert attn_mask is not None # compute once + shift_size_w = window_size_w // 2 + + q = torch.roll(q, shifts=-shift_size_w, dims=1) + k = torch.roll(k, shifts=-shift_size_w, dims=1) + v = torch.roll(v, shifts=-shift_size_w, dims=1) + + q = split_feature_1d(q, num_splits=num_splits) # [B*H*K, W/K, C] + k = split_feature_1d(k, num_splits=num_splits) + v = split_feature_1d(v, num_splits=num_splits) + + scores = torch.matmul(q.view(b_new, -1, c), k.view(b_new, -1, c).permute(0, 2, 1) + ) / scale_factor # [B*H*K, W/K, W/K] + + if with_shift: + # attn_mask: [K, W/K, W/K] + scores += attn_mask.repeat(b * h, 1, 1) # [B*H*K, W/K, W/K] + + attn = torch.softmax(scores, dim=-1) + + out = torch.matmul(attn, v.view(b_new, -1, c)) # [B*H*K, W/K, C] + + out = merge_splits_1d(out, h, num_splits=num_splits) # [B, H, W, C] + + # shift back + if with_shift: + out = torch.roll(out, shifts=shift_size_w, dims=2) + + out = out.view(b, -1, c) + + return out + + +class SelfAttnPropagation(nn.Module): + """ + flow propagation with self-attention on feature + query: feature0, key: feature0, value: flow + """ + + def __init__(self, in_channels, + **kwargs, + ): + super(SelfAttnPropagation, self).__init__() + + self.q_proj = nn.Linear(in_channels, in_channels) + self.k_proj = nn.Linear(in_channels, in_channels) + + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, feature0, flow, + local_window_attn=False, + local_window_radius=1, + **kwargs, + ): + # q, k: feature [B, C, H, W], v: flow [B, 2, H, W] + if local_window_attn: + return self.forward_local_window_attn(feature0, flow, + local_window_radius=local_window_radius) + + b, c, h, w = feature0.size() + + query = feature0.view(b, c, h * w).permute(0, 2, 1) # [B, H*W, C] + + # a note: the ``correct'' implementation should be: + # ``query = self.q_proj(query), key = self.k_proj(query)'' + # this problem is observed while cleaning up the code + # however, this doesn't affect the performance since the projection is a linear operation, + # thus the two projection matrices for key can be merged + # so I just leave it as is in order to not re-train all models :) + query = self.q_proj(query) # [B, H*W, C] + key = self.k_proj(query) # [B, H*W, C] + + value = flow.view(b, flow.size(1), h * w).permute(0, 2, 1) # [B, H*W, 2] + + scores = torch.matmul(query, key.permute(0, 2, 1)) / (c ** 0.5) # [B, H*W, H*W] + prob = torch.softmax(scores, dim=-1) + + out = torch.matmul(prob, value) # [B, H*W, 2] + out = out.view(b, h, w, value.size(-1)).permute(0, 3, 1, 2) # [B, 2, H, W] + + return out + + def forward_local_window_attn(self, feature0, flow, + local_window_radius=1, + ): + assert flow.size(1) == 2 or flow.size(1) == 1 # flow or disparity or depth + assert local_window_radius > 0 + + b, c, h, w = feature0.size() + + value_channel = flow.size(1) + + feature0_reshape = self.q_proj(feature0.view(b, c, -1).permute(0, 2, 1) + ).reshape(b * h * w, 1, c) # [B*H*W, 1, C] + + kernel_size = 2 * local_window_radius + 1 + + feature0_proj = self.k_proj(feature0.view(b, c, -1).permute(0, 2, 1)).permute(0, 2, 1).reshape(b, c, h, w) + + feature0_window = F.unfold(feature0_proj, kernel_size=kernel_size, + padding=local_window_radius) # [B, C*(2R+1)^2), H*W] + + feature0_window = feature0_window.view(b, c, kernel_size ** 2, h, w).permute( + 0, 3, 4, 1, 2).reshape(b * h * w, c, kernel_size ** 2) # [B*H*W, C, (2R+1)^2] + + flow_window = F.unfold(flow, kernel_size=kernel_size, + padding=local_window_radius) # [B, 2*(2R+1)^2), H*W] + + flow_window = flow_window.view(b, value_channel, kernel_size ** 2, h, w).permute( + 0, 3, 4, 2, 1).reshape(b * h * w, kernel_size ** 2, value_channel) # [B*H*W, (2R+1)^2, 2] + + scores = torch.matmul(feature0_reshape, feature0_window) / (c ** 0.5) # [B*H*W, 1, (2R+1)^2] + + prob = torch.softmax(scores, dim=-1) + + out = torch.matmul(prob, flow_window).view(b, h, w, value_channel + ).permute(0, 3, 1, 2).contiguous() # [B, 2, H, W] + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/backbone.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/backbone.py new file mode 100644 index 00000000..a30942ec --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/backbone.py @@ -0,0 +1,117 @@ +import torch.nn as nn + +from .trident_conv import MultiScaleTridentConv + + +class ResidualBlock(nn.Module): + def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d, stride=1, dilation=1, + ): + super(ResidualBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, + dilation=dilation, padding=dilation, stride=stride, bias=False) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, + dilation=dilation, padding=dilation, bias=False) + self.relu = nn.ReLU(inplace=True) + + self.norm1 = norm_layer(planes) + self.norm2 = norm_layer(planes) + if not stride == 1 or in_planes != planes: + self.norm3 = norm_layer(planes) + + if stride == 1 and in_planes == planes: + self.downsample = None + else: + self.downsample = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) + + def forward(self, x): + y = x + y = self.relu(self.norm1(self.conv1(y))) + y = self.relu(self.norm2(self.conv2(y))) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x + y) + + +class CNNEncoder(nn.Module): + def __init__(self, output_dim=128, + norm_layer=nn.InstanceNorm2d, + num_output_scales=1, + **kwargs, + ): + super(CNNEncoder, self).__init__() + self.num_branch = num_output_scales + + feature_dims = [64, 96, 128] + + self.conv1 = nn.Conv2d(3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False) # 1/2 + self.norm1 = norm_layer(feature_dims[0]) + self.relu1 = nn.ReLU(inplace=True) + + self.in_planes = feature_dims[0] + self.layer1 = self._make_layer(feature_dims[0], stride=1, norm_layer=norm_layer) # 1/2 + self.layer2 = self._make_layer(feature_dims[1], stride=2, norm_layer=norm_layer) # 1/4 + + # highest resolution 1/4 or 1/8 + stride = 2 if num_output_scales == 1 else 1 + self.layer3 = self._make_layer(feature_dims[2], stride=stride, + norm_layer=norm_layer, + ) # 1/4 or 1/8 + + self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0) + + if self.num_branch > 1: + if self.num_branch == 4: + strides = (1, 2, 4, 8) + elif self.num_branch == 3: + strides = (1, 2, 4) + elif self.num_branch == 2: + strides = (1, 2) + else: + raise ValueError + + self.trident_conv = MultiScaleTridentConv(output_dim, output_dim, + kernel_size=3, + strides=strides, + paddings=1, + num_branch=self.num_branch, + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): + if m.weight is not None: + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d): + layer1 = ResidualBlock(self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation) + layer2 = ResidualBlock(dim, dim, norm_layer=norm_layer, stride=1, dilation=dilation) + + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.norm1(x) + x = self.relu1(x) + + x = self.layer1(x) # 1/2 + x = self.layer2(x) # 1/4 + x = self.layer3(x) # 1/8 or 1/4 + + x = self.conv2(x) + + if self.num_branch > 1: + out = self.trident_conv([x] * self.num_branch) # high to low res + else: + out = [x] + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/geometry.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/geometry.py new file mode 100644 index 00000000..775a9578 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/geometry.py @@ -0,0 +1,195 @@ +import torch +import torch.nn.functional as F + + +def coords_grid(b, h, w, homogeneous=False, device=None): + y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W] + + stacks = [x, y] + + if homogeneous: + ones = torch.ones_like(x) # [H, W] + stacks.append(ones) + + grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W] + + grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W] + + if device is not None: + grid = grid.to(device) + + return grid + + +def generate_window_grid(h_min, h_max, w_min, w_max, len_h, len_w, device=None): + assert device is not None + + x, y = torch.meshgrid([torch.linspace(w_min, w_max, len_w, device=device), + torch.linspace(h_min, h_max, len_h, device=device)], + ) + grid = torch.stack((x, y), -1).transpose(0, 1).float() # [H, W, 2] + + return grid + + +def normalize_coords(coords, h, w): + # coords: [B, H, W, 2] + c = torch.Tensor([(w - 1) / 2., (h - 1) / 2.]).float().to(coords.device) + return (coords - c) / c # [-1, 1] + + +def bilinear_sample(img, sample_coords, mode='bilinear', padding_mode='zeros', return_mask=False): + # img: [B, C, H, W] + # sample_coords: [B, 2, H, W] in image scale + if sample_coords.size(1) != 2: # [B, H, W, 2] + sample_coords = sample_coords.permute(0, 3, 1, 2) + + b, _, h, w = sample_coords.shape + + # Normalize to [-1, 1] + x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1 + y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1 + + grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2] + + img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True) + + if return_mask: + mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W] + + return img, mask + + return img + + +def flow_warp(feature, flow, mask=False, padding_mode='zeros'): + b, c, h, w = feature.size() + assert flow.size(1) == 2 + + grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W] + + return bilinear_sample(feature, grid, padding_mode=padding_mode, + return_mask=mask) + + +def forward_backward_consistency_check(fwd_flow, bwd_flow, + alpha=0.01, + beta=0.5 + ): + # fwd_flow, bwd_flow: [B, 2, H, W] + # alpha and beta values are following UnFlow (https://arxiv.org/abs/1711.07837) + assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4 + assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2 + flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W] + + warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W] + warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W] + + diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W] + diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1) + + threshold = alpha * flow_mag + beta + + fwd_occ = (diff_fwd > threshold).float() # [B, H, W] + bwd_occ = (diff_bwd > threshold).float() + + return fwd_occ, bwd_occ + + +def back_project(depth, intrinsics): + # Back project 2D pixel coords to 3D points + # depth: [B, H, W] + # intrinsics: [B, 3, 3] + b, h, w = depth.shape + grid = coords_grid(b, h, w, homogeneous=True, device=depth.device) # [B, 3, H, W] + + intrinsics_inv = torch.inverse(intrinsics) # [B, 3, 3] + + points = intrinsics_inv.bmm(grid.view(b, 3, -1)).view(b, 3, h, w) * depth.unsqueeze(1) # [B, 3, H, W] + + return points + + +def camera_transform(points_ref, extrinsics_ref=None, extrinsics_tgt=None, extrinsics_rel=None): + # Transform 3D points from reference camera to target camera + # points_ref: [B, 3, H, W] + # extrinsics_ref: [B, 4, 4] + # extrinsics_tgt: [B, 4, 4] + # extrinsics_rel: [B, 4, 4], relative pose transform + b, _, h, w = points_ref.shape + + if extrinsics_rel is None: + extrinsics_rel = torch.bmm(extrinsics_tgt, torch.inverse(extrinsics_ref)) # [B, 4, 4] + + points_tgt = torch.bmm(extrinsics_rel[:, :3, :3], + points_ref.view(b, 3, -1)) + extrinsics_rel[:, :3, -1:] # [B, 3, H*W] + + points_tgt = points_tgt.view(b, 3, h, w) # [B, 3, H, W] + + return points_tgt + + +def reproject(points_tgt, intrinsics, return_mask=False): + # reproject to target view + # points_tgt: [B, 3, H, W] + # intrinsics: [B, 3, 3] + + b, _, h, w = points_tgt.shape + + proj_points = torch.bmm(intrinsics, points_tgt.view(b, 3, -1)).view(b, 3, h, w) # [B, 3, H, W] + + X = proj_points[:, 0] + Y = proj_points[:, 1] + Z = proj_points[:, 2].clamp(min=1e-3) + + pixel_coords = torch.stack([X / Z, Y / Z], dim=1).view(b, 2, h, w) # [B, 2, H, W] in image scale + + if return_mask: + # valid mask in pixel space + mask = (pixel_coords[:, 0] >= 0) & (pixel_coords[:, 0] <= (w - 1)) & ( + pixel_coords[:, 1] >= 0) & (pixel_coords[:, 1] <= (h - 1)) # [B, H, W] + + return pixel_coords, mask + + return pixel_coords + + +def reproject_coords(depth_ref, intrinsics, extrinsics_ref=None, extrinsics_tgt=None, extrinsics_rel=None, + return_mask=False): + # Compute reprojection sample coords + points_ref = back_project(depth_ref, intrinsics) # [B, 3, H, W] + points_tgt = camera_transform(points_ref, extrinsics_ref, extrinsics_tgt, extrinsics_rel=extrinsics_rel) + + if return_mask: + reproj_coords, mask = reproject(points_tgt, intrinsics, + return_mask=return_mask) # [B, 2, H, W] in image scale + + return reproj_coords, mask + + reproj_coords = reproject(points_tgt, intrinsics, + return_mask=return_mask) # [B, 2, H, W] in image scale + + return reproj_coords + + +def compute_flow_with_depth_pose(depth_ref, intrinsics, + extrinsics_ref=None, extrinsics_tgt=None, extrinsics_rel=None, + return_mask=False): + b, h, w = depth_ref.shape + coords_init = coords_grid(b, h, w, device=depth_ref.device) # [B, 2, H, W] + + if return_mask: + reproj_coords, mask = reproject_coords(depth_ref, intrinsics, extrinsics_ref, extrinsics_tgt, + extrinsics_rel=extrinsics_rel, + return_mask=return_mask) # [B, 2, H, W] + rigid_flow = reproj_coords - coords_init + + return rigid_flow, mask + + reproj_coords = reproject_coords(depth_ref, intrinsics, extrinsics_ref, extrinsics_tgt, + extrinsics_rel=extrinsics_rel, + return_mask=return_mask) # [B, 2, H, W] + + rigid_flow = reproj_coords - coords_init + + return rigid_flow diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/matching.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/matching.py new file mode 100644 index 00000000..595437f2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/matching.py @@ -0,0 +1,279 @@ +import torch +import torch.nn.functional as F + +from .geometry import coords_grid, generate_window_grid, normalize_coords + + +def global_correlation_softmax(feature0, feature1, + pred_bidir_flow=False, + ): + # global correlation + b, c, h, w = feature0.shape + feature0 = feature0.view(b, c, -1).permute(0, 2, 1) # [B, H*W, C] + feature1 = feature1.view(b, c, -1) # [B, C, H*W] + + correlation = torch.matmul(feature0, feature1).view(b, h, w, h, w) / (c ** 0.5) # [B, H, W, H, W] + + # flow from softmax + init_grid = coords_grid(b, h, w).to(correlation.device) # [B, 2, H, W] + grid = init_grid.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2] + + correlation = correlation.view(b, h * w, h * w) # [B, H*W, H*W] + + if pred_bidir_flow: + correlation = torch.cat((correlation, correlation.permute(0, 2, 1)), dim=0) # [2*B, H*W, H*W] + init_grid = init_grid.repeat(2, 1, 1, 1) # [2*B, 2, H, W] + grid = grid.repeat(2, 1, 1) # [2*B, H*W, 2] + b = b * 2 + + prob = F.softmax(correlation, dim=-1) # [B, H*W, H*W] + + correspondence = torch.matmul(prob, grid).view(b, h, w, 2).permute(0, 3, 1, 2) # [B, 2, H, W] + + # when predicting bidirectional flow, flow is the concatenation of forward flow and backward flow + flow = correspondence - init_grid + + return flow, prob + + +def local_correlation_softmax(feature0, feature1, local_radius, + padding_mode='zeros', + ): + b, c, h, w = feature0.size() + coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W] + coords = coords_init.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2] + + local_h = 2 * local_radius + 1 + local_w = 2 * local_radius + 1 + + window_grid = generate_window_grid(-local_radius, local_radius, + -local_radius, local_radius, + local_h, local_w, device=feature0.device) # [2R+1, 2R+1, 2] + window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1)^2, 2] + sample_coords = coords.unsqueeze(-2) + window_grid # [B, H*W, (2R+1)^2, 2] + + sample_coords_softmax = sample_coords + + # exclude coords that are out of image space + valid_x = (sample_coords[:, :, :, 0] >= 0) & (sample_coords[:, :, :, 0] < w) # [B, H*W, (2R+1)^2] + valid_y = (sample_coords[:, :, :, 1] >= 0) & (sample_coords[:, :, :, 1] < h) # [B, H*W, (2R+1)^2] + + valid = valid_x & valid_y # [B, H*W, (2R+1)^2], used to mask out invalid values when softmax + + # normalize coordinates to [-1, 1] + sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1] + window_feature = F.grid_sample(feature1, sample_coords_norm, + padding_mode=padding_mode, align_corners=True + ).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)^2] + feature0_view = feature0.permute(0, 2, 3, 1).view(b, h * w, 1, c) # [B, H*W, 1, C] + + corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)^2] + + # mask invalid locations + corr[~valid] = -1e9 + + prob = F.softmax(corr, -1) # [B, H*W, (2R+1)^2] + + correspondence = torch.matmul(prob.unsqueeze(-2), sample_coords_softmax).squeeze(-2).view( + b, h, w, 2).permute(0, 3, 1, 2) # [B, 2, H, W] + + flow = correspondence - coords_init + match_prob = prob + + return flow, match_prob + + +def local_correlation_with_flow(feature0, feature1, + flow, + local_radius, + padding_mode='zeros', + dilation=1, + ): + b, c, h, w = feature0.size() + coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W] + coords = coords_init.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2] + + local_h = 2 * local_radius + 1 + local_w = 2 * local_radius + 1 + + window_grid = generate_window_grid(-local_radius, local_radius, + -local_radius, local_radius, + local_h, local_w, device=feature0.device) # [2R+1, 2R+1, 2] + window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1)^2, 2] + sample_coords = coords.unsqueeze(-2) + window_grid * dilation # [B, H*W, (2R+1)^2, 2] + + # flow can be zero when using features after transformer + if not isinstance(flow, float): + sample_coords = sample_coords + flow.view( + b, 2, -1).permute(0, 2, 1).unsqueeze(-2) # [B, H*W, (2R+1)^2, 2] + else: + assert flow == 0. + + # normalize coordinates to [-1, 1] + sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1] + window_feature = F.grid_sample(feature1, sample_coords_norm, + padding_mode=padding_mode, align_corners=True + ).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)^2] + feature0_view = feature0.permute(0, 2, 3, 1).view(b, h * w, 1, c) # [B, H*W, 1, C] + + corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)^2] + + corr = corr.view(b, h, w, -1).permute(0, 3, 1, 2).contiguous() # [B, (2R+1)^2, H, W] + + return corr + + +def global_correlation_softmax_stereo(feature0, feature1, + ): + # global correlation on horizontal direction + b, c, h, w = feature0.shape + + x_grid = torch.linspace(0, w - 1, w, device=feature0.device) # [W] + + feature0 = feature0.permute(0, 2, 3, 1) # [B, H, W, C] + feature1 = feature1.permute(0, 2, 1, 3) # [B, H, C, W] + + correlation = torch.matmul(feature0, feature1) / (c ** 0.5) # [B, H, W, W] + + # mask subsequent positions to make disparity positive + mask = torch.triu(torch.ones((w, w)), diagonal=1).type_as(feature0) # [W, W] + valid_mask = (mask == 0).unsqueeze(0).unsqueeze(0).repeat(b, h, 1, 1) # [B, H, W, W] + + correlation[~valid_mask] = -1e9 + + prob = F.softmax(correlation, dim=-1) # [B, H, W, W] + + correspondence = (x_grid.view(1, 1, 1, w) * prob).sum(-1) # [B, H, W] + + # NOTE: unlike flow, disparity is typically positive + disparity = x_grid.view(1, 1, w).repeat(b, h, 1) - correspondence # [B, H, W] + + return disparity.unsqueeze(1), prob # feature resolution + + +def local_correlation_softmax_stereo(feature0, feature1, local_radius, + ): + b, c, h, w = feature0.size() + coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W] + coords = coords_init.view(b, 2, -1).permute(0, 2, 1).contiguous() # [B, H*W, 2] + + local_h = 1 + local_w = 2 * local_radius + 1 + + window_grid = generate_window_grid(0, 0, + -local_radius, local_radius, + local_h, local_w, device=feature0.device) # [1, 2R+1, 2] + window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1), 2] + sample_coords = coords.unsqueeze(-2) + window_grid # [B, H*W, (2R+1), 2] + + sample_coords_softmax = sample_coords + + # exclude coords that are out of image space + valid_x = (sample_coords[:, :, :, 0] >= 0) & (sample_coords[:, :, :, 0] < w) # [B, H*W, (2R+1)^2] + valid_y = (sample_coords[:, :, :, 1] >= 0) & (sample_coords[:, :, :, 1] < h) # [B, H*W, (2R+1)^2] + + valid = valid_x & valid_y # [B, H*W, (2R+1)^2], used to mask out invalid values when softmax + + # normalize coordinates to [-1, 1] + sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1] + window_feature = F.grid_sample(feature1, sample_coords_norm, + padding_mode='zeros', align_corners=True + ).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)] + feature0_view = feature0.permute(0, 2, 3, 1).contiguous().view(b, h * w, 1, c) # [B, H*W, 1, C] + + corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)] + + # mask invalid locations + corr[~valid] = -1e9 + + prob = F.softmax(corr, -1) # [B, H*W, (2R+1)] + + correspondence = torch.matmul(prob.unsqueeze(-2), + sample_coords_softmax).squeeze(-2).view( + b, h, w, 2).permute(0, 3, 1, 2).contiguous() # [B, 2, H, W] + + flow = correspondence - coords_init # flow at feature resolution + match_prob = prob + + flow_x = -flow[:, :1] # [B, 1, H, W] + + return flow_x, match_prob + + +def correlation_softmax_depth(feature0, feature1, + intrinsics, + pose, + depth_candidates, + depth_from_argmax=False, + pred_bidir_depth=False, + ): + b, c, h, w = feature0.size() + assert depth_candidates.dim() == 4 # [B, D, H, W] + scale_factor = c ** 0.5 + + if pred_bidir_depth: + feature0, feature1 = torch.cat((feature0, feature1), dim=0), torch.cat((feature1, feature0), dim=0) + intrinsics = intrinsics.repeat(2, 1, 1) + pose = torch.cat((pose, torch.inverse(pose)), dim=0) + depth_candidates = depth_candidates.repeat(2, 1, 1, 1) + + # depth candidates are actually inverse depth + warped_feature1 = warp_with_pose_depth_candidates(feature1, intrinsics, pose, + 1. / depth_candidates, + ) # [B, C, D, H, W] + + correlation = (feature0.unsqueeze(2) * warped_feature1).sum(1) / scale_factor # [B, D, H, W] + + match_prob = F.softmax(correlation, dim=1) # [B, D, H, W] + + # for cross-task transfer (flow -> depth), extract depth with argmax at test time + if depth_from_argmax: + index = torch.argmax(match_prob, dim=1, keepdim=True) + depth = torch.gather(depth_candidates, dim=1, index=index) + else: + depth = (match_prob * depth_candidates).sum(dim=1, keepdim=True) # [B, 1, H, W] + + return depth, match_prob + + +def warp_with_pose_depth_candidates(feature1, intrinsics, pose, depth, + clamp_min_depth=1e-3, + ): + """ + feature1: [B, C, H, W] + intrinsics: [B, 3, 3] + pose: [B, 4, 4] + depth: [B, D, H, W] + """ + + assert intrinsics.size(1) == intrinsics.size(2) == 3 + assert pose.size(1) == pose.size(2) == 4 + assert depth.dim() == 4 + + b, d, h, w = depth.size() + c = feature1.size(1) + + with torch.no_grad(): + # pixel coordinates + grid = coords_grid(b, h, w, homogeneous=True, device=depth.device) # [B, 3, H, W] + # back project to 3D and transform viewpoint + points = torch.inverse(intrinsics).bmm(grid.view(b, 3, -1)) # [B, 3, H*W] + points = torch.bmm(pose[:, :3, :3], points).unsqueeze(2).repeat( + 1, 1, d, 1) * depth.view(b, 1, d, h * w) # [B, 3, D, H*W] + points = points + pose[:, :3, -1:].unsqueeze(-1) # [B, 3, D, H*W] + # reproject to 2D image plane + points = torch.bmm(intrinsics, points.view(b, 3, -1)).view(b, 3, d, h * w) # [B, 3, D, H*W] + pixel_coords = points[:, :2] / points[:, -1:].clamp(min=clamp_min_depth) # [B, 2, D, H*W] + + # normalize to [-1, 1] + x_grid = 2 * pixel_coords[:, 0] / (w - 1) - 1 + y_grid = 2 * pixel_coords[:, 1] / (h - 1) - 1 + + grid = torch.stack([x_grid, y_grid], dim=-1) # [B, D, H*W, 2] + + # sample features + warped_feature = F.grid_sample(feature1, grid.view(b, d * h, w, 2), mode='bilinear', + padding_mode='zeros', + align_corners=True).view(b, c, d, h, w) # [B, C, D, H, W] + + return warped_feature diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/position.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/position.py new file mode 100644 index 00000000..14a6da43 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/position.py @@ -0,0 +1,46 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# https://github.com/facebookresearch/detr/blob/main/models/position_encoding.py + +import torch +import torch.nn as nn +import math + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=True, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x): + # x = tensor_list.tensors # [B, C, H, W] + # mask = tensor_list.mask # [B, H, W], input with padding, valid as 0 + b, c, h, w = x.size() + mask = torch.ones((b, h, w), device=x.device) # [B, H, W] + y_embed = mask.cumsum(1, dtype=torch.float32) + x_embed = mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/reg_refine.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/reg_refine.py new file mode 100644 index 00000000..47f83da1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/reg_refine.py @@ -0,0 +1,119 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class FlowHead(nn.Module): + def __init__(self, input_dim=128, hidden_dim=256, + out_dim=2, + ): + super(FlowHead, self).__init__() + + self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) + self.conv2 = nn.Conv2d(hidden_dim, out_dim, 3, padding=1) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + out = self.conv2(self.relu(self.conv1(x))) + + return out + + +class SepConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192 + 128, + kernel_size=5, + ): + padding = (kernel_size - 1) // 2 + + super(SepConvGRU, self).__init__() + self.convz1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) + self.convr1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) + self.convq1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) + + self.convz2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) + self.convr2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) + self.convq2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) + + def forward(self, h, x): + # horizontal + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz1(hx)) + r = torch.sigmoid(self.convr1(hx)) + q = torch.tanh(self.convq1(torch.cat([r * h, x], dim=1))) + h = (1 - z) * h + z * q + + # vertical + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz2(hx)) + r = torch.sigmoid(self.convr2(hx)) + q = torch.tanh(self.convq2(torch.cat([r * h, x], dim=1))) + h = (1 - z) * h + z * q + + return h + + +class BasicMotionEncoder(nn.Module): + def __init__(self, corr_channels=324, + flow_channels=2, + ): + super(BasicMotionEncoder, self).__init__() + + self.convc1 = nn.Conv2d(corr_channels, 256, 1, padding=0) + self.convc2 = nn.Conv2d(256, 192, 3, padding=1) + self.convf1 = nn.Conv2d(flow_channels, 128, 7, padding=3) + self.convf2 = nn.Conv2d(128, 64, 3, padding=1) + self.conv = nn.Conv2d(64 + 192, 128 - flow_channels, 3, padding=1) + + def forward(self, flow, corr): + cor = F.relu(self.convc1(corr)) + cor = F.relu(self.convc2(cor)) + flo = F.relu(self.convf1(flow)) + flo = F.relu(self.convf2(flo)) + + cor_flo = torch.cat([cor, flo], dim=1) + out = F.relu(self.conv(cor_flo)) + return torch.cat([out, flow], dim=1) + + +class BasicUpdateBlock(nn.Module): + def __init__(self, corr_channels=324, + hidden_dim=128, + context_dim=128, + downsample_factor=8, + flow_dim=2, + bilinear_up=False, + ): + super(BasicUpdateBlock, self).__init__() + + self.encoder = BasicMotionEncoder(corr_channels=corr_channels, + flow_channels=flow_dim, + ) + + self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=context_dim + hidden_dim) + + self.flow_head = FlowHead(hidden_dim, hidden_dim=256, + out_dim=flow_dim, + ) + + if bilinear_up: + self.mask = None + else: + self.mask = nn.Sequential( + nn.Conv2d(hidden_dim, 256, 3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, downsample_factor ** 2 * 9, 1, padding=0)) + + def forward(self, net, inp, corr, flow): + motion_features = self.encoder(flow, corr) + + inp = torch.cat([inp, motion_features], dim=1) + + net = self.gru(net, inp) + delta_flow = self.flow_head(net) + + if self.mask is not None: + mask = self.mask(net) + else: + mask = None + + return net, mask, delta_flow diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/transformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/transformer.py new file mode 100644 index 00000000..4878e23a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/transformer.py @@ -0,0 +1,294 @@ +import torch +import torch.nn as nn + +from .attention import (single_head_full_attention, single_head_split_window_attention, + single_head_full_attention_1d, single_head_split_window_attention_1d) +from .utils import generate_shift_window_attn_mask, generate_shift_window_attn_mask_1d + + +class TransformerLayer(nn.Module): + def __init__(self, + d_model=128, + nhead=1, + no_ffn=False, + ffn_dim_expansion=4, + ): + super(TransformerLayer, self).__init__() + + self.dim = d_model + self.nhead = nhead + self.no_ffn = no_ffn + + # multi-head attention + self.q_proj = nn.Linear(d_model, d_model, bias=False) + self.k_proj = nn.Linear(d_model, d_model, bias=False) + self.v_proj = nn.Linear(d_model, d_model, bias=False) + + self.merge = nn.Linear(d_model, d_model, bias=False) + + self.norm1 = nn.LayerNorm(d_model) + + # no ffn after self-attn, with ffn after cross-attn + if not self.no_ffn: + in_channels = d_model * 2 + self.mlp = nn.Sequential( + nn.Linear(in_channels, in_channels * ffn_dim_expansion, bias=False), + nn.GELU(), + nn.Linear(in_channels * ffn_dim_expansion, d_model, bias=False), + ) + + self.norm2 = nn.LayerNorm(d_model) + + def forward(self, source, target, + height=None, + width=None, + shifted_window_attn_mask=None, + shifted_window_attn_mask_1d=None, + attn_type='swin', + with_shift=False, + attn_num_splits=None, + ): + # source, target: [B, L, C] + query, key, value = source, target, target + + # for stereo: 2d attn in self-attn, 1d attn in cross-attn + is_self_attn = (query - key).abs().max() < 1e-6 + + # single-head attention + query = self.q_proj(query) # [B, L, C] + key = self.k_proj(key) # [B, L, C] + value = self.v_proj(value) # [B, L, C] + + if attn_type == 'swin' and attn_num_splits > 1: # self, cross-attn: both swin 2d + if self.nhead > 1: + # we observe that multihead attention slows down the speed and increases the memory consumption + # without bringing obvious performance gains and thus the implementation is removed + raise NotImplementedError + else: + message = single_head_split_window_attention(query, key, value, + num_splits=attn_num_splits, + with_shift=with_shift, + h=height, + w=width, + attn_mask=shifted_window_attn_mask, + ) + + elif attn_type == 'self_swin2d_cross_1d': # self-attn: swin 2d, cross-attn: full 1d + if self.nhead > 1: + raise NotImplementedError + else: + if is_self_attn: + if attn_num_splits > 1: + message = single_head_split_window_attention(query, key, value, + num_splits=attn_num_splits, + with_shift=with_shift, + h=height, + w=width, + attn_mask=shifted_window_attn_mask, + ) + else: + # full 2d attn + message = single_head_full_attention(query, key, value) # [N, L, C] + + else: + # cross attn 1d + message = single_head_full_attention_1d(query, key, value, + h=height, + w=width, + ) + + elif attn_type == 'self_swin2d_cross_swin1d': # self-attn: swin 2d, cross-attn: swin 1d + if self.nhead > 1: + raise NotImplementedError + else: + if is_self_attn: + if attn_num_splits > 1: + # self attn shift window + message = single_head_split_window_attention(query, key, value, + num_splits=attn_num_splits, + with_shift=with_shift, + h=height, + w=width, + attn_mask=shifted_window_attn_mask, + ) + else: + # full 2d attn + message = single_head_full_attention(query, key, value) # [N, L, C] + else: + if attn_num_splits > 1: + assert shifted_window_attn_mask_1d is not None + # cross attn 1d shift + message = single_head_split_window_attention_1d(query, key, value, + num_splits=attn_num_splits, + with_shift=with_shift, + h=height, + w=width, + attn_mask=shifted_window_attn_mask_1d, + ) + else: + message = single_head_full_attention_1d(query, key, value, + h=height, + w=width, + ) + + else: + message = single_head_full_attention(query, key, value) # [B, L, C] + + message = self.merge(message) # [B, L, C] + message = self.norm1(message) + + if not self.no_ffn: + message = self.mlp(torch.cat([source, message], dim=-1)) + message = self.norm2(message) + + return source + message + + +class TransformerBlock(nn.Module): + """self attention + cross attention + FFN""" + + def __init__(self, + d_model=128, + nhead=1, + ffn_dim_expansion=4, + ): + super(TransformerBlock, self).__init__() + + self.self_attn = TransformerLayer(d_model=d_model, + nhead=nhead, + no_ffn=True, + ffn_dim_expansion=ffn_dim_expansion, + ) + + self.cross_attn_ffn = TransformerLayer(d_model=d_model, + nhead=nhead, + ffn_dim_expansion=ffn_dim_expansion, + ) + + def forward(self, source, target, + height=None, + width=None, + shifted_window_attn_mask=None, + shifted_window_attn_mask_1d=None, + attn_type='swin', + with_shift=False, + attn_num_splits=None, + ): + # source, target: [B, L, C] + + # self attention + source = self.self_attn(source, source, + height=height, + width=width, + shifted_window_attn_mask=shifted_window_attn_mask, + attn_type=attn_type, + with_shift=with_shift, + attn_num_splits=attn_num_splits, + ) + + # cross attention and ffn + source = self.cross_attn_ffn(source, target, + height=height, + width=width, + shifted_window_attn_mask=shifted_window_attn_mask, + shifted_window_attn_mask_1d=shifted_window_attn_mask_1d, + attn_type=attn_type, + with_shift=with_shift, + attn_num_splits=attn_num_splits, + ) + + return source + + +class FeatureTransformer(nn.Module): + def __init__(self, + num_layers=6, + d_model=128, + nhead=1, + ffn_dim_expansion=4, + ): + super(FeatureTransformer, self).__init__() + + self.d_model = d_model + self.nhead = nhead + + self.layers = nn.ModuleList([ + TransformerBlock(d_model=d_model, + nhead=nhead, + ffn_dim_expansion=ffn_dim_expansion, + ) + for i in range(num_layers)]) + + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, feature0, feature1, + attn_type='swin', + attn_num_splits=None, + **kwargs, + ): + + b, c, h, w = feature0.shape + assert self.d_model == c + + feature0 = feature0.flatten(-2).permute(0, 2, 1) # [B, H*W, C] + feature1 = feature1.flatten(-2).permute(0, 2, 1) # [B, H*W, C] + + # 2d attention + if 'swin' in attn_type and attn_num_splits > 1: + # global and refine use different number of splits + window_size_h = h // attn_num_splits + window_size_w = w // attn_num_splits + + # compute attn mask once + shifted_window_attn_mask = generate_shift_window_attn_mask( + input_resolution=(h, w), + window_size_h=window_size_h, + window_size_w=window_size_w, + shift_size_h=window_size_h // 2, + shift_size_w=window_size_w // 2, + device=feature0.device, + ) # [K*K, H/K*W/K, H/K*W/K] + else: + shifted_window_attn_mask = None + + # 1d attention + if 'swin1d' in attn_type and attn_num_splits > 1: + window_size_w = w // attn_num_splits + + # compute attn mask once + shifted_window_attn_mask_1d = generate_shift_window_attn_mask_1d( + input_w=w, + window_size_w=window_size_w, + shift_size_w=window_size_w // 2, + device=feature0.device, + ) # [K, W/K, W/K] + else: + shifted_window_attn_mask_1d = None + + # concat feature0 and feature1 in batch dimension to compute in parallel + concat0 = torch.cat((feature0, feature1), dim=0) # [2B, H*W, C] + concat1 = torch.cat((feature1, feature0), dim=0) # [2B, H*W, C] + + for i, layer in enumerate(self.layers): + concat0 = layer(concat0, concat1, + height=h, + width=w, + attn_type=attn_type, + with_shift='swin' in attn_type and attn_num_splits > 1 and i % 2 == 1, + attn_num_splits=attn_num_splits, + shifted_window_attn_mask=shifted_window_attn_mask, + shifted_window_attn_mask_1d=shifted_window_attn_mask_1d, + ) + + # update feature1 + concat1 = torch.cat(concat0.chunk(chunks=2, dim=0)[::-1], dim=0) + + feature0, feature1 = concat0.chunk(chunks=2, dim=0) # [B, H*W, C] + + # reshape back + feature0 = feature0.view(b, h, w, c).permute(0, 3, 1, 2).contiguous() # [B, C, H, W] + feature1 = feature1.view(b, h, w, c).permute(0, 3, 1, 2).contiguous() # [B, C, H, W] + + return feature0, feature1 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/trident_conv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/trident_conv.py new file mode 100644 index 00000000..29a2a73e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/trident_conv.py @@ -0,0 +1,90 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# https://github.com/facebookresearch/detectron2/blob/main/projects/TridentNet/tridentnet/trident_conv.py + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.modules.utils import _pair + + +class MultiScaleTridentConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + strides=1, + paddings=0, + dilations=1, + dilation=1, + groups=1, + num_branch=1, + test_branch_idx=-1, + bias=False, + norm=None, + activation=None, + ): + super(MultiScaleTridentConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.num_branch = num_branch + self.stride = _pair(stride) + self.groups = groups + self.with_bias = bias + self.dilation = dilation + if isinstance(paddings, int): + paddings = [paddings] * self.num_branch + if isinstance(dilations, int): + dilations = [dilations] * self.num_branch + if isinstance(strides, int): + strides = [strides] * self.num_branch + self.paddings = [_pair(padding) for padding in paddings] + self.dilations = [_pair(dilation) for dilation in dilations] + self.strides = [_pair(stride) for stride in strides] + self.test_branch_idx = test_branch_idx + self.norm = norm + self.activation = activation + + assert len({self.num_branch, len(self.paddings), len(self.strides)}) == 1 + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, inputs): + num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1 + assert len(inputs) == num_branch + + if self.training or self.test_branch_idx == -1: + outputs = [ + F.conv2d(input, self.weight, self.bias, stride, padding, self.dilation, self.groups) + for input, stride, padding in zip(inputs, self.strides, self.paddings) + ] + else: + outputs = [ + F.conv2d( + inputs[0], + self.weight, + self.bias, + self.strides[self.test_branch_idx] if self.test_branch_idx == -1 else self.strides[-1], + self.paddings[self.test_branch_idx] if self.test_branch_idx == -1 else self.paddings[-1], + self.dilation, + self.groups, + ) + ] + + if self.norm is not None: + outputs = [self.norm(x) for x in outputs] + if self.activation is not None: + outputs = [self.activation(x) for x in outputs] + return outputs diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/unimatch.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/unimatch.py new file mode 100644 index 00000000..c442c674 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/unimatch.py @@ -0,0 +1,367 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .backbone import CNNEncoder +from .transformer import FeatureTransformer +from .matching import (global_correlation_softmax, local_correlation_softmax, local_correlation_with_flow, + global_correlation_softmax_stereo, local_correlation_softmax_stereo, + correlation_softmax_depth) +from .attention import SelfAttnPropagation +from .geometry import flow_warp, compute_flow_with_depth_pose +from .reg_refine import BasicUpdateBlock +from .utils import normalize_img, feature_add_position, upsample_flow_with_mask + + +class UniMatch(nn.Module): + def __init__(self, + num_scales=1, + feature_channels=128, + upsample_factor=8, + num_head=1, + ffn_dim_expansion=4, + num_transformer_layers=6, + reg_refine=False, # optional local regression refinement + task='flow', + ): + super(UniMatch, self).__init__() + + self.feature_channels = feature_channels + self.num_scales = num_scales + self.upsample_factor = upsample_factor + self.reg_refine = reg_refine + + # CNN + self.backbone = CNNEncoder(output_dim=feature_channels, num_output_scales=num_scales) + + # Transformer + self.transformer = FeatureTransformer(num_layers=num_transformer_layers, + d_model=feature_channels, + nhead=num_head, + ffn_dim_expansion=ffn_dim_expansion, + ) + + # propagation with self-attn + self.feature_flow_attn = SelfAttnPropagation(in_channels=feature_channels) + + if not self.reg_refine or task == 'depth': + # convex upsampling simiar to RAFT + # concat feature0 and low res flow as input + self.upsampler = nn.Sequential(nn.Conv2d(2 + feature_channels, 256, 3, 1, 1), + nn.ReLU(inplace=True), + nn.Conv2d(256, upsample_factor ** 2 * 9, 1, 1, 0)) + # thus far, all the learnable parameters are task-agnostic + + if reg_refine: + # optional task-specific local regression refinement + self.refine_proj = nn.Conv2d(128, 256, 1) + self.refine = BasicUpdateBlock(corr_channels=(2 * 4 + 1) ** 2, + downsample_factor=upsample_factor, + flow_dim=2 if task == 'flow' else 1, + bilinear_up=task == 'depth', + ) + + def extract_feature(self, img0, img1): + concat = torch.cat((img0, img1), dim=0) # [2B, C, H, W] + features = self.backbone(concat) # list of [2B, C, H, W], resolution from high to low + + # reverse: resolution from low to high + features = features[::-1] + + feature0, feature1 = [], [] + + for i in range(len(features)): + feature = features[i] + chunks = torch.chunk(feature, 2, 0) # tuple + feature0.append(chunks[0]) + feature1.append(chunks[1]) + + return feature0, feature1 + + def upsample_flow(self, flow, feature, bilinear=False, upsample_factor=8, + is_depth=False): + if bilinear: + multiplier = 1 if is_depth else upsample_factor + up_flow = F.interpolate(flow, scale_factor=upsample_factor, + mode='bilinear', align_corners=True) * multiplier + else: + concat = torch.cat((flow, feature), dim=1) + mask = self.upsampler(concat) + up_flow = upsample_flow_with_mask(flow, mask, upsample_factor=self.upsample_factor, + is_depth=is_depth) + + return up_flow + + def forward(self, img0, img1, + attn_type=None, + attn_splits_list=None, + corr_radius_list=None, + prop_radius_list=None, + num_reg_refine=1, + pred_bidir_flow=False, + task='flow', + intrinsics=None, + pose=None, # relative pose transform + min_depth=1. / 0.5, # inverse depth range + max_depth=1. / 10, + num_depth_candidates=64, + depth_from_argmax=False, + pred_bidir_depth=False, + **kwargs, + ): + + if pred_bidir_flow: + assert task == 'flow' + + if task == 'depth': + assert self.num_scales == 1 # multi-scale depth model is not supported yet + + results_dict = {} + flow_preds = [] + + if task == 'flow': + # stereo and depth tasks have normalized img in dataloader + img0, img1 = normalize_img(img0, img1) # [B, 3, H, W] + + # list of features, resolution low to high + feature0_list, feature1_list = self.extract_feature(img0, img1) # list of features + + flow = None + + if task != 'depth': + assert len(attn_splits_list) == len(corr_radius_list) == len(prop_radius_list) == self.num_scales + else: + assert len(attn_splits_list) == len(prop_radius_list) == self.num_scales == 1 + + for scale_idx in range(self.num_scales): + feature0, feature1 = feature0_list[scale_idx], feature1_list[scale_idx] + + if pred_bidir_flow and scale_idx > 0: + # predicting bidirectional flow with refinement + feature0, feature1 = torch.cat((feature0, feature1), dim=0), torch.cat((feature1, feature0), dim=0) + + feature0_ori, feature1_ori = feature0, feature1 + + upsample_factor = self.upsample_factor * (2 ** (self.num_scales - 1 - scale_idx)) + + if task == 'depth': + # scale intrinsics + intrinsics_curr = intrinsics.clone() + intrinsics_curr[:, :2] = intrinsics_curr[:, :2] / upsample_factor + + if scale_idx > 0: + assert task != 'depth' # not supported for multi-scale depth model + flow = F.interpolate(flow, scale_factor=2, mode='bilinear', align_corners=True) * 2 + + if flow is not None: + assert task != 'depth' + flow = flow.detach() + + if task == 'stereo': + # construct flow vector for disparity + # flow here is actually disparity + zeros = torch.zeros_like(flow) # [B, 1, H, W] + # NOTE: reverse disp, disparity is positive + displace = torch.cat((-flow, zeros), dim=1) # [B, 2, H, W] + feature1 = flow_warp(feature1, displace) # [B, C, H, W] + elif task == 'flow': + feature1 = flow_warp(feature1, flow) # [B, C, H, W] + else: + raise NotImplementedError + + attn_splits = attn_splits_list[scale_idx] + if task != 'depth': + corr_radius = corr_radius_list[scale_idx] + prop_radius = prop_radius_list[scale_idx] + + # add position to features + feature0, feature1 = feature_add_position(feature0, feature1, attn_splits, self.feature_channels) + + # Transformer + feature0, feature1 = self.transformer(feature0, feature1, + attn_type=attn_type, + attn_num_splits=attn_splits, + ) + + # correlation and softmax + if task == 'depth': + # first generate depth candidates + b, _, h, w = feature0.size() + depth_candidates = torch.linspace(min_depth, max_depth, num_depth_candidates).type_as(feature0) + depth_candidates = depth_candidates.view(1, num_depth_candidates, 1, 1).repeat(b, 1, h, + w) # [B, D, H, W] + + flow_pred = correlation_softmax_depth(feature0, feature1, + intrinsics_curr, + pose, + depth_candidates=depth_candidates, + depth_from_argmax=depth_from_argmax, + pred_bidir_depth=pred_bidir_depth, + )[0] + + else: + if corr_radius == -1: # global matching + if task == 'flow': + flow_pred = global_correlation_softmax(feature0, feature1, pred_bidir_flow)[0] + elif task == 'stereo': + flow_pred = global_correlation_softmax_stereo(feature0, feature1)[0] + else: + raise NotImplementedError + else: # local matching + if task == 'flow': + flow_pred = local_correlation_softmax(feature0, feature1, corr_radius)[0] + elif task == 'stereo': + flow_pred = local_correlation_softmax_stereo(feature0, feature1, corr_radius)[0] + else: + raise NotImplementedError + + # flow or residual flow + flow = flow + flow_pred if flow is not None else flow_pred + + if task == 'stereo': + flow = flow.clamp(min=0) # positive disparity + + # upsample to the original resolution for supervison at training time only + if self.training: + flow_bilinear = self.upsample_flow(flow, None, bilinear=True, upsample_factor=upsample_factor, + is_depth=task == 'depth') + flow_preds.append(flow_bilinear) + + # flow propagation with self-attn + if (pred_bidir_flow or pred_bidir_depth) and scale_idx == 0: + feature0 = torch.cat((feature0, feature1), dim=0) # [2*B, C, H, W] for propagation + + flow = self.feature_flow_attn(feature0, flow.detach(), + local_window_attn=prop_radius > 0, + local_window_radius=prop_radius, + ) + + # bilinear exclude the last one + if self.training and scale_idx < self.num_scales - 1: + flow_up = self.upsample_flow(flow, feature0, bilinear=True, + upsample_factor=upsample_factor, + is_depth=task == 'depth') + flow_preds.append(flow_up) + + if scale_idx == self.num_scales - 1: + if not self.reg_refine: + # upsample to the original image resolution + + if task == 'stereo': + flow_pad = torch.cat((-flow, torch.zeros_like(flow)), dim=1) # [B, 2, H, W] + flow_up_pad = self.upsample_flow(flow_pad, feature0) + flow_up = -flow_up_pad[:, :1] # [B, 1, H, W] + elif task == 'depth': + depth_pad = torch.cat((flow, torch.zeros_like(flow)), dim=1) # [B, 2, H, W] + depth_up_pad = self.upsample_flow(depth_pad, feature0, + is_depth=True).clamp(min=min_depth, max=max_depth) + flow_up = depth_up_pad[:, :1] # [B, 1, H, W] + else: + flow_up = self.upsample_flow(flow, feature0) + + flow_preds.append(flow_up) + else: + # task-specific local regression refinement + # supervise current flow + if self.training: + flow_up = self.upsample_flow(flow, feature0, bilinear=True, + upsample_factor=upsample_factor, + is_depth=task == 'depth') + flow_preds.append(flow_up) + + assert num_reg_refine > 0 + for refine_iter_idx in range(num_reg_refine): + flow = flow.detach() + + if task == 'stereo': + zeros = torch.zeros_like(flow) # [B, 1, H, W] + # NOTE: reverse disp, disparity is positive + displace = torch.cat((-flow, zeros), dim=1) # [B, 2, H, W] + correlation = local_correlation_with_flow( + feature0_ori, + feature1_ori, + flow=displace, + local_radius=4, + ) # [B, (2R+1)^2, H, W] + elif task == 'depth': + if pred_bidir_depth and refine_iter_idx == 0: + intrinsics_curr = intrinsics_curr.repeat(2, 1, 1) + pose = torch.cat((pose, torch.inverse(pose)), dim=0) + + feature0_ori, feature1_ori = torch.cat((feature0_ori, feature1_ori), + dim=0), torch.cat((feature1_ori, + feature0_ori), dim=0) + + flow_from_depth = compute_flow_with_depth_pose(1. / flow.squeeze(1), + intrinsics_curr, + extrinsics_rel=pose, + ) + + correlation = local_correlation_with_flow( + feature0_ori, + feature1_ori, + flow=flow_from_depth, + local_radius=4, + ) # [B, (2R+1)^2, H, W] + + else: + correlation = local_correlation_with_flow( + feature0_ori, + feature1_ori, + flow=flow, + local_radius=4, + ) # [B, (2R+1)^2, H, W] + + proj = self.refine_proj(feature0) + + net, inp = torch.chunk(proj, chunks=2, dim=1) + + net = torch.tanh(net) + inp = torch.relu(inp) + + net, up_mask, residual_flow = self.refine(net, inp, correlation, flow.clone(), + ) + + if task == 'depth': + flow = (flow - residual_flow).clamp(min=min_depth, max=max_depth) + else: + flow = flow + residual_flow + + if task == 'stereo': + flow = flow.clamp(min=0) # positive + + if self.training or refine_iter_idx == num_reg_refine - 1: + if task == 'depth': + if refine_iter_idx < num_reg_refine - 1: + # bilinear upsampling + flow_up = self.upsample_flow(flow, feature0, bilinear=True, + upsample_factor=upsample_factor, + is_depth=True) + else: + # last one convex upsampling + # NOTE: clamp depth due to the zero padding in the unfold in the convex upsampling + # pad depth to 2 channels as flow + depth_pad = torch.cat((flow, torch.zeros_like(flow)), dim=1) # [B, 2, H, W] + depth_up_pad = self.upsample_flow(depth_pad, feature0, + is_depth=True).clamp(min=min_depth, + max=max_depth) + flow_up = depth_up_pad[:, :1] # [B, 1, H, W] + + else: + flow_up = upsample_flow_with_mask(flow, up_mask, upsample_factor=self.upsample_factor, + is_depth=task == 'depth') + + flow_preds.append(flow_up) + + if task == 'stereo': + for i in range(len(flow_preds)): + flow_preds[i] = flow_preds[i].squeeze(1) # [B, H, W] + + # convert inverse depth to depth + if task == 'depth': + for i in range(len(flow_preds)): + flow_preds[i] = 1. / flow_preds[i].squeeze(1) # [B, H, W] + + results_dict.update({'flow_preds': flow_preds}) + + return results_dict diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/utils.py new file mode 100644 index 00000000..a51d3882 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/unimatch/utils.py @@ -0,0 +1,216 @@ +import torch +import torch.nn.functional as F +from .position import PositionEmbeddingSine + + +def generate_window_grid(h_min, h_max, w_min, w_max, len_h, len_w, device=None): + assert device is not None + + x, y = torch.meshgrid([torch.linspace(w_min, w_max, len_w, device=device), + torch.linspace(h_min, h_max, len_h, device=device)], + ) + grid = torch.stack((x, y), -1).transpose(0, 1).float() # [H, W, 2] + + return grid + + +def normalize_coords(coords, h, w): + # coords: [B, H, W, 2] + c = torch.Tensor([(w - 1) / 2., (h - 1) / 2.]).float().to(coords.device) + return (coords - c) / c # [-1, 1] + + +def normalize_img(img0, img1): + # loaded images are in [0, 255] + # normalize by ImageNet mean and std + mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(img1.device) + std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(img1.device) + img0 = (img0 / 255. - mean) / std + img1 = (img1 / 255. - mean) / std + + return img0, img1 + + +def split_feature(feature, + num_splits=2, + channel_last=False, + ): + if channel_last: # [B, H, W, C] + b, h, w, c = feature.size() + assert h % num_splits == 0 and w % num_splits == 0 + + b_new = b * num_splits * num_splits + h_new = h // num_splits + w_new = w // num_splits + + feature = feature.view(b, num_splits, h // num_splits, num_splits, w // num_splits, c + ).permute(0, 1, 3, 2, 4, 5).reshape(b_new, h_new, w_new, c) # [B*K*K, H/K, W/K, C] + else: # [B, C, H, W] + b, c, h, w = feature.size() + assert h % num_splits == 0 and w % num_splits == 0 + + b_new = b * num_splits * num_splits + h_new = h // num_splits + w_new = w // num_splits + + feature = feature.view(b, c, num_splits, h // num_splits, num_splits, w // num_splits + ).permute(0, 2, 4, 1, 3, 5).reshape(b_new, c, h_new, w_new) # [B*K*K, C, H/K, W/K] + + return feature + + +def merge_splits(splits, + num_splits=2, + channel_last=False, + ): + if channel_last: # [B*K*K, H/K, W/K, C] + b, h, w, c = splits.size() + new_b = b // num_splits // num_splits + + splits = splits.view(new_b, num_splits, num_splits, h, w, c) + merge = splits.permute(0, 1, 3, 2, 4, 5).contiguous().view( + new_b, num_splits * h, num_splits * w, c) # [B, H, W, C] + else: # [B*K*K, C, H/K, W/K] + b, c, h, w = splits.size() + new_b = b // num_splits // num_splits + + splits = splits.view(new_b, num_splits, num_splits, c, h, w) + merge = splits.permute(0, 3, 1, 4, 2, 5).contiguous().view( + new_b, c, num_splits * h, num_splits * w) # [B, C, H, W] + + return merge + + +def generate_shift_window_attn_mask(input_resolution, window_size_h, window_size_w, + shift_size_h, shift_size_w, device=torch.device('cuda')): + # ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py + # calculate attention mask for SW-MSA + h, w = input_resolution + img_mask = torch.zeros((1, h, w, 1)).to(device) # 1 H W 1 + h_slices = (slice(0, -window_size_h), + slice(-window_size_h, -shift_size_h), + slice(-shift_size_h, None)) + w_slices = (slice(0, -window_size_w), + slice(-window_size_w, -shift_size_w), + slice(-shift_size_w, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = split_feature(img_mask, num_splits=input_resolution[-1] // window_size_w, channel_last=True) + + mask_windows = mask_windows.view(-1, window_size_h * window_size_w) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + +def feature_add_position(feature0, feature1, attn_splits, feature_channels): + pos_enc = PositionEmbeddingSine(num_pos_feats=feature_channels // 2) + + if attn_splits > 1: # add position in splited window + feature0_splits = split_feature(feature0, num_splits=attn_splits) + feature1_splits = split_feature(feature1, num_splits=attn_splits) + + position = pos_enc(feature0_splits) + + feature0_splits = feature0_splits + position + feature1_splits = feature1_splits + position + + feature0 = merge_splits(feature0_splits, num_splits=attn_splits) + feature1 = merge_splits(feature1_splits, num_splits=attn_splits) + else: + position = pos_enc(feature0) + + feature0 = feature0 + position + feature1 = feature1 + position + + return feature0, feature1 + + +def upsample_flow_with_mask(flow, up_mask, upsample_factor, + is_depth=False): + # convex upsampling following raft + + mask = up_mask + b, flow_channel, h, w = flow.shape + mask = mask.view(b, 1, 9, upsample_factor, upsample_factor, h, w) # [B, 1, 9, K, K, H, W] + mask = torch.softmax(mask, dim=2) + + multiplier = 1 if is_depth else upsample_factor + up_flow = F.unfold(multiplier * flow, [3, 3], padding=1) + up_flow = up_flow.view(b, flow_channel, 9, 1, 1, h, w) # [B, 2, 9, 1, 1, H, W] + + up_flow = torch.sum(mask * up_flow, dim=2) # [B, 2, K, K, H, W] + up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) # [B, 2, K, H, K, W] + up_flow = up_flow.reshape(b, flow_channel, upsample_factor * h, + upsample_factor * w) # [B, 2, K*H, K*W] + + return up_flow + + +def split_feature_1d(feature, + num_splits=2, + ): + # feature: [B, W, C] + b, w, c = feature.size() + assert w % num_splits == 0 + + b_new = b * num_splits + w_new = w // num_splits + + feature = feature.view(b, num_splits, w // num_splits, c + ).view(b_new, w_new, c) # [B*K, W/K, C] + + return feature + + +def merge_splits_1d(splits, + h, + num_splits=2, + ): + b, w, c = splits.size() + new_b = b // num_splits // h + + splits = splits.view(new_b, h, num_splits, w, c) + merge = splits.view( + new_b, h, num_splits * w, c) # [B, H, W, C] + + return merge + + +def window_partition_1d(x, window_size_w): + """ + Args: + x: (B, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, C) + """ + B, W, C = x.shape + x = x.view(B, W // window_size_w, window_size_w, C).view(-1, window_size_w, C) + return x + + +def generate_shift_window_attn_mask_1d(input_w, window_size_w, + shift_size_w, device=torch.device('cuda')): + # calculate attention mask for SW-MSA + img_mask = torch.zeros((1, input_w, 1)).to(device) # 1 W 1 + w_slices = (slice(0, -window_size_w), + slice(-window_size_w, -shift_size_w), + slice(-shift_size_w, None)) + cnt = 0 + for w in w_slices: + img_mask[:, w, :] = cnt + cnt += 1 + + mask_windows = window_partition_1d(img_mask, window_size_w) # nW, window_size, 1 + mask_windows = mask_windows.view(-1, window_size_w) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) # nW, window_size, window_size + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/dist_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/dist_utils.py new file mode 100644 index 00000000..9958a48f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/dist_utils.py @@ -0,0 +1,105 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# https://github.com/open-mmlab/mmcv/blob/7540cf73ac7e5d1e14d0ffbd9b6759e83929ecfc/mmcv/runner/dist_utils.py + +import os +import subprocess + +import torch +import torch.multiprocessing as mp +from torch import distributed as dist + + +def init_dist(launcher, backend='nccl', **kwargs): + if mp.get_start_method(allow_none=True) is None: + mp.set_start_method('spawn') + if launcher == 'pytorch': + _init_dist_pytorch(backend, **kwargs) + elif launcher == 'mpi': + _init_dist_mpi(backend, **kwargs) + elif launcher == 'slurm': + _init_dist_slurm(backend, **kwargs) + else: + raise ValueError(f'Invalid launcher type: {launcher}') + + +def _init_dist_pytorch(backend, **kwargs): + # TODO: use local_rank instead of rank % num_gpus + rank = int(os.environ['RANK']) + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) + + +def _init_dist_mpi(backend, **kwargs): + # TODO: use local_rank instead of rank % num_gpus + rank = int(os.environ['OMPI_COMM_WORLD_RANK']) + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) + + +def _init_dist_slurm(backend, port=None): + """Initialize slurm distributed training environment. + If argument ``port`` is not specified, then the master port will be system + environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system + environment variable, then a default port ``29500`` will be used. + Args: + backend (str): Backend of torch.distributed. + port (int, optional): Master port. Defaults to None. + """ + proc_id = int(os.environ['SLURM_PROCID']) + ntasks = int(os.environ['SLURM_NTASKS']) + node_list = os.environ['SLURM_NODELIST'] + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(proc_id % num_gpus) + addr = subprocess.getoutput( + f'scontrol show hostname {node_list} | head -n1') + # specify master port + if port is not None: + os.environ['MASTER_PORT'] = str(port) + elif 'MASTER_PORT' in os.environ: + pass # use MASTER_PORT in the environment variable + else: + # 29500 is torch.distributed default port + os.environ['MASTER_PORT'] = '29500' + # use MASTER_ADDR in the environment variable if it already exists + if 'MASTER_ADDR' not in os.environ: + os.environ['MASTER_ADDR'] = addr + os.environ['WORLD_SIZE'] = str(ntasks) + os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) + os.environ['RANK'] = str(proc_id) + dist.init_process_group(backend=backend) + + +def get_dist_info(): + # if (TORCH_VERSION != 'parrots' + # and digit_version(TORCH_VERSION) < digit_version('1.0')): + # initialized = dist._initialized + # else: + if dist.is_available(): + initialized = dist.is_initialized() + else: + initialized = False + if initialized: + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + return rank, world_size + + +# from DETR repo +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/file_io.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/file_io.py new file mode 100644 index 00000000..4f0a9909 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/file_io.py @@ -0,0 +1,224 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import re +from PIL import Image +import sys +import cv2 +import json +import os + + +def read_img(filename): + # convert to RGB for scene flow finalpass data + img = np.array(Image.open(filename).convert('RGB')).astype(np.float32) + return img + + +def read_disp(filename, subset=False, vkitti2=False, sintel=False, + tartanair=False, instereo2k=False, crestereo=False, + fallingthings=False, + argoverse=False, + raw_disp_png=False, + ): + # Scene Flow dataset + if filename.endswith('pfm'): + # For finalpass and cleanpass, gt disparity is positive, subset is negative + disp = np.ascontiguousarray(_read_pfm(filename)[0]) + if subset: + disp = -disp + # VKITTI2 dataset + elif vkitti2: + disp = _read_vkitti2_disp(filename) + # Sintel + elif sintel: + disp = _read_sintel_disparity(filename) + elif tartanair: + disp = _read_tartanair_disp(filename) + elif instereo2k: + disp = _read_instereo2k_disp(filename) + elif crestereo: + disp = _read_crestereo_disp(filename) + elif fallingthings: + disp = _read_fallingthings_disp(filename) + elif argoverse: + disp = _read_argoverse_disp(filename) + elif raw_disp_png: + disp = np.array(Image.open(filename)).astype(np.float32) + # KITTI + elif filename.endswith('png'): + disp = _read_kitti_disp(filename) + elif filename.endswith('npy'): + disp = np.load(filename) + else: + raise Exception('Invalid disparity file format!') + return disp # [H, W] + + +def _read_pfm(file): + file = open(file, 'rb') + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header.decode("ascii") == 'PF': + color = True + elif header.decode("ascii") == 'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode("ascii")) + if dim_match: + width, height = list(map(int, dim_match.groups())) + else: + raise Exception('Malformed PFM header.') + + scale = float(file.readline().decode("ascii").rstrip()) + if scale < 0: # little-endian + endian = '<' + scale = -scale + else: + endian = '>' # big-endian + + data = np.fromfile(file, endian + 'f') + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + return data, scale + + +def write_pfm(file, image, scale=1): + file = open(file, 'wb') + + color = None + + if image.dtype.name != 'float32': + raise Exception('Image dtype must be float32.') + + image = np.flipud(image) + + if len(image.shape) == 3 and image.shape[2] == 3: # color image + color = True + elif len(image.shape) == 2 or len( + image.shape) == 3 and image.shape[2] == 1: # greyscale + color = False + else: + raise Exception( + 'Image must have H x W x 3, H x W x 1 or H x W dimensions.') + + file.write(b'PF\n' if color else b'Pf\n') + file.write(b'%d %d\n' % (image.shape[1], image.shape[0])) + + endian = image.dtype.byteorder + + if endian == '<' or endian == '=' and sys.byteorder == 'little': + scale = -scale + + file.write(b'%f\n' % scale) + + image.tofile(file) + + +def _read_kitti_disp(filename): + depth = np.array(Image.open(filename)) + depth = depth.astype(np.float32) / 256. + return depth + + +def _read_vkitti2_disp(filename): + # read depth + depth = cv2.imread(filename, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) # in cm + depth = (depth / 100).astype(np.float32) # depth clipped to 655.35m for sky + + valid = (depth > 0) & (depth < 655) # depth clipped to 655.35m for sky + + # convert to disparity + focal_length = 725.0087 # in pixels + baseline = 0.532725 # meter + + disp = baseline * focal_length / depth + + disp[~valid] = 0.000001 # invalid as very small value + + return disp + + +def _read_sintel_disparity(filename): + """ Return disparity read from filename. """ + f_in = np.array(Image.open(filename)) + + d_r = f_in[:, :, 0].astype('float32') + d_g = f_in[:, :, 1].astype('float32') + d_b = f_in[:, :, 2].astype('float32') + + depth = d_r * 4 + d_g / (2 ** 6) + d_b / (2 ** 14) + return depth + + +def _read_tartanair_disp(filename): + # the infinite distant object such as the sky has a large depth value (e.g. 10000) + depth = np.load(filename) + + # change to disparity image + disparity = 80.0 / depth + + return disparity + + +def _read_instereo2k_disp(filename): + disp = np.array(Image.open(filename)) + disp = disp.astype(np.float32) / 100. + return disp + + +def _read_crestereo_disp(filename): + disp = np.array(Image.open(filename)) + return disp.astype(np.float32) / 32. + + +def _read_fallingthings_disp(filename): + depth = np.array(Image.open(filename)) + camera_file = os.path.join(os.path.dirname(filename), '_camera_settings.json') + with open(camera_file, 'r') as f: + intrinsics = json.load(f) + fx = intrinsics['camera_settings'][0]['intrinsic_settings']['fx'] + disp = (fx * 6.0 * 100) / depth.astype(np.float32) + + return disp + + +def _read_argoverse_disp(filename): + disparity_map = cv2.imread(filename, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) + return np.float32(disparity_map) / 256. + + +def extract_video(video_name): + cap = cv2.VideoCapture(video_name) + assert cap.isOpened(), f'Failed to load video file {video_name}' + # get video info + size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), + int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) + fps = cap.get(cv2.CAP_PROP_FPS) + + print('video size (hxw): %dx%d' % (size[1], size[0])) + print('fps: %d' % fps) + + imgs = [] + while cap.isOpened(): + # get frames + flag, img = cap.read() + if not flag: + break + # to rgb format + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + imgs.append(img) + + return imgs, fps diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/flow_viz.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/flow_viz.py new file mode 100644 index 00000000..dbe3f139 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/flow_viz.py @@ -0,0 +1,290 @@ +# MIT License +# +# Copyright (c) 2018 Tom Runia +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to conditions. +# +# Author: Tom Runia +# Date Created: 2018-08-03 + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from PIL import Image + + +def make_colorwheel(): + ''' + Generates a color wheel for optical flow visualization as presented in: + Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007) + URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + ''' + + RY = 15 + YG = 6 + GC = 4 + CB = 11 + BM = 13 + MR = 6 + + ncols = RY + YG + GC + CB + BM + MR + colorwheel = np.zeros((ncols, 3)) + col = 0 + + # RY + colorwheel[0:RY, 0] = 255 + colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY) + col = col + RY + # YG + colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG) + colorwheel[col:col + YG, 1] = 255 + col = col + YG + # GC + colorwheel[col:col + GC, 1] = 255 + colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC) + col = col + GC + # CB + colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB) + colorwheel[col:col + CB, 2] = 255 + col = col + CB + # BM + colorwheel[col:col + BM, 2] = 255 + colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM) + col = col + BM + # MR + colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR) + colorwheel[col:col + MR, 0] = 255 + return colorwheel + + +def flow_compute_color(u, v, convert_to_bgr=False): + ''' + Applies the flow color wheel to (possibly clipped) flow components u and v. + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + :param u: np.ndarray, input horizontal flow + :param v: np.ndarray, input vertical flow + :param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB + :return: + ''' + + flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8) + + colorwheel = make_colorwheel() # shape [55x3] + ncols = colorwheel.shape[0] + + rad = np.sqrt(np.square(u) + np.square(v)) + a = np.arctan2(-v, -u) / np.pi + + fk = (a + 1) / 2 * (ncols - 1) + 1 + k0 = np.floor(fk).astype(np.int32) + k1 = k0 + 1 + k1[k1 == ncols] = 1 + f = fk - k0 + + for i in range(colorwheel.shape[1]): + tmp = colorwheel[:, i] + col0 = tmp[k0] / 255.0 + col1 = tmp[k1] / 255.0 + col = (1 - f) * col0 + f * col1 + + idx = (rad <= 1) + col[idx] = 1 - rad[idx] * (1 - col[idx]) + col[~idx] = col[~idx] * 0.75 # out of range? + + # Note the 2-i => BGR instead of RGB + ch_idx = 2 - i if convert_to_bgr else i + flow_image[:, :, ch_idx] = np.floor(255 * col) + + return flow_image + + +def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False): + ''' + Expects a two dimensional flow image of shape [H,W,2] + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + :param flow_uv: np.ndarray of shape [H,W,2] + :param clip_flow: float, maximum clipping value for flow + :return: + ''' + + assert flow_uv.ndim == 3, 'input flow must have three dimensions' + assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]' + + if clip_flow is not None: + flow_uv = np.clip(flow_uv, 0, clip_flow) + + u = flow_uv[:, :, 0] + v = flow_uv[:, :, 1] + + rad = np.sqrt(np.square(u) + np.square(v)) + rad_max = np.max(rad) + + epsilon = 1e-5 + u = u / (rad_max + epsilon) + v = v / (rad_max + epsilon) + + return flow_compute_color(u, v, convert_to_bgr) + + +UNKNOWN_FLOW_THRESH = 1e7 +SMALLFLOW = 0.0 +LARGEFLOW = 1e8 + + +def make_color_wheel(): + """ + Generate color wheel according Middlebury color code + :return: Color wheel + """ + RY = 15 + YG = 6 + GC = 4 + CB = 11 + BM = 13 + MR = 6 + + ncols = RY + YG + GC + CB + BM + MR + + colorwheel = np.zeros([ncols, 3]) + + col = 0 + + # RY + colorwheel[0:RY, 0] = 255 + colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY)) + col += RY + + # YG + colorwheel[col:col + YG, 0] = 255 - np.transpose(np.floor(255 * np.arange(0, YG) / YG)) + colorwheel[col:col + YG, 1] = 255 + col += YG + + # GC + colorwheel[col:col + GC, 1] = 255 + colorwheel[col:col + GC, 2] = np.transpose(np.floor(255 * np.arange(0, GC) / GC)) + col += GC + + # CB + colorwheel[col:col + CB, 1] = 255 - np.transpose(np.floor(255 * np.arange(0, CB) / CB)) + colorwheel[col:col + CB, 2] = 255 + col += CB + + # BM + colorwheel[col:col + BM, 2] = 255 + colorwheel[col:col + BM, 0] = np.transpose(np.floor(255 * np.arange(0, BM) / BM)) + col += + BM + + # MR + colorwheel[col:col + MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR)) + colorwheel[col:col + MR, 0] = 255 + + return colorwheel + + +def compute_color(u, v): + """ + compute optical flow color map + :param u: optical flow horizontal map + :param v: optical flow vertical map + :return: optical flow in color code + """ + [h, w] = u.shape + img = np.zeros([h, w, 3]) + nanIdx = np.isnan(u) | np.isnan(v) + u[nanIdx] = 0 + v[nanIdx] = 0 + + colorwheel = make_color_wheel() + ncols = np.size(colorwheel, 0) + + rad = np.sqrt(u ** 2 + v ** 2) + + a = np.arctan2(-v, -u) / np.pi + + fk = (a + 1) / 2 * (ncols - 1) + 1 + + k0 = np.floor(fk).astype(int) + + k1 = k0 + 1 + k1[k1 == ncols + 1] = 1 + f = fk - k0 + + for i in range(0, np.size(colorwheel, 1)): + tmp = colorwheel[:, i] + col0 = tmp[k0 - 1] / 255 + col1 = tmp[k1 - 1] / 255 + col = (1 - f) * col0 + f * col1 + + idx = rad <= 1 + col[idx] = 1 - rad[idx] * (1 - col[idx]) + notidx = np.logical_not(idx) + + col[notidx] *= 0.75 + img[:, :, i] = np.uint8(np.floor(255 * col * (1 - nanIdx))) + + return img + + +# from https://github.com/gengshan-y/VCN +def flow_to_image(flow): + """ + Convert flow into middlebury color code image + :param flow: optical flow map + :return: optical flow image in middlebury color + """ + u = flow[:, :, 0] + v = flow[:, :, 1] + + maxu = -999. + maxv = -999. + minu = 999. + minv = 999. + + idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH) + u[idxUnknow] = 0 + v[idxUnknow] = 0 + + maxu = max(maxu, np.max(u)) + minu = min(minu, np.min(u)) + + maxv = max(maxv, np.max(v)) + minv = min(minv, np.min(v)) + + rad = np.sqrt(u ** 2 + v ** 2) + maxrad = max(-1, np.max(rad)) + + u = u / (maxrad + np.finfo(float).eps) + v = v / (maxrad + np.finfo(float).eps) + + img = compute_color(u, v) + + idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2) + img[idx] = 0 + + return np.uint8(img) + + +def save_vis_flow_tofile(flow, output_path): + vis_flow = flow_to_image(flow) + Image.fromarray(vis_flow).save(output_path) + + +def flow_tensor_to_image(flow): + """Used for tensorboard visualization""" + flow = flow.permute(1, 2, 0) # [H, W, 2] + flow = flow.detach().cpu().numpy() + flow = flow_to_image(flow) # [H, W, 3] + flow = np.transpose(flow, (2, 0, 1)) # [3, H, W] + + return flow diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/frame_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/frame_utils.py new file mode 100644 index 00000000..d10d2ee9 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/frame_utils.py @@ -0,0 +1,158 @@ +import numpy as np +from PIL import Image +from os.path import * +import re +import cv2 + +TAG_CHAR = np.array([202021.25], np.float32) + + +def readFlow(fn): + """ Read .flo file in Middlebury format""" + # Code adapted from: + # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy + + # WARNING: this will work on little-endian architectures (eg Intel x86) only! + # print 'fn = %s'%(fn) + with open(fn, 'rb') as f: + magic = np.fromfile(f, np.float32, count=1) + if 202021.25 != magic: + print('Magic number incorrect. Invalid .flo file') + return None + else: + w = np.fromfile(f, np.int32, count=1) + h = np.fromfile(f, np.int32, count=1) + # print 'Reading %d x %d flo file\n' % (w, h) + data = np.fromfile(f, np.float32, count=2 * int(w) * int(h)) + # Reshape testdata into 3D array (columns, rows, bands) + # The reshape here is for visualization, the original code is (w,h,2) + return np.resize(data, (int(h), int(w), 2)) + + +def readPFM(file): + file = open(file, 'rb') + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header == b'PF': + color = True + elif header == b'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline()) + if dim_match: + width, height = map(int, dim_match.groups()) + else: + raise Exception('Malformed PFM header.') + + scale = float(file.readline().rstrip()) + if scale < 0: # little-endian + endian = '<' + scale = -scale + else: + endian = '>' # big-endian + + data = np.fromfile(file, endian + 'f') + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + return data + + +def writeFlow(filename, uv, v=None): + """ Write optical flow to file. + + If v is None, uv is assumed to contain both u and v channels, + stacked in depth. + Original code by Deqing Sun, adapted from Daniel Scharstein. + """ + nBands = 2 + + if v is None: + assert (uv.ndim == 3) + assert (uv.shape[2] == 2) + u = uv[:, :, 0] + v = uv[:, :, 1] + else: + u = uv + + assert (u.shape == v.shape) + height, width = u.shape + f = open(filename, 'wb') + # write the header + f.write(TAG_CHAR) + np.array(width).astype(np.int32).tofile(f) + np.array(height).astype(np.int32).tofile(f) + # arrange into matrix form + tmp = np.zeros((height, width * nBands)) + tmp[:, np.arange(width) * 2] = u + tmp[:, np.arange(width) * 2 + 1] = v + tmp.astype(np.float32).tofile(f) + f.close() + + +def readFlowKITTI(filename): + flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR) + flow = flow[:, :, ::-1].astype(np.float32) + flow, valid = flow[:, :, :2], flow[:, :, 2] + flow = (flow - 2 ** 15) / 64.0 + return flow, valid + + +def readDispKITTI(filename): + disp = cv2.imread(filename, cv2.IMREAD_ANYDEPTH) / 256.0 + valid = disp > 0.0 + flow = np.stack([-disp, np.zeros_like(disp)], -1) + return flow, valid + + +def writeFlowKITTI(filename, uv): + uv = 64.0 * uv + 2 ** 15 + valid = np.ones([uv.shape[0], uv.shape[1], 1]) + uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16) + cv2.imwrite(filename, uv[..., ::-1]) + + +def read_gen(file_name, pil=False): + ext = splitext(file_name)[-1] + if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg': + return Image.open(file_name) + elif ext == '.bin' or ext == '.raw': + return np.load(file_name) + elif ext == '.flo': + return readFlow(file_name).astype(np.float32) + elif ext == '.pfm': + flow = readPFM(file_name).astype(np.float32) + if len(flow.shape) == 2: + return flow + else: + return flow[:, :, :-1] + return [] + + +def read_vkitti2_flow(filename): + # In R, flow along x-axis normalized by image width and quantized to [0;2^16 – 1] + # In G, flow along x-axis normalized by image width and quantized to [0;2^16 – 1] + # B = 0 for invalid flow (e.g., sky pixels) + bgr = cv2.imread(filename, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) + h, w, _c = bgr.shape + assert bgr.dtype == np.uint16 and _c == 3 + # b == invalid flow flag == 0 for sky or other invalid flow + invalid = bgr[:, :, 0] == 0 + # g,r == flow_y,x normalized by height,width and scaled to [0;2**16 – 1] + out_flow = 2.0 / (2 ** 16 - 1.0) * bgr[:, :, 2:0:-1].astype('f4') - 1 # [H, W, 2] + out_flow[..., 0] *= (w - 1) + out_flow[..., 1] *= (h - 1) + + out_flow[invalid] = 0.000001 # invalid as very small value to add supervison on the sky + valid = (np.logical_or(invalid, ~invalid)).astype(np.float32) + + return out_flow, valid diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/logger.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/logger.py new file mode 100644 index 00000000..742b23c5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/logger.py @@ -0,0 +1,104 @@ +import torch + +from utils.flow_viz import flow_tensor_to_image +from .visualization import viz_depth_tensor + + +class Logger: + def __init__(self, lr_scheduler, + summary_writer, + summary_freq=100, + start_step=0, + img_mean=None, + img_std=None, + ): + self.lr_scheduler = lr_scheduler + self.total_steps = start_step + self.running_loss = {} + self.summary_writer = summary_writer + self.summary_freq = summary_freq + + self.img_mean = img_mean + self.img_std = img_std + + def print_training_status(self, mode='train', is_depth=False): + if is_depth: + print('step: %06d \t loss: %.3f' % (self.total_steps, self.running_loss['total_loss'] / self.summary_freq)) + else: + print('step: %06d \t epe: %.3f' % (self.total_steps, self.running_loss['epe'] / self.summary_freq)) + + for k in self.running_loss: + self.summary_writer.add_scalar(mode + '/' + k, + self.running_loss[k] / self.summary_freq, self.total_steps) + self.running_loss[k] = 0.0 + + def lr_summary(self): + lr = self.lr_scheduler.get_last_lr()[0] + self.summary_writer.add_scalar('lr', lr, self.total_steps) + + def add_image_summary(self, img1, img2, flow_preds=None, flow_gt=None, mode='train', + is_depth=False, + ): + if self.total_steps % self.summary_freq == 0: + if is_depth: + img1 = self.unnormalize_image(img1.detach().cpu()) # [3, H, W], range [0, 1] + img2 = self.unnormalize_image(img2.detach().cpu()) + + concat = torch.cat((img1, img2), dim=-1) # [3, H, W*2] + + self.summary_writer.add_image(mode + '/img', concat, self.total_steps) + else: + img_concat = torch.cat((img1[0].detach().cpu(), img2[0].detach().cpu()), dim=-1) + img_concat = img_concat.type(torch.uint8) # convert to uint8 to visualize in tensorboard + + flow_pred = flow_tensor_to_image(flow_preds[-1][0]) + forward_flow_gt = flow_tensor_to_image(flow_gt[0]) + flow_concat = torch.cat((torch.from_numpy(flow_pred), + torch.from_numpy(forward_flow_gt)), dim=-1) + + concat = torch.cat((img_concat, flow_concat), dim=-2) + + self.summary_writer.add_image(mode + '/img_pred_gt', concat, self.total_steps) + + def add_depth_summary(self, depth_pred, depth_gt, mode='train'): + # assert depth_pred.dim() == 2 # [H, W] + if self.total_steps % self.summary_freq == 0 or 'val' in mode: + pred_viz = viz_depth_tensor(depth_pred.detach().cpu()) # [3, H, W] + gt_viz = viz_depth_tensor(depth_gt.detach().cpu()) + + concat = torch.cat((pred_viz, gt_viz), dim=-1) # [3, H, W*2] + + self.summary_writer.add_image(mode + '/depth_pred_gt', concat, self.total_steps) + + def unnormalize_image(self, img): + # img: [3, H, W], used for visualizing image + mean = torch.tensor(self.img_mean).view(3, 1, 1).type_as(img) + std = torch.tensor(self.img_std).view(3, 1, 1).type_as(img) + + out = img * std + mean + + return out + + def push(self, metrics, mode='train', is_depth=False, ): + self.total_steps += 1 + + self.lr_summary() + + for key in metrics: + if key not in self.running_loss: + self.running_loss[key] = 0.0 + + self.running_loss[key] += metrics[key] + + if self.total_steps % self.summary_freq == 0: + self.print_training_status(mode, is_depth=is_depth) + self.running_loss = {} + + def write_dict(self, results): + for key in results: + tag = key.split('_')[0] + tag = tag + '/' + key + self.summary_writer.add_scalar(tag, results[key], self.total_steps) + + def close(self): + self.summary_writer.close() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/misc.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/misc.py new file mode 100644 index 00000000..8a3f8449 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/misc.py @@ -0,0 +1,36 @@ +import os +import sys +import json + + +def read_text_lines(filepath): + with open(filepath, 'r') as f: + lines = f.readlines() + lines = [l.rstrip() for l in lines] + return lines + + +def check_path(path): + if not os.path.exists(path): + os.makedirs(path, exist_ok=True) # explicitly set exist_ok when multi-processing + + +def save_command(save_path, filename='command_train.txt'): + check_path(save_path) + command = sys.argv + save_file = os.path.join(save_path, filename) + # Save all training commands when resuming training + with open(save_file, 'a') as f: + f.write(' '.join(command)) + f.write('\n\n') + + +def save_args(args, filename='args.json'): + args_dict = vars(args) + check_path(args.checkpoint_dir) + save_path = os.path.join(args.checkpoint_dir, filename) + + # save all training args when resuming training + with open(save_path, 'a') as f: + json.dump(args_dict, f, indent=4, sort_keys=False) + f.write('\n\n') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/utils.py new file mode 100644 index 00000000..73d780fa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/utils.py @@ -0,0 +1,157 @@ +import torch +import torch.nn.functional as F +import numpy as np + + +class InputPadder: + """ Pads images such that dimensions are divisible by 8 """ + + def __init__(self, dims, mode='sintel', padding_factor=8): + self.ht, self.wd = dims[-2:] + pad_ht = (((self.ht // padding_factor) + 1) * padding_factor - self.ht) % padding_factor + pad_wd = (((self.wd // padding_factor) + 1) * padding_factor - self.wd) % padding_factor + if mode == 'sintel': + self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2] + else: + self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, 0, pad_ht] + + def pad(self, *inputs): + return [F.pad(x, self._pad, mode='replicate') for x in inputs] + + def unpad(self, x): + ht, wd = x.shape[-2:] + c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]] + return x[..., c[0]:c[1], c[2]:c[3]] + + +def bilinear_sampler(img, coords, mode='bilinear', mask=False, padding_mode='zeros'): + """ Wrapper for grid_sample, uses pixel coordinates """ + if coords.size(-1) != 2: # [B, 2, H, W] -> [B, H, W, 2] + coords = coords.permute(0, 2, 3, 1) + + H, W = img.shape[-2:] + # H = height if height is not None else img.shape[-2] + # W = width if width is not None else img.shape[-1] + + xgrid, ygrid = coords.split([1, 1], dim=-1) + + # To handle H or W equals to 1 by explicitly defining height and width + if H == 1: + assert ygrid.abs().max() < 1e-8 + H = 10 + if W == 1: + assert xgrid.abs().max() < 1e-8 + W = 10 + + xgrid = 2 * xgrid / (W - 1) - 1 + ygrid = 2 * ygrid / (H - 1) - 1 + + grid = torch.cat([xgrid, ygrid], dim=-1) + img = F.grid_sample(img, grid, mode=mode, + padding_mode=padding_mode, + align_corners=True) + + if mask: + mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1) + return img, mask.squeeze(-1).float() + + return img + + +def coords_grid(batch, ht, wd, normalize=False): + if normalize: # [-1, 1] + coords = torch.meshgrid(2 * torch.arange(ht) / (ht - 1) - 1, + 2 * torch.arange(wd) / (wd - 1) - 1) + else: + coords = torch.meshgrid(torch.arange(ht), torch.arange(wd)) + coords = torch.stack(coords[::-1], dim=0).float() + return coords[None].repeat(batch, 1, 1, 1) # [B, 2, H, W] + + +def coords_grid_np(h, w): # used for accumulating high speed sintel flow testdata + coords = np.meshgrid(np.arange(h, dtype=np.float32), + np.arange(w, dtype=np.float32), indexing='ij') + coords = np.stack(coords[::-1], axis=-1) # [H, W, 2] + + return coords + + +def compute_out_of_boundary_mask(flow, downsample_factor=None): + # flow: [B, 2, H, W] + assert flow.dim() == 4 and flow.size(1) == 2 + b, _, h, w = flow.shape + init_coords = coords_grid(b, h, w).to(flow.device) + corres = init_coords + flow # [B, 2, H, W] + + if downsample_factor is not None: + assert w % downsample_factor == 0 and h % downsample_factor == 0 + # the actual max disp can predict is in the downsampled feature resolution, then upsample + max_w = (w // downsample_factor - 1) * downsample_factor + max_h = (h // downsample_factor - 1) * downsample_factor + # print('max_w: %d, max_h: %d' % (max_w, max_h)) + else: + max_w = w - 1 + max_h = h - 1 + + valid_mask = (corres[:, 0] >= 0) & (corres[:, 0] <= max_w) & (corres[:, 1] >= 0) & (corres[:, 1] <= max_h) + + # in case very large flow + flow_mask = (flow[:, 0].abs() <= max_w) & (flow[:, 1].abs() <= max_h) + + valid_mask = valid_mask & flow_mask + + return valid_mask # [B, H, W] + + +def normalize_coords(grid): + """Normalize coordinates of image scale to [-1, 1] + Args: + grid: [B, 2, H, W] + """ + assert grid.size(1) == 2 + h, w = grid.size()[2:] + grid[:, 0, :, :] = 2 * (grid[:, 0, :, :].clone() / (w - 1)) - 1 # x: [-1, 1] + grid[:, 1, :, :] = 2 * (grid[:, 1, :, :].clone() / (h - 1)) - 1 # y: [-1, 1] + # grid = grid.permute((0, 2, 3, 1)) # [B, H, W, 2] + return grid + + +def flow_warp(feature, flow, mask=False, padding_mode='zeros'): + b, c, h, w = feature.size() + assert flow.size(1) == 2 + + grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W] + + return bilinear_sampler(feature, grid, mask=mask, padding_mode=padding_mode) + + +def upflow8(flow, mode='bilinear'): + new_size = (8 * flow.shape[2], 8 * flow.shape[3]) + return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True) + + +def bilinear_upflow(flow, scale_factor=8): + assert flow.size(1) == 2 + flow = F.interpolate(flow, scale_factor=scale_factor, + mode='bilinear', align_corners=True) * scale_factor + + return flow + + +def upsample_flow(flow, img): + if flow.size(-1) != img.size(-1): + scale_factor = img.size(-1) / flow.size(-1) + flow = F.interpolate(flow, size=img.size()[-2:], + mode='bilinear', align_corners=True) * scale_factor + return flow + + +def count_parameters(model): + num = sum(p.numel() for p in model.parameters() if p.requires_grad) + return num + + +def set_bn_eval(m): + classname = m.__class__.__name__ + if classname.find('BatchNorm') != -1: + m.eval() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/visualization.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/visualization.py new file mode 100644 index 00000000..30fe8ae6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/unimatch/utils/visualization.py @@ -0,0 +1,107 @@ +import torch +import torch.utils.data +import numpy as np +import torchvision.utils as vutils +import cv2 +from matplotlib.cm import get_cmap +import matplotlib as mpl +import matplotlib.cm as cm + + +def vis_disparity(disp): + disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0 + disp_vis = disp_vis.astype("uint8") + disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO) + + return disp_vis + + +def gen_error_colormap(): + cols = np.array( + [[0 / 3.0, 0.1875 / 3.0, 49, 54, 149], + [0.1875 / 3.0, 0.375 / 3.0, 69, 117, 180], + [0.375 / 3.0, 0.75 / 3.0, 116, 173, 209], + [0.75 / 3.0, 1.5 / 3.0, 171, 217, 233], + [1.5 / 3.0, 3 / 3.0, 224, 243, 248], + [3 / 3.0, 6 / 3.0, 254, 224, 144], + [6 / 3.0, 12 / 3.0, 253, 174, 97], + [12 / 3.0, 24 / 3.0, 244, 109, 67], + [24 / 3.0, 48 / 3.0, 215, 48, 39], + [48 / 3.0, np.inf, 165, 0, 38]], dtype=np.float32) + cols[:, 2: 5] /= 255. + return cols + + +def disp_error_img(D_est_tensor, D_gt_tensor, abs_thres=3., rel_thres=0.05, dilate_radius=1): + D_gt_np = D_gt_tensor.detach().cpu().numpy() + D_est_np = D_est_tensor.detach().cpu().numpy() + B, H, W = D_gt_np.shape + # valid mask + mask = D_gt_np > 0 + # error in percentage. When error <= 1, the pixel is valid since <= 3px & 5% + error = np.abs(D_gt_np - D_est_np) + error[np.logical_not(mask)] = 0 + error[mask] = np.minimum(error[mask] / abs_thres, (error[mask] / D_gt_np[mask]) / rel_thres) + # get colormap + cols = gen_error_colormap() + # create error image + error_image = np.zeros([B, H, W, 3], dtype=np.float32) + for i in range(cols.shape[0]): + error_image[np.logical_and(error >= cols[i][0], error < cols[i][1])] = cols[i, 2:] + # TODO: imdilate + # error_image = cv2.imdilate(D_err, strel('disk', dilate_radius)); + error_image[np.logical_not(mask)] = 0. + # show color tag in the top-left cornor of the image + for i in range(cols.shape[0]): + distance = 20 + error_image[:, :10, i * distance:(i + 1) * distance, :] = cols[i, 2:] + + return torch.from_numpy(np.ascontiguousarray(error_image.transpose([0, 3, 1, 2]))) + + +def save_images(logger, mode_tag, images_dict, global_step): + images_dict = tensor2numpy(images_dict) + for tag, values in images_dict.items(): + if not isinstance(values, list) and not isinstance(values, tuple): + values = [values] + for idx, value in enumerate(values): + if len(value.shape) == 3: + value = value[:, np.newaxis, :, :] + value = value[:1] + value = torch.from_numpy(value) + + image_name = '{}/{}'.format(mode_tag, tag) + if len(values) > 1: + image_name = image_name + "_" + str(idx) + logger.add_image(image_name, vutils.make_grid(value, padding=0, nrow=1, normalize=True, scale_each=True), + global_step) + + +def tensor2numpy(var_dict): + for key, vars in var_dict.items(): + if isinstance(vars, np.ndarray): + var_dict[key] = vars + elif isinstance(vars, torch.Tensor): + var_dict[key] = vars.data.cpu().numpy() + else: + raise NotImplementedError("invalid input type for tensor2numpy") + + return var_dict + + +def viz_depth_tensor(disp, return_numpy=False, colormap='plasma'): + # visualize inverse depth + assert isinstance(disp, torch.Tensor) + + disp = disp.numpy() + vmax = np.percentile(disp, 95) + normalizer = mpl.colors.Normalize(vmin=disp.min(), vmax=vmax) + mapper = cm.ScalarMappable(norm=normalizer, cmap=colormap) + colormapped_im = (mapper.to_rgba(disp)[:, :, :3] * 255).astype(np.uint8) # [H, W, 3] + + if return_numpy: + return colormapped_im + + viz = torch.from_numpy(colormapped_im).permute(2, 0, 1) # [3, H, W] + + return viz diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/util.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/util.py new file mode 100644 index 00000000..fee98aea --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/util.py @@ -0,0 +1,350 @@ +import os +import random +import tempfile +import warnings +from contextlib import suppress +from pathlib import Path + +import cv2 +import numpy as np +import torch +from huggingface_hub import constants, hf_hub_download +from torch.utils.model_zoo import load_url +from ast import literal_eval + + +HF_MODEL_NAME = "lllyasviel/Annotators" +DWPOSE_MODEL_NAME = "yzd-v/DWPose" +BDS_MODEL_NAME = "bdsqlsz/qinglong_controlnet-lllite" +DENSEPOSE_MODEL_NAME = "LayerNorm/DensePose-TorchScript-with-hint-image" +MESH_GRAPHORMER_MODEL_NAME = "hr16/ControlNet-HandRefiner-pruned" +SAM_MODEL_NAME = "dhkim2810/MobileSAM" +UNIMATCH_MODEL_NAME = "hr16/Unimatch" +DEPTH_ANYTHING_MODEL_NAME = "LiheYoung/Depth-Anything" #HF Space +DIFFUSION_EDGE_MODEL_NAME = "hr16/Diffusion-Edge" +METRIC3D_MODEL_NAME = "JUGGHM/Metric3D" + +DEPTH_ANYTHING_V2_MODEL_NAME_DICT = { + "depth_anything_v2_vits.pth": "depth-anything/Depth-Anything-V2-Small", + "depth_anything_v2_vitb.pth": "depth-anything/Depth-Anything-V2-Base", + "depth_anything_v2_vitl.pth": "depth-anything/Depth-Anything-V2-Large", + "depth_anything_v2_vitg.pth": "depth-anything/Depth-Anything-V2-Giant", + "depth_anything_v2_metric_vkitti_vitl.pth": "depth-anything/Depth-Anything-V2-Metric-VKITTI-Large", + "depth_anything_v2_metric_hypersim_vitl.pth": "depth-anything/Depth-Anything-V2-Metric-Hypersim-Large" +} + +temp_dir = tempfile.gettempdir() +annotator_ckpts_path = os.path.join(Path(__file__).parents[2], 'ckpts') +USE_SYMLINKS = False + +try: + annotator_ckpts_path = os.environ['AUX_ANNOTATOR_CKPTS_PATH'] +except: + warnings.warn("Custom pressesor model path not set successfully.") + pass + +try: + USE_SYMLINKS = literal_eval(os.environ['AUX_USE_SYMLINKS']) +except: + warnings.warn("USE_SYMLINKS not set successfully. Using default value: False to download models.") + pass + +try: + temp_dir = os.environ['AUX_TEMP_DIR'] + if len(temp_dir) >= 60: + warnings.warn(f"custom temp dir is too long. Using default") + temp_dir = tempfile.gettempdir() +except: + warnings.warn(f"custom temp dir not set successfully") + pass + +here = Path(__file__).parent.resolve() + +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + + +def make_noise_disk(H, W, C, F, rng=None): + if rng: + noise = rng.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C)) + else: + noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C)) + noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC) + noise = noise[F: F + H, F: F + W] + noise -= np.min(noise) + noise /= np.max(noise) + if C == 1: + noise = noise[:, :, None] + return noise + + +def nms(x, t, s): + x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) + + f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) + f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) + f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) + f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) + + y = np.zeros_like(x) + + for f in [f1, f2, f3, f4]: + np.putmask(y, cv2.dilate(x, kernel=f) == x, x) + + z = np.zeros_like(y, dtype=np.uint8) + z[y > t] = 255 + return z + +def min_max_norm(x): + x -= np.min(x) + x /= np.maximum(np.max(x), 1e-5) + return x + + +def safe_step(x, step=2): + y = x.astype(np.float32) * float(step + 1) + y = y.astype(np.int32).astype(np.float32) / float(step) + return y + + +def img2mask(img, H, W, low=10, high=90): + assert img.ndim == 3 or img.ndim == 2 + assert img.dtype == np.uint8 + + if img.ndim == 3: + y = img[:, :, random.randrange(0, img.shape[2])] + else: + y = img + + y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC) + + if random.uniform(0, 1) < 0.5: + y = 255 - y + + return y < np.percentile(y, random.randrange(low, high)) + +def safer_memory(x): + # Fix many MAC/AMD problems + return np.ascontiguousarray(x.copy()).copy() + +UPSCALE_METHODS = ["INTER_NEAREST", "INTER_LINEAR", "INTER_AREA", "INTER_CUBIC", "INTER_LANCZOS4"] +def get_upscale_method(method_str): + assert method_str in UPSCALE_METHODS, f"Method {method_str} not found in {UPSCALE_METHODS}" + return getattr(cv2, method_str) + +def pad64(x): + return int(np.ceil(float(x) / 64.0) * 64 - x) + +#https://github.com/Mikubill/sd-webui-controlnet/blob/main/scripts/processor.py#L17 +#Added upscale_method, mode params +def resize_image_with_pad(input_image, resolution, upscale_method = "", skip_hwc3=False, mode='edge'): + if skip_hwc3: + img = input_image + else: + img = HWC3(input_image) + H_raw, W_raw, _ = img.shape + if resolution == 0: + return img, lambda x: x + k = float(resolution) / float(min(H_raw, W_raw)) + H_target = int(np.round(float(H_raw) * k)) + W_target = int(np.round(float(W_raw) * k)) + img = cv2.resize(img, (W_target, H_target), interpolation=get_upscale_method(upscale_method) if k > 1 else cv2.INTER_AREA) + H_pad, W_pad = pad64(H_target), pad64(W_target) + img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode=mode) + + def remove_pad(x): + return safer_memory(x[:H_target, :W_target, ...]) + + return safer_memory(img_padded), remove_pad + +def common_input_validate(input_image, output_type, **kwargs): + if "img" in kwargs: + warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning) + input_image = kwargs.pop("img") + + if "return_pil" in kwargs: + warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) + output_type = "pil" if kwargs["return_pil"] else "np" + + if type(output_type) is bool: + warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") + if output_type: + output_type = "pil" + + if input_image is None: + raise ValueError("input_image must be defined.") + + if not isinstance(input_image, np.ndarray): + input_image = np.array(input_image, dtype=np.uint8) + output_type = output_type or "pil" + else: + output_type = output_type or "np" + + return (input_image, output_type) + +def torch_gc(): + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + +def ade_palette(): + """ADE20K palette that maps each class to RGB values.""" + return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], + [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], + [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], + [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], + [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], + [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], + [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], + [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], + [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], + [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], + [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], + [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], + [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], + [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], + [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], + [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], + [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], + [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], + [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], + [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], + [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], + [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], + [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], + [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], + [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], + [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], + [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], + [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], + [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], + [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], + [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], + [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], + [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], + [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], + [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], + [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], + [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], + [102, 255, 0], [92, 0, 255]] + +#https://stackoverflow.com/a/44873382 +#Assume that the minimum version of Python ppl use is 3.9 +def sha256sum(file_path): + import hashlib + h = hashlib.sha256() + b = bytearray(128*1024) + mv = memoryview(b) + with open(file_path, 'rb', buffering=0) as f: + while n := f.readinto(mv): + h.update(mv[:n]) + return h.hexdigest() + +def check_hash_from_torch_hub(file_path, filename): + basename, _ = filename.split('.') + _, ref_hash = basename.split('-') + curr_hash = sha256sum(file_path) + return curr_hash[:len(ref_hash)] == ref_hash + +def custom_torch_download(filename, ckpts_dir=annotator_ckpts_path): + """Download PyTorch models using PyTorch 2.7's built-in download mechanism.""" + model_url = "https://download.pytorch.org/models/" + filename + + # Use PyTorch's built-in model downloading with custom cache directory + local_dir = os.path.join(ckpts_dir, "torch") + if not os.path.exists(local_dir): + os.makedirs(local_dir, exist_ok=True) + + model_path = os.path.join(local_dir, filename) + + if not os.path.exists(model_path): + print(f"Downloading {filename} from pytorch.org...") + try: + # Use PyTorch 2.7's load_url which handles caching, progress, and hash checking + state_dict = load_url(model_url, model_dir=local_dir, file_name=filename, progress=True, check_hash=True) + # The file is already saved by load_url, we just need the path + except Exception as e: + warnings.warn(f"Download failed with error: {e}") + raise + + print(f"model_path is {model_path}") + return model_path + +def custom_hf_download(pretrained_model_or_path, filename, cache_dir=temp_dir, ckpts_dir=annotator_ckpts_path, subfolder='', use_symlinks=USE_SYMLINKS, repo_type="model"): + + local_dir = os.path.join(ckpts_dir, pretrained_model_or_path) + model_path = Path(local_dir).joinpath(*subfolder.split('/'), filename).__str__() + + if len(str(model_path)) >= 255: + warnings.warn(f"Path {model_path} is too long, \n please change annotator_ckpts_path in config.yaml") + + if not os.path.exists(model_path): + print(f"Failed to find {model_path}.\n Downloading from huggingface.co") + print(f"cacher folder is {cache_dir}, you can change it by custom_tmp_path in config.yaml") + if use_symlinks: + cache_dir_d = constants.HF_HUB_CACHE # use huggingface newer env variables `HF_HUB_CACHE` + if cache_dir_d is None: + import platform + if platform.system() == "Windows": + cache_dir_d = Path(os.getenv("USERPROFILE")).joinpath(".cache", "huggingface", "hub").__str__() + else: + cache_dir_d = os.path.join(os.getenv("HOME"), ".cache", "huggingface", "hub") + try: + # test_link + Path(cache_dir_d).mkdir(parents=True, exist_ok=True) + Path(ckpts_dir).mkdir(parents=True, exist_ok=True) + (Path(cache_dir_d) / f"linktest_{filename}.txt").touch() + # symlink instead of link avoid `invalid cross-device link` error. + os.symlink(os.path.join(cache_dir_d, f"linktest_{filename}.txt"), os.path.join(ckpts_dir, f"linktest_{filename}.txt")) + print("Using symlinks to download models. \n",\ + "Make sure you have enough space on your cache folder. \n",\ + "And do not purge the cache folder after downloading.\n",\ + "Otherwise, you will have to re-download the models every time you run the script.\n",\ + "You can use USE_SYMLINKS: False in config.yaml to avoid this behavior.") + except: + print("Maybe not able to create symlink. Disable using symlinks.") + use_symlinks = False + cache_dir_d = Path(cache_dir).joinpath("ckpts", pretrained_model_or_path).__str__() + finally: # always remove test link files + with suppress(FileNotFoundError): + os.remove(os.path.join(ckpts_dir, f"linktest_{filename}.txt")) + os.remove(os.path.join(cache_dir_d, f"linktest_{filename}.txt")) + else: + cache_dir_d = os.path.join(cache_dir, "ckpts", pretrained_model_or_path) + + model_path = hf_hub_download(repo_id=pretrained_model_or_path, + cache_dir=cache_dir_d, + local_dir=local_dir, + subfolder=subfolder, + filename=filename, + local_dir_use_symlinks=use_symlinks, + resume_download=True, + etag_timeout=100, + repo_type=repo_type + ) + if not use_symlinks: + try: + import shutil + shutil.rmtree(os.path.join(cache_dir, "ckpts")) + except Exception as e : + print(e) + + print(f"model_path is {model_path}") + + return model_path diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/LICENSE new file mode 100644 index 00000000..7a1e90d0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Intelligent Systems Lab Org + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/__init__.py new file mode 100644 index 00000000..e67761de --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/__init__.py @@ -0,0 +1,3 @@ +# Modern ZoeDepth implementation using HuggingFace transformers + +from .transformers import ZoeDetector, ZoeDepthAnythingDetector \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/transformers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/transformers.py new file mode 100644 index 00000000..943ab6b7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_controlnet_aux/zoe/transformers.py @@ -0,0 +1,169 @@ +""" +ZoeDepth implementation using HuggingFace transformers. +Uses official Intel models for depth estimation. +""" + +import numpy as np +import torch +from PIL import Image +from transformers import pipeline, AutoImageProcessor, ZoeDepthForDepthEstimation + +# Local utility functions +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + +def pad64(x): + return int(np.ceil(float(x) / 64.0) * 64 - x) + +def safer_memory(x): + return np.ascontiguousarray(x.copy()).copy() + +def resize_image_with_pad(input_image, resolution, upscale_method="INTER_CUBIC", skip_hwc3=False, mode='edge'): + import cv2 + if skip_hwc3: + img = input_image + else: + img = HWC3(input_image) + H_raw, W_raw, _ = img.shape + if resolution == 0: + return img, lambda x: x + k = float(resolution) / float(min(H_raw, W_raw)) + H_target = int(np.round(float(H_raw) * k)) + W_target = int(np.round(float(W_raw) * k)) + + upscale_methods = {"INTER_NEAREST": cv2.INTER_NEAREST, "INTER_LINEAR": cv2.INTER_LINEAR, + "INTER_AREA": cv2.INTER_AREA, "INTER_CUBIC": cv2.INTER_CUBIC, + "INTER_LANCZOS4": cv2.INTER_LANCZOS4} + method = upscale_methods.get(upscale_method, cv2.INTER_CUBIC) + + img = cv2.resize(img, (W_target, H_target), interpolation=method if k > 1 else cv2.INTER_AREA) + H_pad, W_pad = pad64(H_target), pad64(W_target) + img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode=mode) + + def remove_pad(x): + return safer_memory(x[:H_target, :W_target, ...]) + + return safer_memory(img_padded), remove_pad + +def common_input_validate(input_image, output_type, **kwargs): + import warnings + if "img" in kwargs: + warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning) + input_image = kwargs.pop("img") + + if "return_pil" in kwargs: + warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) + output_type = "pil" if kwargs["return_pil"] else "np" + + if type(output_type) is bool: + warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") + if output_type: + output_type = "pil" + + if input_image is None: + raise ValueError("input_image must be defined.") + + if not isinstance(input_image, np.ndarray): + input_image = np.array(input_image, dtype=np.uint8) + output_type = output_type or "pil" + else: + output_type = output_type or "np" + + return (input_image, output_type) + + +class ZoeDetector: + """ZoeDepth depth estimation using HuggingFace transformers.""" + + def __init__(self, model_name="Intel/zoedepth-nyu-kitti"): + """Initialize ZoeDepth with specified model.""" + self.pipe = pipeline(task="depth-estimation", model=model_name) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path="Intel/zoedepth-nyu-kitti", filename=None, **kwargs): + """Create ZoeDetector from pretrained model.""" + return cls(model_name=pretrained_model_or_path) + + def to(self, device): + """Move model to specified device.""" + self.pipe.model = self.pipe.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + """Perform depth estimation on input image.""" + input_image, output_type = common_input_validate(input_image, output_type, **kwargs) + input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) + + if isinstance(input_image, np.ndarray): + pil_image = Image.fromarray(input_image) + else: + pil_image = input_image + + with torch.no_grad(): + result = self.pipe(pil_image) + depth = result["depth"] + + if isinstance(depth, Image.Image): + depth_array = np.array(depth, dtype=np.float32) + else: + depth_array = np.array(depth) + + vmin = np.percentile(depth_array, 2) + vmax = np.percentile(depth_array, 85) + + depth_array = depth_array - vmin + depth_array = depth_array / (vmax - vmin) + depth_array = 1.0 - depth_array + depth_image = (depth_array * 255.0).clip(0, 255).astype(np.uint8) + + detected_map = remove_pad(HWC3(depth_image)) + + if output_type == "pil": + detected_map = Image.fromarray(detected_map) + + return detected_map + + +class ZoeDepthAnythingDetector: + """ZoeDepthAnything implementation using HuggingFace transformers.""" + + def __init__(self, model_name="Intel/zoedepth-nyu-kitti"): + """Initialize ZoeDepthAnything detector.""" + self.pipe = pipeline(task="depth-estimation", model=model_name) + self.device = "cpu" + + @classmethod + def from_pretrained(cls, pretrained_model_or_path="Intel/zoedepth-nyu-kitti", filename=None, **kwargs): + """Create from pretrained model.""" + return cls(model_name=pretrained_model_or_path) + + def to(self, device): + """Move model to specified device.""" + self.pipe.model = self.pipe.model.to(device) + self.device = device + return self + + def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): + """Perform depth estimation.""" + detector = ZoeDetector(model_name="Intel/zoedepth-nyu-kitti") + detector.pipe = self.pipe + detector.device = self.device + + return detector(input_image, detect_resolution, output_type, upscale_method, **kwargs) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/CHANGES.md b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/CHANGES.md new file mode 100644 index 00000000..27e7d745 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/CHANGES.md @@ -0,0 +1 @@ +* Chumpy is removed \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/LICENSE b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/LICENSE new file mode 100644 index 00000000..e72bfdda --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/__init__.py new file mode 100644 index 00000000..e27cf869 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/__init__.py @@ -0,0 +1 @@ +name = 'manopth' diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/argutils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/argutils.py new file mode 100644 index 00000000..7e86eb02 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/argutils.py @@ -0,0 +1,51 @@ +import datetime +import os +import pickle +import subprocess +import sys + + +def print_args(args): + opts = vars(args) + print('======= Options ========') + for k, v in sorted(opts.items()): + print('{}: {}'.format(k, v)) + print('========================') + + +def save_args(args, save_folder, opt_prefix='opt', verbose=True): + opts = vars(args) + # Create checkpoint folder + if not os.path.exists(save_folder): + os.makedirs(save_folder, exist_ok=True) + + # Save options + opt_filename = '{}.txt'.format(opt_prefix) + opt_path = os.path.join(save_folder, opt_filename) + with open(opt_path, 'a') as opt_file: + opt_file.write('====== Options ======\n') + for k, v in sorted(opts.items()): + opt_file.write( + '{option}: {value}\n'.format(option=str(k), value=str(v))) + opt_file.write('=====================\n') + opt_file.write('launched {} at {}\n'.format( + str(sys.argv[0]), str(datetime.datetime.now()))) + + # Add git info + label = subprocess.check_output(["git", "describe", + "--always"]).strip() + if subprocess.call( + ["git", "branch"], + stderr=subprocess.STDOUT, + stdout=open(os.devnull, 'w')) == 0: + opt_file.write('=== Git info ====\n') + opt_file.write('{}\n'.format(label)) + commit = subprocess.check_output(['git', 'rev-parse', 'HEAD']) + opt_file.write('commit : {}\n'.format(commit.strip())) + + opt_picklename = '{}.pkl'.format(opt_prefix) + opt_picklepath = os.path.join(save_folder, opt_picklename) + with open(opt_picklepath, 'wb') as opt_file: + pickle.dump(opts, opt_file) + if verbose: + print('Saved options to {}'.format(opt_path)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/demo.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/demo.py new file mode 100644 index 00000000..0a250b7c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/demo.py @@ -0,0 +1,59 @@ +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from mpl_toolkits.mplot3d.art3d import Poly3DCollection +import numpy as np +import torch + +from custom_manopth.manolayer import ManoLayer + + +def generate_random_hand(batch_size=1, ncomps=6, mano_root='mano/models'): + nfull_comps = ncomps + 3 # Add global orientation dims to PCA + random_pcapose = torch.rand(batch_size, nfull_comps) + mano_layer = ManoLayer(mano_root=mano_root) + verts, joints = mano_layer(random_pcapose) + return {'verts': verts, 'joints': joints, 'faces': mano_layer.th_faces} + + +def display_hand(hand_info, mano_faces=None, ax=None, alpha=0.2, batch_idx=0, show=True): + """ + Displays hand batch_idx in batch of hand_info, hand_info as returned by + generate_random_hand + """ + if ax is None: + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + verts, joints = hand_info['verts'][batch_idx], hand_info['joints'][ + batch_idx] + if mano_faces is None: + ax.scatter(verts[:, 0], verts[:, 1], verts[:, 2], alpha=0.1) + else: + mesh = Poly3DCollection(verts[mano_faces], alpha=alpha) + face_color = (141 / 255, 184 / 255, 226 / 255) + edge_color = (50 / 255, 50 / 255, 50 / 255) + mesh.set_edgecolor(edge_color) + mesh.set_facecolor(face_color) + ax.add_collection3d(mesh) + ax.scatter(joints[:, 0], joints[:, 1], joints[:, 2], color='r') + cam_equal_aspect_3d(ax, verts.numpy()) + if show: + plt.show() + + +def cam_equal_aspect_3d(ax, verts, flip_x=False): + """ + Centers view on cuboid containing hand and flips y and z axis + and fixes azimuth + """ + extents = np.stack([verts.min(0), verts.max(0)], axis=1) + sz = extents[:, 1] - extents[:, 0] + centers = np.mean(extents, axis=1) + maxsize = max(abs(sz)) + r = maxsize / 2 + if flip_x: + ax.set_xlim(centers[0] + r, centers[0] - r) + else: + ax.set_xlim(centers[0] - r, centers[0] + r) + # Invert y and z axis + ax.set_ylim(centers[1] + r, centers[1] - r) + ax.set_zlim(centers[2] + r, centers[2] - r) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/manolayer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/manolayer.py new file mode 100644 index 00000000..29440d96 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/manolayer.py @@ -0,0 +1,274 @@ +import os + +import numpy as np +import torch +from torch.nn import Module + +from custom_manopth.smpl_handpca_wrapper_HAND_only import ready_arguments +from custom_manopth import rodrigues_layer, rotproj, rot6d +from custom_manopth.tensutils import (th_posemap_axisang, th_with_zeros, th_pack, + subtract_flat_id, make_list) + + +class ManoLayer(Module): + __constants__ = [ + 'use_pca', 'rot', 'ncomps', 'ncomps', 'kintree_parents', 'check', + 'side', 'center_idx', 'joint_rot_mode' + ] + + def __init__(self, + center_idx=None, + flat_hand_mean=True, + ncomps=6, + side='right', + mano_root='mano/models', + use_pca=True, + root_rot_mode='axisang', + joint_rot_mode='axisang', + robust_rot=False): + """ + Args: + center_idx: index of center joint in our computations, + if -1 centers on estimate of palm as middle of base + of middle finger and wrist + flat_hand_mean: if True, (0, 0, 0, ...) pose coefficients match + flat hand, else match average hand pose + mano_root: path to MANO pkl files for left and right hand + ncomps: number of PCA components form pose space (<45) + side: 'right' or 'left' + use_pca: Use PCA decomposition for pose space. + joint_rot_mode: 'axisang' or 'rotmat', ignored if use_pca + """ + super().__init__() + + self.center_idx = center_idx + self.robust_rot = robust_rot + if root_rot_mode == 'axisang': + self.rot = 3 + else: + self.rot = 6 + self.flat_hand_mean = flat_hand_mean + self.side = side + self.use_pca = use_pca + self.joint_rot_mode = joint_rot_mode + self.root_rot_mode = root_rot_mode + if use_pca: + self.ncomps = ncomps + else: + self.ncomps = 45 + + if side == 'right': + self.mano_path = os.path.join(mano_root, 'MANO_RIGHT.pkl') + elif side == 'left': + self.mano_path = os.path.join(mano_root, 'MANO_LEFT.pkl') + + smpl_data = ready_arguments(self.mano_path) + + hands_components = smpl_data['hands_components'] + + self.smpl_data = smpl_data + + self.register_buffer('th_betas', + torch.Tensor(smpl_data['betas']).unsqueeze(0)) + self.register_buffer('th_shapedirs', + torch.Tensor(smpl_data['shapedirs'])) + self.register_buffer('th_posedirs', + torch.Tensor(smpl_data['posedirs'])) + self.register_buffer( + 'th_v_template', + torch.Tensor(smpl_data['v_template']).unsqueeze(0)) + self.register_buffer( + 'th_J_regressor', + torch.Tensor(np.array(smpl_data['J_regressor'].toarray()))) + self.register_buffer('th_weights', + torch.Tensor(smpl_data['weights'])) + self.register_buffer('th_faces', + torch.Tensor(smpl_data['f'].astype(np.int32)).long()) + + # Get hand mean + hands_mean = np.zeros(hands_components.shape[1] + ) if flat_hand_mean else smpl_data['hands_mean'] + hands_mean = hands_mean.copy() + th_hands_mean = torch.Tensor(hands_mean).unsqueeze(0) + if self.use_pca or self.joint_rot_mode == 'axisang': + # Save as axis-angle + self.register_buffer('th_hands_mean', th_hands_mean) + selected_components = hands_components[:ncomps] + self.register_buffer('th_comps', torch.Tensor(hands_components)) + self.register_buffer('th_selected_comps', + torch.Tensor(selected_components)) + else: + th_hands_mean_rotmat = rodrigues_layer.batch_rodrigues( + th_hands_mean.view(15, 3)).reshape(15, 3, 3) + self.register_buffer('th_hands_mean_rotmat', th_hands_mean_rotmat) + + # Kinematic chain params + self.kintree_table = smpl_data['kintree_table'] + parents = list(self.kintree_table[0].tolist()) + self.kintree_parents = parents + + def forward(self, + th_pose_coeffs, + th_betas=torch.zeros(1), + th_trans=torch.zeros(1), + root_palm=torch.Tensor([0]), + share_betas=torch.Tensor([0]), + ): + """ + Args: + th_trans (Tensor (batch_size x ncomps)): if provided, applies trans to joints and vertices + th_betas (Tensor (batch_size x 10)): if provided, uses given shape parameters for hand shape + else centers on root joint (9th joint) + root_palm: return palm as hand root instead of wrist + """ + # if len(th_pose_coeffs) == 0: + # return th_pose_coeffs.new_empty(0), th_pose_coeffs.new_empty(0) + + batch_size = th_pose_coeffs.shape[0] + # Get axis angle from PCA components and coefficients + if self.use_pca or self.joint_rot_mode == 'axisang': + # Remove global rot coeffs + th_hand_pose_coeffs = th_pose_coeffs[:, self.rot:self.rot + + self.ncomps] + if self.use_pca: + # PCA components --> axis angles + th_full_hand_pose = th_hand_pose_coeffs.mm(self.th_selected_comps) + else: + th_full_hand_pose = th_hand_pose_coeffs + + # Concatenate back global rot + th_full_pose = torch.cat([ + th_pose_coeffs[:, :self.rot], + self.th_hands_mean + th_full_hand_pose + ], 1) + if self.root_rot_mode == 'axisang': + # compute rotation matrixes from axis-angle while skipping global rotation + th_pose_map, th_rot_map = th_posemap_axisang(th_full_pose) + root_rot = th_rot_map[:, :9].view(batch_size, 3, 3) + th_rot_map = th_rot_map[:, 9:] + th_pose_map = th_pose_map[:, 9:] + else: + # th_posemap offsets by 3, so add offset or 3 to get to self.rot=6 + th_pose_map, th_rot_map = th_posemap_axisang(th_full_pose[:, 6:]) + if self.robust_rot: + root_rot = rot6d.robust_compute_rotation_matrix_from_ortho6d(th_full_pose[:, :6]) + else: + root_rot = rot6d.compute_rotation_matrix_from_ortho6d(th_full_pose[:, :6]) + else: + assert th_pose_coeffs.dim() == 4, ( + 'When not self.use_pca, ' + 'th_pose_coeffs should have 4 dims, got {}'.format( + th_pose_coeffs.dim())) + assert th_pose_coeffs.shape[2:4] == (3, 3), ( + 'When not self.use_pca, th_pose_coeffs have 3x3 matrix for two' + 'last dims, got {}'.format(th_pose_coeffs.shape[2:4])) + th_pose_rots = rotproj.batch_rotprojs(th_pose_coeffs) + th_rot_map = th_pose_rots[:, 1:].view(batch_size, -1) + th_pose_map = subtract_flat_id(th_rot_map) + root_rot = th_pose_rots[:, 0] + + # Full axis angle representation with root joint + if th_betas is None or th_betas.numel() == 1: + th_v_shaped = torch.matmul(self.th_shapedirs, + self.th_betas.transpose(1, 0)).permute( + 2, 0, 1) + self.th_v_template + th_j = torch.matmul(self.th_J_regressor, th_v_shaped).repeat( + batch_size, 1, 1) + + else: + if share_betas: + th_betas = th_betas.mean(0, keepdim=True).expand(th_betas.shape[0], 10) + th_v_shaped = torch.matmul(self.th_shapedirs, + th_betas.transpose(1, 0)).permute( + 2, 0, 1) + self.th_v_template + th_j = torch.matmul(self.th_J_regressor, th_v_shaped) + # th_pose_map should have shape 20x135 + + th_v_posed = th_v_shaped + torch.matmul( + self.th_posedirs, th_pose_map.transpose(0, 1)).permute(2, 0, 1) + # Final T pose with transformation done ! + + # Global rigid transformation + + root_j = th_j[:, 0, :].contiguous().view(batch_size, 3, 1) + root_trans = th_with_zeros(torch.cat([root_rot, root_j], 2)) + + all_rots = th_rot_map.view(th_rot_map.shape[0], 15, 3, 3) + lev1_idxs = [1, 4, 7, 10, 13] + lev2_idxs = [2, 5, 8, 11, 14] + lev3_idxs = [3, 6, 9, 12, 15] + lev1_rots = all_rots[:, [idx - 1 for idx in lev1_idxs]] + lev2_rots = all_rots[:, [idx - 1 for idx in lev2_idxs]] + lev3_rots = all_rots[:, [idx - 1 for idx in lev3_idxs]] + lev1_j = th_j[:, lev1_idxs] + lev2_j = th_j[:, lev2_idxs] + lev3_j = th_j[:, lev3_idxs] + + # From base to tips + # Get lev1 results + all_transforms = [root_trans.unsqueeze(1)] + lev1_j_rel = lev1_j - root_j.transpose(1, 2) + lev1_rel_transform_flt = th_with_zeros(torch.cat([lev1_rots, lev1_j_rel.unsqueeze(3)], 3).view(-1, 3, 4)) + root_trans_flt = root_trans.unsqueeze(1).repeat(1, 5, 1, 1).view(root_trans.shape[0] * 5, 4, 4) + lev1_flt = torch.matmul(root_trans_flt, lev1_rel_transform_flt) + all_transforms.append(lev1_flt.view(all_rots.shape[0], 5, 4, 4)) + + # Get lev2 results + lev2_j_rel = lev2_j - lev1_j + lev2_rel_transform_flt = th_with_zeros(torch.cat([lev2_rots, lev2_j_rel.unsqueeze(3)], 3).view(-1, 3, 4)) + lev2_flt = torch.matmul(lev1_flt, lev2_rel_transform_flt) + all_transforms.append(lev2_flt.view(all_rots.shape[0], 5, 4, 4)) + + # Get lev3 results + lev3_j_rel = lev3_j - lev2_j + lev3_rel_transform_flt = th_with_zeros(torch.cat([lev3_rots, lev3_j_rel.unsqueeze(3)], 3).view(-1, 3, 4)) + lev3_flt = torch.matmul(lev2_flt, lev3_rel_transform_flt) + all_transforms.append(lev3_flt.view(all_rots.shape[0], 5, 4, 4)) + + reorder_idxs = [0, 1, 6, 11, 2, 7, 12, 3, 8, 13, 4, 9, 14, 5, 10, 15] + th_results = torch.cat(all_transforms, 1)[:, reorder_idxs] + th_results_global = th_results + + joint_js = torch.cat([th_j, th_j.new_zeros(th_j.shape[0], 16, 1)], 2) + tmp2 = torch.matmul(th_results, joint_js.unsqueeze(3)) + th_results2 = (th_results - torch.cat([tmp2.new_zeros(*tmp2.shape[:2], 4, 3), tmp2], 3)).permute(0, 2, 3, 1) + + th_T = torch.matmul(th_results2, self.th_weights.transpose(0, 1)) + + th_rest_shape_h = torch.cat([ + th_v_posed.transpose(2, 1), + torch.ones((batch_size, 1, th_v_posed.shape[1]), + dtype=th_T.dtype, + device=th_T.device), + ], 1) + + th_verts = (th_T * th_rest_shape_h.unsqueeze(1)).sum(2).transpose(2, 1) + th_verts = th_verts[:, :, :3] + th_jtr = th_results_global[:, :, :3, 3] + # In addition to MANO reference joints we sample vertices on each finger + # to serve as finger tips + if self.side == 'right': + tips = th_verts[:, [745, 317, 444, 556, 673]] + else: + tips = th_verts[:, [745, 317, 445, 556, 673]] + if bool(root_palm): + palm = (th_verts[:, 95] + th_verts[:, 22]).unsqueeze(1) / 2 + th_jtr = torch.cat([palm, th_jtr[:, 1:]], 1) + th_jtr = torch.cat([th_jtr, tips], 1) + + # Reorder joints to match visualization utilities + th_jtr = th_jtr[:, [0, 13, 14, 15, 16, 1, 2, 3, 17, 4, 5, 6, 18, 10, 11, 12, 19, 7, 8, 9, 20]] + + if th_trans is None or bool(torch.norm(th_trans) == 0): + if self.center_idx is not None: + center_joint = th_jtr[:, self.center_idx].unsqueeze(1) + th_jtr = th_jtr - center_joint + th_verts = th_verts - center_joint + else: + th_jtr = th_jtr + th_trans.unsqueeze(1) + th_verts = th_verts + th_trans.unsqueeze(1) + + # Scale to milimeters + th_verts = th_verts * 1000 + th_jtr = th_jtr * 1000 + return th_verts, th_jtr diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/posemapper.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/posemapper.py new file mode 100644 index 00000000..9b86ea0a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/posemapper.py @@ -0,0 +1,37 @@ +''' +Copyright 2017 Javier Romero, Dimitrios Tzionas, Michael J Black and the Max Planck Gesellschaft. All rights reserved. +This software is provided for research purposes only. +By using this software you agree to the terms of the MANO/SMPL+H Model license here http://mano.is.tue.mpg.de/license + +More information about MANO/SMPL+H is available at http://mano.is.tue.mpg.de. +For comments or questions, please email us at: mano@tue.mpg.de + + +About this file: +================ +This file defines a wrapper for the loading functions of the MANO model. + +Modules included: +- load_model: + loads the MANO model from a given file location (i.e. a .pkl file location), + or a dictionary object. + +''' + + +import numpy as np +import cv2 + +def lrotmin(p): + if isinstance(p, np.ndarray): + p = p.ravel()[3:] + return np.concatenate( + [(cv2.Rodrigues(np.array(pp))[0] - np.eye(3)).ravel() + for pp in p.reshape((-1, 3))]).ravel() + + +def posemap(s): + if s == 'lrotmin': + return lrotmin + else: + raise Exception('Unknown posemapping: %s' % (str(s), )) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rodrigues_layer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rodrigues_layer.py new file mode 100644 index 00000000..eb7a2565 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rodrigues_layer.py @@ -0,0 +1,89 @@ +""" +This part reuses code from https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py +which is part of a PyTorch port of SMPL. +Thanks to Zhang Xiong (MandyMo) for making this great code available on github ! +""" + +import argparse +from torch.autograd import gradcheck +import torch +from torch.autograd import Variable + +from custom_manopth import argutils + + +def quat2mat(quat): + """Convert quaternion coefficients to rotation matrix. + Args: + quat: size = [batch_size, 4] 4 <===>(w, x, y, z) + Returns: + Rotation matrix corresponding to the quaternion -- size = [batch_size, 3, 3] + """ + norm_quat = quat + norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True) + w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, + 2], norm_quat[:, + 3] + + batch_size = quat.size(0) + + w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) + wx, wy, wz = w * x, w * y, w * z + xy, xz, yz = x * y, x * z, y * z + + rotMat = torch.stack([ + w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy, + w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz, + w2 - x2 - y2 + z2 + ], + dim=1).view(batch_size, 3, 3) + return rotMat + + +def batch_rodrigues(axisang): + #axisang N x 3 + axisang_norm = torch.norm(axisang + 1e-8, p=2, dim=1) + angle = torch.unsqueeze(axisang_norm, -1) + axisang_normalized = torch.div(axisang, angle) + angle = angle * 0.5 + v_cos = torch.cos(angle) + v_sin = torch.sin(angle) + quat = torch.cat([v_cos, v_sin * axisang_normalized], dim=1) + rot_mat = quat2mat(quat) + rot_mat = rot_mat.view(rot_mat.shape[0], 9) + return rot_mat + + +def th_get_axis_angle(vector): + angle = torch.norm(vector, 2, 1) + axes = vector / angle.unsqueeze(1) + return axes, angle + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--batch_size', default=1, type=int) + parser.add_argument('--cuda', action='store_true') + args = parser.parse_args() + + argutils.print_args(args) + + n_components = 6 + rot = 3 + inputs = torch.rand(args.batch_size, rot) + inputs_var = Variable(inputs.double(), requires_grad=True) + if args.cuda: + inputs = inputs.cuda() + # outputs = batch_rodrigues(inputs) + test_function = gradcheck(batch_rodrigues, (inputs_var, )) + print('batch test passed !') + + inputs = torch.rand(rot) + inputs_var = Variable(inputs.double(), requires_grad=True) + test_function = gradcheck(th_cv2_rod_sub_id.apply, (inputs_var, )) + print('th_cv2_rod test passed') + + inputs = torch.rand(rot) + inputs_var = Variable(inputs.double(), requires_grad=True) + test_th = gradcheck(th_cv2_rod.apply, (inputs_var, )) + print('th_cv2_rod_id test passed !') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rot6d.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rot6d.py new file mode 100644 index 00000000..c1d60efb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rot6d.py @@ -0,0 +1,71 @@ +import torch + + +def compute_rotation_matrix_from_ortho6d(poses): + """ + Code from + https://github.com/papagina/RotationContinuity + On the Continuity of Rotation Representations in Neural Networks + Zhou et al. CVPR19 + https://zhouyisjtu.github.io/project_rotation/rotation.html + """ + x_raw = poses[:, 0:3] # batch*3 + y_raw = poses[:, 3:6] # batch*3 + + x = normalize_vector(x_raw) # batch*3 + z = cross_product(x, y_raw) # batch*3 + z = normalize_vector(z) # batch*3 + y = cross_product(z, x) # batch*3 + + x = x.view(-1, 3, 1) + y = y.view(-1, 3, 1) + z = z.view(-1, 3, 1) + matrix = torch.cat((x, y, z), 2) # batch*3*3 + return matrix + +def robust_compute_rotation_matrix_from_ortho6d(poses): + """ + Instead of making 2nd vector orthogonal to first + create a base that takes into account the two predicted + directions equally + """ + x_raw = poses[:, 0:3] # batch*3 + y_raw = poses[:, 3:6] # batch*3 + + x = normalize_vector(x_raw) # batch*3 + y = normalize_vector(y_raw) # batch*3 + middle = normalize_vector(x + y) + orthmid = normalize_vector(x - y) + x = normalize_vector(middle + orthmid) + y = normalize_vector(middle - orthmid) + # Their scalar product should be small ! + # assert torch.einsum("ij,ij->i", [x, y]).abs().max() < 0.00001 + z = normalize_vector(cross_product(x, y)) + + x = x.view(-1, 3, 1) + y = y.view(-1, 3, 1) + z = z.view(-1, 3, 1) + matrix = torch.cat((x, y, z), 2) # batch*3*3 + # Check for reflection in matrix ! If found, flip last vector TODO + assert (torch.stack([torch.det(mat) for mat in matrix ])< 0).sum() == 0 + return matrix + + +def normalize_vector(v): + batch = v.shape[0] + v_mag = torch.sqrt(v.pow(2).sum(1)) # batch + v_mag = torch.max(v_mag, v.new([1e-8])) + v_mag = v_mag.view(batch, 1).expand(batch, v.shape[1]) + v = v/v_mag + return v + + +def cross_product(u, v): + batch = u.shape[0] + i = u[:, 1] * v[:, 2] - u[:, 2] * v[:, 1] + j = u[:, 2] * v[:, 0] - u[:, 0] * v[:, 2] + k = u[:, 0] * v[:, 1] - u[:, 1] * v[:, 0] + + out = torch.cat((i.view(batch, 1), j.view(batch, 1), k.view(batch, 1)), 1) + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rotproj.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rotproj.py new file mode 100644 index 00000000..91a601d5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/rotproj.py @@ -0,0 +1,21 @@ +import torch + + +def batch_rotprojs(batches_rotmats): + proj_rotmats = [] + for batch_idx, batch_rotmats in enumerate(batches_rotmats): + proj_batch_rotmats = [] + for rot_idx, rotmat in enumerate(batch_rotmats): + # GPU implementation of svd is VERY slow + # ~ 2 10^-3 per hit vs 5 10^-5 on cpu + U, S, V = rotmat.cpu().svd() + rotmat = torch.matmul(U, V.transpose(0, 1)) + orth_det = rotmat.det() + # Remove reflection + if orth_det < 0: + rotmat[:, 2] = -1 * rotmat[:, 2] + + rotmat = rotmat.cuda() + proj_batch_rotmats.append(rotmat) + proj_rotmats.append(torch.stack(proj_batch_rotmats)) + return torch.stack(proj_rotmats) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/smpl_handpca_wrapper_HAND_only.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/smpl_handpca_wrapper_HAND_only.py new file mode 100644 index 00000000..e2d15762 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/smpl_handpca_wrapper_HAND_only.py @@ -0,0 +1,155 @@ +''' +Copyright 2017 Javier Romero, Dimitrios Tzionas, Michael J Black and the Max Planck Gesellschaft. All rights reserved. +This software is provided for research purposes only. +By using this software you agree to the terms of the MANO/SMPL+H Model license here http://mano.is.tue.mpg.de/license + +More information about MANO/SMPL+H is available at http://mano.is.tue.mpg.de. +For comments or questions, please email us at: mano@tue.mpg.de + + +About this file: +================ +This file defines a wrapper for the loading functions of the MANO model. + +Modules included: +- load_model: + loads the MANO model from a given file location (i.e. a .pkl file location), + or a dictionary object. + +''' + +def col(A): + return A.reshape((-1, 1)) + +def MatVecMult(mtx, vec): + result = mtx.dot(col(vec.ravel())).ravel() + if len(vec.shape) > 1 and vec.shape[1] > 1: + result = result.reshape((-1, vec.shape[1])) + return result + +def ready_arguments(fname_or_dict, posekey4vposed='pose'): + import numpy as np + import pickle + from custom_manopth.posemapper import posemap + + if not isinstance(fname_or_dict, dict): + dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1') + # dd = pickle.load(open(fname_or_dict, 'rb')) + else: + dd = fname_or_dict + + want_shapemodel = 'shapedirs' in dd + nposeparms = dd['kintree_table'].shape[1] * 3 + + if 'trans' not in dd: + dd['trans'] = np.zeros(3) + if 'pose' not in dd: + dd['pose'] = np.zeros(nposeparms) + if 'shapedirs' in dd and 'betas' not in dd: + dd['betas'] = np.zeros(dd['shapedirs'].shape[-1]) + + for s in [ + 'v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', + 'betas', 'J' + ]: + if (s in dd) and not hasattr(dd[s], 'dterms'): + dd[s] = np.array(dd[s]) + + assert (posekey4vposed in dd) + if want_shapemodel: + dd['v_shaped'] = dd['shapedirs'].dot(dd['betas']) + dd['v_template'] + v_shaped = dd['v_shaped'] + J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:, 0]) + J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:, 1]) + J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:, 2]) + dd['J'] = np.vstack((J_tmpx, J_tmpy, J_tmpz)).T + pose_map_res = posemap(dd['bs_type'])(dd[posekey4vposed]) + dd['v_posed'] = v_shaped + dd['posedirs'].dot(pose_map_res) + else: + pose_map_res = posemap(dd['bs_type'])(dd[posekey4vposed]) + dd_add = dd['posedirs'].dot(pose_map_res) + dd['v_posed'] = dd['v_template'] + dd_add + + return dd + + +def load_model(fname_or_dict, ncomps=6, flat_hand_mean=False, v_template=None): + ''' This model loads the fully articulable HAND SMPL model, + and replaces the pose DOFS by ncomps from PCA''' + + from custom_manopth.verts import verts_core + import numpy as np + import pickle + import scipy.sparse as sp + np.random.seed(1) + + if not isinstance(fname_or_dict, dict): + smpl_data = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1') + # smpl_data = pickle.load(open(fname_or_dict, 'rb')) + else: + smpl_data = fname_or_dict + + rot = 3 # for global orientation!!! + + hands_components = smpl_data['hands_components'] + hands_mean = np.zeros(hands_components.shape[ + 1]) if flat_hand_mean else smpl_data['hands_mean'] + hands_coeffs = smpl_data['hands_coeffs'][:, :ncomps] + + selected_components = np.vstack((hands_components[:ncomps])) + hands_mean = hands_mean.copy() + + pose_coeffs = np.zeros(rot + selected_components.shape[0]) + full_hand_pose = pose_coeffs[rot:(rot + ncomps)].dot(selected_components) + + smpl_data['fullpose'] = np.concatenate((pose_coeffs[:rot], + hands_mean + full_hand_pose)) + smpl_data['pose'] = pose_coeffs + + Jreg = smpl_data['J_regressor'] + if not sp.issparse(Jreg): + smpl_data['J_regressor'] = (sp.csc_matrix( + (Jreg.data, (Jreg.row, Jreg.col)), shape=Jreg.shape)) + + # slightly modify ready_arguments to make sure that it uses the fullpose + # (which will NOT be pose) for the computation of posedirs + dd = ready_arguments(smpl_data, posekey4vposed='fullpose') + + # create the smpl formula with the fullpose, + # but expose the PCA coefficients as smpl.pose for compatibility + args = { + 'pose': dd['fullpose'], + 'v': dd['v_posed'], + 'J': dd['J'], + 'weights': dd['weights'], + 'kintree_table': dd['kintree_table'], + 'xp': np, + 'want_Jtr': True, + 'bs_style': dd['bs_style'], + } + + result_previous, meta = verts_core(**args) + + result = result_previous + dd['trans'].reshape((1, 3)) + result.no_translation = result_previous + + if meta is not None: + for field in ['Jtr', 'A', 'A_global', 'A_weighted']: + if (hasattr(meta, field)): + setattr(result, field, getattr(meta, field)) + + setattr(result, 'Jtr', meta) + if hasattr(result, 'Jtr'): + result.J_transformed = result.Jtr + dd['trans'].reshape((1, 3)) + + for k, v in dd.items(): + setattr(result, k, v) + + if v_template is not None: + result.v_template[:] = v_template + + return result + + +if __name__ == '__main__': + load_model() \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/tensutils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/tensutils.py new file mode 100644 index 00000000..ce627beb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/tensutils.py @@ -0,0 +1,47 @@ +import torch + +from custom_manopth import rodrigues_layer + + +def th_posemap_axisang(pose_vectors): + rot_nb = int(pose_vectors.shape[1] / 3) + pose_vec_reshaped = pose_vectors.contiguous().view(-1, 3) + rot_mats = rodrigues_layer.batch_rodrigues(pose_vec_reshaped) + rot_mats = rot_mats.view(pose_vectors.shape[0], rot_nb * 9) + pose_maps = subtract_flat_id(rot_mats) + return pose_maps, rot_mats + + +def th_with_zeros(tensor): + batch_size = tensor.shape[0] + padding = torch.tensor([0.0, 0.0, 0.0, 1.0], device = tensor.device, dtype = tensor.dtype) + padding.requires_grad = False + + concat_list = [tensor, padding.view(1, 1, 4).repeat(batch_size, 1, 1)] + cat_res = torch.cat(concat_list, 1) + return cat_res + + +def th_pack(tensor): + batch_size = tensor.shape[0] + padding = tensor.new_zeros((batch_size, 4, 3)) + padding.requires_grad = False + pack_list = [padding, tensor] + pack_res = torch.cat(pack_list, 2) + return pack_res + + +def subtract_flat_id(rot_mats): + # Subtracts identity as a flattened tensor + rot_nb = int(rot_mats.shape[1] / 9) + id_flat = torch.eye( + 3, dtype=rot_mats.dtype, device=rot_mats.device).view(1, 9).repeat( + rot_mats.shape[0], rot_nb) + # id_flat.requires_grad = False + results = rot_mats - id_flat + return results + + +def make_list(tensor): + # type: (List[int]) -> List[int] + return tensor diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/verts.py b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/verts.py new file mode 100644 index 00000000..7a9e5c32 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_manopth/verts.py @@ -0,0 +1,117 @@ +''' +Copyright 2017 Javier Romero, Dimitrios Tzionas, Michael J Black and the Max Planck Gesellschaft. All rights reserved. +This software is provided for research purposes only. +By using this software you agree to the terms of the MANO/SMPL+H Model license here http://mano.is.tue.mpg.de/license + +More information about MANO/SMPL+H is available at http://mano.is.tue.mpg.de. +For comments or questions, please email us at: mano@tue.mpg.de + + +About this file: +================ +This file defines a wrapper for the loading functions of the MANO model. + +Modules included: +- load_model: + loads the MANO model from a given file location (i.e. a .pkl file location), + or a dictionary object. + +''' + + +import numpy as np +import mano.webuser.lbs as lbs +from mano.webuser.posemapper import posemap +import scipy.sparse as sp + + +def ischumpy(x): + return hasattr(x, 'dterms') + + +def verts_decorated(trans, + pose, + v_template, + J_regressor, + weights, + kintree_table, + bs_style, + f, + bs_type=None, + posedirs=None, + betas=None, + shapedirs=None, + want_Jtr=False): + + for which in [ + trans, pose, v_template, weights, posedirs, betas, shapedirs + ]: + if which is not None: + assert ischumpy(which) + + v = v_template + + if shapedirs is not None: + if betas is None: + betas = np.zeros(shapedirs.shape[-1]) + v_shaped = v + shapedirs.dot(betas) + else: + v_shaped = v + + if posedirs is not None: + v_posed = v_shaped + posedirs.dot(posemap(bs_type)(pose)) + else: + v_posed = v_shaped + + v = v_posed + + if sp.issparse(J_regressor): + J_tmpx = np.matmul(J_regressor, v_shaped[:, 0]) + J_tmpy = np.matmul(J_regressor, v_shaped[:, 1]) + J_tmpz = np.matmul(J_regressor, v_shaped[:, 2]) + J = np.vstack((J_tmpx, J_tmpy, J_tmpz)).T + else: + assert (ischumpy(J)) + + assert (bs_style == 'lbs') + result, Jtr = lbs.verts_core( + pose, v, J, weights, kintree_table, want_Jtr=True, xp=np) + + tr = trans.reshape((1, 3)) + result = result + tr + Jtr = Jtr + tr + + result.trans = trans + result.f = f + result.pose = pose + result.v_template = v_template + result.J = J + result.J_regressor = J_regressor + result.weights = weights + result.kintree_table = kintree_table + result.bs_style = bs_style + result.bs_type = bs_type + if posedirs is not None: + result.posedirs = posedirs + result.v_posed = v_posed + if shapedirs is not None: + result.shapedirs = shapedirs + result.betas = betas + result.v_shaped = v_shaped + if want_Jtr: + result.J_transformed = Jtr + return result + + +def verts_core(pose, + v, + J, + weights, + kintree_table, + bs_style, + want_Jtr=False, + xp=np): + + assert (bs_style == 'lbs') + result = lbs.verts_core(pose, v, J, weights, kintree_table, want_Jtr, xp) + return result \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/__init__.py new file mode 100644 index 00000000..3dc1f76b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/__init__.py @@ -0,0 +1 @@ + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/build.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/build.py new file mode 100644 index 00000000..e91c2830 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/build.py @@ -0,0 +1,147 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + + +import os.path as op +import torch +import logging +import code +from custom_mesh_graphormer.utils.comm import get_world_size +from custom_mesh_graphormer.datasets.human_mesh_tsv import (MeshTSVDataset, MeshTSVYamlDataset) +from custom_mesh_graphormer.datasets.hand_mesh_tsv import (HandMeshTSVDataset, HandMeshTSVYamlDataset) + + +def build_dataset(yaml_file, args, is_train=True, scale_factor=1): + print(yaml_file) + if not op.isfile(yaml_file): + yaml_file = op.join(args.data_dir, yaml_file) + # code.interact(local=locals()) + assert op.isfile(yaml_file) + return MeshTSVYamlDataset(yaml_file, is_train, False, scale_factor) + + +class IterationBasedBatchSampler(torch.utils.data.sampler.BatchSampler): + """ + Wraps a BatchSampler, resampling from it until + a specified number of iterations have been sampled + """ + + def __init__(self, batch_sampler, num_iterations, start_iter=0): + self.batch_sampler = batch_sampler + self.num_iterations = num_iterations + self.start_iter = start_iter + + def __iter__(self): + iteration = self.start_iter + while iteration <= self.num_iterations: + # if the underlying sampler has a set_epoch method, like + # DistributedSampler, used for making each process see + # a different split of the dataset, then set it + if hasattr(self.batch_sampler.sampler, "set_epoch"): + self.batch_sampler.sampler.set_epoch(iteration) + for batch in self.batch_sampler: + iteration += 1 + if iteration > self.num_iterations: + break + yield batch + + def __len__(self): + return self.num_iterations + + +def make_batch_data_sampler(sampler, images_per_gpu, num_iters=None, start_iter=0): + batch_sampler = torch.utils.data.sampler.BatchSampler( + sampler, images_per_gpu, drop_last=False + ) + if num_iters is not None and num_iters >= 0: + batch_sampler = IterationBasedBatchSampler( + batch_sampler, num_iters, start_iter + ) + return batch_sampler + + +def make_data_sampler(dataset, shuffle, distributed): + if distributed: + return torch.utils.data.distributed.DistributedSampler(dataset, shuffle=shuffle) + if shuffle: + sampler = torch.utils.data.sampler.RandomSampler(dataset) + else: + sampler = torch.utils.data.sampler.SequentialSampler(dataset) + return sampler + + +def make_data_loader(args, yaml_file, is_distributed=True, + is_train=True, start_iter=0, scale_factor=1): + + dataset = build_dataset(yaml_file, args, is_train=is_train, scale_factor=scale_factor) + logger = logging.getLogger(__name__) + if is_train==True: + shuffle = True + images_per_gpu = args.per_gpu_train_batch_size + images_per_batch = images_per_gpu * get_world_size() + iters_per_batch = len(dataset) // images_per_batch + num_iters = iters_per_batch * args.num_train_epochs + logger.info("Train with {} images per GPU.".format(images_per_gpu)) + logger.info("Total batch size {}".format(images_per_batch)) + logger.info("Total training steps {}".format(num_iters)) + else: + shuffle = False + images_per_gpu = args.per_gpu_eval_batch_size + num_iters = None + start_iter = 0 + + sampler = make_data_sampler(dataset, shuffle, is_distributed) + batch_sampler = make_batch_data_sampler( + sampler, images_per_gpu, num_iters, start_iter + ) + data_loader = torch.utils.data.DataLoader( + dataset, num_workers=args.num_workers, batch_sampler=batch_sampler, + pin_memory=True, + ) + return data_loader + + +#============================================================================================== + +def build_hand_dataset(yaml_file, args, is_train=True, scale_factor=1): + print(yaml_file) + if not op.isfile(yaml_file): + yaml_file = op.join(args.data_dir, yaml_file) + # code.interact(local=locals()) + assert op.isfile(yaml_file) + return HandMeshTSVYamlDataset(args, yaml_file, is_train, False, scale_factor) + + +def make_hand_data_loader(args, yaml_file, is_distributed=True, + is_train=True, start_iter=0, scale_factor=1): + + dataset = build_hand_dataset(yaml_file, args, is_train=is_train, scale_factor=scale_factor) + logger = logging.getLogger(__name__) + if is_train==True: + shuffle = True + images_per_gpu = args.per_gpu_train_batch_size + images_per_batch = images_per_gpu * get_world_size() + iters_per_batch = len(dataset) // images_per_batch + num_iters = iters_per_batch * args.num_train_epochs + logger.info("Train with {} images per GPU.".format(images_per_gpu)) + logger.info("Total batch size {}".format(images_per_batch)) + logger.info("Total training steps {}".format(num_iters)) + else: + shuffle = False + images_per_gpu = args.per_gpu_eval_batch_size + num_iters = None + start_iter = 0 + + sampler = make_data_sampler(dataset, shuffle, is_distributed) + batch_sampler = make_batch_data_sampler( + sampler, images_per_gpu, num_iters, start_iter + ) + data_loader = torch.utils.data.DataLoader( + dataset, num_workers=args.num_workers, batch_sampler=batch_sampler, + pin_memory=True, + ) + return data_loader + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/hand_mesh_tsv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/hand_mesh_tsv.py new file mode 100644 index 00000000..d4a83920 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/hand_mesh_tsv.py @@ -0,0 +1,334 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + + +import cv2 +import math +import json +from PIL import Image +import os.path as op +import numpy as np +import code + +from custom_mesh_graphormer.utils.tsv_file import TSVFile, CompositeTSVFile +from custom_mesh_graphormer.utils.tsv_file_ops import load_linelist_file, load_from_yaml_file, find_file_path_in_yaml +from custom_mesh_graphormer.utils.image_ops import img_from_base64, crop, flip_img, flip_pose, flip_kp, transform, rot_aa +import torch +import torchvision.transforms as transforms + + +class HandMeshTSVDataset(object): + def __init__(self, args, img_file, label_file=None, hw_file=None, + linelist_file=None, is_train=True, cv2_output=False, scale_factor=1): + + self.args = args + self.img_file = img_file + self.label_file = label_file + self.hw_file = hw_file + self.linelist_file = linelist_file + self.img_tsv = self.get_tsv_file(img_file) + self.label_tsv = None if label_file is None else self.get_tsv_file(label_file) + self.hw_tsv = None if hw_file is None else self.get_tsv_file(hw_file) + + if self.is_composite: + assert op.isfile(self.linelist_file) + self.line_list = [i for i in range(self.hw_tsv.num_rows())] + else: + self.line_list = load_linelist_file(linelist_file) + + self.cv2_output = cv2_output + self.normalize_img = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.is_train = is_train + self.scale_factor = 0.25 # rescale bounding boxes by a factor of [1-options.scale_factor,1+options.scale_factor] + self.noise_factor = 0.4 + self.rot_factor = 90 # Random rotation in the range [-rot_factor, rot_factor] + self.img_res = 224 + self.image_keys = self.prepare_image_keys() + self.joints_definition = ('Wrist', 'Thumb_1', 'Thumb_2', 'Thumb_3', 'Thumb_4', 'Index_1', 'Index_2', 'Index_3', 'Index_4', 'Middle_1', + 'Middle_2', 'Middle_3', 'Middle_4', 'Ring_1', 'Ring_2', 'Ring_3', 'Ring_4', 'Pinky_1', 'Pinky_2', 'Pinky_3', 'Pinky_4') + self.root_index = self.joints_definition.index('Wrist') + + def get_tsv_file(self, tsv_file): + if tsv_file: + if self.is_composite: + return CompositeTSVFile(tsv_file, self.linelist_file, + root=self.root) + tsv_path = find_file_path_in_yaml(tsv_file, self.root) + return TSVFile(tsv_path) + + def get_valid_tsv(self): + # sorted by file size + if self.hw_tsv: + return self.hw_tsv + if self.label_tsv: + return self.label_tsv + + def prepare_image_keys(self): + tsv = self.get_valid_tsv() + return [tsv.get_key(i) for i in range(tsv.num_rows())] + + def prepare_image_key_to_index(self): + tsv = self.get_valid_tsv() + return {tsv.get_key(i) : i for i in range(tsv.num_rows())} + + + def augm_params(self): + """Get augmentation parameters.""" + flip = 0 # flipping + pn = np.ones(3) # per channel pixel-noise + + if self.args.multiscale_inference == False: + rot = 0 # rotation + sc = 1.0 # scaling + elif self.args.multiscale_inference == True: + rot = self.args.rot + sc = self.args.sc + + if self.is_train: + sc = 1.0 + # Each channel is multiplied with a number + # in the area [1-opt.noiseFactor,1+opt.noiseFactor] + pn = np.random.uniform(1-self.noise_factor, 1+self.noise_factor, 3) + + # The rotation is a number in the area [-2*rotFactor, 2*rotFactor] + rot = min(2*self.rot_factor, + max(-2*self.rot_factor, np.random.randn()*self.rot_factor)) + + # The scale is multiplied with a number + # in the area [1-scaleFactor,1+scaleFactor] + sc = min(1+self.scale_factor, + max(1-self.scale_factor, np.random.randn()*self.scale_factor+1)) + # but it is zero with probability 3/5 + if np.random.uniform() <= 0.6: + rot = 0 + + return flip, pn, rot, sc + + def rgb_processing(self, rgb_img, center, scale, rot, flip, pn): + """Process rgb image and do augmentation.""" + rgb_img = crop(rgb_img, center, scale, + [self.img_res, self.img_res], rot=rot) + # flip the image + if flip: + rgb_img = flip_img(rgb_img) + # in the rgb image we add pixel noise in a channel-wise manner + rgb_img[:,:,0] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,0]*pn[0])) + rgb_img[:,:,1] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,1]*pn[1])) + rgb_img[:,:,2] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,2]*pn[2])) + # (3,224,224),float,[0,1] + rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0 + return rgb_img + + def j2d_processing(self, kp, center, scale, r, f): + """Process gt 2D keypoints and apply all augmentation transforms.""" + nparts = kp.shape[0] + for i in range(nparts): + kp[i,0:2] = transform(kp[i,0:2]+1, center, scale, + [self.img_res, self.img_res], rot=r) + # convert to normalized coordinates + kp[:,:-1] = 2.*kp[:,:-1]/self.img_res - 1. + # flip the x coordinates + if f: + kp = flip_kp(kp) + kp = kp.astype('float32') + return kp + + + def j3d_processing(self, S, r, f): + """Process gt 3D keypoints and apply all augmentation transforms.""" + # in-plane rotation + rot_mat = np.eye(3) + if not r == 0: + rot_rad = -r * np.pi / 180 + sn,cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0,:2] = [cs, -sn] + rot_mat[1,:2] = [sn, cs] + S[:, :-1] = np.einsum('ij,kj->ki', rot_mat, S[:, :-1]) + # flip the x coordinates + if f: + S = flip_kp(S) + S = S.astype('float32') + return S + + def pose_processing(self, pose, r, f): + """Process SMPL theta parameters and apply all augmentation transforms.""" + # rotation or the pose parameters + pose = pose.astype('float32') + pose[:3] = rot_aa(pose[:3], r) + # flip the pose parameters + if f: + pose = flip_pose(pose) + # (72),float + pose = pose.astype('float32') + return pose + + def get_line_no(self, idx): + return idx if self.line_list is None else self.line_list[idx] + + def get_image(self, idx): + line_no = self.get_line_no(idx) + row = self.img_tsv[line_no] + # use -1 to support old format with multiple columns. + cv2_im = img_from_base64(row[-1]) + if self.cv2_output: + return cv2_im.astype(np.float32, copy=True) + cv2_im = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB) + return cv2_im + + def get_annotations(self, idx): + line_no = self.get_line_no(idx) + if self.label_tsv is not None: + row = self.label_tsv[line_no] + annotations = json.loads(row[1]) + return annotations + else: + return [] + + def get_target_from_annotations(self, annotations, img_size, idx): + # This function will be overwritten by each dataset to + # decode the labels to specific formats for each task. + return annotations + + def get_img_info(self, idx): + if self.hw_tsv is not None: + line_no = self.get_line_no(idx) + row = self.hw_tsv[line_no] + try: + # json string format with "height" and "width" being the keys + return json.loads(row[1])[0] + except ValueError: + # list of strings representing height and width in order + hw_str = row[1].split(' ') + hw_dict = {"height": int(hw_str[0]), "width": int(hw_str[1])} + return hw_dict + + def get_img_key(self, idx): + line_no = self.get_line_no(idx) + # based on the overhead of reading each row. + if self.hw_tsv: + return self.hw_tsv[line_no][0] + elif self.label_tsv: + return self.label_tsv[line_no][0] + else: + return self.img_tsv[line_no][0] + + def __len__(self): + if self.line_list is None: + return self.img_tsv.num_rows() + else: + return len(self.line_list) + + def __getitem__(self, idx): + + img = self.get_image(idx) + img_key = self.get_img_key(idx) + annotations = self.get_annotations(idx) + + annotations = annotations[0] + center = annotations['center'] + scale = annotations['scale'] + has_2d_joints = annotations['has_2d_joints'] + has_3d_joints = annotations['has_3d_joints'] + joints_2d = np.asarray(annotations['2d_joints']) + joints_3d = np.asarray(annotations['3d_joints']) + + if joints_2d.ndim==3: + joints_2d = joints_2d[0] + if joints_3d.ndim==3: + joints_3d = joints_3d[0] + + # Get SMPL parameters, if available + has_smpl = np.asarray(annotations['has_smpl']) + pose = np.asarray(annotations['pose']) + betas = np.asarray(annotations['betas']) + + # Get augmentation parameters + flip,pn,rot,sc = self.augm_params() + + # Process image + img = self.rgb_processing(img, center, sc*scale, rot, flip, pn) + img = torch.from_numpy(img).float() + # Store image before normalization to use it in visualization + transfromed_img = self.normalize_img(img) + + # normalize 3d pose by aligning the wrist as the root (at origin) + root_coord = joints_3d[self.root_index,:-1] + joints_3d[:,:-1] = joints_3d[:,:-1] - root_coord[None,:] + # 3d pose augmentation (random flip + rotation, consistent to image and SMPL) + joints_3d_transformed = self.j3d_processing(joints_3d.copy(), rot, flip) + # 2d pose augmentation + joints_2d_transformed = self.j2d_processing(joints_2d.copy(), center, sc*scale, rot, flip) + + ################################### + # Masking percantage + # We observe that 0% or 5% works better for 3D hand mesh + # We think this is probably becasue 3D vertices are quite sparse in the down-sampled hand mesh + mvm_percent = 0.0 # or 0.05 + ################################### + + mjm_mask = np.ones((21,1)) + if self.is_train: + num_joints = 21 + pb = np.random.random_sample() + masked_num = int(pb * mvm_percent * num_joints) # at most x% of the joints could be masked + indices = np.random.choice(np.arange(num_joints),replace=False,size=masked_num) + mjm_mask[indices,:] = 0.0 + mjm_mask = torch.from_numpy(mjm_mask).float() + + mvm_mask = np.ones((195,1)) + if self.is_train: + num_vertices = 195 + pb = np.random.random_sample() + masked_num = int(pb * mvm_percent * num_vertices) # at most x% of the vertices could be masked + indices = np.random.choice(np.arange(num_vertices),replace=False,size=masked_num) + mvm_mask[indices,:] = 0.0 + mvm_mask = torch.from_numpy(mvm_mask).float() + + meta_data = {} + meta_data['ori_img'] = img + meta_data['pose'] = torch.from_numpy(self.pose_processing(pose, rot, flip)).float() + meta_data['betas'] = torch.from_numpy(betas).float() + meta_data['joints_3d'] = torch.from_numpy(joints_3d_transformed).float() + meta_data['has_3d_joints'] = has_3d_joints + meta_data['has_smpl'] = has_smpl + meta_data['mjm_mask'] = mjm_mask + meta_data['mvm_mask'] = mvm_mask + + # Get 2D keypoints and apply augmentation transforms + meta_data['has_2d_joints'] = has_2d_joints + meta_data['joints_2d'] = torch.from_numpy(joints_2d_transformed).float() + + meta_data['scale'] = float(sc * scale) + meta_data['center'] = np.asarray(center).astype(np.float32) + + return img_key, transfromed_img, meta_data + + +class HandMeshTSVYamlDataset(HandMeshTSVDataset): + """ TSVDataset taking a Yaml file for easy function call + """ + def __init__(self, args, yaml_file, is_train=True, cv2_output=False, scale_factor=1): + self.cfg = load_from_yaml_file(yaml_file) + self.is_composite = self.cfg.get('composite', False) + self.root = op.dirname(yaml_file) + + if self.is_composite==False: + img_file = find_file_path_in_yaml(self.cfg['img'], self.root) + label_file = find_file_path_in_yaml(self.cfg.get('label', None), + self.root) + hw_file = find_file_path_in_yaml(self.cfg.get('hw', None), self.root) + linelist_file = find_file_path_in_yaml(self.cfg.get('linelist', None), + self.root) + else: + img_file = self.cfg['img'] + hw_file = self.cfg['hw'] + label_file = self.cfg.get('label', None) + linelist_file = find_file_path_in_yaml(self.cfg.get('linelist', None), + self.root) + + super(HandMeshTSVYamlDataset, self).__init__( + args, img_file, label_file, hw_file, linelist_file, is_train, cv2_output=cv2_output, scale_factor=scale_factor) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/human_mesh_tsv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/human_mesh_tsv.py new file mode 100644 index 00000000..b60e0830 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/datasets/human_mesh_tsv.py @@ -0,0 +1,337 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + +import cv2 +import math +import json +from PIL import Image +import os.path as op +import numpy as np +import code + +from custom_mesh_graphormer.utils.tsv_file import TSVFile, CompositeTSVFile +from custom_mesh_graphormer.utils.tsv_file_ops import load_linelist_file, load_from_yaml_file, find_file_path_in_yaml +from custom_mesh_graphormer.utils.image_ops import img_from_base64, crop, flip_img, flip_pose, flip_kp, transform, rot_aa +import torch +import torchvision.transforms as transforms + + +class MeshTSVDataset(object): + def __init__(self, img_file, label_file=None, hw_file=None, + linelist_file=None, is_train=True, cv2_output=False, scale_factor=1): + + self.img_file = img_file + self.label_file = label_file + self.hw_file = hw_file + self.linelist_file = linelist_file + self.img_tsv = self.get_tsv_file(img_file) + self.label_tsv = None if label_file is None else self.get_tsv_file(label_file) + self.hw_tsv = None if hw_file is None else self.get_tsv_file(hw_file) + + if self.is_composite: + assert op.isfile(self.linelist_file) + self.line_list = [i for i in range(self.hw_tsv.num_rows())] + else: + self.line_list = load_linelist_file(linelist_file) + + self.cv2_output = cv2_output + self.normalize_img = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.is_train = is_train + self.scale_factor = 0.25 # rescale bounding boxes by a factor of [1-options.scale_factor,1+options.scale_factor] + self.noise_factor = 0.4 + self.rot_factor = 30 # Random rotation in the range [-rot_factor, rot_factor] + self.img_res = 224 + + self.image_keys = self.prepare_image_keys() + + self.joints_definition = ('R_Ankle', 'R_Knee', 'R_Hip', 'L_Hip', 'L_Knee', 'L_Ankle', 'R_Wrist', 'R_Elbow', 'R_Shoulder', 'L_Shoulder', + 'L_Elbow','L_Wrist','Neck','Top_of_Head','Pelvis','Thorax','Spine','Jaw','Head','Nose','L_Eye','R_Eye','L_Ear','R_Ear') + self.pelvis_index = self.joints_definition.index('Pelvis') + + def get_tsv_file(self, tsv_file): + if tsv_file: + if self.is_composite: + return CompositeTSVFile(tsv_file, self.linelist_file, + root=self.root) + tsv_path = find_file_path_in_yaml(tsv_file, self.root) + return TSVFile(tsv_path) + + def get_valid_tsv(self): + # sorted by file size + if self.hw_tsv: + return self.hw_tsv + if self.label_tsv: + return self.label_tsv + + def prepare_image_keys(self): + tsv = self.get_valid_tsv() + return [tsv.get_key(i) for i in range(tsv.num_rows())] + + def prepare_image_key_to_index(self): + tsv = self.get_valid_tsv() + return {tsv.get_key(i) : i for i in range(tsv.num_rows())} + + + def augm_params(self): + """Get augmentation parameters.""" + flip = 0 # flipping + pn = np.ones(3) # per channel pixel-noise + rot = 0 # rotation + sc = 1 # scaling + if self.is_train: + # We flip with probability 1/2 + if np.random.uniform() <= 0.5: + flip = 1 + + # Each channel is multiplied with a number + # in the area [1-opt.noiseFactor,1+opt.noiseFactor] + pn = np.random.uniform(1-self.noise_factor, 1+self.noise_factor, 3) + + # The rotation is a number in the area [-2*rotFactor, 2*rotFactor] + rot = min(2*self.rot_factor, + max(-2*self.rot_factor, np.random.randn()*self.rot_factor)) + + # The scale is multiplied with a number + # in the area [1-scaleFactor,1+scaleFactor] + sc = min(1+self.scale_factor, + max(1-self.scale_factor, np.random.randn()*self.scale_factor+1)) + # but it is zero with probability 3/5 + if np.random.uniform() <= 0.6: + rot = 0 + + return flip, pn, rot, sc + + def rgb_processing(self, rgb_img, center, scale, rot, flip, pn): + """Process rgb image and do augmentation.""" + rgb_img = crop(rgb_img, center, scale, + [self.img_res, self.img_res], rot=rot) + # flip the image + if flip: + rgb_img = flip_img(rgb_img) + # in the rgb image we add pixel noise in a channel-wise manner + rgb_img[:,:,0] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,0]*pn[0])) + rgb_img[:,:,1] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,1]*pn[1])) + rgb_img[:,:,2] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,2]*pn[2])) + # (3,224,224),float,[0,1] + rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0 + return rgb_img + + def j2d_processing(self, kp, center, scale, r, f): + """Process gt 2D keypoints and apply all augmentation transforms.""" + nparts = kp.shape[0] + for i in range(nparts): + kp[i,0:2] = transform(kp[i,0:2]+1, center, scale, + [self.img_res, self.img_res], rot=r) + # convert to normalized coordinates + kp[:,:-1] = 2.*kp[:,:-1]/self.img_res - 1. + # flip the x coordinates + if f: + kp = flip_kp(kp) + kp = kp.astype('float32') + return kp + + def j3d_processing(self, S, r, f): + """Process gt 3D keypoints and apply all augmentation transforms.""" + # in-plane rotation + rot_mat = np.eye(3) + if not r == 0: + rot_rad = -r * np.pi / 180 + sn,cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0,:2] = [cs, -sn] + rot_mat[1,:2] = [sn, cs] + S[:, :-1] = np.einsum('ij,kj->ki', rot_mat, S[:, :-1]) + # flip the x coordinates + if f: + S = flip_kp(S) + S = S.astype('float32') + return S + + def pose_processing(self, pose, r, f): + """Process SMPL theta parameters and apply all augmentation transforms.""" + # rotation or the pose parameters + pose = pose.astype('float32') + pose[:3] = rot_aa(pose[:3], r) + # flip the pose parameters + if f: + pose = flip_pose(pose) + # (72),float + pose = pose.astype('float32') + return pose + + def get_line_no(self, idx): + return idx if self.line_list is None else self.line_list[idx] + + def get_image(self, idx): + line_no = self.get_line_no(idx) + row = self.img_tsv[line_no] + # use -1 to support old format with multiple columns. + cv2_im = img_from_base64(row[-1]) + if self.cv2_output: + return cv2_im.astype(np.float32, copy=True) + cv2_im = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB) + + return cv2_im + + def get_annotations(self, idx): + line_no = self.get_line_no(idx) + if self.label_tsv is not None: + row = self.label_tsv[line_no] + annotations = json.loads(row[1]) + return annotations + else: + return [] + + def get_target_from_annotations(self, annotations, img_size, idx): + # This function will be overwritten by each dataset to + # decode the labels to specific formats for each task. + return annotations + + + def get_img_info(self, idx): + if self.hw_tsv is not None: + line_no = self.get_line_no(idx) + row = self.hw_tsv[line_no] + try: + # json string format with "height" and "width" being the keys + return json.loads(row[1])[0] + except ValueError: + # list of strings representing height and width in order + hw_str = row[1].split(' ') + hw_dict = {"height": int(hw_str[0]), "width": int(hw_str[1])} + return hw_dict + + def get_img_key(self, idx): + line_no = self.get_line_no(idx) + # based on the overhead of reading each row. + if self.hw_tsv: + return self.hw_tsv[line_no][0] + elif self.label_tsv: + return self.label_tsv[line_no][0] + else: + return self.img_tsv[line_no][0] + + def __len__(self): + if self.line_list is None: + return self.img_tsv.num_rows() + else: + return len(self.line_list) + + def __getitem__(self, idx): + + img = self.get_image(idx) + img_key = self.get_img_key(idx) + annotations = self.get_annotations(idx) + + annotations = annotations[0] + center = annotations['center'] + scale = annotations['scale'] + has_2d_joints = annotations['has_2d_joints'] + has_3d_joints = annotations['has_3d_joints'] + joints_2d = np.asarray(annotations['2d_joints']) + joints_3d = np.asarray(annotations['3d_joints']) + + if joints_2d.ndim==3: + joints_2d = joints_2d[0] + if joints_3d.ndim==3: + joints_3d = joints_3d[0] + + # Get SMPL parameters, if available + has_smpl = np.asarray(annotations['has_smpl']) + pose = np.asarray(annotations['pose']) + betas = np.asarray(annotations['betas']) + + try: + gender = annotations['gender'] + except KeyError: + gender = 'none' + + # Get augmentation parameters + flip,pn,rot,sc = self.augm_params() + + # Process image + img = self.rgb_processing(img, center, sc*scale, rot, flip, pn) + img = torch.from_numpy(img).float() + # Store image before normalization to use it in visualization + transfromed_img = self.normalize_img(img) + + # normalize 3d pose by aligning the pelvis as the root (at origin) + root_pelvis = joints_3d[self.pelvis_index,:-1] + joints_3d[:,:-1] = joints_3d[:,:-1] - root_pelvis[None,:] + # 3d pose augmentation (random flip + rotation, consistent to image and SMPL) + joints_3d_transformed = self.j3d_processing(joints_3d.copy(), rot, flip) + # 2d pose augmentation + joints_2d_transformed = self.j2d_processing(joints_2d.copy(), center, sc*scale, rot, flip) + + ################################### + # Masking percantage + # We observe that 30% works better for human body mesh. Further details are reported in the paper. + mvm_percent = 0.3 + ################################### + + mjm_mask = np.ones((14,1)) + if self.is_train: + num_joints = 14 + pb = np.random.random_sample() + masked_num = int(pb * mvm_percent * num_joints) # at most x% of the joints could be masked + indices = np.random.choice(np.arange(num_joints),replace=False,size=masked_num) + mjm_mask[indices,:] = 0.0 + mjm_mask = torch.from_numpy(mjm_mask).float() + + mvm_mask = np.ones((431,1)) + if self.is_train: + num_vertices = 431 + pb = np.random.random_sample() + masked_num = int(pb * mvm_percent * num_vertices) # at most x% of the vertices could be masked + indices = np.random.choice(np.arange(num_vertices),replace=False,size=masked_num) + mvm_mask[indices,:] = 0.0 + mvm_mask = torch.from_numpy(mvm_mask).float() + + meta_data = {} + meta_data['ori_img'] = img + meta_data['pose'] = torch.from_numpy(self.pose_processing(pose, rot, flip)).float() + meta_data['betas'] = torch.from_numpy(betas).float() + meta_data['joints_3d'] = torch.from_numpy(joints_3d_transformed).float() + meta_data['has_3d_joints'] = has_3d_joints + meta_data['has_smpl'] = has_smpl + + meta_data['mjm_mask'] = mjm_mask + meta_data['mvm_mask'] = mvm_mask + + # Get 2D keypoints and apply augmentation transforms + meta_data['has_2d_joints'] = has_2d_joints + meta_data['joints_2d'] = torch.from_numpy(joints_2d_transformed).float() + meta_data['scale'] = float(sc * scale) + meta_data['center'] = np.asarray(center).astype(np.float32) + meta_data['gender'] = gender + return img_key, transfromed_img, meta_data + + + +class MeshTSVYamlDataset(MeshTSVDataset): + """ TSVDataset taking a Yaml file for easy function call + """ + def __init__(self, yaml_file, is_train=True, cv2_output=False, scale_factor=1): + self.cfg = load_from_yaml_file(yaml_file) + self.is_composite = self.cfg.get('composite', False) + self.root = op.dirname(yaml_file) + + if self.is_composite==False: + img_file = find_file_path_in_yaml(self.cfg['img'], self.root) + label_file = find_file_path_in_yaml(self.cfg.get('label', None), + self.root) + hw_file = find_file_path_in_yaml(self.cfg.get('hw', None), self.root) + linelist_file = find_file_path_in_yaml(self.cfg.get('linelist', None), + self.root) + else: + img_file = self.cfg['img'] + hw_file = self.cfg['hw'] + label_file = self.cfg.get('label', None) + linelist_file = find_file_path_in_yaml(self.cfg.get('linelist', None), + self.root) + + super(MeshTSVYamlDataset, self).__init__( + img_file, label_file, hw_file, linelist_file, is_train, cv2_output=cv2_output, scale_factor=scale_factor) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_gcnn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_gcnn.py new file mode 100644 index 00000000..f2baaa97 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_gcnn.py @@ -0,0 +1,184 @@ +from __future__ import division +import torch +import torch.nn.functional as F +import numpy as np +import scipy.sparse +import math +from pathlib import Path +data_path = Path(__file__).parent / "data" + +from comfy.model_management import get_torch_device +from wrapper_for_mps import sparse_to_dense +device = get_torch_device() + +class SparseMM(torch.autograd.Function): + """Redefine sparse @ dense matrix multiplication to enable backpropagation. + The builtin matrix multiplication operation does not support backpropagation in some cases. + """ + @staticmethod + def forward(ctx, sparse, dense): + ctx.req_grad = dense.requires_grad + ctx.save_for_backward(sparse) + return torch.matmul(sparse, dense) + + @staticmethod + def backward(ctx, grad_output): + grad_input = None + sparse, = ctx.saved_tensors + if ctx.req_grad: + grad_input = torch.matmul(sparse.t(), grad_output) + return None, grad_input + +def spmm(sparse, dense): + sparse = sparse.to(device) + dense = dense.to(device) + return SparseMM.apply(sparse, dense) + + +def gelu(x): + """Implementation of the gelu activation function. + For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): + 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + Also see https://arxiv.org/abs/1606.08415 + """ + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) + +class BertLayerNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root). + """ + super(BertLayerNorm, self).__init__() + self.weight = torch.nn.Parameter(torch.ones(hidden_size)) + self.bias = torch.nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x + self.bias + + +class GraphResBlock(torch.nn.Module): + """ + Graph Residual Block similar to the Bottleneck Residual Block in ResNet + """ + def __init__(self, in_channels, out_channels, mesh_type='body'): + super(GraphResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lin1 = GraphLinear(in_channels, out_channels // 2) + self.conv = GraphConvolution(out_channels // 2, out_channels // 2, mesh_type) + self.lin2 = GraphLinear(out_channels // 2, out_channels) + self.skip_conv = GraphLinear(in_channels, out_channels) + # print('Use BertLayerNorm in GraphResBlock') + self.pre_norm = BertLayerNorm(in_channels) + self.norm1 = BertLayerNorm(out_channels // 2) + self.norm2 = BertLayerNorm(out_channels // 2) + + def forward(self, x): + trans_y = F.relu(self.pre_norm(x)).transpose(1,2) + y = self.lin1(trans_y).transpose(1,2) + + y = F.relu(self.norm1(y)) + y = self.conv(y) + + trans_y = F.relu(self.norm2(y)).transpose(1,2) + y = self.lin2(trans_y).transpose(1,2) + + z = x+y + + return z + +# class GraphResBlock(torch.nn.Module): +# """ +# Graph Residual Block similar to the Bottleneck Residual Block in ResNet +# """ +# def __init__(self, in_channels, out_channels, mesh_type='body'): +# super(GraphResBlock, self).__init__() +# self.in_channels = in_channels +# self.out_channels = out_channels +# self.conv = GraphConvolution(self.in_channels, self.out_channels, mesh_type) +# print('Use BertLayerNorm and GeLU in GraphResBlock') +# self.norm = BertLayerNorm(self.out_channels) +# def forward(self, x): +# y = self.conv(x) +# y = self.norm(y) +# y = gelu(y) +# z = x+y +# return z + +class GraphLinear(torch.nn.Module): + """ + Generalization of 1x1 convolutions on Graphs + """ + def __init__(self, in_channels, out_channels): + super(GraphLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.W = torch.nn.Parameter(torch.FloatTensor(out_channels, in_channels)) + self.b = torch.nn.Parameter(torch.FloatTensor(out_channels)) + self.reset_parameters() + + def reset_parameters(self): + w_stdv = 1 / (self.in_channels * self.out_channels) + self.W.data.uniform_(-w_stdv, w_stdv) + self.b.data.uniform_(-w_stdv, w_stdv) + + def forward(self, x): + return torch.matmul(self.W[None, :], x) + self.b[None, :, None] + +class GraphConvolution(torch.nn.Module): + """Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.""" + def __init__(self, in_features, out_features, mesh='body', bias=True): + super(GraphConvolution, self).__init__() + self.in_features = in_features + self.out_features = out_features + + if mesh=='body': + adj_indices = torch.load(data_path / 'smpl_431_adjmat_indices.pt') + adj_mat_value = torch.load(data_path / 'smpl_431_adjmat_values.pt') + adj_mat_size = torch.load(data_path / 'smpl_431_adjmat_size.pt') + elif mesh=='hand': + adj_indices = torch.load(data_path / 'mano_195_adjmat_indices.pt') + adj_mat_value = torch.load(data_path / 'mano_195_adjmat_values.pt') + adj_mat_size = torch.load(data_path / 'mano_195_adjmat_size.pt') + + self.adjmat = sparse_to_dense(torch.sparse_coo_tensor(adj_indices, adj_mat_value, size=adj_mat_size)).to(device) + + self.weight = torch.nn.Parameter(torch.FloatTensor(in_features, out_features)) + if bias: + self.bias = torch.nn.Parameter(torch.FloatTensor(out_features)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + + def reset_parameters(self): + # stdv = 1. / math.sqrt(self.weight.size(1)) + stdv = 6. / math.sqrt(self.weight.size(0) + self.weight.size(1)) + self.weight.data.uniform_(-stdv, stdv) + if self.bias is not None: + self.bias.data.uniform_(-stdv, stdv) + + def forward(self, x): + if x.ndimension() == 2: + support = torch.matmul(x, self.weight) + output = torch.matmul(self.adjmat, support) + if self.bias is not None: + output = output + self.bias + return output + else: + output = [] + for i in range(x.shape[0]): + support = torch.matmul(x[i], self.weight) + # output.append(torch.matmul(self.adjmat, support)) + output.append(spmm(self.adjmat, support)) + output = torch.stack(output, dim=0) + if self.bias is not None: + output = output + self.bias + return output + + def __repr__(self): + return self.__class__.__name__ + ' (' \ + + str(self.in_features) + ' -> ' \ + + str(self.out_features) + ')' \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_mano.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_mano.py new file mode 100644 index 00000000..8374b630 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_mano.py @@ -0,0 +1,184 @@ +""" +This file contains the MANO defination and mesh sampling operations for MANO mesh + +Adapted from opensource projects +MANOPTH (https://github.com/hassony2/manopth) +Pose2Mesh (https://github.com/hongsukchoi/Pose2Mesh_RELEASE) +GraphCMR (https://github.com/nkolot/GraphCMR/) +""" + +from __future__ import division +import numpy as np +import torch +import torch.nn as nn +import os.path as osp +import json +import code +from custom_manopth.manolayer import ManoLayer +import scipy.sparse +import custom_mesh_graphormer.modeling.data.config as cfg +from pathlib import Path + +from comfy.model_management import get_torch_device +from wrapper_for_mps import sparse_to_dense +device = get_torch_device() + +class MANO(nn.Module): + def __init__(self): + super(MANO, self).__init__() + + self.mano_dir = str(Path(__file__).parent / "data") + self.layer = self.get_layer() + self.vertex_num = 778 + self.face = self.layer.th_faces.numpy() + self.joint_regressor = self.layer.th_J_regressor.numpy() + + self.joint_num = 21 + self.joints_name = ('Wrist', 'Thumb_1', 'Thumb_2', 'Thumb_3', 'Thumb_4', 'Index_1', 'Index_2', 'Index_3', 'Index_4', 'Middle_1', 'Middle_2', 'Middle_3', 'Middle_4', 'Ring_1', 'Ring_2', 'Ring_3', 'Ring_4', 'Pinky_1', 'Pinky_2', 'Pinky_3', 'Pinky_4') + self.skeleton = ( (0,1), (0,5), (0,9), (0,13), (0,17), (1,2), (2,3), (3,4), (5,6), (6,7), (7,8), (9,10), (10,11), (11,12), (13,14), (14,15), (15,16), (17,18), (18,19), (19,20) ) + self.root_joint_idx = self.joints_name.index('Wrist') + + # add fingertips to joint_regressor + self.fingertip_vertex_idx = [745, 317, 444, 556, 673] # mesh vertex idx (right hand) + thumbtip_onehot = np.array([1 if i == 745 else 0 for i in range(self.joint_regressor.shape[1])], dtype=np.float32).reshape(1,-1) + indextip_onehot = np.array([1 if i == 317 else 0 for i in range(self.joint_regressor.shape[1])], dtype=np.float32).reshape(1,-1) + middletip_onehot = np.array([1 if i == 445 else 0 for i in range(self.joint_regressor.shape[1])], dtype=np.float32).reshape(1,-1) + ringtip_onehot = np.array([1 if i == 556 else 0 for i in range(self.joint_regressor.shape[1])], dtype=np.float32).reshape(1,-1) + pinkytip_onehot = np.array([1 if i == 673 else 0 for i in range(self.joint_regressor.shape[1])], dtype=np.float32).reshape(1,-1) + self.joint_regressor = np.concatenate((self.joint_regressor, thumbtip_onehot, indextip_onehot, middletip_onehot, ringtip_onehot, pinkytip_onehot)) + self.joint_regressor = self.joint_regressor[[0, 13, 14, 15, 16, 1, 2, 3, 17, 4, 5, 6, 18, 10, 11, 12, 19, 7, 8, 9, 20],:] + joint_regressor_torch = torch.from_numpy(self.joint_regressor).float() + self.register_buffer('joint_regressor_torch', joint_regressor_torch) + + def get_layer(self): + return ManoLayer(mano_root=osp.join(self.mano_dir), flat_hand_mean=False, use_pca=False) # load right hand MANO model + + def get_3d_joints(self, vertices): + """ + This method is used to get the joint locations from the SMPL mesh + Input: + vertices: size = (B, 778, 3) + Output: + 3D joints: size = (B, 21, 3) + """ + joints = torch.einsum('bik,ji->bjk', [vertices, self.joint_regressor_torch]) + return joints + + +class SparseMM(torch.autograd.Function): + """Redefine sparse @ dense matrix multiplication to enable backpropagation. + The builtin matrix multiplication operation does not support backpropagation in some cases. + """ + @staticmethod + def forward(ctx, sparse, dense): + ctx.req_grad = dense.requires_grad + ctx.save_for_backward(sparse) + return torch.matmul(sparse, dense) + + @staticmethod + def backward(ctx, grad_output): + grad_input = None + sparse, = ctx.saved_tensors + if ctx.req_grad: + grad_input = torch.matmul(sparse.t(), grad_output) + return None, grad_input + +def spmm(sparse, dense): + sparse = sparse.to(device) + dense = dense.to(device) + return SparseMM.apply(sparse, dense) + + +def scipy_to_pytorch(A, U, D): + """Convert scipy sparse matrices to pytorch sparse matrix.""" + ptU = [] + ptD = [] + + for i in range(len(U)): + u = scipy.sparse.coo_matrix(U[i]) + i = torch.LongTensor(np.array([u.row, u.col])) + v = torch.FloatTensor(u.data) + ptU.append(sparse_to_dense(torch.sparse_coo_tensor(i, v, u.shape))) + + for i in range(len(D)): + d = scipy.sparse.coo_matrix(D[i]) + i = torch.LongTensor(np.array([d.row, d.col])) + v = torch.FloatTensor(d.data) + ptD.append(sparse_to_dense(torch.sparse_coo_tensor(i, v, d.shape))) + + return ptU, ptD + + +def adjmat_sparse(adjmat, nsize=1): + """Create row-normalized sparse graph adjacency matrix.""" + adjmat = scipy.sparse.csr_matrix(adjmat) + if nsize > 1: + orig_adjmat = adjmat.copy() + for _ in range(1, nsize): + adjmat = adjmat * orig_adjmat + adjmat.data = np.ones_like(adjmat.data) + for i in range(adjmat.shape[0]): + adjmat[i,i] = 1 + num_neighbors = np.array(1 / adjmat.sum(axis=-1)) + adjmat = adjmat.multiply(num_neighbors) + adjmat = scipy.sparse.coo_matrix(adjmat) + row = adjmat.row + col = adjmat.col + data = adjmat.data + i = torch.LongTensor(np.array([row, col])) + v = torch.from_numpy(data).float() + adjmat = sparse_to_dense(torch.sparse_coo_tensor(i, v, adjmat.shape)) + return adjmat + +def get_graph_params(filename, nsize=1): + """Load and process graph adjacency matrix and upsampling/downsampling matrices.""" + data = np.load(filename, encoding='latin1', allow_pickle=True) + A = data['A'] + U = data['U'] + D = data['D'] + U, D = scipy_to_pytorch(A, U, D) + A = [adjmat_sparse(a, nsize=nsize) for a in A] + return A, U, D + + +class Mesh(object): + """Mesh object that is used for handling certain graph operations.""" + def __init__(self, filename=cfg.MANO_sampling_matrix, + num_downsampling=1, nsize=1, device=torch.device('cuda')): + self._A, self._U, self._D = get_graph_params(filename=filename, nsize=nsize) + # self._A = [a.to(device) for a in self._A] + self._U = [u.to(device) for u in self._U] + self._D = [d.to(device) for d in self._D] + self.num_downsampling = num_downsampling + + def downsample(self, x, n1=0, n2=None): + """Downsample mesh.""" + if n2 is None: + n2 = self.num_downsampling + if x.ndimension() < 3: + for i in range(n1, n2): + x = spmm(self._D[i], x) + elif x.ndimension() == 3: + out = [] + for i in range(x.shape[0]): + y = x[i] + for j in range(n1, n2): + y = spmm(self._D[j], y) + out.append(y) + x = torch.stack(out, dim=0) + return x + + def upsample(self, x, n1=1, n2=0): + """Upsample mesh.""" + if x.ndimension() < 3: + for i in reversed(range(n2, n1)): + x = spmm(self._U[i], x) + elif x.ndimension() == 3: + out = [] + for i in range(x.shape[0]): + y = x[i] + for j in reversed(range(n2, n1)): + y = spmm(self._U[j], y) + out.append(y) + x = torch.stack(out, dim=0) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_smpl.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_smpl.py new file mode 100644 index 00000000..a5b46413 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/_smpl.py @@ -0,0 +1,283 @@ +""" +This file contains the definition of the SMPL model + +It is adapted from opensource project GraphCMR (https://github.com/nkolot/GraphCMR/) +""" +from __future__ import division + +import torch +import torch.nn as nn +import numpy as np +import scipy.sparse +try: + import cPickle as pickle +except ImportError: + import pickle + +from custom_mesh_graphormer.utils.geometric_layers import rodrigues +import custom_mesh_graphormer.modeling.data.config as cfg + +from comfy.model_management import get_torch_device +from wrapper_for_mps import sparse_to_dense +device = get_torch_device() + +class SMPL(nn.Module): + + def __init__(self, gender='neutral'): + super(SMPL, self).__init__() + + if gender=='m': + model_file=cfg.SMPL_Male + elif gender=='f': + model_file=cfg.SMPL_Female + else: + model_file=cfg.SMPL_FILE + + smpl_model = pickle.load(open(model_file, 'rb'), encoding='latin1') + J_regressor = smpl_model['J_regressor'].tocoo() + row = J_regressor.row + col = J_regressor.col + data = J_regressor.data + i = torch.LongTensor([row, col]) + v = torch.FloatTensor(data) + J_regressor_shape = [24, 6890] + self.register_buffer('J_regressor', torch.sparse_coo_tensor(i, v, J_regressor_shape).to_dense()) + self.register_buffer('weights', torch.FloatTensor(smpl_model['weights'])) + self.register_buffer('posedirs', torch.FloatTensor(smpl_model['posedirs'])) + self.register_buffer('v_template', torch.FloatTensor(smpl_model['v_template'])) + self.register_buffer('shapedirs', torch.FloatTensor(np.array(smpl_model['shapedirs']))) + self.register_buffer('faces', torch.from_numpy(smpl_model['f'].astype(np.int64))) + self.register_buffer('kintree_table', torch.from_numpy(smpl_model['kintree_table'].astype(np.int64))) + id_to_col = {self.kintree_table[1, i].item(): i for i in range(self.kintree_table.shape[1])} + self.register_buffer('parent', torch.LongTensor([id_to_col[self.kintree_table[0, it].item()] for it in range(1, self.kintree_table.shape[1])])) + + self.pose_shape = [24, 3] + self.beta_shape = [10] + self.translation_shape = [3] + + self.pose = torch.zeros(self.pose_shape) + self.beta = torch.zeros(self.beta_shape) + self.translation = torch.zeros(self.translation_shape) + + self.verts = None + self.J = None + self.R = None + + J_regressor_extra = torch.from_numpy(np.load(cfg.JOINT_REGRESSOR_TRAIN_EXTRA)).float() + self.register_buffer('J_regressor_extra', J_regressor_extra) + self.joints_idx = cfg.JOINTS_IDX + + J_regressor_h36m_correct = torch.from_numpy(np.load(cfg.JOINT_REGRESSOR_H36M_correct)).float() + self.register_buffer('J_regressor_h36m_correct', J_regressor_h36m_correct) + + + def forward(self, pose, beta): + device = pose.device + batch_size = pose.shape[0] + v_template = self.v_template[None, :] + shapedirs = self.shapedirs.view(-1,10)[None, :].expand(batch_size, -1, -1) + beta = beta[:, :, None] + v_shaped = torch.matmul(shapedirs, beta).view(-1, 6890, 3) + v_template + # batched sparse matmul not supported in pytorch + J = [] + for i in range(batch_size): + J.append(torch.matmul(self.J_regressor, v_shaped[i])) + J = torch.stack(J, dim=0) + # input it rotmat: (bs,24,3,3) + if pose.ndimension() == 4: + R = pose + # input it rotmat: (bs,72) + elif pose.ndimension() == 2: + pose_cube = pose.view(-1, 3) # (batch_size * 24, 1, 3) + R = rodrigues(pose_cube).view(batch_size, 24, 3, 3) + R = R.view(batch_size, 24, 3, 3) + I_cube = torch.eye(3)[None, None, :].to(device) + # I_cube = torch.eye(3)[None, None, :].expand(theta.shape[0], R.shape[1]-1, -1, -1) + lrotmin = (R[:,1:,:] - I_cube).view(batch_size, -1) + posedirs = self.posedirs.view(-1,207)[None, :].expand(batch_size, -1, -1) + v_posed = v_shaped + torch.matmul(posedirs, lrotmin[:, :, None]).view(-1, 6890, 3) + J_ = J.clone() + J_[:, 1:, :] = J[:, 1:, :] - J[:, self.parent, :] + G_ = torch.cat([R, J_[:, :, :, None]], dim=-1) + pad_row = torch.FloatTensor([0,0,0,1]).to(device).view(1,1,1,4).expand(batch_size, 24, -1, -1) + G_ = torch.cat([G_, pad_row], dim=2) + G = [G_[:, 0].clone()] + for i in range(1, 24): + G.append(torch.matmul(G[self.parent[i-1]], G_[:, i, :, :])) + G = torch.stack(G, dim=1) + + rest = torch.cat([J, torch.zeros(batch_size, 24, 1).to(device)], dim=2).view(batch_size, 24, 4, 1) + zeros = torch.zeros(batch_size, 24, 4, 3).to(device) + rest = torch.cat([zeros, rest], dim=-1) + rest = torch.matmul(G, rest) + G = G - rest + T = torch.matmul(self.weights, G.permute(1,0,2,3).contiguous().view(24,-1)).view(6890, batch_size, 4, 4).transpose(0,1) + rest_shape_h = torch.cat([v_posed, torch.ones_like(v_posed)[:, :, [0]]], dim=-1) + v = torch.matmul(T, rest_shape_h[:, :, :, None])[:, :, :3, 0] + return v + + def get_joints(self, vertices): + """ + This method is used to get the joint locations from the SMPL mesh + Input: + vertices: size = (B, 6890, 3) + Output: + 3D joints: size = (B, 38, 3) + """ + joints = torch.einsum('bik,ji->bjk', [vertices, self.J_regressor]) + joints_extra = torch.einsum('bik,ji->bjk', [vertices, self.J_regressor_extra]) + joints = torch.cat((joints, joints_extra), dim=1) + joints = joints[:, cfg.JOINTS_IDX] + return joints + + def get_h36m_joints(self, vertices): + """ + This method is used to get the joint locations from the SMPL mesh + Input: + vertices: size = (B, 6890, 3) + Output: + 3D joints: size = (B, 24, 3) + """ + joints = torch.einsum('bik,ji->bjk', [vertices, self.J_regressor_h36m_correct]) + return joints + +class SparseMM(torch.autograd.Function): + """Redefine sparse @ dense matrix multiplication to enable backpropagation. + The builtin matrix multiplication operation does not support backpropagation in some cases. + """ + @staticmethod + def forward(ctx, sparse, dense): + ctx.req_grad = dense.requires_grad + ctx.save_for_backward(sparse) + return torch.matmul(sparse, dense) + + @staticmethod + def backward(ctx, grad_output): + grad_input = None + sparse, = ctx.saved_tensors + if ctx.req_grad: + grad_input = torch.matmul(sparse.t(), grad_output) + return None, grad_input + +def spmm(sparse, dense): + sparse = sparse.to(device) + dense = dense.to(device) + return SparseMM.apply(sparse, dense) + + +def scipy_to_pytorch(A, U, D): + """Convert scipy sparse matrices to pytorch sparse matrix.""" + ptU = [] + ptD = [] + + for i in range(len(U)): + u = scipy.sparse.coo_matrix(U[i]) + i = torch.LongTensor(np.array([u.row, u.col])) + v = torch.FloatTensor(u.data) + ptU.append(sparse_to_dense(torch.sparse_coo_tensor(i, v, u.shape))) + + for i in range(len(D)): + d = scipy.sparse.coo_matrix(D[i]) + i = torch.LongTensor(np.array([d.row, d.col])) + v = torch.FloatTensor(d.data) + ptD.append(sparse_to_dense(torch.sparse_coo_tensor(i, v, d.shape))) + + return ptU, ptD + + +def adjmat_sparse(adjmat, nsize=1): + """Create row-normalized sparse graph adjacency matrix.""" + adjmat = scipy.sparse.csr_matrix(adjmat) + if nsize > 1: + orig_adjmat = adjmat.copy() + for _ in range(1, nsize): + adjmat = adjmat * orig_adjmat + adjmat.data = np.ones_like(adjmat.data) + for i in range(adjmat.shape[0]): + adjmat[i,i] = 1 + num_neighbors = np.array(1 / adjmat.sum(axis=-1)) + adjmat = adjmat.multiply(num_neighbors) + adjmat = scipy.sparse.coo_matrix(adjmat) + row = adjmat.row + col = adjmat.col + data = adjmat.data + i = torch.LongTensor(np.array([row, col])) + v = torch.from_numpy(data).float() + adjmat = sparse_to_dense(torch.sparse_coo_tensor(i, v, adjmat.shape)) + return adjmat + +def get_graph_params(filename, nsize=1): + """Load and process graph adjacency matrix and upsampling/downsampling matrices.""" + data = np.load(filename, encoding='latin1', allow_pickle=True) + A = data['A'] + U = data['U'] + D = data['D'] + U, D = scipy_to_pytorch(A, U, D) + A = [adjmat_sparse(a, nsize=nsize) for a in A] + return A, U, D + + +class Mesh(object): + """Mesh object that is used for handling certain graph operations.""" + def __init__(self, filename=cfg.SMPL_sampling_matrix, + num_downsampling=1, nsize=1, device=torch.device('cuda')): + self._A, self._U, self._D = get_graph_params(filename=filename, nsize=nsize) + # self._A = [a.to(device) for a in self._A] + self._U = [u.to(device) for u in self._U] + self._D = [d.to(device) for d in self._D] + self.num_downsampling = num_downsampling + + # load template vertices from SMPL and normalize them + smpl = SMPL() + ref_vertices = smpl.v_template + center = 0.5*(ref_vertices.max(dim=0)[0] + ref_vertices.min(dim=0)[0])[None] + ref_vertices -= center + ref_vertices /= ref_vertices.abs().max().item() + + self._ref_vertices = ref_vertices.to(device) + self.faces = smpl.faces.int().to(device) + + # @property + # def adjmat(self): + # """Return the graph adjacency matrix at the specified subsampling level.""" + # return self._A[self.num_downsampling].float() + + @property + def ref_vertices(self): + """Return the template vertices at the specified subsampling level.""" + ref_vertices = self._ref_vertices + for i in range(self.num_downsampling): + ref_vertices = torch.spmm(self._D[i], ref_vertices) + return ref_vertices + + def downsample(self, x, n1=0, n2=None): + """Downsample mesh.""" + if n2 is None: + n2 = self.num_downsampling + if x.ndimension() < 3: + for i in range(n1, n2): + x = spmm(self._D[i], x) + elif x.ndimension() == 3: + out = [] + for i in range(x.shape[0]): + y = x[i] + for j in range(n1, n2): + y = spmm(self._D[j], y) + out.append(y) + x = torch.stack(out, dim=0) + return x + + def upsample(self, x, n1=1, n2=0): + """Upsample mesh.""" + if x.ndimension() < 3: + for i in reversed(range(n2, n1)): + x = spmm(self._U[i], x) + elif x.ndimension() == 3: + out = [] + for i in range(x.shape[0]): + y = x[i] + for j in reversed(range(n2, n1)): + y = spmm(self._U[j], y) + out.append(y) + x = torch.stack(out, dim=0) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/__init__.py new file mode 100644 index 00000000..197c5b92 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/__init__.py @@ -0,0 +1,17 @@ +__version__ = "1.0.0" + +from .modeling_bert import (BertConfig, BertModel, + load_tf_weights_in_bert) + +from .modeling_graphormer import Graphormer + +from .e2e_body_network import Graphormer_Body_Network + +from .e2e_hand_network import Graphormer_Hand_Network + +CONFIG_NAME = "config.json" + +from .modeling_utils import (WEIGHTS_NAME, TF_WEIGHTS_NAME, + PretrainedConfig, PreTrainedModel, prune_layer, Conv1D) + +from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/bert-base-uncased/config.json b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/bert-base-uncased/config.json new file mode 100644 index 00000000..79276673 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/bert-base-uncased/config.json @@ -0,0 +1,16 @@ +{ + "architectures": [ + "BertForMaskedLM" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "type_vocab_size": 2, + "vocab_size": 30522 +} diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/e2e_body_network.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/e2e_body_network.py new file mode 100644 index 00000000..f570ec58 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/e2e_body_network.py @@ -0,0 +1,103 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + +import torch +import custom_mesh_graphormer.modeling.data.config as cfg +from comfy.model_management import get_torch_device +device = get_torch_device() + +class Graphormer_Body_Network(torch.nn.Module): + ''' + End-to-end Graphormer network for human pose and mesh reconstruction from a single image. + ''' + def __init__(self, args, config, backbone, trans_encoder, mesh_sampler): + super(Graphormer_Body_Network, self).__init__() + self.config = config + self.config.device = device + self.backbone = backbone + self.trans_encoder = trans_encoder + self.upsampling = torch.nn.Linear(431, 1723) + self.upsampling2 = torch.nn.Linear(1723, 6890) + self.cam_param_fc = torch.nn.Linear(3, 1) + self.cam_param_fc2 = torch.nn.Linear(431, 250) + self.cam_param_fc3 = torch.nn.Linear(250, 3) + self.grid_feat_dim = torch.nn.Linear(1024, 2051) + + + def forward(self, images, smpl, mesh_sampler, meta_masks=None, is_train=False): + batch_size = images.size(0) + # Generate T-pose template mesh + template_pose = torch.zeros((1,72)) + template_pose[:,0] = 3.1416 # Rectify "upside down" reference mesh in global coord + template_pose = template_pose.to(device) + template_betas = torch.zeros((1,10)).to(device) + template_vertices = smpl(template_pose, template_betas) + + # template mesh simplification + template_vertices_sub = mesh_sampler.downsample(template_vertices) + template_vertices_sub2 = mesh_sampler.downsample(template_vertices_sub, n1=1, n2=2) + + # template mesh-to-joint regression + template_3d_joints = smpl.get_h36m_joints(template_vertices) + template_pelvis = template_3d_joints[:,cfg.H36M_J17_NAME.index('Pelvis'),:] + template_3d_joints = template_3d_joints[:,cfg.H36M_J17_TO_J14,:] + num_joints = template_3d_joints.shape[1] + + # normalize + template_3d_joints = template_3d_joints - template_pelvis[:, None, :] + template_vertices_sub2 = template_vertices_sub2 - template_pelvis[:, None, :] + + # concatinate template joints and template vertices, and then duplicate to batch size + ref_vertices = torch.cat([template_3d_joints, template_vertices_sub2],dim=1) + ref_vertices = ref_vertices.expand(batch_size, -1, -1) + + # extract grid features and global image features using a CNN backbone + image_feat, grid_feat = self.backbone(images) + # concatinate image feat and 3d mesh template + image_feat = image_feat.view(batch_size, 1, 2048).expand(-1, ref_vertices.shape[-2], -1) + # process grid features + grid_feat = torch.flatten(grid_feat, start_dim=2) + grid_feat = grid_feat.transpose(1,2) + grid_feat = self.grid_feat_dim(grid_feat) + # concatinate image feat and template mesh to form the joint/vertex queries + features = torch.cat([ref_vertices, image_feat], dim=2) + # prepare input tokens including joint/vertex queries and grid features + features = torch.cat([features, grid_feat],dim=1) + + if is_train==True: + # apply mask vertex/joint modeling + # meta_masks is a tensor of all the masks, randomly generated in dataloader + # we pre-define a [MASK] token, which is a floating-value vector with 0.01s + special_token = torch.ones_like(features[:,:-49,:]).to(device)*0.01 + features[:,:-49,:] = features[:,:-49,:]*meta_masks + special_token*(1-meta_masks) + + # forward pass + if self.config.output_attentions==True: + features, hidden_states, att = self.trans_encoder(features) + else: + features = self.trans_encoder(features) + + pred_3d_joints = features[:,:num_joints,:] + pred_vertices_sub2 = features[:,num_joints:-49,:] + + # learn camera parameters + x = self.cam_param_fc(pred_vertices_sub2) + x = x.transpose(1,2) + x = self.cam_param_fc2(x) + x = self.cam_param_fc3(x) + cam_param = x.transpose(1,2) + cam_param = cam_param.squeeze() + + temp_transpose = pred_vertices_sub2.transpose(1,2) + pred_vertices_sub = self.upsampling(temp_transpose) + pred_vertices_full = self.upsampling2(pred_vertices_sub) + pred_vertices_sub = pred_vertices_sub.transpose(1,2) + pred_vertices_full = pred_vertices_full.transpose(1,2) + + if self.config.output_attentions==True: + return cam_param, pred_3d_joints, pred_vertices_sub2, pred_vertices_sub, pred_vertices_full, hidden_states, att + else: + return cam_param, pred_3d_joints, pred_vertices_sub2, pred_vertices_sub, pred_vertices_full \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/e2e_hand_network.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/e2e_hand_network.py new file mode 100644 index 00000000..57cba0a8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/e2e_hand_network.py @@ -0,0 +1,94 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + +import torch +import custom_mesh_graphormer.modeling.data.config as cfg +from comfy.model_management import get_torch_device +device = get_torch_device() + +class Graphormer_Hand_Network(torch.nn.Module): + ''' + End-to-end Graphormer network for hand pose and mesh reconstruction from a single image. + ''' + def __init__(self, args, config, backbone, trans_encoder): + super(Graphormer_Hand_Network, self).__init__() + self.config = config + self.backbone = backbone + self.trans_encoder = trans_encoder + self.upsampling = torch.nn.Linear(195, 778) + self.cam_param_fc = torch.nn.Linear(3, 1) + self.cam_param_fc2 = torch.nn.Linear(195+21, 150) + self.cam_param_fc3 = torch.nn.Linear(150, 3) + self.grid_feat_dim = torch.nn.Linear(1024, 2051) + + def forward(self, images, mesh_model, mesh_sampler, meta_masks=None, is_train=False): + batch_size = images.size(0) + # Generate T-pose template mesh + template_pose = torch.zeros((1,48)) + template_pose = template_pose.to(device) + template_betas = torch.zeros((1,10)).to(device) + template_vertices, template_3d_joints = mesh_model.layer(template_pose, template_betas) + template_vertices = template_vertices/1000.0 + template_3d_joints = template_3d_joints/1000.0 + + template_vertices_sub = mesh_sampler.downsample(template_vertices) + + # normalize + template_root = template_3d_joints[:,cfg.J_NAME.index('Wrist'),:] + template_3d_joints = template_3d_joints - template_root[:, None, :] + template_vertices = template_vertices - template_root[:, None, :] + template_vertices_sub = template_vertices_sub - template_root[:, None, :] + num_joints = template_3d_joints.shape[1] + + # concatinate template joints and template vertices, and then duplicate to batch size + ref_vertices = torch.cat([template_3d_joints, template_vertices_sub],dim=1) + ref_vertices = ref_vertices.expand(batch_size, -1, -1) + + # extract grid features and global image features using a CNN backbone + image_feat, grid_feat = self.backbone(images) + # concatinate image feat and mesh template + image_feat = image_feat.view(batch_size, 1, 2048).expand(-1, ref_vertices.shape[-2], -1) + # process grid features + grid_feat = torch.flatten(grid_feat, start_dim=2) + grid_feat = grid_feat.transpose(1,2) + grid_feat = self.grid_feat_dim(grid_feat) + # concatinate image feat and template mesh to form the joint/vertex queries + features = torch.cat([ref_vertices, image_feat], dim=2) + # prepare input tokens including joint/vertex queries and grid features + features = torch.cat([features, grid_feat],dim=1) + + if is_train==True: + # apply mask vertex/joint modeling + # meta_masks is a tensor of all the masks, randomly generated in dataloader + # we pre-define a [MASK] token, which is a floating-value vector with 0.01s + special_token = torch.ones_like(features[:,:-49,:]).to(device)*0.01 + features[:,:-49,:] = features[:,:-49,:]*meta_masks + special_token*(1-meta_masks) + + # forward pass + if self.config.output_attentions==True: + features, hidden_states, att = self.trans_encoder(features) + else: + features = self.trans_encoder(features) + + pred_3d_joints = features[:,:num_joints,:] + pred_vertices_sub = features[:,num_joints:-49,:] + + # learn camera parameters + x = self.cam_param_fc(features[:,:-49,:]) + x = x.transpose(1,2) + x = self.cam_param_fc2(x) + x = self.cam_param_fc3(x) + cam_param = x.transpose(1,2) + cam_param = cam_param.squeeze() + + temp_transpose = pred_vertices_sub.transpose(1,2) + pred_vertices = self.upsampling(temp_transpose) + pred_vertices = pred_vertices.transpose(1,2) + + if self.config.output_attentions==True: + return cam_param, pred_3d_joints, pred_vertices_sub, pred_vertices, hidden_states, att + else: + return cam_param, pred_3d_joints, pred_vertices_sub, pred_vertices \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/file_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/file_utils.py new file mode 100644 index 00000000..0b26c2ba --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/file_utils.py @@ -0,0 +1 @@ +from transformers.file_utils import * \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_bert.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_bert.py new file mode 100644 index 00000000..4b6a1eee --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_bert.py @@ -0,0 +1,5 @@ +from transformers.models.bert import modeling_bert + +for symbol in dir(modeling_bert): + if not symbol.startswith("_"): + globals()[symbol] = getattr(modeling_bert, symbol) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_graphormer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_graphormer.py new file mode 100644 index 00000000..e7c6e262 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_graphormer.py @@ -0,0 +1,328 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import logging +import math +import os +import code +import torch +from torch import nn +from .modeling_bert import BertPreTrainedModel, BertEmbeddings, BertPooler, BertIntermediate, BertOutput, BertSelfOutput +import custom_mesh_graphormer.modeling.data.config as cfg +from custom_mesh_graphormer.modeling._gcnn import GraphConvolution, GraphResBlock +from .modeling_utils import prune_linear_layer +LayerNormClass = torch.nn.LayerNorm +BertLayerNorm = torch.nn.LayerNorm +from comfy.model_management import get_torch_device +device = get_torch_device() + + +class BertSelfAttention(nn.Module): + def __init__(self, config): + super(BertSelfAttention, self).__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads)) + self.output_attentions = config.output_attentions + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, hidden_states, attention_mask, head_mask=None, + history_state=None): + if history_state is not None: + x_states = torch.cat([history_state, hidden_states], dim=1) + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(x_states) + mixed_value_layer = self.value(x_states) + else: + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,) + return outputs + +class BertAttention(nn.Module): + def __init__(self, config): + super(BertAttention, self).__init__() + self.self = BertSelfAttention(config) + self.output = BertSelfOutput(config) + + def prune_heads(self, heads): + if len(heads) == 0: + return + mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size) + for head in heads: + mask[head] = 0 + mask = mask.view(-1).contiguous().eq(1) + index = torch.arange(len(mask))[mask].long() + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + # Update hyper params + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + + def forward(self, input_tensor, attention_mask, head_mask=None, + history_state=None): + self_outputs = self.self(input_tensor, attention_mask, head_mask, + history_state) + attention_output = self.output(self_outputs[0], input_tensor) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class GraphormerLayer(nn.Module): + def __init__(self, config): + super(GraphormerLayer, self).__init__() + self.attention = BertAttention(config) + self.has_graph_conv = config.graph_conv + self.mesh_type = config.mesh_type + + if self.has_graph_conv == True: + self.graph_conv = GraphResBlock(config.hidden_size, config.hidden_size, mesh_type=self.mesh_type) + + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def MHA_GCN(self, hidden_states, attention_mask, head_mask=None, + history_state=None): + attention_outputs = self.attention(hidden_states, attention_mask, + head_mask, history_state) + attention_output = attention_outputs[0] + + if self.has_graph_conv==True: + if self.mesh_type == 'body': + joints = attention_output[:,0:14,:] + vertices = attention_output[:,14:-49,:] + img_tokens = attention_output[:,-49:,:] + + elif self.mesh_type == 'hand': + joints = attention_output[:,0:21,:] + vertices = attention_output[:,21:-49,:] + img_tokens = attention_output[:,-49:,:] + + vertices = self.graph_conv(vertices) + joints_vertices = torch.cat([joints,vertices,img_tokens],dim=1) + else: + joints_vertices = attention_output + + intermediate_output = self.intermediate(joints_vertices) + layer_output = self.output(intermediate_output, joints_vertices) + outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them + return outputs + + def forward(self, hidden_states, attention_mask, head_mask=None, + history_state=None): + return self.MHA_GCN(hidden_states, attention_mask, head_mask,history_state) + + +class GraphormerEncoder(nn.Module): + def __init__(self, config): + super(GraphormerEncoder, self).__init__() + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.layer = nn.ModuleList([GraphormerLayer(config) for _ in range(config.num_hidden_layers)]) + + def forward(self, hidden_states, attention_mask, head_mask=None, + encoder_history_states=None): + all_hidden_states = () + all_attentions = () + for i, layer_module in enumerate(self.layer): + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + history_state = None if encoder_history_states is None else encoder_history_states[i] + layer_outputs = layer_module( + hidden_states, attention_mask, head_mask[i], + history_state) + hidden_states = layer_outputs[0] + + if self.output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + # Add last layer + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (all_hidden_states,) + if self.output_attentions: + outputs = outputs + (all_attentions,) + + return outputs # outputs, (hidden states), (attentions) + +class EncoderBlock(BertPreTrainedModel): + def __init__(self, config): + super(EncoderBlock, self).__init__(config) + self.config = config + self.embeddings = BertEmbeddings(config) + self.encoder = GraphormerEncoder(config) + self.pooler = BertPooler(config) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.img_dim = config.img_feature_dim + + try: + self.use_img_layernorm = config.use_img_layernorm + except: + self.use_img_layernorm = None + + self.img_embedding = nn.Linear(self.img_dim, self.config.hidden_size, bias=True) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + if self.use_img_layernorm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.img_layer_norm_eps) + + + def _prune_heads(self, heads_to_prune): + """ Prunes heads of the model. + heads_to_prune: dict of {layer_num: list of heads to prune in this layer} + See base class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def forward(self, img_feats, input_ids=None, token_type_ids=None, attention_mask=None, + position_ids=None, head_mask=None): + + batch_size = len(img_feats) + seq_length = len(img_feats[0]) + input_ids = torch.zeros([batch_size, seq_length],dtype=torch.long).to(device) + + if position_ids is None: + position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(input_ids) + + position_embeddings = self.position_embeddings(position_ids) + + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + if attention_mask.dim() == 2: + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + elif attention_mask.dim() == 3: + extended_attention_mask = attention_mask.unsqueeze(1) + else: + raise NotImplementedError + + extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + + if head_mask is not None: + if head_mask.dim() == 1: + head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) + head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) + elif head_mask.dim() == 2: + head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer + head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility + else: + head_mask = [None] * self.config.num_hidden_layers + + # Project input token features to have spcified hidden size + img_embedding_output = self.img_embedding(img_feats) + + # We empirically observe that adding an additional learnable position embedding leads to more stable training + embeddings = position_embeddings + img_embedding_output + + if self.use_img_layernorm: + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + + encoder_outputs = self.encoder(embeddings, + extended_attention_mask, head_mask=head_mask) + sequence_output = encoder_outputs[0] + + outputs = (sequence_output,) + if self.config.output_hidden_states: + all_hidden_states = encoder_outputs[1] + outputs = outputs + (all_hidden_states,) + if self.config.output_attentions: + all_attentions = encoder_outputs[-1] + outputs = outputs + (all_attentions,) + + return outputs + +class Graphormer(BertPreTrainedModel): + ''' + The archtecture of a transformer encoder block we used in Graphormer + ''' + def __init__(self, config): + super(Graphormer, self).__init__(config) + self.config = config + self.bert = EncoderBlock(config) + self.cls_head = nn.Linear(config.hidden_size, self.config.output_feature_dim) + self.residual = nn.Linear(config.img_feature_dim, self.config.output_feature_dim) + + def forward(self, img_feats, input_ids=None, token_type_ids=None, attention_mask=None, masked_lm_labels=None, + next_sentence_label=None, position_ids=None, head_mask=None): + ''' + # self.bert has three outputs + # predictions[0]: output tokens + # predictions[1]: all_hidden_states, if enable "self.config.output_hidden_states" + # predictions[2]: attentions, if enable "self.config.output_attentions" + ''' + predictions = self.bert(img_feats=img_feats, input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, + attention_mask=attention_mask, head_mask=head_mask) + + # We use "self.cls_head" to perform dimensionality reduction. We don't use it for classification. + pred_score = self.cls_head(predictions[0]) + res_img_feats = self.residual(img_feats) + pred_score = pred_score + res_img_feats + + if self.config.output_attentions and self.config.output_hidden_states: + return pred_score, predictions[1], predictions[-1] + else: + return pred_score + + \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_utils.py new file mode 100644 index 00000000..fcfab434 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/bert/modeling_utils.py @@ -0,0 +1 @@ +from transformers.modeling_utils import * \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/J_regressor_extra.npy b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/J_regressor_extra.npy new file mode 100644 index 00000000..d045e083 Binary files /dev/null and b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/J_regressor_extra.npy differ diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/J_regressor_h36m_correct.npy b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/J_regressor_h36m_correct.npy new file mode 100644 index 00000000..8fe4518c Binary files /dev/null and b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/J_regressor_h36m_correct.npy differ diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/MANO_LEFT.pkl b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/MANO_LEFT.pkl new file mode 100644 index 00000000..745210a6 Binary files /dev/null and b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/MANO_LEFT.pkl differ diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/MANO_RIGHT.pkl b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/MANO_RIGHT.pkl new file mode 100644 index 00000000..06b42f33 Binary files /dev/null and b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/MANO_RIGHT.pkl differ diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/README.md b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/README.md new file mode 100644 index 00000000..e7cfc083 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/README.md @@ -0,0 +1,30 @@ + +# Extra data +Adapted from open source project [GraphCMR](https://github.com/nkolot/GraphCMR/) and [Pose2Mesh](https://github.com/hongsukchoi/Pose2Mesh_RELEASE) + +Our code requires additional data to run smoothly. + +### J_regressor_extra.npy +Joints regressor for joints or landmarks that are not included in the standard set of SMPL joints. + +### J_regressor_h36m_correct.npy +Joints regressor reflecting the Human3.6M joints. + +### mesh_downsampling.npz +Extra file with precomputed downsampling for the SMPL body mesh. + +### mano_downsampling.npz +Extra file with precomputed downsampling for the MANO hand mesh. + +### basicModel_neutral_lbs_10_207_0_v1.0.0.pkl +SMPL neutral model. Please visit the official website to download the file [http://smplify.is.tue.mpg.de/](http://smplify.is.tue.mpg.de/) + +### basicModel_m_lbs_10_207_0_v1.0.0.pkl +SMPL male model. Please visit the official website to download the file [https://smpl.is.tue.mpg.de/](https://smpl.is.tue.mpg.de/) + +### basicModel_f_lbs_10_207_0_v1.0.0.pkl +SMPL female model. Please visit the official website to download the file [https://smpl.is.tue.mpg.de/](https://smpl.is.tue.mpg.de/) + +### MANO_RIGHT.pkl +MANO hand model. Please visit the official website to download the file [https://mano.is.tue.mpg.de/](https://mano.is.tue.mpg.de/) + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/config.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/config.py new file mode 100644 index 00000000..b8eb40f7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/data/config.py @@ -0,0 +1,47 @@ +""" +This file contains definitions of useful data stuctures and the paths +for the datasets and data files necessary to run the code. + +Adapted from opensource project GraphCMR (https://github.com/nkolot/GraphCMR/) and Pose2Mesh (https://github.com/hongsukchoi/Pose2Mesh_RELEASE) + +""" + +from pathlib import Path +folder_path = Path(__file__).parent.parent +JOINT_REGRESSOR_TRAIN_EXTRA = folder_path / 'data/J_regressor_extra.npy' +JOINT_REGRESSOR_H36M_correct = folder_path / 'data/J_regressor_h36m_correct.npy' +SMPL_FILE = folder_path / 'data/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl' +SMPL_Male = folder_path / 'data/basicModel_m_lbs_10_207_0_v1.0.0.pkl' +SMPL_Female = folder_path / 'data/basicModel_f_lbs_10_207_0_v1.0.0.pkl' +SMPL_sampling_matrix = folder_path / 'data/mesh_downsampling.npz' +MANO_FILE = folder_path / 'data/MANO_RIGHT.pkl' +MANO_sampling_matrix = folder_path / 'data/mano_downsampling.npz' + +JOINTS_IDX = [8, 5, 29, 30, 4, 7, 21, 19, 17, 16, 18, 20, 31, 32, 33, 34, 35, 36, 37, 24, 26, 25, 28, 27] + + +""" +We follow the body joint definition, loss functions, and evaluation metrics from +open source project GraphCMR (https://github.com/nkolot/GraphCMR/) + +Each dataset uses different sets of joints. +We use a superset of 24 joints such that we include all joints from every dataset. +If a dataset doesn't provide annotations for a specific joint, we simply ignore it. +The joints used here are: +""" +J24_NAME = ('R_Ankle', 'R_Knee', 'R_Hip', 'L_Hip', 'L_Knee', 'L_Ankle', 'R_Wrist', 'R_Elbow', 'R_Shoulder', 'L_Shoulder', +'L_Elbow','L_Wrist','Neck','Top_of_Head','Pelvis','Thorax','Spine','Jaw','Head','Nose','L_Eye','R_Eye','L_Ear','R_Ear') +H36M_J17_NAME = ( 'Pelvis', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Torso', 'Neck', 'Nose', 'Head', + 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Shoulder', 'R_Elbow', 'R_Wrist') +J24_TO_J14 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18] +H36M_J17_TO_J14 = [3, 2, 1, 4, 5, 6, 16, 15, 14, 11, 12, 13, 8, 10] + +""" +We follow the hand joint definition and mesh topology from +open source project Manopth (https://github.com/hassony2/manopth) + +The hand joints used here are: +""" +J_NAME = ('Wrist', 'Thumb_1', 'Thumb_2', 'Thumb_3', 'Thumb_4', 'Index_1', 'Index_2', 'Index_3', 'Index_4', 'Middle_1', +'Middle_2', 'Middle_3', 'Middle_4', 'Ring_1', 'Ring_2', 'Ring_3', 'Ring_4', 'Pinky_1', 'Pinky_2', 'Pinky_3', 'Pinky_4') +ROOT_INDEX = 0 \ No newline at end of file diff --git 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mode 100644 index 00000000..59be7491 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/__init__.py @@ -0,0 +1,9 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from .default import _C as config +from .default import update_config +from .models import MODEL_EXTRAS diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/default.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/default.py new file mode 100644 index 00000000..59b98434 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/default.py @@ -0,0 +1,138 @@ + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Ke Sun (sunk@mail.ustc.edu.cn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from yacs.config import CfgNode as CN + + +_C = CN() + +_C.OUTPUT_DIR = '' +_C.LOG_DIR = '' +_C.DATA_DIR = '' +_C.GPUS = (0,) +_C.WORKERS = 4 +_C.PRINT_FREQ = 20 +_C.AUTO_RESUME = False +_C.PIN_MEMORY = True +_C.RANK = 0 + +# Cudnn related params +_C.CUDNN = CN() +_C.CUDNN.BENCHMARK = True +_C.CUDNN.DETERMINISTIC = False +_C.CUDNN.ENABLED = True + +# common params for NETWORK +_C.MODEL = CN() +_C.MODEL.NAME = 'cls_hrnet' +_C.MODEL.INIT_WEIGHTS = True +_C.MODEL.PRETRAINED = '' +_C.MODEL.NUM_JOINTS = 17 +_C.MODEL.NUM_CLASSES = 1000 +_C.MODEL.TAG_PER_JOINT = True +_C.MODEL.TARGET_TYPE = 'gaussian' +_C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 +_C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 +_C.MODEL.SIGMA = 2 +_C.MODEL.EXTRA = CN(new_allowed=True) + +_C.LOSS = CN() +_C.LOSS.USE_OHKM = False +_C.LOSS.TOPK = 8 +_C.LOSS.USE_TARGET_WEIGHT = True +_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False + +# DATASET related params +_C.DATASET = CN() +_C.DATASET.ROOT = '' +_C.DATASET.DATASET = 'mpii' +_C.DATASET.TRAIN_SET = 'train' +_C.DATASET.TEST_SET = 'valid' +_C.DATASET.DATA_FORMAT = 'jpg' +_C.DATASET.HYBRID_JOINTS_TYPE = '' +_C.DATASET.SELECT_DATA = False + +# training data augmentation +_C.DATASET.FLIP = True +_C.DATASET.SCALE_FACTOR = 0.25 +_C.DATASET.ROT_FACTOR = 30 +_C.DATASET.PROB_HALF_BODY = 0.0 +_C.DATASET.NUM_JOINTS_HALF_BODY = 8 +_C.DATASET.COLOR_RGB = False + +# train +_C.TRAIN = CN() + +_C.TRAIN.LR_FACTOR = 0.1 +_C.TRAIN.LR_STEP = [90, 110] +_C.TRAIN.LR = 0.001 + +_C.TRAIN.OPTIMIZER = 'adam' +_C.TRAIN.MOMENTUM = 0.9 +_C.TRAIN.WD = 0.0001 +_C.TRAIN.NESTEROV = False +_C.TRAIN.GAMMA1 = 0.99 +_C.TRAIN.GAMMA2 = 0.0 + +_C.TRAIN.BEGIN_EPOCH = 0 +_C.TRAIN.END_EPOCH = 140 + +_C.TRAIN.RESUME = False +_C.TRAIN.CHECKPOINT = '' + +_C.TRAIN.BATCH_SIZE_PER_GPU = 32 +_C.TRAIN.SHUFFLE = True + +# testing +_C.TEST = CN() + +# size of images for each device +_C.TEST.BATCH_SIZE_PER_GPU = 32 +# Test Model Epoch +_C.TEST.FLIP_TEST = False +_C.TEST.POST_PROCESS = False +_C.TEST.SHIFT_HEATMAP = False + +_C.TEST.USE_GT_BBOX = False + +# nms +_C.TEST.IMAGE_THRE = 0.1 +_C.TEST.NMS_THRE = 0.6 +_C.TEST.SOFT_NMS = False +_C.TEST.OKS_THRE = 0.5 +_C.TEST.IN_VIS_THRE = 0.0 +_C.TEST.COCO_BBOX_FILE = '' +_C.TEST.BBOX_THRE = 1.0 +_C.TEST.MODEL_FILE = '' + +# debug +_C.DEBUG = CN() +_C.DEBUG.DEBUG = False +_C.DEBUG.SAVE_BATCH_IMAGES_GT = False +_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False +_C.DEBUG.SAVE_HEATMAPS_GT = False +_C.DEBUG.SAVE_HEATMAPS_PRED = False + + +def update_config(cfg, config_file): + cfg.defrost() + cfg.merge_from_file(config_file) + cfg.freeze() + + +if __name__ == '__main__': + import sys + with open(sys.argv[1], 'w') as f: + print(_C, file=f) + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/models.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/models.py new file mode 100644 index 00000000..7a73bc9f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/config/models.py @@ -0,0 +1,47 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Create by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Ke Sun (sunk@mail.ustc.edu.cn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from yacs.config import CfgNode as CN + +# high_resoluton_net related params for classification +POSE_HIGH_RESOLUTION_NET = CN() +POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*'] +POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64 +POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1 +POSE_HIGH_RESOLUTION_NET.WITH_HEAD = True + +POSE_HIGH_RESOLUTION_NET.STAGE2 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64] +POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE3 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128] +POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE4 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] +POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM' + +MODEL_EXTRAS = { + 'cls_hrnet': POSE_HIGH_RESOLUTION_NET, +} diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/hrnet_cls_net.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/hrnet_cls_net.py new file mode 100644 index 00000000..3388c230 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/hrnet_cls_net.py @@ -0,0 +1,523 @@ + + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Ke Sun (sunk@mail.ustc.edu.cn) +# Modified by Kevin Lin (keli@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging +import functools + +import numpy as np + +import torch +import torch.nn as nn +import torch._utils +import torch.nn.functional as F +import code +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(False) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index], stride, downsample)) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index])) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_inchannels[i], + 1, + 1, + 0, + bias=False), + nn.BatchNorm2d(num_inchannels[i], + momentum=BN_MOMENTUM), + nn.Upsample(scale_factor=2**(j-i), mode='nearest'))) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i-j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM))) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM), + nn.ReLU(False))) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = { + 'BASIC': BasicBlock, + 'BOTTLENECK': Bottleneck +} + + +class HighResolutionNet(nn.Module): + + def __init__(self, cfg, **kwargs): + super(HighResolutionNet, self).__init__() + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + + self.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1'] + num_channels = self.stage1_cfg['NUM_CHANNELS'][0] + block = blocks_dict[self.stage1_cfg['BLOCK']] + num_blocks = self.stage1_cfg['NUM_BLOCKS'][0] + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + stage1_out_channel = block.expansion*num_channels + + self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2'] + num_channels = self.stage2_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage2_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition1 = self._make_transition_layer( + [stage1_out_channel], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3'] + num_channels = self.stage3_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage3_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4'] + num_channels = self.stage4_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage4_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=True) + + # Classification Head + self.incre_modules, self.downsamp_modules, \ + self.final_layer = self._make_head(pre_stage_channels) + + self.classifier = nn.Linear(2048, 1000) + + def _make_head(self, pre_stage_channels): + head_block = Bottleneck + head_channels = [32, 64, 128, 256] + + # Increasing the #channels on each resolution + # from C, 2C, 4C, 8C to 128, 256, 512, 1024 + incre_modules = [] + for i, channels in enumerate(pre_stage_channels): + incre_module = self._make_layer(head_block, + channels, + head_channels[i], + 1, + stride=1) + incre_modules.append(incre_module) + incre_modules = nn.ModuleList(incre_modules) + + # downsampling modules + downsamp_modules = [] + for i in range(len(pre_stage_channels)-1): + in_channels = head_channels[i] * head_block.expansion + out_channels = head_channels[i+1] * head_block.expansion + + downsamp_module = nn.Sequential( + nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=2, + padding=1), + nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + downsamp_modules.append(downsamp_module) + downsamp_modules = nn.ModuleList(downsamp_modules) + + final_layer = nn.Sequential( + nn.Conv2d( + in_channels=head_channels[3] * head_block.expansion, + out_channels=2048, + kernel_size=1, + stride=1, + padding=0 + ), + nn.BatchNorm2d(2048, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + return incre_modules, downsamp_modules, final_layer + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append(nn.Sequential( + nn.Conv2d(num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, + 1, + 1, + bias=False), + nn.BatchNorm2d( + num_channels_cur_layer[i], momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i+1-num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i-num_branches_pre else inchannels + conv3x3s.append(nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False), + nn.BatchNorm2d(outchannels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(inplanes, planes, stride, downsample)) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True): + num_modules = layer_config['NUM_MODULES'] + num_branches = layer_config['NUM_BRANCHES'] + num_blocks = layer_config['NUM_BLOCKS'] + num_channels = layer_config['NUM_CHANNELS'] + block = blocks_dict[layer_config['BLOCK']] + fuse_method = layer_config['FUSE_METHOD'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule(num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['NUM_BRANCHES']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['NUM_BRANCHES']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['NUM_BRANCHES']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + # Classification Head + y = self.incre_modules[0](y_list[0]) + for i in range(len(self.downsamp_modules)): + y = self.incre_modules[i+1](y_list[i+1]) + \ + self.downsamp_modules[i](y) + + y = self.final_layer(y) + + if torch._C._get_tracing_state(): + y = y.flatten(start_dim=2).mean(dim=2) + else: + y = F.avg_pool2d(y, kernel_size=y.size() + [2:]).view(y.size(0), -1) + + # y = self.classifier(y) + + return y + + def init_weights(self, pretrained='',): + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_( + m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + if os.path.isfile(pretrained): + pretrained_dict = torch.load(pretrained) + logger.info('=> loading pretrained model {}'.format(pretrained)) + print('=> loading pretrained model {}'.format(pretrained)) + model_dict = self.state_dict() + pretrained_dict = {k: v for k, v in pretrained_dict.items() + if k in model_dict.keys()} + # for k, _ in pretrained_dict.items(): + # logger.info( + # '=> loading {} pretrained model {}'.format(k, pretrained)) + # print('=> loading {} pretrained model {}'.format(k, pretrained)) + model_dict.update(pretrained_dict) + self.load_state_dict(model_dict) + # code.interact(local=locals()) + +def get_cls_net(config, pretrained, **kwargs): + model = HighResolutionNet(config, **kwargs) + model.init_weights(pretrained=pretrained) + return model diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/hrnet_cls_net_gridfeat.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/hrnet_cls_net_gridfeat.py new file mode 100644 index 00000000..6c44c948 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/modeling/hrnet/hrnet_cls_net_gridfeat.py @@ -0,0 +1,524 @@ + + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Ke Sun (sunk@mail.ustc.edu.cn) +# Modified by Kevin Lin (keli@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging +import functools + +import numpy as np + +import torch +import torch.nn as nn +import torch._utils +import torch.nn.functional as F +import code +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(False) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index], stride, downsample)) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index])) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_inchannels[i], + 1, + 1, + 0, + bias=False), + nn.BatchNorm2d(num_inchannels[i], + momentum=BN_MOMENTUM), + nn.Upsample(scale_factor=2**(j-i), mode='nearest'))) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i-j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM))) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM), + nn.ReLU(False))) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = { + 'BASIC': BasicBlock, + 'BOTTLENECK': Bottleneck +} + + +class HighResolutionNet(nn.Module): + + def __init__(self, cfg, **kwargs): + super(HighResolutionNet, self).__init__() + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + + self.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1'] + num_channels = self.stage1_cfg['NUM_CHANNELS'][0] + block = blocks_dict[self.stage1_cfg['BLOCK']] + num_blocks = self.stage1_cfg['NUM_BLOCKS'][0] + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + stage1_out_channel = block.expansion*num_channels + + self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2'] + num_channels = self.stage2_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage2_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition1 = self._make_transition_layer( + [stage1_out_channel], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3'] + num_channels = self.stage3_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage3_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4'] + num_channels = self.stage4_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage4_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=True) + + # Classification Head + self.incre_modules, self.downsamp_modules, \ + self.final_layer = self._make_head(pre_stage_channels) + + self.classifier = nn.Linear(2048, 1000) + + def _make_head(self, pre_stage_channels): + head_block = Bottleneck + head_channels = [32, 64, 128, 256] + + # Increasing the #channels on each resolution + # from C, 2C, 4C, 8C to 128, 256, 512, 1024 + incre_modules = [] + for i, channels in enumerate(pre_stage_channels): + incre_module = self._make_layer(head_block, + channels, + head_channels[i], + 1, + stride=1) + incre_modules.append(incre_module) + incre_modules = nn.ModuleList(incre_modules) + + # downsampling modules + downsamp_modules = [] + for i in range(len(pre_stage_channels)-1): + in_channels = head_channels[i] * head_block.expansion + out_channels = head_channels[i+1] * head_block.expansion + + downsamp_module = nn.Sequential( + nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=2, + padding=1), + nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + downsamp_modules.append(downsamp_module) + downsamp_modules = nn.ModuleList(downsamp_modules) + + final_layer = nn.Sequential( + nn.Conv2d( + in_channels=head_channels[3] * head_block.expansion, + out_channels=2048, + kernel_size=1, + stride=1, + padding=0 + ), + nn.BatchNorm2d(2048, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + return incre_modules, downsamp_modules, final_layer + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append(nn.Sequential( + nn.Conv2d(num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, + 1, + 1, + bias=False), + nn.BatchNorm2d( + num_channels_cur_layer[i], momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i+1-num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i-num_branches_pre else inchannels + conv3x3s.append(nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False), + nn.BatchNorm2d(outchannels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(inplanes, planes, stride, downsample)) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True): + num_modules = layer_config['NUM_MODULES'] + num_branches = layer_config['NUM_BRANCHES'] + num_blocks = layer_config['NUM_BLOCKS'] + num_channels = layer_config['NUM_CHANNELS'] + block = blocks_dict[layer_config['BLOCK']] + fuse_method = layer_config['FUSE_METHOD'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule(num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['NUM_BRANCHES']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['NUM_BRANCHES']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['NUM_BRANCHES']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + # Classification Head + y = self.incre_modules[0](y_list[0]) + for i in range(len(self.downsamp_modules)): + y = self.incre_modules[i+1](y_list[i+1]) + \ + self.downsamp_modules[i](y) + + yy = self.final_layer(y) + + if torch._C._get_tracing_state(): + yy = yy.flatten(start_dim=2).mean(dim=2) + else: + yy = F.avg_pool2d(yy, kernel_size=yy.size() + [2:]).view(yy.size(0), -1) + + # y = self.classifier(y) + return yy, y + + + + def init_weights(self, pretrained='',): + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_( + m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + if os.path.isfile(pretrained): + pretrained_dict = torch.load(pretrained) + logger.info('=> loading pretrained model {}'.format(pretrained)) + print('=> loading pretrained model {}'.format(pretrained)) + model_dict = self.state_dict() + pretrained_dict = {k: v for k, v in pretrained_dict.items() + if k in model_dict.keys()} + # for k, _ in pretrained_dict.items(): + # logger.info( + # '=> loading {} pretrained model {}'.format(k, pretrained)) + # print('=> loading {} pretrained model {}'.format(k, pretrained)) + model_dict.update(pretrained_dict) + self.load_state_dict(model_dict) + # code.interact(local=locals()) + +def get_cls_net_gridfeat(config, pretrained, **kwargs): + model = HighResolutionNet(config, **kwargs) + model.init_weights(pretrained=pretrained) + return model diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_bodymesh.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_bodymesh.py new file mode 100644 index 00000000..83685c4e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_bodymesh.py @@ -0,0 +1,750 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +Training and evaluation codes for +3D human body mesh reconstruction from an image +""" + +from __future__ import absolute_import, division, print_function +import argparse +import os +import os.path as op +import code +import json +import time +import datetime +import torch +import torchvision.models as models +from torchvision.utils import make_grid +import gc +import numpy as np +import cv2 +from custom_mesh_graphormer.modeling.bert import BertConfig, Graphormer +from custom_mesh_graphormer.modeling.bert import Graphormer_Body_Network as Graphormer_Network +from custom_mesh_graphormer.modeling._smpl import SMPL, Mesh +from custom_mesh_graphormer.modeling.hrnet.hrnet_cls_net_gridfeat import get_cls_net_gridfeat +from custom_mesh_graphormer.modeling.hrnet.config import config as hrnet_config +from custom_mesh_graphormer.modeling.hrnet.config import update_config as hrnet_update_config +import custom_mesh_graphormer.modeling.data.config as cfg +from custom_mesh_graphormer.datasets.build import make_data_loader + +from custom_mesh_graphormer.utils.logger import setup_logger +from custom_mesh_graphormer.utils.comm import synchronize, is_main_process, get_rank, get_world_size, all_gather +from custom_mesh_graphormer.utils.miscellaneous import mkdir, set_seed +from custom_mesh_graphormer.utils.metric_logger import AverageMeter, EvalMetricsLogger +from custom_mesh_graphormer.utils.renderer import Renderer, visualize_reconstruction, visualize_reconstruction_test +from custom_mesh_graphormer.utils.metric_pampjpe import reconstruction_error +from custom_mesh_graphormer.utils.geometric_layers import orthographic_projection + +from comfy.model_management import get_torch_device +device = get_torch_device() + +from azureml.core.run import Run +aml_run = Run.get_context() + +def save_checkpoint(model, args, epoch, iteration, num_trial=10): + checkpoint_dir = op.join(args.output_dir, 'checkpoint-{}-{}'.format( + epoch, iteration)) + if not is_main_process(): + return checkpoint_dir + mkdir(checkpoint_dir) + model_to_save = model.module if hasattr(model, 'module') else model + for i in range(num_trial): + try: + torch.save(model_to_save, op.join(checkpoint_dir, 'model.bin')) + torch.save(model_to_save.state_dict(), op.join(checkpoint_dir, 'state_dict.bin')) + torch.save(args, op.join(checkpoint_dir, 'training_args.bin')) + logger.info("Save checkpoint to {}".format(checkpoint_dir)) + break + except: + pass + else: + logger.info("Failed to save checkpoint after {} trails.".format(num_trial)) + return checkpoint_dir + +def save_scores(args, split, mpjpe, pampjpe, mpve): + eval_log = [] + res = {} + res['mPJPE'] = mpjpe + res['PAmPJPE'] = pampjpe + res['mPVE'] = mpve + eval_log.append(res) + with open(op.join(args.output_dir, split+'_eval_logs.json'), 'w') as f: + json.dump(eval_log, f) + logger.info("Save eval scores to {}".format(args.output_dir)) + return + +def adjust_learning_rate(optimizer, epoch, args): + """ + Sets the learning rate to the initial LR decayed by x every y epochs + x = 0.1, y = args.num_train_epochs/2.0 = 100 + """ + lr = args.lr * (0.1 ** (epoch // (args.num_train_epochs/2.0) )) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def mean_per_joint_position_error(pred, gt, has_3d_joints): + """ + Compute mPJPE + """ + gt = gt[has_3d_joints == 1] + gt = gt[:, :, :-1] + pred = pred[has_3d_joints == 1] + + with torch.no_grad(): + gt_pelvis = (gt[:, 2,:] + gt[:, 3,:]) / 2 + gt = gt - gt_pelvis[:, None, :] + pred_pelvis = (pred[:, 2,:] + pred[:, 3,:]) / 2 + pred = pred - pred_pelvis[:, None, :] + error = torch.sqrt( ((pred - gt) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() + return error + +def mean_per_vertex_error(pred, gt, has_smpl): + """ + Compute mPVE + """ + pred = pred[has_smpl == 1] + gt = gt[has_smpl == 1] + with torch.no_grad(): + error = torch.sqrt( ((pred - gt) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() + return error + +def keypoint_2d_loss(criterion_keypoints, pred_keypoints_2d, gt_keypoints_2d, has_pose_2d): + """ + Compute 2D reprojection loss if 2D keypoint annotations are available. + The confidence (conf) is binary and indicates whether the keypoints exist or not. + """ + conf = gt_keypoints_2d[:, :, -1].unsqueeze(-1).clone() + loss = (conf * criterion_keypoints(pred_keypoints_2d, gt_keypoints_2d[:, :, :-1])).mean() + return loss + +def keypoint_3d_loss(criterion_keypoints, pred_keypoints_3d, gt_keypoints_3d, has_pose_3d, device): + """ + Compute 3D keypoint loss if 3D keypoint annotations are available. + """ + conf = gt_keypoints_3d[:, :, -1].unsqueeze(-1).clone() + gt_keypoints_3d = gt_keypoints_3d[:, :, :-1].clone() + gt_keypoints_3d = gt_keypoints_3d[has_pose_3d == 1] + conf = conf[has_pose_3d == 1] + pred_keypoints_3d = pred_keypoints_3d[has_pose_3d == 1] + if len(gt_keypoints_3d) > 0: + gt_pelvis = (gt_keypoints_3d[:, 2,:] + gt_keypoints_3d[:, 3,:]) / 2 + gt_keypoints_3d = gt_keypoints_3d - gt_pelvis[:, None, :] + pred_pelvis = (pred_keypoints_3d[:, 2,:] + pred_keypoints_3d[:, 3,:]) / 2 + pred_keypoints_3d = pred_keypoints_3d - pred_pelvis[:, None, :] + return (conf * criterion_keypoints(pred_keypoints_3d, gt_keypoints_3d)).mean() + else: + return torch.FloatTensor(1).fill_(0.).to(device) + +def vertices_loss(criterion_vertices, pred_vertices, gt_vertices, has_smpl, device): + """ + Compute per-vertex loss if vertex annotations are available. + """ + pred_vertices_with_shape = pred_vertices[has_smpl == 1] + gt_vertices_with_shape = gt_vertices[has_smpl == 1] + if len(gt_vertices_with_shape) > 0: + return criterion_vertices(pred_vertices_with_shape, gt_vertices_with_shape) + else: + return torch.FloatTensor(1).fill_(0.).to(device) + +def rectify_pose(pose): + pose = pose.copy() + R_mod = cv2.Rodrigues(np.array([np.pi, 0, 0]))[0] + R_root = cv2.Rodrigues(pose[:3])[0] + new_root = R_root.dot(R_mod) + pose[:3] = cv2.Rodrigues(new_root)[0].reshape(3) + return pose + +def run(args, train_dataloader, val_dataloader, Graphormer_model, smpl, mesh_sampler, renderer): + smpl.eval() + max_iter = len(train_dataloader) + iters_per_epoch = max_iter // args.num_train_epochs + if iters_per_epoch<1000: + args.logging_steps = 500 + + optimizer = torch.optim.Adam(params=list(Graphormer_model.parameters()), + lr=args.lr, + betas=(0.9, 0.999), + weight_decay=0) + + # define loss function (criterion) and optimizer + criterion_2d_keypoints = torch.nn.MSELoss(reduction='none').to(device) + criterion_keypoints = torch.nn.MSELoss(reduction='none').to(device) + criterion_vertices = torch.nn.L1Loss().to(device) + + if args.distributed: + Graphormer_model = torch.nn.parallel.DistributedDataParallel( + Graphormer_model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True, + ) + + logger.info( + ' '.join( + ['Local rank: {o}', 'Max iteration: {a}', 'iters_per_epoch: {b}','num_train_epochs: {c}',] + ).format(o=args.local_rank, a=max_iter, b=iters_per_epoch, c=args.num_train_epochs) + ) + + start_training_time = time.time() + end = time.time() + Graphormer_model.train() + batch_time = AverageMeter() + data_time = AverageMeter() + log_losses = AverageMeter() + log_loss_2djoints = AverageMeter() + log_loss_3djoints = AverageMeter() + log_loss_vertices = AverageMeter() + log_eval_metrics = EvalMetricsLogger() + + for iteration, (img_keys, images, annotations) in enumerate(train_dataloader): + # gc.collect() + # torch.cuda.empty_cache() + Graphormer_model.train() + iteration += 1 + epoch = iteration // iters_per_epoch + batch_size = images.size(0) + adjust_learning_rate(optimizer, epoch, args) + data_time.update(time.time() - end) + + images = images.to(device) + gt_2d_joints = annotations['joints_2d'].to(device) + gt_2d_joints = gt_2d_joints[:,cfg.J24_TO_J14,:] + has_2d_joints = annotations['has_2d_joints'].to(device) + + gt_3d_joints = annotations['joints_3d'].to(device) + gt_3d_pelvis = gt_3d_joints[:,cfg.J24_NAME.index('Pelvis'),:3] + gt_3d_joints = gt_3d_joints[:,cfg.J24_TO_J14,:] + gt_3d_joints[:,:,:3] = gt_3d_joints[:,:,:3] - gt_3d_pelvis[:, None, :] + has_3d_joints = annotations['has_3d_joints'].to(device) + + gt_pose = annotations['pose'].to(device) + gt_betas = annotations['betas'].to(device) + has_smpl = annotations['has_smpl'].to(device) + mjm_mask = annotations['mjm_mask'].to(device) + mvm_mask = annotations['mvm_mask'].to(device) + + # generate simplified mesh + gt_vertices = smpl(gt_pose, gt_betas) + gt_vertices_sub2 = mesh_sampler.downsample(gt_vertices, n1=0, n2=2) + gt_vertices_sub = mesh_sampler.downsample(gt_vertices) + + # normalize gt based on smpl's pelvis + gt_smpl_3d_joints = smpl.get_h36m_joints(gt_vertices) + gt_smpl_3d_pelvis = gt_smpl_3d_joints[:,cfg.H36M_J17_NAME.index('Pelvis'),:] + gt_vertices_sub2 = gt_vertices_sub2 - gt_smpl_3d_pelvis[:, None, :] + + # prepare masks for mask vertex/joint modeling + mjm_mask_ = mjm_mask.expand(-1,-1,2051) + mvm_mask_ = mvm_mask.expand(-1,-1,2051) + meta_masks = torch.cat([mjm_mask_, mvm_mask_], dim=1) + + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub2, pred_vertices_sub, pred_vertices = Graphormer_model(images, smpl, mesh_sampler, meta_masks=meta_masks, is_train=True) + + # normalize gt based on smpl's pelvis + gt_vertices_sub = gt_vertices_sub - gt_smpl_3d_pelvis[:, None, :] + gt_vertices = gt_vertices - gt_smpl_3d_pelvis[:, None, :] + + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_smpl = smpl.get_h36m_joints(pred_vertices) + pred_3d_joints_from_smpl = pred_3d_joints_from_smpl[:,cfg.H36M_J17_TO_J14,:] + + # obtain 2d joints, which are projected from 3d joints of smpl mesh + pred_2d_joints_from_smpl = orthographic_projection(pred_3d_joints_from_smpl, pred_camera) + pred_2d_joints = orthographic_projection(pred_3d_joints, pred_camera) + + # compute 3d joint loss (where the joints are directly output from transformer) + loss_3d_joints = keypoint_3d_loss(criterion_keypoints, pred_3d_joints, gt_3d_joints, has_3d_joints, args.device) + # compute 3d vertex loss + loss_vertices = ( args.vloss_w_sub2 * vertices_loss(criterion_vertices, pred_vertices_sub2, gt_vertices_sub2, has_smpl, args.device) + \ + args.vloss_w_sub * vertices_loss(criterion_vertices, pred_vertices_sub, gt_vertices_sub, has_smpl, args.device) + \ + args.vloss_w_full * vertices_loss(criterion_vertices, pred_vertices, gt_vertices, has_smpl, args.device) ) + # compute 3d joint loss (where the joints are regressed from full mesh) + loss_reg_3d_joints = keypoint_3d_loss(criterion_keypoints, pred_3d_joints_from_smpl, gt_3d_joints, has_3d_joints, args.device) + # compute 2d joint loss + loss_2d_joints = keypoint_2d_loss(criterion_2d_keypoints, pred_2d_joints, gt_2d_joints, has_2d_joints) + \ + keypoint_2d_loss(criterion_2d_keypoints, pred_2d_joints_from_smpl, gt_2d_joints, has_2d_joints) + + loss_3d_joints = loss_3d_joints + loss_reg_3d_joints + + # we empirically use hyperparameters to balance difference losses + loss = args.joints_loss_weight*loss_3d_joints + \ + args.vertices_loss_weight*loss_vertices + args.vertices_loss_weight*loss_2d_joints + + # update logs + log_loss_2djoints.update(loss_2d_joints.item(), batch_size) + log_loss_3djoints.update(loss_3d_joints.item(), batch_size) + log_loss_vertices.update(loss_vertices.item(), batch_size) + log_losses.update(loss.item(), batch_size) + + # back prop + optimizer.zero_grad() + loss.backward() + optimizer.step() + + batch_time.update(time.time() - end) + end = time.time() + + if iteration % args.logging_steps == 0 or iteration == max_iter: + eta_seconds = batch_time.avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + logger.info( + ' '.join( + ['eta: {eta}', 'epoch: {ep}', 'iter: {iter}', 'max mem : {memory:.0f}',] + ).format(eta=eta_string, ep=epoch, iter=iteration, + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0) + + ' loss: {:.4f}, 2d joint loss: {:.4f}, 3d joint loss: {:.4f}, vertex loss: {:.4f}, compute: {:.4f}, data: {:.4f}, lr: {:.6f}'.format( + log_losses.avg, log_loss_2djoints.avg, log_loss_3djoints.avg, log_loss_vertices.avg, batch_time.avg, data_time.avg, + optimizer.param_groups[0]['lr']) + ) + + aml_run.log(name='Loss', value=float(log_losses.avg)) + aml_run.log(name='3d joint Loss', value=float(log_loss_3djoints.avg)) + aml_run.log(name='2d joint Loss', value=float(log_loss_2djoints.avg)) + aml_run.log(name='vertex Loss', value=float(log_loss_vertices.avg)) + + visual_imgs = visualize_mesh( renderer, + annotations['ori_img'].detach(), + annotations['joints_2d'].detach(), + pred_vertices.detach(), + pred_camera.detach(), + pred_2d_joints_from_smpl.detach()) + visual_imgs = visual_imgs.transpose(0,1) + visual_imgs = visual_imgs.transpose(1,2) + visual_imgs = np.asarray(visual_imgs) + + if is_main_process()==True: + stamp = str(epoch) + '_' + str(iteration) + temp_fname = args.output_dir + 'visual_' + stamp + '.jpg' + cv2.imwrite(temp_fname, np.asarray(visual_imgs[:,:,::-1]*255)) + aml_run.log_image(name='visual results', path=temp_fname) + + if iteration % iters_per_epoch == 0: + val_mPVE, val_mPJPE, val_PAmPJPE, val_count = run_validate(args, val_dataloader, + Graphormer_model, + criterion_keypoints, + criterion_vertices, + epoch, + smpl, + mesh_sampler) + aml_run.log(name='mPVE', value=float(1000*val_mPVE)) + aml_run.log(name='mPJPE', value=float(1000*val_mPJPE)) + aml_run.log(name='PAmPJPE', value=float(1000*val_PAmPJPE)) + logger.info( + ' '.join(['Validation', 'epoch: {ep}',]).format(ep=epoch) + + ' mPVE: {:6.2f}, mPJPE: {:6.2f}, PAmPJPE: {:6.2f}, Data Count: {:6.2f}'.format(1000*val_mPVE, 1000*val_mPJPE, 1000*val_PAmPJPE, val_count) + ) + + if val_PAmPJPE0: + mPVE.update(np.mean(error_vertices), int(torch.sum(has_smpl)) ) + if len(error_joints)>0: + mPJPE.update(np.mean(error_joints), int(torch.sum(has_3d_joints)) ) + if len(error_joints_pa)>0: + PAmPJPE.update(np.mean(error_joints_pa), int(torch.sum(has_3d_joints)) ) + + val_mPVE = all_gather(float(mPVE.avg)) + val_mPVE = sum(val_mPVE)/len(val_mPVE) + val_mPJPE = all_gather(float(mPJPE.avg)) + val_mPJPE = sum(val_mPJPE)/len(val_mPJPE) + + val_PAmPJPE = all_gather(float(PAmPJPE.avg)) + val_PAmPJPE = sum(val_PAmPJPE)/len(val_PAmPJPE) + + val_count = all_gather(float(mPVE.count)) + val_count = sum(val_count) + + return val_mPVE, val_mPJPE, val_PAmPJPE, val_count + + +def visualize_mesh( renderer, + images, + gt_keypoints_2d, + pred_vertices, + pred_camera, + pred_keypoints_2d): + """Tensorboard logging.""" + gt_keypoints_2d = gt_keypoints_2d.cpu().numpy() + to_lsp = list(range(14)) + rend_imgs = [] + batch_size = pred_vertices.shape[0] + # Do visualization for the first 6 images of the batch + for i in range(min(batch_size, 10)): + img = images[i].cpu().numpy().transpose(1,2,0) + # Get LSP keypoints from the full list of keypoints + gt_keypoints_2d_ = gt_keypoints_2d[i, to_lsp] + pred_keypoints_2d_ = pred_keypoints_2d.cpu().numpy()[i, to_lsp] + # Get predict vertices for the particular example + vertices = pred_vertices[i].cpu().numpy() + cam = pred_camera[i].cpu().numpy() + # Visualize reconstruction and detected pose + rend_img = visualize_reconstruction(img, 224, gt_keypoints_2d_, vertices, pred_keypoints_2d_, cam, renderer) + rend_img = rend_img.transpose(2,0,1) + rend_imgs.append(torch.from_numpy(rend_img)) + rend_imgs = make_grid(rend_imgs, nrow=1) + return rend_imgs + +def visualize_mesh_test( renderer, + images, + gt_keypoints_2d, + pred_vertices, + pred_camera, + pred_keypoints_2d, + PAmPJPE_h36m_j14): + """Tensorboard logging.""" + gt_keypoints_2d = gt_keypoints_2d.cpu().numpy() + to_lsp = list(range(14)) + rend_imgs = [] + batch_size = pred_vertices.shape[0] + # Do visualization for the first 6 images of the batch + for i in range(min(batch_size, 10)): + img = images[i].cpu().numpy().transpose(1,2,0) + # Get LSP keypoints from the full list of keypoints + gt_keypoints_2d_ = gt_keypoints_2d[i, to_lsp] + pred_keypoints_2d_ = pred_keypoints_2d.cpu().numpy()[i, to_lsp] + # Get predict vertices for the particular example + vertices = pred_vertices[i].cpu().numpy() + cam = pred_camera[i].cpu().numpy() + score = PAmPJPE_h36m_j14[i] + # Visualize reconstruction and detected pose + rend_img = visualize_reconstruction_test(img, 224, gt_keypoints_2d_, vertices, pred_keypoints_2d_, cam, renderer, score) + rend_img = rend_img.transpose(2,0,1) + rend_imgs.append(torch.from_numpy(rend_img)) + rend_imgs = make_grid(rend_imgs, nrow=1) + return rend_imgs + + +def parse_args(): + parser = argparse.ArgumentParser() + ######################################################### + # Data related arguments + ######################################################### + parser.add_argument("--data_dir", default='datasets', type=str, required=False, + help="Directory with all datasets, each in one subfolder") + parser.add_argument("--train_yaml", default='imagenet2012/train.yaml', type=str, required=False, + help="Yaml file with all data for training.") + parser.add_argument("--val_yaml", default='imagenet2012/test.yaml', type=str, required=False, + help="Yaml file with all data for validation.") + parser.add_argument("--num_workers", default=4, type=int, + help="Workers in dataloader.") + parser.add_argument("--img_scale_factor", default=1, type=int, + help="adjust image resolution.") + ######################################################### + # Loading/saving checkpoints + ######################################################### + parser.add_argument("--model_name_or_path", default='src/modeling/bert/bert-base-uncased/', type=str, required=False, + help="Path to pre-trained transformer model or model type.") + parser.add_argument("--resume_checkpoint", default=None, type=str, required=False, + help="Path to specific checkpoint for resume training.") + parser.add_argument("--output_dir", default='output/', type=str, required=False, + help="The output directory to save checkpoint and test results.") + parser.add_argument("--config_name", default="", type=str, + help="Pretrained config name or path if not the same as model_name.") + ######################################################### + # Training parameters + ######################################################### + parser.add_argument("--per_gpu_train_batch_size", default=30, type=int, + help="Batch size per GPU/CPU for training.") + parser.add_argument("--per_gpu_eval_batch_size", default=30, type=int, + help="Batch size per GPU/CPU for evaluation.") + parser.add_argument('--lr', "--learning_rate", default=1e-4, type=float, + help="The initial lr.") + parser.add_argument("--num_train_epochs", default=200, type=int, + help="Total number of training epochs to perform.") + parser.add_argument("--vertices_loss_weight", default=100.0, type=float) + parser.add_argument("--joints_loss_weight", default=1000.0, type=float) + parser.add_argument("--vloss_w_full", default=0.33, type=float) + parser.add_argument("--vloss_w_sub", default=0.33, type=float) + parser.add_argument("--vloss_w_sub2", default=0.33, type=float) + parser.add_argument("--drop_out", default=0.1, type=float, + help="Drop out ratio in BERT.") + ######################################################### + # Model architectures + ######################################################### + parser.add_argument('-a', '--arch', default='hrnet-w64', + help='CNN backbone architecture: hrnet-w64, hrnet, resnet50') + parser.add_argument("--num_hidden_layers", default=4, type=int, required=False, + help="Update model config if given") + parser.add_argument("--hidden_size", default=-1, type=int, required=False, + help="Update model config if given") + parser.add_argument("--num_attention_heads", default=4, type=int, required=False, + help="Update model config if given. Note that the division of " + "hidden_size / num_attention_heads should be in integer.") + parser.add_argument("--intermediate_size", default=-1, type=int, required=False, + help="Update model config if given.") + parser.add_argument("--input_feat_dim", default='2051,512,128', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--hidden_feat_dim", default='1024,256,64', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--which_gcn", default='0,0,1', type=str, + help="which encoder block to have graph conv. Encoder1, Encoder2, Encoder3. Default: only Encoder3 has graph conv") + parser.add_argument("--mesh_type", default='body', type=str, help="body or hand") + parser.add_argument("--interm_size_scale", default=2, type=int) + ######################################################### + # Others + ######################################################### + parser.add_argument("--run_eval_only", default=False, action='store_true',) + parser.add_argument('--logging_steps', type=int, default=1000, + help="Log every X steps.") + parser.add_argument("--device", type=str, default='cuda', + help="cuda or cpu") + parser.add_argument('--seed', type=int, default=88, + help="random seed for initialization.") + parser.add_argument("--local_rank", type=int, default=0, + help="For distributed training.") + + + args = parser.parse_args() + return args + + +def main(args): + global logger + # Setup CUDA, GPU & distributed training + args.num_gpus = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + os.environ['OMP_NUM_THREADS'] = str(args.num_workers) + print('set os.environ[OMP_NUM_THREADS] to {}'.format(os.environ['OMP_NUM_THREADS'])) + + args.distributed = args.num_gpus > 1 + args.device = torch.device(args.device) + if args.distributed: + print("Init distributed training on local rank {} ({}), rank {}, world size {}".format(args.local_rank, int(os.environ["LOCAL_RANK"]), int(os.environ["NODE_RANK"]), args.num_gpus)) + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group( + backend='nccl', init_method='env://' + ) + local_rank = int(os.environ["LOCAL_RANK"]) + args.device = torch.device("cuda", local_rank) + synchronize() + + mkdir(args.output_dir) + logger = setup_logger("Graphormer", args.output_dir, get_rank()) + set_seed(args.seed, args.num_gpus) + logger.info("Using {} GPUs".format(args.num_gpus)) + + # Mesh and SMPL utils + smpl = SMPL().to(args.device) + mesh_sampler = Mesh() + + # Renderer for visualization + renderer = Renderer(faces=smpl.faces.cpu().numpy()) + + # Load model + trans_encoder = [] + + input_feat_dim = [int(item) for item in args.input_feat_dim.split(',')] + hidden_feat_dim = [int(item) for item in args.hidden_feat_dim.split(',')] + output_feat_dim = input_feat_dim[1:] + [3] + + # which encoder block to have graph convs + which_blk_graph = [int(item) for item in args.which_gcn.split(',')] + + if args.run_eval_only==True and args.resume_checkpoint!=None and args.resume_checkpoint!='None' and 'state_dict' not in args.resume_checkpoint: + # if only run eval, load checkpoint + logger.info("Evaluation: Loading from checkpoint {}".format(args.resume_checkpoint)) + _model = torch.load(args.resume_checkpoint) + else: + # init three transformer-encoder blocks in a loop + for i in range(len(output_feat_dim)): + config_class, model_class = BertConfig, Graphormer + config = config_class.from_pretrained(args.config_name if args.config_name \ + else args.model_name_or_path) + + config.output_attentions = False + config.hidden_dropout_prob = args.drop_out + config.img_feature_dim = input_feat_dim[i] + config.output_feature_dim = output_feat_dim[i] + args.hidden_size = hidden_feat_dim[i] + args.intermediate_size = int(args.hidden_size*args.interm_size_scale) + + if which_blk_graph[i]==1: + config.graph_conv = True + logger.info("Add Graph Conv") + else: + config.graph_conv = False + + config.mesh_type = args.mesh_type + + # update model structure if specified in arguments + update_params = ['num_hidden_layers', 'hidden_size', 'num_attention_heads', 'intermediate_size'] + + for idx, param in enumerate(update_params): + arg_param = getattr(args, param) + config_param = getattr(config, param) + if arg_param > 0 and arg_param != config_param: + logger.info("Update config parameter {}: {} -> {}".format(param, config_param, arg_param)) + setattr(config, param, arg_param) + + # init a transformer encoder and append it to a list + assert config.hidden_size % config.num_attention_heads == 0 + model = model_class(config=config) + logger.info("Init model from scratch.") + trans_encoder.append(model) + + + # init ImageNet pre-trained backbone model + if args.arch=='hrnet': + hrnet_yaml = 'models/hrnet/cls_hrnet_w40_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w40_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w40 model') + elif args.arch=='hrnet-w64': + hrnet_yaml = 'models/hrnet/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w64_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w64 model') + else: + print("=> using pre-trained model '{}'".format(args.arch)) + backbone = models.__dict__[args.arch](pretrained=True) + # remove the last fc layer + backbone = torch.nn.Sequential(*list(backbone.children())[:-2]) + + + trans_encoder = torch.nn.Sequential(*trans_encoder) + total_params = sum(p.numel() for p in trans_encoder.parameters()) + logger.info('Graphormer encoders total parameters: {}'.format(total_params)) + backbone_total_params = sum(p.numel() for p in backbone.parameters()) + logger.info('Backbone total parameters: {}'.format(backbone_total_params)) + + # build end-to-end Graphormer network (CNN backbone + multi-layer graphormer encoder) + _model = Graphormer_Network(args, config, backbone, trans_encoder, mesh_sampler) + + if args.resume_checkpoint!=None and args.resume_checkpoint!='None': + # for fine-tuning or resume training or inference, load weights from checkpoint + logger.info("Loading state dict from checkpoint {}".format(args.resume_checkpoint)) + # workaround approach to load sparse tensor in graph conv. + states = torch.load(args.resume_checkpoint) + # states = checkpoint_loaded.state_dict() + for k, v in states.items(): + states[k] = v.cpu() + # del checkpoint_loaded + _model.load_state_dict(states, strict=False) + del states + gc.collect() + torch.cuda.empty_cache() + + + _model.to(args.device) + logger.info("Training parameters %s", args) + + if args.run_eval_only==True: + val_dataloader = make_data_loader(args, args.val_yaml, + args.distributed, is_train=False, scale_factor=args.img_scale_factor) + run_eval_general(args, val_dataloader, _model, smpl, mesh_sampler) + + else: + train_dataloader = make_data_loader(args, args.train_yaml, + args.distributed, is_train=True, scale_factor=args.img_scale_factor) + val_dataloader = make_data_loader(args, args.val_yaml, + args.distributed, is_train=False, scale_factor=args.img_scale_factor) + run(args, train_dataloader, val_dataloader, _model, smpl, mesh_sampler, renderer) + + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_bodymesh_inference.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_bodymesh_inference.py new file mode 100644 index 00000000..7d67fc92 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_bodymesh_inference.py @@ -0,0 +1,351 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +End-to-end inference codes for +3D human body mesh reconstruction from an image +""" + +from __future__ import absolute_import, division, print_function +import argparse +import os +import os.path as op +import code +import json +import time +import datetime +import torch +import torchvision.models as models +from torchvision.utils import make_grid +import gc +import numpy as np +import cv2 +from custom_mesh_graphormer.modeling.bert import BertConfig, Graphormer +from custom_mesh_graphormer.modeling.bert import Graphormer_Body_Network as Graphormer_Network +from custom_mesh_graphormer.modeling._smpl import SMPL, Mesh +from custom_mesh_graphormer.modeling.hrnet.hrnet_cls_net_gridfeat import get_cls_net_gridfeat +from custom_mesh_graphormer.modeling.hrnet.config import config as hrnet_config +from custom_mesh_graphormer.modeling.hrnet.config import update_config as hrnet_update_config +import custom_mesh_graphormer.modeling.data.config as cfg +from custom_mesh_graphormer.datasets.build import make_data_loader + +from custom_mesh_graphormer.utils.logger import setup_logger +from custom_mesh_graphormer.utils.comm import synchronize, is_main_process, get_rank, get_world_size, all_gather +from custom_mesh_graphormer.utils.miscellaneous import mkdir, set_seed +from custom_mesh_graphormer.utils.metric_logger import AverageMeter, EvalMetricsLogger +from custom_mesh_graphormer.utils.renderer import Renderer, visualize_reconstruction_and_att_local, visualize_reconstruction_no_text +from custom_mesh_graphormer.utils.metric_pampjpe import reconstruction_error +from custom_mesh_graphormer.utils.geometric_layers import orthographic_projection + +from PIL import Image +from torchvision import transforms + +from comfy.model_management import get_torch_device +device = get_torch_device() + +transform = transforms.Compose([ + transforms.Resize(224), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + +transform_visualize = transforms.Compose([ + transforms.Resize(224), + transforms.CenterCrop(224), + transforms.ToTensor()]) + +def run_inference(args, image_list, Graphormer_model, smpl, renderer, mesh_sampler): + # switch to evaluate mode + Graphormer_model.eval() + smpl.eval() + with torch.no_grad(): + for image_file in image_list: + if 'pred' not in image_file: + att_all = [] + img = Image.open(image_file) + img_tensor = transform(img) + img_visual = transform_visualize(img) + + batch_imgs = torch.unsqueeze(img_tensor, 0).to(device) + batch_visual_imgs = torch.unsqueeze(img_visual, 0).to(device) + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub2, pred_vertices_sub, pred_vertices, hidden_states, att = Graphormer_model(batch_imgs, smpl, mesh_sampler) + + # obtain 3d joints from full mesh + pred_3d_joints_from_smpl = smpl.get_h36m_joints(pred_vertices) + + pred_3d_pelvis = pred_3d_joints_from_smpl[:,cfg.H36M_J17_NAME.index('Pelvis'),:] + pred_3d_joints_from_smpl = pred_3d_joints_from_smpl[:,cfg.H36M_J17_TO_J14,:] + pred_3d_joints_from_smpl = pred_3d_joints_from_smpl - pred_3d_pelvis[:, None, :] + pred_vertices = pred_vertices - pred_3d_pelvis[:, None, :] + + # save attantion + att_max_value = att[-1] + att_cpu = np.asarray(att_max_value.cpu().detach()) + att_all.append(att_cpu) + + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_smpl = smpl.get_h36m_joints(pred_vertices) + pred_3d_joints_from_smpl = pred_3d_joints_from_smpl[:,cfg.H36M_J17_TO_J14,:] + # obtain 2d joints, which are projected from 3d joints of smpl mesh + pred_2d_joints_from_smpl = orthographic_projection(pred_3d_joints_from_smpl, pred_camera) + pred_2d_431_vertices_from_smpl = orthographic_projection(pred_vertices_sub2, pred_camera) + visual_imgs_output = visualize_mesh( renderer, batch_visual_imgs[0], + pred_vertices[0].detach(), + pred_camera.detach()) + # visual_imgs_output = visualize_mesh_and_attention( renderer, batch_visual_imgs[0], + # pred_vertices[0].detach(), + # pred_vertices_sub2[0].detach(), + # pred_2d_431_vertices_from_smpl[0].detach(), + # pred_2d_joints_from_smpl[0].detach(), + # pred_camera.detach(), + # att[-1][0].detach()) + + visual_imgs = visual_imgs_output.transpose(1,2,0) + visual_imgs = np.asarray(visual_imgs) + + temp_fname = image_file[:-4] + '_graphormer_pred.jpg' + print('save to ', temp_fname) + cv2.imwrite(temp_fname, np.asarray(visual_imgs[:,:,::-1]*255)) + + return + +def visualize_mesh( renderer, images, + pred_vertices_full, + pred_camera): + img = images.cpu().numpy().transpose(1,2,0) + # Get predict vertices for the particular example + vertices_full = pred_vertices_full.cpu().numpy() + cam = pred_camera.cpu().numpy() + # Visualize only mesh reconstruction + rend_img = visualize_reconstruction_no_text(img, 224, vertices_full, cam, renderer, color='light_blue') + rend_img = rend_img.transpose(2,0,1) + return rend_img + +def visualize_mesh_and_attention( renderer, images, + pred_vertices_full, + pred_vertices, + pred_2d_vertices, + pred_2d_joints, + pred_camera, + attention): + img = images.cpu().numpy().transpose(1,2,0) + # Get predict vertices for the particular example + vertices_full = pred_vertices_full.cpu().numpy() + vertices = pred_vertices.cpu().numpy() + vertices_2d = pred_2d_vertices.cpu().numpy() + joints_2d = pred_2d_joints.cpu().numpy() + cam = pred_camera.cpu().numpy() + att = attention.cpu().numpy() + # Visualize reconstruction and attention + rend_img = visualize_reconstruction_and_att_local(img, 224, vertices_full, vertices, vertices_2d, cam, renderer, joints_2d, att, color='light_blue') + rend_img = rend_img.transpose(2,0,1) + return rend_img + + +def parse_args(): + parser = argparse.ArgumentParser() + ######################################################### + # Data related arguments + ######################################################### + parser.add_argument("--num_workers", default=4, type=int, + help="Workers in dataloader.") + parser.add_argument("--img_scale_factor", default=1, type=int, + help="adjust image resolution.") + parser.add_argument("--image_file_or_path", default='./samples/human-body', type=str, + help="test data") + ######################################################### + # Loading/saving checkpoints + ######################################################### + parser.add_argument("--model_name_or_path", default='src/modeling/bert/bert-base-uncased/', type=str, required=False, + help="Path to pre-trained transformer model or model type.") + parser.add_argument("--resume_checkpoint", default=None, type=str, required=False, + help="Path to specific checkpoint for resume training.") + parser.add_argument("--output_dir", default='output/', type=str, required=False, + help="The output directory to save checkpoint and test results.") + parser.add_argument("--config_name", default="", type=str, + help="Pretrained config name or path if not the same as model_name.") + ######################################################### + # Model architectures + ######################################################### + parser.add_argument('-a', '--arch', default='hrnet-w64', + help='CNN backbone architecture: hrnet-w64, hrnet, resnet50') + parser.add_argument("--num_hidden_layers", default=4, type=int, required=False, + help="Update model config if given") + parser.add_argument("--hidden_size", default=-1, type=int, required=False, + help="Update model config if given") + parser.add_argument("--num_attention_heads", default=4, type=int, required=False, + help="Update model config if given. Note that the division of " + "hidden_size / num_attention_heads should be in integer.") + parser.add_argument("--intermediate_size", default=-1, type=int, required=False, + help="Update model config if given.") + parser.add_argument("--input_feat_dim", default='2051,512,128', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--hidden_feat_dim", default='1024,256,64', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--which_gcn", default='0,0,1', type=str, + help="which encoder block to have graph conv. Encoder1, Encoder2, Encoder3. Default: only Encoder3 has graph conv") + parser.add_argument("--mesh_type", default='body', type=str, help="body or hand") + parser.add_argument("--interm_size_scale", default=2, type=int) + ######################################################### + # Others + ######################################################### + parser.add_argument("--run_eval_only", default=True, action='store_true',) + parser.add_argument("--device", type=str, default='cuda', + help="cuda or cpu") + parser.add_argument('--seed', type=int, default=88, + help="random seed for initialization.") + + args = parser.parse_args() + return args + + +def main(args): + global logger + # Setup CUDA, GPU & distributed training + args.num_gpus = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + os.environ['OMP_NUM_THREADS'] = str(args.num_workers) + print('set os.environ[OMP_NUM_THREADS] to {}'.format(os.environ['OMP_NUM_THREADS'])) + + args.distributed = args.num_gpus > 1 + args.device = torch.device(args.device) + + mkdir(args.output_dir) + logger = setup_logger("Graphormer", args.output_dir, get_rank()) + set_seed(args.seed, args.num_gpus) + logger.info("Using {} GPUs".format(args.num_gpus)) + + # Mesh and SMPL utils + smpl = SMPL().to(args.device) + mesh_sampler = Mesh() + + # Renderer for visualization + renderer = Renderer(faces=smpl.faces.cpu().numpy()) + + # Load model + trans_encoder = [] + + input_feat_dim = [int(item) for item in args.input_feat_dim.split(',')] + hidden_feat_dim = [int(item) for item in args.hidden_feat_dim.split(',')] + output_feat_dim = input_feat_dim[1:] + [3] + + # which encoder block to have graph convs + which_blk_graph = [int(item) for item in args.which_gcn.split(',')] + + if args.run_eval_only==True and args.resume_checkpoint!=None and args.resume_checkpoint!='None' and 'state_dict' not in args.resume_checkpoint: + # if only run eval, load checkpoint + logger.info("Evaluation: Loading from checkpoint {}".format(args.resume_checkpoint)) + _model = torch.load(args.resume_checkpoint) + else: + # init three transformer-encoder blocks in a loop + for i in range(len(output_feat_dim)): + config_class, model_class = BertConfig, Graphormer + config = config_class.from_pretrained(args.config_name if args.config_name \ + else args.model_name_or_path) + + config.output_attentions = False + config.img_feature_dim = input_feat_dim[i] + config.output_feature_dim = output_feat_dim[i] + args.hidden_size = hidden_feat_dim[i] + args.intermediate_size = int(args.hidden_size*args.interm_size_scale) + + if which_blk_graph[i]==1: + config.graph_conv = True + logger.info("Add Graph Conv") + else: + config.graph_conv = False + + config.mesh_type = args.mesh_type + + # update model structure if specified in arguments + update_params = ['num_hidden_layers', 'hidden_size', 'num_attention_heads', 'intermediate_size'] + + for idx, param in enumerate(update_params): + arg_param = getattr(args, param) + config_param = getattr(config, param) + if arg_param > 0 and arg_param != config_param: + logger.info("Update config parameter {}: {} -> {}".format(param, config_param, arg_param)) + setattr(config, param, arg_param) + + # init a transformer encoder and append it to a list + assert config.hidden_size % config.num_attention_heads == 0 + model = model_class(config=config) + logger.info("Init model from scratch.") + trans_encoder.append(model) + + # init ImageNet pre-trained backbone model + if args.arch=='hrnet': + hrnet_yaml = 'models/hrnet/cls_hrnet_w40_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w40_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w40 model') + elif args.arch=='hrnet-w64': + hrnet_yaml = 'models/hrnet/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w64_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w64 model') + else: + print("=> using pre-trained model '{}'".format(args.arch)) + backbone = models.__dict__[args.arch](pretrained=True) + # remove the last fc layer + backbone = torch.nn.Sequential(*list(backbone.children())[:-2]) + + + trans_encoder = torch.nn.Sequential(*trans_encoder) + total_params = sum(p.numel() for p in trans_encoder.parameters()) + logger.info('Graphormer encoders total parameters: {}'.format(total_params)) + backbone_total_params = sum(p.numel() for p in backbone.parameters()) + logger.info('Backbone total parameters: {}'.format(backbone_total_params)) + + # build end-to-end Graphormer network (CNN backbone + multi-layer graphormer encoder) + _model = Graphormer_Network(args, config, backbone, trans_encoder, mesh_sampler) + + if args.resume_checkpoint!=None and args.resume_checkpoint!='None': + # for fine-tuning or resume training or inference, load weights from checkpoint + logger.info("Loading state dict from checkpoint {}".format(args.resume_checkpoint)) + # workaround approach to load sparse tensor in graph conv. + states = torch.load(args.resume_checkpoint) + # states = checkpoint_loaded.state_dict() + for k, v in states.items(): + states[k] = v.cpu() + # del checkpoint_loaded + _model.load_state_dict(states, strict=False) + del states + gc.collect() + torch.cuda.empty_cache() + + # update configs to enable attention outputs + setattr(_model.trans_encoder[-1].config,'output_attentions', True) + setattr(_model.trans_encoder[-1].config,'output_hidden_states', True) + _model.trans_encoder[-1].bert.encoder.output_attentions = True + _model.trans_encoder[-1].bert.encoder.output_hidden_states = True + for iter_layer in range(4): + _model.trans_encoder[-1].bert.encoder.layer[iter_layer].attention.self.output_attentions = True + for inter_block in range(3): + setattr(_model.trans_encoder[-1].config,'device', args.device) + + _model.to(args.device) + logger.info("Run inference") + + image_list = [] + if not args.image_file_or_path: + raise ValueError("image_file_or_path not specified") + if op.isfile(args.image_file_or_path): + image_list = [args.image_file_or_path] + elif op.isdir(args.image_file_or_path): + # should be a path with images only + for filename in os.listdir(args.image_file_or_path): + if filename.endswith(".png") or filename.endswith(".jpg") and 'pred' not in filename: + image_list.append(args.image_file_or_path+'/'+filename) + else: + raise ValueError("Cannot find images at {}".format(args.image_file_or_path)) + + run_inference(args, image_list, _model, smpl, renderer, mesh_sampler) + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_handmesh.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_handmesh.py new file mode 100644 index 00000000..5971f7bb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_handmesh.py @@ -0,0 +1,713 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +Training and evaluation codes for +3D hand mesh reconstruction from an image +""" + +from __future__ import absolute_import, division, print_function +import argparse +import os +import os.path as op +import code +import json +import time +import datetime +import torch +import torchvision.models as models +from torchvision.utils import make_grid +import gc +import numpy as np +import cv2 +from custom_mesh_graphormer.modeling.bert import BertConfig, Graphormer +from custom_mesh_graphormer.modeling.bert import Graphormer_Hand_Network as Graphormer_Network +from custom_mesh_graphormer.modeling._mano import MANO, Mesh +from custom_mesh_graphormer.modeling.hrnet.hrnet_cls_net_gridfeat import get_cls_net_gridfeat +from custom_mesh_graphormer.modeling.hrnet.config import config as hrnet_config +from custom_mesh_graphormer.modeling.hrnet.config import update_config as hrnet_update_config +import custom_mesh_graphormer.modeling.data.config as cfg +from custom_mesh_graphormer.datasets.build import make_hand_data_loader + +from custom_mesh_graphormer.utils.logger import setup_logger +from custom_mesh_graphormer.utils.comm import synchronize, is_main_process, get_rank, get_world_size, all_gather +from custom_mesh_graphormer.utils.miscellaneous import mkdir, set_seed +from custom_mesh_graphormer.utils.metric_logger import AverageMeter +from custom_mesh_graphormer.utils.renderer import Renderer, visualize_reconstruction, visualize_reconstruction_test, visualize_reconstruction_no_text +from custom_mesh_graphormer.utils.metric_pampjpe import reconstruction_error +from custom_mesh_graphormer.utils.geometric_layers import orthographic_projection + +from comfy.model_management import get_torch_device +device = get_torch_device() + +from azureml.core.run import Run +aml_run = Run.get_context() + +def save_checkpoint(model, args, epoch, iteration, num_trial=10): + checkpoint_dir = op.join(args.output_dir, 'checkpoint-{}-{}'.format( + epoch, iteration)) + if not is_main_process(): + return checkpoint_dir + mkdir(checkpoint_dir) + model_to_save = model.module if hasattr(model, 'module') else model + for i in range(num_trial): + try: + torch.save(model_to_save, op.join(checkpoint_dir, 'model.bin')) + torch.save(model_to_save.state_dict(), op.join(checkpoint_dir, 'state_dict.bin')) + torch.save(args, op.join(checkpoint_dir, 'training_args.bin')) + logger.info("Save checkpoint to {}".format(checkpoint_dir)) + break + except: + pass + else: + logger.info("Failed to save checkpoint after {} trails.".format(num_trial)) + return checkpoint_dir + +def adjust_learning_rate(optimizer, epoch, args): + """ + Sets the learning rate to the initial LR decayed by x every y epochs + x = 0.1, y = args.num_train_epochs/2.0 = 100 + """ + lr = args.lr * (0.1 ** (epoch // (args.num_train_epochs/2.0) )) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def keypoint_2d_loss(criterion_keypoints, pred_keypoints_2d, gt_keypoints_2d, has_pose_2d): + """ + Compute 2D reprojection loss if 2D keypoint annotations are available. + The confidence is binary and indicates whether the keypoints exist or not. + """ + conf = gt_keypoints_2d[:, :, -1].unsqueeze(-1).clone() + loss = (conf * criterion_keypoints(pred_keypoints_2d, gt_keypoints_2d[:, :, :-1])).mean() + return loss + +def keypoint_3d_loss(criterion_keypoints, pred_keypoints_3d, gt_keypoints_3d, has_pose_3d): + """ + Compute 3D keypoint loss if 3D keypoint annotations are available. + """ + conf = gt_keypoints_3d[:, :, -1].unsqueeze(-1).clone() + gt_keypoints_3d = gt_keypoints_3d[:, :, :-1].clone() + gt_keypoints_3d = gt_keypoints_3d[has_pose_3d == 1] + conf = conf[has_pose_3d == 1] + pred_keypoints_3d = pred_keypoints_3d[has_pose_3d == 1] + if len(gt_keypoints_3d) > 0: + gt_root = gt_keypoints_3d[:, 0,:] + gt_keypoints_3d = gt_keypoints_3d - gt_root[:, None, :] + pred_root = pred_keypoints_3d[:, 0,:] + pred_keypoints_3d = pred_keypoints_3d - pred_root[:, None, :] + return (conf * criterion_keypoints(pred_keypoints_3d, gt_keypoints_3d)).mean() + else: + return torch.FloatTensor(1).fill_(0.).to(device) + +def vertices_loss(criterion_vertices, pred_vertices, gt_vertices, has_smpl): + """ + Compute per-vertex loss if vertex annotations are available. + """ + pred_vertices_with_shape = pred_vertices[has_smpl == 1] + gt_vertices_with_shape = gt_vertices[has_smpl == 1] + if len(gt_vertices_with_shape) > 0: + return criterion_vertices(pred_vertices_with_shape, gt_vertices_with_shape) + else: + return torch.FloatTensor(1).fill_(0.).to(device) + + +def run(args, train_dataloader, Graphormer_model, mano_model, renderer, mesh_sampler): + + max_iter = len(train_dataloader) + iters_per_epoch = max_iter // args.num_train_epochs + + optimizer = torch.optim.Adam(params=list(Graphormer_model.parameters()), + lr=args.lr, + betas=(0.9, 0.999), + weight_decay=0) + + # define loss function (criterion) and optimizer + criterion_2d_keypoints = torch.nn.MSELoss(reduction='none').to(device) + criterion_keypoints = torch.nn.MSELoss(reduction='none').to(device) + criterion_vertices = torch.nn.L1Loss().to(device) + + if args.distributed: + Graphormer_model = torch.nn.parallel.DistributedDataParallel( + Graphormer_model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True, + ) + + start_training_time = time.time() + end = time.time() + Graphormer_model.train() + batch_time = AverageMeter() + data_time = AverageMeter() + log_losses = AverageMeter() + log_loss_2djoints = AverageMeter() + log_loss_3djoints = AverageMeter() + log_loss_vertices = AverageMeter() + + for iteration, (img_keys, images, annotations) in enumerate(train_dataloader): + + Graphormer_model.train() + iteration += 1 + epoch = iteration // iters_per_epoch + batch_size = images.size(0) + adjust_learning_rate(optimizer, epoch, args) + data_time.update(time.time() - end) + + images = images.to(device) + gt_2d_joints = annotations['joints_2d'].to(device) + gt_pose = annotations['pose'].to(device) + gt_betas = annotations['betas'].to(device) + has_mesh = annotations['has_smpl'].to(device) + has_3d_joints = has_mesh + has_2d_joints = has_mesh + mjm_mask = annotations['mjm_mask'].to(device) + mvm_mask = annotations['mvm_mask'].to(device) + + # generate mesh + gt_vertices, gt_3d_joints = mano_model.layer(gt_pose, gt_betas) + gt_vertices = gt_vertices/1000.0 + gt_3d_joints = gt_3d_joints/1000.0 + + gt_vertices_sub = mesh_sampler.downsample(gt_vertices) + # normalize gt based on hand's wrist + gt_3d_root = gt_3d_joints[:,cfg.J_NAME.index('Wrist'),:] + gt_vertices = gt_vertices - gt_3d_root[:, None, :] + gt_vertices_sub = gt_vertices_sub - gt_3d_root[:, None, :] + gt_3d_joints = gt_3d_joints - gt_3d_root[:, None, :] + gt_3d_joints_with_tag = torch.ones((batch_size,gt_3d_joints.shape[1],4)).to(device) + gt_3d_joints_with_tag[:,:,:3] = gt_3d_joints + + # prepare masks for mask vertex/joint modeling + mjm_mask_ = mjm_mask.expand(-1,-1,2051) + mvm_mask_ = mvm_mask.expand(-1,-1,2051) + meta_masks = torch.cat([mjm_mask_, mvm_mask_], dim=1) + + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub, pred_vertices = Graphormer_model(images, mano_model, mesh_sampler, meta_masks=meta_masks, is_train=True) + + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_mesh = mano_model.get_3d_joints(pred_vertices) + + # obtain 2d joints, which are projected from 3d joints of smpl mesh + pred_2d_joints_from_mesh = orthographic_projection(pred_3d_joints_from_mesh.contiguous(), pred_camera.contiguous()) + pred_2d_joints = orthographic_projection(pred_3d_joints.contiguous(), pred_camera.contiguous()) + + # compute 3d joint loss (where the joints are directly output from transformer) + loss_3d_joints = keypoint_3d_loss(criterion_keypoints, pred_3d_joints, gt_3d_joints_with_tag, has_3d_joints) + + # compute 3d vertex loss + loss_vertices = ( args.vloss_w_sub * vertices_loss(criterion_vertices, pred_vertices_sub, gt_vertices_sub, has_mesh) + \ + args.vloss_w_full * vertices_loss(criterion_vertices, pred_vertices, gt_vertices, has_mesh) ) + + # compute 3d joint loss (where the joints are regressed from full mesh) + loss_reg_3d_joints = keypoint_3d_loss(criterion_keypoints, pred_3d_joints_from_mesh, gt_3d_joints_with_tag, has_3d_joints) + # compute 2d joint loss + loss_2d_joints = keypoint_2d_loss(criterion_2d_keypoints, pred_2d_joints, gt_2d_joints, has_2d_joints) + \ + keypoint_2d_loss(criterion_2d_keypoints, pred_2d_joints_from_mesh, gt_2d_joints, has_2d_joints) + + loss_3d_joints = loss_3d_joints + loss_reg_3d_joints + + # we empirically use hyperparameters to balance difference losses + loss = args.joints_loss_weight*loss_3d_joints + \ + args.vertices_loss_weight*loss_vertices + args.vertices_loss_weight*loss_2d_joints + + # update logs + log_loss_2djoints.update(loss_2d_joints.item(), batch_size) + log_loss_3djoints.update(loss_3d_joints.item(), batch_size) + log_loss_vertices.update(loss_vertices.item(), batch_size) + log_losses.update(loss.item(), batch_size) + + # back prop + optimizer.zero_grad() + loss.backward() + optimizer.step() + + batch_time.update(time.time() - end) + end = time.time() + + if iteration % args.logging_steps == 0 or iteration == max_iter: + eta_seconds = batch_time.avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + logger.info( + ' '.join( + ['eta: {eta}', 'epoch: {ep}', 'iter: {iter}', 'max mem : {memory:.0f}',] + ).format(eta=eta_string, ep=epoch, iter=iteration, + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0) + + ' loss: {:.4f}, 2d joint loss: {:.4f}, 3d joint loss: {:.4f}, vertex loss: {:.4f}, compute: {:.4f}, data: {:.4f}, lr: {:.6f}'.format( + log_losses.avg, log_loss_2djoints.avg, log_loss_3djoints.avg, log_loss_vertices.avg, batch_time.avg, data_time.avg, + optimizer.param_groups[0]['lr']) + ) + + aml_run.log(name='Loss', value=float(log_losses.avg)) + aml_run.log(name='3d joint Loss', value=float(log_loss_3djoints.avg)) + aml_run.log(name='2d joint Loss', value=float(log_loss_2djoints.avg)) + aml_run.log(name='vertex Loss', value=float(log_loss_vertices.avg)) + + visual_imgs = visualize_mesh( renderer, + annotations['ori_img'].detach(), + annotations['joints_2d'].detach(), + pred_vertices.detach(), + pred_camera.detach(), + pred_2d_joints_from_mesh.detach()) + visual_imgs = visual_imgs.transpose(0,1) + visual_imgs = visual_imgs.transpose(1,2) + visual_imgs = np.asarray(visual_imgs) + + if is_main_process()==True: + stamp = str(epoch) + '_' + str(iteration) + temp_fname = args.output_dir + 'visual_' + stamp + '.jpg' + cv2.imwrite(temp_fname, np.asarray(visual_imgs[:,:,::-1]*255)) + aml_run.log_image(name='visual results', path=temp_fname) + + if iteration % iters_per_epoch == 0: + if epoch%10==0: + checkpoint_dir = save_checkpoint(Graphormer_model, args, epoch, iteration) + + total_training_time = time.time() - start_training_time + total_time_str = str(datetime.timedelta(seconds=total_training_time)) + logger.info('Total training time: {} ({:.4f} s / iter)'.format( + total_time_str, total_training_time / max_iter) + ) + checkpoint_dir = save_checkpoint(Graphormer_model, args, epoch, iteration) + +def run_eval_and_save(args, split, val_dataloader, Graphormer_model, mano_model, renderer, mesh_sampler): + + criterion_keypoints = torch.nn.MSELoss(reduction='none').to(device) + criterion_vertices = torch.nn.L1Loss().to(device) + + if args.distributed: + Graphormer_model = torch.nn.parallel.DistributedDataParallel( + Graphormer_model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True, + ) + Graphormer_model.eval() + + if args.aml_eval==True: + run_aml_inference_hand_mesh(args, val_dataloader, + Graphormer_model, + criterion_keypoints, + criterion_vertices, + 0, + mano_model, mesh_sampler, + renderer, split) + else: + run_inference_hand_mesh(args, val_dataloader, + Graphormer_model, + criterion_keypoints, + criterion_vertices, + 0, + mano_model, mesh_sampler, + renderer, split) + checkpoint_dir = save_checkpoint(Graphormer_model, args, 0, 0) + return + +def run_aml_inference_hand_mesh(args, val_loader, Graphormer_model, criterion, criterion_vertices, epoch, mano_model, mesh_sampler, renderer, split): + # switch to evaluate mode + Graphormer_model.eval() + fname_output_save = [] + mesh_output_save = [] + joint_output_save = [] + world_size = get_world_size() + with torch.no_grad(): + for i, (img_keys, images, annotations) in enumerate(val_loader): + batch_size = images.size(0) + # compute output + images = images.to(device) + + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub, pred_vertices = Graphormer_model(images, mano_model, mesh_sampler) + # obtain 3d joints from full mesh + pred_3d_joints_from_mesh = mano_model.get_3d_joints(pred_vertices) + + for j in range(batch_size): + fname_output_save.append(img_keys[j]) + pred_vertices_list = pred_vertices[j].tolist() + mesh_output_save.append(pred_vertices_list) + pred_3d_joints_from_mesh_list = pred_3d_joints_from_mesh[j].tolist() + joint_output_save.append(pred_3d_joints_from_mesh_list) + + if world_size > 1: + torch.distributed.barrier() + print('save results to pred.json') + output_json_file = 'pred.json' + print('save results to ', output_json_file) + with open(output_json_file, 'w') as f: + json.dump([joint_output_save, mesh_output_save], f) + + azure_ckpt_name = '200' # args.resume_checkpoint.split('/')[-2].split('-')[1] + inference_setting = 'sc%02d_rot%s'%(int(args.sc*10),str(int(args.rot))) + output_zip_file = args.output_dir + 'ckpt' + azure_ckpt_name + '-' + inference_setting +'-pred.zip' + + resolved_submit_cmd = 'zip ' + output_zip_file + ' ' + output_json_file + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + resolved_submit_cmd = 'rm %s'%(output_json_file) + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + if world_size > 1: + torch.distributed.barrier() + + return + +def run_inference_hand_mesh(args, val_loader, Graphormer_model, criterion, criterion_vertices, epoch, mano_model, mesh_sampler, renderer, split): + # switch to evaluate mode + Graphormer_model.eval() + fname_output_save = [] + mesh_output_save = [] + joint_output_save = [] + with torch.no_grad(): + for i, (img_keys, images, annotations) in enumerate(val_loader): + batch_size = images.size(0) + # compute output + images = images.to(device) + + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub, pred_vertices = Graphormer_model(images, mano_model, mesh_sampler) + + # obtain 3d joints from full mesh + pred_3d_joints_from_mesh = mano_model.get_3d_joints(pred_vertices) + pred_3d_pelvis = pred_3d_joints_from_mesh[:,cfg.J_NAME.index('Wrist'),:] + pred_3d_joints_from_mesh = pred_3d_joints_from_mesh - pred_3d_pelvis[:, None, :] + pred_vertices = pred_vertices - pred_3d_pelvis[:, None, :] + + for j in range(batch_size): + fname_output_save.append(img_keys[j]) + pred_vertices_list = pred_vertices[j].tolist() + mesh_output_save.append(pred_vertices_list) + pred_3d_joints_from_mesh_list = pred_3d_joints_from_mesh[j].tolist() + joint_output_save.append(pred_3d_joints_from_mesh_list) + + if i%20==0: + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_mesh = mano_model.get_3d_joints(pred_vertices) + # obtain 2d joints, which are projected from 3d joints of mesh + pred_2d_joints_from_mesh = orthographic_projection(pred_3d_joints_from_mesh.contiguous(), pred_camera.contiguous()) + visual_imgs = visualize_mesh( renderer, + annotations['ori_img'].detach(), + annotations['joints_2d'].detach(), + pred_vertices.detach(), + pred_camera.detach(), + pred_2d_joints_from_mesh.detach()) + + visual_imgs = visual_imgs.transpose(0,1) + visual_imgs = visual_imgs.transpose(1,2) + visual_imgs = np.asarray(visual_imgs) + + inference_setting = 'sc%02d_rot%s'%(int(args.sc*10),str(int(args.rot))) + temp_fname = args.output_dir + args.resume_checkpoint[0:-9] + 'freihand_results_'+inference_setting+'_batch'+str(i)+'.jpg' + cv2.imwrite(temp_fname, np.asarray(visual_imgs[:,:,::-1]*255)) + + print('save results to pred.json') + with open('pred.json', 'w') as f: + json.dump([joint_output_save, mesh_output_save], f) + + run_exp_name = args.resume_checkpoint.split('/')[-3] + run_ckpt_name = args.resume_checkpoint.split('/')[-2].split('-')[1] + inference_setting = 'sc%02d_rot%s'%(int(args.sc*10),str(int(args.rot))) + resolved_submit_cmd = 'zip ' + args.output_dir + run_exp_name + '-ckpt'+ run_ckpt_name + '-' + inference_setting +'-pred.zip ' + 'pred.json' + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + resolved_submit_cmd = 'rm pred.json' + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + return + +def visualize_mesh( renderer, + images, + gt_keypoints_2d, + pred_vertices, + pred_camera, + pred_keypoints_2d): + """Tensorboard logging.""" + gt_keypoints_2d = gt_keypoints_2d.cpu().numpy() + to_lsp = list(range(21)) + rend_imgs = [] + batch_size = pred_vertices.shape[0] + # Do visualization for the first 6 images of the batch + for i in range(min(batch_size, 10)): + img = images[i].cpu().numpy().transpose(1,2,0) + # Get LSP keypoints from the full list of keypoints + gt_keypoints_2d_ = gt_keypoints_2d[i, to_lsp] + pred_keypoints_2d_ = pred_keypoints_2d.cpu().numpy()[i, to_lsp] + # Get predict vertices for the particular example + vertices = pred_vertices[i].cpu().numpy() + cam = pred_camera[i].cpu().numpy() + # Visualize reconstruction and detected pose + rend_img = visualize_reconstruction(img, 224, gt_keypoints_2d_, vertices, pred_keypoints_2d_, cam, renderer) + rend_img = rend_img.transpose(2,0,1) + rend_imgs.append(torch.from_numpy(rend_img)) + rend_imgs = make_grid(rend_imgs, nrow=1) + return rend_imgs + +def visualize_mesh_test( renderer, + images, + gt_keypoints_2d, + pred_vertices, + pred_camera, + pred_keypoints_2d, + PAmPJPE): + """Tensorboard logging.""" + gt_keypoints_2d = gt_keypoints_2d.cpu().numpy() + to_lsp = list(range(21)) + rend_imgs = [] + batch_size = pred_vertices.shape[0] + # Do visualization for the first 6 images of the batch + for i in range(min(batch_size, 10)): + img = images[i].cpu().numpy().transpose(1,2,0) + # Get LSP keypoints from the full list of keypoints + gt_keypoints_2d_ = gt_keypoints_2d[i, to_lsp] + pred_keypoints_2d_ = pred_keypoints_2d.cpu().numpy()[i, to_lsp] + # Get predict vertices for the particular example + vertices = pred_vertices[i].cpu().numpy() + cam = pred_camera[i].cpu().numpy() + score = PAmPJPE[i] + # Visualize reconstruction and detected pose + rend_img = visualize_reconstruction_test(img, 224, gt_keypoints_2d_, vertices, pred_keypoints_2d_, cam, renderer, score) + rend_img = rend_img.transpose(2,0,1) + rend_imgs.append(torch.from_numpy(rend_img)) + rend_imgs = make_grid(rend_imgs, nrow=1) + return rend_imgs + +def visualize_mesh_no_text( renderer, + images, + pred_vertices, + pred_camera): + """Tensorboard logging.""" + rend_imgs = [] + batch_size = pred_vertices.shape[0] + # Do visualization for the first 6 images of the batch + for i in range(min(batch_size, 1)): + img = images[i].cpu().numpy().transpose(1,2,0) + # Get predict vertices for the particular example + vertices = pred_vertices[i].cpu().numpy() + cam = pred_camera[i].cpu().numpy() + # Visualize reconstruction only + rend_img = visualize_reconstruction_no_text(img, 224, vertices, cam, renderer, color='hand') + rend_img = rend_img.transpose(2,0,1) + rend_imgs.append(torch.from_numpy(rend_img)) + rend_imgs = make_grid(rend_imgs, nrow=1) + return rend_imgs + +def parse_args(): + parser = argparse.ArgumentParser() + ######################################################### + # Data related arguments + ######################################################### + parser.add_argument("--data_dir", default='datasets', type=str, required=False, + help="Directory with all datasets, each in one subfolder") + parser.add_argument("--train_yaml", default='imagenet2012/train.yaml', type=str, required=False, + help="Yaml file with all data for training.") + parser.add_argument("--val_yaml", default='imagenet2012/test.yaml', type=str, required=False, + help="Yaml file with all data for validation.") + parser.add_argument("--num_workers", default=4, type=int, + help="Workers in dataloader.") + parser.add_argument("--img_scale_factor", default=1, type=int, + help="adjust image resolution.") + ######################################################### + # Loading/saving checkpoints + ######################################################### + parser.add_argument("--model_name_or_path", default='src/modeling/bert/bert-base-uncased/', type=str, required=False, + help="Path to pre-trained transformer model or model type.") + parser.add_argument("--resume_checkpoint", default=None, type=str, required=False, + help="Path to specific checkpoint for resume training.") + parser.add_argument("--output_dir", default='output/', type=str, required=False, + help="The output directory to save checkpoint and test results.") + parser.add_argument("--config_name", default="", type=str, + help="Pretrained config name or path if not the same as model_name.") + parser.add_argument('-a', '--arch', default='hrnet-w64', + help='CNN backbone architecture: hrnet-w64, hrnet, resnet50') + ######################################################### + # Training parameters + ######################################################### + parser.add_argument("--per_gpu_train_batch_size", default=64, type=int, + help="Batch size per GPU/CPU for training.") + parser.add_argument("--per_gpu_eval_batch_size", default=64, type=int, + help="Batch size per GPU/CPU for evaluation.") + parser.add_argument('--lr', "--learning_rate", default=1e-4, type=float, + help="The initial lr.") + parser.add_argument("--num_train_epochs", default=200, type=int, + help="Total number of training epochs to perform.") + parser.add_argument("--vertices_loss_weight", default=1.0, type=float) + parser.add_argument("--joints_loss_weight", default=1.0, type=float) + parser.add_argument("--vloss_w_full", default=0.5, type=float) + parser.add_argument("--vloss_w_sub", default=0.5, type=float) + parser.add_argument("--drop_out", default=0.1, type=float, + help="Drop out ratio in BERT.") + ######################################################### + # Model architectures + ######################################################### + parser.add_argument("--num_hidden_layers", default=-1, type=int, required=False, + help="Update model config if given") + parser.add_argument("--hidden_size", default=-1, type=int, required=False, + help="Update model config if given") + parser.add_argument("--num_attention_heads", default=-1, type=int, required=False, + help="Update model config if given. Note that the division of " + "hidden_size / num_attention_heads should be in integer.") + parser.add_argument("--intermediate_size", default=-1, type=int, required=False, + help="Update model config if given.") + parser.add_argument("--input_feat_dim", default='2051,512,128', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--hidden_feat_dim", default='1024,256,64', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--which_gcn", default='0,0,1', type=str, + help="which encoder block to have graph conv. Encoder1, Encoder2, Encoder3. Default: only Encoder3 has graph conv") + parser.add_argument("--mesh_type", default='hand', type=str, help="body or hand") + + ######################################################### + # Others + ######################################################### + parser.add_argument("--run_eval_only", default=False, action='store_true',) + parser.add_argument("--multiscale_inference", default=False, action='store_true',) + # if enable "multiscale_inference", dataloader will apply transformations to the test image based on + # the rotation "rot" and scale "sc" parameters below + parser.add_argument("--rot", default=0, type=float) + parser.add_argument("--sc", default=1.0, type=float) + parser.add_argument("--aml_eval", default=False, action='store_true',) + + parser.add_argument('--logging_steps', type=int, default=100, + help="Log every X steps.") + parser.add_argument("--device", type=str, default='cuda', + help="cuda or cpu") + parser.add_argument('--seed', type=int, default=88, + help="random seed for initialization.") + parser.add_argument("--local_rank", type=int, default=0, + help="For distributed training.") + args = parser.parse_args() + return args + +def main(args): + global logger + # Setup CUDA, GPU & distributed training + args.num_gpus = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + os.environ['OMP_NUM_THREADS'] = str(args.num_workers) + print('set os.environ[OMP_NUM_THREADS] to {}'.format(os.environ['OMP_NUM_THREADS'])) + + args.distributed = args.num_gpus > 1 + args.device = torch.device(args.device) + if args.distributed: + print("Init distributed training on local rank {}".format(args.local_rank)) + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group( + backend='nccl', init_method='env://' + ) + synchronize() + + mkdir(args.output_dir) + logger = setup_logger("Graphormer", args.output_dir, get_rank()) + set_seed(args.seed, args.num_gpus) + logger.info("Using {} GPUs".format(args.num_gpus)) + + # Mesh and SMPL utils + mano_model = MANO().to(args.device) + mano_model.layer = mano_model.layer.to(device) + mesh_sampler = Mesh() + + # Renderer for visualization + renderer = Renderer(faces=mano_model.face) + + # Load pretrained model + trans_encoder = [] + + input_feat_dim = [int(item) for item in args.input_feat_dim.split(',')] + hidden_feat_dim = [int(item) for item in args.hidden_feat_dim.split(',')] + output_feat_dim = input_feat_dim[1:] + [3] + + # which encoder block to have graph convs + which_blk_graph = [int(item) for item in args.which_gcn.split(',')] + + if args.run_eval_only==True and args.resume_checkpoint!=None and args.resume_checkpoint!='None' and 'state_dict' not in args.resume_checkpoint: + # if only run eval, load checkpoint + logger.info("Evaluation: Loading from checkpoint {}".format(args.resume_checkpoint)) + _model = torch.load(args.resume_checkpoint) + + else: + # init three transformer-encoder blocks in a loop + for i in range(len(output_feat_dim)): + config_class, model_class = BertConfig, Graphormer + config = config_class.from_pretrained(args.config_name if args.config_name \ + else args.model_name_or_path) + + config.output_attentions = False + config.hidden_dropout_prob = args.drop_out + config.img_feature_dim = input_feat_dim[i] + config.output_feature_dim = output_feat_dim[i] + args.hidden_size = hidden_feat_dim[i] + args.intermediate_size = int(args.hidden_size*2) + + if which_blk_graph[i]==1: + config.graph_conv = True + logger.info("Add Graph Conv") + else: + config.graph_conv = False + + config.mesh_type = args.mesh_type + + # update model structure if specified in arguments + update_params = ['num_hidden_layers', 'hidden_size', 'num_attention_heads', 'intermediate_size'] + for idx, param in enumerate(update_params): + arg_param = getattr(args, param) + config_param = getattr(config, param) + if arg_param > 0 and arg_param != config_param: + logger.info("Update config parameter {}: {} -> {}".format(param, config_param, arg_param)) + setattr(config, param, arg_param) + + # init a transformer encoder and append it to a list + assert config.hidden_size % config.num_attention_heads == 0 + model = model_class(config=config) + logger.info("Init model from scratch.") + trans_encoder.append(model) + + # create backbone model + if args.arch=='hrnet': + hrnet_yaml = 'models/hrnet/cls_hrnet_w40_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w40_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w40 model') + elif args.arch=='hrnet-w64': + hrnet_yaml = 'models/hrnet/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w64_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w64 model') + else: + print("=> using pre-trained model '{}'".format(args.arch)) + backbone = models.__dict__[args.arch](pretrained=True) + # remove the last fc layer + backbone = torch.nn.Sequential(*list(backbone.children())[:-1]) + + trans_encoder = torch.nn.Sequential(*trans_encoder) + total_params = sum(p.numel() for p in trans_encoder.parameters()) + logger.info('Graphormer encoders total parameters: {}'.format(total_params)) + backbone_total_params = sum(p.numel() for p in backbone.parameters()) + logger.info('Backbone total parameters: {}'.format(backbone_total_params)) + + # build end-to-end Graphormer network (CNN backbone + multi-layer Graphormer encoder) + _model = Graphormer_Network(args, config, backbone, trans_encoder) + + if args.resume_checkpoint!=None and args.resume_checkpoint!='None': + # for fine-tuning or resume training or inference, load weights from checkpoint + logger.info("Loading state dict from checkpoint {}".format(args.resume_checkpoint)) + # workaround approach to load sparse tensor in graph conv. + state_dict = torch.load(args.resume_checkpoint) + _model.load_state_dict(state_dict, strict=False) + del state_dict + gc.collect() + torch.cuda.empty_cache() + + _model.to(args.device) + logger.info("Training parameters %s", args) + + if args.run_eval_only==True: + val_dataloader = make_hand_data_loader(args, args.val_yaml, + args.distributed, is_train=False, scale_factor=args.img_scale_factor) + run_eval_and_save(args, 'freihand', val_dataloader, _model, mano_model, renderer, mesh_sampler) + + else: + train_dataloader = make_hand_data_loader(args, args.train_yaml, + args.distributed, is_train=True, scale_factor=args.img_scale_factor) + run(args, train_dataloader, _model, mano_model, renderer, mesh_sampler) + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_handmesh_inference.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_handmesh_inference.py new file mode 100644 index 00000000..8b7300a7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_gphmer_handmesh_inference.py @@ -0,0 +1,338 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +End-to-end inference codes for +3D hand mesh reconstruction from an image +""" + +from __future__ import absolute_import, division, print_function +import argparse +import os +import os.path as op +import code +import json +import time +import datetime +import torch +import torchvision.models as models +from torchvision.utils import make_grid +import gc +import numpy as np +import cv2 +from custom_mesh_graphormer.modeling.bert import BertConfig, Graphormer +from custom_mesh_graphormer.modeling.bert import Graphormer_Hand_Network as Graphormer_Network +from custom_mesh_graphormer.modeling._mano import MANO, Mesh +from custom_mesh_graphormer.modeling.hrnet.hrnet_cls_net_gridfeat import get_cls_net_gridfeat +from custom_mesh_graphormer.modeling.hrnet.config import config as hrnet_config +from custom_mesh_graphormer.modeling.hrnet.config import update_config as hrnet_update_config +import custom_mesh_graphormer.modeling.data.config as cfg +from custom_mesh_graphormer.datasets.build import make_hand_data_loader + +from custom_mesh_graphormer.utils.logger import setup_logger +from custom_mesh_graphormer.utils.comm import synchronize, is_main_process, get_rank, get_world_size, all_gather +from custom_mesh_graphormer.utils.miscellaneous import mkdir, set_seed +from custom_mesh_graphormer.utils.metric_logger import AverageMeter +from custom_mesh_graphormer.utils.renderer import Renderer, visualize_reconstruction_and_att_local, visualize_reconstruction_no_text +from custom_mesh_graphormer.utils.metric_pampjpe import reconstruction_error +from custom_mesh_graphormer.utils.geometric_layers import orthographic_projection + +from PIL import Image +from torchvision import transforms + +from comfy.model_management import get_torch_device +device = get_torch_device() + +transform = transforms.Compose([ + transforms.Resize(224), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + +transform_visualize = transforms.Compose([ + transforms.Resize(224), + transforms.CenterCrop(224), + transforms.ToTensor()]) + +def run_inference(args, image_list, Graphormer_model, mano, renderer, mesh_sampler): +# switch to evaluate mode + Graphormer_model.eval() + mano.eval() + with torch.no_grad(): + for image_file in image_list: + if 'pred' not in image_file: + att_all = [] + print(image_file) + img = Image.open(image_file) + img_tensor = transform(img) + img_visual = transform_visualize(img) + + batch_imgs = torch.unsqueeze(img_tensor, 0).to(device) + batch_visual_imgs = torch.unsqueeze(img_visual, 0).to(device) + # forward-pass + pred_camera, pred_3d_joints, pred_vertices_sub, pred_vertices, hidden_states, att = Graphormer_model(batch_imgs, mano, mesh_sampler) + # obtain 3d joints from full mesh + pred_3d_joints_from_mesh = mano.get_3d_joints(pred_vertices) + pred_3d_pelvis = pred_3d_joints_from_mesh[:,cfg.J_NAME.index('Wrist'),:] + pred_3d_joints_from_mesh = pred_3d_joints_from_mesh - pred_3d_pelvis[:, None, :] + pred_vertices = pred_vertices - pred_3d_pelvis[:, None, :] + + # save attantion + att_max_value = att[-1] + att_cpu = np.asarray(att_max_value.cpu().detach()) + att_all.append(att_cpu) + + # obtain 3d joints, which are regressed from the full mesh + pred_3d_joints_from_mesh = mano.get_3d_joints(pred_vertices) + # obtain 2d joints, which are projected from 3d joints of mesh + pred_2d_joints_from_mesh = orthographic_projection(pred_3d_joints_from_mesh.contiguous(), pred_camera.contiguous()) + pred_2d_coarse_vertices_from_mesh = orthographic_projection(pred_vertices_sub.contiguous(), pred_camera.contiguous()) + + + visual_imgs_output = visualize_mesh( renderer, batch_visual_imgs[0], + pred_vertices[0].detach(), + pred_camera.detach()) + # visual_imgs_output = visualize_mesh_and_attention( renderer, batch_visual_imgs[0], + # pred_vertices[0].detach(), + # pred_vertices_sub[0].detach(), + # pred_2d_coarse_vertices_from_mesh[0].detach(), + # pred_2d_joints_from_mesh[0].detach(), + # pred_camera.detach(), + # att[-1][0].detach()) + visual_imgs = visual_imgs_output.transpose(1,2,0) + visual_imgs = np.asarray(visual_imgs) + + temp_fname = image_file[:-4] + '_graphormer_pred.jpg' + print('save to ', temp_fname) + cv2.imwrite(temp_fname, np.asarray(visual_imgs[:,:,::-1]*255)) + return + +def visualize_mesh( renderer, images, + pred_vertices_full, + pred_camera): + img = images.cpu().numpy().transpose(1,2,0) + # Get predict vertices for the particular example + vertices_full = pred_vertices_full.cpu().numpy() + cam = pred_camera.cpu().numpy() + # Visualize only mesh reconstruction + rend_img = visualize_reconstruction_no_text(img, 224, vertices_full, cam, renderer, color='light_blue') + rend_img = rend_img.transpose(2,0,1) + return rend_img + +def visualize_mesh_and_attention( renderer, images, + pred_vertices_full, + pred_vertices, + pred_2d_vertices, + pred_2d_joints, + pred_camera, + attention): + img = images.cpu().numpy().transpose(1,2,0) + # Get predict vertices for the particular example + vertices_full = pred_vertices_full.cpu().numpy() + vertices = pred_vertices.cpu().numpy() + vertices_2d = pred_2d_vertices.cpu().numpy() + joints_2d = pred_2d_joints.cpu().numpy() + cam = pred_camera.cpu().numpy() + att = attention.cpu().numpy() + # Visualize reconstruction and attention + rend_img = visualize_reconstruction_and_att_local(img, 224, vertices_full, vertices, vertices_2d, cam, renderer, joints_2d, att, color='light_blue') + rend_img = rend_img.transpose(2,0,1) + return rend_img + +def parse_args(): + parser = argparse.ArgumentParser() + ######################################################### + # Data related arguments + ######################################################### + parser.add_argument("--num_workers", default=4, type=int, + help="Workers in dataloader.") + parser.add_argument("--img_scale_factor", default=1, type=int, + help="adjust image resolution.") + parser.add_argument("--image_file_or_path", default='./samples/hand', type=str, + help="test data") + ######################################################### + # Loading/saving checkpoints + ######################################################### + parser.add_argument("--model_name_or_path", default='src/modeling/bert/bert-base-uncased/', type=str, required=False, + help="Path to pre-trained transformer model or model type.") + parser.add_argument("--resume_checkpoint", default=None, type=str, required=False, + help="Path to specific checkpoint for resume training.") + parser.add_argument("--output_dir", default='output/', type=str, required=False, + help="The output directory to save checkpoint and test results.") + parser.add_argument("--config_name", default="", type=str, + help="Pretrained config name or path if not the same as model_name.") + parser.add_argument('-a', '--arch', default='hrnet-w64', + help='CNN backbone architecture: hrnet-w64, hrnet, resnet50') + ######################################################### + # Model architectures + ######################################################### + parser.add_argument("--num_hidden_layers", default=4, type=int, required=False, + help="Update model config if given") + parser.add_argument("--hidden_size", default=-1, type=int, required=False, + help="Update model config if given") + parser.add_argument("--num_attention_heads", default=4, type=int, required=False, + help="Update model config if given. Note that the division of " + "hidden_size / num_attention_heads should be in integer.") + parser.add_argument("--intermediate_size", default=-1, type=int, required=False, + help="Update model config if given.") + parser.add_argument("--input_feat_dim", default='2051,512,128', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--hidden_feat_dim", default='1024,256,64', type=str, + help="The Image Feature Dimension.") + parser.add_argument("--which_gcn", default='0,0,1', type=str, + help="which encoder block to have graph conv. Encoder1, Encoder2, Encoder3. Default: only Encoder3 has graph conv") + parser.add_argument("--mesh_type", default='hand', type=str, help="body or hand") + + ######################################################### + # Others + ######################################################### + parser.add_argument("--run_eval_only", default=True, action='store_true',) + parser.add_argument("--device", type=str, default='cuda', + help="cuda or cpu") + parser.add_argument('--seed', type=int, default=88, + help="random seed for initialization.") + args = parser.parse_args() + return args + +def main(args): + global logger + # Setup CUDA, GPU & distributed training + args.num_gpus = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + os.environ['OMP_NUM_THREADS'] = str(args.num_workers) + print('set os.environ[OMP_NUM_THREADS] to {}'.format(os.environ['OMP_NUM_THREADS'])) + + mkdir(args.output_dir) + logger = setup_logger("Graphormer", args.output_dir, get_rank()) + set_seed(args.seed, args.num_gpus) + logger.info("Using {} GPUs".format(args.num_gpus)) + + # Mesh and MANO utils + mano_model = MANO().to(args.device) + mano_model.layer = mano_model.layer.to(device) + mesh_sampler = Mesh() + + # Renderer for visualization + renderer = Renderer(faces=mano_model.face) + + # Load pretrained model + trans_encoder = [] + + input_feat_dim = [int(item) for item in args.input_feat_dim.split(',')] + hidden_feat_dim = [int(item) for item in args.hidden_feat_dim.split(',')] + output_feat_dim = input_feat_dim[1:] + [3] + + # which encoder block to have graph convs + which_blk_graph = [int(item) for item in args.which_gcn.split(',')] + + if args.run_eval_only==True and args.resume_checkpoint!=None and args.resume_checkpoint!='None' and 'state_dict' not in args.resume_checkpoint: + # if only run eval, load checkpoint + logger.info("Evaluation: Loading from checkpoint {}".format(args.resume_checkpoint)) + _model = torch.load(args.resume_checkpoint) + + else: + # init three transformer-encoder blocks in a loop + for i in range(len(output_feat_dim)): + config_class, model_class = BertConfig, Graphormer + config = config_class.from_pretrained(args.config_name if args.config_name \ + else args.model_name_or_path) + + config.output_attentions = False + config.img_feature_dim = input_feat_dim[i] + config.output_feature_dim = output_feat_dim[i] + args.hidden_size = hidden_feat_dim[i] + args.intermediate_size = int(args.hidden_size*2) + + if which_blk_graph[i]==1: + config.graph_conv = True + logger.info("Add Graph Conv") + else: + config.graph_conv = False + + config.mesh_type = args.mesh_type + + # update model structure if specified in arguments + update_params = ['num_hidden_layers', 'hidden_size', 'num_attention_heads', 'intermediate_size'] + for idx, param in enumerate(update_params): + arg_param = getattr(args, param) + config_param = getattr(config, param) + if arg_param > 0 and arg_param != config_param: + logger.info("Update config parameter {}: {} -> {}".format(param, config_param, arg_param)) + setattr(config, param, arg_param) + + # init a transformer encoder and append it to a list + assert config.hidden_size % config.num_attention_heads == 0 + model = model_class(config=config) + logger.info("Init model from scratch.") + trans_encoder.append(model) + + # create backbone model + if args.arch=='hrnet': + hrnet_yaml = 'models/hrnet/cls_hrnet_w40_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w40_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w40 model') + elif args.arch=='hrnet-w64': + hrnet_yaml = 'models/hrnet/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml' + hrnet_checkpoint = 'models/hrnet/hrnetv2_w64_imagenet_pretrained.pth' + hrnet_update_config(hrnet_config, hrnet_yaml) + backbone = get_cls_net_gridfeat(hrnet_config, pretrained=hrnet_checkpoint) + logger.info('=> loading hrnet-v2-w64 model') + else: + print("=> using pre-trained model '{}'".format(args.arch)) + backbone = models.__dict__[args.arch](pretrained=True) + # remove the last fc layer + backbone = torch.nn.Sequential(*list(backbone.children())[:-1]) + + trans_encoder = torch.nn.Sequential(*trans_encoder) + total_params = sum(p.numel() for p in trans_encoder.parameters()) + logger.info('Graphormer encoders total parameters: {}'.format(total_params)) + backbone_total_params = sum(p.numel() for p in backbone.parameters()) + logger.info('Backbone total parameters: {}'.format(backbone_total_params)) + + # build end-to-end Graphormer network (CNN backbone + multi-layer Graphormer encoder) + _model = Graphormer_Network(args, config, backbone, trans_encoder) + + if args.resume_checkpoint!=None and args.resume_checkpoint!='None': + # for fine-tuning or resume training or inference, load weights from checkpoint + logger.info("Loading state dict from checkpoint {}".format(args.resume_checkpoint)) + # workaround approach to load sparse tensor in graph conv. + state_dict = torch.load(args.resume_checkpoint) + _model.load_state_dict(state_dict, strict=False) + del state_dict + gc.collect() + torch.cuda.empty_cache() + + # update configs to enable attention outputs + setattr(_model.trans_encoder[-1].config,'output_attentions', True) + setattr(_model.trans_encoder[-1].config,'output_hidden_states', True) + _model.trans_encoder[-1].bert.encoder.output_attentions = True + _model.trans_encoder[-1].bert.encoder.output_hidden_states = True + for iter_layer in range(4): + _model.trans_encoder[-1].bert.encoder.layer[iter_layer].attention.self.output_attentions = True + for inter_block in range(3): + setattr(_model.trans_encoder[-1].config,'device', args.device) + + _model.to(args.device) + logger.info("Run inference") + + image_list = [] + if not args.image_file_or_path: + raise ValueError("image_file_or_path not specified") + if op.isfile(args.image_file_or_path): + image_list = [args.image_file_or_path] + elif op.isdir(args.image_file_or_path): + # should be a path with images only + for filename in os.listdir(args.image_file_or_path): + if filename.endswith(".png") or filename.endswith(".jpg") and 'pred' not in filename: + image_list.append(args.image_file_or_path+'/'+filename) + else: + raise ValueError("Cannot find images at {}".format(args.image_file_or_path)) + + run_inference(args, image_list, _model, mano_model, renderer, mesh_sampler) + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_hand_multiscale.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_hand_multiscale.py new file mode 100644 index 00000000..d656756b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/tools/run_hand_multiscale.py @@ -0,0 +1,136 @@ +from __future__ import absolute_import, division, print_function + +import argparse +import os +import os.path as op +import code +import json +import zipfile +import torch +import numpy as np +from custom_mesh_graphormer.utils.metric_pampjpe import get_alignMesh + + +def load_pred_json(filepath): + archive = zipfile.ZipFile(filepath, 'r') + jsondata = archive.read('pred.json') + reference = json.loads(jsondata.decode("utf-8")) + return reference[0], reference[1] + + +def multiscale_fusion(output_dir): + s = '10' + filepath = output_dir+'ckpt200-sc10_rot0-pred.zip' + ref_joints, ref_vertices = load_pred_json(filepath) + ref_joints_array = np.asarray(ref_joints) + ref_vertices_array = np.asarray(ref_vertices) + + rotations = [0.0] + for i in range(1,10): + rotations.append(i*10) + rotations.append(i*-10) + + scale = [0.7,0.8,0.9,1.0,1.1] + multiscale_joints = [] + multiscale_vertices = [] + + counter = 0 + for s in scale: + for r in rotations: + setting = 'sc%02d_rot%s'%(int(s*10),str(int(r))) + filepath = output_dir+'ckpt200-'+setting+'-pred.zip' + joints, vertices = load_pred_json(filepath) + joints_array = np.asarray(joints) + vertices_array = np.asarray(vertices) + + pa_joint_error, pa_joint_array, _ = get_alignMesh(joints_array, ref_joints_array, reduction=None) + pa_vertices_error, pa_vertices_array, _ = get_alignMesh(vertices_array, ref_vertices_array, reduction=None) + print('--------------------------') + print('scale:', s, 'rotate', r) + print('PAMPJPE:', 1000*np.mean(pa_joint_error)) + print('PAMPVPE:', 1000*np.mean(pa_vertices_error)) + multiscale_joints.append(pa_joint_array) + multiscale_vertices.append(pa_vertices_array) + counter = counter + 1 + + overall_joints_array = ref_joints_array.copy() + overall_vertices_array = ref_vertices_array.copy() + for i in range(counter): + overall_joints_array += multiscale_joints[i] + overall_vertices_array += multiscale_vertices[i] + + overall_joints_array /= (1+counter) + overall_vertices_array /= (1+counter) + pa_joint_error, pa_joint_array, _ = get_alignMesh(overall_joints_array, ref_joints_array, reduction=None) + pa_vertices_error, pa_vertices_array, _ = get_alignMesh(overall_vertices_array, ref_vertices_array, reduction=None) + print('--------------------------') + print('overall:') + print('PAMPJPE:', 1000*np.mean(pa_joint_error)) + print('PAMPVPE:', 1000*np.mean(pa_vertices_error)) + + joint_output_save = overall_joints_array.tolist() + mesh_output_save = overall_vertices_array.tolist() + + print('save results to pred.json') + with open('pred.json', 'w') as f: + json.dump([joint_output_save, mesh_output_save], f) + + + filepath = output_dir+'ckpt200-multisc-pred.zip' + resolved_submit_cmd = 'zip ' + filepath + ' ' + 'pred.json' + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + resolved_submit_cmd = 'rm pred.json' + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + + +def run_multiscale_inference(model_path, mode, output_dir): + + if mode==True: + rotations = [0.0] + for i in range(1,10): + rotations.append(i*10) + rotations.append(i*-10) + scale = [0.7,0.8,0.9,1.0,1.1] + else: + rotations = [0.0] + scale = [1.0] + + job_cmd = "python ./src/tools/run_gphmer_handmesh.py " \ + "--val_yaml freihand_v3/test.yaml " \ + "--resume_checkpoint %s " \ + "--per_gpu_eval_batch_size 32 --run_eval_only --num_worker 2 " \ + "--multiscale_inference " \ + "--rot %f " \ + "--sc %s " \ + "--arch hrnet-w64 " \ + "--num_hidden_layers 4 " \ + "--num_attention_heads 4 " \ + "--input_feat_dim 2051,512,128 " \ + "--hidden_feat_dim 1024,256,64 " \ + "--output_dir %s" + + for s in scale: + for r in rotations: + resolved_submit_cmd = job_cmd%(model_path, r, s, output_dir) + print(resolved_submit_cmd) + os.system(resolved_submit_cmd) + +def main(args): + model_path = args.model_path + mode = args.multiscale_inference + output_dir = args.output_dir + run_multiscale_inference(model_path, mode, output_dir) + if mode==True: + multiscale_fusion(output_dir) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Evaluate a checkpoint in the folder") + parser.add_argument("--model_path") + parser.add_argument("--multiscale_inference", default=False, action='store_true',) + parser.add_argument("--output_dir", default='output/', type=str, required=False, + help="The output directory to save checkpoint and test results.") + args = parser.parse_args() + main(args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/comm.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/comm.py new file mode 100644 index 00000000..ae053506 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/comm.py @@ -0,0 +1,176 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import pickle +import time + +import torch +import torch.distributed as dist + +from comfy.model_management import get_torch_device +device = get_torch_device() + + +def get_world_size(): + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + dist.barrier() + + +def gather_on_master(data): + """Same as all_gather, but gathers data on master process only, using CPU. + Thus, this does not work with NCCL backend unless they add CPU support. + + The memory consumption of this function is ~ 3x of data size. While in + principal, it should be ~2x, it's not easy to force Python to release + memory immediately and thus, peak memory usage could be up to 3x. + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + # trying to optimize memory, but in fact, it's not guaranteed to be released + del data + storage = torch.ByteStorage.from_buffer(buffer) + del buffer + tensor = torch.ByteTensor(storage) + + # obtain Tensor size of each rank + local_size = torch.LongTensor([tensor.numel()]) + size_list = [torch.LongTensor([0]) for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + if local_size != max_size: + padding = torch.ByteTensor(size=(max_size - local_size,)) + tensor = torch.cat((tensor, padding), dim=0) + del padding + + if is_main_process(): + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.ByteTensor(size=(max_size,))) + dist.gather(tensor, gather_list=tensor_list, dst=0) + del tensor + else: + dist.gather(tensor, gather_list=[], dst=0) + del tensor + return + + data_list = [] + for tensor in tensor_list: + buffer = tensor.cpu().numpy().tobytes() + del tensor + data_list.append(pickle.loads(buffer)) + del buffer + + return data_list + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to(device) + + # obtain Tensor size of each rank + local_size = torch.LongTensor([tensor.numel()]).to(device) + size_list = [torch.LongTensor([0]).to(device) for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.ByteTensor(size=(max_size,)).to(device)) + if local_size != max_size: + padding = torch.ByteTensor(size=(max_size - local_size,)).to(device) + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that process with rank + 0 has the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/dataset_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/dataset_utils.py new file mode 100644 index 00000000..cb66451e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/dataset_utils.py @@ -0,0 +1,66 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +""" + + +import os +import os.path as op +import numpy as np +import base64 +import cv2 +import yaml +from collections import OrderedDict + + +def img_from_base64(imagestring): + try: + jpgbytestring = base64.b64decode(imagestring) + nparr = np.frombuffer(jpgbytestring, np.uint8) + r = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + return r + except: + return None + + +def load_labelmap(labelmap_file): + label_dict = None + if labelmap_file is not None and op.isfile(labelmap_file): + label_dict = OrderedDict() + with open(labelmap_file, 'r') as fp: + for line in fp: + label = line.strip().split('\t')[0] + if label in label_dict: + raise ValueError("Duplicate label " + label + " in labelmap.") + else: + label_dict[label] = len(label_dict) + return label_dict + + +def load_shuffle_file(shuf_file): + shuf_list = None + if shuf_file is not None: + with open(shuf_file, 'r') as fp: + shuf_list = [] + for i in fp: + shuf_list.append(int(i.strip())) + return shuf_list + + +def load_box_shuffle_file(shuf_file): + if shuf_file is not None: + with open(shuf_file, 'r') as fp: + img_shuf_list = [] + box_shuf_list = [] + for i in fp: + idx = [int(_) for _ in i.strip().split('\t')] + img_shuf_list.append(idx[0]) + box_shuf_list.append(idx[1]) + return [img_shuf_list, box_shuf_list] + return None + + +def load_from_yaml_file(file_name): + with open(file_name, 'r') as fp: + return yaml.load(fp, Loader=yaml.CLoader) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/geometric_layers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/geometric_layers.py new file mode 100644 index 00000000..ad4bf9d1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/geometric_layers.py @@ -0,0 +1,58 @@ +""" +Useful geometric operations, e.g. Orthographic projection and a differentiable Rodrigues formula + +Parts of the code are taken from https://github.com/MandyMo/pytorch_HMR +""" +import torch + +def rodrigues(theta): + """Convert axis-angle representation to rotation matrix. + Args: + theta: size = [B, 3] + Returns: + Rotation matrix corresponding to the quaternion -- size = [B, 3, 3] + """ + l1norm = torch.norm(theta + 1e-8, p = 2, dim = 1) + angle = torch.unsqueeze(l1norm, -1) + normalized = torch.div(theta, angle) + angle = angle * 0.5 + v_cos = torch.cos(angle) + v_sin = torch.sin(angle) + quat = torch.cat([v_cos, v_sin * normalized], dim = 1) + return quat2mat(quat) + +def quat2mat(quat): + """Convert quaternion coefficients to rotation matrix. + Args: + quat: size = [B, 4] 4 <===>(w, x, y, z) + Returns: + Rotation matrix corresponding to the quaternion -- size = [B, 3, 3] + """ + norm_quat = quat + norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True) + w, x, y, z = norm_quat[:,0], norm_quat[:,1], norm_quat[:,2], norm_quat[:,3] + + B = quat.size(0) + + w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) + wx, wy, wz = w*x, w*y, w*z + xy, xz, yz = x*y, x*z, y*z + + rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz, + 2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx, + 2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).view(B, 3, 3) + return rotMat + +def orthographic_projection(X, camera): + """Perform orthographic projection of 3D points X using the camera parameters + Args: + X: size = [B, N, 3] + camera: size = [B, 3] + Returns: + Projected 2D points -- size = [B, N, 2] + """ + camera = camera.view(-1, 1, 3) + X_trans = X[:, :, :2] + camera[:, :, 1:] + shape = X_trans.shape + X_2d = (camera[:, :, 0] * X_trans.view(shape[0], -1)).view(shape) + return X_2d diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/image_ops.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/image_ops.py new file mode 100644 index 00000000..eb62d8fa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/image_ops.py @@ -0,0 +1,208 @@ +""" +Image processing tools + +Modified from open source projects: +(https://github.com/nkolot/GraphCMR/) +(https://github.com/open-mmlab/mmdetection) + +""" + +import numpy as np +import base64 +import cv2 +import torch +import scipy.misc + +def img_from_base64(imagestring): + try: + jpgbytestring = base64.b64decode(imagestring) + nparr = np.frombuffer(jpgbytestring, np.uint8) + r = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + return r + except ValueError: + return None + +def myimrotate(img, angle, center=None, scale=1.0, border_value=0, auto_bound=False): + if center is not None and auto_bound: + raise ValueError('`auto_bound` conflicts with `center`') + h, w = img.shape[:2] + if center is None: + center = ((w - 1) * 0.5, (h - 1) * 0.5) + assert isinstance(center, tuple) + + matrix = cv2.getRotationMatrix2D(center, angle, scale) + if auto_bound: + cos = np.abs(matrix[0, 0]) + sin = np.abs(matrix[0, 1]) + new_w = h * sin + w * cos + new_h = h * cos + w * sin + matrix[0, 2] += (new_w - w) * 0.5 + matrix[1, 2] += (new_h - h) * 0.5 + w = int(np.round(new_w)) + h = int(np.round(new_h)) + rotated = cv2.warpAffine(img, matrix, (w, h), borderValue=border_value) + return rotated + +def myimresize(img, size, return_scale=False, interpolation='bilinear'): + + h, w = img.shape[:2] + resized_img = cv2.resize( + img, (size[0],size[1]), interpolation=cv2.INTER_LINEAR) + if not return_scale: + return resized_img + else: + w_scale = size[0] / w + h_scale = size[1] / h + return resized_img, w_scale, h_scale + + +def get_transform(center, scale, res, rot=0): + """Generate transformation matrix.""" + h = 200 * scale + t = np.zeros((3, 3)) + t[0, 0] = float(res[1]) / h + t[1, 1] = float(res[0]) / h + t[0, 2] = res[1] * (-float(center[0]) / h + .5) + t[1, 2] = res[0] * (-float(center[1]) / h + .5) + t[2, 2] = 1 + if not rot == 0: + rot = -rot # To match direction of rotation from cropping + rot_mat = np.zeros((3,3)) + rot_rad = rot * np.pi / 180 + sn,cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0,:2] = [cs, -sn] + rot_mat[1,:2] = [sn, cs] + rot_mat[2,2] = 1 + # Need to rotate around center + t_mat = np.eye(3) + t_mat[0,2] = -res[1]/2 + t_mat[1,2] = -res[0]/2 + t_inv = t_mat.copy() + t_inv[:2,2] *= -1 + t = np.dot(t_inv,np.dot(rot_mat,np.dot(t_mat,t))) + return t + +def transform(pt, center, scale, res, invert=0, rot=0): + """Transform pixel location to different reference.""" + t = get_transform(center, scale, res, rot=rot) + if invert: + # t = np.linalg.inv(t) + t_torch = torch.from_numpy(t) + t_torch = torch.inverse(t_torch) + t = t_torch.numpy() + new_pt = np.array([pt[0]-1, pt[1]-1, 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2].astype(int)+1 + +def crop(img, center, scale, res, rot=0): + """Crop image according to the supplied bounding box.""" + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1))-1 + # Bottom right point + br = np.array(transform([res[0]+1, + res[1]+1], center, scale, res, invert=1))-1 + # Padding so that when rotated proper amount of context is included + pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) + if not rot == 0: + ul -= pad + br += pad + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_img = np.zeros(new_shape) + + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] + new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(len(img[0]), br[0]) + old_y = max(0, ul[1]), min(len(img), br[1]) + + new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], + old_x[0]:old_x[1]] + if not rot == 0: + # Remove padding + # new_img = scipy.misc.imrotate(new_img, rot) + new_img = myimrotate(new_img, rot) + new_img = new_img[pad:-pad, pad:-pad] + + # new_img = scipy.misc.imresize(new_img, res) + new_img = myimresize(new_img, [res[0], res[1]]) + return new_img + +def uncrop(img, center, scale, orig_shape, rot=0, is_rgb=True): + """'Undo' the image cropping/resizing. + This function is used when evaluating mask/part segmentation. + """ + res = img.shape[:2] + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1))-1 + # Bottom right point + br = np.array(transform([res[0]+1,res[1]+1], center, scale, res, invert=1))-1 + # size of cropped image + crop_shape = [br[1] - ul[1], br[0] - ul[0]] + + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_img = np.zeros(orig_shape, dtype=np.uint8) + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], orig_shape[1]) - ul[0] + new_y = max(0, -ul[1]), min(br[1], orig_shape[0]) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(orig_shape[1], br[0]) + old_y = max(0, ul[1]), min(orig_shape[0], br[1]) + # img = scipy.misc.imresize(img, crop_shape, interp='nearest') + img = myimresize(img, [crop_shape[0],crop_shape[1]]) + new_img[old_y[0]:old_y[1], old_x[0]:old_x[1]] = img[new_y[0]:new_y[1], new_x[0]:new_x[1]] + return new_img + +def rot_aa(aa, rot): + """Rotate axis angle parameters.""" + # pose parameters + R = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0], + [np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0], + [0, 0, 1]]) + # find the rotation of the body in camera frame + per_rdg, _ = cv2.Rodrigues(aa) + # apply the global rotation to the global orientation + resrot, _ = cv2.Rodrigues(np.dot(R,per_rdg)) + aa = (resrot.T)[0] + return aa + +def flip_img(img): + """Flip rgb images or masks. + channels come last, e.g. (256,256,3). + """ + img = np.fliplr(img) + return img + +def flip_kp(kp): + """Flip keypoints.""" + flipped_parts = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13, 14, 15, 16, 17, 18, 19, 21, 20, 23, 22] + kp = kp[flipped_parts] + kp[:,0] = - kp[:,0] + return kp + +def flip_pose(pose): + """Flip pose. + The flipping is based on SMPL parameters. + """ + flippedParts = [0, 1, 2, 6, 7, 8, 3, 4, 5, 9, 10, 11, 15, 16, 17, 12, 13, + 14 ,18, 19, 20, 24, 25, 26, 21, 22, 23, 27, 28, 29, 33, + 34, 35, 30, 31, 32, 36, 37, 38, 42, 43, 44, 39, 40, 41, + 45, 46, 47, 51, 52, 53, 48, 49, 50, 57, 58, 59, 54, 55, + 56, 63, 64, 65, 60, 61, 62, 69, 70, 71, 66, 67, 68] + pose = pose[flippedParts] + # we also negate the second and the third dimension of the axis-angle + pose[1::3] = -pose[1::3] + pose[2::3] = -pose[2::3] + return pose + +def flip_aa(aa): + """Flip axis-angle representation. + We negate the second and the third dimension of the axis-angle. + """ + aa[1] = -aa[1] + aa[2] = -aa[2] + return aa \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/logger.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/logger.py new file mode 100644 index 00000000..01313960 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/logger.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import logging +import os +import sys +from logging import StreamHandler, Handler, getLevelName + + +# this class is a copy of logging.FileHandler except we end self.close() +# at the end of each emit. While closing file and reopening file after each +# write is not efficient, it allows us to see partial logs when writing to +# fused Azure blobs, which is very convenient +class FileHandler(StreamHandler): + """ + A handler class which writes formatted logging records to disk files. + """ + def __init__(self, filename, mode='a', encoding=None, delay=False): + """ + Open the specified file and use it as the stream for logging. + """ + # Issue #27493: add support for Path objects to be passed in + filename = os.fspath(filename) + #keep the absolute path, otherwise derived classes which use this + #may come a cropper when the current directory changes + self.baseFilename = os.path.abspath(filename) + self.mode = mode + self.encoding = encoding + self.delay = delay + if delay: + #We don't open the stream, but we still need to call the + #Handler constructor to set level, formatter, lock etc. + Handler.__init__(self) + self.stream = None + else: + StreamHandler.__init__(self, self._open()) + + def close(self): + """ + Closes the stream. + """ + self.acquire() + try: + try: + if self.stream: + try: + self.flush() + finally: + stream = self.stream + self.stream = None + if hasattr(stream, "close"): + stream.close() + finally: + # Issue #19523: call unconditionally to + # prevent a handler leak when delay is set + StreamHandler.close(self) + finally: + self.release() + + def _open(self): + """ + Open the current base file with the (original) mode and encoding. + Return the resulting stream. + """ + return open(self.baseFilename, self.mode, encoding=self.encoding) + + def emit(self, record): + """ + Emit a record. + + If the stream was not opened because 'delay' was specified in the + constructor, open it before calling the superclass's emit. + """ + if self.stream is None: + self.stream = self._open() + StreamHandler.emit(self, record) + self.close() + + def __repr__(self): + level = getLevelName(self.level) + return '<%s %s (%s)>' % (self.__class__.__name__, self.baseFilename, level) + + +def setup_logger(name, save_dir, distributed_rank, filename="log.txt"): + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + # don't log results for the non-master process + if distributed_rank > 0: + return logger + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s: %(message)s") + ch.setFormatter(formatter) + logger.addHandler(ch) + + if save_dir: + fh = FileHandler(os.path.join(save_dir, filename)) + fh.setLevel(logging.DEBUG) + fh.setFormatter(formatter) + logger.addHandler(fh) + + return logger diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/metric_logger.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/metric_logger.py new file mode 100644 index 00000000..ddaa0ab3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/metric_logger.py @@ -0,0 +1,45 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +Basic logger. It Computes and stores the average and current value +""" + +class AverageMeter(object): + + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + + +class EvalMetricsLogger(object): + + def __init__(self): + self.reset() + + def reset(self): + # define a upper-bound performance (worst case) + # numbers are in unit millimeter + self.PAmPJPE = 100.0/1000.0 + self.mPJPE = 100.0/1000.0 + self.mPVE = 100.0/1000.0 + + self.epoch = 0 + + def update(self, mPVE, mPJPE, PAmPJPE, epoch): + self.PAmPJPE = PAmPJPE + self.mPJPE = mPJPE + self.mPVE = mPVE + self.epoch = epoch diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/metric_pampjpe.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/metric_pampjpe.py new file mode 100644 index 00000000..89fe55b7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/metric_pampjpe.py @@ -0,0 +1,99 @@ +""" +Functions for compuing Procrustes alignment and reconstruction error + +Parts of the code are adapted from https://github.com/akanazawa/hmr + +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import numpy as np + +def compute_similarity_transform(S1, S2): + """Computes a similarity transform (sR, t) that takes + a set of 3D points S1 (3 x N) closest to a set of 3D points S2, + where R is an 3x3 rotation matrix, t 3x1 translation, s scale. + i.e. solves the orthogonal Procrutes problem. + """ + transposed = False + if S1.shape[0] != 3 and S1.shape[0] != 2: + S1 = S1.T + S2 = S2.T + transposed = True + assert(S2.shape[1] == S1.shape[1]) + + # 1. Remove mean. + mu1 = S1.mean(axis=1, keepdims=True) + mu2 = S2.mean(axis=1, keepdims=True) + X1 = S1 - mu1 + X2 = S2 - mu2 + + # 2. Compute variance of X1 used for scale. + var1 = np.sum(X1**2) + + # 3. The outer product of X1 and X2. + K = X1.dot(X2.T) + + # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are + # singular vectors of K. + U, s, Vh = np.linalg.svd(K) + V = Vh.T + # Construct Z that fixes the orientation of R to get det(R)=1. + Z = np.eye(U.shape[0]) + Z[-1, -1] *= np.sign(np.linalg.det(U.dot(V.T))) + # Construct R. + R = V.dot(Z.dot(U.T)) + + # 5. Recover scale. + scale = np.trace(R.dot(K)) / var1 + + # 6. Recover translation. + t = mu2 - scale*(R.dot(mu1)) + + # 7. Error: + S1_hat = scale*R.dot(S1) + t + + if transposed: + S1_hat = S1_hat.T + + return S1_hat + +def compute_similarity_transform_batch(S1, S2): + """Batched version of compute_similarity_transform.""" + S1_hat = np.zeros_like(S1) + for i in range(S1.shape[0]): + S1_hat[i] = compute_similarity_transform(S1[i], S2[i]) + return S1_hat + +def reconstruction_error(S1, S2, reduction='mean'): + """Do Procrustes alignment and compute reconstruction error.""" + S1_hat = compute_similarity_transform_batch(S1, S2) + re = np.sqrt( ((S1_hat - S2)** 2).sum(axis=-1)).mean(axis=-1) + if reduction == 'mean': + re = re.mean() + elif reduction == 'sum': + re = re.sum() + return re + + +def reconstruction_error_v2(S1, S2, J24_TO_J14, reduction='mean'): + """Do Procrustes alignment and compute reconstruction error.""" + S1_hat = compute_similarity_transform_batch(S1, S2) + S1_hat = S1_hat[:,J24_TO_J14,:] + S2 = S2[:,J24_TO_J14,:] + re = np.sqrt( ((S1_hat - S2)** 2).sum(axis=-1)).mean(axis=-1) + if reduction == 'mean': + re = re.mean() + elif reduction == 'sum': + re = re.sum() + return re + +def get_alignMesh(S1, S2, reduction='mean'): + """Do Procrustes alignment and compute reconstruction error.""" + S1_hat = compute_similarity_transform_batch(S1, S2) + re = np.sqrt( ((S1_hat - S2)** 2).sum(axis=-1)).mean(axis=-1) + if reduction == 'mean': + re = re.mean() + elif reduction == 'sum': + re = re.sum() + return re, S1_hat, S2 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/miscellaneous.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/miscellaneous.py new file mode 100644 index 00000000..3de72c69 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/miscellaneous.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import errno +import os +import os.path as op +import re +import logging +import numpy as np +import torch +import random +import shutil +from .comm import is_main_process +import yaml + + +def mkdir(path): + # if it is the current folder, skip. + # otherwise the original code will raise FileNotFoundError + if path == '': + return + try: + os.makedirs(path) + except OSError as e: + if e.errno != errno.EEXIST: + raise + + +def save_config(cfg, path): + if is_main_process(): + with open(path, 'w') as f: + f.write(cfg.dump()) + + +def config_iteration(output_dir, max_iter): + save_file = os.path.join(output_dir, 'last_checkpoint') + iteration = -1 + if os.path.exists(save_file): + with open(save_file, 'r') as f: + fname = f.read().strip() + model_name = os.path.basename(fname) + model_path = os.path.dirname(fname) + if model_name.startswith('model_') and len(model_name) == 17: + iteration = int(model_name[-11:-4]) + elif model_name == "model_final": + iteration = max_iter + elif model_path.startswith('checkpoint-') and len(model_path) == 18: + iteration = int(model_path.split('-')[-1]) + return iteration + + +def get_matching_parameters(model, regexp, none_on_empty=True): + """Returns parameters matching regular expression""" + if not regexp: + if none_on_empty: + return {} + else: + return dict(model.named_parameters()) + compiled_pattern = re.compile(regexp) + params = {} + for weight_name, weight in model.named_parameters(): + if compiled_pattern.match(weight_name): + params[weight_name] = weight + return params + + +def freeze_weights(model, regexp): + """Freeze weights based on regular expression.""" + logger = logging.getLogger("maskrcnn_benchmark.trainer") + for weight_name, weight in get_matching_parameters(model, regexp).items(): + weight.requires_grad = False + logger.info("Disabled training of {}".format(weight_name)) + + +def unfreeze_weights(model, regexp, backbone_freeze_at=-1, + is_distributed=False): + """Unfreeze weights based on regular expression. + This is helpful during training to unfreeze freezed weights after + other unfreezed weights have been trained for some iterations. + """ + logger = logging.getLogger("maskrcnn_benchmark.trainer") + for weight_name, weight in get_matching_parameters(model, regexp).items(): + weight.requires_grad = True + logger.info("Enabled training of {}".format(weight_name)) + if backbone_freeze_at >= 0: + logger.info("Freeze backbone at stage: {}".format(backbone_freeze_at)) + if is_distributed: + model.module.backbone.body._freeze_backbone(backbone_freeze_at) + else: + model.backbone.body._freeze_backbone(backbone_freeze_at) + + +def delete_tsv_files(tsvs): + for t in tsvs: + if op.isfile(t): + try_delete(t) + line = op.splitext(t)[0] + '.lineidx' + if op.isfile(line): + try_delete(line) + + +def concat_files(ins, out): + mkdir(op.dirname(out)) + out_tmp = out + '.tmp' + with open(out_tmp, 'wb') as fp_out: + for i, f in enumerate(ins): + logging.info('concating {}/{} - {}'.format(i, len(ins), f)) + with open(f, 'rb') as fp_in: + shutil.copyfileobj(fp_in, fp_out, 1024*1024*10) + os.rename(out_tmp, out) + + +def concat_tsv_files(tsvs, out_tsv): + concat_files(tsvs, out_tsv) + sizes = [os.stat(t).st_size for t in tsvs] + sizes = np.cumsum(sizes) + all_idx = [] + for i, t in enumerate(tsvs): + for idx in load_list_file(op.splitext(t)[0] + '.lineidx'): + if i == 0: + all_idx.append(idx) + else: + all_idx.append(str(int(idx) + sizes[i - 1])) + with open(op.splitext(out_tsv)[0] + '.lineidx', 'w') as f: + f.write('\n'.join(all_idx)) + + +def load_list_file(fname): + with open(fname, 'r') as fp: + lines = fp.readlines() + result = [line.strip() for line in lines] + if len(result) > 0 and result[-1] == '': + result = result[:-1] + return result + + +def try_once(func): + def func_wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + logging.info('ignore error \n{}'.format(str(e))) + return func_wrapper + + +@try_once +def try_delete(f): + os.remove(f) + + +def set_seed(seed, n_gpu): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if n_gpu > 0: + torch.cuda.manual_seed_all(seed) + + +def print_and_run_cmd(cmd): + print(cmd) + os.system(cmd) + + +def write_to_yaml_file(context, file_name): + with open(file_name, 'w') as fp: + yaml.dump(context, fp, encoding='utf-8') + + +def load_from_yaml_file(yaml_file): + with open(yaml_file, 'r') as fp: + return yaml.load(fp, Loader=yaml.CLoader) + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/renderer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/renderer.py new file mode 100644 index 00000000..3b2f8a96 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/renderer.py @@ -0,0 +1,691 @@ +""" +Rendering tools for 3D mesh visualization on 2D image. + +Parts of the code are taken from https://github.com/akanazawa/hmr +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 +import code +from opendr.camera import ProjectPoints +from opendr.renderer import ColoredRenderer, TexturedRenderer +from opendr.lighting import LambertianPointLight +import random + + +# Rotate the points by a specified angle. +def rotateY(points, angle): + ry = np.array([ + [np.cos(angle), 0., np.sin(angle)], [0., 1., 0.], + [-np.sin(angle), 0., np.cos(angle)] + ]) + return np.dot(points, ry) + +def draw_skeleton(input_image, joints, draw_edges=True, vis=None, radius=None): + """ + joints is 3 x 19. but if not will transpose it. + 0: Right ankle + 1: Right knee + 2: Right hip + 3: Left hip + 4: Left knee + 5: Left ankle + 6: Right wrist + 7: Right elbow + 8: Right shoulder + 9: Left shoulder + 10: Left elbow + 11: Left wrist + 12: Neck + 13: Head top + 14: nose + 15: left_eye + 16: right_eye + 17: left_ear + 18: right_ear + """ + + if radius is None: + radius = max(4, (np.mean(input_image.shape[:2]) * 0.01).astype(int)) + + colors = { + 'pink': (197, 27, 125), # L lower leg + 'light_pink': (233, 163, 201), # L upper leg + 'light_green': (161, 215, 106), # L lower arm + 'green': (77, 146, 33), # L upper arm + 'red': (215, 48, 39), # head + 'light_red': (252, 146, 114), # head + 'light_orange': (252, 141, 89), # chest + 'purple': (118, 42, 131), # R lower leg + 'light_purple': (175, 141, 195), # R upper + 'light_blue': (145, 191, 219), # R lower arm + 'blue': (69, 117, 180), # R upper arm + 'gray': (130, 130, 130), # + 'white': (255, 255, 255), # + } + + image = input_image.copy() + input_is_float = False + + if np.issubdtype(image.dtype, np.float): + input_is_float = True + max_val = image.max() + if max_val <= 2.: # should be 1 but sometimes it's slightly above 1 + image = (image * 255).astype(np.uint8) + else: + image = (image).astype(np.uint8) + + if joints.shape[0] != 2: + joints = joints.T + joints = np.round(joints).astype(int) + + jcolors = [ + 'light_pink', 'light_pink', 'light_pink', 'pink', 'pink', 'pink', + 'light_blue', 'light_blue', 'light_blue', 'blue', 'blue', 'blue', + 'purple', 'purple', 'red', 'green', 'green', 'white', 'white', + 'purple', 'purple', 'red', 'green', 'green', 'white', 'white' + ] + + if joints.shape[1] == 19: + # parent indices -1 means no parents + parents = np.array([ + 1, 2, 8, 9, 3, 4, 7, 8, 12, 12, 9, 10, 14, -1, 13, -1, -1, 15, 16 + ]) + # Left is light and right is dark + ecolors = { + 0: 'light_pink', + 1: 'light_pink', + 2: 'light_pink', + 3: 'pink', + 4: 'pink', + 5: 'pink', + 6: 'light_blue', + 7: 'light_blue', + 8: 'light_blue', + 9: 'blue', + 10: 'blue', + 11: 'blue', + 12: 'purple', + 17: 'light_green', + 18: 'light_green', + 14: 'purple' + } + elif joints.shape[1] == 14: + parents = np.array([ + 1, + 2, + 8, + 9, + 3, + 4, + 7, + 8, + -1, + -1, + 9, + 10, + 13, + -1, + ]) + ecolors = { + 0: 'light_pink', + 1: 'light_pink', + 2: 'light_pink', + 3: 'pink', + 4: 'pink', + 5: 'pink', + 6: 'light_blue', + 7: 'light_blue', + 10: 'light_blue', + 11: 'blue', + 12: 'purple' + } + elif joints.shape[1] == 21: # hand + parents = np.array([ + -1, + 0, + 1, + 2, + 3, + 0, + 5, + 6, + 7, + 0, + 9, + 10, + 11, + 0, + 13, + 14, + 15, + 0, + 17, + 18, + 19, + ]) + ecolors = { + 0: 'light_purple', + 1: 'light_green', + 2: 'light_green', + 3: 'light_green', + 4: 'light_green', + 5: 'pink', + 6: 'pink', + 7: 'pink', + 8: 'pink', + 9: 'light_blue', + 10: 'light_blue', + 11: 'light_blue', + 12: 'light_blue', + 13: 'light_red', + 14: 'light_red', + 15: 'light_red', + 16: 'light_red', + 17: 'purple', + 18: 'purple', + 19: 'purple', + 20: 'purple', + } + else: + print('Unknown skeleton!!') + + for child in range(len(parents)): + point = joints[:, child] + # If invisible skip + if vis is not None and vis[child] == 0: + continue + if draw_edges: + cv2.circle(image, (point[0], point[1]), radius, colors['white'], + -1) + cv2.circle(image, (point[0], point[1]), radius - 1, + colors[jcolors[child]], -1) + else: + # cv2.circle(image, (point[0], point[1]), 5, colors['white'], 1) + cv2.circle(image, (point[0], point[1]), radius - 1, + colors[jcolors[child]], 1) + # cv2.circle(image, (point[0], point[1]), 5, colors['gray'], -1) + pa_id = parents[child] + if draw_edges and pa_id >= 0: + if vis is not None and vis[pa_id] == 0: + continue + point_pa = joints[:, pa_id] + cv2.circle(image, (point_pa[0], point_pa[1]), radius - 1, + colors[jcolors[pa_id]], -1) + if child not in ecolors.keys(): + print('bad') + import ipdb + ipdb.set_trace() + cv2.line(image, (point[0], point[1]), (point_pa[0], point_pa[1]), + colors[ecolors[child]], radius - 2) + + # Convert back in original dtype + if input_is_float: + if max_val <= 1.: + image = image.astype(np.float32) / 255. + else: + image = image.astype(np.float32) + + return image + +def draw_text(input_image, content): + """ + content is a dict. draws key: val on image + Assumes key is str, val is float + """ + image = input_image.copy() + input_is_float = False + if np.issubdtype(image.dtype, np.float): + input_is_float = True + image = (image * 255).astype(np.uint8) + + black = (255, 255, 0) + margin = 15 + start_x = 5 + start_y = margin + for key in sorted(content.keys()): + text = "%s: %.2g" % (key, content[key]) + cv2.putText(image, text, (start_x, start_y), 0, 0.45, black) + start_y += margin + + if input_is_float: + image = image.astype(np.float32) / 255. + return image + +def visualize_reconstruction(img, img_size, gt_kp, vertices, pred_kp, camera, renderer, color='pink', focal_length=1000): + """Overlays gt_kp and pred_kp on img. + Draws vert with text. + Renderer is an instance of SMPLRenderer. + """ + gt_vis = gt_kp[:, 2].astype(bool) + loss = np.sum((gt_kp[gt_vis, :2] - pred_kp[gt_vis])**2) + debug_text = {"sc": camera[0], "tx": camera[1], "ty": camera[2], "kpl": loss} + # Fix a flength so i can render this with persp correct scale + res = img.shape[1] + camera_t = np.array([camera[1], camera[2], 2*focal_length/(res * camera[0] +1e-9)]) + rend_img = renderer.render(vertices, camera_t=camera_t, + img=img, use_bg=True, + focal_length=focal_length, + body_color=color) + rend_img = draw_text(rend_img, debug_text) + + # Draw skeleton + gt_joint = ((gt_kp[:, :2] + 1) * 0.5) * img_size + pred_joint = ((pred_kp + 1) * 0.5) * img_size + img_with_gt = draw_skeleton( img, gt_joint, draw_edges=False, vis=gt_vis) + skel_img = draw_skeleton(img_with_gt, pred_joint) + + combined = np.hstack([skel_img, rend_img]) + + return combined + +def visualize_reconstruction_test(img, img_size, gt_kp, vertices, pred_kp, camera, renderer, score, color='pink', focal_length=1000): + """Overlays gt_kp and pred_kp on img. + Draws vert with text. + Renderer is an instance of SMPLRenderer. + """ + gt_vis = gt_kp[:, 2].astype(bool) + loss = np.sum((gt_kp[gt_vis, :2] - pred_kp[gt_vis])**2) + debug_text = {"sc": camera[0], "tx": camera[1], "ty": camera[2], "kpl": loss, "pa-mpjpe": score*1000} + # Fix a flength so i can render this with persp correct scale + res = img.shape[1] + camera_t = np.array([camera[1], camera[2], 2*focal_length/(res * camera[0] +1e-9)]) + rend_img = renderer.render(vertices, camera_t=camera_t, + img=img, use_bg=True, + focal_length=focal_length, + body_color=color) + rend_img = draw_text(rend_img, debug_text) + + # Draw skeleton + gt_joint = ((gt_kp[:, :2] + 1) * 0.5) * img_size + pred_joint = ((pred_kp + 1) * 0.5) * img_size + img_with_gt = draw_skeleton( img, gt_joint, draw_edges=False, vis=gt_vis) + skel_img = draw_skeleton(img_with_gt, pred_joint) + + combined = np.hstack([skel_img, rend_img]) + + return combined + + + +def visualize_reconstruction_and_att(img, img_size, vertices_full, vertices, vertices_2d, camera, renderer, ref_points, attention, focal_length=1000): + """Overlays gt_kp and pred_kp on img. + Draws vert with text. + Renderer is an instance of SMPLRenderer. + """ + # Fix a flength so i can render this with persp correct scale + res = img.shape[1] + camera_t = np.array([camera[1], camera[2], 2*focal_length/(res * camera[0] +1e-9)]) + rend_img = renderer.render(vertices_full, camera_t=camera_t, + img=img, use_bg=True, + focal_length=focal_length, body_color='light_blue') + + + heads_num, vertex_num, _ = attention.shape + + all_head = np.zeros((vertex_num,vertex_num)) + + ###### find max + # for i in range(vertex_num): + # for j in range(vertex_num): + # all_head[i,j] = np.max(attention[:,i,j]) + + ##### find avg + for h in range(4): + att_per_img = attention[h] + all_head = all_head + att_per_img + all_head = all_head/4 + + col_sums = all_head.sum(axis=0) + all_head = all_head / col_sums[np.newaxis, :] + + + # code.interact(local=locals()) + + combined = [] + if vertex_num>400: # body + selected_joints = [6,7,4,5,13] # [6,7,4,5,13,12] + else: # hand + selected_joints = [0, 4, 8, 12, 16, 20] + # Draw attention + for ii in range(len(selected_joints)): + reference_id = selected_joints[ii] + ref_point = ref_points[reference_id] + attention_to_show = all_head[reference_id][14::] + min_v = np.min(attention_to_show) + max_v = np.max(attention_to_show) + norm_attention_to_show = (attention_to_show - min_v)/(max_v-min_v) + + vertices_norm = ((vertices_2d + 1) * 0.5) * img_size + ref_norm = ((ref_point + 1) * 0.5) * img_size + image = np.zeros_like(rend_img) + + for jj in range(vertices_norm.shape[0]): + x = int(vertices_norm[jj,0]) + y = int(vertices_norm[jj,1]) + cv2.circle(image,(x,y), 1, (255,255,255), -1) + + total_to_draw = [] + for jj in range(vertices_norm.shape[0]): + thres = 0.0 + if norm_attention_to_show[jj]>thres: + things = [norm_attention_to_show[jj], ref_norm, vertices_norm[jj]] + total_to_draw.append(things) + # plot_one_line(ref_norm, vertices_norm[jj], image, reference_id, alpha=0.4*(norm_attention_to_show[jj]-thres)/(1-thres) ) + total_to_draw.sort() + max_att_score = total_to_draw[-1][0] + for item in total_to_draw: + attention_score = item[0] + ref_point = item[1] + vertex = item[2] + plot_one_line(ref_point, vertex, image, ii, alpha=(attention_score-thres)/(max_att_score-thres) ) + # code.interact(local=locals()) + if len(combined)==0: + combined = image + else: + combined = np.hstack([combined, image]) + + final = np.hstack([img, combined, rend_img]) + + return final + + +def visualize_reconstruction_and_att_local(img, img_size, vertices_full, vertices, vertices_2d, camera, renderer, ref_points, attention, color='light_blue', focal_length=1000): + """Overlays gt_kp and pred_kp on img. + Draws vert with text. + Renderer is an instance of SMPLRenderer. + """ + # Fix a flength so i can render this with persp correct scale + res = img.shape[1] + camera_t = np.array([camera[1], camera[2], 2*focal_length/(res * camera[0] +1e-9)]) + rend_img = renderer.render(vertices_full, camera_t=camera_t, + img=img, use_bg=True, + focal_length=focal_length, body_color=color) + heads_num, vertex_num, _ = attention.shape + all_head = np.zeros((vertex_num,vertex_num)) + + ##### compute avg attention for 4 attention heads + for h in range(4): + att_per_img = attention[h] + all_head = all_head + att_per_img + all_head = all_head/4 + + col_sums = all_head.sum(axis=0) + all_head = all_head / col_sums[np.newaxis, :] + + combined = [] + if vertex_num>400: # body + selected_joints = [7] # [6,7,4,5,13,12] + else: # hand + selected_joints = [0] # [0, 4, 8, 12, 16, 20] + # Draw attention + for ii in range(len(selected_joints)): + reference_id = selected_joints[ii] + ref_point = ref_points[reference_id] + attention_to_show = all_head[reference_id][14::] + min_v = np.min(attention_to_show) + max_v = np.max(attention_to_show) + norm_attention_to_show = (attention_to_show - min_v)/(max_v-min_v) + vertices_norm = ((vertices_2d + 1) * 0.5) * img_size + ref_norm = ((ref_point + 1) * 0.5) * img_size + image = rend_img*0.4 + + total_to_draw = [] + for jj in range(vertices_norm.shape[0]): + thres = 0.0 + if norm_attention_to_show[jj]>thres: + things = [norm_attention_to_show[jj], ref_norm, vertices_norm[jj]] + total_to_draw.append(things) + total_to_draw.sort() + max_att_score = total_to_draw[-1][0] + for item in total_to_draw: + attention_score = item[0] + ref_point = item[1] + vertex = item[2] + plot_one_line(ref_point, vertex, image, ii, alpha=(attention_score-thres)/(max_att_score-thres) ) + + for jj in range(vertices_norm.shape[0]): + x = int(vertices_norm[jj,0]) + y = int(vertices_norm[jj,1]) + cv2.circle(image,(x,y), 1, (255,255,255), -1) + + if len(combined)==0: + combined = image + else: + combined = np.hstack([combined, image]) + + final = np.hstack([img, combined, rend_img]) + + return final + + +def visualize_reconstruction_no_text(img, img_size, vertices, camera, renderer, color='pink', focal_length=1000): + """Overlays gt_kp and pred_kp on img. + Draws vert with text. + Renderer is an instance of SMPLRenderer. + """ + # Fix a flength so i can render this with persp correct scale + res = img.shape[1] + camera_t = np.array([camera[1], camera[2], 2*focal_length/(res * camera[0] +1e-9)]) + rend_img = renderer.render(vertices, camera_t=camera_t, + img=img, use_bg=True, + focal_length=focal_length, + body_color=color) + + + combined = np.hstack([img, rend_img]) + + return combined + + +def plot_one_line(ref, vertex, img, color_index, alpha=0.0, line_thickness=None): + # 13,6,7,8,3,4,5 + # att_colors = [(255, 221, 104), (255, 255, 0), (255, 215, 227), (210, 240, 119), \ + # (209, 238, 245), (244, 200, 243), (233, 242, 216)] + att_colors = [(255, 255, 0), (244, 200, 243), (210, 243, 119), (209, 238, 255), (200, 208, 255), (250, 238, 215)] + + + overlay = img.copy() + # output = img.copy() + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + + color = list(att_colors[color_index]) + c1, c2 = (int(ref[0]), int(ref[1])), (int(vertex[0]), int(vertex[1])) + cv2.line(overlay, c1, c2, (alpha*float(color[0])/255,alpha*float(color[1])/255,alpha*float(color[2])/255) , thickness=tl, lineType=cv2.LINE_AA) + cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0, img) + + + +def cam2pixel(cam_coord, f, c): + x = cam_coord[:, 0] / (cam_coord[:, 2]) * f[0] + c[0] + y = cam_coord[:, 1] / (cam_coord[:, 2]) * f[1] + c[1] + z = cam_coord[:, 2] + img_coord = np.concatenate((x[:,None], y[:,None], z[:,None]),1) + return img_coord + + +class Renderer(object): + """ + Render mesh using OpenDR for visualization. + """ + + def __init__(self, width=800, height=600, near=0.5, far=1000, faces=None): + self.colors = {'hand': [.9, .9, .9], 'pink': [.9, .7, .7], 'light_blue': [0.65098039, 0.74117647, 0.85882353] } + self.width = width + self.height = height + self.faces = faces + self.renderer = ColoredRenderer() + + def render(self, vertices, faces=None, img=None, + camera_t=np.zeros([3], dtype=np.float32), + camera_rot=np.zeros([3], dtype=np.float32), + camera_center=None, + use_bg=False, + bg_color=(0.0, 0.0, 0.0), + body_color=None, + focal_length=5000, + disp_text=False, + gt_keyp=None, + pred_keyp=None, + **kwargs): + if img is not None: + height, width = img.shape[:2] + else: + height, width = self.height, self.width + + if faces is None: + faces = self.faces + + if camera_center is None: + camera_center = np.array([width * 0.5, + height * 0.5]) + + self.renderer.camera = ProjectPoints(rt=camera_rot, + t=camera_t, + f=focal_length * np.ones(2), + c=camera_center, + k=np.zeros(5)) + dist = np.abs(self.renderer.camera.t.r[2] - + np.mean(vertices, axis=0)[2]) + far = dist + 20 + + self.renderer.frustum = {'near': 1.0, 'far': far, + 'width': width, + 'height': height} + + if img is not None: + if use_bg: + self.renderer.background_image = img + else: + self.renderer.background_image = np.ones_like( + img) * np.array(bg_color) + + if body_color is None: + color = self.colors['light_blue'] + else: + color = self.colors[body_color] + + if isinstance(self.renderer, TexturedRenderer): + color = [1.,1.,1.] + + self.renderer.set(v=vertices, f=faces, + vc=color, bgcolor=np.ones(3)) + albedo = self.renderer.vc + # Construct Back Light (on back right corner) + yrot = np.radians(120) + + self.renderer.vc = LambertianPointLight( + f=self.renderer.f, + v=self.renderer.v, + num_verts=self.renderer.v.shape[0], + light_pos=rotateY(np.array([-200, -100, -100]), yrot), + vc=albedo, + light_color=np.array([1, 1, 1])) + + # Construct Left Light + self.renderer.vc += LambertianPointLight( + f=self.renderer.f, + v=self.renderer.v, + num_verts=self.renderer.v.shape[0], + light_pos=rotateY(np.array([800, 10, 300]), yrot), + vc=albedo, + light_color=np.array([1, 1, 1])) + + # Construct Right Light + self.renderer.vc += LambertianPointLight( + f=self.renderer.f, + v=self.renderer.v, + num_verts=self.renderer.v.shape[0], + light_pos=rotateY(np.array([-500, 500, 1000]), yrot), + vc=albedo, + light_color=np.array([.7, .7, .7])) + + return self.renderer.r + + + def render_vertex_color(self, vertices, faces=None, img=None, + camera_t=np.zeros([3], dtype=np.float32), + camera_rot=np.zeros([3], dtype=np.float32), + camera_center=None, + use_bg=False, + bg_color=(0.0, 0.0, 0.0), + vertex_color=None, + focal_length=5000, + disp_text=False, + gt_keyp=None, + pred_keyp=None, + **kwargs): + if img is not None: + height, width = img.shape[:2] + else: + height, width = self.height, self.width + + if faces is None: + faces = self.faces + + if camera_center is None: + camera_center = np.array([width * 0.5, + height * 0.5]) + + self.renderer.camera = ProjectPoints(rt=camera_rot, + t=camera_t, + f=focal_length * np.ones(2), + c=camera_center, + k=np.zeros(5)) + dist = np.abs(self.renderer.camera.t.r[2] - + np.mean(vertices, axis=0)[2]) + far = dist + 20 + + self.renderer.frustum = {'near': 1.0, 'far': far, + 'width': width, + 'height': height} + + if img is not None: + if use_bg: + self.renderer.background_image = img + else: + self.renderer.background_image = np.ones_like( + img) * np.array(bg_color) + + if vertex_color is None: + vertex_color = self.colors['light_blue'] + + + self.renderer.set(v=vertices, f=faces, + vc=vertex_color, bgcolor=np.ones(3)) + albedo = self.renderer.vc + # Construct Back Light (on back right corner) + yrot = np.radians(120) + + self.renderer.vc = LambertianPointLight( + f=self.renderer.f, + v=self.renderer.v, + num_verts=self.renderer.v.shape[0], + light_pos=rotateY(np.array([-200, -100, -100]), yrot), + vc=albedo, + light_color=np.array([1, 1, 1])) + + # Construct Left Light + self.renderer.vc += LambertianPointLight( + f=self.renderer.f, + v=self.renderer.v, + num_verts=self.renderer.v.shape[0], + light_pos=rotateY(np.array([800, 10, 300]), yrot), + vc=albedo, + light_color=np.array([1, 1, 1])) + + # Construct Right Light + self.renderer.vc += LambertianPointLight( + f=self.renderer.f, + v=self.renderer.v, + num_verts=self.renderer.v.shape[0], + light_pos=rotateY(np.array([-500, 500, 1000]), yrot), + vc=albedo, + light_color=np.array([.7, .7, .7])) + + return self.renderer.r \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/tsv_file.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/tsv_file.py new file mode 100644 index 00000000..8e79a9ec --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/tsv_file.py @@ -0,0 +1,162 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +Definition of TSV class +""" + + +import logging +import os +import os.path as op + + +def generate_lineidx(filein, idxout): + idxout_tmp = idxout + '.tmp' + with open(filein, 'r') as tsvin, open(idxout_tmp,'w') as tsvout: + fsize = os.fstat(tsvin.fileno()).st_size + fpos = 0 + while fpos!=fsize: + tsvout.write(str(fpos)+"\n") + tsvin.readline() + fpos = tsvin.tell() + os.rename(idxout_tmp, idxout) + + +def read_to_character(fp, c): + result = [] + while True: + s = fp.read(32) + assert s != '' + if c in s: + result.append(s[: s.index(c)]) + break + else: + result.append(s) + return ''.join(result) + + +class TSVFile(object): + def __init__(self, tsv_file, generate_lineidx=False): + self.tsv_file = tsv_file + self.lineidx = op.splitext(tsv_file)[0] + '.lineidx' + self._fp = None + self._lineidx = None + # the process always keeps the process which opens the file. + # If the pid is not equal to the currrent pid, we will re-open the file. + self.pid = None + # generate lineidx if not exist + if not op.isfile(self.lineidx) and generate_lineidx: + generate_lineidx(self.tsv_file, self.lineidx) + + def __del__(self): + if self._fp: + self._fp.close() + + def __str__(self): + return "TSVFile(tsv_file='{}')".format(self.tsv_file) + + def __repr__(self): + return str(self) + + def num_rows(self): + self._ensure_lineidx_loaded() + return len(self._lineidx) + + def seek(self, idx): + self._ensure_tsv_opened() + self._ensure_lineidx_loaded() + try: + pos = self._lineidx[idx] + except: + logging.info('{}-{}'.format(self.tsv_file, idx)) + raise + self._fp.seek(pos) + return [s.strip() for s in self._fp.readline().split('\t')] + + def seek_first_column(self, idx): + self._ensure_tsv_opened() + self._ensure_lineidx_loaded() + pos = self._lineidx[idx] + self._fp.seek(pos) + return read_to_character(self._fp, '\t') + + def get_key(self, idx): + return self.seek_first_column(idx) + + def __getitem__(self, index): + return self.seek(index) + + def __len__(self): + return self.num_rows() + + def _ensure_lineidx_loaded(self): + if self._lineidx is None: + logging.info('loading lineidx: {}'.format(self.lineidx)) + with open(self.lineidx, 'r') as fp: + self._lineidx = [int(i.strip()) for i in fp.readlines()] + + def _ensure_tsv_opened(self): + if self._fp is None: + self._fp = open(self.tsv_file, 'r') + self.pid = os.getpid() + + if self.pid != os.getpid(): + logging.info('re-open {} because the process id changed'.format(self.tsv_file)) + self._fp = open(self.tsv_file, 'r') + self.pid = os.getpid() + + +class CompositeTSVFile(): + def __init__(self, file_list, seq_file, root='.'): + if isinstance(file_list, str): + self.file_list = load_list_file(file_list) + else: + assert isinstance(file_list, list) + self.file_list = file_list + + self.seq_file = seq_file + self.root = root + self.initialized = False + self.initialize() + + def get_key(self, index): + idx_source, idx_row = self.seq[index] + k = self.tsvs[idx_source].get_key(idx_row) + return '_'.join([self.file_list[idx_source], k]) + + def num_rows(self): + return len(self.seq) + + def __getitem__(self, index): + idx_source, idx_row = self.seq[index] + return self.tsvs[idx_source].seek(idx_row) + + def __len__(self): + return len(self.seq) + + def initialize(self): + ''' + this function has to be called in init function if cache_policy is + enabled. Thus, let's always call it in init funciton to make it simple. + ''' + if self.initialized: + return + self.seq = [] + with open(self.seq_file, 'r') as fp: + for line in fp: + parts = line.strip().split('\t') + self.seq.append([int(parts[0]), int(parts[1])]) + self.tsvs = [TSVFile(op.join(self.root, f)) for f in self.file_list] + self.initialized = True + + +def load_list_file(fname): + with open(fname, 'r') as fp: + lines = fp.readlines() + result = [line.strip() for line in lines] + if len(result) > 0 and result[-1] == '': + result = result[:-1] + return result + + diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/tsv_file_ops.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/tsv_file_ops.py new file mode 100644 index 00000000..70f2798b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mesh_graphormer/utils/tsv_file_ops.py @@ -0,0 +1,116 @@ +""" +Copyright (c) Microsoft Corporation. +Licensed under the MIT license. + +Basic operations for TSV files +""" + + +import os +import os.path as op +import json +import numpy as np +import base64 +import cv2 +from tqdm import tqdm +import yaml +from custom_mesh_graphormer.utils.miscellaneous import mkdir +from custom_mesh_graphormer.utils.tsv_file import TSVFile + + +def img_from_base64(imagestring): + try: + jpgbytestring = base64.b64decode(imagestring) + nparr = np.frombuffer(jpgbytestring, np.uint8) + r = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + return r + except ValueError: + return None + +def load_linelist_file(linelist_file): + if linelist_file is not None: + line_list = [] + with open(linelist_file, 'r') as fp: + for i in fp: + line_list.append(int(i.strip())) + return line_list + +def tsv_writer(values, tsv_file, sep='\t'): + mkdir(op.dirname(tsv_file)) + lineidx_file = op.splitext(tsv_file)[0] + '.lineidx' + idx = 0 + tsv_file_tmp = tsv_file + '.tmp' + lineidx_file_tmp = lineidx_file + '.tmp' + with open(tsv_file_tmp, 'w') as fp, open(lineidx_file_tmp, 'w') as fpidx: + assert values is not None + for value in values: + assert value is not None + value = [v if type(v)!=bytes else v.decode('utf-8') for v in value] + v = '{0}\n'.format(sep.join(map(str, value))) + fp.write(v) + fpidx.write(str(idx) + '\n') + idx = idx + len(v) + os.rename(tsv_file_tmp, tsv_file) + os.rename(lineidx_file_tmp, lineidx_file) + +def tsv_reader(tsv_file, sep='\t'): + with open(tsv_file, 'r') as fp: + for i, line in enumerate(fp): + yield [x.strip() for x in line.split(sep)] + +def config_save_file(tsv_file, save_file=None, append_str='.new.tsv'): + if save_file is not None: + return save_file + return op.splitext(tsv_file)[0] + append_str + +def get_line_list(linelist_file=None, num_rows=None): + if linelist_file is not None: + return load_linelist_file(linelist_file) + + if num_rows is not None: + return [i for i in range(num_rows)] + +def generate_hw_file(img_file, save_file=None): + rows = tsv_reader(img_file) + def gen_rows(): + for i, row in tqdm(enumerate(rows)): + row1 = [row[0]] + img = img_from_base64(row[-1]) + height = img.shape[0] + width = img.shape[1] + row1.append(json.dumps([{"height":height, "width": width}])) + yield row1 + + save_file = config_save_file(img_file, save_file, '.hw.tsv') + tsv_writer(gen_rows(), save_file) + +def generate_linelist_file(label_file, save_file=None, ignore_attrs=()): + # generate a list of image that has labels + # images with only ignore labels are not selected. + line_list = [] + rows = tsv_reader(label_file) + for i, row in tqdm(enumerate(rows)): + labels = json.loads(row[1]) + if labels: + if ignore_attrs and all([any([lab[attr] for attr in ignore_attrs if attr in lab]) \ + for lab in labels]): + continue + line_list.append([i]) + + save_file = config_save_file(label_file, save_file, '.linelist.tsv') + tsv_writer(line_list, save_file) + +def load_from_yaml_file(yaml_file): + with open(yaml_file, 'r') as fp: + return yaml.load(fp, Loader=yaml.CLoader) + +def find_file_path_in_yaml(fname, root): + if fname is not None: + if op.isfile(fname): + return fname + elif op.isfile(op.join(root, fname)): + return op.join(root, fname) + else: + raise FileNotFoundError( + errno.ENOENT, os.strerror(errno.ENOENT), op.join(root, fname) + ) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/__init__.py new file mode 100644 index 00000000..33e7a7f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/__init__.py @@ -0,0 +1 @@ +#Dummy file ensuring this package will be recognized \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/__init__.py new file mode 100644 index 00000000..210a2989 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# flake8: noqa +from .arraymisc import * +from .fileio import * +from .image import * +from .utils import * +from .version import * +from .video import * +from .visualization import * + +# The following modules are not imported to this level, so mmcv may be used +# without PyTorch. +# - runner +# - parallel +# - op diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/arraymisc/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/arraymisc/__init__.py new file mode 100644 index 00000000..4b4700d6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/arraymisc/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .quantization import dequantize, quantize + +__all__ = ['quantize', 'dequantize'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/arraymisc/quantization.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/arraymisc/quantization.py new file mode 100644 index 00000000..8e47a354 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/arraymisc/quantization.py @@ -0,0 +1,55 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np + + +def quantize(arr, min_val, max_val, levels, dtype=np.int64): + """Quantize an array of (-inf, inf) to [0, levels-1]. + + Args: + arr (ndarray): Input array. + min_val (scalar): Minimum value to be clipped. + max_val (scalar): Maximum value to be clipped. + levels (int): Quantization levels. + dtype (np.type): The type of the quantized array. + + Returns: + tuple: Quantized array. + """ + if not (isinstance(levels, int) and levels > 1): + raise ValueError( + f'levels must be a positive integer, but got {levels}') + if min_val >= max_val: + raise ValueError( + f'min_val ({min_val}) must be smaller than max_val ({max_val})') + + arr = np.clip(arr, min_val, max_val) - min_val + quantized_arr = np.minimum( + np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1) + + return quantized_arr + + +def dequantize(arr, min_val, max_val, levels, dtype=np.float64): + """Dequantize an array. + + Args: + arr (ndarray): Input array. + min_val (scalar): Minimum value to be clipped. + max_val (scalar): Maximum value to be clipped. + levels (int): Quantization levels. + dtype (np.type): The type of the dequantized array. + + Returns: + tuple: Dequantized array. + """ + if not (isinstance(levels, int) and levels > 1): + raise ValueError( + f'levels must be a positive integer, but got {levels}') + if min_val >= max_val: + raise ValueError( + f'min_val ({min_val}) must be smaller than max_val ({max_val})') + + dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - + min_val) / levels + min_val + + return dequantized_arr diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/__init__.py new file mode 100644 index 00000000..7246c897 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/__init__.py @@ -0,0 +1,41 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .alexnet import AlexNet +# yapf: disable +from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS, + PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS, + ContextBlock, Conv2d, Conv3d, ConvAWS2d, ConvModule, + ConvTranspose2d, ConvTranspose3d, ConvWS2d, + DepthwiseSeparableConvModule, GeneralizedAttention, + HSigmoid, HSwish, Linear, MaxPool2d, MaxPool3d, + NonLocal1d, NonLocal2d, NonLocal3d, Scale, Swish, + build_activation_layer, build_conv_layer, + build_norm_layer, build_padding_layer, build_plugin_layer, + build_upsample_layer, conv_ws_2d, is_norm) +from .builder import MODELS, build_model_from_cfg +# yapf: enable +from .resnet import ResNet, make_res_layer +from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit, + NormalInit, PretrainedInit, TruncNormalInit, UniformInit, + XavierInit, bias_init_with_prob, caffe2_xavier_init, + constant_init, fuse_conv_bn, get_model_complexity_info, + initialize, kaiming_init, normal_init, trunc_normal_init, + uniform_init, xavier_init) +from .vgg import VGG, make_vgg_layer + +__all__ = [ + 'AlexNet', 'VGG', 'make_vgg_layer', 'ResNet', 'make_res_layer', + 'constant_init', 'xavier_init', 'normal_init', 'trunc_normal_init', + 'uniform_init', 'kaiming_init', 'caffe2_xavier_init', + 'bias_init_with_prob', 'ConvModule', 'build_activation_layer', + 'build_conv_layer', 'build_norm_layer', 'build_padding_layer', + 'build_upsample_layer', 'build_plugin_layer', 'is_norm', 'NonLocal1d', + 'NonLocal2d', 'NonLocal3d', 'ContextBlock', 'HSigmoid', 'Swish', 'HSwish', + 'GeneralizedAttention', 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS', + 'PADDING_LAYERS', 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale', + 'get_model_complexity_info', 'conv_ws_2d', 'ConvAWS2d', 'ConvWS2d', + 'fuse_conv_bn', 'DepthwiseSeparableConvModule', 'Linear', 'Conv2d', + 'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d', 'MaxPool3d', 'Conv3d', + 'initialize', 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit', + 'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit', + 'Caffe2XavierInit', 'MODELS', 'build_model_from_cfg' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/alexnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/alexnet.py new file mode 100644 index 00000000..89e36b8c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/alexnet.py @@ -0,0 +1,61 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import logging + +import torch.nn as nn + + +class AlexNet(nn.Module): + """AlexNet backbone. + + Args: + num_classes (int): number of classes for classification. + """ + + def __init__(self, num_classes=-1): + super(AlexNet, self).__init__() + self.num_classes = num_classes + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(64, 192, kernel_size=5, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(192, 384, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(384, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + ) + if self.num_classes > 0: + self.classifier = nn.Sequential( + nn.Dropout(), + nn.Linear(256 * 6 * 6, 4096), + nn.ReLU(inplace=True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(inplace=True), + nn.Linear(4096, num_classes), + ) + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = logging.getLogger() + from ..runner import load_checkpoint + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + # use default initializer + pass + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + + x = self.features(x) + if self.num_classes > 0: + x = x.view(x.size(0), 256 * 6 * 6) + x = self.classifier(x) + + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/__init__.py new file mode 100644 index 00000000..0f33124e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/__init__.py @@ -0,0 +1,35 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .activation import build_activation_layer +from .context_block import ContextBlock +from .conv import build_conv_layer +from .conv2d_adaptive_padding import Conv2dAdaptivePadding +from .conv_module import ConvModule +from .conv_ws import ConvAWS2d, ConvWS2d, conv_ws_2d +from .depthwise_separable_conv_module import DepthwiseSeparableConvModule +from .drop import Dropout, DropPath +from .generalized_attention import GeneralizedAttention +from .hsigmoid import HSigmoid +from .hswish import HSwish +from .non_local import NonLocal1d, NonLocal2d, NonLocal3d +from .norm import build_norm_layer, is_norm +from .padding import build_padding_layer +from .plugin import build_plugin_layer +from .registry import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS, + PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS) +from .scale import Scale +from .swish import Swish +from .upsample import build_upsample_layer +from .wrappers import (Conv2d, Conv3d, ConvTranspose2d, ConvTranspose3d, + Linear, MaxPool2d, MaxPool3d) + +__all__ = [ + 'ConvModule', 'build_activation_layer', 'build_conv_layer', + 'build_norm_layer', 'build_padding_layer', 'build_upsample_layer', + 'build_plugin_layer', 'is_norm', 'HSigmoid', 'HSwish', 'NonLocal1d', + 'NonLocal2d', 'NonLocal3d', 'ContextBlock', 'GeneralizedAttention', + 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS', 'PADDING_LAYERS', + 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale', 'ConvAWS2d', 'ConvWS2d', + 'conv_ws_2d', 'DepthwiseSeparableConvModule', 'Swish', 'Linear', + 'Conv2dAdaptivePadding', 'Conv2d', 'ConvTranspose2d', 'MaxPool2d', + 'ConvTranspose3d', 'MaxPool3d', 'Conv3d', 'Dropout', 'DropPath' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/activation.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/activation.py new file mode 100644 index 00000000..0881d720 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/activation.py @@ -0,0 +1,92 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +import torch.nn.functional as F + +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, build_from_cfg, digit_version +from .registry import ACTIVATION_LAYERS + +for module in [ + nn.ReLU, nn.LeakyReLU, nn.PReLU, nn.RReLU, nn.ReLU6, nn.ELU, + nn.Sigmoid, nn.Tanh +]: + ACTIVATION_LAYERS.register_module(module=module) + + +@ACTIVATION_LAYERS.register_module(name='Clip') +@ACTIVATION_LAYERS.register_module() +class Clamp(nn.Module): + """Clamp activation layer. + + This activation function is to clamp the feature map value within + :math:`[min, max]`. More details can be found in ``torch.clamp()``. + + Args: + min (Number | optional): Lower-bound of the range to be clamped to. + Default to -1. + max (Number | optional): Upper-bound of the range to be clamped to. + Default to 1. + """ + + def __init__(self, min=-1., max=1.): + super(Clamp, self).__init__() + self.min = min + self.max = max + + def forward(self, x): + """Forward function. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: Clamped tensor. + """ + return torch.clamp(x, min=self.min, max=self.max) + + +class GELU(nn.Module): + r"""Applies the Gaussian Error Linear Units function: + + .. math:: + \text{GELU}(x) = x * \Phi(x) + where :math:`\Phi(x)` is the Cumulative Distribution Function for + Gaussian Distribution. + + Shape: + - Input: :math:`(N, *)` where `*` means, any number of additional + dimensions + - Output: :math:`(N, *)`, same shape as the input + + .. image:: scripts/activation_images/GELU.png + + Examples:: + + >>> m = nn.GELU() + >>> input = torch.randn(2) + >>> output = m(input) + """ + + def forward(self, input): + return F.gelu(input) + + +if (TORCH_VERSION == 'parrots' + or digit_version(TORCH_VERSION) < digit_version('1.4')): + ACTIVATION_LAYERS.register_module(module=GELU) +else: + ACTIVATION_LAYERS.register_module(module=nn.GELU) + + +def build_activation_layer(cfg): + """Build activation layer. + + Args: + cfg (dict): The activation layer config, which should contain: + - type (str): Layer type. + - layer args: Args needed to instantiate an activation layer. + + Returns: + nn.Module: Created activation layer. + """ + return build_from_cfg(cfg, ACTIVATION_LAYERS) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/context_block.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/context_block.py new file mode 100644 index 00000000..d60fdb90 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/context_block.py @@ -0,0 +1,125 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn + +from ..utils import constant_init, kaiming_init +from .registry import PLUGIN_LAYERS + + +def last_zero_init(m): + if isinstance(m, nn.Sequential): + constant_init(m[-1], val=0) + else: + constant_init(m, val=0) + + +@PLUGIN_LAYERS.register_module() +class ContextBlock(nn.Module): + """ContextBlock module in GCNet. + + See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' + (https://arxiv.org/abs/1904.11492) for details. + + Args: + in_channels (int): Channels of the input feature map. + ratio (float): Ratio of channels of transform bottleneck + pooling_type (str): Pooling method for context modeling. + Options are 'att' and 'avg', stand for attention pooling and + average pooling respectively. Default: 'att'. + fusion_types (Sequence[str]): Fusion method for feature fusion, + Options are 'channels_add', 'channel_mul', stand for channelwise + addition and multiplication respectively. Default: ('channel_add',) + """ + + _abbr_ = 'context_block' + + def __init__(self, + in_channels, + ratio, + pooling_type='att', + fusion_types=('channel_add', )): + super(ContextBlock, self).__init__() + assert pooling_type in ['avg', 'att'] + assert isinstance(fusion_types, (list, tuple)) + valid_fusion_types = ['channel_add', 'channel_mul'] + assert all([f in valid_fusion_types for f in fusion_types]) + assert len(fusion_types) > 0, 'at least one fusion should be used' + self.in_channels = in_channels + self.ratio = ratio + self.planes = int(in_channels * ratio) + self.pooling_type = pooling_type + self.fusion_types = fusion_types + if pooling_type == 'att': + self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1) + self.softmax = nn.Softmax(dim=2) + else: + self.avg_pool = nn.AdaptiveAvgPool2d(1) + if 'channel_add' in fusion_types: + self.channel_add_conv = nn.Sequential( + nn.Conv2d(self.in_channels, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(inplace=True), # yapf: disable + nn.Conv2d(self.planes, self.in_channels, kernel_size=1)) + else: + self.channel_add_conv = None + if 'channel_mul' in fusion_types: + self.channel_mul_conv = nn.Sequential( + nn.Conv2d(self.in_channels, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(inplace=True), # yapf: disable + nn.Conv2d(self.planes, self.in_channels, kernel_size=1)) + else: + self.channel_mul_conv = None + self.reset_parameters() + + def reset_parameters(self): + if self.pooling_type == 'att': + kaiming_init(self.conv_mask, mode='fan_in') + self.conv_mask.inited = True + + if self.channel_add_conv is not None: + last_zero_init(self.channel_add_conv) + if self.channel_mul_conv is not None: + last_zero_init(self.channel_mul_conv) + + def spatial_pool(self, x): + batch, channel, height, width = x.size() + if self.pooling_type == 'att': + input_x = x + # [N, C, H * W] + input_x = input_x.view(batch, channel, height * width) + # [N, 1, C, H * W] + input_x = input_x.unsqueeze(1) + # [N, 1, H, W] + context_mask = self.conv_mask(x) + # [N, 1, H * W] + context_mask = context_mask.view(batch, 1, height * width) + # [N, 1, H * W] + context_mask = self.softmax(context_mask) + # [N, 1, H * W, 1] + context_mask = context_mask.unsqueeze(-1) + # [N, 1, C, 1] + context = torch.matmul(input_x, context_mask) + # [N, C, 1, 1] + context = context.view(batch, channel, 1, 1) + else: + # [N, C, 1, 1] + context = self.avg_pool(x) + + return context + + def forward(self, x): + # [N, C, 1, 1] + context = self.spatial_pool(x) + + out = x + if self.channel_mul_conv is not None: + # [N, C, 1, 1] + channel_mul_term = torch.sigmoid(self.channel_mul_conv(context)) + out = out * channel_mul_term + if self.channel_add_conv is not None: + # [N, C, 1, 1] + channel_add_term = self.channel_add_conv(context) + out = out + channel_add_term + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv.py new file mode 100644 index 00000000..cf544919 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv.py @@ -0,0 +1,44 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from torch import nn + +from .registry import CONV_LAYERS + +CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d) +CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d) +CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d) +CONV_LAYERS.register_module('Conv', module=nn.Conv2d) + + +def build_conv_layer(cfg, *args, **kwargs): + """Build convolution layer. + + Args: + cfg (None or dict): The conv layer config, which should contain: + - type (str): Layer type. + - layer args: Args needed to instantiate an conv layer. + args (argument list): Arguments passed to the `__init__` + method of the corresponding conv layer. + kwargs (keyword arguments): Keyword arguments passed to the `__init__` + method of the corresponding conv layer. + + Returns: + nn.Module: Created conv layer. + """ + if cfg is None: + cfg_ = dict(type='Conv2d') + else: + if not isinstance(cfg, dict): + raise TypeError('cfg must be a dict') + if 'type' not in cfg: + raise KeyError('the cfg dict must contain the key "type"') + cfg_ = cfg.copy() + + layer_type = cfg_.pop('type') + if layer_type not in CONV_LAYERS: + raise KeyError(f'Unrecognized norm type {layer_type}') + else: + conv_layer = CONV_LAYERS.get(layer_type) + + layer = conv_layer(*args, **kwargs, **cfg_) + + return layer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv2d_adaptive_padding.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv2d_adaptive_padding.py new file mode 100644 index 00000000..b45e758a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv2d_adaptive_padding.py @@ -0,0 +1,62 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +from torch import nn +from torch.nn import functional as F + +from .registry import CONV_LAYERS + + +@CONV_LAYERS.register_module() +class Conv2dAdaptivePadding(nn.Conv2d): + """Implementation of 2D convolution in tensorflow with `padding` as "same", + which applies padding to input (if needed) so that input image gets fully + covered by filter and stride you specified. For stride 1, this will ensure + that output image size is same as input. For stride of 2, output dimensions + will be half, for example. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of + the input. Default: 0 + dilation (int or tuple, optional): Spacing between kernel elements. + Default: 1 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If ``True``, adds a learnable bias to the + output. Default: ``True`` + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True): + super().__init__(in_channels, out_channels, kernel_size, stride, 0, + dilation, groups, bias) + + def forward(self, x): + img_h, img_w = x.size()[-2:] + kernel_h, kernel_w = self.weight.size()[-2:] + stride_h, stride_w = self.stride + output_h = math.ceil(img_h / stride_h) + output_w = math.ceil(img_w / stride_w) + pad_h = ( + max((output_h - 1) * self.stride[0] + + (kernel_h - 1) * self.dilation[0] + 1 - img_h, 0)) + pad_w = ( + max((output_w - 1) * self.stride[1] + + (kernel_w - 1) * self.dilation[1] + 1 - img_w, 0)) + if pad_h > 0 or pad_w > 0: + x = F.pad(x, [ + pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2 + ]) + return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, + self.dilation, self.groups) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv_module.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv_module.py new file mode 100644 index 00000000..f9b82b6b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv_module.py @@ -0,0 +1,206 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings + +import torch.nn as nn + +from custom_mmpkg.custom_mmcv.utils import _BatchNorm, _InstanceNorm +from ..utils import constant_init, kaiming_init +from .activation import build_activation_layer +from .conv import build_conv_layer +from .norm import build_norm_layer +from .padding import build_padding_layer +from .registry import PLUGIN_LAYERS + + +@PLUGIN_LAYERS.register_module() +class ConvModule(nn.Module): + """A conv block that bundles conv/norm/activation layers. + + This block simplifies the usage of convolution layers, which are commonly + used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). + It is based upon three build methods: `build_conv_layer()`, + `build_norm_layer()` and `build_activation_layer()`. + + Besides, we add some additional features in this module. + 1. Automatically set `bias` of the conv layer. + 2. Spectral norm is supported. + 3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only + supports zero and circular padding, and we add "reflect" padding mode. + + Args: + in_channels (int): Number of channels in the input feature map. + Same as that in ``nn._ConvNd``. + out_channels (int): Number of channels produced by the convolution. + Same as that in ``nn._ConvNd``. + kernel_size (int | tuple[int]): Size of the convolving kernel. + Same as that in ``nn._ConvNd``. + stride (int | tuple[int]): Stride of the convolution. + Same as that in ``nn._ConvNd``. + padding (int | tuple[int]): Zero-padding added to both sides of + the input. Same as that in ``nn._ConvNd``. + dilation (int | tuple[int]): Spacing between kernel elements. + Same as that in ``nn._ConvNd``. + groups (int): Number of blocked connections from input channels to + output channels. Same as that in ``nn._ConvNd``. + bias (bool | str): If specified as `auto`, it will be decided by the + norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise + False. Default: "auto". + conv_cfg (dict): Config dict for convolution layer. Default: None, + which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. Default: None. + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU'). + inplace (bool): Whether to use inplace mode for activation. + Default: True. + with_spectral_norm (bool): Whether use spectral norm in conv module. + Default: False. + padding_mode (str): If the `padding_mode` has not been supported by + current `Conv2d` in PyTorch, we will use our own padding layer + instead. Currently, we support ['zeros', 'circular'] with official + implementation and ['reflect'] with our own implementation. + Default: 'zeros'. + order (tuple[str]): The order of conv/norm/activation layers. It is a + sequence of "conv", "norm" and "act". Common examples are + ("conv", "norm", "act") and ("act", "conv", "norm"). + Default: ('conv', 'norm', 'act'). + """ + + _abbr_ = 'conv_block' + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias='auto', + conv_cfg=None, + norm_cfg=None, + act_cfg=dict(type='ReLU'), + inplace=True, + with_spectral_norm=False, + padding_mode='zeros', + order=('conv', 'norm', 'act')): + super(ConvModule, self).__init__() + assert conv_cfg is None or isinstance(conv_cfg, dict) + assert norm_cfg is None or isinstance(norm_cfg, dict) + assert act_cfg is None or isinstance(act_cfg, dict) + official_padding_mode = ['zeros', 'circular'] + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.inplace = inplace + self.with_spectral_norm = with_spectral_norm + self.with_explicit_padding = padding_mode not in official_padding_mode + self.order = order + assert isinstance(self.order, tuple) and len(self.order) == 3 + assert set(order) == set(['conv', 'norm', 'act']) + + self.with_norm = norm_cfg is not None + self.with_activation = act_cfg is not None + # if the conv layer is before a norm layer, bias is unnecessary. + if bias == 'auto': + bias = not self.with_norm + self.with_bias = bias + + if self.with_explicit_padding: + pad_cfg = dict(type=padding_mode) + self.padding_layer = build_padding_layer(pad_cfg, padding) + + # reset padding to 0 for conv module + conv_padding = 0 if self.with_explicit_padding else padding + # build convolution layer + self.conv = build_conv_layer( + conv_cfg, + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=conv_padding, + dilation=dilation, + groups=groups, + bias=bias) + # export the attributes of self.conv to a higher level for convenience + self.in_channels = self.conv.in_channels + self.out_channels = self.conv.out_channels + self.kernel_size = self.conv.kernel_size + self.stride = self.conv.stride + self.padding = padding + self.dilation = self.conv.dilation + self.transposed = self.conv.transposed + self.output_padding = self.conv.output_padding + self.groups = self.conv.groups + + if self.with_spectral_norm: + self.conv = nn.utils.spectral_norm(self.conv) + + # build normalization layers + if self.with_norm: + # norm layer is after conv layer + if order.index('norm') > order.index('conv'): + norm_channels = out_channels + else: + norm_channels = in_channels + self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels) + self.add_module(self.norm_name, norm) + if self.with_bias: + if isinstance(norm, (_BatchNorm, _InstanceNorm)): + warnings.warn( + 'Unnecessary conv bias before batch/instance norm') + else: + self.norm_name = None + + # build activation layer + if self.with_activation: + act_cfg_ = act_cfg.copy() + # nn.Tanh has no 'inplace' argument + if act_cfg_['type'] not in [ + 'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish' + ]: + act_cfg_.setdefault('inplace', inplace) + self.activate = build_activation_layer(act_cfg_) + + # Use msra init by default + self.init_weights() + + @property + def norm(self): + if self.norm_name: + return getattr(self, self.norm_name) + else: + return None + + def init_weights(self): + # 1. It is mainly for customized conv layers with their own + # initialization manners by calling their own ``init_weights()``, + # and we do not want ConvModule to override the initialization. + # 2. For customized conv layers without their own initialization + # manners (that is, they don't have their own ``init_weights()``) + # and PyTorch's conv layers, they will be initialized by + # this method with default ``kaiming_init``. + # Note: For PyTorch's conv layers, they will be overwritten by our + # initialization implementation using default ``kaiming_init``. + if not hasattr(self.conv, 'init_weights'): + if self.with_activation and self.act_cfg['type'] == 'LeakyReLU': + nonlinearity = 'leaky_relu' + a = self.act_cfg.get('negative_slope', 0.01) + else: + nonlinearity = 'relu' + a = 0 + kaiming_init(self.conv, a=a, nonlinearity=nonlinearity) + if self.with_norm: + constant_init(self.norm, 1, bias=0) + + def forward(self, x, activate=True, norm=True): + for layer in self.order: + if layer == 'conv': + if self.with_explicit_padding: + x = self.padding_layer(x) + x = self.conv(x) + elif layer == 'norm' and norm and self.with_norm: + x = self.norm(x) + elif layer == 'act' and activate and self.with_activation: + x = self.activate(x) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv_ws.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv_ws.py new file mode 100644 index 00000000..a3941e27 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/conv_ws.py @@ -0,0 +1,148 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .registry import CONV_LAYERS + + +def conv_ws_2d(input, + weight, + bias=None, + stride=1, + padding=0, + dilation=1, + groups=1, + eps=1e-5): + c_in = weight.size(0) + weight_flat = weight.view(c_in, -1) + mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1) + std = weight_flat.std(dim=1, keepdim=True).view(c_in, 1, 1, 1) + weight = (weight - mean) / (std + eps) + return F.conv2d(input, weight, bias, stride, padding, dilation, groups) + + +@CONV_LAYERS.register_module('ConvWS') +class ConvWS2d(nn.Conv2d): + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + eps=1e-5): + super(ConvWS2d, self).__init__( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias) + self.eps = eps + + def forward(self, x): + return conv_ws_2d(x, self.weight, self.bias, self.stride, self.padding, + self.dilation, self.groups, self.eps) + + +@CONV_LAYERS.register_module(name='ConvAWS') +class ConvAWS2d(nn.Conv2d): + """AWS (Adaptive Weight Standardization) + + This is a variant of Weight Standardization + (https://arxiv.org/pdf/1903.10520.pdf) + It is used in DetectoRS to avoid NaN + (https://arxiv.org/pdf/2006.02334.pdf) + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the conv kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of + the input. Default: 0 + dilation (int or tuple, optional): Spacing between kernel elements. + Default: 1 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If set True, adds a learnable bias to the + output. Default: True + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True): + super().__init__( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias) + self.register_buffer('weight_gamma', + torch.ones(self.out_channels, 1, 1, 1)) + self.register_buffer('weight_beta', + torch.zeros(self.out_channels, 1, 1, 1)) + + def _get_weight(self, weight): + weight_flat = weight.view(weight.size(0), -1) + mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1) + std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1) + weight = (weight - mean) / std + weight = self.weight_gamma * weight + self.weight_beta + return weight + + def forward(self, x): + weight = self._get_weight(self.weight) + return F.conv2d(x, weight, self.bias, self.stride, self.padding, + self.dilation, self.groups) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + """Override default load function. + + AWS overrides the function _load_from_state_dict to recover + weight_gamma and weight_beta if they are missing. If weight_gamma and + weight_beta are found in the checkpoint, this function will return + after super()._load_from_state_dict. Otherwise, it will compute the + mean and std of the pretrained weights and store them in weight_beta + and weight_gamma. + """ + + self.weight_gamma.data.fill_(-1) + local_missing_keys = [] + super()._load_from_state_dict(state_dict, prefix, local_metadata, + strict, local_missing_keys, + unexpected_keys, error_msgs) + if self.weight_gamma.data.mean() > 0: + for k in local_missing_keys: + missing_keys.append(k) + return + weight = self.weight.data + weight_flat = weight.view(weight.size(0), -1) + mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1) + std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1) + self.weight_beta.data.copy_(mean) + self.weight_gamma.data.copy_(std) + missing_gamma_beta = [ + k for k in local_missing_keys + if k.endswith('weight_gamma') or k.endswith('weight_beta') + ] + for k in missing_gamma_beta: + local_missing_keys.remove(k) + for k in local_missing_keys: + missing_keys.append(k) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/depthwise_separable_conv_module.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/depthwise_separable_conv_module.py new file mode 100644 index 00000000..722d5d8d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/depthwise_separable_conv_module.py @@ -0,0 +1,96 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn + +from .conv_module import ConvModule + + +class DepthwiseSeparableConvModule(nn.Module): + """Depthwise separable convolution module. + + See https://arxiv.org/pdf/1704.04861.pdf for details. + + This module can replace a ConvModule with the conv block replaced by two + conv block: depthwise conv block and pointwise conv block. The depthwise + conv block contains depthwise-conv/norm/activation layers. The pointwise + conv block contains pointwise-conv/norm/activation layers. It should be + noted that there will be norm/activation layer in the depthwise conv block + if `norm_cfg` and `act_cfg` are specified. + + Args: + in_channels (int): Number of channels in the input feature map. + Same as that in ``nn._ConvNd``. + out_channels (int): Number of channels produced by the convolution. + Same as that in ``nn._ConvNd``. + kernel_size (int | tuple[int]): Size of the convolving kernel. + Same as that in ``nn._ConvNd``. + stride (int | tuple[int]): Stride of the convolution. + Same as that in ``nn._ConvNd``. Default: 1. + padding (int | tuple[int]): Zero-padding added to both sides of + the input. Same as that in ``nn._ConvNd``. Default: 0. + dilation (int | tuple[int]): Spacing between kernel elements. + Same as that in ``nn._ConvNd``. Default: 1. + norm_cfg (dict): Default norm config for both depthwise ConvModule and + pointwise ConvModule. Default: None. + act_cfg (dict): Default activation config for both depthwise ConvModule + and pointwise ConvModule. Default: dict(type='ReLU'). + dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is + 'default', it will be the same as `norm_cfg`. Default: 'default'. + dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is + 'default', it will be the same as `act_cfg`. Default: 'default'. + pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is + 'default', it will be the same as `norm_cfg`. Default: 'default'. + pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is + 'default', it will be the same as `act_cfg`. Default: 'default'. + kwargs (optional): Other shared arguments for depthwise and pointwise + ConvModule. See ConvModule for ref. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + norm_cfg=None, + act_cfg=dict(type='ReLU'), + dw_norm_cfg='default', + dw_act_cfg='default', + pw_norm_cfg='default', + pw_act_cfg='default', + **kwargs): + super(DepthwiseSeparableConvModule, self).__init__() + assert 'groups' not in kwargs, 'groups should not be specified' + + # if norm/activation config of depthwise/pointwise ConvModule is not + # specified, use default config. + dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg + dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg + pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg + pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg + + # depthwise convolution + self.depthwise_conv = ConvModule( + in_channels, + in_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + norm_cfg=dw_norm_cfg, + act_cfg=dw_act_cfg, + **kwargs) + + self.pointwise_conv = ConvModule( + in_channels, + out_channels, + 1, + norm_cfg=pw_norm_cfg, + act_cfg=pw_act_cfg, + **kwargs) + + def forward(self, x): + x = self.depthwise_conv(x) + x = self.pointwise_conv(x) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/drop.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/drop.py new file mode 100644 index 00000000..90d192e3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/drop.py @@ -0,0 +1,65 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn + +from custom_mmpkg.custom_mmcv import build_from_cfg +from .registry import DROPOUT_LAYERS + + +def drop_path(x, drop_prob=0., training=False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of + residual blocks). + + We follow the implementation + https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py # noqa: E501 + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + # handle tensors with different dimensions, not just 4D tensors. + shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) + random_tensor = keep_prob + torch.rand( + shape, dtype=x.dtype, device=x.device) + output = x.div(keep_prob) * random_tensor.floor() + return output + + +@DROPOUT_LAYERS.register_module() +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of + residual blocks). + + We follow the implementation + https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py # noqa: E501 + + Args: + drop_prob (float): Probability of the path to be zeroed. Default: 0.1 + """ + + def __init__(self, drop_prob=0.1): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +@DROPOUT_LAYERS.register_module() +class Dropout(nn.Dropout): + """A wrapper for ``torch.nn.Dropout``, We rename the ``p`` of + ``torch.nn.Dropout`` to ``drop_prob`` so as to be consistent with + ``DropPath`` + + Args: + drop_prob (float): Probability of the elements to be + zeroed. Default: 0.5. + inplace (bool): Do the operation inplace or not. Default: False. + """ + + def __init__(self, drop_prob=0.5, inplace=False): + super().__init__(p=drop_prob, inplace=inplace) + + +def build_dropout(cfg, default_args=None): + """Builder for drop out layers.""" + return build_from_cfg(cfg, DROPOUT_LAYERS, default_args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/generalized_attention.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/generalized_attention.py new file mode 100644 index 00000000..988d9adf --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/generalized_attention.py @@ -0,0 +1,412 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..utils import kaiming_init +from .registry import PLUGIN_LAYERS + + +@PLUGIN_LAYERS.register_module() +class GeneralizedAttention(nn.Module): + """GeneralizedAttention module. + + See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks' + (https://arxiv.org/abs/1711.07971) for details. + + Args: + in_channels (int): Channels of the input feature map. + spatial_range (int): The spatial range. -1 indicates no spatial range + constraint. Default: -1. + num_heads (int): The head number of empirical_attention module. + Default: 9. + position_embedding_dim (int): The position embedding dimension. + Default: -1. + position_magnitude (int): A multiplier acting on coord difference. + Default: 1. + kv_stride (int): The feature stride acting on key/value feature map. + Default: 2. + q_stride (int): The feature stride acting on query feature map. + Default: 1. + attention_type (str): A binary indicator string for indicating which + items in generalized empirical_attention module are used. + Default: '1111'. + + - '1000' indicates 'query and key content' (appr - appr) item, + - '0100' indicates 'query content and relative position' + (appr - position) item, + - '0010' indicates 'key content only' (bias - appr) item, + - '0001' indicates 'relative position only' (bias - position) item. + """ + + _abbr_ = 'gen_attention_block' + + def __init__(self, + in_channels, + spatial_range=-1, + num_heads=9, + position_embedding_dim=-1, + position_magnitude=1, + kv_stride=2, + q_stride=1, + attention_type='1111'): + + super(GeneralizedAttention, self).__init__() + + # hard range means local range for non-local operation + self.position_embedding_dim = ( + position_embedding_dim + if position_embedding_dim > 0 else in_channels) + + self.position_magnitude = position_magnitude + self.num_heads = num_heads + self.in_channels = in_channels + self.spatial_range = spatial_range + self.kv_stride = kv_stride + self.q_stride = q_stride + self.attention_type = [bool(int(_)) for _ in attention_type] + self.qk_embed_dim = in_channels // num_heads + out_c = self.qk_embed_dim * num_heads + + if self.attention_type[0] or self.attention_type[1]: + self.query_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_c, + kernel_size=1, + bias=False) + self.query_conv.kaiming_init = True + + if self.attention_type[0] or self.attention_type[2]: + self.key_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_c, + kernel_size=1, + bias=False) + self.key_conv.kaiming_init = True + + self.v_dim = in_channels // num_heads + self.value_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=self.v_dim * num_heads, + kernel_size=1, + bias=False) + self.value_conv.kaiming_init = True + + if self.attention_type[1] or self.attention_type[3]: + self.appr_geom_fc_x = nn.Linear( + self.position_embedding_dim // 2, out_c, bias=False) + self.appr_geom_fc_x.kaiming_init = True + + self.appr_geom_fc_y = nn.Linear( + self.position_embedding_dim // 2, out_c, bias=False) + self.appr_geom_fc_y.kaiming_init = True + + if self.attention_type[2]: + stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2) + appr_bias_value = -2 * stdv * torch.rand(out_c) + stdv + self.appr_bias = nn.Parameter(appr_bias_value) + + if self.attention_type[3]: + stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2) + geom_bias_value = -2 * stdv * torch.rand(out_c) + stdv + self.geom_bias = nn.Parameter(geom_bias_value) + + self.proj_conv = nn.Conv2d( + in_channels=self.v_dim * num_heads, + out_channels=in_channels, + kernel_size=1, + bias=True) + self.proj_conv.kaiming_init = True + self.gamma = nn.Parameter(torch.zeros(1)) + + if self.spatial_range >= 0: + # only works when non local is after 3*3 conv + if in_channels == 256: + max_len = 84 + elif in_channels == 512: + max_len = 42 + + max_len_kv = int((max_len - 1.0) / self.kv_stride + 1) + local_constraint_map = np.ones( + (max_len, max_len, max_len_kv, max_len_kv), dtype=np.int) + for iy in range(max_len): + for ix in range(max_len): + local_constraint_map[ + iy, ix, + max((iy - self.spatial_range) // + self.kv_stride, 0):min((iy + self.spatial_range + + 1) // self.kv_stride + + 1, max_len), + max((ix - self.spatial_range) // + self.kv_stride, 0):min((ix + self.spatial_range + + 1) // self.kv_stride + + 1, max_len)] = 0 + + self.local_constraint_map = nn.Parameter( + torch.from_numpy(local_constraint_map).byte(), + requires_grad=False) + + if self.q_stride > 1: + self.q_downsample = nn.AvgPool2d( + kernel_size=1, stride=self.q_stride) + else: + self.q_downsample = None + + if self.kv_stride > 1: + self.kv_downsample = nn.AvgPool2d( + kernel_size=1, stride=self.kv_stride) + else: + self.kv_downsample = None + + self.init_weights() + + def get_position_embedding(self, + h, + w, + h_kv, + w_kv, + q_stride, + kv_stride, + device, + dtype, + feat_dim, + wave_length=1000): + # the default type of Tensor is float32, leading to type mismatch + # in fp16 mode. Cast it to support fp16 mode. + h_idxs = torch.linspace(0, h - 1, h).to(device=device, dtype=dtype) + h_idxs = h_idxs.view((h, 1)) * q_stride + + w_idxs = torch.linspace(0, w - 1, w).to(device=device, dtype=dtype) + w_idxs = w_idxs.view((w, 1)) * q_stride + + h_kv_idxs = torch.linspace(0, h_kv - 1, h_kv).to( + device=device, dtype=dtype) + h_kv_idxs = h_kv_idxs.view((h_kv, 1)) * kv_stride + + w_kv_idxs = torch.linspace(0, w_kv - 1, w_kv).to( + device=device, dtype=dtype) + w_kv_idxs = w_kv_idxs.view((w_kv, 1)) * kv_stride + + # (h, h_kv, 1) + h_diff = h_idxs.unsqueeze(1) - h_kv_idxs.unsqueeze(0) + h_diff *= self.position_magnitude + + # (w, w_kv, 1) + w_diff = w_idxs.unsqueeze(1) - w_kv_idxs.unsqueeze(0) + w_diff *= self.position_magnitude + + feat_range = torch.arange(0, feat_dim / 4).to( + device=device, dtype=dtype) + + dim_mat = torch.Tensor([wave_length]).to(device=device, dtype=dtype) + dim_mat = dim_mat**((4. / feat_dim) * feat_range) + dim_mat = dim_mat.view((1, 1, -1)) + + embedding_x = torch.cat( + ((w_diff / dim_mat).sin(), (w_diff / dim_mat).cos()), dim=2) + + embedding_y = torch.cat( + ((h_diff / dim_mat).sin(), (h_diff / dim_mat).cos()), dim=2) + + return embedding_x, embedding_y + + def forward(self, x_input): + num_heads = self.num_heads + + # use empirical_attention + if self.q_downsample is not None: + x_q = self.q_downsample(x_input) + else: + x_q = x_input + n, _, h, w = x_q.shape + + if self.kv_downsample is not None: + x_kv = self.kv_downsample(x_input) + else: + x_kv = x_input + _, _, h_kv, w_kv = x_kv.shape + + if self.attention_type[0] or self.attention_type[1]: + proj_query = self.query_conv(x_q).view( + (n, num_heads, self.qk_embed_dim, h * w)) + proj_query = proj_query.permute(0, 1, 3, 2) + + if self.attention_type[0] or self.attention_type[2]: + proj_key = self.key_conv(x_kv).view( + (n, num_heads, self.qk_embed_dim, h_kv * w_kv)) + + if self.attention_type[1] or self.attention_type[3]: + position_embed_x, position_embed_y = self.get_position_embedding( + h, w, h_kv, w_kv, self.q_stride, self.kv_stride, + x_input.device, x_input.dtype, self.position_embedding_dim) + # (n, num_heads, w, w_kv, dim) + position_feat_x = self.appr_geom_fc_x(position_embed_x).\ + view(1, w, w_kv, num_heads, self.qk_embed_dim).\ + permute(0, 3, 1, 2, 4).\ + repeat(n, 1, 1, 1, 1) + + # (n, num_heads, h, h_kv, dim) + position_feat_y = self.appr_geom_fc_y(position_embed_y).\ + view(1, h, h_kv, num_heads, self.qk_embed_dim).\ + permute(0, 3, 1, 2, 4).\ + repeat(n, 1, 1, 1, 1) + + position_feat_x /= math.sqrt(2) + position_feat_y /= math.sqrt(2) + + # accelerate for saliency only + if (np.sum(self.attention_type) == 1) and self.attention_type[2]: + appr_bias = self.appr_bias.\ + view(1, num_heads, 1, self.qk_embed_dim).\ + repeat(n, 1, 1, 1) + + energy = torch.matmul(appr_bias, proj_key).\ + view(n, num_heads, 1, h_kv * w_kv) + + h = 1 + w = 1 + else: + # (n, num_heads, h*w, h_kv*w_kv), query before key, 540mb for + if not self.attention_type[0]: + energy = torch.zeros( + n, + num_heads, + h, + w, + h_kv, + w_kv, + dtype=x_input.dtype, + device=x_input.device) + + # attention_type[0]: appr - appr + # attention_type[1]: appr - position + # attention_type[2]: bias - appr + # attention_type[3]: bias - position + if self.attention_type[0] or self.attention_type[2]: + if self.attention_type[0] and self.attention_type[2]: + appr_bias = self.appr_bias.\ + view(1, num_heads, 1, self.qk_embed_dim) + energy = torch.matmul(proj_query + appr_bias, proj_key).\ + view(n, num_heads, h, w, h_kv, w_kv) + + elif self.attention_type[0]: + energy = torch.matmul(proj_query, proj_key).\ + view(n, num_heads, h, w, h_kv, w_kv) + + elif self.attention_type[2]: + appr_bias = self.appr_bias.\ + view(1, num_heads, 1, self.qk_embed_dim).\ + repeat(n, 1, 1, 1) + + energy += torch.matmul(appr_bias, proj_key).\ + view(n, num_heads, 1, 1, h_kv, w_kv) + + if self.attention_type[1] or self.attention_type[3]: + if self.attention_type[1] and self.attention_type[3]: + geom_bias = self.geom_bias.\ + view(1, num_heads, 1, self.qk_embed_dim) + + proj_query_reshape = (proj_query + geom_bias).\ + view(n, num_heads, h, w, self.qk_embed_dim) + + energy_x = torch.matmul( + proj_query_reshape.permute(0, 1, 3, 2, 4), + position_feat_x.permute(0, 1, 2, 4, 3)) + energy_x = energy_x.\ + permute(0, 1, 3, 2, 4).unsqueeze(4) + + energy_y = torch.matmul( + proj_query_reshape, + position_feat_y.permute(0, 1, 2, 4, 3)) + energy_y = energy_y.unsqueeze(5) + + energy += energy_x + energy_y + + elif self.attention_type[1]: + proj_query_reshape = proj_query.\ + view(n, num_heads, h, w, self.qk_embed_dim) + proj_query_reshape = proj_query_reshape.\ + permute(0, 1, 3, 2, 4) + position_feat_x_reshape = position_feat_x.\ + permute(0, 1, 2, 4, 3) + position_feat_y_reshape = position_feat_y.\ + permute(0, 1, 2, 4, 3) + + energy_x = torch.matmul(proj_query_reshape, + position_feat_x_reshape) + energy_x = energy_x.permute(0, 1, 3, 2, 4).unsqueeze(4) + + energy_y = torch.matmul(proj_query_reshape, + position_feat_y_reshape) + energy_y = energy_y.unsqueeze(5) + + energy += energy_x + energy_y + + elif self.attention_type[3]: + geom_bias = self.geom_bias.\ + view(1, num_heads, self.qk_embed_dim, 1).\ + repeat(n, 1, 1, 1) + + position_feat_x_reshape = position_feat_x.\ + view(n, num_heads, w*w_kv, self.qk_embed_dim) + + position_feat_y_reshape = position_feat_y.\ + view(n, num_heads, h * h_kv, self.qk_embed_dim) + + energy_x = torch.matmul(position_feat_x_reshape, geom_bias) + energy_x = energy_x.view(n, num_heads, 1, w, 1, w_kv) + + energy_y = torch.matmul(position_feat_y_reshape, geom_bias) + energy_y = energy_y.view(n, num_heads, h, 1, h_kv, 1) + + energy += energy_x + energy_y + + energy = energy.view(n, num_heads, h * w, h_kv * w_kv) + + if self.spatial_range >= 0: + cur_local_constraint_map = \ + self.local_constraint_map[:h, :w, :h_kv, :w_kv].\ + contiguous().\ + view(1, 1, h*w, h_kv*w_kv) + + energy = energy.masked_fill_(cur_local_constraint_map, + float('-inf')) + + attention = F.softmax(energy, 3) + + proj_value = self.value_conv(x_kv) + proj_value_reshape = proj_value.\ + view((n, num_heads, self.v_dim, h_kv * w_kv)).\ + permute(0, 1, 3, 2) + + out = torch.matmul(attention, proj_value_reshape).\ + permute(0, 1, 3, 2).\ + contiguous().\ + view(n, self.v_dim * self.num_heads, h, w) + + out = self.proj_conv(out) + + # output is downsampled, upsample back to input size + if self.q_downsample is not None: + out = F.interpolate( + out, + size=x_input.shape[2:], + mode='bilinear', + align_corners=False) + + out = self.gamma * out + x_input + return out + + def init_weights(self): + for m in self.modules(): + if hasattr(m, 'kaiming_init') and m.kaiming_init: + kaiming_init( + m, + mode='fan_in', + nonlinearity='leaky_relu', + bias=0, + distribution='uniform', + a=1) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/hsigmoid.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/hsigmoid.py new file mode 100644 index 00000000..30b1a3d6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/hsigmoid.py @@ -0,0 +1,34 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn + +from .registry import ACTIVATION_LAYERS + + +@ACTIVATION_LAYERS.register_module() +class HSigmoid(nn.Module): + """Hard Sigmoid Module. Apply the hard sigmoid function: + Hsigmoid(x) = min(max((x + bias) / divisor, min_value), max_value) + Default: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1) + + Args: + bias (float): Bias of the input feature map. Default: 1.0. + divisor (float): Divisor of the input feature map. Default: 2.0. + min_value (float): Lower bound value. Default: 0.0. + max_value (float): Upper bound value. Default: 1.0. + + Returns: + Tensor: The output tensor. + """ + + def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0): + super(HSigmoid, self).__init__() + self.bias = bias + self.divisor = divisor + assert self.divisor != 0 + self.min_value = min_value + self.max_value = max_value + + def forward(self, x): + x = (x + self.bias) / self.divisor + + return x.clamp_(self.min_value, self.max_value) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/hswish.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/hswish.py new file mode 100644 index 00000000..7e0c090f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/hswish.py @@ -0,0 +1,29 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn + +from .registry import ACTIVATION_LAYERS + + +@ACTIVATION_LAYERS.register_module() +class HSwish(nn.Module): + """Hard Swish Module. + + This module applies the hard swish function: + + .. math:: + Hswish(x) = x * ReLU6(x + 3) / 6 + + Args: + inplace (bool): can optionally do the operation in-place. + Default: False. + + Returns: + Tensor: The output tensor. + """ + + def __init__(self, inplace=False): + super(HSwish, self).__init__() + self.act = nn.ReLU6(inplace) + + def forward(self, x): + return x * self.act(x + 3) / 6 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/non_local.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/non_local.py new file mode 100644 index 00000000..92d00155 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/non_local.py @@ -0,0 +1,306 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from abc import ABCMeta + +import torch +import torch.nn as nn + +from ..utils import constant_init, normal_init +from .conv_module import ConvModule +from .registry import PLUGIN_LAYERS + + +class _NonLocalNd(nn.Module, metaclass=ABCMeta): + """Basic Non-local module. + + This module is proposed in + "Non-local Neural Networks" + Paper reference: https://arxiv.org/abs/1711.07971 + Code reference: https://github.com/AlexHex7/Non-local_pytorch + + Args: + in_channels (int): Channels of the input feature map. + reduction (int): Channel reduction ratio. Default: 2. + use_scale (bool): Whether to scale pairwise_weight by + `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`. + Default: True. + conv_cfg (None | dict): The config dict for convolution layers. + If not specified, it will use `nn.Conv2d` for convolution layers. + Default: None. + norm_cfg (None | dict): The config dict for normalization layers. + Default: None. (This parameter is only applicable to conv_out.) + mode (str): Options are `gaussian`, `concatenation`, + `embedded_gaussian` and `dot_product`. Default: embedded_gaussian. + """ + + def __init__(self, + in_channels, + reduction=2, + use_scale=True, + conv_cfg=None, + norm_cfg=None, + mode='embedded_gaussian', + **kwargs): + super(_NonLocalNd, self).__init__() + self.in_channels = in_channels + self.reduction = reduction + self.use_scale = use_scale + self.inter_channels = max(in_channels // reduction, 1) + self.mode = mode + + if mode not in [ + 'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation' + ]: + raise ValueError("Mode should be in 'gaussian', 'concatenation', " + f"'embedded_gaussian' or 'dot_product', but got " + f'{mode} instead.') + + # g, theta, phi are defaulted as `nn.ConvNd`. + # Here we use ConvModule for potential usage. + self.g = ConvModule( + self.in_channels, + self.inter_channels, + kernel_size=1, + conv_cfg=conv_cfg, + act_cfg=None) + self.conv_out = ConvModule( + self.inter_channels, + self.in_channels, + kernel_size=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + if self.mode != 'gaussian': + self.theta = ConvModule( + self.in_channels, + self.inter_channels, + kernel_size=1, + conv_cfg=conv_cfg, + act_cfg=None) + self.phi = ConvModule( + self.in_channels, + self.inter_channels, + kernel_size=1, + conv_cfg=conv_cfg, + act_cfg=None) + + if self.mode == 'concatenation': + self.concat_project = ConvModule( + self.inter_channels * 2, + 1, + kernel_size=1, + stride=1, + padding=0, + bias=False, + act_cfg=dict(type='ReLU')) + + self.init_weights(**kwargs) + + def init_weights(self, std=0.01, zeros_init=True): + if self.mode != 'gaussian': + for m in [self.g, self.theta, self.phi]: + normal_init(m.conv, std=std) + else: + normal_init(self.g.conv, std=std) + if zeros_init: + if self.conv_out.norm_cfg is None: + constant_init(self.conv_out.conv, 0) + else: + constant_init(self.conv_out.norm, 0) + else: + if self.conv_out.norm_cfg is None: + normal_init(self.conv_out.conv, std=std) + else: + normal_init(self.conv_out.norm, std=std) + + def gaussian(self, theta_x, phi_x): + # NonLocal1d pairwise_weight: [N, H, H] + # NonLocal2d pairwise_weight: [N, HxW, HxW] + # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] + pairwise_weight = torch.matmul(theta_x, phi_x) + pairwise_weight = pairwise_weight.softmax(dim=-1) + return pairwise_weight + + def embedded_gaussian(self, theta_x, phi_x): + # NonLocal1d pairwise_weight: [N, H, H] + # NonLocal2d pairwise_weight: [N, HxW, HxW] + # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] + pairwise_weight = torch.matmul(theta_x, phi_x) + if self.use_scale: + # theta_x.shape[-1] is `self.inter_channels` + pairwise_weight /= theta_x.shape[-1]**0.5 + pairwise_weight = pairwise_weight.softmax(dim=-1) + return pairwise_weight + + def dot_product(self, theta_x, phi_x): + # NonLocal1d pairwise_weight: [N, H, H] + # NonLocal2d pairwise_weight: [N, HxW, HxW] + # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] + pairwise_weight = torch.matmul(theta_x, phi_x) + pairwise_weight /= pairwise_weight.shape[-1] + return pairwise_weight + + def concatenation(self, theta_x, phi_x): + # NonLocal1d pairwise_weight: [N, H, H] + # NonLocal2d pairwise_weight: [N, HxW, HxW] + # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] + h = theta_x.size(2) + w = phi_x.size(3) + theta_x = theta_x.repeat(1, 1, 1, w) + phi_x = phi_x.repeat(1, 1, h, 1) + + concat_feature = torch.cat([theta_x, phi_x], dim=1) + pairwise_weight = self.concat_project(concat_feature) + n, _, h, w = pairwise_weight.size() + pairwise_weight = pairwise_weight.view(n, h, w) + pairwise_weight /= pairwise_weight.shape[-1] + + return pairwise_weight + + def forward(self, x): + # Assume `reduction = 1`, then `inter_channels = C` + # or `inter_channels = C` when `mode="gaussian"` + + # NonLocal1d x: [N, C, H] + # NonLocal2d x: [N, C, H, W] + # NonLocal3d x: [N, C, T, H, W] + n = x.size(0) + + # NonLocal1d g_x: [N, H, C] + # NonLocal2d g_x: [N, HxW, C] + # NonLocal3d g_x: [N, TxHxW, C] + g_x = self.g(x).view(n, self.inter_channels, -1) + g_x = g_x.permute(0, 2, 1) + + # NonLocal1d theta_x: [N, H, C], phi_x: [N, C, H] + # NonLocal2d theta_x: [N, HxW, C], phi_x: [N, C, HxW] + # NonLocal3d theta_x: [N, TxHxW, C], phi_x: [N, C, TxHxW] + if self.mode == 'gaussian': + theta_x = x.view(n, self.in_channels, -1) + theta_x = theta_x.permute(0, 2, 1) + if self.sub_sample: + phi_x = self.phi(x).view(n, self.in_channels, -1) + else: + phi_x = x.view(n, self.in_channels, -1) + elif self.mode == 'concatenation': + theta_x = self.theta(x).view(n, self.inter_channels, -1, 1) + phi_x = self.phi(x).view(n, self.inter_channels, 1, -1) + else: + theta_x = self.theta(x).view(n, self.inter_channels, -1) + theta_x = theta_x.permute(0, 2, 1) + phi_x = self.phi(x).view(n, self.inter_channels, -1) + + pairwise_func = getattr(self, self.mode) + # NonLocal1d pairwise_weight: [N, H, H] + # NonLocal2d pairwise_weight: [N, HxW, HxW] + # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] + pairwise_weight = pairwise_func(theta_x, phi_x) + + # NonLocal1d y: [N, H, C] + # NonLocal2d y: [N, HxW, C] + # NonLocal3d y: [N, TxHxW, C] + y = torch.matmul(pairwise_weight, g_x) + # NonLocal1d y: [N, C, H] + # NonLocal2d y: [N, C, H, W] + # NonLocal3d y: [N, C, T, H, W] + y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels, + *x.size()[2:]) + + output = x + self.conv_out(y) + + return output + + +class NonLocal1d(_NonLocalNd): + """1D Non-local module. + + Args: + in_channels (int): Same as `NonLocalND`. + sub_sample (bool): Whether to apply max pooling after pairwise + function (Note that the `sub_sample` is applied on spatial only). + Default: False. + conv_cfg (None | dict): Same as `NonLocalND`. + Default: dict(type='Conv1d'). + """ + + def __init__(self, + in_channels, + sub_sample=False, + conv_cfg=dict(type='Conv1d'), + **kwargs): + super(NonLocal1d, self).__init__( + in_channels, conv_cfg=conv_cfg, **kwargs) + + self.sub_sample = sub_sample + + if sub_sample: + max_pool_layer = nn.MaxPool1d(kernel_size=2) + self.g = nn.Sequential(self.g, max_pool_layer) + if self.mode != 'gaussian': + self.phi = nn.Sequential(self.phi, max_pool_layer) + else: + self.phi = max_pool_layer + + +@PLUGIN_LAYERS.register_module() +class NonLocal2d(_NonLocalNd): + """2D Non-local module. + + Args: + in_channels (int): Same as `NonLocalND`. + sub_sample (bool): Whether to apply max pooling after pairwise + function (Note that the `sub_sample` is applied on spatial only). + Default: False. + conv_cfg (None | dict): Same as `NonLocalND`. + Default: dict(type='Conv2d'). + """ + + _abbr_ = 'nonlocal_block' + + def __init__(self, + in_channels, + sub_sample=False, + conv_cfg=dict(type='Conv2d'), + **kwargs): + super(NonLocal2d, self).__init__( + in_channels, conv_cfg=conv_cfg, **kwargs) + + self.sub_sample = sub_sample + + if sub_sample: + max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2)) + self.g = nn.Sequential(self.g, max_pool_layer) + if self.mode != 'gaussian': + self.phi = nn.Sequential(self.phi, max_pool_layer) + else: + self.phi = max_pool_layer + + +class NonLocal3d(_NonLocalNd): + """3D Non-local module. + + Args: + in_channels (int): Same as `NonLocalND`. + sub_sample (bool): Whether to apply max pooling after pairwise + function (Note that the `sub_sample` is applied on spatial only). + Default: False. + conv_cfg (None | dict): Same as `NonLocalND`. + Default: dict(type='Conv3d'). + """ + + def __init__(self, + in_channels, + sub_sample=False, + conv_cfg=dict(type='Conv3d'), + **kwargs): + super(NonLocal3d, self).__init__( + in_channels, conv_cfg=conv_cfg, **kwargs) + self.sub_sample = sub_sample + + if sub_sample: + max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2)) + self.g = nn.Sequential(self.g, max_pool_layer) + if self.mode != 'gaussian': + self.phi = nn.Sequential(self.phi, max_pool_layer) + else: + self.phi = max_pool_layer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/norm.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/norm.py new file mode 100644 index 00000000..da7a4d5d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/norm.py @@ -0,0 +1,144 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import inspect + +import torch.nn as nn + +from custom_mmpkg.custom_mmcv.utils import is_tuple_of +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm, _InstanceNorm +from .registry import NORM_LAYERS + +NORM_LAYERS.register_module('BN', module=nn.BatchNorm2d) +NORM_LAYERS.register_module('BN1d', module=nn.BatchNorm1d) +NORM_LAYERS.register_module('BN2d', module=nn.BatchNorm2d) +NORM_LAYERS.register_module('BN3d', module=nn.BatchNorm3d) +NORM_LAYERS.register_module('SyncBN', module=SyncBatchNorm) +NORM_LAYERS.register_module('GN', module=nn.GroupNorm) +NORM_LAYERS.register_module('LN', module=nn.LayerNorm) +NORM_LAYERS.register_module('IN', module=nn.InstanceNorm2d) +NORM_LAYERS.register_module('IN1d', module=nn.InstanceNorm1d) +NORM_LAYERS.register_module('IN2d', module=nn.InstanceNorm2d) +NORM_LAYERS.register_module('IN3d', module=nn.InstanceNorm3d) + + +def infer_abbr(class_type): + """Infer abbreviation from the class name. + + When we build a norm layer with `build_norm_layer()`, we want to preserve + the norm type in variable names, e.g, self.bn1, self.gn. This method will + infer the abbreviation to map class types to abbreviations. + + Rule 1: If the class has the property "_abbr_", return the property. + Rule 2: If the parent class is _BatchNorm, GroupNorm, LayerNorm or + InstanceNorm, the abbreviation of this layer will be "bn", "gn", "ln" and + "in" respectively. + Rule 3: If the class name contains "batch", "group", "layer" or "instance", + the abbreviation of this layer will be "bn", "gn", "ln" and "in" + respectively. + Rule 4: Otherwise, the abbreviation falls back to "norm". + + Args: + class_type (type): The norm layer type. + + Returns: + str: The inferred abbreviation. + """ + if not inspect.isclass(class_type): + raise TypeError( + f'class_type must be a type, but got {type(class_type)}') + if hasattr(class_type, '_abbr_'): + return class_type._abbr_ + if issubclass(class_type, _InstanceNorm): # IN is a subclass of BN + return 'in' + elif issubclass(class_type, _BatchNorm): + return 'bn' + elif issubclass(class_type, nn.GroupNorm): + return 'gn' + elif issubclass(class_type, nn.LayerNorm): + return 'ln' + else: + class_name = class_type.__name__.lower() + if 'batch' in class_name: + return 'bn' + elif 'group' in class_name: + return 'gn' + elif 'layer' in class_name: + return 'ln' + elif 'instance' in class_name: + return 'in' + else: + return 'norm_layer' + + +def build_norm_layer(cfg, num_features, postfix=''): + """Build normalization layer. + + Args: + cfg (dict): The norm layer config, which should contain: + + - type (str): Layer type. + - layer args: Args needed to instantiate a norm layer. + - requires_grad (bool, optional): Whether stop gradient updates. + num_features (int): Number of input channels. + postfix (int | str): The postfix to be appended into norm abbreviation + to create named layer. + + Returns: + (str, nn.Module): The first element is the layer name consisting of + abbreviation and postfix, e.g., bn1, gn. The second element is the + created norm layer. + """ + if not isinstance(cfg, dict): + raise TypeError('cfg must be a dict') + if 'type' not in cfg: + raise KeyError('the cfg dict must contain the key "type"') + cfg_ = cfg.copy() + + layer_type = cfg_.pop('type') + if layer_type not in NORM_LAYERS: + raise KeyError(f'Unrecognized norm type {layer_type}') + + norm_layer = NORM_LAYERS.get(layer_type) + abbr = infer_abbr(norm_layer) + + assert isinstance(postfix, (int, str)) + name = abbr + str(postfix) + + requires_grad = cfg_.pop('requires_grad', True) + cfg_.setdefault('eps', 1e-5) + if layer_type != 'GN': + layer = norm_layer(num_features, **cfg_) + if layer_type == 'SyncBN' and hasattr(layer, '_specify_ddp_gpu_num'): + layer._specify_ddp_gpu_num(1) + else: + assert 'num_groups' in cfg_ + layer = norm_layer(num_channels=num_features, **cfg_) + + for param in layer.parameters(): + param.requires_grad = requires_grad + + return name, layer + + +def is_norm(layer, exclude=None): + """Check if a layer is a normalization layer. + + Args: + layer (nn.Module): The layer to be checked. + exclude (type | tuple[type]): Types to be excluded. + + Returns: + bool: Whether the layer is a norm layer. + """ + if exclude is not None: + if not isinstance(exclude, tuple): + exclude = (exclude, ) + if not is_tuple_of(exclude, type): + raise TypeError( + f'"exclude" must be either None or type or a tuple of types, ' + f'but got {type(exclude)}: {exclude}') + + if exclude and isinstance(layer, exclude): + return False + + all_norm_bases = (_BatchNorm, _InstanceNorm, nn.GroupNorm, nn.LayerNorm) + return isinstance(layer, all_norm_bases) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/padding.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/padding.py new file mode 100644 index 00000000..e4ac6b28 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/padding.py @@ -0,0 +1,36 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn + +from .registry import PADDING_LAYERS + +PADDING_LAYERS.register_module('zero', module=nn.ZeroPad2d) +PADDING_LAYERS.register_module('reflect', module=nn.ReflectionPad2d) +PADDING_LAYERS.register_module('replicate', module=nn.ReplicationPad2d) + + +def build_padding_layer(cfg, *args, **kwargs): + """Build padding layer. + + Args: + cfg (None or dict): The padding layer config, which should contain: + - type (str): Layer type. + - layer args: Args needed to instantiate a padding layer. + + Returns: + nn.Module: Created padding layer. + """ + if not isinstance(cfg, dict): + raise TypeError('cfg must be a dict') + if 'type' not in cfg: + raise KeyError('the cfg dict must contain the key "type"') + + cfg_ = cfg.copy() + padding_type = cfg_.pop('type') + if padding_type not in PADDING_LAYERS: + raise KeyError(f'Unrecognized padding type {padding_type}.') + else: + padding_layer = PADDING_LAYERS.get(padding_type) + + layer = padding_layer(*args, **kwargs, **cfg_) + + return layer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/plugin.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/plugin.py new file mode 100644 index 00000000..07c010d4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/plugin.py @@ -0,0 +1,88 @@ +import inspect +import platform + +from .registry import PLUGIN_LAYERS + +if platform.system() == 'Windows': + import regex as re +else: + import re + + +def infer_abbr(class_type): + """Infer abbreviation from the class name. + + This method will infer the abbreviation to map class types to + abbreviations. + + Rule 1: If the class has the property "abbr", return the property. + Rule 2: Otherwise, the abbreviation falls back to snake case of class + name, e.g. the abbreviation of ``FancyBlock`` will be ``fancy_block``. + + Args: + class_type (type): The norm layer type. + + Returns: + str: The inferred abbreviation. + """ + + def camel2snack(word): + """Convert camel case word into snack case. + + Modified from `inflection lib + `_. + + Example:: + + >>> camel2snack("FancyBlock") + 'fancy_block' + """ + + word = re.sub(r'([A-Z]+)([A-Z][a-z])', r'\1_\2', word) + word = re.sub(r'([a-z\d])([A-Z])', r'\1_\2', word) + word = word.replace('-', '_') + return word.lower() + + if not inspect.isclass(class_type): + raise TypeError( + f'class_type must be a type, but got {type(class_type)}') + if hasattr(class_type, '_abbr_'): + return class_type._abbr_ + else: + return camel2snack(class_type.__name__) + + +def build_plugin_layer(cfg, postfix='', **kwargs): + """Build plugin layer. + + Args: + cfg (None or dict): cfg should contain: + type (str): identify plugin layer type. + layer args: args needed to instantiate a plugin layer. + postfix (int, str): appended into norm abbreviation to + create named layer. Default: ''. + + Returns: + tuple[str, nn.Module]: + name (str): abbreviation + postfix + layer (nn.Module): created plugin layer + """ + if not isinstance(cfg, dict): + raise TypeError('cfg must be a dict') + if 'type' not in cfg: + raise KeyError('the cfg dict must contain the key "type"') + cfg_ = cfg.copy() + + layer_type = cfg_.pop('type') + if layer_type not in PLUGIN_LAYERS: + raise KeyError(f'Unrecognized plugin type {layer_type}') + + plugin_layer = PLUGIN_LAYERS.get(layer_type) + abbr = infer_abbr(plugin_layer) + + assert isinstance(postfix, (int, str)) + name = abbr + str(postfix) + + layer = plugin_layer(**kwargs, **cfg_) + + return name, layer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/registry.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/registry.py new file mode 100644 index 00000000..496c1879 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/registry.py @@ -0,0 +1,16 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from custom_mmpkg.custom_mmcv.utils import Registry + +CONV_LAYERS = Registry('conv layer') +NORM_LAYERS = Registry('norm layer') +ACTIVATION_LAYERS = Registry('activation layer') +PADDING_LAYERS = Registry('padding layer') +UPSAMPLE_LAYERS = Registry('upsample layer') +PLUGIN_LAYERS = Registry('plugin layer') + +DROPOUT_LAYERS = Registry('drop out layers') +POSITIONAL_ENCODING = Registry('position encoding') +ATTENTION = Registry('attention') +FEEDFORWARD_NETWORK = Registry('feed-forward Network') +TRANSFORMER_LAYER = Registry('transformerLayer') +TRANSFORMER_LAYER_SEQUENCE = Registry('transformer-layers sequence') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/scale.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/scale.py new file mode 100644 index 00000000..c905fffc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/scale.py @@ -0,0 +1,21 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn + + +class Scale(nn.Module): + """A learnable scale parameter. + + This layer scales the input by a learnable factor. It multiplies a + learnable scale parameter of shape (1,) with input of any shape. + + Args: + scale (float): Initial value of scale factor. Default: 1.0 + """ + + def __init__(self, scale=1.0): + super(Scale, self).__init__() + self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float)) + + def forward(self, x): + return x * self.scale diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/swish.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/swish.py new file mode 100644 index 00000000..e2ca8ed7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/swish.py @@ -0,0 +1,25 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn + +from .registry import ACTIVATION_LAYERS + + +@ACTIVATION_LAYERS.register_module() +class Swish(nn.Module): + """Swish Module. + + This module applies the swish function: + + .. math:: + Swish(x) = x * Sigmoid(x) + + Returns: + Tensor: The output tensor. + """ + + def __init__(self): + super(Swish, self).__init__() + + def forward(self, x): + return x * torch.sigmoid(x) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/transformer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/transformer.py new file mode 100644 index 00000000..d4cd4655 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/transformer.py @@ -0,0 +1,595 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +import warnings + +import torch +import torch.nn as nn + +from custom_mmpkg.custom_mmcv import ConfigDict, deprecated_api_warning +from custom_mmpkg.custom_mmcv.cnn import Linear, build_activation_layer, build_norm_layer +from custom_mmpkg.custom_mmcv.runner.base_module import BaseModule, ModuleList, Sequential +from custom_mmpkg.custom_mmcv.utils import build_from_cfg +from .drop import build_dropout +from .registry import (ATTENTION, FEEDFORWARD_NETWORK, POSITIONAL_ENCODING, + TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE) + +# Avoid BC-breaking of importing MultiScaleDeformableAttention from this file +try: + from custom_mmpkg.custom_mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention # noqa F401 + warnings.warn( + ImportWarning( + '``MultiScaleDeformableAttention`` has been moved to ' + '``mmcv.ops.multi_scale_deform_attn``, please change original path ' # noqa E501 + '``from custom_mmpkg.custom_mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention`` ' # noqa E501 + 'to ``from custom_mmpkg.custom_mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention`` ' # noqa E501 + )) + +except ImportError: + warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from ' + '``mmcv.ops.multi_scale_deform_attn``, ' + 'You should install ``mmcv-full`` if you need this module. ') + + +def build_positional_encoding(cfg, default_args=None): + """Builder for Position Encoding.""" + return build_from_cfg(cfg, POSITIONAL_ENCODING, default_args) + + +def build_attention(cfg, default_args=None): + """Builder for attention.""" + return build_from_cfg(cfg, ATTENTION, default_args) + + +def build_feedforward_network(cfg, default_args=None): + """Builder for feed-forward network (FFN).""" + return build_from_cfg(cfg, FEEDFORWARD_NETWORK, default_args) + + +def build_transformer_layer(cfg, default_args=None): + """Builder for transformer layer.""" + return build_from_cfg(cfg, TRANSFORMER_LAYER, default_args) + + +def build_transformer_layer_sequence(cfg, default_args=None): + """Builder for transformer encoder and transformer decoder.""" + return build_from_cfg(cfg, TRANSFORMER_LAYER_SEQUENCE, default_args) + + +@ATTENTION.register_module() +class MultiheadAttention(BaseModule): + """A wrapper for ``torch.nn.MultiheadAttention``. + + This module implements MultiheadAttention with identity connection, + and positional encoding is also passed as input. + + Args: + embed_dims (int): The embedding dimension. + num_heads (int): Parallel attention heads. + attn_drop (float): A Dropout layer on attn_output_weights. + Default: 0.0. + proj_drop (float): A Dropout layer after `nn.MultiheadAttention`. + Default: 0.0. + dropout_layer (obj:`ConfigDict`): The dropout_layer used + when adding the shortcut. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + batch_first (bool): When it is True, Key, Query and Value are shape of + (batch, n, embed_dim), otherwise (n, batch, embed_dim). + Default to False. + """ + + def __init__(self, + embed_dims, + num_heads, + attn_drop=0., + proj_drop=0., + dropout_layer=dict(type='Dropout', drop_prob=0.), + init_cfg=None, + batch_first=False, + **kwargs): + super(MultiheadAttention, self).__init__(init_cfg) + if 'dropout' in kwargs: + warnings.warn('The arguments `dropout` in MultiheadAttention ' + 'has been deprecated, now you can separately ' + 'set `attn_drop`(float), proj_drop(float), ' + 'and `dropout_layer`(dict) ') + attn_drop = kwargs['dropout'] + dropout_layer['drop_prob'] = kwargs.pop('dropout') + + self.embed_dims = embed_dims + self.num_heads = num_heads + self.batch_first = batch_first + + self.attn = nn.MultiheadAttention(embed_dims, num_heads, attn_drop, + **kwargs) + + self.proj_drop = nn.Dropout(proj_drop) + self.dropout_layer = build_dropout( + dropout_layer) if dropout_layer else nn.Identity() + + @deprecated_api_warning({'residual': 'identity'}, + cls_name='MultiheadAttention') + def forward(self, + query, + key=None, + value=None, + identity=None, + query_pos=None, + key_pos=None, + attn_mask=None, + key_padding_mask=None, + **kwargs): + """Forward function for `MultiheadAttention`. + + **kwargs allow passing a more general data flow when combining + with other operations in `transformerlayer`. + + Args: + query (Tensor): The input query with shape [num_queries, bs, + embed_dims] if self.batch_first is False, else + [bs, num_queries embed_dims]. + key (Tensor): The key tensor with shape [num_keys, bs, + embed_dims] if self.batch_first is False, else + [bs, num_keys, embed_dims] . + If None, the ``query`` will be used. Defaults to None. + value (Tensor): The value tensor with same shape as `key`. + Same in `nn.MultiheadAttention.forward`. Defaults to None. + If None, the `key` will be used. + identity (Tensor): This tensor, with the same shape as x, + will be used for the identity link. + If None, `x` will be used. Defaults to None. + query_pos (Tensor): The positional encoding for query, with + the same shape as `x`. If not None, it will + be added to `x` before forward function. Defaults to None. + key_pos (Tensor): The positional encoding for `key`, with the + same shape as `key`. Defaults to None. If not None, it will + be added to `key` before forward function. If None, and + `query_pos` has the same shape as `key`, then `query_pos` + will be used for `key_pos`. Defaults to None. + attn_mask (Tensor): ByteTensor mask with shape [num_queries, + num_keys]. Same in `nn.MultiheadAttention.forward`. + Defaults to None. + key_padding_mask (Tensor): ByteTensor with shape [bs, num_keys]. + Defaults to None. + + Returns: + Tensor: forwarded results with shape + [num_queries, bs, embed_dims] + if self.batch_first is False, else + [bs, num_queries embed_dims]. + """ + + if key is None: + key = query + if value is None: + value = key + if identity is None: + identity = query + if key_pos is None: + if query_pos is not None: + # use query_pos if key_pos is not available + if query_pos.shape == key.shape: + key_pos = query_pos + else: + warnings.warn(f'position encoding of key is' + f'missing in {self.__class__.__name__}.') + if query_pos is not None: + query = query + query_pos + if key_pos is not None: + key = key + key_pos + + # Because the dataflow('key', 'query', 'value') of + # ``torch.nn.MultiheadAttention`` is (num_query, batch, + # embed_dims), We should adjust the shape of dataflow from + # batch_first (batch, num_query, embed_dims) to num_query_first + # (num_query ,batch, embed_dims), and recover ``attn_output`` + # from num_query_first to batch_first. + if self.batch_first: + query = query.transpose(0, 1) + key = key.transpose(0, 1) + value = value.transpose(0, 1) + + out = self.attn( + query=query, + key=key, + value=value, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask)[0] + + if self.batch_first: + out = out.transpose(0, 1) + + return identity + self.dropout_layer(self.proj_drop(out)) + + +@FEEDFORWARD_NETWORK.register_module() +class FFN(BaseModule): + """Implements feed-forward networks (FFNs) with identity connection. + + Args: + embed_dims (int): The feature dimension. Same as + `MultiheadAttention`. Defaults: 256. + feedforward_channels (int): The hidden dimension of FFNs. + Defaults: 1024. + num_fcs (int, optional): The number of fully-connected layers in + FFNs. Default: 2. + act_cfg (dict, optional): The activation config for FFNs. + Default: dict(type='ReLU') + ffn_drop (float, optional): Probability of an element to be + zeroed in FFN. Default 0.0. + add_identity (bool, optional): Whether to add the + identity connection. Default: `True`. + dropout_layer (obj:`ConfigDict`): The dropout_layer used + when adding the shortcut. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + @deprecated_api_warning( + { + 'dropout': 'ffn_drop', + 'add_residual': 'add_identity' + }, + cls_name='FFN') + def __init__(self, + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0., + dropout_layer=None, + add_identity=True, + init_cfg=None, + **kwargs): + super(FFN, self).__init__(init_cfg) + assert num_fcs >= 2, 'num_fcs should be no less ' \ + f'than 2. got {num_fcs}.' + self.embed_dims = embed_dims + self.feedforward_channels = feedforward_channels + self.num_fcs = num_fcs + self.act_cfg = act_cfg + self.activate = build_activation_layer(act_cfg) + + layers = [] + in_channels = embed_dims + for _ in range(num_fcs - 1): + layers.append( + Sequential( + Linear(in_channels, feedforward_channels), self.activate, + nn.Dropout(ffn_drop))) + in_channels = feedforward_channels + layers.append(Linear(feedforward_channels, embed_dims)) + layers.append(nn.Dropout(ffn_drop)) + self.layers = Sequential(*layers) + self.dropout_layer = build_dropout( + dropout_layer) if dropout_layer else torch.nn.Identity() + self.add_identity = add_identity + + @deprecated_api_warning({'residual': 'identity'}, cls_name='FFN') + def forward(self, x, identity=None): + """Forward function for `FFN`. + + The function would add x to the output tensor if residue is None. + """ + out = self.layers(x) + if not self.add_identity: + return self.dropout_layer(out) + if identity is None: + identity = x + return identity + self.dropout_layer(out) + + +@TRANSFORMER_LAYER.register_module() +class BaseTransformerLayer(BaseModule): + """Base `TransformerLayer` for vision transformer. + + It can be built from `mmcv.ConfigDict` and support more flexible + customization, for example, using any number of `FFN or LN ` and + use different kinds of `attention` by specifying a list of `ConfigDict` + named `attn_cfgs`. It is worth mentioning that it supports `prenorm` + when you specifying `norm` as the first element of `operation_order`. + More details about the `prenorm`: `On Layer Normalization in the + Transformer Architecture `_ . + + Args: + attn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )): + Configs for `self_attention` or `cross_attention` modules, + The order of the configs in the list should be consistent with + corresponding attentions in operation_order. + If it is a dict, all of the attention modules in operation_order + will be built with this config. Default: None. + ffn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )): + Configs for FFN, The order of the configs in the list should be + consistent with corresponding ffn in operation_order. + If it is a dict, all of the attention modules in operation_order + will be built with this config. + operation_order (tuple[str]): The execution order of operation + in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). + Support `prenorm` when you specifying first element as `norm`. + Default：None. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='LN'). + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + batch_first (bool): Key, Query and Value are shape + of (batch, n, embed_dim) + or (n, batch, embed_dim). Default to False. + """ + + def __init__(self, + attn_cfgs=None, + ffn_cfgs=dict( + type='FFN', + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0., + act_cfg=dict(type='ReLU', inplace=True), + ), + operation_order=None, + norm_cfg=dict(type='LN'), + init_cfg=None, + batch_first=False, + **kwargs): + + deprecated_args = dict( + feedforward_channels='feedforward_channels', + ffn_dropout='ffn_drop', + ffn_num_fcs='num_fcs') + for ori_name, new_name in deprecated_args.items(): + if ori_name in kwargs: + warnings.warn( + f'The arguments `{ori_name}` in BaseTransformerLayer ' + f'has been deprecated, now you should set `{new_name}` ' + f'and other FFN related arguments ' + f'to a dict named `ffn_cfgs`. ') + ffn_cfgs[new_name] = kwargs[ori_name] + + super(BaseTransformerLayer, self).__init__(init_cfg) + + self.batch_first = batch_first + + assert set(operation_order) & set( + ['self_attn', 'norm', 'ffn', 'cross_attn']) == \ + set(operation_order), f'The operation_order of' \ + f' {self.__class__.__name__} should ' \ + f'contains all four operation type ' \ + f"{['self_attn', 'norm', 'ffn', 'cross_attn']}" + + num_attn = operation_order.count('self_attn') + operation_order.count( + 'cross_attn') + if isinstance(attn_cfgs, dict): + attn_cfgs = [copy.deepcopy(attn_cfgs) for _ in range(num_attn)] + else: + assert num_attn == len(attn_cfgs), f'The length ' \ + f'of attn_cfg {num_attn} is ' \ + f'not consistent with the number of attention' \ + f'in operation_order {operation_order}.' + + self.num_attn = num_attn + self.operation_order = operation_order + self.norm_cfg = norm_cfg + self.pre_norm = operation_order[0] == 'norm' + self.attentions = ModuleList() + + index = 0 + for operation_name in operation_order: + if operation_name in ['self_attn', 'cross_attn']: + if 'batch_first' in attn_cfgs[index]: + assert self.batch_first == attn_cfgs[index]['batch_first'] + else: + attn_cfgs[index]['batch_first'] = self.batch_first + attention = build_attention(attn_cfgs[index]) + # Some custom attentions used as `self_attn` + # or `cross_attn` can have different behavior. + attention.operation_name = operation_name + self.attentions.append(attention) + index += 1 + + self.embed_dims = self.attentions[0].embed_dims + + self.ffns = ModuleList() + num_ffns = operation_order.count('ffn') + if isinstance(ffn_cfgs, dict): + ffn_cfgs = ConfigDict(ffn_cfgs) + if isinstance(ffn_cfgs, dict): + ffn_cfgs = [copy.deepcopy(ffn_cfgs) for _ in range(num_ffns)] + assert len(ffn_cfgs) == num_ffns + for ffn_index in range(num_ffns): + if 'embed_dims' not in ffn_cfgs[ffn_index]: + ffn_cfgs['embed_dims'] = self.embed_dims + else: + assert ffn_cfgs[ffn_index]['embed_dims'] == self.embed_dims + self.ffns.append( + build_feedforward_network(ffn_cfgs[ffn_index], + dict(type='FFN'))) + + self.norms = ModuleList() + num_norms = operation_order.count('norm') + for _ in range(num_norms): + self.norms.append(build_norm_layer(norm_cfg, self.embed_dims)[1]) + + def forward(self, + query, + key=None, + value=None, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + **kwargs): + """Forward function for `TransformerDecoderLayer`. + + **kwargs contains some specific arguments of attentions. + + Args: + query (Tensor): The input query with shape + [num_queries, bs, embed_dims] if + self.batch_first is False, else + [bs, num_queries embed_dims]. + key (Tensor): The key tensor with shape [num_keys, bs, + embed_dims] if self.batch_first is False, else + [bs, num_keys, embed_dims] . + value (Tensor): The value tensor with same shape as `key`. + query_pos (Tensor): The positional encoding for `query`. + Default: None. + key_pos (Tensor): The positional encoding for `key`. + Default: None. + attn_masks (List[Tensor] | None): 2D Tensor used in + calculation of corresponding attention. The length of + it should equal to the number of `attention` in + `operation_order`. Default: None. + query_key_padding_mask (Tensor): ByteTensor for `query`, with + shape [bs, num_queries]. Only used in `self_attn` layer. + Defaults to None. + key_padding_mask (Tensor): ByteTensor for `query`, with + shape [bs, num_keys]. Default: None. + + Returns: + Tensor: forwarded results with shape [num_queries, bs, embed_dims]. + """ + + norm_index = 0 + attn_index = 0 + ffn_index = 0 + identity = query + if attn_masks is None: + attn_masks = [None for _ in range(self.num_attn)] + elif isinstance(attn_masks, torch.Tensor): + attn_masks = [ + copy.deepcopy(attn_masks) for _ in range(self.num_attn) + ] + warnings.warn(f'Use same attn_mask in all attentions in ' + f'{self.__class__.__name__} ') + else: + assert len(attn_masks) == self.num_attn, f'The length of ' \ + f'attn_masks {len(attn_masks)} must be equal ' \ + f'to the number of attention in ' \ + f'operation_order {self.num_attn}' + + for layer in self.operation_order: + if layer == 'self_attn': + temp_key = temp_value = query + query = self.attentions[attn_index]( + query, + temp_key, + temp_value, + identity if self.pre_norm else None, + query_pos=query_pos, + key_pos=query_pos, + attn_mask=attn_masks[attn_index], + key_padding_mask=query_key_padding_mask, + **kwargs) + attn_index += 1 + identity = query + + elif layer == 'norm': + query = self.norms[norm_index](query) + norm_index += 1 + + elif layer == 'cross_attn': + query = self.attentions[attn_index]( + query, + key, + value, + identity if self.pre_norm else None, + query_pos=query_pos, + key_pos=key_pos, + attn_mask=attn_masks[attn_index], + key_padding_mask=key_padding_mask, + **kwargs) + attn_index += 1 + identity = query + + elif layer == 'ffn': + query = self.ffns[ffn_index]( + query, identity if self.pre_norm else None) + ffn_index += 1 + + return query + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class TransformerLayerSequence(BaseModule): + """Base class for TransformerEncoder and TransformerDecoder in vision + transformer. + + As base-class of Encoder and Decoder in vision transformer. + Support customization such as specifying different kind + of `transformer_layer` in `transformer_coder`. + + Args: + transformerlayer (list[obj:`mmcv.ConfigDict`] | + obj:`mmcv.ConfigDict`): Config of transformerlayer + in TransformerCoder. If it is obj:`mmcv.ConfigDict`, + it would be repeated `num_layer` times to a + list[`mmcv.ConfigDict`]. Default: None. + num_layers (int): The number of `TransformerLayer`. Default: None. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, transformerlayers=None, num_layers=None, init_cfg=None): + super(TransformerLayerSequence, self).__init__(init_cfg) + if isinstance(transformerlayers, dict): + transformerlayers = [ + copy.deepcopy(transformerlayers) for _ in range(num_layers) + ] + else: + assert isinstance(transformerlayers, list) and \ + len(transformerlayers) == num_layers + self.num_layers = num_layers + self.layers = ModuleList() + for i in range(num_layers): + self.layers.append(build_transformer_layer(transformerlayers[i])) + self.embed_dims = self.layers[0].embed_dims + self.pre_norm = self.layers[0].pre_norm + + def forward(self, + query, + key, + value, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + **kwargs): + """Forward function for `TransformerCoder`. + + Args: + query (Tensor): Input query with shape + `(num_queries, bs, embed_dims)`. + key (Tensor): The key tensor with shape + `(num_keys, bs, embed_dims)`. + value (Tensor): The value tensor with shape + `(num_keys, bs, embed_dims)`. + query_pos (Tensor): The positional encoding for `query`. + Default: None. + key_pos (Tensor): The positional encoding for `key`. + Default: None. + attn_masks (List[Tensor], optional): Each element is 2D Tensor + which is used in calculation of corresponding attention in + operation_order. Default: None. + query_key_padding_mask (Tensor): ByteTensor for `query`, with + shape [bs, num_queries]. Only used in self-attention + Default: None. + key_padding_mask (Tensor): ByteTensor for `query`, with + shape [bs, num_keys]. Default: None. + + Returns: + Tensor: results with shape [num_queries, bs, embed_dims]. + """ + for layer in self.layers: + query = layer( + query, + key, + value, + query_pos=query_pos, + key_pos=key_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + **kwargs) + return query diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/upsample.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/upsample.py new file mode 100644 index 00000000..a1a35376 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/upsample.py @@ -0,0 +1,84 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn +import torch.nn.functional as F + +from ..utils import xavier_init +from .registry import UPSAMPLE_LAYERS + +UPSAMPLE_LAYERS.register_module('nearest', module=nn.Upsample) +UPSAMPLE_LAYERS.register_module('bilinear', module=nn.Upsample) + + +@UPSAMPLE_LAYERS.register_module(name='pixel_shuffle') +class PixelShufflePack(nn.Module): + """Pixel Shuffle upsample layer. + + This module packs `F.pixel_shuffle()` and a nn.Conv2d module together to + achieve a simple upsampling with pixel shuffle. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + scale_factor (int): Upsample ratio. + upsample_kernel (int): Kernel size of the conv layer to expand the + channels. + """ + + def __init__(self, in_channels, out_channels, scale_factor, + upsample_kernel): + super(PixelShufflePack, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.scale_factor = scale_factor + self.upsample_kernel = upsample_kernel + self.upsample_conv = nn.Conv2d( + self.in_channels, + self.out_channels * scale_factor * scale_factor, + self.upsample_kernel, + padding=(self.upsample_kernel - 1) // 2) + self.init_weights() + + def init_weights(self): + xavier_init(self.upsample_conv, distribution='uniform') + + def forward(self, x): + x = self.upsample_conv(x) + x = F.pixel_shuffle(x, self.scale_factor) + return x + + +def build_upsample_layer(cfg, *args, **kwargs): + """Build upsample layer. + + Args: + cfg (dict): The upsample layer config, which should contain: + + - type (str): Layer type. + - scale_factor (int): Upsample ratio, which is not applicable to + deconv. + - layer args: Args needed to instantiate a upsample layer. + args (argument list): Arguments passed to the ``__init__`` + method of the corresponding conv layer. + kwargs (keyword arguments): Keyword arguments passed to the + ``__init__`` method of the corresponding conv layer. + + Returns: + nn.Module: Created upsample layer. + """ + if not isinstance(cfg, dict): + raise TypeError(f'cfg must be a dict, but got {type(cfg)}') + if 'type' not in cfg: + raise KeyError( + f'the cfg dict must contain the key "type", but got {cfg}') + cfg_ = cfg.copy() + + layer_type = cfg_.pop('type') + if layer_type not in UPSAMPLE_LAYERS: + raise KeyError(f'Unrecognized upsample type {layer_type}') + else: + upsample = UPSAMPLE_LAYERS.get(layer_type) + + if upsample is nn.Upsample: + cfg_['mode'] = layer_type + layer = upsample(*args, **kwargs, **cfg_) + return layer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/wrappers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/wrappers.py new file mode 100644 index 00000000..8aebf67b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/bricks/wrappers.py @@ -0,0 +1,180 @@ +# Copyright (c) OpenMMLab. All rights reserved. +r"""Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/wrappers.py # noqa: E501 + +Wrap some nn modules to support empty tensor input. Currently, these wrappers +are mainly used in mask heads like fcn_mask_head and maskiou_heads since mask +heads are trained on only positive RoIs. +""" +import math + +import torch +import torch.nn as nn +from torch.nn.modules.utils import _pair, _triple + +from .registry import CONV_LAYERS, UPSAMPLE_LAYERS + +if torch.__version__ == 'parrots': + TORCH_VERSION = torch.__version__ +else: + # torch.__version__ could be 1.3.1+cu92, we only need the first two + # for comparison + TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2]) + + +def obsolete_torch_version(torch_version, version_threshold): + return torch_version == 'parrots' or torch_version <= version_threshold + + +class NewEmptyTensorOp(torch.autograd.Function): + + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return NewEmptyTensorOp.apply(grad, shape), None + + +@CONV_LAYERS.register_module('Conv', force=True) +class Conv2d(nn.Conv2d): + + def forward(self, x): + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)): + out_shape = [x.shape[0], self.out_channels] + for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size, + self.padding, self.stride, self.dilation): + o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1 + out_shape.append(o) + empty = NewEmptyTensorOp.apply(x, out_shape) + if self.training: + # produce dummy gradient to avoid DDP warning. + dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + dummy + else: + return empty + + return super().forward(x) + + +@CONV_LAYERS.register_module('Conv3d', force=True) +class Conv3d(nn.Conv3d): + + def forward(self, x): + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)): + out_shape = [x.shape[0], self.out_channels] + for i, k, p, s, d in zip(x.shape[-3:], self.kernel_size, + self.padding, self.stride, self.dilation): + o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1 + out_shape.append(o) + empty = NewEmptyTensorOp.apply(x, out_shape) + if self.training: + # produce dummy gradient to avoid DDP warning. + dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + dummy + else: + return empty + + return super().forward(x) + + +@CONV_LAYERS.register_module() +@CONV_LAYERS.register_module('deconv') +@UPSAMPLE_LAYERS.register_module('deconv', force=True) +class ConvTranspose2d(nn.ConvTranspose2d): + + def forward(self, x): + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)): + out_shape = [x.shape[0], self.out_channels] + for i, k, p, s, d, op in zip(x.shape[-2:], self.kernel_size, + self.padding, self.stride, + self.dilation, self.output_padding): + out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op) + empty = NewEmptyTensorOp.apply(x, out_shape) + if self.training: + # produce dummy gradient to avoid DDP warning. + dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + dummy + else: + return empty + + return super().forward(x) + + +@CONV_LAYERS.register_module() +@CONV_LAYERS.register_module('deconv3d') +@UPSAMPLE_LAYERS.register_module('deconv3d', force=True) +class ConvTranspose3d(nn.ConvTranspose3d): + + def forward(self, x): + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)): + out_shape = [x.shape[0], self.out_channels] + for i, k, p, s, d, op in zip(x.shape[-3:], self.kernel_size, + self.padding, self.stride, + self.dilation, self.output_padding): + out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op) + empty = NewEmptyTensorOp.apply(x, out_shape) + if self.training: + # produce dummy gradient to avoid DDP warning. + dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + dummy + else: + return empty + + return super().forward(x) + + +class MaxPool2d(nn.MaxPool2d): + + def forward(self, x): + # PyTorch 1.9 does not support empty tensor inference yet + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)): + out_shape = list(x.shape[:2]) + for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size), + _pair(self.padding), _pair(self.stride), + _pair(self.dilation)): + o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1 + o = math.ceil(o) if self.ceil_mode else math.floor(o) + out_shape.append(o) + empty = NewEmptyTensorOp.apply(x, out_shape) + return empty + + return super().forward(x) + + +class MaxPool3d(nn.MaxPool3d): + + def forward(self, x): + # PyTorch 1.9 does not support empty tensor inference yet + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)): + out_shape = list(x.shape[:2]) + for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size), + _triple(self.padding), + _triple(self.stride), + _triple(self.dilation)): + o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1 + o = math.ceil(o) if self.ceil_mode else math.floor(o) + out_shape.append(o) + empty = NewEmptyTensorOp.apply(x, out_shape) + return empty + + return super().forward(x) + + +class Linear(torch.nn.Linear): + + def forward(self, x): + # empty tensor forward of Linear layer is supported in Pytorch 1.6 + if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 5)): + out_shape = [x.shape[0], self.out_features] + empty = NewEmptyTensorOp.apply(x, out_shape) + if self.training: + # produce dummy gradient to avoid DDP warning. + dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + dummy + else: + return empty + + return super().forward(x) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/builder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/builder.py new file mode 100644 index 00000000..7567316c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/builder.py @@ -0,0 +1,30 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ..runner import Sequential +from ..utils import Registry, build_from_cfg + + +def build_model_from_cfg(cfg, registry, default_args=None): + """Build a PyTorch model from config dict(s). Different from + ``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will be built. + + Args: + cfg (dict, list[dict]): The config of modules, is is either a config + dict or a list of config dicts. If cfg is a list, a + the built modules will be wrapped with ``nn.Sequential``. + registry (:obj:`Registry`): A registry the module belongs to. + default_args (dict, optional): Default arguments to build the module. + Defaults to None. + + Returns: + nn.Module: A built nn module. + """ + if isinstance(cfg, list): + modules = [ + build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg + ] + return Sequential(*modules) + else: + return build_from_cfg(cfg, registry, default_args) + + +MODELS = Registry('model', build_func=build_model_from_cfg) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/resnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/resnet.py new file mode 100644 index 00000000..1cb3ac05 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/resnet.py @@ -0,0 +1,316 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import logging + +import torch.nn as nn +import torch.utils.checkpoint as cp + +from .utils import constant_init, kaiming_init + + +def conv3x3(in_planes, out_planes, stride=1, dilation=1): + """3x3 convolution with padding.""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False): + super(BasicBlock, self).__init__() + assert style in ['pytorch', 'caffe'] + self.conv1 = conv3x3(inplanes, planes, stride, dilation) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + assert not with_cp + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False): + """Bottleneck block. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottleneck, self).__init__() + assert style in ['pytorch', 'caffe'] + if style == 'pytorch': + conv1_stride = 1 + conv2_stride = stride + else: + conv1_stride = stride + conv2_stride = 1 + self.conv1 = nn.Conv2d( + inplanes, planes, kernel_size=1, stride=conv1_stride, bias=False) + self.conv2 = nn.Conv2d( + planes, + planes, + kernel_size=3, + stride=conv2_stride, + padding=dilation, + dilation=dilation, + bias=False) + + self.bn1 = nn.BatchNorm2d(planes) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d( + planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + self.with_cp = with_cp + + def forward(self, x): + + def _inner_forward(x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +def make_res_layer(block, + inplanes, + planes, + blocks, + stride=1, + dilation=1, + style='pytorch', + with_cp=False): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + inplanes, + planes, + stride, + dilation, + downsample, + style=style, + with_cp=with_cp)) + inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block(inplanes, planes, 1, dilation, style=style, with_cp=with_cp)) + + return nn.Sequential(*layers) + + +class ResNet(nn.Module): + """ResNet backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. + num_stages (int): Resnet stages, normally 4. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze + running stats (mean and var). + bn_frozen (bool): Whether to freeze weight and bias of BN layers. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + """ + + arch_settings = { + 18: (BasicBlock, (2, 2, 2, 2)), + 34: (BasicBlock, (3, 4, 6, 3)), + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, + depth, + num_stages=4, + strides=(1, 2, 2, 2), + dilations=(1, 1, 1, 1), + out_indices=(0, 1, 2, 3), + style='pytorch', + frozen_stages=-1, + bn_eval=True, + bn_frozen=False, + with_cp=False): + super(ResNet, self).__init__() + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for resnet') + assert num_stages >= 1 and num_stages <= 4 + block, stage_blocks = self.arch_settings[depth] + stage_blocks = stage_blocks[:num_stages] + assert len(strides) == len(dilations) == num_stages + assert max(out_indices) < num_stages + + self.out_indices = out_indices + self.style = style + self.frozen_stages = frozen_stages + self.bn_eval = bn_eval + self.bn_frozen = bn_frozen + self.with_cp = with_cp + + self.inplanes = 64 + self.conv1 = nn.Conv2d( + 3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.res_layers = [] + for i, num_blocks in enumerate(stage_blocks): + stride = strides[i] + dilation = dilations[i] + planes = 64 * 2**i + res_layer = make_res_layer( + block, + self.inplanes, + planes, + num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + with_cp=with_cp) + self.inplanes = planes * block.expansion + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self.feat_dim = block.expansion * 64 * 2**(len(stage_blocks) - 1) + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = logging.getLogger() + from ..runner import load_checkpoint + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, nn.BatchNorm2d): + constant_init(m, 1) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + if len(outs) == 1: + return outs[0] + else: + return tuple(outs) + + def train(self, mode=True): + super(ResNet, self).train(mode) + if self.bn_eval: + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() + if self.bn_frozen: + for params in m.parameters(): + params.requires_grad = False + if mode and self.frozen_stages >= 0: + for param in self.conv1.parameters(): + param.requires_grad = False + for param in self.bn1.parameters(): + param.requires_grad = False + self.bn1.eval() + self.bn1.weight.requires_grad = False + self.bn1.bias.requires_grad = False + for i in range(1, self.frozen_stages + 1): + mod = getattr(self, f'layer{i}') + mod.eval() + for param in mod.parameters(): + param.requires_grad = False diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/__init__.py new file mode 100644 index 00000000..a263e31c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .flops_counter import get_model_complexity_info +from .fuse_conv_bn import fuse_conv_bn +from .sync_bn import revert_sync_batchnorm +from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit, + KaimingInit, NormalInit, PretrainedInit, + TruncNormalInit, UniformInit, XavierInit, + bias_init_with_prob, caffe2_xavier_init, + constant_init, initialize, kaiming_init, normal_init, + trunc_normal_init, uniform_init, xavier_init) + +__all__ = [ + 'get_model_complexity_info', 'bias_init_with_prob', 'caffe2_xavier_init', + 'constant_init', 'kaiming_init', 'normal_init', 'trunc_normal_init', + 'uniform_init', 'xavier_init', 'fuse_conv_bn', 'initialize', + 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit', + 'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit', + 'Caffe2XavierInit', 'revert_sync_batchnorm' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/flops_counter.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/flops_counter.py new file mode 100644 index 00000000..a445d7a0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/flops_counter.py @@ -0,0 +1,599 @@ +# Modified from flops-counter.pytorch by Vladislav Sovrasov +# original repo: https://github.com/sovrasov/flops-counter.pytorch + +# MIT License + +# Copyright (c) 2018 Vladislav Sovrasov + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import sys +from functools import partial + +import numpy as np +import torch +import torch.nn as nn + +import custom_mmpkg.custom_mmcv as mmcv + + +def get_model_complexity_info(model, + input_shape, + print_per_layer_stat=True, + as_strings=True, + input_constructor=None, + flush=False, + ost=sys.stdout): + """Get complexity information of a model. + + This method can calculate FLOPs and parameter counts of a model with + corresponding input shape. It can also print complexity information for + each layer in a model. + + Supported layers are listed as below: + - Convolutions: ``nn.Conv1d``, ``nn.Conv2d``, ``nn.Conv3d``. + - Activations: ``nn.ReLU``, ``nn.PReLU``, ``nn.ELU``, ``nn.LeakyReLU``, + ``nn.ReLU6``. + - Poolings: ``nn.MaxPool1d``, ``nn.MaxPool2d``, ``nn.MaxPool3d``, + ``nn.AvgPool1d``, ``nn.AvgPool2d``, ``nn.AvgPool3d``, + ``nn.AdaptiveMaxPool1d``, ``nn.AdaptiveMaxPool2d``, + ``nn.AdaptiveMaxPool3d``, ``nn.AdaptiveAvgPool1d``, + ``nn.AdaptiveAvgPool2d``, ``nn.AdaptiveAvgPool3d``. + - BatchNorms: ``nn.BatchNorm1d``, ``nn.BatchNorm2d``, + ``nn.BatchNorm3d``, ``nn.GroupNorm``, ``nn.InstanceNorm1d``, + ``InstanceNorm2d``, ``InstanceNorm3d``, ``nn.LayerNorm``. + - Linear: ``nn.Linear``. + - Deconvolution: ``nn.ConvTranspose2d``. + - Upsample: ``nn.Upsample``. + + Args: + model (nn.Module): The model for complexity calculation. + input_shape (tuple): Input shape used for calculation. + print_per_layer_stat (bool): Whether to print complexity information + for each layer in a model. Default: True. + as_strings (bool): Output FLOPs and params counts in a string form. + Default: True. + input_constructor (None | callable): If specified, it takes a callable + method that generates input. otherwise, it will generate a random + tensor with input shape to calculate FLOPs. Default: None. + flush (bool): same as that in :func:`print`. Default: False. + ost (stream): same as ``file`` param in :func:`print`. + Default: sys.stdout. + + Returns: + tuple[float | str]: If ``as_strings`` is set to True, it will return + FLOPs and parameter counts in a string format. otherwise, it will + return those in a float number format. + """ + assert type(input_shape) is tuple + assert len(input_shape) >= 1 + assert isinstance(model, nn.Module) + flops_model = add_flops_counting_methods(model) + flops_model.eval() + flops_model.start_flops_count() + if input_constructor: + input = input_constructor(input_shape) + _ = flops_model(**input) + else: + try: + batch = torch.ones(()).new_empty( + (1, *input_shape), + dtype=next(flops_model.parameters()).dtype, + device=next(flops_model.parameters()).device) + except StopIteration: + # Avoid StopIteration for models which have no parameters, + # like `nn.Relu()`, `nn.AvgPool2d`, etc. + batch = torch.ones(()).new_empty((1, *input_shape)) + + _ = flops_model(batch) + + flops_count, params_count = flops_model.compute_average_flops_cost() + if print_per_layer_stat: + print_model_with_flops( + flops_model, flops_count, params_count, ost=ost, flush=flush) + flops_model.stop_flops_count() + + if as_strings: + return flops_to_string(flops_count), params_to_string(params_count) + + return flops_count, params_count + + +def flops_to_string(flops, units='GFLOPs', precision=2): + """Convert FLOPs number into a string. + + Note that Here we take a multiply-add counts as one FLOP. + + Args: + flops (float): FLOPs number to be converted. + units (str | None): Converted FLOPs units. Options are None, 'GFLOPs', + 'MFLOPs', 'KFLOPs', 'FLOPs'. If set to None, it will automatically + choose the most suitable unit for FLOPs. Default: 'GFLOPs'. + precision (int): Digit number after the decimal point. Default: 2. + + Returns: + str: The converted FLOPs number with units. + + Examples: + >>> flops_to_string(1e9) + '1.0 GFLOPs' + >>> flops_to_string(2e5, 'MFLOPs') + '0.2 MFLOPs' + >>> flops_to_string(3e-9, None) + '3e-09 FLOPs' + """ + if units is None: + if flops // 10**9 > 0: + return str(round(flops / 10.**9, precision)) + ' GFLOPs' + elif flops // 10**6 > 0: + return str(round(flops / 10.**6, precision)) + ' MFLOPs' + elif flops // 10**3 > 0: + return str(round(flops / 10.**3, precision)) + ' KFLOPs' + else: + return str(flops) + ' FLOPs' + else: + if units == 'GFLOPs': + return str(round(flops / 10.**9, precision)) + ' ' + units + elif units == 'MFLOPs': + return str(round(flops / 10.**6, precision)) + ' ' + units + elif units == 'KFLOPs': + return str(round(flops / 10.**3, precision)) + ' ' + units + else: + return str(flops) + ' FLOPs' + + +def params_to_string(num_params, units=None, precision=2): + """Convert parameter number into a string. + + Args: + num_params (float): Parameter number to be converted. + units (str | None): Converted FLOPs units. Options are None, 'M', + 'K' and ''. If set to None, it will automatically choose the most + suitable unit for Parameter number. Default: None. + precision (int): Digit number after the decimal point. Default: 2. + + Returns: + str: The converted parameter number with units. + + Examples: + >>> params_to_string(1e9) + '1000.0 M' + >>> params_to_string(2e5) + '200.0 k' + >>> params_to_string(3e-9) + '3e-09' + """ + if units is None: + if num_params // 10**6 > 0: + return str(round(num_params / 10**6, precision)) + ' M' + elif num_params // 10**3: + return str(round(num_params / 10**3, precision)) + ' k' + else: + return str(num_params) + else: + if units == 'M': + return str(round(num_params / 10.**6, precision)) + ' ' + units + elif units == 'K': + return str(round(num_params / 10.**3, precision)) + ' ' + units + else: + return str(num_params) + + +def print_model_with_flops(model, + total_flops, + total_params, + units='GFLOPs', + precision=3, + ost=sys.stdout, + flush=False): + """Print a model with FLOPs for each layer. + + Args: + model (nn.Module): The model to be printed. + total_flops (float): Total FLOPs of the model. + total_params (float): Total parameter counts of the model. + units (str | None): Converted FLOPs units. Default: 'GFLOPs'. + precision (int): Digit number after the decimal point. Default: 3. + ost (stream): same as `file` param in :func:`print`. + Default: sys.stdout. + flush (bool): same as that in :func:`print`. Default: False. + + Example: + >>> class ExampleModel(nn.Module): + + >>> def __init__(self): + >>> super().__init__() + >>> self.conv1 = nn.Conv2d(3, 8, 3) + >>> self.conv2 = nn.Conv2d(8, 256, 3) + >>> self.conv3 = nn.Conv2d(256, 8, 3) + >>> self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) + >>> self.flatten = nn.Flatten() + >>> self.fc = nn.Linear(8, 1) + + >>> def forward(self, x): + >>> x = self.conv1(x) + >>> x = self.conv2(x) + >>> x = self.conv3(x) + >>> x = self.avg_pool(x) + >>> x = self.flatten(x) + >>> x = self.fc(x) + >>> return x + + >>> model = ExampleModel() + >>> x = (3, 16, 16) + to print the complexity information state for each layer, you can use + >>> get_model_complexity_info(model, x) + or directly use + >>> print_model_with_flops(model, 4579784.0, 37361) + ExampleModel( + 0.037 M, 100.000% Params, 0.005 GFLOPs, 100.000% FLOPs, + (conv1): Conv2d(0.0 M, 0.600% Params, 0.0 GFLOPs, 0.959% FLOPs, 3, 8, kernel_size=(3, 3), stride=(1, 1)) # noqa: E501 + (conv2): Conv2d(0.019 M, 50.020% Params, 0.003 GFLOPs, 58.760% FLOPs, 8, 256, kernel_size=(3, 3), stride=(1, 1)) + (conv3): Conv2d(0.018 M, 49.356% Params, 0.002 GFLOPs, 40.264% FLOPs, 256, 8, kernel_size=(3, 3), stride=(1, 1)) + (avg_pool): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.017% FLOPs, output_size=(1, 1)) + (flatten): Flatten(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, ) + (fc): Linear(0.0 M, 0.024% Params, 0.0 GFLOPs, 0.000% FLOPs, in_features=8, out_features=1, bias=True) + ) + """ + + def accumulate_params(self): + if is_supported_instance(self): + return self.__params__ + else: + sum = 0 + for m in self.children(): + sum += m.accumulate_params() + return sum + + def accumulate_flops(self): + if is_supported_instance(self): + return self.__flops__ / model.__batch_counter__ + else: + sum = 0 + for m in self.children(): + sum += m.accumulate_flops() + return sum + + def flops_repr(self): + accumulated_num_params = self.accumulate_params() + accumulated_flops_cost = self.accumulate_flops() + return ', '.join([ + params_to_string( + accumulated_num_params, units='M', precision=precision), + '{:.3%} Params'.format(accumulated_num_params / total_params), + flops_to_string( + accumulated_flops_cost, units=units, precision=precision), + '{:.3%} FLOPs'.format(accumulated_flops_cost / total_flops), + self.original_extra_repr() + ]) + + def add_extra_repr(m): + m.accumulate_flops = accumulate_flops.__get__(m) + m.accumulate_params = accumulate_params.__get__(m) + flops_extra_repr = flops_repr.__get__(m) + if m.extra_repr != flops_extra_repr: + m.original_extra_repr = m.extra_repr + m.extra_repr = flops_extra_repr + assert m.extra_repr != m.original_extra_repr + + def del_extra_repr(m): + if hasattr(m, 'original_extra_repr'): + m.extra_repr = m.original_extra_repr + del m.original_extra_repr + if hasattr(m, 'accumulate_flops'): + del m.accumulate_flops + + model.apply(add_extra_repr) + print(model, file=ost, flush=flush) + model.apply(del_extra_repr) + + +def get_model_parameters_number(model): + """Calculate parameter number of a model. + + Args: + model (nn.module): The model for parameter number calculation. + + Returns: + float: Parameter number of the model. + """ + num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + return num_params + + +def add_flops_counting_methods(net_main_module): + # adding additional methods to the existing module object, + # this is done this way so that each function has access to self object + net_main_module.start_flops_count = start_flops_count.__get__( + net_main_module) + net_main_module.stop_flops_count = stop_flops_count.__get__( + net_main_module) + net_main_module.reset_flops_count = reset_flops_count.__get__( + net_main_module) + net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__( # noqa: E501 + net_main_module) + + net_main_module.reset_flops_count() + + return net_main_module + + +def compute_average_flops_cost(self): + """Compute average FLOPs cost. + + A method to compute average FLOPs cost, which will be available after + `add_flops_counting_methods()` is called on a desired net object. + + Returns: + float: Current mean flops consumption per image. + """ + batches_count = self.__batch_counter__ + flops_sum = 0 + for module in self.modules(): + if is_supported_instance(module): + flops_sum += module.__flops__ + params_sum = get_model_parameters_number(self) + return flops_sum / batches_count, params_sum + + +def start_flops_count(self): + """Activate the computation of mean flops consumption per image. + + A method to activate the computation of mean flops consumption per image. + which will be available after ``add_flops_counting_methods()`` is called on + a desired net object. It should be called before running the network. + """ + add_batch_counter_hook_function(self) + + def add_flops_counter_hook_function(module): + if is_supported_instance(module): + if hasattr(module, '__flops_handle__'): + return + + else: + handle = module.register_forward_hook( + get_modules_mapping()[type(module)]) + + module.__flops_handle__ = handle + + self.apply(partial(add_flops_counter_hook_function)) + + +def stop_flops_count(self): + """Stop computing the mean flops consumption per image. + + A method to stop computing the mean flops consumption per image, which will + be available after ``add_flops_counting_methods()`` is called on a desired + net object. It can be called to pause the computation whenever. + """ + remove_batch_counter_hook_function(self) + self.apply(remove_flops_counter_hook_function) + + +def reset_flops_count(self): + """Reset statistics computed so far. + + A method to Reset computed statistics, which will be available after + `add_flops_counting_methods()` is called on a desired net object. + """ + add_batch_counter_variables_or_reset(self) + self.apply(add_flops_counter_variable_or_reset) + + +# ---- Internal functions +def empty_flops_counter_hook(module, input, output): + module.__flops__ += 0 + + +def upsample_flops_counter_hook(module, input, output): + output_size = output[0] + batch_size = output_size.shape[0] + output_elements_count = batch_size + for val in output_size.shape[1:]: + output_elements_count *= val + module.__flops__ += int(output_elements_count) + + +def relu_flops_counter_hook(module, input, output): + active_elements_count = output.numel() + module.__flops__ += int(active_elements_count) + + +def linear_flops_counter_hook(module, input, output): + input = input[0] + output_last_dim = output.shape[ + -1] # pytorch checks dimensions, so here we don't care much + module.__flops__ += int(np.prod(input.shape) * output_last_dim) + + +def pool_flops_counter_hook(module, input, output): + input = input[0] + module.__flops__ += int(np.prod(input.shape)) + + +def norm_flops_counter_hook(module, input, output): + input = input[0] + + batch_flops = np.prod(input.shape) + if (getattr(module, 'affine', False) + or getattr(module, 'elementwise_affine', False)): + batch_flops *= 2 + module.__flops__ += int(batch_flops) + + +def deconv_flops_counter_hook(conv_module, input, output): + # Can have multiple inputs, getting the first one + input = input[0] + + batch_size = input.shape[0] + input_height, input_width = input.shape[2:] + + kernel_height, kernel_width = conv_module.kernel_size + in_channels = conv_module.in_channels + out_channels = conv_module.out_channels + groups = conv_module.groups + + filters_per_channel = out_channels // groups + conv_per_position_flops = ( + kernel_height * kernel_width * in_channels * filters_per_channel) + + active_elements_count = batch_size * input_height * input_width + overall_conv_flops = conv_per_position_flops * active_elements_count + bias_flops = 0 + if conv_module.bias is not None: + output_height, output_width = output.shape[2:] + bias_flops = out_channels * batch_size * output_height * output_height + overall_flops = overall_conv_flops + bias_flops + + conv_module.__flops__ += int(overall_flops) + + +def conv_flops_counter_hook(conv_module, input, output): + # Can have multiple inputs, getting the first one + input = input[0] + + batch_size = input.shape[0] + output_dims = list(output.shape[2:]) + + kernel_dims = list(conv_module.kernel_size) + in_channels = conv_module.in_channels + out_channels = conv_module.out_channels + groups = conv_module.groups + + filters_per_channel = out_channels // groups + conv_per_position_flops = int( + np.prod(kernel_dims)) * in_channels * filters_per_channel + + active_elements_count = batch_size * int(np.prod(output_dims)) + + overall_conv_flops = conv_per_position_flops * active_elements_count + + bias_flops = 0 + + if conv_module.bias is not None: + + bias_flops = out_channels * active_elements_count + + overall_flops = overall_conv_flops + bias_flops + + conv_module.__flops__ += int(overall_flops) + + +def batch_counter_hook(module, input, output): + batch_size = 1 + if len(input) > 0: + # Can have multiple inputs, getting the first one + input = input[0] + batch_size = len(input) + else: + pass + print('Warning! No positional inputs found for a module, ' + 'assuming batch size is 1.') + module.__batch_counter__ += batch_size + + +def add_batch_counter_variables_or_reset(module): + + module.__batch_counter__ = 0 + + +def add_batch_counter_hook_function(module): + if hasattr(module, '__batch_counter_handle__'): + return + + handle = module.register_forward_hook(batch_counter_hook) + module.__batch_counter_handle__ = handle + + +def remove_batch_counter_hook_function(module): + if hasattr(module, '__batch_counter_handle__'): + module.__batch_counter_handle__.remove() + del module.__batch_counter_handle__ + + +def add_flops_counter_variable_or_reset(module): + if is_supported_instance(module): + if hasattr(module, '__flops__') or hasattr(module, '__params__'): + print('Warning: variables __flops__ or __params__ are already ' + 'defined for the module' + type(module).__name__ + + ' ptflops can affect your code!') + module.__flops__ = 0 + module.__params__ = get_model_parameters_number(module) + + +def is_supported_instance(module): + if type(module) in get_modules_mapping(): + return True + return False + + +def remove_flops_counter_hook_function(module): + if is_supported_instance(module): + if hasattr(module, '__flops_handle__'): + module.__flops_handle__.remove() + del module.__flops_handle__ + + +def get_modules_mapping(): + return { + # convolutions + nn.Conv1d: conv_flops_counter_hook, + nn.Conv2d: conv_flops_counter_hook, + mmcv.cnn.bricks.Conv2d: conv_flops_counter_hook, + nn.Conv3d: conv_flops_counter_hook, + mmcv.cnn.bricks.Conv3d: conv_flops_counter_hook, + # activations + nn.ReLU: relu_flops_counter_hook, + nn.PReLU: relu_flops_counter_hook, + nn.ELU: relu_flops_counter_hook, + nn.LeakyReLU: relu_flops_counter_hook, + nn.ReLU6: relu_flops_counter_hook, + # poolings + nn.MaxPool1d: pool_flops_counter_hook, + nn.AvgPool1d: pool_flops_counter_hook, + nn.AvgPool2d: pool_flops_counter_hook, + nn.MaxPool2d: pool_flops_counter_hook, + mmcv.cnn.bricks.MaxPool2d: pool_flops_counter_hook, + nn.MaxPool3d: pool_flops_counter_hook, + mmcv.cnn.bricks.MaxPool3d: pool_flops_counter_hook, + nn.AvgPool3d: pool_flops_counter_hook, + nn.AdaptiveMaxPool1d: pool_flops_counter_hook, + nn.AdaptiveAvgPool1d: pool_flops_counter_hook, + nn.AdaptiveMaxPool2d: pool_flops_counter_hook, + nn.AdaptiveAvgPool2d: pool_flops_counter_hook, + nn.AdaptiveMaxPool3d: pool_flops_counter_hook, + nn.AdaptiveAvgPool3d: pool_flops_counter_hook, + # normalizations + nn.BatchNorm1d: norm_flops_counter_hook, + nn.BatchNorm2d: norm_flops_counter_hook, + nn.BatchNorm3d: norm_flops_counter_hook, + nn.GroupNorm: norm_flops_counter_hook, + nn.InstanceNorm1d: norm_flops_counter_hook, + nn.InstanceNorm2d: norm_flops_counter_hook, + nn.InstanceNorm3d: norm_flops_counter_hook, + nn.LayerNorm: norm_flops_counter_hook, + # FC + nn.Linear: linear_flops_counter_hook, + mmcv.cnn.bricks.Linear: linear_flops_counter_hook, + # Upscale + nn.Upsample: upsample_flops_counter_hook, + # Deconvolution + nn.ConvTranspose2d: deconv_flops_counter_hook, + mmcv.cnn.bricks.ConvTranspose2d: deconv_flops_counter_hook, + } diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/fuse_conv_bn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/fuse_conv_bn.py new file mode 100644 index 00000000..cb7076f8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/fuse_conv_bn.py @@ -0,0 +1,59 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn + + +def _fuse_conv_bn(conv, bn): + """Fuse conv and bn into one module. + + Args: + conv (nn.Module): Conv to be fused. + bn (nn.Module): BN to be fused. + + Returns: + nn.Module: Fused module. + """ + conv_w = conv.weight + conv_b = conv.bias if conv.bias is not None else torch.zeros_like( + bn.running_mean) + + factor = bn.weight / torch.sqrt(bn.running_var + bn.eps) + conv.weight = nn.Parameter(conv_w * + factor.reshape([conv.out_channels, 1, 1, 1])) + conv.bias = nn.Parameter((conv_b - bn.running_mean) * factor + bn.bias) + return conv + + +def fuse_conv_bn(module): + """Recursively fuse conv and bn in a module. + + During inference, the functionary of batch norm layers is turned off + but only the mean and var alone channels are used, which exposes the + chance to fuse it with the preceding conv layers to save computations and + simplify network structures. + + Args: + module (nn.Module): Module to be fused. + + Returns: + nn.Module: Fused module. + """ + last_conv = None + last_conv_name = None + + for name, child in module.named_children(): + if isinstance(child, + (nn.modules.batchnorm._BatchNorm, nn.SyncBatchNorm)): + if last_conv is None: # only fuse BN that is after Conv + continue + fused_conv = _fuse_conv_bn(last_conv, child) + module._modules[last_conv_name] = fused_conv + # To reduce changes, set BN as Identity instead of deleting it. + module._modules[name] = nn.Identity() + last_conv = None + elif isinstance(child, nn.Conv2d): + last_conv = child + last_conv_name = name + else: + fuse_conv_bn(child) + return module diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/sync_bn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/sync_bn.py new file mode 100644 index 00000000..f75291da --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/sync_bn.py @@ -0,0 +1,59 @@ +import torch + +import custom_mmpkg.custom_mmcv as mmcv + + +class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm): + """A general BatchNorm layer without input dimension check. + + Reproduced from @kapily's work: + (https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547) + The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc + is `_check_input_dim` that is designed for tensor sanity checks. + The check has been bypassed in this class for the convenience of converting + SyncBatchNorm. + """ + + def _check_input_dim(self, input): + return + + +def revert_sync_batchnorm(module): + """Helper function to convert all `SyncBatchNorm` (SyncBN) and + `mmcv.ops.sync_bn.SyncBatchNorm`(MMSyncBN) layers in the model to + `BatchNormXd` layers. + + Adapted from @kapily's work: + (https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547) + + Args: + module (nn.Module): The module containing `SyncBatchNorm` layers. + + Returns: + module_output: The converted module with `BatchNormXd` layers. + """ + module_output = module + module_checklist = [torch.nn.modules.batchnorm.SyncBatchNorm] + if hasattr(mmcv, 'ops'): + module_checklist.append(mmcv.ops.SyncBatchNorm) + if isinstance(module, tuple(module_checklist)): + module_output = _BatchNormXd(module.num_features, module.eps, + module.momentum, module.affine, + module.track_running_stats) + if module.affine: + # no_grad() may not be needed here but + # just to be consistent with `convert_sync_batchnorm()` + with torch.no_grad(): + module_output.weight = module.weight + module_output.bias = module.bias + module_output.running_mean = module.running_mean + module_output.running_var = module.running_var + module_output.num_batches_tracked = module.num_batches_tracked + module_output.training = module.training + # qconfig exists in quantized models + if hasattr(module, 'qconfig'): + module_output.qconfig = module.qconfig + for name, child in module.named_children(): + module_output.add_module(name, revert_sync_batchnorm(child)) + del module + return module_output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/weight_init.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/weight_init.py new file mode 100644 index 00000000..7a5bb175 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/utils/weight_init.py @@ -0,0 +1,684 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +import math +import warnings + +import numpy as np +import torch +import torch.nn as nn +from torch import Tensor + +from custom_mmpkg.custom_mmcv.utils import Registry, build_from_cfg, get_logger, print_log + +INITIALIZERS = Registry('initializer') + + +def update_init_info(module, init_info): + """Update the `_params_init_info` in the module if the value of parameters + are changed. + + Args: + module (obj:`nn.Module`): The module of PyTorch with a user-defined + attribute `_params_init_info` which records the initialization + information. + init_info (str): The string that describes the initialization. + """ + assert hasattr( + module, + '_params_init_info'), f'Can not find `_params_init_info` in {module}' + for name, param in module.named_parameters(): + + assert param in module._params_init_info, ( + f'Find a new :obj:`Parameter` ' + f'named `{name}` during executing the ' + f'`init_weights` of ' + f'`{module.__class__.__name__}`. ' + f'Please do not add or ' + f'replace parameters during executing ' + f'the `init_weights`. ') + + # The parameter has been changed during executing the + # `init_weights` of module + mean_value = param.data.mean() + if module._params_init_info[param]['tmp_mean_value'] != mean_value: + module._params_init_info[param]['init_info'] = init_info + module._params_init_info[param]['tmp_mean_value'] = mean_value + + +def constant_init(module, val, bias=0): + if hasattr(module, 'weight') and module.weight is not None: + nn.init.constant_(module.weight, val) + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) + + +def xavier_init(module, gain=1, bias=0, distribution='normal'): + assert distribution in ['uniform', 'normal'] + if hasattr(module, 'weight') and module.weight is not None: + if distribution == 'uniform': + nn.init.xavier_uniform_(module.weight, gain=gain) + else: + nn.init.xavier_normal_(module.weight, gain=gain) + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) + + +def normal_init(module, mean=0, std=1, bias=0): + if hasattr(module, 'weight') and module.weight is not None: + nn.init.normal_(module.weight, mean, std) + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) + + +def trunc_normal_init(module: nn.Module, + mean: float = 0, + std: float = 1, + a: float = -2, + b: float = 2, + bias: float = 0) -> None: + if hasattr(module, 'weight') and module.weight is not None: + trunc_normal_(module.weight, mean, std, a, b) # type: ignore + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) # type: ignore + + +def uniform_init(module, a=0, b=1, bias=0): + if hasattr(module, 'weight') and module.weight is not None: + nn.init.uniform_(module.weight, a, b) + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) + + +def kaiming_init(module, + a=0, + mode='fan_out', + nonlinearity='relu', + bias=0, + distribution='normal'): + assert distribution in ['uniform', 'normal'] + if hasattr(module, 'weight') and module.weight is not None: + if distribution == 'uniform': + nn.init.kaiming_uniform_( + module.weight, a=a, mode=mode, nonlinearity=nonlinearity) + else: + nn.init.kaiming_normal_( + module.weight, a=a, mode=mode, nonlinearity=nonlinearity) + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) + + +def caffe2_xavier_init(module, bias=0): + # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch + # Acknowledgment to FAIR's internal code + kaiming_init( + module, + a=1, + mode='fan_in', + nonlinearity='leaky_relu', + bias=bias, + distribution='uniform') + + +def bias_init_with_prob(prior_prob): + """initialize conv/fc bias value according to a given probability value.""" + bias_init = float(-np.log((1 - prior_prob) / prior_prob)) + return bias_init + + +def _get_bases_name(m): + return [b.__name__ for b in m.__class__.__bases__] + + +class BaseInit(object): + + def __init__(self, *, bias=0, bias_prob=None, layer=None): + self.wholemodule = False + if not isinstance(bias, (int, float)): + raise TypeError(f'bias must be a number, but got a {type(bias)}') + + if bias_prob is not None: + if not isinstance(bias_prob, float): + raise TypeError(f'bias_prob type must be float, \ + but got {type(bias_prob)}') + + if layer is not None: + if not isinstance(layer, (str, list)): + raise TypeError(f'layer must be a str or a list of str, \ + but got a {type(layer)}') + else: + layer = [] + + if bias_prob is not None: + self.bias = bias_init_with_prob(bias_prob) + else: + self.bias = bias + self.layer = [layer] if isinstance(layer, str) else layer + + def _get_init_info(self): + info = f'{self.__class__.__name__}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='Constant') +class ConstantInit(BaseInit): + """Initialize module parameters with constant values. + + Args: + val (int | float): the value to fill the weights in the module with + bias (int | float): the value to fill the bias. Defaults to 0. + bias_prob (float, optional): the probability for bias initialization. + Defaults to None. + layer (str | list[str], optional): the layer will be initialized. + Defaults to None. + """ + + def __init__(self, val, **kwargs): + super().__init__(**kwargs) + self.val = val + + def __call__(self, module): + + def init(m): + if self.wholemodule: + constant_init(m, self.val, self.bias) + else: + layername = m.__class__.__name__ + basesname = _get_bases_name(m) + if len(set(self.layer) & set([layername] + basesname)): + constant_init(m, self.val, self.bias) + + module.apply(init) + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: val={self.val}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='Xavier') +class XavierInit(BaseInit): + r"""Initialize module parameters with values according to the method + described in `Understanding the difficulty of training deep feedforward + neural networks - Glorot, X. & Bengio, Y. (2010). + `_ + + Args: + gain (int | float): an optional scaling factor. Defaults to 1. + bias (int | float): the value to fill the bias. Defaults to 0. + bias_prob (float, optional): the probability for bias initialization. + Defaults to None. + distribution (str): distribution either be ``'normal'`` + or ``'uniform'``. Defaults to ``'normal'``. + layer (str | list[str], optional): the layer will be initialized. + Defaults to None. + """ + + def __init__(self, gain=1, distribution='normal', **kwargs): + super().__init__(**kwargs) + self.gain = gain + self.distribution = distribution + + def __call__(self, module): + + def init(m): + if self.wholemodule: + xavier_init(m, self.gain, self.bias, self.distribution) + else: + layername = m.__class__.__name__ + basesname = _get_bases_name(m) + if len(set(self.layer) & set([layername] + basesname)): + xavier_init(m, self.gain, self.bias, self.distribution) + + module.apply(init) + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: gain={self.gain}, ' \ + f'distribution={self.distribution}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='Normal') +class NormalInit(BaseInit): + r"""Initialize module parameters with the values drawn from the normal + distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`. + + Args: + mean (int | float):the mean of the normal distribution. Defaults to 0. + std (int | float): the standard deviation of the normal distribution. + Defaults to 1. + bias (int | float): the value to fill the bias. Defaults to 0. + bias_prob (float, optional): the probability for bias initialization. + Defaults to None. + layer (str | list[str], optional): the layer will be initialized. + Defaults to None. + + """ + + def __init__(self, mean=0, std=1, **kwargs): + super().__init__(**kwargs) + self.mean = mean + self.std = std + + def __call__(self, module): + + def init(m): + if self.wholemodule: + normal_init(m, self.mean, self.std, self.bias) + else: + layername = m.__class__.__name__ + basesname = _get_bases_name(m) + if len(set(self.layer) & set([layername] + basesname)): + normal_init(m, self.mean, self.std, self.bias) + + module.apply(init) + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: mean={self.mean},' \ + f' std={self.std}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='TruncNormal') +class TruncNormalInit(BaseInit): + r"""Initialize module parameters with the values drawn from the normal + distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values + outside :math:`[a, b]`. + + Args: + mean (float): the mean of the normal distribution. Defaults to 0. + std (float): the standard deviation of the normal distribution. + Defaults to 1. + a (float): The minimum cutoff value. + b ( float): The maximum cutoff value. + bias (float): the value to fill the bias. Defaults to 0. + bias_prob (float, optional): the probability for bias initialization. + Defaults to None. + layer (str | list[str], optional): the layer will be initialized. + Defaults to None. + + """ + + def __init__(self, + mean: float = 0, + std: float = 1, + a: float = -2, + b: float = 2, + **kwargs) -> None: + super().__init__(**kwargs) + self.mean = mean + self.std = std + self.a = a + self.b = b + + def __call__(self, module: nn.Module) -> None: + + def init(m): + if self.wholemodule: + trunc_normal_init(m, self.mean, self.std, self.a, self.b, + self.bias) + else: + layername = m.__class__.__name__ + basesname = _get_bases_name(m) + if len(set(self.layer) & set([layername] + basesname)): + trunc_normal_init(m, self.mean, self.std, self.a, self.b, + self.bias) + + module.apply(init) + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: a={self.a}, b={self.b},' \ + f' mean={self.mean}, std={self.std}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='Uniform') +class UniformInit(BaseInit): + r"""Initialize module parameters with values drawn from the uniform + distribution :math:`\mathcal{U}(a, b)`. + + Args: + a (int | float): the lower bound of the uniform distribution. + Defaults to 0. + b (int | float): the upper bound of the uniform distribution. + Defaults to 1. + bias (int | float): the value to fill the bias. Defaults to 0. + bias_prob (float, optional): the probability for bias initialization. + Defaults to None. + layer (str | list[str], optional): the layer will be initialized. + Defaults to None. + """ + + def __init__(self, a=0, b=1, **kwargs): + super().__init__(**kwargs) + self.a = a + self.b = b + + def __call__(self, module): + + def init(m): + if self.wholemodule: + uniform_init(m, self.a, self.b, self.bias) + else: + layername = m.__class__.__name__ + basesname = _get_bases_name(m) + if len(set(self.layer) & set([layername] + basesname)): + uniform_init(m, self.a, self.b, self.bias) + + module.apply(init) + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: a={self.a},' \ + f' b={self.b}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='Kaiming') +class KaimingInit(BaseInit): + r"""Initialize module parameters with the values according to the method + described in `Delving deep into rectifiers: Surpassing human-level + performance on ImageNet classification - He, K. et al. (2015). + `_ + + Args: + a (int | float): the negative slope of the rectifier used after this + layer (only used with ``'leaky_relu'``). Defaults to 0. + mode (str): either ``'fan_in'`` or ``'fan_out'``. Choosing + ``'fan_in'`` preserves the magnitude of the variance of the weights + in the forward pass. Choosing ``'fan_out'`` preserves the + magnitudes in the backwards pass. Defaults to ``'fan_out'``. + nonlinearity (str): the non-linear function (`nn.functional` name), + recommended to use only with ``'relu'`` or ``'leaky_relu'`` . + Defaults to 'relu'. + bias (int | float): the value to fill the bias. Defaults to 0. + bias_prob (float, optional): the probability for bias initialization. + Defaults to None. + distribution (str): distribution either be ``'normal'`` or + ``'uniform'``. Defaults to ``'normal'``. + layer (str | list[str], optional): the layer will be initialized. + Defaults to None. + """ + + def __init__(self, + a=0, + mode='fan_out', + nonlinearity='relu', + distribution='normal', + **kwargs): + super().__init__(**kwargs) + self.a = a + self.mode = mode + self.nonlinearity = nonlinearity + self.distribution = distribution + + def __call__(self, module): + + def init(m): + if self.wholemodule: + kaiming_init(m, self.a, self.mode, self.nonlinearity, + self.bias, self.distribution) + else: + layername = m.__class__.__name__ + basesname = _get_bases_name(m) + if len(set(self.layer) & set([layername] + basesname)): + kaiming_init(m, self.a, self.mode, self.nonlinearity, + self.bias, self.distribution) + + module.apply(init) + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: a={self.a}, mode={self.mode}, ' \ + f'nonlinearity={self.nonlinearity}, ' \ + f'distribution ={self.distribution}, bias={self.bias}' + return info + + +@INITIALIZERS.register_module(name='Caffe2Xavier') +class Caffe2XavierInit(KaimingInit): + # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch + # Acknowledgment to FAIR's internal code + def __init__(self, **kwargs): + super().__init__( + a=1, + mode='fan_in', + nonlinearity='leaky_relu', + distribution='uniform', + **kwargs) + + def __call__(self, module): + super().__call__(module) + + +@INITIALIZERS.register_module(name='Pretrained') +class PretrainedInit(object): + """Initialize module by loading a pretrained model. + + Args: + checkpoint (str): the checkpoint file of the pretrained model should + be load. + prefix (str, optional): the prefix of a sub-module in the pretrained + model. it is for loading a part of the pretrained model to + initialize. For example, if we would like to only load the + backbone of a detector model, we can set ``prefix='backbone.'``. + Defaults to None. + map_location (str): map tensors into proper locations. + """ + + def __init__(self, checkpoint, prefix=None, map_location=None): + self.checkpoint = checkpoint + self.prefix = prefix + self.map_location = map_location + + def __call__(self, module): + from custom_mmpkg.custom_mmcv.runner import (_load_checkpoint_with_prefix, load_checkpoint, + load_state_dict) + logger = get_logger('mmcv') + if self.prefix is None: + print_log(f'load model from: {self.checkpoint}', logger=logger) + load_checkpoint( + module, + self.checkpoint, + map_location=self.map_location, + strict=False, + logger=logger) + else: + print_log( + f'load {self.prefix} in model from: {self.checkpoint}', + logger=logger) + state_dict = _load_checkpoint_with_prefix( + self.prefix, self.checkpoint, map_location=self.map_location) + load_state_dict(module, state_dict, strict=False, logger=logger) + + if hasattr(module, '_params_init_info'): + update_init_info(module, init_info=self._get_init_info()) + + def _get_init_info(self): + info = f'{self.__class__.__name__}: load from {self.checkpoint}' + return info + + +def _initialize(module, cfg, wholemodule=False): + func = build_from_cfg(cfg, INITIALIZERS) + # wholemodule flag is for override mode, there is no layer key in override + # and initializer will give init values for the whole module with the name + # in override. + func.wholemodule = wholemodule + func(module) + + +def _initialize_override(module, override, cfg): + if not isinstance(override, (dict, list)): + raise TypeError(f'override must be a dict or a list of dict, \ + but got {type(override)}') + + override = [override] if isinstance(override, dict) else override + + for override_ in override: + + cp_override = copy.deepcopy(override_) + name = cp_override.pop('name', None) + if name is None: + raise ValueError('`override` must contain the key "name",' + f'but got {cp_override}') + # if override only has name key, it means use args in init_cfg + if not cp_override: + cp_override.update(cfg) + # if override has name key and other args except type key, it will + # raise error + elif 'type' not in cp_override.keys(): + raise ValueError( + f'`override` need "type" key, but got {cp_override}') + + if hasattr(module, name): + _initialize(getattr(module, name), cp_override, wholemodule=True) + else: + raise RuntimeError(f'module did not have attribute {name}, ' + f'but init_cfg is {cp_override}.') + + +def initialize(module, init_cfg): + """Initialize a module. + + Args: + module (``torch.nn.Module``): the module will be initialized. + init_cfg (dict | list[dict]): initialization configuration dict to + define initializer. OpenMMLab has implemented 6 initializers + including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``, + ``Kaiming``, and ``Pretrained``. + Example: + >>> module = nn.Linear(2, 3, bias=True) + >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2) + >>> initialize(module, init_cfg) + + >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2)) + >>> # define key ``'layer'`` for initializing layer with different + >>> # configuration + >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1), + dict(type='Constant', layer='Linear', val=2)] + >>> initialize(module, init_cfg) + + >>> # define key``'override'`` to initialize some specific part in + >>> # module + >>> class FooNet(nn.Module): + >>> def __init__(self): + >>> super().__init__() + >>> self.feat = nn.Conv2d(3, 16, 3) + >>> self.reg = nn.Conv2d(16, 10, 3) + >>> self.cls = nn.Conv2d(16, 5, 3) + >>> model = FooNet() + >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d', + >>> override=dict(type='Constant', name='reg', val=3, bias=4)) + >>> initialize(model, init_cfg) + + >>> model = ResNet(depth=50) + >>> # Initialize weights with the pretrained model. + >>> init_cfg = dict(type='Pretrained', + checkpoint='torchvision://resnet50') + >>> initialize(model, init_cfg) + + >>> # Initialize weights of a sub-module with the specific part of + >>> # a pretrained model by using "prefix". + >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\ + >>> 'retinanet_r50_fpn_1x_coco/'\ + >>> 'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth' + >>> init_cfg = dict(type='Pretrained', + checkpoint=url, prefix='backbone.') + """ + if not isinstance(init_cfg, (dict, list)): + raise TypeError(f'init_cfg must be a dict or a list of dict, \ + but got {type(init_cfg)}') + + if isinstance(init_cfg, dict): + init_cfg = [init_cfg] + + for cfg in init_cfg: + # should deeply copy the original config because cfg may be used by + # other modules, e.g., one init_cfg shared by multiple bottleneck + # blocks, the expected cfg will be changed after pop and will change + # the initialization behavior of other modules + cp_cfg = copy.deepcopy(cfg) + override = cp_cfg.pop('override', None) + _initialize(module, cp_cfg) + + if override is not None: + cp_cfg.pop('layer', None) + _initialize_override(module, override, cp_cfg) + else: + # All attributes in module have same initialization. + pass + + +def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float, + b: float) -> Tensor: + # Method based on + # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + # Modified from + # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' + 'The distribution of values may be incorrect.', + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + lower = norm_cdf((a - mean) / std) + upper = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [lower, upper], then translate + # to [2lower-1, 2upper-1]. + tensor.uniform_(2 * lower - 1, 2 * upper - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor: Tensor, + mean: float = 0., + std: float = 1., + a: float = -2., + b: float = 2.) -> Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + Modified from + https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py + + Args: + tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`. + mean (float): the mean of the normal distribution. + std (float): the standard deviation of the normal distribution. + a (float): the minimum cutoff value. + b (float): the maximum cutoff value. + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/vgg.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/vgg.py new file mode 100644 index 00000000..8778b649 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/cnn/vgg.py @@ -0,0 +1,175 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import logging + +import torch.nn as nn + +from .utils import constant_init, kaiming_init, normal_init + + +def conv3x3(in_planes, out_planes, dilation=1): + """3x3 convolution with padding.""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + padding=dilation, + dilation=dilation) + + +def make_vgg_layer(inplanes, + planes, + num_blocks, + dilation=1, + with_bn=False, + ceil_mode=False): + layers = [] + for _ in range(num_blocks): + layers.append(conv3x3(inplanes, planes, dilation)) + if with_bn: + layers.append(nn.BatchNorm2d(planes)) + layers.append(nn.ReLU(inplace=True)) + inplanes = planes + layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode)) + + return layers + + +class VGG(nn.Module): + """VGG backbone. + + Args: + depth (int): Depth of vgg, from {11, 13, 16, 19}. + with_bn (bool): Use BatchNorm or not. + num_classes (int): number of classes for classification. + num_stages (int): VGG stages, normally 5. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze + running stats (mean and var). + bn_frozen (bool): Whether to freeze weight and bias of BN layers. + """ + + arch_settings = { + 11: (1, 1, 2, 2, 2), + 13: (2, 2, 2, 2, 2), + 16: (2, 2, 3, 3, 3), + 19: (2, 2, 4, 4, 4) + } + + def __init__(self, + depth, + with_bn=False, + num_classes=-1, + num_stages=5, + dilations=(1, 1, 1, 1, 1), + out_indices=(0, 1, 2, 3, 4), + frozen_stages=-1, + bn_eval=True, + bn_frozen=False, + ceil_mode=False, + with_last_pool=True): + super(VGG, self).__init__() + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for vgg') + assert num_stages >= 1 and num_stages <= 5 + stage_blocks = self.arch_settings[depth] + self.stage_blocks = stage_blocks[:num_stages] + assert len(dilations) == num_stages + assert max(out_indices) <= num_stages + + self.num_classes = num_classes + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.bn_eval = bn_eval + self.bn_frozen = bn_frozen + + self.inplanes = 3 + start_idx = 0 + vgg_layers = [] + self.range_sub_modules = [] + for i, num_blocks in enumerate(self.stage_blocks): + num_modules = num_blocks * (2 + with_bn) + 1 + end_idx = start_idx + num_modules + dilation = dilations[i] + planes = 64 * 2**i if i < 4 else 512 + vgg_layer = make_vgg_layer( + self.inplanes, + planes, + num_blocks, + dilation=dilation, + with_bn=with_bn, + ceil_mode=ceil_mode) + vgg_layers.extend(vgg_layer) + self.inplanes = planes + self.range_sub_modules.append([start_idx, end_idx]) + start_idx = end_idx + if not with_last_pool: + vgg_layers.pop(-1) + self.range_sub_modules[-1][1] -= 1 + self.module_name = 'features' + self.add_module(self.module_name, nn.Sequential(*vgg_layers)) + + if self.num_classes > 0: + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, num_classes), + ) + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = logging.getLogger() + from ..runner import load_checkpoint + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, nn.BatchNorm2d): + constant_init(m, 1) + elif isinstance(m, nn.Linear): + normal_init(m, std=0.01) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + outs = [] + vgg_layers = getattr(self, self.module_name) + for i in range(len(self.stage_blocks)): + for j in range(*self.range_sub_modules[i]): + vgg_layer = vgg_layers[j] + x = vgg_layer(x) + if i in self.out_indices: + outs.append(x) + if self.num_classes > 0: + x = x.view(x.size(0), -1) + x = self.classifier(x) + outs.append(x) + if len(outs) == 1: + return outs[0] + else: + return tuple(outs) + + def train(self, mode=True): + super(VGG, self).train(mode) + if self.bn_eval: + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() + if self.bn_frozen: + for params in m.parameters(): + params.requires_grad = False + vgg_layers = getattr(self, self.module_name) + if mode and self.frozen_stages >= 0: + for i in range(self.frozen_stages): + for j in range(*self.range_sub_modules[i]): + mod = vgg_layers[j] + mod.eval() + for param in mod.parameters(): + param.requires_grad = False diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/engine/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/engine/__init__.py new file mode 100644 index 00000000..3193b7f6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/engine/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .test import (collect_results_cpu, collect_results_gpu, multi_gpu_test, + single_gpu_test) + +__all__ = [ + 'collect_results_cpu', 'collect_results_gpu', 'multi_gpu_test', + 'single_gpu_test' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/engine/test.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/engine/test.py new file mode 100644 index 00000000..ac64007f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/engine/test.py @@ -0,0 +1,202 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +import pickle +import shutil +import tempfile +import time + +import torch +import torch.distributed as dist + +import custom_mmpkg.custom_mmcv as mmcv +from custom_mmpkg.custom_mmcv.runner import get_dist_info + + +def single_gpu_test(model, data_loader): + """Test model with a single gpu. + + This method tests model with a single gpu and displays test progress bar. + + Args: + model (nn.Module): Model to be tested. + data_loader (nn.Dataloader): Pytorch data loader. + + Returns: + list: The prediction results. + """ + model.eval() + results = [] + dataset = data_loader.dataset + prog_bar = mmcv.ProgressBar(len(dataset)) + for data in data_loader: + with torch.no_grad(): + result = model(return_loss=False, **data) + results.extend(result) + + # Assume result has the same length of batch_size + # refer to https://github.com/open-mmlab/mmcv/issues/985 + batch_size = len(result) + for _ in range(batch_size): + prog_bar.update() + return results + + +def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False): + """Test model with multiple gpus. + + This method tests model with multiple gpus and collects the results + under two different modes: gpu and cpu modes. By setting + ``gpu_collect=True``, it encodes results to gpu tensors and use gpu + communication for results collection. On cpu mode it saves the results on + different gpus to ``tmpdir`` and collects them by the rank 0 worker. + + Args: + model (nn.Module): Model to be tested. + data_loader (nn.Dataloader): Pytorch data loader. + tmpdir (str): Path of directory to save the temporary results from + different gpus under cpu mode. + gpu_collect (bool): Option to use either gpu or cpu to collect results. + + Returns: + list: The prediction results. + """ + model.eval() + results = [] + dataset = data_loader.dataset + rank, world_size = get_dist_info() + if rank == 0: + prog_bar = mmcv.ProgressBar(len(dataset)) + time.sleep(2) # This line can prevent deadlock problem in some cases. + for i, data in enumerate(data_loader): + with torch.no_grad(): + result = model(return_loss=False, **data) + results.extend(result) + + if rank == 0: + batch_size = len(result) + batch_size_all = batch_size * world_size + if batch_size_all + prog_bar.completed > len(dataset): + batch_size_all = len(dataset) - prog_bar.completed + for _ in range(batch_size_all): + prog_bar.update() + + # collect results from all ranks + if gpu_collect: + results = collect_results_gpu(results, len(dataset)) + else: + results = collect_results_cpu(results, len(dataset), tmpdir) + return results + + +def collect_results_cpu(result_part, size, tmpdir=None): + """Collect results under cpu mode. + + On cpu mode, this function will save the results on different gpus to + ``tmpdir`` and collect them by the rank 0 worker. + + Args: + result_part (list): Result list containing result parts + to be collected. + size (int): Size of the results, commonly equal to length of + the results. + tmpdir (str | None): temporal directory for collected results to + store. If set to None, it will create a random temporal directory + for it. + + Returns: + list: The collected results. + """ + rank, world_size = get_dist_info() + # create a tmp dir if it is not specified + if tmpdir is None: + MAX_LEN = 512 + # 32 is whitespace + dir_tensor = torch.full((MAX_LEN, ), + 32, + dtype=torch.uint8, + device='cuda') + if rank == 0: + mmcv.mkdir_or_exist('.dist_test') + tmpdir = tempfile.mkdtemp(dir='.dist_test') + tmpdir = torch.tensor( + bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda') + dir_tensor[:len(tmpdir)] = tmpdir + dist.broadcast(dir_tensor, 0) + tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip() + else: + mmcv.mkdir_or_exist(tmpdir) + # dump the part result to the dir + mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl')) + dist.barrier() + # collect all parts + if rank != 0: + return None + else: + # load results of all parts from tmp dir + part_list = [] + for i in range(world_size): + part_file = osp.join(tmpdir, f'part_{i}.pkl') + part_result = mmcv.load(part_file) + # When data is severely insufficient, an empty part_result + # on a certain gpu could makes the overall outputs empty. + if part_result: + part_list.append(part_result) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + # remove tmp dir + shutil.rmtree(tmpdir) + return ordered_results + + +def collect_results_gpu(result_part, size): + """Collect results under gpu mode. + + On gpu mode, this function will encode results to gpu tensors and use gpu + communication for results collection. + + Args: + result_part (list): Result list containing result parts + to be collected. + size (int): Size of the results, commonly equal to length of + the results. + + Returns: + list: The collected results. + """ + rank, world_size = get_dist_info() + # dump result part to tensor with pickle + part_tensor = torch.tensor( + bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda') + # gather all result part tensor shape + shape_tensor = torch.tensor(part_tensor.shape, device='cuda') + shape_list = [shape_tensor.clone() for _ in range(world_size)] + dist.all_gather(shape_list, shape_tensor) + # padding result part tensor to max length + shape_max = torch.tensor(shape_list).max() + part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda') + part_send[:shape_tensor[0]] = part_tensor + part_recv_list = [ + part_tensor.new_zeros(shape_max) for _ in range(world_size) + ] + # gather all result part + dist.all_gather(part_recv_list, part_send) + + if rank == 0: + part_list = [] + for recv, shape in zip(part_recv_list, shape_list): + part_result = pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()) + # When data is severely insufficient, an empty part_result + # on a certain gpu could makes the overall outputs empty. + if part_result: + part_list.append(part_result) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + return ordered_results diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/__init__.py new file mode 100644 index 00000000..2051b85f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .file_client import BaseStorageBackend, FileClient +from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler +from .io import dump, load, register_handler +from .parse import dict_from_file, list_from_file + +__all__ = [ + 'BaseStorageBackend', 'FileClient', 'load', 'dump', 'register_handler', + 'BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler', + 'list_from_file', 'dict_from_file' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/file_client.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/file_client.py new file mode 100644 index 00000000..c060e6e8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/file_client.py @@ -0,0 +1,1148 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import inspect +import os +import os.path as osp +import re +import tempfile +import warnings +from abc import ABCMeta, abstractmethod +from contextlib import contextmanager +from pathlib import Path +from typing import Iterable, Iterator, Optional, Tuple, Union +from urllib.request import urlopen + +import custom_mmpkg.custom_mmcv as mmcv +from custom_mmpkg.custom_mmcv.utils.misc import has_method +from custom_mmpkg.custom_mmcv.utils.path import is_filepath + + +class BaseStorageBackend(metaclass=ABCMeta): + """Abstract class of storage backends. + + All backends need to implement two apis: ``get()`` and ``get_text()``. + ``get()`` reads the file as a byte stream and ``get_text()`` reads the file + as texts. + """ + + # a flag to indicate whether the backend can create a symlink for a file + _allow_symlink = False + + @property + def name(self): + return self.__class__.__name__ + + @property + def allow_symlink(self): + return self._allow_symlink + + @abstractmethod + def get(self, filepath): + pass + + @abstractmethod + def get_text(self, filepath): + pass + + +class CephBackend(BaseStorageBackend): + """Ceph storage backend (for internal use). + + Args: + path_mapping (dict|None): path mapping dict from local path to Petrel + path. When ``path_mapping={'src': 'dst'}``, ``src`` in ``filepath`` + will be replaced by ``dst``. Default: None. + + .. warning:: + :class:`mmcv.fileio.file_client.CephBackend` will be deprecated, + please use :class:`mmcv.fileio.file_client.PetrelBackend` instead. + """ + + def __init__(self, path_mapping=None): + try: + import ceph + except ImportError: + raise ImportError('Please install ceph to enable CephBackend.') + + warnings.warn( + 'CephBackend will be deprecated, please use PetrelBackend instead') + self._client = ceph.S3Client() + assert isinstance(path_mapping, dict) or path_mapping is None + self.path_mapping = path_mapping + + def get(self, filepath): + filepath = str(filepath) + if self.path_mapping is not None: + for k, v in self.path_mapping.items(): + filepath = filepath.replace(k, v) + value = self._client.Get(filepath) + value_buf = memoryview(value) + return value_buf + + def get_text(self, filepath, encoding=None): + raise NotImplementedError + + +class PetrelBackend(BaseStorageBackend): + """Petrel storage backend (for internal use). + + PetrelBackend supports reading and writing data to multiple clusters. + If the file path contains the cluster name, PetrelBackend will read data + from specified cluster or write data to it. Otherwise, PetrelBackend will + access the default cluster. + + Args: + path_mapping (dict, optional): Path mapping dict from local path to + Petrel path. When ``path_mapping={'src': 'dst'}``, ``src`` in + ``filepath`` will be replaced by ``dst``. Default: None. + enable_mc (bool, optional): Whether to enable memcached support. + Default: True. + + Examples: + >>> filepath1 = 's3://path/of/file' + >>> filepath2 = 'cluster-name:s3://path/of/file' + >>> client = PetrelBackend() + >>> client.get(filepath1) # get data from default cluster + >>> client.get(filepath2) # get data from 'cluster-name' cluster + """ + + def __init__(self, + path_mapping: Optional[dict] = None, + enable_mc: bool = True): + try: + from petrel_client import client + except ImportError: + raise ImportError('Please install petrel_client to enable ' + 'PetrelBackend.') + + self._client = client.Client(enable_mc=enable_mc) + assert isinstance(path_mapping, dict) or path_mapping is None + self.path_mapping = path_mapping + + def _map_path(self, filepath: Union[str, Path]) -> str: + """Map ``filepath`` to a string path whose prefix will be replaced by + :attr:`self.path_mapping`. + + Args: + filepath (str): Path to be mapped. + """ + filepath = str(filepath) + if self.path_mapping is not None: + for k, v in self.path_mapping.items(): + filepath = filepath.replace(k, v) + return filepath + + def _format_path(self, filepath: str) -> str: + """Convert a ``filepath`` to standard format of petrel oss. + + If the ``filepath`` is concatenated by ``os.path.join``, in a Windows + environment, the ``filepath`` will be the format of + 's3://bucket_name\\image.jpg'. By invoking :meth:`_format_path`, the + above ``filepath`` will be converted to 's3://bucket_name/image.jpg'. + + Args: + filepath (str): Path to be formatted. + """ + return re.sub(r'\\+', '/', filepath) + + def get(self, filepath: Union[str, Path]) -> memoryview: + """Read data from a given ``filepath`` with 'rb' mode. + + Args: + filepath (str or Path): Path to read data. + + Returns: + memoryview: A memory view of expected bytes object to avoid + copying. The memoryview object can be converted to bytes by + ``value_buf.tobytes()``. + """ + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + value = self._client.Get(filepath) + value_buf = memoryview(value) + return value_buf + + def get_text(self, + filepath: Union[str, Path], + encoding: str = 'utf-8') -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + return str(self.get(filepath), encoding=encoding) + + def put(self, obj: bytes, filepath: Union[str, Path]) -> None: + """Save data to a given ``filepath``. + + Args: + obj (bytes): Data to be saved. + filepath (str or Path): Path to write data. + """ + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + self._client.put(filepath, obj) + + def put_text(self, + obj: str, + filepath: Union[str, Path], + encoding: str = 'utf-8') -> None: + """Save data to a given ``filepath``. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str): The encoding format used to encode the ``obj``. + Default: 'utf-8'. + """ + self.put(bytes(obj, encoding=encoding), filepath) + + def remove(self, filepath: Union[str, Path]) -> None: + """Remove a file. + + Args: + filepath (str or Path): Path to be removed. + """ + if not has_method(self._client, 'delete'): + raise NotImplementedError( + ('Current version of Petrel Python SDK has not supported ' + 'the `delete` method, please use a higher version or dev' + ' branch instead.')) + + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + self._client.delete(filepath) + + def exists(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path exists. + + Args: + filepath (str or Path): Path to be checked whether exists. + + Returns: + bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise. + """ + if not (has_method(self._client, 'contains') + and has_method(self._client, 'isdir')): + raise NotImplementedError( + ('Current version of Petrel Python SDK has not supported ' + 'the `contains` and `isdir` methods, please use a higher' + 'version or dev branch instead.')) + + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + return self._client.contains(filepath) or self._client.isdir(filepath) + + def isdir(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path is a directory. + + Args: + filepath (str or Path): Path to be checked whether it is a + directory. + + Returns: + bool: Return ``True`` if ``filepath`` points to a directory, + ``False`` otherwise. + """ + if not has_method(self._client, 'isdir'): + raise NotImplementedError( + ('Current version of Petrel Python SDK has not supported ' + 'the `isdir` method, please use a higher version or dev' + ' branch instead.')) + + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + return self._client.isdir(filepath) + + def isfile(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path is a file. + + Args: + filepath (str or Path): Path to be checked whether it is a file. + + Returns: + bool: Return ``True`` if ``filepath`` points to a file, ``False`` + otherwise. + """ + if not has_method(self._client, 'contains'): + raise NotImplementedError( + ('Current version of Petrel Python SDK has not supported ' + 'the `contains` method, please use a higher version or ' + 'dev branch instead.')) + + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + return self._client.contains(filepath) + + def join_path(self, filepath: Union[str, Path], + *filepaths: Union[str, Path]) -> str: + """Concatenate all file paths. + + Args: + filepath (str or Path): Path to be concatenated. + + Returns: + str: The result after concatenation. + """ + filepath = self._format_path(self._map_path(filepath)) + if filepath.endswith('/'): + filepath = filepath[:-1] + formatted_paths = [filepath] + for path in filepaths: + formatted_paths.append(self._format_path(self._map_path(path))) + return '/'.join(formatted_paths) + + @contextmanager + def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]: + """Download a file from ``filepath`` and return a temporary path. + + ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Args: + filepath (str | Path): Download a file from ``filepath``. + + Examples: + >>> client = PetrelBackend() + >>> # After existing from the ``with`` clause, + >>> # the path will be removed + >>> with client.get_local_path('s3://path/of/your/file') as path: + ... # do something here + + Yields: + Iterable[str]: Only yield one temporary path. + """ + filepath = self._map_path(filepath) + filepath = self._format_path(filepath) + assert self.isfile(filepath) + try: + f = tempfile.NamedTemporaryFile(delete=False) + f.write(self.get(filepath)) + f.close() + yield f.name + finally: + os.remove(f.name) + + def list_dir_or_file(self, + dir_path: Union[str, Path], + list_dir: bool = True, + list_file: bool = True, + suffix: Optional[Union[str, Tuple[str]]] = None, + recursive: bool = False) -> Iterator[str]: + """Scan a directory to find the interested directories or files in + arbitrary order. + + Note: + Petrel has no concept of directories but it simulates the directory + hierarchy in the filesystem through public prefixes. In addition, + if the returned path ends with '/', it means the path is a public + prefix which is a logical directory. + + Note: + :meth:`list_dir_or_file` returns the path relative to ``dir_path``. + In addition, the returned path of directory will not contains the + suffix '/' which is consistent with other backends. + + Args: + dir_path (str | Path): Path of the directory. + list_dir (bool): List the directories. Default: True. + list_file (bool): List the path of files. Default: True. + suffix (str or tuple[str], optional): File suffix + that we are interested in. Default: None. + recursive (bool): If set to True, recursively scan the + directory. Default: False. + + Yields: + Iterable[str]: A relative path to ``dir_path``. + """ + if not has_method(self._client, 'list'): + raise NotImplementedError( + ('Current version of Petrel Python SDK has not supported ' + 'the `list` method, please use a higher version or dev' + ' branch instead.')) + + dir_path = self._map_path(dir_path) + dir_path = self._format_path(dir_path) + if list_dir and suffix is not None: + raise TypeError( + '`list_dir` should be False when `suffix` is not None') + + if (suffix is not None) and not isinstance(suffix, (str, tuple)): + raise TypeError('`suffix` must be a string or tuple of strings') + + # Petrel's simulated directory hierarchy assumes that directory paths + # should end with `/` + if not dir_path.endswith('/'): + dir_path += '/' + + root = dir_path + + def _list_dir_or_file(dir_path, list_dir, list_file, suffix, + recursive): + for path in self._client.list(dir_path): + # the `self.isdir` is not used here to determine whether path + # is a directory, because `self.isdir` relies on + # `self._client.list` + if path.endswith('/'): # a directory path + next_dir_path = self.join_path(dir_path, path) + if list_dir: + # get the relative path and exclude the last + # character '/' + rel_dir = next_dir_path[len(root):-1] + yield rel_dir + if recursive: + yield from _list_dir_or_file(next_dir_path, list_dir, + list_file, suffix, + recursive) + else: # a file path + absolute_path = self.join_path(dir_path, path) + rel_path = absolute_path[len(root):] + if (suffix is None + or rel_path.endswith(suffix)) and list_file: + yield rel_path + + return _list_dir_or_file(dir_path, list_dir, list_file, suffix, + recursive) + + +class MemcachedBackend(BaseStorageBackend): + """Memcached storage backend. + + Attributes: + server_list_cfg (str): Config file for memcached server list. + client_cfg (str): Config file for memcached client. + sys_path (str | None): Additional path to be appended to `sys.path`. + Default: None. + """ + + def __init__(self, server_list_cfg, client_cfg, sys_path=None): + if sys_path is not None: + import sys + sys.path.append(sys_path) + try: + import mc + except ImportError: + raise ImportError( + 'Please install memcached to enable MemcachedBackend.') + + self.server_list_cfg = server_list_cfg + self.client_cfg = client_cfg + self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, + self.client_cfg) + # mc.pyvector servers as a point which points to a memory cache + self._mc_buffer = mc.pyvector() + + def get(self, filepath): + filepath = str(filepath) + import mc + self._client.Get(filepath, self._mc_buffer) + value_buf = mc.ConvertBuffer(self._mc_buffer) + return value_buf + + def get_text(self, filepath, encoding=None): + raise NotImplementedError + + +class LmdbBackend(BaseStorageBackend): + """Lmdb storage backend. + + Args: + db_path (str): Lmdb database path. + readonly (bool, optional): Lmdb environment parameter. If True, + disallow any write operations. Default: True. + lock (bool, optional): Lmdb environment parameter. If False, when + concurrent access occurs, do not lock the database. Default: False. + readahead (bool, optional): Lmdb environment parameter. If False, + disable the OS filesystem readahead mechanism, which may improve + random read performance when a database is larger than RAM. + Default: False. + + Attributes: + db_path (str): Lmdb database path. + """ + + def __init__(self, + db_path, + readonly=True, + lock=False, + readahead=False, + **kwargs): + try: + import lmdb + except ImportError: + raise ImportError('Please install lmdb to enable LmdbBackend.') + + self.db_path = str(db_path) + self._client = lmdb.open( + self.db_path, + readonly=readonly, + lock=lock, + readahead=readahead, + **kwargs) + + def get(self, filepath): + """Get values according to the filepath. + + Args: + filepath (str | obj:`Path`): Here, filepath is the lmdb key. + """ + filepath = str(filepath) + with self._client.begin(write=False) as txn: + value_buf = txn.get(filepath.encode('ascii')) + return value_buf + + def get_text(self, filepath, encoding=None): + raise NotImplementedError + + +class HardDiskBackend(BaseStorageBackend): + """Raw hard disks storage backend.""" + + _allow_symlink = True + + def get(self, filepath: Union[str, Path]) -> bytes: + """Read data from a given ``filepath`` with 'rb' mode. + + Args: + filepath (str or Path): Path to read data. + + Returns: + bytes: Expected bytes object. + """ + with open(filepath, 'rb') as f: + value_buf = f.read() + return value_buf + + def get_text(self, + filepath: Union[str, Path], + encoding: str = 'utf-8') -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + with open(filepath, 'r', encoding=encoding) as f: + value_buf = f.read() + return value_buf + + def put(self, obj: bytes, filepath: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'wb' mode. + + Note: + ``put`` will create a directory if the directory of ``filepath`` + does not exist. + + Args: + obj (bytes): Data to be written. + filepath (str or Path): Path to write data. + """ + mmcv.mkdir_or_exist(osp.dirname(filepath)) + with open(filepath, 'wb') as f: + f.write(obj) + + def put_text(self, + obj: str, + filepath: Union[str, Path], + encoding: str = 'utf-8') -> None: + """Write data to a given ``filepath`` with 'w' mode. + + Note: + ``put_text`` will create a directory if the directory of + ``filepath`` does not exist. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + """ + mmcv.mkdir_or_exist(osp.dirname(filepath)) + with open(filepath, 'w', encoding=encoding) as f: + f.write(obj) + + def remove(self, filepath: Union[str, Path]) -> None: + """Remove a file. + + Args: + filepath (str or Path): Path to be removed. + """ + os.remove(filepath) + + def exists(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path exists. + + Args: + filepath (str or Path): Path to be checked whether exists. + + Returns: + bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise. + """ + return osp.exists(filepath) + + def isdir(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path is a directory. + + Args: + filepath (str or Path): Path to be checked whether it is a + directory. + + Returns: + bool: Return ``True`` if ``filepath`` points to a directory, + ``False`` otherwise. + """ + return osp.isdir(filepath) + + def isfile(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path is a file. + + Args: + filepath (str or Path): Path to be checked whether it is a file. + + Returns: + bool: Return ``True`` if ``filepath`` points to a file, ``False`` + otherwise. + """ + return osp.isfile(filepath) + + def join_path(self, filepath: Union[str, Path], + *filepaths: Union[str, Path]) -> str: + """Concatenate all file paths. + + Join one or more filepath components intelligently. The return value + is the concatenation of filepath and any members of *filepaths. + + Args: + filepath (str or Path): Path to be concatenated. + + Returns: + str: The result of concatenation. + """ + return osp.join(filepath, *filepaths) + + @contextmanager + def get_local_path( + self, filepath: Union[str, Path]) -> Iterable[Union[str, Path]]: + """Only for unified API and do nothing.""" + yield filepath + + def list_dir_or_file(self, + dir_path: Union[str, Path], + list_dir: bool = True, + list_file: bool = True, + suffix: Optional[Union[str, Tuple[str]]] = None, + recursive: bool = False) -> Iterator[str]: + """Scan a directory to find the interested directories or files in + arbitrary order. + + Note: + :meth:`list_dir_or_file` returns the path relative to ``dir_path``. + + Args: + dir_path (str | Path): Path of the directory. + list_dir (bool): List the directories. Default: True. + list_file (bool): List the path of files. Default: True. + suffix (str or tuple[str], optional): File suffix + that we are interested in. Default: None. + recursive (bool): If set to True, recursively scan the + directory. Default: False. + + Yields: + Iterable[str]: A relative path to ``dir_path``. + """ + if list_dir and suffix is not None: + raise TypeError('`suffix` should be None when `list_dir` is True') + + if (suffix is not None) and not isinstance(suffix, (str, tuple)): + raise TypeError('`suffix` must be a string or tuple of strings') + + root = dir_path + + def _list_dir_or_file(dir_path, list_dir, list_file, suffix, + recursive): + for entry in os.scandir(dir_path): + if not entry.name.startswith('.') and entry.is_file(): + rel_path = osp.relpath(entry.path, root) + if (suffix is None + or rel_path.endswith(suffix)) and list_file: + yield rel_path + elif osp.isdir(entry.path): + if list_dir: + rel_dir = osp.relpath(entry.path, root) + yield rel_dir + if recursive: + yield from _list_dir_or_file(entry.path, list_dir, + list_file, suffix, + recursive) + + return _list_dir_or_file(dir_path, list_dir, list_file, suffix, + recursive) + + +class HTTPBackend(BaseStorageBackend): + """HTTP and HTTPS storage bachend.""" + + def get(self, filepath): + value_buf = urlopen(filepath).read() + return value_buf + + def get_text(self, filepath, encoding='utf-8'): + value_buf = urlopen(filepath).read() + return value_buf.decode(encoding) + + @contextmanager + def get_local_path(self, filepath: str) -> Iterable[str]: + """Download a file from ``filepath``. + + ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Args: + filepath (str): Download a file from ``filepath``. + + Examples: + >>> client = HTTPBackend() + >>> # After existing from the ``with`` clause, + >>> # the path will be removed + >>> with client.get_local_path('http://path/of/your/file') as path: + ... # do something here + """ + try: + f = tempfile.NamedTemporaryFile(delete=False) + f.write(self.get(filepath)) + f.close() + yield f.name + finally: + os.remove(f.name) + + +class FileClient: + """A general file client to access files in different backends. + + The client loads a file or text in a specified backend from its path + and returns it as a binary or text file. There are two ways to choose a + backend, the name of backend and the prefix of path. Although both of them + can be used to choose a storage backend, ``backend`` has a higher priority + that is if they are all set, the storage backend will be chosen by the + backend argument. If they are all `None`, the disk backend will be chosen. + Note that It can also register other backend accessor with a given name, + prefixes, and backend class. In addition, We use the singleton pattern to + avoid repeated object creation. If the arguments are the same, the same + object will be returned. + + Args: + backend (str, optional): The storage backend type. Options are "disk", + "ceph", "memcached", "lmdb", "http" and "petrel". Default: None. + prefix (str, optional): The prefix of the registered storage backend. + Options are "s3", "http", "https". Default: None. + + Examples: + >>> # only set backend + >>> file_client = FileClient(backend='petrel') + >>> # only set prefix + >>> file_client = FileClient(prefix='s3') + >>> # set both backend and prefix but use backend to choose client + >>> file_client = FileClient(backend='petrel', prefix='s3') + >>> # if the arguments are the same, the same object is returned + >>> file_client1 = FileClient(backend='petrel') + >>> file_client1 is file_client + True + + Attributes: + client (:obj:`BaseStorageBackend`): The backend object. + """ + + _backends = { + 'disk': HardDiskBackend, + 'ceph': CephBackend, + 'memcached': MemcachedBackend, + 'lmdb': LmdbBackend, + 'petrel': PetrelBackend, + 'http': HTTPBackend, + } + # This collection is used to record the overridden backends, and when a + # backend appears in the collection, the singleton pattern is disabled for + # that backend, because if the singleton pattern is used, then the object + # returned will be the backend before overwriting + _overridden_backends = set() + _prefix_to_backends = { + 's3': PetrelBackend, + 'http': HTTPBackend, + 'https': HTTPBackend, + } + _overridden_prefixes = set() + + _instances = {} + + def __new__(cls, backend=None, prefix=None, **kwargs): + if backend is None and prefix is None: + backend = 'disk' + if backend is not None and backend not in cls._backends: + raise ValueError( + f'Backend {backend} is not supported. Currently supported ones' + f' are {list(cls._backends.keys())}') + if prefix is not None and prefix not in cls._prefix_to_backends: + raise ValueError( + f'prefix {prefix} is not supported. Currently supported ones ' + f'are {list(cls._prefix_to_backends.keys())}') + + # concatenate the arguments to a unique key for determining whether + # objects with the same arguments were created + arg_key = f'{backend}:{prefix}' + for key, value in kwargs.items(): + arg_key += f':{key}:{value}' + + # if a backend was overridden, it will create a new object + if (arg_key in cls._instances + and backend not in cls._overridden_backends + and prefix not in cls._overridden_prefixes): + _instance = cls._instances[arg_key] + else: + # create a new object and put it to _instance + _instance = super().__new__(cls) + if backend is not None: + _instance.client = cls._backends[backend](**kwargs) + else: + _instance.client = cls._prefix_to_backends[prefix](**kwargs) + + cls._instances[arg_key] = _instance + + return _instance + + @property + def name(self): + return self.client.name + + @property + def allow_symlink(self): + return self.client.allow_symlink + + @staticmethod + def parse_uri_prefix(uri: Union[str, Path]) -> Optional[str]: + """Parse the prefix of a uri. + + Args: + uri (str | Path): Uri to be parsed that contains the file prefix. + + Examples: + >>> FileClient.parse_uri_prefix('s3://path/of/your/file') + 's3' + + Returns: + str | None: Return the prefix of uri if the uri contains '://' + else ``None``. + """ + assert is_filepath(uri) + uri = str(uri) + if '://' not in uri: + return None + else: + prefix, _ = uri.split('://') + # In the case of PetrelBackend, the prefix may contains the cluster + # name like clusterName:s3 + if ':' in prefix: + _, prefix = prefix.split(':') + return prefix + + @classmethod + def infer_client(cls, + file_client_args: Optional[dict] = None, + uri: Optional[Union[str, Path]] = None) -> 'FileClient': + """Infer a suitable file client based on the URI and arguments. + + Args: + file_client_args (dict, optional): Arguments to instantiate a + FileClient. Default: None. + uri (str | Path, optional): Uri to be parsed that contains the file + prefix. Default: None. + + Examples: + >>> uri = 's3://path/of/your/file' + >>> file_client = FileClient.infer_client(uri=uri) + >>> file_client_args = {'backend': 'petrel'} + >>> file_client = FileClient.infer_client(file_client_args) + + Returns: + FileClient: Instantiated FileClient object. + """ + assert file_client_args is not None or uri is not None + if file_client_args is None: + file_prefix = cls.parse_uri_prefix(uri) # type: ignore + return cls(prefix=file_prefix) + else: + return cls(**file_client_args) + + @classmethod + def _register_backend(cls, name, backend, force=False, prefixes=None): + if not isinstance(name, str): + raise TypeError('the backend name should be a string, ' + f'but got {type(name)}') + if not inspect.isclass(backend): + raise TypeError( + f'backend should be a class but got {type(backend)}') + if not issubclass(backend, BaseStorageBackend): + raise TypeError( + f'backend {backend} is not a subclass of BaseStorageBackend') + if not force and name in cls._backends: + raise KeyError( + f'{name} is already registered as a storage backend, ' + 'add "force=True" if you want to override it') + + if name in cls._backends and force: + cls._overridden_backends.add(name) + cls._backends[name] = backend + + if prefixes is not None: + if isinstance(prefixes, str): + prefixes = [prefixes] + else: + assert isinstance(prefixes, (list, tuple)) + for prefix in prefixes: + if prefix not in cls._prefix_to_backends: + cls._prefix_to_backends[prefix] = backend + elif (prefix in cls._prefix_to_backends) and force: + cls._overridden_prefixes.add(prefix) + cls._prefix_to_backends[prefix] = backend + else: + raise KeyError( + f'{prefix} is already registered as a storage backend,' + ' add "force=True" if you want to override it') + + @classmethod + def register_backend(cls, name, backend=None, force=False, prefixes=None): + """Register a backend to FileClient. + + This method can be used as a normal class method or a decorator. + + .. code-block:: python + + class NewBackend(BaseStorageBackend): + + def get(self, filepath): + return filepath + + def get_text(self, filepath): + return filepath + + FileClient.register_backend('new', NewBackend) + + or + + .. code-block:: python + + @FileClient.register_backend('new') + class NewBackend(BaseStorageBackend): + + def get(self, filepath): + return filepath + + def get_text(self, filepath): + return filepath + + Args: + name (str): The name of the registered backend. + backend (class, optional): The backend class to be registered, + which must be a subclass of :class:`BaseStorageBackend`. + When this method is used as a decorator, backend is None. + Defaults to None. + force (bool, optional): Whether to override the backend if the name + has already been registered. Defaults to False. + prefixes (str or list[str] or tuple[str], optional): The prefixes + of the registered storage backend. Default: None. + `New in version 1.3.15.` + """ + if backend is not None: + cls._register_backend( + name, backend, force=force, prefixes=prefixes) + return + + def _register(backend_cls): + cls._register_backend( + name, backend_cls, force=force, prefixes=prefixes) + return backend_cls + + return _register + + def get(self, filepath: Union[str, Path]) -> Union[bytes, memoryview]: + """Read data from a given ``filepath`` with 'rb' mode. + + Note: + There are two types of return values for ``get``, one is ``bytes`` + and the other is ``memoryview``. The advantage of using memoryview + is that you can avoid copying, and if you want to convert it to + ``bytes``, you can use ``.tobytes()``. + + Args: + filepath (str or Path): Path to read data. + + Returns: + bytes | memoryview: Expected bytes object or a memory view of the + bytes object. + """ + return self.client.get(filepath) + + def get_text(self, filepath: Union[str, Path], encoding='utf-8') -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + return self.client.get_text(filepath, encoding) + + def put(self, obj: bytes, filepath: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'wb' mode. + + Note: + ``put`` should create a directory if the directory of ``filepath`` + does not exist. + + Args: + obj (bytes): Data to be written. + filepath (str or Path): Path to write data. + """ + self.client.put(obj, filepath) + + def put_text(self, obj: str, filepath: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'w' mode. + + Note: + ``put_text`` should create a directory if the directory of + ``filepath`` does not exist. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str, optional): The encoding format used to open the + `filepath`. Default: 'utf-8'. + """ + self.client.put_text(obj, filepath) + + def remove(self, filepath: Union[str, Path]) -> None: + """Remove a file. + + Args: + filepath (str, Path): Path to be removed. + """ + self.client.remove(filepath) + + def exists(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path exists. + + Args: + filepath (str or Path): Path to be checked whether exists. + + Returns: + bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise. + """ + return self.client.exists(filepath) + + def isdir(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path is a directory. + + Args: + filepath (str or Path): Path to be checked whether it is a + directory. + + Returns: + bool: Return ``True`` if ``filepath`` points to a directory, + ``False`` otherwise. + """ + return self.client.isdir(filepath) + + def isfile(self, filepath: Union[str, Path]) -> bool: + """Check whether a file path is a file. + + Args: + filepath (str or Path): Path to be checked whether it is a file. + + Returns: + bool: Return ``True`` if ``filepath`` points to a file, ``False`` + otherwise. + """ + return self.client.isfile(filepath) + + def join_path(self, filepath: Union[str, Path], + *filepaths: Union[str, Path]) -> str: + """Concatenate all file paths. + + Join one or more filepath components intelligently. The return value + is the concatenation of filepath and any members of *filepaths. + + Args: + filepath (str or Path): Path to be concatenated. + + Returns: + str: The result of concatenation. + """ + return self.client.join_path(filepath, *filepaths) + + @contextmanager + def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]: + """Download data from ``filepath`` and write the data to local path. + + ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Note: + If the ``filepath`` is a local path, just return itself. + + .. warning:: + ``get_local_path`` is an experimental interface that may change in + the future. + + Args: + filepath (str or Path): Path to be read data. + + Examples: + >>> file_client = FileClient(prefix='s3') + >>> with file_client.get_local_path('s3://bucket/abc.jpg') as path: + ... # do something here + + Yields: + Iterable[str]: Only yield one path. + """ + with self.client.get_local_path(str(filepath)) as local_path: + yield local_path + + def list_dir_or_file(self, + dir_path: Union[str, Path], + list_dir: bool = True, + list_file: bool = True, + suffix: Optional[Union[str, Tuple[str]]] = None, + recursive: bool = False) -> Iterator[str]: + """Scan a directory to find the interested directories or files in + arbitrary order. + + Note: + :meth:`list_dir_or_file` returns the path relative to ``dir_path``. + + Args: + dir_path (str | Path): Path of the directory. + list_dir (bool): List the directories. Default: True. + list_file (bool): List the path of files. Default: True. + suffix (str or tuple[str], optional): File suffix + that we are interested in. Default: None. + recursive (bool): If set to True, recursively scan the + directory. Default: False. + + Yields: + Iterable[str]: A relative path to ``dir_path``. + """ + yield from self.client.list_dir_or_file(dir_path, list_dir, list_file, + suffix, recursive) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/__init__.py new file mode 100644 index 00000000..aa24d919 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .base import BaseFileHandler +from .json_handler import JsonHandler +from .pickle_handler import PickleHandler +from .yaml_handler import YamlHandler + +__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/base.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/base.py new file mode 100644 index 00000000..288878bc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/base.py @@ -0,0 +1,30 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from abc import ABCMeta, abstractmethod + + +class BaseFileHandler(metaclass=ABCMeta): + # `str_like` is a flag to indicate whether the type of file object is + # str-like object or bytes-like object. Pickle only processes bytes-like + # objects but json only processes str-like object. If it is str-like + # object, `StringIO` will be used to process the buffer. + str_like = True + + @abstractmethod + def load_from_fileobj(self, file, **kwargs): + pass + + @abstractmethod + def dump_to_fileobj(self, obj, file, **kwargs): + pass + + @abstractmethod + def dump_to_str(self, obj, **kwargs): + pass + + def load_from_path(self, filepath, mode='r', **kwargs): + with open(filepath, mode) as f: + return self.load_from_fileobj(f, **kwargs) + + def dump_to_path(self, obj, filepath, mode='w', **kwargs): + with open(filepath, mode) as f: + self.dump_to_fileobj(obj, f, **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/json_handler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/json_handler.py new file mode 100644 index 00000000..18d4f15f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/json_handler.py @@ -0,0 +1,36 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import json + +import numpy as np + +from .base import BaseFileHandler + + +def set_default(obj): + """Set default json values for non-serializable values. + + It helps convert ``set``, ``range`` and ``np.ndarray`` data types to list. + It also converts ``np.generic`` (including ``np.int32``, ``np.float32``, + etc.) into plain numbers of plain python built-in types. + """ + if isinstance(obj, (set, range)): + return list(obj) + elif isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, np.generic): + return obj.item() + raise TypeError(f'{type(obj)} is unsupported for json dump') + + +class JsonHandler(BaseFileHandler): + + def load_from_fileobj(self, file): + return json.load(file) + + def dump_to_fileobj(self, obj, file, **kwargs): + kwargs.setdefault('default', set_default) + json.dump(obj, file, **kwargs) + + def dump_to_str(self, obj, **kwargs): + kwargs.setdefault('default', set_default) + return json.dumps(obj, **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/pickle_handler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/pickle_handler.py new file mode 100644 index 00000000..b37c79be --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/pickle_handler.py @@ -0,0 +1,28 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import pickle + +from .base import BaseFileHandler + + +class PickleHandler(BaseFileHandler): + + str_like = False + + def load_from_fileobj(self, file, **kwargs): + return pickle.load(file, **kwargs) + + def load_from_path(self, filepath, **kwargs): + return super(PickleHandler, self).load_from_path( + filepath, mode='rb', **kwargs) + + def dump_to_str(self, obj, **kwargs): + kwargs.setdefault('protocol', 2) + return pickle.dumps(obj, **kwargs) + + def dump_to_fileobj(self, obj, file, **kwargs): + kwargs.setdefault('protocol', 2) + pickle.dump(obj, file, **kwargs) + + def dump_to_path(self, obj, filepath, **kwargs): + super(PickleHandler, self).dump_to_path( + obj, filepath, mode='wb', **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/yaml_handler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/yaml_handler.py new file mode 100644 index 00000000..c5aa2eea --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/handlers/yaml_handler.py @@ -0,0 +1,24 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import yaml + +try: + from yaml import CLoader as Loader, CDumper as Dumper +except ImportError: + from yaml import Loader, Dumper + +from .base import BaseFileHandler # isort:skip + + +class YamlHandler(BaseFileHandler): + + def load_from_fileobj(self, file, **kwargs): + kwargs.setdefault('Loader', Loader) + return yaml.load(file, **kwargs) + + def dump_to_fileobj(self, obj, file, **kwargs): + kwargs.setdefault('Dumper', Dumper) + yaml.dump(obj, file, **kwargs) + + def dump_to_str(self, obj, **kwargs): + kwargs.setdefault('Dumper', Dumper) + return yaml.dump(obj, **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/io.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/io.py new file mode 100644 index 00000000..aaefde58 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/io.py @@ -0,0 +1,151 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from io import BytesIO, StringIO +from pathlib import Path + +from ..utils import is_list_of, is_str +from .file_client import FileClient +from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler + +file_handlers = { + 'json': JsonHandler(), + 'yaml': YamlHandler(), + 'yml': YamlHandler(), + 'pickle': PickleHandler(), + 'pkl': PickleHandler() +} + + +def load(file, file_format=None, file_client_args=None, **kwargs): + """Load data from json/yaml/pickle files. + + This method provides a unified api for loading data from serialized files. + + Note: + In v1.3.16 and later, ``load`` supports loading data from serialized + files those can be storaged in different backends. + + Args: + file (str or :obj:`Path` or file-like object): Filename or a file-like + object. + file_format (str, optional): If not specified, the file format will be + inferred from the file extension, otherwise use the specified one. + Currently supported formats include "json", "yaml/yml" and + "pickle/pkl". + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + + Examples: + >>> load('/path/of/your/file') # file is storaged in disk + >>> load('https://path/of/your/file') # file is storaged in Internet + >>> load('s3://path/of/your/file') # file is storaged in petrel + + Returns: + The content from the file. + """ + if isinstance(file, Path): + file = str(file) + if file_format is None and is_str(file): + file_format = file.split('.')[-1] + if file_format not in file_handlers: + raise TypeError(f'Unsupported format: {file_format}') + + handler = file_handlers[file_format] + if is_str(file): + file_client = FileClient.infer_client(file_client_args, file) + if handler.str_like: + with StringIO(file_client.get_text(file)) as f: + obj = handler.load_from_fileobj(f, **kwargs) + else: + with BytesIO(file_client.get(file)) as f: + obj = handler.load_from_fileobj(f, **kwargs) + elif hasattr(file, 'read'): + obj = handler.load_from_fileobj(file, **kwargs) + else: + raise TypeError('"file" must be a filepath str or a file-object') + return obj + + +def dump(obj, file=None, file_format=None, file_client_args=None, **kwargs): + """Dump data to json/yaml/pickle strings or files. + + This method provides a unified api for dumping data as strings or to files, + and also supports custom arguments for each file format. + + Note: + In v1.3.16 and later, ``dump`` supports dumping data as strings or to + files which is saved to different backends. + + Args: + obj (any): The python object to be dumped. + file (str or :obj:`Path` or file-like object, optional): If not + specified, then the object is dumped to a str, otherwise to a file + specified by the filename or file-like object. + file_format (str, optional): Same as :func:`load`. + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + + Examples: + >>> dump('hello world', '/path/of/your/file') # disk + >>> dump('hello world', 's3://path/of/your/file') # ceph or petrel + + Returns: + bool: True for success, False otherwise. + """ + if isinstance(file, Path): + file = str(file) + if file_format is None: + if is_str(file): + file_format = file.split('.')[-1] + elif file is None: + raise ValueError( + 'file_format must be specified since file is None') + if file_format not in file_handlers: + raise TypeError(f'Unsupported format: {file_format}') + + handler = file_handlers[file_format] + if file is None: + return handler.dump_to_str(obj, **kwargs) + elif is_str(file): + file_client = FileClient.infer_client(file_client_args, file) + if handler.str_like: + with StringIO() as f: + handler.dump_to_fileobj(obj, f, **kwargs) + file_client.put_text(f.getvalue(), file) + else: + with BytesIO() as f: + handler.dump_to_fileobj(obj, f, **kwargs) + file_client.put(f.getvalue(), file) + elif hasattr(file, 'write'): + handler.dump_to_fileobj(obj, file, **kwargs) + else: + raise TypeError('"file" must be a filename str or a file-object') + + +def _register_handler(handler, file_formats): + """Register a handler for some file extensions. + + Args: + handler (:obj:`BaseFileHandler`): Handler to be registered. + file_formats (str or list[str]): File formats to be handled by this + handler. + """ + if not isinstance(handler, BaseFileHandler): + raise TypeError( + f'handler must be a child of BaseFileHandler, not {type(handler)}') + if isinstance(file_formats, str): + file_formats = [file_formats] + if not is_list_of(file_formats, str): + raise TypeError('file_formats must be a str or a list of str') + for ext in file_formats: + file_handlers[ext] = handler + + +def register_handler(file_formats, **kwargs): + + def wrap(cls): + _register_handler(cls(**kwargs), file_formats) + return cls + + return wrap diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/parse.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/parse.py new file mode 100644 index 00000000..f60f0d61 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/fileio/parse.py @@ -0,0 +1,97 @@ +# Copyright (c) OpenMMLab. All rights reserved. + +from io import StringIO + +from .file_client import FileClient + + +def list_from_file(filename, + prefix='', + offset=0, + max_num=0, + encoding='utf-8', + file_client_args=None): + """Load a text file and parse the content as a list of strings. + + Note: + In v1.3.16 and later, ``list_from_file`` supports loading a text file + which can be storaged in different backends and parsing the content as + a list for strings. + + Args: + filename (str): Filename. + prefix (str): The prefix to be inserted to the beginning of each item. + offset (int): The offset of lines. + max_num (int): The maximum number of lines to be read, + zeros and negatives mean no limitation. + encoding (str): Encoding used to open the file. Default utf-8. + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + + Examples: + >>> list_from_file('/path/of/your/file') # disk + ['hello', 'world'] + >>> list_from_file('s3://path/of/your/file') # ceph or petrel + ['hello', 'world'] + + Returns: + list[str]: A list of strings. + """ + cnt = 0 + item_list = [] + file_client = FileClient.infer_client(file_client_args, filename) + with StringIO(file_client.get_text(filename, encoding)) as f: + for _ in range(offset): + f.readline() + for line in f: + if 0 < max_num <= cnt: + break + item_list.append(prefix + line.rstrip('\n\r')) + cnt += 1 + return item_list + + +def dict_from_file(filename, + key_type=str, + encoding='utf-8', + file_client_args=None): + """Load a text file and parse the content as a dict. + + Each line of the text file will be two or more columns split by + whitespaces or tabs. The first column will be parsed as dict keys, and + the following columns will be parsed as dict values. + + Note: + In v1.3.16 and later, ``dict_from_file`` supports loading a text file + which can be storaged in different backends and parsing the content as + a dict. + + Args: + filename(str): Filename. + key_type(type): Type of the dict keys. str is user by default and + type conversion will be performed if specified. + encoding (str): Encoding used to open the file. Default utf-8. + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + + Examples: + >>> dict_from_file('/path/of/your/file') # disk + {'key1': 'value1', 'key2': 'value2'} + >>> dict_from_file('s3://path/of/your/file') # ceph or petrel + {'key1': 'value1', 'key2': 'value2'} + + Returns: + dict: The parsed contents. + """ + mapping = {} + file_client = FileClient.infer_client(file_client_args, filename) + with StringIO(file_client.get_text(filename, encoding)) as f: + for line in f: + items = line.rstrip('\n').split() + assert len(items) >= 2 + key = key_type(items[0]) + val = items[1:] if len(items) > 2 else items[1] + mapping[key] = val + return mapping diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/__init__.py new file mode 100644 index 00000000..d0051d60 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .colorspace import (bgr2gray, bgr2hls, bgr2hsv, bgr2rgb, bgr2ycbcr, + gray2bgr, gray2rgb, hls2bgr, hsv2bgr, imconvert, + rgb2bgr, rgb2gray, rgb2ycbcr, ycbcr2bgr, ycbcr2rgb) +from .geometric import (cutout, imcrop, imflip, imflip_, impad, + impad_to_multiple, imrescale, imresize, imresize_like, + imresize_to_multiple, imrotate, imshear, imtranslate, + rescale_size) +from .io import imfrombytes, imread, imwrite, supported_backends, use_backend +from .misc import tensor2imgs +from .photometric import (adjust_brightness, adjust_color, adjust_contrast, + adjust_lighting, adjust_sharpness, auto_contrast, + clahe, imdenormalize, imequalize, iminvert, + imnormalize, imnormalize_, lut_transform, posterize, + solarize) + +__all__ = [ + 'bgr2gray', 'bgr2hls', 'bgr2hsv', 'bgr2rgb', 'gray2bgr', 'gray2rgb', + 'hls2bgr', 'hsv2bgr', 'imconvert', 'rgb2bgr', 'rgb2gray', 'imrescale', + 'imresize', 'imresize_like', 'imresize_to_multiple', 'rescale_size', + 'imcrop', 'imflip', 'imflip_', 'impad', 'impad_to_multiple', 'imrotate', + 'imfrombytes', 'imread', 'imwrite', 'supported_backends', 'use_backend', + 'imdenormalize', 'imnormalize', 'imnormalize_', 'iminvert', 'posterize', + 'solarize', 'rgb2ycbcr', 'bgr2ycbcr', 'ycbcr2rgb', 'ycbcr2bgr', + 'tensor2imgs', 'imshear', 'imtranslate', 'adjust_color', 'imequalize', + 'adjust_brightness', 'adjust_contrast', 'lut_transform', 'clahe', + 'adjust_sharpness', 'auto_contrast', 'cutout', 'adjust_lighting' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/colorspace.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/colorspace.py new file mode 100644 index 00000000..81453395 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/colorspace.py @@ -0,0 +1,306 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import cv2 +import numpy as np + + +def imconvert(img, src, dst): + """Convert an image from the src colorspace to dst colorspace. + + Args: + img (ndarray): The input image. + src (str): The source colorspace, e.g., 'rgb', 'hsv'. + dst (str): The destination colorspace, e.g., 'rgb', 'hsv'. + + Returns: + ndarray: The converted image. + """ + code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}') + out_img = cv2.cvtColor(img, code) + return out_img + + +def bgr2gray(img, keepdim=False): + """Convert a BGR image to grayscale image. + + Args: + img (ndarray): The input image. + keepdim (bool): If False (by default), then return the grayscale image + with 2 dims, otherwise 3 dims. + + Returns: + ndarray: The converted grayscale image. + """ + out_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + if keepdim: + out_img = out_img[..., None] + return out_img + + +def rgb2gray(img, keepdim=False): + """Convert a RGB image to grayscale image. + + Args: + img (ndarray): The input image. + keepdim (bool): If False (by default), then return the grayscale image + with 2 dims, otherwise 3 dims. + + Returns: + ndarray: The converted grayscale image. + """ + out_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) + if keepdim: + out_img = out_img[..., None] + return out_img + + +def gray2bgr(img): + """Convert a grayscale image to BGR image. + + Args: + img (ndarray): The input image. + + Returns: + ndarray: The converted BGR image. + """ + img = img[..., None] if img.ndim == 2 else img + out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + return out_img + + +def gray2rgb(img): + """Convert a grayscale image to RGB image. + + Args: + img (ndarray): The input image. + + Returns: + ndarray: The converted RGB image. + """ + img = img[..., None] if img.ndim == 2 else img + out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) + return out_img + + +def _convert_input_type_range(img): + """Convert the type and range of the input image. + + It converts the input image to np.float32 type and range of [0, 1]. + It is mainly used for pre-processing the input image in colorspace + conversion functions such as rgb2ycbcr and ycbcr2rgb. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + + Returns: + (ndarray): The converted image with type of np.float32 and range of + [0, 1]. + """ + img_type = img.dtype + img = img.astype(np.float32) + if img_type == np.float32: + pass + elif img_type == np.uint8: + img /= 255. + else: + raise TypeError('The img type should be np.float32 or np.uint8, ' + f'but got {img_type}') + return img + + +def _convert_output_type_range(img, dst_type): + """Convert the type and range of the image according to dst_type. + + It converts the image to desired type and range. If `dst_type` is np.uint8, + images will be converted to np.uint8 type with range [0, 255]. If + `dst_type` is np.float32, it converts the image to np.float32 type with + range [0, 1]. + It is mainly used for post-processing images in colorspace conversion + functions such as rgb2ycbcr and ycbcr2rgb. + + Args: + img (ndarray): The image to be converted with np.float32 type and + range [0, 255]. + dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it + converts the image to np.uint8 type with range [0, 255]. If + dst_type is np.float32, it converts the image to np.float32 type + with range [0, 1]. + + Returns: + (ndarray): The converted image with desired type and range. + """ + if dst_type not in (np.uint8, np.float32): + raise TypeError('The dst_type should be np.float32 or np.uint8, ' + f'but got {dst_type}') + if dst_type == np.uint8: + img = img.round() + else: + img /= 255. + return img.astype(dst_type) + + +def rgb2ycbcr(img, y_only=False): + """Convert a RGB image to YCbCr image. + + This function produces the same results as Matlab's `rgb2ycbcr` function. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + y_only (bool): Whether to only return Y channel. Default: False. + + Returns: + ndarray: The converted YCbCr image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) + if y_only: + out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 + else: + out_img = np.matmul( + img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], + [24.966, 112.0, -18.214]]) + [16, 128, 128] + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def bgr2ycbcr(img, y_only=False): + """Convert a BGR image to YCbCr image. + + The bgr version of rgb2ycbcr. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + y_only (bool): Whether to only return Y channel. Default: False. + + Returns: + ndarray: The converted YCbCr image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) + if y_only: + out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 + else: + out_img = np.matmul( + img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], + [65.481, -37.797, 112.0]]) + [16, 128, 128] + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def ycbcr2rgb(img): + """Convert a YCbCr image to RGB image. + + This function produces the same results as Matlab's ycbcr2rgb function. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + + Returns: + ndarray: The converted RGB image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) * 255 + out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], + [0, -0.00153632, 0.00791071], + [0.00625893, -0.00318811, 0]]) * 255.0 + [ + -222.921, 135.576, -276.836 + ] + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def ycbcr2bgr(img): + """Convert a YCbCr image to BGR image. + + The bgr version of ycbcr2rgb. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + + Returns: + ndarray: The converted BGR image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) * 255 + out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], + [0.00791071, -0.00153632, 0], + [0, -0.00318811, 0.00625893]]) * 255.0 + [ + -276.836, 135.576, -222.921 + ] + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def convert_color_factory(src, dst): + + code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}') + + def convert_color(img): + out_img = cv2.cvtColor(img, code) + return out_img + + convert_color.__doc__ = f"""Convert a {src.upper()} image to {dst.upper()} + image. + + Args: + img (ndarray or str): The input image. + + Returns: + ndarray: The converted {dst.upper()} image. + """ + + return convert_color + + +bgr2rgb = convert_color_factory('bgr', 'rgb') + +rgb2bgr = convert_color_factory('rgb', 'bgr') + +bgr2hsv = convert_color_factory('bgr', 'hsv') + +hsv2bgr = convert_color_factory('hsv', 'bgr') + +bgr2hls = convert_color_factory('bgr', 'hls') + +hls2bgr = convert_color_factory('hls', 'bgr') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/geometric.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/geometric.py new file mode 100644 index 00000000..cf97c201 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/geometric.py @@ -0,0 +1,728 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numbers + +import cv2 +import numpy as np + +from ..utils import to_2tuple +from .io import imread_backend + +try: + from PIL import Image +except ImportError: + Image = None + + +def _scale_size(size, scale): + """Rescale a size by a ratio. + + Args: + size (tuple[int]): (w, h). + scale (float | tuple(float)): Scaling factor. + + Returns: + tuple[int]: scaled size. + """ + if isinstance(scale, (float, int)): + scale = (scale, scale) + w, h = size + return int(w * float(scale[0]) + 0.5), int(h * float(scale[1]) + 0.5) + + +cv2_interp_codes = { + 'nearest': cv2.INTER_NEAREST, + 'bilinear': cv2.INTER_LINEAR, + 'bicubic': cv2.INTER_CUBIC, + 'area': cv2.INTER_AREA, + 'lanczos': cv2.INTER_LANCZOS4 +} + +if Image is not None: + pillow_interp_codes = { + 'nearest': Image.NEAREST, + 'bilinear': Image.BILINEAR, + 'bicubic': Image.BICUBIC, + 'box': Image.BOX, + 'lanczos': Image.LANCZOS, + 'hamming': Image.HAMMING + } + + +def imresize(img, + size, + return_scale=False, + interpolation='bilinear', + out=None, + backend=None): + """Resize image to a given size. + + Args: + img (ndarray): The input image. + size (tuple[int]): Target size (w, h). + return_scale (bool): Whether to return `w_scale` and `h_scale`. + interpolation (str): Interpolation method, accepted values are + "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' + backend, "nearest", "bilinear" for 'pillow' backend. + out (ndarray): The output destination. + backend (str | None): The image resize backend type. Options are `cv2`, + `pillow`, `None`. If backend is None, the global imread_backend + specified by ``mmcv.use_backend()`` will be used. Default: None. + + Returns: + tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or + `resized_img`. + """ + h, w = img.shape[:2] + if backend is None: + backend = imread_backend + if backend not in ['cv2', 'pillow']: + raise ValueError(f'backend: {backend} is not supported for resize.' + f"Supported backends are 'cv2', 'pillow'") + + if backend == 'pillow': + assert img.dtype == np.uint8, 'Pillow backend only support uint8 type' + pil_image = Image.fromarray(img) + pil_image = pil_image.resize(size, pillow_interp_codes[interpolation]) + resized_img = np.array(pil_image) + else: + resized_img = cv2.resize( + img, size, dst=out, interpolation=cv2_interp_codes[interpolation]) + if not return_scale: + return resized_img + else: + w_scale = size[0] / w + h_scale = size[1] / h + return resized_img, w_scale, h_scale + + +def imresize_to_multiple(img, + divisor, + size=None, + scale_factor=None, + keep_ratio=False, + return_scale=False, + interpolation='bilinear', + out=None, + backend=None): + """Resize image according to a given size or scale factor and then rounds + up the the resized or rescaled image size to the nearest value that can be + divided by the divisor. + + Args: + img (ndarray): The input image. + divisor (int | tuple): Resized image size will be a multiple of + divisor. If divisor is a tuple, divisor should be + (w_divisor, h_divisor). + size (None | int | tuple[int]): Target size (w, h). Default: None. + scale_factor (None | float | tuple[float]): Multiplier for spatial + size. Should match input size if it is a tuple and the 2D style is + (w_scale_factor, h_scale_factor). Default: None. + keep_ratio (bool): Whether to keep the aspect ratio when resizing the + image. Default: False. + return_scale (bool): Whether to return `w_scale` and `h_scale`. + interpolation (str): Interpolation method, accepted values are + "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' + backend, "nearest", "bilinear" for 'pillow' backend. + out (ndarray): The output destination. + backend (str | None): The image resize backend type. Options are `cv2`, + `pillow`, `None`. If backend is None, the global imread_backend + specified by ``mmcv.use_backend()`` will be used. Default: None. + + Returns: + tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or + `resized_img`. + """ + h, w = img.shape[:2] + if size is not None and scale_factor is not None: + raise ValueError('only one of size or scale_factor should be defined') + elif size is None and scale_factor is None: + raise ValueError('one of size or scale_factor should be defined') + elif size is not None: + size = to_2tuple(size) + if keep_ratio: + size = rescale_size((w, h), size, return_scale=False) + else: + size = _scale_size((w, h), scale_factor) + + divisor = to_2tuple(divisor) + size = tuple([int(np.ceil(s / d)) * d for s, d in zip(size, divisor)]) + resized_img, w_scale, h_scale = imresize( + img, + size, + return_scale=True, + interpolation=interpolation, + out=out, + backend=backend) + if return_scale: + return resized_img, w_scale, h_scale + else: + return resized_img + + +def imresize_like(img, + dst_img, + return_scale=False, + interpolation='bilinear', + backend=None): + """Resize image to the same size of a given image. + + Args: + img (ndarray): The input image. + dst_img (ndarray): The target image. + return_scale (bool): Whether to return `w_scale` and `h_scale`. + interpolation (str): Same as :func:`resize`. + backend (str | None): Same as :func:`resize`. + + Returns: + tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or + `resized_img`. + """ + h, w = dst_img.shape[:2] + return imresize(img, (w, h), return_scale, interpolation, backend=backend) + + +def rescale_size(old_size, scale, return_scale=False): + """Calculate the new size to be rescaled to. + + Args: + old_size (tuple[int]): The old size (w, h) of image. + scale (float | tuple[int]): The scaling factor or maximum size. + If it is a float number, then the image will be rescaled by this + factor, else if it is a tuple of 2 integers, then the image will + be rescaled as large as possible within the scale. + return_scale (bool): Whether to return the scaling factor besides the + rescaled image size. + + Returns: + tuple[int]: The new rescaled image size. + """ + w, h = old_size + if isinstance(scale, (float, int)): + if scale <= 0: + raise ValueError(f'Invalid scale {scale}, must be positive.') + scale_factor = scale + elif isinstance(scale, tuple): + max_long_edge = max(scale) + max_short_edge = min(scale) + scale_factor = min(max_long_edge / max(h, w), + max_short_edge / min(h, w)) + else: + raise TypeError( + f'Scale must be a number or tuple of int, but got {type(scale)}') + + new_size = _scale_size((w, h), scale_factor) + + if return_scale: + return new_size, scale_factor + else: + return new_size + + +def imrescale(img, + scale, + return_scale=False, + interpolation='bilinear', + backend=None): + """Resize image while keeping the aspect ratio. + + Args: + img (ndarray): The input image. + scale (float | tuple[int]): The scaling factor or maximum size. + If it is a float number, then the image will be rescaled by this + factor, else if it is a tuple of 2 integers, then the image will + be rescaled as large as possible within the scale. + return_scale (bool): Whether to return the scaling factor besides the + rescaled image. + interpolation (str): Same as :func:`resize`. + backend (str | None): Same as :func:`resize`. + + Returns: + ndarray: The rescaled image. + """ + h, w = img.shape[:2] + new_size, scale_factor = rescale_size((w, h), scale, return_scale=True) + rescaled_img = imresize( + img, new_size, interpolation=interpolation, backend=backend) + if return_scale: + return rescaled_img, scale_factor + else: + return rescaled_img + + +def imflip(img, direction='horizontal'): + """Flip an image horizontally or vertically. + + Args: + img (ndarray): Image to be flipped. + direction (str): The flip direction, either "horizontal" or + "vertical" or "diagonal". + + Returns: + ndarray: The flipped image. + """ + assert direction in ['horizontal', 'vertical', 'diagonal'] + if direction == 'horizontal': + return np.flip(img, axis=1) + elif direction == 'vertical': + return np.flip(img, axis=0) + else: + return np.flip(img, axis=(0, 1)) + + +def imflip_(img, direction='horizontal'): + """Inplace flip an image horizontally or vertically. + + Args: + img (ndarray): Image to be flipped. + direction (str): The flip direction, either "horizontal" or + "vertical" or "diagonal". + + Returns: + ndarray: The flipped image (inplace). + """ + assert direction in ['horizontal', 'vertical', 'diagonal'] + if direction == 'horizontal': + return cv2.flip(img, 1, img) + elif direction == 'vertical': + return cv2.flip(img, 0, img) + else: + return cv2.flip(img, -1, img) + + +def imrotate(img, + angle, + center=None, + scale=1.0, + border_value=0, + interpolation='bilinear', + auto_bound=False): + """Rotate an image. + + Args: + img (ndarray): Image to be rotated. + angle (float): Rotation angle in degrees, positive values mean + clockwise rotation. + center (tuple[float], optional): Center point (w, h) of the rotation in + the source image. If not specified, the center of the image will be + used. + scale (float): Isotropic scale factor. + border_value (int): Border value. + interpolation (str): Same as :func:`resize`. + auto_bound (bool): Whether to adjust the image size to cover the whole + rotated image. + + Returns: + ndarray: The rotated image. + """ + if center is not None and auto_bound: + raise ValueError('`auto_bound` conflicts with `center`') + h, w = img.shape[:2] + if center is None: + center = ((w - 1) * 0.5, (h - 1) * 0.5) + assert isinstance(center, tuple) + + matrix = cv2.getRotationMatrix2D(center, -angle, scale) + if auto_bound: + cos = np.abs(matrix[0, 0]) + sin = np.abs(matrix[0, 1]) + new_w = h * sin + w * cos + new_h = h * cos + w * sin + matrix[0, 2] += (new_w - w) * 0.5 + matrix[1, 2] += (new_h - h) * 0.5 + w = int(np.round(new_w)) + h = int(np.round(new_h)) + rotated = cv2.warpAffine( + img, + matrix, (w, h), + flags=cv2_interp_codes[interpolation], + borderValue=border_value) + return rotated + + +def bbox_clip(bboxes, img_shape): + """Clip bboxes to fit the image shape. + + Args: + bboxes (ndarray): Shape (..., 4*k) + img_shape (tuple[int]): (height, width) of the image. + + Returns: + ndarray: Clipped bboxes. + """ + assert bboxes.shape[-1] % 4 == 0 + cmin = np.empty(bboxes.shape[-1], dtype=bboxes.dtype) + cmin[0::2] = img_shape[1] - 1 + cmin[1::2] = img_shape[0] - 1 + clipped_bboxes = np.maximum(np.minimum(bboxes, cmin), 0) + return clipped_bboxes + + +def bbox_scaling(bboxes, scale, clip_shape=None): + """Scaling bboxes w.r.t the box center. + + Args: + bboxes (ndarray): Shape(..., 4). + scale (float): Scaling factor. + clip_shape (tuple[int], optional): If specified, bboxes that exceed the + boundary will be clipped according to the given shape (h, w). + + Returns: + ndarray: Scaled bboxes. + """ + if float(scale) == 1.0: + scaled_bboxes = bboxes.copy() + else: + w = bboxes[..., 2] - bboxes[..., 0] + 1 + h = bboxes[..., 3] - bboxes[..., 1] + 1 + dw = (w * (scale - 1)) * 0.5 + dh = (h * (scale - 1)) * 0.5 + scaled_bboxes = bboxes + np.stack((-dw, -dh, dw, dh), axis=-1) + if clip_shape is not None: + return bbox_clip(scaled_bboxes, clip_shape) + else: + return scaled_bboxes + + +def imcrop(img, bboxes, scale=1.0, pad_fill=None): + """Crop image patches. + + 3 steps: scale the bboxes -> clip bboxes -> crop and pad. + + Args: + img (ndarray): Image to be cropped. + bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. + scale (float, optional): Scale ratio of bboxes, the default value + 1.0 means no padding. + pad_fill (Number | list[Number]): Value to be filled for padding. + Default: None, which means no padding. + + Returns: + list[ndarray] | ndarray: The cropped image patches. + """ + chn = 1 if img.ndim == 2 else img.shape[2] + if pad_fill is not None: + if isinstance(pad_fill, (int, float)): + pad_fill = [pad_fill for _ in range(chn)] + assert len(pad_fill) == chn + + _bboxes = bboxes[None, ...] if bboxes.ndim == 1 else bboxes + scaled_bboxes = bbox_scaling(_bboxes, scale).astype(np.int32) + clipped_bbox = bbox_clip(scaled_bboxes, img.shape) + + patches = [] + for i in range(clipped_bbox.shape[0]): + x1, y1, x2, y2 = tuple(clipped_bbox[i, :]) + if pad_fill is None: + patch = img[y1:y2 + 1, x1:x2 + 1, ...] + else: + _x1, _y1, _x2, _y2 = tuple(scaled_bboxes[i, :]) + if chn == 1: + patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1) + else: + patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1, chn) + patch = np.array( + pad_fill, dtype=img.dtype) * np.ones( + patch_shape, dtype=img.dtype) + x_start = 0 if _x1 >= 0 else -_x1 + y_start = 0 if _y1 >= 0 else -_y1 + w = x2 - x1 + 1 + h = y2 - y1 + 1 + patch[y_start:y_start + h, x_start:x_start + w, + ...] = img[y1:y1 + h, x1:x1 + w, ...] + patches.append(patch) + + if bboxes.ndim == 1: + return patches[0] + else: + return patches + + +def impad(img, + *, + shape=None, + padding=None, + pad_val=0, + padding_mode='constant'): + """Pad the given image to a certain shape or pad on all sides with + specified padding mode and padding value. + + Args: + img (ndarray): Image to be padded. + shape (tuple[int]): Expected padding shape (h, w). Default: None. + padding (int or tuple[int]): Padding on each border. If a single int is + provided this is used to pad all borders. If tuple of length 2 is + provided this is the padding on left/right and top/bottom + respectively. If a tuple of length 4 is provided this is the + padding for the left, top, right and bottom borders respectively. + Default: None. Note that `shape` and `padding` can not be both + set. + pad_val (Number | Sequence[Number]): Values to be filled in padding + areas when padding_mode is 'constant'. Default: 0. + padding_mode (str): Type of padding. Should be: constant, edge, + reflect or symmetric. Default: constant. + + - constant: pads with a constant value, this value is specified + with pad_val. + - edge: pads with the last value at the edge of the image. + - reflect: pads with reflection of image without repeating the + last value on the edge. For example, padding [1, 2, 3, 4] + with 2 elements on both sides in reflect mode will result + in [3, 2, 1, 2, 3, 4, 3, 2]. + - symmetric: pads with reflection of image repeating the last + value on the edge. For example, padding [1, 2, 3, 4] with + 2 elements on both sides in symmetric mode will result in + [2, 1, 1, 2, 3, 4, 4, 3] + + Returns: + ndarray: The padded image. + """ + + assert (shape is not None) ^ (padding is not None) + if shape is not None: + padding = (0, 0, shape[1] - img.shape[1], shape[0] - img.shape[0]) + + # check pad_val + if isinstance(pad_val, tuple): + assert len(pad_val) == img.shape[-1] + elif not isinstance(pad_val, numbers.Number): + raise TypeError('pad_val must be a int or a tuple. ' + f'But received {type(pad_val)}') + + # check padding + if isinstance(padding, tuple) and len(padding) in [2, 4]: + if len(padding) == 2: + padding = (padding[0], padding[1], padding[0], padding[1]) + elif isinstance(padding, numbers.Number): + padding = (padding, padding, padding, padding) + else: + raise ValueError('Padding must be a int or a 2, or 4 element tuple.' + f'But received {padding}') + + # check padding mode + assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] + + border_type = { + 'constant': cv2.BORDER_CONSTANT, + 'edge': cv2.BORDER_REPLICATE, + 'reflect': cv2.BORDER_REFLECT_101, + 'symmetric': cv2.BORDER_REFLECT + } + img = cv2.copyMakeBorder( + img, + padding[1], + padding[3], + padding[0], + padding[2], + border_type[padding_mode], + value=pad_val) + + return img + + +def impad_to_multiple(img, divisor, pad_val=0): + """Pad an image to ensure each edge to be multiple to some number. + + Args: + img (ndarray): Image to be padded. + divisor (int): Padded image edges will be multiple to divisor. + pad_val (Number | Sequence[Number]): Same as :func:`impad`. + + Returns: + ndarray: The padded image. + """ + pad_h = int(np.ceil(img.shape[0] / divisor)) * divisor + pad_w = int(np.ceil(img.shape[1] / divisor)) * divisor + return impad(img, shape=(pad_h, pad_w), pad_val=pad_val) + + +def cutout(img, shape, pad_val=0): + """Randomly cut out a rectangle from the original img. + + Args: + img (ndarray): Image to be cutout. + shape (int | tuple[int]): Expected cutout shape (h, w). If given as a + int, the value will be used for both h and w. + pad_val (int | float | tuple[int | float]): Values to be filled in the + cut area. Defaults to 0. + + Returns: + ndarray: The cutout image. + """ + + channels = 1 if img.ndim == 2 else img.shape[2] + if isinstance(shape, int): + cut_h, cut_w = shape, shape + else: + assert isinstance(shape, tuple) and len(shape) == 2, \ + f'shape must be a int or a tuple with length 2, but got type ' \ + f'{type(shape)} instead.' + cut_h, cut_w = shape + if isinstance(pad_val, (int, float)): + pad_val = tuple([pad_val] * channels) + elif isinstance(pad_val, tuple): + assert len(pad_val) == channels, \ + 'Expected the num of elements in tuple equals the channels' \ + 'of input image. Found {} vs {}'.format( + len(pad_val), channels) + else: + raise TypeError(f'Invalid type {type(pad_val)} for `pad_val`') + + img_h, img_w = img.shape[:2] + y0 = np.random.uniform(img_h) + x0 = np.random.uniform(img_w) + + y1 = int(max(0, y0 - cut_h / 2.)) + x1 = int(max(0, x0 - cut_w / 2.)) + y2 = min(img_h, y1 + cut_h) + x2 = min(img_w, x1 + cut_w) + + if img.ndim == 2: + patch_shape = (y2 - y1, x2 - x1) + else: + patch_shape = (y2 - y1, x2 - x1, channels) + + img_cutout = img.copy() + patch = np.array( + pad_val, dtype=img.dtype) * np.ones( + patch_shape, dtype=img.dtype) + img_cutout[y1:y2, x1:x2, ...] = patch + + return img_cutout + + +def _get_shear_matrix(magnitude, direction='horizontal'): + """Generate the shear matrix for transformation. + + Args: + magnitude (int | float): The magnitude used for shear. + direction (str): The flip direction, either "horizontal" + or "vertical". + + Returns: + ndarray: The shear matrix with dtype float32. + """ + if direction == 'horizontal': + shear_matrix = np.float32([[1, magnitude, 0], [0, 1, 0]]) + elif direction == 'vertical': + shear_matrix = np.float32([[1, 0, 0], [magnitude, 1, 0]]) + return shear_matrix + + +def imshear(img, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """Shear an image. + + Args: + img (ndarray): Image to be sheared with format (h, w) + or (h, w, c). + magnitude (int | float): The magnitude used for shear. + direction (str): The flip direction, either "horizontal" + or "vertical". + border_value (int | tuple[int]): Value used in case of a + constant border. + interpolation (str): Same as :func:`resize`. + + Returns: + ndarray: The sheared image. + """ + assert direction in ['horizontal', + 'vertical'], f'Invalid direction: {direction}' + height, width = img.shape[:2] + if img.ndim == 2: + channels = 1 + elif img.ndim == 3: + channels = img.shape[-1] + if isinstance(border_value, int): + border_value = tuple([border_value] * channels) + elif isinstance(border_value, tuple): + assert len(border_value) == channels, \ + 'Expected the num of elements in tuple equals the channels' \ + 'of input image. Found {} vs {}'.format( + len(border_value), channels) + else: + raise ValueError( + f'Invalid type {type(border_value)} for `border_value`') + shear_matrix = _get_shear_matrix(magnitude, direction) + sheared = cv2.warpAffine( + img, + shear_matrix, + (width, height), + # Note case when the number elements in `border_value` + # greater than 3 (e.g. shearing masks whose channels large + # than 3) will raise TypeError in `cv2.warpAffine`. + # Here simply slice the first 3 values in `border_value`. + borderValue=border_value[:3], + flags=cv2_interp_codes[interpolation]) + return sheared + + +def _get_translate_matrix(offset, direction='horizontal'): + """Generate the translate matrix. + + Args: + offset (int | float): The offset used for translate. + direction (str): The translate direction, either + "horizontal" or "vertical". + + Returns: + ndarray: The translate matrix with dtype float32. + """ + if direction == 'horizontal': + translate_matrix = np.float32([[1, 0, offset], [0, 1, 0]]) + elif direction == 'vertical': + translate_matrix = np.float32([[1, 0, 0], [0, 1, offset]]) + return translate_matrix + + +def imtranslate(img, + offset, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """Translate an image. + + Args: + img (ndarray): Image to be translated with format + (h, w) or (h, w, c). + offset (int | float): The offset used for translate. + direction (str): The translate direction, either "horizontal" + or "vertical". + border_value (int | tuple[int]): Value used in case of a + constant border. + interpolation (str): Same as :func:`resize`. + + Returns: + ndarray: The translated image. + """ + assert direction in ['horizontal', + 'vertical'], f'Invalid direction: {direction}' + height, width = img.shape[:2] + if img.ndim == 2: + channels = 1 + elif img.ndim == 3: + channels = img.shape[-1] + if isinstance(border_value, int): + border_value = tuple([border_value] * channels) + elif isinstance(border_value, tuple): + assert len(border_value) == channels, \ + 'Expected the num of elements in tuple equals the channels' \ + 'of input image. Found {} vs {}'.format( + len(border_value), channels) + else: + raise ValueError( + f'Invalid type {type(border_value)} for `border_value`.') + translate_matrix = _get_translate_matrix(offset, direction) + translated = cv2.warpAffine( + img, + translate_matrix, + (width, height), + # Note case when the number elements in `border_value` + # greater than 3 (e.g. translating masks whose channels + # large than 3) will raise TypeError in `cv2.warpAffine`. + # Here simply slice the first 3 values in `border_value`. + borderValue=border_value[:3], + flags=cv2_interp_codes[interpolation]) + return translated diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/io.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/io.py new file mode 100644 index 00000000..8fe4400d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/io.py @@ -0,0 +1,258 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import io +import os.path as osp +from pathlib import Path + +import cv2 +import numpy as np +from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION, + IMREAD_UNCHANGED) + +from custom_mmpkg.custom_mmcv.utils import check_file_exist, is_str, mkdir_or_exist + +try: + from turbojpeg import TJCS_RGB, TJPF_BGR, TJPF_GRAY, TurboJPEG +except ImportError: + TJCS_RGB = TJPF_GRAY = TJPF_BGR = TurboJPEG = None + +try: + from PIL import Image, ImageOps +except ImportError: + Image = None + +try: + import tifffile +except ImportError: + tifffile = None + +jpeg = None +supported_backends = ['cv2', 'turbojpeg', 'pillow', 'tifffile'] + +imread_flags = { + 'color': IMREAD_COLOR, + 'grayscale': IMREAD_GRAYSCALE, + 'unchanged': IMREAD_UNCHANGED, + 'color_ignore_orientation': IMREAD_IGNORE_ORIENTATION | IMREAD_COLOR, + 'grayscale_ignore_orientation': + IMREAD_IGNORE_ORIENTATION | IMREAD_GRAYSCALE +} + +imread_backend = 'cv2' + + +def use_backend(backend): + """Select a backend for image decoding. + + Args: + backend (str): The image decoding backend type. Options are `cv2`, + `pillow`, `turbojpeg` (see https://github.com/lilohuang/PyTurboJPEG) + and `tifffile`. `turbojpeg` is faster but it only supports `.jpeg` + file format. + """ + assert backend in supported_backends + global imread_backend + imread_backend = backend + if imread_backend == 'turbojpeg': + if TurboJPEG is None: + raise ImportError('`PyTurboJPEG` is not installed') + global jpeg + if jpeg is None: + jpeg = TurboJPEG() + elif imread_backend == 'pillow': + if Image is None: + raise ImportError('`Pillow` is not installed') + elif imread_backend == 'tifffile': + if tifffile is None: + raise ImportError('`tifffile` is not installed') + + +def _jpegflag(flag='color', channel_order='bgr'): + channel_order = channel_order.lower() + if channel_order not in ['rgb', 'bgr']: + raise ValueError('channel order must be either "rgb" or "bgr"') + + if flag == 'color': + if channel_order == 'bgr': + return TJPF_BGR + elif channel_order == 'rgb': + return TJCS_RGB + elif flag == 'grayscale': + return TJPF_GRAY + else: + raise ValueError('flag must be "color" or "grayscale"') + + +def _pillow2array(img, flag='color', channel_order='bgr'): + """Convert a pillow image to numpy array. + + Args: + img (:obj:`PIL.Image.Image`): The image loaded using PIL + flag (str): Flags specifying the color type of a loaded image, + candidates are 'color', 'grayscale' and 'unchanged'. + Default to 'color'. + channel_order (str): The channel order of the output image array, + candidates are 'bgr' and 'rgb'. Default to 'bgr'. + + Returns: + np.ndarray: The converted numpy array + """ + channel_order = channel_order.lower() + if channel_order not in ['rgb', 'bgr']: + raise ValueError('channel order must be either "rgb" or "bgr"') + + if flag == 'unchanged': + array = np.array(img) + if array.ndim >= 3 and array.shape[2] >= 3: # color image + array[:, :, :3] = array[:, :, (2, 1, 0)] # RGB to BGR + else: + # Handle exif orientation tag + if flag in ['color', 'grayscale']: + img = ImageOps.exif_transpose(img) + # If the image mode is not 'RGB', convert it to 'RGB' first. + if img.mode != 'RGB': + if img.mode != 'LA': + # Most formats except 'LA' can be directly converted to RGB + img = img.convert('RGB') + else: + # When the mode is 'LA', the default conversion will fill in + # the canvas with black, which sometimes shadows black objects + # in the foreground. + # + # Therefore, a random color (124, 117, 104) is used for canvas + img_rgba = img.convert('RGBA') + img = Image.new('RGB', img_rgba.size, (124, 117, 104)) + img.paste(img_rgba, mask=img_rgba.split()[3]) # 3 is alpha + if flag in ['color', 'color_ignore_orientation']: + array = np.array(img) + if channel_order != 'rgb': + array = array[:, :, ::-1] # RGB to BGR + elif flag in ['grayscale', 'grayscale_ignore_orientation']: + img = img.convert('L') + array = np.array(img) + else: + raise ValueError( + 'flag must be "color", "grayscale", "unchanged", ' + f'"color_ignore_orientation" or "grayscale_ignore_orientation"' + f' but got {flag}') + return array + + +def imread(img_or_path, flag='color', channel_order='bgr', backend=None): + """Read an image. + + Args: + img_or_path (ndarray or str or Path): Either a numpy array or str or + pathlib.Path. If it is a numpy array (loaded image), then + it will be returned as is. + flag (str): Flags specifying the color type of a loaded image, + candidates are `color`, `grayscale`, `unchanged`, + `color_ignore_orientation` and `grayscale_ignore_orientation`. + By default, `cv2` and `pillow` backend would rotate the image + according to its EXIF info unless called with `unchanged` or + `*_ignore_orientation` flags. `turbojpeg` and `tifffile` backend + always ignore image's EXIF info regardless of the flag. + The `turbojpeg` backend only supports `color` and `grayscale`. + channel_order (str): Order of channel, candidates are `bgr` and `rgb`. + backend (str | None): The image decoding backend type. Options are + `cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`. + If backend is None, the global imread_backend specified by + ``mmcv.use_backend()`` will be used. Default: None. + + Returns: + ndarray: Loaded image array. + """ + + if backend is None: + backend = imread_backend + if backend not in supported_backends: + raise ValueError(f'backend: {backend} is not supported. Supported ' + "backends are 'cv2', 'turbojpeg', 'pillow'") + if isinstance(img_or_path, Path): + img_or_path = str(img_or_path) + + if isinstance(img_or_path, np.ndarray): + return img_or_path + elif is_str(img_or_path): + check_file_exist(img_or_path, + f'img file does not exist: {img_or_path}') + if backend == 'turbojpeg': + with open(img_or_path, 'rb') as in_file: + img = jpeg.decode(in_file.read(), + _jpegflag(flag, channel_order)) + if img.shape[-1] == 1: + img = img[:, :, 0] + return img + elif backend == 'pillow': + img = Image.open(img_or_path) + img = _pillow2array(img, flag, channel_order) + return img + elif backend == 'tifffile': + img = tifffile.imread(img_or_path) + return img + else: + flag = imread_flags[flag] if is_str(flag) else flag + img = cv2.imread(img_or_path, flag) + if flag == IMREAD_COLOR and channel_order == 'rgb': + cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) + return img + else: + raise TypeError('"img" must be a numpy array or a str or ' + 'a pathlib.Path object') + + +def imfrombytes(content, flag='color', channel_order='bgr', backend=None): + """Read an image from bytes. + + Args: + content (bytes): Image bytes got from files or other streams. + flag (str): Same as :func:`imread`. + backend (str | None): The image decoding backend type. Options are + `cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the + global imread_backend specified by ``mmcv.use_backend()`` will be + used. Default: None. + + Returns: + ndarray: Loaded image array. + """ + + if backend is None: + backend = imread_backend + if backend not in supported_backends: + raise ValueError(f'backend: {backend} is not supported. Supported ' + "backends are 'cv2', 'turbojpeg', 'pillow'") + if backend == 'turbojpeg': + img = jpeg.decode(content, _jpegflag(flag, channel_order)) + if img.shape[-1] == 1: + img = img[:, :, 0] + return img + elif backend == 'pillow': + buff = io.BytesIO(content) + img = Image.open(buff) + img = _pillow2array(img, flag, channel_order) + return img + else: + img_np = np.frombuffer(content, np.uint8) + flag = imread_flags[flag] if is_str(flag) else flag + img = cv2.imdecode(img_np, flag) + if flag == IMREAD_COLOR and channel_order == 'rgb': + cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) + return img + + +def imwrite(img, file_path, params=None, auto_mkdir=True): + """Write image to file. + + Args: + img (ndarray): Image array to be written. + file_path (str): Image file path. + params (None or list): Same as opencv :func:`imwrite` interface. + auto_mkdir (bool): If the parent folder of `file_path` does not exist, + whether to create it automatically. + + Returns: + bool: Successful or not. + """ + if auto_mkdir: + dir_name = osp.abspath(osp.dirname(file_path)) + mkdir_or_exist(dir_name) + return cv2.imwrite(file_path, img, params) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/misc.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/misc.py new file mode 100644 index 00000000..f8a1aae4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/misc.py @@ -0,0 +1,44 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np + +import custom_mmpkg.custom_mmcv as mmcv + +try: + import torch +except ImportError: + torch = None + + +def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True): + """Convert tensor to 3-channel images. + + Args: + tensor (torch.Tensor): Tensor that contains multiple images, shape ( + N, C, H, W). + mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0). + std (tuple[float], optional): Standard deviation of images. + Defaults to (1, 1, 1). + to_rgb (bool, optional): Whether the tensor was converted to RGB + format in the first place. If so, convert it back to BGR. + Defaults to True. + + Returns: + list[np.ndarray]: A list that contains multiple images. + """ + + if torch is None: + raise RuntimeError('pytorch is not installed') + assert torch.is_tensor(tensor) and tensor.ndim == 4 + assert len(mean) == 3 + assert len(std) == 3 + + num_imgs = tensor.size(0) + mean = np.array(mean, dtype=np.float32) + std = np.array(std, dtype=np.float32) + imgs = [] + for img_id in range(num_imgs): + img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0) + img = mmcv.imdenormalize( + img, mean, std, to_bgr=to_rgb).astype(np.uint8) + imgs.append(np.ascontiguousarray(img)) + return imgs diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/photometric.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/photometric.py new file mode 100644 index 00000000..5085d012 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/image/photometric.py @@ -0,0 +1,428 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import cv2 +import numpy as np + +from ..utils import is_tuple_of +from .colorspace import bgr2gray, gray2bgr + + +def imnormalize(img, mean, std, to_rgb=True): + """Normalize an image with mean and std. + + Args: + img (ndarray): Image to be normalized. + mean (ndarray): The mean to be used for normalize. + std (ndarray): The std to be used for normalize. + to_rgb (bool): Whether to convert to rgb. + + Returns: + ndarray: The normalized image. + """ + img = img.copy().astype(np.float32) + return imnormalize_(img, mean, std, to_rgb) + + +def imnormalize_(img, mean, std, to_rgb=True): + """Inplace normalize an image with mean and std. + + Args: + img (ndarray): Image to be normalized. + mean (ndarray): The mean to be used for normalize. + std (ndarray): The std to be used for normalize. + to_rgb (bool): Whether to convert to rgb. + + Returns: + ndarray: The normalized image. + """ + # cv2 inplace normalization does not accept uint8 + assert img.dtype != np.uint8 + mean = np.float64(mean.reshape(1, -1)) + stdinv = 1 / np.float64(std.reshape(1, -1)) + if to_rgb: + cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace + cv2.subtract(img, mean, img) # inplace + cv2.multiply(img, stdinv, img) # inplace + return img + + +def imdenormalize(img, mean, std, to_bgr=True): + assert img.dtype != np.uint8 + mean = mean.reshape(1, -1).astype(np.float64) + std = std.reshape(1, -1).astype(np.float64) + img = cv2.multiply(img, std) # make a copy + cv2.add(img, mean, img) # inplace + if to_bgr: + cv2.cvtColor(img, cv2.COLOR_RGB2BGR, img) # inplace + return img + + +def iminvert(img): + """Invert (negate) an image. + + Args: + img (ndarray): Image to be inverted. + + Returns: + ndarray: The inverted image. + """ + return np.full_like(img, 255) - img + + +def solarize(img, thr=128): + """Solarize an image (invert all pixel values above a threshold) + + Args: + img (ndarray): Image to be solarized. + thr (int): Threshold for solarizing (0 - 255). + + Returns: + ndarray: The solarized image. + """ + img = np.where(img < thr, img, 255 - img) + return img + + +def posterize(img, bits): + """Posterize an image (reduce the number of bits for each color channel) + + Args: + img (ndarray): Image to be posterized. + bits (int): Number of bits (1 to 8) to use for posterizing. + + Returns: + ndarray: The posterized image. + """ + shift = 8 - bits + img = np.left_shift(np.right_shift(img, shift), shift) + return img + + +def adjust_color(img, alpha=1, beta=None, gamma=0): + r"""It blends the source image and its gray image: + + .. math:: + output = img * alpha + gray\_img * beta + gamma + + Args: + img (ndarray): The input source image. + alpha (int | float): Weight for the source image. Default 1. + beta (int | float): Weight for the converted gray image. + If None, it's assigned the value (1 - `alpha`). + gamma (int | float): Scalar added to each sum. + Same as :func:`cv2.addWeighted`. Default 0. + + Returns: + ndarray: Colored image which has the same size and dtype as input. + """ + gray_img = bgr2gray(img) + gray_img = np.tile(gray_img[..., None], [1, 1, 3]) + if beta is None: + beta = 1 - alpha + colored_img = cv2.addWeighted(img, alpha, gray_img, beta, gamma) + if not colored_img.dtype == np.uint8: + # Note when the dtype of `img` is not the default `np.uint8` + # (e.g. np.float32), the value in `colored_img` got from cv2 + # is not guaranteed to be in range [0, 255], so here clip + # is needed. + colored_img = np.clip(colored_img, 0, 255) + return colored_img + + +def imequalize(img): + """Equalize the image histogram. + + This function applies a non-linear mapping to the input image, + in order to create a uniform distribution of grayscale values + in the output image. + + Args: + img (ndarray): Image to be equalized. + + Returns: + ndarray: The equalized image. + """ + + def _scale_channel(im, c): + """Scale the data in the corresponding channel.""" + im = im[:, :, c] + # Compute the histogram of the image channel. + histo = np.histogram(im, 256, (0, 255))[0] + # For computing the step, filter out the nonzeros. + nonzero_histo = histo[histo > 0] + step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255 + if not step: + lut = np.array(range(256)) + else: + # Compute the cumulative sum, shifted by step // 2 + # and then normalized by step. + lut = (np.cumsum(histo) + (step // 2)) // step + # Shift lut, prepending with 0. + lut = np.concatenate([[0], lut[:-1]], 0) + # handle potential integer overflow + lut[lut > 255] = 255 + # If step is zero, return the original image. + # Otherwise, index from lut. + return np.where(np.equal(step, 0), im, lut[im]) + + # Scales each channel independently and then stacks + # the result. + s1 = _scale_channel(img, 0) + s2 = _scale_channel(img, 1) + s3 = _scale_channel(img, 2) + equalized_img = np.stack([s1, s2, s3], axis=-1) + return equalized_img.astype(img.dtype) + + +def adjust_brightness(img, factor=1.): + """Adjust image brightness. + + This function controls the brightness of an image. An + enhancement factor of 0.0 gives a black image. + A factor of 1.0 gives the original image. This function + blends the source image and the degenerated black image: + + .. math:: + output = img * factor + degenerated * (1 - factor) + + Args: + img (ndarray): Image to be brightened. + factor (float): A value controls the enhancement. + Factor 1.0 returns the original image, lower + factors mean less color (brightness, contrast, + etc), and higher values more. Default 1. + + Returns: + ndarray: The brightened image. + """ + degenerated = np.zeros_like(img) + # Note manually convert the dtype to np.float32, to + # achieve as close results as PIL.ImageEnhance.Brightness. + # Set beta=1-factor, and gamma=0 + brightened_img = cv2.addWeighted( + img.astype(np.float32), factor, degenerated.astype(np.float32), + 1 - factor, 0) + brightened_img = np.clip(brightened_img, 0, 255) + return brightened_img.astype(img.dtype) + + +def adjust_contrast(img, factor=1.): + """Adjust image contrast. + + This function controls the contrast of an image. An + enhancement factor of 0.0 gives a solid grey + image. A factor of 1.0 gives the original image. It + blends the source image and the degenerated mean image: + + .. math:: + output = img * factor + degenerated * (1 - factor) + + Args: + img (ndarray): Image to be contrasted. BGR order. + factor (float): Same as :func:`mmcv.adjust_brightness`. + + Returns: + ndarray: The contrasted image. + """ + gray_img = bgr2gray(img) + hist = np.histogram(gray_img, 256, (0, 255))[0] + mean = round(np.sum(gray_img) / np.sum(hist)) + degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype) + degenerated = gray2bgr(degenerated) + contrasted_img = cv2.addWeighted( + img.astype(np.float32), factor, degenerated.astype(np.float32), + 1 - factor, 0) + contrasted_img = np.clip(contrasted_img, 0, 255) + return contrasted_img.astype(img.dtype) + + +def auto_contrast(img, cutoff=0): + """Auto adjust image contrast. + + This function maximize (normalize) image contrast by first removing cutoff + percent of the lightest and darkest pixels from the histogram and remapping + the image so that the darkest pixel becomes black (0), and the lightest + becomes white (255). + + Args: + img (ndarray): Image to be contrasted. BGR order. + cutoff (int | float | tuple): The cutoff percent of the lightest and + darkest pixels to be removed. If given as tuple, it shall be + (low, high). Otherwise, the single value will be used for both. + Defaults to 0. + + Returns: + ndarray: The contrasted image. + """ + + def _auto_contrast_channel(im, c, cutoff): + im = im[:, :, c] + # Compute the histogram of the image channel. + histo = np.histogram(im, 256, (0, 255))[0] + # Remove cut-off percent pixels from histo + histo_sum = np.cumsum(histo) + cut_low = histo_sum[-1] * cutoff[0] // 100 + cut_high = histo_sum[-1] - histo_sum[-1] * cutoff[1] // 100 + histo_sum = np.clip(histo_sum, cut_low, cut_high) - cut_low + histo = np.concatenate([[histo_sum[0]], np.diff(histo_sum)], 0) + + # Compute mapping + low, high = np.nonzero(histo)[0][0], np.nonzero(histo)[0][-1] + # If all the values have been cut off, return the origin img + if low >= high: + return im + scale = 255.0 / (high - low) + offset = -low * scale + lut = np.array(range(256)) + lut = lut * scale + offset + lut = np.clip(lut, 0, 255) + return lut[im] + + if isinstance(cutoff, (int, float)): + cutoff = (cutoff, cutoff) + else: + assert isinstance(cutoff, tuple), 'cutoff must be of type int, ' \ + f'float or tuple, but got {type(cutoff)} instead.' + # Auto adjusts contrast for each channel independently and then stacks + # the result. + s1 = _auto_contrast_channel(img, 0, cutoff) + s2 = _auto_contrast_channel(img, 1, cutoff) + s3 = _auto_contrast_channel(img, 2, cutoff) + contrasted_img = np.stack([s1, s2, s3], axis=-1) + return contrasted_img.astype(img.dtype) + + +def adjust_sharpness(img, factor=1., kernel=None): + """Adjust image sharpness. + + This function controls the sharpness of an image. An + enhancement factor of 0.0 gives a blurred image. A + factor of 1.0 gives the original image. And a factor + of 2.0 gives a sharpened image. It blends the source + image and the degenerated mean image: + + .. math:: + output = img * factor + degenerated * (1 - factor) + + Args: + img (ndarray): Image to be sharpened. BGR order. + factor (float): Same as :func:`mmcv.adjust_brightness`. + kernel (np.ndarray, optional): Filter kernel to be applied on the img + to obtain the degenerated img. Defaults to None. + + Note: + No value sanity check is enforced on the kernel set by users. So with + an inappropriate kernel, the ``adjust_sharpness`` may fail to perform + the function its name indicates but end up performing whatever + transform determined by the kernel. + + Returns: + ndarray: The sharpened image. + """ + + if kernel is None: + # adopted from PIL.ImageFilter.SMOOTH + kernel = np.array([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]]) / 13 + assert isinstance(kernel, np.ndarray), \ + f'kernel must be of type np.ndarray, but got {type(kernel)} instead.' + assert kernel.ndim == 2, \ + f'kernel must have a dimension of 2, but got {kernel.ndim} instead.' + + degenerated = cv2.filter2D(img, -1, kernel) + sharpened_img = cv2.addWeighted( + img.astype(np.float32), factor, degenerated.astype(np.float32), + 1 - factor, 0) + sharpened_img = np.clip(sharpened_img, 0, 255) + return sharpened_img.astype(img.dtype) + + +def adjust_lighting(img, eigval, eigvec, alphastd=0.1, to_rgb=True): + """AlexNet-style PCA jitter. + + This data augmentation is proposed in `ImageNet Classification with Deep + Convolutional Neural Networks + `_. + + Args: + img (ndarray): Image to be adjusted lighting. BGR order. + eigval (ndarray): the eigenvalue of the convariance matrix of pixel + values, respectively. + eigvec (ndarray): the eigenvector of the convariance matrix of pixel + values, respectively. + alphastd (float): The standard deviation for distribution of alpha. + Defaults to 0.1 + to_rgb (bool): Whether to convert img to rgb. + + Returns: + ndarray: The adjusted image. + """ + assert isinstance(eigval, np.ndarray) and isinstance(eigvec, np.ndarray), \ + f'eigval and eigvec should both be of type np.ndarray, got ' \ + f'{type(eigval)} and {type(eigvec)} instead.' + + assert eigval.ndim == 1 and eigvec.ndim == 2 + assert eigvec.shape == (3, eigval.shape[0]) + n_eigval = eigval.shape[0] + assert isinstance(alphastd, float), 'alphastd should be of type float, ' \ + f'got {type(alphastd)} instead.' + + img = img.copy().astype(np.float32) + if to_rgb: + cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace + + alpha = np.random.normal(0, alphastd, n_eigval) + alter = eigvec \ + * np.broadcast_to(alpha.reshape(1, n_eigval), (3, n_eigval)) \ + * np.broadcast_to(eigval.reshape(1, n_eigval), (3, n_eigval)) + alter = np.broadcast_to(alter.sum(axis=1).reshape(1, 1, 3), img.shape) + img_adjusted = img + alter + return img_adjusted + + +def lut_transform(img, lut_table): + """Transform array by look-up table. + + The function lut_transform fills the output array with values from the + look-up table. Indices of the entries are taken from the input array. + + Args: + img (ndarray): Image to be transformed. + lut_table (ndarray): look-up table of 256 elements; in case of + multi-channel input array, the table should either have a single + channel (in this case the same table is used for all channels) or + the same number of channels as in the input array. + + Returns: + ndarray: The transformed image. + """ + assert isinstance(img, np.ndarray) + assert 0 <= np.min(img) and np.max(img) <= 255 + assert isinstance(lut_table, np.ndarray) + assert lut_table.shape == (256, ) + + return cv2.LUT(np.array(img, dtype=np.uint8), lut_table) + + +def clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)): + """Use CLAHE method to process the image. + + See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J]. + Graphics Gems, 1994:474-485.` for more information. + + Args: + img (ndarray): Image to be processed. + clip_limit (float): Threshold for contrast limiting. Default: 40.0. + tile_grid_size (tuple[int]): Size of grid for histogram equalization. + Input image will be divided into equally sized rectangular tiles. + It defines the number of tiles in row and column. Default: (8, 8). + + Returns: + ndarray: The processed image. + """ + assert isinstance(img, np.ndarray) + assert img.ndim == 2 + assert isinstance(clip_limit, (float, int)) + assert is_tuple_of(tile_grid_size, int) + assert len(tile_grid_size) == 2 + + clahe = cv2.createCLAHE(clip_limit, tile_grid_size) + return clahe.apply(np.array(img, dtype=np.uint8)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/deprecated.json b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/deprecated.json new file mode 100644 index 00000000..25cf6f28 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/deprecated.json @@ -0,0 +1,6 @@ +{ + "resnet50_caffe": "detectron/resnet50_caffe", + "resnet50_caffe_bgr": "detectron2/resnet50_caffe_bgr", + "resnet101_caffe": "detectron/resnet101_caffe", + "resnet101_caffe_bgr": "detectron2/resnet101_caffe_bgr" +} diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/mmcls.json b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/mmcls.json new file mode 100644 index 00000000..bdb311d9 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/mmcls.json @@ -0,0 +1,31 @@ +{ + "vgg11": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth", + "vgg13": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth", + "vgg16": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth", + "vgg19": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth", + "vgg11_bn": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth", + "vgg13_bn": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth", + "vgg16_bn": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth", + "vgg19_bn": "https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth", + "resnet18": "https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth", + "resnet34": "https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth", + "resnet50": "https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth", + "resnet101": "https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth", + "resnet152": "https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth", + "resnet50_v1d": "https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth", + "resnet101_v1d": "https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth", + "resnet152_v1d": "https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth", + "resnext50_32x4d": "https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth", + "resnext101_32x4d": "https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth", + "resnext101_32x8d": "https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth", + "resnext152_32x4d": "https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth", + "se-resnet50": "https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth", + "se-resnet101": "https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth", + "resnest50": "https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth", + "resnest101": "https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth", + "resnest200": "https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth", + "resnest269": "https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth", + "shufflenet_v1": "https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth", + "shufflenet_v2": "https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth", + "mobilenet_v2": "https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth" +} diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/open_mmlab.json b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/open_mmlab.json new file mode 100644 index 00000000..8311db4f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/model_zoo/open_mmlab.json @@ -0,0 +1,50 @@ +{ + "vgg16_caffe": "https://download.openmmlab.com/pretrain/third_party/vgg16_caffe-292e1171.pth", + "detectron/resnet50_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth", + "detectron2/resnet50_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet50_msra-5891d200.pth", + "detectron/resnet101_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth", + "detectron2/resnet101_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet101_msra-6cc46731.pth", + "detectron2/resnext101_32x8d": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth", + "resnext50_32x4d": "https://download.openmmlab.com/pretrain/third_party/resnext50-32x4d-0ab1a123.pth", + "resnext101_32x4d": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d-a5af3160.pth", + "resnext101_64x4d": "https://download.openmmlab.com/pretrain/third_party/resnext101_64x4d-ee2c6f71.pth", + "contrib/resnet50_gn": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn_thangvubk-ad1730dd.pth", + "detectron/resnet50_gn": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn-9186a21c.pth", + "detectron/resnet101_gn": "https://download.openmmlab.com/pretrain/third_party/resnet101_gn-cac0ab98.pth", + "jhu/resnet50_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn_ws-15beedd8.pth", + "jhu/resnet101_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnet101_gn_ws-3e3c308c.pth", + "jhu/resnext50_32x4d_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnext50_32x4d_gn_ws-0d87ac85.pth", + "jhu/resnext101_32x4d_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d_gn_ws-34ac1a9e.pth", + "jhu/resnext50_32x4d_gn": "https://download.openmmlab.com/pretrain/third_party/resnext50_32x4d_gn-c7e8b754.pth", + "jhu/resnext101_32x4d_gn": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d_gn-ac3bb84e.pth", + "msra/hrnetv2_w18_small": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w18_small-b5a04e21.pth", + "msra/hrnetv2_w18": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w18-00eb2006.pth", + "msra/hrnetv2_w32": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w32-dc9eeb4f.pth", + "msra/hrnetv2_w40": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w40-ed0b031c.pth", + "msra/hrnetv2_w48": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w48-d2186c55.pth", + "bninception_caffe": "https://download.openmmlab.com/pretrain/third_party/bn_inception_caffe-ed2e8665.pth", + "kin400/i3d_r50_f32s2_k400": "https://download.openmmlab.com/pretrain/third_party/i3d_r50_f32s2_k400-2c57e077.pth", + "kin400/nl3d_r50_f32s2_k400": "https://download.openmmlab.com/pretrain/third_party/nl3d_r50_f32s2_k400-fa7e7caa.pth", + "res2net101_v1d_26w_4s": "https://download.openmmlab.com/pretrain/third_party/res2net101_v1d_26w_4s_mmdetv2-f0a600f9.pth", + "regnetx_400mf": "https://download.openmmlab.com/pretrain/third_party/regnetx_400mf-a5b10d96.pth", + "regnetx_800mf": "https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth", + "regnetx_1.6gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_1.6gf-5791c176.pth", + "regnetx_3.2gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth", + "regnetx_4.0gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_4.0gf-a88f671e.pth", + "regnetx_6.4gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_6.4gf-006af45d.pth", + "regnetx_8.0gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_8.0gf-3c68abe7.pth", + "regnetx_12gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_12gf-4c2a3350.pth", + "resnet18_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet18_v1c-b5776b93.pth", + "resnet50_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet50_v1c-2cccc1ad.pth", + "resnet101_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet101_v1c-e67eebb6.pth", + "mmedit/vgg16": "https://download.openmmlab.com/mmediting/third_party/vgg_state_dict.pth", + "mmedit/res34_en_nomixup": "https://download.openmmlab.com/mmediting/third_party/model_best_resnet34_En_nomixup.pth", + "mmedit/mobilenet_v2": "https://download.openmmlab.com/mmediting/third_party/mobilenet_v2.pth", + "contrib/mobilenet_v3_large": "https://download.openmmlab.com/pretrain/third_party/mobilenet_v3_large-bc2c3fd3.pth", + "contrib/mobilenet_v3_small": "https://download.openmmlab.com/pretrain/third_party/mobilenet_v3_small-47085aa1.pth", + "resnest50": "https://download.openmmlab.com/pretrain/third_party/resnest50_d2-7497a55b.pth", + "resnest101": "https://download.openmmlab.com/pretrain/third_party/resnest101_d2-f3b931b2.pth", + "resnest200": "https://download.openmmlab.com/pretrain/third_party/resnest200_d2-ca88e41f.pth", + "darknet53": "https://download.openmmlab.com/pretrain/third_party/darknet53-a628ea1b.pth", + "mmdet/mobilenet_v2": "https://download.openmmlab.com/mmdetection/v2.0/third_party/mobilenet_v2_batch256_imagenet-ff34753d.pth" +} diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/__init__.py new file mode 100644 index 00000000..999e090a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/__init__.py @@ -0,0 +1,81 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .assign_score_withk import assign_score_withk +from .ball_query import ball_query +from .bbox import bbox_overlaps +from .border_align import BorderAlign, border_align +from .box_iou_rotated import box_iou_rotated +from .carafe import CARAFE, CARAFENaive, CARAFEPack, carafe, carafe_naive +from .cc_attention import CrissCrossAttention +from .contour_expand import contour_expand +from .corner_pool import CornerPool +from .correlation import Correlation +from .deform_conv import DeformConv2d, DeformConv2dPack, deform_conv2d +from .deform_roi_pool import (DeformRoIPool, DeformRoIPoolPack, + ModulatedDeformRoIPoolPack, deform_roi_pool) +from .deprecated_wrappers import Conv2d_deprecated as Conv2d +from .deprecated_wrappers import ConvTranspose2d_deprecated as ConvTranspose2d +from .deprecated_wrappers import Linear_deprecated as Linear +from .deprecated_wrappers import MaxPool2d_deprecated as MaxPool2d +from .focal_loss import (SigmoidFocalLoss, SoftmaxFocalLoss, + sigmoid_focal_loss, softmax_focal_loss) +from .furthest_point_sample import (furthest_point_sample, + furthest_point_sample_with_dist) +from .fused_bias_leakyrelu import FusedBiasLeakyReLU, fused_bias_leakyrelu +from .gather_points import gather_points +from .group_points import GroupAll, QueryAndGroup, grouping_operation +from .info import (get_compiler_version, get_compiling_cuda_version, + get_onnxruntime_op_path) +from .iou3d import boxes_iou_bev, nms_bev, nms_normal_bev +from .knn import knn +from .masked_conv import MaskedConv2d, masked_conv2d +from .modulated_deform_conv import (ModulatedDeformConv2d, + ModulatedDeformConv2dPack, + modulated_deform_conv2d) +from .multi_scale_deform_attn import MultiScaleDeformableAttention +from .nms import batched_nms, nms, nms_match, nms_rotated, soft_nms +from .pixel_group import pixel_group +from .point_sample import (SimpleRoIAlign, point_sample, + rel_roi_point_to_rel_img_point) +from .points_in_boxes import (points_in_boxes_all, points_in_boxes_cpu, + points_in_boxes_part) +from .points_sampler import PointsSampler +from .psa_mask import PSAMask +from .roi_align import RoIAlign, roi_align +from .roi_align_rotated import RoIAlignRotated, roi_align_rotated +from .roi_pool import RoIPool, roi_pool +from .roiaware_pool3d import RoIAwarePool3d +from .roipoint_pool3d import RoIPointPool3d +from .saconv import SAConv2d +from .scatter_points import DynamicScatter, dynamic_scatter +from .sync_bn import SyncBatchNorm +from .three_interpolate import three_interpolate +from .three_nn import three_nn +from .tin_shift import TINShift, tin_shift +from .upfirdn2d import upfirdn2d +from .voxelize import Voxelization, voxelization + +__all__ = [ + 'bbox_overlaps', 'CARAFE', 'CARAFENaive', 'CARAFEPack', 'carafe', + 'carafe_naive', 'CornerPool', 'DeformConv2d', 'DeformConv2dPack', + 'deform_conv2d', 'DeformRoIPool', 'DeformRoIPoolPack', + 'ModulatedDeformRoIPoolPack', 'deform_roi_pool', 'SigmoidFocalLoss', + 'SoftmaxFocalLoss', 'sigmoid_focal_loss', 'softmax_focal_loss', + 'get_compiler_version', 'get_compiling_cuda_version', + 'get_onnxruntime_op_path', 'MaskedConv2d', 'masked_conv2d', + 'ModulatedDeformConv2d', 'ModulatedDeformConv2dPack', + 'modulated_deform_conv2d', 'batched_nms', 'nms', 'soft_nms', 'nms_match', + 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool', 'SyncBatchNorm', 'Conv2d', + 'ConvTranspose2d', 'Linear', 'MaxPool2d', 'CrissCrossAttention', 'PSAMask', + 'point_sample', 'rel_roi_point_to_rel_img_point', 'SimpleRoIAlign', + 'SAConv2d', 'TINShift', 'tin_shift', 'assign_score_withk', + 'box_iou_rotated', 'RoIPointPool3d', 'nms_rotated', 'knn', 'ball_query', + 'upfirdn2d', 'FusedBiasLeakyReLU', 'fused_bias_leakyrelu', + 'RoIAlignRotated', 'roi_align_rotated', 'pixel_group', 'QueryAndGroup', + 'GroupAll', 'grouping_operation', 'contour_expand', 'three_nn', + 'three_interpolate', 'MultiScaleDeformableAttention', 'BorderAlign', + 'border_align', 'gather_points', 'furthest_point_sample', + 'furthest_point_sample_with_dist', 'PointsSampler', 'Correlation', + 'boxes_iou_bev', 'nms_bev', 'nms_normal_bev', 'Voxelization', + 'voxelization', 'dynamic_scatter', 'DynamicScatter', 'RoIAwarePool3d', + 'points_in_boxes_part', 'points_in_boxes_cpu', 'points_in_boxes_all' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/assign_score_withk.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/assign_score_withk.py new file mode 100644 index 00000000..4906adaa --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/assign_score_withk.py @@ -0,0 +1,123 @@ +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['assign_score_withk_forward', 'assign_score_withk_backward']) + + +class AssignScoreWithK(Function): + r"""Perform weighted sum to generate output features according to scores. + Modified from `PAConv `_. + + This is a memory-efficient CUDA implementation of assign_scores operation, + which first transform all point features with weight bank, then assemble + neighbor features with ``knn_idx`` and perform weighted sum of ``scores``. + + See the `paper `_ appendix Sec. D for + more detailed descriptions. + + Note: + This implementation assumes using ``neighbor`` kernel input, which is + (point_features - center_features, point_features). + See https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/model/ + pointnet2/paconv.py#L128 for more details. + """ + + @staticmethod + def forward(ctx, + scores, + point_features, + center_features, + knn_idx, + aggregate='sum'): + """ + Args: + scores (torch.Tensor): (B, npoint, K, M), predicted scores to + aggregate weight matrices in the weight bank. + ``npoint`` is the number of sampled centers. + ``K`` is the number of queried neighbors. + ``M`` is the number of weight matrices in the weight bank. + point_features (torch.Tensor): (B, N, M, out_dim) + Pre-computed point features to be aggregated. + center_features (torch.Tensor): (B, N, M, out_dim) + Pre-computed center features to be aggregated. + knn_idx (torch.Tensor): (B, npoint, K), index of sampled kNN. + We assume the first idx in each row is the idx of the center. + aggregate (str, optional): Aggregation method. + Can be 'sum', 'avg' or 'max'. Defaults: 'sum'. + + Returns: + torch.Tensor: (B, out_dim, npoint, K), the aggregated features. + """ + agg = {'sum': 0, 'avg': 1, 'max': 2} + + B, N, M, out_dim = point_features.size() + _, npoint, K, _ = scores.size() + + output = point_features.new_zeros((B, out_dim, npoint, K)) + ext_module.assign_score_withk_forward( + point_features.contiguous(), + center_features.contiguous(), + scores.contiguous(), + knn_idx.contiguous(), + output, + B=B, + N0=N, + N1=npoint, + M=M, + K=K, + O=out_dim, + aggregate=agg[aggregate]) + + ctx.save_for_backward(output, point_features, center_features, scores, + knn_idx) + ctx.agg = agg[aggregate] + + return output + + @staticmethod + def backward(ctx, grad_out): + """ + Args: + grad_out (torch.Tensor): (B, out_dim, npoint, K) + + Returns: + grad_scores (torch.Tensor): (B, npoint, K, M) + grad_point_features (torch.Tensor): (B, N, M, out_dim) + grad_center_features (torch.Tensor): (B, N, M, out_dim) + """ + _, point_features, center_features, scores, knn_idx = ctx.saved_tensors + + agg = ctx.agg + + B, N, M, out_dim = point_features.size() + _, npoint, K, _ = scores.size() + + grad_point_features = point_features.new_zeros(point_features.shape) + grad_center_features = center_features.new_zeros(center_features.shape) + grad_scores = scores.new_zeros(scores.shape) + + ext_module.assign_score_withk_backward( + grad_out.contiguous(), + point_features.contiguous(), + center_features.contiguous(), + scores.contiguous(), + knn_idx.contiguous(), + grad_point_features, + grad_center_features, + grad_scores, + B=B, + N0=N, + N1=npoint, + M=M, + K=K, + O=out_dim, + aggregate=agg) + + return grad_scores, grad_point_features, \ + grad_center_features, None, None + + +assign_score_withk = AssignScoreWithK.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/ball_query.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/ball_query.py new file mode 100644 index 00000000..d0466847 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/ball_query.py @@ -0,0 +1,55 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['ball_query_forward']) + + +class BallQuery(Function): + """Find nearby points in spherical space.""" + + @staticmethod + def forward(ctx, min_radius: float, max_radius: float, sample_num: int, + xyz: torch.Tensor, center_xyz: torch.Tensor) -> torch.Tensor: + """ + Args: + min_radius (float): minimum radius of the balls. + max_radius (float): maximum radius of the balls. + sample_num (int): maximum number of features in the balls. + xyz (Tensor): (B, N, 3) xyz coordinates of the features. + center_xyz (Tensor): (B, npoint, 3) centers of the ball query. + + Returns: + Tensor: (B, npoint, nsample) tensor with the indices of + the features that form the query balls. + """ + assert center_xyz.is_contiguous() + assert xyz.is_contiguous() + assert min_radius < max_radius + + B, N, _ = xyz.size() + npoint = center_xyz.size(1) + idx = xyz.new_zeros(B, npoint, sample_num, dtype=torch.int) + + ext_module.ball_query_forward( + center_xyz, + xyz, + idx, + b=B, + n=N, + m=npoint, + min_radius=min_radius, + max_radius=max_radius, + nsample=sample_num) + if torch.__version__ != 'parrots': + ctx.mark_non_differentiable(idx) + return idx + + @staticmethod + def backward(ctx, a=None): + return None, None, None, None + + +ball_query = BallQuery.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/bbox.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/bbox.py new file mode 100644 index 00000000..0c4d58b6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/bbox.py @@ -0,0 +1,72 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps']) + + +def bbox_overlaps(bboxes1, bboxes2, mode='iou', aligned=False, offset=0): + """Calculate overlap between two set of bboxes. + + If ``aligned`` is ``False``, then calculate the ious between each bbox + of bboxes1 and bboxes2, otherwise the ious between each aligned pair of + bboxes1 and bboxes2. + + Args: + bboxes1 (Tensor): shape (m, 4) in format or empty. + bboxes2 (Tensor): shape (n, 4) in format or empty. + If aligned is ``True``, then m and n must be equal. + mode (str): "iou" (intersection over union) or iof (intersection over + foreground). + + Returns: + ious(Tensor): shape (m, n) if aligned == False else shape (m, 1) + + Example: + >>> bboxes1 = torch.FloatTensor([ + >>> [0, 0, 10, 10], + >>> [10, 10, 20, 20], + >>> [32, 32, 38, 42], + >>> ]) + >>> bboxes2 = torch.FloatTensor([ + >>> [0, 0, 10, 20], + >>> [0, 10, 10, 19], + >>> [10, 10, 20, 20], + >>> ]) + >>> bbox_overlaps(bboxes1, bboxes2) + tensor([[0.5000, 0.0000, 0.0000], + [0.0000, 0.0000, 1.0000], + [0.0000, 0.0000, 0.0000]]) + + Example: + >>> empty = torch.FloatTensor([]) + >>> nonempty = torch.FloatTensor([ + >>> [0, 0, 10, 9], + >>> ]) + >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) + >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) + >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) + """ + + mode_dict = {'iou': 0, 'iof': 1} + assert mode in mode_dict.keys() + mode_flag = mode_dict[mode] + # Either the boxes are empty or the length of boxes' last dimension is 4 + assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0) + assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0) + assert offset == 1 or offset == 0 + + rows = bboxes1.size(0) + cols = bboxes2.size(0) + if aligned: + assert rows == cols + + if rows * cols == 0: + return bboxes1.new(rows, 1) if aligned else bboxes1.new(rows, cols) + + if aligned: + ious = bboxes1.new_zeros(rows) + else: + ious = bboxes1.new_zeros((rows, cols)) + ext_module.bbox_overlaps( + bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset) + return ious diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/border_align.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/border_align.py new file mode 100644 index 00000000..ff305be3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/border_align.py @@ -0,0 +1,109 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# modified from +# https://github.com/Megvii-BaseDetection/cvpods/blob/master/cvpods/layers/border_align.py + +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['border_align_forward', 'border_align_backward']) + + +class BorderAlignFunction(Function): + + @staticmethod + def symbolic(g, input, boxes, pool_size): + return g.op( + 'mmcv::MMCVBorderAlign', input, boxes, pool_size_i=pool_size) + + @staticmethod + def forward(ctx, input, boxes, pool_size): + ctx.pool_size = pool_size + ctx.input_shape = input.size() + + assert boxes.ndim == 3, 'boxes must be with shape [B, H*W, 4]' + assert boxes.size(2) == 4, \ + 'the last dimension of boxes must be (x1, y1, x2, y2)' + assert input.size(1) % 4 == 0, \ + 'the channel for input feature must be divisible by factor 4' + + # [B, C//4, H*W, 4] + output_shape = (input.size(0), input.size(1) // 4, boxes.size(1), 4) + output = input.new_zeros(output_shape) + # `argmax_idx` only used for backward + argmax_idx = input.new_zeros(output_shape).to(torch.int) + + ext_module.border_align_forward( + input, boxes, output, argmax_idx, pool_size=ctx.pool_size) + + ctx.save_for_backward(boxes, argmax_idx) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + boxes, argmax_idx = ctx.saved_tensors + grad_input = grad_output.new_zeros(ctx.input_shape) + # complex head architecture may cause grad_output uncontiguous + grad_output = grad_output.contiguous() + ext_module.border_align_backward( + grad_output, + boxes, + argmax_idx, + grad_input, + pool_size=ctx.pool_size) + return grad_input, None, None + + +border_align = BorderAlignFunction.apply + + +class BorderAlign(nn.Module): + r"""Border align pooling layer. + + Applies border_align over the input feature based on predicted bboxes. + The details were described in the paper + `BorderDet: Border Feature for Dense Object Detection + `_. + + For each border line (e.g. top, left, bottom or right) of each box, + border_align does the following: + 1. uniformly samples `pool_size`+1 positions on this line, involving \ + the start and end points. + 2. the corresponding features on these points are computed by \ + bilinear interpolation. + 3. max pooling over all the `pool_size`+1 positions are used for \ + computing pooled feature. + + Args: + pool_size (int): number of positions sampled over the boxes' borders + (e.g. top, bottom, left, right). + + """ + + def __init__(self, pool_size): + super(BorderAlign, self).__init__() + self.pool_size = pool_size + + def forward(self, input, boxes): + """ + Args: + input: Features with shape [N,4C,H,W]. Channels ranged in [0,C), + [C,2C), [2C,3C), [3C,4C) represent the top, left, bottom, + right features respectively. + boxes: Boxes with shape [N,H*W,4]. Coordinate format (x1,y1,x2,y2). + + Returns: + Tensor: Pooled features with shape [N,C,H*W,4]. The order is + (top,left,bottom,right) for the last dimension. + """ + return border_align(input, boxes, self.pool_size) + + def __repr__(self): + s = self.__class__.__name__ + s += f'(pool_size={self.pool_size})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/box_iou_rotated.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/box_iou_rotated.py new file mode 100644 index 00000000..2d78015e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/box_iou_rotated.py @@ -0,0 +1,45 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['box_iou_rotated']) + + +def box_iou_rotated(bboxes1, bboxes2, mode='iou', aligned=False): + """Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in + (x_center, y_center, width, height, angle) format. + + If ``aligned`` is ``False``, then calculate the ious between each bbox + of bboxes1 and bboxes2, otherwise the ious between each aligned pair of + bboxes1 and bboxes2. + + Arguments: + boxes1 (Tensor): rotated bboxes 1. \ + It has shape (N, 5), indicating (x, y, w, h, theta) for each row. + Note that theta is in radian. + boxes2 (Tensor): rotated bboxes 2. \ + It has shape (M, 5), indicating (x, y, w, h, theta) for each row. + Note that theta is in radian. + mode (str): "iou" (intersection over union) or iof (intersection over + foreground). + + Returns: + ious(Tensor): shape (N, M) if aligned == False else shape (N,) + """ + assert mode in ['iou', 'iof'] + mode_dict = {'iou': 0, 'iof': 1} + mode_flag = mode_dict[mode] + rows = bboxes1.size(0) + cols = bboxes2.size(0) + if aligned: + ious = bboxes1.new_zeros(rows) + else: + ious = bboxes1.new_zeros((rows * cols)) + bboxes1 = bboxes1.contiguous() + bboxes2 = bboxes2.contiguous() + ext_module.box_iou_rotated( + bboxes1, bboxes2, ious, mode_flag=mode_flag, aligned=aligned) + if not aligned: + ious = ious.view(rows, cols) + return ious diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/carafe.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/carafe.py new file mode 100644 index 00000000..5154cb3a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/carafe.py @@ -0,0 +1,287 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Function +from torch.nn.modules.module import Module + +from ..cnn import UPSAMPLE_LAYERS, normal_init, xavier_init +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'carafe_naive_forward', 'carafe_naive_backward', 'carafe_forward', + 'carafe_backward' +]) + + +class CARAFENaiveFunction(Function): + + @staticmethod + def symbolic(g, features, masks, kernel_size, group_size, scale_factor): + return g.op( + 'mmcv::MMCVCARAFENaive', + features, + masks, + kernel_size_i=kernel_size, + group_size_i=group_size, + scale_factor_f=scale_factor) + + @staticmethod + def forward(ctx, features, masks, kernel_size, group_size, scale_factor): + assert scale_factor >= 1 + assert masks.size(1) == kernel_size * kernel_size * group_size + assert masks.size(-1) == features.size(-1) * scale_factor + assert masks.size(-2) == features.size(-2) * scale_factor + assert features.size(1) % group_size == 0 + assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1 + ctx.kernel_size = kernel_size + ctx.group_size = group_size + ctx.scale_factor = scale_factor + ctx.feature_size = features.size() + ctx.mask_size = masks.size() + + n, c, h, w = features.size() + output = features.new_zeros((n, c, h * scale_factor, w * scale_factor)) + ext_module.carafe_naive_forward( + features, + masks, + output, + kernel_size=kernel_size, + group_size=group_size, + scale_factor=scale_factor) + + if features.requires_grad or masks.requires_grad: + ctx.save_for_backward(features, masks) + return output + + @staticmethod + def backward(ctx, grad_output): + assert grad_output.is_cuda + + features, masks = ctx.saved_tensors + kernel_size = ctx.kernel_size + group_size = ctx.group_size + scale_factor = ctx.scale_factor + + grad_input = torch.zeros_like(features) + grad_masks = torch.zeros_like(masks) + ext_module.carafe_naive_backward( + grad_output.contiguous(), + features, + masks, + grad_input, + grad_masks, + kernel_size=kernel_size, + group_size=group_size, + scale_factor=scale_factor) + + return grad_input, grad_masks, None, None, None + + +carafe_naive = CARAFENaiveFunction.apply + + +class CARAFENaive(Module): + + def __init__(self, kernel_size, group_size, scale_factor): + super(CARAFENaive, self).__init__() + + assert isinstance(kernel_size, int) and isinstance( + group_size, int) and isinstance(scale_factor, int) + self.kernel_size = kernel_size + self.group_size = group_size + self.scale_factor = scale_factor + + def forward(self, features, masks): + return carafe_naive(features, masks, self.kernel_size, self.group_size, + self.scale_factor) + + +class CARAFEFunction(Function): + + @staticmethod + def symbolic(g, features, masks, kernel_size, group_size, scale_factor): + return g.op( + 'mmcv::MMCVCARAFE', + features, + masks, + kernel_size_i=kernel_size, + group_size_i=group_size, + scale_factor_f=scale_factor) + + @staticmethod + def forward(ctx, features, masks, kernel_size, group_size, scale_factor): + assert scale_factor >= 1 + assert masks.size(1) == kernel_size * kernel_size * group_size + assert masks.size(-1) == features.size(-1) * scale_factor + assert masks.size(-2) == features.size(-2) * scale_factor + assert features.size(1) % group_size == 0 + assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1 + ctx.kernel_size = kernel_size + ctx.group_size = group_size + ctx.scale_factor = scale_factor + ctx.feature_size = features.size() + ctx.mask_size = masks.size() + + n, c, h, w = features.size() + output = features.new_zeros((n, c, h * scale_factor, w * scale_factor)) + routput = features.new_zeros(output.size(), requires_grad=False) + rfeatures = features.new_zeros(features.size(), requires_grad=False) + rmasks = masks.new_zeros(masks.size(), requires_grad=False) + ext_module.carafe_forward( + features, + masks, + rfeatures, + routput, + rmasks, + output, + kernel_size=kernel_size, + group_size=group_size, + scale_factor=scale_factor) + + if features.requires_grad or masks.requires_grad: + ctx.save_for_backward(features, masks, rfeatures) + return output + + @staticmethod + def backward(ctx, grad_output): + assert grad_output.is_cuda + + features, masks, rfeatures = ctx.saved_tensors + kernel_size = ctx.kernel_size + group_size = ctx.group_size + scale_factor = ctx.scale_factor + + rgrad_output = torch.zeros_like(grad_output, requires_grad=False) + rgrad_input_hs = torch.zeros_like(grad_output, requires_grad=False) + rgrad_input = torch.zeros_like(features, requires_grad=False) + rgrad_masks = torch.zeros_like(masks, requires_grad=False) + grad_input = torch.zeros_like(features, requires_grad=False) + grad_masks = torch.zeros_like(masks, requires_grad=False) + ext_module.carafe_backward( + grad_output.contiguous(), + rfeatures, + masks, + rgrad_output, + rgrad_input_hs, + rgrad_input, + rgrad_masks, + grad_input, + grad_masks, + kernel_size=kernel_size, + group_size=group_size, + scale_factor=scale_factor) + return grad_input, grad_masks, None, None, None + + +carafe = CARAFEFunction.apply + + +class CARAFE(Module): + """ CARAFE: Content-Aware ReAssembly of FEatures + + Please refer to https://arxiv.org/abs/1905.02188 for more details. + + Args: + kernel_size (int): reassemble kernel size + group_size (int): reassemble group size + scale_factor (int): upsample ratio + + Returns: + upsampled feature map + """ + + def __init__(self, kernel_size, group_size, scale_factor): + super(CARAFE, self).__init__() + + assert isinstance(kernel_size, int) and isinstance( + group_size, int) and isinstance(scale_factor, int) + self.kernel_size = kernel_size + self.group_size = group_size + self.scale_factor = scale_factor + + def forward(self, features, masks): + return carafe(features, masks, self.kernel_size, self.group_size, + self.scale_factor) + + +@UPSAMPLE_LAYERS.register_module(name='carafe') +class CARAFEPack(nn.Module): + """A unified package of CARAFE upsampler that contains: 1) channel + compressor 2) content encoder 3) CARAFE op. + + Official implementation of ICCV 2019 paper + CARAFE: Content-Aware ReAssembly of FEatures + Please refer to https://arxiv.org/abs/1905.02188 for more details. + + Args: + channels (int): input feature channels + scale_factor (int): upsample ratio + up_kernel (int): kernel size of CARAFE op + up_group (int): group size of CARAFE op + encoder_kernel (int): kernel size of content encoder + encoder_dilation (int): dilation of content encoder + compressed_channels (int): output channels of channels compressor + + Returns: + upsampled feature map + """ + + def __init__(self, + channels, + scale_factor, + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64): + super(CARAFEPack, self).__init__() + self.channels = channels + self.scale_factor = scale_factor + self.up_kernel = up_kernel + self.up_group = up_group + self.encoder_kernel = encoder_kernel + self.encoder_dilation = encoder_dilation + self.compressed_channels = compressed_channels + self.channel_compressor = nn.Conv2d(channels, self.compressed_channels, + 1) + self.content_encoder = nn.Conv2d( + self.compressed_channels, + self.up_kernel * self.up_kernel * self.up_group * + self.scale_factor * self.scale_factor, + self.encoder_kernel, + padding=int((self.encoder_kernel - 1) * self.encoder_dilation / 2), + dilation=self.encoder_dilation, + groups=1) + self.init_weights() + + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + xavier_init(m, distribution='uniform') + normal_init(self.content_encoder, std=0.001) + + def kernel_normalizer(self, mask): + mask = F.pixel_shuffle(mask, self.scale_factor) + n, mask_c, h, w = mask.size() + # use float division explicitly, + # to void inconsistency while exporting to onnx + mask_channel = int(mask_c / float(self.up_kernel**2)) + mask = mask.view(n, mask_channel, -1, h, w) + + mask = F.softmax(mask, dim=2, dtype=mask.dtype) + mask = mask.view(n, mask_c, h, w).contiguous() + + return mask + + def feature_reassemble(self, x, mask): + x = carafe(x, mask, self.up_kernel, self.up_group, self.scale_factor) + return x + + def forward(self, x): + compressed_x = self.channel_compressor(x) + mask = self.content_encoder(compressed_x) + mask = self.kernel_normalizer(mask) + + x = self.feature_reassemble(x, mask) + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/cc_attention.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/cc_attention.py new file mode 100644 index 00000000..d6868974 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/cc_attention.py @@ -0,0 +1,83 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +import torch.nn.functional as F + +from custom_mmpkg.custom_mmcv.cnn import PLUGIN_LAYERS, Scale + + +def NEG_INF_DIAG(n, device): + """Returns a diagonal matrix of size [n, n]. + + The diagonal are all "-inf". This is for avoiding calculating the + overlapped element in the Criss-Cross twice. + """ + return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0) + + +@PLUGIN_LAYERS.register_module() +class CrissCrossAttention(nn.Module): + """Criss-Cross Attention Module. + + .. note:: + Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch + to a pure PyTorch and equivalent implementation. For more + details, please refer to https://github.com/open-mmlab/mmcv/pull/1201. + + Speed comparison for one forward pass + + - Input size: [2,512,97,97] + - Device: 1 NVIDIA GeForce RTX 2080 Ti + + +-----------------------+---------------+------------+---------------+ + | |PyTorch version|CUDA version|Relative speed | + +=======================+===============+============+===============+ + |with torch.no_grad() |0.00554402 s |0.0299619 s |5.4x | + +-----------------------+---------------+------------+---------------+ + |no with torch.no_grad()|0.00562803 s |0.0301349 s |5.4x | + +-----------------------+---------------+------------+---------------+ + + Args: + in_channels (int): Channels of the input feature map. + """ + + def __init__(self, in_channels): + super().__init__() + self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1) + self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1) + self.value_conv = nn.Conv2d(in_channels, in_channels, 1) + self.gamma = Scale(0.) + self.in_channels = in_channels + + def forward(self, x): + """forward function of Criss-Cross Attention. + + Args: + x (Tensor): Input feature. \ + shape (batch_size, in_channels, height, width) + Returns: + Tensor: Output of the layer, with shape of \ + (batch_size, in_channels, height, width) + """ + B, C, H, W = x.size() + query = self.query_conv(x) + key = self.key_conv(x) + value = self.value_conv(x) + energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG( + H, query.device) + energy_H = energy_H.transpose(1, 2) + energy_W = torch.einsum('bchw,bchj->bhwj', query, key) + attn = F.softmax( + torch.cat([energy_H, energy_W], dim=-1), dim=-1) # [B,H,W,(H+W)] + out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H]) + out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:]) + + out = self.gamma(out) + x + out = out.contiguous() + + return out + + def __repr__(self): + s = self.__class__.__name__ + s += f'(in_channels={self.in_channels})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/contour_expand.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/contour_expand.py new file mode 100644 index 00000000..ea1111e1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/contour_expand.py @@ -0,0 +1,49 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np +import torch + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['contour_expand']) + + +def contour_expand(kernel_mask, internal_kernel_label, min_kernel_area, + kernel_num): + """Expand kernel contours so that foreground pixels are assigned into + instances. + + Arguments: + kernel_mask (np.array or Tensor): The instance kernel mask with + size hxw. + internal_kernel_label (np.array or Tensor): The instance internal + kernel label with size hxw. + min_kernel_area (int): The minimum kernel area. + kernel_num (int): The instance kernel number. + + Returns: + label (list): The instance index map with size hxw. + """ + assert isinstance(kernel_mask, (torch.Tensor, np.ndarray)) + assert isinstance(internal_kernel_label, (torch.Tensor, np.ndarray)) + assert isinstance(min_kernel_area, int) + assert isinstance(kernel_num, int) + + if isinstance(kernel_mask, np.ndarray): + kernel_mask = torch.from_numpy(kernel_mask) + if isinstance(internal_kernel_label, np.ndarray): + internal_kernel_label = torch.from_numpy(internal_kernel_label) + + if torch.__version__ == 'parrots': + if kernel_mask.shape[0] == 0 or internal_kernel_label.shape[0] == 0: + label = [] + else: + label = ext_module.contour_expand( + kernel_mask, + internal_kernel_label, + min_kernel_area=min_kernel_area, + kernel_num=kernel_num) + label = label.tolist() + else: + label = ext_module.contour_expand(kernel_mask, internal_kernel_label, + min_kernel_area, kernel_num) + return label diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/corner_pool.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/corner_pool.py new file mode 100644 index 00000000..a33d798b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/corner_pool.py @@ -0,0 +1,161 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'top_pool_forward', 'top_pool_backward', 'bottom_pool_forward', + 'bottom_pool_backward', 'left_pool_forward', 'left_pool_backward', + 'right_pool_forward', 'right_pool_backward' +]) + +_mode_dict = {'top': 0, 'bottom': 1, 'left': 2, 'right': 3} + + +class TopPoolFunction(Function): + + @staticmethod + def symbolic(g, input): + output = g.op( + 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['top'])) + return output + + @staticmethod + def forward(ctx, input): + output = ext_module.top_pool_forward(input) + ctx.save_for_backward(input) + return output + + @staticmethod + def backward(ctx, grad_output): + input, = ctx.saved_tensors + output = ext_module.top_pool_backward(input, grad_output) + return output + + +class BottomPoolFunction(Function): + + @staticmethod + def symbolic(g, input): + output = g.op( + 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['bottom'])) + return output + + @staticmethod + def forward(ctx, input): + output = ext_module.bottom_pool_forward(input) + ctx.save_for_backward(input) + return output + + @staticmethod + def backward(ctx, grad_output): + input, = ctx.saved_tensors + output = ext_module.bottom_pool_backward(input, grad_output) + return output + + +class LeftPoolFunction(Function): + + @staticmethod + def symbolic(g, input): + output = g.op( + 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['left'])) + return output + + @staticmethod + def forward(ctx, input): + output = ext_module.left_pool_forward(input) + ctx.save_for_backward(input) + return output + + @staticmethod + def backward(ctx, grad_output): + input, = ctx.saved_tensors + output = ext_module.left_pool_backward(input, grad_output) + return output + + +class RightPoolFunction(Function): + + @staticmethod + def symbolic(g, input): + output = g.op( + 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['right'])) + return output + + @staticmethod + def forward(ctx, input): + output = ext_module.right_pool_forward(input) + ctx.save_for_backward(input) + return output + + @staticmethod + def backward(ctx, grad_output): + input, = ctx.saved_tensors + output = ext_module.right_pool_backward(input, grad_output) + return output + + +class CornerPool(nn.Module): + """Corner Pooling. + + Corner Pooling is a new type of pooling layer that helps a + convolutional network better localize corners of bounding boxes. + + Please refer to https://arxiv.org/abs/1808.01244 for more details. + Code is modified from https://github.com/princeton-vl/CornerNet-Lite. + + Args: + mode(str): Pooling orientation for the pooling layer + + - 'bottom': Bottom Pooling + - 'left': Left Pooling + - 'right': Right Pooling + - 'top': Top Pooling + + Returns: + Feature map after pooling. + """ + + pool_functions = { + 'bottom': BottomPoolFunction, + 'left': LeftPoolFunction, + 'right': RightPoolFunction, + 'top': TopPoolFunction, + } + + cummax_dim_flip = { + 'bottom': (2, False), + 'left': (3, True), + 'right': (3, False), + 'top': (2, True), + } + + def __init__(self, mode): + super(CornerPool, self).__init__() + assert mode in self.pool_functions + self.mode = mode + self.corner_pool = self.pool_functions[mode] + + def forward(self, x): + if torch.__version__ != 'parrots' and torch.__version__ >= '1.5.0': + if torch.onnx.is_in_onnx_export(): + assert torch.__version__ >= '1.7.0', \ + 'When `cummax` serves as an intermediate component whose '\ + 'outputs is used as inputs for another modules, it\'s '\ + 'expected that pytorch version must be >= 1.7.0, '\ + 'otherwise Error appears like: `RuntimeError: tuple '\ + 'appears in op that does not forward tuples, unsupported '\ + 'kind: prim::PythonOp`.' + + dim, flip = self.cummax_dim_flip[self.mode] + if flip: + x = x.flip(dim) + pool_tensor, _ = torch.cummax(x, dim=dim) + if flip: + pool_tensor = pool_tensor.flip(dim) + return pool_tensor + else: + return self.corner_pool.apply(x) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/correlation.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/correlation.py new file mode 100644 index 00000000..3d0b79c3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/correlation.py @@ -0,0 +1,196 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import Tensor, nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['correlation_forward', 'correlation_backward']) + + +class CorrelationFunction(Function): + + @staticmethod + def forward(ctx, + input1, + input2, + kernel_size=1, + max_displacement=1, + stride=1, + padding=1, + dilation=1, + dilation_patch=1): + + ctx.save_for_backward(input1, input2) + + kH, kW = ctx.kernel_size = _pair(kernel_size) + patch_size = max_displacement * 2 + 1 + ctx.patch_size = patch_size + dH, dW = ctx.stride = _pair(stride) + padH, padW = ctx.padding = _pair(padding) + dilationH, dilationW = ctx.dilation = _pair(dilation) + dilation_patchH, dilation_patchW = ctx.dilation_patch = _pair( + dilation_patch) + + output_size = CorrelationFunction._output_size(ctx, input1) + + output = input1.new_zeros(output_size) + + ext_module.correlation_forward( + input1, + input2, + output, + kH=kH, + kW=kW, + patchH=patch_size, + patchW=patch_size, + padH=padH, + padW=padW, + dilationH=dilationH, + dilationW=dilationW, + dilation_patchH=dilation_patchH, + dilation_patchW=dilation_patchW, + dH=dH, + dW=dW) + + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input1, input2 = ctx.saved_tensors + + kH, kW = ctx.kernel_size + patch_size = ctx.patch_size + padH, padW = ctx.padding + dilationH, dilationW = ctx.dilation + dilation_patchH, dilation_patchW = ctx.dilation_patch + dH, dW = ctx.stride + grad_input1 = torch.zeros_like(input1) + grad_input2 = torch.zeros_like(input2) + + ext_module.correlation_backward( + grad_output, + input1, + input2, + grad_input1, + grad_input2, + kH=kH, + kW=kW, + patchH=patch_size, + patchW=patch_size, + padH=padH, + padW=padW, + dilationH=dilationH, + dilationW=dilationW, + dilation_patchH=dilation_patchH, + dilation_patchW=dilation_patchW, + dH=dH, + dW=dW) + return grad_input1, grad_input2, None, None, None, None, None, None + + @staticmethod + def _output_size(ctx, input1): + iH, iW = input1.size(2), input1.size(3) + batch_size = input1.size(0) + kH, kW = ctx.kernel_size + patch_size = ctx.patch_size + dH, dW = ctx.stride + padH, padW = ctx.padding + dilationH, dilationW = ctx.dilation + dilatedKH = (kH - 1) * dilationH + 1 + dilatedKW = (kW - 1) * dilationW + 1 + + oH = int((iH + 2 * padH - dilatedKH) / dH + 1) + oW = int((iW + 2 * padW - dilatedKW) / dW + 1) + + output_size = (batch_size, patch_size, patch_size, oH, oW) + return output_size + + +class Correlation(nn.Module): + r"""Correlation operator + + This correlation operator works for optical flow correlation computation. + + There are two batched tensors with shape :math:`(N, C, H, W)`, + and the correlation output's shape is :math:`(N, max\_displacement \times + 2 + 1, max\_displacement * 2 + 1, H_{out}, W_{out})` + + where + + .. math:: + H_{out} = \left\lfloor\frac{H_{in} + 2 \times padding - + dilation \times (kernel\_size - 1) - 1} + {stride} + 1\right\rfloor + + .. math:: + W_{out} = \left\lfloor\frac{W_{in} + 2 \times padding - dilation + \times (kernel\_size - 1) - 1} + {stride} + 1\right\rfloor + + the correlation item :math:`(N_i, dy, dx)` is formed by taking the sliding + window convolution between input1 and shifted input2, + + .. math:: + Corr(N_i, dx, dy) = + \sum_{c=0}^{C-1} + input1(N_i, c) \star + \mathcal{S}(input2(N_i, c), dy, dx) + + where :math:`\star` is the valid 2d sliding window convolution operator, + and :math:`\mathcal{S}` means shifting the input features (auto-complete + zero marginal), and :math:`dx, dy` are shifting distance, :math:`dx, dy \in + [-max\_displacement \times dilation\_patch, max\_displacement \times + dilation\_patch]`. + + Args: + kernel_size (int): The size of sliding window i.e. local neighborhood + representing the center points and involved in correlation + computation. Defaults to 1. + max_displacement (int): The radius for computing correlation volume, + but the actual working space can be dilated by dilation_patch. + Defaults to 1. + stride (int): The stride of the sliding blocks in the input spatial + dimensions. Defaults to 1. + padding (int): Zero padding added to all four sides of the input1. + Defaults to 0. + dilation (int): The spacing of local neighborhood that will involved + in correlation. Defaults to 1. + dilation_patch (int): The spacing between position need to compute + correlation. Defaults to 1. + """ + + def __init__(self, + kernel_size: int = 1, + max_displacement: int = 1, + stride: int = 1, + padding: int = 0, + dilation: int = 1, + dilation_patch: int = 1) -> None: + super().__init__() + self.kernel_size = kernel_size + self.max_displacement = max_displacement + self.stride = stride + self.padding = padding + self.dilation = dilation + self.dilation_patch = dilation_patch + + def forward(self, input1: Tensor, input2: Tensor) -> Tensor: + return CorrelationFunction.apply(input1, input2, self.kernel_size, + self.max_displacement, self.stride, + self.padding, self.dilation, + self.dilation_patch) + + def __repr__(self) -> str: + s = self.__class__.__name__ + s += f'(kernel_size={self.kernel_size}, ' + s += f'max_displacement={self.max_displacement}, ' + s += f'stride={self.stride}, ' + s += f'padding={self.padding}, ' + s += f'dilation={self.dilation}, ' + s += f'dilation_patch={self.dilation_patch})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deform_conv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deform_conv.py new file mode 100644 index 00000000..c8e80c5a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deform_conv.py @@ -0,0 +1,405 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair, _single + +from custom_mmpkg.custom_mmcv.utils import deprecated_api_warning +from ..cnn import CONV_LAYERS +from ..utils import ext_loader, print_log + +ext_module = ext_loader.load_ext('_ext', [ + 'deform_conv_forward', 'deform_conv_backward_input', + 'deform_conv_backward_parameters' +]) + + +class DeformConv2dFunction(Function): + + @staticmethod + def symbolic(g, + input, + offset, + weight, + stride, + padding, + dilation, + groups, + deform_groups, + bias=False, + im2col_step=32): + return g.op( + 'mmcv::MMCVDeformConv2d', + input, + offset, + weight, + stride_i=stride, + padding_i=padding, + dilation_i=dilation, + groups_i=groups, + deform_groups_i=deform_groups, + bias_i=bias, + im2col_step_i=im2col_step) + + @staticmethod + def forward(ctx, + input, + offset, + weight, + stride=1, + padding=0, + dilation=1, + groups=1, + deform_groups=1, + bias=False, + im2col_step=32): + if input is not None and input.dim() != 4: + raise ValueError( + f'Expected 4D tensor as input, got {input.dim()}D tensor \ + instead.') + assert bias is False, 'Only support bias is False.' + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deform_groups = deform_groups + ctx.im2col_step = im2col_step + + # When pytorch version >= 1.6.0, amp is adopted for fp16 mode; + # amp won't cast the type of model (float32), but "offset" is cast + # to float16 by nn.Conv2d automatically, leading to the type + # mismatch with input (when it is float32) or weight. + # The flag for whether to use fp16 or amp is the type of "offset", + # we cast weight and input to temporarily support fp16 and amp + # whatever the pytorch version is. + input = input.type_as(offset) + weight = weight.type_as(input) + ctx.save_for_backward(input, offset, weight) + + output = input.new_empty( + DeformConv2dFunction._output_size(ctx, input, weight)) + + ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones + + cur_im2col_step = min(ctx.im2col_step, input.size(0)) + assert (input.size(0) % + cur_im2col_step) == 0, 'im2col step must divide batchsize' + ext_module.deform_conv_forward( + input, + weight, + offset, + output, + ctx.bufs_[0], + ctx.bufs_[1], + kW=weight.size(3), + kH=weight.size(2), + dW=ctx.stride[1], + dH=ctx.stride[0], + padW=ctx.padding[1], + padH=ctx.padding[0], + dilationW=ctx.dilation[1], + dilationH=ctx.dilation[0], + group=ctx.groups, + deformable_group=ctx.deform_groups, + im2col_step=cur_im2col_step) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, weight = ctx.saved_tensors + + grad_input = grad_offset = grad_weight = None + + cur_im2col_step = min(ctx.im2col_step, input.size(0)) + assert (input.size(0) % cur_im2col_step + ) == 0, 'batch size must be divisible by im2col_step' + + grad_output = grad_output.contiguous() + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + ext_module.deform_conv_backward_input( + input, + offset, + grad_output, + grad_input, + grad_offset, + weight, + ctx.bufs_[0], + kW=weight.size(3), + kH=weight.size(2), + dW=ctx.stride[1], + dH=ctx.stride[0], + padW=ctx.padding[1], + padH=ctx.padding[0], + dilationW=ctx.dilation[1], + dilationH=ctx.dilation[0], + group=ctx.groups, + deformable_group=ctx.deform_groups, + im2col_step=cur_im2col_step) + + if ctx.needs_input_grad[2]: + grad_weight = torch.zeros_like(weight) + ext_module.deform_conv_backward_parameters( + input, + offset, + grad_output, + grad_weight, + ctx.bufs_[0], + ctx.bufs_[1], + kW=weight.size(3), + kH=weight.size(2), + dW=ctx.stride[1], + dH=ctx.stride[0], + padW=ctx.padding[1], + padH=ctx.padding[0], + dilationW=ctx.dilation[1], + dilationH=ctx.dilation[0], + group=ctx.groups, + deformable_group=ctx.deform_groups, + scale=1, + im2col_step=cur_im2col_step) + + return grad_input, grad_offset, grad_weight, \ + None, None, None, None, None, None, None + + @staticmethod + def _output_size(ctx, input, weight): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = ctx.padding[d] + kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = ctx.stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError( + 'convolution input is too small (output would be ' + + 'x'.join(map(str, output_size)) + ')') + return output_size + + +deform_conv2d = DeformConv2dFunction.apply + + +class DeformConv2d(nn.Module): + r"""Deformable 2D convolution. + + Applies a deformable 2D convolution over an input signal composed of + several input planes. DeformConv2d was described in the paper + `Deformable Convolutional Networks + `_ + + Note: + The argument ``im2col_step`` was added in version 1.3.17, which means + number of samples processed by the ``im2col_cuda_kernel`` per call. + It enables users to define ``batch_size`` and ``im2col_step`` more + flexibly and solved `issue mmcv#1440 + `_. + + Args: + in_channels (int): Number of channels in the input image. + out_channels (int): Number of channels produced by the convolution. + kernel_size(int, tuple): Size of the convolving kernel. + stride(int, tuple): Stride of the convolution. Default: 1. + padding (int or tuple): Zero-padding added to both sides of the input. + Default: 0. + dilation (int or tuple): Spacing between kernel elements. Default: 1. + groups (int): Number of blocked connections from input. + channels to output channels. Default: 1. + deform_groups (int): Number of deformable group partitions. + bias (bool): If True, adds a learnable bias to the output. + Default: False. + im2col_step (int): Number of samples processed by im2col_cuda_kernel + per call. It will work when ``batch_size`` > ``im2col_step``, but + ``batch_size`` must be divisible by ``im2col_step``. Default: 32. + `New in version 1.3.17.` + """ + + @deprecated_api_warning({'deformable_groups': 'deform_groups'}, + cls_name='DeformConv2d') + def __init__(self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, ...]], + stride: Union[int, Tuple[int, ...]] = 1, + padding: Union[int, Tuple[int, ...]] = 0, + dilation: Union[int, Tuple[int, ...]] = 1, + groups: int = 1, + deform_groups: int = 1, + bias: bool = False, + im2col_step: int = 32) -> None: + super(DeformConv2d, self).__init__() + + assert not bias, \ + f'bias={bias} is not supported in DeformConv2d.' + assert in_channels % groups == 0, \ + f'in_channels {in_channels} cannot be divisible by groups {groups}' + assert out_channels % groups == 0, \ + f'out_channels {out_channels} cannot be divisible by groups \ + {groups}' + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deform_groups = deform_groups + self.im2col_step = im2col_step + # enable compatibility with nn.Conv2d + self.transposed = False + self.output_padding = _single(0) + + # only weight, no bias + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // self.groups, + *self.kernel_size)) + + self.reset_parameters() + + def reset_parameters(self): + # switch the initialization of `self.weight` to the standard kaiming + # method described in `Delving deep into rectifiers: Surpassing + # human-level performance on ImageNet classification` - He, K. et al. + # (2015), using a uniform distribution + nn.init.kaiming_uniform_(self.weight, nonlinearity='relu') + + def forward(self, x: Tensor, offset: Tensor) -> Tensor: + """Deformable Convolutional forward function. + + Args: + x (Tensor): Input feature, shape (B, C_in, H_in, W_in) + offset (Tensor): Offset for deformable convolution, shape + (B, deform_groups*kernel_size[0]*kernel_size[1]*2, + H_out, W_out), H_out, W_out are equal to the output's. + + An offset is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`. + The spatial arrangement is like: + + .. code:: text + + (x0, y0) (x1, y1) (x2, y2) + (x3, y3) (x4, y4) (x5, y5) + (x6, y6) (x7, y7) (x8, y8) + + Returns: + Tensor: Output of the layer. + """ + # To fix an assert error in deform_conv_cuda.cpp:128 + # input image is smaller than kernel + input_pad = (x.size(2) < self.kernel_size[0]) or (x.size(3) < + self.kernel_size[1]) + if input_pad: + pad_h = max(self.kernel_size[0] - x.size(2), 0) + pad_w = max(self.kernel_size[1] - x.size(3), 0) + x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() + offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0) + offset = offset.contiguous() + out = deform_conv2d(x, offset, self.weight, self.stride, self.padding, + self.dilation, self.groups, self.deform_groups, + False, self.im2col_step) + if input_pad: + out = out[:, :, :out.size(2) - pad_h, :out.size(3) - + pad_w].contiguous() + return out + + def __repr__(self): + s = self.__class__.__name__ + s += f'(in_channels={self.in_channels},\n' + s += f'out_channels={self.out_channels},\n' + s += f'kernel_size={self.kernel_size},\n' + s += f'stride={self.stride},\n' + s += f'padding={self.padding},\n' + s += f'dilation={self.dilation},\n' + s += f'groups={self.groups},\n' + s += f'deform_groups={self.deform_groups},\n' + # bias is not supported in DeformConv2d. + s += 'bias=False)' + return s + + +@CONV_LAYERS.register_module('DCN') +class DeformConv2dPack(DeformConv2d): + """A Deformable Conv Encapsulation that acts as normal Conv layers. + + The offset tensor is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`. + The spatial arrangement is like: + + .. code:: text + + (x0, y0) (x1, y1) (x2, y2) + (x3, y3) (x4, y4) (x5, y5) + (x6, y6) (x7, y7) (x8, y8) + + Args: + in_channels (int): Same as nn.Conv2d. + out_channels (int): Same as nn.Conv2d. + kernel_size (int or tuple[int]): Same as nn.Conv2d. + stride (int or tuple[int]): Same as nn.Conv2d. + padding (int or tuple[int]): Same as nn.Conv2d. + dilation (int or tuple[int]): Same as nn.Conv2d. + groups (int): Same as nn.Conv2d. + bias (bool or str): If specified as `auto`, it will be decided by the + norm_cfg. Bias will be set as True if norm_cfg is None, otherwise + False. + """ + + _version = 2 + + def __init__(self, *args, **kwargs): + super(DeformConv2dPack, self).__init__(*args, **kwargs) + self.conv_offset = nn.Conv2d( + self.in_channels, + self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1], + kernel_size=self.kernel_size, + stride=_pair(self.stride), + padding=_pair(self.padding), + dilation=_pair(self.dilation), + bias=True) + self.init_offset() + + def init_offset(self): + self.conv_offset.weight.data.zero_() + self.conv_offset.bias.data.zero_() + + def forward(self, x): + offset = self.conv_offset(x) + return deform_conv2d(x, offset, self.weight, self.stride, self.padding, + self.dilation, self.groups, self.deform_groups, + False, self.im2col_step) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + version = local_metadata.get('version', None) + + if version is None or version < 2: + # the key is different in early versions + # In version < 2, DeformConvPack loads previous benchmark models. + if (prefix + 'conv_offset.weight' not in state_dict + and prefix[:-1] + '_offset.weight' in state_dict): + state_dict[prefix + 'conv_offset.weight'] = state_dict.pop( + prefix[:-1] + '_offset.weight') + if (prefix + 'conv_offset.bias' not in state_dict + and prefix[:-1] + '_offset.bias' in state_dict): + state_dict[prefix + + 'conv_offset.bias'] = state_dict.pop(prefix[:-1] + + '_offset.bias') + + if version is not None and version > 1: + print_log( + f'DeformConv2dPack {prefix.rstrip(".")} is upgraded to ' + 'version 2.', + logger='root') + + super()._load_from_state_dict(state_dict, prefix, local_metadata, + strict, missing_keys, unexpected_keys, + error_msgs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deform_roi_pool.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deform_roi_pool.py new file mode 100644 index 00000000..cc245ba9 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deform_roi_pool.py @@ -0,0 +1,204 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['deform_roi_pool_forward', 'deform_roi_pool_backward']) + + +class DeformRoIPoolFunction(Function): + + @staticmethod + def symbolic(g, input, rois, offset, output_size, spatial_scale, + sampling_ratio, gamma): + return g.op( + 'mmcv::MMCVDeformRoIPool', + input, + rois, + offset, + pooled_height_i=output_size[0], + pooled_width_i=output_size[1], + spatial_scale_f=spatial_scale, + sampling_ratio_f=sampling_ratio, + gamma_f=gamma) + + @staticmethod + def forward(ctx, + input, + rois, + offset, + output_size, + spatial_scale=1.0, + sampling_ratio=0, + gamma=0.1): + if offset is None: + offset = input.new_zeros(0) + ctx.output_size = _pair(output_size) + ctx.spatial_scale = float(spatial_scale) + ctx.sampling_ratio = int(sampling_ratio) + ctx.gamma = float(gamma) + + assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' + + output_shape = (rois.size(0), input.size(1), ctx.output_size[0], + ctx.output_size[1]) + output = input.new_zeros(output_shape) + + ext_module.deform_roi_pool_forward( + input, + rois, + offset, + output, + pooled_height=ctx.output_size[0], + pooled_width=ctx.output_size[1], + spatial_scale=ctx.spatial_scale, + sampling_ratio=ctx.sampling_ratio, + gamma=ctx.gamma) + + ctx.save_for_backward(input, rois, offset) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, rois, offset = ctx.saved_tensors + grad_input = grad_output.new_zeros(input.shape) + grad_offset = grad_output.new_zeros(offset.shape) + + ext_module.deform_roi_pool_backward( + grad_output, + input, + rois, + offset, + grad_input, + grad_offset, + pooled_height=ctx.output_size[0], + pooled_width=ctx.output_size[1], + spatial_scale=ctx.spatial_scale, + sampling_ratio=ctx.sampling_ratio, + gamma=ctx.gamma) + if grad_offset.numel() == 0: + grad_offset = None + return grad_input, None, grad_offset, None, None, None, None + + +deform_roi_pool = DeformRoIPoolFunction.apply + + +class DeformRoIPool(nn.Module): + + def __init__(self, + output_size, + spatial_scale=1.0, + sampling_ratio=0, + gamma=0.1): + super(DeformRoIPool, self).__init__() + self.output_size = _pair(output_size) + self.spatial_scale = float(spatial_scale) + self.sampling_ratio = int(sampling_ratio) + self.gamma = float(gamma) + + def forward(self, input, rois, offset=None): + return deform_roi_pool(input, rois, offset, self.output_size, + self.spatial_scale, self.sampling_ratio, + self.gamma) + + +class DeformRoIPoolPack(DeformRoIPool): + + def __init__(self, + output_size, + output_channels, + deform_fc_channels=1024, + spatial_scale=1.0, + sampling_ratio=0, + gamma=0.1): + super(DeformRoIPoolPack, self).__init__(output_size, spatial_scale, + sampling_ratio, gamma) + + self.output_channels = output_channels + self.deform_fc_channels = deform_fc_channels + + self.offset_fc = nn.Sequential( + nn.Linear( + self.output_size[0] * self.output_size[1] * + self.output_channels, self.deform_fc_channels), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_channels, self.deform_fc_channels), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_channels, + self.output_size[0] * self.output_size[1] * 2)) + self.offset_fc[-1].weight.data.zero_() + self.offset_fc[-1].bias.data.zero_() + + def forward(self, input, rois): + assert input.size(1) == self.output_channels + x = deform_roi_pool(input, rois, None, self.output_size, + self.spatial_scale, self.sampling_ratio, + self.gamma) + rois_num = rois.size(0) + offset = self.offset_fc(x.view(rois_num, -1)) + offset = offset.view(rois_num, 2, self.output_size[0], + self.output_size[1]) + return deform_roi_pool(input, rois, offset, self.output_size, + self.spatial_scale, self.sampling_ratio, + self.gamma) + + +class ModulatedDeformRoIPoolPack(DeformRoIPool): + + def __init__(self, + output_size, + output_channels, + deform_fc_channels=1024, + spatial_scale=1.0, + sampling_ratio=0, + gamma=0.1): + super(ModulatedDeformRoIPoolPack, + self).__init__(output_size, spatial_scale, sampling_ratio, gamma) + + self.output_channels = output_channels + self.deform_fc_channels = deform_fc_channels + + self.offset_fc = nn.Sequential( + nn.Linear( + self.output_size[0] * self.output_size[1] * + self.output_channels, self.deform_fc_channels), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_channels, self.deform_fc_channels), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_channels, + self.output_size[0] * self.output_size[1] * 2)) + self.offset_fc[-1].weight.data.zero_() + self.offset_fc[-1].bias.data.zero_() + + self.mask_fc = nn.Sequential( + nn.Linear( + self.output_size[0] * self.output_size[1] * + self.output_channels, self.deform_fc_channels), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_channels, + self.output_size[0] * self.output_size[1] * 1), + nn.Sigmoid()) + self.mask_fc[2].weight.data.zero_() + self.mask_fc[2].bias.data.zero_() + + def forward(self, input, rois): + assert input.size(1) == self.output_channels + x = deform_roi_pool(input, rois, None, self.output_size, + self.spatial_scale, self.sampling_ratio, + self.gamma) + rois_num = rois.size(0) + offset = self.offset_fc(x.view(rois_num, -1)) + offset = offset.view(rois_num, 2, self.output_size[0], + self.output_size[1]) + mask = self.mask_fc(x.view(rois_num, -1)) + mask = mask.view(rois_num, 1, self.output_size[0], self.output_size[1]) + d = deform_roi_pool(input, rois, offset, self.output_size, + self.spatial_scale, self.sampling_ratio, + self.gamma) + return d * mask diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deprecated_wrappers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deprecated_wrappers.py new file mode 100644 index 00000000..a2e593df --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/deprecated_wrappers.py @@ -0,0 +1,43 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This file is for backward compatibility. +# Module wrappers for empty tensor have been moved to mmcv.cnn.bricks. +import warnings + +from ..cnn.bricks.wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d + + +class Conv2d_deprecated(Conv2d): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + warnings.warn( + 'Importing Conv2d wrapper from "mmcv.ops" will be deprecated in' + ' the future. Please import them from "mmcv.cnn" instead') + + +class ConvTranspose2d_deprecated(ConvTranspose2d): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + warnings.warn( + 'Importing ConvTranspose2d wrapper from "mmcv.ops" will be ' + 'deprecated in the future. Please import them from "mmcv.cnn" ' + 'instead') + + +class MaxPool2d_deprecated(MaxPool2d): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + warnings.warn( + 'Importing MaxPool2d wrapper from "mmcv.ops" will be deprecated in' + ' the future. Please import them from "mmcv.cnn" instead') + + +class Linear_deprecated(Linear): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + warnings.warn( + 'Importing Linear wrapper from "mmcv.ops" will be deprecated in' + ' the future. Please import them from "mmcv.cnn" instead') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/focal_loss.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/focal_loss.py new file mode 100644 index 00000000..763bc93b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/focal_loss.py @@ -0,0 +1,212 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward', + 'softmax_focal_loss_forward', 'softmax_focal_loss_backward' +]) + + +class SigmoidFocalLossFunction(Function): + + @staticmethod + def symbolic(g, input, target, gamma, alpha, weight, reduction): + return g.op( + 'mmcv::MMCVSigmoidFocalLoss', + input, + target, + gamma_f=gamma, + alpha_f=alpha, + weight_f=weight, + reduction_s=reduction) + + @staticmethod + def forward(ctx, + input, + target, + gamma=2.0, + alpha=0.25, + weight=None, + reduction='mean'): + + assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor)) + assert input.dim() == 2 + assert target.dim() == 1 + assert input.size(0) == target.size(0) + if weight is None: + weight = input.new_empty(0) + else: + assert weight.dim() == 1 + assert input.size(1) == weight.size(0) + ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2} + assert reduction in ctx.reduction_dict.keys() + + ctx.gamma = float(gamma) + ctx.alpha = float(alpha) + ctx.reduction = ctx.reduction_dict[reduction] + + output = input.new_zeros(input.size()) + + ext_module.sigmoid_focal_loss_forward( + input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha) + if ctx.reduction == ctx.reduction_dict['mean']: + output = output.sum() / input.size(0) + elif ctx.reduction == ctx.reduction_dict['sum']: + output = output.sum() + ctx.save_for_backward(input, target, weight) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, target, weight = ctx.saved_tensors + + grad_input = input.new_zeros(input.size()) + + ext_module.sigmoid_focal_loss_backward( + input, + target, + weight, + grad_input, + gamma=ctx.gamma, + alpha=ctx.alpha) + + grad_input *= grad_output + if ctx.reduction == ctx.reduction_dict['mean']: + grad_input /= input.size(0) + return grad_input, None, None, None, None, None + + +sigmoid_focal_loss = SigmoidFocalLossFunction.apply + + +class SigmoidFocalLoss(nn.Module): + + def __init__(self, gamma, alpha, weight=None, reduction='mean'): + super(SigmoidFocalLoss, self).__init__() + self.gamma = gamma + self.alpha = alpha + self.register_buffer('weight', weight) + self.reduction = reduction + + def forward(self, input, target): + return sigmoid_focal_loss(input, target, self.gamma, self.alpha, + self.weight, self.reduction) + + def __repr__(self): + s = self.__class__.__name__ + s += f'(gamma={self.gamma}, ' + s += f'alpha={self.alpha}, ' + s += f'reduction={self.reduction})' + return s + + +class SoftmaxFocalLossFunction(Function): + + @staticmethod + def symbolic(g, input, target, gamma, alpha, weight, reduction): + return g.op( + 'mmcv::MMCVSoftmaxFocalLoss', + input, + target, + gamma_f=gamma, + alpha_f=alpha, + weight_f=weight, + reduction_s=reduction) + + @staticmethod + def forward(ctx, + input, + target, + gamma=2.0, + alpha=0.25, + weight=None, + reduction='mean'): + + assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor)) + assert input.dim() == 2 + assert target.dim() == 1 + assert input.size(0) == target.size(0) + if weight is None: + weight = input.new_empty(0) + else: + assert weight.dim() == 1 + assert input.size(1) == weight.size(0) + ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2} + assert reduction in ctx.reduction_dict.keys() + + ctx.gamma = float(gamma) + ctx.alpha = float(alpha) + ctx.reduction = ctx.reduction_dict[reduction] + + channel_stats, _ = torch.max(input, dim=1) + input_softmax = input - channel_stats.unsqueeze(1).expand_as(input) + input_softmax.exp_() + + channel_stats = input_softmax.sum(dim=1) + input_softmax /= channel_stats.unsqueeze(1).expand_as(input) + + output = input.new_zeros(input.size(0)) + ext_module.softmax_focal_loss_forward( + input_softmax, + target, + weight, + output, + gamma=ctx.gamma, + alpha=ctx.alpha) + + if ctx.reduction == ctx.reduction_dict['mean']: + output = output.sum() / input.size(0) + elif ctx.reduction == ctx.reduction_dict['sum']: + output = output.sum() + ctx.save_for_backward(input_softmax, target, weight) + return output + + @staticmethod + def backward(ctx, grad_output): + input_softmax, target, weight = ctx.saved_tensors + buff = input_softmax.new_zeros(input_softmax.size(0)) + grad_input = input_softmax.new_zeros(input_softmax.size()) + + ext_module.softmax_focal_loss_backward( + input_softmax, + target, + weight, + buff, + grad_input, + gamma=ctx.gamma, + alpha=ctx.alpha) + + grad_input *= grad_output + if ctx.reduction == ctx.reduction_dict['mean']: + grad_input /= input_softmax.size(0) + return grad_input, None, None, None, None, None + + +softmax_focal_loss = SoftmaxFocalLossFunction.apply + + +class SoftmaxFocalLoss(nn.Module): + + def __init__(self, gamma, alpha, weight=None, reduction='mean'): + super(SoftmaxFocalLoss, self).__init__() + self.gamma = gamma + self.alpha = alpha + self.register_buffer('weight', weight) + self.reduction = reduction + + def forward(self, input, target): + return softmax_focal_loss(input, target, self.gamma, self.alpha, + self.weight, self.reduction) + + def __repr__(self): + s = self.__class__.__name__ + s += f'(gamma={self.gamma}, ' + s += f'alpha={self.alpha}, ' + s += f'reduction={self.reduction})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/furthest_point_sample.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/furthest_point_sample.py new file mode 100644 index 00000000..374b7a87 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/furthest_point_sample.py @@ -0,0 +1,83 @@ +import torch +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'furthest_point_sampling_forward', + 'furthest_point_sampling_with_dist_forward' +]) + + +class FurthestPointSampling(Function): + """Uses iterative furthest point sampling to select a set of features whose + corresponding points have the furthest distance.""" + + @staticmethod + def forward(ctx, points_xyz: torch.Tensor, + num_points: int) -> torch.Tensor: + """ + Args: + points_xyz (Tensor): (B, N, 3) where N > num_points. + num_points (int): Number of points in the sampled set. + + Returns: + Tensor: (B, num_points) indices of the sampled points. + """ + assert points_xyz.is_contiguous() + + B, N = points_xyz.size()[:2] + output = torch.cuda.IntTensor(B, num_points) + temp = torch.cuda.FloatTensor(B, N).fill_(1e10) + + ext_module.furthest_point_sampling_forward( + points_xyz, + temp, + output, + b=B, + n=N, + m=num_points, + ) + if torch.__version__ != 'parrots': + ctx.mark_non_differentiable(output) + return output + + @staticmethod + def backward(xyz, a=None): + return None, None + + +class FurthestPointSamplingWithDist(Function): + """Uses iterative furthest point sampling to select a set of features whose + corresponding points have the furthest distance.""" + + @staticmethod + def forward(ctx, points_dist: torch.Tensor, + num_points: int) -> torch.Tensor: + """ + Args: + points_dist (Tensor): (B, N, N) Distance between each point pair. + num_points (int): Number of points in the sampled set. + + Returns: + Tensor: (B, num_points) indices of the sampled points. + """ + assert points_dist.is_contiguous() + + B, N, _ = points_dist.size() + output = points_dist.new_zeros([B, num_points], dtype=torch.int32) + temp = points_dist.new_zeros([B, N]).fill_(1e10) + + ext_module.furthest_point_sampling_with_dist_forward( + points_dist, temp, output, b=B, n=N, m=num_points) + if torch.__version__ != 'parrots': + ctx.mark_non_differentiable(output) + return output + + @staticmethod + def backward(xyz, a=None): + return None, None + + +furthest_point_sample = FurthestPointSampling.apply +furthest_point_sample_with_dist = FurthestPointSamplingWithDist.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/fused_bias_leakyrelu.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/fused_bias_leakyrelu.py new file mode 100644 index 00000000..6d125084 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/fused_bias_leakyrelu.py @@ -0,0 +1,268 @@ +# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 + +# Copyright (c) 2021, NVIDIA Corporation. All rights reserved. +# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator +# Augmentation (ADA) +# ======================================================================= + +# 1. Definitions + +# "Licensor" means any person or entity that distributes its Work. + +# "Software" means the original work of authorship made available under +# this License. + +# "Work" means the Software and any additions to or derivative works of +# the Software that are made available under this License. + +# The terms "reproduce," "reproduction," "derivative works," and +# "distribution" have the meaning as provided under U.S. copyright law; +# provided, however, that for the purposes of this License, derivative +# works shall not include works that remain separable from, or merely +# link (or bind by name) to the interfaces of, the Work. + +# Works, including the Software, are "made available" under this License +# by including in or with the Work either (a) a copyright notice +# referencing the applicability of this License to the Work, or (b) a +# copy of this License. + +# 2. License Grants + +# 2.1 Copyright Grant. Subject to the terms and conditions of this +# License, each Licensor grants to you a perpetual, worldwide, +# non-exclusive, royalty-free, copyright license to reproduce, +# prepare derivative works of, publicly display, publicly perform, +# sublicense and distribute its Work and any resulting derivative +# works in any form. + +# 3. Limitations + +# 3.1 Redistribution. You may reproduce or distribute the Work only +# if (a) you do so under this License, (b) you include a complete +# copy of this License with your distribution, and (c) you retain +# without modification any copyright, patent, trademark, or +# attribution notices that are present in the Work. + +# 3.2 Derivative Works. You may specify that additional or different +# terms apply to the use, reproduction, and distribution of your +# derivative works of the Work ("Your Terms") only if (a) Your Terms +# provide that the use limitation in Section 3.3 applies to your +# derivative works, and (b) you identify the specific derivative +# works that are subject to Your Terms. Notwithstanding Your Terms, +# this License (including the redistribution requirements in Section +# 3.1) will continue to apply to the Work itself. + +# 3.3 Use Limitation. The Work and any derivative works thereof only +# may be used or intended for use non-commercially. Notwithstanding +# the foregoing, NVIDIA and its affiliates may use the Work and any +# derivative works commercially. As used herein, "non-commercially" +# means for research or evaluation purposes only. + +# 3.4 Patent Claims. If you bring or threaten to bring a patent claim +# against any Licensor (including any claim, cross-claim or +# counterclaim in a lawsuit) to enforce any patents that you allege +# are infringed by any Work, then your rights under this License from +# such Licensor (including the grant in Section 2.1) will terminate +# immediately. + +# 3.5 Trademarks. This License does not grant any rights to use any +# Licensor’s or its affiliates’ names, logos, or trademarks, except +# as necessary to reproduce the notices described in this License. + +# 3.6 Termination. If you violate any term of this License, then your +# rights under this License (including the grant in Section 2.1) will +# terminate immediately. + +# 4. Disclaimer of Warranty. + +# THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR +# NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER +# THIS LICENSE. + +# 5. Limitation of Liability. + +# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL +# THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE +# SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, +# INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF +# OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK +# (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, +# LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER +# COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF +# THE POSSIBILITY OF SUCH DAMAGES. + +# ======================================================================= + +import torch +import torch.nn.functional as F +from torch import nn +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['fused_bias_leakyrelu']) + + +class FusedBiasLeakyReLUFunctionBackward(Function): + """Calculate second order deviation. + + This function is to compute the second order deviation for the fused leaky + relu operation. + """ + + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = ext_module.fused_bias_leakyrelu( + grad_output, + empty, + out, + act=3, + grad=1, + alpha=negative_slope, + scale=scale) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + out, = ctx.saved_tensors + + # The second order deviation, in fact, contains two parts, while the + # the first part is zero. Thus, we direct consider the second part + # which is similar with the first order deviation in implementation. + gradgrad_out = ext_module.fused_bias_leakyrelu( + gradgrad_input, + gradgrad_bias.to(out.dtype), + out, + act=3, + grad=1, + alpha=ctx.negative_slope, + scale=ctx.scale) + + return gradgrad_out, None, None, None + + +class FusedBiasLeakyReLUFunction(Function): + + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + + out = ext_module.fused_bias_leakyrelu( + input, + bias, + empty, + act=3, + grad=0, + alpha=negative_slope, + scale=scale) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + out, = ctx.saved_tensors + + grad_input, grad_bias = FusedBiasLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale) + + return grad_input, grad_bias, None, None + + +class FusedBiasLeakyReLU(nn.Module): + """Fused bias leaky ReLU. + + This function is introduced in the StyleGAN2: + http://arxiv.org/abs/1912.04958 + + The bias term comes from the convolution operation. In addition, to keep + the variance of the feature map or gradients unchanged, they also adopt a + scale similarly with Kaiming initialization. However, since the + :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the + final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501 + your own scale. + + TODO: Implement the CPU version. + + Args: + channel (int): The channel number of the feature map. + negative_slope (float, optional): Same as nn.LeakyRelu. + Defaults to 0.2. + scale (float, optional): A scalar to adjust the variance of the feature + map. Defaults to 2**0.5. + """ + + def __init__(self, num_channels, negative_slope=0.2, scale=2**0.5): + super(FusedBiasLeakyReLU, self).__init__() + + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_bias_leakyrelu(input, self.bias, self.negative_slope, + self.scale) + + +def fused_bias_leakyrelu(input, bias, negative_slope=0.2, scale=2**0.5): + """Fused bias leaky ReLU function. + + This function is introduced in the StyleGAN2: + http://arxiv.org/abs/1912.04958 + + The bias term comes from the convolution operation. In addition, to keep + the variance of the feature map or gradients unchanged, they also adopt a + scale similarly with Kaiming initialization. However, since the + :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the + final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501 + your own scale. + + Args: + input (torch.Tensor): Input feature map. + bias (nn.Parameter): The bias from convolution operation. + negative_slope (float, optional): Same as nn.LeakyRelu. + Defaults to 0.2. + scale (float, optional): A scalar to adjust the variance of the feature + map. Defaults to 2**0.5. + + Returns: + torch.Tensor: Feature map after non-linear activation. + """ + + if not input.is_cuda: + return bias_leakyrelu_ref(input, bias, negative_slope, scale) + + return FusedBiasLeakyReLUFunction.apply(input, bias.to(input.dtype), + negative_slope, scale) + + +def bias_leakyrelu_ref(x, bias, negative_slope=0.2, scale=2**0.5): + + if bias is not None: + assert bias.ndim == 1 + assert bias.shape[0] == x.shape[1] + x = x + bias.reshape([-1 if i == 1 else 1 for i in range(x.ndim)]) + + x = F.leaky_relu(x, negative_slope) + if scale != 1: + x = x * scale + + return x diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/gather_points.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/gather_points.py new file mode 100644 index 00000000..f52f1677 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/gather_points.py @@ -0,0 +1,57 @@ +import torch +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['gather_points_forward', 'gather_points_backward']) + + +class GatherPoints(Function): + """Gather points with given index.""" + + @staticmethod + def forward(ctx, features: torch.Tensor, + indices: torch.Tensor) -> torch.Tensor: + """ + Args: + features (Tensor): (B, C, N) features to gather. + indices (Tensor): (B, M) where M is the number of points. + + Returns: + Tensor: (B, C, M) where M is the number of points. + """ + assert features.is_contiguous() + assert indices.is_contiguous() + + B, npoint = indices.size() + _, C, N = features.size() + output = torch.cuda.FloatTensor(B, C, npoint) + + ext_module.gather_points_forward( + features, indices, output, b=B, c=C, n=N, npoints=npoint) + + ctx.for_backwards = (indices, C, N) + if torch.__version__ != 'parrots': + ctx.mark_non_differentiable(indices) + return output + + @staticmethod + def backward(ctx, grad_out): + idx, C, N = ctx.for_backwards + B, npoint = idx.size() + + grad_features = torch.cuda.FloatTensor(B, C, N).zero_() + grad_out_data = grad_out.data.contiguous() + ext_module.gather_points_backward( + grad_out_data, + idx, + grad_features.data, + b=B, + c=C, + n=N, + npoints=npoint) + return grad_features, None + + +gather_points = GatherPoints.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/group_points.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/group_points.py new file mode 100644 index 00000000..6c3ec9d7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/group_points.py @@ -0,0 +1,224 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Tuple + +import torch +from torch import nn as nn +from torch.autograd import Function + +from ..utils import ext_loader +from .ball_query import ball_query +from .knn import knn + +ext_module = ext_loader.load_ext( + '_ext', ['group_points_forward', 'group_points_backward']) + + +class QueryAndGroup(nn.Module): + """Groups points with a ball query of radius. + + Args: + max_radius (float): The maximum radius of the balls. + If None is given, we will use kNN sampling instead of ball query. + sample_num (int): Maximum number of features to gather in the ball. + min_radius (float, optional): The minimum radius of the balls. + Default: 0. + use_xyz (bool, optional): Whether to use xyz. + Default: True. + return_grouped_xyz (bool, optional): Whether to return grouped xyz. + Default: False. + normalize_xyz (bool, optional): Whether to normalize xyz. + Default: False. + uniform_sample (bool, optional): Whether to sample uniformly. + Default: False + return_unique_cnt (bool, optional): Whether to return the count of + unique samples. Default: False. + return_grouped_idx (bool, optional): Whether to return grouped idx. + Default: False. + """ + + def __init__(self, + max_radius, + sample_num, + min_radius=0, + use_xyz=True, + return_grouped_xyz=False, + normalize_xyz=False, + uniform_sample=False, + return_unique_cnt=False, + return_grouped_idx=False): + super().__init__() + self.max_radius = max_radius + self.min_radius = min_radius + self.sample_num = sample_num + self.use_xyz = use_xyz + self.return_grouped_xyz = return_grouped_xyz + self.normalize_xyz = normalize_xyz + self.uniform_sample = uniform_sample + self.return_unique_cnt = return_unique_cnt + self.return_grouped_idx = return_grouped_idx + if self.return_unique_cnt: + assert self.uniform_sample, \ + 'uniform_sample should be True when ' \ + 'returning the count of unique samples' + if self.max_radius is None: + assert not self.normalize_xyz, \ + 'can not normalize grouped xyz when max_radius is None' + + def forward(self, points_xyz, center_xyz, features=None): + """ + Args: + points_xyz (Tensor): (B, N, 3) xyz coordinates of the features. + center_xyz (Tensor): (B, npoint, 3) coordinates of the centriods. + features (Tensor): (B, C, N) Descriptors of the features. + + Returns: + Tensor: (B, 3 + C, npoint, sample_num) Grouped feature. + """ + # if self.max_radius is None, we will perform kNN instead of ball query + # idx is of shape [B, npoint, sample_num] + if self.max_radius is None: + idx = knn(self.sample_num, points_xyz, center_xyz, False) + idx = idx.transpose(1, 2).contiguous() + else: + idx = ball_query(self.min_radius, self.max_radius, self.sample_num, + points_xyz, center_xyz) + + if self.uniform_sample: + unique_cnt = torch.zeros((idx.shape[0], idx.shape[1])) + for i_batch in range(idx.shape[0]): + for i_region in range(idx.shape[1]): + unique_ind = torch.unique(idx[i_batch, i_region, :]) + num_unique = unique_ind.shape[0] + unique_cnt[i_batch, i_region] = num_unique + sample_ind = torch.randint( + 0, + num_unique, (self.sample_num - num_unique, ), + dtype=torch.long) + all_ind = torch.cat((unique_ind, unique_ind[sample_ind])) + idx[i_batch, i_region, :] = all_ind + + xyz_trans = points_xyz.transpose(1, 2).contiguous() + # (B, 3, npoint, sample_num) + grouped_xyz = grouping_operation(xyz_trans, idx) + grouped_xyz_diff = grouped_xyz - \ + center_xyz.transpose(1, 2).unsqueeze(-1) # relative offsets + if self.normalize_xyz: + grouped_xyz_diff /= self.max_radius + + if features is not None: + grouped_features = grouping_operation(features, idx) + if self.use_xyz: + # (B, C + 3, npoint, sample_num) + new_features = torch.cat([grouped_xyz_diff, grouped_features], + dim=1) + else: + new_features = grouped_features + else: + assert (self.use_xyz + ), 'Cannot have not features and not use xyz as a feature!' + new_features = grouped_xyz_diff + + ret = [new_features] + if self.return_grouped_xyz: + ret.append(grouped_xyz) + if self.return_unique_cnt: + ret.append(unique_cnt) + if self.return_grouped_idx: + ret.append(idx) + if len(ret) == 1: + return ret[0] + else: + return tuple(ret) + + +class GroupAll(nn.Module): + """Group xyz with feature. + + Args: + use_xyz (bool): Whether to use xyz. + """ + + def __init__(self, use_xyz: bool = True): + super().__init__() + self.use_xyz = use_xyz + + def forward(self, + xyz: torch.Tensor, + new_xyz: torch.Tensor, + features: torch.Tensor = None): + """ + Args: + xyz (Tensor): (B, N, 3) xyz coordinates of the features. + new_xyz (Tensor): new xyz coordinates of the features. + features (Tensor): (B, C, N) features to group. + + Returns: + Tensor: (B, C + 3, 1, N) Grouped feature. + """ + grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) + if features is not None: + grouped_features = features.unsqueeze(2) + if self.use_xyz: + # (B, 3 + C, 1, N) + new_features = torch.cat([grouped_xyz, grouped_features], + dim=1) + else: + new_features = grouped_features + else: + new_features = grouped_xyz + + return new_features + + +class GroupingOperation(Function): + """Group feature with given index.""" + + @staticmethod + def forward(ctx, features: torch.Tensor, + indices: torch.Tensor) -> torch.Tensor: + """ + Args: + features (Tensor): (B, C, N) tensor of features to group. + indices (Tensor): (B, npoint, nsample) the indices of + features to group with. + + Returns: + Tensor: (B, C, npoint, nsample) Grouped features. + """ + features = features.contiguous() + indices = indices.contiguous() + + B, nfeatures, nsample = indices.size() + _, C, N = features.size() + output = torch.cuda.FloatTensor(B, C, nfeatures, nsample) + + ext_module.group_points_forward(B, C, N, nfeatures, nsample, features, + indices, output) + + ctx.for_backwards = (indices, N) + return output + + @staticmethod + def backward(ctx, + grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients + of the output from forward. + + Returns: + Tensor: (B, C, N) gradient of the features. + """ + idx, N = ctx.for_backwards + + B, C, npoint, nsample = grad_out.size() + grad_features = torch.cuda.FloatTensor(B, C, N).zero_() + + grad_out_data = grad_out.data.contiguous() + ext_module.group_points_backward(B, C, N, npoint, nsample, + grad_out_data, idx, + grad_features.data) + return grad_features, None + + +grouping_operation = GroupingOperation.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/info.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/info.py new file mode 100644 index 00000000..29f2e559 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/info.py @@ -0,0 +1,36 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import glob +import os + +import torch + +if torch.__version__ == 'parrots': + import parrots + + def get_compiler_version(): + return 'GCC ' + parrots.version.compiler + + def get_compiling_cuda_version(): + return parrots.version.cuda +else: + from ..utils import ext_loader + ext_module = ext_loader.load_ext( + '_ext', ['get_compiler_version', 'get_compiling_cuda_version']) + + def get_compiler_version(): + return ext_module.get_compiler_version() + + def get_compiling_cuda_version(): + return ext_module.get_compiling_cuda_version() + + +def get_onnxruntime_op_path(): + wildcard = os.path.join( + os.path.abspath(os.path.dirname(os.path.dirname(__file__))), + '_ext_ort.*.so') + + paths = glob.glob(wildcard) + if len(paths) > 0: + return paths[0] + else: + return '' diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/iou3d.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/iou3d.py new file mode 100644 index 00000000..6fc71979 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/iou3d.py @@ -0,0 +1,85 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward', + 'iou3d_nms_normal_forward' +]) + + +def boxes_iou_bev(boxes_a, boxes_b): + """Calculate boxes IoU in the Bird's Eye View. + + Args: + boxes_a (torch.Tensor): Input boxes a with shape (M, 5). + boxes_b (torch.Tensor): Input boxes b with shape (N, 5). + + Returns: + ans_iou (torch.Tensor): IoU result with shape (M, N). + """ + ans_iou = boxes_a.new_zeros( + torch.Size((boxes_a.shape[0], boxes_b.shape[0]))) + + ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(), + boxes_b.contiguous(), ans_iou) + + return ans_iou + + +def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None): + """NMS function GPU implementation (for BEV boxes). The overlap of two + boxes for IoU calculation is defined as the exact overlapping area of the + two boxes. In this function, one can also set ``pre_max_size`` and + ``post_max_size``. + + Args: + boxes (torch.Tensor): Input boxes with the shape of [N, 5] + ([x1, y1, x2, y2, ry]). + scores (torch.Tensor): Scores of boxes with the shape of [N]. + thresh (float): Overlap threshold of NMS. + pre_max_size (int, optional): Max size of boxes before NMS. + Default: None. + post_max_size (int, optional): Max size of boxes after NMS. + Default: None. + + Returns: + torch.Tensor: Indexes after NMS. + """ + assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]' + order = scores.sort(0, descending=True)[1] + + if pre_max_size is not None: + order = order[:pre_max_size] + boxes = boxes[order].contiguous() + + keep = torch.zeros(boxes.size(0), dtype=torch.long) + num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh) + keep = order[keep[:num_out].cuda(boxes.device)].contiguous() + if post_max_size is not None: + keep = keep[:post_max_size] + return keep + + +def nms_normal_bev(boxes, scores, thresh): + """Normal NMS function GPU implementation (for BEV boxes). The overlap of + two boxes for IoU calculation is defined as the exact overlapping area of + the two boxes WITH their yaw angle set to 0. + + Args: + boxes (torch.Tensor): Input boxes with shape (N, 5). + scores (torch.Tensor): Scores of predicted boxes with shape (N). + thresh (float): Overlap threshold of NMS. + + Returns: + torch.Tensor: Remaining indices with scores in descending order. + """ + assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]' + order = scores.sort(0, descending=True)[1] + + boxes = boxes[order].contiguous() + + keep = torch.zeros(boxes.size(0), dtype=torch.long) + num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh) + return order[keep[:num_out].cuda(boxes.device)].contiguous() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/knn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/knn.py new file mode 100644 index 00000000..f3357850 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/knn.py @@ -0,0 +1,77 @@ +import torch +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['knn_forward']) + + +class KNN(Function): + r"""KNN (CUDA) based on heap data structure. + Modified from `PAConv `_. + + Find k-nearest points. + """ + + @staticmethod + def forward(ctx, + k: int, + xyz: torch.Tensor, + center_xyz: torch.Tensor = None, + transposed: bool = False) -> torch.Tensor: + """ + Args: + k (int): number of nearest neighbors. + xyz (Tensor): (B, N, 3) if transposed == False, else (B, 3, N). + xyz coordinates of the features. + center_xyz (Tensor, optional): (B, npoint, 3) if transposed == + False, else (B, 3, npoint). centers of the knn query. + Default: None. + transposed (bool, optional): whether the input tensors are + transposed. Should not explicitly use this keyword when + calling knn (=KNN.apply), just add the fourth param. + Default: False. + + Returns: + Tensor: (B, k, npoint) tensor with the indices of + the features that form k-nearest neighbours. + """ + assert (k > 0) & (k < 100), 'k should be in range(0, 100)' + + if center_xyz is None: + center_xyz = xyz + + if transposed: + xyz = xyz.transpose(2, 1).contiguous() + center_xyz = center_xyz.transpose(2, 1).contiguous() + + assert xyz.is_contiguous() # [B, N, 3] + assert center_xyz.is_contiguous() # [B, npoint, 3] + + center_xyz_device = center_xyz.get_device() + assert center_xyz_device == xyz.get_device(), \ + 'center_xyz and xyz should be put on the same device' + if torch.cuda.current_device() != center_xyz_device: + torch.cuda.set_device(center_xyz_device) + + B, npoint, _ = center_xyz.shape + N = xyz.shape[1] + + idx = center_xyz.new_zeros((B, npoint, k)).int() + dist2 = center_xyz.new_zeros((B, npoint, k)).float() + + ext_module.knn_forward( + xyz, center_xyz, idx, dist2, b=B, n=N, m=npoint, nsample=k) + # idx shape to [B, k, npoint] + idx = idx.transpose(2, 1).contiguous() + if torch.__version__ != 'parrots': + ctx.mark_non_differentiable(idx) + return idx + + @staticmethod + def backward(ctx, a=None): + return None, None, None + + +knn = KNN.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/masked_conv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/masked_conv.py new file mode 100644 index 00000000..cd514cc2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/masked_conv.py @@ -0,0 +1,111 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['masked_im2col_forward', 'masked_col2im_forward']) + + +class MaskedConv2dFunction(Function): + + @staticmethod + def symbolic(g, features, mask, weight, bias, padding, stride): + return g.op( + 'mmcv::MMCVMaskedConv2d', + features, + mask, + weight, + bias, + padding_i=padding, + stride_i=stride) + + @staticmethod + def forward(ctx, features, mask, weight, bias, padding=0, stride=1): + assert mask.dim() == 3 and mask.size(0) == 1 + assert features.dim() == 4 and features.size(0) == 1 + assert features.size()[2:] == mask.size()[1:] + pad_h, pad_w = _pair(padding) + stride_h, stride_w = _pair(stride) + if stride_h != 1 or stride_w != 1: + raise ValueError( + 'Stride could not only be 1 in masked_conv2d currently.') + out_channel, in_channel, kernel_h, kernel_w = weight.size() + + batch_size = features.size(0) + out_h = int( + math.floor((features.size(2) + 2 * pad_h - + (kernel_h - 1) - 1) / stride_h + 1)) + out_w = int( + math.floor((features.size(3) + 2 * pad_w - + (kernel_h - 1) - 1) / stride_w + 1)) + mask_inds = torch.nonzero(mask[0] > 0, as_tuple=False) + output = features.new_zeros(batch_size, out_channel, out_h, out_w) + if mask_inds.numel() > 0: + mask_h_idx = mask_inds[:, 0].contiguous() + mask_w_idx = mask_inds[:, 1].contiguous() + data_col = features.new_zeros(in_channel * kernel_h * kernel_w, + mask_inds.size(0)) + ext_module.masked_im2col_forward( + features, + mask_h_idx, + mask_w_idx, + data_col, + kernel_h=kernel_h, + kernel_w=kernel_w, + pad_h=pad_h, + pad_w=pad_w) + + masked_output = torch.addmm(1, bias[:, None], 1, + weight.view(out_channel, -1), data_col) + ext_module.masked_col2im_forward( + masked_output, + mask_h_idx, + mask_w_idx, + output, + height=out_h, + width=out_w, + channels=out_channel) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + return (None, ) * 5 + + +masked_conv2d = MaskedConv2dFunction.apply + + +class MaskedConv2d(nn.Conv2d): + """A MaskedConv2d which inherits the official Conv2d. + + The masked forward doesn't implement the backward function and only + supports the stride parameter to be 1 currently. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True): + super(MaskedConv2d, + self).__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias) + + def forward(self, input, mask=None): + if mask is None: # fallback to the normal Conv2d + return super(MaskedConv2d, self).forward(input) + else: + return masked_conv2d(input, mask, self.weight, self.bias, + self.padding) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/merge_cells.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/merge_cells.py new file mode 100644 index 00000000..48ca8cc0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/merge_cells.py @@ -0,0 +1,149 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from abc import abstractmethod + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..cnn import ConvModule + + +class BaseMergeCell(nn.Module): + """The basic class for cells used in NAS-FPN and NAS-FCOS. + + BaseMergeCell takes 2 inputs. After applying convolution + on them, they are resized to the target size. Then, + they go through binary_op, which depends on the type of cell. + If with_out_conv is True, the result of output will go through + another convolution layer. + + Args: + in_channels (int): number of input channels in out_conv layer. + out_channels (int): number of output channels in out_conv layer. + with_out_conv (bool): Whether to use out_conv layer + out_conv_cfg (dict): Config dict for convolution layer, which should + contain "groups", "kernel_size", "padding", "bias" to build + out_conv layer. + out_norm_cfg (dict): Config dict for normalization layer in out_conv. + out_conv_order (tuple): The order of conv/norm/activation layers in + out_conv. + with_input1_conv (bool): Whether to use convolution on input1. + with_input2_conv (bool): Whether to use convolution on input2. + input_conv_cfg (dict): Config dict for building input1_conv layer and + input2_conv layer, which is expected to contain the type of + convolution. + Default: None, which means using conv2d. + input_norm_cfg (dict): Config dict for normalization layer in + input1_conv and input2_conv layer. Default: None. + upsample_mode (str): Interpolation method used to resize the output + of input1_conv and input2_conv to target size. Currently, we + support ['nearest', 'bilinear']. Default: 'nearest'. + """ + + def __init__(self, + fused_channels=256, + out_channels=256, + with_out_conv=True, + out_conv_cfg=dict( + groups=1, kernel_size=3, padding=1, bias=True), + out_norm_cfg=None, + out_conv_order=('act', 'conv', 'norm'), + with_input1_conv=False, + with_input2_conv=False, + input_conv_cfg=None, + input_norm_cfg=None, + upsample_mode='nearest'): + super(BaseMergeCell, self).__init__() + assert upsample_mode in ['nearest', 'bilinear'] + self.with_out_conv = with_out_conv + self.with_input1_conv = with_input1_conv + self.with_input2_conv = with_input2_conv + self.upsample_mode = upsample_mode + + if self.with_out_conv: + self.out_conv = ConvModule( + fused_channels, + out_channels, + **out_conv_cfg, + norm_cfg=out_norm_cfg, + order=out_conv_order) + + self.input1_conv = self._build_input_conv( + out_channels, input_conv_cfg, + input_norm_cfg) if with_input1_conv else nn.Sequential() + self.input2_conv = self._build_input_conv( + out_channels, input_conv_cfg, + input_norm_cfg) if with_input2_conv else nn.Sequential() + + def _build_input_conv(self, channel, conv_cfg, norm_cfg): + return ConvModule( + channel, + channel, + 3, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + bias=True) + + @abstractmethod + def _binary_op(self, x1, x2): + pass + + def _resize(self, x, size): + if x.shape[-2:] == size: + return x + elif x.shape[-2:] < size: + return F.interpolate(x, size=size, mode=self.upsample_mode) + else: + assert x.shape[-2] % size[-2] == 0 and x.shape[-1] % size[-1] == 0 + kernel_size = x.shape[-1] // size[-1] + x = F.max_pool2d(x, kernel_size=kernel_size, stride=kernel_size) + return x + + def forward(self, x1, x2, out_size=None): + assert x1.shape[:2] == x2.shape[:2] + assert out_size is None or len(out_size) == 2 + if out_size is None: # resize to larger one + out_size = max(x1.size()[2:], x2.size()[2:]) + + x1 = self.input1_conv(x1) + x2 = self.input2_conv(x2) + + x1 = self._resize(x1, out_size) + x2 = self._resize(x2, out_size) + + x = self._binary_op(x1, x2) + if self.with_out_conv: + x = self.out_conv(x) + return x + + +class SumCell(BaseMergeCell): + + def __init__(self, in_channels, out_channels, **kwargs): + super(SumCell, self).__init__(in_channels, out_channels, **kwargs) + + def _binary_op(self, x1, x2): + return x1 + x2 + + +class ConcatCell(BaseMergeCell): + + def __init__(self, in_channels, out_channels, **kwargs): + super(ConcatCell, self).__init__(in_channels * 2, out_channels, + **kwargs) + + def _binary_op(self, x1, x2): + ret = torch.cat([x1, x2], dim=1) + return ret + + +class GlobalPoolingCell(BaseMergeCell): + + def __init__(self, in_channels=None, out_channels=None, **kwargs): + super().__init__(in_channels, out_channels, **kwargs) + self.global_pool = nn.AdaptiveAvgPool2d((1, 1)) + + def _binary_op(self, x1, x2): + x2_att = self.global_pool(x2).sigmoid() + return x2 + x2_att * x1 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/modulated_deform_conv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/modulated_deform_conv.py new file mode 100644 index 00000000..95b4828e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/modulated_deform_conv.py @@ -0,0 +1,282 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair, _single + +from custom_mmpkg.custom_mmcv.utils import deprecated_api_warning +from ..cnn import CONV_LAYERS +from ..utils import ext_loader, print_log + +ext_module = ext_loader.load_ext( + '_ext', + ['modulated_deform_conv_forward', 'modulated_deform_conv_backward']) + + +class ModulatedDeformConv2dFunction(Function): + + @staticmethod + def symbolic(g, input, offset, mask, weight, bias, stride, padding, + dilation, groups, deform_groups): + input_tensors = [input, offset, mask, weight] + if bias is not None: + input_tensors.append(bias) + return g.op( + 'mmcv::MMCVModulatedDeformConv2d', + *input_tensors, + stride_i=stride, + padding_i=padding, + dilation_i=dilation, + groups_i=groups, + deform_groups_i=deform_groups) + + @staticmethod + def forward(ctx, + input, + offset, + mask, + weight, + bias=None, + stride=1, + padding=0, + dilation=1, + groups=1, + deform_groups=1): + if input is not None and input.dim() != 4: + raise ValueError( + f'Expected 4D tensor as input, got {input.dim()}D tensor \ + instead.') + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deform_groups = deform_groups + ctx.with_bias = bias is not None + if not ctx.with_bias: + bias = input.new_empty(0) # fake tensor + # When pytorch version >= 1.6.0, amp is adopted for fp16 mode; + # amp won't cast the type of model (float32), but "offset" is cast + # to float16 by nn.Conv2d automatically, leading to the type + # mismatch with input (when it is float32) or weight. + # The flag for whether to use fp16 or amp is the type of "offset", + # we cast weight and input to temporarily support fp16 and amp + # whatever the pytorch version is. + input = input.type_as(offset) + weight = weight.type_as(input) + ctx.save_for_backward(input, offset, mask, weight, bias) + output = input.new_empty( + ModulatedDeformConv2dFunction._output_size(ctx, input, weight)) + ctx._bufs = [input.new_empty(0), input.new_empty(0)] + ext_module.modulated_deform_conv_forward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + output, + ctx._bufs[1], + kernel_h=weight.size(2), + kernel_w=weight.size(3), + stride_h=ctx.stride[0], + stride_w=ctx.stride[1], + pad_h=ctx.padding[0], + pad_w=ctx.padding[1], + dilation_h=ctx.dilation[0], + dilation_w=ctx.dilation[1], + group=ctx.groups, + deformable_group=ctx.deform_groups, + with_bias=ctx.with_bias) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + grad_mask = torch.zeros_like(mask) + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(bias) + grad_output = grad_output.contiguous() + ext_module.modulated_deform_conv_backward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + ctx._bufs[1], + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + kernel_h=weight.size(2), + kernel_w=weight.size(3), + stride_h=ctx.stride[0], + stride_w=ctx.stride[1], + pad_h=ctx.padding[0], + pad_w=ctx.padding[1], + dilation_h=ctx.dilation[0], + dilation_w=ctx.dilation[1], + group=ctx.groups, + deformable_group=ctx.deform_groups, + with_bias=ctx.with_bias) + if not ctx.with_bias: + grad_bias = None + + return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, + None, None, None, None, None) + + @staticmethod + def _output_size(ctx, input, weight): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = ctx.padding[d] + kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = ctx.stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError( + 'convolution input is too small (output would be ' + + 'x'.join(map(str, output_size)) + ')') + return output_size + + +modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply + + +class ModulatedDeformConv2d(nn.Module): + + @deprecated_api_warning({'deformable_groups': 'deform_groups'}, + cls_name='ModulatedDeformConv2d') + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deform_groups=1, + bias=True): + super(ModulatedDeformConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deform_groups = deform_groups + # enable compatibility with nn.Conv2d + self.transposed = False + self.output_padding = _single(0) + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // groups, + *self.kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter('bias', None) + self.init_weights() + + def init_weights(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + if self.bias is not None: + self.bias.data.zero_() + + def forward(self, x, offset, mask): + return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias, + self.stride, self.padding, + self.dilation, self.groups, + self.deform_groups) + + +@CONV_LAYERS.register_module('DCNv2') +class ModulatedDeformConv2dPack(ModulatedDeformConv2d): + """A ModulatedDeformable Conv Encapsulation that acts as normal Conv + layers. + + Args: + in_channels (int): Same as nn.Conv2d. + out_channels (int): Same as nn.Conv2d. + kernel_size (int or tuple[int]): Same as nn.Conv2d. + stride (int): Same as nn.Conv2d, while tuple is not supported. + padding (int): Same as nn.Conv2d, while tuple is not supported. + dilation (int): Same as nn.Conv2d, while tuple is not supported. + groups (int): Same as nn.Conv2d. + bias (bool or str): If specified as `auto`, it will be decided by the + norm_cfg. Bias will be set as True if norm_cfg is None, otherwise + False. + """ + + _version = 2 + + def __init__(self, *args, **kwargs): + super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs) + self.conv_offset = nn.Conv2d( + self.in_channels, + self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1], + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + bias=True) + self.init_weights() + + def init_weights(self): + super(ModulatedDeformConv2dPack, self).init_weights() + if hasattr(self, 'conv_offset'): + self.conv_offset.weight.data.zero_() + self.conv_offset.bias.data.zero_() + + def forward(self, x): + out = self.conv_offset(x) + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias, + self.stride, self.padding, + self.dilation, self.groups, + self.deform_groups) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + version = local_metadata.get('version', None) + + if version is None or version < 2: + # the key is different in early versions + # In version < 2, ModulatedDeformConvPack + # loads previous benchmark models. + if (prefix + 'conv_offset.weight' not in state_dict + and prefix[:-1] + '_offset.weight' in state_dict): + state_dict[prefix + 'conv_offset.weight'] = state_dict.pop( + prefix[:-1] + '_offset.weight') + if (prefix + 'conv_offset.bias' not in state_dict + and prefix[:-1] + '_offset.bias' in state_dict): + state_dict[prefix + + 'conv_offset.bias'] = state_dict.pop(prefix[:-1] + + '_offset.bias') + + if version is not None and version > 1: + print_log( + f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to ' + 'version 2.', + logger='root') + + super()._load_from_state_dict(state_dict, prefix, local_metadata, + strict, missing_keys, unexpected_keys, + error_msgs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/multi_scale_deform_attn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/multi_scale_deform_attn.py new file mode 100644 index 00000000..8696322b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/multi_scale_deform_attn.py @@ -0,0 +1,358 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd.function import Function, once_differentiable + +from custom_mmpkg.custom_mmcv import deprecated_api_warning +from custom_mmpkg.custom_mmcv.cnn import constant_init, xavier_init +from custom_mmpkg.custom_mmcv.cnn.bricks.registry import ATTENTION +from custom_mmpkg.custom_mmcv.runner import BaseModule +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward']) + + +class MultiScaleDeformableAttnFunction(Function): + + @staticmethod + def forward(ctx, value, value_spatial_shapes, value_level_start_index, + sampling_locations, attention_weights, im2col_step): + """GPU version of multi-scale deformable attention. + + Args: + value (Tensor): The value has shape + (bs, num_keys, mum_heads, embed_dims//num_heads) + value_spatial_shapes (Tensor): Spatial shape of + each feature map, has shape (num_levels, 2), + last dimension 2 represent (h, w) + sampling_locations (Tensor): The location of sampling points, + has shape + (bs ,num_queries, num_heads, num_levels, num_points, 2), + the last dimension 2 represent (x, y). + attention_weights (Tensor): The weight of sampling points used + when calculate the attention, has shape + (bs ,num_queries, num_heads, num_levels, num_points), + im2col_step (Tensor): The step used in image to column. + + Returns: + Tensor: has shape (bs, num_queries, embed_dims) + """ + + ctx.im2col_step = im2col_step + output = ext_module.ms_deform_attn_forward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + im2col_step=ctx.im2col_step) + ctx.save_for_backward(value, value_spatial_shapes, + value_level_start_index, sampling_locations, + attention_weights) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + """GPU version of backward function. + + Args: + grad_output (Tensor): Gradient + of output tensor of forward. + + Returns: + Tuple[Tensor]: Gradient + of input tensors in forward. + """ + value, value_spatial_shapes, value_level_start_index,\ + sampling_locations, attention_weights = ctx.saved_tensors + grad_value = torch.zeros_like(value) + grad_sampling_loc = torch.zeros_like(sampling_locations) + grad_attn_weight = torch.zeros_like(attention_weights) + + ext_module.ms_deform_attn_backward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + grad_output.contiguous(), + grad_value, + grad_sampling_loc, + grad_attn_weight, + im2col_step=ctx.im2col_step) + + return grad_value, None, None, \ + grad_sampling_loc, grad_attn_weight, None + + +def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes, + sampling_locations, attention_weights): + """CPU version of multi-scale deformable attention. + + Args: + value (Tensor): The value has shape + (bs, num_keys, mum_heads, embed_dims//num_heads) + value_spatial_shapes (Tensor): Spatial shape of + each feature map, has shape (num_levels, 2), + last dimension 2 represent (h, w) + sampling_locations (Tensor): The location of sampling points, + has shape + (bs ,num_queries, num_heads, num_levels, num_points, 2), + the last dimension 2 represent (x, y). + attention_weights (Tensor): The weight of sampling points used + when calculate the attention, has shape + (bs ,num_queries, num_heads, num_levels, num_points), + + Returns: + Tensor: has shape (bs, num_queries, embed_dims) + """ + + bs, _, num_heads, embed_dims = value.shape + _, num_queries, num_heads, num_levels, num_points, _ =\ + sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], + dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level, (H_, W_) in enumerate(value_spatial_shapes): + # bs, H_*W_, num_heads, embed_dims -> + # bs, H_*W_, num_heads*embed_dims -> + # bs, num_heads*embed_dims, H_*W_ -> + # bs*num_heads, embed_dims, H_, W_ + value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape( + bs * num_heads, embed_dims, H_, W_) + # bs, num_queries, num_heads, num_points, 2 -> + # bs, num_heads, num_queries, num_points, 2 -> + # bs*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, + level].transpose(1, 2).flatten(0, 1) + # bs*num_heads, embed_dims, num_queries, num_points + sampling_value_l_ = F.grid_sample( + value_l_, + sampling_grid_l_, + mode='bilinear', + padding_mode='zeros', + align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (bs, num_queries, num_heads, num_levels, num_points) -> + # (bs, num_heads, num_queries, num_levels, num_points) -> + # (bs, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + bs * num_heads, 1, num_queries, num_levels * num_points) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * + attention_weights).sum(-1).view(bs, num_heads * embed_dims, + num_queries) + return output.transpose(1, 2).contiguous() + + +@ATTENTION.register_module() +class MultiScaleDeformableAttention(BaseModule): + """An attention module used in Deformable-Detr. + + `Deformable DETR: Deformable Transformers for End-to-End Object Detection. + `_. + + Args: + embed_dims (int): The embedding dimension of Attention. + Default: 256. + num_heads (int): Parallel attention heads. Default: 64. + num_levels (int): The number of feature map used in + Attention. Default: 4. + num_points (int): The number of sampling points for + each query in each head. Default: 4. + im2col_step (int): The step used in image_to_column. + Default: 64. + dropout (float): A Dropout layer on `inp_identity`. + Default: 0.1. + batch_first (bool): Key, Query and Value are shape of + (batch, n, embed_dim) + or (n, batch, embed_dim). Default to False. + norm_cfg (dict): Config dict for normalization layer. + Default: None. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, + embed_dims=256, + num_heads=8, + num_levels=4, + num_points=4, + im2col_step=64, + dropout=0.1, + batch_first=False, + norm_cfg=None, + init_cfg=None): + super().__init__(init_cfg) + if embed_dims % num_heads != 0: + raise ValueError(f'embed_dims must be divisible by num_heads, ' + f'but got {embed_dims} and {num_heads}') + dim_per_head = embed_dims // num_heads + self.norm_cfg = norm_cfg + self.dropout = nn.Dropout(dropout) + self.batch_first = batch_first + + # you'd better set dim_per_head to a power of 2 + # which is more efficient in the CUDA implementation + def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError( + 'invalid input for _is_power_of_2: {} (type: {})'.format( + n, type(n))) + return (n & (n - 1) == 0) and n != 0 + + if not _is_power_of_2(dim_per_head): + warnings.warn( + "You'd better set embed_dims in " + 'MultiScaleDeformAttention to make ' + 'the dimension of each attention head a power of 2 ' + 'which is more efficient in our CUDA implementation.') + + self.im2col_step = im2col_step + self.embed_dims = embed_dims + self.num_levels = num_levels + self.num_heads = num_heads + self.num_points = num_points + self.sampling_offsets = nn.Linear( + embed_dims, num_heads * num_levels * num_points * 2) + self.attention_weights = nn.Linear(embed_dims, + num_heads * num_levels * num_points) + self.value_proj = nn.Linear(embed_dims, embed_dims) + self.output_proj = nn.Linear(embed_dims, embed_dims) + self.init_weights() + + def init_weights(self): + """Default initialization for Parameters of Module.""" + constant_init(self.sampling_offsets, 0.) + thetas = torch.arange( + self.num_heads, + dtype=torch.float32) * (2.0 * math.pi / self.num_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / + grid_init.abs().max(-1, keepdim=True)[0]).view( + self.num_heads, 1, 1, + 2).repeat(1, self.num_levels, self.num_points, 1) + for i in range(self.num_points): + grid_init[:, :, i, :] *= i + 1 + + self.sampling_offsets.bias.data = grid_init.view(-1) + constant_init(self.attention_weights, val=0., bias=0.) + xavier_init(self.value_proj, distribution='uniform', bias=0.) + xavier_init(self.output_proj, distribution='uniform', bias=0.) + self._is_init = True + + @deprecated_api_warning({'residual': 'identity'}, + cls_name='MultiScaleDeformableAttention') + def forward(self, + query, + key=None, + value=None, + identity=None, + query_pos=None, + key_padding_mask=None, + reference_points=None, + spatial_shapes=None, + level_start_index=None, + **kwargs): + """Forward Function of MultiScaleDeformAttention. + + Args: + query (Tensor): Query of Transformer with shape + (num_query, bs, embed_dims). + key (Tensor): The key tensor with shape + `(num_key, bs, embed_dims)`. + value (Tensor): The value tensor with shape + `(num_key, bs, embed_dims)`. + identity (Tensor): The tensor used for addition, with the + same shape as `query`. Default None. If None, + `query` will be used. + query_pos (Tensor): The positional encoding for `query`. + Default: None. + key_pos (Tensor): The positional encoding for `key`. Default + None. + reference_points (Tensor): The normalized reference + points with shape (bs, num_query, num_levels, 2), + all elements is range in [0, 1], top-left (0,0), + bottom-right (1, 1), including padding area. + or (N, Length_{query}, num_levels, 4), add + additional two dimensions is (w, h) to + form reference boxes. + key_padding_mask (Tensor): ByteTensor for `query`, with + shape [bs, num_key]. + spatial_shapes (Tensor): Spatial shape of features in + different levels. With shape (num_levels, 2), + last dimension represents (h, w). + level_start_index (Tensor): The start index of each level. + A tensor has shape ``(num_levels, )`` and can be represented + as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...]. + + Returns: + Tensor: forwarded results with shape [num_query, bs, embed_dims]. + """ + + if value is None: + value = query + + if identity is None: + identity = query + if query_pos is not None: + query = query + query_pos + if not self.batch_first: + # change to (bs, num_query ,embed_dims) + query = query.permute(1, 0, 2) + value = value.permute(1, 0, 2) + + bs, num_query, _ = query.shape + bs, num_value, _ = value.shape + assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value + + value = self.value_proj(value) + if key_padding_mask is not None: + value = value.masked_fill(key_padding_mask[..., None], 0.0) + value = value.view(bs, num_value, self.num_heads, -1) + sampling_offsets = self.sampling_offsets(query).view( + bs, num_query, self.num_heads, self.num_levels, self.num_points, 2) + attention_weights = self.attention_weights(query).view( + bs, num_query, self.num_heads, self.num_levels * self.num_points) + attention_weights = attention_weights.softmax(-1) + + attention_weights = attention_weights.view(bs, num_query, + self.num_heads, + self.num_levels, + self.num_points) + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack( + [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets \ + / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.num_points \ + * reference_points[:, :, None, :, None, 2:] \ + * 0.5 + else: + raise ValueError( + f'Last dim of reference_points must be' + f' 2 or 4, but get {reference_points.shape[-1]} instead.') + if torch.cuda.is_available() and value.is_cuda: + output = MultiScaleDeformableAttnFunction.apply( + value, spatial_shapes, level_start_index, sampling_locations, + attention_weights, self.im2col_step) + else: + output = multi_scale_deformable_attn_pytorch( + value, spatial_shapes, sampling_locations, attention_weights) + + output = self.output_proj(output) + + if not self.batch_first: + # (num_query, bs ,embed_dims) + output = output.permute(1, 0, 2) + + return self.dropout(output) + identity diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/nms.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/nms.py new file mode 100644 index 00000000..080c0cf0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/nms.py @@ -0,0 +1,417 @@ +import os + +import numpy as np +import torch + +from custom_mmpkg.custom_mmcv.utils import deprecated_api_warning +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated']) + + +# This function is modified from: https://github.com/pytorch/vision/ +class NMSop(torch.autograd.Function): + + @staticmethod + def forward(ctx, bboxes, scores, iou_threshold, offset, score_threshold, + max_num): + is_filtering_by_score = score_threshold > 0 + if is_filtering_by_score: + valid_mask = scores > score_threshold + bboxes, scores = bboxes[valid_mask], scores[valid_mask] + valid_inds = torch.nonzero( + valid_mask, as_tuple=False).squeeze(dim=1) + + inds = ext_module.nms( + bboxes, scores, iou_threshold=float(iou_threshold), offset=offset) + + if max_num > 0: + inds = inds[:max_num] + if is_filtering_by_score: + inds = valid_inds[inds] + return inds + + @staticmethod + def symbolic(g, bboxes, scores, iou_threshold, offset, score_threshold, + max_num): + from ..onnx import is_custom_op_loaded + has_custom_op = is_custom_op_loaded() + # TensorRT nms plugin is aligned with original nms in ONNXRuntime + is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT' + if has_custom_op and (not is_trt_backend): + return g.op( + 'mmcv::NonMaxSuppression', + bboxes, + scores, + iou_threshold_f=float(iou_threshold), + offset_i=int(offset)) + else: + from torch.onnx.symbolic_opset9 import select, squeeze, unsqueeze + from ..onnx.onnx_utils.symbolic_helper import _size_helper + + boxes = unsqueeze(g, bboxes, 0) + scores = unsqueeze(g, unsqueeze(g, scores, 0), 0) + + if max_num > 0: + max_num = g.op( + 'Constant', + value_t=torch.tensor(max_num, dtype=torch.long)) + else: + dim = g.op('Constant', value_t=torch.tensor(0)) + max_num = _size_helper(g, bboxes, dim) + max_output_per_class = max_num + iou_threshold = g.op( + 'Constant', + value_t=torch.tensor([iou_threshold], dtype=torch.float)) + score_threshold = g.op( + 'Constant', + value_t=torch.tensor([score_threshold], dtype=torch.float)) + nms_out = g.op('NonMaxSuppression', boxes, scores, + max_output_per_class, iou_threshold, + score_threshold) + return squeeze( + g, + select( + g, nms_out, 1, + g.op( + 'Constant', + value_t=torch.tensor([2], dtype=torch.long))), 1) + + +class SoftNMSop(torch.autograd.Function): + + @staticmethod + def forward(ctx, boxes, scores, iou_threshold, sigma, min_score, method, + offset): + dets = boxes.new_empty((boxes.size(0), 5), device='cpu') + inds = ext_module.softnms( + boxes.cpu(), + scores.cpu(), + dets.cpu(), + iou_threshold=float(iou_threshold), + sigma=float(sigma), + min_score=float(min_score), + method=int(method), + offset=int(offset)) + return dets, inds + + @staticmethod + def symbolic(g, boxes, scores, iou_threshold, sigma, min_score, method, + offset): + from packaging import version + assert version.parse(torch.__version__) >= version.parse('1.7.0') + nms_out = g.op( + 'mmcv::SoftNonMaxSuppression', + boxes, + scores, + iou_threshold_f=float(iou_threshold), + sigma_f=float(sigma), + min_score_f=float(min_score), + method_i=int(method), + offset_i=int(offset), + outputs=2) + return nms_out + + +@deprecated_api_warning({'iou_thr': 'iou_threshold'}) +def nms(boxes, scores, iou_threshold, offset=0, score_threshold=0, max_num=-1): + """Dispatch to either CPU or GPU NMS implementations. + + The input can be either torch tensor or numpy array. GPU NMS will be used + if the input is gpu tensor, otherwise CPU NMS + will be used. The returned type will always be the same as inputs. + + Arguments: + boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). + scores (torch.Tensor or np.ndarray): scores in shape (N, ). + iou_threshold (float): IoU threshold for NMS. + offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset). + score_threshold (float): score threshold for NMS. + max_num (int): maximum number of boxes after NMS. + + Returns: + tuple: kept dets(boxes and scores) and indice, which is always the \ + same data type as the input. + + Example: + >>> boxes = np.array([[49.1, 32.4, 51.0, 35.9], + >>> [49.3, 32.9, 51.0, 35.3], + >>> [49.2, 31.8, 51.0, 35.4], + >>> [35.1, 11.5, 39.1, 15.7], + >>> [35.6, 11.8, 39.3, 14.2], + >>> [35.3, 11.5, 39.9, 14.5], + >>> [35.2, 11.7, 39.7, 15.7]], dtype=np.float32) + >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.5, 0.4, 0.3],\ + dtype=np.float32) + >>> iou_threshold = 0.6 + >>> dets, inds = nms(boxes, scores, iou_threshold) + >>> assert len(inds) == len(dets) == 3 + """ + assert isinstance(boxes, (torch.Tensor, np.ndarray)) + assert isinstance(scores, (torch.Tensor, np.ndarray)) + is_numpy = False + if isinstance(boxes, np.ndarray): + is_numpy = True + boxes = torch.from_numpy(boxes) + if isinstance(scores, np.ndarray): + scores = torch.from_numpy(scores) + assert boxes.size(1) == 4 + assert boxes.size(0) == scores.size(0) + assert offset in (0, 1) + + if torch.__version__ == 'parrots': + indata_list = [boxes, scores] + indata_dict = { + 'iou_threshold': float(iou_threshold), + 'offset': int(offset) + } + inds = ext_module.nms(*indata_list, **indata_dict) + else: + inds = NMSop.apply(boxes, scores, iou_threshold, offset, + score_threshold, max_num) + dets = torch.cat((boxes[inds], scores[inds].reshape(-1, 1)), dim=1) + if is_numpy: + dets = dets.cpu().numpy() + inds = inds.cpu().numpy() + return dets, inds + + +@deprecated_api_warning({'iou_thr': 'iou_threshold'}) +def soft_nms(boxes, + scores, + iou_threshold=0.3, + sigma=0.5, + min_score=1e-3, + method='linear', + offset=0): + """Dispatch to only CPU Soft NMS implementations. + + The input can be either a torch tensor or numpy array. + The returned type will always be the same as inputs. + + Arguments: + boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). + scores (torch.Tensor or np.ndarray): scores in shape (N, ). + iou_threshold (float): IoU threshold for NMS. + sigma (float): hyperparameter for gaussian method + min_score (float): score filter threshold + method (str): either 'linear' or 'gaussian' + offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset). + + Returns: + tuple: kept dets(boxes and scores) and indice, which is always the \ + same data type as the input. + + Example: + >>> boxes = np.array([[4., 3., 5., 3.], + >>> [4., 3., 5., 4.], + >>> [3., 1., 3., 1.], + >>> [3., 1., 3., 1.], + >>> [3., 1., 3., 1.], + >>> [3., 1., 3., 1.]], dtype=np.float32) + >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.4, 0.0], dtype=np.float32) + >>> iou_threshold = 0.6 + >>> dets, inds = soft_nms(boxes, scores, iou_threshold, sigma=0.5) + >>> assert len(inds) == len(dets) == 5 + """ + + assert isinstance(boxes, (torch.Tensor, np.ndarray)) + assert isinstance(scores, (torch.Tensor, np.ndarray)) + is_numpy = False + if isinstance(boxes, np.ndarray): + is_numpy = True + boxes = torch.from_numpy(boxes) + if isinstance(scores, np.ndarray): + scores = torch.from_numpy(scores) + assert boxes.size(1) == 4 + assert boxes.size(0) == scores.size(0) + assert offset in (0, 1) + method_dict = {'naive': 0, 'linear': 1, 'gaussian': 2} + assert method in method_dict.keys() + + if torch.__version__ == 'parrots': + dets = boxes.new_empty((boxes.size(0), 5), device='cpu') + indata_list = [boxes.cpu(), scores.cpu(), dets.cpu()] + indata_dict = { + 'iou_threshold': float(iou_threshold), + 'sigma': float(sigma), + 'min_score': min_score, + 'method': method_dict[method], + 'offset': int(offset) + } + inds = ext_module.softnms(*indata_list, **indata_dict) + else: + dets, inds = SoftNMSop.apply(boxes.cpu(), scores.cpu(), + float(iou_threshold), float(sigma), + float(min_score), method_dict[method], + int(offset)) + + dets = dets[:inds.size(0)] + + if is_numpy: + dets = dets.cpu().numpy() + inds = inds.cpu().numpy() + return dets, inds + else: + return dets.to(device=boxes.device), inds.to(device=boxes.device) + + +def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False): + """Performs non-maximum suppression in a batched fashion. + + Modified from https://github.com/pytorch/vision/blob + /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39. + In order to perform NMS independently per class, we add an offset to all + the boxes. The offset is dependent only on the class idx, and is large + enough so that boxes from different classes do not overlap. + + Arguments: + boxes (torch.Tensor): boxes in shape (N, 4). + scores (torch.Tensor): scores in shape (N, ). + idxs (torch.Tensor): each index value correspond to a bbox cluster, + and NMS will not be applied between elements of different idxs, + shape (N, ). + nms_cfg (dict): specify nms type and other parameters like iou_thr. + Possible keys includes the following. + + - iou_thr (float): IoU threshold used for NMS. + - split_thr (float): threshold number of boxes. In some cases the + number of boxes is large (e.g., 200k). To avoid OOM during + training, the users could set `split_thr` to a small value. + If the number of boxes is greater than the threshold, it will + perform NMS on each group of boxes separately and sequentially. + Defaults to 10000. + class_agnostic (bool): if true, nms is class agnostic, + i.e. IoU thresholding happens over all boxes, + regardless of the predicted class. + + Returns: + tuple: kept dets and indice. + """ + nms_cfg_ = nms_cfg.copy() + class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic) + if class_agnostic: + boxes_for_nms = boxes + else: + max_coordinate = boxes.max() + offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes)) + boxes_for_nms = boxes + offsets[:, None] + + nms_type = nms_cfg_.pop('type', 'nms') + nms_op = eval(nms_type) + + split_thr = nms_cfg_.pop('split_thr', 10000) + # Won't split to multiple nms nodes when exporting to onnx + if boxes_for_nms.shape[0] < split_thr or torch.onnx.is_in_onnx_export(): + dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_) + boxes = boxes[keep] + # -1 indexing works abnormal in TensorRT + # This assumes `dets` has 5 dimensions where + # the last dimension is score. + # TODO: more elegant way to handle the dimension issue. + # Some type of nms would reweight the score, such as SoftNMS + scores = dets[:, 4] + else: + max_num = nms_cfg_.pop('max_num', -1) + total_mask = scores.new_zeros(scores.size(), dtype=torch.bool) + # Some type of nms would reweight the score, such as SoftNMS + scores_after_nms = scores.new_zeros(scores.size()) + for id in torch.unique(idxs): + mask = (idxs == id).nonzero(as_tuple=False).view(-1) + dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_) + total_mask[mask[keep]] = True + scores_after_nms[mask[keep]] = dets[:, -1] + keep = total_mask.nonzero(as_tuple=False).view(-1) + + scores, inds = scores_after_nms[keep].sort(descending=True) + keep = keep[inds] + boxes = boxes[keep] + + if max_num > 0: + keep = keep[:max_num] + boxes = boxes[:max_num] + scores = scores[:max_num] + + return torch.cat([boxes, scores[:, None]], -1), keep + + +def nms_match(dets, iou_threshold): + """Matched dets into different groups by NMS. + + NMS match is Similar to NMS but when a bbox is suppressed, nms match will + record the indice of suppressed bbox and form a group with the indice of + kept bbox. In each group, indice is sorted as score order. + + Arguments: + dets (torch.Tensor | np.ndarray): Det boxes with scores, shape (N, 5). + iou_thr (float): IoU thresh for NMS. + + Returns: + List[torch.Tensor | np.ndarray]: The outer list corresponds different + matched group, the inner Tensor corresponds the indices for a group + in score order. + """ + if dets.shape[0] == 0: + matched = [] + else: + assert dets.shape[-1] == 5, 'inputs dets.shape should be (N, 5), ' \ + f'but get {dets.shape}' + if isinstance(dets, torch.Tensor): + dets_t = dets.detach().cpu() + else: + dets_t = torch.from_numpy(dets) + indata_list = [dets_t] + indata_dict = {'iou_threshold': float(iou_threshold)} + matched = ext_module.nms_match(*indata_list, **indata_dict) + if torch.__version__ == 'parrots': + matched = matched.tolist() + + if isinstance(dets, torch.Tensor): + return [dets.new_tensor(m, dtype=torch.long) for m in matched] + else: + return [np.array(m, dtype=np.int) for m in matched] + + +def nms_rotated(dets, scores, iou_threshold, labels=None): + """Performs non-maximum suppression (NMS) on the rotated boxes according to + their intersection-over-union (IoU). + + Rotated NMS iteratively removes lower scoring rotated boxes which have an + IoU greater than iou_threshold with another (higher scoring) rotated box. + + Args: + boxes (Tensor): Rotated boxes in shape (N, 5). They are expected to \ + be in (x_ctr, y_ctr, width, height, angle_radian) format. + scores (Tensor): scores in shape (N, ). + iou_threshold (float): IoU thresh for NMS. + labels (Tensor): boxes' label in shape (N,). + + Returns: + tuple: kept dets(boxes and scores) and indice, which is always the \ + same data type as the input. + """ + if dets.shape[0] == 0: + return dets, None + multi_label = labels is not None + if multi_label: + dets_wl = torch.cat((dets, labels.unsqueeze(1)), 1) + else: + dets_wl = dets + _, order = scores.sort(0, descending=True) + dets_sorted = dets_wl.index_select(0, order) + + if torch.__version__ == 'parrots': + keep_inds = ext_module.nms_rotated( + dets_wl, + scores, + order, + dets_sorted, + iou_threshold=iou_threshold, + multi_label=multi_label) + else: + keep_inds = ext_module.nms_rotated(dets_wl, scores, order, dets_sorted, + iou_threshold, multi_label) + dets = torch.cat((dets[keep_inds], scores[keep_inds].reshape(-1, 1)), + dim=1) + return dets, keep_inds diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/pixel_group.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/pixel_group.py new file mode 100644 index 00000000..2143c75f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/pixel_group.py @@ -0,0 +1,75 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np +import torch + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['pixel_group']) + + +def pixel_group(score, mask, embedding, kernel_label, kernel_contour, + kernel_region_num, distance_threshold): + """Group pixels into text instances, which is widely used text detection + methods. + + Arguments: + score (np.array or Tensor): The foreground score with size hxw. + mask (np.array or Tensor): The foreground mask with size hxw. + embedding (np.array or Tensor): The embedding with size hxwxc to + distinguish instances. + kernel_label (np.array or Tensor): The instance kernel index with + size hxw. + kernel_contour (np.array or Tensor): The kernel contour with size hxw. + kernel_region_num (int): The instance kernel region number. + distance_threshold (float): The embedding distance threshold between + kernel and pixel in one instance. + + Returns: + pixel_assignment (List[List[float]]): The instance coordinate list. + Each element consists of averaged confidence, pixel number, and + coordinates (x_i, y_i for all pixels) in order. + """ + assert isinstance(score, (torch.Tensor, np.ndarray)) + assert isinstance(mask, (torch.Tensor, np.ndarray)) + assert isinstance(embedding, (torch.Tensor, np.ndarray)) + assert isinstance(kernel_label, (torch.Tensor, np.ndarray)) + assert isinstance(kernel_contour, (torch.Tensor, np.ndarray)) + assert isinstance(kernel_region_num, int) + assert isinstance(distance_threshold, float) + + if isinstance(score, np.ndarray): + score = torch.from_numpy(score) + if isinstance(mask, np.ndarray): + mask = torch.from_numpy(mask) + if isinstance(embedding, np.ndarray): + embedding = torch.from_numpy(embedding) + if isinstance(kernel_label, np.ndarray): + kernel_label = torch.from_numpy(kernel_label) + if isinstance(kernel_contour, np.ndarray): + kernel_contour = torch.from_numpy(kernel_contour) + + if torch.__version__ == 'parrots': + label = ext_module.pixel_group( + score, + mask, + embedding, + kernel_label, + kernel_contour, + kernel_region_num=kernel_region_num, + distance_threshold=distance_threshold) + label = label.tolist() + label = label[0] + list_index = kernel_region_num + pixel_assignment = [] + for x in range(kernel_region_num): + pixel_assignment.append( + np.array( + label[list_index:list_index + int(label[x])], + dtype=np.float)) + list_index = list_index + int(label[x]) + else: + pixel_assignment = ext_module.pixel_group(score, mask, embedding, + kernel_label, kernel_contour, + kernel_region_num, + distance_threshold) + return pixel_assignment diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/point_sample.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/point_sample.py new file mode 100644 index 00000000..0f09ce3c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/point_sample.py @@ -0,0 +1,336 @@ +# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa + +from os import path as osp + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair +from torch.onnx.operators import shape_as_tensor + + +def bilinear_grid_sample(im, grid, align_corners=False): + """Given an input and a flow-field grid, computes the output using input + values and pixel locations from grid. Supported only bilinear interpolation + method to sample the input pixels. + + Args: + im (torch.Tensor): Input feature map, shape (N, C, H, W) + grid (torch.Tensor): Point coordinates, shape (N, Hg, Wg, 2) + align_corners {bool}: If set to True, the extrema (-1 and 1) are + considered as referring to the center points of the input’s + corner pixels. If set to False, they are instead considered as + referring to the corner points of the input’s corner pixels, + making the sampling more resolution agnostic. + Returns: + torch.Tensor: A tensor with sampled points, shape (N, C, Hg, Wg) + """ + n, c, h, w = im.shape + gn, gh, gw, _ = grid.shape + assert n == gn + + x = grid[:, :, :, 0] + y = grid[:, :, :, 1] + + if align_corners: + x = ((x + 1) / 2) * (w - 1) + y = ((y + 1) / 2) * (h - 1) + else: + x = ((x + 1) * w - 1) / 2 + y = ((y + 1) * h - 1) / 2 + + x = x.view(n, -1) + y = y.view(n, -1) + + x0 = torch.floor(x).long() + y0 = torch.floor(y).long() + x1 = x0 + 1 + y1 = y0 + 1 + + wa = ((x1 - x) * (y1 - y)).unsqueeze(1) + wb = ((x1 - x) * (y - y0)).unsqueeze(1) + wc = ((x - x0) * (y1 - y)).unsqueeze(1) + wd = ((x - x0) * (y - y0)).unsqueeze(1) + + # Apply default for grid_sample function zero padding + im_padded = F.pad(im, pad=[1, 1, 1, 1], mode='constant', value=0) + padded_h = h + 2 + padded_w = w + 2 + # save points positions after padding + x0, x1, y0, y1 = x0 + 1, x1 + 1, y0 + 1, y1 + 1 + + # Clip coordinates to padded image size + x0 = torch.where(x0 < 0, torch.tensor(0), x0) + x0 = torch.where(x0 > padded_w - 1, torch.tensor(padded_w - 1), x0) + x1 = torch.where(x1 < 0, torch.tensor(0), x1) + x1 = torch.where(x1 > padded_w - 1, torch.tensor(padded_w - 1), x1) + y0 = torch.where(y0 < 0, torch.tensor(0), y0) + y0 = torch.where(y0 > padded_h - 1, torch.tensor(padded_h - 1), y0) + y1 = torch.where(y1 < 0, torch.tensor(0), y1) + y1 = torch.where(y1 > padded_h - 1, torch.tensor(padded_h - 1), y1) + + im_padded = im_padded.view(n, c, -1) + + x0_y0 = (x0 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1) + x0_y1 = (x0 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1) + x1_y0 = (x1 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1) + x1_y1 = (x1 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1) + + Ia = torch.gather(im_padded, 2, x0_y0) + Ib = torch.gather(im_padded, 2, x0_y1) + Ic = torch.gather(im_padded, 2, x1_y0) + Id = torch.gather(im_padded, 2, x1_y1) + + return (Ia * wa + Ib * wb + Ic * wc + Id * wd).reshape(n, c, gh, gw) + + +def is_in_onnx_export_without_custom_ops(): + from custom_mmpkg.custom_mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() + return torch.onnx.is_in_onnx_export( + ) and not osp.exists(ort_custom_op_path) + + +def normalize(grid): + """Normalize input grid from [-1, 1] to [0, 1] + Args: + grid (Tensor): The grid to be normalize, range [-1, 1]. + Returns: + Tensor: Normalized grid, range [0, 1]. + """ + + return (grid + 1.0) / 2.0 + + +def denormalize(grid): + """Denormalize input grid from range [0, 1] to [-1, 1] + Args: + grid (Tensor): The grid to be denormalize, range [0, 1]. + Returns: + Tensor: Denormalized grid, range [-1, 1]. + """ + + return grid * 2.0 - 1.0 + + +def generate_grid(num_grid, size, device): + """Generate regular square grid of points in [0, 1] x [0, 1] coordinate + space. + + Args: + num_grid (int): The number of grids to sample, one for each region. + size (tuple(int, int)): The side size of the regular grid. + device (torch.device): Desired device of returned tensor. + + Returns: + (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that + contains coordinates for the regular grids. + """ + + affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device) + grid = F.affine_grid( + affine_trans, torch.Size((1, 1, *size)), align_corners=False) + grid = normalize(grid) + return grid.view(1, -1, 2).expand(num_grid, -1, -1) + + +def rel_roi_point_to_abs_img_point(rois, rel_roi_points): + """Convert roi based relative point coordinates to image based absolute + point coordinates. + + Args: + rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) + rel_roi_points (Tensor): Point coordinates inside RoI, relative to + RoI, location, range (0, 1), shape (N, P, 2) + Returns: + Tensor: Image based absolute point coordinates, shape (N, P, 2) + """ + + with torch.no_grad(): + assert rel_roi_points.size(0) == rois.size(0) + assert rois.dim() == 2 + assert rel_roi_points.dim() == 3 + assert rel_roi_points.size(2) == 2 + # remove batch idx + if rois.size(1) == 5: + rois = rois[:, 1:] + abs_img_points = rel_roi_points.clone() + # To avoid an error during exporting to onnx use independent + # variables instead inplace computation + xs = abs_img_points[:, :, 0] * (rois[:, None, 2] - rois[:, None, 0]) + ys = abs_img_points[:, :, 1] * (rois[:, None, 3] - rois[:, None, 1]) + xs += rois[:, None, 0] + ys += rois[:, None, 1] + abs_img_points = torch.stack([xs, ys], dim=2) + return abs_img_points + + +def get_shape_from_feature_map(x): + """Get spatial resolution of input feature map considering exporting to + onnx mode. + + Args: + x (torch.Tensor): Input tensor, shape (N, C, H, W) + Returns: + torch.Tensor: Spatial resolution (width, height), shape (1, 1, 2) + """ + if torch.onnx.is_in_onnx_export(): + img_shape = shape_as_tensor(x)[2:].flip(0).view(1, 1, 2).to( + x.device).float() + else: + img_shape = torch.tensor(x.shape[2:]).flip(0).view(1, 1, 2).to( + x.device).float() + return img_shape + + +def abs_img_point_to_rel_img_point(abs_img_points, img, spatial_scale=1.): + """Convert image based absolute point coordinates to image based relative + coordinates for sampling. + + Args: + abs_img_points (Tensor): Image based absolute point coordinates, + shape (N, P, 2) + img (tuple/Tensor): (height, width) of image or feature map. + spatial_scale (float): Scale points by this factor. Default: 1. + + Returns: + Tensor: Image based relative point coordinates for sampling, + shape (N, P, 2) + """ + + assert (isinstance(img, tuple) and len(img) == 2) or \ + (isinstance(img, torch.Tensor) and len(img.shape) == 4) + + if isinstance(img, tuple): + h, w = img + scale = torch.tensor([w, h], + dtype=torch.float, + device=abs_img_points.device) + scale = scale.view(1, 1, 2) + else: + scale = get_shape_from_feature_map(img) + + return abs_img_points / scale * spatial_scale + + +def rel_roi_point_to_rel_img_point(rois, + rel_roi_points, + img, + spatial_scale=1.): + """Convert roi based relative point coordinates to image based absolute + point coordinates. + + Args: + rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) + rel_roi_points (Tensor): Point coordinates inside RoI, relative to + RoI, location, range (0, 1), shape (N, P, 2) + img (tuple/Tensor): (height, width) of image or feature map. + spatial_scale (float): Scale points by this factor. Default: 1. + + Returns: + Tensor: Image based relative point coordinates for sampling, + shape (N, P, 2) + """ + + abs_img_point = rel_roi_point_to_abs_img_point(rois, rel_roi_points) + rel_img_point = abs_img_point_to_rel_img_point(abs_img_point, img, + spatial_scale) + + return rel_img_point + + +def point_sample(input, points, align_corners=False, **kwargs): + """A wrapper around :func:`grid_sample` to support 3D point_coords tensors + Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to + lie inside ``[0, 1] x [0, 1]`` square. + + Args: + input (Tensor): Feature map, shape (N, C, H, W). + points (Tensor): Image based absolute point coordinates (normalized), + range [0, 1] x [0, 1], shape (N, P, 2) or (N, Hgrid, Wgrid, 2). + align_corners (bool): Whether align_corners. Default: False + + Returns: + Tensor: Features of `point` on `input`, shape (N, C, P) or + (N, C, Hgrid, Wgrid). + """ + + add_dim = False + if points.dim() == 3: + add_dim = True + points = points.unsqueeze(2) + if is_in_onnx_export_without_custom_ops(): + # If custom ops for onnx runtime not compiled use python + # implementation of grid_sample function to make onnx graph + # with supported nodes + output = bilinear_grid_sample( + input, denormalize(points), align_corners=align_corners) + else: + output = F.grid_sample( + input, denormalize(points), align_corners=align_corners, **kwargs) + if add_dim: + output = output.squeeze(3) + return output + + +class SimpleRoIAlign(nn.Module): + + def __init__(self, output_size, spatial_scale, aligned=True): + """Simple RoI align in PointRend, faster than standard RoIAlign. + + Args: + output_size (tuple[int]): h, w + spatial_scale (float): scale the input boxes by this number + aligned (bool): if False, use the legacy implementation in + MMDetection, align_corners=True will be used in F.grid_sample. + If True, align the results more perfectly. + """ + + super(SimpleRoIAlign, self).__init__() + self.output_size = _pair(output_size) + self.spatial_scale = float(spatial_scale) + # to be consistent with other RoI ops + self.use_torchvision = False + self.aligned = aligned + + def forward(self, features, rois): + num_imgs = features.size(0) + num_rois = rois.size(0) + rel_roi_points = generate_grid( + num_rois, self.output_size, device=rois.device) + + if torch.onnx.is_in_onnx_export(): + rel_img_points = rel_roi_point_to_rel_img_point( + rois, rel_roi_points, features, self.spatial_scale) + rel_img_points = rel_img_points.reshape(num_imgs, -1, + *rel_img_points.shape[1:]) + point_feats = point_sample( + features, rel_img_points, align_corners=not self.aligned) + point_feats = point_feats.transpose(1, 2) + else: + point_feats = [] + for batch_ind in range(num_imgs): + # unravel batch dim + feat = features[batch_ind].unsqueeze(0) + inds = (rois[:, 0].long() == batch_ind) + if inds.any(): + rel_img_points = rel_roi_point_to_rel_img_point( + rois[inds], rel_roi_points[inds], feat, + self.spatial_scale).unsqueeze(0) + point_feat = point_sample( + feat, rel_img_points, align_corners=not self.aligned) + point_feat = point_feat.squeeze(0).transpose(0, 1) + point_feats.append(point_feat) + + point_feats = torch.cat(point_feats, dim=0) + + channels = features.size(1) + roi_feats = point_feats.reshape(num_rois, channels, *self.output_size) + + return roi_feats + + def __repr__(self): + format_str = self.__class__.__name__ + format_str += '(output_size={}, spatial_scale={}'.format( + self.output_size, self.spatial_scale) + return format_str diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/points_in_boxes.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/points_in_boxes.py new file mode 100644 index 00000000..4003173a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/points_in_boxes.py @@ -0,0 +1,133 @@ +import torch + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'points_in_boxes_part_forward', 'points_in_boxes_cpu_forward', + 'points_in_boxes_all_forward' +]) + + +def points_in_boxes_part(points, boxes): + """Find the box in which each point is (CUDA). + + Args: + points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate + boxes (torch.Tensor): [B, T, 7], + num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz] in + LiDAR/DEPTH coordinate, (x, y, z) is the bottom center + + Returns: + box_idxs_of_pts (torch.Tensor): (B, M), default background = -1 + """ + assert points.shape[0] == boxes.shape[0], \ + 'Points and boxes should have the same batch size, ' \ + f'but got {points.shape[0]} and {boxes.shape[0]}' + assert boxes.shape[2] == 7, \ + 'boxes dimension should be 7, ' \ + f'but got unexpected shape {boxes.shape[2]}' + assert points.shape[2] == 3, \ + 'points dimension should be 3, ' \ + f'but got unexpected shape {points.shape[2]}' + batch_size, num_points, _ = points.shape + + box_idxs_of_pts = points.new_zeros((batch_size, num_points), + dtype=torch.int).fill_(-1) + + # If manually put the tensor 'points' or 'boxes' on a device + # which is not the current device, some temporary variables + # will be created on the current device in the cuda op, + # and the output will be incorrect. + # Therefore, we force the current device to be the same + # as the device of the tensors if it was not. + # Please refer to https://github.com/open-mmlab/mmdetection3d/issues/305 + # for the incorrect output before the fix. + points_device = points.get_device() + assert points_device == boxes.get_device(), \ + 'Points and boxes should be put on the same device' + if torch.cuda.current_device() != points_device: + torch.cuda.set_device(points_device) + + ext_module.points_in_boxes_part_forward(boxes.contiguous(), + points.contiguous(), + box_idxs_of_pts) + + return box_idxs_of_pts + + +def points_in_boxes_cpu(points, boxes): + """Find all boxes in which each point is (CPU). The CPU version of + :meth:`points_in_boxes_all`. + + Args: + points (torch.Tensor): [B, M, 3], [x, y, z] in + LiDAR/DEPTH coordinate + boxes (torch.Tensor): [B, T, 7], + num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz], + (x, y, z) is the bottom center. + + Returns: + box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0. + """ + assert points.shape[0] == boxes.shape[0], \ + 'Points and boxes should have the same batch size, ' \ + f'but got {points.shape[0]} and {boxes.shape[0]}' + assert boxes.shape[2] == 7, \ + 'boxes dimension should be 7, ' \ + f'but got unexpected shape {boxes.shape[2]}' + assert points.shape[2] == 3, \ + 'points dimension should be 3, ' \ + f'but got unexpected shape {points.shape[2]}' + batch_size, num_points, _ = points.shape + num_boxes = boxes.shape[1] + + point_indices = points.new_zeros((batch_size, num_boxes, num_points), + dtype=torch.int) + for b in range(batch_size): + ext_module.points_in_boxes_cpu_forward(boxes[b].float().contiguous(), + points[b].float().contiguous(), + point_indices[b]) + point_indices = point_indices.transpose(1, 2) + + return point_indices + + +def points_in_boxes_all(points, boxes): + """Find all boxes in which each point is (CUDA). + + Args: + points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate + boxes (torch.Tensor): [B, T, 7], + num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz], + (x, y, z) is the bottom center. + + Returns: + box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0. + """ + assert boxes.shape[0] == points.shape[0], \ + 'Points and boxes should have the same batch size, ' \ + f'but got {boxes.shape[0]} and {boxes.shape[0]}' + assert boxes.shape[2] == 7, \ + 'boxes dimension should be 7, ' \ + f'but got unexpected shape {boxes.shape[2]}' + assert points.shape[2] == 3, \ + 'points dimension should be 3, ' \ + f'but got unexpected shape {points.shape[2]}' + batch_size, num_points, _ = points.shape + num_boxes = boxes.shape[1] + + box_idxs_of_pts = points.new_zeros((batch_size, num_points, num_boxes), + dtype=torch.int).fill_(0) + + # Same reason as line 25-32 + points_device = points.get_device() + assert points_device == boxes.get_device(), \ + 'Points and boxes should be put on the same device' + if torch.cuda.current_device() != points_device: + torch.cuda.set_device(points_device) + + ext_module.points_in_boxes_all_forward(boxes.contiguous(), + points.contiguous(), + box_idxs_of_pts) + + return box_idxs_of_pts diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/points_sampler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/points_sampler.py new file mode 100644 index 00000000..1df32153 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/points_sampler.py @@ -0,0 +1,177 @@ +from typing import List + +import torch +from torch import nn as nn + +from custom_mmpkg.custom_mmcv.runner import force_fp32 +from .furthest_point_sample import (furthest_point_sample, + furthest_point_sample_with_dist) + + +def calc_square_dist(point_feat_a, point_feat_b, norm=True): + """Calculating square distance between a and b. + + Args: + point_feat_a (Tensor): (B, N, C) Feature vector of each point. + point_feat_b (Tensor): (B, M, C) Feature vector of each point. + norm (Bool, optional): Whether to normalize the distance. + Default: True. + + Returns: + Tensor: (B, N, M) Distance between each pair points. + """ + num_channel = point_feat_a.shape[-1] + # [bs, n, 1] + a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1) + # [bs, 1, m] + b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1) + + corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2)) + + dist = a_square + b_square - 2 * corr_matrix + if norm: + dist = torch.sqrt(dist) / num_channel + return dist + + +def get_sampler_cls(sampler_type): + """Get the type and mode of points sampler. + + Args: + sampler_type (str): The type of points sampler. + The valid value are "D-FPS", "F-FPS", or "FS". + + Returns: + class: Points sampler type. + """ + sampler_mappings = { + 'D-FPS': DFPSSampler, + 'F-FPS': FFPSSampler, + 'FS': FSSampler, + } + try: + return sampler_mappings[sampler_type] + except KeyError: + raise KeyError( + f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \ + {sampler_type}') + + +class PointsSampler(nn.Module): + """Points sampling. + + Args: + num_point (list[int]): Number of sample points. + fps_mod_list (list[str], optional): Type of FPS method, valid mod + ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS']. + F-FPS: using feature distances for FPS. + D-FPS: using Euclidean distances of points for FPS. + FS: using F-FPS and D-FPS simultaneously. + fps_sample_range_list (list[int], optional): + Range of points to apply FPS. Default: [-1]. + """ + + def __init__(self, + num_point: List[int], + fps_mod_list: List[str] = ['D-FPS'], + fps_sample_range_list: List[int] = [-1]): + super().__init__() + # FPS would be applied to different fps_mod in the list, + # so the length of the num_point should be equal to + # fps_mod_list and fps_sample_range_list. + assert len(num_point) == len(fps_mod_list) == len( + fps_sample_range_list) + self.num_point = num_point + self.fps_sample_range_list = fps_sample_range_list + self.samplers = nn.ModuleList() + for fps_mod in fps_mod_list: + self.samplers.append(get_sampler_cls(fps_mod)()) + self.fp16_enabled = False + + @force_fp32() + def forward(self, points_xyz, features): + """ + Args: + points_xyz (Tensor): (B, N, 3) xyz coordinates of the features. + features (Tensor): (B, C, N) Descriptors of the features. + + Returns: + Tensor: (B, npoint, sample_num) Indices of sampled points. + """ + indices = [] + last_fps_end_index = 0 + + for fps_sample_range, sampler, npoint in zip( + self.fps_sample_range_list, self.samplers, self.num_point): + assert fps_sample_range < points_xyz.shape[1] + + if fps_sample_range == -1: + sample_points_xyz = points_xyz[:, last_fps_end_index:] + if features is not None: + sample_features = features[:, :, last_fps_end_index:] + else: + sample_features = None + else: + sample_points_xyz = \ + points_xyz[:, last_fps_end_index:fps_sample_range] + if features is not None: + sample_features = features[:, :, last_fps_end_index: + fps_sample_range] + else: + sample_features = None + + fps_idx = sampler(sample_points_xyz.contiguous(), sample_features, + npoint) + + indices.append(fps_idx + last_fps_end_index) + last_fps_end_index += fps_sample_range + indices = torch.cat(indices, dim=1) + + return indices + + +class DFPSSampler(nn.Module): + """Using Euclidean distances of points for FPS.""" + + def __init__(self): + super().__init__() + + def forward(self, points, features, npoint): + """Sampling points with D-FPS.""" + fps_idx = furthest_point_sample(points.contiguous(), npoint) + return fps_idx + + +class FFPSSampler(nn.Module): + """Using feature distances for FPS.""" + + def __init__(self): + super().__init__() + + def forward(self, points, features, npoint): + """Sampling points with F-FPS.""" + assert features is not None, \ + 'feature input to FFPS_Sampler should not be None' + features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2) + features_dist = calc_square_dist( + features_for_fps, features_for_fps, norm=False) + fps_idx = furthest_point_sample_with_dist(features_dist, npoint) + return fps_idx + + +class FSSampler(nn.Module): + """Using F-FPS and D-FPS simultaneously.""" + + def __init__(self): + super().__init__() + + def forward(self, points, features, npoint): + """Sampling points with FS_Sampling.""" + assert features is not None, \ + 'feature input to FS_Sampler should not be None' + ffps_sampler = FFPSSampler() + dfps_sampler = DFPSSampler() + fps_idx_ffps = ffps_sampler(points, features, npoint) + fps_idx_dfps = dfps_sampler(points, features, npoint) + fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1) + return fps_idx diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/psa_mask.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/psa_mask.py new file mode 100644 index 00000000..cdf14e62 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/psa_mask.py @@ -0,0 +1,92 @@ +# Modified from https://github.com/hszhao/semseg/blob/master/lib/psa +from torch import nn +from torch.autograd import Function +from torch.nn.modules.utils import _pair + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', + ['psamask_forward', 'psamask_backward']) + + +class PSAMaskFunction(Function): + + @staticmethod + def symbolic(g, input, psa_type, mask_size): + return g.op( + 'mmcv::MMCVPSAMask', + input, + psa_type_i=psa_type, + mask_size_i=mask_size) + + @staticmethod + def forward(ctx, input, psa_type, mask_size): + ctx.psa_type = psa_type + ctx.mask_size = _pair(mask_size) + ctx.save_for_backward(input) + + h_mask, w_mask = ctx.mask_size + batch_size, channels, h_feature, w_feature = input.size() + assert channels == h_mask * w_mask + output = input.new_zeros( + (batch_size, h_feature * w_feature, h_feature, w_feature)) + + ext_module.psamask_forward( + input, + output, + psa_type=psa_type, + num_=batch_size, + h_feature=h_feature, + w_feature=w_feature, + h_mask=h_mask, + w_mask=w_mask, + half_h_mask=(h_mask - 1) // 2, + half_w_mask=(w_mask - 1) // 2) + return output + + @staticmethod + def backward(ctx, grad_output): + input = ctx.saved_tensors[0] + psa_type = ctx.psa_type + h_mask, w_mask = ctx.mask_size + batch_size, channels, h_feature, w_feature = input.size() + grad_input = grad_output.new_zeros( + (batch_size, channels, h_feature, w_feature)) + ext_module.psamask_backward( + grad_output, + grad_input, + psa_type=psa_type, + num_=batch_size, + h_feature=h_feature, + w_feature=w_feature, + h_mask=h_mask, + w_mask=w_mask, + half_h_mask=(h_mask - 1) // 2, + half_w_mask=(w_mask - 1) // 2) + return grad_input, None, None, None + + +psa_mask = PSAMaskFunction.apply + + +class PSAMask(nn.Module): + + def __init__(self, psa_type, mask_size=None): + super(PSAMask, self).__init__() + assert psa_type in ['collect', 'distribute'] + if psa_type == 'collect': + psa_type_enum = 0 + else: + psa_type_enum = 1 + self.psa_type_enum = psa_type_enum + self.mask_size = mask_size + self.psa_type = psa_type + + def forward(self, input): + return psa_mask(input, self.psa_type_enum, self.mask_size) + + def __repr__(self): + s = self.__class__.__name__ + s += f'(psa_type={self.psa_type}, ' + s += f'mask_size={self.mask_size})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_align.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_align.py new file mode 100644 index 00000000..0755aefc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_align.py @@ -0,0 +1,223 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from ..utils import deprecated_api_warning, ext_loader + +ext_module = ext_loader.load_ext('_ext', + ['roi_align_forward', 'roi_align_backward']) + + +class RoIAlignFunction(Function): + + @staticmethod + def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio, + pool_mode, aligned): + from ..onnx import is_custom_op_loaded + has_custom_op = is_custom_op_loaded() + if has_custom_op: + return g.op( + 'mmcv::MMCVRoiAlign', + input, + rois, + output_height_i=output_size[0], + output_width_i=output_size[1], + spatial_scale_f=spatial_scale, + sampling_ratio_i=sampling_ratio, + mode_s=pool_mode, + aligned_i=aligned) + else: + from torch.onnx.symbolic_opset9 import sub, squeeze + from torch.onnx.symbolic_helper import _slice_helper + from torch.onnx import TensorProtoDataType + # batch_indices = rois[:, 0].long() + batch_indices = _slice_helper( + g, rois, axes=[1], starts=[0], ends=[1]) + batch_indices = squeeze(g, batch_indices, 1) + batch_indices = g.op( + 'Cast', batch_indices, to_i=TensorProtoDataType.INT64) + # rois = rois[:, 1:] + rois = _slice_helper(g, rois, axes=[1], starts=[1], ends=[5]) + if aligned: + # rois -= 0.5/spatial_scale + aligned_offset = g.op( + 'Constant', + value_t=torch.tensor([0.5 / spatial_scale], + dtype=torch.float32)) + rois = sub(g, rois, aligned_offset) + # roi align + return g.op( + 'RoiAlign', + input, + rois, + batch_indices, + output_height_i=output_size[0], + output_width_i=output_size[1], + spatial_scale_f=spatial_scale, + sampling_ratio_i=max(0, sampling_ratio), + mode_s=pool_mode) + + @staticmethod + def forward(ctx, + input, + rois, + output_size, + spatial_scale=1.0, + sampling_ratio=0, + pool_mode='avg', + aligned=True): + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.sampling_ratio = sampling_ratio + assert pool_mode in ('max', 'avg') + ctx.pool_mode = 0 if pool_mode == 'max' else 1 + ctx.aligned = aligned + ctx.input_shape = input.size() + + assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' + + output_shape = (rois.size(0), input.size(1), ctx.output_size[0], + ctx.output_size[1]) + output = input.new_zeros(output_shape) + if ctx.pool_mode == 0: + argmax_y = input.new_zeros(output_shape) + argmax_x = input.new_zeros(output_shape) + else: + argmax_y = input.new_zeros(0) + argmax_x = input.new_zeros(0) + + ext_module.roi_align_forward( + input, + rois, + output, + argmax_y, + argmax_x, + aligned_height=ctx.output_size[0], + aligned_width=ctx.output_size[1], + spatial_scale=ctx.spatial_scale, + sampling_ratio=ctx.sampling_ratio, + pool_mode=ctx.pool_mode, + aligned=ctx.aligned) + + ctx.save_for_backward(rois, argmax_y, argmax_x) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + rois, argmax_y, argmax_x = ctx.saved_tensors + grad_input = grad_output.new_zeros(ctx.input_shape) + # complex head architecture may cause grad_output uncontiguous. + grad_output = grad_output.contiguous() + ext_module.roi_align_backward( + grad_output, + rois, + argmax_y, + argmax_x, + grad_input, + aligned_height=ctx.output_size[0], + aligned_width=ctx.output_size[1], + spatial_scale=ctx.spatial_scale, + sampling_ratio=ctx.sampling_ratio, + pool_mode=ctx.pool_mode, + aligned=ctx.aligned) + return grad_input, None, None, None, None, None, None + + +roi_align = RoIAlignFunction.apply + + +class RoIAlign(nn.Module): + """RoI align pooling layer. + + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each + output sample. 0 to take samples densely for current models. + pool_mode (str, 'avg' or 'max'): pooling mode in each bin. + aligned (bool): if False, use the legacy implementation in + MMDetection. If True, align the results more perfectly. + use_torchvision (bool): whether to use roi_align from torchvision. + + Note: + The implementation of RoIAlign when aligned=True is modified from + https://github.com/facebookresearch/detectron2/ + + The meaning of aligned=True: + + Given a continuous coordinate c, its two neighboring pixel + indices (in our pixel model) are computed by floor(c - 0.5) and + ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete + indices [0] and [1] (which are sampled from the underlying signal + at continuous coordinates 0.5 and 1.5). But the original roi_align + (aligned=False) does not subtract the 0.5 when computing + neighboring pixel indices and therefore it uses pixels with a + slightly incorrect alignment (relative to our pixel model) when + performing bilinear interpolation. + + With `aligned=True`, + we first appropriately scale the ROI and then shift it by -0.5 + prior to calling roi_align. This produces the correct neighbors; + + The difference does not make a difference to the model's + performance if ROIAlign is used together with conv layers. + """ + + @deprecated_api_warning( + { + 'out_size': 'output_size', + 'sample_num': 'sampling_ratio' + }, + cls_name='RoIAlign') + def __init__(self, + output_size, + spatial_scale=1.0, + sampling_ratio=0, + pool_mode='avg', + aligned=True, + use_torchvision=False): + super(RoIAlign, self).__init__() + + self.output_size = _pair(output_size) + self.spatial_scale = float(spatial_scale) + self.sampling_ratio = int(sampling_ratio) + self.pool_mode = pool_mode + self.aligned = aligned + self.use_torchvision = use_torchvision + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx5 boxes. First column is the index into N.\ + The other 4 columns are xyxy. + """ + if self.use_torchvision: + from torchvision.ops import roi_align as tv_roi_align + if 'aligned' in tv_roi_align.__code__.co_varnames: + return tv_roi_align(input, rois, self.output_size, + self.spatial_scale, self.sampling_ratio, + self.aligned) + else: + if self.aligned: + rois -= rois.new_tensor([0.] + + [0.5 / self.spatial_scale] * 4) + return tv_roi_align(input, rois, self.output_size, + self.spatial_scale, self.sampling_ratio) + else: + return roi_align(input, rois, self.output_size, self.spatial_scale, + self.sampling_ratio, self.pool_mode, self.aligned) + + def __repr__(self): + s = self.__class__.__name__ + s += f'(output_size={self.output_size}, ' + s += f'spatial_scale={self.spatial_scale}, ' + s += f'sampling_ratio={self.sampling_ratio}, ' + s += f'pool_mode={self.pool_mode}, ' + s += f'aligned={self.aligned}, ' + s += f'use_torchvision={self.use_torchvision})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_align_rotated.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_align_rotated.py new file mode 100644 index 00000000..0ce4961a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_align_rotated.py @@ -0,0 +1,177 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['roi_align_rotated_forward', 'roi_align_rotated_backward']) + + +class RoIAlignRotatedFunction(Function): + + @staticmethod + def symbolic(g, features, rois, out_size, spatial_scale, sample_num, + aligned, clockwise): + if isinstance(out_size, int): + out_h = out_size + out_w = out_size + elif isinstance(out_size, tuple): + assert len(out_size) == 2 + assert isinstance(out_size[0], int) + assert isinstance(out_size[1], int) + out_h, out_w = out_size + else: + raise TypeError( + '"out_size" must be an integer or tuple of integers') + return g.op( + 'mmcv::MMCVRoIAlignRotated', + features, + rois, + output_height_i=out_h, + output_width_i=out_h, + spatial_scale_f=spatial_scale, + sampling_ratio_i=sample_num, + aligned_i=aligned, + clockwise_i=clockwise) + + @staticmethod + def forward(ctx, + features, + rois, + out_size, + spatial_scale, + sample_num=0, + aligned=True, + clockwise=False): + if isinstance(out_size, int): + out_h = out_size + out_w = out_size + elif isinstance(out_size, tuple): + assert len(out_size) == 2 + assert isinstance(out_size[0], int) + assert isinstance(out_size[1], int) + out_h, out_w = out_size + else: + raise TypeError( + '"out_size" must be an integer or tuple of integers') + ctx.spatial_scale = spatial_scale + ctx.sample_num = sample_num + ctx.aligned = aligned + ctx.clockwise = clockwise + ctx.save_for_backward(rois) + ctx.feature_size = features.size() + + batch_size, num_channels, data_height, data_width = features.size() + num_rois = rois.size(0) + + output = features.new_zeros(num_rois, num_channels, out_h, out_w) + ext_module.roi_align_rotated_forward( + features, + rois, + output, + pooled_height=out_h, + pooled_width=out_w, + spatial_scale=spatial_scale, + sample_num=sample_num, + aligned=aligned, + clockwise=clockwise) + return output + + @staticmethod + def backward(ctx, grad_output): + feature_size = ctx.feature_size + spatial_scale = ctx.spatial_scale + aligned = ctx.aligned + clockwise = ctx.clockwise + sample_num = ctx.sample_num + rois = ctx.saved_tensors[0] + assert feature_size is not None + batch_size, num_channels, data_height, data_width = feature_size + + out_w = grad_output.size(3) + out_h = grad_output.size(2) + + grad_input = grad_rois = None + + if ctx.needs_input_grad[0]: + grad_input = rois.new_zeros(batch_size, num_channels, data_height, + data_width) + ext_module.roi_align_rotated_backward( + grad_output.contiguous(), + rois, + grad_input, + pooled_height=out_h, + pooled_width=out_w, + spatial_scale=spatial_scale, + sample_num=sample_num, + aligned=aligned, + clockwise=clockwise) + return grad_input, grad_rois, None, None, None, None, None + + +roi_align_rotated = RoIAlignRotatedFunction.apply + + +class RoIAlignRotated(nn.Module): + """RoI align pooling layer for rotated proposals. + + It accepts a feature map of shape (N, C, H, W) and rois with shape + (n, 6) with each roi decoded as (batch_index, center_x, center_y, + w, h, angle). The angle is in radian. + + Args: + out_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sample_num (int): number of inputs samples to take for each + output sample. 0 to take samples densely for current models. + aligned (bool): if False, use the legacy implementation in + MMDetection. If True, align the results more perfectly. + Default: True. + clockwise (bool): If True, the angle in each proposal follows a + clockwise fashion in image space, otherwise, the angle is + counterclockwise. Default: False. + + Note: + The implementation of RoIAlign when aligned=True is modified from + https://github.com/facebookresearch/detectron2/ + + The meaning of aligned=True: + + Given a continuous coordinate c, its two neighboring pixel + indices (in our pixel model) are computed by floor(c - 0.5) and + ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete + indices [0] and [1] (which are sampled from the underlying signal + at continuous coordinates 0.5 and 1.5). But the original roi_align + (aligned=False) does not subtract the 0.5 when computing + neighboring pixel indices and therefore it uses pixels with a + slightly incorrect alignment (relative to our pixel model) when + performing bilinear interpolation. + + With `aligned=True`, + we first appropriately scale the ROI and then shift it by -0.5 + prior to calling roi_align. This produces the correct neighbors; + + The difference does not make a difference to the model's + performance if ROIAlign is used together with conv layers. + """ + + def __init__(self, + out_size, + spatial_scale, + sample_num=0, + aligned=True, + clockwise=False): + super(RoIAlignRotated, self).__init__() + + self.out_size = out_size + self.spatial_scale = float(spatial_scale) + self.sample_num = int(sample_num) + self.aligned = aligned + self.clockwise = clockwise + + def forward(self, features, rois): + return RoIAlignRotatedFunction.apply(features, rois, self.out_size, + self.spatial_scale, + self.sample_num, self.aligned, + self.clockwise) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_pool.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_pool.py new file mode 100644 index 00000000..d339d8f2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roi_pool.py @@ -0,0 +1,86 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', + ['roi_pool_forward', 'roi_pool_backward']) + + +class RoIPoolFunction(Function): + + @staticmethod + def symbolic(g, input, rois, output_size, spatial_scale): + return g.op( + 'MaxRoiPool', + input, + rois, + pooled_shape_i=output_size, + spatial_scale_f=spatial_scale) + + @staticmethod + def forward(ctx, input, rois, output_size, spatial_scale=1.0): + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.input_shape = input.size() + + assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' + + output_shape = (rois.size(0), input.size(1), ctx.output_size[0], + ctx.output_size[1]) + output = input.new_zeros(output_shape) + argmax = input.new_zeros(output_shape, dtype=torch.int) + + ext_module.roi_pool_forward( + input, + rois, + output, + argmax, + pooled_height=ctx.output_size[0], + pooled_width=ctx.output_size[1], + spatial_scale=ctx.spatial_scale) + + ctx.save_for_backward(rois, argmax) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + rois, argmax = ctx.saved_tensors + grad_input = grad_output.new_zeros(ctx.input_shape) + + ext_module.roi_pool_backward( + grad_output, + rois, + argmax, + grad_input, + pooled_height=ctx.output_size[0], + pooled_width=ctx.output_size[1], + spatial_scale=ctx.spatial_scale) + + return grad_input, None, None, None + + +roi_pool = RoIPoolFunction.apply + + +class RoIPool(nn.Module): + + def __init__(self, output_size, spatial_scale=1.0): + super(RoIPool, self).__init__() + + self.output_size = _pair(output_size) + self.spatial_scale = float(spatial_scale) + + def forward(self, input, rois): + return roi_pool(input, rois, self.output_size, self.spatial_scale) + + def __repr__(self): + s = self.__class__.__name__ + s += f'(output_size={self.output_size}, ' + s += f'spatial_scale={self.spatial_scale})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roiaware_pool3d.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roiaware_pool3d.py new file mode 100644 index 00000000..00d8a4d7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roiaware_pool3d.py @@ -0,0 +1,114 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn as nn +from torch.autograd import Function + +import custom_mmpkg.custom_mmcv as mmcv +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['roiaware_pool3d_forward', 'roiaware_pool3d_backward']) + + +class RoIAwarePool3d(nn.Module): + """Encode the geometry-specific features of each 3D proposal. + + Please refer to `PartA2 `_ for more + details. + + Args: + out_size (int or tuple): The size of output features. n or + [n1, n2, n3]. + max_pts_per_voxel (int, optional): The maximum number of points per + voxel. Default: 128. + mode (str, optional): Pooling method of RoIAware, 'max' or 'avg'. + Default: 'max'. + """ + + def __init__(self, out_size, max_pts_per_voxel=128, mode='max'): + super().__init__() + + self.out_size = out_size + self.max_pts_per_voxel = max_pts_per_voxel + assert mode in ['max', 'avg'] + pool_mapping = {'max': 0, 'avg': 1} + self.mode = pool_mapping[mode] + + def forward(self, rois, pts, pts_feature): + """ + Args: + rois (torch.Tensor): [N, 7], in LiDAR coordinate, + (x, y, z) is the bottom center of rois. + pts (torch.Tensor): [npoints, 3], coordinates of input points. + pts_feature (torch.Tensor): [npoints, C], features of input points. + + Returns: + pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C] + """ + + return RoIAwarePool3dFunction.apply(rois, pts, pts_feature, + self.out_size, + self.max_pts_per_voxel, self.mode) + + +class RoIAwarePool3dFunction(Function): + + @staticmethod + def forward(ctx, rois, pts, pts_feature, out_size, max_pts_per_voxel, + mode): + """ + Args: + rois (torch.Tensor): [N, 7], in LiDAR coordinate, + (x, y, z) is the bottom center of rois. + pts (torch.Tensor): [npoints, 3], coordinates of input points. + pts_feature (torch.Tensor): [npoints, C], features of input points. + out_size (int or tuple): The size of output features. n or + [n1, n2, n3]. + max_pts_per_voxel (int): The maximum number of points per voxel. + Default: 128. + mode (int): Pooling method of RoIAware, 0 (max pool) or 1 (average + pool). + + Returns: + pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C], output + pooled features. + """ + + if isinstance(out_size, int): + out_x = out_y = out_z = out_size + else: + assert len(out_size) == 3 + assert mmcv.is_tuple_of(out_size, int) + out_x, out_y, out_z = out_size + + num_rois = rois.shape[0] + num_channels = pts_feature.shape[-1] + num_pts = pts.shape[0] + + pooled_features = pts_feature.new_zeros( + (num_rois, out_x, out_y, out_z, num_channels)) + argmax = pts_feature.new_zeros( + (num_rois, out_x, out_y, out_z, num_channels), dtype=torch.int) + pts_idx_of_voxels = pts_feature.new_zeros( + (num_rois, out_x, out_y, out_z, max_pts_per_voxel), + dtype=torch.int) + + ext_module.roiaware_pool3d_forward(rois, pts, pts_feature, argmax, + pts_idx_of_voxels, pooled_features, + mode) + + ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, mode, + num_pts, num_channels) + return pooled_features + + @staticmethod + def backward(ctx, grad_out): + ret = ctx.roiaware_pool3d_for_backward + pts_idx_of_voxels, argmax, mode, num_pts, num_channels = ret + + grad_in = grad_out.new_zeros((num_pts, num_channels)) + ext_module.roiaware_pool3d_backward(pts_idx_of_voxels, argmax, + grad_out.contiguous(), grad_in, + mode) + + return None, None, grad_in, None, None, None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roipoint_pool3d.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roipoint_pool3d.py new file mode 100644 index 00000000..0a21412c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/roipoint_pool3d.py @@ -0,0 +1,77 @@ +from torch import nn as nn +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['roipoint_pool3d_forward']) + + +class RoIPointPool3d(nn.Module): + """Encode the geometry-specific features of each 3D proposal. + + Please refer to `Paper of PartA2 `_ + for more details. + + Args: + num_sampled_points (int, optional): Number of samples in each roi. + Default: 512. + """ + + def __init__(self, num_sampled_points=512): + super().__init__() + self.num_sampled_points = num_sampled_points + + def forward(self, points, point_features, boxes3d): + """ + Args: + points (torch.Tensor): Input points whose shape is (B, N, C). + point_features (torch.Tensor): Features of input points whose shape + is (B, N, C). + boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7). + + Returns: + pooled_features (torch.Tensor): The output pooled features whose + shape is (B, M, 512, 3 + C). + pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M). + """ + return RoIPointPool3dFunction.apply(points, point_features, boxes3d, + self.num_sampled_points) + + +class RoIPointPool3dFunction(Function): + + @staticmethod + def forward(ctx, points, point_features, boxes3d, num_sampled_points=512): + """ + Args: + points (torch.Tensor): Input points whose shape is (B, N, C). + point_features (torch.Tensor): Features of input points whose shape + is (B, N, C). + boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7). + num_sampled_points (int, optional): The num of sampled points. + Default: 512. + + Returns: + pooled_features (torch.Tensor): The output pooled features whose + shape is (B, M, 512, 3 + C). + pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M). + """ + assert len(points.shape) == 3 and points.shape[2] == 3 + batch_size, boxes_num, feature_len = points.shape[0], boxes3d.shape[ + 1], point_features.shape[2] + pooled_boxes3d = boxes3d.view(batch_size, -1, 7) + pooled_features = point_features.new_zeros( + (batch_size, boxes_num, num_sampled_points, 3 + feature_len)) + pooled_empty_flag = point_features.new_zeros( + (batch_size, boxes_num)).int() + + ext_module.roipoint_pool3d_forward(points.contiguous(), + pooled_boxes3d.contiguous(), + point_features.contiguous(), + pooled_features, pooled_empty_flag) + + return pooled_features, pooled_empty_flag + + @staticmethod + def backward(ctx, grad_out): + raise NotImplementedError diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/saconv.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/saconv.py new file mode 100644 index 00000000..46d26992 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/saconv.py @@ -0,0 +1,145 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn as nn +import torch.nn.functional as F + +from custom_mmpkg.custom_mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init +from custom_mmpkg.custom_mmcv.ops.deform_conv import deform_conv2d +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, digit_version + + +@CONV_LAYERS.register_module(name='SAC') +class SAConv2d(ConvAWS2d): + """SAC (Switchable Atrous Convolution) + + This is an implementation of SAC in DetectoRS + (https://arxiv.org/pdf/2006.02334.pdf). + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of + the input. Default: 0 + padding_mode (string, optional): ``'zeros'``, ``'reflect'``, + ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` + dilation (int or tuple, optional): Spacing between kernel elements. + Default: 1 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If ``True``, adds a learnable bias to the + output. Default: ``True`` + use_deform: If ``True``, replace convolution with deformable + convolution. Default: ``False``. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + use_deform=False): + super().__init__( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias) + self.use_deform = use_deform + self.switch = nn.Conv2d( + self.in_channels, 1, kernel_size=1, stride=stride, bias=True) + self.weight_diff = nn.Parameter(torch.Tensor(self.weight.size())) + self.pre_context = nn.Conv2d( + self.in_channels, self.in_channels, kernel_size=1, bias=True) + self.post_context = nn.Conv2d( + self.out_channels, self.out_channels, kernel_size=1, bias=True) + if self.use_deform: + self.offset_s = nn.Conv2d( + self.in_channels, + 18, + kernel_size=3, + padding=1, + stride=stride, + bias=True) + self.offset_l = nn.Conv2d( + self.in_channels, + 18, + kernel_size=3, + padding=1, + stride=stride, + bias=True) + self.init_weights() + + def init_weights(self): + constant_init(self.switch, 0, bias=1) + self.weight_diff.data.zero_() + constant_init(self.pre_context, 0) + constant_init(self.post_context, 0) + if self.use_deform: + constant_init(self.offset_s, 0) + constant_init(self.offset_l, 0) + + def forward(self, x): + # pre-context + avg_x = F.adaptive_avg_pool2d(x, output_size=1) + avg_x = self.pre_context(avg_x) + avg_x = avg_x.expand_as(x) + x = x + avg_x + # switch + avg_x = F.pad(x, pad=(2, 2, 2, 2), mode='reflect') + avg_x = F.avg_pool2d(avg_x, kernel_size=5, stride=1, padding=0) + switch = self.switch(avg_x) + # sac + weight = self._get_weight(self.weight) + zero_bias = torch.zeros( + self.out_channels, device=weight.device, dtype=weight.dtype) + + if self.use_deform: + offset = self.offset_s(avg_x) + out_s = deform_conv2d(x, offset, weight, self.stride, self.padding, + self.dilation, self.groups, 1) + else: + if (TORCH_VERSION == 'parrots' + or digit_version(TORCH_VERSION) < digit_version('1.5.0')): + out_s = super().conv2d_forward(x, weight) + elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'): + # bias is a required argument of _conv_forward in torch 1.8.0 + out_s = super()._conv_forward(x, weight, zero_bias) + else: + out_s = super()._conv_forward(x, weight) + ori_p = self.padding + ori_d = self.dilation + self.padding = tuple(3 * p for p in self.padding) + self.dilation = tuple(3 * d for d in self.dilation) + weight = weight + self.weight_diff + if self.use_deform: + offset = self.offset_l(avg_x) + out_l = deform_conv2d(x, offset, weight, self.stride, self.padding, + self.dilation, self.groups, 1) + else: + if (TORCH_VERSION == 'parrots' + or digit_version(TORCH_VERSION) < digit_version('1.5.0')): + out_l = super().conv2d_forward(x, weight) + elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'): + # bias is a required argument of _conv_forward in torch 1.8.0 + out_l = super()._conv_forward(x, weight, zero_bias) + else: + out_l = super()._conv_forward(x, weight) + + out = switch * out_s + (1 - switch) * out_l + self.padding = ori_p + self.dilation = ori_d + # post-context + avg_x = F.adaptive_avg_pool2d(out, output_size=1) + avg_x = self.post_context(avg_x) + avg_x = avg_x.expand_as(out) + out = out + avg_x + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/scatter_points.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/scatter_points.py new file mode 100644 index 00000000..2b8aa416 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/scatter_points.py @@ -0,0 +1,135 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', + ['dynamic_point_to_voxel_forward', 'dynamic_point_to_voxel_backward']) + + +class _DynamicScatter(Function): + + @staticmethod + def forward(ctx, feats, coors, reduce_type='max'): + """convert kitti points(N, >=3) to voxels. + + Args: + feats (torch.Tensor): [N, C]. Points features to be reduced + into voxels. + coors (torch.Tensor): [N, ndim]. Corresponding voxel coordinates + (specifically multi-dim voxel index) of each points. + reduce_type (str, optional): Reduce op. support 'max', 'sum' and + 'mean'. Default: 'max'. + + Returns: + voxel_feats (torch.Tensor): [M, C]. Reduced features, input + features that shares the same voxel coordinates are reduced to + one row. + voxel_coors (torch.Tensor): [M, ndim]. Voxel coordinates. + """ + results = ext_module.dynamic_point_to_voxel_forward( + feats, coors, reduce_type) + (voxel_feats, voxel_coors, point2voxel_map, + voxel_points_count) = results + ctx.reduce_type = reduce_type + ctx.save_for_backward(feats, voxel_feats, point2voxel_map, + voxel_points_count) + ctx.mark_non_differentiable(voxel_coors) + return voxel_feats, voxel_coors + + @staticmethod + def backward(ctx, grad_voxel_feats, grad_voxel_coors=None): + (feats, voxel_feats, point2voxel_map, + voxel_points_count) = ctx.saved_tensors + grad_feats = torch.zeros_like(feats) + # TODO: whether to use index put or use cuda_backward + # To use index put, need point to voxel index + ext_module.dynamic_point_to_voxel_backward( + grad_feats, grad_voxel_feats.contiguous(), feats, voxel_feats, + point2voxel_map, voxel_points_count, ctx.reduce_type) + return grad_feats, None, None + + +dynamic_scatter = _DynamicScatter.apply + + +class DynamicScatter(nn.Module): + """Scatters points into voxels, used in the voxel encoder with dynamic + voxelization. + + Note: + The CPU and GPU implementation get the same output, but have numerical + difference after summation and division (e.g., 5e-7). + + Args: + voxel_size (list): list [x, y, z] size of three dimension. + point_cloud_range (list): The coordinate range of points, [x_min, + y_min, z_min, x_max, y_max, z_max]. + average_points (bool): whether to use avg pooling to scatter points + into voxel. + """ + + def __init__(self, voxel_size, point_cloud_range, average_points: bool): + super().__init__() + + self.voxel_size = voxel_size + self.point_cloud_range = point_cloud_range + self.average_points = average_points + + def forward_single(self, points, coors): + """Scatters points into voxels. + + Args: + points (torch.Tensor): Points to be reduced into voxels. + coors (torch.Tensor): Corresponding voxel coordinates (specifically + multi-dim voxel index) of each points. + + Returns: + voxel_feats (torch.Tensor): Reduced features, input features that + shares the same voxel coordinates are reduced to one row. + voxel_coors (torch.Tensor): Voxel coordinates. + """ + reduce = 'mean' if self.average_points else 'max' + return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce) + + def forward(self, points, coors): + """Scatters points/features into voxels. + + Args: + points (torch.Tensor): Points to be reduced into voxels. + coors (torch.Tensor): Corresponding voxel coordinates (specifically + multi-dim voxel index) of each points. + + Returns: + voxel_feats (torch.Tensor): Reduced features, input features that + shares the same voxel coordinates are reduced to one row. + voxel_coors (torch.Tensor): Voxel coordinates. + """ + if coors.size(-1) == 3: + return self.forward_single(points, coors) + else: + batch_size = coors[-1, 0] + 1 + voxels, voxel_coors = [], [] + for i in range(batch_size): + inds = torch.where(coors[:, 0] == i) + voxel, voxel_coor = self.forward_single( + points[inds], coors[inds][:, 1:]) + coor_pad = nn.functional.pad( + voxel_coor, (1, 0), mode='constant', value=i) + voxel_coors.append(coor_pad) + voxels.append(voxel) + features = torch.cat(voxels, dim=0) + feature_coors = torch.cat(voxel_coors, dim=0) + + return features, feature_coors + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += 'voxel_size=' + str(self.voxel_size) + s += ', point_cloud_range=' + str(self.point_cloud_range) + s += ', average_points=' + str(self.average_points) + s += ')' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/sync_bn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/sync_bn.py new file mode 100644 index 00000000..f885caac --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/sync_bn.py @@ -0,0 +1,279 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.module import Module +from torch.nn.parameter import Parameter + +from custom_mmpkg.custom_mmcv.cnn import NORM_LAYERS +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', [ + 'sync_bn_forward_mean', 'sync_bn_forward_var', 'sync_bn_forward_output', + 'sync_bn_backward_param', 'sync_bn_backward_data' +]) + + +class SyncBatchNormFunction(Function): + + @staticmethod + def symbolic(g, input, running_mean, running_var, weight, bias, momentum, + eps, group, group_size, stats_mode): + return g.op( + 'mmcv::MMCVSyncBatchNorm', + input, + running_mean, + running_var, + weight, + bias, + momentum_f=momentum, + eps_f=eps, + group_i=group, + group_size_i=group_size, + stats_mode=stats_mode) + + @staticmethod + def forward(self, input, running_mean, running_var, weight, bias, momentum, + eps, group, group_size, stats_mode): + self.momentum = momentum + self.eps = eps + self.group = group + self.group_size = group_size + self.stats_mode = stats_mode + + assert isinstance( + input, (torch.HalfTensor, torch.FloatTensor, + torch.cuda.HalfTensor, torch.cuda.FloatTensor)), \ + f'only support Half or Float Tensor, but {input.type()}' + output = torch.zeros_like(input) + input3d = input.flatten(start_dim=2) + output3d = output.view_as(input3d) + num_channels = input3d.size(1) + + # ensure mean/var/norm/std are initialized as zeros + # ``torch.empty()`` does not guarantee that + mean = torch.zeros( + num_channels, dtype=torch.float, device=input3d.device) + var = torch.zeros( + num_channels, dtype=torch.float, device=input3d.device) + norm = torch.zeros_like( + input3d, dtype=torch.float, device=input3d.device) + std = torch.zeros( + num_channels, dtype=torch.float, device=input3d.device) + + batch_size = input3d.size(0) + if batch_size > 0: + ext_module.sync_bn_forward_mean(input3d, mean) + batch_flag = torch.ones([1], device=mean.device, dtype=mean.dtype) + else: + # skip updating mean and leave it as zeros when the input is empty + batch_flag = torch.zeros([1], device=mean.device, dtype=mean.dtype) + + # synchronize mean and the batch flag + vec = torch.cat([mean, batch_flag]) + if self.stats_mode == 'N': + vec *= batch_size + if self.group_size > 1: + dist.all_reduce(vec, group=self.group) + total_batch = vec[-1].detach() + mean = vec[:num_channels] + + if self.stats_mode == 'default': + mean = mean / self.group_size + elif self.stats_mode == 'N': + mean = mean / total_batch.clamp(min=1) + else: + raise NotImplementedError + + # leave var as zeros when the input is empty + if batch_size > 0: + ext_module.sync_bn_forward_var(input3d, mean, var) + + if self.stats_mode == 'N': + var *= batch_size + if self.group_size > 1: + dist.all_reduce(var, group=self.group) + + if self.stats_mode == 'default': + var /= self.group_size + elif self.stats_mode == 'N': + var /= total_batch.clamp(min=1) + else: + raise NotImplementedError + + # if the total batch size over all the ranks is zero, + # we should not update the statistics in the current batch + update_flag = total_batch.clamp(max=1) + momentum = update_flag * self.momentum + ext_module.sync_bn_forward_output( + input3d, + mean, + var, + weight, + bias, + running_mean, + running_var, + norm, + std, + output3d, + eps=self.eps, + momentum=momentum, + group_size=self.group_size) + self.save_for_backward(norm, std, weight) + return output + + @staticmethod + @once_differentiable + def backward(self, grad_output): + norm, std, weight = self.saved_tensors + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(weight) + grad_input = torch.zeros_like(grad_output) + grad_output3d = grad_output.flatten(start_dim=2) + grad_input3d = grad_input.view_as(grad_output3d) + + batch_size = grad_input3d.size(0) + if batch_size > 0: + ext_module.sync_bn_backward_param(grad_output3d, norm, grad_weight, + grad_bias) + + # all reduce + if self.group_size > 1: + dist.all_reduce(grad_weight, group=self.group) + dist.all_reduce(grad_bias, group=self.group) + grad_weight /= self.group_size + grad_bias /= self.group_size + + if batch_size > 0: + ext_module.sync_bn_backward_data(grad_output3d, weight, + grad_weight, grad_bias, norm, std, + grad_input3d) + + return grad_input, None, None, grad_weight, grad_bias, \ + None, None, None, None, None + + +@NORM_LAYERS.register_module(name='MMSyncBN') +class SyncBatchNorm(Module): + """Synchronized Batch Normalization. + + Args: + num_features (int): number of features/chennels in input tensor + eps (float, optional): a value added to the denominator for numerical + stability. Defaults to 1e-5. + momentum (float, optional): the value used for the running_mean and + running_var computation. Defaults to 0.1. + affine (bool, optional): whether to use learnable affine parameters. + Defaults to True. + track_running_stats (bool, optional): whether to track the running + mean and variance during training. When set to False, this + module does not track such statistics, and initializes statistics + buffers ``running_mean`` and ``running_var`` as ``None``. When + these buffers are ``None``, this module always uses batch + statistics in both training and eval modes. Defaults to True. + group (int, optional): synchronization of stats happen within + each process group individually. By default it is synchronization + across the whole world. Defaults to None. + stats_mode (str, optional): The statistical mode. Available options + includes ``'default'`` and ``'N'``. Defaults to 'default'. + When ``stats_mode=='default'``, it computes the overall statistics + using those from each worker with equal weight, i.e., the + statistics are synchronized and simply divied by ``group``. This + mode will produce inaccurate statistics when empty tensors occur. + When ``stats_mode=='N'``, it compute the overall statistics using + the total number of batches in each worker ignoring the number of + group, i.e., the statistics are synchronized and then divied by + the total batch ``N``. This mode is beneficial when empty tensors + occur during training, as it average the total mean by the real + number of batch. + """ + + def __init__(self, + num_features, + eps=1e-5, + momentum=0.1, + affine=True, + track_running_stats=True, + group=None, + stats_mode='default'): + super(SyncBatchNorm, self).__init__() + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.affine = affine + self.track_running_stats = track_running_stats + group = dist.group.WORLD if group is None else group + self.group = group + self.group_size = dist.get_world_size(group) + assert stats_mode in ['default', 'N'], \ + f'"stats_mode" only accepts "default" and "N", got "{stats_mode}"' + self.stats_mode = stats_mode + if self.affine: + self.weight = Parameter(torch.Tensor(num_features)) + self.bias = Parameter(torch.Tensor(num_features)) + else: + self.register_parameter('weight', None) + self.register_parameter('bias', None) + if self.track_running_stats: + self.register_buffer('running_mean', torch.zeros(num_features)) + self.register_buffer('running_var', torch.ones(num_features)) + self.register_buffer('num_batches_tracked', + torch.tensor(0, dtype=torch.long)) + else: + self.register_buffer('running_mean', None) + self.register_buffer('running_var', None) + self.register_buffer('num_batches_tracked', None) + self.reset_parameters() + + def reset_running_stats(self): + if self.track_running_stats: + self.running_mean.zero_() + self.running_var.fill_(1) + self.num_batches_tracked.zero_() + + def reset_parameters(self): + self.reset_running_stats() + if self.affine: + self.weight.data.uniform_() # pytorch use ones_() + self.bias.data.zero_() + + def forward(self, input): + if input.dim() < 2: + raise ValueError( + f'expected at least 2D input, got {input.dim()}D input') + if self.momentum is None: + exponential_average_factor = 0.0 + else: + exponential_average_factor = self.momentum + + if self.training and self.track_running_stats: + if self.num_batches_tracked is not None: + self.num_batches_tracked += 1 + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float( + self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + if self.training or not self.track_running_stats: + return SyncBatchNormFunction.apply( + input, self.running_mean, self.running_var, self.weight, + self.bias, exponential_average_factor, self.eps, self.group, + self.group_size, self.stats_mode) + else: + return F.batch_norm(input, self.running_mean, self.running_var, + self.weight, self.bias, False, + exponential_average_factor, self.eps) + + def __repr__(self): + s = self.__class__.__name__ + s += f'({self.num_features}, ' + s += f'eps={self.eps}, ' + s += f'momentum={self.momentum}, ' + s += f'affine={self.affine}, ' + s += f'track_running_stats={self.track_running_stats}, ' + s += f'group_size={self.group_size},' + s += f'stats_mode={self.stats_mode})' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/three_interpolate.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/three_interpolate.py new file mode 100644 index 00000000..203f47f0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/three_interpolate.py @@ -0,0 +1,68 @@ +from typing import Tuple + +import torch +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['three_interpolate_forward', 'three_interpolate_backward']) + + +class ThreeInterpolate(Function): + """Performs weighted linear interpolation on 3 features. + + Please refer to `Paper of PointNet++ `_ + for more details. + """ + + @staticmethod + def forward(ctx, features: torch.Tensor, indices: torch.Tensor, + weight: torch.Tensor) -> torch.Tensor: + """ + Args: + features (Tensor): (B, C, M) Features descriptors to be + interpolated + indices (Tensor): (B, n, 3) index three nearest neighbors + of the target features in features + weight (Tensor): (B, n, 3) weights of interpolation + + Returns: + Tensor: (B, C, N) tensor of the interpolated features + """ + assert features.is_contiguous() + assert indices.is_contiguous() + assert weight.is_contiguous() + + B, c, m = features.size() + n = indices.size(1) + ctx.three_interpolate_for_backward = (indices, weight, m) + output = torch.cuda.FloatTensor(B, c, n) + + ext_module.three_interpolate_forward( + features, indices, weight, output, b=B, c=c, m=m, n=n) + return output + + @staticmethod + def backward( + ctx, grad_out: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Args: + grad_out (Tensor): (B, C, N) tensor with gradients of outputs + + Returns: + Tensor: (B, C, M) tensor with gradients of features + """ + idx, weight, m = ctx.three_interpolate_for_backward + B, c, n = grad_out.size() + + grad_features = torch.cuda.FloatTensor(B, c, m).zero_() + grad_out_data = grad_out.data.contiguous() + + ext_module.three_interpolate_backward( + grad_out_data, idx, weight, grad_features.data, b=B, c=c, n=n, m=m) + return grad_features, None, None + + +three_interpolate = ThreeInterpolate.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/three_nn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/three_nn.py new file mode 100644 index 00000000..2b01047a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/three_nn.py @@ -0,0 +1,51 @@ +from typing import Tuple + +import torch +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', ['three_nn_forward']) + + +class ThreeNN(Function): + """Find the top-3 nearest neighbors of the target set from the source set. + + Please refer to `Paper of PointNet++ `_ + for more details. + """ + + @staticmethod + def forward(ctx, target: torch.Tensor, + source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + target (Tensor): shape (B, N, 3), points set that needs to + find the nearest neighbors. + source (Tensor): shape (B, M, 3), points set that is used + to find the nearest neighbors of points in target set. + + Returns: + Tensor: shape (B, N, 3), L2 distance of each point in target + set to their corresponding nearest neighbors. + """ + target = target.contiguous() + source = source.contiguous() + + B, N, _ = target.size() + m = source.size(1) + dist2 = torch.cuda.FloatTensor(B, N, 3) + idx = torch.cuda.IntTensor(B, N, 3) + + ext_module.three_nn_forward(target, source, dist2, idx, b=B, n=N, m=m) + if torch.__version__ != 'parrots': + ctx.mark_non_differentiable(idx) + + return torch.sqrt(dist2), idx + + @staticmethod + def backward(ctx, a=None, b=None): + return None, None + + +three_nn = ThreeNN.apply diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/tin_shift.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/tin_shift.py new file mode 100644 index 00000000..472c9fcf --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/tin_shift.py @@ -0,0 +1,68 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# Code reference from "Temporal Interlacing Network" +# https://github.com/deepcs233/TIN/blob/master/cuda_shift/rtc_wrap.py +# Hao Shao, Shengju Qian, Yu Liu +# shaoh19@mails.tsinghua.edu.cn, sjqian@cse.cuhk.edu.hk, yuliu@ee.cuhk.edu.hk + +import torch +import torch.nn as nn +from torch.autograd import Function + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext('_ext', + ['tin_shift_forward', 'tin_shift_backward']) + + +class TINShiftFunction(Function): + + @staticmethod + def forward(ctx, input, shift): + C = input.size(2) + num_segments = shift.size(1) + if C // num_segments <= 0 or C % num_segments != 0: + raise ValueError('C should be a multiple of num_segments, ' + f'but got C={C} and num_segments={num_segments}.') + + ctx.save_for_backward(shift) + + out = torch.zeros_like(input) + ext_module.tin_shift_forward(input, shift, out) + + return out + + @staticmethod + def backward(ctx, grad_output): + + shift = ctx.saved_tensors[0] + data_grad_input = grad_output.new(*grad_output.size()).zero_() + shift_grad_input = shift.new(*shift.size()).zero_() + ext_module.tin_shift_backward(grad_output, shift, data_grad_input) + + return data_grad_input, shift_grad_input + + +tin_shift = TINShiftFunction.apply + + +class TINShift(nn.Module): + """Temporal Interlace Shift. + + Temporal Interlace shift is a differentiable temporal-wise frame shifting + which is proposed in "Temporal Interlacing Network" + + Please refer to https://arxiv.org/abs/2001.06499 for more details. + Code is modified from https://github.com/mit-han-lab/temporal-shift-module + """ + + def forward(self, input, shift): + """Perform temporal interlace shift. + + Args: + input (Tensor): Feature map with shape [N, num_segments, C, H * W]. + shift (Tensor): Shift tensor with shape [N, num_segments]. + + Returns: + Feature map after temporal interlace shift. + """ + return tin_shift(input, shift) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/upfirdn2d.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/upfirdn2d.py new file mode 100644 index 00000000..ef4a5236 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/upfirdn2d.py @@ -0,0 +1,330 @@ +# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 + +# Copyright (c) 2021, NVIDIA Corporation. All rights reserved. +# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator +# Augmentation (ADA) +# ======================================================================= + +# 1. 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If you bring or threaten to bring a patent claim +# against any Licensor (including any claim, cross-claim or +# counterclaim in a lawsuit) to enforce any patents that you allege +# are infringed by any Work, then your rights under this License from +# such Licensor (including the grant in Section 2.1) will terminate +# immediately. + +# 3.5 Trademarks. This License does not grant any rights to use any +# Licensor’s or its affiliates’ names, logos, or trademarks, except +# as necessary to reproduce the notices described in this License. + +# 3.6 Termination. If you violate any term of this License, then your +# rights under this License (including the grant in Section 2.1) will +# terminate immediately. + +# 4. Disclaimer of Warranty. + +# THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR +# NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER +# THIS LICENSE. + +# 5. Limitation of Liability. + +# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL +# THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE +# SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, +# INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF +# OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK +# (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, +# LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER +# COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF +# THE POSSIBILITY OF SUCH DAMAGES. + +# ======================================================================= + +import torch +from torch.autograd import Function +from torch.nn import functional as F + +from custom_mmpkg.custom_mmcv.utils import to_2tuple +from ..utils import ext_loader + +upfirdn2d_ext = ext_loader.load_ext('_ext', ['upfirdn2d']) + + +class UpFirDn2dBackward(Function): + + @staticmethod + def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, + in_size, out_size): + + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_ext.upfirdn2d( + grad_output, + grad_kernel, + up_x=down_x, + up_y=down_y, + down_x=up_x, + down_y=up_y, + pad_x0=g_pad_x0, + pad_x1=g_pad_x1, + pad_y0=g_pad_y0, + pad_y1=g_pad_y1) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], + in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + kernel, = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], + ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_ext.upfirdn2d( + gradgrad_input, + kernel, + up_x=ctx.up_x, + up_y=ctx.up_y, + down_x=ctx.down_x, + down_y=ctx.down_y, + pad_x0=ctx.pad_x0, + pad_x1=ctx.pad_x1, + pad_y0=ctx.pad_y0, + pad_y1=ctx.pad_y1) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], + # ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], + ctx.out_size[0], ctx.out_size[1]) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + batch, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_ext.upfirdn2d( + input, + kernel, + up_x=up_x, + up_y=up_y, + down_x=down_x, + down_y=down_y, + pad_x0=pad_x0, + pad_x1=pad_x1, + pad_y0=pad_y0, + pad_y1=pad_y1) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + """UpFRIDn for 2d features. + + UpFIRDn is short for upsample, apply FIR filter and downsample. More + details can be found in: + https://www.mathworks.com/help/signal/ref/upfirdn.html + + Args: + input (Tensor): Tensor with shape of (n, c, h, w). + kernel (Tensor): Filter kernel. + up (int | tuple[int], optional): Upsampling factor. If given a number, + we will use this factor for the both height and width side. + Defaults to 1. + down (int | tuple[int], optional): Downsampling factor. If given a + number, we will use this factor for the both height and width side. + Defaults to 1. + pad (tuple[int], optional): Padding for tensors, (x_pad, y_pad) or + (x_pad_0, x_pad_1, y_pad_0, y_pad_1). Defaults to (0, 0). + + Returns: + Tensor: Tensor after UpFIRDn. + """ + if input.device.type == 'cpu': + if len(pad) == 2: + pad = (pad[0], pad[1], pad[0], pad[1]) + + up = to_2tuple(up) + + down = to_2tuple(down) + + out = upfirdn2d_native(input, kernel, up[0], up[1], down[0], down[1], + pad[0], pad[1], pad[2], pad[3]) + else: + _up = to_2tuple(up) + + _down = to_2tuple(down) + + if len(pad) == 4: + _pad = pad + elif len(pad) == 2: + _pad = (pad[0], pad[1], pad[0], pad[1]) + + out = UpFirDn2d.apply(input, kernel, _up, _down, _pad) + + return out + + +def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, + pad_y0, pad_y1): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, + [0, 0, + max(pad_x0, 0), + max(pad_x1, 0), + max(pad_y0, 0), + max(pad_y1, 0)]) + out = out[:, + max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/voxelize.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/voxelize.py new file mode 100644 index 00000000..ca3226a4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/ops/voxelize.py @@ -0,0 +1,132 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn +from torch.autograd import Function +from torch.nn.modules.utils import _pair + +from ..utils import ext_loader + +ext_module = ext_loader.load_ext( + '_ext', ['dynamic_voxelize_forward', 'hard_voxelize_forward']) + + +class _Voxelization(Function): + + @staticmethod + def forward(ctx, + points, + voxel_size, + coors_range, + max_points=35, + max_voxels=20000): + """Convert kitti points(N, >=3) to voxels. + + Args: + points (torch.Tensor): [N, ndim]. Points[:, :3] contain xyz points + and points[:, 3:] contain other information like reflectivity. + voxel_size (tuple or float): The size of voxel with the shape of + [3]. + coors_range (tuple or float): The coordinate range of voxel with + the shape of [6]. + max_points (int, optional): maximum points contained in a voxel. if + max_points=-1, it means using dynamic_voxelize. Default: 35. + max_voxels (int, optional): maximum voxels this function create. + for second, 20000 is a good choice. Users should shuffle points + before call this function because max_voxels may drop points. + Default: 20000. + + Returns: + voxels_out (torch.Tensor): Output voxels with the shape of [M, + max_points, ndim]. Only contain points and returned when + max_points != -1. + coors_out (torch.Tensor): Output coordinates with the shape of + [M, 3]. + num_points_per_voxel_out (torch.Tensor): Num points per voxel with + the shape of [M]. Only returned when max_points != -1. + """ + if max_points == -1 or max_voxels == -1: + coors = points.new_zeros(size=(points.size(0), 3), dtype=torch.int) + ext_module.dynamic_voxelize_forward(points, coors, voxel_size, + coors_range, 3) + return coors + else: + voxels = points.new_zeros( + size=(max_voxels, max_points, points.size(1))) + coors = points.new_zeros(size=(max_voxels, 3), dtype=torch.int) + num_points_per_voxel = points.new_zeros( + size=(max_voxels, ), dtype=torch.int) + voxel_num = ext_module.hard_voxelize_forward( + points, voxels, coors, num_points_per_voxel, voxel_size, + coors_range, max_points, max_voxels, 3) + # select the valid voxels + voxels_out = voxels[:voxel_num] + coors_out = coors[:voxel_num] + num_points_per_voxel_out = num_points_per_voxel[:voxel_num] + return voxels_out, coors_out, num_points_per_voxel_out + + +voxelization = _Voxelization.apply + + +class Voxelization(nn.Module): + """Convert kitti points(N, >=3) to voxels. + + Please refer to `PVCNN `_ for more + details. + + Args: + voxel_size (tuple or float): The size of voxel with the shape of [3]. + point_cloud_range (tuple or float): The coordinate range of voxel with + the shape of [6]. + max_num_points (int): maximum points contained in a voxel. if + max_points=-1, it means using dynamic_voxelize. + max_voxels (int, optional): maximum voxels this function create. + for second, 20000 is a good choice. Users should shuffle points + before call this function because max_voxels may drop points. + Default: 20000. + """ + + def __init__(self, + voxel_size, + point_cloud_range, + max_num_points, + max_voxels=20000): + super().__init__() + + self.voxel_size = voxel_size + self.point_cloud_range = point_cloud_range + self.max_num_points = max_num_points + if isinstance(max_voxels, tuple): + self.max_voxels = max_voxels + else: + self.max_voxels = _pair(max_voxels) + + point_cloud_range = torch.tensor( + point_cloud_range, dtype=torch.float32) + voxel_size = torch.tensor(voxel_size, dtype=torch.float32) + grid_size = (point_cloud_range[3:] - + point_cloud_range[:3]) / voxel_size + grid_size = torch.round(grid_size).long() + input_feat_shape = grid_size[:2] + self.grid_size = grid_size + # the origin shape is as [x-len, y-len, z-len] + # [w, h, d] -> [d, h, w] + self.pcd_shape = [*input_feat_shape, 1][::-1] + + def forward(self, input): + if self.training: + max_voxels = self.max_voxels[0] + else: + max_voxels = self.max_voxels[1] + + return voxelization(input, self.voxel_size, self.point_cloud_range, + self.max_num_points, max_voxels) + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += 'voxel_size=' + str(self.voxel_size) + s += ', point_cloud_range=' + str(self.point_cloud_range) + s += ', max_num_points=' + str(self.max_num_points) + s += ', max_voxels=' + str(self.max_voxels) + s += ')' + return s diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/__init__.py new file mode 100644 index 00000000..2ed2c17a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .collate import collate +from .data_container import DataContainer +from .data_parallel import MMDataParallel +from .distributed import MMDistributedDataParallel +from .registry import MODULE_WRAPPERS +from .scatter_gather import scatter, scatter_kwargs +from .utils import is_module_wrapper + +__all__ = [ + 'collate', 'DataContainer', 'MMDataParallel', 'MMDistributedDataParallel', + 'scatter', 'scatter_kwargs', 'is_module_wrapper', 'MODULE_WRAPPERS' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/_functions.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/_functions.py new file mode 100644 index 00000000..9b5a8a44 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/_functions.py @@ -0,0 +1,79 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch.nn.parallel._functions import _get_stream + + +def scatter(input, devices, streams=None): + """Scatters tensor across multiple GPUs.""" + if streams is None: + streams = [None] * len(devices) + + if isinstance(input, list): + chunk_size = (len(input) - 1) // len(devices) + 1 + outputs = [ + scatter(input[i], [devices[i // chunk_size]], + [streams[i // chunk_size]]) for i in range(len(input)) + ] + return outputs + elif isinstance(input, torch.Tensor): + output = input.contiguous() + # TODO: copy to a pinned buffer first (if copying from CPU) + stream = streams[0] if output.numel() > 0 else None + if devices != [-1]: + with torch.cuda.device(devices[0]), torch.cuda.stream(stream): + output = output.cuda(devices[0], non_blocking=True) + else: + # unsqueeze the first dimension thus the tensor's shape is the + # same as those scattered with GPU. + output = output.unsqueeze(0) + return output + else: + raise Exception(f'Unknown type {type(input)}.') + + +def synchronize_stream(output, devices, streams): + if isinstance(output, list): + chunk_size = len(output) // len(devices) + for i in range(len(devices)): + for j in range(chunk_size): + synchronize_stream(output[i * chunk_size + j], [devices[i]], + [streams[i]]) + elif isinstance(output, torch.Tensor): + if output.numel() != 0: + with torch.cuda.device(devices[0]): + main_stream = torch.cuda.current_stream() + main_stream.wait_stream(streams[0]) + output.record_stream(main_stream) + else: + raise Exception(f'Unknown type {type(output)}.') + + +def get_input_device(input): + if isinstance(input, list): + for item in input: + input_device = get_input_device(item) + if input_device != -1: + return input_device + return -1 + elif isinstance(input, torch.Tensor): + return input.get_device() if input.is_cuda else -1 + else: + raise Exception(f'Unknown type {type(input)}.') + + +class Scatter: + + @staticmethod + def forward(target_gpus, input): + input_device = get_input_device(input) + streams = None + if input_device == -1 and target_gpus != [-1]: + # Perform CPU to GPU copies in a background stream + streams = [_get_stream(device) for device in target_gpus] + + outputs = scatter(input, target_gpus, streams) + # Synchronize with the copy stream + if streams is not None: + synchronize_stream(outputs, target_gpus, streams) + + return tuple(outputs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/collate.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/collate.py new file mode 100644 index 00000000..ad749197 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/collate.py @@ -0,0 +1,84 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from collections.abc import Mapping, Sequence + +import torch +import torch.nn.functional as F +from torch.utils.data.dataloader import default_collate + +from .data_container import DataContainer + + +def collate(batch, samples_per_gpu=1): + """Puts each data field into a tensor/DataContainer with outer dimension + batch size. + + Extend default_collate to add support for + :type:`~mmcv.parallel.DataContainer`. There are 3 cases. + + 1. cpu_only = True, e.g., meta data + 2. cpu_only = False, stack = True, e.g., images tensors + 3. cpu_only = False, stack = False, e.g., gt bboxes + """ + + if not isinstance(batch, Sequence): + raise TypeError(f'{batch.dtype} is not supported.') + + if isinstance(batch[0], DataContainer): + stacked = [] + if batch[0].cpu_only: + for i in range(0, len(batch), samples_per_gpu): + stacked.append( + [sample.data for sample in batch[i:i + samples_per_gpu]]) + return DataContainer( + stacked, batch[0].stack, batch[0].padding_value, cpu_only=True) + elif batch[0].stack: + for i in range(0, len(batch), samples_per_gpu): + assert isinstance(batch[i].data, torch.Tensor) + + if batch[i].pad_dims is not None: + ndim = batch[i].dim() + assert ndim > batch[i].pad_dims + max_shape = [0 for _ in range(batch[i].pad_dims)] + for dim in range(1, batch[i].pad_dims + 1): + max_shape[dim - 1] = batch[i].size(-dim) + for sample in batch[i:i + samples_per_gpu]: + for dim in range(0, ndim - batch[i].pad_dims): + assert batch[i].size(dim) == sample.size(dim) + for dim in range(1, batch[i].pad_dims + 1): + max_shape[dim - 1] = max(max_shape[dim - 1], + sample.size(-dim)) + padded_samples = [] + for sample in batch[i:i + samples_per_gpu]: + pad = [0 for _ in range(batch[i].pad_dims * 2)] + for dim in range(1, batch[i].pad_dims + 1): + pad[2 * dim - + 1] = max_shape[dim - 1] - sample.size(-dim) + padded_samples.append( + F.pad( + sample.data, pad, value=sample.padding_value)) + stacked.append(default_collate(padded_samples)) + elif batch[i].pad_dims is None: + stacked.append( + default_collate([ + sample.data + for sample in batch[i:i + samples_per_gpu] + ])) + else: + raise ValueError( + 'pad_dims should be either None or integers (1-3)') + + else: + for i in range(0, len(batch), samples_per_gpu): + stacked.append( + [sample.data for sample in batch[i:i + samples_per_gpu]]) + return DataContainer(stacked, batch[0].stack, batch[0].padding_value) + elif isinstance(batch[0], Sequence): + transposed = zip(*batch) + return [collate(samples, samples_per_gpu) for samples in transposed] + elif isinstance(batch[0], Mapping): + return { + key: collate([d[key] for d in batch], samples_per_gpu) + for key in batch[0] + } + else: + return default_collate(batch) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/data_container.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/data_container.py new file mode 100644 index 00000000..cedb0d32 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/data_container.py @@ -0,0 +1,89 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import functools + +import torch + + +def assert_tensor_type(func): + + @functools.wraps(func) + def wrapper(*args, **kwargs): + if not isinstance(args[0].data, torch.Tensor): + raise AttributeError( + f'{args[0].__class__.__name__} has no attribute ' + f'{func.__name__} for type {args[0].datatype}') + return func(*args, **kwargs) + + return wrapper + + +class DataContainer: + """A container for any type of objects. + + Typically tensors will be stacked in the collate function and sliced along + some dimension in the scatter function. This behavior has some limitations. + 1. All tensors have to be the same size. + 2. Types are limited (numpy array or Tensor). + + We design `DataContainer` and `MMDataParallel` to overcome these + limitations. The behavior can be either of the following. + + - copy to GPU, pad all tensors to the same size and stack them + - copy to GPU without stacking + - leave the objects as is and pass it to the model + - pad_dims specifies the number of last few dimensions to do padding + """ + + def __init__(self, + data, + stack=False, + padding_value=0, + cpu_only=False, + pad_dims=2): + self._data = data + self._cpu_only = cpu_only + self._stack = stack + self._padding_value = padding_value + assert pad_dims in [None, 1, 2, 3] + self._pad_dims = pad_dims + + def __repr__(self): + return f'{self.__class__.__name__}({repr(self.data)})' + + def __len__(self): + return len(self._data) + + @property + def data(self): + return self._data + + @property + def datatype(self): + if isinstance(self.data, torch.Tensor): + return self.data.type() + else: + return type(self.data) + + @property + def cpu_only(self): + return self._cpu_only + + @property + def stack(self): + return self._stack + + @property + def padding_value(self): + return self._padding_value + + @property + def pad_dims(self): + return self._pad_dims + + @assert_tensor_type + def size(self, *args, **kwargs): + return self.data.size(*args, **kwargs) + + @assert_tensor_type + def dim(self): + return self.data.dim() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/data_parallel.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/data_parallel.py new file mode 100644 index 00000000..79b5f69b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/data_parallel.py @@ -0,0 +1,89 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from itertools import chain + +from torch.nn.parallel import DataParallel + +from .scatter_gather import scatter_kwargs + + +class MMDataParallel(DataParallel): + """The DataParallel module that supports DataContainer. + + MMDataParallel has two main differences with PyTorch DataParallel: + + - It supports a custom type :class:`DataContainer` which allows more + flexible control of input data during both GPU and CPU inference. + - It implement two more APIs ``train_step()`` and ``val_step()``. + + Args: + module (:class:`nn.Module`): Module to be encapsulated. + device_ids (list[int]): Device IDS of modules to be scattered to. + Defaults to None when GPU is not available. + output_device (str | int): Device ID for output. Defaults to None. + dim (int): Dimension used to scatter the data. Defaults to 0. + """ + + def __init__(self, *args, dim=0, **kwargs): + super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs) + self.dim = dim + + def forward(self, *inputs, **kwargs): + """Override the original forward function. + + The main difference lies in the CPU inference where the data in + :class:`DataContainers` will still be gathered. + """ + if not self.device_ids: + # We add the following line thus the module could gather and + # convert data containers as those in GPU inference + inputs, kwargs = self.scatter(inputs, kwargs, [-1]) + return self.module(*inputs[0], **kwargs[0]) + else: + return super().forward(*inputs, **kwargs) + + def scatter(self, inputs, kwargs, device_ids): + return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) + + def train_step(self, *inputs, **kwargs): + if not self.device_ids: + # We add the following line thus the module could gather and + # convert data containers as those in GPU inference + inputs, kwargs = self.scatter(inputs, kwargs, [-1]) + return self.module.train_step(*inputs[0], **kwargs[0]) + + assert len(self.device_ids) == 1, \ + ('MMDataParallel only supports single GPU training, if you need to' + ' train with multiple GPUs, please use MMDistributedDataParallel' + 'instead.') + + for t in chain(self.module.parameters(), self.module.buffers()): + if t.device != self.src_device_obj: + raise RuntimeError( + 'module must have its parameters and buffers ' + f'on device {self.src_device_obj} (device_ids[0]) but ' + f'found one of them on device: {t.device}') + + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) + return self.module.train_step(*inputs[0], **kwargs[0]) + + def val_step(self, *inputs, **kwargs): + if not self.device_ids: + # We add the following line thus the module could gather and + # convert data containers as those in GPU inference + inputs, kwargs = self.scatter(inputs, kwargs, [-1]) + return self.module.val_step(*inputs[0], **kwargs[0]) + + assert len(self.device_ids) == 1, \ + ('MMDataParallel only supports single GPU training, if you need to' + ' train with multiple GPUs, please use MMDistributedDataParallel' + ' instead.') + + for t in chain(self.module.parameters(), self.module.buffers()): + if t.device != self.src_device_obj: + raise RuntimeError( + 'module must have its parameters and buffers ' + f'on device {self.src_device_obj} (device_ids[0]) but ' + f'found one of them on device: {t.device}') + + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) + return self.module.val_step(*inputs[0], **kwargs[0]) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/distributed.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/distributed.py new file mode 100644 index 00000000..fa1bae90 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/distributed.py @@ -0,0 +1,112 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch.nn.parallel.distributed import (DistributedDataParallel, + _find_tensors) + +from custom_mmpkg.custom_mmcv import print_log +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, digit_version +from .scatter_gather import scatter_kwargs + + +class MMDistributedDataParallel(DistributedDataParallel): + """The DDP module that supports DataContainer. + + MMDDP has two main differences with PyTorch DDP: + + - It supports a custom type :class:`DataContainer` which allows more + flexible control of input data. + - It implement two APIs ``train_step()`` and ``val_step()``. + """ + + def to_kwargs(self, inputs, kwargs, device_id): + # Use `self.to_kwargs` instead of `self.scatter` in pytorch1.8 + # to move all tensors to device_id + return scatter_kwargs(inputs, kwargs, [device_id], dim=self.dim) + + def scatter(self, inputs, kwargs, device_ids): + return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) + + def train_step(self, *inputs, **kwargs): + """train_step() API for module wrapped by DistributedDataParallel. + + This method is basically the same as + ``DistributedDataParallel.forward()``, while replacing + ``self.module.forward()`` with ``self.module.train_step()``. + It is compatible with PyTorch 1.1 - 1.5. + """ + + # In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the + # end of backward to the beginning of forward. + if ('parrots' not in TORCH_VERSION + and digit_version(TORCH_VERSION) >= digit_version('1.7') + and self.reducer._rebuild_buckets()): + print_log( + 'Reducer buckets have been rebuilt in this iteration.', + logger='mmcv') + + if getattr(self, 'require_forward_param_sync', True): + self._sync_params() + if self.device_ids: + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) + if len(self.device_ids) == 1: + output = self.module.train_step(*inputs[0], **kwargs[0]) + else: + outputs = self.parallel_apply( + self._module_copies[:len(inputs)], inputs, kwargs) + output = self.gather(outputs, self.output_device) + else: + output = self.module.train_step(*inputs, **kwargs) + + if torch.is_grad_enabled() and getattr( + self, 'require_backward_grad_sync', True): + if self.find_unused_parameters: + self.reducer.prepare_for_backward(list(_find_tensors(output))) + else: + self.reducer.prepare_for_backward([]) + else: + if ('parrots' not in TORCH_VERSION + and digit_version(TORCH_VERSION) > digit_version('1.2')): + self.require_forward_param_sync = False + return output + + def val_step(self, *inputs, **kwargs): + """val_step() API for module wrapped by DistributedDataParallel. + + This method is basically the same as + ``DistributedDataParallel.forward()``, while replacing + ``self.module.forward()`` with ``self.module.val_step()``. + It is compatible with PyTorch 1.1 - 1.5. + """ + # In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the + # end of backward to the beginning of forward. + if ('parrots' not in TORCH_VERSION + and digit_version(TORCH_VERSION) >= digit_version('1.7') + and self.reducer._rebuild_buckets()): + print_log( + 'Reducer buckets have been rebuilt in this iteration.', + logger='mmcv') + + if getattr(self, 'require_forward_param_sync', True): + self._sync_params() + if self.device_ids: + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) + if len(self.device_ids) == 1: + output = self.module.val_step(*inputs[0], **kwargs[0]) + else: + outputs = self.parallel_apply( + self._module_copies[:len(inputs)], inputs, kwargs) + output = self.gather(outputs, self.output_device) + else: + output = self.module.val_step(*inputs, **kwargs) + + if torch.is_grad_enabled() and getattr( + self, 'require_backward_grad_sync', True): + if self.find_unused_parameters: + self.reducer.prepare_for_backward(list(_find_tensors(output))) + else: + self.reducer.prepare_for_backward([]) + else: + if ('parrots' not in TORCH_VERSION + and digit_version(TORCH_VERSION) > digit_version('1.2')): + self.require_forward_param_sync = False + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/distributed_deprecated.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/distributed_deprecated.py new file mode 100644 index 00000000..d31f7be0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/distributed_deprecated.py @@ -0,0 +1,70 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.distributed as dist +import torch.nn as nn +from torch._utils import (_flatten_dense_tensors, _take_tensors, + _unflatten_dense_tensors) + +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, digit_version +from .registry import MODULE_WRAPPERS +from .scatter_gather import scatter_kwargs + + +@MODULE_WRAPPERS.register_module() +class MMDistributedDataParallel(nn.Module): + + def __init__(self, + module, + dim=0, + broadcast_buffers=True, + bucket_cap_mb=25): + super(MMDistributedDataParallel, self).__init__() + self.module = module + self.dim = dim + self.broadcast_buffers = broadcast_buffers + + self.broadcast_bucket_size = bucket_cap_mb * 1024 * 1024 + self._sync_params() + + def _dist_broadcast_coalesced(self, tensors, buffer_size): + for tensors in _take_tensors(tensors, buffer_size): + flat_tensors = _flatten_dense_tensors(tensors) + dist.broadcast(flat_tensors, 0) + for tensor, synced in zip( + tensors, _unflatten_dense_tensors(flat_tensors, tensors)): + tensor.copy_(synced) + + def _sync_params(self): + module_states = list(self.module.state_dict().values()) + if len(module_states) > 0: + self._dist_broadcast_coalesced(module_states, + self.broadcast_bucket_size) + if self.broadcast_buffers: + if (TORCH_VERSION != 'parrots' + and digit_version(TORCH_VERSION) < digit_version('1.0')): + buffers = [b.data for b in self.module._all_buffers()] + else: + buffers = [b.data for b in self.module.buffers()] + if len(buffers) > 0: + self._dist_broadcast_coalesced(buffers, + self.broadcast_bucket_size) + + def scatter(self, inputs, kwargs, device_ids): + return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) + + def forward(self, *inputs, **kwargs): + inputs, kwargs = self.scatter(inputs, kwargs, + [torch.cuda.current_device()]) + return self.module(*inputs[0], **kwargs[0]) + + def train_step(self, *inputs, **kwargs): + inputs, kwargs = self.scatter(inputs, kwargs, + [torch.cuda.current_device()]) + output = self.module.train_step(*inputs[0], **kwargs[0]) + return output + + def val_step(self, *inputs, **kwargs): + inputs, kwargs = self.scatter(inputs, kwargs, + [torch.cuda.current_device()]) + output = self.module.val_step(*inputs[0], **kwargs[0]) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/registry.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/registry.py new file mode 100644 index 00000000..3a0e9f66 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/registry.py @@ -0,0 +1,8 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from torch.nn.parallel import DataParallel, DistributedDataParallel + +from custom_mmpkg.custom_mmcv.utils import Registry + +MODULE_WRAPPERS = Registry('module wrapper') +MODULE_WRAPPERS.register_module(module=DataParallel) +MODULE_WRAPPERS.register_module(module=DistributedDataParallel) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/scatter_gather.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/scatter_gather.py new file mode 100644 index 00000000..900ff885 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/scatter_gather.py @@ -0,0 +1,59 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch.nn.parallel._functions import Scatter as OrigScatter + +from ._functions import Scatter +from .data_container import DataContainer + + +def scatter(inputs, target_gpus, dim=0): + """Scatter inputs to target gpus. + + The only difference from original :func:`scatter` is to add support for + :type:`~mmcv.parallel.DataContainer`. + """ + + def scatter_map(obj): + if isinstance(obj, torch.Tensor): + if target_gpus != [-1]: + return OrigScatter.apply(target_gpus, None, dim, obj) + else: + # for CPU inference we use self-implemented scatter + return Scatter.forward(target_gpus, obj) + if isinstance(obj, DataContainer): + if obj.cpu_only: + return obj.data + else: + return Scatter.forward(target_gpus, obj.data) + if isinstance(obj, tuple) and len(obj) > 0: + return list(zip(*map(scatter_map, obj))) + if isinstance(obj, list) and len(obj) > 0: + out = list(map(list, zip(*map(scatter_map, obj)))) + return out + if isinstance(obj, dict) and len(obj) > 0: + out = list(map(type(obj), zip(*map(scatter_map, obj.items())))) + return out + return [obj for targets in target_gpus] + + # After scatter_map is called, a scatter_map cell will exist. This cell + # has a reference to the actual function scatter_map, which has references + # to a closure that has a reference to the scatter_map cell (because the + # fn is recursive). To avoid this reference cycle, we set the function to + # None, clearing the cell + try: + return scatter_map(inputs) + finally: + scatter_map = None + + +def scatter_kwargs(inputs, kwargs, target_gpus, dim=0): + """Scatter with support for kwargs dictionary.""" + inputs = scatter(inputs, target_gpus, dim) if inputs else [] + kwargs = scatter(kwargs, target_gpus, dim) if kwargs else [] + if len(inputs) < len(kwargs): + inputs.extend([() for _ in range(len(kwargs) - len(inputs))]) + elif len(kwargs) < len(inputs): + kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))]) + inputs = tuple(inputs) + kwargs = tuple(kwargs) + return inputs, kwargs diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/utils.py new file mode 100644 index 00000000..0f5712cb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/parallel/utils.py @@ -0,0 +1,20 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .registry import MODULE_WRAPPERS + + +def is_module_wrapper(module): + """Check if a module is a module wrapper. + + The following 3 modules in MMCV (and their subclasses) are regarded as + module wrappers: DataParallel, DistributedDataParallel, + MMDistributedDataParallel (the deprecated version). You may add you own + module wrapper by registering it to mmcv.parallel.MODULE_WRAPPERS. + + Args: + module (nn.Module): The module to be checked. + + Returns: + bool: True if the input module is a module wrapper. + """ + module_wrappers = tuple(MODULE_WRAPPERS.module_dict.values()) + return isinstance(module, module_wrappers) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/__init__.py new file mode 100644 index 00000000..52e4b48d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/__init__.py @@ -0,0 +1,47 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .base_module import BaseModule, ModuleList, Sequential +from .base_runner import BaseRunner +from .builder import RUNNERS, build_runner +from .checkpoint import (CheckpointLoader, _load_checkpoint, + _load_checkpoint_with_prefix, load_checkpoint, + load_state_dict, save_checkpoint, weights_to_cpu) +from .default_constructor import DefaultRunnerConstructor +from .dist_utils import (allreduce_grads, allreduce_params, get_dist_info, + init_dist, master_only) +from .epoch_based_runner import EpochBasedRunner, Runner +from .fp16_utils import LossScaler, auto_fp16, force_fp32, wrap_fp16_model +from .hooks import (HOOKS, CheckpointHook, ClosureHook, DistEvalHook, + DistSamplerSeedHook, DvcliveLoggerHook, EMAHook, EvalHook, + Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook, + GradientCumulativeOptimizerHook, Hook, IterTimerHook, + LoggerHook, LrUpdaterHook, MlflowLoggerHook, + NeptuneLoggerHook, OptimizerHook, PaviLoggerHook, + SyncBuffersHook, TensorboardLoggerHook, TextLoggerHook, + WandbLoggerHook) +from .iter_based_runner import IterBasedRunner, IterLoader +from .log_buffer import LogBuffer +from .optimizer import (OPTIMIZER_BUILDERS, OPTIMIZERS, + DefaultOptimizerConstructor, build_optimizer, + build_optimizer_constructor) +from .priority import Priority, get_priority +from .utils import get_host_info, get_time_str, obj_from_dict, set_random_seed + +__all__ = [ + 'BaseRunner', 'Runner', 'EpochBasedRunner', 'IterBasedRunner', 'LogBuffer', + 'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook', + 'OptimizerHook', 'IterTimerHook', 'DistSamplerSeedHook', 'LoggerHook', + 'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook', + 'NeptuneLoggerHook', 'WandbLoggerHook', 'MlflowLoggerHook', + 'DvcliveLoggerHook', '_load_checkpoint', 'load_state_dict', + 'load_checkpoint', 'weights_to_cpu', 'save_checkpoint', 'Priority', + 'get_priority', 'get_host_info', 'get_time_str', 'obj_from_dict', + 'init_dist', 'get_dist_info', 'master_only', 'OPTIMIZER_BUILDERS', + 'OPTIMIZERS', 'DefaultOptimizerConstructor', 'build_optimizer', + 'build_optimizer_constructor', 'IterLoader', 'set_random_seed', + 'auto_fp16', 'force_fp32', 'wrap_fp16_model', 'Fp16OptimizerHook', + 'SyncBuffersHook', 'EMAHook', 'build_runner', 'RUNNERS', 'allreduce_grads', + 'allreduce_params', 'LossScaler', 'CheckpointLoader', 'BaseModule', + '_load_checkpoint_with_prefix', 'EvalHook', 'DistEvalHook', 'Sequential', + 'ModuleList', 'GradientCumulativeOptimizerHook', + 'GradientCumulativeFp16OptimizerHook', 'DefaultRunnerConstructor' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/base_module.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/base_module.py new file mode 100644 index 00000000..362b0ae3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/base_module.py @@ -0,0 +1,195 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +import warnings +from abc import ABCMeta +from collections import defaultdict +from logging import FileHandler + +import torch.nn as nn + +from custom_mmpkg.custom_mmcv.runner.dist_utils import master_only +from custom_mmpkg.custom_mmcv.utils.logging import get_logger, logger_initialized, print_log + + +class BaseModule(nn.Module, metaclass=ABCMeta): + """Base module for all modules in openmmlab. + + ``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional + functionality of parameter initialization. Compared with + ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes. + + - ``init_cfg``: the config to control the initialization. + - ``init_weights``: The function of parameter + initialization and recording initialization + information. + - ``_params_init_info``: Used to track the parameter + initialization information. This attribute only + exists during executing the ``init_weights``. + + Args: + init_cfg (dict, optional): Initialization config dict. + """ + + def __init__(self, init_cfg=None): + """Initialize BaseModule, inherited from `torch.nn.Module`""" + + # NOTE init_cfg can be defined in different levels, but init_cfg + # in low levels has a higher priority. + + super(BaseModule, self).__init__() + # define default value of init_cfg instead of hard code + # in init_weights() function + self._is_init = False + + self.init_cfg = copy.deepcopy(init_cfg) + + # Backward compatibility in derived classes + # if pretrained is not None: + # warnings.warn('DeprecationWarning: pretrained is a deprecated \ + # key, please consider using init_cfg') + # self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + + @property + def is_init(self): + return self._is_init + + def init_weights(self): + """Initialize the weights.""" + + is_top_level_module = False + # check if it is top-level module + if not hasattr(self, '_params_init_info'): + # The `_params_init_info` is used to record the initialization + # information of the parameters + # the key should be the obj:`nn.Parameter` of model and the value + # should be a dict containing + # - init_info (str): The string that describes the initialization. + # - tmp_mean_value (FloatTensor): The mean of the parameter, + # which indicates whether the parameter has been modified. + # this attribute would be deleted after all parameters + # is initialized. + self._params_init_info = defaultdict(dict) + is_top_level_module = True + + # Initialize the `_params_init_info`, + # When detecting the `tmp_mean_value` of + # the corresponding parameter is changed, update related + # initialization information + for name, param in self.named_parameters(): + self._params_init_info[param][ + 'init_info'] = f'The value is the same before and ' \ + f'after calling `init_weights` ' \ + f'of {self.__class__.__name__} ' + self._params_init_info[param][ + 'tmp_mean_value'] = param.data.mean() + + # pass `params_init_info` to all submodules + # All submodules share the same `params_init_info`, + # so it will be updated when parameters are + # modified at any level of the model. + for sub_module in self.modules(): + sub_module._params_init_info = self._params_init_info + + # Get the initialized logger, if not exist, + # create a logger named `mmcv` + logger_names = list(logger_initialized.keys()) + logger_name = logger_names[0] if logger_names else 'mmcv' + + from ..cnn import initialize + from ..cnn.utils.weight_init import update_init_info + module_name = self.__class__.__name__ + if not self._is_init: + if self.init_cfg: + print_log( + f'initialize {module_name} with init_cfg {self.init_cfg}', + logger=logger_name) + initialize(self, self.init_cfg) + if isinstance(self.init_cfg, dict): + # prevent the parameters of + # the pre-trained model + # from being overwritten by + # the `init_weights` + if self.init_cfg['type'] == 'Pretrained': + return + + for m in self.children(): + if hasattr(m, 'init_weights'): + m.init_weights() + # users may overload the `init_weights` + update_init_info( + m, + init_info=f'Initialized by ' + f'user-defined `init_weights`' + f' in {m.__class__.__name__} ') + + self._is_init = True + else: + warnings.warn(f'init_weights of {self.__class__.__name__} has ' + f'been called more than once.') + + if is_top_level_module: + self._dump_init_info(logger_name) + + for sub_module in self.modules(): + del sub_module._params_init_info + + @master_only + def _dump_init_info(self, logger_name): + """Dump the initialization information to a file named + `initialization.log.json` in workdir. + + Args: + logger_name (str): The name of logger. + """ + + logger = get_logger(logger_name) + + with_file_handler = False + # dump the information to the logger file if there is a `FileHandler` + for handler in logger.handlers: + if isinstance(handler, FileHandler): + handler.stream.write( + 'Name of parameter - Initialization information\n') + for name, param in self.named_parameters(): + handler.stream.write( + f'\n{name} - {param.shape}: ' + f"\n{self._params_init_info[param]['init_info']} \n") + handler.stream.flush() + with_file_handler = True + if not with_file_handler: + for name, param in self.named_parameters(): + print_log( + f'\n{name} - {param.shape}: ' + f"\n{self._params_init_info[param]['init_info']} \n ", + logger=logger_name) + + def __repr__(self): + s = super().__repr__() + if self.init_cfg: + s += f'\ninit_cfg={self.init_cfg}' + return s + + +class Sequential(BaseModule, nn.Sequential): + """Sequential module in openmmlab. + + Args: + init_cfg (dict, optional): Initialization config dict. + """ + + def __init__(self, *args, init_cfg=None): + BaseModule.__init__(self, init_cfg) + nn.Sequential.__init__(self, *args) + + +class ModuleList(BaseModule, nn.ModuleList): + """ModuleList in openmmlab. + + Args: + modules (iterable, optional): an iterable of modules to add. + init_cfg (dict, optional): Initialization config dict. + """ + + def __init__(self, modules=None, init_cfg=None): + BaseModule.__init__(self, init_cfg) + nn.ModuleList.__init__(self, modules) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/base_runner.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/base_runner.py new file mode 100644 index 00000000..6f15d719 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/base_runner.py @@ -0,0 +1,542 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +import logging +import os.path as osp +import warnings +from abc import ABCMeta, abstractmethod + +import torch +from torch.optim import Optimizer + +import custom_mmpkg.custom_mmcv as mmcv +from ..parallel import is_module_wrapper +from .checkpoint import load_checkpoint +from .dist_utils import get_dist_info +from .hooks import HOOKS, Hook +from .log_buffer import LogBuffer +from .priority import Priority, get_priority +from .utils import get_time_str + + +class BaseRunner(metaclass=ABCMeta): + """The base class of Runner, a training helper for PyTorch. + + All subclasses should implement the following APIs: + + - ``run()`` + - ``train()`` + - ``val()`` + - ``save_checkpoint()`` + + Args: + model (:obj:`torch.nn.Module`): The model to be run. + batch_processor (callable): A callable method that process a data + batch. The interface of this method should be + `batch_processor(model, data, train_mode) -> dict` + optimizer (dict or :obj:`torch.optim.Optimizer`): It can be either an + optimizer (in most cases) or a dict of optimizers (in models that + requires more than one optimizer, e.g., GAN). + work_dir (str, optional): The working directory to save checkpoints + and logs. Defaults to None. + logger (:obj:`logging.Logger`): Logger used during training. + Defaults to None. (The default value is just for backward + compatibility) + meta (dict | None): A dict records some import information such as + environment info and seed, which will be logged in logger hook. + Defaults to None. + max_epochs (int, optional): Total training epochs. + max_iters (int, optional): Total training iterations. + """ + + def __init__(self, + model, + batch_processor=None, + optimizer=None, + work_dir=None, + logger=None, + meta=None, + max_iters=None, + max_epochs=None): + if batch_processor is not None: + if not callable(batch_processor): + raise TypeError('batch_processor must be callable, ' + f'but got {type(batch_processor)}') + warnings.warn('batch_processor is deprecated, please implement ' + 'train_step() and val_step() in the model instead.') + # raise an error is `batch_processor` is not None and + # `model.train_step()` exists. + if is_module_wrapper(model): + _model = model.module + else: + _model = model + if hasattr(_model, 'train_step') or hasattr(_model, 'val_step'): + raise RuntimeError( + 'batch_processor and model.train_step()/model.val_step() ' + 'cannot be both available.') + else: + assert hasattr(model, 'train_step') + + # check the type of `optimizer` + if isinstance(optimizer, dict): + for name, optim in optimizer.items(): + if not isinstance(optim, Optimizer): + raise TypeError( + f'optimizer must be a dict of torch.optim.Optimizers, ' + f'but optimizer["{name}"] is a {type(optim)}') + elif not isinstance(optimizer, Optimizer) and optimizer is not None: + raise TypeError( + f'optimizer must be a torch.optim.Optimizer object ' + f'or dict or None, but got {type(optimizer)}') + + # check the type of `logger` + if not isinstance(logger, logging.Logger): + raise TypeError(f'logger must be a logging.Logger object, ' + f'but got {type(logger)}') + + # check the type of `meta` + if meta is not None and not isinstance(meta, dict): + raise TypeError( + f'meta must be a dict or None, but got {type(meta)}') + + self.model = model + self.batch_processor = batch_processor + self.optimizer = optimizer + self.logger = logger + self.meta = meta + # create work_dir + if mmcv.is_str(work_dir): + self.work_dir = osp.abspath(work_dir) + mmcv.mkdir_or_exist(self.work_dir) + elif work_dir is None: + self.work_dir = None + else: + raise TypeError('"work_dir" must be a str or None') + + # get model name from the model class + if hasattr(self.model, 'module'): + self._model_name = self.model.module.__class__.__name__ + else: + self._model_name = self.model.__class__.__name__ + + self._rank, self._world_size = get_dist_info() + self.timestamp = get_time_str() + self.mode = None + self._hooks = [] + self._epoch = 0 + self._iter = 0 + self._inner_iter = 0 + + if max_epochs is not None and max_iters is not None: + raise ValueError( + 'Only one of `max_epochs` or `max_iters` can be set.') + + self._max_epochs = max_epochs + self._max_iters = max_iters + # TODO: Redesign LogBuffer, it is not flexible and elegant enough + self.log_buffer = LogBuffer() + + @property + def model_name(self): + """str: Name of the model, usually the module class name.""" + return self._model_name + + @property + def rank(self): + """int: Rank of current process. (distributed training)""" + return self._rank + + @property + def world_size(self): + """int: Number of processes participating in the job. + (distributed training)""" + return self._world_size + + @property + def hooks(self): + """list[:obj:`Hook`]: A list of registered hooks.""" + return self._hooks + + @property + def epoch(self): + """int: Current epoch.""" + return self._epoch + + @property + def iter(self): + """int: Current iteration.""" + return self._iter + + @property + def inner_iter(self): + """int: Iteration in an epoch.""" + return self._inner_iter + + @property + def max_epochs(self): + """int: Maximum training epochs.""" + return self._max_epochs + + @property + def max_iters(self): + """int: Maximum training iterations.""" + return self._max_iters + + @abstractmethod + def train(self): + pass + + @abstractmethod + def val(self): + pass + + @abstractmethod + def run(self, data_loaders, workflow, **kwargs): + pass + + @abstractmethod + def save_checkpoint(self, + out_dir, + filename_tmpl, + save_optimizer=True, + meta=None, + create_symlink=True): + pass + + def current_lr(self): + """Get current learning rates. + + Returns: + list[float] | dict[str, list[float]]: Current learning rates of all + param groups. If the runner has a dict of optimizers, this + method will return a dict. + """ + if isinstance(self.optimizer, torch.optim.Optimizer): + lr = [group['lr'] for group in self.optimizer.param_groups] + elif isinstance(self.optimizer, dict): + lr = dict() + for name, optim in self.optimizer.items(): + lr[name] = [group['lr'] for group in optim.param_groups] + else: + raise RuntimeError( + 'lr is not applicable because optimizer does not exist.') + return lr + + def current_momentum(self): + """Get current momentums. + + Returns: + list[float] | dict[str, list[float]]: Current momentums of all + param groups. If the runner has a dict of optimizers, this + method will return a dict. + """ + + def _get_momentum(optimizer): + momentums = [] + for group in optimizer.param_groups: + if 'momentum' in group.keys(): + momentums.append(group['momentum']) + elif 'betas' in group.keys(): + momentums.append(group['betas'][0]) + else: + momentums.append(0) + return momentums + + if self.optimizer is None: + raise RuntimeError( + 'momentum is not applicable because optimizer does not exist.') + elif isinstance(self.optimizer, torch.optim.Optimizer): + momentums = _get_momentum(self.optimizer) + elif isinstance(self.optimizer, dict): + momentums = dict() + for name, optim in self.optimizer.items(): + momentums[name] = _get_momentum(optim) + return momentums + + def register_hook(self, hook, priority='NORMAL'): + """Register a hook into the hook list. + + The hook will be inserted into a priority queue, with the specified + priority (See :class:`Priority` for details of priorities). + For hooks with the same priority, they will be triggered in the same + order as they are registered. + + Args: + hook (:obj:`Hook`): The hook to be registered. + priority (int or str or :obj:`Priority`): Hook priority. + Lower value means higher priority. + """ + assert isinstance(hook, Hook) + if hasattr(hook, 'priority'): + raise ValueError('"priority" is a reserved attribute for hooks') + priority = get_priority(priority) + hook.priority = priority + # insert the hook to a sorted list + inserted = False + for i in range(len(self._hooks) - 1, -1, -1): + if priority >= self._hooks[i].priority: + self._hooks.insert(i + 1, hook) + inserted = True + break + if not inserted: + self._hooks.insert(0, hook) + + def register_hook_from_cfg(self, hook_cfg): + """Register a hook from its cfg. + + Args: + hook_cfg (dict): Hook config. It should have at least keys 'type' + and 'priority' indicating its type and priority. + + Notes: + The specific hook class to register should not use 'type' and + 'priority' arguments during initialization. + """ + hook_cfg = hook_cfg.copy() + priority = hook_cfg.pop('priority', 'NORMAL') + hook = mmcv.build_from_cfg(hook_cfg, HOOKS) + self.register_hook(hook, priority=priority) + + def call_hook(self, fn_name): + """Call all hooks. + + Args: + fn_name (str): The function name in each hook to be called, such as + "before_train_epoch". + """ + for hook in self._hooks: + getattr(hook, fn_name)(self) + + def get_hook_info(self): + # Get hooks info in each stage + stage_hook_map = {stage: [] for stage in Hook.stages} + for hook in self.hooks: + try: + priority = Priority(hook.priority).name + except ValueError: + priority = hook.priority + classname = hook.__class__.__name__ + hook_info = f'({priority:<12}) {classname:<35}' + for trigger_stage in hook.get_triggered_stages(): + stage_hook_map[trigger_stage].append(hook_info) + + stage_hook_infos = [] + for stage in Hook.stages: + hook_infos = stage_hook_map[stage] + if len(hook_infos) > 0: + info = f'{stage}:\n' + info += '\n'.join(hook_infos) + info += '\n -------------------- ' + stage_hook_infos.append(info) + return '\n'.join(stage_hook_infos) + + def load_checkpoint(self, + filename, + map_location='cpu', + strict=False, + revise_keys=[(r'^module.', '')]): + return load_checkpoint( + self.model, + filename, + map_location, + strict, + self.logger, + revise_keys=revise_keys) + + def resume(self, + checkpoint, + resume_optimizer=True, + map_location='default'): + if map_location == 'default': + if torch.cuda.is_available(): + device_id = torch.cuda.current_device() + checkpoint = self.load_checkpoint( + checkpoint, + map_location=lambda storage, loc: storage.cuda(device_id)) + else: + checkpoint = self.load_checkpoint(checkpoint) + else: + checkpoint = self.load_checkpoint( + checkpoint, map_location=map_location) + + self._epoch = checkpoint['meta']['epoch'] + self._iter = checkpoint['meta']['iter'] + if self.meta is None: + self.meta = {} + self.meta.setdefault('hook_msgs', {}) + # load `last_ckpt`, `best_score`, `best_ckpt`, etc. for hook messages + self.meta['hook_msgs'].update(checkpoint['meta'].get('hook_msgs', {})) + + # Re-calculate the number of iterations when resuming + # models with different number of GPUs + if 'config' in checkpoint['meta']: + config = mmcv.Config.fromstring( + checkpoint['meta']['config'], file_format='.py') + previous_gpu_ids = config.get('gpu_ids', None) + if previous_gpu_ids and len(previous_gpu_ids) > 0 and len( + previous_gpu_ids) != self.world_size: + self._iter = int(self._iter * len(previous_gpu_ids) / + self.world_size) + self.logger.info('the iteration number is changed due to ' + 'change of GPU number') + + # resume meta information meta + self.meta = checkpoint['meta'] + + if 'optimizer' in checkpoint and resume_optimizer: + if isinstance(self.optimizer, Optimizer): + self.optimizer.load_state_dict(checkpoint['optimizer']) + elif isinstance(self.optimizer, dict): + for k in self.optimizer.keys(): + self.optimizer[k].load_state_dict( + checkpoint['optimizer'][k]) + else: + raise TypeError( + 'Optimizer should be dict or torch.optim.Optimizer ' + f'but got {type(self.optimizer)}') + + self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter) + + def register_lr_hook(self, lr_config): + if lr_config is None: + return + elif isinstance(lr_config, dict): + assert 'policy' in lr_config + policy_type = lr_config.pop('policy') + # If the type of policy is all in lower case, e.g., 'cyclic', + # then its first letter will be capitalized, e.g., to be 'Cyclic'. + # This is for the convenient usage of Lr updater. + # Since this is not applicable for ` + # CosineAnnealingLrUpdater`, + # the string will not be changed if it contains capital letters. + if policy_type == policy_type.lower(): + policy_type = policy_type.title() + hook_type = policy_type + 'LrUpdaterHook' + lr_config['type'] = hook_type + hook = mmcv.build_from_cfg(lr_config, HOOKS) + else: + hook = lr_config + self.register_hook(hook, priority='VERY_HIGH') + + def register_momentum_hook(self, momentum_config): + if momentum_config is None: + return + if isinstance(momentum_config, dict): + assert 'policy' in momentum_config + policy_type = momentum_config.pop('policy') + # If the type of policy is all in lower case, e.g., 'cyclic', + # then its first letter will be capitalized, e.g., to be 'Cyclic'. + # This is for the convenient usage of momentum updater. + # Since this is not applicable for + # `CosineAnnealingMomentumUpdater`, + # the string will not be changed if it contains capital letters. + if policy_type == policy_type.lower(): + policy_type = policy_type.title() + hook_type = policy_type + 'MomentumUpdaterHook' + momentum_config['type'] = hook_type + hook = mmcv.build_from_cfg(momentum_config, HOOKS) + else: + hook = momentum_config + self.register_hook(hook, priority='HIGH') + + def register_optimizer_hook(self, optimizer_config): + if optimizer_config is None: + return + if isinstance(optimizer_config, dict): + optimizer_config.setdefault('type', 'OptimizerHook') + hook = mmcv.build_from_cfg(optimizer_config, HOOKS) + else: + hook = optimizer_config + self.register_hook(hook, priority='ABOVE_NORMAL') + + def register_checkpoint_hook(self, checkpoint_config): + if checkpoint_config is None: + return + if isinstance(checkpoint_config, dict): + checkpoint_config.setdefault('type', 'CheckpointHook') + hook = mmcv.build_from_cfg(checkpoint_config, HOOKS) + else: + hook = checkpoint_config + self.register_hook(hook, priority='NORMAL') + + def register_logger_hooks(self, log_config): + if log_config is None: + return + log_interval = log_config['interval'] + for info in log_config['hooks']: + logger_hook = mmcv.build_from_cfg( + info, HOOKS, default_args=dict(interval=log_interval)) + self.register_hook(logger_hook, priority='VERY_LOW') + + def register_timer_hook(self, timer_config): + if timer_config is None: + return + if isinstance(timer_config, dict): + timer_config_ = copy.deepcopy(timer_config) + hook = mmcv.build_from_cfg(timer_config_, HOOKS) + else: + hook = timer_config + self.register_hook(hook, priority='LOW') + + def register_custom_hooks(self, custom_config): + if custom_config is None: + return + + if not isinstance(custom_config, list): + custom_config = [custom_config] + + for item in custom_config: + if isinstance(item, dict): + self.register_hook_from_cfg(item) + else: + self.register_hook(item, priority='NORMAL') + + def register_profiler_hook(self, profiler_config): + if profiler_config is None: + return + if isinstance(profiler_config, dict): + profiler_config.setdefault('type', 'ProfilerHook') + hook = mmcv.build_from_cfg(profiler_config, HOOKS) + else: + hook = profiler_config + self.register_hook(hook) + + def register_training_hooks(self, + lr_config, + optimizer_config=None, + checkpoint_config=None, + log_config=None, + momentum_config=None, + timer_config=dict(type='IterTimerHook'), + custom_hooks_config=None): + """Register default and custom hooks for training. + + Default and custom hooks include: + + +----------------------+-------------------------+ + | Hooks | Priority | + +======================+=========================+ + | LrUpdaterHook | VERY_HIGH (10) | + +----------------------+-------------------------+ + | MomentumUpdaterHook | HIGH (30) | + +----------------------+-------------------------+ + | OptimizerStepperHook | ABOVE_NORMAL (40) | + +----------------------+-------------------------+ + | CheckpointSaverHook | NORMAL (50) | + +----------------------+-------------------------+ + | IterTimerHook | LOW (70) | + +----------------------+-------------------------+ + | LoggerHook(s) | VERY_LOW (90) | + +----------------------+-------------------------+ + | CustomHook(s) | defaults to NORMAL (50) | + +----------------------+-------------------------+ + + If custom hooks have same priority with default hooks, custom hooks + will be triggered after default hooks. + """ + self.register_lr_hook(lr_config) + self.register_momentum_hook(momentum_config) + self.register_optimizer_hook(optimizer_config) + self.register_checkpoint_hook(checkpoint_config) + self.register_timer_hook(timer_config) + self.register_logger_hooks(log_config) + self.register_custom_hooks(custom_hooks_config) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/builder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/builder.py new file mode 100644 index 00000000..77c96ba0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/builder.py @@ -0,0 +1,24 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy + +from ..utils import Registry + +RUNNERS = Registry('runner') +RUNNER_BUILDERS = Registry('runner builder') + + +def build_runner_constructor(cfg): + return RUNNER_BUILDERS.build(cfg) + + +def build_runner(cfg, default_args=None): + runner_cfg = copy.deepcopy(cfg) + constructor_type = runner_cfg.pop('constructor', + 'DefaultRunnerConstructor') + runner_constructor = build_runner_constructor( + dict( + type=constructor_type, + runner_cfg=runner_cfg, + default_args=default_args)) + runner = runner_constructor() + return runner diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/checkpoint.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/checkpoint.py new file mode 100644 index 00000000..da148108 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/checkpoint.py @@ -0,0 +1,707 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import io +import os +import os.path as osp +import pkgutil +import re +import time +import warnings +from collections import OrderedDict +from importlib import import_module +from tempfile import TemporaryDirectory + +import torch +import torchvision +from torch.optim import Optimizer +from torch.utils import model_zoo + +import custom_mmpkg.custom_mmcv as mmcv +from ..fileio import FileClient +from ..fileio import load as load_file +from ..parallel import is_module_wrapper +from ..utils import mkdir_or_exist +from .dist_utils import get_dist_info + +ENV_MMCV_HOME = 'MMCV_HOME' +ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME' +DEFAULT_CACHE_DIR = '~/.cache' + + +def _get_mmcv_home(): + mmcv_home = os.path.expanduser( + os.getenv( + ENV_MMCV_HOME, + os.path.join( + os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv'))) + + mkdir_or_exist(mmcv_home) + return mmcv_home + + +def load_state_dict(module, state_dict, strict=False, logger=None): + """Load state_dict to a module. + + This method is modified from :meth:`torch.nn.Module.load_state_dict`. + Default value for ``strict`` is set to ``False`` and the message for + param mismatch will be shown even if strict is False. + + Args: + module (Module): Module that receives the state_dict. + state_dict (OrderedDict): Weights. + strict (bool): whether to strictly enforce that the keys + in :attr:`state_dict` match the keys returned by this module's + :meth:`~torch.nn.Module.state_dict` function. Default: ``False``. + logger (:obj:`logging.Logger`, optional): Logger to log the error + message. If not specified, print function will be used. + """ + unexpected_keys = [] + all_missing_keys = [] + err_msg = [] + + metadata = getattr(state_dict, '_metadata', None) + state_dict = state_dict.copy() + if metadata is not None: + state_dict._metadata = metadata + + # use _load_from_state_dict to enable checkpoint version control + def load(module, prefix=''): + # recursively check parallel module in case that the model has a + # complicated structure, e.g., nn.Module(nn.Module(DDP)) + if is_module_wrapper(module): + module = module.module + local_metadata = {} if metadata is None else metadata.get( + prefix[:-1], {}) + module._load_from_state_dict(state_dict, prefix, local_metadata, True, + all_missing_keys, unexpected_keys, + err_msg) + for name, child in module._modules.items(): + if child is not None: + load(child, prefix + name + '.') + + load(module) + load = None # break load->load reference cycle + + # ignore "num_batches_tracked" of BN layers + missing_keys = [ + key for key in all_missing_keys if 'num_batches_tracked' not in key + ] + + if unexpected_keys: + err_msg.append('unexpected key in source ' + f'state_dict: {", ".join(unexpected_keys)}\n') + if missing_keys: + err_msg.append( + f'missing keys in source state_dict: {", ".join(missing_keys)}\n') + + rank, _ = get_dist_info() + if len(err_msg) > 0 and rank == 0: + err_msg.insert( + 0, 'The model and loaded state dict do not match exactly\n') + err_msg = '\n'.join(err_msg) + if strict: + raise RuntimeError(err_msg) + elif logger is not None: + logger.warning(err_msg) + else: + print(err_msg) + + +def get_torchvision_models(): + model_urls = dict() + for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__): + if ispkg: + continue + _zoo = import_module(f'torchvision.models.{name}') + if hasattr(_zoo, 'model_urls'): + _urls = getattr(_zoo, 'model_urls') + model_urls.update(_urls) + return model_urls + + +def get_external_models(): + mmcv_home = _get_mmcv_home() + default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json') + default_urls = load_file(default_json_path) + assert isinstance(default_urls, dict) + external_json_path = osp.join(mmcv_home, 'open_mmlab.json') + if osp.exists(external_json_path): + external_urls = load_file(external_json_path) + assert isinstance(external_urls, dict) + default_urls.update(external_urls) + + return default_urls + + +def get_mmcls_models(): + mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json') + mmcls_urls = load_file(mmcls_json_path) + + return mmcls_urls + + +def get_deprecated_model_names(): + deprecate_json_path = osp.join(mmcv.__path__[0], + 'model_zoo/deprecated.json') + deprecate_urls = load_file(deprecate_json_path) + assert isinstance(deprecate_urls, dict) + + return deprecate_urls + + +def _process_mmcls_checkpoint(checkpoint): + state_dict = checkpoint['state_dict'] + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k.startswith('backbone.'): + new_state_dict[k[9:]] = v + new_checkpoint = dict(state_dict=new_state_dict) + + return new_checkpoint + + +class CheckpointLoader: + """A general checkpoint loader to manage all schemes.""" + + _schemes = {} + + @classmethod + def _register_scheme(cls, prefixes, loader, force=False): + if isinstance(prefixes, str): + prefixes = [prefixes] + else: + assert isinstance(prefixes, (list, tuple)) + for prefix in prefixes: + if (prefix not in cls._schemes) or force: + cls._schemes[prefix] = loader + else: + raise KeyError( + f'{prefix} is already registered as a loader backend, ' + 'add "force=True" if you want to override it') + # sort, longer prefixes take priority + cls._schemes = OrderedDict( + sorted(cls._schemes.items(), key=lambda t: t[0], reverse=True)) + + @classmethod + def register_scheme(cls, prefixes, loader=None, force=False): + """Register a loader to CheckpointLoader. + + This method can be used as a normal class method or a decorator. + + Args: + prefixes (str or list[str] or tuple[str]): + The prefix of the registered loader. + loader (function, optional): The loader function to be registered. + When this method is used as a decorator, loader is None. + Defaults to None. + force (bool, optional): Whether to override the loader + if the prefix has already been registered. Defaults to False. + """ + + if loader is not None: + cls._register_scheme(prefixes, loader, force=force) + return + + def _register(loader_cls): + cls._register_scheme(prefixes, loader_cls, force=force) + return loader_cls + + return _register + + @classmethod + def _get_checkpoint_loader(cls, path): + """Finds a loader that supports the given path. Falls back to the local + loader if no other loader is found. + + Args: + path (str): checkpoint path + + Returns: + loader (function): checkpoint loader + """ + + for p in cls._schemes: + if path.startswith(p): + return cls._schemes[p] + + @classmethod + def load_checkpoint(cls, filename, map_location=None, logger=None): + """load checkpoint through URL scheme path. + + Args: + filename (str): checkpoint file name with given prefix + map_location (str, optional): Same as :func:`torch.load`. + Default: None + logger (:mod:`logging.Logger`, optional): The logger for message. + Default: None + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + + checkpoint_loader = cls._get_checkpoint_loader(filename) + class_name = checkpoint_loader.__name__ + mmcv.print_log( + f'load checkpoint from {class_name[10:]} path: {filename}', logger) + return checkpoint_loader(filename, map_location) + + +@CheckpointLoader.register_scheme(prefixes='') +def load_from_local(filename, map_location): + """load checkpoint by local file path. + + Args: + filename (str): local checkpoint file path + map_location (str, optional): Same as :func:`torch.load`. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + + if not osp.isfile(filename): + raise IOError(f'{filename} is not a checkpoint file') + checkpoint = torch.load(filename, map_location=map_location) + return checkpoint + + +@CheckpointLoader.register_scheme(prefixes=('http://', 'https://')) +def load_from_http(filename, map_location=None, model_dir=None): + """load checkpoint through HTTP or HTTPS scheme path. In distributed + setting, this function only download checkpoint at local rank 0. + + Args: + filename (str): checkpoint file path with modelzoo or + torchvision prefix + map_location (str, optional): Same as :func:`torch.load`. + model_dir (string, optional): directory in which to save the object, + Default: None + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + rank, world_size = get_dist_info() + rank = int(os.environ.get('LOCAL_RANK', rank)) + if rank == 0: + checkpoint = model_zoo.load_url( + filename, model_dir=model_dir, map_location=map_location) + if world_size > 1: + torch.distributed.barrier() + if rank > 0: + checkpoint = model_zoo.load_url( + filename, model_dir=model_dir, map_location=map_location) + return checkpoint + + +@CheckpointLoader.register_scheme(prefixes='pavi://') +def load_from_pavi(filename, map_location=None): + """load checkpoint through the file path prefixed with pavi. In distributed + setting, this function download ckpt at all ranks to different temporary + directories. + + Args: + filename (str): checkpoint file path with pavi prefix + map_location (str, optional): Same as :func:`torch.load`. + Default: None + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + assert filename.startswith('pavi://'), \ + f'Expected filename startswith `pavi://`, but get {filename}' + model_path = filename[7:] + + try: + from pavi import modelcloud + except ImportError: + raise ImportError( + 'Please install pavi to load checkpoint from modelcloud.') + + model = modelcloud.get(model_path) + with TemporaryDirectory() as tmp_dir: + downloaded_file = osp.join(tmp_dir, model.name) + model.download(downloaded_file) + checkpoint = torch.load(downloaded_file, map_location=map_location) + return checkpoint + + +@CheckpointLoader.register_scheme(prefixes='s3://') +def load_from_ceph(filename, map_location=None, backend='petrel'): + """load checkpoint through the file path prefixed with s3. In distributed + setting, this function download ckpt at all ranks to different temporary + directories. + + Args: + filename (str): checkpoint file path with s3 prefix + map_location (str, optional): Same as :func:`torch.load`. + backend (str, optional): The storage backend type. Options are 'ceph', + 'petrel'. Default: 'petrel'. + + .. warning:: + :class:`mmcv.fileio.file_client.CephBackend` will be deprecated, + please use :class:`mmcv.fileio.file_client.PetrelBackend` instead. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + allowed_backends = ['ceph', 'petrel'] + if backend not in allowed_backends: + raise ValueError(f'Load from Backend {backend} is not supported.') + + if backend == 'ceph': + warnings.warn( + 'CephBackend will be deprecated, please use PetrelBackend instead') + + # CephClient and PetrelBackend have the same prefix 's3://' and the latter + # will be chosen as default. If PetrelBackend can not be instantiated + # successfully, the CephClient will be chosen. + try: + file_client = FileClient(backend=backend) + except ImportError: + allowed_backends.remove(backend) + file_client = FileClient(backend=allowed_backends[0]) + + with io.BytesIO(file_client.get(filename)) as buffer: + checkpoint = torch.load(buffer, map_location=map_location) + return checkpoint + + +@CheckpointLoader.register_scheme(prefixes=('modelzoo://', 'torchvision://')) +def load_from_torchvision(filename, map_location=None): + """load checkpoint through the file path prefixed with modelzoo or + torchvision. + + Args: + filename (str): checkpoint file path with modelzoo or + torchvision prefix + map_location (str, optional): Same as :func:`torch.load`. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + model_urls = get_torchvision_models() + if filename.startswith('modelzoo://'): + warnings.warn('The URL scheme of "modelzoo://" is deprecated, please ' + 'use "torchvision://" instead') + model_name = filename[11:] + else: + model_name = filename[14:] + return load_from_http(model_urls[model_name], map_location=map_location) + + +@CheckpointLoader.register_scheme(prefixes=('open-mmlab://', 'openmmlab://')) +def load_from_openmmlab(filename, map_location=None): + """load checkpoint through the file path prefixed with open-mmlab or + openmmlab. + + Args: + filename (str): checkpoint file path with open-mmlab or + openmmlab prefix + map_location (str, optional): Same as :func:`torch.load`. + Default: None + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + + model_urls = get_external_models() + prefix_str = 'open-mmlab://' + if filename.startswith(prefix_str): + model_name = filename[13:] + else: + model_name = filename[12:] + prefix_str = 'openmmlab://' + + deprecated_urls = get_deprecated_model_names() + if model_name in deprecated_urls: + warnings.warn(f'{prefix_str}{model_name} is deprecated in favor ' + f'of {prefix_str}{deprecated_urls[model_name]}') + model_name = deprecated_urls[model_name] + model_url = model_urls[model_name] + # check if is url + if model_url.startswith(('http://', 'https://')): + checkpoint = load_from_http(model_url, map_location=map_location) + else: + filename = osp.join(_get_mmcv_home(), model_url) + if not osp.isfile(filename): + raise IOError(f'{filename} is not a checkpoint file') + checkpoint = torch.load(filename, map_location=map_location) + return checkpoint + + +@CheckpointLoader.register_scheme(prefixes='mmcls://') +def load_from_mmcls(filename, map_location=None): + """load checkpoint through the file path prefixed with mmcls. + + Args: + filename (str): checkpoint file path with mmcls prefix + map_location (str, optional): Same as :func:`torch.load`. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + + model_urls = get_mmcls_models() + model_name = filename[8:] + checkpoint = load_from_http( + model_urls[model_name], map_location=map_location) + checkpoint = _process_mmcls_checkpoint(checkpoint) + return checkpoint + + +def _load_checkpoint(filename, map_location=None, logger=None): + """Load checkpoint from somewhere (modelzoo, file, url). + + Args: + filename (str): Accept local filepath, URL, ``torchvision://xxx``, + ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for + details. + map_location (str, optional): Same as :func:`torch.load`. + Default: None. + logger (:mod:`logging.Logger`, optional): The logger for error message. + Default: None + + Returns: + dict or OrderedDict: The loaded checkpoint. It can be either an + OrderedDict storing model weights or a dict containing other + information, which depends on the checkpoint. + """ + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + + +def _load_checkpoint_with_prefix(prefix, filename, map_location=None): + """Load partial pretrained model with specific prefix. + + Args: + prefix (str): The prefix of sub-module. + filename (str): Accept local filepath, URL, ``torchvision://xxx``, + ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for + details. + map_location (str | None): Same as :func:`torch.load`. Default: None. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + + checkpoint = _load_checkpoint(filename, map_location=map_location) + + if 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + else: + state_dict = checkpoint + if not prefix.endswith('.'): + prefix += '.' + prefix_len = len(prefix) + + state_dict = { + k[prefix_len:]: v + for k, v in state_dict.items() if k.startswith(prefix) + } + + assert state_dict, f'{prefix} is not in the pretrained model' + return state_dict + + +def load_checkpoint(model, + filename, + map_location=None, + strict=False, + logger=None, + revise_keys=[(r'^module\.', '')]): + """Load checkpoint from a file or URI. + + Args: + model (Module): Module to load checkpoint. + filename (str): Accept local filepath, URL, ``torchvision://xxx``, + ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for + details. + map_location (str): Same as :func:`torch.load`. + strict (bool): Whether to allow different params for the model and + checkpoint. + logger (:mod:`logging.Logger` or None): The logger for error message. + revise_keys (list): A list of customized keywords to modify the + state_dict in checkpoint. Each item is a (pattern, replacement) + pair of the regular expression operations. Default: strip + the prefix 'module.' by [(r'^module\\.', '')]. + + Returns: + dict or OrderedDict: The loaded checkpoint. + """ + checkpoint = _load_checkpoint(filename, map_location, logger) + # OrderedDict is a subclass of dict + if not isinstance(checkpoint, dict): + raise RuntimeError( + f'No state_dict found in checkpoint file {filename}') + # get state_dict from checkpoint + if 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + else: + state_dict = checkpoint + + # strip prefix of state_dict + metadata = getattr(state_dict, '_metadata', OrderedDict()) + for p, r in revise_keys: + state_dict = OrderedDict( + {re.sub(p, r, k): v + for k, v in state_dict.items()}) + # Keep metadata in state_dict + state_dict._metadata = metadata + + # load state_dict + load_state_dict(model, state_dict, strict, logger) + return checkpoint + + +def weights_to_cpu(state_dict): + """Copy a model state_dict to cpu. + + Args: + state_dict (OrderedDict): Model weights on GPU. + + Returns: + OrderedDict: Model weights on GPU. + """ + state_dict_cpu = OrderedDict() + for key, val in state_dict.items(): + state_dict_cpu[key] = val.cpu() + # Keep metadata in state_dict + state_dict_cpu._metadata = getattr(state_dict, '_metadata', OrderedDict()) + return state_dict_cpu + + +def _save_to_state_dict(module, destination, prefix, keep_vars): + """Saves module state to `destination` dictionary. + + This method is modified from :meth:`torch.nn.Module._save_to_state_dict`. + + Args: + module (nn.Module): The module to generate state_dict. + destination (dict): A dict where state will be stored. + prefix (str): The prefix for parameters and buffers used in this + module. + """ + for name, param in module._parameters.items(): + if param is not None: + destination[prefix + name] = param if keep_vars else param.detach() + for name, buf in module._buffers.items(): + # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d + if buf is not None: + destination[prefix + name] = buf if keep_vars else buf.detach() + + +def get_state_dict(module, destination=None, prefix='', keep_vars=False): + """Returns a dictionary containing a whole state of the module. + + Both parameters and persistent buffers (e.g. running averages) are + included. Keys are corresponding parameter and buffer names. + + This method is modified from :meth:`torch.nn.Module.state_dict` to + recursively check parallel module in case that the model has a complicated + structure, e.g., nn.Module(nn.Module(DDP)). + + Args: + module (nn.Module): The module to generate state_dict. + destination (OrderedDict): Returned dict for the state of the + module. + prefix (str): Prefix of the key. + keep_vars (bool): Whether to keep the variable property of the + parameters. Default: False. + + Returns: + dict: A dictionary containing a whole state of the module. + """ + # recursively check parallel module in case that the model has a + # complicated structure, e.g., nn.Module(nn.Module(DDP)) + if is_module_wrapper(module): + module = module.module + + # below is the same as torch.nn.Module.state_dict() + if destination is None: + destination = OrderedDict() + destination._metadata = OrderedDict() + destination._metadata[prefix[:-1]] = local_metadata = dict( + version=module._version) + _save_to_state_dict(module, destination, prefix, keep_vars) + for name, child in module._modules.items(): + if child is not None: + get_state_dict( + child, destination, prefix + name + '.', keep_vars=keep_vars) + for hook in module._state_dict_hooks.values(): + hook_result = hook(module, destination, prefix, local_metadata) + if hook_result is not None: + destination = hook_result + return destination + + +def save_checkpoint(model, + filename, + optimizer=None, + meta=None, + file_client_args=None): + """Save checkpoint to file. + + The checkpoint will have 3 fields: ``meta``, ``state_dict`` and + ``optimizer``. By default ``meta`` will contain version and time info. + + Args: + model (Module): Module whose params are to be saved. + filename (str): Checkpoint filename. + optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. + meta (dict, optional): Metadata to be saved in checkpoint. + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + `New in version 1.3.16.` + """ + if meta is None: + meta = {} + elif not isinstance(meta, dict): + raise TypeError(f'meta must be a dict or None, but got {type(meta)}') + meta.update(mmcv_version=mmcv.__version__, time=time.asctime()) + + if is_module_wrapper(model): + model = model.module + + if hasattr(model, 'CLASSES') and model.CLASSES is not None: + # save class name to the meta + meta.update(CLASSES=model.CLASSES) + + checkpoint = { + 'meta': meta, + 'state_dict': weights_to_cpu(get_state_dict(model)) + } + # save optimizer state dict in the checkpoint + if isinstance(optimizer, Optimizer): + checkpoint['optimizer'] = optimizer.state_dict() + elif isinstance(optimizer, dict): + checkpoint['optimizer'] = {} + for name, optim in optimizer.items(): + checkpoint['optimizer'][name] = optim.state_dict() + + if filename.startswith('pavi://'): + if file_client_args is not None: + raise ValueError( + 'file_client_args should be "None" if filename starts with' + f'"pavi://", but got {file_client_args}') + try: + from pavi import modelcloud + from pavi import exception + except ImportError: + raise ImportError( + 'Please install pavi to load checkpoint from modelcloud.') + model_path = filename[7:] + root = modelcloud.Folder() + model_dir, model_name = osp.split(model_path) + try: + model = modelcloud.get(model_dir) + except exception.NodeNotFoundError: + model = root.create_training_model(model_dir) + with TemporaryDirectory() as tmp_dir: + checkpoint_file = osp.join(tmp_dir, model_name) + with open(checkpoint_file, 'wb') as f: + torch.save(checkpoint, f) + f.flush() + model.create_file(checkpoint_file, name=model_name) + else: + file_client = FileClient.infer_client(file_client_args, filename) + with io.BytesIO() as f: + torch.save(checkpoint, f) + file_client.put(f.getvalue(), filename) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/default_constructor.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/default_constructor.py new file mode 100644 index 00000000..fed2e0c8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/default_constructor.py @@ -0,0 +1,44 @@ +from .builder import RUNNER_BUILDERS, RUNNERS + + +@RUNNER_BUILDERS.register_module() +class DefaultRunnerConstructor: + """Default constructor for runners. + + Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`. + For example, We can inject some new properties and functions for `Runner`. + + Example: + >>> from custom_mmpkg.custom_mmcv.runner import RUNNER_BUILDERS, build_runner + >>> # Define a new RunnerReconstructor + >>> @RUNNER_BUILDERS.register_module() + >>> class MyRunnerConstructor: + ... def __init__(self, runner_cfg, default_args=None): + ... if not isinstance(runner_cfg, dict): + ... raise TypeError('runner_cfg should be a dict', + ... f'but got {type(runner_cfg)}') + ... self.runner_cfg = runner_cfg + ... self.default_args = default_args + ... + ... def __call__(self): + ... runner = RUNNERS.build(self.runner_cfg, + ... default_args=self.default_args) + ... # Add new properties for existing runner + ... runner.my_name = 'my_runner' + ... runner.my_function = lambda self: print(self.my_name) + ... ... + >>> # build your runner + >>> runner_cfg = dict(type='EpochBasedRunner', max_epochs=40, + ... constructor='MyRunnerConstructor') + >>> runner = build_runner(runner_cfg) + """ + + def __init__(self, runner_cfg, default_args=None): + if not isinstance(runner_cfg, dict): + raise TypeError('runner_cfg should be a dict', + f'but got {type(runner_cfg)}') + self.runner_cfg = runner_cfg + self.default_args = default_args + + def __call__(self): + return RUNNERS.build(self.runner_cfg, default_args=self.default_args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/dist_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/dist_utils.py new file mode 100644 index 00000000..d3a1ef3f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/dist_utils.py @@ -0,0 +1,164 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import functools +import os +import subprocess +from collections import OrderedDict + +import torch +import torch.multiprocessing as mp +from torch import distributed as dist +from torch._utils import (_flatten_dense_tensors, _take_tensors, + _unflatten_dense_tensors) + + +def init_dist(launcher, backend='nccl', **kwargs): + if mp.get_start_method(allow_none=True) is None: + mp.set_start_method('spawn') + if launcher == 'pytorch': + _init_dist_pytorch(backend, **kwargs) + elif launcher == 'mpi': + _init_dist_mpi(backend, **kwargs) + elif launcher == 'slurm': + _init_dist_slurm(backend, **kwargs) + else: + raise ValueError(f'Invalid launcher type: {launcher}') + + +def _init_dist_pytorch(backend, **kwargs): + # TODO: use local_rank instead of rank % num_gpus + rank = int(os.environ['RANK']) + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) + + +def _init_dist_mpi(backend, **kwargs): + # TODO: use local_rank instead of rank % num_gpus + rank = int(os.environ['OMPI_COMM_WORLD_RANK']) + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) + + +def _init_dist_slurm(backend, port=None): + """Initialize slurm distributed training environment. + + If argument ``port`` is not specified, then the master port will be system + environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system + environment variable, then a default port ``29500`` will be used. + + Args: + backend (str): Backend of torch.distributed. + port (int, optional): Master port. Defaults to None. + """ + proc_id = int(os.environ['SLURM_PROCID']) + ntasks = int(os.environ['SLURM_NTASKS']) + node_list = os.environ['SLURM_NODELIST'] + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(proc_id % num_gpus) + addr = subprocess.getoutput( + f'scontrol show hostname {node_list} | head -n1') + # specify master port + if port is not None: + os.environ['MASTER_PORT'] = str(port) + elif 'MASTER_PORT' in os.environ: + pass # use MASTER_PORT in the environment variable + else: + # 29500 is torch.distributed default port + os.environ['MASTER_PORT'] = '29500' + # use MASTER_ADDR in the environment variable if it already exists + if 'MASTER_ADDR' not in os.environ: + os.environ['MASTER_ADDR'] = addr + os.environ['WORLD_SIZE'] = str(ntasks) + os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) + os.environ['RANK'] = str(proc_id) + dist.init_process_group(backend=backend) + + +def get_dist_info(): + if dist.is_available() and dist.is_initialized(): + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + return rank, world_size + + +def master_only(func): + + @functools.wraps(func) + def wrapper(*args, **kwargs): + rank, _ = get_dist_info() + if rank == 0: + return func(*args, **kwargs) + + return wrapper + + +def allreduce_params(params, coalesce=True, bucket_size_mb=-1): + """Allreduce parameters. + + Args: + params (list[torch.Parameters]): List of parameters or buffers of a + model. + coalesce (bool, optional): Whether allreduce parameters as a whole. + Defaults to True. + bucket_size_mb (int, optional): Size of bucket, the unit is MB. + Defaults to -1. + """ + _, world_size = get_dist_info() + if world_size == 1: + return + params = [param.data for param in params] + if coalesce: + _allreduce_coalesced(params, world_size, bucket_size_mb) + else: + for tensor in params: + dist.all_reduce(tensor.div_(world_size)) + + +def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): + """Allreduce gradients. + + Args: + params (list[torch.Parameters]): List of parameters of a model + coalesce (bool, optional): Whether allreduce parameters as a whole. + Defaults to True. + bucket_size_mb (int, optional): Size of bucket, the unit is MB. + Defaults to -1. + """ + grads = [ + param.grad.data for param in params + if param.requires_grad and param.grad is not None + ] + _, world_size = get_dist_info() + if world_size == 1: + return + if coalesce: + _allreduce_coalesced(grads, world_size, bucket_size_mb) + else: + for tensor in grads: + dist.all_reduce(tensor.div_(world_size)) + + +def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): + if bucket_size_mb > 0: + bucket_size_bytes = bucket_size_mb * 1024 * 1024 + buckets = _take_tensors(tensors, bucket_size_bytes) + else: + buckets = OrderedDict() + for tensor in tensors: + tp = tensor.type() + if tp not in buckets: + buckets[tp] = [] + buckets[tp].append(tensor) + buckets = buckets.values() + + for bucket in buckets: + flat_tensors = _flatten_dense_tensors(bucket) + dist.all_reduce(flat_tensors) + flat_tensors.div_(world_size) + for tensor, synced in zip( + bucket, _unflatten_dense_tensors(flat_tensors, bucket)): + tensor.copy_(synced) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/epoch_based_runner.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/epoch_based_runner.py new file mode 100644 index 00000000..46b96618 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/epoch_based_runner.py @@ -0,0 +1,187 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +import platform +import shutil +import time +import warnings + +import torch + +import custom_mmpkg.custom_mmcv as mmcv +from .base_runner import BaseRunner +from .builder import RUNNERS +from .checkpoint import save_checkpoint +from .utils import get_host_info + + +@RUNNERS.register_module() +class EpochBasedRunner(BaseRunner): + """Epoch-based Runner. + + This runner train models epoch by epoch. + """ + + def run_iter(self, data_batch, train_mode, **kwargs): + if self.batch_processor is not None: + outputs = self.batch_processor( + self.model, data_batch, train_mode=train_mode, **kwargs) + elif train_mode: + outputs = self.model.train_step(data_batch, self.optimizer, + **kwargs) + else: + outputs = self.model.val_step(data_batch, self.optimizer, **kwargs) + if not isinstance(outputs, dict): + raise TypeError('"batch_processor()" or "model.train_step()"' + 'and "model.val_step()" must return a dict') + if 'log_vars' in outputs: + self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) + self.outputs = outputs + + def train(self, data_loader, **kwargs): + self.model.train() + self.mode = 'train' + self.data_loader = data_loader + self._max_iters = self._max_epochs * len(self.data_loader) + self.call_hook('before_train_epoch') + time.sleep(2) # Prevent possible deadlock during epoch transition + for i, data_batch in enumerate(self.data_loader): + self._inner_iter = i + self.call_hook('before_train_iter') + self.run_iter(data_batch, train_mode=True, **kwargs) + self.call_hook('after_train_iter') + self._iter += 1 + + self.call_hook('after_train_epoch') + self._epoch += 1 + + @torch.no_grad() + def val(self, data_loader, **kwargs): + self.model.eval() + self.mode = 'val' + self.data_loader = data_loader + self.call_hook('before_val_epoch') + time.sleep(2) # Prevent possible deadlock during epoch transition + for i, data_batch in enumerate(self.data_loader): + self._inner_iter = i + self.call_hook('before_val_iter') + self.run_iter(data_batch, train_mode=False) + self.call_hook('after_val_iter') + + self.call_hook('after_val_epoch') + + def run(self, data_loaders, workflow, max_epochs=None, **kwargs): + """Start running. + + Args: + data_loaders (list[:obj:`DataLoader`]): Dataloaders for training + and validation. + workflow (list[tuple]): A list of (phase, epochs) to specify the + running order and epochs. E.g, [('train', 2), ('val', 1)] means + running 2 epochs for training and 1 epoch for validation, + iteratively. + """ + assert isinstance(data_loaders, list) + assert mmcv.is_list_of(workflow, tuple) + assert len(data_loaders) == len(workflow) + if max_epochs is not None: + warnings.warn( + 'setting max_epochs in run is deprecated, ' + 'please set max_epochs in runner_config', DeprecationWarning) + self._max_epochs = max_epochs + + assert self._max_epochs is not None, ( + 'max_epochs must be specified during instantiation') + + for i, flow in enumerate(workflow): + mode, epochs = flow + if mode == 'train': + self._max_iters = self._max_epochs * len(data_loaders[i]) + break + + work_dir = self.work_dir if self.work_dir is not None else 'NONE' + self.logger.info('Start running, host: %s, work_dir: %s', + get_host_info(), work_dir) + self.logger.info('Hooks will be executed in the following order:\n%s', + self.get_hook_info()) + self.logger.info('workflow: %s, max: %d epochs', workflow, + self._max_epochs) + self.call_hook('before_run') + + while self.epoch < self._max_epochs: + for i, flow in enumerate(workflow): + mode, epochs = flow + if isinstance(mode, str): # self.train() + if not hasattr(self, mode): + raise ValueError( + f'runner has no method named "{mode}" to run an ' + 'epoch') + epoch_runner = getattr(self, mode) + else: + raise TypeError( + 'mode in workflow must be a str, but got {}'.format( + type(mode))) + + for _ in range(epochs): + if mode == 'train' and self.epoch >= self._max_epochs: + break + epoch_runner(data_loaders[i], **kwargs) + + time.sleep(1) # wait for some hooks like loggers to finish + self.call_hook('after_run') + + def save_checkpoint(self, + out_dir, + filename_tmpl='epoch_{}.pth', + save_optimizer=True, + meta=None, + create_symlink=True): + """Save the checkpoint. + + Args: + out_dir (str): The directory that checkpoints are saved. + filename_tmpl (str, optional): The checkpoint filename template, + which contains a placeholder for the epoch number. + Defaults to 'epoch_{}.pth'. + save_optimizer (bool, optional): Whether to save the optimizer to + the checkpoint. Defaults to True. + meta (dict, optional): The meta information to be saved in the + checkpoint. Defaults to None. + create_symlink (bool, optional): Whether to create a symlink + "latest.pth" to point to the latest checkpoint. + Defaults to True. + """ + if meta is None: + meta = {} + elif not isinstance(meta, dict): + raise TypeError( + f'meta should be a dict or None, but got {type(meta)}') + if self.meta is not None: + meta.update(self.meta) + # Note: meta.update(self.meta) should be done before + # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise + # there will be problems with resumed checkpoints. + # More details in https://github.com/open-mmlab/mmcv/pull/1108 + meta.update(epoch=self.epoch + 1, iter=self.iter) + + filename = filename_tmpl.format(self.epoch + 1) + filepath = osp.join(out_dir, filename) + optimizer = self.optimizer if save_optimizer else None + save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta) + # in some environments, `os.symlink` is not supported, you may need to + # set `create_symlink` to False + if create_symlink: + dst_file = osp.join(out_dir, 'latest.pth') + if platform.system() != 'Windows': + mmcv.symlink(filename, dst_file) + else: + shutil.copy(filepath, dst_file) + + +@RUNNERS.register_module() +class Runner(EpochBasedRunner): + """Deprecated name of EpochBasedRunner.""" + + def __init__(self, *args, **kwargs): + warnings.warn( + 'Runner was deprecated, please use EpochBasedRunner instead') + super().__init__(*args, **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/fp16_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/fp16_utils.py new file mode 100644 index 00000000..752e1b23 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/fp16_utils.py @@ -0,0 +1,410 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import functools +import warnings +from collections import abc +from inspect import getfullargspec + +import numpy as np +import torch +import torch.nn as nn + +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, digit_version +from .dist_utils import allreduce_grads as _allreduce_grads + +try: + # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported + # and used; otherwise, auto fp16 will adopt mmcv's implementation. + # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16 + # manually, so the behavior may not be consistent with real amp. + from torch.cuda.amp import autocast +except ImportError: + pass + + +def cast_tensor_type(inputs, src_type, dst_type): + """Recursively convert Tensor in inputs from src_type to dst_type. + + Args: + inputs: Inputs that to be casted. + src_type (torch.dtype): Source type.. + dst_type (torch.dtype): Destination type. + + Returns: + The same type with inputs, but all contained Tensors have been cast. + """ + if isinstance(inputs, nn.Module): + return inputs + elif isinstance(inputs, torch.Tensor): + return inputs.to(dst_type) + elif isinstance(inputs, str): + return inputs + elif isinstance(inputs, np.ndarray): + return inputs + elif isinstance(inputs, abc.Mapping): + return type(inputs)({ + k: cast_tensor_type(v, src_type, dst_type) + for k, v in inputs.items() + }) + elif isinstance(inputs, abc.Iterable): + return type(inputs)( + cast_tensor_type(item, src_type, dst_type) for item in inputs) + else: + return inputs + + +def auto_fp16(apply_to=None, out_fp32=False): + """Decorator to enable fp16 training automatically. + + This decorator is useful when you write custom modules and want to support + mixed precision training. If inputs arguments are fp32 tensors, they will + be converted to fp16 automatically. Arguments other than fp32 tensors are + ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the + backend, otherwise, original mmcv implementation will be adopted. + + Args: + apply_to (Iterable, optional): The argument names to be converted. + `None` indicates all arguments. + out_fp32 (bool): Whether to convert the output back to fp32. + + Example: + + >>> import torch.nn as nn + >>> class MyModule1(nn.Module): + >>> + >>> # Convert x and y to fp16 + >>> @auto_fp16() + >>> def forward(self, x, y): + >>> pass + + >>> import torch.nn as nn + >>> class MyModule2(nn.Module): + >>> + >>> # convert pred to fp16 + >>> @auto_fp16(apply_to=('pred', )) + >>> def do_something(self, pred, others): + >>> pass + """ + + def auto_fp16_wrapper(old_func): + + @functools.wraps(old_func) + def new_func(*args, **kwargs): + # check if the module has set the attribute `fp16_enabled`, if not, + # just fallback to the original method. + if not isinstance(args[0], torch.nn.Module): + raise TypeError('@auto_fp16 can only be used to decorate the ' + 'method of nn.Module') + if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled): + return old_func(*args, **kwargs) + + # get the arg spec of the decorated method + args_info = getfullargspec(old_func) + # get the argument names to be casted + args_to_cast = args_info.args if apply_to is None else apply_to + # convert the args that need to be processed + new_args = [] + # NOTE: default args are not taken into consideration + if args: + arg_names = args_info.args[:len(args)] + for i, arg_name in enumerate(arg_names): + if arg_name in args_to_cast: + new_args.append( + cast_tensor_type(args[i], torch.float, torch.half)) + else: + new_args.append(args[i]) + # convert the kwargs that need to be processed + new_kwargs = {} + if kwargs: + for arg_name, arg_value in kwargs.items(): + if arg_name in args_to_cast: + new_kwargs[arg_name] = cast_tensor_type( + arg_value, torch.float, torch.half) + else: + new_kwargs[arg_name] = arg_value + # apply converted arguments to the decorated method + if (TORCH_VERSION != 'parrots' and + digit_version(TORCH_VERSION) >= digit_version('1.6.0')): + with autocast(enabled=True): + output = old_func(*new_args, **new_kwargs) + else: + output = old_func(*new_args, **new_kwargs) + # cast the results back to fp32 if necessary + if out_fp32: + output = cast_tensor_type(output, torch.half, torch.float) + return output + + return new_func + + return auto_fp16_wrapper + + +def force_fp32(apply_to=None, out_fp16=False): + """Decorator to convert input arguments to fp32 in force. + + This decorator is useful when you write custom modules and want to support + mixed precision training. If there are some inputs that must be processed + in fp32 mode, then this decorator can handle it. If inputs arguments are + fp16 tensors, they will be converted to fp32 automatically. Arguments other + than fp16 tensors are ignored. If you are using PyTorch >= 1.6, + torch.cuda.amp is used as the backend, otherwise, original mmcv + implementation will be adopted. + + Args: + apply_to (Iterable, optional): The argument names to be converted. + `None` indicates all arguments. + out_fp16 (bool): Whether to convert the output back to fp16. + + Example: + + >>> import torch.nn as nn + >>> class MyModule1(nn.Module): + >>> + >>> # Convert x and y to fp32 + >>> @force_fp32() + >>> def loss(self, x, y): + >>> pass + + >>> import torch.nn as nn + >>> class MyModule2(nn.Module): + >>> + >>> # convert pred to fp32 + >>> @force_fp32(apply_to=('pred', )) + >>> def post_process(self, pred, others): + >>> pass + """ + + def force_fp32_wrapper(old_func): + + @functools.wraps(old_func) + def new_func(*args, **kwargs): + # check if the module has set the attribute `fp16_enabled`, if not, + # just fallback to the original method. + if not isinstance(args[0], torch.nn.Module): + raise TypeError('@force_fp32 can only be used to decorate the ' + 'method of nn.Module') + if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled): + return old_func(*args, **kwargs) + # get the arg spec of the decorated method + args_info = getfullargspec(old_func) + # get the argument names to be casted + args_to_cast = args_info.args if apply_to is None else apply_to + # convert the args that need to be processed + new_args = [] + if args: + arg_names = args_info.args[:len(args)] + for i, arg_name in enumerate(arg_names): + if arg_name in args_to_cast: + new_args.append( + cast_tensor_type(args[i], torch.half, torch.float)) + else: + new_args.append(args[i]) + # convert the kwargs that need to be processed + new_kwargs = dict() + if kwargs: + for arg_name, arg_value in kwargs.items(): + if arg_name in args_to_cast: + new_kwargs[arg_name] = cast_tensor_type( + arg_value, torch.half, torch.float) + else: + new_kwargs[arg_name] = arg_value + # apply converted arguments to the decorated method + if (TORCH_VERSION != 'parrots' and + digit_version(TORCH_VERSION) >= digit_version('1.6.0')): + with autocast(enabled=False): + output = old_func(*new_args, **new_kwargs) + else: + output = old_func(*new_args, **new_kwargs) + # cast the results back to fp32 if necessary + if out_fp16: + output = cast_tensor_type(output, torch.float, torch.half) + return output + + return new_func + + return force_fp32_wrapper + + +def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): + warnings.warning( + '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be ' + 'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads') + _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb) + + +def wrap_fp16_model(model): + """Wrap the FP32 model to FP16. + + If you are using PyTorch >= 1.6, torch.cuda.amp is used as the + backend, otherwise, original mmcv implementation will be adopted. + + For PyTorch >= 1.6, this function will + 1. Set fp16 flag inside the model to True. + + Otherwise: + 1. Convert FP32 model to FP16. + 2. Remain some necessary layers to be FP32, e.g., normalization layers. + 3. Set `fp16_enabled` flag inside the model to True. + + Args: + model (nn.Module): Model in FP32. + """ + if (TORCH_VERSION == 'parrots' + or digit_version(TORCH_VERSION) < digit_version('1.6.0')): + # convert model to fp16 + model.half() + # patch the normalization layers to make it work in fp32 mode + patch_norm_fp32(model) + # set `fp16_enabled` flag + for m in model.modules(): + if hasattr(m, 'fp16_enabled'): + m.fp16_enabled = True + + +def patch_norm_fp32(module): + """Recursively convert normalization layers from FP16 to FP32. + + Args: + module (nn.Module): The modules to be converted in FP16. + + Returns: + nn.Module: The converted module, the normalization layers have been + converted to FP32. + """ + if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)): + module.float() + if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3': + module.forward = patch_forward_method(module.forward, torch.half, + torch.float) + for child in module.children(): + patch_norm_fp32(child) + return module + + +def patch_forward_method(func, src_type, dst_type, convert_output=True): + """Patch the forward method of a module. + + Args: + func (callable): The original forward method. + src_type (torch.dtype): Type of input arguments to be converted from. + dst_type (torch.dtype): Type of input arguments to be converted to. + convert_output (bool): Whether to convert the output back to src_type. + + Returns: + callable: The patched forward method. + """ + + def new_forward(*args, **kwargs): + output = func(*cast_tensor_type(args, src_type, dst_type), + **cast_tensor_type(kwargs, src_type, dst_type)) + if convert_output: + output = cast_tensor_type(output, dst_type, src_type) + return output + + return new_forward + + +class LossScaler: + """Class that manages loss scaling in mixed precision training which + supports both dynamic or static mode. + + The implementation refers to + https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py. + Indirectly, by supplying ``mode='dynamic'`` for dynamic loss scaling. + It's important to understand how :class:`LossScaler` operates. + Loss scaling is designed to combat the problem of underflowing + gradients encountered at long times when training fp16 networks. + Dynamic loss scaling begins by attempting a very high loss + scale. Ironically, this may result in OVERflowing gradients. + If overflowing gradients are encountered, :class:`FP16_Optimizer` then + skips the update step for this particular iteration/minibatch, + and :class:`LossScaler` adjusts the loss scale to a lower value. + If a certain number of iterations occur without overflowing gradients + detected,:class:`LossScaler` increases the loss scale once more. + In this way :class:`LossScaler` attempts to "ride the edge" of always + using the highest loss scale possible without incurring overflow. + + Args: + init_scale (float): Initial loss scale value, default: 2**32. + scale_factor (float): Factor used when adjusting the loss scale. + Default: 2. + mode (str): Loss scaling mode. 'dynamic' or 'static' + scale_window (int): Number of consecutive iterations without an + overflow to wait before increasing the loss scale. Default: 1000. + """ + + def __init__(self, + init_scale=2**32, + mode='dynamic', + scale_factor=2., + scale_window=1000): + self.cur_scale = init_scale + self.cur_iter = 0 + assert mode in ('dynamic', + 'static'), 'mode can only be dynamic or static' + self.mode = mode + self.last_overflow_iter = -1 + self.scale_factor = scale_factor + self.scale_window = scale_window + + def has_overflow(self, params): + """Check if params contain overflow.""" + if self.mode != 'dynamic': + return False + for p in params: + if p.grad is not None and LossScaler._has_inf_or_nan(p.grad.data): + return True + return False + + def _has_inf_or_nan(x): + """Check if params contain NaN.""" + try: + cpu_sum = float(x.float().sum()) + except RuntimeError as instance: + if 'value cannot be converted' not in instance.args[0]: + raise + return True + else: + if cpu_sum == float('inf') or cpu_sum == -float('inf') \ + or cpu_sum != cpu_sum: + return True + return False + + def update_scale(self, overflow): + """update the current loss scale value when overflow happens.""" + if self.mode != 'dynamic': + return + if overflow: + self.cur_scale = max(self.cur_scale / self.scale_factor, 1) + self.last_overflow_iter = self.cur_iter + else: + if (self.cur_iter - self.last_overflow_iter) % \ + self.scale_window == 0: + self.cur_scale *= self.scale_factor + self.cur_iter += 1 + + def state_dict(self): + """Returns the state of the scaler as a :class:`dict`.""" + return dict( + cur_scale=self.cur_scale, + cur_iter=self.cur_iter, + mode=self.mode, + last_overflow_iter=self.last_overflow_iter, + scale_factor=self.scale_factor, + scale_window=self.scale_window) + + def load_state_dict(self, state_dict): + """Loads the loss_scaler state dict. + + Args: + state_dict (dict): scaler state. + """ + self.cur_scale = state_dict['cur_scale'] + self.cur_iter = state_dict['cur_iter'] + self.mode = state_dict['mode'] + self.last_overflow_iter = state_dict['last_overflow_iter'] + self.scale_factor = state_dict['scale_factor'] + self.scale_window = state_dict['scale_window'] + + @property + def loss_scale(self): + return self.cur_scale diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/__init__.py new file mode 100644 index 00000000..915af28c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .checkpoint import CheckpointHook +from .closure import ClosureHook +from .ema import EMAHook +from .evaluation import DistEvalHook, EvalHook +from .hook import HOOKS, Hook +from .iter_timer import IterTimerHook +from .logger import (DvcliveLoggerHook, LoggerHook, MlflowLoggerHook, + NeptuneLoggerHook, PaviLoggerHook, TensorboardLoggerHook, + TextLoggerHook, WandbLoggerHook) +from .lr_updater import LrUpdaterHook +from .memory import EmptyCacheHook +from .momentum_updater import MomentumUpdaterHook +from .optimizer import (Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook, + GradientCumulativeOptimizerHook, OptimizerHook) +from .profiler import ProfilerHook +from .sampler_seed import DistSamplerSeedHook +from .sync_buffer import SyncBuffersHook + +__all__ = [ + 'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook', + 'OptimizerHook', 'Fp16OptimizerHook', 'IterTimerHook', + 'DistSamplerSeedHook', 'EmptyCacheHook', 'LoggerHook', 'MlflowLoggerHook', + 'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook', + 'NeptuneLoggerHook', 'WandbLoggerHook', 'DvcliveLoggerHook', + 'MomentumUpdaterHook', 'SyncBuffersHook', 'EMAHook', 'EvalHook', + 'DistEvalHook', 'ProfilerHook', 'GradientCumulativeOptimizerHook', + 'GradientCumulativeFp16OptimizerHook' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/checkpoint.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/checkpoint.py new file mode 100644 index 00000000..0a1b688b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/checkpoint.py @@ -0,0 +1,167 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +import warnings + +from custom_mmpkg.custom_mmcv.fileio import FileClient +from ..dist_utils import allreduce_params, master_only +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class CheckpointHook(Hook): + """Save checkpoints periodically. + + Args: + interval (int): The saving period. If ``by_epoch=True``, interval + indicates epochs, otherwise it indicates iterations. + Default: -1, which means "never". + by_epoch (bool): Saving checkpoints by epoch or by iteration. + Default: True. + save_optimizer (bool): Whether to save optimizer state_dict in the + checkpoint. It is usually used for resuming experiments. + Default: True. + out_dir (str, optional): The root directory to save checkpoints. If not + specified, ``runner.work_dir`` will be used by default. If + specified, the ``out_dir`` will be the concatenation of ``out_dir`` + and the last level directory of ``runner.work_dir``. + `Changed in version 1.3.16.` + max_keep_ckpts (int, optional): The maximum checkpoints to keep. + In some cases we want only the latest few checkpoints and would + like to delete old ones to save the disk space. + Default: -1, which means unlimited. + save_last (bool, optional): Whether to force the last checkpoint to be + saved regardless of interval. Default: True. + sync_buffer (bool, optional): Whether to synchronize buffers in + different gpus. Default: False. + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + `New in version 1.3.16.` + + .. warning:: + Before v1.3.16, the ``out_dir`` argument indicates the path where the + checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the + root directory and the final path to save checkpoint is the + concatenation of ``out_dir`` and the last level directory of + ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A" + and the value of ``runner.work_dir`` is "/path/of/B", then the final + path will be "/path/of/A/B". + """ + + def __init__(self, + interval=-1, + by_epoch=True, + save_optimizer=True, + out_dir=None, + max_keep_ckpts=-1, + save_last=True, + sync_buffer=False, + file_client_args=None, + **kwargs): + self.interval = interval + self.by_epoch = by_epoch + self.save_optimizer = save_optimizer + self.out_dir = out_dir + self.max_keep_ckpts = max_keep_ckpts + self.save_last = save_last + self.args = kwargs + self.sync_buffer = sync_buffer + self.file_client_args = file_client_args + + def before_run(self, runner): + if not self.out_dir: + self.out_dir = runner.work_dir + + self.file_client = FileClient.infer_client(self.file_client_args, + self.out_dir) + + # if `self.out_dir` is not equal to `runner.work_dir`, it means that + # `self.out_dir` is set so the final `self.out_dir` is the + # concatenation of `self.out_dir` and the last level directory of + # `runner.work_dir` + if self.out_dir != runner.work_dir: + basename = osp.basename(runner.work_dir.rstrip(osp.sep)) + self.out_dir = self.file_client.join_path(self.out_dir, basename) + + runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by ' + f'{self.file_client.name}.')) + + # disable the create_symlink option because some file backends do not + # allow to create a symlink + if 'create_symlink' in self.args: + if self.args[ + 'create_symlink'] and not self.file_client.allow_symlink: + self.args['create_symlink'] = False + warnings.warn( + ('create_symlink is set as True by the user but is changed' + 'to be False because creating symbolic link is not ' + f'allowed in {self.file_client.name}')) + else: + self.args['create_symlink'] = self.file_client.allow_symlink + + def after_train_epoch(self, runner): + if not self.by_epoch: + return + + # save checkpoint for following cases: + # 1. every ``self.interval`` epochs + # 2. reach the last epoch of training + if self.every_n_epochs( + runner, self.interval) or (self.save_last + and self.is_last_epoch(runner)): + runner.logger.info( + f'Saving checkpoint at {runner.epoch + 1} epochs') + if self.sync_buffer: + allreduce_params(runner.model.buffers()) + self._save_checkpoint(runner) + + @master_only + def _save_checkpoint(self, runner): + """Save the current checkpoint and delete unwanted checkpoint.""" + runner.save_checkpoint( + self.out_dir, save_optimizer=self.save_optimizer, **self.args) + if runner.meta is not None: + if self.by_epoch: + cur_ckpt_filename = self.args.get( + 'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1) + else: + cur_ckpt_filename = self.args.get( + 'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1) + runner.meta.setdefault('hook_msgs', dict()) + runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path( + self.out_dir, cur_ckpt_filename) + # remove other checkpoints + if self.max_keep_ckpts > 0: + if self.by_epoch: + name = 'epoch_{}.pth' + current_ckpt = runner.epoch + 1 + else: + name = 'iter_{}.pth' + current_ckpt = runner.iter + 1 + redundant_ckpts = range( + current_ckpt - self.max_keep_ckpts * self.interval, 0, + -self.interval) + filename_tmpl = self.args.get('filename_tmpl', name) + for _step in redundant_ckpts: + ckpt_path = self.file_client.join_path( + self.out_dir, filename_tmpl.format(_step)) + if self.file_client.isfile(ckpt_path): + self.file_client.remove(ckpt_path) + else: + break + + def after_train_iter(self, runner): + if self.by_epoch: + return + + # save checkpoint for following cases: + # 1. every ``self.interval`` iterations + # 2. reach the last iteration of training + if self.every_n_iters( + runner, self.interval) or (self.save_last + and self.is_last_iter(runner)): + runner.logger.info( + f'Saving checkpoint at {runner.iter + 1} iterations') + if self.sync_buffer: + allreduce_params(runner.model.buffers()) + self._save_checkpoint(runner) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/closure.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/closure.py new file mode 100644 index 00000000..b955f81f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/closure.py @@ -0,0 +1,11 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class ClosureHook(Hook): + + def __init__(self, fn_name, fn): + assert hasattr(self, fn_name) + assert callable(fn) + setattr(self, fn_name, fn) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/ema.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/ema.py new file mode 100644 index 00000000..15c7e680 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/ema.py @@ -0,0 +1,89 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ...parallel import is_module_wrapper +from ..hooks.hook import HOOKS, Hook + + +@HOOKS.register_module() +class EMAHook(Hook): + r"""Exponential Moving Average Hook. + + Use Exponential Moving Average on all parameters of model in training + process. All parameters have a ema backup, which update by the formula + as below. EMAHook takes priority over EvalHook and CheckpointSaverHook. + + .. math:: + + \text{Xema\_{t+1}} = (1 - \text{momentum}) \times + \text{Xema\_{t}} + \text{momentum} \times X_t + + Args: + momentum (float): The momentum used for updating ema parameter. + Defaults to 0.0002. + interval (int): Update ema parameter every interval iteration. + Defaults to 1. + warm_up (int): During first warm_up steps, we may use smaller momentum + to update ema parameters more slowly. Defaults to 100. + resume_from (str): The checkpoint path. Defaults to None. + """ + + def __init__(self, + momentum=0.0002, + interval=1, + warm_up=100, + resume_from=None): + assert isinstance(interval, int) and interval > 0 + self.warm_up = warm_up + self.interval = interval + assert momentum > 0 and momentum < 1 + self.momentum = momentum**interval + self.checkpoint = resume_from + + def before_run(self, runner): + """To resume model with it's ema parameters more friendly. + + Register ema parameter as ``named_buffer`` to model + """ + model = runner.model + if is_module_wrapper(model): + model = model.module + self.param_ema_buffer = {} + self.model_parameters = dict(model.named_parameters(recurse=True)) + for name, value in self.model_parameters.items(): + # "." is not allowed in module's buffer name + buffer_name = f"ema_{name.replace('.', '_')}" + self.param_ema_buffer[name] = buffer_name + model.register_buffer(buffer_name, value.data.clone()) + self.model_buffers = dict(model.named_buffers(recurse=True)) + if self.checkpoint is not None: + runner.resume(self.checkpoint) + + def after_train_iter(self, runner): + """Update ema parameter every self.interval iterations.""" + curr_step = runner.iter + # We warm up the momentum considering the instability at beginning + momentum = min(self.momentum, + (1 + curr_step) / (self.warm_up + curr_step)) + if curr_step % self.interval != 0: + return + for name, parameter in self.model_parameters.items(): + buffer_name = self.param_ema_buffer[name] + buffer_parameter = self.model_buffers[buffer_name] + buffer_parameter.mul_(1 - momentum).add_(momentum, parameter.data) + + def after_train_epoch(self, runner): + """We load parameter values from ema backup to model before the + EvalHook.""" + self._swap_ema_parameters() + + def before_train_epoch(self, runner): + """We recover model's parameter from ema backup after last epoch's + EvalHook.""" + self._swap_ema_parameters() + + def _swap_ema_parameters(self): + """Swap the parameter of model with parameter in ema_buffer.""" + for name, value in self.model_parameters.items(): + temp = value.data.clone() + ema_buffer = self.model_buffers[self.param_ema_buffer[name]] + value.data.copy_(ema_buffer.data) + ema_buffer.data.copy_(temp) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/evaluation.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/evaluation.py new file mode 100644 index 00000000..1d76699d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/evaluation.py @@ -0,0 +1,509 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +import warnings +from math import inf + +import torch.distributed as dist +from torch.nn.modules.batchnorm import _BatchNorm +from torch.utils.data import DataLoader + +from custom_mmpkg.custom_mmcv.fileio import FileClient +from custom_mmpkg.custom_mmcv.utils import is_seq_of +from .hook import Hook +from .logger import LoggerHook + + +class EvalHook(Hook): + """Non-Distributed evaluation hook. + + This hook will regularly perform evaluation in a given interval when + performing in non-distributed environment. + + Args: + dataloader (DataLoader): A PyTorch dataloader, whose dataset has + implemented ``evaluate`` function. + start (int | None, optional): Evaluation starting epoch. It enables + evaluation before the training starts if ``start`` <= the resuming + epoch. If None, whether to evaluate is merely decided by + ``interval``. Default: None. + interval (int): Evaluation interval. Default: 1. + by_epoch (bool): Determine perform evaluation by epoch or by iteration. + If set to True, it will perform by epoch. Otherwise, by iteration. + Default: True. + save_best (str, optional): If a metric is specified, it would measure + the best checkpoint during evaluation. The information about best + checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep + best score value and best checkpoint path, which will be also + loaded when resume checkpoint. Options are the evaluation metrics + on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox + detection and instance segmentation. ``AR@100`` for proposal + recall. If ``save_best`` is ``auto``, the first key of the returned + ``OrderedDict`` result will be used. Default: None. + rule (str | None, optional): Comparison rule for best score. If set to + None, it will infer a reasonable rule. Keys such as 'acc', 'top' + .etc will be inferred by 'greater' rule. Keys contain 'loss' will + be inferred by 'less' rule. Options are 'greater', 'less', None. + Default: None. + test_fn (callable, optional): test a model with samples from a + dataloader, and return the test results. If ``None``, the default + test function ``mmcv.engine.single_gpu_test`` will be used. + (default: ``None``) + greater_keys (List[str] | None, optional): Metric keys that will be + inferred by 'greater' comparison rule. If ``None``, + _default_greater_keys will be used. (default: ``None``) + less_keys (List[str] | None, optional): Metric keys that will be + inferred by 'less' comparison rule. If ``None``, _default_less_keys + will be used. (default: ``None``) + out_dir (str, optional): The root directory to save checkpoints. If not + specified, `runner.work_dir` will be used by default. If specified, + the `out_dir` will be the concatenation of `out_dir` and the last + level directory of `runner.work_dir`. + `New in version 1.3.16.` + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. Default: None. + `New in version 1.3.16.` + **eval_kwargs: Evaluation arguments fed into the evaluate function of + the dataset. + + Notes: + If new arguments are added for EvalHook, tools/test.py, + tools/eval_metric.py may be affected. + """ + + # Since the key for determine greater or less is related to the downstream + # tasks, downstream repos may need to overwrite the following inner + # variable accordingly. + + rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y} + init_value_map = {'greater': -inf, 'less': inf} + _default_greater_keys = [ + 'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU', + 'mAcc', 'aAcc' + ] + _default_less_keys = ['loss'] + + def __init__(self, + dataloader, + start=None, + interval=1, + by_epoch=True, + save_best=None, + rule=None, + test_fn=None, + greater_keys=None, + less_keys=None, + out_dir=None, + file_client_args=None, + **eval_kwargs): + if not isinstance(dataloader, DataLoader): + raise TypeError(f'dataloader must be a pytorch DataLoader, ' + f'but got {type(dataloader)}') + + if interval <= 0: + raise ValueError(f'interval must be a positive number, ' + f'but got {interval}') + + assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean' + + if start is not None and start < 0: + raise ValueError(f'The evaluation start epoch {start} is smaller ' + f'than 0') + + self.dataloader = dataloader + self.interval = interval + self.start = start + self.by_epoch = by_epoch + + assert isinstance(save_best, str) or save_best is None, \ + '""save_best"" should be a str or None ' \ + f'rather than {type(save_best)}' + self.save_best = save_best + self.eval_kwargs = eval_kwargs + self.initial_flag = True + + if test_fn is None: + from custom_mmpkg.custom_mmcv.engine import single_gpu_test + self.test_fn = single_gpu_test + else: + self.test_fn = test_fn + + if greater_keys is None: + self.greater_keys = self._default_greater_keys + else: + if not isinstance(greater_keys, (list, tuple)): + greater_keys = (greater_keys, ) + assert is_seq_of(greater_keys, str) + self.greater_keys = greater_keys + + if less_keys is None: + self.less_keys = self._default_less_keys + else: + if not isinstance(less_keys, (list, tuple)): + less_keys = (less_keys, ) + assert is_seq_of(less_keys, str) + self.less_keys = less_keys + + if self.save_best is not None: + self.best_ckpt_path = None + self._init_rule(rule, self.save_best) + + self.out_dir = out_dir + self.file_client_args = file_client_args + + def _init_rule(self, rule, key_indicator): + """Initialize rule, key_indicator, comparison_func, and best score. + + Here is the rule to determine which rule is used for key indicator + when the rule is not specific (note that the key indicator matching + is case-insensitive): + 1. If the key indicator is in ``self.greater_keys``, the rule will be + specified as 'greater'. + 2. Or if the key indicator is in ``self.less_keys``, the rule will be + specified as 'less'. + 3. Or if the key indicator is equal to the substring in any one item + in ``self.greater_keys``, the rule will be specified as 'greater'. + 4. Or if the key indicator is equal to the substring in any one item + in ``self.less_keys``, the rule will be specified as 'less'. + + Args: + rule (str | None): Comparison rule for best score. + key_indicator (str | None): Key indicator to determine the + comparison rule. + """ + if rule not in self.rule_map and rule is not None: + raise KeyError(f'rule must be greater, less or None, ' + f'but got {rule}.') + + if rule is None: + if key_indicator != 'auto': + # `_lc` here means we use the lower case of keys for + # case-insensitive matching + key_indicator_lc = key_indicator.lower() + greater_keys = [key.lower() for key in self.greater_keys] + less_keys = [key.lower() for key in self.less_keys] + + if key_indicator_lc in greater_keys: + rule = 'greater' + elif key_indicator_lc in less_keys: + rule = 'less' + elif any(key in key_indicator_lc for key in greater_keys): + rule = 'greater' + elif any(key in key_indicator_lc for key in less_keys): + rule = 'less' + else: + raise ValueError(f'Cannot infer the rule for key ' + f'{key_indicator}, thus a specific rule ' + f'must be specified.') + self.rule = rule + self.key_indicator = key_indicator + if self.rule is not None: + self.compare_func = self.rule_map[self.rule] + + def before_run(self, runner): + if not self.out_dir: + self.out_dir = runner.work_dir + + self.file_client = FileClient.infer_client(self.file_client_args, + self.out_dir) + + # if `self.out_dir` is not equal to `runner.work_dir`, it means that + # `self.out_dir` is set so the final `self.out_dir` is the + # concatenation of `self.out_dir` and the last level directory of + # `runner.work_dir` + if self.out_dir != runner.work_dir: + basename = osp.basename(runner.work_dir.rstrip(osp.sep)) + self.out_dir = self.file_client.join_path(self.out_dir, basename) + runner.logger.info( + (f'The best checkpoint will be saved to {self.out_dir} by ' + f'{self.file_client.name}')) + + if self.save_best is not None: + if runner.meta is None: + warnings.warn('runner.meta is None. Creating an empty one.') + runner.meta = dict() + runner.meta.setdefault('hook_msgs', dict()) + self.best_ckpt_path = runner.meta['hook_msgs'].get( + 'best_ckpt', None) + + def before_train_iter(self, runner): + """Evaluate the model only at the start of training by iteration.""" + if self.by_epoch or not self.initial_flag: + return + if self.start is not None and runner.iter >= self.start: + self.after_train_iter(runner) + self.initial_flag = False + + def before_train_epoch(self, runner): + """Evaluate the model only at the start of training by epoch.""" + if not (self.by_epoch and self.initial_flag): + return + if self.start is not None and runner.epoch >= self.start: + self.after_train_epoch(runner) + self.initial_flag = False + + def after_train_iter(self, runner): + """Called after every training iter to evaluate the results.""" + if not self.by_epoch and self._should_evaluate(runner): + # Because the priority of EvalHook is higher than LoggerHook, the + # training log and the evaluating log are mixed. Therefore, + # we need to dump the training log and clear it before evaluating + # log is generated. In addition, this problem will only appear in + # `IterBasedRunner` whose `self.by_epoch` is False, because + # `EpochBasedRunner` whose `self.by_epoch` is True calls + # `_do_evaluate` in `after_train_epoch` stage, and at this stage + # the training log has been printed, so it will not cause any + # problem. more details at + # https://github.com/open-mmlab/mmsegmentation/issues/694 + for hook in runner._hooks: + if isinstance(hook, LoggerHook): + hook.after_train_iter(runner) + runner.log_buffer.clear() + + self._do_evaluate(runner) + + def after_train_epoch(self, runner): + """Called after every training epoch to evaluate the results.""" + if self.by_epoch and self._should_evaluate(runner): + self._do_evaluate(runner) + + def _do_evaluate(self, runner): + """perform evaluation and save ckpt.""" + results = self.test_fn(runner.model, self.dataloader) + runner.log_buffer.output['eval_iter_num'] = len(self.dataloader) + key_score = self.evaluate(runner, results) + # the key_score may be `None` so it needs to skip the action to save + # the best checkpoint + if self.save_best and key_score: + self._save_ckpt(runner, key_score) + + def _should_evaluate(self, runner): + """Judge whether to perform evaluation. + + Here is the rule to judge whether to perform evaluation: + 1. It will not perform evaluation during the epoch/iteration interval, + which is determined by ``self.interval``. + 2. It will not perform evaluation if the start time is larger than + current time. + 3. It will not perform evaluation when current time is larger than + the start time but during epoch/iteration interval. + + Returns: + bool: The flag indicating whether to perform evaluation. + """ + if self.by_epoch: + current = runner.epoch + check_time = self.every_n_epochs + else: + current = runner.iter + check_time = self.every_n_iters + + if self.start is None: + if not check_time(runner, self.interval): + # No evaluation during the interval. + return False + elif (current + 1) < self.start: + # No evaluation if start is larger than the current time. + return False + else: + # Evaluation only at epochs/iters 3, 5, 7... + # if start==3 and interval==2 + if (current + 1 - self.start) % self.interval: + return False + return True + + def _save_ckpt(self, runner, key_score): + """Save the best checkpoint. + + It will compare the score according to the compare function, write + related information (best score, best checkpoint path) and save the + best checkpoint into ``work_dir``. + """ + if self.by_epoch: + current = f'epoch_{runner.epoch + 1}' + cur_type, cur_time = 'epoch', runner.epoch + 1 + else: + current = f'iter_{runner.iter + 1}' + cur_type, cur_time = 'iter', runner.iter + 1 + + best_score = runner.meta['hook_msgs'].get( + 'best_score', self.init_value_map[self.rule]) + if self.compare_func(key_score, best_score): + best_score = key_score + runner.meta['hook_msgs']['best_score'] = best_score + + if self.best_ckpt_path and self.file_client.isfile( + self.best_ckpt_path): + self.file_client.remove(self.best_ckpt_path) + runner.logger.info( + (f'The previous best checkpoint {self.best_ckpt_path} was ' + 'removed')) + + best_ckpt_name = f'best_{self.key_indicator}_{current}.pth' + self.best_ckpt_path = self.file_client.join_path( + self.out_dir, best_ckpt_name) + runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path + + runner.save_checkpoint( + self.out_dir, best_ckpt_name, create_symlink=False) + runner.logger.info( + f'Now best checkpoint is saved as {best_ckpt_name}.') + runner.logger.info( + f'Best {self.key_indicator} is {best_score:0.4f} ' + f'at {cur_time} {cur_type}.') + + def evaluate(self, runner, results): + """Evaluate the results. + + Args: + runner (:obj:`mmcv.Runner`): The underlined training runner. + results (list): Output results. + """ + eval_res = self.dataloader.dataset.evaluate( + results, logger=runner.logger, **self.eval_kwargs) + + for name, val in eval_res.items(): + runner.log_buffer.output[name] = val + runner.log_buffer.ready = True + + if self.save_best is not None: + # If the performance of model is pool, the `eval_res` may be an + # empty dict and it will raise exception when `self.save_best` is + # not None. More details at + # https://github.com/open-mmlab/mmdetection/issues/6265. + if not eval_res: + warnings.warn( + 'Since `eval_res` is an empty dict, the behavior to save ' + 'the best checkpoint will be skipped in this evaluation.') + return None + + if self.key_indicator == 'auto': + # infer from eval_results + self._init_rule(self.rule, list(eval_res.keys())[0]) + return eval_res[self.key_indicator] + + return None + + +class DistEvalHook(EvalHook): + """Distributed evaluation hook. + + This hook will regularly perform evaluation in a given interval when + performing in distributed environment. + + Args: + dataloader (DataLoader): A PyTorch dataloader, whose dataset has + implemented ``evaluate`` function. + start (int | None, optional): Evaluation starting epoch. It enables + evaluation before the training starts if ``start`` <= the resuming + epoch. If None, whether to evaluate is merely decided by + ``interval``. Default: None. + interval (int): Evaluation interval. Default: 1. + by_epoch (bool): Determine perform evaluation by epoch or by iteration. + If set to True, it will perform by epoch. Otherwise, by iteration. + default: True. + save_best (str, optional): If a metric is specified, it would measure + the best checkpoint during evaluation. The information about best + checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep + best score value and best checkpoint path, which will be also + loaded when resume checkpoint. Options are the evaluation metrics + on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox + detection and instance segmentation. ``AR@100`` for proposal + recall. If ``save_best`` is ``auto``, the first key of the returned + ``OrderedDict`` result will be used. Default: None. + rule (str | None, optional): Comparison rule for best score. If set to + None, it will infer a reasonable rule. Keys such as 'acc', 'top' + .etc will be inferred by 'greater' rule. Keys contain 'loss' will + be inferred by 'less' rule. Options are 'greater', 'less', None. + Default: None. + test_fn (callable, optional): test a model with samples from a + dataloader in a multi-gpu manner, and return the test results. If + ``None``, the default test function ``mmcv.engine.multi_gpu_test`` + will be used. (default: ``None``) + tmpdir (str | None): Temporary directory to save the results of all + processes. Default: None. + gpu_collect (bool): Whether to use gpu or cpu to collect results. + Default: False. + broadcast_bn_buffer (bool): Whether to broadcast the + buffer(running_mean and running_var) of rank 0 to other rank + before evaluation. Default: True. + out_dir (str, optional): The root directory to save checkpoints. If not + specified, `runner.work_dir` will be used by default. If specified, + the `out_dir` will be the concatenation of `out_dir` and the last + level directory of `runner.work_dir`. + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. Default: None. + **eval_kwargs: Evaluation arguments fed into the evaluate function of + the dataset. + """ + + def __init__(self, + dataloader, + start=None, + interval=1, + by_epoch=True, + save_best=None, + rule=None, + test_fn=None, + greater_keys=None, + less_keys=None, + broadcast_bn_buffer=True, + tmpdir=None, + gpu_collect=False, + out_dir=None, + file_client_args=None, + **eval_kwargs): + + if test_fn is None: + from custom_mmpkg.custom_mmcv.engine import multi_gpu_test + test_fn = multi_gpu_test + + super().__init__( + dataloader, + start=start, + interval=interval, + by_epoch=by_epoch, + save_best=save_best, + rule=rule, + test_fn=test_fn, + greater_keys=greater_keys, + less_keys=less_keys, + out_dir=out_dir, + file_client_args=file_client_args, + **eval_kwargs) + + self.broadcast_bn_buffer = broadcast_bn_buffer + self.tmpdir = tmpdir + self.gpu_collect = gpu_collect + + def _do_evaluate(self, runner): + """perform evaluation and save ckpt.""" + # Synchronization of BatchNorm's buffer (running_mean + # and running_var) is not supported in the DDP of pytorch, + # which may cause the inconsistent performance of models in + # different ranks, so we broadcast BatchNorm's buffers + # of rank 0 to other ranks to avoid this. + if self.broadcast_bn_buffer: + model = runner.model + for name, module in model.named_modules(): + if isinstance(module, + _BatchNorm) and module.track_running_stats: + dist.broadcast(module.running_var, 0) + dist.broadcast(module.running_mean, 0) + + tmpdir = self.tmpdir + if tmpdir is None: + tmpdir = osp.join(runner.work_dir, '.eval_hook') + + results = self.test_fn( + runner.model, + self.dataloader, + tmpdir=tmpdir, + gpu_collect=self.gpu_collect) + if runner.rank == 0: + print('\n') + runner.log_buffer.output['eval_iter_num'] = len(self.dataloader) + key_score = self.evaluate(runner, results) + # the key_score may be `None` so it needs to skip the action to + # save the best checkpoint + if self.save_best and key_score: + self._save_ckpt(runner, key_score) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/hook.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/hook.py new file mode 100644 index 00000000..9e497e18 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/hook.py @@ -0,0 +1,92 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from custom_mmpkg.custom_mmcv.utils import Registry, is_method_overridden + +HOOKS = Registry('hook') + + +class Hook: + stages = ('before_run', 'before_train_epoch', 'before_train_iter', + 'after_train_iter', 'after_train_epoch', 'before_val_epoch', + 'before_val_iter', 'after_val_iter', 'after_val_epoch', + 'after_run') + + def before_run(self, runner): + pass + + def after_run(self, runner): + pass + + def before_epoch(self, runner): + pass + + def after_epoch(self, runner): + pass + + def before_iter(self, runner): + pass + + def after_iter(self, runner): + pass + + def before_train_epoch(self, runner): + self.before_epoch(runner) + + def before_val_epoch(self, runner): + self.before_epoch(runner) + + def after_train_epoch(self, runner): + self.after_epoch(runner) + + def after_val_epoch(self, runner): + self.after_epoch(runner) + + def before_train_iter(self, runner): + self.before_iter(runner) + + def before_val_iter(self, runner): + self.before_iter(runner) + + def after_train_iter(self, runner): + self.after_iter(runner) + + def after_val_iter(self, runner): + self.after_iter(runner) + + def every_n_epochs(self, runner, n): + return (runner.epoch + 1) % n == 0 if n > 0 else False + + def every_n_inner_iters(self, runner, n): + return (runner.inner_iter + 1) % n == 0 if n > 0 else False + + def every_n_iters(self, runner, n): + return (runner.iter + 1) % n == 0 if n > 0 else False + + def end_of_epoch(self, runner): + return runner.inner_iter + 1 == len(runner.data_loader) + + def is_last_epoch(self, runner): + return runner.epoch + 1 == runner._max_epochs + + def is_last_iter(self, runner): + return runner.iter + 1 == runner._max_iters + + def get_triggered_stages(self): + trigger_stages = set() + for stage in Hook.stages: + if is_method_overridden(stage, Hook, self): + trigger_stages.add(stage) + + # some methods will be triggered in multi stages + # use this dict to map method to stages. + method_stages_map = { + 'before_epoch': ['before_train_epoch', 'before_val_epoch'], + 'after_epoch': ['after_train_epoch', 'after_val_epoch'], + 'before_iter': ['before_train_iter', 'before_val_iter'], + 'after_iter': ['after_train_iter', 'after_val_iter'], + } + + for method, map_stages in method_stages_map.items(): + if is_method_overridden(method, Hook, self): + trigger_stages.update(map_stages) + + return [stage for stage in Hook.stages if stage in trigger_stages] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/iter_timer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/iter_timer.py new file mode 100644 index 00000000..cfd5002f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/iter_timer.py @@ -0,0 +1,18 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import time + +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class IterTimerHook(Hook): + + def before_epoch(self, runner): + self.t = time.time() + + def before_iter(self, runner): + runner.log_buffer.update({'data_time': time.time() - self.t}) + + def after_iter(self, runner): + runner.log_buffer.update({'time': time.time() - self.t}) + self.t = time.time() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/__init__.py new file mode 100644 index 00000000..a0b6b345 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .base import LoggerHook +from .dvclive import DvcliveLoggerHook +from .mlflow import MlflowLoggerHook +from .neptune import NeptuneLoggerHook +from .pavi import PaviLoggerHook +from .tensorboard import TensorboardLoggerHook +from .text import TextLoggerHook +from .wandb import WandbLoggerHook + +__all__ = [ + 'LoggerHook', 'MlflowLoggerHook', 'PaviLoggerHook', + 'TensorboardLoggerHook', 'TextLoggerHook', 'WandbLoggerHook', + 'NeptuneLoggerHook', 'DvcliveLoggerHook' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/base.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/base.py new file mode 100644 index 00000000..f8452567 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/base.py @@ -0,0 +1,166 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numbers +from abc import ABCMeta, abstractmethod + +import numpy as np +import torch + +from ..hook import Hook + + +class LoggerHook(Hook): + """Base class for logger hooks. + + Args: + interval (int): Logging interval (every k iterations). + ignore_last (bool): Ignore the log of last iterations in each epoch + if less than `interval`. + reset_flag (bool): Whether to clear the output buffer after logging. + by_epoch (bool): Whether EpochBasedRunner is used. + """ + + __metaclass__ = ABCMeta + + def __init__(self, + interval=10, + ignore_last=True, + reset_flag=False, + by_epoch=True): + self.interval = interval + self.ignore_last = ignore_last + self.reset_flag = reset_flag + self.by_epoch = by_epoch + + @abstractmethod + def log(self, runner): + pass + + @staticmethod + def is_scalar(val, include_np=True, include_torch=True): + """Tell the input variable is a scalar or not. + + Args: + val: Input variable. + include_np (bool): Whether include 0-d np.ndarray as a scalar. + include_torch (bool): Whether include 0-d torch.Tensor as a scalar. + + Returns: + bool: True or False. + """ + if isinstance(val, numbers.Number): + return True + elif include_np and isinstance(val, np.ndarray) and val.ndim == 0: + return True + elif include_torch and isinstance(val, torch.Tensor) and len(val) == 1: + return True + else: + return False + + def get_mode(self, runner): + if runner.mode == 'train': + if 'time' in runner.log_buffer.output: + mode = 'train' + else: + mode = 'val' + elif runner.mode == 'val': + mode = 'val' + else: + raise ValueError(f"runner mode should be 'train' or 'val', " + f'but got {runner.mode}') + return mode + + def get_epoch(self, runner): + if runner.mode == 'train': + epoch = runner.epoch + 1 + elif runner.mode == 'val': + # normal val mode + # runner.epoch += 1 has been done before val workflow + epoch = runner.epoch + else: + raise ValueError(f"runner mode should be 'train' or 'val', " + f'but got {runner.mode}') + return epoch + + def get_iter(self, runner, inner_iter=False): + """Get the current training iteration step.""" + if self.by_epoch and inner_iter: + current_iter = runner.inner_iter + 1 + else: + current_iter = runner.iter + 1 + return current_iter + + def get_lr_tags(self, runner): + tags = {} + lrs = runner.current_lr() + if isinstance(lrs, dict): + for name, value in lrs.items(): + tags[f'learning_rate/{name}'] = value[0] + else: + tags['learning_rate'] = lrs[0] + return tags + + def get_momentum_tags(self, runner): + tags = {} + momentums = runner.current_momentum() + if isinstance(momentums, dict): + for name, value in momentums.items(): + tags[f'momentum/{name}'] = value[0] + else: + tags['momentum'] = momentums[0] + return tags + + def get_loggable_tags(self, + runner, + allow_scalar=True, + allow_text=False, + add_mode=True, + tags_to_skip=('time', 'data_time')): + tags = {} + for var, val in runner.log_buffer.output.items(): + if var in tags_to_skip: + continue + if self.is_scalar(val) and not allow_scalar: + continue + if isinstance(val, str) and not allow_text: + continue + if add_mode: + var = f'{self.get_mode(runner)}/{var}' + tags[var] = val + tags.update(self.get_lr_tags(runner)) + tags.update(self.get_momentum_tags(runner)) + return tags + + def before_run(self, runner): + for hook in runner.hooks[::-1]: + if isinstance(hook, LoggerHook): + hook.reset_flag = True + break + + def before_epoch(self, runner): + runner.log_buffer.clear() # clear logs of last epoch + + def after_train_iter(self, runner): + if self.by_epoch and self.every_n_inner_iters(runner, self.interval): + runner.log_buffer.average(self.interval) + elif not self.by_epoch and self.every_n_iters(runner, self.interval): + runner.log_buffer.average(self.interval) + elif self.end_of_epoch(runner) and not self.ignore_last: + # not precise but more stable + runner.log_buffer.average(self.interval) + + if runner.log_buffer.ready: + self.log(runner) + if self.reset_flag: + runner.log_buffer.clear_output() + + def after_train_epoch(self, runner): + if runner.log_buffer.ready: + self.log(runner) + if self.reset_flag: + runner.log_buffer.clear_output() + + def after_val_epoch(self, runner): + runner.log_buffer.average() + self.log(runner) + if self.reset_flag: + runner.log_buffer.clear_output() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/dvclive.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/dvclive.py new file mode 100644 index 00000000..687cdc58 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/dvclive.py @@ -0,0 +1,58 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ...dist_utils import master_only +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class DvcliveLoggerHook(LoggerHook): + """Class to log metrics with dvclive. + + It requires `dvclive`_ to be installed. + + Args: + path (str): Directory where dvclive will write TSV log files. + interval (int): Logging interval (every k iterations). + Default 10. + ignore_last (bool): Ignore the log of last iterations in each epoch + if less than `interval`. + Default: True. + reset_flag (bool): Whether to clear the output buffer after logging. + Default: True. + by_epoch (bool): Whether EpochBasedRunner is used. + Default: True. + + .. _dvclive: + https://dvc.org/doc/dvclive + """ + + def __init__(self, + path, + interval=10, + ignore_last=True, + reset_flag=True, + by_epoch=True): + + super(DvcliveLoggerHook, self).__init__(interval, ignore_last, + reset_flag, by_epoch) + self.path = path + self.import_dvclive() + + def import_dvclive(self): + try: + import dvclive + except ImportError: + raise ImportError( + 'Please run "pip install dvclive" to install dvclive') + self.dvclive = dvclive + + @master_only + def before_run(self, runner): + self.dvclive.init(self.path) + + @master_only + def log(self, runner): + tags = self.get_loggable_tags(runner) + if tags: + for k, v in tags.items(): + self.dvclive.log(k, v, step=self.get_iter(runner)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/mlflow.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/mlflow.py new file mode 100644 index 00000000..f9a72592 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/mlflow.py @@ -0,0 +1,78 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ...dist_utils import master_only +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class MlflowLoggerHook(LoggerHook): + + def __init__(self, + exp_name=None, + tags=None, + log_model=True, + interval=10, + ignore_last=True, + reset_flag=False, + by_epoch=True): + """Class to log metrics and (optionally) a trained model to MLflow. + + It requires `MLflow`_ to be installed. + + Args: + exp_name (str, optional): Name of the experiment to be used. + Default None. + If not None, set the active experiment. + If experiment does not exist, an experiment with provided name + will be created. + tags (dict of str: str, optional): Tags for the current run. + Default None. + If not None, set tags for the current run. + log_model (bool, optional): Whether to log an MLflow artifact. + Default True. + If True, log runner.model as an MLflow artifact + for the current run. + interval (int): Logging interval (every k iterations). + ignore_last (bool): Ignore the log of last iterations in each epoch + if less than `interval`. + reset_flag (bool): Whether to clear the output buffer after logging + by_epoch (bool): Whether EpochBasedRunner is used. + + .. _MLflow: + https://www.mlflow.org/docs/latest/index.html + """ + super(MlflowLoggerHook, self).__init__(interval, ignore_last, + reset_flag, by_epoch) + self.import_mlflow() + self.exp_name = exp_name + self.tags = tags + self.log_model = log_model + + def import_mlflow(self): + try: + import mlflow + import mlflow.pytorch as mlflow_pytorch + except ImportError: + raise ImportError( + 'Please run "pip install mlflow" to install mlflow') + self.mlflow = mlflow + self.mlflow_pytorch = mlflow_pytorch + + @master_only + def before_run(self, runner): + super(MlflowLoggerHook, self).before_run(runner) + if self.exp_name is not None: + self.mlflow.set_experiment(self.exp_name) + if self.tags is not None: + self.mlflow.set_tags(self.tags) + + @master_only + def log(self, runner): + tags = self.get_loggable_tags(runner) + if tags: + self.mlflow.log_metrics(tags, step=self.get_iter(runner)) + + @master_only + def after_run(self, runner): + if self.log_model: + self.mlflow_pytorch.log_model(runner.model, 'models') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/neptune.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/neptune.py new file mode 100644 index 00000000..7a38772b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/neptune.py @@ -0,0 +1,82 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ...dist_utils import master_only +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class NeptuneLoggerHook(LoggerHook): + """Class to log metrics to NeptuneAI. + + It requires `neptune-client` to be installed. + + Args: + init_kwargs (dict): a dict contains the initialization keys as below: + - project (str): Name of a project in a form of + namespace/project_name. If None, the value of + NEPTUNE_PROJECT environment variable will be taken. + - api_token (str): User’s API token. + If None, the value of NEPTUNE_API_TOKEN environment + variable will be taken. Note: It is strongly recommended + to use NEPTUNE_API_TOKEN environment variable rather than + placing your API token in plain text in your source code. + - name (str, optional, default is 'Untitled'): Editable name of + the run. Name is displayed in the run's Details and in + Runs table as a column. + Check https://docs.neptune.ai/api-reference/neptune#init for + more init arguments. + interval (int): Logging interval (every k iterations). + ignore_last (bool): Ignore the log of last iterations in each epoch + if less than `interval`. + reset_flag (bool): Whether to clear the output buffer after logging + by_epoch (bool): Whether EpochBasedRunner is used. + + .. _NeptuneAI: + https://docs.neptune.ai/you-should-know/logging-metadata + """ + + def __init__(self, + init_kwargs=None, + interval=10, + ignore_last=True, + reset_flag=True, + with_step=True, + by_epoch=True): + + super(NeptuneLoggerHook, self).__init__(interval, ignore_last, + reset_flag, by_epoch) + self.import_neptune() + self.init_kwargs = init_kwargs + self.with_step = with_step + + def import_neptune(self): + try: + import neptune.new as neptune + except ImportError: + raise ImportError( + 'Please run "pip install neptune-client" to install neptune') + self.neptune = neptune + self.run = None + + @master_only + def before_run(self, runner): + if self.init_kwargs: + self.run = self.neptune.init(**self.init_kwargs) + else: + self.run = self.neptune.init() + + @master_only + def log(self, runner): + tags = self.get_loggable_tags(runner) + if tags: + for tag_name, tag_value in tags.items(): + if self.with_step: + self.run[tag_name].log( + tag_value, step=self.get_iter(runner)) + else: + tags['global_step'] = self.get_iter(runner) + self.run[tag_name].log(tags) + + @master_only + def after_run(self, runner): + self.run.stop() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/pavi.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/pavi.py new file mode 100644 index 00000000..0c5f1422 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/pavi.py @@ -0,0 +1,117 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import json +import os +import os.path as osp + +import torch +import yaml + +import custom_mmpkg.custom_mmcv as mmcv +from ....parallel.utils import is_module_wrapper +from ...dist_utils import master_only +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class PaviLoggerHook(LoggerHook): + + def __init__(self, + init_kwargs=None, + add_graph=False, + add_last_ckpt=False, + interval=10, + ignore_last=True, + reset_flag=False, + by_epoch=True, + img_key='img_info'): + super(PaviLoggerHook, self).__init__(interval, ignore_last, reset_flag, + by_epoch) + self.init_kwargs = init_kwargs + self.add_graph = add_graph + self.add_last_ckpt = add_last_ckpt + self.img_key = img_key + + @master_only + def before_run(self, runner): + super(PaviLoggerHook, self).before_run(runner) + try: + from pavi import SummaryWriter + except ImportError: + raise ImportError('Please run "pip install pavi" to install pavi.') + + self.run_name = runner.work_dir.split('/')[-1] + + if not self.init_kwargs: + self.init_kwargs = dict() + self.init_kwargs['name'] = self.run_name + self.init_kwargs['model'] = runner._model_name + if runner.meta is not None: + if 'config_dict' in runner.meta: + config_dict = runner.meta['config_dict'] + assert isinstance( + config_dict, + dict), ('meta["config_dict"] has to be of a dict, ' + f'but got {type(config_dict)}') + elif 'config_file' in runner.meta: + config_file = runner.meta['config_file'] + config_dict = dict(mmcv.Config.fromfile(config_file)) + else: + config_dict = None + if config_dict is not None: + # 'max_.*iter' is parsed in pavi sdk as the maximum iterations + # to properly set up the progress bar. + config_dict = config_dict.copy() + config_dict.setdefault('max_iter', runner.max_iters) + # non-serializable values are first converted in + # mmcv.dump to json + config_dict = json.loads( + mmcv.dump(config_dict, file_format='json')) + session_text = yaml.dump(config_dict) + self.init_kwargs['session_text'] = session_text + self.writer = SummaryWriter(**self.init_kwargs) + + def get_step(self, runner): + """Get the total training step/epoch.""" + if self.get_mode(runner) == 'val' and self.by_epoch: + return self.get_epoch(runner) + else: + return self.get_iter(runner) + + @master_only + def log(self, runner): + tags = self.get_loggable_tags(runner, add_mode=False) + if tags: + self.writer.add_scalars( + self.get_mode(runner), tags, self.get_step(runner)) + + @master_only + def after_run(self, runner): + if self.add_last_ckpt: + ckpt_path = osp.join(runner.work_dir, 'latest.pth') + if osp.islink(ckpt_path): + ckpt_path = osp.join(runner.work_dir, os.readlink(ckpt_path)) + + if osp.isfile(ckpt_path): + # runner.epoch += 1 has been done before `after_run`. + iteration = runner.epoch if self.by_epoch else runner.iter + return self.writer.add_snapshot_file( + tag=self.run_name, + snapshot_file_path=ckpt_path, + iteration=iteration) + + # flush the buffer and send a task ending signal to Pavi + self.writer.close() + + @master_only + def before_epoch(self, runner): + if runner.epoch == 0 and self.add_graph: + if is_module_wrapper(runner.model): + _model = runner.model.module + else: + _model = runner.model + device = next(_model.parameters()).device + data = next(iter(runner.data_loader)) + image = data[self.img_key][0:1].to(device) + with torch.no_grad(): + self.writer.add_graph(_model, image) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/tensorboard.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/tensorboard.py new file mode 100644 index 00000000..cc9c727f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/tensorboard.py @@ -0,0 +1,57 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp + +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, digit_version +from ...dist_utils import master_only +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class TensorboardLoggerHook(LoggerHook): + + def __init__(self, + log_dir=None, + interval=10, + ignore_last=True, + reset_flag=False, + by_epoch=True): + super(TensorboardLoggerHook, self).__init__(interval, ignore_last, + reset_flag, by_epoch) + self.log_dir = log_dir + + @master_only + def before_run(self, runner): + super(TensorboardLoggerHook, self).before_run(runner) + if (TORCH_VERSION == 'parrots' + or digit_version(TORCH_VERSION) < digit_version('1.1')): + try: + from tensorboardX import SummaryWriter + except ImportError: + raise ImportError('Please install tensorboardX to use ' + 'TensorboardLoggerHook.') + else: + try: + from torch.utils.tensorboard import SummaryWriter + except ImportError: + raise ImportError( + 'Please run "pip install future tensorboard" to install ' + 'the dependencies to use torch.utils.tensorboard ' + '(applicable to PyTorch 1.1 or higher)') + + if self.log_dir is None: + self.log_dir = osp.join(runner.work_dir, 'tf_logs') + self.writer = SummaryWriter(self.log_dir) + + @master_only + def log(self, runner): + tags = self.get_loggable_tags(runner, allow_text=True) + for tag, val in tags.items(): + if isinstance(val, str): + self.writer.add_text(tag, val, self.get_iter(runner)) + else: + self.writer.add_scalar(tag, val, self.get_iter(runner)) + + @master_only + def after_run(self, runner): + self.writer.close() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/text.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/text.py new file mode 100644 index 00000000..ea12c02a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/text.py @@ -0,0 +1,256 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import datetime +import os +import os.path as osp +from collections import OrderedDict + +import torch +import torch.distributed as dist + +import custom_mmpkg.custom_mmcv as mmcv +from custom_mmpkg.custom_mmcv.fileio.file_client import FileClient +from custom_mmpkg.custom_mmcv.utils import is_tuple_of, scandir +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class TextLoggerHook(LoggerHook): + """Logger hook in text. + + In this logger hook, the information will be printed on terminal and + saved in json file. + + Args: + by_epoch (bool, optional): Whether EpochBasedRunner is used. + Default: True. + interval (int, optional): Logging interval (every k iterations). + Default: 10. + ignore_last (bool, optional): Ignore the log of last iterations in each + epoch if less than :attr:`interval`. Default: True. + reset_flag (bool, optional): Whether to clear the output buffer after + logging. Default: False. + interval_exp_name (int, optional): Logging interval for experiment + name. This feature is to help users conveniently get the experiment + information from screen or log file. Default: 1000. + out_dir (str, optional): Logs are saved in ``runner.work_dir`` default. + If ``out_dir`` is specified, logs will be copied to a new directory + which is the concatenation of ``out_dir`` and the last level + directory of ``runner.work_dir``. Default: None. + `New in version 1.3.16.` + out_suffix (str or tuple[str], optional): Those filenames ending with + ``out_suffix`` will be copied to ``out_dir``. + Default: ('.log.json', '.log', '.py'). + `New in version 1.3.16.` + keep_local (bool, optional): Whether to keep local log when + :attr:`out_dir` is specified. If False, the local log will be + removed. Default: True. + `New in version 1.3.16.` + file_client_args (dict, optional): Arguments to instantiate a + FileClient. See :class:`mmcv.fileio.FileClient` for details. + Default: None. + `New in version 1.3.16.` + """ + + def __init__(self, + by_epoch=True, + interval=10, + ignore_last=True, + reset_flag=False, + interval_exp_name=1000, + out_dir=None, + out_suffix=('.log.json', '.log', '.py'), + keep_local=True, + file_client_args=None): + super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag, + by_epoch) + self.by_epoch = by_epoch + self.time_sec_tot = 0 + self.interval_exp_name = interval_exp_name + + if out_dir is None and file_client_args is not None: + raise ValueError( + 'file_client_args should be "None" when `out_dir` is not' + 'specified.') + self.out_dir = out_dir + + if not (out_dir is None or isinstance(out_dir, str) + or is_tuple_of(out_dir, str)): + raise TypeError('out_dir should be "None" or string or tuple of ' + 'string, but got {out_dir}') + self.out_suffix = out_suffix + + self.keep_local = keep_local + self.file_client_args = file_client_args + if self.out_dir is not None: + self.file_client = FileClient.infer_client(file_client_args, + self.out_dir) + + def before_run(self, runner): + super(TextLoggerHook, self).before_run(runner) + + if self.out_dir is not None: + self.file_client = FileClient.infer_client(self.file_client_args, + self.out_dir) + # The final `self.out_dir` is the concatenation of `self.out_dir` + # and the last level directory of `runner.work_dir` + basename = osp.basename(runner.work_dir.rstrip(osp.sep)) + self.out_dir = self.file_client.join_path(self.out_dir, basename) + runner.logger.info( + (f'Text logs will be saved to {self.out_dir} by ' + f'{self.file_client.name} after the training process.')) + + self.start_iter = runner.iter + self.json_log_path = osp.join(runner.work_dir, + f'{runner.timestamp}.log.json') + if runner.meta is not None: + self._dump_log(runner.meta, runner) + + def _get_max_memory(self, runner): + device = getattr(runner.model, 'output_device', None) + mem = torch.cuda.max_memory_allocated(device=device) + mem_mb = torch.tensor([mem / (1024 * 1024)], + dtype=torch.int, + device=device) + if runner.world_size > 1: + dist.reduce(mem_mb, 0, op=dist.ReduceOp.MAX) + return mem_mb.item() + + def _log_info(self, log_dict, runner): + # print exp name for users to distinguish experiments + # at every ``interval_exp_name`` iterations and the end of each epoch + if runner.meta is not None and 'exp_name' in runner.meta: + if (self.every_n_iters(runner, self.interval_exp_name)) or ( + self.by_epoch and self.end_of_epoch(runner)): + exp_info = f'Exp name: {runner.meta["exp_name"]}' + runner.logger.info(exp_info) + + if log_dict['mode'] == 'train': + if isinstance(log_dict['lr'], dict): + lr_str = [] + for k, val in log_dict['lr'].items(): + lr_str.append(f'lr_{k}: {val:.3e}') + lr_str = ' '.join(lr_str) + else: + lr_str = f'lr: {log_dict["lr"]:.3e}' + + # by epoch: Epoch [4][100/1000] + # by iter: Iter [100/100000] + if self.by_epoch: + log_str = f'Epoch [{log_dict["epoch"]}]' \ + f'[{log_dict["iter"]}/{len(runner.data_loader)}]\t' + else: + log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}]\t' + log_str += f'{lr_str}, ' + + if 'time' in log_dict.keys(): + self.time_sec_tot += (log_dict['time'] * self.interval) + time_sec_avg = self.time_sec_tot / ( + runner.iter - self.start_iter + 1) + eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1) + eta_str = str(datetime.timedelta(seconds=int(eta_sec))) + log_str += f'eta: {eta_str}, ' + log_str += f'time: {log_dict["time"]:.3f}, ' \ + f'data_time: {log_dict["data_time"]:.3f}, ' + # statistic memory + if torch.cuda.is_available(): + log_str += f'memory: {log_dict["memory"]}, ' + else: + # val/test time + # here 1000 is the length of the val dataloader + # by epoch: Epoch[val] [4][1000] + # by iter: Iter[val] [1000] + if self.by_epoch: + log_str = f'Epoch({log_dict["mode"]}) ' \ + f'[{log_dict["epoch"]}][{log_dict["iter"]}]\t' + else: + log_str = f'Iter({log_dict["mode"]}) [{log_dict["iter"]}]\t' + + log_items = [] + for name, val in log_dict.items(): + # TODO: resolve this hack + # these items have been in log_str + if name in [ + 'mode', 'Epoch', 'iter', 'lr', 'time', 'data_time', + 'memory', 'epoch' + ]: + continue + if isinstance(val, float): + val = f'{val:.4f}' + log_items.append(f'{name}: {val}') + log_str += ', '.join(log_items) + + runner.logger.info(log_str) + + def _dump_log(self, log_dict, runner): + # dump log in json format + json_log = OrderedDict() + for k, v in log_dict.items(): + json_log[k] = self._round_float(v) + # only append log at last line + if runner.rank == 0: + with open(self.json_log_path, 'a+') as f: + mmcv.dump(json_log, f, file_format='json') + f.write('\n') + + def _round_float(self, items): + if isinstance(items, list): + return [self._round_float(item) for item in items] + elif isinstance(items, float): + return round(items, 5) + else: + return items + + def log(self, runner): + if 'eval_iter_num' in runner.log_buffer.output: + # this doesn't modify runner.iter and is regardless of by_epoch + cur_iter = runner.log_buffer.output.pop('eval_iter_num') + else: + cur_iter = self.get_iter(runner, inner_iter=True) + + log_dict = OrderedDict( + mode=self.get_mode(runner), + epoch=self.get_epoch(runner), + iter=cur_iter) + + # only record lr of the first param group + cur_lr = runner.current_lr() + if isinstance(cur_lr, list): + log_dict['lr'] = cur_lr[0] + else: + assert isinstance(cur_lr, dict) + log_dict['lr'] = {} + for k, lr_ in cur_lr.items(): + assert isinstance(lr_, list) + log_dict['lr'].update({k: lr_[0]}) + + if 'time' in runner.log_buffer.output: + # statistic memory + if torch.cuda.is_available(): + log_dict['memory'] = self._get_max_memory(runner) + + log_dict = dict(log_dict, **runner.log_buffer.output) + + self._log_info(log_dict, runner) + self._dump_log(log_dict, runner) + return log_dict + + def after_run(self, runner): + # copy or upload logs to self.out_dir + if self.out_dir is not None: + for filename in scandir(runner.work_dir, self.out_suffix, True): + local_filepath = osp.join(runner.work_dir, filename) + out_filepath = self.file_client.join_path( + self.out_dir, filename) + with open(local_filepath, 'r') as f: + self.file_client.put_text(f.read(), out_filepath) + + runner.logger.info( + (f'The file {local_filepath} has been uploaded to ' + f'{out_filepath}.')) + + if not self.keep_local: + os.remove(local_filepath) + runner.logger.info( + (f'{local_filepath} was removed due to the ' + '`self.keep_local=False`')) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/wandb.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/wandb.py new file mode 100644 index 00000000..9f680846 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/logger/wandb.py @@ -0,0 +1,56 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ...dist_utils import master_only +from ..hook import HOOKS +from .base import LoggerHook + + +@HOOKS.register_module() +class WandbLoggerHook(LoggerHook): + + def __init__(self, + init_kwargs=None, + interval=10, + ignore_last=True, + reset_flag=False, + commit=True, + by_epoch=True, + with_step=True): + super(WandbLoggerHook, self).__init__(interval, ignore_last, + reset_flag, by_epoch) + self.import_wandb() + self.init_kwargs = init_kwargs + self.commit = commit + self.with_step = with_step + + def import_wandb(self): + try: + import wandb + except ImportError: + raise ImportError( + 'Please run "pip install wandb" to install wandb') + self.wandb = wandb + + @master_only + def before_run(self, runner): + super(WandbLoggerHook, self).before_run(runner) + if self.wandb is None: + self.import_wandb() + if self.init_kwargs: + self.wandb.init(**self.init_kwargs) + else: + self.wandb.init() + + @master_only + def log(self, runner): + tags = self.get_loggable_tags(runner) + if tags: + if self.with_step: + self.wandb.log( + tags, step=self.get_iter(runner), commit=self.commit) + else: + tags['global_step'] = self.get_iter(runner) + self.wandb.log(tags, commit=self.commit) + + @master_only + def after_run(self, runner): + self.wandb.join() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/lr_updater.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/lr_updater.py new file mode 100644 index 00000000..f3759323 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/lr_updater.py @@ -0,0 +1,670 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numbers +from math import cos, pi + +import custom_mmpkg.custom_mmcv as mmcv +from .hook import HOOKS, Hook + + +class LrUpdaterHook(Hook): + """LR Scheduler in MMCV. + + Args: + by_epoch (bool): LR changes epoch by epoch + warmup (string): Type of warmup used. It can be None(use no warmup), + 'constant', 'linear' or 'exp' + warmup_iters (int): The number of iterations or epochs that warmup + lasts + warmup_ratio (float): LR used at the beginning of warmup equals to + warmup_ratio * initial_lr + warmup_by_epoch (bool): When warmup_by_epoch == True, warmup_iters + means the number of epochs that warmup lasts, otherwise means the + number of iteration that warmup lasts + """ + + def __init__(self, + by_epoch=True, + warmup=None, + warmup_iters=0, + warmup_ratio=0.1, + warmup_by_epoch=False): + # validate the "warmup" argument + if warmup is not None: + if warmup not in ['constant', 'linear', 'exp']: + raise ValueError( + f'"{warmup}" is not a supported type for warming up, valid' + ' types are "constant" and "linear"') + if warmup is not None: + assert warmup_iters > 0, \ + '"warmup_iters" must be a positive integer' + assert 0 < warmup_ratio <= 1.0, \ + '"warmup_ratio" must be in range (0,1]' + + self.by_epoch = by_epoch + self.warmup = warmup + self.warmup_iters = warmup_iters + self.warmup_ratio = warmup_ratio + self.warmup_by_epoch = warmup_by_epoch + + if self.warmup_by_epoch: + self.warmup_epochs = self.warmup_iters + self.warmup_iters = None + else: + self.warmup_epochs = None + + self.base_lr = [] # initial lr for all param groups + self.regular_lr = [] # expected lr if no warming up is performed + + def _set_lr(self, runner, lr_groups): + if isinstance(runner.optimizer, dict): + for k, optim in runner.optimizer.items(): + for param_group, lr in zip(optim.param_groups, lr_groups[k]): + param_group['lr'] = lr + else: + for param_group, lr in zip(runner.optimizer.param_groups, + lr_groups): + param_group['lr'] = lr + + def get_lr(self, runner, base_lr): + raise NotImplementedError + + def get_regular_lr(self, runner): + if isinstance(runner.optimizer, dict): + lr_groups = {} + for k in runner.optimizer.keys(): + _lr_group = [ + self.get_lr(runner, _base_lr) + for _base_lr in self.base_lr[k] + ] + lr_groups.update({k: _lr_group}) + + return lr_groups + else: + return [self.get_lr(runner, _base_lr) for _base_lr in self.base_lr] + + def get_warmup_lr(self, cur_iters): + + def _get_warmup_lr(cur_iters, regular_lr): + if self.warmup == 'constant': + warmup_lr = [_lr * self.warmup_ratio for _lr in regular_lr] + elif self.warmup == 'linear': + k = (1 - cur_iters / self.warmup_iters) * (1 - + self.warmup_ratio) + warmup_lr = [_lr * (1 - k) for _lr in regular_lr] + elif self.warmup == 'exp': + k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters) + warmup_lr = [_lr * k for _lr in regular_lr] + return warmup_lr + + if isinstance(self.regular_lr, dict): + lr_groups = {} + for key, regular_lr in self.regular_lr.items(): + lr_groups[key] = _get_warmup_lr(cur_iters, regular_lr) + return lr_groups + else: + return _get_warmup_lr(cur_iters, self.regular_lr) + + def before_run(self, runner): + # NOTE: when resuming from a checkpoint, if 'initial_lr' is not saved, + # it will be set according to the optimizer params + if isinstance(runner.optimizer, dict): + self.base_lr = {} + for k, optim in runner.optimizer.items(): + for group in optim.param_groups: + group.setdefault('initial_lr', group['lr']) + _base_lr = [ + group['initial_lr'] for group in optim.param_groups + ] + self.base_lr.update({k: _base_lr}) + else: + for group in runner.optimizer.param_groups: + group.setdefault('initial_lr', group['lr']) + self.base_lr = [ + group['initial_lr'] for group in runner.optimizer.param_groups + ] + + def before_train_epoch(self, runner): + if self.warmup_iters is None: + epoch_len = len(runner.data_loader) + self.warmup_iters = self.warmup_epochs * epoch_len + + if not self.by_epoch: + return + + self.regular_lr = self.get_regular_lr(runner) + self._set_lr(runner, self.regular_lr) + + def before_train_iter(self, runner): + cur_iter = runner.iter + if not self.by_epoch: + self.regular_lr = self.get_regular_lr(runner) + if self.warmup is None or cur_iter >= self.warmup_iters: + self._set_lr(runner, self.regular_lr) + else: + warmup_lr = self.get_warmup_lr(cur_iter) + self._set_lr(runner, warmup_lr) + elif self.by_epoch: + if self.warmup is None or cur_iter > self.warmup_iters: + return + elif cur_iter == self.warmup_iters: + self._set_lr(runner, self.regular_lr) + else: + warmup_lr = self.get_warmup_lr(cur_iter) + self._set_lr(runner, warmup_lr) + + +@HOOKS.register_module() +class FixedLrUpdaterHook(LrUpdaterHook): + + def __init__(self, **kwargs): + super(FixedLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + return base_lr + + +@HOOKS.register_module() +class StepLrUpdaterHook(LrUpdaterHook): + """Step LR scheduler with min_lr clipping. + + Args: + step (int | list[int]): Step to decay the LR. If an int value is given, + regard it as the decay interval. If a list is given, decay LR at + these steps. + gamma (float, optional): Decay LR ratio. Default: 0.1. + min_lr (float, optional): Minimum LR value to keep. If LR after decay + is lower than `min_lr`, it will be clipped to this value. If None + is given, we don't perform lr clipping. Default: None. + """ + + def __init__(self, step, gamma=0.1, min_lr=None, **kwargs): + if isinstance(step, list): + assert mmcv.is_list_of(step, int) + assert all([s > 0 for s in step]) + elif isinstance(step, int): + assert step > 0 + else: + raise TypeError('"step" must be a list or integer') + self.step = step + self.gamma = gamma + self.min_lr = min_lr + super(StepLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + progress = runner.epoch if self.by_epoch else runner.iter + + # calculate exponential term + if isinstance(self.step, int): + exp = progress // self.step + else: + exp = len(self.step) + for i, s in enumerate(self.step): + if progress < s: + exp = i + break + + lr = base_lr * (self.gamma**exp) + if self.min_lr is not None: + # clip to a minimum value + lr = max(lr, self.min_lr) + return lr + + +@HOOKS.register_module() +class ExpLrUpdaterHook(LrUpdaterHook): + + def __init__(self, gamma, **kwargs): + self.gamma = gamma + super(ExpLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + progress = runner.epoch if self.by_epoch else runner.iter + return base_lr * self.gamma**progress + + +@HOOKS.register_module() +class PolyLrUpdaterHook(LrUpdaterHook): + + def __init__(self, power=1., min_lr=0., **kwargs): + self.power = power + self.min_lr = min_lr + super(PolyLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + if self.by_epoch: + progress = runner.epoch + max_progress = runner.max_epochs + else: + progress = runner.iter + max_progress = runner.max_iters + coeff = (1 - progress / max_progress)**self.power + return (base_lr - self.min_lr) * coeff + self.min_lr + + +@HOOKS.register_module() +class InvLrUpdaterHook(LrUpdaterHook): + + def __init__(self, gamma, power=1., **kwargs): + self.gamma = gamma + self.power = power + super(InvLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + progress = runner.epoch if self.by_epoch else runner.iter + return base_lr * (1 + self.gamma * progress)**(-self.power) + + +@HOOKS.register_module() +class CosineAnnealingLrUpdaterHook(LrUpdaterHook): + + def __init__(self, min_lr=None, min_lr_ratio=None, **kwargs): + assert (min_lr is None) ^ (min_lr_ratio is None) + self.min_lr = min_lr + self.min_lr_ratio = min_lr_ratio + super(CosineAnnealingLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + if self.by_epoch: + progress = runner.epoch + max_progress = runner.max_epochs + else: + progress = runner.iter + max_progress = runner.max_iters + + if self.min_lr_ratio is not None: + target_lr = base_lr * self.min_lr_ratio + else: + target_lr = self.min_lr + return annealing_cos(base_lr, target_lr, progress / max_progress) + + +@HOOKS.register_module() +class FlatCosineAnnealingLrUpdaterHook(LrUpdaterHook): + """Flat + Cosine lr schedule. + + Modified from https://github.com/fastai/fastai/blob/master/fastai/callback/schedule.py#L128 # noqa: E501 + + Args: + start_percent (float): When to start annealing the learning rate + after the percentage of the total training steps. + The value should be in range [0, 1). + Default: 0.75 + min_lr (float, optional): The minimum lr. Default: None. + min_lr_ratio (float, optional): The ratio of minimum lr to the base lr. + Either `min_lr` or `min_lr_ratio` should be specified. + Default: None. + """ + + def __init__(self, + start_percent=0.75, + min_lr=None, + min_lr_ratio=None, + **kwargs): + assert (min_lr is None) ^ (min_lr_ratio is None) + if start_percent < 0 or start_percent > 1 or not isinstance( + start_percent, float): + raise ValueError( + 'expected float between 0 and 1 start_percent, but ' + f'got {start_percent}') + self.start_percent = start_percent + self.min_lr = min_lr + self.min_lr_ratio = min_lr_ratio + super(FlatCosineAnnealingLrUpdaterHook, self).__init__(**kwargs) + + def get_lr(self, runner, base_lr): + if self.by_epoch: + start = round(runner.max_epochs * self.start_percent) + progress = runner.epoch - start + max_progress = runner.max_epochs - start + else: + start = round(runner.max_iters * self.start_percent) + progress = runner.iter - start + max_progress = runner.max_iters - start + + if self.min_lr_ratio is not None: + target_lr = base_lr * self.min_lr_ratio + else: + target_lr = self.min_lr + + if progress < 0: + return base_lr + else: + return annealing_cos(base_lr, target_lr, progress / max_progress) + + +@HOOKS.register_module() +class CosineRestartLrUpdaterHook(LrUpdaterHook): + """Cosine annealing with restarts learning rate scheme. + + Args: + periods (list[int]): Periods for each cosine anneling cycle. + restart_weights (list[float], optional): Restart weights at each + restart iteration. Default: [1]. + min_lr (float, optional): The minimum lr. Default: None. + min_lr_ratio (float, optional): The ratio of minimum lr to the base lr. + Either `min_lr` or `min_lr_ratio` should be specified. + Default: None. + """ + + def __init__(self, + periods, + restart_weights=[1], + min_lr=None, + min_lr_ratio=None, + **kwargs): + assert (min_lr is None) ^ (min_lr_ratio is None) + self.periods = periods + self.min_lr = min_lr + self.min_lr_ratio = min_lr_ratio + self.restart_weights = restart_weights + assert (len(self.periods) == len(self.restart_weights) + ), 'periods and restart_weights should have the same length.' + super(CosineRestartLrUpdaterHook, self).__init__(**kwargs) + + self.cumulative_periods = [ + sum(self.periods[0:i + 1]) for i in range(0, len(self.periods)) + ] + + def get_lr(self, runner, base_lr): + if self.by_epoch: + progress = runner.epoch + else: + progress = runner.iter + + if self.min_lr_ratio is not None: + target_lr = base_lr * self.min_lr_ratio + else: + target_lr = self.min_lr + + idx = get_position_from_periods(progress, self.cumulative_periods) + current_weight = self.restart_weights[idx] + nearest_restart = 0 if idx == 0 else self.cumulative_periods[idx - 1] + current_periods = self.periods[idx] + + alpha = min((progress - nearest_restart) / current_periods, 1) + return annealing_cos(base_lr, target_lr, alpha, current_weight) + + +def get_position_from_periods(iteration, cumulative_periods): + """Get the position from a period list. + + It will return the index of the right-closest number in the period list. + For example, the cumulative_periods = [100, 200, 300, 400], + if iteration == 50, return 0; + if iteration == 210, return 2; + if iteration == 300, return 3. + + Args: + iteration (int): Current iteration. + cumulative_periods (list[int]): Cumulative period list. + + Returns: + int: The position of the right-closest number in the period list. + """ + for i, period in enumerate(cumulative_periods): + if iteration < period: + return i + raise ValueError(f'Current iteration {iteration} exceeds ' + f'cumulative_periods {cumulative_periods}') + + +@HOOKS.register_module() +class CyclicLrUpdaterHook(LrUpdaterHook): + """Cyclic LR Scheduler. + + Implement the cyclical learning rate policy (CLR) described in + https://arxiv.org/pdf/1506.01186.pdf + + Different from the original paper, we use cosine annealing rather than + triangular policy inside a cycle. This improves the performance in the + 3D detection area. + + Args: + by_epoch (bool): Whether to update LR by epoch. + target_ratio (tuple[float]): Relative ratio of the highest LR and the + lowest LR to the initial LR. + cyclic_times (int): Number of cycles during training + step_ratio_up (float): The ratio of the increasing process of LR in + the total cycle. + anneal_strategy (str): {'cos', 'linear'} + Specifies the annealing strategy: 'cos' for cosine annealing, + 'linear' for linear annealing. Default: 'cos'. + """ + + def __init__(self, + by_epoch=False, + target_ratio=(10, 1e-4), + cyclic_times=1, + step_ratio_up=0.4, + anneal_strategy='cos', + **kwargs): + if isinstance(target_ratio, float): + target_ratio = (target_ratio, target_ratio / 1e5) + elif isinstance(target_ratio, tuple): + target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \ + if len(target_ratio) == 1 else target_ratio + else: + raise ValueError('target_ratio should be either float ' + f'or tuple, got {type(target_ratio)}') + + assert len(target_ratio) == 2, \ + '"target_ratio" must be list or tuple of two floats' + assert 0 <= step_ratio_up < 1.0, \ + '"step_ratio_up" must be in range [0,1)' + + self.target_ratio = target_ratio + self.cyclic_times = cyclic_times + self.step_ratio_up = step_ratio_up + self.lr_phases = [] # init lr_phases + # validate anneal_strategy + if anneal_strategy not in ['cos', 'linear']: + raise ValueError('anneal_strategy must be one of "cos" or ' + f'"linear", instead got {anneal_strategy}') + elif anneal_strategy == 'cos': + self.anneal_func = annealing_cos + elif anneal_strategy == 'linear': + self.anneal_func = annealing_linear + + assert not by_epoch, \ + 'currently only support "by_epoch" = False' + super(CyclicLrUpdaterHook, self).__init__(by_epoch, **kwargs) + + def before_run(self, runner): + super(CyclicLrUpdaterHook, self).before_run(runner) + # initiate lr_phases + # total lr_phases are separated as up and down + max_iter_per_phase = runner.max_iters // self.cyclic_times + iter_up_phase = int(self.step_ratio_up * max_iter_per_phase) + self.lr_phases.append( + [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]]) + self.lr_phases.append([ + iter_up_phase, max_iter_per_phase, max_iter_per_phase, + self.target_ratio[0], self.target_ratio[1] + ]) + + def get_lr(self, runner, base_lr): + curr_iter = runner.iter + for (start_iter, end_iter, max_iter_per_phase, start_ratio, + end_ratio) in self.lr_phases: + curr_iter %= max_iter_per_phase + if start_iter <= curr_iter < end_iter: + progress = curr_iter - start_iter + return self.anneal_func(base_lr * start_ratio, + base_lr * end_ratio, + progress / (end_iter - start_iter)) + + +@HOOKS.register_module() +class OneCycleLrUpdaterHook(LrUpdaterHook): + """One Cycle LR Scheduler. + + The 1cycle learning rate policy changes the learning rate after every + batch. The one cycle learning rate policy is described in + https://arxiv.org/pdf/1708.07120.pdf + + Args: + max_lr (float or list): Upper learning rate boundaries in the cycle + for each parameter group. + total_steps (int, optional): The total number of steps in the cycle. + Note that if a value is not provided here, it will be the max_iter + of runner. Default: None. + pct_start (float): The percentage of the cycle (in number of steps) + spent increasing the learning rate. + Default: 0.3 + anneal_strategy (str): {'cos', 'linear'} + Specifies the annealing strategy: 'cos' for cosine annealing, + 'linear' for linear annealing. + Default: 'cos' + div_factor (float): Determines the initial learning rate via + initial_lr = max_lr/div_factor + Default: 25 + final_div_factor (float): Determines the minimum learning rate via + min_lr = initial_lr/final_div_factor + Default: 1e4 + three_phase (bool): If three_phase is True, use a third phase of the + schedule to annihilate the learning rate according to + final_div_factor instead of modifying the second phase (the first + two phases will be symmetrical about the step indicated by + pct_start). + Default: False + """ + + def __init__(self, + max_lr, + total_steps=None, + pct_start=0.3, + anneal_strategy='cos', + div_factor=25, + final_div_factor=1e4, + three_phase=False, + **kwargs): + # validate by_epoch, currently only support by_epoch = False + if 'by_epoch' not in kwargs: + kwargs['by_epoch'] = False + else: + assert not kwargs['by_epoch'], \ + 'currently only support "by_epoch" = False' + if not isinstance(max_lr, (numbers.Number, list, dict)): + raise ValueError('the type of max_lr must be the one of list or ' + f'dict, but got {type(max_lr)}') + self._max_lr = max_lr + if total_steps is not None: + if not isinstance(total_steps, int): + raise ValueError('the type of total_steps must be int, but' + f'got {type(total_steps)}') + self.total_steps = total_steps + # validate pct_start + if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float): + raise ValueError('expected float between 0 and 1 pct_start, but ' + f'got {pct_start}') + self.pct_start = pct_start + # validate anneal_strategy + if anneal_strategy not in ['cos', 'linear']: + raise ValueError('anneal_strategy must be one of "cos" or ' + f'"linear", instead got {anneal_strategy}') + elif anneal_strategy == 'cos': + self.anneal_func = annealing_cos + elif anneal_strategy == 'linear': + self.anneal_func = annealing_linear + self.div_factor = div_factor + self.final_div_factor = final_div_factor + self.three_phase = three_phase + self.lr_phases = [] # init lr_phases + super(OneCycleLrUpdaterHook, self).__init__(**kwargs) + + def before_run(self, runner): + if hasattr(self, 'total_steps'): + total_steps = self.total_steps + else: + total_steps = runner.max_iters + if total_steps < runner.max_iters: + raise ValueError( + 'The total steps must be greater than or equal to max ' + f'iterations {runner.max_iters} of runner, but total steps ' + f'is {total_steps}.') + + if isinstance(runner.optimizer, dict): + self.base_lr = {} + for k, optim in runner.optimizer.items(): + _max_lr = format_param(k, optim, self._max_lr) + self.base_lr[k] = [lr / self.div_factor for lr in _max_lr] + for group, lr in zip(optim.param_groups, self.base_lr[k]): + group.setdefault('initial_lr', lr) + else: + k = type(runner.optimizer).__name__ + _max_lr = format_param(k, runner.optimizer, self._max_lr) + self.base_lr = [lr / self.div_factor for lr in _max_lr] + for group, lr in zip(runner.optimizer.param_groups, self.base_lr): + group.setdefault('initial_lr', lr) + + if self.three_phase: + self.lr_phases.append( + [float(self.pct_start * total_steps) - 1, 1, self.div_factor]) + self.lr_phases.append([ + float(2 * self.pct_start * total_steps) - 2, self.div_factor, 1 + ]) + self.lr_phases.append( + [total_steps - 1, 1, 1 / self.final_div_factor]) + else: + self.lr_phases.append( + [float(self.pct_start * total_steps) - 1, 1, self.div_factor]) + self.lr_phases.append( + [total_steps - 1, self.div_factor, 1 / self.final_div_factor]) + + def get_lr(self, runner, base_lr): + curr_iter = runner.iter + start_iter = 0 + for i, (end_iter, start_lr, end_lr) in enumerate(self.lr_phases): + if curr_iter <= end_iter: + pct = (curr_iter - start_iter) / (end_iter - start_iter) + lr = self.anneal_func(base_lr * start_lr, base_lr * end_lr, + pct) + break + start_iter = end_iter + return lr + + +def annealing_cos(start, end, factor, weight=1): + """Calculate annealing cos learning rate. + + Cosine anneal from `weight * start + (1 - weight) * end` to `end` as + percentage goes from 0.0 to 1.0. + + Args: + start (float): The starting learning rate of the cosine annealing. + end (float): The ending learing rate of the cosine annealing. + factor (float): The coefficient of `pi` when calculating the current + percentage. Range from 0.0 to 1.0. + weight (float, optional): The combination factor of `start` and `end` + when calculating the actual starting learning rate. Default to 1. + """ + cos_out = cos(pi * factor) + 1 + return end + 0.5 * weight * (start - end) * cos_out + + +def annealing_linear(start, end, factor): + """Calculate annealing linear learning rate. + + Linear anneal from `start` to `end` as percentage goes from 0.0 to 1.0. + + Args: + start (float): The starting learning rate of the linear annealing. + end (float): The ending learing rate of the linear annealing. + factor (float): The coefficient of `pi` when calculating the current + percentage. Range from 0.0 to 1.0. + """ + return start + (end - start) * factor + + +def format_param(name, optim, param): + if isinstance(param, numbers.Number): + return [param] * len(optim.param_groups) + elif isinstance(param, (list, tuple)): # multi param groups + if len(param) != len(optim.param_groups): + raise ValueError(f'expected {len(optim.param_groups)} ' + f'values for {name}, got {len(param)}') + return param + else: # multi optimizers + if name not in param: + raise KeyError(f'{name} is not found in {param.keys()}') + return param[name] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/memory.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/memory.py new file mode 100644 index 00000000..70cf9a83 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/memory.py @@ -0,0 +1,25 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch + +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class EmptyCacheHook(Hook): + + def __init__(self, before_epoch=False, after_epoch=True, after_iter=False): + self._before_epoch = before_epoch + self._after_epoch = after_epoch + self._after_iter = after_iter + + def after_iter(self, runner): + if self._after_iter: + torch.cuda.empty_cache() + + def before_epoch(self, runner): + if self._before_epoch: + torch.cuda.empty_cache() + + def after_epoch(self, runner): + if self._after_epoch: + torch.cuda.empty_cache() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/momentum_updater.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/momentum_updater.py new file mode 100644 index 00000000..29b6c7c5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/momentum_updater.py @@ -0,0 +1,493 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import custom_mmpkg.custom_mmcv as mmcv +from .hook import HOOKS, Hook +from .lr_updater import annealing_cos, annealing_linear, format_param + + +class MomentumUpdaterHook(Hook): + + def __init__(self, + by_epoch=True, + warmup=None, + warmup_iters=0, + warmup_ratio=0.9): + # validate the "warmup" argument + if warmup is not None: + if warmup not in ['constant', 'linear', 'exp']: + raise ValueError( + f'"{warmup}" is not a supported type for warming up, valid' + ' types are "constant" and "linear"') + if warmup is not None: + assert warmup_iters > 0, \ + '"warmup_iters" must be a positive integer' + assert 0 < warmup_ratio <= 1.0, \ + '"warmup_momentum" must be in range (0,1]' + + self.by_epoch = by_epoch + self.warmup = warmup + self.warmup_iters = warmup_iters + self.warmup_ratio = warmup_ratio + + self.base_momentum = [] # initial momentum for all param groups + self.regular_momentum = [ + ] # expected momentum if no warming up is performed + + def _set_momentum(self, runner, momentum_groups): + if isinstance(runner.optimizer, dict): + for k, optim in runner.optimizer.items(): + for param_group, mom in zip(optim.param_groups, + momentum_groups[k]): + if 'momentum' in param_group.keys(): + param_group['momentum'] = mom + elif 'betas' in param_group.keys(): + param_group['betas'] = (mom, param_group['betas'][1]) + else: + for param_group, mom in zip(runner.optimizer.param_groups, + momentum_groups): + if 'momentum' in param_group.keys(): + param_group['momentum'] = mom + elif 'betas' in param_group.keys(): + param_group['betas'] = (mom, param_group['betas'][1]) + + def get_momentum(self, runner, base_momentum): + raise NotImplementedError + + def get_regular_momentum(self, runner): + if isinstance(runner.optimizer, dict): + momentum_groups = {} + for k in runner.optimizer.keys(): + _momentum_group = [ + self.get_momentum(runner, _base_momentum) + for _base_momentum in self.base_momentum[k] + ] + momentum_groups.update({k: _momentum_group}) + return momentum_groups + else: + return [ + self.get_momentum(runner, _base_momentum) + for _base_momentum in self.base_momentum + ] + + def get_warmup_momentum(self, cur_iters): + + def _get_warmup_momentum(cur_iters, regular_momentum): + if self.warmup == 'constant': + warmup_momentum = [ + _momentum / self.warmup_ratio + for _momentum in self.regular_momentum + ] + elif self.warmup == 'linear': + k = (1 - cur_iters / self.warmup_iters) * (1 - + self.warmup_ratio) + warmup_momentum = [ + _momentum / (1 - k) for _momentum in self.regular_mom + ] + elif self.warmup == 'exp': + k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters) + warmup_momentum = [ + _momentum / k for _momentum in self.regular_mom + ] + return warmup_momentum + + if isinstance(self.regular_momentum, dict): + momentum_groups = {} + for key, regular_momentum in self.regular_momentum.items(): + momentum_groups[key] = _get_warmup_momentum( + cur_iters, regular_momentum) + return momentum_groups + else: + return _get_warmup_momentum(cur_iters, self.regular_momentum) + + def before_run(self, runner): + # NOTE: when resuming from a checkpoint, + # if 'initial_momentum' is not saved, + # it will be set according to the optimizer params + if isinstance(runner.optimizer, dict): + self.base_momentum = {} + for k, optim in runner.optimizer.items(): + for group in optim.param_groups: + if 'momentum' in group.keys(): + group.setdefault('initial_momentum', group['momentum']) + else: + group.setdefault('initial_momentum', group['betas'][0]) + _base_momentum = [ + group['initial_momentum'] for group in optim.param_groups + ] + self.base_momentum.update({k: _base_momentum}) + else: + for group in runner.optimizer.param_groups: + if 'momentum' in group.keys(): + group.setdefault('initial_momentum', group['momentum']) + else: + group.setdefault('initial_momentum', group['betas'][0]) + self.base_momentum = [ + group['initial_momentum'] + for group in runner.optimizer.param_groups + ] + + def before_train_epoch(self, runner): + if not self.by_epoch: + return + self.regular_mom = self.get_regular_momentum(runner) + self._set_momentum(runner, self.regular_mom) + + def before_train_iter(self, runner): + cur_iter = runner.iter + if not self.by_epoch: + self.regular_mom = self.get_regular_momentum(runner) + if self.warmup is None or cur_iter >= self.warmup_iters: + self._set_momentum(runner, self.regular_mom) + else: + warmup_momentum = self.get_warmup_momentum(cur_iter) + self._set_momentum(runner, warmup_momentum) + elif self.by_epoch: + if self.warmup is None or cur_iter > self.warmup_iters: + return + elif cur_iter == self.warmup_iters: + self._set_momentum(runner, self.regular_mom) + else: + warmup_momentum = self.get_warmup_momentum(cur_iter) + self._set_momentum(runner, warmup_momentum) + + +@HOOKS.register_module() +class StepMomentumUpdaterHook(MomentumUpdaterHook): + """Step momentum scheduler with min value clipping. + + Args: + step (int | list[int]): Step to decay the momentum. If an int value is + given, regard it as the decay interval. If a list is given, decay + momentum at these steps. + gamma (float, optional): Decay momentum ratio. Default: 0.5. + min_momentum (float, optional): Minimum momentum value to keep. If + momentum after decay is lower than this value, it will be clipped + accordingly. If None is given, we don't perform lr clipping. + Default: None. + """ + + def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs): + if isinstance(step, list): + assert mmcv.is_list_of(step, int) + assert all([s > 0 for s in step]) + elif isinstance(step, int): + assert step > 0 + else: + raise TypeError('"step" must be a list or integer') + self.step = step + self.gamma = gamma + self.min_momentum = min_momentum + super(StepMomentumUpdaterHook, self).__init__(**kwargs) + + def get_momentum(self, runner, base_momentum): + progress = runner.epoch if self.by_epoch else runner.iter + + # calculate exponential term + if isinstance(self.step, int): + exp = progress // self.step + else: + exp = len(self.step) + for i, s in enumerate(self.step): + if progress < s: + exp = i + break + + momentum = base_momentum * (self.gamma**exp) + if self.min_momentum is not None: + # clip to a minimum value + momentum = max(momentum, self.min_momentum) + return momentum + + +@HOOKS.register_module() +class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook): + + def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs): + assert (min_momentum is None) ^ (min_momentum_ratio is None) + self.min_momentum = min_momentum + self.min_momentum_ratio = min_momentum_ratio + super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs) + + def get_momentum(self, runner, base_momentum): + if self.by_epoch: + progress = runner.epoch + max_progress = runner.max_epochs + else: + progress = runner.iter + max_progress = runner.max_iters + if self.min_momentum_ratio is not None: + target_momentum = base_momentum * self.min_momentum_ratio + else: + target_momentum = self.min_momentum + return annealing_cos(base_momentum, target_momentum, + progress / max_progress) + + +@HOOKS.register_module() +class CyclicMomentumUpdaterHook(MomentumUpdaterHook): + """Cyclic momentum Scheduler. + + Implement the cyclical momentum scheduler policy described in + https://arxiv.org/pdf/1708.07120.pdf + + This momentum scheduler usually used together with the CyclicLRUpdater + to improve the performance in the 3D detection area. + + Attributes: + target_ratio (tuple[float]): Relative ratio of the lowest momentum and + the highest momentum to the initial momentum. + cyclic_times (int): Number of cycles during training + step_ratio_up (float): The ratio of the increasing process of momentum + in the total cycle. + by_epoch (bool): Whether to update momentum by epoch. + """ + + def __init__(self, + by_epoch=False, + target_ratio=(0.85 / 0.95, 1), + cyclic_times=1, + step_ratio_up=0.4, + **kwargs): + if isinstance(target_ratio, float): + target_ratio = (target_ratio, target_ratio / 1e5) + elif isinstance(target_ratio, tuple): + target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \ + if len(target_ratio) == 1 else target_ratio + else: + raise ValueError('target_ratio should be either float ' + f'or tuple, got {type(target_ratio)}') + + assert len(target_ratio) == 2, \ + '"target_ratio" must be list or tuple of two floats' + assert 0 <= step_ratio_up < 1.0, \ + '"step_ratio_up" must be in range [0,1)' + + self.target_ratio = target_ratio + self.cyclic_times = cyclic_times + self.step_ratio_up = step_ratio_up + self.momentum_phases = [] # init momentum_phases + # currently only support by_epoch=False + assert not by_epoch, \ + 'currently only support "by_epoch" = False' + super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs) + + def before_run(self, runner): + super(CyclicMomentumUpdaterHook, self).before_run(runner) + # initiate momentum_phases + # total momentum_phases are separated as up and down + max_iter_per_phase = runner.max_iters // self.cyclic_times + iter_up_phase = int(self.step_ratio_up * max_iter_per_phase) + self.momentum_phases.append( + [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]]) + self.momentum_phases.append([ + iter_up_phase, max_iter_per_phase, max_iter_per_phase, + self.target_ratio[0], self.target_ratio[1] + ]) + + def get_momentum(self, runner, base_momentum): + curr_iter = runner.iter + for (start_iter, end_iter, max_iter_per_phase, start_ratio, + end_ratio) in self.momentum_phases: + curr_iter %= max_iter_per_phase + if start_iter <= curr_iter < end_iter: + progress = curr_iter - start_iter + return annealing_cos(base_momentum * start_ratio, + base_momentum * end_ratio, + progress / (end_iter - start_iter)) + + +@HOOKS.register_module() +class OneCycleMomentumUpdaterHook(MomentumUpdaterHook): + """OneCycle momentum Scheduler. + + This momentum scheduler usually used together with the OneCycleLrUpdater + to improve the performance. + + Args: + base_momentum (float or list): Lower momentum boundaries in the cycle + for each parameter group. Note that momentum is cycled inversely + to learning rate; at the peak of a cycle, momentum is + 'base_momentum' and learning rate is 'max_lr'. + Default: 0.85 + max_momentum (float or list): Upper momentum boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (max_momentum - base_momentum). + Note that momentum is cycled inversely + to learning rate; at the start of a cycle, momentum is + 'max_momentum' and learning rate is 'base_lr' + Default: 0.95 + pct_start (float): The percentage of the cycle (in number of steps) + spent increasing the learning rate. + Default: 0.3 + anneal_strategy (str): {'cos', 'linear'} + Specifies the annealing strategy: 'cos' for cosine annealing, + 'linear' for linear annealing. + Default: 'cos' + three_phase (bool): If three_phase is True, use a third phase of the + schedule to annihilate the learning rate according to + final_div_factor instead of modifying the second phase (the first + two phases will be symmetrical about the step indicated by + pct_start). + Default: False + """ + + def __init__(self, + base_momentum=0.85, + max_momentum=0.95, + pct_start=0.3, + anneal_strategy='cos', + three_phase=False, + **kwargs): + # validate by_epoch, currently only support by_epoch=False + if 'by_epoch' not in kwargs: + kwargs['by_epoch'] = False + else: + assert not kwargs['by_epoch'], \ + 'currently only support "by_epoch" = False' + if not isinstance(base_momentum, (float, list, dict)): + raise ValueError('base_momentum must be the type among of float,' + 'list or dict.') + self._base_momentum = base_momentum + if not isinstance(max_momentum, (float, list, dict)): + raise ValueError('max_momentum must be the type among of float,' + 'list or dict.') + self._max_momentum = max_momentum + # validate pct_start + if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float): + raise ValueError('Expected float between 0 and 1 pct_start, but ' + f'got {pct_start}') + self.pct_start = pct_start + # validate anneal_strategy + if anneal_strategy not in ['cos', 'linear']: + raise ValueError('anneal_strategy must by one of "cos" or ' + f'"linear", instead got {anneal_strategy}') + elif anneal_strategy == 'cos': + self.anneal_func = annealing_cos + elif anneal_strategy == 'linear': + self.anneal_func = annealing_linear + self.three_phase = three_phase + self.momentum_phases = [] # init momentum_phases + super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs) + + def before_run(self, runner): + if isinstance(runner.optimizer, dict): + for k, optim in runner.optimizer.items(): + if ('momentum' not in optim.defaults + and 'betas' not in optim.defaults): + raise ValueError('optimizer must support momentum with' + 'option enabled') + self.use_beta1 = 'betas' in optim.defaults + _base_momentum = format_param(k, optim, self._base_momentum) + _max_momentum = format_param(k, optim, self._max_momentum) + for group, b_momentum, m_momentum in zip( + optim.param_groups, _base_momentum, _max_momentum): + if self.use_beta1: + _, beta2 = group['betas'] + group['betas'] = (m_momentum, beta2) + else: + group['momentum'] = m_momentum + group['base_momentum'] = b_momentum + group['max_momentum'] = m_momentum + else: + optim = runner.optimizer + if ('momentum' not in optim.defaults + and 'betas' not in optim.defaults): + raise ValueError('optimizer must support momentum with' + 'option enabled') + self.use_beta1 = 'betas' in optim.defaults + k = type(optim).__name__ + _base_momentum = format_param(k, optim, self._base_momentum) + _max_momentum = format_param(k, optim, self._max_momentum) + for group, b_momentum, m_momentum in zip(optim.param_groups, + _base_momentum, + _max_momentum): + if self.use_beta1: + _, beta2 = group['betas'] + group['betas'] = (m_momentum, beta2) + else: + group['momentum'] = m_momentum + group['base_momentum'] = b_momentum + group['max_momentum'] = m_momentum + + if self.three_phase: + self.momentum_phases.append({ + 'end_iter': + float(self.pct_start * runner.max_iters) - 1, + 'start_momentum': + 'max_momentum', + 'end_momentum': + 'base_momentum' + }) + self.momentum_phases.append({ + 'end_iter': + float(2 * self.pct_start * runner.max_iters) - 2, + 'start_momentum': + 'base_momentum', + 'end_momentum': + 'max_momentum' + }) + self.momentum_phases.append({ + 'end_iter': runner.max_iters - 1, + 'start_momentum': 'max_momentum', + 'end_momentum': 'max_momentum' + }) + else: + self.momentum_phases.append({ + 'end_iter': + float(self.pct_start * runner.max_iters) - 1, + 'start_momentum': + 'max_momentum', + 'end_momentum': + 'base_momentum' + }) + self.momentum_phases.append({ + 'end_iter': runner.max_iters - 1, + 'start_momentum': 'base_momentum', + 'end_momentum': 'max_momentum' + }) + + def _set_momentum(self, runner, momentum_groups): + if isinstance(runner.optimizer, dict): + for k, optim in runner.optimizer.items(): + for param_group, mom in zip(optim.param_groups, + momentum_groups[k]): + if 'momentum' in param_group.keys(): + param_group['momentum'] = mom + elif 'betas' in param_group.keys(): + param_group['betas'] = (mom, param_group['betas'][1]) + else: + for param_group, mom in zip(runner.optimizer.param_groups, + momentum_groups): + if 'momentum' in param_group.keys(): + param_group['momentum'] = mom + elif 'betas' in param_group.keys(): + param_group['betas'] = (mom, param_group['betas'][1]) + + def get_momentum(self, runner, param_group): + curr_iter = runner.iter + start_iter = 0 + for i, phase in enumerate(self.momentum_phases): + end_iter = phase['end_iter'] + if curr_iter <= end_iter or i == len(self.momentum_phases) - 1: + pct = (curr_iter - start_iter) / (end_iter - start_iter) + momentum = self.anneal_func( + param_group[phase['start_momentum']], + param_group[phase['end_momentum']], pct) + break + start_iter = end_iter + return momentum + + def get_regular_momentum(self, runner): + if isinstance(runner.optimizer, dict): + momentum_groups = {} + for k, optim in runner.optimizer.items(): + _momentum_group = [ + self.get_momentum(runner, param_group) + for param_group in optim.param_groups + ] + momentum_groups.update({k: _momentum_group}) + return momentum_groups + else: + momentum_groups = [] + for param_group in runner.optimizer.param_groups: + momentum_groups.append(self.get_momentum(runner, param_group)) + return momentum_groups diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/optimizer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/optimizer.py new file mode 100644 index 00000000..c7f11173 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/optimizer.py @@ -0,0 +1,508 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +from collections import defaultdict +from itertools import chain + +from torch.nn.utils import clip_grad + +from custom_mmpkg.custom_mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version +from ..dist_utils import allreduce_grads +from ..fp16_utils import LossScaler, wrap_fp16_model +from .hook import HOOKS, Hook + +try: + # If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported + # and used; otherwise, auto fp16 will adopt mmcv's implementation. + from torch.cuda.amp import GradScaler +except ImportError: + pass + + +@HOOKS.register_module() +class OptimizerHook(Hook): + + def __init__(self, grad_clip=None): + self.grad_clip = grad_clip + + def clip_grads(self, params): + params = list( + filter(lambda p: p.requires_grad and p.grad is not None, params)) + if len(params) > 0: + return clip_grad.clip_grad_norm_(params, **self.grad_clip) + + def after_train_iter(self, runner): + runner.optimizer.zero_grad() + runner.outputs['loss'].backward() + if self.grad_clip is not None: + grad_norm = self.clip_grads(runner.model.parameters()) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update({'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + runner.optimizer.step() + + +@HOOKS.register_module() +class GradientCumulativeOptimizerHook(OptimizerHook): + """Optimizer Hook implements multi-iters gradient cumulating. + + Args: + cumulative_iters (int, optional): Num of gradient cumulative iters. + The optimizer will step every `cumulative_iters` iters. + Defaults to 1. + + Examples: + >>> # Use cumulative_iters to simulate a large batch size + >>> # It is helpful when the hardware cannot handle a large batch size. + >>> loader = DataLoader(data, batch_size=64) + >>> optim_hook = GradientCumulativeOptimizerHook(cumulative_iters=4) + >>> # almost equals to + >>> loader = DataLoader(data, batch_size=256) + >>> optim_hook = OptimizerHook() + """ + + def __init__(self, cumulative_iters=1, **kwargs): + super(GradientCumulativeOptimizerHook, self).__init__(**kwargs) + + assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \ + f'cumulative_iters only accepts positive int, but got ' \ + f'{type(cumulative_iters)} instead.' + + self.cumulative_iters = cumulative_iters + self.divisible_iters = 0 + self.remainder_iters = 0 + self.initialized = False + + def has_batch_norm(self, module): + if isinstance(module, _BatchNorm): + return True + for m in module.children(): + if self.has_batch_norm(m): + return True + return False + + def _init(self, runner): + if runner.iter % self.cumulative_iters != 0: + runner.logger.warning( + 'Resume iter number is not divisible by cumulative_iters in ' + 'GradientCumulativeOptimizerHook, which means the gradient of ' + 'some iters is lost and the result may be influenced slightly.' + ) + + if self.has_batch_norm(runner.model) and self.cumulative_iters > 1: + runner.logger.warning( + 'GradientCumulativeOptimizerHook may slightly decrease ' + 'performance if the model has BatchNorm layers.') + + residual_iters = runner.max_iters - runner.iter + + self.divisible_iters = ( + residual_iters // self.cumulative_iters * self.cumulative_iters) + self.remainder_iters = residual_iters - self.divisible_iters + + self.initialized = True + + def after_train_iter(self, runner): + if not self.initialized: + self._init(runner) + + if runner.iter < self.divisible_iters: + loss_factor = self.cumulative_iters + else: + loss_factor = self.remainder_iters + loss = runner.outputs['loss'] + loss = loss / loss_factor + loss.backward() + + if (self.every_n_iters(runner, self.cumulative_iters) + or self.is_last_iter(runner)): + + if self.grad_clip is not None: + grad_norm = self.clip_grads(runner.model.parameters()) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update({'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + runner.optimizer.step() + runner.optimizer.zero_grad() + + +if (TORCH_VERSION != 'parrots' + and digit_version(TORCH_VERSION) >= digit_version('1.6.0')): + + @HOOKS.register_module() + class Fp16OptimizerHook(OptimizerHook): + """FP16 optimizer hook (using PyTorch's implementation). + + If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, + to take care of the optimization procedure. + + Args: + loss_scale (float | str | dict): Scale factor configuration. + If loss_scale is a float, static loss scaling will be used with + the specified scale. If loss_scale is a string, it must be + 'dynamic', then dynamic loss scaling will be used. + It can also be a dict containing arguments of GradScalar. + Defaults to 512. For Pytorch >= 1.6, mmcv uses official + implementation of GradScaler. If you use a dict version of + loss_scale to create GradScaler, please refer to: + https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler + for the parameters. + + Examples: + >>> loss_scale = dict( + ... init_scale=65536.0, + ... growth_factor=2.0, + ... backoff_factor=0.5, + ... growth_interval=2000 + ... ) + >>> optimizer_hook = Fp16OptimizerHook(loss_scale=loss_scale) + """ + + def __init__(self, + grad_clip=None, + coalesce=True, + bucket_size_mb=-1, + loss_scale=512., + distributed=True): + self.grad_clip = grad_clip + self.coalesce = coalesce + self.bucket_size_mb = bucket_size_mb + self.distributed = distributed + self._scale_update_param = None + if loss_scale == 'dynamic': + self.loss_scaler = GradScaler() + elif isinstance(loss_scale, float): + self._scale_update_param = loss_scale + self.loss_scaler = GradScaler(init_scale=loss_scale) + elif isinstance(loss_scale, dict): + self.loss_scaler = GradScaler(**loss_scale) + else: + raise ValueError('loss_scale must be of type float, dict, or ' + f'"dynamic", got {loss_scale}') + + def before_run(self, runner): + """Preparing steps before Mixed Precision Training.""" + # wrap model mode to fp16 + wrap_fp16_model(runner.model) + # resume from state dict + if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']: + scaler_state_dict = runner.meta['fp16']['loss_scaler'] + self.loss_scaler.load_state_dict(scaler_state_dict) + + def copy_grads_to_fp32(self, fp16_net, fp32_weights): + """Copy gradients from fp16 model to fp32 weight copy.""" + for fp32_param, fp16_param in zip(fp32_weights, + fp16_net.parameters()): + if fp16_param.grad is not None: + if fp32_param.grad is None: + fp32_param.grad = fp32_param.data.new( + fp32_param.size()) + fp32_param.grad.copy_(fp16_param.grad) + + def copy_params_to_fp16(self, fp16_net, fp32_weights): + """Copy updated params from fp32 weight copy to fp16 model.""" + for fp16_param, fp32_param in zip(fp16_net.parameters(), + fp32_weights): + fp16_param.data.copy_(fp32_param.data) + + def after_train_iter(self, runner): + """Backward optimization steps for Mixed Precision Training. For + dynamic loss scaling, please refer to + https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler. + + 1. Scale the loss by a scale factor. + 2. Backward the loss to obtain the gradients. + 3. Unscale the optimizer’s gradient tensors. + 4. Call optimizer.step() and update scale factor. + 5. Save loss_scaler state_dict for resume purpose. + """ + # clear grads of last iteration + runner.model.zero_grad() + runner.optimizer.zero_grad() + + self.loss_scaler.scale(runner.outputs['loss']).backward() + self.loss_scaler.unscale_(runner.optimizer) + # grad clip + if self.grad_clip is not None: + grad_norm = self.clip_grads(runner.model.parameters()) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update({'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + # backward and update scaler + self.loss_scaler.step(runner.optimizer) + self.loss_scaler.update(self._scale_update_param) + + # save state_dict of loss_scaler + runner.meta.setdefault( + 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() + + @HOOKS.register_module() + class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook, + Fp16OptimizerHook): + """Fp16 optimizer Hook (using PyTorch's implementation) implements + multi-iters gradient cumulating. + + If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, + to take care of the optimization procedure. + """ + + def __init__(self, *args, **kwargs): + super(GradientCumulativeFp16OptimizerHook, + self).__init__(*args, **kwargs) + + def after_train_iter(self, runner): + if not self.initialized: + self._init(runner) + + if runner.iter < self.divisible_iters: + loss_factor = self.cumulative_iters + else: + loss_factor = self.remainder_iters + loss = runner.outputs['loss'] + loss = loss / loss_factor + + self.loss_scaler.scale(loss).backward() + + if (self.every_n_iters(runner, self.cumulative_iters) + or self.is_last_iter(runner)): + + # copy fp16 grads in the model to fp32 params in the optimizer + self.loss_scaler.unscale_(runner.optimizer) + + if self.grad_clip is not None: + grad_norm = self.clip_grads(runner.model.parameters()) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update( + {'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + + # backward and update scaler + self.loss_scaler.step(runner.optimizer) + self.loss_scaler.update(self._scale_update_param) + + # save state_dict of loss_scaler + runner.meta.setdefault( + 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() + + # clear grads + runner.model.zero_grad() + runner.optimizer.zero_grad() + +else: + + @HOOKS.register_module() + class Fp16OptimizerHook(OptimizerHook): + """FP16 optimizer hook (mmcv's implementation). + + The steps of fp16 optimizer is as follows. + 1. Scale the loss value. + 2. BP in the fp16 model. + 2. Copy gradients from fp16 model to fp32 weights. + 3. Update fp32 weights. + 4. Copy updated parameters from fp32 weights to fp16 model. + + Refer to https://arxiv.org/abs/1710.03740 for more details. + + Args: + loss_scale (float | str | dict): Scale factor configuration. + If loss_scale is a float, static loss scaling will be used with + the specified scale. If loss_scale is a string, it must be + 'dynamic', then dynamic loss scaling will be used. + It can also be a dict containing arguments of LossScaler. + Defaults to 512. + """ + + def __init__(self, + grad_clip=None, + coalesce=True, + bucket_size_mb=-1, + loss_scale=512., + distributed=True): + self.grad_clip = grad_clip + self.coalesce = coalesce + self.bucket_size_mb = bucket_size_mb + self.distributed = distributed + if loss_scale == 'dynamic': + self.loss_scaler = LossScaler(mode='dynamic') + elif isinstance(loss_scale, float): + self.loss_scaler = LossScaler( + init_scale=loss_scale, mode='static') + elif isinstance(loss_scale, dict): + self.loss_scaler = LossScaler(**loss_scale) + else: + raise ValueError('loss_scale must be of type float, dict, or ' + f'"dynamic", got {loss_scale}') + + def before_run(self, runner): + """Preparing steps before Mixed Precision Training. + + 1. Make a master copy of fp32 weights for optimization. + 2. Convert the main model from fp32 to fp16. + """ + # keep a copy of fp32 weights + old_groups = runner.optimizer.param_groups + runner.optimizer.param_groups = copy.deepcopy( + runner.optimizer.param_groups) + state = defaultdict(dict) + p_map = { + old_p: p + for old_p, p in zip( + chain(*(g['params'] for g in old_groups)), + chain(*(g['params'] + for g in runner.optimizer.param_groups))) + } + for k, v in runner.optimizer.state.items(): + state[p_map[k]] = v + runner.optimizer.state = state + # convert model to fp16 + wrap_fp16_model(runner.model) + # resume from state dict + if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']: + scaler_state_dict = runner.meta['fp16']['loss_scaler'] + self.loss_scaler.load_state_dict(scaler_state_dict) + + def copy_grads_to_fp32(self, fp16_net, fp32_weights): + """Copy gradients from fp16 model to fp32 weight copy.""" + for fp32_param, fp16_param in zip(fp32_weights, + fp16_net.parameters()): + if fp16_param.grad is not None: + if fp32_param.grad is None: + fp32_param.grad = fp32_param.data.new( + fp32_param.size()) + fp32_param.grad.copy_(fp16_param.grad) + + def copy_params_to_fp16(self, fp16_net, fp32_weights): + """Copy updated params from fp32 weight copy to fp16 model.""" + for fp16_param, fp32_param in zip(fp16_net.parameters(), + fp32_weights): + fp16_param.data.copy_(fp32_param.data) + + def after_train_iter(self, runner): + """Backward optimization steps for Mixed Precision Training. For + dynamic loss scaling, please refer `loss_scalar.py` + + 1. Scale the loss by a scale factor. + 2. Backward the loss to obtain the gradients (fp16). + 3. Copy gradients from the model to the fp32 weight copy. + 4. Scale the gradients back and update the fp32 weight copy. + 5. Copy back the params from fp32 weight copy to the fp16 model. + 6. Save loss_scaler state_dict for resume purpose. + """ + # clear grads of last iteration + runner.model.zero_grad() + runner.optimizer.zero_grad() + # scale the loss value + scaled_loss = runner.outputs['loss'] * self.loss_scaler.loss_scale + scaled_loss.backward() + # copy fp16 grads in the model to fp32 params in the optimizer + + fp32_weights = [] + for param_group in runner.optimizer.param_groups: + fp32_weights += param_group['params'] + self.copy_grads_to_fp32(runner.model, fp32_weights) + # allreduce grads + if self.distributed: + allreduce_grads(fp32_weights, self.coalesce, + self.bucket_size_mb) + + has_overflow = self.loss_scaler.has_overflow(fp32_weights) + # if has overflow, skip this iteration + if not has_overflow: + # scale the gradients back + for param in fp32_weights: + if param.grad is not None: + param.grad.div_(self.loss_scaler.loss_scale) + if self.grad_clip is not None: + grad_norm = self.clip_grads(fp32_weights) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update( + {'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + # update fp32 params + runner.optimizer.step() + # copy fp32 params to the fp16 model + self.copy_params_to_fp16(runner.model, fp32_weights) + self.loss_scaler.update_scale(has_overflow) + if has_overflow: + runner.logger.warning('Check overflow, downscale loss scale ' + f'to {self.loss_scaler.cur_scale}') + + # save state_dict of loss_scaler + runner.meta.setdefault( + 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() + + @HOOKS.register_module() + class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook, + Fp16OptimizerHook): + """Fp16 optimizer Hook (using mmcv implementation) implements multi- + iters gradient cumulating.""" + + def __init__(self, *args, **kwargs): + super(GradientCumulativeFp16OptimizerHook, + self).__init__(*args, **kwargs) + + def after_train_iter(self, runner): + if not self.initialized: + self._init(runner) + + if runner.iter < self.divisible_iters: + loss_factor = self.cumulative_iters + else: + loss_factor = self.remainder_iters + + loss = runner.outputs['loss'] + loss = loss / loss_factor + + # scale the loss value + scaled_loss = loss * self.loss_scaler.loss_scale + scaled_loss.backward() + + if (self.every_n_iters(runner, self.cumulative_iters) + or self.is_last_iter(runner)): + + # copy fp16 grads in the model to fp32 params in the optimizer + fp32_weights = [] + for param_group in runner.optimizer.param_groups: + fp32_weights += param_group['params'] + self.copy_grads_to_fp32(runner.model, fp32_weights) + # allreduce grads + if self.distributed: + allreduce_grads(fp32_weights, self.coalesce, + self.bucket_size_mb) + + has_overflow = self.loss_scaler.has_overflow(fp32_weights) + # if has overflow, skip this iteration + if not has_overflow: + # scale the gradients back + for param in fp32_weights: + if param.grad is not None: + param.grad.div_(self.loss_scaler.loss_scale) + if self.grad_clip is not None: + grad_norm = self.clip_grads(fp32_weights) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update( + {'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + # update fp32 params + runner.optimizer.step() + # copy fp32 params to the fp16 model + self.copy_params_to_fp16(runner.model, fp32_weights) + else: + runner.logger.warning( + 'Check overflow, downscale loss scale ' + f'to {self.loss_scaler.cur_scale}') + + self.loss_scaler.update_scale(has_overflow) + + # save state_dict of loss_scaler + runner.meta.setdefault( + 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() + + # clear grads + runner.model.zero_grad() + runner.optimizer.zero_grad() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/profiler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/profiler.py new file mode 100644 index 00000000..b7023699 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/profiler.py @@ -0,0 +1,180 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings +from typing import Callable, List, Optional, Union + +import torch + +from ..dist_utils import master_only +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class ProfilerHook(Hook): + """Profiler to analyze performance during training. + + PyTorch Profiler is a tool that allows the collection of the performance + metrics during the training. More details on Profiler can be found at + https://pytorch.org/docs/1.8.1/profiler.html#torch.profiler.profile + + Args: + by_epoch (bool): Profile performance by epoch or by iteration. + Default: True. + profile_iters (int): Number of iterations for profiling. + If ``by_epoch=True``, profile_iters indicates that they are the + first profile_iters epochs at the beginning of the + training, otherwise it indicates the first profile_iters + iterations. Default: 1. + activities (list[str]): List of activity groups (CPU, CUDA) to use in + profiling. Default: ['cpu', 'cuda']. + schedule (dict, optional): Config of generating the callable schedule. + if schedule is None, profiler will not add step markers into the + trace and table view. Default: None. + on_trace_ready (callable, dict): Either a handler or a dict of generate + handler. Default: None. + record_shapes (bool): Save information about operator's input shapes. + Default: False. + profile_memory (bool): Track tensor memory allocation/deallocation. + Default: False. + with_stack (bool): Record source information (file and line number) + for the ops. Default: False. + with_flops (bool): Use formula to estimate the FLOPS of specific + operators (matrix multiplication and 2D convolution). + Default: False. + json_trace_path (str, optional): Exports the collected trace in Chrome + JSON format. Default: None. + + Example: + >>> runner = ... # instantiate a Runner + >>> # tensorboard trace + >>> trace_config = dict(type='tb_trace', dir_name='work_dir') + >>> profiler_config = dict(on_trace_ready=trace_config) + >>> runner.register_profiler_hook(profiler_config) + >>> runner.run(data_loaders=[trainloader], workflow=[('train', 1)]) + """ + + def __init__(self, + by_epoch: bool = True, + profile_iters: int = 1, + activities: List[str] = ['cpu', 'cuda'], + schedule: Optional[dict] = None, + on_trace_ready: Optional[Union[Callable, dict]] = None, + record_shapes: bool = False, + profile_memory: bool = False, + with_stack: bool = False, + with_flops: bool = False, + json_trace_path: Optional[str] = None) -> None: + try: + from torch import profiler # torch version >= 1.8.1 + except ImportError: + raise ImportError('profiler is the new feature of torch1.8.1, ' + f'but your version is {torch.__version__}') + + assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean.' + self.by_epoch = by_epoch + + if profile_iters < 1: + raise ValueError('profile_iters should be greater than 0, but got ' + f'{profile_iters}') + self.profile_iters = profile_iters + + if not isinstance(activities, list): + raise ValueError( + f'activities should be list, but got {type(activities)}') + self.activities = [] + for activity in activities: + activity = activity.lower() + if activity == 'cpu': + self.activities.append(profiler.ProfilerActivity.CPU) + elif activity == 'cuda': + self.activities.append(profiler.ProfilerActivity.CUDA) + else: + raise ValueError( + f'activity should be "cpu" or "cuda", but got {activity}') + + if schedule is not None: + self.schedule = profiler.schedule(**schedule) + else: + self.schedule = None + + self.on_trace_ready = on_trace_ready + self.record_shapes = record_shapes + self.profile_memory = profile_memory + self.with_stack = with_stack + self.with_flops = with_flops + self.json_trace_path = json_trace_path + + @master_only + def before_run(self, runner): + if self.by_epoch and runner.max_epochs < self.profile_iters: + raise ValueError('self.profile_iters should not be greater than ' + f'{runner.max_epochs}') + + if not self.by_epoch and runner.max_iters < self.profile_iters: + raise ValueError('self.profile_iters should not be greater than ' + f'{runner.max_iters}') + + if callable(self.on_trace_ready): # handler + _on_trace_ready = self.on_trace_ready + elif isinstance(self.on_trace_ready, dict): # config of handler + trace_cfg = self.on_trace_ready.copy() + trace_type = trace_cfg.pop('type') # log_trace handler + if trace_type == 'log_trace': + + def _log_handler(prof): + print(prof.key_averages().table(**trace_cfg)) + + _on_trace_ready = _log_handler + elif trace_type == 'tb_trace': # tensorboard_trace handler + try: + import torch_tb_profiler # noqa: F401 + except ImportError: + raise ImportError('please run "pip install ' + 'torch-tb-profiler" to install ' + 'torch_tb_profiler') + _on_trace_ready = torch.profiler.tensorboard_trace_handler( + **trace_cfg) + else: + raise ValueError('trace_type should be "log_trace" or ' + f'"tb_trace", but got {trace_type}') + elif self.on_trace_ready is None: + _on_trace_ready = None # type: ignore + else: + raise ValueError('on_trace_ready should be handler, dict or None, ' + f'but got {type(self.on_trace_ready)}') + + if runner.max_epochs > 1: + warnings.warn(f'profiler will profile {runner.max_epochs} epochs ' + 'instead of 1 epoch. Since profiler will slow down ' + 'the training, it is recommended to train 1 epoch ' + 'with ProfilerHook and adjust your setting according' + ' to the profiler summary. During normal training ' + '(epoch > 1), you may disable the ProfilerHook.') + + self.profiler = torch.profiler.profile( + activities=self.activities, + schedule=self.schedule, + on_trace_ready=_on_trace_ready, + record_shapes=self.record_shapes, + profile_memory=self.profile_memory, + with_stack=self.with_stack, + with_flops=self.with_flops) + + self.profiler.__enter__() + runner.logger.info('profiler is profiling...') + + @master_only + def after_train_epoch(self, runner): + if self.by_epoch and runner.epoch == self.profile_iters - 1: + runner.logger.info('profiler may take a few minutes...') + self.profiler.__exit__(None, None, None) + if self.json_trace_path is not None: + self.profiler.export_chrome_trace(self.json_trace_path) + + @master_only + def after_train_iter(self, runner): + self.profiler.step() + if not self.by_epoch and runner.iter == self.profile_iters - 1: + runner.logger.info('profiler may take a few minutes...') + self.profiler.__exit__(None, None, None) + if self.json_trace_path is not None: + self.profiler.export_chrome_trace(self.json_trace_path) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/sampler_seed.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/sampler_seed.py new file mode 100644 index 00000000..ee0dc6bd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/sampler_seed.py @@ -0,0 +1,20 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class DistSamplerSeedHook(Hook): + """Data-loading sampler for distributed training. + + When distributed training, it is only useful in conjunction with + :obj:`EpochBasedRunner`, while :obj:`IterBasedRunner` achieves the same + purpose with :obj:`IterLoader`. + """ + + def before_epoch(self, runner): + if hasattr(runner.data_loader.sampler, 'set_epoch'): + # in case the data loader uses `SequentialSampler` in Pytorch + runner.data_loader.sampler.set_epoch(runner.epoch) + elif hasattr(runner.data_loader.batch_sampler.sampler, 'set_epoch'): + # batch sampler in pytorch warps the sampler as its attributes. + runner.data_loader.batch_sampler.sampler.set_epoch(runner.epoch) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/sync_buffer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/sync_buffer.py new file mode 100644 index 00000000..6376b7ff --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/hooks/sync_buffer.py @@ -0,0 +1,22 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ..dist_utils import allreduce_params +from .hook import HOOKS, Hook + + +@HOOKS.register_module() +class SyncBuffersHook(Hook): + """Synchronize model buffers such as running_mean and running_var in BN at + the end of each epoch. + + Args: + distributed (bool): Whether distributed training is used. It is + effective only for distributed training. Defaults to True. + """ + + def __init__(self, distributed=True): + self.distributed = distributed + + def after_epoch(self, runner): + """All-reduce model buffers at the end of each epoch.""" + if self.distributed: + allreduce_params(runner.model.buffers()) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/iter_based_runner.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/iter_based_runner.py new file mode 100644 index 00000000..075e4b93 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/iter_based_runner.py @@ -0,0 +1,273 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +import platform +import shutil +import time +import warnings + +import torch +from torch.optim import Optimizer + +import custom_mmpkg.custom_mmcv as mmcv +from .base_runner import BaseRunner +from .builder import RUNNERS +from .checkpoint import save_checkpoint +from .hooks import IterTimerHook +from .utils import get_host_info + + +class IterLoader: + + def __init__(self, dataloader): + self._dataloader = dataloader + self.iter_loader = iter(self._dataloader) + self._epoch = 0 + + @property + def epoch(self): + return self._epoch + + def __next__(self): + try: + data = next(self.iter_loader) + except StopIteration: + self._epoch += 1 + if hasattr(self._dataloader.sampler, 'set_epoch'): + self._dataloader.sampler.set_epoch(self._epoch) + time.sleep(2) # Prevent possible deadlock during epoch transition + self.iter_loader = iter(self._dataloader) + data = next(self.iter_loader) + + return data + + def __len__(self): + return len(self._dataloader) + + +@RUNNERS.register_module() +class IterBasedRunner(BaseRunner): + """Iteration-based Runner. + + This runner train models iteration by iteration. + """ + + def train(self, data_loader, **kwargs): + self.model.train() + self.mode = 'train' + self.data_loader = data_loader + self._epoch = data_loader.epoch + data_batch = next(data_loader) + self.call_hook('before_train_iter') + outputs = self.model.train_step(data_batch, self.optimizer, **kwargs) + if not isinstance(outputs, dict): + raise TypeError('model.train_step() must return a dict') + if 'log_vars' in outputs: + self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) + self.outputs = outputs + self.call_hook('after_train_iter') + self._inner_iter += 1 + self._iter += 1 + + @torch.no_grad() + def val(self, data_loader, **kwargs): + self.model.eval() + self.mode = 'val' + self.data_loader = data_loader + data_batch = next(data_loader) + self.call_hook('before_val_iter') + outputs = self.model.val_step(data_batch, **kwargs) + if not isinstance(outputs, dict): + raise TypeError('model.val_step() must return a dict') + if 'log_vars' in outputs: + self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) + self.outputs = outputs + self.call_hook('after_val_iter') + self._inner_iter += 1 + + def run(self, data_loaders, workflow, max_iters=None, **kwargs): + """Start running. + + Args: + data_loaders (list[:obj:`DataLoader`]): Dataloaders for training + and validation. + workflow (list[tuple]): A list of (phase, iters) to specify the + running order and iterations. E.g, [('train', 10000), + ('val', 1000)] means running 10000 iterations for training and + 1000 iterations for validation, iteratively. + """ + assert isinstance(data_loaders, list) + assert mmcv.is_list_of(workflow, tuple) + assert len(data_loaders) == len(workflow) + if max_iters is not None: + warnings.warn( + 'setting max_iters in run is deprecated, ' + 'please set max_iters in runner_config', DeprecationWarning) + self._max_iters = max_iters + assert self._max_iters is not None, ( + 'max_iters must be specified during instantiation') + + work_dir = self.work_dir if self.work_dir is not None else 'NONE' + self.logger.info('Start running, host: %s, work_dir: %s', + get_host_info(), work_dir) + self.logger.info('Hooks will be executed in the following order:\n%s', + self.get_hook_info()) + self.logger.info('workflow: %s, max: %d iters', workflow, + self._max_iters) + self.call_hook('before_run') + + iter_loaders = [IterLoader(x) for x in data_loaders] + + self.call_hook('before_epoch') + + while self.iter < self._max_iters: + for i, flow in enumerate(workflow): + self._inner_iter = 0 + mode, iters = flow + if not isinstance(mode, str) or not hasattr(self, mode): + raise ValueError( + 'runner has no method named "{}" to run a workflow'. + format(mode)) + iter_runner = getattr(self, mode) + for _ in range(iters): + if mode == 'train' and self.iter >= self._max_iters: + break + iter_runner(iter_loaders[i], **kwargs) + + time.sleep(1) # wait for some hooks like loggers to finish + self.call_hook('after_epoch') + self.call_hook('after_run') + + def resume(self, + checkpoint, + resume_optimizer=True, + map_location='default'): + """Resume model from checkpoint. + + Args: + checkpoint (str): Checkpoint to resume from. + resume_optimizer (bool, optional): Whether resume the optimizer(s) + if the checkpoint file includes optimizer(s). Default to True. + map_location (str, optional): Same as :func:`torch.load`. + Default to 'default'. + """ + if map_location == 'default': + device_id = torch.cuda.current_device() + checkpoint = self.load_checkpoint( + checkpoint, + map_location=lambda storage, loc: storage.cuda(device_id)) + else: + checkpoint = self.load_checkpoint( + checkpoint, map_location=map_location) + + self._epoch = checkpoint['meta']['epoch'] + self._iter = checkpoint['meta']['iter'] + self._inner_iter = checkpoint['meta']['iter'] + if 'optimizer' in checkpoint and resume_optimizer: + if isinstance(self.optimizer, Optimizer): + self.optimizer.load_state_dict(checkpoint['optimizer']) + elif isinstance(self.optimizer, dict): + for k in self.optimizer.keys(): + self.optimizer[k].load_state_dict( + checkpoint['optimizer'][k]) + else: + raise TypeError( + 'Optimizer should be dict or torch.optim.Optimizer ' + f'but got {type(self.optimizer)}') + + self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}') + + def save_checkpoint(self, + out_dir, + filename_tmpl='iter_{}.pth', + meta=None, + save_optimizer=True, + create_symlink=True): + """Save checkpoint to file. + + Args: + out_dir (str): Directory to save checkpoint files. + filename_tmpl (str, optional): Checkpoint file template. + Defaults to 'iter_{}.pth'. + meta (dict, optional): Metadata to be saved in checkpoint. + Defaults to None. + save_optimizer (bool, optional): Whether save optimizer. + Defaults to True. + create_symlink (bool, optional): Whether create symlink to the + latest checkpoint file. Defaults to True. + """ + if meta is None: + meta = {} + elif not isinstance(meta, dict): + raise TypeError( + f'meta should be a dict or None, but got {type(meta)}') + if self.meta is not None: + meta.update(self.meta) + # Note: meta.update(self.meta) should be done before + # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise + # there will be problems with resumed checkpoints. + # More details in https://github.com/open-mmlab/mmcv/pull/1108 + meta.update(epoch=self.epoch + 1, iter=self.iter) + + filename = filename_tmpl.format(self.iter + 1) + filepath = osp.join(out_dir, filename) + optimizer = self.optimizer if save_optimizer else None + save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta) + # in some environments, `os.symlink` is not supported, you may need to + # set `create_symlink` to False + if create_symlink: + dst_file = osp.join(out_dir, 'latest.pth') + if platform.system() != 'Windows': + mmcv.symlink(filename, dst_file) + else: + shutil.copy(filepath, dst_file) + + def register_training_hooks(self, + lr_config, + optimizer_config=None, + checkpoint_config=None, + log_config=None, + momentum_config=None, + custom_hooks_config=None): + """Register default hooks for iter-based training. + + Checkpoint hook, optimizer stepper hook and logger hooks will be set to + `by_epoch=False` by default. + + Default hooks include: + + +----------------------+-------------------------+ + | Hooks | Priority | + +======================+=========================+ + | LrUpdaterHook | VERY_HIGH (10) | + +----------------------+-------------------------+ + | MomentumUpdaterHook | HIGH (30) | + +----------------------+-------------------------+ + | OptimizerStepperHook | ABOVE_NORMAL (40) | + +----------------------+-------------------------+ + | CheckpointSaverHook | NORMAL (50) | + +----------------------+-------------------------+ + | IterTimerHook | LOW (70) | + +----------------------+-------------------------+ + | LoggerHook(s) | VERY_LOW (90) | + +----------------------+-------------------------+ + | CustomHook(s) | defaults to NORMAL (50) | + +----------------------+-------------------------+ + + If custom hooks have same priority with default hooks, custom hooks + will be triggered after default hooks. + """ + if checkpoint_config is not None: + checkpoint_config.setdefault('by_epoch', False) + if lr_config is not None: + lr_config.setdefault('by_epoch', False) + if log_config is not None: + for info in log_config['hooks']: + info.setdefault('by_epoch', False) + super(IterBasedRunner, self).register_training_hooks( + lr_config=lr_config, + momentum_config=momentum_config, + optimizer_config=optimizer_config, + checkpoint_config=checkpoint_config, + log_config=log_config, + timer_config=IterTimerHook(), + custom_hooks_config=custom_hooks_config) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/log_buffer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/log_buffer.py new file mode 100644 index 00000000..d949e294 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/log_buffer.py @@ -0,0 +1,41 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from collections import OrderedDict + +import numpy as np + + +class LogBuffer: + + def __init__(self): + self.val_history = OrderedDict() + self.n_history = OrderedDict() + self.output = OrderedDict() + self.ready = False + + def clear(self): + self.val_history.clear() + self.n_history.clear() + self.clear_output() + + def clear_output(self): + self.output.clear() + self.ready = False + + def update(self, vars, count=1): + assert isinstance(vars, dict) + for key, var in vars.items(): + if key not in self.val_history: + self.val_history[key] = [] + self.n_history[key] = [] + self.val_history[key].append(var) + self.n_history[key].append(count) + + def average(self, n=0): + """Average latest n values or all values.""" + assert n >= 0 + for key in self.val_history: + values = np.array(self.val_history[key][-n:]) + nums = np.array(self.n_history[key][-n:]) + avg = np.sum(values * nums) / np.sum(nums) + self.output[key] = avg + self.ready = True diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/__init__.py new file mode 100644 index 00000000..53c34d04 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .builder import (OPTIMIZER_BUILDERS, OPTIMIZERS, build_optimizer, + build_optimizer_constructor) +from .default_constructor import DefaultOptimizerConstructor + +__all__ = [ + 'OPTIMIZER_BUILDERS', 'OPTIMIZERS', 'DefaultOptimizerConstructor', + 'build_optimizer', 'build_optimizer_constructor' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/builder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/builder.py new file mode 100644 index 00000000..f9234eed --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/builder.py @@ -0,0 +1,44 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +import inspect + +import torch + +from ...utils import Registry, build_from_cfg + +OPTIMIZERS = Registry('optimizer') +OPTIMIZER_BUILDERS = Registry('optimizer builder') + + +def register_torch_optimizers(): + torch_optimizers = [] + for module_name in dir(torch.optim): + if module_name.startswith('__'): + continue + _optim = getattr(torch.optim, module_name) + if inspect.isclass(_optim) and issubclass(_optim, + torch.optim.Optimizer): + OPTIMIZERS.register_module()(_optim) + torch_optimizers.append(module_name) + return torch_optimizers + + +TORCH_OPTIMIZERS = register_torch_optimizers() + + +def build_optimizer_constructor(cfg): + return build_from_cfg(cfg, OPTIMIZER_BUILDERS) + + +def build_optimizer(model, cfg): + optimizer_cfg = copy.deepcopy(cfg) + constructor_type = optimizer_cfg.pop('constructor', + 'DefaultOptimizerConstructor') + paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None) + optim_constructor = build_optimizer_constructor( + dict( + type=constructor_type, + optimizer_cfg=optimizer_cfg, + paramwise_cfg=paramwise_cfg)) + optimizer = optim_constructor(model) + return optimizer diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/default_constructor.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/default_constructor.py new file mode 100644 index 00000000..59019558 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/optimizer/default_constructor.py @@ -0,0 +1,249 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings + +import torch +from torch.nn import GroupNorm, LayerNorm + +from custom_mmpkg.custom_mmcv.utils import _BatchNorm, _InstanceNorm, build_from_cfg, is_list_of +from custom_mmpkg.custom_mmcv.utils.ext_loader import check_ops_exist +from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS + + +@OPTIMIZER_BUILDERS.register_module() +class DefaultOptimizerConstructor: + """Default constructor for optimizers. + + By default each parameter share the same optimizer settings, and we + provide an argument ``paramwise_cfg`` to specify parameter-wise settings. + It is a dict and may contain the following fields: + + - ``custom_keys`` (dict): Specified parameters-wise settings by keys. If + one of the keys in ``custom_keys`` is a substring of the name of one + parameter, then the setting of the parameter will be specified by + ``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will + be ignored. It should be noted that the aforementioned ``key`` is the + longest key that is a substring of the name of the parameter. If there + are multiple matched keys with the same length, then the key with lower + alphabet order will be chosen. + ``custom_keys[key]`` should be a dict and may contain fields ``lr_mult`` + and ``decay_mult``. See Example 2 below. + - ``bias_lr_mult`` (float): It will be multiplied to the learning + rate for all bias parameters (except for those in normalization + layers and offset layers of DCN). + - ``bias_decay_mult`` (float): It will be multiplied to the weight + decay for all bias parameters (except for those in + normalization layers, depthwise conv layers, offset layers of DCN). + - ``norm_decay_mult`` (float): It will be multiplied to the weight + decay for all weight and bias parameters of normalization + layers. + - ``dwconv_decay_mult`` (float): It will be multiplied to the weight + decay for all weight and bias parameters of depthwise conv + layers. + - ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning + rate for parameters of offset layer in the deformable convs + of a model. + - ``bypass_duplicate`` (bool): If true, the duplicate parameters + would not be added into optimizer. Default: False. + + Note: + 1. If the option ``dcn_offset_lr_mult`` is used, the constructor will + override the effect of ``bias_lr_mult`` in the bias of offset + layer. So be careful when using both ``bias_lr_mult`` and + ``dcn_offset_lr_mult``. If you wish to apply both of them to the + offset layer in deformable convs, set ``dcn_offset_lr_mult`` + to the original ``dcn_offset_lr_mult`` * ``bias_lr_mult``. + 2. If the option ``dcn_offset_lr_mult`` is used, the constructor will + apply it to all the DCN layers in the model. So be careful when + the model contains multiple DCN layers in places other than + backbone. + + Args: + model (:obj:`nn.Module`): The model with parameters to be optimized. + optimizer_cfg (dict): The config dict of the optimizer. + Positional fields are + + - `type`: class name of the optimizer. + + Optional fields are + + - any arguments of the corresponding optimizer type, e.g., + lr, weight_decay, momentum, etc. + paramwise_cfg (dict, optional): Parameter-wise options. + + Example 1: + >>> model = torch.nn.modules.Conv1d(1, 1, 1) + >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9, + >>> weight_decay=0.0001) + >>> paramwise_cfg = dict(norm_decay_mult=0.) + >>> optim_builder = DefaultOptimizerConstructor( + >>> optimizer_cfg, paramwise_cfg) + >>> optimizer = optim_builder(model) + + Example 2: + >>> # assume model have attribute model.backbone and model.cls_head + >>> optimizer_cfg = dict(type='SGD', lr=0.01, weight_decay=0.95) + >>> paramwise_cfg = dict(custom_keys={ + '.backbone': dict(lr_mult=0.1, decay_mult=0.9)}) + >>> optim_builder = DefaultOptimizerConstructor( + >>> optimizer_cfg, paramwise_cfg) + >>> optimizer = optim_builder(model) + >>> # Then the `lr` and `weight_decay` for model.backbone is + >>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for + >>> # model.cls_head is (0.01, 0.95). + """ + + def __init__(self, optimizer_cfg, paramwise_cfg=None): + if not isinstance(optimizer_cfg, dict): + raise TypeError('optimizer_cfg should be a dict', + f'but got {type(optimizer_cfg)}') + self.optimizer_cfg = optimizer_cfg + self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg + self.base_lr = optimizer_cfg.get('lr', None) + self.base_wd = optimizer_cfg.get('weight_decay', None) + self._validate_cfg() + + def _validate_cfg(self): + if not isinstance(self.paramwise_cfg, dict): + raise TypeError('paramwise_cfg should be None or a dict, ' + f'but got {type(self.paramwise_cfg)}') + + if 'custom_keys' in self.paramwise_cfg: + if not isinstance(self.paramwise_cfg['custom_keys'], dict): + raise TypeError( + 'If specified, custom_keys must be a dict, ' + f'but got {type(self.paramwise_cfg["custom_keys"])}') + if self.base_wd is None: + for key in self.paramwise_cfg['custom_keys']: + if 'decay_mult' in self.paramwise_cfg['custom_keys'][key]: + raise ValueError('base_wd should not be None') + + # get base lr and weight decay + # weight_decay must be explicitly specified if mult is specified + if ('bias_decay_mult' in self.paramwise_cfg + or 'norm_decay_mult' in self.paramwise_cfg + or 'dwconv_decay_mult' in self.paramwise_cfg): + if self.base_wd is None: + raise ValueError('base_wd should not be None') + + def _is_in(self, param_group, param_group_list): + assert is_list_of(param_group_list, dict) + param = set(param_group['params']) + param_set = set() + for group in param_group_list: + param_set.update(set(group['params'])) + + return not param.isdisjoint(param_set) + + def add_params(self, params, module, prefix='', is_dcn_module=None): + """Add all parameters of module to the params list. + + The parameters of the given module will be added to the list of param + groups, with specific rules defined by paramwise_cfg. + + Args: + params (list[dict]): A list of param groups, it will be modified + in place. + module (nn.Module): The module to be added. + prefix (str): The prefix of the module + is_dcn_module (int|float|None): If the current module is a + submodule of DCN, `is_dcn_module` will be passed to + control conv_offset layer's learning rate. Defaults to None. + """ + # get param-wise options + custom_keys = self.paramwise_cfg.get('custom_keys', {}) + # first sort with alphabet order and then sort with reversed len of str + sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True) + + bias_lr_mult = self.paramwise_cfg.get('bias_lr_mult', 1.) + bias_decay_mult = self.paramwise_cfg.get('bias_decay_mult', 1.) + norm_decay_mult = self.paramwise_cfg.get('norm_decay_mult', 1.) + dwconv_decay_mult = self.paramwise_cfg.get('dwconv_decay_mult', 1.) + bypass_duplicate = self.paramwise_cfg.get('bypass_duplicate', False) + dcn_offset_lr_mult = self.paramwise_cfg.get('dcn_offset_lr_mult', 1.) + + # special rules for norm layers and depth-wise conv layers + is_norm = isinstance(module, + (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)) + is_dwconv = ( + isinstance(module, torch.nn.Conv2d) + and module.in_channels == module.groups) + + for name, param in module.named_parameters(recurse=False): + param_group = {'params': [param]} + if not param.requires_grad: + params.append(param_group) + continue + if bypass_duplicate and self._is_in(param_group, params): + warnings.warn(f'{prefix} is duplicate. It is skipped since ' + f'bypass_duplicate={bypass_duplicate}') + continue + # if the parameter match one of the custom keys, ignore other rules + is_custom = False + for key in sorted_keys: + if key in f'{prefix}.{name}': + is_custom = True + lr_mult = custom_keys[key].get('lr_mult', 1.) + param_group['lr'] = self.base_lr * lr_mult + if self.base_wd is not None: + decay_mult = custom_keys[key].get('decay_mult', 1.) + param_group['weight_decay'] = self.base_wd * decay_mult + break + + if not is_custom: + # bias_lr_mult affects all bias parameters + # except for norm.bias dcn.conv_offset.bias + if name == 'bias' and not (is_norm or is_dcn_module): + param_group['lr'] = self.base_lr * bias_lr_mult + + if (prefix.find('conv_offset') != -1 and is_dcn_module + and isinstance(module, torch.nn.Conv2d)): + # deal with both dcn_offset's bias & weight + param_group['lr'] = self.base_lr * dcn_offset_lr_mult + + # apply weight decay policies + if self.base_wd is not None: + # norm decay + if is_norm: + param_group[ + 'weight_decay'] = self.base_wd * norm_decay_mult + # depth-wise conv + elif is_dwconv: + param_group[ + 'weight_decay'] = self.base_wd * dwconv_decay_mult + # bias lr and decay + elif name == 'bias' and not is_dcn_module: + # TODO: current bias_decay_mult will have affect on DCN + param_group[ + 'weight_decay'] = self.base_wd * bias_decay_mult + params.append(param_group) + + if check_ops_exist(): + from custom_mmpkg.custom_mmcv.ops import DeformConv2d, ModulatedDeformConv2d + is_dcn_module = isinstance(module, + (DeformConv2d, ModulatedDeformConv2d)) + else: + is_dcn_module = False + for child_name, child_mod in module.named_children(): + child_prefix = f'{prefix}.{child_name}' if prefix else child_name + self.add_params( + params, + child_mod, + prefix=child_prefix, + is_dcn_module=is_dcn_module) + + def __call__(self, model): + if hasattr(model, 'module'): + model = model.module + + optimizer_cfg = self.optimizer_cfg.copy() + # if no paramwise option is specified, just use the global setting + if not self.paramwise_cfg: + optimizer_cfg['params'] = model.parameters() + return build_from_cfg(optimizer_cfg, OPTIMIZERS) + + # set param-wise lr and weight decay recursively + params = [] + self.add_params(params, model) + optimizer_cfg['params'] = params + + return build_from_cfg(optimizer_cfg, OPTIMIZERS) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/priority.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/priority.py new file mode 100644 index 00000000..64cc4e3a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/priority.py @@ -0,0 +1,60 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from enum import Enum + + +class Priority(Enum): + """Hook priority levels. + + +--------------+------------+ + | Level | Value | + +==============+============+ + | HIGHEST | 0 | + +--------------+------------+ + | VERY_HIGH | 10 | + +--------------+------------+ + | HIGH | 30 | + +--------------+------------+ + | ABOVE_NORMAL | 40 | + +--------------+------------+ + | NORMAL | 50 | + +--------------+------------+ + | BELOW_NORMAL | 60 | + +--------------+------------+ + | LOW | 70 | + +--------------+------------+ + | VERY_LOW | 90 | + +--------------+------------+ + | LOWEST | 100 | + +--------------+------------+ + """ + + HIGHEST = 0 + VERY_HIGH = 10 + HIGH = 30 + ABOVE_NORMAL = 40 + NORMAL = 50 + BELOW_NORMAL = 60 + LOW = 70 + VERY_LOW = 90 + LOWEST = 100 + + +def get_priority(priority): + """Get priority value. + + Args: + priority (int or str or :obj:`Priority`): Priority. + + Returns: + int: The priority value. + """ + if isinstance(priority, int): + if priority < 0 or priority > 100: + raise ValueError('priority must be between 0 and 100') + return priority + elif isinstance(priority, Priority): + return priority.value + elif isinstance(priority, str): + return Priority[priority.upper()].value + else: + raise TypeError('priority must be an integer or Priority enum value') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/utils.py new file mode 100644 index 00000000..32fa4a72 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/runner/utils.py @@ -0,0 +1,93 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os +import random +import sys +import time +import warnings +from getpass import getuser +from socket import gethostname + +import numpy as np +import torch + +import custom_mmpkg.custom_mmcv as mmcv + + +def get_host_info(): + """Get hostname and username. + + Return empty string if exception raised, e.g. ``getpass.getuser()`` will + lead to error in docker container + """ + host = '' + try: + host = f'{getuser()}@{gethostname()}' + except Exception as e: + warnings.warn(f'Host or user not found: {str(e)}') + finally: + return host + + +def get_time_str(): + return time.strftime('%Y%m%d_%H%M%S', time.localtime()) + + +def obj_from_dict(info, parent=None, default_args=None): + """Initialize an object from dict. + + The dict must contain the key "type", which indicates the object type, it + can be either a string or type, such as "list" or ``list``. Remaining + fields are treated as the arguments for constructing the object. + + Args: + info (dict): Object types and arguments. + parent (:class:`module`): Module which may containing expected object + classes. + default_args (dict, optional): Default arguments for initializing the + object. + + Returns: + any type: Object built from the dict. + """ + assert isinstance(info, dict) and 'type' in info + assert isinstance(default_args, dict) or default_args is None + args = info.copy() + obj_type = args.pop('type') + if mmcv.is_str(obj_type): + if parent is not None: + obj_type = getattr(parent, obj_type) + else: + obj_type = sys.modules[obj_type] + elif not isinstance(obj_type, type): + raise TypeError('type must be a str or valid type, but ' + f'got {type(obj_type)}') + if default_args is not None: + for name, value in default_args.items(): + args.setdefault(name, value) + return obj_type(**args) + + +def set_random_seed(seed, deterministic=False, use_rank_shift=False): + """Set random seed. + + Args: + seed (int): Seed to be used. + deterministic (bool): Whether to set the deterministic option for + CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` + to True and `torch.backends.cudnn.benchmark` to False. + Default: False. + rank_shift (bool): Whether to add rank number to the random seed to + have different random seed in different threads. Default: False. + """ + if use_rank_shift: + rank, _ = mmcv.runner.get_dist_info() + seed += rank + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + if deterministic: + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/__init__.py new file mode 100644 index 00000000..378a0068 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/__init__.py @@ -0,0 +1,69 @@ +# flake8: noqa +# Copyright (c) OpenMMLab. All rights reserved. +from .config import Config, ConfigDict, DictAction +from .misc import (check_prerequisites, concat_list, deprecated_api_warning, + has_method, import_modules_from_strings, is_list_of, + is_method_overridden, is_seq_of, is_str, is_tuple_of, + iter_cast, list_cast, requires_executable, requires_package, + slice_list, to_1tuple, to_2tuple, to_3tuple, to_4tuple, + to_ntuple, tuple_cast) +from .path import (check_file_exist, fopen, is_filepath, mkdir_or_exist, + scandir, symlink) +from .progressbar import (ProgressBar, track_iter_progress, + track_parallel_progress, track_progress) +from .testing import (assert_attrs_equal, assert_dict_contains_subset, + assert_dict_has_keys, assert_is_norm_layer, + assert_keys_equal, assert_params_all_zeros, + check_python_script) +from .timer import Timer, TimerError, check_time +from .version_utils import digit_version, get_git_hash + +try: + import torch +except ImportError: + __all__ = [ + 'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast', + 'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of', + 'slice_list', 'concat_list', 'check_prerequisites', 'requires_package', + 'requires_executable', 'is_filepath', 'fopen', 'check_file_exist', + 'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar', + 'track_progress', 'track_iter_progress', 'track_parallel_progress', + 'Timer', 'TimerError', 'check_time', 'deprecated_api_warning', + 'digit_version', 'get_git_hash', 'import_modules_from_strings', + 'assert_dict_contains_subset', 'assert_attrs_equal', + 'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script', + 'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple', + 'is_method_overridden', 'has_method' + ] +else: + from .env import collect_env + from .logging import get_logger, print_log + from .parrots_jit import jit, skip_no_elena + from .parrots_wrapper import ( + TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader, + PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, + _AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm, + _MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home) + from .registry import Registry, build_from_cfg + from .trace import is_jit_tracing + __all__ = [ + 'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger', + 'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast', + 'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list', + 'check_prerequisites', 'requires_package', 'requires_executable', + 'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist', + 'symlink', 'scandir', 'ProgressBar', 'track_progress', + 'track_iter_progress', 'track_parallel_progress', 'Registry', + 'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm', + '_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm', + '_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd', + 'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension', + 'DataLoader', 'PoolDataLoader', 'TORCH_VERSION', + 'deprecated_api_warning', 'digit_version', 'get_git_hash', + 'import_modules_from_strings', 'jit', 'skip_no_elena', + 'assert_dict_contains_subset', 'assert_attrs_equal', + 'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer', + 'assert_params_all_zeros', 'check_python_script', + 'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch', + '_get_cuda_home', 'has_method' + ] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/config.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/config.py new file mode 100644 index 00000000..098a7067 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/config.py @@ -0,0 +1,688 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import ast +import copy +import os +import os.path as osp +import platform +import shutil +import sys +import tempfile +import uuid +import warnings +from argparse import Action, ArgumentParser +from collections import abc +from importlib import import_module + +from addict import Dict +from yapf.yapflib.yapf_api import FormatCode + +from .misc import import_modules_from_strings +from .path import check_file_exist + +if platform.system() == 'Windows': + import regex as re +else: + import re + +BASE_KEY = '_base_' +DELETE_KEY = '_delete_' +DEPRECATION_KEY = '_deprecation_' +RESERVED_KEYS = ['filename', 'text', 'pretty_text'] + + +class ConfigDict(Dict): + + def __missing__(self, name): + raise KeyError(name) + + def __getattr__(self, name): + try: + value = super(ConfigDict, self).__getattr__(name) + except KeyError: + ex = AttributeError(f"'{self.__class__.__name__}' object has no " + f"attribute '{name}'") + except Exception as e: + ex = e + else: + return value + raise ex + + +def add_args(parser, cfg, prefix=''): + for k, v in cfg.items(): + if isinstance(v, str): + parser.add_argument('--' + prefix + k) + elif isinstance(v, int): + parser.add_argument('--' + prefix + k, type=int) + elif isinstance(v, float): + parser.add_argument('--' + prefix + k, type=float) + elif isinstance(v, bool): + parser.add_argument('--' + prefix + k, action='store_true') + elif isinstance(v, dict): + add_args(parser, v, prefix + k + '.') + elif isinstance(v, abc.Iterable): + parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+') + else: + print(f'cannot parse key {prefix + k} of type {type(v)}') + return parser + + +class Config: + """A facility for config and config files. + + It supports common file formats as configs: python/json/yaml. The interface + is the same as a dict object and also allows access config values as + attributes. + + Example: + >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) + >>> cfg.a + 1 + >>> cfg.b + {'b1': [0, 1]} + >>> cfg.b.b1 + [0, 1] + >>> cfg = Config.fromfile('tests/data/config/a.py') + >>> cfg.filename + "/home/kchen/projects/mmcv/tests/data/config/a.py" + >>> cfg.item4 + 'test' + >>> cfg + "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: " + "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" + """ + + @staticmethod + def _validate_py_syntax(filename): + with open(filename, 'r', encoding='utf-8') as f: + # Setting encoding explicitly to resolve coding issue on windows + content = f.read() + try: + ast.parse(content) + except SyntaxError as e: + raise SyntaxError('There are syntax errors in config ' + f'file {filename}: {e}') + + @staticmethod + def _substitute_predefined_vars(filename, temp_config_name): + file_dirname = osp.dirname(filename) + file_basename = osp.basename(filename) + file_basename_no_extension = osp.splitext(file_basename)[0] + file_extname = osp.splitext(filename)[1] + support_templates = dict( + fileDirname=file_dirname, + fileBasename=file_basename, + fileBasenameNoExtension=file_basename_no_extension, + fileExtname=file_extname) + with open(filename, 'r', encoding='utf-8') as f: + # Setting encoding explicitly to resolve coding issue on windows + config_file = f.read() + for key, value in support_templates.items(): + regexp = r'\{\{\s*' + str(key) + r'\s*\}\}' + value = value.replace('\\', '/') + config_file = re.sub(regexp, value, config_file) + with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file: + tmp_config_file.write(config_file) + + @staticmethod + def _pre_substitute_base_vars(filename, temp_config_name): + """Substitute base variable placehoders to string, so that parsing + would work.""" + with open(filename, 'r', encoding='utf-8') as f: + # Setting encoding explicitly to resolve coding issue on windows + config_file = f.read() + base_var_dict = {} + regexp = r'\{\{\s*' + BASE_KEY + r'\.([\w\.]+)\s*\}\}' + base_vars = set(re.findall(regexp, config_file)) + for base_var in base_vars: + randstr = f'_{base_var}_{uuid.uuid4().hex.lower()[:6]}' + base_var_dict[randstr] = base_var + regexp = r'\{\{\s*' + BASE_KEY + r'\.' + base_var + r'\s*\}\}' + config_file = re.sub(regexp, f'"{randstr}"', config_file) + with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file: + tmp_config_file.write(config_file) + return base_var_dict + + @staticmethod + def _substitute_base_vars(cfg, base_var_dict, base_cfg): + """Substitute variable strings to their actual values.""" + cfg = copy.deepcopy(cfg) + + if isinstance(cfg, dict): + for k, v in cfg.items(): + if isinstance(v, str) and v in base_var_dict: + new_v = base_cfg + for new_k in base_var_dict[v].split('.'): + new_v = new_v[new_k] + cfg[k] = new_v + elif isinstance(v, (list, tuple, dict)): + cfg[k] = Config._substitute_base_vars( + v, base_var_dict, base_cfg) + elif isinstance(cfg, tuple): + cfg = tuple( + Config._substitute_base_vars(c, base_var_dict, base_cfg) + for c in cfg) + elif isinstance(cfg, list): + cfg = [ + Config._substitute_base_vars(c, base_var_dict, base_cfg) + for c in cfg + ] + elif isinstance(cfg, str) and cfg in base_var_dict: + new_v = base_cfg + for new_k in base_var_dict[cfg].split('.'): + new_v = new_v[new_k] + cfg = new_v + + return cfg + + @staticmethod + def _file2dict(filename, use_predefined_variables=True): + filename = osp.abspath(osp.expanduser(filename)) + check_file_exist(filename) + fileExtname = osp.splitext(filename)[1] + if fileExtname not in ['.py', '.json', '.yaml', '.yml']: + raise IOError('Only py/yml/yaml/json type are supported now!') + + with tempfile.TemporaryDirectory() as temp_config_dir: + temp_config_file = tempfile.NamedTemporaryFile( + dir=temp_config_dir, suffix=fileExtname) + if platform.system() == 'Windows': + temp_config_file.close() + temp_config_name = osp.basename(temp_config_file.name) + # Substitute predefined variables + if use_predefined_variables: + Config._substitute_predefined_vars(filename, + temp_config_file.name) + else: + shutil.copyfile(filename, temp_config_file.name) + # Substitute base variables from placeholders to strings + base_var_dict = Config._pre_substitute_base_vars( + temp_config_file.name, temp_config_file.name) + + if filename.endswith('.py'): + temp_module_name = osp.splitext(temp_config_name)[0] + sys.path.insert(0, temp_config_dir) + Config._validate_py_syntax(filename) + mod = import_module(temp_module_name) + sys.path.pop(0) + cfg_dict = { + name: value + for name, value in mod.__dict__.items() + if not name.startswith('__') + } + # delete imported module + del sys.modules[temp_module_name] + elif filename.endswith(('.yml', '.yaml', '.json')): + import custom_mmpkg.custom_mmcv as mmcv + cfg_dict = mmcv.load(temp_config_file.name) + # close temp file + temp_config_file.close() + + # check deprecation information + if DEPRECATION_KEY in cfg_dict: + deprecation_info = cfg_dict.pop(DEPRECATION_KEY) + warning_msg = f'The config file {filename} will be deprecated ' \ + 'in the future.' + if 'expected' in deprecation_info: + warning_msg += f' Please use {deprecation_info["expected"]} ' \ + 'instead.' + if 'reference' in deprecation_info: + warning_msg += ' More information can be found at ' \ + f'{deprecation_info["reference"]}' + warnings.warn(warning_msg) + + cfg_text = filename + '\n' + with open(filename, 'r', encoding='utf-8') as f: + # Setting encoding explicitly to resolve coding issue on windows + cfg_text += f.read() + + if BASE_KEY in cfg_dict: + cfg_dir = osp.dirname(filename) + base_filename = cfg_dict.pop(BASE_KEY) + base_filename = base_filename if isinstance( + base_filename, list) else [base_filename] + + cfg_dict_list = list() + cfg_text_list = list() + for f in base_filename: + _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f)) + cfg_dict_list.append(_cfg_dict) + cfg_text_list.append(_cfg_text) + + base_cfg_dict = dict() + for c in cfg_dict_list: + duplicate_keys = base_cfg_dict.keys() & c.keys() + if len(duplicate_keys) > 0: + raise KeyError('Duplicate key is not allowed among bases. ' + f'Duplicate keys: {duplicate_keys}') + base_cfg_dict.update(c) + + # Substitute base variables from strings to their actual values + cfg_dict = Config._substitute_base_vars(cfg_dict, base_var_dict, + base_cfg_dict) + + base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict) + cfg_dict = base_cfg_dict + + # merge cfg_text + cfg_text_list.append(cfg_text) + cfg_text = '\n'.join(cfg_text_list) + + return cfg_dict, cfg_text + + @staticmethod + def _merge_a_into_b(a, b, allow_list_keys=False): + """merge dict ``a`` into dict ``b`` (non-inplace). + + Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid + in-place modifications. + + Args: + a (dict): The source dict to be merged into ``b``. + b (dict): The origin dict to be fetch keys from ``a``. + allow_list_keys (bool): If True, int string keys (e.g. '0', '1') + are allowed in source ``a`` and will replace the element of the + corresponding index in b if b is a list. Default: False. + + Returns: + dict: The modified dict of ``b`` using ``a``. + + Examples: + # Normally merge a into b. + >>> Config._merge_a_into_b( + ... dict(obj=dict(a=2)), dict(obj=dict(a=1))) + {'obj': {'a': 2}} + + # Delete b first and merge a into b. + >>> Config._merge_a_into_b( + ... dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1))) + {'obj': {'a': 2}} + + # b is a list + >>> Config._merge_a_into_b( + ... {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True) + [{'a': 2}, {'b': 2}] + """ + b = b.copy() + for k, v in a.items(): + if allow_list_keys and k.isdigit() and isinstance(b, list): + k = int(k) + if len(b) <= k: + raise KeyError(f'Index {k} exceeds the length of list {b}') + b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys) + elif isinstance(v, + dict) and k in b and not v.pop(DELETE_KEY, False): + allowed_types = (dict, list) if allow_list_keys else dict + if not isinstance(b[k], allowed_types): + raise TypeError( + f'{k}={v} in child config cannot inherit from base ' + f'because {k} is a dict in the child config but is of ' + f'type {type(b[k])} in base config. You may set ' + f'`{DELETE_KEY}=True` to ignore the base config') + b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys) + else: + b[k] = v + return b + + @staticmethod + def fromfile(filename, + use_predefined_variables=True, + import_custom_modules=True): + cfg_dict, cfg_text = Config._file2dict(filename, + use_predefined_variables) + if import_custom_modules and cfg_dict.get('custom_imports', None): + import_modules_from_strings(**cfg_dict['custom_imports']) + return Config(cfg_dict, cfg_text=cfg_text, filename=filename) + + @staticmethod + def fromstring(cfg_str, file_format): + """Generate config from config str. + + Args: + cfg_str (str): Config str. + file_format (str): Config file format corresponding to the + config str. Only py/yml/yaml/json type are supported now! + + Returns: + obj:`Config`: Config obj. + """ + if file_format not in ['.py', '.json', '.yaml', '.yml']: + raise IOError('Only py/yml/yaml/json type are supported now!') + if file_format != '.py' and 'dict(' in cfg_str: + # check if users specify a wrong suffix for python + warnings.warn( + 'Please check "file_format", the file format may be .py') + with tempfile.NamedTemporaryFile( + 'w', encoding='utf-8', suffix=file_format, + delete=False) as temp_file: + temp_file.write(cfg_str) + # on windows, previous implementation cause error + # see PR 1077 for details + cfg = Config.fromfile(temp_file.name) + os.remove(temp_file.name) + return cfg + + @staticmethod + def auto_argparser(description=None): + """Generate argparser from config file automatically (experimental)""" + partial_parser = ArgumentParser(description=description) + partial_parser.add_argument('config', help='config file path') + cfg_file = partial_parser.parse_known_args()[0].config + cfg = Config.fromfile(cfg_file) + parser = ArgumentParser(description=description) + parser.add_argument('config', help='config file path') + add_args(parser, cfg) + return parser, cfg + + def __init__(self, cfg_dict=None, cfg_text=None, filename=None): + if cfg_dict is None: + cfg_dict = dict() + elif not isinstance(cfg_dict, dict): + raise TypeError('cfg_dict must be a dict, but ' + f'got {type(cfg_dict)}') + for key in cfg_dict: + if key in RESERVED_KEYS: + raise KeyError(f'{key} is reserved for config file') + + super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict)) + super(Config, self).__setattr__('_filename', filename) + if cfg_text: + text = cfg_text + elif filename: + with open(filename, 'r') as f: + text = f.read() + else: + text = '' + super(Config, self).__setattr__('_text', text) + + @property + def filename(self): + return self._filename + + @property + def text(self): + return self._text + + @property + def pretty_text(self): + + indent = 4 + + def _indent(s_, num_spaces): + s = s_.split('\n') + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(num_spaces * ' ') + line for line in s] + s = '\n'.join(s) + s = first + '\n' + s + return s + + def _format_basic_types(k, v, use_mapping=False): + if isinstance(v, str): + v_str = f"'{v}'" + else: + v_str = str(v) + + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f'{k_str}: {v_str}' + else: + attr_str = f'{str(k)}={v_str}' + attr_str = _indent(attr_str, indent) + + return attr_str + + def _format_list(k, v, use_mapping=False): + # check if all items in the list are dict + if all(isinstance(_, dict) for _ in v): + v_str = '[\n' + v_str += '\n'.join( + f'dict({_indent(_format_dict(v_), indent)}),' + for v_ in v).rstrip(',') + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f'{k_str}: {v_str}' + else: + attr_str = f'{str(k)}={v_str}' + attr_str = _indent(attr_str, indent) + ']' + else: + attr_str = _format_basic_types(k, v, use_mapping) + return attr_str + + def _contain_invalid_identifier(dict_str): + contain_invalid_identifier = False + for key_name in dict_str: + contain_invalid_identifier |= \ + (not str(key_name).isidentifier()) + return contain_invalid_identifier + + def _format_dict(input_dict, outest_level=False): + r = '' + s = [] + + use_mapping = _contain_invalid_identifier(input_dict) + if use_mapping: + r += '{' + for idx, (k, v) in enumerate(input_dict.items()): + is_last = idx >= len(input_dict) - 1 + end = '' if outest_level or is_last else ',' + if isinstance(v, dict): + v_str = '\n' + _format_dict(v) + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f'{k_str}: dict({v_str}' + else: + attr_str = f'{str(k)}=dict({v_str}' + attr_str = _indent(attr_str, indent) + ')' + end + elif isinstance(v, list): + attr_str = _format_list(k, v, use_mapping) + end + else: + attr_str = _format_basic_types(k, v, use_mapping) + end + + s.append(attr_str) + r += '\n'.join(s) + if use_mapping: + r += '}' + return r + + cfg_dict = self._cfg_dict.to_dict() + text = _format_dict(cfg_dict, outest_level=True) + # copied from setup.cfg + yapf_style = dict( + based_on_style='pep8', + blank_line_before_nested_class_or_def=True, + split_before_expression_after_opening_paren=True) + text, _ = FormatCode(text, style_config=yapf_style, verify=True) + + return text + + def __repr__(self): + return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}' + + def __len__(self): + return len(self._cfg_dict) + + def __getattr__(self, name): + return getattr(self._cfg_dict, name) + + def __getitem__(self, name): + return self._cfg_dict.__getitem__(name) + + def __setattr__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setattr__(name, value) + + def __setitem__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setitem__(name, value) + + def __iter__(self): + return iter(self._cfg_dict) + + def __getstate__(self): + return (self._cfg_dict, self._filename, self._text) + + def __setstate__(self, state): + _cfg_dict, _filename, _text = state + super(Config, self).__setattr__('_cfg_dict', _cfg_dict) + super(Config, self).__setattr__('_filename', _filename) + super(Config, self).__setattr__('_text', _text) + + def dump(self, file=None): + cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict() + if self.filename.endswith('.py'): + if file is None: + return self.pretty_text + else: + with open(file, 'w', encoding='utf-8') as f: + f.write(self.pretty_text) + else: + import custom_mmpkg.custom_mmcv as mmcv + if file is None: + file_format = self.filename.split('.')[-1] + return mmcv.dump(cfg_dict, file_format=file_format) + else: + mmcv.dump(cfg_dict, file) + + def merge_from_dict(self, options, allow_list_keys=True): + """Merge list into cfg_dict. + + Merge the dict parsed by MultipleKVAction into this cfg. + + Examples: + >>> options = {'model.backbone.depth': 50, + ... 'model.backbone.with_cp':True} + >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet')))) + >>> cfg.merge_from_dict(options) + >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + >>> assert cfg_dict == dict( + ... model=dict(backbone=dict(depth=50, with_cp=True))) + + # Merge list element + >>> cfg = Config(dict(pipeline=[ + ... dict(type='LoadImage'), dict(type='LoadAnnotations')])) + >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')}) + >>> cfg.merge_from_dict(options, allow_list_keys=True) + >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + >>> assert cfg_dict == dict(pipeline=[ + ... dict(type='SelfLoadImage'), dict(type='LoadAnnotations')]) + + Args: + options (dict): dict of configs to merge from. + allow_list_keys (bool): If True, int string keys (e.g. '0', '1') + are allowed in ``options`` and will replace the element of the + corresponding index in the config if the config is a list. + Default: True. + """ + option_cfg_dict = {} + for full_key, v in options.items(): + d = option_cfg_dict + key_list = full_key.split('.') + for subkey in key_list[:-1]: + d.setdefault(subkey, ConfigDict()) + d = d[subkey] + subkey = key_list[-1] + d[subkey] = v + + cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + super(Config, self).__setattr__( + '_cfg_dict', + Config._merge_a_into_b( + option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys)) + + +class DictAction(Action): + """ + argparse action to split an argument into KEY=VALUE form + on the first = and append to a dictionary. List options can + be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit + brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build + list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]' + """ + + @staticmethod + def _parse_int_float_bool(val): + try: + return int(val) + except ValueError: + pass + try: + return float(val) + except ValueError: + pass + if val.lower() in ['true', 'false']: + return True if val.lower() == 'true' else False + return val + + @staticmethod + def _parse_iterable(val): + """Parse iterable values in the string. + + All elements inside '()' or '[]' are treated as iterable values. + + Args: + val (str): Value string. + + Returns: + list | tuple: The expanded list or tuple from the string. + + Examples: + >>> DictAction._parse_iterable('1,2,3') + [1, 2, 3] + >>> DictAction._parse_iterable('[a, b, c]') + ['a', 'b', 'c'] + >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]') + [(1, 2, 3), ['a', 'b'], 'c'] + """ + + def find_next_comma(string): + """Find the position of next comma in the string. + + If no ',' is found in the string, return the string length. All + chars inside '()' and '[]' are treated as one element and thus ',' + inside these brackets are ignored. + """ + assert (string.count('(') == string.count(')')) and ( + string.count('[') == string.count(']')), \ + f'Imbalanced brackets exist in {string}' + end = len(string) + for idx, char in enumerate(string): + pre = string[:idx] + # The string before this ',' is balanced + if ((char == ',') and (pre.count('(') == pre.count(')')) + and (pre.count('[') == pre.count(']'))): + end = idx + break + return end + + # Strip ' and " characters and replace whitespace. + val = val.strip('\'\"').replace(' ', '') + is_tuple = False + if val.startswith('(') and val.endswith(')'): + is_tuple = True + val = val[1:-1] + elif val.startswith('[') and val.endswith(']'): + val = val[1:-1] + elif ',' not in val: + # val is a single value + return DictAction._parse_int_float_bool(val) + + values = [] + while len(val) > 0: + comma_idx = find_next_comma(val) + element = DictAction._parse_iterable(val[:comma_idx]) + values.append(element) + val = val[comma_idx + 1:] + if is_tuple: + values = tuple(values) + return values + + def __call__(self, parser, namespace, values, option_string=None): + options = {} + for kv in values: + key, val = kv.split('=', maxsplit=1) + options[key] = self._parse_iterable(val) + setattr(namespace, self.dest, options) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/env.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/env.py new file mode 100644 index 00000000..ffc2e44d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/env.py @@ -0,0 +1,95 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""This file holding some environment constant for sharing by other files.""" + +import os.path as osp +import subprocess +import sys +from collections import defaultdict + +import cv2 +import torch + +import custom_mmpkg.custom_mmcv as mmcv +from .parrots_wrapper import get_build_config + + +def collect_env(): + """Collect the information of the running environments. + + Returns: + dict: The environment information. The following fields are contained. + + - sys.platform: The variable of ``sys.platform``. + - Python: Python version. + - CUDA available: Bool, indicating if CUDA is available. + - GPU devices: Device type of each GPU. + - CUDA_HOME (optional): The env var ``CUDA_HOME``. + - NVCC (optional): NVCC version. + - GCC: GCC version, "n/a" if GCC is not installed. + - PyTorch: PyTorch version. + - PyTorch compiling details: The output of \ + ``torch.__config__.show()``. + - TorchVision (optional): TorchVision version. + - OpenCV: OpenCV version. + - MMCV: MMCV version. + - MMCV Compiler: The GCC version for compiling MMCV ops. + - MMCV CUDA Compiler: The CUDA version for compiling MMCV ops. + """ + env_info = {} + env_info['sys.platform'] = sys.platform + env_info['Python'] = sys.version.replace('\n', '') + + cuda_available = torch.cuda.is_available() + env_info['CUDA available'] = cuda_available + + if cuda_available: + devices = defaultdict(list) + for k in range(torch.cuda.device_count()): + devices[torch.cuda.get_device_name(k)].append(str(k)) + for name, device_ids in devices.items(): + env_info['GPU ' + ','.join(device_ids)] = name + + from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import _get_cuda_home + CUDA_HOME = _get_cuda_home() + env_info['CUDA_HOME'] = CUDA_HOME + + if CUDA_HOME is not None and osp.isdir(CUDA_HOME): + try: + nvcc = osp.join(CUDA_HOME, 'bin/nvcc') + nvcc = subprocess.check_output( + f'"{nvcc}" -V | tail -n1', shell=True) + nvcc = nvcc.decode('utf-8').strip() + except subprocess.SubprocessError: + nvcc = 'Not Available' + env_info['NVCC'] = nvcc + + try: + gcc = subprocess.check_output('gcc --version | head -n1', shell=True) + gcc = gcc.decode('utf-8').strip() + env_info['GCC'] = gcc + except subprocess.CalledProcessError: # gcc is unavailable + env_info['GCC'] = 'n/a' + + env_info['PyTorch'] = torch.__version__ + env_info['PyTorch compiling details'] = get_build_config() + + try: + import torchvision + env_info['TorchVision'] = torchvision.__version__ + except ModuleNotFoundError: + pass + + env_info['OpenCV'] = cv2.__version__ + + env_info['MMCV'] = mmcv.__version__ + + try: + from custom_mmpkg.custom_mmcv.ops import get_compiler_version, get_compiling_cuda_version + except ModuleNotFoundError: + env_info['MMCV Compiler'] = 'n/a' + env_info['MMCV CUDA Compiler'] = 'n/a' + else: + env_info['MMCV Compiler'] = get_compiler_version() + env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version() + + return env_info diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/ext_loader.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/ext_loader.py new file mode 100644 index 00000000..9fbdddf8 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/ext_loader.py @@ -0,0 +1,71 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import importlib +import os +import pkgutil +import warnings +from collections import namedtuple + +import torch + +if torch.__version__ != 'parrots': + + def load_ext(name, funcs): + ext = importlib.import_module('custom_mmcv.' + name) + for fun in funcs: + assert hasattr(ext, fun), f'{fun} miss in module {name}' + return ext +else: + from parrots import extension + from parrots.base import ParrotsException + + has_return_value_ops = [ + 'nms', + 'softnms', + 'nms_match', + 'nms_rotated', + 'top_pool_forward', + 'top_pool_backward', + 'bottom_pool_forward', + 'bottom_pool_backward', + 'left_pool_forward', + 'left_pool_backward', + 'right_pool_forward', + 'right_pool_backward', + 'fused_bias_leakyrelu', + 'upfirdn2d', + 'ms_deform_attn_forward', + 'pixel_group', + 'contour_expand', + ] + + def get_fake_func(name, e): + + def fake_func(*args, **kwargs): + warnings.warn(f'{name} is not supported in parrots now') + raise e + + return fake_func + + def load_ext(name, funcs): + ExtModule = namedtuple('ExtModule', funcs) + ext_list = [] + lib_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + for fun in funcs: + try: + ext_fun = extension.load(fun, name, lib_dir=lib_root) + except ParrotsException as e: + if 'No element registered' not in e.message: + warnings.warn(e.message) + ext_fun = get_fake_func(fun, e) + ext_list.append(ext_fun) + else: + if fun in has_return_value_ops: + ext_list.append(ext_fun.op) + else: + ext_list.append(ext_fun.op_) + return ExtModule(*ext_list) + + +def check_ops_exist(): + ext_loader = pkgutil.find_loader('mmcv._ext') + return ext_loader is not None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/logging.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/logging.py new file mode 100644 index 00000000..4aa0e04b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/logging.py @@ -0,0 +1,110 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import logging + +import torch.distributed as dist + +logger_initialized = {} + + +def get_logger(name, log_file=None, log_level=logging.INFO, file_mode='w'): + """Initialize and get a logger by name. + + If the logger has not been initialized, this method will initialize the + logger by adding one or two handlers, otherwise the initialized logger will + be directly returned. During initialization, a StreamHandler will always be + added. If `log_file` is specified and the process rank is 0, a FileHandler + will also be added. + + Args: + name (str): Logger name. + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the logger. + log_level (int): The logger level. Note that only the process of + rank 0 is affected, and other processes will set the level to + "Error" thus be silent most of the time. + file_mode (str): The file mode used in opening log file. + Defaults to 'w'. + + Returns: + logging.Logger: The expected logger. + """ + logger = logging.getLogger(name) + if name in logger_initialized: + return logger + # handle hierarchical names + # e.g., logger "a" is initialized, then logger "a.b" will skip the + # initialization since it is a child of "a". + for logger_name in logger_initialized: + if name.startswith(logger_name): + return logger + + # handle duplicate logs to the console + # Starting in 1.8.0, PyTorch DDP attaches a StreamHandler (NOTSET) + # to the root logger. As logger.propagate is True by default, this root + # level handler causes logging messages from rank>0 processes to + # unexpectedly show up on the console, creating much unwanted clutter. + # To fix this issue, we set the root logger's StreamHandler, if any, to log + # at the ERROR level. + for handler in logger.root.handlers: + if type(handler) is logging.StreamHandler: + handler.setLevel(logging.ERROR) + + stream_handler = logging.StreamHandler() + handlers = [stream_handler] + + if dist.is_available() and dist.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 + + # only rank 0 will add a FileHandler + if rank == 0 and log_file is not None: + # Here, the default behaviour of the official logger is 'a'. Thus, we + # provide an interface to change the file mode to the default + # behaviour. + file_handler = logging.FileHandler(log_file, file_mode) + handlers.append(file_handler) + + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s') + for handler in handlers: + handler.setFormatter(formatter) + handler.setLevel(log_level) + logger.addHandler(handler) + + if rank == 0: + logger.setLevel(log_level) + else: + logger.setLevel(logging.ERROR) + + logger_initialized[name] = True + + return logger + + +def print_log(msg, logger=None, level=logging.INFO): + """Print a log message. + + Args: + msg (str): The message to be logged. + logger (logging.Logger | str | None): The logger to be used. + Some special loggers are: + - "silent": no message will be printed. + - other str: the logger obtained with `get_root_logger(logger)`. + - None: The `print()` method will be used to print log messages. + level (int): Logging level. Only available when `logger` is a Logger + object or "root". + """ + if logger is None: + print(msg) + elif isinstance(logger, logging.Logger): + logger.log(level, msg) + elif logger == 'silent': + pass + elif isinstance(logger, str): + _logger = get_logger(logger) + _logger.log(level, msg) + else: + raise TypeError( + 'logger should be either a logging.Logger object, str, ' + f'"silent" or None, but got {type(logger)}') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/misc.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/misc.py new file mode 100644 index 00000000..2c58d0d7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/misc.py @@ -0,0 +1,377 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import collections.abc +import functools +import itertools +import subprocess +import warnings +from collections import abc +from importlib import import_module +from inspect import getfullargspec +from itertools import repeat + + +# From PyTorch internals +def _ntuple(n): + + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +def is_str(x): + """Whether the input is an string instance. + + Note: This method is deprecated since python 2 is no longer supported. + """ + return isinstance(x, str) + + +def import_modules_from_strings(imports, allow_failed_imports=False): + """Import modules from the given list of strings. + + Args: + imports (list | str | None): The given module names to be imported. + allow_failed_imports (bool): If True, the failed imports will return + None. Otherwise, an ImportError is raise. Default: False. + + Returns: + list[module] | module | None: The imported modules. + + Examples: + >>> osp, sys = import_modules_from_strings( + ... ['os.path', 'sys']) + >>> import os.path as osp_ + >>> import sys as sys_ + >>> assert osp == osp_ + >>> assert sys == sys_ + """ + if not imports: + return + single_import = False + if isinstance(imports, str): + single_import = True + imports = [imports] + if not isinstance(imports, list): + raise TypeError( + f'custom_imports must be a list but got type {type(imports)}') + imported = [] + for imp in imports: + if not isinstance(imp, str): + raise TypeError( + f'{imp} is of type {type(imp)} and cannot be imported.') + try: + imported_tmp = import_module(imp) + except ImportError: + if allow_failed_imports: + warnings.warn(f'{imp} failed to import and is ignored.', + UserWarning) + imported_tmp = None + else: + raise ImportError + imported.append(imported_tmp) + if single_import: + imported = imported[0] + return imported + + +def iter_cast(inputs, dst_type, return_type=None): + """Cast elements of an iterable object into some type. + + Args: + inputs (Iterable): The input object. + dst_type (type): Destination type. + return_type (type, optional): If specified, the output object will be + converted to this type, otherwise an iterator. + + Returns: + iterator or specified type: The converted object. + """ + if not isinstance(inputs, abc.Iterable): + raise TypeError('inputs must be an iterable object') + if not isinstance(dst_type, type): + raise TypeError('"dst_type" must be a valid type') + + out_iterable = map(dst_type, inputs) + + if return_type is None: + return out_iterable + else: + return return_type(out_iterable) + + +def list_cast(inputs, dst_type): + """Cast elements of an iterable object into a list of some type. + + A partial method of :func:`iter_cast`. + """ + return iter_cast(inputs, dst_type, return_type=list) + + +def tuple_cast(inputs, dst_type): + """Cast elements of an iterable object into a tuple of some type. + + A partial method of :func:`iter_cast`. + """ + return iter_cast(inputs, dst_type, return_type=tuple) + + +def is_seq_of(seq, expected_type, seq_type=None): + """Check whether it is a sequence of some type. + + Args: + seq (Sequence): The sequence to be checked. + expected_type (type): Expected type of sequence items. + seq_type (type, optional): Expected sequence type. + + Returns: + bool: Whether the sequence is valid. + """ + if seq_type is None: + exp_seq_type = abc.Sequence + else: + assert isinstance(seq_type, type) + exp_seq_type = seq_type + if not isinstance(seq, exp_seq_type): + return False + for item in seq: + if not isinstance(item, expected_type): + return False + return True + + +def is_list_of(seq, expected_type): + """Check whether it is a list of some type. + + A partial method of :func:`is_seq_of`. + """ + return is_seq_of(seq, expected_type, seq_type=list) + + +def is_tuple_of(seq, expected_type): + """Check whether it is a tuple of some type. + + A partial method of :func:`is_seq_of`. + """ + return is_seq_of(seq, expected_type, seq_type=tuple) + + +def slice_list(in_list, lens): + """Slice a list into several sub lists by a list of given length. + + Args: + in_list (list): The list to be sliced. + lens(int or list): The expected length of each out list. + + Returns: + list: A list of sliced list. + """ + if isinstance(lens, int): + assert len(in_list) % lens == 0 + lens = [lens] * int(len(in_list) / lens) + if not isinstance(lens, list): + raise TypeError('"indices" must be an integer or a list of integers') + elif sum(lens) != len(in_list): + raise ValueError('sum of lens and list length does not ' + f'match: {sum(lens)} != {len(in_list)}') + out_list = [] + idx = 0 + for i in range(len(lens)): + out_list.append(in_list[idx:idx + lens[i]]) + idx += lens[i] + return out_list + + +def concat_list(in_list): + """Concatenate a list of list into a single list. + + Args: + in_list (list): The list of list to be merged. + + Returns: + list: The concatenated flat list. + """ + return list(itertools.chain(*in_list)) + + +def check_prerequisites( + prerequisites, + checker, + msg_tmpl='Prerequisites "{}" are required in method "{}" but not ' + 'found, please install them first.'): # yapf: disable + """A decorator factory to check if prerequisites are satisfied. + + Args: + prerequisites (str of list[str]): Prerequisites to be checked. + checker (callable): The checker method that returns True if a + prerequisite is meet, False otherwise. + msg_tmpl (str): The message template with two variables. + + Returns: + decorator: A specific decorator. + """ + + def wrap(func): + + @functools.wraps(func) + def wrapped_func(*args, **kwargs): + requirements = [prerequisites] if isinstance( + prerequisites, str) else prerequisites + missing = [] + for item in requirements: + if not checker(item): + missing.append(item) + if missing: + print(msg_tmpl.format(', '.join(missing), func.__name__)) + raise RuntimeError('Prerequisites not meet.') + else: + return func(*args, **kwargs) + + return wrapped_func + + return wrap + + +def _check_py_package(package): + try: + import_module(package) + except ImportError: + return False + else: + return True + + +def _check_executable(cmd): + if subprocess.call(f'which {cmd}', shell=True) != 0: + return False + else: + return True + + +def requires_package(prerequisites): + """A decorator to check if some python packages are installed. + + Example: + >>> @requires_package('numpy') + >>> func(arg1, args): + >>> return numpy.zeros(1) + array([0.]) + >>> @requires_package(['numpy', 'non_package']) + >>> func(arg1, args): + >>> return numpy.zeros(1) + ImportError + """ + return check_prerequisites(prerequisites, checker=_check_py_package) + + +def requires_executable(prerequisites): + """A decorator to check if some executable files are installed. + + Example: + >>> @requires_executable('ffmpeg') + >>> func(arg1, args): + >>> print(1) + 1 + """ + return check_prerequisites(prerequisites, checker=_check_executable) + + +def deprecated_api_warning(name_dict, cls_name=None): + """A decorator to check if some arguments are deprecate and try to replace + deprecate src_arg_name to dst_arg_name. + + Args: + name_dict(dict): + key (str): Deprecate argument names. + val (str): Expected argument names. + + Returns: + func: New function. + """ + + def api_warning_wrapper(old_func): + + @functools.wraps(old_func) + def new_func(*args, **kwargs): + # get the arg spec of the decorated method + args_info = getfullargspec(old_func) + # get name of the function + func_name = old_func.__name__ + if cls_name is not None: + func_name = f'{cls_name}.{func_name}' + if args: + arg_names = args_info.args[:len(args)] + for src_arg_name, dst_arg_name in name_dict.items(): + if src_arg_name in arg_names: + warnings.warn( + f'"{src_arg_name}" is deprecated in ' + f'`{func_name}`, please use "{dst_arg_name}" ' + 'instead') + arg_names[arg_names.index(src_arg_name)] = dst_arg_name + if kwargs: + for src_arg_name, dst_arg_name in name_dict.items(): + if src_arg_name in kwargs: + + assert dst_arg_name not in kwargs, ( + f'The expected behavior is to replace ' + f'the deprecated key `{src_arg_name}` to ' + f'new key `{dst_arg_name}`, but got them ' + f'in the arguments at the same time, which ' + f'is confusing. `{src_arg_name} will be ' + f'deprecated in the future, please ' + f'use `{dst_arg_name}` instead.') + + warnings.warn( + f'"{src_arg_name}" is deprecated in ' + f'`{func_name}`, please use "{dst_arg_name}" ' + 'instead') + kwargs[dst_arg_name] = kwargs.pop(src_arg_name) + + # apply converted arguments to the decorated method + output = old_func(*args, **kwargs) + return output + + return new_func + + return api_warning_wrapper + + +def is_method_overridden(method, base_class, derived_class): + """Check if a method of base class is overridden in derived class. + + Args: + method (str): the method name to check. + base_class (type): the class of the base class. + derived_class (type | Any): the class or instance of the derived class. + """ + assert isinstance(base_class, type), \ + "base_class doesn't accept instance, Please pass class instead." + + if not isinstance(derived_class, type): + derived_class = derived_class.__class__ + + base_method = getattr(base_class, method) + derived_method = getattr(derived_class, method) + return derived_method != base_method + + +def has_method(obj: object, method: str) -> bool: + """Check whether the object has a method. + + Args: + method (str): The method name to check. + obj (object): The object to check. + + Returns: + bool: True if the object has the method else False. + """ + return hasattr(obj, method) and callable(getattr(obj, method)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/parrots_jit.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/parrots_jit.py new file mode 100644 index 00000000..61873f6d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/parrots_jit.py @@ -0,0 +1,41 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os + +from .parrots_wrapper import TORCH_VERSION + +parrots_jit_option = os.getenv('PARROTS_JIT_OPTION') + +if TORCH_VERSION == 'parrots' and parrots_jit_option == 'ON': + from parrots.jit import pat as jit +else: + + def jit(func=None, + check_input=None, + full_shape=True, + derivate=False, + coderize=False, + optimize=False): + + def wrapper(func): + + def wrapper_inner(*args, **kargs): + return func(*args, **kargs) + + return wrapper_inner + + if func is None: + return wrapper + else: + return func + + +if TORCH_VERSION == 'parrots': + from parrots.utils.tester import skip_no_elena +else: + + def skip_no_elena(func): + + def wrapper(*args, **kargs): + return func(*args, **kargs) + + return wrapper diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/parrots_wrapper.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/parrots_wrapper.py new file mode 100644 index 00000000..93c97640 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/parrots_wrapper.py @@ -0,0 +1,107 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from functools import partial + +import torch + +TORCH_VERSION = torch.__version__ + + +def is_rocm_pytorch() -> bool: + is_rocm = False + if TORCH_VERSION != 'parrots': + try: + from torch.utils.cpp_extension import ROCM_HOME + is_rocm = True if ((torch.version.hip is not None) and + (ROCM_HOME is not None)) else False + except ImportError: + pass + return is_rocm + + +def _get_cuda_home(): + if TORCH_VERSION == 'parrots': + from parrots.utils.build_extension import CUDA_HOME + else: + if is_rocm_pytorch(): + from torch.utils.cpp_extension import ROCM_HOME + CUDA_HOME = ROCM_HOME + else: + from torch.utils.cpp_extension import CUDA_HOME + return CUDA_HOME + + +def get_build_config(): + if TORCH_VERSION == 'parrots': + from parrots.config import get_build_info + return get_build_info() + else: + return torch.__config__.show() + + +def _get_conv(): + if TORCH_VERSION == 'parrots': + from parrots.nn.modules.conv import _ConvNd, _ConvTransposeMixin + else: + from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin + return _ConvNd, _ConvTransposeMixin + + +def _get_dataloader(): + if TORCH_VERSION == 'parrots': + from torch.utils.data import DataLoader, PoolDataLoader + else: + from torch.utils.data import DataLoader + PoolDataLoader = DataLoader + return DataLoader, PoolDataLoader + + +def _get_extension(): + if TORCH_VERSION == 'parrots': + from parrots.utils.build_extension import BuildExtension, Extension + CppExtension = partial(Extension, cuda=False) + CUDAExtension = partial(Extension, cuda=True) + else: + from torch.utils.cpp_extension import (BuildExtension, CppExtension, + CUDAExtension) + return BuildExtension, CppExtension, CUDAExtension + + +def _get_pool(): + if TORCH_VERSION == 'parrots': + from parrots.nn.modules.pool import (_AdaptiveAvgPoolNd, + _AdaptiveMaxPoolNd, _AvgPoolNd, + _MaxPoolNd) + else: + from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd, + _AdaptiveMaxPoolNd, _AvgPoolNd, + _MaxPoolNd) + return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd + + +def _get_norm(): + if TORCH_VERSION == 'parrots': + from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm + SyncBatchNorm_ = torch.nn.SyncBatchNorm2d + else: + from torch.nn.modules.instancenorm import _InstanceNorm + from torch.nn.modules.batchnorm import _BatchNorm + SyncBatchNorm_ = torch.nn.SyncBatchNorm + return _BatchNorm, _InstanceNorm, SyncBatchNorm_ + + +_ConvNd, _ConvTransposeMixin = _get_conv() +DataLoader, PoolDataLoader = _get_dataloader() +BuildExtension, CppExtension, CUDAExtension = _get_extension() +_BatchNorm, _InstanceNorm, SyncBatchNorm_ = _get_norm() +_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool() + + +class SyncBatchNorm(SyncBatchNorm_): + + def _check_input_dim(self, input): + if TORCH_VERSION == 'parrots': + if input.dim() < 2: + raise ValueError( + f'expected at least 2D input (got {input.dim()}D input)') + else: + super()._check_input_dim(input) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/path.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/path.py new file mode 100644 index 00000000..7dab4b30 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/path.py @@ -0,0 +1,101 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os +import os.path as osp +from pathlib import Path + +from .misc import is_str + + +def is_filepath(x): + return is_str(x) or isinstance(x, Path) + + +def fopen(filepath, *args, **kwargs): + if is_str(filepath): + return open(filepath, *args, **kwargs) + elif isinstance(filepath, Path): + return filepath.open(*args, **kwargs) + raise ValueError('`filepath` should be a string or a Path') + + +def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): + if not osp.isfile(filename): + raise FileNotFoundError(msg_tmpl.format(filename)) + + +def mkdir_or_exist(dir_name, mode=0o777): + if dir_name == '': + return + dir_name = osp.expanduser(dir_name) + os.makedirs(dir_name, mode=mode, exist_ok=True) + + +def symlink(src, dst, overwrite=True, **kwargs): + if os.path.lexists(dst) and overwrite: + os.remove(dst) + os.symlink(src, dst, **kwargs) + + +def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True): + """Scan a directory to find the interested files. + + Args: + dir_path (str | obj:`Path`): Path of the directory. + suffix (str | tuple(str), optional): File suffix that we are + interested in. Default: None. + recursive (bool, optional): If set to True, recursively scan the + directory. Default: False. + case_sensitive (bool, optional) : If set to False, ignore the case of + suffix. Default: True. + + Returns: + A generator for all the interested files with relative paths. + """ + if isinstance(dir_path, (str, Path)): + dir_path = str(dir_path) + else: + raise TypeError('"dir_path" must be a string or Path object') + + if (suffix is not None) and not isinstance(suffix, (str, tuple)): + raise TypeError('"suffix" must be a string or tuple of strings') + + if suffix is not None and not case_sensitive: + suffix = suffix.lower() if isinstance(suffix, str) else tuple( + item.lower() for item in suffix) + + root = dir_path + + def _scandir(dir_path, suffix, recursive, case_sensitive): + for entry in os.scandir(dir_path): + if not entry.name.startswith('.') and entry.is_file(): + rel_path = osp.relpath(entry.path, root) + _rel_path = rel_path if case_sensitive else rel_path.lower() + if suffix is None or _rel_path.endswith(suffix): + yield rel_path + elif recursive and os.path.isdir(entry.path): + # scan recursively if entry.path is a directory + yield from _scandir(entry.path, suffix, recursive, + case_sensitive) + + return _scandir(dir_path, suffix, recursive, case_sensitive) + + +def find_vcs_root(path, markers=('.git', )): + """Finds the root directory (including itself) of specified markers. + + Args: + path (str): Path of directory or file. + markers (list[str], optional): List of file or directory names. + + Returns: + The directory contained one of the markers or None if not found. + """ + if osp.isfile(path): + path = osp.dirname(path) + + prev, cur = None, osp.abspath(osp.expanduser(path)) + while cur != prev: + if any(osp.exists(osp.join(cur, marker)) for marker in markers): + return cur + prev, cur = cur, osp.split(cur)[0] + return None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/progressbar.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/progressbar.py new file mode 100644 index 00000000..0062f670 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/progressbar.py @@ -0,0 +1,208 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import sys +from collections.abc import Iterable +from multiprocessing import Pool +from shutil import get_terminal_size + +from .timer import Timer + + +class ProgressBar: + """A progress bar which can print the progress.""" + + def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout): + self.task_num = task_num + self.bar_width = bar_width + self.completed = 0 + self.file = file + if start: + self.start() + + @property + def terminal_width(self): + width, _ = get_terminal_size() + return width + + def start(self): + if self.task_num > 0: + self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, ' + 'elapsed: 0s, ETA:') + else: + self.file.write('completed: 0, elapsed: 0s') + self.file.flush() + self.timer = Timer() + + def update(self, num_tasks=1): + assert num_tasks > 0 + self.completed += num_tasks + elapsed = self.timer.since_start() + if elapsed > 0: + fps = self.completed / elapsed + else: + fps = float('inf') + if self.task_num > 0: + percentage = self.completed / float(self.task_num) + eta = int(elapsed * (1 - percentage) / percentage + 0.5) + msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \ + f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \ + f'ETA: {eta:5}s' + + bar_width = min(self.bar_width, + int(self.terminal_width - len(msg)) + 2, + int(self.terminal_width * 0.6)) + bar_width = max(2, bar_width) + mark_width = int(bar_width * percentage) + bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width) + self.file.write(msg.format(bar_chars)) + else: + self.file.write( + f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,' + f' {fps:.1f} tasks/s') + self.file.flush() + + +def track_progress(func, tasks, bar_width=50, file=sys.stdout, **kwargs): + """Track the progress of tasks execution with a progress bar. + + Tasks are done with a simple for-loop. + + Args: + func (callable): The function to be applied to each task. + tasks (list or tuple[Iterable, int]): A list of tasks or + (tasks, total num). + bar_width (int): Width of progress bar. + + Returns: + list: The task results. + """ + if isinstance(tasks, tuple): + assert len(tasks) == 2 + assert isinstance(tasks[0], Iterable) + assert isinstance(tasks[1], int) + task_num = tasks[1] + tasks = tasks[0] + elif isinstance(tasks, Iterable): + task_num = len(tasks) + else: + raise TypeError( + '"tasks" must be an iterable object or a (iterator, int) tuple') + prog_bar = ProgressBar(task_num, bar_width, file=file) + results = [] + for task in tasks: + results.append(func(task, **kwargs)) + prog_bar.update() + prog_bar.file.write('\n') + return results + + +def init_pool(process_num, initializer=None, initargs=None): + if initializer is None: + return Pool(process_num) + elif initargs is None: + return Pool(process_num, initializer) + else: + if not isinstance(initargs, tuple): + raise TypeError('"initargs" must be a tuple') + return Pool(process_num, initializer, initargs) + + +def track_parallel_progress(func, + tasks, + nproc, + initializer=None, + initargs=None, + bar_width=50, + chunksize=1, + skip_first=False, + keep_order=True, + file=sys.stdout): + """Track the progress of parallel task execution with a progress bar. + + The built-in :mod:`multiprocessing` module is used for process pools and + tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`. + + Args: + func (callable): The function to be applied to each task. + tasks (list or tuple[Iterable, int]): A list of tasks or + (tasks, total num). + nproc (int): Process (worker) number. + initializer (None or callable): Refer to :class:`multiprocessing.Pool` + for details. + initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for + details. + chunksize (int): Refer to :class:`multiprocessing.Pool` for details. + bar_width (int): Width of progress bar. + skip_first (bool): Whether to skip the first sample for each worker + when estimating fps, since the initialization step may takes + longer. + keep_order (bool): If True, :func:`Pool.imap` is used, otherwise + :func:`Pool.imap_unordered` is used. + + Returns: + list: The task results. + """ + if isinstance(tasks, tuple): + assert len(tasks) == 2 + assert isinstance(tasks[0], Iterable) + assert isinstance(tasks[1], int) + task_num = tasks[1] + tasks = tasks[0] + elif isinstance(tasks, Iterable): + task_num = len(tasks) + else: + raise TypeError( + '"tasks" must be an iterable object or a (iterator, int) tuple') + pool = init_pool(nproc, initializer, initargs) + start = not skip_first + task_num -= nproc * chunksize * int(skip_first) + prog_bar = ProgressBar(task_num, bar_width, start, file=file) + results = [] + if keep_order: + gen = pool.imap(func, tasks, chunksize) + else: + gen = pool.imap_unordered(func, tasks, chunksize) + for result in gen: + results.append(result) + if skip_first: + if len(results) < nproc * chunksize: + continue + elif len(results) == nproc * chunksize: + prog_bar.start() + continue + prog_bar.update() + prog_bar.file.write('\n') + pool.close() + pool.join() + return results + + +def track_iter_progress(tasks, bar_width=50, file=sys.stdout): + """Track the progress of tasks iteration or enumeration with a progress + bar. + + Tasks are yielded with a simple for-loop. + + Args: + tasks (list or tuple[Iterable, int]): A list of tasks or + (tasks, total num). + bar_width (int): Width of progress bar. + + Yields: + list: The task results. + """ + if isinstance(tasks, tuple): + assert len(tasks) == 2 + assert isinstance(tasks[0], Iterable) + assert isinstance(tasks[1], int) + task_num = tasks[1] + tasks = tasks[0] + elif isinstance(tasks, Iterable): + task_num = len(tasks) + else: + raise TypeError( + '"tasks" must be an iterable object or a (iterator, int) tuple') + prog_bar = ProgressBar(task_num, bar_width, file=file) + for task in tasks: + yield task + prog_bar.update() + prog_bar.file.write('\n') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/registry.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/registry.py new file mode 100644 index 00000000..fa9df39b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/registry.py @@ -0,0 +1,315 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import inspect +import warnings +from functools import partial + +from .misc import is_seq_of + + +def build_from_cfg(cfg, registry, default_args=None): + """Build a module from config dict. + + Args: + cfg (dict): Config dict. It should at least contain the key "type". + registry (:obj:`Registry`): The registry to search the type from. + default_args (dict, optional): Default initialization arguments. + + Returns: + object: The constructed object. + """ + if not isinstance(cfg, dict): + raise TypeError(f'cfg must be a dict, but got {type(cfg)}') + if 'type' not in cfg: + if default_args is None or 'type' not in default_args: + raise KeyError( + '`cfg` or `default_args` must contain the key "type", ' + f'but got {cfg}\n{default_args}') + if not isinstance(registry, Registry): + raise TypeError('registry must be an mmcv.Registry object, ' + f'but got {type(registry)}') + if not (isinstance(default_args, dict) or default_args is None): + raise TypeError('default_args must be a dict or None, ' + f'but got {type(default_args)}') + + args = cfg.copy() + + if default_args is not None: + for name, value in default_args.items(): + args.setdefault(name, value) + + obj_type = args.pop('type') + if isinstance(obj_type, str): + obj_cls = registry.get(obj_type) + if obj_cls is None: + raise KeyError( + f'{obj_type} is not in the {registry.name} registry') + elif inspect.isclass(obj_type): + obj_cls = obj_type + else: + raise TypeError( + f'type must be a str or valid type, but got {type(obj_type)}') + try: + return obj_cls(**args) + except Exception as e: + # Normal TypeError does not print class name. + raise type(e)(f'{obj_cls.__name__}: {e}') + + +class Registry: + """A registry to map strings to classes. + + Registered object could be built from registry. + Example: + >>> MODELS = Registry('models') + >>> @MODELS.register_module() + >>> class ResNet: + >>> pass + >>> resnet = MODELS.build(dict(type='ResNet')) + + Please refer to + https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for + advanced usage. + + Args: + name (str): Registry name. + build_func(func, optional): Build function to construct instance from + Registry, func:`build_from_cfg` is used if neither ``parent`` or + ``build_func`` is specified. If ``parent`` is specified and + ``build_func`` is not given, ``build_func`` will be inherited + from ``parent``. Default: None. + parent (Registry, optional): Parent registry. The class registered in + children registry could be built from parent. Default: None. + scope (str, optional): The scope of registry. It is the key to search + for children registry. If not specified, scope will be the name of + the package where class is defined, e.g. mmdet, mmcls, mmseg. + Default: None. + """ + + def __init__(self, name, build_func=None, parent=None, scope=None): + self._name = name + self._module_dict = dict() + self._children = dict() + self._scope = self.infer_scope() if scope is None else scope + + # self.build_func will be set with the following priority: + # 1. build_func + # 2. parent.build_func + # 3. build_from_cfg + if build_func is None: + if parent is not None: + self.build_func = parent.build_func + else: + self.build_func = build_from_cfg + else: + self.build_func = build_func + if parent is not None: + assert isinstance(parent, Registry) + parent._add_children(self) + self.parent = parent + else: + self.parent = None + + def __len__(self): + return len(self._module_dict) + + def __contains__(self, key): + return self.get(key) is not None + + def __repr__(self): + format_str = self.__class__.__name__ + \ + f'(name={self._name}, ' \ + f'items={self._module_dict})' + return format_str + + @staticmethod + def infer_scope(): + """Infer the scope of registry. + + The name of the package where registry is defined will be returned. + + Example: + # in mmdet/models/backbone/resnet.py + >>> MODELS = Registry('models') + >>> @MODELS.register_module() + >>> class ResNet: + >>> pass + The scope of ``ResNet`` will be ``mmdet``. + + + Returns: + scope (str): The inferred scope name. + """ + # inspect.stack() trace where this function is called, the index-2 + # indicates the frame where `infer_scope()` is called + filename = inspect.getmodule(inspect.stack()[2][0]).__name__ + split_filename = filename.split('.') + return split_filename[0] + + @staticmethod + def split_scope_key(key): + """Split scope and key. + + The first scope will be split from key. + + Examples: + >>> Registry.split_scope_key('mmdet.ResNet') + 'mmdet', 'ResNet' + >>> Registry.split_scope_key('ResNet') + None, 'ResNet' + + Return: + scope (str, None): The first scope. + key (str): The remaining key. + """ + split_index = key.find('.') + if split_index != -1: + return key[:split_index], key[split_index + 1:] + else: + return None, key + + @property + def name(self): + return self._name + + @property + def scope(self): + return self._scope + + @property + def module_dict(self): + return self._module_dict + + @property + def children(self): + return self._children + + def get(self, key): + """Get the registry record. + + Args: + key (str): The class name in string format. + + Returns: + class: The corresponding class. + """ + scope, real_key = self.split_scope_key(key) + if scope is None or scope == self._scope: + # get from self + if real_key in self._module_dict: + return self._module_dict[real_key] + else: + # get from self._children + if scope in self._children: + return self._children[scope].get(real_key) + else: + # goto root + parent = self.parent + while parent.parent is not None: + parent = parent.parent + return parent.get(key) + + def build(self, *args, **kwargs): + return self.build_func(*args, **kwargs, registry=self) + + def _add_children(self, registry): + """Add children for a registry. + + The ``registry`` will be added as children based on its scope. + The parent registry could build objects from children registry. + + Example: + >>> models = Registry('models') + >>> mmdet_models = Registry('models', parent=models) + >>> @mmdet_models.register_module() + >>> class ResNet: + >>> pass + >>> resnet = models.build(dict(type='mmdet.ResNet')) + """ + + assert isinstance(registry, Registry) + assert registry.scope is not None + assert registry.scope not in self.children, \ + f'scope {registry.scope} exists in {self.name} registry' + self.children[registry.scope] = registry + + def _register_module(self, module_class, module_name=None, force=False): + if not inspect.isclass(module_class): + raise TypeError('module must be a class, ' + f'but got {type(module_class)}') + + if module_name is None: + module_name = module_class.__name__ + if isinstance(module_name, str): + module_name = [module_name] + for name in module_name: + if not force and name in self._module_dict: + raise KeyError(f'{name} is already registered ' + f'in {self.name}') + self._module_dict[name] = module_class + + def deprecated_register_module(self, cls=None, force=False): + warnings.warn( + 'The old API of register_module(module, force=False) ' + 'is deprecated and will be removed, please use the new API ' + 'register_module(name=None, force=False, module=None) instead.') + if cls is None: + return partial(self.deprecated_register_module, force=force) + self._register_module(cls, force=force) + return cls + + def register_module(self, name=None, force=False, module=None): + """Register a module. + + A record will be added to `self._module_dict`, whose key is the class + name or the specified name, and value is the class itself. + It can be used as a decorator or a normal function. + + Example: + >>> backbones = Registry('backbone') + >>> @backbones.register_module() + >>> class ResNet: + >>> pass + + >>> backbones = Registry('backbone') + >>> @backbones.register_module(name='mnet') + >>> class MobileNet: + >>> pass + + >>> backbones = Registry('backbone') + >>> class ResNet: + >>> pass + >>> backbones.register_module(ResNet) + + Args: + name (str | None): The module name to be registered. If not + specified, the class name will be used. + force (bool, optional): Whether to override an existing class with + the same name. Default: False. + module (type): Module class to be registered. + """ + if not isinstance(force, bool): + raise TypeError(f'force must be a boolean, but got {type(force)}') + # NOTE: This is a walkaround to be compatible with the old api, + # while it may introduce unexpected bugs. + if isinstance(name, type): + return self.deprecated_register_module(name, force=force) + + # raise the error ahead of time + if not (name is None or isinstance(name, str) or is_seq_of(name, str)): + raise TypeError( + 'name must be either of None, an instance of str or a sequence' + f' of str, but got {type(name)}') + + # use it as a normal method: x.register_module(module=SomeClass) + if module is not None: + self._register_module( + module_class=module, module_name=name, force=force) + return module + + # use it as a decorator: @x.register_module() + def _register(cls): + self._register_module( + module_class=cls, module_name=name, force=force) + return cls + + return _register diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/testing.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/testing.py new file mode 100644 index 00000000..a27f936d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/testing.py @@ -0,0 +1,140 @@ +# Copyright (c) Open-MMLab. +import sys +from collections.abc import Iterable +from runpy import run_path +from shlex import split +from typing import Any, Dict, List +from unittest.mock import patch + + +def check_python_script(cmd): + """Run the python cmd script with `__main__`. The difference between + `os.system` is that, this function exectues code in the current process, so + that it can be tracked by coverage tools. Currently it supports two forms: + + - ./tests/data/scripts/hello.py zz + - python tests/data/scripts/hello.py zz + """ + args = split(cmd) + if args[0] == 'python': + args = args[1:] + with patch.object(sys, 'argv', args): + run_path(args[0], run_name='__main__') + + +def _any(judge_result): + """Since built-in ``any`` works only when the element of iterable is not + iterable, implement the function.""" + if not isinstance(judge_result, Iterable): + return judge_result + + try: + for element in judge_result: + if _any(element): + return True + except TypeError: + # Maybe encounter the case: torch.tensor(True) | torch.tensor(False) + if judge_result: + return True + return False + + +def assert_dict_contains_subset(dict_obj: Dict[Any, Any], + expected_subset: Dict[Any, Any]) -> bool: + """Check if the dict_obj contains the expected_subset. + + Args: + dict_obj (Dict[Any, Any]): Dict object to be checked. + expected_subset (Dict[Any, Any]): Subset expected to be contained in + dict_obj. + + Returns: + bool: Whether the dict_obj contains the expected_subset. + """ + + for key, value in expected_subset.items(): + if key not in dict_obj.keys() or _any(dict_obj[key] != value): + return False + return True + + +def assert_attrs_equal(obj: Any, expected_attrs: Dict[str, Any]) -> bool: + """Check if attribute of class object is correct. + + Args: + obj (object): Class object to be checked. + expected_attrs (Dict[str, Any]): Dict of the expected attrs. + + Returns: + bool: Whether the attribute of class object is correct. + """ + for attr, value in expected_attrs.items(): + if not hasattr(obj, attr) or _any(getattr(obj, attr) != value): + return False + return True + + +def assert_dict_has_keys(obj: Dict[str, Any], + expected_keys: List[str]) -> bool: + """Check if the obj has all the expected_keys. + + Args: + obj (Dict[str, Any]): Object to be checked. + expected_keys (List[str]): Keys expected to contained in the keys of + the obj. + + Returns: + bool: Whether the obj has the expected keys. + """ + return set(expected_keys).issubset(set(obj.keys())) + + +def assert_keys_equal(result_keys: List[str], target_keys: List[str]) -> bool: + """Check if target_keys is equal to result_keys. + + Args: + result_keys (List[str]): Result keys to be checked. + target_keys (List[str]): Target keys to be checked. + + Returns: + bool: Whether target_keys is equal to result_keys. + """ + return set(result_keys) == set(target_keys) + + +def assert_is_norm_layer(module) -> bool: + """Check if the module is a norm layer. + + Args: + module (nn.Module): The module to be checked. + + Returns: + bool: Whether the module is a norm layer. + """ + from .parrots_wrapper import _BatchNorm, _InstanceNorm + from torch.nn import GroupNorm, LayerNorm + norm_layer_candidates = (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm) + return isinstance(module, norm_layer_candidates) + + +def assert_params_all_zeros(module) -> bool: + """Check if the parameters of the module is all zeros. + + Args: + module (nn.Module): The module to be checked. + + Returns: + bool: Whether the parameters of the module is all zeros. + """ + weight_data = module.weight.data + is_weight_zero = weight_data.allclose( + weight_data.new_zeros(weight_data.size())) + + if hasattr(module, 'bias') and module.bias is not None: + bias_data = module.bias.data + is_bias_zero = bias_data.allclose( + bias_data.new_zeros(bias_data.size())) + else: + is_bias_zero = True + + return is_weight_zero and is_bias_zero diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/timer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/timer.py new file mode 100644 index 00000000..5907e0ed --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/timer.py @@ -0,0 +1,118 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from time import time + + +class TimerError(Exception): + + def __init__(self, message): + self.message = message + super(TimerError, self).__init__(message) + + +class Timer: + """A flexible Timer class. + + :Example: + + >>> import time + >>> import custom_mmpkg.custom_mmcv as mmcv + >>> with mmcv.Timer(): + >>> # simulate a code block that will run for 1s + >>> time.sleep(1) + 1.000 + >>> with mmcv.Timer(print_tmpl='it takes {:.1f} seconds'): + >>> # simulate a code block that will run for 1s + >>> time.sleep(1) + it takes 1.0 seconds + >>> timer = mmcv.Timer() + >>> time.sleep(0.5) + >>> print(timer.since_start()) + 0.500 + >>> time.sleep(0.5) + >>> print(timer.since_last_check()) + 0.500 + >>> print(timer.since_start()) + 1.000 + """ + + def __init__(self, start=True, print_tmpl=None): + self._is_running = False + self.print_tmpl = print_tmpl if print_tmpl else '{:.3f}' + if start: + self.start() + + @property + def is_running(self): + """bool: indicate whether the timer is running""" + return self._is_running + + def __enter__(self): + self.start() + return self + + def __exit__(self, type, value, traceback): + print(self.print_tmpl.format(self.since_last_check())) + self._is_running = False + + def start(self): + """Start the timer.""" + if not self._is_running: + self._t_start = time() + self._is_running = True + self._t_last = time() + + def since_start(self): + """Total time since the timer is started. + + Returns (float): Time in seconds. + """ + if not self._is_running: + raise TimerError('timer is not running') + self._t_last = time() + return self._t_last - self._t_start + + def since_last_check(self): + """Time since the last checking. + + Either :func:`since_start` or :func:`since_last_check` is a checking + operation. + + Returns (float): Time in seconds. + """ + if not self._is_running: + raise TimerError('timer is not running') + dur = time() - self._t_last + self._t_last = time() + return dur + + +_g_timers = {} # global timers + + +def check_time(timer_id): + """Add check points in a single line. + + This method is suitable for running a task on a list of items. A timer will + be registered when the method is called for the first time. + + :Example: + + >>> import time + >>> import custom_mmpkg.custom_mmcv as mmcv + >>> for i in range(1, 6): + >>> # simulate a code block + >>> time.sleep(i) + >>> mmcv.check_time('task1') + 2.000 + 3.000 + 4.000 + 5.000 + + Args: + timer_id (str): Timer identifier. + """ + if timer_id not in _g_timers: + _g_timers[timer_id] = Timer() + return 0 + else: + return _g_timers[timer_id].since_last_check() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/trace.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/trace.py new file mode 100644 index 00000000..3907185b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/trace.py @@ -0,0 +1,23 @@ +import warnings + +import torch + +from custom_mmpkg.custom_mmcv.utils import digit_version + + +def is_jit_tracing() -> bool: + if (torch.__version__ != 'parrots' + and digit_version(torch.__version__) >= digit_version('1.6.0')): + on_trace = torch.jit.is_tracing() + # In PyTorch 1.6, torch.jit.is_tracing has a bug. + # Refers to https://github.com/pytorch/pytorch/issues/42448 + if isinstance(on_trace, bool): + return on_trace + else: + return torch._C._is_tracing() + else: + warnings.warn( + 'torch.jit.is_tracing is only supported after v1.6.0. ' + 'Therefore is_tracing returns False automatically. Please ' + 'set on_trace manually if you are using trace.', UserWarning) + return False diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/version_utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/version_utils.py new file mode 100644 index 00000000..963c45a2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/utils/version_utils.py @@ -0,0 +1,90 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os +import subprocess +import warnings + +from packaging.version import parse + + +def digit_version(version_str: str, length: int = 4): + """Convert a version string into a tuple of integers. + + This method is usually used for comparing two versions. For pre-release + versions: alpha < beta < rc. + + Args: + version_str (str): The version string. + length (int): The maximum number of version levels. Default: 4. + + Returns: + tuple[int]: The version info in digits (integers). + """ + assert 'parrots' not in version_str + version = parse(version_str) + assert version.release, f'failed to parse version {version_str}' + release = list(version.release) + release = release[:length] + if len(release) < length: + release = release + [0] * (length - len(release)) + if version.is_prerelease: + mapping = {'a': -3, 'b': -2, 'rc': -1} + val = -4 + # version.pre can be None + if version.pre: + if version.pre[0] not in mapping: + warnings.warn(f'unknown prerelease version {version.pre[0]}, ' + 'version checking may go wrong') + else: + val = mapping[version.pre[0]] + release.extend([val, version.pre[-1]]) + else: + release.extend([val, 0]) + + elif version.is_postrelease: + release.extend([1, version.post]) + else: + release.extend([0, 0]) + return tuple(release) + + +def _minimal_ext_cmd(cmd): + # construct minimal environment + env = {} + for k in ['SYSTEMROOT', 'PATH', 'HOME']: + v = os.environ.get(k) + if v is not None: + env[k] = v + # LANGUAGE is used on win32 + env['LANGUAGE'] = 'C' + env['LANG'] = 'C' + env['LC_ALL'] = 'C' + out = subprocess.Popen( + cmd, stdout=subprocess.PIPE, env=env).communicate()[0] + return out + + +def get_git_hash(fallback='unknown', digits=None): + """Get the git hash of the current repo. + + Args: + fallback (str, optional): The fallback string when git hash is + unavailable. Defaults to 'unknown'. + digits (int, optional): kept digits of the hash. Defaults to None, + meaning all digits are kept. + + Returns: + str: Git commit hash. + """ + + if digits is not None and not isinstance(digits, int): + raise TypeError('digits must be None or an integer') + + try: + out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD']) + sha = out.strip().decode('ascii') + if digits is not None: + sha = sha[:digits] + except OSError: + sha = fallback + + return sha diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/version.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/version.py new file mode 100644 index 00000000..1cce4e50 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/version.py @@ -0,0 +1,35 @@ +# Copyright (c) OpenMMLab. All rights reserved. +__version__ = '1.3.17' + + +def parse_version_info(version_str: str, length: int = 4) -> tuple: + """Parse a version string into a tuple. + + Args: + version_str (str): The version string. + length (int): The maximum number of version levels. Default: 4. + + Returns: + tuple[int | str]: The version info, e.g., "1.3.0" is parsed into + (1, 3, 0, 0, 0, 0), and "2.0.0rc1" is parsed into + (2, 0, 0, 0, 'rc', 1) (when length is set to 4). + """ + from packaging.version import parse + version = parse(version_str) + assert version.release, f'failed to parse version {version_str}' + release = list(version.release) + release = release[:length] + if len(release) < length: + release = release + [0] * (length - len(release)) + if version.is_prerelease: + release.extend(list(version.pre)) + elif version.is_postrelease: + release.extend(list(version.post)) + else: + release.extend([0, 0]) + return tuple(release) + + +version_info = tuple(int(x) for x in __version__.split('.')[:3]) + +__all__ = ['__version__', 'version_info', 'parse_version_info'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/__init__.py new file mode 100644 index 00000000..73199b01 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .io import Cache, VideoReader, frames2video +from .optflow import (dequantize_flow, flow_from_bytes, flow_warp, flowread, + flowwrite, quantize_flow, sparse_flow_from_bytes) +from .processing import concat_video, convert_video, cut_video, resize_video + +__all__ = [ + 'Cache', 'VideoReader', 'frames2video', 'convert_video', 'resize_video', + 'cut_video', 'concat_video', 'flowread', 'flowwrite', 'quantize_flow', + 'dequantize_flow', 'flow_warp', 'flow_from_bytes', 'sparse_flow_from_bytes' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/io.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/io.py new file mode 100644 index 00000000..f9c20cee --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/io.py @@ -0,0 +1,318 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from collections import OrderedDict + +import cv2 +from cv2 import (CAP_PROP_FOURCC, CAP_PROP_FPS, CAP_PROP_FRAME_COUNT, + CAP_PROP_FRAME_HEIGHT, CAP_PROP_FRAME_WIDTH, + CAP_PROP_POS_FRAMES, VideoWriter_fourcc) + +from custom_mmpkg.custom_mmcv.utils import (check_file_exist, mkdir_or_exist, scandir, + track_progress) + + +class Cache: + + def __init__(self, capacity): + self._cache = OrderedDict() + self._capacity = int(capacity) + if capacity <= 0: + raise ValueError('capacity must be a positive integer') + + @property + def capacity(self): + return self._capacity + + @property + def size(self): + return len(self._cache) + + def put(self, key, val): + if key in self._cache: + return + if len(self._cache) >= self.capacity: + self._cache.popitem(last=False) + self._cache[key] = val + + def get(self, key, default=None): + val = self._cache[key] if key in self._cache else default + return val + + +class VideoReader: + """Video class with similar usage to a list object. + + This video warpper class provides convenient apis to access frames. + There exists an issue of OpenCV's VideoCapture class that jumping to a + certain frame may be inaccurate. It is fixed in this class by checking + the position after jumping each time. + Cache is used when decoding videos. So if the same frame is visited for + the second time, there is no need to decode again if it is stored in the + cache. + + :Example: + + >>> import custom_mmpkg.custom_mmcv as mmcv + >>> v = mmcv.VideoReader('sample.mp4') + >>> len(v) # get the total frame number with `len()` + 120 + >>> for img in v: # v is iterable + >>> mmcv.imshow(img) + >>> v[5] # get the 6th frame + """ + + def __init__(self, filename, cache_capacity=10): + # Check whether the video path is a url + if not filename.startswith(('https://', 'http://')): + check_file_exist(filename, 'Video file not found: ' + filename) + self._vcap = cv2.VideoCapture(filename) + assert cache_capacity > 0 + self._cache = Cache(cache_capacity) + self._position = 0 + # get basic info + self._width = int(self._vcap.get(CAP_PROP_FRAME_WIDTH)) + self._height = int(self._vcap.get(CAP_PROP_FRAME_HEIGHT)) + self._fps = self._vcap.get(CAP_PROP_FPS) + self._frame_cnt = int(self._vcap.get(CAP_PROP_FRAME_COUNT)) + self._fourcc = self._vcap.get(CAP_PROP_FOURCC) + + @property + def vcap(self): + """:obj:`cv2.VideoCapture`: The raw VideoCapture object.""" + return self._vcap + + @property + def opened(self): + """bool: Indicate whether the video is opened.""" + return self._vcap.isOpened() + + @property + def width(self): + """int: Width of video frames.""" + return self._width + + @property + def height(self): + """int: Height of video frames.""" + return self._height + + @property + def resolution(self): + """tuple: Video resolution (width, height).""" + return (self._width, self._height) + + @property + def fps(self): + """float: FPS of the video.""" + return self._fps + + @property + def frame_cnt(self): + """int: Total frames of the video.""" + return self._frame_cnt + + @property + def fourcc(self): + """str: "Four character code" of the video.""" + return self._fourcc + + @property + def position(self): + """int: Current cursor position, indicating frame decoded.""" + return self._position + + def _get_real_position(self): + return int(round(self._vcap.get(CAP_PROP_POS_FRAMES))) + + def _set_real_position(self, frame_id): + self._vcap.set(CAP_PROP_POS_FRAMES, frame_id) + pos = self._get_real_position() + for _ in range(frame_id - pos): + self._vcap.read() + self._position = frame_id + + def read(self): + """Read the next frame. + + If the next frame have been decoded before and in the cache, then + return it directly, otherwise decode, cache and return it. + + Returns: + ndarray or None: Return the frame if successful, otherwise None. + """ + # pos = self._position + if self._cache: + img = self._cache.get(self._position) + if img is not None: + ret = True + else: + if self._position != self._get_real_position(): + self._set_real_position(self._position) + ret, img = self._vcap.read() + if ret: + self._cache.put(self._position, img) + else: + ret, img = self._vcap.read() + if ret: + self._position += 1 + return img + + def get_frame(self, frame_id): + """Get frame by index. + + Args: + frame_id (int): Index of the expected frame, 0-based. + + Returns: + ndarray or None: Return the frame if successful, otherwise None. + """ + if frame_id < 0 or frame_id >= self._frame_cnt: + raise IndexError( + f'"frame_id" must be between 0 and {self._frame_cnt - 1}') + if frame_id == self._position: + return self.read() + if self._cache: + img = self._cache.get(frame_id) + if img is not None: + self._position = frame_id + 1 + return img + self._set_real_position(frame_id) + ret, img = self._vcap.read() + if ret: + if self._cache: + self._cache.put(self._position, img) + self._position += 1 + return img + + def current_frame(self): + """Get the current frame (frame that is just visited). + + Returns: + ndarray or None: If the video is fresh, return None, otherwise + return the frame. + """ + if self._position == 0: + return None + return self._cache.get(self._position - 1) + + def cvt2frames(self, + frame_dir, + file_start=0, + filename_tmpl='{:06d}.jpg', + start=0, + max_num=0, + show_progress=True): + """Convert a video to frame images. + + Args: + frame_dir (str): Output directory to store all the frame images. + file_start (int): Filenames will start from the specified number. + filename_tmpl (str): Filename template with the index as the + placeholder. + start (int): The starting frame index. + max_num (int): Maximum number of frames to be written. + show_progress (bool): Whether to show a progress bar. + """ + mkdir_or_exist(frame_dir) + if max_num == 0: + task_num = self.frame_cnt - start + else: + task_num = min(self.frame_cnt - start, max_num) + if task_num <= 0: + raise ValueError('start must be less than total frame number') + if start > 0: + self._set_real_position(start) + + def write_frame(file_idx): + img = self.read() + if img is None: + return + filename = osp.join(frame_dir, filename_tmpl.format(file_idx)) + cv2.imwrite(filename, img) + + if show_progress: + track_progress(write_frame, range(file_start, + file_start + task_num)) + else: + for i in range(task_num): + write_frame(file_start + i) + + def __len__(self): + return self.frame_cnt + + def __getitem__(self, index): + if isinstance(index, slice): + return [ + self.get_frame(i) + for i in range(*index.indices(self.frame_cnt)) + ] + # support negative indexing + if index < 0: + index += self.frame_cnt + if index < 0: + raise IndexError('index out of range') + return self.get_frame(index) + + def __iter__(self): + self._set_real_position(0) + return self + + def __next__(self): + img = self.read() + if img is not None: + return img + else: + raise StopIteration + + next = __next__ + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self._vcap.release() + + +def frames2video(frame_dir, + video_file, + fps=30, + fourcc='XVID', + filename_tmpl='{:06d}.jpg', + start=0, + end=0, + show_progress=True): + """Read the frame images from a directory and join them as a video. + + Args: + frame_dir (str): The directory containing video frames. + video_file (str): Output filename. + fps (float): FPS of the output video. + fourcc (str): Fourcc of the output video, this should be compatible + with the output file type. + filename_tmpl (str): Filename template with the index as the variable. + start (int): Starting frame index. + end (int): Ending frame index. + show_progress (bool): Whether to show a progress bar. + """ + if end == 0: + ext = filename_tmpl.split('.')[-1] + end = len([name for name in scandir(frame_dir, ext)]) + first_file = osp.join(frame_dir, filename_tmpl.format(start)) + check_file_exist(first_file, 'The start frame not found: ' + first_file) + img = cv2.imread(first_file) + height, width = img.shape[:2] + resolution = (width, height) + vwriter = cv2.VideoWriter(video_file, VideoWriter_fourcc(*fourcc), fps, + resolution) + + def write_frame(file_idx): + filename = osp.join(frame_dir, filename_tmpl.format(file_idx)) + img = cv2.imread(filename) + vwriter.write(img) + + if show_progress: + track_progress(write_frame, range(start, end)) + else: + for i in range(start, end): + write_frame(i) + vwriter.release() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/optflow.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/optflow.py new file mode 100644 index 00000000..71c7cc1c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/optflow.py @@ -0,0 +1,254 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings + +import cv2 +import numpy as np + +from custom_mmpkg.custom_mmcv.arraymisc import dequantize, quantize +from custom_mmpkg.custom_mmcv.image import imread, imwrite +from custom_mmpkg.custom_mmcv.utils import is_str + + +def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs): + """Read an optical flow map. + + Args: + flow_or_path (ndarray or str): A flow map or filepath. + quantize (bool): whether to read quantized pair, if set to True, + remaining args will be passed to :func:`dequantize_flow`. + concat_axis (int): The axis that dx and dy are concatenated, + can be either 0 or 1. Ignored if quantize is False. + + Returns: + ndarray: Optical flow represented as a (h, w, 2) numpy array + """ + if isinstance(flow_or_path, np.ndarray): + if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2): + raise ValueError(f'Invalid flow with shape {flow_or_path.shape}') + return flow_or_path + elif not is_str(flow_or_path): + raise TypeError(f'"flow_or_path" must be a filename or numpy array, ' + f'not {type(flow_or_path)}') + + if not quantize: + with open(flow_or_path, 'rb') as f: + try: + header = f.read(4).decode('utf-8') + except Exception: + raise IOError(f'Invalid flow file: {flow_or_path}') + else: + if header != 'PIEH': + raise IOError(f'Invalid flow file: {flow_or_path}, ' + 'header does not contain PIEH') + + w = np.fromfile(f, np.int32, 1).squeeze() + h = np.fromfile(f, np.int32, 1).squeeze() + flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2)) + else: + assert concat_axis in [0, 1] + cat_flow = imread(flow_or_path, flag='unchanged') + if cat_flow.ndim != 2: + raise IOError( + f'{flow_or_path} is not a valid quantized flow file, ' + f'its dimension is {cat_flow.ndim}.') + assert cat_flow.shape[concat_axis] % 2 == 0 + dx, dy = np.split(cat_flow, 2, axis=concat_axis) + flow = dequantize_flow(dx, dy, *args, **kwargs) + + return flow.astype(np.float32) + + +def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): + """Write optical flow to file. + + If the flow is not quantized, it will be saved as a .flo file losslessly, + otherwise a jpeg image which is lossy but of much smaller size. (dx and dy + will be concatenated horizontally into a single image if quantize is True.) + + Args: + flow (ndarray): (h, w, 2) array of optical flow. + filename (str): Output filepath. + quantize (bool): Whether to quantize the flow and save it to 2 jpeg + images. If set to True, remaining args will be passed to + :func:`quantize_flow`. + concat_axis (int): The axis that dx and dy are concatenated, + can be either 0 or 1. Ignored if quantize is False. + """ + if not quantize: + with open(filename, 'wb') as f: + f.write('PIEH'.encode('utf-8')) + np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) + flow = flow.astype(np.float32) + flow.tofile(f) + f.flush() + else: + assert concat_axis in [0, 1] + dx, dy = quantize_flow(flow, *args, **kwargs) + dxdy = np.concatenate((dx, dy), axis=concat_axis) + imwrite(dxdy, filename) + + +def quantize_flow(flow, max_val=0.02, norm=True): + """Quantize flow to [0, 255]. + + After this step, the size of flow will be much smaller, and can be + dumped as jpeg images. + + Args: + flow (ndarray): (h, w, 2) array of optical flow. + max_val (float): Maximum value of flow, values beyond + [-max_val, max_val] will be truncated. + norm (bool): Whether to divide flow values by image width/height. + + Returns: + tuple[ndarray]: Quantized dx and dy. + """ + h, w, _ = flow.shape + dx = flow[..., 0] + dy = flow[..., 1] + if norm: + dx = dx / w # avoid inplace operations + dy = dy / h + # use 255 levels instead of 256 to make sure 0 is 0 after dequantization. + flow_comps = [ + quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy] + ] + return tuple(flow_comps) + + +def dequantize_flow(dx, dy, max_val=0.02, denorm=True): + """Recover from quantized flow. + + Args: + dx (ndarray): Quantized dx. + dy (ndarray): Quantized dy. + max_val (float): Maximum value used when quantizing. + denorm (bool): Whether to multiply flow values with width/height. + + Returns: + ndarray: Dequantized flow. + """ + assert dx.shape == dy.shape + assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1) + + dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]] + + if denorm: + dx *= dx.shape[1] + dy *= dx.shape[0] + flow = np.dstack((dx, dy)) + return flow + + +def flow_warp(img, flow, filling_value=0, interpolate_mode='nearest'): + """Use flow to warp img. + + Args: + img (ndarray, float or uint8): Image to be warped. + flow (ndarray, float): Optical Flow. + filling_value (int): The missing pixels will be set with filling_value. + interpolate_mode (str): bilinear -> Bilinear Interpolation; + nearest -> Nearest Neighbor. + + Returns: + ndarray: Warped image with the same shape of img + """ + warnings.warn('This function is just for prototyping and cannot ' + 'guarantee the computational efficiency.') + assert flow.ndim == 3, 'Flow must be in 3D arrays.' + height = flow.shape[0] + width = flow.shape[1] + channels = img.shape[2] + + output = np.ones( + (height, width, channels), dtype=img.dtype) * filling_value + + grid = np.indices((height, width)).swapaxes(0, 1).swapaxes(1, 2) + dx = grid[:, :, 0] + flow[:, :, 1] + dy = grid[:, :, 1] + flow[:, :, 0] + sx = np.floor(dx).astype(int) + sy = np.floor(dy).astype(int) + valid = (sx >= 0) & (sx < height - 1) & (sy >= 0) & (sy < width - 1) + + if interpolate_mode == 'nearest': + output[valid, :] = img[dx[valid].round().astype(int), + dy[valid].round().astype(int), :] + elif interpolate_mode == 'bilinear': + # dirty walkround for integer positions + eps_ = 1e-6 + dx, dy = dx + eps_, dy + eps_ + left_top_ = img[np.floor(dx[valid]).astype(int), + np.floor(dy[valid]).astype(int), :] * ( + np.ceil(dx[valid]) - dx[valid])[:, None] * ( + np.ceil(dy[valid]) - dy[valid])[:, None] + left_down_ = img[np.ceil(dx[valid]).astype(int), + np.floor(dy[valid]).astype(int), :] * ( + dx[valid] - np.floor(dx[valid]))[:, None] * ( + np.ceil(dy[valid]) - dy[valid])[:, None] + right_top_ = img[np.floor(dx[valid]).astype(int), + np.ceil(dy[valid]).astype(int), :] * ( + np.ceil(dx[valid]) - dx[valid])[:, None] * ( + dy[valid] - np.floor(dy[valid]))[:, None] + right_down_ = img[np.ceil(dx[valid]).astype(int), + np.ceil(dy[valid]).astype(int), :] * ( + dx[valid] - np.floor(dx[valid]))[:, None] * ( + dy[valid] - np.floor(dy[valid]))[:, None] + output[valid, :] = left_top_ + left_down_ + right_top_ + right_down_ + else: + raise NotImplementedError( + 'We only support interpolation modes of nearest and bilinear, ' + f'but got {interpolate_mode}.') + return output.astype(img.dtype) + + +def flow_from_bytes(content): + """Read dense optical flow from bytes. + + .. note:: + This load optical flow function works for FlyingChairs, FlyingThings3D, + Sintel, FlyingChairsOcc datasets, but cannot load the data from + ChairsSDHom. + + Args: + content (bytes): Optical flow bytes got from files or other streams. + + Returns: + ndarray: Loaded optical flow with the shape (H, W, 2). + """ + + # header in first 4 bytes + header = content[:4] + if header.decode('utf-8') != 'PIEH': + raise Exception('Flow file header does not contain PIEH') + # width in second 4 bytes + width = np.frombuffer(content[4:], np.int32, 1).squeeze() + # height in third 4 bytes + height = np.frombuffer(content[8:], np.int32, 1).squeeze() + # after first 12 bytes, all bytes are flow + flow = np.frombuffer(content[12:], np.float32, width * height * 2).reshape( + (height, width, 2)) + + return flow + + +def sparse_flow_from_bytes(content): + """Read the optical flow in KITTI datasets from bytes. + + This function is modified from RAFT load the `KITTI datasets + `_. + + Args: + content (bytes): Optical flow bytes got from files or other streams. + + Returns: + Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2) + and flow valid mask with the shape (H, W). + """ # nopa + + content = np.frombuffer(content, np.uint8) + flow = cv2.imdecode(content, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR) + flow = flow[:, :, ::-1].astype(np.float32) + # flow shape (H, W, 2) valid shape (H, W) + flow, valid = flow[:, :, :2], flow[:, :, 2] + flow = (flow - 2**15) / 64.0 + return flow, valid diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/processing.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/processing.py new file mode 100644 index 00000000..72865d90 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/video/processing.py @@ -0,0 +1,160 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os +import os.path as osp +import subprocess +import tempfile + +from custom_mmpkg.custom_mmcv.utils import requires_executable + + +@requires_executable('ffmpeg') +def convert_video(in_file, + out_file, + print_cmd=False, + pre_options='', + **kwargs): + """Convert a video with ffmpeg. + + This provides a general api to ffmpeg, the executed command is:: + + `ffmpeg -y -i ` + + Options(kwargs) are mapped to ffmpeg commands with the following rules: + + - key=val: "-key val" + - key=True: "-key" + - key=False: "" + + Args: + in_file (str): Input video filename. + out_file (str): Output video filename. + pre_options (str): Options appears before "-i ". + print_cmd (bool): Whether to print the final ffmpeg command. + """ + options = [] + for k, v in kwargs.items(): + if isinstance(v, bool): + if v: + options.append(f'-{k}') + elif k == 'log_level': + assert v in [ + 'quiet', 'panic', 'fatal', 'error', 'warning', 'info', + 'verbose', 'debug', 'trace' + ] + options.append(f'-loglevel {v}') + else: + options.append(f'-{k} {v}') + cmd = f'ffmpeg -y {pre_options} -i {in_file} {" ".join(options)} ' \ + f'{out_file}' + if print_cmd: + print(cmd) + subprocess.call(cmd, shell=True) + + +@requires_executable('ffmpeg') +def resize_video(in_file, + out_file, + size=None, + ratio=None, + keep_ar=False, + log_level='info', + print_cmd=False): + """Resize a video. + + Args: + in_file (str): Input video filename. + out_file (str): Output video filename. + size (tuple): Expected size (w, h), eg, (320, 240) or (320, -1). + ratio (tuple or float): Expected resize ratio, (2, 0.5) means + (w*2, h*0.5). + keep_ar (bool): Whether to keep original aspect ratio. + log_level (str): Logging level of ffmpeg. + print_cmd (bool): Whether to print the final ffmpeg command. + """ + if size is None and ratio is None: + raise ValueError('expected size or ratio must be specified') + if size is not None and ratio is not None: + raise ValueError('size and ratio cannot be specified at the same time') + options = {'log_level': log_level} + if size: + if not keep_ar: + options['vf'] = f'scale={size[0]}:{size[1]}' + else: + options['vf'] = f'scale=w={size[0]}:h={size[1]}:' \ + 'force_original_aspect_ratio=decrease' + else: + if not isinstance(ratio, tuple): + ratio = (ratio, ratio) + options['vf'] = f'scale="trunc(iw*{ratio[0]}):trunc(ih*{ratio[1]})"' + convert_video(in_file, out_file, print_cmd, **options) + + +@requires_executable('ffmpeg') +def cut_video(in_file, + out_file, + start=None, + end=None, + vcodec=None, + acodec=None, + log_level='info', + print_cmd=False): + """Cut a clip from a video. + + Args: + in_file (str): Input video filename. + out_file (str): Output video filename. + start (None or float): Start time (in seconds). + end (None or float): End time (in seconds). + vcodec (None or str): Output video codec, None for unchanged. + acodec (None or str): Output audio codec, None for unchanged. + log_level (str): Logging level of ffmpeg. + print_cmd (bool): Whether to print the final ffmpeg command. + """ + options = {'log_level': log_level} + if vcodec is None: + options['vcodec'] = 'copy' + if acodec is None: + options['acodec'] = 'copy' + if start: + options['ss'] = start + else: + start = 0 + if end: + options['t'] = end - start + convert_video(in_file, out_file, print_cmd, **options) + + +@requires_executable('ffmpeg') +def concat_video(video_list, + out_file, + vcodec=None, + acodec=None, + log_level='info', + print_cmd=False): + """Concatenate multiple videos into a single one. + + Args: + video_list (list): A list of video filenames + out_file (str): Output video filename + vcodec (None or str): Output video codec, None for unchanged + acodec (None or str): Output audio codec, None for unchanged + log_level (str): Logging level of ffmpeg. + print_cmd (bool): Whether to print the final ffmpeg command. + """ + tmp_filehandler, tmp_filename = tempfile.mkstemp(suffix='.txt', text=True) + with open(tmp_filename, 'w') as f: + for filename in video_list: + f.write(f'file {osp.abspath(filename)}\n') + options = {'log_level': log_level} + if vcodec is None: + options['vcodec'] = 'copy' + if acodec is None: + options['acodec'] = 'copy' + convert_video( + tmp_filename, + out_file, + print_cmd, + pre_options='-f concat -safe 0', + **options) + os.close(tmp_filehandler) + os.remove(tmp_filename) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/__init__.py new file mode 100644 index 00000000..835df136 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .color import Color, color_val +from .image import imshow, imshow_bboxes, imshow_det_bboxes +from .optflow import flow2rgb, flowshow, make_color_wheel + +__all__ = [ + 'Color', 'color_val', 'imshow', 'imshow_bboxes', 'imshow_det_bboxes', + 'flowshow', 'flow2rgb', 'make_color_wheel' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/color.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/color.py new file mode 100644 index 00000000..2bff8a9d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/color.py @@ -0,0 +1,51 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from enum import Enum + +import numpy as np + +from custom_mmpkg.custom_mmcv.utils import is_str + + +class Color(Enum): + """An enum that defines common colors. + + Contains red, green, blue, cyan, yellow, magenta, white and black. + """ + red = (0, 0, 255) + green = (0, 255, 0) + blue = (255, 0, 0) + cyan = (255, 255, 0) + yellow = (0, 255, 255) + magenta = (255, 0, 255) + white = (255, 255, 255) + black = (0, 0, 0) + + +def color_val(color): + """Convert various input to color tuples. + + Args: + color (:obj:`Color`/str/tuple/int/ndarray): Color inputs + + Returns: + tuple[int]: A tuple of 3 integers indicating BGR channels. + """ + if is_str(color): + return Color[color].value + elif isinstance(color, Color): + return color.value + elif isinstance(color, tuple): + assert len(color) == 3 + for channel in color: + assert 0 <= channel <= 255 + return color + elif isinstance(color, int): + assert 0 <= color <= 255 + return color, color, color + elif isinstance(color, np.ndarray): + assert color.ndim == 1 and color.size == 3 + assert np.all((color >= 0) & (color <= 255)) + color = color.astype(np.uint8) + return tuple(color) + else: + raise TypeError(f'Invalid type for color: {type(color)}') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/image.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/image.py new file mode 100644 index 00000000..3f77c6d1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/image.py @@ -0,0 +1,152 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import cv2 +import numpy as np + +from custom_mmpkg.custom_mmcv.image import imread, imwrite +from .color import color_val + + +def imshow(img, win_name='', wait_time=0): + """Show an image. + + Args: + img (str or ndarray): The image to be displayed. + win_name (str): The window name. + wait_time (int): Value of waitKey param. + """ + cv2.imshow(win_name, imread(img)) + if wait_time == 0: # prevent from hanging if windows was closed + while True: + ret = cv2.waitKey(1) + + closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1 + # if user closed window or if some key pressed + if closed or ret != -1: + break + else: + ret = cv2.waitKey(wait_time) + + +def imshow_bboxes(img, + bboxes, + colors='green', + top_k=-1, + thickness=1, + show=True, + win_name='', + wait_time=0, + out_file=None): + """Draw bboxes on an image. + + Args: + img (str or ndarray): The image to be displayed. + bboxes (list or ndarray): A list of ndarray of shape (k, 4). + colors (list[str or tuple or Color]): A list of colors. + top_k (int): Plot the first k bboxes only if set positive. + thickness (int): Thickness of lines. + show (bool): Whether to show the image. + win_name (str): The window name. + wait_time (int): Value of waitKey param. + out_file (str, optional): The filename to write the image. + + Returns: + ndarray: The image with bboxes drawn on it. + """ + img = imread(img) + img = np.ascontiguousarray(img) + + if isinstance(bboxes, np.ndarray): + bboxes = [bboxes] + if not isinstance(colors, list): + colors = [colors for _ in range(len(bboxes))] + colors = [color_val(c) for c in colors] + assert len(bboxes) == len(colors) + + for i, _bboxes in enumerate(bboxes): + _bboxes = _bboxes.astype(np.int32) + if top_k <= 0: + _top_k = _bboxes.shape[0] + else: + _top_k = min(top_k, _bboxes.shape[0]) + for j in range(_top_k): + left_top = (_bboxes[j, 0], _bboxes[j, 1]) + right_bottom = (_bboxes[j, 2], _bboxes[j, 3]) + cv2.rectangle( + img, left_top, right_bottom, colors[i], thickness=thickness) + + if show: + imshow(img, win_name, wait_time) + if out_file is not None: + imwrite(img, out_file) + return img + + +def imshow_det_bboxes(img, + bboxes, + labels, + class_names=None, + score_thr=0, + bbox_color='green', + text_color='green', + thickness=1, + font_scale=0.5, + show=True, + win_name='', + wait_time=0, + out_file=None): + """Draw bboxes and class labels (with scores) on an image. + + Args: + img (str or ndarray): The image to be displayed. + bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or + (n, 5). + labels (ndarray): Labels of bboxes. + class_names (list[str]): Names of each classes. + score_thr (float): Minimum score of bboxes to be shown. + bbox_color (str or tuple or :obj:`Color`): Color of bbox lines. + text_color (str or tuple or :obj:`Color`): Color of texts. + thickness (int): Thickness of lines. + font_scale (float): Font scales of texts. + show (bool): Whether to show the image. + win_name (str): The window name. + wait_time (int): Value of waitKey param. + out_file (str or None): The filename to write the image. + + Returns: + ndarray: The image with bboxes drawn on it. + """ + assert bboxes.ndim == 2 + assert labels.ndim == 1 + assert bboxes.shape[0] == labels.shape[0] + assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5 + img = imread(img) + img = np.ascontiguousarray(img) + + if score_thr > 0: + assert bboxes.shape[1] == 5 + scores = bboxes[:, -1] + inds = scores > score_thr + bboxes = bboxes[inds, :] + labels = labels[inds] + + bbox_color = color_val(bbox_color) + text_color = color_val(text_color) + + for bbox, label in zip(bboxes, labels): + bbox_int = bbox.astype(np.int32) + left_top = (bbox_int[0], bbox_int[1]) + right_bottom = (bbox_int[2], bbox_int[3]) + cv2.rectangle( + img, left_top, right_bottom, bbox_color, thickness=thickness) + label_text = class_names[ + label] if class_names is not None else f'cls {label}' + if len(bbox) > 4: + label_text += f'|{bbox[-1]:.02f}' + cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2), + cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color) + + if show: + imshow(img, win_name, wait_time) + if out_file is not None: + imwrite(img, out_file) + return img diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/optflow.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/optflow.py new file mode 100644 index 00000000..8b13b411 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmcv/visualization/optflow.py @@ -0,0 +1,112 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from __future__ import division + +import numpy as np + +from custom_mmpkg.custom_mmcv.image import rgb2bgr +from custom_mmpkg.custom_mmcv.video import flowread +from .image import imshow + + +def flowshow(flow, win_name='', wait_time=0): + """Show optical flow. + + Args: + flow (ndarray or str): The optical flow to be displayed. + win_name (str): The window name. + wait_time (int): Value of waitKey param. + """ + flow = flowread(flow) + flow_img = flow2rgb(flow) + imshow(rgb2bgr(flow_img), win_name, wait_time) + + +def flow2rgb(flow, color_wheel=None, unknown_thr=1e6): + """Convert flow map to RGB image. + + Args: + flow (ndarray): Array of optical flow. + color_wheel (ndarray or None): Color wheel used to map flow field to + RGB colorspace. Default color wheel will be used if not specified. + unknown_thr (str): Values above this threshold will be marked as + unknown and thus ignored. + + Returns: + ndarray: RGB image that can be visualized. + """ + assert flow.ndim == 3 and flow.shape[-1] == 2 + if color_wheel is None: + color_wheel = make_color_wheel() + assert color_wheel.ndim == 2 and color_wheel.shape[1] == 3 + num_bins = color_wheel.shape[0] + + dx = flow[:, :, 0].copy() + dy = flow[:, :, 1].copy() + + ignore_inds = ( + np.isnan(dx) | np.isnan(dy) | (np.abs(dx) > unknown_thr) | + (np.abs(dy) > unknown_thr)) + dx[ignore_inds] = 0 + dy[ignore_inds] = 0 + + rad = np.sqrt(dx**2 + dy**2) + if np.any(rad > np.finfo(float).eps): + max_rad = np.max(rad) + dx /= max_rad + dy /= max_rad + + rad = np.sqrt(dx**2 + dy**2) + angle = np.arctan2(-dy, -dx) / np.pi + + bin_real = (angle + 1) / 2 * (num_bins - 1) + bin_left = np.floor(bin_real).astype(int) + bin_right = (bin_left + 1) % num_bins + w = (bin_real - bin_left.astype(np.float32))[..., None] + flow_img = (1 - + w) * color_wheel[bin_left, :] + w * color_wheel[bin_right, :] + small_ind = rad <= 1 + flow_img[small_ind] = 1 - rad[small_ind, None] * (1 - flow_img[small_ind]) + flow_img[np.logical_not(small_ind)] *= 0.75 + + flow_img[ignore_inds, :] = 0 + + return flow_img + + +def make_color_wheel(bins=None): + """Build a color wheel. + + Args: + bins(list or tuple, optional): Specify the number of bins for each + color range, corresponding to six ranges: red -> yellow, + yellow -> green, green -> cyan, cyan -> blue, blue -> magenta, + magenta -> red. [15, 6, 4, 11, 13, 6] is used for default + (see Middlebury). + + Returns: + ndarray: Color wheel of shape (total_bins, 3). + """ + if bins is None: + bins = [15, 6, 4, 11, 13, 6] + assert len(bins) == 6 + + RY, YG, GC, CB, BM, MR = tuple(bins) + + ry = [1, np.arange(RY) / RY, 0] + yg = [1 - np.arange(YG) / YG, 1, 0] + gc = [0, 1, np.arange(GC) / GC] + cb = [0, 1 - np.arange(CB) / CB, 1] + bm = [np.arange(BM) / BM, 0, 1] + mr = [1, 0, 1 - np.arange(MR) / MR] + + num_bins = RY + YG + GC + CB + BM + MR + + color_wheel = np.zeros((3, num_bins), dtype=np.float32) + + col = 0 + for i, color in enumerate([ry, yg, gc, cb, bm, mr]): + for j in range(3): + color_wheel[j, col:col + bins[i]] = color[j] + col += bins[i] + + return color_wheel.T diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/__init__.py new file mode 100644 index 00000000..170724be --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/__init__.py @@ -0,0 +1,9 @@ +from .inference import inference_segmentor, init_segmentor, show_result_pyplot +from .test import multi_gpu_test, single_gpu_test +from .train import get_root_logger, set_random_seed, train_segmentor + +__all__ = [ + 'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor', + 'inference_segmentor', 'multi_gpu_test', 'single_gpu_test', + 'show_result_pyplot' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/inference.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/inference.py new file mode 100644 index 00000000..0ee57d61 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/inference.py @@ -0,0 +1,137 @@ +import matplotlib.pyplot as plt +import custom_mmpkg.custom_mmcv as mmcv +import torch +from custom_mmpkg.custom_mmcv.parallel import collate, scatter +from custom_mmpkg.custom_mmcv.runner import load_checkpoint + +from custom_mmpkg.custom_mmseg.datasets.pipelines import Compose +from custom_mmpkg.custom_mmseg.models import build_segmentor + + +def init_segmentor(config, checkpoint=None, device="cpu"): + """Initialize a segmentor from config file. + + Args: + config (str or :obj:`mmcv.Config`): Config file path or the config + object. + checkpoint (str, optional): Checkpoint path. If left as None, the model + will not load any weights. + device (str, optional) CPU/CUDA device option. Default 'cuda:0'. + Use 'cpu' for loading model on CPU. + Returns: + nn.Module: The constructed segmentor. + """ + if isinstance(config, str): + config = mmcv.Config.fromfile(config) + elif not isinstance(config, mmcv.Config): + raise TypeError('config must be a filename or Config object, ' + 'but got {}'.format(type(config))) + config.model.pretrained = None + config.model.train_cfg = None + model = build_segmentor(config.model, test_cfg=config.get('test_cfg')) + if checkpoint is not None: + checkpoint = load_checkpoint(model, checkpoint, map_location='cpu') + model.CLASSES = checkpoint['meta']['CLASSES'] + model.PALETTE = checkpoint['meta']['PALETTE'] + model.cfg = config # save the config in the model for convenience + model.to(device) + model.eval() + return model + + +class LoadImage: + """A simple pipeline to load image.""" + + def __call__(self, results): + """Call function to load images into results. + + Args: + results (dict): A result dict contains the file name + of the image to be read. + + Returns: + dict: ``results`` will be returned containing loaded image. + """ + + if isinstance(results['img'], str): + results['filename'] = results['img'] + results['ori_filename'] = results['img'] + else: + results['filename'] = None + results['ori_filename'] = None + img = mmcv.imread(results['img']) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + return results + + +def inference_segmentor(model, img): + """Inference image(s) with the segmentor. + + Args: + model (nn.Module): The loaded segmentor. + imgs (str/ndarray or list[str/ndarray]): Either image files or loaded + images. + + Returns: + (list[Tensor]): The segmentation result. + """ + cfg = model.cfg + device = next(model.parameters()).device # model device + # build the data pipeline + test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:] + test_pipeline = Compose(test_pipeline) + # prepare data + data = dict(img=img) + data = test_pipeline(data) + data = collate([data], samples_per_gpu=1) + if next(model.parameters()).is_cuda: + # scatter to specified GPU + data = scatter(data, [device])[0] + else: + data['img'][0] = data['img'][0].to(device) + data['img_metas'] = [i.data[0] for i in data['img_metas']] + + # forward the model + with torch.no_grad(): + result = model(return_loss=False, rescale=True, **data) + return result + + +def show_result_pyplot(model, + img, + result, + palette=None, + fig_size=(15, 10), + opacity=0.5, + title='', + block=True): + """Visualize the segmentation results on the image. + + Args: + model (nn.Module): The loaded segmentor. + img (str or np.ndarray): Image filename or loaded image. + result (list): The segmentation result. + palette (list[list[int]]] | None): The palette of segmentation + map. If None is given, random palette will be generated. + Default: None + fig_size (tuple): Figure size of the pyplot figure. + opacity(float): Opacity of painted segmentation map. + Default 0.5. + Must be in (0, 1] range. + title (str): The title of pyplot figure. + Default is ''. + block (bool): Whether to block the pyplot figure. + Default is True. + """ + if hasattr(model, 'module'): + model = model.module + img = model.show_result( + img, result, palette=palette, show=False, opacity=opacity) + # plt.figure(figsize=fig_size) + # plt.imshow(mmcv.bgr2rgb(img)) + # plt.title(title) + # plt.tight_layout() + # plt.show(block=block) + return mmcv.bgr2rgb(img) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/test.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/test.py new file mode 100644 index 00000000..7d0078b5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/test.py @@ -0,0 +1,238 @@ +import os.path as osp +import pickle +import shutil +import tempfile + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +import torch +import torch.distributed as dist +from custom_mmpkg.custom_mmcv.image import tensor2imgs +from custom_mmpkg.custom_mmcv.runner import get_dist_info + + +def np2tmp(array, temp_file_name=None): + """Save ndarray to local numpy file. + + Args: + array (ndarray): Ndarray to save. + temp_file_name (str): Numpy file name. If 'temp_file_name=None', this + function will generate a file name with tempfile.NamedTemporaryFile + to save ndarray. Default: None. + + Returns: + str: The numpy file name. + """ + + if temp_file_name is None: + temp_file_name = tempfile.NamedTemporaryFile( + suffix='.npy', delete=False).name + np.save(temp_file_name, array) + return temp_file_name + + +def single_gpu_test(model, + data_loader, + show=False, + out_dir=None, + efficient_test=False, + opacity=0.5): + """Test with single GPU. + + Args: + model (nn.Module): Model to be tested. + data_loader (utils.data.Dataloader): Pytorch data loader. + show (bool): Whether show results during inference. Default: False. + out_dir (str, optional): If specified, the results will be dumped into + the directory to save output results. + efficient_test (bool): Whether save the results as local numpy files to + save CPU memory during evaluation. Default: False. + opacity(float): Opacity of painted segmentation map. + Default 0.5. + Must be in (0, 1] range. + Returns: + list: The prediction results. + """ + + model.eval() + results = [] + dataset = data_loader.dataset + prog_bar = mmcv.ProgressBar(len(dataset)) + for i, data in enumerate(data_loader): + with torch.no_grad(): + result = model(return_loss=False, **data) + + if show or out_dir: + img_tensor = data['img'][0] + img_metas = data['img_metas'][0].data[0] + imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg']) + assert len(imgs) == len(img_metas) + + for img, img_meta in zip(imgs, img_metas): + h, w, _ = img_meta['img_shape'] + img_show = img[:h, :w, :] + + ori_h, ori_w = img_meta['ori_shape'][:-1] + img_show = mmcv.imresize(img_show, (ori_w, ori_h)) + + if out_dir: + out_file = osp.join(out_dir, img_meta['ori_filename']) + else: + out_file = None + + model.module.show_result( + img_show, + result, + palette=dataset.PALETTE, + show=show, + out_file=out_file, + opacity=opacity) + + if isinstance(result, list): + if efficient_test: + result = [np2tmp(_) for _ in result] + results.extend(result) + else: + if efficient_test: + result = np2tmp(result) + results.append(result) + + batch_size = len(result) + for _ in range(batch_size): + prog_bar.update() + return results + + +def multi_gpu_test(model, + data_loader, + tmpdir=None, + gpu_collect=False, + efficient_test=False): + """Test model with multiple gpus. + + This method tests model with multiple gpus and collects the results + under two different modes: gpu and cpu modes. By setting 'gpu_collect=True' + it encodes results to gpu tensors and use gpu communication for results + collection. On cpu mode it saves the results on different gpus to 'tmpdir' + and collects them by the rank 0 worker. + + Args: + model (nn.Module): Model to be tested. + data_loader (utils.data.Dataloader): Pytorch data loader. + tmpdir (str): Path of directory to save the temporary results from + different gpus under cpu mode. + gpu_collect (bool): Option to use either gpu or cpu to collect results. + efficient_test (bool): Whether save the results as local numpy files to + save CPU memory during evaluation. Default: False. + + Returns: + list: The prediction results. + """ + + model.eval() + results = [] + dataset = data_loader.dataset + rank, world_size = get_dist_info() + if rank == 0: + prog_bar = mmcv.ProgressBar(len(dataset)) + for i, data in enumerate(data_loader): + with torch.no_grad(): + result = model(return_loss=False, rescale=True, **data) + + if isinstance(result, list): + if efficient_test: + result = [np2tmp(_) for _ in result] + results.extend(result) + else: + if efficient_test: + result = np2tmp(result) + results.append(result) + + if rank == 0: + batch_size = data['img'][0].size(0) + for _ in range(batch_size * world_size): + prog_bar.update() + + # collect results from all ranks + if gpu_collect: + results = collect_results_gpu(results, len(dataset)) + else: + results = collect_results_cpu(results, len(dataset), tmpdir) + return results + + +def collect_results_cpu(result_part, size, tmpdir=None): + """Collect results with CPU.""" + rank, world_size = get_dist_info() + # create a tmp dir if it is not specified + if tmpdir is None: + MAX_LEN = 512 + # 32 is whitespace + dir_tensor = torch.full((MAX_LEN, ), + 32, + dtype=torch.uint8, + device='cuda') + if rank == 0: + tmpdir = tempfile.mkdtemp() + tmpdir = torch.tensor( + bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda') + dir_tensor[:len(tmpdir)] = tmpdir + dist.broadcast(dir_tensor, 0) + tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip() + else: + mmcv.mkdir_or_exist(tmpdir) + # dump the part result to the dir + mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank))) + dist.barrier() + # collect all parts + if rank != 0: + return None + else: + # load results of all parts from tmp dir + part_list = [] + for i in range(world_size): + part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i)) + part_list.append(mmcv.load(part_file)) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + # remove tmp dir + shutil.rmtree(tmpdir) + return ordered_results + + +def collect_results_gpu(result_part, size): + """Collect results with GPU.""" + rank, world_size = get_dist_info() + # dump result part to tensor with pickle + part_tensor = torch.tensor( + bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda') + # gather all result part tensor shape + shape_tensor = torch.tensor(part_tensor.shape, device='cuda') + shape_list = [shape_tensor.clone() for _ in range(world_size)] + dist.all_gather(shape_list, shape_tensor) + # padding result part tensor to max length + shape_max = torch.tensor(shape_list).max() + part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda') + part_send[:shape_tensor[0]] = part_tensor + part_recv_list = [ + part_tensor.new_zeros(shape_max) for _ in range(world_size) + ] + # gather all result part + dist.all_gather(part_recv_list, part_send) + + if rank == 0: + part_list = [] + for recv, shape in zip(part_recv_list, shape_list): + part_list.append( + pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + return ordered_results diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/train.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/train.py new file mode 100644 index 00000000..61eb4768 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/apis/train.py @@ -0,0 +1,116 @@ +import random +import warnings + +import numpy as np +import torch +from custom_mmpkg.custom_mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from custom_mmpkg.custom_mmcv.runner import build_optimizer, build_runner + +from custom_mmpkg.custom_mmseg.core import DistEvalHook, EvalHook +from custom_mmpkg.custom_mmseg.datasets import build_dataloader, build_dataset +from custom_mmpkg.custom_mmseg.utils import get_root_logger + + +def set_random_seed(seed, deterministic=False): + """Set random seed. + + Args: + seed (int): Seed to be used. + deterministic (bool): Whether to set the deterministic option for + CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` + to True and `torch.backends.cudnn.benchmark` to False. + Default: False. + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if deterministic: + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def train_segmentor(model, + dataset, + cfg, + distributed=False, + validate=False, + timestamp=None, + meta=None): + """Launch segmentor training.""" + logger = get_root_logger(cfg.log_level) + + # prepare data loaders + dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] + data_loaders = [ + build_dataloader( + ds, + cfg.data.samples_per_gpu, + cfg.data.workers_per_gpu, + # cfg.gpus will be ignored if distributed + len(cfg.gpu_ids), + dist=distributed, + seed=cfg.seed, + drop_last=True) for ds in dataset + ] + + # put model on gpus + if distributed: + find_unused_parameters = cfg.get('find_unused_parameters', False) + # Sets the `find_unused_parameters` parameter in + # torch.nn.parallel.DistributedDataParallel + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False, + find_unused_parameters=find_unused_parameters) + else: + model = MMDataParallel( + model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids) + + # build runner + optimizer = build_optimizer(model, cfg.optimizer) + + if cfg.get('runner') is None: + cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters} + warnings.warn( + 'config is now expected to have a `runner` section, ' + 'please set `runner` in your config.', UserWarning) + + runner = build_runner( + cfg.runner, + default_args=dict( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=cfg.work_dir, + logger=logger, + meta=meta)) + + # register hooks + runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config, + cfg.checkpoint_config, cfg.log_config, + cfg.get('momentum_config', None)) + + # an ugly walkaround to make the .log and .log.json filenames the same + runner.timestamp = timestamp + + # register eval hooks + if validate: + val_dataset = build_dataset(cfg.data.val, dict(test_mode=True)) + val_dataloader = build_dataloader( + val_dataset, + samples_per_gpu=1, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=distributed, + shuffle=False) + eval_cfg = cfg.get('evaluation', {}) + eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner' + eval_hook = DistEvalHook if distributed else EvalHook + runner.register_hook(eval_hook(val_dataloader, **eval_cfg), priority='LOW') + + if cfg.resume_from: + runner.resume(cfg.resume_from) + elif cfg.load_from: + runner.load_checkpoint(cfg.load_from) + runner.run(data_loaders, cfg.workflow) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/__init__.py new file mode 100644 index 00000000..96560558 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/__init__.py @@ -0,0 +1,3 @@ +from .evaluation import * # noqa: F401, F403 +from .seg import * # noqa: F401, F403 +from .utils import * # noqa: F401, F403 diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/__init__.py new file mode 100644 index 00000000..f7cc4b23 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/__init__.py @@ -0,0 +1,8 @@ +from .class_names import get_classes, get_palette +from .eval_hooks import DistEvalHook, EvalHook +from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou + +__all__ = [ + 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore', + 'eval_metrics', 'get_classes', 'get_palette' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/class_names.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/class_names.py new file mode 100644 index 00000000..3e790829 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/class_names.py @@ -0,0 +1,152 @@ +import custom_mmpkg.custom_mmcv as mmcv + + +def cityscapes_classes(): + """Cityscapes class names for external use.""" + return [ + 'road', 'sidewalk', 'building', 'wall', 'fence', 'pole', + 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', + 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', + 'bicycle' + ] + + +def ade_classes(): + """ADE20K class names for external use.""" + return [ + 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ', + 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth', + 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car', + 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug', + 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe', + 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column', + 'signboard', 'chest of drawers', 'counter', 'sand', 'sink', + 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path', + 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door', + 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table', + 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove', + 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar', + 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower', + 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver', + 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister', + 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van', + 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything', + 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent', + 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank', + 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake', + 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce', + 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen', + 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass', + 'clock', 'flag' + ] + + +def voc_classes(): + """Pascal VOC class names for external use.""" + return [ + 'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', + 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor' + ] + + +def cityscapes_palette(): + """Cityscapes palette for external use.""" + return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], + [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0], + [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60], + [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100], + [0, 0, 230], [119, 11, 32]] + + +def ade_palette(): + """ADE20K palette for external use.""" + return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], + [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], + [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], + [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], + [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], + [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], + [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], + [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], + [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], + [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], + [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], + [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], + [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], + [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], + [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], + [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], + [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], + [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], + [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], + [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], + [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], + [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], + [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], + [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], + [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], + [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], + [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], + [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], + [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], + [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], + [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], + [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], + [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], + [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], + [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], + [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], + [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], + [102, 255, 0], [92, 0, 255]] + + +def voc_palette(): + """Pascal VOC palette for external use.""" + return [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], + [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], + [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], + [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], + [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]] + + +dataset_aliases = { + 'cityscapes': ['cityscapes'], + 'ade': ['ade', 'ade20k'], + 'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'] +} + + +def get_classes(dataset): + """Get class names of a dataset.""" + alias2name = {} + for name, aliases in dataset_aliases.items(): + for alias in aliases: + alias2name[alias] = name + + if mmcv.is_str(dataset): + if dataset in alias2name: + labels = eval(alias2name[dataset] + '_classes()') + else: + raise ValueError(f'Unrecognized dataset: {dataset}') + else: + raise TypeError(f'dataset must a str, but got {type(dataset)}') + return labels + + +def get_palette(dataset): + """Get class palette (RGB) of a dataset.""" + alias2name = {} + for name, aliases in dataset_aliases.items(): + for alias in aliases: + alias2name[alias] = name + + if mmcv.is_str(dataset): + if dataset in alias2name: + labels = eval(alias2name[dataset] + '_palette()') + else: + raise ValueError(f'Unrecognized dataset: {dataset}') + else: + raise TypeError(f'dataset must a str, but got {type(dataset)}') + return labels diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/eval_hooks.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/eval_hooks.py new file mode 100644 index 00000000..684fd6c2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/eval_hooks.py @@ -0,0 +1,109 @@ +import os.path as osp + +from custom_mmpkg.custom_mmcv.runner import DistEvalHook as _DistEvalHook +from custom_mmpkg.custom_mmcv.runner import EvalHook as _EvalHook + + +class EvalHook(_EvalHook): + """Single GPU EvalHook, with efficient test support. + + Args: + by_epoch (bool): Determine perform evaluation by epoch or by iteration. + If set to True, it will perform by epoch. Otherwise, by iteration. + Default: False. + efficient_test (bool): Whether save the results as local numpy files to + save CPU memory during evaluation. Default: False. + Returns: + list: The prediction results. + """ + + greater_keys = ['mIoU', 'mAcc', 'aAcc'] + + def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs): + super().__init__(*args, by_epoch=by_epoch, **kwargs) + self.efficient_test = efficient_test + + def after_train_iter(self, runner): + """After train epoch hook. + + Override default ``single_gpu_test``. + """ + if self.by_epoch or not self.every_n_iters(runner, self.interval): + return + from custom_mmpkg.custom_mmseg.apis import single_gpu_test + runner.log_buffer.clear() + results = single_gpu_test( + runner.model, + self.dataloader, + show=False, + efficient_test=self.efficient_test) + self.evaluate(runner, results) + + def after_train_epoch(self, runner): + """After train epoch hook. + + Override default ``single_gpu_test``. + """ + if not self.by_epoch or not self.every_n_epochs(runner, self.interval): + return + from custom_mmpkg.custom_mmseg.apis import single_gpu_test + runner.log_buffer.clear() + results = single_gpu_test(runner.model, self.dataloader, show=False) + self.evaluate(runner, results) + + +class DistEvalHook(_DistEvalHook): + """Distributed EvalHook, with efficient test support. + + Args: + by_epoch (bool): Determine perform evaluation by epoch or by iteration. + If set to True, it will perform by epoch. Otherwise, by iteration. + Default: False. + efficient_test (bool): Whether save the results as local numpy files to + save CPU memory during evaluation. Default: False. + Returns: + list: The prediction results. + """ + + greater_keys = ['mIoU', 'mAcc', 'aAcc'] + + def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs): + super().__init__(*args, by_epoch=by_epoch, **kwargs) + self.efficient_test = efficient_test + + def after_train_iter(self, runner): + """After train epoch hook. + + Override default ``multi_gpu_test``. + """ + if self.by_epoch or not self.every_n_iters(runner, self.interval): + return + from custom_mmpkg.custom_mmseg.apis import multi_gpu_test + runner.log_buffer.clear() + results = multi_gpu_test( + runner.model, + self.dataloader, + tmpdir=osp.join(runner.work_dir, '.eval_hook'), + gpu_collect=self.gpu_collect, + efficient_test=self.efficient_test) + if runner.rank == 0: + print('\n') + self.evaluate(runner, results) + + def after_train_epoch(self, runner): + """After train epoch hook. + + Override default ``multi_gpu_test``. + """ + if not self.by_epoch or not self.every_n_epochs(runner, self.interval): + return + from custom_mmpkg.custom_mmseg.apis import multi_gpu_test + runner.log_buffer.clear() + results = multi_gpu_test( + runner.model, + self.dataloader, + tmpdir=osp.join(runner.work_dir, '.eval_hook'), + gpu_collect=self.gpu_collect) + if runner.rank == 0: + print('\n') + self.evaluate(runner, results) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/metrics.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/metrics.py new file mode 100644 index 00000000..db4b29f6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/evaluation/metrics.py @@ -0,0 +1,326 @@ +from collections import OrderedDict + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +import torch + + +def f_score(precision, recall, beta=1): + """calcuate the f-score value. + + Args: + precision (float | torch.Tensor): The precision value. + recall (float | torch.Tensor): The recall value. + beta (int): Determines the weight of recall in the combined score. + Default: False. + + Returns: + [torch.tensor]: The f-score value. + """ + score = (1 + beta**2) * (precision * recall) / ( + (beta**2 * precision) + recall) + return score + + +def intersect_and_union(pred_label, + label, + num_classes, + ignore_index, + label_map=dict(), + reduce_zero_label=False): + """Calculate intersection and Union. + + Args: + pred_label (ndarray | str): Prediction segmentation map + or predict result filename. + label (ndarray | str): Ground truth segmentation map + or label filename. + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + label_map (dict): Mapping old labels to new labels. The parameter will + work only when label is str. Default: dict(). + reduce_zero_label (bool): Whether ignore zero label. The parameter will + work only when label is str. Default: False. + + Returns: + torch.Tensor: The intersection of prediction and ground truth + histogram on all classes. + torch.Tensor: The union of prediction and ground truth histogram on + all classes. + torch.Tensor: The prediction histogram on all classes. + torch.Tensor: The ground truth histogram on all classes. + """ + + if isinstance(pred_label, str): + pred_label = torch.from_numpy(np.load(pred_label)) + else: + pred_label = torch.from_numpy((pred_label)) + + if isinstance(label, str): + label = torch.from_numpy( + mmcv.imread(label, flag='unchanged', backend='pillow')) + else: + label = torch.from_numpy(label) + + if label_map is not None: + for old_id, new_id in label_map.items(): + label[label == old_id] = new_id + if reduce_zero_label: + label[label == 0] = 255 + label = label - 1 + label[label == 254] = 255 + + mask = (label != ignore_index) + pred_label = pred_label[mask] + label = label[mask] + + intersect = pred_label[pred_label == label] + area_intersect = torch.histc( + intersect.float(), bins=(num_classes), min=0, max=num_classes - 1) + area_pred_label = torch.histc( + pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1) + area_label = torch.histc( + label.float(), bins=(num_classes), min=0, max=num_classes - 1) + area_union = area_pred_label + area_label - area_intersect + return area_intersect, area_union, area_pred_label, area_label + + +def total_intersect_and_union(results, + gt_seg_maps, + num_classes, + ignore_index, + label_map=dict(), + reduce_zero_label=False): + """Calculate Total Intersection and Union. + + Args: + results (list[ndarray] | list[str]): List of prediction segmentation + maps or list of prediction result filenames. + gt_seg_maps (list[ndarray] | list[str]): list of ground truth + segmentation maps or list of label filenames. + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + label_map (dict): Mapping old labels to new labels. Default: dict(). + reduce_zero_label (bool): Whether ignore zero label. Default: False. + + Returns: + ndarray: The intersection of prediction and ground truth histogram + on all classes. + ndarray: The union of prediction and ground truth histogram on all + classes. + ndarray: The prediction histogram on all classes. + ndarray: The ground truth histogram on all classes. + """ + num_imgs = len(results) + assert len(gt_seg_maps) == num_imgs + total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64) + total_area_union = torch.zeros((num_classes, ), dtype=torch.float64) + total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64) + total_area_label = torch.zeros((num_classes, ), dtype=torch.float64) + for i in range(num_imgs): + area_intersect, area_union, area_pred_label, area_label = \ + intersect_and_union( + results[i], gt_seg_maps[i], num_classes, ignore_index, + label_map, reduce_zero_label) + total_area_intersect += area_intersect + total_area_union += area_union + total_area_pred_label += area_pred_label + total_area_label += area_label + return total_area_intersect, total_area_union, total_area_pred_label, \ + total_area_label + + +def mean_iou(results, + gt_seg_maps, + num_classes, + ignore_index, + nan_to_num=None, + label_map=dict(), + reduce_zero_label=False): + """Calculate Mean Intersection and Union (mIoU) + + Args: + results (list[ndarray] | list[str]): List of prediction segmentation + maps or list of prediction result filenames. + gt_seg_maps (list[ndarray] | list[str]): list of ground truth + segmentation maps or list of label filenames. + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + label_map (dict): Mapping old labels to new labels. Default: dict(). + reduce_zero_label (bool): Whether ignore zero label. Default: False. + + Returns: + dict[str, float | ndarray]: + float: Overall accuracy on all images. + ndarray: Per category accuracy, shape (num_classes, ). + ndarray: Per category IoU, shape (num_classes, ). + """ + iou_result = eval_metrics( + results=results, + gt_seg_maps=gt_seg_maps, + num_classes=num_classes, + ignore_index=ignore_index, + metrics=['mIoU'], + nan_to_num=nan_to_num, + label_map=label_map, + reduce_zero_label=reduce_zero_label) + return iou_result + + +def mean_dice(results, + gt_seg_maps, + num_classes, + ignore_index, + nan_to_num=None, + label_map=dict(), + reduce_zero_label=False): + """Calculate Mean Dice (mDice) + + Args: + results (list[ndarray] | list[str]): List of prediction segmentation + maps or list of prediction result filenames. + gt_seg_maps (list[ndarray] | list[str]): list of ground truth + segmentation maps or list of label filenames. + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + label_map (dict): Mapping old labels to new labels. Default: dict(). + reduce_zero_label (bool): Whether ignore zero label. Default: False. + + Returns: + dict[str, float | ndarray]: Default metrics. + float: Overall accuracy on all images. + ndarray: Per category accuracy, shape (num_classes, ). + ndarray: Per category dice, shape (num_classes, ). + """ + + dice_result = eval_metrics( + results=results, + gt_seg_maps=gt_seg_maps, + num_classes=num_classes, + ignore_index=ignore_index, + metrics=['mDice'], + nan_to_num=nan_to_num, + label_map=label_map, + reduce_zero_label=reduce_zero_label) + return dice_result + + +def mean_fscore(results, + gt_seg_maps, + num_classes, + ignore_index, + nan_to_num=None, + label_map=dict(), + reduce_zero_label=False, + beta=1): + """Calculate Mean Intersection and Union (mIoU) + + Args: + results (list[ndarray] | list[str]): List of prediction segmentation + maps or list of prediction result filenames. + gt_seg_maps (list[ndarray] | list[str]): list of ground truth + segmentation maps or list of label filenames. + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + label_map (dict): Mapping old labels to new labels. Default: dict(). + reduce_zero_label (bool): Whether ignore zero label. Default: False. + beta (int): Determines the weight of recall in the combined score. + Default: False. + + + Returns: + dict[str, float | ndarray]: Default metrics. + float: Overall accuracy on all images. + ndarray: Per category recall, shape (num_classes, ). + ndarray: Per category precision, shape (num_classes, ). + ndarray: Per category f-score, shape (num_classes, ). + """ + fscore_result = eval_metrics( + results=results, + gt_seg_maps=gt_seg_maps, + num_classes=num_classes, + ignore_index=ignore_index, + metrics=['mFscore'], + nan_to_num=nan_to_num, + label_map=label_map, + reduce_zero_label=reduce_zero_label, + beta=beta) + return fscore_result + + +def eval_metrics(results, + gt_seg_maps, + num_classes, + ignore_index, + metrics=['mIoU'], + nan_to_num=None, + label_map=dict(), + reduce_zero_label=False, + beta=1): + """Calculate evaluation metrics + Args: + results (list[ndarray] | list[str]): List of prediction segmentation + maps or list of prediction result filenames. + gt_seg_maps (list[ndarray] | list[str]): list of ground truth + segmentation maps or list of label filenames. + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + label_map (dict): Mapping old labels to new labels. Default: dict(). + reduce_zero_label (bool): Whether ignore zero label. Default: False. + Returns: + float: Overall accuracy on all images. + ndarray: Per category accuracy, shape (num_classes, ). + ndarray: Per category evaluation metrics, shape (num_classes, ). + """ + if isinstance(metrics, str): + metrics = [metrics] + allowed_metrics = ['mIoU', 'mDice', 'mFscore'] + if not set(metrics).issubset(set(allowed_metrics)): + raise KeyError('metrics {} is not supported'.format(metrics)) + + total_area_intersect, total_area_union, total_area_pred_label, \ + total_area_label = total_intersect_and_union( + results, gt_seg_maps, num_classes, ignore_index, label_map, + reduce_zero_label) + all_acc = total_area_intersect.sum() / total_area_label.sum() + ret_metrics = OrderedDict({'aAcc': all_acc}) + for metric in metrics: + if metric == 'mIoU': + iou = total_area_intersect / total_area_union + acc = total_area_intersect / total_area_label + ret_metrics['IoU'] = iou + ret_metrics['Acc'] = acc + elif metric == 'mDice': + dice = 2 * total_area_intersect / ( + total_area_pred_label + total_area_label) + acc = total_area_intersect / total_area_label + ret_metrics['Dice'] = dice + ret_metrics['Acc'] = acc + elif metric == 'mFscore': + precision = total_area_intersect / total_area_pred_label + recall = total_area_intersect / total_area_label + f_value = torch.tensor( + [f_score(x[0], x[1], beta) for x in zip(precision, recall)]) + ret_metrics['Fscore'] = f_value + ret_metrics['Precision'] = precision + ret_metrics['Recall'] = recall + + ret_metrics = { + metric: value.numpy() + for metric, value in ret_metrics.items() + } + if nan_to_num is not None: + ret_metrics = OrderedDict({ + metric: np.nan_to_num(metric_value, nan=nan_to_num) + for metric, metric_value in ret_metrics.items() + }) + return ret_metrics diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/__init__.py new file mode 100644 index 00000000..93bc129b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/__init__.py @@ -0,0 +1,4 @@ +from .builder import build_pixel_sampler +from .sampler import BasePixelSampler, OHEMPixelSampler + +__all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/builder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/builder.py new file mode 100644 index 00000000..8c6971fc --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/builder.py @@ -0,0 +1,8 @@ +from custom_mmpkg.custom_mmcv.utils import Registry, build_from_cfg + +PIXEL_SAMPLERS = Registry('pixel sampler') + + +def build_pixel_sampler(cfg, **default_args): + """Build pixel sampler for segmentation map.""" + return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/__init__.py new file mode 100644 index 00000000..332b242c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/__init__.py @@ -0,0 +1,4 @@ +from .base_pixel_sampler import BasePixelSampler +from .ohem_pixel_sampler import OHEMPixelSampler + +__all__ = ['BasePixelSampler', 'OHEMPixelSampler'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/base_pixel_sampler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/base_pixel_sampler.py new file mode 100644 index 00000000..b75b1566 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/base_pixel_sampler.py @@ -0,0 +1,12 @@ +from abc import ABCMeta, abstractmethod + + +class BasePixelSampler(metaclass=ABCMeta): + """Base class of pixel sampler.""" + + def __init__(self, **kwargs): + pass + + @abstractmethod + def sample(self, seg_logit, seg_label): + """Placeholder for sample function.""" diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/ohem_pixel_sampler.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/ohem_pixel_sampler.py new file mode 100644 index 00000000..88bb10d4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/seg/sampler/ohem_pixel_sampler.py @@ -0,0 +1,76 @@ +import torch +import torch.nn.functional as F + +from ..builder import PIXEL_SAMPLERS +from .base_pixel_sampler import BasePixelSampler + + +@PIXEL_SAMPLERS.register_module() +class OHEMPixelSampler(BasePixelSampler): + """Online Hard Example Mining Sampler for segmentation. + + Args: + context (nn.Module): The context of sampler, subclass of + :obj:`BaseDecodeHead`. + thresh (float, optional): The threshold for hard example selection. + Below which, are prediction with low confidence. If not + specified, the hard examples will be pixels of top ``min_kept`` + loss. Default: None. + min_kept (int, optional): The minimum number of predictions to keep. + Default: 100000. + """ + + def __init__(self, context, thresh=None, min_kept=100000): + super(OHEMPixelSampler, self).__init__() + self.context = context + assert min_kept > 1 + self.thresh = thresh + self.min_kept = min_kept + + def sample(self, seg_logit, seg_label): + """Sample pixels that have high loss or with low prediction confidence. + + Args: + seg_logit (torch.Tensor): segmentation logits, shape (N, C, H, W) + seg_label (torch.Tensor): segmentation label, shape (N, 1, H, W) + + Returns: + torch.Tensor: segmentation weight, shape (N, H, W) + """ + with torch.no_grad(): + assert seg_logit.shape[2:] == seg_label.shape[2:] + assert seg_label.shape[1] == 1 + seg_label = seg_label.squeeze(1).long() + batch_kept = self.min_kept * seg_label.size(0) + valid_mask = seg_label != self.context.ignore_index + seg_weight = seg_logit.new_zeros(size=seg_label.size()) + valid_seg_weight = seg_weight[valid_mask] + if self.thresh is not None: + seg_prob = F.softmax(seg_logit, dim=1) + + tmp_seg_label = seg_label.clone().unsqueeze(1) + tmp_seg_label[tmp_seg_label == self.context.ignore_index] = 0 + seg_prob = seg_prob.gather(1, tmp_seg_label).squeeze(1) + sort_prob, sort_indices = seg_prob[valid_mask].sort() + + if sort_prob.numel() > 0: + min_threshold = sort_prob[min(batch_kept, + sort_prob.numel() - 1)] + else: + min_threshold = 0.0 + threshold = max(min_threshold, self.thresh) + valid_seg_weight[seg_prob[valid_mask] < threshold] = 1. + else: + losses = self.context.loss_decode( + seg_logit, + seg_label, + weight=None, + ignore_index=self.context.ignore_index, + reduction_override='none') + # faster than topk according to https://github.com/pytorch/pytorch/issues/22812 # noqa + _, sort_indices = losses[valid_mask].sort(descending=True) + valid_seg_weight[sort_indices[:batch_kept]] = 1. + + seg_weight[valid_mask] = valid_seg_weight + + return seg_weight diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/utils/__init__.py new file mode 100644 index 00000000..f2678b32 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/utils/__init__.py @@ -0,0 +1,3 @@ +from .misc import add_prefix + +__all__ = ['add_prefix'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/utils/misc.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/utils/misc.py new file mode 100644 index 00000000..eb862a82 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/core/utils/misc.py @@ -0,0 +1,17 @@ +def add_prefix(inputs, prefix): + """Add prefix for dict. + + Args: + inputs (dict): The input dict with str keys. + prefix (str): The prefix to add. + + Returns: + + dict: The dict with keys updated with ``prefix``. + """ + + outputs = dict() + for name, value in inputs.items(): + outputs[f'{prefix}.{name}'] = value + + return outputs diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/__init__.py new file mode 100644 index 00000000..ebeaef4a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/__init__.py @@ -0,0 +1,19 @@ +from .ade import ADE20KDataset +from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset +from .chase_db1 import ChaseDB1Dataset +from .cityscapes import CityscapesDataset +from .custom import CustomDataset +from .dataset_wrappers import ConcatDataset, RepeatDataset +from .drive import DRIVEDataset +from .hrf import HRFDataset +from .pascal_context import PascalContextDataset, PascalContextDataset59 +from .stare import STAREDataset +from .voc import PascalVOCDataset + +__all__ = [ + 'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset', + 'DATASETS', 'build_dataset', 'PIPELINES', 'CityscapesDataset', + 'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset', + 'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset', + 'STAREDataset' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/ade.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/ade.py new file mode 100644 index 00000000..5913e437 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/ade.py @@ -0,0 +1,84 @@ +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class ADE20KDataset(CustomDataset): + """ADE20K dataset. + + In segmentation map annotation for ADE20K, 0 stands for background, which + is not included in 150 categories. ``reduce_zero_label`` is fixed to True. + The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to + '.png'. + """ + CLASSES = ( + 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ', + 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth', + 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car', + 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug', + 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe', + 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column', + 'signboard', 'chest of drawers', 'counter', 'sand', 'sink', + 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path', + 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door', + 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table', + 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove', + 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar', + 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower', + 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver', + 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister', + 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van', + 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything', + 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent', + 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank', + 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake', + 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce', + 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen', + 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass', + 'clock', 'flag') + + PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], + [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], + [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], + [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], + [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], + [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], + [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], + [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], + [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], + [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], + [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], + [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], + [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], + [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], + [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], + [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], + [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], + [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], + [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], + [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], + [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], + [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], + [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], + [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], + [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], + [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], + [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], + [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], + [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], + [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], + [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], + [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], + [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], + [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], + [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], + [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], + [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], + [102, 255, 0], [92, 0, 255]] + + def __init__(self, **kwargs): + super(ADE20KDataset, self).__init__( + img_suffix='.jpg', + seg_map_suffix='.png', + reduce_zero_label=True, + **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/builder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/builder.py new file mode 100644 index 00000000..6d09e961 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/builder.py @@ -0,0 +1,161 @@ +import copy +import platform +import random +from functools import partial + +import numpy as np +from custom_mmpkg.custom_mmcv.parallel import collate +from custom_mmpkg.custom_mmcv.runner import get_dist_info +from custom_mmpkg.custom_mmcv.utils import Registry, build_from_cfg +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import DataLoader, PoolDataLoader +from torch.utils.data import DistributedSampler + +DATASETS = Registry('dataset') +PIPELINES = Registry('pipeline') + + +def _concat_dataset(cfg, default_args=None): + """Build :obj:`ConcatDataset by.""" + from .dataset_wrappers import ConcatDataset + img_dir = cfg['img_dir'] + ann_dir = cfg.get('ann_dir', None) + split = cfg.get('split', None) + num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1 + if ann_dir is not None: + num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1 + else: + num_ann_dir = 0 + if split is not None: + num_split = len(split) if isinstance(split, (list, tuple)) else 1 + else: + num_split = 0 + if num_img_dir > 1: + assert num_img_dir == num_ann_dir or num_ann_dir == 0 + assert num_img_dir == num_split or num_split == 0 + else: + assert num_split == num_ann_dir or num_ann_dir <= 1 + num_dset = max(num_split, num_img_dir) + + datasets = [] + for i in range(num_dset): + data_cfg = copy.deepcopy(cfg) + if isinstance(img_dir, (list, tuple)): + data_cfg['img_dir'] = img_dir[i] + if isinstance(ann_dir, (list, tuple)): + data_cfg['ann_dir'] = ann_dir[i] + if isinstance(split, (list, tuple)): + data_cfg['split'] = split[i] + datasets.append(build_dataset(data_cfg, default_args)) + + return ConcatDataset(datasets) + + +def build_dataset(cfg, default_args=None): + """Build datasets.""" + from .dataset_wrappers import ConcatDataset, RepeatDataset + if isinstance(cfg, (list, tuple)): + dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg]) + elif cfg['type'] == 'RepeatDataset': + dataset = RepeatDataset( + build_dataset(cfg['dataset'], default_args), cfg['times']) + elif isinstance(cfg.get('img_dir'), (list, tuple)) or isinstance( + cfg.get('split', None), (list, tuple)): + dataset = _concat_dataset(cfg, default_args) + else: + dataset = build_from_cfg(cfg, DATASETS, default_args) + + return dataset + + +def build_dataloader(dataset, + samples_per_gpu, + workers_per_gpu, + num_gpus=1, + dist=True, + shuffle=True, + seed=None, + drop_last=False, + pin_memory=True, + dataloader_type='PoolDataLoader', + **kwargs): + """Build PyTorch DataLoader. + + In distributed training, each GPU/process has a dataloader. + In non-distributed training, there is only one dataloader for all GPUs. + + Args: + dataset (Dataset): A PyTorch dataset. + samples_per_gpu (int): Number of training samples on each GPU, i.e., + batch size of each GPU. + workers_per_gpu (int): How many subprocesses to use for data loading + for each GPU. + num_gpus (int): Number of GPUs. Only used in non-distributed training. + dist (bool): Distributed training/test or not. Default: True. + shuffle (bool): Whether to shuffle the data at every epoch. + Default: True. + seed (int | None): Seed to be used. Default: None. + drop_last (bool): Whether to drop the last incomplete batch in epoch. + Default: False + pin_memory (bool): Whether to use pin_memory in DataLoader. + Default: True + dataloader_type (str): Type of dataloader. Default: 'PoolDataLoader' + kwargs: any keyword argument to be used to initialize DataLoader + + Returns: + DataLoader: A PyTorch dataloader. + """ + rank, world_size = get_dist_info() + if dist: + sampler = DistributedSampler( + dataset, world_size, rank, shuffle=shuffle) + shuffle = False + batch_size = samples_per_gpu + num_workers = workers_per_gpu + else: + sampler = None + batch_size = num_gpus * samples_per_gpu + num_workers = num_gpus * workers_per_gpu + + init_fn = partial( + worker_init_fn, num_workers=num_workers, rank=rank, + seed=seed) if seed is not None else None + + assert dataloader_type in ( + 'DataLoader', + 'PoolDataLoader'), f'unsupported dataloader {dataloader_type}' + + if dataloader_type == 'PoolDataLoader': + dataloader = PoolDataLoader + elif dataloader_type == 'DataLoader': + dataloader = DataLoader + + data_loader = dataloader( + dataset, + batch_size=batch_size, + sampler=sampler, + num_workers=num_workers, + collate_fn=partial(collate, samples_per_gpu=samples_per_gpu), + pin_memory=pin_memory, + shuffle=shuffle, + worker_init_fn=init_fn, + drop_last=drop_last, + **kwargs) + + return data_loader + + +def worker_init_fn(worker_id, num_workers, rank, seed): + """Worker init func for dataloader. + + The seed of each worker equals to num_worker * rank + worker_id + user_seed + + Args: + worker_id (int): Worker id. + num_workers (int): Number of workers. + rank (int): The rank of current process. + seed (int): The random seed to use. + """ + + worker_seed = num_workers * rank + worker_id + seed + np.random.seed(worker_seed) + random.seed(worker_seed) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/chase_db1.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/chase_db1.py new file mode 100644 index 00000000..8bc29bea --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/chase_db1.py @@ -0,0 +1,27 @@ +import os.path as osp + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class ChaseDB1Dataset(CustomDataset): + """Chase_db1 dataset. + + In segmentation map annotation for Chase_db1, 0 stands for background, + which is included in 2 categories. ``reduce_zero_label`` is fixed to False. + The ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to + '_1stHO.png'. + """ + + CLASSES = ('background', 'vessel') + + PALETTE = [[120, 120, 120], [6, 230, 230]] + + def __init__(self, **kwargs): + super(ChaseDB1Dataset, self).__init__( + img_suffix='.png', + seg_map_suffix='_1stHO.png', + reduce_zero_label=False, + **kwargs) + assert osp.exists(self.img_dir) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/cityscapes.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/cityscapes.py new file mode 100644 index 00000000..4c4d0937 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/cityscapes.py @@ -0,0 +1,217 @@ +import os.path as osp +import tempfile + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +from custom_mmpkg.custom_mmcv.utils import print_log +from PIL import Image + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class CityscapesDataset(CustomDataset): + """Cityscapes dataset. + + The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is + fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset. + """ + + CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole', + 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', + 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', + 'bicycle') + + PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], + [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0], + [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60], + [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], + [0, 80, 100], [0, 0, 230], [119, 11, 32]] + + def __init__(self, **kwargs): + super(CityscapesDataset, self).__init__( + img_suffix='_leftImg8bit.png', + seg_map_suffix='_gtFine_labelTrainIds.png', + **kwargs) + + @staticmethod + def _convert_to_label_id(result): + """Convert trainId to id for cityscapes.""" + if isinstance(result, str): + result = np.load(result) + import cityscapesscripts.helpers.labels as CSLabels + result_copy = result.copy() + for trainId, label in CSLabels.trainId2label.items(): + result_copy[result == trainId] = label.id + + return result_copy + + def results2img(self, results, imgfile_prefix, to_label_id): + """Write the segmentation results to images. + + Args: + results (list[list | tuple | ndarray]): Testing results of the + dataset. + imgfile_prefix (str): The filename prefix of the png files. + If the prefix is "somepath/xxx", + the png files will be named "somepath/xxx.png". + to_label_id (bool): whether convert output to label_id for + submission + + Returns: + list[str: str]: result txt files which contains corresponding + semantic segmentation images. + """ + mmcv.mkdir_or_exist(imgfile_prefix) + result_files = [] + prog_bar = mmcv.ProgressBar(len(self)) + for idx in range(len(self)): + result = results[idx] + if to_label_id: + result = self._convert_to_label_id(result) + filename = self.img_infos[idx]['filename'] + basename = osp.splitext(osp.basename(filename))[0] + + png_filename = osp.join(imgfile_prefix, f'{basename}.png') + + output = Image.fromarray(result.astype(np.uint8)).convert('P') + import cityscapesscripts.helpers.labels as CSLabels + palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8) + for label_id, label in CSLabels.id2label.items(): + palette[label_id] = label.color + + output.putpalette(palette) + output.save(png_filename) + result_files.append(png_filename) + prog_bar.update() + + return result_files + + def format_results(self, results, imgfile_prefix=None, to_label_id=True): + """Format the results into dir (standard format for Cityscapes + evaluation). + + Args: + results (list): Testing results of the dataset. + imgfile_prefix (str | None): The prefix of images files. It + includes the file path and the prefix of filename, e.g., + "a/b/prefix". If not specified, a temp file will be created. + Default: None. + to_label_id (bool): whether convert output to label_id for + submission. Default: False + + Returns: + tuple: (result_files, tmp_dir), result_files is a list containing + the image paths, tmp_dir is the temporal directory created + for saving json/png files when img_prefix is not specified. + """ + + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: ' + f'{len(results)} != {len(self)}') + + if imgfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + imgfile_prefix = tmp_dir.name + else: + tmp_dir = None + result_files = self.results2img(results, imgfile_prefix, to_label_id) + + return result_files, tmp_dir + + def evaluate(self, + results, + metric='mIoU', + logger=None, + imgfile_prefix=None, + efficient_test=False): + """Evaluation in Cityscapes/default protocol. + + Args: + results (list): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. + logger (logging.Logger | None | str): Logger used for printing + related information during evaluation. Default: None. + imgfile_prefix (str | None): The prefix of output image file, + for cityscapes evaluation only. It includes the file path and + the prefix of filename, e.g., "a/b/prefix". + If results are evaluated with cityscapes protocol, it would be + the prefix of output png files. The output files would be + png images under folder "a/b/prefix/xxx.png", where "xxx" is + the image name of cityscapes. If not specified, a temp file + will be created for evaluation. + Default: None. + + Returns: + dict[str, float]: Cityscapes/default metrics. + """ + + eval_results = dict() + metrics = metric.copy() if isinstance(metric, list) else [metric] + if 'cityscapes' in metrics: + eval_results.update( + self._evaluate_cityscapes(results, logger, imgfile_prefix)) + metrics.remove('cityscapes') + if len(metrics) > 0: + eval_results.update( + super(CityscapesDataset, + self).evaluate(results, metrics, logger, efficient_test)) + + return eval_results + + def _evaluate_cityscapes(self, results, logger, imgfile_prefix): + """Evaluation in Cityscapes protocol. + + Args: + results (list): Testing results of the dataset. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + imgfile_prefix (str | None): The prefix of output image file + + Returns: + dict[str: float]: Cityscapes evaluation results. + """ + try: + import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval # noqa + except ImportError: + raise ImportError('Please run "pip install cityscapesscripts" to ' + 'install cityscapesscripts first.') + msg = 'Evaluating in Cityscapes style' + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + result_files, tmp_dir = self.format_results(results, imgfile_prefix) + + if tmp_dir is None: + result_dir = imgfile_prefix + else: + result_dir = tmp_dir.name + + eval_results = dict() + print_log(f'Evaluating results under {result_dir} ...', logger=logger) + + CSEval.args.evalInstLevelScore = True + CSEval.args.predictionPath = osp.abspath(result_dir) + CSEval.args.evalPixelAccuracy = True + CSEval.args.JSONOutput = False + + seg_map_list = [] + pred_list = [] + + # when evaluating with official cityscapesscripts, + # **_gtFine_labelIds.png is used + for seg_map in mmcv.scandir( + self.ann_dir, 'gtFine_labelIds.png', recursive=True): + seg_map_list.append(osp.join(self.ann_dir, seg_map)) + pred_list.append(CSEval.getPrediction(CSEval.args, seg_map)) + + eval_results.update( + CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args)) + + if tmp_dir is not None: + tmp_dir.cleanup() + + return eval_results diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/custom.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/custom.py new file mode 100644 index 00000000..5096a1d7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/custom.py @@ -0,0 +1,403 @@ +import os +import os.path as osp +from collections import OrderedDict +from functools import reduce + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +from custom_mmpkg.custom_mmcv.utils import print_log +from torch.utils.data import Dataset + +from custom_mmpkg.custom_mmseg.core import eval_metrics +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from .builder import DATASETS +from .pipelines import Compose + + +@DATASETS.register_module() +class CustomDataset(Dataset): + """Custom dataset for semantic segmentation. An example of file structure + is as followed. + + .. code-block:: none + + β”œβ”€β”€ data + β”‚ β”œβ”€β”€ my_dataset + β”‚ β”‚ β”œβ”€β”€ img_dir + β”‚ β”‚ β”‚ β”œβ”€β”€ train + β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ xxx{img_suffix} + β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ yyy{img_suffix} + β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ zzz{img_suffix} + β”‚ β”‚ β”‚ β”œβ”€β”€ val + β”‚ β”‚ β”œβ”€β”€ ann_dir + β”‚ β”‚ β”‚ β”œβ”€β”€ train + β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ xxx{seg_map_suffix} + β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ yyy{seg_map_suffix} + β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ zzz{seg_map_suffix} + β”‚ β”‚ β”‚ β”œβ”€β”€ val + + The img/gt_semantic_seg pair of CustomDataset should be of the same + except suffix. A valid img/gt_semantic_seg filename pair should be like + ``xxx{img_suffix}`` and ``xxx{seg_map_suffix}`` (extension is also included + in the suffix). If split is given, then ``xxx`` is specified in txt file. + Otherwise, all files in ``img_dir/``and ``ann_dir`` will be loaded. + Please refer to ``docs/tutorials/new_dataset.md`` for more details. + + + Args: + pipeline (list[dict]): Processing pipeline + img_dir (str): Path to image directory + img_suffix (str): Suffix of images. Default: '.jpg' + ann_dir (str, optional): Path to annotation directory. Default: None + seg_map_suffix (str): Suffix of segmentation maps. Default: '.png' + split (str, optional): Split txt file. If split is specified, only + file with suffix in the splits will be loaded. Otherwise, all + images in img_dir/ann_dir will be loaded. Default: None + data_root (str, optional): Data root for img_dir/ann_dir. Default: + None. + test_mode (bool): If test_mode=True, gt wouldn't be loaded. + ignore_index (int): The label index to be ignored. Default: 255 + reduce_zero_label (bool): Whether to mark label zero as ignored. + Default: False + classes (str | Sequence[str], optional): Specify classes to load. + If is None, ``cls.CLASSES`` will be used. Default: None. + palette (Sequence[Sequence[int]]] | np.ndarray | None): + The palette of segmentation map. If None is given, and + self.PALETTE is None, random palette will be generated. + Default: None + """ + + CLASSES = None + + PALETTE = None + + def __init__(self, + pipeline, + img_dir, + img_suffix='.jpg', + ann_dir=None, + seg_map_suffix='.png', + split=None, + data_root=None, + test_mode=False, + ignore_index=255, + reduce_zero_label=False, + classes=None, + palette=None): + self.pipeline = Compose(pipeline) + self.img_dir = img_dir + self.img_suffix = img_suffix + self.ann_dir = ann_dir + self.seg_map_suffix = seg_map_suffix + self.split = split + self.data_root = data_root + self.test_mode = test_mode + self.ignore_index = ignore_index + self.reduce_zero_label = reduce_zero_label + self.label_map = None + self.CLASSES, self.PALETTE = self.get_classes_and_palette( + classes, palette) + + # join paths if data_root is specified + if self.data_root is not None: + if not osp.isabs(self.img_dir): + self.img_dir = osp.join(self.data_root, self.img_dir) + if not (self.ann_dir is None or osp.isabs(self.ann_dir)): + self.ann_dir = osp.join(self.data_root, self.ann_dir) + if not (self.split is None or osp.isabs(self.split)): + self.split = osp.join(self.data_root, self.split) + + # load annotations + self.img_infos = self.load_annotations(self.img_dir, self.img_suffix, + self.ann_dir, + self.seg_map_suffix, self.split) + + def __len__(self): + """Total number of samples of data.""" + return len(self.img_infos) + + def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix, + split): + """Load annotation from directory. + + Args: + img_dir (str): Path to image directory + img_suffix (str): Suffix of images. + ann_dir (str|None): Path to annotation directory. + seg_map_suffix (str|None): Suffix of segmentation maps. + split (str|None): Split txt file. If split is specified, only file + with suffix in the splits will be loaded. Otherwise, all images + in img_dir/ann_dir will be loaded. Default: None + + Returns: + list[dict]: All image info of dataset. + """ + + img_infos = [] + if split is not None: + with open(split) as f: + for line in f: + img_name = line.strip() + img_info = dict(filename=img_name + img_suffix) + if ann_dir is not None: + seg_map = img_name + seg_map_suffix + img_info['ann'] = dict(seg_map=seg_map) + img_infos.append(img_info) + else: + for img in mmcv.scandir(img_dir, img_suffix, recursive=True): + img_info = dict(filename=img) + if ann_dir is not None: + seg_map = img.replace(img_suffix, seg_map_suffix) + img_info['ann'] = dict(seg_map=seg_map) + img_infos.append(img_info) + + print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger()) + return img_infos + + def get_ann_info(self, idx): + """Get annotation by index. + + Args: + idx (int): Index of data. + + Returns: + dict: Annotation info of specified index. + """ + + return self.img_infos[idx]['ann'] + + def pre_pipeline(self, results): + """Prepare results dict for pipeline.""" + results['seg_fields'] = [] + results['img_prefix'] = self.img_dir + results['seg_prefix'] = self.ann_dir + if self.custom_classes: + results['label_map'] = self.label_map + + def __getitem__(self, idx): + """Get training/test data after pipeline. + + Args: + idx (int): Index of data. + + Returns: + dict: Training/test data (with annotation if `test_mode` is set + False). + """ + + if self.test_mode: + return self.prepare_test_img(idx) + else: + return self.prepare_train_img(idx) + + def prepare_train_img(self, idx): + """Get training data and annotations after pipeline. + + Args: + idx (int): Index of data. + + Returns: + dict: Training data and annotation after pipeline with new keys + introduced by pipeline. + """ + + img_info = self.img_infos[idx] + ann_info = self.get_ann_info(idx) + results = dict(img_info=img_info, ann_info=ann_info) + self.pre_pipeline(results) + return self.pipeline(results) + + def prepare_test_img(self, idx): + """Get testing data after pipeline. + + Args: + idx (int): Index of data. + + Returns: + dict: Testing data after pipeline with new keys introduced by + pipeline. + """ + + img_info = self.img_infos[idx] + results = dict(img_info=img_info) + self.pre_pipeline(results) + return self.pipeline(results) + + def format_results(self, results, **kwargs): + """Place holder to format result to dataset specific output.""" + + def get_gt_seg_maps(self, efficient_test=False): + """Get ground truth segmentation maps for evaluation.""" + gt_seg_maps = [] + for img_info in self.img_infos: + seg_map = osp.join(self.ann_dir, img_info['ann']['seg_map']) + if efficient_test: + gt_seg_map = seg_map + else: + gt_seg_map = mmcv.imread( + seg_map, flag='unchanged', backend='pillow') + gt_seg_maps.append(gt_seg_map) + return gt_seg_maps + + def get_classes_and_palette(self, classes=None, palette=None): + """Get class names of current dataset. + + Args: + classes (Sequence[str] | str | None): If classes is None, use + default CLASSES defined by builtin dataset. If classes is a + string, take it as a file name. The file contains the name of + classes where each line contains one class name. If classes is + a tuple or list, override the CLASSES defined by the dataset. + palette (Sequence[Sequence[int]]] | np.ndarray | None): + The palette of segmentation map. If None is given, random + palette will be generated. Default: None + """ + if classes is None: + self.custom_classes = False + return self.CLASSES, self.PALETTE + + self.custom_classes = True + if isinstance(classes, str): + # take it as a file path + class_names = mmcv.list_from_file(classes) + elif isinstance(classes, (tuple, list)): + class_names = classes + else: + raise ValueError(f'Unsupported type {type(classes)} of classes.') + + if self.CLASSES: + if not set(classes).issubset(self.CLASSES): + raise ValueError('classes is not a subset of CLASSES.') + + # dictionary, its keys are the old label ids and its values + # are the new label ids. + # used for changing pixel labels in load_annotations. + self.label_map = {} + for i, c in enumerate(self.CLASSES): + if c not in class_names: + self.label_map[i] = -1 + else: + self.label_map[i] = classes.index(c) + + palette = self.get_palette_for_custom_classes(class_names, palette) + + return class_names, palette + + def get_palette_for_custom_classes(self, class_names, palette=None): + + if self.label_map is not None: + # return subset of palette + palette = [] + for old_id, new_id in sorted( + self.label_map.items(), key=lambda x: x[1]): + if new_id != -1: + palette.append(self.PALETTE[old_id]) + palette = type(self.PALETTE)(palette) + + elif palette is None: + if self.PALETTE is None: + palette = np.random.randint(0, 255, size=(len(class_names), 3)) + else: + palette = self.PALETTE + + return palette + + def evaluate(self, + results, + metric='mIoU', + logger=None, + efficient_test=False, + **kwargs): + """Evaluate the dataset. + + Args: + results (list): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. 'mIoU', + 'mDice' and 'mFscore' are supported. + logger (logging.Logger | None | str): Logger used for printing + related information during evaluation. Default: None. + + Returns: + dict[str, float]: Default metrics. + """ + + if isinstance(metric, str): + metric = [metric] + allowed_metrics = ['mIoU', 'mDice', 'mFscore'] + if not set(metric).issubset(set(allowed_metrics)): + raise KeyError('metric {} is not supported'.format(metric)) + eval_results = {} + gt_seg_maps = self.get_gt_seg_maps(efficient_test) + if self.CLASSES is None: + num_classes = len( + reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps])) + else: + num_classes = len(self.CLASSES) + ret_metrics = eval_metrics( + results, + gt_seg_maps, + num_classes, + self.ignore_index, + metric, + label_map=self.label_map, + reduce_zero_label=self.reduce_zero_label) + + if self.CLASSES is None: + class_names = tuple(range(num_classes)) + else: + class_names = self.CLASSES + + # summary table + ret_metrics_summary = OrderedDict({ + ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + + # each class table + ret_metrics.pop('aAcc', None) + ret_metrics_class = OrderedDict({ + ret_metric: np.round(ret_metric_value * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + ret_metrics_class.update({'Class': class_names}) + ret_metrics_class.move_to_end('Class', last=False) + + try: + from prettytable import PrettyTable + # for logger + class_table_data = PrettyTable() + for key, val in ret_metrics_class.items(): + class_table_data.add_column(key, val) + + summary_table_data = PrettyTable() + for key, val in ret_metrics_summary.items(): + if key == 'aAcc': + summary_table_data.add_column(key, [val]) + else: + summary_table_data.add_column('m' + key, [val]) + + print_log('per class results:', logger) + print_log('\n' + class_table_data.get_string(), logger=logger) + print_log('Summary:', logger) + print_log('\n' + summary_table_data.get_string(), logger=logger) + except ImportError: # prettytable is not installed + pass + + # each metric dict + for key, value in ret_metrics_summary.items(): + if key == 'aAcc': + eval_results[key] = value / 100.0 + else: + eval_results['m' + key] = value / 100.0 + + ret_metrics_class.pop('Class', None) + for key, value in ret_metrics_class.items(): + eval_results.update({ + key + '.' + str(name): value[idx] / 100.0 + for idx, name in enumerate(class_names) + }) + + if mmcv.is_list_of(results, str): + for file_name in results: + os.remove(file_name) + return eval_results diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/dataset_wrappers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/dataset_wrappers.py new file mode 100644 index 00000000..d6a5e957 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/dataset_wrappers.py @@ -0,0 +1,50 @@ +from torch.utils.data.dataset import ConcatDataset as _ConcatDataset + +from .builder import DATASETS + + +@DATASETS.register_module() +class ConcatDataset(_ConcatDataset): + """A wrapper of concatenated dataset. + + Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but + concat the group flag for image aspect ratio. + + Args: + datasets (list[:obj:`Dataset`]): A list of datasets. + """ + + def __init__(self, datasets): + super(ConcatDataset, self).__init__(datasets) + self.CLASSES = datasets[0].CLASSES + self.PALETTE = datasets[0].PALETTE + + +@DATASETS.register_module() +class RepeatDataset(object): + """A wrapper of repeated dataset. + + The length of repeated dataset will be `times` larger than the original + dataset. This is useful when the data loading time is long but the dataset + is small. Using RepeatDataset can reduce the data loading time between + epochs. + + Args: + dataset (:obj:`Dataset`): The dataset to be repeated. + times (int): Repeat times. + """ + + def __init__(self, dataset, times): + self.dataset = dataset + self.times = times + self.CLASSES = dataset.CLASSES + self.PALETTE = dataset.PALETTE + self._ori_len = len(self.dataset) + + def __getitem__(self, idx): + """Get item from original dataset.""" + return self.dataset[idx % self._ori_len] + + def __len__(self): + """The length is multiplied by ``times``""" + return self.times * self._ori_len diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/drive.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/drive.py new file mode 100644 index 00000000..3cbfda8a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/drive.py @@ -0,0 +1,27 @@ +import os.path as osp + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class DRIVEDataset(CustomDataset): + """DRIVE dataset. + + In segmentation map annotation for DRIVE, 0 stands for background, which is + included in 2 categories. ``reduce_zero_label`` is fixed to False. The + ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to + '_manual1.png'. + """ + + CLASSES = ('background', 'vessel') + + PALETTE = [[120, 120, 120], [6, 230, 230]] + + def __init__(self, **kwargs): + super(DRIVEDataset, self).__init__( + img_suffix='.png', + seg_map_suffix='_manual1.png', + reduce_zero_label=False, + **kwargs) + assert osp.exists(self.img_dir) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/hrf.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/hrf.py new file mode 100644 index 00000000..923203b5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/hrf.py @@ -0,0 +1,27 @@ +import os.path as osp + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class HRFDataset(CustomDataset): + """HRF dataset. + + In segmentation map annotation for HRF, 0 stands for background, which is + included in 2 categories. ``reduce_zero_label`` is fixed to False. The + ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to + '.png'. + """ + + CLASSES = ('background', 'vessel') + + PALETTE = [[120, 120, 120], [6, 230, 230]] + + def __init__(self, **kwargs): + super(HRFDataset, self).__init__( + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) + assert osp.exists(self.img_dir) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pascal_context.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pascal_context.py new file mode 100644 index 00000000..541a63c6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pascal_context.py @@ -0,0 +1,103 @@ +import os.path as osp + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class PascalContextDataset(CustomDataset): + """PascalContext dataset. + + In segmentation map annotation for PascalContext, 0 stands for background, + which is included in 60 categories. ``reduce_zero_label`` is fixed to + False. The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is + fixed to '.png'. + + Args: + split (str): Split txt file for PascalContext. + """ + + CLASSES = ('background', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench', + 'bicycle', 'bird', 'boat', 'book', 'bottle', 'building', 'bus', + 'cabinet', 'car', 'cat', 'ceiling', 'chair', 'cloth', + 'computer', 'cow', 'cup', 'curtain', 'dog', 'door', 'fence', + 'floor', 'flower', 'food', 'grass', 'ground', 'horse', + 'keyboard', 'light', 'motorbike', 'mountain', 'mouse', 'person', + 'plate', 'platform', 'pottedplant', 'road', 'rock', 'sheep', + 'shelves', 'sidewalk', 'sign', 'sky', 'snow', 'sofa', 'table', + 'track', 'train', 'tree', 'truck', 'tvmonitor', 'wall', 'water', + 'window', 'wood') + + PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], + [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], + [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], + [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], + [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], + [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], + [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], + [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], + [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], + [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], + [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], + [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], + [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], + [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], + [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255]] + + def __init__(self, split, **kwargs): + super(PascalContextDataset, self).__init__( + img_suffix='.jpg', + seg_map_suffix='.png', + split=split, + reduce_zero_label=False, + **kwargs) + assert osp.exists(self.img_dir) and self.split is not None + + +@DATASETS.register_module() +class PascalContextDataset59(CustomDataset): + """PascalContext dataset. + + In segmentation map annotation for PascalContext, 0 stands for background, + which is included in 60 categories. ``reduce_zero_label`` is fixed to + False. The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is + fixed to '.png'. + + Args: + split (str): Split txt file for PascalContext. + """ + + CLASSES = ('aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle', + 'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet', + 'car', 'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow', + 'cup', 'curtain', 'dog', 'door', 'fence', 'floor', 'flower', + 'food', 'grass', 'ground', 'horse', 'keyboard', 'light', + 'motorbike', 'mountain', 'mouse', 'person', 'plate', 'platform', + 'pottedplant', 'road', 'rock', 'sheep', 'shelves', 'sidewalk', + 'sign', 'sky', 'snow', 'sofa', 'table', 'track', 'train', + 'tree', 'truck', 'tvmonitor', 'wall', 'water', 'window', 'wood') + + PALETTE = [[180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], + [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], + [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], + [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], + [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], + [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], + [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], + [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], + [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], + [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], + [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], + [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], + [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], + [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0], + [0, 235, 255], [0, 173, 255], [31, 0, 255]] + + def __init__(self, split, **kwargs): + super(PascalContextDataset59, self).__init__( + img_suffix='.jpg', + seg_map_suffix='.png', + split=split, + reduce_zero_label=True, + **kwargs) + assert osp.exists(self.img_dir) and self.split is not None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/__init__.py new file mode 100644 index 00000000..8b9046b0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/__init__.py @@ -0,0 +1,16 @@ +from .compose import Compose +from .formating import (Collect, ImageToTensor, ToDataContainer, ToTensor, + Transpose, to_tensor) +from .loading import LoadAnnotations, LoadImageFromFile +from .test_time_aug import MultiScaleFlipAug +from .transforms import (CLAHE, AdjustGamma, Normalize, Pad, + PhotoMetricDistortion, RandomCrop, RandomFlip, + RandomRotate, Rerange, Resize, RGB2Gray, SegRescale) + +__all__ = [ + 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer', + 'Transpose', 'Collect', 'LoadAnnotations', 'LoadImageFromFile', + 'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop', + 'Normalize', 'SegRescale', 'PhotoMetricDistortion', 'RandomRotate', + 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/compose.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/compose.py new file mode 100644 index 00000000..a9c8027c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/compose.py @@ -0,0 +1,51 @@ +import collections + +from custom_mmpkg.custom_mmcv.utils import build_from_cfg + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class Compose(object): + """Compose multiple transforms sequentially. + + Args: + transforms (Sequence[dict | callable]): Sequence of transform object or + config dict to be composed. + """ + + def __init__(self, transforms): + assert isinstance(transforms, collections.abc.Sequence) + self.transforms = [] + for transform in transforms: + if isinstance(transform, dict): + transform = build_from_cfg(transform, PIPELINES) + self.transforms.append(transform) + elif callable(transform): + self.transforms.append(transform) + else: + raise TypeError('transform must be callable or a dict') + + def __call__(self, data): + """Call function to apply transforms sequentially. + + Args: + data (dict): A result dict contains the data to transform. + + Returns: + dict: Transformed data. + """ + + for t in self.transforms: + data = t(data) + if data is None: + return None + return data + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += f' {t}' + format_string += '\n)' + return format_string diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/formating.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/formating.py new file mode 100644 index 00000000..0c259f18 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/formating.py @@ -0,0 +1,288 @@ +from collections.abc import Sequence + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +import torch +from custom_mmpkg.custom_mmcv.parallel import DataContainer as DC + +from ..builder import PIPELINES + + +def to_tensor(data): + """Convert objects of various python types to :obj:`torch.Tensor`. + + Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, + :class:`Sequence`, :class:`int` and :class:`float`. + + Args: + data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to + be converted. + """ + + if isinstance(data, torch.Tensor): + return data + elif isinstance(data, np.ndarray): + return torch.from_numpy(data) + elif isinstance(data, Sequence) and not mmcv.is_str(data): + return torch.tensor(data) + elif isinstance(data, int): + return torch.LongTensor([data]) + elif isinstance(data, float): + return torch.FloatTensor([data]) + else: + raise TypeError(f'type {type(data)} cannot be converted to tensor.') + + +@PIPELINES.register_module() +class ToTensor(object): + """Convert some results to :obj:`torch.Tensor` by given keys. + + Args: + keys (Sequence[str]): Keys that need to be converted to Tensor. + """ + + def __init__(self, keys): + self.keys = keys + + def __call__(self, results): + """Call function to convert data in results to :obj:`torch.Tensor`. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + dict: The result dict contains the data converted + to :obj:`torch.Tensor`. + """ + + for key in self.keys: + results[key] = to_tensor(results[key]) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(keys={self.keys})' + + +@PIPELINES.register_module() +class ImageToTensor(object): + """Convert image to :obj:`torch.Tensor` by given keys. + + The dimension order of input image is (H, W, C). The pipeline will convert + it to (C, H, W). If only 2 dimension (H, W) is given, the output would be + (1, H, W). + + Args: + keys (Sequence[str]): Key of images to be converted to Tensor. + """ + + def __init__(self, keys): + self.keys = keys + + def __call__(self, results): + """Call function to convert image in results to :obj:`torch.Tensor` and + transpose the channel order. + + Args: + results (dict): Result dict contains the image data to convert. + + Returns: + dict: The result dict contains the image converted + to :obj:`torch.Tensor` and transposed to (C, H, W) order. + """ + + for key in self.keys: + img = results[key] + if len(img.shape) < 3: + img = np.expand_dims(img, -1) + results[key] = to_tensor(img.transpose(2, 0, 1)) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(keys={self.keys})' + + +@PIPELINES.register_module() +class Transpose(object): + """Transpose some results by given keys. + + Args: + keys (Sequence[str]): Keys of results to be transposed. + order (Sequence[int]): Order of transpose. + """ + + def __init__(self, keys, order): + self.keys = keys + self.order = order + + def __call__(self, results): + """Call function to convert image in results to :obj:`torch.Tensor` and + transpose the channel order. + + Args: + results (dict): Result dict contains the image data to convert. + + Returns: + dict: The result dict contains the image converted + to :obj:`torch.Tensor` and transposed to (C, H, W) order. + """ + + for key in self.keys: + results[key] = results[key].transpose(self.order) + return results + + def __repr__(self): + return self.__class__.__name__ + \ + f'(keys={self.keys}, order={self.order})' + + +@PIPELINES.register_module() +class ToDataContainer(object): + """Convert results to :obj:`mmcv.DataContainer` by given fields. + + Args: + fields (Sequence[dict]): Each field is a dict like + ``dict(key='xxx', **kwargs)``. The ``key`` in result will + be converted to :obj:`mmcv.DataContainer` with ``**kwargs``. + Default: ``(dict(key='img', stack=True), + dict(key='gt_semantic_seg'))``. + """ + + def __init__(self, + fields=(dict(key='img', + stack=True), dict(key='gt_semantic_seg'))): + self.fields = fields + + def __call__(self, results): + """Call function to convert data in results to + :obj:`mmcv.DataContainer`. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + dict: The result dict contains the data converted to + :obj:`mmcv.DataContainer`. + """ + + for field in self.fields: + field = field.copy() + key = field.pop('key') + results[key] = DC(results[key], **field) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(fields={self.fields})' + + +@PIPELINES.register_module() +class DefaultFormatBundle(object): + """Default formatting bundle. + + It simplifies the pipeline of formatting common fields, including "img" + and "gt_semantic_seg". These fields are formatted as follows. + + - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True) + - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, + (3)to DataContainer (stack=True) + """ + + def __call__(self, results): + """Call function to transform and format common fields in results. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + dict: The result dict contains the data that is formatted with + default bundle. + """ + + if 'img' in results: + img = results['img'] + if len(img.shape) < 3: + img = np.expand_dims(img, -1) + img = np.ascontiguousarray(img.transpose(2, 0, 1)) + results['img'] = DC(to_tensor(img), stack=True) + if 'gt_semantic_seg' in results: + # convert to long + results['gt_semantic_seg'] = DC( + to_tensor(results['gt_semantic_seg'][None, + ...].astype(np.int64)), + stack=True) + return results + + def __repr__(self): + return self.__class__.__name__ + + +@PIPELINES.register_module() +class Collect(object): + """Collect data from the loader relevant to the specific task. + + This is usually the last stage of the data loader pipeline. Typically keys + is set to some subset of "img", "gt_semantic_seg". + + The "img_meta" item is always populated. The contents of the "img_meta" + dictionary depends on "meta_keys". By default this includes: + + - "img_shape": shape of the image input to the network as a tuple + (h, w, c). Note that images may be zero padded on the bottom/right + if the batch tensor is larger than this shape. + + - "scale_factor": a float indicating the preprocessing scale + + - "flip": a boolean indicating if image flip transform was used + + - "filename": path to the image file + + - "ori_shape": original shape of the image as a tuple (h, w, c) + + - "pad_shape": image shape after padding + + - "img_norm_cfg": a dict of normalization information: + - mean - per channel mean subtraction + - std - per channel std divisor + - to_rgb - bool indicating if bgr was converted to rgb + + Args: + keys (Sequence[str]): Keys of results to be collected in ``data``. + meta_keys (Sequence[str], optional): Meta keys to be converted to + ``mmcv.DataContainer`` and collected in ``data[img_metas]``. + Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape', + 'pad_shape', 'scale_factor', 'flip', 'flip_direction', + 'img_norm_cfg')`` + """ + + def __init__(self, + keys, + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg')): + self.keys = keys + self.meta_keys = meta_keys + + def __call__(self, results): + """Call function to collect keys in results. The keys in ``meta_keys`` + will be converted to :obj:mmcv.DataContainer. + + Args: + results (dict): Result dict contains the data to collect. + + Returns: + dict: The result dict contains the following keys + - keys in``self.keys`` + - ``img_metas`` + """ + + data = {} + img_meta = {} + for key in self.meta_keys: + img_meta[key] = results[key] + data['img_metas'] = DC(img_meta, cpu_only=True) + for key in self.keys: + data[key] = results[key] + return data + + def __repr__(self): + return self.__class__.__name__ + \ + f'(keys={self.keys}, meta_keys={self.meta_keys})' diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/loading.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/loading.py new file mode 100644 index 00000000..0ef470c7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/loading.py @@ -0,0 +1,153 @@ +import os.path as osp + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class LoadImageFromFile(object): + """Load an image from file. + + Required keys are "img_prefix" and "img_info" (a dict that must contain the + key "filename"). Added or updated keys are "filename", "img", "img_shape", + "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`), + "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1). + + Args: + to_float32 (bool): Whether to convert the loaded image to a float32 + numpy array. If set to False, the loaded image is an uint8 array. + Defaults to False. + color_type (str): The flag argument for :func:`mmcv.imfrombytes`. + Defaults to 'color'. + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. + Defaults to ``dict(backend='disk')``. + imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default: + 'cv2' + """ + + def __init__(self, + to_float32=False, + color_type='color', + file_client_args=dict(backend='disk'), + imdecode_backend='cv2'): + self.to_float32 = to_float32 + self.color_type = color_type + self.file_client_args = file_client_args.copy() + self.file_client = None + self.imdecode_backend = imdecode_backend + + def __call__(self, results): + """Call functions to load image and get image meta information. + + Args: + results (dict): Result dict from :obj:`mmseg.CustomDataset`. + + Returns: + dict: The dict contains loaded image and meta information. + """ + + if self.file_client is None: + self.file_client = mmcv.FileClient(**self.file_client_args) + + if results.get('img_prefix') is not None: + filename = osp.join(results['img_prefix'], + results['img_info']['filename']) + else: + filename = results['img_info']['filename'] + img_bytes = self.file_client.get(filename) + img = mmcv.imfrombytes( + img_bytes, flag=self.color_type, backend=self.imdecode_backend) + if self.to_float32: + img = img.astype(np.float32) + + results['filename'] = filename + results['ori_filename'] = results['img_info']['filename'] + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + num_channels = 1 if len(img.shape) < 3 else img.shape[2] + results['img_norm_cfg'] = dict( + mean=np.zeros(num_channels, dtype=np.float32), + std=np.ones(num_channels, dtype=np.float32), + to_rgb=False) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(to_float32={self.to_float32},' + repr_str += f"color_type='{self.color_type}'," + repr_str += f"imdecode_backend='{self.imdecode_backend}')" + return repr_str + + +@PIPELINES.register_module() +class LoadAnnotations(object): + """Load annotations for semantic segmentation. + + Args: + reduce_zero_label (bool): Whether reduce all label value by 1. + Usually used for datasets where 0 is background label. + Default: False. + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. + Defaults to ``dict(backend='disk')``. + imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default: + 'pillow' + """ + + def __init__(self, + reduce_zero_label=False, + file_client_args=dict(backend='disk'), + imdecode_backend='pillow'): + self.reduce_zero_label = reduce_zero_label + self.file_client_args = file_client_args.copy() + self.file_client = None + self.imdecode_backend = imdecode_backend + + def __call__(self, results): + """Call function to load multiple types annotations. + + Args: + results (dict): Result dict from :obj:`mmseg.CustomDataset`. + + Returns: + dict: The dict contains loaded semantic segmentation annotations. + """ + + if self.file_client is None: + self.file_client = mmcv.FileClient(**self.file_client_args) + + if results.get('seg_prefix', None) is not None: + filename = osp.join(results['seg_prefix'], + results['ann_info']['seg_map']) + else: + filename = results['ann_info']['seg_map'] + img_bytes = self.file_client.get(filename) + gt_semantic_seg = mmcv.imfrombytes( + img_bytes, flag='unchanged', + backend=self.imdecode_backend).squeeze().astype(np.uint8) + # modify if custom classes + if results.get('label_map', None) is not None: + for old_id, new_id in results['label_map'].items(): + gt_semantic_seg[gt_semantic_seg == old_id] = new_id + # reduce zero_label + if self.reduce_zero_label: + # avoid using underflow conversion + gt_semantic_seg[gt_semantic_seg == 0] = 255 + gt_semantic_seg = gt_semantic_seg - 1 + gt_semantic_seg[gt_semantic_seg == 254] = 255 + results['gt_semantic_seg'] = gt_semantic_seg + results['seg_fields'].append('gt_semantic_seg') + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(reduce_zero_label={self.reduce_zero_label},' + repr_str += f"imdecode_backend='{self.imdecode_backend}')" + return repr_str diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/test_time_aug.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/test_time_aug.py new file mode 100644 index 00000000..93fe2143 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/test_time_aug.py @@ -0,0 +1,133 @@ +import warnings + +import custom_mmpkg.custom_mmcv as mmcv + +from ..builder import PIPELINES +from .compose import Compose + + +@PIPELINES.register_module() +class MultiScaleFlipAug(object): + """Test-time augmentation with multiple scales and flipping. + + An example configuration is as followed: + + .. code-block:: + + img_scale=(2048, 1024), + img_ratios=[0.5, 1.0], + flip=True, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ] + + After MultiScaleFLipAug with above configuration, the results are wrapped + into lists of the same length as followed: + + .. code-block:: + + dict( + img=[...], + img_shape=[...], + scale=[(1024, 512), (1024, 512), (2048, 1024), (2048, 1024)] + flip=[False, True, False, True] + ... + ) + + Args: + transforms (list[dict]): Transforms to apply in each augmentation. + img_scale (None | tuple | list[tuple]): Images scales for resizing. + img_ratios (float | list[float]): Image ratios for resizing + flip (bool): Whether apply flip augmentation. Default: False. + flip_direction (str | list[str]): Flip augmentation directions, + options are "horizontal" and "vertical". If flip_direction is list, + multiple flip augmentations will be applied. + It has no effect when flip == False. Default: "horizontal". + """ + + def __init__(self, + transforms, + img_scale, + img_ratios=None, + flip=False, + flip_direction='horizontal'): + self.transforms = Compose(transforms) + if img_ratios is not None: + img_ratios = img_ratios if isinstance(img_ratios, + list) else [img_ratios] + assert mmcv.is_list_of(img_ratios, float) + if img_scale is None: + # mode 1: given img_scale=None and a range of image ratio + self.img_scale = None + assert mmcv.is_list_of(img_ratios, float) + elif isinstance(img_scale, tuple) and mmcv.is_list_of( + img_ratios, float): + assert len(img_scale) == 2 + # mode 2: given a scale and a range of image ratio + self.img_scale = [(int(img_scale[0] * ratio), + int(img_scale[1] * ratio)) + for ratio in img_ratios] + else: + # mode 3: given multiple scales + self.img_scale = img_scale if isinstance(img_scale, + list) else [img_scale] + assert mmcv.is_list_of(self.img_scale, tuple) or self.img_scale is None + self.flip = flip + self.img_ratios = img_ratios + self.flip_direction = flip_direction if isinstance( + flip_direction, list) else [flip_direction] + assert mmcv.is_list_of(self.flip_direction, str) + if not self.flip and self.flip_direction != ['horizontal']: + warnings.warn( + 'flip_direction has no effect when flip is set to False') + if (self.flip + and not any([t['type'] == 'RandomFlip' for t in transforms])): + warnings.warn( + 'flip has no effect when RandomFlip is not in transforms') + + def __call__(self, results): + """Call function to apply test time augment transforms on results. + + Args: + results (dict): Result dict contains the data to transform. + + Returns: + dict[str: list]: The augmented data, where each value is wrapped + into a list. + """ + + aug_data = [] + if self.img_scale is None and mmcv.is_list_of(self.img_ratios, float): + h, w = results['img'].shape[:2] + img_scale = [(int(w * ratio), int(h * ratio)) + for ratio in self.img_ratios] + else: + img_scale = self.img_scale + flip_aug = [False, True] if self.flip else [False] + for scale in img_scale: + for flip in flip_aug: + for direction in self.flip_direction: + _results = results.copy() + _results['scale'] = scale + _results['flip'] = flip + _results['flip_direction'] = direction + data = self.transforms(_results) + aug_data.append(data) + # list of dict to dict of list + aug_data_dict = {key: [] for key in aug_data[0]} + for data in aug_data: + for key, val in data.items(): + aug_data_dict[key].append(val) + return aug_data_dict + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(transforms={self.transforms}, ' + repr_str += f'img_scale={self.img_scale}, flip={self.flip})' + repr_str += f'flip_direction={self.flip_direction}' + return repr_str diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/transforms.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/transforms.py new file mode 100644 index 00000000..677191de --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/pipelines/transforms.py @@ -0,0 +1,889 @@ +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +from custom_mmpkg.custom_mmcv.utils import deprecated_api_warning, is_tuple_of +from numpy import random + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class Resize(object): + """Resize images & seg. + + This transform resizes the input image to some scale. If the input dict + contains the key "scale", then the scale in the input dict is used, + otherwise the specified scale in the init method is used. + + ``img_scale`` can be None, a tuple (single-scale) or a list of tuple + (multi-scale). There are 4 multiscale modes: + + - ``ratio_range is not None``: + 1. When img_scale is None, img_scale is the shape of image in results + (img_scale = results['img'].shape[:2]) and the image is resized based + on the original size. (mode 1) + 2. When img_scale is a tuple (single-scale), randomly sample a ratio from + the ratio range and multiply it with the image scale. (mode 2) + + - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a + scale from the a range. (mode 3) + + - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a + scale from multiple scales. (mode 4) + + Args: + img_scale (tuple or list[tuple]): Images scales for resizing. + multiscale_mode (str): Either "range" or "value". + ratio_range (tuple[float]): (min_ratio, max_ratio) + keep_ratio (bool): Whether to keep the aspect ratio when resizing the + image. + """ + + def __init__(self, + img_scale=None, + multiscale_mode='range', + ratio_range=None, + keep_ratio=True): + if img_scale is None: + self.img_scale = None + else: + if isinstance(img_scale, list): + self.img_scale = img_scale + else: + self.img_scale = [img_scale] + assert mmcv.is_list_of(self.img_scale, tuple) + + if ratio_range is not None: + # mode 1: given img_scale=None and a range of image ratio + # mode 2: given a scale and a range of image ratio + assert self.img_scale is None or len(self.img_scale) == 1 + else: + # mode 3 and 4: given multiple scales or a range of scales + assert multiscale_mode in ['value', 'range'] + + self.multiscale_mode = multiscale_mode + self.ratio_range = ratio_range + self.keep_ratio = keep_ratio + + @staticmethod + def random_select(img_scales): + """Randomly select an img_scale from given candidates. + + Args: + img_scales (list[tuple]): Images scales for selection. + + Returns: + (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, + where ``img_scale`` is the selected image scale and + ``scale_idx`` is the selected index in the given candidates. + """ + + assert mmcv.is_list_of(img_scales, tuple) + scale_idx = np.random.randint(len(img_scales)) + img_scale = img_scales[scale_idx] + return img_scale, scale_idx + + @staticmethod + def random_sample(img_scales): + """Randomly sample an img_scale when ``multiscale_mode=='range'``. + + Args: + img_scales (list[tuple]): Images scale range for sampling. + There must be two tuples in img_scales, which specify the lower + and upper bound of image scales. + + Returns: + (tuple, None): Returns a tuple ``(img_scale, None)``, where + ``img_scale`` is sampled scale and None is just a placeholder + to be consistent with :func:`random_select`. + """ + + assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2 + img_scale_long = [max(s) for s in img_scales] + img_scale_short = [min(s) for s in img_scales] + long_edge = np.random.randint( + min(img_scale_long), + max(img_scale_long) + 1) + short_edge = np.random.randint( + min(img_scale_short), + max(img_scale_short) + 1) + img_scale = (long_edge, short_edge) + return img_scale, None + + @staticmethod + def random_sample_ratio(img_scale, ratio_range): + """Randomly sample an img_scale when ``ratio_range`` is specified. + + A ratio will be randomly sampled from the range specified by + ``ratio_range``. Then it would be multiplied with ``img_scale`` to + generate sampled scale. + + Args: + img_scale (tuple): Images scale base to multiply with ratio. + ratio_range (tuple[float]): The minimum and maximum ratio to scale + the ``img_scale``. + + Returns: + (tuple, None): Returns a tuple ``(scale, None)``, where + ``scale`` is sampled ratio multiplied with ``img_scale`` and + None is just a placeholder to be consistent with + :func:`random_select`. + """ + + assert isinstance(img_scale, tuple) and len(img_scale) == 2 + min_ratio, max_ratio = ratio_range + assert min_ratio <= max_ratio + ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio + scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio) + return scale, None + + def _random_scale(self, results): + """Randomly sample an img_scale according to ``ratio_range`` and + ``multiscale_mode``. + + If ``ratio_range`` is specified, a ratio will be sampled and be + multiplied with ``img_scale``. + If multiple scales are specified by ``img_scale``, a scale will be + sampled according to ``multiscale_mode``. + Otherwise, single scale will be used. + + Args: + results (dict): Result dict from :obj:`dataset`. + + Returns: + dict: Two new keys 'scale` and 'scale_idx` are added into + ``results``, which would be used by subsequent pipelines. + """ + + if self.ratio_range is not None: + if self.img_scale is None: + h, w = results['img'].shape[:2] + scale, scale_idx = self.random_sample_ratio((w, h), + self.ratio_range) + else: + scale, scale_idx = self.random_sample_ratio( + self.img_scale[0], self.ratio_range) + elif len(self.img_scale) == 1: + scale, scale_idx = self.img_scale[0], 0 + elif self.multiscale_mode == 'range': + scale, scale_idx = self.random_sample(self.img_scale) + elif self.multiscale_mode == 'value': + scale, scale_idx = self.random_select(self.img_scale) + else: + raise NotImplementedError + + results['scale'] = scale + results['scale_idx'] = scale_idx + + def _resize_img(self, results): + """Resize images with ``results['scale']``.""" + if self.keep_ratio: + img, scale_factor = mmcv.imrescale( + results['img'], results['scale'], return_scale=True) + # the w_scale and h_scale has minor difference + # a real fix should be done in the mmcv.imrescale in the future + new_h, new_w = img.shape[:2] + h, w = results['img'].shape[:2] + w_scale = new_w / w + h_scale = new_h / h + else: + img, w_scale, h_scale = mmcv.imresize( + results['img'], results['scale'], return_scale=True) + scale_factor = np.array([w_scale, h_scale, w_scale, h_scale], + dtype=np.float32) + results['img'] = img + results['img_shape'] = img.shape + results['pad_shape'] = img.shape # in case that there is no padding + results['scale_factor'] = scale_factor + results['keep_ratio'] = self.keep_ratio + + def _resize_seg(self, results): + """Resize semantic segmentation map with ``results['scale']``.""" + for key in results.get('seg_fields', []): + if self.keep_ratio: + gt_seg = mmcv.imrescale( + results[key], results['scale'], interpolation='nearest') + else: + gt_seg = mmcv.imresize( + results[key], results['scale'], interpolation='nearest') + results[key] = gt_seg + + def __call__(self, results): + """Call function to resize images, bounding boxes, masks, semantic + segmentation map. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', + 'keep_ratio' keys are added into result dict. + """ + + if 'scale' not in results: + self._random_scale(results) + self._resize_img(results) + self._resize_seg(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += (f'(img_scale={self.img_scale}, ' + f'multiscale_mode={self.multiscale_mode}, ' + f'ratio_range={self.ratio_range}, ' + f'keep_ratio={self.keep_ratio})') + return repr_str + + +@PIPELINES.register_module() +class RandomFlip(object): + """Flip the image & seg. + + If the input dict contains the key "flip", then the flag will be used, + otherwise it will be randomly decided by a ratio specified in the init + method. + + Args: + prob (float, optional): The flipping probability. Default: None. + direction(str, optional): The flipping direction. Options are + 'horizontal' and 'vertical'. Default: 'horizontal'. + """ + + @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip') + def __init__(self, prob=None, direction='horizontal'): + self.prob = prob + self.direction = direction + if prob is not None: + assert prob >= 0 and prob <= 1 + assert direction in ['horizontal', 'vertical'] + + def __call__(self, results): + """Call function to flip bounding boxes, masks, semantic segmentation + maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Flipped results, 'flip', 'flip_direction' keys are added into + result dict. + """ + + if 'flip' not in results: + flip = True if np.random.rand() < self.prob else False + results['flip'] = flip + if 'flip_direction' not in results: + results['flip_direction'] = self.direction + if results['flip']: + # flip image + results['img'] = mmcv.imflip( + results['img'], direction=results['flip_direction']) + + # flip segs + for key in results.get('seg_fields', []): + # use copy() to make numpy stride positive + results[key] = mmcv.imflip( + results[key], direction=results['flip_direction']).copy() + return results + + def __repr__(self): + return self.__class__.__name__ + f'(prob={self.prob})' + + +@PIPELINES.register_module() +class Pad(object): + """Pad the image & mask. + + There are two padding modes: (1) pad to a fixed size and (2) pad to the + minimum size that is divisible by some number. + Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor", + + Args: + size (tuple, optional): Fixed padding size. + size_divisor (int, optional): The divisor of padded size. + pad_val (float, optional): Padding value. Default: 0. + seg_pad_val (float, optional): Padding value of segmentation map. + Default: 255. + """ + + def __init__(self, + size=None, + size_divisor=None, + pad_val=0, + seg_pad_val=255): + self.size = size + self.size_divisor = size_divisor + self.pad_val = pad_val + self.seg_pad_val = seg_pad_val + # only one of size and size_divisor should be valid + assert size is not None or size_divisor is not None + assert size is None or size_divisor is None + + def _pad_img(self, results): + """Pad images according to ``self.size``.""" + if self.size is not None: + padded_img = mmcv.impad( + results['img'], shape=self.size, pad_val=self.pad_val) + elif self.size_divisor is not None: + padded_img = mmcv.impad_to_multiple( + results['img'], self.size_divisor, pad_val=self.pad_val) + results['img'] = padded_img + results['pad_shape'] = padded_img.shape + results['pad_fixed_size'] = self.size + results['pad_size_divisor'] = self.size_divisor + + def _pad_seg(self, results): + """Pad masks according to ``results['pad_shape']``.""" + for key in results.get('seg_fields', []): + results[key] = mmcv.impad( + results[key], + shape=results['pad_shape'][:2], + pad_val=self.seg_pad_val) + + def __call__(self, results): + """Call function to pad images, masks, semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Updated result dict. + """ + + self._pad_img(results) + self._pad_seg(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \ + f'pad_val={self.pad_val})' + return repr_str + + +@PIPELINES.register_module() +class Normalize(object): + """Normalize the image. + + Added key is "img_norm_cfg". + + Args: + mean (sequence): Mean values of 3 channels. + std (sequence): Std values of 3 channels. + to_rgb (bool): Whether to convert the image from BGR to RGB, + default is true. + """ + + def __init__(self, mean, std, to_rgb=True): + self.mean = np.array(mean, dtype=np.float32) + self.std = np.array(std, dtype=np.float32) + self.to_rgb = to_rgb + + def __call__(self, results): + """Call function to normalize images. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Normalized results, 'img_norm_cfg' key is added into + result dict. + """ + + results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std, + self.to_rgb) + results['img_norm_cfg'] = dict( + mean=self.mean, std=self.std, to_rgb=self.to_rgb) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \ + f'{self.to_rgb})' + return repr_str + + +@PIPELINES.register_module() +class Rerange(object): + """Rerange the image pixel value. + + Args: + min_value (float or int): Minimum value of the reranged image. + Default: 0. + max_value (float or int): Maximum value of the reranged image. + Default: 255. + """ + + def __init__(self, min_value=0, max_value=255): + assert isinstance(min_value, float) or isinstance(min_value, int) + assert isinstance(max_value, float) or isinstance(max_value, int) + assert min_value < max_value + self.min_value = min_value + self.max_value = max_value + + def __call__(self, results): + """Call function to rerange images. + + Args: + results (dict): Result dict from loading pipeline. + Returns: + dict: Reranged results. + """ + + img = results['img'] + img_min_value = np.min(img) + img_max_value = np.max(img) + + assert img_min_value < img_max_value + # rerange to [0, 1] + img = (img - img_min_value) / (img_max_value - img_min_value) + # rerange to [min_value, max_value] + img = img * (self.max_value - self.min_value) + self.min_value + results['img'] = img + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(min_value={self.min_value}, max_value={self.max_value})' + return repr_str + + +@PIPELINES.register_module() +class CLAHE(object): + """Use CLAHE method to process the image. + + See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J]. + Graphics Gems, 1994:474-485.` for more information. + + Args: + clip_limit (float): Threshold for contrast limiting. Default: 40.0. + tile_grid_size (tuple[int]): Size of grid for histogram equalization. + Input image will be divided into equally sized rectangular tiles. + It defines the number of tiles in row and column. Default: (8, 8). + """ + + def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)): + assert isinstance(clip_limit, (float, int)) + self.clip_limit = clip_limit + assert is_tuple_of(tile_grid_size, int) + assert len(tile_grid_size) == 2 + self.tile_grid_size = tile_grid_size + + def __call__(self, results): + """Call function to Use CLAHE method process images. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Processed results. + """ + + for i in range(results['img'].shape[2]): + results['img'][:, :, i] = mmcv.clahe( + np.array(results['img'][:, :, i], dtype=np.uint8), + self.clip_limit, self.tile_grid_size) + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(clip_limit={self.clip_limit}, '\ + f'tile_grid_size={self.tile_grid_size})' + return repr_str + + +@PIPELINES.register_module() +class RandomCrop(object): + """Random crop the image & seg. + + Args: + crop_size (tuple): Expected size after cropping, (h, w). + cat_max_ratio (float): The maximum ratio that single category could + occupy. + """ + + def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255): + assert crop_size[0] > 0 and crop_size[1] > 0 + self.crop_size = crop_size + self.cat_max_ratio = cat_max_ratio + self.ignore_index = ignore_index + + def get_crop_bbox(self, img): + """Randomly get a crop bounding box.""" + margin_h = max(img.shape[0] - self.crop_size[0], 0) + margin_w = max(img.shape[1] - self.crop_size[1], 0) + offset_h = np.random.randint(0, margin_h + 1) + offset_w = np.random.randint(0, margin_w + 1) + crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0] + crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1] + + return crop_y1, crop_y2, crop_x1, crop_x2 + + def crop(self, img, crop_bbox): + """Crop from ``img``""" + crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox + img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...] + return img + + def __call__(self, results): + """Call function to randomly crop images, semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Randomly cropped results, 'img_shape' key in result dict is + updated according to crop size. + """ + + img = results['img'] + crop_bbox = self.get_crop_bbox(img) + if self.cat_max_ratio < 1.: + # Repeat 10 times + for _ in range(10): + seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox) + labels, cnt = np.unique(seg_temp, return_counts=True) + cnt = cnt[labels != self.ignore_index] + if len(cnt) > 1 and np.max(cnt) / np.sum( + cnt) < self.cat_max_ratio: + break + crop_bbox = self.get_crop_bbox(img) + + # crop the image + img = self.crop(img, crop_bbox) + img_shape = img.shape + results['img'] = img + results['img_shape'] = img_shape + + # crop semantic seg + for key in results.get('seg_fields', []): + results[key] = self.crop(results[key], crop_bbox) + + return results + + def __repr__(self): + return self.__class__.__name__ + f'(crop_size={self.crop_size})' + + +@PIPELINES.register_module() +class RandomRotate(object): + """Rotate the image & seg. + + Args: + prob (float): The rotation probability. + degree (float, tuple[float]): Range of degrees to select from. If + degree is a number instead of tuple like (min, max), + the range of degree will be (``-degree``, ``+degree``) + pad_val (float, optional): Padding value of image. Default: 0. + seg_pad_val (float, optional): Padding value of segmentation map. + Default: 255. + center (tuple[float], optional): Center point (w, h) of the rotation in + the source image. If not specified, the center of the image will be + used. Default: None. + auto_bound (bool): Whether to adjust the image size to cover the whole + rotated image. Default: False + """ + + def __init__(self, + prob, + degree, + pad_val=0, + seg_pad_val=255, + center=None, + auto_bound=False): + self.prob = prob + assert prob >= 0 and prob <= 1 + if isinstance(degree, (float, int)): + assert degree > 0, f'degree {degree} should be positive' + self.degree = (-degree, degree) + else: + self.degree = degree + assert len(self.degree) == 2, f'degree {self.degree} should be a ' \ + f'tuple of (min, max)' + self.pal_val = pad_val + self.seg_pad_val = seg_pad_val + self.center = center + self.auto_bound = auto_bound + + def __call__(self, results): + """Call function to rotate image, semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Rotated results. + """ + + rotate = True if np.random.rand() < self.prob else False + degree = np.random.uniform(min(*self.degree), max(*self.degree)) + if rotate: + # rotate image + results['img'] = mmcv.imrotate( + results['img'], + angle=degree, + border_value=self.pal_val, + center=self.center, + auto_bound=self.auto_bound) + + # rotate segs + for key in results.get('seg_fields', []): + results[key] = mmcv.imrotate( + results[key], + angle=degree, + border_value=self.seg_pad_val, + center=self.center, + auto_bound=self.auto_bound, + interpolation='nearest') + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(prob={self.prob}, ' \ + f'degree={self.degree}, ' \ + f'pad_val={self.pal_val}, ' \ + f'seg_pad_val={self.seg_pad_val}, ' \ + f'center={self.center}, ' \ + f'auto_bound={self.auto_bound})' + return repr_str + + +@PIPELINES.register_module() +class RGB2Gray(object): + """Convert RGB image to grayscale image. + + This transform calculate the weighted mean of input image channels with + ``weights`` and then expand the channels to ``out_channels``. When + ``out_channels`` is None, the number of output channels is the same as + input channels. + + Args: + out_channels (int): Expected number of output channels after + transforming. Default: None. + weights (tuple[float]): The weights to calculate the weighted mean. + Default: (0.299, 0.587, 0.114). + """ + + def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)): + assert out_channels is None or out_channels > 0 + self.out_channels = out_channels + assert isinstance(weights, tuple) + for item in weights: + assert isinstance(item, (float, int)) + self.weights = weights + + def __call__(self, results): + """Call function to convert RGB image to grayscale image. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with grayscale image. + """ + img = results['img'] + assert len(img.shape) == 3 + assert img.shape[2] == len(self.weights) + weights = np.array(self.weights).reshape((1, 1, -1)) + img = (img * weights).sum(2, keepdims=True) + if self.out_channels is None: + img = img.repeat(weights.shape[2], axis=2) + else: + img = img.repeat(self.out_channels, axis=2) + + results['img'] = img + results['img_shape'] = img.shape + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(out_channels={self.out_channels}, ' \ + f'weights={self.weights})' + return repr_str + + +@PIPELINES.register_module() +class AdjustGamma(object): + """Using gamma correction to process the image. + + Args: + gamma (float or int): Gamma value used in gamma correction. + Default: 1.0. + """ + + def __init__(self, gamma=1.0): + assert isinstance(gamma, float) or isinstance(gamma, int) + assert gamma > 0 + self.gamma = gamma + inv_gamma = 1.0 / gamma + self.table = np.array([(i / 255.0)**inv_gamma * 255 + for i in np.arange(256)]).astype('uint8') + + def __call__(self, results): + """Call function to process the image with gamma correction. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Processed results. + """ + + results['img'] = mmcv.lut_transform( + np.array(results['img'], dtype=np.uint8), self.table) + + return results + + def __repr__(self): + return self.__class__.__name__ + f'(gamma={self.gamma})' + + +@PIPELINES.register_module() +class SegRescale(object): + """Rescale semantic segmentation maps. + + Args: + scale_factor (float): The scale factor of the final output. + """ + + def __init__(self, scale_factor=1): + self.scale_factor = scale_factor + + def __call__(self, results): + """Call function to scale the semantic segmentation map. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with semantic segmentation map scaled. + """ + for key in results.get('seg_fields', []): + if self.scale_factor != 1: + results[key] = mmcv.imrescale( + results[key], self.scale_factor, interpolation='nearest') + return results + + def __repr__(self): + return self.__class__.__name__ + f'(scale_factor={self.scale_factor})' + + +@PIPELINES.register_module() +class PhotoMetricDistortion(object): + """Apply photometric distortion to image sequentially, every transformation + is applied with a probability of 0.5. The position of random contrast is in + second or second to last. + + 1. random brightness + 2. random contrast (mode 0) + 3. convert color from BGR to HSV + 4. random saturation + 5. random hue + 6. convert color from HSV to BGR + 7. random contrast (mode 1) + + Args: + brightness_delta (int): delta of brightness. + contrast_range (tuple): range of contrast. + saturation_range (tuple): range of saturation. + hue_delta (int): delta of hue. + """ + + def __init__(self, + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18): + self.brightness_delta = brightness_delta + self.contrast_lower, self.contrast_upper = contrast_range + self.saturation_lower, self.saturation_upper = saturation_range + self.hue_delta = hue_delta + + def convert(self, img, alpha=1, beta=0): + """Multiple with alpha and add beat with clip.""" + img = img.astype(np.float32) * alpha + beta + img = np.clip(img, 0, 255) + return img.astype(np.uint8) + + def brightness(self, img): + """Brightness distortion.""" + if random.randint(2): + return self.convert( + img, + beta=random.uniform(-self.brightness_delta, + self.brightness_delta)) + return img + + def contrast(self, img): + """Contrast distortion.""" + if random.randint(2): + return self.convert( + img, + alpha=random.uniform(self.contrast_lower, self.contrast_upper)) + return img + + def saturation(self, img): + """Saturation distortion.""" + if random.randint(2): + img = mmcv.bgr2hsv(img) + img[:, :, 1] = self.convert( + img[:, :, 1], + alpha=random.uniform(self.saturation_lower, + self.saturation_upper)) + img = mmcv.hsv2bgr(img) + return img + + def hue(self, img): + """Hue distortion.""" + if random.randint(2): + img = mmcv.bgr2hsv(img) + img[:, :, + 0] = (img[:, :, 0].astype(int) + + random.randint(-self.hue_delta, self.hue_delta)) % 180 + img = mmcv.hsv2bgr(img) + return img + + def __call__(self, results): + """Call function to perform photometric distortion on images. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images distorted. + """ + + img = results['img'] + # random brightness + img = self.brightness(img) + + # mode == 0 --> do random contrast first + # mode == 1 --> do random contrast last + mode = random.randint(2) + if mode == 1: + img = self.contrast(img) + + # random saturation + img = self.saturation(img) + + # random hue + img = self.hue(img) + + # random contrast + if mode == 0: + img = self.contrast(img) + + results['img'] = img + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += (f'(brightness_delta={self.brightness_delta}, ' + f'contrast_range=({self.contrast_lower}, ' + f'{self.contrast_upper}), ' + f'saturation_range=({self.saturation_lower}, ' + f'{self.saturation_upper}), ' + f'hue_delta={self.hue_delta})') + return repr_str diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/stare.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/stare.py new file mode 100644 index 00000000..cbd14e09 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/stare.py @@ -0,0 +1,27 @@ +import os.path as osp + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class STAREDataset(CustomDataset): + """STARE dataset. + + In segmentation map annotation for STARE, 0 stands for background, which is + included in 2 categories. ``reduce_zero_label`` is fixed to False. The + ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to + '.ah.png'. + """ + + CLASSES = ('background', 'vessel') + + PALETTE = [[120, 120, 120], [6, 230, 230]] + + def __init__(self, **kwargs): + super(STAREDataset, self).__init__( + img_suffix='.png', + seg_map_suffix='.ah.png', + reduce_zero_label=False, + **kwargs) + assert osp.exists(self.img_dir) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/voc.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/voc.py new file mode 100644 index 00000000..a8855203 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/datasets/voc.py @@ -0,0 +1,29 @@ +import os.path as osp + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class PascalVOCDataset(CustomDataset): + """Pascal VOC dataset. + + Args: + split (str): Split txt file for Pascal VOC. + """ + + CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', + 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', + 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', + 'train', 'tvmonitor') + + PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], + [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], + [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], + [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], + [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]] + + def __init__(self, split, **kwargs): + super(PascalVOCDataset, self).__init__( + img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs) + assert osp.exists(self.img_dir) and self.split is not None diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/__init__.py new file mode 100644 index 00000000..3cf93f8b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/__init__.py @@ -0,0 +1,12 @@ +from .backbones import * # noqa: F401,F403 +from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone, + build_head, build_loss, build_segmentor) +from .decode_heads import * # noqa: F401,F403 +from .losses import * # noqa: F401,F403 +from .necks import * # noqa: F401,F403 +from .segmentors import * # noqa: F401,F403 + +__all__ = [ + 'BACKBONES', 'HEADS', 'LOSSES', 'SEGMENTORS', 'build_backbone', + 'build_head', 'build_loss', 'build_segmentor' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/__init__.py new file mode 100644 index 00000000..a1116c00 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/__init__.py @@ -0,0 +1,16 @@ +from .cgnet import CGNet +# from .fast_scnn import FastSCNN +from .hrnet import HRNet +from .mobilenet_v2 import MobileNetV2 +from .mobilenet_v3 import MobileNetV3 +from .resnest import ResNeSt +from .resnet import ResNet, ResNetV1c, ResNetV1d +from .resnext import ResNeXt +from .unet import UNet +from .vit import VisionTransformer + +__all__ = [ + 'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', + 'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3', + 'VisionTransformer' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/cgnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/cgnet.py new file mode 100644 index 00000000..18b158be --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/cgnet.py @@ -0,0 +1,367 @@ +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp +from custom_mmpkg.custom_mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer, + constant_init, kaiming_init) +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import _BatchNorm + +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from ..builder import BACKBONES + + +class GlobalContextExtractor(nn.Module): + """Global Context Extractor for CGNet. + + This class is employed to refine the joint feature of both local feature + and surrounding context. + + Args: + channel (int): Number of input feature channels. + reduction (int): Reductions for global context extractor. Default: 16. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + """ + + def __init__(self, channel, reduction=16, with_cp=False): + super(GlobalContextExtractor, self).__init__() + self.channel = channel + self.reduction = reduction + assert reduction >= 1 and channel >= reduction + self.with_cp = with_cp + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction), nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel), nn.Sigmoid()) + + def forward(self, x): + + def _inner_forward(x): + num_batch, num_channel = x.size()[:2] + y = self.avg_pool(x).view(num_batch, num_channel) + y = self.fc(y).view(num_batch, num_channel, 1, 1) + return x * y + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + return out + + +class ContextGuidedBlock(nn.Module): + """Context Guided Block for CGNet. + + This class consists of four components: local feature extractor, + surrounding feature extractor, joint feature extractor and global + context extractor. + + Args: + in_channels (int): Number of input feature channels. + out_channels (int): Number of output feature channels. + dilation (int): Dilation rate for surrounding context extractor. + Default: 2. + reduction (int): Reduction for global context extractor. Default: 16. + skip_connect (bool): Add input to output or not. Default: True. + downsample (bool): Downsample the input to 1/2 or not. Default: False. + conv_cfg (dict): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN', requires_grad=True). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='PReLU'). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + """ + + def __init__(self, + in_channels, + out_channels, + dilation=2, + reduction=16, + skip_connect=True, + downsample=False, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='PReLU'), + with_cp=False): + super(ContextGuidedBlock, self).__init__() + self.with_cp = with_cp + self.downsample = downsample + + channels = out_channels if downsample else out_channels // 2 + if 'type' in act_cfg and act_cfg['type'] == 'PReLU': + act_cfg['num_parameters'] = channels + kernel_size = 3 if downsample else 1 + stride = 2 if downsample else 1 + padding = (kernel_size - 1) // 2 + + self.conv1x1 = ConvModule( + in_channels, + channels, + kernel_size, + stride, + padding, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + self.f_loc = build_conv_layer( + conv_cfg, + channels, + channels, + kernel_size=3, + padding=1, + groups=channels, + bias=False) + self.f_sur = build_conv_layer( + conv_cfg, + channels, + channels, + kernel_size=3, + padding=dilation, + groups=channels, + dilation=dilation, + bias=False) + + self.bn = build_norm_layer(norm_cfg, 2 * channels)[1] + self.activate = nn.PReLU(2 * channels) + + if downsample: + self.bottleneck = build_conv_layer( + conv_cfg, + 2 * channels, + out_channels, + kernel_size=1, + bias=False) + + self.skip_connect = skip_connect and not downsample + self.f_glo = GlobalContextExtractor(out_channels, reduction, with_cp) + + def forward(self, x): + + def _inner_forward(x): + out = self.conv1x1(x) + loc = self.f_loc(out) + sur = self.f_sur(out) + + joi_feat = torch.cat([loc, sur], 1) # the joint feature + joi_feat = self.bn(joi_feat) + joi_feat = self.activate(joi_feat) + if self.downsample: + joi_feat = self.bottleneck(joi_feat) # channel = out_channels + # f_glo is employed to refine the joint feature + out = self.f_glo(joi_feat) + + if self.skip_connect: + return x + out + else: + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + return out + + +class InputInjection(nn.Module): + """Downsampling module for CGNet.""" + + def __init__(self, num_downsampling): + super(InputInjection, self).__init__() + self.pool = nn.ModuleList() + for i in range(num_downsampling): + self.pool.append(nn.AvgPool2d(3, stride=2, padding=1)) + + def forward(self, x): + for pool in self.pool: + x = pool(x) + return x + + +@BACKBONES.register_module() +class CGNet(nn.Module): + """CGNet backbone. + + A Light-weight Context Guided Network for Semantic Segmentation + arXiv: https://arxiv.org/abs/1811.08201 + + Args: + in_channels (int): Number of input image channels. Normally 3. + num_channels (tuple[int]): Numbers of feature channels at each stages. + Default: (32, 64, 128). + num_blocks (tuple[int]): Numbers of CG blocks at stage 1 and stage 2. + Default: (3, 21). + dilations (tuple[int]): Dilation rate for surrounding context + extractors at stage 1 and stage 2. Default: (2, 4). + reductions (tuple[int]): Reductions for global context extractors at + stage 1 and stage 2. Default: (8, 16). + conv_cfg (dict): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN', requires_grad=True). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='PReLU'). + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + """ + + def __init__(self, + in_channels=3, + num_channels=(32, 64, 128), + num_blocks=(3, 21), + dilations=(2, 4), + reductions=(8, 16), + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='PReLU'), + norm_eval=False, + with_cp=False): + + super(CGNet, self).__init__() + self.in_channels = in_channels + self.num_channels = num_channels + assert isinstance(self.num_channels, tuple) and len( + self.num_channels) == 3 + self.num_blocks = num_blocks + assert isinstance(self.num_blocks, tuple) and len(self.num_blocks) == 2 + self.dilations = dilations + assert isinstance(self.dilations, tuple) and len(self.dilations) == 2 + self.reductions = reductions + assert isinstance(self.reductions, tuple) and len(self.reductions) == 2 + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + if 'type' in self.act_cfg and self.act_cfg['type'] == 'PReLU': + self.act_cfg['num_parameters'] = num_channels[0] + self.norm_eval = norm_eval + self.with_cp = with_cp + + cur_channels = in_channels + self.stem = nn.ModuleList() + for i in range(3): + self.stem.append( + ConvModule( + cur_channels, + num_channels[0], + 3, + 2 if i == 0 else 1, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + cur_channels = num_channels[0] + + self.inject_2x = InputInjection(1) # down-sample for Input, factor=2 + self.inject_4x = InputInjection(2) # down-sample for Input, factor=4 + + cur_channels += in_channels + self.norm_prelu_0 = nn.Sequential( + build_norm_layer(norm_cfg, cur_channels)[1], + nn.PReLU(cur_channels)) + + # stage 1 + self.level1 = nn.ModuleList() + for i in range(num_blocks[0]): + self.level1.append( + ContextGuidedBlock( + cur_channels if i == 0 else num_channels[1], + num_channels[1], + dilations[0], + reductions[0], + downsample=(i == 0), + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + with_cp=with_cp)) # CG block + + cur_channels = 2 * num_channels[1] + in_channels + self.norm_prelu_1 = nn.Sequential( + build_norm_layer(norm_cfg, cur_channels)[1], + nn.PReLU(cur_channels)) + + # stage 2 + self.level2 = nn.ModuleList() + for i in range(num_blocks[1]): + self.level2.append( + ContextGuidedBlock( + cur_channels if i == 0 else num_channels[2], + num_channels[2], + dilations[1], + reductions[1], + downsample=(i == 0), + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + with_cp=with_cp)) # CG block + + cur_channels = 2 * num_channels[2] + self.norm_prelu_2 = nn.Sequential( + build_norm_layer(norm_cfg, cur_channels)[1], + nn.PReLU(cur_channels)) + + def forward(self, x): + output = [] + + # stage 0 + inp_2x = self.inject_2x(x) + inp_4x = self.inject_4x(x) + for layer in self.stem: + x = layer(x) + x = self.norm_prelu_0(torch.cat([x, inp_2x], 1)) + output.append(x) + + # stage 1 + for i, layer in enumerate(self.level1): + x = layer(x) + if i == 0: + down1 = x + x = self.norm_prelu_1(torch.cat([x, down1, inp_4x], 1)) + output.append(x) + + # stage 2 + for i, layer in enumerate(self.level2): + x = layer(x) + if i == 0: + down2 = x + x = self.norm_prelu_2(torch.cat([down2, x], 1)) + output.append(x) + + return output + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + elif isinstance(m, nn.PReLU): + constant_init(m, 0) + else: + raise TypeError('pretrained must be a str or None') + + def train(self, mode=True): + """Convert the model into training mode will keeping the normalization + layer freezed.""" + super(CGNet, self).train(mode) + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/fast_scnn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/fast_scnn.py new file mode 100644 index 00000000..0d06faa7 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/fast_scnn.py @@ -0,0 +1,375 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, constant_init, + kaiming_init) +from torch.nn.modules.batchnorm import _BatchNorm + +from custom_mmpkg.custom_mmseg.models.decode_heads.psp_head import PPM +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import BACKBONES +from ..utils.inverted_residual import InvertedResidual + + +class LearningToDownsample(nn.Module): + """Learning to downsample module. + + Args: + in_channels (int): Number of input channels. + dw_channels (tuple[int]): Number of output channels of the first and + the second depthwise conv (dwconv) layers. + out_channels (int): Number of output channels of the whole + 'learning to downsample' module. + conv_cfg (dict | None): Config of conv layers. Default: None + norm_cfg (dict | None): Config of norm layers. Default: + dict(type='BN') + act_cfg (dict): Config of activation layers. Default: + dict(type='ReLU') + """ + + def __init__(self, + in_channels, + dw_channels, + out_channels, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU')): + super(LearningToDownsample, self).__init__() + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + dw_channels1 = dw_channels[0] + dw_channels2 = dw_channels[1] + + self.conv = ConvModule( + in_channels, + dw_channels1, + 3, + stride=2, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.dsconv1 = DepthwiseSeparableConvModule( + dw_channels1, + dw_channels2, + kernel_size=3, + stride=2, + padding=1, + norm_cfg=self.norm_cfg) + self.dsconv2 = DepthwiseSeparableConvModule( + dw_channels2, + out_channels, + kernel_size=3, + stride=2, + padding=1, + norm_cfg=self.norm_cfg) + + def forward(self, x): + x = self.conv(x) + x = self.dsconv1(x) + x = self.dsconv2(x) + return x + + +class GlobalFeatureExtractor(nn.Module): + """Global feature extractor module. + + Args: + in_channels (int): Number of input channels of the GFE module. + Default: 64 + block_channels (tuple[int]): Tuple of ints. Each int specifies the + number of output channels of each Inverted Residual module. + Default: (64, 96, 128) + out_channels(int): Number of output channels of the GFE module. + Default: 128 + expand_ratio (int): Adjusts number of channels of the hidden layer + in InvertedResidual by this amount. + Default: 6 + num_blocks (tuple[int]): Tuple of ints. Each int specifies the + number of times each Inverted Residual module is repeated. + The repeated Inverted Residual modules are called a 'group'. + Default: (3, 3, 3) + strides (tuple[int]): Tuple of ints. Each int specifies + the downsampling factor of each 'group'. + Default: (2, 2, 1) + pool_scales (tuple[int]): Tuple of ints. Each int specifies + the parameter required in 'global average pooling' within PPM. + Default: (1, 2, 3, 6) + conv_cfg (dict | None): Config of conv layers. Default: None + norm_cfg (dict | None): Config of norm layers. Default: + dict(type='BN') + act_cfg (dict): Config of activation layers. Default: + dict(type='ReLU') + align_corners (bool): align_corners argument of F.interpolate. + Default: False + """ + + def __init__(self, + in_channels=64, + block_channels=(64, 96, 128), + out_channels=128, + expand_ratio=6, + num_blocks=(3, 3, 3), + strides=(2, 2, 1), + pool_scales=(1, 2, 3, 6), + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + align_corners=False): + super(GlobalFeatureExtractor, self).__init__() + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + assert len(block_channels) == len(num_blocks) == 3 + self.bottleneck1 = self._make_layer(in_channels, block_channels[0], + num_blocks[0], strides[0], + expand_ratio) + self.bottleneck2 = self._make_layer(block_channels[0], + block_channels[1], num_blocks[1], + strides[1], expand_ratio) + self.bottleneck3 = self._make_layer(block_channels[1], + block_channels[2], num_blocks[2], + strides[2], expand_ratio) + self.ppm = PPM( + pool_scales, + block_channels[2], + block_channels[2] // 4, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + align_corners=align_corners) + self.out = ConvModule( + block_channels[2] * 2, + out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def _make_layer(self, + in_channels, + out_channels, + blocks, + stride=1, + expand_ratio=6): + layers = [ + InvertedResidual( + in_channels, + out_channels, + stride, + expand_ratio, + norm_cfg=self.norm_cfg) + ] + for i in range(1, blocks): + layers.append( + InvertedResidual( + out_channels, + out_channels, + 1, + expand_ratio, + norm_cfg=self.norm_cfg)) + return nn.Sequential(*layers) + + def forward(self, x): + x = self.bottleneck1(x) + x = self.bottleneck2(x) + x = self.bottleneck3(x) + x = torch.cat([x, *self.ppm(x)], dim=1) + x = self.out(x) + return x + + +class FeatureFusionModule(nn.Module): + """Feature fusion module. + + Args: + higher_in_channels (int): Number of input channels of the + higher-resolution branch. + lower_in_channels (int): Number of input channels of the + lower-resolution branch. + out_channels (int): Number of output channels. + conv_cfg (dict | None): Config of conv layers. Default: None + norm_cfg (dict | None): Config of norm layers. Default: + dict(type='BN') + act_cfg (dict): Config of activation layers. Default: + dict(type='ReLU') + align_corners (bool): align_corners argument of F.interpolate. + Default: False + """ + + def __init__(self, + higher_in_channels, + lower_in_channels, + out_channels, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + align_corners=False): + super(FeatureFusionModule, self).__init__() + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.align_corners = align_corners + self.dwconv = ConvModule( + lower_in_channels, + out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.conv_lower_res = ConvModule( + out_channels, + out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=None) + self.conv_higher_res = ConvModule( + higher_in_channels, + out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=None) + self.relu = nn.ReLU(True) + + def forward(self, higher_res_feature, lower_res_feature): + lower_res_feature = resize( + lower_res_feature, + size=higher_res_feature.size()[2:], + mode='bilinear', + align_corners=self.align_corners) + lower_res_feature = self.dwconv(lower_res_feature) + lower_res_feature = self.conv_lower_res(lower_res_feature) + + higher_res_feature = self.conv_higher_res(higher_res_feature) + out = higher_res_feature + lower_res_feature + return self.relu(out) + + +@BACKBONES.register_module() +class FastSCNN(nn.Module): + """Fast-SCNN Backbone. + + Args: + in_channels (int): Number of input image channels. Default: 3. + downsample_dw_channels (tuple[int]): Number of output channels after + the first conv layer & the second conv layer in + Learning-To-Downsample (LTD) module. + Default: (32, 48). + global_in_channels (int): Number of input channels of + Global Feature Extractor(GFE). + Equal to number of output channels of LTD. + Default: 64. + global_block_channels (tuple[int]): Tuple of integers that describe + the output channels for each of the MobileNet-v2 bottleneck + residual blocks in GFE. + Default: (64, 96, 128). + global_block_strides (tuple[int]): Tuple of integers + that describe the strides (downsampling factors) for each of the + MobileNet-v2 bottleneck residual blocks in GFE. + Default: (2, 2, 1). + global_out_channels (int): Number of output channels of GFE. + Default: 128. + higher_in_channels (int): Number of input channels of the higher + resolution branch in FFM. + Equal to global_in_channels. + Default: 64. + lower_in_channels (int): Number of input channels of the lower + resolution branch in FFM. + Equal to global_out_channels. + Default: 128. + fusion_out_channels (int): Number of output channels of FFM. + Default: 128. + out_indices (tuple): Tuple of indices of list + [higher_res_features, lower_res_features, fusion_output]. + Often set to (0,1,2) to enable aux. heads. + Default: (0, 1, 2). + conv_cfg (dict | None): Config of conv layers. Default: None + norm_cfg (dict | None): Config of norm layers. Default: + dict(type='BN') + act_cfg (dict): Config of activation layers. Default: + dict(type='ReLU') + align_corners (bool): align_corners argument of F.interpolate. + Default: False + """ + + def __init__(self, + in_channels=3, + downsample_dw_channels=(32, 48), + global_in_channels=64, + global_block_channels=(64, 96, 128), + global_block_strides=(2, 2, 1), + global_out_channels=128, + higher_in_channels=64, + lower_in_channels=128, + fusion_out_channels=128, + out_indices=(0, 1, 2), + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + align_corners=False): + + super(FastSCNN, self).__init__() + if global_in_channels != higher_in_channels: + raise AssertionError('Global Input Channels must be the same \ + with Higher Input Channels!') + elif global_out_channels != lower_in_channels: + raise AssertionError('Global Output Channels must be the same \ + with Lower Input Channels!') + + self.in_channels = in_channels + self.downsample_dw_channels1 = downsample_dw_channels[0] + self.downsample_dw_channels2 = downsample_dw_channels[1] + self.global_in_channels = global_in_channels + self.global_block_channels = global_block_channels + self.global_block_strides = global_block_strides + self.global_out_channels = global_out_channels + self.higher_in_channels = higher_in_channels + self.lower_in_channels = lower_in_channels + self.fusion_out_channels = fusion_out_channels + self.out_indices = out_indices + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.align_corners = align_corners + self.learning_to_downsample = LearningToDownsample( + in_channels, + downsample_dw_channels, + global_in_channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.global_feature_extractor = GlobalFeatureExtractor( + global_in_channels, + global_block_channels, + global_out_channels, + strides=self.global_block_strides, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + align_corners=self.align_corners) + self.feature_fusion = FeatureFusionModule( + higher_in_channels, + lower_in_channels, + fusion_out_channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + align_corners=self.align_corners) + + def init_weights(self, pretrained=None): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + + def forward(self, x): + higher_res_features = self.learning_to_downsample(x) + lower_res_features = self.global_feature_extractor(higher_res_features) + fusion_output = self.feature_fusion(higher_res_features, + lower_res_features) + + outs = [higher_res_features, lower_res_features, fusion_output] + outs = [outs[i] for i in self.out_indices] + return tuple(outs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/hrnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/hrnet.py new file mode 100644 index 00000000..7df19e7b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/hrnet.py @@ -0,0 +1,555 @@ +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init, + kaiming_init) +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import _BatchNorm + +from custom_mmpkg.custom_mmseg.ops import Upsample, resize +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from ..builder import BACKBONES +from .resnet import BasicBlock, Bottleneck + + +class HRModule(nn.Module): + """High-Resolution Module for HRNet. + + In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange + is in this module. + """ + + def __init__(self, + num_branches, + blocks, + num_blocks, + in_channels, + num_channels, + multiscale_output=True, + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True)): + super(HRModule, self).__init__() + self._check_branches(num_branches, num_blocks, in_channels, + num_channels) + + self.in_channels = in_channels + self.num_branches = num_branches + + self.multiscale_output = multiscale_output + self.norm_cfg = norm_cfg + self.conv_cfg = conv_cfg + self.with_cp = with_cp + self.branches = self._make_branches(num_branches, blocks, num_blocks, + num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(inplace=False) + + def _check_branches(self, num_branches, num_blocks, in_channels, + num_channels): + """Check branches configuration.""" + if num_branches != len(num_blocks): + error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_BLOCKS(' \ + f'{len(num_blocks)})' + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_CHANNELS(' \ + f'{len(num_channels)})' + raise ValueError(error_msg) + + if num_branches != len(in_channels): + error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_INCHANNELS(' \ + f'{len(in_channels)})' + raise ValueError(error_msg) + + def _make_one_branch(self, + branch_index, + block, + num_blocks, + num_channels, + stride=1): + """Build one branch.""" + downsample = None + if stride != 1 or \ + self.in_channels[branch_index] != \ + num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + build_conv_layer( + self.conv_cfg, + self.in_channels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + build_norm_layer(self.norm_cfg, num_channels[branch_index] * + block.expansion)[1]) + + layers = [] + layers.append( + block( + self.in_channels[branch_index], + num_channels[branch_index], + stride, + downsample=downsample, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg)) + self.in_channels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append( + block( + self.in_channels[branch_index], + num_channels[branch_index], + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg)) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + """Build multiple branch.""" + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + """Build fuse layer.""" + if self.num_branches == 1: + return None + + num_branches = self.num_branches + in_channels = self.in_channels + fuse_layers = [] + num_out_branches = num_branches if self.multiscale_output else 1 + for i in range(num_out_branches): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[i], + kernel_size=1, + stride=1, + padding=0, + bias=False), + build_norm_layer(self.norm_cfg, in_channels[i])[1], + # we set align_corners=False for HRNet + Upsample( + scale_factor=2**(j - i), + mode='bilinear', + align_corners=False))) + elif j == i: + fuse_layer.append(None) + else: + conv_downsamples = [] + for k in range(i - j): + if k == i - j - 1: + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[i], + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + in_channels[i])[1])) + else: + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[j], + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + in_channels[j])[1], + nn.ReLU(inplace=False))) + fuse_layer.append(nn.Sequential(*conv_downsamples)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def forward(self, x): + """Forward function.""" + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = 0 + for j in range(self.num_branches): + if i == j: + y += x[j] + elif j > i: + y = y + resize( + self.fuse_layers[i][j](x[j]), + size=x[i].shape[2:], + mode='bilinear', + align_corners=False) + else: + y += self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + return x_fuse + + +@BACKBONES.register_module() +class HRNet(nn.Module): + """HRNet backbone. + + High-Resolution Representations for Labeling Pixels and Regions + arXiv: https://arxiv.org/abs/1904.04514 + + Args: + extra (dict): detailed configuration for each stage of HRNet. + in_channels (int): Number of input image channels. Normally 3. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + + Example: + >>> from custom_mmpkg.custom_mmseg.models import HRNet + >>> import torch + >>> extra = dict( + >>> stage1=dict( + >>> num_modules=1, + >>> num_branches=1, + >>> block='BOTTLENECK', + >>> num_blocks=(4, ), + >>> num_channels=(64, )), + >>> stage2=dict( + >>> num_modules=1, + >>> num_branches=2, + >>> block='BASIC', + >>> num_blocks=(4, 4), + >>> num_channels=(32, 64)), + >>> stage3=dict( + >>> num_modules=4, + >>> num_branches=3, + >>> block='BASIC', + >>> num_blocks=(4, 4, 4), + >>> num_channels=(32, 64, 128)), + >>> stage4=dict( + >>> num_modules=3, + >>> num_branches=4, + >>> block='BASIC', + >>> num_blocks=(4, 4, 4, 4), + >>> num_channels=(32, 64, 128, 256))) + >>> self = HRNet(extra, in_channels=1) + >>> self.eval() + >>> inputs = torch.rand(1, 1, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 32, 8, 8) + (1, 64, 4, 4) + (1, 128, 2, 2) + (1, 256, 1, 1) + """ + + blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} + + def __init__(self, + extra, + in_channels=3, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=False, + with_cp=False, + zero_init_residual=False): + super(HRNet, self).__init__() + self.extra = extra + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.norm_eval = norm_eval + self.with_cp = with_cp + self.zero_init_residual = zero_init_residual + + # stem net + self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1) + self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2) + + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + 64, + kernel_size=3, + stride=2, + padding=1, + bias=False) + + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + self.conv_cfg, + 64, + 64, + kernel_size=3, + stride=2, + padding=1, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.relu = nn.ReLU(inplace=True) + + # stage 1 + self.stage1_cfg = self.extra['stage1'] + num_channels = self.stage1_cfg['num_channels'][0] + block_type = self.stage1_cfg['block'] + num_blocks = self.stage1_cfg['num_blocks'][0] + + block = self.blocks_dict[block_type] + stage1_out_channels = num_channels * block.expansion + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + + # stage 2 + self.stage2_cfg = self.extra['stage2'] + num_channels = self.stage2_cfg['num_channels'] + block_type = self.stage2_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition1 = self._make_transition_layer([stage1_out_channels], + num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + # stage 3 + self.stage3_cfg = self.extra['stage3'] + num_channels = self.stage3_cfg['num_channels'] + block_type = self.stage3_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition2 = self._make_transition_layer(pre_stage_channels, + num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + # stage 4 + self.stage4_cfg = self.extra['stage4'] + num_channels = self.stage4_cfg['num_channels'] + block_type = self.stage4_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition3 = self._make_transition_layer(pre_stage_channels, + num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: the normalization layer named "norm2" """ + return getattr(self, self.norm2_name) + + def _make_transition_layer(self, num_channels_pre_layer, + num_channels_cur_layer): + """Make transition layer.""" + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + num_channels_pre_layer[i], + num_channels_cur_layer[i], + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + num_channels_cur_layer[i])[1], + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv_downsamples = [] + for j in range(i + 1 - num_branches_pre): + in_channels = num_channels_pre_layer[-1] + out_channels = num_channels_cur_layer[i] \ + if j == i - num_branches_pre else in_channels + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels, + out_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, out_channels)[1], + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv_downsamples)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + """Make each layer.""" + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + build_conv_layer( + self.conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + build_norm_layer(self.norm_cfg, planes * block.expansion)[1]) + + layers = [] + layers.append( + block( + inplanes, + planes, + stride, + downsample=downsample, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg)) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append( + block( + inplanes, + planes, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, in_channels, multiscale_output=True): + """Make each stage.""" + num_modules = layer_config['num_modules'] + num_branches = layer_config['num_branches'] + num_blocks = layer_config['num_blocks'] + num_channels = layer_config['num_channels'] + block = self.blocks_dict[layer_config['block']] + + hr_modules = [] + for i in range(num_modules): + # multi_scale_output is only used for the last module + if not multiscale_output and i == num_modules - 1: + reset_multiscale_output = False + else: + reset_multiscale_output = True + + hr_modules.append( + HRModule( + num_branches, + block, + num_blocks, + in_channels, + num_channels, + reset_multiscale_output, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg)) + + return nn.Sequential(*hr_modules), in_channels + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + + if self.zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + constant_init(m.norm3, 0) + elif isinstance(m, BasicBlock): + constant_init(m.norm2, 0) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + """Forward function.""" + + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.norm2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['num_branches']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['num_branches']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['num_branches']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + return y_list + + def train(self, mode=True): + """Convert the model into training mode will keeping the normalization + layer freezed.""" + super(HRNet, self).train(mode) + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/mobilenet_v2.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/mobilenet_v2.py new file mode 100644 index 00000000..bcec93a2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/mobilenet_v2.py @@ -0,0 +1,180 @@ +import logging + +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule, constant_init, kaiming_init +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from torch.nn.modules.batchnorm import _BatchNorm + +from ..builder import BACKBONES +from ..utils import InvertedResidual, make_divisible + + +@BACKBONES.register_module() +class MobileNetV2(nn.Module): + """MobileNetV2 backbone. + + Args: + widen_factor (float): Width multiplier, multiply number of + channels in each layer by this amount. Default: 1.0. + strides (Sequence[int], optional): Strides of the first block of each + layer. If not specified, default config in ``arch_setting`` will + be used. + dilations (Sequence[int]): Dilation of each layer. + out_indices (None or Sequence[int]): Output from which stages. + Default: (7, ). + frozen_stages (int): Stages to be frozen (all param fixed). + Default: -1, which means not freezing any parameters. + conv_cfg (dict): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU6'). + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + """ + + # Parameters to build layers. 3 parameters are needed to construct a + # layer, from left to right: expand_ratio, channel, num_blocks. + arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4], + [6, 96, 3], [6, 160, 3], [6, 320, 1]] + + def __init__(self, + widen_factor=1., + strides=(1, 2, 2, 2, 1, 2, 1), + dilations=(1, 1, 1, 1, 1, 1, 1), + out_indices=(1, 2, 4, 6), + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU6'), + norm_eval=False, + with_cp=False): + super(MobileNetV2, self).__init__() + self.widen_factor = widen_factor + self.strides = strides + self.dilations = dilations + assert len(strides) == len(dilations) == len(self.arch_settings) + self.out_indices = out_indices + for index in out_indices: + if index not in range(0, 7): + raise ValueError('the item in out_indices must in ' + f'range(0, 8). But received {index}') + + if frozen_stages not in range(-1, 7): + raise ValueError('frozen_stages must be in range(-1, 7). ' + f'But received {frozen_stages}') + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.norm_eval = norm_eval + self.with_cp = with_cp + + self.in_channels = make_divisible(32 * widen_factor, 8) + + self.conv1 = ConvModule( + in_channels=3, + out_channels=self.in_channels, + kernel_size=3, + stride=2, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.layers = [] + + for i, layer_cfg in enumerate(self.arch_settings): + expand_ratio, channel, num_blocks = layer_cfg + stride = self.strides[i] + dilation = self.dilations[i] + out_channels = make_divisible(channel * widen_factor, 8) + inverted_res_layer = self.make_layer( + out_channels=out_channels, + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + expand_ratio=expand_ratio) + layer_name = f'layer{i + 1}' + self.add_module(layer_name, inverted_res_layer) + self.layers.append(layer_name) + + def make_layer(self, out_channels, num_blocks, stride, dilation, + expand_ratio): + """Stack InvertedResidual blocks to build a layer for MobileNetV2. + + Args: + out_channels (int): out_channels of block. + num_blocks (int): Number of blocks. + stride (int): Stride of the first block. + dilation (int): Dilation of the first block. + expand_ratio (int): Expand the number of channels of the + hidden layer in InvertedResidual by this ratio. + """ + layers = [] + for i in range(num_blocks): + layers.append( + InvertedResidual( + self.in_channels, + out_channels, + stride if i == 0 else 1, + expand_ratio=expand_ratio, + dilation=dilation if i == 0 else 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + with_cp=self.with_cp)) + self.in_channels = out_channels + + return nn.Sequential(*layers) + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = logging.getLogger() + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + x = self.conv1(x) + + outs = [] + for i, layer_name in enumerate(self.layers): + layer = getattr(self, layer_name) + x = layer(x) + if i in self.out_indices: + outs.append(x) + + if len(outs) == 1: + return outs[0] + else: + return tuple(outs) + + def _freeze_stages(self): + if self.frozen_stages >= 0: + for param in self.conv1.parameters(): + param.requires_grad = False + for i in range(1, self.frozen_stages + 1): + layer = getattr(self, f'layer{i}') + layer.eval() + for param in layer.parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(MobileNetV2, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/mobilenet_v3.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/mobilenet_v3.py new file mode 100644 index 00000000..17210327 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/mobilenet_v3.py @@ -0,0 +1,255 @@ +import logging + +import custom_mmpkg.custom_mmcv as mmcv +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule, constant_init, kaiming_init +from custom_mmpkg.custom_mmcv.cnn.bricks import Conv2dAdaptivePadding +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from torch.nn.modules.batchnorm import _BatchNorm + +from ..builder import BACKBONES +from ..utils import InvertedResidualV3 as InvertedResidual + + +@BACKBONES.register_module() +class MobileNetV3(nn.Module): + """MobileNetV3 backbone. + + This backbone is the improved implementation of `Searching for MobileNetV3 + `_. + + Args: + arch (str): Architecture of mobilnetv3, from {'small', 'large'}. + Default: 'small'. + conv_cfg (dict): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN'). + out_indices (tuple[int]): Output from which layer. + Default: (0, 1, 12). + frozen_stages (int): Stages to be frozen (all param fixed). + Default: -1, which means not freezing any parameters. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + with_cp (bool): Use checkpoint or not. Using checkpoint will save + some memory while slowing down the training speed. + Default: False. + """ + # Parameters to build each block: + # [kernel size, mid channels, out channels, with_se, act type, stride] + arch_settings = { + 'small': [[3, 16, 16, True, 'ReLU', 2], # block0 layer1 os=4 + [3, 72, 24, False, 'ReLU', 2], # block1 layer2 os=8 + [3, 88, 24, False, 'ReLU', 1], + [5, 96, 40, True, 'HSwish', 2], # block2 layer4 os=16 + [5, 240, 40, True, 'HSwish', 1], + [5, 240, 40, True, 'HSwish', 1], + [5, 120, 48, True, 'HSwish', 1], # block3 layer7 os=16 + [5, 144, 48, True, 'HSwish', 1], + [5, 288, 96, True, 'HSwish', 2], # block4 layer9 os=32 + [5, 576, 96, True, 'HSwish', 1], + [5, 576, 96, True, 'HSwish', 1]], + 'large': [[3, 16, 16, False, 'ReLU', 1], # block0 layer1 os=2 + [3, 64, 24, False, 'ReLU', 2], # block1 layer2 os=4 + [3, 72, 24, False, 'ReLU', 1], + [5, 72, 40, True, 'ReLU', 2], # block2 layer4 os=8 + [5, 120, 40, True, 'ReLU', 1], + [5, 120, 40, True, 'ReLU', 1], + [3, 240, 80, False, 'HSwish', 2], # block3 layer7 os=16 + [3, 200, 80, False, 'HSwish', 1], + [3, 184, 80, False, 'HSwish', 1], + [3, 184, 80, False, 'HSwish', 1], + [3, 480, 112, True, 'HSwish', 1], # block4 layer11 os=16 + [3, 672, 112, True, 'HSwish', 1], + [5, 672, 160, True, 'HSwish', 2], # block5 layer13 os=32 + [5, 960, 160, True, 'HSwish', 1], + [5, 960, 160, True, 'HSwish', 1]] + } # yapf: disable + + def __init__(self, + arch='small', + conv_cfg=None, + norm_cfg=dict(type='BN'), + out_indices=(0, 1, 12), + frozen_stages=-1, + reduction_factor=1, + norm_eval=False, + with_cp=False): + super(MobileNetV3, self).__init__() + assert arch in self.arch_settings + assert isinstance(reduction_factor, int) and reduction_factor > 0 + assert mmcv.is_tuple_of(out_indices, int) + for index in out_indices: + if index not in range(0, len(self.arch_settings[arch]) + 2): + raise ValueError( + 'the item in out_indices must in ' + f'range(0, {len(self.arch_settings[arch])+2}). ' + f'But received {index}') + + if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2): + raise ValueError('frozen_stages must be in range(-1, ' + f'{len(self.arch_settings[arch])+2}). ' + f'But received {frozen_stages}') + self.arch = arch + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.reduction_factor = reduction_factor + self.norm_eval = norm_eval + self.with_cp = with_cp + self.layers = self._make_layer() + + def _make_layer(self): + layers = [] + + # build the first layer (layer0) + in_channels = 16 + layer = ConvModule( + in_channels=3, + out_channels=in_channels, + kernel_size=3, + stride=2, + padding=1, + conv_cfg=dict(type='Conv2dAdaptivePadding'), + norm_cfg=self.norm_cfg, + act_cfg=dict(type='HSwish')) + self.add_module('layer0', layer) + layers.append('layer0') + + layer_setting = self.arch_settings[self.arch] + for i, params in enumerate(layer_setting): + (kernel_size, mid_channels, out_channels, with_se, act, + stride) = params + + if self.arch == 'large' and i >= 12 or self.arch == 'small' and \ + i >= 8: + mid_channels = mid_channels // self.reduction_factor + out_channels = out_channels // self.reduction_factor + + if with_se: + se_cfg = dict( + channels=mid_channels, + ratio=4, + act_cfg=(dict(type='ReLU'), + dict(type='HSigmoid', bias=3.0, divisor=6.0))) + else: + se_cfg = None + + layer = InvertedResidual( + in_channels=in_channels, + out_channels=out_channels, + mid_channels=mid_channels, + kernel_size=kernel_size, + stride=stride, + se_cfg=se_cfg, + with_expand_conv=(in_channels != mid_channels), + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=dict(type=act), + with_cp=self.with_cp) + in_channels = out_channels + layer_name = 'layer{}'.format(i + 1) + self.add_module(layer_name, layer) + layers.append(layer_name) + + # build the last layer + # block5 layer12 os=32 for small model + # block6 layer16 os=32 for large model + layer = ConvModule( + in_channels=in_channels, + out_channels=576 if self.arch == 'small' else 960, + kernel_size=1, + stride=1, + dilation=4, + padding=0, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=dict(type='HSwish')) + layer_name = 'layer{}'.format(len(layer_setting) + 1) + self.add_module(layer_name, layer) + layers.append(layer_name) + + # next, convert backbone MobileNetV3 to a semantic segmentation version + if self.arch == 'small': + self.layer4.depthwise_conv.conv.stride = (1, 1) + self.layer9.depthwise_conv.conv.stride = (1, 1) + for i in range(4, len(layers)): + layer = getattr(self, layers[i]) + if isinstance(layer, InvertedResidual): + modified_module = layer.depthwise_conv.conv + else: + modified_module = layer.conv + + if i < 9: + modified_module.dilation = (2, 2) + pad = 2 + else: + modified_module.dilation = (4, 4) + pad = 4 + + if not isinstance(modified_module, Conv2dAdaptivePadding): + # Adjust padding + pad *= (modified_module.kernel_size[0] - 1) // 2 + modified_module.padding = (pad, pad) + else: + self.layer7.depthwise_conv.conv.stride = (1, 1) + self.layer13.depthwise_conv.conv.stride = (1, 1) + for i in range(7, len(layers)): + layer = getattr(self, layers[i]) + if isinstance(layer, InvertedResidual): + modified_module = layer.depthwise_conv.conv + else: + modified_module = layer.conv + + if i < 13: + modified_module.dilation = (2, 2) + pad = 2 + else: + modified_module.dilation = (4, 4) + pad = 4 + + if not isinstance(modified_module, Conv2dAdaptivePadding): + # Adjust padding + pad *= (modified_module.kernel_size[0] - 1) // 2 + modified_module.padding = (pad, pad) + + return layers + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = logging.getLogger() + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, nn.BatchNorm2d): + constant_init(m, 1) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + outs = [] + for i, layer_name in enumerate(self.layers): + layer = getattr(self, layer_name) + x = layer(x) + if i in self.out_indices: + outs.append(x) + return outs + + def _freeze_stages(self): + for i in range(self.frozen_stages + 1): + layer = getattr(self, f'layer{i}') + layer.eval() + for param in layer.parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(MobileNetV3, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnest.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnest.py new file mode 100644 index 00000000..3ea8fbe3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnest.py @@ -0,0 +1,314 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from custom_mmpkg.custom_mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNetV1d + + +class RSoftmax(nn.Module): + """Radix Softmax module in ``SplitAttentionConv2d``. + + Args: + radix (int): Radix of input. + groups (int): Groups of input. + """ + + def __init__(self, radix, groups): + super().__init__() + self.radix = radix + self.groups = groups + + def forward(self, x): + batch = x.size(0) + if self.radix > 1: + x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2) + x = F.softmax(x, dim=1) + x = x.reshape(batch, -1) + else: + x = torch.sigmoid(x) + return x + + +class SplitAttentionConv2d(nn.Module): + """Split-Attention Conv2d in ResNeSt. + + Args: + in_channels (int): Same as nn.Conv2d. + out_channels (int): Same as nn.Conv2d. + kernel_size (int | tuple[int]): Same as nn.Conv2d. + stride (int | tuple[int]): Same as nn.Conv2d. + padding (int | tuple[int]): Same as nn.Conv2d. + dilation (int | tuple[int]): Same as nn.Conv2d. + groups (int): Same as nn.Conv2d. + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels. Default: 4. + conv_cfg (dict): Config dict for convolution layer. Default: None, + which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. Default: None. + dcn (dict): Config dict for DCN. Default: None. + """ + + def __init__(self, + in_channels, + channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + radix=2, + reduction_factor=4, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None): + super(SplitAttentionConv2d, self).__init__() + inter_channels = max(in_channels * radix // reduction_factor, 32) + self.radix = radix + self.groups = groups + self.channels = channels + self.with_dcn = dcn is not None + self.dcn = dcn + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if self.with_dcn and not fallback_on_stride: + assert conv_cfg is None, 'conv_cfg must be None for DCN' + conv_cfg = dcn + self.conv = build_conv_layer( + conv_cfg, + in_channels, + channels * radix, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups * radix, + bias=False) + self.norm0_name, norm0 = build_norm_layer( + norm_cfg, channels * radix, postfix=0) + self.add_module(self.norm0_name, norm0) + self.relu = nn.ReLU(inplace=True) + self.fc1 = build_conv_layer( + None, channels, inter_channels, 1, groups=self.groups) + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, inter_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.fc2 = build_conv_layer( + None, inter_channels, channels * radix, 1, groups=self.groups) + self.rsoftmax = RSoftmax(radix, groups) + + @property + def norm0(self): + """nn.Module: the normalization layer named "norm0" """ + return getattr(self, self.norm0_name) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + def forward(self, x): + x = self.conv(x) + x = self.norm0(x) + x = self.relu(x) + + batch, rchannel = x.shape[:2] + batch = x.size(0) + if self.radix > 1: + splits = x.view(batch, self.radix, -1, *x.shape[2:]) + gap = splits.sum(dim=1) + else: + gap = x + gap = F.adaptive_avg_pool2d(gap, 1) + gap = self.fc1(gap) + + gap = self.norm1(gap) + gap = self.relu(gap) + + atten = self.fc2(gap) + atten = self.rsoftmax(atten).view(batch, -1, 1, 1) + + if self.radix > 1: + attens = atten.view(batch, self.radix, -1, *atten.shape[2:]) + out = torch.sum(attens * splits, dim=1) + else: + out = atten * x + return out.contiguous() + + +class Bottleneck(_Bottleneck): + """Bottleneck block for ResNeSt. + + Args: + inplane (int): Input planes of this block. + planes (int): Middle planes of this block. + groups (int): Groups of conv2. + width_per_group (int): Width per group of conv2. 64x4d indicates + ``groups=64, width_per_group=4`` and 32x8d indicates + ``groups=32, width_per_group=8``. + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels in + SplitAttentionConv2d. Default: 4. + avg_down_stride (bool): Whether to use average pool for stride in + Bottleneck. Default: True. + kwargs (dict): Key word arguments for base class. + """ + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + radix=2, + reduction_factor=4, + avg_down_stride=True, + **kwargs): + """Bottleneck block for ResNeSt.""" + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.avg_down_stride = avg_down_stride and self.conv2_stride > 1 + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + self.with_modulated_dcn = False + self.conv2 = SplitAttentionConv2d( + width, + width, + kernel_size=3, + stride=1 if self.avg_down_stride else self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + radix=radix, + reduction_factor=reduction_factor, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=self.dcn) + delattr(self, self.norm2_name) + + if self.avg_down_stride: + self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1) + + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + def forward(self, x): + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + + if self.avg_down_stride: + out = self.avd_layer(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +@BACKBONES.register_module() +class ResNeSt(ResNetV1d): + """ResNeSt backbone. + + Args: + groups (int): Number of groups of Bottleneck. Default: 1 + base_width (int): Base width of Bottleneck. Default: 4 + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels in + SplitAttentionConv2d. Default: 4. + avg_down_stride (bool): Whether to use average pool for stride in + Bottleneck. Default: True. + kwargs (dict): Keyword arguments for ResNet. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)), + 200: (Bottleneck, (3, 24, 36, 3)) + } + + def __init__(self, + groups=1, + base_width=4, + radix=2, + reduction_factor=4, + avg_down_stride=True, + **kwargs): + self.groups = groups + self.base_width = base_width + self.radix = radix + self.reduction_factor = reduction_factor + self.avg_down_stride = avg_down_stride + super(ResNeSt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``.""" + return ResLayer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + radix=self.radix, + reduction_factor=self.reduction_factor, + avg_down_stride=self.avg_down_stride, + **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnet.py new file mode 100644 index 00000000..9585254c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnet.py @@ -0,0 +1,688 @@ +import torch.nn as nn +import torch.utils.checkpoint as cp +from custom_mmpkg.custom_mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer, + constant_init, kaiming_init) +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import _BatchNorm + +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from ..builder import BACKBONES +from ..utils import ResLayer + + +class BasicBlock(nn.Module): + """Basic block for ResNet.""" + + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None): + super(BasicBlock, self).__init__() + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + + self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) + self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) + + self.conv1 = build_conv_layer( + conv_cfg, + inplanes, + planes, + 3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=False) + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + conv_cfg, planes, planes, 3, padding=1, bias=False) + self.add_module(self.norm2_name, norm2) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + self.with_cp = with_cp + + @property + def norm1(self): + """nn.Module: normalization layer after the first convolution layer""" + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: normalization layer after the second convolution layer""" + return getattr(self, self.norm2_name) + + def forward(self, x): + """Forward function.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.norm2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block for ResNet. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is + "caffe", the stride-two layer is the first 1x1 conv layer. + """ + + expansion = 4 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None): + super(Bottleneck, self).__init__() + assert style in ['pytorch', 'caffe'] + assert dcn is None or isinstance(dcn, dict) + assert plugins is None or isinstance(plugins, list) + if plugins is not None: + allowed_position = ['after_conv1', 'after_conv2', 'after_conv3'] + assert all(p['position'] in allowed_position for p in plugins) + + self.inplanes = inplanes + self.planes = planes + self.stride = stride + self.dilation = dilation + self.style = style + self.with_cp = with_cp + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.dcn = dcn + self.with_dcn = dcn is not None + self.plugins = plugins + self.with_plugins = plugins is not None + + if self.with_plugins: + # collect plugins for conv1/conv2/conv3 + self.after_conv1_plugins = [ + plugin['cfg'] for plugin in plugins + if plugin['position'] == 'after_conv1' + ] + self.after_conv2_plugins = [ + plugin['cfg'] for plugin in plugins + if plugin['position'] == 'after_conv2' + ] + self.after_conv3_plugins = [ + plugin['cfg'] for plugin in plugins + if plugin['position'] == 'after_conv3' + ] + + if self.style == 'pytorch': + self.conv1_stride = 1 + self.conv2_stride = stride + else: + self.conv1_stride = stride + self.conv2_stride = 1 + + self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) + self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + norm_cfg, planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + conv_cfg, + inplanes, + planes, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + conv_cfg, + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=dilation, + dilation=dilation, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + dcn, + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=dilation, + dilation=dilation, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + conv_cfg, + planes, + planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + + if self.with_plugins: + self.after_conv1_plugin_names = self.make_block_plugins( + planes, self.after_conv1_plugins) + self.after_conv2_plugin_names = self.make_block_plugins( + planes, self.after_conv2_plugins) + self.after_conv3_plugin_names = self.make_block_plugins( + planes * self.expansion, self.after_conv3_plugins) + + def make_block_plugins(self, in_channels, plugins): + """make plugins for block. + + Args: + in_channels (int): Input channels of plugin. + plugins (list[dict]): List of plugins cfg to build. + + Returns: + list[str]: List of the names of plugin. + """ + assert isinstance(plugins, list) + plugin_names = [] + for plugin in plugins: + plugin = plugin.copy() + name, layer = build_plugin_layer( + plugin, + in_channels=in_channels, + postfix=plugin.pop('postfix', '')) + assert not hasattr(self, name), f'duplicate plugin {name}' + self.add_module(name, layer) + plugin_names.append(name) + return plugin_names + + def forward_plugin(self, x, plugin_names): + """Forward function for plugins.""" + out = x + for name in plugin_names: + out = getattr(self, name)(x) + return out + + @property + def norm1(self): + """nn.Module: normalization layer after the first convolution layer""" + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: normalization layer after the second convolution layer""" + return getattr(self, self.norm2_name) + + @property + def norm3(self): + """nn.Module: normalization layer after the third convolution layer""" + return getattr(self, self.norm3_name) + + def forward(self, x): + """Forward function.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + out = self.norm2(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +@BACKBONES.register_module() +class ResNet(nn.Module): + """ResNet backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. + in_channels (int): Number of input image channels. Default" 3. + stem_channels (int): Number of stem channels. Default: 64. + base_channels (int): Number of base channels of res layer. Default: 64. + num_stages (int): Resnet stages, normally 4. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottleneck. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + norm_cfg (dict): Dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + plugins (list[dict]): List of plugins for stages, each dict contains: + + - cfg (dict, required): Cfg dict to build plugin. + + - position (str, required): Position inside block to insert plugin, + options: 'after_conv1', 'after_conv2', 'after_conv3'. + + - stages (tuple[bool], optional): Stages to apply plugin, length + should be same as 'num_stages' + multi_grid (Sequence[int]|None): Multi grid dilation rates of last + stage. Default: None + contract_dilation (bool): Whether contract first dilation of each layer + Default: False + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): Whether to use zero init for last norm layer + in resblocks to let them behave as identity. + + Example: + >>> from custom_mmpkg.custom_mmseg.models import ResNet + >>> import torch + >>> self = ResNet(depth=18) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 64, 8, 8) + (1, 128, 4, 4) + (1, 256, 2, 2) + (1, 512, 1, 1) + """ + + arch_settings = { + 18: (BasicBlock, (2, 2, 2, 2)), + 34: (BasicBlock, (3, 4, 6, 3)), + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, + depth, + in_channels=3, + stem_channels=64, + base_channels=64, + num_stages=4, + strides=(1, 2, 2, 2), + dilations=(1, 1, 1, 1), + out_indices=(0, 1, 2, 3), + style='pytorch', + deep_stem=False, + avg_down=False, + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=False, + dcn=None, + stage_with_dcn=(False, False, False, False), + plugins=None, + multi_grid=None, + contract_dilation=False, + with_cp=False, + zero_init_residual=True): + super(ResNet, self).__init__() + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for resnet') + self.depth = depth + self.stem_channels = stem_channels + self.base_channels = base_channels + self.num_stages = num_stages + assert num_stages >= 1 and num_stages <= 4 + self.strides = strides + self.dilations = dilations + assert len(strides) == len(dilations) == num_stages + self.out_indices = out_indices + assert max(out_indices) < num_stages + self.style = style + self.deep_stem = deep_stem + self.avg_down = avg_down + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.with_cp = with_cp + self.norm_eval = norm_eval + self.dcn = dcn + self.stage_with_dcn = stage_with_dcn + if dcn is not None: + assert len(stage_with_dcn) == num_stages + self.plugins = plugins + self.multi_grid = multi_grid + self.contract_dilation = contract_dilation + self.zero_init_residual = zero_init_residual + self.block, stage_blocks = self.arch_settings[depth] + self.stage_blocks = stage_blocks[:num_stages] + self.inplanes = stem_channels + + self._make_stem_layer(in_channels, stem_channels) + + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = strides[i] + dilation = dilations[i] + dcn = self.dcn if self.stage_with_dcn[i] else None + if plugins is not None: + stage_plugins = self.make_stage_plugins(plugins, i) + else: + stage_plugins = None + # multi grid is applied to last layer only + stage_multi_grid = multi_grid if i == len( + self.stage_blocks) - 1 else None + planes = base_channels * 2**i + res_layer = self.make_res_layer( + block=self.block, + inplanes=self.inplanes, + planes=planes, + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + avg_down=self.avg_down, + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + dcn=dcn, + plugins=stage_plugins, + multi_grid=stage_multi_grid, + contract_dilation=contract_dilation) + self.inplanes = planes * self.block.expansion + layer_name = f'layer{i+1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self._freeze_stages() + + self.feat_dim = self.block.expansion * base_channels * 2**( + len(self.stage_blocks) - 1) + + def make_stage_plugins(self, plugins, stage_idx): + """make plugins for ResNet 'stage_idx'th stage . + + Currently we support to insert 'context_block', + 'empirical_attention_block', 'nonlocal_block' into the backbone like + ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of + Bottleneck. + + An example of plugins format could be : + >>> plugins=[ + ... dict(cfg=dict(type='xxx', arg1='xxx'), + ... stages=(False, True, True, True), + ... position='after_conv2'), + ... dict(cfg=dict(type='yyy'), + ... stages=(True, True, True, True), + ... position='after_conv3'), + ... dict(cfg=dict(type='zzz', postfix='1'), + ... stages=(True, True, True, True), + ... position='after_conv3'), + ... dict(cfg=dict(type='zzz', postfix='2'), + ... stages=(True, True, True, True), + ... position='after_conv3') + ... ] + >>> self = ResNet(depth=18) + >>> stage_plugins = self.make_stage_plugins(plugins, 0) + >>> assert len(stage_plugins) == 3 + + Suppose 'stage_idx=0', the structure of blocks in the stage would be: + conv1-> conv2->conv3->yyy->zzz1->zzz2 + Suppose 'stage_idx=1', the structure of blocks in the stage would be: + conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2 + + If stages is missing, the plugin would be applied to all stages. + + Args: + plugins (list[dict]): List of plugins cfg to build. The postfix is + required if multiple same type plugins are inserted. + stage_idx (int): Index of stage to build + + Returns: + list[dict]: Plugins for current stage + """ + stage_plugins = [] + for plugin in plugins: + plugin = plugin.copy() + stages = plugin.pop('stages', None) + assert stages is None or len(stages) == self.num_stages + # whether to insert plugin into current stage + if stages is None or stages[stage_idx]: + stage_plugins.append(plugin) + + return stage_plugins + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``.""" + return ResLayer(**kwargs) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + def _make_stem_layer(self, in_channels, stem_channels): + """Make stem layer for ResNet.""" + if self.deep_stem: + self.stem = nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels, + stem_channels // 2, + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, stem_channels // 2)[1], + nn.ReLU(inplace=True), + build_conv_layer( + self.conv_cfg, + stem_channels // 2, + stem_channels // 2, + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, stem_channels // 2)[1], + nn.ReLU(inplace=True), + build_conv_layer( + self.conv_cfg, + stem_channels // 2, + stem_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, stem_channels)[1], + nn.ReLU(inplace=True)) + else: + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + stem_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False) + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, stem_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + def _freeze_stages(self): + """Freeze stages param and norm stats.""" + if self.frozen_stages >= 0: + if self.deep_stem: + self.stem.eval() + for param in self.stem.parameters(): + param.requires_grad = False + else: + self.norm1.eval() + for m in [self.conv1, self.norm1]: + for param in m.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = getattr(self, f'layer{i}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + + if self.dcn is not None: + for m in self.modules(): + if isinstance(m, Bottleneck) and hasattr( + m, 'conv2_offset'): + constant_init(m.conv2_offset, 0) + + if self.zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + constant_init(m.norm3, 0) + elif isinstance(m, BasicBlock): + constant_init(m.norm2, 0) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + """Forward function.""" + if self.deep_stem: + x = self.stem(x) + else: + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.maxpool(x) + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + return tuple(outs) + + def train(self, mode=True): + """Convert the model into training mode while keep normalization layer + freezed.""" + super(ResNet, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() + + +@BACKBONES.register_module() +class ResNetV1c(ResNet): + """ResNetV1c variant described in [1]_. + + Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv + in the input stem with three 3x3 convs. + + References: + .. [1] https://arxiv.org/pdf/1812.01187.pdf + """ + + def __init__(self, **kwargs): + super(ResNetV1c, self).__init__( + deep_stem=True, avg_down=False, **kwargs) + + +@BACKBONES.register_module() +class ResNetV1d(ResNet): + """ResNetV1d variant described in [1]_. + + Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in + the input stem with three 3x3 convs. And in the downsampling block, a 2x2 + avg_pool with stride 2 is added before conv, whose stride is changed to 1. + """ + + def __init__(self, **kwargs): + super(ResNetV1d, self).__init__( + deep_stem=True, avg_down=True, **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnext.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnext.py new file mode 100644 index 00000000..d6a29100 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/resnext.py @@ -0,0 +1,145 @@ +import math + +from custom_mmpkg.custom_mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottleneck(_Bottleneck): + """Bottleneck block for ResNeXt. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is + "caffe", the stride-two layer is the first 1x1 conv layer. + """ + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + **kwargs): + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm2_name, norm2 = build_norm_layer( + self.norm_cfg, width, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + self.with_modulated_dcn = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + +@BACKBONES.register_module() +class ResNeXt(ResNet): + """ResNeXt backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. + in_channels (int): Number of input image channels. Normally 3. + num_stages (int): Resnet stages, normally 4. + groups (int): Group of resnext. + base_width (int): Base width of resnext. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + + Example: + >>> from custom_mmpkg.custom_mmseg.models import ResNeXt + >>> import torch + >>> self = ResNeXt(depth=50) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 256, 8, 8) + (1, 512, 4, 4) + (1, 1024, 2, 2) + (1, 2048, 1, 1) + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, groups=1, base_width=4, **kwargs): + self.groups = groups + self.base_width = base_width + super(ResNeXt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``""" + return ResLayer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/unet.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/unet.py new file mode 100644 index 00000000..69427211 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/unet.py @@ -0,0 +1,429 @@ +import torch.nn as nn +import torch.utils.checkpoint as cp +from custom_mmpkg.custom_mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer, + build_norm_layer, constant_init, kaiming_init) +from custom_mmpkg.custom_mmcv.runner import load_checkpoint +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import _BatchNorm + +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from ..builder import BACKBONES +from ..utils import UpConvBlock + + +class BasicConvBlock(nn.Module): + """Basic convolutional block for UNet. + + This module consists of several plain convolutional layers. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + num_convs (int): Number of convolutional layers. Default: 2. + stride (int): Whether use stride convolution to downsample + the input feature map. If stride=2, it only uses stride convolution + in the first convolutional layer to downsample the input feature + map. Options are 1 or 2. Default: 1. + dilation (int): Whether use dilated convolution to expand the + receptive field. Set dilation rate of each convolutional layer and + the dilation rate of the first convolutional layer is always 1. + Default: 1. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + conv_cfg (dict | None): Config dict for convolution layer. + Default: None. + norm_cfg (dict | None): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict | None): Config dict for activation layer in ConvModule. + Default: dict(type='ReLU'). + dcn (bool): Use deformable convolution in convolutional layer or not. + Default: None. + plugins (dict): plugins for convolutional layers. Default: None. + """ + + def __init__(self, + in_channels, + out_channels, + num_convs=2, + stride=1, + dilation=1, + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + dcn=None, + plugins=None): + super(BasicConvBlock, self).__init__() + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + + self.with_cp = with_cp + convs = [] + for i in range(num_convs): + convs.append( + ConvModule( + in_channels=in_channels if i == 0 else out_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride if i == 0 else 1, + dilation=1 if i == 0 else dilation, + padding=1 if i == 0 else dilation, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + + self.convs = nn.Sequential(*convs) + + def forward(self, x): + """Forward function.""" + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(self.convs, x) + else: + out = self.convs(x) + return out + + +@UPSAMPLE_LAYERS.register_module() +class DeconvModule(nn.Module): + """Deconvolution upsample module in decoder for UNet (2X upsample). + + This module uses deconvolution to upsample feature map in the decoder + of UNet. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + norm_cfg (dict | None): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict | None): Config dict for activation layer in ConvModule. + Default: dict(type='ReLU'). + kernel_size (int): Kernel size of the convolutional layer. Default: 4. + """ + + def __init__(self, + in_channels, + out_channels, + with_cp=False, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + *, + kernel_size=4, + scale_factor=2): + super(DeconvModule, self).__init__() + + assert (kernel_size - scale_factor >= 0) and\ + (kernel_size - scale_factor) % 2 == 0,\ + f'kernel_size should be greater than or equal to scale_factor '\ + f'and (kernel_size - scale_factor) should be even numbers, '\ + f'while the kernel size is {kernel_size} and scale_factor is '\ + f'{scale_factor}.' + + stride = scale_factor + padding = (kernel_size - scale_factor) // 2 + self.with_cp = with_cp + deconv = nn.ConvTranspose2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding) + + norm_name, norm = build_norm_layer(norm_cfg, out_channels) + activate = build_activation_layer(act_cfg) + self.deconv_upsamping = nn.Sequential(deconv, norm, activate) + + def forward(self, x): + """Forward function.""" + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(self.deconv_upsamping, x) + else: + out = self.deconv_upsamping(x) + return out + + +@UPSAMPLE_LAYERS.register_module() +class InterpConv(nn.Module): + """Interpolation upsample module in decoder for UNet. + + This module uses interpolation to upsample feature map in the decoder + of UNet. It consists of one interpolation upsample layer and one + convolutional layer. It can be one interpolation upsample layer followed + by one convolutional layer (conv_first=False) or one convolutional layer + followed by one interpolation upsample layer (conv_first=True). + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + norm_cfg (dict | None): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict | None): Config dict for activation layer in ConvModule. + Default: dict(type='ReLU'). + conv_cfg (dict | None): Config dict for convolution layer. + Default: None. + conv_first (bool): Whether convolutional layer or interpolation + upsample layer first. Default: False. It means interpolation + upsample layer followed by one convolutional layer. + kernel_size (int): Kernel size of the convolutional layer. Default: 1. + stride (int): Stride of the convolutional layer. Default: 1. + padding (int): Padding of the convolutional layer. Default: 1. + upsample_cfg (dict): Interpolation config of the upsample layer. + Default: dict( + scale_factor=2, mode='bilinear', align_corners=False). + """ + + def __init__(self, + in_channels, + out_channels, + with_cp=False, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + *, + conv_cfg=None, + conv_first=False, + kernel_size=1, + stride=1, + padding=0, + upsample_cfg=dict( + scale_factor=2, mode='bilinear', align_corners=False)): + super(InterpConv, self).__init__() + + self.with_cp = with_cp + conv = ConvModule( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + upsample = nn.Upsample(**upsample_cfg) + if conv_first: + self.interp_upsample = nn.Sequential(conv, upsample) + else: + self.interp_upsample = nn.Sequential(upsample, conv) + + def forward(self, x): + """Forward function.""" + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(self.interp_upsample, x) + else: + out = self.interp_upsample(x) + return out + + +@BACKBONES.register_module() +class UNet(nn.Module): + """UNet backbone. + U-Net: Convolutional Networks for Biomedical Image Segmentation. + https://arxiv.org/pdf/1505.04597.pdf + + Args: + in_channels (int): Number of input image channels. Default" 3. + base_channels (int): Number of base channels of each stage. + The output channels of the first stage. Default: 64. + num_stages (int): Number of stages in encoder, normally 5. Default: 5. + strides (Sequence[int 1 | 2]): Strides of each stage in encoder. + len(strides) is equal to num_stages. Normally the stride of the + first stage in encoder is 1. If strides[i]=2, it uses stride + convolution to downsample in the correspondence encoder stage. + Default: (1, 1, 1, 1, 1). + enc_num_convs (Sequence[int]): Number of convolutional layers in the + convolution block of the correspondence encoder stage. + Default: (2, 2, 2, 2, 2). + dec_num_convs (Sequence[int]): Number of convolutional layers in the + convolution block of the correspondence decoder stage. + Default: (2, 2, 2, 2). + downsamples (Sequence[int]): Whether use MaxPool to downsample the + feature map after the first stage of encoder + (stages: [1, num_stages)). If the correspondence encoder stage use + stride convolution (strides[i]=2), it will never use MaxPool to + downsample, even downsamples[i-1]=True. + Default: (True, True, True, True). + enc_dilations (Sequence[int]): Dilation rate of each stage in encoder. + Default: (1, 1, 1, 1, 1). + dec_dilations (Sequence[int]): Dilation rate of each stage in decoder. + Default: (1, 1, 1, 1). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + conv_cfg (dict | None): Config dict for convolution layer. + Default: None. + norm_cfg (dict | None): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict | None): Config dict for activation layer in ConvModule. + Default: dict(type='ReLU'). + upsample_cfg (dict): The upsample config of the upsample module in + decoder. Default: dict(type='InterpConv'). + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + dcn (bool): Use deformable convolution in convolutional layer or not. + Default: None. + plugins (dict): plugins for convolutional layers. Default: None. + + Notice: + The input image size should be divisible by the whole downsample rate + of the encoder. More detail of the whole downsample rate can be found + in UNet._check_input_divisible. + + """ + + def __init__(self, + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False, + dcn=None, + plugins=None): + super(UNet, self).__init__() + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + assert len(strides) == num_stages, \ + 'The length of strides should be equal to num_stages, '\ + f'while the strides is {strides}, the length of '\ + f'strides is {len(strides)}, and the num_stages is '\ + f'{num_stages}.' + assert len(enc_num_convs) == num_stages, \ + 'The length of enc_num_convs should be equal to num_stages, '\ + f'while the enc_num_convs is {enc_num_convs}, the length of '\ + f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '\ + f'{num_stages}.' + assert len(dec_num_convs) == (num_stages-1), \ + 'The length of dec_num_convs should be equal to (num_stages-1), '\ + f'while the dec_num_convs is {dec_num_convs}, the length of '\ + f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '\ + f'{num_stages}.' + assert len(downsamples) == (num_stages-1), \ + 'The length of downsamples should be equal to (num_stages-1), '\ + f'while the downsamples is {downsamples}, the length of '\ + f'downsamples is {len(downsamples)}, and the num_stages is '\ + f'{num_stages}.' + assert len(enc_dilations) == num_stages, \ + 'The length of enc_dilations should be equal to num_stages, '\ + f'while the enc_dilations is {enc_dilations}, the length of '\ + f'enc_dilations is {len(enc_dilations)}, and the num_stages is '\ + f'{num_stages}.' + assert len(dec_dilations) == (num_stages-1), \ + 'The length of dec_dilations should be equal to (num_stages-1), '\ + f'while the dec_dilations is {dec_dilations}, the length of '\ + f'dec_dilations is {len(dec_dilations)}, and the num_stages is '\ + f'{num_stages}.' + self.num_stages = num_stages + self.strides = strides + self.downsamples = downsamples + self.norm_eval = norm_eval + self.base_channels = base_channels + + self.encoder = nn.ModuleList() + self.decoder = nn.ModuleList() + + for i in range(num_stages): + enc_conv_block = [] + if i != 0: + if strides[i] == 1 and downsamples[i - 1]: + enc_conv_block.append(nn.MaxPool2d(kernel_size=2)) + upsample = (strides[i] != 1 or downsamples[i - 1]) + self.decoder.append( + UpConvBlock( + conv_block=BasicConvBlock, + in_channels=base_channels * 2**i, + skip_channels=base_channels * 2**(i - 1), + out_channels=base_channels * 2**(i - 1), + num_convs=dec_num_convs[i - 1], + stride=1, + dilation=dec_dilations[i - 1], + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + upsample_cfg=upsample_cfg if upsample else None, + dcn=None, + plugins=None)) + + enc_conv_block.append( + BasicConvBlock( + in_channels=in_channels, + out_channels=base_channels * 2**i, + num_convs=enc_num_convs[i], + stride=strides[i], + dilation=enc_dilations[i], + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + dcn=None, + plugins=None)) + self.encoder.append((nn.Sequential(*enc_conv_block))) + in_channels = base_channels * 2**i + + def forward(self, x): + self._check_input_divisible(x) + enc_outs = [] + for enc in self.encoder: + x = enc(x) + enc_outs.append(x) + dec_outs = [x] + for i in reversed(range(len(self.decoder))): + x = self.decoder[i](enc_outs[i], x) + dec_outs.append(x) + + return dec_outs + + def train(self, mode=True): + """Convert the model into training mode while keep normalization layer + freezed.""" + super(UNet, self).train(mode) + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() + + def _check_input_divisible(self, x): + h, w = x.shape[-2:] + whole_downsample_rate = 1 + for i in range(1, self.num_stages): + if self.strides[i] == 2 or self.downsamples[i - 1]: + whole_downsample_rate *= 2 + assert (h % whole_downsample_rate == 0) \ + and (w % whole_downsample_rate == 0),\ + f'The input image size {(h, w)} should be divisible by the whole '\ + f'downsample rate {whole_downsample_rate}, when num_stages is '\ + f'{self.num_stages}, strides is {self.strides}, and downsamples '\ + f'is {self.downsamples}.' + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, strict=False, logger=logger) + elif pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + else: + raise TypeError('pretrained must be a str or None') diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/vit.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/vit.py new file mode 100644 index 00000000..93bae384 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/backbones/vit.py @@ -0,0 +1,459 @@ +"""Modified from https://github.com/rwightman/pytorch-image- +models/blob/master/timm/models/vision_transformer.py.""" + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from custom_mmpkg.custom_mmcv.cnn import (Conv2d, Linear, build_activation_layer, build_norm_layer, + constant_init, kaiming_init, normal_init) +from custom_mmpkg.custom_mmcv.runner import _load_checkpoint +from custom_mmpkg.custom_mmcv.utils.parrots_wrapper import _BatchNorm + +from custom_mmpkg.custom_mmseg.utils import get_root_logger +from ..builder import BACKBONES +from ..utils import DropPath, trunc_normal_ + + +class Mlp(nn.Module): + """MLP layer for Encoder block. + + Args: + in_features(int): Input dimension for the first fully + connected layer. + hidden_features(int): Output dimension for the first fully + connected layer. + out_features(int): Output dementsion for the second fully + connected layer. + act_cfg(dict): Config dict for activation layer. + Default: dict(type='GELU'). + drop(float): Drop rate for the dropout layer. Dropout rate has + to be between 0 and 1. Default: 0. + """ + + def __init__(self, + in_features, + hidden_features=None, + out_features=None, + act_cfg=dict(type='GELU'), + drop=0.): + super(Mlp, self).__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = Linear(in_features, hidden_features) + self.act = build_activation_layer(act_cfg) + self.fc2 = Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + """Attention layer for Encoder block. + + Args: + dim (int): Dimension for the input vector. + num_heads (int): Number of parallel attention heads. + qkv_bias (bool): Enable bias for qkv if True. Default: False. + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + attn_drop (float): Drop rate for attention output weights. + Default: 0. + proj_drop (float): Drop rate for output weights. Default: 0. + """ + + def __init__(self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0.): + super(Attention, self).__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + b, n, c = x.shape + qkv = self.qkv(x).reshape(b, n, 3, self.num_heads, + c // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(b, n, c) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + """Implements encoder block with residual connection. + + Args: + dim (int): The feature dimension. + num_heads (int): Number of parallel attention heads. + mlp_ratio (int): Ratio of mlp hidden dim to embedding dim. + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop (float): Drop rate for mlp output weights. Default: 0. + attn_drop (float): Drop rate for attention output weights. + Default: 0. + proj_drop (float): Drop rate for attn layer output weights. + Default: 0. + drop_path (float): Drop rate for paths of model. + Default: 0. + act_cfg (dict): Config dict for activation layer. + Default: dict(type='GELU'). + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='LN', requires_grad=True). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + """ + + def __init__(self, + dim, + num_heads, + mlp_ratio=4, + qkv_bias=False, + qk_scale=None, + drop=0., + attn_drop=0., + proj_drop=0., + drop_path=0., + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='LN', eps=1e-6), + with_cp=False): + super(Block, self).__init__() + self.with_cp = with_cp + _, self.norm1 = build_norm_layer(norm_cfg, dim) + self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, + proj_drop) + self.drop_path = DropPath( + drop_path) if drop_path > 0. else nn.Identity() + _, self.norm2 = build_norm_layer(norm_cfg, dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_cfg=act_cfg, + drop=drop) + + def forward(self, x): + + def _inner_forward(x): + out = x + self.drop_path(self.attn(self.norm1(x))) + out = out + self.drop_path(self.mlp(self.norm2(out))) + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + return out + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding. + + Args: + img_size (int | tuple): Input image size. + default: 224. + patch_size (int): Width and height for a patch. + default: 16. + in_channels (int): Input channels for images. Default: 3. + embed_dim (int): The embedding dimension. Default: 768. + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_channels=3, + embed_dim=768): + super(PatchEmbed, self).__init__() + if isinstance(img_size, int): + self.img_size = (img_size, img_size) + elif isinstance(img_size, tuple): + self.img_size = img_size + else: + raise TypeError('img_size must be type of int or tuple') + h, w = self.img_size + self.patch_size = (patch_size, patch_size) + self.num_patches = (h // patch_size) * (w // patch_size) + self.proj = Conv2d( + in_channels, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + return self.proj(x).flatten(2).transpose(1, 2) + + +@BACKBONES.register_module() +class VisionTransformer(nn.Module): + """Vision transformer backbone. + + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for + Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 + + Args: + img_size (tuple): input image size. Default: (224, 224). + patch_size (int, tuple): patch size. Default: 16. + in_channels (int): number of input channels. Default: 3. + embed_dim (int): embedding dimension. Default: 768. + depth (int): depth of transformer. Default: 12. + num_heads (int): number of attention heads. Default: 12. + mlp_ratio (int): ratio of mlp hidden dim to embedding dim. + Default: 4. + out_indices (list | tuple | int): Output from which stages. + Default: -1. + qkv_bias (bool): enable bias for qkv if True. Default: True. + qk_scale (float): override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): dropout rate. Default: 0. + attn_drop_rate (float): attention dropout rate. Default: 0. + drop_path_rate (float): Rate of DropPath. Default: 0. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='LN', eps=1e-6, requires_grad=True). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='GELU'). + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + final_norm (bool): Whether to add a additional layer to normalize + final feature map. Default: False. + interpolate_mode (str): Select the interpolate mode for position + embeding vector resize. Default: bicubic. + with_cls_token (bool): If concatenating class token into image tokens + as transformer input. Default: True. + with_cp (bool): Use checkpoint or not. Using checkpoint + will save some memory while slowing down the training speed. + Default: False. + """ + + def __init__(self, + img_size=(224, 224), + patch_size=16, + in_channels=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + out_indices=11, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_cfg=dict(type='LN', eps=1e-6, requires_grad=True), + act_cfg=dict(type='GELU'), + norm_eval=False, + final_norm=False, + with_cls_token=True, + interpolate_mode='bicubic', + with_cp=False): + super(VisionTransformer, self).__init__() + self.img_size = img_size + self.patch_size = patch_size + self.features = self.embed_dim = embed_dim + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=embed_dim) + + self.with_cls_token = with_cls_token + self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) + self.pos_embed = nn.Parameter( + torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + if isinstance(out_indices, int): + self.out_indices = [out_indices] + elif isinstance(out_indices, list) or isinstance(out_indices, tuple): + self.out_indices = out_indices + else: + raise TypeError('out_indices must be type of int, list or tuple') + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=dpr[i], + attn_drop=attn_drop_rate, + act_cfg=act_cfg, + norm_cfg=norm_cfg, + with_cp=with_cp) for i in range(depth) + ]) + + self.interpolate_mode = interpolate_mode + self.final_norm = final_norm + if final_norm: + _, self.norm = build_norm_layer(norm_cfg, embed_dim) + + self.norm_eval = norm_eval + self.with_cp = with_cp + + def init_weights(self, pretrained=None): + if isinstance(pretrained, str): + logger = get_root_logger() + checkpoint = _load_checkpoint(pretrained, logger=logger) + if 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + else: + state_dict = checkpoint + + if 'pos_embed' in state_dict.keys(): + if self.pos_embed.shape != state_dict['pos_embed'].shape: + logger.info(msg=f'Resize the pos_embed shape from \ +{state_dict["pos_embed"].shape} to {self.pos_embed.shape}') + h, w = self.img_size + pos_size = int( + math.sqrt(state_dict['pos_embed'].shape[1] - 1)) + state_dict['pos_embed'] = self.resize_pos_embed( + state_dict['pos_embed'], (h, w), (pos_size, pos_size), + self.patch_size, self.interpolate_mode) + + self.load_state_dict(state_dict, False) + + elif pretrained is None: + # We only implement the 'jax_impl' initialization implemented at + # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501 + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + for n, m in self.named_modules(): + if isinstance(m, Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + if 'mlp' in n: + normal_init(m.bias, std=1e-6) + else: + constant_init(m.bias, 0) + elif isinstance(m, Conv2d): + kaiming_init(m.weight, mode='fan_in') + if m.bias is not None: + constant_init(m.bias, 0) + elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)): + constant_init(m.bias, 0) + constant_init(m.weight, 1.0) + else: + raise TypeError('pretrained must be a str or None') + + def _pos_embeding(self, img, patched_img, pos_embed): + """Positiong embeding method. + + Resize the pos_embed, if the input image size doesn't match + the training size. + Args: + img (torch.Tensor): The inference image tensor, the shape + must be [B, C, H, W]. + patched_img (torch.Tensor): The patched image, it should be + shape of [B, L1, C]. + pos_embed (torch.Tensor): The pos_embed weighs, it should be + shape of [B, L2, c]. + Return: + torch.Tensor: The pos encoded image feature. + """ + assert patched_img.ndim == 3 and pos_embed.ndim == 3, \ + 'the shapes of patched_img and pos_embed must be [B, L, C]' + x_len, pos_len = patched_img.shape[1], pos_embed.shape[1] + if x_len != pos_len: + if pos_len == (self.img_size[0] // self.patch_size) * ( + self.img_size[1] // self.patch_size) + 1: + pos_h = self.img_size[0] // self.patch_size + pos_w = self.img_size[1] // self.patch_size + else: + raise ValueError( + 'Unexpected shape of pos_embed, got {}.'.format( + pos_embed.shape)) + pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:], + (pos_h, pos_w), self.patch_size, + self.interpolate_mode) + return self.pos_drop(patched_img + pos_embed) + + @staticmethod + def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size, mode): + """Resize pos_embed weights. + + Resize pos_embed using bicubic interpolate method. + Args: + pos_embed (torch.Tensor): pos_embed weights. + input_shpae (tuple): Tuple for (input_h, intput_w). + pos_shape (tuple): Tuple for (pos_h, pos_w). + patch_size (int): Patch size. + Return: + torch.Tensor: The resized pos_embed of shape [B, L_new, C] + """ + assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]' + input_h, input_w = input_shpae + pos_h, pos_w = pos_shape + cls_token_weight = pos_embed[:, 0] + pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):] + pos_embed_weight = pos_embed_weight.reshape( + 1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2) + pos_embed_weight = F.interpolate( + pos_embed_weight, + size=[input_h // patch_size, input_w // patch_size], + align_corners=False, + mode=mode) + cls_token_weight = cls_token_weight.unsqueeze(1) + pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2) + pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1) + return pos_embed + + def forward(self, inputs): + B = inputs.shape[0] + + x = self.patch_embed(inputs) + + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + x = self._pos_embeding(inputs, x, self.pos_embed) + + if not self.with_cls_token: + # Remove class token for transformer input + x = x[:, 1:] + + outs = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if i == len(self.blocks) - 1: + if self.final_norm: + x = self.norm(x) + if i in self.out_indices: + if self.with_cls_token: + # Remove class token and reshape token for decoder head + out = x[:, 1:] + else: + out = x + B, _, C = out.shape + out = out.reshape(B, inputs.shape[2] // self.patch_size, + inputs.shape[3] // self.patch_size, + C).permute(0, 3, 1, 2) + outs.append(out) + + return tuple(outs) + + def train(self, mode=True): + super(VisionTransformer, self).train(mode) + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, nn.LayerNorm): + m.eval() diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/builder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/builder.py new file mode 100644 index 00000000..e6e5920e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/builder.py @@ -0,0 +1,46 @@ +import warnings + +from custom_mmpkg.custom_mmcv.cnn import MODELS as MMCV_MODELS +from custom_mmpkg.custom_mmcv.utils import Registry + +MODELS = Registry('models', parent=MMCV_MODELS) + +BACKBONES = MODELS +NECKS = MODELS +HEADS = MODELS +LOSSES = MODELS +SEGMENTORS = MODELS + + +def build_backbone(cfg): + """Build backbone.""" + return BACKBONES.build(cfg) + + +def build_neck(cfg): + """Build neck.""" + return NECKS.build(cfg) + + +def build_head(cfg): + """Build head.""" + return HEADS.build(cfg) + + +def build_loss(cfg): + """Build loss.""" + return LOSSES.build(cfg) + + +def build_segmentor(cfg, train_cfg=None, test_cfg=None): + """Build segmentor.""" + if train_cfg is not None or test_cfg is not None: + warnings.warn( + 'train_cfg and test_cfg is deprecated, ' + 'please specify them in model', UserWarning) + assert cfg.get('train_cfg') is None or train_cfg is None, \ + 'train_cfg specified in both outer field and model field ' + assert cfg.get('test_cfg') is None or test_cfg is None, \ + 'test_cfg specified in both outer field and model field ' + return SEGMENTORS.build( + cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/__init__.py new file mode 100644 index 00000000..ac66d3cf --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/__init__.py @@ -0,0 +1,28 @@ +from .ann_head import ANNHead +from .apc_head import APCHead +from .aspp_head import ASPPHead +from .cc_head import CCHead +from .da_head import DAHead +from .dm_head import DMHead +from .dnl_head import DNLHead +from .ema_head import EMAHead +from .enc_head import EncHead +from .fcn_head import FCNHead +from .fpn_head import FPNHead +from .gc_head import GCHead +from .lraspp_head import LRASPPHead +from .nl_head import NLHead +from .ocr_head import OCRHead +# from .point_head import PointHead +from .psa_head import PSAHead +from .psp_head import PSPHead +from .sep_aspp_head import DepthwiseSeparableASPPHead +from .sep_fcn_head import DepthwiseSeparableFCNHead +from .uper_head import UPerHead + +__all__ = [ + 'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead', + 'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead', + 'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead', + 'APCHead', 'DMHead', 'LRASPPHead' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ann_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ann_head.py new file mode 100644 index 00000000..bebbc4f1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ann_head.py @@ -0,0 +1,245 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from ..builder import HEADS +from ..utils import SelfAttentionBlock as _SelfAttentionBlock +from .decode_head import BaseDecodeHead + + +class PPMConcat(nn.ModuleList): + """Pyramid Pooling Module that only concat the features of each layer. + + Args: + pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module. + """ + + def __init__(self, pool_scales=(1, 3, 6, 8)): + super(PPMConcat, self).__init__( + [nn.AdaptiveAvgPool2d(pool_scale) for pool_scale in pool_scales]) + + def forward(self, feats): + """Forward function.""" + ppm_outs = [] + for ppm in self: + ppm_out = ppm(feats) + ppm_outs.append(ppm_out.view(*feats.shape[:2], -1)) + concat_outs = torch.cat(ppm_outs, dim=2) + return concat_outs + + +class SelfAttentionBlock(_SelfAttentionBlock): + """Make a ANN used SelfAttentionBlock. + + Args: + low_in_channels (int): Input channels of lower level feature, + which is the key feature for self-attention. + high_in_channels (int): Input channels of higher level feature, + which is the query feature for self-attention. + channels (int): Output channels of key/query transform. + out_channels (int): Output channels. + share_key_query (bool): Whether share projection weight between key + and query projection. + query_scale (int): The scale of query feature map. + key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module of key feature. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict|None): Config of activation layers. + """ + + def __init__(self, low_in_channels, high_in_channels, channels, + out_channels, share_key_query, query_scale, key_pool_scales, + conv_cfg, norm_cfg, act_cfg): + key_psp = PPMConcat(key_pool_scales) + if query_scale > 1: + query_downsample = nn.MaxPool2d(kernel_size=query_scale) + else: + query_downsample = None + super(SelfAttentionBlock, self).__init__( + key_in_channels=low_in_channels, + query_in_channels=high_in_channels, + channels=channels, + out_channels=out_channels, + share_key_query=share_key_query, + query_downsample=query_downsample, + key_downsample=key_psp, + key_query_num_convs=1, + key_query_norm=True, + value_out_num_convs=1, + value_out_norm=False, + matmul_norm=True, + with_out=True, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + +class AFNB(nn.Module): + """Asymmetric Fusion Non-local Block(AFNB) + + Args: + low_in_channels (int): Input channels of lower level feature, + which is the key feature for self-attention. + high_in_channels (int): Input channels of higher level feature, + which is the query feature for self-attention. + channels (int): Output channels of key/query transform. + out_channels (int): Output channels. + and query projection. + query_scales (tuple[int]): The scales of query feature map. + Default: (1,) + key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module of key feature. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict|None): Config of activation layers. + """ + + def __init__(self, low_in_channels, high_in_channels, channels, + out_channels, query_scales, key_pool_scales, conv_cfg, + norm_cfg, act_cfg): + super(AFNB, self).__init__() + self.stages = nn.ModuleList() + for query_scale in query_scales: + self.stages.append( + SelfAttentionBlock( + low_in_channels=low_in_channels, + high_in_channels=high_in_channels, + channels=channels, + out_channels=out_channels, + share_key_query=False, + query_scale=query_scale, + key_pool_scales=key_pool_scales, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + self.bottleneck = ConvModule( + out_channels + high_in_channels, + out_channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + def forward(self, low_feats, high_feats): + """Forward function.""" + priors = [stage(high_feats, low_feats) for stage in self.stages] + context = torch.stack(priors, dim=0).sum(dim=0) + output = self.bottleneck(torch.cat([context, high_feats], 1)) + return output + + +class APNB(nn.Module): + """Asymmetric Pyramid Non-local Block (APNB) + + Args: + in_channels (int): Input channels of key/query feature, + which is the key feature for self-attention. + channels (int): Output channels of key/query transform. + out_channels (int): Output channels. + query_scales (tuple[int]): The scales of query feature map. + Default: (1,) + key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module of key feature. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict|None): Config of activation layers. + """ + + def __init__(self, in_channels, channels, out_channels, query_scales, + key_pool_scales, conv_cfg, norm_cfg, act_cfg): + super(APNB, self).__init__() + self.stages = nn.ModuleList() + for query_scale in query_scales: + self.stages.append( + SelfAttentionBlock( + low_in_channels=in_channels, + high_in_channels=in_channels, + channels=channels, + out_channels=out_channels, + share_key_query=True, + query_scale=query_scale, + key_pool_scales=key_pool_scales, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + self.bottleneck = ConvModule( + 2 * in_channels, + out_channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + def forward(self, feats): + """Forward function.""" + priors = [stage(feats, feats) for stage in self.stages] + context = torch.stack(priors, dim=0).sum(dim=0) + output = self.bottleneck(torch.cat([context, feats], 1)) + return output + + +@HEADS.register_module() +class ANNHead(BaseDecodeHead): + """Asymmetric Non-local Neural Networks for Semantic Segmentation. + + This head is the implementation of `ANNNet + `_. + + Args: + project_channels (int): Projection channels for Nonlocal. + query_scales (tuple[int]): The scales of query feature map. + Default: (1,) + key_pool_scales (tuple[int]): The pooling scales of key feature map. + Default: (1, 3, 6, 8). + """ + + def __init__(self, + project_channels, + query_scales=(1, ), + key_pool_scales=(1, 3, 6, 8), + **kwargs): + super(ANNHead, self).__init__( + input_transform='multiple_select', **kwargs) + assert len(self.in_channels) == 2 + low_in_channels, high_in_channels = self.in_channels + self.project_channels = project_channels + self.fusion = AFNB( + low_in_channels=low_in_channels, + high_in_channels=high_in_channels, + out_channels=high_in_channels, + channels=project_channels, + query_scales=query_scales, + key_pool_scales=key_pool_scales, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.bottleneck = ConvModule( + high_in_channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.context = APNB( + in_channels=self.channels, + out_channels=self.channels, + channels=project_channels, + query_scales=query_scales, + key_pool_scales=key_pool_scales, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + low_feats, high_feats = self._transform_inputs(inputs) + output = self.fusion(low_feats, high_feats) + output = self.dropout(output) + output = self.bottleneck(output) + output = self.context(output) + output = self.cls_seg(output) + + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/apc_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/apc_head.py new file mode 100644 index 00000000..119c083a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/apc_head.py @@ -0,0 +1,158 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +class ACM(nn.Module): + """Adaptive Context Module used in APCNet. + + Args: + pool_scale (int): Pooling scale used in Adaptive Context + Module to extract region features. + fusion (bool): Add one conv to fuse residual feature. + in_channels (int): Input channels. + channels (int): Channels after modules, before conv_seg. + conv_cfg (dict | None): Config of conv layers. + norm_cfg (dict | None): Config of norm layers. + act_cfg (dict): Config of activation layers. + """ + + def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg, + norm_cfg, act_cfg): + super(ACM, self).__init__() + self.pool_scale = pool_scale + self.fusion = fusion + self.in_channels = in_channels + self.channels = channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.pooled_redu_conv = ConvModule( + self.in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.input_redu_conv = ConvModule( + self.in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.global_info = ConvModule( + self.channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0) + + self.residual_conv = ConvModule( + self.channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + if self.fusion: + self.fusion_conv = ConvModule( + self.channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, x): + """Forward function.""" + pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale) + # [batch_size, channels, h, w] + x = self.input_redu_conv(x) + # [batch_size, channels, pool_scale, pool_scale] + pooled_x = self.pooled_redu_conv(pooled_x) + batch_size = x.size(0) + # [batch_size, pool_scale * pool_scale, channels] + pooled_x = pooled_x.view(batch_size, self.channels, + -1).permute(0, 2, 1).contiguous() + # [batch_size, h * w, pool_scale * pool_scale] + affinity_matrix = self.gla(x + resize( + self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:]) + ).permute(0, 2, 3, 1).reshape( + batch_size, -1, self.pool_scale**2) + affinity_matrix = F.sigmoid(affinity_matrix) + # [batch_size, h * w, channels] + z_out = torch.matmul(affinity_matrix, pooled_x) + # [batch_size, channels, h * w] + z_out = z_out.permute(0, 2, 1).contiguous() + # [batch_size, channels, h, w] + z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3)) + z_out = self.residual_conv(z_out) + z_out = F.relu(z_out + x) + if self.fusion: + z_out = self.fusion_conv(z_out) + + return z_out + + +@HEADS.register_module() +class APCHead(BaseDecodeHead): + """Adaptive Pyramid Context Network for Semantic Segmentation. + + This head is the implementation of + `APCNet `_. + + Args: + pool_scales (tuple[int]): Pooling scales used in Adaptive Context + Module. Default: (1, 2, 3, 6). + fusion (bool): Add one conv to fuse residual feature. + """ + + def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs): + super(APCHead, self).__init__(**kwargs) + assert isinstance(pool_scales, (list, tuple)) + self.pool_scales = pool_scales + self.fusion = fusion + acm_modules = [] + for pool_scale in self.pool_scales: + acm_modules.append( + ACM(pool_scale, + self.fusion, + self.in_channels, + self.channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + self.acm_modules = nn.ModuleList(acm_modules) + self.bottleneck = ConvModule( + self.in_channels + len(pool_scales) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + acm_outs = [x] + for acm_module in self.acm_modules: + acm_outs.append(acm_module(x)) + acm_outs = torch.cat(acm_outs, dim=1) + output = self.bottleneck(acm_outs) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/aspp_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/aspp_head.py new file mode 100644 index 00000000..5b251f26 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/aspp_head.py @@ -0,0 +1,107 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +class ASPPModule(nn.ModuleList): + """Atrous Spatial Pyramid Pooling (ASPP) Module. + + Args: + dilations (tuple[int]): Dilation rate of each layer. + in_channels (int): Input channels. + channels (int): Channels after modules, before conv_seg. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict): Config of activation layers. + """ + + def __init__(self, dilations, in_channels, channels, conv_cfg, norm_cfg, + act_cfg): + super(ASPPModule, self).__init__() + self.dilations = dilations + self.in_channels = in_channels + self.channels = channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + for dilation in dilations: + self.append( + ConvModule( + self.in_channels, + self.channels, + 1 if dilation == 1 else 3, + dilation=dilation, + padding=0 if dilation == 1 else dilation, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + + def forward(self, x): + """Forward function.""" + aspp_outs = [] + for aspp_module in self: + aspp_outs.append(aspp_module(x)) + + return aspp_outs + + +@HEADS.register_module() +class ASPPHead(BaseDecodeHead): + """Rethinking Atrous Convolution for Semantic Image Segmentation. + + This head is the implementation of `DeepLabV3 + `_. + + Args: + dilations (tuple[int]): Dilation rates for ASPP module. + Default: (1, 6, 12, 18). + """ + + def __init__(self, dilations=(1, 6, 12, 18), **kwargs): + super(ASPPHead, self).__init__(**kwargs) + assert isinstance(dilations, (list, tuple)) + self.dilations = dilations + self.image_pool = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + ConvModule( + self.in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + self.aspp_modules = ASPPModule( + dilations, + self.in_channels, + self.channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.bottleneck = ConvModule( + (len(dilations) + 1) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + aspp_outs = [ + resize( + self.image_pool(x), + size=x.size()[2:], + mode='bilinear', + align_corners=self.align_corners) + ] + aspp_outs.extend(self.aspp_modules(x)) + aspp_outs = torch.cat(aspp_outs, dim=1) + output = self.bottleneck(aspp_outs) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/cascade_decode_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/cascade_decode_head.py new file mode 100644 index 00000000..d02122ca --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/cascade_decode_head.py @@ -0,0 +1,57 @@ +from abc import ABCMeta, abstractmethod + +from .decode_head import BaseDecodeHead + + +class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta): + """Base class for cascade decode head used in + :class:`CascadeEncoderDecoder.""" + + def __init__(self, *args, **kwargs): + super(BaseCascadeDecodeHead, self).__init__(*args, **kwargs) + + @abstractmethod + def forward(self, inputs, prev_output): + """Placeholder of forward function.""" + pass + + def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg, + train_cfg): + """Forward function for training. + Args: + inputs (list[Tensor]): List of multi-level img features. + prev_output (Tensor): The output of previous decode head. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + gt_semantic_seg (Tensor): Semantic segmentation masks + used if the architecture supports semantic segmentation task. + train_cfg (dict): The training config. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + seg_logits = self.forward(inputs, prev_output) + losses = self.losses(seg_logits, gt_semantic_seg) + + return losses + + def forward_test(self, inputs, prev_output, img_metas, test_cfg): + """Forward function for testing. + + Args: + inputs (list[Tensor]): List of multi-level img features. + prev_output (Tensor): The output of previous decode head. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + test_cfg (dict): The testing config. + + Returns: + Tensor: Output segmentation map. + """ + return self.forward(inputs, prev_output) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/cc_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/cc_head.py new file mode 100644 index 00000000..a5827184 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/cc_head.py @@ -0,0 +1,42 @@ +import torch + +from ..builder import HEADS +from .fcn_head import FCNHead + +try: + from custom_mmpkg.custom_mmcv.ops import CrissCrossAttention +except ModuleNotFoundError: + CrissCrossAttention = None + + +@HEADS.register_module() +class CCHead(FCNHead): + """CCNet: Criss-Cross Attention for Semantic Segmentation. + + This head is the implementation of `CCNet + `_. + + Args: + recurrence (int): Number of recurrence of Criss Cross Attention + module. Default: 2. + """ + + def __init__(self, recurrence=2, **kwargs): + if CrissCrossAttention is None: + raise RuntimeError('Please install mmcv-full for ' + 'CrissCrossAttention ops') + super(CCHead, self).__init__(num_convs=2, **kwargs) + self.recurrence = recurrence + self.cca = CrissCrossAttention(self.channels) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + output = self.convs[0](x) + for _ in range(self.recurrence): + output = self.cca(output) + output = self.convs[1](output) + if self.concat_input: + output = self.conv_cat(torch.cat([x, output], dim=1)) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/da_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/da_head.py new file mode 100644 index 00000000..ce384a5f --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/da_head.py @@ -0,0 +1,178 @@ +import torch +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule, Scale +from torch import nn + +from custom_mmpkg.custom_mmseg.core import add_prefix +from ..builder import HEADS +from ..utils import SelfAttentionBlock as _SelfAttentionBlock +from .decode_head import BaseDecodeHead + + +class PAM(_SelfAttentionBlock): + """Position Attention Module (PAM) + + Args: + in_channels (int): Input channels of key/query feature. + channels (int): Output channels of key/query transform. + """ + + def __init__(self, in_channels, channels): + super(PAM, self).__init__( + key_in_channels=in_channels, + query_in_channels=in_channels, + channels=channels, + out_channels=in_channels, + share_key_query=False, + query_downsample=None, + key_downsample=None, + key_query_num_convs=1, + key_query_norm=False, + value_out_num_convs=1, + value_out_norm=False, + matmul_norm=False, + with_out=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=None) + + self.gamma = Scale(0) + + def forward(self, x): + """Forward function.""" + out = super(PAM, self).forward(x, x) + + out = self.gamma(out) + x + return out + + +class CAM(nn.Module): + """Channel Attention Module (CAM)""" + + def __init__(self): + super(CAM, self).__init__() + self.gamma = Scale(0) + + def forward(self, x): + """Forward function.""" + batch_size, channels, height, width = x.size() + proj_query = x.view(batch_size, channels, -1) + proj_key = x.view(batch_size, channels, -1).permute(0, 2, 1) + energy = torch.bmm(proj_query, proj_key) + energy_new = torch.max( + energy, -1, keepdim=True)[0].expand_as(energy) - energy + attention = F.softmax(energy_new, dim=-1) + proj_value = x.view(batch_size, channels, -1) + + out = torch.bmm(attention, proj_value) + out = out.view(batch_size, channels, height, width) + + out = self.gamma(out) + x + return out + + +@HEADS.register_module() +class DAHead(BaseDecodeHead): + """Dual Attention Network for Scene Segmentation. + + This head is the implementation of `DANet + `_. + + Args: + pam_channels (int): The channels of Position Attention Module(PAM). + """ + + def __init__(self, pam_channels, **kwargs): + super(DAHead, self).__init__(**kwargs) + self.pam_channels = pam_channels + self.pam_in_conv = ConvModule( + self.in_channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.pam = PAM(self.channels, pam_channels) + self.pam_out_conv = ConvModule( + self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.pam_conv_seg = nn.Conv2d( + self.channels, self.num_classes, kernel_size=1) + + self.cam_in_conv = ConvModule( + self.in_channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.cam = CAM() + self.cam_out_conv = ConvModule( + self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.cam_conv_seg = nn.Conv2d( + self.channels, self.num_classes, kernel_size=1) + + def pam_cls_seg(self, feat): + """PAM feature classification.""" + if self.dropout is not None: + feat = self.dropout(feat) + output = self.pam_conv_seg(feat) + return output + + def cam_cls_seg(self, feat): + """CAM feature classification.""" + if self.dropout is not None: + feat = self.dropout(feat) + output = self.cam_conv_seg(feat) + return output + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + pam_feat = self.pam_in_conv(x) + pam_feat = self.pam(pam_feat) + pam_feat = self.pam_out_conv(pam_feat) + pam_out = self.pam_cls_seg(pam_feat) + + cam_feat = self.cam_in_conv(x) + cam_feat = self.cam(cam_feat) + cam_feat = self.cam_out_conv(cam_feat) + cam_out = self.cam_cls_seg(cam_feat) + + feat_sum = pam_feat + cam_feat + pam_cam_out = self.cls_seg(feat_sum) + + return pam_cam_out, pam_out, cam_out + + def forward_test(self, inputs, img_metas, test_cfg): + """Forward function for testing, only ``pam_cam`` is used.""" + return self.forward(inputs)[0] + + def losses(self, seg_logit, seg_label): + """Compute ``pam_cam``, ``pam``, ``cam`` loss.""" + pam_cam_seg_logit, pam_seg_logit, cam_seg_logit = seg_logit + loss = dict() + loss.update( + add_prefix( + super(DAHead, self).losses(pam_cam_seg_logit, seg_label), + 'pam_cam')) + loss.update( + add_prefix( + super(DAHead, self).losses(pam_seg_logit, seg_label), 'pam')) + loss.update( + add_prefix( + super(DAHead, self).losses(cam_seg_logit, seg_label), 'cam')) + return loss diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/decode_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/decode_head.py new file mode 100644 index 00000000..2ed88037 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/decode_head.py @@ -0,0 +1,234 @@ +from abc import ABCMeta, abstractmethod + +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import normal_init +from custom_mmpkg.custom_mmcv.runner import auto_fp16, force_fp32 + +from custom_mmpkg.custom_mmseg.core import build_pixel_sampler +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import build_loss +from ..losses import accuracy + + +class BaseDecodeHead(nn.Module, metaclass=ABCMeta): + """Base class for BaseDecodeHead. + + Args: + in_channels (int|Sequence[int]): Input channels. + channels (int): Channels after modules, before conv_seg. + num_classes (int): Number of classes. + dropout_ratio (float): Ratio of dropout layer. Default: 0.1. + conv_cfg (dict|None): Config of conv layers. Default: None. + norm_cfg (dict|None): Config of norm layers. Default: None. + act_cfg (dict): Config of activation layers. + Default: dict(type='ReLU') + in_index (int|Sequence[int]): Input feature index. Default: -1 + input_transform (str|None): Transformation type of input features. + Options: 'resize_concat', 'multiple_select', None. + 'resize_concat': Multiple feature maps will be resize to the + same size as first one and than concat together. + Usually used in FCN head of HRNet. + 'multiple_select': Multiple feature maps will be bundle into + a list and passed into decode head. + None: Only one select feature map is allowed. + Default: None. + loss_decode (dict): Config of decode loss. + Default: dict(type='CrossEntropyLoss'). + ignore_index (int | None): The label index to be ignored. When using + masked BCE loss, ignore_index should be set to None. Default: 255 + sampler (dict|None): The config of segmentation map sampler. + Default: None. + align_corners (bool): align_corners argument of F.interpolate. + Default: False. + """ + + def __init__(self, + in_channels, + channels, + *, + num_classes, + dropout_ratio=0.1, + conv_cfg=None, + norm_cfg=None, + act_cfg=dict(type='ReLU'), + in_index=-1, + input_transform=None, + loss_decode=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + ignore_index=255, + sampler=None, + align_corners=False): + super(BaseDecodeHead, self).__init__() + self._init_inputs(in_channels, in_index, input_transform) + self.channels = channels + self.num_classes = num_classes + self.dropout_ratio = dropout_ratio + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.in_index = in_index + self.loss_decode = build_loss(loss_decode) + self.ignore_index = ignore_index + self.align_corners = align_corners + if sampler is not None: + self.sampler = build_pixel_sampler(sampler, context=self) + else: + self.sampler = None + + self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1) + if dropout_ratio > 0: + self.dropout = nn.Dropout2d(dropout_ratio) + else: + self.dropout = None + self.fp16_enabled = False + + def extra_repr(self): + """Extra repr.""" + s = f'input_transform={self.input_transform}, ' \ + f'ignore_index={self.ignore_index}, ' \ + f'align_corners={self.align_corners}' + return s + + def _init_inputs(self, in_channels, in_index, input_transform): + """Check and initialize input transforms. + + The in_channels, in_index and input_transform must match. + Specifically, when input_transform is None, only single feature map + will be selected. So in_channels and in_index must be of type int. + When input_transform + + Args: + in_channels (int|Sequence[int]): Input channels. + in_index (int|Sequence[int]): Input feature index. + input_transform (str|None): Transformation type of input features. + Options: 'resize_concat', 'multiple_select', None. + 'resize_concat': Multiple feature maps will be resize to the + same size as first one and than concat together. + Usually used in FCN head of HRNet. + 'multiple_select': Multiple feature maps will be bundle into + a list and passed into decode head. + None: Only one select feature map is allowed. + """ + + if input_transform is not None: + assert input_transform in ['resize_concat', 'multiple_select'] + self.input_transform = input_transform + self.in_index = in_index + if input_transform is not None: + assert isinstance(in_channels, (list, tuple)) + assert isinstance(in_index, (list, tuple)) + assert len(in_channels) == len(in_index) + if input_transform == 'resize_concat': + self.in_channels = sum(in_channels) + else: + self.in_channels = in_channels + else: + assert isinstance(in_channels, int) + assert isinstance(in_index, int) + self.in_channels = in_channels + + def init_weights(self): + """Initialize weights of classification layer.""" + normal_init(self.conv_seg, mean=0, std=0.01) + + def _transform_inputs(self, inputs): + """Transform inputs for decoder. + + Args: + inputs (list[Tensor]): List of multi-level img features. + + Returns: + Tensor: The transformed inputs + """ + + if self.input_transform == 'resize_concat': + inputs = [inputs[i] for i in self.in_index] + upsampled_inputs = [ + resize( + input=x, + size=inputs[0].shape[2:], + mode='bilinear', + align_corners=self.align_corners) for x in inputs + ] + inputs = torch.cat(upsampled_inputs, dim=1) + elif self.input_transform == 'multiple_select': + inputs = [inputs[i] for i in self.in_index] + else: + inputs = inputs[self.in_index] + + return inputs + + @auto_fp16() + @abstractmethod + def forward(self, inputs): + """Placeholder of forward function.""" + pass + + def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg): + """Forward function for training. + Args: + inputs (list[Tensor]): List of multi-level img features. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + gt_semantic_seg (Tensor): Semantic segmentation masks + used if the architecture supports semantic segmentation task. + train_cfg (dict): The training config. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + seg_logits = self.forward(inputs) + losses = self.losses(seg_logits, gt_semantic_seg) + return losses + + def forward_test(self, inputs, img_metas, test_cfg): + """Forward function for testing. + + Args: + inputs (list[Tensor]): List of multi-level img features. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + test_cfg (dict): The testing config. + + Returns: + Tensor: Output segmentation map. + """ + return self.forward(inputs) + + def cls_seg(self, feat): + """Classify each pixel.""" + if self.dropout is not None: + feat = self.dropout(feat) + output = self.conv_seg(feat) + return output + + @force_fp32(apply_to=('seg_logit', )) + def losses(self, seg_logit, seg_label): + """Compute segmentation loss.""" + loss = dict() + seg_logit = resize( + input=seg_logit, + size=seg_label.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + if self.sampler is not None: + seg_weight = self.sampler.sample(seg_logit, seg_label) + else: + seg_weight = None + seg_label = seg_label.squeeze(1) + loss['loss_seg'] = self.loss_decode( + seg_logit, + seg_label, + weight=seg_weight, + ignore_index=self.ignore_index) + loss['acc_seg'] = accuracy(seg_logit, seg_label) + return loss diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/dm_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/dm_head.py new file mode 100644 index 00000000..607cd3dd --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/dm_head.py @@ -0,0 +1,140 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer + +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +class DCM(nn.Module): + """Dynamic Convolutional Module used in DMNet. + + Args: + filter_size (int): The filter size of generated convolution kernel + used in Dynamic Convolutional Module. + fusion (bool): Add one conv to fuse DCM output feature. + in_channels (int): Input channels. + channels (int): Channels after modules, before conv_seg. + conv_cfg (dict | None): Config of conv layers. + norm_cfg (dict | None): Config of norm layers. + act_cfg (dict): Config of activation layers. + """ + + def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg, + norm_cfg, act_cfg): + super(DCM, self).__init__() + self.filter_size = filter_size + self.fusion = fusion + self.in_channels = in_channels + self.channels = channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1, + 0) + + self.input_redu_conv = ConvModule( + self.in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + if self.norm_cfg is not None: + self.norm = build_norm_layer(self.norm_cfg, self.channels)[1] + else: + self.norm = None + self.activate = build_activation_layer(self.act_cfg) + + if self.fusion: + self.fusion_conv = ConvModule( + self.channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, x): + """Forward function.""" + generated_filter = self.filter_gen_conv( + F.adaptive_avg_pool2d(x, self.filter_size)) + x = self.input_redu_conv(x) + b, c, h, w = x.shape + # [1, b * c, h, w], c = self.channels + x = x.view(1, b * c, h, w) + # [b * c, 1, filter_size, filter_size] + generated_filter = generated_filter.view(b * c, 1, self.filter_size, + self.filter_size) + pad = (self.filter_size - 1) // 2 + if (self.filter_size - 1) % 2 == 0: + p2d = (pad, pad, pad, pad) + else: + p2d = (pad + 1, pad, pad + 1, pad) + x = F.pad(input=x, pad=p2d, mode='constant', value=0) + # [1, b * c, h, w] + output = F.conv2d(input=x, weight=generated_filter, groups=b * c) + # [b, c, h, w] + output = output.view(b, c, h, w) + if self.norm is not None: + output = self.norm(output) + output = self.activate(output) + + if self.fusion: + output = self.fusion_conv(output) + + return output + + +@HEADS.register_module() +class DMHead(BaseDecodeHead): + """Dynamic Multi-scale Filters for Semantic Segmentation. + + This head is the implementation of + `DMNet `_. + + Args: + filter_sizes (tuple[int]): The size of generated convolutional filters + used in Dynamic Convolutional Module. Default: (1, 3, 5, 7). + fusion (bool): Add one conv to fuse DCM output feature. + """ + + def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs): + super(DMHead, self).__init__(**kwargs) + assert isinstance(filter_sizes, (list, tuple)) + self.filter_sizes = filter_sizes + self.fusion = fusion + dcm_modules = [] + for filter_size in self.filter_sizes: + dcm_modules.append( + DCM(filter_size, + self.fusion, + self.in_channels, + self.channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + self.dcm_modules = nn.ModuleList(dcm_modules) + self.bottleneck = ConvModule( + self.in_channels + len(filter_sizes) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + dcm_outs = [x] + for dcm_module in self.dcm_modules: + dcm_outs.append(dcm_module(x)) + dcm_outs = torch.cat(dcm_outs, dim=1) + output = self.bottleneck(dcm_outs) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/dnl_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/dnl_head.py new file mode 100644 index 00000000..ed5b7c19 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/dnl_head.py @@ -0,0 +1,131 @@ +import torch +from custom_mmpkg.custom_mmcv.cnn import NonLocal2d +from torch import nn + +from ..builder import HEADS +from .fcn_head import FCNHead + + +class DisentangledNonLocal2d(NonLocal2d): + """Disentangled Non-Local Blocks. + + Args: + temperature (float): Temperature to adjust attention. Default: 0.05 + """ + + def __init__(self, *arg, temperature, **kwargs): + super().__init__(*arg, **kwargs) + self.temperature = temperature + self.conv_mask = nn.Conv2d(self.in_channels, 1, kernel_size=1) + + def embedded_gaussian(self, theta_x, phi_x): + """Embedded gaussian with temperature.""" + + # NonLocal2d pairwise_weight: [N, HxW, HxW] + pairwise_weight = torch.matmul(theta_x, phi_x) + if self.use_scale: + # theta_x.shape[-1] is `self.inter_channels` + pairwise_weight /= theta_x.shape[-1]**0.5 + pairwise_weight /= self.temperature + pairwise_weight = pairwise_weight.softmax(dim=-1) + return pairwise_weight + + def forward(self, x): + # x: [N, C, H, W] + n = x.size(0) + + # g_x: [N, HxW, C] + g_x = self.g(x).view(n, self.inter_channels, -1) + g_x = g_x.permute(0, 2, 1) + + # theta_x: [N, HxW, C], phi_x: [N, C, HxW] + if self.mode == 'gaussian': + theta_x = x.view(n, self.in_channels, -1) + theta_x = theta_x.permute(0, 2, 1) + if self.sub_sample: + phi_x = self.phi(x).view(n, self.in_channels, -1) + else: + phi_x = x.view(n, self.in_channels, -1) + elif self.mode == 'concatenation': + theta_x = self.theta(x).view(n, self.inter_channels, -1, 1) + phi_x = self.phi(x).view(n, self.inter_channels, 1, -1) + else: + theta_x = self.theta(x).view(n, self.inter_channels, -1) + theta_x = theta_x.permute(0, 2, 1) + phi_x = self.phi(x).view(n, self.inter_channels, -1) + + # subtract mean + theta_x -= theta_x.mean(dim=-2, keepdim=True) + phi_x -= phi_x.mean(dim=-1, keepdim=True) + + pairwise_func = getattr(self, self.mode) + # pairwise_weight: [N, HxW, HxW] + pairwise_weight = pairwise_func(theta_x, phi_x) + + # y: [N, HxW, C] + y = torch.matmul(pairwise_weight, g_x) + # y: [N, C, H, W] + y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels, + *x.size()[2:]) + + # unary_mask: [N, 1, HxW] + unary_mask = self.conv_mask(x) + unary_mask = unary_mask.view(n, 1, -1) + unary_mask = unary_mask.softmax(dim=-1) + # unary_x: [N, 1, C] + unary_x = torch.matmul(unary_mask, g_x) + # unary_x: [N, C, 1, 1] + unary_x = unary_x.permute(0, 2, 1).contiguous().reshape( + n, self.inter_channels, 1, 1) + + output = x + self.conv_out(y + unary_x) + + return output + + +@HEADS.register_module() +class DNLHead(FCNHead): + """Disentangled Non-Local Neural Networks. + + This head is the implementation of `DNLNet + `_. + + Args: + reduction (int): Reduction factor of projection transform. Default: 2. + use_scale (bool): Whether to scale pairwise_weight by + sqrt(1/inter_channels). Default: False. + mode (str): The nonlocal mode. Options are 'embedded_gaussian', + 'dot_product'. Default: 'embedded_gaussian.'. + temperature (float): Temperature to adjust attention. Default: 0.05 + """ + + def __init__(self, + reduction=2, + use_scale=True, + mode='embedded_gaussian', + temperature=0.05, + **kwargs): + super(DNLHead, self).__init__(num_convs=2, **kwargs) + self.reduction = reduction + self.use_scale = use_scale + self.mode = mode + self.temperature = temperature + self.dnl_block = DisentangledNonLocal2d( + in_channels=self.channels, + reduction=self.reduction, + use_scale=self.use_scale, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + mode=self.mode, + temperature=self.temperature) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + output = self.convs[0](x) + output = self.dnl_block(output) + output = self.convs[1](output) + if self.concat_input: + output = self.conv_cat(torch.cat([x, output], dim=1)) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ema_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ema_head.py new file mode 100644 index 00000000..e2279d53 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ema_head.py @@ -0,0 +1,168 @@ +import math + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +def reduce_mean(tensor): + """Reduce mean when distributed training.""" + if not (dist.is_available() and dist.is_initialized()): + return tensor + tensor = tensor.clone() + dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM) + return tensor + + +class EMAModule(nn.Module): + """Expectation Maximization Attention Module used in EMANet. + + Args: + channels (int): Channels of the whole module. + num_bases (int): Number of bases. + num_stages (int): Number of the EM iterations. + """ + + def __init__(self, channels, num_bases, num_stages, momentum): + super(EMAModule, self).__init__() + assert num_stages >= 1, 'num_stages must be at least 1!' + self.num_bases = num_bases + self.num_stages = num_stages + self.momentum = momentum + + bases = torch.zeros(1, channels, self.num_bases) + bases.normal_(0, math.sqrt(2. / self.num_bases)) + # [1, channels, num_bases] + bases = F.normalize(bases, dim=1, p=2) + self.register_buffer('bases', bases) + + def forward(self, feats): + """Forward function.""" + batch_size, channels, height, width = feats.size() + # [batch_size, channels, height*width] + feats = feats.view(batch_size, channels, height * width) + # [batch_size, channels, num_bases] + bases = self.bases.repeat(batch_size, 1, 1) + + with torch.no_grad(): + for i in range(self.num_stages): + # [batch_size, height*width, num_bases] + attention = torch.einsum('bcn,bck->bnk', feats, bases) + attention = F.softmax(attention, dim=2) + # l1 norm + attention_normed = F.normalize(attention, dim=1, p=1) + # [batch_size, channels, num_bases] + bases = torch.einsum('bcn,bnk->bck', feats, attention_normed) + # l2 norm + bases = F.normalize(bases, dim=1, p=2) + + feats_recon = torch.einsum('bck,bnk->bcn', bases, attention) + feats_recon = feats_recon.view(batch_size, channels, height, width) + + if self.training: + bases = bases.mean(dim=0, keepdim=True) + bases = reduce_mean(bases) + # l2 norm + bases = F.normalize(bases, dim=1, p=2) + self.bases = (1 - + self.momentum) * self.bases + self.momentum * bases + + return feats_recon + + +@HEADS.register_module() +class EMAHead(BaseDecodeHead): + """Expectation Maximization Attention Networks for Semantic Segmentation. + + This head is the implementation of `EMANet + `_. + + Args: + ema_channels (int): EMA module channels + num_bases (int): Number of bases. + num_stages (int): Number of the EM iterations. + concat_input (bool): Whether concat the input and output of convs + before classification layer. Default: True + momentum (float): Momentum to update the base. Default: 0.1. + """ + + def __init__(self, + ema_channels, + num_bases, + num_stages, + concat_input=True, + momentum=0.1, + **kwargs): + super(EMAHead, self).__init__(**kwargs) + self.ema_channels = ema_channels + self.num_bases = num_bases + self.num_stages = num_stages + self.concat_input = concat_input + self.momentum = momentum + self.ema_module = EMAModule(self.ema_channels, self.num_bases, + self.num_stages, self.momentum) + + self.ema_in_conv = ConvModule( + self.in_channels, + self.ema_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + # project (0, inf) -> (-inf, inf) + self.ema_mid_conv = ConvModule( + self.ema_channels, + self.ema_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=None, + act_cfg=None) + for param in self.ema_mid_conv.parameters(): + param.requires_grad = False + + self.ema_out_conv = ConvModule( + self.ema_channels, + self.ema_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=None) + self.bottleneck = ConvModule( + self.ema_channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + if self.concat_input: + self.conv_cat = ConvModule( + self.in_channels + self.channels, + self.channels, + kernel_size=3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + feats = self.ema_in_conv(x) + identity = feats + feats = self.ema_mid_conv(feats) + recon = self.ema_module(feats) + recon = F.relu(recon, inplace=True) + recon = self.ema_out_conv(recon) + output = F.relu(identity + recon, inplace=True) + output = self.bottleneck(output) + if self.concat_input: + output = self.conv_cat(torch.cat([x, output], dim=1)) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/enc_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/enc_head.py new file mode 100644 index 00000000..ee8ecd74 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/enc_head.py @@ -0,0 +1,187 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule, build_norm_layer + +from custom_mmpkg.custom_mmseg.ops import Encoding, resize +from ..builder import HEADS, build_loss +from .decode_head import BaseDecodeHead + + +class EncModule(nn.Module): + """Encoding Module used in EncNet. + + Args: + in_channels (int): Input channels. + num_codes (int): Number of code words. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict): Config of activation layers. + """ + + def __init__(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg): + super(EncModule, self).__init__() + self.encoding_project = ConvModule( + in_channels, + in_channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + # TODO: resolve this hack + # change to 1d + if norm_cfg is not None: + encoding_norm_cfg = norm_cfg.copy() + if encoding_norm_cfg['type'] in ['BN', 'IN']: + encoding_norm_cfg['type'] += '1d' + else: + encoding_norm_cfg['type'] = encoding_norm_cfg['type'].replace( + '2d', '1d') + else: + # fallback to BN1d + encoding_norm_cfg = dict(type='BN1d') + self.encoding = nn.Sequential( + Encoding(channels=in_channels, num_codes=num_codes), + build_norm_layer(encoding_norm_cfg, num_codes)[1], + nn.ReLU(inplace=True)) + self.fc = nn.Sequential( + nn.Linear(in_channels, in_channels), nn.Sigmoid()) + + def forward(self, x): + """Forward function.""" + encoding_projection = self.encoding_project(x) + encoding_feat = self.encoding(encoding_projection).mean(dim=1) + batch_size, channels, _, _ = x.size() + gamma = self.fc(encoding_feat) + y = gamma.view(batch_size, channels, 1, 1) + output = F.relu_(x + x * y) + return encoding_feat, output + + +@HEADS.register_module() +class EncHead(BaseDecodeHead): + """Context Encoding for Semantic Segmentation. + + This head is the implementation of `EncNet + `_. + + Args: + num_codes (int): Number of code words. Default: 32. + use_se_loss (bool): Whether use Semantic Encoding Loss (SE-loss) to + regularize the training. Default: True. + add_lateral (bool): Whether use lateral connection to fuse features. + Default: False. + loss_se_decode (dict): Config of decode loss. + Default: dict(type='CrossEntropyLoss', use_sigmoid=True). + """ + + def __init__(self, + num_codes=32, + use_se_loss=True, + add_lateral=False, + loss_se_decode=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=0.2), + **kwargs): + super(EncHead, self).__init__( + input_transform='multiple_select', **kwargs) + self.use_se_loss = use_se_loss + self.add_lateral = add_lateral + self.num_codes = num_codes + self.bottleneck = ConvModule( + self.in_channels[-1], + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + if add_lateral: + self.lateral_convs = nn.ModuleList() + for in_channels in self.in_channels[:-1]: # skip the last one + self.lateral_convs.append( + ConvModule( + in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + self.fusion = ConvModule( + len(self.in_channels) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.enc_module = EncModule( + self.channels, + num_codes=num_codes, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + if self.use_se_loss: + self.loss_se_decode = build_loss(loss_se_decode) + self.se_layer = nn.Linear(self.channels, self.num_classes) + + def forward(self, inputs): + """Forward function.""" + inputs = self._transform_inputs(inputs) + feat = self.bottleneck(inputs[-1]) + if self.add_lateral: + laterals = [ + resize( + lateral_conv(inputs[i]), + size=feat.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + feat = self.fusion(torch.cat([feat, *laterals], 1)) + encode_feat, output = self.enc_module(feat) + output = self.cls_seg(output) + if self.use_se_loss: + se_output = self.se_layer(encode_feat) + return output, se_output + else: + return output + + def forward_test(self, inputs, img_metas, test_cfg): + """Forward function for testing, ignore se_loss.""" + if self.use_se_loss: + return self.forward(inputs)[0] + else: + return self.forward(inputs) + + @staticmethod + def _convert_to_onehot_labels(seg_label, num_classes): + """Convert segmentation label to onehot. + + Args: + seg_label (Tensor): Segmentation label of shape (N, H, W). + num_classes (int): Number of classes. + + Returns: + Tensor: Onehot labels of shape (N, num_classes). + """ + + batch_size = seg_label.size(0) + onehot_labels = seg_label.new_zeros((batch_size, num_classes)) + for i in range(batch_size): + hist = seg_label[i].float().histc( + bins=num_classes, min=0, max=num_classes - 1) + onehot_labels[i] = hist > 0 + return onehot_labels + + def losses(self, seg_logit, seg_label): + """Compute segmentation and semantic encoding loss.""" + seg_logit, se_seg_logit = seg_logit + loss = dict() + loss.update(super(EncHead, self).losses(seg_logit, seg_label)) + se_loss = self.loss_se_decode( + se_seg_logit, + self._convert_to_onehot_labels(seg_label, self.num_classes)) + loss['loss_se'] = se_loss + return loss diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/fcn_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/fcn_head.py new file mode 100644 index 00000000..7f0c3843 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/fcn_head.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +@HEADS.register_module() +class FCNHead(BaseDecodeHead): + """Fully Convolution Networks for Semantic Segmentation. + + This head is implemented of `FCNNet `_. + + Args: + num_convs (int): Number of convs in the head. Default: 2. + kernel_size (int): The kernel size for convs in the head. Default: 3. + concat_input (bool): Whether concat the input and output of convs + before classification layer. + dilation (int): The dilation rate for convs in the head. Default: 1. + """ + + def __init__(self, + num_convs=2, + kernel_size=3, + concat_input=True, + dilation=1, + **kwargs): + assert num_convs >= 0 and dilation > 0 and isinstance(dilation, int) + self.num_convs = num_convs + self.concat_input = concat_input + self.kernel_size = kernel_size + super(FCNHead, self).__init__(**kwargs) + if num_convs == 0: + assert self.in_channels == self.channels + + conv_padding = (kernel_size // 2) * dilation + convs = [] + convs.append( + ConvModule( + self.in_channels, + self.channels, + kernel_size=kernel_size, + padding=conv_padding, + dilation=dilation, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + for i in range(num_convs - 1): + convs.append( + ConvModule( + self.channels, + self.channels, + kernel_size=kernel_size, + padding=conv_padding, + dilation=dilation, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + if num_convs == 0: + self.convs = nn.Identity() + else: + self.convs = nn.Sequential(*convs) + if self.concat_input: + self.conv_cat = ConvModule( + self.in_channels + self.channels, + self.channels, + kernel_size=kernel_size, + padding=kernel_size // 2, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + output = self.convs(x) + if self.concat_input: + output = self.conv_cat(torch.cat([x, output], dim=1)) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/fpn_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/fpn_head.py new file mode 100644 index 00000000..26d0849b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/fpn_head.py @@ -0,0 +1,68 @@ +import numpy as np +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +@HEADS.register_module() +class FPNHead(BaseDecodeHead): + """Panoptic Feature Pyramid Networks. + + This head is the implementation of `Semantic FPN + `_. + + Args: + feature_strides (tuple[int]): The strides for input feature maps. + stack_lateral. All strides suppose to be power of 2. The first + one is of largest resolution. + """ + + def __init__(self, feature_strides, **kwargs): + super(FPNHead, self).__init__( + input_transform='multiple_select', **kwargs) + assert len(feature_strides) == len(self.in_channels) + assert min(feature_strides) == feature_strides[0] + self.feature_strides = feature_strides + + self.scale_heads = nn.ModuleList() + for i in range(len(feature_strides)): + head_length = max( + 1, + int(np.log2(feature_strides[i]) - np.log2(feature_strides[0]))) + scale_head = [] + for k in range(head_length): + scale_head.append( + ConvModule( + self.in_channels[i] if k == 0 else self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + if feature_strides[i] != feature_strides[0]: + scale_head.append( + nn.Upsample( + scale_factor=2, + mode='bilinear', + align_corners=self.align_corners)) + self.scale_heads.append(nn.Sequential(*scale_head)) + + def forward(self, inputs): + + x = self._transform_inputs(inputs) + + output = self.scale_heads[0](x[0]) + for i in range(1, len(self.feature_strides)): + # non inplace + output = output + resize( + self.scale_heads[i](x[i]), + size=output.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/gc_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/gc_head.py new file mode 100644 index 00000000..60004999 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/gc_head.py @@ -0,0 +1,47 @@ +import torch +from custom_mmpkg.custom_mmcv.cnn import ContextBlock + +from ..builder import HEADS +from .fcn_head import FCNHead + + +@HEADS.register_module() +class GCHead(FCNHead): + """GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond. + + This head is the implementation of `GCNet + `_. + + Args: + ratio (float): Multiplier of channels ratio. Default: 1/4. + pooling_type (str): The pooling type of context aggregation. + Options are 'att', 'avg'. Default: 'avg'. + fusion_types (tuple[str]): The fusion type for feature fusion. + Options are 'channel_add', 'channel_mul'. Default: ('channel_add',) + """ + + def __init__(self, + ratio=1 / 4., + pooling_type='att', + fusion_types=('channel_add', ), + **kwargs): + super(GCHead, self).__init__(num_convs=2, **kwargs) + self.ratio = ratio + self.pooling_type = pooling_type + self.fusion_types = fusion_types + self.gc_block = ContextBlock( + in_channels=self.channels, + ratio=self.ratio, + pooling_type=self.pooling_type, + fusion_types=self.fusion_types) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + output = self.convs[0](x) + output = self.gc_block(output) + output = self.convs[1](output) + if self.concat_input: + output = self.conv_cat(torch.cat([x, output], dim=1)) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/lraspp_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/lraspp_head.py new file mode 100644 index 00000000..5395a8f5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/lraspp_head.py @@ -0,0 +1,90 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv import is_tuple_of +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +@HEADS.register_module() +class LRASPPHead(BaseDecodeHead): + """Lite R-ASPP (LRASPP) head is proposed in Searching for MobileNetV3. + + This head is the improved implementation of `Searching for MobileNetV3 + `_. + + Args: + branch_channels (tuple[int]): The number of output channels in every + each branch. Default: (32, 64). + """ + + def __init__(self, branch_channels=(32, 64), **kwargs): + super(LRASPPHead, self).__init__(**kwargs) + if self.input_transform != 'multiple_select': + raise ValueError('in Lite R-ASPP (LRASPP) head, input_transform ' + f'must be \'multiple_select\'. But received ' + f'\'{self.input_transform}\'') + assert is_tuple_of(branch_channels, int) + assert len(branch_channels) == len(self.in_channels) - 1 + self.branch_channels = branch_channels + + self.convs = nn.Sequential() + self.conv_ups = nn.Sequential() + for i in range(len(branch_channels)): + self.convs.add_module( + f'conv{i}', + nn.Conv2d( + self.in_channels[i], branch_channels[i], 1, bias=False)) + self.conv_ups.add_module( + f'conv_up{i}', + ConvModule( + self.channels + branch_channels[i], + self.channels, + 1, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + bias=False)) + + self.conv_up_input = nn.Conv2d(self.channels, self.channels, 1) + + self.aspp_conv = ConvModule( + self.in_channels[-1], + self.channels, + 1, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + bias=False) + self.image_pool = nn.Sequential( + nn.AvgPool2d(kernel_size=49, stride=(16, 20)), + ConvModule( + self.in_channels[2], + self.channels, + 1, + act_cfg=dict(type='Sigmoid'), + bias=False)) + + def forward(self, inputs): + """Forward function.""" + inputs = self._transform_inputs(inputs) + + x = inputs[-1] + + x = self.aspp_conv(x) * resize( + self.image_pool(x), + size=x.size()[2:], + mode='bilinear', + align_corners=self.align_corners) + x = self.conv_up_input(x) + + for i in range(len(self.branch_channels) - 1, -1, -1): + x = resize( + x, + size=inputs[i].size()[2:], + mode='bilinear', + align_corners=self.align_corners) + x = torch.cat([x, self.convs[i](inputs[i])], 1) + x = self.conv_ups[i](x) + + return self.cls_seg(x) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/nl_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/nl_head.py new file mode 100644 index 00000000..9010d303 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/nl_head.py @@ -0,0 +1,49 @@ +import torch +from custom_mmpkg.custom_mmcv.cnn import NonLocal2d + +from ..builder import HEADS +from .fcn_head import FCNHead + + +@HEADS.register_module() +class NLHead(FCNHead): + """Non-local Neural Networks. + + This head is the implementation of `NLNet + `_. + + Args: + reduction (int): Reduction factor of projection transform. Default: 2. + use_scale (bool): Whether to scale pairwise_weight by + sqrt(1/inter_channels). Default: True. + mode (str): The nonlocal mode. Options are 'embedded_gaussian', + 'dot_product'. Default: 'embedded_gaussian.'. + """ + + def __init__(self, + reduction=2, + use_scale=True, + mode='embedded_gaussian', + **kwargs): + super(NLHead, self).__init__(num_convs=2, **kwargs) + self.reduction = reduction + self.use_scale = use_scale + self.mode = mode + self.nl_block = NonLocal2d( + in_channels=self.channels, + reduction=self.reduction, + use_scale=self.use_scale, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + mode=self.mode) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + output = self.convs[0](x) + output = self.nl_block(output) + output = self.convs[1](output) + if self.concat_input: + output = self.conv_cat(torch.cat([x, output], dim=1)) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ocr_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ocr_head.py new file mode 100644 index 00000000..b31f0023 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/ocr_head.py @@ -0,0 +1,127 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from ..utils import SelfAttentionBlock as _SelfAttentionBlock +from .cascade_decode_head import BaseCascadeDecodeHead + + +class SpatialGatherModule(nn.Module): + """Aggregate the context features according to the initial predicted + probability distribution. + + Employ the soft-weighted method to aggregate the context. + """ + + def __init__(self, scale): + super(SpatialGatherModule, self).__init__() + self.scale = scale + + def forward(self, feats, probs): + """Forward function.""" + batch_size, num_classes, height, width = probs.size() + channels = feats.size(1) + probs = probs.view(batch_size, num_classes, -1) + feats = feats.view(batch_size, channels, -1) + # [batch_size, height*width, num_classes] + feats = feats.permute(0, 2, 1) + # [batch_size, channels, height*width] + probs = F.softmax(self.scale * probs, dim=2) + # [batch_size, channels, num_classes] + ocr_context = torch.matmul(probs, feats) + ocr_context = ocr_context.permute(0, 2, 1).contiguous().unsqueeze(3) + return ocr_context + + +class ObjectAttentionBlock(_SelfAttentionBlock): + """Make a OCR used SelfAttentionBlock.""" + + def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg, + act_cfg): + if scale > 1: + query_downsample = nn.MaxPool2d(kernel_size=scale) + else: + query_downsample = None + super(ObjectAttentionBlock, self).__init__( + key_in_channels=in_channels, + query_in_channels=in_channels, + channels=channels, + out_channels=in_channels, + share_key_query=False, + query_downsample=query_downsample, + key_downsample=None, + key_query_num_convs=2, + key_query_norm=True, + value_out_num_convs=1, + value_out_norm=True, + matmul_norm=True, + with_out=True, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.bottleneck = ConvModule( + in_channels * 2, + in_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, query_feats, key_feats): + """Forward function.""" + context = super(ObjectAttentionBlock, + self).forward(query_feats, key_feats) + output = self.bottleneck(torch.cat([context, query_feats], dim=1)) + if self.query_downsample is not None: + output = resize(query_feats) + + return output + + +@HEADS.register_module() +class OCRHead(BaseCascadeDecodeHead): + """Object-Contextual Representations for Semantic Segmentation. + + This head is the implementation of `OCRNet + `_. + + Args: + ocr_channels (int): The intermediate channels of OCR block. + scale (int): The scale of probability map in SpatialGatherModule in + Default: 1. + """ + + def __init__(self, ocr_channels, scale=1, **kwargs): + super(OCRHead, self).__init__(**kwargs) + self.ocr_channels = ocr_channels + self.scale = scale + self.object_context_block = ObjectAttentionBlock( + self.channels, + self.ocr_channels, + self.scale, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.spatial_gather_module = SpatialGatherModule(self.scale) + + self.bottleneck = ConvModule( + self.in_channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs, prev_output): + """Forward function.""" + x = self._transform_inputs(inputs) + feats = self.bottleneck(x) + context = self.spatial_gather_module(feats, prev_output) + object_context = self.object_context_block(feats, context) + output = self.cls_seg(object_context) + + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/point_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/point_head.py new file mode 100644 index 00000000..71c9f8e0 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/point_head.py @@ -0,0 +1,350 @@ +# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa + +import torch +import torch.nn as nn + +from custom_mmpkg.custom_mmcv.cnn import ConvModule, normal_init +from custom_mmpkg.custom_mmcv.ops import point_sample + +from custom_mmpkg.custom_mmseg.models.builder import HEADS +from custom_mmpkg.custom_mmseg.ops import resize +from ..losses import accuracy +from .cascade_decode_head import BaseCascadeDecodeHead + + +def calculate_uncertainty(seg_logits): + """Estimate uncertainty based on seg logits. + + For each location of the prediction ``seg_logits`` we estimate + uncertainty as the difference between top first and top second + predicted logits. + + Args: + seg_logits (Tensor): Semantic segmentation logits, + shape (batch_size, num_classes, height, width). + + Returns: + scores (Tensor): T uncertainty scores with the most uncertain + locations having the highest uncertainty score, shape ( + batch_size, 1, height, width) + """ + top2_scores = torch.topk(seg_logits, k=2, dim=1)[0] + return (top2_scores[:, 1] - top2_scores[:, 0]).unsqueeze(1) + + +@HEADS.register_module() +class PointHead(BaseCascadeDecodeHead): + """A mask point head use in PointRend. + + ``PointHead`` use shared multi-layer perceptron (equivalent to + nn.Conv1d) to predict the logit of input points. The fine-grained feature + and coarse feature will be concatenate together for predication. + + Args: + num_fcs (int): Number of fc layers in the head. Default: 3. + in_channels (int): Number of input channels. Default: 256. + fc_channels (int): Number of fc channels. Default: 256. + num_classes (int): Number of classes for logits. Default: 80. + class_agnostic (bool): Whether use class agnostic classification. + If so, the output channels of logits will be 1. Default: False. + coarse_pred_each_layer (bool): Whether concatenate coarse feature with + the output of each fc layer. Default: True. + conv_cfg (dict|None): Dictionary to construct and config conv layer. + Default: dict(type='Conv1d')) + norm_cfg (dict|None): Dictionary to construct and config norm layer. + Default: None. + loss_point (dict): Dictionary to construct and config loss layer of + point head. Default: dict(type='CrossEntropyLoss', use_mask=True, + loss_weight=1.0). + """ + + def __init__(self, + num_fcs=3, + coarse_pred_each_layer=True, + conv_cfg=dict(type='Conv1d'), + norm_cfg=None, + act_cfg=dict(type='ReLU', inplace=False), + **kwargs): + super(PointHead, self).__init__( + input_transform='multiple_select', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + **kwargs) + + self.num_fcs = num_fcs + self.coarse_pred_each_layer = coarse_pred_each_layer + + fc_in_channels = sum(self.in_channels) + self.num_classes + fc_channels = self.channels + self.fcs = nn.ModuleList() + for k in range(num_fcs): + fc = ConvModule( + fc_in_channels, + fc_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.fcs.append(fc) + fc_in_channels = fc_channels + fc_in_channels += self.num_classes if self.coarse_pred_each_layer \ + else 0 + self.fc_seg = nn.Conv1d( + fc_in_channels, + self.num_classes, + kernel_size=1, + stride=1, + padding=0) + if self.dropout_ratio > 0: + self.dropout = nn.Dropout(self.dropout_ratio) + delattr(self, 'conv_seg') + + def init_weights(self): + """Initialize weights of classification layer.""" + normal_init(self.fc_seg, std=0.001) + + def cls_seg(self, feat): + """Classify each pixel with fc.""" + if self.dropout is not None: + feat = self.dropout(feat) + output = self.fc_seg(feat) + return output + + def forward(self, fine_grained_point_feats, coarse_point_feats): + x = torch.cat([fine_grained_point_feats, coarse_point_feats], dim=1) + for fc in self.fcs: + x = fc(x) + if self.coarse_pred_each_layer: + x = torch.cat((x, coarse_point_feats), dim=1) + return self.cls_seg(x) + + def _get_fine_grained_point_feats(self, x, points): + """Sample from fine grained features. + + Args: + x (list[Tensor]): Feature pyramid from by neck or backbone. + points (Tensor): Point coordinates, shape (batch_size, + num_points, 2). + + Returns: + fine_grained_feats (Tensor): Sampled fine grained feature, + shape (batch_size, sum(channels of x), num_points). + """ + + fine_grained_feats_list = [ + point_sample(_, points, align_corners=self.align_corners) + for _ in x + ] + if len(fine_grained_feats_list) > 1: + fine_grained_feats = torch.cat(fine_grained_feats_list, dim=1) + else: + fine_grained_feats = fine_grained_feats_list[0] + + return fine_grained_feats + + def _get_coarse_point_feats(self, prev_output, points): + """Sample from fine grained features. + + Args: + prev_output (list[Tensor]): Prediction of previous decode head. + points (Tensor): Point coordinates, shape (batch_size, + num_points, 2). + + Returns: + coarse_feats (Tensor): Sampled coarse feature, shape (batch_size, + num_classes, num_points). + """ + + coarse_feats = point_sample( + prev_output, points, align_corners=self.align_corners) + + return coarse_feats + + def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg, + train_cfg): + """Forward function for training. + Args: + inputs (list[Tensor]): List of multi-level img features. + prev_output (Tensor): The output of previous decode head. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + gt_semantic_seg (Tensor): Semantic segmentation masks + used if the architecture supports semantic segmentation task. + train_cfg (dict): The training config. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + x = self._transform_inputs(inputs) + with torch.no_grad(): + points = self.get_points_train( + prev_output, calculate_uncertainty, cfg=train_cfg) + fine_grained_point_feats = self._get_fine_grained_point_feats( + x, points) + coarse_point_feats = self._get_coarse_point_feats(prev_output, points) + point_logits = self.forward(fine_grained_point_feats, + coarse_point_feats) + point_label = point_sample( + gt_semantic_seg.float(), + points, + mode='nearest', + align_corners=self.align_corners) + point_label = point_label.squeeze(1).long() + + losses = self.losses(point_logits, point_label) + + return losses + + def forward_test(self, inputs, prev_output, img_metas, test_cfg): + """Forward function for testing. + + Args: + inputs (list[Tensor]): List of multi-level img features. + prev_output (Tensor): The output of previous decode head. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + test_cfg (dict): The testing config. + + Returns: + Tensor: Output segmentation map. + """ + + x = self._transform_inputs(inputs) + refined_seg_logits = prev_output.clone() + for _ in range(test_cfg.subdivision_steps): + refined_seg_logits = resize( + refined_seg_logits, + scale_factor=test_cfg.scale_factor, + mode='bilinear', + align_corners=self.align_corners) + batch_size, channels, height, width = refined_seg_logits.shape + point_indices, points = self.get_points_test( + refined_seg_logits, calculate_uncertainty, cfg=test_cfg) + fine_grained_point_feats = self._get_fine_grained_point_feats( + x, points) + coarse_point_feats = self._get_coarse_point_feats( + prev_output, points) + point_logits = self.forward(fine_grained_point_feats, + coarse_point_feats) + + point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1) + refined_seg_logits = refined_seg_logits.reshape( + batch_size, channels, height * width) + refined_seg_logits = refined_seg_logits.scatter_( + 2, point_indices, point_logits) + refined_seg_logits = refined_seg_logits.view( + batch_size, channels, height, width) + + return refined_seg_logits + + def losses(self, point_logits, point_label): + """Compute segmentation loss.""" + loss = dict() + loss['loss_point'] = self.loss_decode( + point_logits, point_label, ignore_index=self.ignore_index) + loss['acc_point'] = accuracy(point_logits, point_label) + return loss + + def get_points_train(self, seg_logits, uncertainty_func, cfg): + """Sample points for training. + + Sample points in [0, 1] x [0, 1] coordinate space based on their + uncertainty. The uncertainties are calculated for each point using + 'uncertainty_func' function that takes point's logit prediction as + input. + + Args: + seg_logits (Tensor): Semantic segmentation logits, shape ( + batch_size, num_classes, height, width). + uncertainty_func (func): uncertainty calculation function. + cfg (dict): Training config of point head. + + Returns: + point_coords (Tensor): A tensor of shape (batch_size, num_points, + 2) that contains the coordinates of ``num_points`` sampled + points. + """ + num_points = cfg.num_points + oversample_ratio = cfg.oversample_ratio + importance_sample_ratio = cfg.importance_sample_ratio + assert oversample_ratio >= 1 + assert 0 <= importance_sample_ratio <= 1 + batch_size = seg_logits.shape[0] + num_sampled = int(num_points * oversample_ratio) + point_coords = torch.rand( + batch_size, num_sampled, 2, device=seg_logits.device) + point_logits = point_sample(seg_logits, point_coords) + # It is crucial to calculate uncertainty based on the sampled + # prediction value for the points. Calculating uncertainties of the + # coarse predictions first and sampling them for points leads to + # incorrect results. To illustrate this: assume uncertainty func( + # logits)=-abs(logits), a sampled point between two coarse + # predictions with -1 and 1 logits has 0 logits, and therefore 0 + # uncertainty value. However, if we calculate uncertainties for the + # coarse predictions first, both will have -1 uncertainty, + # and sampled point will get -1 uncertainty. + point_uncertainties = uncertainty_func(point_logits) + num_uncertain_points = int(importance_sample_ratio * num_points) + num_random_points = num_points - num_uncertain_points + idx = torch.topk( + point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1] + shift = num_sampled * torch.arange( + batch_size, dtype=torch.long, device=seg_logits.device) + idx += shift[:, None] + point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view( + batch_size, num_uncertain_points, 2) + if num_random_points > 0: + rand_point_coords = torch.rand( + batch_size, num_random_points, 2, device=seg_logits.device) + point_coords = torch.cat((point_coords, rand_point_coords), dim=1) + return point_coords + + def get_points_test(self, seg_logits, uncertainty_func, cfg): + """Sample points for testing. + + Find ``num_points`` most uncertain points from ``uncertainty_map``. + + Args: + seg_logits (Tensor): A tensor of shape (batch_size, num_classes, + height, width) for class-specific or class-agnostic prediction. + uncertainty_func (func): uncertainty calculation function. + cfg (dict): Testing config of point head. + + Returns: + point_indices (Tensor): A tensor of shape (batch_size, num_points) + that contains indices from [0, height x width) of the most + uncertain points. + point_coords (Tensor): A tensor of shape (batch_size, num_points, + 2) that contains [0, 1] x [0, 1] normalized coordinates of the + most uncertain points from the ``height x width`` grid . + """ + + num_points = cfg.subdivision_num_points + uncertainty_map = uncertainty_func(seg_logits) + batch_size, _, height, width = uncertainty_map.shape + h_step = 1.0 / height + w_step = 1.0 / width + + uncertainty_map = uncertainty_map.view(batch_size, height * width) + num_points = min(height * width, num_points) + point_indices = uncertainty_map.topk(num_points, dim=1)[1] + point_coords = torch.zeros( + batch_size, + num_points, + 2, + dtype=torch.float, + device=seg_logits.device) + point_coords[:, :, 0] = w_step / 2.0 + (point_indices % + width).float() * w_step + point_coords[:, :, 1] = h_step / 2.0 + (point_indices // + width).float() * h_step + return point_indices, point_coords diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/psa_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/psa_head.py new file mode 100644 index 00000000..c1424a85 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/psa_head.py @@ -0,0 +1,196 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead + +try: + from custom_mmpkg.custom_mmcv.ops import PSAMask +except ModuleNotFoundError: + PSAMask = None + + +@HEADS.register_module() +class PSAHead(BaseDecodeHead): + """Point-wise Spatial Attention Network for Scene Parsing. + + This head is the implementation of `PSANet + `_. + + Args: + mask_size (tuple[int]): The PSA mask size. It usually equals input + size. + psa_type (str): The type of psa module. Options are 'collect', + 'distribute', 'bi-direction'. Default: 'bi-direction' + compact (bool): Whether use compact map for 'collect' mode. + Default: True. + shrink_factor (int): The downsample factors of psa mask. Default: 2. + normalization_factor (float): The normalize factor of attention. + psa_softmax (bool): Whether use softmax for attention. + """ + + def __init__(self, + mask_size, + psa_type='bi-direction', + compact=False, + shrink_factor=2, + normalization_factor=1.0, + psa_softmax=True, + **kwargs): + if PSAMask is None: + raise RuntimeError('Please install mmcv-full for PSAMask ops') + super(PSAHead, self).__init__(**kwargs) + assert psa_type in ['collect', 'distribute', 'bi-direction'] + self.psa_type = psa_type + self.compact = compact + self.shrink_factor = shrink_factor + self.mask_size = mask_size + mask_h, mask_w = mask_size + self.psa_softmax = psa_softmax + if normalization_factor is None: + normalization_factor = mask_h * mask_w + self.normalization_factor = normalization_factor + + self.reduce = ConvModule( + self.in_channels, + self.channels, + kernel_size=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.attention = nn.Sequential( + ConvModule( + self.channels, + self.channels, + kernel_size=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg), + nn.Conv2d( + self.channels, mask_h * mask_w, kernel_size=1, bias=False)) + if psa_type == 'bi-direction': + self.reduce_p = ConvModule( + self.in_channels, + self.channels, + kernel_size=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.attention_p = nn.Sequential( + ConvModule( + self.channels, + self.channels, + kernel_size=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg), + nn.Conv2d( + self.channels, mask_h * mask_w, kernel_size=1, bias=False)) + self.psamask_collect = PSAMask('collect', mask_size) + self.psamask_distribute = PSAMask('distribute', mask_size) + else: + self.psamask = PSAMask(psa_type, mask_size) + self.proj = ConvModule( + self.channels * (2 if psa_type == 'bi-direction' else 1), + self.in_channels, + kernel_size=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.bottleneck = ConvModule( + self.in_channels * 2, + self.channels, + kernel_size=3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + identity = x + align_corners = self.align_corners + if self.psa_type in ['collect', 'distribute']: + out = self.reduce(x) + n, c, h, w = out.size() + if self.shrink_factor != 1: + if h % self.shrink_factor and w % self.shrink_factor: + h = (h - 1) // self.shrink_factor + 1 + w = (w - 1) // self.shrink_factor + 1 + align_corners = True + else: + h = h // self.shrink_factor + w = w // self.shrink_factor + align_corners = False + out = resize( + out, + size=(h, w), + mode='bilinear', + align_corners=align_corners) + y = self.attention(out) + if self.compact: + if self.psa_type == 'collect': + y = y.view(n, h * w, + h * w).transpose(1, 2).view(n, h * w, h, w) + else: + y = self.psamask(y) + if self.psa_softmax: + y = F.softmax(y, dim=1) + out = torch.bmm( + out.view(n, c, h * w), y.view(n, h * w, h * w)).view( + n, c, h, w) * (1.0 / self.normalization_factor) + else: + x_col = self.reduce(x) + x_dis = self.reduce_p(x) + n, c, h, w = x_col.size() + if self.shrink_factor != 1: + if h % self.shrink_factor and w % self.shrink_factor: + h = (h - 1) // self.shrink_factor + 1 + w = (w - 1) // self.shrink_factor + 1 + align_corners = True + else: + h = h // self.shrink_factor + w = w // self.shrink_factor + align_corners = False + x_col = resize( + x_col, + size=(h, w), + mode='bilinear', + align_corners=align_corners) + x_dis = resize( + x_dis, + size=(h, w), + mode='bilinear', + align_corners=align_corners) + y_col = self.attention(x_col) + y_dis = self.attention_p(x_dis) + if self.compact: + y_dis = y_dis.view(n, h * w, + h * w).transpose(1, 2).view(n, h * w, h, w) + else: + y_col = self.psamask_collect(y_col) + y_dis = self.psamask_distribute(y_dis) + if self.psa_softmax: + y_col = F.softmax(y_col, dim=1) + y_dis = F.softmax(y_dis, dim=1) + x_col = torch.bmm( + x_col.view(n, c, h * w), y_col.view(n, h * w, h * w)).view( + n, c, h, w) * (1.0 / self.normalization_factor) + x_dis = torch.bmm( + x_dis.view(n, c, h * w), y_dis.view(n, h * w, h * w)).view( + n, c, h, w) * (1.0 / self.normalization_factor) + out = torch.cat([x_col, x_dis], 1) + out = self.proj(out) + out = resize( + out, + size=identity.shape[2:], + mode='bilinear', + align_corners=align_corners) + out = self.bottleneck(torch.cat((identity, out), dim=1)) + out = self.cls_seg(out) + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/psp_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/psp_head.py new file mode 100644 index 00000000..0f7880f2 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/psp_head.py @@ -0,0 +1,101 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead + + +class PPM(nn.ModuleList): + """Pooling Pyramid Module used in PSPNet. + + Args: + pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module. + in_channels (int): Input channels. + channels (int): Channels after modules, before conv_seg. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict): Config of activation layers. + align_corners (bool): align_corners argument of F.interpolate. + """ + + def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg, + act_cfg, align_corners): + super(PPM, self).__init__() + self.pool_scales = pool_scales + self.align_corners = align_corners + self.in_channels = in_channels + self.channels = channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + for pool_scale in pool_scales: + self.append( + nn.Sequential( + nn.AdaptiveAvgPool2d(pool_scale), + ConvModule( + self.in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg))) + + def forward(self, x): + """Forward function.""" + ppm_outs = [] + for ppm in self: + ppm_out = ppm(x) + upsampled_ppm_out = resize( + ppm_out, + size=x.size()[2:], + mode='bilinear', + align_corners=self.align_corners) + ppm_outs.append(upsampled_ppm_out) + return ppm_outs + + +@HEADS.register_module() +class PSPHead(BaseDecodeHead): + """Pyramid Scene Parsing Network. + + This head is the implementation of + `PSPNet `_. + + Args: + pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module. Default: (1, 2, 3, 6). + """ + + def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs): + super(PSPHead, self).__init__(**kwargs) + assert isinstance(pool_scales, (list, tuple)) + self.pool_scales = pool_scales + self.psp_modules = PPM( + self.pool_scales, + self.in_channels, + self.channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + align_corners=self.align_corners) + self.bottleneck = ConvModule( + self.in_channels + len(pool_scales) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + psp_outs = [x] + psp_outs.extend(self.psp_modules(x)) + psp_outs = torch.cat(psp_outs, dim=1) + output = self.bottleneck(psp_outs) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/sep_aspp_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/sep_aspp_head.py new file mode 100644 index 00000000..26942ae1 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/sep_aspp_head.py @@ -0,0 +1,101 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule, DepthwiseSeparableConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .aspp_head import ASPPHead, ASPPModule + + +class DepthwiseSeparableASPPModule(ASPPModule): + """Atrous Spatial Pyramid Pooling (ASPP) Module with depthwise separable + conv.""" + + def __init__(self, **kwargs): + super(DepthwiseSeparableASPPModule, self).__init__(**kwargs) + for i, dilation in enumerate(self.dilations): + if dilation > 1: + self[i] = DepthwiseSeparableConvModule( + self.in_channels, + self.channels, + 3, + dilation=dilation, + padding=dilation, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + +@HEADS.register_module() +class DepthwiseSeparableASPPHead(ASPPHead): + """Encoder-Decoder with Atrous Separable Convolution for Semantic Image + Segmentation. + + This head is the implementation of `DeepLabV3+ + `_. + + Args: + c1_in_channels (int): The input channels of c1 decoder. If is 0, + the no decoder will be used. + c1_channels (int): The intermediate channels of c1 decoder. + """ + + def __init__(self, c1_in_channels, c1_channels, **kwargs): + super(DepthwiseSeparableASPPHead, self).__init__(**kwargs) + assert c1_in_channels >= 0 + self.aspp_modules = DepthwiseSeparableASPPModule( + dilations=self.dilations, + in_channels=self.in_channels, + channels=self.channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + if c1_in_channels > 0: + self.c1_bottleneck = ConvModule( + c1_in_channels, + c1_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + else: + self.c1_bottleneck = None + self.sep_bottleneck = nn.Sequential( + DepthwiseSeparableConvModule( + self.channels + c1_channels, + self.channels, + 3, + padding=1, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg), + DepthwiseSeparableConvModule( + self.channels, + self.channels, + 3, + padding=1, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + + def forward(self, inputs): + """Forward function.""" + x = self._transform_inputs(inputs) + aspp_outs = [ + resize( + self.image_pool(x), + size=x.size()[2:], + mode='bilinear', + align_corners=self.align_corners) + ] + aspp_outs.extend(self.aspp_modules(x)) + aspp_outs = torch.cat(aspp_outs, dim=1) + output = self.bottleneck(aspp_outs) + if self.c1_bottleneck is not None: + c1_output = self.c1_bottleneck(inputs[0]) + output = resize( + input=output, + size=c1_output.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + output = torch.cat([output, c1_output], dim=1) + output = self.sep_bottleneck(output) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/sep_fcn_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/sep_fcn_head.py new file mode 100644 index 00000000..fabb624a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/sep_fcn_head.py @@ -0,0 +1,51 @@ +from custom_mmpkg.custom_mmcv.cnn import DepthwiseSeparableConvModule + +from ..builder import HEADS +from .fcn_head import FCNHead + + +@HEADS.register_module() +class DepthwiseSeparableFCNHead(FCNHead): + """Depthwise-Separable Fully Convolutional Network for Semantic + Segmentation. + + This head is implemented according to Fast-SCNN paper. + Args: + in_channels(int): Number of output channels of FFM. + channels(int): Number of middle-stage channels in the decode head. + concat_input(bool): Whether to concatenate original decode input into + the result of several consecutive convolution layers. + Default: True. + num_classes(int): Used to determine the dimension of + final prediction tensor. + in_index(int): Correspond with 'out_indices' in FastSCNN backbone. + norm_cfg (dict | None): Config of norm layers. + align_corners (bool): align_corners argument of F.interpolate. + Default: False. + loss_decode(dict): Config of loss type and some + relevant additional options. + """ + + def __init__(self, **kwargs): + super(DepthwiseSeparableFCNHead, self).__init__(**kwargs) + self.convs[0] = DepthwiseSeparableConvModule( + self.in_channels, + self.channels, + kernel_size=self.kernel_size, + padding=self.kernel_size // 2, + norm_cfg=self.norm_cfg) + for i in range(1, self.num_convs): + self.convs[i] = DepthwiseSeparableConvModule( + self.channels, + self.channels, + kernel_size=self.kernel_size, + padding=self.kernel_size // 2, + norm_cfg=self.norm_cfg) + + if self.concat_input: + self.conv_cat = DepthwiseSeparableConvModule( + self.in_channels + self.channels, + self.channels, + kernel_size=self.kernel_size, + padding=self.kernel_size // 2, + norm_cfg=self.norm_cfg) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/uper_head.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/uper_head.py new file mode 100644 index 00000000..d4990010 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/decode_heads/uper_head.py @@ -0,0 +1,126 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from custom_mmpkg.custom_mmseg.ops import resize +from ..builder import HEADS +from .decode_head import BaseDecodeHead +from .psp_head import PPM + + +@HEADS.register_module() +class UPerHead(BaseDecodeHead): + """Unified Perceptual Parsing for Scene Understanding. + + This head is the implementation of `UPerNet + `_. + + Args: + pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid + Module applied on the last feature. Default: (1, 2, 3, 6). + """ + + def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs): + super(UPerHead, self).__init__( + input_transform='multiple_select', **kwargs) + # PSP Module + self.psp_modules = PPM( + pool_scales, + self.in_channels[-1], + self.channels, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + align_corners=self.align_corners) + self.bottleneck = ConvModule( + self.in_channels[-1] + len(pool_scales) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + # FPN Module + self.lateral_convs = nn.ModuleList() + self.fpn_convs = nn.ModuleList() + for in_channels in self.in_channels[:-1]: # skip the top layer + l_conv = ConvModule( + in_channels, + self.channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + inplace=False) + fpn_conv = ConvModule( + self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + inplace=False) + self.lateral_convs.append(l_conv) + self.fpn_convs.append(fpn_conv) + + self.fpn_bottleneck = ConvModule( + len(self.in_channels) * self.channels, + self.channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def psp_forward(self, inputs): + """Forward function of PSP module.""" + x = inputs[-1] + psp_outs = [x] + psp_outs.extend(self.psp_modules(x)) + psp_outs = torch.cat(psp_outs, dim=1) + output = self.bottleneck(psp_outs) + + return output + + def forward(self, inputs): + """Forward function.""" + + inputs = self._transform_inputs(inputs) + + # build laterals + laterals = [ + lateral_conv(inputs[i]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + + laterals.append(self.psp_forward(inputs)) + + # build top-down path + used_backbone_levels = len(laterals) + for i in range(used_backbone_levels - 1, 0, -1): + prev_shape = laterals[i - 1].shape[2:] + laterals[i - 1] += resize( + laterals[i], + size=prev_shape, + mode='bilinear', + align_corners=self.align_corners) + + # build outputs + fpn_outs = [ + self.fpn_convs[i](laterals[i]) + for i in range(used_backbone_levels - 1) + ] + # append psp feature + fpn_outs.append(laterals[-1]) + + for i in range(used_backbone_levels - 1, 0, -1): + fpn_outs[i] = resize( + fpn_outs[i], + size=fpn_outs[0].shape[2:], + mode='bilinear', + align_corners=self.align_corners) + fpn_outs = torch.cat(fpn_outs, dim=1) + output = self.fpn_bottleneck(fpn_outs) + output = self.cls_seg(output) + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/__init__.py new file mode 100644 index 00000000..beca7204 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/__init__.py @@ -0,0 +1,12 @@ +from .accuracy import Accuracy, accuracy +from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy, + cross_entropy, mask_cross_entropy) +from .dice_loss import DiceLoss +from .lovasz_loss import LovaszLoss +from .utils import reduce_loss, weight_reduce_loss, weighted_loss + +__all__ = [ + 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy', + 'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss', + 'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/accuracy.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/accuracy.py new file mode 100644 index 00000000..c0fd2e7e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/accuracy.py @@ -0,0 +1,78 @@ +import torch.nn as nn + + +def accuracy(pred, target, topk=1, thresh=None): + """Calculate accuracy according to the prediction and target. + + Args: + pred (torch.Tensor): The model prediction, shape (N, num_class, ...) + target (torch.Tensor): The target of each prediction, shape (N, , ...) + topk (int | tuple[int], optional): If the predictions in ``topk`` + matches the target, the predictions will be regarded as + correct ones. Defaults to 1. + thresh (float, optional): If not None, predictions with scores under + this threshold are considered incorrect. Default to None. + + Returns: + float | tuple[float]: If the input ``topk`` is a single integer, + the function will return a single float as accuracy. If + ``topk`` is a tuple containing multiple integers, the + function will return a tuple containing accuracies of + each ``topk`` number. + """ + assert isinstance(topk, (int, tuple)) + if isinstance(topk, int): + topk = (topk, ) + return_single = True + else: + return_single = False + + maxk = max(topk) + if pred.size(0) == 0: + accu = [pred.new_tensor(0.) for i in range(len(topk))] + return accu[0] if return_single else accu + assert pred.ndim == target.ndim + 1 + assert pred.size(0) == target.size(0) + assert maxk <= pred.size(1), \ + f'maxk {maxk} exceeds pred dimension {pred.size(1)}' + pred_value, pred_label = pred.topk(maxk, dim=1) + # transpose to shape (maxk, N, ...) + pred_label = pred_label.transpose(0, 1) + correct = pred_label.eq(target.unsqueeze(0).expand_as(pred_label)) + if thresh is not None: + # Only prediction values larger than thresh are counted as correct + correct = correct & (pred_value > thresh).t() + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / target.numel())) + return res[0] if return_single else res + + +class Accuracy(nn.Module): + """Accuracy calculation module.""" + + def __init__(self, topk=(1, ), thresh=None): + """Module to calculate the accuracy. + + Args: + topk (tuple, optional): The criterion used to calculate the + accuracy. Defaults to (1,). + thresh (float, optional): If not None, predictions with scores + under this threshold are considered incorrect. Default to None. + """ + super().__init__() + self.topk = topk + self.thresh = thresh + + def forward(self, pred, target): + """Forward function to calculate accuracy. + + Args: + pred (torch.Tensor): Prediction of models. + target (torch.Tensor): Target for each prediction. + + Returns: + tuple[float]: The accuracies under different topk criterions. + """ + return accuracy(pred, target, self.topk, self.thresh) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/cross_entropy_loss.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/cross_entropy_loss.py new file mode 100644 index 00000000..42c0790c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/cross_entropy_loss.py @@ -0,0 +1,198 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import get_class_weight, weight_reduce_loss + + +def cross_entropy(pred, + label, + weight=None, + class_weight=None, + reduction='mean', + avg_factor=None, + ignore_index=-100): + """The wrapper function for :func:`F.cross_entropy`""" + # class_weight is a manual rescaling weight given to each class. + # If given, has to be a Tensor of size C element-wise losses + loss = F.cross_entropy( + pred, + label, + weight=class_weight, + reduction='none', + ignore_index=ignore_index) + + # apply weights and do the reduction + if weight is not None: + weight = weight.float() + loss = weight_reduce_loss( + loss, weight=weight, reduction=reduction, avg_factor=avg_factor) + + return loss + + +def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index): + """Expand onehot labels to match the size of prediction.""" + bin_labels = labels.new_zeros(target_shape) + valid_mask = (labels >= 0) & (labels != ignore_index) + inds = torch.nonzero(valid_mask, as_tuple=True) + + if inds[0].numel() > 0: + if labels.dim() == 3: + bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1 + else: + bin_labels[inds[0], labels[valid_mask]] = 1 + + valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float() + if label_weights is None: + bin_label_weights = valid_mask + else: + bin_label_weights = label_weights.unsqueeze(1).expand(target_shape) + bin_label_weights *= valid_mask + + return bin_labels, bin_label_weights + + +def binary_cross_entropy(pred, + label, + weight=None, + reduction='mean', + avg_factor=None, + class_weight=None, + ignore_index=255): + """Calculate the binary CrossEntropy loss. + + Args: + pred (torch.Tensor): The prediction with shape (N, 1). + label (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + ignore_index (int | None): The label index to be ignored. Default: 255 + + Returns: + torch.Tensor: The calculated loss + """ + if pred.dim() != label.dim(): + assert (pred.dim() == 2 and label.dim() == 1) or ( + pred.dim() == 4 and label.dim() == 3), \ + 'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \ + 'H, W], label shape [N, H, W] are supported' + label, weight = _expand_onehot_labels(label, weight, pred.shape, + ignore_index) + + # weighted element-wise losses + if weight is not None: + weight = weight.float() + loss = F.binary_cross_entropy_with_logits( + pred, label.float(), pos_weight=class_weight, reduction='none') + # do the reduction for the weighted loss + loss = weight_reduce_loss( + loss, weight, reduction=reduction, avg_factor=avg_factor) + + return loss + + +def mask_cross_entropy(pred, + target, + label, + reduction='mean', + avg_factor=None, + class_weight=None, + ignore_index=None): + """Calculate the CrossEntropy loss for masks. + + Args: + pred (torch.Tensor): The prediction with shape (N, C), C is the number + of classes. + target (torch.Tensor): The learning label of the prediction. + label (torch.Tensor): ``label`` indicates the class label of the mask' + corresponding object. This will be used to select the mask in the + of the class which the object belongs to when the mask prediction + if not class-agnostic. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + ignore_index (None): Placeholder, to be consistent with other loss. + Default: None. + + Returns: + torch.Tensor: The calculated loss + """ + assert ignore_index is None, 'BCE loss does not support ignore_index' + # TODO: handle these two reserved arguments + assert reduction == 'mean' and avg_factor is None + num_rois = pred.size()[0] + inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device) + pred_slice = pred[inds, label].squeeze(1) + return F.binary_cross_entropy_with_logits( + pred_slice, target, weight=class_weight, reduction='mean')[None] + + +@LOSSES.register_module() +class CrossEntropyLoss(nn.Module): + """CrossEntropyLoss. + + Args: + use_sigmoid (bool, optional): Whether the prediction uses sigmoid + of softmax. Defaults to False. + use_mask (bool, optional): Whether to use mask cross entropy loss. + Defaults to False. + reduction (str, optional): . Defaults to 'mean'. + Options are "none", "mean" and "sum". + class_weight (list[float] | str, optional): Weight of each class. If in + str format, read them from a file. Defaults to None. + loss_weight (float, optional): Weight of the loss. Defaults to 1.0. + """ + + def __init__(self, + use_sigmoid=False, + use_mask=False, + reduction='mean', + class_weight=None, + loss_weight=1.0): + super(CrossEntropyLoss, self).__init__() + assert (use_sigmoid is False) or (use_mask is False) + self.use_sigmoid = use_sigmoid + self.use_mask = use_mask + self.reduction = reduction + self.loss_weight = loss_weight + self.class_weight = get_class_weight(class_weight) + + if self.use_sigmoid: + self.cls_criterion = binary_cross_entropy + elif self.use_mask: + self.cls_criterion = mask_cross_entropy + else: + self.cls_criterion = cross_entropy + + def forward(self, + cls_score, + label, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + """Forward function.""" + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.class_weight is not None: + class_weight = cls_score.new_tensor(self.class_weight) + else: + class_weight = None + loss_cls = self.loss_weight * self.cls_criterion( + cls_score, + label, + weight, + class_weight=class_weight, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss_cls diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/dice_loss.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/dice_loss.py new file mode 100644 index 00000000..27a77b96 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/dice_loss.py @@ -0,0 +1,119 @@ +"""Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/ +segmentron/solver/loss.py (Apache-2.0 License)""" +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import get_class_weight, weighted_loss + + +@weighted_loss +def dice_loss(pred, + target, + valid_mask, + smooth=1, + exponent=2, + class_weight=None, + ignore_index=255): + assert pred.shape[0] == target.shape[0] + total_loss = 0 + num_classes = pred.shape[1] + for i in range(num_classes): + if i != ignore_index: + dice_loss = binary_dice_loss( + pred[:, i], + target[..., i], + valid_mask=valid_mask, + smooth=smooth, + exponent=exponent) + if class_weight is not None: + dice_loss *= class_weight[i] + total_loss += dice_loss + return total_loss / num_classes + + +@weighted_loss +def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards): + assert pred.shape[0] == target.shape[0] + pred = pred.reshape(pred.shape[0], -1) + target = target.reshape(target.shape[0], -1) + valid_mask = valid_mask.reshape(valid_mask.shape[0], -1) + + num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth + den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth + + return 1 - num / den + + +@LOSSES.register_module() +class DiceLoss(nn.Module): + """DiceLoss. + + This loss is proposed in `V-Net: Fully Convolutional Neural Networks for + Volumetric Medical Image Segmentation `_. + + Args: + loss_type (str, optional): Binary or multi-class loss. + Default: 'multi_class'. Options are "binary" and "multi_class". + smooth (float): A float number to smooth loss, and avoid NaN error. + Default: 1 + exponent (float): An float number to calculate denominator + value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2. + reduction (str, optional): The method used to reduce the loss. Options + are "none", "mean" and "sum". This parameter only works when + per_image is True. Default: 'mean'. + class_weight (list[float] | str, optional): Weight of each class. If in + str format, read them from a file. Defaults to None. + loss_weight (float, optional): Weight of the loss. Default to 1.0. + ignore_index (int | None): The label index to be ignored. Default: 255. + """ + + def __init__(self, + smooth=1, + exponent=2, + reduction='mean', + class_weight=None, + loss_weight=1.0, + ignore_index=255, + **kwards): + super(DiceLoss, self).__init__() + self.smooth = smooth + self.exponent = exponent + self.reduction = reduction + self.class_weight = get_class_weight(class_weight) + self.loss_weight = loss_weight + self.ignore_index = ignore_index + + def forward(self, + pred, + target, + avg_factor=None, + reduction_override=None, + **kwards): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.class_weight is not None: + class_weight = pred.new_tensor(self.class_weight) + else: + class_weight = None + + pred = F.softmax(pred, dim=1) + num_classes = pred.shape[1] + one_hot_target = F.one_hot( + torch.clamp(target.long(), 0, num_classes - 1), + num_classes=num_classes) + valid_mask = (target != self.ignore_index).long() + + loss = self.loss_weight * dice_loss( + pred, + one_hot_target, + valid_mask=valid_mask, + reduction=reduction, + avg_factor=avg_factor, + smooth=self.smooth, + exponent=self.exponent, + class_weight=class_weight, + ignore_index=self.ignore_index) + return loss diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/lovasz_loss.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/lovasz_loss.py new file mode 100644 index 00000000..e3813785 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/lovasz_loss.py @@ -0,0 +1,303 @@ +"""Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor +ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim +Berman 2018 ESAT-PSI KU Leuven (MIT License)""" + +import custom_mmpkg.custom_mmcv as mmcv +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import get_class_weight, weight_reduce_loss + + +def lovasz_grad(gt_sorted): + """Computes gradient of the Lovasz extension w.r.t sorted errors. + + See Alg. 1 in paper. + """ + p = len(gt_sorted) + gts = gt_sorted.sum() + intersection = gts - gt_sorted.float().cumsum(0) + union = gts + (1 - gt_sorted).float().cumsum(0) + jaccard = 1. - intersection / union + if p > 1: # cover 1-pixel case + jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] + return jaccard + + +def flatten_binary_logits(logits, labels, ignore_index=None): + """Flattens predictions in the batch (binary case) Remove labels equal to + 'ignore_index'.""" + logits = logits.view(-1) + labels = labels.view(-1) + if ignore_index is None: + return logits, labels + valid = (labels != ignore_index) + vlogits = logits[valid] + vlabels = labels[valid] + return vlogits, vlabels + + +def flatten_probs(probs, labels, ignore_index=None): + """Flattens predictions in the batch.""" + if probs.dim() == 3: + # assumes output of a sigmoid layer + B, H, W = probs.size() + probs = probs.view(B, 1, H, W) + B, C, H, W = probs.size() + probs = probs.permute(0, 2, 3, 1).contiguous().view(-1, C) # B*H*W, C=P,C + labels = labels.view(-1) + if ignore_index is None: + return probs, labels + valid = (labels != ignore_index) + vprobs = probs[valid.nonzero().squeeze()] + vlabels = labels[valid] + return vprobs, vlabels + + +def lovasz_hinge_flat(logits, labels): + """Binary Lovasz hinge loss. + + Args: + logits (torch.Tensor): [P], logits at each prediction + (between -infty and +infty). + labels (torch.Tensor): [P], binary ground truth labels (0 or 1). + + Returns: + torch.Tensor: The calculated loss. + """ + if len(labels) == 0: + # only void pixels, the gradients should be 0 + return logits.sum() * 0. + signs = 2. * labels.float() - 1. + errors = (1. - logits * signs) + errors_sorted, perm = torch.sort(errors, dim=0, descending=True) + perm = perm.data + gt_sorted = labels[perm] + grad = lovasz_grad(gt_sorted) + loss = torch.dot(F.relu(errors_sorted), grad) + return loss + + +def lovasz_hinge(logits, + labels, + classes='present', + per_image=False, + class_weight=None, + reduction='mean', + avg_factor=None, + ignore_index=255): + """Binary Lovasz hinge loss. + + Args: + logits (torch.Tensor): [B, H, W], logits at each pixel + (between -infty and +infty). + labels (torch.Tensor): [B, H, W], binary ground truth masks (0 or 1). + classes (str | list[int], optional): Placeholder, to be consistent with + other loss. Default: None. + per_image (bool, optional): If per_image is True, compute the loss per + image instead of per batch. Default: False. + class_weight (list[float], optional): Placeholder, to be consistent + with other loss. Default: None. + reduction (str, optional): The method used to reduce the loss. Options + are "none", "mean" and "sum". This parameter only works when + per_image is True. Default: 'mean'. + avg_factor (int, optional): Average factor that is used to average + the loss. This parameter only works when per_image is True. + Default: None. + ignore_index (int | None): The label index to be ignored. Default: 255. + + Returns: + torch.Tensor: The calculated loss. + """ + if per_image: + loss = [ + lovasz_hinge_flat(*flatten_binary_logits( + logit.unsqueeze(0), label.unsqueeze(0), ignore_index)) + for logit, label in zip(logits, labels) + ] + loss = weight_reduce_loss( + torch.stack(loss), None, reduction, avg_factor) + else: + loss = lovasz_hinge_flat( + *flatten_binary_logits(logits, labels, ignore_index)) + return loss + + +def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None): + """Multi-class Lovasz-Softmax loss. + + Args: + probs (torch.Tensor): [P, C], class probabilities at each prediction + (between 0 and 1). + labels (torch.Tensor): [P], ground truth labels (between 0 and C - 1). + classes (str | list[int], optional): Classes chosen to calculate loss. + 'all' for all classes, 'present' for classes present in labels, or + a list of classes to average. Default: 'present'. + class_weight (list[float], optional): The weight for each class. + Default: None. + + Returns: + torch.Tensor: The calculated loss. + """ + if probs.numel() == 0: + # only void pixels, the gradients should be 0 + return probs * 0. + C = probs.size(1) + losses = [] + class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes + for c in class_to_sum: + fg = (labels == c).float() # foreground for class c + if (classes == 'present' and fg.sum() == 0): + continue + if C == 1: + if len(classes) > 1: + raise ValueError('Sigmoid output possible only with 1 class') + class_pred = probs[:, 0] + else: + class_pred = probs[:, c] + errors = (fg - class_pred).abs() + errors_sorted, perm = torch.sort(errors, 0, descending=True) + perm = perm.data + fg_sorted = fg[perm] + loss = torch.dot(errors_sorted, lovasz_grad(fg_sorted)) + if class_weight is not None: + loss *= class_weight[c] + losses.append(loss) + return torch.stack(losses).mean() + + +def lovasz_softmax(probs, + labels, + classes='present', + per_image=False, + class_weight=None, + reduction='mean', + avg_factor=None, + ignore_index=255): + """Multi-class Lovasz-Softmax loss. + + Args: + probs (torch.Tensor): [B, C, H, W], class probabilities at each + prediction (between 0 and 1). + labels (torch.Tensor): [B, H, W], ground truth labels (between 0 and + C - 1). + classes (str | list[int], optional): Classes chosen to calculate loss. + 'all' for all classes, 'present' for classes present in labels, or + a list of classes to average. Default: 'present'. + per_image (bool, optional): If per_image is True, compute the loss per + image instead of per batch. Default: False. + class_weight (list[float], optional): The weight for each class. + Default: None. + reduction (str, optional): The method used to reduce the loss. Options + are "none", "mean" and "sum". This parameter only works when + per_image is True. Default: 'mean'. + avg_factor (int, optional): Average factor that is used to average + the loss. This parameter only works when per_image is True. + Default: None. + ignore_index (int | None): The label index to be ignored. Default: 255. + + Returns: + torch.Tensor: The calculated loss. + """ + + if per_image: + loss = [ + lovasz_softmax_flat( + *flatten_probs( + prob.unsqueeze(0), label.unsqueeze(0), ignore_index), + classes=classes, + class_weight=class_weight) + for prob, label in zip(probs, labels) + ] + loss = weight_reduce_loss( + torch.stack(loss), None, reduction, avg_factor) + else: + loss = lovasz_softmax_flat( + *flatten_probs(probs, labels, ignore_index), + classes=classes, + class_weight=class_weight) + return loss + + +@LOSSES.register_module() +class LovaszLoss(nn.Module): + """LovaszLoss. + + This loss is proposed in `The Lovasz-Softmax loss: A tractable surrogate + for the optimization of the intersection-over-union measure in neural + networks `_. + + Args: + loss_type (str, optional): Binary or multi-class loss. + Default: 'multi_class'. Options are "binary" and "multi_class". + classes (str | list[int], optional): Classes chosen to calculate loss. + 'all' for all classes, 'present' for classes present in labels, or + a list of classes to average. Default: 'present'. + per_image (bool, optional): If per_image is True, compute the loss per + image instead of per batch. Default: False. + reduction (str, optional): The method used to reduce the loss. Options + are "none", "mean" and "sum". This parameter only works when + per_image is True. Default: 'mean'. + class_weight (list[float] | str, optional): Weight of each class. If in + str format, read them from a file. Defaults to None. + loss_weight (float, optional): Weight of the loss. Defaults to 1.0. + """ + + def __init__(self, + loss_type='multi_class', + classes='present', + per_image=False, + reduction='mean', + class_weight=None, + loss_weight=1.0): + super(LovaszLoss, self).__init__() + assert loss_type in ('binary', 'multi_class'), "loss_type should be \ + 'binary' or 'multi_class'." + + if loss_type == 'binary': + self.cls_criterion = lovasz_hinge + else: + self.cls_criterion = lovasz_softmax + assert classes in ('all', 'present') or mmcv.is_list_of(classes, int) + if not per_image: + assert reduction == 'none', "reduction should be 'none' when \ + per_image is False." + + self.classes = classes + self.per_image = per_image + self.reduction = reduction + self.loss_weight = loss_weight + self.class_weight = get_class_weight(class_weight) + + def forward(self, + cls_score, + label, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + """Forward function.""" + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.class_weight is not None: + class_weight = cls_score.new_tensor(self.class_weight) + else: + class_weight = None + + # if multi-class loss, transform logits to probs + if self.cls_criterion == lovasz_softmax: + cls_score = F.softmax(cls_score, dim=1) + + loss_cls = self.loss_weight * self.cls_criterion( + cls_score, + label, + self.classes, + self.per_image, + class_weight=class_weight, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss_cls diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/utils.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/utils.py new file mode 100644 index 00000000..cdfbd436 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/losses/utils.py @@ -0,0 +1,121 @@ +import functools + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +import torch.nn.functional as F + + +def get_class_weight(class_weight): + """Get class weight for loss function. + + Args: + class_weight (list[float] | str | None): If class_weight is a str, + take it as a file name and read from it. + """ + if isinstance(class_weight, str): + # take it as a file path + if class_weight.endswith('.npy'): + class_weight = np.load(class_weight) + else: + # pkl, json or yaml + class_weight = mmcv.load(class_weight) + + return class_weight + + +def reduce_loss(loss, reduction): + """Reduce loss as specified. + + Args: + loss (Tensor): Elementwise loss tensor. + reduction (str): Options are "none", "mean" and "sum". + + Return: + Tensor: Reduced loss tensor. + """ + reduction_enum = F._Reduction.get_enum(reduction) + # none: 0, elementwise_mean:1, sum: 2 + if reduction_enum == 0: + return loss + elif reduction_enum == 1: + return loss.mean() + elif reduction_enum == 2: + return loss.sum() + + +def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None): + """Apply element-wise weight and reduce loss. + + Args: + loss (Tensor): Element-wise loss. + weight (Tensor): Element-wise weights. + reduction (str): Same as built-in losses of PyTorch. + avg_factor (float): Avarage factor when computing the mean of losses. + + Returns: + Tensor: Processed loss values. + """ + # if weight is specified, apply element-wise weight + if weight is not None: + assert weight.dim() == loss.dim() + if weight.dim() > 1: + assert weight.size(1) == 1 or weight.size(1) == loss.size(1) + loss = loss * weight + + # if avg_factor is not specified, just reduce the loss + if avg_factor is None: + loss = reduce_loss(loss, reduction) + else: + # if reduction is mean, then average the loss by avg_factor + if reduction == 'mean': + loss = loss.sum() / avg_factor + # if reduction is 'none', then do nothing, otherwise raise an error + elif reduction != 'none': + raise ValueError('avg_factor can not be used with reduction="sum"') + return loss + + +def weighted_loss(loss_func): + """Create a weighted version of a given loss function. + + To use this decorator, the loss function must have the signature like + `loss_func(pred, target, **kwargs)`. The function only needs to compute + element-wise loss without any reduction. This decorator will add weight + and reduction arguments to the function. The decorated function will have + the signature like `loss_func(pred, target, weight=None, reduction='mean', + avg_factor=None, **kwargs)`. + + :Example: + + >>> import torch + >>> @weighted_loss + >>> def l1_loss(pred, target): + >>> return (pred - target).abs() + + >>> pred = torch.Tensor([0, 2, 3]) + >>> target = torch.Tensor([1, 1, 1]) + >>> weight = torch.Tensor([1, 0, 1]) + + >>> l1_loss(pred, target) + tensor(1.3333) + >>> l1_loss(pred, target, weight) + tensor(1.) + >>> l1_loss(pred, target, reduction='none') + tensor([1., 1., 2.]) + >>> l1_loss(pred, target, weight, avg_factor=2) + tensor(1.5000) + """ + + @functools.wraps(loss_func) + def wrapper(pred, + target, + weight=None, + reduction='mean', + avg_factor=None, + **kwargs): + # get element-wise loss + loss = loss_func(pred, target, **kwargs) + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + return wrapper diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/__init__.py new file mode 100644 index 00000000..9b9d3d5b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/__init__.py @@ -0,0 +1,4 @@ +from .fpn import FPN +from .multilevel_neck import MultiLevelNeck + +__all__ = ['FPN', 'MultiLevelNeck'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/fpn.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/fpn.py new file mode 100644 index 00000000..3c32cc5e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/fpn.py @@ -0,0 +1,212 @@ +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule, xavier_init + +from ..builder import NECKS + + +@NECKS.register_module() +class FPN(nn.Module): + """Feature Pyramid Network. + + This is an implementation of - Feature Pyramid Networks for Object + Detection (https://arxiv.org/abs/1612.03144) + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool | str): If bool, it decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + If str, it specifies the source feature map of the extra convs. + Only the following options are allowed + + - 'on_input': Last feat map of neck inputs (i.e. backbone feature). + - 'on_lateral': Last feature map after lateral convs. + - 'on_output': The last output feature map after fpn convs. + extra_convs_on_inputs (bool, deprecated): Whether to apply extra convs + on the original feature from the backbone. If True, + it is equivalent to `add_extra_convs='on_input'`. If False, it is + equivalent to set `add_extra_convs='on_output'`. Default to True. + relu_before_extra_convs (bool): Whether to apply relu before the extra + conv. Default: False. + no_norm_on_lateral (bool): Whether to apply norm on lateral. + Default: False. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Config dict for normalization layer. Default: None. + act_cfg (str): Config dict for activation layer in ConvModule. + Default: None. + upsample_cfg (dict): Config dict for interpolate layer. + Default: `dict(mode='nearest')` + + Example: + >>> import torch + >>> in_channels = [2, 3, 5, 7] + >>> scales = [340, 170, 84, 43] + >>> inputs = [torch.rand(1, c, s, s) + ... for c, s in zip(in_channels, scales)] + >>> self = FPN(in_channels, 11, len(in_channels)).eval() + >>> outputs = self.forward(inputs) + >>> for i in range(len(outputs)): + ... print(f'outputs[{i}].shape = {outputs[i].shape}') + outputs[0].shape = torch.Size([1, 11, 340, 340]) + outputs[1].shape = torch.Size([1, 11, 170, 170]) + outputs[2].shape = torch.Size([1, 11, 84, 84]) + outputs[3].shape = torch.Size([1, 11, 43, 43]) + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False, + extra_convs_on_inputs=False, + relu_before_extra_convs=False, + no_norm_on_lateral=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=None, + upsample_cfg=dict(mode='nearest')): + super(FPN, self).__init__() + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.relu_before_extra_convs = relu_before_extra_convs + self.no_norm_on_lateral = no_norm_on_lateral + self.fp16_enabled = False + self.upsample_cfg = upsample_cfg.copy() + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + assert isinstance(add_extra_convs, (str, bool)) + if isinstance(add_extra_convs, str): + # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output' + assert add_extra_convs in ('on_input', 'on_lateral', 'on_output') + elif add_extra_convs: # True + if extra_convs_on_inputs: + # For compatibility with previous release + # TODO: deprecate `extra_convs_on_inputs` + self.add_extra_convs = 'on_input' + else: + self.add_extra_convs = 'on_output' + + self.lateral_convs = nn.ModuleList() + self.fpn_convs = nn.ModuleList() + + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, + act_cfg=act_cfg, + inplace=False) + fpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + + self.lateral_convs.append(l_conv) + self.fpn_convs.append(fpn_conv) + + # add extra conv layers (e.g., RetinaNet) + extra_levels = num_outs - self.backbone_end_level + self.start_level + if self.add_extra_convs and extra_levels >= 1: + for i in range(extra_levels): + if i == 0 and self.add_extra_convs == 'on_input': + in_channels = self.in_channels[self.backbone_end_level - 1] + else: + in_channels = out_channels + extra_fpn_conv = ConvModule( + in_channels, + out_channels, + 3, + stride=2, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + self.fpn_convs.append(extra_fpn_conv) + + # default init_weights for conv(msra) and norm in ConvModule + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + xavier_init(m, distribution='uniform') + + def forward(self, inputs): + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + + # build top-down path + used_backbone_levels = len(laterals) + for i in range(used_backbone_levels - 1, 0, -1): + # In some cases, fixing `scale factor` (e.g. 2) is preferred, but + # it cannot co-exist with `size` in `F.interpolate`. + if 'scale_factor' in self.upsample_cfg: + laterals[i - 1] += F.interpolate(laterals[i], + **self.upsample_cfg) + else: + prev_shape = laterals[i - 1].shape[2:] + laterals[i - 1] += F.interpolate( + laterals[i], size=prev_shape, **self.upsample_cfg) + + # build outputs + # part 1: from original levels + outs = [ + self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) + ] + # part 2: add extra levels + if self.num_outs > len(outs): + # use max pool to get more levels on top of outputs + # (e.g., Faster R-CNN, Mask R-CNN) + if not self.add_extra_convs: + for i in range(self.num_outs - used_backbone_levels): + outs.append(F.max_pool2d(outs[-1], 1, stride=2)) + # add conv layers on top of original feature maps (RetinaNet) + else: + if self.add_extra_convs == 'on_input': + extra_source = inputs[self.backbone_end_level - 1] + elif self.add_extra_convs == 'on_lateral': + extra_source = laterals[-1] + elif self.add_extra_convs == 'on_output': + extra_source = outs[-1] + else: + raise NotImplementedError + outs.append(self.fpn_convs[used_backbone_levels](extra_source)) + for i in range(used_backbone_levels + 1, self.num_outs): + if self.relu_before_extra_convs: + outs.append(self.fpn_convs[i](F.relu(outs[-1]))) + else: + outs.append(self.fpn_convs[i](outs[-1])) + return tuple(outs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/multilevel_neck.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/multilevel_neck.py new file mode 100644 index 00000000..ce5e8563 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/necks/multilevel_neck.py @@ -0,0 +1,70 @@ +import torch.nn as nn +import torch.nn.functional as F +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from ..builder import NECKS + + +@NECKS.register_module() +class MultiLevelNeck(nn.Module): + """MultiLevelNeck. + + A neck structure connect vit backbone and decoder_heads. + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale). + scales (List[int]): Scale factors for each input feature map. + norm_cfg (dict): Config dict for normalization layer. Default: None. + act_cfg (dict): Config dict for activation layer in ConvModule. + Default: None. + """ + + def __init__(self, + in_channels, + out_channels, + scales=[0.5, 1, 2, 4], + norm_cfg=None, + act_cfg=None): + super(MultiLevelNeck, self).__init__() + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.scales = scales + self.num_outs = len(scales) + self.lateral_convs = nn.ModuleList() + self.convs = nn.ModuleList() + for in_channel in in_channels: + self.lateral_convs.append( + ConvModule( + in_channel, + out_channels, + kernel_size=1, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + for _ in range(self.num_outs): + self.convs.append( + ConvModule( + out_channels, + out_channels, + kernel_size=3, + padding=1, + stride=1, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + + def forward(self, inputs): + assert len(inputs) == len(self.in_channels) + print(inputs[0].shape) + inputs = [ + lateral_conv(inputs[i]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + # for len(inputs) not equal to self.num_outs + if len(inputs) == 1: + inputs = [inputs[0] for _ in range(self.num_outs)] + outs = [] + for i in range(self.num_outs): + x_resize = F.interpolate( + inputs[i], scale_factor=self.scales[i], mode='bilinear') + outs.append(self.convs[i](x_resize)) + return tuple(outs) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/__init__.py new file mode 100644 index 00000000..dca2f094 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/__init__.py @@ -0,0 +1,5 @@ +from .base import BaseSegmentor +from .cascade_encoder_decoder import CascadeEncoderDecoder +from .encoder_decoder import EncoderDecoder + +__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/base.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/base.py new file mode 100644 index 00000000..5fd073f4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/base.py @@ -0,0 +1,273 @@ +import logging +import warnings +from abc import ABCMeta, abstractmethod +from collections import OrderedDict + +import custom_mmpkg.custom_mmcv as mmcv +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from custom_mmpkg.custom_mmcv.runner import auto_fp16 + + +class BaseSegmentor(nn.Module): + """Base class for segmentors.""" + + __metaclass__ = ABCMeta + + def __init__(self): + super(BaseSegmentor, self).__init__() + self.fp16_enabled = False + + @property + def with_neck(self): + """bool: whether the segmentor has neck""" + return hasattr(self, 'neck') and self.neck is not None + + @property + def with_auxiliary_head(self): + """bool: whether the segmentor has auxiliary head""" + return hasattr(self, + 'auxiliary_head') and self.auxiliary_head is not None + + @property + def with_decode_head(self): + """bool: whether the segmentor has decode head""" + return hasattr(self, 'decode_head') and self.decode_head is not None + + @abstractmethod + def extract_feat(self, imgs): + """Placeholder for extract features from images.""" + pass + + @abstractmethod + def encode_decode(self, img, img_metas): + """Placeholder for encode images with backbone and decode into a + semantic segmentation map of the same size as input.""" + pass + + @abstractmethod + def forward_train(self, imgs, img_metas, **kwargs): + """Placeholder for Forward function for training.""" + pass + + @abstractmethod + def simple_test(self, img, img_meta, **kwargs): + """Placeholder for single image test.""" + pass + + @abstractmethod + def aug_test(self, imgs, img_metas, **kwargs): + """Placeholder for augmentation test.""" + pass + + def init_weights(self, pretrained=None): + """Initialize the weights in segmentor. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + if pretrained is not None: + logger = logging.getLogger() + logger.info(f'load model from: {pretrained}') + + def forward_test(self, imgs, img_metas, **kwargs): + """ + Args: + imgs (List[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains all images in the batch. + img_metas (List[List[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. + """ + for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: + if not isinstance(var, list): + raise TypeError(f'{name} must be a list, but got ' + f'{type(var)}') + + num_augs = len(imgs) + if num_augs != len(img_metas): + raise ValueError(f'num of augmentations ({len(imgs)}) != ' + f'num of image meta ({len(img_metas)})') + # all images in the same aug batch all of the same ori_shape and pad + # shape + for img_meta in img_metas: + ori_shapes = [_['ori_shape'] for _ in img_meta] + assert all(shape == ori_shapes[0] for shape in ori_shapes) + img_shapes = [_['img_shape'] for _ in img_meta] + assert all(shape == img_shapes[0] for shape in img_shapes) + pad_shapes = [_['pad_shape'] for _ in img_meta] + assert all(shape == pad_shapes[0] for shape in pad_shapes) + + if num_augs == 1: + return self.simple_test(imgs[0], img_metas[0], **kwargs) + else: + return self.aug_test(imgs, img_metas, **kwargs) + + @auto_fp16(apply_to=('img', )) + def forward(self, img, img_metas, return_loss=True, **kwargs): + """Calls either :func:`forward_train` or :func:`forward_test` depending + on whether ``return_loss`` is ``True``. + + Note this setting will change the expected inputs. When + ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor + and List[dict]), and when ``resturn_loss=False``, img and img_meta + should be double nested (i.e. List[Tensor], List[List[dict]]), with + the outer list indicating test time augmentations. + """ + if return_loss: + return self.forward_train(img, img_metas, **kwargs) + else: + return self.forward_test(img, img_metas, **kwargs) + + def train_step(self, data_batch, optimizer, **kwargs): + """The iteration step during training. + + This method defines an iteration step during training, except for the + back propagation and optimizer updating, which are done in an optimizer + hook. Note that in some complicated cases or models, the whole process + including back propagation and optimizer updating is also defined in + this method, such as GAN. + + Args: + data (dict): The output of dataloader. + optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of + runner is passed to ``train_step()``. This argument is unused + and reserved. + + Returns: + dict: It should contain at least 3 keys: ``loss``, ``log_vars``, + ``num_samples``. + ``loss`` is a tensor for back propagation, which can be a + weighted sum of multiple losses. + ``log_vars`` contains all the variables to be sent to the + logger. + ``num_samples`` indicates the batch size (when the model is + DDP, it means the batch size on each GPU), which is used for + averaging the logs. + """ + losses = self(**data_batch) + loss, log_vars = self._parse_losses(losses) + + outputs = dict( + loss=loss, + log_vars=log_vars, + num_samples=len(data_batch['img_metas'])) + + return outputs + + def val_step(self, data_batch, **kwargs): + """The iteration step during validation. + + This method shares the same signature as :func:`train_step`, but used + during val epochs. Note that the evaluation after training epochs is + not implemented with this method, but an evaluation hook. + """ + output = self(**data_batch, **kwargs) + return output + + @staticmethod + def _parse_losses(losses): + """Parse the raw outputs (losses) of the network. + + Args: + losses (dict): Raw output of the network, which usually contain + losses and other necessary information. + + Returns: + tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor + which may be a weighted sum of all losses, log_vars contains + all the variables to be sent to the logger. + """ + log_vars = OrderedDict() + for loss_name, loss_value in losses.items(): + if isinstance(loss_value, torch.Tensor): + log_vars[loss_name] = loss_value.mean() + elif isinstance(loss_value, list): + log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) + else: + raise TypeError( + f'{loss_name} is not a tensor or list of tensors') + + loss = sum(_value for _key, _value in log_vars.items() + if 'loss' in _key) + + log_vars['loss'] = loss + for loss_name, loss_value in log_vars.items(): + # reduce loss when distributed training + if dist.is_available() and dist.is_initialized(): + loss_value = loss_value.data.clone() + dist.all_reduce(loss_value.div_(dist.get_world_size())) + log_vars[loss_name] = loss_value.item() + + return loss, log_vars + + def show_result(self, + img, + result, + palette=None, + win_name='', + show=False, + wait_time=0, + out_file=None, + opacity=0.5): + """Draw `result` over `img`. + + Args: + img (str or Tensor): The image to be displayed. + result (Tensor): The semantic segmentation results to draw over + `img`. + palette (list[list[int]]] | np.ndarray | None): The palette of + segmentation map. If None is given, random palette will be + generated. Default: None + win_name (str): The window name. + wait_time (int): Value of waitKey param. + Default: 0. + show (bool): Whether to show the image. + Default: False. + out_file (str or None): The filename to write the image. + Default: None. + opacity(float): Opacity of painted segmentation map. + Default 0.5. + Must be in (0, 1] range. + Returns: + img (Tensor): Only if not `show` or `out_file` + """ + img = mmcv.imread(img) + img = img.copy() + seg = result[0] + if palette is None: + if self.PALETTE is None: + palette = np.random.randint( + 0, 255, size=(len(self.CLASSES), 3)) + else: + palette = self.PALETTE + palette = np.array(palette) + assert palette.shape[0] == len(self.CLASSES) + assert palette.shape[1] == 3 + assert len(palette.shape) == 2 + assert 0 < opacity <= 1.0 + color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) + for label, color in enumerate(palette): + color_seg[seg == label, :] = color + # convert to BGR + color_seg = color_seg[..., ::-1] + + img = img * (1 - opacity) + color_seg * opacity + img = img.astype(np.uint8) + # if out_file specified, do not show image in window + if out_file is not None: + show = False + + if show: + mmcv.imshow(img, win_name, wait_time) + if out_file is not None: + mmcv.imwrite(img, out_file) + + if not (show or out_file): + warnings.warn('show==False and out_file is not specified, only ' + 'result image will be returned') + return img diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/cascade_encoder_decoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/cascade_encoder_decoder.py new file mode 100644 index 00000000..cdece0ac --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/cascade_encoder_decoder.py @@ -0,0 +1,98 @@ +from torch import nn + +from custom_mmpkg.custom_mmseg.core import add_prefix +from custom_mmpkg.custom_mmseg.ops import resize +from .. import builder +from ..builder import SEGMENTORS +from .encoder_decoder import EncoderDecoder + + +@SEGMENTORS.register_module() +class CascadeEncoderDecoder(EncoderDecoder): + """Cascade Encoder Decoder segmentors. + + CascadeEncoderDecoder almost the same as EncoderDecoder, while decoders of + CascadeEncoderDecoder are cascaded. The output of previous decoder_head + will be the input of next decoder_head. + """ + + def __init__(self, + num_stages, + backbone, + decode_head, + neck=None, + auxiliary_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None): + self.num_stages = num_stages + super(CascadeEncoderDecoder, self).__init__( + backbone=backbone, + decode_head=decode_head, + neck=neck, + auxiliary_head=auxiliary_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained) + + def _init_decode_head(self, decode_head): + """Initialize ``decode_head``""" + assert isinstance(decode_head, list) + assert len(decode_head) == self.num_stages + self.decode_head = nn.ModuleList() + for i in range(self.num_stages): + self.decode_head.append(builder.build_head(decode_head[i])) + self.align_corners = self.decode_head[-1].align_corners + self.num_classes = self.decode_head[-1].num_classes + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone and heads. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + self.backbone.init_weights(pretrained=pretrained) + for i in range(self.num_stages): + self.decode_head[i].init_weights() + if self.with_auxiliary_head: + if isinstance(self.auxiliary_head, nn.ModuleList): + for aux_head in self.auxiliary_head: + aux_head.init_weights() + else: + self.auxiliary_head.init_weights() + + def encode_decode(self, img, img_metas): + """Encode images with backbone and decode into a semantic segmentation + map of the same size as input.""" + x = self.extract_feat(img) + out = self.decode_head[0].forward_test(x, img_metas, self.test_cfg) + for i in range(1, self.num_stages): + out = self.decode_head[i].forward_test(x, out, img_metas, + self.test_cfg) + out = resize( + input=out, + size=img.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + return out + + def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg): + """Run forward function and calculate loss for decode head in + training.""" + losses = dict() + + loss_decode = self.decode_head[0].forward_train( + x, img_metas, gt_semantic_seg, self.train_cfg) + + losses.update(add_prefix(loss_decode, 'decode_0')) + + for i in range(1, self.num_stages): + # forward test again, maybe unnecessary for most methods. + prev_outputs = self.decode_head[i - 1].forward_test( + x, img_metas, self.test_cfg) + loss_decode = self.decode_head[i].forward_train( + x, prev_outputs, img_metas, gt_semantic_seg, self.train_cfg) + losses.update(add_prefix(loss_decode, f'decode_{i}')) + + return losses diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/encoder_decoder.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/encoder_decoder.py new file mode 100644 index 00000000..b8e42be5 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/segmentors/encoder_decoder.py @@ -0,0 +1,298 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from custom_mmpkg.custom_mmseg.core import add_prefix +from custom_mmpkg.custom_mmseg.ops import resize +from .. import builder +from ..builder import SEGMENTORS +from .base import BaseSegmentor + + +@SEGMENTORS.register_module() +class EncoderDecoder(BaseSegmentor): + """Encoder Decoder segmentors. + + EncoderDecoder typically consists of backbone, decode_head, auxiliary_head. + Note that auxiliary_head is only used for deep supervision during training, + which could be dumped during inference. + """ + + def __init__(self, + backbone, + decode_head, + neck=None, + auxiliary_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None): + super(EncoderDecoder, self).__init__() + self.backbone = builder.build_backbone(backbone) + if neck is not None: + self.neck = builder.build_neck(neck) + self._init_decode_head(decode_head) + self._init_auxiliary_head(auxiliary_head) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + self.init_weights(pretrained=pretrained) + + assert self.with_decode_head + + def _init_decode_head(self, decode_head): + """Initialize ``decode_head``""" + self.decode_head = builder.build_head(decode_head) + self.align_corners = self.decode_head.align_corners + self.num_classes = self.decode_head.num_classes + + def _init_auxiliary_head(self, auxiliary_head): + """Initialize ``auxiliary_head``""" + if auxiliary_head is not None: + if isinstance(auxiliary_head, list): + self.auxiliary_head = nn.ModuleList() + for head_cfg in auxiliary_head: + self.auxiliary_head.append(builder.build_head(head_cfg)) + else: + self.auxiliary_head = builder.build_head(auxiliary_head) + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone and heads. + + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + super(EncoderDecoder, self).init_weights(pretrained) + self.backbone.init_weights(pretrained=pretrained) + self.decode_head.init_weights() + if self.with_auxiliary_head: + if isinstance(self.auxiliary_head, nn.ModuleList): + for aux_head in self.auxiliary_head: + aux_head.init_weights() + else: + self.auxiliary_head.init_weights() + + def extract_feat(self, img): + """Extract features from images.""" + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def encode_decode(self, img, img_metas): + """Encode images with backbone and decode into a semantic segmentation + map of the same size as input.""" + x = self.extract_feat(img) + out = self._decode_head_forward_test(x, img_metas) + out = resize( + input=out, + size=img.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + return out + + def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg): + """Run forward function and calculate loss for decode head in + training.""" + losses = dict() + loss_decode = self.decode_head.forward_train(x, img_metas, + gt_semantic_seg, + self.train_cfg) + + losses.update(add_prefix(loss_decode, 'decode')) + return losses + + def _decode_head_forward_test(self, x, img_metas): + """Run forward function and calculate loss for decode head in + inference.""" + seg_logits = self.decode_head.forward_test(x, img_metas, self.test_cfg) + return seg_logits + + def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg): + """Run forward function and calculate loss for auxiliary head in + training.""" + losses = dict() + if isinstance(self.auxiliary_head, nn.ModuleList): + for idx, aux_head in enumerate(self.auxiliary_head): + loss_aux = aux_head.forward_train(x, img_metas, + gt_semantic_seg, + self.train_cfg) + losses.update(add_prefix(loss_aux, f'aux_{idx}')) + else: + loss_aux = self.auxiliary_head.forward_train( + x, img_metas, gt_semantic_seg, self.train_cfg) + losses.update(add_prefix(loss_aux, 'aux')) + + return losses + + def forward_dummy(self, img): + """Dummy forward function.""" + seg_logit = self.encode_decode(img, None) + + return seg_logit + + def forward_train(self, img, img_metas, gt_semantic_seg): + """Forward function for training. + + Args: + img (Tensor): Input images. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + gt_semantic_seg (Tensor): Semantic segmentation masks + used if the architecture supports semantic segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + + x = self.extract_feat(img) + + losses = dict() + + loss_decode = self._decode_head_forward_train(x, img_metas, + gt_semantic_seg) + losses.update(loss_decode) + + if self.with_auxiliary_head: + loss_aux = self._auxiliary_head_forward_train( + x, img_metas, gt_semantic_seg) + losses.update(loss_aux) + + return losses + + # TODO refactor + def slide_inference(self, img, img_meta, rescale): + """Inference by sliding-window with overlap. + + If h_crop > h_img or w_crop > w_img, the small patch will be used to + decode without padding. + """ + + h_stride, w_stride = self.test_cfg.stride + h_crop, w_crop = self.test_cfg.crop_size + batch_size, _, h_img, w_img = img.size() + num_classes = self.num_classes + h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 + w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 + preds = img.new_zeros((batch_size, num_classes, h_img, w_img)) + count_mat = img.new_zeros((batch_size, 1, h_img, w_img)) + for h_idx in range(h_grids): + for w_idx in range(w_grids): + y1 = h_idx * h_stride + x1 = w_idx * w_stride + y2 = min(y1 + h_crop, h_img) + x2 = min(x1 + w_crop, w_img) + y1 = max(y2 - h_crop, 0) + x1 = max(x2 - w_crop, 0) + crop_img = img[:, :, y1:y2, x1:x2] + crop_seg_logit = self.encode_decode(crop_img, img_meta) + preds += F.pad(crop_seg_logit, + (int(x1), int(preds.shape[3] - x2), int(y1), + int(preds.shape[2] - y2))) + + count_mat[:, :, y1:y2, x1:x2] += 1 + assert (count_mat == 0).sum() == 0 + if torch.onnx.is_in_onnx_export(): + # cast count_mat to constant while exporting to ONNX + count_mat = torch.from_numpy( + count_mat.cpu().detach().numpy()).to(device=img.device) + preds = preds / count_mat + if rescale: + preds = resize( + preds, + size=img_meta[0]['ori_shape'][:2], + mode='bilinear', + align_corners=self.align_corners, + warning=False) + return preds + + def whole_inference(self, img, img_meta, rescale): + """Inference with full image.""" + + seg_logit = self.encode_decode(img, img_meta) + if rescale: + # support dynamic shape for onnx + if torch.onnx.is_in_onnx_export(): + size = img.shape[2:] + else: + size = img_meta[0]['ori_shape'][:2] + seg_logit = resize( + seg_logit, + size=size, + mode='bilinear', + align_corners=self.align_corners, + warning=False) + + return seg_logit + + def inference(self, img, img_meta, rescale): + """Inference with slide/whole style. + + Args: + img (Tensor): The input image of shape (N, 3, H, W). + img_meta (dict): Image info dict where each dict has: 'img_shape', + 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmseg/datasets/pipelines/formatting.py:Collect`. + rescale (bool): Whether rescale back to original shape. + + Returns: + Tensor: The output segmentation map. + """ + + assert self.test_cfg.mode in ['slide', 'whole'] + ori_shape = img_meta[0]['ori_shape'] + assert all(_['ori_shape'] == ori_shape for _ in img_meta) + if self.test_cfg.mode == 'slide': + seg_logit = self.slide_inference(img, img_meta, rescale) + else: + seg_logit = self.whole_inference(img, img_meta, rescale) + output = F.softmax(seg_logit, dim=1) + flip = img_meta[0]['flip'] + if flip: + flip_direction = img_meta[0]['flip_direction'] + assert flip_direction in ['horizontal', 'vertical'] + if flip_direction == 'horizontal': + output = output.flip(dims=(3, )) + elif flip_direction == 'vertical': + output = output.flip(dims=(2, )) + + return output + + def simple_test(self, img, img_meta, rescale=True): + """Simple test with single image.""" + seg_logit = self.inference(img, img_meta, rescale) + seg_pred = seg_logit.argmax(dim=1) + if torch.onnx.is_in_onnx_export(): + # our inference backend only support 4D output + seg_pred = seg_pred.unsqueeze(0) + return seg_pred + seg_pred = seg_pred.cpu().numpy() + # unravel batch dim + seg_pred = list(seg_pred) + return seg_pred + + def aug_test(self, imgs, img_metas, rescale=True): + """Test with augmentations. + + Only rescale=True is supported. + """ + # aug_test rescale all imgs back to ori_shape for now + assert rescale + # to save memory, we get augmented seg logit inplace + seg_logit = self.inference(imgs[0], img_metas[0], rescale) + for i in range(1, len(imgs)): + cur_seg_logit = self.inference(imgs[i], img_metas[i], rescale) + seg_logit += cur_seg_logit + seg_logit /= len(imgs) + seg_pred = seg_logit.argmax(dim=1) + seg_pred = seg_pred.cpu().numpy() + # unravel batch dim + seg_pred = list(seg_pred) + return seg_pred diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/__init__.py new file mode 100644 index 00000000..3d3bdd34 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/__init__.py @@ -0,0 +1,13 @@ +from .drop import DropPath +from .inverted_residual import InvertedResidual, InvertedResidualV3 +from .make_divisible import make_divisible +from .res_layer import ResLayer +from .se_layer import SELayer +from .self_attention_block import SelfAttentionBlock +from .up_conv_block import UpConvBlock +from .weight_init import trunc_normal_ + +__all__ = [ + 'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual', + 'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'DropPath', 'trunc_normal_' +] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/drop.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/drop.py new file mode 100644 index 00000000..4520b0ff --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/drop.py @@ -0,0 +1,31 @@ +"""Modified from https://github.com/rwightman/pytorch-image- +models/blob/master/timm/models/layers/drop.py.""" + +import torch +from torch import nn + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of + residual blocks). + + Args: + drop_prob (float): Drop rate for paths of model. Dropout rate has + to be between 0 and 1. Default: 0. + """ + + def __init__(self, drop_prob=0.): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.keep_prob = 1 - drop_prob + + def forward(self, x): + if self.drop_prob == 0. or not self.training: + return x + shape = (x.shape[0], ) + (1, ) * ( + x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = self.keep_prob + torch.rand( + shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(self.keep_prob) * random_tensor + return output diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/inverted_residual.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/inverted_residual.py new file mode 100644 index 00000000..0fb93391 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/inverted_residual.py @@ -0,0 +1,208 @@ +from custom_mmpkg.custom_mmcv.cnn import ConvModule +from torch import nn +from torch.utils import checkpoint as cp + +from .se_layer import SELayer + + +class InvertedResidual(nn.Module): + """InvertedResidual block for MobileNetV2. + + Args: + in_channels (int): The input channels of the InvertedResidual block. + out_channels (int): The output channels of the InvertedResidual block. + stride (int): Stride of the middle (first) 3x3 convolution. + expand_ratio (int): Adjusts number of channels of the hidden layer + in InvertedResidual by this amount. + dilation (int): Dilation rate of depthwise conv. Default: 1 + conv_cfg (dict): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU6'). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + + Returns: + Tensor: The output tensor. + """ + + def __init__(self, + in_channels, + out_channels, + stride, + expand_ratio, + dilation=1, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU6'), + with_cp=False): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2], f'stride must in [1, 2]. ' \ + f'But received {stride}.' + self.with_cp = with_cp + self.use_res_connect = self.stride == 1 and in_channels == out_channels + hidden_dim = int(round(in_channels * expand_ratio)) + + layers = [] + if expand_ratio != 1: + layers.append( + ConvModule( + in_channels=in_channels, + out_channels=hidden_dim, + kernel_size=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + layers.extend([ + ConvModule( + in_channels=hidden_dim, + out_channels=hidden_dim, + kernel_size=3, + stride=stride, + padding=dilation, + dilation=dilation, + groups=hidden_dim, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg), + ConvModule( + in_channels=hidden_dim, + out_channels=out_channels, + kernel_size=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + ]) + self.conv = nn.Sequential(*layers) + + def forward(self, x): + + def _inner_forward(x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + return out + + +class InvertedResidualV3(nn.Module): + """Inverted Residual Block for MobileNetV3. + + Args: + in_channels (int): The input channels of this Module. + out_channels (int): The output channels of this Module. + mid_channels (int): The input channels of the depthwise convolution. + kernel_size (int): The kernel size of the depthwise convolution. + Default: 3. + stride (int): The stride of the depthwise convolution. Default: 1. + se_cfg (dict): Config dict for se layer. Default: None, which means no + se layer. + with_expand_conv (bool): Use expand conv or not. If set False, + mid_channels must be the same with in_channels. Default: True. + conv_cfg (dict): Config dict for convolution layer. Default: None, + which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU'). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + + Returns: + Tensor: The output tensor. + """ + + def __init__(self, + in_channels, + out_channels, + mid_channels, + kernel_size=3, + stride=1, + se_cfg=None, + with_expand_conv=True, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + with_cp=False): + super(InvertedResidualV3, self).__init__() + self.with_res_shortcut = (stride == 1 and in_channels == out_channels) + assert stride in [1, 2] + self.with_cp = with_cp + self.with_se = se_cfg is not None + self.with_expand_conv = with_expand_conv + + if self.with_se: + assert isinstance(se_cfg, dict) + if not self.with_expand_conv: + assert mid_channels == in_channels + + if self.with_expand_conv: + self.expand_conv = ConvModule( + in_channels=in_channels, + out_channels=mid_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.depthwise_conv = ConvModule( + in_channels=mid_channels, + out_channels=mid_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + groups=mid_channels, + conv_cfg=dict( + type='Conv2dAdaptivePadding') if stride == 2 else conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + if self.with_se: + self.se = SELayer(**se_cfg) + + self.linear_conv = ConvModule( + in_channels=mid_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + def forward(self, x): + + def _inner_forward(x): + out = x + + if self.with_expand_conv: + out = self.expand_conv(out) + + out = self.depthwise_conv(out) + + if self.with_se: + out = self.se(out) + + out = self.linear_conv(out) + + if self.with_res_shortcut: + return x + out + else: + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/make_divisible.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/make_divisible.py new file mode 100644 index 00000000..75ad7560 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/make_divisible.py @@ -0,0 +1,27 @@ +def make_divisible(value, divisor, min_value=None, min_ratio=0.9): + """Make divisible function. + + This function rounds the channel number to the nearest value that can be + divisible by the divisor. It is taken from the original tf repo. It ensures + that all layers have a channel number that is divisible by divisor. It can + be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py # noqa + + Args: + value (int): The original channel number. + divisor (int): The divisor to fully divide the channel number. + min_value (int): The minimum value of the output channel. + Default: None, means that the minimum value equal to the divisor. + min_ratio (float): The minimum ratio of the rounded channel number to + the original channel number. Default: 0.9. + + Returns: + int: The modified output channel number. + """ + + if min_value is None: + min_value = divisor + new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than (1-min_ratio). + if new_value < min_ratio * value: + new_value += divisor + return new_value diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/res_layer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/res_layer.py new file mode 100644 index 00000000..1379e62e --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/res_layer.py @@ -0,0 +1,94 @@ +from custom_mmpkg.custom_mmcv.cnn import build_conv_layer, build_norm_layer +from torch import nn as nn + + +class ResLayer(nn.Sequential): + """ResLayer to build ResNet style backbone. + + Args: + block (nn.Module): block used to build ResLayer. + inplanes (int): inplanes of block. + planes (int): planes of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottleneck. Default: False + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN') + multi_grid (int | None): Multi grid dilation rates of last + stage. Default: None + contract_dilation (bool): Whether contract first dilation of each layer + Default: False + """ + + def __init__(self, + block, + inplanes, + planes, + num_blocks, + stride=1, + dilation=1, + avg_down=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + multi_grid=None, + contract_dilation=False, + **kwargs): + self.block = block + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = [] + conv_stride = stride + if avg_down: + conv_stride = 1 + downsample.append( + nn.AvgPool2d( + kernel_size=stride, + stride=stride, + ceil_mode=True, + count_include_pad=False)) + downsample.extend([ + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=conv_stride, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1] + ]) + downsample = nn.Sequential(*downsample) + + layers = [] + if multi_grid is None: + if dilation > 1 and contract_dilation: + first_dilation = dilation // 2 + else: + first_dilation = dilation + else: + first_dilation = multi_grid[0] + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + dilation=first_dilation, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + inplanes = planes * block.expansion + for i in range(1, num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=1, + dilation=dilation if multi_grid is None else multi_grid[i], + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + super(ResLayer, self).__init__(*layers) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/se_layer.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/se_layer.py new file mode 100644 index 00000000..7ec17f8d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/se_layer.py @@ -0,0 +1,57 @@ +import custom_mmpkg.custom_mmcv as mmcv +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule + +from .make_divisible import make_divisible + + +class SELayer(nn.Module): + """Squeeze-and-Excitation Module. + + Args: + channels (int): The input (and output) channels of the SE layer. + ratio (int): Squeeze ratio in SELayer, the intermediate channel will be + ``int(channels/ratio)``. Default: 16. + conv_cfg (None or dict): Config dict for convolution layer. + Default: None, which means using conv2d. + act_cfg (dict or Sequence[dict]): Config dict for activation layer. + If act_cfg is a dict, two activation layers will be configured + by this dict. If act_cfg is a sequence of dicts, the first + activation layer will be configured by the first dict and the + second activation layer will be configured by the second dict. + Default: (dict(type='ReLU'), dict(type='HSigmoid', bias=3.0, + divisor=6.0)). + """ + + def __init__(self, + channels, + ratio=16, + conv_cfg=None, + act_cfg=(dict(type='ReLU'), + dict(type='HSigmoid', bias=3.0, divisor=6.0))): + super(SELayer, self).__init__() + if isinstance(act_cfg, dict): + act_cfg = (act_cfg, act_cfg) + assert len(act_cfg) == 2 + assert mmcv.is_tuple_of(act_cfg, dict) + self.global_avgpool = nn.AdaptiveAvgPool2d(1) + self.conv1 = ConvModule( + in_channels=channels, + out_channels=make_divisible(channels // ratio, 8), + kernel_size=1, + stride=1, + conv_cfg=conv_cfg, + act_cfg=act_cfg[0]) + self.conv2 = ConvModule( + in_channels=make_divisible(channels // ratio, 8), + out_channels=channels, + kernel_size=1, + stride=1, + conv_cfg=conv_cfg, + act_cfg=act_cfg[1]) + + def forward(self, x): + out = self.global_avgpool(x) + out = self.conv1(out) + out = self.conv2(out) + return x * out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/self_attention_block.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/self_attention_block.py new file mode 100644 index 00000000..ad20ca6c --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/self_attention_block.py @@ -0,0 +1,159 @@ +import torch +from custom_mmpkg.custom_mmcv.cnn import ConvModule, constant_init +from torch import nn as nn +from torch.nn import functional as F + + +class SelfAttentionBlock(nn.Module): + """General self-attention block/non-local block. + + Please refer to https://arxiv.org/abs/1706.03762 for details about key, + query and value. + + Args: + key_in_channels (int): Input channels of key feature. + query_in_channels (int): Input channels of query feature. + channels (int): Output channels of key/query transform. + out_channels (int): Output channels. + share_key_query (bool): Whether share projection weight between key + and query projection. + query_downsample (nn.Module): Query downsample module. + key_downsample (nn.Module): Key downsample module. + key_query_num_convs (int): Number of convs for key/query projection. + value_num_convs (int): Number of convs for value projection. + matmul_norm (bool): Whether normalize attention map with sqrt of + channels + with_out (bool): Whether use out projection. + conv_cfg (dict|None): Config of conv layers. + norm_cfg (dict|None): Config of norm layers. + act_cfg (dict|None): Config of activation layers. + """ + + def __init__(self, key_in_channels, query_in_channels, channels, + out_channels, share_key_query, query_downsample, + key_downsample, key_query_num_convs, value_out_num_convs, + key_query_norm, value_out_norm, matmul_norm, with_out, + conv_cfg, norm_cfg, act_cfg): + super(SelfAttentionBlock, self).__init__() + if share_key_query: + assert key_in_channels == query_in_channels + self.key_in_channels = key_in_channels + self.query_in_channels = query_in_channels + self.out_channels = out_channels + self.channels = channels + self.share_key_query = share_key_query + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.key_project = self.build_project( + key_in_channels, + channels, + num_convs=key_query_num_convs, + use_conv_module=key_query_norm, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + if share_key_query: + self.query_project = self.key_project + else: + self.query_project = self.build_project( + query_in_channels, + channels, + num_convs=key_query_num_convs, + use_conv_module=key_query_norm, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.value_project = self.build_project( + key_in_channels, + channels if with_out else out_channels, + num_convs=value_out_num_convs, + use_conv_module=value_out_norm, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + if with_out: + self.out_project = self.build_project( + channels, + out_channels, + num_convs=value_out_num_convs, + use_conv_module=value_out_norm, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + else: + self.out_project = None + + self.query_downsample = query_downsample + self.key_downsample = key_downsample + self.matmul_norm = matmul_norm + + self.init_weights() + + def init_weights(self): + """Initialize weight of later layer.""" + if self.out_project is not None: + if not isinstance(self.out_project, ConvModule): + constant_init(self.out_project, 0) + + def build_project(self, in_channels, channels, num_convs, use_conv_module, + conv_cfg, norm_cfg, act_cfg): + """Build projection layer for key/query/value/out.""" + if use_conv_module: + convs = [ + ConvModule( + in_channels, + channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + ] + for _ in range(num_convs - 1): + convs.append( + ConvModule( + channels, + channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + else: + convs = [nn.Conv2d(in_channels, channels, 1)] + for _ in range(num_convs - 1): + convs.append(nn.Conv2d(channels, channels, 1)) + if len(convs) > 1: + convs = nn.Sequential(*convs) + else: + convs = convs[0] + return convs + + def forward(self, query_feats, key_feats): + """Forward function.""" + batch_size = query_feats.size(0) + query = self.query_project(query_feats) + if self.query_downsample is not None: + query = self.query_downsample(query) + query = query.reshape(*query.shape[:2], -1) + query = query.permute(0, 2, 1).contiguous() + + key = self.key_project(key_feats) + value = self.value_project(key_feats) + if self.key_downsample is not None: + key = self.key_downsample(key) + value = self.key_downsample(value) + key = key.reshape(*key.shape[:2], -1) + value = value.reshape(*value.shape[:2], -1) + value = value.permute(0, 2, 1).contiguous() + + sim_map = torch.matmul(query, key) + if self.matmul_norm: + sim_map = (self.channels**-.5) * sim_map + sim_map = F.softmax(sim_map, dim=-1) + + context = torch.matmul(sim_map, value) + context = context.permute(0, 2, 1).contiguous() + context = context.reshape(batch_size, -1, *query_feats.shape[2:]) + if self.out_project is not None: + context = self.out_project(context) + return context diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/up_conv_block.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/up_conv_block.py new file mode 100644 index 00000000..a4320e26 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/up_conv_block.py @@ -0,0 +1,101 @@ +import torch +import torch.nn as nn +from custom_mmpkg.custom_mmcv.cnn import ConvModule, build_upsample_layer + + +class UpConvBlock(nn.Module): + """Upsample convolution block in decoder for UNet. + + This upsample convolution block consists of one upsample module + followed by one convolution block. The upsample module expands the + high-level low-resolution feature map and the convolution block fuses + the upsampled high-level low-resolution feature map and the low-level + high-resolution feature map from encoder. + + Args: + conv_block (nn.Sequential): Sequential of convolutional layers. + in_channels (int): Number of input channels of the high-level + skip_channels (int): Number of input channels of the low-level + high-resolution feature map from encoder. + out_channels (int): Number of output channels. + num_convs (int): Number of convolutional layers in the conv_block. + Default: 2. + stride (int): Stride of convolutional layer in conv_block. Default: 1. + dilation (int): Dilation rate of convolutional layer in conv_block. + Default: 1. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + conv_cfg (dict | None): Config dict for convolution layer. + Default: None. + norm_cfg (dict | None): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict | None): Config dict for activation layer in ConvModule. + Default: dict(type='ReLU'). + upsample_cfg (dict): The upsample config of the upsample module in + decoder. Default: dict(type='InterpConv'). If the size of + high-level feature map is the same as that of skip feature map + (low-level feature map from encoder), it does not need upsample the + high-level feature map and the upsample_cfg is None. + dcn (bool): Use deformable convolution in convolutional layer or not. + Default: None. + plugins (dict): plugins for convolutional layers. Default: None. + """ + + def __init__(self, + conv_block, + in_channels, + skip_channels, + out_channels, + num_convs=2, + stride=1, + dilation=1, + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + dcn=None, + plugins=None): + super(UpConvBlock, self).__init__() + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + + self.conv_block = conv_block( + in_channels=2 * skip_channels, + out_channels=out_channels, + num_convs=num_convs, + stride=stride, + dilation=dilation, + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + dcn=None, + plugins=None) + if upsample_cfg is not None: + self.upsample = build_upsample_layer( + cfg=upsample_cfg, + in_channels=in_channels, + out_channels=skip_channels, + with_cp=with_cp, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + else: + self.upsample = ConvModule( + in_channels, + skip_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + def forward(self, skip, x): + """Forward function.""" + + x = self.upsample(x) + out = torch.cat([skip, x], dim=1) + out = self.conv_block(out) + + return out diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/weight_init.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/weight_init.py new file mode 100644 index 00000000..38141ba3 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/models/utils/weight_init.py @@ -0,0 +1,62 @@ +"""Modified from https://github.com/rwightman/pytorch-image- +models/blob/master/timm/models/layers/drop.py.""" + +import math +import warnings + +import torch + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + """Reference: https://people.sc.fsu.edu/~jburkardt/presentations + /truncated_normal.pdf""" + + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' + 'The distribution of values may be incorrect.', + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + lower_bound = norm_cdf((a - mean) / std) + upper_bound = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * lower_bound - 1, 2 * upper_bound - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + Args: + tensor (``torch.Tensor``): an n-dimensional `torch.Tensor` + mean (float): the mean of the normal distribution + std (float): the standard deviation of the normal distribution + a (float): the minimum cutoff value + b (float): the maximum cutoff value + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/__init__.py new file mode 100644 index 00000000..bec51c75 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/__init__.py @@ -0,0 +1,4 @@ +from .encoding import Encoding +from .wrappers import Upsample, resize + +__all__ = ['Upsample', 'resize', 'Encoding'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/encoding.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/encoding.py new file mode 100644 index 00000000..7eb3629a --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/encoding.py @@ -0,0 +1,74 @@ +import torch +from torch import nn +from torch.nn import functional as F + + +class Encoding(nn.Module): + """Encoding Layer: a learnable residual encoder. + + Input is of shape (batch_size, channels, height, width). + Output is of shape (batch_size, num_codes, channels). + + Args: + channels: dimension of the features or feature channels + num_codes: number of code words + """ + + def __init__(self, channels, num_codes): + super(Encoding, self).__init__() + # init codewords and smoothing factor + self.channels, self.num_codes = channels, num_codes + std = 1. / ((num_codes * channels)**0.5) + # [num_codes, channels] + self.codewords = nn.Parameter( + torch.empty(num_codes, channels, + dtype=torch.float).uniform_(-std, std), + requires_grad=True) + # [num_codes] + self.scale = nn.Parameter( + torch.empty(num_codes, dtype=torch.float).uniform_(-1, 0), + requires_grad=True) + + @staticmethod + def scaled_l2(x, codewords, scale): + num_codes, channels = codewords.size() + batch_size = x.size(0) + reshaped_scale = scale.view((1, 1, num_codes)) + expanded_x = x.unsqueeze(2).expand( + (batch_size, x.size(1), num_codes, channels)) + reshaped_codewords = codewords.view((1, 1, num_codes, channels)) + + scaled_l2_norm = reshaped_scale * ( + expanded_x - reshaped_codewords).pow(2).sum(dim=3) + return scaled_l2_norm + + @staticmethod + def aggregate(assignment_weights, x, codewords): + num_codes, channels = codewords.size() + reshaped_codewords = codewords.view((1, 1, num_codes, channels)) + batch_size = x.size(0) + + expanded_x = x.unsqueeze(2).expand( + (batch_size, x.size(1), num_codes, channels)) + encoded_feat = (assignment_weights.unsqueeze(3) * + (expanded_x - reshaped_codewords)).sum(dim=1) + return encoded_feat + + def forward(self, x): + assert x.dim() == 4 and x.size(1) == self.channels + # [batch_size, channels, height, width] + batch_size = x.size(0) + # [batch_size, height x width, channels] + x = x.view(batch_size, self.channels, -1).transpose(1, 2).contiguous() + # assignment_weights: [batch_size, channels, num_codes] + assignment_weights = F.softmax( + self.scaled_l2(x, self.codewords, self.scale), dim=2) + # aggregate + encoded_feat = self.aggregate(assignment_weights, x, self.codewords) + return encoded_feat + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(Nx{self.channels}xHxW =>Nx{self.num_codes}' \ + f'x{self.channels})' + return repr_str diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/wrappers.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/wrappers.py new file mode 100644 index 00000000..0ed9a0cb --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/ops/wrappers.py @@ -0,0 +1,50 @@ +import warnings + +import torch.nn as nn +import torch.nn.functional as F + + +def resize(input, + size=None, + scale_factor=None, + mode='nearest', + align_corners=None, + warning=True): + if warning: + if size is not None and align_corners: + input_h, input_w = tuple(int(x) for x in input.shape[2:]) + output_h, output_w = tuple(int(x) for x in size) + if output_h > input_h or output_w > output_h: + if ((output_h > 1 and output_w > 1 and input_h > 1 + and input_w > 1) and (output_h - 1) % (input_h - 1) + and (output_w - 1) % (input_w - 1)): + warnings.warn( + f'When align_corners={align_corners}, ' + 'the output would more aligned if ' + f'input size {(input_h, input_w)} is `x+1` and ' + f'out size {(output_h, output_w)} is `nx+1`') + return F.interpolate(input, size, scale_factor, mode, align_corners) + + +class Upsample(nn.Module): + + def __init__(self, + size=None, + scale_factor=None, + mode='nearest', + align_corners=None): + super(Upsample, self).__init__() + self.size = size + if isinstance(scale_factor, tuple): + self.scale_factor = tuple(float(factor) for factor in scale_factor) + else: + self.scale_factor = float(scale_factor) if scale_factor else None + self.mode = mode + self.align_corners = align_corners + + def forward(self, x): + if not self.size: + size = [int(t * self.scale_factor) for t in x.shape[-2:]] + else: + size = self.size + return resize(x, size, None, self.mode, self.align_corners) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/__init__.py new file mode 100644 index 00000000..ac489e2d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/__init__.py @@ -0,0 +1,4 @@ +from .collect_env import collect_env +from .logger import get_root_logger + +__all__ = ['get_root_logger', 'collect_env'] diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/collect_env.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/collect_env.py new file mode 100644 index 00000000..ce5cd6ff --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/collect_env.py @@ -0,0 +1,17 @@ +from custom_mmpkg.custom_mmcv.utils import collect_env as collect_base_env +from custom_mmpkg.custom_mmcv.utils import get_git_hash + +import custom_mmpkg.custom_mmseg as mmseg + + +def collect_env(): + """Collect the information of the running environments.""" + env_info = collect_base_env() + env_info['MMSegmentation'] = f'{mmseg.__version__}+{get_git_hash()[:7]}' + + return env_info + + +if __name__ == '__main__': + for name, val in collect_env().items(): + print('{}: {}'.format(name, val)) diff --git a/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/logger.py b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/logger.py new file mode 100644 index 00000000..bc2ac05b --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/custom_mmpkg/custom_mmseg/utils/logger.py @@ -0,0 +1,27 @@ +import logging + +from custom_mmpkg.custom_mmcv.utils import get_logger + + +def get_root_logger(log_file=None, log_level=logging.INFO): + """Get the root logger. + + The logger will be initialized if it has not been initialized. By default a + StreamHandler will be added. If `log_file` is specified, a FileHandler will + also be added. The name of the root logger is the top-level package name, + e.g., "mmseg". + + Args: + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the root logger. + log_level (int): The root logger level. Note that only the process of + rank 0 is affected, while other processes will set the level to + "Error" and be silent most of the time. + + Returns: + logging.Logger: The root logger. + """ + + logger = get_logger(name='mmseg', log_file=log_file, log_level=log_level) + + return logger diff --git a/custom_nodes/comfyui_controlnet_aux/src/wrapper_for_mps/__init__.py b/custom_nodes/comfyui_controlnet_aux/src/wrapper_for_mps/__init__.py new file mode 100644 index 00000000..d40bcf5d --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/src/wrapper_for_mps/__init__.py @@ -0,0 +1,7 @@ +import torch +from comfy.model_management import get_torch_device + +device = get_torch_device() +#https://github.com/microsoft/DirectML/issues/414#issuecomment-1541319479 +def sparse_to_dense(sparse_tensor): + return sparse_tensor.to_dense() \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/tests/pose.png b/custom_nodes/comfyui_controlnet_aux/tests/pose.png new file mode 100644 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44, + 1, + 0, + 44, + 0, + "IMAGE" + ] + ], + "groups": [], + "config": {}, + "extra": {}, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/tests/test_controlnet_aux.py b/custom_nodes/comfyui_controlnet_aux/tests/test_controlnet_aux.py new file mode 100644 index 00000000..5fdaaca4 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/tests/test_controlnet_aux.py @@ -0,0 +1,126 @@ +import os +import shutil +from io import BytesIO + +import numpy as np +import pytest +import requests +from PIL import Image + +from custom_controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector, + LeresDetector, LineartAnimeDetector, + LineartDetector, MediapipeFaceDetector, + MidasDetector, MLSDdetector, NormalBaeDetector, + OpenposeDetector, PidiNetDetector, SamDetector, + ZoeDetector, TileDetector) + +OUTPUT_DIR = "tests/outputs" + +def output(name, img): + img.save(os.path.join(OUTPUT_DIR, "{:s}.png".format(name))) + +def common(name, processor, img): + output(name, processor(img)) + output(name + "_pil_np", Image.fromarray(processor(img, output_type="np"))) + output(name + "_np_np", Image.fromarray(processor(np.array(img, dtype=np.uint8), output_type="np"))) + output(name + "_np_pil", processor(np.array(img, dtype=np.uint8), output_type="pil")) + output(name + "_scaled", processor(img, detect_resolution=640, image_resolution=768)) + +def return_pil(name, processor, img): + output(name + "_pil_false", Image.fromarray(processor(img, return_pil=False))) + output(name + "_pil_true", processor(img, return_pil=True)) + +@pytest.fixture(scope="module") +def img(): + if os.path.exists(OUTPUT_DIR): + shutil.rmtree(OUTPUT_DIR) + os.mkdir(OUTPUT_DIR) + url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png" + response = requests.get(url) + img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512)) + return img + +def test_canny(img): + canny = CannyDetector() + common("canny", canny, img) + output("canny_img", canny(img=img)) + +def test_hed(img): + hed = HEDdetector.from_pretrained("lllyasviel/Annotators") + common("hed", hed, img) + return_pil("hed", hed, img) + output("hed_safe", hed(img, safe=True)) + output("hed_scribble", hed(img, scribble=True)) + +def test_leres(img): + leres = LeresDetector.from_pretrained("lllyasviel/Annotators") + common("leres", leres, img) + output("leres_boost", leres(img, boost=True)) + +def test_lineart(img): + lineart = LineartDetector.from_pretrained("lllyasviel/Annotators") + common("lineart", lineart, img) + return_pil("lineart", lineart, img) + output("lineart_coarse", lineart(img, coarse=True)) + +def test_lineart_anime(img): + lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators") + common("lineart_anime", lineart_anime, img) + return_pil("lineart_anime", lineart_anime, img) + +def test_mediapipe_face(img): + mediapipe = MediapipeFaceDetector() + common("mediapipe", mediapipe, img) + output("mediapipe_image", mediapipe(image=img)) + +def test_midas(img): + midas = MidasDetector.from_pretrained("lllyasviel/Annotators") + common("midas", midas, img) + output("midas_normal", midas(img, depth_and_normal=True)[1]) + +def test_mlsd(img): + mlsd = MLSDdetector.from_pretrained("lllyasviel/Annotators") + common("mlsd", mlsd, img) + return_pil("mlsd", mlsd, img) + +def test_normalbae(img): + normal_bae = NormalBaeDetector.from_pretrained("lllyasviel/Annotators") + common("normal_bae", normal_bae, img) + return_pil("normal_bae", normal_bae, img) + +def test_openpose(img): + openpose = OpenposeDetector.from_pretrained("lllyasviel/Annotators") + common("openpose", openpose, img) + return_pil("openpose", openpose, img) + output("openpose_hand_and_face_false", openpose(img, hand_and_face=False)) + output("openpose_hand_and_face_true", openpose(img, hand_and_face=True)) + output("openpose_face", openpose(img, include_body=True, include_hand=False, include_face=True)) + output("openpose_faceonly", openpose(img, include_body=False, include_hand=False, include_face=True)) + output("openpose_full", openpose(img, include_body=True, include_hand=True, include_face=True)) + output("openpose_hand", openpose(img, include_body=True, include_hand=True, include_face=False)) + +def test_pidi(img): + pidi = PidiNetDetector.from_pretrained("lllyasviel/Annotators") + common("pidi", pidi, img) + return_pil("pidi", pidi, img) + output("pidi_safe", pidi(img, safe=True)) + output("pidi_scribble", pidi(img, scribble=True)) + +def test_sam(img): + sam = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints") + common("sam", sam, img) + output("sam_image", sam(image=img)) + +def test_shuffle(img): + shuffle = ContentShuffleDetector() + common("shuffle", shuffle, img) + return_pil("shuffle", shuffle, img) + +def test_zoe(img): + zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators") + common("zoe", zoe, img) + +def test_tile(img): + tile = TileDetector() + common("tile", tile, img) + output("tile_img", tile(img)) \ No newline at end of file diff --git a/custom_nodes/comfyui_controlnet_aux/utils.py b/custom_nodes/comfyui_controlnet_aux/utils.py new file mode 100644 index 00000000..cf3dd6e6 --- /dev/null +++ b/custom_nodes/comfyui_controlnet_aux/utils.py @@ -0,0 +1,250 @@ +import torch +import numpy as np +import os +import cv2 +import yaml +from pathlib import Path +from enum import Enum +from .log import log +import subprocess +import threading +import comfy +import tempfile + +here = Path(__file__).parent.resolve() + +config_path = Path(here, "config.yaml") + +if os.path.exists(config_path): + config = yaml.load(open(config_path, "r"), Loader=yaml.FullLoader) + + annotator_ckpts_path = str(Path(here, config["annotator_ckpts_path"])) + TEMP_DIR = config["custom_temp_path"] + USE_SYMLINKS = config["USE_SYMLINKS"] + ORT_PROVIDERS = config["EP_list"] + + if USE_SYMLINKS is None or type(USE_SYMLINKS) != bool: + log.error("USE_SYMLINKS must be a boolean. Using False by default.") + USE_SYMLINKS = False + + if TEMP_DIR is None: + TEMP_DIR = tempfile.gettempdir() + elif not os.path.isdir(TEMP_DIR): + try: + os.makedirs(TEMP_DIR) + except: + log.error("Failed to create custom temp directory. Using default.") + TEMP_DIR = tempfile.gettempdir() + + if not os.path.isdir(annotator_ckpts_path): + try: + os.makedirs(annotator_ckpts_path) + except: + log.error("Failed to create config ckpts directory. Using default.") + annotator_ckpts_path = str(Path(here, "./ckpts")) +else: + annotator_ckpts_path = str(Path(here, "./ckpts")) + TEMP_DIR = tempfile.gettempdir() + USE_SYMLINKS = False + ORT_PROVIDERS = ["CUDAExecutionProvider", "DirectMLExecutionProvider", "OpenVINOExecutionProvider", "ROCMExecutionProvider", "CPUExecutionProvider", "CoreMLExecutionProvider"] + +os.environ['AUX_ANNOTATOR_CKPTS_PATH'] = os.getenv('AUX_ANNOTATOR_CKPTS_PATH', annotator_ckpts_path) +os.environ['AUX_TEMP_DIR'] = os.getenv('AUX_TEMP_DIR', str(TEMP_DIR)) +os.environ['AUX_USE_SYMLINKS'] = os.getenv('AUX_USE_SYMLINKS', str(USE_SYMLINKS)) +os.environ['AUX_ORT_PROVIDERS'] = os.getenv('AUX_ORT_PROVIDERS', str(",".join(ORT_PROVIDERS))) + +log.info(f"Using ckpts path: {annotator_ckpts_path}") +log.info(f"Using symlinks: {USE_SYMLINKS}") +log.info(f"Using ort providers: {ORT_PROVIDERS}") + +# Sync with theoritical limit from Comfy base +# https://github.com/comfyanonymous/ComfyUI/blob/eecd69b53a896343775bcb02a4f8349e7442ffd1/nodes.py#L45 +MAX_RESOLUTION=16384 + +def common_annotator_call(model, tensor_image, input_batch=False, show_pbar=True, **kwargs): + if "detect_resolution" in kwargs: + del kwargs["detect_resolution"] #Prevent weird case? + + if "resolution" in kwargs: + detect_resolution = kwargs["resolution"] if type(kwargs["resolution"]) == int and kwargs["resolution"] >= 64 else 512 + del kwargs["resolution"] + else: + detect_resolution = 512 + + if input_batch: + np_images = np.asarray(tensor_image * 255., dtype=np.uint8) + np_results = model(np_images, output_type="np", detect_resolution=detect_resolution, **kwargs) + return torch.from_numpy(np_results.astype(np.float32) / 255.0) + + batch_size = tensor_image.shape[0] + if show_pbar: + pbar = comfy.utils.ProgressBar(batch_size) + out_tensor = None + for i, image in enumerate(tensor_image): + np_image = np.asarray(image.cpu() * 255., dtype=np.uint8) + np_result = model(np_image, output_type="np", detect_resolution=detect_resolution, **kwargs) + out = torch.from_numpy(np_result.astype(np.float32) / 255.0) + if out_tensor is None: + out_tensor = torch.zeros(batch_size, *out.shape, dtype=torch.float32) + out_tensor[i] = out + if show_pbar: + pbar.update(1) + return out_tensor + +def define_preprocessor_inputs(**arguments): + return dict( + required=dict(image=INPUT.IMAGE()), + optional=arguments + ) + +class INPUT(Enum): + def IMAGE(): + return ("IMAGE",) + def LATENT(): + return ("LATENT",) + def MASK(): + return ("MASK",) + def SEED(default=0): + return ("INT", dict(default=default, min=0, max=0xffffffffffffffff)) + def RESOLUTION(default=512, min=64, max=MAX_RESOLUTION, step=64): + return ("INT", dict(default=default, min=min, max=max, step=step)) + def INT(default=0, min=0, max=MAX_RESOLUTION, step=1): + return ("INT", dict(default=default, min=min, max=max, step=step)) + def FLOAT(default=0, min=0, max=1, step=0.01): + return ("FLOAT", dict(default=default, min=min, max=max, step=step)) + def STRING(default='', multiline=False): + return ("STRING", dict(default=default, multiline=multiline)) + def COMBO(values, default=None): + return (values, dict(default=values[0] if default is None else default)) + def BOOLEAN(default=True): + return ("BOOLEAN", dict(default=default)) + + + +class ResizeMode(Enum): + """ + Resize modes for ControlNet input images. + """ + + RESIZE = "Just Resize" + INNER_FIT = "Crop and Resize" + OUTER_FIT = "Resize and Fill" + + def int_value(self): + if self == ResizeMode.RESIZE: + return 0 + elif self == ResizeMode.INNER_FIT: + return 1 + elif self == ResizeMode.OUTER_FIT: + return 2 + assert False, "NOTREACHED" + +#https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/internal_controlnet/external_code.py#L89 +#Replaced logger with internal log +def pixel_perfect_resolution( + image: np.ndarray, + target_H: int, + target_W: int, + resize_mode: ResizeMode, +) -> int: + """ + Calculate the estimated resolution for resizing an image while preserving aspect ratio. + + The function first calculates scaling factors for height and width of the image based on the target + height and width. Then, based on the chosen resize mode, it either takes the smaller or the larger + scaling factor to estimate the new resolution. + + If the resize mode is OUTER_FIT, the function uses the smaller scaling factor, ensuring the whole image + fits within the target dimensions, potentially leaving some empty space. + + If the resize mode is not OUTER_FIT, the function uses the larger scaling factor, ensuring the target + dimensions are fully filled, potentially cropping the image. + + After calculating the estimated resolution, the function prints some debugging information. + + Args: + image (np.ndarray): A 3D numpy array representing an image. The dimensions represent [height, width, channels]. + target_H (int): The target height for the image. + target_W (int): The target width for the image. + resize_mode (ResizeMode): The mode for resizing. + + Returns: + int: The estimated resolution after resizing. + """ + raw_H, raw_W, _ = image.shape + + k0 = float(target_H) / float(raw_H) + k1 = float(target_W) / float(raw_W) + + if resize_mode == ResizeMode.OUTER_FIT: + estimation = min(k0, k1) * float(min(raw_H, raw_W)) + else: + estimation = max(k0, k1) * float(min(raw_H, raw_W)) + + log.debug(f"Pixel Perfect Computation:") + log.debug(f"resize_mode = {resize_mode}") + log.debug(f"raw_H = {raw_H}") + log.debug(f"raw_W = {raw_W}") + log.debug(f"target_H = {target_H}") + log.debug(f"target_W = {target_W}") + log.debug(f"estimation = {estimation}") + + return int(np.round(estimation)) + +#https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/scripts/controlnet.py#L404 +def safe_numpy(x): + # A very safe method to make sure that Apple/Mac works + y = x + + # below is very boring but do not change these. If you change these Apple or Mac may fail. + y = y.copy() + y = np.ascontiguousarray(y) + y = y.copy() + return y + +#https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/scripts/utils.py#L140 +def get_unique_axis0(data): + arr = np.asanyarray(data) + idxs = np.lexsort(arr.T) + arr = arr[idxs] + unique_idxs = np.empty(len(arr), dtype=np.bool_) + unique_idxs[:1] = True + unique_idxs[1:] = np.any(arr[:-1, :] != arr[1:, :], axis=-1) + return arr[unique_idxs] + +#Ref: https://github.com/ltdrdata/ComfyUI-Manager/blob/284e90dc8296a2e1e4f14b4b2d10fba2f52f0e53/__init__.py#L14 +def handle_stream(stream, prefix): + for line in stream: + print(prefix, line, end="") + + +def run_script(cmd, cwd='.'): + process = subprocess.Popen(cmd, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1) + + stdout_thread = threading.Thread(target=handle_stream, args=(process.stdout, "")) + stderr_thread = threading.Thread(target=handle_stream, args=(process.stderr, "[!]")) + + stdout_thread.start() + stderr_thread.start() + + stdout_thread.join() + stderr_thread.join() + + return process.wait() + +def nms(x, t, s): + x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) + + f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) + f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) + f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) + f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) + + y = np.zeros_like(x) + + for f in [f1, f2, f3, f4]: + np.putmask(y, cv2.dilate(x, kernel=f) == x, x) + + z = np.zeros_like(y, dtype=np.uint8) + z[y > t] = 255 + return z diff --git a/custom_nodes/comfyui_pulid_flux_ll/.gitignore b/custom_nodes/comfyui_pulid_flux_ll/.gitignore new file mode 100644 index 00000000..82f92755 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; 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model.model_options["transformer_options"] = to + else: + to["patches_replace"]["dit"][key].add(pulid_patch, **patch_kwargs) + +def pulid_patch(img, pulid_model=None, ca_idx=None, weight=1.0, embedding=None, mask=None, transformer_options={}): + pulid_img = weight * pulid_model.model.pulid_ca[ca_idx].to(img.device)(embedding, img) + if mask is not None: + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + latent_image_shape = pulid_temp_attrs.get("latent_image_shape", None) + if latent_image_shape is not None: + bs, c, h, w = latent_image_shape + mask = comfy.sampler_helpers.prepare_mask(mask, (bs, c, h, w), img.device) + patch_size = transformer_options[PatchKeys.running_net_model].patch_size + mask = comfy.ldm.common_dit.pad_to_patch_size(mask, (patch_size, patch_size)) + mask = rearrange(mask, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + # (b, seq_len, _) =>(b, seq_len, pulid.dim) + mask = mask[..., 0].unsqueeze(-1).repeat(1, 1, pulid_img.shape[-1]).to(dtype=pulid_img.dtype) + del patch_size, latent_image_shape + + pulid_img = pulid_img * mask + + del mask, pulid_temp_attrs + + return pulid_img + +class DitDoubleBlockReplace: + def __init__(self, callback, **kwargs): + self.callback = [callback] + self.kwargs = [kwargs] + + def add(self, callback, **kwargs): + self.callback.append(callback) + self.kwargs.append(kwargs) + + for key, value in kwargs.items(): + setattr(self, key, value) + + def __call__(self, input_args, extra_options): + transformer_options = extra_options["transformer_options"] + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + sigma = pulid_temp_attrs["timesteps"][0].detach().cpu().item() + out = extra_options["original_block"](input_args) + img = out['img'] + temp_img = img + for i, callback in enumerate(self.callback): + if self.kwargs[i]["sigma_start"] >= sigma >= self.kwargs[i]["sigma_end"]: + img = img + callback(temp_img, + pulid_model=self.kwargs[i]['pulid_model'], + ca_idx=self.kwargs[i]['ca_idx'], + weight=self.kwargs[i]['weight'], + embedding=self.kwargs[i]['embedding'], + mask = self.kwargs[i]['mask'], + transformer_options=transformer_options + ) + out['img'] = img + + del temp_img, pulid_temp_attrs, sigma, transformer_options, img + + return out + + +class LastDitDoubleBlockReplace(DitDoubleBlockReplace): + def __call__(self, input_args, extra_options): + out = super().__call__(input_args, extra_options) + transformer_options = extra_options["transformer_options"] + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + pulid_temp_attrs["double_blocks_txt"] = out['txt'] + return out + +class DitSingleBlockReplace: + def __init__(self, callback, **kwargs): + self.callback = [callback] + self.kwargs = [kwargs] + + def add(self, callback, **kwargs): + self.callback.append(callback) + self.kwargs.append(kwargs) + + for key, value in kwargs.items(): + setattr(self, key, value) + + def __call__(self, input_args, extra_options): + transformer_options = extra_options["transformer_options"] + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + + out = extra_options["original_block"](input_args) + + sigma = pulid_temp_attrs["timesteps"][0].detach().cpu().item() + img = out['img'] + txt = pulid_temp_attrs['double_blocks_txt'] + real_img, txt = img[:, txt.shape[1]:, ...], img[:, :txt.shape[1], ...] + temp_img = real_img + for i, callback in enumerate(self.callback): + if self.kwargs[i]["sigma_start"] >= sigma >= self.kwargs[i]["sigma_end"]: + real_img = real_img + callback(temp_img, + pulid_model=self.kwargs[i]['pulid_model'], + ca_idx=self.kwargs[i]['ca_idx'], + weight=self.kwargs[i]['weight'], + embedding=self.kwargs[i]['embedding'], + mask=self.kwargs[i]['mask'], + transformer_options = transformer_options, + ) + + img = torch.cat((txt, real_img), 1) + + out['img'] = img + + del temp_img, pulid_temp_attrs, sigma, transformer_options, real_img, img + + return out + +def pulid_forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, +) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + transformer_options[PatchKeys.running_net_model] = self + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + + for i, block in enumerate(self.double_blocks): + # 0 -> 18 + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask + }, + { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + # 0 -> 37 + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask + }, + { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1]:, ...] += add + + img = img[:, txt.shape[1]:, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + + del transformer_options[PatchKeys.running_net_model] + + return img + + +def pulid_enter(img, img_ids, txt, txt_ids, timesteps, y, guidance, control, attn_mask, transformer_options): + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + pulid_temp_attrs['timesteps'] = timesteps + return img, img_ids, txt, txt_ids, timesteps, y, guidance, control, attn_mask + + +def pulid_patch_double_blocks_after(img, txt, transformer_options): + pulid_temp_attrs = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + pulid_temp_attrs['double_blocks_txt'] = txt + return img, txt diff --git a/custom_nodes/comfyui_pulid_flux_ll/README.md b/custom_nodes/comfyui_pulid_flux_ll/README.md new file mode 100644 index 00000000..f3b1c93d --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/README.md @@ -0,0 +1,108 @@ +[δΈ­ζ–‡ζ–‡ζ‘£](README_CN.md) + +- Solved [ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) model pollution problem. +- Supported use with `TeaCache` (Need use with [ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)). +- Supported use with [Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed), supported by `Comfy-WaveSpeed` in [commit-36ba3c8](https://github.com/chengzeyi/Comfy-WaveSpeed/commit/36ba3c8b74735d4521828507a4bf323df1a9a9d0). +- Supported simple use with `First Block Cache` (Can use with [ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)). + +Must uninstall or disable `ComfyUI-PuLID-Flux` and other PuLID-Flux nodes before install this plugin. Due to certain reasons, I used the same node's name `ApplyPulidFlux`. + +Need upgrade ComfyUI Version>=0.3.7 + +## Update logs +### 2025.02.19 +- Fix: when selecting a face from multiple faces as a reference, embeddings and alignment features maybe not from the same face. +### 2025.02.18 +- Supported selecting a face from multiple faces as a reference. [Example workflow](examples/PuLID_select_ref_face.png). +### 2025.01.27 +- Changed the model path of facexlib to `ComfyUI/models/facexlib/`. +- When automatically downloading, modify the path of Antelope v2 model to `ComfyUI/models/insightface/models/antelopev2/`. +- Changed the model path of EVA_CLIP_L_14_336 to `ComfyUI/models/clip/`. + +## Preview (Image with WorkFlow) +![save api extended](examples/PuLID_with_speedup.png) +![save api extended](examples/PuLID_with_attn_mask.png) +![save api extended](examples/PuLID_select_ref_face.png) + +## Install + +- Manual +```shell + cd custom_nodes + git clone https://github.com/lldacing/ComfyUI_PuLID_Flux_ll.git + cd ComfyUI_PuLID_Flux_ll + pip install -r requirements.txt + # restart ComfyUI +``` + +Tips: + +- If you use `ComfyUI_windows_portable` and encounter the following error, please see https://github.com/deepinsight/insightface/issues/2576 +``` +insightface/thirdparty/face3d/mesh/cython/mesh_core_cython.cpp(36): fatal error C1083: ζ— ζ³•ζ‰“εΌ€εŒ…ζ‹¬ζ–‡δ»Ά: β€œPython.h”: No such file or directory + error: command 'd:\\installed\\Microsoft Visual Studio\\2022\\BuildTools\\VC\\Tools\\MSVC\\14.42.34433\\bin\\HostX86\\x64\\cl.exe' failed with exit code 2 + [end of output] + + note: This error originates from a subprocess, and is likely not a problem with pip. + ERROR: Failed building wheel for insightface +Failed to build insightface +``` + +## Models +### Available Flux models +- 32bit/16bit (~22GB VRAM): [model](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors) +- 8bit gguf (~12GB VRAM): [model](https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q8_0.gguf), [encoder](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/blob/main/t5-v1_1-xxl-encoder-Q8_0.gguf) +- 8 bit FP8 e5m2 (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e5m2.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- 8 bit FP8 e4m3fn (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e4m3fn.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- Clip and VAE (for all models): [clip](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors), [vae](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors) + +#### For GGUF models you will need to install [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) + +### PuLID models +- Download [PuLID-Flux](https://huggingface.co/guozinan/PuLID/resolve/main/pulid_flux_v0.9.1.safetensors?download=true) => `ComfyUI/models/pulid/`. +- (Support auto-download) Download [EVA02-CLIP-L-14-336](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt?download=true) => `ComfyUI/models/clip/`. +- (Support auto-download) Download all models like `*.onnx` from [AntelopeV2](https://huggingface.co/MonsterMMORPG/tools/tree/main) => `ComfyUI/models/insightface/models/antelopev2/`. +- (Support auto-download) Download [parsing_bisenet](https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth), [parsing_parsenet](https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth) and [Resnet50](https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth) => `ComfyUI/models/facexlib/`. + +## Nodes +- PulidFluxModelLoader + - See chapter [PuLID models](#pulid-models) +- PulidFluxInsightFaceLoader + - See chapter [PuLID models](#pulid-models) +- PulidFluxEvaClipLoader + - See chapter [PuLID models](#pulid-models) +- ApplyPulidFlux + - Solved the model pollution problem of the original plugin ComfyUI-PuLID-Flux + - `attn_mask` ~~may not work correctly (I have no idea how to apply it, I have tried multiple methods and the results have been not satisfactory)~~ works now. + - If you want use with [TeaCache](https://github.com/ali-vilab/TeaCache), must put it before node [`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll). + - If you want use with [Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed), must put it before node `ApplyFBCacheOnModel`. +- FixPulidFluxPatch (Deprecated) + - If you want use with [TeaCache](https://github.com/ali-vilab/TeaCache), must link it after node `ApplyPulidFlux`, and link node [`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll) after it. +- PulidFluxOptions + - `input_faces_order` - Sorting rule for detected bboxes. + - `left-right`: Sort the left boundary of bbox by column from left to right. + - `right-left`: Reverse order of `left-right` (Sort the left boundary of bbox by column from right to left). + - `top-bottom`: Sort the top boundary of bbox by row from top to bottom. + - `bottom-top`: Reverse order of `top-bottom` (Sort the top boundary of bbox by row from bottom to top). + - `small-large`: Sort the area of bbox from small to large. + - `large-small`: Sort the area of bbox from large to small. + - `input_faces_index` - The target index of the sorted bboxes. + - `input_faces_align_mode` - Choose the detection method for aligning facial features. + - `0`: Old version method, When there is a face in an image, the selected facial embedding amount and alignment features maybe not consistent. + - `1`: Keep the selected facial embedding amount and alignment features consistent. + - There is a slight difference between the two mode, with the `align_face` value of `1` resulting smaller area than the `embed_face` value of `0`. +- PulidFluxFaceDetector + - Can check the facial features applied in `ApplyPulidFlux`. + - When `input_faces_align_mode = 0`, the `embed_face` and `align_face` should be the same face, but they are generated by different detectors, and the number detected may be not consistent, so they may be not the same face. + - When `input_faces_align_mode = 1`, the `embed_face` and `align_face` are always the same face, they are generated by same detectors. + - `face_bbox_image` - Draw the detected facial bounding box (the result of the `embed_face`'s detector). + +## Thanks + +[ToTheBeginning/PuLID](https://github.com/ToTheBeginning/PuLID) + +[ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) + +[TeaCache](https://github.com/ali-vilab/TeaCache) + +[Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed) diff --git a/custom_nodes/comfyui_pulid_flux_ll/README_CN.md b/custom_nodes/comfyui_pulid_flux_ll/README_CN.md new file mode 100644 index 00000000..391c08c8 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/README_CN.md @@ -0,0 +1,104 @@ +[English](README.md) + +- 解决插仢 [ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) ε­˜εœ¨ηš„ζ¨‘εž‹ζ±‘ζŸ“ι—ι’˜γ€‚ +- ζ”―ζŒδ½Ώη”¨[TeaCache](https://github.com/ali-vilab/TeaCache)εŠ ι€ŸοΌˆ`TeaCache`εŠ ι€Ÿιœ€θ¦ι…εˆ[ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)使用）。 +- ζ”―ζŒδ½Ώη”¨[Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed)εŠ ι€Ÿ, Comfy-WaveSpeed在[提亀θ°ε½•-36ba3c8](https://github.com/chengzeyi/Comfy-WaveSpeed/commit/36ba3c8b74735d4521828507a4bf323df1a9a9d0)δΈ­ζδΎ›ζ”―ζŒγ€‚ +- ζ”―ζŒδ½Ώη”¨η€ε•ηš„`First Block Cache`εŠ ι€ŸοΌˆε―δ»₯ι…εˆ[ComfyUI_Patches_ll](https://github.com/lldacing/ComfyUI_Patches_ll)使用）。 + +在ε‰θ£…ζ­€ζ’δ»ΆδΉ‹ε‰οΌŒεΏ…ι‘»εΈθ½½ζˆ–η¦η”¨`ComfyUI-PuLID-Flux`ε’Œε…Άδ»–PuLID FluxθŠ‚η‚Ή, ε› δΈΊη”±δΊŽζŸδΊ›εŽŸε› οΌŒζˆ‘δ½Ώη”¨δΊ†εŒζ ·ηš„θŠ‚η‚Ήε`ApplyPulidFlux` + +ComfyUIδΈ»δ½“η‰ˆζœ¬ιœ€θ¦>=0.3.7 + +## ζ›΄ζ–°ζ—₯εΏ— +### 2025.02.19 +- θ§£ε†³ε€šεΌ δΊΊθ„Έζ—Άι€‰ζ‹©ηš„δΊΊθ„Έε΅Œε…₯ι‡ε’Œε―Ήι½η‰ΉεΎδΈζ˜―εŒδΈ€δΈͺδΊΊθ„Έηš„ι—ι’˜γ€‚ +### 2025.02.18 +- ζ”―ζŒδ»Žε«ζœ‰ε€šεΌ θ„Έηš„ε›Ύη‰‡δΈ­ι€‰ζ‹©δΈ€εΌ θ„Έδ½œδΈΊε‚θ€ƒγ€‚[η€ΊδΎ‹ε·₯作桁](examples/PuLID_select_ref_face.png). +### 2025.01.27 +- δΏζ”Ή facexlib ηš„ζ¨‘εž‹θ·―εΎ„δΈΊ `ComfyUI/models/facexlib/`. +- θ‡ͺεŠ¨δΈ‹θ½½ζ—Ά δΏζ”Ή Antelopev2 ζ¨‘εž‹ηš„θ·―εΎ„δΈΊ `ComfyUI/models/insightface/models/antelopev2/`. +- δΏζ”Ή EVA_CLIP_L_14_336 ηš„ζ¨‘εž‹θ·―εΎ„δΈΊ `ComfyUI/models/clip/`. + +## ι’„θ§ˆ (图片含ε·₯作桁) +![save api extended](examples/PuLID_with_speedup.png) +![save api extended](examples/PuLID_with_attn_mask.png) +![save api extended](examples/PuLID_select_ref_face.png) + +## ε‰θ£… + +- ζ‰‹εŠ¨ +```shell + cd custom_nodes + git clone https://github.com/lldacing/ComfyUI_PuLID_Flux_ll.git + cd ComfyUI_PuLID_Flux_ll + pip install -r requirements.txt + # 重启 ComfyUI +``` +ε‰θ£…ι—ι’˜: + +- ε¦‚ζžœδ½Ώη”¨`ComfyUI_windows_portable`εΉΆι‡εˆ°δ»₯δΈ‹ι”™θ――, θ―·ζŸ₯ηœ‹ https://github.com/deepinsight/insightface/issues/2576 +``` +insightface/thirdparty/face3d/mesh/cython/mesh_core_cython.cpp(36): fatal error C1083: ζ— ζ³•ζ‰“εΌ€εŒ…ζ‹¬ζ–‡δ»Ά: β€œPython.h”: No such file or directory + error: command 'd:\\installed\\Microsoft Visual Studio\\2022\\BuildTools\\VC\\Tools\\MSVC\\14.42.34433\\bin\\HostX86\\x64\\cl.exe' failed with exit code 2 + [end of output] + + note: This error originates from a subprocess, and is likely not a problem with pip. + ERROR: Failed building wheel for insightface +Failed to build insightface +``` + +## ζ¨‘εž‹ +### ε―η”¨ηš„ Flux ζ¨‘εž‹ +- 32bit/16bit (~22GB VRAM): [model](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors) +- 8bit gguf (~12GB VRAM): [model](https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q8_0.gguf), [encoder](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/blob/main/t5-v1_1-xxl-encoder-Q8_0.gguf) +- 8 bit FP8 e5m2 (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e5m2.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- 8 bit FP8 e4m3fn (~12GB VRAM): [model](https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8-e4m3fn.safetensors), [encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp8_e4m3fn.safetensors) +- Clip and VAE (for all models): [clip](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors), [vae](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors) + +#### θ‹₯使用 GGUF ιœ€θ¦ε‰θ£… [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) + +### PuLID ζ¨‘εž‹ +- δΈ‹θ½½ [PuLID-Flux](https://huggingface.co/guozinan/PuLID/resolve/main/pulid_flux_v0.9.1.safetensors?download=true) 到η›ε½• `ComfyUI/models/pulid/` +- οΌˆζ”―ζŒθ‡ͺεŠ¨δΈ‹θ½½οΌ‰δΈ‹θ½½ [EVA02-CLIP-L-14-336](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt?download=true) 到η›ε½• `ComfyUI/models/clip/` +- οΌˆζ”―ζŒθ‡ͺεŠ¨δΈ‹θ½½οΌ‰δ»Ž [AntelopeV2](https://huggingface.co/MonsterMMORPG/tools/tree/main) δΈ‹θ½½ζ‰€ζœ‰`*.onnx`ζ¨‘εž‹ζ–‡δ»Άεˆ°η›ε½• `ComfyUI/models/insightface/models/antelopev2/`. +- οΌˆζ”―ζŒθ‡ͺεŠ¨δΈ‹θ½½οΌ‰δΈ‹θ½½ [parsing_bisenet](https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth), [parsing_parsenet](https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth) and [Resnet50](https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth) 到η›ε½• `ComfyUI/models/facexlib/`. + +## θŠ‚η‚Ή +- PulidFluxModelLoader +- PulidFluxInsightFaceLoader +- PulidFluxEvaClipLoader +- ApplyPulidFlux + - θ§£ε†³δΊ†εŽŸζ’δ»ΆδΈ­ζ¨‘εž‹ζ±‘ζŸ“ηš„ι—ι’˜ + - `attn_mask`~~可能不能正η‘ε·₯作， ε› δΈΊζˆ‘δΈηŸ₯道如何εžηŽ°εƒοΌŒ ε°θ―•δΊ†ε€šη§ζ–ΉεΌζ•ˆζžœιƒ½ζœͺθƒ½θΎΎεˆ°ι’„ζœŸ~~，可δ»₯ζ­£εΈΈε·₯δ½œδΊ†γ€‚ + - 使用 [TeaCache](https://github.com/ali-vilab/TeaCache)εŠ ι€Ÿ, εΏ…ι‘»εŠ εœ¨[`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll)之前. + - 使用 [Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed)εŠ ι€Ÿ, εΏ…ι‘»εŠ εœ¨[`ApplyFBCacheOnModel`](https://github.com/lldacing/ComfyUI_Patches_ll)之前. +- FixPulidFluxPatch (已弃用) + - ε¦‚ζžœζƒ³δ½Ώη”¨ [TeaCache](https://github.com/ali-vilab/TeaCache)εŠ ι€Ÿ, εΏ…ι‘»εŠ εœ¨ `ApplyPulidFlux` θŠ‚η‚ΉδΉ‹εŽ, 幢在后青连ζŽ₯θŠ‚η‚Ή [`FluxForwardOverrider` and `ApplyTeaCachePatch`](https://github.com/lldacing/ComfyUI_Patches_ll). +- PulidFluxOptions + - `input_faces_order` - ε―Ήζ£€ζ΅‹εˆ°ηš„θ„Έιƒ¨θΎΉη•Œζ‘†ηš„ζŽ’εΊθ§„εˆ™γ€‚ + - `left-right`: ζŒ‰εˆ—δ»Žε·¦εˆ°ε³ε―Ήbboxηš„ε·¦θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊγ€‚ + - `right-left`: `left-right`ηš„ε€’εΊοΌˆζŒ‰εˆ—δ»Žε³εˆ°ε·¦ε―Ήbboxηš„ε·¦θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊοΌ‰γ€‚ + - `top-bottom`: ζŒ‰θ‘Œδ»ŽδΈŠεˆ°δΈ‹ε―Ήbboxηš„ι‘Άιƒ¨θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊγ€‚ + - `bottom-top`: `top-bottom`ηš„ε€’εΊοΌˆζŒ‰θ‘Œδ»ŽδΈ‹εˆ°δΈŠε―Ήbboxηš„ι‘Άιƒ¨θΎΉη•ŒθΏ›θ‘ŒζŽ’εΊοΌ‰γ€‚ + - `small-large`: ζŒ‰bboxηš„ι’η§―δ»Žε°εˆ°ε€§ζŽ’εΊγ€‚ + - `large-small`: ζŒ‰bboxηš„ι’η§―δ»Žε€§εˆ°ε°ζŽ’εΊγ€‚ + - `input_faces_index` - δ»ŽζŽ’εΊεŽηš„bboxι€‰ε–ηš„η΄’εΌ•ε·γ€‚ + - `input_faces_align_mode` - ι€‰ζ‹©ε―Ήι½θ„Έιƒ¨η‰ΉεΎηš„ζ£€ζ΅‹ζ–ΉεΌγ€‚ + - `0`: ζ—§η‰ˆζœ¬ζ–ΉεΌοΌŒδΈ€εΌ ε›Ύη‰‡δΈ­ζœ‰εΌ θ„Έζ—Άι€‰ζ‹©ηš„θ„Έιƒ¨ε΅Œε…₯ι‡ε’Œε―Ήι½η‰ΉεΎε―θƒ½δΈδΈ€θ‡΄γ€‚ + - `1`: δΏζŒι€‰ζ‹©ηš„θ„Έιƒ¨ε΅Œε…₯ι‡ε’Œε―Ήι½η‰ΉεΎδΈ€θ‡΄γ€‚ + - δΈ€η§ε‡Ίε›Ύζœ‰η»†εΎε·εˆ«οΌŒε€Ό`1`ηš„`align_face`η»“ζžœε›Ύζ―”`0`ηš„`embed_face`θŒƒε›΄ε°δΈ€η‚Ήγ€‚ +- PulidFluxFaceDetector + - 用ζ₯ζ£€ζŸ₯在`ApplyPulidFlux`εžι™…δ½Ώη”¨ηš„ι’部特征。 + - `input_faces_align_mode = 0`ζ—ΆοΌŒ`embed_face` ε’Œ `align_face` 理θΊδΈŠεΊ”θ―₯ζ˜―εŒδΈ€εΌ θ„ΈοΌŒδ½†εƒδ»¬η”±δΈεŒηš„ζ£€ζ΅‹ε™¨δΊ§η”ŸοΌŒε―θƒ½ζ£€ζ΅‹εˆ°ηš„ζ•°ι‡δΈδΈ€θ‡΄οΌŒε› ζ­€δΈ€εΌ ε›Ύε―θƒ½δΈζ˜―εŒδΈ€εΌ θ„Έγ€‚ + - `input_faces_align_mode = 1`ζ—ΆοΌŒ`embed_face` ε’Œ `align_face` η”±η›ΈεŒηš„ζ£€ζ΅‹ε™¨δΊ§η”ŸοΌŒδΈ€εΌ ε›Ύε§‹η»ˆζ˜―εŒδΈ€εΌ θ„Έγ€‚ + - `face_bbox_image` - η”»ε‡Ίζ£€ζ΅‹εˆ°ηš„θ„Έιƒ¨θΎΉη•Œζ‘†οΌˆ`embed_face`ηš„ζ£€ζ΅‹ε™¨η»“ζžœοΌ‰γ€‚ + +## ζ„Ÿθ°’ + +[ToTheBeginning/PuLID](https://github.com/ToTheBeginning/PuLID) + +[ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux) + +[TeaCache](https://github.com/ali-vilab/TeaCache) + +[Comfy-WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed) \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/__init__.py b/custom_nodes/comfyui_pulid_flux_ll/__init__.py new file mode 100644 index 00000000..7ca69bca --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/__init__.py @@ -0,0 +1,3 @@ +from .pulidflux import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/comfyui_pulid_flux_ll/encoders_flux.py b/custom_nodes/comfyui_pulid_flux_ll/encoders_flux.py new file mode 100644 index 00000000..c4c87bd0 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/encoders_flux.py @@ -0,0 +1,213 @@ +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttentionCA(nn.Module): + def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents, mask=None): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + k, v = self.to_kv(x).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + # if mask is not None: + # not sure + # weight.shape (bs, n_heads, seq_len, seq_len) + # mask.shape (bs, seq_len, _) -> (bs, 1, 1, seq_len) + # mask = mask[:,:, :1].view(b, 1, 1, -1) + # weight = weight.masked_fill(mask == 0, float('-inf')) + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class IDFormer(nn.Module): + """ + - perceiver resampler like arch (compared with previous MLP-like arch) + - we concat id embedding (generated by arcface) and query tokens as latents + - latents will attend each other and interact with vit features through cross-attention + - vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two + IDFormer layers + """ + def __init__( + self, + dim=1024, + depth=10, + dim_head=64, + heads=16, + num_id_token=5, + num_queries=32, + output_dim=2048, + ff_mult=4, + ): + super().__init__() + + self.num_id_token = num_id_token + self.dim = dim + self.num_queries = num_queries + assert depth % 5 == 0 + self.depth = depth // 5 + scale = dim ** -0.5 + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale) + self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim)) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + for i in range(5): + setattr( + self, + f'mapping_{i}', + nn.Sequential( + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim), + ), + ) + + self.id_embedding_mapping = nn.Sequential( + nn.Linear(1280, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim * num_id_token), + ) + + def forward(self, x, y): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.id_embedding_mapping(x) + x = x.reshape(-1, self.num_id_token, self.dim) + + latents = torch.cat((latents, x), dim=1) + + for i in range(5): + vit_feature = getattr(self, f'mapping_{i}')(y[i]) + ctx_feature = torch.cat((x, vit_feature), dim=1) + for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]: + latents = attn(ctx_feature, latents) + latents + latents = ff(latents) + latents + + latents = latents[:, :self.num_queries] + latents = latents @ self.proj_out + return latents diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/__init__.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/__init__.py new file mode 100644 index 00000000..fa2d014b --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/__init__.py @@ -0,0 +1,11 @@ +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_transforms +from .factory import list_models, add_model_config, get_model_config, load_checkpoint +from .loss import ClipLoss +from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg,\ + convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\ + get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +from .tokenizer import SimpleTokenizer, tokenize +from .transform import image_transform \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/bpe_simple_vocab_16e6.txt.gz b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 00000000..7b5088a5 Binary files /dev/null and b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/bpe_simple_vocab_16e6.txt.gz differ diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/constants.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/constants.py new file mode 100644 index 00000000..a670bb3f --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/constants.py @@ -0,0 +1,2 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/eva_vit_model.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/eva_vit_model.py new file mode 100644 index 00000000..51db88cf --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/eva_vit_model.py @@ -0,0 +1,548 @@ +# -------------------------------------------------------- +# Adapted from https://github.com/microsoft/unilm/tree/master/beit +# -------------------------------------------------------- +import math +import os +from functools import partial +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +except: + from timm.layers import drop_path, to_2tuple, trunc_normal_ + +from .transformer import PatchDropout +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers + import xformers.ops as xops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + drop=0., + subln=False, + + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.ffn_ln(x) + + x = self.fc2(x) + x = self.drop(x) + return x + +class SwiGLU(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., + norm_layer=nn.LayerNorm, subln=False): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.subln = subln + if self.subln: + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + else: + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() + # self.proj = nn.Linear(all_head_dim, all_head_dim) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + self.rope = rope + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + B, N, C = x.shape + if self.subln: + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + else: + + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C + q, k, v = qkv[0], qkv[1], qkv[2] + + if self.rope: + # slightly fast impl + q_t = q[:, :, 1:, :] + ro_q_t = self.rope(q_t) + q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) + + k_t = k[:, :, 1:, :] + ro_k_t = self.rope(k_t) + k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) + + if self.xattn: + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale, + ) + x = x.reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias.type_as(attn) + + if attn_mask is not None: + attn_mask = attn_mask.bool() + attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf")) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False, + subln=False, naiveswiglu=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, + xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + if naiveswiglu: + self.mlp = SwiGLU( + in_features=dim, + hidden_features=mlp_hidden_dim, + subln=subln, + norm_layer=norm_layer, + ) + else: + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + subln=subln, + drop=drop + ) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + self.postnorm = postnorm + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + if self.gamma_1 is None: + if self.postnorm: + x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + if self.postnorm: + x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class EVAVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0., + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False, + use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False, + pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False): + super().__init__() + + if not XFORMERS_IS_AVAILBLE: + xattn = False + + self.image_size = img_size + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + + if rope: + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + self.rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + # patch_dropout=patch_dropout + ) + else: + self.rope = None + + self.naiveswiglu = naiveswiglu + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, + xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu) + for i in range(depth)]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) + + self.apply(self._init_weights) + self.fix_init_weight() + + if isinstance(self.head, nn.Linear): + trunc_normal_(self.head.weight, std=.02) + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.grad_checkpointing = grad_checkpointing + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + if self.naiveswiglu: + rescale(layer.mlp.w3.weight.data, layer_id + 1) + else: + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def get_cast_dtype(self) -> torch.dtype: + return self.blocks[0].mlp.fc2.weight.dtype + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, return_all_features=False, return_hidden=False, shuffle=False): + + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + if shuffle: + idx = torch.randperm(x.shape[1]) + 1 + zero = torch.LongTensor([0, ]) + idx = torch.cat([zero, idx]) + pos_embed = self.pos_embed[:, idx] + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if shuffle: + x = x + pos_embed + elif self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + if os.getenv('RoPE') == '1': + if self.training and not isinstance(self.patch_dropout, nn.Identity): + x, patch_indices_keep = self.patch_dropout(x) + self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep) + else: + self.rope.forward = partial(self.rope.forward, patch_indices_keep=None) + x = self.patch_dropout(x) + else: + x = self.patch_dropout(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + hidden_states = [] + for idx, blk in enumerate(self.blocks): + if (0 < idx <= 20) and (idx % 4 == 0) and return_hidden: + hidden_states.append(x) + if self.grad_checkpointing: + x = checkpoint(blk, x, (rel_pos_bias,)) + else: + x = blk(x, rel_pos_bias=rel_pos_bias) + + if not return_all_features: + x = self.norm(x) + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)), hidden_states + else: + return x[:, 0], hidden_states + return x + + def forward(self, x, return_all_features=False, return_hidden=False, shuffle=False): + if return_all_features: + return self.forward_features(x, return_all_features, return_hidden, shuffle) + x, hidden_states = self.forward_features(x, return_all_features, return_hidden, shuffle) + x = self.head(x) + if return_hidden: + return x, hidden_states + return x diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/factory.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/factory.py new file mode 100644 index 00000000..c717b211 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/factory.py @@ -0,0 +1,520 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +from typing import Optional, Tuple, Union, Dict, Any +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + get_cast_dtype +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model +from .transform import image_transform +from .tokenizer import HFTokenizer, tokenize +from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed + + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, "r", encoding="utf8") as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))) + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name): + config = get_model_config(model_name) + tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize + return tokenizer + + +# loading openai CLIP weights when is_openai=True for training +def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]): + if is_openai: + model = torch.jit.load(checkpoint_path, map_location="cpu").eval() + state_dict = model.state_dict() + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + else: + checkpoint = torch.load(checkpoint_path, map_location=map_location, weights_only=False) + for mk in model_key.split('|'): + if isinstance(checkpoint, dict) and mk in checkpoint: + state_dict = checkpoint[mk] + break + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + for k in skip_list: + if k in list(state_dict.keys()): + logging.info(f"Removing key {k} from pretrained checkpoint") + del state_dict[k] + + if os.getenv('RoPE') == '1': + for k in list(state_dict.keys()): + if 'freqs_cos' in k or 'freqs_sin' in k: + del state_dict[k] + return state_dict + + + +def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True): + state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'): + state_dict['logit_scale'] = state_dict['text.logit_scale'] + del state_dict['text.logit_scale'] + + # resize_clip_pos_embed for CLIP and open CLIP + if 'visual.positional_embedding' in state_dict: + resize_clip_pos_embed(state_dict, model) + # specified to eva_vit_model + elif 'visual.pos_embed' in state_dict: + resize_evaclip_pos_embed(state_dict, model) + + # resize_clip_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}") + return incompatible_keys + +def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if not k.startswith('visual.'): + del state_dict[k] + for k in list(state_dict.keys()): + if k.startswith('visual.'): + new_k = k[7:] + state_dict[new_k] = state_dict[k] + del state_dict[k] + return state_dict + +def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if k.startswith('visual.'): + del state_dict[k] + return state_dict + +def get_pretrained_tag(pretrained_model): + pretrained_model = pretrained_model.lower() + if "laion" in pretrained_model or "open_clip" in pretrained_model: + return "open_clip" + elif "openai" in pretrained_model: + return "clip" + elif "eva" in pretrained_model and "clip" in pretrained_model: + return "eva_clip" + else: + return "other" + +def load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=True, + visual_model=None, + text_model=None, + model_key="model|module|state_dict", + skip_list=[]): + visual_tag = get_pretrained_tag(visual_model) + text_tag = get_pretrained_tag(text_model) + + logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}") + visual_incompatible_keys, text_incompatible_keys = None, None + if visual_checkpoint_path: + if visual_tag == "eva_clip" or visual_tag == "open_clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list) + elif visual_tag == "clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + # resize_clip_pos_embed for CLIP and open CLIP + if 'positional_embedding' in visual_state_dict: + resize_visual_pos_embed(visual_state_dict, model) + # specified to EVA model + elif 'pos_embed' in visual_state_dict: + resize_eva_pos_embed(visual_state_dict, model) + + visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict) + logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}") + logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}") + + if text_checkpoint_path: + if text_tag == "eva_clip" or text_tag == "open_clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list) + elif text_tag == "clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict) + + logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}") + logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}") + + return visual_incompatible_keys, text_incompatible_keys + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + cache_dir: Optional[str] = None, + local_dir: Optional[str] = None, + skip_list: list = [], +): + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + else: + model_cfg = get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if 'rope' in model_cfg.get('vision_cfg', {}): + if model_cfg['vision_cfg']['rope']: + os.environ['RoPE'] = "1" + else: + os.environ['RoPE'] = "0" + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout + + cast_dtype = get_cast_dtype(precision) + custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg']) + + + if custom_clip: + if 'hf_model_name' in model_cfg.get('text_cfg', {}): + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_cfg = {} + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir, local_dir=local_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, + checkpoint_path, + model_key="model|module|state_dict", + strict=False + ) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + else: + visual_checkpoint_path = '' + text_checkpoint_path = '' + + if pretrained_image: + pretrained_visual_model = pretrained_visual_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image) + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + elif pretrained_image_cfg: + visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_image): + visual_checkpoint_path = pretrained_image + else: + logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + + if pretrained_text: + pretrained_text_model = pretrained_text_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text) + if pretrained_image_cfg: + text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_text): + text_checkpoint_path = pretrained_text + else: + logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + + if visual_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).') + if text_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).') + + if visual_checkpoint_path or text_checkpoint_path: + load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=False, + visual_model=pretrained_visual_model, + text_model=pretrained_text_model, + model_key="model|module|state_dict", + skip_list=skip_list + ) + + if "fp16" in precision or "bf16" in precision: + logging.info(f'convert precision to {precision}') + model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16) + + model.to(device=device) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + if jit: + model = torch.jit.script(model) + + return model + + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + local_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + local_dir=local_dir, + skip_list=skip_list, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + + +def create_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + del model + + return preprocess_train, preprocess_val + +def create_model_from_pretrained( + model_name: str, + pretrained: str, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + is_frozen: bool = False, +): + if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained): + raise RuntimeError( + f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.' + f' Use open_clip.list_pretrained() to find one.') + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + cache_dir=cache_dir, + ) + + if is_frozen: + for param in model.parameters(): + param.requires_grad = False + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/hf_configs.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/hf_configs.py new file mode 100644 index 00000000..a8c9b704 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/hf_configs.py @@ -0,0 +1,57 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, + "bert": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + } +} diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/hf_model.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/hf_model.py new file mode 100644 index 00000000..c4b9fd85 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/hf_model.py @@ -0,0 +1,248 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch import TensorType +try: + import transformers + from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + +# utils +def _camel2snake(s): + return re.sub(r'(? TensorType: + # image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(x.device) + # attn_mask = (x != self.config.pad_token_id).long() + # out = self.transformer( + # input_ids=x, + # attention_mask=attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # ) + # pooled_out = self.pooler(out, attn_mask) + + # return self.itm_proj(pooled_out) + + def mask(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None): + if masked_indices is None: + masked_indices = torch.bernoulli(probability_matrix).bool() + + masked_indices[input_ids == self.tokenizer.pad_token_id] = False + masked_indices[input_ids == self.tokenizer.cls_token_id] = False + + if targets is not None: + targets[~masked_indices] = -100 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(input_ids.shape, 0.8)).bool() & masked_indices + input_ids[indices_replaced] = self.tokenizer.mask_token_id + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(input_ids.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(vocab_size, input_ids.shape, dtype=torch.long).to(device) + input_ids[indices_random] = random_words[indices_random] + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + + if targets is not None: + return input_ids, targets + else: + return input_ids + + def forward_mlm(self, input_ids, image_embeds, mlm_probability=0.25): + labels = input_ids.clone() + attn_mask = (input_ids != self.config.pad_token_id).long() + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(input_ids.device) + vocab_size = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["vocab_size"]) + probability_matrix = torch.full(labels.shape, mlm_probability) + input_ids, labels = self.mask(input_ids, vocab_size, input_ids.device, targets=labels, + probability_matrix = probability_matrix) + mlm_output = self.transformer(input_ids, + attention_mask = attn_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + labels = labels, + ) + return mlm_output.loss + # mlm_output = self.transformer(input_ids, + # attention_mask = attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # return_dict = True, + # ).last_hidden_state + # logits = self.mlm_proj(mlm_output) + + # # logits = logits[:, :-1, :].contiguous().view(-1, vocab_size) + # logits = logits[:, 1:, :].contiguous().view(-1, vocab_size) + # labels = labels[:, 1:].contiguous().view(-1) + + # mlm_loss = F.cross_entropy( + # logits, + # labels, + # # label_smoothing=0.1, + # ) + # return mlm_loss + + + def forward(self, x:TensorType) -> TensorType: + attn_mask = (x != self.config.pad_token_id).long() + out = self.transformer(input_ids=x, attention_mask=attn_mask) + pooled_out = self.pooler(out, attn_mask) + + return self.proj(pooled_out) + + def lock(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + if not unlocked_layers: # full freezing + for n, p in self.transformer.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + return + + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model") + embeddings = getattr( + self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"]) + modules = [embeddings, *layer_list][:-unlocked_layers] + # freeze layers + for module in modules: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.gradient_checkpointing_enable() + + def get_num_layers(self): + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + return len(layer_list) + + def init_parameters(self): + pass diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/loss.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/loss.py new file mode 100644 index 00000000..473f60d9 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/loss.py @@ -0,0 +1,138 @@ +import math +import torch +import torch.nn as nn +from torch.nn import functional as F + +try: + import torch.distributed.nn + from torch import distributed as dist + has_distributed = True +except ImportError: + has_distributed = False + +try: + import horovod.torch as hvd +except ImportError: + hvd = None + +from timm.loss import LabelSmoothingCrossEntropy + + +def gather_features( + image_features, + text_features, + local_loss=False, + gather_with_grad=False, + rank=0, + world_size=1, + use_horovod=False +): + assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' + if use_horovod: + assert hvd is not None, 'Please install horovod' + if gather_with_grad: + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + else: + with torch.no_grad(): + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) + gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + else: + # We gather tensors from all gpus + if gather_with_grad: + all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0) + # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0) + else: + gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] + dist.all_gather(gathered_image_features, image_features) + dist.all_gather(gathered_text_features, text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + + return all_image_features, all_text_features + + +class ClipLoss(nn.Module): + + def __init__( + self, + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + smoothing=0., + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + self.use_horovod = use_horovod + self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None + + # cache state + self.prev_num_logits = 0 + self.labels = {} + + def forward(self, image_features, text_features, logit_scale=1.): + device = image_features.device + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + # calculated ground-truth and cache if enabled + num_logits = logits_per_image.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + if self.label_smoothing_cross_entropy: + total_loss = ( + self.label_smoothing_cross_entropy(logits_per_image, labels) + + self.label_smoothing_cross_entropy(logits_per_text, labels) + ) / 2 + else: + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + acc = None + i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image) + t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text) + acc = {"i2t": i2t_acc, "t2i": t2i_acc} + return total_loss, acc \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model.py new file mode 100644 index 00000000..90f34b91 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model.py @@ -0,0 +1,439 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union +from functools import partial + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +try: + from .hf_model import HFTextEncoder +except: + HFTextEncoder = None +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .eva_vit_model import EVAVisionTransformer +from .transformer import LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + print("Nvidia APEX normalization not installed, using PyTorch LayerNorm") + +try: + import xformers.ops as xops +except ImportError: + xops = None + #print("Please 'pip install xformers'") + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + drop_path_rate: Optional[float] = None # drop path rate + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size + qkv_bias: bool = True + fusedLN: bool = False + xattn: bool = False + postnorm: bool = False + rope: bool = False + pt_hw_seq_len: int = 16 # 224/14 + intp_freq: bool = False + naiveswiglu: bool = False + subln: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + masked_language_modeling: bool = False + fusedLN: bool = False + xattn: bool = False + attn_mask: bool = True + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.eva_model_name: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNorm + + visual = EVAVisionTransformer( + img_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + num_classes=embed_dim, + use_mean_pooling=vision_cfg.global_average_pool, #False + init_values=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + embed_dim=vision_cfg.width, + depth=vision_cfg.layers, + num_heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + qkv_bias=vision_cfg.qkv_bias, + drop_path_rate=vision_cfg.drop_path_rate, + norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6), + xattn=vision_cfg.xattn, + rope=vision_cfg.rope, + postnorm=vision_cfg.postnorm, + pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14 + intp_freq= vision_cfg.intp_freq, + naiveswiglu= vision_cfg.naiveswiglu, + subln= vision_cfg.subln + ) + elif vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + embed_dim=embed_dim, + image_size=vision_cfg.image_size + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + global_average_pool=vision_cfg.global_average_pool, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + tokenizer_name=text_cfg.hf_tokenizer_name, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + masked_language_modeling=text_cfg.masked_language_modeling + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer= FusedLayerNorm if text_cfg.fusedLN else norm_layer, + xattn=text_cfg.xattn, + attn_mask=text_cfg.attn_mask, + ) + return text + +class CLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +class CustomCLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + itm_task: bool = False, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr, None) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + if isinstance(l, nn.Parameter): + l.data = l.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name) and isinstance(l, nn.Parameter): + attr = getattr(l, name, None) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + 'logit_scale' + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-B-16.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-B-16.json new file mode 100644 index 00000000..aad20580 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-B-16.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16, + "eva_model_name": "eva-clip-b-16", + "ls_init_value": 0.1, + "drop_path_rate": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json new file mode 100644 index 00000000..10027957 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-g-14.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-g-14.json new file mode 100644 index 00000000..5d338b4e --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA01-CLIP-g-14.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0.4, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-B-16.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-B-16.json new file mode 100644 index 00000000..e4a6e723 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-B-16.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "head_width": 64, + "patch_size": 16, + "mlp_ratio": 2.6667, + "eva_model_name": "eva-clip-b-16-X", + "drop_path_rate": 0.0, + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "xattn": true, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-L-14-336.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-L-14-336.json new file mode 100644 index 00000000..3e1d124e --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-L-14-336.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14-336", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-L-14.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-L-14.json new file mode 100644 index 00000000..03b22ad3 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-L-14.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json new file mode 100644 index 00000000..aa04e254 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32, + "xattn": false, + "fusedLN": true + } +} diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-bigE-14.json b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-bigE-14.json new file mode 100644 index 00000000..747ffccc --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/model_configs/EVA02-CLIP-bigE-14.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/modified_resnet.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/modified_resnet.py new file mode 100644 index 00000000..6a8d3aed --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/openai.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/openai.py new file mode 100644 index 00000000..cc4e13e8 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/pretrained.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/pretrained.py new file mode 100644 index 00000000..5077a047 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/pretrained.py @@ -0,0 +1,340 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +try: + from huggingface_hub import hf_hub_download + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_EVAB16 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_EVAL14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_EVAL14_336 = dict( + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), + eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_EVAg14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), +) + +_EVAg14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_EVAbigE14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), +) + +_EVAbigE14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), +) + + +_PRETRAINED = { + # "ViT-B-32": _VITB32, + "OpenaiCLIP-B-32": _VITB32, + "OpenCLIP-B-32": _VITB32, + + # "ViT-B-32-quickgelu": _VITB32_quickgelu, + "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, + "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, + + # "ViT-B-16": _VITB16, + "OpenaiCLIP-B-16": _VITB16, + "OpenCLIP-B-16": _VITB16, + + "EVA02-B-16": _EVAB16, + "EVA02-CLIP-B-16": _EVAB16, + + # "ViT-B-16-plus-240": _VITB16_PLUS_240, + "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, + + # "ViT-L-14": _VITL14, + "OpenaiCLIP-L-14": _VITL14, + "OpenCLIP-L-14": _VITL14, + + "EVA02-L-14": _EVAL14, + "EVA02-CLIP-L-14": _EVAL14, + + # "ViT-L-14-336": _VITL14_336, + "OpenaiCLIP-L-14-336": _VITL14_336, + + "EVA02-CLIP-L-14-336": _EVAL14_336, + + # "ViT-H-14": _VITH14, + # "ViT-g-14": _VITg14, + "OpenCLIP-H-14": _VITH14, + "OpenCLIP-g-14": _VITg14, + + "EVA01-CLIP-g-14": _EVAg14, + "EVA01-CLIP-g-14-plus": _EVAg14_PLUS, + + # "ViT-bigG-14": _VITbigG14, + "OpenCLIP-bigG-14": _VITbigG14, + + "EVA02-CLIP-bigE-14": _EVAbigE14, + "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, + local_dir: Union[str, None] = None, +): + cache_dir = local_dir if not local_dir else cache_dir + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, + local_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir, local_dir=local_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, + local_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir, local_dir=local_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if local_dir is not None: + full_model_path = os.path.join(local_dir, filename) + if os.path.exists(full_model_path): + return full_model_path + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir, local_dir=local_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir, local_dir=local_dir) + + return target diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/rope.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/rope.py new file mode 100644 index 00000000..69030c35 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/rope.py @@ -0,0 +1,137 @@ +from math import pi +import torch +from torch import nn +from einops import rearrange, repeat +import logging + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + + return torch.cat((t_left, t, t_right), dim = -1) + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + patch_dropout = 0. + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) + freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) + + self.patch_dropout = patch_dropout + + self.register_buffer("freqs_cos", freqs_cos) + self.register_buffer("freqs_sin", freqs_sin) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, patch_indices_keep=None): + if patch_indices_keep is not None: + batch = t.size()[0] + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + + freqs_cos = freqs_cos[batch_indices, patch_indices_keep] + freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j') + freqs_sin = freqs_sin[batch_indices, patch_indices_keep] + freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j') + + return t * freqs_cos + rotate_half(t) * freqs_sin + + return t * self.freqs_cos + rotate_half(t) * self.freqs_sin \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/timm_model.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/timm_model.py new file mode 100644 index 00000000..b58122c0 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/timm_model.py @@ -0,0 +1,122 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + pretrained=False): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + self.trunk = timm.create_model(model_name, pretrained=pretrained) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/tokenizer.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/tokenizer.py new file mode 100644 index 00000000..41482f82 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/tokenizer.py @@ -0,0 +1,201 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("Β‘"), ord("Β¬")+1))+list(range(ord("Β"), ord("ΓΏ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + +_tokenizer = SimpleTokenizer() + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder[""] + eot_token = _tokenizer.encoder[""] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + +class HFTokenizer: + "HuggingFace tokenizer wrapper" + def __init__(self, tokenizer_name:str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids + return input_ids diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/transform.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/transform.py new file mode 100644 index 00000000..39f3e4cf --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/transform.py @@ -0,0 +1,103 @@ +from typing import Optional, Sequence, Tuple + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F + +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[:2] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +# class CatGen(nn.Module): +# def __init__(self, num=4): +# self.num = num +# def mixgen_batch(image, text): +# batch_size = image.shape[0] +# index = np.random.permutation(batch_size) + +# cat_images = [] +# for i in range(batch_size): +# # image mixup +# image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:] +# # text concat +# text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0] +# text = torch.stack(text) +# return image, text + + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + normalize = Normalize(mean=mean, std=std) + if is_train: + return Compose([ + RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), + _convert_to_rgb, + ToTensor(), + normalize, + ]) + else: + if resize_longest_max: + transforms = [ + ResizeMaxSize(image_size, fill=fill_color) + ] + else: + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend([ + _convert_to_rgb, + ToTensor(), + normalize, + ]) + return Compose(transforms) diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/transformer.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/transformer.py new file mode 100644 index 00000000..33e89ff7 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/transformer.py @@ -0,0 +1,737 @@ +import os +import logging +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +try: + from timm.models.layers import trunc_normal_ +except: + from timm.layers import trunc_normal_ + +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast +from .utils import to_2tuple + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + import deepspeed + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + print("Please 'pip install deepspeed'") + deepspeed = None + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: torch.Tensor): + output = F.layer_norm( + x.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(x) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}") + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + if self.training and os.getenv('RoPE') == '1': + return x, patch_indices_keep + + return x + + +def _in_projection_packed( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + w: torch.Tensor, + b: Optional[torch.Tensor] = None, + ): + """ + https://github.com/pytorch/pytorch/blob/db2a237763eb8693a20788be94f8c192e762baa8/torch/nn/functional.py#L4726 + """ + E = q.size(-1) + if k is v: + if q is k: + # self-attention + return F.linear(q, w, b).chunk(3, dim=-1) + else: + # encoder-decoder attention + w_q, w_kv = w.split([E, E * 2]) + if b is None: + b_q = b_kv = None + else: + b_q, b_kv = b.split([E, E * 2]) + return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1) + else: + w_q, w_k, w_v = w.chunk(3) + if b is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = b.chunk(3) + return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v) + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False, + rope=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + self.rope = rope + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + if self.xattn: + q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None, + ) + else: + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=True, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias) + N_q, B_q, C_q = q.shape + N_k, B_k, C_k = k.shape + N_v, B_v, C_v = v.shape + if self.xattn: + # B, N, C -> B, N, num_heads, C + q = q.permute(1, 0, 2).reshape(B_q, N_q, self.num_heads, -1) + k = k.permute(1, 0, 2).reshape(B_k, N_k, self.num_heads, -1) + v = v.permute(1, 0, 2).reshape(B_v, N_v, self.num_heads, -1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None + ) + else: + # B*H, L, C + q = q.contiguous().view(N_q, B_q * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(N_k, B_k * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(N_v, B_v * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + # B*H, N_q, N_k + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(B_q, self.num_heads, N_q, N_k) * logit_scale + attn = attn.view(-1, N_q, N_k) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(B_q, self.num_heads, N_q, C_q) * self.head_scale + x = x.view(-1, N_q, C_q) + x = x.transpose(0, 1).reshape(N_q, B_q, C_q) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.ln_1_k = norm_layer(d_model) if cross_attn else self.ln_1 + self.ln_1_v = norm_layer(d_model) if cross_attn else self.ln_1 + self.attn = CustomAttention( + d_model, n_head, + qkv_bias=True, + attn_drop=0., + proj_drop=0., + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + xattn=xattn + ) + + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q = q + self.ls_1(self.ln_attn(self.attn(self.ln_1(q), self.ln_1_k(k), self.ln_1_v(v), attn_mask=attn_mask))) + q = q + self.ls_2(self.mlp(self.ln_2(q))) + return q + +class CustomTransformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = True, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + self.xattn = xattn + + self.resblocks = nn.ModuleList([ + CustomResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + scale_cosine_attn=scale_cosine_attn, + scale_heads=scale_heads, + scale_attn=scale_attn, + scale_fc=scale_fc, + cross_attn=cross_attn, + xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, q: torch.Tensor, k: torch.Tensor = None, v: torch.Tensor = None, attn_mask: Optional[torch.Tensor] = None): + if k is None and v is None: + k = v = q + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + q = checkpoint(r, q, k, v, attn_mask) + else: + q = r(q, k, v, attn_mask=attn_mask) + return q + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + if xattn: + self.attn = Attention(d_model, n_head, xattn=True) + else: + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + self.xattn = xattn + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None + if self.xattn: + return self.attn(x, attn_mask=attn_mask) + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + patch_dropout: float = 0., + global_average_pool: bool = False, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.image_size = to_2tuple(image_size) + self.patch_size = to_2tuple(patch_size) + self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1]) + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + self.ln_pre = norm_layer(width) + + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.global_average_pool = global_average_pool + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def get_num_layers(self): + return self.transformer.layers + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'positional_embedding', 'class_embedding'} + + def forward(self, x: torch.Tensor, return_all_features: bool=False): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if not return_all_features: + if self.global_average_pool: + x = x.mean(dim=1) #x = x[:,1:,:].mean(dim=1) + else: + x = x[:, 0] + + x = self.ln_post(x) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class TextTransformer(nn.Module): + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool= False, + attn_mask: bool = True + ): + super().__init__() + self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.xattn = xattn + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if attn_mask: + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + else: + self.attn_mask = None + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + # return {'positional_embedding', 'token_embedding'} + return {'positional_embedding'} + + def get_num_layers(self): + return self.transformer.layers + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text, return_all_features: bool=False): + cast_dtype = self.transformer.get_cast_dtype() + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + # x = self.transformer(x) # no attention mask is applied + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if not return_all_features: + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x diff --git a/custom_nodes/comfyui_pulid_flux_ll/eva_clip/utils.py b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/utils.py new file mode 100644 index 00000000..bdc5a7a4 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/eva_clip/utils.py @@ -0,0 +1,326 @@ +from itertools import repeat +import collections.abc +import logging +import math +import numpy as np + +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d +import torch.nn.functional as F + +# open CLIP +def resize_clip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed + + +def resize_visual_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['positional_embedding'] = new_pos_embed + +def resize_evaclip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'visual.pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['visual.pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['visual.pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['visual.patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['visual.patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_eva_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + for key in all_keys: + if "relative_position_index" in key: + state_dict.pop(key) + + if "relative_position_bias_table" in key: + rel_pos_bias = state_dict[key] + src_num_pos, num_attn_heads = rel_pos_bias.size() + dst_num_pos, _ = model.visual.state_dict()[key].size() + dst_patch_shape = model.visual.patch_embed.patch_shape + if dst_patch_shape[0] != dst_patch_shape[1]: + raise NotImplementedError() + num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) + src_size = int((src_num_pos - num_extra_tokens) ** 0.5) + dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) + if src_size != dst_size: + print("Position interpolate for %s from %dx%d to %dx%d" % ( + key, src_size, src_size, dst_size, dst_size)) + extra_tokens = rel_pos_bias[-num_extra_tokens:, :] + rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] + + def geometric_progression(a, r, n): + return a * (1.0 - r ** n) / (1.0 - r) + + left, right = 1.01, 1.5 + while right - left > 1e-6: + q = (left + right) / 2.0 + gp = geometric_progression(1, q, src_size // 2) + if gp > dst_size // 2: + right = q + else: + left = q + + # if q > 1.090307: + # q = 1.090307 + + dis = [] + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + + x = r_ids + [0] + dis + y = r_ids + [0] + dis + + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + dy = np.arange(-t, t + 0.1, 1.0) + + print("Original positions = %s" % str(x)) + print("Target positions = %s" % str(dx)) + + all_rel_pos_bias = [] + + for i in range(num_attn_heads): + z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() + f = F.interpolate.interp2d(x, y, z, kind='cubic') + all_rel_pos_bias.append( + torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) + + rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) + + new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) + state_dict[key] = new_rel_pos_bias + + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) + + +def is_logging(args): + def is_global_master(args): + return args.rank == 0 + + def is_local_master(args): + return args.local_rank == 0 + + def is_master(args, local=False): + return is_local_master(args) if local else is_global_master(args) + return is_master + + +class AllGather(torch.autograd.Function): + """An autograd function that performs allgather on a tensor. + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + + @staticmethod + def forward(ctx, tensor, rank, world_size): + tensors_gather = [torch.empty_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(tensors_gather, tensor) + ctx.rank = rank + ctx.batch_size = tensor.shape[0] + return torch.cat(tensors_gather, 0) + + @staticmethod + def backward(ctx, grad_output): + return ( + grad_output[ctx.batch_size * ctx.rank: ctx.batch_size * (ctx.rank + 1)], + None, + None + ) + +allgather = AllGather.apply \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_select_ref_face.png b/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_select_ref_face.png new file mode 100644 index 00000000..d6b22fbd Binary files /dev/null and b/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_select_ref_face.png differ diff --git a/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_with_attn_mask.png b/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_with_attn_mask.png new file mode 100644 index 00000000..84940bba Binary files /dev/null and b/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_with_attn_mask.png differ diff --git a/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_with_speedup.png b/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_with_speedup.png new file mode 100644 index 00000000..7ae71dd0 Binary files /dev/null and b/custom_nodes/comfyui_pulid_flux_ll/examples/PuLID_with_speedup.png differ diff --git a/custom_nodes/comfyui_pulid_flux_ll/face_restoration_helper.py b/custom_nodes/comfyui_pulid_flux_ll/face_restoration_helper.py new file mode 100644 index 00000000..cc4e28b5 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/face_restoration_helper.py @@ -0,0 +1,456 @@ +import cv2 +import numpy as np +import os +import torch +from torchvision.transforms.functional import normalize +from torchvision.ops import box_iou + +from facexlib.detection import init_detection_model +from facexlib.parsing import init_parsing_model +from facexlib.utils.misc import img2tensor, imwrite + +def get_face_by_index(det_faces, face_sort_rule, face_index=0): + if det_faces is None: + return None, None + + has_bbox_attr = hasattr(det_faces[0], 'bbox') + + # εˆ›ε»ΊεΈ¦η΄’εΌ•ηš„εˆ—θ‘¨ [(original_index, face), ...] + indexed_faces = list(enumerate(det_faces)) + + # εšδΉ‰ζŽ’εΊθ§„εˆ™ + if face_sort_rule == 'left-right': + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[0] if has_bbox_attr else x[1][0]) + elif face_sort_rule == "right-left": + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[0] if has_bbox_attr else x[1][0], reverse=True) + elif face_sort_rule == "top-bottom": + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[1] if has_bbox_attr else x[1][1]) + elif face_sort_rule == "bottom-top": + sorted_faces = sorted(indexed_faces, key=lambda x: x[1].bbox[1] if has_bbox_attr else x[1][1], reverse=True) + elif face_sort_rule == "small-large": + sorted_faces = sorted(indexed_faces, key=lambda x: (x[1].bbox[2] - x[1].bbox[0]) * (x[1].bbox[3] - x[1].bbox[1]) if has_bbox_attr else (x[1][2] - x[1][0]) * (x[1][3] - x[1][1])) + elif face_sort_rule == "large-small": + sorted_faces = sorted(indexed_faces, key=lambda x: (x[1].bbox[2] - x[1].bbox[0]) * (x[1].bbox[3] - x[1].bbox[1]) if has_bbox_attr else (x[1][2] - x[1][0]) * (x[1][3] - x[1][1]), reverse=True) + else: + sorted_faces = indexed_faces + # θΏ”ε›žεŽŸε§‹η΄’εΌ• + if not 0 <= face_index < len(sorted_faces): + # θΏ”ε›žη¬¬δΈ€δΈͺ + face_index = 0 + # θΏ”ε›žι€‰ζ‹©ηš„θ„Έιƒ¨γ€εŽŸε§‹η΄’εΌ•ε€Όε’ŒζŽ’εΊεŽηš„εˆ—θ‘¨ + return sorted_faces[face_index][1], sorted_faces[face_index][0], [face[1] for face in sorted_faces] + + +def get_largest_face(det_faces, h, w): + + def get_location(val, length): + if val < 0: + return 0 + elif val > length: + return length + else: + return val + + face_areas = [] + for det_face in det_faces: + left = get_location(det_face[0], w) + right = get_location(det_face[2], w) + top = get_location(det_face[1], h) + bottom = get_location(det_face[3], h) + face_area = (right - left) * (bottom - top) + face_areas.append(face_area) + largest_idx = face_areas.index(max(face_areas)) + return det_faces[largest_idx], largest_idx + + +def get_center_face(det_faces, h=0, w=0, center=None): + if center is not None: + center = np.array(center) + else: + center = np.array([w / 2, h / 2]) + center_dist = [] + for det_face in det_faces: + face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2]) + dist = np.linalg.norm(face_center - center) + center_dist.append(dist) + center_idx = center_dist.index(min(center_dist)) + return det_faces[center_idx], center_idx + + +class FaceRestoreHelper(object): + """Helper for the face restoration pipeline (base class).""" + + def __init__(self, + upscale_factor, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + parsing_model='bisenet', + save_ext='png', + template_3points=False, + pad_blur=False, + use_parse=False, + device=None, + model_rootpath=None): + self.template_3points = template_3points # improve robustness + self.upscale_factor = upscale_factor + # the cropped face ratio based on the square face + self.crop_ratio = crop_ratio # (h, w) + assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1' + self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0])) + + if self.template_3points: + self.face_template = np.array([[192, 240], [319, 240], [257, 371]]) + else: + # standard 5 landmarks for FFHQ faces with 512 x 512 + self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935], + [201.26117, 371.41043], [313.08905, 371.15118]]) + self.face_template = self.face_template * (face_size / 512.0) + if self.crop_ratio[0] > 1: + self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2 + if self.crop_ratio[1] > 1: + self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2 + self.save_ext = save_ext + self.pad_blur = pad_blur + if self.pad_blur is True: + self.template_3points = False + + self.all_landmarks_5 = [] + self.det_faces = [] + self.affine_matrices = [] + self.inverse_affine_matrices = [] + self.cropped_faces = [] + self.restored_faces = [] + self.pad_input_imgs = [] + + if device is None: + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = device + + # init face detection model + self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath) + + # init face parsing model + self.use_parse = use_parse + self.face_parse = init_parsing_model(model_name=parsing_model, device=self.device, model_rootpath=model_rootpath) + + def set_upscale_factor(self, upscale_factor): + self.upscale_factor = upscale_factor + + def read_image(self, img): + """img can be image path or cv2 loaded image.""" + # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255] + if isinstance(img, str): + img = cv2.imread(img) + + if np.max(img) > 256: # 16-bit image + img = img / 65535 * 255 + if len(img.shape) == 2: # gray image + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + elif img.shape[2] == 4: # RGBA image with alpha channel + img = img[:, :, 0:3] + + self.input_img = img + + def get_face_landmarks_5(self, + only_keep_largest=False, + only_center_face=False, + resize=None, + blur_ratio=0.01, + eye_dist_threshold=None, + face_sort_rule=None, + ref_sort_bboxes=None, + face_index=None): + if resize is None: + scale = 1 + input_img = self.input_img + else: + h, w = self.input_img.shape[0:2] + scale = min(h, w) / resize + h, w = int(h / scale), int(w / scale) + input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4) + + with torch.no_grad(): + # use 0.5 (old value is 0.97), keep consistent with Insightface, but still cannot ensure consistent quantity of bboxes. + bboxes = self.face_det.detect_faces(input_img, 0.5) * scale + for bbox in bboxes: + # remove faces with too small eye distance: side faces or too small faces + eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]]) + if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold): + continue + + if self.template_3points: + landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)]) + else: + landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)]) + self.all_landmarks_5.append(landmark) + self.det_faces.append(bbox[0:5]) + if len(self.det_faces) == 0: + return 0 + if ref_sort_bboxes is not None: + if len(self.det_faces) != len(ref_sort_bboxes): + return 0 + iou = box_iou(torch.tensor(ref_sort_bboxes), torch.tensor(self.det_faces)[:, :4]) + indices = torch.max(iou, dim=1).indices + self.det_faces = [self.det_faces[idx] for idx in indices] + if face_index is None or face_index >= len(self.det_faces): + face_index = 0 + self.all_landmarks_5 = [self.all_landmarks_5[indices[face_index]]] + elif face_sort_rule is not None: + self.det_faces, center_idx, _ = get_face_by_index(self.det_faces, face_sort_rule=face_sort_rule, face_index=face_index) + self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] + elif only_keep_largest: + h, w, _ = self.input_img.shape + self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w) + self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]] + elif only_center_face: + h, w, _ = self.input_img.shape + self.det_faces, center_idx = get_center_face(self.det_faces, h, w) + self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] + + # pad blurry images + if self.pad_blur: + self.pad_input_imgs = [] + for landmarks in self.all_landmarks_5: + # get landmarks + eye_left = landmarks[0, :] + eye_right = landmarks[1, :] + eye_avg = (eye_left + eye_right) * 0.5 + mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5 + eye_to_eye = eye_right - eye_left + eye_to_mouth = mouth_avg - eye_avg + + # Get the oriented crop rectangle + # x: half width of the oriented crop rectangle + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise + # norm with the hypotenuse: get the direction + x /= np.hypot(*x) # get the hypotenuse of a right triangle + rect_scale = 1.5 + x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) + # y: half height of the oriented crop rectangle + y = np.flipud(x) * [-1, 1] + + # c: center + c = eye_avg + eye_to_mouth * 0.1 + # quad: (left_top, left_bottom, right_bottom, right_top) + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + # qsize: side length of the square + qsize = np.hypot(*x) * 2 + border = max(int(np.rint(qsize * 0.1)), 3) + + # get pad + # pad: (width_left, height_top, width_right, height_bottom) + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = [ + max(-pad[0] + border, 1), + max(-pad[1] + border, 1), + max(pad[2] - self.input_img.shape[0] + border, 1), + max(pad[3] - self.input_img.shape[1] + border, 1) + ] + + if max(pad) > 1: + # pad image + pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + # modify landmark coords + landmarks[:, 0] += pad[0] + landmarks[:, 1] += pad[1] + # blur pad images + h, w, _ = pad_img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], + np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], + np.float32(h - 1 - y) / pad[3])) + blur = int(qsize * blur_ratio) + if blur % 2 == 0: + blur += 1 + blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur)) + # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0) + + pad_img = pad_img.astype('float32') + pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0) + pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255] + self.pad_input_imgs.append(pad_img) + else: + self.pad_input_imgs.append(np.copy(self.input_img)) + + return len(self.all_landmarks_5) + + def align_warp_face(self, save_cropped_path=None, border_mode='constant'): + """Align and warp faces with face template. + """ + if self.pad_blur: + assert len(self.pad_input_imgs) == len( + self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}' + for idx, landmark in enumerate(self.all_landmarks_5): + # use 5 landmarks to get affine matrix + # use cv2.LMEDS method for the equivalence to skimage transform + # ref: https://blog.csdn.net/yichxi/article/details/115827338 + affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] + self.affine_matrices.append(affine_matrix) + # warp and crop faces + if border_mode == 'constant': + border_mode = cv2.BORDER_CONSTANT + elif border_mode == 'reflect101': + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == 'reflect': + border_mode = cv2.BORDER_REFLECT + if self.pad_blur: + input_img = self.pad_input_imgs[idx] + else: + input_img = self.input_img + cropped_face = cv2.warpAffine( + input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray + self.cropped_faces.append(cropped_face) + # save the cropped face + if save_cropped_path is not None: + path = os.path.splitext(save_cropped_path)[0] + save_path = f'{path}_{idx:02d}.{self.save_ext}' + imwrite(cropped_face, save_path) + + def get_inverse_affine(self, save_inverse_affine_path=None): + """Get inverse affine matrix.""" + for idx, affine_matrix in enumerate(self.affine_matrices): + inverse_affine = cv2.invertAffineTransform(affine_matrix) + inverse_affine *= self.upscale_factor + self.inverse_affine_matrices.append(inverse_affine) + # save inverse affine matrices + if save_inverse_affine_path is not None: + path, _ = os.path.splitext(save_inverse_affine_path) + save_path = f'{path}_{idx:02d}.pth' + torch.save(inverse_affine, save_path) + + def add_restored_face(self, face): + self.restored_faces.append(face) + + def paste_faces_to_input_image(self, save_path=None, upsample_img=None): + h, w, _ = self.input_img.shape + h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) + + if upsample_img is None: + # simply resize the background + upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + else: + upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + + assert len(self.restored_faces) == len( + self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') + for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): + # Add an offset to inverse affine matrix, for more precise back alignment + if self.upscale_factor > 1: + extra_offset = 0.5 * self.upscale_factor + else: + extra_offset = 0 + inverse_affine[:, 2] += extra_offset + inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) + + if self.use_parse: + # inference + face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR) + face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True) + normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + face_input = torch.unsqueeze(face_input, 0).to(self.device) + with torch.no_grad(): + out = self.face_parse(face_input)[0] + out = out.argmax(dim=1).squeeze().cpu().numpy() + + mask = np.zeros(out.shape) + MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] + for idx, color in enumerate(MASK_COLORMAP): + mask[out == idx] = color + # blur the mask + mask = cv2.GaussianBlur(mask, (101, 101), 11) + mask = cv2.GaussianBlur(mask, (101, 101), 11) + # remove the black borders + thres = 10 + mask[:thres, :] = 0 + mask[-thres:, :] = 0 + mask[:, :thres] = 0 + mask[:, -thres:] = 0 + mask = mask / 255. + + mask = cv2.resize(mask, restored_face.shape[:2]) + mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3) + inv_soft_mask = mask[:, :, None] + pasted_face = inv_restored + + else: # use square parse maps + mask = np.ones(self.face_size, dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) + # remove the black borders + inv_mask_erosion = cv2.erode( + inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8)) + pasted_face = inv_mask_erosion[:, :, None] * inv_restored + total_face_area = np.sum(inv_mask_erosion) # // 3 + # compute the fusion edge based on the area of face + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + if len(upsample_img.shape) == 2: # upsample_img is gray image + upsample_img = upsample_img[:, :, None] + inv_soft_mask = inv_soft_mask[:, :, None] + + if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel + alpha = upsample_img[:, :, 3:] + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3] + upsample_img = np.concatenate((upsample_img, alpha), axis=2) + else: + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img + + if np.max(upsample_img) > 256: # 16-bit image + upsample_img = upsample_img.astype(np.uint16) + else: + upsample_img = upsample_img.astype(np.uint8) + if save_path is not None: + path = os.path.splitext(save_path)[0] + save_path = f'{path}.{self.save_ext}' + imwrite(upsample_img, save_path) + return upsample_img + + def clean_all(self): + self.all_landmarks_5 = [] + self.restored_faces = [] + self.affine_matrices = [] + self.cropped_faces = [] + self.inverse_affine_matrices = [] + self.det_faces = [] + self.pad_input_imgs = [] + +def draw_on(img, faces): + dimg = img.copy() + for i in range(len(faces)): + face = faces[i] + box = face.bbox.astype(np.int32) + color = (0, 0, 255) + cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2) + if face.kps is not None: + kps = face.kps.astype(np.int32) + #print(landmark.shape) + for l in range(kps.shape[0]): + color = (0, 0, 255) + if l == 0 or l == 3: + color = (0, 255, 0) + cv2.circle(dimg, (kps[l][0], kps[l][1]), 1, color, + 2) + + cv2.putText(dimg,'index: %d'%i, (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1) + + # if face.gender is not None and face.age is not None: + # cv2.putText(dimg,'%s,%d'%(face.sex,face.age), (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1) + + #for key, value in face.items(): + # if key.startswith('landmark_3d'): + # print(key, value.shape) + # print(value[0:10,:]) + # lmk = np.round(value).astype(np.int) + # for l in range(lmk.shape[0]): + # color = (255, 0, 0) + # cv2.circle(dimg, (lmk[l][0], lmk[l][1]), 1, color, + # 2) + return dimg \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/patch_util.py b/custom_nodes/comfyui_pulid_flux_ll/patch_util.py new file mode 100644 index 00000000..3437d2b3 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/patch_util.py @@ -0,0 +1,41 @@ +class PatchKeys: + ################## transformer_options patches ################## + running_net_model = "running_net_model" + options_key = "patches_point" + # patches_pointδΈ‹ζ”―ζŒθΎη½ηš„θ‘₯丁 + dit_enter = "patch_dit_enter" + dit_blocks_before = "patch_dit_blocks_before" + dit_double_blocks_replace = "patch_dit_double_blocks_replace" + dit_double_blocks_after = "patch_dit_double_blocks_after" + dit_blocks_transition_replace = "patch_dit_blocks_transition_replace" + dit_single_blocks_before = "patch_dit_single_blocks_before" + dit_single_blocks_replace = "patch_dit_single_blocks_replace" + dit_blocks_after = "patch_dit_blocks_after" + dit_blocks_after_transition_replace = "patch_dit_final_layer_before_replace" + dit_final_layer_before = "patch_dit_final_layer_before" + dit_exit = "patch_dit_exit" + ################## transformer_options patches ################## + + # pulid + pulid_patch_key_attrs = "pulid_temp_attr" + + +def set_model_patch(model_patcher, options_key, patch, name): + to = model_patcher.model_options["transformer_options"] + if options_key not in to: + to[options_key] = {} + to[options_key][name] = to[options_key].get(name, []) + [patch] + +def set_model_patch_replace(model_patcher, options_key, patch, name): + to = model_patcher.model_options["transformer_options"] + if options_key not in to: + to[options_key] = {} + to[options_key][name] = patch + +def add_model_patch_option(model, patch_key): + if 'transformer_options' not in model.model_options: + model.model_options['transformer_options'] = {} + to = model.model_options['transformer_options'] + if patch_key not in to: + to[patch_key] = {} + return to[patch_key] \ No newline at end of file diff --git a/custom_nodes/comfyui_pulid_flux_ll/pulidflux.py b/custom_nodes/comfyui_pulid_flux_ll/pulidflux.py new file mode 100644 index 00000000..3d1f9734 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/pulidflux.py @@ -0,0 +1,664 @@ +import types +import zipfile + +import cv2 +import torch +from insightface.utils.download import download_file +from insightface.utils.storage import BASE_REPO_URL +from insightface.utils import face_align +from torch import nn +from torchvision import transforms +from torchvision.transforms import functional +import os +import logging +import folder_paths +import comfy +from insightface.app import FaceAnalysis +from .face_restoration_helper import FaceRestoreHelper, get_face_by_index, draw_on + +from comfy import model_management +from .eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .encoders_flux import IDFormer, PerceiverAttentionCA + +from .PulidFluxHook import pulid_forward_orig, set_model_dit_patch_replace, pulid_enter, pulid_patch_double_blocks_after +from .patch_util import PatchKeys, add_model_patch_option, set_model_patch + + +def set_extra_config_model_path(extra_config_models_dir_key, models_dir_name:str): + models_dir_default = os.path.join(folder_paths.models_dir, models_dir_name) + if extra_config_models_dir_key not in folder_paths.folder_names_and_paths: + folder_paths.folder_names_and_paths[extra_config_models_dir_key] = ( + [os.path.join(folder_paths.models_dir, models_dir_name)], folder_paths.supported_pt_extensions) + else: + if not os.path.exists(models_dir_default): + os.makedirs(models_dir_default, exist_ok=True) + folder_paths.add_model_folder_path(extra_config_models_dir_key, models_dir_default, is_default=True) + +set_extra_config_model_path("pulid", "pulid") +set_extra_config_model_path("insightface", "insightface") +set_extra_config_model_path("facexlib", "facexlib") + +INSIGHTFACE_DIR = folder_paths.get_folder_paths("insightface")[0] +FACEXLIB_DIR = folder_paths.get_folder_paths("facexlib")[0] + +# MODELS_DIR = os.path.join(folder_paths.models_dir, "pulid") +# if "pulid" not in folder_paths.folder_names_and_paths: +# current_paths = [MODELS_DIR] +# else: +# current_paths, _ = folder_paths.folder_names_and_paths["pulid"] +# folder_paths.folder_names_and_paths["pulid"] = (current_paths, folder_paths.supported_pt_extensions) + +class PulidFluxModel(nn.Module): + def __init__(self): + super().__init__() + + self.double_interval = 2 + self.single_interval = 4 + + # Init encoder + self.pulid_encoder = IDFormer() + + # Init attention + num_ca = 19 // self.double_interval + 38 // self.single_interval + if 19 % self.double_interval != 0: + num_ca += 1 + if 38 % self.single_interval != 0: + num_ca += 1 + self.pulid_ca = nn.ModuleList([ + PerceiverAttentionCA() for _ in range(num_ca) + ]) + + def from_pretrained(self, path: str): + state_dict = comfy.utils.load_torch_file(path, safe_load=True) + state_dict_dict = {} + for k, v in state_dict.items(): + module = k.split('.')[0] + state_dict_dict.setdefault(module, {}) + new_k = k[len(module) + 1:] + state_dict_dict[module][new_k] = v + + for module in state_dict_dict: + getattr(self, module).load_state_dict(state_dict_dict[module], strict=True) + + del state_dict + del state_dict_dict + + def get_embeds(self, face_embed, clip_embeds): + return self.pulid_encoder(face_embed, clip_embeds) + +def tensor_to_image(tensor): + image = tensor.mul(255).clamp(0, 255).byte().cpu() + image = image[..., [2, 1, 0]].numpy() + return image + +def image_to_tensor(image): + tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1) + tensor = tensor[..., [2, 1, 0]] + return tensor + +def to_gray(img): + x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3] + x = x.repeat(1, 3, 1, 1) + return x + +""" +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Nodes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +wrappers_name = "PULID_wrappers" + +class PulidFluxModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pulid_file": (folder_paths.get_filename_list("pulid"), )}} + + RETURN_TYPES = ("PULIDFLUX",) + FUNCTION = "load_model" + CATEGORY = "pulid" + + def load_model(self, pulid_file): + model_path = folder_paths.get_full_path("pulid", pulid_file) + + # Also initialize the model, takes longer to load but then it doesn't have to be done every time you change parameters in the apply node + offload_device = model_management.unet_offload_device() + load_device = model_management.get_torch_device() + + model = PulidFluxModel() + + logging.info("Loading PuLID-Flux model.") + model.from_pretrained(path=model_path) + + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) + del model + + return (model_patcher,) + +def download_insightface_model(sub_dir, name, force=False, root='~/.insightface'): + # Copied and modified from insightface.utils.storage.download + # Solve https://github.com/deepinsight/insightface/issues/2711 + _root = os.path.expanduser(root) + dir_path = os.path.join(_root, sub_dir, name) + if os.path.exists(dir_path) and not force: + return dir_path + print('download_path:', dir_path) + zip_file_path = os.path.join(_root, sub_dir, name + '.zip') + model_url = "%s/%s.zip"%(BASE_REPO_URL, name) + download_file(model_url, + path=zip_file_path, + overwrite=True) + if not os.path.exists(dir_path): + os.makedirs(dir_path) + + # zip file has contains ${name} + real_dir_path = os.path.join(_root, sub_dir) + with zipfile.ZipFile(zip_file_path) as zf: + zf.extractall(real_dir_path) + #os.remove(zip_file_path) + return dir_path + +class PulidFluxInsightFaceLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "provider": (["CPU", "CUDA", "ROCM"], ), + }, + } + + RETURN_TYPES = ("FACEANALYSIS",) + FUNCTION = "load_insightface" + CATEGORY = "pulid" + + def load_insightface(self, provider): + name = "antelopev2" + download_insightface_model("models", name, root=INSIGHTFACE_DIR) + model = FaceAnalysis(name=name, root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider', ]) # alternative to buffalo_l + model.prepare(ctx_id=0, det_size=(640, 640)) + + return (model,) + +class PulidFluxEvaClipLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": {}, + } + + RETURN_TYPES = ("EVA_CLIP",) + FUNCTION = "load_eva_clip" + CATEGORY = "pulid" + + def load_eva_clip(self): + from .eva_clip.factory import create_model_and_transforms + + clip_file_path = folder_paths.get_full_path("text_encoders", 'EVA02_CLIP_L_336_psz14_s6B.pt') + if clip_file_path is None: + clip_dir = os.path.join(folder_paths.models_dir, "clip") + else: + clip_dir = os.path.dirname(clip_file_path) + model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, local_dir=clip_dir) + + model = model.visual + + eva_transform_mean = getattr(model, 'image_mean', OPENAI_DATASET_MEAN) + eva_transform_std = getattr(model, 'image_std', OPENAI_DATASET_STD) + if not isinstance(eva_transform_mean, (list, tuple)): + model["image_mean"] = (eva_transform_mean,) * 3 + if not isinstance(eva_transform_std, (list, tuple)): + model["image_std"] = (eva_transform_std,) * 3 + + return (model,) + +class ApplyPulidFlux: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", ), + "pulid_flux": ("PULIDFLUX", ), + "eva_clip": ("EVA_CLIP", ), + "face_analysis": ("FACEANALYSIS", ), + "image": ("IMAGE", ), + "weight": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05 }), + "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }), + }, + "optional": { + "attn_mask": ("MASK", ), + "options": ("OPTIONS",), + }, + "hidden": { + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply_pulid_flux" + CATEGORY = "pulid" + + def apply_pulid_flux(self, model, pulid_flux, eva_clip, face_analysis, image, weight, start_at, end_at, attn_mask=None, options={}, unique_id=None): + model = model.clone() + + device = comfy.model_management.get_torch_device() + # Why should I care what args say, when the unet model has a different dtype?! + # Am I missing something?! + #dtype = comfy.model_management.unet_dtype() + dtype = model.model.diffusion_model.dtype + # Because of 8bit models we must check what cast type does the unet uses + # ZLUDA (Intel, AMD) & GPUs with compute capability < 8.0 don't support bfloat16 etc. + # Issue: https://github.com/balazik/ComfyUI-PuLID-Flux/issues/6 + if model.model.manual_cast_dtype is not None: + dtype = model.model.manual_cast_dtype + + eva_clip.to(device, dtype=dtype) + pulid_flux.model.to(dtype=dtype) + model_management.load_models_gpu([pulid_flux], force_full_load=True) + # model_management.load_model_gpu(pulid_flux) + + if attn_mask is not None: + if attn_mask.dim() > 3: + attn_mask = attn_mask.squeeze(-1) + elif attn_mask.dim() < 3: + attn_mask = attn_mask.unsqueeze(0) + # attn_mask = attn_mask.to(device, dtype=dtype) + + image = tensor_to_image(image) + + face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + parsing_model='bisenet', + save_ext='png', + device=device, + model_rootpath=FACEXLIB_DIR + ) + + bg_label = [0, 16, 18, 7, 8, 9, 14, 15] + cond = [] + + input_face_sort = options.get('input_faces_order', "large-small") + input_face_index = options.get('input_faces_index', 0) + input_face_align_mode = options.get('input_faces_align_mode', 1) + # Analyse multiple images at multiple sizes and combine largest area embeddings + for i in range(image.shape[0]): + # get insightface embeddings + bboxes = [] + iface_embeds = None + for size in [(size, size) for size in range(640, 256, -64)]: + face_analysis.det_model.input_size = size + face_info = face_analysis.get(image[i]) + if face_info: + face_info, index, sorted_faces = get_face_by_index(face_info, face_sort_rule=input_face_sort, face_index=input_face_index) + bboxes = [face.bbox for face in sorted_faces] + iface_embeds = torch.from_numpy(face_info.embedding).unsqueeze(0).to(device, dtype=dtype) + break + else: + # No face detected, skip this image + logging.warning(f'Warning: No face detected in image {str(i)}') + continue + + if input_face_align_mode == 1: + image_size = 512 + M = face_align.estimate_norm(face_info.kps, image_size=image_size) + align_face = cv2.warpAffine(image[i], M, (image_size, image_size), borderMode=cv2.BORDER_CONSTANT, + borderValue=(135, 133, 132)) + # align_face = face_align.norm_crop(image[i], landmark=face_info.kps, image_size=image_size) + del M + else: + # get eva_clip embeddings + face_helper.clean_all() + face_helper.read_image(image[i]) + face_helper.get_face_landmarks_5(ref_sort_bboxes=bboxes, face_index=input_face_index) + face_helper.align_warp_face() + + if len(face_helper.cropped_faces) == 0: + # No face detected, skip this image + continue + + # Get aligned face image + align_face = face_helper.cropped_faces[0] + # Convert bgr face image to tensor + align_face = image_to_tensor(align_face).unsqueeze(0).permute(0, 3, 1, 2).to(device) + parsing_out = face_helper.face_parse(functional.normalize(align_face, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] + parsing_out = parsing_out.argmax(dim=1, keepdim=True) + bg = sum(parsing_out == i for i in bg_label).bool() + white_image = torch.ones_like(align_face) + # Only keep the face features + face_features_image = torch.where(bg, white_image, to_gray(align_face)) + + # Transform img before sending to eva_clip + # Apparently MPS only supports NEAREST interpolation? + face_features_image = functional.resize(face_features_image, eva_clip.image_size, transforms.InterpolationMode.BICUBIC if 'cuda' in device.type else transforms.InterpolationMode.NEAREST).to(device, dtype=dtype) + face_features_image = functional.normalize(face_features_image, eva_clip.image_mean, eva_clip.image_std) + + # eva_clip + id_cond_vit, id_vit_hidden = eva_clip(face_features_image, return_all_features=False, return_hidden=True, shuffle=False) + id_cond_vit = id_cond_vit.to(device, dtype=dtype) + for idx in range(len(id_vit_hidden)): + id_vit_hidden[idx] = id_vit_hidden[idx].to(device, dtype=dtype) + + id_cond_vit = torch.div(id_cond_vit, torch.norm(id_cond_vit, 2, 1, True)) + + # Combine embeddings + id_cond = torch.cat([iface_embeds, id_cond_vit], dim=-1) + + # Pulid_encoder + cond.append(pulid_flux.model.get_embeds(id_cond, id_vit_hidden)) + + eva_clip.to(torch.device('cpu')) + if not cond: + # No faces detected, return the original model + logging.warning("PuLID warning: No faces detected in any of the given images, returning unmodified model.") + del eva_clip, face_analysis, pulid_flux, face_helper, attn_mask + return (model,) + + # average embeddings + cond = torch.cat(cond).to(device, dtype=dtype) + if cond.shape[0] > 1: + cond = torch.mean(cond, dim=0, keepdim=True) + + sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at) + sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at) + + patch_kwargs = { + "pulid_model": pulid_flux, + "weight": weight, + "embedding": cond, + "sigma_start": sigma_start, + "sigma_end": sigma_end, + "mask": attn_mask + } + + ca_idx = 0 + for i in range(19): + if i % pulid_flux.model.double_interval == 0: + patch_kwargs["ca_idx"] = ca_idx + set_model_dit_patch_replace(model, patch_kwargs, ("double_block", i)) + ca_idx += 1 + for i in range(38): + if i % pulid_flux.model.single_interval == 0: + patch_kwargs["ca_idx"] = ca_idx + set_model_dit_patch_replace(model, patch_kwargs, ("single_block", i)) + ca_idx += 1 + + if len(model.get_additional_models_with_key("pulid_flux_model_patcher")) == 0: + model.set_additional_models("pulid_flux_model_patcher", [pulid_flux]) + + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name)) == 0: + # Just add it once when connecting in series + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name, pulid_outer_sample_wrappers_with_override) + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name)) == 0: + # Just add it once when connecting in series + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name, pulid_apply_model_wrappers) + + del eva_clip, face_analysis, pulid_flux, face_helper, attn_mask + return (model,) + + +class FixPulidFluxPatch: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "fix_pulid_patch" + CATEGORY = "pulid" + + def fix_pulid_patch(self, model): + model = model.clone() + + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name)) > 0: + model.remove_wrappers_with_key(comfy.patcher_extension.WrappersMP.APPLY_MODEL, wrappers_name) + + if len(model.get_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name)) > 0: + model.remove_wrappers_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, wrappers_name, pulid_outer_sample_wrappers) + + set_model_patch(model, PatchKeys.options_key, pulid_enter, PatchKeys.dit_enter) + set_model_patch(model, PatchKeys.options_key, pulid_patch_double_blocks_after, PatchKeys.dit_double_blocks_after) + + return (model,) + + +class PulidFluxOptions: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input_faces_order": ( + ["left-right","right-left","top-bottom","bottom-top","small-large","large-small"], + { + "default": "large-small", + "tooltip": "left-right: Sort the left boundary of bbox by column from left to right.\n" + "right-left: Reverse order of left-right (Sort the left boundary of bbox by column from right to left).\n" + "top-bottom: Sort the top boundary of bbox by row from top to bottom.\n" + "bottom-top: Reverse order of top-bottom (Sort the top boundary of bbox by row from bottom to top).\n" + "small-large: Sort the area of bbox from small to large.\n" + "large-small: Sort the area of bbox from large to small." + } + ), + "input_faces_index": ("INT", + { + "default": 0, "min": 0, "max": 1000, "step": 1, + "tooltip": "If the value is greater than the size of bboxes, will set value to 0." + }), + "input_faces_align_mode": ("INT", + { + "default": 1, "min": 0, "max": 1, "step": 1, + "tooltip": "Align face mode.\n" + "0: align_face and embed_face use different detectors. The results maybe different.\n" + "1: align_face and embed_face use the same detector." + }), + } + } + + RETURN_TYPES = ("OPTIONS",) + FUNCTION = "execute" + CATEGORY = "pulid" + + def execute(self,input_faces_order, input_faces_index, input_faces_align_mode=1): + options: dict = { + "input_faces_order": input_faces_order, + "input_faces_index": input_faces_index, + "input_faces_align_mode": input_faces_align_mode, + } + return (options, ) + + +class PulidFluxFaceDetector: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "face_analysis": ("FACEANALYSIS", ), + "image": ("IMAGE",), + "options": ("OPTIONS",), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE",) + RETURN_NAMES = ("embed_face", "align_face", "face_bbox_image",) + FUNCTION = "execute" + CATEGORY = "pulid" + OUTPUT_IS_LIST = (True, True, True,) + + def execute(self, face_analysis, image, options): + + device = comfy.model_management.get_torch_device() + + input_face_sort = options.get('input_faces_order', "large-small") + input_face_index = options.get('input_faces_index', 0) + input_face_align_mode = options.get('input_faces_align_mode', 1) + + if input_face_align_mode == 0: + face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + parsing_model='bisenet', + save_ext='png', + device=device, + model_rootpath=FACEXLIB_DIR + ) + + # Analyse multiple images at multiple sizes and combine largest area embeddings + embed_faces=[] + align_faces=[] + draw_embed_face_bbox=[] + image = tensor_to_image(image) + for i in range(image.shape[0]): + bboxes = [] + for size in [(size, size) for size in range(640, 256, -64)]: + face_analysis.det_model.input_size = size + face_info = face_analysis.get(image[i]) + if face_info: + face_info, index, sorted_faces = get_face_by_index(face_info, face_sort_rule=input_face_sort, + face_index=input_face_index) + bboxes = [face.bbox for face in sorted_faces] + embed_faces.append(crop_image(image[i], face_info.bbox, margin=10)) + draw_embed_face_bbox.append(image_to_tensor(draw_on(image[i], sorted_faces)).unsqueeze(0)) + break + else: + # No face detected, skip this image + logging.warning(f'Warning: No face detected in image {str(i)}') + continue + + if input_face_align_mode == 1: + image_size = 512 + M = face_align.estimate_norm(face_info.kps, image_size=image_size) + align_face = cv2.warpAffine(image[i], M, (image_size, image_size), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) + # align_face = face_align.norm_crop(image[i], landmark=face_info.kps, image_size=image_size) + del M + else: + # get eva_clip embeddings + face_helper.clean_all() + face_helper.read_image(image[i]) + face_helper.get_face_landmarks_5(ref_sort_bboxes=bboxes, face_index=input_face_index) + face_helper.align_warp_face() + + if len(face_helper.cropped_faces) == 0: + # No face detected, skip this image + continue + + # Get aligned face image + align_face = face_helper.cropped_faces[0] + del face_helper + align_faces.append(image_to_tensor(align_face).unsqueeze(0)) + del bboxes, align_face + del image + if len(embed_faces) == 0: + # No face detected, skip this image + logging.warning(f'Warning: No embed face detected in image') + if len(align_faces) == 0: + logging.warning(f'Warning: No align face detected in image') + return embed_faces, align_faces, draw_embed_face_bbox, + + +def crop_image(image, bbox, margin=0): + if len(image.shape) == 3: + image = image[None, ...] + image = image_to_tensor(image) + x, y, x1, y1 = bbox.astype(int) + w = x1 - x + h = y1 - y + image_height = image.shape[1] + image_width = image.shape[2] + # ε·¦δΈŠθ§’εζ ‡ + x = min(x, image_width) + y = min(y, image_height) + # 右下角坐标 + to_x = min(w + x + margin, image_width) + to_y = min(h + y + margin, image_height) + # ι˜²ζ­’θΆŠη•Œ + x = max(0, x - margin) + y = max(0, y - margin) + to_x = max(0, to_x) + to_y = max(0, to_y) + # ζŒ‰εŒΊεŸŸζˆͺ取图片 + crop_img = image[:, y:to_y, x:to_x, :] + return crop_img + + +def set_hook(diffusion_model, target_forward_orig): + # comfy.ldm.flux.model.Flux.old_forward_orig_for_pulid = comfy.ldm.flux.model.Flux.forward_orig + # comfy.ldm.flux.model.Flux.forward_orig = pulid_forward_orig + diffusion_model.old_forward_orig_for_pulid = diffusion_model.forward_orig + diffusion_model.forward_orig = types.MethodType(target_forward_orig, diffusion_model) + +def clean_hook(diffusion_model): + # if hasattr(comfy.ldm.flux.model.Flux, 'old_forward_orig_for_pulid'): + # comfy.ldm.flux.model.Flux.forward_orig = comfy.ldm.flux.model.Flux.old_forward_orig_for_pulid + # del comfy.ldm.flux.model.Flux.old_forward_orig_for_pulid + if hasattr(diffusion_model, 'old_forward_orig_for_pulid'): + diffusion_model.forward_orig = diffusion_model.old_forward_orig_for_pulid + del diffusion_model.old_forward_orig_for_pulid + +def pulid_outer_sample_wrappers_with_override(wrapper_executor, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs): + cfg_guider = wrapper_executor.class_obj + PULID_model_patch = add_model_patch_option(cfg_guider, PatchKeys.pulid_patch_key_attrs) + PULID_model_patch['latent_image_shape'] = latent_image.shape + + diffusion_model = cfg_guider.model_patcher.model.diffusion_model + set_hook(diffusion_model, pulid_forward_orig) + try : + out = wrapper_executor(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs) + finally: + del PULID_model_patch['latent_image_shape'] + clean_hook(diffusion_model) + del diffusion_model, cfg_guider + + return out + +def pulid_outer_sample_wrappers(wrapper_executor, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs): + cfg_guider = wrapper_executor.class_obj + PULID_model_patch = add_model_patch_option(cfg_guider, PatchKeys.pulid_patch_key_attrs) + PULID_model_patch['latent_image_shape'] = latent_image.shape + try: + out = wrapper_executor(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs) + finally: + del PULID_model_patch['latent_image_shape'] + + return out + +def pulid_apply_model_wrappers(wrapper_executor, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): + base_model = wrapper_executor.class_obj + PULID_model_patch = transformer_options.get(PatchKeys.pulid_patch_key_attrs, {}) + PULID_model_patch['timesteps'] = base_model.model_sampling.timestep(t).float() + try: + transformer_options[PatchKeys.running_net_model] = base_model.diffusion_model + out = wrapper_executor(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) + finally: + if PatchKeys.running_net_model in transformer_options: + del transformer_options[PatchKeys.running_net_model] + del PULID_model_patch['timesteps'], base_model + + return out + +NODE_CLASS_MAPPINGS = { + "PulidFluxModelLoader": PulidFluxModelLoader, + "PulidFluxInsightFaceLoader": PulidFluxInsightFaceLoader, + "PulidFluxEvaClipLoader": PulidFluxEvaClipLoader, + "ApplyPulidFlux": ApplyPulidFlux, + "FixPulidFluxPatch": FixPulidFluxPatch, + "PulidFluxOptions": PulidFluxOptions, + "PulidFluxFaceDetector": PulidFluxFaceDetector, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PulidFluxModelLoader": "Load PuLID Flux Model", + "PulidFluxInsightFaceLoader": "Load InsightFace (PuLID Flux)", + "PulidFluxEvaClipLoader": "Load Eva Clip (PuLID Flux)", + "ApplyPulidFlux": "Apply PuLID Flux", + "FixPulidFluxPatch": "Fix PuLID Flux Patch", + "PulidFluxOptions": "Pulid Flux Options", + "PulidFluxFaceDetector": "Pulid Flux Face Detector", +} diff --git a/custom_nodes/comfyui_pulid_flux_ll/pyproject.toml b/custom_nodes/comfyui_pulid_flux_ll/pyproject.toml new file mode 100644 index 00000000..d9743543 --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui_pulid_flux_ll" +description = "The implementation for PuLID-Flux, support use with TeaCache and WaveSpeed, no model pollution." +version = "1.1.4" +license = {file = "LICENSE"} +dependencies = ['cython', 'facexlib', 'insightface', 'onnxruntime', 'onnxruntime-gpu; sys_platform != "darwin" and (platform_machine == "x86_64" or platform_machine == "AMD64")', 'ftfy', 'timm'] + +[project.urls] +Repository = "https://github.com/lldacing/ComfyUI_PuLID_Flux_ll" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "lldacing" +DisplayName = "ComfyUI_PuLID_Flux_ll" +Icon = "" diff --git a/custom_nodes/comfyui_pulid_flux_ll/requirements.txt b/custom_nodes/comfyui_pulid_flux_ll/requirements.txt new file mode 100644 index 00000000..cc32c09c --- /dev/null +++ b/custom_nodes/comfyui_pulid_flux_ll/requirements.txt @@ -0,0 +1,7 @@ +cython +facexlib +insightface +onnxruntime +onnxruntime-gpu; sys_platform != 'darwin' and (platform_machine == 'x86_64' or platform_machine == 'AMD64') +ftfy +timm diff --git a/custom_nodes/comfyui_ultimatesdupscale/.gitignore b/custom_nodes/comfyui_ultimatesdupscale/.gitignore new file mode 100644 index 00000000..b2f0659f --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/.gitignore @@ -0,0 +1,159 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/comfyui_ultimatesdupscale/README.md b/custom_nodes/comfyui_ultimatesdupscale/README.md new file mode 100644 index 00000000..8a833d3c --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/README.md @@ -0,0 +1,44 @@ +# ComfyUI_UltimateSDUpscale + +[ComfyUI](https://github.com/comfyanonymous/ComfyUI) nodes for performing the image-to-image diffusion process on large images in tiles. This approach improves the details that is commonly found on upscaled images while reducing hardware requirements and maintaining an image size that the diffusion model is trained on. + +## Installation + + +### Using Git +1. Git must be installed on your system. Verify by running `git -v` in a terminal. +2. Enter the following command from the terminal starting in ComfyUI/custom_nodes/ + ``` + git clone https://github.com/ssitu/ComfyUI_UltimateSDUpscale + ``` + +### ComfyUI Manager +1. [ComfyUI Manager](https://github.com/Comfy-Org/ComfyUI-Manager) must be installed. +2. After launching ComfyUI, open ComfyUI Manager and select the "Custom Nodes Manager" option. +3. Search for "UltimateSDUpscale" and install the node. Select latest for the most up-to-date version. +4. Follow any prompts to restart ComfyUI. + +### comfy-cli + +1. [comfy-cli](https://github.com/Comfy-Org/comfy-cli) must be installed. +2. Run this command from the terminal: `comfy node install comfyui_ultimatesdupscale` + +### Manual Download +1. Download the zip file from https://registry.comfy.org/nodes/comfyui_ultimatesdupscale to select the version you want, or obtain the current nightly version by clicking the green "Code" button on the GitHub repository page and selecting "Download ZIP". +2. Create a new folder in the `ComfyUI/custom_nodes/` directory to hold the extracted files (e.g. `ComfyUI/custom_nodes/ComfyUI_UltimateSDUpscale`). +3. Extract the contents of the zip file into the `ComfyUI/custom_nodes/ComfyUI_UltimateSDUpscale` folder. + + +## Usage + +Nodes can be found in the node menu under `image/upscaling`. + +Documentation for the nodes can be found in the [`js/docs/`](js/docs/) folder, or viewed within the application by right-clicking the relevant node and selecting the info icon. + +Details about most of the parameters can be found [here](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/FAQ#parameters-descriptions). + +Example workflows can be found in the [`example_workflows/`](example_workflows/) folder. You can also find them in the ComfyUI application under the Templates menu, scroll down the left sidebar to find the Extensions section, then selecting this repository. + +## References +* Ultimate Stable Diffusion Upscale script for the Automatic1111 Web UI: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111 +* ComfyUI: https://github.com/comfyanonymous/ComfyUI \ No newline at end of file diff --git a/custom_nodes/comfyui_ultimatesdupscale/__init__.py b/custom_nodes/comfyui_ultimatesdupscale/__init__.py new file mode 100644 index 00000000..073b7707 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/__init__.py @@ -0,0 +1,77 @@ +import sys +import os + +# Check for original USDU script +current_dir = os.path.dirname(os.path.realpath(__file__)) +repos_dir = os.path.join(current_dir, "repositories") +usdu_dir = os.path.join(repos_dir, "ultimate_sd_upscale") +if not len(os.listdir(usdu_dir)): + print("[USDU] Original USDU script not found, downloading it from https://github.com/Coyote-A/ultimate-upscale-for-automatic1111") + import urllib.request + import zipfile + import shutil + + url = "https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/archive/master.zip" + zip_path = os.path.join(current_dir, "usdu_temp.zip") + + urllib.request.urlretrieve(url, zip_path) + + with zipfile.ZipFile(zip_path, "r") as zip_ref: + top_folder = zip_ref.namelist()[0].split('/')[0] + '/' + for member in zip_ref.namelist(): + if member.startswith(top_folder) and not member.endswith('/'): + target_path = os.path.join(usdu_dir, member[len(top_folder):]) + os.makedirs(os.path.dirname(target := os.path.join(usdu_dir, member[len(top_folder):])), exist_ok=True) + with zip_ref.open(member) as source, open(target, 'wb') as target_file: + shutil.copyfileobj(fsrc=zip_ref.open(member), fdst=target_file) + + os.remove(zip_path) + print("[USDU] Original USDU script downloaded successfully") + +# Remove other custom_node paths from sys.path to avoid conflicts +custom_node_paths = [path for path in sys.path if "custom_node" in path] +original_sys_path = sys.path.copy() +for path in custom_node_paths: + sys.path.remove(path) + +# Add this repository's path to sys.path for third-party imports +repo_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.insert(0, repo_dir) +original_modules = sys.modules.copy() + +# Place aside potentially conflicting modules +modules_used = [ + "modules", + "modules.devices", + "modules.images", + "modules.processing", + "modules.scripts", + "modules.shared", + "modules.upscaler", + "utils", +] +original_imported_modules = {} +for module in modules_used: + if module in sys.modules: + original_imported_modules[module] = sys.modules.pop(module) + +# Proceed with node setup +from .usdu_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +WEB_DIRECTORY = "./js" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] + +# Clean up imports +# Remove any new modules +modules_to_remove = [] +for module in sys.modules: + if module not in original_modules: + modules_to_remove.append(module) +for module in modules_to_remove: + del sys.modules[module] + +# Restore original modules +sys.modules.update(original_imported_modules) + +# Restore original sys.path +sys.path = original_sys_path diff --git a/custom_nodes/comfyui_ultimatesdupscale/config.json.example b/custom_nodes/comfyui_ultimatesdupscale/config.json.example new file mode 100644 index 00000000..beeda353 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/config.json.example @@ -0,0 +1,3 @@ +{ + "per_tile_progress": true +} \ No newline at end of file diff --git a/custom_nodes/comfyui_ultimatesdupscale/example_workflows/basic-usdu.jpg b/custom_nodes/comfyui_ultimatesdupscale/example_workflows/basic-usdu.jpg new file mode 100644 index 00000000..d82ef107 Binary files /dev/null and b/custom_nodes/comfyui_ultimatesdupscale/example_workflows/basic-usdu.jpg differ diff --git a/custom_nodes/comfyui_ultimatesdupscale/example_workflows/basic-usdu.json b/custom_nodes/comfyui_ultimatesdupscale/example_workflows/basic-usdu.json new file mode 100644 index 00000000..9920ea4d --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/example_workflows/basic-usdu.json @@ -0,0 +1 @@ +{"id":"2ba0b800-2f13-4f21-b8d6-c6cdb0152cae","revision":0,"last_node_id":19,"last_link_id":17,"nodes":[{"id":5,"type":"EmptyLatentImage","pos":[520,690],"size":[315,106],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"width","name":"width","type":"INT","widget":{"name":"width"},"link":null},{"localized_name":"height","name":"height","type":"INT","widget":{"name":"height"},"link":null},{"localized_name":"batch_size","name":"batch_size","type":"INT","widget":{"name":"batch_size"},"link":null}],"outputs":[{"localized_name":"LATENT","name":"LATENT","type":"LATENT","slot_index":0,"links":[2]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.65","Node name for S&R":"EmptyLatentImage"},"widgets_values":[512,512,1]},{"id":16,"type":"PreviewImage","pos":[1119.6224671232608,289.841055926025],"size":[432.0242653727887,496.8408860195624],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":10}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.4.0","Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":8,"type":"VAEDecode","pos":[1119.091462077578,206.84562991483833],"size":[140,46],"flags":{},"order":6,"mode":0,"inputs":[{"localized_name":"samples","name":"samples","type":"LATENT","link":7},{"localized_name":"vae","name":"vae","type":"VAE","link":8}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","slot_index":0,"links":[10,11]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.65","Node name for S&R":"VAEDecode"},"widgets_values":[]},{"id":7,"type":"CLIPTextEncode","pos":[410,410],"size":[425.27801513671875,180.6060791015625],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":5},{"localized_name":"text","name":"text","type":"STRING","widget":{"name":"text"},"link":null}],"outputs":[{"localized_name":"CONDITIONING","name":"CONDITIONING","type":"CONDITIONING","slot_index":0,"links":[6,14]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.65","Node name for S&R":"CLIPTextEncode"},"widgets_values":["text, watermark"],"color":"#223","bgcolor":"#335"},{"id":4,"type":"CheckpointLoaderSimple","pos":[60,200],"size":[315,98],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"ckpt_name","name":"ckpt_name","type":"COMBO","widget":{"name":"ckpt_name"},"link":null}],"outputs":[{"localized_name":"MODEL","name":"MODEL","type":"MODEL","slot_index":0,"links":[1,12]},{"localized_name":"CLIP","name":"CLIP","type":"CLIP","slot_index":1,"links":[3,5]},{"localized_name":"VAE","name":"VAE","type":"VAE","slot_index":2,"links":[8,15]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.65","Node name for S&R":"CheckpointLoaderSimple","models":[{"name":"v1-5-pruned-emaonly-fp16.safetensors","url":"https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors?download=true","directory":"checkpoints"}]},"widgets_values":["v1-5-pruned-emaonly-fp16.safetensors"]},{"id":18,"type":"UpscaleModelLoader","pos":[1249.0009242529084,889.5563144136845],"size":[307.35852275958496,72.00944603484436],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"model_name","name":"model_name","type":"COMBO","widget":{"name":"model_name"},"link":null}],"outputs":[{"localized_name":"UPSCALE_MODEL","name":"UPSCALE_MODEL","type":"UPSCALE_MODEL","links":[16]}],"properties":{"cnr_id":"comfy-core","ver":"0.4.0","Node name for S&R":"UpscaleModelLoader"},"widgets_values":["4x-UltraSharp.pth"]},{"id":6,"type":"CLIPTextEncode","pos":[411.21649169921875,203.68695068359375],"size":[422.84503173828125,164.31304931640625],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":3},{"localized_name":"text","name":"text","type":"STRING","widget":{"name":"text"},"link":null}],"outputs":[{"localized_name":"CONDITIONING","name":"CONDITIONING","type":"CONDITIONING","slot_index":0,"links":[4,13]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.65","Node name for S&R":"CLIPTextEncode"},"widgets_values":["close up photo of a white lion, best quality, side profile, bright 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@@ -0,0 +1 @@ +# Empty gradio module for the ultimate-upscale.py import because gradio is not needed \ No newline at end of file diff --git a/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscale.md b/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscale.md new file mode 100644 index 00000000..191acb02 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscale.md @@ -0,0 +1,71 @@ +`Ultimate SD Upscale` combines image upscaling with tiled image-to-image processing to create high-quality, detail-enhanced upscaled images. + +This is the main node that interfaces with the original code for the Ultimate SD Upscale script. An image is supplied for upscaling, determined by the `upscale_by` parameter. The upscale is performed using the upscale model input. + +After the upscaling, the image goes through the redraw step if the tiling order is not set to "None". A tile is selected from the image, defined by the tiling order and tile parameters from the node widgets. The tile is used as input for an image-to-image process, using the sampling-related parameters given by the node widgets. The tile is then pasted back onto the upscaled image at the appropriate position. This continues until all tiles have been processed. + +After the redraw step, the seam fix step is applied if enabled. There are various strategies for fixing seams, defined by the `seam_fix_mode` parameter from the node widgets. The seam fix step uses the same image-to-image process as the redraw step, but applied to areas between tiles from the redraw step. + +## Inputs + +| Parameter | Data Type | Input Method | Default | Range | Description | +|-----------|-----------|--------------|---------|--------|-------------| +| `image` | IMAGE | Image Input | None | - | The image to upscale. | +| `model` | MODEL | Model Selection | None | - | The model to use for image-to-image processing on each tile. | +| `positive` | CONDITIONING | Conditioning Input | None | - | The positive conditioning for each tile during the redraw step. | +| `negative` | CONDITIONING | Conditioning Input | None | - | The negative conditioning for each tile during the redraw step. | +| `vae` | VAE | Model Selection | None | - | The VAE model to use for encoding and decoding tiles. | +| `upscale_by` | FLOAT | Slider | 2.0 | 0.05-4.0 (step 0.05) | The factor to multiply the height and width of the input image(s) by. | +| `seed` | INT | Number Input | 0 | 0-18446744073709551615 | The seed to use for image-to-image processing, ensuring reproducible results. | +| `steps` | INT | Number Input | 20 | 1-10000 | The number of sampling steps to use for each tile during the redraw step and seam fix step. | +| `cfg` | FLOAT | Slider | 8.0 | 0.0-100.0 | The CFG (Classifier Free Guidance) scale to use for each tile. Higher values make the output follow the prompt more closely. The recommended values depend on the model. | +| `sampler_name` | COMBO | Dropdown | - | Available samplers | The sampler to use for each tile during the image-to-image process. | +| `scheduler` | COMBO | Dropdown | - | Available schedulers | The scheduler to use for each tile during the sampling process. | +| `denoise` | FLOAT | Slider | 0.2 | 0.0-1.0 (step 0.01) | The denoising strength to use for each tile. Higher values allow more creative changes, but more chance of seams. | +| `upscale_model` | UPSCALE_MODEL | Model Selection | None | - | The upscaler model for upscaling the image before the tiled redraw step. | +| `mode_type` | COMBO | Dropdown | - | Linear, Chess, None | The tiling order to use for the redraw step. Linear processes tiles row by row, Chess uses a checkerboard pattern, and None skips the redraw step. | +| `tile_width` | INT | Number Input | 512 | 64-8192 (step 8) | The base width of each tile during the redraw step. | +| `tile_height` | INT | Number Input | 512 | 64-8192 (step 8) | The base height of each tile during the redraw step. | +| `mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the mask applied to tiles, helping blend tiles seamlessly. A higher value means more of the original image is retained near the seams when pasting the refined tiles back on the upscaled image. | +| `tile_padding` | INT | Number Input | 32 | 0-8192 (step 8) | The padding to apply to tiles, providing more context for better blending. Adds to tile size (e.g. (`tile_width` + `tile_padding`)x(`tile_height` + `tile_padding`)). | +| `seam_fix_mode` | COMBO | Dropdown | - | None, Band Pass, Half Tile, Half Tile + Intersections | The seam fix mode to use. Different modes apply different strategies to fix visible seams between tiles. | +| `seam_fix_denoise` | FLOAT | Slider | 1.0 | 0.0-1.0 (step 0.01) | The denoising strength to use for the seam fix step. | +| `seam_fix_width` | INT | Number Input | 64 | 0-8192 (step 8) | The width of the bands used for the Band Pass seam fix mode. | +| `seam_fix_mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the seam fix mask, ensuring smooth blending. | +| `seam_fix_padding` | INT | Number Input | 16 | 0-8192 (step 8) | The padding to apply for the seam fix step. Adds to tile size. | +| `force_uniform_tiles` | BOOLEAN | Toggle | True | True/False | If enabled, tiles that would be cut off by the edges of the image will expand using context around the tile to keep the same tile size determined by `tile_width`, `tile_height`, and `tile_padding`. This is what happens in the A1111 Web UI. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes. | +| `tiled_decode` | BOOLEAN | Toggle | False | True/False | Whether to use tiled decoding when decoding tiles. Useful when you know the ComfyUI engine will attempt a normal decode and run into an Out Of Memory error, and resorts to tiled decoding anyway. | + +## Outputs + +| Output Name | Data Type | Description | +|-------------|-----------|-------------| +| `IMAGE` | IMAGE | The final upscaled image. | + +## Usage Tips + +1. **Basic Usage** + - Typical tile sizes are based on the model resolutions that it is trained on, such as 512x512 for SD1.5 models. If you can generate a coherent image at that resolution, then it is a good choice for the tile size. + - If the workflow involves generating a base image, then using USDU to upscale and refine, it is common to take the base image size as the tile size for the USDU node. For example, generating a 512x512 image, then using USDU with 2x upscale and 512x512 tiles to get a final 1024x1024 image. + - If you want to specify an exact output size, use the "No Upscale" variant of the node and perform the upscaling separately (e.g., ImageUpscaleWithModel -> ImageScale -> UltimateSDUpscaleNoUpscale). + +2. **Tiling Modes** + - **Linear**: Processes tiles sequentially row by row. + - **Chess**: Uses a checkerboard pattern, processing every other tile first. Can help reduce visible seams. + - **None**: Skips the redraw step entirely, only performs the initial upscale. Useful if you have an image upscaled by USDU and see seams, and only want to use the seam fix step. + +3. **Denoise Settings** + - Use a lower denoise (0.05-0.2) to refine the upscaled image to be less blurry, while avoiding seams and hallucinations. + - Higher denoise values are only usable when using something like a ControlNet tile model to avoid tiles and seams. + +4. **Seam Fix Modes** + - **None**: No seam fixing applied + - **Band Pass**: Applies processing to band-like areas between tiles + - **Half Tile**: Processes half-tile overlapping regions + - **Half Tile + Intersections**: Most thorough, processes half-tiles and their intersections + +5. **Performance Optimization** + - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding. + - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed. + - Disable `force_uniform_tiles` to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled.6. **Important Notes** + - The seam fix step significantly increases processing time. If seams are a problem, it may be better to reduce the denoise or increase tile size instead to avoid the increase in processing time. \ No newline at end of file diff --git a/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleCustomSample.md b/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleCustomSample.md new file mode 100644 index 00000000..50c4f6ea --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleCustomSample.md @@ -0,0 +1,106 @@ +`Ultimate SD Upscale (Custom Sample)` combines image upscaling with tiled image-to-image processing using custom samplers and sigmas for advanced control over the sampling process. + +This variant of the Ultimate SD Upscale node is designed for advanced users who want to use custom samplers and sigma schedules instead of the built-in ComfyUI samplers. This allows for more experimental and fine-tuned control over the sampling process during the tiled redraw and seam fix steps. The upscale model is optional; if not provided, the Lanczos algorithm will be used instead. + +An image is supplied for upscaling, determined by the `upscale_by` parameter. The upscale is performed using the upscale model input if provided, otherwise a Lanczos scaling is applied. + +After the upscaling, the image goes through the redraw step if the tiling order is not set to "None". A tile is selected from the image, defined by the tiling order and tile parameters from the node widgets. The tile is used as input for an image-to-image process, using the sampling-related parameters given by the node widgets, including custom sampler and sigmas if provided. The tile is then pasted back onto the upscaled image at the appropriate position. This continues until all tiles have been processed. + +After the redraw step, the seam fix step is applied if enabled. There are various strategies for fixing seams, defined by the `seam_fix_mode` parameter from the node widgets. The seam fix step uses the same image-to-image process as the redraw step, but applied to areas between tiles from the redraw step. + +## Inputs + +### Required Inputs + +| Parameter | Data Type | Input Method | Default | Range | Description | +|-----------|-----------|--------------|---------|--------|-------------| +| `image` | IMAGE | Image Input | None | - | The image to upscale. | +| `model` | MODEL | Model Selection | None | - | The model to use for image-to-image processing on each tile. | +| `positive` | CONDITIONING | Conditioning Input | None | - | The positive conditioning for each tile during the redraw step. | +| `negative` | CONDITIONING | Conditioning Input | None | - | The negative conditioning for each tile during the redraw step. | +| `vae` | VAE | Model Selection | None | - | The VAE model to use for encoding and decoding tiles. | +| `upscale_by` | FLOAT | Slider | 2.0 | 0.05-4.0 (step 0.05) | The factor to multiply the height and width of the input image(s) by. | +| `seed` | INT | Number Input | 0 | 0-18446744073709551615 | The seed to use for image-to-image processing, ensuring reproducible results. | +| `steps` | INT | Number Input | 20 | 1-10000 | The number of sampling steps to use for each tile during the redraw step and seam fix step. | +| `cfg` | FLOAT | Slider | 8.0 | 0.0-100.0 | The CFG (Classifier Free Guidance) scale to use for each tile. Higher values make the output follow the prompt more closely. The recommended values depend on the model. | +| `sampler_name` | COMBO | Dropdown | - | Available samplers | The sampler to use for each tile during the image-to-image process. | +| `scheduler` | COMBO | Dropdown | - | Available schedulers | The scheduler to use for each tile during the sampling process. | +| `denoise` | FLOAT | Slider | 0.2 | 0.0-1.0 (step 0.01) | The denoising strength to use for each tile. Higher values allow more creative changes, but more chance of seams. | +| `mode_type` | COMBO | Dropdown | - | Linear, Chess, None | The tiling order to use for the redraw step. Linear processes tiles row by row, Chess uses a checkerboard pattern, and None skips the redraw step. | +| `tile_width` | INT | Number Input | 512 | 64-8192 (step 8) | The base width of each tile during the redraw step. | +| `tile_height` | INT | Number Input | 512 | 64-8192 (step 8) | The base height of each tile during the redraw step. | +| `mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the mask applied to tiles, helping blend tiles seamlessly. A higher value means more of the original image is retained near the seams when pasting the refined tiles back on the upscaled image. | +| `tile_padding` | INT | Number Input | 32 | 0-8192 (step 8) | The padding to apply to tiles, providing more context for better blending. Adds to tile size (e.g. (`tile_width` + `tile_padding`)x(`tile_height` + `tile_padding`)). | +| `seam_fix_mode` | COMBO | Dropdown | - | None, Band Pass, Half Tile, Half Tile + Intersections | The seam fix mode to use. Different modes apply different strategies to fix visible seams between tiles. | +| `seam_fix_denoise` | FLOAT | Slider | 1.0 | 0.0-1.0 (step 0.01) | The denoising strength to use for the seam fix step. | +| `seam_fix_width` | INT | Number Input | 64 | 0-8192 (step 8) | The width of the bands used for the Band Pass seam fix mode. | +| `seam_fix_mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the seam fix mask, ensuring smooth blending. | +| `seam_fix_padding` | INT | Number Input | 16 | 0-8192 (step 8) | The padding to apply for the seam fix step. Adds to tile size. | +| `force_uniform_tiles` | BOOLEAN | Toggle | True | True/False | If enabled, tiles that would be cut off by the edges of the image will expand using context around the tile to keep the same tile size determined by `tile_width`, `tile_height`, and `tile_padding`. This is what happens in the A1111 Web UI. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes. | +| `tiled_decode` | BOOLEAN | Toggle | False | True/False | Whether to use tiled decoding when decoding tiles. Useful when you know the ComfyUI engine will attempt a normal decode and run into an Out Of Memory error, and resorts to tiled decoding anyway. | + +### Optional Inputs + +| Parameter | Data Type | Input Method | Default | Description | +|-----------|-----------|--------------|---------|-------------| +| `upscale_model` | UPSCALE_MODEL | Model Selection | None | The upscaler model for upscaling the image before the tiled redraw step. If not provided, the Lanczos algorithm will be used instead. | +| `custom_sampler` | SAMPLER | Sampler Input | None | A custom sampler to use instead of the built-in ComfyUI sampler specified by `sampler_name`. Only used if both `custom_sampler` and `custom_sigmas` are provided. | +| `custom_sigmas` | SIGMAS | Sigmas Input | None | A custom noise schedule to use during sampling. Only used if both `custom_sampler` and `custom_sigmas` are provided. | + +## Outputs + +| Output Name | Data Type | Description | +|-------------|-----------|-------------| +| `IMAGE` | IMAGE | The final upscaled image. | + +## Usage Tips + +1. **When to Use This Node** + - You want to experiment with custom samplers not available in the standard node. + - You need precise control over sigma schedules. + - You're working with advanced sampling techniques or research implementations. + - You want the flexibility to skip the upscale model and use Lanczos instead. + - You're combining USDU with custom sampling workflows. + +2. **Basic Usage** + - Typical tile sizes are based on the model resolutions that it is trained on, such as 512x512 for SD1.5 models. If you can generate a coherent image at that resolution, then it is probably a good choice for the tile size. + - If the workflow involves generating a base image, then using USDU to upscale and refine, it is common to take the base image size as the tile size for the USDU node. For example, generating a 512x512 image, then using USDU with 2x upscale and 512x512 tiles to get a final 1024x1024 image. + +3. **Custom Sampler Usage** + - When both `custom_sampler` and `custom_sigmas` are provided, `custom_sampler` will be used instead of the `sampler_name` parameter + - Custom samplers can implement experimental or specialized sampling algorithms + - Ensure your custom sampler is compatible with the model and VAE being used + - Custom samplers typically require `custom_sigmas` to be provided as well + +4. **Custom Sigmas Usage** + - When both `custom_sampler` and `custom_sigmas` are provided, `custom_sigmas` will be used instead of the default noise schedule. + - Sigma schedules control the noise levels during the denoising process + - Custom sigmas allow you to fine-tune the denoising trajectory + - Different sigma schedules can produce different aesthetic results + - When using `custom_sigmas`, ensure they're appropriate for your `steps` parameter + +5. **Tiling Modes** + - **Linear**: Processes tiles sequentially row by row. + - **Chess**: Uses a checkerboard pattern, processing every other tile first. Can help reduce visible seams. + - **None**: Skips the redraw step entirely, only performs the initial upscale. Useful if you have an image upscaled by USDU and see seams, and only want to use the seam fix step. + +6. **Denoise Settings** + - Use a lower denoise (0.05-0.2) to refine the upscaled image to be less blurry, while avoiding seams and hallucinations. + - Higher denoise values are only usable when using something like a ControlNet tile model to avoid tiles and seams. + +7. **Seam Fix Modes** + - **None**: No seam fixing applied + - **Band Pass**: Applies processing to band-like areas between tiles + - **Half Tile**: Processes half-tile overlapping regions + - **Half Tile + Intersections**: Most thorough, processes half-tiles and their intersections + +8. **Performance Optimization** + - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding. + - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed. + - Disable `force_uniform_tiles` to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled. + +9. **Important Notes** + - When no `upscale_model` is provided, Lanczos is used to scale by the `upscale_by` factor instead. + - Custom sampler and sigmas should be compatible with each other, and also the model being used. + - `custom_sampler` and `custom_sigmas` are both optional, but must be provided together to take effect. + - The seam fix step significantly increases processing time. If seams are a problem, it may be better to reduce the denoise or increase tile size instead to avoid the increase in processing time. diff --git a/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleNoUpscale.md b/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleNoUpscale.md new file mode 100644 index 00000000..46fe37b9 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleNoUpscale.md @@ -0,0 +1,80 @@ +`Ultimate SD Upscale (No Upscale)` applies tiled image-to-image proc5. **Performance Optimization** + - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding. + - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed. + - Disable `force_uniform_tiles` to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled.ng to an already upscaled image to enhance details and fix seams, without performing the initial upscaling step with an upscale model. + +This variant of the Ultimate SD Upscale node is designed for situations where you already have an upscaled image and only want to apply the tiled redraw and seam fix steps. This is useful when you've upscaled an image using a different method or upscaler and want to use USDU's tiled refinement capabilities to add details and remove artifacts. + +The image goes through the redraw step if the tiling order is not set to "None". A tile is selected from the image, defined by the tiling order and tile parameters from the node widgets. The tile is used as input for an image-to-image process, using the sampling-related parameters given by the node widgets. The tile is then pasted back onto the image at the appropriate position. This continues until all tiles have been processed. + +After the redraw step, the seam fix step is applied if enabled. There are various strategies for fixing seams, defined by the `seam_fix_mode` parameter from the node widgets. The seam fix step uses the same image-to-image process as the redraw step, but applied to areas between tiles from the redraw step. + +## Inputs + +| Parameter | Data Type | Input Method | Default | Range | Description | +|-----------|-----------|--------------|---------|--------|-------------| +| `upscaled_image` | IMAGE | Image Input | None | - | The already upscaled image to refine with tiled processing. | +| `model` | MODEL | Model Selection | None | - | The model to use for image-to-image processing on each tile. | +| `positive` | CONDITIONING | Conditioning Input | None | - | The positive conditioning for each tile during the redraw step. | +| `negative` | CONDITIONING | Conditioning Input | None | - | The negative conditioning for each tile during the redraw step. | +| `vae` | VAE | Model Selection | None | - | The VAE model to use for encoding and decoding tiles. | +| `seed` | INT | Number Input | 0 | 0-18446744073709551615 | The seed to use for image-to-image processing, ensuring reproducible results. | +| `steps` | INT | Number Input | 20 | 1-10000 | The number of sampling steps to use for each tile during the redraw step and seam fix step. | +| `cfg` | FLOAT | Slider | 8.0 | 0.0-100.0 | The CFG (Classifier Free Guidance) scale to use for each tile. Higher values make the output follow the prompt more closely. The recommended values depend on the model. | +| `sampler_name` | COMBO | Dropdown | - | Available samplers | The sampler to use for each tile during the image-to-image process. | +| `scheduler` | COMBO | Dropdown | - | Available schedulers | The scheduler to use for each tile during the sampling process. | +| `denoise` | FLOAT | Slider | 0.2 | 0.0-1.0 (step 0.01) | The denoising strength to use for each tile. Higher values allow more creative changes, but more chance of seams. | +| `mode_type` | COMBO | Dropdown | - | Linear, Chess, None | The tiling order to use for the redraw step. Linear processes tiles row by row, Chess uses a checkerboard pattern, and None skips the redraw step. | +| `tile_width` | INT | Number Input | 512 | 64-8192 (step 8) | The base width of each tile during the redraw step. | +| `tile_height` | INT | Number Input | 512 | 64-8192 (step 8) | The base height of each tile during the redraw step. | +| `mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the mask applied to tiles, helping blend tiles seamlessly. A higher value means more of the original image is retained near the seams when pasting the refined tiles back on the upscaled image. | +| `tile_padding` | INT | Number Input | 32 | 0-8192 (step 8) | The padding to apply to tiles, providing more context for better blending. Adds to tile size (e.g. (`tile_width` + `tile_padding`)x(`tile_height` + `tile_padding`)). | +| `seam_fix_mode` | COMBO | Dropdown | - | None, Band Pass, Half Tile, Half Tile + Intersections | The seam fix mode to use. Different modes apply different strategies to fix visible seams between tiles. | +| `seam_fix_denoise` | FLOAT | Slider | 1.0 | 0.0-1.0 (step 0.01) | The denoising strength to use for the seam fix step. | +| `seam_fix_width` | INT | Number Input | 64 | 0-8192 (step 8) | The width of the bands used for the Band Pass seam fix mode. | +| `seam_fix_mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the seam fix mask, ensuring smooth blending. | +| `seam_fix_padding` | INT | Number Input | 16 | 0-8192 (step 8) | The padding to apply for the seam fix step. Adds to tile size. | +| `force_uniform_tiles` | BOOLEAN | Toggle | True | True/False | If enabled, tiles that would be cut off by the edges of the image will expand using context around the tile to keep the same tile size determined by `tile_width`, `tile_height`, and `tile_padding`. This is what happens in the A1111 Web UI. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes. | +| `tiled_decode` | BOOLEAN | Toggle | False | True/False | Whether to use tiled decoding when decoding tiles. Useful when you know the ComfyUI engine will attempt a normal decode and run into an Out Of Memory error, and resorts to tiled decoding anyway. | + +## Outputs + +| Output Name | Data Type | Description | +|-------------|-----------|-------------| +| `IMAGE` | IMAGE | The final refined image. | + +## Usage Tips + +1. **When to Use This Node** + - You've already upscaled an image with a different upscaler and want to add details. + - You want to fix seams or artifacts in an existing high-resolution image. + - You want more control by separating the upscaling and refinement steps. + - You want to skip the use of an upscale model, and do a simple upscale with an algorithm like Lanczos or Nearest Neighbor beforehand. + +2. **Basic Usage** + - Typical tile sizes are based on the model resolutions that it is trained on, such as 512x512 for SD1.5 models. If you can generate a coherent image at that resolution, then it is probably a good choice for the tile size. + +3. **Tiling Modes** + - **Linear**: Processes tiles sequentially row by row. + - **Chess**: Uses a checkerboard pattern, processing every other tile first. Can help reduce visible seams. + - **None**: Skips the redraw step entirely. Useful if you only want to use the seam fix step to fix visible seams without adding new details. + +4. **Denoise Settings** + - Use a lower denoise (0.05-0.2) to refine the upscaled image to be less blurry, while avoiding seams and hallucinations. + - Higher denoise values are only usable when using something like a ControlNet tile model to avoid tiles and seams. + +5. **Seam Fix Modes** + - **None**: No seam fixing applied + - **Band Pass**: Applies processing to band-like areas between tiles + - **Half Tile**: Processes half-tile overlapping regions + - **Half Tile + Intersections**: Most thorough, processes half-tiles and their intersections + +6. **Performance Optimization** + - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding. + - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed. + - Disable force_uniform_tiles to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled. + +7. **Important Notes** + - This node does not perform any upscaling; it expects an already upscaled image as input + - The input image size determines the output size (no scaling is applied) + - The seam fix step significantly increases processing time. If seams are a problem, it may be better to reduce the denoise or increase tile size instead to avoid the increase in processing time. diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/__init__.py b/custom_nodes/comfyui_ultimatesdupscale/modules/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/devices.py b/custom_nodes/comfyui_ultimatesdupscale/modules/devices.py new file mode 100644 index 00000000..3e37b88d --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/modules/devices.py @@ -0,0 +1,2 @@ +def torch_gc(): + pass diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/images.py b/custom_nodes/comfyui_ultimatesdupscale/modules/images.py new file mode 100644 index 00000000..502c819a --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/modules/images.py @@ -0,0 +1,8 @@ +from PIL import Image + + +def flatten(img, bgcolor): + # Replace transparency with bgcolor + if img.mode in ("RGB"): + return img + return Image.alpha_composite(Image.new("RGBA", img.size, bgcolor), img).convert("RGB") diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/processing.py b/custom_nodes/comfyui_ultimatesdupscale/modules/processing.py new file mode 100644 index 00000000..3572bbb2 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/modules/processing.py @@ -0,0 +1,277 @@ +from PIL import Image, ImageFilter +import torch +import math +from nodes import common_ksampler, VAEEncode, VAEDecode, VAEDecodeTiled +from comfy_extras.nodes_custom_sampler import SamplerCustom +from usdu_utils import pil_to_tensor, tensor_to_pil, get_crop_region, expand_crop, crop_cond +from modules import shared +from tqdm import tqdm +import comfy +from enum import Enum +import json +import os + +if (not hasattr(Image, 'Resampling')): # For older versions of Pillow + Image.Resampling = Image + +# Taken from the USDU script +class USDUMode(Enum): + LINEAR = 0 + CHESS = 1 + NONE = 2 + +class USDUSFMode(Enum): + NONE = 0 + BAND_PASS = 1 + HALF_TILE = 2 + HALF_TILE_PLUS_INTERSECTIONS = 3 + +class StableDiffusionProcessing: + + def __init__( + self, + init_img, + model, + positive, + negative, + vae, + seed, + steps, + cfg, + sampler_name, + scheduler, + denoise, + upscale_by, + uniform_tile_mode, + tiled_decode, + tile_width, + tile_height, + redraw_mode, + seam_fix_mode, + custom_sampler=None, + custom_sigmas=None, + ): + # Variables used by the USDU script + self.init_images = [init_img] + self.image_mask = None + self.mask_blur = 0 + self.inpaint_full_res_padding = 0 + self.width = init_img.width * upscale_by + self.height = init_img.height * upscale_by + self.rows = round(self.height / tile_height) + self.cols = round(self.width / tile_width) + + # ComfyUI Sampler inputs + self.model = model + self.positive = positive + self.negative = negative + self.vae = vae + self.seed = seed + self.steps = steps + self.cfg = cfg + self.sampler_name = sampler_name + self.scheduler = scheduler + self.denoise = denoise + + # Optional custom sampler and sigmas + self.custom_sampler = custom_sampler + self.custom_sigmas = custom_sigmas + + if (custom_sampler is not None) ^ (custom_sigmas is not None): + print("[USDU] Both custom sampler and custom sigmas must be provided, defaulting to widget sampler and sigmas") + + # Variables used only by this script + self.init_size = init_img.width, init_img.height + self.upscale_by = upscale_by + self.uniform_tile_mode = uniform_tile_mode + self.tiled_decode = tiled_decode + self.vae_decoder = VAEDecode() + self.vae_encoder = VAEEncode() + self.vae_decoder_tiled = VAEDecodeTiled() + + if self.tiled_decode: + print("[USDU] Using tiled decode") + + # Other required A1111 variables for the USDU script that is currently unused in this script + self.extra_generation_params = {} + + # Load config file for USDU + config_path = os.path.join(os.path.dirname(__file__), os.pardir, 'config.json') + config = {} + if os.path.exists(config_path): + with open(config_path, 'r') as f: + config = json.load(f) + + # Progress bar for the entire process instead of per tile + self.progress_bar_enabled = False + if comfy.utils.PROGRESS_BAR_ENABLED: + self.progress_bar_enabled = True + comfy.utils.PROGRESS_BAR_ENABLED = config.get('per_tile_progress', True) + self.tiles = 0 + if redraw_mode.value != USDUMode.NONE.value: + self.tiles += self.rows * self.cols + if seam_fix_mode.value == USDUSFMode.BAND_PASS.value: + self.tiles += (self.rows - 1) + (self.cols - 1) + elif seam_fix_mode.value == USDUSFMode.HALF_TILE.value: + self.tiles += (self.rows - 1) * self.cols + (self.cols - 1) * self.rows + elif seam_fix_mode.value == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS.value: + self.tiles += (self.rows - 1) * self.cols + (self.cols - 1) * self.rows + (self.rows - 1) * (self.cols - 1) + self.pbar = None + # self.pbar = tqdm(total=self.tiles, desc='USDU') # Creating the pbar here will cause an empty progress bar to be displayed + + def __del__(self): + # Undo changes to progress bar flag when node is done or cancelled + if self.progress_bar_enabled: + comfy.utils.PROGRESS_BAR_ENABLED = True + +class Processed: + + def __init__(self, p: StableDiffusionProcessing, images: list, seed: int, info: str): + self.images = images + self.seed = seed + self.info = info + + def infotext(self, p: StableDiffusionProcessing, index): + return None + + +def fix_seed(p: StableDiffusionProcessing): + pass + + +def sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise, custom_sampler, custom_sigmas): + # Choose way to sample based on given inputs + + # Custom sampler and sigmas + if custom_sampler is not None and custom_sigmas is not None: + kwargs = dict( + model=model, + add_noise=True, + noise_seed=seed, + cfg=cfg, + positive=positive, + negative=negative, + sampler=custom_sampler, + sigmas=custom_sigmas, + latent_image=latent + ) + if "execute" in dir(SamplerCustom): + (samples, _) = SamplerCustom.execute(**kwargs) + else: + custom_sample = SamplerCustom() + (samples, _) = getattr(custom_sample, custom_sample.FUNCTION)(**kwargs) + return samples + + # Default + (samples,) = common_ksampler(model, seed, steps, cfg, sampler_name, + scheduler, positive, negative, latent, denoise=denoise) + return samples + + +def process_images(p: StableDiffusionProcessing) -> Processed: + # Where the main image generation happens in A1111 + + # Show the progress bar + if p.progress_bar_enabled and p.pbar is None: + p.pbar = tqdm(total=p.tiles, desc='USDU', unit='tile') + + # Setup + image_mask = p.image_mask.convert('L') + init_image = p.init_images[0] + + # Locate the white region of the mask outlining the tile and add padding + crop_region = get_crop_region(image_mask, p.inpaint_full_res_padding) + + if p.uniform_tile_mode: + # Expand the crop region to match the processing size ratio and then resize it to the processing size + x1, y1, x2, y2 = crop_region + crop_width = x2 - x1 + crop_height = y2 - y1 + crop_ratio = crop_width / crop_height + p_ratio = p.width / p.height + if crop_ratio > p_ratio: + target_width = crop_width + target_height = round(crop_width / p_ratio) + else: + target_width = round(crop_height * p_ratio) + target_height = crop_height + crop_region, _ = expand_crop(crop_region, image_mask.width, image_mask.height, target_width, target_height) + tile_size = p.width, p.height + else: + # Uses the minimal size that can fit the mask, minimizes tile size but may lead to image sizes that the model is not trained on + x1, y1, x2, y2 = crop_region + crop_width = x2 - x1 + crop_height = y2 - y1 + target_width = math.ceil(crop_width / 8) * 8 + target_height = math.ceil(crop_height / 8) * 8 + crop_region, tile_size = expand_crop(crop_region, image_mask.width, + image_mask.height, target_width, target_height) + + # Blur the mask + if p.mask_blur > 0: + image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur)) + + # Crop the images to get the tiles that will be used for generation + tiles = [img.crop(crop_region) for img in shared.batch] + + # Assume the same size for all images in the batch + initial_tile_size = tiles[0].size + + # Resize if necessary + for i, tile in enumerate(tiles): + if tile.size != tile_size: + tiles[i] = tile.resize(tile_size, Image.Resampling.LANCZOS) + + # Crop conditioning + positive_cropped = crop_cond(p.positive, crop_region, p.init_size, init_image.size, tile_size) + negative_cropped = crop_cond(p.negative, crop_region, p.init_size, init_image.size, tile_size) + + # Encode the image + batched_tiles = torch.cat([pil_to_tensor(tile) for tile in tiles], dim=0) + (latent,) = p.vae_encoder.encode(p.vae, batched_tiles) + + # Generate samples + samples = sample(p.model, p.seed, p.steps, p.cfg, p.sampler_name, p.scheduler, positive_cropped, + negative_cropped, latent, p.denoise, p.custom_sampler, p.custom_sigmas) + + # Update the progress bar + if p.progress_bar_enabled: + p.pbar.update(1) + + # Decode the sample + if not p.tiled_decode: + (decoded,) = p.vae_decoder.decode(p.vae, samples) + else: + (decoded,) = p.vae_decoder_tiled.decode(p.vae, samples, 512) # Default tile size is 512 + + # Convert the sample to a PIL image + tiles_sampled = [tensor_to_pil(decoded, i) for i in range(len(decoded))] + + for i, tile_sampled in enumerate(tiles_sampled): + init_image = shared.batch[i] + + # Resize back to the original size + if tile_sampled.size != initial_tile_size: + tile_sampled = tile_sampled.resize(initial_tile_size, Image.Resampling.LANCZOS) + + # Put the tile into position + image_tile_only = Image.new('RGBA', init_image.size) + image_tile_only.paste(tile_sampled, crop_region[:2]) + + # Add the mask as an alpha channel + # Must make a copy due to the possibility of an edge becoming black + temp = image_tile_only.copy() + temp.putalpha(image_mask) + image_tile_only.paste(temp, image_tile_only) + + # Add back the tile to the initial image according to the mask in the alpha channel + result = init_image.convert('RGBA') + result.alpha_composite(image_tile_only) + + # Convert back to RGB + result = result.convert('RGB') + + shared.batch[i] = result + + processed = Processed(p, [shared.batch[0]], p.seed, None) + return processed diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/scripts.py b/custom_nodes/comfyui_ultimatesdupscale/modules/scripts.py new file mode 100644 index 00000000..5cbd134f --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/modules/scripts.py @@ -0,0 +1,2 @@ +class Script: + pass diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/shared.py b/custom_nodes/comfyui_ultimatesdupscale/modules/shared.py new file mode 100644 index 00000000..4222630f --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/modules/shared.py @@ -0,0 +1,25 @@ +class Options: + img2img_background_color = "#ffffff" # Set to white for now + + +class State: + interrupted = False + + def begin(self): + pass + + def end(self): + pass + + +opts = Options() +state = State() + +# Will only ever hold 1 upscaler +sd_upscalers = [None] +# The upscaler usable by ComfyUI nodes +actual_upscaler = None + +# Batch of images to upscale +batch = None +batch_as_tensor = None diff --git a/custom_nodes/comfyui_ultimatesdupscale/modules/upscaler.py b/custom_nodes/comfyui_ultimatesdupscale/modules/upscaler.py new file mode 100644 index 00000000..da1456a5 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/modules/upscaler.py @@ -0,0 +1,31 @@ +from PIL import Image +from usdu_utils import tensor_to_pil, pil_to_tensor +from comfy_extras.nodes_upscale_model import ImageUpscaleWithModel +from modules import shared + +if (not hasattr(Image, 'Resampling')): # For older versions of Pillow + Image.Resampling = Image + + +class Upscaler: + + def upscale(self, img: Image, scale, selected_model: str = None): + if scale == 1.0: + return img + if (shared.actual_upscaler is None): + return img.resize((img.width * scale, img.height * scale), Image.Resampling.LANCZOS) + if "execute" in dir(ImageUpscaleWithModel): + # V3 schema: https://github.com/comfyanonymous/ComfyUI/pull/10149 + (upscaled,) = ImageUpscaleWithModel.execute(shared.actual_upscaler, shared.batch_as_tensor) + else: + (upscaled,) = ImageUpscaleWithModel().upscale(shared.actual_upscaler, shared.batch_as_tensor) + shared.batch = [tensor_to_pil(upscaled, i) for i in range(len(upscaled))] + return shared.batch[0] + + +class UpscalerData: + name = "" + data_path = "" + + def __init__(self): + self.scaler = Upscaler() diff --git a/custom_nodes/comfyui_ultimatesdupscale/pyproject.toml b/custom_nodes/comfyui_ultimatesdupscale/pyproject.toml new file mode 100644 index 00000000..1d797829 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "comfyui_ultimatesdupscale" +description = "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A." +version = "1.6.5" +license = { file = "LICENSE" } + +[project.optional-dependencies] +test = [ + "pytest>=9.0.0", + "pytest-cov>=7.0.0", +] + +[project.urls] +Repository = "https://github.com/ssitu/ComfyUI_UltimateSDUpscale" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "ssit" +DisplayName = "ComfyUI_UltimateSDUpscale" +Icon = "" +includes = ["/repositories/ultimate_sd_upscale/"] + +[tool.setuptools.packages.find] +where = ["."] +include = ["ComfyUI_UltimateSDUpscale"] diff --git a/custom_nodes/comfyui_ultimatesdupscale/repositories/__init__.py b/custom_nodes/comfyui_ultimatesdupscale/repositories/__init__.py new file mode 100644 index 00000000..a30c5650 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/repositories/__init__.py @@ -0,0 +1,14 @@ +import os +import sys +import importlib.util + +repositories_path = os.path.dirname(os.path.realpath(__file__)) + +# Import the script +script_name = os.path.join("scripts", "ultimate-upscale") +repo_name = "ultimate_sd_upscale" +script_path = os.path.join(repositories_path, repo_name, f"{script_name}.py") +spec = importlib.util.spec_from_file_location(script_name, script_path) +ultimate_upscale = importlib.util.module_from_spec(spec) +sys.modules[script_name] = ultimate_upscale +spec.loader.exec_module(ultimate_upscale) diff --git a/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/.gitignore b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/.gitignore new file mode 100644 index 00000000..600d2d33 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/.gitignore @@ -0,0 +1 @@ +.vscode \ No newline at end of file diff --git a/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/LICENSE b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/LICENSE new file mode 100644 index 00000000..ebfe3f52 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/README.md b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/README.md new file mode 100644 index 00000000..3c73378c --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/README.md @@ -0,0 +1,119 @@ +# Ultimate SD Upscale extension for [AUTOMATIC1111 Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) +Now you have the opportunity to use a large denoise (0.3-0.5) and not spawn many artifacts. Works on any video card, since you can use a 512x512 tile size and the image will converge. + +News channel: https://t.me/usdunews + +# Instructions +All instructions can be found on the project's [wiki](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki). + +# Refs + +https://github.com/ssitu/ComfyUI_UltimateSDUpscale - Implementation for ComfyUI + +# Examples +More on [wiki page](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/Examples) + +
    + E1 + Original image + + ![Original](https://i.imgur.com/J8mRYOD.png) + + 2k upscaled. **Tile size**: 512, **Padding**: 32, **Mask blur**: 16, **Denoise**: 0.4 + ![2k upscale](https://i.imgur.com/0aKua4r.png) +
    + +
    + E2 + Original image + + ![Original](https://i.imgur.com/aALNI2w.png) + + 2k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35 + ![2k upscale](https://i.imgur.com/B5PHz0J.png) + + 4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35 + ![4k upscale](https://i.imgur.com/tIUQ7TJ.jpg) +
    + +
    + E3 + Original image + + ![Original](https://i.imgur.com/AGtszA8.png) + + 4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.4 + ![4k upscale](https://i.imgur.com/LCYLfCs.jpg) +
    + +# API Usage + +```javascript +{ +"script_name" : "ultimate sd upscale", +"script_args" : [ + null, // _ (not used) + 512, // tile_width + 512, // tile_height + 8, // mask_blur + 32, // padding + 64, // seams_fix_width + 0.35, // seams_fix_denoise + 32, // seams_fix_padding + 0, // upscaler_index + true, // save_upscaled_image a.k.a Upscaled + 0, // redraw_mode + false, // save_seams_fix_image a.k.a Seams fix + 8, // seams_fix_mask_blur + 0, // seams_fix_type + 0, // target_size_type + 2048, // custom_width + 2048, // custom_height + 2 // custom_scale +] +} +``` +upscaler_index +| Value | | +|:-------------:| -----:| +| 0 | None | +| 1 | Lanczos | +| 2 | Nearest | +| 3 | ESRGAN_4x | +| 4 | LDSR | +| 5 | R-ESRGAN_4x+ | +| 6 | R-ESRGAN 4x+ Anime6B | +| 7 | ScuNET GAN | +| 8 | ScuNET PSNR | +| 9 | SwinIR 4x | + +redraw_mode +| Value | | +|:-------------:| -----:| +| 0 | Linear | +| 1 | Chess | +| 2 | None | + +seams_fix_mask_blur +| Value | | +|:-------------:| -----:| +| 0 | None | +| 1 | BAND_PASS | +| 2 | HALF_TILE | +| 3 | HALF_TILE_PLUS_INTERSECTIONS | + +seams_fix_type +| Value | | +|:-------------:| -----:| +| 0 | None | +| 1 | Band pass | +| 2 | Half tile offset pass | +| 3 | Half tile offset pass + intersections | + +seams_fix_type +| Value | | +|:-------------:| -----:| +| 0 | From img2img2 settings | +| 1 | Custom size | +| 2 | Scale from image size | + diff --git a/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/scripts/ultimate-upscale.py b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/scripts/ultimate-upscale.py new file mode 100644 index 00000000..fe695465 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/scripts/ultimate-upscale.py @@ -0,0 +1,569 @@ +import math +import gradio as gr +from PIL import Image, ImageDraw, ImageOps +from modules import processing, shared, images, devices, scripts +from modules.processing import StableDiffusionProcessing +from modules.processing import Processed +from modules.shared import opts, state +from enum import Enum + +elem_id_prefix = "ultimateupscale" + +class USDUMode(Enum): + LINEAR = 0 + CHESS = 1 + NONE = 2 + +class USDUSFMode(Enum): + NONE = 0 + BAND_PASS = 1 + HALF_TILE = 2 + HALF_TILE_PLUS_INTERSECTIONS = 3 + +class USDUpscaler(): + + def __init__(self, p, image, upscaler_index:int, save_redraw, save_seams_fix, tile_width, tile_height) -> None: + self.p:StableDiffusionProcessing = p + self.image:Image = image + self.scale_factor = math.ceil(max(p.width, p.height) / max(image.width, image.height)) + self.upscaler = shared.sd_upscalers[upscaler_index] + self.redraw = USDURedraw() + self.redraw.save = save_redraw + self.redraw.tile_width = tile_width if tile_width > 0 else tile_height + self.redraw.tile_height = tile_height if tile_height > 0 else tile_width + self.seams_fix = USDUSeamsFix() + self.seams_fix.save = save_seams_fix + self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height + self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width + self.initial_info = None + self.rows = math.ceil(self.p.height / self.redraw.tile_height) + self.cols = math.ceil(self.p.width / self.redraw.tile_width) + + def get_factor(self, num): + # Its just return, don't need elif + if num == 1: + return 2 + if num % 4 == 0: + return 4 + if num % 3 == 0: + return 3 + if num % 2 == 0: + return 2 + return 0 + + def get_factors(self): + scales = [] + current_scale = 1 + current_scale_factor = self.get_factor(self.scale_factor) + while current_scale_factor == 0: + self.scale_factor += 1 + current_scale_factor = self.get_factor(self.scale_factor) + while current_scale < self.scale_factor: + current_scale_factor = self.get_factor(self.scale_factor // current_scale) + scales.append(current_scale_factor) + current_scale = current_scale * current_scale_factor + if current_scale_factor == 0: + break + self.scales = enumerate(scales) + + def upscale(self): + # Log info + print(f"Canva size: {self.p.width}x{self.p.height}") + print(f"Image size: {self.image.width}x{self.image.height}") + print(f"Scale factor: {self.scale_factor}") + # Check upscaler is not empty + if self.upscaler.name == "None": + self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS) + return + # Get list with scale factors + self.get_factors() + # Upscaling image over all factors + for index, value in self.scales: + print(f"Upscaling iteration {index+1} with scale factor {value}") + self.image = self.upscaler.scaler.upscale(self.image, value, self.upscaler.data_path) + # Resize image to set values + self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS) + + def setup_redraw(self, redraw_mode, padding, mask_blur): + self.redraw.mode = USDUMode(redraw_mode) + self.redraw.enabled = self.redraw.mode != USDUMode.NONE + self.redraw.padding = padding + self.p.mask_blur = mask_blur + + def setup_seams_fix(self, padding, denoise, mask_blur, width, mode): + self.seams_fix.padding = padding + self.seams_fix.denoise = denoise + self.seams_fix.mask_blur = mask_blur + self.seams_fix.width = width + self.seams_fix.mode = USDUSFMode(mode) + self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE + + def save_image(self): + if type(self.p.prompt) != list: + images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt, opts.samples_format, info=self.initial_info, p=self.p) + else: + images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt[0], opts.samples_format, info=self.initial_info, p=self.p) + + def calc_jobs_count(self): + redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0 + seams_job_count = 0 + if self.seams_fix.mode == USDUSFMode.BAND_PASS: + seams_job_count = self.rows + self.cols - 2 + elif self.seams_fix.mode == USDUSFMode.HALF_TILE: + seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + elif self.seams_fix.mode == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS: + seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + (self.rows - 1) * (self.cols - 1) + + state.job_count = redraw_job_count + seams_job_count + + def print_info(self): + print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}") + print(f"Tiles amount: {self.rows * self.cols}") + print(f"Grid: {self.rows}x{self.cols}") + print(f"Redraw enabled: {self.redraw.enabled}") + print(f"Seams fix mode: {self.seams_fix.mode.name}") + + def add_extra_info(self): + self.p.extra_generation_params["Ultimate SD upscale upscaler"] = self.upscaler.name + self.p.extra_generation_params["Ultimate SD upscale tile_width"] = self.redraw.tile_width + self.p.extra_generation_params["Ultimate SD upscale tile_height"] = self.redraw.tile_height + self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = self.p.mask_blur + self.p.extra_generation_params["Ultimate SD upscale padding"] = self.redraw.padding + + def process(self): + state.begin() + self.calc_jobs_count() + self.result_images = [] + if self.redraw.enabled: + self.image = self.redraw.start(self.p, self.image, self.rows, self.cols) + self.initial_info = self.redraw.initial_info + self.result_images.append(self.image) + if self.redraw.save: + self.save_image() + + if self.seams_fix.enabled: + self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols) + self.initial_info = self.seams_fix.initial_info + self.result_images.append(self.image) + if self.seams_fix.save: + self.save_image() + state.end() + +class USDURedraw(): + + def init_draw(self, p, width, height): + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + p.width = math.ceil((self.tile_width+self.padding) / 64) * 64 + p.height = math.ceil((self.tile_height+self.padding) / 64) * 64 + mask = Image.new("L", (width, height), "black") + draw = ImageDraw.Draw(mask) + return mask, draw + + def calc_rectangle(self, xi, yi): + x1 = xi * self.tile_width + y1 = yi * self.tile_height + x2 = xi * self.tile_width + self.tile_width + y2 = yi * self.tile_height + self.tile_height + + return x1, y1, x2, y2 + + def linear_process(self, p, image, rows, cols): + mask, draw = self.init_draw(p, image.width, image.height) + for yi in range(rows): + for xi in range(cols): + if state.interrupted: + break + draw.rectangle(self.calc_rectangle(xi, yi), fill="white") + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + draw.rectangle(self.calc_rectangle(xi, yi), fill="black") + if (len(processed.images) > 0): + image = processed.images[0] + + p.width = image.width + p.height = image.height + self.initial_info = processed.infotext(p, 0) + + return image + + def chess_process(self, p, image, rows, cols): + mask, draw = self.init_draw(p, image.width, image.height) + tiles = [] + # calc tiles colors + for yi in range(rows): + for xi in range(cols): + if state.interrupted: + break + if xi == 0: + tiles.append([]) + color = xi % 2 == 0 + if yi > 0 and yi % 2 != 0: + color = not color + tiles[yi].append(color) + + for yi in range(len(tiles)): + for xi in range(len(tiles[yi])): + if state.interrupted: + break + if not tiles[yi][xi]: + tiles[yi][xi] = not tiles[yi][xi] + continue + tiles[yi][xi] = not tiles[yi][xi] + draw.rectangle(self.calc_rectangle(xi, yi), fill="white") + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + draw.rectangle(self.calc_rectangle(xi, yi), fill="black") + if (len(processed.images) > 0): + image = processed.images[0] + + for yi in range(len(tiles)): + for xi in range(len(tiles[yi])): + if state.interrupted: + break + if not tiles[yi][xi]: + continue + draw.rectangle(self.calc_rectangle(xi, yi), fill="white") + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + draw.rectangle(self.calc_rectangle(xi, yi), fill="black") + if (len(processed.images) > 0): + image = processed.images[0] + + p.width = image.width + p.height = image.height + self.initial_info = processed.infotext(p, 0) + + return image + + def start(self, p, image, rows, cols): + self.initial_info = None + if self.mode == USDUMode.LINEAR: + return self.linear_process(p, image, rows, cols) + if self.mode == USDUMode.CHESS: + return self.chess_process(p, image, rows, cols) + +class USDUSeamsFix(): + + def init_draw(self, p): + self.initial_info = None + p.width = math.ceil((self.tile_width+self.padding) / 64) * 64 + p.height = math.ceil((self.tile_height+self.padding) / 64) * 64 + + def half_tile_process(self, p, image, rows, cols): + + self.init_draw(p) + processed = None + + gradient = Image.linear_gradient("L") + row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black") + row_gradient.paste(gradient.resize( + (self.tile_width, self.tile_height//2), resample=Image.BICUBIC), (0, 0)) + row_gradient.paste(gradient.rotate(180).resize( + (self.tile_width, self.tile_height//2), resample=Image.BICUBIC), + (0, self.tile_height//2)) + col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black") + col_gradient.paste(gradient.rotate(90).resize( + (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (0, 0)) + col_gradient.paste(gradient.rotate(270).resize( + (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (self.tile_width//2, 0)) + + p.denoising_strength = self.denoise + p.mask_blur = self.mask_blur + + for yi in range(rows-1): + for xi in range(cols): + if state.interrupted: + break + p.width = self.tile_width + p.height = self.tile_height + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + mask = Image.new("L", (image.width, image.height), "black") + mask.paste(row_gradient, (xi*self.tile_width, yi*self.tile_height + self.tile_height//2)) + + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + if (len(processed.images) > 0): + image = processed.images[0] + + for yi in range(rows): + for xi in range(cols-1): + if state.interrupted: + break + p.width = self.tile_width + p.height = self.tile_height + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + mask = Image.new("L", (image.width, image.height), "black") + mask.paste(col_gradient, (xi*self.tile_width+self.tile_width//2, yi*self.tile_height)) + + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + if (len(processed.images) > 0): + image = processed.images[0] + + p.width = image.width + p.height = image.height + if processed is not None: + self.initial_info = processed.infotext(p, 0) + + return image + + def half_tile_process_corners(self, p, image, rows, cols): + fixed_image = self.half_tile_process(p, image, rows, cols) + processed = None + self.init_draw(p) + gradient = Image.radial_gradient("L").resize( + (self.tile_width, self.tile_height), resample=Image.BICUBIC) + gradient = ImageOps.invert(gradient) + p.denoising_strength = self.denoise + #p.mask_blur = 0 + p.mask_blur = self.mask_blur + + for yi in range(rows-1): + for xi in range(cols-1): + if state.interrupted: + break + p.width = self.tile_width + p.height = self.tile_height + p.inpaint_full_res = True + p.inpaint_full_res_padding = 0 + mask = Image.new("L", (fixed_image.width, fixed_image.height), "black") + mask.paste(gradient, (xi*self.tile_width + self.tile_width//2, + yi*self.tile_height + self.tile_height//2)) + + p.init_images = [fixed_image] + p.image_mask = mask + processed = processing.process_images(p) + if (len(processed.images) > 0): + fixed_image = processed.images[0] + + p.width = fixed_image.width + p.height = fixed_image.height + if processed is not None: + self.initial_info = processed.infotext(p, 0) + + return fixed_image + + def band_pass_process(self, p, image, cols, rows): + + self.init_draw(p) + processed = None + + p.denoising_strength = self.denoise + p.mask_blur = 0 + + gradient = Image.linear_gradient("L") + mirror_gradient = Image.new("L", (256, 256), "black") + mirror_gradient.paste(gradient.resize((256, 128), resample=Image.BICUBIC), (0, 0)) + mirror_gradient.paste(gradient.rotate(180).resize((256, 128), resample=Image.BICUBIC), (0, 128)) + + row_gradient = mirror_gradient.resize((image.width, self.width), resample=Image.BICUBIC) + col_gradient = mirror_gradient.rotate(90).resize((self.width, image.height), resample=Image.BICUBIC) + + for xi in range(1, rows): + if state.interrupted: + break + p.width = self.width + self.padding * 2 + p.height = image.height + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + mask = Image.new("L", (image.width, image.height), "black") + mask.paste(col_gradient, (xi * self.tile_width - self.width // 2, 0)) + + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + if (len(processed.images) > 0): + image = processed.images[0] + for yi in range(1, cols): + if state.interrupted: + break + p.width = image.width + p.height = self.width + self.padding * 2 + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + mask = Image.new("L", (image.width, image.height), "black") + mask.paste(row_gradient, (0, yi * self.tile_height - self.width // 2)) + + p.init_images = [image] + p.image_mask = mask + processed = processing.process_images(p) + if (len(processed.images) > 0): + image = processed.images[0] + + p.width = image.width + p.height = image.height + if processed is not None: + self.initial_info = processed.infotext(p, 0) + + return image + + def start(self, p, image, rows, cols): + if USDUSFMode(self.mode) == USDUSFMode.BAND_PASS: + return self.band_pass_process(p, image, rows, cols) + elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE: + return self.half_tile_process(p, image, rows, cols) + elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS: + return self.half_tile_process_corners(p, image, rows, cols) + else: + return image + +class Script(scripts.Script): + def title(self): + return "Ultimate SD upscale" + + def show(self, is_img2img): + return is_img2img + + def ui(self, is_img2img): + + target_size_types = [ + "From img2img2 settings", + "Custom size", + "Scale from image size" + ] + + seams_fix_types = [ + "None", + "Band pass", + "Half tile offset pass", + "Half tile offset pass + intersections" + ] + + redrow_modes = [ + "Linear", + "Chess", + "None" + ] + + info = gr.HTML( + "

    Will upscale the image depending on the selected target size type

    ") + + with gr.Row(): + target_size_type = gr.Dropdown(label="Target size type", elem_id=f"{elem_id_prefix}_target_size_type", choices=[k for k in target_size_types], type="index", + value=next(iter(target_size_types))) + + custom_width = gr.Slider(label='Custom width', elem_id=f"{elem_id_prefix}_custom_width", minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True) + custom_height = gr.Slider(label='Custom height', elem_id=f"{elem_id_prefix}_custom_height", minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True) + custom_scale = gr.Slider(label='Scale', elem_id=f"{elem_id_prefix}_custom_scale", minimum=1, maximum=16, step=0.01, value=2, visible=False, interactive=True) + + gr.HTML("

    Redraw options:

    ") + with gr.Row(): + upscaler_index = gr.Radio(label='Upscaler', elem_id=f"{elem_id_prefix}_upscaler_index", choices=[x.name for x in shared.sd_upscalers], + value=shared.sd_upscalers[0].name, type="index") + with gr.Row(): + redraw_mode = gr.Dropdown(label="Type", elem_id=f"{elem_id_prefix}_redraw_mode", choices=[k for k in redrow_modes], type="index", value=next(iter(redrow_modes))) + tile_width = gr.Slider(elem_id=f"{elem_id_prefix}_tile_width", minimum=0, maximum=2048, step=64, label='Tile width', value=512) + tile_height = gr.Slider(elem_id=f"{elem_id_prefix}_tile_height", minimum=0, maximum=2048, step=64, label='Tile height', value=0) + mask_blur = gr.Slider(elem_id=f"{elem_id_prefix}_mask_blur", label='Mask blur', minimum=0, maximum=64, step=1, value=8) + padding = gr.Slider(elem_id=f"{elem_id_prefix}_padding", label='Padding', minimum=0, maximum=512, step=1, value=32) + gr.HTML("

    Seams fix:

    ") + with gr.Row(): + seams_fix_type = gr.Dropdown(label="Type", elem_id=f"{elem_id_prefix}_seams_fix_type", choices=[k for k in seams_fix_types], type="index", value=next(iter(seams_fix_types))) + seams_fix_denoise = gr.Slider(label='Denoise', elem_id=f"{elem_id_prefix}_seams_fix_denoise", minimum=0, maximum=1, step=0.01, value=0.35, visible=False, interactive=True) + seams_fix_width = gr.Slider(label='Width', elem_id=f"{elem_id_prefix}_seams_fix_width", minimum=0, maximum=128, step=1, value=64, visible=False, interactive=True) + seams_fix_mask_blur = gr.Slider(label='Mask blur', elem_id=f"{elem_id_prefix}_seams_fix_mask_blur", minimum=0, maximum=64, step=1, value=4, visible=False, interactive=True) + seams_fix_padding = gr.Slider(label='Padding', elem_id=f"{elem_id_prefix}_seams_fix_padding", minimum=0, maximum=128, step=1, value=16, visible=False, interactive=True) + gr.HTML("

    Save options:

    ") + with gr.Row(): + save_upscaled_image = gr.Checkbox(label="Upscaled", elem_id=f"{elem_id_prefix}_save_upscaled_image", value=True) + save_seams_fix_image = gr.Checkbox(label="Seams fix", elem_id=f"{elem_id_prefix}_save_seams_fix_image", value=False) + + def select_fix_type(fix_index): + all_visible = fix_index != 0 + mask_blur_visible = fix_index == 2 or fix_index == 3 + width_visible = fix_index == 1 + + return [gr.update(visible=all_visible), + gr.update(visible=width_visible), + gr.update(visible=mask_blur_visible), + gr.update(visible=all_visible)] + + seams_fix_type.change( + fn=select_fix_type, + inputs=seams_fix_type, + outputs=[seams_fix_denoise, seams_fix_width, seams_fix_mask_blur, seams_fix_padding] + ) + + def select_scale_type(scale_index): + is_custom_size = scale_index == 1 + is_custom_scale = scale_index == 2 + + return [gr.update(visible=is_custom_size), + gr.update(visible=is_custom_size), + gr.update(visible=is_custom_scale), + ] + + target_size_type.change( + fn=select_scale_type, + inputs=target_size_type, + outputs=[custom_width, custom_height, custom_scale] + ) + + def init_field(scale_name): + try: + scale_index = target_size_types.index(scale_name) + custom_width.visible = custom_height.visible = scale_index == 1 + custom_scale.visible = scale_index == 2 + except: + pass + + target_size_type.init_field = init_field + + return [info, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding, + upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur, + seams_fix_type, target_size_type, custom_width, custom_height, custom_scale] + + def run(self, p, _, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding, + upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur, + seams_fix_type, target_size_type, custom_width, custom_height, custom_scale): + + # Init + processing.fix_seed(p) + devices.torch_gc() + + p.do_not_save_grid = True + p.do_not_save_samples = True + p.inpaint_full_res = False + + p.inpainting_fill = 1 + p.n_iter = 1 + p.batch_size = 1 + + seed = p.seed + + # Init image + init_img = p.init_images[0] + if init_img == None: + return Processed(p, [], seed, "Empty image") + init_img = images.flatten(init_img, opts.img2img_background_color) + + #override size + if target_size_type == 1: + p.width = custom_width + p.height = custom_height + if target_size_type == 2: + p.width = math.ceil((init_img.width * custom_scale) / 64) * 64 + p.height = math.ceil((init_img.height * custom_scale) / 64) * 64 + + # Upscaling + upscaler = USDUpscaler(p, init_img, upscaler_index, save_upscaled_image, save_seams_fix_image, tile_width, tile_height) + upscaler.upscale() + + # Drawing + upscaler.setup_redraw(redraw_mode, padding, mask_blur) + upscaler.setup_seams_fix(seams_fix_padding, seams_fix_denoise, seams_fix_mask_blur, seams_fix_width, seams_fix_type) + upscaler.print_info() + upscaler.add_extra_info() + upscaler.process() + result_images = upscaler.result_images + + return Processed(p, result_images, seed, upscaler.initial_info if upscaler.initial_info is not None else "") + diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/.gitignore b/custom_nodes/comfyui_ultimatesdupscale/test/.gitignore new file mode 100644 index 00000000..add05095 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/.gitignore @@ -0,0 +1 @@ +sample_images/ \ No newline at end of file diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/README.md b/custom_nodes/comfyui_ultimatesdupscale/test/README.md new file mode 100644 index 00000000..abe42df0 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/README.md @@ -0,0 +1,49 @@ +# Running Tests + +This directory contains tests for ComfyUI_UltimateSDUpscale. + +## Prerequisites + +- These tests assume that ComfyUI is installed using a virtual environment +- Activate the ComfyUI virtual environment before running tests +- The checkpoint `v1-5-pruned-emaonly-fp16.safetensors` is available +- The upscale model `4x-UltraSharp.pth` is available + +## Running Tests + +### Using the convenience scripts (works from repo root or test directory): + +**Linux/Mac (Bash):** +```bash +./test/run_tests.sh # From repo root +./run_tests.sh # From test directory +``` +run_tests.sh will forward all arguments into pytest. + +### Using pytest directly (must be in test directory): + +```bash +cd test +pytest # Run all tests +pytest -v # Verbose +``` + +### Common pytest options: + +- `-v` - Verbose output +- `-s` - Show print statements +- `--log-cli-level=INFO` - Show info-level logs +- `-k PATTERN` - Run tests matching pattern +- `--lf` - Run last failed tests + +## Test Structure + +- `conftest.py` - Pytest configuration, fixtures, and path setup +- `sample_images/` - Generated test images for visual inspection +- `test_images/` - Reference images used as inputs or expected outputs + +## Troubleshooting + +If you encounter import errors: +1. Make sure you're running from the `test/` directory +2. Verify the virtual environment is activated diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/configs.py b/custom_nodes/comfyui_ultimatesdupscale/test/configs.py new file mode 100644 index 00000000..c7c81274 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/configs.py @@ -0,0 +1,9 @@ +import pathlib + + +class DirectoryConfig: + """Helper class for test directories.""" + + def __init__(self, test_images: pathlib.Path, sample_images: pathlib.Path): + self.test_images = test_images + self.sample_images = sample_images diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/conftest.py b/custom_nodes/comfyui_ultimatesdupscale/test/conftest.py new file mode 100644 index 00000000..2cf86c83 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/conftest.py @@ -0,0 +1,174 @@ +""" +Setup for the ComfyUI engine and shared test fixtures. +""" + +import os +import sys +from pathlib import Path +import pytest +import asyncio +import logging + +from setup_utils import SilenceLogs, execute +from hf_downloader import download_test_images +from configs import DirectoryConfig +# Because of manipulations to sys.path, non-packaged imports should be delayed to avoid import issues + + +# +# # Configuration +# +TEST_CHECKPOINT = "v1-5-pruned-emaonly-fp16.safetensors" +TEST_UPSCALE_MODEL = "4x-UltraSharp.pth" +SAMPLE_IMAGE_SUBDIR = "sample_images" +TEST_IMAGE_SUBDIR = "test_images" +# conftest.py is in repo_root/test/ directory +REPO_ROOT = Path(__file__).parent.parent.resolve() +COMFYUI_ROOT = REPO_ROOT.parent.parent.resolve() + +# Make sure the repo root is in sys.path for imports +# Ensure submodule root is in path for test imports +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) +# Ensure ComfyUI path is set up +if str(COMFYUI_ROOT) not in sys.path: + sys.path.insert(0, str(COMFYUI_ROOT)) + + +# Session scoped fixtures +from fixtures_images import base_image + + +def pytest_configure(config): + """Called before test collection begins.""" + # Download test images + download_test_images( + repo_id="ssitu/ultimatesdupscale_test", + save_dir=(REPO_ROOT / "test" / "test_images").resolve(), + repo_folder="test_images", + ) + + from comfy.cli_args import args + + # args.cpu = True # Force CPU mode for tests + # args.force_fp16 = True # Force float16 mode for tests + args.disable_all_custom_nodes = True + # Assumes the name of the custom node folder is ComfyUI_UltimateSDUpscale + args.whitelist_custom_nodes = ["ComfyUI_UltimateSDUpscale"] + + +# +# # Path Setup +# +def _setup_comfyui_paths(): + """Configure ComfyUI folder paths for testing.""" + # Ensure modules containing a utils.py are NOT in sys.path + # The comfy directory must be removed to prevent comfy/utils.py from shadowing + # ComfyUI's utils/ package directory when we import utils.extra_config + to_remove = [ + str(COMFYUI_ROOT / "comfy"), + ] + for path_to_remove in to_remove: + while path_to_remove in sys.path: + sys.path.remove(path_to_remove) + + # Ensure ComfyUI is in path + if str(COMFYUI_ROOT) not in sys.path: + sys.path.insert(0, str(COMFYUI_ROOT)) + + # Apply custom paths + # main.py will trigger a warning that torch was already imported, probably by pytest. Shouldn't be a problem as far as I know. + from main import apply_custom_paths + + apply_custom_paths() + + +# +# # Fixtures +# +@pytest.fixture(scope="session") +def comfyui_initialized(): + """Initialize ComfyUI nodes once per test session.""" + from nodes import init_extra_nodes + + _setup_comfyui_paths() + + async def _init(): + with SilenceLogs(): + await init_extra_nodes(init_api_nodes=False) + + asyncio.run(_init()) + + yield True + + +@pytest.fixture(scope="session") +def node_classes(comfyui_initialized): + """Get ComfyUI node class mappings.""" + from nodes import NODE_CLASS_MAPPINGS + + return NODE_CLASS_MAPPINGS + + +@pytest.fixture(scope="session") +def test_checkpoint(): + """Find and return a valid test checkpoint.""" + import folder_paths + + checkpoints = folder_paths.get_filename_list("checkpoints") + # TODO: Should probably use a hash instead of matching the filename + if TEST_CHECKPOINT not in checkpoints: + pytest.skip(f"No test checkpoint found. Please add {TEST_CHECKPOINT}") + + return TEST_CHECKPOINT + + +@pytest.fixture(scope="session") +def loaded_checkpoint(comfyui_initialized, test_checkpoint, node_classes): + """Load checkpoint and return (model, clip, vae) tuple.""" + import torch + + with torch.inference_mode(): + CheckpointLoaderSimple = node_classes["CheckpointLoaderSimple"] + model, clip, vae = execute(CheckpointLoaderSimple, test_checkpoint) + + return model, clip, vae + + +@pytest.fixture(scope="session") +def upscale_model(comfyui_initialized, node_classes): + """Load the first available upscale model.""" + import torch + import folder_paths + + UpscaleModelLoader = node_classes["UpscaleModelLoader"] + + upscale_models = folder_paths.get_filename_list("upscale_models") + # TODO: Should probably use a hash instead of matching the filename + if TEST_UPSCALE_MODEL not in upscale_models: + pytest.skip("No upscale models found") + + model_name = upscale_models[0] + with torch.inference_mode(): + (model,) = execute(UpscaleModelLoader, model_name) + + return model + + +@pytest.fixture(scope="session") +def test_dirs(): + """Return paths to test and sample image directories.""" + test_dir = REPO_ROOT / "test" + test_image_dir = test_dir / TEST_IMAGE_SUBDIR + sample_image_dir = test_dir / SAMPLE_IMAGE_SUBDIR + sample_image_dir.mkdir(exist_ok=True) + return DirectoryConfig( + test_images=test_image_dir, + sample_images=sample_image_dir, + ) + + +@pytest.fixture(scope="session") +def seed(): + """Default seed for reproducible tests.""" + return 1 diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/fixtures_images.py b/custom_nodes/comfyui_ultimatesdupscale/test/fixtures_images.py new file mode 100644 index 00000000..deb3c0ac --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/fixtures_images.py @@ -0,0 +1,70 @@ +""" +Fixtures for base images. +""" + +import pathlib +import pytest +import torch + +from setup_utils import execute +from io_utils import save_image, load_image +from configs import DirectoryConfig + +# Image file names +EXT = ".jpg" +CATEGORY = pathlib.Path("base_images") +BASE_IMAGE_1_NAME = "main1_sd15" + EXT +BASE_IMAGE_2_NAME = "main2_sd15" + EXT + +# Prepend category path +BASE_IMAGE_1 = CATEGORY / BASE_IMAGE_1_NAME +BASE_IMAGE_2 = CATEGORY / BASE_IMAGE_2_NAME + + +@pytest.fixture(scope="session") +def base_image(loaded_checkpoint, seed, test_dirs: DirectoryConfig, node_classes): + """Generate a base image for upscaling tests.""" + EmptyLatentImage = node_classes["EmptyLatentImage"] + CLIPTextEncode = node_classes["CLIPTextEncode"] + KSampler = node_classes["KSampler"] + VAEDecode = node_classes["VAEDecode"] + + model, clip, vae = loaded_checkpoint + + with torch.inference_mode(): + (empty_latent,) = execute(EmptyLatentImage, width=512, height=512, batch_size=2) + + (positive,) = execute( + CLIPTextEncode, + text="beautiful scenery nature glass bottle landscape, , purple galaxy bottle,", + clip=clip, + ) + + (negative,) = execute(CLIPTextEncode, text="text, watermark", clip=clip) + + (samples,) = execute( + KSampler, + model=model, + positive=positive, + negative=negative, + latent_image=empty_latent, + seed=seed, + steps=10, + cfg=8, + sampler_name="dpmpp_2m", + scheduler="karras", + denoise=1.0, + ) + + (image,) = execute(VAEDecode, samples=samples, vae=vae) + + # Save base images + sample_dir = test_dirs.sample_images + base_img1_path = sample_dir / BASE_IMAGE_1 + base_img2_path = sample_dir / BASE_IMAGE_2 + save_image(image[0:1], base_img1_path) + save_image(image[1:2], base_img2_path) + + # Load images back as tensors to account for compression + image = torch.cat([load_image(base_img1_path), load_image(base_img2_path)]) + return image, positive, negative diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/hf_downloader.py b/custom_nodes/comfyui_ultimatesdupscale/test/hf_downloader.py new file mode 100644 index 00000000..a17d216f --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/hf_downloader.py @@ -0,0 +1,85 @@ +import logging +import re +import urllib.parse +import urllib.request +from pathlib import Path + +logging.basicConfig(level=logging.INFO) + + +def _fetch_hf_html(repo_id: str, folder_path: str) -> str: + """Fetch HTML from HuggingFace tree page.""" + url = f"https://huggingface.co/datasets/{repo_id}/tree/main/{folder_path}" + with urllib.request.urlopen(url) as response: + return response.read().decode("utf-8") + + +def list_hf_subfolders(repo_id: str, folder_path: str) -> list[str]: + """List subfolders in a HuggingFace dataset folder.""" + try: + html = _fetch_hf_html(repo_id, folder_path) + pattern = rf'/datasets/{repo_id}/tree/main/({folder_path}/[^"/?]+)' + return sorted(set(re.findall(pattern, html))) + except Exception as e: + logging.error(f"Failed to list subfolders in {folder_path}: {e}") + return [] + + +def list_hf_files( + repo_id: str, + folder_path: str, + extensions: tuple = (".jpg", ".jpeg", ".png", ".webp"), +) -> list[str]: + """List image files in a HuggingFace dataset folder.""" + try: + html = _fetch_hf_html(repo_id, folder_path) + pattern = rf'/datasets/{repo_id}/blob/main/({folder_path}/[^"]+?({"|".join(e for e in extensions)}))' + return [urllib.parse.unquote(match[0]) for match in re.findall(pattern, html)] + except Exception as e: + logging.error(f"Failed to list files in {folder_path}: {e}") + return [] + + +def download_test_images(save_dir: Path, repo_folder: str, repo_id: str) -> Path: + """Download the test_images/ folder from the HF test dataset repo""" + # Discover all subfolders and collect files + subfolders = list_hf_subfolders(repo_id, repo_folder) + if not subfolders: + logging.warning(f"No subfolders found in {repo_folder}") + return save_dir + + all_files = [f for folder in subfolders for f in list_hf_files(repo_id, folder)] + if not all_files: + logging.warning(f"No image files found in {repo_folder}") + return save_dir + + logging.info(f"Found {len(all_files)} files from {len(subfolders)} folders") + # Download files, preserving folder structure + save_dir_path = Path(save_dir) + downloaded = 0 + skipped = 0 + for file_path in all_files: + relative_path = Path(file_path).relative_to(repo_folder) + save_path = save_dir_path / relative_path + if save_path.exists(): + logging.info(f"Skipping {relative_path} (already exists)") + skipped += 1 + continue + + save_path.parent.mkdir(parents=True, exist_ok=True) + url = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{file_path}" + logging.info(f"Downloading {relative_path}...") + urllib.request.urlretrieve(url, save_path) + downloaded += 1 + + logging.info(f"Downloaded {downloaded} files, skipped {skipped} existing files") + return save_dir_path + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + download_test_images( + repo_id="ssitu/ultimatesdupscale_test", + save_dir=Path("./test/test_images/"), + repo_folder="test_images", + ) diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/io_utils.py b/custom_nodes/comfyui_ultimatesdupscale/test/io_utils.py new file mode 100644 index 00000000..8f4dabf9 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/io_utils.py @@ -0,0 +1,20 @@ +import pathlib +from PIL import Image +import usdu_utils + + +def save_image(tensor, path: pathlib.Path): + """The goto function to save a tensor image to the sampled images directory.""" + assert tensor.ndim == 3 or (tensor.ndim == 4 and tensor.shape[0] == 1), ( + f"Expected a 3D tensor (H, W, C) or (1, H, W, C), got {tensor.ndim=}" + ) + if tensor.ndim == 3: + tensor = tensor.unsqueeze(0) + image = usdu_utils.tensor_to_pil(tensor.cpu()) + path.parent.mkdir(parents=True, exist_ok=True) + image.save(path, quality=75, optimize=True) + + +def load_image(path: pathlib.Path, device=None): + """Load an image from disk and convert it to a tensor.""" + return usdu_utils.pil_to_tensor(Image.open(path)).to(device=device) diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/pytest.ini b/custom_nodes/comfyui_ultimatesdupscale/test/pytest.ini new file mode 100644 index 00000000..eb76a44d --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/pytest.ini @@ -0,0 +1,9 @@ +[pytest] + +# Filter out warnings that are unavoidable or from external libraries +filterwarnings = + # Ignore CUDA compatibility warnings (hardware limitation) + ignore::UserWarning:torch.cuda + # Ignore Swig type warnings from importlib by pytest + ignore:builtin type.*has no __module__ attribute:DeprecationWarning:importlib._bootstrap:488 + ignore:builtin type.*has no __module__ attribute:DeprecationWarning:sys:0 diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/run_tests.sh b/custom_nodes/comfyui_ultimatesdupscale/test/run_tests.sh new file mode 100644 index 00000000..477ce4bd --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/run_tests.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# Can be run from either the repo root or the test directory +# Example usage: sh ./run_tests.sh [additional pytest arguments] + +# Get the script directory +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" + +# Determine the test directory +if [[ "$(basename "$SCRIPT_DIR")" == "test" ]]; then + # Script is in test directory + TEST_DIR="$SCRIPT_DIR" +else + # Script is in repo root + TEST_DIR="$SCRIPT_DIR/test" +fi + +cd "$TEST_DIR" +python -m pytest "$@" diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/setup_utils.py b/custom_nodes/comfyui_ultimatesdupscale/test/setup_utils.py new file mode 100644 index 00000000..95d5a067 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/setup_utils.py @@ -0,0 +1,20 @@ +import logging + + +class SilenceLogs: + """Context manager to temporarily silence logging.""" + + def __enter__(self): + logging.disable(logging.CRITICAL) + return self + + def __exit__(self, exc_type, exc_value, traceback): + logging.disable(logging.NOTSET) + + +def execute(node, *args, **kwargs): + """Execute a ComfyUI node, handling both V3 and legacy schemas.""" + if hasattr(node, "execute"): + return node.execute(*args, **kwargs) + else: + return getattr(node(), node.FUNCTION)(*args, **kwargs) diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/tensor_utils.py b/custom_nodes/comfyui_ultimatesdupscale/test/tensor_utils.py new file mode 100644 index 00000000..9a8b53ae --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/tensor_utils.py @@ -0,0 +1,27 @@ +import torchvision.transforms.functional as TF + + +def img_tensor_mae(tensor1, tensor2): + """Calculate the mean absolute difference between two image tensors.""" + # Remove batch dimensions if present + tensor1 = tensor1.squeeze(0).cpu() + tensor2 = tensor2.squeeze(0).cpu() + if tensor1.shape != tensor2.shape: + raise ValueError( + f"Tensors must have the same shape for comparison. Got {tensor1.shape=} and {tensor2.shape=}." + ) + return (tensor1 - tensor2).abs().mean().item() + + +def blur(tensor, kernel_size=9, sigma=None): + """Apply Gaussian blur to an image tensor.""" + # [1, H, W, C] -> [1, C, H, W] + if tensor.ndim == 4: + tensor = tensor.permute(0, 3, 1, 2) + elif tensor.ndim == 3: + tensor = tensor.permute(2, 0, 1).unsqueeze(0) + else: + raise ValueError(f"Expected a 3D or 4D tensor, got {tensor.ndim=}") + return TF.gaussian_blur(tensor, kernel_size=kernel_size, sigma=sigma).permute( # type: ignore + 0, 2, 3, 1 + ) diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/test_base_images.py b/custom_nodes/comfyui_ultimatesdupscale/test/test_base_images.py new file mode 100644 index 00000000..0f3642bf --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/test_base_images.py @@ -0,0 +1,31 @@ +""" +Tests for base image generation. +""" + +import logging +from configs import DirectoryConfig +from tensor_utils import img_tensor_mae, blur +from io_utils import load_image +from fixtures_images import BASE_IMAGE_1, BASE_IMAGE_2 + + +def test_base_image_matches_reference(base_image, test_dirs: DirectoryConfig): + """ + Verify generated base images match reference images. + This is just to check if the checkpoint and generation pipeline are as expected for the tests dependent on their behavior. + """ + logger = logging.getLogger("test_base_image_matches_reference") + image, _, _ = base_image + test_image_dir = test_dirs.test_images + im1 = image[0:1] + im2 = image[1:2] + + test_im1 = load_image(test_image_dir / BASE_IMAGE_1) + test_im2 = load_image(test_image_dir / BASE_IMAGE_2) + + # Reduce high-frequency noise differences with gaussian blur. Using perceptual metrics are probably overkill. + diff1 = img_tensor_mae(blur(im1), blur(test_im1)) + diff2 = img_tensor_mae(blur(im2), blur(test_im2)) + logger.info(f"Base Image Diff1: {diff1}, Diff2: {diff2}") + assert diff1 < 0.05, "Image 1 does not match its test image." + assert diff2 < 0.05, "Image 2 does not match its test image." diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/test_controlnet.py b/custom_nodes/comfyui_ultimatesdupscale/test/test_controlnet.py new file mode 100644 index 00000000..acd1afc8 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/test_controlnet.py @@ -0,0 +1,96 @@ +""" +Test using controlnet in the upscaling workflow. +""" + +import logging +import pathlib +import pytest +import torch + +from setup_utils import execute +from tensor_utils import img_tensor_mae, blur +from io_utils import save_image, load_image +from configs import DirectoryConfig +from fixtures_images import EXT + +CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_")) +CONTROLNET_TILE_OUTPUT_IMAGE = "controlnet_tile" + EXT +TEST_CONTROLNET_TILE_MODEL = "control_v11f1e_sd15_tile.pth" + + +class TestControlNet: + """Integration tests for the upscaling workflow with ControlNet.""" + + @pytest.fixture(scope="class") + def controlnet_upscaled_image( + self, + base_image, + loaded_checkpoint, + upscale_model, + node_classes, + seed, + test_dirs, + ): + """Generate upscaled images using ControlNet.""" + image, positive, negative = base_image + model, clip, vae = loaded_checkpoint + image = image[0:1] + + (controlnet_tile_model,) = execute( + node_classes["ControlNetLoader"], TEST_CONTROLNET_TILE_MODEL + ) + (positive,) = execute( + node_classes["ControlNetApply"], positive, controlnet_tile_model, image, 1.0 + ) + + with torch.inference_mode(): + # Run upscale with ControlNet + usdu = node_classes["UltimateSDUpscale"] + (upscaled,) = usdu().upscale( + image=image, + model=model, + positive=positive, + negative=negative, + vae=vae, + upscale_by=2.0, + seed=seed, + steps=5, + cfg=8, + sampler_name="euler", + scheduler="normal", + denoise=1.0, + upscale_model=None, + mode_type="Chess", + tile_width=512, + tile_height=512, + mask_blur=8, + tile_padding=32, + seam_fix_mode="None", + seam_fix_denoise=1.0, + seam_fix_width=64, + seam_fix_mask_blur=8, + seam_fix_padding=16, + force_uniform_tiles=True, + tiled_decode=False, + ) + # Save and reload sample image + sample_dir = test_dirs.sample_images + filename = CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE + save_image(upscaled[0], sample_dir / filename) + upscaled = load_image(sample_dir / filename) + return upscaled + + def test_controlnet_upscaled_image_matches_reference( + self, controlnet_upscaled_image, test_dirs: DirectoryConfig + ): + """ + Verify ControlNet upscaled images match reference images. + """ + logger = logging.getLogger("test_controlnet_upscaled_image_matches_reference") + test_img_dir = test_dirs.test_images + test_img = load_image(test_img_dir / CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE) + + # Reduce high-frequency noise differences with gaussian blur + diff = img_tensor_mae(blur(controlnet_upscaled_image), blur(test_img)) + logger.info(f"ControlNet Upscaled Image Diff: {diff}") + assert diff < 0.05, "ControlNet upscaled image does not match its test image." diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/test_main_workflow.py b/custom_nodes/comfyui_ultimatesdupscale/test/test_main_workflow.py new file mode 100644 index 00000000..275655fb --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/test_main_workflow.py @@ -0,0 +1,246 @@ +""" +Tests a common workflow for UltimateSDUpscale. +""" + +import logging +import pathlib +import torch + +from setup_utils import execute +from tensor_utils import img_tensor_mae, blur +from io_utils import save_image, load_image +from configs import DirectoryConfig +from fixtures_images import EXT + +# Image file names +CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_")) +IMAGE_1 = CATEGORY / ("main1_sd15_upscaled" + EXT) +IMAGE_2 = CATEGORY / ("main2_sd15_upscaled" + EXT) +NO_UPSCALE_IMAGE_1 = CATEGORY / ("main1_sd15_upscaled_no_upscale" + EXT) +NO_UPSCALE_IMAGE_2 = CATEGORY / ("main2_sd15_upscaled_no_upscale" + EXT) +CUSTOM_SAMPLER_IMAGE_1 = CATEGORY / ("main1_sd15_upscaled_custom_sampler" + EXT) +CUSTOM_SAMPLER_IMAGE_2 = CATEGORY / ("main2_sd15_upscaled_custom_sampler" + EXT) + + +class TestMainWorkflow: + """Integration tests for the main upscaling workflow.""" + + def test_upscale( + self, + base_image, + loaded_checkpoint, + upscale_model, + node_classes, + seed, + test_dirs: DirectoryConfig, + ): + """Generate upscaled images using standard workflow.""" + image, positive, negative = base_image + model, clip, vae = loaded_checkpoint + + with torch.inference_mode(): + usdu = node_classes["UltimateSDUpscale"] + (upscaled,) = usdu().upscale( + image=image, + model=model, + positive=positive, + negative=negative, + vae=vae, + upscale_by=2.00000004, # Test small float difference doesn't add extra tiles + seed=seed, + steps=10, + cfg=8, + sampler_name="euler", + scheduler="normal", + denoise=0.2, + upscale_model=upscale_model, + mode_type="Chess", + tile_width=512, + tile_height=512, + mask_blur=8, + tile_padding=32, + seam_fix_mode="None", + seam_fix_denoise=1.0, + seam_fix_width=64, + seam_fix_mask_blur=8, + seam_fix_padding=16, + force_uniform_tiles=True, + tiled_decode=False, + ) + # Save images + sample_dir = test_dirs.sample_images + upscaled_img1_path = sample_dir / IMAGE_1 + upscaled_img2_path = sample_dir / IMAGE_2 + save_image(upscaled[0], upscaled_img1_path) + save_image(upscaled[1], upscaled_img2_path) + # Load to account for compression + upscaled = torch.cat( + [load_image(upscaled_img1_path), load_image(upscaled_img2_path)] + ) + # Verify results + logger = logging.getLogger("test_upscale") + test_image_dir = test_dirs.test_images + im1_upscaled = upscaled[0] + im2_upscaled = upscaled[1] + + test_im1_upscaled = load_image(test_image_dir / IMAGE_1) + test_im2_upscaled = load_image(test_image_dir / IMAGE_2) + + diff1 = img_tensor_mae(blur(im1_upscaled), blur(test_im1_upscaled)) + diff2 = img_tensor_mae(blur(im2_upscaled), blur(test_im2_upscaled)) + + # This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences. + logger.info(f"Diff1: {diff1}, Diff2: {diff2}") + assert diff1 < 0.05, "Upscaled Image 1 doesn't match its test image." + assert diff2 < 0.05, "Upscaled Image 2 doesn't match its test image." + + def test_upscale_no_upscale( + self, + base_image, + loaded_checkpoint, + node_classes, + seed, + test_dirs: DirectoryConfig, + ): + """Generate upscaled images using standard workflow using the no upscale node.""" + image, positive, negative = base_image + model, clip, vae = loaded_checkpoint + (image,) = execute( + node_classes["ImageScaleBy"], + image=image, + upscale_method="lanczos", + scale_by=2.0, + ) + + with torch.inference_mode(): + usdu = node_classes["UltimateSDUpscaleNoUpscale"] + (upscaled,) = usdu().upscale( + upscaled_image=image, + model=model, + positive=positive, + negative=negative, + vae=vae, + seed=seed, + steps=10, + cfg=8, + sampler_name="euler", + scheduler="normal", + denoise=0.2, + mode_type="Chess", + tile_width=512, + tile_height=512, + mask_blur=8, + tile_padding=32, + seam_fix_mode="None", + seam_fix_denoise=1.0, + seam_fix_width=64, + seam_fix_mask_blur=8, + seam_fix_padding=16, + force_uniform_tiles=True, + tiled_decode=False, + ) + # Save images + sample_dir = test_dirs.sample_images + upscaled_img1_path = sample_dir / NO_UPSCALE_IMAGE_1 + upscaled_img2_path = sample_dir / NO_UPSCALE_IMAGE_2 + save_image(upscaled[0], upscaled_img1_path) + save_image(upscaled[1], upscaled_img2_path) + # Load to account for compression + upscaled = torch.cat( + [load_image(upscaled_img1_path), load_image(upscaled_img2_path)] + ) + # Verify results + logger = logging.getLogger("test_upscale_no_upscale") + test_image_dir = test_dirs.test_images + im1_upscaled = upscaled[0] + im2_upscaled = upscaled[1] + + test_im1_upscaled = load_image(test_image_dir / NO_UPSCALE_IMAGE_1) + test_im2_upscaled = load_image(test_image_dir / NO_UPSCALE_IMAGE_2) + + diff1 = img_tensor_mae(blur(im1_upscaled), blur(test_im1_upscaled)) + diff2 = img_tensor_mae(blur(im2_upscaled), blur(test_im2_upscaled)) + # This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences. + logger.info(f"Diff1: {diff1}, Diff2: {diff2}") + assert diff1 < 0.05, "No Upscale Image 1 doesn't match its test image." + assert diff2 < 0.05, "No Upscale Image 2 doesn't match its test image." + + def test_upscale_with_custom_sampler( + self, + base_image, + loaded_checkpoint, + upscale_model, + node_classes, + seed, + test_dirs: DirectoryConfig, + ): + """Generate upscaled images using standard workflow using the custom sampler node.""" + image, positive, negative = base_image + model, clip, vae = loaded_checkpoint + + with torch.inference_mode(): + # Setup custom scheduler and sampler + custom_scheduler = node_classes["KarrasScheduler"] + (sigmas,) = execute(custom_scheduler, 20, 14.614642, 0.0291675, 7.0) + (_, sigmas) = execute(node_classes["SplitSigmasDenoise"], sigmas, 0.15) + + custom_sampler = node_classes["KSamplerSelect"] + (sampler,) = execute(custom_sampler, "dpmpp_2m") + + # Run upscale + usdu = node_classes["UltimateSDUpscaleCustomSample"] + (upscaled,) = usdu().upscale( + image=image, + model=model, + positive=positive, + negative=negative, + vae=vae, + upscale_by=2.0, + seed=seed, + steps=10, + cfg=8, + sampler_name="euler", + scheduler="normal", + denoise=0.2, + upscale_model=upscale_model, + mode_type="Chess", + tile_width=512, + tile_height=512, + mask_blur=8, + tile_padding=32, + seam_fix_mode="None", + seam_fix_denoise=1.0, + seam_fix_width=64, + seam_fix_mask_blur=8, + seam_fix_padding=16, + force_uniform_tiles=True, + tiled_decode=False, + custom_sampler=sampler, + custom_sigmas=sigmas, + ) + # Save images + sample_dir = test_dirs.sample_images + upscaled_img1_path = sample_dir / CUSTOM_SAMPLER_IMAGE_1 + upscaled_img2_path = sample_dir / CUSTOM_SAMPLER_IMAGE_2 + save_image(upscaled[0], upscaled_img1_path) + save_image(upscaled[1], upscaled_img2_path) + # Load to account for compression + upscaled = torch.cat( + [load_image(upscaled_img1_path), load_image(upscaled_img2_path)] + ) + # Verify results + logger = logging.getLogger("test_upscale_with_custom_sampler") + test_image_dir = test_dirs.test_images + im1_upscaled = upscaled[0] + im2_upscaled = upscaled[1] + + test_im1_upscaled = load_image(test_image_dir / CUSTOM_SAMPLER_IMAGE_1) + test_im2_upscaled = load_image(test_image_dir / CUSTOM_SAMPLER_IMAGE_2) + + diff1 = img_tensor_mae(blur(im1_upscaled), blur(test_im1_upscaled)) + diff2 = img_tensor_mae(blur(im2_upscaled), blur(test_im2_upscaled)) + + # This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences. + logger.info(f"Diff1: {diff1}, Diff2: {diff2}") + assert diff1 < 0.05, "Upscaled Image 1 doesn't match its test image." + assert diff2 < 0.05, "Upscaled Image 2 doesn't match its test image." diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/test_settings.py b/custom_nodes/comfyui_ultimatesdupscale/test/test_settings.py new file mode 100644 index 00000000..84641ac0 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/test_settings.py @@ -0,0 +1,69 @@ +""" +Test for other settings included in the upscaling nodes. +""" + +import logging +import pathlib +import pytest +import torch + +from tensor_utils import img_tensor_mae, blur +from io_utils import save_image, load_image +from configs import DirectoryConfig +from fixtures_images import EXT + +# Image file names +CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_")) + + +def test_minimal_tile_sizes( + base_image, loaded_checkpoint, node_classes, seed, test_dirs: DirectoryConfig +): + """Test upscaling with minimal tile sizes.""" + filename = "non_uniform_tiles" + image, positive, negative = base_image + model, clip, vae = loaded_checkpoint + + with torch.inference_mode(): + usdu = node_classes["UltimateSDUpscale"] + (upscaled,) = usdu().upscale( + image=image[0:1], + model=model, + positive=positive, + negative=negative, + vae=vae, + upscale_by=1.5, + seed=seed, + steps=5, + cfg=8, + sampler_name="euler", + scheduler="normal", + denoise=0.15, + upscale_model=None, + mode_type="Chess", + tile_width=512, + tile_height=512, + mask_blur=8, + tile_padding=8, + seam_fix_mode="None", + seam_fix_denoise=1.0, + seam_fix_width=16, + seam_fix_mask_blur=8, + seam_fix_padding=4, + force_uniform_tiles=False, + tiled_decode=False, + ) + + # Save and reload sample image + sample_dir = test_dirs.sample_images + filename_path = CATEGORY / (filename + EXT) + save_image(upscaled[0], sample_dir / filename_path) + upscaled = load_image(sample_dir / filename_path) + + # Compare with reference + test_image_dir = test_dirs.test_images + test_image = load_image(test_image_dir / filename_path) + diff = img_tensor_mae(blur(upscaled), blur(test_image)) + logger = logging.getLogger(__name__) + logger.info(f"{filename} MAE: {diff}") + assert diff < 0.05, f"{filename} output doesn't match reference" diff --git a/custom_nodes/comfyui_ultimatesdupscale/test/test_tiling_modes.py b/custom_nodes/comfyui_ultimatesdupscale/test/test_tiling_modes.py new file mode 100644 index 00000000..b25437c6 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/test/test_tiling_modes.py @@ -0,0 +1,134 @@ +""" +Tests for different upscaling modes and seam fix modes. +""" + +import logging +import pathlib +import pytest +import torch + +from tensor_utils import img_tensor_mae, blur +from io_utils import save_image, load_image +from configs import DirectoryConfig +from fixtures_images import EXT + +# Image file names +CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_")) + + +def image_name_format(prefix: str, mode: str) -> str: + """Helper for the image name format for the tests below.""" + return f"{prefix}_{mode.lower().replace(' ', '_')}{EXT}" + + +class TestTilingModes: + def _test_upscale_variant( + self, + base_image, + loaded_checkpoint, + node_classes, + seed, + test_dirs: DirectoryConfig, + mode_type, + seam_fix_mode, + seam_fix_denoise, + filename_prefix, + ): + """Helper method to test upscale variants with different parameters.""" + logger = logging.getLogger(f"test_{filename_prefix}") + image, positive, negative = base_image + model, clip, vae = loaded_checkpoint + + with torch.inference_mode(): + usdu = node_classes["UltimateSDUpscale"] + (upscaled,) = usdu().upscale( + image=image[0:1], + model=model, + positive=positive, + negative=negative, + vae=vae, + upscale_by=2.0, + seed=seed, + steps=3, + cfg=8, + sampler_name="euler", + scheduler="normal", + denoise=0.2, + upscale_model=None, + mode_type=mode_type, + tile_width=512, + tile_height=512, + mask_blur=8, + tile_padding=32, + seam_fix_mode=seam_fix_mode, + seam_fix_denoise=seam_fix_denoise, + seam_fix_width=64, + seam_fix_mask_blur=8, + seam_fix_padding=16, + force_uniform_tiles=True, + tiled_decode=False, + ) + + # Save and reload sample image + sample_dir = test_dirs.sample_images + filename = CATEGORY / filename_prefix + save_image(upscaled[0], sample_dir / filename) + upscaled = load_image(sample_dir / filename) + + # Compare with reference + test_image_dir = test_dirs.test_images + test_image = load_image(test_image_dir / filename) + diff = img_tensor_mae(blur(upscaled), blur(test_image)) + logger.info(f"{filename_prefix} MAE: {diff}") + assert diff < 0.05, f"{filename_prefix} output doesn't match reference" + + # "Chess" is tested in the main workflow test + @pytest.mark.parametrize("mode_type", ["Linear", "None"]) + def test_mode_types( + self, + base_image, + loaded_checkpoint, + node_classes, + seed, + mode_type, + test_dirs: DirectoryConfig, + ): + """Test different tiling mode types.""" + filename = image_name_format("mode", mode_type) + self._test_upscale_variant( + base_image, + loaded_checkpoint, + node_classes, + seed, + test_dirs, + mode_type=mode_type, + seam_fix_mode="None", + seam_fix_denoise=1.0, + filename_prefix=filename, + ) + + @pytest.mark.parametrize( + "seam_fix_mode", ["None", "Band Pass", "Half Tile", "Half Tile + Intersections"] + ) + def test_seam_fix_modes( + self, + base_image, + loaded_checkpoint, + node_classes, + seed, + seam_fix_mode, + test_dirs: DirectoryConfig, + ): + """Test different seam fix modes.""" + filename = image_name_format("seamfix", seam_fix_mode) + self._test_upscale_variant( + base_image, + loaded_checkpoint, + node_classes, + seed, + test_dirs, + mode_type="None", + seam_fix_mode=seam_fix_mode, + seam_fix_denoise=0.5, + filename_prefix=filename, + ) diff --git a/custom_nodes/comfyui_ultimatesdupscale/usdu_nodes.py b/custom_nodes/comfyui_ultimatesdupscale/usdu_nodes.py new file mode 100644 index 00000000..b840e913 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/usdu_nodes.py @@ -0,0 +1,243 @@ +# ComfyUI Node for Ultimate SD Upscale by Coyote-A: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111 + +import logging +import torch +import comfy +from usdu_patch import usdu +from usdu_utils import tensor_to_pil, pil_to_tensor +from modules.processing import StableDiffusionProcessing +import modules.shared as shared +from modules.upscaler import UpscalerData + +MAX_RESOLUTION = 8192 +# The modes available for Ultimate SD Upscale +MODES = { + "Linear": usdu.USDUMode.LINEAR, + "Chess": usdu.USDUMode.CHESS, + "None": usdu.USDUMode.NONE, +} +# The seam fix modes +SEAM_FIX_MODES = { + "None": usdu.USDUSFMode.NONE, + "Band Pass": usdu.USDUSFMode.BAND_PASS, + "Half Tile": usdu.USDUSFMode.HALF_TILE, + "Half Tile + Intersections": usdu.USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS, +} + + +def USDU_base_inputs(): + required = [ + ("image", ("IMAGE", {"tooltip": "The image to upscale."})), + # Sampling Params + ("model", ("MODEL", {"tooltip": "The model to use for image-to-image."})), + ("positive", ("CONDITIONING", {"tooltip": "The positive conditioning for each tile."})), + ("negative", ("CONDITIONING", {"tooltip": "The negative conditioning for each tile."})), + ("vae", ("VAE", {"tooltip": "The VAE model to use for tiles."})), + ("upscale_by", ("FLOAT", {"default": 2, "min": 0.05, "max": 4, "step": 0.05, "tooltip": "The factor to upscale the image by."})), + ("seed", ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The seed to use for image-to-image."})), + ("steps", ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1, "tooltip": "The number of steps to use for each tile."})), + ("cfg", ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "The CFG scale to use for each tile."})), + ("sampler_name", (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The sampler to use for each tile."})), + ("scheduler", (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler to use for each tile."})), + ("denoise", ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The denoising strength to use for each tile."})), + # Upscale Params + ("upscale_model", ("UPSCALE_MODEL", {"tooltip": "The upscaler model for upscaling the image."})), + ("mode_type", (list(MODES.keys()), {"tooltip": "The tiling order to use for the redraw step."})), + ("tile_width", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of each tile."})), + ("tile_height", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of each tile."})), + ("mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1, "tooltip": "The blur radius for the mask."})), + ("tile_padding", ("INT", {"default": 32, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The padding to apply between tiles."})), + # Seam fix params + ("seam_fix_mode", (list(SEAM_FIX_MODES.keys()), {"tooltip": "The seam fix mode to use."})), + ("seam_fix_denoise", ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The denoising strength to use for the seam fix."})), + ("seam_fix_width", ("INT", {"default": 64, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the bands used for the Band Pass seam fix mode."})), + ("seam_fix_mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1, "tooltip": "The blur radius for the seam fix mask."})), + ("seam_fix_padding", ("INT", {"default": 16, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The padding to apply for the seam fix tiles."})), + # Misc + ("force_uniform_tiles", ("BOOLEAN", {"default": True, "tooltip": "Force all tiles to be the same as the set tile size, even when tiles could be smaller. This can help prevent the model from working with irregular tile sizes."})), + ("tiled_decode", ("BOOLEAN", {"default": False, "tooltip": "Whether to use tiled decoding when decoding tiles."})), + ] + + optional = [] + + return required, optional + + +def prepare_inputs(required: list, optional: list = None): + inputs = {} + if required: + inputs["required"] = {} + for name, type in required: + inputs["required"][name] = type + if optional: + inputs["optional"] = {} + for name, type in optional: + inputs["optional"][name] = type + return inputs + + +def remove_input(inputs: list, input_name: str): + for i, (n, _) in enumerate(inputs): + if n == input_name: + del inputs[i] + break + + +def rename_input(inputs: list, old_name: str, new_name: str): + for i, (n, t) in enumerate(inputs): + if n == old_name: + inputs[i] = (new_name, t) + break + + +class UltimateSDUpscale: + @classmethod + def INPUT_TYPES(s): + required, optional = USDU_base_inputs() + return prepare_inputs(required, optional) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + OUTPUT_TOOLTIPS = ("The final upscaled image.",) + DESCRIPTION = "Upscales an image and runs image-to-image on tiles from the input image." + + def upscale(self, image, model, positive, negative, vae, upscale_by, seed, + steps, cfg, sampler_name, scheduler, denoise, upscale_model, + mode_type, tile_width, tile_height, mask_blur, tile_padding, + seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur, + seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode, + custom_sampler=None, custom_sigmas=None): + # Store params + self.tile_width = tile_width + self.tile_height = tile_height + self.mask_blur = mask_blur + self.tile_padding = tile_padding + self.seam_fix_width = seam_fix_width + self.seam_fix_denoise = seam_fix_denoise + self.seam_fix_padding = seam_fix_padding + self.seam_fix_mode = seam_fix_mode + self.mode_type = mode_type + self.upscale_by = upscale_by + self.seam_fix_mask_blur = seam_fix_mask_blur + + # + # Set up A1111 patches + # + + # Upscaler + # An object that the script works with + shared.sd_upscalers[0] = UpscalerData() + # Where the actual upscaler is stored, will be used when the script upscales using the Upscaler in UpscalerData + shared.actual_upscaler = upscale_model + + # Set the batch of images + shared.batch = [tensor_to_pil(image, i) for i in range(len(image))] + shared.batch_as_tensor = image + + # Processing + sdprocessing = StableDiffusionProcessing( + shared.batch[0], model, positive, negative, vae, + seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tiles, tiled_decode, + tile_width, tile_height, MODES[self.mode_type], SEAM_FIX_MODES[self.seam_fix_mode], + custom_sampler, custom_sigmas, + ) + + # Disable logging + logger = logging.getLogger() + old_level = logger.getEffectiveLevel() + logger.setLevel(logging.CRITICAL + 1) + try: + # + # Running the script + # + script = usdu.Script() + processed = script.run(p=sdprocessing, _=None, tile_width=self.tile_width, tile_height=self.tile_height, + mask_blur=self.mask_blur, padding=self.tile_padding, seams_fix_width=self.seam_fix_width, + seams_fix_denoise=self.seam_fix_denoise, seams_fix_padding=self.seam_fix_padding, + upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[self.mode_type], + save_seams_fix_image=False, seams_fix_mask_blur=self.seam_fix_mask_blur, + seams_fix_type=SEAM_FIX_MODES[self.seam_fix_mode], target_size_type=2, + custom_width=None, custom_height=None, custom_scale=self.upscale_by) + + # Return the resulting images + images = [pil_to_tensor(img) for img in shared.batch] + tensor = torch.cat(images, dim=0) + return (tensor,) + finally: + # Restore the original logging level + logger.setLevel(old_level) + +class UltimateSDUpscaleNoUpscale(UltimateSDUpscale): + @classmethod + def INPUT_TYPES(s): + required, optional = USDU_base_inputs() + remove_input(required, "upscale_model") + remove_input(required, "upscale_by") + rename_input(required, "image", "upscaled_image") + return prepare_inputs(required, optional) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + CATEGORY = "image/upscaling" + OUTPUT_TOOLTIPS = ("The final refined image.",) + DESCRIPTION = "Runs image-to-image on tiles from the input image." + + def upscale(self, upscaled_image, model, positive, negative, vae, seed, + steps, cfg, sampler_name, scheduler, denoise, + mode_type, tile_width, tile_height, mask_blur, tile_padding, + seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur, + seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode): + upscale_by = 1.0 + return super().upscale(upscaled_image, model, positive, negative, vae, upscale_by, seed, + steps, cfg, sampler_name, scheduler, denoise, None, + mode_type, tile_width, tile_height, mask_blur, tile_padding, + seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur, + seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode) + +class UltimateSDUpscaleCustomSample(UltimateSDUpscale): + @classmethod + def INPUT_TYPES(s): + required, optional = USDU_base_inputs() + remove_input(required, "upscale_model") + optional.append(("upscale_model", ("UPSCALE_MODEL", {"tooltip": "The model to use for upscaling the image. If not provided, a simple Lanczos scaling will be used instead."}))) + optional.append(("custom_sampler", ("SAMPLER", {"tooltip": "A custom sampler to use instead of the built-in ComfyUI sampler specified by sampler_name. Only used if both custom_sampler and custom_sigmas are provided."}))) + optional.append(("custom_sigmas", ("SIGMAS", {"tooltip": "A custom noise schedule to use during sampling. Only used if both custom_sampler and custom_sigmas are provided."}))) + return prepare_inputs(required, optional) + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + CATEGORY = "image/upscaling" + OUTPUT_TOOLTIPS = ("The final upscaled image.",) + DESCRIPTION = "Runs image-to-image on tiles from the input image." + + def upscale(self, image, model, positive, negative, vae, upscale_by, seed, + steps, cfg, sampler_name, scheduler, denoise, + mode_type, tile_width, tile_height, mask_blur, tile_padding, + seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur, + seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode, + upscale_model=None, + custom_sampler=None, custom_sigmas=None): + return super().upscale(image, model, positive, negative, vae, upscale_by, seed, + steps, cfg, sampler_name, scheduler, denoise, upscale_model, + mode_type, tile_width, tile_height, mask_blur, tile_padding, + seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur, + seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode, + custom_sampler, custom_sigmas) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "UltimateSDUpscale": UltimateSDUpscale, + "UltimateSDUpscaleNoUpscale": UltimateSDUpscaleNoUpscale, + "UltimateSDUpscaleCustomSample": UltimateSDUpscaleCustomSample +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "UltimateSDUpscale": "Ultimate SD Upscale", + "UltimateSDUpscaleNoUpscale": "Ultimate SD Upscale (No Upscale)", + "UltimateSDUpscaleCustomSample": "Ultimate SD Upscale (Custom Sample)" +} diff --git a/custom_nodes/comfyui_ultimatesdupscale/usdu_patch.py b/custom_nodes/comfyui_ultimatesdupscale/usdu_patch.py new file mode 100644 index 00000000..f96f07e3 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/usdu_patch.py @@ -0,0 +1,71 @@ +# Make some patches to the script +from repositories import ultimate_upscale as usdu +import modules.shared as shared +import math +from PIL import Image + + +if (not hasattr(Image, 'Resampling')): # For older versions of Pillow + Image.Resampling = Image + +# +# Instead of using multiples of 64, use multiples of 8 +# + + +def round_length(length, multiple=8): + return round(length / multiple) * multiple + + +# Upscaler +old_init = usdu.USDUpscaler.__init__ + + +def new_init(self, p, image, upscaler_index, save_redraw, save_seams_fix, tile_width, tile_height): + p.width = round_length(image.width * p.upscale_by) + p.height = round_length(image.height * p.upscale_by) + old_init(self, p, image, upscaler_index, save_redraw, save_seams_fix, tile_width, tile_height) + + +usdu.USDUpscaler.__init__ = new_init + +# Redraw +old_setup_redraw = usdu.USDURedraw.init_draw + + +def new_setup_redraw(self, p, width, height): + mask, draw = old_setup_redraw(self, p, width, height) + p.width = round_length(self.tile_width + self.padding) + p.height = round_length(self.tile_height + self.padding) + return mask, draw + + +usdu.USDURedraw.init_draw = new_setup_redraw + +# Seams fix +old_setup_seams_fix = usdu.USDUSeamsFix.init_draw + + +def new_setup_seams_fix(self, p): + old_setup_seams_fix(self, p) + p.width = round_length(self.tile_width + self.padding) + p.height = round_length(self.tile_height + self.padding) + + +usdu.USDUSeamsFix.init_draw = new_setup_seams_fix + + +# +# Make the script upscale on a batch of images instead of one image +# + +old_upscale = usdu.USDUpscaler.upscale + + +def new_upscale(self): + old_upscale(self) + shared.batch = [self.image] + \ + [img.resize((self.p.width, self.p.height), resample=Image.LANCZOS) for img in shared.batch[1:]] + + +usdu.USDUpscaler.upscale = new_upscale diff --git a/custom_nodes/comfyui_ultimatesdupscale/usdu_utils.py b/custom_nodes/comfyui_ultimatesdupscale/usdu_utils.py new file mode 100644 index 00000000..479dd3d7 --- /dev/null +++ b/custom_nodes/comfyui_ultimatesdupscale/usdu_utils.py @@ -0,0 +1,517 @@ +import numpy as np +from PIL import Image, ImageFilter +import torch +import torch.nn.functional as F +from torchvision.transforms import GaussianBlur +import math + +if (not hasattr(Image, 'Resampling')): # For older versions of Pillow + Image.Resampling = Image + +BLUR_KERNEL_SIZE = 15 + + +def tensor_to_pil(img_tensor, batch_index=0): + # Takes a batch of images in the form of a tensor of shape [batch_size, height, width, channels] + # and returns an RGB PIL Image. Assumes channels=3 + safe_tensor = torch.nan_to_num(img_tensor[batch_index]) + return Image.fromarray((255 * safe_tensor.cpu().numpy()).astype(np.uint8)) + + +def pil_to_tensor(image): + # Takes a PIL image and returns a tensor of shape [1, height, width, channels] + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image).unsqueeze(0) + if len(image.shape) == 3: # If the image is grayscale, add a channel dimension + image = image.unsqueeze(-1) + return image + + +def controlnet_hint_to_pil(tensor, batch_index=0): + return tensor_to_pil(tensor.movedim(1, -1), batch_index) + + +def pil_to_controlnet_hint(img): + return pil_to_tensor(img).movedim(-1, 1) + + +def crop_tensor(tensor, region): + # Takes a tensor of shape [batch_size, height, width, channels] and crops it to the given region + x1, y1, x2, y2 = region + return tensor[:, y1:y2, x1:x2, :] + + +def resize_tensor(tensor, size, mode="nearest-exact"): + # Takes a tensor of shape [B, C, H, W] and resizes + # it to a shape of [B, C, size[0], size[1]] using the given mode + return torch.nn.functional.interpolate(tensor, size=size, mode=mode) + + +def get_crop_region(mask, pad=0): + # Takes a black and white PIL image in 'L' mode and returns the coordinates of the white rectangular mask region + # Should be equivalent to the get_crop_region function from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/masking.py + coordinates = mask.getbbox() + if coordinates is not None: + x1, y1, x2, y2 = coordinates + else: + x1, y1, x2, y2 = mask.width, mask.height, 0, 0 + # Apply padding + x1 = max(x1 - pad, 0) + y1 = max(y1 - pad, 0) + x2 = min(x2 + pad, mask.width) + y2 = min(y2 + pad, mask.height) + return fix_crop_region((x1, y1, x2, y2), (mask.width, mask.height)) + + +def fix_crop_region(region, image_size): + # Remove the extra pixel added by the get_crop_region function + image_width, image_height = image_size + x1, y1, x2, y2 = region + if x2 < image_width: + x2 -= 1 + if y2 < image_height: + y2 -= 1 + return x1, y1, x2, y2 + + +def expand_crop(region, width, height, target_width, target_height): + ''' + Expands a crop region to a specified target size. + :param region: A tuple of the form (x1, y1, x2, y2) denoting the upper left and the lower right points + of the rectangular region. Expected to have x2 > x1 and y2 > y1. + :param width: The width of the image the crop region is from. + :param height: The height of the image the crop region is from. + :param target_width: The desired width of the crop region. + :param target_height: The desired height of the crop region. + ''' + x1, y1, x2, y2 = region + actual_width = x2 - x1 + actual_height = y2 - y1 + # target_width = math.ceil(actual_width / 8) * 8 + # target_height = math.ceil(actual_height / 8) * 8 + + # Try to expand region to the right of half the difference + width_diff = target_width - actual_width + x2 = min(x2 + width_diff // 2, width) + # Expand region to the left of the difference including the pixels that could not be expanded to the right + width_diff = target_width - (x2 - x1) + x1 = max(x1 - width_diff, 0) + # Try the right again + width_diff = target_width - (x2 - x1) + x2 = min(x2 + width_diff, width) + + # Try to expand region to the bottom of half the difference + height_diff = target_height - actual_height + y2 = min(y2 + height_diff // 2, height) + # Expand region to the top of the difference including the pixels that could not be expanded to the bottom + height_diff = target_height - (y2 - y1) + y1 = max(y1 - height_diff, 0) + # Try the bottom again + height_diff = target_height - (y2 - y1) + y2 = min(y2 + height_diff, height) + + return (x1, y1, x2, y2), (target_width, target_height) + + +def resize_region(region, init_size, resize_size): + # Resize a crop so that it fits an image that was resized to the given width and height + x1, y1, x2, y2 = region + init_width, init_height = init_size + resize_width, resize_height = resize_size + x1 = math.floor(x1 * resize_width / init_width) + x2 = math.ceil(x2 * resize_width / init_width) + y1 = math.floor(y1 * resize_height / init_height) + y2 = math.ceil(y2 * resize_height / init_height) + return (x1, y1, x2, y2) + + +def pad_image(image, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False): + ''' + Pads an image with the given number of pixels on each side and fills the padding with data from the edges. + :param image: A PIL image + :param left_pad: The number of pixels to pad on the left side + :param right_pad: The number of pixels to pad on the right side + :param top_pad: The number of pixels to pad on the top side + :param bottom_pad: The number of pixels to pad on the bottom side + :param blur: Whether to blur the padded edges + :return: A PIL image with size (image.width + left_pad + right_pad, image.height + top_pad + bottom_pad) + ''' + left_edge = image.crop((0, 1, 1, image.height - 1)) + right_edge = image.crop((image.width - 1, 1, image.width, image.height - 1)) + top_edge = image.crop((1, 0, image.width - 1, 1)) + bottom_edge = image.crop((1, image.height - 1, image.width - 1, image.height)) + new_width = image.width + left_pad + right_pad + new_height = image.height + top_pad + bottom_pad + padded_image = Image.new(image.mode, (new_width, new_height)) + padded_image.paste(image, (left_pad, top_pad)) + if fill: + for i in range(left_pad): + edge = left_edge.resize( + (1, new_height - i * (top_pad + bottom_pad) // left_pad), resample=Image.Resampling.NEAREST) + padded_image.paste(edge, (i, i * top_pad // left_pad)) + for i in range(right_pad): + edge = right_edge.resize( + (1, new_height - i * (top_pad + bottom_pad) // right_pad), resample=Image.Resampling.NEAREST) + padded_image.paste(edge, (new_width - 1 - i, i * top_pad // right_pad)) + for i in range(top_pad): + edge = top_edge.resize( + (new_width - i * (left_pad + right_pad) // top_pad, 1), resample=Image.Resampling.NEAREST) + padded_image.paste(edge, (i * left_pad // top_pad, i)) + for i in range(bottom_pad): + edge = bottom_edge.resize( + (new_width - i * (left_pad + right_pad) // bottom_pad, 1), resample=Image.Resampling.NEAREST) + padded_image.paste(edge, (i * left_pad // bottom_pad, new_height - 1 - i)) + if blur and not (left_pad == right_pad == top_pad == bottom_pad == 0): + padded_image = padded_image.filter(ImageFilter.GaussianBlur(BLUR_KERNEL_SIZE)) + padded_image.paste(image, (left_pad, top_pad)) + return padded_image + + +def pad_image2(image, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False): + ''' + Pads an image with the given number of pixels on each side and fills the padding with data from the edges. + Faster than pad_image, but only pads with edge data in straight lines. + :param image: A PIL image + :param left_pad: The number of pixels to pad on the left side + :param right_pad: The number of pixels to pad on the right side + :param top_pad: The number of pixels to pad on the top side + :param bottom_pad: The number of pixels to pad on the bottom side + :param blur: Whether to blur the padded edges + :return: A PIL image with size (image.width + left_pad + right_pad, image.height + top_pad + bottom_pad) + ''' + left_edge = image.crop((0, 1, 1, image.height - 1)) + right_edge = image.crop((image.width - 1, 1, image.width, image.height - 1)) + top_edge = image.crop((1, 0, image.width - 1, 1)) + bottom_edge = image.crop((1, image.height - 1, image.width - 1, image.height)) + new_width = image.width + left_pad + right_pad + new_height = image.height + top_pad + bottom_pad + padded_image = Image.new(image.mode, (new_width, new_height)) + padded_image.paste(image, (left_pad, top_pad)) + if fill: + if left_pad > 0: + padded_image.paste(left_edge.resize((left_pad, new_height), resample=Image.Resampling.NEAREST), (0, 0)) + if right_pad > 0: + padded_image.paste(right_edge.resize((right_pad, new_height), + resample=Image.Resampling.NEAREST), (new_width - right_pad, 0)) + if top_pad > 0: + padded_image.paste(top_edge.resize((new_width, top_pad), resample=Image.Resampling.NEAREST), (0, 0)) + if bottom_pad > 0: + padded_image.paste(bottom_edge.resize((new_width, bottom_pad), + resample=Image.Resampling.NEAREST), (0, new_height - bottom_pad)) + if blur and not (left_pad == right_pad == top_pad == bottom_pad == 0): + padded_image = padded_image.filter(ImageFilter.GaussianBlur(BLUR_KERNEL_SIZE)) + padded_image.paste(image, (left_pad, top_pad)) + return padded_image + + +def pad_tensor(tensor, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False): + ''' + Pads an image tensor with the given number of pixels on each side and fills the padding with data from the edges. + :param tensor: A tensor of shape [B, H, W, C] + :param left_pad: The number of pixels to pad on the left side + :param right_pad: The number of pixels to pad on the right side + :param top_pad: The number of pixels to pad on the top side + :param bottom_pad: The number of pixels to pad on the bottom side + :param blur: Whether to blur the padded edges + :return: A tensor of shape [B, H + top_pad + bottom_pad, W + left_pad + right_pad, C] + ''' + batch_size, channels, height, width = tensor.shape + h_pad = left_pad + right_pad + v_pad = top_pad + bottom_pad + new_width = width + h_pad + new_height = height + v_pad + + # Create empty image + padded = torch.zeros((batch_size, channels, new_height, new_width), dtype=tensor.dtype) + + # Copy the original image into the centor of the padded tensor + padded[:, :, top_pad:top_pad + height, left_pad:left_pad + width] = tensor + + # Duplicate the edges of the original image into the padding + if top_pad > 0: + padded[:, :, :top_pad, :] = padded[:, :, top_pad:top_pad + 1, :] # Top edge + if bottom_pad > 0: + padded[:, :, -bottom_pad:, :] = padded[:, :, -bottom_pad - 1:-bottom_pad, :] # Bottom edge + if left_pad > 0: + padded[:, :, :, :left_pad] = padded[:, :, :, left_pad:left_pad + 1] # Left edge + if right_pad > 0: + padded[:, :, :, -right_pad:] = padded[:, :, :, -right_pad - 1:-right_pad] # Right edge + + return padded + + +def resize_and_pad_image(image, width, height, fill=False, blur=False): + ''' + Resizes an image to the given width and height and pads it to the given width and height. + :param image: A PIL image + :param width: The width of the resized image + :param height: The height of the resized image + :param fill: Whether to fill the padding with data from the edges + :param blur: Whether to blur the padded edges + :return: A PIL image of size (width, height) + ''' + width_ratio = width / image.width + height_ratio = height / image.height + if height_ratio > width_ratio: + resize_ratio = width_ratio + else: + resize_ratio = height_ratio + resize_width = round(image.width * resize_ratio) + resize_height = round(image.height * resize_ratio) + resized = image.resize((resize_width, resize_height), resample=Image.Resampling.LANCZOS) + # Pad the sides of the image to get the image to the desired size that wasn't covered by the resize + horizontal_pad = (width - resize_width) // 2 + vertical_pad = (height - resize_height) // 2 + result = pad_image2(resized, horizontal_pad, horizontal_pad, vertical_pad, vertical_pad, fill, blur) + result = result.resize((width, height), resample=Image.Resampling.LANCZOS) + return result, (horizontal_pad, vertical_pad) + + +def resize_and_pad_tensor(tensor, width, height, fill=False, blur=False): + ''' + Resizes an image tensor to the given width and height and pads it to the given width and height. + :param tensor: A tensor of shape [B, H, W, C] + :param width: The width of the resized image + :param height: The height of the resized image + :param fill: Whether to fill the padding with data from the edges + :param blur: Whether to blur the padded edges + :return: A tensor of shape [B, height, width, C] + ''' + # Resize the image to the closest size that maintains the aspect ratio + width_ratio = width / tensor.shape[3] + height_ratio = height / tensor.shape[2] + if height_ratio > width_ratio: + resize_ratio = width_ratio + else: + resize_ratio = height_ratio + resize_width = round(tensor.shape[3] * resize_ratio) + resize_height = round(tensor.shape[2] * resize_ratio) + resized = F.interpolate(tensor, size=(resize_height, resize_width), mode='nearest-exact') + # Pad the sides of the image to get the image to the desired size that wasn't covered by the resize + horizontal_pad = (width - resize_width) // 2 + vertical_pad = (height - resize_height) // 2 + result = pad_tensor(resized, horizontal_pad, horizontal_pad, vertical_pad, vertical_pad, fill, blur) + result = F.interpolate(result, size=(height, width), mode='nearest-exact') + return result + + +def crop_controlnet(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad): + if "control" not in cond_dict: + return + c = cond_dict["control"] + controlnet = c.copy() + cond_dict["control"] = controlnet + while c is not None: + # hint is shape (B, C, H, W) + hint = controlnet.cond_hint_original + resized_crop = resize_region(region, canvas_size, hint.shape[:-3:-1]) + hint = crop_tensor(hint.movedim(1, -1), resized_crop).movedim(-1, 1) + hint = resize_tensor(hint, tile_size[::-1]) + controlnet.cond_hint_original = hint + c = c.previous_controlnet + controlnet.set_previous_controlnet(c.copy() if c is not None else None) + controlnet = controlnet.previous_controlnet + + +def region_intersection(region1, region2): + """ + Returns the coordinates of the intersection of two rectangular regions. + :param region1: A tuple of the form (x1, y1, x2, y2) denoting the upper left and the lower right points + of the first rectangular region. Expected to have x2 > x1 and y2 > y1. + :param region2: The second rectangular region with the same format as the first. + :return: A tuple of the form (x1, y1, x2, y2) denoting the rectangular intersection. + None if there is no intersection. + """ + x1, y1, x2, y2 = region1 + x1_, y1_, x2_, y2_ = region2 + x1 = max(x1, x1_) + y1 = max(y1, y1_) + x2 = min(x2, x2_) + y2 = min(y2, y2_) + if x1 >= x2 or y1 >= y2: + return None + return (x1, y1, x2, y2) + + +def crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad): + if "gligen" not in cond_dict: + return + type, model, cond = cond_dict["gligen"] + if type != "position": + from warnings import warn + warn(f"Unknown gligen type {type}") + return + cropped = [] + for c in cond: + emb, h, w, y, x = c + # Get the coordinates of the box in the upscaled image + x1 = x * 8 + y1 = y * 8 + x2 = x1 + w * 8 + y2 = y1 + h * 8 + gligen_upscaled_box = resize_region((x1, y1, x2, y2), init_size, canvas_size) + + # Calculate the intersection of the gligen box and the region + intersection = region_intersection(gligen_upscaled_box, region) + if intersection is None: + continue + x1, y1, x2, y2 = intersection + + # Offset the gligen box so that the origin is at the top left of the tile region + x1 -= region[0] + y1 -= region[1] + x2 -= region[0] + y2 -= region[1] + + # Add the padding + x1 += w_pad + y1 += h_pad + x2 += w_pad + y2 += h_pad + + # Set the new position params + h = (y2 - y1) // 8 + w = (x2 - x1) // 8 + x = x1 // 8 + y = y1 // 8 + cropped.append((emb, h, w, y, x)) + + cond_dict["gligen"] = (type, model, cropped) + + +def crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad): + if "area" not in cond_dict: + return + + # Resize the area conditioning to the canvas size and confine it to the tile region + h, w, y, x = cond_dict["area"] + w, h, x, y = 8 * w, 8 * h, 8 * x, 8 * y + x1, y1, x2, y2 = resize_region((x, y, x + w, y + h), init_size, canvas_size) + intersection = region_intersection((x1, y1, x2, y2), region) + if intersection is None: + del cond_dict["area"] + del cond_dict["strength"] + return + x1, y1, x2, y2 = intersection + + # Offset origin to the top left of the tile + x1 -= region[0] + y1 -= region[1] + x2 -= region[0] + y2 -= region[1] + + # Add the padding + x1 += w_pad + y1 += h_pad + x2 += w_pad + y2 += h_pad + + # Set the params for tile + w, h = (x2 - x1) // 8, (y2 - y1) // 8 + x, y = x1 // 8, y1 // 8 + + cond_dict["area"] = (h, w, y, x) + + +def crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad): + if "mask" not in cond_dict: + return + mask_tensor = cond_dict["mask"] # (B, H, W) + masks = [] + for i in range(mask_tensor.shape[0]): + # Convert to PIL image + mask = tensor_to_pil(mask_tensor, i) # W x H + + # Resize the mask to the canvas size + mask = mask.resize(canvas_size, Image.Resampling.BICUBIC) + + # Crop the mask to the region + mask = mask.crop(region) + + # Add padding + mask, _ = resize_and_pad_image(mask, tile_size[0], tile_size[1], fill=True) + + # Resize the mask to the tile size + if tile_size != mask.size: + mask = mask.resize(tile_size, Image.Resampling.BICUBIC) + + # Convert back to tensor + mask = pil_to_tensor(mask) # (1, H, W, 1) + mask = mask.squeeze(-1) # (1, H, W) + masks.append(mask) + + cond_dict["mask"] = torch.cat(masks, dim=0) # (B, H, W) + +# Added Flux-Kontext Support crop_reference_latents by TBG ETUR +def crop_reference_latents(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad): + """ + 1. Resize each latent to `canvas_size` in latent units. + 2. Crop the rectangle `region` (pixel coordinates). + 3. Down-sample the crop to latent-space `tile_size`. + Expects a list of BCHW tensors under "reference_latents". + """ + + latents = cond_dict.get("reference_latents") + if not isinstance(latents, list): + return # nothing to do + + k = 8 # down-sample factor from pixel space β†’ latent space (SD-type models) + + W_can_px, H_can_px = canvas_size + # canvas size expressed in latent units + W_can_lat, H_can_lat = W_can_px // k, H_can_px // k + + W_tile_px, H_tile_px = tile_size + W_tile_lat, H_tile_lat = max(1, W_tile_px // k), max(1, H_tile_px // k) + + x1_px, y1_px, x2_px, y2_px = region + + new_latents = [] + for t in latents: # (B,C,H_lat_in,W_lat_in) + has_5d = False + if t.ndim == 5: # (B,C,1,H_lat_in,W_lat_in) + has_5d = True + t = t.squeeze(2) + if t.ndim != 4: + raise ValueError(f"expected BCHW, got {t.shape}") + + # 1. Resize to canvas resolution in latent units only if needed + if t.shape[-2:] != (H_can_lat, W_can_lat): + t = F.interpolate(t, + size=(H_can_lat, W_can_lat), + mode="bilinear", + align_corners=False) + + # 2. Convert pixel crop β†’ latent slice + w0_lat = int(round(x1_px / k)) + w1_lat = int(round(x2_px / k)) + h0_lat = int(round(y1_px / k)) + h1_lat = int(round(y2_px / k)) + + cropped = t[:, :, h0_lat:h1_lat, w0_lat:w1_lat] # view + + # 3. Down-sample to latent-tile size + cropped = F.interpolate(cropped, + size=(H_tile_lat, W_tile_lat), + mode="bilinear", + align_corners=False) + if has_5d: + cropped = cropped.unsqueeze(2) + new_latents.append(cropped) + + cond_dict["reference_latents"] = new_latents + + + +def crop_cond(cond, region, init_size, canvas_size, tile_size, w_pad=0, h_pad=0): + cropped = [] + for emb, x in cond: + cond_dict = x.copy() + n = [emb, cond_dict] + crop_controlnet(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad) + crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad) + crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad) + crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad) + crop_reference_latents(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad) + cropped.append(n) + return cropped diff --git a/custom_nodes/controlaltai-nodes/.gitignore b/custom_nodes/controlaltai-nodes/.gitignore new file mode 100644 index 00000000..00983fea --- /dev/null +++ b/custom_nodes/controlaltai-nodes/.gitignore @@ -0,0 +1 @@ +/__pycache__/ \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/LICENSE b/custom_nodes/controlaltai-nodes/LICENSE new file mode 100644 index 00000000..ee0c9b04 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 ControlAltAI + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/controlaltai-nodes/README.md b/custom_nodes/controlaltai-nodes/README.md new file mode 100644 index 00000000..128d491e --- /dev/null +++ b/custom_nodes/controlaltai-nodes/README.md @@ -0,0 +1,288 @@ +### Requirements Update: 8 Dec 2024: Flux Attention Control Node requires XFormers. Check your version of PyTorch and install a compatible version of XFormers. Please follow the instructions here: xformers_instructions + +# ComfyUI ControlAltAI Nodes + +This repository contains custom nodes designed for the ComfyUI framework, focusing on quality-of-life improvements. These nodes aim to make tasks easier and more efficient. Two Flux nodes are available to enhance functionality and streamline workflows within ComfyUI. + +## Nodes + +### List of Nodes: +- Flux + - Flux Resolution Calculator (Updated May 2025) + - Flux Sampler + - Flux Union ControlNet Apply +- Logic + - Boolean Basic + - Boolean Reverse + - Integer Settings + - Choose Upscale Model +- Image + - Get Image Size & Ratio + - Noise Plus Blend +- Flux Region + - Region Mask Generator + - Region Mask Processor + - Region Mask Validator + - Region Mask Conditioning + - Flux Attention Control + - Region Overlay Visualizer + - Flux Attention Cleanup + +### Flux Resolution Calculator + +The Flux Resolution Calculator is designed to determine the optimal image resolution for outputs generated using the Flux model, which is notably more oriented towards megapixels. Unlike traditional methods that rely on standard SDXL resolutions, this calculator operates based on user-specified megapixel inputs. Users can select their desired megapixel count, ranging from 0.1 to 2.0 megapixels, and aspect ratio. The calculator then provides the exact image dimensions necessary for optimal performance with the Flux model. This approach ensures that the generated images meet specific quality and size requirements tailored to the user's needs. Additionally, while the official limit is 2.0 megapixels, during testing, I have successfully generated images at higher resolutions, indicating the model's flexibility in accommodating various image dimensions without compromising quality. + +- **Supported Megapixels:** 0.1 MP - 2.5 MP (change stepping to 0.1 for fine-tuned selection) +- **Note:** Generations above 1 MP may appear slightly blurry, but resolutions of 3k+ have been successfully tested on the Flux1.Dev model. +- **Custom Ratio:** Custom Ratio is now supported. Enable or Disable the Custom Ratio and input any ratio. (Example: 4:9). +- **Preview:** The preview node is just a visual representation of the ratio. +- **Divisible By:** You can now choose the divisibility by 8/16/32/64. By default, it is 64. To get fine-tuned results, choose divisibility by 8. Divisibility by 32/64 is recommended for Flux Dev 1. + +### Flux Sampler + +The Flux Sampler node combines the functionality of the CustomSamplerAdvance node and input nodes into a single, streamlined node. + +- **CFG Setting:** The CFG is fixed at 1. +- **Conditioning Input:** Only positive conditioning is supported. +- **Compatibility:** Only the samplers and schedulers compatible with the Flux model are included. +- **Latent Compatibility:** Use SD3 Empty Latent Image only. The normal empty latent image node is not compatible. + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-7361.png) +![ComfyUI Screenshot](https://gseth.com/images/SNAG-7362.png) +![ComfyUI Screenshot](https://gseth.com/images/SNAG-7363.png) + +### Flux Union ControlNet Apply + +The Flux Union ControlNet Apply node is an all-in-one node compatible with InstanX Union Pro ControlNet. It has been tested extensively with the union controlnet type and works as intended. You can combine two ControlNet Union units and get good results. Not recommended to combine more than two. The ControlNet is tested only on the Flux 1.Dev Model. + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4402.png) + +**Recommended Settings:**
    +strength: 0.15-0.65.
    +end percentage: 0.200 - 0.900. + +**Recommended PreProcessors:**
    +Canny: Canny Edge (ControlNet Aux).
    +Tile: Tile (ControlNet Aux).
    +Depth: Depth Anything V2 Relative (ControlNet Aux).
    +Blue: Direct Input (Blurry Image) or Tile (ControlNet Aux).
    +Pose: DWPose Estimator (ControlNet Aux).
    +Gray: Image Desaturate (Comfy Essentials Custom Node).
    +Low Quality: Direct Input. + +Results: (Canny and Depth Examples not included. They are straightforward.)

    +**Pixel Low Resolution to High Resolution**

    +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4386.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4343.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4387.png) + +**Photo Restoration**

    +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4375.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4376.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4381.png) + +**Game Asset Low Resolution Upscale**

    +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4389.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4340.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4341a.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4342.png) + +**Blur to UnBlur**

    +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4364.png) + +**Re-Color**

    +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4390.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4392.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4394.png) + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4395.png) + +**YouTube tutorial Union ControlNet Usage: Video Tutorial** + +**Shakker Labs & InstantX Flux ControlNet Union Pro Model Download:** Hugging Face Link + +### Get Image Size & Ratio +This node is designed to get the image resolution in width, height, and ratio. The node can be further connected to the Flux Resolution Calculator. To do so, follow the following steps: +- Right-click on the Flux Resolution Calculator -- > Convert widget to input -- > Convert custom_aspect_ratio to input. +- Connect Ratio output to custom_aspect_ratio input. + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-3959.png) + +### Integer Setting +This node is designed to give output as a raw value of 1 or 2 integers. Enable = 2, Disable = 1. + +Use case: This can be set up before a two-way switch, allowing workflow logical control to flow in one or the other direction. As of now, it only controls two logical flows. In the future, we will upgrade the node to support three or more logical switch flows. + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4239.png) + +### Choose Upscale Model +A simple node that can be connected with a boolean logic. A true response will use upscale model 1, and a false response will use upscale model 2. + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4240.png) + +### Noise Plus Blend +This node will generate a Gaussian blur noise based on the dimensions of the input image and will blend the noise into the entire image or only the mask region. + +**Issue:** Generated faces/landscapes are realistic, but during upscale, the AI model smoothens the skin or texture, making it look plastic or adding smooth fine lines. + +**Solution:** For upscaling, auto segment or manually mask the face or specified regions and add noise. Then, pass the blended image output to the K-Sampler and denoise at 0.20 - 0.50. + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4241.png) + +You can see the noise has been applied only to the face as per the mask. This will maintain the smooth bokeh and preserve the facial details during upscale. +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4242.png) + +Denoise the image using Flux or SDXL sampler. Recommended sampler denoise: 0.10 - 0.50 +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4243.png) + +**Settings:**
    +noise_scale: 0.30 - 0.50.
    +blend_opacity: 10-25. + +If you find too many artifacts on the skin or other textures, reduce both values. Increase the values if upscaling output results in plastic, velvet-like smooth lines. + +**Best Setting for AI-generated Faces:**
    +noise_scale: 0.40-0.50.
    +blend_opacity: 15-25. + +**Best Setting for AI-generated texture (landscapes):**
    +noise_scale: 0.30.
    +blend_opacity: 12-15. + +Results: +**Example 1**
    +Without Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/without_noise_blend_1.png) + +With Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/with_noise_blend_1.png) + +**Example 2**
    +Without Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/without_noise_blend_2.png) + +With Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/with_noise_blend_2.png) + +**Example 3**
    +Without Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/without_noise_blend_3a.png) + +With Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/with_noise_blend_3.png) + +**Example 4**
    +Without Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/without_noise_blend_4.png) + +With Noise Blend: +![ComfyUI Screenshot](https://gseth.com/images/with_noise_blend_4.png) + +### Flux Region (Spatial Control) + +The node pipeline is as follows: Region Mask Generator --> Region Mask Processor --> Region Mask Validator --> Flux Region Conditioning --> Flux Attention Control --> Flux Overlay Visualizer (optional) --> Flux Attention Cleanup.
    +*Note: Watching the video tutorial is a must. The learning curve is a bit high to use Flux Region Spatial Control.* + +**Region Mask Generator:** This node generates the regions in mask and bbox format. This information is then passed on to the Mask Processor.
    + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4945.png)
    + +**Region Mask Processor:** This node processes the generated mask and applies Gaussian Blur and feathering. This pre-processor node preprocesses the mask and sends the preprocessed information in the pipeline.
    + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4947.png)
    + +**Region Mask Validator:** This node calculates the validity of the regions. The "is valid" message will be true if there are no overlaps. The validation message would show you detailed information on the overlapping regions and the overlap percentage. Although the methodology used requires zero overlaps, the issue is resolved in the flux attention control with feathering. Overlapping will only be an issue if it is excessive, beyond 40-50%.
    + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4946.png)
    + +**Region Mask Conditioning:** Up to three separate conditioning can be connected. The node will process based on the number of regions defined rather than the actual conditioning connections. The strength values are independent for each region. Strength 1 for Region 1, Strength 2 for Region 2, and Strength 3 for Region 3. The strength value range is from 0 to 10 with an increment/decrement step of 0.1. At Value 1, the region strength will match the base conditioning strength, which is always set at 1 as a global value. Strength Values are not only relative to the base conditioning value but are also relative to each other. They are also affected by the Region % area in the canvas and the feathering value in the attention control. Please note. Only use the dual clip and flux conditioning in comfy. The base + region flux guidance should be set to 1.
    + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4950.png)
    + +**Flux Attention Control:** The node takes the region conditioning + base conditioning + the feathering strengths and all the previous information in the pipeline and overrides the Flux Attention. When disabled, it only passes through the base conditioning to the Sampler.
    + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4951.png)
    + +**Region Overlay Visualizer:** This node overlays the region on the final output for visual purposes only.
    + +**Flux Attention Cleanup:** Since the attention is overridden in the model, a tensor mismatch error will occur when you switch the workflow. We also do not want the attention to be cleaned up in the existing workflow. This node automatically will preserve attention during re-runs in the existing workflow, but when switching workflow will do a fresh clean up and restore flux original attention. This process is achieved without a model unload or manual cache cleanup, as they will not work.
    + +![ComfyUI Screenshot](https://gseth.com/images/SNAG-4957.png)
    + +**Xformers & Token/Attention Limits:** The pipeline uses an advanced attention mechanism that combines text tokens from your prompts with spatial information from defined regions. As you increase prompt length or add multiple, complex regions, you create larger attention matrices. While xFormers helps optimize memory usage, there is still a practical limit on how many tokens and spatial positions the model can handle without causing dimension or shape alignment errors. + +Example Error: 'Invalid shape for attention bias: torch.Size([1, 24, 5264, 5264]) (expected (1, 24, 5118, 5118))' + +This limit isn’t about a fixed β€œ5,000 x 5,000” size or a strict VRAM cap. Instead, it’s determined by the model’s architecture and how tokens are combined with spatial positions. Extremely long prompts or too many intricate regions can produce attention shapes that the model’s code cannot process, resulting in shape mismatch errors rather than running out of memory. If you encounter these errors, try shortening your prompt or reducing the complexity of your regional conditioning. There isn’t a simple formula linking VRAM size directly to token count. Instead, it’s about balancing your prompt length and region definitions to keep the attention mechanism within workable limits. Testing with the Flux model and T5-XXL in FP16 on a 4090 shows that keeping prompts relatively short (each clip under 80 tokens) and regions manageable helps avoid such issues. + +**GGUF & CivitAI fine-tune models:** The Flux Region Pipeline was tested with GGUF models without issues. Third-party CivitAI Copax Timeless XPlus 3 Flux models also worked without problems. + +**LoRA Support:** LoRA is supported and will apply to all attention. At this stage, using different LoRA for different Regions is not possible. Research work is still ongoing. + +**ControlNet Support:** Currently not tested. Research work is still ongoing. + +Results: +**Example 1**
    +3 Region Split Blend using Advance LLM: Base Conditioning (ignored) + 3 Regions +![ComfyUI Screenshot](https://gseth.com/images/region_control_11.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_12.png) + +**Example 2**
    +Style manipulation: Base Conditioning + 1 Region +![ComfyUI Screenshot](https://gseth.com/images/region_control_1.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_2.png) + +**Example 3**
    +Simple Splitting Contrast: Base Conditioning (ignored) + 2 Regions +![ComfyUI Screenshot](https://gseth.com/images/region_control_3.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_4.png) + +**Example 4**
    +Simple Splitting Blend: Base Conditioning + 1 Region +![ComfyUI Screenshot](https://gseth.com/images/region_control_5.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_6.png) + +**Example 5**
    +3 Region Split Blend: Base Conditioning (ignored) + 3 Regions +![ComfyUI Screenshot](https://gseth.com/images/region_control_7.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_8.png) + +**Example 6**
    +3 Region Split Blend using Advance LLM: Base Conditioning (ignored) + 3 Regions +![ComfyUI Screenshot](https://gseth.com/images/region_control_13.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_14.png) + +**Example 7**
    +Color Manipulation: Base Conditioning (ignored) + 2 Regions +![ComfyUI Screenshot](https://gseth.com/images/region_control_15.png) +![ComfyUI Screenshot](https://gseth.com/images/region_control_16.png) + +**YouTube tutorial Flux Region Usage: Flux Region Spatial Control Tutorial** + +### YouTube ComfyUI Tutorials + +We are a team of two and create extensive tutorials for ComfyUI. Check out our YouTube channel:
    +ControlAltAI YouTube Tutorials + +### Black Forest Labs AI + +Black Forest Labs, a pioneering AI research organization, has developed the Flux model series, which includes the Flux1.[dev] and Flux1.[schnell] models. These models are designed to push the boundaries of image generation through advanced deep-learning techniques. + +For more details on these models, their capabilities, and licensing information, you can visit the Black Forest Labs website + +### Flux Regional Spatial Control Acknowledgment + +Inspired from: Flux Region Attention by Attashe + +### License + +This project is licensed under the MIT License. diff --git a/custom_nodes/controlaltai-nodes/__init__.py b/custom_nodes/controlaltai-nodes/__init__.py new file mode 100644 index 00000000..fdea274f --- /dev/null +++ b/custom_nodes/controlaltai-nodes/__init__.py @@ -0,0 +1,79 @@ +print("\n\033[32mInitializing ControlAltAI Nodes\033[0m") # Fixed green reset + +from .flux_resolution_cal_node import FluxResolutionNode +from .flux_sampler_node import FluxSampler +from .flux_union_controlnet_node import FluxUnionControlNetApply +from .boolean_basic_node import BooleanBasic +from .boolean_reverse_node import BooleanReverse +from .get_image_size_ratio_node import GetImageSizeRatio +from .noise_plus_blend_node import NoisePlusBlend +from .integer_settings_node import IntegerSettings +from .integer_settings_advanced_node import IntegerSettingsAdvanced +from .choose_upscale_model_node import ChooseUpscaleModel +from .region_mask_generator_node import RegionMaskGenerator +from .region_mask_validator_node import RegionMaskValidator +from .region_mask_processor_node import RegionMaskProcessor +from .region_mask_conditioning_node import RegionMaskConditioning +from .flux_attention_control_node import FluxAttentionControl +from .region_overlay_visualizer_node import RegionOverlayVisualizer +from .flux_attention_cleanup_node import FluxAttentionCleanup +from .hidream_resolution_node import HiDreamResolutionNode +from .perturbation_texture_node import PerturbationTexture +from .text_bridge_node import TextBridge +from .two_way_switch_node import TwoWaySwitch +from .three_way_switch_node import ThreeWaySwitch + +NODE_CLASS_MAPPINGS = { + "FluxResolutionNode": FluxResolutionNode, + "FluxSampler": FluxSampler, + "FluxUnionControlNetApply": FluxUnionControlNetApply, + "BooleanBasic": BooleanBasic, + "BooleanReverse": BooleanReverse, + "GetImageSizeRatio": GetImageSizeRatio, + "NoisePlusBlend": NoisePlusBlend, + "IntegerSettings": IntegerSettings, + "IntegerSettingsAdvanced": IntegerSettingsAdvanced, + "ChooseUpscaleModel": ChooseUpscaleModel, + "RegionMaskGenerator": RegionMaskGenerator, + "RegionMaskValidator": RegionMaskValidator, + "RegionMaskProcessor": RegionMaskProcessor, + "RegionMaskConditioning": RegionMaskConditioning, + "FluxAttentionControl": FluxAttentionControl, + "RegionOverlayVisualizer": RegionOverlayVisualizer, + "FluxAttentionCleanup": FluxAttentionCleanup, + "HiDreamResolutionNode": HiDreamResolutionNode, + "PerturbationTexture": PerturbationTexture, + "TextBridge": TextBridge, + "TwoWaySwitch": TwoWaySwitch, + "ThreeWaySwitch": ThreeWaySwitch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxResolutionNode": "Flux Resolution Calc", + "FluxSampler": "Flux Sampler", + "FluxUnionControlNetApply": "Flux Union ControlNet Apply", + "BooleanBasic": "Boolean Basic", + "BooleanReverse": "Boolean Reverse", + "GetImageSizeRatio": "Get Image Size Ratio", + "NoisePlusBlend": "Noise Plus Blend", + "IntegerSettings": "Integer Settings", + "IntegerSettingsAdvanced": "Integer Settings Advanced", + "ChooseUpscaleModel": "Choose Upscale Model", + "RegionMaskGenerator": "Region Mask Generator", + "RegionMaskValidator": "Region Mask Validator", + "RegionMaskProcessor": "Region Mask Processor", + "RegionMaskConditioning": "Region Mask Conditioning", + "FluxAttentionControl": "Flux Attention Control", + "RegionOverlayVisualizer": "Region Overlay Visualizer", + "FluxAttentionCleanup": "Flux Attention Cleanup", + "HiDreamResolutionNode": "HiDream Resolution", + "PerturbationTexture": "Perturbation Texture", + "TextBridge": "Text Bridge", + "TwoWaySwitch": "Switch (Two Way)", + "ThreeWaySwitch": "Switch (Three Way)", +} + +# Tell ComfyUI where to find JavaScript files +WEB_DIRECTORY = "./web" + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/boolean_basic_node.py b/custom_nodes/controlaltai-nodes/boolean_basic_node.py new file mode 100644 index 00000000..7da6dc3a --- /dev/null +++ b/custom_nodes/controlaltai-nodes/boolean_basic_node.py @@ -0,0 +1,23 @@ +class BooleanBasic: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": ("BOOLEAN", {"default": False}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "process_boolean" + CATEGORY = "ControlAltAI Nodes/Logic" + + def process_boolean(self, boolean): + return (boolean,) + +NODE_CLASS_MAPPINGS = { + "BooleanBasic": BooleanBasic, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "BooleanBasic": "Boolean Basic", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/boolean_reverse_node.py b/custom_nodes/controlaltai-nodes/boolean_reverse_node.py new file mode 100644 index 00000000..454700a6 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/boolean_reverse_node.py @@ -0,0 +1,23 @@ +class BooleanReverse: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "reverse_boolean" + CATEGORY = "ControlAltAI Nodes/Logic" + + def reverse_boolean(self, boolean): + return (not boolean,) + +NODE_CLASS_MAPPINGS = { + "BooleanReverse": BooleanReverse, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "BooleanReverse": "Boolean Reverse", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/choose_upscale_model_node.py b/custom_nodes/controlaltai-nodes/choose_upscale_model_node.py new file mode 100644 index 00000000..dc467433 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/choose_upscale_model_node.py @@ -0,0 +1,30 @@ +class ChooseUpscaleModel: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "upscale_model_1": ("UPSCALE_MODEL",), + "upscale_model_2": ("UPSCALE_MODEL",), + "use_model_1": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("UPSCALE_MODEL",) + RETURN_NAMES = ("upscale_model",) + FUNCTION = "choose_upscale_model" + + CATEGORY = "ControlAltAI Nodes/Logic" + + def choose_upscale_model(self, upscale_model_1, upscale_model_2, use_model_1): + if use_model_1: + return (upscale_model_1,) + else: + return (upscale_model_2,) + +NODE_CLASS_MAPPINGS = { + "ChooseUpscaleModel": ChooseUpscaleModel, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ChooseUpscaleModel": "Choose Upscale Model", +} diff --git a/custom_nodes/controlaltai-nodes/flux_attention_cleanup_node.py b/custom_nodes/controlaltai-nodes/flux_attention_cleanup_node.py new file mode 100644 index 00000000..ab7e49f3 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/flux_attention_cleanup_node.py @@ -0,0 +1,75 @@ +import torch +from comfy.ldm.modules import attention as comfy_attention +from comfy.ldm.flux import math as flux_math +from comfy.ldm.flux import layers as flux_layers + +class AnyType(str): + """A special class that is always equal in not equal comparisons""" + def __ne__(self, __value: object) -> bool: + return False + +any_type = AnyType("*") + +class FluxAttentionCleanup: + def __init__(self): + self.original_attention = comfy_attention.optimized_attention + self.original_flux_attention = flux_math.attention + self.original_flux_layers_attention = flux_layers.attention + self.current_attn_mask = None + print("FluxAttentionCleanup initialized") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any_input": (any_type, {}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("message",) + FUNCTION = "cleanup_attention" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def cleanup_attention(self, any_input): + """Skip cleanup during normal operation, but clean on workflow switch""" + message = "Attention preserved for current workflow. Will clean on workflow switch." + print("\n" + message) + return (message,) + + def __del__(self): + """Clean up attention when switching workflows""" + try: + print("\nStarting attention cleanup for workflow switch...") + + # Reset attention functions to original state + flux_math.attention = self.original_flux_attention + flux_layers.attention = self.original_flux_layers_attention + + # Clear attention mask + if hasattr(flux_math.attention, 'keywords'): + if 'attn_mask' in flux_math.attention.keywords: + flux_math.attention.keywords['attn_mask'] = None + + # Clear stored mask + if self.current_attn_mask is not None: + del self.current_attn_mask + self.current_attn_mask = None + + # Force CUDA cleanup + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + print("Workflow switch: Region Attention Cleanup Successful") + except: + pass + +# Node class mappings +NODE_CLASS_MAPPINGS = { + "FluxAttentionCleanup": FluxAttentionCleanup +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxAttentionCleanup": "Flux Attention Cleanup" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/flux_attention_control_node.py b/custom_nodes/controlaltai-nodes/flux_attention_control_node.py new file mode 100644 index 00000000..de241119 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/flux_attention_control_node.py @@ -0,0 +1,325 @@ +import torch +from torch import Tensor +import torch.nn.functional as F +from typing import List, Dict, Optional, Tuple +from einops import rearrange +import comfy.model_management as model_management +from comfy.ldm.modules import attention as comfy_attention +from comfy.ldm.flux import math as flux_math +from comfy.ldm.flux import layers as flux_layers +import numpy as np +from PIL import Image, ImageFilter, ImageDraw +from functools import partial + +# Protected xformers import +try: + from xformers.ops import memory_efficient_attention as xattention + has_xformers = True +except ImportError: + has_xformers = False + xattention = None + +class FluxAttentionControl: + def __init__(self): + self.original_attention = comfy_attention.optimized_attention + self.original_flux_attention = flux_math.attention + self.original_flux_layers_attention = flux_layers.attention + if not has_xformers: + print("\n" + "="*70) + print("\033[94mControlAltAI-Nodes: This node requires xformers to function.\033[0m") + print("\033[33mPlease check \"xformers_instructions.txt\" in ComfyUI\\custom_nodes\\ControlAltAI-Nodes for how to install XFormers\033[0m") + print("="*70 + "\n") + print("FluxAttentionControl initialized") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL",), + "condition": ("CONDITIONING",), + "latent_dimensions": ("LATENT",), + "region1": ("REGION",), + "number_of_regions": ("INT", { + "default": 1, + "min": 1, + "max": 3, + "step": 1, + "display": "Number of Regions" + }), + "enabled": ("BOOLEAN", { + "default": True, + "display": "Enable Regional Control" + }), + "feather_radius1": ("FLOAT", { + "default": 0.0, + "min": 0.0, + "max": 100.0, + "step": 1.0, + "display": "Feather Radius for Region 1" + }), + }, + "optional": { + "region2": ("REGION",), + "feather_radius2": ("FLOAT", { + "default": 0.0, + "min": 0.0, + "max": 100.0, + "step": 1.0, + "display": "Feather Radius for Region 2" + }), + "region3": ("REGION",), + "feather_radius3": ("FLOAT", { + "default": 0.0, + "min": 0.0, + "max": 100.0, + "step": 1.0, + "display": "Feather Radius for Region 3" + }), + } + } + + RETURN_TYPES = ("MODEL", "CONDITIONING",) + RETURN_NAMES = ("model", "conditioning",) + FUNCTION = "apply_attention_control" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def generate_region_mask(self, region: Dict, width: int, height: int, feather_radius: float) -> Image.Image: + if region.get('bbox') is not None: + x1, y1, x2, y2 = region['bbox'] + x1_px = int(x1 * width) + y1_px = int(y1 * height) + x2_px = int(x2 * width) + y2_px = int(y2 * height) + mask = Image.new('L', (width, height), 0) + mask_draw = ImageDraw.Draw(mask) + mask_draw.rectangle([x1_px, y1_px, x2_px, y2_px], fill=255) + if feather_radius > 0: + mask = mask.filter(ImageFilter.GaussianBlur(radius=feather_radius)) + print(f'Generating masks with {width}x{height} and [{x1}, {y1}, {x2}, {y2}], feather_radius={feather_radius}') + return mask + elif region.get('mask') is not None: + mask = region['mask'][0].cpu().numpy() + mask = (mask * 255).astype(np.uint8) + mask = Image.fromarray(mask) + mask = mask.resize((width, height)) + if feather_radius > 0: + mask = mask.filter(ImageFilter.GaussianBlur(radius=feather_radius)) + return mask + else: + raise Exception('Unknown region type') + + def generate_test_mask(self, masks: List[Image.Image], height: int, width: int): + hH, hW = int(height) // 16, int(width) // 16 + print(f'{width} {height} -> {hW} {hH}') + + lin_masks = [] + for mask in masks: + mask = mask.convert('L') + mask = torch.tensor(np.array(mask)).unsqueeze(0).unsqueeze(0) / 255.0 # Normalize to 0-1 + mask = F.interpolate(mask, (hH, hW), mode='bilinear', align_corners=False).flatten() + lin_masks.append(mask) + return lin_masks, hH, hW + + def prepare_attention_mask(self, lin_masks: List[torch.Tensor], region_strengths: List[float], Nx: int, emb_size: int, emb_len: int): + """Prepare attention mask for three regions with per-region strengths.""" + total_len = emb_len + Nx + n_regs = len(lin_masks) + + # Initialize attention mask and scales + cross_mask = torch.zeros(total_len, total_len) + q_scale = torch.ones(total_len) + k_scale = torch.ones(total_len) + + # Indices for embeddings + main_prompt_start = 0 + main_prompt_end = emb_size + + # Subprompt indices + subprompt_starts = [emb_size * (i + 1) for i in range(n_regs)] + subprompt_ends = [emb_size * (i + 2) for i in range(n_regs)] + + # Initialize position masks + position_masks = torch.stack(lin_masks) # Shape: [n_regs, Nx] + + # Normalize masks so that overlapping areas sum to 1 + position_masks_sum = position_masks.sum(dim=0) + position_masks_normalized = position_masks / (position_masks_sum + 1e-8) + + # Build attention masks and scales + for i in range(n_regs): + sp_start = subprompt_starts[i] + sp_end = subprompt_ends[i] + mask_i = position_masks_normalized[i] + + # Scale embeddings based on mask and per-region strength + strength = region_strengths[i] + q_scale[sp_start:sp_end] = mask_i.mean() * strength + k_scale[sp_start:sp_end] = mask_i.mean() * strength + + # Create mask including tokens and positions + m_with_tokens = torch.cat([torch.ones(emb_len), mask_i]) + mb = m_with_tokens > 0.0 # Include positions where mask > 0 + + # Block attention between positions not in mask and subprompt + cross_mask[~mb, sp_start:sp_end] = 1 + cross_mask[sp_start:sp_end, ~mb] = 1 + + # Block attention between positions in region and main prompt + positions_idx = (mask_i > 0.0).nonzero(as_tuple=True)[0] + emb_len + cross_mask[positions_idx[:, None], main_prompt_start:main_prompt_end] = 1 + cross_mask[main_prompt_start:main_prompt_end, positions_idx[None, :]] = 1 + + # Block attention between subprompts + for j in range(n_regs): + if i != j: + other_sp_start = subprompt_starts[j] + other_sp_end = subprompt_ends[j] + cross_mask[sp_start:sp_end, other_sp_start:other_sp_end] = 1 + cross_mask[other_sp_start:other_sp_end, sp_start:sp_end] = 1 + + # Ensure self-attention is allowed + cross_mask.fill_diagonal_(0) + + # Prepare scales for GPU + q_scale = q_scale.reshape(1, 1, -1, 1).cuda() + k_scale = k_scale.reshape(1, 1, -1, 1).cuda() + + return cross_mask, q_scale, k_scale + + def xformers_attention(self, q: Tensor, k: Tensor, v: Tensor, pe: Tensor, + attn_mask: Optional[Tensor] = None, + mask: Optional[Tensor] = None) -> Tensor: # Added mask parameter + q, k = flux_math.apply_rope(q, k, pe) + q = rearrange(q, "B H L D -> B L H D") + k = rearrange(k, "B H L D -> B L H D") + v = rearrange(v, "B H L D -> B L H D") + + # Use attn_mask if provided, otherwise use the mask parameter + attention_bias = attn_mask if attn_mask is not None else mask + + if attention_bias is not None: + x = xattention(q, k, v, attn_bias=attention_bias) + else: + x = xattention(q, k, v) + + x = rearrange(x, "B L H D -> B L (H D)") + return x + + def apply_attention_control(self, + model: object, + condition: List, + latent_dimensions: Dict, + region1: Dict, + number_of_regions: int, + enabled: bool, + feather_radius1: float = 0.0, + region2: Optional[Dict] = None, + feather_radius2: Optional[float] = 0.0, + region3: Optional[Dict] = None, + feather_radius3: Optional[float] = 0.0): + + # Extract dimensions and embeddings first (moved before enabled check) + latent = latent_dimensions["samples"] + bs_l, n_ch, lH, lW = latent.shape + text_emb = condition[0][0].clone() + clip_emb = condition[0][1]['pooled_output'].clone() + bs, emb_size, emb_dim = text_emb.shape + iH, iW = lH * 8, lW * 8 + + if not enabled: + # Restore original attention functions + flux_math.attention = self.original_flux_attention + flux_layers.attention = self.original_flux_layers_attention + print("Regional control disabled. Restored original attention functions.") + return (model, condition) # Return original condition when disabled + + if enabled and not has_xformers: + raise RuntimeError("Xformers is required for this node when enabled. Please install xformers.") + + print(f'Region attention Node enabled: {enabled}, regions: {number_of_regions}') + + # Extract dimensions and embeddings + latent = latent_dimensions["samples"] + bs_l, n_ch, lH, lW = latent.shape + text_emb = condition[0][0].clone() + clip_emb = condition[0][1]['pooled_output'].clone() + bs, emb_size, emb_dim = text_emb.shape + iH, iW = lH * 8, lW * 8 + + # Process active regions + subprompts_embeds = [] + masks = [] + region_strengths = [] + + # Collect regions and feather radii + regions = [region1, region2, region3] + feather_radii = [feather_radius1, feather_radius2, feather_radius3] + + for idx, region in enumerate(regions[:number_of_regions]): + if region is not None and region.get('conditioning') is not None: + # Get 'strength' from region or default to 1.0 + strength = region.get('strength', 1.0) + region_strengths.append(strength) + subprompt_emb = region['conditioning'][0][0] + subprompts_embeds.append(subprompt_emb) + # Use per-region feather_radius + feather_radius = feather_radii[idx] if feather_radii[idx] is not None else 0.0 + masks.append(self.generate_region_mask(region, iW, iH, feather_radius)) + else: + print(f"Region {idx+1} is None or has no conditioning") + + if not subprompts_embeds: + print("No active regions with conditioning found.") + # Restore original attention functions + flux_math.attention = self.original_flux_attention + flux_layers.attention = self.original_flux_layers_attention + return (model, condition) + + n_regs = len(subprompts_embeds) + + # Generate attention components + lin_masks, hH, hW = self.generate_test_mask(masks, iH, iW) + Nx = int(hH * hW) + emb_len = emb_size * (n_regs + 1) # +1 for main prompt + + # Create attention mask + attn_mask, q_scale, k_scale = self.prepare_attention_mask( + lin_masks, region_strengths, Nx, emb_size, emb_len) + + # Format for xFormers + device = torch.device('cuda') + attn_dtype = torch.bfloat16 if model_management.should_use_bf16(device=device) else torch.float16 + + if attn_mask is not None: + print(f'Applying attention masks: torch.Size([{attn_mask.shape[0]}, {attn_mask.shape[1]}])') + L = attn_mask.shape[0] + H = 24 # Number of heads in FLUX model + pad = (8 - L % 8) % 8 # Ensure pad is between 0 and 7 + pad_L = L + pad + mask_out = torch.zeros([bs, H, pad_L, pad_L], dtype=attn_dtype, device=device) + mask_out[:, :, :L, :L] = attn_mask.to(device, dtype=attn_dtype) + attn_mask = mask_out[:, :, :pad_L, :pad_L] + + # Prepare final mask + attn_mask_bool = attn_mask > 0.5 + attn_mask.masked_fill_(attn_mask_bool, float('-inf')) + + # Override attention + attn_mask_arg = attn_mask if enabled else None + override_attention = partial(self.xformers_attention, attn_mask=attn_mask_arg) + flux_math.attention = override_attention + flux_layers.attention = override_attention + + # Create extended conditioning + extended_condition = torch.cat([text_emb] + subprompts_embeds, dim=1) + + return (model, [[extended_condition, {'pooled_output': clip_emb}]]) + +# Node class mappings +NODE_CLASS_MAPPINGS = { + "FluxAttentionControl": FluxAttentionControl +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxAttentionControl": "Flux Attention Control" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/flux_controlnet_node.py b/custom_nodes/controlaltai-nodes/flux_controlnet_node.py new file mode 100644 index 00000000..4c62d477 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/flux_controlnet_node.py @@ -0,0 +1,40 @@ +class FluxControlNetApply: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "conditioning": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) + } + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "flux_controlnet" + CATEGORY = "ControlAltAI Nodes/Flux" + + def flux_controlnet(self, conditioning, control_net, image, strength): + if strength == 0: + return (conditioning,) + + c = [] + control_hint = image.movedim(-1, 1) + for t in conditioning: + n = [t[0], t[1].copy()] + c_net = control_net.copy().set_cond_hint(control_hint, strength) + if 'control' in t[1]: + c_net.set_previous_controlnet(t[1]['control']) + n[1]['control'] = c_net + n[1]['control_apply_to_uncond'] = False # This ensures it's only applied to positive + c.append(n) + + return (c,) + +NODE_CLASS_MAPPINGS = { + "FluxControlNetApply": FluxControlNetApply, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxControlNetApply": "Flux ControlNet", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/flux_resolution_cal_node.py b/custom_nodes/controlaltai-nodes/flux_resolution_cal_node.py new file mode 100644 index 00000000..e5ef00c6 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/flux_resolution_cal_node.py @@ -0,0 +1,143 @@ +from PIL import Image, ImageDraw, ImageFont +import numpy as np +import torch + +def pil2tensor(image): + """Convert PIL image to tensor in the correct format""" + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +class FluxResolutionNode: + @classmethod + def INPUT_TYPES(cls): + # Generate megapixel options from 0.1 to 2.5 with 0.1 increments + megapixel_options = [f"{i/10:.1f}" for i in range(1, 26)] # 0.1 to 2.5 + + return { + "required": { + "megapixel": (megapixel_options, {"default": "1.0"}), + "aspect_ratio": ([ + "1:1 (Perfect Square)", + "2:3 (Classic Portrait)", "3:4 (Golden Ratio)", "3:5 (Elegant Vertical)", "4:5 (Artistic Frame)", "5:7 (Balanced Portrait)", "5:8 (Tall Portrait)", + "7:9 (Modern Portrait)", "9:16 (Slim Vertical)", "9:19 (Tall Slim)", "9:21 (Ultra Tall)", "9:32 (Skyline)", + "3:2 (Golden Landscape)", "4:3 (Classic Landscape)", "5:3 (Wide Horizon)", "5:4 (Balanced Frame)", "7:5 (Elegant Landscape)", "8:5 (Cinematic View)", + "9:7 (Artful Horizon)", "16:9 (Panorama)", "19:9 (Cinematic Ultrawide)", "21:9 (Epic Ultrawide)", "32:9 (Extreme Ultrawide)" + ], {"default": "1:1 (Perfect Square)"}), + "divisible_by": (["8", "16", "32", "64"], {"default": "64"}), + "custom_ratio": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable"}), + }, + "optional": { + "custom_aspect_ratio": ("STRING", {"default": "1:1"}), + } + } + + RETURN_TYPES = ("INT", "INT", "STRING", "IMAGE") + RETURN_NAMES = ("width", "height", "resolution", "preview") + FUNCTION = "calculate_dimensions" + CATEGORY = "ControlAltAI Nodes/Flux" + OUTPUT_NODE = True + + def create_preview_image(self, width, height, resolution, ratio_display): + # 1024x1024 preview size + preview_size = (1024, 1024) + image = Image.new('RGB', preview_size, (0, 0, 0)) # Black background + draw = ImageDraw.Draw(image) + + # Draw grid with grey lines + grid_color = '#333333' # Dark grey for grid + grid_spacing = 50 # Adjusted grid spacing + for x in range(0, preview_size[0], grid_spacing): + draw.line([(x, 0), (x, preview_size[1])], fill=grid_color) + for y in range(0, preview_size[1], grid_spacing): + draw.line([(0, y), (preview_size[0], y)], fill=grid_color) + + # Calculate preview box dimensions + preview_width = 800 # Increased size + preview_height = int(preview_width * (height / width)) + + # Adjust if height is too tall + if preview_height > 800: # Adjusted for larger preview + preview_height = 800 + preview_width = int(preview_height * (width / height)) + + # Calculate center position + x_offset = (preview_size[0] - preview_width) // 2 + y_offset = (preview_size[1] - preview_height) // 2 + + # Draw the aspect ratio box with thicker outline + draw.rectangle( + [(x_offset, y_offset), (x_offset + preview_width, y_offset + preview_height)], + outline='red', + width=4 # Thicker outline + ) + + # Add text with larger font sizes + try: + # Draw text (centered) + text_y = y_offset + preview_height//2 + + # Resolution text in red + draw.text((preview_size[0]//2, text_y), + f"{width}x{height}", + fill='red', + anchor="mm", + font=ImageFont.truetype("arial.ttf", 48)) + + # Aspect ratio text in red + draw.text((preview_size[0]//2, text_y + 60), + f"({ratio_display})", + fill='red', + anchor="mm", + font=ImageFont.truetype("arial.ttf", 36)) + + # Resolution text at bottom in white + draw.text((preview_size[0]//2, y_offset + preview_height + 60), + f"Resolution: {resolution}", + fill='white', # Changed to white + anchor="mm", + font=ImageFont.truetype("arial.ttf", 32)) + + except: + # Fallback if font loading fails + draw.text((preview_size[0]//2, text_y), f"{width}x{height}", fill='red', anchor="mm") + draw.text((preview_size[0]//2, text_y + 60), f"({ratio_display})", fill='red', anchor="mm") + draw.text((preview_size[0]//2, y_offset + preview_height + 60), f"Resolution: {resolution}", fill='white', anchor="mm") + + # Convert to tensor using the helper function + return pil2tensor(image) + + def calculate_dimensions(self, megapixel, aspect_ratio, divisible_by, custom_ratio, custom_aspect_ratio=None): + megapixel = float(megapixel) + round_to = int(divisible_by) + + if custom_ratio and custom_aspect_ratio: + numeric_ratio = custom_aspect_ratio + ratio_display = custom_aspect_ratio # Keep original format for display + else: + numeric_ratio = aspect_ratio.split(' ')[0] + ratio_display = numeric_ratio # Keep original format for display + + width_ratio, height_ratio = map(int, numeric_ratio.split(':')) + + total_pixels = megapixel * 1_000_000 + dimension = (total_pixels / (width_ratio * height_ratio)) ** 0.5 + width = int(dimension * width_ratio) + height = int(dimension * height_ratio) + + # Apply user-selected rounding + width = round(width / round_to) * round_to + height = round(height / round_to) * round_to + + resolution = f"{width} x {height}" + + # Generate preview image with original ratio format + preview = self.create_preview_image(width, height, resolution, ratio_display) + + return width, height, resolution, preview + +NODE_CLASS_MAPPINGS = { + "FluxResolutionNode": FluxResolutionNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxResolutionNode": "Flux Resolution Calculator", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/flux_sampler_node.py b/custom_nodes/controlaltai-nodes/flux_sampler_node.py new file mode 100644 index 00000000..5be1cfc4 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/flux_sampler_node.py @@ -0,0 +1,66 @@ +import comfy.samplers +import torch +import comfy.sample +import latent_preview + +FLUX_SAMPLER_NAMES = [ + "euler", "heun", "heunpp2", "dpm_2", "lms", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2m", + "ipndm", "ipndm_v", "deis", "ddim", "uni_pc", "uni_pc_bh2" +] + +FLUX_SCHEDULER_NAMES = ["simple", "normal", "sgm_uniform", "ddim_uniform", "beta"] + +class FluxSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "conditioning": ("CONDITIONING",), + "latent_image": ("LATENT",), + "sampler_name": (FLUX_SAMPLER_NAMES, {"default": "euler"}), + "scheduler": (FLUX_SCHEDULER_NAMES, {"default": "beta"}), + "steps": ("INT", {"default": 30, "min": 1, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "noise_seed": ("INT", {"default": 143220275975594, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent",) + FUNCTION = "sample" + CATEGORY = "ControlAltAI Nodes/Flux" + + def sample(self, model, conditioning, latent_image, sampler_name, scheduler, steps, denoise, noise_seed): + device = comfy.model_management.get_torch_device() + sampler = comfy.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise) + + latent = latent_image.copy() + latent_image = latent["samples"] + + # Handle noise_mask if present + noise_mask = latent.get("noise_mask", None) + + noise = comfy.sample.prepare_noise(latent_image, noise_seed) + + positive = conditioning + negative = [] # Empty list for negative conditioning + + callback = latent_preview.prepare_callback(model, steps) + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + + samples = sampler.sample(noise, positive, negative, cfg=1.0, latent_image=latent_image, + force_full_denoise=True, denoise_mask=noise_mask, callback=callback, + disable_pbar=disable_pbar, seed=noise_seed) + + out = latent.copy() + out["samples"] = samples + return (out,) + +NODE_CLASS_MAPPINGS = { + "FluxSampler": FluxSampler +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxSampler": "Flux Sampler" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/flux_union_controlnet_node.py b/custom_nodes/controlaltai-nodes/flux_union_controlnet_node.py new file mode 100644 index 00000000..df06ed7a --- /dev/null +++ b/custom_nodes/controlaltai-nodes/flux_union_controlnet_node.py @@ -0,0 +1,98 @@ +import torch +import comfy +import folder_paths + +class FluxUnionControlNetApply: + # Correct UNION_CONTROLNET_TYPES mapping + UNION_CONTROLNET_TYPES = { + "canny": 0, + "tile": 1, + "depth": 2, + "blur": 3, + "pose": 4, + "gray": 5, + "low quality": 6, + } + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "conditioning": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "union_controlnet_type": (list(s.UNION_CONTROLNET_TYPES.keys()), ), + "strength": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.01 + }), + "start_percent": ("FLOAT", { + "default": 0.0, + "min": 0.0, + "max": 1.0, + "step": 0.001 + }), + "end_percent": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.001 + }), + "vae": ("VAE", ), + }, + } + + RETURN_TYPES = ("CONDITIONING", "VAE") + FUNCTION = "apply_flux_union_controlnet" + CATEGORY = "ControlAltAI Nodes/Flux" + + def apply_flux_union_controlnet(self, conditioning, control_net, image, union_controlnet_type, strength, start_percent, end_percent, vae): + if strength == 0: + return (conditioning, vae) + + # Map the 'union_controlnet_type' to 'control_type' + control_type = self.UNION_CONTROLNET_TYPES[union_controlnet_type] + control_type_list = [control_type] + + # Set the 'control_type' using 'set_extra_arg' + control_net = control_net.copy() + control_net.set_extra_arg("control_type", control_type_list) + + # Process the image to get 'control_hint' + control_hint = image.movedim(-1, 1) # Assuming the image is in HWC format + + # Apply the ControlNet to the positive conditioning + cnets = {} + c = [] + for t in conditioning: + d = t[1].copy() + prev_cnet = d.get('control', None) + + # Create a unique key for caching + cache_key = (prev_cnet, tuple(control_net.extra_args.get('control_type', []))) + + if cache_key in cnets: + c_net_instance = cnets[cache_key] + else: + # Create a copy of the 'control_net' and set the conditional hint + c_net_instance = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae) + c_net_instance.set_previous_controlnet(prev_cnet) + cnets[cache_key] = c_net_instance + + d['control'] = c_net_instance + d['control_apply_to_uncond'] = False + + n = [t[0], d] + c.append(n) + + return (c, vae) + +NODE_CLASS_MAPPINGS = { + "FluxUnionControlNetApply": FluxUnionControlNetApply, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxUnionControlNetApply": "Flux Union ControlNet", +} diff --git a/custom_nodes/controlaltai-nodes/get_image_size_ratio_node.py b/custom_nodes/controlaltai-nodes/get_image_size_ratio_node.py new file mode 100644 index 00000000..c3781fc0 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/get_image_size_ratio_node.py @@ -0,0 +1,38 @@ +class GetImageSizeRatio: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",) + } + } + + RETURN_TYPES = ("INT", "INT", "STRING") + RETURN_NAMES = ("width", "height", "ratio") + FUNCTION = "get_image_size_ratio" + + CATEGORY = "ControlAltAI Nodes/Image" + + def get_image_size_ratio(self, image): + _, height, width, _ = image.shape + + gcd = self.greatest_common_divisor(width, height) + ratio_width = width // gcd + ratio_height = height // gcd + + ratio = f"{ratio_width}:{ratio_height}" + + return width, height, ratio + + def greatest_common_divisor(self, a, b): + while b != 0: + a, b = b, a % b + return a + +NODE_CLASS_MAPPINGS = { + "GetImageSizeRatio": GetImageSizeRatio, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "GetImageSizeRatio": "Get Image Size & Ratio", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/hidream_resolution_node.py b/custom_nodes/controlaltai-nodes/hidream_resolution_node.py new file mode 100644 index 00000000..47ecbb85 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/hidream_resolution_node.py @@ -0,0 +1,120 @@ +from PIL import Image, ImageDraw, ImageFont +import numpy as np +import torch + +def pil2tensor(image): + """Convert PIL image to tensor in the correct format""" + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +class HiDreamResolutionNode: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "resolution": ([ + "1:1 (Perfect Square)", + "3:4 (Standard Portrait)", + "2:3 (Classic Portrait)", + "9:16 (Widescreen Portrait)", + "4:3 (Standard Landscape)", + "3:2 (Classic Landscape)", + "16:9 (Widescreen Landscape)", + ], {"default": "1:1 (Perfect Square)"}), + } + } + + RETURN_TYPES = ("INT", "INT", "STRING", "IMAGE") + RETURN_NAMES = ("width", "height", "resolution", "preview") + FUNCTION = "get_dimensions" + CATEGORY = "ControlAltAI Nodes/HiDream" + OUTPUT_NODE = True + + def create_preview_image(self, width, height, resolution): + # 1024x1024 preview size + preview_size = (1024, 1024) + image = Image.new('RGB', preview_size, (0, 0, 0)) # Black background + draw = ImageDraw.Draw(image) + + # Draw grid with grey lines + grid_color = '#333333' # Dark grey for grid + grid_spacing = 50 # Adjusted grid spacing + for x in range(0, preview_size[0], grid_spacing): + draw.line([(x, 0), (x, preview_size[1])], fill=grid_color) + for y in range(0, preview_size[1], grid_spacing): + draw.line([(0, y), (preview_size[0], y)], fill=grid_color) + + # Calculate preview box dimensions + preview_width = 800 # Increased size + preview_height = int(preview_width * (height / width)) + + # Adjust if height is too tall + if preview_height > 800: # Adjusted for larger preview + preview_height = 800 + preview_width = int(preview_height * (width / height)) + + # Calculate center position + x_offset = (preview_size[0] - preview_width) // 2 + y_offset = (preview_size[1] - preview_height) // 2 + + # Draw the aspect ratio box with thicker outline + draw.rectangle( + [(x_offset, y_offset), (x_offset + preview_width, y_offset + preview_height)], + outline='red', + width=4 # Thicker outline + ) + + # Add text with larger font sizes + try: + # Draw text (centered) + text_y = y_offset + preview_height//2 + + # Resolution text in red + draw.text((preview_size[0]//2, text_y), + f"{width}x{height}", + fill='red', + anchor="mm", + font=ImageFont.truetype("arial.ttf", 48)) + + except: + # Fallback if font loading fails + draw.text((preview_size[0]//2, text_y), f"{width}x{height}", fill='red', anchor="mm") + + # Convert to tensor using the helper function + return pil2tensor(image) + + def get_dimensions(self, resolution): + # Map from aspect ratio to actual dimensions + resolution_map = { + "1:1 (Perfect Square)": (1024, 1024), + "3:4 (Standard Portrait)": (880, 1168), + "2:3 (Classic Portrait)": (832, 1248), + "9:16 (Widescreen Portrait)": (768, 1360), + "4:3 (Standard Landscape)": (1168, 880), + "3:2 (Classic Landscape)": (1248, 832), + "16:9 (Widescreen Landscape)": (1360, 768) + } + + # Get dimensions from the map + width, height = resolution_map[resolution] + + # Resolution as string + resolution_str = f"{width} x {height}" + + # Generate preview image + preview = self.create_preview_image(width, height, resolution_str) + + return width, height, resolution_str, preview + +def gcd(a, b): + """Calculate the Greatest Common Divisor of a and b.""" + while b: + a, b = b, a % b + return a + +NODE_CLASS_MAPPINGS = { + "HiDreamResolutionNode": HiDreamResolutionNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "HiDreamResolutionNode": "HiDream Resolution", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/integer_settings_advanced_node.py b/custom_nodes/controlaltai-nodes/integer_settings_advanced_node.py new file mode 100644 index 00000000..384878d6 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/integer_settings_advanced_node.py @@ -0,0 +1,38 @@ +class IntegerSettingsAdvanced: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "setting_1": ("BOOLEAN", {"default": True, "label_on": "Enable", "label_off": "Disable"}), + "setting_2": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable"}), + "setting_3": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable"}), + }, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("setting_value",) + FUNCTION = "integer_settings_advanced" + + CATEGORY = "ControlAltAI Nodes/Logic" + + def integer_settings_advanced(self, setting_1, setting_2, setting_3): + """ + Returns integer based on which setting is enabled. + Due to mutual exclusion (handled by JS), only one should be True. + Priority order: setting_3 > setting_2 > setting_1 + """ + if setting_3: + return (3,) + elif setting_2: + return (2,) + else: + # Default to 1 (setting_1 or fallback) + return (1,) + +NODE_CLASS_MAPPINGS = { + "IntegerSettingsAdvanced": IntegerSettingsAdvanced, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "IntegerSettingsAdvanced": "Integer Settings Advanced", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/integer_settings_node.py b/custom_nodes/controlaltai-nodes/integer_settings_node.py new file mode 100644 index 00000000..55f22bdd --- /dev/null +++ b/custom_nodes/controlaltai-nodes/integer_settings_node.py @@ -0,0 +1,28 @@ +class IntegerSettings: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "setting": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable"}), + }, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("setting_value",) + FUNCTION = "integer_settings" + + CATEGORY = "ControlAltAI Nodes/Logic" + + def integer_settings(self, setting): + # Handle the single boolean setting + status = 2 if setting else 1 + return (status,) + + +NODE_CLASS_MAPPINGS = { + "IntegerSettings": IntegerSettings, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "IntegerSettings": "Integer Settings", +} diff --git a/custom_nodes/controlaltai-nodes/noise_plus_blend_node.py b/custom_nodes/controlaltai-nodes/noise_plus_blend_node.py new file mode 100644 index 00000000..1a0292d6 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/noise_plus_blend_node.py @@ -0,0 +1,89 @@ +import numpy as np +from PIL import Image, ImageChops +import torch + +class NoisePlusBlend: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "noise_scale": ("FLOAT", {"default": 0.40, "min": 0.00, "max": 100.00, "step": 0.01}), + "blend_opacity": ("INT", {"default": 20, "min": 0, "max": 100}), + }, + "optional": { + "mask": ("MASK",), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("blended_image_output", "noise_output") + FUNCTION = "noise_plus_blend" + + CATEGORY = "ControlAltAI Nodes/Image" + + def tensor_to_pil(self, tensor_image): + """Converts tensor to a PIL Image""" + tensor_image = tensor_image.squeeze(0) # Remove batch dimension if it exists + pil_image = Image.fromarray((tensor_image.cpu().numpy() * 255).astype(np.uint8)) + return pil_image + + def pil_to_tensor(self, pil_image): + """Converts a PIL image back to a tensor""" + return torch.from_numpy(np.array(pil_image).astype(np.float32) / 255).unsqueeze(0) + + def generate_gaussian_noise(self, width, height, noise_scale=0.05): + """Generates Gaussian noise with a given scale.""" + noise = np.random.normal(128, 128 * noise_scale, (height, width, 3)).astype(np.uint8) + return Image.fromarray(noise) + + def soft_light_blend(self, base_image, noise_image, mask=None, opacity=15): + """Blends noise over the base image using soft light, applying mask if present.""" + # Resize noise to match base image size + noise_image = noise_image.resize(base_image.size) + + base_image = base_image.convert('RGB') + noise_image = noise_image.convert('RGB') + + noise_blended = ImageChops.soft_light(base_image, noise_image) + blended_image = Image.blend(base_image, noise_blended, opacity / 100) + + # Apply mask only if it's provided, valid, and contains more than a single value + if mask is not None: + mask_pil = self.tensor_to_pil(mask).convert('L') + mask_resized = mask_pil.resize(base_image.size) + + # Invert the mask by subtracting from 255 + inverted_mask = ImageChops.invert(mask_resized) + + # Apply the inverted mask to the composite blending + blended_image = Image.composite(base_image, blended_image, inverted_mask) + + return blended_image + + def noise_plus_blend(self, image, noise_scale=0.05, blend_opacity=15, mask=None): + """Main function to generate noise, blend, and return results.""" + # Convert Tensor image to PIL + base_image = self.tensor_to_pil(image) + image_size = base_image.size + + # Generate Gaussian noise with the size of the input image + noise_image = self.generate_gaussian_noise(image_size[0], image_size[1], noise_scale) + + # Blend the noise with the base image using soft light + blended_image = self.soft_light_blend(base_image, noise_image, mask, blend_opacity) + + # Convert the final blended image back to tensor + noise_tensor = self.pil_to_tensor(noise_image) + blended_tensor = self.pil_to_tensor(blended_image) + + # Return both the noise and blended image as tensors + return blended_tensor, noise_tensor + +NODE_CLASS_MAPPINGS = { + "NoisePlusBlend": NoisePlusBlend, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "NoisePlusBlend": "Noise Plus Blend", +} diff --git a/custom_nodes/controlaltai-nodes/perturbation_texture_node.py b/custom_nodes/controlaltai-nodes/perturbation_texture_node.py new file mode 100644 index 00000000..d8874a68 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/perturbation_texture_node.py @@ -0,0 +1,229 @@ +import numpy as np +from PIL import Image, ImageChops +import torch + +class PerturbationTexture: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "noise_scale": ("FLOAT", {"default": 0.5, "min": 0.00, "max": 1.00, "step": 0.01}), + "texture_strength": ("INT", {"default": 50, "min": 0, "max": 100}), + "texture_type": (["Film Grain", "Skin Pore", "Natural", "Fine Detail"], {"default": "Skin Pore"}), + "frequency": ("FLOAT", {"default": 1.0, "min": 0.2, "max": 5.0, "step": 0.1}), + "perturbation_factor": ("FLOAT", {"default": 0.30, "min": 0.01, "max": 0.5, "step": 0.01}), + "use_mask": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + "seed": ("INT", {"default": -1}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("textured_image_output", "texture_layer") + FUNCTION = "apply_perturbation_texture" + + CATEGORY = "ControlAltAI Nodes/Image" + + def tensor_to_pil(self, tensor_image): + """Converts tensor to a PIL Image""" + tensor_image = tensor_image.squeeze(0) # Remove batch dimension if it exists + pil_image = Image.fromarray((tensor_image.cpu().numpy() * 255).astype(np.uint8)) + return pil_image + + def pil_to_tensor(self, pil_image): + """Converts a PIL image back to a tensor""" + return torch.from_numpy(np.array(pil_image).astype(np.float32) / 255).unsqueeze(0) + + def generate_adaptive_texture(self, base_image, noise_scale, texture_type, frequency, perturbation_factor, texture_strength, seed=None): + """Generate texture with adaptive color matching.""" + width, height = base_image.size + + # Set seed for reproducibility if provided + if seed is not None and seed >= 0: + np.random.seed(seed) + + # Convert base image to numpy array + base_np = np.array(base_image).astype(np.float32) / 255.0 + + # Generate noise patterns based on texture type + noise_patterns = self.generate_noise_patterns(width, height, noise_scale, texture_type, frequency) + + # Convert noise to -1 to 1 range for proper mixing + noise_normalized = (noise_patterns.astype(np.float32) - 128.0) / 128.0 + + # Apply perturbation with texture_strength controlling the final intensity + effective_perturbation = perturbation_factor * (texture_strength / 100.0) + + # Apply noise as color-matched variations around the base color + result = base_np + (noise_normalized * effective_perturbation) + + # Clamp to valid range + result = np.clip(result, 0, 1) + + # Create a more visible texture layer for preview/debugging + texture_layer = base_np + (noise_normalized * perturbation_factor * 2.0) + texture_layer = np.clip(texture_layer, 0, 1) + + final_image = Image.fromarray((result * 255).astype(np.uint8)) + texture_image = Image.fromarray((texture_layer * 255).astype(np.uint8)) + + return final_image, texture_image + + def generate_noise_patterns(self, width, height, noise_scale, texture_type, frequency): + """Generates noise patterns optimized for each texture type.""" + + # Safe resize function for noise scaling + def safe_resize(arr, target_height, target_width): + from PIL import Image + if arr.ndim == 2: + img = Image.fromarray((arr * 255 / arr.max()).astype(np.uint8)) + else: + img = Image.fromarray(arr.astype(np.uint8)) + img = img.resize((target_width, target_height), Image.LANCZOS) + return np.array(img).astype(np.float32) / 255.0 * arr.max() + + if texture_type == "Film Grain": + # Film grain - larger, more irregular pattern with RGB variation + base_noise_r = np.random.normal(128, 64 * noise_scale, (height, width)) + base_noise_g = np.random.normal(128, 64 * noise_scale, (height, width)) + base_noise_b = np.random.normal(128, 64 * noise_scale, (height, width)) + + # Add larger scale variation for film-like clustering + large_scale_h = max(4, int(height/(4*frequency))) + large_scale_w = max(4, int(width/(4*frequency))) + large_scale_r = np.random.normal(0, 30 * noise_scale, (large_scale_h, large_scale_w)) + large_scale_g = np.random.normal(0, 30 * noise_scale, (large_scale_h, large_scale_w)) + large_scale_b = np.random.normal(0, 30 * noise_scale, (large_scale_h, large_scale_w)) + + large_scale_r = safe_resize(large_scale_r, height, width) + large_scale_g = safe_resize(large_scale_g, height, width) + large_scale_b = safe_resize(large_scale_b, height, width) + + combined_r = np.clip(base_noise_r * 0.7 + large_scale_r * 0.3, 0, 255) + combined_g = np.clip(base_noise_g * 0.7 + large_scale_g * 0.3, 0, 255) + combined_b = np.clip(base_noise_b * 0.7 + large_scale_b * 0.3, 0, 255) + + elif texture_type == "Skin Pore": + # Fine, subtle texture optimized for skin with reduced intensity + base_scale = noise_scale * 0.6 # More subtle for natural skin texture + + # Create subtle RGB variations for realistic skin texture + base_noise_r = np.random.normal(128, 32 * base_scale, (height, width)) + base_noise_g = np.random.normal(128, 28 * base_scale, (height, width)) + base_noise_b = np.random.normal(128, 24 * base_scale, (height, width)) + + # Fine pore-like details at higher frequency + fine_h = max(4, int(height*frequency*1.5)) + fine_w = max(4, int(width*frequency*1.5)) + fine_noise_r = np.random.normal(0, 20 * base_scale, (fine_h, fine_w)) + fine_noise_g = np.random.normal(0, 18 * base_scale, (fine_h, fine_w)) + fine_noise_b = np.random.normal(0, 16 * base_scale, (fine_h, fine_w)) + + fine_noise_r = safe_resize(fine_noise_r, height, width) + fine_noise_g = safe_resize(fine_noise_g, height, width) + fine_noise_b = safe_resize(fine_noise_b, height, width) + + combined_r = np.clip(base_noise_r + fine_noise_r * 0.8, 0, 255) + combined_g = np.clip(base_noise_g + fine_noise_g * 0.8, 0, 255) + combined_b = np.clip(base_noise_b + fine_noise_b * 0.8, 0, 255) + + elif texture_type == "Natural": + # Multi-layered natural texture with organic frequency distribution + base_noise_r = np.random.normal(128, 48 * noise_scale, (height, width)) + base_noise_g = np.random.normal(128, 44 * noise_scale, (height, width)) + base_noise_b = np.random.normal(128, 40 * noise_scale, (height, width)) + + # Multiple frequency layers for natural complexity + frequencies = [frequency*2, frequency, frequency/3] + weights = [0.5, 0.3, 0.2] + + combined_r = base_noise_r.copy() + combined_g = base_noise_g.copy() + combined_b = base_noise_b.copy() + + for freq, weight in zip(frequencies, weights): + f_h = max(4, int(height*freq)) + f_w = max(4, int(width*freq)) + + layer_r = np.random.normal(0, 30 * noise_scale * weight, (f_h, f_w)) + layer_g = np.random.normal(0, 28 * noise_scale * weight, (f_h, f_w)) + layer_b = np.random.normal(0, 26 * noise_scale * weight, (f_h, f_w)) + + layer_r = safe_resize(layer_r, height, width) + layer_g = safe_resize(layer_g, height, width) + layer_b = safe_resize(layer_b, height, width) + + combined_r += layer_r * weight + combined_g += layer_g * weight + combined_b += layer_b * weight + + combined_r = np.clip(combined_r, 0, 255) + combined_g = np.clip(combined_g, 0, 255) + combined_b = np.clip(combined_b, 0, 255) + + else: # Fine Detail + # High-frequency detailed texture for micro-details + high_freq = frequency * 2.5 + + base_noise_r = np.random.normal(128, 40 * noise_scale, (height, width)) + base_noise_g = np.random.normal(128, 38 * noise_scale, (height, width)) + base_noise_b = np.random.normal(128, 36 * noise_scale, (height, width)) + + # High-frequency fine details + fine_h = max(4, int(height*high_freq)) + fine_w = max(4, int(width*high_freq)) + fine_detail_r = np.random.normal(0, 25 * noise_scale, (fine_h, fine_w)) + fine_detail_g = np.random.normal(0, 23 * noise_scale, (fine_h, fine_w)) + fine_detail_b = np.random.normal(0, 21 * noise_scale, (fine_h, fine_w)) + + fine_detail_r = safe_resize(fine_detail_r, height, width) + fine_detail_g = safe_resize(fine_detail_g, height, width) + fine_detail_b = safe_resize(fine_detail_b, height, width) + + combined_r = np.clip(base_noise_r + fine_detail_r * 0.7, 0, 255) + combined_g = np.clip(base_noise_g + fine_detail_g * 0.7, 0, 255) + combined_b = np.clip(base_noise_b + fine_detail_b * 0.7, 0, 255) + + # Stack RGB channels into final noise pattern + return np.stack([combined_r, combined_g, combined_b], axis=2) + + def apply_perturbation_texture(self, image, noise_scale=0.5, texture_strength=50, texture_type="Skin Pore", + frequency=1.0, perturbation_factor=0.15, use_mask=False, mask=None, seed=-1): + """Main function to apply adaptive color-matched texture.""" + # Convert tensor image to PIL + base_image = self.tensor_to_pil(image) + + # Use provided seed or generate random if -1 + seed_value = seed if seed >= 0 else None + + # Generate adaptive texture + textured_image, texture_layer = self.generate_adaptive_texture( + base_image, noise_scale, texture_type, frequency, + perturbation_factor, texture_strength, seed_value + ) + + # Apply mask if specified + if use_mask and mask is not None: + mask_pil = self.tensor_to_pil(mask).convert('L') + mask_resized = mask_pil.resize(base_image.size) + # Invert mask so white areas get texture, black areas are protected + inverted_mask = ImageChops.invert(mask_resized) + # Composite: base where mask is black, textured where mask is white + textured_image = Image.composite(base_image, textured_image, inverted_mask) + + # Convert results back to tensors + texture_tensor = self.pil_to_tensor(texture_layer) + textured_tensor = self.pil_to_tensor(textured_image) + + return textured_tensor, texture_tensor + +NODE_CLASS_MAPPINGS = { + "PerturbationTexture": PerturbationTexture, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PerturbationTexture": "Perturbation Texture", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/pyproject.toml b/custom_nodes/controlaltai-nodes/pyproject.toml new file mode 100644 index 00000000..cc14a535 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "controlaltai-nodes" +description = "Quality of Life Nodes from ControlAltAI. Flux Resolution Calculator, Flux Sampler, Flux Union ControlNet Apply, Noise Plus Blend, Boolean Logic, and Flux Region Nodes." +version = "1.1.4" +license = {file = "LICENSE"} + +[project.urls] +Repository = "https://github.com/gseth/ControlAltAI-Nodes" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "controlaltai" +DisplayName = "ControlAltAI_Nodes" +Icon = "" diff --git a/custom_nodes/controlaltai-nodes/region_mask_conditioning_node.py b/custom_nodes/controlaltai-nodes/region_mask_conditioning_node.py new file mode 100644 index 00000000..0c77b80e --- /dev/null +++ b/custom_nodes/controlaltai-nodes/region_mask_conditioning_node.py @@ -0,0 +1,301 @@ +import torch +import numpy as np +from typing import Dict, List, Optional, Tuple +from PIL import Image, ImageDraw + +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +class RegionMaskConditioning: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask1": ("MASK",), + "bbox1": ("BBOX",), + "conditioning1": ("CONDITIONING",), + "number_of_regions": ("INT", { + "default": 1, + "min": 1, + "max": 3, + "step": 1, + "display": "Number of Regions" + }), + "strength1": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.1, + "display": "Strength for Region 1" + }), + }, + "optional": { + "mask2": ("MASK",), + "bbox2": ("BBOX",), + "conditioning2": ("CONDITIONING",), + "strength2": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.1, + "display": "Strength for Region 2" + }), + "mask3": ("MASK",), + "bbox3": ("BBOX",), + "conditioning3": ("CONDITIONING",), + "strength3": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.1, + "display": "Strength for Region 3" + }), + } + } + + RETURN_TYPES = ("REGION", "REGION", "REGION", "INT", "IMAGE") + RETURN_NAMES = ("region1", "region2", "region3", + "region_count", "preview_image") + FUNCTION = "create_conditioned_regions" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def validate_bbox(self, bbox: Dict) -> bool: + """Validate bbox coordinates and structure""" + print(f"\n=== Validating BBox ===") + print(f"Input bbox: {bbox}") + + if bbox is None or not isinstance(bbox, dict): + print("Failed: Invalid bbox type") + return False + + required_keys = ["x1", "y1", "x2", "y2"] + if not all(k in bbox for k in required_keys): + print(f"Failed: Missing keys. Required: {required_keys}") + return False + + # Validate coordinate values + if not all(isinstance(bbox[k], (int, float)) for k in required_keys): + print("Failed: Invalid coordinate types") + return False + + # Validate coordinate ranges + if not all(0 <= bbox[k] <= 1.0 for k in required_keys): + print("Failed: Coordinates out of range [0,1]") + return False + + # Validate proper ordering + if bbox["x1"] >= bbox["x2"] or bbox["y1"] >= bbox["y2"]: + print("Failed: Invalid coordinate ordering") + return False + + print("Passed: BBox validation successful") + return True + + def scale_conditioning(self, conditioning: List, strength: float) -> List: + """Scale conditioning tensors by strength""" + print(f"\n=== Scaling Conditioning ===") + print(f"Strength: {strength}") + + try: + if not conditioning or not isinstance(conditioning, list): + print("Failed: Invalid conditioning format") + raise ValueError("Invalid conditioning format") + + # Get the conditioning tensors and dict + cond_tensors = conditioning[0][0] + cond_dict = conditioning[0][1] + + print(f"Input tensor shape: {cond_tensors.shape}") + print(f"Conditioning keys: {list(cond_dict.keys())}") + print(f"Input tensor stats: min={cond_tensors.min():.3f}, max={cond_tensors.max():.3f}, mean={cond_tensors.mean():.3f}") + + # Scale the tensors + scaled_tensors = cond_tensors.clone() * strength + print(f"Scaled tensor stats: min={scaled_tensors.min():.3f}, max={scaled_tensors.max():.3f}, mean={scaled_tensors.mean():.3f}") + + return [[scaled_tensors, cond_dict]] + + except Exception as e: + print(f"Error in scale_conditioning: {str(e)}") + import traceback + traceback.print_exc() + return conditioning + + def create_region(self, mask: Optional[torch.Tensor], bbox: Optional[Dict], + conditioning: Optional[List], strength: float, region_idx: int) -> Dict: + """Create a single region with its conditioning""" + print(f"\n=== Creating Region {region_idx} ===") + + # Debug inputs + print("Input validation:") + print(f"- Mask: {type(mask)}, shape={mask.shape if mask is not None else None}") + print(f"- BBox: {bbox}") + print(f"- Conditioning type: {type(conditioning)}") + print(f"- Strength: {strength}") + + # Default empty region + empty_region = { + "conditioning": None, + "bbox": [0.0, 0.0, 0.0, 0.0], # Array format for empty + "is_active": False, + "strength": 1.0 + } + + try: + # Validate inputs + if mask is None or bbox is None or conditioning is None: + print(f"Region {region_idx}: Missing components") + return empty_region + + if not self.validate_bbox(bbox): + print(f"Region {region_idx}: Invalid bbox") + return empty_region + + # Scale conditioning + scaled_conditioning = self.scale_conditioning(conditioning, strength) + + # Create region output - bbox array, conditioning, and strength + region = { + "conditioning": scaled_conditioning, + "bbox": [bbox["x1"], bbox["y1"], bbox["x2"], bbox["y2"]], # Array format + "is_active": True, + "strength": strength + } + + print(f"\nSuccessfully created region {region_idx}") + return region + + except Exception as e: + print(f"Error creating region {region_idx}: {str(e)}") + import traceback + traceback.print_exc() + return empty_region + + def create_preview(self, masks: List[torch.Tensor], bboxes: List[Dict], + number_of_regions: int) -> torch.Tensor: + """Create preview of conditioned regions""" + print("\n=== Creating Preview ===") + + if not masks: + print("No masks provided") + return torch.zeros((3, 64, 64), dtype=torch.float32) + + height, width = masks[0].shape + print(f"Preview dimensions: {width}x{height}") + + # Create PIL Image for preview + preview = Image.new("RGB", (width, height), (0, 0, 0)) + draw = ImageDraw.Draw(preview) + + # Define colors for 3 regions + colors = [ + (255, 0, 0), # Red + (0, 255, 0), # Green + (255, 255, 0), # Yellow + ] + + # Draw each region + for i, (mask, bbox) in enumerate(zip(masks[:number_of_regions], bboxes[:number_of_regions])): + validation_result = self.validate_bbox(bbox) + if validation_result and mask is not None: + print(f"\nDrawing region {i+1}:") + # Get pixel coordinates + x1 = int(bbox["x1"] * width) + y1 = int(bbox["y1"] * height) + x2 = int(bbox["x2"] * width) + y2 = int(bbox["y2"] * height) + + print(f"Region {i+1} coordinates: ({x1},{y1}) to ({x2},{y2})") + + # Draw region outline + draw.rectangle([x1, y1, x2, y2], outline=colors[i], width=4) + + return pil2tensor(preview) + + def create_conditioned_regions(self, + mask1: torch.Tensor, + bbox1: Dict, + conditioning1: List, + number_of_regions: int, + strength1: float, + mask2: Optional[torch.Tensor] = None, + bbox2: Optional[Dict] = None, + conditioning2: Optional[List] = None, + strength2: Optional[float] = 1.0, + mask3: Optional[torch.Tensor] = None, + bbox3: Optional[Dict] = None, + conditioning3: Optional[List] = None, + strength3: Optional[float] = 1.0) -> Tuple: + print("\n=== Creating Conditioned Regions ===") + print(f"Number of regions: {number_of_regions}") + + try: + # Create regions + regions = [] + active_count = 0 + + # Process required number of regions + inputs = [ + (mask1, bbox1, conditioning1, strength1), + (mask2, bbox2, conditioning2, strength2), + (mask3, bbox3, conditioning3, strength3) + ] + + # Store masks and bboxes for preview only + preview_masks = [] + preview_bboxes = [] + + for i, (mask, bbox, conditioning, strength) in enumerate(inputs[:number_of_regions]): + # Create region with per-region strength + region = self.create_region(mask, bbox, conditioning, strength, i+1) + if region["is_active"]: + active_count += 1 + regions.append(region) + print(f"Processed region {i+1}: active={region['is_active']}") + + # Store for preview + preview_masks.append(mask) + preview_bboxes.append(bbox) + + # Fill remaining slots with empty regions + empty_region = { + "conditioning": None, + "bbox": [0.0, 0.0, 0.0, 0.0], # Array format + "is_active": False, + "strength": 1.0 + } + + while len(regions) < 3: + idx = len(regions) + 1 + print(f"Adding empty region {idx}") + regions.append(empty_region) + + print(f"\nCreated {active_count} active regions out of {number_of_regions} requested") + + # Create preview using stored masks and bboxes + preview = self.create_preview(preview_masks, preview_bboxes, number_of_regions) + + return (*regions, active_count, preview) + + except Exception as e: + print(f"Error in create_conditioned_regions: {str(e)}") + import traceback + traceback.print_exc() + + empty_region = { + "conditioning": None, + "bbox": [0.0, 0.0, 0.0, 0.0], # Array format + "is_active": False, + "strength": 1.0 + } + empty_preview = torch.zeros((3, mask1.shape[0], mask1.shape[1]), dtype=torch.float32) + return (empty_region, empty_region, empty_region, 0, empty_preview) + +# Node class mappings +NODE_CLASS_MAPPINGS = { + "RegionMaskConditioning": RegionMaskConditioning +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RegionMaskConditioning": "Region Mask Conditioning" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/region_mask_generator_node.py b/custom_nodes/controlaltai-nodes/region_mask_generator_node.py new file mode 100644 index 00000000..a732a42e --- /dev/null +++ b/custom_nodes/controlaltai-nodes/region_mask_generator_node.py @@ -0,0 +1,164 @@ +import torch +import numpy as np +from typing import Dict, List, Optional, Tuple +from PIL import Image, ImageDraw + +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +class RegionMaskGenerator: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 1024}), + "height": ("INT", {"default": 1024}), + "number_of_regions": ("INT", {"default": 1, "min": 1, "max": 3}), + # Region 1 + "region1_x1": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "region1_y1": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "region1_x2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "region1_y2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + # Region 2 + "region2_x1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "region2_y1": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "region2_x2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "region2_y2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + # Region 3 + "region3_x1": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "region3_y1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "region3_x2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "region3_y2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "MASK", "MASK", "MASK", "INT", "BBOX", "BBOX", "BBOX") + RETURN_NAMES = ("colored_regions_image", "bbox_preview", + "mask1", "mask2", "mask3", + "number_of_regions", + "bbox1", "bbox2", "bbox3") + FUNCTION = "generate_regions" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def create_bbox(self, x1: float, y1: float, x2: float, y2: float) -> Dict: + """Create bbox with debug output""" + print(f"Creating BBOX: x1={x1:.3f}, y1={y1:.3f}, x2={x2:.3f}, y2={y2:.3f}") + return { + "x1": x1, + "y1": y1, + "x2": x2, + "y2": y2, + "active": True + } + + def create_mask_from_bbox(self, bbox: Dict, width: int, height: int) -> torch.Tensor: + """Create mask from bbox with debug output""" + mask = torch.zeros((height, width), dtype=torch.float32) + if bbox["active"]: + x1 = int(bbox["x1"] * width) + y1 = int(bbox["y1"] * height) + x2 = int(bbox["x2"] * width) + y2 = int(bbox["y2"] * height) + print(f"Creating mask at pixels: x1={x1}, y1={y1}, x2={x2}, y2={y2}") + mask[y1:y2, x1:x2] = 1.0 + return mask + + def create_preview(self, masks: List[torch.Tensor], bboxes: List[Dict], + number_of_regions: int) -> Tuple[torch.Tensor, torch.Tensor]: + """Create preview images with debug info""" + if not masks: + return torch.zeros((3, 64, 64), dtype=torch.float32), torch.zeros((3, 64, 64), dtype=torch.float32) + + height, width = masks[0].shape + + # Create both preview images + region_preview = Image.new("RGB", (width, height), (0, 0, 0)) + bbox_preview = Image.new("RGB", (width, height), (0, 0, 0)) + region_draw = ImageDraw.Draw(region_preview) + bbox_draw = ImageDraw.Draw(bbox_preview) + + colors = [ + (255, 0, 0), # Red - Region 1 + (0, 255, 0), # Green - Region 2 + (255, 255, 0), # Yellow - Region 3 + ] + + # Store regions for ordered preview + preview_regions = [] + for i in range(number_of_regions): + if bboxes[i]["active"]: + mask_np = masks[i].cpu().numpy() > 0.5 + if mask_np.any(): + preview_regions.append((i, mask_np, bboxes[i])) + + # Draw regions in reverse order (Region 3 first, Region 1 last) + for i, mask_np, bbox in sorted(preview_regions, reverse=True): + # Draw on region preview + color_array = np.zeros((height, width, 3), dtype=np.uint8) + for c in range(3): + color_array[mask_np, c] = colors[i][c] + preview_np = np.array(region_preview) + preview_np[mask_np] = color_array[mask_np] + region_preview = Image.fromarray(preview_np) + + # Draw on bbox preview - maintaining original bbox drawing order + x1 = int(bbox["x1"] * width) + y1 = int(bbox["y1"] * height) + x2 = int(bbox["x2"] * width) + y2 = int(bbox["y2"] * height) + print(f"Drawing preview for region {i}: x1={x1}, y1={y1}, x2={x2}, y2={y2}") + bbox_draw.rectangle([x1, y1, x2, y2], outline=colors[i], width=2) + + return pil2tensor(region_preview), pil2tensor(bbox_preview) + + def generate_regions(self, + width: int, + height: int, + number_of_regions: int, + **kwargs) -> Tuple: + try: + print(f"\nGenerating {number_of_regions} regions for {width}x{height} image") + bboxes = [] + masks = [] + + # Create regions + for i in range(3): + if i < number_of_regions: + print(f"\nProcessing region {i+1}:") + bbox = self.create_bbox( + kwargs[f"region{i+1}_x1"], + kwargs[f"region{i+1}_y1"], + kwargs[f"region{i+1}_x2"], + kwargs[f"region{i+1}_y2"] + ) + mask = self.create_mask_from_bbox(bbox, width, height) + bboxes.append(bbox) + masks.append(mask) + else: + print(f"Creating empty region {i+1}") + empty_bbox = {"x1": 0.0, "y1": 0.0, "x2": 0.0, "y2": 0.0, "active": False} + bboxes.append(empty_bbox) + masks.append(torch.zeros((height, width), dtype=torch.float32)) + + # Create previews + region_preview, bbox_preview = self.create_preview(masks, bboxes, number_of_regions) + + return (region_preview, bbox_preview, *masks, number_of_regions, *bboxes) + + except Exception as e: + print(f"Error in generate_regions: {str(e)}") + empty_mask = torch.zeros((height, width), dtype=torch.float32) + empty_bbox = {"x1": 0.0, "y1": 0.0, "x2": 0.0, "y2": 0.0, "active": False} + empty_preview = torch.zeros((3, height, width), dtype=torch.float32) + return (empty_preview, empty_preview, + empty_mask, empty_mask, empty_mask, + 0, empty_bbox, empty_bbox, empty_bbox) + +# Node class mappings +NODE_CLASS_MAPPINGS = { + "RegionMaskGenerator": RegionMaskGenerator +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RegionMaskGenerator": "Region Mask Generator" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/region_mask_processor_node.py b/custom_nodes/controlaltai-nodes/region_mask_processor_node.py new file mode 100644 index 00000000..e2f0e543 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/region_mask_processor_node.py @@ -0,0 +1,230 @@ +import torch +import torch.nn.functional as F +from typing import Tuple, Dict, Optional, List +import numpy as np +from PIL import Image, ImageDraw + +def pil2tensor(image): + """Convert a PIL image to a PyTorch tensor in the expected format.""" + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +class RegionMaskProcessor: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask1": ("MASK",), + "bbox1": ("BBOX",), + "blur_radius": ("INT", { + "default": 5, + "min": 0, + "max": 32, + "step": 1, + "display": "Blur Radius" + }), + "threshold": ("FLOAT", { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.1, + "display": "Mask Threshold" + }), + "feather_edges": ("BOOLEAN", { + "default": True, + "display": "Feather Edges" + }), + "number_of_regions": ("INT", { + "default": 1, + "min": 1, + "max": 3, + "display": "Number of Regions" + }), + }, + "optional": { + "mask2": ("MASK",), + "bbox2": ("BBOX",), + "mask3": ("MASK",), + "bbox3": ("BBOX",), + } + } + + RETURN_TYPES = ("MASK", "BBOX", "MASK", "BBOX", "MASK", "BBOX", "IMAGE", "INT") + RETURN_NAMES = ("processed_mask1", "processed_bbox1", + "processed_mask2", "processed_bbox2", + "processed_mask3", "processed_bbox3", + "preview_image", "region_count") + FUNCTION = "process_regions" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def apply_gaussian_blur(self, mask: torch.Tensor, radius: int) -> torch.Tensor: + """Apply gaussian blur to mask edges""" + if radius <= 0: + return mask + + kernel_size = 2 * radius + 1 + sigma = radius / 3.0 + + if len(mask.shape) == 2: + mask = mask.unsqueeze(0).unsqueeze(0) + + kernel_1d = torch.exp(torch.linspace(-radius, radius, kernel_size).pow(2) / (-2 * sigma ** 2)) + kernel_1d = kernel_1d / kernel_1d.sum() + + padding = radius + kernel_h = kernel_1d.unsqueeze(0).unsqueeze(0).unsqueeze(0).to(mask.device) + kernel_v = kernel_1d.unsqueeze(0).unsqueeze(0).unsqueeze(-1).to(mask.device) + + mask = F.pad(mask, (padding, padding, 0, 0), mode='reflect') + mask = F.conv2d(mask, kernel_h) + mask = F.pad(mask, (0, 0, padding, padding), mode='reflect') + mask = F.conv2d(mask, kernel_v) + + return mask.squeeze() + + def apply_feathering(self, mask: torch.Tensor, bbox: Dict, radius: int) -> Tuple[torch.Tensor, Dict]: + """Apply feathering to mask edges while preserving bbox boundaries""" + if radius <= 0 or not bbox["active"]: + return mask, bbox + + height, width = mask.shape + x1 = int(bbox["x1"] * width) + y1 = int(bbox["y1"] * height) + x2 = int(bbox["x2"] * width) + y2 = int(bbox["y2"] * height) + + inner_mask = torch.zeros_like(mask) + inner_mask[y1+radius:y2-radius, x1+radius:x2-radius] = 1.0 + edge_mask = mask - inner_mask + + if edge_mask.any(): + blurred = self.apply_gaussian_blur(mask, radius) + result = mask.clone() + result[edge_mask > 0] = blurred[edge_mask > 0] + else: + result = mask + + return result, bbox + + def process_single_region(self, + mask: torch.Tensor, + bbox: Dict, + blur_radius: int, + threshold: float, + feather_edges: bool) -> Tuple[torch.Tensor, Dict]: + """Process a single mask-bbox pair""" + if mask is None or not bbox["active"]: + return mask, bbox + + try: + processed = (mask > threshold).float() + + if feather_edges and blur_radius > 0: + processed, bbox = self.apply_feathering(processed, bbox, blur_radius) + elif blur_radius > 0: + processed = self.apply_gaussian_blur(processed, blur_radius) + + return processed, bbox + + except Exception as e: + print(f"Error processing region: {str(e)}") + return mask, bbox + + def create_preview(self, masks: List[torch.Tensor], bboxes: List[Dict], + number_of_regions: int) -> torch.Tensor: + """Create preview of processed regions with PIL for consistent coloring""" + if not masks: + return torch.zeros((3, 64, 64), dtype=torch.float32) + + height, width = masks[0].shape + + # Create PIL Image for preview + preview = Image.new("RGB", (width, height), (0, 0, 0)) + + colors = [ + (255, 0, 0), # Red - Region 1 + (0, 255, 0), # Green - Region 2 + (255, 255, 0), # Yellow - Region 3 + ] + + # Store regions for ordered preview + preview_regions = [] + for i in range(number_of_regions): + if bboxes[i]["active"] and masks[i] is not None: + mask_np = masks[i].cpu().numpy() > 0.5 + preview_regions.append((i, mask_np)) + + # Draw regions in reverse order (Region 3 first, Region 1 last) + for i, mask_np in sorted(preview_regions, reverse=True): + color_array = np.zeros((height, width, 3), dtype=np.uint8) + color_array[mask_np] = colors[i] + + # Convert to PIL and composite + region_img = Image.fromarray(color_array, 'RGB') + preview = Image.alpha_composite( + preview.convert('RGBA'), + Image.merge('RGBA', (*region_img.split(), Image.fromarray((mask_np * 255).astype(np.uint8)))) + ) + + return pil2tensor(preview.convert('RGB')) + + def process_regions(self, + mask1: torch.Tensor, + bbox1: Dict, + blur_radius: int, + threshold: float, + feather_edges: bool, + number_of_regions: int, + mask2: Optional[torch.Tensor] = None, + bbox2: Optional[Dict] = None, + mask3: Optional[torch.Tensor] = None, + bbox3: Optional[Dict] = None) -> Tuple: + try: + # Process each mask-bbox pair + mask_bbox_pairs = [ + (mask1, bbox1), + (mask2, bbox2) if mask2 is not None else (None, None), + (mask3, bbox3) if mask3 is not None else (None, None), + ] + + processed_masks = [] + processed_bboxes = [] + active_count = 0 + + for i, (mask, bbox) in enumerate(mask_bbox_pairs): + if i < number_of_regions and mask is not None and bbox is not None: + proc_mask, proc_bbox = self.process_single_region( + mask, bbox, blur_radius, threshold, feather_edges + ) + if proc_bbox["active"]: + active_count += 1 + processed_masks.append(proc_mask) + processed_bboxes.append(proc_bbox) + else: + empty_mask = torch.zeros_like(mask1) + empty_bbox = {"x1": 0.0, "y1": 0.0, "x2": 0.0, "y2": 0.0, "active": False} + processed_masks.append(empty_mask) + processed_bboxes.append(empty_bbox) + + # Create preview + preview = self.create_preview(processed_masks, processed_bboxes, number_of_regions) + + return (*[item for pair in zip(processed_masks, processed_bboxes) for item in pair], + preview, active_count) + + except Exception as e: + print(f"Error processing regions: {str(e)}") + empty_mask = torch.zeros_like(mask1) + empty_bbox = {"x1": 0.0, "y1": 0.0, "x2": 0.0, "y2": 0.0, "active": False} + empty_preview = torch.zeros((3, mask1.shape[0], mask1.shape[1]), dtype=torch.float32) + return (empty_mask, empty_bbox, empty_mask, empty_bbox, + empty_mask, empty_bbox, + empty_preview, 0) + +# Node class mappings +NODE_CLASS_MAPPINGS = { + "RegionMaskProcessor": RegionMaskProcessor +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RegionMaskProcessor": "Region Mask Processor" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/region_mask_validator_node.py b/custom_nodes/controlaltai-nodes/region_mask_validator_node.py new file mode 100644 index 00000000..b08d96fa --- /dev/null +++ b/custom_nodes/controlaltai-nodes/region_mask_validator_node.py @@ -0,0 +1,270 @@ +import torch +from typing import Tuple, Dict, Optional, List +import numpy as np +from PIL import Image, ImageDraw + +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +class RegionMaskValidator: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask1": ("MASK",), + "bbox1": ("BBOX",), + "number_of_regions": ("INT", { + "default": 1, + "min": 1, + "max": 3, + "step": 1 + }), + "min_region_size": ("INT", { + "default": 64, + "min": 32, + "max": 512, + "step": 32, + "display": "Minimum Region Size (px)" + }), + "max_overlap": ("FLOAT", { + "default": 0.1, + "min": 0.0, + "max": 0.5, + "step": 0.01, + "display": "Maximum Region Overlap" + }), + }, + "optional": { + "mask2": ("MASK",), + "bbox2": ("BBOX",), + "mask3": ("MASK",), + "bbox3": ("BBOX",), + } + } + + RETURN_TYPES = ("MASK", "BBOX", "MASK", "BBOX", "MASK", "BBOX", + "INT", "BOOLEAN", "STRING", "IMAGE") + RETURN_NAMES = ("valid_mask1", "valid_bbox1", + "valid_mask2", "valid_bbox2", + "valid_mask3", "valid_bbox3", + "valid_region_count", "is_valid", "validation_message", + "validation_preview") + FUNCTION = "validate_regions" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def get_region_dimensions(self, bbox: Dict, width: int, height: int) -> Tuple[int, int, Tuple[int, int]]: + """Calculate region dimensions in pixels""" + if not bbox["active"]: + return 0, (0, 0) + + x1 = int(bbox["x1"] * width) + y1 = int(bbox["y1"] * height) + x2 = int(bbox["x2"] * width) + y2 = int(bbox["y2"] * height) + + w = x2 - x1 + h = y2 - y1 + area = w * h + print(f"Region dimensions: {w}x{h} pixels") + return area, (w, h) + + def calculate_overlap(self, bbox1: Dict, bbox2: Dict, width: int, height: int) -> Tuple[Tuple[int, int], float]: + """Calculate overlap dimensions and ratio""" + if not (bbox1["active"] and bbox2["active"]): + return (0, 0), 0.0 + + # Convert to pixel coordinates + x1_1 = int(bbox1["x1"] * width) + y1_1 = int(bbox1["y1"] * height) + x2_1 = int(bbox1["x2"] * width) + y2_1 = int(bbox1["y2"] * height) + + x1_2 = int(bbox2["x1"] * width) + y1_2 = int(bbox2["y1"] * height) + x2_2 = int(bbox2["x2"] * width) + y2_2 = int(bbox2["y2"] * height) + + # Calculate intersection + x_left = max(x1_1, x1_2) + y_top = max(y1_1, y1_2) + x_right = min(x2_1, x2_2) + y_bottom = min(y2_1, y2_2) + + if x_right > x_left and y_bottom > y_top: + overlap_width = x_right - x_left + overlap_height = y_bottom - y_top + overlap_area = overlap_width * overlap_height + + area1 = (x2_1 - x1_1) * (y2_1 - y1_1) + area2 = (x2_2 - x1_2) * (y2_2 - y1_2) + smaller_area = min(area1, area2) + overlap_ratio = overlap_area / smaller_area + + print(f"Overlap dimensions: {overlap_width}x{overlap_height} pixels ({overlap_ratio:.1%})") + return (overlap_width, overlap_height), overlap_ratio + + return (0, 0), 0.0 + + def create_validation_preview(self, masks: List[torch.Tensor], bboxes: List[Dict], + number_of_regions: int, is_valid: bool, + messages: List[str], img_width: int, img_height: int) -> torch.Tensor: + """Create visual validation feedback with improved text rendering""" + if not masks: + return torch.zeros((3, 64, 64), dtype=torch.float32) + + preview = Image.new("RGB", (img_width, img_height), (0, 0, 0)) + draw = ImageDraw.Draw(preview) + + # Colors for valid/invalid regions + colors = { + 'valid': [(0, 255, 0), (0, 200, 0), (0, 150, 0)], # Green shades + 'invalid': [(255, 0, 0), (200, 0, 0), (150, 0, 0)] # Red shades + } + + # Draw regions with validation status and improved text + for i, (mask, bbox) in enumerate(zip(masks[:number_of_regions], bboxes[:number_of_regions])): + if bbox["active"]: + x1 = int(bbox["x1"] * img_width) + y1 = int(bbox["y1"] * img_height) + x2 = int(bbox["x2"] * img_width) + y2 = int(bbox["y2"] * img_height) + + w = x2 - x1 + h = y2 - y1 + color = colors['valid' if is_valid else 'invalid'][i] + + # Draw thicker rectangle outline + draw.rectangle([x1, y1, x2, y2], outline=color, width=4) + + # Improved region label with dimensions + label = f"R{i+1}: {w}x{h}" + # Position text with offset from corner and draw twice for better visibility + text_x = x1 + 10 + text_y = y1 + 10 + + # Draw text shadow/outline for better contrast + shadow_offset = 2 + shadow_color = (0, 0, 0) + for dx in [-shadow_offset, shadow_offset]: + for dy in [-shadow_offset, shadow_offset]: + draw.text((text_x + dx, text_y + dy), label, fill=shadow_color, font=None, size=64) + + # Draw main text + draw.text((text_x, text_y), label, fill=color, font=None, size=64) + + # If region is invalid, add error message below the label + if not is_valid and i < len(messages): + error_y = text_y + 30 # Position error message below label + # Draw error message with shadow for contrast + for dx in [-shadow_offset, shadow_offset]: + for dy in [-shadow_offset, shadow_offset]: + draw.text((text_x + dx, error_y + dy), messages[i], fill=shadow_color, font=None, size=20) + draw.text((text_x, error_y), messages[i], fill=color, font=None, size=20) + + return pil2tensor(preview) + + def validate_regions(self, + mask1: torch.Tensor, + bbox1: Dict, + number_of_regions: int, + min_region_size: int, + max_overlap: float, + mask2: Optional[torch.Tensor] = None, + bbox2: Optional[Dict] = None, + mask3: Optional[torch.Tensor] = None, + bbox3: Optional[Dict] = None) -> Tuple: + try: + print(f"\nValidating {number_of_regions} regions:") + messages = [] + is_valid = True + height, width = mask1.shape + print(f"Canvas size: {width}x{height} pixels") + + # Collect regions + regions = [ + (mask1, bbox1), + (mask2, bbox2) if mask2 is not None else (None, None), + (mask3, bbox3) if mask3 is not None else (None, None), + ] + + # Validate each region + valid_regions = [] + valid_count = 0 + for i, (mask, bbox) in enumerate(regions): + if i < number_of_regions and mask is not None and bbox is not None: + print(f"\nValidating Region {i+1}:") + # Check region size + _, (w, h) = self.get_region_dimensions(bbox, width, height) + + if w < min_region_size or h < min_region_size: + message = f"Region {i+1} too small: {w}x{h} pixels (minimum: {min_region_size}x{min_region_size})" + print(f"Failed: {message}") + messages.append(message) + is_valid = False + bbox = bbox.copy() + bbox["active"] = False + else: + print(f"Passed: Region {i+1} size check ({w}x{h} pixels)") + valid_count += 1 + + valid_regions.append((mask, bbox)) + else: + valid_regions.append(( + torch.zeros_like(mask1), + {"x1": 0.0, "y1": 0.0, "x2": 0.0, "y2": 0.0, "active": False} + )) + + # Check overlaps + if valid_count > 1: + print("\nChecking region overlaps:") + for i in range(len(valid_regions)): + for j in range(i + 1, len(valid_regions)): + mask_i, bbox_i = valid_regions[i] + mask_j, bbox_j = valid_regions[j] + + if bbox_i["active"] and bbox_j["active"]: + print(f"Checking overlap between regions {i+1} and {j+1}:") + (ow, oh), overlap_ratio = self.calculate_overlap(bbox_i, bbox_j, width, height) + + if overlap_ratio > max_overlap: + message = f"Excessive overlap ({ow}x{oh} pixels, {overlap_ratio:.1%}) between regions {i+1} and {j+1}" + print(f"Failed: {message}") + messages.append(message) + is_valid = False + + # Create validation message + validation_message = "All regions valid" if is_valid else "\n".join(messages) + print(f"\nValidation {'passed' if is_valid else 'failed'}:") + print(validation_message) + + # Create validation preview + preview = self.create_validation_preview( + [r[0] for r in valid_regions], + [r[1] for r in valid_regions], + number_of_regions, + is_valid, + messages, + width, + height + ) + + return (*[item for region in valid_regions for item in region], + valid_count, is_valid, validation_message, preview) + + except Exception as e: + print(f"Validation error: {str(e)}") + empty_mask = torch.zeros_like(mask1) + empty_bbox = {"x1": 0.0, "y1": 0.0, "x2": 0.0, "y2": 0.0, "active": False} + empty_preview = torch.zeros((3, height, width), dtype=torch.float32) + return (empty_mask, empty_bbox, empty_mask, empty_bbox, + empty_mask, empty_bbox, + 0, False, f"Validation error: {str(e)}", empty_preview) + +# Node class mappings +NODE_CLASS_MAPPINGS = { + "RegionMaskValidator": RegionMaskValidator +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RegionMaskValidator": "Region Mask Validator" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/region_overlay_visualizer_node.py b/custom_nodes/controlaltai-nodes/region_overlay_visualizer_node.py new file mode 100644 index 00000000..758f03f8 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/region_overlay_visualizer_node.py @@ -0,0 +1,90 @@ +import torch +import numpy as np +from typing import Tuple + +class RegionOverlayVisualizer: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "region_preview": ("IMAGE",), + "opacity": ("FLOAT", { + "default": 0.3, + "min": 0.0, + "max": 1.0, + "step": 0.1, + "display": "Overlay Opacity" + }), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "visualize_regions" + CATEGORY = "ControlAltAI Nodes/Flux Region" + + def visualize_regions( + self, + image: torch.Tensor, + region_preview: torch.Tensor, + opacity: float, + ) -> Tuple[torch.Tensor]: + try: + print("\n=== Starting Region Overlay Visualization ===") + print(f"Initial shapes - Image: {image.shape}, Preview: {region_preview.shape}") + + # Ensure input tensors are in [B, H, W, C] format + if len(image.shape) == 3: + image = image.unsqueeze(0) + if len(region_preview.shape) == 3: + region_preview = region_preview.unsqueeze(0) + + # Get working copies + base_image = image.clone() + preview = region_preview.clone() + + # Convert to numpy for mask creation (keeping batch and HWC format) + preview_np = (preview * 255).byte().cpu().numpy() + + # Create mask based on preview content (operating on the last dimension - channels) + color_sum = np.sum(preview_np, axis=-1) # Sum across color channels + max_channel = np.max(preview_np, axis=-1) + min_channel = np.min(preview_np, axis=-1) + + # Create binary mask where content exists + mask = ( + (color_sum > 50) & + (max_channel > 30) & + ((max_channel - min_channel) > 10) + ) + + # Expand mask to match input dimensions + mask = mask[..., None] # Add channel dimension back + mask = torch.from_numpy(mask).to(image.device) + + print(f"Mask shape: {mask.shape}") + print(f"Masked pixels: {mask.sum().item()}/{mask.numel()} ({mask.sum().item()/mask.numel()*100:.2f}%)") + + # Apply blending only where mask is True + result = torch.where( + mask.bool(), + (1 - opacity) * base_image + opacity * preview, + base_image + ) + + print(f"Final shape: {result.shape}") + return (result,) + + except Exception as e: + print(f"Error in visualization: {str(e)}") + import traceback + traceback.print_exc() + return (image,) + +NODE_CLASS_MAPPINGS = { + "RegionOverlayVisualizer": RegionOverlayVisualizer +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RegionOverlayVisualizer": "Region Overlay Visualizer" +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/text_bridge_node.py b/custom_nodes/controlaltai-nodes/text_bridge_node.py new file mode 100644 index 00000000..e5bf5f74 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/text_bridge_node.py @@ -0,0 +1,40 @@ +class TextBridge: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text_input": ("STRING", {"default": "", "multiline": True}), + }, + "optional": { + "passthrough_text": ("STRING", {"forceInput": True}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("text_output",) + FUNCTION = "bridge_text" + + CATEGORY = "ControlAltAI Nodes/Utility" + + def bridge_text(self, text_input="", passthrough_text=""): + """ + Bridge function that allows editing of input text and passes it through as output. + If passthrough_text is connected, it uses that as the base text. + The text_input field allows manual editing/override. + """ + # If passthrough_text is provided and text_input is empty or default, use passthrough + if passthrough_text and (not text_input or text_input == ""): + output_text = passthrough_text + else: + # Use the manually entered/edited text + output_text = text_input + + return (output_text,) + +NODE_CLASS_MAPPINGS = { + "TextBridge": TextBridge, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TextBridge": "Text Bridge", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/three_way_switch_node.py b/custom_nodes/controlaltai-nodes/three_way_switch_node.py new file mode 100644 index 00000000..b25428ba --- /dev/null +++ b/custom_nodes/controlaltai-nodes/three_way_switch_node.py @@ -0,0 +1,62 @@ +class AnyType(str): + """A special string subclass that equals any other type for ComfyUI type checking.""" + def __ne__(self, __value: object) -> bool: + return False + +# Create an instance to use as the any type +any_type = AnyType("*") + +class ThreeWaySwitch: + """Three-way switch that selects between three inputs based on selection setting.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "selection_setting": ("INT", {"default": 1, "min": 1, "max": 3}), + }, + "optional": { + "input_1": (any_type,), + "input_2": (any_type,), + "input_3": (any_type,), + } + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("output",) + FUNCTION = "switch_inputs" + + CATEGORY = "ControlAltAI Nodes/Logic" + + @classmethod + def VALIDATE_INPUTS(cls, **kwargs): + """Allow any input types.""" + return True + + def switch_inputs(self, selection_setting=1, input_1=None, input_2=None, input_3=None): + """ + Three-way switch that selects between three inputs based on the selection_setting. + Compatible with IntegerSettingsAdvanced node: + - selection_setting = 1: selects input_1 + - selection_setting = 2: selects input_2 + - selection_setting = 3: selects input_3 + """ + if selection_setting == 2: + # Second option - select input_2, fallback to input_1, then input_3 + selected_output = input_2 if input_2 is not None else (input_1 if input_1 is not None else input_3) + elif selection_setting == 3: + # Third option - select input_3, fallback to input_1, then input_2 + selected_output = input_3 if input_3 is not None else (input_1 if input_1 is not None else input_2) + else: + # Default/First option (1) - select input_1, fallback to input_2, then input_3 + selected_output = input_1 if input_1 is not None else (input_2 if input_2 is not None else input_3) + + return (selected_output,) + +NODE_CLASS_MAPPINGS = { + "ThreeWaySwitch": ThreeWaySwitch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ThreeWaySwitch": "Switch (Three Way)", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/two_way_switch_node.py b/custom_nodes/controlaltai-nodes/two_way_switch_node.py new file mode 100644 index 00000000..9c3059b7 --- /dev/null +++ b/custom_nodes/controlaltai-nodes/two_way_switch_node.py @@ -0,0 +1,57 @@ +class AnyType(str): + """A special string subclass that equals any other type for ComfyUI type checking.""" + def __ne__(self, __value: object) -> bool: + return False + +# Create an instance to use as the any type +any_type = AnyType("*") + +class TwoWaySwitch: + """Two-way switch that selects between two inputs based on selection setting.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "selection_setting": ("INT", {"default": 1, "min": 1, "max": 2}), + }, + "optional": { + "input_1": (any_type,), + "input_2": (any_type,), + } + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ("output",) + FUNCTION = "switch_inputs" + + CATEGORY = "ControlAltAI Nodes/Logic" + + @classmethod + def VALIDATE_INPUTS(cls, **kwargs): + """Allow any input types.""" + return True + + def switch_inputs(self, selection_setting=1, input_1=None, input_2=None): + """ + Two-way switch that selects between two inputs based on the selection_setting. + Compatible with IntegerSettings node: + - selection_setting = 1 (Disable): selects input_1 + - selection_setting = 2 (Enable): selects input_2 + """ + if selection_setting == 2: + # Enable state - select second input + selected_output = input_2 if input_2 is not None else input_1 + else: + # Disable state (1) or any other value - select first input + selected_output = input_1 if input_1 is not None else input_2 + + return (selected_output,) + +NODE_CLASS_MAPPINGS = { + "TwoWaySwitch": TwoWaySwitch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TwoWaySwitch": "Switch (Two Way)", +} \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/web/js/integer_settings_advanced.js b/custom_nodes/controlaltai-nodes/web/js/integer_settings_advanced.js new file mode 100644 index 00000000..206eda4f --- /dev/null +++ b/custom_nodes/controlaltai-nodes/web/js/integer_settings_advanced.js @@ -0,0 +1,78 @@ +import { app } from "/scripts/app.js"; + +// Register the extension for IntegerSettingsAdvanced node +app.registerExtension({ + name: "ControlAltAI.IntegerSettingsAdvanced", + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "IntegerSettingsAdvanced") { + console.log("Registering IntegerSettingsAdvanced mutual exclusion behavior"); + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const result = onNodeCreated?.apply(this, arguments); + + // Store reference to the node + const node = this; + + // Function to enforce mutual exclusion + function enforceMutualExclusion(activeWidget) { + // Get all boolean widgets + const booleanWidgets = node.widgets.filter(w => + w.type === "toggle" && + (w.name === "setting_1" || w.name === "setting_2" || w.name === "setting_3") + ); + + // If a widget is being set to true, set others to false + if (activeWidget.value === true) { + booleanWidgets.forEach(widget => { + if (widget !== activeWidget) { + widget.value = false; + } + }); + } + + // Always ensure at least one is true (always one behavior) + const anyEnabled = booleanWidgets.some(w => w.value === true); + if (!anyEnabled) { + // If none are enabled, enable setting_1 as default + const setting1Widget = booleanWidgets.find(w => w.name === "setting_1"); + if (setting1Widget) { + setting1Widget.value = true; + } + } + + // Trigger canvas redraw + if (app.graph) { + app.graph.setDirtyCanvas(true, false); + } + } + + // Hook into widget callbacks after node is fully created + setTimeout(() => { + node.widgets.forEach(widget => { + if (widget.type === "toggle" && + (widget.name === "setting_1" || widget.name === "setting_2" || widget.name === "setting_3")) { + + // Store original callback + const originalCallback = widget.callback; + + // Override with mutual exclusion logic + widget.callback = function(value) { + // Call original callback first + if (originalCallback) { + originalCallback.call(this, value); + } + + // Apply mutual exclusion + enforceMutualExclusion(this); + }; + } + }); + }, 10); + + return result; + }; + } + } +}); \ No newline at end of file diff --git a/custom_nodes/controlaltai-nodes/xformers_instructions.txt b/custom_nodes/controlaltai-nodes/xformers_instructions.txt new file mode 100644 index 00000000..e2c8813c --- /dev/null +++ b/custom_nodes/controlaltai-nodes/xformers_instructions.txt @@ -0,0 +1,26 @@ +XFormers is now needed for the flux region spatial control to work. + +Go to your python_embeded folder and Check your pytorch and cuda version: + +python.exe -c "import torch; print(torch.__version__)" + +check for xformers if installed: + +python.exe -m pip show xformers + +Please go to: https://github.com/facebookresearch/xformers/releases + +Check for the latest Xformers version that is compatible with your installed Pytorch version. + +You can Install the latest version of xformers using this command: + +python.exe -m pip install xformers==PUTVERSIONHERE --index-url https://download.pytorch.org/whl/cuVERSION + +example For PyTorch 2.5.1 with CUDA 12.4: +python.exe -m pip install xformers==0.0.28.post3 --index-url https://download.pytorch.org/whl/cu124 + +As of 8th December 2024: +Recommended: +xformers==0.0.28.post3 +PyTorch 2.5.1 +CUDA version: cu124 (for CUDA 12.4) \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/.gitignore b/custom_nodes/rgthree-comfy/.gitignore new file mode 100644 index 00000000..1526efa2 --- /dev/null +++ b/custom_nodes/rgthree-comfy/.gitignore @@ -0,0 +1,13 @@ +__pycache__ +*.ini +wildcards/** +.vscode/ +.idea/ +node_modules/ +rgthree_config.json +web/rgthree_config.js +web/comfyui/rgthree_config.js +userdata/ +userdata/** +web/comfyui/testing/ +web/comfyui/tests/ \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/LICENSE b/custom_nodes/rgthree-comfy/LICENSE new file mode 100644 index 00000000..5a4eb87c --- /dev/null +++ b/custom_nodes/rgthree-comfy/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Regis Gaughan, III (rgthree) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/rgthree-comfy/README.md b/custom_nodes/rgthree-comfy/README.md new file mode 100644 index 00000000..89034746 --- /dev/null +++ b/custom_nodes/rgthree-comfy/README.md @@ -0,0 +1,434 @@ +

    + rgthree-comfy +
    + Making ComfyUI more comfortable! +
    +

    +

    + The Nodes   |   Improvements & Features   |   Link Fixer +

    +
    + +A collection of nodes and improvements created while messing around with ComfyUI. I made them for myself to make my workflow cleaner, easier, and faster. You're welcome to try them out. But remember, I made them for my own use cases :) + +![Context Node](./docs/rgthree_advanced.png) + +# Get Started + +## Install + +1. Install the great [ComfyUi](https://github.com/comfyanonymous/ComfyUI). +2. Clone this repo into `custom_modules`: + ``` + cd ComfyUI/custom_nodes + git clone https://github.com/rgthree/rgthree-comfy.git + ``` +3. Start up ComfyUI. + +## Settings + +You can configure certain aspect of rgthree-comfy. For instance, perhaps a future ComfyUI change breaks rgthree-comfy, or you already have another extension that does something similar and you want to turn it off for rgthree-comfy. + +You can get to rgthree-settings by right-clicking on the empty part of the graph, and selecting `rgthree-comfy > Settings (rgthree-comfy)` or by clicking the `rgthree-comfy settings` in the ComfyUI settings dialog. + +_(Note, settings are stored in an `rgthree_config.json` in the `rgthree-comfy` directory. There are other advanced settings that can only be configured there; You can copy default settings from `rgthree_config.json.default` before `rgthree_config.json` before modifying)_. + +
    + +# ✴️ The Nodes + +Note, you can right-click on a bunch of the rgthree-comfy nodes and select `πŸ›Ÿ Node Help` menu item for in-app help when available. + +## Seed +> An intuitive seed control node for ComfyUI that works very much like Automatic1111's seed control. +>
    +> ℹ️ See More Information +> +> - Set the seed value to "-1" to use a random seed every time +> - Set any other number in there to use as a static/fixed seed +> - Quick actions to randomize, or (re-)use the last queued seed. +> - Images metadata will store the seed value _(so dragging an image in, will have the seed field already fixed to its seed)_. +> - _Secret Features_: You can manually set the seed value to "-2" or "-3" to increment or decrement the last seed value. If there was not last seed value, it will randomly use on first. +> +> ![Router Node](./docs/rgthree_seed.png) +>
    + + +## Reroute +> Keep your workflow neat with this much improved Reroute node with, like, actual rerouting with multiple directions and sizes. +>
    +> ℹ️ More Information +> +> - Use the right-click context menu to change the width, height and connection layout +> - Also toggle resizability (min size is 40x43 if resizing though), and title/type display. +> +> ![Router Node](./docs/rgthree_router.png) +>
    + +## Bookmark (πŸ”–) +> Place the bookmark node anywhere on screen to quickly navigate to that with a shortcut key. +>
    +> ℹ️ See More Information +> +> - Define the `shortcut_key` to press to go right to that bookmark node, anchored in the top left. +> - You can also define the zoom level as well! +> - Pro tip: `shortcut_key` can be multiple keys. For instance "alt + shift + !" would require +> pressing the alt key, the shift key, and the "!" (as in the "1" key, but with shift pressed) +> in order to trigger. +>
    + + +## Context / Context Big +> Pass along in general flow properties, and merge in new data. Similar to some other node suites "pipes" but easier merging, is more easily interoperable with standard nodes by both combining and exploding all in a single node. +>
    +> ℹ️ More Information +> +> - Context and Context Big are backwards compatible with each other. That is, an input connected to a Context Big will be passed through the CONTEXT outputs through normal Context nodes and available as an output on either (or, Context Big if the output is only on that node, like "steps"). +> - Pro Tip: When dragging a Context output over a nother node, hold down "ctrl" and release to automatically connect the other Context outputs to the hovered node. +> - Pro Tip: You can change between Context and Context Big nodes from the menu. +> +> ![Context Node](./docs/rgthree_context.png) +>
    + +## Image Comparer +> The Image Comparer node compares two images on top of each other. +>
    +> ℹ️ More Information +> +> - **Note:** The right-click menu may show image options (Open Image, Save Image, etc.) which will correspond to the first image (image_a) if clicked on the left-half of the node, or the second image if on the right half of the node. +> - **Inputs:** +> - `image_a` _Required._ The first image to use to compare. If image_b is not supplied and image_a is a batch, the comparer will use the first two images of image_a. +> - `image_b` _Optional._ The second image to use to compare. Optional only if image_a is a batch with two images. +> - **Properties:** You can change the following properties (by right-clicking on the node, and select "Properties" or "Properties Panel" from the menu): +> - `comparer_mode` - Choose between "Slide" and "Click". Defaults to "Slide". + + +## Image Inset Crop +> The node that lets you crop an input image by either pixel value, or percentage value. + + +## Display Any +> Displays most any piece of text data from the backend _after execution_. + +## Power Lora Loader +> A super-simply Lora Loader node that can load multiple Loras at once, and quick toggle each, all in an ultra-condensed node. +>
    +> ℹ️ More Information +> +> - Add as many Lora's as you would like by clicking the "+ Add Lora" button. There's no real limit! +> - Right-click on a Lora widget for special options to move the lora up or down +> _(no affect on image, just presentation)_, toggle it on/off, or delete the row all together. +> - from the properties, change the `Show Strengths` to choose between showing a single, simple +> strength value (which will be used for both model and clip), or a more advanced view with +> both model and clip strengths being modifiable. +>
    + + +## ~~Lora Loader Stack~~ +> _**Deprecated.** Used the `Power Lora Loader` instead._ +> +> A simplified Lora Loader stack. Much like other suites, but more interoperable with standard inputs/outputs. + + +## Power Prompt +> Power up your prompt and get drop downs for adding your embeddings, loras, and even have saved prompt snippets. +>
    +> ℹ️ More Information +> +> - At the core, you can use Power Prompt almost as a String Primitive node with additional features of dropdowns for choosing your embeddings, and even loras, with no further processing. This will output just the raw `TEXT` to another node for any lora processing, CLIP Encoding, etc. +> - Connect a `CLIP` to the input to encode the text, with both the `CLIP` and `CONDITIONING` output right from the node. +> - Connect a `MODEL` to the input to parse and load any `` tags in the text automatically, without +> needing a separate Lora Loaders +>
    + +## Power Prompt - Simple +> Same as Power Prompt above, but without LORA support; made for a slightly cleaner negative prompt _(since negative prompts do not support loras)_. + +## SDXL Power Prompt - Positive +> The SDXL sibling to the Power Prompt above. It contains the text_g and text_l as separate text inputs, as well a couple more input slots necessary to ensure proper clipe encoding. Combine with + +## SDXL Power Prompt - Simple +> Like the non-SDXL `Power Prompt - Simple` node, this one is essentially the same as the SDXL Power Prompt but without lora support for either non-lora positive prompts or SDXL negative prompts _(since negative prompts do not support loras)_. + +## SDXL Config +> Just some configuration fields for SDXL prompting. Honestly, could be used for non SDXL too. + +## Context Switch / Context Switch Big +> A powerful node to branch your workflow. Works by choosing the first Context input that is not null/empty. +>
    +> ℹ️ More Information +> +> - Pass in several context nodes and the Context Switch will automatically choose the first non-null context to continue onward with. +> - Wondering how to toggle contexts to null? Use in conjuction with the **Fast Muter** or **Fast Groups Muter** +> +>
    + +## Any Switch +> A powerful node to similar to the Context Switch above, that chooses the first input that is not null/empty. +>
    +> ℹ️ More Information +> +> - Pass in several inmputs of the same type and the Any Switch will automatically choose the first non-null value to continue onward with. +> - Wondering how to toggle contexts to null? Use in conjuction with the **Fast Muter** or **Fast Groups Muter** +> +>
    + + +## Power Primitive +> A single node that can output primitives (STRING, INT, FLOAT, BOOLEAN). If connecting an input, it will cast/convert the primitive input to the desired output. +>
    +> ℹ️ More Information +> +> - You can hide the type selector input from the right-click menu or properties. +> - You can also fast-switch the output type from the right-click menu as well. +> +>
    + + +## Power Puter +> A powerful and versatile node that opens the door for a wide range of utility by offering mult-line code parsing for output. This node can be used for simple string concatenation, or math operations; to an image dimension or a node's widgets with advanced list comprehension. If you want to output something in your workflow, this is the node to do it. +> +> Additional documentation available in the [wiki](https://github.com/rgthree/rgthree-comfy/wiki/Node:-Power-Puter) +>
    +> ℹ️ More Information +> +> - Evaluate almost any kind of input and more, and choose your output from INT, FLOAT, STRING, or BOOLEAN. +> - Connect some nodes and do simply math operations like `a + b` or `ceil(1 / 2)`. +> - Or do more advanced things, like input an image, and get the width like `a.shape[2]`. +> - Even more powerful, you can target nodes in the prompt that's sent to the backend. For instance; if you have a Power Lora Loader node at id #5, and want to get a comma-delimited list of the enabled loras, you could enter: +> +> ``` +> loras = [v.lora for v in node(5).inputs.values() if 'lora' in v and v.on] +> ', '.join(loras) +> ``` +> +>
    + +## Fast Groups Muter +> The Fast Groups Muter is an input-less node that automatically collects all groups in your current workflow and allows you to quickly mute and unmute all nodes within the group. +>
    +> ℹ️ More Information +> +> - Groups will automatically be shown, though you can filter, sort and more from the **node Properties** _(by right-clicking on the node, and select "Properties" or "Properties Panel" from the menu)_. Properties include: +> - `matchColors` - Only add groups that match the provided colors. Can be ComfyUI colors (red, pale_blue) or hex codes (#a4d399). Multiple can be added, comma delimited. +> - `matchTitle` - Filter the list of toggles by title match (string match, or regular expression). +> - `showNav` - Add / remove a quick navigation arrow to take you to the group. (default: true) +> - `showAllGraphs` - Show groups from all [sub]graphs in the workflow. (default: true) +> - `sort` - Sort the toggles' order by "alphanumeric", graph "position", or "custom alphabet". (default: "position") +> - `customSortAlphabet` - When the sort property is "custom alphabet" you can define the alphabet to use here, which will match the beginning of each group name and sort against it. If group titles do not match any custom alphabet entry, then they will be put after groups that do, ordered alphanumerically. +> +> This can be a list of single characters, like "zyxw..." or comma delimited strings for more control, like "sdxl,pro,sd,n,p". +> +> Note, when two group title match the same custom alphabet entry, the normal alphanumeric alphabet breaks the tie. For instance, a custom alphabet of "e,s,d" will order groups names like "SDXL, SEGS, Detailer" eventhough the custom alphabet has an "e" before "d" (where one may expect "SE" to be before "SD"). +> +> To have "SEGS" appear before "SDXL" you can use longer strings. For instance, the custom alphabet value of "se,s,f" would work here. +> - `toggleRestriction` - Optionally, attempt to restrict the number of widgets that can be enabled to a maximum of one, or always one. +> +> _Note: If using "max one" or "always one" then this is only enforced when clicking a toggle on this node; if nodes within groups are changed outside of the initial toggle click, then these restriction will not be enforced, and could result in a state where more than one toggle is enabled. This could also happen if nodes are overlapped with multiple groups._ +>
    + +## Fast Groups Bypasser +> _Same as **Fast Groups Muter** above, but sets the connected nodes to "Bypass" instead of "Mute"_ + + +## Fast Muter +> A powerful 'control panel' node to quickly toggle connected nodes allowing them to quickly be muted or enabled +>
    +> ℹ️ More Information +> +> - Add a collection of all connected nodes allowing a single-spot as a "dashboard" to quickly enable and disable nodes. Two distinct nodes; one for "Muting" connected nodes, and one for "Bypassing" connected nodes. +>
    + + +## Fast Bypasser +> Same as Fast Muter but sets the connected nodes to "Bypass" + +## Fast Actions Button +> Oh boy, this node allows you to semi-automate connected nodes and/or ConfyUI. +>
    +> ℹ️ More Information +> +> - Connect nodes and, at the least, mute, bypass or enable them when the button is pressed. +> - Certain nodes expose additional actions. For instance, the `Seed` node you can set `Randomize Each Time` or `Use Last Queued Seed` when the button is pressed. +> - Also, from the node properties, set a shortcut key to toggle the button actions, without needing a click! +>
    + + +## Node Collector +> Used to cleanup noodles, this will accept any number of input nodes and passes it along to another node. +> +> ⚠️ *Currently, this should really only be connected to **Fast Muter**, **Fast Bypasser**, or **Mute / Bypass Relay**.* + + +## Mute / Bypass Repeater +> A powerful node that will dispatch its Mute/Bypass/Active mode to all connected input nodes or, if in a group w/o any connected inputs, will dispatch its Mute/Bypass/Active mode to all nodes in that group. +>
    +> ℹ️ More Information +> +> - πŸ’‘ Pro Tip #1: Connect this node's output to a **Fast Muter** or **Fast Bypasser** to have a single toggle there that can mute/bypass/enable many nodes with one click. +> +> - πŸ’‘ Pro Tip #2: Connect a **Mute / Bypass Relay** node to this node's inputs to have the relay automatically dispatch a mute/bypass/enable change to the repeater. +>
    + + +## Mute / Bypass Relay +> An advanced node that, when working with a **Mute / Bypass Repeater**, will relay its input nodes' +> modes (Mute, Bypass, or Active) to a connected repeater (which would then repeat that mode change +> to all of its inputs). +>
    +> ℹ️ More Information +> +> - When all connected input nodes are muted, the relay will set a connected repeater to mute (by +> default). +> - When all connected input nodes are bypassed, the relay will set a connected repeater to +> bypass (by default). +> - When _any_ connected input nodes are active, the relay will set a connected repeater to +> active (by default). +> - **Note:** If no inputs are connected, the relay will set a connected repeater to its mode +> _when its own mode is changed_. **Note**, if any inputs are connected, then the above bullets +> will occur and the Relay's mode does not matter. +> - **Pro Tip:** You can change which signals get sent on the above in the `Properties`. +> For instance, you could configure an inverse relay which will send a MUTE when any of its +> inputs are active (instead of sending an ACTIVE signal), and send an ACTIVE signal when all +> of its inputs are muted (instead of sending a MUTE signal), etc. +>
    + + +## Random Unmuter +> An advanced node used to unmute one of its inputs randomly when the graph is queued (and, immediately mute it back). +>
    +> ℹ️ More Information +> +> - **Note:** All input nodes MUST be muted to start; if not this node will not randomly unmute another. (This is powerful, as the generated image can be dragged in and the chosen input will already by unmuted and work w/o any further action.) +> - **Tip:** Connect a Repeater's output to this nodes input and place that Repeater on a group without any other inputs, and it will mute/unmute the entire group. +>
    + + +## Label +> A purely visual node, this allows you to add a floating label to your workflow. +>
    +> ℹ️ More Information +> +> - The text shown is the "Title" of the node and you can adjust the the font size, font family, +> font color, text alignment as well as a background color, padding, background border +> radius, and angle (in degrees) from the node's properties. You can double-click the node to +> open the properties panel. +> - The Title also supports the literal sequence "\\n" to insert a newline when drawing the label. +> - ~**Pro Tip #1:** You can add multiline text from the properties panel _(because ComfyUI let's +> you shift + enter there, only)._~ +> - **Pro Tip #2:** You can use ComfyUI's native "pin" option in the right-click menu to make the +> label stick to the workflow and clicks to "go through". You can right-click at any time to +> unpin. +> - **Pro Tip #3:** Color values are hexidecimal strings, like "#FFFFFF" for white, or "#660000" +> for dark red. You can supply a 7th & 8th value (or 5th if using shorthand) to create a +> transluscent color. For instance, "#FFFFFF88" is semi-transparent white. +>
    + + +# Advanced Techniques + +## First, a word on muting + +A lot of the power of these nodes comes from *Muting*. Muting is the basis of correctly implementing multiple paths for a workflow utlizing the Context Switch node. + +While other extensions may provide switches, they often get it wrong causing your workflow to do more work than is needed. While other switches may have a selector to choose which input to pass along, they don't stop the execution of the other inputs, which will result in wasted work. Instead, Context Switch works by choosing the first non-empty context to pass along and correctly Muting is one way to make a previous node empty, and causes no extra work to be done when set up correctly. + +### To understand muting, is to understand the graph flow + +Muting, and therefore using Switches, can often confuse people at first because it _feels_ like muting a node, or using a switch, should be able to stop or direct the _forward_ flow of the graph. However, this is not the case and, in fact, the graph actually starts working backwards. + +If you have a workflow that has a path like `... > Context > KSampler > VAE Decode > Save Image` it may initially _feel_ like you should be able to mute that first Context node and the graph would stop there when moving forward and skip the rest of that workflow. + +But you'll quickly find that will cause an error, becase the graph doesn't actually move forward. When a workflow is processed, it _first moves backwards_ starting at each "Output Node" (Preview Image, Save Image, even "Display String" etc.) and then walking backwards to all possible paths to get there. + +So, with that `... > Context > KSampler > VAE Decode > Save Image` example from above, we actually want to mute the `Save Image` node to stop this path. Once we do, since the output node is gone, none of these nodes will be run. + +Let's take a look at an example. + +### A powerful combination: Using Context, Context Switch, & Fast Muter + +![Context Node](./docs/rgthree_advanced.png) + +1. Using the **Context Switch** (aqua colored in screenshot) feed context inputs in order of preference. In the workflow above, the `Upscale Out` context is first so, if that one is enabled, it will be chosen for the output. If not, the second input slot which comes from the context rerouted from above (before the Upscaler booth) will be chosen. + + - Notice the `Upscale Preview` is _after_ the `Upscale Out` context node, using the image from it instead of the image from the upscale `VAE Decoder`. This is on purpose so, when we disable the `Upscale Out` context, none of the Upscaler nodes will run, saving precious GPU cycles. If we had the preview hooked up directly to the `VAE Decoder` the upscaler would always run to generate the preview, even if we had the `Upscale Out` context node disabled. + +2. We can now disable the `Upscale Out` context node by _muting_ it. Highlighting it and pressing `ctrl + m` will work. By doing so, it's output will be None, and it will not pass anthing onto the further nodes. In the diagram you can see the `Upscale Preview` is red, but that's OK; there are no actual errors to stop execution. + +3. Now, let's hook it up to the `Fast Muter` node. `The Fast Muter` node works as dashboard by adding quick toggles for any connected node (ignoring reroutes). In the diagram, we have both the `Upscaler Out` context node, and the `Save File` context node hooked up. So, we can quickly enable and disable those. + + - The workflow seen here would be a common one where we can generate a handful of base previews cheaply with a random seed, and then choose one to upscale and save to disk. + +4. Lastly, and optionally, you can see the `Node Collector`. Use it to clean up noodles if you want and connect it to the muter. You can connect anything to it, but doing so may break your workflow's execution. + +
    + +# ⚑ Improvements & Features + +rgthree-comfy adds several improvements, features, and optimizations to ComfyUI that are not directly tied to nodes. + +## Progress Bar +> A minimal progress bar that run alongs the top of the app window that shows the queue size, the current progress of the a prompt execution (within the same window), and the progress of multi-step nodes as well. +> +> You can remove/enable from rgthree-comfy settings, as well as configure the height/size. + + +## ~~ComfyUI Recursive Optimization~~ +> πŸŽ‰ The newest version of ComfyUI no longer suffers from poor execution recursion! This feature +> has been removed from rgthree-comfy. + + +## "Queue Selected Output Nodes" in right-click menu +> Sometimes you want to just queue one or two paths to specific output node(s) without executing the entire workflow. Well, now you can do just that by right-clicking on an output node and selecting `Queue Selected Output Nodes (rgthree)`. +> +>
    +> ℹ️ More Information +> +> - Select the _output_ nodes you want to execute. +> +> - Note: Only output nodes are captured and traversed, not all selected nodes. So if you select an output AND a node from a different path, only the path connected to the output will be executed and not non-output nodes, even if they were selected. +> +> - Note: The whole workflow is serialized, and then we trim what we don't want for the backend. So things like all seed random/increment/decrement will run even if that node isn't being sent in the end, etc. +> +>
    + + +## Auto-Nest Subdirectories in long Combos +> _(Off by default while experimenting, turn on in rgthree-comfy settings)_. +> +> Automatically detect top-level subdirectories in long combo lists (like, Load Checkpoint) and break out into sub directories. + + +## Quick Mute/Bypass Toggles in Group Headers +> _(Off by default while experimenting, turn on in rgthree-comfy settings)_. +> +> Adds a mute and/or bypass toggle icons in the top-right of Group Headers for one-click toggling of groups you may be currently looking at. + + +## Import Individual Node Widgets (Drag & Drop) +> _(Off by default while experimenting, turn on in rgthree-comfy settings)_. +> +> Allows dragging and dropping an image/JSON workflow from a previous generation and overriding the same node's widgets +> (that match with the same id & type). This is useful if you have several generations using the same general workflow +> and would like to import just some data, like a previous generation's seed, or prompt, etc. + + + +## "Copy Image" in right-click menu +> Right clicking on a node that has an image should have a context-menu item of "Copy Image" will allow you to copy the image right to your clipboard +> +> πŸŽ“ I believe this has graduated, with ComfyUI recently adding this setting too. You won't get two menu items; my code checks that there isn't already a "Copy Image" item there before adding it. + + +## Other/Smaller Fixes +- Fixed the width of ultra-wide node chooser on double click. +- Fixed z-indexes for textareas that would overlap above other elements, like Properties Panel, or @pythongosssss's image viewer. +- Check for bad links when loading a workflow and log to console, by default. _(See Link Fixer below)._ + +
    + +# πŸ“„ Link Fixer + +If your workflows sometimes have missing connections, or even errors on load, start up ComfyUI and go to http://127.0.0.1:8188/rgthree/link_fixer which will allow you to drop in an image or workflow json file and check for and fix any bad links. + +You can also enable a link fixer check in the rgthree-comfy settings to give you an alert if you load a workflow with bad linking data to start. diff --git a/custom_nodes/rgthree-comfy/__build__.py b/custom_nodes/rgthree-comfy/__build__.py new file mode 100644 index 00000000..32b78d04 --- /dev/null +++ b/custom_nodes/rgthree-comfy/__build__.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 + +import subprocess +import os +from shutil import rmtree, copytree, ignore_patterns +from glob import glob +import time +import re +import argparse + +from py.log import COLORS +from py.config import RGTHREE_CONFIG + +step_msg = '' +step_start = 0 +step_infos = [] + +def log_step(msg=None, status=None): + """ Logs a step keeping track of timing and initial msg. """ + global step_msg # pylint: disable=W0601 + global step_start # pylint: disable=W0601 + global step_infos # pylint: disable=W0601 + if msg: + tag = f'{COLORS["YELLOW"]}[ Notice ]' if status == 'Notice' else f'{COLORS["RESET"]}[Starting]' + step_msg = f'β–» {tag}{COLORS["RESET"]} {msg}...' + step_start = time.time() + step_infos = [] + print(step_msg, end="\r") + elif status: + if status != 'Error': + warnings = [w for w in step_infos if w["type"] == 'warn'] + status = "Warn" if warnings else status + step_time = round(time.time() - step_start, 3) + if status == 'Error': + status_msg = f'{COLORS["RED"]}‫ {status}{COLORS["RESET"]}' + elif status == 'Warn': + status_msg = f'{COLORS["YELLOW"]}! {status}{COLORS["RESET"]}' + else: + status_msg = f'{COLORS["BRIGHT_GREEN"]}πŸ—Έ {status}{COLORS["RESET"]}' + print(f'{step_msg.ljust(64, ".")} {status_msg} ({step_time}s)') + for info in step_infos: + print(info["msg"]) + +def log_step_info(msg:str, status='info'): + global step_infos # pylint: disable=W0601 + step_infos.append({"msg": f' - {msg}', "type": status}) + + +def build(without_tests = True, fix = False): + + THIS_DIR = os.path.dirname(os.path.abspath(__file__)) + DIR_SRC_WEB = os.path.abspath(f'{THIS_DIR}/src_web/') + DIR_WEB = os.path.abspath(f'{THIS_DIR}/web/') + DIR_WEB_COMFYUI = os.path.abspath(f'{DIR_WEB}/comfyui/') + + if fix: + tss = glob(os.path.join(DIR_SRC_WEB, "**", "*.ts"), recursive=True) + log_step(msg=f'Fixing {len(tss)} ts files') + for ts in tss: + with open(ts, 'r', encoding="utf-8") as f: + content = f.read() + # (\s*from\s*['"](?!.*[.]js['"]).*?)(['"];) in vscode. + content, n = re.subn(r'(\s*from [\'"](?!.*[.]js[\'"]).*?)([\'"];)', '\\1.js\\2', content) + if n > 0: + filename = os.path.basename(ts) + log_step_info( + f'{filename} has {n} import{"s" if n > 1 else ""} that do not end in ".js"', 'warn') + with open(ts, 'w', encoding="utf-8") as f: + f.write(content) + log_step(status="Done") + + log_step(msg='Copying web directory') + rmtree(DIR_WEB) + copytree(DIR_SRC_WEB, DIR_WEB, ignore=ignore_patterns("typings*", "*.ts", "*.scss")) + log_step(status="Done") + + ts_version_result = subprocess.run(["node", "./node_modules/typescript/bin/tsc", "-v"], + capture_output=True, + text=True, + check=True) + ts_version = re.sub(r'^.*Version\s*([\d\.]+).*', 'v\\1', ts_version_result.stdout, flags=re.DOTALL) + + log_step(msg=f'TypeScript ({ts_version})') + checked = subprocess.run(["node", "./node_modules/typescript/bin/tsc"], check=True) + log_step(status="Done") + + if not without_tests: + log_step(msg='Removing directories (KEEPING TESTING)', status="Notice") + else: + log_step(msg='Removing unneeded directories') + test_path = os.path.join(DIR_WEB, 'comfyui', 'tests') + if os.path.exists(test_path): + rmtree(test_path) + rmtree(os.path.join(DIR_WEB, 'comfyui', 'testing')) + # Always remove the dummy scripts_comfy directory + rmtree(os.path.join(DIR_WEB, 'scripts_comfy')) + log_step(status="Done") + + scsss = glob(os.path.join(DIR_SRC_WEB, "**", "*.scss"), recursive=True) + log_step(msg=f'SASS for {len(scsss)} files') + scsss = [i.replace(THIS_DIR, '.') for i in scsss] + cmds = ["node", "./node_modules/sass/sass"] + for scss in scsss: + out = scss.replace('src_web', 'web').replace('.scss', '.css') + cmds.append(f'{scss}:{out}') + cmds.append('--no-source-map') + checked = subprocess.run(cmds, check=True) + log_step(status="Done") + + # Handle the common directories. Because ComfyUI loads under /extensions/rgthree-comfy we can't + # easily share sources outside of the `DIR_WEB_COMFYUI` _and_ allow typescript to resolve them in + # src view, so we set the path in the tsconfig to map an import of "rgthree/common" to the + # "src_web/common" directory, but then need to rewrite the comfyui JS files to load from + # "../../rgthree/common" (which we map correctly in rgthree_server.py). + log_step(msg='Cleaning Imports') + js_files = glob(os.path.join(DIR_WEB, '**', '*.js'), recursive=True) + for file in js_files: + rel_path = file.replace(f'{DIR_WEB}/', "") + with open(file, 'r', encoding="utf-8") as f: + filedata = f.read() + num = rel_path.count(os.sep) + if rel_path.startswith('comfyui'): + filedata = re.sub(r'(from\s+["\'])rgthree/', f'\\1{"../" * (num + 1)}rgthree/', filedata) + filedata = re.sub(r'(from\s+["\'])scripts/', f'\\1{"../" * (num + 1)}scripts/', filedata) + # Dynamic Imports + filedata = re.sub(r'(import\(["\'])rgthree/', f'\\1{"../" * (num + 1)}rgthree/', filedata) + else: + filedata = re.sub(r'(from\s+["\'])rgthree/', f'\\1{"../" * num}', filedata) + filedata = re.sub(r'(from\s+["\'])scripts/', f'\\1{"../" * (num + 1)}scripts/', filedata) + # Dynamic Imports + filedata = re.sub(r'(import\(["\'])rgthree/', f'\\1{"../" * num}', filedata) + + filedata, n = re.subn(r'(\s*from [\'"](?!.*[.]js[\'"]).*?)([\'"];)', '\\1.js\\2', filedata) + if n > 0: + filename = file.split('rgthree-comfy')[1] + log_step_info( + f'{filename} has {n} import{"s" if n > 1 else ""} that do not end in ".js"', 'warn') + with open(file, 'w', encoding="utf-8") as f: + f.write(filedata) + log_step(status="Done") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-t", "--no-tests", default=False, action="store_true") + parser.add_argument("-f", "--fix", default=False, action="store_true") + args = parser.parse_args() + + start = time.time() + build(without_tests=args.no_tests, fix=args.fix) + print(f'Finished all in {round(time.time() - start, 3)}s') diff --git a/custom_nodes/rgthree-comfy/__commit__.py b/custom_nodes/rgthree-comfy/__commit__.py new file mode 100644 index 00000000..bab14170 --- /dev/null +++ b/custom_nodes/rgthree-comfy/__commit__.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 + +import subprocess +import os +import re +import datetime +import time +import argparse + +from __build__ import build, log_step, log_step_info + +_THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +_FILE_PY_PROJECT = os.path.join(_THIS_DIR, 'pyproject.toml') + +parser = argparse.ArgumentParser() +parser.add_argument( + "-m", "--message", help="The git commit message", required=True, action="store", type=str +) +args = parser.parse_args() + +start = time.time() +build() + +log_step(msg='Updating version in pyproject.toml') +py_project = '' +with open(_FILE_PY_PROJECT, "r", encoding='utf-8') as f: + py_project = f.read() + +version = re.search(r'^\s*version\s*=\s*"(.*?)"', py_project, flags=re.MULTILINE) +version_old = version[1] + +now = datetime.datetime.now() +version_new = version_old.split('.') +version_new[-1] = f'{str(now.year)[2:]}{now.month:02}{now.day:02}{now.hour:02}{now.minute:02}' +version_new = '.'.join(version_new) + +log_step_info(f'Updating from v{version_old} to v{version_new}') +py_project = py_project.replace(version_old, version_new) +with open(_FILE_PY_PROJECT, "w", encoding='utf-8') as f: + f.write(py_project) +log_step(status="Done") + +log_step('Running git add') +process = subprocess.Popen(['git', 'add', '.'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) +stdout, stderr = process.communicate() +log_step(status="Done") + +log_step('Running git commit') +process = subprocess.Popen(['git', 'commit', '-a', '-v', '-m', args.message], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE) +stdout, stderr = process.communicate() +log_step(status="Done") + +print(f'Finished all in {round(time.time() - start, 3)}s') diff --git a/custom_nodes/rgthree-comfy/__init__.py b/custom_nodes/rgthree-comfy/__init__.py new file mode 100644 index 00000000..edf954cb --- /dev/null +++ b/custom_nodes/rgthree-comfy/__init__.py @@ -0,0 +1,126 @@ +""" +@author: rgthree +@title: Comfy Nodes +@nickname: rgthree +@description: A bunch of nodes I created that I also find useful. +""" + +from glob import glob +import json +import os +import shutil +import re +import random + +import execution + +from .py.log import log +from .py.config import get_config_value +from .py.server.rgthree_server import * + +from .py.context import RgthreeContext +from .py.context_switch import RgthreeContextSwitch +from .py.context_switch_big import RgthreeContextSwitchBig +from .py.display_any import RgthreeDisplayAny, RgthreeDisplayInt +from .py.lora_stack import RgthreeLoraLoaderStack +from .py.seed import RgthreeSeed +from .py.sdxl_empty_latent_image import RgthreeSDXLEmptyLatentImage +from .py.power_prompt import RgthreePowerPrompt +from .py.power_prompt_simple import RgthreePowerPromptSimple +from .py.image_inset_crop import RgthreeImageInsetCrop +from .py.context_big import RgthreeBigContext +from .py.dynamic_context import RgthreeDynamicContext +from .py.dynamic_context_switch import RgthreeDynamicContextSwitch +from .py.ksampler_config import RgthreeKSamplerConfig +from .py.sdxl_power_prompt_postive import RgthreeSDXLPowerPromptPositive +from .py.sdxl_power_prompt_simple import RgthreeSDXLPowerPromptSimple +from .py.any_switch import RgthreeAnySwitch +from .py.context_merge import RgthreeContextMerge +from .py.context_merge_big import RgthreeContextMergeBig +from .py.image_comparer import RgthreeImageComparer +from .py.power_lora_loader import RgthreePowerLoraLoader +from .py.power_primitive import RgthreePowerPrimitive +from .py.image_or_latent_size import RgthreeImageOrLatentSize +from .py.image_resize import RgthreeImageResize +from .py.power_puter import RgthreePowerPuter + +NODE_CLASS_MAPPINGS = { + RgthreeBigContext.NAME: RgthreeBigContext, + RgthreeContext.NAME: RgthreeContext, + RgthreeContextSwitch.NAME: RgthreeContextSwitch, + RgthreeContextSwitchBig.NAME: RgthreeContextSwitchBig, + RgthreeContextMerge.NAME: RgthreeContextMerge, + RgthreeContextMergeBig.NAME: RgthreeContextMergeBig, + RgthreeDisplayInt.NAME: RgthreeDisplayInt, + RgthreeDisplayAny.NAME: RgthreeDisplayAny, + RgthreeLoraLoaderStack.NAME: RgthreeLoraLoaderStack, + RgthreeSeed.NAME: RgthreeSeed, + RgthreeImageInsetCrop.NAME: RgthreeImageInsetCrop, + RgthreePowerPrompt.NAME: RgthreePowerPrompt, + RgthreePowerPromptSimple.NAME: RgthreePowerPromptSimple, + RgthreeKSamplerConfig.NAME: RgthreeKSamplerConfig, + RgthreeSDXLEmptyLatentImage.NAME: RgthreeSDXLEmptyLatentImage, + RgthreeSDXLPowerPromptPositive.NAME: RgthreeSDXLPowerPromptPositive, + RgthreeSDXLPowerPromptSimple.NAME: RgthreeSDXLPowerPromptSimple, + RgthreeAnySwitch.NAME: RgthreeAnySwitch, + RgthreeImageComparer.NAME: RgthreeImageComparer, + RgthreePowerLoraLoader.NAME: RgthreePowerLoraLoader, + RgthreePowerPrimitive.NAME: RgthreePowerPrimitive, + RgthreeImageOrLatentSize.NAME: RgthreeImageOrLatentSize, + RgthreeImageResize.NAME: RgthreeImageResize, + RgthreePowerPuter.NAME: RgthreePowerPuter, +} + +if get_config_value('unreleased.dynamic_context.enabled') is True: + NODE_CLASS_MAPPINGS[RgthreeDynamicContext.NAME] = RgthreeDynamicContext + NODE_CLASS_MAPPINGS[RgthreeDynamicContextSwitch.NAME] = RgthreeDynamicContextSwitch + +# WEB_DIRECTORY is the comfyui nodes directory that ComfyUI will link and auto-load. +WEB_DIRECTORY = "./web/comfyui" + +THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +DIR_WEB = os.path.abspath(f'{THIS_DIR}/{WEB_DIRECTORY}') +DIR_PY = os.path.abspath(f'{THIS_DIR}/py') + +# remove old directories +OLD_DIRS = [ + os.path.abspath(f'{THIS_DIR}/../../web/extensions/rgthree'), + os.path.abspath(f'{THIS_DIR}/../../web/extensions/rgthree-comfy'), +] +for old_dir in OLD_DIRS: + if os.path.exists(old_dir): + shutil.rmtree(old_dir) + +__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY'] + +NOT_NODES = ['constants', 'log', 'utils', 'rgthree', 'rgthree_server', 'image_clipbaord', 'config'] + +nodes = [] +for file in glob(os.path.join(DIR_PY, '*.py')) + glob(os.path.join(DIR_WEB, '*.js')): + name = os.path.splitext(os.path.basename(file))[0] + if name in NOT_NODES or name in nodes: + continue + if name.startswith('_') or name.startswith('base') or 'utils' in name: + continue + nodes.append(name) + if name == 'display_any': + nodes.append('display_int') + +print() +adjs = ['exciting', 'extraordinary', 'epic', 'fantastic', 'magnificent'] +log(f'Loaded {len(nodes)} {random.choice(adjs)} nodes. πŸŽ‰', color='BRIGHT_GREEN') +print() + +if get_config_value('announcements.comfy-nodes-20.incompatible', True): + message = ( + "ComfyUI's new Node 2.0 rendering may be incompatible with some rgthree-comfy nodes " + "and features, breaking some rendering as well as losing the ability to " + "access a node's properties (a vital part of many nodes). It also appears to run MUCH more " + "slowly spiking CPU usage and causing jankiness and unresponsiveness, especially with large " + "workflows. Personally I am not planning to use the new Nodes 2.0 and, unfortunately, am not " + "able to invest the time to investigate and overhaul rgthree-comfy where needed. " + "If you have issues when Nodes 2.0 is enabled, I'd urge you to switch it off as well and " + "join me in hoping ComfyUI is not planning to deprecate the existing, stable canvas rendering " + "all together.\n" + ) + log(message, color='YELLOW', id='announcements.comfy-nodes-20.incompatible', at_most_secs=60) diff --git a/custom_nodes/rgthree-comfy/__update_comfy__.py b/custom_nodes/rgthree-comfy/__update_comfy__.py new file mode 100644 index 00000000..262f1129 --- /dev/null +++ b/custom_nodes/rgthree-comfy/__update_comfy__.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python3 + +# A nicer output for git pulling custom nodes (and ComfyUI). +# Quick shell version: ls | xargs -I % sh -c 'echo; echo %; git -C % pull' + +import os +from subprocess import Popen, PIPE, STDOUT + + +def pull_path(path): + p = Popen(["git", "-C", path, "pull"], stdout=PIPE, stderr=STDOUT) + output, error = p.communicate() + return output.decode() + +THIS_DIR=os.path.dirname(os.path.abspath(__file__)) + +def show_output(output): + if output.startswith('Already up to date'): + print(f' \33[32mπŸ—Έ {output}\33[0m', end ='') + elif output.startswith('error:'): + print(f' \33[31m🞫 Error.\33[0m \n {output}') + else: + print(f' \33[33mπŸ‘… Needs update.\33[0m \n {output}', end='') + + +os.chdir(THIS_DIR) +os.chdir("../") + +# Get the list or custom nodes, so we can format the output a little more nicely. +custom_extensions = [] +custom_extensions_name_max = 0 +for directory in os.listdir(os.getcwd()): + if os.path.isdir(directory) and directory != "__pycache__": #and directory != "rgthree-comfy" : + custom_extensions.append({ + 'directory': directory + }) + if len(directory) > custom_extensions_name_max: + custom_extensions_name_max = len(directory) + +if len(custom_extensions) == 0: + custom_extensions_name_max = 15 +else: + custom_extensions_name_max += 6 + +# Update ComfyUI itself. +label = "{0:.<{max}}".format('Updating ComfyUI ', max=custom_extensions_name_max) +print(label, end = '') +show_output(pull_path('../')) + +# If we have custom nodes, update them as well. +if len(custom_extensions) > 0: + print(f'\nUpdating custom_nodes ({len(custom_extensions)}):') + for custom_extension in custom_extensions: + directory = custom_extension['directory'] + label = "{0:.<{max}}".format(f'πŸ—€ {directory} ', max=custom_extensions_name_max) + print(label, end = '') + show_output(pull_path(directory)) diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_advanced.png b/custom_nodes/rgthree-comfy/docs/rgthree_advanced.png new file mode 100644 index 00000000..59c77cf2 Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_advanced.png differ diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_advanced_metadata.png b/custom_nodes/rgthree-comfy/docs/rgthree_advanced_metadata.png new file mode 100644 index 00000000..a9809a5d Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_advanced_metadata.png differ diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_context.png b/custom_nodes/rgthree-comfy/docs/rgthree_context.png new file mode 100644 index 00000000..ee203e59 Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_context.png differ diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_context_metadata.png b/custom_nodes/rgthree-comfy/docs/rgthree_context_metadata.png new file mode 100644 index 00000000..a5fcc34f Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_context_metadata.png differ diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_router.png b/custom_nodes/rgthree-comfy/docs/rgthree_router.png new file mode 100644 index 00000000..f3581d70 Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_router.png differ diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_seed.png b/custom_nodes/rgthree-comfy/docs/rgthree_seed.png new file mode 100644 index 00000000..32c392fb Binary files /dev/null and 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UI should fail gracefully. +# TODO: See if anyone actually used this. +# folder_paths.folder_names_and_paths['saved_prompts'] = ([], set(['.txt'])) diff --git a/custom_nodes/rgthree-comfy/py/__init__.py b/custom_nodes/rgthree-comfy/py/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/rgthree-comfy/py/any_switch.py b/custom_nodes/rgthree-comfy/py/any_switch.py new file mode 100644 index 00000000..99713faf --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/any_switch.py @@ -0,0 +1,38 @@ +from .context_utils import is_context_empty +from .constants import get_category, get_name +from .utils import FlexibleOptionalInputType, any_type + + +def is_none(value): + """Checks if a value is none. Pulled out in case we want to expand what 'None' means.""" + if value is not None: + if isinstance(value, dict) and 'model' in value and 'clip' in value: + return is_context_empty(value) + return value is None + + +class RgthreeAnySwitch: + """The dynamic Any Switch. """ + + NAME = get_name("Any Switch") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType(any_type), + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ('*',) + FUNCTION = "switch" + + def switch(self, **kwargs): + """Chooses the first non-empty item to output.""" + any_value = None + for key, value in kwargs.items(): + if key.startswith('any_') and not is_none(value): + any_value = value + break + return (any_value,) diff --git a/custom_nodes/rgthree-comfy/py/config.py b/custom_nodes/rgthree-comfy/py/config.py new file mode 100644 index 00000000..a50c4929 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/config.py @@ -0,0 +1,111 @@ +import os +import json + +from .utils import get_dict_value, set_dict_value, dict_has_key, load_json_file +from .pyproject import VERSION + + +def get_config_value(key, default=None): + return get_dict_value(RGTHREE_CONFIG, key, default) + + +def extend_config(default_config, user_config): + """ Returns a new config dict combining user_config into defined keys for default_config.""" + cfg = {} + for key, value in default_config.items(): + if key not in user_config: + cfg[key] = value + elif isinstance(value, dict): + cfg[key] = extend_config(value, user_config[key]) + else: + cfg[key] = user_config[key] if key in user_config else value + return cfg + + +def set_user_config(data: dict): + """ Sets the user configuration.""" + count = 0 + for key, value in data.items(): + if dict_has_key(DEFAULT_CONFIG, key): + set_dict_value(USER_CONFIG, key, value) + set_dict_value(RGTHREE_CONFIG, key, value) + count += 1 + if count > 0: + write_user_config() + + +def get_rgthree_default_config(): + """ Gets the default configuration.""" + return load_json_file(DEFAULT_CONFIG_FILE, default={}) + + +def get_rgthree_user_config(): + """ Gets the user configuration.""" + return load_json_file(USER_CONFIG_FILE, default={}) + + +def write_user_config(): + """ Writes the user configuration.""" + with open(USER_CONFIG_FILE, 'w+', encoding='UTF-8') as file: + json.dump(USER_CONFIG, file, sort_keys=True, indent=2, separators=(",", ": ")) + + +THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +DEFAULT_CONFIG_FILE = os.path.join(THIS_DIR, '..', 'rgthree_config.json.default') +USER_CONFIG_FILE = os.path.join(THIS_DIR, '..', 'rgthree_config.json') + +DEFAULT_CONFIG = {} +USER_CONFIG = {} +RGTHREE_CONFIG = {} + + +def refresh_config(): + """Refreshes the config.""" + global DEFAULT_CONFIG, USER_CONFIG, RGTHREE_CONFIG + DEFAULT_CONFIG = get_rgthree_default_config() + USER_CONFIG = get_rgthree_user_config() + + # Migrate old config options into "features" + needs_to_write_user_config = False + if 'patch_recursive_execution' in USER_CONFIG: + del USER_CONFIG['patch_recursive_execution'] + needs_to_write_user_config = True + + if 'features' in USER_CONFIG and 'patch_recursive_execution' in USER_CONFIG['features']: + del USER_CONFIG['features']['patch_recursive_execution'] + needs_to_write_user_config = True + + if 'show_alerts_for_corrupt_workflows' in USER_CONFIG: + if 'features' not in USER_CONFIG: + USER_CONFIG['features'] = {} + USER_CONFIG['features']['show_alerts_for_corrupt_workflows'] = USER_CONFIG[ + 'show_alerts_for_corrupt_workflows'] + del USER_CONFIG['show_alerts_for_corrupt_workflows'] + needs_to_write_user_config = True + + if 'monitor_for_corrupt_links' in USER_CONFIG: + if 'features' not in USER_CONFIG: + USER_CONFIG['features'] = {} + USER_CONFIG['features']['monitor_for_corrupt_links'] = USER_CONFIG['monitor_for_corrupt_links'] + del USER_CONFIG['monitor_for_corrupt_links'] + needs_to_write_user_config = True + + if needs_to_write_user_config is True: + print('writing new user config.') + write_user_config() + + RGTHREE_CONFIG = {"version": VERSION} | extend_config(DEFAULT_CONFIG, USER_CONFIG) + + if "unreleased" in USER_CONFIG and "unreleased" not in RGTHREE_CONFIG: + RGTHREE_CONFIG["unreleased"] = USER_CONFIG["unreleased"] + + if "debug" in USER_CONFIG and "debug" not in RGTHREE_CONFIG: + RGTHREE_CONFIG["debug"] = USER_CONFIG["debug"] + + +def get_config(): + """Returns the congfig.""" + return RGTHREE_CONFIG + + +refresh_config() diff --git a/custom_nodes/rgthree-comfy/py/constants.py b/custom_nodes/rgthree-comfy/py/constants.py new file mode 100644 index 00000000..73b7d687 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/constants.py @@ -0,0 +1,11 @@ + +NAMESPACE='rgthree' + +def get_name(name): + return '{} ({})'.format(name, NAMESPACE) + +def get_category(sub_dirs = None): + if sub_dirs is None: + return NAMESPACE + else: + return "{}/utils".format(NAMESPACE) diff --git a/custom_nodes/rgthree-comfy/py/context.py b/custom_nodes/rgthree-comfy/py/context.py new file mode 100644 index 00000000..e2c94f6a --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context.py @@ -0,0 +1,33 @@ +"""The Context node.""" +from .context_utils import (ORIG_CTX_OPTIONAL_INPUTS, ORIG_CTX_RETURN_NAMES, ORIG_CTX_RETURN_TYPES, + get_orig_context_return_tuple, new_context) +from .constants import get_category, get_name + + +class RgthreeContext: + """The initial Context node. + + For now, this nodes' outputs will remain as-is, as they are perfect for most 1.5 application, but + is also backwards compatible with other Context nodes. + """ + + NAME = get_name("Context") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": ORIG_CTX_OPTIONAL_INPUTS, + "hidden": { + "version": "FLOAT" + }, + } + + RETURN_TYPES = ORIG_CTX_RETURN_TYPES + RETURN_NAMES = ORIG_CTX_RETURN_NAMES + FUNCTION = "convert" + + def convert(self, base_ctx=None, **kwargs): # pylint: disable = missing-function-docstring + ctx = new_context(base_ctx, **kwargs) + return get_orig_context_return_tuple(ctx) diff --git a/custom_nodes/rgthree-comfy/py/context_big.py b/custom_nodes/rgthree-comfy/py/context_big.py new file mode 100644 index 00000000..411cdc8f --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context_big.py @@ -0,0 +1,31 @@ +"""The Conmtext big node.""" +from .constants import get_category, get_name +from .context_utils import (ALL_CTX_OPTIONAL_INPUTS, ALL_CTX_RETURN_NAMES, ALL_CTX_RETURN_TYPES, + new_context, get_context_return_tuple) + + +class RgthreeBigContext: + """The Context Big node. + + This context node will expose all context fields as inputs and outputs. It is backwards compatible + with other context nodes and can be intertwined with them. + """ + + NAME = get_name("Context Big") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name,missing-function-docstring + return { + "required": {}, + "optional": ALL_CTX_OPTIONAL_INPUTS, + "hidden": {}, + } + + RETURN_TYPES = ALL_CTX_RETURN_TYPES + RETURN_NAMES = ALL_CTX_RETURN_NAMES + FUNCTION = "convert" + + def convert(self, base_ctx=None, **kwargs): # pylint: disable = missing-function-docstring + ctx = new_context(base_ctx, **kwargs) + return get_context_return_tuple(ctx) diff --git a/custom_nodes/rgthree-comfy/py/context_merge.py b/custom_nodes/rgthree-comfy/py/context_merge.py new file mode 100644 index 00000000..02d0afe6 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context_merge.py @@ -0,0 +1,37 @@ +"""The Context Switch (Big).""" +from .constants import get_category, get_name +from .context_utils import (ORIG_CTX_RETURN_TYPES, ORIG_CTX_RETURN_NAMES, merge_new_context, + get_orig_context_return_tuple, is_context_empty) +from .utils import FlexibleOptionalInputType + + +class RgthreeContextMerge: + """The Context Merge node.""" + + NAME = get_name("Context Merge") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType("RGTHREE_CONTEXT"), + } + + RETURN_TYPES = ORIG_CTX_RETURN_TYPES + RETURN_NAMES = ORIG_CTX_RETURN_NAMES + FUNCTION = "merge" + + def get_return_tuple(self, ctx): + """Returns the context data. Separated so it can be overridden.""" + return get_orig_context_return_tuple(ctx) + + def merge(self, **kwargs): + """Merges any non-null passed contexts; later ones overriding earlier.""" + ctxs = [ + value for key, value in kwargs.items() + if key.startswith('ctx_') and not is_context_empty(value) + ] + ctx = merge_new_context(*ctxs) + + return self.get_return_tuple(ctx) diff --git a/custom_nodes/rgthree-comfy/py/context_merge_big.py b/custom_nodes/rgthree-comfy/py/context_merge_big.py new file mode 100644 index 00000000..f4039e4e --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context_merge_big.py @@ -0,0 +1,16 @@ +"""The Context Switch (Big).""" +from .constants import get_category, get_name +from .context_utils import (ALL_CTX_RETURN_TYPES, ALL_CTX_RETURN_NAMES, get_context_return_tuple) +from .context_merge import RgthreeContextMerge + + +class RgthreeContextMergeBig(RgthreeContextMerge): + """The Context Merge Big node.""" + + NAME = get_name("Context Merge Big") + RETURN_TYPES = ALL_CTX_RETURN_TYPES + RETURN_NAMES = ALL_CTX_RETURN_NAMES + + def get_return_tuple(self, ctx): + """Returns the context data. Separated so it can be overridden.""" + return get_context_return_tuple(ctx) diff --git a/custom_nodes/rgthree-comfy/py/context_switch.py b/custom_nodes/rgthree-comfy/py/context_switch.py new file mode 100644 index 00000000..67e02e14 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context_switch.py @@ -0,0 +1,36 @@ +"""The original Context Switch.""" +from .constants import get_category, get_name +from .context_utils import (ORIG_CTX_RETURN_TYPES, ORIG_CTX_RETURN_NAMES, is_context_empty, + get_orig_context_return_tuple) +from .utils import FlexibleOptionalInputType + + +class RgthreeContextSwitch: + """The (original) Context Switch node.""" + + NAME = get_name("Context Switch") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType("RGTHREE_CONTEXT"), + } + + RETURN_TYPES = ORIG_CTX_RETURN_TYPES + RETURN_NAMES = ORIG_CTX_RETURN_NAMES + FUNCTION = "switch" + + def get_return_tuple(self, ctx): + """Returns the context data. Separated so it can be overridden.""" + return get_orig_context_return_tuple(ctx) + + def switch(self, **kwargs): + """Chooses the first non-empty Context to output.""" + ctx = None + for key, value in kwargs.items(): + if key.startswith('ctx_') and not is_context_empty(value): + ctx = value + break + return self.get_return_tuple(ctx) diff --git a/custom_nodes/rgthree-comfy/py/context_switch_big.py b/custom_nodes/rgthree-comfy/py/context_switch_big.py new file mode 100644 index 00000000..4382e302 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context_switch_big.py @@ -0,0 +1,16 @@ +"""The Context Switch (Big).""" +from .constants import get_category, get_name +from .context_utils import (ALL_CTX_RETURN_TYPES, ALL_CTX_RETURN_NAMES, get_context_return_tuple) +from .context_switch import RgthreeContextSwitch + + +class RgthreeContextSwitchBig(RgthreeContextSwitch): + """The Context Switch Big node.""" + + NAME = get_name("Context Switch Big") + RETURN_TYPES = ALL_CTX_RETURN_TYPES + RETURN_NAMES = ALL_CTX_RETURN_NAMES + + def get_return_tuple(self, ctx): + """Overrides the RgthreeContextSwitch `get_return_tuple` to return big context data.""" + return get_context_return_tuple(ctx) diff --git a/custom_nodes/rgthree-comfy/py/context_utils.py b/custom_nodes/rgthree-comfy/py/context_utils.py new file mode 100644 index 00000000..6c2e6847 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/context_utils.py @@ -0,0 +1,118 @@ +"""A set of constants and utilities for handling contexts. + +Sets up the inputs and outputs for the Context going forward, with additional functions for +creating and exporting context objects. +""" +import comfy.samplers +import folder_paths + +_all_context_input_output_data = { + "base_ctx": ("base_ctx", "RGTHREE_CONTEXT", "CONTEXT"), + "model": ("model", "MODEL", "MODEL"), + "clip": ("clip", "CLIP", "CLIP"), + "vae": ("vae", "VAE", "VAE"), + "positive": ("positive", "CONDITIONING", "POSITIVE"), + "negative": ("negative", "CONDITIONING", "NEGATIVE"), + "latent": ("latent", "LATENT", "LATENT"), + "images": ("images", "IMAGE", "IMAGE"), + "seed": ("seed", "INT", "SEED"), + "steps": ("steps", "INT", "STEPS"), + "step_refiner": ("step_refiner", "INT", "STEP_REFINER"), + "cfg": ("cfg", "FLOAT", "CFG"), + "ckpt_name": ("ckpt_name", folder_paths.get_filename_list("checkpoints"), "CKPT_NAME"), + "sampler": ("sampler", comfy.samplers.KSampler.SAMPLERS, "SAMPLER"), + "scheduler": ("scheduler", comfy.samplers.KSampler.SCHEDULERS, "SCHEDULER"), + "clip_width": ("clip_width", "INT", "CLIP_WIDTH"), + "clip_height": ("clip_height", "INT", "CLIP_HEIGHT"), + "text_pos_g": ("text_pos_g", "STRING", "TEXT_POS_G"), + "text_pos_l": ("text_pos_l", "STRING", "TEXT_POS_L"), + "text_neg_g": ("text_neg_g", "STRING", "TEXT_NEG_G"), + "text_neg_l": ("text_neg_l", "STRING", "TEXT_NEG_L"), + "mask": ("mask", "MASK", "MASK"), + "control_net": ("control_net", "CONTROL_NET", "CONTROL_NET"), +} + +force_input_types = ["INT", "STRING", "FLOAT"] +force_input_names = ["sampler", "scheduler", "ckpt_name"] + + +def _create_context_data(input_list=None): + """Returns a tuple of context inputs, return types, and return names to use in a node"s def""" + if input_list is None: + input_list = _all_context_input_output_data.keys() + list_ctx_return_types = [] + list_ctx_return_names = [] + ctx_optional_inputs = {} + for inp in input_list: + data = _all_context_input_output_data[inp] + list_ctx_return_types.append(data[1]) + list_ctx_return_names.append(data[2]) + ctx_optional_inputs[data[0]] = tuple([data[1]] + ([{ + "forceInput": True + }] if data[1] in force_input_types or data[0] in force_input_names else [])) + + ctx_return_types = tuple(list_ctx_return_types) + ctx_return_names = tuple(list_ctx_return_names) + return (ctx_optional_inputs, ctx_return_types, ctx_return_names) + + +ALL_CTX_OPTIONAL_INPUTS, ALL_CTX_RETURN_TYPES, ALL_CTX_RETURN_NAMES = _create_context_data() + +_original_ctx_inputs_list = [ + "base_ctx", "model", "clip", "vae", "positive", "negative", "latent", "images", "seed" +] +ORIG_CTX_OPTIONAL_INPUTS, ORIG_CTX_RETURN_TYPES, ORIG_CTX_RETURN_NAMES = _create_context_data( + _original_ctx_inputs_list) + + +def new_context(base_ctx, **kwargs): + """Creates a new context from the provided data, with an optional base ctx to start.""" + context = base_ctx if base_ctx is not None else None + new_ctx = {} + for key in _all_context_input_output_data: + if key == "base_ctx": + continue + v = kwargs[key] if key in kwargs else None + new_ctx[key] = v if v is not None else context[ + key] if context is not None and key in context else None + return new_ctx + + +def merge_new_context(*args): + """Creates a new context by merging provided contexts with the latter overriding same fields.""" + new_ctx = {} + for key in _all_context_input_output_data: + if key == "base_ctx": + continue + v = None + # Move backwards through the passed contexts until we find a value and use it. + for ctx in reversed(args): + v = ctx[key] if not is_context_empty(ctx) and key in ctx else None + if v is not None: + break + new_ctx[key] = v + return new_ctx + + +def get_context_return_tuple(ctx, inputs_list=None): + """Returns a tuple for returning in the order of the inputs list.""" + if inputs_list is None: + inputs_list = _all_context_input_output_data.keys() + tup_list = [ + ctx, + ] + for key in inputs_list: + if key == "base_ctx": + continue + tup_list.append(ctx[key] if ctx is not None and key in ctx else None) + return tuple(tup_list) + + +def get_orig_context_return_tuple(ctx): + """Returns a tuple for returning from a node with only the original context keys.""" + return get_context_return_tuple(ctx, _original_ctx_inputs_list) + + +def is_context_empty(ctx): + """Checks if the provided ctx is None or contains just None values.""" + return not ctx or all(v is None for v in ctx.values()) diff --git a/custom_nodes/rgthree-comfy/py/display_any.py b/custom_nodes/rgthree-comfy/py/display_any.py new file mode 100644 index 00000000..19238dba --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/display_any.py @@ -0,0 +1,77 @@ +import json +from .constants import get_category, get_name +from .utils import any_type, get_dict_value + + +class RgthreeDisplayAny: + """Display any data node.""" + + NAME = get_name('Display Any') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "source": (any_type, {}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + RETURN_TYPES = () + FUNCTION = "main" + OUTPUT_NODE = True + + def main(self, source=None, unique_id=None, extra_pnginfo=None): + value = 'None' + if isinstance(source, str): + value = source + elif isinstance(source, (int, float, bool)): + value = str(source) + elif source is not None: + try: + value = json.dumps(source) + except Exception: + try: + value = str(source) + except Exception: + value = 'source exists, but could not be serialized.' + + # Save the output to the pnginfo so it's pre-filled when loading the data. + if extra_pnginfo and unique_id: + for node in get_dict_value(extra_pnginfo, 'workflow.nodes', []): + if str(node['id']) == str(unique_id): + node['widgets_values'] = [value] + break + + return {"ui": {"text": (value,)}} + + +class RgthreeDisplayInt: + """Old DisplayInt node. + + Can be ported over to DisplayAny if https://github.com/comfyanonymous/ComfyUI/issues/1527 fixed. + """ + + NAME = get_name('Display Int') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input": ("INT", { + "forceInput": True + }), + }, + } + + RETURN_TYPES = () + FUNCTION = "main" + OUTPUT_NODE = True + + def main(self, input=None): + return {"ui": {"text": (input,)}} diff --git a/custom_nodes/rgthree-comfy/py/dynamic_context.py b/custom_nodes/rgthree-comfy/py/dynamic_context.py new file mode 100644 index 00000000..411210d0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/dynamic_context.py @@ -0,0 +1,56 @@ +"""The Dynamic Context node.""" +from mimetypes import add_type +from .constants import get_category, get_name +from .utils import ByPassTypeTuple, FlexibleOptionalInputType + + +class RgthreeDynamicContext: + """The Dynamic Context node. + + Similar to the static Context and Context Big nodes, this allows users to add any number and + variety of inputs to a Dynamic Context node, and return the outputs by key name. + """ + + NAME = get_name("Dynamic Context") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name,missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType(add_type), + "hidden": {}, + } + + RETURN_TYPES = ByPassTypeTuple(("RGTHREE_DYNAMIC_CONTEXT",)) + RETURN_NAMES = ByPassTypeTuple(("CONTEXT",)) + FUNCTION = "main" + + def main(self, **kwargs): + """Creates a new context from the provided data, with an optional base ctx to start. + + This node takes a list of named inputs that are the named keys (with an optional "+ " prefix) + which are to be stored within the ctx dict as well as a list of keys contained in `output_keys` + to determine the list of output data. + """ + + base_ctx = kwargs.get('base_ctx', None) + output_keys = kwargs.get('output_keys', None) + + new_ctx = base_ctx.copy() if base_ctx is not None else {} + + for key_raw, value in kwargs.items(): + if key_raw in ['base_ctx', 'output_keys']: + continue + key = key_raw.upper() + if key.startswith('+ '): + key = key[2:] + new_ctx[key] = value + + print(new_ctx) + + res = [new_ctx] + output_keys = output_keys.split(',') if output_keys is not None else [] + for key in output_keys: + res.append(new_ctx[key] if key in new_ctx else None) + return tuple(res) diff --git a/custom_nodes/rgthree-comfy/py/dynamic_context_switch.py b/custom_nodes/rgthree-comfy/py/dynamic_context_switch.py new file mode 100644 index 00000000..3d1b9e6a --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/dynamic_context_switch.py @@ -0,0 +1,39 @@ +"""The original Context Switch.""" +from .constants import get_category, get_name +from .context_utils import is_context_empty +from .utils import ByPassTypeTuple, FlexibleOptionalInputType + + +class RgthreeDynamicContextSwitch: + """The initial Context Switch node.""" + + NAME = get_name("Dynamic Context Switch") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType("RGTHREE_DYNAMIC_CONTEXT"), + } + + RETURN_TYPES = ByPassTypeTuple(("RGTHREE_DYNAMIC_CONTEXT",)) + RETURN_NAMES = ByPassTypeTuple(("CONTEXT",)) + FUNCTION = "switch" + + def switch(self, **kwargs): + """Chooses the first non-empty Context to output.""" + + output_keys = kwargs.get('output_keys', None) + + ctx = None + for key, value in kwargs.items(): + if key.startswith('ctx_') and not is_context_empty(value): + ctx = value + break + + res = [ctx] + output_keys = output_keys.split(',') if output_keys is not None else [] + for key in output_keys: + res.append(ctx[key] if ctx is not None and key in ctx else None) + return tuple(res) diff --git a/custom_nodes/rgthree-comfy/py/image_comparer.py b/custom_nodes/rgthree-comfy/py/image_comparer.py new file mode 100644 index 00000000..079ebdbf --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/image_comparer.py @@ -0,0 +1,42 @@ +from nodes import PreviewImage + +from .constants import get_category, get_name + + +class RgthreeImageComparer(PreviewImage): + """A node that compares two images in the UI.""" + + NAME = get_name('Image Comparer') + CATEGORY = get_category() + FUNCTION = "compare_images" + DESCRIPTION = "Compares two images with a hover slider, or click from properties." + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + def compare_images(self, + image_a=None, + image_b=None, + filename_prefix="rgthree.compare.", + prompt=None, + extra_pnginfo=None): + + result = { "ui": { "a_images":[], "b_images": [] } } + if image_a is not None and len(image_a) > 0: + result['ui']['a_images'] = self.save_images(image_a, filename_prefix, prompt, extra_pnginfo)['ui']['images'] + + if image_b is not None and len(image_b) > 0: + result['ui']['b_images'] = self.save_images(image_b, filename_prefix, prompt, extra_pnginfo)['ui']['images'] + + return result \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/py/image_inset_crop.py b/custom_nodes/rgthree-comfy/py/image_inset_crop.py new file mode 100644 index 00000000..5f5f29ad --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/image_inset_crop.py @@ -0,0 +1,93 @@ +"""Image Inset Crop, with percentages.""" +from .log import log_node_info +from .constants import get_category, get_name +from nodes import MAX_RESOLUTION + + +def get_new_bounds(width, height, left, right, top, bottom): + """Returns the new bounds for an image with inset crop data.""" + left = 0 + left + right = width - right + top = 0 + top + bottom = height - bottom + return (left, right, top, bottom) + + +class RgthreeImageInsetCrop: + """Image Inset Crop, with percentages.""" + + NAME = get_name('Image Inset Crop') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "image": ("IMAGE",), + "measurement": (['Pixels', 'Percentage'],), + "left": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + "right": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + "top": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + "bottom": ("INT", { + "default": 0, + "min": 0, + "max": MAX_RESOLUTION, + "step": 8 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crop" + + # pylint: disable = too-many-arguments + def crop(self, measurement, left, right, top, bottom, image=None): + """Does the crop.""" + + _, height, width, _ = image.shape + + if measurement == 'Percentage': + left = int(width - (width * (100 - left) / 100)) + right = int(width - (width * (100 - right) / 100)) + top = int(height - (height * (100 - top) / 100)) + bottom = int(height - (height * (100 - bottom) / 100)) + + # Snap to 8 pixels + left = left // 8 * 8 + right = right // 8 * 8 + top = top // 8 * 8 + bottom = bottom // 8 * 8 + + if left == 0 and right == 0 and bottom == 0 and top == 0: + return (image,) + + inset_left, inset_right, inset_top, inset_bottom = get_new_bounds(width, height, left, right, + top, bottom) + if inset_top > inset_bottom: + raise ValueError( + f"Invalid cropping dimensions top ({inset_top}) exceeds bottom ({inset_bottom})") + if inset_left > inset_right: + raise ValueError( + f"Invalid cropping dimensions left ({inset_left}) exceeds right ({inset_right})") + + log_node_info( + self.NAME, f'Cropping image {width}x{height} width inset by {inset_left},{inset_right}, ' + + f'and height inset by {inset_top}, {inset_bottom}') + image = image[:, inset_top:inset_bottom, inset_left:inset_right, :] + + return (image,) diff --git a/custom_nodes/rgthree-comfy/py/image_or_latent_size.py b/custom_nodes/rgthree-comfy/py/image_or_latent_size.py new file mode 100644 index 00000000..65f62f86 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/image_or_latent_size.py @@ -0,0 +1,31 @@ +from .utils import FlexibleOptionalInputType, any_type +from .constants import get_category, get_name + + +class RgthreeImageOrLatentSize: + """The ImageOrLatentSize Node.""" + + NAME = get_name('Image or Latent Size') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType(any_type), + } + + RETURN_TYPES = ("INT", "INT") + RETURN_NAMES = ('WIDTH', 'HEIGHT') + FUNCTION = "main" + + def main(self, **kwargs): + """Does the node's work.""" + image_or_latent_or_mask = kwargs.get('input', None) + + if isinstance(image_or_latent_or_mask, dict) and 'samples' in image_or_latent_or_mask: + count, _, height, width = image_or_latent_or_mask['samples'].shape + return (width * 8, height * 8) + + batch, height, width, channel = image_or_latent_or_mask.shape + return (width, height) diff --git a/custom_nodes/rgthree-comfy/py/image_resize.py b/custom_nodes/rgthree-comfy/py/image_resize.py new file mode 100644 index 00000000..a21001b3 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/image_resize.py @@ -0,0 +1,117 @@ +import torch +import comfy.utils +import nodes + +from .constants import get_category, get_name + + +class RgthreeImageResize: + """Image Resize.""" + + NAME = get_name("Image Resize") + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "image": ("IMAGE",), + "measurement": (["pixels", "percentage"],), + "width": ( + "INT", { + "default": 0, + "min": 0, + "max": nodes.MAX_RESOLUTION, + "step": 1, + "tooltip": ( + "The width of the desired resize. A pixel value if measurement is 'pixels' or a" + " 100% scale percentage value if measurement is 'percentage'. Passing '0' will" + " calculate the dimension based on the height." + ), + }, + ), + "height": ("INT", { + "default": 0, + "min": 0, + "max": nodes.MAX_RESOLUTION, + "step": 1 + }), + "fit": (["crop", "pad", "contain"], { + "tooltip": ( + "'crop' resizes so the image covers the desired width and height, and center-crops the" + " excess, returning exactly the desired width and height." + "\n'pad' resizes so the image fits inside the desired width and height, and fills the" + " empty space returning exactly the desired width and height." + "\n'contain' resizes so the image fits inside the desired width and height, and" + " returns the image with it's new size, with one side liekly smaller than the desired." + "\n\nNote, if either width or height is '0', the effective fit is 'contain'." + ) + }, + ), + "method": (nodes.ImageScale.upscale_methods,), + }, + } + + RETURN_TYPES = ("IMAGE", "INT", "INT",) + RETURN_NAMES = ("IMAGE", "WIDTH", "HEIGHT",) + FUNCTION = "main" + DESCRIPTION = """Resize the image.""" + + def main(self, image, measurement, width, height, method, fit): + """Resizes the image.""" + _, H, W, _ = image.shape + + if measurement == "percentage": + width = round(width * W / 100) + height = round(height * H / 100) + + if (width == 0 and height == 0) or (width == W and height == H): + return (image, W, H) + + # If one dimension is 0, then calculate the desired value from the ratio of the set dimension. + # This also implies a 'contain' fit since the width and height will be scaled with a locked + # aspect ratio. + if width == 0 or height == 0: + width = round(height / H * W) if width == 0 else width + height = round(width / W * H) if height == 0 else height + fit = "contain" + + # At this point, width and height are our output height, but our resize sizes will be different. + resized_width = width + resized_height = height + if fit == "crop": + # If we resize against the opposite ratio, then choose the ratio that has the overhang. + if (height / H * W) > width: + resized_width = round(height / H * W) + elif (width / W * H) > height: + resized_height = round(width / W * H) + elif fit == "contain" or fit == "pad": + # If we resize against the opposite ratio, then choose the ratio that has the overhang. + if (height / H * W) > width: + resized_height = round(width / W * H) + elif (width / W * H) > height: + resized_width = round(height / H * W) + + out_image = comfy.utils.common_upscale( + image.clone().movedim(-1, 1), resized_width, resized_height, method, crop="disabled" + ).movedim(1, -1) + OB, OH, OW, OC = out_image.shape + + if fit != "contain": + # First, we crop, then we pad; no need to check fit (other than not 'contain') since the size + # should already be correct. + if OW > width: + out_image = out_image.narrow(-2, (OW - width) // 2, width) + if OH > height: + out_image = out_image.narrow(-3, (OH - height) // 2, height) + + OB, OH, OW, OC = out_image.shape + if width != OW or height != OH: + padded_image = torch.zeros((OB, height, width, OC), dtype=image.dtype, device=image.device) + x = (width - OW) // 2 + y = (height - OH) // 2 + for b in range(OB): + padded_image[b, y:y + OH, x:x + OW, :] = out_image[b] + out_image = padded_image + + return (out_image, out_image.shape[2], out_image.shape[1]) diff --git a/custom_nodes/rgthree-comfy/py/ksampler_config.py b/custom_nodes/rgthree-comfy/py/ksampler_config.py new file mode 100644 index 00000000..043ea49e --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/ksampler_config.py @@ -0,0 +1,56 @@ +"""Some basic config stuff I use for SDXL.""" + +from .constants import get_category, get_name +from nodes import MAX_RESOLUTION +import comfy.samplers + + +class RgthreeKSamplerConfig: + """Some basic config stuff I started using for SDXL, but useful in other spots too.""" + + NAME = get_name('KSampler Config') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "steps_total": ("INT", { + "default": 30, + "min": 1, + "max": MAX_RESOLUTION, + "step": 1, + }), + "refiner_step": ("INT", { + "default": 24, + "min": 1, + "max": MAX_RESOLUTION, + "step": 1, + }), + "cfg": ("FLOAT", { + "default": 8.0, + "min": 0.0, + "max": 100.0, + "step": 0.5, + }), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS,), + #"refiner_ascore_pos": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + #"refiner_ascore_neg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("INT", "INT", "FLOAT", comfy.samplers.KSampler.SAMPLERS, + comfy.samplers.KSampler.SCHEDULERS) + RETURN_NAMES = ("STEPS", "REFINER_STEP", "CFG", "SAMPLER", "SCHEDULER") + FUNCTION = "main" + + def main(self, steps_total, refiner_step, cfg, sampler_name, scheduler): + """main""" + return ( + steps_total, + refiner_step, + cfg, + sampler_name, + scheduler, + ) diff --git a/custom_nodes/rgthree-comfy/py/log.py b/custom_nodes/rgthree-comfy/py/log.py new file mode 100644 index 00000000..4f41675e --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/log.py @@ -0,0 +1,100 @@ +import datetime +import time +from .pyproject import NAME + +# https://stackoverflow.com/questions/4842424/list-of-ansi-color-escape-sequences +# https://en.wikipedia.org/wiki/ANSI_escape_code#3-bit_and_4-bit +COLORS = { + 'BLACK': '\33[30m', + 'RED': '\33[31m', + 'GREEN': '\33[32m', + 'YELLOW': '\33[33m', + 'BLUE': '\33[34m', + 'MAGENTA': '\33[35m', + 'CYAN': '\33[36m', + 'WHITE': '\33[37m', + 'GREY': '\33[90m', + 'BRIGHT_RED': '\33[91m', + 'BRIGHT_GREEN': '\33[92m', + 'BRIGHT_YELLOW': '\33[93m', + 'BRIGHT_BLUE': '\33[94m', + 'BRIGHT_MAGENTA': '\33[95m', + 'BRIGHT_CYAN': '\33[96m', + 'BRIGHT_WHITE': '\33[97m', + # Styles. + 'RESET': '\33[0m', # Note, Portainer doesn't like 00 here, so we'll use 0. Should be fine... + 'BOLD': '\33[01m', + 'NORMAL': '\33[22m', + 'ITALIC': '\33[03m', + 'UNDERLINE': '\33[04m', + 'BLINK': '\33[05m', + 'BLINK2': '\33[06m', + 'SELECTED': '\33[07m', + # Backgrounds + 'BG_BLACK': '\33[40m', + 'BG_RED': '\33[41m', + 'BG_GREEN': '\33[42m', + 'BG_YELLOW': '\33[43m', + 'BG_BLUE': '\33[44m', + 'BG_MAGENTA': '\33[45m', + 'BG_CYAN': '\33[46m', + 'BG_WHITE': '\33[47m', + 'BG_GREY': '\33[100m', + 'BG_BRIGHT_RED': '\33[101m', + 'BG_BRIGHT_GREEN': '\33[102m', + 'BG_BRIGHT_YELLOW': '\33[103m', + 'BG_BRIGHT_BLUE': '\33[104m', + 'BG_BRIGHT_MAGENTA': '\33[105m', + 'BG_BRIGHT_CYAN': '\33[106m', + 'BG_BRIGHT_WHITE': '\33[107m', +} + + +def log_node_success(node_name, message, msg_color='RESET'): + """Logs a success message.""" + _log_node("BRIGHT_GREEN", node_name, message, msg_color=msg_color) + + +def log_node_info(node_name, message, msg_color='RESET'): + """Logs an info message.""" + _log_node("CYAN", node_name, message, msg_color=msg_color) + + +def log_node_error(node_name, message, msg_color='RESET'): + """Logs an info message.""" + _log_node("RED", node_name, message, msg_color=msg_color) + + +def log_node_warn(node_name, message, msg_color='RESET'): + """Logs an warn message.""" + _log_node("YELLOW", node_name, message, msg_color=msg_color) + + +def log_node(node_name, message, msg_color='RESET'): + """Logs a message.""" + _log_node("CYAN", node_name, message, msg_color=msg_color) + + +def _log_node(color, node_name, message, msg_color='RESET'): + """Logs for a node message.""" + log(message, color=color, prefix=node_name.replace(" (rgthree)", ""), msg_color=msg_color) + +LOGGED = {} + +def log(message, color=None, msg_color=None, prefix=None, id=None, at_most_secs=None): + """Basic logging.""" + now = int(time.time()) + if id: + if at_most_secs is None: + raise ValueError('at_most_secs should be set if an id is set.') + if id in LOGGED: + last_logged = LOGGED[id] + if now < last_logged + at_most_secs: + return + LOGGED[id] = now + color = COLORS[color] if color is not None and color in COLORS else COLORS["BRIGHT_GREEN"] + msg_color = COLORS[msg_color] if msg_color is not None and msg_color in COLORS else '' + prefix = f'[{prefix}]' if prefix is not None else '' + msg = f'{color}[{NAME}]{prefix}' + msg += f'{msg_color} {message}{COLORS["RESET"]}' + print(msg) diff --git a/custom_nodes/rgthree-comfy/py/lora_stack.py b/custom_nodes/rgthree-comfy/py/lora_stack.py new file mode 100644 index 00000000..c6d947aa --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/lora_stack.py @@ -0,0 +1,46 @@ +from .constants import get_category, get_name +from nodes import LoraLoader +import folder_paths + + +class RgthreeLoraLoaderStack: + + NAME = get_name('Lora Loader Stack') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "model": ("MODEL",), + "clip": ("CLIP", ), + + "lora_01": (['None'] + folder_paths.get_filename_list("loras"), ), + "strength_01":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "lora_02": (['None'] + folder_paths.get_filename_list("loras"), ), + "strength_02":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "lora_03": (['None'] + folder_paths.get_filename_list("loras"), ), + "strength_03":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + + "lora_04": (['None'] + folder_paths.get_filename_list("loras"), ), + "strength_04":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "load_lora" + + def load_lora(self, model, clip, lora_01, strength_01, lora_02, strength_02, lora_03, strength_03, lora_04, strength_04): + if lora_01 != "None" and strength_01 != 0: + model, clip = LoraLoader().load_lora(model, clip, lora_01, strength_01, strength_01) + if lora_02 != "None" and strength_02 != 0: + model, clip = LoraLoader().load_lora(model, clip, lora_02, strength_02, strength_02) + if lora_03 != "None" and strength_03 != 0: + model, clip = LoraLoader().load_lora(model, clip, lora_03, strength_03, strength_03) + if lora_04 != "None" and strength_04 != 0: + model, clip = LoraLoader().load_lora(model, clip, lora_04, strength_04, strength_04) + + return (model, clip) + diff --git a/custom_nodes/rgthree-comfy/py/power_lora_loader.py b/custom_nodes/rgthree-comfy/py/power_lora_loader.py new file mode 100644 index 00000000..1d11ac54 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/power_lora_loader.py @@ -0,0 +1,101 @@ +import folder_paths + +from typing import Union + +from nodes import LoraLoader +from .constants import get_category, get_name +from .power_prompt_utils import get_lora_by_filename +from .utils import FlexibleOptionalInputType, any_type +from .server.utils_info import get_model_info_file_data +from .log import log_node_warn + +NODE_NAME = get_name('Power Lora Loader') + + +class RgthreePowerLoraLoader: + """ The Power Lora Loader is a powerful, flexible node to add multiple loras to a model/clip.""" + + NAME = NODE_NAME + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + }, + # Since we will pass any number of loras in from the UI, this needs to always allow an + "optional": FlexibleOptionalInputType(type=any_type, data={ + "model": ("MODEL",), + "clip": ("CLIP",), + }), + "hidden": {}, + } + + RETURN_TYPES = ("MODEL", "CLIP") + RETURN_NAMES = ("MODEL", "CLIP") + FUNCTION = "load_loras" + + def load_loras(self, model=None, clip=None, **kwargs): + """Loops over the provided loras in kwargs and applies valid ones.""" + for key, value in kwargs.items(): + key = key.upper() + if key.startswith('LORA_') and 'on' in value and 'lora' in value and 'strength' in value: + strength_model = value['strength'] + # If we just passed one strength value, then use it for both, if we passed a strengthTwo + # as well, then our `strength` will be for the model, and `strengthTwo` for clip. + strength_clip = value['strengthTwo'] if 'strengthTwo' in value else None + if clip is None: + if strength_clip is not None and strength_clip != 0: + log_node_warn(NODE_NAME, 'Recieved clip strength eventhough no clip supplied!') + strength_clip = 0 + else: + strength_clip = strength_clip if strength_clip is not None else strength_model + if value['on'] and (strength_model != 0 or strength_clip != 0): + lora = get_lora_by_filename(value['lora'], log_node=self.NAME) + if model is not None and lora is not None: + model, clip = LoraLoader().load_lora(model, clip, lora, strength_model, strength_clip) + + return (model, clip) + + @classmethod + def get_enabled_loras_from_prompt_node(cls, + prompt_node: dict) -> list[dict[str, Union[str, float]]]: + """Gets enabled loras of a node within a server prompt.""" + result = [] + for name, lora in prompt_node['inputs'].items(): + if name.startswith('lora_') and lora['on']: + lora_file = get_lora_by_filename(lora['lora'], log_node=cls.NAME) + if lora_file is not None: # Add the same safety check + lora_dict = { + 'name': lora['lora'], + 'strength': lora['strength'], + 'path': folder_paths.get_full_path("loras", lora_file) + } + if 'strengthTwo' in lora: + lora_dict['strength_clip'] = lora['strengthTwo'] + result.append(lora_dict) + return result + + @classmethod + def get_enabled_triggers_from_prompt_node(cls, prompt_node: dict, max_each: int = 1): + """Gets trigger words up to the max for enabled loras of a node within a server prompt.""" + loras = [l['name'] for l in cls.get_enabled_loras_from_prompt_node(prompt_node)] + trained_words = [] + for lora in loras: + info = get_model_info_file_data(lora, 'loras', default={}) + if not info or not info.keys(): + log_node_warn( + NODE_NAME, + f'No info found for lora {lora} when grabbing triggers. Have you generated an info file' + ' from the Power Lora Loader "Show Info" dialog?' + ) + continue + if 'trainedWords' not in info or not info['trainedWords']: + log_node_warn( + NODE_NAME, + f'No trained words for lora {lora} when grabbing triggers. Have you fetched data from' + 'civitai or manually added words?' + ) + continue + trained_words += [w for wi in info['trainedWords'][:max_each] if (wi and (w := wi['word']))] + return trained_words diff --git a/custom_nodes/rgthree-comfy/py/power_primitive.py b/custom_nodes/rgthree-comfy/py/power_primitive.py new file mode 100644 index 00000000..c8deea1c --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/power_primitive.py @@ -0,0 +1,83 @@ +import re + +from .utils import FlexibleOptionalInputType, any_type +from .constants import get_category, get_name + + +def cast_to_str(x): + """Handles our cast to a string.""" + if x is None: + return '' + try: + return str(x) + except (ValueError, TypeError): + return '' + + +def cast_to_float(x): + """Handles our cast to a float.""" + try: + return float(x) + except (ValueError, TypeError): + return 0.0 + + +def cast_to_bool(x): + """Handles our cast to a bool.""" + try: + return bool(float(x)) + except (ValueError, TypeError): + return str(x).lower() not in ['0', 'false', 'null', 'none', ''] + + +output_to_type = { + 'STRING': { + 'cast': cast_to_str, + 'null': '', + }, + 'FLOAT': { + 'cast': cast_to_float, + 'null': 0.0, + }, + 'INT': { + 'cast': lambda x: int(cast_to_float(x)), + 'null': 0, + }, + 'BOOLEAN': { + 'cast': cast_to_bool, + 'null': False, + }, + # This can be removed soon, there was a bug where this should have been BOOLEAN + 'BOOL': { + 'cast': cast_to_bool, + 'null': False, + }, +} + + +class RgthreePowerPrimitive: + """The Power Primitive Node.""" + + NAME = get_name('Power Primitive') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": {}, + "optional": FlexibleOptionalInputType(any_type), + } + + RETURN_TYPES = (any_type,) + RETURN_NAMES = ('*',) + FUNCTION = "main" + + def main(self, **kwargs): + """Outputs the expected type.""" + output = kwargs.get('value', None) + output_type = re.sub(r'\s*\([^\)]*\)\s*$', '', kwargs.get('type', '')) + output_type = output_to_type[output_type] + cast = output_type['cast'] + output = cast(output) + + return (output,) diff --git a/custom_nodes/rgthree-comfy/py/power_prompt.py b/custom_nodes/rgthree-comfy/py/power_prompt.py new file mode 100644 index 00000000..5fa75bf0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/power_prompt.py @@ -0,0 +1,95 @@ +import os + +from .log import log_node_warn, log_node_info, log_node_success + +from .constants import get_category, get_name +from .power_prompt_utils import get_and_strip_loras +from nodes import LoraLoader, CLIPTextEncode +import folder_paths + +NODE_NAME = get_name('Power Prompt') + + +class RgthreePowerPrompt: + + NAME = NODE_NAME + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully, + # TODO: Rip out saved prompt input data + SAVED_PROMPTS_FILES=[] + SAVED_PROMPTS_CONTENT=[] + return { + 'required': { + 'prompt': ('STRING', { + 'multiline': True, + 'dynamicPrompts': True + }), + }, + 'optional': { + "opt_model": ("MODEL",), + "opt_clip": ("CLIP",), + 'insert_lora': (['CHOOSE', 'DISABLE LORAS'] + + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('loras')],), + 'insert_embedding': ([ + 'CHOOSE', + ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],), + 'insert_saved': ([ + 'CHOOSE', + ] + SAVED_PROMPTS_FILES,), + }, + 'hidden': { + 'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,), + } + } + + RETURN_TYPES = ( + 'CONDITIONING', + 'MODEL', + 'CLIP', + 'STRING', + ) + RETURN_NAMES = ( + 'CONDITIONING', + 'MODEL', + 'CLIP', + 'TEXT', + ) + FUNCTION = 'main' + + def main(self, + prompt, + opt_model=None, + opt_clip=None, + insert_lora=None, + insert_embedding=None, + insert_saved=None, + values_insert_saved=None): + if insert_lora == 'DISABLE LORAS': + prompt, loras, skipped, unfound = get_and_strip_loras(prompt, log_node=NODE_NAME, silent=True) + log_node_info( + NODE_NAME, + f'Disabling all found loras ({len(loras)}) and stripping lora tags for TEXT output.') + elif opt_model is not None and opt_clip is not None: + prompt, loras, skipped, unfound = get_and_strip_loras(prompt, log_node=NODE_NAME) + if len(loras) > 0: + for lora in loras: + opt_model, opt_clip = LoraLoader().load_lora(opt_model, opt_clip, lora['lora'], + lora['strength'], lora['strength']) + log_node_success(NODE_NAME, f'Loaded "{lora["lora"]}" from prompt') + log_node_info(NODE_NAME, f'{len(loras)} Loras processed; stripping tags for TEXT output.') + elif ']*?)(?::(-?\d*(?:\.\d*)?))?>' + lora_paths = folder_paths.get_filename_list('loras') + + matches = re.findall(pattern, prompt) + + loras = [] + unfound_loras = [] + skipped_loras = [] + for match in matches: + tag_path = match[0] + + strength = float(match[1] if len(match) > 1 and len(match[1]) else 1.0) + if strength == 0: + if not silent: + log_node_info(log_node, f'Skipping "{tag_path}" with strength of zero') + skipped_loras.append({'lora': tag_path, 'strength': strength}) + continue + + lora_path = get_lora_by_filename(tag_path, lora_paths, log_node=None if silent else log_node) + if lora_path is None: + unfound_loras.append({'lora': tag_path, 'strength': strength}) + continue + + loras.append({'lora': lora_path, 'strength': strength}) + + return (re.sub(pattern, '', prompt), loras, skipped_loras, unfound_loras) + + +# pylint: disable = too-many-return-statements, too-many-branches +def get_lora_by_filename(file_path, lora_paths=None, log_node=None): + """Returns a lora by filename, looking for exactl paths and then fuzzier matching.""" + lora_paths = lora_paths if lora_paths is not None else folder_paths.get_filename_list('loras') + + if file_path in lora_paths: + return file_path + + lora_paths_no_ext = [os.path.splitext(x)[0] for x in lora_paths] + + # See if we've entered the exact path, but without the extension + if file_path in lora_paths_no_ext: + found = lora_paths[lora_paths_no_ext.index(file_path)] + return found + + # Same check, but ensure file_path is without extension. + file_path_force_no_ext = os.path.splitext(file_path)[0] + if file_path_force_no_ext in lora_paths_no_ext: + found = lora_paths[lora_paths_no_ext.index(file_path_force_no_ext)] + return found + + # See if we passed just the name, without paths. + lora_filenames_only = [os.path.basename(x) for x in lora_paths] + if file_path in lora_filenames_only: + found = lora_paths[lora_filenames_only.index(file_path)] + if log_node is not None: + log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".') + return found + + # Same, but force the input to be without paths + file_path_force_filename = os.path.basename(file_path) + lora_filenames_only = [os.path.basename(x) for x in lora_paths] + if file_path_force_filename in lora_filenames_only: + found = lora_paths[lora_filenames_only.index(file_path_force_filename)] + if log_node is not None: + log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".') + return found + + # Check the filenames and without extension. + lora_filenames_and_no_ext = [os.path.splitext(os.path.basename(x))[0] for x in lora_paths] + if file_path in lora_filenames_and_no_ext: + found = lora_paths[lora_filenames_and_no_ext.index(file_path)] + if log_node is not None: + log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".') + return found + + # And, one last forcing the input to be the same + file_path_force_filename_and_no_ext = os.path.splitext(os.path.basename(file_path))[0] + if file_path_force_filename_and_no_ext in lora_filenames_and_no_ext: + found = lora_paths[lora_filenames_and_no_ext.index(file_path_force_filename_and_no_ext)] + if log_node is not None: + log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".') + return found + + # Finally, super fuzzy, we'll just check if the input exists in the path at all. + for index, lora_path in enumerate(lora_paths): + if file_path in lora_path: + found = lora_paths[index] + if log_node is not None: + log_node_warn(log_node, f'Fuzzy-matched Lora input "{file_path}" to "{found}".') + return found + + if log_node is not None: + log_node_warn(log_node, f'Lora "{file_path}" not found, skipping.') + + return None diff --git a/custom_nodes/rgthree-comfy/py/power_puter.py b/custom_nodes/rgthree-comfy/py/power_puter.py new file mode 100644 index 00000000..bbf31f31 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/power_puter.py @@ -0,0 +1,842 @@ +"""The Power Puter is a powerful node that can compute and evaluate Python-like code safely allowing +for complex operations for primitives and workflow items for output. From string concatenation, to +math operations, list comprehension, and node value output. + +Originally based off https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/aac13aa7ce35b07d43633c3bbe654a38c00d74f5/py/math_expression.py +under an MIT License https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/aac13aa7ce35b07d43633c3bbe654a38c00d74f5/LICENSE +""" + +import math +import ast +import json +import random +import dataclasses +import re +import time +import operator as op +import datetime +import numpy as np + +from typing import Any, Callable, Iterable, Optional, Union +from types import MappingProxyType + +from .constants import get_category, get_name +from .utils import ByPassTypeTuple, FlexibleOptionalInputType, any_type, get_dict_value +from .log import log_node_error, log_node_warn, log_node_info + +from .power_lora_loader import RgthreePowerLoraLoader + +from nodes import ImageBatch +from comfy_extras.nodes_latent import LatentBatch + + +class LoopBreak(Exception): + """A special error type that is caught in a loop for correct breaking behavior.""" + + def __init__(self): + super().__init__('Cannot use "break" outside of a loop.') + + +class LoopContinue(Exception): + """A special error type that is caught in a loop for correct continue behavior.""" + + def __init__(self): + super().__init__('Cannot use "continue" outside of a loop.') + + +@dataclasses.dataclass(frozen=True) # Note, kw_only=True is only python 3.10+ +class Function(): + """Function data. + + Attributes: + name: The name of the function as called from the node. + call: The callable (reference, lambda, etc), or a string if on _Puter instance. + args: A tuple that represents the minimum and maximum number of args (or arg for no limit). + """ + + name: str + call: Union[Callable, str] + args: tuple[int, Optional[int]] + + +def purge_vram(purge_models=True): + """Purges vram and, optionally, unloads models.""" + import gc + import torch + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + if purge_models: + import comfy + comfy.model_management.unload_all_models() + comfy.model_management.soft_empty_cache() + + +def batch(*args): + """Batches multiple image or latents together.""" + + def check_is_latent(item) -> bool: + return isinstance(item, dict) and 'samples' in item + + args = list(args) + result = args.pop(0) + is_latent = check_is_latent(result) + node = LatentBatch() if is_latent else ImageBatch() + + for arg in args: + if is_latent != check_is_latent(arg): + raise ValueError( + f'batch() error: Expecting "{"LATENT" if is_latent else "IMAGE"}"' + f' but got "{"IMAGE" if is_latent else "LATENT"}".' + ) + result = node.batch(result, arg)[0] + return result + + +_BUILTIN_FN_PREFIX = '__rgthreefn.' + + +def _get_built_in_fn_key(fn: Function) -> str: + """Returns a key for a built-in function.""" + return f'{_BUILTIN_FN_PREFIX}{hash(fn.name)}' + + +def _get_built_in_fn_by_key(fn_key: str): + """Returns the `Function` for the provided key (purposefully, not name).""" + if not fn_key.startswith(_BUILTIN_FN_PREFIX) or fn_key not in _BUILT_INS_BY_NAME_AND_KEY: + raise ValueError('No built in function found.') + return _BUILT_INS_BY_NAME_AND_KEY[fn_key] + + +_BUILT_IN_FNS_LIST = [ + Function(name="round", call=round, args=(1, 2)), + Function(name="ceil", call=math.ceil, args=(1, 1)), + Function(name="floor", call=math.floor, args=(1, 1)), + Function(name="sqrt", call=math.sqrt, args=(1, 1)), + Function(name="min", call=min, args=(2, None)), + Function(name="max", call=max, args=(2, None)), + Function(name=".random_int", call=random.randint, args=(2, 2)), + Function(name=".random_choice", call=random.choice, args=(1, 1)), + Function(name=".random_seed", call=random.seed, args=(1, 1)), + Function(name="re", call=re.compile, args=(1, 1)), + Function(name="len", call=len, args=(1, 1)), + Function(name="enumerate", call=enumerate, args=(1, 1)), + Function(name="range", call=range, args=(1, 3)), + # Casts + Function(name="int", call=int, args=(1, 1)), + Function(name="float", call=float, args=(1, 1)), + Function(name="str", call=str, args=(1, 1)), + Function(name="bool", call=bool, args=(1, 1)), + Function(name="list", call=list, args=(1, 1)), + Function(name="tuple", call=tuple, args=(1, 1)), + # Special + Function(name="dir", call=dir, args=(1, 1)), + Function(name="type", call=type, args=(1, 1)), + Function(name="print", call=print, args=(0, None)), + # Comfy Specials + Function(name="node", call='_get_node', args=(0, 1)), + Function(name="nodes", call='_get_nodes', args=(0, 1)), + Function(name="input_node", call='_get_input_node', args=(0, 1)), + Function(name="purge_vram", call=purge_vram, args=(0, 1)), + Function(name="batch", call=batch, args=(2, None)), +] + +_BUILT_INS_BY_NAME_AND_KEY = { + fn.name: fn for fn in _BUILT_IN_FNS_LIST +} | { + key: fn for fn in _BUILT_IN_FNS_LIST if (key := _get_built_in_fn_key(fn)) +} + +_BUILT_INS = MappingProxyType( + {fn.name: key for fn in _BUILT_IN_FNS_LIST if (key := _get_built_in_fn_key(fn))} | { + 'random': + MappingProxyType({ + 'int': _get_built_in_fn_key(_BUILT_INS_BY_NAME_AND_KEY['.random_int']), + 'choice': _get_built_in_fn_key(_BUILT_INS_BY_NAME_AND_KEY['.random_choice']), + 'seed': _get_built_in_fn_key(_BUILT_INS_BY_NAME_AND_KEY['.random_seed']), + }), + } +) + +# A dict of types to blocked attributes/methods. Used to disallow file system access or other +# invocations we may want to block. Necessary for any instance type that is possible to create from +# the code or standard ComfyUI inputs. +# +# For instance, a user does not have access to the numpy module directly, so they cannot invoke +# `numpy.save`. However, a user can access a numpy.ndarray instance from a tensor and, from there, +# an attempt to call `tofile` or `dump` etc. would need to be blocked. +_BLOCKED_METHODS_OR_ATTRS = MappingProxyType({np.ndarray: ['tofile', 'dump']}) + +# Special functions by class type (called from the Attrs.) +_SPECIAL_FUNCTIONS = { + RgthreePowerLoraLoader.NAME: { + # Get a list of the enabled loras from a power lora loader. + "loras": RgthreePowerLoraLoader.get_enabled_loras_from_prompt_node, + "triggers": RgthreePowerLoraLoader.get_enabled_triggers_from_prompt_node, + } +} + +# Series of regex checks for usage of a non-deterministic function. Using these is fine, but means +# the output can't be cached because it's either random, or is associated with another node that is +# not connected to ours (like looking up a node in the prompt). Using these means downstream nodes +# would always be run; that is fine for something like a final JSON output, but less so for a prompt +# text. +_NON_DETERMINISTIC_FUNCTION_CHECKS = [r'(? has_rand_int_or_choice.span()[0]: + log_node_warn( + _NODE_NAME, + f"Note, Power Puter (node #{kwargs['unique_id']}) cannot be cached b/c it's using a" + " non-deterministic function call. Matches function call for" + f" `{has_rand_int_or_choice.group(1)}`." + ) + return time.time() + if has_rand_seed: + log_node_info( + _NODE_NAME, + f"Power Puter node #{kwargs['unique_id']} WILL be cached eventhough it's using" + f" a non-deterministic random call `{has_rand_int_or_choice.group(1)}` because it also" + f" calls `random.seed` first. NOTE: Please ensure that the seed value is deterministic." + ) + + return 42 + + def main(self, **kwargs): + """Does the nodes' work.""" + code = kwargs['code'] + unique_id = kwargs['unique_id'] + pnginfo = kwargs['extra_pnginfo'] + workflow = pnginfo["workflow"] if "workflow" in pnginfo else {"nodes": []} + prompt = kwargs['prompt'] + dynprompt = kwargs['dynprompt'] + + outputs = get_dict_value(kwargs, 'outputs.outputs', None) + if not outputs: + output = kwargs.get('output', None) + if not output: + output = 'STRING' + outputs = [output] + + ctx = {} + # Set variable names, defaulting to None instead of KeyErrors + for c in list('abcdefghijklmnopqrstuvwxyz'): + ctx[c] = kwargs[c] if c in kwargs else None + + code = _update_code(kwargs['code'], unique_id=kwargs['unique_id'], log=True) + + eva = _Puter( + code=code, + ctx=ctx, + workflow=workflow, + prompt=prompt, + dynprompt=dynprompt, + unique_id=unique_id + ) + values = eva.execute() + + # Check if we have multiple outputs that the returned value is a tuple and raise if not. + if len(outputs) > 1 and not isinstance(values, tuple): + t = re.sub(r'^<[a-z]*\s(.*?)>$', r'\1', str(type(values))) + msg = ( + f"When using multiple node outputs, the value from the code should be a 'tuple' with the" + f" number of items equal to the number of outputs. But value from code was of type {t}." + ) + log_node_error(_NODE_NAME, f'{msg}\n') + raise ValueError(msg) + + if len(outputs) == 1: + values = (values,) + + if len(values) > len(outputs): + log_node_warn( + _NODE_NAME, + f"Expected value from code to be tuple with {len(outputs)} items, but value from code had" + f" {len(values)} items. Extra values will be dropped." + ) + elif len(values) < len(outputs): + log_node_warn( + _NODE_NAME, + f"Expected value from code to be tuple with {len(outputs)} items, but value from code had" + f" {len(values)} items. Extra outputs will be null." + ) + + # Now, we'll go over out return tuple, and cast as the output types. + response = [] + for i, output in enumerate(outputs): + value = values[i] if len(values) > i else None + if value is not None: + if output == 'INT': + value = int(value) + elif output == 'FLOAT': + value = float(value) + # Accidentally defined "BOOL" when should have been "BOOLEAN." + # TODO: Can prob get rid of BOOl after a bit when UIs would be updated from sending + # BOOL incorrectly. + elif output in ('BOOL', 'BOOLEAN'): + value = bool(value) + elif output == 'STRING': + if isinstance(value, (dict, list)): + value = json.dumps(value, indent=2) + else: + value = str(value) + elif output == '*': + # Do nothing, the output will be passed as-is. This could be anything and it's up to the + # user to control the intended output, like passing through an input value, etc. + pass + response.append(value) + return tuple(response) + + +class _Puter: + """The main computation evaluator, using ast.parse the code. + + See https://www.basicexamples.com/example/python/ast for examples. + """ + + def __init__(self, *, code: str, ctx: dict[str, Any], workflow, prompt, dynprompt, unique_id): + ctx = ctx or {} + self._ctx = {**ctx} + self._code = code + self._workflow = workflow + self._prompt = prompt + self._unique_id = unique_id + self._dynprompt = dynprompt + # These are now expanded lazily when needed. + self._prompt_nodes = None + self._prompt_node = None + + def execute(self, code=Optional[str]) -> Any: + """Evaluates a the code block.""" + + # Always store random state and initialize a new seed. We'll restore the state later. + initial_random_state = random.getstate() + random.seed(datetime.datetime.now().timestamp()) + last_value = None + try: + code = code or self._code + node = ast.parse(self._code) + ctx = {**self._ctx} + for body in node.body: + last_value = self._eval_statement(body, ctx) + # If we got a return, then that's it folks. + if isinstance(body, ast.Return): + break + except: + random.setstate(initial_random_state) + raise + random.setstate(initial_random_state) + return last_value + + def _get_prompt_nodes(self): + """Expands the prompt nodes lazily from the dynamic prompt. + + https://github.com/comfyanonymous/ComfyUI/blob/fc657f471a29d07696ca16b566000e8e555d67d1/comfy_execution/graph.py#L22 + """ + if self._prompt_nodes is None: + self._prompt_nodes = [] + if self._dynprompt: + all_ids = self._dynprompt.all_node_ids() + self._prompt_nodes = [{'id': k} | {**self._dynprompt.get_node(k)} for k in all_ids] + return self._prompt_nodes + + def _get_prompt_node(self): + if self._prompt_nodes is None: + self._prompt_node = [n for n in self._get_prompt_nodes() if n['id'] == self._unique_id][0] + return self._prompt_node + + def _get_nodes(self, node_id: Union[int, str, re.Pattern, None] = None) -> list[Any]: + """Get a list of the nodes that match the node_id, or all the nodes in the prompt.""" + nodes = self._get_prompt_nodes().copy() + if not node_id: + return nodes + + if isinstance(node_id, re.Pattern): + found = [n for n in nodes if re.search(node_id, get_dict_value(n, '_meta.title', ''))] + else: + node_id = str(node_id) + found = None + if re.match(r'\d+$', node_id): + found = [n for n in nodes if node_id == n['id']] + if not found: + found = [n for n in nodes if node_id == get_dict_value(n, '_meta.title', '')] + return found + + def _get_node(self, node_id: Union[int, str, re.Pattern, None] = None) -> Union[Any, None]: + """Returns a prompt-node from the hidden prompt.""" + if node_id is None: + return self._get_prompt_node() + nodes = self._get_nodes(node_id) + if nodes and len(nodes) > 1: + log_node_warn(_NODE_NAME, f"More than one node found for '{node_id}'. Returning first.") + return nodes[0] if nodes else None + + def _get_input_node(self, input_name, node=None): + """Gets the (non-muted) node of an input connection from a node (default to the power puter).""" + node = node if node else self._get_prompt_node() + try: + connected_node_id = node['inputs'][input_name][0] + return [n for n in self._get_prompt_nodes() if n['id'] == connected_node_id][0] + except (TypeError, IndexError, KeyError): + log_node_warn(_NODE_NAME, f'No input node found for "{input_name}". ') + return None + + def _eval_statement(self, stmt: ast.AST, ctx: dict, prev_stmt: Union[ast.AST, None] = None): + """Evaluates an ast.stmt.""" + + if '__returned__' in ctx: + return ctx['__returned__'] + + # print('\n\n----: _eval_statement') + # print(type(stmt)) + # print(ctx) + + if isinstance(stmt, (ast.FormattedValue, ast.Expr)): + return self._eval_statement(stmt.value, ctx=ctx) + + if isinstance(stmt, (ast.Constant, ast.Num)): + return stmt.n + + if isinstance(stmt, ast.BinOp): + left = self._eval_statement(stmt.left, ctx=ctx) + right = self._eval_statement(stmt.right, ctx=ctx) + return _OPERATORS[type(stmt.op)](left, right) + + if isinstance(stmt, ast.BoolOp): + is_and = isinstance(stmt.op, ast.And) + is_or = isinstance(stmt.op, ast.Or) + stmt_value_eval = None + for stmt_value in stmt.values: + stmt_value_eval = self._eval_statement(stmt_value, ctx=ctx) + # If we're an and operator and have a falsyt value, then we stop and return. Likewise, if + # we're an or operator and have a truthy value, we can stop and return. + if (is_and and not stmt_value_eval) or (is_or and stmt_value_eval): + return stmt_value_eval + # Always return the last if we made it here w/o success. + return stmt_value_eval + + if isinstance(stmt, ast.UnaryOp): + return _OPERATORS[type(stmt.op)](self._eval_statement(stmt.operand, ctx=ctx)) + + if isinstance(stmt, (ast.Attribute, ast.Subscript)): + # Like: node(14).inputs.sampler_name (Attribute) + # Like: node(14)['inputs']['sampler_name'] (Subscript) + item = self._eval_statement(stmt.value, ctx=ctx) + attr = None + # if hasattr(stmt, 'attr'): + if isinstance(stmt, ast.Attribute): + attr = stmt.attr + else: + # Slice could be a name or a constant; evaluate it + attr = self._eval_statement(stmt.slice, ctx=ctx) + # Check if we're blocking access to this attribute/method on this item type. + for typ, names in _BLOCKED_METHODS_OR_ATTRS.items(): + if isinstance(item, typ) and isinstance(attr, str) and attr in names: + raise ValueError(f'Disallowed access to "{attr}" for type {typ}.') + try: + val = item[attr] + except (TypeError, IndexError, KeyError): + try: + val = getattr(item, attr) + except AttributeError: + # If we're a dict, then just return None instead of error; saves time. + if isinstance(item, dict): + # Any special cases in the _SPECIAL_FUNCTIONS + class_type = get_dict_value(item, "class_type") + if class_type in _SPECIAL_FUNCTIONS and attr in _SPECIAL_FUNCTIONS[class_type]: + val = _SPECIAL_FUNCTIONS[class_type][attr] + # If our previous statment was a Call, then send back a tuple of the callable and + # the evaluated item, and it will make the call; perhaps also adding other arguments + # only it knows about. + if isinstance(prev_stmt, ast.Call): + return (val, item) + val = val(item) + else: + val = None + else: + raise + return val + + if isinstance(stmt, (ast.List, ast.Tuple)): + value = [] + for elt in stmt.elts: + value.append(self._eval_statement(elt, ctx=ctx)) + return tuple(value) if isinstance(stmt, ast.Tuple) else value + + if isinstance(stmt, ast.Dict): + the_dict = {} + if stmt.keys: + if len(stmt.keys) != len(stmt.values): + raise ValueError('Expected same number of keys as values for dict.') + for i, k in enumerate(stmt.keys): + item_key = self._eval_statement(k, ctx=ctx) + item_value = self._eval_statement(stmt.values[i], ctx=ctx) + the_dict[item_key] = item_value + return the_dict + + # f-strings: https://www.basicexamples.com/example/python/ast-JoinedStr + # Note, this will str() all evaluated items in the fstrings, and doesn't handle f-string + # directives, like padding, etc. + if isinstance(stmt, ast.JoinedStr): + vals = [str(self._eval_statement(v, ctx=ctx)) for v in stmt.values] + val = ''.join(vals) + return val + + if isinstance(stmt, ast.Slice): + if not stmt.lower or not stmt.upper: + raise ValueError('Unhandled Slice w/o lower or upper.') + slice_lower = self._eval_statement(stmt.lower, ctx=ctx) + slice_upper = self._eval_statement(stmt.upper, ctx=ctx) + if stmt.step: + slice_step = self._eval_statement(stmt.step, ctx=ctx) + return slice(slice_lower, slice_upper, slice_step) + return slice(slice_lower, slice_upper) + + if isinstance(stmt, ast.Name): + if stmt.id in ctx: + val = ctx[stmt.id] + return val + if stmt.id in _BUILT_INS: + val = _BUILT_INS[stmt.id] + return val + raise NameError(f"Name not found: {stmt.id}") + + if isinstance(stmt, ast.For): + for_iter = self._eval_statement(stmt.iter, ctx=ctx) + for item in for_iter: + # Set the for var(s) + if isinstance(stmt.target, ast.Name): + ctx[stmt.target.id] = item + elif isinstance(stmt.target, ast.Tuple): # dict, like `for k, v in d.entries()` + for i, elt in enumerate(stmt.target.elts): + ctx[elt.id] = item[i] + bodies = stmt.body if isinstance(stmt.body, list) else [stmt.body] + breaked = False + for body in bodies: + # Catch any breaks or continues and handle inside the loop normally. + try: + value = self._eval_statement(body, ctx=ctx) + except (LoopBreak, LoopContinue) as e: + breaked = isinstance(e, LoopBreak) + break + if breaked: + break + return None + + if isinstance(stmt, ast.While): + while self._eval_statement(stmt.test, ctx=ctx): + bodies = stmt.body if isinstance(stmt.body, list) else [stmt.body] + breaked = False + for body in bodies: + # Catch any breaks or continues and handle inside the loop normally. + try: + value = self._eval_statement(body, ctx=ctx) + except (LoopBreak, LoopContinue) as e: + breaked = isinstance(e, LoopBreak) + break + if breaked: + break + return None + + if isinstance(stmt, ast.ListComp): + # Like: [v.lora for name, v in node(19).inputs.items() if name.startswith('lora_')] + # Like: [v.lower() for v in lora_list] + # Like: [v for v in l if v.startswith('B')] + # Like: [v.lower() for v in l if v.startswith('B') or v.startswith('F')] + # --- + # Like: [l for n in nodes(re('Loras')).values() if (l := n.loras)] + final_list = [] + + gen_ctx = {**ctx} + + generators = [*stmt.generators] + + def handle_gen(generators: list[ast.comprehension]): + gen = generators.pop(0) + if isinstance(gen.target, ast.Name): + gen_ctx[gen.target.id] = None + elif isinstance(gen.target, ast.Tuple): # dict, like `for k, v in d.entries()` + for elt in gen.target.elts: + gen_ctx[elt.id] = None + else: + raise ValueError('Na') + + gen_iters = None + # A call, like my_dct.items(), or a named ctx list + if isinstance(gen.iter, ast.Call): + gen_iters = self._eval_statement(gen.iter, ctx=gen_ctx) + elif isinstance(gen.iter, (ast.Name, ast.Attribute, ast.List, ast.Tuple)): + gen_iters = self._eval_statement(gen.iter, ctx=gen_ctx) + + if not isinstance(gen_iters, Iterable): + raise ValueError('No iteraors found for list comprehension') + + for gen_iter in gen_iters: + if_ctx = {**gen_ctx} + if isinstance(gen.target, ast.Tuple): # dict, like `for k, v in d.entries()` + for i, elt in enumerate(gen.target.elts): + if_ctx[elt.id] = gen_iter[i] + else: + if_ctx[gen.target.id] = gen_iter + good = True + for ifcall in gen.ifs: + if not self._eval_statement(ifcall, ctx=if_ctx): + good = False + break + if not good: + continue + gen_ctx.update(if_ctx) + if len(generators): + handle_gen(generators) + else: + final_list.append(self._eval_statement(stmt.elt, gen_ctx)) + generators.insert(0, gen) + + handle_gen(generators) + return final_list + + if isinstance(stmt, ast.Call): + call = None + args = [] + kwargs = {} + if isinstance(stmt.func, ast.Attribute): + call = self._eval_statement(stmt.func, prev_stmt=stmt, ctx=ctx) + if isinstance(call, tuple): + args.append(call[1]) + call = call[0] + if not call: + raise ValueError(f'No call for ast.Call {stmt.func}') + + name = '' + if isinstance(stmt.func, ast.Name): + name = stmt.func.id + if name in _BUILT_INS: + call = _BUILT_INS[name] + + if isinstance(call, str) and call.startswith(_BUILTIN_FN_PREFIX): + fn = _get_built_in_fn_by_key(call) + call = fn.call + if isinstance(call, str): + call = getattr(self, call) + num_args = len(stmt.args) + if num_args < fn.args[0] or (fn.args[1] is not None and num_args > fn.args[1]): + toErr = " or more" if fn.args[1] is None else f" to {fn.args[1]}" + raise SyntaxError(f"Invalid function call: {fn.name} requires {fn.args[0]}{toErr} args") + + if not call: + raise ValueError(f'No call for ast.Call {name}') + + for arg in stmt.args: + args.append(self._eval_statement(arg, ctx=ctx)) + for kwarg in stmt.keywords: + kwargs[kwarg.arg] = self._eval_statement(kwarg.value, ctx=ctx) + return call(*args, **kwargs) + + if isinstance(stmt, ast.Compare): + l = self._eval_statement(stmt.left, ctx=ctx) + r = self._eval_statement(stmt.comparators[0], ctx=ctx) + if isinstance(stmt.ops[0], ast.Eq): + return 1 if l == r else 0 + if isinstance(stmt.ops[0], ast.NotEq): + return 1 if l != r else 0 + if isinstance(stmt.ops[0], ast.Gt): + return 1 if l > r else 0 + if isinstance(stmt.ops[0], ast.GtE): + return 1 if l >= r else 0 + if isinstance(stmt.ops[0], ast.Lt): + return 1 if l < r else 0 + if isinstance(stmt.ops[0], ast.LtE): + return 1 if l <= r else 0 + if isinstance(stmt.ops[0], ast.In): + return 1 if l in r else 0 + if isinstance(stmt.ops[0], ast.Is): + return 1 if l is r else 0 + if isinstance(stmt.ops[0], ast.IsNot): + return 1 if l is not r else 0 + raise NotImplementedError("Operator " + stmt.ops[0].__class__.__name__ + " not supported.") + + if isinstance(stmt, (ast.If, ast.IfExp)): + value = self._eval_statement(stmt.test, ctx=ctx) + if value: + # ast.If is a list, ast.IfExp is an object. + bodies = stmt.body if isinstance(stmt.body, list) else [stmt.body] + for body in bodies: + value = self._eval_statement(body, ctx=ctx) + elif stmt.orelse: + # ast.If is a list, ast.IfExp is an object. TBH, I don't know why the If is a list, it's + # only ever one item AFAICT. + orelses = stmt.orelse if isinstance(stmt.orelse, list) else [stmt.orelse] + for orelse in orelses: + value = self._eval_statement(orelse, ctx=ctx) + return value + + # Assign a variable and add it to our ctx. + if isinstance(stmt, (ast.Assign, ast.AugAssign)): + if isinstance(stmt, ast.AugAssign): + left = self._eval_statement(stmt.target, ctx=ctx) + right = self._eval_statement(stmt.value, ctx=ctx) + value = _OPERATORS[type(stmt.op)](left, right) + target = stmt.target + else: + value = self._eval_statement(stmt.value, ctx=ctx) + if len(stmt.targets) != 1: + raise ValueError('Expected length of assign targets to be 1') + target = stmt.targets[0] + + if isinstance(target, ast.Tuple): # like `a, z = (1,2)` (ast.Assign only) + for i, elt in enumerate(target.elts): + ctx[elt.id] = value[i] + elif isinstance(target, ast.Name): # like `a = 1`` + ctx[target.id] = value + elif isinstance(target, ast.Subscript) and isinstance(target.value, ast.Name): # `a[0] = 1` + ctx[target.value.id][self._eval_statement(target.slice, ctx=ctx)] = value + else: + raise ValueError('Unhandled target type for Assign.') + return value + + # For assigning a var in a list comprehension. + # Like [name for node in node_list if (name := node.name)] + if isinstance(stmt, ast.NamedExpr): + value = self._eval_statement(stmt.value, ctx=ctx) + ctx[stmt.target.id] = value + return value + + if isinstance(stmt, ast.Return): + if stmt.value is None: + value = None + else: + value = self._eval_statement(stmt.value, ctx=ctx) + # Mark that we have a return value, as we may be deeper in evaluation, like going through an + # if condition's body. + ctx['__returned__'] = value + return value + + # Raise an error for break or continue, which should be caught and handled inside of loops, + # otherwise the error will be raised (which is desired when used outside of a loop). + if isinstance(stmt, ast.Break): + raise LoopBreak() + if isinstance(stmt, ast.Continue): + raise LoopContinue() + + # Literally nothing. + if isinstance(stmt, ast.Pass): + return None + + raise TypeError(stmt) diff --git a/custom_nodes/rgthree-comfy/py/pyproject.py b/custom_nodes/rgthree-comfy/py/pyproject.py new file mode 100644 index 00000000..26eab343 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/pyproject.py @@ -0,0 +1,70 @@ +import os +import re +import json + +from .utils import set_dict_value + +_THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +_FILE_PY_PROJECT = os.path.join(_THIS_DIR, '..', 'pyproject.toml') + + +def read_pyproject(): + """Reads the pyproject.toml file""" + data = {} + last_key = '' + lines = [] + # I'd like to use tomllib/tomli, but I'd much rather not introduce dependencies since I've yet to + # need to and not everyone may have 3.11. We've got a controlled config file anyway. + with open(_FILE_PY_PROJECT, "r", encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + line = line.strip() + if re.match(r'\[([^\]]+)\]$', line): + last_key = line[1:-1] + set_dict_value(data, last_key, data[last_key] if last_key in data else {}) + continue + value_matches = re.match(r'^([^\s\=]+)\s*=\s*(.*)$', line) + if value_matches: + try: + set_dict_value(data, f'{last_key}.{value_matches[1]}', json.loads(value_matches[2])) + except json.decoder.JSONDecodeError: + # We don't handle multiline arrays or curly brackets; that's ok, we know the file. + pass + + return data + + +_DATA = read_pyproject() + +# We would want these to fail if they don't exist, so assume they do. +VERSION: str = _DATA['project']['version'] +NAME: str = _DATA['project']['name'] +LOGO_URL: str = _DATA['tool']['comfy']['Icon'] + +if not LOGO_URL.endswith('.svg'): + raise ValueError('Bad logo url.') + +LOGO_SVG = None +async def get_logo_svg(): + import aiohttp + global LOGO_SVG + if LOGO_SVG is not None: + return LOGO_SVG + # Fetch the logo so we have any updated markup. + try: + async with aiohttp.ClientSession( + trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=True) + ) as session: + headers = { + "user-agent": f"rgthree-comfy/{VERSION}", + 'Cache-Control': 'no-cache', + 'Pragma': 'no-cache', + 'Expires': '0' + } + async with session.get(LOGO_URL, headers=headers) as resp: + LOGO_SVG = await resp.text() + LOGO_SVG = re.sub(r'(id="bg".*fill=)"[^\"]+"', r'\1"{bg}"', LOGO_SVG) + LOGO_SVG = re.sub(r'(id="fg".*fill=)"[^\"]+"', r'\1"{fg}"', LOGO_SVG) + except Exception: + LOGO_SVG = '' + return LOGO_SVG diff --git a/custom_nodes/rgthree-comfy/py/sdxl_empty_latent_image.py b/custom_nodes/rgthree-comfy/py/sdxl_empty_latent_image.py new file mode 100644 index 00000000..c367aaa6 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/sdxl_empty_latent_image.py @@ -0,0 +1,63 @@ +from nodes import EmptyLatentImage +from .constants import get_category, get_name + + +class RgthreeSDXLEmptyLatentImage: + + NAME = get_name('SDXL Empty Latent Image') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "dimensions": ( + [ + # 'Custom', + '1536 x 640 (landscape)', + '1344 x 768 (landscape)', + '1216 x 832 (landscape)', + '1152 x 896 (landscape)', + '1024 x 1024 (square)', + ' 896 x 1152 (portrait)', + ' 832 x 1216 (portrait)', + ' 768 x 1344 (portrait)', + ' 640 x 1536 (portrait)', + ], + { + "default": '1024 x 1024 (square)' + }), + "clip_scale": ("FLOAT", { + "default": 2.0, + "min": 1.0, + "max": 10.0, + "step": .5 + }), + "batch_size": ("INT", { + "default": 1, + "min": 1, + "max": 64 + }), + }, + # "optional": { + # "custom_width": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}), + # "custom_height": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}), + # } + } + + RETURN_TYPES = ("LATENT", "INT", "INT") + RETURN_NAMES = ("LATENT", "CLIP_WIDTH", "CLIP_HEIGHT") + FUNCTION = "generate" + + def generate(self, dimensions, clip_scale, batch_size): + """Generates the latent and exposes the clip_width and clip_height""" + if True: + result = [x.strip() for x in dimensions.split('x')] + width = int(result[0]) + height = int(result[1].split(' ')[0]) + latent = EmptyLatentImage().generate(width, height, batch_size)[0] + return ( + latent, + int(width * clip_scale), + int(height * clip_scale), + ) diff --git a/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_postive.py b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_postive.py new file mode 100644 index 00000000..d070da03 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_postive.py @@ -0,0 +1,178 @@ +import os +import re +from nodes import MAX_RESOLUTION +from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL + +from .log import log_node_warn, log_node_info, log_node_success +from .constants import get_category, get_name +from .power_prompt_utils import get_and_strip_loras +from nodes import LoraLoader, CLIPTextEncode +import folder_paths + +NODE_NAME = get_name('SDXL Power Prompt - Positive') + + +class RgthreeSDXLPowerPromptPositive: + """The Power Prompt for positive conditioning.""" + + NAME = NODE_NAME + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully, + # TODO: Rip out saved prompt input data + SAVED_PROMPTS_FILES=[] + SAVED_PROMPTS_CONTENT=[] + return { + 'required': { + 'prompt_g': ('STRING', { + 'multiline': True, + 'dynamicPrompts': True + }), + 'prompt_l': ('STRING', { + 'multiline': True, + 'dynamicPrompts': True + }), + }, + 'optional': { + "opt_model": ("MODEL",), + "opt_clip": ("CLIP",), + "opt_clip_width": ("INT", { + "forceInput": True, + "default": 1024.0, + "min": 0, + "max": MAX_RESOLUTION + }), + "opt_clip_height": ("INT", { + "forceInput": True, + "default": 1024.0, + "min": 0, + "max": MAX_RESOLUTION + }), + 'insert_lora': (['CHOOSE', 'DISABLE LORAS'] + + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('loras')],), + 'insert_embedding': ([ + 'CHOOSE', + ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],), + 'insert_saved': ([ + 'CHOOSE', + ] + SAVED_PROMPTS_FILES,), + # We'll hide these in the UI for now. + "target_width": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + "target_height": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + "crop_width": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + "crop_height": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + }, + 'hidden': { + 'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,), + } + } + + RETURN_TYPES = ('CONDITIONING', 'MODEL', 'CLIP', 'STRING', 'STRING') + RETURN_NAMES = ('CONDITIONING', 'MODEL', 'CLIP', 'TEXT_G', 'TEXT_L') + FUNCTION = 'main' + + def main(self, + prompt_g, + prompt_l, + opt_model=None, + opt_clip=None, + opt_clip_width=None, + opt_clip_height=None, + insert_lora=None, + insert_embedding=None, + insert_saved=None, + target_width=-1, + target_height=-1, + crop_width=-1, + crop_height=-1, + values_insert_saved=None): + + if insert_lora == 'DISABLE LORAS': + prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, + True, + log_node=self.NAME) + prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, + True, + log_node=self.NAME) + loras = loras_g + loras_l + log_node_info( + NODE_NAME, + f'Disabling all found loras ({len(loras)}) and stripping lora tags for TEXT output.') + elif opt_model is not None and opt_clip is not None: + prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, log_node=self.NAME) + prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, log_node=self.NAME) + loras = loras_g + loras_l + if len(loras) > 0: + for lora in loras: + opt_model, opt_clip = LoraLoader().load_lora(opt_model, opt_clip, lora['lora'], + lora['strength'], lora['strength']) + log_node_success(NODE_NAME, f'Loaded "{lora["lora"]}" from prompt') + log_node_info(NODE_NAME, f'{len(loras)} Loras processed; stripping tags for TEXT output.') + elif ' 0 else opt_clip_width + target_height = target_height if target_height and target_height > 0 else opt_clip_height + crop_width = crop_width if crop_width and crop_width > 0 else 0 + crop_height = crop_height if crop_height and crop_height > 0 else 0 + try: + conditioning = CLIPTextEncodeSDXL().encode(opt_clip, opt_clip_width, opt_clip_height, + crop_width, crop_height, target_width, + target_height, prompt_g, prompt_l)[0] + except Exception: + do_regular_clip_text_encode = True + log_node_info( + self.NAME, + 'Exception while attempting to CLIPTextEncodeSDXL, will fall back to standard encoding.' + ) + else: + log_node_info( + self.NAME, + 'CLIP supplied, but not CLIP_WIDTH and CLIP_HEIGHT. Text encoding will use standard ' + + 'encoding with prompt_g and prompt_l concatenated.') + + if not do_regular_clip_text_encode: + conditioning = CLIPTextEncode().encode( + opt_clip, f'{prompt_g if prompt_g else ""}\n{prompt_l if prompt_l else ""}')[0] + return conditioning diff --git a/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_simple.py b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_simple.py new file mode 100644 index 00000000..9774d878 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_simple.py @@ -0,0 +1,106 @@ +"""A simpler SDXL Power Prompt that doesn't load Loras, like for negative.""" +import os +import re +import folder_paths +from nodes import MAX_RESOLUTION, LoraLoader +from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL +from .sdxl_power_prompt_postive import RgthreeSDXLPowerPromptPositive + +from .log import log_node_warn, log_node_info, log_node_success + +from .constants import get_category, get_name + +NODE_NAME = get_name('SDXL Power Prompt - Simple / Negative') + + +class RgthreeSDXLPowerPromptSimple(RgthreeSDXLPowerPromptPositive): + """A simpler SDXL Power Prompt that doesn't handle Loras.""" + + NAME = NODE_NAME + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully, + # TODO: Rip out saved prompt input data + SAVED_PROMPTS_FILES=[] + SAVED_PROMPTS_CONTENT=[] + return { + 'required': { + 'prompt_g': ('STRING', { + 'multiline': True, + 'dynamicPrompts': True + }), + 'prompt_l': ('STRING', { + 'multiline': True, + 'dynamicPrompts': True + }), + }, + 'optional': { + "opt_clip": ("CLIP",), + "opt_clip_width": ("INT", { + "forceInput": True, + "default": 1024.0, + "min": 0, + "max": MAX_RESOLUTION + }), + "opt_clip_height": ("INT", { + "forceInput": True, + "default": 1024.0, + "min": 0, + "max": MAX_RESOLUTION + }), + 'insert_embedding': ([ + 'CHOOSE', + ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],), + 'insert_saved': ([ + 'CHOOSE', + ] + SAVED_PROMPTS_FILES,), + # We'll hide these in the UI for now. + "target_width": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + "target_height": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + "crop_width": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + "crop_height": ("INT", { + "default": -1, + "min": -1, + "max": MAX_RESOLUTION + }), + }, + 'hidden': { + 'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,), + } + } + + RETURN_TYPES = ('CONDITIONING', 'STRING', 'STRING') + RETURN_NAMES = ('CONDITIONING', 'TEXT_G', 'TEXT_L') + FUNCTION = 'main' + + def main(self, + prompt_g, + prompt_l, + opt_clip=None, + opt_clip_width=None, + opt_clip_height=None, + insert_embedding=None, + insert_saved=None, + target_width=-1, + target_height=-1, + crop_width=-1, + crop_height=-1, + values_insert_saved=None): + + conditioning = self.get_conditioning(prompt_g, prompt_l, opt_clip, opt_clip_width, + opt_clip_height, target_width, target_height, crop_width, crop_height) + return (conditioning, prompt_g, prompt_l) diff --git a/custom_nodes/rgthree-comfy/py/seed.py b/custom_nodes/rgthree-comfy/py/seed.py new file mode 100644 index 00000000..d86c54dc --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/seed.py @@ -0,0 +1,123 @@ +"""See node.""" +import random +from datetime import datetime + +from .constants import get_category, get_name +from .log import log_node_warn, log_node_info + +# Some extension must be setting a seed as server-generated seeds were not random. We'll set a new +# seed and use that state going forward. +initial_random_state = random.getstate() +random.seed(datetime.now().timestamp()) +rgthree_seed_random_state = random.getstate() +random.setstate(initial_random_state) + + +def new_random_seed(): + """ Gets a new random seed from the rgthree_seed_random_state and resetting the previous state.""" + global rgthree_seed_random_state + prev_random_state = random.getstate() + random.setstate(rgthree_seed_random_state) + seed = random.randint(1, 1125899906842624) + rgthree_seed_random_state = random.getstate() + random.setstate(prev_random_state) + return seed + + +class RgthreeSeed: + """Seed node.""" + + NAME = get_name('Seed') + CATEGORY = get_category() + + @classmethod + def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring + return { + "required": { + "seed": ("INT", { + "default": 0, + "min": -1125899906842624, + "max": 1125899906842624 + }), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("SEED",) + FUNCTION = "main" + + @classmethod + def IS_CHANGED(cls, seed, prompt=None, extra_pnginfo=None, unique_id=None): + """Forces a changed state if we happen to get a special seed, as if from the API directly.""" + if seed in (-1, -2, -3): + # This isn't used, but a different value than previous will force it to be "changed" + return new_random_seed() + return seed + + def main(self, seed=0, prompt=None, extra_pnginfo=None, unique_id=None): + """Returns the passed seed on execution.""" + + # We generate random seeds on the frontend in the seed node before sending the workflow in for + # many reasons. However, if we want to use this in an API call without changing the seed before + # sending, then users _could_ pass in "-1" and get a random seed used and added to the metadata. + # Though, this should likely be discouraged for several reasons (thus, a lot of logging). + if seed in (-1, -2, -3): + log_node_warn(self.NAME, + f'Got "{seed}" as passed seed. ' + + 'This shouldn\'t happen when queueing from the ComfyUI frontend.', + msg_color="YELLOW") + if seed in (-2, -3): + log_node_warn(self.NAME, + f'Cannot {"increment" if seed == -2 else "decrement"} seed from ' + + 'server, but will generate a new random seed.', + msg_color="YELLOW") + + original_seed = seed + seed = new_random_seed() + log_node_info(self.NAME, f'Server-generated random seed {seed} and saving to workflow.') + log_node_warn( + self.NAME, + 'NOTE: Re-queues passing in "{seed}" and server-generated random seed won\'t be cached.', + msg_color="YELLOW") + + if unique_id is None: + log_node_warn( + self.NAME, 'Cannot save server-generated seed to image metadata because ' + + 'the node\'s id was not provided.') + else: + if extra_pnginfo is None: + log_node_warn( + self.NAME, 'Cannot save server-generated seed to image workflow ' + + 'metadata because workflow was not provided.') + else: + workflow_node = next( + (x for x in extra_pnginfo['workflow']['nodes'] if str(x['id']) == str(unique_id)), None) + if workflow_node is None or 'widgets_values' not in workflow_node: + log_node_warn( + self.NAME, 'Cannot save server-generated seed to image workflow ' + + 'metadata because node was not found in the provided workflow.') + else: + for index, widget_value in enumerate(workflow_node['widgets_values']): + if widget_value == original_seed: + workflow_node['widgets_values'][index] = seed + + if prompt is None: + log_node_warn( + self.NAME, 'Cannot save server-generated seed to image API prompt ' + + 'metadata because prompt was not provided.') + else: + prompt_node = prompt[str(unique_id)] + if prompt_node is None or 'inputs' not in prompt_node or 'seed' not in prompt_node[ + 'inputs']: + log_node_warn( + self.NAME, 'Cannot save server-generated seed to image workflow ' + + 'metadata because node was not found in the provided workflow.') + else: + prompt_node['inputs']['seed'] = seed + + return (seed,) diff --git a/custom_nodes/rgthree-comfy/py/server/rgthree_server.py b/custom_nodes/rgthree-comfy/py/server/rgthree_server.py new file mode 100644 index 00000000..fe42165e --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/server/rgthree_server.py @@ -0,0 +1,48 @@ +import os +from aiohttp import web +from server import PromptServer + +from ..config import get_config_value +from ..log import log +from .utils_server import set_default_page_resources, set_default_page_routes, get_param +from .routes_config import * +from .routes_model_info import * + +THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +DIR_WEB = os.path.abspath(f'{THIS_DIR}/../../web/') + +routes = PromptServer.instance.routes + +# Sometimes other pages (link_fixer, etc.) may want to import JS from the comfyui +# directory. To allows TS to resolve like '../comfyui/file.js', we'll also resolve any module HTTP +# to these routes. +set_default_page_resources("comfyui", routes) +set_default_page_resources("common", routes) +set_default_page_resources("lib", routes) + +set_default_page_routes("link_fixer", routes) +if get_config_value('unreleased.models_page.enabled') is True: + set_default_page_routes("models", routes) + + +@routes.get('/rgthree/api/print') +async def api_print(request): + """Logs a user message to the terminal.""" + + message_type = get_param(request, 'type') + if message_type == 'PRIMITIVE_REROUTE': + log( + "You are using rgthree-comfy reroutes with a ComfyUI Primitive node. Unfortunately, ComfyUI " + "has removed support for this. While rgthree-comfy has a best-effort support fallback for " + "now, it may no longer work as expected and is strongly recommended you either replace the " + "Reroute node using ComfyUI's reroute node, or refrain from using the Primitive node " + "(you can always use the rgthree-comfy \"Power Primitive\" for non-combo primitives).", + prefix="Reroute", + color="YELLOW", + id=message_type, + at_most_secs=20 + ) + else: + log("Unknown log type from api", prefix="rgthree-comfy",color ="YELLOW") + + return web.json_response({}) diff --git a/custom_nodes/rgthree-comfy/py/server/routes_config.py b/custom_nodes/rgthree-comfy/py/server/routes_config.py new file mode 100644 index 00000000..74e7e1e2 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/server/routes_config.py @@ -0,0 +1,67 @@ +import json +import re +from aiohttp import web + +from server import PromptServer + +from ..pyproject import get_logo_svg +from .utils_server import is_param_truthy, get_param +from ..config import get_config, set_user_config, refresh_config + +routes = PromptServer.instance.routes + + +@routes.get('/rgthree/config.js') +def api_get_user_config_file(request): + """ Returns the user configuration as a javascript file. """ + data_str = json.dumps(get_config(), sort_keys=True, indent=2, separators=(",", ": ")) + text = f'export const rgthreeConfig = {data_str}' + return web.Response(text=text, content_type='application/javascript') + + +@routes.get('/rgthree/api/config') +def api_get_user_config(request): + """ Returns the user configuration. """ + if is_param_truthy(request, 'refresh'): + refresh_config() + return web.json_response(get_config()) + + +@routes.post('/rgthree/api/config') +async def api_set_user_config(request): + """ Returns the user configuration. """ + post = await request.post() + data = json.loads(post.get("json")) + set_user_config(data) + return web.json_response({"status": "ok"}) + + +@routes.get('/rgthree/logo.svg') +async def get_logo(request, as_markup=False): + """ Returns the rgthree logo with color config. """ + bg = get_param(request, 'bg', 'transparent') + fg = get_param(request, 'fg', '#111111') + w = get_param(request, 'w') + h = get_param(request, 'h') + css_class = get_param(request, 'cssClass') + svg = await get_logo_svg() + resp = svg.format(bg=bg, fg=fg) + if w is not None: + resp = re.sub(r'(]*?)width="[^\"]+"', r'\1', resp) + if str(w).isnumeric(): + resp = re.sub(r']*?)height="[^\"]+"', r'\1', resp) + if str(h).isnumeric(): + resp = re.sub(r' 1 else '.'} " + "ComfyUI thinks they exist, but they were not found on the filesystem.", + prefix="Power Lora Loader", + color="YELLOW", + id=f'no_file_details_{model_type}', + at_most_secs=30 + ) + return web.json_response(response) + + return web.json_response(list(files)) + + +@routes.get('/rgthree/api/{type}/info') +async def api_get_models_info(request): + """Returns a list model info; either all or a specific ones if provided a 'files' param. + + If a `light` param is specified and not falsy, no metadata will be fetched. + """ + if _check_valid_model_type(request): + return _check_valid_model_type(request) + + model_type = request.match_info['type'] + files_param = get_param(request, 'files') + maybe_fetch_metadata = files_param is not None + if not is_param_falsy(request, 'light'): + maybe_fetch_metadata = False + api_response = await models_info_response( + request, model_type, maybe_fetch_metadata=maybe_fetch_metadata + ) + return web.json_response(api_response) + + +@routes.get('/rgthree/api/{type}/info/refresh') +async def api_get_refresh_get_models_info(request): + """Refreshes model info; either all or specific ones if provided a 'files' param. """ + if _check_valid_model_type(request): + return _check_valid_model_type(request) + + model_type = request.match_info['type'] + api_response = await models_info_response( + request, model_type, maybe_fetch_civitai=True, maybe_fetch_metadata=True + ) + return web.json_response(api_response) + + +@routes.get('/rgthree/api/{type}/info/clear') +async def api_get_delete_model_info(request): + """Clears model info from the filesystem for the provided file.""" + if _check_valid_model_type(request): + return _check_valid_model_type(request) + + api_response = {'status': 200} + model_type = request.match_info['type'] + files_param = get_param(request, 'files') + if files_param is not None: + files_param = files_param.split(',') + del_info = not is_param_falsy(request, 'del_info') + del_metadata = not is_param_falsy(request, 'del_metadata') + del_civitai = not is_param_falsy(request, 'del_civitai') + if not files_param: + api_response['status'] = '404' + api_response['error'] = f'No file provided. Please pass files=ALL to clear {model_type} info.' + else: + if len(files_param) == 1 and files_param[ + 0] == "ALL": # Force the user to supply files=ALL to trigger all clearing. + files_param = folder_paths.get_filename_list(model_type) + for file_param in files_param: + await delete_model_info( + file_param, + model_type, + del_info=del_info, + del_metadata=del_metadata, + del_civitai=del_civitai + ) + return web.json_response(api_response) + + +@routes.post('/rgthree/api/{type}/info') +async def api_post_save_model_data(request): + """Saves data to a model by name. """ + if _check_valid_model_type(request): + return _check_valid_model_type(request) + + model_type = request.match_info['type'] + api_response = {'status': 200} + file_param = get_param(request, 'file') + if file_param is None: + api_response['status'] = '404' + api_response['error'] = 'No model found at path' + else: + post = await request.post() + await set_model_info_partial(file_param, model_type, json.loads(post.get("json"))) + info_data = await get_model_info(file_param, model_type) + api_response['data'] = info_data + return web.json_response(api_response) + + +@routes.get('/rgthree/api/{type}/img') +async def api_get_models_info_img(request): + """ Returns an image response if one exists for the model. """ + if _check_valid_model_type(request): + return _check_valid_model_type(request) + + model_type = request.match_info['type'] + file_param = get_param(request, 'file') + file_path = folder_paths.get_full_path(model_type, file_param) + if not path_exists(file_path): + file_path = abspath(file_path) + img_path = None + for ext in ['jpg', 'png', 'jpeg']: + try_path = f'{os.path.splitext(file_path)[0]}.{ext}' + if path_exists(try_path): + img_path = try_path + break + + if not path_exists(img_path): + api_response = {} + api_response['status'] = '404' + api_response['error'] = 'No model found at path' + return web.json_response(api_response) + + return web.FileResponse(img_path) + + +async def models_info_response( + request, model_type, maybe_fetch_civitai=False, maybe_fetch_metadata=False +): + """Gets model info for all or a single model type.""" + api_response = {'status': 200, 'data': []} + light = not is_param_falsy(request, 'light') + files_param = get_param(request, 'files') + if files_param is not None: + files_param = files_param.split(',') + else: + files_param = folder_paths.get_filename_list(model_type) + for file_param in files_param: + info_data = await get_model_info( + file_param, + model_type, + maybe_fetch_civitai=maybe_fetch_civitai, + maybe_fetch_metadata=maybe_fetch_metadata, + light=light + ) + api_response['data'].append(info_data) + return api_response diff --git a/custom_nodes/rgthree-comfy/py/server/utils_info.py b/custom_nodes/rgthree-comfy/py/server/utils_info.py new file mode 100644 index 00000000..4723a664 --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/server/utils_info.py @@ -0,0 +1,452 @@ +import hashlib +import json +import os +import re +from datetime import datetime + +import requests + +from server import PromptServer +import folder_paths + +from ..utils import abspath, get_dict_value, load_json_file, file_exists, remove_path, save_json_file +from ..utils_userdata import read_userdata_json, save_userdata_json, delete_userdata_file + + +def _get_info_cache_file(data_type: str, file_hash: str): + return f'info/{file_hash}.{data_type}.json' + + +async def delete_model_info( + file: str, model_type, del_info=True, del_metadata=True, del_civitai=True +): + """Delete the info json, and the civitai & metadata caches.""" + file_path = get_folder_path(file, model_type) + if file_path is None: + return + if del_info: + remove_path(get_info_file(file_path)) + if del_civitai or del_metadata: + file_hash = _get_sha256_hash(file_path) + if del_civitai: + json_file_path = _get_info_cache_file(file_hash, 'civitai') + delete_userdata_file(json_file_path) + if del_metadata: + json_file_path = _get_info_cache_file(file_hash, 'metadata') + delete_userdata_file(json_file_path) + + +def get_file_info(file: str, model_type): + """Gets basic file info, like created or modified date.""" + file_path = get_folder_path(file, model_type) + if file_path is None: + return None + return { + 'file': file, + 'path': file_path, + 'modified': os.path.getmtime(file_path) * 1000, # millis + 'imageLocal': f'/rgthree/api/{model_type}/img?file={file}' if get_img_file(file_path) else None, + 'hasInfoFile': get_info_file(file_path) is not None, + } + + +def get_info_file(file_path: str, force=False): + # Try to load a rgthree-info.json file next to the file. + info_path = f'{file_path}.rgthree-info.json' + return info_path if file_exists(info_path) or force else None + + +def get_img_file(file_path: str, force=False): + for ext in ['jpg', 'png', 'jpeg', 'webp']: + try_path = f'{os.path.splitext(file_path)[0]}.{ext}' + if file_exists(try_path): + return try_path + + +def get_model_info_file_data(file: str, model_type, default=None): + """Returns the data from the info file, or a default value if it doesn't exist.""" + file_path = get_folder_path(file, model_type) + if file_path is None: + return default + return load_json_file(get_info_file(file_path), default=default) + + +async def get_model_info( + file: str, + model_type, + default=None, + maybe_fetch_civitai=False, + force_fetch_civitai=False, + maybe_fetch_metadata=False, + force_fetch_metadata=False, + light=False +): + """Compiles a model info given a stored file next to the model, and/or metadata/civitai.""" + + file_path = get_folder_path(file, model_type) + if file_path is None: + return default + + should_save = False + # basic data + basic_data = get_file_info(file, model_type) + # Try to load a rgthree-info.json file next to the file. + info_data = get_model_info_file_data(file, model_type, default={}) + + for key in ['file', 'path', 'modified', 'imageLocal', 'hasInfoFile']: + if key in basic_data and basic_data[key] and ( + key not in info_data or info_data[key] != basic_data[key] + ): + info_data[key] = basic_data[key] + should_save = True + + # Check if we have an image next to the file and, if so, add it to the front of the images + # (if it isn't already). + img_next_to_file = basic_data['imageLocal'] + + if 'images' not in info_data: + info_data['images'] = [] + should_save = True + + if img_next_to_file: + if len(info_data['images']) == 0 or info_data['images'][0]['url'] != img_next_to_file: + info_data['images'].insert(0, {'url': img_next_to_file}) + should_save = True + + # If we just want light data then bail now with just existing data, plus file, path and img if + # next to the file. + if light and not maybe_fetch_metadata and not force_fetch_metadata and not maybe_fetch_civitai and not force_fetch_civitai: + return info_data + + if 'raw' not in info_data: + info_data['raw'] = {} + should_save = True + + should_save = _update_data(info_data) or should_save + + should_fetch_civitai = force_fetch_civitai is True or ( + maybe_fetch_civitai is True and 'civitai' not in info_data['raw'] + ) + should_fetch_metadata = force_fetch_metadata is True or ( + maybe_fetch_metadata is True and 'metadata' not in info_data['raw'] + ) + + if should_fetch_metadata: + data_meta = _get_model_metadata(file, model_type, default={}, refresh=force_fetch_metadata) + should_save = _merge_metadata(info_data, data_meta) or should_save + + if should_fetch_civitai: + data_civitai = _get_model_civitai_data( + file, model_type, default={}, refresh=force_fetch_civitai + ) + should_save = _merge_civitai_data(info_data, data_civitai) or should_save + + if 'sha256' not in info_data: + file_hash = _get_sha256_hash(file_path) + if file_hash is not None: + info_data['sha256'] = file_hash + should_save = True + + if should_save: + if 'trainedWords' in info_data: + # Sort by count; if it doesn't exist, then assume it's a top item from civitai or elsewhere. + info_data['trainedWords'] = sorted( + info_data['trainedWords'], + key=lambda w: w['count'] if 'count' in w else 99999, + reverse=True + ) + save_model_info(file, info_data, model_type) + + # If we're saving, then the UI is likely waiting to see if the refreshed data is coming in. + await PromptServer.instance.send(f"rgthree-refreshed-{model_type}-info", {"data": info_data}) + + return info_data + + +def _update_data(info_data: dict) -> bool: + """Ports old data to new data if necessary.""" + should_save = False + # If we have "triggerWords" then move them over to "trainedWords" + if 'triggerWords' in info_data and len(info_data['triggerWords']) > 0: + civitai_words = ','.join(( + get_dict_value(info_data, 'raw.civitai.triggerWords', default=[]) + + get_dict_value(info_data, 'raw.civitai.trainedWords', default=[]) + )) + if 'trainedWords' not in info_data: + info_data['trainedWords'] = [] + for trigger_word in info_data['triggerWords']: + word_data = next((data for data in info_data['trainedWords'] if data['word'] == trigger_word), + None) + if word_data is None: + word_data = {'word': trigger_word} + info_data['trainedWords'].append(word_data) + if trigger_word in civitai_words: + word_data['civitai'] = True + else: + word_data['user'] = True + + del info_data['triggerWords'] + should_save = True + return should_save + + +def _merge_metadata(info_data: dict, data_meta: dict) -> bool: + """Returns true if data was saved.""" + should_save = False + + base_model_file = get_dict_value(data_meta, 'ss_sd_model_name', None) + if base_model_file: + info_data['baseModelFile'] = base_model_file + + # Loop over metadata tags + trained_words = {} + if 'ss_tag_frequency' in data_meta and isinstance(data_meta['ss_tag_frequency'], dict): + for bucket_value in data_meta['ss_tag_frequency'].values(): + if isinstance(bucket_value, dict): + for tag, count in bucket_value.items(): + if tag not in trained_words: + trained_words[tag] = {'word': tag, 'count': 0, 'metadata': True} + trained_words[tag]['count'] = trained_words[tag]['count'] + count + + if 'trainedWords' not in info_data: + info_data['trainedWords'] = list(trained_words.values()) + should_save = True + else: + # We can't merge, because the list may have other data, like it's part of civitaidata. + merged_dict = {} + for existing_word_data in info_data['trainedWords']: + merged_dict[existing_word_data['word']] = existing_word_data + for new_key, new_word_data in trained_words.items(): + if new_key not in merged_dict: + merged_dict[new_key] = {} + merged_dict[new_key] = {**merged_dict[new_key], **new_word_data} + info_data['trainedWords'] = list(merged_dict.values()) + should_save = True + + # trained_words = list(trained_words.values()) + # info_data['meta_trained_words'] = trained_words + info_data['raw']['metadata'] = data_meta + should_save = True + + if 'sha256' not in info_data and '_sha256' in data_meta: + info_data['sha256'] = data_meta['_sha256'] + should_save = True + + return should_save + + +def _merge_civitai_data(info_data: dict, data_civitai: dict) -> bool: + """Returns true if data was saved.""" + should_save = False + + if 'name' not in info_data: + info_data['name'] = get_dict_value(data_civitai, 'model.name', '') + should_save = True + version_name = get_dict_value(data_civitai, 'name') + if version_name is not None: + info_data['name'] += f' - {version_name}' + + if 'type' not in info_data: + info_data['type'] = get_dict_value(data_civitai, 'model.type') + should_save = True + if 'baseModel' not in info_data: + info_data['baseModel'] = get_dict_value(data_civitai, 'baseModel') + should_save = True + + # We always want to merge triggerword. + civitai_trigger = get_dict_value(data_civitai, 'triggerWords', default=[]) + civitai_trained = get_dict_value(data_civitai, 'trainedWords', default=[]) + civitai_words = ','.join(civitai_trigger + civitai_trained) + if civitai_words: + civitai_words = re.sub(r"\s*,\s*", ",", civitai_words) + civitai_words = re.sub(r",+", ",", civitai_words) + civitai_words = re.sub(r"^,", "", civitai_words) + civitai_words = re.sub(r",$", "", civitai_words) + if civitai_words: + civitai_words = civitai_words.split(',') + if 'trainedWords' not in info_data: + info_data['trainedWords'] = [] + for trigger_word in civitai_words: + word_data = next( + (data for data in info_data['trainedWords'] if data['word'] == trigger_word), None + ) + if word_data is None: + word_data = {'word': trigger_word} + info_data['trainedWords'].append(word_data) + word_data['civitai'] = True + + if 'sha256' not in info_data: + info_data['sha256'] = data_civitai['_sha256'] + should_save = True + + if 'modelId' in data_civitai: + info_data['links'] = info_data['links'] if 'links' in info_data else [] + civitai_link = f'https://civitai.com/models/{get_dict_value(data_civitai, "modelId")}' + if get_dict_value(data_civitai, "id"): + civitai_link += f'?modelVersionId={get_dict_value(data_civitai, "id")}' + info_data['links'].append(civitai_link) + info_data['links'].append(data_civitai['_civitai_api']) + should_save = True + + # Take images from civitai + if 'images' in data_civitai: + info_data_image_urls = list( + map(lambda i: i['url'] if 'url' in i else None, info_data['images']) + ) + for img in data_civitai['images']: + img_url = get_dict_value(img, 'url') + if img_url is not None and img_url not in info_data_image_urls: + img_id = os.path.splitext(os.path.basename(img_url))[0] if img_url is not None else None + img_data = { + 'url': img_url, + 'civitaiUrl': f'https://civitai.com/images/{img_id}' if img_id is not None else None, + 'width': get_dict_value(img, 'width'), + 'height': get_dict_value(img, 'height'), + 'type': get_dict_value(img, 'type'), + 'nsfwLevel': get_dict_value(img, 'nsfwLevel'), + 'seed': get_dict_value(img, 'meta.seed'), + 'positive': get_dict_value(img, 'meta.prompt'), + 'negative': get_dict_value(img, 'meta.negativePrompt'), + 'steps': get_dict_value(img, 'meta.steps'), + 'sampler': get_dict_value(img, 'meta.sampler'), + 'cfg': get_dict_value(img, 'meta.cfgScale'), + 'model': get_dict_value(img, 'meta.Model'), + 'resources': get_dict_value(img, 'meta.resources'), + } + info_data['images'].append(img_data) + should_save = True + + # The raw data + if 'civitai' not in info_data['raw']: + info_data['raw']['civitai'] = data_civitai + should_save = True + + return should_save + + +def _get_model_civitai_data(file: str, model_type, default=None, refresh=False): + """Gets the civitai data, either cached from the user directory, or from civitai api.""" + file_hash = _get_sha256_hash(get_folder_path(file, model_type)) + if file_hash is None: + return None + + json_file_path = _get_info_cache_file(file_hash, 'civitai') + + api_url = f'https://civitai.com/api/v1/model-versions/by-hash/{file_hash}' + file_data = read_userdata_json(json_file_path) + if file_data is None or refresh is True: + try: + response = requests.get(api_url, timeout=5000) + data = response.json() + save_userdata_json( + json_file_path, { + 'url': api_url, + 'timestamp': datetime.now().timestamp(), + 'response': data + } + ) + file_data = read_userdata_json(json_file_path) + except requests.exceptions.RequestException as e: # This is the correct syntax + print(e) + response = file_data['response'] if file_data is not None and 'response' in file_data else None + if response is not None: + response['_sha256'] = file_hash + response['_civitai_api'] = api_url + return response if response is not None else default + + +def _get_model_metadata(file: str, model_type, default=None, refresh=False): + """Gets the metadata from the file itself.""" + file_path = get_folder_path(file, model_type) + file_hash = _get_sha256_hash(file_path) + if file_hash is None: + return default + + json_file_path = _get_info_cache_file(file_hash, 'metadata') + + file_data = read_userdata_json(json_file_path) + if file_data is None or refresh is True: + data = _read_file_metadata_from_header(file_path) + if data is not None: + file_data = {'url': file, 'timestamp': datetime.now().timestamp(), 'response': data} + save_userdata_json(json_file_path, file_data) + response = file_data['response'] if file_data is not None and 'response' in file_data else None + if response is not None: + response['_sha256'] = file_hash + return response if response is not None else default + + +def _read_file_metadata_from_header(file_path: str) -> dict: + """Reads the file's header and returns a JSON dict metdata if available.""" + data = None + try: + if file_path.endswith('.safetensors'): + with open(file_path, "rb") as file: + # https://github.com/huggingface/safetensors#format + # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header + header_size = int.from_bytes(file.read(8), "little", signed=False) + + if header_size <= 0: + raise BufferError("Invalid header size") + + header = file.read(header_size) + if header is None: + raise BufferError("Invalid header") + + header_json = json.loads(header) + data = header_json["__metadata__"] if "__metadata__" in header_json else None + + if data is not None: + for key, value in data.items(): + if isinstance(value, str) and value.startswith('{') and value.endswith('}'): + try: + value_as_json = json.loads(value) + data[key] = value_as_json + except Exception: + print(f'metdata for field {key} did not parse as json') + except requests.exceptions.RequestException as e: + print(e) + data = None + + return data + + +def get_folder_path(file: str, model_type) -> str | None: + """Gets the file path ensuring it exists.""" + file_path = folder_paths.get_full_path(model_type, file) + if not file_exists(file_path): + file_path = abspath(file_path) + if not file_exists(file_path): + file_path = None + return file_path + + +def _get_sha256_hash(file_path: str | None): + """Returns the hash for the file.""" + if not file_path or not file_exists(file_path): + return None + BUF_SIZE = 1024 * 128 # lets read stuff in 64kb chunks! + file_hash = None + sha256_hash = hashlib.sha256() + with open(file_path, "rb") as f: + # Read and update hash string value in blocks of BUF_SIZE + for byte_block in iter(lambda: f.read(BUF_SIZE), b""): + sha256_hash.update(byte_block) + file_hash = sha256_hash.hexdigest() + return file_hash + + +async def set_model_info_partial(file: str, model_type: str, info_data_partial): + """Sets partial data into the existing model info data.""" + info_data = await get_model_info(file, model_type, default={}) + info_data = {**info_data, **info_data_partial} + save_model_info(file, info_data, model_type) + + +def save_model_info(file: str, info_data, model_type): + """Saves the model info alongside the model itself.""" + file_path = get_folder_path(file, model_type) + if file_path is None: + return + info_path = get_info_file(file_path, force=True) + save_json_file(info_path, info_data) diff --git a/custom_nodes/rgthree-comfy/py/server/utils_server.py b/custom_nodes/rgthree-comfy/py/server/utils_server.py new file mode 100644 index 00000000..4b7b938d --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/server/utils_server.py @@ -0,0 +1,56 @@ +import os +from aiohttp import web + +THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +DIR_WEB = os.path.abspath(f'{THIS_DIR}/../../web/') + + +def get_param(request, param, default=None): + """Gets a param from a request.""" + return request.rel_url.query[param] if param in request.rel_url.query else default + + +def is_param_falsy(request, param): + """Determines if a param is explicitly 0 or false.""" + val = get_param(request, param) + return val is not None and (val == "0" or val.upper() == "FALSE") + + +def is_param_truthy(request, param): + """Determines if a param is explicitly 0 or false.""" + val = get_param(request, param) + return val is not None and not is_param_falsy(request, param) + + +def set_default_page_resources(path, routes): + """ Sets up routes for handling static files under a path.""" + + @routes.get(f'/rgthree/{path}/{{file}}') + async def get_resource(request): + """ Returns a resource file. """ + return web.FileResponse(os.path.join(DIR_WEB, path, request.match_info['file'])) + + @routes.get(f'/rgthree/{path}/{{subdir}}/{{file}}') + async def get_resource_subdir(request): + """ Returns a resource file. """ + return web.FileResponse( + os.path.join(DIR_WEB, path, request.match_info['subdir'], request.match_info['file'])) + + +def set_default_page_routes(path, routes): + """ Sets default path handling for a hosted rgthree page. """ + + @routes.get(f'/rgthree/{path}') + async def get_path_redir(request): + """ Redirects to the path adding a trailing slash. """ + raise web.HTTPFound(f'{request.path}/') + + @routes.get(f'/rgthree/{path}/') + async def get_path_index(request): + """ Handles the page's index loading. """ + html = '' + with open(os.path.join(DIR_WEB, path, 'index.html'), 'r', encoding='UTF-8') as file: + html = file.read() + return web.Response(text=html, content_type='text/html') + + set_default_page_resources(path, routes) diff --git a/custom_nodes/rgthree-comfy/py/utils.py b/custom_nodes/rgthree-comfy/py/utils.py new file mode 100644 index 00000000..f3927e3a --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/utils.py @@ -0,0 +1,168 @@ +import json +import os +import re + +from typing import Union + + +class AnyType(str): + """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" + + def __ne__(self, __value: object) -> bool: + return False + + +class FlexibleOptionalInputType(dict): + """A special class to make flexible nodes that pass data to our python handlers. + + Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs + (like for Any Switch, Context Switch, Context Merge, Power Lora Loader, etc). + + Initially, ComfyUI only needed to return True for `__contains__` below, which told ComfyUI that + our node will handle the input, regardless of what it is. + + However, after https://github.com/comfyanonymous/ComfyUI/pull/2666 ComdyUI's execution changed + also checking the data for the key; specifcially, the type which is the first tuple entry. This + type is supplied to our FlexibleOptionalInputType and returned for any non-data key. This can be a + real type, or use the AnyType for additional flexibility. + """ + + def __init__(self, type, data: Union[dict, None] = None): + """Initializes the FlexibleOptionalInputType. + + Args: + type: The flexible type to use when ComfyUI retrieves an unknown key (via `__getitem__`). + data: An optional dict to use as the basis. This is stored both in a `data` attribute, so we + can look it up without hitting our overrides, as well as iterated over and adding its key + and values to our `self` keys. This way, when looked at, we will appear to represent this + data. When used in an "optional" INPUT_TYPES, these are the starting optional node types. + """ + self.type = type + self.data = data + if self.data is not None: + for k, v in self.data.items(): + self[k] = v + + def __getitem__(self, key): + # If we have this key in the initial data, then return it. Otherwise return the tuple with our + # flexible type. + if self.data is not None and key in self.data: + val = self.data[key] + return val + return (self.type,) + + def __contains__(self, key): + """Always contain a key, and we'll always return the tuple above when asked for it.""" + return True + + +any_type = AnyType("*") + + +def is_dict_value_falsy(data: dict, dict_key: str): + """Checks if a dict value is falsy.""" + val = get_dict_value(data, dict_key) + return not val + + +def get_dict_value(data: dict, dict_key: str, default=None): + """Gets a deeply nested value given a dot-delimited key.""" + keys = dict_key.split('.') + key = keys.pop(0) if len(keys) > 0 else None + found = data[key] if key in data else None + if found is not None and len(keys) > 0: + return get_dict_value(found, '.'.join(keys), default) + return found if found is not None else default + + +def set_dict_value(data: dict, dict_key: str, value, create_missing_objects=True): + """Sets a deeply nested value given a dot-delimited key.""" + keys = dict_key.split('.') + key = keys.pop(0) if len(keys) > 0 else None + if key not in data: + if create_missing_objects is False: + return data + data[key] = {} + if len(keys) == 0: + data[key] = value + else: + set_dict_value(data[key], '.'.join(keys), value, create_missing_objects) + + return data + + +def dict_has_key(data: dict, dict_key): + """Checks if a dict has a deeply nested dot-delimited key.""" + keys = dict_key.split('.') + key = keys.pop(0) if len(keys) > 0 else None + if key is None or key not in data: + return False + if len(keys) == 0: + return True + return dict_has_key(data[key], '.'.join(keys)) + + +def load_json_file(file: str, default=None): + """Reads a json file and returns the json dict, stripping out "//" comments first.""" + if path_exists(file): + with open(file, 'r', encoding='UTF-8') as file: + config = file.read() + try: + return json.loads(config) + except json.decoder.JSONDecodeError: + try: + config = re.sub(r"^\s*//\s.*", "", config, flags=re.MULTILINE) + return json.loads(config) + except json.decoder.JSONDecodeError: + try: + config = re.sub(r"(?:^|\s)//.*", "", config, flags=re.MULTILINE) + return json.loads(config) + except json.decoder.JSONDecodeError: + pass + return default + + +def save_json_file(file_path: str, data: dict): + """Saves a json file.""" + os.makedirs(os.path.dirname(file_path), exist_ok=True) + with open(file_path, 'w+', encoding='UTF-8') as file: + json.dump(data, file, sort_keys=False, indent=2, separators=(",", ": ")) + + +def path_exists(path): + """Checks if a path exists, accepting None type.""" + if path is not None: + return os.path.exists(path) + return False + + +def file_exists(path): + """Checks if a file exists, accepting None type.""" + if path is not None: + return os.path.isfile(path) + return False + + +def remove_path(path): + """Removes a path, if it exists.""" + if path_exists(path): + os.remove(path) + return True + return False + +def abspath(file_path: str): + """Resolves the abspath of a file, resolving symlinks and user dirs.""" + if file_path and not path_exists(file_path): + maybe_path = os.path.abspath(os.path.realpath(os.path.expanduser(file_path))) + file_path = maybe_path if path_exists(maybe_path) else file_path + return file_path + +class ByPassTypeTuple(tuple): + """A special class that will return additional "AnyType" strings beyond defined values. + Credit to Trung0246 + """ + + def __getitem__(self, index): + if index > len(self) - 1: + return AnyType("*") + return super().__getitem__(index) diff --git a/custom_nodes/rgthree-comfy/py/utils_userdata.py b/custom_nodes/rgthree-comfy/py/utils_userdata.py new file mode 100644 index 00000000..b10b757a --- /dev/null +++ b/custom_nodes/rgthree-comfy/py/utils_userdata.py @@ -0,0 +1,50 @@ +import os + +from .utils import load_json_file, path_exists, save_json_file + +THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +USERDATA = os.path.join(THIS_DIR, '..', 'userdata') + + +def read_userdata_file(rel_path: str): + """Reads a file from the userdata directory.""" + file_path = clean_path(rel_path) + if path_exists(file_path): + with open(file_path, 'r', encoding='UTF-8') as file: + return file.read() + return None + + +def save_userdata_file(rel_path: str, content: str): + """Saves a file from the userdata directory.""" + file_path = clean_path(rel_path) + with open(file_path, 'w+', encoding='UTF-8') as file: + file.write(content) + + +def delete_userdata_file(rel_path: str): + """Deletes a file from the userdata directory.""" + file_path = clean_path(rel_path) + if os.path.isfile(file_path): + os.remove(file_path) + + +def read_userdata_json(rel_path: str): + """Reads a json file from the userdata directory.""" + file_path = clean_path(rel_path) + return load_json_file(file_path) + + +def save_userdata_json(rel_path: str, data: dict): + """Saves a json file from the userdata directory.""" + file_path = clean_path(rel_path) + return save_json_file(file_path, data) + + +def clean_path(rel_path: str): + """Cleans a relative path by splitting on forward slash and os.path.joining.""" + cleaned = USERDATA + paths = rel_path.split('/') + for path in paths: + cleaned = os.path.join(cleaned, path) + return cleaned diff --git a/custom_nodes/rgthree-comfy/pyproject.toml b/custom_nodes/rgthree-comfy/pyproject.toml new file mode 100644 index 00000000..d9a717ff --- /dev/null +++ b/custom_nodes/rgthree-comfy/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "rgthree-comfy" +description = "Making ComfyUI more comfortable." +version = "1.0.2512112053" +license = { file = "LICENSE" } +dependencies = [] + +[project.urls] +Repository = "https://github.com/rgthree/rgthree-comfy" + +[tool.comfy] +PublisherId = "rgthree" +DisplayName = "rgthree-comfy" +Icon = "https://comfy.rgthree.com/media/rgthree.svg" diff --git a/custom_nodes/rgthree-comfy/requirements.txt b/custom_nodes/rgthree-comfy/requirements.txt new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/rgthree-comfy/rgthree_config.json.default b/custom_nodes/rgthree-comfy/rgthree_config.json.default new file mode 100644 index 00000000..f8c30d87 --- /dev/null +++ b/custom_nodes/rgthree-comfy/rgthree_config.json.default @@ -0,0 +1,65 @@ +// COPY THIS FILE BEFORE MAKING CHANGES TO: rgthree_config.json +{ + "log_level": "WARN", + "features": { + "show_alerts_for_corrupt_workflows": false, + "monitor_for_corrupt_links": false, + "menu_queue_selected_nodes": true, + "menu_auto_nest": { + "subdirs": null, + "threshold": 20 + }, + "menu_bookmarks": { + "enabled": true + }, + "group_header_fast_toggle": { + "enabled": null, + "toggles": ["queue", "bypass", "mute"], + "show": "hover" + }, + "progress_bar": { + "enabled": true, + "height": 16, + "position": "top" + }, + "comfy_top_bar_menu": { + "enabled": true, + "button_bookmarks": { + "enabled": true + } + }, + // Allows for dragging and dropping a workflow (image, json) onto an individual node to import + // that specific node's widgets if it also exists in the dropped workflow (same id, type). + "import_individual_nodes": { + "enabled": null + }, + // Enables invokeExtensionsAsync for rgthree-nodes allowing other extensions to hook into the + // nodes like the default ComfyNodes. This was not possible before Apr 2024, so it's a config + // entry in case it causes issues. This is only for the nodeCreated event/function as of now. + "invoke_extensions_async": { + "node_created": true + } + }, + "nodes": { + "reroute": { + "default_width": 40, + "default_height": 30, + "default_resizable": false, + "default_layout": ["Left", "Right"], + "fast_reroute": { + "enabled": true, + "key_create_while_dragging_link" : "Shift + R", + "key_rotate": "Shift + A", + "key_resize": "Shift + X", + "key_move": "Shift + Z", + "key_connections_input": "Shift + S", + "key_connections_output": "Shift + D" + } + } + }, + "announcements": { + "comfy-nodes-20": { + "incompatible": true + } + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/any_switch.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/any_switch.ts new file mode 100644 index 00000000..9e72642b --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/any_switch.ts @@ -0,0 +1,103 @@ +import type { + ComfyApp, + INodeInputSlot, + INodeOutputSlot, + LGraphNode, + LLink, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {IoDirection, addConnectionLayoutSupport, followConnectionUntilType} from "./utils.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js"; +import {debounce} from "rgthree/common/shared_utils.js"; + +class RgthreeAnySwitch extends RgthreeBaseServerNode { + static override title = NodeTypesString.ANY_SWITCH; + static override type = NodeTypesString.ANY_SWITCH; + static comfyClass = NodeTypesString.ANY_SWITCH; + + private stabilizeBound = this.stabilize.bind(this); + private nodeType: string | string[] | null = null; + + constructor(title = RgthreeAnySwitch.title) { + super(title); + // Adding five. Note, configure will add as many as was in the stored workflow automatically. + this.addAnyInput(5); + } + + override onConnectionsChange( + type: number, + slotIndex: number, + isConnected: boolean, + linkInfo: LLink, + ioSlot: INodeOutputSlot | INodeInputSlot, + ) { + super.onConnectionsChange?.(type, slotIndex, isConnected, linkInfo, ioSlot); + this.scheduleStabilize(); + } + + onConnectionsChainChange() { + this.scheduleStabilize(); + } + + scheduleStabilize(ms = 64) { + return debounce(this.stabilizeBound, ms); + } + + private addAnyInput(num = 1) { + for (let i = 0; i < num; i++) { + this.addInput( + `any_${String(this.inputs.length + 1).padStart(2, "0")}`, + (this.nodeType || "*") as string, + ); + } + } + + stabilize() { + // First, clean up the dynamic number of inputs. + removeUnusedInputsFromEnd(this, 4); + this.addAnyInput(); + + // We prefer the inputs, then the output. + let connectedType = followConnectionUntilType(this, IoDirection.INPUT, undefined, true); + if (!connectedType) { + connectedType = followConnectionUntilType(this, IoDirection.OUTPUT, undefined, true); + } + // TODO: What this doesn't do is broadcast to other nodes when its type changes. Reroute node + // does, but, for now, if this was connected to another Any Switch, say, the second one wouldn't + // change its type when the first does. The user would need to change the connections. + this.nodeType = connectedType?.type || "*"; + for (const input of this.inputs) { + input.type = this.nodeType as string; // So, types can indeed be arrays,, + } + for (const output of this.outputs) { + output.type = this.nodeType as string; // So, types can indeed be arrays,, + output.label = + output.type === "RGTHREE_CONTEXT" + ? "CONTEXT" + : Array.isArray(this.nodeType) || this.nodeType.includes(",") + ? connectedType?.label || String(this.nodeType) + : String(this.nodeType); + } + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeAnySwitch); + addConnectionLayoutSupport(RgthreeAnySwitch, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + } +} + +app.registerExtension({ + name: "rgthree.AnySwitch", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: any, app: ComfyApp) { + if (nodeData.name === "Any Switch (rgthree)") { + RgthreeAnySwitch.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_any_input_connected_node.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_any_input_connected_node.ts new file mode 100644 index 00000000..d54279a9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_any_input_connected_node.ts @@ -0,0 +1,354 @@ +import type { + Vector2, + LLink, + INodeInputSlot, + INodeOutputSlot, + LGraphNode as TLGraphNode, + ISlotType, + ConnectByTypeOptions, + TWidgetType, + IWidgetOptions, + IWidget, + IBaseWidget, + WidgetTypeMap, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseVirtualNode} from "./base_node.js"; +import {rgthree} from "./rgthree.js"; +import { + PassThroughFollowing, + addConnectionLayoutSupport, + addMenuItem, + getConnectedInputNodes, + getConnectedInputNodesAndFilterPassThroughs, + getConnectedOutputNodes, + getConnectedOutputNodesAndFilterPassThroughs, +} from "./utils.js"; + +/** + * A Virtual Node that allows any node's output to connect to it. + */ +export class BaseAnyInputConnectedNode extends RgthreeBaseVirtualNode { + override isVirtualNode = true; + + /** + * Whether inputs show the immediate nodes, or follow and show connected nodes through + * passthrough nodes. + */ + readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.NONE; + + debouncerTempWidth: number = 0; + schedulePromise: Promise | null = null; + + constructor(title = BaseAnyInputConnectedNode.title) { + super(title); + } + + override onConstructed() { + this.addInput("", "*"); + return super.onConstructed(); + } + + override clone() { + const cloned = super.clone()!; + // Copying to clipboard (and also, creating node templates) work by cloning nodes and, for some + // reason, it manually manipulates the cloned data. So, we want to keep the present input slots + // so if it's pasted/templatized the data is correct. Otherwise, clear the inputs and so the new + // node is ready to go, fresh. + if (!rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes) { + while (cloned.inputs.length > 1) { + cloned.removeInput(cloned.inputs.length - 1); + } + if (cloned.inputs[0]) { + cloned.inputs[0].label = ""; + } + } + return cloned; + } + + /** + * Schedules a promise to run a stabilization, debouncing duplicate requests. + */ + scheduleStabilizeWidgets(ms = 100) { + if (!this.schedulePromise) { + this.schedulePromise = new Promise((resolve) => { + setTimeout(() => { + this.schedulePromise = null; + this.doStablization(); + resolve(); + }, ms); + }); + } + return this.schedulePromise; + } + + /** + * Ensures we have at least one empty input at the end, returns true if changes were made, or false + * if no changes were needed. + */ + private stabilizeInputsOutputs(): boolean { + let changed = false; + const hasEmptyInput = !this.inputs[this.inputs.length - 1]?.link; + if (!hasEmptyInput) { + this.addInput("", "*"); + changed = true; + } + for (let index = this.inputs.length - 2; index >= 0; index--) { + const input = this.inputs[index]!; + if (!input.link) { + this.removeInput(index); + changed = true; + } else { + const node = getConnectedInputNodesAndFilterPassThroughs( + this, + this, + index, + this.inputsPassThroughFollowing, + )[0]; + const newName = node?.title || ""; + if (input.name !== newName) { + input.name = node?.title || ""; + changed = true; + } + } + } + return changed; + } + + /** + * Stabilizes the node's inputs and widgets. + */ + private doStablization() { + if (!this.graph) { + return; + } + let dirty = false; + + // When we add/remove widgets, litegraph is going to mess up the size, so we + // store it so we can retrieve it in computeSize. Hacky.. + (this as any)._tempWidth = this.size[0]; + + dirty = this.stabilizeInputsOutputs(); + const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this); + dirty = this.handleLinkedNodesStabilization(linkedNodes) || dirty; + + // Only mark dirty if something's changed. + if (dirty) { + this.graph.setDirtyCanvas(true, true); + } + + // Schedule another stabilization in the future. + this.scheduleStabilizeWidgets(500); + } + + /** + * Handles stabilization of linked nodes. To be overridden. Should return true if changes were + * made, or false if no changes were needed. + */ + handleLinkedNodesStabilization(linkedNodes: TLGraphNode[]): boolean { + linkedNodes; // No-op, but makes overridding in VSCode cleaner. + throw new Error("handleLinkedNodesStabilization should be overridden."); + } + + onConnectionsChainChange() { + this.scheduleStabilizeWidgets(); + } + + override onConnectionsChange( + type: number, + index: number, + connected: boolean, + linkInfo: LLink, + ioSlot: INodeOutputSlot | INodeInputSlot, + ) { + super.onConnectionsChange && + super.onConnectionsChange(type, index, connected, linkInfo, ioSlot); + if (!linkInfo) return; + // Follow outputs to see if we need to trigger an onConnectionChange. + const connectedNodes = getConnectedOutputNodesAndFilterPassThroughs(this); + for (const node of connectedNodes) { + if ((node as BaseAnyInputConnectedNode).onConnectionsChainChange) { + (node as BaseAnyInputConnectedNode).onConnectionsChainChange(); + } + } + this.scheduleStabilizeWidgets(); + } + + override removeInput(slot: number) { + (this as any)._tempWidth = this.size[0]; + return super.removeInput(slot); + } + + override addInput>( + name: string, + type: ISlotType, + extra_info?: TProperties | undefined, + ): INodeInputSlot & TProperties { + (this as any)._tempWidth = this.size[0]; + return super.addInput(name, type, extra_info); + } + + override addWidget( + type: Type, + name: string, + value: TValue, + callback: IBaseWidget["callback"] | string | null, + options?: IWidgetOptions | string, + ): + | IBaseWidget> + | WidgetTypeMap[Type] { + (this as any)._tempWidth = this.size[0]; + return super.addWidget(type, name, value, callback, options); + } + + override removeWidget(widget: IBaseWidget | IWidget | number | undefined): void { + (this as any)._tempWidth = this.size[0]; + super.removeWidget(widget); + } + + override computeSize(out: Vector2) { + let size = super.computeSize(out); + if ((this as any)._tempWidth) { + size[0] = (this as any)._tempWidth; + // We sometimes get repeated calls to compute size, so debounce before clearing. + this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth); + this.debouncerTempWidth = setTimeout(() => { + (this as any)._tempWidth = null; + }, 32); + } + // If we're collapsed, then subtract the total calculated height of the other input slots. + if (this.properties["collapse_connections"]) { + const rows = Math.max(this.inputs?.length || 0, this.outputs?.length || 0, 1) - 1; + size[1] = size[1] - rows * LiteGraph.NODE_SLOT_HEIGHT; + } + setTimeout(() => { + this.graph?.setDirtyCanvas(true, true); + }, 16); + return size; + } + + /** + * When we connect our output, check our inputs and make sure we're not trying to connect a loop. + */ + override onConnectOutput( + outputIndex: number, + inputType: string | -1, + inputSlot: INodeInputSlot, + inputNode: TLGraphNode, + inputIndex: number, + ): boolean { + let canConnect = true; + if (super.onConnectOutput) { + canConnect = super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex); + } + if (canConnect) { + const nodes = getConnectedInputNodes(this); // We want passthrough nodes, since they will loop. + if (nodes.includes(inputNode)) { + alert( + `Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` + + `a situation that could create a time paradox, the results of which could cause a ` + + `chain reaction that would unravel the very fabric of the space time continuum, ` + + `and destroy the entire universe!`, + ); + canConnect = false; + } + } + return canConnect; + } + + override onConnectInput( + inputIndex: number, + outputType: string | -1, + outputSlot: INodeOutputSlot, + outputNode: TLGraphNode, + outputIndex: number, + ): boolean { + let canConnect = true; + if (super.onConnectInput) { + canConnect = super.onConnectInput( + inputIndex, + outputType, + outputSlot, + outputNode, + outputIndex, + ); + } + if (canConnect) { + const nodes = getConnectedOutputNodes(this); // We want passthrough nodes, since they will loop. + if (nodes.includes(outputNode)) { + alert( + `Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` + + `a situation that could create a time paradox, the results of which could cause a ` + + `chain reaction that would unravel the very fabric of the space time continuum, ` + + `and destroy the entire universe!`, + ); + canConnect = false; + } + } + return canConnect; + } + + /** + * If something is dropped on us, just add it to the bottom. onConnectInput should already cancel + * if it's disallowed. + */ + override connectByTypeOutput( + slot: number | string, + sourceNode: TLGraphNode, + sourceSlotType: ISlotType, + optsIn?: ConnectByTypeOptions, + ): LLink | null { + const lastInput = this.inputs[this.inputs.length - 1]; + if (!lastInput?.link && lastInput?.type === "*") { + var sourceSlot = sourceNode.findOutputSlotByType(sourceSlotType, false, true); + return sourceNode.connect(sourceSlot, this, slot); + } + return super.connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn); + } + + static override setUp() { + super.setUp(); + addConnectionLayoutSupport(this, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + addMenuItem(this, app, { + name: (node) => + `${node.properties?.["collapse_connections"] ? "Show" : "Collapse"} Connections`, + property: "collapse_connections", + prepareValue: (_value, node) => !node.properties?.["collapse_connections"], + callback: (_node) => { + app.canvas.getCurrentGraph()?.setDirtyCanvas(true, true); + }, + }); + } +} + +// Ok, hack time! LGraphNode's connectByType is powerful, but for our nodes, that have multiple "*" +// input types, it seems it just takes the first one, and disconnects it. I'd rather we don't do +// that and instead take the next free one. If that doesn't work, then we'll give it to the old +// method. +const oldLGraphNodeConnectByType = LGraphNode.prototype.connectByType; +LGraphNode.prototype.connectByType = function connectByType( + slot: number | string, + targetNode: TLGraphNode, + targetSlotType: ISlotType, + optsIn?: ConnectByTypeOptions, +): LLink | null { + // If we're dropping on a node, and the last input is free and an "*" type, then connect there + // first... + if (targetNode.inputs) { + for (const [index, input] of targetNode.inputs.entries()) { + if (!input.link && input.type === "*") { + this.connect(slot, targetNode, index); + return null; + } + } + } + return ( + (oldLGraphNodeConnectByType && + oldLGraphNodeConnectByType.call(this, slot, targetNode, targetSlotType, optsIn)) || + null + ); +}; diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_node.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node.ts new file mode 100644 index 00000000..be4e1be5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node.ts @@ -0,0 +1,504 @@ +import type { + IWidget, + LGraphCanvas, + IContextMenuValue, + IFoundSlot, + LGraphEventMode, + LGraphNodeConstructor, + ISerialisedNode, + IBaseWidget, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; +import type {RgthreeBaseServerNodeConstructor} from "typings/rgthree.js"; + +import {app} from "scripts/app.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js"; +import {LogLevel, rgthree} from "./rgthree.js"; +import {addHelpMenuItem} from "./utils.js"; +import {RgthreeHelpDialog} from "rgthree/common/dialog.js"; +import { + importIndividualNodesInnerOnDragDrop, + importIndividualNodesInnerOnDragOver, +} from "./feature_import_individual_nodes.js"; +import {defineProperty, moveArrayItem} from "rgthree/common/shared_utils.js"; + +/** + * A base node with standard methods, directly extending the LGraphNode. + * This can be used for ui-nodes and a further base for server nodes. + */ +export abstract class RgthreeBaseNode extends LGraphNode { + /** + * Action strings that can be exposed and triggered from other nodes, like Fast Actions Button. + */ + static exposedActions: string[] = []; + + static override title: string = "__NEED_CLASS_TITLE__"; + static override type: string = "__NEED_CLASS_TYPE__"; + static override category = "rgthree"; + static _category = "rgthree"; // `category` seems to get reset by comfy, so reset to this after. + + /** Our constructor ensures there's a widget array, so we get rid of the nullability. */ + override widgets!: IWidget[]; + + /** + * The comfyClass is property ComfyUI and extensions may care about, even through it is only for + * server nodes. RgthreeBaseServerNode below overrides this with the expected value and we just + * set it here so extensions that are none the wiser don't break on some unchecked string method + * call on an undefined calue. + */ + override comfyClass: string = "__NEED_COMFY_CLASS__"; + + /** Used by the ComfyUI-Manager badge. */ + readonly nickname = "rgthree"; + /** Are we a virtual node? */ + override readonly isVirtualNode: boolean = false; + /** Are we able to be dropped on (if config is enabled too). */ + isDropEnabled = false; + /** A state member determining if we're currently removed. */ + removed = false; + /** A state member determining if we're currently "configuring."" */ + configuring = false; + /** A temporary width value that can be used to ensure compute size operates correctly. */ + _tempWidth = 0; + + /** Private Mode member so we can override the setter/getter and call an `onModeChange`. */ + private rgthree_mode?: LGraphEventMode; + + /** An internal bool set when `onConstructed` is run. */ + private __constructed__ = false; + /** The help dialog. */ + private helpDialog: RgthreeHelpDialog | null = null; + + constructor(title = RgthreeBaseNode.title, skipOnConstructedCall = true) { + super(title); + if (title == "__NEED_CLASS_TITLE__") { + throw new Error("RgthreeBaseNode needs overrides."); + } + // Ensure these exist since some other extensions will break in their onNodeCreated. + this.widgets = this.widgets || []; + this.properties = this.properties || {}; + + // Some checks we want to do after we're constructed, looking that data is set correctly and + // that our base's `onConstructed` was called (if not, set a DEV warning). + setTimeout(() => { + // Check we have a comfyClass defined. + if (this.comfyClass == "__NEED_COMFY_CLASS__") { + throw new Error("RgthreeBaseNode needs a comfy class override."); + } + if (this.constructor.type == "__NEED_CLASS_TYPE__") { + throw new Error("RgthreeBaseNode needs overrides."); + } + // Ensure we've called onConstructed before we got here. + this.checkAndRunOnConstructed(); + }); + + defineProperty(this, "mode", { + get: () => { + return this.rgthree_mode; + }, + set: (mode: LGraphEventMode) => { + if (this.rgthree_mode != mode) { + const oldMode = this.rgthree_mode; + this.rgthree_mode = mode; + this.onModeChange(oldMode, mode); + } + }, + }); + } + + private checkAndRunOnConstructed() { + if (!this.__constructed__) { + this.onConstructed(); + const [n, v] = rgthree.logger.logParts( + LogLevel.DEV, + `[RgthreeBaseNode] Child class did not call onConstructed for "${this.type}.`, + ); + console[n]?.(...v); + } + return this.__constructed__; + } + + override onDragOver(e: DragEvent): boolean { + if (!this.isDropEnabled) return false; + return importIndividualNodesInnerOnDragOver(this, e); + } + + override async onDragDrop(e: DragEvent): Promise { + if (!this.isDropEnabled) return false; + return importIndividualNodesInnerOnDragDrop(this, e); + } + + /** + * When a node is finished with construction, we must call this. Failure to do so will result in + * an error message from the timeout in this base class. This is broken out and becomes the + * responsibility of the child class because + */ + onConstructed() { + if (this.__constructed__) return false; + // This is kinda a hack, but if this.type is still null, then set it to undefined to match. + this.type = this.type ?? undefined; + this.__constructed__ = true; + rgthree.invokeExtensionsAsync("nodeCreated", this); + return this.__constructed__; + } + + override configure(info: ISerialisedNode): void { + this.configuring = true; + super.configure(info); + // Fix https://github.com/comfyanonymous/ComfyUI/issues/1448 locally. + // Can removed when fixed and adopted. + for (const w of this.widgets || []) { + w.last_y = w.last_y || 0; + } + this.configuring = false; + } + + /** + * Override clone for, at the least, deep-copying properties. + */ + override clone() { + const cloned = super.clone()!; + // This is wild, but LiteGraph doesn't deep clone data, so we will. We'll use structured clone, + // which most browsers in 2022 support, but but we'll check. + if (cloned?.properties && !!window.structuredClone) { + cloned.properties = structuredClone(cloned.properties); + } + // [πŸ€] https://github.com/Comfy-Org/ComfyUI_frontend/issues/5037 + // ComfyUI started throwing errors when some of our nodes wanted to remove inputs when cloning + // (like our dynamic inputs) because the disconnect method that's automatically called assumes + // there should be a graph. For now, I _think_ we can simply assign the current graph to avoid + // the error, which would then be overwritten when placed... + cloned.graph = this.graph; + return cloned; + } + + /** When a mode change, we want all connected nodes to match. */ + onModeChange(from: LGraphEventMode | undefined, to: LGraphEventMode) { + // Override + } + + /** + * Given a string, do something. At the least, handle any `exposedActions` that may be called and + * passed into from other nodes, like Fast Actions Button + */ + async handleAction(action: string) { + action; // No-op. Should be overridden but OK if not. + } + + /** + * This didn't exist in LiteGraph/Comfy, but now it's added. Ours was a bit more flexible, though. + */ + override removeWidget(widget: IBaseWidget | IWidget | number | undefined): void { + if (typeof widget === "number") { + widget = this.widgets[widget]; + } + if (!widget) return; + + // Comfy added their own removeWidget, but it's not fully rolled out to stable, so keep our + // original implementation. + // TODO: Actually, scratch that. The ComfyUI impl doesn't call widtget.onRemove?.() and so + // we shouldn't switch to it yet. See: https://github.com/Comfy-Org/ComfyUI_frontend/issues/5090 + const canUseComfyUIRemoveWidget = false; + if (canUseComfyUIRemoveWidget && typeof super.removeWidget === 'function') { + super.removeWidget(widget as IBaseWidget); + } else { + const index = this.widgets.indexOf(widget as IWidget); + if (index > -1) { + this.widgets.splice(index, 1); + } + widget.onRemove?.(); + } + } + + /** + * Replaces an existing widget. + */ + replaceWidget(widgetOrSlot: IWidget | number | undefined, newWidget: IWidget) { + let index = null; + if (widgetOrSlot) { + index = typeof widgetOrSlot === "number" ? widgetOrSlot : this.widgets.indexOf(widgetOrSlot); + this.removeWidget(this.widgets[index]!); + } + index = index != null ? index : this.widgets.length - 1; + if (this.widgets.includes(newWidget)) { + moveArrayItem(this.widgets, newWidget, index); + } else { + this.widgets.splice(index, 0, newWidget); + } + } + + /** + * A default version of the logive when a node does not set `getSlotMenuOptions`. This is + * necessary because child nodes may want to define getSlotMenuOptions but LiteGraph then won't do + * it's default logic. This bakes it so child nodes can call this instead (and this doesn't set + * getSlotMenuOptions for all child nodes in case it doesn't exist). + */ + defaultGetSlotMenuOptions(slot: IFoundSlot): IContextMenuValue[] { + const menu_info: IContextMenuValue[] = []; + if (slot?.output?.links?.length) { + menu_info.push({content: "Disconnect Links", slot}); + } + let inputOrOutput = slot.input || slot.output; + if (inputOrOutput) { + if (inputOrOutput.removable) { + menu_info.push( + inputOrOutput.locked ? {content: "Cannot remove"} : {content: "Remove Slot", slot}, + ); + } + if (!inputOrOutput.nameLocked) { + menu_info.push({content: "Rename Slot", slot}); + } + } + return menu_info; + } + + override onRemoved(): void { + super.onRemoved?.(); + this.removed = true; + } + + static setUp(...args: any[]) { + // No-op. + } + + /** + * A function to provide help text to be overridden. + */ + getHelp() { + return ""; + } + + showHelp() { + const help = this.getHelp() || (this.constructor as any).help; + if (help) { + this.helpDialog = new RgthreeHelpDialog(this, help).show(); + this.helpDialog.addEventListener("close", (e) => { + this.helpDialog = null; + }); + } + } + + override onKeyDown(event: KeyboardEvent): void { + KEY_EVENT_SERVICE.handleKeyDownOrUp(event); + if (event.key == "?" && !this.helpDialog) { + this.showHelp(); + } + } + + override onKeyUp(event: KeyboardEvent): void { + KEY_EVENT_SERVICE.handleKeyDownOrUp(event); + } + + override getExtraMenuOptions( + canvas: LGraphCanvas, + options: (IContextMenuValue | null)[], + ): (IContextMenuValue | null)[] { + // Some other extensions override getExtraMenuOptions on the nodeType as it comes through from + // the server, so we can call out to that if we don't have our own. + if (super.getExtraMenuOptions) { + super.getExtraMenuOptions?.apply(this, [canvas, options]); + } else if (this.constructor.nodeType?.prototype?.getExtraMenuOptions) { + this.constructor.nodeType?.prototype?.getExtraMenuOptions?.apply(this, [canvas, options]); + } + // If we have help content, then add a menu item. + const help = this.getHelp() || (this.constructor as any).help; + if (help) { + addHelpMenuItem(this, help, options); + } + return []; + } +} + +/** + * A virtual node. Right now, this is just a wrapper for RgthreeBaseNode (which was the initial + * base virtual node). + */ +export class RgthreeBaseVirtualNode extends RgthreeBaseNode { + override isVirtualNode = true; + + constructor(title = RgthreeBaseNode.title) { + super(title, false); + } + + static override setUp() { + if (!this.type) { + throw new Error(`Missing type for RgthreeBaseVirtualNode: ${this.title}`); + } + LiteGraph.registerNodeType(this.type, this); + if (this._category) { + this.category = this._category; + } + } +} + +/** + * A base node with standard methods, extending the LGraphNode. + * This is somewhat experimental, but if comfyui is going to keep breaking widgets and inputs, it + * seems safer than NOT overriding. + */ +export class RgthreeBaseServerNode extends RgthreeBaseNode { + static nodeType: LGraphNodeConstructor | null = null; + static nodeData: ComfyNodeDef | null = null; + + // Drop is enabled by default for server nodes. + override isDropEnabled = true; + + constructor(title: string) { + super(title, true); + this.serialize_widgets = true; + this.setupFromServerNodeData(); + this.onConstructed(); + } + + getWidgets() { + return ComfyWidgets; + } + + /** + * This takes the server data and builds out the inputs, outputs and widgets. It's similar to the + * ComfyNode constructor in registerNodes in ComfyUI's app.js, but is more stable and thus + * shouldn't break as often when it modifyies widgets and types. + */ + async setupFromServerNodeData() { + const nodeData = (this.constructor as any).nodeData; + if (!nodeData) { + throw Error("No node data"); + } + + // Necessary for serialization so Comfy backend can check types. + // Serialized as `class_type`. See app.js#graphToPrompt + this.comfyClass = nodeData.name; + + let inputs = nodeData["input"]["required"]; + if (nodeData["input"]["optional"] != undefined) { + inputs = Object.assign({}, inputs, nodeData["input"]["optional"]); + } + + const WIDGETS = this.getWidgets(); + + const config: {minWidth: number; minHeight: number; widget?: null | {options: any}} = { + minWidth: 1, + minHeight: 1, + widget: null, + }; + for (const inputName in inputs) { + const inputData = inputs[inputName]; + const type = inputData[0]; + // If we're forcing the input, just do it now and forget all that widget stuff. + // This is one of the differences from ComfyNode and provides smoother experience for inputs + // that are going to remain inputs anyway. + // Also, it fixes https://github.com/comfyanonymous/ComfyUI/issues/1404 (for rgthree nodes) + if (inputData[1]?.forceInput) { + this.addInput(inputName, type); + } else { + let widgetCreated = true; + if (Array.isArray(type)) { + // Enums + Object.assign(config, WIDGETS.COMBO(this, inputName, inputData, app) || {}); + } else if (`${type}:${inputName}` in WIDGETS) { + // Support custom widgets by Type:Name + Object.assign( + config, + WIDGETS[`${type}:${inputName}`]!(this, inputName, inputData, app) || {}, + ); + } else if (type in WIDGETS) { + // Standard type widgets + Object.assign(config, WIDGETS[type]!(this, inputName, inputData, app) || {}); + } else { + // Node connection inputs + this.addInput(inputName, type); + widgetCreated = false; + } + + // Don't actually need this right now, but ported it over from ComfyWidget. + if (widgetCreated && inputData[1]?.forceInput && config?.widget) { + if (!config.widget.options) config.widget.options = {}; + config.widget.options.forceInput = inputData[1].forceInput; + } + if (widgetCreated && inputData[1]?.defaultInput && config?.widget) { + if (!config.widget.options) config.widget.options = {}; + config.widget.options.defaultInput = inputData[1].defaultInput; + } + } + } + + for (const o in nodeData["output"]) { + let output = nodeData["output"][o]; + if (output instanceof Array) output = "COMBO"; + const outputName = nodeData["output_name"][o] || output; + const outputShape = nodeData["output_is_list"][o] + ? LiteGraph.GRID_SHAPE + : LiteGraph.CIRCLE_SHAPE; + this.addOutput(outputName, output, {shape: outputShape}); + } + + const s = this.computeSize(); + // Sometime around v1.12.6 this broke as `minWidth` and `minHeight` were being explicitly set + // to `undefined` in the above Object.assign call (specifically for `WIDGETS[INT]`. We can avoid + // that by ensureing we're at a number in that case. + // See https://github.com/Comfy-Org/ComfyUI_frontend/issues/3045 + s[0] = Math.max(config.minWidth ?? 1, s[0] * 1.5); + s[1] = Math.max(config.minHeight ?? 1, s[1]); + this.size = s; + this.serialize_widgets = true; + } + + static __registeredForOverride__: boolean = false; + static registerForOverride( + comfyClass: typeof LGraphNode, + nodeData: ComfyNodeDef, + rgthreeClass: RgthreeBaseServerNodeConstructor, + ) { + if (OVERRIDDEN_SERVER_NODES.has(comfyClass)) { + throw Error( + `Already have a class to override ${ + comfyClass.type || comfyClass.name || comfyClass.title + }`, + ); + } + OVERRIDDEN_SERVER_NODES.set(comfyClass, rgthreeClass); + // Mark the rgthreeClass as `__registeredForOverride__` because ComfyUI will repeatedly call + // this and certain setups will only want to setup once (like adding context menus, etc). + if (!rgthreeClass.__registeredForOverride__) { + rgthreeClass.__registeredForOverride__ = true; + rgthreeClass.nodeType = comfyClass; + rgthreeClass.nodeData = nodeData; + rgthreeClass.onRegisteredForOverride(comfyClass, rgthreeClass); + } + } + + static onRegisteredForOverride(comfyClass: any, rgthreeClass: any) { + // To be overridden + } +} + +/** + * Keeps track of the rgthree-comfy nodes that come from the server (and want to be ComfyNodes) that + * we override into a own, more flexible and cleaner nodes. + */ +const OVERRIDDEN_SERVER_NODES = new Map(); + +const oldregisterNodeType = LiteGraph.registerNodeType; +/** + * ComfyUI calls registerNodeType with its ComfyNode, but we don't trust that will remain stable, so + * we need to identify it, intercept it, and supply our own class for the node. + */ +LiteGraph.registerNodeType = async function (nodeId: string, baseClass: any) { + const clazz = OVERRIDDEN_SERVER_NODES.get(baseClass) || baseClass; + if (clazz !== baseClass) { + const classLabel = clazz.type || clazz.name || clazz.title; + const [n, v] = rgthree.logger.logParts( + LogLevel.DEBUG, + `${nodeId}: replacing default ComfyNode implementation with custom ${classLabel} class.`, + ); + console[n]?.(...v); + // Note, we don't currently call our rgthree.invokeExtensionsAsync w/ beforeRegisterNodeDef as + // this runs right after that. However, this does mean that extensions cannot actually change + // anything about overriden server rgthree nodes in their beforeRegisterNodeDef (as when comfy + // calls it, it's for the wrong ComfyNode class). Calling it here, however, would re-run + // everything causing more issues than not. If we wanted to support beforeRegisterNodeDef then + // it would mean rewriting ComfyUI's registerNodeDef which, frankly, is not worth it. + } + return oldregisterNodeType.call(LiteGraph, nodeId, clazz); +}; diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_collector.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_collector.ts new file mode 100644 index 00000000..30076301 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_collector.ts @@ -0,0 +1,99 @@ +import type {INodeOutputSlot, LGraphNode} from "@comfyorg/frontend"; + +import {rgthree} from "./rgthree.js"; +import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js"; +import { + PassThroughFollowing, + getConnectedInputNodes, + getConnectedInputNodesAndFilterPassThroughs, + shouldPassThrough, +} from "./utils.js"; + +/** + * Base collector node that monitors changing inputs and outputs. + */ +export class BaseCollectorNode extends BaseAnyInputConnectedNode { + /** + * We only want to show nodes through re_route nodes, other pass through nodes show each input. + */ + override readonly inputsPassThroughFollowing: PassThroughFollowing = + PassThroughFollowing.REROUTE_ONLY; + + readonly logger = rgthree.newLogSession("[BaseCollectorNode]"); + + constructor(title?: string) { + super(title); + } + + override clone() { + const cloned = super.clone()!; + return cloned; + } + + override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) { + return false; // No-op, no widgets. + } + + /** + * When we connect an input, check to see if it's already connected and cancel it. + */ + override onConnectInput( + inputIndex: number, + outputType: string | -1, + outputSlot: INodeOutputSlot, + outputNode: LGraphNode, + outputIndex: number, + ): boolean { + let canConnect = super.onConnectInput( + inputIndex, + outputType, + outputSlot, + outputNode, + outputIndex, + ); + if (canConnect) { + const allConnectedNodes = getConnectedInputNodes(this); // We want passthrough nodes, since they will loop. + const nodesAlreadyInSlot = getConnectedInputNodes(this, undefined, inputIndex); + if (allConnectedNodes.includes(outputNode)) { + // If we're connecting to the same slot, then allow it by replacing the one we have. + // const slotsOriginNode = getOriginNodeByLink(this.inputs[inputIndex]?.link); + const [n, v] = this.logger.debugParts( + `${outputNode.title} is already connected to ${this.title}.`, + ); + console[n]?.(...v); + if (nodesAlreadyInSlot.includes(outputNode)) { + const [n, v] = this.logger.debugParts( + `... but letting it slide since it's for the same slot.`, + ); + console[n]?.(...v); + } else { + canConnect = false; + } + } + if (canConnect && shouldPassThrough(outputNode, PassThroughFollowing.REROUTE_ONLY)) { + const connectedNode = getConnectedInputNodesAndFilterPassThroughs( + outputNode, + undefined, + undefined, + PassThroughFollowing.REROUTE_ONLY, + )[0]; + if (connectedNode && allConnectedNodes.includes(connectedNode)) { + // If we're connecting to the same slot, then allow it by replacing the one we have. + const [n, v] = this.logger.debugParts( + `${connectedNode.title} is already connected to ${this.title}.`, + ); + console[n]?.(...v); + if (nodesAlreadyInSlot.includes(connectedNode)) { + const [n, v] = this.logger.debugParts( + `... but letting it slide since it's for the same slot.`, + ); + console[n]?.(...v); + } else { + canConnect = false; + } + } + } + } + return canConnect; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_mode_changer.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_mode_changer.ts new file mode 100644 index 00000000..ae1afeaf --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_mode_changer.ts @@ -0,0 +1,106 @@ +import type {LGraphNode, IWidget} from "@comfyorg/frontend"; + +import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js"; +import {changeModeOfNodes, PassThroughFollowing} from "./utils.js"; +import {wait} from "rgthree/common/shared_utils.js"; + +export class BaseNodeModeChanger extends BaseAnyInputConnectedNode { + override readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.ALL; + + static collapsible = false; + override isVirtualNode = true; + + // These Must be overriden + readonly modeOn: number = -1; + readonly modeOff: number = -1; + + static "@toggleRestriction" = { + type: "combo", + values: ["default", "max one", "always one"], + }; + + constructor(title?: string) { + super(title); + this.properties["toggleRestriction"] = "default"; + } + + override onConstructed(): boolean { + wait(10).then(() => { + if (this.modeOn < 0 || this.modeOff < 0) { + throw new Error("modeOn and modeOff must be overridden."); + } + }); + this.addOutput("OPT_CONNECTION", "*"); + return super.onConstructed(); + } + + override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) { + let changed = false; + for (const [index, node] of linkedNodes.entries()) { + let widget: IWidget | undefined = this.widgets && this.widgets[index]; + if (!widget) { + // When we add a widget, litegraph is going to mess up the size, so we + // store it so we can retrieve it in computeSize. Hacky.. + (this as any)._tempWidth = this.size[0]; + widget = this.addWidget("toggle", "", false, "", {on: "yes", off: "no"}) as IWidget; + changed = true; + } + if (node) { + changed = this.setWidget(widget, node) || changed; + } + } + if (this.widgets && this.widgets.length > linkedNodes.length) { + this.widgets.length = linkedNodes.length; + changed = true; + } + return changed; + } + + private setWidget(widget: IWidget, linkedNode: LGraphNode, forceValue?: boolean) { + let changed = false; + const value = forceValue == null ? linkedNode.mode === this.modeOn : forceValue; + let name = `Enable ${linkedNode.title}`; + // Need to set initally + if (widget.name !== name) { + widget.name = `Enable ${linkedNode.title}`; + widget.options = {on: "yes", off: "no"}; + widget.value = value; + (widget as any).doModeChange = (forceValue?: boolean, skipOtherNodeCheck?: boolean) => { + let newValue = forceValue == null ? linkedNode.mode === this.modeOff : forceValue; + if (skipOtherNodeCheck !== true) { + if (newValue && (this.properties?.["toggleRestriction"] as string)?.includes(" one")) { + for (const widget of this.widgets) { + (widget as any).doModeChange(false, true); + } + } else if (!newValue && this.properties?.["toggleRestriction"] === "always one") { + newValue = this.widgets.every((w) => !w.value || w === widget); + } + } + changeModeOfNodes(linkedNode, (newValue ? this.modeOn : this.modeOff)) + widget.value = newValue; + }; + widget.callback = () => { + (widget as any).doModeChange(); + }; + changed = true; + } + if (forceValue != null) { + const newMode = (forceValue ? this.modeOn : this.modeOff) as 1 | 2 | 3 | 4; + if (linkedNode.mode !== newMode) { + changeModeOfNodes(linkedNode, newMode); + changed = true; + } + } + return changed; + } + + forceWidgetOff(widget: IWidget, skipOtherNodeCheck?: boolean) { + (widget as any).doModeChange(false, skipOtherNodeCheck); + } + forceWidgetOn(widget: IWidget, skipOtherNodeCheck?: boolean) { + (widget as any).doModeChange(true, skipOtherNodeCheck); + } + forceWidgetToggle(widget: IWidget, skipOtherNodeCheck?: boolean) { + (widget as any).doModeChange(!widget.value, skipOtherNodeCheck); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_power_prompt.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_power_prompt.ts new file mode 100644 index 00000000..02a161b7 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_power_prompt.ts @@ -0,0 +1,366 @@ +import type { + LLink, + LGraphNode, + INodeOutputSlot, + INodeInputSlot, + ISerialisedNode, + IComboWidget, + IBaseWidget, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {api} from "scripts/api.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {rgthree} from "./rgthree.js"; + +/** Wraps a node instance keeping closure without mucking the finicky types. */ +export class PowerPrompt { + readonly isSimple: boolean; + readonly node: LGraphNode; + readonly promptEl: HTMLTextAreaElement; + nodeData: ComfyNodeDef; + readonly combos: {[key: string]: IComboWidget} = {}; + readonly combosValues: {[key: string]: string[]} = {}; + boundOnFreshNodeDefs!: (event: CustomEvent) => void; + + private configuring = false; + + constructor(node: LGraphNode, nodeData: ComfyNodeDef) { + this.node = node; + this.node.properties = this.node.properties || {}; + + this.node.properties["combos_filter"] = ""; + + this.nodeData = nodeData; + this.isSimple = this.nodeData.name.includes("Simple"); + + this.promptEl = (node.widgets![0]! as any).inputEl; + this.addAndHandleKeyboardLoraEditWeight(); + + this.patchNodeRefresh(); + + const oldConfigure = this.node.configure; + this.node.configure = (info: ISerialisedNode) => { + this.configuring = true; + oldConfigure?.apply(this.node, [info]); + this.configuring = false; + }; + + const oldOnConnectionsChange = this.node.onConnectionsChange; + this.node.onConnectionsChange = ( + type: number, + slotIndex: number, + isConnected: boolean, + link_info: LLink, + _ioSlot: INodeOutputSlot | INodeInputSlot, + ) => { + oldOnConnectionsChange?.apply(this.node, [type, slotIndex, isConnected, link_info, _ioSlot]); + this.onNodeConnectionsChange(type, slotIndex, isConnected, link_info, _ioSlot); + }; + + const oldOnConnectInput = this.node.onConnectInput; + this.node.onConnectInput = ( + inputIndex: number, + outputType: INodeOutputSlot["type"], + outputSlot: INodeOutputSlot, + outputNode: LGraphNode, + outputIndex: number, + ) => { + let canConnect = true; + if (oldOnConnectInput) { + canConnect = oldOnConnectInput.apply(this.node, [ + inputIndex, + outputType, + outputSlot, + outputNode, + outputIndex, + ]); + } + return ( + this.configuring || + !!rgthree.loadingApiJson || + (canConnect && !this.node.inputs[inputIndex]!.disabled) + ); + }; + + const oldOnConnectOutput = this.node.onConnectOutput; + this.node.onConnectOutput = ( + outputIndex: number, + inputType: INodeInputSlot["type"], + inputSlot: INodeInputSlot, + inputNode: LGraphNode, + inputIndex: number, + ) => { + let canConnect = true; + if (oldOnConnectOutput) { + canConnect = oldOnConnectOutput?.apply(this.node, [ + outputIndex, + inputType, + inputSlot, + inputNode, + inputIndex, + ]); + } + return ( + this.configuring || + !!rgthree.loadingApiJson || + (canConnect && !this.node.outputs[outputIndex]!.disabled) + ); + }; + + const onPropertyChanged = this.node.onPropertyChanged; + this.node.onPropertyChanged = (property: string, value: any, prevValue: any) => { + const v = onPropertyChanged && onPropertyChanged.call(this.node, property, value, prevValue); + if (property === "combos_filter") { + this.refreshCombos(this.nodeData); + } + return v ?? true; + }; + + // Strip all widgets but prompt (we'll re-add them in refreshCombos) + // this.node.widgets.splice(1); + for (let i = this.node.widgets!.length - 1; i >= 0; i--) { + if (this.shouldRemoveServerWidget(this.node.widgets![i]!)) { + this.node.widgets!.splice(i, 1); + } + } + + this.refreshCombos(nodeData); + setTimeout(() => { + this.stabilizeInputsOutputs(); + }, 32); + } + + /** + * Cleans up optional out puts when we don't have the optional input. Purely a vanity function. + */ + onNodeConnectionsChange( + _type: number, + _slotIndex: number, + _isConnected: boolean, + _linkInfo: LLink, + _ioSlot: INodeOutputSlot | INodeInputSlot, + ) { + this.stabilizeInputsOutputs(); + } + + private stabilizeInputsOutputs() { + // If we are currently "configuring" then skip this stabilization. The connected nodes may + // not yet be configured. + if (this.configuring || rgthree.loadingApiJson) { + return; + } + // If our first input is connected, then we can show the proper output. + const clipLinked = this.node.inputs.some((i) => i.name.includes("clip") && !!i.link); + const modelLinked = this.node.inputs.some((i) => i.name.includes("model") && !!i.link); + for (const output of this.node.outputs) { + const type = (output.type as string).toLowerCase(); + if (type.includes("model")) { + output.disabled = !modelLinked; + } else if (type.includes("conditioning")) { + output.disabled = !clipLinked; + } else if (type.includes("clip")) { + output.disabled = !clipLinked; + } else if (type.includes("string")) { + // Our text prompt is always enabled, but let's color it so it stands out + // if the others are disabled. #7F7 is Litegraph's default. + output.color_off = "#7F7"; + output.color_on = "#7F7"; + } + if (output.disabled) { + // this.node.disconnectOutput(index); + } + } + } + + onFreshNodeDefs(event: CustomEvent) { + this.refreshCombos(event.detail[this.nodeData.name]); + } + + shouldRemoveServerWidget(widget: IBaseWidget) { + return ( + widget.name?.startsWith("insert_") || + widget.name?.startsWith("target_") || + widget.name?.startsWith("crop_") || + widget.name?.startsWith("values_") + ); + } + + refreshCombos(nodeData: ComfyNodeDef) { + this.nodeData = nodeData; + let filter: RegExp | null = null; + if ((this.node.properties["combos_filter"] as string)?.trim()) { + try { + filter = new RegExp((this.node.properties["combos_filter"] as string).trim(), "i"); + } catch (e) { + console.error(`Could not parse "${filter}" for Regular Expression`, e); + filter = null; + } + } + + // Add the combo for hidden inputs of nodeData + let data = Object.assign( + {}, + this.nodeData.input?.optional || {}, + this.nodeData.input?.hidden || {}, + ); + + for (const [key, value] of Object.entries(data)) { + //Object.entries(this.nodeData.input?.hidden || {})) { + if (Array.isArray(value[0])) { + let values = value[0] as string[]; + if (key.startsWith("insert")) { + values = filter + ? values.filter( + (v, i) => i < 1 || (i == 1 && v.match(/^disable\s[a-z]/i)) || filter?.test(v), + ) + : values; + const shouldShow = + values.length > 2 || (values.length > 1 && !values[1]!.match(/^disable\s[a-z]/i)); + if (shouldShow) { + if (!this.combos[key]) { + this.combos[key] = this.node.addWidget( + "combo", + key, + values[0]!, + (selected) => { + if (selected !== values[0] && !selected.match(/^disable\s[a-z]/i)) { + // We wait a frame because if we use a keydown event to call, it'll wipe out + // the selection. + wait().then(() => { + if (key.includes("embedding")) { + this.insertSelectionText(`embedding:${selected}`); + } else if (key.includes("saved")) { + this.insertSelectionText( + this.combosValues[`values_${key}`]![values.indexOf(selected)]!, + ); + } else if (key.includes("lora")) { + this.insertSelectionText(``); + } + this.combos[key]!.value = values[0]!; + }); + } + }, + { + values, + serialize: true, // Don't include this in prompt. + }, + ) as IComboWidget; + (this.combos[key]! as any).oldComputeSize = this.combos[key]!.computeSize; + let node = this.node; + this.combos[key]!.computeSize = function (width: number) { + const size = (this as any).oldComputeSize?.(width) || [ + width, + LiteGraph.NODE_WIDGET_HEIGHT, + ]; + if (this === node.widgets![node.widgets!.length - 1]) { + size[1] += 10; + } + return size; + }; + } + this.combos[key]!.options!.values = values; + this.combos[key]!.value = values[0]!; + } else if (!shouldShow && this.combos[key]) { + this.node.widgets!.splice(this.node.widgets!.indexOf(this.combos[key]!), 1); + delete this.combos[key]; + } + } else if (key.startsWith("values")) { + this.combosValues[key] = values; + } + } + } + } + + insertSelectionText(text: string) { + if (!this.promptEl) { + console.error("Asked to insert text, but no textbox found."); + return; + } + let prompt = this.promptEl.value; + // Use selectionEnd as the split; if we have highlighted text, then we likely don't want to + // overwrite it (we could have just deleted it more easily). + let first = prompt.substring(0, this.promptEl.selectionEnd).replace(/ +$/, ""); + first = first + (["\n"].includes(first[first.length - 1]!) ? "" : first.length ? " " : ""); + let second = prompt.substring(this.promptEl.selectionEnd).replace(/^ +/, ""); + second = (["\n"].includes(second[0]!) ? "" : second.length ? " " : "") + second; + this.promptEl.value = first + text + second; + this.promptEl.focus(); + this.promptEl.selectionStart = first.length; + this.promptEl.selectionEnd = first.length + text.length; + } + + /** + * Adds a keydown event listener to our prompt so we can see if we're using the + * ctrl/cmd + up/down arrows shortcut. This kind of competes with the core extension + * "Comfy.EditAttention" but since that only handles parenthesis and listens on window, we should + * be able to intercept and cancel the bubble if we're doing the same action within the lora tag. + */ + addAndHandleKeyboardLoraEditWeight() { + this.promptEl.addEventListener("keydown", (event: KeyboardEvent) => { + // If we're not doing a ctrl/cmd + arrow key, then bail. + if (!(event.key === "ArrowUp" || event.key === "ArrowDown")) return; + if (!event.ctrlKey && !event.metaKey) return; + // Unfortunately, we can't see Comfy.EditAttention delta in settings, so we hardcode to 0.01. + // We can acutally do better too, let's make it .1 by default, and .01 if also holding shift. + const delta = event.shiftKey ? 0.01 : 0.1; + + let start = this.promptEl.selectionStart; + let end = this.promptEl.selectionEnd; + let fullText = this.promptEl.value; + let selectedText = fullText.substring(start, end); + + // We don't care about fully rewriting Comfy.EditAttention, we just want to see if our + // selected text is a lora, which will always start with "") { + start -= 2; + end -= 2; + } + if (fullText[end - 1] == "<") { + start += 2; + end += 2; + } + while (!stopOn.includes(fullText[start]!) && start > 0) { + start--; + } + while (!stopOn.includes(fullText[end - 1]!) && end < fullText.length) { + end++; + } + selectedText = fullText.substring(start, end); + } + + // Bail if this isn't a lora. + if (!selectedText.startsWith("")) { + return; + } + + let weight = Number(selectedText.match(/:(-?\d*(\.\d*)?)>$/)?.[1]) ?? 1; + weight += event.key === "ArrowUp" ? delta : -delta; + const updatedText = selectedText.replace(/(:-?\d*(\.\d*)?)?>$/, `:${weight.toFixed(2)}>`); + + // Handle the new value and cancel the bubble so Comfy.EditAttention doesn't also try. + this.promptEl.setRangeText(updatedText, start, end, "select"); + event.preventDefault(); + event.stopPropagation(); + }); + } + + /** + * Patches over api.getNodeDefs in comfy's api.js to fire a custom event that we can listen to + * here and manually refresh our combos when a request comes in to fetch the node data; which + * only happens once at startup (but before custom nodes js runs), and then after clicking + * the "Refresh" button in the floating menu, which is what we care about. + */ + patchNodeRefresh() { + this.boundOnFreshNodeDefs = this.onFreshNodeDefs.bind(this); + api.addEventListener("fresh-node-defs", this.boundOnFreshNodeDefs as EventListener); + const oldNodeRemoved = this.node.onRemoved; + this.node.onRemoved = () => { + oldNodeRemoved?.call(this.node); + api.removeEventListener("fresh-node-defs", this.boundOnFreshNodeDefs as EventListener); + }; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/bookmark.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/bookmark.ts new file mode 100644 index 00000000..8eb74230 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/bookmark.ts @@ -0,0 +1,163 @@ +import type { + LGraph, + LGraphCanvas, + LGraphNode, + Point, + CanvasMouseEvent, + Subgraph, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseVirtualNode} from "./base_node.js"; +import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js"; +import {SERVICE as BOOKMARKS_SERVICE} from "./services/bookmarks_services.js"; +import {NodeTypesString} from "./constants.js"; +import {getClosestOrSelf, query} from "rgthree/common/utils_dom.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {findFromNodeForSubgraph} from "./utils.js"; + +/** + * A bookmark node. Can be placed anywhere in the workflow, and given a shortcut key that will + * navigate to that node, with it in the top-left corner. + */ +export class Bookmark extends RgthreeBaseVirtualNode { + static override type = NodeTypesString.BOOKMARK; + static override title = NodeTypesString.BOOKMARK; + override comfyClass = NodeTypesString.BOOKMARK; + + // Really silly, but Litegraph assumes we have at least one input/output... so we need to + // counteract it's computeSize calculation by offsetting the start. + static slot_start_y = -20; + + // LiteGraph adds mroe spacing than we want when calculating a nodes' `_collapsed_width`, so we'll + // override it with a setter and re-set it measured exactly as we want. + ___collapsed_width: number = 0; + + override isVirtualNode = true; + override serialize_widgets = true; + + //@ts-ignore - TS Doesn't like us overriding a property with accessors but, too bad. + override get _collapsed_width() { + return this.___collapsed_width; + } + + override set _collapsed_width(width: number) { + const canvas = app.canvas as LGraphCanvas; + const ctx = canvas.canvas.getContext("2d")!; + const oldFont = ctx.font; + ctx.font = canvas.title_text_font; + this.___collapsed_width = 40 + ctx.measureText(this.title).width; + ctx.font = oldFont; + } + + readonly keypressBound; + + constructor(title = Bookmark.title) { + super(title); + const nextShortcutChar = BOOKMARKS_SERVICE.getNextShortcut(); + this.addWidget( + "text", + "shortcut_key", + nextShortcutChar, + (value: string, ...args) => { + value = value.trim()[0] || "1"; + }, + { + y: 8, + }, + ); + this.addWidget("number", "zoom", 1, (value: number) => {}, { + y: 8 + LiteGraph.NODE_WIDGET_HEIGHT + 4, + max: 2, + min: 0.5, + precision: 2, + }); + this.keypressBound = this.onKeypress.bind(this); + this.title = "πŸ”–"; + this.onConstructed(); + } + + // override computeSize(out?: Vector2 | undefined): Vector2 { + // super.computeSize(out); + // const minHeight = (this.widgets?.length || 0) * (LiteGraph.NODE_WIDGET_HEIGHT + 4) + 16; + // this.size[1] = Math.max(minHeight, this.size[1]); + // } + + get shortcutKey(): string { + return (this.widgets[0]?.value as string)?.toLocaleLowerCase() ?? ""; + } + + override onAdded(graph: LGraph): void { + KEY_EVENT_SERVICE.addEventListener("keydown", this.keypressBound as EventListener); + } + + override onRemoved(): void { + KEY_EVENT_SERVICE.removeEventListener("keydown", this.keypressBound as EventListener); + } + + onKeypress(event: CustomEvent<{originalEvent: KeyboardEvent}>) { + const originalEvent = event.detail.originalEvent; + const target = (originalEvent.target as HTMLElement)!; + if (getClosestOrSelf(target, 'input,textarea,[contenteditable="true"]')) { + return; + } + + // Only the shortcut keys are held down, optionally including "shift". + if (KEY_EVENT_SERVICE.areOnlyKeysDown(this.widgets[0]!.value as string, true)) { + this.canvasToBookmark(); + originalEvent.preventDefault(); + originalEvent.stopPropagation(); + } + } + + /** + * Called from LiteGraph's `processMouseDown` after it would invoke the input box for the + * shortcut_key, so we check if it exists and then add our own event listener so we can track the + * keys down for the user. Note, blocks drag if the return is truthy. + */ + override onMouseDown(event: CanvasMouseEvent, pos: Point, graphCanvas: LGraphCanvas): boolean { + const input = query(".graphdialog > input.value"); + if (input && input.value === this.widgets[0]?.value) { + input.addEventListener("keydown", (e) => { + // ComfyUI swallows keydown on inputs, so we need to call out to rgthree to use downkeys. + KEY_EVENT_SERVICE.handleKeyDownOrUp(e); + e.preventDefault(); + e.stopPropagation(); + input.value = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" + "); + }); + } + return false; + } + + async canvasToBookmark() { + const canvas = app.canvas as LGraphCanvas; + if (this.graph !== app.canvas.getCurrentGraph()) { + const subgraph = this.graph as Subgraph; + // At some point, ComfyUI made a second param for openSubgraph which appears to be the node + // that id double-clicked on to open the subgraph. We don't have that in the bookmark, so + // we'll look for it. Note, that when opening the root graph, this will be null (since there's + // no such node). It seems to still navigate fine, though there's a console error about + // proxyWidgets or something.. + const fromNode = findFromNodeForSubgraph(subgraph.id); + canvas.openSubgraph(subgraph, fromNode!); + await wait(16); + } + // ComfyUI seemed to break us again, but couldn't repro. No reason to not check, I guess. + // https://github.com/rgthree/rgthree-comfy/issues/71 + if (canvas?.ds?.offset) { + canvas.ds.offset[0] = -this.pos[0] + 16; + canvas.ds.offset[1] = -this.pos[1] + 40; + } + if (canvas?.ds?.scale != null) { + canvas.ds.scale = Number(this.widgets[1]!.value || 1); + } + canvas.setDirty(true, true); + } +} + +app.registerExtension({ + name: "rgthree.Bookmark", + registerCustomNodes() { + Bookmark.setUp(); + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/bypasser.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/bypasser.ts new file mode 100644 index 00000000..0cfb51f8 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/bypasser.ts @@ -0,0 +1,52 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {BaseNodeModeChanger} from "./base_node_mode_changer.js"; +import {NodeTypesString} from "./constants.js"; + +const MODE_BYPASS = 4; +const MODE_ALWAYS = 0; + +class BypasserNode extends BaseNodeModeChanger { + static override exposedActions = ["Bypass all", "Enable all", "Toggle all"]; + + static override type = NodeTypesString.FAST_BYPASSER; + static override title = NodeTypesString.FAST_BYPASSER; + override comfyClass = NodeTypesString.FAST_BYPASSER; + + override readonly modeOn = MODE_ALWAYS; + override readonly modeOff = MODE_BYPASS; + + constructor(title = BypasserNode.title) { + super(title); + this.onConstructed(); + } + + override async handleAction(action: string) { + if (action === "Bypass all") { + for (const widget of this.widgets || []) { + this.forceWidgetOff(widget, true); + } + } else if (action === "Enable all") { + for (const widget of this.widgets || []) { + this.forceWidgetOn(widget, true); + } + } else if (action === "Toggle all") { + for (const widget of this.widgets || []) { + this.forceWidgetToggle(widget, true); + } + } + } +} + +app.registerExtension({ + name: "rgthree.Bypasser", + registerCustomNodes() { + BypasserNode.setUp(); + }, + loadedGraphNode(node: LGraphNode) { + if (node.type == BypasserNode.title) { + (node as any)._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/comfy_ui_bar.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/comfy_ui_bar.ts new file mode 100644 index 00000000..af79cc95 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/comfy_ui_bar.ts @@ -0,0 +1,264 @@ +import {app} from "scripts/app.js"; +import {iconGear, iconStarFilled, logoRgthreeAsync} from "rgthree/common/media/svgs.js"; +import {$el, empty} from "rgthree/common/utils_dom.js"; +import {SERVICE as BOOKMARKS_SERVICE} from "./services/bookmarks_services.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; +import {RgthreeConfigDialog} from "./config.js"; +import {wait} from "rgthree/common/shared_utils.js"; + +let rgthreeButtonGroup: RgthreeComfyButtonGroup | null = null; + +function addRgthreeTopBarButtons() { + if (!CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.enabled")) { + if (rgthreeButtonGroup?.element?.parentElement) { + rgthreeButtonGroup.element.parentElement.removeChild(rgthreeButtonGroup.element); + } + return; + } else if (rgthreeButtonGroup) { + app.menu?.settingsGroup.element.before(rgthreeButtonGroup.element); + return; + } + + const buttons = []; + + const rgthreeButton = new RgthreeComfyButton({ + icon: "", + tooltip: "rgthree-comfy", + primary: true, + // content: 'rgthree-comfy', + // app, + enabled: true, + classList: "comfyui-button comfyui-menu-mobile-collapse primary", + }); + buttons.push(rgthreeButton); + logoRgthreeAsync().then((t) => { + rgthreeButton.setIcon(t); + }); + + rgthreeButton.withPopup( + new RgthreeComfyPopup( + {target: rgthreeButton.element}, + $el("menu.rgthree-menu.rgthree-top-menu", { + children: [ + $el("li", { + child: $el("button.rgthree-button-reset", { + html: iconGear + "Settings (rgthree-comfy)", + onclick: () => new RgthreeConfigDialog().show(), + }), + }), + $el("li", { + child: $el("button.rgthree-button-reset", { + html: iconStarFilled + "Star on Github", + onclick: () => window.open("https://github.com/rgthree/rgthree-comfy", "_blank"), + }), + }), + ], + }), + ), + "click", + ); + + if (CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.button_bookmarks.enabled")) { + const bookmarksListEl = $el("menu.rgthree-menu.rgthree-top-menu"); + bookmarksListEl.appendChild( + $el("li.rgthree-message", { + child: $el("span", {text: "No bookmarks in current workflow."}), + }), + ); + const bookmarksButton = new RgthreeComfyButton({ + icon: "bookmark", + tooltip: "Workflow Bookmarks (rgthree-comfy)", + // app, + }); + const bookmarksPopup = new RgthreeComfyPopup( + {target: bookmarksButton.element, modal: false}, + bookmarksListEl, + ); + bookmarksPopup.onOpen(() => { + const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks(); + empty(bookmarksListEl); + if (bookmarks.length) { + for (const b of bookmarks) { + bookmarksListEl.appendChild( + $el("li", { + child: $el("button.rgthree-button-reset", { + text: `[${b.shortcutKey}] ${b.title}`, + onclick: () => { + b.canvasToBookmark(); + }, + }), + }), + ); + } + } else { + bookmarksListEl.appendChild( + $el("li.rgthree-message", { + child: $el("span", {text: "No bookmarks in current workflow."}), + }), + ); + } + // bookmarksPopup.update(); + }); + bookmarksButton.withPopup(bookmarksPopup, "hover"); + buttons.push(bookmarksButton); + } + + rgthreeButtonGroup = new RgthreeComfyButtonGroup(...buttons); + app.menu?.settingsGroup.element.before(rgthreeButtonGroup.element); +} + +app.registerExtension({ + name: "rgthree.TopMenu", + async setup() { + addRgthreeTopBarButtons(); + + CONFIG_SERVICE.addEventListener("config-change", ((e: CustomEvent) => { + if (e.detail?.key?.includes("features.comfy_top_bar_menu")) { + addRgthreeTopBarButtons(); + } + }) as EventListener); + }, +}); + +// The following are rough hacks since ComfyUI took away their button/buttongroup/popup +// functionality. TODO: Find a better spot to add rgthree controls to the UI, I suppose. + +class RgthreeComfyButtonGroup { + element = $el("div.rgthree-comfybar-top-button-group"); + buttons: RgthreeComfyButton[]; + + constructor(...buttons: RgthreeComfyButton[]) { + this.buttons = buttons; + this.update(); + } + + insert(button: RgthreeComfyButton, index: number) { + this.buttons.splice(index, 0, button); + this.update(); + } + + append(button: RgthreeComfyButton) { + this.buttons.push(button); + this.update(); + } + + remove(indexOrButton: RgthreeComfyButton | number) { + if (typeof indexOrButton !== "number") { + indexOrButton = this.buttons.indexOf(indexOrButton); + } + if (indexOrButton > -1) { + const btn = this.buttons.splice(indexOrButton, 1); + this.update(); + return btn; + } + return null; + } + + update() { + this.element.replaceChildren(...this.buttons.map((b) => b["element"] ?? b)); + } +} + +interface RgthreeComfyButtonOptions { + icon?: string; + primary?: boolean; + overIcon?: string; + iconSize?: number; + content?: string | HTMLElement; + tooltip?: string; + enabled?: boolean; + action?: (e: Event, btn: RgthreeComfyButton) => void; + classList?: string; + visibilitySetting?: {id: string; showValue: any}; + // app?: ComfyApp; +} + +class RgthreeComfyButton { + element = $el("button.rgthree-comfybar-top-button.rgthree-button-reset.rgthree-button"); + iconElement = $el("span.rgthree-button-icon"); + constructor(opts: RgthreeComfyButtonOptions) { + opts.icon && this.setIcon(opts.icon); + opts.tooltip && this.element.setAttribute("title", opts.tooltip); + opts.primary && this.element.classList.add("-primary"); + } + + setIcon(iconOrMarkup: string) { + const markup = iconOrMarkup.startsWith("<") + ? iconOrMarkup + : ``; + this.iconElement.innerHTML = markup; + if (!this.iconElement.parentElement) { + this.element.appendChild(this.iconElement); + } + } + + withPopup(popup: RgthreeComfyPopup, trigger: "click" | "hover") { + if (trigger === "click") { + this.element.addEventListener("click", () => { + popup.open(); + }); + } + if (trigger === "hover") { + this.element.addEventListener("pointerenter", () => { + popup.open(); + }); + } + } +} + +interface RgthreeComfyPopupOptions { + target: HTMLElement; + classList?: string; + modal?: boolean; +} + +class RgthreeComfyPopup { + element: HTMLElement; + target?: HTMLElement; + onOpenFn: (() => Promise | void) | null = null; + opts: RgthreeComfyPopupOptions; + + onWindowClickBound = this.onWindowClick.bind(this); + + constructor(opts: RgthreeComfyPopupOptions, element: HTMLElement) { + this.element = element; + this.opts = opts; + opts.target && (this.target = opts.target); + opts.modal && this.element.classList.add("-modal"); + } + + async open() { + if (!this.target) { + throw new Error("No target for RgthreeComfyPopup"); + } + if (this.onOpenFn) { + await this.onOpenFn(); + } + await wait(16); + const rect = this.target.getBoundingClientRect(); + this.element.setAttribute("state", "measuring"); + document.body.appendChild(this.element); + this.element.style.position = "fixed"; + this.element.style.left = `${rect.left}px`; + this.element.style.top = `${rect.top + rect.height}px`; + this.element.setAttribute("state", "open"); + if (this.opts.modal) { + document.body.classList.add("rgthree-modal-menu-open"); + } + window.addEventListener("click", this.onWindowClickBound); + } + + close() { + this.element.remove(); + document.body.classList.remove("rgthree-modal-menu-open"); + window.removeEventListener("click", this.onWindowClickBound); + } + + onOpen(fn: (() => void) | null) { + this.onOpenFn = fn; + } + + onWindowClick() { + this.close(); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/config.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/config.ts new file mode 100644 index 00000000..1932eaab --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/config.ts @@ -0,0 +1,407 @@ +import {app} from "scripts/app.js"; +import { RgthreeDialog, RgthreeDialogOptions } from "rgthree/common/dialog.js"; +import { createElement as $el, queryAll as $$ } from "rgthree/common/utils_dom.js"; +import { checkmark, logoRgthree } from "rgthree/common/media/svgs.js"; +import { LogLevel, rgthree } from "./rgthree.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; + +/** Types of config used as a hint for the form handling. */ +enum ConfigType { + UNKNOWN, + BOOLEAN, + STRING, + NUMBER, + ARRAY, +} + +enum ConfigInputType { + UNKNOWN, + CHECKLIST, // Which is a multiselect array. +} + +const TYPE_TO_STRING = { + [ConfigType.UNKNOWN]: "unknown", + [ConfigType.BOOLEAN]: "boolean", + [ConfigType.STRING]: "string", + [ConfigType.NUMBER]: "number", + [ConfigType.ARRAY]: "array", +}; + +type ConfigurationSchema = { + key: string; + type: ConfigType; + label: string; + inputType?: ConfigInputType, + options?: string[] | number[] | ConfigurationSchemaOption[]; + description?: string; + subconfig?: ConfigurationSchema[]; + isDevOnly?: boolean; + onSave?: (value: any) => void; +}; + +type ConfigurationSchemaOption = { value: any; label: string }; + +/** + * A static schema of sorts to layout options found in the config. + */ +const CONFIGURABLE: { [key: string]: ConfigurationSchema[] } = { + features: [ + { + key: "features.progress_bar.enabled", + type: ConfigType.BOOLEAN, + label: "Prompt Progress Bar", + description: `Shows a minimal progress bar for nodes and steps at the top of the app.`, + subconfig: [ + { + key: "features.progress_bar.height", + type: ConfigType.NUMBER, + label: "Height of the bar", + }, + { + key: "features.progress_bar.position", + type: ConfigType.STRING, + label: "Position at top or bottom of window", + options: ["top", "bottom"], + }, + ], + }, + { + key: "features.import_individual_nodes.enabled", + type: ConfigType.BOOLEAN, + label: "Import Individual Nodes Widgets", + description: + "Dragging & Dropping a similar image/JSON workflow onto (most) current workflow nodes" + + "will allow you to import that workflow's node's widgets when it has the same " + + "id and type. This is useful when you have several images and you'd like to import just " + + "one part of a previous iteration, like a seed, or prompt.", + }, + ], + menus: [ + { + key: "features.comfy_top_bar_menu.enabled", + type: ConfigType.BOOLEAN, + label: "Enable Top Bar Menu", + description: + "Have quick access from ComfyUI's new top bar to rgthree-comfy bookmarks, settings " + + "(and more to come).", + }, + { + key: "features.menu_queue_selected_nodes", + type: ConfigType.BOOLEAN, + label: "Show 'Queue Selected Output Nodes'", + description: + "Will show a menu item in the right-click context menus to queue (only) the selected " + + "output nodes.", + }, + { + key: "features.menu_auto_nest.subdirs", + type: ConfigType.BOOLEAN, + label: "Auto Nest Subdirectories in Menus", + description: + "When a large, flat list of values contain sub-directories, auto nest them. (Like, for " + + "a large list of checkpoints).", + subconfig: [ + { + key: "features.menu_auto_nest.threshold", + type: ConfigType.NUMBER, + label: "Number of items needed to trigger nesting.", + }, + ], + }, + { + key: "features.menu_bookmarks.enabled", + type: ConfigType.BOOLEAN, + label: "Show Bookmarks in context menu", + description: "Will list bookmarks in the rgthree-comfy right-click context menu.", + }, + ], + groups: [ + { + key: "features.group_header_fast_toggle.enabled", + type: ConfigType.BOOLEAN, + label: "Show fast toggles in Group Headers", + description: "Show quick toggles in Groups' Headers to quickly mute, bypass or queue.", + subconfig: [ + { + key: "features.group_header_fast_toggle.toggles", + type: ConfigType.ARRAY, + label: "Which toggles to show.", + inputType: ConfigInputType.CHECKLIST, + options: [ + { value: "queue", label: "queue" }, + { value: "bypass", label: "bypass" }, + { value: "mute", label: "mute" }, + ], + }, + { + key: "features.group_header_fast_toggle.show", + type: ConfigType.STRING, + label: "When to show them.", + options: [ + { value: "hover", label: "on hover" }, + { value: "always", label: "always" }, + ], + }, + ], + }, + ], + advanced: [ + { + key: "features.show_alerts_for_corrupt_workflows", + type: ConfigType.BOOLEAN, + label: "Detect Corrupt Workflows", + description: + "Will show a message at the top of the screen when loading a workflow that has " + + "corrupt linking data.", + }, + { + key: "log_level", + type: ConfigType.STRING, + label: "Log level for browser dev console.", + description: + "Further down the list, the more verbose logs to the console will be. For instance, " + + "selecting 'IMPORTANT' means only important message will be logged to the browser " + + "console, while selecting 'WARN' will log all messages at or higher than WARN, including " + + "'ERROR' and 'IMPORTANT' etc.", + options: ["IMPORTANT", "ERROR", "WARN", "INFO", "DEBUG", "DEV"], + isDevOnly: true, + onSave: function (value: LogLevel) { + rgthree.setLogLevel(value); + }, + }, + { + key: "features.invoke_extensions_async.node_created", + type: ConfigType.BOOLEAN, + label: "Allow other extensions to call nodeCreated on rgthree-nodes.", + isDevOnly: true, + description: + "Do not disable unless you are having trouble (and then file an issue at rgthree-comfy)." + + "Prior to Apr 2024 it was not possible for other extensions to invoke their nodeCreated " + + "event on some rgthree-comfy nodes. Now it's possible and this option is only here in " + + "for easy if something is wrong.", + }, + ], +}; + +/** + * Creates a new fieldrow for main or sub configuration items. + */ +function fieldrow(item: ConfigurationSchema) { + const initialValue = CONFIG_SERVICE.getConfigValue(item.key); + const container = $el(`div.fieldrow.-type-${TYPE_TO_STRING[item.type]}`, { + dataset: { + name: item.key, + initial: initialValue, + type: item.type, + }, + }); + + $el(`label[for="${item.key}"]`, { + children: [ + $el(`span[text="${item.label}"]`), + item.description ? $el("small", { html: item.description }) : null, + ], + parent: container, + }); + + let input; + if (item.options?.length) { + if (item.inputType === ConfigInputType.CHECKLIST) { + const initialValueList = initialValue || []; + input = $el(`fieldset.rgthree-checklist-group[id="${item.key}"]`, { + parent: container, + children: item.options.map((o) => { + const label = (o as ConfigurationSchemaOption).label || String(o); + const value = (o as ConfigurationSchemaOption).value || o; + const id = `${item.key}_${value}`; + return $el(`span.rgthree-checklist-item`, { + children: [ + $el(`input[type="checkbox"][value="${value}"]`, { + id, + checked: initialValueList.includes(value), + }), + $el(`label`, { + for: id, + text: label, + }) + ] + }); + }), + }); + } else { + input = $el(`select[id="${item.key}"]`, { + parent: container, + children: item.options.map((o) => { + const label = (o as ConfigurationSchemaOption).label || String(o); + const value = (o as ConfigurationSchemaOption).value || o; + const valueSerialized = JSON.stringify({ value: value }); + return $el(`option[value="${valueSerialized}"]`, { + text: label, + selected: valueSerialized === JSON.stringify({ value: initialValue }), + }); + }), + }); + } + } else if (item.type === ConfigType.BOOLEAN) { + container.classList.toggle("-checked", !!initialValue); + input = $el(`input[type="checkbox"][id="${item.key}"]`, { + parent: container, + checked: initialValue, + }); + } else { + input = $el(`input[id="${item.key}"]`, { + parent: container, + value: initialValue, + }); + } + $el("div.fieldrow-value", { children: [input], parent: container }); + return container; +} + +/** + * A dialog to edit rgthree-comfy settings and config. + */ +export class RgthreeConfigDialog extends RgthreeDialog { + constructor() { + const content = $el("div"); + + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["features"]!, "Features")); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["menus"]!, "Menus")); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["groups"]!, "Groups")); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["advanced"]!, "Advanced")); + + content.addEventListener("input", (e) => { + const changed = this.getChangedFormData(); + ($$(".save-button", this.element)[0] as HTMLButtonElement).disabled = + !Object.keys(changed).length; + }); + content.addEventListener("change", (e) => { + const changed = this.getChangedFormData(); + ($$(".save-button", this.element)[0] as HTMLButtonElement).disabled = + !Object.keys(changed).length; + }); + + const dialogOptions: RgthreeDialogOptions = { + class: "-iconed -settings", + title: logoRgthree + `

    Settings - rgthree-comfy

    `, + content, + onBeforeClose: () => { + const changed = this.getChangedFormData(); + if (Object.keys(changed).length) { + return confirm("Looks like there are unsaved changes. Are you sure you want close?"); + } + return true; + }, + buttons: [ + { + label: "Save", + disabled: true, + className: "rgthree-button save-button -blue", + callback: async (e) => { + const changed = this.getChangedFormData(); + if (!Object.keys(changed).length) { + this.close(); + return; + } + const success = await CONFIG_SERVICE.setConfigValues(changed); + if (success) { + for (const key of Object.keys(changed)) { + Object.values(CONFIGURABLE) + .flat() + .find((f) => f.key === key) + ?.onSave?.(changed[key]); + } + this.close(); + rgthree.showMessage({ + id: "config-success", + message: `${checkmark} Successfully saved rgthree-comfy settings!`, + timeout: 4000, + }); + ($$(".save-button", this.element)[0] as HTMLButtonElement).disabled = true; + } else { + alert("There was an error saving rgthree-comfy configuration."); + } + }, + }, + ], + }; + super(dialogOptions); + } + + private static buildFieldset(datas: ConfigurationSchema[], label: string) { + const fieldset = $el(`fieldset`, { children: [$el(`legend[text="${label}"]`)] }); + for (const data of datas) { + if (data.isDevOnly && !rgthree.isDevMode()) { + continue; + } + const container = $el("div.formrow"); + container.appendChild(fieldrow(data)); + + if (data.subconfig) { + for (const subfeature of data.subconfig) { + container.appendChild(fieldrow(subfeature)); + } + } + fieldset.appendChild(container); + } + return fieldset; + } + + getChangedFormData() { + return $$("[data-name]", this.contentElement).reduce((acc: { [key: string]: any }, el) => { + const name = el.dataset["name"]!; + const type = el.dataset["type"]!; + const initialValue = CONFIG_SERVICE.getConfigValue(name); + let currentValueEl = $$("fieldset.rgthree-checklist-group, input, textarea, select", el)[0] as HTMLInputElement; + let currentValue: any = null; + if (type === String(ConfigType.BOOLEAN)) { + currentValue = currentValueEl.checked; + // Not sure I like this side effect in here, but it's easy to just do it now. + el.classList.toggle("-checked", currentValue); + } else { + currentValue = currentValueEl?.value; + if (currentValueEl.nodeName === "SELECT") { + currentValue = JSON.parse(currentValue).value; + } else if (currentValueEl.classList.contains('rgthree-checklist-group')) { + currentValue = []; + for (const check of $$('input[type="checkbox"]', currentValueEl)) { + if (check.checked) { + currentValue.push(check.value); + } + } + } else if (type === String(ConfigType.NUMBER)) { + currentValue = Number(currentValue) || initialValue; + } + } + if (JSON.stringify(currentValue) !== JSON.stringify(initialValue)) { + acc[name] = currentValue; + } + return acc; + }, {}); + } +} + +app.ui.settings.addSetting({ + id: "rgthree.config", + defaultValue: null, + name: "Open rgthree-comfy config", + type: () => { + // Adds a row to open the dialog from the ComfyUI settings. + return $el("tr.rgthree-comfyui-settings-row", { + children: [ + $el("td", { + child: `
    ${logoRgthree} [rgthree-comfy] configuration / settings
    `, + }), + $el("td", { + child: $el('button.rgthree-button.-blue[text="rgthree-comfy settings"]', { + events: { + click: (e: PointerEvent) => { + new RgthreeConfigDialog().show(); + }, + }, + }), + }), + ], + }); + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/constants.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/constants.ts new file mode 100644 index 00000000..4e6a13f8 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/constants.ts @@ -0,0 +1,72 @@ +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; + +export function addRgthree(str: string) { + return str + " (rgthree)"; +} + +export function stripRgthree(str: string) { + return str.replace(/\s*\(rgthree\)$/, ""); +} + +export const NodeTypesString = { + ANY_SWITCH: addRgthree("Any Switch"), + CONTEXT: addRgthree("Context"), + CONTEXT_BIG: addRgthree("Context Big"), + CONTEXT_SWITCH: addRgthree("Context Switch"), + CONTEXT_SWITCH_BIG: addRgthree("Context Switch Big"), + CONTEXT_MERGE: addRgthree("Context Merge"), + CONTEXT_MERGE_BIG: addRgthree("Context Merge Big"), + DYNAMIC_CONTEXT: addRgthree("Dynamic Context"), + DYNAMIC_CONTEXT_SWITCH: addRgthree("Dynamic Context Switch"), + DISPLAY_ANY: addRgthree("Display Any"), + IMAGE_OR_LATENT_SIZE: addRgthree("Image or Latent Size"), + + NODE_MODE_RELAY: addRgthree("Mute / Bypass Relay"), + NODE_MODE_REPEATER: addRgthree("Mute / Bypass Repeater"), + FAST_MUTER: addRgthree("Fast Muter"), + FAST_BYPASSER: addRgthree("Fast Bypasser"), + FAST_GROUPS_MUTER: addRgthree("Fast Groups Muter"), + FAST_GROUPS_BYPASSER: addRgthree("Fast Groups Bypasser"), + FAST_ACTIONS_BUTTON: addRgthree("Fast Actions Button"), + LABEL: addRgthree("Label"), + POWER_PRIMITIVE: addRgthree("Power Primitive"), + POWER_PROMPT: addRgthree("Power Prompt"), + POWER_PROMPT_SIMPLE: addRgthree("Power Prompt - Simple"), + POWER_PUTER: addRgthree("Power Puter"), + POWER_CONDUCTOR: addRgthree("Power Conductor"), + SDXL_EMPTY_LATENT_IMAGE: addRgthree("SDXL Empty Latent Image"), + SDXL_POWER_PROMPT_POSITIVE: addRgthree("SDXL Power Prompt - Positive"), + SDXL_POWER_PROMPT_NEGATIVE: addRgthree("SDXL Power Prompt - Simple / Negative"), + POWER_LORA_LOADER: addRgthree("Power Lora Loader"), + KSAMPLER_CONFIG: addRgthree("KSampler Config"), + NODE_COLLECTOR: addRgthree("Node Collector"), + REROUTE: addRgthree("Reroute"), + RANDOM_UNMUTER: addRgthree("Random Unmuter"), + SEED: addRgthree("Seed"), + BOOKMARK: addRgthree("Bookmark"), + IMAGE_COMPARER: addRgthree("Image Comparer"), + IMAGE_INSET_CROP: addRgthree("Image Inset Crop"), +}; + +const UNRELEASED_KEYS = { + [NodeTypesString.DYNAMIC_CONTEXT]: "dynamic_context", + [NodeTypesString.DYNAMIC_CONTEXT_SWITCH]: "dynamic_context", + [NodeTypesString.POWER_CONDUCTOR]: "power_conductor", +}; + + +/** + * Gets the list of nodes from NoteTypeString above, filtering any that are not applicable. + */ +export function getNodeTypeStrings() { + const unreleasedKeys = Object.keys(UNRELEASED_KEYS); + return Object.values(NodeTypesString) + .map((i) => stripRgthree(i)) + .filter((i) => { + if (unreleasedKeys.includes(i)) { + return !!CONFIG_SERVICE.getConfigValue(`unreleased.${UNRELEASED_KEYS[i]}.enabled`) + } + return true; + }) + .sort(); +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/context.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/context.ts new file mode 100644 index 00000000..a49b7a9e --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/context.ts @@ -0,0 +1,488 @@ +import type { + INodeInputSlot, + INodeOutputSlot, + LGraphCanvas as TLGraphCanvas, + LGraphNode as TLGraphNode, + LLink, + ISlotType, + ConnectByTypeOptions, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import { + IoDirection, + addConnectionLayoutSupport, + addMenuItem, + matchLocalSlotsToServer, + replaceNode, +} from "./utils.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js"; +import {RgthreeBaseServerNodeConstructor} from "typings/rgthree.js"; +import {debounce, wait} from "rgthree/common/shared_utils.js"; +import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js"; +import {NodeTypesString} from "./constants.js"; + +/** + * Takes a non-context node and determins for its input or output slot, if there is a valid + * connection for an opposite context output or input slot. + */ +function findMatchingIndexByTypeOrName( + otherNode: TLGraphNode, + otherSlot: INodeInputSlot | INodeOutputSlot, + ctxSlots: INodeInputSlot[] | INodeOutputSlot[], +) { + const otherNodeType = (otherNode.type || "").toUpperCase(); + const otherNodeName = (otherNode.title || "").toUpperCase(); + let otherSlotType = otherSlot.type as string; + if (Array.isArray(otherSlotType) || otherSlotType.includes(",")) { + otherSlotType = "COMBO"; + } + const otherSlotName = otherSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", ""); + let ctxSlotIndex = -1; + if (["CONDITIONING", "INT", "STRING", "FLOAT", "COMBO"].includes(otherSlotType)) { + ctxSlotIndex = ctxSlots.findIndex((ctxSlot) => { + const ctxSlotName = ctxSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", ""); + let ctxSlotType = ctxSlot.type as string; + if (Array.isArray(ctxSlotType) || ctxSlotType.includes(",")) { + ctxSlotType = "COMBO"; + } + if (ctxSlotType !== otherSlotType) { + return false; + } + // Straightforward matches. + if ( + ctxSlotName === otherSlotName || + (ctxSlotName === "SEED" && otherSlotName.includes("SEED")) || + (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("AT_STEP")) || + (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("REFINER_STEP")) + ) { + return true; + } + // If postive other node, try to match conditining and text. + if ( + (otherNodeType.includes("POSITIVE") || otherNodeName.includes("POSITIVE")) && + ((ctxSlotName === "POSITIVE" && otherSlotType === "CONDITIONING") || + (ctxSlotName === "TEXT_POS_G" && otherSlotName.includes("TEXT_G")) || + (ctxSlotName === "TEXT_POS_L" && otherSlotName.includes("TEXT_L"))) + ) { + return true; + } + if ( + (otherNodeType.includes("NEGATIVE") || otherNodeName.includes("NEGATIVE")) && + ((ctxSlotName === "NEGATIVE" && otherSlotType === "CONDITIONING") || + (ctxSlotName === "TEXT_NEG_G" && otherSlotName.includes("TEXT_G")) || + (ctxSlotName === "TEXT_NEG_L" && otherSlotName.includes("TEXT_L"))) + ) { + return true; + } + return false; + }); + } else { + ctxSlotIndex = ctxSlots.map((s) => s.type).indexOf(otherSlotType); + } + return ctxSlotIndex; +} + +/** + * A Base Context node for other context based nodes to extend. + */ +export class BaseContextNode extends RgthreeBaseServerNode { + constructor(title: string) { + super(title); + } + + // LiteGraph adds more spacing than we want when calculating a nodes' `_collapsed_width`, so we'll + // override it with a setter and re-set it measured exactly as we want. + ___collapsed_width: number = 0; + + //@ts-ignore - TS Doesn't like us overriding a property with accessors but, too bad. + override get _collapsed_width() { + return this.___collapsed_width; + } + + override set _collapsed_width(width: number) { + const canvas = app.canvas as TLGraphCanvas; + const ctx = canvas.canvas.getContext("2d")!; + const oldFont = ctx.font; + ctx.font = canvas.title_text_font; + let title = this.title.trim(); + this.___collapsed_width = 30 + (title ? 10 + ctx.measureText(title).width : 0); + ctx.font = oldFont; + } + + override connectByType( + slot: number | string, + targetNode: TLGraphNode, + targetSlotType: ISlotType, + optsIn?: ConnectByTypeOptions, + ): LLink | null { + let canConnect = super.connectByType?.call(this, slot, targetNode, targetSlotType, optsIn); + if (!super.connectByType) { + canConnect = LGraphNode.prototype.connectByType.call( + this, + slot, + targetNode, + targetSlotType, + optsIn, + ); + } + if (!canConnect && slot === 0) { + const ctrlKey = KEY_EVENT_SERVICE.ctrlKey; + // Okay, we've dragged a context and it can't connect.. let's connect all the other nodes. + // Unfortunately, we don't know which are null now, so we'll just connect any that are + // not already connected. + for (const [index, input] of (targetNode.inputs || []).entries()) { + if (input.link && !ctrlKey) { + continue; + } + const thisOutputSlot = findMatchingIndexByTypeOrName(targetNode, input, this.outputs); + if (thisOutputSlot > -1) { + this.connect(thisOutputSlot, targetNode, index); + } + } + } + return null; + } + + override connectByTypeOutput( + slot: number | string, + sourceNode: TLGraphNode, + sourceSlotType: ISlotType, + optsIn?: ConnectByTypeOptions, + ): LLink | null { + let canConnect = super.connectByTypeOutput?.call( + this, + slot, + sourceNode, + sourceSlotType, + optsIn, + ); + if (!super.connectByType) { + canConnect = LGraphNode.prototype.connectByTypeOutput.call( + this, + slot, + sourceNode, + sourceSlotType, + optsIn, + ); + } + if (!canConnect && slot === 0) { + const ctrlKey = KEY_EVENT_SERVICE.ctrlKey; + // Okay, we've dragged a context and it can't connect.. let's connect all the other nodes. + // Unfortunately, we don't know which are null now, so we'll just connect any that are + // not already connected. + for (const [index, output] of (sourceNode.outputs || []).entries()) { + if (output.links?.length && !ctrlKey) { + continue; + } + const thisInputSlot = findMatchingIndexByTypeOrName(sourceNode, output, this.inputs); + if (thisInputSlot > -1) { + sourceNode.connect(index, this, thisInputSlot); + } + } + } + return null; + } + + static override setUp( + comfyClass: typeof LGraphNode, + nodeData: ComfyNodeDef, + ctxClass: RgthreeBaseServerNodeConstructor, + ) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ctxClass); + // [πŸ€] ComfyUI only adds "required" inputs to the outputs list when dragging an output to + // empty space, but since RGTHREE_CONTEXT is optional, it doesn't get added to the menu because + // ...of course. So, we'll manually add it. Of course, we also have to do this in a timeout + // because ComfyUI clears out `LiteGraph.slot_types_default_out` in its own 'Comfy.SlotDefaults' + // extension and we need to wait for that to happen. + wait(500).then(() => { + LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] = + LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] || []; + LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"].push((comfyClass as any).comfyClass); + }); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + addConnectionLayoutSupport(ctxClass, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + ctxClass.category = comfyClass.category; + }); + } +} + +/** + * The original Context node. + */ +class ContextNode extends BaseContextNode { + static override title = NodeTypesString.CONTEXT; + static override type = NodeTypesString.CONTEXT; + static comfyClass = NodeTypesString.CONTEXT; + + constructor(title = ContextNode.title) { + super(title); + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + BaseContextNode.setUp(comfyClass, nodeData, ContextNode); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextNode, app, { + name: "Convert To Context Big", + callback: (node) => { + replaceNode(node, ContextBigNode.type); + }, + }); + } +} + +/** + * The Context Big node. + */ +class ContextBigNode extends BaseContextNode { + static override title = NodeTypesString.CONTEXT_BIG; + static override type = NodeTypesString.CONTEXT_BIG; + static comfyClass = NodeTypesString.CONTEXT_BIG; + + constructor(title = ContextBigNode.title) { + super(title); + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + BaseContextNode.setUp(comfyClass, nodeData, ContextBigNode); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextBigNode, app, { + name: "Convert To Context (Original)", + callback: (node) => { + replaceNode(node, ContextNode.type); + }, + }); + } +} + +/** + * A base node for Context Switche nodes and Context Merges nodes that will always add another empty + * ctx input, no less than five. + */ +class BaseContextMultiCtxInputNode extends BaseContextNode { + private stabilizeBound = this.stabilize.bind(this); + + constructor(title: string) { + super(title); + // Adding five. Note, configure will add as many as was in the stored workflow automatically. + this.addContextInput(5); + } + + private addContextInput(num = 1) { + for (let i = 0; i < num; i++) { + this.addInput(`ctx_${String(this.inputs.length + 1).padStart(2, "0")}`, "RGTHREE_CONTEXT"); + } + } + + override onConnectionsChange( + type: number, + slotIndex: number, + isConnected: boolean, + link: LLink, + ioSlot: INodeInputSlot | INodeOutputSlot, + ): void { + super.onConnectionsChange?.apply(this, [...arguments] as any); + if (type === LiteGraph.INPUT) { + this.scheduleStabilize(); + } + } + + private scheduleStabilize(ms = 64) { + return debounce(this.stabilizeBound, 64); + } + + /** + * Stabilizes the inputs; removing any disconnected ones from the bottom, then adding an empty + * one to the end so we always have one empty one to expand. + */ + private stabilize() { + removeUnusedInputsFromEnd(this, 4); + this.addContextInput(); + } +} + +/** + * The Context Switch (original) node. + */ +class ContextSwitchNode extends BaseContextMultiCtxInputNode { + static override title = NodeTypesString.CONTEXT_SWITCH; + static override type = NodeTypesString.CONTEXT_SWITCH; + static comfyClass = NodeTypesString.CONTEXT_SWITCH; + + constructor(title = ContextSwitchNode.title) { + super(title); + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchNode); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextSwitchNode, app, { + name: "Convert To Context Switch Big", + callback: (node) => { + replaceNode(node, ContextSwitchBigNode.type); + }, + }); + } +} + +/** + * The Context Switch Big node. + */ +class ContextSwitchBigNode extends BaseContextMultiCtxInputNode { + static override title = NodeTypesString.CONTEXT_SWITCH_BIG; + static override type = NodeTypesString.CONTEXT_SWITCH_BIG; + static comfyClass = NodeTypesString.CONTEXT_SWITCH_BIG; + + constructor(title = ContextSwitchBigNode.title) { + super(title); + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchBigNode); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextSwitchBigNode, app, { + name: "Convert To Context Switch", + callback: (node) => { + replaceNode(node, ContextSwitchNode.type); + }, + }); + } +} + +/** + * The Context Merge (original) node. + */ +class ContextMergeNode extends BaseContextMultiCtxInputNode { + static override title = NodeTypesString.CONTEXT_MERGE; + static override type = NodeTypesString.CONTEXT_MERGE; + static comfyClass = NodeTypesString.CONTEXT_MERGE; + + constructor(title = ContextMergeNode.title) { + super(title); + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + BaseContextNode.setUp(comfyClass, nodeData, ContextMergeNode); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextMergeNode, app, { + name: "Convert To Context Merge Big", + callback: (node) => { + replaceNode(node, ContextMergeBigNode.type); + }, + }); + } +} + +/** + * The Context Switch Big node. + */ +class ContextMergeBigNode extends BaseContextMultiCtxInputNode { + static override title = NodeTypesString.CONTEXT_MERGE_BIG; + static override type = NodeTypesString.CONTEXT_MERGE_BIG; + static comfyClass = NodeTypesString.CONTEXT_MERGE_BIG; + + constructor(title = ContextMergeBigNode.title) { + super(title); + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + BaseContextNode.setUp(comfyClass, nodeData, ContextMergeBigNode); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextMergeBigNode, app, { + name: "Convert To Context Switch", + callback: (node) => { + replaceNode(node, ContextMergeNode.type); + }, + }); + } +} + +const contextNodes = [ + ContextNode, + ContextBigNode, + ContextSwitchNode, + ContextSwitchBigNode, + ContextMergeNode, + ContextMergeBigNode, +]; +const contextTypeToServerDef: {[type: string]: ComfyNodeDef} = {}; + +function fixBadConfigs(node: ContextNode) { + // Dumb mistake, but let's fix our mispelling. This will probably need to stay in perpetuity to + // keep any old workflows operating. + const wrongName = node.outputs.find((o, i) => o.name === "CLIP_HEIGTH"); + if (wrongName) { + wrongName.name = "CLIP_HEIGHT"; + } +} + +app.registerExtension({ + name: "rgthree.Context", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + // Loop over out context nodes and see if any match the server data. + for (const ctxClass of contextNodes) { + if (nodeData.name === ctxClass.type) { + contextTypeToServerDef[ctxClass.type] = nodeData; + ctxClass.setUp(nodeType, nodeData); + break; + } + } + }, + + async nodeCreated(node: TLGraphNode) { + const type = node.type || (node.constructor as any).type; + const serverDef = type && contextTypeToServerDef[type]; + if (serverDef) { + fixBadConfigs(node as ContextNode); + matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef); + // Switches don't need to change inputs, only context outputs + if (!type!.includes("Switch") && !type!.includes("Merge")) { + matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef); + } + // }, 100); + } + }, + + /** + * When we're loaded from the server, check if we're using an out of date version and update our + * inputs / outputs to match. + */ + async loadedGraphNode(node: TLGraphNode) { + const type = node.type || (node.constructor as any).type; + const serverDef = type && contextTypeToServerDef[type]; + if (serverDef) { + fixBadConfigs(node as ContextNode); + matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef); + // Switches don't need to change inputs, only context outputs + if (!type!.includes("Switch") && !type!.includes("Merge")) { + matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef); + } + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dialog_info.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dialog_info.ts new file mode 100644 index 00000000..6c8b02a4 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dialog_info.ts @@ -0,0 +1,436 @@ +import {RgthreeDialog, RgthreeDialogOptions} from "rgthree/common/dialog.js"; +import { + createElement as $el, + empty, + appendChildren, + getClosestOrSelf, + query, + queryAll, + setAttributes, +} from "rgthree/common/utils_dom.js"; +import { + logoCivitai, + link, + pencilColored, + diskColored, + dotdotdot, +} from "rgthree/common/media/svgs.js"; +import {RgthreeModelInfo} from "typings/rgthree.js"; +import {CHECKPOINT_INFO_SERVICE, LORA_INFO_SERVICE} from "rgthree/common/model_info_service.js"; +import {rgthree} from "./rgthree.js"; +import {MenuButton} from "rgthree/common/menu.js"; +import {generateId, injectCss} from "rgthree/common/shared_utils.js"; +import {rgthreeApi} from "rgthree/common/rgthree_api.js"; + +/** + * A dialog that displays information about a model/lora/etc. + */ +abstract class RgthreeInfoDialog extends RgthreeDialog { + private modifiedModelData = false; + private modelInfo: RgthreeModelInfo | null = null; + + constructor(file: string) { + const dialogOptions: RgthreeDialogOptions = { + class: "rgthree-info-dialog", + title: `

    Loading...

    `, + content: "
    Loading..
    ", + onBeforeClose: () => { + return true; + }, + }; + super(dialogOptions); + this.init(file); + } + + abstract getModelInfo(file: string): Promise; + abstract refreshModelInfo(file: string): Promise; + abstract clearModelInfo(file: string): Promise; + + private async init(file: string) { + const cssPromise = injectCss("rgthree/common/css/dialog_model_info.css"); + this.modelInfo = await this.getModelInfo(file); + await cssPromise; + this.setContent(this.getInfoContent()); + this.setTitle(this.modelInfo?.["name"] || this.modelInfo?.["file"] || "Unknown"); + this.attachEvents(); + } + + protected override getCloseEventDetail(): {detail: any} { + const detail = { + dirty: this.modifiedModelData, + }; + return {detail}; + } + + private attachEvents() { + this.contentElement.addEventListener("click", async (e: MouseEvent) => { + const target = getClosestOrSelf(e.target as HTMLElement, "[data-action]"); + const action = target?.getAttribute("data-action"); + if (!target || !action) { + return; + } + await this.handleEventAction(action, target, e); + }); + } + + private async handleEventAction(action: string, target: HTMLElement, e?: Event) { + const info = this.modelInfo!; + if (!info?.file) { + return; + } + if (action === "fetch-civitai") { + this.modelInfo = await this.refreshModelInfo(info.file); + this.setContent(this.getInfoContent()); + this.setTitle(this.modelInfo?.["name"] || this.modelInfo?.["file"] || "Unknown"); + } else if (action === "copy-trained-words") { + const selected = queryAll(".-rgthree-is-selected", target.closest("tr")!); + const text = selected.map((el) => el.getAttribute("data-word")).join(", "); + await navigator.clipboard.writeText(text); + rgthree.showMessage({ + id: "copy-trained-words-" + generateId(4), + type: "success", + message: `Successfully copied ${selected.length} key word${ + selected.length === 1 ? "" : "s" + }.`, + timeout: 4000, + }); + } else if (action === "toggle-trained-word") { + target?.classList.toggle("-rgthree-is-selected"); + const tr = target.closest("tr"); + if (tr) { + const span = query("td:first-child > *", tr)!; + let small = query("small", span); + if (!small) { + small = $el("small", {parent: span}); + } + const num = queryAll(".-rgthree-is-selected", tr).length; + small.innerHTML = num + ? `${num} selected | Copy` + : ""; + // this.handleEventAction('copy-trained-words', target, e); + } + } else if (action === "edit-row") { + const tr = target!.closest("tr")!; + const td = query("td:nth-child(2)", tr)!; + const input = td.querySelector("input,textarea"); + if (!input) { + const fieldName = tr.dataset["fieldName"] as string; + tr.classList.add("-rgthree-editing"); + const isTextarea = fieldName === "userNote"; + const input = $el(`${isTextarea ? "textarea" : 'input[type="text"]'}`, { + value: td.textContent, + }); + input.addEventListener("keydown", (e) => { + if (!isTextarea && e.key === "Enter") { + const modified = saveEditableRow(info!, tr, true); + this.modifiedModelData = this.modifiedModelData || modified; + e.stopPropagation(); + e.preventDefault(); + } else if (e.key === "Escape") { + const modified = saveEditableRow(info!, tr, false); + this.modifiedModelData = this.modifiedModelData || modified; + e.stopPropagation(); + e.preventDefault(); + } + }); + appendChildren(empty(td), [input]); + input.focus(); + } else if (target!.nodeName.toLowerCase() === "button") { + const modified = saveEditableRow(info!, tr, true); + this.modifiedModelData = this.modifiedModelData || modified; + } + e?.preventDefault(); + e?.stopPropagation(); + } + } + + private getInfoContent() { + const info = this.modelInfo || {}; + const civitaiLink = info.links?.find((i) => i.includes("civitai.com/models")); + const html = ` +
      +
    • ${info.type || ""}
      • +
    • ${info.baseModel || ""}
      • +
    • + ${ + "" + // !civitaiLink + // ? "" + // : ` + //
    • Civitai ${link}
      • + // ` + } +
    + + + ${infoTableRow("File", info.file || "")} + ${infoTableRow("Hash (sha256)", info.sha256 || "")} + ${ + civitaiLink + ? infoTableRow( + "Civitai", + `${logoCivitai}View on Civitai`, + ) + : info.raw?.civitai?.error === "Model not found" + ? infoTableRow( + "Civitai", + 'Model not found', + ) + : info.raw?.civitai?.error + ? infoTableRow("Civitai", info.raw?.civitai?.error) + : !info.raw?.civitai + ? infoTableRow( + "Civitai", + ``, + ) + : "" + } + + ${infoTableRow( + "Name", + info.name || info.raw?.metadata?.ss_output_name || "", + "The name for display.", + "name", + )} + + ${ + !info.baseModelFile && !info.baseModelFile + ? "" + : infoTableRow( + "Base Model", + (info.baseModel || "") + (info.baseModelFile ? ` (${info.baseModelFile})` : ""), + ) + } + + + ${ + !info.trainedWords?.length + ? "" + : infoTableRow( + "Trained Words", + getTrainedWordsMarkup(info.trainedWords) ?? "", + "Trained words from the metadata and/or civitai. Click to select for copy.", + ) + } + + ${ + !info.raw?.metadata?.ss_clip_skip || info.raw?.metadata?.ss_clip_skip == "None" + ? "" + : infoTableRow("Clip Skip", info.raw?.metadata?.ss_clip_skip) + } + ${infoTableRow( + "Strength Min", + info.strengthMin ?? "", + "The recommended minimum strength, In the Power Lora Loader node, strength will signal when it is below this threshold.", + "strengthMin", + )} + ${infoTableRow( + "Strength Max", + info.strengthMax ?? "", + "The recommended maximum strength. In the Power Lora Loader node, strength will signal when it is above this threshold.", + "strengthMax", + )} + ${ + "" /*infoTableRow( + "User Tags", + info.userTags?.join(", ") ?? "", + "A list of tags to make filtering easier in the Power Lora Chooser.", + "userTags", + )*/ + } + ${infoTableRow( + "Additional Notes", + info.userNote ?? "", + "Additional notes you'd like to keep and reference in the info dialog.", + "userNote", + )} + +
    + +
      ${ + info.images + ?.map( + (img) => ` +
    • +
      ${ + img.type === 'video' + ? `` + : `` + } +
      ${imgInfoField( + "", + img.civitaiUrl + ? `civitai${link}` + : undefined, + )}${imgInfoField("seed", img.seed)}${imgInfoField("steps", img.steps)}${imgInfoField("cfg", img.cfg)}${imgInfoField("sampler", img.sampler)}${imgInfoField("model", img.model)}${imgInfoField("positive", img.positive)}${imgInfoField("negative", img.negative)}
      +
      +
      • `, + ) + .join("") ?? "" + }
    + `; + + const div = $el("div", {html}); + + if (rgthree.isDevMode()) { + setAttributes(query('[stub="menu"]', div)!, { + children: [ + new MenuButton({ + icon: dotdotdot, + options: [ + {label: "More Actions", type: "title"}, + { + label: "Open API JSON", + callback: async (e: PointerEvent) => { + if (this.modelInfo?.file) { + window.open( + `rgthree/api/loras/info?file=${encodeURIComponent(this.modelInfo.file)}`, + ); + } + }, + }, + { + label: "Clear all local info", + callback: async (e: PointerEvent) => { + if (this.modelInfo?.file) { + this.modelInfo = await LORA_INFO_SERVICE.clearFetchedInfo(this.modelInfo.file); + this.setContent(this.getInfoContent()); + this.setTitle( + this.modelInfo?.["name"] || this.modelInfo?.["file"] || "Unknown", + ); + } + }, + }, + ], + }), + ], + }); + } + + return div; + } +} + +export class RgthreeLoraInfoDialog extends RgthreeInfoDialog { + override async getModelInfo(file: string) { + return LORA_INFO_SERVICE.getInfo(file, false, false); + } + override async refreshModelInfo(file: string) { + return LORA_INFO_SERVICE.refreshInfo(file); + } + override async clearModelInfo(file: string) { + return LORA_INFO_SERVICE.clearFetchedInfo(file); + } +} + +export class RgthreeCheckpointInfoDialog extends RgthreeInfoDialog { + override async getModelInfo(file: string) { + return CHECKPOINT_INFO_SERVICE.getInfo(file, false, false); + } + override async refreshModelInfo(file: string) { + return CHECKPOINT_INFO_SERVICE.refreshInfo(file); + } + override async clearModelInfo(file: string) { + return CHECKPOINT_INFO_SERVICE.clearFetchedInfo(file); + } +} + +/** + * Generates a uniform markup string for a table row. + */ +function infoTableRow( + name: string, + value: string | number, + help: string = "", + editableFieldName = "", +) { + return ` + + ${name} ${help ? `` : ""} + ${ + String(value).startsWith("<") ? value : `${value}` + } + ${ + editableFieldName + ? `` + : "" + } + `; +} + +function getTrainedWordsMarkup(words: RgthreeModelInfo["trainedWords"]) { + let markup = `
      `; + for (const wordData of words || []) { + markup += `
    • + ${wordData.word} + ${wordData.civitai ? logoCivitai : ""} + ${wordData.count != null ? `${wordData.count}` : ""} +
      • `; + } + markup += `
    `; + return markup; +} + +/** + * Saves / cancels an editable row. Returns a boolean if the data was modified. + */ +function saveEditableRow(info: RgthreeModelInfo, tr: HTMLElement, saving = true): boolean { + const fieldName = tr.dataset["fieldName"] as "file"; + const input = query("input,textarea", tr)!; + let newValue = info[fieldName] ?? ""; + let modified = false; + if (saving) { + newValue = input!.value; + if (fieldName.startsWith("strength")) { + if (Number.isNaN(Number(newValue))) { + alert(`You must enter a number into the ${fieldName} field.`); + return false; + } + newValue = (Math.round(Number(newValue) * 100) / 100).toFixed(2); + } + LORA_INFO_SERVICE.savePartialInfo(info.file!, {[fieldName]: newValue}); + modified = true; + } + tr.classList.remove("-rgthree-editing"); + const td = query("td:nth-child(2)", tr)!; + appendChildren(empty(td), [$el("span", {text: newValue})]); + return modified; +} + +function imgInfoField(label: string, value?: string | number) { + return value != null ? `${label ? `` : ""}${value}` : ""; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/display_any.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/display_any.ts new file mode 100644 index 00000000..f2c725a5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/display_any.ts @@ -0,0 +1,71 @@ +import type {LGraphNodeConstructor, LGraphNode as TLGraphNode} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; +import type {ComfyApp} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {addConnectionLayoutSupport} from "./utils.js"; +import {rgthree} from "./rgthree.js"; + +let hasShownAlertForUpdatingInt = false; + +app.registerExtension({ + name: "rgthree.DisplayAny", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef, app: ComfyApp) { + if (nodeData.name === "Display Any (rgthree)" || nodeData.name === "Display Int (rgthree)") { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + + (this as any).showValueWidget = ComfyWidgets["STRING"]( + this, + "output", + ["STRING", {multiline: true}], + app, + ).widget; + (this as any).showValueWidget.inputEl!.readOnly = true; + (this as any).showValueWidget.serializeValue = async (node: TLGraphNode, index: number) => { + const n = + rgthree.getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(node); + if (n) { + // Since we need a round trip to get the value, the serizalized value means nothing, and + // saving it to the metadata would just be confusing. So, we clear it here. + n.widgets_values![index] = ""; + } else { + console.warn( + "No serialized node found in workflow. May be attributed to " + + "https://github.com/comfyanonymous/ComfyUI/issues/2193", + ); + } + return ""; + }; + }; + + addConnectionLayoutSupport(nodeType as LGraphNodeConstructor, app, [["Left"], ["Right"]]); + + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message: any) { + onExecuted?.apply(this, [message]); + (this as any).showValueWidget.value = message.text[0]; + }; + } + }, + + // This ports Display Int to DisplayAny, but ComfyUI still shows an error. + // If https://github.com/comfyanonymous/ComfyUI/issues/1527 is fixed, this could work. + // async loadedGraphNode(node: TLGraphNode) { + // if (node.type === "Display Int (rgthree)") { + // replaceNode(node, "Display Any (rgthree)", new Map([["input", "source"]])); + // if (!hasShownAlertForUpdatingInt) { + // hasShownAlertForUpdatingInt = true; + // setTimeout(() => { + // alert( + // "Don't worry, your 'Display Int' nodes have been updated to the new " + + // "'Display Any' nodes! You can ignore the error message underneath (for that node)." + + // "\n\nThanks.\n- rgthree", + // ); + // }, 128); + // } + // } + // }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context.ts new file mode 100644 index 00000000..a964d620 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context.ts @@ -0,0 +1,302 @@ +import type { + IContextMenuValue, + IFoundSlot, + INodeInputSlot, + INodeOutputSlot, + ISlotType, + LGraphNode, + LLink, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import { + IoDirection, + followConnectionUntilType, + getConnectedInputInfosAndFilterPassThroughs, +} from "./utils.js"; +import {rgthree} from "./rgthree.js"; +import { + SERVICE as CONTEXT_SERVICE, + InputMutation, + InputMutationOperation, +} from "./services/context_service.js"; +import {NodeTypesString} from "./constants.js"; +import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js"; +import {DynamicContextNodeBase} from "./dynamic_context_base.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; + +const OWNED_PREFIX = "+"; +const REGEX_OWNED_PREFIX = /^\+\s*/; +const REGEX_EMPTY_INPUT = /^\+\s*$/; + +/** + * The Dynamic Context node. + */ +export class DynamicContextNode extends DynamicContextNodeBase { + static override title = NodeTypesString.DYNAMIC_CONTEXT; + static override type = NodeTypesString.DYNAMIC_CONTEXT; + static comfyClass = NodeTypesString.DYNAMIC_CONTEXT; + + constructor(title = DynamicContextNode.title) { + super(title); + } + + override onNodeCreated() { + this.addInput("base_ctx", "RGTHREE_DYNAMIC_CONTEXT"); + this.ensureOneRemainingNewInputSlot(); + super.onNodeCreated(); + } + + override onConnectionsChange( + type: ISlotType, + slotIndex: number, + isConnected: boolean, + link: LLink | null | undefined, + ioSlot: INodeInputSlot | INodeOutputSlot, + ): void { + super.onConnectionsChange?.call(this, type, slotIndex, isConnected, link, ioSlot); + if (this.configuring) { + return; + } + if (type === LiteGraph.INPUT) { + if (isConnected) { + this.handleInputConnected(slotIndex); + } else { + this.handleInputDisconnected(slotIndex); + } + } + } + + override onConnectInput( + inputIndex: number, + outputType: INodeOutputSlot["type"], + outputSlot: INodeOutputSlot, + outputNode: LGraphNode, + outputIndex: number, + ): boolean { + let canConnect = true; + if (super.onConnectInput) { + canConnect = super.onConnectInput.apply(this, [...arguments] as any); + } + if ( + canConnect && + outputNode instanceof DynamicContextNode && + outputIndex === 0 && + inputIndex !== 0 + ) { + const [n, v] = rgthree.logger.warnParts( + "Currently, you can only connect a context node in the first slot.", + ); + console[n]?.call(console, ...v); + canConnect = false; + } + return canConnect; + } + + handleInputConnected(slotIndex: number) { + const ioSlot = this.inputs[slotIndex]; + const connectedIndexes = []; + if (slotIndex === 0) { + let baseNodeInfos = getConnectedInputInfosAndFilterPassThroughs(this, this, 0); + const baseNodes = baseNodeInfos.map((n) => n.node)!; + const baseNodesDynamicCtx = baseNodes[0] as DynamicContextNodeBase; + if (baseNodesDynamicCtx?.provideInputsData) { + const inputsData = CONTEXT_SERVICE.getDynamicContextInputsData(baseNodesDynamicCtx); + console.log("inputsData", inputsData); + for (const input of baseNodesDynamicCtx.provideInputsData()) { + if (input.name === "base_ctx" || input.name === "+") { + continue; + } + this.addContextInput(input.name, input.type, input.index); + this.stabilizeNames(); + } + } + } else if (this.isInputSlotForNewInput(slotIndex)) { + this.handleNewInputConnected(slotIndex); + } + } + + isInputSlotForNewInput(slotIndex: number) { + const ioSlot = this.inputs[slotIndex]; + return ioSlot && ioSlot.name === "+" && ioSlot.type === "*"; + } + + handleNewInputConnected(slotIndex: number) { + if (!this.isInputSlotForNewInput(slotIndex)) { + throw new Error('Expected the incoming slot index to be the "new input" input.'); + } + const ioSlot = this.inputs[slotIndex]!; + let cxn = null; + if (ioSlot.link != null) { + cxn = followConnectionUntilType(this, IoDirection.INPUT, slotIndex, true); + } + if (cxn?.type && cxn?.name) { + let name = this.addOwnedPrefix(this.getNextUniqueNameForThisNode(cxn.name)); + if (name.match(/^\+\s*[A-Z_]+(\.\d+)?$/)) { + name = name.toLowerCase(); + } + ioSlot.name = name; + ioSlot.type = cxn.type as string; + ioSlot.removable = true; + while (!this.outputs[slotIndex]) { + this.addOutput("*", "*"); + } + this.outputs[slotIndex]!.type = cxn.type as string; + this.outputs[slotIndex]!.name = this.stripOwnedPrefix(name).toLocaleUpperCase(); + // This is a dumb override for ComfyUI's widgetinputs issues. + if (cxn.type === "COMBO" || cxn.type.includes(",") || Array.isArray(cxn.type)) { + (this.outputs[slotIndex] as any).widget = true; + } + this.inputsMutated({ + operation: InputMutationOperation.ADDED, + node: this, + slotIndex, + slot: ioSlot, + }); + this.stabilizeNames(); + this.ensureOneRemainingNewInputSlot(); + } + } + + handleInputDisconnected(slotIndex: number) { + const inputs = this.getContextInputsList(); + if (slotIndex === 0) { + for (let index = inputs.length - 1; index > 0; index--) { + if (index === 0 || index === inputs.length - 1) { + continue; + } + const input = inputs[index]!; + if (!this.isOwnedInput(input.name)) { + if (input.link || this.outputs[index]?.links?.length) { + this.renameContextInput(index, input.name, true); + } else { + this.removeContextInput(index); + } + } + } + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + } + } + + ensureOneRemainingNewInputSlot() { + removeUnusedInputsFromEnd(this, 1, REGEX_EMPTY_INPUT); + this.addInput(OWNED_PREFIX, "*"); + } + + getNextUniqueNameForThisNode(desiredName: string) { + const inputs = this.getContextInputsList(); + const allExistingKeys = inputs.map((i) => this.stripOwnedPrefix(i.name).toLocaleUpperCase()); + desiredName = this.stripOwnedPrefix(desiredName); + let newName = desiredName; + let n = 0; + while (allExistingKeys.includes(newName.toLocaleUpperCase())) { + newName = `${desiredName}.${++n}`; + } + return newName; + } + + override removeInput(slotIndex: number) { + const slot = this.inputs[slotIndex]!; + super.removeInput(slotIndex); + if (this.outputs[slotIndex]) { + this.removeOutput(slotIndex); + } + this.inputsMutated({operation: InputMutationOperation.REMOVED, node: this, slotIndex, slot}); + this.stabilizeNames(); + } + + stabilizeNames() { + const inputs = this.getContextInputsList(); + const names: string[] = []; + for (const [index, input] of inputs.entries()) { + if (index === 0 || index === inputs.length - 1) { + continue; + } + input.label = undefined; + this.outputs[index]!.label = undefined; + let origName = this.stripOwnedPrefix(input.name).replace(/\.\d+$/, ""); + let name = input.name; + if (!this.isOwnedInput(name)) { + names.push(name.toLocaleUpperCase()); + } else { + let n = 0; + name = this.addOwnedPrefix(origName); + while (names.includes(this.stripOwnedPrefix(name).toLocaleUpperCase())) { + name = `${this.addOwnedPrefix(origName)}.${++n}`; + } + names.push(this.stripOwnedPrefix(name).toLocaleUpperCase()); + if (input.name !== name) { + this.renameContextInput(index, name); + } + } + } + } + + override getSlotMenuOptions(slot: IFoundSlot): IContextMenuValue[] { + const editable = this.isOwnedInput(slot.input!.name) && this.type !== "*"; + return [ + { + content: "✏️ Rename Input", + disabled: !editable, + callback: () => { + var dialog = app.canvas.createDialog( + "Name", + {}, + ); + var dialogInput = dialog.querySelector("input")!; + if (dialogInput) { + dialogInput.value = this.stripOwnedPrefix(slot.input!.name || ""); + } + var inner = () => { + this.handleContextMenuRenameInputDialog(slot.slot, dialogInput.value); + dialog.close(); + }; + dialog.querySelector("button")!.addEventListener("click", inner); + dialogInput.addEventListener("keydown", (e) => { + dialog.is_modified = true; + if (e.keyCode == 27) { + dialog.close(); + } else if (e.keyCode == 13) { + inner(); + } else if (e.keyCode != 13 && (e.target as HTMLElement)?.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + dialogInput.focus(); + }, + }, + { + content: "πŸ—‘οΈ Delete Input", + disabled: !editable, + callback: () => { + this.removeInput(slot.slot); + }, + }, + ]; + } + + handleContextMenuRenameInputDialog(slotIndex: number, value: string) { + app.graph.beforeChange(); + this.renameContextInput(slotIndex, value); + this.stabilizeNames(); + this.setDirtyCanvas(true, true); + app.graph.afterChange(); + } +} + +const contextDynamicNodes = [DynamicContextNode]; +app.registerExtension({ + name: "rgthree.DynamicContext", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) { + return; + } + if (nodeData.name === DynamicContextNode.type) { + DynamicContextNode.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_base.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_base.ts new file mode 100644 index 00000000..a983ac30 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_base.ts @@ -0,0 +1,241 @@ +import type {INodeInputSlot, LGraphNodeConstructor} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {BaseContextNode} from "./context.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {moveArrayItem, wait} from "rgthree/common/shared_utils.js"; +import {RgthreeInvisibleWidget} from "./utils_widgets.js"; +import { + getContextOutputName, + InputMutation, + InputMutationOperation, +} from "./services/context_service.js"; +import {SERVICE as CONTEXT_SERVICE} from "./services/context_service.js"; + +const OWNED_PREFIX = "+"; +const REGEX_OWNED_PREFIX = /^\+\s*/; +const REGEX_EMPTY_INPUT = /^\+\s*$/; + +export type InputLike = { + name: string; + type: number | string; + label?: string; + link: number | null; + removable?: boolean; + boundingRect: any; +}; + +/** + * The base context node that contains some shared between DynamicContext nodes. Not labels + * `abstract` so we can reference `this` in static methods. + */ +export class DynamicContextNodeBase extends BaseContextNode { + protected readonly hasShadowInputs: boolean = false; + + getContextInputsList(): InputLike[] { + return this.inputs; + } + + provideInputsData() { + const inputs = this.getContextInputsList(); + return inputs + .map((input, index) => ({ + name: this.stripOwnedPrefix(input.name), + type: String(input.type), + index, + })) + .filter((i) => i.type !== "*"); + } + + addOwnedPrefix(name: string) { + return `+ ${this.stripOwnedPrefix(name)}`; + } + + isOwnedInput(inputOrName: string | null | INodeInputSlot) { + const name = typeof inputOrName == "string" ? inputOrName : inputOrName?.name || ""; + return REGEX_OWNED_PREFIX.test(name); + } + + stripOwnedPrefix(name: string) { + return name.replace(REGEX_OWNED_PREFIX, ""); + } + + // handleUpstreamMutation(mutation: InputMutation) { + // throw new Error('handleUpstreamMutation not overridden!') + // } + + handleUpstreamMutation(mutation: InputMutation) { + console.log(`[node ${this.id}] handleUpstreamMutation`, mutation); + if (mutation.operation === InputMutationOperation.ADDED) { + const slot = mutation.slot; + if (!slot) { + throw new Error("Cannot have an ADDED mutation without a provided slot data."); + } + this.addContextInput( + this.stripOwnedPrefix(slot.name), + slot.type as string, + mutation.slotIndex, + ); + return; + } + if (mutation.operation === InputMutationOperation.REMOVED) { + const slot = mutation.slot; + if (!slot) { + throw new Error("Cannot have an REMOVED mutation without a provided slot data."); + } + this.removeContextInput(mutation.slotIndex); + return; + } + if (mutation.operation === InputMutationOperation.RENAMED) { + const slot = mutation.slot; + if (!slot) { + throw new Error("Cannot have an RENAMED mutation without a provided slot data."); + } + this.renameContextInput(mutation.slotIndex, slot.name); + return; + } + } + override clone() { + const cloned = super.clone()! as DynamicContextNodeBase; + while (cloned.inputs.length > 1) { + cloned.removeInput(cloned.inputs.length - 1); + } + while (cloned.widgets.length > 1) { + cloned.removeWidget(cloned.widgets.length - 1); + } + while (cloned.outputs.length > 1) { + cloned.removeOutput(cloned.outputs.length - 1); + } + return cloned; + } + + /** + * Adds the basic output_keys widget. Should be called _after_ specific nodes setup their inputs + * or widgets. + */ + override onNodeCreated() { + const node = this; + this.addCustomWidget( + new RgthreeInvisibleWidget("output_keys", "RGTHREE_DYNAMIC_CONTEXT_OUTPUTS", "", () => { + return (node.outputs || []) + .map((o, i) => i > 0 && o.name) + .filter((n) => n !== false) + .join(","); + }), + ); + } + + addContextInput(name: string, type: string, slot = -1) { + const inputs = this.getContextInputsList(); + if (this.hasShadowInputs) { + inputs.push({name, type, link: null, boundingRect: null}); + } else { + this.addInput(name, type); + } + if (slot > -1) { + moveArrayItem(inputs, inputs.length - 1, slot); + } else { + slot = inputs.length - 1; + } + if (type !== "*") { + const output = this.addOutput(getContextOutputName(name), type); + if (type === "COMBO" || String(type).includes(",") || Array.isArray(type)) { + (output as any).widget = true; + } + if (slot > -1) { + moveArrayItem(this.outputs, this.outputs.length - 1, slot); + } + } + this.fixInputsOutputsLinkSlots(); + this.inputsMutated({ + operation: InputMutationOperation.ADDED, + node: this, + slotIndex: slot, + slot: inputs[slot]!, + }); + } + + removeContextInput(slotIndex: number) { + if (this.hasShadowInputs) { + const inputs = this.getContextInputsList(); + const input = inputs.splice(slotIndex, 1)[0]; + if (this.outputs[slotIndex]) { + this.removeOutput(slotIndex); + } + } else { + this.removeInput(slotIndex); + } + } + + renameContextInput(index: number, newName: string, forceOwnBool: boolean | null = null) { + const inputs = this.getContextInputsList(); + const input = inputs[index]!; + const oldName = input.name; + newName = this.stripOwnedPrefix(newName.trim() || this.getSlotDefaultInputLabel(index)); + if (forceOwnBool === true || (this.isOwnedInput(oldName) && forceOwnBool !== false)) { + newName = this.addOwnedPrefix(newName); + } + if (oldName !== newName) { + input.name = newName; + input.removable = this.isOwnedInput(newName); + this.outputs[index]!.name = getContextOutputName(inputs[index]!.name); + this.inputsMutated({ + node: this, + operation: InputMutationOperation.RENAMED, + slotIndex: index, + slot: input, + }); + } + } + + getSlotDefaultInputLabel(slotIndex: number) { + const inputs = this.getContextInputsList(); + const input = inputs[slotIndex]!; + let defaultLabel = this.stripOwnedPrefix(input.name).toLowerCase(); + return defaultLabel.toLocaleLowerCase(); + } + + inputsMutated(mutation: InputMutation) { + CONTEXT_SERVICE.onInputChanges(this, mutation); + } + + fixInputsOutputsLinkSlots() { + if (!this.hasShadowInputs) { + const inputs = this.getContextInputsList(); + for (let index = inputs.length - 1; index > 0; index--) { + const input = inputs[index]!; + if ((input === null || input === void 0 ? void 0 : input.link) != null) { + app.graph.links[input.link!]!.target_slot = index; + } + } + } + const outputs = this.outputs; + for (let index = outputs.length - 1; index > 0; index--) { + const output = outputs[index]; + if (output) { + output.nameLocked = true; + for (const link of output.links || []) { + app.graph.links[link!]!.origin_slot = index; + } + } + } + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, this); + // [πŸ€] ComfyUI only adds "required" inputs to the outputs list when dragging an output to + // empty space, but since RGTHREE_CONTEXT is optional, it doesn't get added to the menu because + // ...of course. So, we'll manually add it. Of course, we also have to do this in a timeout + // because ComfyUI clears out `LiteGraph.slot_types_default_out` in its own 'Comfy.SlotDefaults' + // extension and we need to wait for that to happen. + wait(500).then(() => { + LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] = + LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] || []; + const comfyClassStr = (comfyClass as LGraphNodeConstructor).comfyClass; + if (comfyClassStr) { + LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"].push(comfyClassStr); + } + }); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_switch.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_switch.ts new file mode 100644 index 00000000..b2f090ba --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_switch.ts @@ -0,0 +1,214 @@ +import type { + LGraphNode, + LLink, + LGraphCanvas, + INodeInputSlot, + INodeOutputSlot, + ISlotType, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {DynamicContextNodeBase, InputLike} from "./dynamic_context_base.js"; +import {NodeTypesString} from "./constants.js"; +import { + InputMutation, + SERVICE as CONTEXT_SERVICE, + getContextOutputName, +} from "./services/context_service.js"; +import {getConnectedInputNodesAndFilterPassThroughs} from "./utils.js"; +import {debounce, moveArrayItem} from "rgthree/common/shared_utils.js"; +import {measureText} from "./utils_canvas.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; + +type ShadowInputData = { + node: LGraphNode; + slot: number; + shadowIndex: number; + shadowIndexIfShownSingularly: number; + shadowIndexFull: number; + nodeIndex: number; + type: string | -1; + name: string; + key: string; + // isDuplicatedBefore: boolean, + duplicatesBefore: number[]; + duplicatesAfter: number[]; +}; + +/** + * The Context Switch node. + */ +class DynamicContextSwitchNode extends DynamicContextNodeBase { + static override title = NodeTypesString.DYNAMIC_CONTEXT_SWITCH; + static override type = NodeTypesString.DYNAMIC_CONTEXT_SWITCH; + static comfyClass = NodeTypesString.DYNAMIC_CONTEXT_SWITCH; + + protected override readonly hasShadowInputs = true; + + // override hasShadowInputs = true; + + /** + * We should be able to assume that `lastInputsList` is the input list after the last, major + * synchronous change. Which should mean, if we're handling a change that is currently live, but + * not represented in our node (like, an upstream node has already removed an input), then we + * should be able to compar the current InputList to this `lastInputsList`. + */ + lastInputsList: ShadowInputData[] = []; + + private shadowInputs: (InputLike & {count: number})[] = [ + {name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null}, + ]; + + constructor(title = DynamicContextSwitchNode.title) { + super(title); + } + + override getContextInputsList() { + return this.shadowInputs; + } + override handleUpstreamMutation(mutation: InputMutation) { + this.scheduleHardRefresh(); + } + + override onConnectionsChange( + type: ISlotType, + slotIndex: number, + isConnected: boolean, + link: LLink | null | undefined, + inputOrOutput: INodeInputSlot | INodeOutputSlot, + ): void { + super.onConnectionsChange?.call(this, type, slotIndex, isConnected, link, inputOrOutput); + if (this.configuring) { + return; + } + if (type === LiteGraph.INPUT) { + this.scheduleHardRefresh(); + } + } + + scheduleHardRefresh(ms = 64) { + return debounce(() => { + this.refreshInputsAndOutputs(); + }, ms); + } + + override onNodeCreated() { + this.addInput("ctx_1", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_2", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_3", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_4", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_5", "RGTHREE_DYNAMIC_CONTEXT"); + super.onNodeCreated(); + } + + override addContextInput(name: string, type: string, slot?: number): void {} + + /** + * This is a "hard" refresh of the list, but looping over the actual context inputs, and + * recompiling the shadowInputs and outputs. + */ + private refreshInputsAndOutputs() { + const inputs: (InputLike & {count: number})[] = [ + {name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null}, + ]; + let numConnected = 0; + for (let i = 0; i < this.inputs.length; i++) { + const childCtxs = getConnectedInputNodesAndFilterPassThroughs( + this, + this, + i, + ) as DynamicContextNodeBase[]; + if (childCtxs.length > 1) { + throw new Error("How is there more than one input?"); + } + const ctx = childCtxs[0]; + if (!ctx) continue; + numConnected++; + const slotsData = CONTEXT_SERVICE.getDynamicContextInputsData(ctx); + console.log(slotsData); + for (const slotData of slotsData) { + const found = inputs.find( + (n) => getContextOutputName(slotData.name) === getContextOutputName(n.name), + ); + if (found) { + found.count += 1; + continue; + } + inputs.push({ + name: slotData.name, + type: slotData.type, + link: null, + count: 1, + boundingRect: null, + }); + } + } + this.shadowInputs = inputs; + // First output is always CONTEXT, so "p" is the offset. + let i = 0; + for (i; i < this.shadowInputs.length; i++) { + const data = this.shadowInputs[i]!; + let existing = this.outputs.find( + (o) => getContextOutputName(o.name) === getContextOutputName(data.name), + ); + if (!existing) { + existing = this.addOutput(getContextOutputName(data.name), data.type); + } + moveArrayItem(this.outputs, existing, i); + delete existing.rgthree_status; + if (data.count !== numConnected) { + existing.rgthree_status = "WARN"; + } + } + while (this.outputs[i]) { + const output = this.outputs[i]; + if (output?.links?.length) { + output.rgthree_status = "ERROR"; + i++; + } else { + this.removeOutput(i); + } + } + this.fixInputsOutputsLinkSlots(); + } + + override onDrawForeground(ctx: CanvasRenderingContext2D, canvas: LGraphCanvas): void { + const low_quality = (canvas?.ds?.scale ?? 1) < 0.6; + if (low_quality || this.size[0] <= 10) { + return; + } + let y = LiteGraph.NODE_SLOT_HEIGHT - 1; + const w = this.size[0]; + ctx.save(); + ctx.font = "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial"; + ctx.textAlign = "right"; + + for (const output of this.outputs) { + if (!output.rgthree_status) { + y += LiteGraph.NODE_SLOT_HEIGHT; + continue; + } + const x = w - 20 - measureText(ctx, output.name); + if (output.rgthree_status === "ERROR") { + ctx.fillText("πŸ›‘", x, y); + } else if (output.rgthree_status === "WARN") { + ctx.fillText("⚠️", x, y); + } + y += LiteGraph.NODE_SLOT_HEIGHT; + } + ctx.restore(); + } +} + +app.registerExtension({ + name: "rgthree.DynamicContextSwitch", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) { + return; + } + if (nodeData.name === DynamicContextSwitchNode.type) { + DynamicContextSwitchNode.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/fast_actions_button.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_actions_button.ts new file mode 100644 index 00000000..890c05ac --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_actions_button.ts @@ -0,0 +1,359 @@ +import type { + LGraph, + LGraphNode, + ISerialisedNode, + IButtonWidget, + IComboWidget, + IWidget, + IBaseWidget, +} from "@comfyorg/frontend"; +import type {ComfyApp} from "@comfyorg/frontend"; +import type {RgthreeBaseVirtualNode} from "./base_node.js"; + +import {app} from "scripts/app.js"; +import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js"; +import {NodeTypesString} from "./constants.js"; +import {addMenuItem, changeModeOfNodes} from "./utils.js"; +import {rgthree} from "./rgthree.js"; + +const MODE_ALWAYS = 0; +const MODE_MUTE = 2; +const MODE_BYPASS = 4; + +/** + * The Fast Actions Button. + * + * This adds a button that the user can connect any node to and then choose an action to take on + * that node when the button is pressed. Default actions are "Mute," "Bypass," and "Enable," but + * Nodes can expose actions additional actions that can then be called back. + */ +class FastActionsButton extends BaseAnyInputConnectedNode { + static override type = NodeTypesString.FAST_ACTIONS_BUTTON; + static override title = NodeTypesString.FAST_ACTIONS_BUTTON; + override comfyClass = NodeTypesString.FAST_ACTIONS_BUTTON; + + readonly logger = rgthree.newLogSession("[FastActionsButton]"); + + static "@buttonText" = {type: "string"}; + static "@shortcutModifier" = { + type: "combo", + values: ["ctrl", "alt", "shift"], + }; + static "@shortcutKey" = {type: "string"}; + + static collapsible = false; + + override readonly isVirtualNode = true; + + override serialize_widgets = true; + + readonly buttonWidget: IButtonWidget; + + readonly widgetToData = new Map(); + readonly nodeIdtoFunctionCache = new Map(); + + readonly keypressBound; + readonly keyupBound; + + private executingFromShortcut = false; + + override properties!: BaseAnyInputConnectedNode["properties"] & { + buttonText: string; + shortcutModifier: string; + shortcutKey: string; + }; + + constructor(title?: string) { + super(title); + this.properties["buttonText"] = "🎬 Action!"; + this.properties["shortcutModifier"] = "alt"; + this.properties["shortcutKey"] = ""; + this.buttonWidget = this.addWidget( + "button", + this.properties["buttonText"], + "", + () => { + this.executeConnectedNodes(); + }, + {serialize: false}, + ) as IButtonWidget; + + this.keypressBound = this.onKeypress.bind(this); + this.keyupBound = this.onKeyup.bind(this); + this.onConstructed(); + } + + /** When we're given data to configure, like from a PNG or JSON. */ + override configure(info: ISerialisedNode): void { + super.configure(info); + // Since we add the widgets dynamically, we need to wait to set their values + // with a short timeout. + setTimeout(() => { + if (info.widgets_values) { + for (let [index, value] of info.widgets_values.entries()) { + if (index > 0) { + if (typeof value === "string" && value.startsWith("comfy_action:")) { + value = value.replace("comfy_action:", ""); + this.addComfyActionWidget(index, value); + } + if (this.widgets[index]) { + this.widgets[index]!.value = value; + } + } + } + } + }, 100); + } + + override clone() { + const cloned = super.clone()!; + cloned.properties["buttonText"] = "🎬 Action!"; + cloned.properties["shortcutKey"] = ""; + return cloned; + } + + override onAdded(graph: LGraph): void { + window.addEventListener("keydown", this.keypressBound); + window.addEventListener("keyup", this.keyupBound); + } + + override onRemoved(): void { + window.removeEventListener("keydown", this.keypressBound); + window.removeEventListener("keyup", this.keyupBound); + } + + async onKeypress(event: KeyboardEvent) { + const target = (event.target as HTMLElement)!; + if ( + this.executingFromShortcut || + target.localName == "input" || + target.localName == "textarea" + ) { + return; + } + if ( + this.properties["shortcutKey"].trim() && + this.properties["shortcutKey"].toLowerCase() === event.key.toLowerCase() + ) { + const shortcutModifier = this.properties["shortcutModifier"]; + let good = shortcutModifier === "ctrl" && event.ctrlKey; + good = good || (shortcutModifier === "alt" && event.altKey); + good = good || (shortcutModifier === "shift" && event.shiftKey); + good = good || (shortcutModifier === "meta" && event.metaKey); + if (good) { + setTimeout(() => { + this.executeConnectedNodes(); + }, 20); + this.executingFromShortcut = true; + event.preventDefault(); + event.stopImmediatePropagation(); + app.canvas.dirty_canvas = true; + return false; + } + } + return; + } + + onKeyup(event: KeyboardEvent) { + const target = (event.target as HTMLElement)!; + if (target.localName == "input" || target.localName == "textarea") { + return; + } + this.executingFromShortcut = false; + } + + override onPropertyChanged(property: string, value: unknown, prevValue?: unknown) { + if (property == "buttonText" && typeof value === "string") { + this.buttonWidget.name = value; + } + if (property == "shortcutKey" && typeof value === "string") { + this.properties["shortcutKey"] = value.trim()[0]?.toLowerCase() ?? ""; + } + return true; + } + + override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) { + let changed = false; + // Remove any widgets and data for widgets that are no longer linked. + for (const [widget, data] of this.widgetToData.entries()) { + if (!data.node) { + continue; + } + if (!linkedNodes.includes(data.node)) { + const index = this.widgets.indexOf(widget); + if (index > -1) { + this.widgetToData.delete(widget); + this.removeWidget(widget); + changed = true; + } else { + const [m, a] = this.logger.debugParts("Connected widget is not in widgets... weird."); + console[m]?.(...a); + } + } + } + + const badNodes: LGraphNode[] = []; // Nodes that are deleted elsewhere may not exist in linkedNodes. + let indexOffset = 1; // Start with button, increment when we hit a non-node widget (like comfy) + for (const [index, node] of linkedNodes.entries()) { + // Sometimes linkedNodes is stale. + if (!node) { + const [m, a] = this.logger.debugParts("linkedNode provided that does not exist. "); + console[m]?.(...a); + badNodes.push(node); + continue; + } + let widgetAtSlot = this.widgets[index + indexOffset]; + if (widgetAtSlot && this.widgetToData.get(widgetAtSlot)?.comfy) { + indexOffset++; + widgetAtSlot = this.widgets[index + indexOffset]; + } + + if (!widgetAtSlot || this.widgetToData.get(widgetAtSlot)?.node?.id !== node.id) { + // Find the next widget that matches the node. + let widget: IWidget | null = null; + for (let i = index + indexOffset; i < this.widgets.length; i++) { + if (this.widgetToData.get(this.widgets[i]!)?.node?.id === node.id) { + widget = this.widgets.splice(i, 1)[0]!; + this.widgets.splice(index + indexOffset, 0, widget); + changed = true; + break; + } + } + if (!widget) { + // Add a widget at this spot. + const exposedActions: string[] = (node.constructor as any).exposedActions || []; + widget = this.addWidget("combo", node.title, "None", "", { + values: ["None", "Mute", "Bypass", "Enable", ...exposedActions], + }) as IWidget; + widget.serializeValue = async (_node: LGraphNode, _index: number) => { + return widget?.value; + }; + this.widgetToData.set(widget, {node}); + changed = true; + } + } + } + + // Go backwards through widgets, and remove any that are not in out widgetToData + for (let i = this.widgets.length - 1; i > linkedNodes.length + indexOffset - 1; i--) { + const widgetAtSlot = this.widgets[i]; + if (widgetAtSlot && this.widgetToData.get(widgetAtSlot)?.comfy) { + continue; + } + this.removeWidget(widgetAtSlot); + changed = true; + } + return changed; + } + + override removeWidget(widget: IBaseWidget | IWidget | number | undefined): void { + widget = typeof widget === "number" ? this.widgets[widget] : widget; + if (widget && this.widgetToData.has(widget as IWidget)) { + this.widgetToData.delete(widget as IWidget); + } + super.removeWidget(widget); + } + + /** + * Runs through the widgets, and executes the actions. + */ + async executeConnectedNodes() { + for (const widget of this.widgets) { + if (widget == this.buttonWidget) { + continue; + } + const action = widget.value; + const {comfy, node} = this.widgetToData.get(widget) ?? {}; + if (comfy) { + if (action === "Queue Prompt") { + await comfy.queuePrompt(0); + } + continue; + } + if (node) { + if (action === "Mute") { + changeModeOfNodes(node, MODE_MUTE); + } else if (action === "Bypass") { + changeModeOfNodes(node, MODE_BYPASS); + } else if (action === "Enable") { + changeModeOfNodes(node, MODE_ALWAYS); + } + // If there's a handleAction, always call it. + if ((node as RgthreeBaseVirtualNode).handleAction) { + if (typeof action !== "string") { + throw new Error("Fast Actions Button action should be a string: " + action); + } + await (node as RgthreeBaseVirtualNode).handleAction(action); + } + this.graph?.change(); + continue; + } + console.warn("Fast Actions Button has a widget without correct data."); + } + } + + /** + * Adds a ComfyActionWidget at the provided slot (or end). + */ + addComfyActionWidget(slot?: number, value?: string) { + let widget = this.addWidget( + "combo", + "Comfy Action", + "None", + () => { + if (String(widget.value).startsWith("MOVE ")) { + this.widgets.push(this.widgets.splice(this.widgets.indexOf(widget), 1)[0]!); + widget.value = String(widget.rgthree_lastValue); + } else if (String(widget.value).startsWith("REMOVE ")) { + this.removeWidget(widget); + } + widget.rgthree_lastValue = widget.value; + }, + { + values: ["None", "Queue Prompt", "REMOVE Comfy Action", "MOVE to end"], + }, + ) as IComboWidget; + widget.rgthree_lastValue = value; + + widget.serializeValue = async (_node: LGraphNode, _index: number) => { + return `comfy_app:${widget?.value}`; + }; + this.widgetToData.set(widget, {comfy: app}); + + if (slot != null) { + this.widgets.splice(slot, 0, this.widgets.splice(this.widgets.indexOf(widget), 1)[0]!); + } + return widget; + } + + override onSerialize(serialised: ISerialisedNode) { + super.onSerialize?.(serialised); + for (let [index, value] of (serialised.widgets_values || []).entries()) { + if (this.widgets[index]?.name === "Comfy Action") { + serialised.widgets_values![index] = `comfy_action:${value}`; + } + } + } + + static override setUp() { + super.setUp(); + addMenuItem(this, app, { + name: "βž• Append a Comfy Action", + callback: (nodeArg: LGraphNode) => { + (nodeArg as FastActionsButton).addComfyActionWidget(); + }, + }); + } +} + +app.registerExtension({ + name: "rgthree.FastActionsButton", + registerCustomNodes() { + FastActionsButton.setUp(); + }, + loadedGraphNode(node: LGraphNode) { + if (node.type == FastActionsButton.title) { + (node as FastActionsButton)._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_bypasser.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_bypasser.ts new file mode 100644 index 00000000..4e18b07f --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_bypasser.ts @@ -0,0 +1,38 @@ +import type {Size} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {NodeTypesString} from "./constants.js"; +import {BaseFastGroupsModeChanger} from "./fast_groups_muter.js"; + +/** + * Fast Bypasser implementation that looks for groups in the workflow and adds toggles to mute them. + */ +export class FastGroupsBypasser extends BaseFastGroupsModeChanger { + static override type = NodeTypesString.FAST_GROUPS_BYPASSER; + static override title = NodeTypesString.FAST_GROUPS_BYPASSER; + override comfyClass = NodeTypesString.FAST_GROUPS_BYPASSER; + + static override exposedActions = ["Bypass all", "Enable all", "Toggle all"]; + + protected override helpActions = "bypass and enable"; + + override readonly modeOn = LiteGraph.ALWAYS; + override readonly modeOff = 4; // Used by Comfy for "bypass" + + constructor(title = FastGroupsBypasser.title) { + super(title); + this.onConstructed(); + } +} + +app.registerExtension({ + name: "rgthree.FastGroupsBypasser", + registerCustomNodes() { + FastGroupsBypasser.setUp(); + }, + loadedGraphNode(node: FastGroupsBypasser) { + if (node.type == FastGroupsBypasser.title) { + node.tempSize = [...node.size] as Size; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_muter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_muter.ts new file mode 100644 index 00000000..4eee7f8c --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_muter.ts @@ -0,0 +1,542 @@ +import type { + LGraphNode, + LGraph as TLGraph, + LGraphCanvas as TLGraphCanvas, + Vector2, + Size, + LGraphGroup, + CanvasMouseEvent, + Point, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseVirtualNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {SERVICE as FAST_GROUPS_SERVICE} from "./services/fast_groups_service.js"; +import {drawNodeWidget, fitString} from "./utils_canvas.js"; +import {RgthreeBaseWidget} from "./utils_widgets.js"; +import { changeModeOfNodes, getGroupNodes } from "./utils.js"; + +const PROPERTY_SORT = "sort"; +const PROPERTY_SORT_CUSTOM_ALPHA = "customSortAlphabet"; +const PROPERTY_MATCH_COLORS = "matchColors"; +const PROPERTY_MATCH_TITLE = "matchTitle"; +const PROPERTY_SHOW_NAV = "showNav"; +const PROPERTY_SHOW_ALL_GRAPHS = "showAllGraphs"; +const PROPERTY_RESTRICTION = "toggleRestriction"; + +/** + * Fast Muter implementation that looks for groups in the workflow and adds toggles to mute them. + */ +export abstract class BaseFastGroupsModeChanger extends RgthreeBaseVirtualNode { + static override type = NodeTypesString.FAST_GROUPS_MUTER; + static override title = NodeTypesString.FAST_GROUPS_MUTER; + + static override exposedActions = ["Mute all", "Enable all", "Toggle all"]; + + readonly modeOn: number = LiteGraph.ALWAYS; + readonly modeOff: number = LiteGraph.NEVER; + + private debouncerTempWidth: number = 0; + tempSize: Vector2 | null = null; + + // We don't need to serizalize since we'll just be checking group data on startup anyway + override serialize_widgets = false; + + protected helpActions = "mute and unmute"; + + static "@matchColors" = {type: "string"}; + static "@matchTitle" = {type: "string"}; + static "@showNav" = {type: "boolean"}; + static "@showAllGraphs" = {type: "boolean"}; + static "@sort" = { + type: "combo", + values: ["position", "alphanumeric", "custom alphabet"], + }; + static "@customSortAlphabet" = {type: "string"}; + + override properties!: RgthreeBaseVirtualNode["properties"] & { + [PROPERTY_MATCH_COLORS]: string; + [PROPERTY_MATCH_TITLE]: string; + [PROPERTY_SHOW_NAV]: boolean; + [PROPERTY_SHOW_ALL_GRAPHS]: boolean; + [PROPERTY_SORT]: string; + [PROPERTY_SORT_CUSTOM_ALPHA]: string; + [PROPERTY_RESTRICTION]: string; + }; + + static "@toggleRestriction" = { + type: "combo", + values: ["default", "max one", "always one"], + }; + + constructor(title = FastGroupsMuter.title) { + super(title); + this.properties[PROPERTY_MATCH_COLORS] = ""; + this.properties[PROPERTY_MATCH_TITLE] = ""; + this.properties[PROPERTY_SHOW_NAV] = true; + this.properties[PROPERTY_SHOW_ALL_GRAPHS] = true; + this.properties[PROPERTY_SORT] = "position"; + this.properties[PROPERTY_SORT_CUSTOM_ALPHA] = ""; + this.properties[PROPERTY_RESTRICTION] = "default"; + } + + override onConstructed(): boolean { + this.addOutput("OPT_CONNECTION", "*"); + return super.onConstructed(); + } + + override onAdded(graph: TLGraph): void { + FAST_GROUPS_SERVICE.addFastGroupNode(this); + } + + override onRemoved(): void { + FAST_GROUPS_SERVICE.removeFastGroupNode(this); + } + + refreshWidgets() { + const canvas = app.canvas as TLGraphCanvas; + let sort = this.properties?.[PROPERTY_SORT] || "position"; + let customAlphabet: string[] | null = null; + if (sort === "custom alphabet") { + const customAlphaStr = this.properties?.[PROPERTY_SORT_CUSTOM_ALPHA]?.replace(/\n/g, ""); + if (customAlphaStr && customAlphaStr.trim()) { + customAlphabet = customAlphaStr.includes(",") + ? customAlphaStr.toLocaleLowerCase().split(",") + : customAlphaStr.toLocaleLowerCase().trim().split(""); + } + if (!customAlphabet?.length) { + sort = "alphanumeric"; + customAlphabet = null; + } + } + + const groups = [...FAST_GROUPS_SERVICE.getGroups(sort)]; + // The service will return pre-sorted groups for alphanumeric and position. If this node has a + // custom sort, then we need to sort it manually. + if (customAlphabet?.length) { + groups.sort((a, b) => { + let aIndex = -1; + let bIndex = -1; + // Loop and find indexes. As we're finding multiple, a single for loop is more efficient. + for (const [index, alpha] of customAlphabet!.entries()) { + aIndex = + aIndex < 0 ? (a.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : aIndex; + bIndex = + bIndex < 0 ? (b.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : bIndex; + if (aIndex > -1 && bIndex > -1) { + break; + } + } + // Now compare. + if (aIndex > -1 && bIndex > -1) { + const ret = aIndex - bIndex; + if (ret === 0) { + return a.title.localeCompare(b.title); + } + return ret; + } else if (aIndex > -1) { + return -1; + } else if (bIndex > -1) { + return 1; + } + return a.title.localeCompare(b.title); + }); + } + + // See if we're filtering by colors, and match against the built-in keywords and actuial hex + // values. + let filterColors = ( + (this.properties?.[PROPERTY_MATCH_COLORS] as string)?.split(",") || [] + ).filter((c) => c.trim()); + if (filterColors.length) { + filterColors = filterColors.map((color) => { + color = color.trim().toLocaleLowerCase(); + if (LGraphCanvas.node_colors[color]) { + color = LGraphCanvas.node_colors[color]!.groupcolor; + } + color = color.replace("#", "").toLocaleLowerCase(); + if (color.length === 3) { + color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3"); + } + return `#${color}`; + }); + } + + // Go over the groups + let index = 0; + for (const group of groups) { + if (filterColors.length) { + let groupColor = group.color?.replace("#", "").trim().toLocaleLowerCase(); + if (!groupColor) { + continue; + } + if (groupColor.length === 3) { + groupColor = groupColor.replace(/(.)(.)(.)/, "$1$1$2$2$3$3"); + } + groupColor = `#${groupColor}`; + if (!filterColors.includes(groupColor)) { + continue; + } + } + if (this.properties?.[PROPERTY_MATCH_TITLE]?.trim()) { + try { + if (!new RegExp(this.properties[PROPERTY_MATCH_TITLE], "i").exec(group.title)) { + continue; + } + } catch (e) { + console.error(e); + continue; + } + } + const showAllGraphs = this.properties?.[PROPERTY_SHOW_ALL_GRAPHS]; + if (!showAllGraphs && group.graph !== app.canvas.getCurrentGraph()) { + continue; + } + let isDirty = false; + const widgetLabel = `Enable ${group.title}`; + let widget = this.widgets.find((w) => w.label === widgetLabel) as FastGroupsToggleRowWidget; + if (!widget) { + // When we add a widget, litegraph is going to mess up the size, so we + // store it so we can retrieve it in computeSize. Hacky.. + this.tempSize = [...this.size] as Size; + widget = this.addCustomWidget( + new FastGroupsToggleRowWidget(group, this), + ) as FastGroupsToggleRowWidget; + this.setSize(this.computeSize()); + isDirty = true; + } + if (widget.label != widgetLabel) { + widget.label = widgetLabel; + isDirty = true; + } + if ( + group.rgthree_hasAnyActiveNode != null && + widget.toggled != group.rgthree_hasAnyActiveNode + ) { + widget.toggled = group.rgthree_hasAnyActiveNode; + isDirty = true; + } + if (this.widgets[index] !== widget) { + const oldIndex = this.widgets.findIndex((w) => w === widget); + this.widgets.splice(index, 0, this.widgets.splice(oldIndex, 1)[0]!); + isDirty = true; + } + if (isDirty) { + this.setDirtyCanvas(true, false); + } + index++; + } + + // Everything should now be in order, so let's remove all remaining widgets. + while ((this.widgets || [])[index]) { + this.removeWidget(index++); + } + } + + override computeSize(out?: Vector2) { + let size = super.computeSize(out); + if (this.tempSize) { + size[0] = Math.max(this.tempSize[0], size[0]); + size[1] = Math.max(this.tempSize[1], size[1]); + // We sometimes get repeated calls to compute size, so debounce before clearing. + this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth); + this.debouncerTempWidth = setTimeout(() => { + this.tempSize = null; + }, 32); + } + setTimeout(() => { + this.graph?.setDirtyCanvas(true, true); + }, 16); + return size; + } + + override async handleAction(action: string) { + if (action === "Mute all" || action === "Bypass all") { + const alwaysOne = this.properties?.[PROPERTY_RESTRICTION] === "always one"; + for (const [index, widget] of this.widgets.entries()) { + (widget as any)?.doModeChange(alwaysOne && !index ? true : false, true); + } + } else if (action === "Enable all") { + const onlyOne = this.properties?.[PROPERTY_RESTRICTION].includes(" one"); + for (const [index, widget] of this.widgets.entries()) { + (widget as any)?.doModeChange(onlyOne && index > 0 ? false : true, true); + } + } else if (action === "Toggle all") { + const onlyOne = this.properties?.[PROPERTY_RESTRICTION].includes(" one"); + let foundOne = false; + for (const [index, widget] of this.widgets.entries()) { + // If you have only one, then we'll stop at the first. + let newValue: boolean = onlyOne && foundOne ? false : !widget.value; + foundOne = foundOne || newValue; + (widget as any)?.doModeChange(newValue, true); + } + // And if you have always one, then we'll flip the last + if (!foundOne && this.properties?.[PROPERTY_RESTRICTION] === "always one") { + (this.widgets[this.widgets.length - 1] as any)?.doModeChange(true, true); + } + } + } + + override getHelp() { + return ` +

    The ${this.type!.replace( + "(rgthree)", + "", + )} is an input-less node that automatically collects all groups in your current + workflow and allows you to quickly ${this.helpActions} all nodes within the group.

    +
      +
    • +

      + Properties. You can change the following properties (by right-clicking + on the node, and select "Properties" or "Properties Panel" from the menu): +

      +
        +

      • + ${PROPERTY_MATCH_COLORS} - Only add groups that match the provided + colors. Can be ComfyUI colors (red, pale_blue) or hex codes (#a4d399). Multiple can be + added, comma delimited. +

        • +

      • + ${PROPERTY_MATCH_TITLE} - Filter the list of toggles by title match + (string match, or regular expression). +

        • +

      • + ${PROPERTY_SHOW_NAV} - Add / remove a quick navigation arrow to take you + to the group. (default: true) +

        • +

      • + ${PROPERTY_SHOW_ALL_GRAPHS} - Show groups from all [sub]graphs in the + workflow. (default: true) +

        • +

      • + ${PROPERTY_SORT} - Sort the toggles' order by "alphanumeric", graph + "position", or "custom alphabet". (default: "position") +

        • +
      • +

        + ${PROPERTY_SORT_CUSTOM_ALPHA} - When the + ${PROPERTY_SORT} property is "custom alphabet" you can define the + alphabet to use here, which will match the beginning of each group name and + sort against it. If group titles do not match any custom alphabet entry, then they + will be put after groups that do, ordered alphanumerically. +

        +

        + This can be a list of single characters, like "zyxw..." or comma delimited strings + for more control, like "sdxl,pro,sd,n,p". +

        +

        + Note, when two group title match the same custom alphabet entry, the normal + alphanumeric alphabet breaks the tie. For instance, a custom alphabet of + "e,s,d" will order groups names like "SDXL, SEGS, Detailer" eventhough the custom + alphabet has an "e" before "d" (where one may expect "SE" to be before "SD"). +

        +

        + To have "SEGS" appear before "SDXL" you can use longer strings. For instance, the + custom alphabet value of "se,s,f" would work here. +

        +
        • +

      • + ${PROPERTY_RESTRICTION} - Optionally, attempt to restrict the number of + widgets that can be enabled to a maximum of one, or always one. +

        +

        Note: If using "max one" or "always one" then this is only + enforced when clicking a toggle on this node; if nodes within groups are changed + outside of the initial toggle click, then these restriction will not be enforced, and + could result in a state where more than one toggle is enabled. This could also happen + if nodes are overlapped with multiple groups. +

        • + +
      +
      • +
    `; + } +} + +/** + * Fast Bypasser implementation that looks for groups in the workflow and adds toggles to mute them. + */ +export class FastGroupsMuter extends BaseFastGroupsModeChanger { + static override type = NodeTypesString.FAST_GROUPS_MUTER; + static override title = NodeTypesString.FAST_GROUPS_MUTER; + override comfyClass = NodeTypesString.FAST_GROUPS_MUTER; + + static override exposedActions = ["Bypass all", "Enable all", "Toggle all"]; + + protected override helpActions = "mute and unmute"; + + override readonly modeOn: number = LiteGraph.ALWAYS; + override readonly modeOff: number = LiteGraph.NEVER; + + constructor(title = FastGroupsMuter.title) { + super(title); + this.onConstructed(); + } +} + +/** + * The PowerLoraLoaderHeaderWidget that renders a toggle all switch, as well as some title info + * (more necessary for the double model & clip strengths to label them). + */ +class FastGroupsToggleRowWidget extends RgthreeBaseWidget<{toggled: boolean}> { + override value = {toggled: false}; + override options = {on: "yes", off: "no"}; + override readonly type = "custom"; + + label: string = ""; + group: LGraphGroup; + node: BaseFastGroupsModeChanger; + + constructor(group: LGraphGroup, node: BaseFastGroupsModeChanger) { + super("RGTHREE_TOGGLE_AND_NAV"); + this.group = group; + this.node = node; + } + + doModeChange(force?: boolean, skipOtherNodeCheck?: boolean) { + this.group.recomputeInsideNodes(); + const hasAnyActiveNodes = getGroupNodes(this.group).some((n) => n.mode === LiteGraph.ALWAYS); + let newValue = force != null ? force : !hasAnyActiveNodes; + if (skipOtherNodeCheck !== true) { + // TODO: This work should probably live in BaseFastGroupsModeChanger instead of the widgets. + if (newValue && this.node.properties?.[PROPERTY_RESTRICTION]?.includes(" one")) { + for (const widget of this.node.widgets) { + if (widget instanceof FastGroupsToggleRowWidget) { + widget.doModeChange(false, true); + } + } + } else if (!newValue && this.node.properties?.[PROPERTY_RESTRICTION] === "always one") { + newValue = this.node.widgets.every((w) => !w.value || w === this); + } + } + changeModeOfNodes(getGroupNodes(this.group), (newValue ? this.node.modeOn : this.node.modeOff)); + this.group.rgthree_hasAnyActiveNode = newValue; + this.toggled = newValue; + this.group.graph?.setDirtyCanvas(true, false); + } + + get toggled() { + return this.value.toggled; + } + set toggled(value: boolean) { + this.value.toggled = value; + } + + toggle(value?: boolean) { + value = value == null ? !this.toggled : value; + if (value !== this.toggled) { + this.value.toggled = value; + this.doModeChange(); + } + } + + draw( + ctx: CanvasRenderingContext2D, + node: FastGroupsMuter, + width: number, + posY: number, + height: number, + ) { + const widgetData = drawNodeWidget(ctx, {size: [width, height], pos: [15, posY]}); + + const showNav = node.properties?.[PROPERTY_SHOW_NAV] !== false; + + // Render from right to left, since the text on left will take available space. + // `currentX` markes the current x position moving backwards. + let currentX = widgetData.width - widgetData.margin; + + // The nav arrow + if (!widgetData.lowQuality && showNav) { + currentX -= 7; // Arrow space margin + const midY = widgetData.posY + widgetData.height * 0.5; + ctx.fillStyle = ctx.strokeStyle = "#89A"; + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + const arrow = new Path2D(`M${currentX} ${midY} l -7 6 v -3 h -7 v -6 h 7 v -3 z`); + ctx.fill(arrow); + ctx.stroke(arrow); + currentX -= 14; + + currentX -= 7; + ctx.strokeStyle = widgetData.colorOutline; + ctx.stroke(new Path2D(`M ${currentX} ${widgetData.posY} v ${widgetData.height}`)); + } else if (widgetData.lowQuality && showNav) { + currentX -= 28; + } + + // The toggle itself. + currentX -= 7; + ctx.fillStyle = this.toggled ? "#89A" : "#333"; + ctx.beginPath(); + const toggleRadius = height * 0.36; + ctx.arc(currentX - toggleRadius, posY + height * 0.5, toggleRadius, 0, Math.PI * 2); + ctx.fill(); + currentX -= toggleRadius * 2; + + if (!widgetData.lowQuality) { + currentX -= 4; + ctx.textAlign = "right"; + ctx.fillStyle = this.toggled ? widgetData.colorText : widgetData.colorTextSecondary; + const label = this.label; + const toggleLabelOn = this.options.on || "true"; + const toggleLabelOff = this.options.off || "false"; + ctx.fillText(this.toggled ? toggleLabelOn : toggleLabelOff, currentX, posY + height * 0.7); + currentX -= Math.max( + ctx.measureText(toggleLabelOn).width, + ctx.measureText(toggleLabelOff).width, + ); + + currentX -= 7; + ctx.textAlign = "left"; + let maxLabelWidth = widgetData.width - widgetData.margin - 10 - (widgetData.width - currentX); + if (label != null) { + ctx.fillText( + fitString(ctx, label, maxLabelWidth), + widgetData.margin + 10, + posY + height * 0.7, + ); + } + } + } + + override serializeValue(node: LGraphNode, index: number) { + return this.value; + } + + override mouse(event: CanvasMouseEvent, pos: Vector2, node: LGraphNode): boolean { + if (event.type == "pointerdown") { + if (node.properties?.[PROPERTY_SHOW_NAV] !== false && pos[0] >= node.size[0] - 15 - 28 - 1) { + const canvas = app.canvas as TLGraphCanvas; + const lowQuality = (canvas.ds?.scale || 1) <= 0.5; + if (!lowQuality) { + // Clicked on right half with nav arrow, go to the group, center on group and set + // zoom to see it all. + canvas.centerOnNode(this.group); + const zoomCurrent = canvas.ds?.scale || 1; + const zoomX = canvas.canvas.width / this.group._size[0] - 0.02; + const zoomY = canvas.canvas.height / this.group._size[1] - 0.02; + canvas.setZoom(Math.min(zoomCurrent, zoomX, zoomY), [ + canvas.canvas.width / 2, + canvas.canvas.height / 2, + ]); + canvas.setDirty(true, true); + } + } else { + this.toggle(); + } + } + return true; + } +} + +app.registerExtension({ + name: "rgthree.FastGroupsMuter", + registerCustomNodes() { + FastGroupsMuter.setUp(); + }, + loadedGraphNode(node: LGraphNode) { + if (node.type == FastGroupsMuter.title) { + (node as FastGroupsMuter).tempSize = [...node.size] as Point; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/feature_group_fast_toggle.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_group_fast_toggle.ts new file mode 100644 index 00000000..260d7cba --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_group_fast_toggle.ts @@ -0,0 +1,305 @@ +import type { + LGraphCanvas as TLGraphCanvas, + LGraphGroup as TLGraphGroup, + LGraph as TLGraph, + Vector2, + CanvasMouseEvent, +} from "@comfyorg/frontend"; +import type {AdjustedMouseCustomEvent} from "typings/rgthree.js"; + +import {app} from "scripts/app.js"; +import {rgthree} from "./rgthree.js"; +import {changeModeOfNodes, getGroupNodes, getOutputNodes} from "./utils.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; + +const BTN_SIZE = 20; +const BTN_MARGIN: Vector2 = [6, 6]; +const BTN_SPACING = 8; +const BTN_GRID = BTN_SIZE / 8; + +const TOGGLE_TO_MODE = new Map([ + ["MUTE", LiteGraph.NEVER], + ["BYPASS", 4], +]); + +function getToggles() { + return [...CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.toggles", [])].reverse(); +} + +/** + * Determines if the user clicked on an fast header icon. + */ +function clickedOnToggleButton(e: CanvasMouseEvent, group: TLGraphGroup): string | null { + const toggles = getToggles(); + const pos = group.pos; + const size = group.size; + for (let i = 0; i < toggles.length; i++) { + const toggle = toggles[i]; + if ( + LiteGraph.isInsideRectangle( + e.canvasX, + e.canvasY, + pos[0] + size[0] - (BTN_SIZE + BTN_MARGIN[0]) * (i + 1), + pos[1] + BTN_MARGIN[1], + BTN_SIZE, + BTN_SIZE, + ) + ) { + return toggle; + } + } + return null; +} + +/** + * Registers the GroupHeaderToggles which places a mute and/or bypass icons in groups headers for + * quick, single-click ability to mute/bypass. + */ +app.registerExtension({ + name: "rgthree.GroupHeaderToggles", + async setup() { + /** + * LiteGraph won't call `drawGroups` unless the canvas is dirty. Other nodes will do this, but + * in small workflows, we'll want to trigger it dirty so we can be drawn if we're in hover mode. + */ + setInterval(() => { + if ( + CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") && + CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always" + ) { + app.canvas.setDirty(true, true); + } + }, 250); + + /** + * Handles a click on the icon area if the user has the extension enable from settings. + * Hooks into the already overriden mouse down processor from rgthree. + */ + rgthree.addEventListener("on-process-mouse-down", ((e: AdjustedMouseCustomEvent) => { + if (!CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled")) return; + + const canvas = app.canvas as TLGraphCanvas; + if (canvas.selected_group) { + const originalEvent = e.detail.originalEvent; + const group = canvas.selected_group; + const clickedOnToggle = clickedOnToggleButton(originalEvent, group) || ""; + const toggleAction = clickedOnToggle?.toLocaleUpperCase(); + if (toggleAction) { + console.log(toggleAction); + const nodes = getGroupNodes(group); + if (toggleAction === "QUEUE") { + const outputNodes = getOutputNodes(nodes); + if (!outputNodes?.length) { + rgthree.showMessage({ + id: "no-output-in-group", + type: "warn", + timeout: 4000, + message: "No output nodes for group!", + }); + } else { + rgthree.queueOutputNodes(outputNodes); + } + } else { + const toggleMode = TOGGLE_TO_MODE.get(toggleAction); + if (toggleMode) { + group.recomputeInsideNodes(); + const hasAnyActiveNodes = nodes.some((n) => n.mode === LiteGraph.ALWAYS); + const isAllMuted = + !hasAnyActiveNodes && nodes.every((n) => n.mode === LiteGraph.NEVER); + const isAllBypassed = + !hasAnyActiveNodes && !isAllMuted && nodes.every((n) => n.mode === 4); + + let newMode: 0 | 1 | 2 | 3 | 4 = LiteGraph.ALWAYS; + if (toggleMode === LiteGraph.NEVER) { + newMode = isAllMuted ? LiteGraph.ALWAYS : LiteGraph.NEVER; + } else { + newMode = isAllBypassed ? LiteGraph.ALWAYS : 4; + } + changeModeOfNodes(nodes, newMode); + } + } + // Make it such that we're not then moving the group on drag. + canvas.selected_group = null; + canvas.dragging_canvas = false; + } + } + }) as EventListener); + + /** + * Overrides LiteGraph's Canvas method for drawingGroups and, after calling the original, checks + * that the user has enabled fast toggles and draws them on the top-right of the app.. + */ + const drawGroups = LGraphCanvas.prototype.drawGroups; + LGraphCanvas.prototype.drawGroups = function ( + canvasEl: HTMLCanvasElement, + ctx: CanvasRenderingContext2D, + ) { + drawGroups.apply(this, [...arguments] as any); + + if ( + !CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") || + !rgthree.lastCanvasMouseEvent + ) { + return; + } + + const graph = app.canvas.graph as TLGraph; + + let groups: TLGraphGroup[]; + // Default to hover if not always. + if (CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always") { + const hoverGroup = graph.getGroupOnPos( + rgthree.lastCanvasMouseEvent.canvasX, + rgthree.lastCanvasMouseEvent.canvasY, + ); + groups = hoverGroup ? [hoverGroup] : []; + } else { + groups = graph._groups || []; + } + + if (!groups.length) { + return; + } + + const toggles = getToggles(); + + ctx.save(); + for (const group of groups || []) { + const nodes = getGroupNodes(group); + let anyActive = false; + let allMuted = !!nodes.length; + let allBypassed = allMuted; + + // Find the current state of the group's nodes. + for (const node of nodes) { + if (!(node instanceof LGraphNode)) continue; + anyActive = anyActive || node.mode === LiteGraph.ALWAYS; + allMuted = allMuted && node.mode === LiteGraph.NEVER; + allBypassed = allBypassed && node.mode === 4; + if (anyActive || (!allMuted && !allBypassed)) { + break; + } + } + + // Display each toggle. + for (let i = 0; i < toggles.length; i++) { + const toggle = toggles[i]; + const pos = group._pos; + const size = group._size; + ctx.fillStyle = ctx.strokeStyle = group.color || "#335"; + const x = pos[0] + size[0] - BTN_MARGIN[0] - BTN_SIZE - (BTN_SPACING + BTN_SIZE) * i; + const y = pos[1] + BTN_MARGIN[1]; + const midX = x + BTN_SIZE / 2; + const midY = y + BTN_SIZE / 2; + if (toggle === "queue") { + const outputNodes = getOutputNodes(nodes); + const oldGlobalAlpha = ctx.globalAlpha; + if (!outputNodes?.length) { + ctx.globalAlpha = 0.5; + } + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + const arrowSizeX = BTN_SIZE * 0.6; + const arrowSizeY = BTN_SIZE * 0.7; + const arrow = new Path2D( + `M ${x + arrowSizeX / 2} ${midY} l 0 -${arrowSizeY / 2} l ${arrowSizeX} ${arrowSizeY / 2} l -${arrowSizeX} ${arrowSizeY / 2} z`, + ); + ctx.stroke(arrow); + if (outputNodes?.length) { + ctx.fill(arrow); + } + ctx.globalAlpha = oldGlobalAlpha; + } else { + const on = toggle === "bypass" ? allBypassed : allMuted; + + ctx.beginPath(); + ctx.lineJoin = "round"; + ctx.rect(x, y, BTN_SIZE, BTN_SIZE); + + ctx.lineWidth = 2; + if (toggle === "mute") { + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + + if (on) { + ctx.stroke( + new Path2D(` + ${eyeFrame(midX, midY)} + ${eyeLashes(midX, midY)} + `), + ); + } else { + const radius = BTN_GRID * 1.5; + + // Eyeball fill + ctx.fill( + new Path2D(` + ${eyeFrame(midX, midY)} + ${eyeFrame(midX, midY, -1)} + ${circlePath(midX, midY, radius)} + ${circlePath(midX + BTN_GRID / 2, midY - BTN_GRID / 2, BTN_GRID * 0.375)} + `), + "evenodd", + ); + + // Eye Outline Stroke + ctx.stroke(new Path2D(`${eyeFrame(midX, midY)} ${eyeFrame(midX, midY, -1)}`)); + + // Eye lashes (faded) + ctx.globalAlpha = this.editor_alpha * 0.5; + ctx.stroke(new Path2D(`${eyeLashes(midX, midY)} ${eyeLashes(midX, midY, -1)}`)); + ctx.globalAlpha = this.editor_alpha; + } + } else { + const lineChanges = on + ? `a ${BTN_GRID * 3}, ${BTN_GRID * 3} 0 1, 1 ${BTN_GRID * 3 * 2},0 + l ${BTN_GRID * 2.0} 0` + : `l ${BTN_GRID * 8} 0`; + + ctx.stroke( + new Path2D(` + M ${x} ${midY} + ${lineChanges} + M ${x + BTN_SIZE} ${midY} l -2 2 + M ${x + BTN_SIZE} ${midY} l -2 -2 + `), + ); + ctx.fill(new Path2D(`${circlePath(x + BTN_GRID * 3, midY, BTN_GRID * 1.8)}`)); + } + } + } + } + ctx.restore(); + }; + }, +}); + +function eyeFrame(midX: number, midY: number, yFlip = 1) { + return ` + M ${midX - BTN_SIZE / 2} ${midY} + c ${BTN_GRID * 1.5} ${yFlip * BTN_GRID * 2.5}, ${BTN_GRID * (8 - 1.5)} ${ + yFlip * BTN_GRID * 2.5 + }, ${BTN_GRID * 8} 0 + `; +} + +function eyeLashes(midX: number, midY: number, yFlip = 1) { + return ` + M ${midX - BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l -1.15 ${1.25 * yFlip} + M ${midX - BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l -0.90 ${1.5 * yFlip} + M ${midX - BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l -0.50 ${1.75 * yFlip} + M ${midX + BTN_GRID * 0.0} ${midY + yFlip * BTN_GRID * 2.0} l 0.00 ${2.0 * yFlip} + M ${midX + BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l 0.50 ${1.75 * yFlip} + M ${midX + BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l 0.90 ${1.5 * yFlip} + M ${midX + BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l 1.15 ${1.25 * yFlip} +`; +} + +function circlePath(cx: number, cy: number, radius: number) { + return ` + M ${cx} ${cy} + m ${radius}, 0 + a ${radius},${radius} 0 1, 1 -${radius * 2},0 + a ${radius},${radius} 0 1, 1 ${radius * 2},0 + `; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/feature_import_individual_nodes.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_import_individual_nodes.ts new file mode 100644 index 00000000..f01aa87d --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_import_individual_nodes.ts @@ -0,0 +1,85 @@ +import type {INodeSlot, LGraphNode, LGraphNodeConstructor} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {tryToGetWorkflowDataFromEvent} from "rgthree/common/utils_workflow.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; +import {NodeTypesString} from "./constants.js"; + +/** + * Registers the GroupHeaderToggles which places a mute and/or bypass icons in groups headers for + * quick, single-click ability to mute/bypass. + */ +app.registerExtension({ + name: "rgthree.ImportIndividualNodes", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + const onDragOver = nodeType.prototype.onDragOver; + nodeType.prototype.onDragOver = function (e: DragEvent) { + let handled = onDragOver?.apply?.(this, [...arguments] as any); + if (handled != null) { + return handled; + } + return importIndividualNodesInnerOnDragOver(this, e); + }; + + const onDragDrop = nodeType.prototype.onDragDrop; + nodeType.prototype.onDragDrop = async function (e: DragEvent) { + const alreadyHandled = await onDragDrop?.apply?.(this, [...arguments] as any); + if (alreadyHandled) { + return alreadyHandled; + } + return importIndividualNodesInnerOnDragDrop(this, e); + }; + }, +}); + +export function importIndividualNodesInnerOnDragOver(node: LGraphNode, e: DragEvent): boolean { + return ( + (node.widgets?.length && !!CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) || + false + ); +} + +export async function importIndividualNodesInnerOnDragDrop(node: LGraphNode, e: DragEvent) { + if (!node.widgets?.length || !CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) { + return false; + } + + const dynamicWidgetLengthNodes = [NodeTypesString.POWER_LORA_LOADER]; + + let handled = false; + const {workflow, prompt} = await tryToGetWorkflowDataFromEvent(e); + const exact = (workflow?.nodes || []).find( + (n: any) => + n.id === node.id && + n.type === node.type && + (dynamicWidgetLengthNodes.includes(node.type) || + n.widgets_values?.length === node.widgets_values?.length), + ); + if (!exact) { + // If we tried, but didn't find an exact match, then allow user to stop the default behavior. + handled = !confirm( + "[rgthree-comfy] Could not find a matching node (same id & type) in the dropped workflow." + + " Would you like to continue with the default drop behaviour instead?", + ); + } else if (!exact.widgets_values?.length) { + handled = !confirm( + "[rgthree-comfy] Matching node found (same id & type) but there's no widgets to set." + + " Would you like to continue with the default drop behaviour instead?", + ); + } else if ( + confirm( + "[rgthree-comfy] Found a matching node (same id & type) in the dropped workflow." + + " Would you like to set the widget values?", + ) + ) { + node.configure({ + // Title is overridden if it's not supplied; set it to the current then. + title: node.title, + widgets_values: [...(exact?.widgets_values || [])], + mode: exact.mode, + } as any); + handled = true; + } + return handled; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/image_comparer.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/image_comparer.ts new file mode 100644 index 00000000..3c648f8f --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/image_comparer.ts @@ -0,0 +1,480 @@ +import { + LGraphCanvas, + LGraphNode, + Vector2, + LGraphNodeConstructor, + CanvasMouseEvent, + ISerialisedNode, + Point, + CanvasPointerEvent, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {api} from "scripts/api.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {addConnectionLayoutSupport} from "./utils.js"; +import {RgthreeBaseHitAreas, RgthreeBaseWidget, RgthreeBaseWidgetBounds} from "./utils_widgets.js"; +import {measureText} from "./utils_canvas.js"; + +type ComfyImageServerData = {filename: string; type: string; subfolder: string}; +type ComfyImageData = {name: string; selected: boolean; url: string; img?: HTMLImageElement}; +type OldExecutedPayload = { + images: ComfyImageServerData[]; +}; +type ExecutedPayload = { + a_images?: ComfyImageServerData[]; + b_images?: ComfyImageServerData[]; +}; + +function imageDataToUrl(data: ComfyImageServerData) { + return api.apiURL( + `/view?filename=${encodeURIComponent(data.filename)}&type=${data.type}&subfolder=${ + data.subfolder + }${app.getPreviewFormatParam()}${app.getRandParam()}`, + ); +} + +/** + * Compares two images in one canvas node. + */ +export class RgthreeImageComparer extends RgthreeBaseServerNode { + static override title = NodeTypesString.IMAGE_COMPARER; + static override type = NodeTypesString.IMAGE_COMPARER; + static comfyClass = NodeTypesString.IMAGE_COMPARER; + + // These is what the core preview image node uses to show the context menu. May not be that helpful + // since it likely will always be "0" when a context menu is invoked without manually changing + // something. + override imageIndex: number = 0; + override imgs: InstanceType[] = []; + + override serialize_widgets = true; + + isPointerDown = false; + isPointerOver = false; + pointerOverPos: Vector2 = [0, 0]; + + private canvasWidget: RgthreeImageComparerWidget | null = null; + + static "@comparer_mode" = { + type: "combo", + values: ["Slide", "Click"], + }; + + constructor(title = RgthreeImageComparer.title) { + super(title); + this.properties["comparer_mode"] = "Slide"; + } + + override onExecuted(output: ExecutedPayload | OldExecutedPayload) { + super.onExecuted?.(output); + if ("images" in output) { + this.canvasWidget!.value = { + images: (output.images || []).map((d, i) => { + return { + name: i === 0 ? "A" : "B", + selected: true, + url: imageDataToUrl(d), + }; + }), + }; + } else { + output.a_images = output.a_images || []; + output.b_images = output.b_images || []; + const imagesToChoose: ComfyImageData[] = []; + const multiple = output.a_images.length + output.b_images.length > 2; + for (const [i, d] of output.a_images.entries()) { + imagesToChoose.push({ + name: output.a_images.length > 1 || multiple ? `A${i + 1}` : "A", + selected: i === 0, + url: imageDataToUrl(d), + }); + } + for (const [i, d] of output.b_images.entries()) { + imagesToChoose.push({ + name: output.b_images.length > 1 || multiple ? `B${i + 1}` : "B", + selected: i === 0, + url: imageDataToUrl(d), + }); + } + this.canvasWidget!.value = {images: imagesToChoose}; + } + } + + override onSerialize(serialised: ISerialisedNode) { + super.onSerialize && super.onSerialize(serialised); + for (let [index, widget_value] of (serialised.widgets_values || []).entries()) { + if (this.widgets[index]?.name === "rgthree_comparer") { + serialised.widgets_values![index] = ( + this.widgets[index] as unknown as RgthreeImageComparerWidget + ).value.images.map((d) => { + d = {...d}; + delete d.img; + return d; + }); + } + } + } + + override onNodeCreated() { + this.canvasWidget = this.addCustomWidget( + new RgthreeImageComparerWidget("rgthree_comparer", this), + ) as RgthreeImageComparerWidget; + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + } + + /** + * Sets mouse as down or up based on param. If it's down, we also loop to check pointer is still + * down. This is because LiteGraph doesn't fire `onMouseUp` every time there's a mouse up, so we + * need to manually monitor `pointer_is_down` and, when it's no longer true, set mouse as up here. + */ + private setIsPointerDown(down: boolean = this.isPointerDown) { + const newIsDown = down && !!app.canvas.pointer_is_down; + if (this.isPointerDown !== newIsDown) { + this.isPointerDown = newIsDown; + this.setDirtyCanvas(true, false); + } + this.imageIndex = this.isPointerDown ? 1 : 0; + if (this.isPointerDown) { + requestAnimationFrame(() => { + this.setIsPointerDown(); + }); + } + } + + override onMouseDown(event: CanvasPointerEvent, pos: Point, canvas: LGraphCanvas): boolean { + super.onMouseDown?.(event, pos, canvas); + this.setIsPointerDown(true); + return false; + } + + override onMouseEnter(event: CanvasPointerEvent): void { + super.onMouseEnter?.(event); + this.setIsPointerDown(!!app.canvas.pointer_is_down); + this.isPointerOver = true; + } + + override onMouseLeave(event: CanvasPointerEvent): void { + super.onMouseLeave?.(event); + this.setIsPointerDown(false); + this.isPointerOver = false; + } + + override onMouseMove(event: CanvasPointerEvent, pos: Point, canvas: LGraphCanvas): void { + super.onMouseMove?.(event, pos, canvas); + this.pointerOverPos = [...pos] as Point; + this.imageIndex = this.pointerOverPos[0] > this.size[0] / 2 ? 1 : 0; + } + + override getHelp(): string { + return ` +

    + The ${this.type!.replace("(rgthree)", "")} node compares two images on top of each other. +

    +
      +
    • +

      + Notes +

      +
        +

      • + The right-click menu may show image options (Open Image, Save Image, etc.) which will + correspond to the first image (image_a) if clicked on the left-half of the node, or + the second image if on the right half of the node. +

        • +
      +
      • +
    • +

      + Inputs +

      +
        +

      • + image_a Optional. The first image to use to compare. + image_a. +

        • +

      • + image_b Optional. The second image to use to compare. +

        • +

      • + Note image_a and image_b work best when a single + image is provided. However, if each/either are a batch, you can choose which item + from each batch are chosen to be compared. If either image_a or + image_b are not provided, the node will choose the first two from the + provided input if it's a batch, otherwise only show the single image (just as + Preview Image would). +

        • +
      +
      • +
    • +

      + Properties. You can change the following properties (by right-clicking + on the node, and select "Properties" or "Properties Panel" from the menu): +

      +
        +

      • + comparer_mode - Choose between "Slide" and "Click". Defaults to "Slide". +

        • +
      +
      • +
    `; + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeImageComparer); + } + + static override onRegisteredForOverride(comfyClass: any) { + addConnectionLayoutSupport(RgthreeImageComparer, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + RgthreeImageComparer.category = comfyClass.category; + }); + } +} + +type RgthreeImageComparerWidgetValue = { + images: ComfyImageData[]; +}; + +class RgthreeImageComparerWidget extends RgthreeBaseWidget { + override readonly type = "custom"; + + private node: RgthreeImageComparer; + + protected override hitAreas: RgthreeBaseHitAreas = { + // We dynamically set this when/if we draw the labels. + }; + + private selected: [ComfyImageData?, ComfyImageData?] = []; + + constructor(name: string, node: RgthreeImageComparer) { + super(name); + this.node = node; + } + + private _value: RgthreeImageComparerWidgetValue = {images: []}; + + set value(v: RgthreeImageComparerWidgetValue) { + // Despite `v` typed as RgthreeImageComparerWidgetValue, we may have gotten an array of strings + // from previous versions. We can handle that gracefully. + let cleanedVal; + if (Array.isArray(v)) { + cleanedVal = v.map((d, i) => { + if (!d || typeof d === "string") { + // We usually only have two here, so they're selected. + d = {url: d, name: i == 0 ? "A" : "B", selected: true}; + } + return d; + }); + } else { + cleanedVal = v.images || []; + } + + // If we have multiple items in our sent value but we don't have both an "A" and a "B" then + // just simplify it down to the first two in the list. + if (cleanedVal.length > 2) { + const hasAAndB = + cleanedVal.some((i) => i.name.startsWith("A")) && + cleanedVal.some((i) => i.name.startsWith("B")); + if (!hasAAndB) { + cleanedVal = [cleanedVal[0], cleanedVal[1]]; + } + } + + let selected = cleanedVal.filter((d) => d.selected); + // None are selected. + if (!selected.length && cleanedVal.length) { + cleanedVal[0]!.selected = true; + } + + selected = cleanedVal.filter((d) => d.selected); + if (selected.length === 1 && cleanedVal.length > 1) { + cleanedVal.find((d) => !d.selected)!.selected = true; + } + + this._value.images = cleanedVal; + + selected = cleanedVal.filter((d) => d.selected); + this.setSelected(selected as [ComfyImageData, ComfyImageData]); + } + + get value() { + return this._value; + } + + setSelected(selected: [ComfyImageData, ComfyImageData]) { + this._value.images.forEach((d) => (d.selected = false)); + this.node.imgs.length = 0; + for (const sel of selected) { + if (!sel.img) { + sel.img = new Image(); + sel.img.src = sel.url; + this.node.imgs.push(sel.img); + } + sel.selected = true; + } + this.selected = selected; + } + + draw(ctx: CanvasRenderingContext2D, node: RgthreeImageComparer, width: number, y: number) { + this.hitAreas = {}; + if (this.value.images.length > 2) { + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.font = `14px Arial`; + // Let's calculate the widths of all the labels. + const drawData: any = []; + const spacing = 5; + let x = 0; + for (const img of this.value.images) { + const width = measureText(ctx, img.name); + drawData.push({ + img, + text: img.name, + x, + width: measureText(ctx, img.name), + }); + x += width + spacing; + } + x = (node.size[0] - (x - spacing)) / 2; + for (const d of drawData) { + ctx.fillStyle = d.img.selected ? "rgba(180, 180, 180, 1)" : "rgba(180, 180, 180, 0.5)"; + ctx.fillText(d.text, x, y); + this.hitAreas[d.text] = { + bounds: [x, y, d.width, 14], + data: d.img, + onDown: this.onSelectionDown, + }; + x += d.width + spacing; + } + y += 20; + } + + if (node.properties?.["comparer_mode"] === "Click") { + this.drawImage(ctx, this.selected[this.node.isPointerDown ? 1 : 0], y); + } else { + this.drawImage(ctx, this.selected[0], y); + if (node.isPointerOver) { + this.drawImage(ctx, this.selected[1], y, this.node.pointerOverPos[0]); + } + } + } + + private onSelectionDown( + event: CanvasMouseEvent, + pos: Vector2, + node: LGraphNode, + bounds?: RgthreeBaseWidgetBounds, + ) { + const selected = [...this.selected]; + if (bounds?.data.name.startsWith("A")) { + selected[0] = bounds.data; + } else if (bounds?.data.name.startsWith("B")) { + selected[1] = bounds.data; + } + this.setSelected(selected as [ComfyImageData, ComfyImageData]); + } + + private drawImage( + ctx: CanvasRenderingContext2D, + image: ComfyImageData | undefined, + y: number, + cropX?: number, + ) { + if (!image?.img?.naturalWidth || !image?.img?.naturalHeight) { + return; + } + let [nodeWidth, nodeHeight] = this.node.size as [number, number]; + const imageAspect = image?.img.naturalWidth / image?.img.naturalHeight; + let height = nodeHeight - y; + const widgetAspect = nodeWidth / height; + let targetWidth, targetHeight; + let offsetX = 0; + if (imageAspect > widgetAspect) { + targetWidth = nodeWidth; + targetHeight = nodeWidth / imageAspect; + } else { + targetHeight = height; + targetWidth = height * imageAspect; + offsetX = (nodeWidth - targetWidth) / 2; + } + const widthMultiplier = image?.img.naturalWidth / targetWidth; + + const sourceX = 0; + const sourceY = 0; + const sourceWidth = + cropX != null ? (cropX - offsetX) * widthMultiplier : image?.img.naturalWidth; + const sourceHeight = image?.img.naturalHeight; + const destX = (nodeWidth - targetWidth) / 2; + const destY = y + (height - targetHeight) / 2; + const destWidth = cropX != null ? cropX - offsetX : targetWidth; + const destHeight = targetHeight; + ctx.save(); + ctx.beginPath(); + let globalCompositeOperation = ctx.globalCompositeOperation; + if (cropX) { + ctx.rect(destX, destY, destWidth, destHeight); + ctx.clip(); + } + ctx.drawImage( + image?.img, + sourceX, + sourceY, + sourceWidth, + sourceHeight, + destX, + destY, + destWidth, + destHeight, + ); + // Shows a label overlayed on the image. Not perfect, keeping commented out. + // ctx.globalCompositeOperation = "difference"; + // ctx.fillStyle = "rgba(180, 180, 180, 1)"; + // ctx.textAlign = "center"; + // ctx.font = `32px Arial`; + // ctx.fillText(image.name, nodeWidth / 2, y + 32); + if (cropX != null && cropX >= (nodeWidth - targetWidth) / 2 && cropX <= targetWidth + offsetX) { + ctx.beginPath(); + ctx.moveTo(cropX, destY); + ctx.lineTo(cropX, destY + destHeight); + ctx.globalCompositeOperation = "difference"; + ctx.strokeStyle = "rgba(255,255,255, 1)"; + ctx.stroke(); + } + ctx.globalCompositeOperation = globalCompositeOperation; + ctx.restore(); + } + + computeSize(width: number): Vector2 { + return [width, 20]; + } + + override serializeValue( + node: LGraphNode, + index: number, + ): RgthreeImageComparerWidgetValue | Promise { + const v = []; + for (const data of this._value.images) { + // Remove the img since it can't serialize. + const d = {...data}; + delete d.img; + v.push(d); + } + return {images: v}; + } +} + +app.registerExtension({ + name: "rgthree.ImageComparer", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === RgthreeImageComparer.type) { + RgthreeImageComparer.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/image_inset_crop.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/image_inset_crop.ts new file mode 100644 index 00000000..631ceaf0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/image_inset_crop.ts @@ -0,0 +1,70 @@ +import type {LGraph, LGraphNodeConstructor, ISerialisedNode} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; + +class ImageInsetCrop extends RgthreeBaseServerNode { + static override title = NodeTypesString.IMAGE_INSET_CROP; + static override type = NodeTypesString.IMAGE_INSET_CROP; + static comfyClass = NodeTypesString.IMAGE_INSET_CROP; + + static override exposedActions = ["Reset Crop"]; + static maxResolution = 8192; + + constructor(title = ImageInsetCrop.title) { + super(title); + } + + override onAdded(graph: LGraph): void { + const measurementWidget = this.widgets[0]!; + let callback = measurementWidget.callback; + measurementWidget.callback = (...args) => { + this.setWidgetStep(); + callback && callback.apply(measurementWidget, [...args]); + }; + this.setWidgetStep(); + } + + override configure(info: ISerialisedNode): void { + super.configure(info); + this.setWidgetStep(); + } + + private setWidgetStep() { + const measurementWidget = this.widgets[0]!; + for (let i = 1; i <= 4; i++) { + if (measurementWidget.value === "Pixels") { + this.widgets[i]!.options.step = 80; + this.widgets[i]!.options.max = ImageInsetCrop.maxResolution; + } else { + this.widgets[i]!.options.step = 10; + this.widgets[i]!.options.max = 99; + } + } + } + + override async handleAction(action: string): Promise { + if (action === "Reset Crop") { + for (const widget of this.widgets) { + if (["left", "right", "top", "bottom"].includes(widget.name!)) { + widget.value = 0; + } + } + } + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ImageInsetCrop); + } +} + +app.registerExtension({ + name: "rgthree.ImageInsetCrop", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === NodeTypesString.IMAGE_INSET_CROP) { + ImageInsetCrop.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/image_or_latent_size.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/image_or_latent_size.ts new file mode 100644 index 00000000..d52a1e49 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/image_or_latent_size.ts @@ -0,0 +1,51 @@ +import type {ISerialisedNode} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; + +class RgthreeImageOrLatentSize extends RgthreeBaseServerNode { + static override title = NodeTypesString.IMAGE_OR_LATENT_SIZE; + static override type = NodeTypesString.IMAGE_OR_LATENT_SIZE; + static comfyClass = NodeTypesString.IMAGE_OR_LATENT_SIZE; + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + + constructor(title = NODE_CLASS.title) { + super(title); + } + + override onNodeCreated() { + super.onNodeCreated?.(); + + // Litegraph uses an array of acceptable input types, even though ComfyUI's types don't type + // it out that way. + this.addInput("input", ["IMAGE", "LATENT", "MASK"] as any); + } + + override configure(info: ISerialisedNode): void { + super.configure(info); + + if (this.inputs?.length) { + // Litegraph uses an array of acceptable input types, even though ComfyUI's types don't type + // it out that way. + this.inputs[0]!.type = ["IMAGE", "LATENT", "MASK"] as any; + } + } +} + +/** An uniformed name reference to the node class. */ +const NODE_CLASS = RgthreeImageOrLatentSize; + +/** Register the node. */ +app.registerExtension({ + name: "rgthree.ImageOrLatentSize", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/label.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/label.ts new file mode 100644 index 00000000..4d240e08 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/label.ts @@ -0,0 +1,229 @@ +import type { + LGraphCanvas as TLGraphCanvas, + LGraphNode, + Vector2, + CanvasMouseEvent, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseVirtualNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {rgthree} from "./rgthree.js"; + +/** + * A label node that allows you to put floating text anywhere on the graph. The text is the `Title` + * and the font size, family, color, alignment as well as a background color, padding, and + * background border radius can all be adjusted in the properties. Multiline text can be added from + * the properties panel (because ComfyUI let's you shift + enter there, only). + */ +export class Label extends RgthreeBaseVirtualNode { + static override type = NodeTypesString.LABEL; + static override title = NodeTypesString.LABEL; + override comfyClass = NodeTypesString.LABEL; + + static readonly title_mode = LiteGraph.NO_TITLE; + static collapsable = false; + + static "@fontSize" = {type: "number"}; + static "@fontFamily" = {type: "string"}; + static "@fontColor" = {type: "string"}; + static "@textAlign" = {type: "combo", values: ["left", "center", "right"]}; + static "@backgroundColor" = {type: "string"}; + static "@padding" = {type: "number"}; + static "@borderRadius" = {type: "number"}; + static "@angle" = {type: "number"}; + + override properties!: RgthreeBaseVirtualNode["properties"] & { + fontSize: number; + fontFamily: string; + fontColor: string; + textAlign: string; + backgroundColor: string; + padding: number; + borderRadius: number; + angle: number; + }; + + override resizable = false; + + constructor(title = Label.title) { + super(title); + this.properties["fontSize"] = 12; + this.properties["fontFamily"] = "Arial"; + this.properties["fontColor"] = "#ffffff"; + this.properties["textAlign"] = "left"; + this.properties["backgroundColor"] = "transparent"; + this.properties["padding"] = 0; + this.properties["borderRadius"] = 0; + this.properties["angle"] = 0; + this.color = "#fff0"; + this.bgcolor = "#fff0"; + + this.onConstructed(); + } + + draw(ctx: CanvasRenderingContext2D) { + this.flags = this.flags || {}; + this.flags.allow_interaction = !this.flags.pinned; + ctx.save(); + this.color = "#fff0"; + this.bgcolor = "#fff0"; + const fontColor = this.properties["fontColor"] || "#ffffff"; + const backgroundColor = this.properties["backgroundColor"] || ""; + ctx.font = `${Math.max(this.properties["fontSize"] || 0, 1)}px ${ + this.properties["fontFamily"] ?? "Arial" + }`; + const padding = Number(this.properties["padding"]) ?? 0; + + // Support literal "\\n" sequences as newlines and trim trailing newlines + const processedTitle = (this.title ?? "").replace(/\\n/g, "\n").replace(/\n*$/, ""); + const lines = processedTitle.split("\n"); + + const maxWidth = Math.max(...lines.map((s) => ctx.measureText(s).width)); + this.size[0] = maxWidth + padding * 2; + this.size[1] = this.properties["fontSize"] * lines.length + padding * 2; + + // Apply rotation around the center, if angle provided + const angleDeg = parseInt(String(this.properties["angle"] ?? 0)) || 0; + if (angleDeg) { + const cx = this.size[0] / 2; + const cy = this.size[1] / 2; + ctx.translate(cx, cy); + ctx.rotate((angleDeg * Math.PI) / 180); + ctx.translate(-cx, -cy); + } + + if (backgroundColor) { + ctx.beginPath(); + const borderRadius = Number(this.properties["borderRadius"]) || 0; + ctx.roundRect(0, 0, this.size[0], this.size[1], [borderRadius]); + ctx.fillStyle = backgroundColor; + ctx.fill(); + } + ctx.textAlign = "left"; + let textX = padding; + if (this.properties["textAlign"] === "center") { + ctx.textAlign = "center"; + textX = this.size[0] / 2; + } else if (this.properties["textAlign"] === "right") { + ctx.textAlign = "right"; + textX = this.size[0] - padding; + } + ctx.textBaseline = "top"; + ctx.fillStyle = fontColor; + let currentY = padding; + for (let i = 0; i < lines.length; i++) { + ctx.fillText(lines[i] || " ", textX, currentY); + currentY += this.properties["fontSize"]; + } + ctx.restore(); + } + + override onDblClick(event: CanvasMouseEvent, pos: Vector2, canvas: TLGraphCanvas) { + // Since everything we can do here is in the properties, let's pop open the properties panel. + LGraphCanvas.active_canvas.showShowNodePanel(this); + } + + override onShowCustomPanelInfo(panel: HTMLElement) { + panel.querySelector('div.property[data-property="Mode"]')?.remove(); + panel.querySelector('div.property[data-property="Color"]')?.remove(); + } + + override inResizeCorner(x: number, y: number) { + // A little ridiculous there's both a resizable property and this method separately to draw the + // resize icon... + return this.resizable; + } + + override getHelp() { + return ` +

    + The rgthree-comfy ${this.type!.replace("(rgthree)", "")} node allows you to add a floating + label to your workflow. +

    +

    + The text shown is the "Title" of the node and you can adjust the the font size, font family, + font color, text alignment as well as a background color, padding, and background border + radius from the node's properties. You can double-click the node to open the properties + panel. +

    +

      +
    • +

      + Pro tip #1: You can add multiline text from the properties panel + (because ComfyUI let's you shift + enter there, only). +

      +
      • +
    • +

      + Pro tip #2: You can use ComfyUI's native "pin" option in the + right-click menu to make the label stick to the workflow and clicks to "go through". + You can right-click at any time to unpin. +

      +
      • +
    • +

      + Pro tip #3: Color values are hexidecimal strings, like "#FFFFFF" for + white, or "#660000" for dark red. You can supply a 7th & 8th value (or 5th if using + shorthand) to create a transluscent color. For instance, "#FFFFFF88" is semi-transparent + white. +

      +
      • +
    `; + } +} + +/** + * We override the drawNode to see if we're drawing our label and, if so, hijack it so we can draw + * it like we want. We also do call out to oldDrawNode, which takes care of very minimal things, + * like a select box. + */ +const oldDrawNode = LGraphCanvas.prototype.drawNode; +LGraphCanvas.prototype.drawNode = function (node: LGraphNode, ctx: CanvasRenderingContext2D) { + if (node.constructor === Label.prototype.constructor) { + // These get set very aggressively; maybe an extension is doing it. We'll just clear them out + // each time. + (node as Label).bgcolor = "transparent"; + (node as Label).color = "transparent"; + const v = oldDrawNode.apply(this, arguments as any); + (node as Label).draw(ctx); + return v; + } + + const v = oldDrawNode.apply(this, arguments as any); + return v; +}; + +/** + * We override LGraph getNodeOnPos to see if we're being called while also processing a mouse down + * and, if so, filter out any label nodes on labels that are pinned. This makes the click go + * "through" the label. We still allow right clicking (so you can unpin) and double click for the + * properties panel, though that takes two double clicks (one to select, one to actually double + * click). + */ +const oldGetNodeOnPos = LGraph.prototype.getNodeOnPos; +LGraph.prototype.getNodeOnPos = function (x: number, y: number, nodes_list?: LGraphNode[]) { + if ( + // processMouseDown always passes in the nodes_list + nodes_list && + rgthree.processingMouseDown && + rgthree.lastCanvasMouseEvent?.type.includes("down") && + rgthree.lastCanvasMouseEvent?.which === 1 + ) { + // Using the same logic from LGraphCanvas processMouseDown, let's see if we consider this a + // double click. + let isDoubleClick = LiteGraph.getTime() - LGraphCanvas.active_canvas.last_mouseclick < 300; + if (!isDoubleClick) { + nodes_list = [...nodes_list].filter((n) => !(n instanceof Label) || !n.flags?.pinned); + } + } + return oldGetNodeOnPos.apply(this, [x, y, nodes_list]); +}; + +// Register the extension. +app.registerExtension({ + name: "rgthree.Label", + registerCustomNodes() { + Label.setUp(); + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/menu_auto_nest.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_auto_nest.ts new file mode 100644 index 00000000..79d0d289 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_auto_nest.ts @@ -0,0 +1,156 @@ +import type { + LGraphNode, + ContextMenu, + IContextMenuOptions, + IContextMenuValue, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {rgthree} from "./rgthree.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; + +const SPECIAL_ENTRIES = [/^(CHOOSE|NONE|DISABLE|OPEN)(\s|$)/i, /^\p{Extended_Pictographic}/gu]; + +/** + * Handles a large, flat list of string values given ContextMenu and breaks it up into subfolder, if + * they exist. This is experimental and initially built to work for CheckpointLoaderSimple. + */ +app.registerExtension({ + name: "rgthree.ContextMenuAutoNest", + async setup() { + const logger = rgthree.newLogSession("[ContextMenuAutoNest]"); + + const existingContextMenu = LiteGraph.ContextMenu; + + // @ts-ignore: TypeScript doesn't like this override. + LiteGraph.ContextMenu = function ( + values: IContextMenuValue[], + options: IContextMenuOptions, + ) { + const threshold = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.threshold", 20); + const enabled = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.subdirs", false); + + // If we're not enabled, or are incompatible, then just call out safely. + let incompatible: string | boolean = !enabled || !!options?.extra?.rgthree_doNotNest; + if (!incompatible) { + if (values.length <= threshold) { + incompatible = `Skipping context menu auto nesting b/c threshold is not met (${threshold})`; + } + // If there's a rgthree_originalCallback, then we're nested and don't need to check things + // we only expect on the first nesting. + if (!(options.parentMenu?.options as any)?.rgthree_originalCallback) { + // On first context menu, we require a callback and a flat list of options as strings. + if (!options?.callback) { + incompatible = `Skipping context menu auto nesting b/c a callback was expected.`; + } else if (values.some((i) => typeof i !== "string")) { + incompatible = `Skipping context menu auto nesting b/c not all values were strings.`; + } + } + } + if (incompatible) { + if (enabled) { + const [n, v] = logger.infoParts( + "Skipping context menu auto nesting for incompatible menu.", + ); + console[n]?.(...v); + } + return existingContextMenu.apply(this as any, [...arguments] as any); + } + + const folders: {[key: string]: IContextMenuValue[]} = {}; + const specialOps: IContextMenuValue[] = []; + const folderless: IContextMenuValue[] = []; + for (const value of values) { + if (!value) { + folderless.push(value); + continue; + } + const newValue = typeof value === "string" ? {content: value} : Object.assign({}, value); + (newValue as any).rgthree_originalValue = (value as any).rgthree_originalValue || value; + const valueContent = newValue.content || ""; + const splitBy = valueContent.indexOf("/") > -1 ? "/" : "\\"; + const valueSplit = valueContent.split(splitBy); + if (valueSplit.length > 1) { + const key = valueSplit.shift()!; + newValue.content = valueSplit.join(splitBy); + folders[key] = folders[key] || []; + folders[key]!.push(newValue); + } else if (SPECIAL_ENTRIES.some((r) => r.test(valueContent))) { + specialOps.push(newValue); + } else { + folderless.push(newValue); + } + } + + const foldersCount = Object.values(folders).length; + if (foldersCount > 0) { + // Propogate the original callback down through the options. + (options as any).rgthree_originalCallback = + (options as any).rgthree_originalCallback || + (options.parentMenu?.options as any)?.rgthree_originalCallback || + options.callback; + const oldCallback = (options as any)?.rgthree_originalCallback; + options.callback = undefined; + const newCallback = ( + item: IContextMenuValue, + options: IContextMenuOptions, + event: MouseEvent, + parentMenu: ContextMenu | undefined, + node: LGraphNode, + ) => { + oldCallback?.((item as any)?.rgthree_originalValue!, options, event, undefined, node); + }; + const [n, v] = logger.infoParts(`Nested folders found (${foldersCount}).`); + console[n]?.(...v); + const newValues: IContextMenuValue[] = []; + for (const [folderName, folderValues] of Object.entries(folders)) { + newValues.push({ + content: `πŸ“ ${folderName}`, + has_submenu: true, + callback: () => { + /* no-op, use the item callback. */ + }, + submenu: { + options: folderValues.map((value) => { + value!.callback = newCallback; + return value; + }), + }, + }); + } + values = ([] as IContextMenuValue[]).concat( + specialOps.map((f) => { + if (typeof f === "string") { + f = {content: f}; + } + f!.callback = newCallback; + return f; + }), + newValues, + folderless.map((f) => { + if (typeof f === "string") { + f = {content: f}; + } + f!.callback = newCallback; + return f; + }), + ); + } + if (options.scale == null) { + options.scale = Math.max(app.canvas.ds?.scale || 1, 1); + } + + const oldCtrResponse = existingContextMenu.call(this as any, values, options as any); + // For some reason, LiteGraph calls submenus with "this.constructor" which no longer allows + // us to continue building deep nesting, as well as skips many other extensions (even + // ComfyUI's core extensions like translations) from working on submenus. It also removes + // search, etc. While this is a recent-ish issue, I can't seem to find the culpit as it looks + // like old litegraph always did this. Perhaps changing it to a Class? Anyway, this fixes it; + // Hopefully without issues. + if ((oldCtrResponse as any)?.constructor) { + (oldCtrResponse as any).constructor = LiteGraph.ContextMenu; + } + return this; + }; + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/menu_copy_image.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_copy_image.ts new file mode 100644 index 00000000..b4c5dac8 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_copy_image.ts @@ -0,0 +1,75 @@ +import type {IContextMenuValue, LGraphCanvas} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; + +const clipboardSupportedPromise = new Promise(async (resolve) => { + try { + // MDN says to check this, but it doesn't work in Mozilla... however, in secure contexts + // (localhost included), it's given by default if the user has it flagged.. so we should be + // able to check in the latter ClipboardItem too. + const result = await navigator.permissions.query({name: "clipboard-write"} as any); + resolve(result.state === "granted"); + return; + } catch (e) { + try { + if (!navigator.clipboard.write) { + throw new Error(); + } + new ClipboardItem({"image/png": new Blob([], {type: "image/png"})}); + resolve(true); + return; + } catch (e) { + resolve(false); + } + } +}); + +/** + * Adds a "Copy Image" to images in similar fashion to the "native" Open Image and Save Image + * options. + */ +app.registerExtension({ + name: "rgthree.CopyImageToClipboard", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name.toLowerCase().includes("image")) { + if (await clipboardSupportedPromise) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function ( + canvas: LGraphCanvas, + options: (IContextMenuValue | null)[], + ): (IContextMenuValue | null)[] { + options = getExtraMenuOptions?.call(this, canvas, options) ?? options; + // If we already have a copy image somehow, then let's skip ours. + if (this.imgs?.length) { + let img = + this.imgs[this.imageIndex || 0] || this.imgs[this.overIndex || 0] || this.imgs[0]; + const foundIdx = options.findIndex((option) => option?.content?.includes("Copy Image")); + if (img && foundIdx === -1) { + const menuItem: IContextMenuValue = { + content: "Copy Image (rgthree)", + callback: () => { + const canvas = document.createElement("canvas"); + const ctx = canvas.getContext("2d")!; + canvas.width = img.naturalWidth; + canvas.height = img.naturalHeight; + ctx.drawImage(img, 0, 0, img.naturalWidth, img.naturalHeight); + canvas.toBlob((blob) => { + navigator.clipboard.write([new ClipboardItem({"image/png": blob!})]); + }); + }, + }; + let idx = options.findIndex((option) => option?.content?.includes("Open Image")) + 1; + if (idx != null) { + options.splice(idx, 0, menuItem); + } else { + options.unshift(menuItem); + } + } + } + return []; + }; + } + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/menu_queue_node.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_queue_node.ts new file mode 100644 index 00000000..c8e5507c --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_queue_node.ts @@ -0,0 +1,93 @@ +import type { + IContextMenuValue, + LGraphCanvas as TLGraphCanvas, + LGraphNode, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {rgthree} from "./rgthree.js"; +import {getGroupNodes, getOutputNodes} from "./utils.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; + +function showQueueNodesMenuIfOutputNodesAreSelected( + existingOptions: (IContextMenuValue | null)[], +) { + if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) { + return; + } + const outputNodes = getOutputNodes(Object.values(app.canvas.selected_nodes)); + const menuItem = { + content: `Queue Selected Output Nodes (rgthree)  `, + className: "rgthree-contextmenu-item", + callback: () => { + rgthree.queueOutputNodes(outputNodes); + }, + disabled: !outputNodes.length, + }; + + let idx = existingOptions.findIndex((o) => o?.content === "Outputs") + 1; + idx = idx || existingOptions.findIndex((o) => o?.content === "Align") + 1; + idx = idx || 3; + existingOptions.splice(idx, 0, menuItem); +} + +function showQueueGroupNodesMenuIfGroupIsSelected( + existingOptions: (IContextMenuValue | null)[], +) { + if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) { + return; + } + const group = + rgthree.lastCanvasMouseEvent && + (app.canvas.getCurrentGraph() || app.graph).getGroupOnPos( + rgthree.lastCanvasMouseEvent.canvasX, + rgthree.lastCanvasMouseEvent.canvasY, + ); + + const outputNodes: LGraphNode[] | null = (group && getOutputNodes(getGroupNodes(group))) || null; + const menuItem = { + content: `Queue Group Output Nodes (rgthree)  `, + className: "rgthree-contextmenu-item", + callback: () => { + outputNodes && rgthree.queueOutputNodes(outputNodes); + }, + disabled: !outputNodes?.length, + }; + + let idx = existingOptions.findIndex((o) => o?.content?.startsWith("Queue Selected ")) + 1; + idx = idx || existingOptions.findIndex((o) => o?.content === "Outputs") + 1; + idx = idx || existingOptions.findIndex((o) => o?.content === "Align") + 1; + idx = idx || 3; + existingOptions.splice(idx, 0, menuItem); +} + +/** + * Adds a "Queue Node" menu item to all output nodes, working with `rgthree.queueOutputNode` to + * execute only a single node's path. + */ +app.registerExtension({ + name: "rgthree.QueueNode", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function ( + canvas: TLGraphCanvas, + options: (IContextMenuValue | null)[], + ): (IContextMenuValue | null)[] { + const extraOptions = getExtraMenuOptions?.call(this, canvas, options) ?? []; + showQueueNodesMenuIfOutputNodesAreSelected(options); + showQueueGroupNodesMenuIfGroupIsSelected(options); + return extraOptions; + }; + }, + + async setup() { + const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function (...args: any[]) { + const options = getCanvasMenuOptions.apply(this, [...args] as any); + showQueueNodesMenuIfOutputNodesAreSelected(options); + showQueueGroupNodesMenuIfGroupIsSelected(options); + return options; + }; + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/muter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/muter.ts new file mode 100644 index 00000000..89fe00f2 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/muter.ts @@ -0,0 +1,51 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {BaseNodeModeChanger} from "./base_node_mode_changer.js"; +import {NodeTypesString} from "./constants.js"; + +const MODE_MUTE = 2; +const MODE_ALWAYS = 0; + +class MuterNode extends BaseNodeModeChanger { + static override exposedActions = ["Mute all", "Enable all", "Toggle all"]; + + static override type = NodeTypesString.FAST_MUTER; + static override title = NodeTypesString.FAST_MUTER; + override comfyClass = NodeTypesString.FAST_MUTER; + override readonly modeOn = MODE_ALWAYS; + override readonly modeOff = MODE_MUTE; + + constructor(title = MuterNode.title) { + super(title); + this.onConstructed(); + } + + override async handleAction(action: string) { + if (action === "Mute all") { + for (const widget of this.widgets) { + this.forceWidgetOff(widget, true); + } + } else if (action === "Enable all") { + for (const widget of this.widgets) { + this.forceWidgetOn(widget, true); + } + } else if (action === "Toggle all") { + for (const widget of this.widgets) { + this.forceWidgetToggle(widget, true); + } + } + } +} + +app.registerExtension({ + name: "rgthree.Muter", + registerCustomNodes() { + MuterNode.setUp(); + }, + loadedGraphNode(node: LGraphNode) { + if (node.type == MuterNode.title) { + (node as any)._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/node_collector.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/node_collector.ts new file mode 100644 index 00000000..87bb6ba8 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/node_collector.ts @@ -0,0 +1,168 @@ +import type { + LLink, + LGraph, + LGraphCanvas, + LGraphNode as TLGraphNode, + IContextMenuOptions, + ContextMenu, + IContextMenuValue, + Size, + ISerialisedNode, + Point, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {addConnectionLayoutSupport} from "./utils.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {BaseCollectorNode} from "./base_node_collector.js"; +import {NodeTypesString} from "./constants.js"; + +/** + * The Collector Node. Takes any number of inputs as connections for nodes and collects them into + * one outputs. The next node will decide what to do with them. + * + * Currently only works with the Fast Muter, Fast Bypasser, and Fast Actions Button. + */ +class CollectorNode extends BaseCollectorNode { + static override type = NodeTypesString.NODE_COLLECTOR; + static override title = NodeTypesString.NODE_COLLECTOR; + override comfyClass = NodeTypesString.NODE_COLLECTOR; + + constructor(title = CollectorNode.title) { + super(title); + this.onConstructed(); + } + + override onConstructed(): boolean { + this.addOutput("Output", "*"); + return super.onConstructed(); + } +} + +/** Legacy "Combiner" */ +class CombinerNode extends CollectorNode { + static legacyType = "Node Combiner (rgthree)"; + static override title = "‼️ Node Combiner [DEPRECATED]"; + + constructor(title = CombinerNode.title) { + super(title); + + const note = ComfyWidgets["STRING"]( + this, + "last_seed", + ["STRING", {multiline: true}], + app, + ).widget; + note.inputEl!.value = + 'The Node Combiner has been renamed to Node Collector. You can right-click and select "Update to Node Collector" to attempt to automatically update.'; + note.inputEl!.readOnly = true; + note.inputEl!.style.backgroundColor = "#332222"; + note.inputEl!.style.fontWeight = "bold"; + note.inputEl!.style.fontStyle = "italic"; + note.inputEl!.style.opacity = "0.8"; + + this.getExtraMenuOptions = ( + canvas: LGraphCanvas, + options: (IContextMenuValue | null)[], + ): (IContextMenuValue | null)[] => { + options.splice(options.length - 1, 0, { + content: "‼️ Update to Node Collector", + callback: ( + _value: IContextMenuValue, + _options: IContextMenuOptions, + _event: MouseEvent, + _parentMenu: ContextMenu | undefined, + _node: TLGraphNode, + ) => { + updateCombinerToCollector(this); + }, + }); + return []; + }; + } + + override configure(info: ISerialisedNode) { + super.configure(info); + if (this.title != CombinerNode.title && !this.title.startsWith("‼️")) { + this.title = "‼️ " + this.title; + } + } +} + +/** + * Updates a Node Combiner to a Node Collector. + */ +async function updateCombinerToCollector(node: TLGraphNode) { + if (node.type === CombinerNode.legacyType) { + // Create a new CollectorNode. + const newNode = new CollectorNode(); + if (node.title != CombinerNode.title) { + newNode.title = node.title.replace("‼️ ", ""); + } + // Port the position, size, and properties from the old node. + newNode.pos = [...node.pos] as Point; + newNode.size = [...node.size] as Size; + newNode.properties = {...node.properties}; + // We now collect the links data, inputs and outputs, of the old node since these will be + // lost when we remove it. + const links: any[] = []; + const graph = (node.graph || app.graph); + for (const [index, output] of node.outputs.entries()) { + for (const linkId of output.links || []) { + const link: LLink = graph.links[linkId]!; + if (!link) continue; + const targetNode = graph.getNodeById(link.target_id); + links.push({node: newNode, slot: index, targetNode, targetSlot: link.target_slot}); + } + } + for (const [index, input] of node.inputs.entries()) { + const linkId = input.link; + if (linkId) { + const link: LLink = graph.links[linkId]!; + const originNode = graph.getNodeById(link.origin_id); + links.push({ + node: originNode, + slot: link.origin_slot, + targetNode: newNode, + targetSlot: index, + }); + } + } + // Add the new node, remove the old node. + graph.add(newNode); + await wait(); + // Now go through and connect the other nodes up as they were. + for (const link of links) { + link.node.connect(link.slot, link.targetNode, link.targetSlot); + } + await wait(); + graph.remove(node); + } +} + +app.registerExtension({ + name: "rgthree.NodeCollector", + registerCustomNodes() { + addConnectionLayoutSupport(CollectorNode, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + + LiteGraph.registerNodeType(CollectorNode.title, CollectorNode); + CollectorNode.category = CollectorNode._category; + }, +}); + +app.registerExtension({ + name: "rgthree.NodeCombiner", + registerCustomNodes() { + addConnectionLayoutSupport(CombinerNode, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + + LiteGraph.registerNodeType(CombinerNode.legacyType, CombinerNode); + CombinerNode.category = CombinerNode._category; + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_relay.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_relay.ts new file mode 100644 index 00000000..aac47b4c --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_relay.ts @@ -0,0 +1,280 @@ +import type { + INodeInputSlot, + INodeOutputSlot, + LGraphCanvas, + LGraphEventMode, + LGraphNode, + LLink, + Vector2, + ISerialisedNode, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import { + PassThroughFollowing, + addConnectionLayoutSupport, + changeModeOfNodes, + getConnectedInputNodesAndFilterPassThroughs, + getConnectedOutputNodesAndFilterPassThroughs, +} from "./utils.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {BaseCollectorNode} from "./base_node_collector.js"; +import {NodeTypesString, stripRgthree} from "./constants.js"; +import {fitString} from "./utils_canvas.js"; +import {rgthree} from "./rgthree.js"; + +const MODE_ALWAYS = 0; +const MODE_MUTE = 2; +const MODE_BYPASS = 4; +const MODE_REPEATS = [MODE_MUTE, MODE_BYPASS]; +const MODE_NOTHING = -99; // MADE THIS UP. + +const MODE_TO_OPTION = new Map([ + [MODE_ALWAYS, "ACTIVE"], + [MODE_MUTE, "MUTE"], + [MODE_BYPASS, "BYPASS"], + [MODE_NOTHING, "NOTHING"], +]); + +const OPTION_TO_MODE = new Map([ + ["ACTIVE", MODE_ALWAYS], + ["MUTE", MODE_MUTE], + ["BYPASS", MODE_BYPASS], + ["NOTHING", MODE_NOTHING], +]); + +const MODE_TO_PROPERTY = new Map([ + [MODE_MUTE, "on_muted_inputs"], + [MODE_BYPASS, "on_bypassed_inputs"], + [MODE_ALWAYS, "on_any_active_inputs"], +]); + +const logger = rgthree.newLogSession("[NodeModeRelay]"); + +/** + * Like a BaseCollectorNode, this relay node connects to a Repeater node and _relays_ mode changes + * changes to the repeater (so it can go on to modify its connections). + */ +class NodeModeRelay extends BaseCollectorNode { + override readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.ALL; + + static override type = NodeTypesString.NODE_MODE_RELAY; + static override title = NodeTypesString.NODE_MODE_RELAY; + override comfyClass = NodeTypesString.NODE_MODE_RELAY; + + static "@on_muted_inputs" = { + type: "combo", + values: ["MUTE", "ACTIVE", "BYPASS", "NOTHING"], + }; + + static "@on_bypassed_inputs" = { + type: "combo", + values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"], + }; + + static "@on_any_active_inputs" = { + type: "combo", + values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"], + }; + + constructor(title?: string) { + super(title); + this.properties["on_muted_inputs"] = "MUTE"; + this.properties["on_bypassed_inputs"] = "BYPASS"; + this.properties["on_any_active_inputs"] = "ACTIVE"; + + this.onConstructed(); + } + + override onConstructed() { + this.addOutput("REPEATER", "_NODE_REPEATER_", { + color_on: "#Fc0", + color_off: "#a80", + shape: LiteGraph.ARROW_SHAPE, + }); + + setTimeout(() => { + this.stabilize(); + }, 500); + return super.onConstructed(); + } + + override onModeChange(from: LGraphEventMode | undefined, to: LGraphEventMode) { + super.onModeChange(from, to); + // If we aren't connected to anything, then we'll use our mode to relay when it changes. + if (this.inputs.length <= 1 && !this.isInputConnected(0) && this.isAnyOutputConnected()) { + const [n, v] = logger.infoParts(`Mode change without any inputs; relaying our mode.`); + console[n]?.(...v); + // Pass "to" since there may be other getters in the way to access this.mode directly. + this.dispatchModeToRepeater(to); + } + } + + override onDrawForeground(ctx: CanvasRenderingContext2D, canvas: LGraphCanvas): void { + if (this.flags?.collapsed) { + return; + } + if ( + this.properties["on_muted_inputs"] !== "MUTE" || + this.properties["on_bypassed_inputs"] !== "BYPASS" || + this.properties["on_any_active_inputs"] != "ACTIVE" + ) { + let margin = 15; + ctx.textAlign = "left"; + let label = `*(MUTE > ${this.properties["on_muted_inputs"]}, `; + label += `BYPASS > ${this.properties["on_bypassed_inputs"]}, `; + label += `ACTIVE > ${this.properties["on_any_active_inputs"]})`; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + const oldFont = ctx.font; + ctx.font = "italic " + (LiteGraph.NODE_SUBTEXT_SIZE - 2) + "px Arial"; + ctx.fillText(fitString(ctx, label, this.size[0] - 20), 15, this.size[1] - 6); + ctx.font = oldFont; + } + } + + override computeSize(out: Vector2) { + let size = super.computeSize(out); + if ( + this.properties["on_muted_inputs"] !== "MUTE" || + this.properties["on_bypassed_inputs"] !== "BYPASS" || + this.properties["on_any_active_inputs"] != "ACTIVE" + ) { + size[1] += 17; + } + return size; + } + override onConnectOutput( + outputIndex: number, + inputType: string | -1, + inputSlot: INodeInputSlot, + inputNode: LGraphNode, + inputIndex: number, + ): boolean { + let canConnect = super.onConnectOutput?.( + outputIndex, + inputType, + inputSlot, + inputNode, + inputIndex, + ); + let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0] ?? inputNode; + return canConnect && nextNode.type === NodeTypesString.NODE_MODE_REPEATER; + } + + override onConnectionsChange( + type: number, + slotIndex: number, + isConnected: boolean, + link_info: LLink, + ioSlot: INodeOutputSlot | INodeInputSlot, + ): void { + super.onConnectionsChange(type, slotIndex, isConnected, link_info, ioSlot); + setTimeout(() => { + this.stabilize(); + }, 500); + } + + stabilize() { + // If we aren't connected to a repeater, then theres no sense in checking. And if we are, but + // have no inputs, then we're also not ready. + if (!this.graph || !this.isAnyOutputConnected() || !this.isInputConnected(0)) { + return; + } + const inputNodes = getConnectedInputNodesAndFilterPassThroughs( + this, + this, + -1, + this.inputsPassThroughFollowing, + ); + let mode: LGraphEventMode | -99 | undefined = undefined; + for (const inputNode of inputNodes) { + // If we haven't set our mode to be, then let's set it. Otherwise, mode will stick if it + // remains constant, otherwise, if we hit an ALWAYS, then we'll unmute all repeaters and + // if not then we won't do anything. + if (mode === undefined) { + mode = inputNode.mode; + } else if (mode === inputNode.mode && MODE_REPEATS.includes(mode)) { + continue; + } else if (inputNode.mode === MODE_ALWAYS || mode === MODE_ALWAYS) { + mode = MODE_ALWAYS; + } else { + mode = undefined; + } + } + + this.dispatchModeToRepeater(mode); + setTimeout(() => { + this.stabilize(); + }, 500); + } + + /** + * Sends the mode to the repeater, checking to see if we're modifying our mode. + */ + private dispatchModeToRepeater(mode?: LGraphEventMode | -99 | null) { + if (mode != null) { + const propertyVal = this.properties?.[MODE_TO_PROPERTY.get(mode) || ""]; + const newMode = OPTION_TO_MODE.get(propertyVal as string); + mode = (newMode !== null ? newMode : mode) as LGraphEventMode | -99; + if (mode !== null && mode !== MODE_NOTHING) { + if (this.outputs?.length) { + const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this); + for (const outputNode of outputNodes) { + changeModeOfNodes(outputNode, mode); + wait(16).then(() => { + outputNode.setDirtyCanvas(true, true); + }); + } + } + } + } + } + + override getHelp() { + return ` +

    + This node will relay its input nodes' modes (Mute, Bypass, or Active) to a connected + ${stripRgthree(NodeTypesString.NODE_MODE_REPEATER)} (which would then repeat that mode + change to all of its inputs). +

    +
      +

    • + When all connected input nodes are muted, the relay will set a connected repeater to + mute (by default). +

      • +

    • + When all connected input nodes are bypassed, the relay will set a connected repeater to + bypass (by default). +

      • +

    • + When any connected input nodes are active, the relay will set a connected repeater to + active (by default). +

      • +

    • + If no inputs are connected, the relay will set a connected repeater to its mode when + its own mode is changed. Note, if any inputs are connected, then the above + will occur and the Relay's mode does not matter. +

      • +
    +

    + Note, you can change which signals get sent on the above in the Properties. + For instance, you could configure an inverse relay which will send a MUTE when any of its + inputs are active (instead of sending an ACTIVE signal), and send an ACTIVE signal when all + of its inputs are muted (instead of sending a MUTE signal), etc. +

    + `; + } +} + +app.registerExtension({ + name: "rgthree.NodeModeRepeaterHelper", + registerCustomNodes() { + addConnectionLayoutSupport(NodeModeRelay, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + + LiteGraph.registerNodeType(NodeModeRelay.type, NodeModeRelay); + NodeModeRelay.category = NodeModeRelay._category; + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_repeater.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_repeater.ts new file mode 100644 index 00000000..452b8d75 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_repeater.ts @@ -0,0 +1,216 @@ +import type { + INodeInputSlot, + INodeOutputSlot, + LGraphEventMode, + LGraphGroup, + LGraphNode, + LLink, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {BaseCollectorNode} from "./base_node_collector.js"; +import {NodeTypesString, stripRgthree} from "./constants.js"; +import { + PassThroughFollowing, + addConnectionLayoutSupport, + changeModeOfNodes, + getConnectedInputNodesAndFilterPassThroughs, + getConnectedOutputNodesAndFilterPassThroughs, + getGroupNodes, +} from "./utils.js"; + +class NodeModeRepeater extends BaseCollectorNode { + override readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.ALL; + + static override type = NodeTypesString.NODE_MODE_REPEATER; + static override title = NodeTypesString.NODE_MODE_REPEATER; + override comfyClass = NodeTypesString.NODE_MODE_REPEATER; + + private hasRelayInput = false; + private hasTogglerOutput = false; + + constructor(title?: string) { + super(title); + this.onConstructed(); + } + + override onConstructed(): boolean { + this.addOutput("OPT_CONNECTION", "*", { + color_on: "#Fc0", + color_off: "#a80", + }); + + return super.onConstructed(); + } + + override onConnectOutput( + outputIndex: number, + inputType: string | -1, + inputSlot: INodeInputSlot, + inputNode: LGraphNode, + inputIndex: number, + ): boolean { + // We can only connect to a a FAST_MUTER or FAST_BYPASSER if we aren't connectged to a relay, since the relay wins. + let canConnect = !this.hasRelayInput; + canConnect = + canConnect && super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex); + // Output can only connect to a FAST MUTER, FAST BYPASSER, NODE_COLLECTOR OR ACTION BUTTON + let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0] || inputNode; + return ( + canConnect && + [ + NodeTypesString.FAST_MUTER, + NodeTypesString.FAST_BYPASSER, + NodeTypesString.NODE_COLLECTOR, + NodeTypesString.FAST_ACTIONS_BUTTON, + NodeTypesString.REROUTE, + NodeTypesString.RANDOM_UNMUTER, + ].includes(nextNode.type || "") + ); + } + + override onConnectInput( + inputIndex: number, + outputType: string | -1, + outputSlot: INodeOutputSlot, + outputNode: LGraphNode, + outputIndex: number, + ): boolean { + // We can only connect to a a FAST_MUTER or FAST_BYPASSER if we aren't connectged to a relay, since the relay wins. + let canConnect = super.onConnectInput?.( + inputIndex, + outputType, + outputSlot, + outputNode, + outputIndex, + ); + // Output can only connect to a FAST MUTER or FAST BYPASSER + let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, outputNode)[0] || outputNode; + const isNextNodeRelay = nextNode.type === NodeTypesString.NODE_MODE_RELAY; + return canConnect && (!isNextNodeRelay || !this.hasTogglerOutput); + } + + override onConnectionsChange( + type: number, + slotIndex: number, + isConnected: boolean, + linkInfo: LLink, + ioSlot: INodeOutputSlot | INodeInputSlot, + ): void { + super.onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot); + + let hasTogglerOutput = false; + let hasRelayInput = false; + + const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this); + for (const outputNode of outputNodes) { + if ( + outputNode?.type === NodeTypesString.FAST_MUTER || + outputNode?.type === NodeTypesString.FAST_BYPASSER + ) { + hasTogglerOutput = true; + break; + } + } + + const inputNodes = getConnectedInputNodesAndFilterPassThroughs(this); + for (const [index, inputNode] of inputNodes.entries()) { + if (inputNode?.type === NodeTypesString.NODE_MODE_RELAY) { + // We can't be connected to a relay if we're connected to a toggler. Something has gone wrong. + if (hasTogglerOutput) { + console.log(`Can't be connected to a Relay if also output to a toggler.`); + this.disconnectInput(index); + } else { + hasRelayInput = true; + if (this.inputs[index]) { + this.inputs[index]!.color_on = "#FC0"; + this.inputs[index]!.color_off = "#a80"; + } + } + } else { + changeModeOfNodes(inputNode, this.mode); + } + } + + this.hasTogglerOutput = hasTogglerOutput; + this.hasRelayInput = hasRelayInput; + + // If we have a relay input, then we should remove the toggler output, or add it if not. + if (this.hasRelayInput) { + if (this.outputs[0]) { + this.disconnectOutput(0); + this.removeOutput(0); + } + } else if (!this.outputs[0]) { + this.addOutput("OPT_CONNECTION", "*", { + color_on: "#Fc0", + color_off: "#a80", + }); + } + } + + /** When a mode change, we want all connected nodes to match except for connected relays. */ + override onModeChange(from: LGraphEventMode | undefined, to: LGraphEventMode) { + super.onModeChange(from, to); + const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this).filter( + (node) => node.type !== NodeTypesString.NODE_MODE_RELAY, + ); + if (linkedNodes.length) { + for (const node of linkedNodes) { + if (node.type !== NodeTypesString.NODE_MODE_RELAY) { + // Use "to" as there may be other getters in the way to access this.mode directly. + changeModeOfNodes(node, to); + } + } + } else if (this.graph?._groups?.length) { + // No linked nodes.. check if we're in a group. + for (const group of this.graph._groups as LGraphGroup[]) { + group.recomputeInsideNodes(); + const groupNodes = getGroupNodes(group); + if (groupNodes?.includes(this)) { + for (const node of groupNodes) { + if (node !== this) { + // Use "to" as there may be other getters in the way to access this.mode directly. + changeModeOfNodes(node, to); + } + } + } + } + } + } + + override getHelp(): string { + return ` +

    + When this node's mode (Mute, Bypass, Active) changes, it will "repeat" that mode to all + connected input nodes, or, if there are no connected nodes AND it is overlapping a group, + "repeat" it's mode to all nodes in that group. +

    +
      +

    • + Optionally, connect this mode's output to a ${stripRgthree(NodeTypesString.FAST_MUTER)} + or ${stripRgthree(NodeTypesString.FAST_BYPASSER)} for a single toggle to quickly + mute/bypass all its connected nodes. +

      • +

    • + Optionally, connect a ${stripRgthree(NodeTypesString.NODE_MODE_RELAY)} to this nodes + inputs to have it automatically toggle its mode. If connected, this will always take + precedence (and disconnect any connected fast togglers). +

      • +
    + `; + } +} + +app.registerExtension({ + name: "rgthree.NodeModeRepeater", + registerCustomNodes() { + addConnectionLayoutSupport(NodeModeRepeater, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + + LiteGraph.registerNodeType(NodeModeRepeater.type, NodeModeRepeater); + NodeModeRepeater.category = NodeModeRepeater._category; + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_conductor.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_conductor.ts new file mode 100644 index 00000000..9fb57cfe --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_conductor.ts @@ -0,0 +1,137 @@ +import type {Parser, Node, Tree} from "web-tree-sitter"; +import type {IStringWidget, IWidget} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {Exposed, execute, PyTuple} from "rgthree/common/py_parser.js"; +import {RgthreeBaseVirtualNode} from "./base_node.js"; +import {RgthreeBetterButtonWidget} from "./utils_widgets.js"; +import {NodeTypesString} from "./constants.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; +import { changeModeOfNodes, getNodeById } from "./utils.js"; + +const BUILT_INS = { + node: { + fn: (query: string | number) => { + if (typeof query === "number" || /^\d+(\.\d+)?/.exec(query)) { + return new ComfyNodeWrapper(Number(query)); + } + return null; + }, + }, +}; + +class RgthreePowerConductor extends RgthreeBaseVirtualNode { + static override title = NodeTypesString.POWER_CONDUCTOR; + static override type = NodeTypesString.POWER_CONDUCTOR; + override comfyClass = NodeTypesString.POWER_CONDUCTOR; + + override serialize_widgets = true; + + private codeWidget: IStringWidget; + private buttonWidget: RgthreeBetterButtonWidget; + + constructor(title = RgthreePowerConductor.title) { + super(title); + + this.codeWidget = ComfyWidgets.STRING(this, "", ["STRING", {multiline: true}], app).widget; + this.addCustomWidget(this.codeWidget); + + (this.buttonWidget = new RgthreeBetterButtonWidget("Run", (...args: any[]) => { + this.execute(); + })), + this.addCustomWidget(this.buttonWidget); + + this.onConstructed(); + } + + private execute() { + execute(this.codeWidget.value, {}, BUILT_INS); + } +} + +const NODE_CLASS = RgthreePowerConductor; + +/** + * A wrapper around nodes to add helpers and control the exposure of properties and methods. + */ +class ComfyNodeWrapper { + #id: number; + + constructor(id: number) { + this.#id = id; + } + + private getNode() { + return getNodeById(this.#id)!; + } + + @Exposed get id() { + return this.getNode().id; + } + + @Exposed get title() { + return this.getNode().title; + } + set title(value: string) { + this.getNode().title = value; + } + + @Exposed get widgets() { + return new PyTuple(this.getNode().widgets?.map((w) => new ComfyWidgetWrapper(w as IWidget))); + } + + @Exposed get mode() { + return this.getNode().mode; + } + + @Exposed mute() { + changeModeOfNodes(this.getNode(), 2); + } + + @Exposed bypass() { + changeModeOfNodes(this.getNode(), 4); + } + + @Exposed enable() { + changeModeOfNodes(this.getNode(), 0); + } +} + +/** + * A wrapper around widgets to add helpers and control the exposure of properties and methods. + */ +class ComfyWidgetWrapper { + #widget: IWidget; + + constructor(widget: IWidget) { + this.#widget = widget; + } + + @Exposed get value() { + return this.#widget.value; + } + + @Exposed get label() { + return this.#widget.label; + } + + @Exposed toggle(value?: boolean) { + // IF the widget has a "toggle" method, then call it. + if (typeof (this.#widget as any)["toggle"] === "function") { + (this.#widget as any)["toggle"](value); + } else { + // Error. + } + } +} + +/** Register the node. */ +app.registerExtension({ + name: "rgthree.PowerConductor", + registerCustomNodes() { + if (CONFIG_SERVICE.getConfigValue("unreleased.power_conductor.enabled")) { + NODE_CLASS.setUp(); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_lora_loader.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_lora_loader.ts new file mode 100644 index 00000000..02b2ff88 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_lora_loader.ts @@ -0,0 +1,851 @@ +import type { + LGraphNode as TLGraphNode, + LGraphCanvas, + Vector2, + IContextMenuValue, + IFoundSlot, + CanvasMouseEvent, + ISerialisedNode, + ICustomWidget, + CanvasPointerEvent, +} from "@comfyorg/frontend"; +import type {ComfyApiFormat, ComfyNodeDef} from "typings/comfy.js"; +import type {RgthreeModelInfo} from "typings/rgthree.js"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {rgthree} from "./rgthree.js"; +import {addConnectionLayoutSupport} from "./utils.js"; +import {NodeTypesString} from "./constants.js"; +import { + drawInfoIcon, + drawNumberWidgetPart, + drawRoundedRectangle, + drawTogglePart, + fitString, + isLowQuality, +} from "./utils_canvas.js"; +import { + RgthreeBaseHitAreas, + RgthreeBaseWidget, + RgthreeBetterButtonWidget, + RgthreeDividerWidget, +} from "./utils_widgets.js"; +import {rgthreeApi} from "rgthree/common/rgthree_api.js"; +import {showLoraChooser} from "./utils_menu.js"; +import {moveArrayItem, removeArrayItem} from "rgthree/common/shared_utils.js"; +import {RgthreeLoraInfoDialog} from "./dialog_info.js"; +import {LORA_INFO_SERVICE} from "rgthree/common/model_info_service.js"; +// import { RgthreePowerLoraChooserDialog } from "./dialog_power_lora_chooser.js"; + +const PROP_LABEL_SHOW_STRENGTHS = "Show Strengths"; +const PROP_LABEL_SHOW_STRENGTHS_STATIC = `@${PROP_LABEL_SHOW_STRENGTHS}`; +const PROP_VALUE_SHOW_STRENGTHS_SINGLE = "Single Strength"; +const PROP_VALUE_SHOW_STRENGTHS_SEPARATE = "Separate Model & Clip"; + +/** + * The Power Lora Loader is a super-simply Lora Loader node that can load multiple Loras at once + * in an ultra-condensed node allowing fast toggling, and advanced strength setting. + */ +class RgthreePowerLoraLoader extends RgthreeBaseServerNode { + static override title = NodeTypesString.POWER_LORA_LOADER; + static override type = NodeTypesString.POWER_LORA_LOADER; + static comfyClass = NodeTypesString.POWER_LORA_LOADER; + + override serialize_widgets = true; + + private logger = rgthree.newLogSession(`[Power Lora Stack]`); + + static [PROP_LABEL_SHOW_STRENGTHS_STATIC] = { + type: "combo", + values: [PROP_VALUE_SHOW_STRENGTHS_SINGLE, PROP_VALUE_SHOW_STRENGTHS_SEPARATE], + }; + + /** Counts the number of lora widgets. This is used to give unique names. */ + private loraWidgetsCounter = 0; + + /** Keep track of the spacer, new lora widgets will go before it when it exists. */ + private widgetButtonSpacer: ICustomWidget | null = null; + + constructor(title = NODE_CLASS.title) { + super(title); + + this.properties[PROP_LABEL_SHOW_STRENGTHS] = PROP_VALUE_SHOW_STRENGTHS_SINGLE; + + // Prefetch loras list. + rgthreeApi.getLoras(); + + // [πŸ€] If ComfyUI is loading from API JSON it doesn't pass us the actual information at all + // (like, in a `configure` call) and tries to set the widget data on its own. Unfortunately, + // since Power Lora Loader has dynamic widgets, this fails on ComfyUI's side. We can do so after + // the fact but, unfortuntely, we need to do it after a timeout since we don't have any + // information at this point to be able to tell what data we need (like, even the node id, let + // alone the actual data). + if (rgthree.loadingApiJson) { + const fullApiJson = rgthree.loadingApiJson; + setTimeout(() => { + this.configureFromApiJson(fullApiJson); + }, 16); + } + } + + private configureFromApiJson(fullApiJson: ComfyApiFormat) { + if (this.id == null) { + const [n, v] = this.logger.errorParts("Cannot load from API JSON without node id."); + console[n]?.(...v); + return; + } + const nodeData = + fullApiJson[this.id] || fullApiJson[String(this.id)] || fullApiJson[Number(this.id)]; + if (nodeData == null) { + const [n, v] = this.logger.errorParts(`No node found in API JSON for node id ${this.id}.`); + console[n]?.(...v); + return; + } + this.configure({ + widgets_values: Object.values(nodeData.inputs).filter( + (input) => typeof (input as any)?.["lora"] === "string", + ), + }); + } + + /** + * Handles configuration from a saved workflow by first removing our default widgets that were + * added in `onNodeCreated`, letting `super.configure` and do nothing, then create our lora + * widgets and, finally, add back in our default widgets. + */ + override configure( + info: ISerialisedNode | {widgets_values: ISerialisedNode["widgets_values"]}, + ): void { + while (this.widgets?.length) this.removeWidget(0); + this.widgetButtonSpacer = null; + // Since we may be calling into configure manually for just widgets_values setting (like, from + // API JSON) we want to only call the parent class's configure with a real ISerialisedNode data. + if ((info as ISerialisedNode).id != null) { + super.configure(info as ISerialisedNode); + } + + (this as any)._tempWidth = this.size[0]; + (this as any)._tempHeight = this.size[1]; + for (const widgetValue of info.widgets_values || []) { + if ((widgetValue as PowerLoraLoaderWidgetValue)?.lora !== undefined) { + const widget = this.addNewLoraWidget(); + widget.value = {...(widgetValue as PowerLoraLoaderWidgetValue)}; + } + } + this.addNonLoraWidgets(); + this.size[0] = (this as any)._tempWidth; + this.size[1] = Math.max((this as any)._tempHeight, this.computeSize()[1]); + } + + /** + * Adds the non-lora widgets. If we'll be configured then we remove them and add them back, so + * this is really only for newly created nodes in the current session. + */ + override onNodeCreated() { + super.onNodeCreated?.(); + this.addNonLoraWidgets(); + const computed = this.computeSize(); + this.size = this.size || [0, 0]; + this.size[0] = Math.max(this.size[0], computed[0]); + this.size[1] = Math.max(this.size[1], computed[1]); + this.setDirtyCanvas(true, true); + } + + /** Adds a new lora widget in the proper slot. */ + private addNewLoraWidget(lora?: string) { + this.loraWidgetsCounter++; + const widget = this.addCustomWidget( + new PowerLoraLoaderWidget("lora_" + this.loraWidgetsCounter), + ) as PowerLoraLoaderWidget; + if (lora) widget.setLora(lora); + if (this.widgetButtonSpacer) { + moveArrayItem(this.widgets, widget, this.widgets.indexOf(this.widgetButtonSpacer)); + } + + return widget; + } + + /** Adds the non-lora widgets around any lora ones that may be there from configuration. */ + private addNonLoraWidgets() { + moveArrayItem( + this.widgets, + this.addCustomWidget(new RgthreeDividerWidget({marginTop: 4, marginBottom: 0, thickness: 0})), + 0, + ); + moveArrayItem(this.widgets, this.addCustomWidget(new PowerLoraLoaderHeaderWidget()), 1); + + this.widgetButtonSpacer = this.addCustomWidget( + new RgthreeDividerWidget({marginTop: 4, marginBottom: 0, thickness: 0}), + ) as RgthreeDividerWidget; + + this.addCustomWidget( + new RgthreeBetterButtonWidget( + "βž• Add Lora", + (event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) => { + rgthreeApi.getLoras().then((lorasDetails) => { + const loras = lorasDetails.map((l) => l.file); + showLoraChooser( + event as MouseEvent, + (value: IContextMenuValue | string) => { + if (typeof value === "string") { + if (value.includes("Power Lora Chooser")) { + // new RgthreePowerLoraChooserDialog().show(); + } else if (value !== "NONE") { + this.addNewLoraWidget(value); + const computed = this.computeSize(); + const tempHeight = (this as any)._tempHeight ?? 15; + this.size[1] = Math.max(tempHeight, computed[1]); + this.setDirtyCanvas(true, true); + } + } + // }, null, ["⚑️ Power Lora Chooser", ...loras]); + }, + null, + [...loras], + ); + }); + return true; + }, + ), + ); + } + + /** + * Hacks the `getSlotInPosition` call made from LiteGraph so we can show a custom context menu + * for widgets. + * + * Normally this method, called from LiteGraph's processContextMenu, will only get Inputs or + * Outputs. But that's not good enough because we we also want to provide a custom menu when + * clicking a widget for this node... so we are left to HACK once again! + * + * To achieve this: + * - Here, in LiteGraph's processContextMenu it asks the clicked node to tell it which input or + * output the user clicked on in `getSlotInPosition` + * - We check, and if we didn't, then we see if we clicked a widget and, if so, pass back some + * data that looks like we clicked an output to fool LiteGraph like a silly child. + * - As LiteGraph continues in its `processContextMenu`, it will then immediately call + * the clicked node's `getSlotMenuOptions` when `getSlotInPosition` returns data. + * - So, just below, we can then give LiteGraph the ContextMenu options we have. + * + * The only issue is that LiteGraph also checks `input/output.type` to set the ContextMenu title, + * so we need to supply that property (and set it to what we want our title). Otherwise, this + * should be pretty clean. + */ + override getSlotInPosition(canvasX: number, canvasY: number): any { + const slot = super.getSlotInPosition(canvasX, canvasY); + // No slot, let's see if it's a widget. + if (!slot) { + let lastWidget = null; + for (const widget of this.widgets) { + // If last_y isn't set, something is wrong. Bail. + if (!widget.last_y) return; + if (canvasY > this.pos[1] + widget.last_y) { + lastWidget = widget; + continue; + } + break; + } + // Only care about lora widget clicks. + if (lastWidget?.name?.startsWith("lora_")) { + return {widget: lastWidget, output: {type: "LORA WIDGET"}}; + } + } + return slot; + } + + /** + * Working with the overridden `getSlotInPosition` above, this method checks if the passed in + * option is actually a widget from it and then hijacks the context menu all together. + */ + override getSlotMenuOptions(slot: IFoundSlot) { + // Oddly, LiteGraph doesn't call back into our node with a custom menu (even though it let's us + // define a custom menu to begin with... wtf?). So, we'll return null so the default is not + // triggered and then we'll just show one ourselves because.. yea. + if (slot?.widget?.name?.startsWith("lora_")) { + const widget = slot.widget as PowerLoraLoaderWidget; + const index = this.widgets.indexOf(widget); + const canMoveUp = !!this.widgets[index - 1]?.name?.startsWith("lora_"); + const canMoveDown = !!this.widgets[index + 1]?.name?.startsWith("lora_"); + const menuItems: (IContextMenuValue | null)[] = [ + { + content: `ℹ️ Show Info`, + callback: () => { + widget.showLoraInfoDialog(); + }, + }, + null, // Divider + { + content: `${widget.value.on ? "⚫" : "🟒"} Toggle ${widget.value.on ? "Off" : "On"}`, + callback: () => { + widget.value.on = !widget.value.on; + }, + }, + { + content: `⬆️ Move Up`, + disabled: !canMoveUp, + callback: () => { + moveArrayItem(this.widgets, widget, index - 1); + }, + }, + { + content: `⬇️ Move Down`, + disabled: !canMoveDown, + callback: () => { + moveArrayItem(this.widgets, widget, index + 1); + }, + }, + { + content: `πŸ—‘οΈ Remove`, + callback: () => { + removeArrayItem(this.widgets, widget); + }, + }, + ]; + new LiteGraph.ContextMenu(menuItems, { + title: "LORA WIDGET", + event: rgthree.lastCanvasMouseEvent!, + }); + + // [πŸ€] ComfyUI doesn't have a possible return type as falsy, even though the impl skips the + // menu when the return is falsy. Casting as any. + return undefined as any; + } + return this.defaultGetSlotMenuOptions(slot); + } + + /** + * When `refreshComboInNode` is called from ComfyUI, then we'll kick off a fresh loras fetch. + */ + override refreshComboInNode(defs: any) { + rgthreeApi.getLoras(true); + } + + /** + * Returns true if there are any Lora Widgets. Useful for widgets to ask as they render. + */ + hasLoraWidgets() { + return !!this.widgets?.find((w) => w.name?.startsWith("lora_")); + } + + /** + * This will return true when all lora widgets are on, false when all are off, or null if it's + * mixed. + */ + allLorasState() { + let allOn = true; + let allOff = true; + for (const widget of this.widgets) { + if (widget.name?.startsWith("lora_")) { + const on = (widget.value as any)?.on; + allOn = allOn && on === true; + allOff = allOff && on === false; + if (!allOn && !allOff) { + return null; + } + } + } + return allOn && this.widgets?.length ? true : false; + } + + /** + * Toggles all the loras on or off. + */ + toggleAllLoras() { + const allOn = this.allLorasState(); + const toggledTo = !allOn ? true : false; + for (const widget of this.widgets) { + if (widget.name?.startsWith("lora_") && (widget.value as any)?.on != null) { + (widget.value as any).on = toggledTo; + } + } + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + addConnectionLayoutSupport(NODE_CLASS, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + NODE_CLASS.category = comfyClass.category; + }); + } + + override getHelp() { + return ` +

    + The ${this.type!.replace("(rgthree)", "")} is a powerful node that condenses 100s of pixels + of functionality in a single, dynamic node that allows you to add loras, change strengths, + and quickly toggle on/off all without taking up half your screen. +

    +
      +

    • + Add as many Lora's as you would like by clicking the "+ Add Lora" button. + There's no real limit! +

      • +

    • + Right-click on a Lora widget for special options to move the lora up or down + (no image affect, only presentational), toggle it on/off, or delete the row all together. +

      • +
    • +

      + Properties. You can change the following properties (by right-clicking + on the node, and select "Properties" or "Properties Panel" from the menu): +

      +
        +

      • + ${PROP_LABEL_SHOW_STRENGTHS} - Change between showing a single, simple + strength (which will be used for both model and clip), or a more advanced view with + both model and clip strengths being modifiable. +

        • +
      +
      • +
    `; + } +} + +/** + * The PowerLoraLoaderHeaderWidget that renders a toggle all switch, as well as some title info + * (more necessary for the double model & clip strengths to label them). + */ +class PowerLoraLoaderHeaderWidget extends RgthreeBaseWidget<{type: string}> { + override value = {type: "PowerLoraLoaderHeaderWidget"}; + override readonly type = "custom"; + + protected override hitAreas: RgthreeBaseHitAreas<"toggle"> = { + toggle: {bounds: [0, 0] as Vector2, onDown: this.onToggleDown}, + }; + + private showModelAndClip: boolean | null = null; + + constructor(name: string = "PowerLoraLoaderHeaderWidget") { + super(name); + } + + draw( + ctx: CanvasRenderingContext2D, + node: RgthreePowerLoraLoader, + w: number, + posY: number, + height: number, + ) { + if (!node.hasLoraWidgets()) { + return; + } + // Since draw is the loop that runs, this is where we'll check the property state (rather than + // expect the node to tell us it's state etc). + this.showModelAndClip = + node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE; + const margin = 10; + const innerMargin = margin * 0.33; + const lowQuality = isLowQuality(); + const allLoraState = node.allLorasState(); + + // Move slightly down. We don't have a border and this feels a bit nicer. + posY += 2; + const midY = posY + height * 0.5; + let posX = 10; + ctx.save(); + this.hitAreas.toggle.bounds = drawTogglePart(ctx, {posX, posY, height, value: allLoraState}); + + if (!lowQuality) { + posX += this.hitAreas.toggle.bounds[1] + innerMargin; + + ctx.globalAlpha = app.canvas.editor_alpha * 0.55; + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillText("Toggle All", posX, midY); + + let rposX = node.size[0] - margin - innerMargin - innerMargin; + ctx.textAlign = "center"; + ctx.fillText( + this.showModelAndClip ? "Clip" : "Strength", + rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, + midY, + ); + if (this.showModelAndClip) { + rposX = rposX - drawNumberWidgetPart.WIDTH_TOTAL - innerMargin * 2; + ctx.fillText("Model", rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, midY); + } + } + ctx.restore(); + } + + /** + * Handles a pointer down on the toggle's defined hit area. + */ + onToggleDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + (node as RgthreePowerLoraLoader).toggleAllLoras(); + this.cancelMouseDown(); + return true; + } +} + +const DEFAULT_LORA_WIDGET_DATA: PowerLoraLoaderWidgetValue = { + on: true, + lora: null as string | null, + strength: 1, + strengthTwo: null as number | null, +}; + +type PowerLoraLoaderWidgetValue = { + on: boolean; + lora: string | null; + strength: number; + strengthTwo: number | null; +}; + +/** + * The PowerLoaderWidget that combines several custom drawing and functionality in a single row. + */ +class PowerLoraLoaderWidget extends RgthreeBaseWidget { + override readonly type = "custom"; + + /** Whether the strength has changed with mouse move (to cancel mouse up). */ + private haveMouseMovedStrength = false; + private loraInfoPromise: Promise | null = null; + private loraInfo: RgthreeModelInfo | null = null; + + private showModelAndClip: boolean | null = null; + + protected override hitAreas: RgthreeBaseHitAreas< + | "toggle" + | "lora" + // | "info" + | "strengthDec" + | "strengthVal" + | "strengthInc" + | "strengthAny" + | "strengthTwoDec" + | "strengthTwoVal" + | "strengthTwoInc" + | "strengthTwoAny" + > = { + toggle: {bounds: [0, 0] as Vector2, onDown: this.onToggleDown}, + lora: {bounds: [0, 0] as Vector2, onClick: this.onLoraClick}, + // info: { bounds: [0, 0] as Vector2, onDown: this.onInfoDown }, + + strengthDec: {bounds: [0, 0] as Vector2, onClick: this.onStrengthDecDown}, + strengthVal: {bounds: [0, 0] as Vector2, onClick: this.onStrengthValUp}, + strengthInc: {bounds: [0, 0] as Vector2, onClick: this.onStrengthIncDown}, + strengthAny: {bounds: [0, 0] as Vector2, onMove: this.onStrengthAnyMove}, + + strengthTwoDec: {bounds: [0, 0] as Vector2, onClick: this.onStrengthTwoDecDown}, + strengthTwoVal: {bounds: [0, 0] as Vector2, onClick: this.onStrengthTwoValUp}, + strengthTwoInc: {bounds: [0, 0] as Vector2, onClick: this.onStrengthTwoIncDown}, + strengthTwoAny: {bounds: [0, 0] as Vector2, onMove: this.onStrengthTwoAnyMove}, + }; + + constructor(name: string) { + super(name); + } + + private _value = { + on: true, + lora: null as string | null, + strength: 1, + strengthTwo: null as number | null, + }; + + set value(v) { + this._value = v; + // In case widgets are messed up, we can correct course here. + if (typeof this._value !== "object") { + this._value = {...DEFAULT_LORA_WIDGET_DATA}; + if (this.showModelAndClip) { + this._value.strengthTwo = this._value.strength; + } + } + this.getLoraInfo(); + } + + get value() { + return this._value; + } + + setLora(lora: string) { + this._value.lora = lora; + this.getLoraInfo(); + } + + /** Draws our widget with a toggle, lora selector, and number selector all in a single row. */ + draw(ctx: CanvasRenderingContext2D, node: TLGraphNode, w: number, posY: number, height: number) { + // Since draw is the loop that runs, this is where we'll check the property state (rather than + // expect the node to tell us it's state etc). + let currentShowModelAndClip = + node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE; + if (this.showModelAndClip !== currentShowModelAndClip) { + let oldShowModelAndClip = this.showModelAndClip; + this.showModelAndClip = currentShowModelAndClip; + if (this.showModelAndClip) { + // If we're setting show both AND we're not null, then re-set to the current strength. + if (oldShowModelAndClip != null) { + this.value.strengthTwo = this.value.strength ?? 1; + } + } else { + this.value.strengthTwo = null; + this.hitAreas.strengthTwoDec.bounds = [0, -1]; + this.hitAreas.strengthTwoVal.bounds = [0, -1]; + this.hitAreas.strengthTwoInc.bounds = [0, -1]; + this.hitAreas.strengthTwoAny.bounds = [0, -1]; + } + } + + ctx.save(); + const margin = 10; + const innerMargin = margin * 0.33; + const lowQuality = isLowQuality(); + const midY = posY + height * 0.5; + + // We'll move posX along as we draw things. + let posX = margin; + + // Draw the background. + drawRoundedRectangle(ctx, {pos: [posX, posY], size: [node.size[0] - margin * 2, height]}); + + // Draw the toggle + this.hitAreas.toggle.bounds = drawTogglePart(ctx, {posX, posY, height, value: this.value.on}); + posX += this.hitAreas.toggle.bounds[1] + innerMargin; + + // If low quality, then we're done rendering. + if (lowQuality) { + ctx.restore(); + return; + } + + // If we're not toggled on, then make everything after faded. + if (!this.value.on) { + ctx.globalAlpha = app.canvas.editor_alpha * 0.4; + } + + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + + // Now, we draw the strength number part on the right, so we know the width of it to draw the + // lora label as flexible. + let rposX = node.size[0] - margin - innerMargin - innerMargin; + + const strengthValue = this.showModelAndClip + ? (this.value.strengthTwo ?? 1) + : (this.value.strength ?? 1); + + let textColor: string | undefined = undefined; + if (this.loraInfo?.strengthMax != null && strengthValue > this.loraInfo?.strengthMax) { + textColor = "#c66"; + } else if (this.loraInfo?.strengthMin != null && strengthValue < this.loraInfo?.strengthMin) { + textColor = "#c66"; + } + + const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, { + posX: node.size[0] - margin - innerMargin - innerMargin, + posY, + height, + value: strengthValue, + direction: -1, + textColor, + }); + + this.hitAreas.strengthDec.bounds = leftArrow; + this.hitAreas.strengthVal.bounds = text; + this.hitAreas.strengthInc.bounds = rightArrow; + this.hitAreas.strengthAny.bounds = [leftArrow[0], rightArrow[0] + rightArrow[1] - leftArrow[0]]; + + rposX = leftArrow[0] - innerMargin; + + if (this.showModelAndClip) { + rposX -= innerMargin; + // If we're showing both, then the rightmost we just drew is our "strengthTwo", so reset and + // then draw our model ("strength" one) to the left. + this.hitAreas.strengthTwoDec.bounds = this.hitAreas.strengthDec.bounds; + this.hitAreas.strengthTwoVal.bounds = this.hitAreas.strengthVal.bounds; + this.hitAreas.strengthTwoInc.bounds = this.hitAreas.strengthInc.bounds; + this.hitAreas.strengthTwoAny.bounds = this.hitAreas.strengthAny.bounds; + + let textColor: string | undefined = undefined; + if (this.loraInfo?.strengthMax != null && this.value.strength > this.loraInfo?.strengthMax) { + textColor = "#c66"; + } else if ( + this.loraInfo?.strengthMin != null && + this.value.strength < this.loraInfo?.strengthMin + ) { + textColor = "#c66"; + } + const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, { + posX: rposX, + posY, + height, + value: this.value.strength ?? 1, + direction: -1, + textColor, + }); + this.hitAreas.strengthDec.bounds = leftArrow; + this.hitAreas.strengthVal.bounds = text; + this.hitAreas.strengthInc.bounds = rightArrow; + this.hitAreas.strengthAny.bounds = [ + leftArrow[0], + rightArrow[0] + rightArrow[1] - leftArrow[0], + ]; + rposX = leftArrow[0] - innerMargin; + } + + const infoIconSize = height * 0.66; + const infoWidth = infoIconSize + innerMargin + innerMargin; + // Draw an info emoji; if checks if it's enabled (to quickly turn it on or off) + if ((this.hitAreas as any)["info"]) { + rposX -= innerMargin; + drawInfoIcon(ctx, rposX - infoIconSize, posY + (height - infoIconSize) / 2, infoIconSize); + // ctx.fillText('β„Ή', posX, midY); + (this.hitAreas as any).info.bounds = [rposX - infoIconSize, infoWidth]; + rposX = rposX - infoIconSize - innerMargin; + } + + // Draw lora label + const loraWidth = rposX - posX; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + const loraLabel = String(this.value?.lora || "None"); + ctx.fillText(fitString(ctx, loraLabel, loraWidth), posX, midY); + + this.hitAreas.lora.bounds = [posX, loraWidth]; + posX += loraWidth + innerMargin; + + ctx.globalAlpha = app.canvas.editor_alpha; + ctx.restore(); + } + + override serializeValue( + node: TLGraphNode, + index: number, + ): PowerLoraLoaderWidgetValue | Promise { + const v = {...this.value}; + // Never send the second value to the backend if we're not showing it, otherwise, let's just + // make sure it's not null. + if (!this.showModelAndClip) { + delete (v as any).strengthTwo; + } else { + this.value.strengthTwo = this.value.strengthTwo ?? 1; + v.strengthTwo = this.value.strengthTwo; + } + return v; + } + + onToggleDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.value.on = !this.value.on; + this.cancelMouseDown(); // Clear the down since we handle it. + return true; + } + + onInfoDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.showLoraInfoDialog(); + } + + onLoraClick(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + showLoraChooser(event, (value: IContextMenuValue) => { + if (typeof value === "string") { + this.value.lora = value; + this.loraInfo = null; + this.getLoraInfo(); + } + node.setDirtyCanvas(true, true); + }); + this.cancelMouseDown(); + } + + onStrengthDecDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.stepStrength(-1, false); + } + onStrengthIncDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.stepStrength(1, false); + } + onStrengthTwoDecDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.stepStrength(-1, true); + } + onStrengthTwoIncDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.stepStrength(1, true); + } + + onStrengthAnyMove(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.doOnStrengthAnyMove(event, false); + } + + onStrengthTwoAnyMove(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) { + this.doOnStrengthAnyMove(event, true); + } + + private doOnStrengthAnyMove(event: CanvasMouseEvent, isTwo = false) { + if (event.deltaX) { + let prop: "strengthTwo" | "strength" = isTwo ? "strengthTwo" : "strength"; + this.haveMouseMovedStrength = true; + this.value[prop] = (this.value[prop] ?? 1) + event.deltaX * 0.05; + } + } + + onStrengthValUp(event: CanvasPointerEvent, pos: Vector2, node: TLGraphNode) { + this.doOnStrengthValUp(event, false); + } + + onStrengthTwoValUp(event: CanvasPointerEvent, pos: Vector2, node: TLGraphNode) { + this.doOnStrengthValUp(event, true); + } + + private doOnStrengthValUp(event: CanvasPointerEvent, isTwo = false) { + if (this.haveMouseMovedStrength) return; + let prop: "strengthTwo" | "strength" = isTwo ? "strengthTwo" : "strength"; + const canvas = app.canvas as LGraphCanvas; + canvas.prompt("Value", this.value[prop], (v: string) => (this.value[prop] = Number(v)), event); + } + + override onMouseUp(event: CanvasPointerEvent, pos: Vector2, node: TLGraphNode): boolean | void { + super.onMouseUp(event, pos, node); + this.haveMouseMovedStrength = false; + } + + showLoraInfoDialog() { + if (!this.value.lora || this.value.lora === "None") { + return; + } + const infoDialog = new RgthreeLoraInfoDialog(this.value.lora).show(); + infoDialog.addEventListener("close", ((e: CustomEvent<{dirty: boolean}>) => { + if (e.detail.dirty) { + this.getLoraInfo(true); + } + }) as EventListener); + } + + private stepStrength(direction: -1 | 1, isTwo = false) { + let step = 0.05; + let prop: "strengthTwo" | "strength" = isTwo ? "strengthTwo" : "strength"; + let strength = (this.value[prop] ?? 1) + step * direction; + this.value[prop] = Math.round(strength * 100) / 100; + } + + private getLoraInfo(force = false) { + if (!this.loraInfoPromise || force == true) { + let promise; + if (this.value.lora && this.value.lora != "None") { + promise = LORA_INFO_SERVICE.getInfo(this.value.lora, force, true); + } else { + promise = Promise.resolve(null); + } + this.loraInfoPromise = promise.then((v) => (this.loraInfo = v)); + } + return this.loraInfoPromise; + } +} + +/** An uniformed name reference to the node class. */ +const NODE_CLASS = RgthreePowerLoraLoader; + +/** Register the node. */ +app.registerExtension({ + name: "rgthree.PowerLoraLoader", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_primitive.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_primitive.ts new file mode 100644 index 00000000..a4e48470 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_primitive.ts @@ -0,0 +1,254 @@ +import type { + IWidget, + INodeInputSlot, + LGraphCanvas as TLGraphCanvas, + LGraphNodeConstructor, + IContextMenuValue, + INodeOutputSlot, + ISlotType, + ISerialisedNode, + LLink, + IBaseWidget, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {moveArrayItem} from "rgthree/common/shared_utils.js"; + +const PROPERTY_HIDE_TYPE_SELECTOR = "hideTypeSelector"; +const PRIMITIVES = { + STRING: "STRING", + // "STRING (multiline)": "STRING", + INT: "INT", + FLOAT: "FLOAT", + BOOLEAN: "BOOLEAN", +}; + +class RgthreePowerPrimitive extends RgthreeBaseServerNode { + static override title = NodeTypesString.POWER_PRIMITIVE; + static override type = NodeTypesString.POWER_PRIMITIVE; + static comfyClass = NodeTypesString.POWER_PRIMITIVE; + + private outputTypeWidget!: IWidget; + private valueWidget!: IWidget; + private typeState: string = ''; + + static "@hideTypeSelector" = {type: "boolean"}; + + override properties!: RgthreeBaseServerNode["properties"] & { + [PROPERTY_HIDE_TYPE_SELECTOR]: boolean; + }; + + constructor(title = NODE_CLASS.title) { + super(title); + this.properties[PROPERTY_HIDE_TYPE_SELECTOR] = false; + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + + /** + * Adds the non-lora widgets. If we'll be configured then we remove them and add them back, so + * this is really only for newly created nodes in the current session. + */ + override onNodeCreated() { + super.onNodeCreated?.(); + this.addInitialWidgets(); + } + + /** + * Ensures to set the type widget whenever we configure. + */ + override configure(info: ISerialisedNode): void { + super.configure(info); + // Update BOOL to BOOLEAN due to a bug using BOOL instead of BOOLEAN. + if (this.outputTypeWidget.value === 'BOOL') { + this.outputTypeWidget.value = 'BOOLEAN'; + } + setTimeout(() => { + this.setTypedData(); + }); + } + + /** + * Adds menu options for the node: quick toggle to show/hide the first widget, and a menu-option + * to change the type (for easier changing when hiding the first widget). + */ + override getExtraMenuOptions( + canvas: TLGraphCanvas, + options: (IContextMenuValue | null)[], + ) { + const that = this; + super.getExtraMenuOptions(canvas, options); + const isHidden = !!this.properties[PROPERTY_HIDE_TYPE_SELECTOR]; + + const menuItems = [ + { + content: `${isHidden ? "Show" : "Hide"} Type Selector Widget`, + callback: (...args: any[]) => { + this.setProperty( + PROPERTY_HIDE_TYPE_SELECTOR, + !this.properties[PROPERTY_HIDE_TYPE_SELECTOR], + ); + }, + }, + { + content: `Set type`, + submenu: { + options: Object.keys(PRIMITIVES), + callback(value: any, ...args: any[]) { + that.outputTypeWidget.value = value; + that.setTypedData(); + }, + }, + }, + ]; + + options.splice(0, 0, ...menuItems, null); + return []; + } + + private addInitialWidgets() { + if (!this.outputTypeWidget) { + this.outputTypeWidget = this.addWidget( + "combo", + "type", + "STRING", + (...args) => { + this.setTypedData(); + }, + { + values: Object.keys(PRIMITIVES), + }, + ) as IWidget; + this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR]; + } + this.setTypedData(); + } + + /** + * Sets the correct inputs, outputs, and widgets for the designated type (with the + * `outputTypeWidget`) being the source of truth. + */ + private setTypedData() { + const name = "value"; + const type = this.outputTypeWidget.value as string; + const linked = !!this.inputs?.[0]?.link; + const newTypeState = `${type}|${linked}`; + if (this.typeState == newTypeState) return; + this.typeState = newTypeState; + + let value = this.valueWidget?.value ?? null; + let newWidget: IWidget | null= null; + // If we're linked, then set the UI to an empty string widget input, since the ComfyUI is rather + // confusing by showing a value that is not the actual value used (from the input). + if (linked) { + newWidget = ComfyWidgets["STRING"](this, name, ["STRING"], app).widget; + newWidget.value = ""; + } else if (type == "STRING") { + newWidget = ComfyWidgets["STRING"](this, name, ["STRING", {multiline: true}], app).widget; + newWidget.value = value ? "" : String(value); + } else if (type === "INT" || type === "FLOAT") { + const isFloat = type === "FLOAT"; + newWidget = this.addWidget("number", name, value ?? 1 as any, undefined, { + precision: isFloat ? 1 : 0, + step2: isFloat ? 0.1 : 0, + }) as IWidget; + value = Number(value); + value = value == null || isNaN(value) ? 0 : value; + newWidget.value = value; + } else if (type === "BOOLEAN") { + newWidget = this.addWidget("toggle", name, !!(value ?? true), undefined, { + on: "true", + off: "false", + }) as IWidget; + if (typeof value === "string") { + value = !["false", "null", "None", "", "0"].includes(value.toLowerCase()); + } + newWidget.value = !!value; + } + if (newWidget == null) { + throw new Error(`Unsupported type "${type}".`); + } + + if (this.valueWidget) { + this.replaceWidget(this.valueWidget, newWidget); + } else { + if (!this.widgets.includes(newWidget)) { + this.addCustomWidget(newWidget); + } + moveArrayItem(this.widgets, newWidget, 1); + } + this.valueWidget = newWidget; + + // Set the input data. + if (!this.inputs?.length) { + this.addInput("value", "*", {widget: this.valueWidget as any}); + } else { + this.inputs[0]!.widget = this.valueWidget as any; + } + + // Set the output data. + const output = this.outputs[0]!; + const outputLabel = output.label === "*" || output.label === output.type ? null : output.label; + output.type = type; + output.label = outputLabel || output.type; + } + + /** + * Sets the correct typed data when we change any connections (really care about + * onnecting/disconnecting the value input.) + */ + override onConnectionsChange( + type: ISlotType, + index: number, + isConnected: boolean, + link_info: LLink | null | undefined, + inputOrOutput: INodeInputSlot | INodeOutputSlot, + ): void { + super.onConnectionsChange?.apply(this, [...arguments] as any); + if (this.inputs.includes(inputOrOutput as INodeInputSlot)) { + this.setTypedData(); + } + } + + /** + * Sets the correct output type widget state when our `PROPERTY_HIDE_TYPE_SELECTOR` changes. + */ + override onPropertyChanged(name: string, value: unknown, prev_value?: unknown): boolean { + if (name === PROPERTY_HIDE_TYPE_SELECTOR) { + if (!this.outputTypeWidget) { + return true; + } + this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR]; + if (this.outputTypeWidget.hidden) { + this.outputTypeWidget.computeLayoutSize = () => ({ + minHeight: 0, + minWidth: 0, + maxHeight: 0, + maxWidth: 0, + }); + } else { + this.outputTypeWidget.computeLayoutSize = undefined; + } + } + return true; + } +} + +/** An uniformed name reference to the node class. */ +const NODE_CLASS = RgthreePowerPrimitive; + +/** Register the node. */ +app.registerExtension({ + name: "rgthree.PowerPrimitive", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_prompt.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_prompt.ts new file mode 100644 index 00000000..6eac6d22 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_prompt.ts @@ -0,0 +1,48 @@ +import type {LGraphNode, LGraphNodeConstructor} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {addConnectionLayoutSupport} from "./utils.js"; +import {PowerPrompt} from "./base_power_prompt.js"; +import {NodeTypesString} from "./constants.js"; + +let nodeData: ComfyNodeDef | null = null; +app.registerExtension({ + name: "rgthree.PowerPrompt", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, passedNodeData: ComfyNodeDef) { + if (passedNodeData.name.includes("Power Prompt") && passedNodeData.name.includes("rgthree")) { + nodeData = passedNodeData; + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + (this as any).powerPrompt = new PowerPrompt(this as LGraphNode, passedNodeData); + }; + addConnectionLayoutSupport(nodeType as LGraphNodeConstructor, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + } + }, + async loadedGraphNode(node: LGraphNode) { + if (node.type === NodeTypesString.POWER_PROMPT) { + setTimeout(() => { + // If the first output is STRING, then it's the text output from the initial launch. + // Let's port it to the new + if (node.outputs[0]!.type === "STRING") { + if (node.outputs[0]!.links) { + node.outputs[3]!.links = node.outputs[3]!.links || []; + for (const link of node.outputs[0]!.links) { + node.outputs[3]!.links.push(link); + (node.graph || app.graph).links[link]!.origin_slot = 3; + } + node.outputs[0]!.links = null; + } + node.outputs[0]!.type = nodeData!.output![0] as string; + node.outputs[0]!.name = nodeData!.output_name![0] || (node.outputs[0]!.type as string); + node.outputs[0]!.color_on = undefined; + node.outputs[0]!.color_off = undefined; + } + }, 50); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_puter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_puter.ts new file mode 100644 index 00000000..74fc7686 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_puter.ts @@ -0,0 +1,461 @@ +import type { + LGraphNode, + IWidget, + Vector2, + CanvasMouseEvent, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js"; +import {debounce} from "rgthree/common/shared_utils.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {RgthreeBaseHitAreas, RgthreeBaseWidget, RgthreeBaseWidgetBounds} from "./utils_widgets.js"; +import { + drawPlusIcon, + drawRoundedRectangle, + drawWidgetButton, + isLowQuality, + measureText, +} from "./utils_canvas.js"; +import {rgthree} from "./rgthree.js"; + +type Vector4 = [number, number, number, number]; + +const ALPHABET = "abcdefghijklmnopqrstuv".split(""); + +const OUTPUT_TYPES = ["STRING", "INT", "FLOAT", "BOOLEAN", "*"]; + +class RgthreePowerPuter extends RgthreeBaseServerNode { + static override title = NodeTypesString.POWER_PUTER; + static override type = NodeTypesString.POWER_PUTER; + static comfyClass = NodeTypesString.POWER_PUTER; + + private outputTypeWidget!: OutputsWidget; + private expressionWidget!: IWidget; + private stabilizeBound = this.stabilize.bind(this); + + constructor(title = NODE_CLASS.title) { + super(title); + // Note, configure will add as many as was in the stored workflow automatically. + this.addAnyInput(2); + this.addInitialWidgets(); + } + + // /** + // * We need to patch in the configure to fix a bug where Power Puter was using BOOL instead of + // * BOOLEAN. + // */ + // override configure(info: ISerialisedNode): void { + // super.configure(info); + // // Update BOOL to BOOLEAN due to a bug using BOOL instead of BOOLEAN. + // this.outputTypeWidget + // } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + + override onConnectionsChange(...args: any[]): void { + super.onConnectionsChange?.apply(this, [...arguments] as any); + this.scheduleStabilize(); + } + + scheduleStabilize(ms = 64) { + return debounce(this.stabilizeBound, ms); + } + + stabilize() { + removeUnusedInputsFromEnd(this, 1); + this.addAnyInput(); + this.setOutputs(); + } + + private addInitialWidgets() { + if (!this.outputTypeWidget) { + this.outputTypeWidget = this.addCustomWidget( + new OutputsWidget("outputs", this), + ) as OutputsWidget; + this.expressionWidget = ComfyWidgets["STRING"]( + this, + "code", + ["STRING", {multiline: true}], + app, + ).widget; + } + } + + private addAnyInput(num = 1) { + for (let i = 0; i < num; i++) { + this.addInput(ALPHABET[this.inputs.length]!, "*" as string); + } + } + + private setOutputs() { + const desiredOutputs = this.outputTypeWidget.value.outputs; + for (let i = 0; i < Math.max(this.outputs.length, desiredOutputs.length); i++) { + const desired = desiredOutputs[i]; + let output = this.outputs[i]; + if (!desired && output) { + this.disconnectOutput(i); + this.removeOutput(i); + continue; + } + output = output || this.addOutput("", ""); + const outputLabel = + output.label === "*" || output.label === output.type ? null : output.label; + output.type = String(desired); + output.label = outputLabel || output.type; + } + } + + override getHelp() { + return ` +

    + The ${this.type!.replace("(rgthree)", "")} is a powerful and versatile node that opens the + door for a wide range of utility by offering mult-line code parsing for output. This node + can be used for simple string concatenation, or math operations; to an image dimension or a + node's widgets with advanced list comprehension. + If you want to output something in your workflow, this is the node to do it. +

    + +
      +

    • + Evaluate almost any kind of input and more, and choose your output from INT, FLOAT, + STRING, or BOOLEAN. +

      • +

    • + Connect some nodes and do simply math operations like a + b or + ceil(1 / 2). +

      • +

    • + Or do more advanced things, like input an image, and get the width like + a.shape[2]. +

      • +

    • + Even more powerful, you can target nodes in the prompt that's sent to the backend. For + instance; if you have a Power Lora Loader node at id #5, and want to get a comma-delimited + list of the enabled loras, you could enter + ', '.join([v.lora for v in node(5).inputs.values() if 'lora' in v and v.on]). +

      • +

    • + See more at the rgthree-comfy + wiki. +

      • +
    `; + } +} + +/** An uniformed name reference to the node class. */ +const NODE_CLASS = RgthreePowerPuter; + +type OutputsWidgetValue = { + outputs: string[]; +}; + +const OUTPUTS_WIDGET_CHIP_HEIGHT = LiteGraph.NODE_WIDGET_HEIGHT - 4; +const OUTPUTS_WIDGET_CHIP_SPACE = 4; + +const OUTPUTS_WIDGET_CHIP_ARROW_WIDTH = 5.5; +const OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT = 4; + +/** + * The OutputsWidget is an advanced widget that has a background similar to others, but then a + * series of "chips" that correspond to the outputs of the node. The chips are dynamic and wrap to + * additional rows as space is needed. Additionally, there is a "+" chip to add more. + */ +class OutputsWidget extends RgthreeBaseWidget { + override readonly type = "custom"; + private _value: OutputsWidgetValue = {outputs: ["STRING"]}; + + private rows = 1; + private neededHeight = LiteGraph.NODE_WIDGET_HEIGHT + 8; + private node!: RgthreePowerPuter; + + protected override hitAreas: RgthreeBaseHitAreas< + | "add" + | "output0" + | "output1" + | "output2" + | "output3" + | "output4" + | "output5" + | "output6" + | "output7" + | "output8" + | "output9" + > = { + add: {bounds: [0, 0] as Vector2, onClick: this.onAddChipDown}, + output0: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 0}}, + output1: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 1}}, + output2: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 2}}, + output3: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 3}}, + output4: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 4}}, + output5: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 5}}, + output6: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 6}}, + output7: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 7}}, + output8: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 8}}, + output9: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 9}}, + }; + + constructor(name: string, node: RgthreePowerPuter) { + super(name); + this.node = node; + } + + set value(v: OutputsWidgetValue) { + // Handle a string being passed in, as the original Power Puter output widget was a string. + let outputs = typeof v === "string" ? [v] : [...v.outputs]; + // Handle a case where the initial version used "BOOL" instead of "BOOLEAN" incorrectly. + outputs = outputs.map((o) => (o === "BOOL" ? "BOOLEAN" : o)); + this._value.outputs = outputs; + } + + get value(): OutputsWidgetValue { + return this._value; + } + + /** Displays the menu to choose a new output type. */ + onAddChipDown( + event: CanvasMouseEvent, + pos: Vector2, + node: LGraphNode, + bounds: RgthreeBaseWidgetBounds, + ) { + new LiteGraph.ContextMenu(OUTPUT_TYPES, { + event: event, + title: "Add an output", + className: "rgthree-dark", + callback: (value) => { + if (isLowQuality()) return; + if (typeof value === "string" && OUTPUT_TYPES.includes(value)) { + this._value.outputs.push(value); + this.node.scheduleStabilize(); + } + }, + }); + + this.cancelMouseDown(); + return true; + } + + /** Displays a context menu tied to an output chip within our widget. */ + onOutputChipDown( + event: CanvasMouseEvent, + pos: Vector2, + node: LGraphNode, + bounds: RgthreeBaseWidgetBounds, + ) { + const options: Array = [...OUTPUT_TYPES]; + if (this.value.outputs.length > 1) { + options.push(null, "πŸ—‘οΈ Delete"); + } + + new LiteGraph.ContextMenu(options, { + event: event, + title: `Edit output #${bounds.data.index + 1}`, + className: "rgthree-dark", + callback: (value) => { + const index = bounds.data.index; + if (typeof value !== "string" || value === this._value.outputs[index] || isLowQuality()) { + return; + } + const output = this.node.outputs[index]!; + if (value.toLocaleLowerCase().includes("delete")) { + if (output.links?.length) { + rgthree.showMessage({ + id: "puter-remove-linked-output", + type: "warn", + message: "[Power Puter] Removed and disconnected output from that was connected!", + timeout: 3000, + }); + this.node.disconnectOutput(index); + } + this.node.removeOutput(index); + this._value.outputs.splice(index, 1); + this.node.scheduleStabilize(); + return; + } + if (output.links?.length && value !== "*") { + rgthree.showMessage({ + id: "puter-remove-linked-output", + type: "warn", + message: + "[Power Puter] Changing output type of linked output! You should check for" + + " compatibility.", + timeout: 3000, + }); + } + this._value.outputs[index] = value; + this.node.scheduleStabilize(); + }, + }); + + this.cancelMouseDown(); + return true; + } + + /** + * Computes the layout size to ensure the height is what we need to accomodate all the chips; + * specifically, SPACE on the top, plus the CHIP_HEIGHT + SPACE underneath multiplied by the + * number of rows necessary. + */ + computeLayoutSize(node: LGraphNode) { + this.neededHeight = + OUTPUTS_WIDGET_CHIP_SPACE + + (OUTPUTS_WIDGET_CHIP_HEIGHT + OUTPUTS_WIDGET_CHIP_SPACE) * this.rows; + return { + minHeight: this.neededHeight, + maxHeight: this.neededHeight, + minWidth: 0, // Need just zero here to be flexible with the width. + }; + } + + /** + * Draws our nifty, advanced widget keeping track of the space and wrapping to multiple lines when + * more chips than can fit are shown. + */ + draw(ctx: CanvasRenderingContext2D, node: LGraphNode, w: number, posY: number, height: number) { + ctx.save(); + // Despite what `height` was passed in, which is often not our actual height, we'll use oun + // calculated needed height. + height = this.neededHeight; + const margin = 10; + const innerMargin = margin * 0.33; + const width = node.size[0] - margin * 2; + let borderRadius = LiteGraph.NODE_WIDGET_HEIGHT * 0.5; + let midY = posY + height * 0.5; + let posX = margin; + let rposX = node.size[0] - margin; + + // Draw the background encompassing everything, and move our current posX's to create space from + // the border. + + drawRoundedRectangle(ctx, {pos: [posX, posY], size: [width, height], borderRadius}); + posX += innerMargin * 2; + rposX -= innerMargin * 2; + + // If low quality, then we're done. + if (isLowQuality()) { + ctx.restore(); + return; + } + + // Add put our "outputs" label, and a divider line. + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillText("outputs", posX, midY); + posX += measureText(ctx, "outputs") + innerMargin * 2; + ctx.stroke(new Path2D(`M ${posX} ${posY} v ${height}`)); + posX += 1 + innerMargin * 2; + + // Now, prepare our values for the chips; adjust the posY to be within the space, the height to + // be that of the chips, and the new midY for the chips. + const inititalPosX = posX; + posY += OUTPUTS_WIDGET_CHIP_SPACE; + height = OUTPUTS_WIDGET_CHIP_HEIGHT; + borderRadius = height * 0.5; + midY = posY + height / 2; + ctx.textAlign = "center"; + ctx.lineJoin = ctx.lineCap = "round"; + ctx.fillStyle = ctx.strokeStyle = LiteGraph.WIDGET_TEXT_COLOR; + let rows = 1; + const values = this.value?.outputs ?? []; + const fontSize = ctx.font.match(/(\d+)px/); + if (fontSize?.[1]) { + ctx.font = ctx.font.replace(fontSize[1], `${Number(fontSize[1]) - 2}`); + } + + // Loop over our values, and add them from left to right, measuring the width before placing to + // see if we need to wrap the the next line, and updating the hitAreas of the chips. + let i = 0; + for (i; i < values.length; i++) { + const hitArea = this.hitAreas[`output${i}` as "output1"]; + const isClicking = !!hitArea.wasMouseClickedAndIsOver; + hitArea.data.index = i; + + const text = values[i]!; + const textWidth = measureText(ctx, text) + innerMargin * 2; + const width = textWidth + OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 5; + + // If our width is too long, then wrap the values and increment our rows. + if (posX + width >= rposX) { + posX = inititalPosX; + posY = posY + height + 4; + midY = posY + height / 2; + rows++; + } + + drawWidgetButton( + ctx, + {pos: [posX, posY], size: [width, height], borderRadius}, + null, + isClicking, + ); + const startX = posX; + posX += innerMargin * 2; + const newMidY = midY + (isClicking ? 1 : 0); + ctx.fillText(text, posX + textWidth / 2, newMidY); + posX += textWidth + innerMargin; + const arrow = new Path2D( + `M${posX} ${newMidY - OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT / 2} + h${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH} + l-${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH / 2} ${OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT} z`, + ); + ctx.fill(arrow); + ctx.stroke(arrow); + posX += OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 2; + hitArea.bounds = [startX, posY, width, height] as Vector4; + posX += OUTPUTS_WIDGET_CHIP_SPACE; // Space Between + } + // Zero out and following hitAreas. + for (i; i < 9; i++) { + const hitArea = this.hitAreas[`output${i}` as "output1"]; + if (hitArea.bounds[0] > 0) { + hitArea.bounds = [0, 0, 0, 0] as Vector4; + } + } + + // Draw the add arrow, if we're not at the max. + const addHitArea = this.hitAreas["add"]; + if (this.value.outputs.length < 10) { + const isClicking = !!addHitArea.wasMouseClickedAndIsOver; + const plusSize = 10; + let plusWidth = innerMargin * 2 + plusSize + innerMargin * 2; + if (posX + plusWidth >= rposX) { + posX = inititalPosX; + posY = posY + height + 4; + midY = posY + height / 2; + rows++; + } + drawWidgetButton( + ctx, + {size: [plusWidth, height], pos: [posX, posY], borderRadius}, + null, + isClicking, + ); + drawPlusIcon(ctx, posX + innerMargin * 2, midY + (isClicking ? 1 : 0), plusSize); + addHitArea.bounds = [posX, posY, plusWidth, height] as Vector4; + } else { + addHitArea.bounds = [0, 0, 0, 0] as Vector4; + } + + // Set the rows now that we're drawn. + this.rows = rows; + ctx.restore(); + } +} + +/** Register the node. */ +app.registerExtension({ + name: "rgthree.PowerPuter", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/random_unmuter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/random_unmuter.ts new file mode 100644 index 00000000..1f951a94 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/random_unmuter.ts @@ -0,0 +1,117 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js"; +import {NodeTypesString} from "./constants.js"; +import {rgthree} from "./rgthree.js"; +import {changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs} from "./utils.js"; + +const MODE_MUTE = 2; +const MODE_ALWAYS = 0; + +class RandomUnmuterNode extends BaseAnyInputConnectedNode { + static override exposedActions = ["Mute all", "Enable all"]; + + static override type = NodeTypesString.RANDOM_UNMUTER; + override comfyClass = NodeTypesString.RANDOM_UNMUTER; + static override title = RandomUnmuterNode.type; + readonly modeOn = MODE_ALWAYS; + readonly modeOff = MODE_MUTE; + + tempEnabledNode: LGraphNode | null = null; + processingQueue: boolean = false; + + onQueueBound = this.onQueue.bind(this); + onQueueEndBound = this.onQueueEnd.bind(this); + onGraphtoPromptBound = this.onGraphtoPrompt.bind(this); + onGraphtoPromptEndBound = this.onGraphtoPromptEnd.bind(this); + + constructor(title = RandomUnmuterNode.title) { + super(title); + + rgthree.addEventListener("queue", this.onQueueBound); + rgthree.addEventListener("queue-end", this.onQueueEndBound); + rgthree.addEventListener("graph-to-prompt", this.onGraphtoPromptBound); + rgthree.addEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound); + this.onConstructed(); + } + + override onRemoved() { + rgthree.removeEventListener("queue", this.onQueueBound); + rgthree.removeEventListener("queue-end", this.onQueueEndBound); + rgthree.removeEventListener("graph-to-prompt", this.onGraphtoPromptBound); + rgthree.removeEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound); + } + + onQueue(event: Event) { + this.processingQueue = true; + } + onQueueEnd(event: Event) { + this.processingQueue = false; + } + onGraphtoPrompt(event: Event) { + if (!this.processingQueue) { + return; + } + this.tempEnabledNode = null; + // Check that all are muted and, if so, choose one to unmute. + const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this); + let allMuted = true; + if (linkedNodes.length) { + for (const node of linkedNodes) { + if (node.mode !== this.modeOff) { + allMuted = false; + break; + } + } + if (allMuted) { + this.tempEnabledNode = linkedNodes[Math.floor(Math.random() * linkedNodes.length)] || null; + if (this.tempEnabledNode) { + changeModeOfNodes(this.tempEnabledNode, this.modeOn); + } + } + } + } + onGraphtoPromptEnd(event: Event) { + if (this.tempEnabledNode) { + changeModeOfNodes(this.tempEnabledNode, this.modeOff); + this.tempEnabledNode = null; + } + } + + override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) { + return false; // No-op, no widgets. + } + + override getHelp(): string { + return ` +

    + Use this node to unmute on of its inputs randomly when the graph is queued (and, immediately + mute it back). +

    +
      +

    • + NOTE: All input nodes MUST be muted to start; if not this node will not randomly unmute + another. (This is powerful, as the generated image can be dragged in and the chosen input + will already by unmuted and work w/o any further action.) +

      • +

    • + TIP: Connect a Repeater's output to this nodes input and place that Repeater on a group + without any other inputs, and it will mute/unmute the entire group. +

      • +
    + `; + } +} + +app.registerExtension({ + name: "rgthree.RandomUnmuter", + registerCustomNodes() { + RandomUnmuterNode.setUp(); + }, + loadedGraphNode(node: LGraphNode) { + if (node.type == RandomUnmuterNode.title) { + (node as any)._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/reroute.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/reroute.ts new file mode 100644 index 00000000..de0155ae --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/reroute.ts @@ -0,0 +1,1285 @@ +import type { + Vector2, + LLink, + LGraphCanvas as TLGraphCanvas, + LGraph as TLGraph, + INodeInputSlot, + INodeOutputSlot, + LGraphNode as TLGraphNode, + LinkDirection, + ISerialisedNode, + Point, + Size, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +// @ts-ignore +import {rgthreeConfig} from "rgthree/config.js"; +import {rgthree} from "./rgthree.js"; +import { + IoDirection, + LAYOUT_CLOCKWISE, + LAYOUT_LABEL_OPPOSITES, + LAYOUT_LABEL_TO_DATA, + addConnectionLayoutSupport, + addMenuItem, + getSlotLinks, + isValidConnection, + setConnectionsLayout, + waitForCanvas, +} from "./utils.js"; +import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {RgthreeBaseVirtualNode} from "./base_node.js"; +import {NodeTypesString} from "./constants.js"; +import {rgthreeApi} from "rgthree/common/rgthree_api.js"; +import {getWidgetConfig, mergeIfValid, setWidgetConfig} from "./utils_deprecated_comfyui.js"; + +const CONFIG_REROUTE = rgthreeConfig?.["nodes"]?.["reroute"] || {}; + +const CONFIG_FAST_REROUTE = CONFIG_REROUTE["fast_reroute"]; +const CONFIG_FAST_REROUTE_ENABLED = CONFIG_FAST_REROUTE["enabled"] ?? false; +const CONFIG_KEY_CREATE_WHILE_LINKING = CONFIG_FAST_REROUTE["key_create_while_dragging_link"]; +const CONFIG_KEY_ROTATE = CONFIG_FAST_REROUTE["key_rotate"]; +const CONFIG_KEY_RESIZE = CONFIG_FAST_REROUTE["key_resize"]; +const CONFIG_KEY_MOVE = CONFIG_FAST_REROUTE["key_move"]; +const CONFIG_KEY_CXN_INPUT = CONFIG_FAST_REROUTE["key_connections_input"]; +const CONFIG_KEY_CXN_OUTPUT = CONFIG_FAST_REROUTE["key_connections_output"]; + +let configWidth = Math.max( + Math.round((Number(CONFIG_REROUTE["default_width"]) || 40) / 10) * 10, + 10, +); +let configHeight = Math.max( + Math.round((Number(CONFIG_REROUTE["default_height"]) || 30) / 10) * 10, + 10, +); +// Don't allow too small sizes. Granted, 400 is too small, but at least you can right click and +// resize... 10x10 you cannot. +while (configWidth * configHeight < 400) { + configWidth += 10; + configHeight += 10; +} +const configDefaultSize = [configWidth, configHeight] as Vector2; +const configResizable = !!CONFIG_REROUTE["default_resizable"]; +let configLayout: [string, string] = CONFIG_REROUTE["default_layout"]; +if (!Array.isArray(configLayout)) { + configLayout = ["Left", "Right"]; +} +if (!LAYOUT_LABEL_TO_DATA[configLayout[0]]) { + configLayout[0] = "Left"; +} +if (!LAYOUT_LABEL_TO_DATA[configLayout[1]] || configLayout[0] == configLayout[1]) { + configLayout[1] = LAYOUT_LABEL_OPPOSITES[configLayout[0]]!; +} + +type FastRerouteEntryCtx = { + node: TLGraphNode; + input?: INodeInputSlot; + output?: INodeOutputSlot; + slot: number; + pos: Vector2; + direction?: LinkDirection; +}; + +type FastRerouteEntry = { + node: RerouteNode; + previous: FastRerouteEntryCtx; + current?: FastRerouteEntryCtx; +}; + +/** + * RerouteService handles any coordination between reroute nodes and the system. Mostly, it's for + * fast-rerouting that can create a new reroute nodes while dragging a link. + */ +class RerouteService { + private isFastLinking = false; + private handledNewRerouteKeypress = false; + private connectingData: FastRerouteEntryCtx | null = null; + private fastReroutesHistory: FastRerouteEntry[] = []; + + private handleLinkingKeydownBound = this.handleLinkingKeydown.bind(this); + private handleLinkingKeyupBound = this.handleLinkingKeyup.bind(this); + + constructor() { + if (CONFIG_FAST_REROUTE_ENABLED && CONFIG_KEY_CREATE_WHILE_LINKING?.trim()) { + this.onCanvasSetUpListenerForLinking(); + } + } + + /** + * Waits for canvas to be available, then sets up a property accessor for `connecting_node` so + * we can start/stop monitoring for shortcut keys. + */ + async onCanvasSetUpListenerForLinking() { + const canvas = await waitForCanvas(); + + // With the new UI released in August 2024, ComfyUI changed LiteGraph's code, removing + // connecting_node, connecting_node, connecting_node, and connecting_node properties and instead + // using an array of connecting_links. We can try to accomodate both for a while. + const canvasProperty = true ? "connecting_links" : "connecting_node"; + (canvas as any)[`_${canvasProperty}`]; + const thisService = this; + Object.defineProperty(canvas, canvasProperty, { + get: function () { + return this[`_${canvasProperty}`]; + }, + set: function (value) { + const isValNull = !value || !value?.length; + const isPropNull = !this[`_${canvasProperty}`] || !this[`_${canvasProperty}`]?.length; + const isStartingLinking = !isValNull && isPropNull; + const isStoppingLinking = !isPropNull && isValNull; + this[`_${canvasProperty}`] = value; + if (isStartingLinking) { + thisService.startingLinking(); + } + if (isStoppingLinking) { + thisService.stoppingLinking(); + thisService.connectingData = null; + } + }, + }); + } + + /** + * When the user is actively dragging a link, listens for keydown events so we can enable + * shortcuts. + * + * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and + * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty. + */ + private startingLinking() { + this.isFastLinking = true; + KEY_EVENT_SERVICE.addEventListener("keydown", this.handleLinkingKeydownBound as EventListener); + KEY_EVENT_SERVICE.addEventListener("keyup", this.handleLinkingKeyupBound as EventListener); + } + + /** + * When the user stops actively dragging a link, cleans up motnioring data and events. + * + * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and + * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty. + */ + private stoppingLinking() { + this.isFastLinking = false; + this.fastReroutesHistory = []; + KEY_EVENT_SERVICE.removeEventListener( + "keydown", + this.handleLinkingKeydownBound as EventListener, + ); + KEY_EVENT_SERVICE.removeEventListener("keyup", this.handleLinkingKeyupBound as EventListener); + } + + /** + * Handles the keydown event. + * + * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and + * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty. + */ + private handleLinkingKeydown(event: KeyboardEvent) { + if ( + !this.handledNewRerouteKeypress && + KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING) + ) { + this.handledNewRerouteKeypress = true; + this.insertNewRerouteWhileLinking(); + } + } + + /** + * Handles the keyup event. + * + * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and + * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty. + */ + private handleLinkingKeyup(event: KeyboardEvent) { + if ( + this.handledNewRerouteKeypress && + !KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING) + ) { + this.handledNewRerouteKeypress = false; + } + } + + private getConnectingData(): FastRerouteEntryCtx { + const oldCanvas = app.canvas as any; + if ( + oldCanvas.connecting_node && + oldCanvas.connecting_slot != null && + oldCanvas.connecting_pos?.length + ) { + return { + node: oldCanvas.connecting_node, + input: oldCanvas.connecting_input, + output: oldCanvas.connecting_output, + slot: oldCanvas.connecting_slot, + pos: [...oldCanvas.connecting_pos] as Vector2, + }; + } + const canvas = app.canvas; + if (canvas.connecting_links?.length) { + // Assume just the first. + const link = canvas.connecting_links[0]!; + return { + node: link.node, + input: link.input ?? undefined, + output: link.output ?? undefined, + slot: link.slot, + pos: [...link.pos] as Point, + }; + } + throw new Error("Error, handling linking keydown, but there's no link."); + } + + private setCanvasConnectingData(ctx: FastRerouteEntryCtx) { + const oldCanvas = app.canvas as any; + if ( + oldCanvas.connecting_node && + oldCanvas.connecting_slot != null && + oldCanvas.connecting_pos?.length + ) { + oldCanvas.connecting_node = ctx.node; + oldCanvas.connecting_input = ctx.input; + oldCanvas.connecting_output = ctx.output; + oldCanvas.connecting_slot = ctx.slot; + oldCanvas.connecting_pos = ctx.pos; + } + const canvas = app.canvas; + if (canvas.connecting_links?.length) { + // Assume just the first. + const link = canvas.connecting_links[0]!; + link.node = ctx.node; + link.input = ctx.input; + link.output = ctx.output; + link.slot = ctx.slot; + link.pos = ctx.pos; + } + // const newCanvas = app.canvas as unknown as TypedLGraphCanvas; + // if (newCanvas.linkConnector.renderLinks?.length) { + // newCanvas.linkConnector.reset(); + // newCanvas.linkConnector.dragNewFromOutput(app.graph as any, ctx.node as any, ctx.output as any); + // } + + // const link = newCanvas.linkConnector.renderLinks[0]! as any; + // link.node = ctx.node; + // link.fromSlot = ctx.output || ctx.input; + // link.fromSlotIndex = ctx.slot; + // link.fromPos = ctx.pos; + // link.fromDirection = ctx.direction || 4; + // } + } + + /** + * Inserts a new reroute (while linking) as called from key down handler. + * + * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and + * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty. + */ + private insertNewRerouteWhileLinking() { + const canvas = app.canvas; + this.connectingData = this.getConnectingData(); + if (!this.connectingData) { + throw new Error("Error, handling linking keydown, but there's no link."); + } + + const data = this.connectingData; + const node = LiteGraph.createNode("Reroute (rgthree)") as RerouteNode; + const entry: FastRerouteEntry = { + node, + previous: {...this.connectingData}, + current: undefined, + }; + this.fastReroutesHistory.push(entry); + + let connectingDir = (data.input || data.output)?.dir; + if (!connectingDir) { + connectingDir = data.input ? LiteGraph.LEFT : LiteGraph.RIGHT; + } + + let newPos = canvas.convertEventToCanvasOffset({ + clientX: Math.round(canvas.last_mouse_position[0] / 10) * 10, + clientY: Math.round(canvas.last_mouse_position[1] / 10) * 10, + } as MouseEvent); + entry.node.pos = newPos; + canvas.graph!.add(entry.node); + canvas.selectNode(entry.node); + + // Find out which direction we're generally moving. + const distX = entry.node.pos[0] - data.pos[0]; + const distY = entry.node.pos[1] - data.pos[1]; + + const layout: [string, string] = ["Left", "Right"]; + if (distX > 0 && Math.abs(distX) > Math.abs(distY)) { + // To the right, and further right than up or down. + layout[0] = data.output ? "Left" : "Right"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!; + node.pos[0] -= node.size[0] + 10; + node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10; + } else if (distX < 0 && Math.abs(distX) > Math.abs(distY)) { + // To the left, and further right than up or down. + layout[0] = data.output ? "Right" : "Left"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!; + node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10; + } else if (distY < 0 && Math.abs(distY) > Math.abs(distX)) { + // Above and further above than left or right. + layout[0] = data.output ? "Bottom" : "Top"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!; + node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10; + } else if (distY > 0 && Math.abs(distY) > Math.abs(distX)) { + // Below and further below than left or right. + layout[0] = data.output ? "Top" : "Bottom"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!; + node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10; + node.pos[1] -= node.size[1] + 10; + } + setConnectionsLayout(entry.node, layout); + + if (data.output) { + data.node.connect(data.slot, entry.node, 0); + data.node = entry.node; + data.output = entry.node.outputs[0]!; + data.slot = 0; + data.pos = entry.node.getConnectionPos(false, 0); + data.direction = + layout[0] === "Top" ? 2 : layout[0] === "Bottom" ? 1 : layout[0] === "Left" ? 4 : 3; + } else { + entry.node.connect(0, data.node, data.slot); + data.node = entry.node; + data.input = entry.node.inputs[0]!; + data.slot = 0; + data.pos = entry.node.getConnectionPos(true, 0); + data.direction = + layout[1] === "Top" ? 2 : layout[1] === "Bottom" ? 1 : layout[1] === "Left" ? 4 : 3; + } + this.setCanvasConnectingData(data); + entry.current = {...this.connectingData}; + + app.graph.setDirtyCanvas(true, true); + } + + /** + * Is called from a reroute node when it is resized or moved so the service can check if we're + * actively linking to it, and it can update the linking data so the connection moves too, by + * updating `connecting_pos`. + */ + handleMoveOrResizeNodeMaybeWhileDragging(node: RerouteNode) { + const data = this.connectingData!; + if (this.isFastLinking && node === data?.node) { + const entry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1]; + if (entry) { + data.pos = entry.node.getConnectionPos(!!data.input, 0); + this.setCanvasConnectingData(data); + } + } + } + + /** + * Is called from a reroute node when it is deleted so the service can check if we're actively + * linking to it and go "back" in history to the previous node. + */ + handleRemovedNodeMaybeWhileDragging(node: RerouteNode) { + const currentEntry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1]; + if (currentEntry?.node === node) { + this.setCanvasConnectingData(currentEntry.previous); + this.fastReroutesHistory.splice(this.fastReroutesHistory.length - 1, 1); + if (currentEntry.previous.node) { + app.canvas.selectNode(currentEntry.previous.node); + } + } + } +} + +const SERVICE = new RerouteService(); + +/** + * The famous ReroutNode, that has true multidirectional, expansive sizes, etc. + */ +class RerouteNode extends RgthreeBaseVirtualNode { + static override title = NodeTypesString.REROUTE; + static override type = NodeTypesString.REROUTE; + override comfyClass = NodeTypesString.REROUTE; + + static readonly title_mode = LiteGraph.NO_TITLE; + + static collapsable = false; + static layout_slot_offset = 5; + static size: Vector2 = configDefaultSize; // Starting size, read from within litegraph.core + + override isVirtualNode = true; + readonly hideSlotLabels = true; + + private schedulePromise: Promise | null = null; + + defaultConnectionsLayout = Array.from(configLayout); + + /** Shortcuts defined in the config. */ + private shortcuts = { + rotate: {keys: CONFIG_KEY_ROTATE, state: false}, + connection_input: {keys: CONFIG_KEY_CXN_INPUT, state: false}, + connection_output: {keys: CONFIG_KEY_CXN_OUTPUT, state: false}, + resize: { + keys: CONFIG_KEY_RESIZE, + state: false, + initialMousePos: [-1, -1] as Vector2, + initialNodeSize: [-1, -1] as Vector2, + initialNodePos: [-1, -1] as Vector2, + resizeOnSide: [-1, -1] as Vector2, + }, + move: { + keys: CONFIG_KEY_MOVE, + state: false, + initialMousePos: [-1, -1] as Vector2, + initialNodePos: [-1, -1] as Vector2, + }, + }; + + constructor(title = RerouteNode.title) { + super(title); + this.onConstructed(); + } + + override onConstructed(): boolean { + this.setResizable(!!(this.properties["resizable"] ?? configResizable)); + this.size = RerouteNode.size; // Starting size. + this.addInput("", "*"); + this.addOutput("", "*"); + setTimeout(() => this.applyNodeSize(), 20); + return super.onConstructed(); + } + + override configure(info: ISerialisedNode): void { + if (info.inputs?.length) { + info.inputs.length = 1; + } + super.configure(info); + this.configuring = true; + this.setResizable(!!(this.properties["resizable"] ?? configResizable)); + this.applyNodeSize(); + this.configuring = false; + } + + setResizable(resizable: boolean) { + this.properties["resizable"] = !!resizable; + this.resizable = this.properties["resizable"]; + } + + override clone() { + const cloned = super.clone()!; + cloned.inputs[0]!.type = "*"; + cloned.outputs[0]!.type = "*"; + return cloned; + } + + /** + * Copied a good bunch of this from the original reroute included with comfy. + */ + override onConnectionsChange( + type: number, + _slotIndex: number, + connected: boolean, + _link_info: LLink, + _ioSlot: INodeOutputSlot | INodeInputSlot, + ) { + // Prevent multiple connections to different types when we have no input + if (connected && type === LiteGraph.OUTPUT) { + // Ignore wildcard nodes as these will be updated to real types + const types = new Set( + this.outputs[0]!.links!.map((l) => app.graph.links[l]?.type).filter((t) => t && t !== "*"), + ); + if (types.size > 1) { + const linksToDisconnect = []; + for (let i = 0; i < this.outputs[0]!.links!.length - 1; i++) { + const linkId = this.outputs[0]!.links![i]!; + const link = app.graph.links[linkId]; + linksToDisconnect.push(link); + } + for (const link of linksToDisconnect) { + const node = app.graph.getNodeById(link!.target_id)!; + node.disconnectInput(link!.target_slot); + } + } + } + this.scheduleStabilize(); + } + + override onDrawForeground(ctx: CanvasRenderingContext2D, canvas: TLGraphCanvas): void { + if (this.properties?.["showLabel"]) { + // ComfyUI seemed to break us again, but couldn't repro. No reason to not check, I guess. + // https://github.com/rgthree/rgthree-comfy/issues/71 + const low_quality = canvas?.ds?.scale && canvas.ds.scale < 0.6; + if (low_quality || this.size[0] <= 10) { + return; + } + const fontSize = Math.min(14, (this.size[1] * 0.65) | 0); + ctx.save(); + ctx.fillStyle = "#888"; + ctx.font = `${fontSize}px Arial`; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText( + String( + this.title && this.title !== RerouteNode.title + ? this.title + : this.outputs?.[0]?.type || "", + ), + this.size[0] / 2, + this.size[1] / 2, + this.size[0] - 30, + ); + ctx.restore(); + } + } + + /** Finds the input slot; since we only ever have one, this is always 0. */ + override findInputSlot(name: string, returnObj?: TReturn): number; + override findInputSlot(name: string, returnObj?: TReturn): INodeInputSlot; + override findInputSlot(name: string, returnObj: boolean = false): number | INodeInputSlot { + return returnObj ? this.inputs[0]! : 0; + } + + /** Finds the output slot; since we only ever have one, this is always 0. */ + override findOutputSlot(name: string, returnObj?: TReturn): number; + override findOutputSlot(name: string, returnObj?: TReturn): INodeOutputSlot; + override findOutputSlot(name: unknown, returnObj?: unknown): number | INodeOutputSlot { + return returnObj ? this.outputs[0]! : 0; + } + + override disconnectOutput(slot: string | number, targetNode?: TLGraphNode | undefined): boolean { + return super.disconnectOutput(slot, targetNode); + } + + override disconnectInput(slot: string | number): boolean { + // [πŸ€] ComfyUI's reroute nodes will disconnect our input if it doesn't yet match (ours being + // "*" and it's being a type. This mostly happens if we're converting reroutes to rgthree + // reroutes, the old reroute does a check and calls disconnectInput. Luckily, we can be smarter + // and check if we're being asked to disconnect from an old reroute while we're replacing + // reroute nodes (via rgthree.replacingReroute state). + if (rgthree.replacingReroute != null && this.inputs[0]?.link) { + const graph = app.graph as TLGraph; + const link = graph.links[this.inputs[0].link]; + const node = link?.origin_id != null ? graph.getNodeById(link.origin_id) : null; + // We'll also be asked to disconnect when the old one is removed, so we only want to stop a + // disconnect when the connected node is NOT the one being removed/replaced. + if (rgthree.replacingReroute !== node?.id) { + return false; + } + } + return super.disconnectInput(slot); + } + + scheduleStabilize(ms = 64) { + if (!this.schedulePromise) { + this.schedulePromise = new Promise((resolve) => { + setTimeout(() => { + this.schedulePromise = null; + this.stabilize(); + resolve(); + }, ms); + }); + } + return this.schedulePromise; + } + + stabilize() { + // If we are currently "configuring" then skip this stabilization. The connected nodes may + // not yet be configured. + if (this.configuring) { + return; + } + // Find root input + let currentNode: TLGraphNode | null = this; + let updateNodes = []; + let input = null; + let inputType = null; + let inputNode = null; + let inputNodeOutputSlot = null; + while (currentNode) { + updateNodes.unshift(currentNode); + const linkId: number | null = currentNode.inputs[0]!.link; + if (linkId !== null) { + const link: LLink = (app.graph as TLGraph).links[linkId]!; + const node: TLGraphNode = (app.graph as TLGraph).getNodeById(link.origin_id)!; + if (!node) { + // Bummer, somthing happened.. should we cleanup? + app.graph.removeLink(linkId); + currentNode = null; + break; + } + const type = (node.constructor as typeof TLGraphNode).type; + if (type?.includes("Reroute")) { + if (node === this) { + // We've found a circle + currentNode.disconnectInput(link.target_slot); + currentNode = null; + } else { + // Move the previous node + currentNode = node; + } + } else { + // We've found the end + inputNode = node; + inputNodeOutputSlot = link.origin_slot; + input = node.outputs[inputNodeOutputSlot] ?? null; + inputType = input?.type ?? null; + break; + } + } else { + // This path has no input node + currentNode = null; + break; + } + } + + // Find all outputs + const nodes: TLGraphNode[] = [this]; + let outputNode = null; + let outputType = null; + // For primitive nodes, which look at the widget to dsplay themselves. + let outputWidgetConfig = null; + let outputWidget = null; + while (nodes.length) { + currentNode = nodes.pop()!; + const outputs = (currentNode.outputs ? currentNode.outputs[0]!.links : []) || []; + if (outputs.length) { + for (const linkId of outputs) { + const link = app.graph.links[linkId]; + + // When disconnecting sometimes the link is still registered + if (!link) continue; + + const node = app.graph.getNodeById(link.target_id) as TLGraphNode; + // Don't know why this ever happens.. but it did around the repeater.. + if (!node) continue; + const type = (node.constructor as any).type; + if (type?.includes("Reroute")) { + // Follow reroute nodes + nodes.push(node); + updateNodes.push(node); + } else { + // We've found an output + const output = node.inputs?.[link.target_slot] ?? null; + const nodeOutType = output?.type; + if (nodeOutType == null) { + console.warn( + `[rgthree] Reroute - Connected node ${node.id} does not have type information for ` + + `slot ${link.target_slot}. Skipping connection enforcement, but something is odd ` + + `with that node.`, + ); + } else if ( + inputType && + inputType !== "*" && + nodeOutType !== "*" && + !isValidConnection(input, output) + ) { + // The output doesnt match our input so disconnect it + console.warn( + `[rgthree] Reroute - Disconnecting connected node's input (${node.id}.${ + link.target_slot + }) (${node.type}) because its type (${String( + nodeOutType, + )}) does not match the reroute type (${String(inputType)})`, + ); + node.disconnectInput(link.target_slot); + } else { + outputType = nodeOutType; + outputNode = node; + outputWidgetConfig = null; + outputWidget = null; + // For primitive nodes, which look at the widget to dsplay themselves. + if (output?.widget) { + rgthreeApi.print("PRIMITIVE_REROUTE"); + try { + const config = getWidgetConfig(output); + if (!outputWidgetConfig && config) { + outputWidgetConfig = config[1] ?? {}; + outputType = config[0]; + if (!outputWidget) { + outputWidget = outputNode.widgets?.find( + (w) => w.name === output?.widget?.name, + ); + } + const merged = mergeIfValid(output, [config[0], outputWidgetConfig]); + if (merged?.customConfig) { + outputWidgetConfig = merged.customConfig; + } + } + } catch (e) { + // Something happened, probably because comfyUI changes their methods. + console.error( + "[rgthree] Could not propagate widget infor for reroute; maybe ComfyUI updated?", + ); + outputWidgetConfig = null; + outputWidget = null; + } + } + } + } + } + } else { + // No more outputs for this path + } + } + + const displayType = inputType || outputType || "*"; + const color = LGraphCanvas.link_type_colors[displayType]; + + // Update the types of each node + for (const node of updateNodes) { + // If we dont have an input type we are always wildcard but we'll show the output type + // This lets you change the output link to a different type and all nodes will update + node.outputs[0]!.type = inputType || "*"; + (node as any).__outputType = displayType; + node.outputs[0]!.name = input?.name || ""; + node.size = node.computeSize(); + (node as any).applyNodeSize?.(); + + for (const l of node.outputs[0]!.links || []) { + const link = app.graph.links[l]; + if (link && color) { + link.color = color; + } + } + + try { + // For primitive nodes, which look at the widget to dsplay themselves; we get by with just + // an object with 'name'. + if (outputWidgetConfig && outputWidget && outputType) { + rgthreeApi.print("PRIMITIVE_REROUTE"); + node.inputs[0]!.widget = {name: "value"} as any; + setWidgetConfig( + node.inputs[0], + [outputType ?? displayType, outputWidgetConfig], + // outputWidget, // This never existed? + ); + } else { + setWidgetConfig(node.inputs[0], null); + } + } catch (e) { + // Something happened, probably because comfyUI changes their methods. + console.error("[rgthree] Could not set widget config for reroute; maybe ComfyUI updated?"); + outputWidgetConfig = null; + outputWidget = null; + if (node.inputs[0]?.widget) { + delete node.inputs[0].widget; + } + } + } + + if (inputNode && inputNodeOutputSlot != null) { + const links = inputNode.outputs[inputNodeOutputSlot]!.links; + for (const l of links || []) { + const link = app.graph.links[l]; + if (link && color) { + link.color = color; + } + } + } + (inputNode as any)?.onConnectionsChainChange?.(); + (outputNode as any)?.onConnectionsChainChange?.(); + app.graph.setDirtyCanvas(true, true); + } + + /** + * When called, sets the node size, and the properties size, and calls out to `stabilizeLayout`. + */ + override setSize(size: Vector2): void { + const oldSize = [...this.size] as Size; + const newSize = [...size] as Size; + super.setSize(newSize); + this.properties["size"] = [...this.size]; + this.stabilizeLayout(oldSize, newSize); + } + + /** + * Looks at the current layout and determins if we also need to set a `connections_dir` based on + * the size of the node (and what that connections_dir should be). + */ + private stabilizeLayout(oldSize: Vector2, newSize: Vector2) { + if (newSize[0] === 10 || newSize[1] === 10) { + const props = this.properties; + props["connections_layout"] = props["connections_layout"] || ["Left", "Right"]; + props["connections_dir"] = props["connections_dir"] || [-1, -1]; + + const layout = props["connections_layout"] as [string, string]; + const dir = props["connections_dir"] as [number, number]; + + if (oldSize[0] > 10 && newSize[0] === 10) { + dir[0] = LiteGraph.DOWN; + dir[1] = LiteGraph.UP; + if (layout[0] === "Bottom") { + layout[1] = "Top"; + } else if (layout[1] === "Top") { + layout[0] = "Bottom"; + } else { + layout[0] = "Top"; + layout[1] = "Bottom"; + dir[0] = LiteGraph.UP; + dir[1] = LiteGraph.DOWN; + } + this.setDirtyCanvas(true, true); + } else if (oldSize[1] > 10 && newSize[1] === 10) { + dir[0] = LiteGraph.RIGHT; + dir[1] = LiteGraph.LEFT; + if (layout[0] === "Right") { + layout[1] = "Left"; + } else if (layout[1] === "Left") { + layout[0] = "Right"; + } else { + layout[0] = "Left"; + layout[1] = "Right"; + dir[0] = LiteGraph.LEFT; + dir[1] = LiteGraph.RIGHT; + } + this.setDirtyCanvas(true, true); + } + } + SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this); + } + + applyNodeSize() { + this.properties["size"] = this.properties["size"] || RerouteNode.size; + this.properties["size"] = [ + Number((this.properties["size"] as Size)[0]), + Number((this.properties["size"] as Size)[1]), + ]; + this.size = this.properties["size"] as Size; + app.graph.setDirtyCanvas(true, true); + } + + /** + * Rotates the node, including changing size and moving input's and output's layouts. + */ + rotate(degrees: 90 | -90 | 180) { + const w = this.size[0]; + const h = this.size[1]; + this.properties["connections_layout"] = + this.properties["connections_layout"] || (this as RerouteNode).defaultConnectionsLayout; + + const connections_layout = this.properties["connections_layout"] as [string, string]; + const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]); + const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]); + if (degrees == 90 || degrees === -90) { + if (degrees === -90) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex - 1) % 4) + 4) % 4]!; + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex - 1) % 4) + 4) % 4]!; + } else { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 1) % 4) + 4) % 4]!; + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 1) % 4) + 4) % 4]!; + } + } else if (degrees === 180) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]!; + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]!; + } + this.setSize([h, w]); + } + + /** + * Manually handles a move called from `onMouseMove` while the resize shortcut is active. + */ + private manuallyHandleMove(event: PointerEvent) { + const shortcut = this.shortcuts.move; + if (shortcut.state) { + const diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10; + const diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10; + this.pos[0] = shortcut.initialNodePos[0] + diffX; + this.pos[1] = shortcut.initialNodePos[1] + diffY; + this.setDirtyCanvas(true, true); + SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this); + } + } + + /** + * Manually handles a resize called from `onMouseMove` while the resize shortcut is active. + */ + private manuallyHandleResize(event: PointerEvent) { + const shortcut = this.shortcuts.resize; + if (shortcut.state) { + let diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10; + let diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10; + diffX *= shortcut.resizeOnSide[0] === LiteGraph.LEFT ? -1 : 1; + diffY *= shortcut.resizeOnSide[1] === LiteGraph.UP ? -1 : 1; + const oldSize = [...this.size] as Size; + this.setSize([ + Math.max(10, shortcut.initialNodeSize[0] + diffX), + Math.max(10, shortcut.initialNodeSize[1] + diffY), + ]); + if (shortcut.resizeOnSide[0] === LiteGraph.LEFT && oldSize[0] > 10) { + this.pos[0] = shortcut.initialNodePos[0] - diffX; + } + if (shortcut.resizeOnSide[1] === LiteGraph.UP && oldSize[1] > 10) { + this.pos[1] = shortcut.initialNodePos[1] - diffY; + } + this.setDirtyCanvas(true, true); + } + } + + /** + * Cycles the connection (input or output) to the next available layout. Note, when the width or + * height is only 10px, then layout sticks to the ends of the longer size, and we move a + * `connections_dir` property which is only paid attention to in `utils` when size of one axis + * is equal to 10. + * `manuallyHandleResize` handles the reset of `connections_dir` when a node is resized. + */ + private cycleConnection(ioDir: IoDirection) { + const props = this.properties; + props["connections_layout"] = props["connections_layout"] || ["Left", "Right"]; + const connections_layout = this.properties["connections_layout"] as [string, string]; + + const propIdx = ioDir == IoDirection.INPUT ? 0 : 1; + const oppositeIdx = propIdx ? 0 : 1; + let currentLayout = connections_layout[propIdx]; + let oppositeLayout = connections_layout[oppositeIdx]; + + if (this.size[0] === 10 || this.size[1] === 10) { + props["connections_dir"] = props["connections_dir"] || [-1, -1]; + const connections_dir = this.properties["connections_dir"] as [number, number]; + let currentDir = connections_dir[propIdx] as number; + // let oppositeDir = connections_dir[oppositeIdx]; + const options: number[] = + this.size[0] === 10 + ? currentLayout === "Bottom" + ? [LiteGraph.DOWN, LiteGraph.RIGHT, LiteGraph.LEFT] + : [LiteGraph.UP, LiteGraph.LEFT, LiteGraph.RIGHT] + : currentLayout === "Right" + ? [LiteGraph.RIGHT, LiteGraph.DOWN, LiteGraph.UP] + : [LiteGraph.LEFT, LiteGraph.UP, LiteGraph.DOWN]; + let idx = options.indexOf(currentDir); + let next = options[idx + 1] ?? options[0]!; + connections_dir[propIdx] = next; + return; + } + + let next = currentLayout; + do { + let idx = LAYOUT_CLOCKWISE.indexOf(next); + next = LAYOUT_CLOCKWISE[idx + 1] ?? LAYOUT_CLOCKWISE[0]!; + } while (next === oppositeLayout); + connections_layout[propIdx] = next; + this.setDirtyCanvas(true, true); + } + + /** + * Handles a mouse move while this node is selected. Note, though, that the actual work here is + * processed bycause the move and resize shortcuts set `canvas.node_capturing_input` to this node + * when they start (otherwise onMouseMove only fires when the mouse moves within the node's + * bounds). + */ + override onMouseMove(event: PointerEvent): void { + if (this.shortcuts.move.state) { + const shortcut = this.shortcuts.move; + if (shortcut.initialMousePos[0] === -1) { + shortcut.initialMousePos[0] = event.clientX; + shortcut.initialMousePos[1] = event.clientY; + shortcut.initialNodePos[0] = this.pos[0]; + shortcut.initialNodePos[1] = this.pos[1]; + } + this.manuallyHandleMove(event); + } else if (this.shortcuts.resize.state) { + const shortcut = this.shortcuts.resize; + if (shortcut.initialMousePos[0] === -1) { + shortcut.initialMousePos[0] = event.clientX; + shortcut.initialMousePos[1] = event.clientY; + shortcut.initialNodeSize[0] = this.size[0]; + shortcut.initialNodeSize[1] = this.size[1]; + shortcut.initialNodePos[0] = this.pos[0]; + shortcut.initialNodePos[1] = this.pos[1]; + const canvas = app.canvas as TLGraphCanvas; + const offset = canvas.convertEventToCanvasOffset(event); + shortcut.resizeOnSide[0] = this.pos[0] > offset[0] ? LiteGraph.LEFT : LiteGraph.RIGHT; + shortcut.resizeOnSide[1] = this.pos[1] > offset[1] ? LiteGraph.UP : LiteGraph.DOWN; + } + this.manuallyHandleResize(event); + } + } + + /** + * Handles a key down while this node is selected, starting a shortcut if the keys are newly + * pressed. + */ + override onKeyDown(event: KeyboardEvent) { + super.onKeyDown(event); + const canvas = app.canvas as TLGraphCanvas; + + // Only handle shortcuts while we're enabled in the config. + if (CONFIG_FAST_REROUTE_ENABLED) { + for (const [key, shortcut] of Object.entries(this.shortcuts)) { + if (!shortcut.state) { + const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys); + if (keys) { + shortcut.state = true; + if (key === "rotate") { + this.rotate(90); + } else if (key.includes("connection")) { + this.cycleConnection(key.includes("input") ? IoDirection.INPUT : IoDirection.OUTPUT); + } + if ((shortcut as any).initialMousePos) { + canvas.node_capturing_input = this; + } + } + } + } + } + } + + /** + * Handles a key up while this node is selected, canceling any current shortcut. + */ + override onKeyUp(event: KeyboardEvent) { + super.onKeyUp(event); + const canvas = app.canvas as TLGraphCanvas; + + // Only handle shortcuts while we're enabled in the config. + if (CONFIG_FAST_REROUTE_ENABLED) { + for (const [key, shortcut] of Object.entries(this.shortcuts)) { + if (shortcut.state) { + const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys); + if (!keys) { + shortcut.state = false; + if ((shortcut as any).initialMousePos) { + (shortcut as any).initialMousePos = [-1, -1]; + if ((canvas.node_capturing_input = this)) { + canvas.node_capturing_input = null; + } + this.setDirtyCanvas(true, true); + } + } + } + } + } + } + + /** + * Handles a deselection of the node, canceling any current shortcut. + */ + override onDeselected(): void { + super.onDeselected?.(); + const canvas = app.canvas as TLGraphCanvas; + for (const [key, shortcut] of Object.entries(this.shortcuts)) { + shortcut.state = false; + if ((shortcut as any).initialMousePos) { + (shortcut as any).initialMousePos = [-1, -1]; + if ((canvas.node_capturing_input = this)) { + canvas.node_capturing_input = null; + } + this.setDirtyCanvas(true, true); + } + } + } + + override onRemoved(): void { + super.onRemoved?.(); + // If we're removed, let's call out to the link dragging above. In a settimeout because this is + // called as we're removing with further cleanup Litegraph does, and we want the handler to + // cleanup further, afterwards + setTimeout(() => { + SERVICE.handleRemovedNodeMaybeWhileDragging(this); + }, 32); + } + + override getHelp() { + return ` +

    + Finally, a comfortable, powerful reroute node with true multi-direction and powerful + shortcuts to bring your workflow to the next level. +

    + + ${ + !CONFIG_FAST_REROUTE_ENABLED + ? `

    Fast Shortcuts are currently disabled.` + : ` +

      +

    • + ${CONFIG_KEY_CREATE_WHILE_LINKING} Create a new reroute node while dragging + a link, connecting it to the link in the place and continuing the link. +

      • +

    • + ${CONFIG_KEY_ROTATE} Rotate the selected reroute node counter clockwise 90 + degrees. +

      • +

    • + ${CONFIG_KEY_RESIZE} Resize the selected reroute node from the nearest + corner by holding down and moving your mouse. +

      • +

    • + ${CONFIG_KEY_MOVE} Move the selected reroute node by holding down and + moving your mouse. +

      • +

    • + ${CONFIG_KEY_CXN_INPUT} Change the input layout/direction of the selected + reroute node. +

      • +

    • + ${CONFIG_KEY_CXN_OUTPUT} Change the output layout/direction of the selected + reroute node. +

      • +
    + ` + } +

    + To change, ${!CONFIG_FAST_REROUTE_ENABLED ? "enable" : "disable"} or configure sohrtcuts, + make a copy of + /custom_nodes/rgthree-comfy/rgthree_config.json.default to + /custom_nodes/rgthree-comfy/rgthree_config.json and configure under + nodes > reroute > fast_reroute. +

    + `; + } +} + +addMenuItem(RerouteNode, app, { + name: (node) => `${node.properties?.["showLabel"] ? "Hide" : "Show"} Label/Title`, + property: "showLabel", + callback: async (node, value) => { + app.graph.setDirtyCanvas(true, true); + }, +}); + +addMenuItem(RerouteNode, app, { + name: (node) => `${node.resizable ? "No" : "Allow"} Resizing`, + callback: (node) => { + (node as RerouteNode).setResizable(!node.resizable); + node.size[0] = Math.max(40, node.size[0]); + node.size[1] = Math.max(30, node.size[1]); + (node as RerouteNode).applyNodeSize(); + }, +}); + +addMenuItem(RerouteNode, app, { + name: "Static Width", + property: "size", + subMenuOptions: (() => { + const options = []; + for (let w = 8; w > 0; w--) { + options.push(`${w * 10}`); + } + return options; + })(), + prepareValue: (value, node) => [Number(value), node.size[1]], + callback: (node) => { + (node as RerouteNode).setResizable(false); + (node as RerouteNode).applyNodeSize(); + }, +}); + +addMenuItem(RerouteNode, app, { + name: "Static Height", + property: "size", + subMenuOptions: (() => { + const options = []; + for (let w = 8; w > 0; w--) { + options.push(`${w * 10}`); + } + return options; + })(), + prepareValue: (value, node) => [node.size[0], Number(value)], + callback: (node) => { + (node as RerouteNode).setResizable(false); + (node as RerouteNode).applyNodeSize(); + }, +}); + +addConnectionLayoutSupport( + RerouteNode, + app, + [ + ["Left", "Right"], + ["Left", "Top"], + ["Left", "Bottom"], + ["Right", "Left"], + ["Right", "Top"], + ["Right", "Bottom"], + ["Top", "Left"], + ["Top", "Right"], + ["Top", "Bottom"], + ["Bottom", "Left"], + ["Bottom", "Right"], + ["Bottom", "Top"], + ], + (node) => { + (node as RerouteNode).applyNodeSize(); + }, +); + +addMenuItem(RerouteNode, app, { + name: "Rotate", + subMenuOptions: [ + "Rotate 90Β° Clockwise", + "Rotate 90Β° Counter-Clockwise", + "Rotate 180Β°", + null, + "Flip Horizontally", + "Flip Vertically", + ], + callback: (node_: TLGraphNode, value) => { + const node = node_ as RerouteNode; + if (value?.startsWith("Rotate 90Β° Clockwise")) { + node.rotate(90); + } else if (value?.startsWith("Rotate 90Β° Counter-Clockwise")) { + node.rotate(-90); + } else if (value?.startsWith("Rotate 180Β°")) { + node.rotate(180); + } else { + const connections_layout = node.properties["connections_layout"] as [string, string]; + const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]); + const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]); + if (value?.startsWith("Flip Horizontally")) { + if (["Left", "Right"].includes(connections_layout[0])) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]!; + } + if (["Left", "Right"].includes(connections_layout[1])) { + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]!; + } + } else if (value?.startsWith("Flip Vertically")) { + if (["Top", "Bottom"].includes(connections_layout[0])) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]!; + } + if (["Top", "Bottom"].includes(connections_layout[1])) { + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]!; + } + } + } + }, +}); + +addMenuItem(RerouteNode, app, { + name: "Clone New Reroute...", + subMenuOptions: ["Before", "After"], + callback: async (node, value) => { + const clone = node.clone()!; + const pos = [...node.pos]; + if (value === "Before") { + clone.pos = [pos[0]! - 20, pos[1]! - 20]; + app.graph.add(clone); + await wait(); + const inputLinks = getSlotLinks(node.inputs[0]); + for (const inputLink of inputLinks) { + const link = inputLink.link; + const linkedNode = app.graph.getNodeById(link.origin_id) as TLGraphNode; + if (linkedNode) { + linkedNode.connect(0, clone, 0); + } + } + clone.connect(0, node, 0); + } else { + clone.pos = [pos[0]! + 20, pos[1]! + 20]; + app.graph.add(clone); + await wait(); + const outputLinks = getSlotLinks(node.outputs[0]); + node.connect(0, clone, 0); + for (const outputLink of outputLinks) { + const link = outputLink.link; + const linkedNode = app.graph.getNodeById(link.target_id) as TLGraphNode; + if (linkedNode) { + clone.connect(0, linkedNode, link.target_slot); + } + } + } + }, +}); + +app.registerExtension({ + name: "rgthree.Reroute", + registerCustomNodes() { + RerouteNode.setUp(); + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss new file mode 100644 index 00000000..4df9c3a9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss @@ -0,0 +1,328 @@ +.rgthree-top-messages-container { + position: fixed; + z-index: 9999; + top: 0; + left: 0; + width: 100%; + height: 0; + display: flex; + flex-direction: column; + align-items: center; + justify-content: start; +} + +.rgthree-top-messages-container > div { + position: relative; + height: fit-content; + padding: 4px; + margin-top: -100px; /* re-set by JS */ + opacity: 0; + transition: all 0.33s ease-in-out; + z-index: 3; +} +.rgthree-top-messages-container > div:last-child { + z-index: 2; +} +.rgthree-top-messages-container > div:not(.-show) { + z-index: 1; +} + +.rgthree-top-messages-container > div.-show { + opacity: 1; + margin-top: 0px !important; +} + +.rgthree-top-messages-container > div.-show { + opacity: 1; + transform: translateY(0%); +} + +.rgthree-top-messages-container > div > div { + position: relative; + background: #353535; + color: #fff; + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; + height: fit-content; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.88); + padding: 6px 12px; + border-radius: 4px; + font-family: Arial, sans-serif; + font-size: 14px; +} +.rgthree-top-messages-container > div > div > span { + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; +} +.rgthree-top-messages-container > div > div > span svg { + width: 20px; + height: auto; + margin-right: 8px; +} +.rgthree-top-messages-container > div > div > span svg.icon-checkmark { + fill: #2e9720; +} + +.rgthree-top-messages-container [type="warn"]::before, +.rgthree-top-messages-container [type="success"]::before { + content: '⚠️'; + display: inline-block; + flex: 0 0 auto; + font-size: 18px; + margin-right: 4px; + line-height: 1; +} +.rgthree-top-messages-container [type="success"]::before { + content: 'πŸŽ‰'; +} + +.rgthree-top-messages-container a { + cursor: pointer; + text-decoration: underline; + color: #fc0; + margin-left: 4px; + display: inline-block; + line-height: 1; +} + +.rgthree-top-messages-container a:hover { + color: #fc0; + text-decoration: none; +} + +/* Fix node selector being crazy long b/c of array types. */ +.litegraph.litesearchbox input, +.litegraph.litesearchbox select { + max-width: 250px; +} + +/* There's no reason for this z-index to be so high. It layers on top of things it shouldn't, + (like pythongssss' image gallery, the properties panel, etc.) */ +.comfy-multiline-input { + z-index: 1 !important; +} +.comfy-multiline-input:focus { + z-index: 2 !important; +} +.litegraph .dialog { + z-index: 3 !important; /* This is set to 1, but goes under the multi-line inputs, so bump it. */ +} + + +@import '../common/css/buttons.scss'; +@import '../common/css/dialog.scss'; +@import '../common/css/menu.scss'; + +.rgthree-dialog.-settings { + width: 100%; +} +.rgthree-dialog.-settings fieldset { + border: 1px solid rgba(255, 255, 255, 0.25); + padding: 0 12px 8px; + margin-bottom: 16px; +} +.rgthree-dialog.-settings fieldset > legend { + margin-left: 8px; + padding: 0 8px; + opacity: 0.5; +} +.rgthree-dialog.-settings .formrow { + display: flex; + flex-direction: column; +} +.rgthree-dialog.-settings .formrow + .formrow { + border-top: 1px solid rgba(255, 255, 255, 0.25); +} +.rgthree-dialog.-settings .fieldrow { + display: flex; + flex-direction: row; +} +.rgthree-dialog.-settings .fieldrow > label { + flex: 1 1 auto; + user-select: none; + padding: 8px 12px 12px; +} +.rgthree-dialog.-settings .fieldrow > label span { + font-weight: bold; +} +.rgthree-dialog.-settings .fieldrow > label small { + display: block; + margin-top: 4px; + font-size: calc(11rem / 16); + opacity: 0.75; + padding-left: 16px; +} +.rgthree-dialog.-settings .fieldrow ~ .fieldrow { + font-size: 0.9rem; + border-top: 1px dotted rgba(255, 255, 255, 0.25); +} +.rgthree-dialog.-settings .fieldrow ~ .fieldrow label { + padding-left: 28px; +} +.rgthree-dialog.-settings .fieldrow:first-child:not(.-checked) ~ .fieldrow { + display: none; +} +.rgthree-dialog.-settings .fieldrow:hover { + background: rgba(255,255,255,0.1); +} +.rgthree-dialog.-settings .fieldrow ~ .fieldrow span { + font-weight: normal; +} + +.rgthree-dialog.-settings .fieldrow > .fieldrow-value { + display: flex; + align-items: center; + justify-content: end; + flex: 0 0 auto; + width: 50%; + max-width: 230px; +} +.rgthree-dialog.-settings .fieldrow.-type-boolean > .fieldrow-value { + max-width: 64px; +} +.rgthree-dialog.-settings .fieldrow.-type-number input { + width: 48px; + text-align: right; +} + +.rgthree-dialog.-settings .fieldrow input[type="checkbox"] { + width: 24px; + height: 24px; + cursor: pointer; +} + +.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group { + padding: 0; + border: 0; + margin: 0; + + > span.rgthree-checklist-item { + display: inline-block; + white-space: nowrap; + padding-right: 6px; + vertical-align: middle; + + input[type="checkbox"] { + width: 16px; + height: 16px; + } + + label { + padding-left: 4px; + text-align: left; + cursor: pointer; + } + } +} + +.rgthree-comfyui-settings-row div { + display: flex; + flex-direction: row; + align-items: center; + justify-content: end; +} +.rgthree-comfyui-settings-row div svg { + width: 36px; + height: 36px; + margin-right: 16px; +} + + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy, +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item { + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy svg, +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg { + fill: currentColor; + width: auto; + height: 16px; + margin-right: 6px; +} +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg.github-star { + fill: rgb(227, 179, 65); +} + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy, +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-label { + color: #dde; + background-color: #212121 !important; + margin: 0; + padding: 2px; + cursor: default; + opacity: 1; + padding: 4px; + font-weight: bold; +} +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy { + font-size: 1.1em; + color: #fff; + background-color: #090909 !important; + justify-content: center; + padding: 4px 8px; +} + +rgthree-progress-bar { + display: block; + position: relative; + z-index: 999; + top: 0; + left: 0; + height: 14px; + font-size: 10px; + width: 100%; + overflow: hidden; + box-shadow: 0px 0px 3px rgba(0, 0, 0, 0.25); + box-shadow: + inset 0px -1px 0px rgba(0, 0, 0, 0.25), + 0px 1px 0px rgba(255, 255, 255, 0.125); + +} + +* ~ rgthree-progress-bar, +.comfyui-body-bottom rgthree-progress-bar { + box-shadow: + 0px -1px 0px rgba(0, 0, 0, 1), + inset 0px 1px 0px rgba(255, 255, 255, 0.15), inset 0px -1px 0px rgba(0, 0, 0, 0.25), 0px 1px 0px rgba(255, 255, 255, 0.125); +} + +body:not([style*=grid]):not([class*=grid]) { + rgthree-progress-bar { + position: fixed; + top: 0px; + bottom: auto; + } + rgthree-progress-bar.rgthree-pos-bottom { + top: auto; + bottom: 0px; + } +} + + +.rgthree-debug-keydowns { + display: block; + position: fixed; + z-index: 1050; + top: 3px; + right: 8px; + font-size: 10px; + color: #fff; + font-family: sans-serif; + pointer-events: none; +} + + +.rgthree-comfy-about-badge-logo { + width: 20px; + height: 20px; + background: url(/rgthree/logo.svg?bg=transparent&fg=%2393c5fd); + background-size: 100% 100%; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts new file mode 100644 index 00000000..206cb938 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts @@ -0,0 +1,1062 @@ +import type { + LGraphCanvas as TLGraphCanvas, + LGraphNode, + IContextMenuValue, + LGraph as TLGraph, + IContextMenuOptions, + ISerialisedGraph, + CanvasMouseEvent, + CanvasPointerExtensions, + NodeId, + ISerialisedNode, + Positionable, + ComfyApp, +} from "@comfyorg/frontend"; +import type {ComfyApiFormat, ComfyApiPrompt} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {api} from "scripts/api.js"; +import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; +import {SERVICE as BOOKMARKS_SERVICE} from "./services/bookmarks_services.js"; +import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js"; +import {WorkflowLinkFixer} from "rgthree/common/link_fixer.js"; +import {injectCss, wait} from "rgthree/common/shared_utils.js"; +import {replaceNode, waitForCanvas, waitForGraph} from "./utils.js"; +import {NodeTypesString, addRgthree, getNodeTypeStrings} from "./constants.js"; +import {RgthreeProgressBar} from "rgthree/common/progress_bar.js"; +import {RgthreeConfigDialog} from "./config.js"; +import { + iconGear, + iconNode, + iconReplace, + iconStarFilled, + logoRgthree, +} from "rgthree/common/media/svgs.js"; +import {createElement, queryAll, query} from "rgthree/common/utils_dom.js"; + +export enum LogLevel { + IMPORTANT = 1, + ERROR, + WARN, + INFO, + DEBUG, + DEV, +} + +const LogLevelKeyToLogLevel: {[key: string]: LogLevel} = { + IMPORTANT: LogLevel.IMPORTANT, + ERROR: LogLevel.ERROR, + WARN: LogLevel.WARN, + INFO: LogLevel.INFO, + DEBUG: LogLevel.DEBUG, + DEV: LogLevel.DEV, +}; + +type ConsoleLogFns = "log" | "error" | "warn" | "debug" | "info"; +const LogLevelToMethod: {[key in LogLevel]: ConsoleLogFns} = { + [LogLevel.IMPORTANT]: "log", + [LogLevel.ERROR]: "error", + [LogLevel.WARN]: "warn", + [LogLevel.INFO]: "info", + [LogLevel.DEBUG]: "log", + [LogLevel.DEV]: "log", +}; +const LogLevelToCSS: {[key in LogLevel]: string} = { + [LogLevel.IMPORTANT]: "font-weight: bold; color: blue;", + [LogLevel.ERROR]: "", + [LogLevel.WARN]: "", + [LogLevel.INFO]: "font-style: italic; color: blue;", + [LogLevel.DEBUG]: "font-style: italic; color: #444;", + [LogLevel.DEV]: "color: #004b68;", +}; + +let GLOBAL_LOG_LEVEL = LogLevel.ERROR; + +/** + * At some point in Summer of 2024 ComfyUI broke third-party api calls by assuming api paths follow + * a certain structure. However, rgthree-comfy wants an `/rgthree/` prefix for that same reason, so + * we overwrite the apiUrl method to fix. + */ +const apiURL = api.apiURL; +api.apiURL = function (route: string): string { + if (route.includes("rgthree/")) { + return (this.api_base + "/" + route).replace(/\/\//g, "/"); + } + return apiURL.apply(this, arguments as any); +}; + +/** + * A blocklist of extensions to disallow hooking into rgthree's base classes when calling the + * `rgthree.invokeExtensionsAsync` method (which runs outside of ComfyNode's + * `app.invokeExtensionsAsync` which is private). + * + * In Apr 2024 the base rgthree node class added support for other extensions using `nodeCreated` + * and `beforeRegisterNodeDef` which allows other extensions to modify the class. However, since it + * had been months since divorcing the ComfyNode in rgthree-comfy due to instability and + * inflexibility, this was a bit risky as other extensions hadn't ever run with this ability. This + * list attempts to block extensions from being able to call into rgthree-comfy nodes via the + * `nodeCreated` and `beforeRegisterNodeDef` callbacks now that rgthree-comfy is utilizing them + * because they do not work. Oddly, it's ComfyUI's own extension that is broken. + */ +const INVOKE_EXTENSIONS_BLOCKLIST = [ + { + name: "Comfy.WidgetInputs", + reason: + "Major conflict with rgthree-comfy nodes' inputs causing instability and " + + "repeated link disconnections.", + }, + { + name: "efficiency.widgethider", + reason: + "Overrides value getter before widget getter is prepared. Can be lifted if/when " + + "https://github.com/jags111/efficiency-nodes-comfyui/pull/203 is pulled.", + }, +]; + +/** A basic wrapper around logger. */ +class Logger { + /** Logs a message to the console if it meets the current log level. */ + log(level: LogLevel, message: string, ...args: any[]) { + const [n, v] = this.logParts(level, message, ...args); + console[n]?.(...v); + } + + /** + * Returns a tuple of the console function and its arguments. Useful for callers to make the + * actual console. call to gain benefits of DevTools knowing the source line. + * + * If the input is invalid or the level doesn't meet the configuration level, then the return + * value is an unknown function and empty set of values. Callers can use optionla chaining + * successfully: + * + * const [fn, values] = logger.logPars(LogLevel.INFO, 'my message'); + * console[fn]?.(...values); // Will work even if INFO won't be logged. + * + */ + logParts(level: LogLevel, message: string, ...args: any[]): [ConsoleLogFns, any[]] { + if (level <= GLOBAL_LOG_LEVEL) { + const css = LogLevelToCSS[level] || ""; + if (level === LogLevel.DEV) { + message = `πŸ”§ ${message}`; + } + return [LogLevelToMethod[level], [`%c${message}`, css, ...args]]; + } + return ["none" as "info", []]; + } +} + +/** + * A log session, with the name as the prefix. A new session will stack prefixes. + */ +class LogSession { + readonly logger = new Logger(); + readonly logsCache: {[key: string]: {lastShownTime: number}} = {}; + + constructor(readonly name?: string) {} + + /** + * Returns the console log method to use and the arguments to pass so the call site can log from + * there. This extra work at the call site allows for easier debugging in the dev console. + * + * const [logMethod, logArgs] = logger.logParts(LogLevel.DEBUG, message, ...args); + * console[logMethod]?.(...logArgs); + */ + logParts(level: LogLevel, message?: string, ...args: any[]): [ConsoleLogFns, any[]] { + message = `${this.name || ""}${message ? " " + message : ""}`; + return this.logger.logParts(level, message, ...args); + } + + logPartsOnceForTime( + level: LogLevel, + time: number, + message?: string, + ...args: any[] + ): [ConsoleLogFns, any[]] { + message = `${this.name || ""}${message ? " " + message : ""}`; + const cacheKey = `${level}:${message}`; + const cacheEntry = this.logsCache[cacheKey]; + const now = +new Date(); + if (cacheEntry && cacheEntry.lastShownTime + time > now) { + return ["none" as "info", []]; + } + const parts = this.logger.logParts(level, message, ...args); + if (console[parts[0]]) { + this.logsCache[cacheKey] = this.logsCache[cacheKey] || ({} as {lastShownTime: number}); + this.logsCache[cacheKey]!.lastShownTime = now; + } + return parts; + } + + debugParts(message?: string, ...args: any[]) { + return this.logParts(LogLevel.DEBUG, message, ...args); + } + + infoParts(message?: string, ...args: any[]) { + return this.logParts(LogLevel.INFO, message, ...args); + } + + warnParts(message?: string, ...args: any[]) { + return this.logParts(LogLevel.WARN, message, ...args); + } + + errorParts(message?: string, ...args: any[]) { + return this.logParts(LogLevel.ERROR, message, ...args); + } + + newSession(name?: string) { + return new LogSession(`${this.name}${name}`); + } +} + +export type RgthreeUiMessage = { + id: string; + message: string; + type?: "warn" | "info" | "success" | null; + timeout?: number; + // closeable?: boolean; // TODO + actions?: Array<{ + label: string; + href?: string; + callback?: (event: MouseEvent) => void; + }>; +}; + +/** + * A global class as 'rgthree'; exposed on wiindow. Lots can go in here. + */ +class Rgthree extends EventTarget { + /** Exposes the ComfyUI api instance on rgthree. */ + readonly api = api; + private settingsDialog: RgthreeConfigDialog | null = null; + private progressBarEl: RgthreeProgressBar | null = null; + private rgthreeCssPromise: Promise; + + /** Stores a node id that we will use to queu only that output node (with `queueOutputNode`). */ + private queueNodeIds: NodeId[] | null = null; + + readonly version = CONFIG_SERVICE.getConfigValue("version"); + + logger = new LogSession("[rgthree]"); + + monitorBadLinksAlerted = false; + monitorLinkTimeout: number | null = null; + + processingQueue = false; + /** + * The API Json currently being loaded, or null. Can be used as a falsy boolean to determine if + * `app.loadApiJson` is currently executing. + */ + loadingApiJson: ComfyApiFormat | null = null; + replacingReroute: NodeId | null = null; + processingMouseDown = false; + processingMouseUp = false; + processingMouseMove = false; + lastCanvasMouseEvent: CanvasMouseEvent | null = null; + + // Comfy/LiteGraph states so nodes and tell what the hell is going on. + canvasCurrentlyCopyingToClipboard = false; + canvasCurrentlyCopyingToClipboardWithMultipleNodes = false; + canvasCurrentlyPastingFromClipboard = false; + canvasCurrentlyPastingFromClipboardWithMultipleNodes = false; + initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff: any = null; + + private readonly isMac: boolean = !!( + navigator.platform?.toLocaleUpperCase().startsWith("MAC") || + (navigator as any).userAgentData?.platform?.toLocaleUpperCase().startsWith("MAC") + ); + + constructor() { + super(); + + const logLevel = + LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")] ?? GLOBAL_LOG_LEVEL; + this.setLogLevel(logLevel); + + this.initializeGraphAndCanvasHooks(); + this.initializeComfyUIHooks(); + this.initializeContextMenu(); + + this.rgthreeCssPromise = injectCss("extensions/rgthree-comfy/rgthree.css"); + + this.initializeProgressBar(); + + CONFIG_SERVICE.addEventListener("config-change", ((e: CustomEvent) => { + if (e.detail?.key?.includes("features.progress_bar")) { + this.initializeProgressBar(); + } + }) as EventListener); + + if (CONFIG_SERVICE.getConfigValue("debug.keys_down.enabled")) { + const elDebugKeydowns = createElement("div.rgthree-debug-keydowns", { + parent: document.body, + }); + const updateDebugKeyDown = () => { + elDebugKeydowns.innerText = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" "); + }; + KEY_EVENT_SERVICE.addEventListener("keydown", updateDebugKeyDown); + KEY_EVENT_SERVICE.addEventListener("keyup", updateDebugKeyDown); + } + } + + /** + * Initializes the top progress bar, if it's configured. + */ + async initializeProgressBar() { + if (CONFIG_SERVICE.getConfigValue("features.progress_bar.enabled")) { + await this.rgthreeCssPromise; + if (!this.progressBarEl) { + this.progressBarEl = RgthreeProgressBar.create(); + this.progressBarEl.setAttribute( + "title", + "Progress Bar by rgthree. right-click for rgthree menu.", + ); + + this.progressBarEl.addEventListener("contextmenu", async (e) => { + e.stopPropagation(); + e.preventDefault(); + }); + + this.progressBarEl.addEventListener("pointerdown", async (e) => { + LiteGraph.closeAllContextMenus(); + if (e.button == 2) { + const canvas = await waitForCanvas(); + new LiteGraph.ContextMenu(this.getRgthreeIContextMenuValues(), { + title: `
    ${logoRgthree} rgthree-comfy
    `, + left: e.clientX, + top: 5, + }); + return; + } + if (e.button == 0) { + const nodeId = this.progressBarEl?.currentNodeId; + if (nodeId) { + const [canvas, graph] = await Promise.all([waitForCanvas(), waitForGraph()]); + const node = graph.getNodeById(Number(nodeId)); + if (node) { + canvas.centerOnNode(node); + e.stopPropagation(); + e.preventDefault(); + } + } + return; + } + }); + } + // Handle both cases in case someone hasn't updated. Can probably just assume + // `isUpdatedComfyBodyClasses` is true in the near future. + const isUpdatedComfyBodyClasses = !!query(".comfyui-body-top"); + const position = CONFIG_SERVICE.getConfigValue("features.progress_bar.position"); + this.progressBarEl.classList.toggle("rgthree-pos-bottom", position === "bottom"); + // If ComfyUI is updated with the body segments, then use that. + if (isUpdatedComfyBodyClasses) { + if (position === "bottom") { + query(".comfyui-body-bottom")!.appendChild(this.progressBarEl); + } else { + query(".comfyui-body-top")!.appendChild(this.progressBarEl); + } + } else { + document.body.appendChild(this.progressBarEl); + } + const height = CONFIG_SERVICE.getConfigValue("features.progress_bar.height") || 14; + this.progressBarEl.style.height = `${height}px`; + const fontSize = Math.max(10, Number(height) - 10); + this.progressBarEl.style.fontSize = `${fontSize}px`; + this.progressBarEl.style.fontWeight = fontSize <= 12 ? "bold" : "normal"; + } else { + this.progressBarEl?.remove(); + } + } + + /** + * Initialize a bunch of hooks into LiteGraph itself so we can either keep state or context on + * what's happening so nodes can respond appropriately. This is usually to fix broken assumptions + * in the unowned code [πŸ€], but sometimes to add features or enhancements too [⭐]. + */ + private async initializeGraphAndCanvasHooks() { + const rgthree = this; + + // [πŸ€] To mitigate changes from https://github.com/rgthree/rgthree-comfy/issues/69 + // and https://github.com/comfyanonymous/ComfyUI/issues/2193 we can try to store the workflow + // node so our nodes can find the seralized node. Works with method + // `getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff` to find a node + // while serializing. What a way to work around... + const graphSerialize = LGraph.prototype.serialize; + LGraph.prototype.serialize = function () { + const response = graphSerialize.apply(this, [...arguments] as any) as any; + rgthree.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff = response; + return response; + }; + + // Overrides LiteGraphs' processMouseDown to both keep state as well as dispatch a custom event. + const processMouseDown = LGraphCanvas.prototype.processMouseDown; + LGraphCanvas.prototype.processMouseDown = function (e: PointerEvent) { + rgthree.processingMouseDown = true; + const returnVal = processMouseDown.apply(this, [...arguments] as any); + rgthree.dispatchCustomEvent("on-process-mouse-down", {originalEvent: e}); + rgthree.processingMouseDown = false; + return returnVal; + }; + + // Overrides LiteGraph's `adjustMouseEvent` to capture the last even coming in and out. Useful + // to capture the last `canvasX` and `canvasY` properties, which are not the same as LiteGraph's + // `canvas.last_mouse_position`, unfortunately. + const adjustMouseEvent = LGraphCanvas.prototype.adjustMouseEvent; + LGraphCanvas.prototype.adjustMouseEvent = function ( + e: T & Partial, + ): asserts e is T & CanvasMouseEvent { + adjustMouseEvent.apply(this, [...arguments] as any); + rgthree.lastCanvasMouseEvent = e as CanvasMouseEvent; + }; + + // [πŸ€] Copying to clipboard clones nodes and then manipulates the linking data manually which + // does not allow a node to handle connections. This harms nodes that manually handle inputs, + // like our any-input nodes that may start with one input, and manually add new ones when one is + // attached. + const copyToClipboard = LGraphCanvas.prototype.copyToClipboard; + LGraphCanvas.prototype.copyToClipboard = function (items?: Iterable) { + rgthree.canvasCurrentlyCopyingToClipboard = true; + rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes = + Object.values(items || this.selected_nodes || []).length > 1; + const value = copyToClipboard.apply(this, [...arguments] as any); + rgthree.canvasCurrentlyCopyingToClipboard = false; + rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes = false; + return value; + }; + + // [πŸ€] Pasting from clipboard. + const pasteFromClipboard = LGraphCanvas.prototype.pasteFromClipboard; + LGraphCanvas.prototype.pasteFromClipboard = function (...args: any[]) { + rgthree.canvasCurrentlyPastingFromClipboard = true; + pasteFromClipboard.apply(this, [...arguments] as any); + rgthree.canvasCurrentlyPastingFromClipboard = false; + }; + + // [⭐] Make it so when we add a group, we get to name it immediately. + const onGroupAdd = LGraphCanvas.onGroupAdd; + LGraphCanvas.onGroupAdd = function (...args: any[]) { + const graph = app.canvas.getCurrentGraph()!; + onGroupAdd.apply(this, [...args] as any); + // [πŸ€] Bad typing here.. especially the last arg; it is LGraphNode but can really be anything + // with pos or size... pity. See more in our litegraph.d.ts. + LGraphCanvas.onShowPropertyEditor( + {} as any, + null as any, + null as any, + null as any, + graph._groups[graph._groups.length - 1]! as unknown as LGraphNode, + ); + }; + } + + /** + * [πŸ€] Handles the same exact thing as ComfyApp's `invokeExtensionsAsync`, but done here since + * it is #private in ComfyApp because... of course it us. This is necessary since we purposefully + * avoid using the ComfyNode due to historical instability and inflexibility for all the advanced + * ui stuff rgthree-comfy nodes do, but we can still have other custom nodes know what's happening + * with rgthree-comfy; specifically, for `nodeCreated` as of now. + */ + async invokeExtensionsAsync(method: "nodeCreated", ...args: any[]) { + const comfyapp = app as ComfyApp; + if (CONFIG_SERVICE.getConfigValue("features.invoke_extensions_async.node_created") === false) { + const [m, a] = this.logParts( + LogLevel.INFO, + `Skipping invokeExtensionsAsync for applicable rgthree-comfy nodes`, + ); + console[m]?.(...a); + return Promise.resolve(); + } + return await Promise.all( + comfyapp.extensions.map(async (ext) => { + if (ext?.[method]) { + try { + const blocked = INVOKE_EXTENSIONS_BLOCKLIST.find((block) => + ext.name.toLowerCase().startsWith(block.name.toLowerCase()), + ); + if (blocked) { + const [n, v] = this.logger.logPartsOnceForTime( + LogLevel.WARN, + 5000, + `Blocked extension '${ext.name}' method '${method}' for rgthree-nodes because: ${blocked.reason}`, + ); + console[n]?.(...v); + return Promise.resolve(); + } + return await (ext[method] as Function)(...args, comfyapp); + } catch (error) { + const [n, v] = this.logParts( + LogLevel.ERROR, + `Error calling extension '${ext.name}' method '${method}' for rgthree-node.`, + {error}, + {extension: ext}, + {args}, + ); + console[n]?.(...v); + } + } + }), + ); + } + + /** + * Wraps `dispatchEvent` for easier CustomEvent dispatching. + */ + private dispatchCustomEvent(event: string, detail?: any) { + if (detail != null) { + return this.dispatchEvent(new CustomEvent(event, {detail})); + } + return this.dispatchEvent(new CustomEvent(event)); + } + + /** + * Initializes hooks specific to an rgthree-comfy context menu on the root menu. + */ + private async initializeContextMenu() { + const that = this; + setTimeout(async () => { + const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function (...args: any[]) { + let existingOptions = getCanvasMenuOptions.apply(this, [...args] as any); + + const options: (IContextMenuValue | null)[] = []; + options.push(null); // Divider + options.push(null); // Divider + options.push(null); // Divider + options.push({ + content: logoRgthree + `rgthree-comfy`, + className: "rgthree-contextmenu-item rgthree-contextmenu-main-item-rgthree-comfy", + submenu: { + options: that.getRgthreeIContextMenuValues(), + }, + }); + options.push(null); // Divider + options.push(null); // Divider + + let idx = null; + idx = idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Queue Group")) + 1; + idx = + idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Queue Selected")) + 1; + idx = idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Convert to Group")); + idx = idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Arrange (")); + idx = idx || existingOptions.findIndex((o) => !o) + 1; + idx = idx || 3; + // [πŸ€] existingOptions is typed as IContextMenuValue even though it need not be + // a string due to the crazy typing from the original litegraph. oh well. + (existingOptions as (IContextMenuValue | null)[]).splice(idx, 0, ...options); + for (let i = existingOptions.length; i > 0; i--) { + if (existingOptions[i] === null && existingOptions[i + 1] === null) { + existingOptions.splice(i, 1); + } + } + + return existingOptions; + }; + }, 1016); + } + + /** + * Returns the standard menu items for an rgthree-comfy context menu. + */ + private getRgthreeIContextMenuValues(): IContextMenuValue[] { + const [canvas, graph] = [app.canvas as TLGraphCanvas, app.canvas.getCurrentGraph()!]; + const selectedNodes = Object.values(canvas.selected_nodes || {}); + let rerouteNodes: LGraphNode[] = []; + if (selectedNodes.length) { + rerouteNodes = selectedNodes.filter((n) => n.type === "Reroute"); + } else { + rerouteNodes = graph._nodes.filter((n) => n.type == "Reroute"); + } + const rerouteLabel = selectedNodes.length ? "selected" : "all"; + + const showBookmarks = CONFIG_SERVICE.getFeatureValue("menu_bookmarks.enabled"); + const bookmarkMenuItems = showBookmarks ? getBookmarks() : []; + + return [ + { + content: "Nodes", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + { + content: iconNode + "All", + className: "rgthree-contextmenu-item", + has_submenu: true, + submenu: { + options: getNodeTypeStrings() as unknown as IContextMenuValue[], + callback: ( + value: string | IContextMenuValue, + options: IContextMenuOptions, + event: MouseEvent, + ) => { + const node = LiteGraph.createNode(addRgthree(value as string)); + if (node) { + node.pos = [ + rgthree.lastCanvasMouseEvent!.canvasX, + rgthree.lastCanvasMouseEvent!.canvasY, + ]; + canvas.graph!.add(node); + canvas.selectNode(node); + graph.setDirtyCanvas(true, true); + } + }, + extra: {rgthree_doNotNest: true}, + }, + }, + + { + content: "Actions", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + { + content: iconGear + "Settings (rgthree-comfy)", + disabled: !!this.settingsDialog, + className: "rgthree-contextmenu-item", + callback: (...args: any[]) => { + this.settingsDialog = new RgthreeConfigDialog().show(); + this.settingsDialog.addEventListener("close", (e) => { + this.settingsDialog = null; + }); + }, + }, + { + content: iconReplace + ` Convert ${rerouteLabel} Reroutes`, + disabled: !rerouteNodes.length, + className: "rgthree-contextmenu-item", + callback: (...args: any[]) => { + const msg = + `Convert ${rerouteLabel} ComfyUI Reroutes to Reroute (rgthree) nodes? \n` + + `(First save a copy of your workflow & check reroute connections afterwards)`; + if (!window.confirm(msg)) { + return; + } + (async () => { + for (const node of [...rerouteNodes]) { + if (node.type == "Reroute") { + this.replacingReroute = node.id; + await replaceNode(node, NodeTypesString.REROUTE); + this.replacingReroute = null; + } + } + })(); + }, + }, + ...bookmarkMenuItems, + { + content: "More...", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + { + content: iconStarFilled + "Star on Github", + className: "rgthree-contextmenu-item rgthree-contextmenu-github", + callback: (...args: any[]) => { + window.open("https://github.com/rgthree/rgthree-comfy", "_blank"); + }, + }, + ]; + } + + /** + * Wraps an `app.queuePrompt` call setting a specific node id that we will inspect and change the + * serialized graph right before being sent (below, in our `api.queuePrompt` override). + */ + async queueOutputNodes(nodes: LGraphNode[]) { + // We can use just these next two lines when + // https://github.com/ltdrdata/ComfyUI-Inspire-Pack/pull/258 is pulled. Until then, we'll keep + // our custom logic as ComfyUI-Inspire-Pack would cause it not to work. + // app.canvas.selectItems(nodes); + // app.extensionManager.command.execute('Comfy.QueueSelectedOutputNodes'); + const nodeIds = nodes.map((n) => n.id); + try { + this.queueNodeIds = nodeIds; + await app.queuePrompt(0); + } catch (e) { + const [n, v] = this.logParts( + LogLevel.ERROR, + `There was an error queuing nodes ${nodeIds}`, + e, + ); + console[n]?.(...v); + } finally { + this.queueNodeIds = null; + } + } + + /** + * Recusively walks backwards from a node adding its inputs to the `newOutput` from `oldOutput`. + */ + private recursiveAddNodes(nodeId: string, oldOutput: ComfyApiFormat, newOutput: ComfyApiFormat) { + let currentId = nodeId; + let currentNode = oldOutput[currentId]!; + if (newOutput[currentId] == null) { + newOutput[currentId] = currentNode; + for (const inputValue of Object.values(currentNode.inputs || [])) { + if (Array.isArray(inputValue)) { + this.recursiveAddNodes(inputValue[0], oldOutput, newOutput); + } + } + } + return newOutput; + } + + /** + * Initialize a bunch of hooks into ComfyUI and/or LiteGraph itself so we can either keep state or + * context on what's happening so nodes can respond appropriately. This is usually to fix broken + * assumptions in the unowned code [πŸ€], but sometimes to add features or enhancements too [⭐]. + */ + private initializeComfyUIHooks() { + const rgthree = this; + + // Keep state for when the app is queuing the prompt. For instance, this is used for seed to + // understand if we're serializing because we're queueing (and return the random seed to use) or + // for saving the workflow (and keep -1, etc.). + const queuePrompt = app.queuePrompt as Function; + app.queuePrompt = async function (number: number, batchCount?: number) { + rgthree.processingQueue = true; + rgthree.dispatchCustomEvent("queue"); + try { + return await queuePrompt.apply(app, [...arguments]); + } finally { + rgthree.processingQueue = false; + rgthree.dispatchCustomEvent("queue-end"); + } + }; + + // Keep state for when the app is in the middle of loading from an api JSON file. + const loadApiJson = app.loadApiJson; + app.loadApiJson = async function (apiData: any, fileName: string) { + rgthree.loadingApiJson = apiData as ComfyApiFormat; + try { + loadApiJson.apply(app, [...arguments] as any); + } finally { + rgthree.loadingApiJson = null; + } + }; + + // Keep state for when the app is serizalizing the graph to prompt. + const graphToPrompt = app.graphToPrompt; + app.graphToPrompt = async function () { + rgthree.dispatchCustomEvent("graph-to-prompt"); + let promise = graphToPrompt.apply(app, [...arguments] as any); + await promise; + rgthree.dispatchCustomEvent("graph-to-prompt-end"); + return promise; + }; + + // Override the queuePrompt for api to intercept the prompt output and, if queueNodeIds is set, + // then we only want to queue those nodes, by rewriting the api format (prompt 'output' field) + // so only those are evaluated. + const apiQueuePrompt = api.queuePrompt as Function; + api.queuePrompt = async function (index: number, prompt: ComfyApiPrompt, ...args: any[]) { + if (rgthree.queueNodeIds?.length && prompt.output) { + const oldOutput = prompt.output; + let newOutput = {}; + for (const queueNodeId of rgthree.queueNodeIds) { + rgthree.recursiveAddNodes(String(queueNodeId), oldOutput, newOutput); + } + prompt.output = newOutput; + } + rgthree.dispatchCustomEvent("comfy-api-queue-prompt-before", { + workflow: prompt.workflow, + output: prompt.output, + }); + const response = apiQueuePrompt.apply(app, [index, prompt, ...args]); + rgthree.dispatchCustomEvent("comfy-api-queue-prompt-end"); + return response; + }; + + // Hook into a clean call; allow us to clear and rgthree messages. + const clean = app.clean; + app.clean = function () { + rgthree.clearAllMessages(); + clean && clean.apply(app, [...arguments] as any); + }; + + // Hook into a data load, like from an image or JSON drop-in. This is (currently) used to + // monitor for bad linking data. + const loadGraphData = app.loadGraphData; + // NOTE: This was "serializedLGraph" in pre-litegraph types, which maps to `ISerialisedGraph` + // now; though, @comfyorg/comfyui-frontend-types have the signature as `ComfyWorkflowJSON` which + // is not exported and a zod type. Looks like there's mostly an overlap with ISerialisedGraph. + app.loadGraphData = function (graph: ISerialisedGraph) { + if (rgthree.monitorLinkTimeout) { + clearTimeout(rgthree.monitorLinkTimeout); + rgthree.monitorLinkTimeout = null; + } + rgthree.clearAllMessages(); + // Try to make a copy to use, because ComfyUI's loadGraphData will modify it. + let graphCopy: ISerialisedGraph | null; + try { + graphCopy = JSON.parse(JSON.stringify(graph)); + } catch (e) { + graphCopy = null; + } + setTimeout(() => { + const wasLoadingAborted = document + .querySelector(".comfy-modal-content") + ?.textContent?.includes("Loading aborted due"); + const graphToUse = wasLoadingAborted ? graphCopy || graph : app.graph; + const fixer = WorkflowLinkFixer.create(graphToUse as unknown as TLGraph); + const fixBadLinksResult = fixer.check(); + if (fixBadLinksResult.hasBadLinks) { + const [n, v] = rgthree.logParts( + LogLevel.WARN, + `The workflow you've loaded has corrupt linking data. Open ${ + new URL(location.href).origin + }/rgthree/link_fixer to try to fix.`, + ); + console[n]?.(...v); + if (CONFIG_SERVICE.getConfigValue("features.show_alerts_for_corrupt_workflows")) { + rgthree.showMessage({ + id: "bad-links", + type: "warn", + message: + "The workflow you've loaded has corrupt linking data that may be able to be fixed.", + actions: [ + { + label: "Open fixer", + href: "/rgthree/link_fixer", + }, + { + label: "Fix in place", + href: "/rgthree/link_fixer", + callback: (event) => { + event.stopPropagation(); + event.preventDefault(); + if ( + confirm( + "This will attempt to fix in place. Please make sure to have a saved copy of your workflow.", + ) + ) { + try { + const fixBadLinksResult = fixer.fix(); + if (!fixBadLinksResult.hasBadLinks) { + rgthree.hideMessage("bad-links"); + alert( + "Success! It's possible some valid links may have been affected. Please check and verify your workflow.", + ); + wasLoadingAborted && app.loadGraphData(fixBadLinksResult.graph); + if ( + CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") || + CONFIG_SERVICE.getConfigValue("features.monitor_bad_links") + ) { + rgthree.monitorLinkTimeout = setTimeout(() => { + rgthree.monitorBadLinks(); + }, 5000); + } + } + } catch (e) { + console.error(e); + alert("Unsuccessful at fixing corrupt data. :("); + rgthree.hideMessage("bad-links"); + } + } + }, + }, + ], + }); + } + } else if ( + CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") || + CONFIG_SERVICE.getConfigValue("features.monitor_bad_links") + ) { + rgthree.monitorLinkTimeout = setTimeout(() => { + rgthree.monitorBadLinks(); + }, 5000); + } + }, 100); + return loadGraphData && loadGraphData.apply(app, [...arguments] as any); + }; + } + + /** + * [πŸ€] Finds a node in the currently serializing workflow from the hook setup above. This is to + * mitigate breakages from https://github.com/comfyanonymous/ComfyUI/issues/2193 we can try to + * store the workflow node so our nodes can find the seralized node. + */ + getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff( + node: LGraphNode, + ): ISerialisedNode | null { + return ( + this.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff?.nodes?.find( + (n: ISerialisedNode) => n.id === node.id, + ) ?? null + ); + } + + /** + * Shows a message in the UI. + */ + async showMessage(data: RgthreeUiMessage) { + let container = document.querySelector(".rgthree-top-messages-container"); + if (!container) { + container = document.createElement("div"); + container.classList.add("rgthree-top-messages-container"); + document.body.appendChild(container); + } + // If we have a dialog open then we want to append the message to the dialog so they show over + // the modal. + const dialogs = queryAll("dialog[open]"); + if (dialogs.length) { + let dialog = dialogs[dialogs.length - 1]!; + dialog.appendChild(container); + dialog.addEventListener("close", (e) => { + document.body.appendChild(container!); + }); + } + // Hide if we exist. + await this.hideMessage(data.id); + + const messageContainer = document.createElement("div"); + messageContainer.setAttribute("type", data.type || "info"); + + const message = document.createElement("span"); + message.innerHTML = data.message; + messageContainer.appendChild(message); + + for (let a = 0; a < (data.actions || []).length; a++) { + const action = data.actions![a]!; + if (a > 0) { + const sep = document.createElement("span"); + sep.innerHTML = " | "; + messageContainer.appendChild(sep); + } + + const actionEl = document.createElement("a"); + actionEl.innerText = action.label; + if (action.href) { + actionEl.target = "_blank"; + actionEl.href = action.href; + } + if (action.callback) { + actionEl.onclick = (e) => { + return action.callback!(e); + }; + } + messageContainer.appendChild(actionEl); + } + + const messageAnimContainer = document.createElement("div"); + messageAnimContainer.setAttribute("msg-id", data.id); + messageAnimContainer.appendChild(messageContainer); + container.appendChild(messageAnimContainer); + + // Add. Wait. Measure. Wait. Anim. + await wait(64); + messageAnimContainer.style.marginTop = `-${messageAnimContainer.offsetHeight}px`; + await wait(64); + messageAnimContainer.classList.add("-show"); + + if (data.timeout) { + await wait(data.timeout); + this.hideMessage(data.id); + } + } + + /** + * Hides a message in the UI. + */ + async hideMessage(id: string) { + const msg = document.querySelector(`.rgthree-top-messages-container > [msg-id="${id}"]`); + if (msg?.classList.contains("-show")) { + msg.classList.remove("-show"); + await wait(750); + } + msg && msg.remove(); + } + + /** + * Clears all messages in the UI. + */ + async clearAllMessages() { + let container = document.querySelector(".rgthree-top-messages-container"); + container && (container.innerHTML = ""); + } + + setLogLevel(level?: LogLevel | string) { + if (typeof level === "string") { + level = LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")]; + } + if (level != null) { + GLOBAL_LOG_LEVEL = level; + } + } + + logParts(level: LogLevel, message?: string, ...args: any[]) { + return this.logger.logParts(level, message, ...args); + } + + newLogSession(name?: string) { + return this.logger.newSession(name); + } + + isDebugMode() { + if (window.location.href.includes("rgthree-debug=false")) { + return false; + } + return GLOBAL_LOG_LEVEL >= LogLevel.DEBUG || window.location.href.includes("rgthree-debug"); + } + + isDevMode() { + if (window.location.href.includes("rgthree-dev=false")) { + return false; + } + return GLOBAL_LOG_LEVEL >= LogLevel.DEV || window.location.href.includes("rgthree-dev"); + } + + monitorBadLinks() { + const badLinksFound = WorkflowLinkFixer.create(app.graph).check(); + if (badLinksFound.hasBadLinks && !this.monitorBadLinksAlerted) { + this.monitorBadLinksAlerted = true; + alert( + `Problematic links just found in live data. Can you save your workflow and file a bug with ` + + `the last few steps you took to trigger this at ` + + `https://github.com/rgthree/rgthree-comfy/issues. Thank you!`, + ); + } else if (!badLinksFound.hasBadLinks) { + // Clear the alert once fixed so we can alert again. + this.monitorBadLinksAlerted = false; + } + this.monitorLinkTimeout = setTimeout(() => { + this.monitorBadLinks(); + }, 5000); + } +} + +function getBookmarks(): IContextMenuValue[] { + const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks(); + const bookmarkItems = bookmarks.map((n) => ({ + content: `[${n.shortcutKey}] ${n.title}`, + className: "rgthree-contextmenu-item", + callback: () => { + n.canvasToBookmark(); + }, + })); + + return !bookmarkItems.length + ? [] + : [ + { + content: "πŸ”– Bookmarks", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + ...bookmarkItems, + ]; +} + +export const rgthree = new Rgthree(); +// Expose it on window because, why not. +(window as any).rgthree = rgthree; + +app.registerExtension({ + name: "Comfy.RgthreeComfy", + + aboutPageBadges: [ + { + label: `rgthree-comfy v${rgthree.version}`, + url: "https://github.com/rgthree/rgthree-comfy", + icon: "rgthree-comfy-about-badge-logo", + }, + ], +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/seed.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/seed.ts new file mode 100644 index 00000000..2d44f29d --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/seed.ts @@ -0,0 +1,308 @@ +import type { + IContextMenuOptions, + ContextMenu, + LGraphNode as TLGraphNode, + IWidget, + LGraphCanvas, + IContextMenuValue, + LGraphNodeConstructor, + ISerialisedNode, + IButtonWidget, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef, ComfyApiPrompt} from "typings/comfy.js"; + +import {app} from "scripts/app.js"; +import {ComfyWidgets} from "scripts/widgets.js"; +import {RgthreeBaseServerNode} from "./base_node.js"; +import {rgthree} from "./rgthree.js"; +import {addConnectionLayoutSupport} from "./utils.js"; +import {NodeTypesString} from "./constants.js"; + +const LAST_SEED_BUTTON_LABEL = "♻️ (Use Last Queued Seed)"; + +const SPECIAL_SEED_RANDOM = -1; +const SPECIAL_SEED_INCREMENT = -2; +const SPECIAL_SEED_DECREMENT = -3; +const SPECIAL_SEEDS = [SPECIAL_SEED_RANDOM, SPECIAL_SEED_INCREMENT, SPECIAL_SEED_DECREMENT]; + +interface SeedSerializedCtx { + inputSeed?: number; + seedUsed?: number; +} + +class RgthreeSeed extends RgthreeBaseServerNode { + static override title = NodeTypesString.SEED; + static override type = NodeTypesString.SEED; + static comfyClass = NodeTypesString.SEED; + + override serialize_widgets = true; + + private logger = rgthree.newLogSession(`[Seed]`); + + static override exposedActions = ["Randomize Each Time", "Use Last Queued Seed"]; + + static "@randomMax" = {type: "number"}; + static "@randomMin" = {type: "number"}; + + lastSeed?: number = undefined; + serializedCtx: SeedSerializedCtx = {}; + seedWidget!: IWidget; + lastSeedButton!: IWidget; + lastSeedValue: IWidget | null = null; + + private handleApiHijackingBound = this.handleApiHijacking.bind(this); + + constructor(title = RgthreeSeed.title) { + super(title); + + this.properties["randomMax"] = 1125899906842624; + // We can have a full range of seeds, including negative. But, for the randomRange we'll + // only generate positives, since that's what folks assume. + this.properties["randomMin"] = 0; + + rgthree.addEventListener( + "comfy-api-queue-prompt-before", + this.handleApiHijackingBound as EventListener, + ); + } + + override onPropertyChanged(prop: string, value: unknown, prevValue?: unknown): boolean { + if (prop === 'randomMax') { + this.properties["randomMax"] = Math.min(1125899906842624, Number(value as number)); + } else if (prop === 'randomMin') { + this.properties["randomMin"] = Math.max(-1125899906842624, Number(value as number)); + } + return true; + } + + override onRemoved() { + rgthree.addEventListener( + "comfy-api-queue-prompt-before", + this.handleApiHijackingBound as EventListener, + ); + } + + override configure(info: ISerialisedNode): void { + super.configure(info); + if (this.properties?.["showLastSeed"]) { + this.addLastSeedValue(); + } + } + + override async handleAction(action: string) { + if (action === "Randomize Each Time") { + this.seedWidget.value = SPECIAL_SEED_RANDOM; + } else if (action === "Use Last Queued Seed") { + this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value; + this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL; + this.lastSeedButton.disabled = true; + } + } + + override onNodeCreated() { + super.onNodeCreated?.(); + // Grab the already available widgets, and remove the built-in control_after_generate + for (const [i, w] of this.widgets.entries()) { + if (w.name === "seed") { + this.seedWidget = w; // as ComfyWidget; + this.seedWidget.value = SPECIAL_SEED_RANDOM; + } else if (w.name === "control_after_generate") { + this.widgets.splice(i, 1); + } + } + + this.addWidget( + "button", + "🎲 Randomize Each Time", + "", + () => { + this.seedWidget.value = SPECIAL_SEED_RANDOM; + }, + {serialize: false}, + ); + + this.addWidget( + "button", + "🎲 New Fixed Random", + "", + () => { + this.seedWidget.value = this.generateRandomSeed(); + }, + {serialize: false}, + ); + + this.lastSeedButton = this.addWidget( + "button", + LAST_SEED_BUTTON_LABEL, + "", + () => { + this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value; + this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL; + this.lastSeedButton.disabled = true; + }, + {width: 50, serialize: false} as any, + ) as IButtonWidget; + this.lastSeedButton.disabled = true; + } + + generateRandomSeed() { + let step = this.seedWidget.options.step || 1; + const randomMin = Number(this.properties['randomMin'] || 0); + const randomMax = Number(this.properties['randomMax'] || 1125899906842624); + const randomRange = (randomMax - randomMin) / (step / 10); + let seed = Math.floor(Math.random() * randomRange) * (step / 10) + randomMin; + if (SPECIAL_SEEDS.includes(seed)) { + seed = 0; + } + return seed; + } + + override getExtraMenuOptions(canvas: LGraphCanvas, options: IContextMenuValue[]) { + super.getExtraMenuOptions?.apply(this, [...arguments] as any); + options.splice(options.length - 1, 0, { + content: "Show/Hide Last Seed Value", + callback: ( + _value: IContextMenuValue, + _options: IContextMenuOptions, + _event: MouseEvent, + _parentMenu: ContextMenu | undefined, + _node: TLGraphNode, + ) => { + this.properties["showLastSeed"] = !this.properties["showLastSeed"]; + if (this.properties["showLastSeed"]) { + this.addLastSeedValue(); + } else { + this.removeLastSeedValue(); + } + }, + }); + return []; + } + + addLastSeedValue() { + if (this.lastSeedValue) return; + this.lastSeedValue = ComfyWidgets["STRING"]( + this, + "last_seed", + ["STRING", {multiline: true}], + app, + ).widget as unknown as IWidget; + this.lastSeedValue!.inputEl!.readOnly = true; + this.lastSeedValue!.inputEl!.style.fontSize = "0.75rem"; + this.lastSeedValue!.inputEl!.style.textAlign = "center"; + this.computeSize(); + } + + removeLastSeedValue() { + if (!this.lastSeedValue) return; + this.lastSeedValue!.inputEl!.remove(); + this.widgets.splice(this.widgets.indexOf(this.lastSeedValue), 1); + this.lastSeedValue = null; + this.computeSize(); + } + + /** + * Intercepts the prompt right before ComfyUI sends it to the server (as fired from rgthree) so we + * can inspect the prompt and workflow data and change swap in the seeds. + * + * Note, the original implementation tried to change the widget value itself when the graph was + * queued (and the relied on ComfyUI serializing the data changed data) and then changing it back. + * This worked well until other extensions kept calling graphToPrompt during asynchronous + * operations within, causing the widget to get confused without a reliable state to reflect upon. + */ + handleApiHijacking(e: CustomEvent) { + // Don't do any work if we're muted/bypassed. + if (this.mode === LiteGraph.NEVER || this.mode === 4) { + return; + } + + const workflow = e.detail.workflow; + const output = e.detail.output; + + let workflowNode = workflow?.nodes?.find((n: ISerialisedNode) => n.id === this.id) ?? null; + let outputInputs = output?.[this.id]?.inputs; + + if ( + !workflowNode || + !outputInputs || + outputInputs[this.seedWidget.name || "seed"] === undefined + ) { + const [n, v] = this.logger.warnParts( + `Node ${this.id} not found in prompt data sent to server. This may be fine if only ` + + `queuing part of the workflow. If not, then this could be a bug.`, + ); + console[n]?.(...v); + return; + } + + const seedToUse = this.getSeedToUse(); + const seedWidgetndex = this.widgets.indexOf(this.seedWidget); + + workflowNode.widgets_values![seedWidgetndex] = seedToUse; + outputInputs[this.seedWidget.name || "seed"] = seedToUse; + + this.lastSeed = seedToUse; + if (seedToUse != this.seedWidget.value) { + this.lastSeedButton.name = `♻️ ${this.lastSeed}`; + this.lastSeedButton.disabled = false; + } else { + this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL; + this.lastSeedButton.disabled = true; + } + if (this.lastSeedValue) { + this.lastSeedValue.value = `Last Seed: ${this.lastSeed}`; + } + } + + /** + * Determines a seed to use depending on the seed widget's current value and the last used seed. + * There are no sideffects to calling this method. + */ + private getSeedToUse() { + const inputSeed = Number(this.seedWidget.value); + let seedToUse: number | null = null; + + // If our input seed was a special seed, then handle it. + if (SPECIAL_SEEDS.includes(inputSeed)) { + // If the last seed was not a special seed and we have increment/decrement, then do that on + // the last seed. + if (typeof this.lastSeed === "number" && !SPECIAL_SEEDS.includes(this.lastSeed)) { + if (inputSeed === SPECIAL_SEED_INCREMENT) { + seedToUse = this.lastSeed + 1; + } else if (inputSeed === SPECIAL_SEED_DECREMENT) { + seedToUse = this.lastSeed - 1; + } + } + // If we don't have a seed to use, or it's special seed (like we incremented into one), then + // we randomize. + if (seedToUse == null || SPECIAL_SEEDS.includes(seedToUse)) { + seedToUse = this.generateRandomSeed(); + } + } + + return seedToUse ?? inputSeed; + } + + static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeSeed); + } + + static override onRegisteredForOverride(comfyClass: any, ctxClass: any) { + addConnectionLayoutSupport(RgthreeSeed, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + RgthreeSeed.category = comfyClass.category; + }); + } +} + +app.registerExtension({ + name: "rgthree.Seed", + async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { + if (nodeData.name === RgthreeSeed.type) { + RgthreeSeed.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/bookmarks_services.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/bookmarks_services.ts new file mode 100644 index 00000000..25a34c7a --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/bookmarks_services.ts @@ -0,0 +1,34 @@ +import type {Bookmark} from "../bookmark.js"; + +import {app} from "scripts/app.js"; +import {NodeTypesString} from "../constants.js"; +import {reduceNodesDepthFirst} from "../utils.js"; + +const SHORTCUT_DEFAULTS = "1234567890abcdefghijklmnopqrstuvwxyz".split(""); + +class BookmarksService { + /** + * Gets a list of the current bookmarks within the current workflow. + */ + getCurrentBookmarks(): Bookmark[] { + return reduceNodesDepthFirst(app.graph.nodes, (n, acc) => { + if (n.type === NodeTypesString.BOOKMARK) { + acc.push(n as Bookmark); + } + }, []).sort((a, b) => a.title.localeCompare(b.title)); + } + + getExistingShortcuts() { + const bookmarkNodes = this.getCurrentBookmarks(); + const usedShortcuts = new Set(bookmarkNodes.map((n) => n.shortcutKey)); + return usedShortcuts; + } + + getNextShortcut() { + const existingShortcuts = this.getExistingShortcuts(); + return SHORTCUT_DEFAULTS.find((char) => !existingShortcuts.has(char)) ?? "1"; + } +} + +/** The BookmarksService singleton. */ +export const SERVICE = new BookmarksService(); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/config_service.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/config_service.ts new file mode 100644 index 00000000..4737ab11 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/config_service.ts @@ -0,0 +1,40 @@ +// @ts-ignore +import {rgthreeConfig} from "rgthree/config.js"; +import {getObjectValue, setObjectValue} from "rgthree/common/shared_utils.js"; +import {rgthreeApi} from "rgthree/common/rgthree_api.js"; + +/** + * A singleton service exported as `SERVICE` to handle configuration routines. + */ +class ConfigService extends EventTarget { + getConfigValue(key: string, def?: any) { + return getObjectValue(rgthreeConfig, key, def); + } + + getFeatureValue(key: string, def?: any) { + key = "features." + key.replace(/^features\./, ""); + return getObjectValue(rgthreeConfig, key, def); + } + + /** + * Given an object of key:value changes it will send to the server and wait for a successful + * response before setting the values on the local rgthreeConfig. + */ + async setConfigValues(changed: {[key: string]: any}) { + const body = new FormData(); + body.append("json", JSON.stringify(changed)); + const response = await rgthreeApi.fetchJson("/config", {method: "POST", body}); + if (response.status === "ok") { + for (const [key, value] of Object.entries(changed)) { + setObjectValue(rgthreeConfig, key, value); + this.dispatchEvent(new CustomEvent("config-change", {detail: {key, value}})); + } + } else { + return false; + } + return true; + } +} + +/** The ConfigService singleton. */ +export const SERVICE = new ConfigService(); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/context_service.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/context_service.ts new file mode 100644 index 00000000..3a3cfaff --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/context_service.ts @@ -0,0 +1,74 @@ +import type {DynamicContextNodeBase} from "../dynamic_context_base.js"; + +import {NodeTypesString} from "../constants.js"; +import {getConnectedOutputNodesAndFilterPassThroughs} from "../utils.js"; +import {INodeInputSlot, INodeOutputSlot, INodeSlot, LGraphNode} from "@comfyorg/frontend"; + +export let SERVICE: ContextService; + +const OWNED_PREFIX = "+"; +const REGEX_PREFIX = /^[\+⚠️]\s*/; +const REGEX_EMPTY_INPUT = /^\+\s*$/; + +export function stripContextInputPrefixes(name: string) { + return name.replace(REGEX_PREFIX, ""); +} + +export function getContextOutputName(inputName: string) { + if (inputName === "base_ctx") return "CONTEXT"; + return stripContextInputPrefixes(inputName).toUpperCase(); +} + +export enum InputMutationOperation { + "UNKNOWN", + "ADDED", + "REMOVED", + "RENAMED", +} + +export type InputMutation = { + operation: InputMutationOperation; + node: DynamicContextNodeBase; + slotIndex: number; + slot: INodeSlot; +}; + +export class ContextService { + constructor() { + if (SERVICE) { + throw new Error("ContextService was already instantiated."); + } + } + + onInputChanges(node: any, mutation: InputMutation) { + const childCtxs = getConnectedOutputNodesAndFilterPassThroughs( + node, + node, + 0, + ) as DynamicContextNodeBase[]; + for (const childCtx of childCtxs) { + childCtx.handleUpstreamMutation(mutation); + } + } + + getDynamicContextInputsData(node: DynamicContextNodeBase) { + return node + .getContextInputsList() + .map((input: INodeInputSlot, index: number) => ({ + name: stripContextInputPrefixes(input.name), + type: String(input.type), + index, + })) + .filter((i) => i.type !== "*"); + } + + getDynamicContextOutputsData(node: LGraphNode) { + return node.outputs.map((output: INodeOutputSlot, index: number) => ({ + name: stripContextInputPrefixes(output.name), + type: String(output.type), + index, + })); + } +} + +SERVICE = new ContextService(); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/fast_groups_service.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/fast_groups_service.ts new file mode 100644 index 00000000..dc765e8f --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/fast_groups_service.ts @@ -0,0 +1,231 @@ +import type {LGraphGroup as TLGraphGroup} from "@comfyorg/frontend"; +import type {BaseFastGroupsModeChanger} from "../fast_groups_muter.js"; + +import {app} from "scripts/app.js"; +import {getGraphDependantNodeKey, getGroupNodes, reduceNodesDepthFirst} from "../utils.js"; + +type Vector4 = [number, number, number, number]; + + + +/** + * A service that keeps global state that can be shared by multiple FastGroupsMuter or + * FastGroupsBypasser nodes rather than calculate it on it's own. + */ +class FastGroupsService { + private msThreshold = 400; + private msLastUnsorted = 0; + private msLastAlpha = 0; + private msLastPosition = 0; + + private groupsUnsorted: TLGraphGroup[] = []; + private groupsSortedAlpha: TLGraphGroup[] = []; + private groupsSortedPosition: TLGraphGroup[] = []; + + private readonly fastGroupNodes: BaseFastGroupsModeChanger[] = []; + + private runScheduledForMs: number | null = null; + private runScheduleTimeout: number | null = null; + private runScheduleAnimation: number | null = null; + + private cachedNodeBoundings: {[key: string]: Vector4} | null = null; + + constructor() { + // Don't need to do anything, wait until a signal. + } + + addFastGroupNode(node: BaseFastGroupsModeChanger) { + this.fastGroupNodes.push(node); + // Schedule it because the node may not be ready to refreshWidgets (like, when added it may + // not have cloned properties to filter against, etc.). + this.scheduleRun(8); + } + + removeFastGroupNode(node: BaseFastGroupsModeChanger) { + const index = this.fastGroupNodes.indexOf(node); + if (index > -1) { + this.fastGroupNodes.splice(index, 1); + } + // If we have no more group nodes, then clear out data; it could be because of a canvas clear. + if (!this.fastGroupNodes?.length) { + this.clearScheduledRun(); + this.groupsUnsorted = []; + this.groupsSortedAlpha = []; + this.groupsSortedPosition = []; + } + } + + private run() { + // We only run if we're scheduled, so if we're not, then bail. + if (!this.runScheduledForMs) { + return; + } + for (const node of this.fastGroupNodes) { + node.refreshWidgets(); + } + this.clearScheduledRun(); + this.scheduleRun(); + } + + private scheduleRun(ms = 500) { + // If we got a request for an immediate schedule and already have on scheduled for longer, then + // cancel the long one to expediate a fast one. + if (this.runScheduledForMs && ms < this.runScheduledForMs) { + this.clearScheduledRun(); + } + if (!this.runScheduledForMs && this.fastGroupNodes.length) { + this.runScheduledForMs = ms; + this.runScheduleTimeout = setTimeout(() => { + this.runScheduleAnimation = requestAnimationFrame(() => this.run()); + }, ms); + } + } + + private clearScheduledRun() { + this.runScheduleTimeout && clearTimeout(this.runScheduleTimeout); + this.runScheduleAnimation && cancelAnimationFrame(this.runScheduleAnimation); + this.runScheduleTimeout = null; + this.runScheduleAnimation = null; + this.runScheduledForMs = null; + } + + /** + * Returns the boundings for all nodes on the graph, then clears it after a short delay. This is + * to increase efficiency by caching the nodes' boundings when multiple groups are on the page. + */ + getBoundingsForAllNodes() { + if (!this.cachedNodeBoundings) { + this.cachedNodeBoundings = reduceNodesDepthFirst( + app.graph._nodes, + (node, acc) => { + let bounds = node.getBounding(); + // If the bounds are zero'ed out, then we could be a subgraph that hasn't rendered yet and + // need to update them. + if (bounds[0] === 0 && bounds[1] === 0 && bounds[2] === 0 && bounds[3] === 0) { + const ctx = node.graph?.primaryCanvas?.canvas.getContext("2d"); + if (ctx) { + node.updateArea(ctx); + bounds = node.getBounding(); + } + } + acc[getGraphDependantNodeKey(node)] = bounds as Vector4; + }, + {} as {[key: string]: Vector4}, + ); + setTimeout(() => { + this.cachedNodeBoundings = null; + }, 50); + } + return this.cachedNodeBoundings; + } + + /** + * This overrides `TLGraphGroup.prototype.recomputeInsideNodes` to be much more efficient when + * calculating for many groups at once (only compute all nodes once in `getBoundingsForAllNodes`). + */ + recomputeInsideNodesForGroup(group: TLGraphGroup) { + // If the canvas is currently being dragged (includes if a group is being dragged around) then + // don't recompute anything. + if (app.canvas.isDragging) return; + const cachedBoundings = this.getBoundingsForAllNodes(); + const nodes = group.graph!.nodes; + group._children.clear(); + group.nodes.length = 0; + + for (const node of nodes) { + const nodeBounding = cachedBoundings[getGraphDependantNodeKey(node)]; + const nodeCenter = + nodeBounding && + ([nodeBounding[0] + nodeBounding[2] * 0.5, nodeBounding[1] + nodeBounding[3] * 0.5] as [ + number, + number, + ]); + if (nodeCenter) { + const grouBounds = group._bounding as unknown as [number, number, number, number]; + if ( + nodeCenter[0] >= grouBounds[0] && + nodeCenter[0] < grouBounds[0] + grouBounds[2] && + nodeCenter[1] >= grouBounds[1] && + nodeCenter[1] < grouBounds[1] + grouBounds[3] + ) { + group._children.add(node); + group.nodes.push(node); + } + } + } + } + + /** + * Everything goes through getGroupsUnsorted, so we only get groups once. However, LiteGraph's + * `recomputeInsideNodes` is inefficient when calling multiple groups (it iterates over all nodes + * each time). So, we'll do our own dang thing, once. + */ + private getGroupsUnsorted(now: number) { + const canvas = app.canvas; + const graph = canvas.getCurrentGraph() ?? app.graph; + + if ( + // Don't recalculate nodes if we're moving a group (added by ComfyUI in app.js) + // TODO: This doesn't look available anymore... ? + !canvas.selected_group_moving && + (!this.groupsUnsorted.length || now - this.msLastUnsorted > this.msThreshold) + ) { + this.groupsUnsorted = [...graph._groups]; + const subgraphs = graph.subgraphs?.values(); + if (subgraphs) { + let s; + while ((s = subgraphs.next().value)) this.groupsUnsorted.push(...(s.groups ?? [])); + } + for (const group of this.groupsUnsorted) { + this.recomputeInsideNodesForGroup(group); + group.rgthree_hasAnyActiveNode = getGroupNodes(group).some( + (n) => n.mode === LiteGraph.ALWAYS, + ); + } + this.msLastUnsorted = now; + } + return this.groupsUnsorted; + } + + private getGroupsAlpha(now: number) { + if (!this.groupsSortedAlpha.length || now - this.msLastAlpha > this.msThreshold) { + this.groupsSortedAlpha = [...this.getGroupsUnsorted(now)].sort((a, b) => { + return a.title.localeCompare(b.title); + }); + this.msLastAlpha = now; + } + return this.groupsSortedAlpha; + } + + private getGroupsPosition(now: number) { + if (!this.groupsSortedPosition.length || now - this.msLastPosition > this.msThreshold) { + this.groupsSortedPosition = [...this.getGroupsUnsorted(now)].sort((a, b) => { + // Sort by y, then x, clamped to 30. + const aY = Math.floor(a._pos[1] / 30); + const bY = Math.floor(b._pos[1] / 30); + if (aY == bY) { + const aX = Math.floor(a._pos[0] / 30); + const bX = Math.floor(b._pos[0] / 30); + return aX - bX; + } + return aY - bY; + }); + this.msLastPosition = now; + } + return this.groupsSortedPosition; + } + + getGroups(sort?: string) { + const now = +new Date(); + if (sort === "alphanumeric") { + return this.getGroupsAlpha(now); + } + if (sort === "position") { + return this.getGroupsPosition(now); + } + return this.getGroupsUnsorted(now); + } +} + +/** The FastGroupsService singleton. */ +export const SERVICE = new FastGroupsService(); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/key_events_services.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/key_events_services.ts new file mode 100644 index 00000000..6d9a3321 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/key_events_services.ts @@ -0,0 +1,184 @@ +/** + * A service responsible for capturing keys within LiteGraph's canvas, and outside of it, allowing + * nodes and other services to confidently determine what's going on. + */ +class KeyEventService extends EventTarget { + readonly downKeys: { [key: string]: boolean } = {}; + readonly shiftDownKeys: { [key: string]: boolean } = {}; + + ctrlKey = false; + altKey = false; + metaKey = false; + shiftKey = false; + + private readonly isMac: boolean = !!( + navigator.platform?.toLocaleUpperCase().startsWith("MAC") || + (navigator as any).userAgentData?.platform?.toLocaleUpperCase().startsWith("MAC") + ); + + constructor() { + super(); + this.initialize(); + } + + initialize() { + const that = this; + // [πŸ€] Sometimes ComfyUI and/or LiteGraph stop propagation of key events which makes it hard + // to determine if keys are currently pressed. To attempt to get around this, we'll hijack + // LiteGraph's processKey to try to get better consistency. + const processKey = LGraphCanvas.prototype.processKey; + LGraphCanvas.prototype.processKey = function (e: KeyboardEvent) { + if (e.type === "keydown" || e.type === "keyup") { + that.handleKeyDownOrUp(e); + } + return processKey.apply(this, [...arguments] as any) as any; + }; + + // Now that ComfyUI has more non-canvas UI (like the top bar), we listen on window as well, and + // de-dupe when we get multiple events from both window and/or LiteGraph. + window.addEventListener("keydown", (e) => { + that.handleKeyDownOrUp(e); + }); + window.addEventListener("keyup", (e) => { + that.handleKeyDownOrUp(e); + }); + + // If we get a visibilitychange, then clear the keys since we can't listen for keys up/down when + // not visible. + document.addEventListener("visibilitychange", (e) => { + this.clearKeydowns(); + }); + + // If we get a blur, then also clear the keys since we can't listen for keys up/down when + // blurred. This can happen w/o a visibilitychange, like a browser alert. + window.addEventListener("blur", (e) => { + this.clearKeydowns(); + }); + } + + /** + * Adds a new queue item, unless the last is the same. + */ + handleKeyDownOrUp(e: KeyboardEvent) { + const key = e.key.toLocaleUpperCase(); + // If we're already down, or already up, then ignore and don't fire. + if ((e.type === 'keydown' && this.downKeys[key] === true) + || (e.type === 'keyup' && this.downKeys[key] === undefined)) { + return; + } + + this.ctrlKey = !!e.ctrlKey; + this.altKey = !!e.altKey; + this.metaKey = !!e.metaKey; + this.shiftKey = !!e.shiftKey; + if (e.type === "keydown") { + this.downKeys[key] = true; + this.dispatchCustomEvent("keydown", { originalEvent: e }); + + // If SHIFT is pressed down as well, then we need to keep track of this separetly to "release" + // it once SHIFT is also released. + if (this.shiftKey && key !== 'SHIFT') { + this.shiftDownKeys[key] = true; + } + } else if (e.type === "keyup") { + // See https://github.com/rgthree/rgthree-comfy/issues/238 + // A little bit of a hack, but Mac reportedly does something odd with copy/paste. ComfyUI + // gobbles the copy event propagation, but it happens for paste too and reportedly 'Enter' which + // I can't find a reason for in LiteGraph/comfy. So, for Mac only, whenever we lift a Command + // (META) key, we'll also clear any other keys. + if (key === "META" && this.isMac) { + this.clearKeydowns(); + } else { + delete this.downKeys[key]; + } + + // If we're releasing the SHIFT key, then we may also be releasing all other keys we pressed + // during the SHIFT key as well. We should get an additional keydown for them after. + if (key === 'SHIFT') { + for (const key in this.shiftDownKeys) { + delete this.downKeys[key]; + delete this.shiftDownKeys[key]; + } + } + this.dispatchCustomEvent("keyup", { originalEvent: e }); + } + + } + + private clearKeydowns() { + this.ctrlKey = false; + this.altKey = false; + this.metaKey = false; + this.shiftKey = false; + for (const key in this.downKeys) delete this.downKeys[key]; + } + + /** + * Wraps `dispatchEvent` for easier CustomEvent dispatching. + */ + private dispatchCustomEvent(event: string, detail?: any) { + if (detail != null) { + return this.dispatchEvent(new CustomEvent(event, { detail })); + } + return this.dispatchEvent(new CustomEvent(event)); + } + + /** + * Parses a shortcut string. + * + * - 's' => ['S'] + * - 'shift + c' => ['SHIFT', 'C'] + * - 'shift + meta + @' => ['SHIFT', 'META', '@'] + * - 'shift + + + @' => ['SHIFT', '__PLUS__', '='] + * - '+ + p' => ['__PLUS__', 'P'] + */ + private getKeysFromShortcut(shortcut: string | string[]) { + let keys; + if (typeof shortcut === "string") { + // Rip all spaces out. Note, Comfy swallows space, so we don't have to handle it. Otherwise, + // we would require space to be fed as "Space" or "Spacebar" instead of " ". + shortcut = shortcut.replace(/\s/g, ""); + // Change a real "+" to something we can encode. + shortcut = shortcut.replace(/^\+/, "__PLUS__").replace(/\+\+/, "+__PLUS__"); + keys = shortcut.split("+").map((i) => i.replace("__PLUS__", "+")); + } else { + keys = [...shortcut]; + } + return keys.map((k) => k.toLocaleUpperCase()); + } + + /** + * Checks if all keys passed in are down. + */ + areAllKeysDown(keys: string | string[]) { + keys = this.getKeysFromShortcut(keys); + return keys.every((k) => { + return this.downKeys[k]; + }); + } + + /** + * Checks if only the keys passed in are down; optionally and additionally allowing "shift" key. + */ + areOnlyKeysDown(keys: string | string[], alsoAllowShift = false) { + keys = this.getKeysFromShortcut(keys); + const allKeysDown = this.areAllKeysDown(keys); + const downKeysLength = Object.values(this.downKeys).length; + // All keys are down and they're the only ones. + if (allKeysDown && keys.length === downKeysLength) { + return true; + } + // Special case allowing the shift key in addition to the shortcut keys. This helps when a user + // may had originally defined "$" as a shortcut, but needs to press "shift + $" since it's an + // upper key character, etc. + if (alsoAllowShift && !keys.includes("SHIFT") && keys.length === downKeysLength - 1) { + // If we're holding down shift, have one extra key held down, and the original keys don't + // include shift, then we're good to go. + return allKeysDown && this.areAllKeysDown(["SHIFT"]); + } + return false; + } +} + +/** The KeyEventService singleton. */ +export const SERVICE = new KeyEventService(); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/testing/comfyui_env.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/comfyui_env.ts new file mode 100644 index 00000000..23d88331 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/comfyui_env.ts @@ -0,0 +1,71 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {NodeTypesString} from "../constants.js"; + +type addNodeOptions = { + placement?: string; +}; + +/** + * A testing environment to make setting up, clearing, and queuing more predictable in an + * integration test environment. + */ +export class ComfyUITestEnvironment { + private lastNode: LGraphNode | null = null; + private maxY = 0; + + constructor() {} + + wait = wait; + + async addNode(nodeString: string, options: addNodeOptions = {}) { + const [canvas, graph] = [app.canvas, app.graph]; + const node = LiteGraph.createNode(nodeString)!; + let x = 0; + let y = 30; + if (this.lastNode) { + const placement = options.placement || "right"; + if (placement === "under") { + x = this.lastNode.pos[0]; + y = this.lastNode.pos[1] + this.lastNode.size[1] + 30; + } else if (placement === "right") { + x = this.lastNode.pos[0] + this.lastNode.size[0] + 100; + y = this.lastNode.pos[1]; + } else if (placement === "start") { + x = 0; + y = this.maxY + 50; + } + } + canvas.graph!.add(node); + node.pos = [x, y]; + canvas.selectNode(node); + app.graph.setDirtyCanvas(true, true); + await wait(); + this.lastNode = node; + this.maxY = Math.max(this.maxY, y + this.lastNode.size[1]); + return (this.lastNode = node); + } + + async clear() { + app.clean(); + app.graph.clear(); + const nodeConfig = await this.addNode(NodeTypesString.KSAMPLER_CONFIG); + const displayAny = await this.addNode(NodeTypesString.DISPLAY_ANY); + nodeConfig.widgets = nodeConfig.widgets || []; + nodeConfig.widgets[0]!.value = Math.round(Math.random() * 100); + nodeConfig.connect(0, displayAny, 0); + await this.queuePrompt(); + app.clean(); + app.graph.clear(); + this.lastNode = null; + this.maxY = 0; + await wait(); + } + + async queuePrompt() { + await app.queuePrompt(0); + await wait(150); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/testing/runner.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/runner.ts new file mode 100644 index 00000000..2cf36d48 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/runner.ts @@ -0,0 +1,167 @@ +/** + * @fileoverview A set of methods that mimic a bit of the Jasmine testing library, but simpler and + * more succinct for manipulating a comfy integration test. + * + * Tests are not bundled by default, to test build with "--with-tests" and then invoke from the + * dev console like `rgthree_tests.TestDescribeLabel()`. The output is in the test itself. + */ +import { wait } from "rgthree/common/shared_utils.js"; + +declare global { + interface Window { + rgthree_tests: { + [key: string]: any; + }; + } +} + + +window.rgthree_tests = window.rgthree_tests || {}; + +type TestContext = { + label?: string; + beforeEach?: Function[]; +}; + +let contexts: TestContext[] = []; + +export function describe(label: string, fn: Function) { + if (!label.startsWith('Test')) { + throw new Error('Test labels should start with "Test"'); + } + window.rgthree_tests[label] = async () => { + await describeRun(label, fn); + }; + return window.rgthree_tests[label]; +} + +export async function describeRun(label: string, fn: Function) { + await wait(); + contexts.push({ label }); + console.group(`[Start] ${contexts[contexts.length - 1]!.label}`); + await fn(); + contexts.pop(); + console.groupEnd(); +} + +export async function should(declaration: string, fn: Function) { + if (!contexts[contexts.length - 1]) { + throw Error("Called should outside of a describe."); + } + console.group(`...should ${declaration}`); + try { + for (const context of contexts) { + for (const beforeEachFn of context?.beforeEach || []) { + await beforeEachFn(); + } + } + await fn(); + } catch (e: any) { + fail(e); + } + console.groupEnd(); +} + +export async function beforeEach(fn: Function) { + if (!contexts[contexts.length - 1]) { + throw Error("Called beforeEach outside of a describe."); + } + const last = contexts[contexts.length - 1]!; + last.beforeEach = last?.beforeEach || []; + last.beforeEach.push(fn); +} + +export function fail(e: Error) { + log(`X Failure: ${e}`, "color:#600; background:#fdd; padding: 2px 6px;"); +} + +function log(msg: string, styles: string) { + if (styles) { + console.log(`%c ${msg}`, styles); + } else { + console.log(msg); + } +} + +class Expectation { + private propertyLabel: string | null = ""; + private expectedLabel: string | null = ""; + private verbLabel: string | null = "be"; + private expectedFn!: (v: any) => boolean; + private value: any; + + constructor(value: any) { + this.value = value; + } + + toBe(labelOrExpected: any, maybeExpected?: any) { + const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected; + this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null; + this.expectedLabel = JSON.stringify(expected); + this.expectedFn = (v) => v == expected; + return this.toBeEval(); + } + toMatchJson(labelOrExpected: any, maybeExpected?: any) { + const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected; + this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null; + this.expectedLabel = JSON.stringify(expected); + this.expectedFn = (v) => JSON.stringify(JSON.parse(v)) == JSON.stringify(JSON.parse(expected)); + return this.toBeEval(); + } + toBeUndefined(propertyLabel: string) { + this.expectedFn = (v) => v === undefined; + this.propertyLabel = propertyLabel || ""; + this.expectedLabel = "undefined"; + return this.toBeEval(true); + } + toBeNullOrUndefined(propertyLabel: string) { + this.expectedFn = (v) => v == null; + this.propertyLabel = propertyLabel || ""; + this.expectedLabel = "null or undefined"; + return this.toBeEval(true); + } + toBeTruthy(propertyLabel: string) { + this.expectedFn = (v) => !v; + this.propertyLabel = propertyLabel || ""; + this.expectedLabel = "truthy"; + return this.toBeEval(false); + } + toBeANumber(propertyLabel: string) { + this.expectedFn = (v) => typeof v === "number"; + this.propertyLabel = propertyLabel || ""; + this.expectedLabel = "a number"; + return this.toBeEval(); + } + toContain(labelOrExpected: any, maybeExpected?: any) { + const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected; + this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null; + this.verbLabel = 'contain'; + this.expectedLabel = JSON.stringify(expected); + this.expectedFn = (v) => v.includes(expected); + return this.toBeEval(); + } + toBeEval(strict = false) { + let evaluation = this.expectedFn(this.value); + let msg = `Expected ${this.propertyLabel ? this.propertyLabel + ` to ${this.verbLabel} ` : ""}${ + this.expectedLabel + }`; + msg += evaluation ? "." : `, but was ${JSON.stringify(this.value)}`; + this.log(evaluation, msg); + return evaluation; + } + log(value: boolean, msg: string) { + if (value) { + log(`πŸ—Έ ${msg}`, "color:#060; background:#cec; padding: 2px 6px;"); + } else { + log(`X ${msg}`, "color:#600; background:#fdd; padding: 2px 6px;"); + } + } +} + +export function expect(value: any, msg?: string) { + const expectation = new Expectation(value); + if (msg) { + expectation.log(value, msg); + } + return expectation; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/testing/utils_test.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/utils_test.ts new file mode 100644 index 00000000..9c805f2c --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/utils_test.ts @@ -0,0 +1,41 @@ +import type {LGraphNode} from "@comfyorg/frontend"; +import type {ComfyUITestEnvironment} from "./comfyui_env"; + +export const PNG_1x1 = + "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVQIW2P4v5ThPwAG7wKklwQ/bwAAAABJRU5ErkJggg=="; +export const PNG_1x2 = + "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAACCAYAAACZgbYnAAAAEElEQVQIW2NgYGD4D8QM/wEHAwH/OMSHKAAAAABJRU5ErkJggg=="; +export const PNG_2x1 = + "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAIAAAABCAYAAAD0In+KAAAAD0lEQVQIW2NkYGD4D8QMAAUNAQFqjhCLAAAAAElFTkSuQmCC"; + +export async function pasteImageToLoadImageNode( + env: ComfyUITestEnvironment, + dataUrl?: string, + node?: LGraphNode, +) : Promise { + const dataArr = (dataUrl ?? PNG_1x1).split(","); + const mime = dataArr[0]!.match(/:(.*?);/)![1]; + const bstr = atob(dataArr[1]!); + let n = bstr.length; + const u8arr = new Uint8Array(n); + while (n--) { + u8arr[n] = bstr.charCodeAt(n); + } + const filename = `test_image_${+new Date()}.png`; + const file = new File([u8arr], filename, {type: mime}); + if (!node) { + node = await env.addNode("LoadImage"); + } + await (node as any).pasteFiles([file]); + let i = 0; + let good = false; + while (i++ < 10 || good) { + good = node.widgets![0]!.value === filename; + if (good) break; + await env.wait(100); + } + if (!good) { + throw new Error("Expected file not loaded."); + } + return node; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/tests/context_dynamic_tests.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/context_dynamic_tests.ts new file mode 100644 index 00000000..45c2b2c8 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/context_dynamic_tests.ts @@ -0,0 +1,182 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {NodeTypesString} from "../constants.js"; +import {wait} from "rgthree/common/shared_utils.js"; +import {describe, should, beforeEach, expect, describeRun} from "../testing/runner.js"; +import {ComfyUITestEnvironment} from "../testing/comfyui_env.js"; + +const env = new ComfyUITestEnvironment(); + +function verifyInputAndOutputName( + node: LGraphNode, + index: number, + inputName: string | null, + isLinked?: boolean, +) { + if (inputName != null) { + expect(node.inputs[index]!.name).toBe(`input ${index} name`, inputName); + } + if (isLinked) { + expect(node.inputs[index]!.link).toBeANumber(`input ${index} connection`); + } else if (isLinked === false) { + expect(node.inputs[index]!.link).toBeNullOrUndefined(`input ${index} connection`); + } + if (inputName != null) { + if (inputName === "+") { + expect(node.outputs[index]).toBeUndefined(`output ${index}`); + } else { + let outputName = + inputName === "base_ctx" ? "CONTEXT" : inputName.replace(/^\+\s/, "").toUpperCase(); + expect(node.outputs[index]!.name).toBe(`output ${index} name`, outputName); + } + } +} + +function vertifyInputsStructure(node: LGraphNode, expectedLength: number) { + expect(node.inputs.length).toBe("inputs length", expectedLength); + expect(node.outputs.length).toBe("outputs length", expectedLength - 1); + verifyInputAndOutputName(node, expectedLength - 1, "+", false); +} + +describe("TestContextDynamic", async () => { + let nodeConfig!: LGraphNode; + let nodeCtx!: LGraphNode; + + let lastNode: LGraphNode | null = null; + + await beforeEach(async () => { + await env.clear(); + lastNode = nodeConfig = await env.addNode(NodeTypesString.KSAMPLER_CONFIG); + lastNode = nodeCtx = await env.addNode(NodeTypesString.DYNAMIC_CONTEXT); + nodeConfig.connect(0, nodeCtx, 1); // steps + nodeConfig.connect(2, nodeCtx, 2); // cfg + nodeConfig.connect(4, nodeCtx, 3); // scheduler + nodeConfig.connect(0, nodeCtx, 4); // This is the step.1 + nodeConfig.connect(0, nodeCtx, 5); // This is the step.2 + nodeCtx.disconnectInput(2); + nodeCtx.disconnectInput(5); + nodeConfig.connect(0, nodeCtx, 6); // This is the step.3 + nodeCtx.disconnectInput(6); + await wait(); + }); + + await should("add correct inputs", async () => { + vertifyInputsStructure(nodeCtx, 8); + let i = 0; + verifyInputAndOutputName(nodeCtx, i++, "base_ctx", false); + verifyInputAndOutputName(nodeCtx, i++, "+ steps", true); + verifyInputAndOutputName(nodeCtx, i++, "+ cfg", false); + verifyInputAndOutputName(nodeCtx, i++, "+ scheduler", true); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.1", true); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.2", false); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.3", false); + }); + + await should("add evaluate correct outputs", async () => { + const displayAny1 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "right"}); + const displayAny2 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "under"}); + const displayAny3 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "under"}); + const displayAny4 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "under"}); + + nodeCtx.connect(1, displayAny1, 0); // steps + nodeCtx.connect(3, displayAny2, 0); // scheduler + nodeCtx.connect(4, displayAny3, 0); // steps.1 + nodeCtx.connect(6, displayAny4, 0); // steps.3 (unlinked) + + await env.queuePrompt(); + + expect(displayAny1.widgets![0]!.value).toBe("output 1", 30); + expect(displayAny2.widgets![0]!.value).toBe("output 3", '"normal"'); + expect(displayAny3.widgets![0]!.value).toBe("output 4", 30); + expect(displayAny4.widgets![0]!.value).toBe("output 6", "None"); + }); + + await describeRun("Nested", async () => { + let nodeConfig2!: LGraphNode; + let nodeCtx2!: LGraphNode; + + await beforeEach(async () => { + nodeConfig2 = await env.addNode(NodeTypesString.KSAMPLER_CONFIG, {placement: "start"}); + nodeConfig2.widgets = nodeConfig2.widgets || []; + nodeConfig2.widgets[0]!.value = 111; + nodeConfig2.widgets[2]!.value = 11.1; + nodeCtx2 = await env.addNode(NodeTypesString.DYNAMIC_CONTEXT, {placement: "right"}); + nodeConfig2.connect(0, nodeCtx2, 1); // steps + nodeConfig2.connect(2, nodeCtx2, 2); // cfg + nodeConfig2.connect(3, nodeCtx2, 3); // sampler + nodeConfig2.connect(2, nodeCtx2, 4); // This is the cfg.1 + nodeConfig2.connect(0, nodeCtx2, 5); // This is the steps.1 + nodeCtx2.disconnectInput(2); + nodeCtx2.disconnectInput(5); + nodeConfig2.connect(2, nodeCtx2, 6); // This is the cfg.2 + nodeCtx2.disconnectInput(6); + + await wait(); + }); + + await should("disallow context node to be connected to non-first spot.", async () => { + // Connect to first node. + let expectedInputs = 8; + + nodeCtx2.connect(0, nodeCtx, expectedInputs - 1); + console.log(nodeCtx.inputs); + + vertifyInputsStructure(nodeCtx, expectedInputs); + verifyInputAndOutputName(nodeCtx, 0, "base_ctx", false); + verifyInputAndOutputName(nodeCtx, nodeCtx.inputs.length - 1, null, false); + + nodeCtx2.connect(0, nodeCtx, 0); + expectedInputs = 14; + vertifyInputsStructure(nodeCtx, expectedInputs); + verifyInputAndOutputName(nodeCtx, 0, "base_ctx", true); + verifyInputAndOutputName(nodeCtx, expectedInputs - 1, null, false); + }); + + await should("add inputs from connected above owned.", async () => { + // Connect to first node. + nodeCtx2.connect(0, nodeCtx, 0); + + let expectedInputs = 14; + vertifyInputsStructure(nodeCtx, expectedInputs); + let i = 0; + verifyInputAndOutputName(nodeCtx, i++, "base_ctx", true); + verifyInputAndOutputName(nodeCtx, i++, "steps", false); + verifyInputAndOutputName(nodeCtx, i++, "cfg", false); + verifyInputAndOutputName(nodeCtx, i++, "sampler", false); + verifyInputAndOutputName(nodeCtx, i++, "cfg.1", false); + verifyInputAndOutputName(nodeCtx, i++, "steps.1", false); + verifyInputAndOutputName(nodeCtx, i++, "cfg.2", false); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.2", true); + verifyInputAndOutputName(nodeCtx, i++, "+ cfg.3", false); + verifyInputAndOutputName(nodeCtx, i++, "+ scheduler", true); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.3", true); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.4", false); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.5", false); + verifyInputAndOutputName(nodeCtx, i++, "+", false); + }); + + await should("add then remove inputs when disconnected.", async () => { + // Connect to first node. + nodeCtx2.connect(0, nodeCtx, 0); + + let expectedInputs = 14; + expect(nodeCtx.inputs.length).toBe("inputs length", expectedInputs); + expect(nodeCtx.outputs.length).toBe("outputs length", expectedInputs - 1); + + nodeCtx.disconnectInput(0); + + expectedInputs = 8; + expect(nodeCtx.inputs.length).toBe("inputs length", expectedInputs); + expect(nodeCtx.outputs.length).toBe("outputs length", expectedInputs - 1); + let i = 0; + verifyInputAndOutputName(nodeCtx, i++, "base_ctx", false); + verifyInputAndOutputName(nodeCtx, i++, "+ steps", true); + verifyInputAndOutputName(nodeCtx, i++, "+ cfg", false); + verifyInputAndOutputName(nodeCtx, i++, "+ scheduler", true); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.1", true); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.2", false); + verifyInputAndOutputName(nodeCtx, i++, "+ steps.3", false); + verifyInputAndOutputName(nodeCtx, i++, "+", false); + }); + }); +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/tests/image_or_latent_size_tests.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/image_or_latent_size_tests.ts new file mode 100644 index 00000000..0cb932bc --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/image_or_latent_size_tests.ts @@ -0,0 +1,103 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {NodeTypesString} from "../constants"; +import {ComfyUITestEnvironment} from "../testing/comfyui_env"; +import {describe, should, beforeEach, expect, describeRun} from "../testing/runner.js"; +import {pasteImageToLoadImageNode, PNG_1x1, PNG_1x2, PNG_2x1} from "../testing/utils_test.js"; + +const env = new ComfyUITestEnvironment(); + +describe("TestImageOrLatentSize", async () => { + await beforeEach(async () => { + await env.clear(); + }); + + await describeRun("LoadImage", async () => { + let imageNode: LGraphNode; + let displayAnyW: LGraphNode; + let displayAnyH: LGraphNode; + + await beforeEach(async () => { + await env.clear(); + imageNode = await env.addNode("LoadImage"); + const sizeNode = await env.addNode(NodeTypesString.IMAGE_OR_LATENT_SIZE); + displayAnyW = await env.addNode(NodeTypesString.DISPLAY_ANY); + displayAnyH = await env.addNode(NodeTypesString.DISPLAY_ANY); + imageNode.connect(0, sizeNode, 0); + sizeNode.connect(0, displayAnyW, 0); + sizeNode.connect(1, displayAnyH, 0); + await env.wait(); + }); + + await should("get correct size for a 1x1 image", async () => { + await pasteImageToLoadImageNode(env, PNG_1x1, imageNode); + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 1); + expect(displayAnyH.widgets![0]!.value).toBe("height", 1); + }); + + await should("get correct size for a 1x2 image", async () => { + await pasteImageToLoadImageNode(env, PNG_1x2, imageNode); + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 1); + expect(displayAnyH.widgets![0]!.value).toBe("height", 2); + }); + + await should("get correct size for a 2x1 image", async () => { + await pasteImageToLoadImageNode(env, PNG_2x1, imageNode); + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 2); + expect(displayAnyH.widgets![0]!.value).toBe("height", 1); + }); + }); + + await describeRun("Latent", async () => { + let latentNode: LGraphNode; + let displayAnyW: LGraphNode; + let displayAnyH: LGraphNode; + + await beforeEach(async () => { + await env.clear(); + latentNode = await env.addNode("EmptyLatentImage"); + const sizeNode = await env.addNode(NodeTypesString.IMAGE_OR_LATENT_SIZE); + displayAnyW = await env.addNode(NodeTypesString.DISPLAY_ANY); + displayAnyH = await env.addNode(NodeTypesString.DISPLAY_ANY); + latentNode.connect(0, sizeNode, 0); + sizeNode.connect(0, displayAnyW, 0); + sizeNode.connect(1, displayAnyH, 0); + await env.wait(); + latentNode.widgets![0]!.value = 16; // Width + latentNode.widgets![1]!.value = 16; // Height + latentNode.widgets![2]!.value = 1; // Batch + await env.wait(); + }); + + await should("get correct size for a 16x16 latent", async () => { + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 16); + expect(displayAnyH.widgets![0]!.value).toBe("height", 16); + }); + + await should("get correct size for a 16x32 latent", async () => { + latentNode.widgets![1]!.value = 32; + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 16); + expect(displayAnyH.widgets![0]!.value).toBe("height", 32); + }); + + await should("get correct size for a 32x16 image", async () => { + latentNode.widgets![0]!.value = 32; + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 32); + expect(displayAnyH.widgets![0]!.value).toBe("height", 16); + }); + + await should("get correct size with a batch", async () => { + latentNode.widgets![0]!.value = 32; + latentNode.widgets![2]!.value = 2; + await env.queuePrompt(); + expect(displayAnyW.widgets![0]!.value).toBe("width", 32); + expect(displayAnyH.widgets![0]!.value).toBe("height", 16); + }); + }); +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/tests/power_puter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/power_puter.ts new file mode 100644 index 00000000..4ed57599 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/power_puter.ts @@ -0,0 +1,162 @@ +import type {LGraphNode} from "@comfyorg/frontend"; + +import {NodeTypesString} from "../constants"; +import {ComfyUITestEnvironment} from "../testing/comfyui_env"; +import {describe, should, beforeEach, expect, describeRun} from "../testing/runner.js"; +import {pasteImageToLoadImageNode, PNG_1x1, PNG_1x2, PNG_2x1} from "../testing/utils_test.js"; + +const env = new ComfyUITestEnvironment(); + +function setPowerPuterValue(node: LGraphNode, outputType: string, value: string) { + // Strip as much whitespace on first non-empty line from all lines. + if (value.includes("\n")) { + value = value.replace(/^\n/gm, ""); + const strip = value.match(/^(.*?)\S/)?.[1]?.length; + if (strip) { + value = value.replace(new RegExp(`^.{${strip}}`, "mg"), ""); + } + } + node.widgets![1]!.value = value; + node.widgets![0]!.value = outputType; +} + +describe("TestPowerPuter", async () => { + let powerPuter!: LGraphNode; + let displayAny!: LGraphNode; + + await beforeEach(async () => { + await env.clear(); + powerPuter = await env.addNode(NodeTypesString.POWER_PUTER); + displayAny = await env.addNode(NodeTypesString.DISPLAY_ANY); + powerPuter.connect(0, displayAny, 0); + await env.wait(); + }); + + await should("output constants and concatenation", async () => { + const checks: Array<[string, string, string]> = [ + ["1", "1", "STRING"], + ['"abc"', "abc", "STRING"], + ["1 + 2", "3", "STRING"], + ['"abc" + "xyz"', "abcxyz", "STRING"], + // INT + ["1", "1", "INT"], + ["1 + 2", "3", "INT"], + // FLOAT + ["1", "1.0", "FLOAT"], + ["1.3 + 2.8", "4.1", "FLOAT"], + // BOOLEAN + ["1", "True", "BOOLEAN"], + ["1 - 1", "False", "BOOLEAN"], + ]; + for (const data of checks) { + setPowerPuterValue(powerPuter, data[2], data[0]); + await env.queuePrompt(); + expect(displayAny.widgets![0]!.value).toBe(data[0], data[1]); + } + }); + + await should("handle inputs", async () => { + // TODO + }); + + await should("handle complex inputs", async () => { + // TODO + }); + + await should("handle a for loop", async () => { + setPowerPuterValue( + powerPuter, + "STRING", + ` + a = 0 + b = '' + for n in range(4): + a += n + for m in range(2): + b += f'{str(n)}-{str(m)}.' + f'a:{a} b:{b}' + `, + ); + await env.queuePrompt(); + expect(displayAny.widgets![0]!.value).toBe("a:6 b:0-0.0-1.1-0.1-1.2-0.2-1.3-0.3-1."); + }); + + await should("handle assigning with a subscript slice", async () => { + setPowerPuterValue( + powerPuter, + "STRING", + ` + a = [1,2,0] + a[a[2]] = 3 + tuple(a) + `, + ); + await env.queuePrompt(); + expect(displayAny.widgets![0]!.value).toBe("(3, 2, 0)"); + }); + + await should("handle aug assigning with a subscript slice", async () => { + setPowerPuterValue( + powerPuter, + "STRING", + ` + a = [1,2,0] + a[a[2]] += 3 + tuple(a) + `, + ); + await env.queuePrompt(); + expect(displayAny.widgets![0]!.value).toBe("(4, 2, 0)"); + }); + + await should("disallow calls to some methods", async () => { + const imageNode = await pasteImageToLoadImageNode(env); + imageNode.connect(0, powerPuter, 0); + setPowerPuterValue( + powerPuter, + "STRING", + `a.numpy().tofile('/tmp/test') + `, + ); + await env.queuePrompt(); + + // Check to see if there's an error. + expect(document.querySelector(".p-dialog-mask .p-card-body")!.textContent).toContain( + "error message", + "Disallowed access to \"tofile\" for type ", + ); + (document.querySelector(".p-dialog-mask .p-dialog-close-button")! as HTMLButtonElement).click(); + }); + + await should("handle boolean operators correctly", async () => { + const checks: Array<[string, string, string, ('toMatchJson'|'toBe')?]> = [ + // And operator all success + ["1 and 42", "42", "STRING"], + ["True and [42]", "[42]", "STRING", "toMatchJson"], + ["a = 42\nTrue and [a]", "[42]", "STRING", "toMatchJson"], + ["1 and 3 and True and [1] and 42", "42", "STRING"], + // And operator w/ a failure + ["1 and 3 and True and [] and 42", "[]", "STRING", "toMatchJson"], + ["1 and 0 and True and [] and 42", "0", "STRING"], + ["1 and 2 and False and [] and 42", "False", "STRING"], + ["b = None\n1 and 2 and True and b and 42", "None", "STRING"], + // Or operator + ["1 or 42", "1", "STRING"], + ["0 or 42", "42", "STRING"], + ["0 or None or False or [] or 42", "42", "STRING"], + ["b=42\n0 or None or False or [] or b", "42", "STRING"], + ["b=42\n0 or None or False or [b] or b", "[42]", "STRING", "toMatchJson"], + ["b=42\n0 or None or True or [b] or b", "True", "STRING"], + // Mix + ["1 and 2 and 0 or 5", "5", "STRING"], + ["None and 1 or True", "True", "STRING"], + ["0 or False and True", "False", "STRING"], + + ]; + for (const data of checks) { + setPowerPuterValue(powerPuter, data[2], data[0]); + await env.queuePrompt(); + expect(displayAny.widgets![0]!.value)[data[3] || 'toBe'](data[0], data[1]); + } + }); +}); diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils.ts new file mode 100644 index 00000000..b9f99031 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils.ts @@ -0,0 +1,1115 @@ +import type { + Vector2, + LGraphCanvas as TLGraphCanvas, + LLink, + LGraph, + IContextMenuOptions, + ContextMenu, + LGraphNode as TLGraphNode, + INodeSlot, + INodeInputSlot, + INodeOutputSlot, + IContextMenuValue, + ISlotType, + LGraphNodeConstructor, + LGraphEventMode, + NodeProperty, + LinkDirection, + Point, + ComfyApp, + LGraphGroup, + NodeId, + Subgraph, + SubgraphNode, +} from "@comfyorg/frontend"; +import type {ComfyNodeDef} from "typings/comfy.js"; +import type {Constructor} from "typings/rgthree"; + +import {app} from "scripts/app.js"; +import {api} from "scripts/api.js"; +import {Resolver, getResolver, wait} from "rgthree/common/shared_utils.js"; +import {RgthreeHelpDialog} from "rgthree/common/dialog.js"; + +/** + * Override the api.getNodeDefs call to add a hook for refreshing node defs. + * This is necessary for power prompt's custom combos. Since API implements + * add/removeEventListener already, this is rather trivial. + */ +const oldApiGetNodeDefs = api.getNodeDefs; +api.getNodeDefs = async function () { + const defs = await oldApiGetNodeDefs.call(api); + this.dispatchEvent(new CustomEvent("fresh-node-defs", {detail: defs})); + return defs; +}; + +export enum IoDirection { + INPUT, + OUTPUT, +} + +const PADDING = 0; + +type LiteGraphDir = + | typeof LiteGraph.LEFT + | typeof LiteGraph.RIGHT + | typeof LiteGraph.UP + | typeof LiteGraph.DOWN; +export const LAYOUT_LABEL_TO_DATA: {[label: string]: [LiteGraphDir, Vector2, Vector2]} = { + Left: [LiteGraph.LEFT, [0, 0.5], [PADDING, 0]], + Right: [LiteGraph.RIGHT, [1, 0.5], [-PADDING, 0]], + Top: [LiteGraph.UP, [0.5, 0], [0, PADDING]], + Bottom: [LiteGraph.DOWN, [0.5, 1], [0, -PADDING]], +}; +export const LAYOUT_LABEL_OPPOSITES: {[label: string]: string} = { + Left: "Right", + Right: "Left", + Top: "Bottom", + Bottom: "Top", +}; +export const LAYOUT_CLOCKWISE = ["Top", "Right", "Bottom", "Left"]; + +interface MenuConfig { + name: string | ((node: TLGraphNode) => string); + property?: string; + prepareValue?: (value: NodeProperty | undefined, node: TLGraphNode) => any; + callback?: (node: TLGraphNode, value?: string) => void; + subMenuOptions?: (string | null)[] | ((node: TLGraphNode) => (string | null)[]); +} + +export function addMenuItem( + node: LGraphNodeConstructor, + _app: ComfyApp, + config: MenuConfig, + after = "Shape", +) { + const oldGetExtraMenuOptions = node.prototype.getExtraMenuOptions; + node.prototype.getExtraMenuOptions = function ( + canvas: TLGraphCanvas, + menuOptions: IContextMenuValue[], + ) { + oldGetExtraMenuOptions && oldGetExtraMenuOptions.apply(this, [canvas, menuOptions]); + addMenuItemOnExtraMenuOptions(this, config, menuOptions, after); + }; +} + +/** + * Waits for the canvas to be available on app using a single promise. + */ +let canvasResolver: Resolver | null = null; +export function waitForCanvas() { + if (canvasResolver === null) { + canvasResolver = getResolver(); + function _waitForCanvas() { + if (!canvasResolver!.completed) { + if (app?.canvas) { + canvasResolver!.resolve(app.canvas); + } else { + requestAnimationFrame(_waitForCanvas); + } + } + } + _waitForCanvas(); + } + return canvasResolver.promise; +} + +/** + * Waits for the graph to be available on app using a single promise. + */ +let graphResolver: Resolver | null = null; +export function waitForGraph() { + if (graphResolver === null) { + graphResolver = getResolver(); + function _wait() { + if (!graphResolver!.completed) { + if (app?.graph) { + graphResolver!.resolve(app.graph); + } else { + requestAnimationFrame(_wait); + } + } + } + _wait(); + } + return graphResolver.promise; +} + +export function addMenuItemOnExtraMenuOptions( + node: TLGraphNode, + config: MenuConfig, + menuOptions: (IContextMenuValue | null)[], + after = "Shape", +) { + let idx = menuOptions + .slice() + .reverse() + .findIndex((option) => (option as any)?.isRgthree); + if (idx == -1) { + idx = menuOptions.findIndex((option) => option?.content?.includes(after)) + 1; + if (!idx) { + idx = menuOptions.length - 1; + } + // Add a separator, and move to the next one. + menuOptions.splice(idx, 0, null); + idx++; + } else { + idx = menuOptions.length - idx; + } + + const subMenuOptions = + typeof config.subMenuOptions === "function" + ? config.subMenuOptions(node) + : config.subMenuOptions; + + menuOptions.splice(idx, 0, { + content: typeof config.name == "function" ? config.name(node) : config.name, + has_submenu: !!subMenuOptions?.length, + isRgthree: true, // Mark it, so we can find it. + callback: ( + value: IContextMenuValue, + _options: IContextMenuOptions, + event: MouseEvent, + parentMenu: ContextMenu | undefined, + _node: TLGraphNode, + ) => { + if (!!subMenuOptions?.length) { + new LiteGraph.ContextMenu( + subMenuOptions.map((option) => (option ? {content: option} : null)), + { + event, + parentMenu, + callback: ( + subValue: IContextMenuValue, + _options: IContextMenuOptions, + _event: MouseEvent, + _parentMenu: ContextMenu | undefined, + _node: TLGraphNode, + ) => { + if (config.property) { + node.properties = node.properties || {}; + node.properties[config.property] = config.prepareValue + ? config.prepareValue(subValue!.content || "", node) + : subValue!.content || ""; + } + config.callback && config.callback(node, subValue?.content); + }, + }, + ); + return; + } + if (config.property) { + node.properties = node.properties || {}; + node.properties[config.property] = config.prepareValue + ? config.prepareValue(node.properties[config.property], node) + : !node.properties[config.property]; + } + config.callback && config.callback(node, value?.content); + }, + } as IContextMenuValue); +} + +export function addConnectionLayoutSupport( + node: LGraphNodeConstructor, + app: ComfyApp, + options = [ + ["Left", "Right"], + ["Right", "Left"], + ], + callback?: (node: TLGraphNode) => void, +) { + addMenuItem(node, app, { + name: "Connections Layout", + property: "connections_layout", + subMenuOptions: options.map((option) => option[0] + (option[1] ? " -> " + option[1] : "")), + prepareValue: (value, node) => { + const values = String(value).split(" -> "); + if (!values[1] && !node.outputs?.length) { + values[1] = LAYOUT_LABEL_OPPOSITES[values[0]!]!; + } + if (!LAYOUT_LABEL_TO_DATA[values[0]!] || !LAYOUT_LABEL_TO_DATA[values[1]!]) { + throw new Error(`New Layout invalid: [${values[0]}, ${values[1]}]`); + } + return values; + }, + callback: (node) => { + callback && callback(node); + node.graph?.setDirtyCanvas(true, true); + }, + }); + + // [πŸ€] This is deprecated in a https://github.com/Comfy-Org/litegraph.js/pull/716 @v0.9.9 and + // replaced by getInputPos and getOutputPos conveniently added below. + node.prototype.getConnectionPos = function (isInput: boolean, slotNumber: number, out: Vector2) { + // Purposefully do not need to call the old one. + return getConnectionPosForLayout(this, isInput, slotNumber, out); + }; + node.prototype.getInputPos = function (slotNumber: number): Vector2 { + // Purposefully do not need to call the existing one because we're overriding. + return getConnectionPosForLayout(this, true, slotNumber, [0, 0]); + }; + node.prototype.getOutputPos = function (slotNumber: number): Vector2 { + // Purposefully do not need to call the existing one because we're overriding. + return getConnectionPosForLayout(this, false, slotNumber, [0, 0]); + }; +} + +export function setConnectionsLayout(node: TLGraphNode, newLayout: [string, string]) { + newLayout = newLayout || (node as any).defaultConnectionsLayout || ["Left", "Right"]; + // If we didn't supply an output layout, and there's no outputs, then just choose the opposite of the + // input as a safety. + if (!newLayout[1] && !node.outputs?.length) { + newLayout[1] = LAYOUT_LABEL_OPPOSITES[newLayout[0]!]!; + } + if (!LAYOUT_LABEL_TO_DATA[newLayout[0]] || !LAYOUT_LABEL_TO_DATA[newLayout[1]]) { + throw new Error(`New Layout invalid: [${newLayout[0]}, ${newLayout[1]}]`); + } + node.properties = node.properties || {}; + node.properties["connections_layout"] = newLayout; +} + +/** Allows collapsing of connections into one. Pretty unusable, unless you're the muter. */ +export function setConnectionsCollapse( + node: TLGraphNode, + collapseConnections: boolean | null = null, +) { + node.properties = node.properties || {}; + collapseConnections = + collapseConnections !== null ? collapseConnections : !node.properties["collapse_connections"]; + node.properties["collapse_connections"] = collapseConnections; +} + +export function getConnectionPosForLayout( + node: TLGraphNode, + isInput: boolean, + slotNumber: number, + out: Vector2, +) { + out = out || new Float32Array(2); + node.properties = node.properties || {}; + const layout = node.properties["connections_layout"] || + (node as any).defaultConnectionsLayout || ["Left", "Right"]; + const collapseConnections = (node.properties["collapse_connections"] as boolean) || false; + const offset = (node.constructor as any).layout_slot_offset ?? LiteGraph.NODE_SLOT_HEIGHT * 0.5; + let side = isInput ? layout[0] : layout[1]; + const otherSide = isInput ? layout[1] : layout[0]; + let data = LAYOUT_LABEL_TO_DATA[side]!; // || LAYOUT_LABEL_TO_DATA[isInput ? 'Left' : 'Right']; + const slotList = node[isInput ? "inputs" : "outputs"]; + const cxn = slotList[slotNumber]; + if (!cxn) { + console.log("No connection found.. weird", isInput, slotNumber); + return out; + } + // Experimental; doesn't work without node.clip_area set (so it won't draw outside), + // but litegraph.core inexplicably clips the title off which we want... so, no go. + // if (cxn.hidden) { + // out[0] = node.pos[0] - 100000 + // out[1] = node.pos[1] - 100000 + // return out + // } + if (cxn.disabled) { + // Let's store the original colors if have them and haven't yet overridden + if (cxn.color_on !== "#666665") { + (cxn as any)._color_on_org = (cxn as any)._color_on_org || cxn.color_on; + (cxn as any)._color_off_org = (cxn as any)._color_off_org || cxn.color_off; + } + cxn.color_on = "#666665"; + cxn.color_off = "#666665"; + } else if (cxn.color_on === "#666665") { + cxn.color_on = (cxn as any)._color_on_org || undefined; + cxn.color_off = (cxn as any)._color_off_org || undefined; + } + const displaySlot = collapseConnections + ? 0 + : slotNumber - + slotList.reduce((count, ioput, index) => { + count += index < slotNumber && ioput.hidden ? 1 : 0; + return count; + }, 0); + // Set the direction first. This is how the connection line will be drawn. + cxn.dir = data[0]; + + // If we are only 10px tall or wide, then look at connections_dir for the direction. + const connections_dir = node.properties["connections_dir"] as [LinkDirection, LinkDirection]; + if ((node.size[0] == 10 || node.size[1] == 10) && connections_dir) { + cxn.dir = connections_dir[isInput ? 0 : 1]!; + } + + if (side === "Left") { + if (node.flags.collapsed) { + var w = (node as any)._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + out[0] = node.pos[0]; + out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } else { + // If we're an output, then the litegraph.core hates us; we need to blank out the name + // because it's not flexible enough to put the text on the inside. + toggleConnectionLabel(cxn, !isInput || collapseConnections || !!(node as any).hideSlotLabels); + out[0] = node.pos[0] + offset; + if ((node.constructor as any)?.type.includes("Reroute")) { + out[1] = node.pos[1] + node.size[1] * 0.5; + } else { + out[1] = + node.pos[1] + + (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + ((node.constructor as any).slot_start_y || 0); + } + } + } else if (side === "Right") { + if (node.flags.collapsed) { + var w = (node as any)._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + out[0] = node.pos[0] + w; + out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } else { + // If we're an input, then the litegraph.core hates us; we need to blank out the name + // because it's not flexible enough to put the text on the inside. + toggleConnectionLabel(cxn, isInput || collapseConnections || !!(node as any).hideSlotLabels); + out[0] = node.pos[0] + node.size[0] + 1 - offset; + if ((node.constructor as any)?.type.includes("Reroute")) { + out[1] = node.pos[1] + node.size[1] * 0.5; + } else { + out[1] = + node.pos[1] + + (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + ((node.constructor as any).slot_start_y || 0); + } + } + + // Right now, only reroute uses top/bottom, so this may not work for other nodes + // (like, applying to nodes with titles, collapsed, multiple inputs/outputs, etc). + } else if (side === "Top") { + if (!(cxn as any).has_old_label) { + (cxn as any).has_old_label = true; + (cxn as any).old_label = cxn.label; + cxn.label = " "; + } + out[0] = node.pos[0] + node.size[0] * 0.5; + out[1] = node.pos[1] + offset; + } else if (side === "Bottom") { + if (!(cxn as any).has_old_label) { + (cxn as any).has_old_label = true; + (cxn as any).old_label = cxn.label; + cxn.label = " "; + } + out[0] = node.pos[0] + node.size[0] * 0.5; + out[1] = node.pos[1] + node.size[1] - offset; + } + return out; +} + +function toggleConnectionLabel(cxn: any, hide = true) { + if (hide) { + if (!(cxn as any).has_old_label) { + (cxn as any).has_old_label = true; + (cxn as any).old_label = cxn.label; + } + cxn.label = " "; + } else if (!hide && (cxn as any).has_old_label) { + (cxn as any).has_old_label = false; + cxn.label = (cxn as any).old_label; + (cxn as any).old_label = undefined; + } + return cxn; +} + +export function addHelpMenuItem( + node: TLGraphNode, + content: string, + menuOptions: (IContextMenuValue | null)[], +) { + addMenuItemOnExtraMenuOptions( + node, + { + name: "πŸ›Ÿ Node Help", + callback: (node) => { + if ((node as any).showHelp) { + (node as any).showHelp(); + } else { + new RgthreeHelpDialog(node, content).show(); + } + }, + }, + menuOptions, + "Properties Panel", + ); +} + +export enum PassThroughFollowing { + ALL, + NONE, + REROUTE_ONLY, +} + +/** + * Determines if, when doing a chain lookup for connected nodes, we want to pass through this node, + * like reroutes, etc. + */ +export function shouldPassThrough( + node?: TLGraphNode | null, + passThroughFollowing = PassThroughFollowing.ALL, +) { + const type = (node?.constructor as typeof TLGraphNode)?.type; + if (!type || passThroughFollowing === PassThroughFollowing.NONE) { + return false; + } + if (passThroughFollowing === PassThroughFollowing.REROUTE_ONLY) { + return type.includes("Reroute"); + } + return ( + type.includes("Reroute") || type.includes("Node Combiner") || type.includes("Node Collector") + ); +} + +function filterOutPassthroughNodes( + infos: ConnectedNodeInfo[], + passThroughFollowing = PassThroughFollowing.ALL, +) { + return infos.filter((i) => !shouldPassThrough(i.node, passThroughFollowing)); +} + +/** + * Looks through the immediate chain of a node to collect all connected nodes, passing through nodes + * like reroute, etc. Will also disconnect duplicate nodes from a provided node + */ +export function getConnectedInputNodes( + startNode: TLGraphNode, + currentNode?: TLGraphNode, + slot?: number, + passThroughFollowing = PassThroughFollowing.ALL, +): TLGraphNode[] { + return getConnectedNodesInfo( + startNode, + IoDirection.INPUT, + currentNode, + slot, + passThroughFollowing, + ).map((n) => n.node); +} +export function getConnectedInputInfosAndFilterPassThroughs( + startNode: TLGraphNode, + currentNode?: TLGraphNode, + slot?: number, + passThroughFollowing = PassThroughFollowing.ALL, +) { + return filterOutPassthroughNodes( + getConnectedNodesInfo(startNode, IoDirection.INPUT, currentNode, slot, passThroughFollowing), + passThroughFollowing, + ); +} +export function getConnectedInputNodesAndFilterPassThroughs( + startNode: TLGraphNode, + currentNode?: TLGraphNode, + slot?: number, + passThroughFollowing = PassThroughFollowing.ALL, +): TLGraphNode[] { + return getConnectedInputInfosAndFilterPassThroughs( + startNode, + currentNode, + slot, + passThroughFollowing, + ).map((n) => n.node); +} + +export function getConnectedOutputNodes( + startNode: TLGraphNode, + currentNode?: TLGraphNode, + slot?: number, + passThroughFollowing = PassThroughFollowing.ALL, +): TLGraphNode[] { + return getConnectedNodesInfo( + startNode, + IoDirection.OUTPUT, + currentNode, + slot, + passThroughFollowing, + ).map((n) => n.node); +} + +export function getConnectedOutputNodesAndFilterPassThroughs( + startNode: TLGraphNode, + currentNode?: TLGraphNode, + slot?: number, + passThroughFollowing = PassThroughFollowing.ALL, +): TLGraphNode[] { + return filterOutPassthroughNodes( + getConnectedNodesInfo(startNode, IoDirection.OUTPUT, currentNode, slot, passThroughFollowing), + passThroughFollowing, + ).map((n) => n.node); +} + +export type ConnectedNodeInfo = { + node: TLGraphNode; + travelFromSlot: number; + travelToSlot: number; + originTravelFromSlot: number; +}; + +export function getConnectedNodesInfo( + startNode: TLGraphNode, + dir = IoDirection.INPUT, + currentNode?: TLGraphNode, + slot?: number, + passThroughFollowing = PassThroughFollowing.ALL, + originTravelFromSlot?: number, +): ConnectedNodeInfo[] { + currentNode = currentNode || startNode; + let rootNodes: ConnectedNodeInfo[] = []; + if (startNode === currentNode || shouldPassThrough(currentNode, passThroughFollowing)) { + let linkIds: Array; + + slot = slot != null && slot > -1 ? slot : undefined; + if (dir == IoDirection.OUTPUT) { + if (slot != null) { + linkIds = [...(currentNode.outputs?.[slot]?.links || [])]; + } else { + linkIds = currentNode.outputs?.flatMap((i) => i.links) || []; + } + } else { + if (slot != null) { + linkIds = [currentNode.inputs?.[slot]?.link]; + } else { + linkIds = currentNode.inputs?.map((i) => i.link) || []; + } + } + const graph = currentNode.graph ?? app.graph; + for (const linkId of linkIds) { + let link: LLink | null = null; + if (typeof linkId == "number") { + link = graph.links[linkId] ?? null; + } + if (!link) { + continue; + } + const travelFromSlot = dir == IoDirection.OUTPUT ? link.origin_slot : link.target_slot; + const connectedId = dir == IoDirection.OUTPUT ? link.target_id : link.origin_id; + const travelToSlot = dir == IoDirection.OUTPUT ? link.target_slot : link.origin_slot; + originTravelFromSlot = originTravelFromSlot != null ? originTravelFromSlot : travelFromSlot; + const originNode: TLGraphNode = graph.getNodeById(connectedId)!; + if (!link) { + console.error("No connected node found... weird"); + continue; + } + if (rootNodes.some((n) => n.node == originNode)) { + console.log( + `${startNode.title} (${startNode.id}) seems to have two links to ${originNode.title} (${ + originNode.id + }). One may be stale: ${linkIds.join(", ")}`, + ); + } else { + // Add the node and, if it's a pass through, let's collect all its nodes as well. + rootNodes.push({node: originNode, travelFromSlot, travelToSlot, originTravelFromSlot}); + if (shouldPassThrough(originNode, passThroughFollowing)) { + for (const foundNode of getConnectedNodesInfo( + startNode, + dir, + originNode, + undefined, + undefined, + originTravelFromSlot, + )) { + if (!rootNodes.map((n) => n.node).includes(foundNode.node)) { + rootNodes.push(foundNode); + } + } + } + } + } + } + return rootNodes; +} + +export type ConnectionType = { + type: string | string[]; + name: string | undefined; + label: string | undefined; +}; + +/** + * Follows a connection until we find a type associated with a slot. + * `skipSelf` skips the current slot, useful when we may have a dynamic slot that we want to start + * from, but find a type _after_ it (in case it needs to change). + */ +export function followConnectionUntilType( + node: TLGraphNode, + dir: IoDirection, + slotNum?: number, + skipSelf = false, +): ConnectionType | null { + const slots = dir === IoDirection.OUTPUT ? node.outputs : node.inputs; + if (!slots || !slots.length) { + return null; + } + let type: ConnectionType | null = null; + if (slotNum) { + if (!slots[slotNum]) { + return null; + } + type = getTypeFromSlot(slots[slotNum], dir, skipSelf); + } else { + for (const slot of slots) { + type = getTypeFromSlot(slot, dir, skipSelf); + if (type) { + break; + } + } + } + return type; +} + +/** + * Gets the type from a slot. If the type is '*' then it will follow the node to find the next slot. + */ +function getTypeFromSlot( + slot: INodeInputSlot | INodeOutputSlot | undefined, + dir: IoDirection, + skipSelf = false, +): ConnectionType | null { + let graph = app.canvas.getCurrentGraph()!; + let type = slot?.type; + if (!skipSelf && type != null && type != "*") { + return {type: type as string, label: slot?.label, name: slot?.name}; + } + const links = getSlotLinks(slot); + for (const link of links) { + const connectedId = dir == IoDirection.OUTPUT ? link.link.target_id : link.link.origin_id; + const connectedSlotNum = + dir == IoDirection.OUTPUT ? link.link.target_slot : link.link.origin_slot; + const connectedNode: TLGraphNode = graph.getNodeById(connectedId)!; + // Reversed since if we're traveling down the output we want the connected node's input, etc. + const connectedSlots = + dir === IoDirection.OUTPUT ? connectedNode.inputs : connectedNode.outputs; + let connectedSlot = connectedSlots[connectedSlotNum]; + if (connectedSlot?.type != null && connectedSlot?.type != "*") { + return { + type: connectedSlot.type as string, + label: connectedSlot?.label, + name: connectedSlot?.name, + }; + } else if (connectedSlot?.type == "*") { + return followConnectionUntilType(connectedNode, dir); + } + } + return null; +} + +export async function replaceNode( + existingNode: TLGraphNode, + typeOrNewNode: string | TLGraphNode, + inputNameMap?: Map, +) { + const existingCtor = existingNode.constructor as typeof TLGraphNode; + + const newNode = + typeof typeOrNewNode === "string" ? LiteGraph.createNode(typeOrNewNode)! : typeOrNewNode; + // Port title (maybe) the position, size, and properties from the old node. + if (existingNode.title != existingCtor.title) { + newNode.title = existingNode.title; + } + newNode.pos = [...existingNode.pos] as Point; + newNode.properties = {...existingNode.properties}; + const oldComputeSize = [...existingNode.computeSize()]; + // oldSize to use. If we match the smallest size (computeSize) then don't record and we'll use + // the smalles side after conversion. + const oldSize = [ + existingNode.size[0] === oldComputeSize[0] ? null : existingNode.size[0], + existingNode.size[1] === oldComputeSize[1] ? null : existingNode.size[1], + ]; + + let setSizeIters = 0; + const setSizeFn = () => { + // Size gets messed up when ComfyUI adds the text widget, so reset after a delay. + // Since we could be adding many more slots, let's take the larger of the two. + const newComputesize = newNode.computeSize(); + newNode.size[0] = Math.max(oldSize[0] || 0, newComputesize[0]); + newNode.size[1] = Math.max(oldSize[1] || 0, newComputesize[1]); + setSizeIters++; + if (setSizeIters > 10) { + requestAnimationFrame(setSizeFn); + } + }; + setSizeFn(); + + // We now collect the links data, inputs and outputs, of the old node since these will be + // lost when we remove it. + const links: { + node: TLGraphNode; + slot: number | string; + targetNode: TLGraphNode; + targetSlot: number | string; + }[] = []; + const graph = existingNode.graph || app.graph; + for (const [index, output] of existingNode.outputs.entries()) { + for (const linkId of output.links || []) { + const link: LLink = graph.links[linkId]!; + if (!link) continue; + const targetNode = graph.getNodeById(link.target_id)!; + links.push({node: newNode, slot: output.name, targetNode, targetSlot: link.target_slot}); + } + } + for (const [index, input] of existingNode.inputs.entries()) { + const linkId = input.link; + if (linkId) { + const link: LLink = graph.links[linkId]!; + const originNode = graph.getNodeById(link.origin_id)!; + links.push({ + node: originNode, + slot: link.origin_slot, + targetNode: newNode, + targetSlot: inputNameMap?.has(input.name) + ? inputNameMap.get(input.name)! + : input.name || index, + }); + } + } + // Add the new node, remove the old node. + graph.add(newNode); + await wait(); + // Now go through and connect the other nodes up as they were. + for (const link of links) { + link.node.connect(link.slot, link.targetNode, link.targetSlot); + } + await wait(); + graph.remove(existingNode); + newNode.size = newNode.computeSize(); + newNode.setDirtyCanvas(true, true); + return newNode; +} + +export function getOriginNodeByLink(linkId?: number | null) { + let node: TLGraphNode | null = null; + if (linkId != null) { + const link = getLinkById(linkId); + node = (link != null && getNodeById(link.origin_id)) || null; + } + return node; +} + +/** + * Gets a link by id across all graphs and subgraphs. + */ +export function getLinkById(linkId?: number | null) { + if (linkId == null) return null; + let link: LLink | null = app.graph.links[linkId] ?? null; + link = link ?? app.canvas.getCurrentGraph()?.links[linkId] ?? null; + return link || findSomethingInAllSubgraphs((subgraph) => subgraph?.links[linkId] ?? null); +} + +/** + * Gets a node by id across all graphs and subgraphs. + */ +export function getNodeById(id: NodeId) { + if (id == null) return null; + let node = app.graph.getNodeById(id); + node = node ?? app.canvas.getCurrentGraph()?.getNodeById(id) ?? null; + return node || findSomethingInAllSubgraphs((subgraph) => subgraph?.getNodeById(id) ?? null); +} + +/** + * [πŸ€] At some point ComfyUI added a second param for `openSubgraph` which is the node representing + * the subgraph in the UI that a user would double click (at least, from what I can tell). + * Unfortunately, we sometimes want to open a subgraph without knowing what that node is (like, from + * a bookmark). + * + * While that doesn't break right now, it does throw an error. As a best-attempt to fix their + * no longer allowing a subgraph to just open, we can search all nodes for subgraphs for the one + * that represents the subgraph itself. + */ +export function findFromNodeForSubgraph(subgraphId: string): SubgraphNode | null { + const node = + findSomethingInAllSubgraphs((subgraph) => + subgraph.nodes + .filter((node) => node.isSubgraphNode()) + .find((node) => node.subgraph.id === subgraphId), + ) ?? null; + return node; +} + +/** + * Finds something across all the graphs and subgraphs. + */ +function findSomethingInAllSubgraphs(fn: (subgraph: Subgraph) => T | null): T | null { + const rootGraph = app.rootGraph ?? app.graph.rootGraph; + const subgraphs = [rootGraph, ...rootGraph.subgraphs?.values()] as Subgraph[]; + for (const subgraph of subgraphs) { + const thing = fn(subgraph); + if (thing) return thing; + } + return null; +} + +export function applyMixins(original: Constructor, constructors: any[]) { + constructors.forEach((baseCtor) => { + Object.getOwnPropertyNames(baseCtor.prototype).forEach((name) => { + Object.defineProperty( + original.prototype, + name, + Object.getOwnPropertyDescriptor(baseCtor.prototype, name) || Object.create(null), + ); + }); + }); +} + +/** + * Retruns a list of `{id: number, link: LLlink}` for a given input or output. + * + * Obviously, for an input, this will be a max of one. + */ +export function getSlotLinks(inputOrOutput?: INodeInputSlot | INodeOutputSlot | null) { + const links: {id: number; link: LLink}[] = []; + if (!inputOrOutput) { + return links; + } + if ((inputOrOutput as INodeOutputSlot).links?.length) { + const output = inputOrOutput as INodeOutputSlot; + for (const linkId of output.links || []) { + const link: LLink = (app.graph as LGraph).links[linkId]!; + if (link) { + links.push({id: linkId, link: link}); + } + } + } + if ((inputOrOutput as INodeInputSlot).link) { + const input = inputOrOutput as INodeInputSlot; + const link: LLink = (app.graph as LGraph).links[input.link!]!; + if (link) { + links.push({id: input.link!, link: link}); + } + } + return links; +} + +/** + * Given a node, whether we're dealing with INPUTS or OUTPUTS, and the server data, re-arrange then + * slots to match the order. + */ +export async function matchLocalSlotsToServer( + node: TLGraphNode, + direction: IoDirection, + serverNodeData: ComfyNodeDef, +) { + const serverSlotNames = + direction == IoDirection.INPUT + ? Object.keys(serverNodeData.input?.optional || {}) + : serverNodeData.output_name; + const serverSlotTypes = + direction == IoDirection.INPUT + ? (Object.values(serverNodeData.input?.optional || {}).map((i) => i[0]) as string[]) + : serverNodeData.output; + const slots = direction == IoDirection.INPUT ? node.inputs : node.outputs; + + // Let's go through the node data names and make sure our current ones match, and update if not. + let firstIndex = slots.findIndex((o, i) => i !== serverSlotNames.indexOf(o.name)); + if (firstIndex > -1) { + // Have mismatches. First, let's go through and save all our links by name. + const links: {[key: string]: {id: number; link: LLink}[]} = {}; + slots.map((slot) => { + // There's a chance we have duplicate names on an upgrade, so we'll collect all links to one + // name so we don't ovewrite our list per name. + links[slot.name] = links[slot.name] || []; + links[slot.name]?.push(...getSlotLinks(slot)); + }); + + // Now, go through and rearrange outputs by splicing + for (const [index, serverSlotName] of serverSlotNames.entries()) { + const currentNodeSlot = slots.map((s) => s.name).indexOf(serverSlotName); + if (currentNodeSlot > -1) { + if (currentNodeSlot != index) { + const splicedItem = slots.splice(currentNodeSlot, 1)[0]!; + slots.splice(index, 0, splicedItem as any); + } + } else if (currentNodeSlot === -1) { + const splicedItem = { + name: serverSlotName, + type: serverSlotTypes![index], + links: [], + }; + slots.splice(index, 0, splicedItem as any); + } + } + + if (slots.length > serverSlotNames.length) { + for (let i = slots.length - 1; i > serverSlotNames.length - 1; i--) { + if (direction == IoDirection.INPUT) { + node.disconnectInput(i); + node.removeInput(i); + } else { + node.disconnectOutput(i); + node.removeOutput(i); + } + } + } + + // Now, go through the link data again and make sure the origin_slot is the correct slot. + for (const [name, slotLinks] of Object.entries(links)) { + let currentNodeSlot = slots.map((s) => s.name).indexOf(name); + if (currentNodeSlot > -1) { + for (const linkData of slotLinks) { + if (direction == IoDirection.INPUT) { + linkData.link.target_slot = currentNodeSlot; + } else { + linkData.link.origin_slot = currentNodeSlot; + // If our next node is a Reroute, then let's get it to update the type. + const nextNode = app.graph.getNodeById(linkData.link.target_id); + // (Check nextNode, as sometimes graphs seem to have very stale data and that node id + // doesn't exist). + if (nextNode && (nextNode.constructor as any)?.type!.includes("Reroute")) { + (nextNode as any).stabilize && (nextNode as any).stabilize(); + } + } + } + } + } + } +} + +export function isValidConnection(ioA?: INodeSlot | null, ioB?: INodeSlot | null) { + if (!ioA || !ioB) { + return false; + } + const typeA = String(ioA.type); + const typeB = String(ioB.type); + // What does litegraph think, which includes looking at array values. + let isValid = LiteGraph.isValidConnection(typeA, typeB); + + // This is here to fix the churn happening in list types in comfyui itself.. + // https://github.com/comfyanonymous/ComfyUI/issues/1674 + if (!isValid) { + let areCombos = + (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(",")); + // We don't want to let any old combo connect to any old combo, so we'll look at the names too. + if (areCombos) { + // Some nodes use "_name" and some use "model" and "ckpt", so normalize + const nameA = ioA.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL"); + const nameB = ioB.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL"); + isValid = nameA.includes(nameB) || nameB.includes(nameA); + } + } + return isValid; +} + +/** + * Patches the LiteGraph.isValidConnection so old nodes can connect to this new COMBO type for all + * lists (without users needing to go through and re-create all their nodes one by one). + */ +const oldIsValidConnection = LiteGraph.isValidConnection; +LiteGraph.isValidConnection = function (typeA: ISlotType, typeB: ISlotType): boolean { + let isValid = oldIsValidConnection.call(LiteGraph, typeA, typeB); + if (!isValid) { + typeA = String(typeA); + typeB = String(typeB); + // This is waaaay too liberal and now any combos can connect to any combos. But we only have the + // types (not names like my util above), and connecting too liberally is better than old nodes + // with lists not being able to connect to this new COMBO type. And, anyway, it matches the + // current behavior today with new nodes anyway, where all lists are COMBO types. + // Refs: https://github.com/comfyanonymous/ComfyUI/issues/1674 + // https://github.com/comfyanonymous/ComfyUI/pull/1675 + let areCombos = + (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(",")); + isValid = areCombos; + } + return isValid; +}; + +/** + * Returns a list of output nodes given a list of nodes. + */ +export function getOutputNodes(nodes: TLGraphNode[]) { + return ( + nodes?.filter((n) => { + return ( + n.mode != LiteGraph.NEVER && ((n.constructor as any).nodeData as ComfyNodeDef)?.output_node + ); + }) || [] + ); +} + +/** + * Changes the mode of a node. We must go through this to change a node's mode after the + * introduction of subgraphs, as ComfyUI doesn't update the mode of a node in a subgraph on its own. + */ +export function changeModeOfNodes(nodeOrNodes: TLGraphNode | TLGraphNode[], mode: LGraphEventMode) { + reduceNodesDepthFirst(nodeOrNodes, (n) => { + n.mode = mode; + }); +} + +/** + * Performs depth-first traversal of nodes and their subgraphs. + * Adapted from ComfyUI Frontend's method. + */ +export function reduceNodesDepthFirst( + nodeOrNodes: TLGraphNode | TLGraphNode[], + reduceFn: (node: TLGraphNode) => void, +): void; +export function reduceNodesDepthFirst( + nodeOrNodes: TLGraphNode | TLGraphNode[], + reduceFn: (node: TLGraphNode, reduceTo: T) => T | void, + reduceTo: T, +): T; +export function reduceNodesDepthFirst( + nodeOrNodes: TLGraphNode | TLGraphNode[], + reduceFn: (node: TLGraphNode, reduceTo: T) => T | void, + reduceTo?: T, +): T { + const nodes = Array.isArray(nodeOrNodes) ? nodeOrNodes : [nodeOrNodes]; + const stack: Array<{node: TLGraphNode}> = nodes.map((node) => ({node})); + + // Process stack iteratively (DFS) + while (stack.length > 0) { + const {node} = stack.pop()!; + const result = reduceFn(node, reduceTo as T); + if (result !== undefined && result !== reduceTo) { + reduceTo = result; + } + + // If it's a subgraph and we should expand, add children to stack + if (node.isSubgraphNode?.() && node.subgraph) { + // Process children in reverse order to maintain left-to-right DFS processing + // when popping from stack (LIFO). Iterate backwards to avoid array reversal. + const children = node.subgraph.nodes; + for (let i = children.length - 1; i >= 0; i--) { + stack.push({node: children[i]!}); + } + } + } + return reduceTo as T; +} + +/** + * Found an issue where group._nodes had nodes that weren't in the actual group. group._nodes is + * marked deprecated, so we'll go ahead and use _children and filter. + */ +export function getGroupNodes(group: LGraphGroup): TLGraphNode[] { + return Array.from(group._children).filter((c) => c instanceof LGraphNode); +} + +/** + * Gets a node identifier alongside the graph identifier. + * + * Perhaps a bug, but it appears the same node id _could_ be in different subgraphs (or, at least, + * was definitively reported to be in the main graph, and a subgraph). So, if we're trying + * identifying a node alongside other nodes (like, a cache map), we need to keep the graph id along + * side it as well. + */ +export function getGraphDependantNodeKey(node: TLGraphNode): string { + const graph = node.graph ?? app.graph; + return `${graph.id}:${node.id}`; +} + +/** + * Gets a full color string, including parsing from the LGraphCanvas data. + */ +export function getFullColor( + color?: string, + liteGraphKey: "groupcolor" | "color" | "bgcolor" = "color", +) { + if (!color) { + return ""; + } + if (LGraphCanvas.node_colors[color]) { + color = LGraphCanvas.node_colors[color]![liteGraphKey]; + } + color = color.replace("#", "").toLocaleLowerCase(); + if (color.length === 3) { + color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3"); + } + return `#${color}`; +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_canvas.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_canvas.ts new file mode 100644 index 00000000..68432dfd --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_canvas.ts @@ -0,0 +1,389 @@ +import type {LGraphCanvas as TLGraphCanvas, Vector2} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; + +function binarySearch(max: number, getValue: (n: number) => number, match: number) { + let min = 0; + + while (min <= max) { + let guess = Math.floor((min + max) / 2); + const compareVal = getValue(guess); + + if (compareVal === match) return guess; + if (compareVal < match) min = guess + 1; + else max = guess - 1; + } + + return max; +} + +/** + * Fits a string against a max width for a ctx. Font should be defined on ctx beforehand. + */ +export function fitString(ctx: CanvasRenderingContext2D, str: string, maxWidth: number) { + let width = ctx.measureText(str).width; + const ellipsis = "…"; + const ellipsisWidth = measureText(ctx, ellipsis); + if (width <= maxWidth || width <= ellipsisWidth) { + return str; + } + + const index = binarySearch( + str.length, + (guess) => measureText(ctx, str.substring(0, guess)), + maxWidth - ellipsisWidth, + ); + + return str.substring(0, index) + ellipsis; +} + +/** Measures the width of text for a canvas context. */ +export function measureText(ctx: CanvasRenderingContext2D, str: string) { + return ctx.measureText(str).width; +} + +export type WidgetRenderingOptionsPart = { + type?: "toggle" | "custom"; + margin?: number; + fillStyle?: string; + strokeStyle?: string; + lowQuality?: boolean; + draw?(ctx: CanvasRenderingContext2D, x: number, lowQuality: boolean): number; +}; + +type WidgetRenderingOptions = { + size: [number, number]; + pos: [number, number]; + borderRadius?: number; + colorStroke?: string; + colorBackground?: string; +}; + +export function isLowQuality() { + const canvas = app.canvas as TLGraphCanvas; + return (canvas.ds?.scale || 1) <= 0.5; +} + +export function drawNodeWidget(ctx: CanvasRenderingContext2D, options: WidgetRenderingOptions) { + const lowQuality = isLowQuality(); + + const data = { + width: options.size[0], + height: options.size[1], + posY: options.pos[1], + lowQuality, + margin: 15, + colorOutline: LiteGraph.WIDGET_OUTLINE_COLOR, + colorBackground: LiteGraph.WIDGET_BGCOLOR, + colorText: LiteGraph.WIDGET_TEXT_COLOR, + colorTextSecondary: LiteGraph.WIDGET_SECONDARY_TEXT_COLOR, + }; + + // Draw background. + ctx.strokeStyle = options.colorStroke || data.colorOutline; + ctx.fillStyle = options.colorBackground || data.colorBackground; + ctx.beginPath(); + ctx.roundRect( + data.margin, + data.posY, + data.width - data.margin * 2, + data.height, + lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5], + ); + ctx.fill(); + if (!lowQuality) { + ctx.stroke(); + } + + return data; +} + +/** Draws a rounded rectangle. */ +export function drawRoundedRectangle( + ctx: CanvasRenderingContext2D, + options: WidgetRenderingOptions, +) { + const lowQuality = isLowQuality(); + options = {...options}; + ctx.save(); + ctx.strokeStyle = options.colorStroke || LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.fillStyle = options.colorBackground || LiteGraph.WIDGET_BGCOLOR; + ctx.beginPath(); + ctx.roundRect( + ...options.pos, + ...options.size, + lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5], + ); + ctx.fill(); + !lowQuality && ctx.stroke(); + ctx.restore(); +} + +type DrawNumberWidgetPartOptions = { + posX: number; + posY: number; + height: number; + value: number; + direction?: 1 | -1; + textColor?: string; +}; + +/** + * Draws a number picker with arrows off to each side. + * + * This is for internal widgets that may have many hit areas (full-width, default number widgets put + * the arrows on either side of the full-width row). + */ +export function drawNumberWidgetPart( + ctx: CanvasRenderingContext2D, + options: DrawNumberWidgetPartOptions, +): [Vector2, Vector2, Vector2] { + const arrowWidth = 9; + const arrowHeight = 10; + const innerMargin = 3; + const numberWidth = 32; + + const xBoundsArrowLess: Vector2 = [0, 0]; + const xBoundsNumber: Vector2 = [0, 0]; + const xBoundsArrowMore: Vector2 = [0, 0]; + + ctx.save(); + + let posX = options.posX; + const {posY, height, value, textColor} = options; + const midY = posY + height / 2; + + // If we're drawing parts from right to left (usually when something in the middle will be + // flexible), then we can simply move left the expected width of our widget and draw forwards. + if (options.direction === -1) { + posX = posX - arrowWidth - innerMargin - numberWidth - innerMargin - arrowWidth; + } + + // Draw the strength left arrow. + ctx.fill( + new Path2D( + `M ${posX} ${midY} l ${arrowWidth} ${ + arrowHeight / 2 + } l 0 -${arrowHeight} L ${posX} ${midY} z`, + ), + ); + + xBoundsArrowLess[0] = posX; + xBoundsArrowLess[1] = arrowWidth; + posX += arrowWidth + innerMargin; + + // Draw the strength text. + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + const oldTextcolor = ctx.fillStyle; + if (textColor) { + ctx.fillStyle = textColor; + } + ctx.fillText(fitString(ctx, value.toFixed(2), numberWidth), posX + numberWidth / 2, midY); + ctx.fillStyle = oldTextcolor; + + xBoundsNumber[0] = posX; + xBoundsNumber[1] = numberWidth; + posX += numberWidth + innerMargin; + + // Draw the strength right arrow. + ctx.fill( + new Path2D( + `M ${posX} ${midY - arrowHeight / 2} l ${arrowWidth} ${arrowHeight / 2} l -${arrowWidth} ${ + arrowHeight / 2 + } v -${arrowHeight} z`, + ), + ); + + xBoundsArrowMore[0] = posX; + xBoundsArrowMore[1] = arrowWidth; + + ctx.restore(); + + return [xBoundsArrowLess, xBoundsNumber, xBoundsArrowMore]; +} +drawNumberWidgetPart.WIDTH_TOTAL = 9 + 3 + 32 + 3 + 9; + +type DrawTogglePartOptions = { + posX: number; + posY: number; + height: number; + value: boolean | null; +}; + +/** + * Draws a toggle for a widget. The toggle is a three-way switch with left being false, right being + * true, and a middle state being null. + */ +export function drawTogglePart( + ctx: CanvasRenderingContext2D, + options: DrawTogglePartOptions, +): Vector2 { + const lowQuality = isLowQuality(); + ctx.save(); + + const {posX, posY, height, value} = options; + + const toggleRadius = height * 0.36; // This is the standard toggle height calc. + const toggleBgWidth = height * 1.5; // We don't draw a separate bg, but this would be it. + + // Toggle Track + if (!lowQuality) { + ctx.beginPath(); + ctx.roundRect(posX + 4, posY + 4, toggleBgWidth - 8, height - 8, [height * 0.5]); + ctx.globalAlpha = app.canvas.editor_alpha * 0.25; + ctx.fillStyle = "rgba(255,255,255,0.45)"; + ctx.fill(); + ctx.globalAlpha = app.canvas.editor_alpha; + } + + // Toggle itself + ctx.fillStyle = value === true ? "#89B" : "#888"; + const toggleX = + lowQuality || value === false + ? posX + height * 0.5 + : value === true + ? posX + height + : posX + height * 0.75; + ctx.beginPath(); + ctx.arc(toggleX, posY + height * 0.5, toggleRadius, 0, Math.PI * 2); + ctx.fill(); + + ctx.restore(); + + return [posX, toggleBgWidth]; +} + +export function drawInfoIcon( + ctx: CanvasRenderingContext2D, + x: number, + y: number, + size: number = 12, +) { + ctx.save(); + ctx.beginPath(); + ctx.roundRect(x, y, size, size, [size * 0.1]); + ctx.fillStyle = "#2f82ec"; + ctx.strokeStyle = "#0f2a5e"; + ctx.fill(); + // ctx.stroke(); + ctx.strokeStyle = "#FFF"; + ctx.lineWidth = 2; + // ctx.lineCap = 'round'; + const midX = x + size / 2; + const serifSize = size * 0.175; + ctx.stroke( + new Path2D(` + M ${midX} ${y + size * 0.15} + v 2 + M ${midX - serifSize} ${y + size * 0.45} + h ${serifSize} + v ${size * 0.325} + h ${serifSize} + h -${serifSize * 2} + `), + ); + ctx.restore(); +} + +export function drawPlusIcon( + ctx: CanvasRenderingContext2D, + x: number, + midY: number, + size: number = 12, +) { + ctx.save(); + const s = size / 3; + const plus = new Path2D(` + M ${x} ${midY + s / 2} + v-${s} h${s} v-${s} h${s} + v${s} h${s} v${s} h-${s} + v${s} h-${s} v-${s} h-${s} + z + `); + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + ctx.fillStyle = "#3a3"; + ctx.strokeStyle = "#383"; + ctx.fill(plus); + ctx.stroke(plus); + + ctx.restore(); +} + +/** + * Draws a better button. + */ +export function drawWidgetButton( + ctx: CanvasRenderingContext2D, + options: WidgetRenderingOptions, + text: string | null = null, + isMouseDownedAndOver: boolean = false, +) { + const borderRadius = isLowQuality() ? 0 : (options.borderRadius ?? 4); + ctx.save(); + + if (!isLowQuality() && !isMouseDownedAndOver) { + drawRoundedRectangle(ctx, { + size: [options.size[0] - 2, options.size[1]], + pos: [options.pos[0] + 1, options.pos[1] + 1], + borderRadius, + colorBackground: "#000000aa", + colorStroke: "#000000aa", + }); + } + + // BG + drawRoundedRectangle(ctx, { + size: options.size, + pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)], + borderRadius, + colorBackground: isMouseDownedAndOver ? "#444" : LiteGraph.WIDGET_BGCOLOR, + colorStroke: "transparent", + }); + + if (isLowQuality()) { + ctx.restore(); + return; + } + + if (!isMouseDownedAndOver) { + // Shadow + drawRoundedRectangle(ctx, { + size: [options.size[0] - 0.75, options.size[1] - 0.75], + pos: options.pos, + borderRadius: borderRadius - 0.5, + colorBackground: "transparent", + colorStroke: "#00000044", + }); + + // Highlight + drawRoundedRectangle(ctx, { + size: [options.size[0] - 0.75, options.size[1] - 0.75], + pos: [options.pos[0] + 0.75, options.pos[1] + 0.75], + borderRadius: borderRadius - 0.5, + colorBackground: "transparent", + colorStroke: "#ffffff11", + }); + } + + // Stroke + drawRoundedRectangle(ctx, { + size: options.size, + pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)], + borderRadius, + colorBackground: "transparent", + }); + + if (!isLowQuality() && text) { + ctx.textBaseline = "middle"; + ctx.textAlign = "center"; + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + ctx.fillText( + text, + options.size[0] / 2, + options.pos[1] + options.size[1] / 2 + (isMouseDownedAndOver ? 1 : 0), + ); + } + ctx.restore(); +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_deprecated_comfyui.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_deprecated_comfyui.ts new file mode 100644 index 00000000..2077e6e9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_deprecated_comfyui.ts @@ -0,0 +1,294 @@ +/** + * [πŸ€] ComfyUI started deprecating the use of their legacy JavaScript files. These are ports/shims + * since we relied on them at one point. + * + * TODO: Should probably remove these all together at some point. + */ + +import {app} from "scripts/app.js"; + +import type {INodeInputSlot, INodeOutputSlot, InputSpec, LGraphNode} from "@comfyorg/frontend"; + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts#L462 */ +interface PrimitiveNode extends LGraphNode { + recreateWidget(): void; + onLastDisconnect(): void; +} + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/renderer/utils/nodeTypeGuards.ts */ +function isPrimitiveNode(node: LGraphNode): node is PrimitiveNode { + return node.type === "PrimitiveNode"; +} + +/** + * CONFIG and GET_CONFIG in https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/services/litegraphService.ts + * are not accessible publicly, so we need to look at a slot.widget's symbols and check to see if it + * matches rather than access it directly. Cool... + */ +function getWidgetGetConfigSymbols(slot: INodeOutputSlot | INodeInputSlot): { + CONFIG?: symbol; + GET_CONFIG?: symbol; +} { + const widget = slot?.widget; + if (!widget) return {}; + const syms = Object.getOwnPropertySymbols(widget || {}); + for (const sym of syms) { + const symVal = widget![sym]; + const isGetConfig = typeof symVal === "function"; + let maybeCfg = isGetConfig ? symVal() : symVal; + if ( + Array.isArray(maybeCfg) && + maybeCfg.length >= 2 && + typeof maybeCfg[0] === "string" && + (maybeCfg[0] === "*" || typeof maybeCfg[1]?.type === "string") + ) { + return isGetConfig ? {GET_CONFIG: sym} : {CONFIG: sym}; + } + } + return {}; +} + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts + */ +export function getWidgetConfig(slot: INodeOutputSlot | INodeInputSlot): InputSpec { + const configSyms = getWidgetGetConfigSymbols(slot); + const widget = slot.widget || ({} as any); + return ( + (configSyms.CONFIG && widget[configSyms.CONFIG]) ?? + (configSyms.GET_CONFIG && widget[configSyms.GET_CONFIG]?.()) ?? ["*", {}] + ); +} + +/** + * This is lossy, since we don't have access to GET_CONFIG Symbol, we cannot accurately set it. As a + * best-chance we can look for a function that seems to return a + * + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts + */ +export function setWidgetConfig( + slot: INodeOutputSlot | INodeInputSlot | undefined, + config: InputSpec, +) { + if (!slot?.widget) return; + if (config) { + const configSyms = getWidgetGetConfigSymbols(slot); + const widget = slot.widget || ({} as any); + if (configSyms.GET_CONFIG) { + widget[configSyms.GET_CONFIG] = () => config; + } else if (configSyms.CONFIG) { + widget[configSyms.CONFIG] = config; + } else { + console.error( + "Cannot set widget Config. This is due to ComfyUI removing the ability to call legacy " + + "JavaScript APIs that are now deprecated without new, supported APIs. It's possible " + + "some things in rgthree-comfy do not work correctly. If you see this, please file a bug.", + ); + } + } else { + delete slot.widget; + } + + if ("link" in slot) { + const link = app.graph.links[(slot as INodeInputSlot)?.link ?? -1]; + if (link) { + const originNode = app.graph.getNodeById(link.origin_id); + if (originNode && isPrimitiveNode(originNode)) { + if (config) { + originNode.recreateWidget(); + } else if (!app.configuringGraph) { + originNode.disconnectOutput(0); + originNode.onLastDisconnect(); + } + } + } + } +} + +/** + * A slimmed-down version of `mergeIfValid` for only what was needed in rgthree-comfy. + * + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts + */ +export function mergeIfValid( + output: INodeOutputSlot | INodeInputSlot, + config2: InputSpec, +): InputSpec[1] | null { + const config1 = getWidgetConfig(output); + const customSpec = mergeInputSpec(config1, config2); + if (customSpec) { + setWidgetConfig(output, customSpec); + } + return customSpec?.[1] ?? null; +} + +/** + * Merges two input specs. + * + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts + */ +const mergeInputSpec = (spec1: InputSpec, spec2: InputSpec): InputSpec | null => { + const type1 = getInputSpecType(spec1); + const type2 = getInputSpecType(spec2); + + if (type1 !== type2) { + return null; + } + + if (isIntInputSpec(spec1) || isFloatInputSpec(spec1)) { + return mergeNumericInputSpec(spec1, spec2 as typeof spec1); + } + + if (isComboInputSpec(spec1)) { + return mergeComboInputSpec(spec1, spec2 as typeof spec1); + } + + return mergeCommonInputSpec(spec1, spec2); +}; + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function getInputSpecType(inputSpec: InputSpec): string { + return isComboInputSpec(inputSpec) ? "COMBO" : inputSpec[0]; +} + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function isComboInputSpecV1(inputSpec: InputSpec) { + return Array.isArray(inputSpec[0]); +} + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function isIntInputSpec(inputSpec: InputSpec) { + return inputSpec[0] === "INT"; +} + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function isFloatInputSpec(inputSpec: InputSpec) { + return inputSpec[0] === "FLOAT"; +} + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function isComboInputSpecV2(inputSpec: InputSpec) { + return inputSpec[0] === "COMBO"; +} + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function isComboInputSpec(inputSpec: InputSpec) { + return isComboInputSpecV1(inputSpec) || isComboInputSpecV2(inputSpec); +} + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */ +const getRange = (options: any) => { + const min = options.min ?? -Infinity; + const max = options.max ?? Infinity; + return {min, max}; +}; + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */ +const mergeNumericInputSpec = (spec1: any, spec2: any): T | null => { + const type = spec1[0]; + const options1 = spec1[1] ?? {}; + const options2 = spec2[1] ?? {}; + + const range1 = getRange(options1); + const range2 = getRange(options2); + + // If the ranges do not overlap, return null + if (range1.min > range2.max || range1.max < range2.min) { + return null; + } + + const step1 = options1.step ?? 1; + const step2 = options2.step ?? 1; + + const mergedOptions = { + // Take intersection of ranges + min: Math.max(range1.min, range2.min), + max: Math.min(range1.max, range2.max), + step: lcm(step1, step2), + }; + + return mergeCommonInputSpec( + [type, {...options1, ...mergedOptions}] as T, + [type, {...options2, ...mergedOptions}] as T, + ); +}; + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */ +const mergeComboInputSpec = (spec1: any, spec2: any): T | null => { + const options1 = spec1[1] ?? {}; + const options2 = spec2[1] ?? {}; + + const comboOptions1 = getComboSpecComboOptions(spec1); + const comboOptions2 = getComboSpecComboOptions(spec2); + + const intersection = comboOptions1.filter((value) => comboOptions2.includes(value)); + + // If the intersection is empty, return null + if (intersection.length === 0) { + return null; + } + + return mergeCommonInputSpec( + ["COMBO", {...options1, options: intersection}] as T, + ["COMBO", {...options2, options: intersection}] as T, + ); +}; + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */ +const mergeCommonInputSpec = (spec1: T, spec2: T): T | null => { + const type = getInputSpecType(spec1); + const options1 = spec1[1] ?? {}; + const options2 = spec2[1] ?? {}; + + const compareKeys = [...new Set([...Object.keys(options1), ...Object.keys(options2)])].filter( + (key: string) => !IGNORE_KEYS.has(key), + ); + + const mergeIsValid = compareKeys.every((key: string) => { + const value1 = options1[key]; + const value2 = options2[key]; + return value1 === value2 || (value1 == null && value2 == null); + }); + + return mergeIsValid ? ([type, {...options1, ...options2}] as T) : null; +}; + +/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */ +const IGNORE_KEYS = new Set([ + "default", + "forceInput", + "defaultInput", + "control_after_generate", + "multiline", + "tooltip", + "dynamicPrompts", +]); + +/** + * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts + */ +function getComboSpecComboOptions(inputSpec: any): (number | string)[] { + return (isComboInputSpecV2(inputSpec) ? inputSpec[1]?.options : inputSpec[0]) ?? []; +} + +/** Taken from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/mathUtil.ts */ +const lcm = (a: number, b: number): number => { + return Math.abs(a * b) / gcd(a, b); +}; + +/** Taken from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/mathUtil.ts */ +const gcd = (a: number, b: number): number => { + return b === 0 ? a : gcd(b, a % b); +}; diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_inputs_outputs.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_inputs_outputs.ts new file mode 100644 index 00000000..69a215d6 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_inputs_outputs.ts @@ -0,0 +1,18 @@ +import type {LGraphNode} from "@comfyorg/frontend"; +import { RgthreeBaseNode } from "./base_node"; + +/** Removes all inputs from the end. */ +export function removeUnusedInputsFromEnd(node: LGraphNode, minNumber = 1, nameMatch?: RegExp) { + // No need to remove inputs from nodes that have been removed. This can happen because we may + // have debounced cleanup tasks. + if ((node as RgthreeBaseNode).removed) return; + for (let i = node.inputs.length - 1; i >= minNumber; i--) { + if (!node.inputs[i]?.link) { + if (!nameMatch || nameMatch.test(node.inputs[i]!.name)) { + node.removeInput(i); + } + continue; + } + break; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_menu.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_menu.ts new file mode 100644 index 00000000..fdf921a0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_menu.ts @@ -0,0 +1,98 @@ +import type { + LGraphCanvas as TLGraphCanvas, + LGraphNode, + ContextMenu, + IContextMenuValue, + IBaseWidget, + IContextMenuOptions, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {rgthreeApi} from "rgthree/common/rgthree_api.js"; + +const PASS_THROUGH = function (item: T) { + return item as T; +}; + +/** + * Shows a lora chooser context menu. + */ +export async function showLoraChooser( + event: PointerEvent | MouseEvent, + callback: IContextMenuOptions["callback"], + parentMenu?: ContextMenu | null, + loras?: string[], +) { + const canvas = app.canvas as TLGraphCanvas; + if (!loras) { + loras = ["None", ...(await rgthreeApi.getLoras().then((loras) => loras.map((l) => l.file)))]; + } + new LiteGraph.ContextMenu(loras, { + event: event, + parentMenu: parentMenu != null ? parentMenu : undefined, + title: "Choose a lora", + scale: Math.max(1, canvas.ds?.scale ?? 1), + className: "dark", + callback, + }); +} + +/** + * Shows a context menu chooser of nodes. + * + * @param mapFn The function used to map each node to the context menu item. If null is returned + * it will be filtered out (rather than use a separate filter method). + */ +export function showNodesChooser( + event: PointerEvent | MouseEvent, + mapFn: (n: LGraphNode) => T | null, + callback: IContextMenuOptions["callback"], + parentMenu?: ContextMenu, +) { + const canvas = app.canvas as TLGraphCanvas; + const nodesOptions: T[] = (app.graph._nodes as LGraphNode[]) + .map(mapFn) + .filter((e): e is NonNullable => e != null); + + nodesOptions.sort((a: any, b: any) => { + return a.value - b.value; + }); + + new LiteGraph.ContextMenu(nodesOptions, { + event: event, + parentMenu, + title: "Choose a node id", + scale: Math.max(1, canvas.ds?.scale ?? 1), + className: "dark", + callback, + }); +} + +/** + * Shows a conmtext menu chooser for a specific node. + * + * @param mapFn The function used to map each node to the context menu item. If null is returned + * it will be filtered out (rather than use a separate filter method). + */ +export function showWidgetsChooser( + event: PointerEvent | MouseEvent, + node: LGraphNode, + mapFn: (n: IBaseWidget) => T | null, + callback: IContextMenuOptions["callback"], + parentMenu?: ContextMenu, +) { + const options: T[] = (node.widgets || []) + .map(mapFn) + .filter((e): e is NonNullable => e != null); + if (options.length) { + const canvas = app.canvas as TLGraphCanvas; + new LiteGraph.ContextMenu(options, { + event, + parentMenu, + title: "Choose an input/widget", + scale: Math.max(1, canvas.ds?.scale ?? 1), + className: "dark", + callback, + }); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_widgets.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_widgets.ts new file mode 100644 index 00000000..f9c8b1c9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_widgets.ts @@ -0,0 +1,505 @@ +import type { + LGraphNode, + LGraphCanvas as TLGraphCanvas, + Vector2, + ICustomWidget, + IWidgetOptions, + CanvasPointerEvent, +} from "@comfyorg/frontend"; + +import {app} from "scripts/app.js"; +import {drawNodeWidget, drawWidgetButton, fitString, isLowQuality} from "./utils_canvas.js"; + +type Vector4 = [number, number, number, number]; + +/** + * Draws a label on teft, and a value on the right, ellipsizing when out of space. + */ +export function drawLabelAndValue( + ctx: CanvasRenderingContext2D, + label: string, + value: string, + width: number, + posY: number, + height: number, + options?: {offsetLeft: number}, +) { + const outerMargin = 15; + const innerMargin = 10; + const midY = posY + height / 2; + ctx.save(); + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + const labelX = outerMargin + innerMargin + (options?.offsetLeft ?? 0); + ctx.fillText(label, labelX, midY); + + const valueXLeft = labelX + ctx.measureText(label).width + 7; + const valueXRight = width - (outerMargin + innerMargin); + + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + ctx.textAlign = "right"; + ctx.fillText(fitString(ctx, value, valueXRight - valueXLeft), valueXRight, midY); + ctx.restore(); +} + +export type RgthreeBaseWidgetBounds = { + /** The bounds, either [x, width] assuming the full height, or [x, y, width, height] if height. */ + bounds: Vector2 | Vector4; + onDown?( + event: CanvasPointerEvent, + pos: Vector2, + node: LGraphNode, + bounds: RgthreeBaseWidgetBounds, + ): boolean | void; + onUp?( + event: CanvasPointerEvent, + pos: Vector2, + node: LGraphNode, + bounds: RgthreeBaseWidgetBounds, + ): boolean | void; + onMove?( + event: CanvasPointerEvent, + pos: Vector2, + node: LGraphNode, + bounds: RgthreeBaseWidgetBounds, + ): boolean | void; + onClick?( + event: CanvasPointerEvent, + pos: Vector2, + node: LGraphNode, + bounds: RgthreeBaseWidgetBounds, + ): boolean | void; + data?: any; + wasMouseClickedAndIsOver?: boolean; +}; + +export type RgthreeBaseHitAreas = { + [K in Keys]: RgthreeBaseWidgetBounds; +}; + +type NotArray = T extends Array ? never : T; + +/** + * A base widget that handles mouse events more properly. + */ +export abstract class RgthreeBaseWidget implements ICustomWidget { + // Needed here b/c it was added to ComfyUI's types for IBaseWidget. No idea what they use it for, + // or why it's only boolean. + [symbol: symbol]: boolean; + + // We don't want our value to be an array as a widget will be serialized as an "input" for the API + // which uses an array value to represent a link. To keep things simpler, we'll avoid using an + // array at all. + abstract value: NotArray; + + type: ICustomWidget["type"] = "custom"; + name: string; + options: IWidgetOptions = {}; + y: number = 0; + last_y: number = 0; + + protected mouseDowned: Vector2 | null = null; + protected isMouseDownedAndOver: boolean = false; + + // protected hitAreas: {[key: string]: RgthreeBaseWidgetBounds} = {}; + protected readonly hitAreas: RgthreeBaseHitAreas = {}; + private downedHitAreasForMove: RgthreeBaseWidgetBounds[] = []; + private downedHitAreasForClick: RgthreeBaseWidgetBounds[] = []; + + constructor(name: string) { + this.name = name; + } + + serializeValue(node: LGraphNode, index: number): Promise | V { + return this.value; + } + + private clickWasWithinBounds(pos: Vector2, bounds: Vector2 | Vector4) { + let xStart = bounds[0]; + let xEnd = xStart + (bounds.length > 2 ? bounds[2]! : bounds[1]!); + const clickedX = pos[0] >= xStart && pos[0] <= xEnd; + if (bounds.length === 2) { + return clickedX; + } + return clickedX && pos[1] >= bounds[1] && pos[1] <= bounds[1] + bounds[3]!; + } + + mouse(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode) { + const canvas = app.canvas as TLGraphCanvas; + + if (event.type == "pointerdown") { + this.mouseDowned = [...pos] as Vector2; + this.isMouseDownedAndOver = true; + this.downedHitAreasForMove.length = 0; + this.downedHitAreasForClick.length = 0; + // Loop over out bounds data and call any specifics. + let anyHandled = false; + for (const part of Object.values(this.hitAreas)) { + if (this.clickWasWithinBounds(pos, part.bounds)) { + if (part.onMove) { + this.downedHitAreasForMove.push(part); + } + if (part.onClick) { + this.downedHitAreasForClick.push(part); + } + if (part.onDown) { + const thisHandled = part.onDown.apply(this, [event, pos, node, part]); + anyHandled = anyHandled || thisHandled == true; + } + part.wasMouseClickedAndIsOver = true; + } + } + return this.onMouseDown(event, pos, node) ?? anyHandled; + } + + // This only fires when LiteGraph has a node_widget (meaning it's pressed), but we may not be + // the original widget pressed, so we still need `mouseDowned`. + if (event.type == "pointerup") { + if (!this.mouseDowned) return true; + this.downedHitAreasForMove.length = 0; + const wasMouseDownedAndOver = this.isMouseDownedAndOver; + this.cancelMouseDown(); + let anyHandled = false; + for (const part of Object.values(this.hitAreas)) { + if (part.onUp && this.clickWasWithinBounds(pos, part.bounds)) { + const thisHandled = part.onUp.apply(this, [event, pos, node, part]); + anyHandled = anyHandled || thisHandled == true; + } + part.wasMouseClickedAndIsOver = false; + } + for (const part of this.downedHitAreasForClick) { + if (this.clickWasWithinBounds(pos, part.bounds)) { + const thisHandled = part.onClick!.apply(this, [event, pos, node, part]); + anyHandled = anyHandled || thisHandled == true; + } + } + this.downedHitAreasForClick.length = 0; + if (wasMouseDownedAndOver) { + const thisHandled = this.onMouseClick(event, pos, node); + anyHandled = anyHandled || thisHandled == true; + } + return this.onMouseUp(event, pos, node) ?? anyHandled; + } + + // This only fires when LiteGraph has a node_widget (meaning it's pressed). + if (event.type == "pointermove") { + this.isMouseDownedAndOver = !!this.mouseDowned; + // If we've moved off the button while pressing, then consider us no longer pressing. + if ( + this.mouseDowned && + (pos[0] < 15 || + pos[0] > node.size[0] - 15 || + pos[1] < this.last_y || + pos[1] > this.last_y + LiteGraph.NODE_WIDGET_HEIGHT) + ) { + this.isMouseDownedAndOver = false; + } + for (const part of Object.values(this.hitAreas)) { + if (this.downedHitAreasForMove.includes(part)) { + part.onMove!.apply(this, [event, pos, node, part]); + } + if (this.downedHitAreasForClick.includes(part)) { + part.wasMouseClickedAndIsOver = this.clickWasWithinBounds(pos, part.bounds); + } + } + return this.onMouseMove(event, pos, node) ?? true; + } + return false; + } + + /** Sometimes we want to cancel a mouse down, so that an up/move aren't fired. */ + cancelMouseDown() { + this.mouseDowned = null; + this.isMouseDownedAndOver = false; + this.downedHitAreasForMove.length = 0; + } + + /** An event that fires when the pointer is pressed down (once). */ + onMouseDown(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void { + return; + } + + /** + * An event that fires when the pointer is let go. Only fires if this was the widget that was + * originally pressed down. + */ + onMouseUp(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void { + return; + } + + /** + * An event that fires when the pointer is let go _over the widget_ and when the widget that was + * originally pressed down. + */ + onMouseClick(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void { + return; + } + + /** + * An event that fires when the pointer is moving after pressing down. Will fire both on and off + * of the widget. Check `isMouseDownedAndOver` to determine if the mouse is currently over the + * widget or not. + */ + onMouseMove(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void { + return; + } +} + +/** + * A better implementation of the LiteGraph button widget. + */ +export class RgthreeBetterButtonWidget extends RgthreeBaseWidget { + override readonly type = "custom"; + + value: string = ""; + label: string = ""; + mouseClickCallback: (event: CanvasPointerEvent, pos: Vector2, node: LGraphNode) => boolean | void; + + constructor( + name: string, + mouseClickCallback: ( + event: CanvasPointerEvent, + pos: Vector2, + node: LGraphNode, + ) => boolean | void, + label?: string, + ) { + super(name); + this.mouseClickCallback = mouseClickCallback; + this.label = label || name; + } + + draw(ctx: CanvasRenderingContext2D, node: LGraphNode, width: number, y: number, height: number) { + drawWidgetButton( + ctx, + {size: [width - 30, height], pos: [15, y]}, + this.label, + this.isMouseDownedAndOver, + ); + } + + override onMouseClick(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode) { + return this.mouseClickCallback(event, pos, node); + } +} + +/** + * A better implementation of the LiteGraph text widget, including auto ellipsis. + */ +export class RgthreeBetterTextWidget extends RgthreeBaseWidget { + value: string; + + constructor(name: string, value: string) { + super(name); + this.name = name; + this.value = value; + } + + draw(ctx: CanvasRenderingContext2D, node: LGraphNode, width: number, y: number, height: number) { + const widgetData = drawNodeWidget(ctx, {size: [width, height], pos: [15, y]}); + + if (!widgetData.lowQuality) { + drawLabelAndValue(ctx, this.name, this.value, width, y, height); + } + } + + override mouse(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean { + const canvas = app.canvas as TLGraphCanvas; + if (event.type == "pointerdown") { + canvas.prompt("Label", this.value, (v: string) => (this.value = v), event); + return true; + } + return false; + } +} + +/** + * Options for the Divider Widget. + */ +type RgthreeDividerWidgetOptions = { + marginTop: number; + marginBottom: number; + marginLeft: number; + marginRight: number; + color: string; + thickness: number; +}; + +/** + * A divider widget; can also be used as a spacer if fed a 0 thickness. + */ +export class RgthreeDividerWidget extends RgthreeBaseWidget<{}> { + override value = {}; + override options = {serialize: false}; + override readonly type = "custom"; + + private readonly widgetOptions: RgthreeDividerWidgetOptions = { + marginTop: 7, + marginBottom: 7, + marginLeft: 15, + marginRight: 15, + color: LiteGraph.WIDGET_OUTLINE_COLOR, + thickness: 1, + }; + + constructor(widgetOptions?: Partial) { + super("divider"); + Object.assign(this.widgetOptions, widgetOptions || {}); + } + + draw(ctx: CanvasRenderingContext2D, node: LGraphNode, width: number, posY: number, h: number) { + if (this.widgetOptions.thickness) { + ctx.strokeStyle = this.widgetOptions.color; + const x = this.widgetOptions.marginLeft; + const y = posY + this.widgetOptions.marginTop; + const w = width - this.widgetOptions.marginLeft - this.widgetOptions.marginRight; + ctx.stroke(new Path2D(`M ${x} ${y} h ${w}`)); + } + } + + computeSize(width: number): [number, number] { + return [ + width, + this.widgetOptions.marginTop + this.widgetOptions.marginBottom + this.widgetOptions.thickness, + ]; + } +} + +/** + * Options for the Label Widget. + */ +export type RgthreeLabelWidgetOptions = { + align?: "left" | "center" | "right"; + color?: string; + italic?: boolean; + size?: number; + text?: string | (() => string); // Text, or fall back to the name. + + /** A label to put on the right side. */ + actionLabel?: "__PLUS_ICON__" | string; + actionCallback?: (event: PointerEvent | CanvasPointerEvent) => void; +}; + +/** + * A simple label widget, drawn with no background. + */ +export class RgthreeLabelWidget extends RgthreeBaseWidget { + override readonly type = "custom"; + override options = {serialize: false}; + value = ""; + + private readonly widgetOptions: RgthreeLabelWidgetOptions = {}; + private posY: number = 0; + + constructor(name: string, widgetOptions?: RgthreeLabelWidgetOptions) { + super(name); + Object.assign(this.widgetOptions, widgetOptions); + } + + update(widgetOptions: RgthreeLabelWidgetOptions) { + Object.assign(this.widgetOptions, widgetOptions); + } + + draw( + ctx: CanvasRenderingContext2D, + node: LGraphNode, + width: number, + posY: number, + height: number, + ) { + this.posY = posY; + ctx.save(); + + let text = this.widgetOptions.text ?? this.name; + if (typeof text === "function") { + text = text(); + } + + ctx.textAlign = this.widgetOptions.align || "left"; + ctx.fillStyle = this.widgetOptions.color || LiteGraph.WIDGET_TEXT_COLOR; + const oldFont = ctx.font; + if (this.widgetOptions.italic) { + ctx.font = "italic " + ctx.font; + } + if (this.widgetOptions.size) { + ctx.font = ctx.font.replace(/\d+px/, `${this.widgetOptions.size}px`); + } + + const midY = posY + height / 2; + ctx.textBaseline = "middle"; + + if (this.widgetOptions.align === "center") { + ctx.fillText(text, node.size[0] / 2, midY); + } else { + ctx.fillText(text, 15, midY); + } // TODO(right); + + ctx.font = oldFont; + + if (this.widgetOptions.actionLabel === "__PLUS_ICON__") { + const plus = new Path2D( + `M${node.size[0] - 15 - 2} ${posY + 7} v4 h-4 v4 h-4 v-4 h-4 v-4 h4 v-4 h4 v4 h4 z`, + ); + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + ctx.fillStyle = "#3a3"; + ctx.strokeStyle = "#383"; + ctx.fill(plus); + ctx.stroke(plus); + } + ctx.restore(); + } + + override mouse(event: CanvasPointerEvent, nodePos: Vector2, node: LGraphNode): boolean { + if ( + event.type !== "pointerdown" || + isLowQuality() || + !this.widgetOptions.actionLabel || + !this.widgetOptions.actionCallback + ) { + return false; + } + + const pos: Vector2 = [nodePos[0], nodePos[1] - this.posY]; + const rightX = node.size[0] - 15; + if (pos[0] > rightX || pos[0] < rightX - 16) { + return false; + } + this.widgetOptions.actionCallback(event); + return true; + } +} + +/** An invisible widget. */ +export class RgthreeInvisibleWidget extends RgthreeBaseWidget { + override readonly type = "custom"; + + value: NotArray; + private serializeValueFn?: (node: LGraphNode, index: number) => Promise | T; + + constructor( + name: string, + type: string, + value: NotArray, + serializeValueFn?: (node: LGraphNode, index: number) => Promise | T, + ) { + super(name); + // this.type = type; + this.value = value; + this.serializeValueFn = serializeValueFn; + } + + draw() { + return; + } + computeSize(width: number): Vector2 { + return [0, 0]; + } + + override serializeValue(node: LGraphNode, index: number): T | Promise { + return this.serializeValueFn != null + ? this.serializeValueFn(node, index) + : super.serializeValue(node, index); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim.ts b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim.ts new file mode 100644 index 00000000..45befcf3 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim.ts @@ -0,0 +1,49 @@ +/** + * [πŸ€] At some point the new ComfyUI frontend stopped loading it's source as modules and started + * bundling them together. This removed the ability for to import the individual modules (like the + * api.js or pnginfo.js) in stand alone pages, like link_fixer. + * + * So, what do we have to do? Well, we have to fork, hardcode, or port what we would want to load + * from ComfyUI as our own, independant files again, which is unforunate for several reasons; + * duplicate code, risk of falling behind, etc... + * + * Anyway, this file is a shim that will either detect we're in the ComfyUI app and pass through the + * bundled module from the ComfyUI global or load from our own code when that's not available + * (because we're not in the actual ComfyUI UI). + */ + +import type {getPngMetadata, getWebpMetadata} from "typings/comfy.js"; + +const shimCache = new Map(); + +async function shimComfyUiModule(moduleName: string, prop?: string) { + let module = shimCache.get(moduleName); + if (!module) { + if (window.comfyAPI?.[moduleName]) { + module = window.comfyAPI?.[moduleName]; + } else { + module = await import(`./comfyui_shim_${moduleName}.js`); + } + if (!module) { + throw new Error(`Module ${moduleName} could not be loaded.`); + } + shimCache.set(moduleName, module); + } + if (prop) { + if (!module[prop]) { + throw new Error(`Property ${prop} on module ${moduleName} could not be loaded.`); + } + return module[prop]; + } + return module; +} + +export async function getPngMetadata(file: File | Blob) { + const fn = (await shimComfyUiModule("pnginfo", "getPngMetadata")) as getPngMetadata; + return fn(file); +} + +export async function getWebpMetadata(file: File | Blob) { + const fn = (await shimComfyUiModule("pnginfo", "getWebpMetadata")) as getWebpMetadata; + return fn(file); +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim_pnginfo.ts b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim_pnginfo.ts new file mode 100644 index 00000000..e3d46397 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim_pnginfo.ts @@ -0,0 +1,541 @@ + +/** + * [πŸ€] See `./comfyui_shim.ts`. + * + * This code has been forked from https://github.com/Comfy-Org/ComfyUI_frontend/blob/0937c1f2cd5026f390a6efa64f630e01ea414d1d/src/scripts/pnginfo.ts + * with some modifications made, such as removing unneeded exported functions, cleaning up trivial + * typing, etc. + */ + +import { rgthreeApi } from "./rgthree_api.js"; + +/** + * [πŸ€] A type to add to untypd portions of the code below where they were not yet typed in Comfy's + * code. + */ +type lazyComfyAny = any; + + +// [πŸ€] A shim for ComfyAPI getEmbeddings. +const api = { + async getEmbeddings(): Promise { + const resp = await rgthreeApi.fetchComfyApi('/embeddings', { cache: 'no-store' }) + return await resp.json(); + } +} + + +function getFromPngBuffer(buffer: ArrayBuffer) { + // Get the PNG data as a Uint8Array + const pngData = new Uint8Array(buffer) + const dataView = new DataView(pngData.buffer) + + // Check that the PNG signature is present + if (dataView.getUint32(0) !== 0x89504e47) { + console.error('Not a valid PNG file') + return + } + + // Start searching for chunks after the PNG signature + let offset = 8 + let txt_chunks: Record = {} + // Loop through the chunks in the PNG file + while (offset < pngData.length) { + // Get the length of the chunk + const length = dataView.getUint32(offset) + // Get the chunk type + const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)) + if (type === 'tEXt' || type == 'comf' || type === 'iTXt') { + // Get the keyword + let keyword_end = offset + 8 + while (pngData[keyword_end] !== 0) { + keyword_end++ + } + const keyword = String.fromCharCode( + ...pngData.slice(offset + 8, keyword_end) + ) + // Get the text + const contentArraySegment = pngData.slice( + keyword_end + 1, + offset + 8 + length + ) + const contentJson = new TextDecoder('utf-8').decode(contentArraySegment) + txt_chunks[keyword] = contentJson + } + + offset += 12 + length + } + return txt_chunks +} + +function getFromPngFile(file: File) { + return new Promise>((r) => { + const reader = new FileReader() + reader.onload = (event) => { + r(getFromPngBuffer((event.target as lazyComfyAny).result as ArrayBuffer) as lazyComfyAny) + } + + reader.readAsArrayBuffer(file) + }) +} + +function parseExifData(exifData: lazyComfyAny) { + // Check for the correct TIFF header (0x4949 for little-endian or 0x4D4D for big-endian) + const isLittleEndian = String.fromCharCode(...exifData.slice(0, 2)) === 'II' + + // Function to read 16-bit and 32-bit integers from binary data + function readInt(offset: lazyComfyAny, isLittleEndian: lazyComfyAny, length: lazyComfyAny) { + let arr = exifData.slice(offset, offset + length) + if (length === 2) { + return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint16( + 0, + isLittleEndian + ) + } else if (length === 4) { + return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint32( + 0, + isLittleEndian + ) + } + // lazyComfyAny + throw new Error('Shouldn\'t get here.'); + } + + // Read the offset to the first IFD (Image File Directory) + const ifdOffset = readInt(4, isLittleEndian, 4) + + function parseIFD(offset: lazyComfyAny) { + const numEntries = readInt(offset, isLittleEndian, 2) as lazyComfyAny; + const result = {} as lazyComfyAny + + for (let i = 0; i < numEntries; i++) { + const entryOffset = offset + 2 + i * 12 + const tag = readInt(entryOffset, isLittleEndian, 2) as lazyComfyAny + const type = readInt(entryOffset + 2, isLittleEndian, 2) + const numValues = readInt(entryOffset + 4, isLittleEndian, 4) + const valueOffset = readInt(entryOffset + 8, isLittleEndian, 4) as lazyComfyAny; + + // Read the value(s) based on the data type + let value + if (type === 2) { + // ASCII string + value = new TextDecoder('utf-8').decode( + exifData.subarray(valueOffset, valueOffset + numValues - 1) + ) + } + + result[tag] = value + } + + return result + } + + // Parse the first IFD + const ifdData = parseIFD(ifdOffset) + return ifdData +} + +function splitValues(input: lazyComfyAny) { + var output = {} as lazyComfyAny + for (var key in input) { + var value = input[key] + var splitValues = value.split(':', 2) + output[splitValues[0]] = splitValues[1] + } + return output +} + +export function getPngMetadata(file: File): Promise> { + return getFromPngFile(file) +} + +export function getWebpMetadata(file: lazyComfyAny) { + return new Promise>((r) => { + const reader = new FileReader() + reader.onload = (event) => { + const webp = new Uint8Array((event.target as lazyComfyAny).result as ArrayBuffer) + const dataView = new DataView(webp.buffer) + + // Check that the WEBP signature is present + if ( + dataView.getUint32(0) !== 0x52494646 || + dataView.getUint32(8) !== 0x57454250 + ) { + console.error('Not a valid WEBP file') + r({}) + return + } + + // Start searching for chunks after the WEBP signature + let offset = 12 + let txt_chunks = {} as lazyComfyAny + // Loop through the chunks in the WEBP file + while (offset < webp.length) { + const chunk_length = dataView.getUint32(offset + 4, true) + const chunk_type = String.fromCharCode( + ...webp.slice(offset, offset + 4) + ) + if (chunk_type === 'EXIF') { + if ( + String.fromCharCode(...webp.slice(offset + 8, offset + 8 + 6)) == + 'Exif\0\0' + ) { + offset += 6 + } + let data = parseExifData( + webp.slice(offset + 8, offset + 8 + chunk_length) + ) + for (var key in data) { + const value = data[key] as string + if (typeof value === 'string') { + const index = value.indexOf(':') + txt_chunks[value.slice(0, index)] = value.slice(index + 1) + } + } + break + } + + offset += 8 + chunk_length + } + + r(txt_chunks) + } + + reader.readAsArrayBuffer(file) + }) +} + +export function getLatentMetadata(file: lazyComfyAny) { + return new Promise((r) => { + const reader = new FileReader() + reader.onload = (event) => { + const safetensorsData = new Uint8Array((event.target as lazyComfyAny).result as ArrayBuffer) + const dataView = new DataView(safetensorsData.buffer) + let header_size = dataView.getUint32(0, true) + let offset = 8 + let header = JSON.parse( + new TextDecoder().decode( + safetensorsData.slice(offset, offset + header_size) + ) + ) + r(header.__metadata__) + } + + var slice = file.slice(0, 1024 * 1024 * 4) + reader.readAsArrayBuffer(slice) + }) +} + + +export async function importA1111(graph: lazyComfyAny, parameters: lazyComfyAny) { + const p = parameters.lastIndexOf('\nSteps:') + if (p > -1) { + const embeddings = await api.getEmbeddings() + const opts = parameters + .substr(p) + .split('\n')[1] + .match( + new RegExp('\\s*([^:]+:\\s*([^"\\{].*?|".*?"|\\{.*?\\}))\\s*(,|$)', 'g') + ) + .reduce((p: lazyComfyAny, n: lazyComfyAny) => { + const s = n.split(':') + if (s[1].endsWith(',')) { + s[1] = s[1].substr(0, s[1].length - 1) + } + p[s[0].trim().toLowerCase()] = s[1].trim() + return p + }, {}) + const p2 = parameters.lastIndexOf('\nNegative prompt:', p) + if (p2 > -1) { + let positive = parameters.substr(0, p2).trim() + let negative = parameters.substring(p2 + 18, p).trim() + + const ckptNode = LiteGraph.createNode('CheckpointLoaderSimple')! + const clipSkipNode = LiteGraph.createNode('CLIPSetLastLayer')! + const positiveNode = LiteGraph.createNode('CLIPTextEncode')! + const negativeNode = LiteGraph.createNode('CLIPTextEncode')! + const samplerNode = LiteGraph.createNode('KSampler')! + const imageNode = LiteGraph.createNode('EmptyLatentImage')! + const vaeNode = LiteGraph.createNode('VAEDecode')! + const vaeLoaderNode = LiteGraph.createNode('VAELoader')! + const saveNode = LiteGraph.createNode('SaveImage')! + let hrSamplerNode = null as lazyComfyAny + let hrSteps = null + + const ceil64 = (v: lazyComfyAny) => Math.ceil(v / 64) * 64 + + const getWidget = (node: lazyComfyAny, name: lazyComfyAny) => { + return node.widgets.find((w: lazyComfyAny) => w.name === name) + } + + const setWidgetValue = (node: lazyComfyAny, name: lazyComfyAny, value: lazyComfyAny, isOptionPrefix?: lazyComfyAny) => { + const w = getWidget(node, name) + if (isOptionPrefix) { + const o = w.options.values.find((w: lazyComfyAny) => w.startsWith(value)) + if (o) { + w.value = o + } else { + console.warn(`Unknown value '${value}' for widget '${name}'`, node) + w.value = value + } + } else { + w.value = value + } + } + + const createLoraNodes = (clipNode:lazyComfyAny, text: lazyComfyAny, prevClip: lazyComfyAny, prevModel: lazyComfyAny) => { + const loras = [] as lazyComfyAny + text = text.replace(/]+)>/g, function (m: lazyComfyAny, c: lazyComfyAny) { + const s = c.split(':') + const weight = parseFloat(s[1]) + if (isNaN(weight)) { + console.warn('Invalid LORA', m) + } else { + loras.push({ name: s[0], weight }) + } + return '' + }) + + for (const l of loras) { + const loraNode = LiteGraph.createNode('LoraLoader') + graph.add(loraNode) + setWidgetValue(loraNode, 'lora_name', l.name, true) + setWidgetValue(loraNode, 'strength_model', l.weight) + setWidgetValue(loraNode, 'strength_clip', l.weight) + prevModel.node.connect(prevModel.index, loraNode, 0) + prevClip.node.connect(prevClip.index, loraNode, 1) + prevModel = { node: loraNode, index: 0 } + prevClip = { node: loraNode, index: 1 } + } + + prevClip.node.connect(1, clipNode, 0) + prevModel.node.connect(0, samplerNode, 0) + if (hrSamplerNode) { + prevModel.node.connect(0, hrSamplerNode, 0) + } + + return { text, prevModel, prevClip } + } + + const replaceEmbeddings = (text: lazyComfyAny) => { + if (!embeddings.length) return text + return text.replaceAll( + new RegExp( + '\\b(' + + embeddings + .map((e: lazyComfyAny) => e.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')) + .join('\\b|\\b') + + ')\\b', + 'ig' + ), + 'embedding:$1' + ) + } + + const popOpt = (name: lazyComfyAny) => { + const v = opts[name] + delete opts[name] + return v + } + + graph.clear() + graph.add(ckptNode) + graph.add(clipSkipNode) + graph.add(positiveNode) + graph.add(negativeNode) + graph.add(samplerNode) + graph.add(imageNode) + graph.add(vaeNode) + graph.add(vaeLoaderNode) + graph.add(saveNode) + + ckptNode.connect(1, clipSkipNode, 0) + clipSkipNode.connect(0, positiveNode, 0) + clipSkipNode.connect(0, negativeNode, 0) + ckptNode.connect(0, samplerNode, 0) + positiveNode.connect(0, samplerNode, 1) + negativeNode.connect(0, samplerNode, 2) + imageNode.connect(0, samplerNode, 3) + vaeNode.connect(0, saveNode, 0) + samplerNode.connect(0, vaeNode, 0) + vaeLoaderNode.connect(0, vaeNode, 1) + + const handlers = { + model(v: lazyComfyAny) { + setWidgetValue(ckptNode, 'ckpt_name', v, true) + }, + vae(v: lazyComfyAny) { + setWidgetValue(vaeLoaderNode, 'vae_name', v, true) + }, + 'cfg scale'(v: lazyComfyAny) { + setWidgetValue(samplerNode, 'cfg', +v) + }, + 'clip skip'(v: lazyComfyAny) { + setWidgetValue(clipSkipNode, 'stop_at_clip_layer', -v) + }, + sampler(v: lazyComfyAny) { + let name = v.toLowerCase().replace('++', 'pp').replaceAll(' ', '_') + if (name.includes('karras')) { + name = name.replace('karras', '').replace(/_+$/, '') + setWidgetValue(samplerNode, 'scheduler', 'karras') + } else { + setWidgetValue(samplerNode, 'scheduler', 'normal') + } + const w = getWidget(samplerNode, 'sampler_name') + const o = w.options.values.find( + (w: lazyComfyAny) => w === name || w === 'sample_' + name + ) + if (o) { + setWidgetValue(samplerNode, 'sampler_name', o) + } + }, + size(v: lazyComfyAny) { + const wxh = v.split('x') + const w = ceil64(+wxh[0]) + const h = ceil64(+wxh[1]) + const hrUp = popOpt('hires upscale') + const hrSz = popOpt('hires resize') + hrSteps = popOpt('hires steps') + let hrMethod = popOpt('hires upscaler') + + setWidgetValue(imageNode, 'width', w) + setWidgetValue(imageNode, 'height', h) + + if (hrUp || hrSz) { + let uw, uh + if (hrUp) { + uw = w * hrUp + uh = h * hrUp + } else { + const s = hrSz.split('x') + uw = +s[0] + uh = +s[1] + } + + let upscaleNode + let latentNode + + if (hrMethod.startsWith('Latent')) { + latentNode = upscaleNode = LiteGraph.createNode('LatentUpscale')! + graph.add(upscaleNode) + samplerNode.connect(0, upscaleNode, 0) + + switch (hrMethod) { + case 'Latent (nearest-exact)': + hrMethod = 'nearest-exact' + break + } + setWidgetValue(upscaleNode, 'upscale_method', hrMethod, true) + } else { + const decode = LiteGraph.createNode('VAEDecodeTiled')! + graph.add(decode) + samplerNode.connect(0, decode, 0) + vaeLoaderNode.connect(0, decode, 1) + + const upscaleLoaderNode = LiteGraph.createNode('UpscaleModelLoader')! + graph.add(upscaleLoaderNode) + setWidgetValue(upscaleLoaderNode, 'model_name', hrMethod, true) + + const modelUpscaleNode = LiteGraph.createNode( + 'ImageUpscaleWithModel' + )! + graph.add(modelUpscaleNode) + decode.connect(0, modelUpscaleNode, 1) + upscaleLoaderNode.connect(0, modelUpscaleNode, 0) + + upscaleNode = LiteGraph.createNode('ImageScale')! + graph.add(upscaleNode) + modelUpscaleNode.connect(0, upscaleNode, 0) + + const vaeEncodeNode = (latentNode = + LiteGraph.createNode('VAEEncodeTiled')!)! + graph.add(vaeEncodeNode) + upscaleNode.connect(0, vaeEncodeNode, 0) + vaeLoaderNode.connect(0, vaeEncodeNode, 1) + } + + setWidgetValue(upscaleNode, 'width', ceil64(uw)) + setWidgetValue(upscaleNode, 'height', ceil64(uh)) + + hrSamplerNode = LiteGraph.createNode('KSampler')! + graph.add(hrSamplerNode) + ckptNode.connect(0, hrSamplerNode, 0) + positiveNode.connect(0, hrSamplerNode, 1) + negativeNode.connect(0, hrSamplerNode, 2) + latentNode.connect(0, hrSamplerNode, 3) + hrSamplerNode.connect(0, vaeNode, 0) + } + }, + steps(v: lazyComfyAny) { + setWidgetValue(samplerNode, 'steps', +v) + }, + seed(v: lazyComfyAny) { + setWidgetValue(samplerNode, 'seed', +v) + } + } + + for (const opt in opts) { + if (opt in handlers) { + ((handlers as lazyComfyAny)[opt] as lazyComfyAny)(popOpt(opt)) + } + } + + if (hrSamplerNode) { + setWidgetValue( + hrSamplerNode, + 'steps', + hrSteps ? +hrSteps : getWidget(samplerNode, 'steps').value + ) + setWidgetValue( + hrSamplerNode, + 'cfg', + getWidget(samplerNode, 'cfg').value + ) + setWidgetValue( + hrSamplerNode, + 'scheduler', + getWidget(samplerNode, 'scheduler').value + ) + setWidgetValue( + hrSamplerNode, + 'sampler_name', + getWidget(samplerNode, 'sampler_name').value + ) + setWidgetValue( + hrSamplerNode, + 'denoise', + +(popOpt('denoising strength') || '1') + ) + } + + let n = createLoraNodes( + positiveNode, + positive, + { node: clipSkipNode, index: 0 }, + { node: ckptNode, index: 0 } + ) + positive = n.text + n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel) + negative = n.text + + setWidgetValue(positiveNode, 'text', replaceEmbeddings(positive)) + setWidgetValue(negativeNode, 'text', replaceEmbeddings(negative)) + + graph.arrange() + + for (const opt of [ + 'model hash', + 'ensd', + 'version', + 'vae hash', + 'ti hashes', + 'lora hashes', + 'hashes' + ]) { + delete opts[opt] + } + + console.warn('Unhandled parameters:', opts) + } + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/components/base_custom_element.ts b/custom_nodes/rgthree-comfy/src_web/common/components/base_custom_element.ts new file mode 100644 index 00000000..659bee59 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/components/base_custom_element.ts @@ -0,0 +1,252 @@ +import {$el, getActionEls} from "rgthree/common/utils_dom.js"; +import {bind, register} from "../utils_templates"; + +const CSS_STYLE_SHEETS = new Map(); +const CSS_STYLE_SHEETS_ADDED = new Map(); +const HTML_TEMPLATE_FILES = new Map(); + +function getCommonPath(name: string, extension: string) { + return `rgthree/common/components/${name.replace("rgthree-", "").replace(/\-/g, "_")}.${extension}`; +} + +/** + * Fetches the stylesheet for the component, matched by the element name (minus the "rgthree-" + * prefix). + */ +async function getStyleSheet(name: string, markupOrPath: string) { + if (markupOrPath.includes("{")) { + return markupOrPath; + } + if (!CSS_STYLE_SHEETS.has(name)) { + try { + const path = markupOrPath || getCommonPath(name, "css"); + const text = await (await fetch(path)).text(); + CSS_STYLE_SHEETS.set(name, text); + } catch (e) { + // alert("Error loading rgthree custom component css."); + } + } + return CSS_STYLE_SHEETS.get(name)!; +} + +/** + * Adds the stylesheet to the page, once. + */ +async function addStyleSheet(name: string, markupOrPath: string) { + if (markupOrPath.includes("{")) { + throw new Error("Page-level stylesheets should be passed a path."); + } + if (!CSS_STYLE_SHEETS_ADDED.has(name)) { + const link = document.createElement("link"); + link.rel = "stylesheet"; + link.href = markupOrPath; + document.head.appendChild(link); + CSS_STYLE_SHEETS_ADDED.set(name, link); + } + return CSS_STYLE_SHEETS_ADDED.get(name)!; +} + +/** + * Fetches the stylesheet for the component, matched by the element name (minus the "rgthree-" + * prefix). + */ +async function getTemplateMarkup(name: string, markupOrPath: string) { + if (markupOrPath.includes("(): T { + if (this.NAME === "rgthree-override") { + throw new Error("Must override component NAME"); + } + if (!window.customElements.get(this.NAME)) { + window.customElements.define(this.NAME, this as unknown as CustomElementConstructor); + } + return document.createElement(this.NAME) as T; + } + + protected ctor = this.constructor as typeof RgthreeCustomElement; + protected hasBeenConnected: boolean = false; + protected connected: boolean = false; + protected root!: ShadowRoot | HTMLElement; + protected readonly templates = new Map(); + protected firstConnectedPromiseResolver!: Function; + protected firstConnectedPromise = new Promise( + (resolve) => (this.firstConnectedPromiseResolver = resolve), + ); + + onFirstConnected(): void { + // Optionally overridden. + } + onReconnected(): void { + // Optionally overridden. + } + onConnected(): void { + // Optionally overridden. + } + onDisconnected(): void { + // Optionally overridden. + } + onAction(action: string, e?: Event): void { + console.log("onAction", action, e); + // Optionally overridden. + } + + getElement(query: string) { + const el = this.querySelector(query); + if (!el) { + throw new Error("No element found for query: " + query); + } + return el as E; + } + + private onActionInternal(action: string, e?: Event): void { + if (typeof (this as any)[action] === "function") { + (this as any)[action](e); + } else { + this.onAction(action, e); + } + } + + private onConnectedInternal(): void { + this.connectActionElements(); + this.onConnected(); + } + + private onDisconnectedInternal(): void { + this.disconnectActionElements(); + this.onDisconnected(); + } + + async connectedCallback() { + const elementName = this.ctor.NAME; + const wasConnected = this.connected; + if (!wasConnected) { + this.connected = true; + } + if (!this.hasBeenConnected) { + const [stylesheet, markup] = await Promise.all([ + this.ctor.USE_SHADOW + ? getStyleSheet(elementName, this.ctor.CSS) + : addStyleSheet(elementName, this.ctor.CSS), + getTemplateMarkup(elementName, this.ctor.TEMPLATES), + ]); + + if (markup) { + const temp = $el("div"); + const templatesMarkup = markup.match(//gm) || []; + for (const markup of templatesMarkup) { + temp.innerHTML = markup; + const template = temp.children[0]; + if (!(template instanceof HTMLTemplateElement)) { + throw new Error("Not a template element."); + } + let id = template.getAttribute("id"); + if (!id) { + id = this.ctor.NAME; + // throw new Error("Not template id."); + } + this.templates.set(id, template); + } + } + + // If we're using a shadow, then it's our root as a ShadowRoot. If we're not, then the root is + // the custom element itself. This allows easy binding on "this.root" but it also means if we + // want to set an atrtibute or otherwise access the actual custom element, we should use + // "this" to be compatible with both. + if (this.ctor.USE_SHADOW) { + this.root = this.attachShadow({mode: "open"}); + if (typeof stylesheet === "string") { + const sheet = new CSSStyleSheet(); + sheet.replaceSync(stylesheet); + this.root.adoptedStyleSheets = [sheet]; + } + } else { + this.root = this; + } + + let template: HTMLTemplateElement | undefined; + if (this.templates.has(elementName)) { + template = this.templates.get(elementName); + } else if (this.templates.has(elementName.replace("rgthree-", ""))) { + template = this.templates.get(elementName.replace("rgthree-", "")); + } + if (template) { + this.root.appendChild(template.content.cloneNode(true)); + for (const name of template.getAttributeNames()) { + if (name != "id" && template.getAttribute(name)) { + this.setAttribute(name, template.getAttribute(name)!); + } + } + } + + this.onFirstConnected(); + this.hasBeenConnected = true; + this.firstConnectedPromiseResolver(); + } else { + this.onReconnected(); + } + this.onConnectedInternal(); + } + + disconnectedCallback() { + this.connected = false; + this.onDisconnected(); + } + + private readonly eventElements = new Map(); + + private connectActionElements() { + const data = getActionEls(this); + for (const dataItem of Object.values(data)) { + const mapItem = this.eventElements.get(dataItem.el) || {}; + for (const [event, action] of Object.entries(dataItem.actions)) { + if (mapItem[event]) { + console.warn(`Element already has an event for ${event}`); + continue; + } + mapItem[event] = (e: Event) => { + this.onActionInternal(action, e); + }; + dataItem.el.addEventListener(event as keyof ElementEventMap, mapItem[event]); + } + } + } + + private disconnectActionElements() { + for (const [el, eventData] of this.eventElements.entries()) { + for (const [event, fn] of Object.entries(eventData)) { + el.removeEventListener(event, fn); + } + } + } + + async bindWhenConnected(data: any, el?: HTMLElement | ShadowRoot) { + await this.firstConnectedPromise; + this.bind(data, el); + } + + bind(data: any, el?: HTMLElement | ShadowRoot) { + bind(el || this.root, data); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/buttons.scss b/custom_nodes/rgthree-comfy/src_web/common/css/buttons.scss new file mode 100644 index 00000000..569d380a --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/css/buttons.scss @@ -0,0 +1,156 @@ +:not(#fakeid) .rgthree-button-reset { + position: relative; + appearance: none; + cursor: pointer; + border: 0; + background: transparent; + color: inherit; + padding: 0; + margin: 0; +} + +$borderRadius: 0.33rem; + +:not(#fakeid) .rgthree-button { + --padding-top: 7px; + --padding-bottom: 9px; + --padding-x: 16px; + position: relative; + cursor: pointer; + border: 0; + border-radius: $borderRadius; + background: rgba(0, 0, 0, 0.5); + color: white; + font-family: system-ui, sans-serif; + font-size: calc(16rem / 16); + line-height: 1; + white-space: nowrap; + text-decoration: none; + margin: 0.25rem; + box-shadow: 0px 0px 2px rgb(0, 0, 0); + background: #212121; + transition: all 0.1s ease-in-out; + padding: var(--padding-top) var(--padding-x) var(--padding-bottom); + display: inline-flex; + flex-direction: row; + align-items: center; + justify-content: center; + + &::before, + &::after { + content: ""; + display: block; + position: absolute; + border-radius: $borderRadius; + left: 0; + top: 0; + width: 100%; + height: 100%; + box-shadow: + inset 1px 1px 0px rgba(255, 255, 255, 0.12), + inset -1px -1px 0px rgba(0, 0, 0, 0.75); + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)); + mix-blend-mode: screen; + } + + &::after { + mix-blend-mode: multiply; + } + + &:hover { + background: #303030; + } + &:active { + box-shadow: 0px 0px 0px rgba(0, 0, 0, 0); + background: #121212; + padding: calc(var(--padding-top) + 1px) calc(var(--padding-x) - 1px) + calc(var(--padding-bottom) - 1px) calc(var(--padding-x) + 1px); + } + + &:active::before, + &:active::after { + box-shadow: + 1px 1px 0px rgba(255, 255, 255, 0.15), + inset 1px 1px 0px rgba(0, 0, 0, 0.5), + inset 1px 3px 5px rgba(0, 0, 0, 0.33); + } + + &.-blue { + background: #346599 !important; + } + &.-blue:hover { + background: #3b77b8 !important; + } + &.-blue:active { + background: #1d5086 !important; + } + + &.-green { + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #14580b; + } + &.-green:hover { + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #1a6d0f; + } + &.-green:active { + background: linear-gradient(to bottom, rgba(0, 0, 0, 0.15), rgba(255, 255, 255, 0.06)), #0f3f09; + } + + &[disabled] { + box-shadow: none; + background: #666 !important; + color: #aaa; + pointer-events: none; + } + &[disabled]::before, + &[disabled]::after { + display: none; + } +} + +:not(#fakeid) .rgthree-comfybar-top-button-group { + font-size: 0; + flex: 1 1 auto; + display: flex; + align-items: stretch; + + .rgthree-comfybar-top-button { + margin: 0; + flex: 1 1; + height: 36px; + padding: 0 12px; + border-radius: 0; + background: var(--p-button-secondary-background); + color: var(--p-button-secondary-color); + + &.-primary { + background: var(--p-button-primary-background); + color: var(--p-button-primary-color); + } + + &::before, + &::after { + border-radius: 0; + } + + + svg { + fill: currentColor; + width: 28px; + height: 28px; + } + } + + .rgthree-comfybar-top-button:first-of-type, + .rgthree-comfybar-top-button:first-of-type::before, + .rgthree-comfybar-top-button:first-of-type::after { + border-top-left-radius: 0.33rem; + border-bottom-left-radius: 0.33rem; + + } + .rgthree-comfybar-top-button:last-of-type, + .rgthree-comfybar-top-button:last-of-type::before, + .rgthree-comfybar-top-button:last-of-type::after { + border-top-right-radius: 0.33rem; + border-bottom-right-radius: 0.33rem; + } +} \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/dialog.scss b/custom_nodes/rgthree-comfy/src_web/common/css/dialog.scss new file mode 100644 index 00000000..c47e2f9d --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/css/dialog.scss @@ -0,0 +1,129 @@ + +.rgthree-dialog { + outline: 0; + border: 0; + border-radius: 6px; + background: #414141; + color: #fff; + box-shadow: + inset 1px 1px 0px rgba(255, 255, 255, 0.05), + inset -1px -1px 0px rgba(0, 0, 0, 0.5), + 2px 2px 20px rgb(0, 0, 0); + max-width: 800px; + box-sizing: border-box; + font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif; + font-size: 1rem; + padding: 0; + max-height: calc(100% - 32px); + + *, *::before, *::after { + box-sizing: inherit; + } +} + +.rgthree-dialog-container { + // padding: 16px; + > * { + padding: 8px 16px; + + &:first-child { + padding-top: 16px; + } + &:last-child { + padding-bottom: 16px; + } + } +} + +.rgthree-dialog.-iconed::after { + content: ""; + font-size: 276px; + position: absolute; + right: 0px; + bottom: 0px; + opacity: 0.15; + display: block; + width: 237px; + overflow: hidden; + height: 186px; + line-height: 1; + pointer-events: none; + z-index: -1; +} +.rgthree-dialog.-iconed.-help::after { + content: "πŸ›Ÿ"; +} +.rgthree-dialog.-iconed.-settings::after { + content: "βš™οΈ"; +} + +@media (max-width: 832px) { + .rgthree-dialog { + max-width: calc(100% - 32px); + } +} + +.rgthree-dialog-container-title { + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} +.rgthree-dialog-container-title > svg:first-child { + width: 36px; + height: 36px; + margin-right: 16px; +} +.rgthree-dialog-container-title h2 { + font-size: calc(22rem / 16); + margin: 0; + font-weight: bold; +} + +.rgthree-dialog-container-title h2 small { + font-size: calc(13rem / 16); + font-weight: normal; + opacity: 0.75; +} + +.rgthree-dialog-container-content { + overflow: auto; + max-height: calc(100vh - 200px); /* Arbitrary height to copensate for margin, title, and footer.*/ +} +.rgthree-dialog-container-content p { + font-size: calc(13rem / 16); + margin-top: 0; +} + +.rgthree-dialog-container-content ul li p { + margin-bottom: 4px; +} + +.rgthree-dialog-container-content ul li p + p { + margin-top: 0.5em; +} + +.rgthree-dialog-container-content ul li ul { + margin-top: 0.5em; + margin-bottom: 1em; +} + +.rgthree-dialog-container-content p code { + display: inline-block; + padding: 2px 4px; + margin: 0px 2px; + border: 1px solid rgba(255, 255, 255, 0.25); + border-radius: 3px; + background: rgba(255, 255, 255, 0.1); +} + +.rgthree-dialog-container-footer { + display: flex; + align-items: center; + justify-content: center; +} + +body.rgthree-dialog-open > *:not(.rgthree-dialog):not(.rgthree-top-messages-container) { + filter: blur(5px); +} + diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/dialog_lora_chooser.scss b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_lora_chooser.scss new file mode 100644 index 00000000..373152d1 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_lora_chooser.scss @@ -0,0 +1,161 @@ + +.rgthree-lora-chooser-dialog { + max-width: 100%; + + + .rgthree-dialog-container-title { + display: flex; + flex-direction: column; + } + .rgthree-dialog-container-title h2 { + display: flex; + width: 100%; + } + .rgthree-lora-chooser-search { + margin-left: auto; + border-radius: 50px; + width: 50%; + max-width: 170px; + padding: 2px 8px; + } + + .rgthree-lora-chooser-header { + display: flex; + flex-direction: row; + } + + .rgthree-lora-filters-container { + svg {width: 16px; height: 16px;} + } + + .rgthree-dialog-container-content { + width: 80vw; + height: 80vh; + } + + .rgthree-button-reset { + width: 32px; + height: 32px; + > svg {width: 100%; height: 100%;} + + } + + ul.rgthree-lora-chooser-list { + list-style: none; + margin: 0; + padding: 0; + position: relative; + display: flex; + flex-direction: row; + flex-wrap: wrap; + align-items: start; + justify-content: space-around; + + > li { + position: relative; + flex: 0 0 auto; + width: 170px; + max-width: 100%; + margin: 8px 8px 16px; + + label { + position: absolute; + display: block; + inset: 0; + z-index: 3; + cursor: pointer; + } + input[type="checkbox"] { + position: absolute; + right: 8px; + top: 8px; + margin: 0; + z-index: 2; + appearance: none; + background-color: #fff; + width: 48px; + height: 48px; + border-radius: 4px; + border: 1px solid rgba(120,120,120,1); + opacity: 0; + transition: opacity 0.15s ease-in-out; + + &:checked { + opacity: 1; + background: #0060df; + &::before { + content: ""; + display: block; + width: 100%; + height: 100%; + box-shadow: inset 100px 100px #fff; + clip-path: polygon(40.13% 68.39%, 23.05% 51.31%, 17.83% 48.26%, 12.61% 49.57%, 9.57% 53.04%, 8% 60%, 34.13% 85.87%, 39.82% 89.57%, 45.88% 86.73%, 90.66% 32.39%, 88.92% 26.1%, 83.03% 22.17%, 76.94% 22.62%) + } + } + } + + + figure { + position: relative; + display: block; + margin: 0 0 8px; + padding: 0; + border: 1px solid rgba(120, 120, 120, .8); + background: rgba(120, 120, 120, .5); + width: 100%; + padding-top: 120%; + transition: box-shadow 0.15s ease-in-out; + opacity: 0.75; + &::after { + content: ''; + display: block; + position: absolute; + inset: 0; + } + + &:empty { + &::before { + content: 'No image.'; + color: rgba(200, 200, 200, .8); + position: absolute; + display: block; + inset: 0; + font-size: 1.2em; + text-align: center; + display: flex; + align-items: center; + justify-content: center; + } + } + + > img, > video { + position: absolute; + width: 100%; + height: 100%; + top: 0; + left: 0; + object-fit: cover; + } + } + div { + word-wrap: break-word; + font-size: 0.8rem; + opacity: 0.75; + } + + &:hover figure::after{ + box-shadow: 0px 2px 6px rgba(0,0,0,0.75); + } + :checked ~ figure::after { + box-shadow: 0 0 5px #fff, 0px 0px 15px rgba(49, 131, 255, 0.88), inset 0 0 3px #fff, inset 0px 0px 5px rgba(49, 131, 255, 0.88) + } + + &:hover *, + &:hover input[type="checkbox"], + :checked ~ * { + opacity: 1 + } + + } + } +} \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/dialog_model_info.scss b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_model_info.scss new file mode 100644 index 00000000..247cdc08 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_model_info.scss @@ -0,0 +1,400 @@ + +.rgthree-info-dialog { + + width: 90vw; + max-width: 960px; + + .rgthree-info-area { + list-style: none; + padding: 0; + margin: 0; + display: flex; + + > li { + display: inline-flex; + margin: 0; + vertical-align: top; + + + li { + margin-left: 6px; + } + &:not(.-link) + li.-link { + margin-left: auto; + } + + &.rgthree-info-tag > * { + min-height: 24px; + border-radius: 4px; + line-height: 1; + color: rgba(255,255,255,0.85); + background: rgb(69, 92, 85);; + font-size: 14px; + font-weight: bold; + text-decoration: none; + display: flex; + height: 1.6em; + padding-left: .5em; + padding-right: .5em; + padding-bottom: .1em; + align-content: center; + justify-content: center; + align-items: center; + box-shadow: inset 0px 0px 0 1px rgba(0, 0, 0, 0.5); + + > svg { + width: 16px; + height: 16px; + + &:last-child { + margin-left: .5em; + } + } + + + &[href] { + box-shadow: inset 0px 1px 0px rgba(255,255,255,0.25), inset 0px -1px 0px rgba(0,0,0,0.66); + } + + &:empty { + display: none; + } + } + + // &.-civitai > * { + // color: #ddd; + // background: #1b65aa; + // transition: all 0.15s ease-in-out; + // &:hover { + // color: #fff; + // border-color: #1971c2; + // background: #1971c2; + // } + // } + &.-type > * { + background: rgb(73, 54, 94); + color: rgb(228, 209, 248); + } + + &.rgthree-info-menu { + margin-left: auto; + + :not(#fakeid) & .rgthree-button { + margin: 0; + min-height: 24px; + padding: 0 12px; + } + + svg { + width: 16px; + height: 16px; + } + } + } + } + + .rgthree-info-table { + border-collapse: collapse; + margin: 16px 0px; + width: 100%; + font-size: 12px; + + tr.editable button { + display: flex; + width: 28px; + height: 28px; + align-items: center; + justify-content: center; + + svg + svg {display: none;} + } + tr.editable.-rgthree-editing button { + svg {display: none;} + svg + svg {display: inline-block;} + } + + td { + position: relative; + border: 1px solid rgba(255,255,255,0.25); + padding: 0; + vertical-align: top; + + &:first-child { + background: rgba(255,255,255,0.075); + width: 10px; // Small, so it doesn't adjust. + > *:first-child { + white-space: nowrap; + padding-right: 32px; + } + + small { + display: block; + margin-top: 2px; + opacity: 0.75; + + > [data-action] { + text-decoration: underline; + cursor: pointer; + &:hover { + text-decoration: none; + } + } + } + } + + a, a:hover, a:visited { + color: inherit; + } + + svg { + width: 1.3333em; + height: 1.3333em; + vertical-align: -0.285em; + + &.logo-civitai { + margin-right: 0.3333em; + } + } + + > *:first-child { + display: block; + padding: 6px 10px; + } + + > input, > textarea{ + padding: 5px 10px; + border: 0; + box-shadow: inset 1px 1px 5px 0px rgba(0,0,0,0.5); + font: inherit; + appearance: none; + background: #fff; + color: #121212; + resize: vertical; + + &:only-child { + width: 100%; + } + } + + :not(#fakeid) & .rgthree-button[data-action="fetch-civitai"] { + font-size: inherit; + padding: 6px 16px; + margin: 2px; + } + } + + tr[data-field-name="userNote"] td > span:first-child { + white-space: pre; + } + + tr.rgthree-info-table-break-row td { + border: 0; + background: transparent; + padding: 12px 4px 4px; + font-size: 1.2em; + + > small { + font-style: italic; + opacity: 0.66; + } + + &:empty { + padding: 4px; + } + } + + td .-help { + border: 1px solid currentColor; + position: absolute; + right: 5px; + top: 6px; + line-height: 1; + font-size: 11px; + width: 12px; + height: 12px; + border-radius: 8px; + display: flex; + align-content: center; + justify-content: center; + cursor: help; + &::before { + content: '?'; + } + + } + + td > ul.rgthree-info-trained-words-list { + list-style: none; + padding: 2px 8px; + margin: 0; + display: flex; + flex-direction: row; + flex-wrap: wrap; + max-height: 15vh; + overflow: auto; + + > li { + display: inline-flex; + margin: 2px; + vertical-align: top; + border-radius: 4px; + line-height: 1; + color: rgba(255,255,255,0.85); + background: rgb(73, 91, 106); + font-size: 1.2em; + font-weight: 600; + text-decoration: none; + display: flex; + height: 1.6em; + align-content: center; + justify-content: center; + align-items: center; + box-shadow: inset 0px 0px 0 1px rgba(0, 0, 0, 0.5); + cursor: pointer; + white-space: nowrap; + max-width: 183px; + + &:hover { + background: rgb(68, 109, 142); + } + + > svg { + width: auto; + height: 1.2em; + } + + > span { + padding-left: .5em; + padding-right: .5em; + padding-bottom: .1em; + text-overflow: ellipsis; + overflow: hidden; + } + + > small { + align-self: stretch; + display: flex; + align-items: center; + justify-content: center; + padding: 0 0.5em; + background: rgba(0,0,0,0.2); + } + + &.-rgthree-is-selected { + background: rgb(42, 126, 193); + } + } + } + } + + .rgthree-info-images { + list-style:none; + padding:0; + margin:0; + scroll-snap-type: x mandatory; + display:flex; + flex-direction:row; + overflow: auto; + + > li { + scroll-snap-align: start; + max-width: 90%; + flex: 0 0 auto; + display: flex; + align-items: center; + justify-content: center; + flex-direction: column; + overflow: hidden; + padding: 0; + margin: 6px; + font-size: 0; + position: relative; + + figure { + margin: 0; + position: static; + + video, img { + max-height: 45vh; + } + + figcaption { + position: absolute; + left: 0; + width: 100%; + bottom: 0; + padding: 12px; + font-size: 12px; + background: rgba(0,0,0,0.85); + opacity: 0; + transform: translateY(50px); + transition: all 0.25s ease-in-out; + + > span { + display: inline-block; + padding: 2px 4px; + margin: 2px; + border-radius: 2px; + border: 1px solid rgba(255,255,255,0.2); + word-break: break-word; + + label { + display: inline; + padding: 0; + margin: 0; + opacity: 0.5; + pointer-events: none; + user-select: none; + } + a { + color: inherit; + text-decoration: underline; + &:hover { + text-decoration: none; + } + + svg { + height: 10px; + margin-left: 4px; + fill: currentColor; + } + } + } + &:empty { + text-align: center; + + &::before { + content: 'No data.'; + } + } + } + } + + &:hover figure figcaption { + opacity: 1; + transform: translateY(0px); + } + + .rgthree-info-table { + width: calc(100% - 16px); + } + } + } + + .rgthree-info-civitai-link { + margin: 8px; + color: #eee; + + a, a:hover, a:visited { + color: inherit; + text-decoration: none; + } + + > svg { + width: 16px; + height: 16px; + margin-right: 8px; + } + } +} + + diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/menu.scss b/custom_nodes/rgthree-comfy/src_web/common/css/menu.scss new file mode 100644 index 00000000..31b661a1 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/css/menu.scss @@ -0,0 +1,122 @@ + + +.rgthree-menu { + list-style: none; + padding: 0; + margin: 0; + position: fixed; + z-index: 999999; + pointer-events: none; + opacity: 0; + transition: opacity 0.08s ease-in-out; + + color: #dde; + background-color: #111; + font-size: 12px; + box-shadow: 0 0 10px black !important; + + > li { + position: relative; + padding: 4px 6px; + z-index: 9999; + white-space: nowrap; + + &[role="button"] { + background-color: var(--comfy-menu-bg) !important; + color: var(--input-text); + cursor: pointer; + &:hover { + filter: brightness(155%); + } + } + } + + &[state^="measuring"] { + display: block; + opacity: 0; + } + &[state="open"] { + display: block; + opacity: 1; + pointer-events: all; + } +} + + +.rgthree-top-menu { + box-sizing: border-box; + white-space: nowrap; + background: var(--content-bg); + color: var(--content-fg); + display: flex; + flex-direction: column; + list-style: none; + padding: 0; + margin: 0; + * { + box-sizing: inherit; + } + + + > li:not(#fakeid) { + list-style: none; + padding: 0; + margin: 0; + position: relative; + z-index: 2; + + > button { + cursor: pointer; + padding: 8px 12px 8px 8px; + width: 100%; + text-align: start; + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; + + &:hover { + background-color: var(--comfy-input-bg); + } + + svg { + height: 16px; + width: auto; + margin-inline-end: 0.6em; + + &.github-star { + fill: rgb(227, 179, 65); + } + } + } + + &.rgthree-message { + // ComfyUI's code has strange behavior that that always puts the popupat to if its less than + // 30px... we'll force our message to be at least 32px tall so it won't do that unless it's + // actually on the bottom. + min-height: 32px; + > span { + padding: 8px 12px; + display: block; + width: 100%; + text-align: center; + font-style: italic; + font-size: 12px; + } + } + } + + &.-modal::after { + content: ''; + display: block; + position: fixed; + z-index: 1; + inset: 0; + background: #0001; + } +} + +body.rgthree-modal-menu-open > *:not(.rgthree-menu):not(.rgthree-top-messages-container) { + filter: blur(2px); +} + diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/pages_base.scss b/custom_nodes/rgthree-comfy/src_web/common/css/pages_base.scss new file mode 100644 index 00000000..09f292ed --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/css/pages_base.scss @@ -0,0 +1,69 @@ + +html, body { + +} +html { + font-size: 100%; + overflow-y: scroll; + -webkit-text-size-adjust: 100%; + -ms-text-size-adjust: 100%; + box-sizing: border-box; +} +*, *:before, *:after { + box-sizing: inherit +} + +:root { + --header-height: 56px; + --progress-height: 12px; +} + +button { + all: unset; +} + +.-bevel { + position: relative; +} +.-bevel::before { + content: ''; + position: absolute; + left: 0; + top: 0; + width: 100%; + height: 100%; + border: 1px solid red; + border-color: rgba(255,255,255,0.15) rgba(255,255,255,0.15) rgba(0,0,0,0.5) rgba(0,0,0,0.5); + z-index: 5; + pointer-events: none; +} + + +body { + background: #202020; + font-family: Arial, sans-serif; + font-size: calc(16 * 0.0625rem); + font-weight: 400; + margin: 0; + padding-top: calc(var(--header-height) + var(--progress-height)); + color: #ffffff; + display: flex; + flex-direction: column; + align-items: center; + justify-content: start; +} + +.app-header { + height: var( --header-height); + padding: 0; + position: fixed; + z-index: 99; + top: 0; + left: 0; + width: 100%; + background: #353535; + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/dialog.ts b/custom_nodes/rgthree-comfy/src_web/common/dialog.ts new file mode 100644 index 00000000..2f107106 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/dialog.ts @@ -0,0 +1,165 @@ +import type { LGraphNode, LGraphNodeConstructor } from "@comfyorg/frontend"; +import { createElement as $el, getClosestOrSelf, setAttributes } from "./utils_dom.js"; + +type RgthreeDialogButton = { + label: string; + className?: string; + closes?: boolean; + disabled?: boolean; + callback?: (e: PointerEvent | MouseEvent) => void; +}; + +export type RgthreeDialogOptions = { + content: string | HTMLElement | HTMLElement[]; + class?: string | string[]; + title?: string | HTMLElement | HTMLElement[]; + closeX?: boolean; + closeOnEsc?: boolean; + closeOnModalClick?: boolean; + closeButtonLabel?: string | boolean; + buttons?: RgthreeDialogButton[]; + onBeforeClose?: () => Promise | boolean; +}; + +/** + * A Dialog that shows content, and closes. + */ +export class RgthreeDialog extends EventTarget { + element: HTMLDialogElement; + contentElement: HTMLDivElement; + titleElement: HTMLDivElement; + options: RgthreeDialogOptions; + + constructor(options: RgthreeDialogOptions) { + super(); + this.options = options; + let container = $el("div.rgthree-dialog-container"); + this.element = $el("dialog", { + classes: ["rgthree-dialog", options.class || ""], + child: container, + parent: document.body, + events: { + click: (event: MouseEvent) => { + // Close the dialog if we've clicked outside of our container. The dialog modal will + // report itself as the dialog itself, so we use the inner container div (and CSS to + // remove default padding from the dialog element). + if ( + !this.element.open || + event.target === container || + getClosestOrSelf(event.target, `.rgthree-dialog-container`) === container + ) { + return; + } + return this.close(); + }, + }, + }); + this.element.addEventListener("close", (event) => { + this.onDialogElementClose(); + }); + + this.titleElement = $el("div.rgthree-dialog-container-title", { + parent: container, + children: !options.title + ? null + : options.title instanceof Element || Array.isArray(options.title) + ? options.title + : typeof options.title === "string" + ? !options.title.includes(" { + button.callback?.(e); + }, + }, + }); + } + + if (options.closeButtonLabel !== false) { + $el("button", { + text: options.closeButtonLabel || "Close", + className: "rgthree-button", + parent: footerEl, + events: { + click: (e: MouseEvent) => { + this.close(e); + }, + }, + }); + } + } + + setTitle(content: string | HTMLElement | HTMLElement[]) { + const title = + typeof content !== "string" || content.includes(" = {}, + ) { + const title = (node.type || node.title || "").replace( + /\s*\(rgthree\).*/, + " by rgthree", + ); + const options = Object.assign({}, opts, { + class: "-iconed -help", + title, + content, + }); + super(options); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/link_fixer.ts b/custom_nodes/rgthree-comfy/src_web/common/link_fixer.ts new file mode 100644 index 00000000..1ec3cf71 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/link_fixer.ts @@ -0,0 +1,529 @@ +import type { + LGraph, + LGraphNode, + LLink, + ISlotType, + INodeOutputSlot, + INodeInputSlot, + SerialisedLLinkArray, + LinkId, + ISerialisedNode, + ISerialisedGraph, + NodeId, +} from "@comfyorg/frontend"; + +/** + * The bad links data returned from either a fixer `check()`, or the results of a `fix()` call. + */ +export interface BadLinksData { + hasBadLinks: boolean; + graph: T; + patches: number; + deletes: number; +} + +enum IoDirection { + INPUT, + OUTPUT, +} + +/** + * Data interface that mimics a nodes `inputs` and `outputs` holding the _to be_ mutated node data + * during a check. + */ +interface PatchedNodeSlots { + [nodeId: string]: { + inputs?: {[slot: number]: number | null}; + outputs?: { + [slots: number]: { + links: number[]; + changes: {[linkId: number]: "ADD" | "REMOVE"}; + }; + }; + }; +} + +/** + * Link data derived from either a ISerialisedGraph or LGraph `links` property. + */ +interface LinkData { + id: LinkId; + origin_id: NodeId; + origin_slot: number; + target_id: NodeId; + target_slot: number; + type: ISlotType; +} + +/** + * Returns a list of links data for the given links type; either from an LGraph or SerializedGraph. + */ +function getLinksData( + links: ISerialisedGraph["links"] | LGraph["links"] | {[key: string]: LLink}, +): LinkData[] { + if (links instanceof Map) { + const data: LinkData[] = []; + for (const [key, llink] of links.entries()) { + if (!llink) continue; + data.push(llink); + } + return data; + } + // This is apparently marked deprecated in ComfyUI but who knows if we would get stale data in + // here that's not a map (handled above). Go ahead and handle it anyway. + if (!Array.isArray(links)) { + const data: LinkData[] = []; + for (const key in links) { + const llink = (links.hasOwnProperty(key) && links[key]) || null; + if (!llink) continue; + data.push(llink); + } + return data; + } + return links.map((link: SerialisedLLinkArray) => ({ + id: link[0], + origin_id: link[1], + origin_slot: link[2], + target_id: link[3], + target_slot: link[4], + type: link[5], + })); +} + +/** The instruction data for fixing a node's inputs or outputs. */ +interface WorkflowLinkFixerNodeInstruction { + node: ISerialisedNode | LGraphNode; + op: "REMOVE" | "ADD"; + dir: IoDirection; + slot: number; + linkId: number; + linkIdToUse: number | null; +} + +/** The instruction data for fixing a link from a workflow links. */ +interface WorkflowLinkFixerLinksInstruction { + op: "DELETE"; + linkId: number; + reason: string; +} + +type WorkflowLinkFixerInstruction = + | WorkflowLinkFixerNodeInstruction + | WorkflowLinkFixerLinksInstruction; + +/** + * The WorkflowLinkFixer for either ISerialisedGraph or a live LGraph. + * + * Use `WorkflowLinkFixer.create(graph: ISerialisedGraph | LGraph)` to create a new instance. + */ +export abstract class WorkflowLinkFixer< + G extends ISerialisedGraph | LGraph, + N extends ISerialisedNode | LGraphNode, +> { + silent: boolean = false; + checkedData: BadLinksData | null = null; + + protected logger: {log: (...args: any[]) => void} = console; + protected graph: G; + protected patchedNodeSlots: PatchedNodeSlots = {}; + protected instructions: WorkflowLinkFixerInstruction[] = []; + + /** + * Creates the WorkflowLinkFixer for the given graph type. + */ + static create(graph: ISerialisedGraph): WorkflowLinkFixerSerialized; + static create(graph: LGraph): WorkflowLinkFixerGraph; + static create( + graph: ISerialisedGraph | LGraph, + ): WorkflowLinkFixerSerialized | WorkflowLinkFixerGraph { + if (typeof (graph as LGraph).getNodeById === "function") { + return new WorkflowLinkFixerGraph(graph as LGraph); + } + return new WorkflowLinkFixerSerialized(graph as ISerialisedGraph); + } + + protected constructor(graph: G) { + this.graph = graph; + } + + abstract getNodeById(id: NodeId): N | null; + abstract deleteGraphLink(id: LinkId): true | string; + + /** + * Checks the current graph data for any bad links. + */ + check(force: boolean = false): BadLinksData { + if (this.checkedData && !force) { + return {...this.checkedData}; + } + this.instructions = []; + this.patchedNodeSlots = {}; + + const instructions: (WorkflowLinkFixerInstruction | null)[] = []; + + const links: LinkData[] = getLinksData(this.graph.links); + links.reverse(); + for (const link of links) { + if (!link) continue; + + const originNode = this.getNodeById(link.origin_id); + const originHasLink = () => + this.nodeHasLinkId(originNode!, IoDirection.OUTPUT, link.origin_slot, link.id); + const patchOrigin = (op: "ADD" | "REMOVE", id = link.id) => + this.getNodePatchInstruction(originNode!, IoDirection.OUTPUT, link.origin_slot, id, op); + + const targetNode = this.getNodeById(link.target_id); + const targetHasLink = () => + this.nodeHasLinkId(targetNode!, IoDirection.INPUT, link.target_slot, link.id); + const targetHasAnyLink = () => + this.nodeHasAnyLink(targetNode!, IoDirection.INPUT, link.target_slot); + const patchTarget = (op: "ADD" | "REMOVE", id = link.id) => + this.getNodePatchInstruction(targetNode!, IoDirection.INPUT, link.target_slot, id, op); + + const originLog = `origin(${link.origin_id}).outputs[${link.origin_slot}].links`; + const targetLog = `target(${link.target_id}).inputs[${link.target_slot}].link`; + + if (!originNode || !targetNode) { + if (!originNode && !targetNode) { + // This can fall through and continue; we remove it after this loop. + } else if (!originNode && targetNode) { + this.log( + `Link ${link.id} is funky... ` + + `origin ${link.origin_id} does not exist, but target ${link.target_id} does.`, + ); + if (targetHasLink()) { + this.log(` > [PATCH] ${targetLog} does have link, will remove the inputs' link first.`); + instructions.push(patchTarget("REMOVE", -1)); + } + } else if (!targetNode && originNode) { + this.log( + `Link ${link.id} is funky... ` + + `target ${link.target_id} does not exist, but origin ${link.origin_id} does.`, + ); + if (originHasLink()) { + this.log(` > [PATCH] Origin's links' has ${link.id}; will remove the link first.`); + instructions.push(patchOrigin("REMOVE")); + } + } + continue; + } + + if (targetHasLink() || originHasLink()) { + if (!originHasLink()) { + this.log( + `${link.id} is funky... ${originLog} does NOT contain it, but ${targetLog} does.`, + ); + this.log(` > [PATCH] Attempt a fix by adding this ${link.id} to ${originLog}.`); + instructions.push(patchOrigin("ADD")); + } else if (!targetHasLink()) { + this.log( + `${link.id} is funky... ${targetLog} is NOT correct (is ${ + targetNode.inputs?.[link.target_slot]?.link + }), but ${originLog} contains it`, + ); + if (!targetHasAnyLink()) { + this.log(` > [PATCH] ${targetLog} is not defined, will set to ${link.id}.`); + let instruction = patchTarget("ADD"); + if (!instruction) { + this.log( + ` > [PATCH] Nvm, ${targetLog} already patched. Removing ${link.id} from ${originLog}.`, + ); + instruction = patchOrigin("REMOVE"); + } + instructions.push(instruction); + } else { + this.log(` > [PATCH] ${targetLog} is defined, removing ${link.id} from ${originLog}.`); + instructions.push(patchOrigin("REMOVE")); + } + } + } + } + + // Now that we've cleaned up the inputs, outputs, run through it looking for dangling links., + for (let link of links) { + if (!link) continue; + const originNode = this.getNodeById(link.origin_id); + const targetNode = this.getNodeById(link.target_id); + if (!originNode && !targetNode) { + instructions.push({ + op: "DELETE", + linkId: link.id, + reason: `Both nodes #${link.origin_id} & #${link.target_id} are removed`, + }); + } + // Now that we've manipulated the linking, check again if they both exist. + if ( + (!originNode || + !this.nodeHasLinkId(originNode, IoDirection.OUTPUT, link.origin_slot, link.id)) && + (!targetNode || + !this.nodeHasLinkId(targetNode, IoDirection.INPUT, link.target_slot, link.id)) + ) { + instructions.push({ + op: "DELETE", + linkId: link.id, + reason: + `both origin node #${link.origin_id} ` + + `${!originNode ? "is removed" : `is missing link id output slot ${link.origin_slot}`}` + + `and target node #${link.target_id} ` + + `${!targetNode ? "is removed" : `is missing link id input slot ${link.target_slot}`}.`, + }); + continue; + } + } + + this.instructions = instructions.filter((i) => !!i); + this.checkedData = { + hasBadLinks: !!this.instructions.length, + graph: this.graph, + patches: this.instructions.filter((i) => !!(i as WorkflowLinkFixerNodeInstruction).node) + .length, + deletes: this.instructions.filter((i) => i.op === "DELETE").length, + }; + return {...this.checkedData}; + } + + /** + * Fixes a checked graph by running through the instructions generated during the check run. Also + * double-checks for inconsistencies after the fix, recursively calling itself up to five times + * before giving up. + */ + fix(force: boolean = false, times?: number): BadLinksData { + if (!this.checkedData || force) { + this.check(force); + } + let patches = 0; + let deletes = 0; + for (const instruction of this.instructions) { + if ((instruction as WorkflowLinkFixerNodeInstruction).node) { + let {node, slot, linkIdToUse, dir, op} = instruction as WorkflowLinkFixerNodeInstruction; + if (dir == IoDirection.INPUT) { + node.inputs = node.inputs || []; + const old = node.inputs[slot]?.link; + node.inputs[slot] = node.inputs[slot] || ({} as INodeInputSlot); + node.inputs[slot].link = linkIdToUse; + this.log(`Node #${node.id}: Set link ${linkIdToUse} to input slot ${slot} (was ${old})`); + } else if (op === "ADD" && linkIdToUse != null) { + node.outputs = node.outputs || []; + node.outputs[slot] = node.outputs[slot] || ({} as INodeOutputSlot); + node.outputs[slot].links = node.outputs[slot].links || []; + node.outputs[slot].links.push(linkIdToUse); + this.log(`Node #${node.id}: Add link ${linkIdToUse} to output slot #${slot}`); + } else if (op === "REMOVE" && linkIdToUse != null) { + // We should never not have this data since the check call would have found it to be + // removed, but we can be safe and appease TS compiler at the same time. + if (node.outputs?.[slot]?.links?.length === undefined) { + this.log( + `Node #${node.id}: Couldn't remove link ${linkIdToUse} from output slot #${slot}` + + ` because it didn't exist.`, + ); + } else { + let linkIdIndex = node.outputs![slot].links.indexOf(linkIdToUse); + node.outputs[slot].links.splice(linkIdIndex, 1); + this.log(`Node #${node.id}: Remove link ${linkIdToUse} from output slot #${slot}`); + } + } else { + throw new Error("Unhandled Node Instruction"); + } + patches++; + } else if (instruction.op === "DELETE") { + const wasDeleted = this.deleteGraphLink(instruction.linkId); + if (wasDeleted === true) { + this.log(`Link #${instruction.linkId}: Removed workflow link b/c ${instruction.reason}`); + } else { + this.log(`Error Link #${instruction.linkId} was not removed!`); + } + deletes += wasDeleted ? 1 : 0; + } else { + throw new Error("Unhandled Instruction"); + } + } + + const newCheck = this.check(force); + times = times == null ? 5 : times; + let newFix = null; + // If we still have bad links, then recurse (up to five times). + if (newCheck.hasBadLinks && times > 0) { + newFix = this.fix(true, times - 1); + } + + return { + hasBadLinks: newFix?.hasBadLinks ?? newCheck.hasBadLinks, + graph: this.graph, + patches: patches + (newFix?.patches ?? 0), + deletes: deletes + (newFix?.deletes ?? 0), + }; + } + + /** Logs if not silent. */ + protected log(...args: any[]) { + if (this.silent) return; + this.logger.log(...args); + } + + /** + * Patches a node for a check run, returning the instruction that would be made. + */ + private getNodePatchInstruction( + node: N, + ioDir: IoDirection, + slot: number, + linkId: number, + op: "ADD" | "REMOVE", + ): WorkflowLinkFixerNodeInstruction | null { + const nodeId = node.id; + this.patchedNodeSlots[nodeId] = this.patchedNodeSlots[nodeId] || {}; + const patchedNode = this.patchedNodeSlots[nodeId]; + if (ioDir == IoDirection.INPUT) { + patchedNode["inputs"] = patchedNode["inputs"] || {}; + // We can set to null (delete), so undefined means we haven't set it at all. + if (patchedNode["inputs"][slot] !== undefined) { + this.log( + ` > Already set ${nodeId}.inputs[${slot}] to ${patchedNode["inputs"][slot]} Skipping.`, + ); + return null; + } + let linkIdToUse = op === "REMOVE" ? null : linkId; + patchedNode["inputs"][slot] = linkIdToUse; + return {node, dir: ioDir, op, slot, linkId, linkIdToUse}; + } + + patchedNode["outputs"] = patchedNode["outputs"] || {}; + patchedNode["outputs"][slot] = patchedNode["outputs"][slot] || { + links: [...(node.outputs?.[slot]?.links || [])], + changes: {}, + }; + if (patchedNode["outputs"][slot]["changes"][linkId] !== undefined) { + this.log( + ` > Already set ${nodeId}.outputs[${slot}] to ${patchedNode["outputs"][slot]}! Skipping.`, + ); + return null; + } + patchedNode["outputs"][slot]["changes"][linkId] = op; + if (op === "ADD") { + let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId); + if (linkIdIndex !== -1) { + this.log(` > Hmmm.. asked to add ${linkId} but it is already in list...`); + return null; + } + patchedNode["outputs"][slot]["links"].push(linkId); + return {node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId}; + } + + let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId); + if (linkIdIndex === -1) { + this.log(` > Hmmm.. asked to remove ${linkId} but it doesn't exist...`); + return null; + } + patchedNode["outputs"][slot]["links"].splice(linkIdIndex, 1); + return {node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId}; + } + + /** Checks if a node (or patched data) has a linkId. */ + private nodeHasLinkId(node: N, ioDir: IoDirection, slot: number, linkId: number) { + const nodeId = node.id; + let has = false; + if (ioDir === IoDirection.INPUT) { + let nodeHasIt = node.inputs?.[slot]?.link === linkId; + if (this.patchedNodeSlots[nodeId]?.["inputs"]) { + let patchedHasIt = this.patchedNodeSlots[nodeId]["inputs"][slot] === linkId; + has = patchedHasIt; + } else { + has = nodeHasIt; + } + } else { + let nodeHasIt = node.outputs?.[slot]?.links?.includes(linkId); + if (this.patchedNodeSlots[nodeId]?.["outputs"]?.[slot]?.["changes"][linkId]) { + let patchedHasIt = this.patchedNodeSlots[nodeId]["outputs"][slot].links.includes(linkId); + has = !!patchedHasIt; + } else { + has = !!nodeHasIt; + } + } + return has; + } + + /** Checks if a node (or patched data) has a linkId. */ + private nodeHasAnyLink(node: N, ioDir: IoDirection, slot: number) { + // Patched data should be canonical. We can double check if fixing too. + const nodeId = node.id; + let hasAny = false; + if (ioDir === IoDirection.INPUT) { + let nodeHasAny = node.inputs?.[slot]?.link != null; + if (this.patchedNodeSlots[nodeId]?.["inputs"]) { + let patchedHasAny = this.patchedNodeSlots[nodeId]["inputs"][slot] != null; + hasAny = patchedHasAny; + } else { + hasAny = !!nodeHasAny; + } + } else { + let nodeHasAny = node.outputs?.[slot]?.links?.length; + if (this.patchedNodeSlots[nodeId]?.["outputs"]?.[slot]?.["changes"]) { + let patchedHasAny = this.patchedNodeSlots[nodeId]["outputs"][slot].links?.length; + hasAny = !!patchedHasAny; + } else { + hasAny = !!nodeHasAny; + } + } + return hasAny; + } +} + +/** + * A WorkflowLinkFixer for serialized data. + */ +class WorkflowLinkFixerSerialized extends WorkflowLinkFixer { + constructor(graph: ISerialisedGraph) { + super(graph); + } + + getNodeById(id: NodeId) { + return this.graph.nodes.find((node) => Number(node.id) === id) ?? null; + } + + override fix(force: boolean = false, times?: number) { + const ret = super.fix(force, times); + // If we're a serialized graph, we can filter out the links because it's just an array. + this.graph.links = this.graph.links.filter((l) => !!l); + return ret; + } + + deleteGraphLink(id: LinkId) { + // Sometimes we got objects instead of serializzed array for links if passed after ComfyUI's + // loadGraphData modifies the data. Let's find the id handling the bastardized objects just in + // case. + const idx = this.graph.links.findIndex((l) => l && (l[0] === id || (l as any).id === id)); + if (idx === -1) { + return `Link #${id} not found in workflow links.`; + } + this.graph.links.splice(idx, 1); + return true; + } +} + +/** + * A WorkflowLinkFixer for live LGraph data. + */ +class WorkflowLinkFixerGraph extends WorkflowLinkFixer { + constructor(graph: LGraph) { + super(graph); + } + + getNodeById(id: NodeId) { + return this.graph.getNodeById(id) ?? null; + } + + deleteGraphLink(id: LinkId) { + if (this.graph.links instanceof Map) { + if (!this.graph.links.has(id)) { + return `Link #${id} not found in workflow links.`; + } + this.graph.links.delete(id); + return true; + } + if (this.graph.links[id] == null) { + return `Link #${id} not found in workflow links.`; + } + delete this.graph.links[id]; + return true; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg b/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg new file mode 100644 index 00000000..766a7615 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/common/media/svgs.ts b/custom_nodes/rgthree-comfy/src_web/common/media/svgs.ts new file mode 100644 index 00000000..b468cd0e --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/media/svgs.ts @@ -0,0 +1,210 @@ +import {createElement as $el} from "../utils_dom.js"; + +// Some svg repo : https://www.svgrepo.com/svg/326731/open-outline + +export let logoRgthree: string = ""; + +export async function logoRgthreeAsync(): Promise { + if (logoRgthree) return logoRgthree; + let baseUrl = null; + if (window.location.pathname.includes("/rgthree/")) { + // Try to find how many relatives paths we need to go back to hit ./rgthree/api + const parts = window.location.pathname.split("/rgthree/")[1]?.split("/"); + if (parts && parts.length) { + baseUrl = parts.map(() => "../").join("") + "rgthree"; + } + } + baseUrl = baseUrl || "./rgthree"; + return fetch(`${baseUrl}/logo_markup.svg?fg=currentColor&cssClass=rgthree-logo&w=auto&h=auto`) + .then((r) => r.text()) + .then((t) => { + if (t.length < 100) { + t = ``; + } + logoRgthree = t; + return t; + }); +} +// Kick it off to cache upfront. +logoRgthreeAsync(); + +export const github = ``; + +export const iconStarFilled = ` + + `; + +export const iconReplace = ` + + + + + `; + +export const iconNode = ` + + + `; + +export const iconGear = ` + + `; + +export const checkmark = ` + + + + `; + +export const logoCivitai = ` + + + + + + + + + + `; + +export const iconOutLink = ` + + `; + +export const link = ` + + `; + +export const pencil = ` + + `; + +export const dotdotdot = ` + + + +`; + +export const models = ` + + + + +`; + +/** https://www.svgrepo.com/svg/402308/pencil */ +export const pencilColored = ` + + + + + + + + + `; + +/** https://www.svgrepo.com/svg/395640/save */ +export const diskColored = ` + + + + + + + +`; + +/** https://www.svgrepo.com/svg/229838/folder */ +export const folderColored = ` + + +`; + +export const modelsColored = ` + + + + +`; + +export const legoBlocksColored = ` + + + + + + + + + + + + +`; + +export const legoBlockColored = ` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +`; + +export const gearColored = ` + + + + +`; + +export function $svg(markup: string, attrs: {[key: string]: string}) { + if (!markup.match(/^\s* Promise> = new Map(); + + private handleWindowPointerDownBound = this.handleWindowPointerDown.bind(this); + + constructor(options: MenuOption[]) { + this.setOptions(options); + this.element.addEventListener('pointerup', async (e) => { + const target = getClosestOrSelf(e.target as HTMLElement, "[data-callback],menu"); + if (e.which !== 1) { + return; + } + const callback = target?.dataset?.['callback']; + if (callback) { + const halt = await this.callbacks.get(callback)?.(e); + if (halt !== false) { + this.close(); + } + } + e.preventDefault(); + e.stopPropagation(); + e.stopImmediatePropagation(); + }); + } + + setOptions(options: MenuOption[]) { + for (const option of options) { + if (option.type === 'title') { + this.element.appendChild($el(`li`, { + html: option.label + })); + } else { + const id = generateId(8); + this.callbacks.set(id, async (e: PointerEvent) => { return option?.callback?.(e); }); + this.element.appendChild($el(`li[role="button"][data-callback="${id}"]`, { + html: option.label + })); + } + } + } + + toElement() { + return this.element; + } + + async open(e: PointerEvent) { + const parent = (e.target as HTMLElement).closest('div,dialog,body') as HTMLElement + parent.appendChild(this.element); + setAttributes(this.element, { + style: { + left: `${e.clientX + 16}px`, + top: `${e.clientY - 16}px`, + } + }); + this.element.setAttribute('state', 'measuring-open'); + await wait(16); + const rect = this.element.getBoundingClientRect(); + if (rect.right > window.innerWidth) { + this.element.style.left = `${e.clientX - rect.width - 16}px`; + await wait(16); + } + this.element.setAttribute('state', 'open'); + setTimeout(() => { + window.addEventListener('pointerdown', this.handleWindowPointerDownBound); + }); + } + + handleWindowPointerDown(e:PointerEvent) { + if (!this.element.contains(e.target as HTMLElement)) { + this.close(); + } + } + + async close() { + window.removeEventListener('pointerdown', this.handleWindowPointerDownBound); + this.element.setAttribute('state', 'measuring-closed'); + await wait(16); + this.element.setAttribute('state', 'closed'); + this.element.remove(); + } + + isOpen() { + return (this.element.getAttribute('state') || '').includes('open'); + } + +} + +type MenuOption = { + label: string; + type?: 'title'|'item'|'separator'; + callback?: (e: PointerEvent) => void; +} + +type MenuButtonOptions = { + icon: string; + options: MenuOption[]; +} + +export class MenuButton { + + private options: MenuButtonOptions; + private menu: Menu; + + private element: HTMLButtonElement = $el('button.rgthree-button[data-action="open-menu"]') + + constructor(options: MenuButtonOptions) { + this.options = options; + this.element.innerHTML = options.icon; + this.menu = new Menu(options.options); + + this.element.addEventListener('pointerdown', (e) => { + if (!this.menu.isOpen()) { + this.menu.open(e); + } + }); + } + + toElement() { + return this.element; + } + +} \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/common/model_info_service.ts b/custom_nodes/rgthree-comfy/src_web/common/model_info_service.ts new file mode 100644 index 00000000..efa36866 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/model_info_service.ts @@ -0,0 +1,99 @@ +import type {RgthreeModelInfo} from "typings/rgthree.js"; +import {ModelInfoType, rgthreeApi} from "./rgthree_api.js"; +import {api} from "scripts/api.js"; + +/** + * Abstract class defining information syncing for different types. + */ +abstract class BaseModelInfoService extends EventTarget { + private readonly fileToInfo = new Map(); + protected abstract readonly modelInfoType: ModelInfoType; + + protected abstract readonly apiRefreshEventString: string; + + constructor() { + super(); + this.init(); + } + + private init() { + api.addEventListener( + this.apiRefreshEventString, + this.handleAsyncUpdate.bind(this) as EventListener, + ); + } + + async getInfo(file: string, refresh: boolean, light: boolean) { + if (this.fileToInfo.has(file) && !refresh) { + return this.fileToInfo.get(file)!; + } + return this.fetchInfo(file, refresh, light); + } + + async refreshInfo(file: string) { + return this.fetchInfo(file, true); + } + + async clearFetchedInfo(file: string) { + await rgthreeApi.clearModelsInfo({type: this.modelInfoType, files: [file]}); + this.fileToInfo.delete(file); + return null; + } + + async savePartialInfo(file: string, data: Partial) { + let info = await rgthreeApi.saveModelInfo(this.modelInfoType, file, data); + this.fileToInfo.set(file, info); + return info; + } + + handleAsyncUpdate(event: CustomEvent<{data: RgthreeModelInfo}>) { + const info = event.detail?.data as RgthreeModelInfo; + if (info?.file) { + this.setFreshInfo(info.file, info); + } + } + + private async fetchInfo(file: string, refresh = false, light = false) { + let info = null; + if (!refresh) { + info = await rgthreeApi.getModelsInfo({type: this.modelInfoType, files: [file], light}); + } else { + info = await rgthreeApi.refreshModelsInfo({type: this.modelInfoType, files: [file]}); + } + info = info?.[0] ?? null; + if (!light) { + this.fileToInfo.set(file, info); + } + return info; + } + + /** + * Single point to set data into the info cache, and fire an event. Note, this doesn't determine + * if the data is actually different. + */ + private setFreshInfo(file: string, info: RgthreeModelInfo) { + this.fileToInfo.set(file, info); + // this.dispatchEvent( + // new CustomEvent("rgthree-model-service-lora-details", { detail: { lora: info } }), + // ); + } +} + +/** + * Lora type implementation of ModelInfoTypeService. + */ +class LoraInfoService extends BaseModelInfoService { + protected override readonly apiRefreshEventString = "rgthree-refreshed-loras-info"; + protected override readonly modelInfoType = 'loras'; +} + +/** + * Checkpoint type implementation of ModelInfoTypeService. + */ +class CheckpointInfoService extends BaseModelInfoService { + protected override readonly apiRefreshEventString = "rgthree-refreshed-checkpoints-info"; + protected override readonly modelInfoType = 'checkpoints'; +} + +export const LORA_INFO_SERVICE = new LoraInfoService(); +export const CHECKPOINT_INFO_SERVICE = new CheckpointInfoService(); diff --git a/custom_nodes/rgthree-comfy/src_web/common/progress_bar.ts b/custom_nodes/rgthree-comfy/src_web/common/progress_bar.ts new file mode 100644 index 00000000..af55edab --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/progress_bar.ts @@ -0,0 +1,218 @@ +/** + * Progress bar web component. + */ + +import { SERVICE as PROMPT_SERVICE, type PromptExecution } from "rgthree/common/prompt_service.js"; +import { createElement } from "./utils_dom.js"; + +/** + * The progress bar web component. + */ +export class RgthreeProgressBar extends HTMLElement { + static NAME = "rgthree-progress-bar"; + + static create(): RgthreeProgressBar { + return document.createElement(RgthreeProgressBar.NAME) as RgthreeProgressBar; + } + + private shadow: ShadowRoot | null = null; + private progressNodesEl!: HTMLDivElement; + private progressStepsEl!: HTMLDivElement; + private progressTextEl!: HTMLSpanElement; + + private currentPromptExecution: PromptExecution | null = null; + + private readonly onProgressUpdateBound = this.onProgressUpdate.bind(this); + + private connected: boolean = false; + + /** The currentNodeId so outside callers can see what we're currently executing against. */ + get currentNodeId() { + const prompt = this.currentPromptExecution; + const nodeId = prompt?.errorDetails?.node_id || prompt?.currentlyExecuting?.nodeId; + return nodeId || null; + } + + constructor() { + super(); + } + + private onProgressUpdate(e: CustomEvent<{ queue: number; prompt: PromptExecution }>) { + if (!this.connected) return; + + const prompt = e.detail.prompt; + this.currentPromptExecution = prompt; + + if (prompt?.errorDetails) { + let progressText = `${prompt.errorDetails?.exception_type} ${ + prompt.errorDetails?.node_id || "" + } ${prompt.errorDetails?.node_type || ""}`; + this.progressTextEl.innerText = progressText; + this.progressNodesEl.classList.add("-error"); + this.progressStepsEl.classList.add("-error"); + return; + } + if (prompt?.currentlyExecuting) { + this.progressNodesEl.classList.remove("-error"); + this.progressStepsEl.classList.remove("-error"); + + const current = prompt?.currentlyExecuting; + + let progressText = `(${e.detail.queue}) `; + + // Sometimes we may get status updates for a workflow that was already running. In that case + // we don't know totalNodes. + if (!prompt.totalNodes) { + progressText += `??%`; + this.progressNodesEl.style.width = `0%`; + } else { + const percent = (prompt.executedNodeIds.length / prompt.totalNodes) * 100; + this.progressNodesEl.style.width = `${Math.max(2, percent)}%`; + // progressText += `Node ${prompt.executedNodeIds.length + 1} of ${prompt.totalNodes || "?"}`; + progressText += `${Math.round(percent)}%`; + } + + let nodeLabel = current.nodeLabel?.trim(); + let stepsLabel = ""; + if (current.step != null && current.maxSteps) { + const percent = (current.step / current.maxSteps) * 100; + this.progressStepsEl.style.width = `${percent}%`; + // stepsLabel += `Step ${current.step} of ${current.maxSteps}`; + if (current.pass > 1 || current.maxPasses != null) { + stepsLabel += `#${current.pass}`; + if (current.maxPasses && current.maxPasses > 0) { + stepsLabel += `/${current.maxPasses}`; + } + stepsLabel += ` - `; + } + stepsLabel += `${Math.round(percent)}%`; + } + + if (nodeLabel || stepsLabel) { + progressText += ` - ${nodeLabel || "???"}${stepsLabel ? ` (${stepsLabel})` : ""}`; + } + if (!stepsLabel) { + this.progressStepsEl.style.width = `0%`; + } + this.progressTextEl.innerText = progressText; + } else { + if (e?.detail.queue) { + this.progressTextEl.innerText = `(${e.detail.queue}) Running... in another tab`; + } else { + this.progressTextEl.innerText = "Idle"; + } + this.progressNodesEl.style.width = `0%`; + this.progressStepsEl.style.width = `0%`; + } + } + + connectedCallback() { + if (!this.connected) { + PROMPT_SERVICE.addEventListener( + "progress-update", + this.onProgressUpdateBound as EventListener, + ); + this.connected = true; + } + // We were already connected, so we just need to reset. + if (this.shadow) { + this.progressTextEl.innerText = "Idle"; + this.progressNodesEl.style.width = `0%`; + this.progressStepsEl.style.width = `0%`; + return; + } + + this.shadow = this.attachShadow({ mode: "open" }); + const sheet = new CSSStyleSheet(); + sheet.replaceSync(` + + :host { + position: relative; + overflow: hidden; + box-sizing: border-box; + background: var(--rgthree-progress-bg-color); + --rgthree-progress-bg-color: rgba(23, 23, 23, 0.9); + --rgthree-progress-nodes-bg-color: rgb(0, 128, 0); + --rgthree-progress-steps-bg-color: rgb(0, 128, 0); + --rgthree-progress-error-bg-color: rgb(128, 0, 0); + --rgthree-progress-text-color: #fff; + } + :host * { + box-sizing: inherit; + } + + :host > div.bar { + background: var(--rgthree-progress-nodes-bg-color); + position: absolute; + left: 0; + top: 0; + width: 0%; + height: 50%; + z-index: 1; + transition: width 50ms ease-in-out; + } + :host > div.bar + div.bar { + background: var(--rgthree-progress-steps-bg-color); + top: 50%; + height: 50%; + z-index: 2; + } + :host > div.bar.-error { + background: var(--rgthree-progress-error-bg-color); + } + + :host > .overlay { + position: absolute; + left: 0; + top: 0; + width: 100%; + height: 100%; + z-index: 5; + background: linear-gradient(to bottom, rgba(255,255,255,0.25), rgba(0,0,0,0.25)); + mix-blend-mode: overlay; + } + + :host > span { + position: relative; + z-index: 4; + text-align: left; + font-size: inherit; + height: 100%; + font-family: sans-serif; + text-shadow: 1px 1px 0px #000; + display: flex; + flex-direction: row; + padding: 0 6px; + align-items: center; + justify-content: start; + color: var(--rgthree-progress-text-color); + text-shadow: black 0px 0px 2px; + } + + :host > div.bar[style*="width: 0%"]:first-child, + :host > div.bar[style*="width:0%"]:first-child { + height: 0%; + } + :host > div.bar[style*="width: 0%"]:first-child + div, + :host > div.bar[style*="width:0%"]:first-child + div { + bottom: 0%; + } + `); + this.shadow.adoptedStyleSheets = [sheet]; + + const overlayEl = createElement(`div.overlay[part="overlay"]`, { parent: this.shadow }); + this.progressNodesEl = createElement(`div.bar[part="progress-nodes"]`, { parent: this.shadow }); + this.progressStepsEl = createElement(`div.bar[part="progress-steps"]`, { parent: this.shadow }); + this.progressTextEl = createElement(`span[part="text"]`, { text: "Idle", parent: this.shadow }); + } + + disconnectedCallback() { + this.connected = false; + PROMPT_SERVICE.removeEventListener( + "progress-update", + this.onProgressUpdateBound as EventListener, + ); + } +} + +customElements.define(RgthreeProgressBar.NAME, RgthreeProgressBar); diff --git a/custom_nodes/rgthree-comfy/src_web/common/prompt_service.ts b/custom_nodes/rgthree-comfy/src_web/common/prompt_service.ts new file mode 100644 index 00000000..4ed8b40f --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/prompt_service.ts @@ -0,0 +1,274 @@ +import type { + ComfyApiEventDetailCached, + ComfyApiEventDetailError, + ComfyApiEventDetailExecuted, + ComfyApiEventDetailExecuting, + ComfyApiEventDetailExecutionStart, + ComfyApiEventDetailProgress, + ComfyApiEventDetailStatus, + ComfyApiFormat, + ComfyApiPrompt, +} from "typings/comfy.js"; +import { api } from "scripts/api.js"; +import type { LGraph as TLGraph, LGraphCanvas as TLGraphCanvas } from "@comfyorg/frontend"; +import { Resolver, getResolver } from "./shared_utils.js"; + +/** + * Wraps general data of a prompt's execution. + */ +export class PromptExecution { + id: string; + promptApi: ComfyApiFormat | null = null; + executedNodeIds: string[] = []; + totalNodes: number = 0; + currentlyExecuting: { + nodeId: string; + nodeLabel?: string; + step?: number; + maxSteps?: number; + /** The current pass, for nodes with multiple progress passes. */ + pass: number; + /** + * The max num of passes. Can be calculated for some nodes, or set to -1 when known there will + * be multiple passes, but the number cannot be calculated. + */ + maxPasses?: number; + } | null = null; + errorDetails: any | null = null; + + apiPrompt: Resolver = getResolver(); + + constructor(id: string) { + this.id = id; + } + + /** + * Sets the prompt and prompt-related data. This can technically come in lazily, like if the web + * socket fires the 'execution-start' event before we actually get a response back from the + * initial prompt call. + */ + setPrompt(prompt: ComfyApiPrompt) { + this.promptApi = prompt.output; + this.totalNodes = Object.keys(this.promptApi).length; + this.apiPrompt.resolve(null); + } + + getApiNode(nodeId: string | number) { + return this.promptApi?.[String(nodeId)] || null; + } + + private getNodeLabel(nodeId: string | number) { + const apiNode = this.getApiNode(nodeId); + let label = apiNode?._meta?.title || apiNode?.class_type || undefined; + if (!label) { + const graphNode = this.maybeGetComfyGraph()?.getNodeById(Number(nodeId)); + label = graphNode?.title || graphNode?.type || undefined; + } + return label; + } + + /** + * Updates the execution data depending on the passed data, fed from api events. + */ + executing(nodeId: string | null, step?: number, maxSteps?: number) { + if (nodeId == null) { + // We're done, any left over nodes must be skipped... + this.currentlyExecuting = null; + return; + } + if (this.currentlyExecuting?.nodeId !== nodeId) { + if (this.currentlyExecuting != null) { + this.executedNodeIds.push(nodeId); + } + this.currentlyExecuting = { nodeId, nodeLabel: this.getNodeLabel(nodeId), pass: 0 }; + // We'll see if we're known node for multiple passes, that will come in as generic 'progress' + // updates from the api. If we're known to have multiple passes, then we'll pre-set data to + // allow the progress bar to handle intial rendering. If we're not, that's OK, the data will + // be shown with the second pass. + this.apiPrompt.promise.then(() => { + // If we execute with a null node id and clear the currently executing, then we can just + // move on. This seems to only happen with a super-fast execution (like, just seed node + // and display any for testing). + if (this.currentlyExecuting == null) { + return; + } + const apiNode = this.getApiNode(nodeId); + if (!this.currentlyExecuting.nodeLabel) { + this.currentlyExecuting.nodeLabel = this.getNodeLabel(nodeId); + } + if (apiNode?.class_type === "UltimateSDUpscale") { + // From what I can tell, UltimateSDUpscale, does an initial pass that isn't actually a + // tile. It seems to always be 4 steps... We'll start our pass at -1, so this prepass is + // "0" and "1" will start with the first tile. This way, a user knows they have 4 tiles, + // know this pass counter will go to 4 (and not 5). Also, we cannot calculate maxPasses + // for 'UltimateSDUpscale' :( + this.currentlyExecuting.pass--; + this.currentlyExecuting.maxPasses = -1; + } else if (apiNode?.class_type === "IterativeImageUpscale") { + this.currentlyExecuting.maxPasses = (apiNode?.inputs["steps"] as number) ?? -1; + } + }); + } + if (step != null) { + // If we haven't had any stpes before, or the passes step is lower than the previous, then + // increase the passes. + if (!this.currentlyExecuting!.step || step < this.currentlyExecuting!.step) { + this.currentlyExecuting!.pass!++; + } + this.currentlyExecuting!.step = step; + this.currentlyExecuting!.maxSteps = maxSteps; + } + } + + /** + * If there's an error, we add the details. + */ + error(details: any) { + this.errorDetails = details; + } + + private maybeGetComfyGraph(): TLGraph | null { + return ((window as any)?.app?.graph as TLGraph) || null; + } +} + +/** + * A singleton service that wraps the Comfy API and simplifies the event data being fired. + */ +class PromptService extends EventTarget { + promptsMap: Map = new Map(); + currentExecution: PromptExecution | null = null; + lastQueueRemaining = 0; + + constructor(api: any) { + super(); + const that = this; + + // Patch the queuePrompt method so we can capture new data going through. + const queuePrompt = api.queuePrompt; + api.queuePrompt = async function (num: number, prompt: ComfyApiPrompt, ...args: any[]) { + let response; + try { + response = await queuePrompt.apply(api, [...arguments]); + } catch (e) { + const promptExecution = that.getOrMakePrompt("error"); + promptExecution.error({ exception_type: "Unknown." }); + // console.log("ERROR QUEUE PROMPT", response, arguments); + throw e; + } + // console.log("QUEUE PROMPT", response, arguments); + const promptExecution = that.getOrMakePrompt(response.prompt_id); + promptExecution.setPrompt(prompt); + if (!that.currentExecution) { + that.currentExecution = promptExecution; + } + that.promptsMap.set(response.prompt_id, promptExecution); + that.dispatchEvent( + new CustomEvent("queue-prompt", { + detail: { + prompt: promptExecution, + }, + }), + ); + return response; + }; + + api.addEventListener("status", (e: CustomEvent) => { + // console.log("status", JSON.stringify(e.detail)); + // Sometimes a status message is fired when the app loades w/o any details. + if (!e.detail?.exec_info) return; + this.lastQueueRemaining = e.detail.exec_info.queue_remaining; + this.dispatchProgressUpdate(); + }); + + api.addEventListener("execution_start", (e: CustomEvent) => { + // console.log("execution_start", JSON.stringify(e.detail)); + if (!this.promptsMap.has(e.detail.prompt_id)) { + console.warn("'execution_start' fired before prompt was made."); + } + const prompt = this.getOrMakePrompt(e.detail.prompt_id); + this.currentExecution = prompt; + this.dispatchProgressUpdate(); + }); + + api.addEventListener("executing", (e: CustomEvent) => { + // console.log("executing", JSON.stringify(e.detail)); + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt("unknown"); + console.warn("'executing' fired before prompt was made."); + } + this.currentExecution.executing(e.detail); + this.dispatchProgressUpdate(); + if (e.detail == null) { + this.currentExecution = null; + } + }); + + api.addEventListener("progress", (e: CustomEvent) => { + // console.log("progress", JSON.stringify(e.detail)); + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'progress' fired before prompt was made."); + } + this.currentExecution.executing(e.detail.node, e.detail.value, e.detail.max); + this.dispatchProgressUpdate(); + }); + + api.addEventListener("execution_cached", (e: CustomEvent) => { + // console.log("execution_cached", JSON.stringify(e.detail)); + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'execution_cached' fired before prompt was made."); + } + for (const cached of e.detail.nodes) { + this.currentExecution.executing(cached); + } + this.dispatchProgressUpdate(); + }); + + api.addEventListener("executed", (e: CustomEvent) => { + // console.log("executed", JSON.stringify(e.detail)); + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'executed' fired before prompt was made."); + } + }); + + api.addEventListener("execution_error", (e: CustomEvent) => { + // console.log("execution_error", e.detail); + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'execution_error' fired before prompt was made."); + } + this.currentExecution?.error(e.detail); + this.dispatchProgressUpdate(); + }); + } + + /** A helper method, since we extend/override api.queuePrompt above anyway. */ + async queuePrompt(prompt: ComfyApiPrompt) { + return await api.queuePrompt(-1, prompt); + } + + dispatchProgressUpdate() { + this.dispatchEvent( + new CustomEvent("progress-update", { + detail: { + queue: this.lastQueueRemaining, + prompt: this.currentExecution, + }, + }), + ); + } + + getOrMakePrompt(id: string) { + let prompt = this.promptsMap.get(id); + if (!prompt) { + prompt = new PromptExecution(id); + this.promptsMap.set(id, prompt); + } + return prompt; + } +} + +export const SERVICE = new PromptService(api); diff --git a/custom_nodes/rgthree-comfy/src_web/common/py_parser.ts b/custom_nodes/rgthree-comfy/src_web/common/py_parser.ts new file mode 100644 index 00000000..22d69d28 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/py_parser.ts @@ -0,0 +1,983 @@ +/** + * @fileoverview An AST executor using the TreeSitter parser to parse python-like code and execute + * in JS. This parser is self-contained and isolated from other parts of the app (like Comfy-UI + * specific types, etc). Instead, additional handlers, builtins, and types can be passed into the + * pure functions below. + */ +import type {Parser, Node as TreeSitterNode, Tree} from "web-tree-sitter"; + +import {check, deepFreeze} from "./shared_utils.js"; + +// Hacky memoization because I don't feel like writing a decorator. +const MEMOIZED = {parser: null as unknown as Parser}; + +interface Dict extends Object { + [k: string]: unknown; +} +interface ExecutionContextData extends Object { + [k: string]: unknown; +} +class ExecuteContext implements ExecutionContextData { + [k: string]: unknown; + + constructor(existing: Object = {}) { + Object.assign(this, !!window.structuredClone ? structuredClone(existing) : {...existing}); + } +} +class InitialExecuteContext extends ExecuteContext {} + +type NodeHandlerArgs = [ExecutionContextData, BuiltInFns]; +type NodeHandler = (node: Node, ...args: NodeHandlerArgs) => Promise; + +const TYPE_TO_HANDLER = new Map([ + ["module", handleChildren], + ["expression_statement", handleChildren], + ["interpolation", handleInterpolation], + ["block", handleChildren], // Block of code, like in a for loop + + ["comment", handleSwallow], + ["return_statement", handleReturn], + + ["assignment", handleAssignment], + ["named_expression", handleNamedExpression], + + ["identifier", handleIdentifier], + ["attribute", handleAttribute], + ["subscript", handleSubscript], + + ["call", handleCall], + ["argument_list", handleArgumentsList], + + ["for_statement", handleForStatement], + ["list_comprehension", handleListComprehension], + + ["comparison_operator", handleComparisonOperator], + ["boolean_operator", handleBooleanOperator], + ["binary_operator", handleBinaryOperator], + ["not_operator", handleNotOperator], + ["unary_operator", handleUnaryOperator], + + // Types + ["integer", handleNumber], + ["float", handleNumber], + ["string", handleString], + ["tuple", handleList], + ["list", handleList], + ["dictionary", handleDictionary], + ["pair", handleDictionaryPair], + ["true", async (...args: any[]) => true], + ["false", async (...args: any[]) => false], +]); + +type BuiltInFn = {fn: Function}; +type BuiltInFns = {[key: string]: BuiltInFn}; + +const DEFAULT_BUILT_INS: BuiltInFns = { + round: {fn: (n: any) => Math.round(Number(n))}, + ceil: {fn: (n: any) => Math.ceil(Number(n))}, + floor: {fn: (n: any) => Math.floor(Number(n))}, + // Function(name="sqrt", call=math.sqrt, args=(1, 1)), + // Function(name="min", call=min, args=(2, None)), + // Function(name="max", call=max, args=(2, None)), + // Function(name=".random_int", call=random.randint, args=(2, 2)), + // Function(name=".random_choice", call=random.choice, args=(1, 1)), + // Function(name=".random_seed", call=random.seed, args=(1, 1)), + // Function(name="re", call=re.compile, args=(1, 1)), + len: {fn: (n: any) => n?.__len__?.() ?? n?.length}, + // Function(name="enumerate", call=enumerate, args=(1, 1)), + // Function(name="range", call=range, args=(1, 3)), + + // Types + int: {fn: (n: any) => Math.floor(Number(n))}, + float: {fn: (n: any) => Number(n)}, + str: {fn: (n: any) => String(n)}, + bool: {fn: (n: any) => !!n}, + list: {fn: (tupl: any[] = []) => new PyList(tupl)}, + tuple: {fn: (list: any[] = []) => new PyTuple(list)}, + dict: {fn: (dict: Dict = {}) => new PyDict(dict)}, + + // Special + dir: {fn: (...args: any[]) => console.dir(...__unwrap__(...args))}, + print: {fn: (...args: any[]) => console.log(...__unwrap__(...args))}, + log: {fn: (...args: any[]) => console.log(...__unwrap__(...args))}, +}; + +/** + * The main entry point to parse code. + */ +export async function execute( + code: string, + ctx: ExecutionContextData, + additionalBuiltins?: BuiltInFns, +) { + const builtIns = deepFreeze({...DEFAULT_BUILT_INS, ...(additionalBuiltins ?? {})}); + // When we start the execution, we create an InitialExecuteContext as an instance so we can check + // if we're the initial, global context during execution (as we may pass in a new context in the + // like if evaluating a list comprehension, or setting on an object). + ctx = new InitialExecuteContext(ctx); + + const root = (await parse(code)).rootNode; + const value = await handleNode(new Node(root), ctx, builtIns); + + console.log("====="); + console.log(`value`, value?.__unwrap__?.() ?? value); + console.log("context", ctx); + + return value; +} + +/** + * Parses a code string to a `Tree`. + */ +async function parse(code: string): Promise { + if (!MEMOIZED.parser) { + // @ts-ignore - Path is rewritten. + const TreeSitter = (await import("rgthree/lib/tree-sitter.js")) as TreeSitter; + await TreeSitter.Parser.init(); + const lang = await TreeSitter.Language.load("rgthree/lib/tree-sitter-python.wasm"); + MEMOIZED.parser = new TreeSitter.Parser() as Parser; + MEMOIZED.parser.setLanguage(lang); + } + return MEMOIZED.parser.parse(code)!; +} + +/** + * The generic node handler, calls out to specific handlers based on the node type. This is + * recursively called from other handlers. + */ +async function handleNode( + node: Node, + ctx: ExecutionContextData, + builtIns: BuiltInFns, +): Promise { + const type = node.type as string; + + // If we have a returned value, then just return it, which should recursively settle. + if (ctx.hasOwnProperty("__returned__")) return ctx["__returned__"]; + + // console.log(`-----`); + // console.log(`eval_node`); + // console.log(`type: ${type}`); + // console.log(`text: ${node.text}`); + // console.log(`children: ${node.children?.length ?? 0}`); + // console.log(ctx); + // console.log(node); + + const handler = TYPE_TO_HANDLER.get(type); + check(handler, "Unhandled type: " + type, node); + return handler(node, ctx, builtIns); +} + +/** + * Generic handler to loop over children of a node, and evaluate each. + */ +async function handleChildren(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + let lastValue = null; + for (const child of node.children) { + if (!child) continue; + lastValue = await handleNode(child, ctx, builtIns); + } + return lastValue; +} + +/** + * Swallows the execution. Likely just to allow development. + */ +async function handleSwallow(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + // No op +} + +/** + * Handles a return statement. + */ +async function handleReturn(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const value = node.children.length > 1 ? handleNode(node.child(1), ctx, builtIns) : undefined; + // Mark that we have a return value, as we may be deeper in evaluation, like going through an + // if condition's body. + ctx["__returned__"] = value; + return value; +} + +/** + * Handles the retrieval of a variable identifier, already be set in the context. + */ +async function handleIdentifier(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + let value = ctx[node.text]; + if (value === undefined) { + value = builtIns[node.text]?.fn ?? undefined; + } + return maybeWrapValue(value); +} + +async function handleAttribute(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const children = node.children; + check(children.length === 3, "Expected 3 children for attribute."); + check(children[1]!.type === ".", "Expected middle child to be '.' for attribute."); + const inst = await handleNode(children[0]!, ctx, builtIns); + // const attr = await handleNode(node.child(2), inst); + // console.log('handleAttribute', inst, attr); + const attr = children[2]!.text; + checkAttributeAccessibility(inst, attr); + let attribute = maybeWrapValue(inst[attr]); + // check(attribute !== undefined, `"${attr}" not found on instance of type ${typeof inst}.`); + // If the attribute is a function, then bind it to the instance. + return typeof attribute === "function" ? attribute.bind(inst) : attribute; +} + +async function handleSubscript(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const children = node.children; + check(children.length === 4, "Expected 4 children for subscript."); + check(children[1]!.type === "[", "Expected 2nd child to be '[' for subscript."); + check(children[3]!.type === "]", "Expected 4thd child to be ']' for subscript."); + const inst = await handleNode(children[0]!, ctx, builtIns); + const attr = await handleNode(children[2]!, ctx, builtIns); + if (inst instanceof PyTuple && isInt(attr)) { + return maybeWrapValue(inst.__at__(attr)); + } + if (inst instanceof PyDict && typeof attr === "string") { + return maybeWrapValue(inst.get(attr)); + } + checkAttributeAccessibility(inst, attr); + let attribute = maybeWrapValue(inst[attr]); + return typeof attribute === "function" ? attribute.bind(inst) : attribute; +} + +/** + * Handles the assignment. + */ +async function handleAssignment(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + check( + node.children.length === 3, + "Expected 3 children for assignment: identifier/attr, =, and value.", + ); + check(node.children[1]!.type === "=", "Expected middle child to be an '='."); + + let right = await handleNode(node.children[2]!, ctx, builtIns); + const leftNode = node.children[0]!; + let leftObj: any = ctx; + let leftProp: string | number = ""; + if (leftNode.type === "identifier") { + leftProp = leftNode.text; + } else if (leftNode.type === "attribute") { + leftObj = await handleNode(leftNode.children[0]!, ctx, builtIns); + check( + leftNode.children[2]!.type === "identifier", + "Expected left hand assignment attribute to be an identifier.", + leftNode, + ); + leftProp = leftNode.children[2]!.text; + } else if (leftNode.type === "subscript") { + leftObj = await handleNode(leftNode.children[0]!, ctx, builtIns); + check(leftNode.children[1]!.type === "["); + check(leftNode.children[3]!.type === "]"); + leftProp = await handleNode(leftNode.children[2]!, ctx, builtIns); + } else { + throw new Error(`Unhandled left-hand assignement type: ${leftNode.type}`); + } + + if (leftProp == null) { + throw new Error(`No property to assign value`); + } + // If we're a PyTuple or extended from, then try add like a list (PyTuple will fail, PyList will + // allow). + if (leftObj instanceof PyTuple) { + check(isInt(leftProp), "Expected an int for list assignment"); + leftObj.__put__(leftProp, right); + } else if (leftObj instanceof PyDict) { + check(typeof leftProp === "string", "Expected a string for dict assignment"); + leftObj.__put__(leftProp, right); + } else { + check(typeof leftProp === "string", "Expected a string for object assignment"); + // InitialExecutionContext can have anything added, otherwise we're a specific context and + // should check for attribute accessibility. + if (!(leftObj instanceof InitialExecuteContext)) { + checkAttributeAccessibility(leftObj, leftProp); + } + leftObj[leftProp] = right; + } + return right; +} + +/** + * Handles a named expression, like assigning a var in a list comprehension with: + * `[name for node in node_list if (name := node.name)]` + */ +async function handleNamedExpression(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + check(node.children.length === 3, "Expected three children for named expression."); + check(node.child(0).type === "identifier", "Expected identifier first in named expression."); + const varName = node.child(0).text; + ctx[varName] = await handleNode(node.child(2), ctx, builtIns); + return maybeWrapValue(ctx[varName]); +} + +/** + * Handles a function call. + */ +async function handleCall(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + check(node.children.length === 2, "Expected 2 children for call, identifier and arguments."); + const fn = await handleNode(node.children[0]!, ctx, builtIns); + const args = await handleNode(node.children[1]!, ctx, builtIns); + console.log("handleCall", fn, args); + return fn(...args); +} + +async function handleArgumentsList(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const args = (await handleList(node, ctx, builtIns)).__unwrap__(false); + return [...args]; +} + +/** + * Handles a simple for...in loop. + */ +async function handleForStatement(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const childs = node.children; + check(childs.length === 6); + check(childs[4]!.type === ":"); + check(childs[5]!.type === "block"); + await helperGetLoopForIn(node, ctx, builtIns, async (forCtx) => { + await handleNode(childs[5]!, forCtx, builtIns); + }); +} + +async function handleListComprehension( + node: Node, + ctx: ExecutionContextData, + builtIns: BuiltInFns, +) { + // Create a new context that we don't want to pollute our outer one. + const finalList = new PyList(); + const newCtx = {...ctx}; + + let finalEntryNode; + const loopNodes: {forIn: Node; if?: Node}[] = []; + + for (const child of node.children) { + if (!child || ["[", "]"].includes(child.type)) continue; + if (child.type === "identifier" || child.type === "attribute") { + if (finalEntryNode) { + throw Error("Already have a list comprehension finalEntryNode."); + } + finalEntryNode = child; + } else if (child.type === "for_in_clause") { + loopNodes.push({forIn: child}); + } else if (child.type === "if_clause") { + loopNodes[loopNodes.length - 1]!["if"] = child; + } + } + if (!finalEntryNode) { + throw Error("No list comprehension finalEntryNode."); + } + + console.log(`handleListComprehension.loopNodes`, loopNodes); + + const handleLoop = async (loopNodes: {forIn: Node; if?: Node}[]) => { + const loopNode = loopNodes.shift()!; + await helperGetLoopForIn( + loopNode.forIn, + newCtx, + builtIns, + async (forCtx) => { + if (loopNode.if) { + const ifNode = loopNode.if; + check(ifNode.children.length === 2, "Expected 2 children for if_clause."); + check(ifNode.child(0).text === "if", "Expected first child to be 'if'."); + const good = await handleNode(ifNode.child(1), forCtx, builtIns); + if (!good) return; + } + Object.assign(newCtx, forCtx); + if (loopNodes.length) { + await handleLoop(loopNodes); + } else { + finalList.append(await handleNode(finalEntryNode, newCtx, builtIns)); + } + }, + () => ({...newCtx}), + ); + loopNodes.unshift(loopNode); + }; + + await handleLoop(loopNodes); + return finalList; +} + +/** + * Handles the identifiers, iterable, and initial looping with context setting. Handles both simple + * identifiers (like `for item in items`) or a pattern list (like `for key, val in mydict.items()`). + * + * @param eachFn The function to call for each iteration. Will be passed the current context with + * the identifiers assigned. + * @param provideForCtx An optional function that can provide an `ctx`. If not supplied the passed + * `ctx` param will be used. This is useful for providing a new ctx to use for cases like an + * if condition in a list comprhension where we don't want to add to the current context unless + * the condition is met. + */ +async function helperGetLoopForIn( + node: Node, + ctx: ExecutionContextData, + builtIns: BuiltInFns, + eachFn: (forCtx: ExecutionContextData) => Promise, + provideForCtx?: () => ExecutionContextData, +) { + const childs = node.children; + check(childs.length >= 3); + check(childs[0]!.type === "for"); + check( + ["identifier", "pattern_list"].includes(childs[1]!.type), + "Expected identifier for for loop.", + ); + check(childs[2]!.type === "in"); + + let identifiers: string[]; + if (childs[1]!.type === "identifier") { + // identifier: for k in my_list + identifiers = [childs[1]!.text]; + } else { + // pattern_list: for k,v in my_dict.items() + identifiers = childs[1]!.children + .map((n) => { + if (n.type === ",") return null; + check(n.type === "identifier"); + return node.text; + }) + .filter((n) => n != null); + } + const iterable = await handleNode(childs[3]!, ctx, builtIns); + check(iterable instanceof PyTuple, "Expected for loop instance to be a list/tuple."); + + for (const item of iterable.__unwrap__(false)) { + const forCtx = provideForCtx?.() ?? ctx; + if (identifiers.length === 1) { + forCtx[identifiers[0]!] = item; + } else { + check( + Array.isArray(item) && identifiers.length === item.length, + "Expected iterable to be a list, like using dict.items()", + ); + for (let i = 0; i < identifiers.length; i++) { + forCtx[identifiers[i]!] = item[i]; + } + } + await eachFn(forCtx); + } +} + +async function handleNumber(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + return Number(node.text); +} + +async function handleString(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + // check(node.children.length === 3, "Expected 3 children for str (quotes and value)."); + let str = ""; + for (const child of node.children) { + if (!child || ["string_start", "string_end"].includes(child.type)) continue; + if (child.type === "string_content") { + str += child.text; + } else if (child.type === "interpolation") { + check(child.children.length === 3, "Expected interpolation"); + str += await handleNode(child, ctx, builtIns); + } + } + return str; +} + +async function handleInterpolation(node: Node, ...args: NodeHandlerArgs) { + check(node.children.length === 3, "Expected interpolation to be three nodes length."); + check( + node.children[0]!.type === "{" && node.children[2]!.type === "}", + 'Expected interpolation to be wrapped in "{" and "}".', + ); + return await handleNode(node.children[1]!, ...args); +} + +async function handleList(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const list = []; + for (const child of node.children) { + if (!child || ["(", "[", ",", "]", ")"].includes(child.type)) continue; + list.push(await handleNode(child, ctx, builtIns)); + } + if (node.type === "tuple") { + return new PyTuple(list); + } + return new PyList(list); +} + +async function handleComparisonOperator( + node: Node, + ctx: ExecutionContextData, + builtIns: BuiltInFns, +) { + const op = node.child(1).text; + const left = await handleNode(node.child(0), ctx, builtIns); + const right = await handleNode(node.child(2), ctx, builtIns); + if (op === "==") return left === right; // Python '==' is equiv to '===' in JS. + if (op === "!=") return left !== right; + if (op === ">") return left > right; + if (op === ">=") return left >= right; + if (op === "<") return left < right; + if (op === "<=") return left <= right; + if (op === "in") return (right.__unwrap__ ? right.__unwrap__(false) : right).includes(left); + throw new Error(`Comparison not handled: "${op}"`); +} +async function handleBooleanOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const op = node.child(1).text; + const left = await handleNode(node.child(0), ctx, builtIns); + // If we're an AND and already false, then don't even evaluate the right. + if (!left && op === "and") return left; + const right = await handleNode(node.child(2), ctx, builtIns); + if (op === "and") return left && right; + if (op === "or") return left || right; +} + +async function handleBinaryOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const op = node.child(1).text; + const left = await handleNode(node.child(0), ctx, builtIns); + const right = await handleNode(node.child(2), ctx, builtIns); + if (left.constructor !== right.constructor) { + throw new Error(`Can only run ${op} operator on same type.`); + } + if (op === "+") return left.__add__ ? left.__add__(right) : left + right; + if (op === "-") return left - right; + if (op === "/") return left / right; + if (op === "//") return Math.floor(left / right); + if (op === "*") return left * right; + if (op === "%") return left % right; + if (op === "&") return left & right; + if (op === "|") return left | right; + if (op === "^") return left ^ right; + if (op === "<<") return left << right; + if (op === ">>") return left >> right; + throw new Error(`Comparison not handled: "${op}"`); +} + +async function handleNotOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + check(node.children.length === 2, "Expected 2 children for not operator."); + check(node.child(0).text === "not", "Expected first child to be 'not'."); + const value = await handleNode(node.child(1), ctx, builtIns); + return !value; +} + +async function handleUnaryOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + check(node.children.length === 2, "Expected 2 children for not operator."); + const value = await handleNode(node.child(1), ctx, builtIns); + const op = node.child(0).text; + if (op === "-") return value * -1; + console.warn(`Unhandled unary operator: ${op}`); + return value; +} + +async function handleDictionary(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + const dict = new PyDict(); + for (const child of node.children) { + if (!child || ["{", ",", "}"].includes(child.type)) continue; + check(child.type === "pair", "Expected a pair type for dict."); + const pair = await handleNode(child, ctx, builtIns); + dict.__put__(pair[0], pair[1]); + } + return dict; +} + +async function handleDictionaryPair(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) { + check(node.children.length === 3, "Expected 3 children for dict pair."); + let varName = await handleNode(node.child(0)!, ctx, builtIns); + let varValue = await handleNode(node.child(2)!, ctx, builtIns); + check(typeof varName === "string", "Expected varname to be string."); + return [varName, varValue]; +} + +/** + * Wraps some common functionality of a TreeSitterNode. + */ +class Node { + type: string; + text: string; + children: Node[]; + private node: TreeSitterNode; + + constructor(node: TreeSitterNode) { + this.type = node.type; + this.text = node.text; + if (this.type === "ERROR") { + throw new Error(`Error found in parsing near "${this.text}"`); + } + this.children = []; + for (const child of node.children) { + this.children.push(new Node(child!)); + } + this.node = node; + } + + child(index: number): Node { + const child = this.children[index]; + if (!child) throw Error(`No child at index ${index}.`); + return child; + } + + log(tab = "", showNode = false) { + console.log(`${tab}--- Node`); + console.log(`${tab} type: ${this.type}`); + console.log(`${tab} text: ${this.text}`); + console.log(`${tab} children:`, this.children); + if (showNode) { + console.log(`${tab} node:`, this.node); + } + } +} + +/** + * A type that mimics a Python Tuple. + */ +export class PyTuple { + protected list: any[]; + constructor(...args: any[]) { + if (args.length === 1 && args[0] instanceof PyTuple) { + args = args[0].__unwrap__(false); + } + if (args.length === 1 && Array.isArray(args[0])) { + args = [...args[0]]; + } + this.list = [...args]; + } + + @Exposed count(v: any) { + // TODO + } + + @Exposed index() { + // TODO + } + + __at__(index: number) { + index = this.__get_relative_index__(index); + return this.list[index]; + } + + __len__() { + return this.list.length; + } + + __add__(v: any) { + if (!(v instanceof PyTuple)) { + throw new Error("Can only concatenate tuple to tuple."); + } + return new PyTuple(this.__unwrap__(false).concat(v.__unwrap__(false))); + } + + /** Puts the value to the current, existing index. Not available for Tuple. */ + __put__(index: number, v: any) { + throw new Error("Tuple does not support item assignment"); + } + + /** Gets the index for the current list, with negative index support. Throws if out of range. */ + protected __get_relative_index__(index: number) { + if (index >= 0) { + check(this.list.length > index, `Index ${index} out of range.`); + return index; + } + const relIndex = this.list.length + index; + check(relIndex >= 0, `Index ${index} out of range.`); + return relIndex; + } + + /** + * Recursively unwraps the PyTuple returning an Array. + */ + __unwrap__(deep = true) { + const l = [...this.list]; + if (deep) { + for (let i = 0; i < l.length; i++) { + l[i] = l[i]?.__unwrap__ ? l[i].__unwrap__(deep) : l[i]; + } + } + return l; + } + + // a = [ + // "__add__", + // "__class__", + // "__class_getitem__", + // "__contains__", + // "__delattr__", + // "__dir__", + // "__doc__", + // "__eq__", + // "__format__", + // "__ge__", + // "__getattribute__", + // "__getitem__", + // "__getnewargs__", + // "__gt__", + // "__hash__", + // "__init__", + // "__init_subclass__", + // "__iter__", + // "__le__", + // "__len__", + // "__lt__", + // "__mul__", + // "__ne__", + // "__new__", + // "__reduce__", + // "__reduce_ex__", + // "__repr__", + // "__rmul__", + // "__setattr__", + // "__sizeof__", + // "__str__", + // "__subclasshook__", + // "count", + // "index", + // ]; +} + +/** + * A type that mimics a Python List. + */ +export class PyList extends PyTuple { + @Exposed append(...args: any[]) { + this.list.push(...args); + } + + @Exposed clear() { + this.list.length = 0; + } + + @Exposed copy() { + // TODO + } + + @Exposed override count() { + // TODO + } + @Exposed extend() { + // TODO + } + @Exposed override index() { + // TODO + } + @Exposed insert() { + // TODO + } + @Exposed pop() { + // TODO + } + @Exposed remove() { + // TODO + } + @Exposed reverse() { + // TODO + } + @Exposed sort() { + // TODO + } + + override __add__(v: any) { + if (!(v instanceof PyList)) { + throw new Error("Can only concatenate list to list."); + } + return new PyList(this.__unwrap__(false).concat(v.__unwrap__(false))); + } + + /** Assigns an element to the current, existing index. Overriden for support on lists. */ + override __put__(index: number, v: any) { + index = this.__get_relative_index__(index); + this.list[index] = v; + } + + // aa = [ + // "__add__", + // "__class__", + // "__class_getitem__", + // "__contains__", + // "__delattr__", + // "__delitem__", + // "__dir__", + // "__doc__", + // "__eq__", + // "__format__", + // "__ge__", + // "__getattribute__", + // "__getitem__", + // "__gt__", + // "__hash__", + // "__iadd__", + // "__imul__", + // "__init__", + // "__init_subclass__", + // "__iter__", + // "__le__", + // "__len__", + // "__lt__", + // "__mul__", + // "__ne__", + // "__new__", + // "__reduce__", + // "__reduce_ex__", + // "__repr__", + // "__reversed__", + // "__rmul__", + // "__setattr__", + // "__setitem__", + // "__sizeof__", + // "__str__", + // "__subclasshook__", + // ]; +} + +class PyInt {} + +class PyDict { + #dict: {[key: string]: any}; + constructor(dict?: {[key: string]: any}) { + this.#dict = {...(dict ?? {})}; + } + + @Exposed clear() {} // Removes all the elements from the dictionary + @Exposed copy() {} // Returns a copy of the dictionary + @Exposed fromkeys() {} // Returns a dictionary with the specified keys and value + /** Returns the value of the specified key. */ + @Exposed get(key: string) { + return this.#dict[key]; + } + /** Returns a list containing a tuple for each key value pair. */ + @Exposed items() { + return new PyTuple(Object.entries(this.#dict).map((e) => new PyTuple(e))); + } + @Exposed keys() {} // Returns a list containing the dictionary's keys + @Exposed pop() {} // Removes the element with the specified key + @Exposed popitem() {} // Removes the last inserted key-value pair + @Exposed setdefault() {} // Returns the value of the specified key. If the key does not exist: insert the key, with the specified value + @Exposed update() {} // Updates the dictionary with the specified key-value pairs + @Exposed values() {} // Returns a list of all the values in the dictionary + + __put__(key: string, v: any) { + this.#dict[key] = v; + } + + __len__() { + return Object.keys(this.#dict).length; + } + + // a = [ + // "__class__", + // "__class_getitem__", + // "__contains__", + // "__delattr__", + // "__delitem__", + // "__dir__", + // "__doc__", + // "__eq__", + // "__format__", + // "__ge__", + // "__getattribute__", + // "__getitem__", + // "__gt__", + // "__hash__", + // "__init__", + // "__init_subclass__", + // "__ior__", + // "__iter__", + // "__le__", + // "__lt__", + // "__ne__", + // "__new__", + // "__or__", + // "__reduce__", + // "__reduce_ex__", + // "__repr__", + // "__reversed__", + // "__ror__", + // "__setattr__", + // "__setitem__", + // "__sizeof__", + // "__str__", + // "__subclasshook__", + // ]; + + /** + * Recursively unwraps the PyDict returning an Object. + */ + __unwrap__(deep = true) { + const d = {...this.#dict}; + if (deep) { + for (let k of Object.keys(d)) { + d[k] = d[k]?.__unwrap__ ? d[k].__unwrap__(deep) : d[k]; + } + } + return d; + } +} + +/** + * Deeply unwraps a list of values. + */ +function __unwrap__(...args: any[]) { + for (let i = 0; i < args.length; i++) { + args[i] = args[i]?.__unwrap__ ? args[i].__unwrap__(true) : args[i]; + } + return args; +} + +/** + * Checks if access to the attribute/method is allowed. + */ +function checkAttributeAccessibility(inst: any, attr: string) { + const instType = typeof inst; + check( + instType === "object" || instType === "function", + `Instance of type ${instType} does not have attributes.`, + ); + + // If the attr starts and ends with a "__" then consider it unaccessible. + check(!attr.startsWith("__") && !attr.endsWith("__"), `"${attr}" is not accessible.`); + + const attrType = typeof inst[attr]; + if (attrType === "function") { + const allowedMethods = inst.constructor?.__ALLOWED_METHODS__ ?? inst.__ALLOWED_METHODS__ ?? []; + check(allowedMethods.includes(attr), `Method ${attr} is not accessible.`); + } else { + const allowedProps = + inst.constructor?.__ALLOWED_PROPERTIES__ ?? inst.__ALLOWED_PROPERTIES__ ?? []; + check(allowedProps.includes(attr), `Property ${attr} is not accessible.`); + } +} + +function maybeWrapValue(value: any) { + if (Array.isArray(value)) { + return new PyList(value); + } + return value; +} + +function isInt(value: any): value is number { + return typeof value === "number" && Math.round(value) === value; +} + +function isIntLike(value: any): boolean { + let is = isInt(value); + if (!is) { + is = typeof value === "string" && !!/^\d+$/.exec(value); + } + return is; +} + +/** + * An experimental decorator to add allowed properties and methods to an instance. Decorated + * properties and methods on a class, and they'll be added to a static __ALLOWED_PROPERTIES__ and + * __ALLOWED_METHODS__ lists, which can then be checked while parsing to ensure entered code + * cannot end up calling something more. + * + * Note: The decorator does no work on static members; only on instance properties, methods, and + * getters (or setters). If you wish to allow access to only a getter and not setter, then you'll + * need not define the setter (or vice-versa), as adding `@Exposed` to a getter/setter decorates + * the property entirely, not just that individual getter/setter. + */ +export function Exposed(target: any, key: string) { + const descriptor = Object.getOwnPropertyDescriptor(target, key); + if (typeof descriptor?.value === "function") { + target.constructor.__ALLOWED_METHODS__ = target.constructor.__ALLOWED_METHODS__ || []; + target.constructor.__ALLOWED_METHODS__.push(key); + } else { + target.constructor.__ALLOWED_PROPERTIES__ = target.constructor.__ALLOWED_PROPERTIES__ || []; + target.constructor.__ALLOWED_PROPERTIES__.push(key); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts b/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts new file mode 100644 index 00000000..c9e36779 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts @@ -0,0 +1,209 @@ +import type {RgthreeModelInfo} from "typings/rgthree.js"; + +export type ModelInfoType = "loras" | "checkpoints"; + +type ModelsOptions = { + type: ModelInfoType; + files?: string[]; +}; + +type GetModelsOptions = ModelsOptions & { + type: ModelInfoType; + files?: string[]; + format?: null | "details"; +}; + +type GetModelsInfoOptions = GetModelsOptions & { + light?: boolean; +}; + +type GetModelsResponseDetails = { + file: string; + modified: number; + has_info: boolean; + image?: string; +}; + +class RgthreeApi { + private baseUrl!: string; + private comfyBaseUrl!: string; + getCheckpointsPromise: Promise | null = null; + getSamplersPromise: Promise | null = null; + getSchedulersPromise: Promise | null = null; + getLorasPromise: Promise | null = null; + getWorkflowsPromise: Promise | null = null; + + constructor(baseUrl?: string) { + this.setBaseUrl(baseUrl); + } + + setBaseUrl(baseUrlArg?: string) { + let baseUrl = null; + if (baseUrlArg) { + baseUrl = baseUrlArg; + } else if (window.location.pathname.includes("/rgthree/")) { + // Try to find how many relatives paths we need to go back to hit ./rgthree/api + const parts = window.location.pathname.split("/rgthree/")[1]?.split("/"); + if (parts && parts.length) { + baseUrl = parts.map(() => "../").join("") + "rgthree/api"; + } + } + this.baseUrl = baseUrl || "./rgthree/api"; + + // Calculate the comfyUI api base path by checkin gif we're on an rgthree independant page (as + // we'll always use '/rgthree/' prefix) and, if so, assume the path before `/rgthree/` is the + // base path. If we're not, then just use the same pathname logic as the ComfyUI api.js uses. + const comfyBasePathname = location.pathname.includes("/rgthree/") + ? location.pathname.split("rgthree/")[0]! + : location.pathname; + this.comfyBaseUrl = comfyBasePathname.split("/").slice(0, -1).join("/"); + } + + apiURL(route: string) { + return `${this.baseUrl}${route}`; + } + + fetchApi(route: string, options?: RequestInit) { + return fetch(this.apiURL(route), options); + } + + async fetchJson(route: string, options?: RequestInit) { + const r = await this.fetchApi(route, options); + return await r.json(); + } + + async postJson(route: string, json: any) { + const body = new FormData(); + body.append("json", JSON.stringify(json)); + return await rgthreeApi.fetchJson(route, {method: "POST", body}); + } + + getLoras(force = false) { + if (!this.getLorasPromise || force) { + this.getLorasPromise = this.fetchJson("/loras?format=details", {cache: "no-store"}); + } + return this.getLorasPromise; + } + + async fetchApiJsonOrNull(route: string, options?: RequestInit) { + const response = await this.fetchJson(route, options); + if (response.status === 200 && response.data) { + return (response.data as T) || null; + } + return null; + } + + /** + * Fetches the lora information. + * + * @param light Whether or not to generate a json file if there isn't one. This isn't necessary if + * we're just checking for values, but is more necessary when opening an info dialog. + */ + + async getModelsInfo(options: GetModelsInfoOptions): Promise { + const params = new URLSearchParams(); + if (options.files?.length) { + params.set("files", options.files.join(",")); + } + if (options.light) { + params.set("light", "1"); + } + if (options.format) { + params.set("format", options.format); + } + const path = `/${options.type}/info?` + params.toString(); + return (await this.fetchApiJsonOrNull(path)) || []; + } + async getLorasInfo(options: Omit = {}) { + return this.getModelsInfo({type: "loras", ...options}); + } + async getCheckpointsInfo(options: Omit = {}) { + return this.getModelsInfo({type: "checkpoints", ...options}); + } + + async refreshModelsInfo(options: ModelsOptions) { + const params = new URLSearchParams(); + if (options.files?.length) { + params.set("files", options.files.join(",")); + } + const path = `/${options.type}/info/refresh?` + params.toString(); + const infos = await this.fetchApiJsonOrNull(path); + return infos; + } + async refreshLorasInfo(options: Omit = {}) { + return this.refreshModelsInfo({type: "loras", ...options}); + } + async refreshCheckpointsInfo(options: Omit = {}) { + return this.refreshModelsInfo({type: "checkpoints", ...options}); + } + + async clearModelsInfo(options: ModelsOptions) { + const params = new URLSearchParams(); + if (options.files?.length) { + // encodeURIComponent ? + params.set("files", options.files.join(",")); + } + const path = `/${options.type}/info/clear?` + params.toString(); + await this.fetchApiJsonOrNull(path); + return; + } + async clearLorasInfo(options: Omit = {}) { + return this.clearModelsInfo({type: "loras", ...options}); + } + async clearCheckpointsInfo(options: Omit = {}) { + return this.clearModelsInfo({type: "checkpoints", ...options}); + } + + /** + * Saves partial data sending it to the backend.. + */ + async saveModelInfo( + type: ModelInfoType, + file: string, + data: Partial, + ): Promise { + const body = new FormData(); + body.append("json", JSON.stringify(data)); + return await this.fetchApiJsonOrNull( + `/${type}/info?file=${encodeURIComponent(file)}`, + {cache: "no-store", method: "POST", body}, + ); + } + + async saveLoraInfo( + file: string, + data: Partial, + ): Promise { + return this.saveModelInfo("loras", file, data); + } + + async saveCheckpointsInfo( + file: string, + data: Partial, + ): Promise { + return this.saveModelInfo("checkpoints", file, data); + } + + /** + * [πŸ€] Fetches from the ComfyUI given a similar functionality to the real ComfyUI API + * implementation, but can be available on independant pages outside of the ComfyUI UI. This is + * because ComfyUI frontend stopped serving its modules independantly and opted for a giant bundle + * instead which no longer allows us to load its `api.js` file separately. + */ + fetchComfyApi(route: string, options?: any): Promise { + const url = this.comfyBaseUrl + "/api" + route; + options = options || {}; + options.headers = options.headers || {}; + options.cache = options.cache || "no-cache"; + return fetch(url, options); + } + + /** + * A way to log to the terminal from JS. + */ + print(messageType: string) { + this.fetchApi(`/print?type=${messageType}`, {}) + } +} + +export const rgthreeApi = new RgthreeApi(); diff --git a/custom_nodes/rgthree-comfy/src_web/common/shared_utils.ts b/custom_nodes/rgthree-comfy/src_web/common/shared_utils.ts new file mode 100644 index 00000000..4adea7ed --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/shared_utils.ts @@ -0,0 +1,581 @@ +/** + * @fileoverview + * A bunch of shared utils that can be used in ComfyUI, as well as in any single-HTML pages. + */ + +export type Resolver = { + id: string; + completed: boolean; + resolved: boolean; + rejected: boolean; + promise: Promise; + resolve: (data: T) => void; + reject: (e?: Error) => void; + timeout: number | null; + deferment?: {data?: any; timeout?: number | null; signal?: string}; +}; + +/** + * Returns a new `Resolver` type that allows creating a "disconnected" `Promise` that can be + * returned and resolved separately. + */ +export function getResolver(timeout: number = 5000): Resolver { + const resolver: Partial> = {}; + resolver.id = generateId(8); + resolver.completed = false; + resolver.resolved = false; + resolver.rejected = false; + resolver.promise = new Promise((resolve, reject) => { + resolver.reject = (e?: Error) => { + resolver.completed = true; + resolver.rejected = true; + reject(e); + }; + resolver.resolve = (data: T) => { + resolver.completed = true; + resolver.resolved = true; + resolve(data); + }; + }); + resolver.timeout = setTimeout(() => { + if (!resolver.completed) { + resolver.reject!(); + } + }, timeout); + return resolver as Resolver; +} + +/** The WeakMap for debounced functions. */ +const DEBOUNCE_FN_TO_PROMISE: WeakMap> = new WeakMap(); + +/** + * Debounces a function call so it is only called once in the initially provided ms even if asked + * to be called multiple times within that period. + */ +export function debounce(fn: Function, ms = 64) { + if (!DEBOUNCE_FN_TO_PROMISE.get(fn)) { + DEBOUNCE_FN_TO_PROMISE.set( + fn, + wait(ms).then(() => { + DEBOUNCE_FN_TO_PROMISE.delete(fn); + fn(); + }), + ); + } + return DEBOUNCE_FN_TO_PROMISE.get(fn); +} + +/** Checks that a value is not falsy. */ +export function check(value: any, msg = "", ...args: any[]): asserts value { + if (!value) { + console.error(msg, ...(args || [])); + throw new Error(msg || "Error"); + } +} + +/** Waits a certain number of ms, as a `Promise.` */ +export function wait(ms = 16): Promise { + // Special logic, if we're waiting 16ms, then trigger on next frame. + if (ms === 16) { + return new Promise((resolve) => { + requestAnimationFrame(() => { + resolve(); + }); + }); + } + return new Promise((resolve) => { + setTimeout(() => { + resolve(); + }, ms); + }); +} + +/** Deeply freezes the passed in object. */ +export function deepFreeze(obj: T): T { + // Retrieve the property names defined on object + const propNames = Reflect.ownKeys(obj); + + // Freeze properties before freezing self + for (const name of propNames) { + const value = (obj as any)[name]; + if ((value && typeof value === "object") || typeof value === "function") { + deepFreeze(value); + } + } + return Object.freeze(obj); +} + +function dec2hex(dec: number) { + return dec.toString(16).padStart(2, "0"); +} + +/** Generates an unique id of a specific length. */ +export function generateId(length: number) { + const arr = new Uint8Array(length / 2); + crypto.getRandomValues(arr); + return Array.from(arr, dec2hex).join(""); +} + +/** + * Returns the deep value of an object given a dot-delimited key. + */ +export function getObjectValue(obj: {[key: string]: any}, objKey: string, def?: any) { + if (!obj || !objKey) return def; + + const keys = objKey.split("."); + const key = keys.shift()!; + const found = obj[key]; + if (keys.length) { + return getObjectValue(found, keys.join("."), def); + } + return found; +} + +/** + * Sets the deep value of an object given a dot-delimited key. + * + * By default, missing objects will be created while settng the path. If `createMissingObjects` is + * set to false, then the setting will be abandoned if the key path is missing an intermediate + * value. For example: + * + * setObjectValue({a: {z: false}}, 'a.b.c', true); // {a: {z: false, b: {c: true } } } + * setObjectValue({a: {z: false}}, 'a.b.c', true, false); // {a: {z: false}} + * + */ +export function setObjectValue(obj: any, objKey: string, value: any, createMissingObjects = true) { + if (!obj || !objKey) return obj; + + const keys = objKey.split("."); + const key = keys.shift()!; + if (obj[key] === undefined) { + if (!createMissingObjects) { + return; + } + obj[key] = {}; + } + if (!keys.length) { + obj[key] = value; + } else { + if (typeof obj[key] != "object") { + obj[key] = {}; + } + setObjectValue(obj[key], keys.join("."), value, createMissingObjects); + } + return obj; +} + +/** + * Moves an item in an array (by item or its index) to another index. + */ +export function moveArrayItem(arr: any[], itemOrFrom: any, to: number) { + const from = typeof itemOrFrom === "number" ? itemOrFrom : arr.indexOf(itemOrFrom); + arr.splice(to, 0, arr.splice(from, 1)[0]!); +} + +/** + * Moves an item in an array (by item or its index) to another index. + */ +export function removeArrayItem(arr: T[], itemOrIndex: T | number) { + const index = typeof itemOrIndex === "number" ? itemOrIndex : arr.indexOf(itemOrIndex); + arr.splice(index, 1); +} + +/** + * Injects CSS into the page with a promise when complete. + */ +export function injectCss(href: string): Promise { + if (document.querySelector(`link[href^="${href}"]`)) { + return Promise.resolve(); + } + return new Promise((resolve) => { + const link = document.createElement("link"); + link.setAttribute("rel", "stylesheet"); + link.setAttribute("type", "text/css"); + const timeout = setTimeout(resolve, 1000); + link.addEventListener("load", (e) => { + clearInterval(timeout); + resolve(); + }); + link.href = href; + document.head.appendChild(link); + }); +} + +/** + * Calls `Object.defineProperty` with special care around getters and setters to call out to a + * parent getter or setter (like a super.set call) to ensure any side effects up the chain + * are still invoked. + */ +export function defineProperty(instance: any, property: string, desc: PropertyDescriptor) { + const existingDesc = Object.getOwnPropertyDescriptor(instance, property); + if (existingDesc?.configurable === false) { + throw new Error(`Error: rgthree-comfy cannot define un-configurable property "${property}"`); + } + + if (existingDesc?.get && desc.get) { + const descGet = desc.get; + desc.get = () => { + existingDesc.get!.apply(instance, []); + return descGet!.apply(instance, []); + }; + } + if (existingDesc?.set && desc.set) { + const descSet = desc.set; + desc.set = (v: any) => { + existingDesc.set!.apply(instance, [v]); + return descSet!.apply(instance, [v]); + }; + } + + desc.enumerable = desc.enumerable ?? existingDesc?.enumerable ?? true; + desc.configurable = desc.configurable ?? existingDesc?.configurable ?? true; + if (!desc.get && !desc.set) { + desc.writable = desc.writable ?? existingDesc?.writable ?? true; + } + return Object.defineProperty(instance, property, desc); +} + +/** + * Determines if two DataViews are equal. + */ +export function areDataViewsEqual(a: DataView, b: DataView) { + if (a.byteLength !== b.byteLength) { + return false; + } + for (let i = 0; i < a.byteLength; i++) { + if (a.getUint8(i) !== b.getUint8(i)) { + return false; + } + } + return true; +} + +/** + * A cheap check if the source looks like base64. + */ +function looksLikeBase64(source: string) { + return source.length > 500 || source.startsWith("data:") || source.includes(";base64,"); +} + +/** + * Determines if two ArrayBuffers are equal. + */ +export function areArrayBuffersEqual(a?: ArrayBuffer | null, b?: ArrayBuffer | null) { + if (a == b || !a || !b) { + return a == b; + } + return areDataViewsEqual(new DataView(a), new DataView(b)); +} + +export function newCanvas( + widthOrPtOrImage: number | {width: number; height: number} | HTMLImageElement, + height?: number, +) { + let width: number; + if (typeof widthOrPtOrImage !== "number") { + width = widthOrPtOrImage.width; + height = widthOrPtOrImage.height; + } else { + width = widthOrPtOrImage; + height = height; + } + if (height == null) { + throw new Error("Invalid height supplied when creating new canvas object."); + } + const canvas = document.createElement("canvas"); + canvas.width = width; + canvas.height = height; + if (widthOrPtOrImage instanceof HTMLImageElement) { + const ctx = canvas.getContext("2d")!; + ctx.drawImage(widthOrPtOrImage, 0, 0, width, height); + } + return canvas; +} + +/** + * Returns canvas image data for an HTML Image. + */ +export function getCanvasImageData( + image: HTMLImageElement, +): [HTMLCanvasElement, CanvasRenderingContext2D, ImageData] { + const canvas = newCanvas(image); + const ctx = canvas.getContext("2d")!; + const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height); + return [canvas, ctx, imageData]; +} + +/** Union of types for image conversion. */ +type ImageConverstionTypes = string | Blob | ArrayBuffer | HTMLImageElement | HTMLCanvasElement; + +/** + * Converts an ImageConverstionTypes to a base64 string. + */ +export async function convertToBase64( + source: ImageConverstionTypes | Promise, +): Promise { + if (source instanceof Promise) { + source = await source; + } + if (typeof source === "string" && looksLikeBase64(source)) { + return source; + } + if (typeof source === "string" || source instanceof Blob || source instanceof ArrayBuffer) { + return convertToBase64(await loadImage(source)); + } + if (source instanceof HTMLImageElement) { + if (looksLikeBase64(source.src)) { + return source.src; + } + const [canvas, ctx, imageData] = getCanvasImageData(source); + return convertToBase64(canvas); + } + if (source instanceof HTMLCanvasElement) { + return source.toDataURL("image/png"); + } + throw Error("Unknown source to convert to base64."); +} + +/** + * Converts an ImageConverstionTypes to an image array buffer. + */ +export async function convertToArrayBuffer( + source: ImageConverstionTypes | Promise, +): Promise { + if (source instanceof Promise) { + source = await source; + } + if (source instanceof ArrayBuffer) { + return source; + } + if (typeof source === "string") { + if (looksLikeBase64(source)) { + var binaryString = atob(source.replace(/^.*?;base64,/, "")); + var bytes = new Uint8Array(binaryString.length); + for (var i = 0; i < binaryString.length; i++) { + bytes[i] = binaryString.charCodeAt(i); + } + return bytes.buffer; + } + return convertToArrayBuffer(await loadImage(source)); + } + if (source instanceof HTMLImageElement) { + const [canvas, ctx, imageData] = getCanvasImageData(source); + return convertToArrayBuffer(canvas); + } + if (source instanceof HTMLCanvasElement) { + return convertToArrayBuffer(source.toDataURL()); + } + if (source instanceof Blob) { + return source.arrayBuffer(); + } + throw Error("Unknown source to convert to arraybuffer."); +} + +/** + * Loads an image into an HTMLImageElement. + */ +export async function loadImage( + source: ImageConverstionTypes | Promise, +): Promise { + if (source instanceof Promise) { + source = await source; + } + if (source instanceof HTMLImageElement) { + return loadImage(source.src); + } + if (source instanceof Blob) { + return loadImage(source.arrayBuffer()); + } + if (source instanceof HTMLCanvasElement) { + return loadImage(source.toDataURL()); + } + if (source instanceof ArrayBuffer) { + var binary = ""; + var bytes = new Uint8Array(source); + var len = bytes.byteLength; + for (var i = 0; i < len; i++) { + binary += String.fromCharCode(bytes[i]!); + } + return loadImage(`data:${getMimeTypeFromArrayBuffer(bytes)};base64,${btoa(binary)}`); + } + return new Promise((resolve, reject) => { + const img = new Image(); + img.addEventListener("load", () => { + resolve(img); + }); + img.addEventListener("error", () => { + reject(img); + }); + img.src = source; + }); +} + +/** + * Determines the mime type from an array buffer. + */ +function getMimeTypeFromArrayBuffer(buffer: Uint8Array) { + const len = 4; + if (buffer.length >= len) { + let signatureArr = new Array(len); + for (let i = 0; i < len; i++) signatureArr[i] = buffer[i]!.toString(16); + const signature = signatureArr.join("").toUpperCase(); + switch (signature) { + case "89504E47": + return "image/png"; + case "47494638": + return "image/gif"; + case "25504446": + return "application/pdf"; + case "FFD8FFDB": + case "FFD8FFE0": + return "image/jpeg"; + case "504B0304": + return "application/zip"; + default: + return null; + } + } + return null; +} + +type BroadcasterMessage = { + id: string; + replyId?: string; + action: string; + window: Window; + port: MessagePort; + payload?: T; +}; + +type BroadcasterMessageOptions = { + timeout?: number; + listenForReply?: boolean; +}; + +/** + * A Broadcaster is a wrapper around a BroadcastChannel for communication with other windows. + */ +export class Broadcaster extends EventTarget { + private channel: BroadcastChannel; + private queue: {[key: string]: Resolver} = {}; + + constructor(channelName: string) { + super(); + this.queue = {}; + this.channel = new BroadcastChannel(channelName); + this.channel.addEventListener("message", (e) => { + this.onMessage(e); + }); + } + + /** + * Returns a unique id within the queue. + */ + private getId() { + let id: string; + do { + id = generateId(6); + } while (this.queue[id]); + return id; + } + + /** + * Broadcasts an action, and waits for a response, with a timeout before cancelling. + */ + async broadcastAndWait( + action: string, + payload?: OutPayload, + options?: BroadcasterMessageOptions, + ): Promise { + const id = this.getId(); + this.queue[id] = getResolver(options?.timeout); + this.channel.postMessage({ + id, + action, + payload, + }); + let response: InPayload[]; + try { + response = await this.queue[id]!.promise; + } catch (e) { + console.log("CAUGHT", e); + response = []; + } + return response; + } + + broadcast(action: string, payload?: OutPayload) { + this.channel.postMessage({ + id: this.getId(), + action, + payload, + }); + } + + reply(replyId: string, action: string, payload?: OutPayload) { + this.channel.postMessage({ + id: this.getId(), + replyId, + action, + payload, + }); + } + + openWindowAndWaitForMessage(rgthreePath: string, windowName?: string) { + const id = this.getId(); + this.queue[id] = getResolver(); + const win = window.open(`/rgthree/${rgthreePath}#broadcastLoadMsgId=${id}`, windowName); + return {window: win, promise: this.queue[id]!.promise}; + } + + onMessage(e: MessageEvent>) { + const msgId = e.data?.replyId || ""; + const queueItem = this.queue[msgId]; + if (queueItem) { + if (queueItem.completed) { + console.error(`${msgId} already completed..`); + } + queueItem.deferment = queueItem.deferment || {data: []}; + queueItem.deferment.data.push(e.data.payload); + queueItem.deferment.timeout && clearTimeout(queueItem.deferment.timeout); + queueItem.deferment.timeout = setTimeout(() => { + queueItem.resolve(queueItem.deferment!.data); + }, 250); + } else { + this.dispatchEvent( + new CustomEvent("rgthree-broadcast-message", { + detail: Object.assign({replyTo: e.data?.id}, e.data), + }), + ); + } + } + + addMessageListener(callback: EventListener, options?: any) { + return super.addEventListener("rgthree-broadcast-message", callback, options); + } +} + +const broadcastChannelMap: Map}> = new Map(); + +export function broadcastOnChannel( + channel: BroadcastChannel, + action: string, + payload?: T, +) { + let queue = broadcastChannelMap.get(channel); + if (!queue) { + broadcastChannelMap.set(channel, {}); + queue = broadcastChannelMap.get(channel)!; + } + let id: string; + do { + id = generateId(6); + } while (queue[id]); + queue[id] = getResolver(); + channel.postMessage({ + id, + action, + payload, + }); + return queue[id]!.promise; +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/utils_dom.ts b/custom_nodes/rgthree-comfy/src_web/common/utils_dom.ts new file mode 100644 index 00000000..143b68c5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/utils_dom.ts @@ -0,0 +1,429 @@ +/** + * Various dom manipulation utils that have followed me around. + */ +const DIRECT_ATTRIBUTE_MAP: {[name: string]: string} = { + cellpadding: "cellPadding", + cellspacing: "cellSpacing", + colspan: "colSpan", + frameborder: "frameBorder", + height: "height", + maxlength: "maxLength", + nonce: "nonce", + role: "role", + rowspan: "rowSpan", + type: "type", + usemap: "useMap", + valign: "vAlign", + width: "width", +}; + +const RGX_NUMERIC_STYLE_UNIT = "px"; +const RGX_NUMERIC_STYLE = + /^((max|min)?(width|height)|margin|padding|(margin|padding)?(left|top|bottom|right)|fontsize|borderwidth)$/i; +const RGX_DEFAULT_VALUE_PROP = /input|textarea|select/i; + +function localAssertNotFalsy(input?: T | null, errorMsg = `Input is not of type.`): T { + if (input == null) { + throw new Error(errorMsg); + } + return input; +} + +const RGX_STRING_VALID = "[a-z0-9_-]"; +const RGX_TAG = new RegExp(`^([a-z]${RGX_STRING_VALID}*)(\\.|\\[|\\#|$)`, "i"); +const RGX_ATTR_ID = new RegExp(`#(${RGX_STRING_VALID}+)`, "gi"); +const RGX_ATTR_CLASS = new RegExp(`(^|\\S)\\.([a-z0-9_\\-\\.]+)`, "gi"); +const RGX_STRING_CONTENT_TO_SQUARES = "(.*?)(\\[|\\])"; +const RGX_ATTRS_MAYBE_OPEN = new RegExp(`\\[${RGX_STRING_CONTENT_TO_SQUARES}`, "gi"); +const RGX_ATTRS_FOLLOW_OPEN = new RegExp(`^${RGX_STRING_CONTENT_TO_SQUARES}`, "gi"); + +type QueryParent = HTMLElement | Document | DocumentFragment; + +export function queryAll( + selectors: K, + parent?: QueryParent, +): Array; +export function queryAll( + selectors: K, + parent?: QueryParent, +): Array; +export function queryAll( + selectors: K, + parent?: QueryParent, +): Array; +export function queryAll(selectors: string, parent?: QueryParent): Array; +export function queryAll(selectors: string, parent: QueryParent = document) { + return Array.from(parent.querySelectorAll(selectors)).filter((n) => !!n); +} + +export function query( + selectors: K, + parent?: QueryParent, +): HTMLElementTagNameMap[K] | null; +export function query( + selectors: K, + parent?: QueryParent, +): SVGElementTagNameMap[K] | null; +export function query( + selectors: K, + parent?: QueryParent, +): MathMLElementTagNameMap[K] | null; +export function query(selectors: string, parent?: QueryParent): T | null; +export function query(selectors: string, parent: QueryParent = document) { + return parent.querySelector(selectors) ?? null; +} + +export function createText(text: string) { + return document.createTextNode(text); +} + +export function getClosestOrSelf( + element: EventTarget | HTMLElement | null, + query: string, +): HTMLElement | null { + const el = element as HTMLElement; + return (el?.closest && (((el.matches(query) && el) || el.closest(query)) as HTMLElement)) || null; +} + +export function containsOrSelf( + parent: EventTarget | HTMLElement | null, + contained: EventTarget | HTMLElement | null, +): boolean { + return ( + parent === contained || (parent as HTMLElement)?.contains?.(contained as HTMLElement) || false + ); +} + +type Attrs = { + [name: string]: any; +}; + +export function createElement(selectorOrMarkup: string, attrs?: Attrs) { + const frag = getHtmlFragment(selectorOrMarkup); + let element = frag?.firstElementChild as HTMLElement; + let selector = ""; + if (!element) { + selector = selectorOrMarkup.replace(/[\r\n]\s*/g, ""); + const tag = getSelectorTag(selector) || "div"; + element = document.createElement(tag); + selector = selector.replace(RGX_TAG, "$2"); + const brackets = selector.match(/(\[[^\]]+\])/g) || []; + for (const bracket of brackets) { + selector = selector.replace(bracket, ""); + } + // Turn id and classname into [attr]s that can be nested + selector = selector.replace(RGX_ATTR_ID, '[id="$1"]'); + selector = selector.replace( + RGX_ATTR_CLASS, + (match, p1, p2) => `${p1}[class="${p2.replace(/\./g, " ")}"]`, + ); + selector += brackets.join(""); + } + + const selectorAttrs = getSelectorAttributes(selector); + if (selectorAttrs) { + for (const attr of selectorAttrs) { + let matches = attr.substring(1, attr.length - 1).split("="); + let key = localAssertNotFalsy(matches.shift()); + let value: string = matches.join("="); + if (value === undefined) { + setAttribute(element, key, true); + } else { + value = value.replace(/^['"](.*)['"]$/, "$1"); + setAttribute(element, key, value); + } + } + } + if (attrs) { + setAttributes(element, attrs); + } + return element as T; +} +export const $el = createElement; + +function getSelectorTag(str: string) { + return tryMatch(str, RGX_TAG); +} + +function getSelectorAttributes(selector: string) { + RGX_ATTRS_MAYBE_OPEN.lastIndex = 0; + let attrs: string[] = []; + let result; + while ((result = RGX_ATTRS_MAYBE_OPEN.exec(selector))) { + let attr = result[0]; + if (attr.endsWith("]")) { + attrs.push(attr); + } else { + attr = + result[0] + getOpenAttributesRecursive(selector.substr(RGX_ATTRS_MAYBE_OPEN.lastIndex), 2); + RGX_ATTRS_MAYBE_OPEN.lastIndex += attr.length - result[0].length; + attrs.push(attr); + } + } + return attrs; +} + +function getOpenAttributesRecursive(selectorSubstring: string, openCount: number) { + let matches = selectorSubstring.match(RGX_ATTRS_FOLLOW_OPEN); + let result = ""; + if (matches && matches.length) { + result = matches[0]; + openCount += result.endsWith("]") ? -1 : 1; + if (openCount > 0) { + result += getOpenAttributesRecursive(selectorSubstring.substr(result.length), openCount); + } + } + return result; +} + +function tryMatch(str: string, rgx: RegExp, index = 1) { + let found = ""; + try { + found = str.match(rgx)?.[index] || ""; + } catch (e) { + found = ""; + } + return found; +} + +export function setAttributes(element: HTMLElement, data: {[name: string]: any}) { + let attr; + for (attr in data) { + if (data.hasOwnProperty(attr)) { + setAttribute(element, attr, data[attr]); + } + } +} + +function getHtmlFragment(value: string) { + if (value.match(/^\s*<.*?>[\s\S]*<\/[a-z0-9]+>\s*$/)) { + return document.createRange().createContextualFragment(value.trim()); + } + return null; +} + +function getChild(value: any): HTMLElement | DocumentFragment | Text | null { + if (value instanceof Node) { + return value as HTMLElement; + } + if (typeof value === "string") { + let child = getHtmlFragment(value); + if (child) { + return child; + } + if (getSelectorTag(value)) { + return createElement(value); + } + return createText(value); + } + if (value && typeof value.toElement === "function") { + return value.toElement() as HTMLElement; + } + return null; +} + +export function setAttribute(element: HTMLElement, attribute: string, value: any) { + let isRemoving = value == null; + + if (attribute === "default") { + attribute = RGX_DEFAULT_VALUE_PROP.test(element.nodeName) ? "value" : "text"; + } + + if (attribute === "text") { + empty(element).appendChild(createText(value != null ? String(value) : "")); + } else if (attribute === "html") { + empty(element).innerHTML += value != null ? String(value) : ""; + } else if (attribute == "style") { + if (typeof value === "string") { + element.style.cssText = isRemoving ? "" : value != null ? String(value) : ""; + } else { + for (const [styleKey, styleValue] of Object.entries(value as {[key: string]: any})) { + element.style[styleKey as "display"] = styleValue; + } + } + } else if (attribute == "events") { + for (const [key, fn] of Object.entries(value as {[key: string]: (e: Event) => void})) { + addEvent(element, key, fn); + } + } else if (attribute === "parent") { + value.appendChild(element); + } else if (attribute === "child" || attribute === "children") { + // Try to handle an array, like [li,li,li]. Not nested brackets, though + if (typeof value === "string" && /^\[[^\[\]]+\]$/.test(value)) { + const parseable = value.replace(/^\[([^\[\]]+)\]$/, '["$1"]').replace(/,/g, '","'); + try { + const parsed = JSON.parse(parseable); + value = parsed; + } catch (e) { + console.error(e); + } + } + + // "children" is a replace of the children, while "child" appends a new child if others exist. + if (attribute === "children") { + empty(element); + } + + let children = value instanceof Array ? value : [value]; + for (let child of children) { + child = getChild(child); + if (child instanceof Node) { + if (element instanceof HTMLTemplateElement) { + element.content.appendChild(child); + } else { + element.appendChild(child); + } + } + } + } else if (attribute == "for") { + (element as HTMLLabelElement).htmlFor = value != null ? String(value) : ""; + if (isRemoving) { + // delete (element as HTMLLabelElement).htmlFor; + element.removeAttribute("for"); + } + } else if (attribute === "class" || attribute === "className" || attribute === "classes") { + element.className = isRemoving ? "" : Array.isArray(value) ? value.join(" ") : String(value); + } else if (attribute === "dataset") { + if (typeof value !== "object") { + console.error("Expecting an object for dataset"); + return; + } + for (const [key, val] of Object.entries(value)) { + element.dataset[key] = String(val); + } + } else if (attribute.startsWith("on") && typeof value === "function") { + element.addEventListener(attribute.substring(2), value); + } else if (["checked", "disabled", "readonly", "required", "selected"].includes(attribute)) { + // Could be input, button, etc. We are not discriminate. + (element as HTMLInputElement)[attribute as "checked"] = !!value; + if (!value) { + (element as HTMLInputElement).removeAttribute(attribute); + } else { + (element as HTMLInputElement).setAttribute(attribute, attribute); + } + } else if (DIRECT_ATTRIBUTE_MAP.hasOwnProperty(attribute)) { + if (isRemoving) { + element.removeAttribute(DIRECT_ATTRIBUTE_MAP[attribute]!); + } else { + element.setAttribute(DIRECT_ATTRIBUTE_MAP[attribute]!, String(value)); + } + } else if (isRemoving) { + element.removeAttribute(attribute); + } else { + let oldVal = element.getAttribute(attribute); + if (oldVal !== value) { + element.setAttribute(attribute, String(value)); + } + } +} + +function addEvent(element: HTMLElement, key: string, fn: (e: Event) => void) { + element.addEventListener(key, fn); +} + +function setStyles(element: HTMLElement, styles: {[name: string]: string | number} | null = null) { + if (styles) { + for (let name in styles) { + setStyle(element, name, styles[name]!); + } + } + return element; +} + +export function setStyle(element: HTMLElement, name: string, value: string | number | null) { + // Note: Old IE uses 'styleFloat' + name = name.indexOf("float") > -1 ? "cssFloat" : name; + // Camelcase + if (name.indexOf("-") != -1) { + name = name.replace(/-\D/g, (match) => { + return match.charAt(1).toUpperCase(); + }); + } + if (value == String(Number(value)) && RGX_NUMERIC_STYLE.test(name)) { + value = value + RGX_NUMERIC_STYLE_UNIT; + } + if (name === "display" && typeof value !== "string") { + value = !!value ? null : "none"; + } + (element.style as any)[name] = value === null ? null : String(value); + return element; +} + +export function empty(element: HTMLElement) { + while (element.firstChild) { + element.removeChild(element.firstChild); + } + return element; +} + +export function remove(element: HTMLElement) { + while (element.parentElement) { + element.parentElement.removeChild(element); + } + return element; +} + +export function replaceChild(oldChildNode: Node, newNode: Node) { + oldChildNode.parentNode!.replaceChild(newNode, oldChildNode); + return newNode; +} + +type ChildType = HTMLElement | DocumentFragment | Text | string | null; +export function appendChildren(el: HTMLElement, children: ChildType | ChildType[]) { + children = !Array.isArray(children) ? [children] : children; + for (let child of children) { + child = getChild(child); + if (child instanceof Node) { + if (el instanceof HTMLTemplateElement) { + el.content.appendChild(child); + } else { + el.appendChild(child); + } + } + } +} + +/** + * Returns elements and their actions. + * + * data-action="click:action-signal" + */ +export function getActionEls(parent: Element | Document = document) { + const els = Array.from(parent.querySelectorAll("[data-action],[on-action],[on]")); + if (parent instanceof Element) { + els.unshift(parent); + } + return els + .map((actionEl) => { + const actions: {[action: string]: string} = {}; + const actionSegments = ( + actionEl.getAttribute("data-action") || + actionEl.getAttribute("on-action") || + actionEl.getAttribute("on") || + "" + ).split(";"); + for (let segment of actionSegments) { + let actionsData = segment + .trim() + .split(/\s*:\s*/g) + .filter((i) => !!i.trim()) as [string, string?]; + if (!actionsData.length) continue; + if (actionsData.length === 1) { + if (actionEl instanceof HTMLInputElement) { + actionsData.unshift("input"); + } else { + actionsData.unshift("click"); + } + } + if (actionsData[0] && actionsData[1]) { + actions[actionsData[0]] = actionsData[1]; + // actionEl.addEventListener(actionsData[0], (e) => {this.handleAction(actionsData[1]!, actionEl, e);}); + } + } + return { + el: actionEl, + actions, + }; + }) + .filter((el) => !!el); +} diff --git a/custom_nodes/rgthree-comfy/src_web/common/utils_templates.ts b/custom_nodes/rgthree-comfy/src_web/common/utils_templates.ts new file mode 100644 index 00000000..897f4289 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/common/utils_templates.ts @@ -0,0 +1,879 @@ +import * as dom from "./utils_dom"; +import {getObjectValue} from "./shared_utils"; + +const CONFIG_DEFAULT = { + attrBind: "data-bind", + attrIf: "data-if", + attrIfIs: "data-if-is", +}; + +const CONFIG = Object.assign({}, CONFIG_DEFAULT, { + attrBind: "bind", + attrIf: "if", + attrIfIs: "if-is", +}); + +export interface BindOptions { + /** + * If true then only those data-bind keys in the data map will be bound, + * and no `data-bind` fields will be unbound. + */ + onlyDefined?: boolean; + + /** If true, then binding/init will not be called on nested templates. */ + singleScoped?: boolean; + + /** Context elemnt. */ + contextElement?: HTMLElement; +} + +export interface InflateOptions { + skipInit?: boolean; + bindOptions?: BindOptions; +} + +export interface TemplateData { + fragment: DocumentFragment; + preProcessScript: (data: any) => void; +} + +export interface BindingContext { + data: any; + contextElement: HTMLElement; + currentElement: HTMLElement; +} + +// const RGX_COMPARISON = /^\(?([a-z0-9\.\-\[\]'"]+)((?:<|>|==)=?)([a-z0-9\.\-\[\]'"]+)\)?$/i; +const RGX_COMPARISON = (() => { + // /^\(?([a-z0-9\.\-\[\]'"]+)((?:<|>|==)=?)([a-z0-9\.\-\[\]'"]+)\)?$/i; + let value = "((?:\\!*)[_a-z0-9\\.\\-\\[\\]'\"]+)"; + let comparison = "((?:<|>|==|\\!=)=?)"; + return new RegExp(`^(?:\\!*)\\(?${value}\\s*${comparison}\\s*${value}\\)?$`, "i"); +})(); + +const RGXPART_BIND_FN_TEMPLATE_STRING = "template|tpl"; +const RGXPART_BIND_FN_ELEMENT_STRING = "element|el"; +const RGX_BIND_FN_TEMPLATE = new RegExp( + `^(?:${RGXPART_BIND_FN_TEMPLATE_STRING})\\(([^\\)]+)\\)`, + "i", +); +const RGX_BIND_FN_ELEMENT = new RegExp( + `^(?:${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`, + "i", +); +const RGX_BIND_FN_TEMPLATE_OR_ELEMENT = new RegExp( + `^(?:${RGXPART_BIND_FN_TEMPLATE_STRING}|${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`, + "i", +); +const RGX_BIND_FN_LENGTH = /^(?:length|len|size)\(([^\)]+)\)/i; +const RGX_BIND_FN_FORMAT = /^(?:format|fmt)\(([^\,]+),([^\)]+)\)/i; +const RGX_BIND_FN_CALL = /^([^\(]+)\(([^\)]*)\)/i; + +const EMPTY_PREPROCESS_FN = (data: any) => data; + +// This is used within exec, so we don't need to check the first part since it's +// always the lastIndex start position +// const RGX_BIND_DECLARATIONS = /\s*((?:[\$_a-z0-9-\.]|\?\?|\|\|)+(?:\([^\)]+\))?)(?::(.*?))?(\s|$)/ig; +const RGX_BIND_DECLARATIONS = + /\s*(\!*(?:[\$_a-z0-9-\.\'\"]|\?\?|\|\||\&\&|(?:(?:<|>|==|\!=)=?))+(?:\`[^\`]+\`)?(?:\([^\)]*\))?)(?::(.*?))?(\s|$)/gi; + +/** + * Asserts that something is not null of undefined. + */ +function localAssertNotFalsy(input?: T | null, errorMsg = `Input is not of type.`): T { + if (input == null) { + throw new Error(errorMsg); + } + return input; +} + +/** + * Cleans a key. + */ +function cleanKey(key: string) { + return key.toLowerCase().trim().replace(/\s/g, ""); +} + +/** + * Ensures the value is an array, converting array-like items to an array. + */ +function toArray(value: any | any[]): any[] { + if (Array.isArray(value)) { + return value; + } + if (value instanceof Set) { + return Array.from(value); + } + // Array-like. + if (typeof value === "object" && typeof value.length === "number") { + return [].slice.call(value); + } + return [value]; +} + +/** + * Flattens an array. + */ +function flattenArray(arr: any | any[]): any[] { + return toArray(arr).reduce((acc, val) => { + return acc.concat(Array.isArray(val) ? flattenArray(val) : val); + }, []); +} + +/** + * Gets an object value by a string lookup. + */ +function getObjValue(lookup: string, obj: any): any { + // If we want to cast as a boolean via a /!+/ prefix. + let booleanMatch: string[] = lookup.match(/^(\!+)(.+?)$/i) || []; + let booleanNots: string[] = []; + if (booleanMatch[1] && booleanMatch[2]) { + booleanNots = booleanMatch[1].split(""); + lookup = booleanMatch[2]; + } + + let value = getObjectValue(obj, lookup); + while (booleanNots.length) { + value = !value; + booleanNots.shift(); + } + return value; +} + +/** + * Gets a primotove or object value. + */ +function getPrimitiveOrObjValue(stringValue: string | null | undefined, data: any) { + let value; + if (stringValue == null) { + return stringValue; + } + let negate = getNegates(stringValue); + if (negate != null) { + stringValue = stringValue.replace(/^\!+/, ""); + } + try { + const cleanedStringValue = stringValue.replace(/^'(.*)'$/, '"$1"'); + value = JSON.parse(cleanedStringValue); + } catch (e) { + value = getObjValue(stringValue, data); + } + value = negate !== null ? (negate === 1 ? !value : !!value) : value; + return value; +} + +/** + * Get the negates for a string. A `null` value means there are no negates, + * otherwise a `1` means it should be negated, and a `0` means double-negate + * (e.g. cast as boolean). + * + * 'boolVar' => null + * '!boolVar' => 1 + * '!!boolVar' => 0 + * '!!!boolVar' => 1 + * '!!!!boolVar' => 0 + */ +function getNegates(stringValue: string): number | null { + let negate = null; + let negateMatches = stringValue.match(/^(\!+)(.*)/); + if (negateMatches && negateMatches.length >= 3) { + negate = negateMatches[1]!.length % 2; + } + return negate; +} + +/** + * Returns the boolean for a comparison of object values. A `null` value would + * be if the experssion does not match a comparison, and undefined if there was + * but it's not a known comparison (===, ==, <=, >=, !==, !=, <, >). + */ +function getStringComparisonExpression( + bindingPropName: string, + data: any, +): boolean | null | undefined { + let comparisonMatches = bindingPropName.match(RGX_COMPARISON); + if (!comparisonMatches?.length) { + return null; + } + let a = getPrimitiveOrObjValue(comparisonMatches[1]!, data); + let b = getPrimitiveOrObjValue(comparisonMatches[3]!, data); + let c = comparisonMatches[2]; + let value = (() => { + switch (c) { + case "===": + return a === b; + case "==": + return a == b; + case "<=": + return a <= b; + case ">=": + return a >= b; + case "!==": + return a !== b; + case "!=": + return a != b; + case "<": + return a < b; + case ">": + return a > b; + default: + return undefined; + } + })(); + return value; +} + +/** + * Replaces a element with children with special attribute copies for + * single children. + */ +function replaceTplElementWithChildren( + tplEl: HTMLElement, + fragOrElOrEls: DocumentFragment | HTMLElement | Array, +) { + const els = Array.isArray(fragOrElOrEls) ? fragOrElOrEls : [fragOrElOrEls]; + tplEl.replaceWith(...els); + // dom.replaceChild(tplEl, fragOrElOrEls); + const numOfChildren = Array.isArray(fragOrElOrEls) + ? fragOrElOrEls.length + : fragOrElOrEls.childElementCount; + if (numOfChildren === 1) { + const firstChild = Array.isArray(fragOrElOrEls) + ? fragOrElOrEls[0] + : fragOrElOrEls.firstElementChild; + if (firstChild instanceof Element) { + if (tplEl.className.length) { + firstChild.className += ` ${tplEl.className}`; + } + let attr = tplEl.getAttribute("data"); + if (attr) { + firstChild.setAttribute("data", attr); + } + attr = tplEl.getAttribute(CONFIG.attrBind); + if (attr) { + firstChild.setAttribute(CONFIG.attrBind, attr); + } + } + } +} + +/** + * Entry point to get data from a binding name. Checks for null coelescing and + * logical-or operators. + * + * Handles templates as well: + * + * my.value`some string ${value} is cool.` + */ +function getValueForBinding(bindingPropName: string, context: BindingContext) { + console.log("getValueForBinding", bindingPropName, context); + const data = context.data; + let stringTemplate = null; + let stringTemplates = /^(.*?)\`([^\`]*)\`$/.exec(bindingPropName.trim()); + if (stringTemplates?.length === 3) { + bindingPropName = stringTemplates[1]!; + stringTemplate = stringTemplates[2]; + } + let value = null; + + let hadALogicalOp = false; + const opsToValidation = new Map([ + [/\s*\?\?\s*/, (v: any) => v != null], + [/\s*\|\|\s*/, (v: any) => !!v], + [/\s*\&\&\s*/, (v: any) => !v], + ]); + for (const [op, fn] of opsToValidation.entries()) { + if (bindingPropName.match(op)) { + hadALogicalOp = true; + const bindingPropNames = bindingPropName.split(op); + for (const propName of bindingPropNames) { + value = getValueForBindingPropName(propName, context); + if (fn(value)) { + break; + } + } + break; + } + } + + if (!hadALogicalOp) { + value = getValueForBindingPropName(bindingPropName, context); + } + + return stringTemplate && value != null + ? stringTemplate.replace(/\$\{value\}/g, String(value)) + : value; +} + +/** + * Gets the value for a binding prop name. + */ +function getValueForBindingPropName(bindingPropName: string, context: BindingContext) { + const data = context.data; + let negate = getNegates(bindingPropName); + if (negate != null) { + bindingPropName = bindingPropName.replace(/^\!+/, ""); + } + let value; + RGX_COMPARISON.lastIndex = 0; + if (RGX_COMPARISON.test(bindingPropName)) { + value = getStringComparisonExpression(bindingPropName, data); + } else if (RGX_BIND_FN_LENGTH.test(bindingPropName)) { + bindingPropName = RGX_BIND_FN_LENGTH.exec(bindingPropName)![1]!; + value = getPrimitiveOrObjValue(bindingPropName, data); + value = (value && value.length) || 0; + } else if (RGX_BIND_FN_FORMAT.test(bindingPropName)) { + let matches = RGX_BIND_FN_FORMAT.exec(bindingPropName); + bindingPropName = matches![1]!; + value = getPrimitiveOrObjValue(bindingPropName, data); + value = matches![2]!.replace(/^['"]/, "").replace(/['"]$/, "").replace(/\$1/g, value); + } else if (RGX_BIND_FN_CALL.test(bindingPropName)) { + console.log("-----"); + console.log(bindingPropName); + let matches = RGX_BIND_FN_CALL.exec(bindingPropName); + const functionName = matches![1]!; + const maybeDataName = matches![2] ?? null; + value = getPrimitiveOrObjValue(maybeDataName, data); + console.log(functionName, maybeDataName, value); + // First, see if the instance has this call + if (typeof value?.[functionName] === "function") { + value = value[functionName](value, data, context.currentElement, context.contextElement); + } else if (typeof data?.[functionName] === "function") { + value = data[functionName](value, data, context.currentElement, context.contextElement); + } else if (typeof (context.currentElement as any)?.[functionName] === "function") { + value = (context.currentElement as any)[functionName]( + value, + data, + context.currentElement, + context.contextElement, + ); + } else if (typeof (context.contextElement as any)?.[functionName] === "function") { + value = (context.contextElement as any)[functionName]( + value, + data, + context.currentElement, + context.contextElement, + ); + } else { + console.error( + `No method named ${functionName} on data or element instance. Just calling regular value.`, + ); + value = getPrimitiveOrObjValue(bindingPropName, data); + } + } else { + value = getPrimitiveOrObjValue(bindingPropName, data); + } + + if (value !== undefined) { + value = negate !== null ? (negate === 1 ? !value : !!value) : value; + } + return value; +} + +/** + * Removes data-bind attributes, ostensibly "freezing" the current element. + * + * @param deep Will remove all data-bind attributes when true. default behavior + * is only up to the next data-tpl. + */ +function removeBindingAttributes( + elOrEls: DocumentFragment | HTMLElement | HTMLElement[], + deep = false, +) { + flattenArray(elOrEls || []).forEach((el) => { + el.removeAttribute(CONFIG.attrBind); + const innerBinds = dom.queryAll(`:scope [${CONFIG.attrBind}]`, el); + // If we're deep, then pretend there are no data-tpl. + const innerTplBinds = deep ? [] : dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}]`); + + innerBinds.forEach((el) => { + if (deep || !innerTplBinds.includes(el)) { + el.removeAttribute(CONFIG.attrBind); + } + }); + }); +} + +const templateCache: {[key: string]: TemplateData} = {}; + +/** + * Checks if a template exists. + */ +export function checkKey(key: string) { + return !!templateCache[cleanKey(key)]; +} + +/** + * Register a template to it's key and a DocumentFragment to store the markup. + * Uses ` + rgthree's comfy: Workflow Link Fixer + + + + +
    +

    rgthree's Workflow Link Fixer

    +

    + Drag and drop a comfy-generated image or workflow json into this window to check its + serialized links and attempt to fix. +

    + + Sometimes as you have complex workflows the internal data can become corrupt, and the + ComfyUI doesn't always understand or display correctly. Maybe links disappear, or reconnect + to another node when changing something. Load it here to detect and, if possible, attempt + to fix it (sometimes, however, fixing it just isn't feasible, so fingers crossed). + + +
    + + + +
    +
    + +
    + + + +
    + + + + \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/link_fixer/link_page.ts b/custom_nodes/rgthree-comfy/src_web/link_fixer/link_page.ts new file mode 100644 index 00000000..024ff6bc --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/link_fixer/link_page.ts @@ -0,0 +1,225 @@ +import type {ISerialisedGraph} from "@comfyorg/frontend"; +import type {BadLinksData} from "../common/link_fixer.js"; + +import {WorkflowLinkFixer} from "../common/link_fixer.js"; +import {getPngMetadata} from "../common/comfyui_shim.js"; + +function wait(ms = 16, value?: any) { + return new Promise((resolve) => { + setTimeout(() => { + resolve(value); + }, ms); + }); +} + +const logger = { + logTo: console as Console | HTMLElement, + log: (...args: any[]) => { + logger.logTo === console + ? console.log(...args) + : ((logger.logTo as HTMLElement).innerText += args.join(",") + "\n"); + }, +}; + +export class LinkPage { + private containerEl: HTMLDivElement; + private figcaptionEl: HTMLElement; + private btnFix: HTMLButtonElement; + private outputeMessageEl: HTMLDivElement; + private outputImageEl: HTMLImageElement; + + private file?: File | Blob; + private graph?: ISerialisedGraph; + private graphResults?: BadLinksData; + private graphFinalResults?: BadLinksData; + + private fixer: WorkflowLinkFixer | null = null; + + constructor() { + // const consoleEl = document.getElementById("console")!; + this.containerEl = document.querySelector(".box")!; + this.figcaptionEl = document.querySelector("figcaption")!; + this.outputeMessageEl = document.querySelector(".output")!; + this.outputImageEl = document.querySelector(".output-image")!; + this.btnFix = document.querySelector(".btn-fix")!; + + // Need to prevent on dragover to allow drop... + document.addEventListener( + "dragover", + (e) => { + e.preventDefault(); + }, + false, + ); + document.addEventListener("drop", (e) => { + this.onDrop(e); + }); + this.btnFix.addEventListener("click", (e) => { + this.onFixClick(e); + }); + } + + private async onFixClick(e: MouseEvent) { + if (!this.fixer?.checkedData || !this.graph) { + this.updateUi("β›” Fix button click without results."); + return; + } + this.graphFinalResults = this.fixer.fix(); + + if (this.graphFinalResults.hasBadLinks) { + this.updateUi( + "β›” Hmm... Still detecting bad links. Can you file an issue at https://github.com/rgthree/rgthree-comfy/issues with your image/workflow.", + ); + } else { + this.updateUi( + "βœ… Workflow fixed.

    Please load new saved workflow json and double check linking and execution.", + ); + } + await wait(16); + await this.saveFixedWorkflow(); + } + + private async onDrop(event: DragEvent) { + if (!event.dataTransfer) { + return; + } + this.reset(); + + event.preventDefault(); + event.stopPropagation(); + + // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that + if (event.dataTransfer.files.length && event.dataTransfer.files?.[0]?.type !== "image/bmp") { + await this.handleFile(event.dataTransfer.files[0]!); + return; + } + + // Try loading the first URI in the transfer list + const validTypes = ["text/uri-list", "text/x-moz-url"]; + const match = [...event.dataTransfer.types].find((t) => validTypes.find((v) => t === v)); + if (match) { + const uri = event.dataTransfer.getData(match)?.split("\n")?.[0]; + if (uri) { + await this.handleFile(await (await fetch(uri)).blob()); + } + } + } + + reset() { + this.file = undefined; + this.graph = undefined; + this.graphResults = undefined; + this.graphFinalResults = undefined; + this.updateUi(); + } + + private updateUi(msg?: string) { + this.outputeMessageEl.innerHTML = ""; + if (this.file && !this.containerEl.classList.contains("-has-file")) { + this.containerEl.classList.add("-has-file"); + this.figcaptionEl.innerHTML = (this.file as File).name || this.file.type; + if (this.file.type === "application/json") { + this.outputImageEl.src = "icon_file_json.png"; + } else { + const reader = new FileReader(); + reader.onload = () => (this.outputImageEl.src = reader.result as string); + reader.readAsDataURL(this.file); + } + } else if (!this.file && this.containerEl.classList.contains("-has-file")) { + this.containerEl.classList.remove("-has-file"); + this.outputImageEl.src = ""; + this.outputImageEl.removeAttribute("src"); + } + + if (this.graphResults) { + this.containerEl.classList.add("-has-results"); + if (!this.graphResults.patches && !this.graphResults.deletes) { + this.outputeMessageEl.innerHTML = "βœ… No bad links detected in the workflow."; + } else { + this.containerEl.classList.add("-has-fixable-results"); + this.outputeMessageEl.innerHTML = `⚠️ Found ${this.graphResults.patches} links to fix, and ${this.graphResults.deletes} to be removed.`; + } + } else { + this.containerEl.classList.remove("-has-results"); + this.containerEl.classList.remove("-has-fixable-results"); + } + + if (msg) { + this.outputeMessageEl.innerHTML = msg; + } + } + + private async handleFile(file: File | Blob) { + this.file = file; + this.updateUi(); + + let workflow: string | undefined | null = null; + if (file.type.startsWith("image/")) { + const pngInfo = await getPngMetadata(file); + workflow = pngInfo?.workflow; + } else if ( + file.type === "application/json" || + (file instanceof File && file.name.endsWith(".json")) + ) { + workflow = await new Promise((resolve) => { + const reader = new FileReader(); + reader.onload = () => { + resolve(reader.result as string); + }; + reader.readAsText(file); + }); + } + if (!workflow) { + this.updateUi("β›” No workflow found in dropped item."); + } else { + try { + this.graph = JSON.parse(workflow); + } catch (e) { + this.graph = undefined; + } + if (!this.graph) { + this.updateUi("β›” Invalid workflow found in dropped item."); + } else { + this.loadGraphData(this.graph); + } + } + } + + private async loadGraphData(graphData: ISerialisedGraph) { + this.fixer = WorkflowLinkFixer.create(graphData); + this.graphResults = this.fixer.check(); + this.updateUi(); + } + + private async saveFixedWorkflow() { + if (!this.graphFinalResults) { + this.updateUi("β›” Save w/o final graph patched."); + return false; + } + + let filename: string | null = (this.file as File).name || "workflow.json"; + let filenames = filename.split("."); + filenames.pop(); + filename = filenames.join("."); + filename += "_fixed.json"; + filename = prompt("Save workflow as:", filename); + if (!filename) return false; + if (!filename.toLowerCase().endsWith(".json")) { + filename += ".json"; + } + const json = JSON.stringify(this.graphFinalResults.graph, null, 2); + const blob = new Blob([json], {type: "application/json"}); + const url = URL.createObjectURL(blob); + const anchor = document.createElement("a"); + anchor.download = filename; + anchor.href = url; + anchor.style.display = "none"; + document.body.appendChild(anchor); + await wait(); + anchor.click(); + await wait(); + anchor.remove(); + window.URL.revokeObjectURL(url); + return true; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.html b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.html new file mode 100644 index 00000000..3be75c9d --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.html @@ -0,0 +1,18 @@ + + + diff --git a/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.scss b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.scss new file mode 100644 index 00000000..82e085a5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.scss @@ -0,0 +1,42 @@ +$space: 8px; + +.rgthree-model-info-card { + display: block; + padding: $space; +} + +.-is-hidden { + display: none; +} + + +.rgthree-model-info-card { + display: flex; + flex-direction: row; + + >.rgthree-model-info-card-media-container { + width: 100px; + height: auto; + display: block; + margin: 0 $space 0 0; + padding: 0; + flex: 0 0 auto; + + >img, + >video { + width: 100%; + height: 100%; + object-fit: contain; + } + } + + >.rgthree-model-info-card-data-container { + + [bind*="name:"] { + font-size: 1.3em; + margin-bottom: calc($space / 2); + font-weight: bold; + } + + } +} \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.ts b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.ts new file mode 100644 index 00000000..3d5cdea9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.ts @@ -0,0 +1,53 @@ +import {RgthreeCustomElement} from "rgthree/common/components/base_custom_element"; +import {RgthreeModelInfo} from "typings/rgthree"; + +export class RgthreeModelInfoCard extends RgthreeCustomElement { + static override readonly NAME = "rgthree-model-info-card"; + static override readonly TEMPLATES = "components/model-info-card.html"; + static override readonly CSS = "components/model-info-card.css"; + static override readonly USE_SHADOW = false; + + private data: RgthreeModelInfo = {}; + + getModified( + value: number, + data: any, + currentElement: HTMLElement, + contextElement: RgthreeModelInfoCard, + ) { + const date = new Date(value); + return String(`${date.toLocaleDateString()} ${date.toLocaleTimeString()}`); + } + + getCivitaiLink(links: string[] | undefined) { + return links?.find((i) => i.includes("civitai.com/models")) || null; + } + + setModelData(data: RgthreeModelInfo) { + this.data = data; + } + + hasBaseModel(baseModel: string) { + return this.data.baseModel === baseModel; + } + + hasData(field: string) { + // return !!this.data.hasInfoFile; + if (field === "civitai") { + return !!this.getCivitaiLink(this.data.links)?.length; + } + return !!(this.data as any)[field]; + } + + matchesQueryText(query: string) { + return (this.data.name || this.data.file)?.includes(query); + } + + hide() { + this.classList.add("-is-hidden"); + } + + show() { + this.classList.remove("-is-hidden"); + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/models/index.html b/custom_nodes/rgthree-comfy/src_web/models/index.html new file mode 100644 index 00000000..137a1f72 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/models/index.html @@ -0,0 +1,18 @@ + + + + rgthree-comfy: Models Manager + + + + +
    + +
    +
      + + + \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/models/models.scss b/custom_nodes/rgthree-comfy/src_web/models/models.scss new file mode 100644 index 00000000..160ff174 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/models/models.scss @@ -0,0 +1,40 @@ +$space: 8px; + +:root { + --rgthree-bg-color: rgba(23, 23, 23, 0.9); + --rgthree-on-bg-color: rgba(48, 48, 48, 0.9); +} + +html, body { + margin: 0; + padding: 0; + font-family: Arial, sans-serif; + box-sizing: border-body; + background: var(--rgthree-bg-color); +} + +*, *::before, *::after { + box-sizing: inherit; +} + +[if-is="false"] { + display: none; +} + +.models-list { + list-style: none; + margin: 0; + padding: 0; +} + +.model-item { + display: block; + margin: 8px; +} + +rgthree-model-info-card { + background: var(--rgthree-on-bg-color); + margin: $space * 2; + display: block; + border-radius: 4px; +} \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/models/models_info_page.ts b/custom_nodes/rgthree-comfy/src_web/models/models_info_page.ts new file mode 100644 index 00000000..df6e38e4 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/models/models_info_page.ts @@ -0,0 +1,136 @@ +import {createElement, getActionEls, query, queryAll} from "../common/utils_dom.js"; + +import {rgthreeApi} from "rgthree/common/rgthree_api.js"; +import {RgthreeModelInfoCard} from "./components/model-info-card.js"; + +function parseQuery(query: string) { + // Split on spaces + const matches = query.match(/"[^\"]+"/g) || []; + for (const match of matches) { + let cleaned = match.substring(1, match.length - 1); + cleaned = cleaned.replace(/\s+/g, " ").trim().replace(/\s/g, "__SPACE__"); + query = query.replace(match, ` ${cleaned} `); + } + const queryParts = query + .replace(/\s+/g, " ") + .trim() + .split(" ") + .map((p) => p.replace(/__SPACE__/g, " ")); + return queryParts; +} + +export class ModelsInfoPage { + private readonly selectBaseModel: HTMLSelectElement = createElement( + 'select[name="baseModel"][on="change:filter"]', + ); + private readonly searchbox: HTMLInputElement = query("#searchbox")!; + private readonly modelsList: HTMLUListElement = query("#models-list")!; + private queryLast = ""; + private doSearchDebounce: number = 0; + + constructor() { + console.log("hello model page"); + // rgthreeApi.setBaseUrl('../api'); + this.init(); + } + + async init() { + this.searchbox.addEventListener("input", (e) => { + if (this.doSearchDebounce) { + return; + } + this.doSearchDebounce = setTimeout(() => { + this.doSearch(); + this.doSearchDebounce = 0; + }, 250); + }); + + const loras = await rgthreeApi.getLorasInfo({light: true}); + console.log(loras); + + const baseModels = new Set(); + + for (const lora of loras) { + const el = RgthreeModelInfoCard.create(); + el.setModelData(lora); + el.bindWhenConnected(lora); + console.log(el); + lora.baseModel && baseModels.add(lora.baseModel); + this.modelsList.appendChild(createElement("li.model-item", {child: el})); + } + + if (baseModels.size > 1) { + createElement(`option[value="ALL"][text="Choose base model."]`, { + parent: this.selectBaseModel, + }); + for (const baseModel of baseModels.values()) { + createElement(`option[value="${baseModel}"][text="${baseModel}"]`, { + parent: this.selectBaseModel, + }); + } + this.searchbox.insertAdjacentElement("afterend", this.selectBaseModel); + } + + const data = getActionEls(document.body); + for (const dataItem of Object.values(data)) { + for (const [event, action] of Object.entries(dataItem.actions)) { + dataItem.el.addEventListener(event as keyof ElementEventMap, (e) => { + if (typeof (this as any)[action] != "function") { + throw new Error(`"${action}" does not exist on instance.`); + } + (this as any)[action](e); + }); + } + } + } + + filter() { + const parts = parseQuery(this.queryLast); + const baseModel = this.selectBaseModel.value; + const els = queryAll(RgthreeModelInfoCard.NAME); + const shouldHide = (el: RgthreeModelInfoCard) => { + let hide = baseModel !== "ALL" && !el.hasBaseModel(baseModel); + if (!hide) { + for (let part of parts) { + let negate = false; + if (part.startsWith("-")) { + negate = true; + part = part.substring(1); + } + if (!part) continue; + + if (part.startsWith("has:")) { + if (part === "has:civitai") { + hide = !el.hasData(part.replace("has:", "")); + } + } else { + hide = !el.matchesQueryText(part); + } + hide = negate ? !hide : hide; + if (hide) { + break; + } + } + } + return hide; + }; + for (const el of els) { + const hide = shouldHide(el); + if (hide) { + el.hide(); + } else { + el.show(); + } + } + + console.log("filter"); + } + + doSearch() { + const query = this.searchbox.value.trim(); + if (this.queryLast != query) { + this.queryLast = query; + this.filter(); + } + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/README.md b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/README.md new file mode 100644 index 00000000..9b5b4225 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/README.md @@ -0,0 +1,6 @@ +Here lies dummy ts files that decalre/export ComfyUI's own scripts files as typed types w/o needing +to symlink to the actual implementation. + +Actual code in the comfyui/ directory can import these like `import {app} from "/scripts/app.js"` +and have access to `app` as the fully typed `ComfyApp`. The `__build__.py` script will rewrite these +to the relative browser path. \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/api.ts b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/api.ts new file mode 100644 index 00000000..dc440085 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/api.ts @@ -0,0 +1,10 @@ +interface ComfyApi extends EventTarget { + api_base: string; + getNodeDefs(): any; + apiURL(url: string): string; + queuePrompt(num: number, data: { output: {}; workflow: {} }): Promise<{}>; + fetchApi(route: string, options?: RequestInit) : Promise; + interrupt(): void; +} + +export declare const api: ComfyApi; diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/app.ts b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/app.ts new file mode 100644 index 00000000..9bec3ee9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/app.ts @@ -0,0 +1,7 @@ +import {ComfyApp} from "@comfyorg/frontend"; + +/** + * A dummy ComfyApp that we can import from our code, which we'll rewrite later to the comfyui + * hosted app.js + */ +export declare const app: ComfyApp; diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/widgets.ts b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/widgets.ts new file mode 100644 index 00000000..7a6508d1 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/widgets.ts @@ -0,0 +1,18 @@ +import type {ComfyApp, IStringWidget, IComboWidget, IWidget, LGraphNode} from "@comfyorg/frontend"; + +type ComfyWidgetFn = ( + node: LGraphNode, + inputName: string, + inputData: any, + app: ComfyApp, +) => {widget: WidgetType}; + +/** + * A dummy ComfyWidgets that we can import from our code, which we'll rewrite later to the comfyui + * hosted widgets.js + */ +export declare const ComfyWidgets: { + COMBO: ComfyWidgetFn; + STRING: ComfyWidgetFn; + [key: string]: ComfyWidgetFn; +}; diff --git a/custom_nodes/rgthree-comfy/src_web/typings/comfy.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/comfy.d.ts new file mode 100644 index 00000000..f6f2e1b5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/typings/comfy.d.ts @@ -0,0 +1,153 @@ +/** + * These contain types from ComfyUI (or LiteGraph) that are either copied or manually determined + * added here mostly because @comfyorg/comfyui-frontend-types hasn't exported them oir they weren't + * available. + */ +import type {SerializedGraph} from './index.js'; + +export type getPngMetadata = (file: File | Blob) => { workflow?: string; prompt?: string }; +export type getWebpMetadata = (file: File | Blob) => { + Workflow?: string; + workflow?: string; + Prompt?: string; + prompt?: string; +}; + +// Below are types derived from the Serialized version of a workflow. + +// export type SerializedLink = [ +// number, // this.id, +// number, // this.origin_id, +// number, // this.origin_slot, +// number, // this.target_id, +// number, // this.target_slot, +// string, // this.type +// ]; + +// interface SerializedNodeInput { +// name: string; +// type: string; +// link: number; +// } +// interface SerializedNodeOutput extends SerializedNodeInput { +// slot_index: number; +// links: number[]; +// } + +// export interface SerializedNode { +// // id: number; +// // inputs: SerializedNodeInput[]; +// // outputs: SerializedNodeOutput[]; +// mode: number; +// order: number; +// pos: [number, number]; +// properties: any; +// size: [number, number]; +// type: string; +// widgets_values: Array; +// } + +// export interface SerializedGraph { + // config: any; + // extra: any; + // groups: any; + // last_link_id: number; + // last_node_id: number; + // links: SerializedLink[]; + // nodes: SerializedNode[]; +// } + + +/** + * ComfyUI-Frontend defines a ComfyNodeDef from Zod, but doesn't expose it. This is a shim. + */ +export type ComfyNodeDef = { + name: string; + display_name?: string; + description?: string; + category: string; + input?: { + required?: Record; + optional?: Record; + hidden?: Record; + }; + output?: string[]; + output_name: string[]; + // @rgthree + output_node?: boolean; +}; + + + +// Below are types derived from the formats for the ComfyAPI. + +// @rgthree +type ComfyApiInputLink = [ + /** The id string of the connected node. */ + string, + /** The output index. */ + number, +] + + +type ComfyApiFormatNode = { + "inputs": { + [input_name: string]: string|number|boolean|ComfyApiInputLink, + }, + "class_type": string, + "_meta": { + "title": string, + } +} + +export type ComfyApiFormat = { + [node_id: string]: ComfyApiFormatNode +} + +export type ComfyApiPrompt = { + workflow: SerializedGraph, + output: ComfyApiFormat, +} + +export type ComfyApiEventDetailStatus = { + exec_info: { + queue_remaining: number; + }; +}; + +export type ComfyApiEventDetailExecutionStart = { + prompt_id: string; +}; + +export type ComfyApiEventDetailExecuting = null | string; + +export type ComfyApiEventDetailProgress = { + node: string; + prompt_id: string; + max: number; + value: number; +}; + +export type ComfyApiEventDetailExecuted = { + node: string; + prompt_id: string; + output: any; +}; + +export type ComfyApiEventDetailCached = { + nodes: string[]; + prompt_id: string; +}; + +export type ComfyApiEventDetailError = { + prompt_id: string; + exception_type: string; + exception_message: string; + node_id: string; + node_type: string; + node_id: string; + traceback: string; + executed: any[]; + current_inputs: {[key: string]: (number[]|string[])}; + current_outputs: {[key: string]: (number[]|string[])}; +} diff --git a/custom_nodes/rgthree-comfy/src_web/typings/index.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/index.d.ts new file mode 100644 index 00000000..ac6f9f01 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/typings/index.d.ts @@ -0,0 +1,30 @@ +/** + * Declare any global properties and import our other typings here. + */ +import "@comfyorg/frontend"; +import "./litegraph"; +import "./rgthree"; +import "./comfy"; + +import type { + LGraphGroup as TLGraphGroup, + LGraphNode as TLGraphNode, + LGraph as TLGraph, + LGraphCanvas as TLGraphCanvas, + LiteGraph as TLiteGraph, +} from "@comfyorg/frontend"; + +declare global { + const LiteGraph: typeof TLiteGraph; + const LGraph: typeof TLGraph; + const LGraphNode: typeof TLGraphNode; + const LGraphCanvas: typeof TLGraphCanvas; + const LGraphGroup: typeof TLGraphGroup; + interface Window { + // Used in the common/comfyui_shim to determine if we're in the app or not. + comfyAPI: { + // So much more stuffed in here, add as needed. + [key: string]: any; + }; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/typings/litegraph.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/litegraph.d.ts new file mode 100644 index 00000000..24b171a7 --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/typings/litegraph.d.ts @@ -0,0 +1,193 @@ +/** + * This used to augment the LiteGraph types, either to fix them for how they actually behave + * (e.g. marking args that are typed as required as optional because they actually are, etc.) or + * adding properties/methods that rgthree-comfy adds/uses. Mostly the latter are prefixed 'rgthree_' + * but not always. + */ +import "@comfyorg/frontend"; + +declare module "@comfyorg/frontend" { + interface INodeSlot { + // @rgthree: Hides a slot for rgthree-comfy draw methods. + hidden?: boolean; + + // @rgthree: Used to "disable" an input/output. Used in PowerPrompt to disallow connecting + // an output if there's no optional corresponding input (since, that would just break). + disabled?: boolean; + + // @rgthree: A status we put on some nodes so we can draw things around it. + rgthree_status?: "WARN" | "ERROR"; + } + + interface LGraph { + // @rgthree (Fix): `result` arg is optional in impl. + findNodesByType(type: string, result?: LGraphNode[]): LGraphNode[]; + } + + interface LGraphNode { + // @rgthree: rgthree-comfy added this before comfyui did and it was a bit more flexible. + removeWidget(widget: IBaseWidget | IWidget | number | undefined): void; + + // @rgthree (Fix): Implementation allows a falsy value to be returned and it will suppress the + // menu all together. + // NOTE: [πŸ€] We can't actually augment this because it's a return.. but keeping here because + // this is how it's actually implemented. + // getSlotMenuOptions?(this: LGraphNode, slot: IFoundSlot): IContextMenuValue[] | void; + + // @rgthree (Fix): Implementation allows a falsy value to be returned and it will not add items. + // NOTE: [πŸ€] We can't actually augment this because it's a return.. but keeping here because + // this is how it's actually implemented. + // getExtraMenuOptions?( + // canvas: LGraphCanvas, + // options: (IContextMenuValue | null)[], + // ): (IContextMenuValue | null)[] | void; + + /** + * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts + * + * Callback invoked when the node is dragged over from an external source, i.e. + * a file or another HTML element. + * @param e The drag event + * @returns {boolean} True if the drag event should be handled by this node, false otherwise + */ + onDragOver?(e: DragEvent): boolean; + + /** + * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts + * + * Callback invoked when the node is dropped from an external source, i.e. + * a file or another HTML element. + * @param e The drag event + * @returns {boolean} True if the drag event should be handled by this node, false otherwise + */ + onDragDrop?(e: DragEvent): Promise | boolean; + + /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */ + onExecuted?(output: any): void; + + /** + * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts + * + * Index of the currently selected image on a multi-image node such as Preview Image + */ + imageIndex?: number | null; + + /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */ + overIndex?: number | null; + + /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */ + imgs?: HTMLImageElement[]; + + /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */ + refreshComboInNode?(defs: Record); + + /** + * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts + * + * widgets_values is set to LGraphNode by `LGraphNode.configure`, but it is not + * used by litegraph internally. We should remove the dependency on it later. + */ + widgets_values?: unknown[]; + } + + /** + * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts + * + * Only used by the Primitive node. Primitive node is using the widget property + * to store/access the widget config. + * We should remove this hacky solution once we have a proper solution. + * + * @rgthree - Changed this to add `widget?: IWidgetLocator` to INodeOutputSlot (which matches + * INodeInputSlot) and then `[key: symbol]: unknown` to IWidgetLocator as that's how it's + * used for CONFIG and GET_CONFIG symbols. + */ + interface INodeOutputSlot { + widget?: IWidgetLocator; //{name: string; [key: symbol]: unknown}; + } + // @rgthree - See above. + interface IWidgetLocator { + [key: symbol]: unknown; + } + + interface LGraphGroup { + // @rgthree: Track whether a group has any active node from the fast group mode changers. + rgthree_hasAnyActiveNode?: boolean; + } + + interface LGraphCanvas { + // @rgthree (Fix): At one point this was in ComfyUI's app.js. I don't see it now... perhaps it's + // been removed? We were using it in rgthree-comfy. + selected_group_moving?: boolean; + + // @rgthree (Fix): Allows LGraphGroup to be passed (it could be `{size: Point, pos: Point}`). + centerOnNode(node: LGraphNode | LGraphGroup); + + // @rgthree (Fix): Makes item's fields optiona, and other params nullable, as well as adds + // LGraphGroup to the node, since the implementation accomodates all of these as typed below. + // NOTE: [πŸ€] We can't actually augment this because it's static.. but keeping here because + // this is how it's actually implemented. + // static onShowPropertyEditor( + // item: { + // property?: keyof LGraphNode | undefined; + // type?: string; + // }, + // options: IContextMenuOptions | null, + // e: MouseEvent | null, + // menu: ContextMenu | null, + // node: LGraphNode | LGraphGroup, + // ): void; + } + + interface LGraphNodeConstructor { + // @rgthree (Fix): Fixes ComfyUI-Frontend which marks this as required, even though elsewhere it + // defines it as optional (like for the actual for LGraphNode). Our virtual nodes do not have + // a comfyClass since there's nothing to tie it back to. + comfyClass?: string; + + // @rgthree: reference the original nodeType data as sometimes extensions clobber it. + nodeType?: LGraphNodeConstructor | null; + } +} + +declare module "@/lib/litegraph/src/types/widgets" { + interface IBaseWidget { + // @rgthree (Fix): Where is this in Comfy types? + inputEl?: HTMLInputElement; + + // @rgthree: A status we put on some nodes so we can draw things around it. + rgthree_lastValue?: any; + + /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */ + onRemove?(): void; + /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */ + serializeValue?(node: LGraphNode, index: number): Promise | unknown; + } + + interface IWidgetOptions { + /** + * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts + * + * Controls whether the widget's value is included in the API workflow/prompt. + * - If false, the value will be excluded from the API workflow but still serialized as part of the graph state + * - If true or undefined, the value will be included in both the API workflow and graph state * + * @default true + * @use {@link IBaseWidget.serialize} if you don't want the widget value to be included in both + * the API workflow and graph state. + */ + serialize?: boolean; + } +} + +declare module "@/lib/litegraph/src/interfaces" { + // @rgthree (Fix): widget is (or was?) available when inputs were moved from a widget. + interface IFoundSlot { + widget?: IBaseWidget; + } +} + +declare module "@comfyorg/litegraph/dist/LiteGraphGlobal" { + interface LiteGraphGlobal { + // @rgthree (Fix): Window is actually optional in the code. + closeAllContextMenus(ref_window?: Window): void; + } +} diff --git a/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts new file mode 100644 index 00000000..e1573a0c --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts @@ -0,0 +1,75 @@ +/** + * Typings specific to rgthree-comfy. + */ +import type { LGraphNode, Vector2 } from "@comfyorg/frontend"; +import type { CanvasMouseEvent } from "@comfyorg/litegraph/dist/types/events.js"; +import type { RgthreeBaseNode, RgthreeBaseVirtualNode } from '../comfyui/base_node.js' + +export type AdjustedMouseCustomEvent = CustomEvent<{ originalEvent: CanvasMouseEvent }>; + +export type Constructor = new(...args: any[]) => T; + +export interface RgthreeBaseNodeConstructor extends Constructor { + static type: string; + static category: string; + static comfyClass: string; + static exposedActions: string[]; +} + +export interface RgthreeBaseVirtualNodeConstructor extends Constructor { + static type: string; + static category: string; + static _category: string; +} + +export interface RgthreeBaseServerNodeConstructor extends Constructor { + static nodeType: ComfyNodeConstructor; + static nodeData: ComfyObjectInfo; + static __registeredForOverride__: boolean; + onRegisteredForOverride(comfyClass: any, rgthreeClass: any) : void; +} + +export type RgthreeModelInfoDetails = { + file?: string; + path?: string; + hasInfoFile?: boolean; + image?: string; +} + +export type RgthreeModelInfo = RgthreeModelInfoDetails & { + name?: string; + type?: string; + baseModel?: string; + baseModelFile?: string; + links?: string[]; + strengthMin?: number; + strengthMax?: number; + triggerWords?: string[]; + trainedWords?: { + word: string; + count?: number; + civitai?: boolean + user?: boolean + }[]; + description?: string; + sha256?: string; + path?: string; + images?: { + url: string; + civitaiUrl?: string; + steps?: string|number; + cfg?: string|number; + type?: 'image'|'video'; + sampler?: string; + model?: string; + seed?: string; + negative?: string; + positive?: string; + resources?: {name?: string, type?: string, weight?: string|number}[]; + }[] + userTags?: string[]; + userNote?: string; + raw?: any; + // This one is just on the client. + filterDir?: string; +} diff --git a/custom_nodes/rgthree-comfy/src_web/typings/web-tree-sitter.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/web-tree-sitter.d.ts new file mode 100644 index 00000000..87b3100f --- /dev/null +++ b/custom_nodes/rgthree-comfy/src_web/typings/web-tree-sitter.d.ts @@ -0,0 +1 @@ +import 'web-tree-sitter'; diff --git a/custom_nodes/rgthree-comfy/tsconfig.json b/custom_nodes/rgthree-comfy/tsconfig.json new file mode 100644 index 00000000..fd7d330d --- /dev/null +++ b/custom_nodes/rgthree-comfy/tsconfig.json @@ -0,0 +1,57 @@ +{ + "compilerOptions": { + "target": "es2019", + "module": "ESNext", + // "typeRoots": [ + // "./ts/typings", + // ], + "baseUrl": "./", + "paths": { + // Main comfyui typedefs. + "@comfyorg/frontend": ["node_modules/@comfyorg/comfyui-frontend-types/index.d.ts"], + // ComfyUI augments old litegraph types with these module names, so we need to map them back + // to itself to "see" these augmentations in our code. + "@/lib/litegraph/src/types/widgets": ["node_modules/@comfyorg/comfyui-frontend-types/index.d.ts"], + "@/lib/litegraph/src/interfaces": ["node_modules/@comfyorg/comfyui-frontend-types/index.d.ts"], + "@/lib/litegraph/src/litegraph": ["node_modules/@comfyorg/comfyui-frontend-types/index.d.ts"], + // Tree Sitter, for parsing python in js. + "web-tree-sitter": ["node_modules/web-tree-sitter/web-tree-sitter.d.ts"], + "typings/*": ["src_web/typings/*"], + "rgthree/common/*": ["src_web/common/*"], + "rgthree/lib/*": ["src_web/lib/*"], + "node_modules": ["node_modules/*"], + "scripts/*": ["src_web/scripts_comfy/*"], + }, + "outDir": "web/", + "removeComments": true, + "strict": true, + "noImplicitAny": true, + "strictNullChecks": true, + "strictFunctionTypes": true, + "strictBindCallApply": true, + "strictPropertyInitialization": true, + "noImplicitThis": true, + "useUnknownInCatchVariables": true, + "alwaysStrict": true, + // "noUnusedLocals": true, + // "noUnusedParameters": true, + "exactOptionalPropertyTypes": false, + "noImplicitReturns": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedIndexedAccess": true, + "noImplicitOverride": true, + "noPropertyAccessFromIndexSignature": true, + "allowUnusedLabels": true, + "skipLibCheck": true, + "experimentalDecorators": true, + "types": [] + }, + "include": [ + "src_web/*.ts", "src_web/**/*.ts", "src_web/typings/index.d.ts", + ], + "exclude": [ + "**/*.spec.ts", + "**/*.d.ts", + "node_modules/**/*.ts" + ] +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/any_switch.js b/custom_nodes/rgthree-comfy/web/comfyui/any_switch.js new file mode 100644 index 00000000..50e0d796 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/any_switch.js @@ -0,0 +1,69 @@ +import { app } from "../../scripts/app.js"; +import { IoDirection, addConnectionLayoutSupport, followConnectionUntilType } from "./utils.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js"; +import { debounce } from "../../rgthree/common/shared_utils.js"; +class RgthreeAnySwitch extends RgthreeBaseServerNode { + constructor(title = RgthreeAnySwitch.title) { + super(title); + this.stabilizeBound = this.stabilize.bind(this); + this.nodeType = null; + this.addAnyInput(5); + } + onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot) { + var _a; + (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.call(this, type, slotIndex, isConnected, linkInfo, ioSlot); + this.scheduleStabilize(); + } + onConnectionsChainChange() { + this.scheduleStabilize(); + } + scheduleStabilize(ms = 64) { + return debounce(this.stabilizeBound, ms); + } + addAnyInput(num = 1) { + for (let i = 0; i < num; i++) { + this.addInput(`any_${String(this.inputs.length + 1).padStart(2, "0")}`, (this.nodeType || "*")); + } + } + stabilize() { + removeUnusedInputsFromEnd(this, 4); + this.addAnyInput(); + let connectedType = followConnectionUntilType(this, IoDirection.INPUT, undefined, true); + if (!connectedType) { + connectedType = followConnectionUntilType(this, IoDirection.OUTPUT, undefined, true); + } + this.nodeType = (connectedType === null || connectedType === void 0 ? void 0 : connectedType.type) || "*"; + for (const input of this.inputs) { + input.type = this.nodeType; + } + for (const output of this.outputs) { + output.type = this.nodeType; + output.label = + output.type === "RGTHREE_CONTEXT" + ? "CONTEXT" + : Array.isArray(this.nodeType) || this.nodeType.includes(",") + ? (connectedType === null || connectedType === void 0 ? void 0 : connectedType.label) || String(this.nodeType) + : String(this.nodeType); + } + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeAnySwitch); + addConnectionLayoutSupport(RgthreeAnySwitch, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + } +} +RgthreeAnySwitch.title = NodeTypesString.ANY_SWITCH; +RgthreeAnySwitch.type = NodeTypesString.ANY_SWITCH; +RgthreeAnySwitch.comfyClass = NodeTypesString.ANY_SWITCH; +app.registerExtension({ + name: "rgthree.AnySwitch", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "Any Switch (rgthree)") { + RgthreeAnySwitch.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_any_input_connected_node.js b/custom_nodes/rgthree-comfy/web/comfyui/base_any_input_connected_node.js new file mode 100644 index 00000000..09e2950c --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/base_any_input_connected_node.js @@ -0,0 +1,208 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseVirtualNode } from "./base_node.js"; +import { rgthree } from "./rgthree.js"; +import { PassThroughFollowing, addConnectionLayoutSupport, addMenuItem, getConnectedInputNodes, getConnectedInputNodesAndFilterPassThroughs, getConnectedOutputNodes, getConnectedOutputNodesAndFilterPassThroughs, } from "./utils.js"; +export class BaseAnyInputConnectedNode extends RgthreeBaseVirtualNode { + constructor(title = BaseAnyInputConnectedNode.title) { + super(title); + this.isVirtualNode = true; + this.inputsPassThroughFollowing = PassThroughFollowing.NONE; + this.debouncerTempWidth = 0; + this.schedulePromise = null; + } + onConstructed() { + this.addInput("", "*"); + return super.onConstructed(); + } + clone() { + const cloned = super.clone(); + if (!rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes) { + while (cloned.inputs.length > 1) { + cloned.removeInput(cloned.inputs.length - 1); + } + if (cloned.inputs[0]) { + cloned.inputs[0].label = ""; + } + } + return cloned; + } + scheduleStabilizeWidgets(ms = 100) { + if (!this.schedulePromise) { + this.schedulePromise = new Promise((resolve) => { + setTimeout(() => { + this.schedulePromise = null; + this.doStablization(); + resolve(); + }, ms); + }); + } + return this.schedulePromise; + } + stabilizeInputsOutputs() { + var _a; + let changed = false; + const hasEmptyInput = !((_a = this.inputs[this.inputs.length - 1]) === null || _a === void 0 ? void 0 : _a.link); + if (!hasEmptyInput) { + this.addInput("", "*"); + changed = true; + } + for (let index = this.inputs.length - 2; index >= 0; index--) { + const input = this.inputs[index]; + if (!input.link) { + this.removeInput(index); + changed = true; + } + else { + const node = getConnectedInputNodesAndFilterPassThroughs(this, this, index, this.inputsPassThroughFollowing)[0]; + const newName = (node === null || node === void 0 ? void 0 : node.title) || ""; + if (input.name !== newName) { + input.name = (node === null || node === void 0 ? void 0 : node.title) || ""; + changed = true; + } + } + } + return changed; + } + doStablization() { + if (!this.graph) { + return; + } + let dirty = false; + this._tempWidth = this.size[0]; + dirty = this.stabilizeInputsOutputs(); + const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this); + dirty = this.handleLinkedNodesStabilization(linkedNodes) || dirty; + if (dirty) { + this.graph.setDirtyCanvas(true, true); + } + this.scheduleStabilizeWidgets(500); + } + handleLinkedNodesStabilization(linkedNodes) { + linkedNodes; + throw new Error("handleLinkedNodesStabilization should be overridden."); + } + onConnectionsChainChange() { + this.scheduleStabilizeWidgets(); + } + onConnectionsChange(type, index, connected, linkInfo, ioSlot) { + super.onConnectionsChange && + super.onConnectionsChange(type, index, connected, linkInfo, ioSlot); + if (!linkInfo) + return; + const connectedNodes = getConnectedOutputNodesAndFilterPassThroughs(this); + for (const node of connectedNodes) { + if (node.onConnectionsChainChange) { + node.onConnectionsChainChange(); + } + } + this.scheduleStabilizeWidgets(); + } + removeInput(slot) { + this._tempWidth = this.size[0]; + return super.removeInput(slot); + } + addInput(name, type, extra_info) { + this._tempWidth = this.size[0]; + return super.addInput(name, type, extra_info); + } + addWidget(type, name, value, callback, options) { + this._tempWidth = this.size[0]; + return super.addWidget(type, name, value, callback, options); + } + removeWidget(widget) { + this._tempWidth = this.size[0]; + super.removeWidget(widget); + } + computeSize(out) { + var _a, _b; + let size = super.computeSize(out); + if (this._tempWidth) { + size[0] = this._tempWidth; + this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth); + this.debouncerTempWidth = setTimeout(() => { + this._tempWidth = null; + }, 32); + } + if (this.properties["collapse_connections"]) { + const rows = Math.max(((_a = this.inputs) === null || _a === void 0 ? void 0 : _a.length) || 0, ((_b = this.outputs) === null || _b === void 0 ? void 0 : _b.length) || 0, 1) - 1; + size[1] = size[1] - rows * LiteGraph.NODE_SLOT_HEIGHT; + } + setTimeout(() => { + var _a; + (_a = this.graph) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true); + }, 16); + return size; + } + onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex) { + let canConnect = true; + if (super.onConnectOutput) { + canConnect = super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex); + } + if (canConnect) { + const nodes = getConnectedInputNodes(this); + if (nodes.includes(inputNode)) { + alert(`Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` + + `a situation that could create a time paradox, the results of which could cause a ` + + `chain reaction that would unravel the very fabric of the space time continuum, ` + + `and destroy the entire universe!`); + canConnect = false; + } + } + return canConnect; + } + onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) { + let canConnect = true; + if (super.onConnectInput) { + canConnect = super.onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex); + } + if (canConnect) { + const nodes = getConnectedOutputNodes(this); + if (nodes.includes(outputNode)) { + alert(`Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` + + `a situation that could create a time paradox, the results of which could cause a ` + + `chain reaction that would unravel the very fabric of the space time continuum, ` + + `and destroy the entire universe!`); + canConnect = false; + } + } + return canConnect; + } + connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn) { + const lastInput = this.inputs[this.inputs.length - 1]; + if (!(lastInput === null || lastInput === void 0 ? void 0 : lastInput.link) && (lastInput === null || lastInput === void 0 ? void 0 : lastInput.type) === "*") { + var sourceSlot = sourceNode.findOutputSlotByType(sourceSlotType, false, true); + return sourceNode.connect(sourceSlot, this, slot); + } + return super.connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn); + } + static setUp() { + super.setUp(); + addConnectionLayoutSupport(this, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + addMenuItem(this, app, { + name: (node) => { var _a; return `${((_a = node.properties) === null || _a === void 0 ? void 0 : _a["collapse_connections"]) ? "Show" : "Collapse"} Connections`; }, + property: "collapse_connections", + prepareValue: (_value, node) => { var _a; return !((_a = node.properties) === null || _a === void 0 ? void 0 : _a["collapse_connections"]); }, + callback: (_node) => { + var _a; + (_a = app.canvas.getCurrentGraph()) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true); + }, + }); + } +} +const oldLGraphNodeConnectByType = LGraphNode.prototype.connectByType; +LGraphNode.prototype.connectByType = function connectByType(slot, targetNode, targetSlotType, optsIn) { + if (targetNode.inputs) { + for (const [index, input] of targetNode.inputs.entries()) { + if (!input.link && input.type === "*") { + this.connect(slot, targetNode, index); + return null; + } + } + } + return ((oldLGraphNodeConnectByType && + oldLGraphNodeConnectByType.call(this, slot, targetNode, targetSlotType, optsIn)) || + null); +}; diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_node.js b/custom_nodes/rgthree-comfy/web/comfyui/base_node.js new file mode 100644 index 00000000..a004ca9f --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/base_node.js @@ -0,0 +1,316 @@ +import { app } from "../../scripts/app.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js"; +import { LogLevel, rgthree } from "./rgthree.js"; +import { addHelpMenuItem } from "./utils.js"; +import { RgthreeHelpDialog } from "../../rgthree/common/dialog.js"; +import { importIndividualNodesInnerOnDragDrop, importIndividualNodesInnerOnDragOver, } from "./feature_import_individual_nodes.js"; +import { defineProperty, moveArrayItem } from "../../rgthree/common/shared_utils.js"; +export class RgthreeBaseNode extends LGraphNode { + constructor(title = RgthreeBaseNode.title, skipOnConstructedCall = true) { + super(title); + this.comfyClass = "__NEED_COMFY_CLASS__"; + this.nickname = "rgthree"; + this.isVirtualNode = false; + this.isDropEnabled = false; + this.removed = false; + this.configuring = false; + this._tempWidth = 0; + this.__constructed__ = false; + this.helpDialog = null; + if (title == "__NEED_CLASS_TITLE__") { + throw new Error("RgthreeBaseNode needs overrides."); + } + this.widgets = this.widgets || []; + this.properties = this.properties || {}; + setTimeout(() => { + if (this.comfyClass == "__NEED_COMFY_CLASS__") { + throw new Error("RgthreeBaseNode needs a comfy class override."); + } + if (this.constructor.type == "__NEED_CLASS_TYPE__") { + throw new Error("RgthreeBaseNode needs overrides."); + } + this.checkAndRunOnConstructed(); + }); + defineProperty(this, "mode", { + get: () => { + return this.rgthree_mode; + }, + set: (mode) => { + if (this.rgthree_mode != mode) { + const oldMode = this.rgthree_mode; + this.rgthree_mode = mode; + this.onModeChange(oldMode, mode); + } + }, + }); + } + checkAndRunOnConstructed() { + var _a; + if (!this.__constructed__) { + this.onConstructed(); + const [n, v] = rgthree.logger.logParts(LogLevel.DEV, `[RgthreeBaseNode] Child class did not call onConstructed for "${this.type}.`); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + } + return this.__constructed__; + } + onDragOver(e) { + if (!this.isDropEnabled) + return false; + return importIndividualNodesInnerOnDragOver(this, e); + } + async onDragDrop(e) { + if (!this.isDropEnabled) + return false; + return importIndividualNodesInnerOnDragDrop(this, e); + } + onConstructed() { + var _a; + if (this.__constructed__) + return false; + this.type = (_a = this.type) !== null && _a !== void 0 ? _a : undefined; + this.__constructed__ = true; + rgthree.invokeExtensionsAsync("nodeCreated", this); + return this.__constructed__; + } + configure(info) { + this.configuring = true; + super.configure(info); + for (const w of this.widgets || []) { + w.last_y = w.last_y || 0; + } + this.configuring = false; + } + clone() { + const cloned = super.clone(); + if ((cloned === null || cloned === void 0 ? void 0 : cloned.properties) && !!window.structuredClone) { + cloned.properties = structuredClone(cloned.properties); + } + cloned.graph = this.graph; + return cloned; + } + onModeChange(from, to) { + } + async handleAction(action) { + action; + } + removeWidget(widget) { + var _a; + if (typeof widget === "number") { + widget = this.widgets[widget]; + } + if (!widget) + return; + const canUseComfyUIRemoveWidget = false; + if (canUseComfyUIRemoveWidget && typeof super.removeWidget === 'function') { + super.removeWidget(widget); + } + else { + const index = this.widgets.indexOf(widget); + if (index > -1) { + this.widgets.splice(index, 1); + } + (_a = widget.onRemove) === null || _a === void 0 ? void 0 : _a.call(widget); + } + } + replaceWidget(widgetOrSlot, newWidget) { + let index = null; + if (widgetOrSlot) { + index = typeof widgetOrSlot === "number" ? widgetOrSlot : this.widgets.indexOf(widgetOrSlot); + this.removeWidget(this.widgets[index]); + } + index = index != null ? index : this.widgets.length - 1; + if (this.widgets.includes(newWidget)) { + moveArrayItem(this.widgets, newWidget, index); + } + else { + this.widgets.splice(index, 0, newWidget); + } + } + defaultGetSlotMenuOptions(slot) { + var _a, _b; + const menu_info = []; + if ((_b = (_a = slot === null || slot === void 0 ? void 0 : slot.output) === null || _a === void 0 ? void 0 : _a.links) === null || _b === void 0 ? void 0 : _b.length) { + menu_info.push({ content: "Disconnect Links", slot }); + } + let inputOrOutput = slot.input || slot.output; + if (inputOrOutput) { + if (inputOrOutput.removable) { + menu_info.push(inputOrOutput.locked ? { content: "Cannot remove" } : { content: "Remove Slot", slot }); + } + if (!inputOrOutput.nameLocked) { + menu_info.push({ content: "Rename Slot", slot }); + } + } + return menu_info; + } + onRemoved() { + var _a; + (_a = super.onRemoved) === null || _a === void 0 ? void 0 : _a.call(this); + this.removed = true; + } + static setUp(...args) { + } + getHelp() { + return ""; + } + showHelp() { + const help = this.getHelp() || this.constructor.help; + if (help) { + this.helpDialog = new RgthreeHelpDialog(this, help).show(); + this.helpDialog.addEventListener("close", (e) => { + this.helpDialog = null; + }); + } + } + onKeyDown(event) { + KEY_EVENT_SERVICE.handleKeyDownOrUp(event); + if (event.key == "?" && !this.helpDialog) { + this.showHelp(); + } + } + onKeyUp(event) { + KEY_EVENT_SERVICE.handleKeyDownOrUp(event); + } + getExtraMenuOptions(canvas, options) { + var _a, _b, _c, _d, _e, _f; + if (super.getExtraMenuOptions) { + (_a = super.getExtraMenuOptions) === null || _a === void 0 ? void 0 : _a.apply(this, [canvas, options]); + } + else if ((_c = (_b = this.constructor.nodeType) === null || _b === void 0 ? void 0 : _b.prototype) === null || _c === void 0 ? void 0 : _c.getExtraMenuOptions) { + (_f = (_e = (_d = this.constructor.nodeType) === null || _d === void 0 ? void 0 : _d.prototype) === null || _e === void 0 ? void 0 : _e.getExtraMenuOptions) === null || _f === void 0 ? void 0 : _f.apply(this, [canvas, options]); + } + const help = this.getHelp() || this.constructor.help; + if (help) { + addHelpMenuItem(this, help, options); + } + return []; + } +} +RgthreeBaseNode.exposedActions = []; +RgthreeBaseNode.title = "__NEED_CLASS_TITLE__"; +RgthreeBaseNode.type = "__NEED_CLASS_TYPE__"; +RgthreeBaseNode.category = "rgthree"; +RgthreeBaseNode._category = "rgthree"; +export class RgthreeBaseVirtualNode extends RgthreeBaseNode { + constructor(title = RgthreeBaseNode.title) { + super(title, false); + this.isVirtualNode = true; + } + static setUp() { + if (!this.type) { + throw new Error(`Missing type for RgthreeBaseVirtualNode: ${this.title}`); + } + LiteGraph.registerNodeType(this.type, this); + if (this._category) { + this.category = this._category; + } + } +} +export class RgthreeBaseServerNode extends RgthreeBaseNode { + constructor(title) { + super(title, true); + this.isDropEnabled = true; + this.serialize_widgets = true; + this.setupFromServerNodeData(); + this.onConstructed(); + } + getWidgets() { + return ComfyWidgets; + } + async setupFromServerNodeData() { + var _a, _b, _c, _d, _e; + const nodeData = this.constructor.nodeData; + if (!nodeData) { + throw Error("No node data"); + } + this.comfyClass = nodeData.name; + let inputs = nodeData["input"]["required"]; + if (nodeData["input"]["optional"] != undefined) { + inputs = Object.assign({}, inputs, nodeData["input"]["optional"]); + } + const WIDGETS = this.getWidgets(); + const config = { + minWidth: 1, + minHeight: 1, + widget: null, + }; + for (const inputName in inputs) { + const inputData = inputs[inputName]; + const type = inputData[0]; + if ((_a = inputData[1]) === null || _a === void 0 ? void 0 : _a.forceInput) { + this.addInput(inputName, type); + } + else { + let widgetCreated = true; + if (Array.isArray(type)) { + Object.assign(config, WIDGETS.COMBO(this, inputName, inputData, app) || {}); + } + else if (`${type}:${inputName}` in WIDGETS) { + Object.assign(config, WIDGETS[`${type}:${inputName}`](this, inputName, inputData, app) || {}); + } + else if (type in WIDGETS) { + Object.assign(config, WIDGETS[type](this, inputName, inputData, app) || {}); + } + else { + this.addInput(inputName, type); + widgetCreated = false; + } + if (widgetCreated && ((_b = inputData[1]) === null || _b === void 0 ? void 0 : _b.forceInput) && (config === null || config === void 0 ? void 0 : config.widget)) { + if (!config.widget.options) + config.widget.options = {}; + config.widget.options.forceInput = inputData[1].forceInput; + } + if (widgetCreated && ((_c = inputData[1]) === null || _c === void 0 ? void 0 : _c.defaultInput) && (config === null || config === void 0 ? void 0 : config.widget)) { + if (!config.widget.options) + config.widget.options = {}; + config.widget.options.defaultInput = inputData[1].defaultInput; + } + } + } + for (const o in nodeData["output"]) { + let output = nodeData["output"][o]; + if (output instanceof Array) + output = "COMBO"; + const outputName = nodeData["output_name"][o] || output; + const outputShape = nodeData["output_is_list"][o] + ? LiteGraph.GRID_SHAPE + : LiteGraph.CIRCLE_SHAPE; + this.addOutput(outputName, output, { shape: outputShape }); + } + const s = this.computeSize(); + s[0] = Math.max((_d = config.minWidth) !== null && _d !== void 0 ? _d : 1, s[0] * 1.5); + s[1] = Math.max((_e = config.minHeight) !== null && _e !== void 0 ? _e : 1, s[1]); + this.size = s; + this.serialize_widgets = true; + } + static registerForOverride(comfyClass, nodeData, rgthreeClass) { + if (OVERRIDDEN_SERVER_NODES.has(comfyClass)) { + throw Error(`Already have a class to override ${comfyClass.type || comfyClass.name || comfyClass.title}`); + } + OVERRIDDEN_SERVER_NODES.set(comfyClass, rgthreeClass); + if (!rgthreeClass.__registeredForOverride__) { + rgthreeClass.__registeredForOverride__ = true; + rgthreeClass.nodeType = comfyClass; + rgthreeClass.nodeData = nodeData; + rgthreeClass.onRegisteredForOverride(comfyClass, rgthreeClass); + } + } + static onRegisteredForOverride(comfyClass, rgthreeClass) { + } +} +RgthreeBaseServerNode.nodeType = null; +RgthreeBaseServerNode.nodeData = null; +RgthreeBaseServerNode.__registeredForOverride__ = false; +const OVERRIDDEN_SERVER_NODES = new Map(); +const oldregisterNodeType = LiteGraph.registerNodeType; +LiteGraph.registerNodeType = async function (nodeId, baseClass) { + var _a; + const clazz = OVERRIDDEN_SERVER_NODES.get(baseClass) || baseClass; + if (clazz !== baseClass) { + const classLabel = clazz.type || clazz.name || clazz.title; + const [n, v] = rgthree.logger.logParts(LogLevel.DEBUG, `${nodeId}: replacing default ComfyNode implementation with custom ${classLabel} class.`); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + } + return oldregisterNodeType.call(LiteGraph, nodeId, clazz); +}; diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_node_collector.js b/custom_nodes/rgthree-comfy/web/comfyui/base_node_collector.js new file mode 100644 index 00000000..3dd924f7 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/base_node_collector.js @@ -0,0 +1,51 @@ +import { rgthree } from "./rgthree.js"; +import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js"; +import { PassThroughFollowing, getConnectedInputNodes, getConnectedInputNodesAndFilterPassThroughs, shouldPassThrough, } from "./utils.js"; +export class BaseCollectorNode extends BaseAnyInputConnectedNode { + constructor(title) { + super(title); + this.inputsPassThroughFollowing = PassThroughFollowing.REROUTE_ONLY; + this.logger = rgthree.newLogSession("[BaseCollectorNode]"); + } + clone() { + const cloned = super.clone(); + return cloned; + } + handleLinkedNodesStabilization(linkedNodes) { + return false; + } + onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) { + var _a, _b, _c, _d; + let canConnect = super.onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex); + if (canConnect) { + const allConnectedNodes = getConnectedInputNodes(this); + const nodesAlreadyInSlot = getConnectedInputNodes(this, undefined, inputIndex); + if (allConnectedNodes.includes(outputNode)) { + const [n, v] = this.logger.debugParts(`${outputNode.title} is already connected to ${this.title}.`); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + if (nodesAlreadyInSlot.includes(outputNode)) { + const [n, v] = this.logger.debugParts(`... but letting it slide since it's for the same slot.`); + (_b = console[n]) === null || _b === void 0 ? void 0 : _b.call(console, ...v); + } + else { + canConnect = false; + } + } + if (canConnect && shouldPassThrough(outputNode, PassThroughFollowing.REROUTE_ONLY)) { + const connectedNode = getConnectedInputNodesAndFilterPassThroughs(outputNode, undefined, undefined, PassThroughFollowing.REROUTE_ONLY)[0]; + if (connectedNode && allConnectedNodes.includes(connectedNode)) { + const [n, v] = this.logger.debugParts(`${connectedNode.title} is already connected to ${this.title}.`); + (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v); + if (nodesAlreadyInSlot.includes(connectedNode)) { + const [n, v] = this.logger.debugParts(`... but letting it slide since it's for the same slot.`); + (_d = console[n]) === null || _d === void 0 ? void 0 : _d.call(console, ...v); + } + else { + canConnect = false; + } + } + } + } + return canConnect; + } +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_node_mode_changer.js b/custom_nodes/rgthree-comfy/web/comfyui/base_node_mode_changer.js new file mode 100644 index 00000000..8d2b2078 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/base_node_mode_changer.js @@ -0,0 +1,93 @@ +import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js"; +import { changeModeOfNodes, PassThroughFollowing } from "./utils.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +export class BaseNodeModeChanger extends BaseAnyInputConnectedNode { + constructor(title) { + super(title); + this.inputsPassThroughFollowing = PassThroughFollowing.ALL; + this.isVirtualNode = true; + this.modeOn = -1; + this.modeOff = -1; + this.properties["toggleRestriction"] = "default"; + } + onConstructed() { + wait(10).then(() => { + if (this.modeOn < 0 || this.modeOff < 0) { + throw new Error("modeOn and modeOff must be overridden."); + } + }); + this.addOutput("OPT_CONNECTION", "*"); + return super.onConstructed(); + } + handleLinkedNodesStabilization(linkedNodes) { + let changed = false; + for (const [index, node] of linkedNodes.entries()) { + let widget = this.widgets && this.widgets[index]; + if (!widget) { + this._tempWidth = this.size[0]; + widget = this.addWidget("toggle", "", false, "", { on: "yes", off: "no" }); + changed = true; + } + if (node) { + changed = this.setWidget(widget, node) || changed; + } + } + if (this.widgets && this.widgets.length > linkedNodes.length) { + this.widgets.length = linkedNodes.length; + changed = true; + } + return changed; + } + setWidget(widget, linkedNode, forceValue) { + let changed = false; + const value = forceValue == null ? linkedNode.mode === this.modeOn : forceValue; + let name = `Enable ${linkedNode.title}`; + if (widget.name !== name) { + widget.name = `Enable ${linkedNode.title}`; + widget.options = { on: "yes", off: "no" }; + widget.value = value; + widget.doModeChange = (forceValue, skipOtherNodeCheck) => { + var _a, _b, _c; + let newValue = forceValue == null ? linkedNode.mode === this.modeOff : forceValue; + if (skipOtherNodeCheck !== true) { + if (newValue && ((_b = (_a = this.properties) === null || _a === void 0 ? void 0 : _a["toggleRestriction"]) === null || _b === void 0 ? void 0 : _b.includes(" one"))) { + for (const widget of this.widgets) { + widget.doModeChange(false, true); + } + } + else if (!newValue && ((_c = this.properties) === null || _c === void 0 ? void 0 : _c["toggleRestriction"]) === "always one") { + newValue = this.widgets.every((w) => !w.value || w === widget); + } + } + changeModeOfNodes(linkedNode, (newValue ? this.modeOn : this.modeOff)); + widget.value = newValue; + }; + widget.callback = () => { + widget.doModeChange(); + }; + changed = true; + } + if (forceValue != null) { + const newMode = (forceValue ? this.modeOn : this.modeOff); + if (linkedNode.mode !== newMode) { + changeModeOfNodes(linkedNode, newMode); + changed = true; + } + } + return changed; + } + forceWidgetOff(widget, skipOtherNodeCheck) { + widget.doModeChange(false, skipOtherNodeCheck); + } + forceWidgetOn(widget, skipOtherNodeCheck) { + widget.doModeChange(true, skipOtherNodeCheck); + } + forceWidgetToggle(widget, skipOtherNodeCheck) { + widget.doModeChange(!widget.value, skipOtherNodeCheck); + } +} +BaseNodeModeChanger.collapsible = false; +BaseNodeModeChanger["@toggleRestriction"] = { + type: "combo", + values: ["default", "max one", "always one"], +}; diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_power_prompt.js b/custom_nodes/rgthree-comfy/web/comfyui/base_power_prompt.js new file mode 100644 index 00000000..c682f237 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/base_power_prompt.js @@ -0,0 +1,252 @@ +import { api } from "../../scripts/api.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +import { rgthree } from "./rgthree.js"; +export class PowerPrompt { + constructor(node, nodeData) { + this.combos = {}; + this.combosValues = {}; + this.configuring = false; + this.node = node; + this.node.properties = this.node.properties || {}; + this.node.properties["combos_filter"] = ""; + this.nodeData = nodeData; + this.isSimple = this.nodeData.name.includes("Simple"); + this.promptEl = node.widgets[0].inputEl; + this.addAndHandleKeyboardLoraEditWeight(); + this.patchNodeRefresh(); + const oldConfigure = this.node.configure; + this.node.configure = (info) => { + this.configuring = true; + oldConfigure === null || oldConfigure === void 0 ? void 0 : oldConfigure.apply(this.node, [info]); + this.configuring = false; + }; + const oldOnConnectionsChange = this.node.onConnectionsChange; + this.node.onConnectionsChange = (type, slotIndex, isConnected, link_info, _ioSlot) => { + oldOnConnectionsChange === null || oldOnConnectionsChange === void 0 ? void 0 : oldOnConnectionsChange.apply(this.node, [type, slotIndex, isConnected, link_info, _ioSlot]); + this.onNodeConnectionsChange(type, slotIndex, isConnected, link_info, _ioSlot); + }; + const oldOnConnectInput = this.node.onConnectInput; + this.node.onConnectInput = (inputIndex, outputType, outputSlot, outputNode, outputIndex) => { + let canConnect = true; + if (oldOnConnectInput) { + canConnect = oldOnConnectInput.apply(this.node, [ + inputIndex, + outputType, + outputSlot, + outputNode, + outputIndex, + ]); + } + return (this.configuring || + !!rgthree.loadingApiJson || + (canConnect && !this.node.inputs[inputIndex].disabled)); + }; + const oldOnConnectOutput = this.node.onConnectOutput; + this.node.onConnectOutput = (outputIndex, inputType, inputSlot, inputNode, inputIndex) => { + let canConnect = true; + if (oldOnConnectOutput) { + canConnect = oldOnConnectOutput === null || oldOnConnectOutput === void 0 ? void 0 : oldOnConnectOutput.apply(this.node, [ + outputIndex, + inputType, + inputSlot, + inputNode, + inputIndex, + ]); + } + return (this.configuring || + !!rgthree.loadingApiJson || + (canConnect && !this.node.outputs[outputIndex].disabled)); + }; + const onPropertyChanged = this.node.onPropertyChanged; + this.node.onPropertyChanged = (property, value, prevValue) => { + const v = onPropertyChanged && onPropertyChanged.call(this.node, property, value, prevValue); + if (property === "combos_filter") { + this.refreshCombos(this.nodeData); + } + return v !== null && v !== void 0 ? v : true; + }; + for (let i = this.node.widgets.length - 1; i >= 0; i--) { + if (this.shouldRemoveServerWidget(this.node.widgets[i])) { + this.node.widgets.splice(i, 1); + } + } + this.refreshCombos(nodeData); + setTimeout(() => { + this.stabilizeInputsOutputs(); + }, 32); + } + onNodeConnectionsChange(_type, _slotIndex, _isConnected, _linkInfo, _ioSlot) { + this.stabilizeInputsOutputs(); + } + stabilizeInputsOutputs() { + if (this.configuring || rgthree.loadingApiJson) { + return; + } + const clipLinked = this.node.inputs.some((i) => i.name.includes("clip") && !!i.link); + const modelLinked = this.node.inputs.some((i) => i.name.includes("model") && !!i.link); + for (const output of this.node.outputs) { + const type = output.type.toLowerCase(); + if (type.includes("model")) { + output.disabled = !modelLinked; + } + else if (type.includes("conditioning")) { + output.disabled = !clipLinked; + } + else if (type.includes("clip")) { + output.disabled = !clipLinked; + } + else if (type.includes("string")) { + output.color_off = "#7F7"; + output.color_on = "#7F7"; + } + if (output.disabled) { + } + } + } + onFreshNodeDefs(event) { + this.refreshCombos(event.detail[this.nodeData.name]); + } + shouldRemoveServerWidget(widget) { + var _a, _b, _c, _d; + return (((_a = widget.name) === null || _a === void 0 ? void 0 : _a.startsWith("insert_")) || + ((_b = widget.name) === null || _b === void 0 ? void 0 : _b.startsWith("target_")) || + ((_c = widget.name) === null || _c === void 0 ? void 0 : _c.startsWith("crop_")) || + ((_d = widget.name) === null || _d === void 0 ? void 0 : _d.startsWith("values_"))); + } + refreshCombos(nodeData) { + var _a, _b, _c; + this.nodeData = nodeData; + let filter = null; + if ((_a = this.node.properties["combos_filter"]) === null || _a === void 0 ? void 0 : _a.trim()) { + try { + filter = new RegExp(this.node.properties["combos_filter"].trim(), "i"); + } + catch (e) { + console.error(`Could not parse "${filter}" for Regular Expression`, e); + filter = null; + } + } + let data = Object.assign({}, ((_b = this.nodeData.input) === null || _b === void 0 ? void 0 : _b.optional) || {}, ((_c = this.nodeData.input) === null || _c === void 0 ? void 0 : _c.hidden) || {}); + for (const [key, value] of Object.entries(data)) { + if (Array.isArray(value[0])) { + let values = value[0]; + if (key.startsWith("insert")) { + values = filter + ? values.filter((v, i) => i < 1 || (i == 1 && v.match(/^disable\s[a-z]/i)) || (filter === null || filter === void 0 ? void 0 : filter.test(v))) + : values; + const shouldShow = values.length > 2 || (values.length > 1 && !values[1].match(/^disable\s[a-z]/i)); + if (shouldShow) { + if (!this.combos[key]) { + this.combos[key] = this.node.addWidget("combo", key, values[0], (selected) => { + if (selected !== values[0] && !selected.match(/^disable\s[a-z]/i)) { + wait().then(() => { + if (key.includes("embedding")) { + this.insertSelectionText(`embedding:${selected}`); + } + else if (key.includes("saved")) { + this.insertSelectionText(this.combosValues[`values_${key}`][values.indexOf(selected)]); + } + else if (key.includes("lora")) { + this.insertSelectionText(``); + } + this.combos[key].value = values[0]; + }); + } + }, { + values, + serialize: true, + }); + this.combos[key].oldComputeSize = this.combos[key].computeSize; + let node = this.node; + this.combos[key].computeSize = function (width) { + var _a, _b; + const size = ((_b = (_a = this).oldComputeSize) === null || _b === void 0 ? void 0 : _b.call(_a, width)) || [ + width, + LiteGraph.NODE_WIDGET_HEIGHT, + ]; + if (this === node.widgets[node.widgets.length - 1]) { + size[1] += 10; + } + return size; + }; + } + this.combos[key].options.values = values; + this.combos[key].value = values[0]; + } + else if (!shouldShow && this.combos[key]) { + this.node.widgets.splice(this.node.widgets.indexOf(this.combos[key]), 1); + delete this.combos[key]; + } + } + else if (key.startsWith("values")) { + this.combosValues[key] = values; + } + } + } + } + insertSelectionText(text) { + if (!this.promptEl) { + console.error("Asked to insert text, but no textbox found."); + return; + } + let prompt = this.promptEl.value; + let first = prompt.substring(0, this.promptEl.selectionEnd).replace(/ +$/, ""); + first = first + (["\n"].includes(first[first.length - 1]) ? "" : first.length ? " " : ""); + let second = prompt.substring(this.promptEl.selectionEnd).replace(/^ +/, ""); + second = (["\n"].includes(second[0]) ? "" : second.length ? " " : "") + second; + this.promptEl.value = first + text + second; + this.promptEl.focus(); + this.promptEl.selectionStart = first.length; + this.promptEl.selectionEnd = first.length + text.length; + } + addAndHandleKeyboardLoraEditWeight() { + this.promptEl.addEventListener("keydown", (event) => { + var _a, _b; + if (!(event.key === "ArrowUp" || event.key === "ArrowDown")) + return; + if (!event.ctrlKey && !event.metaKey) + return; + const delta = event.shiftKey ? 0.01 : 0.1; + let start = this.promptEl.selectionStart; + let end = this.promptEl.selectionEnd; + let fullText = this.promptEl.value; + let selectedText = fullText.substring(start, end); + if (!selectedText) { + const stopOn = "<>()\r\n\t"; + if (fullText[start] == ">") { + start -= 2; + end -= 2; + } + if (fullText[end - 1] == "<") { + start += 2; + end += 2; + } + while (!stopOn.includes(fullText[start]) && start > 0) { + start--; + } + while (!stopOn.includes(fullText[end - 1]) && end < fullText.length) { + end++; + } + selectedText = fullText.substring(start, end); + } + if (!selectedText.startsWith("")) { + return; + } + let weight = (_b = Number((_a = selectedText.match(/:(-?\d*(\.\d*)?)>$/)) === null || _a === void 0 ? void 0 : _a[1])) !== null && _b !== void 0 ? _b : 1; + weight += event.key === "ArrowUp" ? delta : -delta; + const updatedText = selectedText.replace(/(:-?\d*(\.\d*)?)?>$/, `:${weight.toFixed(2)}>`); + this.promptEl.setRangeText(updatedText, start, end, "select"); + event.preventDefault(); + event.stopPropagation(); + }); + } + patchNodeRefresh() { + this.boundOnFreshNodeDefs = this.onFreshNodeDefs.bind(this); + api.addEventListener("fresh-node-defs", this.boundOnFreshNodeDefs); + const oldNodeRemoved = this.node.onRemoved; + this.node.onRemoved = () => { + oldNodeRemoved === null || oldNodeRemoved === void 0 ? void 0 : oldNodeRemoved.call(this.node); + api.removeEventListener("fresh-node-defs", this.boundOnFreshNodeDefs); + }; + } +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/bookmark.js b/custom_nodes/rgthree-comfy/web/comfyui/bookmark.js new file mode 100644 index 00000000..c4887718 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/bookmark.js @@ -0,0 +1,105 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseVirtualNode } from "./base_node.js"; +import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js"; +import { SERVICE as BOOKMARKS_SERVICE } from "./services/bookmarks_services.js"; +import { NodeTypesString } from "./constants.js"; +import { getClosestOrSelf, query } from "../../rgthree/common/utils_dom.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +import { findFromNodeForSubgraph } from "./utils.js"; +export class Bookmark extends RgthreeBaseVirtualNode { + get _collapsed_width() { + return this.___collapsed_width; + } + set _collapsed_width(width) { + const canvas = app.canvas; + const ctx = canvas.canvas.getContext("2d"); + const oldFont = ctx.font; + ctx.font = canvas.title_text_font; + this.___collapsed_width = 40 + ctx.measureText(this.title).width; + ctx.font = oldFont; + } + constructor(title = Bookmark.title) { + super(title); + this.comfyClass = NodeTypesString.BOOKMARK; + this.___collapsed_width = 0; + this.isVirtualNode = true; + this.serialize_widgets = true; + const nextShortcutChar = BOOKMARKS_SERVICE.getNextShortcut(); + this.addWidget("text", "shortcut_key", nextShortcutChar, (value, ...args) => { + value = value.trim()[0] || "1"; + }, { + y: 8, + }); + this.addWidget("number", "zoom", 1, (value) => { }, { + y: 8 + LiteGraph.NODE_WIDGET_HEIGHT + 4, + max: 2, + min: 0.5, + precision: 2, + }); + this.keypressBound = this.onKeypress.bind(this); + this.title = "πŸ”–"; + this.onConstructed(); + } + get shortcutKey() { + var _a, _b, _c; + return (_c = (_b = (_a = this.widgets[0]) === null || _a === void 0 ? void 0 : _a.value) === null || _b === void 0 ? void 0 : _b.toLocaleLowerCase()) !== null && _c !== void 0 ? _c : ""; + } + onAdded(graph) { + KEY_EVENT_SERVICE.addEventListener("keydown", this.keypressBound); + } + onRemoved() { + KEY_EVENT_SERVICE.removeEventListener("keydown", this.keypressBound); + } + onKeypress(event) { + const originalEvent = event.detail.originalEvent; + const target = originalEvent.target; + if (getClosestOrSelf(target, 'input,textarea,[contenteditable="true"]')) { + return; + } + if (KEY_EVENT_SERVICE.areOnlyKeysDown(this.widgets[0].value, true)) { + this.canvasToBookmark(); + originalEvent.preventDefault(); + originalEvent.stopPropagation(); + } + } + onMouseDown(event, pos, graphCanvas) { + var _a; + const input = query(".graphdialog > input.value"); + if (input && input.value === ((_a = this.widgets[0]) === null || _a === void 0 ? void 0 : _a.value)) { + input.addEventListener("keydown", (e) => { + KEY_EVENT_SERVICE.handleKeyDownOrUp(e); + e.preventDefault(); + e.stopPropagation(); + input.value = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" + "); + }); + } + return false; + } + async canvasToBookmark() { + var _a, _b; + const canvas = app.canvas; + if (this.graph !== app.canvas.getCurrentGraph()) { + const subgraph = this.graph; + const fromNode = findFromNodeForSubgraph(subgraph.id); + canvas.openSubgraph(subgraph, fromNode); + await wait(16); + } + if ((_a = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _a === void 0 ? void 0 : _a.offset) { + canvas.ds.offset[0] = -this.pos[0] + 16; + canvas.ds.offset[1] = -this.pos[1] + 40; + } + if (((_b = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _b === void 0 ? void 0 : _b.scale) != null) { + canvas.ds.scale = Number(this.widgets[1].value || 1); + } + canvas.setDirty(true, true); + } +} +Bookmark.type = NodeTypesString.BOOKMARK; +Bookmark.title = NodeTypesString.BOOKMARK; +Bookmark.slot_start_y = -20; +app.registerExtension({ + name: "rgthree.Bookmark", + registerCustomNodes() { + Bookmark.setUp(); + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/bypasser.js b/custom_nodes/rgthree-comfy/web/comfyui/bypasser.js new file mode 100644 index 00000000..96e47e21 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/bypasser.js @@ -0,0 +1,45 @@ +import { app } from "../../scripts/app.js"; +import { BaseNodeModeChanger } from "./base_node_mode_changer.js"; +import { NodeTypesString } from "./constants.js"; +const MODE_BYPASS = 4; +const MODE_ALWAYS = 0; +class BypasserNode extends BaseNodeModeChanger { + constructor(title = BypasserNode.title) { + super(title); + this.comfyClass = NodeTypesString.FAST_BYPASSER; + this.modeOn = MODE_ALWAYS; + this.modeOff = MODE_BYPASS; + this.onConstructed(); + } + async handleAction(action) { + if (action === "Bypass all") { + for (const widget of this.widgets || []) { + this.forceWidgetOff(widget, true); + } + } + else if (action === "Enable all") { + for (const widget of this.widgets || []) { + this.forceWidgetOn(widget, true); + } + } + else if (action === "Toggle all") { + for (const widget of this.widgets || []) { + this.forceWidgetToggle(widget, true); + } + } + } +} +BypasserNode.exposedActions = ["Bypass all", "Enable all", "Toggle all"]; +BypasserNode.type = NodeTypesString.FAST_BYPASSER; +BypasserNode.title = NodeTypesString.FAST_BYPASSER; +app.registerExtension({ + name: "rgthree.Bypasser", + registerCustomNodes() { + BypasserNode.setUp(); + }, + loadedGraphNode(node) { + if (node.type == BypasserNode.title) { + node._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/comfy_ui_bar.js b/custom_nodes/rgthree-comfy/web/comfyui/comfy_ui_bar.js new file mode 100644 index 00000000..a95fb0ff --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/comfy_ui_bar.js @@ -0,0 +1,197 @@ +import { app } from "../../scripts/app.js"; +import { iconGear, iconStarFilled, logoRgthreeAsync } from "../../rgthree/common/media/svgs.js"; +import { $el, empty } from "../../rgthree/common/utils_dom.js"; +import { SERVICE as BOOKMARKS_SERVICE } from "./services/bookmarks_services.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +import { RgthreeConfigDialog } from "./config.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +let rgthreeButtonGroup = null; +function addRgthreeTopBarButtons() { + var _a, _b, _c; + if (!CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.enabled")) { + if ((_a = rgthreeButtonGroup === null || rgthreeButtonGroup === void 0 ? void 0 : rgthreeButtonGroup.element) === null || _a === void 0 ? void 0 : _a.parentElement) { + rgthreeButtonGroup.element.parentElement.removeChild(rgthreeButtonGroup.element); + } + return; + } + else if (rgthreeButtonGroup) { + (_b = app.menu) === null || _b === void 0 ? void 0 : _b.settingsGroup.element.before(rgthreeButtonGroup.element); + return; + } + const buttons = []; + const rgthreeButton = new RgthreeComfyButton({ + icon: "", + tooltip: "rgthree-comfy", + primary: true, + enabled: true, + classList: "comfyui-button comfyui-menu-mobile-collapse primary", + }); + buttons.push(rgthreeButton); + logoRgthreeAsync().then((t) => { + rgthreeButton.setIcon(t); + }); + rgthreeButton.withPopup(new RgthreeComfyPopup({ target: rgthreeButton.element }, $el("menu.rgthree-menu.rgthree-top-menu", { + children: [ + $el("li", { + child: $el("button.rgthree-button-reset", { + html: iconGear + "Settings (rgthree-comfy)", + onclick: () => new RgthreeConfigDialog().show(), + }), + }), + $el("li", { + child: $el("button.rgthree-button-reset", { + html: iconStarFilled + "Star on Github", + onclick: () => window.open("https://github.com/rgthree/rgthree-comfy", "_blank"), + }), + }), + ], + })), "click"); + if (CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.button_bookmarks.enabled")) { + const bookmarksListEl = $el("menu.rgthree-menu.rgthree-top-menu"); + bookmarksListEl.appendChild($el("li.rgthree-message", { + child: $el("span", { text: "No bookmarks in current workflow." }), + })); + const bookmarksButton = new RgthreeComfyButton({ + icon: "bookmark", + tooltip: "Workflow Bookmarks (rgthree-comfy)", + }); + const bookmarksPopup = new RgthreeComfyPopup({ target: bookmarksButton.element, modal: false }, bookmarksListEl); + bookmarksPopup.onOpen(() => { + const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks(); + empty(bookmarksListEl); + if (bookmarks.length) { + for (const b of bookmarks) { + bookmarksListEl.appendChild($el("li", { + child: $el("button.rgthree-button-reset", { + text: `[${b.shortcutKey}] ${b.title}`, + onclick: () => { + b.canvasToBookmark(); + }, + }), + })); + } + } + else { + bookmarksListEl.appendChild($el("li.rgthree-message", { + child: $el("span", { text: "No bookmarks in current workflow." }), + })); + } + }); + bookmarksButton.withPopup(bookmarksPopup, "hover"); + buttons.push(bookmarksButton); + } + rgthreeButtonGroup = new RgthreeComfyButtonGroup(...buttons); + (_c = app.menu) === null || _c === void 0 ? void 0 : _c.settingsGroup.element.before(rgthreeButtonGroup.element); +} +app.registerExtension({ + name: "rgthree.TopMenu", + async setup() { + addRgthreeTopBarButtons(); + CONFIG_SERVICE.addEventListener("config-change", ((e) => { + var _a, _b; + if ((_b = (_a = e.detail) === null || _a === void 0 ? void 0 : _a.key) === null || _b === void 0 ? void 0 : _b.includes("features.comfy_top_bar_menu")) { + addRgthreeTopBarButtons(); + } + })); + }, +}); +class RgthreeComfyButtonGroup { + constructor(...buttons) { + this.element = $el("div.rgthree-comfybar-top-button-group"); + this.buttons = buttons; + this.update(); + } + insert(button, index) { + this.buttons.splice(index, 0, button); + this.update(); + } + append(button) { + this.buttons.push(button); + this.update(); + } + remove(indexOrButton) { + if (typeof indexOrButton !== "number") { + indexOrButton = this.buttons.indexOf(indexOrButton); + } + if (indexOrButton > -1) { + const btn = this.buttons.splice(indexOrButton, 1); + this.update(); + return btn; + } + return null; + } + update() { + this.element.replaceChildren(...this.buttons.map((b) => { var _a; return (_a = b["element"]) !== null && _a !== void 0 ? _a : b; })); + } +} +class RgthreeComfyButton { + constructor(opts) { + this.element = $el("button.rgthree-comfybar-top-button.rgthree-button-reset.rgthree-button"); + this.iconElement = $el("span.rgthree-button-icon"); + opts.icon && this.setIcon(opts.icon); + opts.tooltip && this.element.setAttribute("title", opts.tooltip); + opts.primary && this.element.classList.add("-primary"); + } + setIcon(iconOrMarkup) { + const markup = iconOrMarkup.startsWith("<") + ? iconOrMarkup + : ``; + this.iconElement.innerHTML = markup; + if (!this.iconElement.parentElement) { + this.element.appendChild(this.iconElement); + } + } + withPopup(popup, trigger) { + if (trigger === "click") { + this.element.addEventListener("click", () => { + popup.open(); + }); + } + if (trigger === "hover") { + this.element.addEventListener("pointerenter", () => { + popup.open(); + }); + } + } +} +class RgthreeComfyPopup { + constructor(opts, element) { + this.onOpenFn = null; + this.onWindowClickBound = this.onWindowClick.bind(this); + this.element = element; + this.opts = opts; + opts.target && (this.target = opts.target); + opts.modal && this.element.classList.add("-modal"); + } + async open() { + if (!this.target) { + throw new Error("No target for RgthreeComfyPopup"); + } + if (this.onOpenFn) { + await this.onOpenFn(); + } + await wait(16); + const rect = this.target.getBoundingClientRect(); + this.element.setAttribute("state", "measuring"); + document.body.appendChild(this.element); + this.element.style.position = "fixed"; + this.element.style.left = `${rect.left}px`; + this.element.style.top = `${rect.top + rect.height}px`; + this.element.setAttribute("state", "open"); + if (this.opts.modal) { + document.body.classList.add("rgthree-modal-menu-open"); + } + window.addEventListener("click", this.onWindowClickBound); + } + close() { + this.element.remove(); + document.body.classList.remove("rgthree-modal-menu-open"); + window.removeEventListener("click", this.onWindowClickBound); + } + onOpen(fn) { + this.onOpenFn = fn; + } + onWindowClick() { + this.close(); + } +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/config.js b/custom_nodes/rgthree-comfy/web/comfyui/config.js new file mode 100644 index 00000000..f92995d2 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/config.js @@ -0,0 +1,369 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeDialog } from "../../rgthree/common/dialog.js"; +import { createElement as $el, queryAll as $$ } from "../../rgthree/common/utils_dom.js"; +import { checkmark, logoRgthree } from "../../rgthree/common/media/svgs.js"; +import { rgthree } from "./rgthree.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +var ConfigType; +(function (ConfigType) { + ConfigType[ConfigType["UNKNOWN"] = 0] = "UNKNOWN"; + ConfigType[ConfigType["BOOLEAN"] = 1] = "BOOLEAN"; + ConfigType[ConfigType["STRING"] = 2] = "STRING"; + ConfigType[ConfigType["NUMBER"] = 3] = "NUMBER"; + ConfigType[ConfigType["ARRAY"] = 4] = "ARRAY"; +})(ConfigType || (ConfigType = {})); +var ConfigInputType; +(function (ConfigInputType) { + ConfigInputType[ConfigInputType["UNKNOWN"] = 0] = "UNKNOWN"; + ConfigInputType[ConfigInputType["CHECKLIST"] = 1] = "CHECKLIST"; +})(ConfigInputType || (ConfigInputType = {})); +const TYPE_TO_STRING = { + [ConfigType.UNKNOWN]: "unknown", + [ConfigType.BOOLEAN]: "boolean", + [ConfigType.STRING]: "string", + [ConfigType.NUMBER]: "number", + [ConfigType.ARRAY]: "array", +}; +const CONFIGURABLE = { + features: [ + { + key: "features.progress_bar.enabled", + type: ConfigType.BOOLEAN, + label: "Prompt Progress Bar", + description: `Shows a minimal progress bar for nodes and steps at the top of the app.`, + subconfig: [ + { + key: "features.progress_bar.height", + type: ConfigType.NUMBER, + label: "Height of the bar", + }, + { + key: "features.progress_bar.position", + type: ConfigType.STRING, + label: "Position at top or bottom of window", + options: ["top", "bottom"], + }, + ], + }, + { + key: "features.import_individual_nodes.enabled", + type: ConfigType.BOOLEAN, + label: "Import Individual Nodes Widgets", + description: "Dragging & Dropping a similar image/JSON workflow onto (most) current workflow nodes" + + "will allow you to import that workflow's node's widgets when it has the same " + + "id and type. This is useful when you have several images and you'd like to import just " + + "one part of a previous iteration, like a seed, or prompt.", + }, + ], + menus: [ + { + key: "features.comfy_top_bar_menu.enabled", + type: ConfigType.BOOLEAN, + label: "Enable Top Bar Menu", + description: "Have quick access from ComfyUI's new top bar to rgthree-comfy bookmarks, settings " + + "(and more to come).", + }, + { + key: "features.menu_queue_selected_nodes", + type: ConfigType.BOOLEAN, + label: "Show 'Queue Selected Output Nodes'", + description: "Will show a menu item in the right-click context menus to queue (only) the selected " + + "output nodes.", + }, + { + key: "features.menu_auto_nest.subdirs", + type: ConfigType.BOOLEAN, + label: "Auto Nest Subdirectories in Menus", + description: "When a large, flat list of values contain sub-directories, auto nest them. (Like, for " + + "a large list of checkpoints).", + subconfig: [ + { + key: "features.menu_auto_nest.threshold", + type: ConfigType.NUMBER, + label: "Number of items needed to trigger nesting.", + }, + ], + }, + { + key: "features.menu_bookmarks.enabled", + type: ConfigType.BOOLEAN, + label: "Show Bookmarks in context menu", + description: "Will list bookmarks in the rgthree-comfy right-click context menu.", + }, + ], + groups: [ + { + key: "features.group_header_fast_toggle.enabled", + type: ConfigType.BOOLEAN, + label: "Show fast toggles in Group Headers", + description: "Show quick toggles in Groups' Headers to quickly mute, bypass or queue.", + subconfig: [ + { + key: "features.group_header_fast_toggle.toggles", + type: ConfigType.ARRAY, + label: "Which toggles to show.", + inputType: ConfigInputType.CHECKLIST, + options: [ + { value: "queue", label: "queue" }, + { value: "bypass", label: "bypass" }, + { value: "mute", label: "mute" }, + ], + }, + { + key: "features.group_header_fast_toggle.show", + type: ConfigType.STRING, + label: "When to show them.", + options: [ + { value: "hover", label: "on hover" }, + { value: "always", label: "always" }, + ], + }, + ], + }, + ], + advanced: [ + { + key: "features.show_alerts_for_corrupt_workflows", + type: ConfigType.BOOLEAN, + label: "Detect Corrupt Workflows", + description: "Will show a message at the top of the screen when loading a workflow that has " + + "corrupt linking data.", + }, + { + key: "log_level", + type: ConfigType.STRING, + label: "Log level for browser dev console.", + description: "Further down the list, the more verbose logs to the console will be. For instance, " + + "selecting 'IMPORTANT' means only important message will be logged to the browser " + + "console, while selecting 'WARN' will log all messages at or higher than WARN, including " + + "'ERROR' and 'IMPORTANT' etc.", + options: ["IMPORTANT", "ERROR", "WARN", "INFO", "DEBUG", "DEV"], + isDevOnly: true, + onSave: function (value) { + rgthree.setLogLevel(value); + }, + }, + { + key: "features.invoke_extensions_async.node_created", + type: ConfigType.BOOLEAN, + label: "Allow other extensions to call nodeCreated on rgthree-nodes.", + isDevOnly: true, + description: "Do not disable unless you are having trouble (and then file an issue at rgthree-comfy)." + + "Prior to Apr 2024 it was not possible for other extensions to invoke their nodeCreated " + + "event on some rgthree-comfy nodes. Now it's possible and this option is only here in " + + "for easy if something is wrong.", + }, + ], +}; +function fieldrow(item) { + var _a; + const initialValue = CONFIG_SERVICE.getConfigValue(item.key); + const container = $el(`div.fieldrow.-type-${TYPE_TO_STRING[item.type]}`, { + dataset: { + name: item.key, + initial: initialValue, + type: item.type, + }, + }); + $el(`label[for="${item.key}"]`, { + children: [ + $el(`span[text="${item.label}"]`), + item.description ? $el("small", { html: item.description }) : null, + ], + parent: container, + }); + let input; + if ((_a = item.options) === null || _a === void 0 ? void 0 : _a.length) { + if (item.inputType === ConfigInputType.CHECKLIST) { + const initialValueList = initialValue || []; + input = $el(`fieldset.rgthree-checklist-group[id="${item.key}"]`, { + parent: container, + children: item.options.map((o) => { + const label = o.label || String(o); + const value = o.value || o; + const id = `${item.key}_${value}`; + return $el(`span.rgthree-checklist-item`, { + children: [ + $el(`input[type="checkbox"][value="${value}"]`, { + id, + checked: initialValueList.includes(value), + }), + $el(`label`, { + for: id, + text: label, + }) + ] + }); + }), + }); + } + else { + input = $el(`select[id="${item.key}"]`, { + parent: container, + children: item.options.map((o) => { + const label = o.label || String(o); + const value = o.value || o; + const valueSerialized = JSON.stringify({ value: value }); + return $el(`option[value="${valueSerialized}"]`, { + text: label, + selected: valueSerialized === JSON.stringify({ value: initialValue }), + }); + }), + }); + } + } + else if (item.type === ConfigType.BOOLEAN) { + container.classList.toggle("-checked", !!initialValue); + input = $el(`input[type="checkbox"][id="${item.key}"]`, { + parent: container, + checked: initialValue, + }); + } + else { + input = $el(`input[id="${item.key}"]`, { + parent: container, + value: initialValue, + }); + } + $el("div.fieldrow-value", { children: [input], parent: container }); + return container; +} +export class RgthreeConfigDialog extends RgthreeDialog { + constructor() { + const content = $el("div"); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["features"], "Features")); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["menus"], "Menus")); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["groups"], "Groups")); + content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["advanced"], "Advanced")); + content.addEventListener("input", (e) => { + const changed = this.getChangedFormData(); + $$(".save-button", this.element)[0].disabled = + !Object.keys(changed).length; + }); + content.addEventListener("change", (e) => { + const changed = this.getChangedFormData(); + $$(".save-button", this.element)[0].disabled = + !Object.keys(changed).length; + }); + const dialogOptions = { + class: "-iconed -settings", + title: logoRgthree + `

      Settings - rgthree-comfy

      `, + content, + onBeforeClose: () => { + const changed = this.getChangedFormData(); + if (Object.keys(changed).length) { + return confirm("Looks like there are unsaved changes. Are you sure you want close?"); + } + return true; + }, + buttons: [ + { + label: "Save", + disabled: true, + className: "rgthree-button save-button -blue", + callback: async (e) => { + var _a, _b; + const changed = this.getChangedFormData(); + if (!Object.keys(changed).length) { + this.close(); + return; + } + const success = await CONFIG_SERVICE.setConfigValues(changed); + if (success) { + for (const key of Object.keys(changed)) { + (_b = (_a = Object.values(CONFIGURABLE) + .flat() + .find((f) => f.key === key)) === null || _a === void 0 ? void 0 : _a.onSave) === null || _b === void 0 ? void 0 : _b.call(_a, changed[key]); + } + this.close(); + rgthree.showMessage({ + id: "config-success", + message: `${checkmark} Successfully saved rgthree-comfy settings!`, + timeout: 4000, + }); + $$(".save-button", this.element)[0].disabled = true; + } + else { + alert("There was an error saving rgthree-comfy configuration."); + } + }, + }, + ], + }; + super(dialogOptions); + } + static buildFieldset(datas, label) { + const fieldset = $el(`fieldset`, { children: [$el(`legend[text="${label}"]`)] }); + for (const data of datas) { + if (data.isDevOnly && !rgthree.isDevMode()) { + continue; + } + const container = $el("div.formrow"); + container.appendChild(fieldrow(data)); + if (data.subconfig) { + for (const subfeature of data.subconfig) { + container.appendChild(fieldrow(subfeature)); + } + } + fieldset.appendChild(container); + } + return fieldset; + } + getChangedFormData() { + return $$("[data-name]", this.contentElement).reduce((acc, el) => { + const name = el.dataset["name"]; + const type = el.dataset["type"]; + const initialValue = CONFIG_SERVICE.getConfigValue(name); + let currentValueEl = $$("fieldset.rgthree-checklist-group, input, textarea, select", el)[0]; + let currentValue = null; + if (type === String(ConfigType.BOOLEAN)) { + currentValue = currentValueEl.checked; + el.classList.toggle("-checked", currentValue); + } + else { + currentValue = currentValueEl === null || currentValueEl === void 0 ? void 0 : currentValueEl.value; + if (currentValueEl.nodeName === "SELECT") { + currentValue = JSON.parse(currentValue).value; + } + else if (currentValueEl.classList.contains('rgthree-checklist-group')) { + currentValue = []; + for (const check of $$('input[type="checkbox"]', currentValueEl)) { + if (check.checked) { + currentValue.push(check.value); + } + } + } + else if (type === String(ConfigType.NUMBER)) { + currentValue = Number(currentValue) || initialValue; + } + } + if (JSON.stringify(currentValue) !== JSON.stringify(initialValue)) { + acc[name] = currentValue; + } + return acc; + }, {}); + } +} +app.ui.settings.addSetting({ + id: "rgthree.config", + defaultValue: null, + name: "Open rgthree-comfy config", + type: () => { + return $el("tr.rgthree-comfyui-settings-row", { + children: [ + $el("td", { + child: `
      ${logoRgthree} [rgthree-comfy] configuration / settings
      `, + }), + $el("td", { + child: $el('button.rgthree-button.-blue[text="rgthree-comfy settings"]', { + events: { + click: (e) => { + new RgthreeConfigDialog().show(); + }, + }, + }), + }), + ], + }); + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/constants.js b/custom_nodes/rgthree-comfy/web/comfyui/constants.js new file mode 100644 index 00000000..b05bf4bd --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/constants.js @@ -0,0 +1,62 @@ +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +export function addRgthree(str) { + return str + " (rgthree)"; +} +export function stripRgthree(str) { + return str.replace(/\s*\(rgthree\)$/, ""); +} +export const NodeTypesString = { + ANY_SWITCH: addRgthree("Any Switch"), + CONTEXT: addRgthree("Context"), + CONTEXT_BIG: addRgthree("Context Big"), + CONTEXT_SWITCH: addRgthree("Context Switch"), + CONTEXT_SWITCH_BIG: addRgthree("Context Switch Big"), + CONTEXT_MERGE: addRgthree("Context Merge"), + CONTEXT_MERGE_BIG: addRgthree("Context Merge Big"), + DYNAMIC_CONTEXT: addRgthree("Dynamic Context"), + DYNAMIC_CONTEXT_SWITCH: addRgthree("Dynamic Context Switch"), + DISPLAY_ANY: addRgthree("Display Any"), + IMAGE_OR_LATENT_SIZE: addRgthree("Image or Latent Size"), + NODE_MODE_RELAY: addRgthree("Mute / Bypass Relay"), + NODE_MODE_REPEATER: addRgthree("Mute / Bypass Repeater"), + FAST_MUTER: addRgthree("Fast Muter"), + FAST_BYPASSER: addRgthree("Fast Bypasser"), + FAST_GROUPS_MUTER: addRgthree("Fast Groups Muter"), + FAST_GROUPS_BYPASSER: addRgthree("Fast Groups Bypasser"), + FAST_ACTIONS_BUTTON: addRgthree("Fast Actions Button"), + LABEL: addRgthree("Label"), + POWER_PRIMITIVE: addRgthree("Power Primitive"), + POWER_PROMPT: addRgthree("Power Prompt"), + POWER_PROMPT_SIMPLE: addRgthree("Power Prompt - Simple"), + POWER_PUTER: addRgthree("Power Puter"), + POWER_CONDUCTOR: addRgthree("Power Conductor"), + SDXL_EMPTY_LATENT_IMAGE: addRgthree("SDXL Empty Latent Image"), + SDXL_POWER_PROMPT_POSITIVE: addRgthree("SDXL Power Prompt - Positive"), + SDXL_POWER_PROMPT_NEGATIVE: addRgthree("SDXL Power Prompt - Simple / Negative"), + POWER_LORA_LOADER: addRgthree("Power Lora Loader"), + KSAMPLER_CONFIG: addRgthree("KSampler Config"), + NODE_COLLECTOR: addRgthree("Node Collector"), + REROUTE: addRgthree("Reroute"), + RANDOM_UNMUTER: addRgthree("Random Unmuter"), + SEED: addRgthree("Seed"), + BOOKMARK: addRgthree("Bookmark"), + IMAGE_COMPARER: addRgthree("Image Comparer"), + IMAGE_INSET_CROP: addRgthree("Image Inset Crop"), +}; +const UNRELEASED_KEYS = { + [NodeTypesString.DYNAMIC_CONTEXT]: "dynamic_context", + [NodeTypesString.DYNAMIC_CONTEXT_SWITCH]: "dynamic_context", + [NodeTypesString.POWER_CONDUCTOR]: "power_conductor", +}; +export function getNodeTypeStrings() { + const unreleasedKeys = Object.keys(UNRELEASED_KEYS); + return Object.values(NodeTypesString) + .map((i) => stripRgthree(i)) + .filter((i) => { + if (unreleasedKeys.includes(i)) { + return !!CONFIG_SERVICE.getConfigValue(`unreleased.${UNRELEASED_KEYS[i]}.enabled`); + } + return true; + }) + .sort(); +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/context.js b/custom_nodes/rgthree-comfy/web/comfyui/context.js new file mode 100644 index 00000000..e6185dad --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/context.js @@ -0,0 +1,322 @@ +import { app } from "../../scripts/app.js"; +import { IoDirection, addConnectionLayoutSupport, addMenuItem, matchLocalSlotsToServer, replaceNode, } from "./utils.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js"; +import { debounce, wait } from "../../rgthree/common/shared_utils.js"; +import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js"; +import { NodeTypesString } from "./constants.js"; +function findMatchingIndexByTypeOrName(otherNode, otherSlot, ctxSlots) { + const otherNodeType = (otherNode.type || "").toUpperCase(); + const otherNodeName = (otherNode.title || "").toUpperCase(); + let otherSlotType = otherSlot.type; + if (Array.isArray(otherSlotType) || otherSlotType.includes(",")) { + otherSlotType = "COMBO"; + } + const otherSlotName = otherSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", ""); + let ctxSlotIndex = -1; + if (["CONDITIONING", "INT", "STRING", "FLOAT", "COMBO"].includes(otherSlotType)) { + ctxSlotIndex = ctxSlots.findIndex((ctxSlot) => { + const ctxSlotName = ctxSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", ""); + let ctxSlotType = ctxSlot.type; + if (Array.isArray(ctxSlotType) || ctxSlotType.includes(",")) { + ctxSlotType = "COMBO"; + } + if (ctxSlotType !== otherSlotType) { + return false; + } + if (ctxSlotName === otherSlotName || + (ctxSlotName === "SEED" && otherSlotName.includes("SEED")) || + (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("AT_STEP")) || + (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("REFINER_STEP"))) { + return true; + } + if ((otherNodeType.includes("POSITIVE") || otherNodeName.includes("POSITIVE")) && + ((ctxSlotName === "POSITIVE" && otherSlotType === "CONDITIONING") || + (ctxSlotName === "TEXT_POS_G" && otherSlotName.includes("TEXT_G")) || + (ctxSlotName === "TEXT_POS_L" && otherSlotName.includes("TEXT_L")))) { + return true; + } + if ((otherNodeType.includes("NEGATIVE") || otherNodeName.includes("NEGATIVE")) && + ((ctxSlotName === "NEGATIVE" && otherSlotType === "CONDITIONING") || + (ctxSlotName === "TEXT_NEG_G" && otherSlotName.includes("TEXT_G")) || + (ctxSlotName === "TEXT_NEG_L" && otherSlotName.includes("TEXT_L")))) { + return true; + } + return false; + }); + } + else { + ctxSlotIndex = ctxSlots.map((s) => s.type).indexOf(otherSlotType); + } + return ctxSlotIndex; +} +export class BaseContextNode extends RgthreeBaseServerNode { + constructor(title) { + super(title); + this.___collapsed_width = 0; + } + get _collapsed_width() { + return this.___collapsed_width; + } + set _collapsed_width(width) { + const canvas = app.canvas; + const ctx = canvas.canvas.getContext("2d"); + const oldFont = ctx.font; + ctx.font = canvas.title_text_font; + let title = this.title.trim(); + this.___collapsed_width = 30 + (title ? 10 + ctx.measureText(title).width : 0); + ctx.font = oldFont; + } + connectByType(slot, targetNode, targetSlotType, optsIn) { + var _a; + let canConnect = (_a = super.connectByType) === null || _a === void 0 ? void 0 : _a.call(this, slot, targetNode, targetSlotType, optsIn); + if (!super.connectByType) { + canConnect = LGraphNode.prototype.connectByType.call(this, slot, targetNode, targetSlotType, optsIn); + } + if (!canConnect && slot === 0) { + const ctrlKey = KEY_EVENT_SERVICE.ctrlKey; + for (const [index, input] of (targetNode.inputs || []).entries()) { + if (input.link && !ctrlKey) { + continue; + } + const thisOutputSlot = findMatchingIndexByTypeOrName(targetNode, input, this.outputs); + if (thisOutputSlot > -1) { + this.connect(thisOutputSlot, targetNode, index); + } + } + } + return null; + } + connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn) { + var _a, _b; + let canConnect = (_a = super.connectByTypeOutput) === null || _a === void 0 ? void 0 : _a.call(this, slot, sourceNode, sourceSlotType, optsIn); + if (!super.connectByType) { + canConnect = LGraphNode.prototype.connectByTypeOutput.call(this, slot, sourceNode, sourceSlotType, optsIn); + } + if (!canConnect && slot === 0) { + const ctrlKey = KEY_EVENT_SERVICE.ctrlKey; + for (const [index, output] of (sourceNode.outputs || []).entries()) { + if (((_b = output.links) === null || _b === void 0 ? void 0 : _b.length) && !ctrlKey) { + continue; + } + const thisInputSlot = findMatchingIndexByTypeOrName(sourceNode, output, this.inputs); + if (thisInputSlot > -1) { + sourceNode.connect(index, this, thisInputSlot); + } + } + } + return null; + } + static setUp(comfyClass, nodeData, ctxClass) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ctxClass); + wait(500).then(() => { + LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] = + LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] || []; + LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"].push(comfyClass.comfyClass); + }); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + addConnectionLayoutSupport(ctxClass, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + ctxClass.category = comfyClass.category; + }); + } +} +class ContextNode extends BaseContextNode { + constructor(title = ContextNode.title) { + super(title); + } + static setUp(comfyClass, nodeData) { + BaseContextNode.setUp(comfyClass, nodeData, ContextNode); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextNode, app, { + name: "Convert To Context Big", + callback: (node) => { + replaceNode(node, ContextBigNode.type); + }, + }); + } +} +ContextNode.title = NodeTypesString.CONTEXT; +ContextNode.type = NodeTypesString.CONTEXT; +ContextNode.comfyClass = NodeTypesString.CONTEXT; +class ContextBigNode extends BaseContextNode { + constructor(title = ContextBigNode.title) { + super(title); + } + static setUp(comfyClass, nodeData) { + BaseContextNode.setUp(comfyClass, nodeData, ContextBigNode); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextBigNode, app, { + name: "Convert To Context (Original)", + callback: (node) => { + replaceNode(node, ContextNode.type); + }, + }); + } +} +ContextBigNode.title = NodeTypesString.CONTEXT_BIG; +ContextBigNode.type = NodeTypesString.CONTEXT_BIG; +ContextBigNode.comfyClass = NodeTypesString.CONTEXT_BIG; +class BaseContextMultiCtxInputNode extends BaseContextNode { + constructor(title) { + super(title); + this.stabilizeBound = this.stabilize.bind(this); + this.addContextInput(5); + } + addContextInput(num = 1) { + for (let i = 0; i < num; i++) { + this.addInput(`ctx_${String(this.inputs.length + 1).padStart(2, "0")}`, "RGTHREE_CONTEXT"); + } + } + onConnectionsChange(type, slotIndex, isConnected, link, ioSlot) { + var _a; + (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]); + if (type === LiteGraph.INPUT) { + this.scheduleStabilize(); + } + } + scheduleStabilize(ms = 64) { + return debounce(this.stabilizeBound, 64); + } + stabilize() { + removeUnusedInputsFromEnd(this, 4); + this.addContextInput(); + } +} +class ContextSwitchNode extends BaseContextMultiCtxInputNode { + constructor(title = ContextSwitchNode.title) { + super(title); + } + static setUp(comfyClass, nodeData) { + BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchNode); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextSwitchNode, app, { + name: "Convert To Context Switch Big", + callback: (node) => { + replaceNode(node, ContextSwitchBigNode.type); + }, + }); + } +} +ContextSwitchNode.title = NodeTypesString.CONTEXT_SWITCH; +ContextSwitchNode.type = NodeTypesString.CONTEXT_SWITCH; +ContextSwitchNode.comfyClass = NodeTypesString.CONTEXT_SWITCH; +class ContextSwitchBigNode extends BaseContextMultiCtxInputNode { + constructor(title = ContextSwitchBigNode.title) { + super(title); + } + static setUp(comfyClass, nodeData) { + BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchBigNode); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextSwitchBigNode, app, { + name: "Convert To Context Switch", + callback: (node) => { + replaceNode(node, ContextSwitchNode.type); + }, + }); + } +} +ContextSwitchBigNode.title = NodeTypesString.CONTEXT_SWITCH_BIG; +ContextSwitchBigNode.type = NodeTypesString.CONTEXT_SWITCH_BIG; +ContextSwitchBigNode.comfyClass = NodeTypesString.CONTEXT_SWITCH_BIG; +class ContextMergeNode extends BaseContextMultiCtxInputNode { + constructor(title = ContextMergeNode.title) { + super(title); + } + static setUp(comfyClass, nodeData) { + BaseContextNode.setUp(comfyClass, nodeData, ContextMergeNode); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextMergeNode, app, { + name: "Convert To Context Merge Big", + callback: (node) => { + replaceNode(node, ContextMergeBigNode.type); + }, + }); + } +} +ContextMergeNode.title = NodeTypesString.CONTEXT_MERGE; +ContextMergeNode.type = NodeTypesString.CONTEXT_MERGE; +ContextMergeNode.comfyClass = NodeTypesString.CONTEXT_MERGE; +class ContextMergeBigNode extends BaseContextMultiCtxInputNode { + constructor(title = ContextMergeBigNode.title) { + super(title); + } + static setUp(comfyClass, nodeData) { + BaseContextNode.setUp(comfyClass, nodeData, ContextMergeBigNode); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass); + addMenuItem(ContextMergeBigNode, app, { + name: "Convert To Context Switch", + callback: (node) => { + replaceNode(node, ContextMergeNode.type); + }, + }); + } +} +ContextMergeBigNode.title = NodeTypesString.CONTEXT_MERGE_BIG; +ContextMergeBigNode.type = NodeTypesString.CONTEXT_MERGE_BIG; +ContextMergeBigNode.comfyClass = NodeTypesString.CONTEXT_MERGE_BIG; +const contextNodes = [ + ContextNode, + ContextBigNode, + ContextSwitchNode, + ContextSwitchBigNode, + ContextMergeNode, + ContextMergeBigNode, +]; +const contextTypeToServerDef = {}; +function fixBadConfigs(node) { + const wrongName = node.outputs.find((o, i) => o.name === "CLIP_HEIGTH"); + if (wrongName) { + wrongName.name = "CLIP_HEIGHT"; + } +} +app.registerExtension({ + name: "rgthree.Context", + async beforeRegisterNodeDef(nodeType, nodeData) { + for (const ctxClass of contextNodes) { + if (nodeData.name === ctxClass.type) { + contextTypeToServerDef[ctxClass.type] = nodeData; + ctxClass.setUp(nodeType, nodeData); + break; + } + } + }, + async nodeCreated(node) { + const type = node.type || node.constructor.type; + const serverDef = type && contextTypeToServerDef[type]; + if (serverDef) { + fixBadConfigs(node); + matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef); + if (!type.includes("Switch") && !type.includes("Merge")) { + matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef); + } + } + }, + async loadedGraphNode(node) { + const type = node.type || node.constructor.type; + const serverDef = type && contextTypeToServerDef[type]; + if (serverDef) { + fixBadConfigs(node); + matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef); + if (!type.includes("Switch") && !type.includes("Merge")) { + matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef); + } + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dialog_info.js b/custom_nodes/rgthree-comfy/web/comfyui/dialog_info.js new file mode 100644 index 00000000..f2679b6c --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/dialog_info.js @@ -0,0 +1,292 @@ +import { RgthreeDialog } from "../../rgthree/common/dialog.js"; +import { createElement as $el, empty, appendChildren, getClosestOrSelf, query, queryAll, setAttributes, } from "../../rgthree/common/utils_dom.js"; +import { logoCivitai, link, pencilColored, diskColored, dotdotdot, } from "../../rgthree/common/media/svgs.js"; +import { CHECKPOINT_INFO_SERVICE, LORA_INFO_SERVICE } from "../../rgthree/common/model_info_service.js"; +import { rgthree } from "./rgthree.js"; +import { MenuButton } from "../../rgthree/common/menu.js"; +import { generateId, injectCss } from "../../rgthree/common/shared_utils.js"; +class RgthreeInfoDialog extends RgthreeDialog { + constructor(file) { + const dialogOptions = { + class: "rgthree-info-dialog", + title: `

      Loading...

      `, + content: "
      Loading..
      ", + onBeforeClose: () => { + return true; + }, + }; + super(dialogOptions); + this.modifiedModelData = false; + this.modelInfo = null; + this.init(file); + } + async init(file) { + var _a, _b; + const cssPromise = injectCss("rgthree/common/css/dialog_model_info.css"); + this.modelInfo = await this.getModelInfo(file); + await cssPromise; + this.setContent(this.getInfoContent()); + this.setTitle(((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a["name"]) || ((_b = this.modelInfo) === null || _b === void 0 ? void 0 : _b["file"]) || "Unknown"); + this.attachEvents(); + } + getCloseEventDetail() { + const detail = { + dirty: this.modifiedModelData, + }; + return { detail }; + } + attachEvents() { + this.contentElement.addEventListener("click", async (e) => { + const target = getClosestOrSelf(e.target, "[data-action]"); + const action = target === null || target === void 0 ? void 0 : target.getAttribute("data-action"); + if (!target || !action) { + return; + } + await this.handleEventAction(action, target, e); + }); + } + async handleEventAction(action, target, e) { + var _a, _b; + const info = this.modelInfo; + if (!(info === null || info === void 0 ? void 0 : info.file)) { + return; + } + if (action === "fetch-civitai") { + this.modelInfo = await this.refreshModelInfo(info.file); + this.setContent(this.getInfoContent()); + this.setTitle(((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a["name"]) || ((_b = this.modelInfo) === null || _b === void 0 ? void 0 : _b["file"]) || "Unknown"); + } + else if (action === "copy-trained-words") { + const selected = queryAll(".-rgthree-is-selected", target.closest("tr")); + const text = selected.map((el) => el.getAttribute("data-word")).join(", "); + await navigator.clipboard.writeText(text); + rgthree.showMessage({ + id: "copy-trained-words-" + generateId(4), + type: "success", + message: `Successfully copied ${selected.length} key word${selected.length === 1 ? "" : "s"}.`, + timeout: 4000, + }); + } + else if (action === "toggle-trained-word") { + target === null || target === void 0 ? void 0 : target.classList.toggle("-rgthree-is-selected"); + const tr = target.closest("tr"); + if (tr) { + const span = query("td:first-child > *", tr); + let small = query("small", span); + if (!small) { + small = $el("small", { parent: span }); + } + const num = queryAll(".-rgthree-is-selected", tr).length; + small.innerHTML = num + ? `${num} selected | Copy` + : ""; + } + } + else if (action === "edit-row") { + const tr = target.closest("tr"); + const td = query("td:nth-child(2)", tr); + const input = td.querySelector("input,textarea"); + if (!input) { + const fieldName = tr.dataset["fieldName"]; + tr.classList.add("-rgthree-editing"); + const isTextarea = fieldName === "userNote"; + const input = $el(`${isTextarea ? "textarea" : 'input[type="text"]'}`, { + value: td.textContent, + }); + input.addEventListener("keydown", (e) => { + if (!isTextarea && e.key === "Enter") { + const modified = saveEditableRow(info, tr, true); + this.modifiedModelData = this.modifiedModelData || modified; + e.stopPropagation(); + e.preventDefault(); + } + else if (e.key === "Escape") { + const modified = saveEditableRow(info, tr, false); + this.modifiedModelData = this.modifiedModelData || modified; + e.stopPropagation(); + e.preventDefault(); + } + }); + appendChildren(empty(td), [input]); + input.focus(); + } + else if (target.nodeName.toLowerCase() === "button") { + const modified = saveEditableRow(info, tr, true); + this.modifiedModelData = this.modifiedModelData || modified; + } + e === null || e === void 0 ? void 0 : e.preventDefault(); + e === null || e === void 0 ? void 0 : e.stopPropagation(); + } + } + getInfoContent() { + var _a, _b, _c, _d, _e, _f, _g, _h, _j, _k, _l, _m, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y; + const info = this.modelInfo || {}; + const civitaiLink = (_a = info.links) === null || _a === void 0 ? void 0 : _a.find((i) => i.includes("civitai.com/models")); + const html = ` +
        +
      • ${info.type || ""}
        • +
      • ${info.baseModel || ""}
        • +
      • + ${""} +
      + + + ${infoTableRow("File", info.file || "")} + ${infoTableRow("Hash (sha256)", info.sha256 || "")} + ${civitaiLink + ? infoTableRow("Civitai", `${logoCivitai}View on Civitai`) + : ((_c = (_b = info.raw) === null || _b === void 0 ? void 0 : _b.civitai) === null || _c === void 0 ? void 0 : _c.error) === "Model not found" + ? infoTableRow("Civitai", 'Model not found') + : ((_e = (_d = info.raw) === null || _d === void 0 ? void 0 : _d.civitai) === null || _e === void 0 ? void 0 : _e.error) + ? infoTableRow("Civitai", (_g = (_f = info.raw) === null || _f === void 0 ? void 0 : _f.civitai) === null || _g === void 0 ? void 0 : _g.error) + : !((_h = info.raw) === null || _h === void 0 ? void 0 : _h.civitai) + ? infoTableRow("Civitai", ``) + : ""} + + ${infoTableRow("Name", info.name || ((_k = (_j = info.raw) === null || _j === void 0 ? void 0 : _j.metadata) === null || _k === void 0 ? void 0 : _k.ss_output_name) || "", "The name for display.", "name")} + + ${!info.baseModelFile && !info.baseModelFile + ? "" + : infoTableRow("Base Model", (info.baseModel || "") + (info.baseModelFile ? ` (${info.baseModelFile})` : ""))} + + + ${!((_l = info.trainedWords) === null || _l === void 0 ? void 0 : _l.length) + ? "" + : infoTableRow("Trained Words", (_m = getTrainedWordsMarkup(info.trainedWords)) !== null && _m !== void 0 ? _m : "", "Trained words from the metadata and/or civitai. Click to select for copy.")} + + ${!((_p = (_o = info.raw) === null || _o === void 0 ? void 0 : _o.metadata) === null || _p === void 0 ? void 0 : _p.ss_clip_skip) || ((_r = (_q = info.raw) === null || _q === void 0 ? void 0 : _q.metadata) === null || _r === void 0 ? void 0 : _r.ss_clip_skip) == "None" + ? "" + : infoTableRow("Clip Skip", (_t = (_s = info.raw) === null || _s === void 0 ? void 0 : _s.metadata) === null || _t === void 0 ? void 0 : _t.ss_clip_skip)} + ${infoTableRow("Strength Min", (_u = info.strengthMin) !== null && _u !== void 0 ? _u : "", "The recommended minimum strength, In the Power Lora Loader node, strength will signal when it is below this threshold.", "strengthMin")} + ${infoTableRow("Strength Max", (_v = info.strengthMax) !== null && _v !== void 0 ? _v : "", "The recommended maximum strength. In the Power Lora Loader node, strength will signal when it is above this threshold.", "strengthMax")} + ${""} + ${infoTableRow("Additional Notes", (_w = info.userNote) !== null && _w !== void 0 ? _w : "", "Additional notes you'd like to keep and reference in the info dialog.", "userNote")} + +
      + +
        ${(_y = (_x = info.images) === null || _x === void 0 ? void 0 : _x.map((img) => ` +
      • +
        ${img.type === 'video' + ? `` + : ``} +
        ${imgInfoField("", img.civitaiUrl + ? `civitai${link}` + : undefined)}${imgInfoField("seed", img.seed)}${imgInfoField("steps", img.steps)}${imgInfoField("cfg", img.cfg)}${imgInfoField("sampler", img.sampler)}${imgInfoField("model", img.model)}${imgInfoField("positive", img.positive)}${imgInfoField("negative", img.negative)}
        +
        +
        • `).join("")) !== null && _y !== void 0 ? _y : ""}
      + `; + const div = $el("div", { html }); + if (rgthree.isDevMode()) { + setAttributes(query('[stub="menu"]', div), { + children: [ + new MenuButton({ + icon: dotdotdot, + options: [ + { label: "More Actions", type: "title" }, + { + label: "Open API JSON", + callback: async (e) => { + var _a; + if ((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a.file) { + window.open(`rgthree/api/loras/info?file=${encodeURIComponent(this.modelInfo.file)}`); + } + }, + }, + { + label: "Clear all local info", + callback: async (e) => { + var _a, _b, _c; + if ((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a.file) { + this.modelInfo = await LORA_INFO_SERVICE.clearFetchedInfo(this.modelInfo.file); + this.setContent(this.getInfoContent()); + this.setTitle(((_b = this.modelInfo) === null || _b === void 0 ? void 0 : _b["name"]) || ((_c = this.modelInfo) === null || _c === void 0 ? void 0 : _c["file"]) || "Unknown"); + } + }, + }, + ], + }), + ], + }); + } + return div; + } +} +export class RgthreeLoraInfoDialog extends RgthreeInfoDialog { + async getModelInfo(file) { + return LORA_INFO_SERVICE.getInfo(file, false, false); + } + async refreshModelInfo(file) { + return LORA_INFO_SERVICE.refreshInfo(file); + } + async clearModelInfo(file) { + return LORA_INFO_SERVICE.clearFetchedInfo(file); + } +} +export class RgthreeCheckpointInfoDialog extends RgthreeInfoDialog { + async getModelInfo(file) { + return CHECKPOINT_INFO_SERVICE.getInfo(file, false, false); + } + async refreshModelInfo(file) { + return CHECKPOINT_INFO_SERVICE.refreshInfo(file); + } + async clearModelInfo(file) { + return CHECKPOINT_INFO_SERVICE.clearFetchedInfo(file); + } +} +function infoTableRow(name, value, help = "", editableFieldName = "") { + return ` + + ${name} ${help ? `` : ""} + ${String(value).startsWith("<") ? value : `${value}`} + ${editableFieldName + ? `` + : ""} + `; +} +function getTrainedWordsMarkup(words) { + let markup = `
        `; + for (const wordData of words || []) { + markup += `
      • + ${wordData.word} + ${wordData.civitai ? logoCivitai : ""} + ${wordData.count != null ? `${wordData.count}` : ""} +
        • `; + } + markup += `
      `; + return markup; +} +function saveEditableRow(info, tr, saving = true) { + var _a; + const fieldName = tr.dataset["fieldName"]; + const input = query("input,textarea", tr); + let newValue = (_a = info[fieldName]) !== null && _a !== void 0 ? _a : ""; + let modified = false; + if (saving) { + newValue = input.value; + if (fieldName.startsWith("strength")) { + if (Number.isNaN(Number(newValue))) { + alert(`You must enter a number into the ${fieldName} field.`); + return false; + } + newValue = (Math.round(Number(newValue) * 100) / 100).toFixed(2); + } + LORA_INFO_SERVICE.savePartialInfo(info.file, { [fieldName]: newValue }); + modified = true; + } + tr.classList.remove("-rgthree-editing"); + const td = query("td:nth-child(2)", tr); + appendChildren(empty(td), [$el("span", { text: newValue })]); + return modified; +} +function imgInfoField(label, value) { + return value != null ? `${label ? `` : ""}${value}` : ""; +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/display_any.js b/custom_nodes/rgthree-comfy/web/comfyui/display_any.js new file mode 100644 index 00000000..cff00f92 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/display_any.js @@ -0,0 +1,35 @@ +import { app } from "../../scripts/app.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { addConnectionLayoutSupport } from "./utils.js"; +import { rgthree } from "./rgthree.js"; +let hasShownAlertForUpdatingInt = false; +app.registerExtension({ + name: "rgthree.DisplayAny", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "Display Any (rgthree)" || nodeData.name === "Display Int (rgthree)") { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + this.showValueWidget = ComfyWidgets["STRING"](this, "output", ["STRING", { multiline: true }], app).widget; + this.showValueWidget.inputEl.readOnly = true; + this.showValueWidget.serializeValue = async (node, index) => { + const n = rgthree.getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(node); + if (n) { + n.widgets_values[index] = ""; + } + else { + console.warn("No serialized node found in workflow. May be attributed to " + + "https://github.com/comfyanonymous/ComfyUI/issues/2193"); + } + return ""; + }; + }; + addConnectionLayoutSupport(nodeType, app, [["Left"], ["Right"]]); + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted === null || onExecuted === void 0 ? void 0 : onExecuted.apply(this, [message]); + this.showValueWidget.value = message.text[0]; + }; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context.js b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context.js new file mode 100644 index 00000000..5a2bc16a --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context.js @@ -0,0 +1,253 @@ +import { app } from "../../scripts/app.js"; +import { IoDirection, followConnectionUntilType, getConnectedInputInfosAndFilterPassThroughs, } from "./utils.js"; +import { rgthree } from "./rgthree.js"; +import { SERVICE as CONTEXT_SERVICE, InputMutationOperation, } from "./services/context_service.js"; +import { NodeTypesString } from "./constants.js"; +import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js"; +import { DynamicContextNodeBase } from "./dynamic_context_base.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +const OWNED_PREFIX = "+"; +const REGEX_OWNED_PREFIX = /^\+\s*/; +const REGEX_EMPTY_INPUT = /^\+\s*$/; +export class DynamicContextNode extends DynamicContextNodeBase { + constructor(title = DynamicContextNode.title) { + super(title); + } + onNodeCreated() { + this.addInput("base_ctx", "RGTHREE_DYNAMIC_CONTEXT"); + this.ensureOneRemainingNewInputSlot(); + super.onNodeCreated(); + } + onConnectionsChange(type, slotIndex, isConnected, link, ioSlot) { + var _a; + (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.call(this, type, slotIndex, isConnected, link, ioSlot); + if (this.configuring) { + return; + } + if (type === LiteGraph.INPUT) { + if (isConnected) { + this.handleInputConnected(slotIndex); + } + else { + this.handleInputDisconnected(slotIndex); + } + } + } + onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) { + var _a; + let canConnect = true; + if (super.onConnectInput) { + canConnect = super.onConnectInput.apply(this, [...arguments]); + } + if (canConnect && + outputNode instanceof DynamicContextNode && + outputIndex === 0 && + inputIndex !== 0) { + const [n, v] = rgthree.logger.warnParts("Currently, you can only connect a context node in the first slot."); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + canConnect = false; + } + return canConnect; + } + handleInputConnected(slotIndex) { + const ioSlot = this.inputs[slotIndex]; + const connectedIndexes = []; + if (slotIndex === 0) { + let baseNodeInfos = getConnectedInputInfosAndFilterPassThroughs(this, this, 0); + const baseNodes = baseNodeInfos.map((n) => n.node); + const baseNodesDynamicCtx = baseNodes[0]; + if (baseNodesDynamicCtx === null || baseNodesDynamicCtx === void 0 ? void 0 : baseNodesDynamicCtx.provideInputsData) { + const inputsData = CONTEXT_SERVICE.getDynamicContextInputsData(baseNodesDynamicCtx); + console.log("inputsData", inputsData); + for (const input of baseNodesDynamicCtx.provideInputsData()) { + if (input.name === "base_ctx" || input.name === "+") { + continue; + } + this.addContextInput(input.name, input.type, input.index); + this.stabilizeNames(); + } + } + } + else if (this.isInputSlotForNewInput(slotIndex)) { + this.handleNewInputConnected(slotIndex); + } + } + isInputSlotForNewInput(slotIndex) { + const ioSlot = this.inputs[slotIndex]; + return ioSlot && ioSlot.name === "+" && ioSlot.type === "*"; + } + handleNewInputConnected(slotIndex) { + if (!this.isInputSlotForNewInput(slotIndex)) { + throw new Error('Expected the incoming slot index to be the "new input" input.'); + } + const ioSlot = this.inputs[slotIndex]; + let cxn = null; + if (ioSlot.link != null) { + cxn = followConnectionUntilType(this, IoDirection.INPUT, slotIndex, true); + } + if ((cxn === null || cxn === void 0 ? void 0 : cxn.type) && (cxn === null || cxn === void 0 ? void 0 : cxn.name)) { + let name = this.addOwnedPrefix(this.getNextUniqueNameForThisNode(cxn.name)); + if (name.match(/^\+\s*[A-Z_]+(\.\d+)?$/)) { + name = name.toLowerCase(); + } + ioSlot.name = name; + ioSlot.type = cxn.type; + ioSlot.removable = true; + while (!this.outputs[slotIndex]) { + this.addOutput("*", "*"); + } + this.outputs[slotIndex].type = cxn.type; + this.outputs[slotIndex].name = this.stripOwnedPrefix(name).toLocaleUpperCase(); + if (cxn.type === "COMBO" || cxn.type.includes(",") || Array.isArray(cxn.type)) { + this.outputs[slotIndex].widget = true; + } + this.inputsMutated({ + operation: InputMutationOperation.ADDED, + node: this, + slotIndex, + slot: ioSlot, + }); + this.stabilizeNames(); + this.ensureOneRemainingNewInputSlot(); + } + } + handleInputDisconnected(slotIndex) { + var _a, _b; + const inputs = this.getContextInputsList(); + if (slotIndex === 0) { + for (let index = inputs.length - 1; index > 0; index--) { + if (index === 0 || index === inputs.length - 1) { + continue; + } + const input = inputs[index]; + if (!this.isOwnedInput(input.name)) { + if (input.link || ((_b = (_a = this.outputs[index]) === null || _a === void 0 ? void 0 : _a.links) === null || _b === void 0 ? void 0 : _b.length)) { + this.renameContextInput(index, input.name, true); + } + else { + this.removeContextInput(index); + } + } + } + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + } + } + ensureOneRemainingNewInputSlot() { + removeUnusedInputsFromEnd(this, 1, REGEX_EMPTY_INPUT); + this.addInput(OWNED_PREFIX, "*"); + } + getNextUniqueNameForThisNode(desiredName) { + const inputs = this.getContextInputsList(); + const allExistingKeys = inputs.map((i) => this.stripOwnedPrefix(i.name).toLocaleUpperCase()); + desiredName = this.stripOwnedPrefix(desiredName); + let newName = desiredName; + let n = 0; + while (allExistingKeys.includes(newName.toLocaleUpperCase())) { + newName = `${desiredName}.${++n}`; + } + return newName; + } + removeInput(slotIndex) { + const slot = this.inputs[slotIndex]; + super.removeInput(slotIndex); + if (this.outputs[slotIndex]) { + this.removeOutput(slotIndex); + } + this.inputsMutated({ operation: InputMutationOperation.REMOVED, node: this, slotIndex, slot }); + this.stabilizeNames(); + } + stabilizeNames() { + const inputs = this.getContextInputsList(); + const names = []; + for (const [index, input] of inputs.entries()) { + if (index === 0 || index === inputs.length - 1) { + continue; + } + input.label = undefined; + this.outputs[index].label = undefined; + let origName = this.stripOwnedPrefix(input.name).replace(/\.\d+$/, ""); + let name = input.name; + if (!this.isOwnedInput(name)) { + names.push(name.toLocaleUpperCase()); + } + else { + let n = 0; + name = this.addOwnedPrefix(origName); + while (names.includes(this.stripOwnedPrefix(name).toLocaleUpperCase())) { + name = `${this.addOwnedPrefix(origName)}.${++n}`; + } + names.push(this.stripOwnedPrefix(name).toLocaleUpperCase()); + if (input.name !== name) { + this.renameContextInput(index, name); + } + } + } + } + getSlotMenuOptions(slot) { + const editable = this.isOwnedInput(slot.input.name) && this.type !== "*"; + return [ + { + content: "✏️ Rename Input", + disabled: !editable, + callback: () => { + var dialog = app.canvas.createDialog("Name", {}); + var dialogInput = dialog.querySelector("input"); + if (dialogInput) { + dialogInput.value = this.stripOwnedPrefix(slot.input.name || ""); + } + var inner = () => { + this.handleContextMenuRenameInputDialog(slot.slot, dialogInput.value); + dialog.close(); + }; + dialog.querySelector("button").addEventListener("click", inner); + dialogInput.addEventListener("keydown", (e) => { + var _a; + dialog.is_modified = true; + if (e.keyCode == 27) { + dialog.close(); + } + else if (e.keyCode == 13) { + inner(); + } + else if (e.keyCode != 13 && ((_a = e.target) === null || _a === void 0 ? void 0 : _a.localName) != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + dialogInput.focus(); + }, + }, + { + content: "πŸ—‘οΈ Delete Input", + disabled: !editable, + callback: () => { + this.removeInput(slot.slot); + }, + }, + ]; + } + handleContextMenuRenameInputDialog(slotIndex, value) { + app.graph.beforeChange(); + this.renameContextInput(slotIndex, value); + this.stabilizeNames(); + this.setDirtyCanvas(true, true); + app.graph.afterChange(); + } +} +DynamicContextNode.title = NodeTypesString.DYNAMIC_CONTEXT; +DynamicContextNode.type = NodeTypesString.DYNAMIC_CONTEXT; +DynamicContextNode.comfyClass = NodeTypesString.DYNAMIC_CONTEXT; +const contextDynamicNodes = [DynamicContextNode]; +app.registerExtension({ + name: "rgthree.DynamicContext", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) { + return; + } + if (nodeData.name === DynamicContextNode.type) { + DynamicContextNode.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_base.js b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_base.js new file mode 100644 index 00000000..4ead4a61 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_base.js @@ -0,0 +1,192 @@ +import { app } from "../../scripts/app.js"; +import { BaseContextNode } from "./context.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { moveArrayItem, wait } from "../../rgthree/common/shared_utils.js"; +import { RgthreeInvisibleWidget } from "./utils_widgets.js"; +import { getContextOutputName, InputMutationOperation, } from "./services/context_service.js"; +import { SERVICE as CONTEXT_SERVICE } from "./services/context_service.js"; +const OWNED_PREFIX = "+"; +const REGEX_OWNED_PREFIX = /^\+\s*/; +const REGEX_EMPTY_INPUT = /^\+\s*$/; +export class DynamicContextNodeBase extends BaseContextNode { + constructor() { + super(...arguments); + this.hasShadowInputs = false; + } + getContextInputsList() { + return this.inputs; + } + provideInputsData() { + const inputs = this.getContextInputsList(); + return inputs + .map((input, index) => ({ + name: this.stripOwnedPrefix(input.name), + type: String(input.type), + index, + })) + .filter((i) => i.type !== "*"); + } + addOwnedPrefix(name) { + return `+ ${this.stripOwnedPrefix(name)}`; + } + isOwnedInput(inputOrName) { + const name = typeof inputOrName == "string" ? inputOrName : (inputOrName === null || inputOrName === void 0 ? void 0 : inputOrName.name) || ""; + return REGEX_OWNED_PREFIX.test(name); + } + stripOwnedPrefix(name) { + return name.replace(REGEX_OWNED_PREFIX, ""); + } + handleUpstreamMutation(mutation) { + console.log(`[node ${this.id}] handleUpstreamMutation`, mutation); + if (mutation.operation === InputMutationOperation.ADDED) { + const slot = mutation.slot; + if (!slot) { + throw new Error("Cannot have an ADDED mutation without a provided slot data."); + } + this.addContextInput(this.stripOwnedPrefix(slot.name), slot.type, mutation.slotIndex); + return; + } + if (mutation.operation === InputMutationOperation.REMOVED) { + const slot = mutation.slot; + if (!slot) { + throw new Error("Cannot have an REMOVED mutation without a provided slot data."); + } + this.removeContextInput(mutation.slotIndex); + return; + } + if (mutation.operation === InputMutationOperation.RENAMED) { + const slot = mutation.slot; + if (!slot) { + throw new Error("Cannot have an RENAMED mutation without a provided slot data."); + } + this.renameContextInput(mutation.slotIndex, slot.name); + return; + } + } + clone() { + const cloned = super.clone(); + while (cloned.inputs.length > 1) { + cloned.removeInput(cloned.inputs.length - 1); + } + while (cloned.widgets.length > 1) { + cloned.removeWidget(cloned.widgets.length - 1); + } + while (cloned.outputs.length > 1) { + cloned.removeOutput(cloned.outputs.length - 1); + } + return cloned; + } + onNodeCreated() { + const node = this; + this.addCustomWidget(new RgthreeInvisibleWidget("output_keys", "RGTHREE_DYNAMIC_CONTEXT_OUTPUTS", "", () => { + return (node.outputs || []) + .map((o, i) => i > 0 && o.name) + .filter((n) => n !== false) + .join(","); + })); + } + addContextInput(name, type, slot = -1) { + const inputs = this.getContextInputsList(); + if (this.hasShadowInputs) { + inputs.push({ name, type, link: null, boundingRect: null }); + } + else { + this.addInput(name, type); + } + if (slot > -1) { + moveArrayItem(inputs, inputs.length - 1, slot); + } + else { + slot = inputs.length - 1; + } + if (type !== "*") { + const output = this.addOutput(getContextOutputName(name), type); + if (type === "COMBO" || String(type).includes(",") || Array.isArray(type)) { + output.widget = true; + } + if (slot > -1) { + moveArrayItem(this.outputs, this.outputs.length - 1, slot); + } + } + this.fixInputsOutputsLinkSlots(); + this.inputsMutated({ + operation: InputMutationOperation.ADDED, + node: this, + slotIndex: slot, + slot: inputs[slot], + }); + } + removeContextInput(slotIndex) { + if (this.hasShadowInputs) { + const inputs = this.getContextInputsList(); + const input = inputs.splice(slotIndex, 1)[0]; + if (this.outputs[slotIndex]) { + this.removeOutput(slotIndex); + } + } + else { + this.removeInput(slotIndex); + } + } + renameContextInput(index, newName, forceOwnBool = null) { + const inputs = this.getContextInputsList(); + const input = inputs[index]; + const oldName = input.name; + newName = this.stripOwnedPrefix(newName.trim() || this.getSlotDefaultInputLabel(index)); + if (forceOwnBool === true || (this.isOwnedInput(oldName) && forceOwnBool !== false)) { + newName = this.addOwnedPrefix(newName); + } + if (oldName !== newName) { + input.name = newName; + input.removable = this.isOwnedInput(newName); + this.outputs[index].name = getContextOutputName(inputs[index].name); + this.inputsMutated({ + node: this, + operation: InputMutationOperation.RENAMED, + slotIndex: index, + slot: input, + }); + } + } + getSlotDefaultInputLabel(slotIndex) { + const inputs = this.getContextInputsList(); + const input = inputs[slotIndex]; + let defaultLabel = this.stripOwnedPrefix(input.name).toLowerCase(); + return defaultLabel.toLocaleLowerCase(); + } + inputsMutated(mutation) { + CONTEXT_SERVICE.onInputChanges(this, mutation); + } + fixInputsOutputsLinkSlots() { + if (!this.hasShadowInputs) { + const inputs = this.getContextInputsList(); + for (let index = inputs.length - 1; index > 0; index--) { + const input = inputs[index]; + if ((input === null || input === void 0 ? void 0 : input.link) != null) { + app.graph.links[input.link].target_slot = index; + } + } + } + const outputs = this.outputs; + for (let index = outputs.length - 1; index > 0; index--) { + const output = outputs[index]; + if (output) { + output.nameLocked = true; + for (const link of output.links || []) { + app.graph.links[link].origin_slot = index; + } + } + } + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, this); + wait(500).then(() => { + LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] = + LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] || []; + const comfyClassStr = comfyClass.comfyClass; + if (comfyClassStr) { + LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"].push(comfyClassStr); + } + }); + } +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_switch.js b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_switch.js new file mode 100644 index 00000000..a91197e5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_switch.js @@ -0,0 +1,147 @@ +import { app } from "../../scripts/app.js"; +import { DynamicContextNodeBase } from "./dynamic_context_base.js"; +import { NodeTypesString } from "./constants.js"; +import { SERVICE as CONTEXT_SERVICE, getContextOutputName, } from "./services/context_service.js"; +import { getConnectedInputNodesAndFilterPassThroughs } from "./utils.js"; +import { debounce, moveArrayItem } from "../../rgthree/common/shared_utils.js"; +import { measureText } from "./utils_canvas.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +class DynamicContextSwitchNode extends DynamicContextNodeBase { + constructor(title = DynamicContextSwitchNode.title) { + super(title); + this.hasShadowInputs = true; + this.lastInputsList = []; + this.shadowInputs = [ + { name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null }, + ]; + } + getContextInputsList() { + return this.shadowInputs; + } + handleUpstreamMutation(mutation) { + this.scheduleHardRefresh(); + } + onConnectionsChange(type, slotIndex, isConnected, link, inputOrOutput) { + var _a; + (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.call(this, type, slotIndex, isConnected, link, inputOrOutput); + if (this.configuring) { + return; + } + if (type === LiteGraph.INPUT) { + this.scheduleHardRefresh(); + } + } + scheduleHardRefresh(ms = 64) { + return debounce(() => { + this.refreshInputsAndOutputs(); + }, ms); + } + onNodeCreated() { + this.addInput("ctx_1", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_2", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_3", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_4", "RGTHREE_DYNAMIC_CONTEXT"); + this.addInput("ctx_5", "RGTHREE_DYNAMIC_CONTEXT"); + super.onNodeCreated(); + } + addContextInput(name, type, slot) { } + refreshInputsAndOutputs() { + var _a; + const inputs = [ + { name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null }, + ]; + let numConnected = 0; + for (let i = 0; i < this.inputs.length; i++) { + const childCtxs = getConnectedInputNodesAndFilterPassThroughs(this, this, i); + if (childCtxs.length > 1) { + throw new Error("How is there more than one input?"); + } + const ctx = childCtxs[0]; + if (!ctx) + continue; + numConnected++; + const slotsData = CONTEXT_SERVICE.getDynamicContextInputsData(ctx); + console.log(slotsData); + for (const slotData of slotsData) { + const found = inputs.find((n) => getContextOutputName(slotData.name) === getContextOutputName(n.name)); + if (found) { + found.count += 1; + continue; + } + inputs.push({ + name: slotData.name, + type: slotData.type, + link: null, + count: 1, + boundingRect: null, + }); + } + } + this.shadowInputs = inputs; + let i = 0; + for (i; i < this.shadowInputs.length; i++) { + const data = this.shadowInputs[i]; + let existing = this.outputs.find((o) => getContextOutputName(o.name) === getContextOutputName(data.name)); + if (!existing) { + existing = this.addOutput(getContextOutputName(data.name), data.type); + } + moveArrayItem(this.outputs, existing, i); + delete existing.rgthree_status; + if (data.count !== numConnected) { + existing.rgthree_status = "WARN"; + } + } + while (this.outputs[i]) { + const output = this.outputs[i]; + if ((_a = output === null || output === void 0 ? void 0 : output.links) === null || _a === void 0 ? void 0 : _a.length) { + output.rgthree_status = "ERROR"; + i++; + } + else { + this.removeOutput(i); + } + } + this.fixInputsOutputsLinkSlots(); + } + onDrawForeground(ctx, canvas) { + var _a, _b; + const low_quality = ((_b = (_a = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1) < 0.6; + if (low_quality || this.size[0] <= 10) { + return; + } + let y = LiteGraph.NODE_SLOT_HEIGHT - 1; + const w = this.size[0]; + ctx.save(); + ctx.font = "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial"; + ctx.textAlign = "right"; + for (const output of this.outputs) { + if (!output.rgthree_status) { + y += LiteGraph.NODE_SLOT_HEIGHT; + continue; + } + const x = w - 20 - measureText(ctx, output.name); + if (output.rgthree_status === "ERROR") { + ctx.fillText("πŸ›‘", x, y); + } + else if (output.rgthree_status === "WARN") { + ctx.fillText("⚠️", x, y); + } + y += LiteGraph.NODE_SLOT_HEIGHT; + } + ctx.restore(); + } +} +DynamicContextSwitchNode.title = NodeTypesString.DYNAMIC_CONTEXT_SWITCH; +DynamicContextSwitchNode.type = NodeTypesString.DYNAMIC_CONTEXT_SWITCH; +DynamicContextSwitchNode.comfyClass = NodeTypesString.DYNAMIC_CONTEXT_SWITCH; +app.registerExtension({ + name: "rgthree.DynamicContextSwitch", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) { + return; + } + if (nodeData.name === DynamicContextSwitchNode.type) { + DynamicContextSwitchNode.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/fast_actions_button.js b/custom_nodes/rgthree-comfy/web/comfyui/fast_actions_button.js new file mode 100644 index 00000000..62d3bac5 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/fast_actions_button.js @@ -0,0 +1,276 @@ +import { app } from "../../scripts/app.js"; +import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js"; +import { NodeTypesString } from "./constants.js"; +import { addMenuItem, changeModeOfNodes } from "./utils.js"; +import { rgthree } from "./rgthree.js"; +const MODE_ALWAYS = 0; +const MODE_MUTE = 2; +const MODE_BYPASS = 4; +class FastActionsButton extends BaseAnyInputConnectedNode { + constructor(title) { + super(title); + this.comfyClass = NodeTypesString.FAST_ACTIONS_BUTTON; + this.logger = rgthree.newLogSession("[FastActionsButton]"); + this.isVirtualNode = true; + this.serialize_widgets = true; + this.widgetToData = new Map(); + this.nodeIdtoFunctionCache = new Map(); + this.executingFromShortcut = false; + this.properties["buttonText"] = "🎬 Action!"; + this.properties["shortcutModifier"] = "alt"; + this.properties["shortcutKey"] = ""; + this.buttonWidget = this.addWidget("button", this.properties["buttonText"], "", () => { + this.executeConnectedNodes(); + }, { serialize: false }); + this.keypressBound = this.onKeypress.bind(this); + this.keyupBound = this.onKeyup.bind(this); + this.onConstructed(); + } + configure(info) { + super.configure(info); + setTimeout(() => { + if (info.widgets_values) { + for (let [index, value] of info.widgets_values.entries()) { + if (index > 0) { + if (typeof value === "string" && value.startsWith("comfy_action:")) { + value = value.replace("comfy_action:", ""); + this.addComfyActionWidget(index, value); + } + if (this.widgets[index]) { + this.widgets[index].value = value; + } + } + } + } + }, 100); + } + clone() { + const cloned = super.clone(); + cloned.properties["buttonText"] = "🎬 Action!"; + cloned.properties["shortcutKey"] = ""; + return cloned; + } + onAdded(graph) { + window.addEventListener("keydown", this.keypressBound); + window.addEventListener("keyup", this.keyupBound); + } + onRemoved() { + window.removeEventListener("keydown", this.keypressBound); + window.removeEventListener("keyup", this.keyupBound); + } + async onKeypress(event) { + const target = event.target; + if (this.executingFromShortcut || + target.localName == "input" || + target.localName == "textarea") { + return; + } + if (this.properties["shortcutKey"].trim() && + this.properties["shortcutKey"].toLowerCase() === event.key.toLowerCase()) { + const shortcutModifier = this.properties["shortcutModifier"]; + let good = shortcutModifier === "ctrl" && event.ctrlKey; + good = good || (shortcutModifier === "alt" && event.altKey); + good = good || (shortcutModifier === "shift" && event.shiftKey); + good = good || (shortcutModifier === "meta" && event.metaKey); + if (good) { + setTimeout(() => { + this.executeConnectedNodes(); + }, 20); + this.executingFromShortcut = true; + event.preventDefault(); + event.stopImmediatePropagation(); + app.canvas.dirty_canvas = true; + return false; + } + } + return; + } + onKeyup(event) { + const target = event.target; + if (target.localName == "input" || target.localName == "textarea") { + return; + } + this.executingFromShortcut = false; + } + onPropertyChanged(property, value, prevValue) { + var _a, _b; + if (property == "buttonText" && typeof value === "string") { + this.buttonWidget.name = value; + } + if (property == "shortcutKey" && typeof value === "string") { + this.properties["shortcutKey"] = (_b = (_a = value.trim()[0]) === null || _a === void 0 ? void 0 : _a.toLowerCase()) !== null && _b !== void 0 ? _b : ""; + } + return true; + } + handleLinkedNodesStabilization(linkedNodes) { + var _a, _b, _c, _d, _e, _f, _g, _h; + let changed = false; + for (const [widget, data] of this.widgetToData.entries()) { + if (!data.node) { + continue; + } + if (!linkedNodes.includes(data.node)) { + const index = this.widgets.indexOf(widget); + if (index > -1) { + this.widgetToData.delete(widget); + this.removeWidget(widget); + changed = true; + } + else { + const [m, a] = this.logger.debugParts("Connected widget is not in widgets... weird."); + (_a = console[m]) === null || _a === void 0 ? void 0 : _a.call(console, ...a); + } + } + } + const badNodes = []; + let indexOffset = 1; + for (const [index, node] of linkedNodes.entries()) { + if (!node) { + const [m, a] = this.logger.debugParts("linkedNode provided that does not exist. "); + (_b = console[m]) === null || _b === void 0 ? void 0 : _b.call(console, ...a); + badNodes.push(node); + continue; + } + let widgetAtSlot = this.widgets[index + indexOffset]; + if (widgetAtSlot && ((_c = this.widgetToData.get(widgetAtSlot)) === null || _c === void 0 ? void 0 : _c.comfy)) { + indexOffset++; + widgetAtSlot = this.widgets[index + indexOffset]; + } + if (!widgetAtSlot || ((_e = (_d = this.widgetToData.get(widgetAtSlot)) === null || _d === void 0 ? void 0 : _d.node) === null || _e === void 0 ? void 0 : _e.id) !== node.id) { + let widget = null; + for (let i = index + indexOffset; i < this.widgets.length; i++) { + if (((_g = (_f = this.widgetToData.get(this.widgets[i])) === null || _f === void 0 ? void 0 : _f.node) === null || _g === void 0 ? void 0 : _g.id) === node.id) { + widget = this.widgets.splice(i, 1)[0]; + this.widgets.splice(index + indexOffset, 0, widget); + changed = true; + break; + } + } + if (!widget) { + const exposedActions = node.constructor.exposedActions || []; + widget = this.addWidget("combo", node.title, "None", "", { + values: ["None", "Mute", "Bypass", "Enable", ...exposedActions], + }); + widget.serializeValue = async (_node, _index) => { + return widget === null || widget === void 0 ? void 0 : widget.value; + }; + this.widgetToData.set(widget, { node }); + changed = true; + } + } + } + for (let i = this.widgets.length - 1; i > linkedNodes.length + indexOffset - 1; i--) { + const widgetAtSlot = this.widgets[i]; + if (widgetAtSlot && ((_h = this.widgetToData.get(widgetAtSlot)) === null || _h === void 0 ? void 0 : _h.comfy)) { + continue; + } + this.removeWidget(widgetAtSlot); + changed = true; + } + return changed; + } + removeWidget(widget) { + widget = typeof widget === "number" ? this.widgets[widget] : widget; + if (widget && this.widgetToData.has(widget)) { + this.widgetToData.delete(widget); + } + super.removeWidget(widget); + } + async executeConnectedNodes() { + var _a, _b; + for (const widget of this.widgets) { + if (widget == this.buttonWidget) { + continue; + } + const action = widget.value; + const { comfy, node } = (_a = this.widgetToData.get(widget)) !== null && _a !== void 0 ? _a : {}; + if (comfy) { + if (action === "Queue Prompt") { + await comfy.queuePrompt(0); + } + continue; + } + if (node) { + if (action === "Mute") { + changeModeOfNodes(node, MODE_MUTE); + } + else if (action === "Bypass") { + changeModeOfNodes(node, MODE_BYPASS); + } + else if (action === "Enable") { + changeModeOfNodes(node, MODE_ALWAYS); + } + if (node.handleAction) { + if (typeof action !== "string") { + throw new Error("Fast Actions Button action should be a string: " + action); + } + await node.handleAction(action); + } + (_b = this.graph) === null || _b === void 0 ? void 0 : _b.change(); + continue; + } + console.warn("Fast Actions Button has a widget without correct data."); + } + } + addComfyActionWidget(slot, value) { + let widget = this.addWidget("combo", "Comfy Action", "None", () => { + if (String(widget.value).startsWith("MOVE ")) { + this.widgets.push(this.widgets.splice(this.widgets.indexOf(widget), 1)[0]); + widget.value = String(widget.rgthree_lastValue); + } + else if (String(widget.value).startsWith("REMOVE ")) { + this.removeWidget(widget); + } + widget.rgthree_lastValue = widget.value; + }, { + values: ["None", "Queue Prompt", "REMOVE Comfy Action", "MOVE to end"], + }); + widget.rgthree_lastValue = value; + widget.serializeValue = async (_node, _index) => { + return `comfy_app:${widget === null || widget === void 0 ? void 0 : widget.value}`; + }; + this.widgetToData.set(widget, { comfy: app }); + if (slot != null) { + this.widgets.splice(slot, 0, this.widgets.splice(this.widgets.indexOf(widget), 1)[0]); + } + return widget; + } + onSerialize(serialised) { + var _a, _b; + (_a = super.onSerialize) === null || _a === void 0 ? void 0 : _a.call(this, serialised); + for (let [index, value] of (serialised.widgets_values || []).entries()) { + if (((_b = this.widgets[index]) === null || _b === void 0 ? void 0 : _b.name) === "Comfy Action") { + serialised.widgets_values[index] = `comfy_action:${value}`; + } + } + } + static setUp() { + super.setUp(); + addMenuItem(this, app, { + name: "βž• Append a Comfy Action", + callback: (nodeArg) => { + nodeArg.addComfyActionWidget(); + }, + }); + } +} +FastActionsButton.type = NodeTypesString.FAST_ACTIONS_BUTTON; +FastActionsButton.title = NodeTypesString.FAST_ACTIONS_BUTTON; +FastActionsButton["@buttonText"] = { type: "string" }; +FastActionsButton["@shortcutModifier"] = { + type: "combo", + values: ["ctrl", "alt", "shift"], +}; +FastActionsButton["@shortcutKey"] = { type: "string" }; +FastActionsButton.collapsible = false; +app.registerExtension({ + name: "rgthree.FastActionsButton", + registerCustomNodes() { + FastActionsButton.setUp(); + }, + loadedGraphNode(node) { + if (node.type == FastActionsButton.title) { + node._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_bypasser.js b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_bypasser.js new file mode 100644 index 00000000..80ee9910 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_bypasser.js @@ -0,0 +1,27 @@ +import { app } from "../../scripts/app.js"; +import { NodeTypesString } from "./constants.js"; +import { BaseFastGroupsModeChanger } from "./fast_groups_muter.js"; +export class FastGroupsBypasser extends BaseFastGroupsModeChanger { + constructor(title = FastGroupsBypasser.title) { + super(title); + this.comfyClass = NodeTypesString.FAST_GROUPS_BYPASSER; + this.helpActions = "bypass and enable"; + this.modeOn = LiteGraph.ALWAYS; + this.modeOff = 4; + this.onConstructed(); + } +} +FastGroupsBypasser.type = NodeTypesString.FAST_GROUPS_BYPASSER; +FastGroupsBypasser.title = NodeTypesString.FAST_GROUPS_BYPASSER; +FastGroupsBypasser.exposedActions = ["Bypass all", "Enable all", "Toggle all"]; +app.registerExtension({ + name: "rgthree.FastGroupsBypasser", + registerCustomNodes() { + FastGroupsBypasser.setUp(); + }, + loadedGraphNode(node) { + if (node.type == FastGroupsBypasser.title) { + node.tempSize = [...node.size]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_muter.js b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_muter.js new file mode 100644 index 00000000..11ee58f0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_muter.js @@ -0,0 +1,437 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseVirtualNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { SERVICE as FAST_GROUPS_SERVICE } from "./services/fast_groups_service.js"; +import { drawNodeWidget, fitString } from "./utils_canvas.js"; +import { RgthreeBaseWidget } from "./utils_widgets.js"; +import { changeModeOfNodes, getGroupNodes } from "./utils.js"; +const PROPERTY_SORT = "sort"; +const PROPERTY_SORT_CUSTOM_ALPHA = "customSortAlphabet"; +const PROPERTY_MATCH_COLORS = "matchColors"; +const PROPERTY_MATCH_TITLE = "matchTitle"; +const PROPERTY_SHOW_NAV = "showNav"; +const PROPERTY_SHOW_ALL_GRAPHS = "showAllGraphs"; +const PROPERTY_RESTRICTION = "toggleRestriction"; +export class BaseFastGroupsModeChanger extends RgthreeBaseVirtualNode { + constructor(title = FastGroupsMuter.title) { + super(title); + this.modeOn = LiteGraph.ALWAYS; + this.modeOff = LiteGraph.NEVER; + this.debouncerTempWidth = 0; + this.tempSize = null; + this.serialize_widgets = false; + this.helpActions = "mute and unmute"; + this.properties[PROPERTY_MATCH_COLORS] = ""; + this.properties[PROPERTY_MATCH_TITLE] = ""; + this.properties[PROPERTY_SHOW_NAV] = true; + this.properties[PROPERTY_SHOW_ALL_GRAPHS] = true; + this.properties[PROPERTY_SORT] = "position"; + this.properties[PROPERTY_SORT_CUSTOM_ALPHA] = ""; + this.properties[PROPERTY_RESTRICTION] = "default"; + } + onConstructed() { + this.addOutput("OPT_CONNECTION", "*"); + return super.onConstructed(); + } + onAdded(graph) { + FAST_GROUPS_SERVICE.addFastGroupNode(this); + } + onRemoved() { + FAST_GROUPS_SERVICE.removeFastGroupNode(this); + } + refreshWidgets() { + var _a, _b, _c, _d, _e, _f, _g, _h, _j; + const canvas = app.canvas; + let sort = ((_a = this.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_SORT]) || "position"; + let customAlphabet = null; + if (sort === "custom alphabet") { + const customAlphaStr = (_c = (_b = this.properties) === null || _b === void 0 ? void 0 : _b[PROPERTY_SORT_CUSTOM_ALPHA]) === null || _c === void 0 ? void 0 : _c.replace(/\n/g, ""); + if (customAlphaStr && customAlphaStr.trim()) { + customAlphabet = customAlphaStr.includes(",") + ? customAlphaStr.toLocaleLowerCase().split(",") + : customAlphaStr.toLocaleLowerCase().trim().split(""); + } + if (!(customAlphabet === null || customAlphabet === void 0 ? void 0 : customAlphabet.length)) { + sort = "alphanumeric"; + customAlphabet = null; + } + } + const groups = [...FAST_GROUPS_SERVICE.getGroups(sort)]; + if (customAlphabet === null || customAlphabet === void 0 ? void 0 : customAlphabet.length) { + groups.sort((a, b) => { + let aIndex = -1; + let bIndex = -1; + for (const [index, alpha] of customAlphabet.entries()) { + aIndex = + aIndex < 0 ? (a.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : aIndex; + bIndex = + bIndex < 0 ? (b.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : bIndex; + if (aIndex > -1 && bIndex > -1) { + break; + } + } + if (aIndex > -1 && bIndex > -1) { + const ret = aIndex - bIndex; + if (ret === 0) { + return a.title.localeCompare(b.title); + } + return ret; + } + else if (aIndex > -1) { + return -1; + } + else if (bIndex > -1) { + return 1; + } + return a.title.localeCompare(b.title); + }); + } + let filterColors = (((_e = (_d = this.properties) === null || _d === void 0 ? void 0 : _d[PROPERTY_MATCH_COLORS]) === null || _e === void 0 ? void 0 : _e.split(",")) || []).filter((c) => c.trim()); + if (filterColors.length) { + filterColors = filterColors.map((color) => { + color = color.trim().toLocaleLowerCase(); + if (LGraphCanvas.node_colors[color]) { + color = LGraphCanvas.node_colors[color].groupcolor; + } + color = color.replace("#", "").toLocaleLowerCase(); + if (color.length === 3) { + color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3"); + } + return `#${color}`; + }); + } + let index = 0; + for (const group of groups) { + if (filterColors.length) { + let groupColor = (_f = group.color) === null || _f === void 0 ? void 0 : _f.replace("#", "").trim().toLocaleLowerCase(); + if (!groupColor) { + continue; + } + if (groupColor.length === 3) { + groupColor = groupColor.replace(/(.)(.)(.)/, "$1$1$2$2$3$3"); + } + groupColor = `#${groupColor}`; + if (!filterColors.includes(groupColor)) { + continue; + } + } + if ((_h = (_g = this.properties) === null || _g === void 0 ? void 0 : _g[PROPERTY_MATCH_TITLE]) === null || _h === void 0 ? void 0 : _h.trim()) { + try { + if (!new RegExp(this.properties[PROPERTY_MATCH_TITLE], "i").exec(group.title)) { + continue; + } + } + catch (e) { + console.error(e); + continue; + } + } + const showAllGraphs = (_j = this.properties) === null || _j === void 0 ? void 0 : _j[PROPERTY_SHOW_ALL_GRAPHS]; + if (!showAllGraphs && group.graph !== app.canvas.getCurrentGraph()) { + continue; + } + let isDirty = false; + const widgetLabel = `Enable ${group.title}`; + let widget = this.widgets.find((w) => w.label === widgetLabel); + if (!widget) { + this.tempSize = [...this.size]; + widget = this.addCustomWidget(new FastGroupsToggleRowWidget(group, this)); + this.setSize(this.computeSize()); + isDirty = true; + } + if (widget.label != widgetLabel) { + widget.label = widgetLabel; + isDirty = true; + } + if (group.rgthree_hasAnyActiveNode != null && + widget.toggled != group.rgthree_hasAnyActiveNode) { + widget.toggled = group.rgthree_hasAnyActiveNode; + isDirty = true; + } + if (this.widgets[index] !== widget) { + const oldIndex = this.widgets.findIndex((w) => w === widget); + this.widgets.splice(index, 0, this.widgets.splice(oldIndex, 1)[0]); + isDirty = true; + } + if (isDirty) { + this.setDirtyCanvas(true, false); + } + index++; + } + while ((this.widgets || [])[index]) { + this.removeWidget(index++); + } + } + computeSize(out) { + let size = super.computeSize(out); + if (this.tempSize) { + size[0] = Math.max(this.tempSize[0], size[0]); + size[1] = Math.max(this.tempSize[1], size[1]); + this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth); + this.debouncerTempWidth = setTimeout(() => { + this.tempSize = null; + }, 32); + } + setTimeout(() => { + var _a; + (_a = this.graph) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true); + }, 16); + return size; + } + async handleAction(action) { + var _a, _b, _c, _d, _e; + if (action === "Mute all" || action === "Bypass all") { + const alwaysOne = ((_a = this.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_RESTRICTION]) === "always one"; + for (const [index, widget] of this.widgets.entries()) { + widget === null || widget === void 0 ? void 0 : widget.doModeChange(alwaysOne && !index ? true : false, true); + } + } + else if (action === "Enable all") { + const onlyOne = (_b = this.properties) === null || _b === void 0 ? void 0 : _b[PROPERTY_RESTRICTION].includes(" one"); + for (const [index, widget] of this.widgets.entries()) { + widget === null || widget === void 0 ? void 0 : widget.doModeChange(onlyOne && index > 0 ? false : true, true); + } + } + else if (action === "Toggle all") { + const onlyOne = (_c = this.properties) === null || _c === void 0 ? void 0 : _c[PROPERTY_RESTRICTION].includes(" one"); + let foundOne = false; + for (const [index, widget] of this.widgets.entries()) { + let newValue = onlyOne && foundOne ? false : !widget.value; + foundOne = foundOne || newValue; + widget === null || widget === void 0 ? void 0 : widget.doModeChange(newValue, true); + } + if (!foundOne && ((_d = this.properties) === null || _d === void 0 ? void 0 : _d[PROPERTY_RESTRICTION]) === "always one") { + (_e = this.widgets[this.widgets.length - 1]) === null || _e === void 0 ? void 0 : _e.doModeChange(true, true); + } + } + } + getHelp() { + return ` +

      The ${this.type.replace("(rgthree)", "")} is an input-less node that automatically collects all groups in your current + workflow and allows you to quickly ${this.helpActions} all nodes within the group.

      +
        +
      • +

        + Properties. You can change the following properties (by right-clicking + on the node, and select "Properties" or "Properties Panel" from the menu): +

        +
          +

        • + ${PROPERTY_MATCH_COLORS} - Only add groups that match the provided + colors. Can be ComfyUI colors (red, pale_blue) or hex codes (#a4d399). Multiple can be + added, comma delimited. +

          • +

        • + ${PROPERTY_MATCH_TITLE} - Filter the list of toggles by title match + (string match, or regular expression). +

          • +

        • + ${PROPERTY_SHOW_NAV} - Add / remove a quick navigation arrow to take you + to the group. (default: true) +

          • +

        • + ${PROPERTY_SHOW_ALL_GRAPHS} - Show groups from all [sub]graphs in the + workflow. (default: true) +

          • +

        • + ${PROPERTY_SORT} - Sort the toggles' order by "alphanumeric", graph + "position", or "custom alphabet". (default: "position") +

          • +
        • +

          + ${PROPERTY_SORT_CUSTOM_ALPHA} - When the + ${PROPERTY_SORT} property is "custom alphabet" you can define the + alphabet to use here, which will match the beginning of each group name and + sort against it. If group titles do not match any custom alphabet entry, then they + will be put after groups that do, ordered alphanumerically. +

          +

          + This can be a list of single characters, like "zyxw..." or comma delimited strings + for more control, like "sdxl,pro,sd,n,p". +

          +

          + Note, when two group title match the same custom alphabet entry, the normal + alphanumeric alphabet breaks the tie. For instance, a custom alphabet of + "e,s,d" will order groups names like "SDXL, SEGS, Detailer" eventhough the custom + alphabet has an "e" before "d" (where one may expect "SE" to be before "SD"). +

          +

          + To have "SEGS" appear before "SDXL" you can use longer strings. For instance, the + custom alphabet value of "se,s,f" would work here. +

          +
          • +

        • + ${PROPERTY_RESTRICTION} - Optionally, attempt to restrict the number of + widgets that can be enabled to a maximum of one, or always one. +

          +

          Note: If using "max one" or "always one" then this is only + enforced when clicking a toggle on this node; if nodes within groups are changed + outside of the initial toggle click, then these restriction will not be enforced, and + could result in a state where more than one toggle is enabled. This could also happen + if nodes are overlapped with multiple groups. +

          • + +
        +
        • +
      `; + } +} +BaseFastGroupsModeChanger.type = NodeTypesString.FAST_GROUPS_MUTER; +BaseFastGroupsModeChanger.title = NodeTypesString.FAST_GROUPS_MUTER; +BaseFastGroupsModeChanger.exposedActions = ["Mute all", "Enable all", "Toggle all"]; +BaseFastGroupsModeChanger["@matchColors"] = { type: "string" }; +BaseFastGroupsModeChanger["@matchTitle"] = { type: "string" }; +BaseFastGroupsModeChanger["@showNav"] = { type: "boolean" }; +BaseFastGroupsModeChanger["@showAllGraphs"] = { type: "boolean" }; +BaseFastGroupsModeChanger["@sort"] = { + type: "combo", + values: ["position", "alphanumeric", "custom alphabet"], +}; +BaseFastGroupsModeChanger["@customSortAlphabet"] = { type: "string" }; +BaseFastGroupsModeChanger["@toggleRestriction"] = { + type: "combo", + values: ["default", "max one", "always one"], +}; +export class FastGroupsMuter extends BaseFastGroupsModeChanger { + constructor(title = FastGroupsMuter.title) { + super(title); + this.comfyClass = NodeTypesString.FAST_GROUPS_MUTER; + this.helpActions = "mute and unmute"; + this.modeOn = LiteGraph.ALWAYS; + this.modeOff = LiteGraph.NEVER; + this.onConstructed(); + } +} +FastGroupsMuter.type = NodeTypesString.FAST_GROUPS_MUTER; +FastGroupsMuter.title = NodeTypesString.FAST_GROUPS_MUTER; +FastGroupsMuter.exposedActions = ["Bypass all", "Enable all", "Toggle all"]; +class FastGroupsToggleRowWidget extends RgthreeBaseWidget { + constructor(group, node) { + super("RGTHREE_TOGGLE_AND_NAV"); + this.value = { toggled: false }; + this.options = { on: "yes", off: "no" }; + this.type = "custom"; + this.label = ""; + this.group = group; + this.node = node; + } + doModeChange(force, skipOtherNodeCheck) { + var _a, _b, _c, _d; + this.group.recomputeInsideNodes(); + const hasAnyActiveNodes = getGroupNodes(this.group).some((n) => n.mode === LiteGraph.ALWAYS); + let newValue = force != null ? force : !hasAnyActiveNodes; + if (skipOtherNodeCheck !== true) { + if (newValue && ((_b = (_a = this.node.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_RESTRICTION]) === null || _b === void 0 ? void 0 : _b.includes(" one"))) { + for (const widget of this.node.widgets) { + if (widget instanceof FastGroupsToggleRowWidget) { + widget.doModeChange(false, true); + } + } + } + else if (!newValue && ((_c = this.node.properties) === null || _c === void 0 ? void 0 : _c[PROPERTY_RESTRICTION]) === "always one") { + newValue = this.node.widgets.every((w) => !w.value || w === this); + } + } + changeModeOfNodes(getGroupNodes(this.group), (newValue ? this.node.modeOn : this.node.modeOff)); + this.group.rgthree_hasAnyActiveNode = newValue; + this.toggled = newValue; + (_d = this.group.graph) === null || _d === void 0 ? void 0 : _d.setDirtyCanvas(true, false); + } + get toggled() { + return this.value.toggled; + } + set toggled(value) { + this.value.toggled = value; + } + toggle(value) { + value = value == null ? !this.toggled : value; + if (value !== this.toggled) { + this.value.toggled = value; + this.doModeChange(); + } + } + draw(ctx, node, width, posY, height) { + var _a; + const widgetData = drawNodeWidget(ctx, { size: [width, height], pos: [15, posY] }); + const showNav = ((_a = node.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_SHOW_NAV]) !== false; + let currentX = widgetData.width - widgetData.margin; + if (!widgetData.lowQuality && showNav) { + currentX -= 7; + const midY = widgetData.posY + widgetData.height * 0.5; + ctx.fillStyle = ctx.strokeStyle = "#89A"; + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + const arrow = new Path2D(`M${currentX} ${midY} l -7 6 v -3 h -7 v -6 h 7 v -3 z`); + ctx.fill(arrow); + ctx.stroke(arrow); + currentX -= 14; + currentX -= 7; + ctx.strokeStyle = widgetData.colorOutline; + ctx.stroke(new Path2D(`M ${currentX} ${widgetData.posY} v ${widgetData.height}`)); + } + else if (widgetData.lowQuality && showNav) { + currentX -= 28; + } + currentX -= 7; + ctx.fillStyle = this.toggled ? "#89A" : "#333"; + ctx.beginPath(); + const toggleRadius = height * 0.36; + ctx.arc(currentX - toggleRadius, posY + height * 0.5, toggleRadius, 0, Math.PI * 2); + ctx.fill(); + currentX -= toggleRadius * 2; + if (!widgetData.lowQuality) { + currentX -= 4; + ctx.textAlign = "right"; + ctx.fillStyle = this.toggled ? widgetData.colorText : widgetData.colorTextSecondary; + const label = this.label; + const toggleLabelOn = this.options.on || "true"; + const toggleLabelOff = this.options.off || "false"; + ctx.fillText(this.toggled ? toggleLabelOn : toggleLabelOff, currentX, posY + height * 0.7); + currentX -= Math.max(ctx.measureText(toggleLabelOn).width, ctx.measureText(toggleLabelOff).width); + currentX -= 7; + ctx.textAlign = "left"; + let maxLabelWidth = widgetData.width - widgetData.margin - 10 - (widgetData.width - currentX); + if (label != null) { + ctx.fillText(fitString(ctx, label, maxLabelWidth), widgetData.margin + 10, posY + height * 0.7); + } + } + } + serializeValue(node, index) { + return this.value; + } + mouse(event, pos, node) { + var _a, _b, _c; + if (event.type == "pointerdown") { + if (((_a = node.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_SHOW_NAV]) !== false && pos[0] >= node.size[0] - 15 - 28 - 1) { + const canvas = app.canvas; + const lowQuality = (((_b = canvas.ds) === null || _b === void 0 ? void 0 : _b.scale) || 1) <= 0.5; + if (!lowQuality) { + canvas.centerOnNode(this.group); + const zoomCurrent = ((_c = canvas.ds) === null || _c === void 0 ? void 0 : _c.scale) || 1; + const zoomX = canvas.canvas.width / this.group._size[0] - 0.02; + const zoomY = canvas.canvas.height / this.group._size[1] - 0.02; + canvas.setZoom(Math.min(zoomCurrent, zoomX, zoomY), [ + canvas.canvas.width / 2, + canvas.canvas.height / 2, + ]); + canvas.setDirty(true, true); + } + } + else { + this.toggle(); + } + } + return true; + } +} +app.registerExtension({ + name: "rgthree.FastGroupsMuter", + registerCustomNodes() { + FastGroupsMuter.setUp(); + }, + loadedGraphNode(node) { + if (node.type == FastGroupsMuter.title) { + node.tempSize = [...node.size]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/feature_group_fast_toggle.js b/custom_nodes/rgthree-comfy/web/comfyui/feature_group_fast_toggle.js new file mode 100644 index 00000000..8058a87b --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/feature_group_fast_toggle.js @@ -0,0 +1,220 @@ +import { app } from "../../scripts/app.js"; +import { rgthree } from "./rgthree.js"; +import { changeModeOfNodes, getGroupNodes, getOutputNodes } from "./utils.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +const BTN_SIZE = 20; +const BTN_MARGIN = [6, 6]; +const BTN_SPACING = 8; +const BTN_GRID = BTN_SIZE / 8; +const TOGGLE_TO_MODE = new Map([ + ["MUTE", LiteGraph.NEVER], + ["BYPASS", 4], +]); +function getToggles() { + return [...CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.toggles", [])].reverse(); +} +function clickedOnToggleButton(e, group) { + const toggles = getToggles(); + const pos = group.pos; + const size = group.size; + for (let i = 0; i < toggles.length; i++) { + const toggle = toggles[i]; + if (LiteGraph.isInsideRectangle(e.canvasX, e.canvasY, pos[0] + size[0] - (BTN_SIZE + BTN_MARGIN[0]) * (i + 1), pos[1] + BTN_MARGIN[1], BTN_SIZE, BTN_SIZE)) { + return toggle; + } + } + return null; +} +app.registerExtension({ + name: "rgthree.GroupHeaderToggles", + async setup() { + setInterval(() => { + if (CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") && + CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always") { + app.canvas.setDirty(true, true); + } + }, 250); + rgthree.addEventListener("on-process-mouse-down", ((e) => { + if (!CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled")) + return; + const canvas = app.canvas; + if (canvas.selected_group) { + const originalEvent = e.detail.originalEvent; + const group = canvas.selected_group; + const clickedOnToggle = clickedOnToggleButton(originalEvent, group) || ""; + const toggleAction = clickedOnToggle === null || clickedOnToggle === void 0 ? void 0 : clickedOnToggle.toLocaleUpperCase(); + if (toggleAction) { + console.log(toggleAction); + const nodes = getGroupNodes(group); + if (toggleAction === "QUEUE") { + const outputNodes = getOutputNodes(nodes); + if (!(outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length)) { + rgthree.showMessage({ + id: "no-output-in-group", + type: "warn", + timeout: 4000, + message: "No output nodes for group!", + }); + } + else { + rgthree.queueOutputNodes(outputNodes); + } + } + else { + const toggleMode = TOGGLE_TO_MODE.get(toggleAction); + if (toggleMode) { + group.recomputeInsideNodes(); + const hasAnyActiveNodes = nodes.some((n) => n.mode === LiteGraph.ALWAYS); + const isAllMuted = !hasAnyActiveNodes && nodes.every((n) => n.mode === LiteGraph.NEVER); + const isAllBypassed = !hasAnyActiveNodes && !isAllMuted && nodes.every((n) => n.mode === 4); + let newMode = LiteGraph.ALWAYS; + if (toggleMode === LiteGraph.NEVER) { + newMode = isAllMuted ? LiteGraph.ALWAYS : LiteGraph.NEVER; + } + else { + newMode = isAllBypassed ? LiteGraph.ALWAYS : 4; + } + changeModeOfNodes(nodes, newMode); + } + } + canvas.selected_group = null; + canvas.dragging_canvas = false; + } + } + })); + const drawGroups = LGraphCanvas.prototype.drawGroups; + LGraphCanvas.prototype.drawGroups = function (canvasEl, ctx) { + drawGroups.apply(this, [...arguments]); + if (!CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") || + !rgthree.lastCanvasMouseEvent) { + return; + } + const graph = app.canvas.graph; + let groups; + if (CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always") { + const hoverGroup = graph.getGroupOnPos(rgthree.lastCanvasMouseEvent.canvasX, rgthree.lastCanvasMouseEvent.canvasY); + groups = hoverGroup ? [hoverGroup] : []; + } + else { + groups = graph._groups || []; + } + if (!groups.length) { + return; + } + const toggles = getToggles(); + ctx.save(); + for (const group of groups || []) { + const nodes = getGroupNodes(group); + let anyActive = false; + let allMuted = !!nodes.length; + let allBypassed = allMuted; + for (const node of nodes) { + if (!(node instanceof LGraphNode)) + continue; + anyActive = anyActive || node.mode === LiteGraph.ALWAYS; + allMuted = allMuted && node.mode === LiteGraph.NEVER; + allBypassed = allBypassed && node.mode === 4; + if (anyActive || (!allMuted && !allBypassed)) { + break; + } + } + for (let i = 0; i < toggles.length; i++) { + const toggle = toggles[i]; + const pos = group._pos; + const size = group._size; + ctx.fillStyle = ctx.strokeStyle = group.color || "#335"; + const x = pos[0] + size[0] - BTN_MARGIN[0] - BTN_SIZE - (BTN_SPACING + BTN_SIZE) * i; + const y = pos[1] + BTN_MARGIN[1]; + const midX = x + BTN_SIZE / 2; + const midY = y + BTN_SIZE / 2; + if (toggle === "queue") { + const outputNodes = getOutputNodes(nodes); + const oldGlobalAlpha = ctx.globalAlpha; + if (!(outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length)) { + ctx.globalAlpha = 0.5; + } + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + const arrowSizeX = BTN_SIZE * 0.6; + const arrowSizeY = BTN_SIZE * 0.7; + const arrow = new Path2D(`M ${x + arrowSizeX / 2} ${midY} l 0 -${arrowSizeY / 2} l ${arrowSizeX} ${arrowSizeY / 2} l -${arrowSizeX} ${arrowSizeY / 2} z`); + ctx.stroke(arrow); + if (outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length) { + ctx.fill(arrow); + } + ctx.globalAlpha = oldGlobalAlpha; + } + else { + const on = toggle === "bypass" ? allBypassed : allMuted; + ctx.beginPath(); + ctx.lineJoin = "round"; + ctx.rect(x, y, BTN_SIZE, BTN_SIZE); + ctx.lineWidth = 2; + if (toggle === "mute") { + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + if (on) { + ctx.stroke(new Path2D(` + ${eyeFrame(midX, midY)} + ${eyeLashes(midX, midY)} + `)); + } + else { + const radius = BTN_GRID * 1.5; + ctx.fill(new Path2D(` + ${eyeFrame(midX, midY)} + ${eyeFrame(midX, midY, -1)} + ${circlePath(midX, midY, radius)} + ${circlePath(midX + BTN_GRID / 2, midY - BTN_GRID / 2, BTN_GRID * 0.375)} + `), "evenodd"); + ctx.stroke(new Path2D(`${eyeFrame(midX, midY)} ${eyeFrame(midX, midY, -1)}`)); + ctx.globalAlpha = this.editor_alpha * 0.5; + ctx.stroke(new Path2D(`${eyeLashes(midX, midY)} ${eyeLashes(midX, midY, -1)}`)); + ctx.globalAlpha = this.editor_alpha; + } + } + else { + const lineChanges = on + ? `a ${BTN_GRID * 3}, ${BTN_GRID * 3} 0 1, 1 ${BTN_GRID * 3 * 2},0 + l ${BTN_GRID * 2.0} 0` + : `l ${BTN_GRID * 8} 0`; + ctx.stroke(new Path2D(` + M ${x} ${midY} + ${lineChanges} + M ${x + BTN_SIZE} ${midY} l -2 2 + M ${x + BTN_SIZE} ${midY} l -2 -2 + `)); + ctx.fill(new Path2D(`${circlePath(x + BTN_GRID * 3, midY, BTN_GRID * 1.8)}`)); + } + } + } + } + ctx.restore(); + }; + }, +}); +function eyeFrame(midX, midY, yFlip = 1) { + return ` + M ${midX - BTN_SIZE / 2} ${midY} + c ${BTN_GRID * 1.5} ${yFlip * BTN_GRID * 2.5}, ${BTN_GRID * (8 - 1.5)} ${yFlip * BTN_GRID * 2.5}, ${BTN_GRID * 8} 0 + `; +} +function eyeLashes(midX, midY, yFlip = 1) { + return ` + M ${midX - BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l -1.15 ${1.25 * yFlip} + M ${midX - BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l -0.90 ${1.5 * yFlip} + M ${midX - BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l -0.50 ${1.75 * yFlip} + M ${midX + BTN_GRID * 0.0} ${midY + yFlip * BTN_GRID * 2.0} l 0.00 ${2.0 * yFlip} + M ${midX + BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l 0.50 ${1.75 * yFlip} + M ${midX + BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l 0.90 ${1.5 * yFlip} + M ${midX + BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l 1.15 ${1.25 * yFlip} +`; +} +function circlePath(cx, cy, radius) { + return ` + M ${cx} ${cy} + m ${radius}, 0 + a ${radius},${radius} 0 1, 1 -${radius * 2},0 + a ${radius},${radius} 0 1, 1 ${radius * 2},0 + `; +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/feature_import_individual_nodes.js b/custom_nodes/rgthree-comfy/web/comfyui/feature_import_individual_nodes.js new file mode 100644 index 00000000..ca5058aa --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/feature_import_individual_nodes.js @@ -0,0 +1,66 @@ +import { app } from "../../scripts/app.js"; +import { tryToGetWorkflowDataFromEvent } from "../../rgthree/common/utils_workflow.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +import { NodeTypesString } from "./constants.js"; +app.registerExtension({ + name: "rgthree.ImportIndividualNodes", + async beforeRegisterNodeDef(nodeType, nodeData) { + const onDragOver = nodeType.prototype.onDragOver; + nodeType.prototype.onDragOver = function (e) { + var _a; + let handled = (_a = onDragOver === null || onDragOver === void 0 ? void 0 : onDragOver.apply) === null || _a === void 0 ? void 0 : _a.call(onDragOver, this, [...arguments]); + if (handled != null) { + return handled; + } + return importIndividualNodesInnerOnDragOver(this, e); + }; + const onDragDrop = nodeType.prototype.onDragDrop; + nodeType.prototype.onDragDrop = async function (e) { + var _a; + const alreadyHandled = await ((_a = onDragDrop === null || onDragDrop === void 0 ? void 0 : onDragDrop.apply) === null || _a === void 0 ? void 0 : _a.call(onDragDrop, this, [...arguments])); + if (alreadyHandled) { + return alreadyHandled; + } + return importIndividualNodesInnerOnDragDrop(this, e); + }; + }, +}); +export function importIndividualNodesInnerOnDragOver(node, e) { + var _a; + return ((((_a = node.widgets) === null || _a === void 0 ? void 0 : _a.length) && !!CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) || + false); +} +export async function importIndividualNodesInnerOnDragDrop(node, e) { + var _a, _b; + if (!((_a = node.widgets) === null || _a === void 0 ? void 0 : _a.length) || !CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) { + return false; + } + const dynamicWidgetLengthNodes = [NodeTypesString.POWER_LORA_LOADER]; + let handled = false; + const { workflow, prompt } = await tryToGetWorkflowDataFromEvent(e); + const exact = ((workflow === null || workflow === void 0 ? void 0 : workflow.nodes) || []).find((n) => { + var _a, _b; + return n.id === node.id && + n.type === node.type && + (dynamicWidgetLengthNodes.includes(node.type) || + ((_a = n.widgets_values) === null || _a === void 0 ? void 0 : _a.length) === ((_b = node.widgets_values) === null || _b === void 0 ? void 0 : _b.length)); + }); + if (!exact) { + handled = !confirm("[rgthree-comfy] Could not find a matching node (same id & type) in the dropped workflow." + + " Would you like to continue with the default drop behaviour instead?"); + } + else if (!((_b = exact.widgets_values) === null || _b === void 0 ? void 0 : _b.length)) { + handled = !confirm("[rgthree-comfy] Matching node found (same id & type) but there's no widgets to set." + + " Would you like to continue with the default drop behaviour instead?"); + } + else if (confirm("[rgthree-comfy] Found a matching node (same id & type) in the dropped workflow." + + " Would you like to set the widget values?")) { + node.configure({ + title: node.title, + widgets_values: [...((exact === null || exact === void 0 ? void 0 : exact.widgets_values) || [])], + mode: exact.mode, + }); + handled = true; + } + return handled; +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/image_comparer.js b/custom_nodes/rgthree-comfy/web/comfyui/image_comparer.js new file mode 100644 index 00000000..4c5d6b8d --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/image_comparer.js @@ -0,0 +1,365 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { addConnectionLayoutSupport } from "./utils.js"; +import { RgthreeBaseWidget } from "./utils_widgets.js"; +import { measureText } from "./utils_canvas.js"; +function imageDataToUrl(data) { + return api.apiURL(`/view?filename=${encodeURIComponent(data.filename)}&type=${data.type}&subfolder=${data.subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`); +} +export class RgthreeImageComparer extends RgthreeBaseServerNode { + constructor(title = RgthreeImageComparer.title) { + super(title); + this.imageIndex = 0; + this.imgs = []; + this.serialize_widgets = true; + this.isPointerDown = false; + this.isPointerOver = false; + this.pointerOverPos = [0, 0]; + this.canvasWidget = null; + this.properties["comparer_mode"] = "Slide"; + } + onExecuted(output) { + var _a; + (_a = super.onExecuted) === null || _a === void 0 ? void 0 : _a.call(this, output); + if ("images" in output) { + this.canvasWidget.value = { + images: (output.images || []).map((d, i) => { + return { + name: i === 0 ? "A" : "B", + selected: true, + url: imageDataToUrl(d), + }; + }), + }; + } + else { + output.a_images = output.a_images || []; + output.b_images = output.b_images || []; + const imagesToChoose = []; + const multiple = output.a_images.length + output.b_images.length > 2; + for (const [i, d] of output.a_images.entries()) { + imagesToChoose.push({ + name: output.a_images.length > 1 || multiple ? `A${i + 1}` : "A", + selected: i === 0, + url: imageDataToUrl(d), + }); + } + for (const [i, d] of output.b_images.entries()) { + imagesToChoose.push({ + name: output.b_images.length > 1 || multiple ? `B${i + 1}` : "B", + selected: i === 0, + url: imageDataToUrl(d), + }); + } + this.canvasWidget.value = { images: imagesToChoose }; + } + } + onSerialize(serialised) { + var _a; + super.onSerialize && super.onSerialize(serialised); + for (let [index, widget_value] of (serialised.widgets_values || []).entries()) { + if (((_a = this.widgets[index]) === null || _a === void 0 ? void 0 : _a.name) === "rgthree_comparer") { + serialised.widgets_values[index] = this.widgets[index].value.images.map((d) => { + d = { ...d }; + delete d.img; + return d; + }); + } + } + } + onNodeCreated() { + this.canvasWidget = this.addCustomWidget(new RgthreeImageComparerWidget("rgthree_comparer", this)); + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + } + setIsPointerDown(down = this.isPointerDown) { + const newIsDown = down && !!app.canvas.pointer_is_down; + if (this.isPointerDown !== newIsDown) { + this.isPointerDown = newIsDown; + this.setDirtyCanvas(true, false); + } + this.imageIndex = this.isPointerDown ? 1 : 0; + if (this.isPointerDown) { + requestAnimationFrame(() => { + this.setIsPointerDown(); + }); + } + } + onMouseDown(event, pos, canvas) { + var _a; + (_a = super.onMouseDown) === null || _a === void 0 ? void 0 : _a.call(this, event, pos, canvas); + this.setIsPointerDown(true); + return false; + } + onMouseEnter(event) { + var _a; + (_a = super.onMouseEnter) === null || _a === void 0 ? void 0 : _a.call(this, event); + this.setIsPointerDown(!!app.canvas.pointer_is_down); + this.isPointerOver = true; + } + onMouseLeave(event) { + var _a; + (_a = super.onMouseLeave) === null || _a === void 0 ? void 0 : _a.call(this, event); + this.setIsPointerDown(false); + this.isPointerOver = false; + } + onMouseMove(event, pos, canvas) { + var _a; + (_a = super.onMouseMove) === null || _a === void 0 ? void 0 : _a.call(this, event, pos, canvas); + this.pointerOverPos = [...pos]; + this.imageIndex = this.pointerOverPos[0] > this.size[0] / 2 ? 1 : 0; + } + getHelp() { + return ` +

      + The ${this.type.replace("(rgthree)", "")} node compares two images on top of each other. +

      +
        +
      • +

        + Notes +

        +
          +

        • + The right-click menu may show image options (Open Image, Save Image, etc.) which will + correspond to the first image (image_a) if clicked on the left-half of the node, or + the second image if on the right half of the node. +

          • +
        +
        • +
      • +

        + Inputs +

        +
          +

        • + image_a Optional. The first image to use to compare. + image_a. +

          • +

        • + image_b Optional. The second image to use to compare. +

          • +

        • + Note image_a and image_b work best when a single + image is provided. However, if each/either are a batch, you can choose which item + from each batch are chosen to be compared. If either image_a or + image_b are not provided, the node will choose the first two from the + provided input if it's a batch, otherwise only show the single image (just as + Preview Image would). +

          • +
        +
        • +
      • +

        + Properties. You can change the following properties (by right-clicking + on the node, and select "Properties" or "Properties Panel" from the menu): +

        +
          +

        • + comparer_mode - Choose between "Slide" and "Click". Defaults to "Slide". +

          • +
        +
        • +
      `; + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeImageComparer); + } + static onRegisteredForOverride(comfyClass) { + addConnectionLayoutSupport(RgthreeImageComparer, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + RgthreeImageComparer.category = comfyClass.category; + }); + } +} +RgthreeImageComparer.title = NodeTypesString.IMAGE_COMPARER; +RgthreeImageComparer.type = NodeTypesString.IMAGE_COMPARER; +RgthreeImageComparer.comfyClass = NodeTypesString.IMAGE_COMPARER; +RgthreeImageComparer["@comparer_mode"] = { + type: "combo", + values: ["Slide", "Click"], +}; +class RgthreeImageComparerWidget extends RgthreeBaseWidget { + constructor(name, node) { + super(name); + this.type = "custom"; + this.hitAreas = {}; + this.selected = []; + this._value = { images: [] }; + this.node = node; + } + set value(v) { + let cleanedVal; + if (Array.isArray(v)) { + cleanedVal = v.map((d, i) => { + if (!d || typeof d === "string") { + d = { url: d, name: i == 0 ? "A" : "B", selected: true }; + } + return d; + }); + } + else { + cleanedVal = v.images || []; + } + if (cleanedVal.length > 2) { + const hasAAndB = cleanedVal.some((i) => i.name.startsWith("A")) && + cleanedVal.some((i) => i.name.startsWith("B")); + if (!hasAAndB) { + cleanedVal = [cleanedVal[0], cleanedVal[1]]; + } + } + let selected = cleanedVal.filter((d) => d.selected); + if (!selected.length && cleanedVal.length) { + cleanedVal[0].selected = true; + } + selected = cleanedVal.filter((d) => d.selected); + if (selected.length === 1 && cleanedVal.length > 1) { + cleanedVal.find((d) => !d.selected).selected = true; + } + this._value.images = cleanedVal; + selected = cleanedVal.filter((d) => d.selected); + this.setSelected(selected); + } + get value() { + return this._value; + } + setSelected(selected) { + this._value.images.forEach((d) => (d.selected = false)); + this.node.imgs.length = 0; + for (const sel of selected) { + if (!sel.img) { + sel.img = new Image(); + sel.img.src = sel.url; + this.node.imgs.push(sel.img); + } + sel.selected = true; + } + this.selected = selected; + } + draw(ctx, node, width, y) { + var _a; + this.hitAreas = {}; + if (this.value.images.length > 2) { + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.font = `14px Arial`; + const drawData = []; + const spacing = 5; + let x = 0; + for (const img of this.value.images) { + const width = measureText(ctx, img.name); + drawData.push({ + img, + text: img.name, + x, + width: measureText(ctx, img.name), + }); + x += width + spacing; + } + x = (node.size[0] - (x - spacing)) / 2; + for (const d of drawData) { + ctx.fillStyle = d.img.selected ? "rgba(180, 180, 180, 1)" : "rgba(180, 180, 180, 0.5)"; + ctx.fillText(d.text, x, y); + this.hitAreas[d.text] = { + bounds: [x, y, d.width, 14], + data: d.img, + onDown: this.onSelectionDown, + }; + x += d.width + spacing; + } + y += 20; + } + if (((_a = node.properties) === null || _a === void 0 ? void 0 : _a["comparer_mode"]) === "Click") { + this.drawImage(ctx, this.selected[this.node.isPointerDown ? 1 : 0], y); + } + else { + this.drawImage(ctx, this.selected[0], y); + if (node.isPointerOver) { + this.drawImage(ctx, this.selected[1], y, this.node.pointerOverPos[0]); + } + } + } + onSelectionDown(event, pos, node, bounds) { + const selected = [...this.selected]; + if (bounds === null || bounds === void 0 ? void 0 : bounds.data.name.startsWith("A")) { + selected[0] = bounds.data; + } + else if (bounds === null || bounds === void 0 ? void 0 : bounds.data.name.startsWith("B")) { + selected[1] = bounds.data; + } + this.setSelected(selected); + } + drawImage(ctx, image, y, cropX) { + var _a, _b; + if (!((_a = image === null || image === void 0 ? void 0 : image.img) === null || _a === void 0 ? void 0 : _a.naturalWidth) || !((_b = image === null || image === void 0 ? void 0 : image.img) === null || _b === void 0 ? void 0 : _b.naturalHeight)) { + return; + } + let [nodeWidth, nodeHeight] = this.node.size; + const imageAspect = (image === null || image === void 0 ? void 0 : image.img.naturalWidth) / (image === null || image === void 0 ? void 0 : image.img.naturalHeight); + let height = nodeHeight - y; + const widgetAspect = nodeWidth / height; + let targetWidth, targetHeight; + let offsetX = 0; + if (imageAspect > widgetAspect) { + targetWidth = nodeWidth; + targetHeight = nodeWidth / imageAspect; + } + else { + targetHeight = height; + targetWidth = height * imageAspect; + offsetX = (nodeWidth - targetWidth) / 2; + } + const widthMultiplier = (image === null || image === void 0 ? void 0 : image.img.naturalWidth) / targetWidth; + const sourceX = 0; + const sourceY = 0; + const sourceWidth = cropX != null ? (cropX - offsetX) * widthMultiplier : image === null || image === void 0 ? void 0 : image.img.naturalWidth; + const sourceHeight = image === null || image === void 0 ? void 0 : image.img.naturalHeight; + const destX = (nodeWidth - targetWidth) / 2; + const destY = y + (height - targetHeight) / 2; + const destWidth = cropX != null ? cropX - offsetX : targetWidth; + const destHeight = targetHeight; + ctx.save(); + ctx.beginPath(); + let globalCompositeOperation = ctx.globalCompositeOperation; + if (cropX) { + ctx.rect(destX, destY, destWidth, destHeight); + ctx.clip(); + } + ctx.drawImage(image === null || image === void 0 ? void 0 : image.img, sourceX, sourceY, sourceWidth, sourceHeight, destX, destY, destWidth, destHeight); + if (cropX != null && cropX >= (nodeWidth - targetWidth) / 2 && cropX <= targetWidth + offsetX) { + ctx.beginPath(); + ctx.moveTo(cropX, destY); + ctx.lineTo(cropX, destY + destHeight); + ctx.globalCompositeOperation = "difference"; + ctx.strokeStyle = "rgba(255,255,255, 1)"; + ctx.stroke(); + } + ctx.globalCompositeOperation = globalCompositeOperation; + ctx.restore(); + } + computeSize(width) { + return [width, 20]; + } + serializeValue(node, index) { + const v = []; + for (const data of this._value.images) { + const d = { ...data }; + delete d.img; + v.push(d); + } + return { images: v }; + } +} +app.registerExtension({ + name: "rgthree.ImageComparer", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === RgthreeImageComparer.type) { + RgthreeImageComparer.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/image_inset_crop.js b/custom_nodes/rgthree-comfy/web/comfyui/image_inset_crop.js new file mode 100644 index 00000000..186e5c14 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/image_inset_crop.js @@ -0,0 +1,59 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +class ImageInsetCrop extends RgthreeBaseServerNode { + constructor(title = ImageInsetCrop.title) { + super(title); + } + onAdded(graph) { + const measurementWidget = this.widgets[0]; + let callback = measurementWidget.callback; + measurementWidget.callback = (...args) => { + this.setWidgetStep(); + callback && callback.apply(measurementWidget, [...args]); + }; + this.setWidgetStep(); + } + configure(info) { + super.configure(info); + this.setWidgetStep(); + } + setWidgetStep() { + const measurementWidget = this.widgets[0]; + for (let i = 1; i <= 4; i++) { + if (measurementWidget.value === "Pixels") { + this.widgets[i].options.step = 80; + this.widgets[i].options.max = ImageInsetCrop.maxResolution; + } + else { + this.widgets[i].options.step = 10; + this.widgets[i].options.max = 99; + } + } + } + async handleAction(action) { + if (action === "Reset Crop") { + for (const widget of this.widgets) { + if (["left", "right", "top", "bottom"].includes(widget.name)) { + widget.value = 0; + } + } + } + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ImageInsetCrop); + } +} +ImageInsetCrop.title = NodeTypesString.IMAGE_INSET_CROP; +ImageInsetCrop.type = NodeTypesString.IMAGE_INSET_CROP; +ImageInsetCrop.comfyClass = NodeTypesString.IMAGE_INSET_CROP; +ImageInsetCrop.exposedActions = ["Reset Crop"]; +ImageInsetCrop.maxResolution = 8192; +app.registerExtension({ + name: "rgthree.ImageInsetCrop", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === NodeTypesString.IMAGE_INSET_CROP) { + ImageInsetCrop.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/image_or_latent_size.js b/custom_nodes/rgthree-comfy/web/comfyui/image_or_latent_size.js new file mode 100644 index 00000000..3e12fc62 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/image_or_latent_size.js @@ -0,0 +1,35 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +class RgthreeImageOrLatentSize extends RgthreeBaseServerNode { + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + constructor(title = NODE_CLASS.title) { + super(title); + } + onNodeCreated() { + var _a; + (_a = super.onNodeCreated) === null || _a === void 0 ? void 0 : _a.call(this); + this.addInput("input", ["IMAGE", "LATENT", "MASK"]); + } + configure(info) { + var _a; + super.configure(info); + if ((_a = this.inputs) === null || _a === void 0 ? void 0 : _a.length) { + this.inputs[0].type = ["IMAGE", "LATENT", "MASK"]; + } + } +} +RgthreeImageOrLatentSize.title = NodeTypesString.IMAGE_OR_LATENT_SIZE; +RgthreeImageOrLatentSize.type = NodeTypesString.IMAGE_OR_LATENT_SIZE; +RgthreeImageOrLatentSize.comfyClass = NodeTypesString.IMAGE_OR_LATENT_SIZE; +const NODE_CLASS = RgthreeImageOrLatentSize; +app.registerExtension({ + name: "rgthree.ImageOrLatentSize", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/label.js b/custom_nodes/rgthree-comfy/web/comfyui/label.js new file mode 100644 index 00000000..b6dedad3 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/label.js @@ -0,0 +1,163 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseVirtualNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { rgthree } from "./rgthree.js"; +export class Label extends RgthreeBaseVirtualNode { + constructor(title = Label.title) { + super(title); + this.comfyClass = NodeTypesString.LABEL; + this.resizable = false; + this.properties["fontSize"] = 12; + this.properties["fontFamily"] = "Arial"; + this.properties["fontColor"] = "#ffffff"; + this.properties["textAlign"] = "left"; + this.properties["backgroundColor"] = "transparent"; + this.properties["padding"] = 0; + this.properties["borderRadius"] = 0; + this.properties["angle"] = 0; + this.color = "#fff0"; + this.bgcolor = "#fff0"; + this.onConstructed(); + } + draw(ctx) { + var _a, _b, _c, _d; + this.flags = this.flags || {}; + this.flags.allow_interaction = !this.flags.pinned; + ctx.save(); + this.color = "#fff0"; + this.bgcolor = "#fff0"; + const fontColor = this.properties["fontColor"] || "#ffffff"; + const backgroundColor = this.properties["backgroundColor"] || ""; + ctx.font = `${Math.max(this.properties["fontSize"] || 0, 1)}px ${(_a = this.properties["fontFamily"]) !== null && _a !== void 0 ? _a : "Arial"}`; + const padding = (_b = Number(this.properties["padding"])) !== null && _b !== void 0 ? _b : 0; + const processedTitle = ((_c = this.title) !== null && _c !== void 0 ? _c : "").replace(/\\n/g, "\n").replace(/\n*$/, ""); + const lines = processedTitle.split("\n"); + const maxWidth = Math.max(...lines.map((s) => ctx.measureText(s).width)); + this.size[0] = maxWidth + padding * 2; + this.size[1] = this.properties["fontSize"] * lines.length + padding * 2; + const angleDeg = parseInt(String((_d = this.properties["angle"]) !== null && _d !== void 0 ? _d : 0)) || 0; + if (angleDeg) { + const cx = this.size[0] / 2; + const cy = this.size[1] / 2; + ctx.translate(cx, cy); + ctx.rotate((angleDeg * Math.PI) / 180); + ctx.translate(-cx, -cy); + } + if (backgroundColor) { + ctx.beginPath(); + const borderRadius = Number(this.properties["borderRadius"]) || 0; + ctx.roundRect(0, 0, this.size[0], this.size[1], [borderRadius]); + ctx.fillStyle = backgroundColor; + ctx.fill(); + } + ctx.textAlign = "left"; + let textX = padding; + if (this.properties["textAlign"] === "center") { + ctx.textAlign = "center"; + textX = this.size[0] / 2; + } + else if (this.properties["textAlign"] === "right") { + ctx.textAlign = "right"; + textX = this.size[0] - padding; + } + ctx.textBaseline = "top"; + ctx.fillStyle = fontColor; + let currentY = padding; + for (let i = 0; i < lines.length; i++) { + ctx.fillText(lines[i] || " ", textX, currentY); + currentY += this.properties["fontSize"]; + } + ctx.restore(); + } + onDblClick(event, pos, canvas) { + LGraphCanvas.active_canvas.showShowNodePanel(this); + } + onShowCustomPanelInfo(panel) { + var _a, _b; + (_a = panel.querySelector('div.property[data-property="Mode"]')) === null || _a === void 0 ? void 0 : _a.remove(); + (_b = panel.querySelector('div.property[data-property="Color"]')) === null || _b === void 0 ? void 0 : _b.remove(); + } + inResizeCorner(x, y) { + return this.resizable; + } + getHelp() { + return ` +

      + The rgthree-comfy ${this.type.replace("(rgthree)", "")} node allows you to add a floating + label to your workflow. +

      +

      + The text shown is the "Title" of the node and you can adjust the the font size, font family, + font color, text alignment as well as a background color, padding, and background border + radius from the node's properties. You can double-click the node to open the properties + panel. +

      +

        +
      • +

        + Pro tip #1: You can add multiline text from the properties panel + (because ComfyUI let's you shift + enter there, only). +

        +
        • +
      • +

        + Pro tip #2: You can use ComfyUI's native "pin" option in the + right-click menu to make the label stick to the workflow and clicks to "go through". + You can right-click at any time to unpin. +

        +
        • +
      • +

        + Pro tip #3: Color values are hexidecimal strings, like "#FFFFFF" for + white, or "#660000" for dark red. You can supply a 7th & 8th value (or 5th if using + shorthand) to create a transluscent color. For instance, "#FFFFFF88" is semi-transparent + white. +

        +
        • +
      `; + } +} +Label.type = NodeTypesString.LABEL; +Label.title = NodeTypesString.LABEL; +Label.title_mode = LiteGraph.NO_TITLE; +Label.collapsable = false; +Label["@fontSize"] = { type: "number" }; +Label["@fontFamily"] = { type: "string" }; +Label["@fontColor"] = { type: "string" }; +Label["@textAlign"] = { type: "combo", values: ["left", "center", "right"] }; +Label["@backgroundColor"] = { type: "string" }; +Label["@padding"] = { type: "number" }; +Label["@borderRadius"] = { type: "number" }; +Label["@angle"] = { type: "number" }; +const oldDrawNode = LGraphCanvas.prototype.drawNode; +LGraphCanvas.prototype.drawNode = function (node, ctx) { + if (node.constructor === Label.prototype.constructor) { + node.bgcolor = "transparent"; + node.color = "transparent"; + const v = oldDrawNode.apply(this, arguments); + node.draw(ctx); + return v; + } + const v = oldDrawNode.apply(this, arguments); + return v; +}; +const oldGetNodeOnPos = LGraph.prototype.getNodeOnPos; +LGraph.prototype.getNodeOnPos = function (x, y, nodes_list) { + var _a, _b; + if (nodes_list && + rgthree.processingMouseDown && + ((_a = rgthree.lastCanvasMouseEvent) === null || _a === void 0 ? void 0 : _a.type.includes("down")) && + ((_b = rgthree.lastCanvasMouseEvent) === null || _b === void 0 ? void 0 : _b.which) === 1) { + let isDoubleClick = LiteGraph.getTime() - LGraphCanvas.active_canvas.last_mouseclick < 300; + if (!isDoubleClick) { + nodes_list = [...nodes_list].filter((n) => { var _a; return !(n instanceof Label) || !((_a = n.flags) === null || _a === void 0 ? void 0 : _a.pinned); }); + } + } + return oldGetNodeOnPos.apply(this, [x, y, nodes_list]); +}; +app.registerExtension({ + name: "rgthree.Label", + registerCustomNodes() { + Label.setUp(); + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/menu_auto_nest.js b/custom_nodes/rgthree-comfy/web/comfyui/menu_auto_nest.js new file mode 100644 index 00000000..d720ee8f --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/menu_auto_nest.js @@ -0,0 +1,113 @@ +import { app } from "../../scripts/app.js"; +import { rgthree } from "./rgthree.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +const SPECIAL_ENTRIES = [/^(CHOOSE|NONE|DISABLE|OPEN)(\s|$)/i, /^\p{Extended_Pictographic}/gu]; +app.registerExtension({ + name: "rgthree.ContextMenuAutoNest", + async setup() { + const logger = rgthree.newLogSession("[ContextMenuAutoNest]"); + const existingContextMenu = LiteGraph.ContextMenu; + LiteGraph.ContextMenu = function (values, options) { + var _a, _b, _c, _d, _e, _f, _g, _h; + const threshold = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.threshold", 20); + const enabled = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.subdirs", false); + let incompatible = !enabled || !!((_a = options === null || options === void 0 ? void 0 : options.extra) === null || _a === void 0 ? void 0 : _a.rgthree_doNotNest); + if (!incompatible) { + if (values.length <= threshold) { + incompatible = `Skipping context menu auto nesting b/c threshold is not met (${threshold})`; + } + if (!((_c = (_b = options.parentMenu) === null || _b === void 0 ? void 0 : _b.options) === null || _c === void 0 ? void 0 : _c.rgthree_originalCallback)) { + if (!(options === null || options === void 0 ? void 0 : options.callback)) { + incompatible = `Skipping context menu auto nesting b/c a callback was expected.`; + } + else if (values.some((i) => typeof i !== "string")) { + incompatible = `Skipping context menu auto nesting b/c not all values were strings.`; + } + } + } + if (incompatible) { + if (enabled) { + const [n, v] = logger.infoParts("Skipping context menu auto nesting for incompatible menu."); + (_d = console[n]) === null || _d === void 0 ? void 0 : _d.call(console, ...v); + } + return existingContextMenu.apply(this, [...arguments]); + } + const folders = {}; + const specialOps = []; + const folderless = []; + for (const value of values) { + if (!value) { + folderless.push(value); + continue; + } + const newValue = typeof value === "string" ? { content: value } : Object.assign({}, value); + newValue.rgthree_originalValue = value.rgthree_originalValue || value; + const valueContent = newValue.content || ""; + const splitBy = valueContent.indexOf("/") > -1 ? "/" : "\\"; + const valueSplit = valueContent.split(splitBy); + if (valueSplit.length > 1) { + const key = valueSplit.shift(); + newValue.content = valueSplit.join(splitBy); + folders[key] = folders[key] || []; + folders[key].push(newValue); + } + else if (SPECIAL_ENTRIES.some((r) => r.test(valueContent))) { + specialOps.push(newValue); + } + else { + folderless.push(newValue); + } + } + const foldersCount = Object.values(folders).length; + if (foldersCount > 0) { + options.rgthree_originalCallback = + options.rgthree_originalCallback || + ((_f = (_e = options.parentMenu) === null || _e === void 0 ? void 0 : _e.options) === null || _f === void 0 ? void 0 : _f.rgthree_originalCallback) || + options.callback; + const oldCallback = options === null || options === void 0 ? void 0 : options.rgthree_originalCallback; + options.callback = undefined; + const newCallback = (item, options, event, parentMenu, node) => { + oldCallback === null || oldCallback === void 0 ? void 0 : oldCallback(item === null || item === void 0 ? void 0 : item.rgthree_originalValue, options, event, undefined, node); + }; + const [n, v] = logger.infoParts(`Nested folders found (${foldersCount}).`); + (_g = console[n]) === null || _g === void 0 ? void 0 : _g.call(console, ...v); + const newValues = []; + for (const [folderName, folderValues] of Object.entries(folders)) { + newValues.push({ + content: `πŸ“ ${folderName}`, + has_submenu: true, + callback: () => { + }, + submenu: { + options: folderValues.map((value) => { + value.callback = newCallback; + return value; + }), + }, + }); + } + values = [].concat(specialOps.map((f) => { + if (typeof f === "string") { + f = { content: f }; + } + f.callback = newCallback; + return f; + }), newValues, folderless.map((f) => { + if (typeof f === "string") { + f = { content: f }; + } + f.callback = newCallback; + return f; + })); + } + if (options.scale == null) { + options.scale = Math.max(((_h = app.canvas.ds) === null || _h === void 0 ? void 0 : _h.scale) || 1, 1); + } + const oldCtrResponse = existingContextMenu.call(this, values, options); + if (oldCtrResponse === null || oldCtrResponse === void 0 ? void 0 : oldCtrResponse.constructor) { + oldCtrResponse.constructor = LiteGraph.ContextMenu; + } + return this; + }; + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/menu_copy_image.js b/custom_nodes/rgthree-comfy/web/comfyui/menu_copy_image.js new file mode 100644 index 00000000..30d034f0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/menu_copy_image.js @@ -0,0 +1,62 @@ +import { app } from "../../scripts/app.js"; +const clipboardSupportedPromise = new Promise(async (resolve) => { + try { + const result = await navigator.permissions.query({ name: "clipboard-write" }); + resolve(result.state === "granted"); + return; + } + catch (e) { + try { + if (!navigator.clipboard.write) { + throw new Error(); + } + new ClipboardItem({ "image/png": new Blob([], { type: "image/png" }) }); + resolve(true); + return; + } + catch (e) { + resolve(false); + } + } +}); +app.registerExtension({ + name: "rgthree.CopyImageToClipboard", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name.toLowerCase().includes("image")) { + if (await clipboardSupportedPromise) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (canvas, options) { + var _a, _b; + options = (_a = getExtraMenuOptions === null || getExtraMenuOptions === void 0 ? void 0 : getExtraMenuOptions.call(this, canvas, options)) !== null && _a !== void 0 ? _a : options; + if ((_b = this.imgs) === null || _b === void 0 ? void 0 : _b.length) { + let img = this.imgs[this.imageIndex || 0] || this.imgs[this.overIndex || 0] || this.imgs[0]; + const foundIdx = options.findIndex((option) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.content) === null || _a === void 0 ? void 0 : _a.includes("Copy Image"); }); + if (img && foundIdx === -1) { + const menuItem = { + content: "Copy Image (rgthree)", + callback: () => { + const canvas = document.createElement("canvas"); + const ctx = canvas.getContext("2d"); + canvas.width = img.naturalWidth; + canvas.height = img.naturalHeight; + ctx.drawImage(img, 0, 0, img.naturalWidth, img.naturalHeight); + canvas.toBlob((blob) => { + navigator.clipboard.write([new ClipboardItem({ "image/png": blob })]); + }); + }, + }; + let idx = options.findIndex((option) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.content) === null || _a === void 0 ? void 0 : _a.includes("Open Image"); }) + 1; + if (idx != null) { + options.splice(idx, 0, menuItem); + } + else { + options.unshift(menuItem); + } + } + } + return []; + }; + } + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/menu_queue_node.js b/custom_nodes/rgthree-comfy/web/comfyui/menu_queue_node.js new file mode 100644 index 00000000..a53250a4 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/menu_queue_node.js @@ -0,0 +1,65 @@ +import { app } from "../../scripts/app.js"; +import { rgthree } from "./rgthree.js"; +import { getGroupNodes, getOutputNodes } from "./utils.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +function showQueueNodesMenuIfOutputNodesAreSelected(existingOptions) { + if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) { + return; + } + const outputNodes = getOutputNodes(Object.values(app.canvas.selected_nodes)); + const menuItem = { + content: `Queue Selected Output Nodes (rgthree)  `, + className: "rgthree-contextmenu-item", + callback: () => { + rgthree.queueOutputNodes(outputNodes); + }, + disabled: !outputNodes.length, + }; + let idx = existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Outputs") + 1; + idx = idx || existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Align") + 1; + idx = idx || 3; + existingOptions.splice(idx, 0, menuItem); +} +function showQueueGroupNodesMenuIfGroupIsSelected(existingOptions) { + if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) { + return; + } + const group = rgthree.lastCanvasMouseEvent && + (app.canvas.getCurrentGraph() || app.graph).getGroupOnPos(rgthree.lastCanvasMouseEvent.canvasX, rgthree.lastCanvasMouseEvent.canvasY); + const outputNodes = (group && getOutputNodes(getGroupNodes(group))) || null; + const menuItem = { + content: `Queue Group Output Nodes (rgthree)  `, + className: "rgthree-contextmenu-item", + callback: () => { + outputNodes && rgthree.queueOutputNodes(outputNodes); + }, + disabled: !(outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length), + }; + let idx = existingOptions.findIndex((o) => { var _a; return (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith("Queue Selected "); }) + 1; + idx = idx || existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Outputs") + 1; + idx = idx || existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Align") + 1; + idx = idx || 3; + existingOptions.splice(idx, 0, menuItem); +} +app.registerExtension({ + name: "rgthree.QueueNode", + async beforeRegisterNodeDef(nodeType, nodeData) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (canvas, options) { + var _a; + const extraOptions = (_a = getExtraMenuOptions === null || getExtraMenuOptions === void 0 ? void 0 : getExtraMenuOptions.call(this, canvas, options)) !== null && _a !== void 0 ? _a : []; + showQueueNodesMenuIfOutputNodesAreSelected(options); + showQueueGroupNodesMenuIfGroupIsSelected(options); + return extraOptions; + }; + }, + async setup() { + const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function (...args) { + const options = getCanvasMenuOptions.apply(this, [...args]); + showQueueNodesMenuIfOutputNodesAreSelected(options); + showQueueGroupNodesMenuIfGroupIsSelected(options); + return options; + }; + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/muter.js b/custom_nodes/rgthree-comfy/web/comfyui/muter.js new file mode 100644 index 00000000..f18210f6 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/muter.js @@ -0,0 +1,45 @@ +import { app } from "../../scripts/app.js"; +import { BaseNodeModeChanger } from "./base_node_mode_changer.js"; +import { NodeTypesString } from "./constants.js"; +const MODE_MUTE = 2; +const MODE_ALWAYS = 0; +class MuterNode extends BaseNodeModeChanger { + constructor(title = MuterNode.title) { + super(title); + this.comfyClass = NodeTypesString.FAST_MUTER; + this.modeOn = MODE_ALWAYS; + this.modeOff = MODE_MUTE; + this.onConstructed(); + } + async handleAction(action) { + if (action === "Mute all") { + for (const widget of this.widgets) { + this.forceWidgetOff(widget, true); + } + } + else if (action === "Enable all") { + for (const widget of this.widgets) { + this.forceWidgetOn(widget, true); + } + } + else if (action === "Toggle all") { + for (const widget of this.widgets) { + this.forceWidgetToggle(widget, true); + } + } + } +} +MuterNode.exposedActions = ["Mute all", "Enable all", "Toggle all"]; +MuterNode.type = NodeTypesString.FAST_MUTER; +MuterNode.title = NodeTypesString.FAST_MUTER; +app.registerExtension({ + name: "rgthree.Muter", + registerCustomNodes() { + MuterNode.setUp(); + }, + loadedGraphNode(node) { + if (node.type == MuterNode.title) { + node._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/node_collector.js b/custom_nodes/rgthree-comfy/web/comfyui/node_collector.js new file mode 100644 index 00000000..477bf868 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/node_collector.js @@ -0,0 +1,113 @@ +import { app } from "../../scripts/app.js"; +import { addConnectionLayoutSupport } from "./utils.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { BaseCollectorNode } from "./base_node_collector.js"; +import { NodeTypesString } from "./constants.js"; +class CollectorNode extends BaseCollectorNode { + constructor(title = CollectorNode.title) { + super(title); + this.comfyClass = NodeTypesString.NODE_COLLECTOR; + this.onConstructed(); + } + onConstructed() { + this.addOutput("Output", "*"); + return super.onConstructed(); + } +} +CollectorNode.type = NodeTypesString.NODE_COLLECTOR; +CollectorNode.title = NodeTypesString.NODE_COLLECTOR; +class CombinerNode extends CollectorNode { + constructor(title = CombinerNode.title) { + super(title); + const note = ComfyWidgets["STRING"](this, "last_seed", ["STRING", { multiline: true }], app).widget; + note.inputEl.value = + 'The Node Combiner has been renamed to Node Collector. You can right-click and select "Update to Node Collector" to attempt to automatically update.'; + note.inputEl.readOnly = true; + note.inputEl.style.backgroundColor = "#332222"; + note.inputEl.style.fontWeight = "bold"; + note.inputEl.style.fontStyle = "italic"; + note.inputEl.style.opacity = "0.8"; + this.getExtraMenuOptions = (canvas, options) => { + options.splice(options.length - 1, 0, { + content: "‼️ Update to Node Collector", + callback: (_value, _options, _event, _parentMenu, _node) => { + updateCombinerToCollector(this); + }, + }); + return []; + }; + } + configure(info) { + super.configure(info); + if (this.title != CombinerNode.title && !this.title.startsWith("‼️")) { + this.title = "‼️ " + this.title; + } + } +} +CombinerNode.legacyType = "Node Combiner (rgthree)"; +CombinerNode.title = "‼️ Node Combiner [DEPRECATED]"; +async function updateCombinerToCollector(node) { + if (node.type === CombinerNode.legacyType) { + const newNode = new CollectorNode(); + if (node.title != CombinerNode.title) { + newNode.title = node.title.replace("‼️ ", ""); + } + newNode.pos = [...node.pos]; + newNode.size = [...node.size]; + newNode.properties = { ...node.properties }; + const links = []; + const graph = (node.graph || app.graph); + for (const [index, output] of node.outputs.entries()) { + for (const linkId of output.links || []) { + const link = graph.links[linkId]; + if (!link) + continue; + const targetNode = graph.getNodeById(link.target_id); + links.push({ node: newNode, slot: index, targetNode, targetSlot: link.target_slot }); + } + } + for (const [index, input] of node.inputs.entries()) { + const linkId = input.link; + if (linkId) { + const link = graph.links[linkId]; + const originNode = graph.getNodeById(link.origin_id); + links.push({ + node: originNode, + slot: link.origin_slot, + targetNode: newNode, + targetSlot: index, + }); + } + } + graph.add(newNode); + await wait(); + for (const link of links) { + link.node.connect(link.slot, link.targetNode, link.targetSlot); + } + await wait(); + graph.remove(node); + } +} +app.registerExtension({ + name: "rgthree.NodeCollector", + registerCustomNodes() { + addConnectionLayoutSupport(CollectorNode, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + LiteGraph.registerNodeType(CollectorNode.title, CollectorNode); + CollectorNode.category = CollectorNode._category; + }, +}); +app.registerExtension({ + name: "rgthree.NodeCombiner", + registerCustomNodes() { + addConnectionLayoutSupport(CombinerNode, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + LiteGraph.registerNodeType(CombinerNode.legacyType, CombinerNode); + CombinerNode.category = CombinerNode._category; + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/node_mode_relay.js b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_relay.js new file mode 100644 index 00000000..5daeffbe --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_relay.js @@ -0,0 +1,205 @@ +import { app } from "../../scripts/app.js"; +import { PassThroughFollowing, addConnectionLayoutSupport, changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs, getConnectedOutputNodesAndFilterPassThroughs, } from "./utils.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +import { BaseCollectorNode } from "./base_node_collector.js"; +import { NodeTypesString, stripRgthree } from "./constants.js"; +import { fitString } from "./utils_canvas.js"; +import { rgthree } from "./rgthree.js"; +const MODE_ALWAYS = 0; +const MODE_MUTE = 2; +const MODE_BYPASS = 4; +const MODE_REPEATS = [MODE_MUTE, MODE_BYPASS]; +const MODE_NOTHING = -99; +const MODE_TO_OPTION = new Map([ + [MODE_ALWAYS, "ACTIVE"], + [MODE_MUTE, "MUTE"], + [MODE_BYPASS, "BYPASS"], + [MODE_NOTHING, "NOTHING"], +]); +const OPTION_TO_MODE = new Map([ + ["ACTIVE", MODE_ALWAYS], + ["MUTE", MODE_MUTE], + ["BYPASS", MODE_BYPASS], + ["NOTHING", MODE_NOTHING], +]); +const MODE_TO_PROPERTY = new Map([ + [MODE_MUTE, "on_muted_inputs"], + [MODE_BYPASS, "on_bypassed_inputs"], + [MODE_ALWAYS, "on_any_active_inputs"], +]); +const logger = rgthree.newLogSession("[NodeModeRelay]"); +class NodeModeRelay extends BaseCollectorNode { + constructor(title) { + super(title); + this.inputsPassThroughFollowing = PassThroughFollowing.ALL; + this.comfyClass = NodeTypesString.NODE_MODE_RELAY; + this.properties["on_muted_inputs"] = "MUTE"; + this.properties["on_bypassed_inputs"] = "BYPASS"; + this.properties["on_any_active_inputs"] = "ACTIVE"; + this.onConstructed(); + } + onConstructed() { + this.addOutput("REPEATER", "_NODE_REPEATER_", { + color_on: "#Fc0", + color_off: "#a80", + shape: LiteGraph.ARROW_SHAPE, + }); + setTimeout(() => { + this.stabilize(); + }, 500); + return super.onConstructed(); + } + onModeChange(from, to) { + var _a; + super.onModeChange(from, to); + if (this.inputs.length <= 1 && !this.isInputConnected(0) && this.isAnyOutputConnected()) { + const [n, v] = logger.infoParts(`Mode change without any inputs; relaying our mode.`); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + this.dispatchModeToRepeater(to); + } + } + onDrawForeground(ctx, canvas) { + var _a; + if ((_a = this.flags) === null || _a === void 0 ? void 0 : _a.collapsed) { + return; + } + if (this.properties["on_muted_inputs"] !== "MUTE" || + this.properties["on_bypassed_inputs"] !== "BYPASS" || + this.properties["on_any_active_inputs"] != "ACTIVE") { + let margin = 15; + ctx.textAlign = "left"; + let label = `*(MUTE > ${this.properties["on_muted_inputs"]}, `; + label += `BYPASS > ${this.properties["on_bypassed_inputs"]}, `; + label += `ACTIVE > ${this.properties["on_any_active_inputs"]})`; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + const oldFont = ctx.font; + ctx.font = "italic " + (LiteGraph.NODE_SUBTEXT_SIZE - 2) + "px Arial"; + ctx.fillText(fitString(ctx, label, this.size[0] - 20), 15, this.size[1] - 6); + ctx.font = oldFont; + } + } + computeSize(out) { + let size = super.computeSize(out); + if (this.properties["on_muted_inputs"] !== "MUTE" || + this.properties["on_bypassed_inputs"] !== "BYPASS" || + this.properties["on_any_active_inputs"] != "ACTIVE") { + size[1] += 17; + } + return size; + } + onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex) { + var _a, _b; + let canConnect = (_a = super.onConnectOutput) === null || _a === void 0 ? void 0 : _a.call(this, outputIndex, inputType, inputSlot, inputNode, inputIndex); + let nextNode = (_b = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0]) !== null && _b !== void 0 ? _b : inputNode; + return canConnect && nextNode.type === NodeTypesString.NODE_MODE_REPEATER; + } + onConnectionsChange(type, slotIndex, isConnected, link_info, ioSlot) { + super.onConnectionsChange(type, slotIndex, isConnected, link_info, ioSlot); + setTimeout(() => { + this.stabilize(); + }, 500); + } + stabilize() { + if (!this.graph || !this.isAnyOutputConnected() || !this.isInputConnected(0)) { + return; + } + const inputNodes = getConnectedInputNodesAndFilterPassThroughs(this, this, -1, this.inputsPassThroughFollowing); + let mode = undefined; + for (const inputNode of inputNodes) { + if (mode === undefined) { + mode = inputNode.mode; + } + else if (mode === inputNode.mode && MODE_REPEATS.includes(mode)) { + continue; + } + else if (inputNode.mode === MODE_ALWAYS || mode === MODE_ALWAYS) { + mode = MODE_ALWAYS; + } + else { + mode = undefined; + } + } + this.dispatchModeToRepeater(mode); + setTimeout(() => { + this.stabilize(); + }, 500); + } + dispatchModeToRepeater(mode) { + var _a, _b; + if (mode != null) { + const propertyVal = (_a = this.properties) === null || _a === void 0 ? void 0 : _a[MODE_TO_PROPERTY.get(mode) || ""]; + const newMode = OPTION_TO_MODE.get(propertyVal); + mode = (newMode !== null ? newMode : mode); + if (mode !== null && mode !== MODE_NOTHING) { + if ((_b = this.outputs) === null || _b === void 0 ? void 0 : _b.length) { + const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this); + for (const outputNode of outputNodes) { + changeModeOfNodes(outputNode, mode); + wait(16).then(() => { + outputNode.setDirtyCanvas(true, true); + }); + } + } + } + } + } + getHelp() { + return ` +

      + This node will relay its input nodes' modes (Mute, Bypass, or Active) to a connected + ${stripRgthree(NodeTypesString.NODE_MODE_REPEATER)} (which would then repeat that mode + change to all of its inputs). +

      +
        +

      • + When all connected input nodes are muted, the relay will set a connected repeater to + mute (by default). +

        • +

      • + When all connected input nodes are bypassed, the relay will set a connected repeater to + bypass (by default). +

        • +

      • + When any connected input nodes are active, the relay will set a connected repeater to + active (by default). +

        • +

      • + If no inputs are connected, the relay will set a connected repeater to its mode when + its own mode is changed. Note, if any inputs are connected, then the above + will occur and the Relay's mode does not matter. +

        • +
      +

      + Note, you can change which signals get sent on the above in the Properties. + For instance, you could configure an inverse relay which will send a MUTE when any of its + inputs are active (instead of sending an ACTIVE signal), and send an ACTIVE signal when all + of its inputs are muted (instead of sending a MUTE signal), etc. +

      + `; + } +} +NodeModeRelay.type = NodeTypesString.NODE_MODE_RELAY; +NodeModeRelay.title = NodeTypesString.NODE_MODE_RELAY; +NodeModeRelay["@on_muted_inputs"] = { + type: "combo", + values: ["MUTE", "ACTIVE", "BYPASS", "NOTHING"], +}; +NodeModeRelay["@on_bypassed_inputs"] = { + type: "combo", + values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"], +}; +NodeModeRelay["@on_any_active_inputs"] = { + type: "combo", + values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"], +}; +app.registerExtension({ + name: "rgthree.NodeModeRepeaterHelper", + registerCustomNodes() { + addConnectionLayoutSupport(NodeModeRelay, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + LiteGraph.registerNodeType(NodeModeRelay.type, NodeModeRelay); + NodeModeRelay.category = NodeModeRelay._category; + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/node_mode_repeater.js b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_repeater.js new file mode 100644 index 00000000..22300d11 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_repeater.js @@ -0,0 +1,148 @@ +import { app } from "../../scripts/app.js"; +import { BaseCollectorNode } from "./base_node_collector.js"; +import { NodeTypesString, stripRgthree } from "./constants.js"; +import { PassThroughFollowing, addConnectionLayoutSupport, changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs, getConnectedOutputNodesAndFilterPassThroughs, getGroupNodes, } from "./utils.js"; +class NodeModeRepeater extends BaseCollectorNode { + constructor(title) { + super(title); + this.inputsPassThroughFollowing = PassThroughFollowing.ALL; + this.comfyClass = NodeTypesString.NODE_MODE_REPEATER; + this.hasRelayInput = false; + this.hasTogglerOutput = false; + this.onConstructed(); + } + onConstructed() { + this.addOutput("OPT_CONNECTION", "*", { + color_on: "#Fc0", + color_off: "#a80", + }); + return super.onConstructed(); + } + onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex) { + let canConnect = !this.hasRelayInput; + canConnect = + canConnect && super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex); + let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0] || inputNode; + return (canConnect && + [ + NodeTypesString.FAST_MUTER, + NodeTypesString.FAST_BYPASSER, + NodeTypesString.NODE_COLLECTOR, + NodeTypesString.FAST_ACTIONS_BUTTON, + NodeTypesString.REROUTE, + NodeTypesString.RANDOM_UNMUTER, + ].includes(nextNode.type || "")); + } + onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) { + var _a; + let canConnect = (_a = super.onConnectInput) === null || _a === void 0 ? void 0 : _a.call(this, inputIndex, outputType, outputSlot, outputNode, outputIndex); + let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, outputNode)[0] || outputNode; + const isNextNodeRelay = nextNode.type === NodeTypesString.NODE_MODE_RELAY; + return canConnect && (!isNextNodeRelay || !this.hasTogglerOutput); + } + onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot) { + super.onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot); + let hasTogglerOutput = false; + let hasRelayInput = false; + const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this); + for (const outputNode of outputNodes) { + if ((outputNode === null || outputNode === void 0 ? void 0 : outputNode.type) === NodeTypesString.FAST_MUTER || + (outputNode === null || outputNode === void 0 ? void 0 : outputNode.type) === NodeTypesString.FAST_BYPASSER) { + hasTogglerOutput = true; + break; + } + } + const inputNodes = getConnectedInputNodesAndFilterPassThroughs(this); + for (const [index, inputNode] of inputNodes.entries()) { + if ((inputNode === null || inputNode === void 0 ? void 0 : inputNode.type) === NodeTypesString.NODE_MODE_RELAY) { + if (hasTogglerOutput) { + console.log(`Can't be connected to a Relay if also output to a toggler.`); + this.disconnectInput(index); + } + else { + hasRelayInput = true; + if (this.inputs[index]) { + this.inputs[index].color_on = "#FC0"; + this.inputs[index].color_off = "#a80"; + } + } + } + else { + changeModeOfNodes(inputNode, this.mode); + } + } + this.hasTogglerOutput = hasTogglerOutput; + this.hasRelayInput = hasRelayInput; + if (this.hasRelayInput) { + if (this.outputs[0]) { + this.disconnectOutput(0); + this.removeOutput(0); + } + } + else if (!this.outputs[0]) { + this.addOutput("OPT_CONNECTION", "*", { + color_on: "#Fc0", + color_off: "#a80", + }); + } + } + onModeChange(from, to) { + var _a, _b; + super.onModeChange(from, to); + const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this).filter((node) => node.type !== NodeTypesString.NODE_MODE_RELAY); + if (linkedNodes.length) { + for (const node of linkedNodes) { + if (node.type !== NodeTypesString.NODE_MODE_RELAY) { + changeModeOfNodes(node, to); + } + } + } + else if ((_b = (_a = this.graph) === null || _a === void 0 ? void 0 : _a._groups) === null || _b === void 0 ? void 0 : _b.length) { + for (const group of this.graph._groups) { + group.recomputeInsideNodes(); + const groupNodes = getGroupNodes(group); + if (groupNodes === null || groupNodes === void 0 ? void 0 : groupNodes.includes(this)) { + for (const node of groupNodes) { + if (node !== this) { + changeModeOfNodes(node, to); + } + } + } + } + } + } + getHelp() { + return ` +

      + When this node's mode (Mute, Bypass, Active) changes, it will "repeat" that mode to all + connected input nodes, or, if there are no connected nodes AND it is overlapping a group, + "repeat" it's mode to all nodes in that group. +

      +
        +

      • + Optionally, connect this mode's output to a ${stripRgthree(NodeTypesString.FAST_MUTER)} + or ${stripRgthree(NodeTypesString.FAST_BYPASSER)} for a single toggle to quickly + mute/bypass all its connected nodes. +

        • +

      • + Optionally, connect a ${stripRgthree(NodeTypesString.NODE_MODE_RELAY)} to this nodes + inputs to have it automatically toggle its mode. If connected, this will always take + precedence (and disconnect any connected fast togglers). +

        • +
      + `; + } +} +NodeModeRepeater.type = NodeTypesString.NODE_MODE_REPEATER; +NodeModeRepeater.title = NodeTypesString.NODE_MODE_REPEATER; +app.registerExtension({ + name: "rgthree.NodeModeRepeater", + registerCustomNodes() { + addConnectionLayoutSupport(NodeModeRepeater, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + LiteGraph.registerNodeType(NodeModeRepeater.type, NodeModeRepeater); + NodeModeRepeater.category = NodeModeRepeater._category; + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_conductor.js b/custom_nodes/rgthree-comfy/web/comfyui/power_conductor.js new file mode 100644 index 00000000..9c51a2f7 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/power_conductor.js @@ -0,0 +1,149 @@ +var __decorate = (this && this.__decorate) || function (decorators, target, key, desc) { + var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d; + if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc); + else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r; + return c > 3 && r && Object.defineProperty(target, key, r), r; +}; +var __classPrivateFieldSet = (this && this.__classPrivateFieldSet) || function (receiver, state, value, kind, f) { + if (kind === "m") throw new TypeError("Private method is not writable"); + if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a setter"); + if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot write private member to an object whose class did not declare it"); + return (kind === "a" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value; +}; +var __classPrivateFieldGet = (this && this.__classPrivateFieldGet) || function (receiver, state, kind, f) { + if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a getter"); + if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot read private member from an object whose class did not declare it"); + return kind === "m" ? f : kind === "a" ? f.call(receiver) : f ? f.value : state.get(receiver); +}; +var _ComfyNodeWrapper_id, _ComfyWidgetWrapper_widget; +import { app } from "../../scripts/app.js"; +import { Exposed, execute, PyTuple } from "../../rgthree/common/py_parser.js"; +import { RgthreeBaseVirtualNode } from "./base_node.js"; +import { RgthreeBetterButtonWidget } from "./utils_widgets.js"; +import { NodeTypesString } from "./constants.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +import { changeModeOfNodes, getNodeById } from "./utils.js"; +const BUILT_INS = { + node: { + fn: (query) => { + if (typeof query === "number" || /^\d+(\.\d+)?/.exec(query)) { + return new ComfyNodeWrapper(Number(query)); + } + return null; + }, + }, +}; +class RgthreePowerConductor extends RgthreeBaseVirtualNode { + constructor(title = RgthreePowerConductor.title) { + super(title); + this.comfyClass = NodeTypesString.POWER_CONDUCTOR; + this.serialize_widgets = true; + this.codeWidget = ComfyWidgets.STRING(this, "", ["STRING", { multiline: true }], app).widget; + this.addCustomWidget(this.codeWidget); + (this.buttonWidget = new RgthreeBetterButtonWidget("Run", (...args) => { + this.execute(); + })), + this.addCustomWidget(this.buttonWidget); + this.onConstructed(); + } + execute() { + execute(this.codeWidget.value, {}, BUILT_INS); + } +} +RgthreePowerConductor.title = NodeTypesString.POWER_CONDUCTOR; +RgthreePowerConductor.type = NodeTypesString.POWER_CONDUCTOR; +const NODE_CLASS = RgthreePowerConductor; +class ComfyNodeWrapper { + constructor(id) { + _ComfyNodeWrapper_id.set(this, void 0); + __classPrivateFieldSet(this, _ComfyNodeWrapper_id, id, "f"); + } + getNode() { + return getNodeById(__classPrivateFieldGet(this, _ComfyNodeWrapper_id, "f")); + } + get id() { + return this.getNode().id; + } + get title() { + return this.getNode().title; + } + set title(value) { + this.getNode().title = value; + } + get widgets() { + var _a; + return new PyTuple((_a = this.getNode().widgets) === null || _a === void 0 ? void 0 : _a.map((w) => new ComfyWidgetWrapper(w))); + } + get mode() { + return this.getNode().mode; + } + mute() { + changeModeOfNodes(this.getNode(), 2); + } + bypass() { + changeModeOfNodes(this.getNode(), 4); + } + enable() { + changeModeOfNodes(this.getNode(), 0); + } +} +_ComfyNodeWrapper_id = new WeakMap(); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "id", null); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "title", null); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "widgets", null); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "mode", null); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "mute", null); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "bypass", null); +__decorate([ + Exposed +], ComfyNodeWrapper.prototype, "enable", null); +class ComfyWidgetWrapper { + constructor(widget) { + _ComfyWidgetWrapper_widget.set(this, void 0); + __classPrivateFieldSet(this, _ComfyWidgetWrapper_widget, widget, "f"); + } + get value() { + return __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f").value; + } + get label() { + return __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f").label; + } + toggle(value) { + if (typeof __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f")["toggle"] === "function") { + __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f")["toggle"](value); + } + else { + } + } +} +_ComfyWidgetWrapper_widget = new WeakMap(); +__decorate([ + Exposed +], ComfyWidgetWrapper.prototype, "value", null); +__decorate([ + Exposed +], ComfyWidgetWrapper.prototype, "label", null); +__decorate([ + Exposed +], ComfyWidgetWrapper.prototype, "toggle", null); +app.registerExtension({ + name: "rgthree.PowerConductor", + registerCustomNodes() { + if (CONFIG_SERVICE.getConfigValue("unreleased.power_conductor.enabled")) { + NODE_CLASS.setUp(); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_lora_loader.js b/custom_nodes/rgthree-comfy/web/comfyui/power_lora_loader.js new file mode 100644 index 00000000..9380aa71 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/power_lora_loader.js @@ -0,0 +1,592 @@ +var _a; +import { app } from "../../scripts/app.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { rgthree } from "./rgthree.js"; +import { addConnectionLayoutSupport } from "./utils.js"; +import { NodeTypesString } from "./constants.js"; +import { drawInfoIcon, drawNumberWidgetPart, drawRoundedRectangle, drawTogglePart, fitString, isLowQuality, } from "./utils_canvas.js"; +import { RgthreeBaseWidget, RgthreeBetterButtonWidget, RgthreeDividerWidget, } from "./utils_widgets.js"; +import { rgthreeApi } from "../../rgthree/common/rgthree_api.js"; +import { showLoraChooser } from "./utils_menu.js"; +import { moveArrayItem, removeArrayItem } from "../../rgthree/common/shared_utils.js"; +import { RgthreeLoraInfoDialog } from "./dialog_info.js"; +import { LORA_INFO_SERVICE } from "../../rgthree/common/model_info_service.js"; +const PROP_LABEL_SHOW_STRENGTHS = "Show Strengths"; +const PROP_LABEL_SHOW_STRENGTHS_STATIC = `@${PROP_LABEL_SHOW_STRENGTHS}`; +const PROP_VALUE_SHOW_STRENGTHS_SINGLE = "Single Strength"; +const PROP_VALUE_SHOW_STRENGTHS_SEPARATE = "Separate Model & Clip"; +class RgthreePowerLoraLoader extends RgthreeBaseServerNode { + constructor(title = NODE_CLASS.title) { + super(title); + this.serialize_widgets = true; + this.logger = rgthree.newLogSession(`[Power Lora Stack]`); + this.loraWidgetsCounter = 0; + this.widgetButtonSpacer = null; + this.properties[PROP_LABEL_SHOW_STRENGTHS] = PROP_VALUE_SHOW_STRENGTHS_SINGLE; + rgthreeApi.getLoras(); + if (rgthree.loadingApiJson) { + const fullApiJson = rgthree.loadingApiJson; + setTimeout(() => { + this.configureFromApiJson(fullApiJson); + }, 16); + } + } + configureFromApiJson(fullApiJson) { + var _b, _c; + if (this.id == null) { + const [n, v] = this.logger.errorParts("Cannot load from API JSON without node id."); + (_b = console[n]) === null || _b === void 0 ? void 0 : _b.call(console, ...v); + return; + } + const nodeData = fullApiJson[this.id] || fullApiJson[String(this.id)] || fullApiJson[Number(this.id)]; + if (nodeData == null) { + const [n, v] = this.logger.errorParts(`No node found in API JSON for node id ${this.id}.`); + (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v); + return; + } + this.configure({ + widgets_values: Object.values(nodeData.inputs).filter((input) => typeof (input === null || input === void 0 ? void 0 : input["lora"]) === "string"), + }); + } + configure(info) { + var _b; + while ((_b = this.widgets) === null || _b === void 0 ? void 0 : _b.length) + this.removeWidget(0); + this.widgetButtonSpacer = null; + if (info.id != null) { + super.configure(info); + } + this._tempWidth = this.size[0]; + this._tempHeight = this.size[1]; + for (const widgetValue of info.widgets_values || []) { + if ((widgetValue === null || widgetValue === void 0 ? void 0 : widgetValue.lora) !== undefined) { + const widget = this.addNewLoraWidget(); + widget.value = { ...widgetValue }; + } + } + this.addNonLoraWidgets(); + this.size[0] = this._tempWidth; + this.size[1] = Math.max(this._tempHeight, this.computeSize()[1]); + } + onNodeCreated() { + var _b; + (_b = super.onNodeCreated) === null || _b === void 0 ? void 0 : _b.call(this); + this.addNonLoraWidgets(); + const computed = this.computeSize(); + this.size = this.size || [0, 0]; + this.size[0] = Math.max(this.size[0], computed[0]); + this.size[1] = Math.max(this.size[1], computed[1]); + this.setDirtyCanvas(true, true); + } + addNewLoraWidget(lora) { + this.loraWidgetsCounter++; + const widget = this.addCustomWidget(new PowerLoraLoaderWidget("lora_" + this.loraWidgetsCounter)); + if (lora) + widget.setLora(lora); + if (this.widgetButtonSpacer) { + moveArrayItem(this.widgets, widget, this.widgets.indexOf(this.widgetButtonSpacer)); + } + return widget; + } + addNonLoraWidgets() { + moveArrayItem(this.widgets, this.addCustomWidget(new RgthreeDividerWidget({ marginTop: 4, marginBottom: 0, thickness: 0 })), 0); + moveArrayItem(this.widgets, this.addCustomWidget(new PowerLoraLoaderHeaderWidget()), 1); + this.widgetButtonSpacer = this.addCustomWidget(new RgthreeDividerWidget({ marginTop: 4, marginBottom: 0, thickness: 0 })); + this.addCustomWidget(new RgthreeBetterButtonWidget("βž• Add Lora", (event, pos, node) => { + rgthreeApi.getLoras().then((lorasDetails) => { + const loras = lorasDetails.map((l) => l.file); + showLoraChooser(event, (value) => { + var _b; + if (typeof value === "string") { + if (value.includes("Power Lora Chooser")) { + } + else if (value !== "NONE") { + this.addNewLoraWidget(value); + const computed = this.computeSize(); + const tempHeight = (_b = this._tempHeight) !== null && _b !== void 0 ? _b : 15; + this.size[1] = Math.max(tempHeight, computed[1]); + this.setDirtyCanvas(true, true); + } + } + }, null, [...loras]); + }); + return true; + })); + } + getSlotInPosition(canvasX, canvasY) { + var _b; + const slot = super.getSlotInPosition(canvasX, canvasY); + if (!slot) { + let lastWidget = null; + for (const widget of this.widgets) { + if (!widget.last_y) + return; + if (canvasY > this.pos[1] + widget.last_y) { + lastWidget = widget; + continue; + } + break; + } + if ((_b = lastWidget === null || lastWidget === void 0 ? void 0 : lastWidget.name) === null || _b === void 0 ? void 0 : _b.startsWith("lora_")) { + return { widget: lastWidget, output: { type: "LORA WIDGET" } }; + } + } + return slot; + } + getSlotMenuOptions(slot) { + var _b, _c, _d, _e, _f, _g; + if ((_c = (_b = slot === null || slot === void 0 ? void 0 : slot.widget) === null || _b === void 0 ? void 0 : _b.name) === null || _c === void 0 ? void 0 : _c.startsWith("lora_")) { + const widget = slot.widget; + const index = this.widgets.indexOf(widget); + const canMoveUp = !!((_e = (_d = this.widgets[index - 1]) === null || _d === void 0 ? void 0 : _d.name) === null || _e === void 0 ? void 0 : _e.startsWith("lora_")); + const canMoveDown = !!((_g = (_f = this.widgets[index + 1]) === null || _f === void 0 ? void 0 : _f.name) === null || _g === void 0 ? void 0 : _g.startsWith("lora_")); + const menuItems = [ + { + content: `ℹ️ Show Info`, + callback: () => { + widget.showLoraInfoDialog(); + }, + }, + null, + { + content: `${widget.value.on ? "⚫" : "🟒"} Toggle ${widget.value.on ? "Off" : "On"}`, + callback: () => { + widget.value.on = !widget.value.on; + }, + }, + { + content: `⬆️ Move Up`, + disabled: !canMoveUp, + callback: () => { + moveArrayItem(this.widgets, widget, index - 1); + }, + }, + { + content: `⬇️ Move Down`, + disabled: !canMoveDown, + callback: () => { + moveArrayItem(this.widgets, widget, index + 1); + }, + }, + { + content: `πŸ—‘οΈ Remove`, + callback: () => { + removeArrayItem(this.widgets, widget); + }, + }, + ]; + new LiteGraph.ContextMenu(menuItems, { + title: "LORA WIDGET", + event: rgthree.lastCanvasMouseEvent, + }); + return undefined; + } + return this.defaultGetSlotMenuOptions(slot); + } + refreshComboInNode(defs) { + rgthreeApi.getLoras(true); + } + hasLoraWidgets() { + var _b; + return !!((_b = this.widgets) === null || _b === void 0 ? void 0 : _b.find((w) => { var _b; return (_b = w.name) === null || _b === void 0 ? void 0 : _b.startsWith("lora_"); })); + } + allLorasState() { + var _b, _c, _d; + let allOn = true; + let allOff = true; + for (const widget of this.widgets) { + if ((_b = widget.name) === null || _b === void 0 ? void 0 : _b.startsWith("lora_")) { + const on = (_c = widget.value) === null || _c === void 0 ? void 0 : _c.on; + allOn = allOn && on === true; + allOff = allOff && on === false; + if (!allOn && !allOff) { + return null; + } + } + } + return allOn && ((_d = this.widgets) === null || _d === void 0 ? void 0 : _d.length) ? true : false; + } + toggleAllLoras() { + var _b, _c; + const allOn = this.allLorasState(); + const toggledTo = !allOn ? true : false; + for (const widget of this.widgets) { + if (((_b = widget.name) === null || _b === void 0 ? void 0 : _b.startsWith("lora_")) && ((_c = widget.value) === null || _c === void 0 ? void 0 : _c.on) != null) { + widget.value.on = toggledTo; + } + } + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + addConnectionLayoutSupport(NODE_CLASS, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + NODE_CLASS.category = comfyClass.category; + }); + } + getHelp() { + return ` +

      + The ${this.type.replace("(rgthree)", "")} is a powerful node that condenses 100s of pixels + of functionality in a single, dynamic node that allows you to add loras, change strengths, + and quickly toggle on/off all without taking up half your screen. +

      +
        +

      • + Add as many Lora's as you would like by clicking the "+ Add Lora" button. + There's no real limit! +

        • +

      • + Right-click on a Lora widget for special options to move the lora up or down + (no image affect, only presentational), toggle it on/off, or delete the row all together. +

        • +
      • +

        + Properties. You can change the following properties (by right-clicking + on the node, and select "Properties" or "Properties Panel" from the menu): +

        +
          +

        • + ${PROP_LABEL_SHOW_STRENGTHS} - Change between showing a single, simple + strength (which will be used for both model and clip), or a more advanced view with + both model and clip strengths being modifiable. +

          • +
        +
        • +
      `; + } +} +_a = PROP_LABEL_SHOW_STRENGTHS_STATIC; +RgthreePowerLoraLoader.title = NodeTypesString.POWER_LORA_LOADER; +RgthreePowerLoraLoader.type = NodeTypesString.POWER_LORA_LOADER; +RgthreePowerLoraLoader.comfyClass = NodeTypesString.POWER_LORA_LOADER; +RgthreePowerLoraLoader[_a] = { + type: "combo", + values: [PROP_VALUE_SHOW_STRENGTHS_SINGLE, PROP_VALUE_SHOW_STRENGTHS_SEPARATE], +}; +class PowerLoraLoaderHeaderWidget extends RgthreeBaseWidget { + constructor(name = "PowerLoraLoaderHeaderWidget") { + super(name); + this.value = { type: "PowerLoraLoaderHeaderWidget" }; + this.type = "custom"; + this.hitAreas = { + toggle: { bounds: [0, 0], onDown: this.onToggleDown }, + }; + this.showModelAndClip = null; + } + draw(ctx, node, w, posY, height) { + if (!node.hasLoraWidgets()) { + return; + } + this.showModelAndClip = + node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE; + const margin = 10; + const innerMargin = margin * 0.33; + const lowQuality = isLowQuality(); + const allLoraState = node.allLorasState(); + posY += 2; + const midY = posY + height * 0.5; + let posX = 10; + ctx.save(); + this.hitAreas.toggle.bounds = drawTogglePart(ctx, { posX, posY, height, value: allLoraState }); + if (!lowQuality) { + posX += this.hitAreas.toggle.bounds[1] + innerMargin; + ctx.globalAlpha = app.canvas.editor_alpha * 0.55; + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillText("Toggle All", posX, midY); + let rposX = node.size[0] - margin - innerMargin - innerMargin; + ctx.textAlign = "center"; + ctx.fillText(this.showModelAndClip ? "Clip" : "Strength", rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, midY); + if (this.showModelAndClip) { + rposX = rposX - drawNumberWidgetPart.WIDTH_TOTAL - innerMargin * 2; + ctx.fillText("Model", rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, midY); + } + } + ctx.restore(); + } + onToggleDown(event, pos, node) { + node.toggleAllLoras(); + this.cancelMouseDown(); + return true; + } +} +const DEFAULT_LORA_WIDGET_DATA = { + on: true, + lora: null, + strength: 1, + strengthTwo: null, +}; +class PowerLoraLoaderWidget extends RgthreeBaseWidget { + constructor(name) { + super(name); + this.type = "custom"; + this.haveMouseMovedStrength = false; + this.loraInfoPromise = null; + this.loraInfo = null; + this.showModelAndClip = null; + this.hitAreas = { + toggle: { bounds: [0, 0], onDown: this.onToggleDown }, + lora: { bounds: [0, 0], onClick: this.onLoraClick }, + strengthDec: { bounds: [0, 0], onClick: this.onStrengthDecDown }, + strengthVal: { bounds: [0, 0], onClick: this.onStrengthValUp }, + strengthInc: { bounds: [0, 0], onClick: this.onStrengthIncDown }, + strengthAny: { bounds: [0, 0], onMove: this.onStrengthAnyMove }, + strengthTwoDec: { bounds: [0, 0], onClick: this.onStrengthTwoDecDown }, + strengthTwoVal: { bounds: [0, 0], onClick: this.onStrengthTwoValUp }, + strengthTwoInc: { bounds: [0, 0], onClick: this.onStrengthTwoIncDown }, + strengthTwoAny: { bounds: [0, 0], onMove: this.onStrengthTwoAnyMove }, + }; + this._value = { + on: true, + lora: null, + strength: 1, + strengthTwo: null, + }; + } + set value(v) { + this._value = v; + if (typeof this._value !== "object") { + this._value = { ...DEFAULT_LORA_WIDGET_DATA }; + if (this.showModelAndClip) { + this._value.strengthTwo = this._value.strength; + } + } + this.getLoraInfo(); + } + get value() { + return this._value; + } + setLora(lora) { + this._value.lora = lora; + this.getLoraInfo(); + } + draw(ctx, node, w, posY, height) { + var _b, _c, _d, _e, _f, _g, _h, _j, _k, _l, _m, _o, _p; + let currentShowModelAndClip = node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE; + if (this.showModelAndClip !== currentShowModelAndClip) { + let oldShowModelAndClip = this.showModelAndClip; + this.showModelAndClip = currentShowModelAndClip; + if (this.showModelAndClip) { + if (oldShowModelAndClip != null) { + this.value.strengthTwo = (_b = this.value.strength) !== null && _b !== void 0 ? _b : 1; + } + } + else { + this.value.strengthTwo = null; + this.hitAreas.strengthTwoDec.bounds = [0, -1]; + this.hitAreas.strengthTwoVal.bounds = [0, -1]; + this.hitAreas.strengthTwoInc.bounds = [0, -1]; + this.hitAreas.strengthTwoAny.bounds = [0, -1]; + } + } + ctx.save(); + const margin = 10; + const innerMargin = margin * 0.33; + const lowQuality = isLowQuality(); + const midY = posY + height * 0.5; + let posX = margin; + drawRoundedRectangle(ctx, { pos: [posX, posY], size: [node.size[0] - margin * 2, height] }); + this.hitAreas.toggle.bounds = drawTogglePart(ctx, { posX, posY, height, value: this.value.on }); + posX += this.hitAreas.toggle.bounds[1] + innerMargin; + if (lowQuality) { + ctx.restore(); + return; + } + if (!this.value.on) { + ctx.globalAlpha = app.canvas.editor_alpha * 0.4; + } + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + let rposX = node.size[0] - margin - innerMargin - innerMargin; + const strengthValue = this.showModelAndClip + ? ((_c = this.value.strengthTwo) !== null && _c !== void 0 ? _c : 1) + : ((_d = this.value.strength) !== null && _d !== void 0 ? _d : 1); + let textColor = undefined; + if (((_e = this.loraInfo) === null || _e === void 0 ? void 0 : _e.strengthMax) != null && strengthValue > ((_f = this.loraInfo) === null || _f === void 0 ? void 0 : _f.strengthMax)) { + textColor = "#c66"; + } + else if (((_g = this.loraInfo) === null || _g === void 0 ? void 0 : _g.strengthMin) != null && strengthValue < ((_h = this.loraInfo) === null || _h === void 0 ? void 0 : _h.strengthMin)) { + textColor = "#c66"; + } + const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, { + posX: node.size[0] - margin - innerMargin - innerMargin, + posY, + height, + value: strengthValue, + direction: -1, + textColor, + }); + this.hitAreas.strengthDec.bounds = leftArrow; + this.hitAreas.strengthVal.bounds = text; + this.hitAreas.strengthInc.bounds = rightArrow; + this.hitAreas.strengthAny.bounds = [leftArrow[0], rightArrow[0] + rightArrow[1] - leftArrow[0]]; + rposX = leftArrow[0] - innerMargin; + if (this.showModelAndClip) { + rposX -= innerMargin; + this.hitAreas.strengthTwoDec.bounds = this.hitAreas.strengthDec.bounds; + this.hitAreas.strengthTwoVal.bounds = this.hitAreas.strengthVal.bounds; + this.hitAreas.strengthTwoInc.bounds = this.hitAreas.strengthInc.bounds; + this.hitAreas.strengthTwoAny.bounds = this.hitAreas.strengthAny.bounds; + let textColor = undefined; + if (((_j = this.loraInfo) === null || _j === void 0 ? void 0 : _j.strengthMax) != null && this.value.strength > ((_k = this.loraInfo) === null || _k === void 0 ? void 0 : _k.strengthMax)) { + textColor = "#c66"; + } + else if (((_l = this.loraInfo) === null || _l === void 0 ? void 0 : _l.strengthMin) != null && + this.value.strength < ((_m = this.loraInfo) === null || _m === void 0 ? void 0 : _m.strengthMin)) { + textColor = "#c66"; + } + const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, { + posX: rposX, + posY, + height, + value: (_o = this.value.strength) !== null && _o !== void 0 ? _o : 1, + direction: -1, + textColor, + }); + this.hitAreas.strengthDec.bounds = leftArrow; + this.hitAreas.strengthVal.bounds = text; + this.hitAreas.strengthInc.bounds = rightArrow; + this.hitAreas.strengthAny.bounds = [ + leftArrow[0], + rightArrow[0] + rightArrow[1] - leftArrow[0], + ]; + rposX = leftArrow[0] - innerMargin; + } + const infoIconSize = height * 0.66; + const infoWidth = infoIconSize + innerMargin + innerMargin; + if (this.hitAreas["info"]) { + rposX -= innerMargin; + drawInfoIcon(ctx, rposX - infoIconSize, posY + (height - infoIconSize) / 2, infoIconSize); + this.hitAreas.info.bounds = [rposX - infoIconSize, infoWidth]; + rposX = rposX - infoIconSize - innerMargin; + } + const loraWidth = rposX - posX; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + const loraLabel = String(((_p = this.value) === null || _p === void 0 ? void 0 : _p.lora) || "None"); + ctx.fillText(fitString(ctx, loraLabel, loraWidth), posX, midY); + this.hitAreas.lora.bounds = [posX, loraWidth]; + posX += loraWidth + innerMargin; + ctx.globalAlpha = app.canvas.editor_alpha; + ctx.restore(); + } + serializeValue(node, index) { + var _b; + const v = { ...this.value }; + if (!this.showModelAndClip) { + delete v.strengthTwo; + } + else { + this.value.strengthTwo = (_b = this.value.strengthTwo) !== null && _b !== void 0 ? _b : 1; + v.strengthTwo = this.value.strengthTwo; + } + return v; + } + onToggleDown(event, pos, node) { + this.value.on = !this.value.on; + this.cancelMouseDown(); + return true; + } + onInfoDown(event, pos, node) { + this.showLoraInfoDialog(); + } + onLoraClick(event, pos, node) { + showLoraChooser(event, (value) => { + if (typeof value === "string") { + this.value.lora = value; + this.loraInfo = null; + this.getLoraInfo(); + } + node.setDirtyCanvas(true, true); + }); + this.cancelMouseDown(); + } + onStrengthDecDown(event, pos, node) { + this.stepStrength(-1, false); + } + onStrengthIncDown(event, pos, node) { + this.stepStrength(1, false); + } + onStrengthTwoDecDown(event, pos, node) { + this.stepStrength(-1, true); + } + onStrengthTwoIncDown(event, pos, node) { + this.stepStrength(1, true); + } + onStrengthAnyMove(event, pos, node) { + this.doOnStrengthAnyMove(event, false); + } + onStrengthTwoAnyMove(event, pos, node) { + this.doOnStrengthAnyMove(event, true); + } + doOnStrengthAnyMove(event, isTwo = false) { + var _b; + if (event.deltaX) { + let prop = isTwo ? "strengthTwo" : "strength"; + this.haveMouseMovedStrength = true; + this.value[prop] = ((_b = this.value[prop]) !== null && _b !== void 0 ? _b : 1) + event.deltaX * 0.05; + } + } + onStrengthValUp(event, pos, node) { + this.doOnStrengthValUp(event, false); + } + onStrengthTwoValUp(event, pos, node) { + this.doOnStrengthValUp(event, true); + } + doOnStrengthValUp(event, isTwo = false) { + if (this.haveMouseMovedStrength) + return; + let prop = isTwo ? "strengthTwo" : "strength"; + const canvas = app.canvas; + canvas.prompt("Value", this.value[prop], (v) => (this.value[prop] = Number(v)), event); + } + onMouseUp(event, pos, node) { + super.onMouseUp(event, pos, node); + this.haveMouseMovedStrength = false; + } + showLoraInfoDialog() { + if (!this.value.lora || this.value.lora === "None") { + return; + } + const infoDialog = new RgthreeLoraInfoDialog(this.value.lora).show(); + infoDialog.addEventListener("close", ((e) => { + if (e.detail.dirty) { + this.getLoraInfo(true); + } + })); + } + stepStrength(direction, isTwo = false) { + var _b; + let step = 0.05; + let prop = isTwo ? "strengthTwo" : "strength"; + let strength = ((_b = this.value[prop]) !== null && _b !== void 0 ? _b : 1) + step * direction; + this.value[prop] = Math.round(strength * 100) / 100; + } + getLoraInfo(force = false) { + if (!this.loraInfoPromise || force == true) { + let promise; + if (this.value.lora && this.value.lora != "None") { + promise = LORA_INFO_SERVICE.getInfo(this.value.lora, force, true); + } + else { + promise = Promise.resolve(null); + } + this.loraInfoPromise = promise.then((v) => (this.loraInfo = v)); + } + return this.loraInfoPromise; + } +} +const NODE_CLASS = RgthreePowerLoraLoader; +app.registerExtension({ + name: "rgthree.PowerLoraLoader", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_primitive.js b/custom_nodes/rgthree-comfy/web/comfyui/power_primitive.js new file mode 100644 index 00000000..eb55e8e9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/power_primitive.js @@ -0,0 +1,175 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { moveArrayItem } from "../../rgthree/common/shared_utils.js"; +const PROPERTY_HIDE_TYPE_SELECTOR = "hideTypeSelector"; +const PRIMITIVES = { + STRING: "STRING", + INT: "INT", + FLOAT: "FLOAT", + BOOLEAN: "BOOLEAN", +}; +class RgthreePowerPrimitive extends RgthreeBaseServerNode { + constructor(title = NODE_CLASS.title) { + super(title); + this.typeState = ''; + this.properties[PROPERTY_HIDE_TYPE_SELECTOR] = false; + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + onNodeCreated() { + var _a; + (_a = super.onNodeCreated) === null || _a === void 0 ? void 0 : _a.call(this); + this.addInitialWidgets(); + } + configure(info) { + super.configure(info); + if (this.outputTypeWidget.value === 'BOOL') { + this.outputTypeWidget.value = 'BOOLEAN'; + } + setTimeout(() => { + this.setTypedData(); + }); + } + getExtraMenuOptions(canvas, options) { + const that = this; + super.getExtraMenuOptions(canvas, options); + const isHidden = !!this.properties[PROPERTY_HIDE_TYPE_SELECTOR]; + const menuItems = [ + { + content: `${isHidden ? "Show" : "Hide"} Type Selector Widget`, + callback: (...args) => { + this.setProperty(PROPERTY_HIDE_TYPE_SELECTOR, !this.properties[PROPERTY_HIDE_TYPE_SELECTOR]); + }, + }, + { + content: `Set type`, + submenu: { + options: Object.keys(PRIMITIVES), + callback(value, ...args) { + that.outputTypeWidget.value = value; + that.setTypedData(); + }, + }, + }, + ]; + options.splice(0, 0, ...menuItems, null); + return []; + } + addInitialWidgets() { + if (!this.outputTypeWidget) { + this.outputTypeWidget = this.addWidget("combo", "type", "STRING", (...args) => { + this.setTypedData(); + }, { + values: Object.keys(PRIMITIVES), + }); + this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR]; + } + this.setTypedData(); + } + setTypedData() { + var _a, _b, _c, _d, _e; + const name = "value"; + const type = this.outputTypeWidget.value; + const linked = !!((_b = (_a = this.inputs) === null || _a === void 0 ? void 0 : _a[0]) === null || _b === void 0 ? void 0 : _b.link); + const newTypeState = `${type}|${linked}`; + if (this.typeState == newTypeState) + return; + this.typeState = newTypeState; + let value = (_d = (_c = this.valueWidget) === null || _c === void 0 ? void 0 : _c.value) !== null && _d !== void 0 ? _d : null; + let newWidget = null; + if (linked) { + newWidget = ComfyWidgets["STRING"](this, name, ["STRING"], app).widget; + newWidget.value = ""; + } + else if (type == "STRING") { + newWidget = ComfyWidgets["STRING"](this, name, ["STRING", { multiline: true }], app).widget; + newWidget.value = value ? "" : String(value); + } + else if (type === "INT" || type === "FLOAT") { + const isFloat = type === "FLOAT"; + newWidget = this.addWidget("number", name, value !== null && value !== void 0 ? value : 1, undefined, { + precision: isFloat ? 1 : 0, + step2: isFloat ? 0.1 : 0, + }); + value = Number(value); + value = value == null || isNaN(value) ? 0 : value; + newWidget.value = value; + } + else if (type === "BOOLEAN") { + newWidget = this.addWidget("toggle", name, !!(value !== null && value !== void 0 ? value : true), undefined, { + on: "true", + off: "false", + }); + if (typeof value === "string") { + value = !["false", "null", "None", "", "0"].includes(value.toLowerCase()); + } + newWidget.value = !!value; + } + if (newWidget == null) { + throw new Error(`Unsupported type "${type}".`); + } + if (this.valueWidget) { + this.replaceWidget(this.valueWidget, newWidget); + } + else { + if (!this.widgets.includes(newWidget)) { + this.addCustomWidget(newWidget); + } + moveArrayItem(this.widgets, newWidget, 1); + } + this.valueWidget = newWidget; + if (!((_e = this.inputs) === null || _e === void 0 ? void 0 : _e.length)) { + this.addInput("value", "*", { widget: this.valueWidget }); + } + else { + this.inputs[0].widget = this.valueWidget; + } + const output = this.outputs[0]; + const outputLabel = output.label === "*" || output.label === output.type ? null : output.label; + output.type = type; + output.label = outputLabel || output.type; + } + onConnectionsChange(type, index, isConnected, link_info, inputOrOutput) { + var _a; + (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]); + if (this.inputs.includes(inputOrOutput)) { + this.setTypedData(); + } + } + onPropertyChanged(name, value, prev_value) { + if (name === PROPERTY_HIDE_TYPE_SELECTOR) { + if (!this.outputTypeWidget) { + return true; + } + this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR]; + if (this.outputTypeWidget.hidden) { + this.outputTypeWidget.computeLayoutSize = () => ({ + minHeight: 0, + minWidth: 0, + maxHeight: 0, + maxWidth: 0, + }); + } + else { + this.outputTypeWidget.computeLayoutSize = undefined; + } + } + return true; + } +} +RgthreePowerPrimitive.title = NodeTypesString.POWER_PRIMITIVE; +RgthreePowerPrimitive.type = NodeTypesString.POWER_PRIMITIVE; +RgthreePowerPrimitive.comfyClass = NodeTypesString.POWER_PRIMITIVE; +RgthreePowerPrimitive["@hideTypeSelector"] = { type: "boolean" }; +const NODE_CLASS = RgthreePowerPrimitive; +app.registerExtension({ + name: "rgthree.PowerPrimitive", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_prompt.js b/custom_nodes/rgthree-comfy/web/comfyui/power_prompt.js new file mode 100644 index 00000000..c82ac944 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/power_prompt.js @@ -0,0 +1,42 @@ +import { app } from "../../scripts/app.js"; +import { addConnectionLayoutSupport } from "./utils.js"; +import { PowerPrompt } from "./base_power_prompt.js"; +import { NodeTypesString } from "./constants.js"; +let nodeData = null; +app.registerExtension({ + name: "rgthree.PowerPrompt", + async beforeRegisterNodeDef(nodeType, passedNodeData) { + if (passedNodeData.name.includes("Power Prompt") && passedNodeData.name.includes("rgthree")) { + nodeData = passedNodeData; + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + this.powerPrompt = new PowerPrompt(this, passedNodeData); + }; + addConnectionLayoutSupport(nodeType, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + } + }, + async loadedGraphNode(node) { + if (node.type === NodeTypesString.POWER_PROMPT) { + setTimeout(() => { + if (node.outputs[0].type === "STRING") { + if (node.outputs[0].links) { + node.outputs[3].links = node.outputs[3].links || []; + for (const link of node.outputs[0].links) { + node.outputs[3].links.push(link); + (node.graph || app.graph).links[link].origin_slot = 3; + } + node.outputs[0].links = null; + } + node.outputs[0].type = nodeData.output[0]; + node.outputs[0].name = nodeData.output_name[0] || node.outputs[0].type; + node.outputs[0].color_on = undefined; + node.outputs[0].color_off = undefined; + } + }, 50); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_puter.js b/custom_nodes/rgthree-comfy/web/comfyui/power_puter.js new file mode 100644 index 00000000..52231b3a --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/power_puter.js @@ -0,0 +1,314 @@ +import { app } from "../../scripts/app.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js"; +import { debounce } from "../../rgthree/common/shared_utils.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { RgthreeBaseWidget } from "./utils_widgets.js"; +import { drawPlusIcon, drawRoundedRectangle, drawWidgetButton, isLowQuality, measureText, } from "./utils_canvas.js"; +import { rgthree } from "./rgthree.js"; +const ALPHABET = "abcdefghijklmnopqrstuv".split(""); +const OUTPUT_TYPES = ["STRING", "INT", "FLOAT", "BOOLEAN", "*"]; +class RgthreePowerPuter extends RgthreeBaseServerNode { + constructor(title = NODE_CLASS.title) { + super(title); + this.stabilizeBound = this.stabilize.bind(this); + this.addAnyInput(2); + this.addInitialWidgets(); + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS); + } + onConnectionsChange(...args) { + var _a; + (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]); + this.scheduleStabilize(); + } + scheduleStabilize(ms = 64) { + return debounce(this.stabilizeBound, ms); + } + stabilize() { + removeUnusedInputsFromEnd(this, 1); + this.addAnyInput(); + this.setOutputs(); + } + addInitialWidgets() { + if (!this.outputTypeWidget) { + this.outputTypeWidget = this.addCustomWidget(new OutputsWidget("outputs", this)); + this.expressionWidget = ComfyWidgets["STRING"](this, "code", ["STRING", { multiline: true }], app).widget; + } + } + addAnyInput(num = 1) { + for (let i = 0; i < num; i++) { + this.addInput(ALPHABET[this.inputs.length], "*"); + } + } + setOutputs() { + const desiredOutputs = this.outputTypeWidget.value.outputs; + for (let i = 0; i < Math.max(this.outputs.length, desiredOutputs.length); i++) { + const desired = desiredOutputs[i]; + let output = this.outputs[i]; + if (!desired && output) { + this.disconnectOutput(i); + this.removeOutput(i); + continue; + } + output = output || this.addOutput("", ""); + const outputLabel = output.label === "*" || output.label === output.type ? null : output.label; + output.type = String(desired); + output.label = outputLabel || output.type; + } + } + getHelp() { + return ` +

      + The ${this.type.replace("(rgthree)", "")} is a powerful and versatile node that opens the + door for a wide range of utility by offering mult-line code parsing for output. This node + can be used for simple string concatenation, or math operations; to an image dimension or a + node's widgets with advanced list comprehension. + If you want to output something in your workflow, this is the node to do it. +

      + +
        +

      • + Evaluate almost any kind of input and more, and choose your output from INT, FLOAT, + STRING, or BOOLEAN. +

        • +

      • + Connect some nodes and do simply math operations like a + b or + ceil(1 / 2). +

        • +

      • + Or do more advanced things, like input an image, and get the width like + a.shape[2]. +

        • +

      • + Even more powerful, you can target nodes in the prompt that's sent to the backend. For + instance; if you have a Power Lora Loader node at id #5, and want to get a comma-delimited + list of the enabled loras, you could enter + ', '.join([v.lora for v in node(5).inputs.values() if 'lora' in v and v.on]). +

        • +

      • + See more at the rgthree-comfy + wiki. +

        • +
      `; + } +} +RgthreePowerPuter.title = NodeTypesString.POWER_PUTER; +RgthreePowerPuter.type = NodeTypesString.POWER_PUTER; +RgthreePowerPuter.comfyClass = NodeTypesString.POWER_PUTER; +const NODE_CLASS = RgthreePowerPuter; +const OUTPUTS_WIDGET_CHIP_HEIGHT = LiteGraph.NODE_WIDGET_HEIGHT - 4; +const OUTPUTS_WIDGET_CHIP_SPACE = 4; +const OUTPUTS_WIDGET_CHIP_ARROW_WIDTH = 5.5; +const OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT = 4; +class OutputsWidget extends RgthreeBaseWidget { + constructor(name, node) { + super(name); + this.type = "custom"; + this._value = { outputs: ["STRING"] }; + this.rows = 1; + this.neededHeight = LiteGraph.NODE_WIDGET_HEIGHT + 8; + this.hitAreas = { + add: { bounds: [0, 0], onClick: this.onAddChipDown }, + output0: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 0 } }, + output1: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 1 } }, + output2: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 2 } }, + output3: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 3 } }, + output4: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 4 } }, + output5: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 5 } }, + output6: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 6 } }, + output7: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 7 } }, + output8: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 8 } }, + output9: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 9 } }, + }; + this.node = node; + } + set value(v) { + let outputs = typeof v === "string" ? [v] : [...v.outputs]; + outputs = outputs.map((o) => (o === "BOOL" ? "BOOLEAN" : o)); + this._value.outputs = outputs; + } + get value() { + return this._value; + } + onAddChipDown(event, pos, node, bounds) { + new LiteGraph.ContextMenu(OUTPUT_TYPES, { + event: event, + title: "Add an output", + className: "rgthree-dark", + callback: (value) => { + if (isLowQuality()) + return; + if (typeof value === "string" && OUTPUT_TYPES.includes(value)) { + this._value.outputs.push(value); + this.node.scheduleStabilize(); + } + }, + }); + this.cancelMouseDown(); + return true; + } + onOutputChipDown(event, pos, node, bounds) { + const options = [...OUTPUT_TYPES]; + if (this.value.outputs.length > 1) { + options.push(null, "πŸ—‘οΈ Delete"); + } + new LiteGraph.ContextMenu(options, { + event: event, + title: `Edit output #${bounds.data.index + 1}`, + className: "rgthree-dark", + callback: (value) => { + var _a, _b; + const index = bounds.data.index; + if (typeof value !== "string" || value === this._value.outputs[index] || isLowQuality()) { + return; + } + const output = this.node.outputs[index]; + if (value.toLocaleLowerCase().includes("delete")) { + if ((_a = output.links) === null || _a === void 0 ? void 0 : _a.length) { + rgthree.showMessage({ + id: "puter-remove-linked-output", + type: "warn", + message: "[Power Puter] Removed and disconnected output from that was connected!", + timeout: 3000, + }); + this.node.disconnectOutput(index); + } + this.node.removeOutput(index); + this._value.outputs.splice(index, 1); + this.node.scheduleStabilize(); + return; + } + if (((_b = output.links) === null || _b === void 0 ? void 0 : _b.length) && value !== "*") { + rgthree.showMessage({ + id: "puter-remove-linked-output", + type: "warn", + message: "[Power Puter] Changing output type of linked output! You should check for" + + " compatibility.", + timeout: 3000, + }); + } + this._value.outputs[index] = value; + this.node.scheduleStabilize(); + }, + }); + this.cancelMouseDown(); + return true; + } + computeLayoutSize(node) { + this.neededHeight = + OUTPUTS_WIDGET_CHIP_SPACE + + (OUTPUTS_WIDGET_CHIP_HEIGHT + OUTPUTS_WIDGET_CHIP_SPACE) * this.rows; + return { + minHeight: this.neededHeight, + maxHeight: this.neededHeight, + minWidth: 0, + }; + } + draw(ctx, node, w, posY, height) { + var _a, _b; + ctx.save(); + height = this.neededHeight; + const margin = 10; + const innerMargin = margin * 0.33; + const width = node.size[0] - margin * 2; + let borderRadius = LiteGraph.NODE_WIDGET_HEIGHT * 0.5; + let midY = posY + height * 0.5; + let posX = margin; + let rposX = node.size[0] - margin; + drawRoundedRectangle(ctx, { pos: [posX, posY], size: [width, height], borderRadius }); + posX += innerMargin * 2; + rposX -= innerMargin * 2; + if (isLowQuality()) { + ctx.restore(); + return; + } + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillText("outputs", posX, midY); + posX += measureText(ctx, "outputs") + innerMargin * 2; + ctx.stroke(new Path2D(`M ${posX} ${posY} v ${height}`)); + posX += 1 + innerMargin * 2; + const inititalPosX = posX; + posY += OUTPUTS_WIDGET_CHIP_SPACE; + height = OUTPUTS_WIDGET_CHIP_HEIGHT; + borderRadius = height * 0.5; + midY = posY + height / 2; + ctx.textAlign = "center"; + ctx.lineJoin = ctx.lineCap = "round"; + ctx.fillStyle = ctx.strokeStyle = LiteGraph.WIDGET_TEXT_COLOR; + let rows = 1; + const values = (_b = (_a = this.value) === null || _a === void 0 ? void 0 : _a.outputs) !== null && _b !== void 0 ? _b : []; + const fontSize = ctx.font.match(/(\d+)px/); + if (fontSize === null || fontSize === void 0 ? void 0 : fontSize[1]) { + ctx.font = ctx.font.replace(fontSize[1], `${Number(fontSize[1]) - 2}`); + } + let i = 0; + for (i; i < values.length; i++) { + const hitArea = this.hitAreas[`output${i}`]; + const isClicking = !!hitArea.wasMouseClickedAndIsOver; + hitArea.data.index = i; + const text = values[i]; + const textWidth = measureText(ctx, text) + innerMargin * 2; + const width = textWidth + OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 5; + if (posX + width >= rposX) { + posX = inititalPosX; + posY = posY + height + 4; + midY = posY + height / 2; + rows++; + } + drawWidgetButton(ctx, { pos: [posX, posY], size: [width, height], borderRadius }, null, isClicking); + const startX = posX; + posX += innerMargin * 2; + const newMidY = midY + (isClicking ? 1 : 0); + ctx.fillText(text, posX + textWidth / 2, newMidY); + posX += textWidth + innerMargin; + const arrow = new Path2D(`M${posX} ${newMidY - OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT / 2} + h${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH} + l-${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH / 2} ${OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT} z`); + ctx.fill(arrow); + ctx.stroke(arrow); + posX += OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 2; + hitArea.bounds = [startX, posY, width, height]; + posX += OUTPUTS_WIDGET_CHIP_SPACE; + } + for (i; i < 9; i++) { + const hitArea = this.hitAreas[`output${i}`]; + if (hitArea.bounds[0] > 0) { + hitArea.bounds = [0, 0, 0, 0]; + } + } + const addHitArea = this.hitAreas["add"]; + if (this.value.outputs.length < 10) { + const isClicking = !!addHitArea.wasMouseClickedAndIsOver; + const plusSize = 10; + let plusWidth = innerMargin * 2 + plusSize + innerMargin * 2; + if (posX + plusWidth >= rposX) { + posX = inititalPosX; + posY = posY + height + 4; + midY = posY + height / 2; + rows++; + } + drawWidgetButton(ctx, { size: [plusWidth, height], pos: [posX, posY], borderRadius }, null, isClicking); + drawPlusIcon(ctx, posX + innerMargin * 2, midY + (isClicking ? 1 : 0), plusSize); + addHitArea.bounds = [posX, posY, plusWidth, height]; + } + else { + addHitArea.bounds = [0, 0, 0, 0]; + } + this.rows = rows; + ctx.restore(); + } +} +app.registerExtension({ + name: "rgthree.PowerPuter", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === NODE_CLASS.type) { + NODE_CLASS.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/random_unmuter.js b/custom_nodes/rgthree-comfy/web/comfyui/random_unmuter.js new file mode 100644 index 00000000..213aa8c0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/random_unmuter.js @@ -0,0 +1,102 @@ +import { app } from "../../scripts/app.js"; +import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js"; +import { NodeTypesString } from "./constants.js"; +import { rgthree } from "./rgthree.js"; +import { changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs } from "./utils.js"; +const MODE_MUTE = 2; +const MODE_ALWAYS = 0; +class RandomUnmuterNode extends BaseAnyInputConnectedNode { + constructor(title = RandomUnmuterNode.title) { + super(title); + this.comfyClass = NodeTypesString.RANDOM_UNMUTER; + this.modeOn = MODE_ALWAYS; + this.modeOff = MODE_MUTE; + this.tempEnabledNode = null; + this.processingQueue = false; + this.onQueueBound = this.onQueue.bind(this); + this.onQueueEndBound = this.onQueueEnd.bind(this); + this.onGraphtoPromptBound = this.onGraphtoPrompt.bind(this); + this.onGraphtoPromptEndBound = this.onGraphtoPromptEnd.bind(this); + rgthree.addEventListener("queue", this.onQueueBound); + rgthree.addEventListener("queue-end", this.onQueueEndBound); + rgthree.addEventListener("graph-to-prompt", this.onGraphtoPromptBound); + rgthree.addEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound); + this.onConstructed(); + } + onRemoved() { + rgthree.removeEventListener("queue", this.onQueueBound); + rgthree.removeEventListener("queue-end", this.onQueueEndBound); + rgthree.removeEventListener("graph-to-prompt", this.onGraphtoPromptBound); + rgthree.removeEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound); + } + onQueue(event) { + this.processingQueue = true; + } + onQueueEnd(event) { + this.processingQueue = false; + } + onGraphtoPrompt(event) { + if (!this.processingQueue) { + return; + } + this.tempEnabledNode = null; + const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this); + let allMuted = true; + if (linkedNodes.length) { + for (const node of linkedNodes) { + if (node.mode !== this.modeOff) { + allMuted = false; + break; + } + } + if (allMuted) { + this.tempEnabledNode = linkedNodes[Math.floor(Math.random() * linkedNodes.length)] || null; + if (this.tempEnabledNode) { + changeModeOfNodes(this.tempEnabledNode, this.modeOn); + } + } + } + } + onGraphtoPromptEnd(event) { + if (this.tempEnabledNode) { + changeModeOfNodes(this.tempEnabledNode, this.modeOff); + this.tempEnabledNode = null; + } + } + handleLinkedNodesStabilization(linkedNodes) { + return false; + } + getHelp() { + return ` +

      + Use this node to unmute on of its inputs randomly when the graph is queued (and, immediately + mute it back). +

      +
        +

      • + NOTE: All input nodes MUST be muted to start; if not this node will not randomly unmute + another. (This is powerful, as the generated image can be dragged in and the chosen input + will already by unmuted and work w/o any further action.) +

        • +

      • + TIP: Connect a Repeater's output to this nodes input and place that Repeater on a group + without any other inputs, and it will mute/unmute the entire group. +

        • +
      + `; + } +} +RandomUnmuterNode.exposedActions = ["Mute all", "Enable all"]; +RandomUnmuterNode.type = NodeTypesString.RANDOM_UNMUTER; +RandomUnmuterNode.title = RandomUnmuterNode.type; +app.registerExtension({ + name: "rgthree.RandomUnmuter", + registerCustomNodes() { + RandomUnmuterNode.setUp(); + }, + loadedGraphNode(node) { + if (node.type == RandomUnmuterNode.title) { + node._tempWidth = node.size[0]; + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/reroute.js b/custom_nodes/rgthree-comfy/web/comfyui/reroute.js new file mode 100644 index 00000000..7d741cee --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/reroute.js @@ -0,0 +1,981 @@ +var _a, _b; +import { app } from "../../scripts/app.js"; +import { rgthreeConfig } from "../../rgthree/config.js"; +import { rgthree } from "./rgthree.js"; +import { IoDirection, LAYOUT_CLOCKWISE, LAYOUT_LABEL_OPPOSITES, LAYOUT_LABEL_TO_DATA, addConnectionLayoutSupport, addMenuItem, getSlotLinks, isValidConnection, setConnectionsLayout, waitForCanvas, } from "./utils.js"; +import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js"; +import { wait } from "../../rgthree/common/shared_utils.js"; +import { RgthreeBaseVirtualNode } from "./base_node.js"; +import { NodeTypesString } from "./constants.js"; +import { rgthreeApi } from "../../rgthree/common/rgthree_api.js"; +import { getWidgetConfig, mergeIfValid, setWidgetConfig } from "./utils_deprecated_comfyui.js"; +const CONFIG_REROUTE = ((_a = rgthreeConfig === null || rgthreeConfig === void 0 ? void 0 : rgthreeConfig["nodes"]) === null || _a === void 0 ? void 0 : _a["reroute"]) || {}; +const CONFIG_FAST_REROUTE = CONFIG_REROUTE["fast_reroute"]; +const CONFIG_FAST_REROUTE_ENABLED = (_b = CONFIG_FAST_REROUTE["enabled"]) !== null && _b !== void 0 ? _b : false; +const CONFIG_KEY_CREATE_WHILE_LINKING = CONFIG_FAST_REROUTE["key_create_while_dragging_link"]; +const CONFIG_KEY_ROTATE = CONFIG_FAST_REROUTE["key_rotate"]; +const CONFIG_KEY_RESIZE = CONFIG_FAST_REROUTE["key_resize"]; +const CONFIG_KEY_MOVE = CONFIG_FAST_REROUTE["key_move"]; +const CONFIG_KEY_CXN_INPUT = CONFIG_FAST_REROUTE["key_connections_input"]; +const CONFIG_KEY_CXN_OUTPUT = CONFIG_FAST_REROUTE["key_connections_output"]; +let configWidth = Math.max(Math.round((Number(CONFIG_REROUTE["default_width"]) || 40) / 10) * 10, 10); +let configHeight = Math.max(Math.round((Number(CONFIG_REROUTE["default_height"]) || 30) / 10) * 10, 10); +while (configWidth * configHeight < 400) { + configWidth += 10; + configHeight += 10; +} +const configDefaultSize = [configWidth, configHeight]; +const configResizable = !!CONFIG_REROUTE["default_resizable"]; +let configLayout = CONFIG_REROUTE["default_layout"]; +if (!Array.isArray(configLayout)) { + configLayout = ["Left", "Right"]; +} +if (!LAYOUT_LABEL_TO_DATA[configLayout[0]]) { + configLayout[0] = "Left"; +} +if (!LAYOUT_LABEL_TO_DATA[configLayout[1]] || configLayout[0] == configLayout[1]) { + configLayout[1] = LAYOUT_LABEL_OPPOSITES[configLayout[0]]; +} +class RerouteService { + constructor() { + this.isFastLinking = false; + this.handledNewRerouteKeypress = false; + this.connectingData = null; + this.fastReroutesHistory = []; + this.handleLinkingKeydownBound = this.handleLinkingKeydown.bind(this); + this.handleLinkingKeyupBound = this.handleLinkingKeyup.bind(this); + if (CONFIG_FAST_REROUTE_ENABLED && (CONFIG_KEY_CREATE_WHILE_LINKING === null || CONFIG_KEY_CREATE_WHILE_LINKING === void 0 ? void 0 : CONFIG_KEY_CREATE_WHILE_LINKING.trim())) { + this.onCanvasSetUpListenerForLinking(); + } + } + async onCanvasSetUpListenerForLinking() { + const canvas = await waitForCanvas(); + const canvasProperty = true ? "connecting_links" : "connecting_node"; + canvas[`_${canvasProperty}`]; + const thisService = this; + Object.defineProperty(canvas, canvasProperty, { + get: function () { + return this[`_${canvasProperty}`]; + }, + set: function (value) { + var _a; + const isValNull = !value || !(value === null || value === void 0 ? void 0 : value.length); + const isPropNull = !this[`_${canvasProperty}`] || !((_a = this[`_${canvasProperty}`]) === null || _a === void 0 ? void 0 : _a.length); + const isStartingLinking = !isValNull && isPropNull; + const isStoppingLinking = !isPropNull && isValNull; + this[`_${canvasProperty}`] = value; + if (isStartingLinking) { + thisService.startingLinking(); + } + if (isStoppingLinking) { + thisService.stoppingLinking(); + thisService.connectingData = null; + } + }, + }); + } + startingLinking() { + this.isFastLinking = true; + KEY_EVENT_SERVICE.addEventListener("keydown", this.handleLinkingKeydownBound); + KEY_EVENT_SERVICE.addEventListener("keyup", this.handleLinkingKeyupBound); + } + stoppingLinking() { + this.isFastLinking = false; + this.fastReroutesHistory = []; + KEY_EVENT_SERVICE.removeEventListener("keydown", this.handleLinkingKeydownBound); + KEY_EVENT_SERVICE.removeEventListener("keyup", this.handleLinkingKeyupBound); + } + handleLinkingKeydown(event) { + if (!this.handledNewRerouteKeypress && + KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING)) { + this.handledNewRerouteKeypress = true; + this.insertNewRerouteWhileLinking(); + } + } + handleLinkingKeyup(event) { + if (this.handledNewRerouteKeypress && + !KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING)) { + this.handledNewRerouteKeypress = false; + } + } + getConnectingData() { + var _a, _b, _c, _d; + const oldCanvas = app.canvas; + if (oldCanvas.connecting_node && + oldCanvas.connecting_slot != null && + ((_a = oldCanvas.connecting_pos) === null || _a === void 0 ? void 0 : _a.length)) { + return { + node: oldCanvas.connecting_node, + input: oldCanvas.connecting_input, + output: oldCanvas.connecting_output, + slot: oldCanvas.connecting_slot, + pos: [...oldCanvas.connecting_pos], + }; + } + const canvas = app.canvas; + if ((_b = canvas.connecting_links) === null || _b === void 0 ? void 0 : _b.length) { + const link = canvas.connecting_links[0]; + return { + node: link.node, + input: (_c = link.input) !== null && _c !== void 0 ? _c : undefined, + output: (_d = link.output) !== null && _d !== void 0 ? _d : undefined, + slot: link.slot, + pos: [...link.pos], + }; + } + throw new Error("Error, handling linking keydown, but there's no link."); + } + setCanvasConnectingData(ctx) { + var _a, _b; + const oldCanvas = app.canvas; + if (oldCanvas.connecting_node && + oldCanvas.connecting_slot != null && + ((_a = oldCanvas.connecting_pos) === null || _a === void 0 ? void 0 : _a.length)) { + oldCanvas.connecting_node = ctx.node; + oldCanvas.connecting_input = ctx.input; + oldCanvas.connecting_output = ctx.output; + oldCanvas.connecting_slot = ctx.slot; + oldCanvas.connecting_pos = ctx.pos; + } + const canvas = app.canvas; + if ((_b = canvas.connecting_links) === null || _b === void 0 ? void 0 : _b.length) { + const link = canvas.connecting_links[0]; + link.node = ctx.node; + link.input = ctx.input; + link.output = ctx.output; + link.slot = ctx.slot; + link.pos = ctx.pos; + } + } + insertNewRerouteWhileLinking() { + var _a; + const canvas = app.canvas; + this.connectingData = this.getConnectingData(); + if (!this.connectingData) { + throw new Error("Error, handling linking keydown, but there's no link."); + } + const data = this.connectingData; + const node = LiteGraph.createNode("Reroute (rgthree)"); + const entry = { + node, + previous: { ...this.connectingData }, + current: undefined, + }; + this.fastReroutesHistory.push(entry); + let connectingDir = (_a = (data.input || data.output)) === null || _a === void 0 ? void 0 : _a.dir; + if (!connectingDir) { + connectingDir = data.input ? LiteGraph.LEFT : LiteGraph.RIGHT; + } + let newPos = canvas.convertEventToCanvasOffset({ + clientX: Math.round(canvas.last_mouse_position[0] / 10) * 10, + clientY: Math.round(canvas.last_mouse_position[1] / 10) * 10, + }); + entry.node.pos = newPos; + canvas.graph.add(entry.node); + canvas.selectNode(entry.node); + const distX = entry.node.pos[0] - data.pos[0]; + const distY = entry.node.pos[1] - data.pos[1]; + const layout = ["Left", "Right"]; + if (distX > 0 && Math.abs(distX) > Math.abs(distY)) { + layout[0] = data.output ? "Left" : "Right"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]; + node.pos[0] -= node.size[0] + 10; + node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10; + } + else if (distX < 0 && Math.abs(distX) > Math.abs(distY)) { + layout[0] = data.output ? "Right" : "Left"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]; + node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10; + } + else if (distY < 0 && Math.abs(distY) > Math.abs(distX)) { + layout[0] = data.output ? "Bottom" : "Top"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]; + node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10; + } + else if (distY > 0 && Math.abs(distY) > Math.abs(distX)) { + layout[0] = data.output ? "Top" : "Bottom"; + layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]; + node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10; + node.pos[1] -= node.size[1] + 10; + } + setConnectionsLayout(entry.node, layout); + if (data.output) { + data.node.connect(data.slot, entry.node, 0); + data.node = entry.node; + data.output = entry.node.outputs[0]; + data.slot = 0; + data.pos = entry.node.getConnectionPos(false, 0); + data.direction = + layout[0] === "Top" ? 2 : layout[0] === "Bottom" ? 1 : layout[0] === "Left" ? 4 : 3; + } + else { + entry.node.connect(0, data.node, data.slot); + data.node = entry.node; + data.input = entry.node.inputs[0]; + data.slot = 0; + data.pos = entry.node.getConnectionPos(true, 0); + data.direction = + layout[1] === "Top" ? 2 : layout[1] === "Bottom" ? 1 : layout[1] === "Left" ? 4 : 3; + } + this.setCanvasConnectingData(data); + entry.current = { ...this.connectingData }; + app.graph.setDirtyCanvas(true, true); + } + handleMoveOrResizeNodeMaybeWhileDragging(node) { + const data = this.connectingData; + if (this.isFastLinking && node === (data === null || data === void 0 ? void 0 : data.node)) { + const entry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1]; + if (entry) { + data.pos = entry.node.getConnectionPos(!!data.input, 0); + this.setCanvasConnectingData(data); + } + } + } + handleRemovedNodeMaybeWhileDragging(node) { + const currentEntry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1]; + if ((currentEntry === null || currentEntry === void 0 ? void 0 : currentEntry.node) === node) { + this.setCanvasConnectingData(currentEntry.previous); + this.fastReroutesHistory.splice(this.fastReroutesHistory.length - 1, 1); + if (currentEntry.previous.node) { + app.canvas.selectNode(currentEntry.previous.node); + } + } + } +} +const SERVICE = new RerouteService(); +class RerouteNode extends RgthreeBaseVirtualNode { + constructor(title = RerouteNode.title) { + super(title); + this.comfyClass = NodeTypesString.REROUTE; + this.isVirtualNode = true; + this.hideSlotLabels = true; + this.schedulePromise = null; + this.defaultConnectionsLayout = Array.from(configLayout); + this.shortcuts = { + rotate: { keys: CONFIG_KEY_ROTATE, state: false }, + connection_input: { keys: CONFIG_KEY_CXN_INPUT, state: false }, + connection_output: { keys: CONFIG_KEY_CXN_OUTPUT, state: false }, + resize: { + keys: CONFIG_KEY_RESIZE, + state: false, + initialMousePos: [-1, -1], + initialNodeSize: [-1, -1], + initialNodePos: [-1, -1], + resizeOnSide: [-1, -1], + }, + move: { + keys: CONFIG_KEY_MOVE, + state: false, + initialMousePos: [-1, -1], + initialNodePos: [-1, -1], + }, + }; + this.onConstructed(); + } + onConstructed() { + var _a; + this.setResizable(!!((_a = this.properties["resizable"]) !== null && _a !== void 0 ? _a : configResizable)); + this.size = RerouteNode.size; + this.addInput("", "*"); + this.addOutput("", "*"); + setTimeout(() => this.applyNodeSize(), 20); + return super.onConstructed(); + } + configure(info) { + var _a, _b; + if ((_a = info.inputs) === null || _a === void 0 ? void 0 : _a.length) { + info.inputs.length = 1; + } + super.configure(info); + this.configuring = true; + this.setResizable(!!((_b = this.properties["resizable"]) !== null && _b !== void 0 ? _b : configResizable)); + this.applyNodeSize(); + this.configuring = false; + } + setResizable(resizable) { + this.properties["resizable"] = !!resizable; + this.resizable = this.properties["resizable"]; + } + clone() { + const cloned = super.clone(); + cloned.inputs[0].type = "*"; + cloned.outputs[0].type = "*"; + return cloned; + } + onConnectionsChange(type, _slotIndex, connected, _link_info, _ioSlot) { + if (connected && type === LiteGraph.OUTPUT) { + const types = new Set(this.outputs[0].links.map((l) => { var _a; return (_a = app.graph.links[l]) === null || _a === void 0 ? void 0 : _a.type; }).filter((t) => t && t !== "*")); + if (types.size > 1) { + const linksToDisconnect = []; + for (let i = 0; i < this.outputs[0].links.length - 1; i++) { + const linkId = this.outputs[0].links[i]; + const link = app.graph.links[linkId]; + linksToDisconnect.push(link); + } + for (const link of linksToDisconnect) { + const node = app.graph.getNodeById(link.target_id); + node.disconnectInput(link.target_slot); + } + } + } + this.scheduleStabilize(); + } + onDrawForeground(ctx, canvas) { + var _a, _b, _c, _d; + if ((_a = this.properties) === null || _a === void 0 ? void 0 : _a["showLabel"]) { + const low_quality = ((_b = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _b === void 0 ? void 0 : _b.scale) && canvas.ds.scale < 0.6; + if (low_quality || this.size[0] <= 10) { + return; + } + const fontSize = Math.min(14, (this.size[1] * 0.65) | 0); + ctx.save(); + ctx.fillStyle = "#888"; + ctx.font = `${fontSize}px Arial`; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText(String(this.title && this.title !== RerouteNode.title + ? this.title + : ((_d = (_c = this.outputs) === null || _c === void 0 ? void 0 : _c[0]) === null || _d === void 0 ? void 0 : _d.type) || ""), this.size[0] / 2, this.size[1] / 2, this.size[0] - 30); + ctx.restore(); + } + } + findInputSlot(name, returnObj = false) { + return returnObj ? this.inputs[0] : 0; + } + findOutputSlot(name, returnObj) { + return returnObj ? this.outputs[0] : 0; + } + disconnectOutput(slot, targetNode) { + return super.disconnectOutput(slot, targetNode); + } + disconnectInput(slot) { + var _a; + if (rgthree.replacingReroute != null && ((_a = this.inputs[0]) === null || _a === void 0 ? void 0 : _a.link)) { + const graph = app.graph; + const link = graph.links[this.inputs[0].link]; + const node = (link === null || link === void 0 ? void 0 : link.origin_id) != null ? graph.getNodeById(link.origin_id) : null; + if (rgthree.replacingReroute !== (node === null || node === void 0 ? void 0 : node.id)) { + return false; + } + } + return super.disconnectInput(slot); + } + scheduleStabilize(ms = 64) { + if (!this.schedulePromise) { + this.schedulePromise = new Promise((resolve) => { + setTimeout(() => { + this.schedulePromise = null; + this.stabilize(); + resolve(); + }, ms); + }); + } + return this.schedulePromise; + } + stabilize() { + var _a, _b, _c, _d, _e, _f, _g, _h, _j, _k, _l; + if (this.configuring) { + return; + } + let currentNode = this; + let updateNodes = []; + let input = null; + let inputType = null; + let inputNode = null; + let inputNodeOutputSlot = null; + while (currentNode) { + updateNodes.unshift(currentNode); + const linkId = currentNode.inputs[0].link; + if (linkId !== null) { + const link = app.graph.links[linkId]; + const node = app.graph.getNodeById(link.origin_id); + if (!node) { + app.graph.removeLink(linkId); + currentNode = null; + break; + } + const type = node.constructor.type; + if (type === null || type === void 0 ? void 0 : type.includes("Reroute")) { + if (node === this) { + currentNode.disconnectInput(link.target_slot); + currentNode = null; + } + else { + currentNode = node; + } + } + else { + inputNode = node; + inputNodeOutputSlot = link.origin_slot; + input = (_a = node.outputs[inputNodeOutputSlot]) !== null && _a !== void 0 ? _a : null; + inputType = (_b = input === null || input === void 0 ? void 0 : input.type) !== null && _b !== void 0 ? _b : null; + break; + } + } + else { + currentNode = null; + break; + } + } + const nodes = [this]; + let outputNode = null; + let outputType = null; + let outputWidgetConfig = null; + let outputWidget = null; + while (nodes.length) { + currentNode = nodes.pop(); + const outputs = (currentNode.outputs ? currentNode.outputs[0].links : []) || []; + if (outputs.length) { + for (const linkId of outputs) { + const link = app.graph.links[linkId]; + if (!link) + continue; + const node = app.graph.getNodeById(link.target_id); + if (!node) + continue; + const type = node.constructor.type; + if (type === null || type === void 0 ? void 0 : type.includes("Reroute")) { + nodes.push(node); + updateNodes.push(node); + } + else { + const output = (_d = (_c = node.inputs) === null || _c === void 0 ? void 0 : _c[link.target_slot]) !== null && _d !== void 0 ? _d : null; + const nodeOutType = output === null || output === void 0 ? void 0 : output.type; + if (nodeOutType == null) { + console.warn(`[rgthree] Reroute - Connected node ${node.id} does not have type information for ` + + `slot ${link.target_slot}. Skipping connection enforcement, but something is odd ` + + `with that node.`); + } + else if (inputType && + inputType !== "*" && + nodeOutType !== "*" && + !isValidConnection(input, output)) { + console.warn(`[rgthree] Reroute - Disconnecting connected node's input (${node.id}.${link.target_slot}) (${node.type}) because its type (${String(nodeOutType)}) does not match the reroute type (${String(inputType)})`); + node.disconnectInput(link.target_slot); + } + else { + outputType = nodeOutType; + outputNode = node; + outputWidgetConfig = null; + outputWidget = null; + if (output === null || output === void 0 ? void 0 : output.widget) { + rgthreeApi.print("PRIMITIVE_REROUTE"); + try { + const config = getWidgetConfig(output); + if (!outputWidgetConfig && config) { + outputWidgetConfig = (_e = config[1]) !== null && _e !== void 0 ? _e : {}; + outputType = config[0]; + if (!outputWidget) { + outputWidget = (_f = outputNode.widgets) === null || _f === void 0 ? void 0 : _f.find((w) => { var _a; return w.name === ((_a = output === null || output === void 0 ? void 0 : output.widget) === null || _a === void 0 ? void 0 : _a.name); }); + } + const merged = mergeIfValid(output, [config[0], outputWidgetConfig]); + if (merged === null || merged === void 0 ? void 0 : merged.customConfig) { + outputWidgetConfig = merged.customConfig; + } + } + } + catch (e) { + console.error("[rgthree] Could not propagate widget infor for reroute; maybe ComfyUI updated?"); + outputWidgetConfig = null; + outputWidget = null; + } + } + } + } + } + } + else { + } + } + const displayType = inputType || outputType || "*"; + const color = LGraphCanvas.link_type_colors[displayType]; + for (const node of updateNodes) { + node.outputs[0].type = inputType || "*"; + node.__outputType = displayType; + node.outputs[0].name = (input === null || input === void 0 ? void 0 : input.name) || ""; + node.size = node.computeSize(); + (_h = (_g = node).applyNodeSize) === null || _h === void 0 ? void 0 : _h.call(_g); + for (const l of node.outputs[0].links || []) { + const link = app.graph.links[l]; + if (link && color) { + link.color = color; + } + } + try { + if (outputWidgetConfig && outputWidget && outputType) { + rgthreeApi.print("PRIMITIVE_REROUTE"); + node.inputs[0].widget = { name: "value" }; + setWidgetConfig(node.inputs[0], [outputType !== null && outputType !== void 0 ? outputType : displayType, outputWidgetConfig]); + } + else { + setWidgetConfig(node.inputs[0], null); + } + } + catch (e) { + console.error("[rgthree] Could not set widget config for reroute; maybe ComfyUI updated?"); + outputWidgetConfig = null; + outputWidget = null; + if ((_j = node.inputs[0]) === null || _j === void 0 ? void 0 : _j.widget) { + delete node.inputs[0].widget; + } + } + } + if (inputNode && inputNodeOutputSlot != null) { + const links = inputNode.outputs[inputNodeOutputSlot].links; + for (const l of links || []) { + const link = app.graph.links[l]; + if (link && color) { + link.color = color; + } + } + } + (_k = inputNode === null || inputNode === void 0 ? void 0 : inputNode.onConnectionsChainChange) === null || _k === void 0 ? void 0 : _k.call(inputNode); + (_l = outputNode === null || outputNode === void 0 ? void 0 : outputNode.onConnectionsChainChange) === null || _l === void 0 ? void 0 : _l.call(outputNode); + app.graph.setDirtyCanvas(true, true); + } + setSize(size) { + const oldSize = [...this.size]; + const newSize = [...size]; + super.setSize(newSize); + this.properties["size"] = [...this.size]; + this.stabilizeLayout(oldSize, newSize); + } + stabilizeLayout(oldSize, newSize) { + if (newSize[0] === 10 || newSize[1] === 10) { + const props = this.properties; + props["connections_layout"] = props["connections_layout"] || ["Left", "Right"]; + props["connections_dir"] = props["connections_dir"] || [-1, -1]; + const layout = props["connections_layout"]; + const dir = props["connections_dir"]; + if (oldSize[0] > 10 && newSize[0] === 10) { + dir[0] = LiteGraph.DOWN; + dir[1] = LiteGraph.UP; + if (layout[0] === "Bottom") { + layout[1] = "Top"; + } + else if (layout[1] === "Top") { + layout[0] = "Bottom"; + } + else { + layout[0] = "Top"; + layout[1] = "Bottom"; + dir[0] = LiteGraph.UP; + dir[1] = LiteGraph.DOWN; + } + this.setDirtyCanvas(true, true); + } + else if (oldSize[1] > 10 && newSize[1] === 10) { + dir[0] = LiteGraph.RIGHT; + dir[1] = LiteGraph.LEFT; + if (layout[0] === "Right") { + layout[1] = "Left"; + } + else if (layout[1] === "Left") { + layout[0] = "Right"; + } + else { + layout[0] = "Left"; + layout[1] = "Right"; + dir[0] = LiteGraph.LEFT; + dir[1] = LiteGraph.RIGHT; + } + this.setDirtyCanvas(true, true); + } + } + SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this); + } + applyNodeSize() { + this.properties["size"] = this.properties["size"] || RerouteNode.size; + this.properties["size"] = [ + Number(this.properties["size"][0]), + Number(this.properties["size"][1]), + ]; + this.size = this.properties["size"]; + app.graph.setDirtyCanvas(true, true); + } + rotate(degrees) { + const w = this.size[0]; + const h = this.size[1]; + this.properties["connections_layout"] = + this.properties["connections_layout"] || this.defaultConnectionsLayout; + const connections_layout = this.properties["connections_layout"]; + const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]); + const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]); + if (degrees == 90 || degrees === -90) { + if (degrees === -90) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex - 1) % 4) + 4) % 4]; + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex - 1) % 4) + 4) % 4]; + } + else { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 1) % 4) + 4) % 4]; + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 1) % 4) + 4) % 4]; + } + } + else if (degrees === 180) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]; + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]; + } + this.setSize([h, w]); + } + manuallyHandleMove(event) { + const shortcut = this.shortcuts.move; + if (shortcut.state) { + const diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10; + const diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10; + this.pos[0] = shortcut.initialNodePos[0] + diffX; + this.pos[1] = shortcut.initialNodePos[1] + diffY; + this.setDirtyCanvas(true, true); + SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this); + } + } + manuallyHandleResize(event) { + const shortcut = this.shortcuts.resize; + if (shortcut.state) { + let diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10; + let diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10; + diffX *= shortcut.resizeOnSide[0] === LiteGraph.LEFT ? -1 : 1; + diffY *= shortcut.resizeOnSide[1] === LiteGraph.UP ? -1 : 1; + const oldSize = [...this.size]; + this.setSize([ + Math.max(10, shortcut.initialNodeSize[0] + diffX), + Math.max(10, shortcut.initialNodeSize[1] + diffY), + ]); + if (shortcut.resizeOnSide[0] === LiteGraph.LEFT && oldSize[0] > 10) { + this.pos[0] = shortcut.initialNodePos[0] - diffX; + } + if (shortcut.resizeOnSide[1] === LiteGraph.UP && oldSize[1] > 10) { + this.pos[1] = shortcut.initialNodePos[1] - diffY; + } + this.setDirtyCanvas(true, true); + } + } + cycleConnection(ioDir) { + var _a, _b; + const props = this.properties; + props["connections_layout"] = props["connections_layout"] || ["Left", "Right"]; + const connections_layout = this.properties["connections_layout"]; + const propIdx = ioDir == IoDirection.INPUT ? 0 : 1; + const oppositeIdx = propIdx ? 0 : 1; + let currentLayout = connections_layout[propIdx]; + let oppositeLayout = connections_layout[oppositeIdx]; + if (this.size[0] === 10 || this.size[1] === 10) { + props["connections_dir"] = props["connections_dir"] || [-1, -1]; + const connections_dir = this.properties["connections_dir"]; + let currentDir = connections_dir[propIdx]; + const options = this.size[0] === 10 + ? currentLayout === "Bottom" + ? [LiteGraph.DOWN, LiteGraph.RIGHT, LiteGraph.LEFT] + : [LiteGraph.UP, LiteGraph.LEFT, LiteGraph.RIGHT] + : currentLayout === "Right" + ? [LiteGraph.RIGHT, LiteGraph.DOWN, LiteGraph.UP] + : [LiteGraph.LEFT, LiteGraph.UP, LiteGraph.DOWN]; + let idx = options.indexOf(currentDir); + let next = (_a = options[idx + 1]) !== null && _a !== void 0 ? _a : options[0]; + connections_dir[propIdx] = next; + return; + } + let next = currentLayout; + do { + let idx = LAYOUT_CLOCKWISE.indexOf(next); + next = (_b = LAYOUT_CLOCKWISE[idx + 1]) !== null && _b !== void 0 ? _b : LAYOUT_CLOCKWISE[0]; + } while (next === oppositeLayout); + connections_layout[propIdx] = next; + this.setDirtyCanvas(true, true); + } + onMouseMove(event) { + if (this.shortcuts.move.state) { + const shortcut = this.shortcuts.move; + if (shortcut.initialMousePos[0] === -1) { + shortcut.initialMousePos[0] = event.clientX; + shortcut.initialMousePos[1] = event.clientY; + shortcut.initialNodePos[0] = this.pos[0]; + shortcut.initialNodePos[1] = this.pos[1]; + } + this.manuallyHandleMove(event); + } + else if (this.shortcuts.resize.state) { + const shortcut = this.shortcuts.resize; + if (shortcut.initialMousePos[0] === -1) { + shortcut.initialMousePos[0] = event.clientX; + shortcut.initialMousePos[1] = event.clientY; + shortcut.initialNodeSize[0] = this.size[0]; + shortcut.initialNodeSize[1] = this.size[1]; + shortcut.initialNodePos[0] = this.pos[0]; + shortcut.initialNodePos[1] = this.pos[1]; + const canvas = app.canvas; + const offset = canvas.convertEventToCanvasOffset(event); + shortcut.resizeOnSide[0] = this.pos[0] > offset[0] ? LiteGraph.LEFT : LiteGraph.RIGHT; + shortcut.resizeOnSide[1] = this.pos[1] > offset[1] ? LiteGraph.UP : LiteGraph.DOWN; + } + this.manuallyHandleResize(event); + } + } + onKeyDown(event) { + super.onKeyDown(event); + const canvas = app.canvas; + if (CONFIG_FAST_REROUTE_ENABLED) { + for (const [key, shortcut] of Object.entries(this.shortcuts)) { + if (!shortcut.state) { + const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys); + if (keys) { + shortcut.state = true; + if (key === "rotate") { + this.rotate(90); + } + else if (key.includes("connection")) { + this.cycleConnection(key.includes("input") ? IoDirection.INPUT : IoDirection.OUTPUT); + } + if (shortcut.initialMousePos) { + canvas.node_capturing_input = this; + } + } + } + } + } + } + onKeyUp(event) { + super.onKeyUp(event); + const canvas = app.canvas; + if (CONFIG_FAST_REROUTE_ENABLED) { + for (const [key, shortcut] of Object.entries(this.shortcuts)) { + if (shortcut.state) { + const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys); + if (!keys) { + shortcut.state = false; + if (shortcut.initialMousePos) { + shortcut.initialMousePos = [-1, -1]; + if ((canvas.node_capturing_input = this)) { + canvas.node_capturing_input = null; + } + this.setDirtyCanvas(true, true); + } + } + } + } + } + } + onDeselected() { + var _a; + (_a = super.onDeselected) === null || _a === void 0 ? void 0 : _a.call(this); + const canvas = app.canvas; + for (const [key, shortcut] of Object.entries(this.shortcuts)) { + shortcut.state = false; + if (shortcut.initialMousePos) { + shortcut.initialMousePos = [-1, -1]; + if ((canvas.node_capturing_input = this)) { + canvas.node_capturing_input = null; + } + this.setDirtyCanvas(true, true); + } + } + } + onRemoved() { + var _a; + (_a = super.onRemoved) === null || _a === void 0 ? void 0 : _a.call(this); + setTimeout(() => { + SERVICE.handleRemovedNodeMaybeWhileDragging(this); + }, 32); + } + getHelp() { + return ` +

      + Finally, a comfortable, powerful reroute node with true multi-direction and powerful + shortcuts to bring your workflow to the next level. +

      + + ${!CONFIG_FAST_REROUTE_ENABLED + ? `

      Fast Shortcuts are currently disabled.` + : ` +

        +

      • + ${CONFIG_KEY_CREATE_WHILE_LINKING} Create a new reroute node while dragging + a link, connecting it to the link in the place and continuing the link. +

        • +

      • + ${CONFIG_KEY_ROTATE} Rotate the selected reroute node counter clockwise 90 + degrees. +

        • +

      • + ${CONFIG_KEY_RESIZE} Resize the selected reroute node from the nearest + corner by holding down and moving your mouse. +

        • +

      • + ${CONFIG_KEY_MOVE} Move the selected reroute node by holding down and + moving your mouse. +

        • +

      • + ${CONFIG_KEY_CXN_INPUT} Change the input layout/direction of the selected + reroute node. +

        • +

      • + ${CONFIG_KEY_CXN_OUTPUT} Change the output layout/direction of the selected + reroute node. +

        • +
      + `} +

      + To change, ${!CONFIG_FAST_REROUTE_ENABLED ? "enable" : "disable"} or configure sohrtcuts, + make a copy of + /custom_nodes/rgthree-comfy/rgthree_config.json.default to + /custom_nodes/rgthree-comfy/rgthree_config.json and configure under + nodes > reroute > fast_reroute. +

      + `; + } +} +RerouteNode.title = NodeTypesString.REROUTE; +RerouteNode.type = NodeTypesString.REROUTE; +RerouteNode.title_mode = LiteGraph.NO_TITLE; +RerouteNode.collapsable = false; +RerouteNode.layout_slot_offset = 5; +RerouteNode.size = configDefaultSize; +addMenuItem(RerouteNode, app, { + name: (node) => { var _a; return `${((_a = node.properties) === null || _a === void 0 ? void 0 : _a["showLabel"]) ? "Hide" : "Show"} Label/Title`; }, + property: "showLabel", + callback: async (node, value) => { + app.graph.setDirtyCanvas(true, true); + }, +}); +addMenuItem(RerouteNode, app, { + name: (node) => `${node.resizable ? "No" : "Allow"} Resizing`, + callback: (node) => { + node.setResizable(!node.resizable); + node.size[0] = Math.max(40, node.size[0]); + node.size[1] = Math.max(30, node.size[1]); + node.applyNodeSize(); + }, +}); +addMenuItem(RerouteNode, app, { + name: "Static Width", + property: "size", + subMenuOptions: (() => { + const options = []; + for (let w = 8; w > 0; w--) { + options.push(`${w * 10}`); + } + return options; + })(), + prepareValue: (value, node) => [Number(value), node.size[1]], + callback: (node) => { + node.setResizable(false); + node.applyNodeSize(); + }, +}); +addMenuItem(RerouteNode, app, { + name: "Static Height", + property: "size", + subMenuOptions: (() => { + const options = []; + for (let w = 8; w > 0; w--) { + options.push(`${w * 10}`); + } + return options; + })(), + prepareValue: (value, node) => [node.size[0], Number(value)], + callback: (node) => { + node.setResizable(false); + node.applyNodeSize(); + }, +}); +addConnectionLayoutSupport(RerouteNode, app, [ + ["Left", "Right"], + ["Left", "Top"], + ["Left", "Bottom"], + ["Right", "Left"], + ["Right", "Top"], + ["Right", "Bottom"], + ["Top", "Left"], + ["Top", "Right"], + ["Top", "Bottom"], + ["Bottom", "Left"], + ["Bottom", "Right"], + ["Bottom", "Top"], +], (node) => { + node.applyNodeSize(); +}); +addMenuItem(RerouteNode, app, { + name: "Rotate", + subMenuOptions: [ + "Rotate 90Β° Clockwise", + "Rotate 90Β° Counter-Clockwise", + "Rotate 180Β°", + null, + "Flip Horizontally", + "Flip Vertically", + ], + callback: (node_, value) => { + const node = node_; + if (value === null || value === void 0 ? void 0 : value.startsWith("Rotate 90Β° Clockwise")) { + node.rotate(90); + } + else if (value === null || value === void 0 ? void 0 : value.startsWith("Rotate 90Β° Counter-Clockwise")) { + node.rotate(-90); + } + else if (value === null || value === void 0 ? void 0 : value.startsWith("Rotate 180Β°")) { + node.rotate(180); + } + else { + const connections_layout = node.properties["connections_layout"]; + const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]); + const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]); + if (value === null || value === void 0 ? void 0 : value.startsWith("Flip Horizontally")) { + if (["Left", "Right"].includes(connections_layout[0])) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]; + } + if (["Left", "Right"].includes(connections_layout[1])) { + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]; + } + } + else if (value === null || value === void 0 ? void 0 : value.startsWith("Flip Vertically")) { + if (["Top", "Bottom"].includes(connections_layout[0])) { + connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]; + } + if (["Top", "Bottom"].includes(connections_layout[1])) { + connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]; + } + } + } + }, +}); +addMenuItem(RerouteNode, app, { + name: "Clone New Reroute...", + subMenuOptions: ["Before", "After"], + callback: async (node, value) => { + const clone = node.clone(); + const pos = [...node.pos]; + if (value === "Before") { + clone.pos = [pos[0] - 20, pos[1] - 20]; + app.graph.add(clone); + await wait(); + const inputLinks = getSlotLinks(node.inputs[0]); + for (const inputLink of inputLinks) { + const link = inputLink.link; + const linkedNode = app.graph.getNodeById(link.origin_id); + if (linkedNode) { + linkedNode.connect(0, clone, 0); + } + } + clone.connect(0, node, 0); + } + else { + clone.pos = [pos[0] + 20, pos[1] + 20]; + app.graph.add(clone); + await wait(); + const outputLinks = getSlotLinks(node.outputs[0]); + node.connect(0, clone, 0); + for (const outputLink of outputLinks) { + const link = outputLink.link; + const linkedNode = app.graph.getNodeById(link.target_id); + if (linkedNode) { + clone.connect(0, linkedNode, link.target_slot); + } + } + } + }, +}); +app.registerExtension({ + name: "rgthree.Reroute", + registerCustomNodes() { + RerouteNode.setUp(); + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css new file mode 100644 index 00000000..a0493ebf --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css @@ -0,0 +1,696 @@ +@charset "UTF-8"; +.rgthree-top-messages-container { + position: fixed; + z-index: 9999; + top: 0; + left: 0; + width: 100%; + height: 0; + display: flex; + flex-direction: column; + align-items: center; + justify-content: start; +} + +.rgthree-top-messages-container > div { + position: relative; + height: fit-content; + padding: 4px; + margin-top: -100px; /* re-set by JS */ + opacity: 0; + transition: all 0.33s ease-in-out; + z-index: 3; +} + +.rgthree-top-messages-container > div:last-child { + z-index: 2; +} + +.rgthree-top-messages-container > div:not(.-show) { + z-index: 1; +} + +.rgthree-top-messages-container > div.-show { + opacity: 1; + margin-top: 0px !important; +} + +.rgthree-top-messages-container > div.-show { + opacity: 1; + transform: translateY(0%); +} + +.rgthree-top-messages-container > div > div { + position: relative; + background: #353535; + color: #fff; + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; + height: fit-content; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.88); + padding: 6px 12px; + border-radius: 4px; + font-family: Arial, sans-serif; + font-size: 14px; +} + +.rgthree-top-messages-container > div > div > span { + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; +} + +.rgthree-top-messages-container > div > div > span svg { + width: 20px; + height: auto; + margin-right: 8px; +} + +.rgthree-top-messages-container > div > div > span svg.icon-checkmark { + fill: #2e9720; +} + +.rgthree-top-messages-container [type=warn]::before, +.rgthree-top-messages-container [type=success]::before { + content: "⚠️"; + display: inline-block; + flex: 0 0 auto; + font-size: 18px; + margin-right: 4px; + line-height: 1; +} + +.rgthree-top-messages-container [type=success]::before { + content: "πŸŽ‰"; +} + +.rgthree-top-messages-container a { + cursor: pointer; + text-decoration: underline; + color: #fc0; + margin-left: 4px; + display: inline-block; + line-height: 1; +} + +.rgthree-top-messages-container a:hover { + color: #fc0; + text-decoration: none; +} + +/* Fix node selector being crazy long b/c of array types. */ +.litegraph.litesearchbox input, +.litegraph.litesearchbox select { + max-width: 250px; +} + +/* There's no reason for this z-index to be so high. It layers on top of things it shouldn't, + (like pythongssss' image gallery, the properties panel, etc.) */ +.comfy-multiline-input { + z-index: 1 !important; +} + +.comfy-multiline-input:focus { + z-index: 2 !important; +} + +.litegraph .dialog { + z-index: 3 !important; /* This is set to 1, but goes under the multi-line inputs, so bump it. */ +} + +:not(#fakeid) .rgthree-button-reset { + position: relative; + appearance: none; + cursor: pointer; + border: 0; + background: transparent; + color: inherit; + padding: 0; + margin: 0; +} + +:not(#fakeid) .rgthree-button { + --padding-top: 7px; + --padding-bottom: 9px; + --padding-x: 16px; + position: relative; + cursor: pointer; + border: 0; + border-radius: 0.33rem; + background: rgba(0, 0, 0, 0.5); + color: white; + font-family: system-ui, sans-serif; + font-size: 1rem; + line-height: 1; + white-space: nowrap; + text-decoration: none; + margin: 0.25rem; + box-shadow: 0px 0px 2px rgb(0, 0, 0); + background: #212121; + transition: all 0.1s ease-in-out; + padding: var(--padding-top) var(--padding-x) var(--padding-bottom); + display: inline-flex; + flex-direction: row; + align-items: center; + justify-content: center; +} +:not(#fakeid) .rgthree-button::before, :not(#fakeid) .rgthree-button::after { + content: ""; + display: block; + position: absolute; + border-radius: 0.33rem; + left: 0; + top: 0; + width: 100%; + height: 100%; + box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.12), inset -1px -1px 0px rgba(0, 0, 0, 0.75); + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)); + mix-blend-mode: screen; +} +:not(#fakeid) .rgthree-button::after { + mix-blend-mode: multiply; +} +:not(#fakeid) .rgthree-button:hover { + background: #303030; +} +:not(#fakeid) .rgthree-button:active { + box-shadow: 0px 0px 0px rgba(0, 0, 0, 0); + background: #121212; + padding: calc(var(--padding-top) + 1px) calc(var(--padding-x) - 1px) calc(var(--padding-bottom) - 1px) calc(var(--padding-x) + 1px); +} +:not(#fakeid) .rgthree-button:active::before, :not(#fakeid) .rgthree-button:active::after { + box-shadow: 1px 1px 0px rgba(255, 255, 255, 0.15), inset 1px 1px 0px rgba(0, 0, 0, 0.5), inset 1px 3px 5px rgba(0, 0, 0, 0.33); +} +:not(#fakeid) .rgthree-button.-blue { + background: #346599 !important; +} +:not(#fakeid) .rgthree-button.-blue:hover { + background: #3b77b8 !important; +} +:not(#fakeid) .rgthree-button.-blue:active { + background: #1d5086 !important; +} +:not(#fakeid) .rgthree-button.-green { + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #14580b; +} +:not(#fakeid) .rgthree-button.-green:hover { + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #1a6d0f; +} +:not(#fakeid) .rgthree-button.-green:active { + background: linear-gradient(to bottom, rgba(0, 0, 0, 0.15), rgba(255, 255, 255, 0.06)), #0f3f09; +} +:not(#fakeid) .rgthree-button[disabled] { + box-shadow: none; + background: #666 !important; + color: #aaa; + pointer-events: none; +} +:not(#fakeid) .rgthree-button[disabled]::before, :not(#fakeid) .rgthree-button[disabled]::after { + display: none; +} + +:not(#fakeid) .rgthree-comfybar-top-button-group { + font-size: 0; + flex: 1 1 auto; + display: flex; + align-items: stretch; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button { + margin: 0; + flex: 1 1; + height: 36px; + padding: 0 12px; + border-radius: 0; + background: var(--p-button-secondary-background); + color: var(--p-button-secondary-color); +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button.-primary { + background: var(--p-button-primary-background); + color: var(--p-button-primary-color); +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::before, :not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::after { + border-radius: 0; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button svg { + fill: currentColor; + width: 28px; + height: 28px; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::before, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::after { + border-top-left-radius: 0.33rem; + border-bottom-left-radius: 0.33rem; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::before, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::after { + border-top-right-radius: 0.33rem; + border-bottom-right-radius: 0.33rem; +} + +.rgthree-dialog { + outline: 0; + border: 0; + border-radius: 6px; + background: #414141; + color: #fff; + box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.05), inset -1px -1px 0px rgba(0, 0, 0, 0.5), 2px 2px 20px rgb(0, 0, 0); + max-width: 800px; + box-sizing: border-box; + font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif; + font-size: 1rem; + padding: 0; + max-height: calc(100% - 32px); +} +.rgthree-dialog *, .rgthree-dialog *::before, .rgthree-dialog *::after { + box-sizing: inherit; +} + +.rgthree-dialog-container > * { + padding: 8px 16px; +} +.rgthree-dialog-container > *:first-child { + padding-top: 16px; +} +.rgthree-dialog-container > *:last-child { + padding-bottom: 16px; +} + +.rgthree-dialog.-iconed::after { + content: ""; + font-size: 276px; + position: absolute; + right: 0px; + bottom: 0px; + opacity: 0.15; + display: block; + width: 237px; + overflow: hidden; + height: 186px; + line-height: 1; + pointer-events: none; + z-index: -1; +} + +.rgthree-dialog.-iconed.-help::after { + content: "πŸ›Ÿ"; +} + +.rgthree-dialog.-iconed.-settings::after { + content: "βš™οΈ"; +} + +@media (max-width: 832px) { + .rgthree-dialog { + max-width: calc(100% - 32px); + } +} +.rgthree-dialog-container-title { + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} + +.rgthree-dialog-container-title > svg:first-child { + width: 36px; + height: 36px; + margin-right: 16px; +} + +.rgthree-dialog-container-title h2 { + font-size: 1.375rem; + margin: 0; + font-weight: bold; +} + +.rgthree-dialog-container-title h2 small { + font-size: 0.8125rem; + font-weight: normal; + opacity: 0.75; +} + +.rgthree-dialog-container-content { + overflow: auto; + max-height: calc(100vh - 200px); /* Arbitrary height to copensate for margin, title, and footer.*/ +} + +.rgthree-dialog-container-content p { + font-size: 0.8125rem; + margin-top: 0; +} + +.rgthree-dialog-container-content ul li p { + margin-bottom: 4px; +} + +.rgthree-dialog-container-content ul li p + p { + margin-top: 0.5em; +} + +.rgthree-dialog-container-content ul li ul { + margin-top: 0.5em; + margin-bottom: 1em; +} + +.rgthree-dialog-container-content p code { + display: inline-block; + padding: 2px 4px; + margin: 0px 2px; + border: 1px solid rgba(255, 255, 255, 0.25); + border-radius: 3px; + background: rgba(255, 255, 255, 0.1); +} + +.rgthree-dialog-container-footer { + display: flex; + align-items: center; + justify-content: center; +} + +body.rgthree-dialog-open > *:not(.rgthree-dialog):not(.rgthree-top-messages-container) { + filter: blur(5px); +} + +.rgthree-menu { + list-style: none; + padding: 0; + margin: 0; + position: fixed; + z-index: 999999; + pointer-events: none; + opacity: 0; + transition: opacity 0.08s ease-in-out; + color: #dde; + background-color: #111; + font-size: 12px; + box-shadow: 0 0 10px black !important; +} +.rgthree-menu > li { + position: relative; + padding: 4px 6px; + z-index: 9999; + white-space: nowrap; +} +.rgthree-menu > li[role=button] { + background-color: var(--comfy-menu-bg) !important; + color: var(--input-text); + cursor: pointer; +} +.rgthree-menu > li[role=button]:hover { + filter: brightness(155%); +} +.rgthree-menu[state^=measuring] { + display: block; + opacity: 0; +} +.rgthree-menu[state=open] { + display: block; + opacity: 1; + pointer-events: all; +} + +.rgthree-top-menu { + box-sizing: border-box; + white-space: nowrap; + background: var(--content-bg); + color: var(--content-fg); + display: flex; + flex-direction: column; + list-style: none; + padding: 0; + margin: 0; +} +.rgthree-top-menu * { + box-sizing: inherit; +} +.rgthree-top-menu > li:not(#fakeid) { + list-style: none; + padding: 0; + margin: 0; + position: relative; + z-index: 2; +} +.rgthree-top-menu > li:not(#fakeid) > button { + cursor: pointer; + padding: 8px 12px 8px 8px; + width: 100%; + text-align: start; + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} +.rgthree-top-menu > li:not(#fakeid) > button:hover { + background-color: var(--comfy-input-bg); +} +.rgthree-top-menu > li:not(#fakeid) > button svg { + height: 16px; + width: auto; + margin-inline-end: 0.6em; +} +.rgthree-top-menu > li:not(#fakeid) > button svg.github-star { + fill: rgb(227, 179, 65); +} +.rgthree-top-menu > li:not(#fakeid).rgthree-message { + min-height: 32px; +} +.rgthree-top-menu > li:not(#fakeid).rgthree-message > span { + padding: 8px 12px; + display: block; + width: 100%; + text-align: center; + font-style: italic; + font-size: 12px; +} +.rgthree-top-menu.-modal::after { + content: ""; + display: block; + position: fixed; + z-index: 1; + inset: 0; + background: rgba(0, 0, 0, 0.0666666667); +} + +body.rgthree-modal-menu-open > *:not(.rgthree-menu):not(.rgthree-top-messages-container) { + filter: blur(2px); +} + +.rgthree-dialog.-settings { + width: 100%; +} + +.rgthree-dialog.-settings fieldset { + border: 1px solid rgba(255, 255, 255, 0.25); + padding: 0 12px 8px; + margin-bottom: 16px; +} + +.rgthree-dialog.-settings fieldset > legend { + margin-left: 8px; + padding: 0 8px; + opacity: 0.5; +} + +.rgthree-dialog.-settings .formrow { + display: flex; + flex-direction: column; +} + +.rgthree-dialog.-settings .formrow + .formrow { + border-top: 1px solid rgba(255, 255, 255, 0.25); +} + +.rgthree-dialog.-settings .fieldrow { + display: flex; + flex-direction: row; +} + +.rgthree-dialog.-settings .fieldrow > label { + flex: 1 1 auto; + user-select: none; + padding: 8px 12px 12px; +} + +.rgthree-dialog.-settings .fieldrow > label span { + font-weight: bold; +} + +.rgthree-dialog.-settings .fieldrow > label small { + display: block; + margin-top: 4px; + font-size: 0.6875rem; + opacity: 0.75; + padding-left: 16px; +} + +.rgthree-dialog.-settings .fieldrow ~ .fieldrow { + font-size: 0.9rem; + border-top: 1px dotted rgba(255, 255, 255, 0.25); +} + +.rgthree-dialog.-settings .fieldrow ~ .fieldrow label { + padding-left: 28px; +} + +.rgthree-dialog.-settings .fieldrow:first-child:not(.-checked) ~ .fieldrow { + display: none; +} + +.rgthree-dialog.-settings .fieldrow:hover { + background: rgba(255, 255, 255, 0.1); +} + +.rgthree-dialog.-settings .fieldrow ~ .fieldrow span { + font-weight: normal; +} + +.rgthree-dialog.-settings .fieldrow > .fieldrow-value { + display: flex; + align-items: center; + justify-content: end; + flex: 0 0 auto; + width: 50%; + max-width: 230px; +} + +.rgthree-dialog.-settings .fieldrow.-type-boolean > .fieldrow-value { + max-width: 64px; +} + +.rgthree-dialog.-settings .fieldrow.-type-number input { + width: 48px; + text-align: right; +} + +.rgthree-dialog.-settings .fieldrow input[type=checkbox] { + width: 24px; + height: 24px; + cursor: pointer; +} + +.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group { + padding: 0; + border: 0; + margin: 0; +} +.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group > span.rgthree-checklist-item { + display: inline-block; + white-space: nowrap; + padding-right: 6px; + vertical-align: middle; +} +.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group > span.rgthree-checklist-item input[type=checkbox] { + width: 16px; + height: 16px; +} +.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group > span.rgthree-checklist-item label { + padding-left: 4px; + text-align: left; + cursor: pointer; +} + +.rgthree-comfyui-settings-row div { + display: flex; + flex-direction: row; + align-items: center; + justify-content: end; +} + +.rgthree-comfyui-settings-row div svg { + width: 36px; + height: 36px; + margin-right: 16px; +} + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy, +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item { + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy svg, +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg { + fill: currentColor; + width: auto; + height: 16px; + margin-right: 6px; +} + +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg.github-star { + fill: rgb(227, 179, 65); +} + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy, +.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-label { + color: #dde; + background-color: #212121 !important; + margin: 0; + padding: 2px; + cursor: default; + opacity: 1; + padding: 4px; + font-weight: bold; +} + +.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy { + font-size: 1.1em; + color: #fff; + background-color: #090909 !important; + justify-content: center; + padding: 4px 8px; +} + +rgthree-progress-bar { + display: block; + position: relative; + z-index: 999; + top: 0; + left: 0; + height: 14px; + font-size: 10px; + width: 100%; + overflow: hidden; + box-shadow: 0px 0px 3px rgba(0, 0, 0, 0.25); + box-shadow: inset 0px -1px 0px rgba(0, 0, 0, 0.25), 0px 1px 0px rgba(255, 255, 255, 0.125); +} + +* ~ rgthree-progress-bar, +.comfyui-body-bottom rgthree-progress-bar { + box-shadow: 0px -1px 0px rgb(0, 0, 0), inset 0px 1px 0px rgba(255, 255, 255, 0.15), inset 0px -1px 0px rgba(0, 0, 0, 0.25), 0px 1px 0px rgba(255, 255, 255, 0.125); +} + +body:not([style*=grid]):not([class*=grid]) rgthree-progress-bar { + position: fixed; + top: 0px; + bottom: auto; +} +body:not([style*=grid]):not([class*=grid]) rgthree-progress-bar.rgthree-pos-bottom { + top: auto; + bottom: 0px; +} + +.rgthree-debug-keydowns { + display: block; + position: fixed; + z-index: 1050; + top: 3px; + right: 8px; + font-size: 10px; + color: #fff; + font-family: sans-serif; + pointer-events: none; +} + +.rgthree-comfy-about-badge-logo { + width: 20px; + height: 20px; + background: url(/rgthree/logo.svg?bg=transparent&fg=%2393c5fd); + background-size: 100% 100%; +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js new file mode 100644 index 00000000..ec4bfa73 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js @@ -0,0 +1,747 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js"; +import { SERVICE as BOOKMARKS_SERVICE } from "./services/bookmarks_services.js"; +import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js"; +import { WorkflowLinkFixer } from "../../rgthree/common/link_fixer.js"; +import { injectCss, wait } from "../../rgthree/common/shared_utils.js"; +import { replaceNode, waitForCanvas, waitForGraph } from "./utils.js"; +import { NodeTypesString, addRgthree, getNodeTypeStrings } from "./constants.js"; +import { RgthreeProgressBar } from "../../rgthree/common/progress_bar.js"; +import { RgthreeConfigDialog } from "./config.js"; +import { iconGear, iconNode, iconReplace, iconStarFilled, logoRgthree, } from "../../rgthree/common/media/svgs.js"; +import { createElement, queryAll, query } from "../../rgthree/common/utils_dom.js"; +export var LogLevel; +(function (LogLevel) { + LogLevel[LogLevel["IMPORTANT"] = 1] = "IMPORTANT"; + LogLevel[LogLevel["ERROR"] = 2] = "ERROR"; + LogLevel[LogLevel["WARN"] = 3] = "WARN"; + LogLevel[LogLevel["INFO"] = 4] = "INFO"; + LogLevel[LogLevel["DEBUG"] = 5] = "DEBUG"; + LogLevel[LogLevel["DEV"] = 6] = "DEV"; +})(LogLevel || (LogLevel = {})); +const LogLevelKeyToLogLevel = { + IMPORTANT: LogLevel.IMPORTANT, + ERROR: LogLevel.ERROR, + WARN: LogLevel.WARN, + INFO: LogLevel.INFO, + DEBUG: LogLevel.DEBUG, + DEV: LogLevel.DEV, +}; +const LogLevelToMethod = { + [LogLevel.IMPORTANT]: "log", + [LogLevel.ERROR]: "error", + [LogLevel.WARN]: "warn", + [LogLevel.INFO]: "info", + [LogLevel.DEBUG]: "log", + [LogLevel.DEV]: "log", +}; +const LogLevelToCSS = { + [LogLevel.IMPORTANT]: "font-weight: bold; color: blue;", + [LogLevel.ERROR]: "", + [LogLevel.WARN]: "", + [LogLevel.INFO]: "font-style: italic; color: blue;", + [LogLevel.DEBUG]: "font-style: italic; color: #444;", + [LogLevel.DEV]: "color: #004b68;", +}; +let GLOBAL_LOG_LEVEL = LogLevel.ERROR; +const apiURL = api.apiURL; +api.apiURL = function (route) { + if (route.includes("rgthree/")) { + return (this.api_base + "/" + route).replace(/\/\//g, "/"); + } + return apiURL.apply(this, arguments); +}; +const INVOKE_EXTENSIONS_BLOCKLIST = [ + { + name: "Comfy.WidgetInputs", + reason: "Major conflict with rgthree-comfy nodes' inputs causing instability and " + + "repeated link disconnections.", + }, + { + name: "efficiency.widgethider", + reason: "Overrides value getter before widget getter is prepared. Can be lifted if/when " + + "https://github.com/jags111/efficiency-nodes-comfyui/pull/203 is pulled.", + }, +]; +class Logger { + log(level, message, ...args) { + var _a; + const [n, v] = this.logParts(level, message, ...args); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + } + logParts(level, message, ...args) { + if (level <= GLOBAL_LOG_LEVEL) { + const css = LogLevelToCSS[level] || ""; + if (level === LogLevel.DEV) { + message = `πŸ”§ ${message}`; + } + return [LogLevelToMethod[level], [`%c${message}`, css, ...args]]; + } + return ["none", []]; + } +} +class LogSession { + constructor(name) { + this.name = name; + this.logger = new Logger(); + this.logsCache = {}; + } + logParts(level, message, ...args) { + message = `${this.name || ""}${message ? " " + message : ""}`; + return this.logger.logParts(level, message, ...args); + } + logPartsOnceForTime(level, time, message, ...args) { + message = `${this.name || ""}${message ? " " + message : ""}`; + const cacheKey = `${level}:${message}`; + const cacheEntry = this.logsCache[cacheKey]; + const now = +new Date(); + if (cacheEntry && cacheEntry.lastShownTime + time > now) { + return ["none", []]; + } + const parts = this.logger.logParts(level, message, ...args); + if (console[parts[0]]) { + this.logsCache[cacheKey] = this.logsCache[cacheKey] || {}; + this.logsCache[cacheKey].lastShownTime = now; + } + return parts; + } + debugParts(message, ...args) { + return this.logParts(LogLevel.DEBUG, message, ...args); + } + infoParts(message, ...args) { + return this.logParts(LogLevel.INFO, message, ...args); + } + warnParts(message, ...args) { + return this.logParts(LogLevel.WARN, message, ...args); + } + errorParts(message, ...args) { + return this.logParts(LogLevel.ERROR, message, ...args); + } + newSession(name) { + return new LogSession(`${this.name}${name}`); + } +} +class Rgthree extends EventTarget { + constructor() { + var _a, _b, _c, _d; + super(); + this.api = api; + this.settingsDialog = null; + this.progressBarEl = null; + this.queueNodeIds = null; + this.version = CONFIG_SERVICE.getConfigValue("version"); + this.logger = new LogSession("[rgthree]"); + this.monitorBadLinksAlerted = false; + this.monitorLinkTimeout = null; + this.processingQueue = false; + this.loadingApiJson = null; + this.replacingReroute = null; + this.processingMouseDown = false; + this.processingMouseUp = false; + this.processingMouseMove = false; + this.lastCanvasMouseEvent = null; + this.canvasCurrentlyCopyingToClipboard = false; + this.canvasCurrentlyCopyingToClipboardWithMultipleNodes = false; + this.canvasCurrentlyPastingFromClipboard = false; + this.canvasCurrentlyPastingFromClipboardWithMultipleNodes = false; + this.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff = null; + this.isMac = !!(((_a = navigator.platform) === null || _a === void 0 ? void 0 : _a.toLocaleUpperCase().startsWith("MAC")) || + ((_c = (_b = navigator.userAgentData) === null || _b === void 0 ? void 0 : _b.platform) === null || _c === void 0 ? void 0 : _c.toLocaleUpperCase().startsWith("MAC"))); + const logLevel = (_d = LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")]) !== null && _d !== void 0 ? _d : GLOBAL_LOG_LEVEL; + this.setLogLevel(logLevel); + this.initializeGraphAndCanvasHooks(); + this.initializeComfyUIHooks(); + this.initializeContextMenu(); + this.rgthreeCssPromise = injectCss("extensions/rgthree-comfy/rgthree.css"); + this.initializeProgressBar(); + CONFIG_SERVICE.addEventListener("config-change", ((e) => { + var _a, _b; + if ((_b = (_a = e.detail) === null || _a === void 0 ? void 0 : _a.key) === null || _b === void 0 ? void 0 : _b.includes("features.progress_bar")) { + this.initializeProgressBar(); + } + })); + if (CONFIG_SERVICE.getConfigValue("debug.keys_down.enabled")) { + const elDebugKeydowns = createElement("div.rgthree-debug-keydowns", { + parent: document.body, + }); + const updateDebugKeyDown = () => { + elDebugKeydowns.innerText = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" "); + }; + KEY_EVENT_SERVICE.addEventListener("keydown", updateDebugKeyDown); + KEY_EVENT_SERVICE.addEventListener("keyup", updateDebugKeyDown); + } + } + async initializeProgressBar() { + var _a; + if (CONFIG_SERVICE.getConfigValue("features.progress_bar.enabled")) { + await this.rgthreeCssPromise; + if (!this.progressBarEl) { + this.progressBarEl = RgthreeProgressBar.create(); + this.progressBarEl.setAttribute("title", "Progress Bar by rgthree. right-click for rgthree menu."); + this.progressBarEl.addEventListener("contextmenu", async (e) => { + e.stopPropagation(); + e.preventDefault(); + }); + this.progressBarEl.addEventListener("pointerdown", async (e) => { + var _a; + LiteGraph.closeAllContextMenus(); + if (e.button == 2) { + const canvas = await waitForCanvas(); + new LiteGraph.ContextMenu(this.getRgthreeIContextMenuValues(), { + title: `
      ${logoRgthree} rgthree-comfy
      `, + left: e.clientX, + top: 5, + }); + return; + } + if (e.button == 0) { + const nodeId = (_a = this.progressBarEl) === null || _a === void 0 ? void 0 : _a.currentNodeId; + if (nodeId) { + const [canvas, graph] = await Promise.all([waitForCanvas(), waitForGraph()]); + const node = graph.getNodeById(Number(nodeId)); + if (node) { + canvas.centerOnNode(node); + e.stopPropagation(); + e.preventDefault(); + } + } + return; + } + }); + } + const isUpdatedComfyBodyClasses = !!query(".comfyui-body-top"); + const position = CONFIG_SERVICE.getConfigValue("features.progress_bar.position"); + this.progressBarEl.classList.toggle("rgthree-pos-bottom", position === "bottom"); + if (isUpdatedComfyBodyClasses) { + if (position === "bottom") { + query(".comfyui-body-bottom").appendChild(this.progressBarEl); + } + else { + query(".comfyui-body-top").appendChild(this.progressBarEl); + } + } + else { + document.body.appendChild(this.progressBarEl); + } + const height = CONFIG_SERVICE.getConfigValue("features.progress_bar.height") || 14; + this.progressBarEl.style.height = `${height}px`; + const fontSize = Math.max(10, Number(height) - 10); + this.progressBarEl.style.fontSize = `${fontSize}px`; + this.progressBarEl.style.fontWeight = fontSize <= 12 ? "bold" : "normal"; + } + else { + (_a = this.progressBarEl) === null || _a === void 0 ? void 0 : _a.remove(); + } + } + async initializeGraphAndCanvasHooks() { + const rgthree = this; + const graphSerialize = LGraph.prototype.serialize; + LGraph.prototype.serialize = function () { + const response = graphSerialize.apply(this, [...arguments]); + rgthree.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff = response; + return response; + }; + const processMouseDown = LGraphCanvas.prototype.processMouseDown; + LGraphCanvas.prototype.processMouseDown = function (e) { + rgthree.processingMouseDown = true; + const returnVal = processMouseDown.apply(this, [...arguments]); + rgthree.dispatchCustomEvent("on-process-mouse-down", { originalEvent: e }); + rgthree.processingMouseDown = false; + return returnVal; + }; + const adjustMouseEvent = LGraphCanvas.prototype.adjustMouseEvent; + LGraphCanvas.prototype.adjustMouseEvent = function (e) { + adjustMouseEvent.apply(this, [...arguments]); + rgthree.lastCanvasMouseEvent = e; + }; + const copyToClipboard = LGraphCanvas.prototype.copyToClipboard; + LGraphCanvas.prototype.copyToClipboard = function (items) { + rgthree.canvasCurrentlyCopyingToClipboard = true; + rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes = + Object.values(items || this.selected_nodes || []).length > 1; + const value = copyToClipboard.apply(this, [...arguments]); + rgthree.canvasCurrentlyCopyingToClipboard = false; + rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes = false; + return value; + }; + const pasteFromClipboard = LGraphCanvas.prototype.pasteFromClipboard; + LGraphCanvas.prototype.pasteFromClipboard = function (...args) { + rgthree.canvasCurrentlyPastingFromClipboard = true; + pasteFromClipboard.apply(this, [...arguments]); + rgthree.canvasCurrentlyPastingFromClipboard = false; + }; + const onGroupAdd = LGraphCanvas.onGroupAdd; + LGraphCanvas.onGroupAdd = function (...args) { + const graph = app.canvas.getCurrentGraph(); + onGroupAdd.apply(this, [...args]); + LGraphCanvas.onShowPropertyEditor({}, null, null, null, graph._groups[graph._groups.length - 1]); + }; + } + async invokeExtensionsAsync(method, ...args) { + var _a; + const comfyapp = app; + if (CONFIG_SERVICE.getConfigValue("features.invoke_extensions_async.node_created") === false) { + const [m, a] = this.logParts(LogLevel.INFO, `Skipping invokeExtensionsAsync for applicable rgthree-comfy nodes`); + (_a = console[m]) === null || _a === void 0 ? void 0 : _a.call(console, ...a); + return Promise.resolve(); + } + return await Promise.all(comfyapp.extensions.map(async (ext) => { + var _a, _b; + if (ext === null || ext === void 0 ? void 0 : ext[method]) { + try { + const blocked = INVOKE_EXTENSIONS_BLOCKLIST.find((block) => ext.name.toLowerCase().startsWith(block.name.toLowerCase())); + if (blocked) { + const [n, v] = this.logger.logPartsOnceForTime(LogLevel.WARN, 5000, `Blocked extension '${ext.name}' method '${method}' for rgthree-nodes because: ${blocked.reason}`); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + return Promise.resolve(); + } + return await ext[method](...args, comfyapp); + } + catch (error) { + const [n, v] = this.logParts(LogLevel.ERROR, `Error calling extension '${ext.name}' method '${method}' for rgthree-node.`, { error }, { extension: ext }, { args }); + (_b = console[n]) === null || _b === void 0 ? void 0 : _b.call(console, ...v); + } + } + })); + } + dispatchCustomEvent(event, detail) { + if (detail != null) { + return this.dispatchEvent(new CustomEvent(event, { detail })); + } + return this.dispatchEvent(new CustomEvent(event)); + } + async initializeContextMenu() { + const that = this; + setTimeout(async () => { + const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function (...args) { + let existingOptions = getCanvasMenuOptions.apply(this, [...args]); + const options = []; + options.push(null); + options.push(null); + options.push(null); + options.push({ + content: logoRgthree + `rgthree-comfy`, + className: "rgthree-contextmenu-item rgthree-contextmenu-main-item-rgthree-comfy", + submenu: { + options: that.getRgthreeIContextMenuValues(), + }, + }); + options.push(null); + options.push(null); + let idx = null; + idx = idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Queue Group"); }) + 1; + idx = + idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Queue Selected"); }) + 1; + idx = idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Convert to Group"); }); + idx = idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Arrange ("); }); + idx = idx || existingOptions.findIndex((o) => !o) + 1; + idx = idx || 3; + existingOptions.splice(idx, 0, ...options); + for (let i = existingOptions.length; i > 0; i--) { + if (existingOptions[i] === null && existingOptions[i + 1] === null) { + existingOptions.splice(i, 1); + } + } + return existingOptions; + }; + }, 1016); + } + getRgthreeIContextMenuValues() { + const [canvas, graph] = [app.canvas, app.canvas.getCurrentGraph()]; + const selectedNodes = Object.values(canvas.selected_nodes || {}); + let rerouteNodes = []; + if (selectedNodes.length) { + rerouteNodes = selectedNodes.filter((n) => n.type === "Reroute"); + } + else { + rerouteNodes = graph._nodes.filter((n) => n.type == "Reroute"); + } + const rerouteLabel = selectedNodes.length ? "selected" : "all"; + const showBookmarks = CONFIG_SERVICE.getFeatureValue("menu_bookmarks.enabled"); + const bookmarkMenuItems = showBookmarks ? getBookmarks() : []; + return [ + { + content: "Nodes", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + { + content: iconNode + "All", + className: "rgthree-contextmenu-item", + has_submenu: true, + submenu: { + options: getNodeTypeStrings(), + callback: (value, options, event) => { + const node = LiteGraph.createNode(addRgthree(value)); + if (node) { + node.pos = [ + rgthree.lastCanvasMouseEvent.canvasX, + rgthree.lastCanvasMouseEvent.canvasY, + ]; + canvas.graph.add(node); + canvas.selectNode(node); + graph.setDirtyCanvas(true, true); + } + }, + extra: { rgthree_doNotNest: true }, + }, + }, + { + content: "Actions", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + { + content: iconGear + "Settings (rgthree-comfy)", + disabled: !!this.settingsDialog, + className: "rgthree-contextmenu-item", + callback: (...args) => { + this.settingsDialog = new RgthreeConfigDialog().show(); + this.settingsDialog.addEventListener("close", (e) => { + this.settingsDialog = null; + }); + }, + }, + { + content: iconReplace + ` Convert ${rerouteLabel} Reroutes`, + disabled: !rerouteNodes.length, + className: "rgthree-contextmenu-item", + callback: (...args) => { + const msg = `Convert ${rerouteLabel} ComfyUI Reroutes to Reroute (rgthree) nodes? \n` + + `(First save a copy of your workflow & check reroute connections afterwards)`; + if (!window.confirm(msg)) { + return; + } + (async () => { + for (const node of [...rerouteNodes]) { + if (node.type == "Reroute") { + this.replacingReroute = node.id; + await replaceNode(node, NodeTypesString.REROUTE); + this.replacingReroute = null; + } + } + })(); + }, + }, + ...bookmarkMenuItems, + { + content: "More...", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + { + content: iconStarFilled + "Star on Github", + className: "rgthree-contextmenu-item rgthree-contextmenu-github", + callback: (...args) => { + window.open("https://github.com/rgthree/rgthree-comfy", "_blank"); + }, + }, + ]; + } + async queueOutputNodes(nodes) { + var _a; + const nodeIds = nodes.map((n) => n.id); + try { + this.queueNodeIds = nodeIds; + await app.queuePrompt(0); + } + catch (e) { + const [n, v] = this.logParts(LogLevel.ERROR, `There was an error queuing nodes ${nodeIds}`, e); + (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v); + } + finally { + this.queueNodeIds = null; + } + } + recursiveAddNodes(nodeId, oldOutput, newOutput) { + let currentId = nodeId; + let currentNode = oldOutput[currentId]; + if (newOutput[currentId] == null) { + newOutput[currentId] = currentNode; + for (const inputValue of Object.values(currentNode.inputs || [])) { + if (Array.isArray(inputValue)) { + this.recursiveAddNodes(inputValue[0], oldOutput, newOutput); + } + } + } + return newOutput; + } + initializeComfyUIHooks() { + const rgthree = this; + const queuePrompt = app.queuePrompt; + app.queuePrompt = async function (number, batchCount) { + rgthree.processingQueue = true; + rgthree.dispatchCustomEvent("queue"); + try { + return await queuePrompt.apply(app, [...arguments]); + } + finally { + rgthree.processingQueue = false; + rgthree.dispatchCustomEvent("queue-end"); + } + }; + const loadApiJson = app.loadApiJson; + app.loadApiJson = async function (apiData, fileName) { + rgthree.loadingApiJson = apiData; + try { + loadApiJson.apply(app, [...arguments]); + } + finally { + rgthree.loadingApiJson = null; + } + }; + const graphToPrompt = app.graphToPrompt; + app.graphToPrompt = async function () { + rgthree.dispatchCustomEvent("graph-to-prompt"); + let promise = graphToPrompt.apply(app, [...arguments]); + await promise; + rgthree.dispatchCustomEvent("graph-to-prompt-end"); + return promise; + }; + const apiQueuePrompt = api.queuePrompt; + api.queuePrompt = async function (index, prompt, ...args) { + var _a; + if (((_a = rgthree.queueNodeIds) === null || _a === void 0 ? void 0 : _a.length) && prompt.output) { + const oldOutput = prompt.output; + let newOutput = {}; + for (const queueNodeId of rgthree.queueNodeIds) { + rgthree.recursiveAddNodes(String(queueNodeId), oldOutput, newOutput); + } + prompt.output = newOutput; + } + rgthree.dispatchCustomEvent("comfy-api-queue-prompt-before", { + workflow: prompt.workflow, + output: prompt.output, + }); + const response = apiQueuePrompt.apply(app, [index, prompt, ...args]); + rgthree.dispatchCustomEvent("comfy-api-queue-prompt-end"); + return response; + }; + const clean = app.clean; + app.clean = function () { + rgthree.clearAllMessages(); + clean && clean.apply(app, [...arguments]); + }; + const loadGraphData = app.loadGraphData; + app.loadGraphData = function (graph) { + if (rgthree.monitorLinkTimeout) { + clearTimeout(rgthree.monitorLinkTimeout); + rgthree.monitorLinkTimeout = null; + } + rgthree.clearAllMessages(); + let graphCopy; + try { + graphCopy = JSON.parse(JSON.stringify(graph)); + } + catch (e) { + graphCopy = null; + } + setTimeout(() => { + var _a, _b, _c; + const wasLoadingAborted = (_b = (_a = document + .querySelector(".comfy-modal-content")) === null || _a === void 0 ? void 0 : _a.textContent) === null || _b === void 0 ? void 0 : _b.includes("Loading aborted due"); + const graphToUse = wasLoadingAborted ? graphCopy || graph : app.graph; + const fixer = WorkflowLinkFixer.create(graphToUse); + const fixBadLinksResult = fixer.check(); + if (fixBadLinksResult.hasBadLinks) { + const [n, v] = rgthree.logParts(LogLevel.WARN, `The workflow you've loaded has corrupt linking data. Open ${new URL(location.href).origin}/rgthree/link_fixer to try to fix.`); + (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v); + if (CONFIG_SERVICE.getConfigValue("features.show_alerts_for_corrupt_workflows")) { + rgthree.showMessage({ + id: "bad-links", + type: "warn", + message: "The workflow you've loaded has corrupt linking data that may be able to be fixed.", + actions: [ + { + label: "Open fixer", + href: "/rgthree/link_fixer", + }, + { + label: "Fix in place", + href: "/rgthree/link_fixer", + callback: (event) => { + event.stopPropagation(); + event.preventDefault(); + if (confirm("This will attempt to fix in place. Please make sure to have a saved copy of your workflow.")) { + try { + const fixBadLinksResult = fixer.fix(); + if (!fixBadLinksResult.hasBadLinks) { + rgthree.hideMessage("bad-links"); + alert("Success! It's possible some valid links may have been affected. Please check and verify your workflow."); + wasLoadingAborted && app.loadGraphData(fixBadLinksResult.graph); + if (CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") || + CONFIG_SERVICE.getConfigValue("features.monitor_bad_links")) { + rgthree.monitorLinkTimeout = setTimeout(() => { + rgthree.monitorBadLinks(); + }, 5000); + } + } + } + catch (e) { + console.error(e); + alert("Unsuccessful at fixing corrupt data. :("); + rgthree.hideMessage("bad-links"); + } + } + }, + }, + ], + }); + } + } + else if (CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") || + CONFIG_SERVICE.getConfigValue("features.monitor_bad_links")) { + rgthree.monitorLinkTimeout = setTimeout(() => { + rgthree.monitorBadLinks(); + }, 5000); + } + }, 100); + return loadGraphData && loadGraphData.apply(app, [...arguments]); + }; + } + getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(node) { + var _a, _b, _c; + return ((_c = (_b = (_a = this.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff) === null || _a === void 0 ? void 0 : _a.nodes) === null || _b === void 0 ? void 0 : _b.find((n) => n.id === node.id)) !== null && _c !== void 0 ? _c : null); + } + async showMessage(data) { + let container = document.querySelector(".rgthree-top-messages-container"); + if (!container) { + container = document.createElement("div"); + container.classList.add("rgthree-top-messages-container"); + document.body.appendChild(container); + } + const dialogs = queryAll("dialog[open]"); + if (dialogs.length) { + let dialog = dialogs[dialogs.length - 1]; + dialog.appendChild(container); + dialog.addEventListener("close", (e) => { + document.body.appendChild(container); + }); + } + await this.hideMessage(data.id); + const messageContainer = document.createElement("div"); + messageContainer.setAttribute("type", data.type || "info"); + const message = document.createElement("span"); + message.innerHTML = data.message; + messageContainer.appendChild(message); + for (let a = 0; a < (data.actions || []).length; a++) { + const action = data.actions[a]; + if (a > 0) { + const sep = document.createElement("span"); + sep.innerHTML = " | "; + messageContainer.appendChild(sep); + } + const actionEl = document.createElement("a"); + actionEl.innerText = action.label; + if (action.href) { + actionEl.target = "_blank"; + actionEl.href = action.href; + } + if (action.callback) { + actionEl.onclick = (e) => { + return action.callback(e); + }; + } + messageContainer.appendChild(actionEl); + } + const messageAnimContainer = document.createElement("div"); + messageAnimContainer.setAttribute("msg-id", data.id); + messageAnimContainer.appendChild(messageContainer); + container.appendChild(messageAnimContainer); + await wait(64); + messageAnimContainer.style.marginTop = `-${messageAnimContainer.offsetHeight}px`; + await wait(64); + messageAnimContainer.classList.add("-show"); + if (data.timeout) { + await wait(data.timeout); + this.hideMessage(data.id); + } + } + async hideMessage(id) { + const msg = document.querySelector(`.rgthree-top-messages-container > [msg-id="${id}"]`); + if (msg === null || msg === void 0 ? void 0 : msg.classList.contains("-show")) { + msg.classList.remove("-show"); + await wait(750); + } + msg && msg.remove(); + } + async clearAllMessages() { + let container = document.querySelector(".rgthree-top-messages-container"); + container && (container.innerHTML = ""); + } + setLogLevel(level) { + if (typeof level === "string") { + level = LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")]; + } + if (level != null) { + GLOBAL_LOG_LEVEL = level; + } + } + logParts(level, message, ...args) { + return this.logger.logParts(level, message, ...args); + } + newLogSession(name) { + return this.logger.newSession(name); + } + isDebugMode() { + if (window.location.href.includes("rgthree-debug=false")) { + return false; + } + return GLOBAL_LOG_LEVEL >= LogLevel.DEBUG || window.location.href.includes("rgthree-debug"); + } + isDevMode() { + if (window.location.href.includes("rgthree-dev=false")) { + return false; + } + return GLOBAL_LOG_LEVEL >= LogLevel.DEV || window.location.href.includes("rgthree-dev"); + } + monitorBadLinks() { + const badLinksFound = WorkflowLinkFixer.create(app.graph).check(); + if (badLinksFound.hasBadLinks && !this.monitorBadLinksAlerted) { + this.monitorBadLinksAlerted = true; + alert(`Problematic links just found in live data. Can you save your workflow and file a bug with ` + + `the last few steps you took to trigger this at ` + + `https://github.com/rgthree/rgthree-comfy/issues. Thank you!`); + } + else if (!badLinksFound.hasBadLinks) { + this.monitorBadLinksAlerted = false; + } + this.monitorLinkTimeout = setTimeout(() => { + this.monitorBadLinks(); + }, 5000); + } +} +function getBookmarks() { + const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks(); + const bookmarkItems = bookmarks.map((n) => ({ + content: `[${n.shortcutKey}] ${n.title}`, + className: "rgthree-contextmenu-item", + callback: () => { + n.canvasToBookmark(); + }, + })); + return !bookmarkItems.length + ? [] + : [ + { + content: "πŸ”– Bookmarks", + disabled: true, + className: "rgthree-contextmenu-item rgthree-contextmenu-label", + }, + ...bookmarkItems, + ]; +} +export const rgthree = new Rgthree(); +window.rgthree = rgthree; +app.registerExtension({ + name: "Comfy.RgthreeComfy", + aboutPageBadges: [ + { + label: `rgthree-comfy v${rgthree.version}`, + url: "https://github.com/rgthree/rgthree-comfy", + icon: "rgthree-comfy-about-badge-logo", + }, + ], +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/seed.js b/custom_nodes/rgthree-comfy/web/comfyui/seed.js new file mode 100644 index 00000000..e23fa073 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/seed.js @@ -0,0 +1,202 @@ +import { app } from "../../scripts/app.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +import { RgthreeBaseServerNode } from "./base_node.js"; +import { rgthree } from "./rgthree.js"; +import { addConnectionLayoutSupport } from "./utils.js"; +import { NodeTypesString } from "./constants.js"; +const LAST_SEED_BUTTON_LABEL = "♻️ (Use Last Queued Seed)"; +const SPECIAL_SEED_RANDOM = -1; +const SPECIAL_SEED_INCREMENT = -2; +const SPECIAL_SEED_DECREMENT = -3; +const SPECIAL_SEEDS = [SPECIAL_SEED_RANDOM, SPECIAL_SEED_INCREMENT, SPECIAL_SEED_DECREMENT]; +class RgthreeSeed extends RgthreeBaseServerNode { + constructor(title = RgthreeSeed.title) { + super(title); + this.serialize_widgets = true; + this.logger = rgthree.newLogSession(`[Seed]`); + this.lastSeed = undefined; + this.serializedCtx = {}; + this.lastSeedValue = null; + this.handleApiHijackingBound = this.handleApiHijacking.bind(this); + this.properties["randomMax"] = 1125899906842624; + this.properties["randomMin"] = 0; + rgthree.addEventListener("comfy-api-queue-prompt-before", this.handleApiHijackingBound); + } + onPropertyChanged(prop, value, prevValue) { + if (prop === 'randomMax') { + this.properties["randomMax"] = Math.min(1125899906842624, Number(value)); + } + else if (prop === 'randomMin') { + this.properties["randomMin"] = Math.max(-1125899906842624, Number(value)); + } + return true; + } + onRemoved() { + rgthree.addEventListener("comfy-api-queue-prompt-before", this.handleApiHijackingBound); + } + configure(info) { + var _a; + super.configure(info); + if ((_a = this.properties) === null || _a === void 0 ? void 0 : _a["showLastSeed"]) { + this.addLastSeedValue(); + } + } + async handleAction(action) { + if (action === "Randomize Each Time") { + this.seedWidget.value = SPECIAL_SEED_RANDOM; + } + else if (action === "Use Last Queued Seed") { + this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value; + this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL; + this.lastSeedButton.disabled = true; + } + } + onNodeCreated() { + var _a; + (_a = super.onNodeCreated) === null || _a === void 0 ? void 0 : _a.call(this); + for (const [i, w] of this.widgets.entries()) { + if (w.name === "seed") { + this.seedWidget = w; + this.seedWidget.value = SPECIAL_SEED_RANDOM; + } + else if (w.name === "control_after_generate") { + this.widgets.splice(i, 1); + } + } + this.addWidget("button", "🎲 Randomize Each Time", "", () => { + this.seedWidget.value = SPECIAL_SEED_RANDOM; + }, { serialize: false }); + this.addWidget("button", "🎲 New Fixed Random", "", () => { + this.seedWidget.value = this.generateRandomSeed(); + }, { serialize: false }); + this.lastSeedButton = this.addWidget("button", LAST_SEED_BUTTON_LABEL, "", () => { + this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value; + this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL; + this.lastSeedButton.disabled = true; + }, { width: 50, serialize: false }); + this.lastSeedButton.disabled = true; + } + generateRandomSeed() { + let step = this.seedWidget.options.step || 1; + const randomMin = Number(this.properties['randomMin'] || 0); + const randomMax = Number(this.properties['randomMax'] || 1125899906842624); + const randomRange = (randomMax - randomMin) / (step / 10); + let seed = Math.floor(Math.random() * randomRange) * (step / 10) + randomMin; + if (SPECIAL_SEEDS.includes(seed)) { + seed = 0; + } + return seed; + } + getExtraMenuOptions(canvas, options) { + var _a; + (_a = super.getExtraMenuOptions) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]); + options.splice(options.length - 1, 0, { + content: "Show/Hide Last Seed Value", + callback: (_value, _options, _event, _parentMenu, _node) => { + this.properties["showLastSeed"] = !this.properties["showLastSeed"]; + if (this.properties["showLastSeed"]) { + this.addLastSeedValue(); + } + else { + this.removeLastSeedValue(); + } + }, + }); + return []; + } + addLastSeedValue() { + if (this.lastSeedValue) + return; + this.lastSeedValue = ComfyWidgets["STRING"](this, "last_seed", ["STRING", { multiline: true }], app).widget; + this.lastSeedValue.inputEl.readOnly = true; + this.lastSeedValue.inputEl.style.fontSize = "0.75rem"; + this.lastSeedValue.inputEl.style.textAlign = "center"; + this.computeSize(); + } + removeLastSeedValue() { + if (!this.lastSeedValue) + return; + this.lastSeedValue.inputEl.remove(); + this.widgets.splice(this.widgets.indexOf(this.lastSeedValue), 1); + this.lastSeedValue = null; + this.computeSize(); + } + handleApiHijacking(e) { + var _a, _b, _c, _d; + if (this.mode === LiteGraph.NEVER || this.mode === 4) { + return; + } + const workflow = e.detail.workflow; + const output = e.detail.output; + let workflowNode = (_b = (_a = workflow === null || workflow === void 0 ? void 0 : workflow.nodes) === null || _a === void 0 ? void 0 : _a.find((n) => n.id === this.id)) !== null && _b !== void 0 ? _b : null; + let outputInputs = (_c = output === null || output === void 0 ? void 0 : output[this.id]) === null || _c === void 0 ? void 0 : _c.inputs; + if (!workflowNode || + !outputInputs || + outputInputs[this.seedWidget.name || "seed"] === undefined) { + const [n, v] = this.logger.warnParts(`Node ${this.id} not found in prompt data sent to server. This may be fine if only ` + + `queuing part of the workflow. If not, then this could be a bug.`); + (_d = console[n]) === null || _d === void 0 ? void 0 : _d.call(console, ...v); + return; + } + const seedToUse = this.getSeedToUse(); + const seedWidgetndex = this.widgets.indexOf(this.seedWidget); + workflowNode.widgets_values[seedWidgetndex] = seedToUse; + outputInputs[this.seedWidget.name || "seed"] = seedToUse; + this.lastSeed = seedToUse; + if (seedToUse != this.seedWidget.value) { + this.lastSeedButton.name = `♻️ ${this.lastSeed}`; + this.lastSeedButton.disabled = false; + } + else { + this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL; + this.lastSeedButton.disabled = true; + } + if (this.lastSeedValue) { + this.lastSeedValue.value = `Last Seed: ${this.lastSeed}`; + } + } + getSeedToUse() { + const inputSeed = Number(this.seedWidget.value); + let seedToUse = null; + if (SPECIAL_SEEDS.includes(inputSeed)) { + if (typeof this.lastSeed === "number" && !SPECIAL_SEEDS.includes(this.lastSeed)) { + if (inputSeed === SPECIAL_SEED_INCREMENT) { + seedToUse = this.lastSeed + 1; + } + else if (inputSeed === SPECIAL_SEED_DECREMENT) { + seedToUse = this.lastSeed - 1; + } + } + if (seedToUse == null || SPECIAL_SEEDS.includes(seedToUse)) { + seedToUse = this.generateRandomSeed(); + } + } + return seedToUse !== null && seedToUse !== void 0 ? seedToUse : inputSeed; + } + static setUp(comfyClass, nodeData) { + RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeSeed); + } + static onRegisteredForOverride(comfyClass, ctxClass) { + addConnectionLayoutSupport(RgthreeSeed, app, [ + ["Left", "Right"], + ["Right", "Left"], + ]); + setTimeout(() => { + RgthreeSeed.category = comfyClass.category; + }); + } +} +RgthreeSeed.title = NodeTypesString.SEED; +RgthreeSeed.type = NodeTypesString.SEED; +RgthreeSeed.comfyClass = NodeTypesString.SEED; +RgthreeSeed.exposedActions = ["Randomize Each Time", "Use Last Queued Seed"]; +RgthreeSeed["@randomMax"] = { type: "number" }; +RgthreeSeed["@randomMin"] = { type: "number" }; +app.registerExtension({ + name: "rgthree.Seed", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData.name === RgthreeSeed.type) { + RgthreeSeed.setUp(nodeType, nodeData); + } + }, +}); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/bookmarks_services.js b/custom_nodes/rgthree-comfy/web/comfyui/services/bookmarks_services.js new file mode 100644 index 00000000..839df30e --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/services/bookmarks_services.js @@ -0,0 +1,24 @@ +import { app } from "../../../scripts/app.js"; +import { NodeTypesString } from "../constants.js"; +import { reduceNodesDepthFirst } from "../utils.js"; +const SHORTCUT_DEFAULTS = "1234567890abcdefghijklmnopqrstuvwxyz".split(""); +class BookmarksService { + getCurrentBookmarks() { + return reduceNodesDepthFirst(app.graph.nodes, (n, acc) => { + if (n.type === NodeTypesString.BOOKMARK) { + acc.push(n); + } + }, []).sort((a, b) => a.title.localeCompare(b.title)); + } + getExistingShortcuts() { + const bookmarkNodes = this.getCurrentBookmarks(); + const usedShortcuts = new Set(bookmarkNodes.map((n) => n.shortcutKey)); + return usedShortcuts; + } + getNextShortcut() { + var _a; + const existingShortcuts = this.getExistingShortcuts(); + return (_a = SHORTCUT_DEFAULTS.find((char) => !existingShortcuts.has(char))) !== null && _a !== void 0 ? _a : "1"; + } +} +export const SERVICE = new BookmarksService(); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/config_service.js b/custom_nodes/rgthree-comfy/web/comfyui/services/config_service.js new file mode 100644 index 00000000..34f10871 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/services/config_service.js @@ -0,0 +1,28 @@ +import { rgthreeConfig } from "../../../rgthree/config.js"; +import { getObjectValue, setObjectValue } from "../../../rgthree/common/shared_utils.js"; +import { rgthreeApi } from "../../../rgthree/common/rgthree_api.js"; +class ConfigService extends EventTarget { + getConfigValue(key, def) { + return getObjectValue(rgthreeConfig, key, def); + } + getFeatureValue(key, def) { + key = "features." + key.replace(/^features\./, ""); + return getObjectValue(rgthreeConfig, key, def); + } + async setConfigValues(changed) { + const body = new FormData(); + body.append("json", JSON.stringify(changed)); + const response = await rgthreeApi.fetchJson("/config", { method: "POST", body }); + if (response.status === "ok") { + for (const [key, value] of Object.entries(changed)) { + setObjectValue(rgthreeConfig, key, value); + this.dispatchEvent(new CustomEvent("config-change", { detail: { key, value } })); + } + } + else { + return false; + } + return true; + } +} +export const SERVICE = new ConfigService(); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/context_service.js b/custom_nodes/rgthree-comfy/web/comfyui/services/context_service.js new file mode 100644 index 00000000..27e19658 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/services/context_service.js @@ -0,0 +1,51 @@ +import { getConnectedOutputNodesAndFilterPassThroughs } from "../utils.js"; +export let SERVICE; +const OWNED_PREFIX = "+"; +const REGEX_PREFIX = /^[\+⚠️]\s*/; +const REGEX_EMPTY_INPUT = /^\+\s*$/; +export function stripContextInputPrefixes(name) { + return name.replace(REGEX_PREFIX, ""); +} +export function getContextOutputName(inputName) { + if (inputName === "base_ctx") + return "CONTEXT"; + return stripContextInputPrefixes(inputName).toUpperCase(); +} +export var InputMutationOperation; +(function (InputMutationOperation) { + InputMutationOperation[InputMutationOperation["UNKNOWN"] = 0] = "UNKNOWN"; + InputMutationOperation[InputMutationOperation["ADDED"] = 1] = "ADDED"; + InputMutationOperation[InputMutationOperation["REMOVED"] = 2] = "REMOVED"; + InputMutationOperation[InputMutationOperation["RENAMED"] = 3] = "RENAMED"; +})(InputMutationOperation || (InputMutationOperation = {})); +export class ContextService { + constructor() { + if (SERVICE) { + throw new Error("ContextService was already instantiated."); + } + } + onInputChanges(node, mutation) { + const childCtxs = getConnectedOutputNodesAndFilterPassThroughs(node, node, 0); + for (const childCtx of childCtxs) { + childCtx.handleUpstreamMutation(mutation); + } + } + getDynamicContextInputsData(node) { + return node + .getContextInputsList() + .map((input, index) => ({ + name: stripContextInputPrefixes(input.name), + type: String(input.type), + index, + })) + .filter((i) => i.type !== "*"); + } + getDynamicContextOutputsData(node) { + return node.outputs.map((output, index) => ({ + name: stripContextInputPrefixes(output.name), + type: String(output.type), + index, + })); + } +} +SERVICE = new ContextService(); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/fast_groups_service.js b/custom_nodes/rgthree-comfy/web/comfyui/services/fast_groups_service.js new file mode 100644 index 00000000..3ee8778f --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/services/fast_groups_service.js @@ -0,0 +1,163 @@ +import { app } from "../../../scripts/app.js"; +import { getGraphDependantNodeKey, getGroupNodes, reduceNodesDepthFirst } from "../utils.js"; +class FastGroupsService { + constructor() { + this.msThreshold = 400; + this.msLastUnsorted = 0; + this.msLastAlpha = 0; + this.msLastPosition = 0; + this.groupsUnsorted = []; + this.groupsSortedAlpha = []; + this.groupsSortedPosition = []; + this.fastGroupNodes = []; + this.runScheduledForMs = null; + this.runScheduleTimeout = null; + this.runScheduleAnimation = null; + this.cachedNodeBoundings = null; + } + addFastGroupNode(node) { + this.fastGroupNodes.push(node); + this.scheduleRun(8); + } + removeFastGroupNode(node) { + var _a; + const index = this.fastGroupNodes.indexOf(node); + if (index > -1) { + this.fastGroupNodes.splice(index, 1); + } + if (!((_a = this.fastGroupNodes) === null || _a === void 0 ? void 0 : _a.length)) { + this.clearScheduledRun(); + this.groupsUnsorted = []; + this.groupsSortedAlpha = []; + this.groupsSortedPosition = []; + } + } + run() { + if (!this.runScheduledForMs) { + return; + } + for (const node of this.fastGroupNodes) { + node.refreshWidgets(); + } + this.clearScheduledRun(); + this.scheduleRun(); + } + scheduleRun(ms = 500) { + if (this.runScheduledForMs && ms < this.runScheduledForMs) { + this.clearScheduledRun(); + } + if (!this.runScheduledForMs && this.fastGroupNodes.length) { + this.runScheduledForMs = ms; + this.runScheduleTimeout = setTimeout(() => { + this.runScheduleAnimation = requestAnimationFrame(() => this.run()); + }, ms); + } + } + clearScheduledRun() { + this.runScheduleTimeout && clearTimeout(this.runScheduleTimeout); + this.runScheduleAnimation && cancelAnimationFrame(this.runScheduleAnimation); + this.runScheduleTimeout = null; + this.runScheduleAnimation = null; + this.runScheduledForMs = null; + } + getBoundingsForAllNodes() { + if (!this.cachedNodeBoundings) { + this.cachedNodeBoundings = reduceNodesDepthFirst(app.graph._nodes, (node, acc) => { + var _a, _b; + let bounds = node.getBounding(); + if (bounds[0] === 0 && bounds[1] === 0 && bounds[2] === 0 && bounds[3] === 0) { + const ctx = (_b = (_a = node.graph) === null || _a === void 0 ? void 0 : _a.primaryCanvas) === null || _b === void 0 ? void 0 : _b.canvas.getContext("2d"); + if (ctx) { + node.updateArea(ctx); + bounds = node.getBounding(); + } + } + acc[getGraphDependantNodeKey(node)] = bounds; + }, {}); + setTimeout(() => { + this.cachedNodeBoundings = null; + }, 50); + } + return this.cachedNodeBoundings; + } + recomputeInsideNodesForGroup(group) { + if (app.canvas.isDragging) + return; + const cachedBoundings = this.getBoundingsForAllNodes(); + const nodes = group.graph.nodes; + group._children.clear(); + group.nodes.length = 0; + for (const node of nodes) { + const nodeBounding = cachedBoundings[getGraphDependantNodeKey(node)]; + const nodeCenter = nodeBounding && + [nodeBounding[0] + nodeBounding[2] * 0.5, nodeBounding[1] + nodeBounding[3] * 0.5]; + if (nodeCenter) { + const grouBounds = group._bounding; + if (nodeCenter[0] >= grouBounds[0] && + nodeCenter[0] < grouBounds[0] + grouBounds[2] && + nodeCenter[1] >= grouBounds[1] && + nodeCenter[1] < grouBounds[1] + grouBounds[3]) { + group._children.add(node); + group.nodes.push(node); + } + } + } + } + getGroupsUnsorted(now) { + var _a, _b, _c; + const canvas = app.canvas; + const graph = (_a = canvas.getCurrentGraph()) !== null && _a !== void 0 ? _a : app.graph; + if (!canvas.selected_group_moving && + (!this.groupsUnsorted.length || now - this.msLastUnsorted > this.msThreshold)) { + this.groupsUnsorted = [...graph._groups]; + const subgraphs = (_b = graph.subgraphs) === null || _b === void 0 ? void 0 : _b.values(); + if (subgraphs) { + let s; + while ((s = subgraphs.next().value)) + this.groupsUnsorted.push(...((_c = s.groups) !== null && _c !== void 0 ? _c : [])); + } + for (const group of this.groupsUnsorted) { + this.recomputeInsideNodesForGroup(group); + group.rgthree_hasAnyActiveNode = getGroupNodes(group).some((n) => n.mode === LiteGraph.ALWAYS); + } + this.msLastUnsorted = now; + } + return this.groupsUnsorted; + } + getGroupsAlpha(now) { + if (!this.groupsSortedAlpha.length || now - this.msLastAlpha > this.msThreshold) { + this.groupsSortedAlpha = [...this.getGroupsUnsorted(now)].sort((a, b) => { + return a.title.localeCompare(b.title); + }); + this.msLastAlpha = now; + } + return this.groupsSortedAlpha; + } + getGroupsPosition(now) { + if (!this.groupsSortedPosition.length || now - this.msLastPosition > this.msThreshold) { + this.groupsSortedPosition = [...this.getGroupsUnsorted(now)].sort((a, b) => { + const aY = Math.floor(a._pos[1] / 30); + const bY = Math.floor(b._pos[1] / 30); + if (aY == bY) { + const aX = Math.floor(a._pos[0] / 30); + const bX = Math.floor(b._pos[0] / 30); + return aX - bX; + } + return aY - bY; + }); + this.msLastPosition = now; + } + return this.groupsSortedPosition; + } + getGroups(sort) { + const now = +new Date(); + if (sort === "alphanumeric") { + return this.getGroupsAlpha(now); + } + if (sort === "position") { + return this.getGroupsPosition(now); + } + return this.getGroupsUnsorted(now); + } +} +export const SERVICE = new FastGroupsService(); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/key_events_services.js b/custom_nodes/rgthree-comfy/web/comfyui/services/key_events_services.js new file mode 100644 index 00000000..af155048 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/services/key_events_services.js @@ -0,0 +1,115 @@ +class KeyEventService extends EventTarget { + constructor() { + var _a, _b, _c; + super(); + this.downKeys = {}; + this.shiftDownKeys = {}; + this.ctrlKey = false; + this.altKey = false; + this.metaKey = false; + this.shiftKey = false; + this.isMac = !!(((_a = navigator.platform) === null || _a === void 0 ? void 0 : _a.toLocaleUpperCase().startsWith("MAC")) || + ((_c = (_b = navigator.userAgentData) === null || _b === void 0 ? void 0 : _b.platform) === null || _c === void 0 ? void 0 : _c.toLocaleUpperCase().startsWith("MAC"))); + this.initialize(); + } + initialize() { + const that = this; + const processKey = LGraphCanvas.prototype.processKey; + LGraphCanvas.prototype.processKey = function (e) { + if (e.type === "keydown" || e.type === "keyup") { + that.handleKeyDownOrUp(e); + } + return processKey.apply(this, [...arguments]); + }; + window.addEventListener("keydown", (e) => { + that.handleKeyDownOrUp(e); + }); + window.addEventListener("keyup", (e) => { + that.handleKeyDownOrUp(e); + }); + document.addEventListener("visibilitychange", (e) => { + this.clearKeydowns(); + }); + window.addEventListener("blur", (e) => { + this.clearKeydowns(); + }); + } + handleKeyDownOrUp(e) { + const key = e.key.toLocaleUpperCase(); + if ((e.type === 'keydown' && this.downKeys[key] === true) + || (e.type === 'keyup' && this.downKeys[key] === undefined)) { + return; + } + this.ctrlKey = !!e.ctrlKey; + this.altKey = !!e.altKey; + this.metaKey = !!e.metaKey; + this.shiftKey = !!e.shiftKey; + if (e.type === "keydown") { + this.downKeys[key] = true; + this.dispatchCustomEvent("keydown", { originalEvent: e }); + if (this.shiftKey && key !== 'SHIFT') { + this.shiftDownKeys[key] = true; + } + } + else if (e.type === "keyup") { + if (key === "META" && this.isMac) { + this.clearKeydowns(); + } + else { + delete this.downKeys[key]; + } + if (key === 'SHIFT') { + for (const key in this.shiftDownKeys) { + delete this.downKeys[key]; + delete this.shiftDownKeys[key]; + } + } + this.dispatchCustomEvent("keyup", { originalEvent: e }); + } + } + clearKeydowns() { + this.ctrlKey = false; + this.altKey = false; + this.metaKey = false; + this.shiftKey = false; + for (const key in this.downKeys) + delete this.downKeys[key]; + } + dispatchCustomEvent(event, detail) { + if (detail != null) { + return this.dispatchEvent(new CustomEvent(event, { detail })); + } + return this.dispatchEvent(new CustomEvent(event)); + } + getKeysFromShortcut(shortcut) { + let keys; + if (typeof shortcut === "string") { + shortcut = shortcut.replace(/\s/g, ""); + shortcut = shortcut.replace(/^\+/, "__PLUS__").replace(/\+\+/, "+__PLUS__"); + keys = shortcut.split("+").map((i) => i.replace("__PLUS__", "+")); + } + else { + keys = [...shortcut]; + } + return keys.map((k) => k.toLocaleUpperCase()); + } + areAllKeysDown(keys) { + keys = this.getKeysFromShortcut(keys); + return keys.every((k) => { + return this.downKeys[k]; + }); + } + areOnlyKeysDown(keys, alsoAllowShift = false) { + keys = this.getKeysFromShortcut(keys); + const allKeysDown = this.areAllKeysDown(keys); + const downKeysLength = Object.values(this.downKeys).length; + if (allKeysDown && keys.length === downKeysLength) { + return true; + } + if (alsoAllowShift && !keys.includes("SHIFT") && keys.length === downKeysLength - 1) { + return allKeysDown && this.areAllKeysDown(["SHIFT"]); + } + return false; + } +} +export const SERVICE = new KeyEventService(); diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils.js b/custom_nodes/rgthree-comfy/web/comfyui/utils.js new file mode 100644 index 00000000..a58d938e --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/utils.js @@ -0,0 +1,723 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { getResolver, wait } from "../../rgthree/common/shared_utils.js"; +import { RgthreeHelpDialog } from "../../rgthree/common/dialog.js"; +const oldApiGetNodeDefs = api.getNodeDefs; +api.getNodeDefs = async function () { + const defs = await oldApiGetNodeDefs.call(api); + this.dispatchEvent(new CustomEvent("fresh-node-defs", { detail: defs })); + return defs; +}; +export var IoDirection; +(function (IoDirection) { + IoDirection[IoDirection["INPUT"] = 0] = "INPUT"; + IoDirection[IoDirection["OUTPUT"] = 1] = "OUTPUT"; +})(IoDirection || (IoDirection = {})); +const PADDING = 0; +export const LAYOUT_LABEL_TO_DATA = { + Left: [LiteGraph.LEFT, [0, 0.5], [PADDING, 0]], + Right: [LiteGraph.RIGHT, [1, 0.5], [-PADDING, 0]], + Top: [LiteGraph.UP, [0.5, 0], [0, PADDING]], + Bottom: [LiteGraph.DOWN, [0.5, 1], [0, -PADDING]], +}; +export const LAYOUT_LABEL_OPPOSITES = { + Left: "Right", + Right: "Left", + Top: "Bottom", + Bottom: "Top", +}; +export const LAYOUT_CLOCKWISE = ["Top", "Right", "Bottom", "Left"]; +export function addMenuItem(node, _app, config, after = "Shape") { + const oldGetExtraMenuOptions = node.prototype.getExtraMenuOptions; + node.prototype.getExtraMenuOptions = function (canvas, menuOptions) { + oldGetExtraMenuOptions && oldGetExtraMenuOptions.apply(this, [canvas, menuOptions]); + addMenuItemOnExtraMenuOptions(this, config, menuOptions, after); + }; +} +let canvasResolver = null; +export function waitForCanvas() { + if (canvasResolver === null) { + canvasResolver = getResolver(); + function _waitForCanvas() { + if (!canvasResolver.completed) { + if (app === null || app === void 0 ? void 0 : app.canvas) { + canvasResolver.resolve(app.canvas); + } + else { + requestAnimationFrame(_waitForCanvas); + } + } + } + _waitForCanvas(); + } + return canvasResolver.promise; +} +let graphResolver = null; +export function waitForGraph() { + if (graphResolver === null) { + graphResolver = getResolver(); + function _wait() { + if (!graphResolver.completed) { + if (app === null || app === void 0 ? void 0 : app.graph) { + graphResolver.resolve(app.graph); + } + else { + requestAnimationFrame(_wait); + } + } + } + _wait(); + } + return graphResolver.promise; +} +export function addMenuItemOnExtraMenuOptions(node, config, menuOptions, after = "Shape") { + let idx = menuOptions + .slice() + .reverse() + .findIndex((option) => option === null || option === void 0 ? void 0 : option.isRgthree); + if (idx == -1) { + idx = menuOptions.findIndex((option) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.content) === null || _a === void 0 ? void 0 : _a.includes(after); }) + 1; + if (!idx) { + idx = menuOptions.length - 1; + } + menuOptions.splice(idx, 0, null); + idx++; + } + else { + idx = menuOptions.length - idx; + } + const subMenuOptions = typeof config.subMenuOptions === "function" + ? config.subMenuOptions(node) + : config.subMenuOptions; + menuOptions.splice(idx, 0, { + content: typeof config.name == "function" ? config.name(node) : config.name, + has_submenu: !!(subMenuOptions === null || subMenuOptions === void 0 ? void 0 : subMenuOptions.length), + isRgthree: true, + callback: (value, _options, event, parentMenu, _node) => { + if (!!(subMenuOptions === null || subMenuOptions === void 0 ? void 0 : subMenuOptions.length)) { + new LiteGraph.ContextMenu(subMenuOptions.map((option) => (option ? { content: option } : null)), { + event, + parentMenu, + callback: (subValue, _options, _event, _parentMenu, _node) => { + if (config.property) { + node.properties = node.properties || {}; + node.properties[config.property] = config.prepareValue + ? config.prepareValue(subValue.content || "", node) + : subValue.content || ""; + } + config.callback && config.callback(node, subValue === null || subValue === void 0 ? void 0 : subValue.content); + }, + }); + return; + } + if (config.property) { + node.properties = node.properties || {}; + node.properties[config.property] = config.prepareValue + ? config.prepareValue(node.properties[config.property], node) + : !node.properties[config.property]; + } + config.callback && config.callback(node, value === null || value === void 0 ? void 0 : value.content); + }, + }); +} +export function addConnectionLayoutSupport(node, app, options = [ + ["Left", "Right"], + ["Right", "Left"], +], callback) { + addMenuItem(node, app, { + name: "Connections Layout", + property: "connections_layout", + subMenuOptions: options.map((option) => option[0] + (option[1] ? " -> " + option[1] : "")), + prepareValue: (value, node) => { + var _a; + const values = String(value).split(" -> "); + if (!values[1] && !((_a = node.outputs) === null || _a === void 0 ? void 0 : _a.length)) { + values[1] = LAYOUT_LABEL_OPPOSITES[values[0]]; + } + if (!LAYOUT_LABEL_TO_DATA[values[0]] || !LAYOUT_LABEL_TO_DATA[values[1]]) { + throw new Error(`New Layout invalid: [${values[0]}, ${values[1]}]`); + } + return values; + }, + callback: (node) => { + var _a; + callback && callback(node); + (_a = node.graph) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true); + }, + }); + node.prototype.getConnectionPos = function (isInput, slotNumber, out) { + return getConnectionPosForLayout(this, isInput, slotNumber, out); + }; + node.prototype.getInputPos = function (slotNumber) { + return getConnectionPosForLayout(this, true, slotNumber, [0, 0]); + }; + node.prototype.getOutputPos = function (slotNumber) { + return getConnectionPosForLayout(this, false, slotNumber, [0, 0]); + }; +} +export function setConnectionsLayout(node, newLayout) { + var _a; + newLayout = newLayout || node.defaultConnectionsLayout || ["Left", "Right"]; + if (!newLayout[1] && !((_a = node.outputs) === null || _a === void 0 ? void 0 : _a.length)) { + newLayout[1] = LAYOUT_LABEL_OPPOSITES[newLayout[0]]; + } + if (!LAYOUT_LABEL_TO_DATA[newLayout[0]] || !LAYOUT_LABEL_TO_DATA[newLayout[1]]) { + throw new Error(`New Layout invalid: [${newLayout[0]}, ${newLayout[1]}]`); + } + node.properties = node.properties || {}; + node.properties["connections_layout"] = newLayout; +} +export function setConnectionsCollapse(node, collapseConnections = null) { + node.properties = node.properties || {}; + collapseConnections = + collapseConnections !== null ? collapseConnections : !node.properties["collapse_connections"]; + node.properties["collapse_connections"] = collapseConnections; +} +export function getConnectionPosForLayout(node, isInput, slotNumber, out) { + var _a, _b, _c; + out = out || new Float32Array(2); + node.properties = node.properties || {}; + const layout = node.properties["connections_layout"] || + node.defaultConnectionsLayout || ["Left", "Right"]; + const collapseConnections = node.properties["collapse_connections"] || false; + const offset = (_a = node.constructor.layout_slot_offset) !== null && _a !== void 0 ? _a : LiteGraph.NODE_SLOT_HEIGHT * 0.5; + let side = isInput ? layout[0] : layout[1]; + const otherSide = isInput ? layout[1] : layout[0]; + let data = LAYOUT_LABEL_TO_DATA[side]; + const slotList = node[isInput ? "inputs" : "outputs"]; + const cxn = slotList[slotNumber]; + if (!cxn) { + console.log("No connection found.. weird", isInput, slotNumber); + return out; + } + if (cxn.disabled) { + if (cxn.color_on !== "#666665") { + cxn._color_on_org = cxn._color_on_org || cxn.color_on; + cxn._color_off_org = cxn._color_off_org || cxn.color_off; + } + cxn.color_on = "#666665"; + cxn.color_off = "#666665"; + } + else if (cxn.color_on === "#666665") { + cxn.color_on = cxn._color_on_org || undefined; + cxn.color_off = cxn._color_off_org || undefined; + } + const displaySlot = collapseConnections + ? 0 + : slotNumber - + slotList.reduce((count, ioput, index) => { + count += index < slotNumber && ioput.hidden ? 1 : 0; + return count; + }, 0); + cxn.dir = data[0]; + const connections_dir = node.properties["connections_dir"]; + if ((node.size[0] == 10 || node.size[1] == 10) && connections_dir) { + cxn.dir = connections_dir[isInput ? 0 : 1]; + } + if (side === "Left") { + if (node.flags.collapsed) { + var w = node._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + out[0] = node.pos[0]; + out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } + else { + toggleConnectionLabel(cxn, !isInput || collapseConnections || !!node.hideSlotLabels); + out[0] = node.pos[0] + offset; + if ((_b = node.constructor) === null || _b === void 0 ? void 0 : _b.type.includes("Reroute")) { + out[1] = node.pos[1] + node.size[1] * 0.5; + } + else { + out[1] = + node.pos[1] + + (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + (node.constructor.slot_start_y || 0); + } + } + } + else if (side === "Right") { + if (node.flags.collapsed) { + var w = node._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + out[0] = node.pos[0] + w; + out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } + else { + toggleConnectionLabel(cxn, isInput || collapseConnections || !!node.hideSlotLabels); + out[0] = node.pos[0] + node.size[0] + 1 - offset; + if ((_c = node.constructor) === null || _c === void 0 ? void 0 : _c.type.includes("Reroute")) { + out[1] = node.pos[1] + node.size[1] * 0.5; + } + else { + out[1] = + node.pos[1] + + (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + (node.constructor.slot_start_y || 0); + } + } + } + else if (side === "Top") { + if (!cxn.has_old_label) { + cxn.has_old_label = true; + cxn.old_label = cxn.label; + cxn.label = " "; + } + out[0] = node.pos[0] + node.size[0] * 0.5; + out[1] = node.pos[1] + offset; + } + else if (side === "Bottom") { + if (!cxn.has_old_label) { + cxn.has_old_label = true; + cxn.old_label = cxn.label; + cxn.label = " "; + } + out[0] = node.pos[0] + node.size[0] * 0.5; + out[1] = node.pos[1] + node.size[1] - offset; + } + return out; +} +function toggleConnectionLabel(cxn, hide = true) { + if (hide) { + if (!cxn.has_old_label) { + cxn.has_old_label = true; + cxn.old_label = cxn.label; + } + cxn.label = " "; + } + else if (!hide && cxn.has_old_label) { + cxn.has_old_label = false; + cxn.label = cxn.old_label; + cxn.old_label = undefined; + } + return cxn; +} +export function addHelpMenuItem(node, content, menuOptions) { + addMenuItemOnExtraMenuOptions(node, { + name: "πŸ›Ÿ Node Help", + callback: (node) => { + if (node.showHelp) { + node.showHelp(); + } + else { + new RgthreeHelpDialog(node, content).show(); + } + }, + }, menuOptions, "Properties Panel"); +} +export var PassThroughFollowing; +(function (PassThroughFollowing) { + PassThroughFollowing[PassThroughFollowing["ALL"] = 0] = "ALL"; + PassThroughFollowing[PassThroughFollowing["NONE"] = 1] = "NONE"; + PassThroughFollowing[PassThroughFollowing["REROUTE_ONLY"] = 2] = "REROUTE_ONLY"; +})(PassThroughFollowing || (PassThroughFollowing = {})); +export function shouldPassThrough(node, passThroughFollowing = PassThroughFollowing.ALL) { + var _a; + const type = (_a = node === null || node === void 0 ? void 0 : node.constructor) === null || _a === void 0 ? void 0 : _a.type; + if (!type || passThroughFollowing === PassThroughFollowing.NONE) { + return false; + } + if (passThroughFollowing === PassThroughFollowing.REROUTE_ONLY) { + return type.includes("Reroute"); + } + return (type.includes("Reroute") || type.includes("Node Combiner") || type.includes("Node Collector")); +} +function filterOutPassthroughNodes(infos, passThroughFollowing = PassThroughFollowing.ALL) { + return infos.filter((i) => !shouldPassThrough(i.node, passThroughFollowing)); +} +export function getConnectedInputNodes(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) { + return getConnectedNodesInfo(startNode, IoDirection.INPUT, currentNode, slot, passThroughFollowing).map((n) => n.node); +} +export function getConnectedInputInfosAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) { + return filterOutPassthroughNodes(getConnectedNodesInfo(startNode, IoDirection.INPUT, currentNode, slot, passThroughFollowing), passThroughFollowing); +} +export function getConnectedInputNodesAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) { + return getConnectedInputInfosAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing).map((n) => n.node); +} +export function getConnectedOutputNodes(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) { + return getConnectedNodesInfo(startNode, IoDirection.OUTPUT, currentNode, slot, passThroughFollowing).map((n) => n.node); +} +export function getConnectedOutputNodesAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) { + return filterOutPassthroughNodes(getConnectedNodesInfo(startNode, IoDirection.OUTPUT, currentNode, slot, passThroughFollowing), passThroughFollowing).map((n) => n.node); +} +export function getConnectedNodesInfo(startNode, dir = IoDirection.INPUT, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL, originTravelFromSlot) { + var _a, _b, _c, _d, _e, _f, _g, _h; + currentNode = currentNode || startNode; + let rootNodes = []; + if (startNode === currentNode || shouldPassThrough(currentNode, passThroughFollowing)) { + let linkIds; + slot = slot != null && slot > -1 ? slot : undefined; + if (dir == IoDirection.OUTPUT) { + if (slot != null) { + linkIds = [...(((_b = (_a = currentNode.outputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.links) || [])]; + } + else { + linkIds = ((_c = currentNode.outputs) === null || _c === void 0 ? void 0 : _c.flatMap((i) => i.links)) || []; + } + } + else { + if (slot != null) { + linkIds = [(_e = (_d = currentNode.inputs) === null || _d === void 0 ? void 0 : _d[slot]) === null || _e === void 0 ? void 0 : _e.link]; + } + else { + linkIds = ((_f = currentNode.inputs) === null || _f === void 0 ? void 0 : _f.map((i) => i.link)) || []; + } + } + const graph = (_g = currentNode.graph) !== null && _g !== void 0 ? _g : app.graph; + for (const linkId of linkIds) { + let link = null; + if (typeof linkId == "number") { + link = (_h = graph.links[linkId]) !== null && _h !== void 0 ? _h : null; + } + if (!link) { + continue; + } + const travelFromSlot = dir == IoDirection.OUTPUT ? link.origin_slot : link.target_slot; + const connectedId = dir == IoDirection.OUTPUT ? link.target_id : link.origin_id; + const travelToSlot = dir == IoDirection.OUTPUT ? link.target_slot : link.origin_slot; + originTravelFromSlot = originTravelFromSlot != null ? originTravelFromSlot : travelFromSlot; + const originNode = graph.getNodeById(connectedId); + if (!link) { + console.error("No connected node found... weird"); + continue; + } + if (rootNodes.some((n) => n.node == originNode)) { + console.log(`${startNode.title} (${startNode.id}) seems to have two links to ${originNode.title} (${originNode.id}). One may be stale: ${linkIds.join(", ")}`); + } + else { + rootNodes.push({ node: originNode, travelFromSlot, travelToSlot, originTravelFromSlot }); + if (shouldPassThrough(originNode, passThroughFollowing)) { + for (const foundNode of getConnectedNodesInfo(startNode, dir, originNode, undefined, undefined, originTravelFromSlot)) { + if (!rootNodes.map((n) => n.node).includes(foundNode.node)) { + rootNodes.push(foundNode); + } + } + } + } + } + } + return rootNodes; +} +export function followConnectionUntilType(node, dir, slotNum, skipSelf = false) { + const slots = dir === IoDirection.OUTPUT ? node.outputs : node.inputs; + if (!slots || !slots.length) { + return null; + } + let type = null; + if (slotNum) { + if (!slots[slotNum]) { + return null; + } + type = getTypeFromSlot(slots[slotNum], dir, skipSelf); + } + else { + for (const slot of slots) { + type = getTypeFromSlot(slot, dir, skipSelf); + if (type) { + break; + } + } + } + return type; +} +function getTypeFromSlot(slot, dir, skipSelf = false) { + let graph = app.canvas.getCurrentGraph(); + let type = slot === null || slot === void 0 ? void 0 : slot.type; + if (!skipSelf && type != null && type != "*") { + return { type: type, label: slot === null || slot === void 0 ? void 0 : slot.label, name: slot === null || slot === void 0 ? void 0 : slot.name }; + } + const links = getSlotLinks(slot); + for (const link of links) { + const connectedId = dir == IoDirection.OUTPUT ? link.link.target_id : link.link.origin_id; + const connectedSlotNum = dir == IoDirection.OUTPUT ? link.link.target_slot : link.link.origin_slot; + const connectedNode = graph.getNodeById(connectedId); + const connectedSlots = dir === IoDirection.OUTPUT ? connectedNode.inputs : connectedNode.outputs; + let connectedSlot = connectedSlots[connectedSlotNum]; + if ((connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.type) != null && (connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.type) != "*") { + return { + type: connectedSlot.type, + label: connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.label, + name: connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.name, + }; + } + else if ((connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.type) == "*") { + return followConnectionUntilType(connectedNode, dir); + } + } + return null; +} +export async function replaceNode(existingNode, typeOrNewNode, inputNameMap) { + const existingCtor = existingNode.constructor; + const newNode = typeof typeOrNewNode === "string" ? LiteGraph.createNode(typeOrNewNode) : typeOrNewNode; + if (existingNode.title != existingCtor.title) { + newNode.title = existingNode.title; + } + newNode.pos = [...existingNode.pos]; + newNode.properties = { ...existingNode.properties }; + const oldComputeSize = [...existingNode.computeSize()]; + const oldSize = [ + existingNode.size[0] === oldComputeSize[0] ? null : existingNode.size[0], + existingNode.size[1] === oldComputeSize[1] ? null : existingNode.size[1], + ]; + let setSizeIters = 0; + const setSizeFn = () => { + const newComputesize = newNode.computeSize(); + newNode.size[0] = Math.max(oldSize[0] || 0, newComputesize[0]); + newNode.size[1] = Math.max(oldSize[1] || 0, newComputesize[1]); + setSizeIters++; + if (setSizeIters > 10) { + requestAnimationFrame(setSizeFn); + } + }; + setSizeFn(); + const links = []; + const graph = existingNode.graph || app.graph; + for (const [index, output] of existingNode.outputs.entries()) { + for (const linkId of output.links || []) { + const link = graph.links[linkId]; + if (!link) + continue; + const targetNode = graph.getNodeById(link.target_id); + links.push({ node: newNode, slot: output.name, targetNode, targetSlot: link.target_slot }); + } + } + for (const [index, input] of existingNode.inputs.entries()) { + const linkId = input.link; + if (linkId) { + const link = graph.links[linkId]; + const originNode = graph.getNodeById(link.origin_id); + links.push({ + node: originNode, + slot: link.origin_slot, + targetNode: newNode, + targetSlot: (inputNameMap === null || inputNameMap === void 0 ? void 0 : inputNameMap.has(input.name)) + ? inputNameMap.get(input.name) + : input.name || index, + }); + } + } + graph.add(newNode); + await wait(); + for (const link of links) { + link.node.connect(link.slot, link.targetNode, link.targetSlot); + } + await wait(); + graph.remove(existingNode); + newNode.size = newNode.computeSize(); + newNode.setDirtyCanvas(true, true); + return newNode; +} +export function getOriginNodeByLink(linkId) { + let node = null; + if (linkId != null) { + const link = getLinkById(linkId); + node = (link != null && getNodeById(link.origin_id)) || null; + } + return node; +} +export function getLinkById(linkId) { + var _a, _b, _c; + if (linkId == null) + return null; + let link = (_a = app.graph.links[linkId]) !== null && _a !== void 0 ? _a : null; + link = (_c = link !== null && link !== void 0 ? link : (_b = app.canvas.getCurrentGraph()) === null || _b === void 0 ? void 0 : _b.links[linkId]) !== null && _c !== void 0 ? _c : null; + return link || findSomethingInAllSubgraphs((subgraph) => { var _a; return (_a = subgraph === null || subgraph === void 0 ? void 0 : subgraph.links[linkId]) !== null && _a !== void 0 ? _a : null; }); +} +export function getNodeById(id) { + var _a, _b; + if (id == null) + return null; + let node = app.graph.getNodeById(id); + node = (_b = node !== null && node !== void 0 ? node : (_a = app.canvas.getCurrentGraph()) === null || _a === void 0 ? void 0 : _a.getNodeById(id)) !== null && _b !== void 0 ? _b : null; + return node || findSomethingInAllSubgraphs((subgraph) => { var _a; return (_a = subgraph === null || subgraph === void 0 ? void 0 : subgraph.getNodeById(id)) !== null && _a !== void 0 ? _a : null; }); +} +export function findFromNodeForSubgraph(subgraphId) { + var _a; + const node = (_a = findSomethingInAllSubgraphs((subgraph) => subgraph.nodes + .filter((node) => node.isSubgraphNode()) + .find((node) => node.subgraph.id === subgraphId))) !== null && _a !== void 0 ? _a : null; + return node; +} +function findSomethingInAllSubgraphs(fn) { + var _a, _b; + const rootGraph = (_a = app.rootGraph) !== null && _a !== void 0 ? _a : app.graph.rootGraph; + const subgraphs = [rootGraph, ...(_b = rootGraph.subgraphs) === null || _b === void 0 ? void 0 : _b.values()]; + for (const subgraph of subgraphs) { + const thing = fn(subgraph); + if (thing) + return thing; + } + return null; +} +export function applyMixins(original, constructors) { + constructors.forEach((baseCtor) => { + Object.getOwnPropertyNames(baseCtor.prototype).forEach((name) => { + Object.defineProperty(original.prototype, name, Object.getOwnPropertyDescriptor(baseCtor.prototype, name) || Object.create(null)); + }); + }); +} +export function getSlotLinks(inputOrOutput) { + var _a; + const links = []; + if (!inputOrOutput) { + return links; + } + if ((_a = inputOrOutput.links) === null || _a === void 0 ? void 0 : _a.length) { + const output = inputOrOutput; + for (const linkId of output.links || []) { + const link = app.graph.links[linkId]; + if (link) { + links.push({ id: linkId, link: link }); + } + } + } + if (inputOrOutput.link) { + const input = inputOrOutput; + const link = app.graph.links[input.link]; + if (link) { + links.push({ id: input.link, link: link }); + } + } + return links; +} +export async function matchLocalSlotsToServer(node, direction, serverNodeData) { + var _a, _b, _c; + const serverSlotNames = direction == IoDirection.INPUT + ? Object.keys(((_a = serverNodeData.input) === null || _a === void 0 ? void 0 : _a.optional) || {}) + : serverNodeData.output_name; + const serverSlotTypes = direction == IoDirection.INPUT + ? Object.values(((_b = serverNodeData.input) === null || _b === void 0 ? void 0 : _b.optional) || {}).map((i) => i[0]) + : serverNodeData.output; + const slots = direction == IoDirection.INPUT ? node.inputs : node.outputs; + let firstIndex = slots.findIndex((o, i) => i !== serverSlotNames.indexOf(o.name)); + if (firstIndex > -1) { + const links = {}; + slots.map((slot) => { + var _a; + links[slot.name] = links[slot.name] || []; + (_a = links[slot.name]) === null || _a === void 0 ? void 0 : _a.push(...getSlotLinks(slot)); + }); + for (const [index, serverSlotName] of serverSlotNames.entries()) { + const currentNodeSlot = slots.map((s) => s.name).indexOf(serverSlotName); + if (currentNodeSlot > -1) { + if (currentNodeSlot != index) { + const splicedItem = slots.splice(currentNodeSlot, 1)[0]; + slots.splice(index, 0, splicedItem); + } + } + else if (currentNodeSlot === -1) { + const splicedItem = { + name: serverSlotName, + type: serverSlotTypes[index], + links: [], + }; + slots.splice(index, 0, splicedItem); + } + } + if (slots.length > serverSlotNames.length) { + for (let i = slots.length - 1; i > serverSlotNames.length - 1; i--) { + if (direction == IoDirection.INPUT) { + node.disconnectInput(i); + node.removeInput(i); + } + else { + node.disconnectOutput(i); + node.removeOutput(i); + } + } + } + for (const [name, slotLinks] of Object.entries(links)) { + let currentNodeSlot = slots.map((s) => s.name).indexOf(name); + if (currentNodeSlot > -1) { + for (const linkData of slotLinks) { + if (direction == IoDirection.INPUT) { + linkData.link.target_slot = currentNodeSlot; + } + else { + linkData.link.origin_slot = currentNodeSlot; + const nextNode = app.graph.getNodeById(linkData.link.target_id); + if (nextNode && ((_c = nextNode.constructor) === null || _c === void 0 ? void 0 : _c.type.includes("Reroute"))) { + nextNode.stabilize && nextNode.stabilize(); + } + } + } + } + } + } +} +export function isValidConnection(ioA, ioB) { + if (!ioA || !ioB) { + return false; + } + const typeA = String(ioA.type); + const typeB = String(ioB.type); + let isValid = LiteGraph.isValidConnection(typeA, typeB); + if (!isValid) { + let areCombos = (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(",")); + if (areCombos) { + const nameA = ioA.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL"); + const nameB = ioB.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL"); + isValid = nameA.includes(nameB) || nameB.includes(nameA); + } + } + return isValid; +} +const oldIsValidConnection = LiteGraph.isValidConnection; +LiteGraph.isValidConnection = function (typeA, typeB) { + let isValid = oldIsValidConnection.call(LiteGraph, typeA, typeB); + if (!isValid) { + typeA = String(typeA); + typeB = String(typeB); + let areCombos = (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(",")); + isValid = areCombos; + } + return isValid; +}; +export function getOutputNodes(nodes) { + return ((nodes === null || nodes === void 0 ? void 0 : nodes.filter((n) => { + var _a; + return (n.mode != LiteGraph.NEVER && ((_a = n.constructor.nodeData) === null || _a === void 0 ? void 0 : _a.output_node)); + })) || []); +} +export function changeModeOfNodes(nodeOrNodes, mode) { + reduceNodesDepthFirst(nodeOrNodes, (n) => { + n.mode = mode; + }); +} +export function reduceNodesDepthFirst(nodeOrNodes, reduceFn, reduceTo) { + var _a; + const nodes = Array.isArray(nodeOrNodes) ? nodeOrNodes : [nodeOrNodes]; + const stack = nodes.map((node) => ({ node })); + while (stack.length > 0) { + const { node } = stack.pop(); + const result = reduceFn(node, reduceTo); + if (result !== undefined && result !== reduceTo) { + reduceTo = result; + } + if (((_a = node.isSubgraphNode) === null || _a === void 0 ? void 0 : _a.call(node)) && node.subgraph) { + const children = node.subgraph.nodes; + for (let i = children.length - 1; i >= 0; i--) { + stack.push({ node: children[i] }); + } + } + } + return reduceTo; +} +export function getGroupNodes(group) { + return Array.from(group._children).filter((c) => c instanceof LGraphNode); +} +export function getGraphDependantNodeKey(node) { + var _a; + const graph = (_a = node.graph) !== null && _a !== void 0 ? _a : app.graph; + return `${graph.id}:${node.id}`; +} +export function getFullColor(color, liteGraphKey = "color") { + if (!color) { + return ""; + } + if (LGraphCanvas.node_colors[color]) { + color = LGraphCanvas.node_colors[color][liteGraphKey]; + } + color = color.replace("#", "").toLocaleLowerCase(); + if (color.length === 3) { + color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3"); + } + return `#${color}`; +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_canvas.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_canvas.js new file mode 100644 index 00000000..dc09972e --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_canvas.js @@ -0,0 +1,225 @@ +import { app } from "../../scripts/app.js"; +function binarySearch(max, getValue, match) { + let min = 0; + while (min <= max) { + let guess = Math.floor((min + max) / 2); + const compareVal = getValue(guess); + if (compareVal === match) + return guess; + if (compareVal < match) + min = guess + 1; + else + max = guess - 1; + } + return max; +} +export function fitString(ctx, str, maxWidth) { + let width = ctx.measureText(str).width; + const ellipsis = "…"; + const ellipsisWidth = measureText(ctx, ellipsis); + if (width <= maxWidth || width <= ellipsisWidth) { + return str; + } + const index = binarySearch(str.length, (guess) => measureText(ctx, str.substring(0, guess)), maxWidth - ellipsisWidth); + return str.substring(0, index) + ellipsis; +} +export function measureText(ctx, str) { + return ctx.measureText(str).width; +} +export function isLowQuality() { + var _a; + const canvas = app.canvas; + return (((_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) || 1) <= 0.5; +} +export function drawNodeWidget(ctx, options) { + const lowQuality = isLowQuality(); + const data = { + width: options.size[0], + height: options.size[1], + posY: options.pos[1], + lowQuality, + margin: 15, + colorOutline: LiteGraph.WIDGET_OUTLINE_COLOR, + colorBackground: LiteGraph.WIDGET_BGCOLOR, + colorText: LiteGraph.WIDGET_TEXT_COLOR, + colorTextSecondary: LiteGraph.WIDGET_SECONDARY_TEXT_COLOR, + }; + ctx.strokeStyle = options.colorStroke || data.colorOutline; + ctx.fillStyle = options.colorBackground || data.colorBackground; + ctx.beginPath(); + ctx.roundRect(data.margin, data.posY, data.width - data.margin * 2, data.height, lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5]); + ctx.fill(); + if (!lowQuality) { + ctx.stroke(); + } + return data; +} +export function drawRoundedRectangle(ctx, options) { + const lowQuality = isLowQuality(); + options = { ...options }; + ctx.save(); + ctx.strokeStyle = options.colorStroke || LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.fillStyle = options.colorBackground || LiteGraph.WIDGET_BGCOLOR; + ctx.beginPath(); + ctx.roundRect(...options.pos, ...options.size, lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5]); + ctx.fill(); + !lowQuality && ctx.stroke(); + ctx.restore(); +} +export function drawNumberWidgetPart(ctx, options) { + const arrowWidth = 9; + const arrowHeight = 10; + const innerMargin = 3; + const numberWidth = 32; + const xBoundsArrowLess = [0, 0]; + const xBoundsNumber = [0, 0]; + const xBoundsArrowMore = [0, 0]; + ctx.save(); + let posX = options.posX; + const { posY, height, value, textColor } = options; + const midY = posY + height / 2; + if (options.direction === -1) { + posX = posX - arrowWidth - innerMargin - numberWidth - innerMargin - arrowWidth; + } + ctx.fill(new Path2D(`M ${posX} ${midY} l ${arrowWidth} ${arrowHeight / 2} l 0 -${arrowHeight} L ${posX} ${midY} z`)); + xBoundsArrowLess[0] = posX; + xBoundsArrowLess[1] = arrowWidth; + posX += arrowWidth + innerMargin; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + const oldTextcolor = ctx.fillStyle; + if (textColor) { + ctx.fillStyle = textColor; + } + ctx.fillText(fitString(ctx, value.toFixed(2), numberWidth), posX + numberWidth / 2, midY); + ctx.fillStyle = oldTextcolor; + xBoundsNumber[0] = posX; + xBoundsNumber[1] = numberWidth; + posX += numberWidth + innerMargin; + ctx.fill(new Path2D(`M ${posX} ${midY - arrowHeight / 2} l ${arrowWidth} ${arrowHeight / 2} l -${arrowWidth} ${arrowHeight / 2} v -${arrowHeight} z`)); + xBoundsArrowMore[0] = posX; + xBoundsArrowMore[1] = arrowWidth; + ctx.restore(); + return [xBoundsArrowLess, xBoundsNumber, xBoundsArrowMore]; +} +drawNumberWidgetPart.WIDTH_TOTAL = 9 + 3 + 32 + 3 + 9; +export function drawTogglePart(ctx, options) { + const lowQuality = isLowQuality(); + ctx.save(); + const { posX, posY, height, value } = options; + const toggleRadius = height * 0.36; + const toggleBgWidth = height * 1.5; + if (!lowQuality) { + ctx.beginPath(); + ctx.roundRect(posX + 4, posY + 4, toggleBgWidth - 8, height - 8, [height * 0.5]); + ctx.globalAlpha = app.canvas.editor_alpha * 0.25; + ctx.fillStyle = "rgba(255,255,255,0.45)"; + ctx.fill(); + ctx.globalAlpha = app.canvas.editor_alpha; + } + ctx.fillStyle = value === true ? "#89B" : "#888"; + const toggleX = lowQuality || value === false + ? posX + height * 0.5 + : value === true + ? posX + height + : posX + height * 0.75; + ctx.beginPath(); + ctx.arc(toggleX, posY + height * 0.5, toggleRadius, 0, Math.PI * 2); + ctx.fill(); + ctx.restore(); + return [posX, toggleBgWidth]; +} +export function drawInfoIcon(ctx, x, y, size = 12) { + ctx.save(); + ctx.beginPath(); + ctx.roundRect(x, y, size, size, [size * 0.1]); + ctx.fillStyle = "#2f82ec"; + ctx.strokeStyle = "#0f2a5e"; + ctx.fill(); + ctx.strokeStyle = "#FFF"; + ctx.lineWidth = 2; + const midX = x + size / 2; + const serifSize = size * 0.175; + ctx.stroke(new Path2D(` + M ${midX} ${y + size * 0.15} + v 2 + M ${midX - serifSize} ${y + size * 0.45} + h ${serifSize} + v ${size * 0.325} + h ${serifSize} + h -${serifSize * 2} + `)); + ctx.restore(); +} +export function drawPlusIcon(ctx, x, midY, size = 12) { + ctx.save(); + const s = size / 3; + const plus = new Path2D(` + M ${x} ${midY + s / 2} + v-${s} h${s} v-${s} h${s} + v${s} h${s} v${s} h-${s} + v${s} h-${s} v-${s} h-${s} + z + `); + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + ctx.fillStyle = "#3a3"; + ctx.strokeStyle = "#383"; + ctx.fill(plus); + ctx.stroke(plus); + ctx.restore(); +} +export function drawWidgetButton(ctx, options, text = null, isMouseDownedAndOver = false) { + var _a; + const borderRadius = isLowQuality() ? 0 : ((_a = options.borderRadius) !== null && _a !== void 0 ? _a : 4); + ctx.save(); + if (!isLowQuality() && !isMouseDownedAndOver) { + drawRoundedRectangle(ctx, { + size: [options.size[0] - 2, options.size[1]], + pos: [options.pos[0] + 1, options.pos[1] + 1], + borderRadius, + colorBackground: "#000000aa", + colorStroke: "#000000aa", + }); + } + drawRoundedRectangle(ctx, { + size: options.size, + pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)], + borderRadius, + colorBackground: isMouseDownedAndOver ? "#444" : LiteGraph.WIDGET_BGCOLOR, + colorStroke: "transparent", + }); + if (isLowQuality()) { + ctx.restore(); + return; + } + if (!isMouseDownedAndOver) { + drawRoundedRectangle(ctx, { + size: [options.size[0] - 0.75, options.size[1] - 0.75], + pos: options.pos, + borderRadius: borderRadius - 0.5, + colorBackground: "transparent", + colorStroke: "#00000044", + }); + drawRoundedRectangle(ctx, { + size: [options.size[0] - 0.75, options.size[1] - 0.75], + pos: [options.pos[0] + 0.75, options.pos[1] + 0.75], + borderRadius: borderRadius - 0.5, + colorBackground: "transparent", + colorStroke: "#ffffff11", + }); + } + drawRoundedRectangle(ctx, { + size: options.size, + pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)], + borderRadius, + colorBackground: "transparent", + }); + if (!isLowQuality() && text) { + ctx.textBaseline = "middle"; + ctx.textAlign = "center"; + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + ctx.fillText(text, options.size[0] / 2, options.pos[1] + options.size[1] / 2 + (isMouseDownedAndOver ? 1 : 0)); + } + ctx.restore(); +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_deprecated_comfyui.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_deprecated_comfyui.js new file mode 100644 index 00000000..a15f692b --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_deprecated_comfyui.js @@ -0,0 +1,177 @@ +import { app } from "../../scripts/app.js"; +function isPrimitiveNode(node) { + return node.type === "PrimitiveNode"; +} +function getWidgetGetConfigSymbols(slot) { + var _a; + const widget = slot === null || slot === void 0 ? void 0 : slot.widget; + if (!widget) + return {}; + const syms = Object.getOwnPropertySymbols(widget || {}); + for (const sym of syms) { + const symVal = widget[sym]; + const isGetConfig = typeof symVal === "function"; + let maybeCfg = isGetConfig ? symVal() : symVal; + if (Array.isArray(maybeCfg) && + maybeCfg.length >= 2 && + typeof maybeCfg[0] === "string" && + (maybeCfg[0] === "*" || typeof ((_a = maybeCfg[1]) === null || _a === void 0 ? void 0 : _a.type) === "string")) { + return isGetConfig ? { GET_CONFIG: sym } : { CONFIG: sym }; + } + } + return {}; +} +export function getWidgetConfig(slot) { + var _a, _b, _c; + const configSyms = getWidgetGetConfigSymbols(slot); + const widget = slot.widget || {}; + return ((_c = (_a = (configSyms.CONFIG && widget[configSyms.CONFIG])) !== null && _a !== void 0 ? _a : (configSyms.GET_CONFIG && ((_b = widget[configSyms.GET_CONFIG]) === null || _b === void 0 ? void 0 : _b.call(widget)))) !== null && _c !== void 0 ? _c : ["*", {}]); +} +export function setWidgetConfig(slot, config) { + var _a; + if (!(slot === null || slot === void 0 ? void 0 : slot.widget)) + return; + if (config) { + const configSyms = getWidgetGetConfigSymbols(slot); + const widget = slot.widget || {}; + if (configSyms.GET_CONFIG) { + widget[configSyms.GET_CONFIG] = () => config; + } + else if (configSyms.CONFIG) { + widget[configSyms.CONFIG] = config; + } + else { + console.error("Cannot set widget Config. This is due to ComfyUI removing the ability to call legacy " + + "JavaScript APIs that are now deprecated without new, supported APIs. It's possible " + + "some things in rgthree-comfy do not work correctly. If you see this, please file a bug."); + } + } + else { + delete slot.widget; + } + if ("link" in slot) { + const link = app.graph.links[(_a = slot === null || slot === void 0 ? void 0 : slot.link) !== null && _a !== void 0 ? _a : -1]; + if (link) { + const originNode = app.graph.getNodeById(link.origin_id); + if (originNode && isPrimitiveNode(originNode)) { + if (config) { + originNode.recreateWidget(); + } + else if (!app.configuringGraph) { + originNode.disconnectOutput(0); + originNode.onLastDisconnect(); + } + } + } + } +} +export function mergeIfValid(output, config2) { + var _a; + const config1 = getWidgetConfig(output); + const customSpec = mergeInputSpec(config1, config2); + if (customSpec) { + setWidgetConfig(output, customSpec); + } + return (_a = customSpec === null || customSpec === void 0 ? void 0 : customSpec[1]) !== null && _a !== void 0 ? _a : null; +} +const mergeInputSpec = (spec1, spec2) => { + const type1 = getInputSpecType(spec1); + const type2 = getInputSpecType(spec2); + if (type1 !== type2) { + return null; + } + if (isIntInputSpec(spec1) || isFloatInputSpec(spec1)) { + return mergeNumericInputSpec(spec1, spec2); + } + if (isComboInputSpec(spec1)) { + return mergeComboInputSpec(spec1, spec2); + } + return mergeCommonInputSpec(spec1, spec2); +}; +function getInputSpecType(inputSpec) { + return isComboInputSpec(inputSpec) ? "COMBO" : inputSpec[0]; +} +function isComboInputSpecV1(inputSpec) { + return Array.isArray(inputSpec[0]); +} +function isIntInputSpec(inputSpec) { + return inputSpec[0] === "INT"; +} +function isFloatInputSpec(inputSpec) { + return inputSpec[0] === "FLOAT"; +} +function isComboInputSpecV2(inputSpec) { + return inputSpec[0] === "COMBO"; +} +function isComboInputSpec(inputSpec) { + return isComboInputSpecV1(inputSpec) || isComboInputSpecV2(inputSpec); +} +const getRange = (options) => { + var _a, _b; + const min = (_a = options.min) !== null && _a !== void 0 ? _a : -Infinity; + const max = (_b = options.max) !== null && _b !== void 0 ? _b : Infinity; + return { min, max }; +}; +const mergeNumericInputSpec = (spec1, spec2) => { + var _a, _b, _c, _d; + const type = spec1[0]; + const options1 = (_a = spec1[1]) !== null && _a !== void 0 ? _a : {}; + const options2 = (_b = spec2[1]) !== null && _b !== void 0 ? _b : {}; + const range1 = getRange(options1); + const range2 = getRange(options2); + if (range1.min > range2.max || range1.max < range2.min) { + return null; + } + const step1 = (_c = options1.step) !== null && _c !== void 0 ? _c : 1; + const step2 = (_d = options2.step) !== null && _d !== void 0 ? _d : 1; + const mergedOptions = { + min: Math.max(range1.min, range2.min), + max: Math.min(range1.max, range2.max), + step: lcm(step1, step2), + }; + return mergeCommonInputSpec([type, { ...options1, ...mergedOptions }], [type, { ...options2, ...mergedOptions }]); +}; +const mergeComboInputSpec = (spec1, spec2) => { + var _a, _b; + const options1 = (_a = spec1[1]) !== null && _a !== void 0 ? _a : {}; + const options2 = (_b = spec2[1]) !== null && _b !== void 0 ? _b : {}; + const comboOptions1 = getComboSpecComboOptions(spec1); + const comboOptions2 = getComboSpecComboOptions(spec2); + const intersection = comboOptions1.filter((value) => comboOptions2.includes(value)); + if (intersection.length === 0) { + return null; + } + return mergeCommonInputSpec(["COMBO", { ...options1, options: intersection }], ["COMBO", { ...options2, options: intersection }]); +}; +const mergeCommonInputSpec = (spec1, spec2) => { + var _a, _b; + const type = getInputSpecType(spec1); + const options1 = (_a = spec1[1]) !== null && _a !== void 0 ? _a : {}; + const options2 = (_b = spec2[1]) !== null && _b !== void 0 ? _b : {}; + const compareKeys = [...new Set([...Object.keys(options1), ...Object.keys(options2)])].filter((key) => !IGNORE_KEYS.has(key)); + const mergeIsValid = compareKeys.every((key) => { + const value1 = options1[key]; + const value2 = options2[key]; + return value1 === value2 || (value1 == null && value2 == null); + }); + return mergeIsValid ? [type, { ...options1, ...options2 }] : null; +}; +const IGNORE_KEYS = new Set([ + "default", + "forceInput", + "defaultInput", + "control_after_generate", + "multiline", + "tooltip", + "dynamicPrompts", +]); +function getComboSpecComboOptions(inputSpec) { + var _a, _b; + return (_b = (isComboInputSpecV2(inputSpec) ? (_a = inputSpec[1]) === null || _a === void 0 ? void 0 : _a.options : inputSpec[0])) !== null && _b !== void 0 ? _b : []; +} +const lcm = (a, b) => { + return Math.abs(a * b) / gcd(a, b); +}; +const gcd = (a, b) => { + return b === 0 ? a : gcd(b, a % b); +}; diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_inputs_outputs.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_inputs_outputs.js new file mode 100644 index 00000000..e58b8411 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_inputs_outputs.js @@ -0,0 +1,14 @@ +export function removeUnusedInputsFromEnd(node, minNumber = 1, nameMatch) { + var _a; + if (node.removed) + return; + for (let i = node.inputs.length - 1; i >= minNumber; i--) { + if (!((_a = node.inputs[i]) === null || _a === void 0 ? void 0 : _a.link)) { + if (!nameMatch || nameMatch.test(node.inputs[i].name)) { + node.removeInput(i); + } + continue; + } + break; + } +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_menu.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_menu.js new file mode 100644 index 00000000..c8dc405d --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_menu.js @@ -0,0 +1,55 @@ +import { app } from "../../scripts/app.js"; +import { rgthreeApi } from "../../rgthree/common/rgthree_api.js"; +const PASS_THROUGH = function (item) { + return item; +}; +export async function showLoraChooser(event, callback, parentMenu, loras) { + var _a, _b; + const canvas = app.canvas; + if (!loras) { + loras = ["None", ...(await rgthreeApi.getLoras().then((loras) => loras.map((l) => l.file)))]; + } + new LiteGraph.ContextMenu(loras, { + event: event, + parentMenu: parentMenu != null ? parentMenu : undefined, + title: "Choose a lora", + scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1), + className: "dark", + callback, + }); +} +export function showNodesChooser(event, mapFn, callback, parentMenu) { + var _a, _b; + const canvas = app.canvas; + const nodesOptions = app.graph._nodes + .map(mapFn) + .filter((e) => e != null); + nodesOptions.sort((a, b) => { + return a.value - b.value; + }); + new LiteGraph.ContextMenu(nodesOptions, { + event: event, + parentMenu, + title: "Choose a node id", + scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1), + className: "dark", + callback, + }); +} +export function showWidgetsChooser(event, node, mapFn, callback, parentMenu) { + var _a, _b; + const options = (node.widgets || []) + .map(mapFn) + .filter((e) => e != null); + if (options.length) { + const canvas = app.canvas; + new LiteGraph.ContextMenu(options, { + event, + parentMenu, + title: "Choose an input/widget", + scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1), + className: "dark", + callback, + }); + } +} diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_widgets.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_widgets.js new file mode 100644 index 00000000..dabce087 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_widgets.js @@ -0,0 +1,291 @@ +import { app } from "../../scripts/app.js"; +import { drawNodeWidget, drawWidgetButton, fitString, isLowQuality } from "./utils_canvas.js"; +export function drawLabelAndValue(ctx, label, value, width, posY, height, options) { + var _a; + const outerMargin = 15; + const innerMargin = 10; + const midY = posY + height / 2; + ctx.save(); + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + const labelX = outerMargin + innerMargin + ((_a = options === null || options === void 0 ? void 0 : options.offsetLeft) !== null && _a !== void 0 ? _a : 0); + ctx.fillText(label, labelX, midY); + const valueXLeft = labelX + ctx.measureText(label).width + 7; + const valueXRight = width - (outerMargin + innerMargin); + ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR; + ctx.textAlign = "right"; + ctx.fillText(fitString(ctx, value, valueXRight - valueXLeft), valueXRight, midY); + ctx.restore(); +} +export class RgthreeBaseWidget { + constructor(name) { + this.type = "custom"; + this.options = {}; + this.y = 0; + this.last_y = 0; + this.mouseDowned = null; + this.isMouseDownedAndOver = false; + this.hitAreas = {}; + this.downedHitAreasForMove = []; + this.downedHitAreasForClick = []; + this.name = name; + } + serializeValue(node, index) { + return this.value; + } + clickWasWithinBounds(pos, bounds) { + let xStart = bounds[0]; + let xEnd = xStart + (bounds.length > 2 ? bounds[2] : bounds[1]); + const clickedX = pos[0] >= xStart && pos[0] <= xEnd; + if (bounds.length === 2) { + return clickedX; + } + return clickedX && pos[1] >= bounds[1] && pos[1] <= bounds[1] + bounds[3]; + } + mouse(event, pos, node) { + var _a, _b, _c; + const canvas = app.canvas; + if (event.type == "pointerdown") { + this.mouseDowned = [...pos]; + this.isMouseDownedAndOver = true; + this.downedHitAreasForMove.length = 0; + this.downedHitAreasForClick.length = 0; + let anyHandled = false; + for (const part of Object.values(this.hitAreas)) { + if (this.clickWasWithinBounds(pos, part.bounds)) { + if (part.onMove) { + this.downedHitAreasForMove.push(part); + } + if (part.onClick) { + this.downedHitAreasForClick.push(part); + } + if (part.onDown) { + const thisHandled = part.onDown.apply(this, [event, pos, node, part]); + anyHandled = anyHandled || thisHandled == true; + } + part.wasMouseClickedAndIsOver = true; + } + } + return (_a = this.onMouseDown(event, pos, node)) !== null && _a !== void 0 ? _a : anyHandled; + } + if (event.type == "pointerup") { + if (!this.mouseDowned) + return true; + this.downedHitAreasForMove.length = 0; + const wasMouseDownedAndOver = this.isMouseDownedAndOver; + this.cancelMouseDown(); + let anyHandled = false; + for (const part of Object.values(this.hitAreas)) { + if (part.onUp && this.clickWasWithinBounds(pos, part.bounds)) { + const thisHandled = part.onUp.apply(this, [event, pos, node, part]); + anyHandled = anyHandled || thisHandled == true; + } + part.wasMouseClickedAndIsOver = false; + } + for (const part of this.downedHitAreasForClick) { + if (this.clickWasWithinBounds(pos, part.bounds)) { + const thisHandled = part.onClick.apply(this, [event, pos, node, part]); + anyHandled = anyHandled || thisHandled == true; + } + } + this.downedHitAreasForClick.length = 0; + if (wasMouseDownedAndOver) { + const thisHandled = this.onMouseClick(event, pos, node); + anyHandled = anyHandled || thisHandled == true; + } + return (_b = this.onMouseUp(event, pos, node)) !== null && _b !== void 0 ? _b : anyHandled; + } + if (event.type == "pointermove") { + this.isMouseDownedAndOver = !!this.mouseDowned; + if (this.mouseDowned && + (pos[0] < 15 || + pos[0] > node.size[0] - 15 || + pos[1] < this.last_y || + pos[1] > this.last_y + LiteGraph.NODE_WIDGET_HEIGHT)) { + this.isMouseDownedAndOver = false; + } + for (const part of Object.values(this.hitAreas)) { + if (this.downedHitAreasForMove.includes(part)) { + part.onMove.apply(this, [event, pos, node, part]); + } + if (this.downedHitAreasForClick.includes(part)) { + part.wasMouseClickedAndIsOver = this.clickWasWithinBounds(pos, part.bounds); + } + } + return (_c = this.onMouseMove(event, pos, node)) !== null && _c !== void 0 ? _c : true; + } + return false; + } + cancelMouseDown() { + this.mouseDowned = null; + this.isMouseDownedAndOver = false; + this.downedHitAreasForMove.length = 0; + } + onMouseDown(event, pos, node) { + return; + } + onMouseUp(event, pos, node) { + return; + } + onMouseClick(event, pos, node) { + return; + } + onMouseMove(event, pos, node) { + return; + } +} +export class RgthreeBetterButtonWidget extends RgthreeBaseWidget { + constructor(name, mouseClickCallback, label) { + super(name); + this.type = "custom"; + this.value = ""; + this.label = ""; + this.mouseClickCallback = mouseClickCallback; + this.label = label || name; + } + draw(ctx, node, width, y, height) { + drawWidgetButton(ctx, { size: [width - 30, height], pos: [15, y] }, this.label, this.isMouseDownedAndOver); + } + onMouseClick(event, pos, node) { + return this.mouseClickCallback(event, pos, node); + } +} +export class RgthreeBetterTextWidget extends RgthreeBaseWidget { + constructor(name, value) { + super(name); + this.name = name; + this.value = value; + } + draw(ctx, node, width, y, height) { + const widgetData = drawNodeWidget(ctx, { size: [width, height], pos: [15, y] }); + if (!widgetData.lowQuality) { + drawLabelAndValue(ctx, this.name, this.value, width, y, height); + } + } + mouse(event, pos, node) { + const canvas = app.canvas; + if (event.type == "pointerdown") { + canvas.prompt("Label", this.value, (v) => (this.value = v), event); + return true; + } + return false; + } +} +export class RgthreeDividerWidget extends RgthreeBaseWidget { + constructor(widgetOptions) { + super("divider"); + this.value = {}; + this.options = { serialize: false }; + this.type = "custom"; + this.widgetOptions = { + marginTop: 7, + marginBottom: 7, + marginLeft: 15, + marginRight: 15, + color: LiteGraph.WIDGET_OUTLINE_COLOR, + thickness: 1, + }; + Object.assign(this.widgetOptions, widgetOptions || {}); + } + draw(ctx, node, width, posY, h) { + if (this.widgetOptions.thickness) { + ctx.strokeStyle = this.widgetOptions.color; + const x = this.widgetOptions.marginLeft; + const y = posY + this.widgetOptions.marginTop; + const w = width - this.widgetOptions.marginLeft - this.widgetOptions.marginRight; + ctx.stroke(new Path2D(`M ${x} ${y} h ${w}`)); + } + } + computeSize(width) { + return [ + width, + this.widgetOptions.marginTop + this.widgetOptions.marginBottom + this.widgetOptions.thickness, + ]; + } +} +export class RgthreeLabelWidget extends RgthreeBaseWidget { + constructor(name, widgetOptions) { + super(name); + this.type = "custom"; + this.options = { serialize: false }; + this.value = ""; + this.widgetOptions = {}; + this.posY = 0; + Object.assign(this.widgetOptions, widgetOptions); + } + update(widgetOptions) { + Object.assign(this.widgetOptions, widgetOptions); + } + draw(ctx, node, width, posY, height) { + var _a; + this.posY = posY; + ctx.save(); + let text = (_a = this.widgetOptions.text) !== null && _a !== void 0 ? _a : this.name; + if (typeof text === "function") { + text = text(); + } + ctx.textAlign = this.widgetOptions.align || "left"; + ctx.fillStyle = this.widgetOptions.color || LiteGraph.WIDGET_TEXT_COLOR; + const oldFont = ctx.font; + if (this.widgetOptions.italic) { + ctx.font = "italic " + ctx.font; + } + if (this.widgetOptions.size) { + ctx.font = ctx.font.replace(/\d+px/, `${this.widgetOptions.size}px`); + } + const midY = posY + height / 2; + ctx.textBaseline = "middle"; + if (this.widgetOptions.align === "center") { + ctx.fillText(text, node.size[0] / 2, midY); + } + else { + ctx.fillText(text, 15, midY); + } + ctx.font = oldFont; + if (this.widgetOptions.actionLabel === "__PLUS_ICON__") { + const plus = new Path2D(`M${node.size[0] - 15 - 2} ${posY + 7} v4 h-4 v4 h-4 v-4 h-4 v-4 h4 v-4 h4 v4 h4 z`); + ctx.lineJoin = "round"; + ctx.lineCap = "round"; + ctx.fillStyle = "#3a3"; + ctx.strokeStyle = "#383"; + ctx.fill(plus); + ctx.stroke(plus); + } + ctx.restore(); + } + mouse(event, nodePos, node) { + if (event.type !== "pointerdown" || + isLowQuality() || + !this.widgetOptions.actionLabel || + !this.widgetOptions.actionCallback) { + return false; + } + const pos = [nodePos[0], nodePos[1] - this.posY]; + const rightX = node.size[0] - 15; + if (pos[0] > rightX || pos[0] < rightX - 16) { + return false; + } + this.widgetOptions.actionCallback(event); + return true; + } +} +export class RgthreeInvisibleWidget extends RgthreeBaseWidget { + constructor(name, type, value, serializeValueFn) { + super(name); + this.type = "custom"; + this.value = value; + this.serializeValueFn = serializeValueFn; + } + draw() { + return; + } + computeSize(width) { + return [0, 0]; + } + serializeValue(node, index) { + return this.serializeValueFn != null + ? this.serializeValueFn(node, index) + : super.serializeValue(node, index); + } +} diff --git a/custom_nodes/rgthree-comfy/web/common/comfyui_shim.js b/custom_nodes/rgthree-comfy/web/common/comfyui_shim.js new file mode 100644 index 00000000..54007baf --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/comfyui_shim.js @@ -0,0 +1,32 @@ +const shimCache = new Map(); +async function shimComfyUiModule(moduleName, prop) { + var _a, _b; + let module = shimCache.get(moduleName); + if (!module) { + if ((_a = window.comfyAPI) === null || _a === void 0 ? void 0 : _a[moduleName]) { + module = (_b = window.comfyAPI) === null || _b === void 0 ? void 0 : _b[moduleName]; + } + else { + module = await import(`./comfyui_shim_${moduleName}.js`); + } + if (!module) { + throw new Error(`Module ${moduleName} could not be loaded.`); + } + shimCache.set(moduleName, module); + } + if (prop) { + if (!module[prop]) { + throw new Error(`Property ${prop} on module ${moduleName} could not be loaded.`); + } + return module[prop]; + } + return module; +} +export async function getPngMetadata(file) { + const fn = (await shimComfyUiModule("pnginfo", "getPngMetadata")); + return fn(file); +} +export async function getWebpMetadata(file) { + const fn = (await shimComfyUiModule("pnginfo", "getWebpMetadata")); + return fn(file); +} diff --git a/custom_nodes/rgthree-comfy/web/common/comfyui_shim_pnginfo.js b/custom_nodes/rgthree-comfy/web/common/comfyui_shim_pnginfo.js new file mode 100644 index 00000000..7ef8e6b0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/comfyui_shim_pnginfo.js @@ -0,0 +1,392 @@ +import { rgthreeApi } from "./rgthree_api.js"; +const api = { + async getEmbeddings() { + const resp = await rgthreeApi.fetchComfyApi('/embeddings', { cache: 'no-store' }); + return await resp.json(); + } +}; +function getFromPngBuffer(buffer) { + const pngData = new Uint8Array(buffer); + const dataView = new DataView(pngData.buffer); + if (dataView.getUint32(0) !== 0x89504e47) { + console.error('Not a valid PNG file'); + return; + } + let offset = 8; + let txt_chunks = {}; + while (offset < pngData.length) { + const length = dataView.getUint32(offset); + const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)); + if (type === 'tEXt' || type == 'comf' || type === 'iTXt') { + let keyword_end = offset + 8; + while (pngData[keyword_end] !== 0) { + keyword_end++; + } + const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end)); + const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length); + const contentJson = new TextDecoder('utf-8').decode(contentArraySegment); + txt_chunks[keyword] = contentJson; + } + offset += 12 + length; + } + return txt_chunks; +} +function getFromPngFile(file) { + return new Promise((r) => { + const reader = new FileReader(); + reader.onload = (event) => { + r(getFromPngBuffer(event.target.result)); + }; + reader.readAsArrayBuffer(file); + }); +} +function parseExifData(exifData) { + const isLittleEndian = String.fromCharCode(...exifData.slice(0, 2)) === 'II'; + function readInt(offset, isLittleEndian, length) { + let arr = exifData.slice(offset, offset + length); + if (length === 2) { + return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint16(0, isLittleEndian); + } + else if (length === 4) { + return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint32(0, isLittleEndian); + } + throw new Error('Shouldn\'t get here.'); + } + const ifdOffset = readInt(4, isLittleEndian, 4); + function parseIFD(offset) { + const numEntries = readInt(offset, isLittleEndian, 2); + const result = {}; + for (let i = 0; i < numEntries; i++) { + const entryOffset = offset + 2 + i * 12; + const tag = readInt(entryOffset, isLittleEndian, 2); + const type = readInt(entryOffset + 2, isLittleEndian, 2); + const numValues = readInt(entryOffset + 4, isLittleEndian, 4); + const valueOffset = readInt(entryOffset + 8, isLittleEndian, 4); + let value; + if (type === 2) { + value = new TextDecoder('utf-8').decode(exifData.subarray(valueOffset, valueOffset + numValues - 1)); + } + result[tag] = value; + } + return result; + } + const ifdData = parseIFD(ifdOffset); + return ifdData; +} +function splitValues(input) { + var output = {}; + for (var key in input) { + var value = input[key]; + var splitValues = value.split(':', 2); + output[splitValues[0]] = splitValues[1]; + } + return output; +} +export function getPngMetadata(file) { + return getFromPngFile(file); +} +export function getWebpMetadata(file) { + return new Promise((r) => { + const reader = new FileReader(); + reader.onload = (event) => { + const webp = new Uint8Array(event.target.result); + const dataView = new DataView(webp.buffer); + if (dataView.getUint32(0) !== 0x52494646 || + dataView.getUint32(8) !== 0x57454250) { + console.error('Not a valid WEBP file'); + r({}); + return; + } + let offset = 12; + let txt_chunks = {}; + while (offset < webp.length) { + const chunk_length = dataView.getUint32(offset + 4, true); + const chunk_type = String.fromCharCode(...webp.slice(offset, offset + 4)); + if (chunk_type === 'EXIF') { + if (String.fromCharCode(...webp.slice(offset + 8, offset + 8 + 6)) == + 'Exif\0\0') { + offset += 6; + } + let data = parseExifData(webp.slice(offset + 8, offset + 8 + chunk_length)); + for (var key in data) { + const value = data[key]; + if (typeof value === 'string') { + const index = value.indexOf(':'); + txt_chunks[value.slice(0, index)] = value.slice(index + 1); + } + } + break; + } + offset += 8 + chunk_length; + } + r(txt_chunks); + }; + reader.readAsArrayBuffer(file); + }); +} +export function getLatentMetadata(file) { + return new Promise((r) => { + const reader = new FileReader(); + reader.onload = (event) => { + const safetensorsData = new Uint8Array(event.target.result); + const dataView = new DataView(safetensorsData.buffer); + let header_size = dataView.getUint32(0, true); + let offset = 8; + let header = JSON.parse(new TextDecoder().decode(safetensorsData.slice(offset, offset + header_size))); + r(header.__metadata__); + }; + var slice = file.slice(0, 1024 * 1024 * 4); + reader.readAsArrayBuffer(slice); + }); +} +export async function importA1111(graph, parameters) { + const p = parameters.lastIndexOf('\nSteps:'); + if (p > -1) { + const embeddings = await api.getEmbeddings(); + const opts = parameters + .substr(p) + .split('\n')[1] + .match(new RegExp('\\s*([^:]+:\\s*([^"\\{].*?|".*?"|\\{.*?\\}))\\s*(,|$)', 'g')) + .reduce((p, n) => { + const s = n.split(':'); + if (s[1].endsWith(',')) { + s[1] = s[1].substr(0, s[1].length - 1); + } + p[s[0].trim().toLowerCase()] = s[1].trim(); + return p; + }, {}); + const p2 = parameters.lastIndexOf('\nNegative prompt:', p); + if (p2 > -1) { + let positive = parameters.substr(0, p2).trim(); + let negative = parameters.substring(p2 + 18, p).trim(); + const ckptNode = LiteGraph.createNode('CheckpointLoaderSimple'); + const clipSkipNode = LiteGraph.createNode('CLIPSetLastLayer'); + const positiveNode = LiteGraph.createNode('CLIPTextEncode'); + const negativeNode = LiteGraph.createNode('CLIPTextEncode'); + const samplerNode = LiteGraph.createNode('KSampler'); + const imageNode = LiteGraph.createNode('EmptyLatentImage'); + const vaeNode = LiteGraph.createNode('VAEDecode'); + const vaeLoaderNode = LiteGraph.createNode('VAELoader'); + const saveNode = LiteGraph.createNode('SaveImage'); + let hrSamplerNode = null; + let hrSteps = null; + const ceil64 = (v) => Math.ceil(v / 64) * 64; + const getWidget = (node, name) => { + return node.widgets.find((w) => w.name === name); + }; + const setWidgetValue = (node, name, value, isOptionPrefix) => { + const w = getWidget(node, name); + if (isOptionPrefix) { + const o = w.options.values.find((w) => w.startsWith(value)); + if (o) { + w.value = o; + } + else { + console.warn(`Unknown value '${value}' for widget '${name}'`, node); + w.value = value; + } + } + else { + w.value = value; + } + }; + const createLoraNodes = (clipNode, text, prevClip, prevModel) => { + const loras = []; + text = text.replace(/]+)>/g, function (m, c) { + const s = c.split(':'); + const weight = parseFloat(s[1]); + if (isNaN(weight)) { + console.warn('Invalid LORA', m); + } + else { + loras.push({ name: s[0], weight }); + } + return ''; + }); + for (const l of loras) { + const loraNode = LiteGraph.createNode('LoraLoader'); + graph.add(loraNode); + setWidgetValue(loraNode, 'lora_name', l.name, true); + setWidgetValue(loraNode, 'strength_model', l.weight); + setWidgetValue(loraNode, 'strength_clip', l.weight); + prevModel.node.connect(prevModel.index, loraNode, 0); + prevClip.node.connect(prevClip.index, loraNode, 1); + prevModel = { node: loraNode, index: 0 }; + prevClip = { node: loraNode, index: 1 }; + } + prevClip.node.connect(1, clipNode, 0); + prevModel.node.connect(0, samplerNode, 0); + if (hrSamplerNode) { + prevModel.node.connect(0, hrSamplerNode, 0); + } + return { text, prevModel, prevClip }; + }; + const replaceEmbeddings = (text) => { + if (!embeddings.length) + return text; + return text.replaceAll(new RegExp('\\b(' + + embeddings + .map((e) => e.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')) + .join('\\b|\\b') + + ')\\b', 'ig'), 'embedding:$1'); + }; + const popOpt = (name) => { + const v = opts[name]; + delete opts[name]; + return v; + }; + graph.clear(); + graph.add(ckptNode); + graph.add(clipSkipNode); + graph.add(positiveNode); + graph.add(negativeNode); + graph.add(samplerNode); + graph.add(imageNode); + graph.add(vaeNode); + graph.add(vaeLoaderNode); + graph.add(saveNode); + ckptNode.connect(1, clipSkipNode, 0); + clipSkipNode.connect(0, positiveNode, 0); + clipSkipNode.connect(0, negativeNode, 0); + ckptNode.connect(0, samplerNode, 0); + positiveNode.connect(0, samplerNode, 1); + negativeNode.connect(0, samplerNode, 2); + imageNode.connect(0, samplerNode, 3); + vaeNode.connect(0, saveNode, 0); + samplerNode.connect(0, vaeNode, 0); + vaeLoaderNode.connect(0, vaeNode, 1); + const handlers = { + model(v) { + setWidgetValue(ckptNode, 'ckpt_name', v, true); + }, + vae(v) { + setWidgetValue(vaeLoaderNode, 'vae_name', v, true); + }, + 'cfg scale'(v) { + setWidgetValue(samplerNode, 'cfg', +v); + }, + 'clip skip'(v) { + setWidgetValue(clipSkipNode, 'stop_at_clip_layer', -v); + }, + sampler(v) { + let name = v.toLowerCase().replace('++', 'pp').replaceAll(' ', '_'); + if (name.includes('karras')) { + name = name.replace('karras', '').replace(/_+$/, ''); + setWidgetValue(samplerNode, 'scheduler', 'karras'); + } + else { + setWidgetValue(samplerNode, 'scheduler', 'normal'); + } + const w = getWidget(samplerNode, 'sampler_name'); + const o = w.options.values.find((w) => w === name || w === 'sample_' + name); + if (o) { + setWidgetValue(samplerNode, 'sampler_name', o); + } + }, + size(v) { + const wxh = v.split('x'); + const w = ceil64(+wxh[0]); + const h = ceil64(+wxh[1]); + const hrUp = popOpt('hires upscale'); + const hrSz = popOpt('hires resize'); + hrSteps = popOpt('hires steps'); + let hrMethod = popOpt('hires upscaler'); + setWidgetValue(imageNode, 'width', w); + setWidgetValue(imageNode, 'height', h); + if (hrUp || hrSz) { + let uw, uh; + if (hrUp) { + uw = w * hrUp; + uh = h * hrUp; + } + else { + const s = hrSz.split('x'); + uw = +s[0]; + uh = +s[1]; + } + let upscaleNode; + let latentNode; + if (hrMethod.startsWith('Latent')) { + latentNode = upscaleNode = LiteGraph.createNode('LatentUpscale'); + graph.add(upscaleNode); + samplerNode.connect(0, upscaleNode, 0); + switch (hrMethod) { + case 'Latent (nearest-exact)': + hrMethod = 'nearest-exact'; + break; + } + setWidgetValue(upscaleNode, 'upscale_method', hrMethod, true); + } + else { + const decode = LiteGraph.createNode('VAEDecodeTiled'); + graph.add(decode); + samplerNode.connect(0, decode, 0); + vaeLoaderNode.connect(0, decode, 1); + const upscaleLoaderNode = LiteGraph.createNode('UpscaleModelLoader'); + graph.add(upscaleLoaderNode); + setWidgetValue(upscaleLoaderNode, 'model_name', hrMethod, true); + const modelUpscaleNode = LiteGraph.createNode('ImageUpscaleWithModel'); + graph.add(modelUpscaleNode); + decode.connect(0, modelUpscaleNode, 1); + upscaleLoaderNode.connect(0, modelUpscaleNode, 0); + upscaleNode = LiteGraph.createNode('ImageScale'); + graph.add(upscaleNode); + modelUpscaleNode.connect(0, upscaleNode, 0); + const vaeEncodeNode = (latentNode = + LiteGraph.createNode('VAEEncodeTiled')); + graph.add(vaeEncodeNode); + upscaleNode.connect(0, vaeEncodeNode, 0); + vaeLoaderNode.connect(0, vaeEncodeNode, 1); + } + setWidgetValue(upscaleNode, 'width', ceil64(uw)); + setWidgetValue(upscaleNode, 'height', ceil64(uh)); + hrSamplerNode = LiteGraph.createNode('KSampler'); + graph.add(hrSamplerNode); + ckptNode.connect(0, hrSamplerNode, 0); + positiveNode.connect(0, hrSamplerNode, 1); + negativeNode.connect(0, hrSamplerNode, 2); + latentNode.connect(0, hrSamplerNode, 3); + hrSamplerNode.connect(0, vaeNode, 0); + } + }, + steps(v) { + setWidgetValue(samplerNode, 'steps', +v); + }, + seed(v) { + setWidgetValue(samplerNode, 'seed', +v); + } + }; + for (const opt in opts) { + if (opt in handlers) { + handlers[opt](popOpt(opt)); + } + } + if (hrSamplerNode) { + setWidgetValue(hrSamplerNode, 'steps', hrSteps ? +hrSteps : getWidget(samplerNode, 'steps').value); + setWidgetValue(hrSamplerNode, 'cfg', getWidget(samplerNode, 'cfg').value); + setWidgetValue(hrSamplerNode, 'scheduler', getWidget(samplerNode, 'scheduler').value); + setWidgetValue(hrSamplerNode, 'sampler_name', getWidget(samplerNode, 'sampler_name').value); + setWidgetValue(hrSamplerNode, 'denoise', +(popOpt('denoising strength') || '1')); + } + let n = createLoraNodes(positiveNode, positive, { node: clipSkipNode, index: 0 }, { node: ckptNode, index: 0 }); + positive = n.text; + n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel); + negative = n.text; + setWidgetValue(positiveNode, 'text', replaceEmbeddings(positive)); + setWidgetValue(negativeNode, 'text', replaceEmbeddings(negative)); + graph.arrange(); + for (const opt of [ + 'model hash', + 'ensd', + 'version', + 'vae hash', + 'ti hashes', + 'lora hashes', + 'hashes' + ]) { + delete opts[opt]; + } + console.warn('Unhandled parameters:', opts); + } + } +} diff --git a/custom_nodes/rgthree-comfy/web/common/components/base_custom_element.js b/custom_nodes/rgthree-comfy/web/common/components/base_custom_element.js new file mode 100644 index 00000000..d389ffc2 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/components/base_custom_element.js @@ -0,0 +1,207 @@ +import { $el, getActionEls } from "../../common/utils_dom.js"; +import { bind } from "../utils_templates.js"; +const CSS_STYLE_SHEETS = new Map(); +const CSS_STYLE_SHEETS_ADDED = new Map(); +const HTML_TEMPLATE_FILES = new Map(); +function getCommonPath(name, extension) { + return `rgthree/common/components/${name.replace("rgthree-", "").replace(/\-/g, "_")}.${extension}`; +} +async function getStyleSheet(name, markupOrPath) { + if (markupOrPath.includes("{")) { + return markupOrPath; + } + if (!CSS_STYLE_SHEETS.has(name)) { + try { + const path = markupOrPath || getCommonPath(name, "css"); + const text = await (await fetch(path)).text(); + CSS_STYLE_SHEETS.set(name, text); + } + catch (e) { + } + } + return CSS_STYLE_SHEETS.get(name); +} +async function addStyleSheet(name, markupOrPath) { + if (markupOrPath.includes("{")) { + throw new Error("Page-level stylesheets should be passed a path."); + } + if (!CSS_STYLE_SHEETS_ADDED.has(name)) { + const link = document.createElement("link"); + link.rel = "stylesheet"; + link.href = markupOrPath; + document.head.appendChild(link); + CSS_STYLE_SHEETS_ADDED.set(name, link); + } + return CSS_STYLE_SHEETS_ADDED.get(name); +} +async function getTemplateMarkup(name, markupOrPath) { + if (markupOrPath.includes(" (this.firstConnectedPromiseResolver = resolve)); + this.eventElements = new Map(); + } + static create() { + if (this.NAME === "rgthree-override") { + throw new Error("Must override component NAME"); + } + if (!window.customElements.get(this.NAME)) { + window.customElements.define(this.NAME, this); + } + return document.createElement(this.NAME); + } + onFirstConnected() { + } + onReconnected() { + } + onConnected() { + } + onDisconnected() { + } + onAction(action, e) { + console.log("onAction", action, e); + } + getElement(query) { + const el = this.querySelector(query); + if (!el) { + throw new Error("No element found for query: " + query); + } + return el; + } + onActionInternal(action, e) { + if (typeof this[action] === "function") { + this[action](e); + } + else { + this.onAction(action, e); + } + } + onConnectedInternal() { + this.connectActionElements(); + this.onConnected(); + } + onDisconnectedInternal() { + this.disconnectActionElements(); + this.onDisconnected(); + } + async connectedCallback() { + const elementName = this.ctor.NAME; + const wasConnected = this.connected; + if (!wasConnected) { + this.connected = true; + } + if (!this.hasBeenConnected) { + const [stylesheet, markup] = await Promise.all([ + this.ctor.USE_SHADOW + ? getStyleSheet(elementName, this.ctor.CSS) + : addStyleSheet(elementName, this.ctor.CSS), + getTemplateMarkup(elementName, this.ctor.TEMPLATES), + ]); + if (markup) { + const temp = $el("div"); + const templatesMarkup = markup.match(//gm) || []; + for (const markup of templatesMarkup) { + temp.innerHTML = markup; + const template = temp.children[0]; + if (!(template instanceof HTMLTemplateElement)) { + throw new Error("Not a template element."); + } + let id = template.getAttribute("id"); + if (!id) { + id = this.ctor.NAME; + } + this.templates.set(id, template); + } + } + if (this.ctor.USE_SHADOW) { + this.root = this.attachShadow({ mode: "open" }); + if (typeof stylesheet === "string") { + const sheet = new CSSStyleSheet(); + sheet.replaceSync(stylesheet); + this.root.adoptedStyleSheets = [sheet]; + } + } + else { + this.root = this; + } + let template; + if (this.templates.has(elementName)) { + template = this.templates.get(elementName); + } + else if (this.templates.has(elementName.replace("rgthree-", ""))) { + template = this.templates.get(elementName.replace("rgthree-", "")); + } + if (template) { + this.root.appendChild(template.content.cloneNode(true)); + for (const name of template.getAttributeNames()) { + if (name != "id" && template.getAttribute(name)) { + this.setAttribute(name, template.getAttribute(name)); + } + } + } + this.onFirstConnected(); + this.hasBeenConnected = true; + this.firstConnectedPromiseResolver(); + } + else { + this.onReconnected(); + } + this.onConnectedInternal(); + } + disconnectedCallback() { + this.connected = false; + this.onDisconnected(); + } + connectActionElements() { + const data = getActionEls(this); + for (const dataItem of Object.values(data)) { + const mapItem = this.eventElements.get(dataItem.el) || {}; + for (const [event, action] of Object.entries(dataItem.actions)) { + if (mapItem[event]) { + console.warn(`Element already has an event for ${event}`); + continue; + } + mapItem[event] = (e) => { + this.onActionInternal(action, e); + }; + dataItem.el.addEventListener(event, mapItem[event]); + } + } + } + disconnectActionElements() { + for (const [el, eventData] of this.eventElements.entries()) { + for (const [event, fn] of Object.entries(eventData)) { + el.removeEventListener(event, fn); + } + } + } + async bindWhenConnected(data, el) { + await this.firstConnectedPromise; + this.bind(data, el); + } + bind(data, el) { + bind(el || this.root, data); + } +} +RgthreeCustomElement.NAME = "rgthree-override"; +RgthreeCustomElement.USE_SHADOW = true; +RgthreeCustomElement.TEMPLATES = ""; +RgthreeCustomElement.CSS = ""; diff --git a/custom_nodes/rgthree-comfy/web/common/css/buttons.css b/custom_nodes/rgthree-comfy/web/common/css/buttons.css new file mode 100644 index 00000000..2c9f13c0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/css/buttons.css @@ -0,0 +1,130 @@ +:not(#fakeid) .rgthree-button-reset { + position: relative; + appearance: none; + cursor: pointer; + border: 0; + background: transparent; + color: inherit; + padding: 0; + margin: 0; +} + +:not(#fakeid) .rgthree-button { + --padding-top: 7px; + --padding-bottom: 9px; + --padding-x: 16px; + position: relative; + cursor: pointer; + border: 0; + border-radius: 0.33rem; + background: rgba(0, 0, 0, 0.5); + color: white; + font-family: system-ui, sans-serif; + font-size: 1rem; + line-height: 1; + white-space: nowrap; + text-decoration: none; + margin: 0.25rem; + box-shadow: 0px 0px 2px rgb(0, 0, 0); + background: #212121; + transition: all 0.1s ease-in-out; + padding: var(--padding-top) var(--padding-x) var(--padding-bottom); + display: inline-flex; + flex-direction: row; + align-items: center; + justify-content: center; +} +:not(#fakeid) .rgthree-button::before, :not(#fakeid) .rgthree-button::after { + content: ""; + display: block; + position: absolute; + border-radius: 0.33rem; + left: 0; + top: 0; + width: 100%; + height: 100%; + box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.12), inset -1px -1px 0px rgba(0, 0, 0, 0.75); + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)); + mix-blend-mode: screen; +} +:not(#fakeid) .rgthree-button::after { + mix-blend-mode: multiply; +} +:not(#fakeid) .rgthree-button:hover { + background: #303030; +} +:not(#fakeid) .rgthree-button:active { + box-shadow: 0px 0px 0px rgba(0, 0, 0, 0); + background: #121212; + padding: calc(var(--padding-top) + 1px) calc(var(--padding-x) - 1px) calc(var(--padding-bottom) - 1px) calc(var(--padding-x) + 1px); +} +:not(#fakeid) .rgthree-button:active::before, :not(#fakeid) .rgthree-button:active::after { + box-shadow: 1px 1px 0px rgba(255, 255, 255, 0.15), inset 1px 1px 0px rgba(0, 0, 0, 0.5), inset 1px 3px 5px rgba(0, 0, 0, 0.33); +} +:not(#fakeid) .rgthree-button.-blue { + background: #346599 !important; +} +:not(#fakeid) .rgthree-button.-blue:hover { + background: #3b77b8 !important; +} +:not(#fakeid) .rgthree-button.-blue:active { + background: #1d5086 !important; +} +:not(#fakeid) .rgthree-button.-green { + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #14580b; +} +:not(#fakeid) .rgthree-button.-green:hover { + background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #1a6d0f; +} +:not(#fakeid) .rgthree-button.-green:active { + background: linear-gradient(to bottom, rgba(0, 0, 0, 0.15), rgba(255, 255, 255, 0.06)), #0f3f09; +} +:not(#fakeid) .rgthree-button[disabled] { + box-shadow: none; + background: #666 !important; + color: #aaa; + pointer-events: none; +} +:not(#fakeid) .rgthree-button[disabled]::before, :not(#fakeid) .rgthree-button[disabled]::after { + display: none; +} + +:not(#fakeid) .rgthree-comfybar-top-button-group { + font-size: 0; + flex: 1 1 auto; + display: flex; + align-items: stretch; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button { + margin: 0; + flex: 1 1; + height: 36px; + padding: 0 12px; + border-radius: 0; + background: var(--p-button-secondary-background); + color: var(--p-button-secondary-color); +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button.-primary { + background: var(--p-button-primary-background); + color: var(--p-button-primary-color); +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::before, :not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::after { + border-radius: 0; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button svg { + fill: currentColor; + width: 28px; + height: 28px; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::before, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::after { + border-top-left-radius: 0.33rem; + border-bottom-left-radius: 0.33rem; +} +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::before, +:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::after { + border-top-right-radius: 0.33rem; + border-bottom-right-radius: 0.33rem; +} diff --git a/custom_nodes/rgthree-comfy/web/common/css/dialog.css b/custom_nodes/rgthree-comfy/web/common/css/dialog.css new file mode 100644 index 00000000..c9efdf67 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/css/dialog.css @@ -0,0 +1,124 @@ +@charset "UTF-8"; +.rgthree-dialog { + outline: 0; + border: 0; + border-radius: 6px; + background: #414141; + color: #fff; + box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.05), inset -1px -1px 0px rgba(0, 0, 0, 0.5), 2px 2px 20px rgb(0, 0, 0); + max-width: 800px; + box-sizing: border-box; + font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif; + font-size: 1rem; + padding: 0; + max-height: calc(100% - 32px); +} +.rgthree-dialog *, .rgthree-dialog *::before, .rgthree-dialog *::after { + box-sizing: inherit; +} + +.rgthree-dialog-container > * { + padding: 8px 16px; +} +.rgthree-dialog-container > *:first-child { + padding-top: 16px; +} +.rgthree-dialog-container > *:last-child { + padding-bottom: 16px; +} + +.rgthree-dialog.-iconed::after { + content: ""; + font-size: 276px; + position: absolute; + right: 0px; + bottom: 0px; + opacity: 0.15; + display: block; + width: 237px; + overflow: hidden; + height: 186px; + line-height: 1; + pointer-events: none; + z-index: -1; +} + +.rgthree-dialog.-iconed.-help::after { + content: "πŸ›Ÿ"; +} + +.rgthree-dialog.-iconed.-settings::after { + content: "βš™οΈ"; +} + +@media (max-width: 832px) { + .rgthree-dialog { + max-width: calc(100% - 32px); + } +} +.rgthree-dialog-container-title { + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} + +.rgthree-dialog-container-title > svg:first-child { + width: 36px; + height: 36px; + margin-right: 16px; +} + +.rgthree-dialog-container-title h2 { + font-size: 1.375rem; + margin: 0; + font-weight: bold; +} + +.rgthree-dialog-container-title h2 small { + font-size: 0.8125rem; + font-weight: normal; + opacity: 0.75; +} + +.rgthree-dialog-container-content { + overflow: auto; + max-height: calc(100vh - 200px); /* Arbitrary height to copensate for margin, title, and footer.*/ +} + +.rgthree-dialog-container-content p { + font-size: 0.8125rem; + margin-top: 0; +} + +.rgthree-dialog-container-content ul li p { + margin-bottom: 4px; +} + +.rgthree-dialog-container-content ul li p + p { + margin-top: 0.5em; +} + +.rgthree-dialog-container-content ul li ul { + margin-top: 0.5em; + margin-bottom: 1em; +} + +.rgthree-dialog-container-content p code { + display: inline-block; + padding: 2px 4px; + margin: 0px 2px; + border: 1px solid rgba(255, 255, 255, 0.25); + border-radius: 3px; + background: rgba(255, 255, 255, 0.1); +} + +.rgthree-dialog-container-footer { + display: flex; + align-items: center; + justify-content: center; +} + +body.rgthree-dialog-open > *:not(.rgthree-dialog):not(.rgthree-top-messages-container) { + filter: blur(5px); +} diff --git a/custom_nodes/rgthree-comfy/web/common/css/dialog_lora_chooser.css b/custom_nodes/rgthree-comfy/web/common/css/dialog_lora_chooser.css new file mode 100644 index 00000000..0142ab10 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/css/dialog_lora_chooser.css @@ -0,0 +1,143 @@ +.rgthree-lora-chooser-dialog { + max-width: 100%; +} +.rgthree-lora-chooser-dialog .rgthree-dialog-container-title { + display: flex; + flex-direction: column; +} +.rgthree-lora-chooser-dialog .rgthree-dialog-container-title h2 { + display: flex; + width: 100%; +} +.rgthree-lora-chooser-dialog .rgthree-lora-chooser-search { + margin-left: auto; + border-radius: 50px; + width: 50%; + max-width: 170px; + padding: 2px 8px; +} +.rgthree-lora-chooser-dialog .rgthree-lora-chooser-header { + display: flex; + flex-direction: row; +} +.rgthree-lora-chooser-dialog .rgthree-lora-filters-container svg { + width: 16px; + height: 16px; +} +.rgthree-lora-chooser-dialog .rgthree-dialog-container-content { + width: 80vw; + height: 80vh; +} +.rgthree-lora-chooser-dialog .rgthree-button-reset { + width: 32px; + height: 32px; +} +.rgthree-lora-chooser-dialog .rgthree-button-reset > svg { + width: 100%; + height: 100%; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list { + list-style: none; + margin: 0; + padding: 0; + position: relative; + display: flex; + flex-direction: row; + flex-wrap: wrap; + align-items: start; + justify-content: space-around; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li { + position: relative; + flex: 0 0 auto; + width: 170px; + max-width: 100%; + margin: 8px 8px 16px; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li label { + position: absolute; + display: block; + inset: 0; + z-index: 3; + cursor: pointer; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li input[type=checkbox] { + position: absolute; + right: 8px; + top: 8px; + margin: 0; + z-index: 2; + appearance: none; + background-color: #fff; + width: 48px; + height: 48px; + border-radius: 4px; + border: 1px solid rgb(120, 120, 120); + opacity: 0; + transition: opacity 0.15s ease-in-out; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li input[type=checkbox]:checked { + opacity: 1; + background: #0060df; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li input[type=checkbox]:checked::before { + content: ""; + display: block; + width: 100%; + height: 100%; + box-shadow: inset 100px 100px #fff; + clip-path: polygon(40.13% 68.39%, 23.05% 51.31%, 17.83% 48.26%, 12.61% 49.57%, 9.57% 53.04%, 8% 60%, 34.13% 85.87%, 39.82% 89.57%, 45.88% 86.73%, 90.66% 32.39%, 88.92% 26.1%, 83.03% 22.17%, 76.94% 22.62%); +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure { + position: relative; + display: block; + margin: 0 0 8px; + padding: 0; + border: 1px solid rgba(120, 120, 120, 0.8); + background: rgba(120, 120, 120, 0.5); + width: 100%; + padding-top: 120%; + transition: box-shadow 0.15s ease-in-out; + opacity: 0.75; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure::after { + content: ""; + display: block; + position: absolute; + inset: 0; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure:empty::before { + content: "No image."; + color: rgba(200, 200, 200, 0.8); + position: absolute; + display: block; + inset: 0; + font-size: 1.2em; + text-align: center; + display: flex; + align-items: center; + justify-content: center; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure > img, .rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure > video { + position: absolute; + width: 100%; + height: 100%; + top: 0; + left: 0; + object-fit: cover; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li div { + word-wrap: break-word; + font-size: 0.8rem; + opacity: 0.75; +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li:hover figure::after { + box-shadow: 0px 2px 6px rgba(0, 0, 0, 0.75); +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li :checked ~ figure::after { + box-shadow: 0 0 5px #fff, 0px 0px 15px rgba(49, 131, 255, 0.88), inset 0 0 3px #fff, inset 0px 0px 5px rgba(49, 131, 255, 0.88); +} +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li:hover *, .rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li:hover input[type=checkbox], +.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li :checked ~ * { + opacity: 1; +} diff --git a/custom_nodes/rgthree-comfy/web/common/css/dialog_model_info.css b/custom_nodes/rgthree-comfy/web/common/css/dialog_model_info.css new file mode 100644 index 00000000..5edee06a --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/css/dialog_model_info.css @@ -0,0 +1,336 @@ +.rgthree-info-dialog { + width: 90vw; + max-width: 960px; +} +.rgthree-info-dialog .rgthree-info-area { + list-style: none; + padding: 0; + margin: 0; + display: flex; +} +.rgthree-info-dialog .rgthree-info-area > li { + display: inline-flex; + margin: 0; + vertical-align: top; +} +.rgthree-info-dialog .rgthree-info-area > li + li { + margin-left: 6px; +} +.rgthree-info-dialog .rgthree-info-area > li:not(.-link) + li.-link { + margin-left: auto; +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > * { + min-height: 24px; + border-radius: 4px; + line-height: 1; + color: rgba(255, 255, 255, 0.85); + background: rgb(69, 92, 85); + font-size: 14px; + font-weight: bold; + text-decoration: none; + display: flex; + height: 1.6em; + padding-left: 0.5em; + padding-right: 0.5em; + padding-bottom: 0.1em; + align-content: center; + justify-content: center; + align-items: center; + box-shadow: inset 0px 0px 0 1px rgba(0, 0, 0, 0.5); +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > * > svg { + width: 16px; + height: 16px; +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > * > svg:last-child { + margin-left: 0.5em; +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > *[href] { + box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.25), inset 0px -1px 0px rgba(0, 0, 0, 0.66); +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > *:empty { + display: none; +} +.rgthree-info-dialog .rgthree-info-area > li.-type > * { + background: rgb(73, 54, 94); + color: rgb(228, 209, 248); +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-menu { + margin-left: auto; +} +:not(#fakeid) .rgthree-info-dialog .rgthree-info-area > li.rgthree-info-menu .rgthree-button { + margin: 0; + min-height: 24px; + padding: 0 12px; +} +.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-menu svg { + width: 16px; + height: 16px; +} +.rgthree-info-dialog .rgthree-info-table { + border-collapse: collapse; + margin: 16px 0px; + width: 100%; + font-size: 12px; +} +.rgthree-info-dialog .rgthree-info-table tr.editable button { + display: flex; + width: 28px; + height: 28px; + align-items: center; + justify-content: center; +} +.rgthree-info-dialog .rgthree-info-table tr.editable button svg + svg { + display: none; +} +.rgthree-info-dialog .rgthree-info-table tr.editable.-rgthree-editing button svg { + display: none; +} +.rgthree-info-dialog .rgthree-info-table tr.editable.-rgthree-editing button svg + svg { + display: inline-block; +} +.rgthree-info-dialog .rgthree-info-table td { + position: relative; + border: 1px solid rgba(255, 255, 255, 0.25); + padding: 0; + vertical-align: top; +} +.rgthree-info-dialog .rgthree-info-table td:first-child { + background: rgba(255, 255, 255, 0.075); + width: 10px; +} +.rgthree-info-dialog .rgthree-info-table td:first-child > *:first-child { + white-space: nowrap; + padding-right: 32px; +} +.rgthree-info-dialog .rgthree-info-table td:first-child small { + display: block; + margin-top: 2px; + opacity: 0.75; +} +.rgthree-info-dialog .rgthree-info-table td:first-child small > [data-action] { + text-decoration: underline; + cursor: pointer; +} +.rgthree-info-dialog .rgthree-info-table td:first-child small > [data-action]:hover { + text-decoration: none; +} +.rgthree-info-dialog .rgthree-info-table td a, .rgthree-info-dialog .rgthree-info-table td a:hover, .rgthree-info-dialog .rgthree-info-table td a:visited { + color: inherit; +} +.rgthree-info-dialog .rgthree-info-table td svg { + width: 1.3333em; + height: 1.3333em; + vertical-align: -0.285em; +} +.rgthree-info-dialog .rgthree-info-table td svg.logo-civitai { + margin-right: 0.3333em; +} +.rgthree-info-dialog .rgthree-info-table td > *:first-child { + display: block; + padding: 6px 10px; +} +.rgthree-info-dialog .rgthree-info-table td > input, .rgthree-info-dialog .rgthree-info-table td > textarea { + padding: 5px 10px; + border: 0; + box-shadow: inset 1px 1px 5px 0px rgba(0, 0, 0, 0.5); + font: inherit; + appearance: none; + background: #fff; + color: #121212; + resize: vertical; +} +.rgthree-info-dialog .rgthree-info-table td > input:only-child, .rgthree-info-dialog .rgthree-info-table td > textarea:only-child { + width: 100%; +} +:not(#fakeid) .rgthree-info-dialog .rgthree-info-table td .rgthree-button[data-action=fetch-civitai] { + font-size: inherit; + padding: 6px 16px; + margin: 2px; +} +.rgthree-info-dialog .rgthree-info-table tr[data-field-name=userNote] td > span:first-child { + white-space: pre; +} +.rgthree-info-dialog .rgthree-info-table tr.rgthree-info-table-break-row td { + border: 0; + background: transparent; + padding: 12px 4px 4px; + font-size: 1.2em; +} +.rgthree-info-dialog .rgthree-info-table tr.rgthree-info-table-break-row td > small { + font-style: italic; + opacity: 0.66; +} +.rgthree-info-dialog .rgthree-info-table tr.rgthree-info-table-break-row td:empty { + padding: 4px; +} +.rgthree-info-dialog .rgthree-info-table td .-help { + border: 1px solid currentColor; + position: absolute; + right: 5px; + top: 6px; + line-height: 1; + font-size: 11px; + width: 12px; + height: 12px; + border-radius: 8px; + display: flex; + align-content: center; + justify-content: center; + cursor: help; +} +.rgthree-info-dialog .rgthree-info-table td .-help::before { + content: "?"; +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list { + list-style: none; + padding: 2px 8px; + margin: 0; + display: flex; + flex-direction: row; + flex-wrap: wrap; + max-height: 15vh; + overflow: auto; +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li { + display: inline-flex; + margin: 2px; + vertical-align: top; + border-radius: 4px; + line-height: 1; + color: rgba(255, 255, 255, 0.85); + background: rgb(73, 91, 106); + font-size: 1.2em; + font-weight: 600; + text-decoration: none; + display: flex; + height: 1.6em; + align-content: center; + justify-content: center; + align-items: center; + box-shadow: inset 0px 0px 0 1px rgba(0, 0, 0, 0.5); + cursor: pointer; + white-space: nowrap; + max-width: 183px; +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li:hover { + background: rgb(68, 109, 142); +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li > svg { + width: auto; + height: 1.2em; +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li > span { + padding-left: 0.5em; + padding-right: 0.5em; + padding-bottom: 0.1em; + text-overflow: ellipsis; + overflow: hidden; +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li > small { + align-self: stretch; + display: flex; + align-items: center; + justify-content: center; + padding: 0 0.5em; + background: rgba(0, 0, 0, 0.2); +} +.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li.-rgthree-is-selected { + background: rgb(42, 126, 193); +} +.rgthree-info-dialog .rgthree-info-images { + list-style: none; + padding: 0; + margin: 0; + scroll-snap-type: x mandatory; + display: flex; + flex-direction: row; + overflow: auto; +} +.rgthree-info-dialog .rgthree-info-images > li { + scroll-snap-align: start; + max-width: 90%; + flex: 0 0 auto; + display: flex; + align-items: center; + justify-content: center; + flex-direction: column; + overflow: hidden; + padding: 0; + margin: 6px; + font-size: 0; + position: relative; +} +.rgthree-info-dialog .rgthree-info-images > li figure { + margin: 0; + position: static; +} +.rgthree-info-dialog .rgthree-info-images > li figure video, .rgthree-info-dialog .rgthree-info-images > li figure img { + max-height: 45vh; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption { + position: absolute; + left: 0; + width: 100%; + bottom: 0; + padding: 12px; + font-size: 12px; + background: rgba(0, 0, 0, 0.85); + opacity: 0; + transform: translateY(50px); + transition: all 0.25s ease-in-out; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span { + display: inline-block; + padding: 2px 4px; + margin: 2px; + border-radius: 2px; + border: 1px solid rgba(255, 255, 255, 0.2); + word-break: break-word; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span label { + display: inline; + padding: 0; + margin: 0; + opacity: 0.5; + pointer-events: none; + user-select: none; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span a { + color: inherit; + text-decoration: underline; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span a:hover { + text-decoration: none; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span a svg { + height: 10px; + margin-left: 4px; + fill: currentColor; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption:empty { + text-align: center; +} +.rgthree-info-dialog .rgthree-info-images > li figure figcaption:empty::before { + content: "No data."; +} +.rgthree-info-dialog .rgthree-info-images > li:hover figure figcaption { + opacity: 1; + transform: translateY(0px); +} +.rgthree-info-dialog .rgthree-info-images > li .rgthree-info-table { + width: calc(100% - 16px); +} +.rgthree-info-dialog .rgthree-info-civitai-link { + margin: 8px; + color: #eee; +} +.rgthree-info-dialog .rgthree-info-civitai-link a, .rgthree-info-dialog .rgthree-info-civitai-link a:hover, .rgthree-info-dialog .rgthree-info-civitai-link a:visited { + color: inherit; + text-decoration: none; +} +.rgthree-info-dialog .rgthree-info-civitai-link > svg { + width: 16px; + height: 16px; + margin-right: 8px; +} diff --git a/custom_nodes/rgthree-comfy/web/common/css/menu.css b/custom_nodes/rgthree-comfy/web/common/css/menu.css new file mode 100644 index 00000000..426469e7 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/css/menu.css @@ -0,0 +1,103 @@ +.rgthree-menu { + list-style: none; + padding: 0; + margin: 0; + position: fixed; + z-index: 999999; + pointer-events: none; + opacity: 0; + transition: opacity 0.08s ease-in-out; + color: #dde; + background-color: #111; + font-size: 12px; + box-shadow: 0 0 10px black !important; +} +.rgthree-menu > li { + position: relative; + padding: 4px 6px; + z-index: 9999; + white-space: nowrap; +} +.rgthree-menu > li[role=button] { + background-color: var(--comfy-menu-bg) !important; + color: var(--input-text); + cursor: pointer; +} +.rgthree-menu > li[role=button]:hover { + filter: brightness(155%); +} +.rgthree-menu[state^=measuring] { + display: block; + opacity: 0; +} +.rgthree-menu[state=open] { + display: block; + opacity: 1; + pointer-events: all; +} + +.rgthree-top-menu { + box-sizing: border-box; + white-space: nowrap; + background: var(--content-bg); + color: var(--content-fg); + display: flex; + flex-direction: column; + list-style: none; + padding: 0; + margin: 0; +} +.rgthree-top-menu * { + box-sizing: inherit; +} +.rgthree-top-menu > li:not(#fakeid) { + list-style: none; + padding: 0; + margin: 0; + position: relative; + z-index: 2; +} +.rgthree-top-menu > li:not(#fakeid) > button { + cursor: pointer; + padding: 8px 12px 8px 8px; + width: 100%; + text-align: start; + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} +.rgthree-top-menu > li:not(#fakeid) > button:hover { + background-color: var(--comfy-input-bg); +} +.rgthree-top-menu > li:not(#fakeid) > button svg { + height: 16px; + width: auto; + margin-inline-end: 0.6em; +} +.rgthree-top-menu > li:not(#fakeid) > button svg.github-star { + fill: rgb(227, 179, 65); +} +.rgthree-top-menu > li:not(#fakeid).rgthree-message { + min-height: 32px; +} +.rgthree-top-menu > li:not(#fakeid).rgthree-message > span { + padding: 8px 12px; + display: block; + width: 100%; + text-align: center; + font-style: italic; + font-size: 12px; +} +.rgthree-top-menu.-modal::after { + content: ""; + display: block; + position: fixed; + z-index: 1; + inset: 0; + background: rgba(0, 0, 0, 0.0666666667); +} + +body.rgthree-modal-menu-open > *:not(.rgthree-menu):not(.rgthree-top-messages-container) { + filter: blur(2px); +} diff --git a/custom_nodes/rgthree-comfy/web/common/css/pages_base.css b/custom_nodes/rgthree-comfy/web/common/css/pages_base.css new file mode 100644 index 00000000..49233231 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/css/pages_base.css @@ -0,0 +1,66 @@ +html { + font-size: 100%; + overflow-y: scroll; + -webkit-text-size-adjust: 100%; + -ms-text-size-adjust: 100%; + box-sizing: border-box; +} + +*, *:before, *:after { + box-sizing: inherit; +} + +:root { + --header-height: 56px; + --progress-height: 12px; +} + +button { + all: unset; +} + +.-bevel { + position: relative; +} + +.-bevel::before { + content: ""; + position: absolute; + left: 0; + top: 0; + width: 100%; + height: 100%; + border: 1px solid red; + border-color: rgba(255, 255, 255, 0.15) rgba(255, 255, 255, 0.15) rgba(0, 0, 0, 0.5) rgba(0, 0, 0, 0.5); + z-index: 5; + pointer-events: none; +} + +body { + background: #202020; + font-family: Arial, sans-serif; + font-size: 1rem; + font-weight: 400; + margin: 0; + padding-top: calc(var(--header-height) + var(--progress-height)); + color: #ffffff; + display: flex; + flex-direction: column; + align-items: center; + justify-content: start; +} + +.app-header { + height: var(--header-height); + padding: 0; + position: fixed; + z-index: 99; + top: 0; + left: 0; + width: 100%; + background: #353535; + display: flex; + flex-direction: row; + align-items: center; + justify-content: start; +} diff --git a/custom_nodes/rgthree-comfy/web/common/dialog.js b/custom_nodes/rgthree-comfy/web/common/dialog.js new file mode 100644 index 00000000..6c1a6ead --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/dialog.js @@ -0,0 +1,109 @@ +import { createElement as $el, getClosestOrSelf, setAttributes } from "./utils_dom.js"; +export class RgthreeDialog extends EventTarget { + constructor(options) { + super(); + this.options = options; + let container = $el("div.rgthree-dialog-container"); + this.element = $el("dialog", { + classes: ["rgthree-dialog", options.class || ""], + child: container, + parent: document.body, + events: { + click: (event) => { + if (!this.element.open || + event.target === container || + getClosestOrSelf(event.target, `.rgthree-dialog-container`) === container) { + return; + } + return this.close(); + }, + }, + }); + this.element.addEventListener("close", (event) => { + this.onDialogElementClose(); + }); + this.titleElement = $el("div.rgthree-dialog-container-title", { + parent: container, + children: !options.title + ? null + : options.title instanceof Element || Array.isArray(options.title) + ? options.title + : typeof options.title === "string" + ? !options.title.includes(" { + var _a; + (_a = button.callback) === null || _a === void 0 ? void 0 : _a.call(button, e); + }, + }, + }); + } + if (options.closeButtonLabel !== false) { + $el("button", { + text: options.closeButtonLabel || "Close", + className: "rgthree-button", + parent: footerEl, + events: { + click: (e) => { + this.close(e); + }, + }, + }); + } + } + setTitle(content) { + const title = typeof content !== "string" || content.includes("by rgthree"); + const options = Object.assign({}, opts, { + class: "-iconed -help", + title, + content, + }); + super(options); + } +} diff --git a/custom_nodes/rgthree-comfy/web/common/link_fixer.js b/custom_nodes/rgthree-comfy/web/common/link_fixer.js new file mode 100644 index 00000000..eed3eee0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/link_fixer.js @@ -0,0 +1,365 @@ +var IoDirection; +(function (IoDirection) { + IoDirection[IoDirection["INPUT"] = 0] = "INPUT"; + IoDirection[IoDirection["OUTPUT"] = 1] = "OUTPUT"; +})(IoDirection || (IoDirection = {})); +function getLinksData(links) { + if (links instanceof Map) { + const data = []; + for (const [key, llink] of links.entries()) { + if (!llink) + continue; + data.push(llink); + } + return data; + } + if (!Array.isArray(links)) { + const data = []; + for (const key in links) { + const llink = (links.hasOwnProperty(key) && links[key]) || null; + if (!llink) + continue; + data.push(llink); + } + return data; + } + return links.map((link) => ({ + id: link[0], + origin_id: link[1], + origin_slot: link[2], + target_id: link[3], + target_slot: link[4], + type: link[5], + })); +} +export class WorkflowLinkFixer { + static create(graph) { + if (typeof graph.getNodeById === "function") { + return new WorkflowLinkFixerGraph(graph); + } + return new WorkflowLinkFixerSerialized(graph); + } + constructor(graph) { + this.silent = false; + this.checkedData = null; + this.logger = console; + this.patchedNodeSlots = {}; + this.instructions = []; + this.graph = graph; + } + check(force = false) { + var _a, _b; + if (this.checkedData && !force) { + return { ...this.checkedData }; + } + this.instructions = []; + this.patchedNodeSlots = {}; + const instructions = []; + const links = getLinksData(this.graph.links); + links.reverse(); + for (const link of links) { + if (!link) + continue; + const originNode = this.getNodeById(link.origin_id); + const originHasLink = () => this.nodeHasLinkId(originNode, IoDirection.OUTPUT, link.origin_slot, link.id); + const patchOrigin = (op, id = link.id) => this.getNodePatchInstruction(originNode, IoDirection.OUTPUT, link.origin_slot, id, op); + const targetNode = this.getNodeById(link.target_id); + const targetHasLink = () => this.nodeHasLinkId(targetNode, IoDirection.INPUT, link.target_slot, link.id); + const targetHasAnyLink = () => this.nodeHasAnyLink(targetNode, IoDirection.INPUT, link.target_slot); + const patchTarget = (op, id = link.id) => this.getNodePatchInstruction(targetNode, IoDirection.INPUT, link.target_slot, id, op); + const originLog = `origin(${link.origin_id}).outputs[${link.origin_slot}].links`; + const targetLog = `target(${link.target_id}).inputs[${link.target_slot}].link`; + if (!originNode || !targetNode) { + if (!originNode && !targetNode) { + } + else if (!originNode && targetNode) { + this.log(`Link ${link.id} is funky... ` + + `origin ${link.origin_id} does not exist, but target ${link.target_id} does.`); + if (targetHasLink()) { + this.log(` > [PATCH] ${targetLog} does have link, will remove the inputs' link first.`); + instructions.push(patchTarget("REMOVE", -1)); + } + } + else if (!targetNode && originNode) { + this.log(`Link ${link.id} is funky... ` + + `target ${link.target_id} does not exist, but origin ${link.origin_id} does.`); + if (originHasLink()) { + this.log(` > [PATCH] Origin's links' has ${link.id}; will remove the link first.`); + instructions.push(patchOrigin("REMOVE")); + } + } + continue; + } + if (targetHasLink() || originHasLink()) { + if (!originHasLink()) { + this.log(`${link.id} is funky... ${originLog} does NOT contain it, but ${targetLog} does.`); + this.log(` > [PATCH] Attempt a fix by adding this ${link.id} to ${originLog}.`); + instructions.push(patchOrigin("ADD")); + } + else if (!targetHasLink()) { + this.log(`${link.id} is funky... ${targetLog} is NOT correct (is ${(_b = (_a = targetNode.inputs) === null || _a === void 0 ? void 0 : _a[link.target_slot]) === null || _b === void 0 ? void 0 : _b.link}), but ${originLog} contains it`); + if (!targetHasAnyLink()) { + this.log(` > [PATCH] ${targetLog} is not defined, will set to ${link.id}.`); + let instruction = patchTarget("ADD"); + if (!instruction) { + this.log(` > [PATCH] Nvm, ${targetLog} already patched. Removing ${link.id} from ${originLog}.`); + instruction = patchOrigin("REMOVE"); + } + instructions.push(instruction); + } + else { + this.log(` > [PATCH] ${targetLog} is defined, removing ${link.id} from ${originLog}.`); + instructions.push(patchOrigin("REMOVE")); + } + } + } + } + for (let link of links) { + if (!link) + continue; + const originNode = this.getNodeById(link.origin_id); + const targetNode = this.getNodeById(link.target_id); + if (!originNode && !targetNode) { + instructions.push({ + op: "DELETE", + linkId: link.id, + reason: `Both nodes #${link.origin_id} & #${link.target_id} are removed`, + }); + } + if ((!originNode || + !this.nodeHasLinkId(originNode, IoDirection.OUTPUT, link.origin_slot, link.id)) && + (!targetNode || + !this.nodeHasLinkId(targetNode, IoDirection.INPUT, link.target_slot, link.id))) { + instructions.push({ + op: "DELETE", + linkId: link.id, + reason: `both origin node #${link.origin_id} ` + + `${!originNode ? "is removed" : `is missing link id output slot ${link.origin_slot}`}` + + `and target node #${link.target_id} ` + + `${!targetNode ? "is removed" : `is missing link id input slot ${link.target_slot}`}.`, + }); + continue; + } + } + this.instructions = instructions.filter((i) => !!i); + this.checkedData = { + hasBadLinks: !!this.instructions.length, + graph: this.graph, + patches: this.instructions.filter((i) => !!i.node) + .length, + deletes: this.instructions.filter((i) => i.op === "DELETE").length, + }; + return { ...this.checkedData }; + } + fix(force = false, times) { + var _a, _b, _c, _d, _e, _f, _g; + if (!this.checkedData || force) { + this.check(force); + } + let patches = 0; + let deletes = 0; + for (const instruction of this.instructions) { + if (instruction.node) { + let { node, slot, linkIdToUse, dir, op } = instruction; + if (dir == IoDirection.INPUT) { + node.inputs = node.inputs || []; + const old = (_a = node.inputs[slot]) === null || _a === void 0 ? void 0 : _a.link; + node.inputs[slot] = node.inputs[slot] || {}; + node.inputs[slot].link = linkIdToUse; + this.log(`Node #${node.id}: Set link ${linkIdToUse} to input slot ${slot} (was ${old})`); + } + else if (op === "ADD" && linkIdToUse != null) { + node.outputs = node.outputs || []; + node.outputs[slot] = node.outputs[slot] || {}; + node.outputs[slot].links = node.outputs[slot].links || []; + node.outputs[slot].links.push(linkIdToUse); + this.log(`Node #${node.id}: Add link ${linkIdToUse} to output slot #${slot}`); + } + else if (op === "REMOVE" && linkIdToUse != null) { + if (((_d = (_c = (_b = node.outputs) === null || _b === void 0 ? void 0 : _b[slot]) === null || _c === void 0 ? void 0 : _c.links) === null || _d === void 0 ? void 0 : _d.length) === undefined) { + this.log(`Node #${node.id}: Couldn't remove link ${linkIdToUse} from output slot #${slot}` + + ` because it didn't exist.`); + } + else { + let linkIdIndex = node.outputs[slot].links.indexOf(linkIdToUse); + node.outputs[slot].links.splice(linkIdIndex, 1); + this.log(`Node #${node.id}: Remove link ${linkIdToUse} from output slot #${slot}`); + } + } + else { + throw new Error("Unhandled Node Instruction"); + } + patches++; + } + else if (instruction.op === "DELETE") { + const wasDeleted = this.deleteGraphLink(instruction.linkId); + if (wasDeleted === true) { + this.log(`Link #${instruction.linkId}: Removed workflow link b/c ${instruction.reason}`); + } + else { + this.log(`Error Link #${instruction.linkId} was not removed!`); + } + deletes += wasDeleted ? 1 : 0; + } + else { + throw new Error("Unhandled Instruction"); + } + } + const newCheck = this.check(force); + times = times == null ? 5 : times; + let newFix = null; + if (newCheck.hasBadLinks && times > 0) { + newFix = this.fix(true, times - 1); + } + return { + hasBadLinks: (_e = newFix === null || newFix === void 0 ? void 0 : newFix.hasBadLinks) !== null && _e !== void 0 ? _e : newCheck.hasBadLinks, + graph: this.graph, + patches: patches + ((_f = newFix === null || newFix === void 0 ? void 0 : newFix.patches) !== null && _f !== void 0 ? _f : 0), + deletes: deletes + ((_g = newFix === null || newFix === void 0 ? void 0 : newFix.deletes) !== null && _g !== void 0 ? _g : 0), + }; + } + log(...args) { + if (this.silent) + return; + this.logger.log(...args); + } + getNodePatchInstruction(node, ioDir, slot, linkId, op) { + var _a, _b; + const nodeId = node.id; + this.patchedNodeSlots[nodeId] = this.patchedNodeSlots[nodeId] || {}; + const patchedNode = this.patchedNodeSlots[nodeId]; + if (ioDir == IoDirection.INPUT) { + patchedNode["inputs"] = patchedNode["inputs"] || {}; + if (patchedNode["inputs"][slot] !== undefined) { + this.log(` > Already set ${nodeId}.inputs[${slot}] to ${patchedNode["inputs"][slot]} Skipping.`); + return null; + } + let linkIdToUse = op === "REMOVE" ? null : linkId; + patchedNode["inputs"][slot] = linkIdToUse; + return { node, dir: ioDir, op, slot, linkId, linkIdToUse }; + } + patchedNode["outputs"] = patchedNode["outputs"] || {}; + patchedNode["outputs"][slot] = patchedNode["outputs"][slot] || { + links: [...(((_b = (_a = node.outputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.links) || [])], + changes: {}, + }; + if (patchedNode["outputs"][slot]["changes"][linkId] !== undefined) { + this.log(` > Already set ${nodeId}.outputs[${slot}] to ${patchedNode["outputs"][slot]}! Skipping.`); + return null; + } + patchedNode["outputs"][slot]["changes"][linkId] = op; + if (op === "ADD") { + let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId); + if (linkIdIndex !== -1) { + this.log(` > Hmmm.. asked to add ${linkId} but it is already in list...`); + return null; + } + patchedNode["outputs"][slot]["links"].push(linkId); + return { node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId }; + } + let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId); + if (linkIdIndex === -1) { + this.log(` > Hmmm.. asked to remove ${linkId} but it doesn't exist...`); + return null; + } + patchedNode["outputs"][slot]["links"].splice(linkIdIndex, 1); + return { node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId }; + } + nodeHasLinkId(node, ioDir, slot, linkId) { + var _a, _b, _c, _d, _e, _f, _g, _h, _j; + const nodeId = node.id; + let has = false; + if (ioDir === IoDirection.INPUT) { + let nodeHasIt = ((_b = (_a = node.inputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.link) === linkId; + if ((_c = this.patchedNodeSlots[nodeId]) === null || _c === void 0 ? void 0 : _c["inputs"]) { + let patchedHasIt = this.patchedNodeSlots[nodeId]["inputs"][slot] === linkId; + has = patchedHasIt; + } + else { + has = nodeHasIt; + } + } + else { + let nodeHasIt = (_f = (_e = (_d = node.outputs) === null || _d === void 0 ? void 0 : _d[slot]) === null || _e === void 0 ? void 0 : _e.links) === null || _f === void 0 ? void 0 : _f.includes(linkId); + if ((_j = (_h = (_g = this.patchedNodeSlots[nodeId]) === null || _g === void 0 ? void 0 : _g["outputs"]) === null || _h === void 0 ? void 0 : _h[slot]) === null || _j === void 0 ? void 0 : _j["changes"][linkId]) { + let patchedHasIt = this.patchedNodeSlots[nodeId]["outputs"][slot].links.includes(linkId); + has = !!patchedHasIt; + } + else { + has = !!nodeHasIt; + } + } + return has; + } + nodeHasAnyLink(node, ioDir, slot) { + var _a, _b, _c, _d, _e, _f, _g, _h, _j, _k; + const nodeId = node.id; + let hasAny = false; + if (ioDir === IoDirection.INPUT) { + let nodeHasAny = ((_b = (_a = node.inputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.link) != null; + if ((_c = this.patchedNodeSlots[nodeId]) === null || _c === void 0 ? void 0 : _c["inputs"]) { + let patchedHasAny = this.patchedNodeSlots[nodeId]["inputs"][slot] != null; + hasAny = patchedHasAny; + } + else { + hasAny = !!nodeHasAny; + } + } + else { + let nodeHasAny = (_f = (_e = (_d = node.outputs) === null || _d === void 0 ? void 0 : _d[slot]) === null || _e === void 0 ? void 0 : _e.links) === null || _f === void 0 ? void 0 : _f.length; + if ((_j = (_h = (_g = this.patchedNodeSlots[nodeId]) === null || _g === void 0 ? void 0 : _g["outputs"]) === null || _h === void 0 ? void 0 : _h[slot]) === null || _j === void 0 ? void 0 : _j["changes"]) { + let patchedHasAny = (_k = this.patchedNodeSlots[nodeId]["outputs"][slot].links) === null || _k === void 0 ? void 0 : _k.length; + hasAny = !!patchedHasAny; + } + else { + hasAny = !!nodeHasAny; + } + } + return hasAny; + } +} +class WorkflowLinkFixerSerialized extends WorkflowLinkFixer { + constructor(graph) { + super(graph); + } + getNodeById(id) { + var _a; + return (_a = this.graph.nodes.find((node) => Number(node.id) === id)) !== null && _a !== void 0 ? _a : null; + } + fix(force = false, times) { + const ret = super.fix(force, times); + this.graph.links = this.graph.links.filter((l) => !!l); + return ret; + } + deleteGraphLink(id) { + const idx = this.graph.links.findIndex((l) => l && (l[0] === id || l.id === id)); + if (idx === -1) { + return `Link #${id} not found in workflow links.`; + } + this.graph.links.splice(idx, 1); + return true; + } +} +class WorkflowLinkFixerGraph extends WorkflowLinkFixer { + constructor(graph) { + super(graph); + } + getNodeById(id) { + var _a; + return (_a = this.graph.getNodeById(id)) !== null && _a !== void 0 ? _a : null; + } + deleteGraphLink(id) { + if (this.graph.links instanceof Map) { + if (!this.graph.links.has(id)) { + return `Link #${id} not found in workflow links.`; + } + this.graph.links.delete(id); + return true; + } + if (this.graph.links[id] == null) { + return `Link #${id} not found in workflow links.`; + } + delete this.graph.links[id]; + return true; + } +} diff --git a/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg b/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg new file mode 100644 index 00000000..766a7615 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/web/common/media/svgs.js b/custom_nodes/rgthree-comfy/web/common/media/svgs.js new file mode 100644 index 00000000..2bd25227 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/media/svgs.js @@ -0,0 +1,183 @@ +import { createElement as $el } from "../utils_dom.js"; +export let logoRgthree = ""; +export async function logoRgthreeAsync() { + var _a; + if (logoRgthree) + return logoRgthree; + let baseUrl = null; + if (window.location.pathname.includes("/rgthree/")) { + const parts = (_a = window.location.pathname.split("/rgthree/")[1]) === null || _a === void 0 ? void 0 : _a.split("/"); + if (parts && parts.length) { + baseUrl = parts.map(() => "../").join("") + "rgthree"; + } + } + baseUrl = baseUrl || "./rgthree"; + return fetch(`${baseUrl}/logo_markup.svg?fg=currentColor&cssClass=rgthree-logo&w=auto&h=auto`) + .then((r) => r.text()) + .then((t) => { + if (t.length < 100) { + t = ``; + } + logoRgthree = t; + return t; + }); +} +logoRgthreeAsync(); +export const github = ``; +export const iconStarFilled = ` + + `; +export const iconReplace = ` + + + + + `; +export const iconNode = ` + + + `; +export const iconGear = ` + + `; +export const checkmark = ` + + + + `; +export const logoCivitai = ` + + + + + + + + + + `; +export const iconOutLink = ` + + `; +export const link = ` + + `; +export const pencil = ` + + `; +export const dotdotdot = ` + + + +`; +export const models = ` + + + + +`; +export const pencilColored = ` + + + + + + + + + `; +export const diskColored = ` + + + + + + + +`; +export const folderColored = ` + + +`; +export const modelsColored = ` + + + + +`; +export const legoBlocksColored = ` + + + + + + + + + + + + +`; +export const legoBlockColored = ` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +`; +export const gearColored = ` + + + + +`; +export function $svg(markup, attrs) { + if (!markup.match(/^\s* { + var _a, _b; + const target = getClosestOrSelf(e.target, "[data-callback],menu"); + if (e.which !== 1) { + return; + } + const callback = (_a = target === null || target === void 0 ? void 0 : target.dataset) === null || _a === void 0 ? void 0 : _a['callback']; + if (callback) { + const halt = await ((_b = this.callbacks.get(callback)) === null || _b === void 0 ? void 0 : _b(e)); + if (halt !== false) { + this.close(); + } + } + e.preventDefault(); + e.stopPropagation(); + e.stopImmediatePropagation(); + }); + } + setOptions(options) { + for (const option of options) { + if (option.type === 'title') { + this.element.appendChild($el(`li`, { + html: option.label + })); + } + else { + const id = generateId(8); + this.callbacks.set(id, async (e) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.callback) === null || _a === void 0 ? void 0 : _a.call(option, e); }); + this.element.appendChild($el(`li[role="button"][data-callback="${id}"]`, { + html: option.label + })); + } + } + } + toElement() { + return this.element; + } + async open(e) { + const parent = e.target.closest('div,dialog,body'); + parent.appendChild(this.element); + setAttributes(this.element, { + style: { + left: `${e.clientX + 16}px`, + top: `${e.clientY - 16}px`, + } + }); + this.element.setAttribute('state', 'measuring-open'); + await wait(16); + const rect = this.element.getBoundingClientRect(); + if (rect.right > window.innerWidth) { + this.element.style.left = `${e.clientX - rect.width - 16}px`; + await wait(16); + } + this.element.setAttribute('state', 'open'); + setTimeout(() => { + window.addEventListener('pointerdown', this.handleWindowPointerDownBound); + }); + } + handleWindowPointerDown(e) { + if (!this.element.contains(e.target)) { + this.close(); + } + } + async close() { + window.removeEventListener('pointerdown', this.handleWindowPointerDownBound); + this.element.setAttribute('state', 'measuring-closed'); + await wait(16); + this.element.setAttribute('state', 'closed'); + this.element.remove(); + } + isOpen() { + return (this.element.getAttribute('state') || '').includes('open'); + } +} +export class MenuButton { + constructor(options) { + this.element = $el('button.rgthree-button[data-action="open-menu"]'); + this.options = options; + this.element.innerHTML = options.icon; + this.menu = new Menu(options.options); + this.element.addEventListener('pointerdown', (e) => { + if (!this.menu.isOpen()) { + this.menu.open(e); + } + }); + } + toElement() { + return this.element; + } +} diff --git a/custom_nodes/rgthree-comfy/web/common/model_info_service.js b/custom_nodes/rgthree-comfy/web/common/model_info_service.js new file mode 100644 index 00000000..e7ec8082 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/model_info_service.js @@ -0,0 +1,72 @@ +import { rgthreeApi } from "./rgthree_api.js"; +import { api } from "../../scripts/api.js"; +class BaseModelInfoService extends EventTarget { + constructor() { + super(); + this.fileToInfo = new Map(); + this.init(); + } + init() { + api.addEventListener(this.apiRefreshEventString, this.handleAsyncUpdate.bind(this)); + } + async getInfo(file, refresh, light) { + if (this.fileToInfo.has(file) && !refresh) { + return this.fileToInfo.get(file); + } + return this.fetchInfo(file, refresh, light); + } + async refreshInfo(file) { + return this.fetchInfo(file, true); + } + async clearFetchedInfo(file) { + await rgthreeApi.clearModelsInfo({ type: this.modelInfoType, files: [file] }); + this.fileToInfo.delete(file); + return null; + } + async savePartialInfo(file, data) { + let info = await rgthreeApi.saveModelInfo(this.modelInfoType, file, data); + this.fileToInfo.set(file, info); + return info; + } + handleAsyncUpdate(event) { + var _a; + const info = (_a = event.detail) === null || _a === void 0 ? void 0 : _a.data; + if (info === null || info === void 0 ? void 0 : info.file) { + this.setFreshInfo(info.file, info); + } + } + async fetchInfo(file, refresh = false, light = false) { + var _a; + let info = null; + if (!refresh) { + info = await rgthreeApi.getModelsInfo({ type: this.modelInfoType, files: [file], light }); + } + else { + info = await rgthreeApi.refreshModelsInfo({ type: this.modelInfoType, files: [file] }); + } + info = (_a = info === null || info === void 0 ? void 0 : info[0]) !== null && _a !== void 0 ? _a : null; + if (!light) { + this.fileToInfo.set(file, info); + } + return info; + } + setFreshInfo(file, info) { + this.fileToInfo.set(file, info); + } +} +class LoraInfoService extends BaseModelInfoService { + constructor() { + super(...arguments); + this.apiRefreshEventString = "rgthree-refreshed-loras-info"; + this.modelInfoType = 'loras'; + } +} +class CheckpointInfoService extends BaseModelInfoService { + constructor() { + super(...arguments); + this.apiRefreshEventString = "rgthree-refreshed-checkpoints-info"; + this.modelInfoType = 'checkpoints'; + } +} +export const LORA_INFO_SERVICE = new LoraInfoService(); +export const CHECKPOINT_INFO_SERVICE = new CheckpointInfoService(); diff --git a/custom_nodes/rgthree-comfy/web/common/progress_bar.js b/custom_nodes/rgthree-comfy/web/common/progress_bar.js new file mode 100644 index 00000000..e342f0e9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/progress_bar.js @@ -0,0 +1,179 @@ +import { SERVICE as PROMPT_SERVICE } from "../common/prompt_service.js"; +import { createElement } from "./utils_dom.js"; +export class RgthreeProgressBar extends HTMLElement { + static create() { + return document.createElement(RgthreeProgressBar.NAME); + } + get currentNodeId() { + var _a, _b; + const prompt = this.currentPromptExecution; + const nodeId = ((_a = prompt === null || prompt === void 0 ? void 0 : prompt.errorDetails) === null || _a === void 0 ? void 0 : _a.node_id) || ((_b = prompt === null || prompt === void 0 ? void 0 : prompt.currentlyExecuting) === null || _b === void 0 ? void 0 : _b.nodeId); + return nodeId || null; + } + constructor() { + super(); + this.shadow = null; + this.currentPromptExecution = null; + this.onProgressUpdateBound = this.onProgressUpdate.bind(this); + this.connected = false; + } + onProgressUpdate(e) { + var _a, _b, _c, _d; + if (!this.connected) + return; + const prompt = e.detail.prompt; + this.currentPromptExecution = prompt; + if (prompt === null || prompt === void 0 ? void 0 : prompt.errorDetails) { + let progressText = `${(_a = prompt.errorDetails) === null || _a === void 0 ? void 0 : _a.exception_type} ${((_b = prompt.errorDetails) === null || _b === void 0 ? void 0 : _b.node_id) || ""} ${((_c = prompt.errorDetails) === null || _c === void 0 ? void 0 : _c.node_type) || ""}`; + this.progressTextEl.innerText = progressText; + this.progressNodesEl.classList.add("-error"); + this.progressStepsEl.classList.add("-error"); + return; + } + if (prompt === null || prompt === void 0 ? void 0 : prompt.currentlyExecuting) { + this.progressNodesEl.classList.remove("-error"); + this.progressStepsEl.classList.remove("-error"); + const current = prompt === null || prompt === void 0 ? void 0 : prompt.currentlyExecuting; + let progressText = `(${e.detail.queue}) `; + if (!prompt.totalNodes) { + progressText += `??%`; + this.progressNodesEl.style.width = `0%`; + } + else { + const percent = (prompt.executedNodeIds.length / prompt.totalNodes) * 100; + this.progressNodesEl.style.width = `${Math.max(2, percent)}%`; + progressText += `${Math.round(percent)}%`; + } + let nodeLabel = (_d = current.nodeLabel) === null || _d === void 0 ? void 0 : _d.trim(); + let stepsLabel = ""; + if (current.step != null && current.maxSteps) { + const percent = (current.step / current.maxSteps) * 100; + this.progressStepsEl.style.width = `${percent}%`; + if (current.pass > 1 || current.maxPasses != null) { + stepsLabel += `#${current.pass}`; + if (current.maxPasses && current.maxPasses > 0) { + stepsLabel += `/${current.maxPasses}`; + } + stepsLabel += ` - `; + } + stepsLabel += `${Math.round(percent)}%`; + } + if (nodeLabel || stepsLabel) { + progressText += ` - ${nodeLabel || "???"}${stepsLabel ? ` (${stepsLabel})` : ""}`; + } + if (!stepsLabel) { + this.progressStepsEl.style.width = `0%`; + } + this.progressTextEl.innerText = progressText; + } + else { + if (e === null || e === void 0 ? void 0 : e.detail.queue) { + this.progressTextEl.innerText = `(${e.detail.queue}) Running... in another tab`; + } + else { + this.progressTextEl.innerText = "Idle"; + } + this.progressNodesEl.style.width = `0%`; + this.progressStepsEl.style.width = `0%`; + } + } + connectedCallback() { + if (!this.connected) { + PROMPT_SERVICE.addEventListener("progress-update", this.onProgressUpdateBound); + this.connected = true; + } + if (this.shadow) { + this.progressTextEl.innerText = "Idle"; + this.progressNodesEl.style.width = `0%`; + this.progressStepsEl.style.width = `0%`; + return; + } + this.shadow = this.attachShadow({ mode: "open" }); + const sheet = new CSSStyleSheet(); + sheet.replaceSync(` + + :host { + position: relative; + overflow: hidden; + box-sizing: border-box; + background: var(--rgthree-progress-bg-color); + --rgthree-progress-bg-color: rgba(23, 23, 23, 0.9); + --rgthree-progress-nodes-bg-color: rgb(0, 128, 0); + --rgthree-progress-steps-bg-color: rgb(0, 128, 0); + --rgthree-progress-error-bg-color: rgb(128, 0, 0); + --rgthree-progress-text-color: #fff; + } + :host * { + box-sizing: inherit; + } + + :host > div.bar { + background: var(--rgthree-progress-nodes-bg-color); + position: absolute; + left: 0; + top: 0; + width: 0%; + height: 50%; + z-index: 1; + transition: width 50ms ease-in-out; + } + :host > div.bar + div.bar { + background: var(--rgthree-progress-steps-bg-color); + top: 50%; + height: 50%; + z-index: 2; + } + :host > div.bar.-error { + background: var(--rgthree-progress-error-bg-color); + } + + :host > .overlay { + position: absolute; + left: 0; + top: 0; + width: 100%; + height: 100%; + z-index: 5; + background: linear-gradient(to bottom, rgba(255,255,255,0.25), rgba(0,0,0,0.25)); + mix-blend-mode: overlay; + } + + :host > span { + position: relative; + z-index: 4; + text-align: left; + font-size: inherit; + height: 100%; + font-family: sans-serif; + text-shadow: 1px 1px 0px #000; + display: flex; + flex-direction: row; + padding: 0 6px; + align-items: center; + justify-content: start; + color: var(--rgthree-progress-text-color); + text-shadow: black 0px 0px 2px; + } + + :host > div.bar[style*="width: 0%"]:first-child, + :host > div.bar[style*="width:0%"]:first-child { + height: 0%; + } + :host > div.bar[style*="width: 0%"]:first-child + div, + :host > div.bar[style*="width:0%"]:first-child + div { + bottom: 0%; + } + `); + this.shadow.adoptedStyleSheets = [sheet]; + const overlayEl = createElement(`div.overlay[part="overlay"]`, { parent: this.shadow }); + this.progressNodesEl = createElement(`div.bar[part="progress-nodes"]`, { parent: this.shadow }); + this.progressStepsEl = createElement(`div.bar[part="progress-steps"]`, { parent: this.shadow }); + this.progressTextEl = createElement(`span[part="text"]`, { text: "Idle", parent: this.shadow }); + } + disconnectedCallback() { + this.connected = false; + PROMPT_SERVICE.removeEventListener("progress-update", this.onProgressUpdateBound); + } +} +RgthreeProgressBar.NAME = "rgthree-progress-bar"; +customElements.define(RgthreeProgressBar.NAME, RgthreeProgressBar); diff --git a/custom_nodes/rgthree-comfy/web/common/prompt_service.js b/custom_nodes/rgthree-comfy/web/common/prompt_service.js new file mode 100644 index 00000000..1f238889 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/prompt_service.js @@ -0,0 +1,188 @@ +import { api } from "../../scripts/api.js"; +import { getResolver } from "./shared_utils.js"; +export class PromptExecution { + constructor(id) { + this.promptApi = null; + this.executedNodeIds = []; + this.totalNodes = 0; + this.currentlyExecuting = null; + this.errorDetails = null; + this.apiPrompt = getResolver(); + this.id = id; + } + setPrompt(prompt) { + this.promptApi = prompt.output; + this.totalNodes = Object.keys(this.promptApi).length; + this.apiPrompt.resolve(null); + } + getApiNode(nodeId) { + var _a; + return ((_a = this.promptApi) === null || _a === void 0 ? void 0 : _a[String(nodeId)]) || null; + } + getNodeLabel(nodeId) { + var _a, _b; + const apiNode = this.getApiNode(nodeId); + let label = ((_a = apiNode === null || apiNode === void 0 ? void 0 : apiNode._meta) === null || _a === void 0 ? void 0 : _a.title) || (apiNode === null || apiNode === void 0 ? void 0 : apiNode.class_type) || undefined; + if (!label) { + const graphNode = (_b = this.maybeGetComfyGraph()) === null || _b === void 0 ? void 0 : _b.getNodeById(Number(nodeId)); + label = (graphNode === null || graphNode === void 0 ? void 0 : graphNode.title) || (graphNode === null || graphNode === void 0 ? void 0 : graphNode.type) || undefined; + } + return label; + } + executing(nodeId, step, maxSteps) { + var _a; + if (nodeId == null) { + this.currentlyExecuting = null; + return; + } + if (((_a = this.currentlyExecuting) === null || _a === void 0 ? void 0 : _a.nodeId) !== nodeId) { + if (this.currentlyExecuting != null) { + this.executedNodeIds.push(nodeId); + } + this.currentlyExecuting = { nodeId, nodeLabel: this.getNodeLabel(nodeId), pass: 0 }; + this.apiPrompt.promise.then(() => { + var _a; + if (this.currentlyExecuting == null) { + return; + } + const apiNode = this.getApiNode(nodeId); + if (!this.currentlyExecuting.nodeLabel) { + this.currentlyExecuting.nodeLabel = this.getNodeLabel(nodeId); + } + if ((apiNode === null || apiNode === void 0 ? void 0 : apiNode.class_type) === "UltimateSDUpscale") { + this.currentlyExecuting.pass--; + this.currentlyExecuting.maxPasses = -1; + } + else if ((apiNode === null || apiNode === void 0 ? void 0 : apiNode.class_type) === "IterativeImageUpscale") { + this.currentlyExecuting.maxPasses = (_a = apiNode === null || apiNode === void 0 ? void 0 : apiNode.inputs["steps"]) !== null && _a !== void 0 ? _a : -1; + } + }); + } + if (step != null) { + if (!this.currentlyExecuting.step || step < this.currentlyExecuting.step) { + this.currentlyExecuting.pass++; + } + this.currentlyExecuting.step = step; + this.currentlyExecuting.maxSteps = maxSteps; + } + } + error(details) { + this.errorDetails = details; + } + maybeGetComfyGraph() { + var _a; + return ((_a = window === null || window === void 0 ? void 0 : window.app) === null || _a === void 0 ? void 0 : _a.graph) || null; + } +} +class PromptService extends EventTarget { + constructor(api) { + super(); + this.promptsMap = new Map(); + this.currentExecution = null; + this.lastQueueRemaining = 0; + const that = this; + const queuePrompt = api.queuePrompt; + api.queuePrompt = async function (num, prompt, ...args) { + let response; + try { + response = await queuePrompt.apply(api, [...arguments]); + } + catch (e) { + const promptExecution = that.getOrMakePrompt("error"); + promptExecution.error({ exception_type: "Unknown." }); + throw e; + } + const promptExecution = that.getOrMakePrompt(response.prompt_id); + promptExecution.setPrompt(prompt); + if (!that.currentExecution) { + that.currentExecution = promptExecution; + } + that.promptsMap.set(response.prompt_id, promptExecution); + that.dispatchEvent(new CustomEvent("queue-prompt", { + detail: { + prompt: promptExecution, + }, + })); + return response; + }; + api.addEventListener("status", (e) => { + var _a; + if (!((_a = e.detail) === null || _a === void 0 ? void 0 : _a.exec_info)) + return; + this.lastQueueRemaining = e.detail.exec_info.queue_remaining; + this.dispatchProgressUpdate(); + }); + api.addEventListener("execution_start", (e) => { + if (!this.promptsMap.has(e.detail.prompt_id)) { + console.warn("'execution_start' fired before prompt was made."); + } + const prompt = this.getOrMakePrompt(e.detail.prompt_id); + this.currentExecution = prompt; + this.dispatchProgressUpdate(); + }); + api.addEventListener("executing", (e) => { + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt("unknown"); + console.warn("'executing' fired before prompt was made."); + } + this.currentExecution.executing(e.detail); + this.dispatchProgressUpdate(); + if (e.detail == null) { + this.currentExecution = null; + } + }); + api.addEventListener("progress", (e) => { + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'progress' fired before prompt was made."); + } + this.currentExecution.executing(e.detail.node, e.detail.value, e.detail.max); + this.dispatchProgressUpdate(); + }); + api.addEventListener("execution_cached", (e) => { + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'execution_cached' fired before prompt was made."); + } + for (const cached of e.detail.nodes) { + this.currentExecution.executing(cached); + } + this.dispatchProgressUpdate(); + }); + api.addEventListener("executed", (e) => { + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'executed' fired before prompt was made."); + } + }); + api.addEventListener("execution_error", (e) => { + var _a; + if (!this.currentExecution) { + this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id); + console.warn("'execution_error' fired before prompt was made."); + } + (_a = this.currentExecution) === null || _a === void 0 ? void 0 : _a.error(e.detail); + this.dispatchProgressUpdate(); + }); + } + async queuePrompt(prompt) { + return await api.queuePrompt(-1, prompt); + } + dispatchProgressUpdate() { + this.dispatchEvent(new CustomEvent("progress-update", { + detail: { + queue: this.lastQueueRemaining, + prompt: this.currentExecution, + }, + })); + } + getOrMakePrompt(id) { + let prompt = this.promptsMap.get(id); + if (!prompt) { + prompt = new PromptExecution(id); + this.promptsMap.set(id, prompt); + } + return prompt; + } +} +export const SERVICE = new PromptService(api); diff --git a/custom_nodes/rgthree-comfy/web/common/py_parser.js b/custom_nodes/rgthree-comfy/web/common/py_parser.js new file mode 100644 index 00000000..0251c168 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/py_parser.js @@ -0,0 +1,717 @@ +var __decorate = (this && this.__decorate) || function (decorators, target, key, desc) { + var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d; + if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc); + else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r; + return c > 3 && r && Object.defineProperty(target, key, r), r; +}; +var __classPrivateFieldSet = (this && this.__classPrivateFieldSet) || function (receiver, state, value, kind, f) { + if (kind === "m") throw new TypeError("Private method is not writable"); + if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a setter"); + if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot write private member to an object whose class did not declare it"); + return (kind === "a" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value; +}; +var __classPrivateFieldGet = (this && this.__classPrivateFieldGet) || function (receiver, state, kind, f) { + if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a getter"); + if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot read private member from an object whose class did not declare it"); + return kind === "m" ? f : kind === "a" ? f.call(receiver) : f ? f.value : state.get(receiver); +}; +var _PyDict_dict; +import { check, deepFreeze } from "./shared_utils.js"; +const MEMOIZED = { parser: null }; +class ExecuteContext { + constructor(existing = {}) { + Object.assign(this, !!window.structuredClone ? structuredClone(existing) : { ...existing }); + } +} +class InitialExecuteContext extends ExecuteContext { +} +const TYPE_TO_HANDLER = new Map([ + ["module", handleChildren], + ["expression_statement", handleChildren], + ["interpolation", handleInterpolation], + ["block", handleChildren], + ["comment", handleSwallow], + ["return_statement", handleReturn], + ["assignment", handleAssignment], + ["named_expression", handleNamedExpression], + ["identifier", handleIdentifier], + ["attribute", handleAttribute], + ["subscript", handleSubscript], + ["call", handleCall], + ["argument_list", handleArgumentsList], + ["for_statement", handleForStatement], + ["list_comprehension", handleListComprehension], + ["comparison_operator", handleComparisonOperator], + ["boolean_operator", handleBooleanOperator], + ["binary_operator", handleBinaryOperator], + ["not_operator", handleNotOperator], + ["unary_operator", handleUnaryOperator], + ["integer", handleNumber], + ["float", handleNumber], + ["string", handleString], + ["tuple", handleList], + ["list", handleList], + ["dictionary", handleDictionary], + ["pair", handleDictionaryPair], + ["true", async (...args) => true], + ["false", async (...args) => false], +]); +const DEFAULT_BUILT_INS = { + round: { fn: (n) => Math.round(Number(n)) }, + ceil: { fn: (n) => Math.ceil(Number(n)) }, + floor: { fn: (n) => Math.floor(Number(n)) }, + len: { fn: (n) => { var _a, _b; return (_b = (_a = n === null || n === void 0 ? void 0 : n.__len__) === null || _a === void 0 ? void 0 : _a.call(n)) !== null && _b !== void 0 ? _b : n === null || n === void 0 ? void 0 : n.length; } }, + int: { fn: (n) => Math.floor(Number(n)) }, + float: { fn: (n) => Number(n) }, + str: { fn: (n) => String(n) }, + bool: { fn: (n) => !!n }, + list: { fn: (tupl = []) => new PyList(tupl) }, + tuple: { fn: (list = []) => new PyTuple(list) }, + dict: { fn: (dict = {}) => new PyDict(dict) }, + dir: { fn: (...args) => console.dir(...__unwrap__(...args)) }, + print: { fn: (...args) => console.log(...__unwrap__(...args)) }, + log: { fn: (...args) => console.log(...__unwrap__(...args)) }, +}; +export async function execute(code, ctx, additionalBuiltins) { + var _a, _b; + const builtIns = deepFreeze({ ...DEFAULT_BUILT_INS, ...(additionalBuiltins !== null && additionalBuiltins !== void 0 ? additionalBuiltins : {}) }); + ctx = new InitialExecuteContext(ctx); + const root = (await parse(code)).rootNode; + const value = await handleNode(new Node(root), ctx, builtIns); + console.log("====="); + console.log(`value`, (_b = (_a = value === null || value === void 0 ? void 0 : value.__unwrap__) === null || _a === void 0 ? void 0 : _a.call(value)) !== null && _b !== void 0 ? _b : value); + console.log("context", ctx); + return value; +} +async function parse(code) { + if (!MEMOIZED.parser) { + const TreeSitter = (await import("../lib/tree-sitter.js")); + await TreeSitter.Parser.init(); + const lang = await TreeSitter.Language.load("rgthree/lib/tree-sitter-python.wasm"); + MEMOIZED.parser = new TreeSitter.Parser(); + MEMOIZED.parser.setLanguage(lang); + } + return MEMOIZED.parser.parse(code); +} +async function handleNode(node, ctx, builtIns) { + const type = node.type; + if (ctx.hasOwnProperty("__returned__")) + return ctx["__returned__"]; + const handler = TYPE_TO_HANDLER.get(type); + check(handler, "Unhandled type: " + type, node); + return handler(node, ctx, builtIns); +} +async function handleChildren(node, ctx, builtIns) { + let lastValue = null; + for (const child of node.children) { + if (!child) + continue; + lastValue = await handleNode(child, ctx, builtIns); + } + return lastValue; +} +async function handleSwallow(node, ctx, builtIns) { +} +async function handleReturn(node, ctx, builtIns) { + const value = node.children.length > 1 ? handleNode(node.child(1), ctx, builtIns) : undefined; + ctx["__returned__"] = value; + return value; +} +async function handleIdentifier(node, ctx, builtIns) { + var _a, _b; + let value = ctx[node.text]; + if (value === undefined) { + value = (_b = (_a = builtIns[node.text]) === null || _a === void 0 ? void 0 : _a.fn) !== null && _b !== void 0 ? _b : undefined; + } + return maybeWrapValue(value); +} +async function handleAttribute(node, ctx, builtIns) { + const children = node.children; + check(children.length === 3, "Expected 3 children for attribute."); + check(children[1].type === ".", "Expected middle child to be '.' for attribute."); + const inst = await handleNode(children[0], ctx, builtIns); + const attr = children[2].text; + checkAttributeAccessibility(inst, attr); + let attribute = maybeWrapValue(inst[attr]); + return typeof attribute === "function" ? attribute.bind(inst) : attribute; +} +async function handleSubscript(node, ctx, builtIns) { + const children = node.children; + check(children.length === 4, "Expected 4 children for subscript."); + check(children[1].type === "[", "Expected 2nd child to be '[' for subscript."); + check(children[3].type === "]", "Expected 4thd child to be ']' for subscript."); + const inst = await handleNode(children[0], ctx, builtIns); + const attr = await handleNode(children[2], ctx, builtIns); + if (inst instanceof PyTuple && isInt(attr)) { + return maybeWrapValue(inst.__at__(attr)); + } + if (inst instanceof PyDict && typeof attr === "string") { + return maybeWrapValue(inst.get(attr)); + } + checkAttributeAccessibility(inst, attr); + let attribute = maybeWrapValue(inst[attr]); + return typeof attribute === "function" ? attribute.bind(inst) : attribute; +} +async function handleAssignment(node, ctx, builtIns) { + check(node.children.length === 3, "Expected 3 children for assignment: identifier/attr, =, and value."); + check(node.children[1].type === "=", "Expected middle child to be an '='."); + let right = await handleNode(node.children[2], ctx, builtIns); + const leftNode = node.children[0]; + let leftObj = ctx; + let leftProp = ""; + if (leftNode.type === "identifier") { + leftProp = leftNode.text; + } + else if (leftNode.type === "attribute") { + leftObj = await handleNode(leftNode.children[0], ctx, builtIns); + check(leftNode.children[2].type === "identifier", "Expected left hand assignment attribute to be an identifier.", leftNode); + leftProp = leftNode.children[2].text; + } + else if (leftNode.type === "subscript") { + leftObj = await handleNode(leftNode.children[0], ctx, builtIns); + check(leftNode.children[1].type === "["); + check(leftNode.children[3].type === "]"); + leftProp = await handleNode(leftNode.children[2], ctx, builtIns); + } + else { + throw new Error(`Unhandled left-hand assignement type: ${leftNode.type}`); + } + if (leftProp == null) { + throw new Error(`No property to assign value`); + } + if (leftObj instanceof PyTuple) { + check(isInt(leftProp), "Expected an int for list assignment"); + leftObj.__put__(leftProp, right); + } + else if (leftObj instanceof PyDict) { + check(typeof leftProp === "string", "Expected a string for dict assignment"); + leftObj.__put__(leftProp, right); + } + else { + check(typeof leftProp === "string", "Expected a string for object assignment"); + if (!(leftObj instanceof InitialExecuteContext)) { + checkAttributeAccessibility(leftObj, leftProp); + } + leftObj[leftProp] = right; + } + return right; +} +async function handleNamedExpression(node, ctx, builtIns) { + check(node.children.length === 3, "Expected three children for named expression."); + check(node.child(0).type === "identifier", "Expected identifier first in named expression."); + const varName = node.child(0).text; + ctx[varName] = await handleNode(node.child(2), ctx, builtIns); + return maybeWrapValue(ctx[varName]); +} +async function handleCall(node, ctx, builtIns) { + check(node.children.length === 2, "Expected 2 children for call, identifier and arguments."); + const fn = await handleNode(node.children[0], ctx, builtIns); + const args = await handleNode(node.children[1], ctx, builtIns); + console.log("handleCall", fn, args); + return fn(...args); +} +async function handleArgumentsList(node, ctx, builtIns) { + const args = (await handleList(node, ctx, builtIns)).__unwrap__(false); + return [...args]; +} +async function handleForStatement(node, ctx, builtIns) { + const childs = node.children; + check(childs.length === 6); + check(childs[4].type === ":"); + check(childs[5].type === "block"); + await helperGetLoopForIn(node, ctx, builtIns, async (forCtx) => { + await handleNode(childs[5], forCtx, builtIns); + }); +} +async function handleListComprehension(node, ctx, builtIns) { + const finalList = new PyList(); + const newCtx = { ...ctx }; + let finalEntryNode; + const loopNodes = []; + for (const child of node.children) { + if (!child || ["[", "]"].includes(child.type)) + continue; + if (child.type === "identifier" || child.type === "attribute") { + if (finalEntryNode) { + throw Error("Already have a list comprehension finalEntryNode."); + } + finalEntryNode = child; + } + else if (child.type === "for_in_clause") { + loopNodes.push({ forIn: child }); + } + else if (child.type === "if_clause") { + loopNodes[loopNodes.length - 1]["if"] = child; + } + } + if (!finalEntryNode) { + throw Error("No list comprehension finalEntryNode."); + } + console.log(`handleListComprehension.loopNodes`, loopNodes); + const handleLoop = async (loopNodes) => { + const loopNode = loopNodes.shift(); + await helperGetLoopForIn(loopNode.forIn, newCtx, builtIns, async (forCtx) => { + if (loopNode.if) { + const ifNode = loopNode.if; + check(ifNode.children.length === 2, "Expected 2 children for if_clause."); + check(ifNode.child(0).text === "if", "Expected first child to be 'if'."); + const good = await handleNode(ifNode.child(1), forCtx, builtIns); + if (!good) + return; + } + Object.assign(newCtx, forCtx); + if (loopNodes.length) { + await handleLoop(loopNodes); + } + else { + finalList.append(await handleNode(finalEntryNode, newCtx, builtIns)); + } + }, () => ({ ...newCtx })); + loopNodes.unshift(loopNode); + }; + await handleLoop(loopNodes); + return finalList; +} +async function helperGetLoopForIn(node, ctx, builtIns, eachFn, provideForCtx) { + var _a; + const childs = node.children; + check(childs.length >= 3); + check(childs[0].type === "for"); + check(["identifier", "pattern_list"].includes(childs[1].type), "Expected identifier for for loop."); + check(childs[2].type === "in"); + let identifiers; + if (childs[1].type === "identifier") { + identifiers = [childs[1].text]; + } + else { + identifiers = childs[1].children + .map((n) => { + if (n.type === ",") + return null; + check(n.type === "identifier"); + return node.text; + }) + .filter((n) => n != null); + } + const iterable = await handleNode(childs[3], ctx, builtIns); + check(iterable instanceof PyTuple, "Expected for loop instance to be a list/tuple."); + for (const item of iterable.__unwrap__(false)) { + const forCtx = (_a = provideForCtx === null || provideForCtx === void 0 ? void 0 : provideForCtx()) !== null && _a !== void 0 ? _a : ctx; + if (identifiers.length === 1) { + forCtx[identifiers[0]] = item; + } + else { + check(Array.isArray(item) && identifiers.length === item.length, "Expected iterable to be a list, like using dict.items()"); + for (let i = 0; i < identifiers.length; i++) { + forCtx[identifiers[i]] = item[i]; + } + } + await eachFn(forCtx); + } +} +async function handleNumber(node, ctx, builtIns) { + return Number(node.text); +} +async function handleString(node, ctx, builtIns) { + let str = ""; + for (const child of node.children) { + if (!child || ["string_start", "string_end"].includes(child.type)) + continue; + if (child.type === "string_content") { + str += child.text; + } + else if (child.type === "interpolation") { + check(child.children.length === 3, "Expected interpolation"); + str += await handleNode(child, ctx, builtIns); + } + } + return str; +} +async function handleInterpolation(node, ...args) { + check(node.children.length === 3, "Expected interpolation to be three nodes length."); + check(node.children[0].type === "{" && node.children[2].type === "}", 'Expected interpolation to be wrapped in "{" and "}".'); + return await handleNode(node.children[1], ...args); +} +async function handleList(node, ctx, builtIns) { + const list = []; + for (const child of node.children) { + if (!child || ["(", "[", ",", "]", ")"].includes(child.type)) + continue; + list.push(await handleNode(child, ctx, builtIns)); + } + if (node.type === "tuple") { + return new PyTuple(list); + } + return new PyList(list); +} +async function handleComparisonOperator(node, ctx, builtIns) { + const op = node.child(1).text; + const left = await handleNode(node.child(0), ctx, builtIns); + const right = await handleNode(node.child(2), ctx, builtIns); + if (op === "==") + return left === right; + if (op === "!=") + return left !== right; + if (op === ">") + return left > right; + if (op === ">=") + return left >= right; + if (op === "<") + return left < right; + if (op === "<=") + return left <= right; + if (op === "in") + return (right.__unwrap__ ? right.__unwrap__(false) : right).includes(left); + throw new Error(`Comparison not handled: "${op}"`); +} +async function handleBooleanOperator(node, ctx, builtIns) { + const op = node.child(1).text; + const left = await handleNode(node.child(0), ctx, builtIns); + if (!left && op === "and") + return left; + const right = await handleNode(node.child(2), ctx, builtIns); + if (op === "and") + return left && right; + if (op === "or") + return left || right; +} +async function handleBinaryOperator(node, ctx, builtIns) { + const op = node.child(1).text; + const left = await handleNode(node.child(0), ctx, builtIns); + const right = await handleNode(node.child(2), ctx, builtIns); + if (left.constructor !== right.constructor) { + throw new Error(`Can only run ${op} operator on same type.`); + } + if (op === "+") + return left.__add__ ? left.__add__(right) : left + right; + if (op === "-") + return left - right; + if (op === "/") + return left / right; + if (op === "//") + return Math.floor(left / right); + if (op === "*") + return left * right; + if (op === "%") + return left % right; + if (op === "&") + return left & right; + if (op === "|") + return left | right; + if (op === "^") + return left ^ right; + if (op === "<<") + return left << right; + if (op === ">>") + return left >> right; + throw new Error(`Comparison not handled: "${op}"`); +} +async function handleNotOperator(node, ctx, builtIns) { + check(node.children.length === 2, "Expected 2 children for not operator."); + check(node.child(0).text === "not", "Expected first child to be 'not'."); + const value = await handleNode(node.child(1), ctx, builtIns); + return !value; +} +async function handleUnaryOperator(node, ctx, builtIns) { + check(node.children.length === 2, "Expected 2 children for not operator."); + const value = await handleNode(node.child(1), ctx, builtIns); + const op = node.child(0).text; + if (op === "-") + return value * -1; + console.warn(`Unhandled unary operator: ${op}`); + return value; +} +async function handleDictionary(node, ctx, builtIns) { + const dict = new PyDict(); + for (const child of node.children) { + if (!child || ["{", ",", "}"].includes(child.type)) + continue; + check(child.type === "pair", "Expected a pair type for dict."); + const pair = await handleNode(child, ctx, builtIns); + dict.__put__(pair[0], pair[1]); + } + return dict; +} +async function handleDictionaryPair(node, ctx, builtIns) { + check(node.children.length === 3, "Expected 3 children for dict pair."); + let varName = await handleNode(node.child(0), ctx, builtIns); + let varValue = await handleNode(node.child(2), ctx, builtIns); + check(typeof varName === "string", "Expected varname to be string."); + return [varName, varValue]; +} +class Node { + constructor(node) { + this.type = node.type; + this.text = node.text; + if (this.type === "ERROR") { + throw new Error(`Error found in parsing near "${this.text}"`); + } + this.children = []; + for (const child of node.children) { + this.children.push(new Node(child)); + } + this.node = node; + } + child(index) { + const child = this.children[index]; + if (!child) + throw Error(`No child at index ${index}.`); + return child; + } + log(tab = "", showNode = false) { + console.log(`${tab}--- Node`); + console.log(`${tab} type: ${this.type}`); + console.log(`${tab} text: ${this.text}`); + console.log(`${tab} children:`, this.children); + if (showNode) { + console.log(`${tab} node:`, this.node); + } + } +} +export class PyTuple { + constructor(...args) { + if (args.length === 1 && args[0] instanceof PyTuple) { + args = args[0].__unwrap__(false); + } + if (args.length === 1 && Array.isArray(args[0])) { + args = [...args[0]]; + } + this.list = [...args]; + } + count(v) { + } + index() { + } + __at__(index) { + index = this.__get_relative_index__(index); + return this.list[index]; + } + __len__() { + return this.list.length; + } + __add__(v) { + if (!(v instanceof PyTuple)) { + throw new Error("Can only concatenate tuple to tuple."); + } + return new PyTuple(this.__unwrap__(false).concat(v.__unwrap__(false))); + } + __put__(index, v) { + throw new Error("Tuple does not support item assignment"); + } + __get_relative_index__(index) { + if (index >= 0) { + check(this.list.length > index, `Index ${index} out of range.`); + return index; + } + const relIndex = this.list.length + index; + check(relIndex >= 0, `Index ${index} out of range.`); + return relIndex; + } + __unwrap__(deep = true) { + var _a; + const l = [...this.list]; + if (deep) { + for (let i = 0; i < l.length; i++) { + l[i] = ((_a = l[i]) === null || _a === void 0 ? void 0 : _a.__unwrap__) ? l[i].__unwrap__(deep) : l[i]; + } + } + return l; + } +} +__decorate([ + Exposed +], PyTuple.prototype, "count", null); +__decorate([ + Exposed +], PyTuple.prototype, "index", null); +export class PyList extends PyTuple { + append(...args) { + this.list.push(...args); + } + clear() { + this.list.length = 0; + } + copy() { + } + count() { + } + extend() { + } + index() { + } + insert() { + } + pop() { + } + remove() { + } + reverse() { + } + sort() { + } + __add__(v) { + if (!(v instanceof PyList)) { + throw new Error("Can only concatenate list to list."); + } + return new PyList(this.__unwrap__(false).concat(v.__unwrap__(false))); + } + __put__(index, v) { + index = this.__get_relative_index__(index); + this.list[index] = v; + } +} +__decorate([ + Exposed +], PyList.prototype, "append", null); +__decorate([ + Exposed +], PyList.prototype, "clear", null); +__decorate([ + Exposed +], PyList.prototype, "copy", null); +__decorate([ + Exposed +], PyList.prototype, "count", null); +__decorate([ + Exposed +], PyList.prototype, "extend", null); +__decorate([ + Exposed +], PyList.prototype, "index", null); +__decorate([ + Exposed +], PyList.prototype, "insert", null); +__decorate([ + Exposed +], PyList.prototype, "pop", null); +__decorate([ + Exposed +], PyList.prototype, "remove", null); +__decorate([ + Exposed +], PyList.prototype, "reverse", null); +__decorate([ + Exposed +], PyList.prototype, "sort", null); +class PyInt { +} +class PyDict { + constructor(dict) { + _PyDict_dict.set(this, void 0); + __classPrivateFieldSet(this, _PyDict_dict, { ...(dict !== null && dict !== void 0 ? dict : {}) }, "f"); + } + clear() { } + copy() { } + fromkeys() { } + get(key) { + return __classPrivateFieldGet(this, _PyDict_dict, "f")[key]; + } + items() { + return new PyTuple(Object.entries(__classPrivateFieldGet(this, _PyDict_dict, "f")).map((e) => new PyTuple(e))); + } + keys() { } + pop() { } + popitem() { } + setdefault() { } + update() { } + values() { } + __put__(key, v) { + __classPrivateFieldGet(this, _PyDict_dict, "f")[key] = v; + } + __len__() { + return Object.keys(__classPrivateFieldGet(this, _PyDict_dict, "f")).length; + } + __unwrap__(deep = true) { + var _a; + const d = { ...__classPrivateFieldGet(this, _PyDict_dict, "f") }; + if (deep) { + for (let k of Object.keys(d)) { + d[k] = ((_a = d[k]) === null || _a === void 0 ? void 0 : _a.__unwrap__) ? d[k].__unwrap__(deep) : d[k]; + } + } + return d; + } +} +_PyDict_dict = new WeakMap(); +__decorate([ + Exposed +], PyDict.prototype, "clear", null); +__decorate([ + Exposed +], PyDict.prototype, "copy", null); +__decorate([ + Exposed +], PyDict.prototype, "fromkeys", null); +__decorate([ + Exposed +], PyDict.prototype, "get", null); +__decorate([ + Exposed +], PyDict.prototype, "items", null); +__decorate([ + Exposed +], PyDict.prototype, "keys", null); +__decorate([ + Exposed +], PyDict.prototype, "pop", null); +__decorate([ + Exposed +], PyDict.prototype, "popitem", null); +__decorate([ + Exposed +], PyDict.prototype, "setdefault", null); +__decorate([ + Exposed +], PyDict.prototype, "update", null); +__decorate([ + Exposed +], PyDict.prototype, "values", null); +function __unwrap__(...args) { + var _a; + for (let i = 0; i < args.length; i++) { + args[i] = ((_a = args[i]) === null || _a === void 0 ? void 0 : _a.__unwrap__) ? args[i].__unwrap__(true) : args[i]; + } + return args; +} +function checkAttributeAccessibility(inst, attr) { + var _a, _b, _c, _d, _e, _f; + const instType = typeof inst; + check(instType === "object" || instType === "function", `Instance of type ${instType} does not have attributes.`); + check(!attr.startsWith("__") && !attr.endsWith("__"), `"${attr}" is not accessible.`); + const attrType = typeof inst[attr]; + if (attrType === "function") { + const allowedMethods = (_c = (_b = (_a = inst.constructor) === null || _a === void 0 ? void 0 : _a.__ALLOWED_METHODS__) !== null && _b !== void 0 ? _b : inst.__ALLOWED_METHODS__) !== null && _c !== void 0 ? _c : []; + check(allowedMethods.includes(attr), `Method ${attr} is not accessible.`); + } + else { + const allowedProps = (_f = (_e = (_d = inst.constructor) === null || _d === void 0 ? void 0 : _d.__ALLOWED_PROPERTIES__) !== null && _e !== void 0 ? _e : inst.__ALLOWED_PROPERTIES__) !== null && _f !== void 0 ? _f : []; + check(allowedProps.includes(attr), `Property ${attr} is not accessible.`); + } +} +function maybeWrapValue(value) { + if (Array.isArray(value)) { + return new PyList(value); + } + return value; +} +function isInt(value) { + return typeof value === "number" && Math.round(value) === value; +} +function isIntLike(value) { + let is = isInt(value); + if (!is) { + is = typeof value === "string" && !!/^\d+$/.exec(value); + } + return is; +} +export function Exposed(target, key) { + const descriptor = Object.getOwnPropertyDescriptor(target, key); + if (typeof (descriptor === null || descriptor === void 0 ? void 0 : descriptor.value) === "function") { + target.constructor.__ALLOWED_METHODS__ = target.constructor.__ALLOWED_METHODS__ || []; + target.constructor.__ALLOWED_METHODS__.push(key); + } + else { + target.constructor.__ALLOWED_PROPERTIES__ = target.constructor.__ALLOWED_PROPERTIES__ || []; + target.constructor.__ALLOWED_PROPERTIES__.push(key); + } +} diff --git a/custom_nodes/rgthree-comfy/web/common/rgthree_api.js b/custom_nodes/rgthree-comfy/web/common/rgthree_api.js new file mode 100644 index 00000000..eae650f4 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/rgthree_api.js @@ -0,0 +1,131 @@ +class RgthreeApi { + constructor(baseUrl) { + this.getCheckpointsPromise = null; + this.getSamplersPromise = null; + this.getSchedulersPromise = null; + this.getLorasPromise = null; + this.getWorkflowsPromise = null; + this.setBaseUrl(baseUrl); + } + setBaseUrl(baseUrlArg) { + var _a; + let baseUrl = null; + if (baseUrlArg) { + baseUrl = baseUrlArg; + } + else if (window.location.pathname.includes("/rgthree/")) { + const parts = (_a = window.location.pathname.split("/rgthree/")[1]) === null || _a === void 0 ? void 0 : _a.split("/"); + if (parts && parts.length) { + baseUrl = parts.map(() => "../").join("") + "rgthree/api"; + } + } + this.baseUrl = baseUrl || "./rgthree/api"; + const comfyBasePathname = location.pathname.includes("/rgthree/") + ? location.pathname.split("rgthree/")[0] + : location.pathname; + this.comfyBaseUrl = comfyBasePathname.split("/").slice(0, -1).join("/"); + } + apiURL(route) { + return `${this.baseUrl}${route}`; + } + fetchApi(route, options) { + return fetch(this.apiURL(route), options); + } + async fetchJson(route, options) { + const r = await this.fetchApi(route, options); + return await r.json(); + } + async postJson(route, json) { + const body = new FormData(); + body.append("json", JSON.stringify(json)); + return await rgthreeApi.fetchJson(route, { method: "POST", body }); + } + getLoras(force = false) { + if (!this.getLorasPromise || force) { + this.getLorasPromise = this.fetchJson("/loras?format=details", { cache: "no-store" }); + } + return this.getLorasPromise; + } + async fetchApiJsonOrNull(route, options) { + const response = await this.fetchJson(route, options); + if (response.status === 200 && response.data) { + return response.data || null; + } + return null; + } + async getModelsInfo(options) { + var _a; + const params = new URLSearchParams(); + if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) { + params.set("files", options.files.join(",")); + } + if (options.light) { + params.set("light", "1"); + } + if (options.format) { + params.set("format", options.format); + } + const path = `/${options.type}/info?` + params.toString(); + return (await this.fetchApiJsonOrNull(path)) || []; + } + async getLorasInfo(options = {}) { + return this.getModelsInfo({ type: "loras", ...options }); + } + async getCheckpointsInfo(options = {}) { + return this.getModelsInfo({ type: "checkpoints", ...options }); + } + async refreshModelsInfo(options) { + var _a; + const params = new URLSearchParams(); + if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) { + params.set("files", options.files.join(",")); + } + const path = `/${options.type}/info/refresh?` + params.toString(); + const infos = await this.fetchApiJsonOrNull(path); + return infos; + } + async refreshLorasInfo(options = {}) { + return this.refreshModelsInfo({ type: "loras", ...options }); + } + async refreshCheckpointsInfo(options = {}) { + return this.refreshModelsInfo({ type: "checkpoints", ...options }); + } + async clearModelsInfo(options) { + var _a; + const params = new URLSearchParams(); + if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) { + params.set("files", options.files.join(",")); + } + const path = `/${options.type}/info/clear?` + params.toString(); + await this.fetchApiJsonOrNull(path); + return; + } + async clearLorasInfo(options = {}) { + return this.clearModelsInfo({ type: "loras", ...options }); + } + async clearCheckpointsInfo(options = {}) { + return this.clearModelsInfo({ type: "checkpoints", ...options }); + } + async saveModelInfo(type, file, data) { + const body = new FormData(); + body.append("json", JSON.stringify(data)); + return await this.fetchApiJsonOrNull(`/${type}/info?file=${encodeURIComponent(file)}`, { cache: "no-store", method: "POST", body }); + } + async saveLoraInfo(file, data) { + return this.saveModelInfo("loras", file, data); + } + async saveCheckpointsInfo(file, data) { + return this.saveModelInfo("checkpoints", file, data); + } + fetchComfyApi(route, options) { + const url = this.comfyBaseUrl + "/api" + route; + options = options || {}; + options.headers = options.headers || {}; + options.cache = options.cache || "no-cache"; + return fetch(url, options); + } + print(messageType) { + this.fetchApi(`/print?type=${messageType}`, {}); + } +} +export const rgthreeApi = new RgthreeApi(); diff --git a/custom_nodes/rgthree-comfy/web/common/shared_utils.js b/custom_nodes/rgthree-comfy/web/common/shared_utils.js new file mode 100644 index 00000000..61ba549c --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/shared_utils.js @@ -0,0 +1,416 @@ +export function getResolver(timeout = 5000) { + const resolver = {}; + resolver.id = generateId(8); + resolver.completed = false; + resolver.resolved = false; + resolver.rejected = false; + resolver.promise = new Promise((resolve, reject) => { + resolver.reject = (e) => { + resolver.completed = true; + resolver.rejected = true; + reject(e); + }; + resolver.resolve = (data) => { + resolver.completed = true; + resolver.resolved = true; + resolve(data); + }; + }); + resolver.timeout = setTimeout(() => { + if (!resolver.completed) { + resolver.reject(); + } + }, timeout); + return resolver; +} +const DEBOUNCE_FN_TO_PROMISE = new WeakMap(); +export function debounce(fn, ms = 64) { + if (!DEBOUNCE_FN_TO_PROMISE.get(fn)) { + DEBOUNCE_FN_TO_PROMISE.set(fn, wait(ms).then(() => { + DEBOUNCE_FN_TO_PROMISE.delete(fn); + fn(); + })); + } + return DEBOUNCE_FN_TO_PROMISE.get(fn); +} +export function check(value, msg = "", ...args) { + if (!value) { + console.error(msg, ...(args || [])); + throw new Error(msg || "Error"); + } +} +export function wait(ms = 16) { + if (ms === 16) { + return new Promise((resolve) => { + requestAnimationFrame(() => { + resolve(); + }); + }); + } + return new Promise((resolve) => { + setTimeout(() => { + resolve(); + }, ms); + }); +} +export function deepFreeze(obj) { + const propNames = Reflect.ownKeys(obj); + for (const name of propNames) { + const value = obj[name]; + if ((value && typeof value === "object") || typeof value === "function") { + deepFreeze(value); + } + } + return Object.freeze(obj); +} +function dec2hex(dec) { + return dec.toString(16).padStart(2, "0"); +} +export function generateId(length) { + const arr = new Uint8Array(length / 2); + crypto.getRandomValues(arr); + return Array.from(arr, dec2hex).join(""); +} +export function getObjectValue(obj, objKey, def) { + if (!obj || !objKey) + return def; + const keys = objKey.split("."); + const key = keys.shift(); + const found = obj[key]; + if (keys.length) { + return getObjectValue(found, keys.join("."), def); + } + return found; +} +export function setObjectValue(obj, objKey, value, createMissingObjects = true) { + if (!obj || !objKey) + return obj; + const keys = objKey.split("."); + const key = keys.shift(); + if (obj[key] === undefined) { + if (!createMissingObjects) { + return; + } + obj[key] = {}; + } + if (!keys.length) { + obj[key] = value; + } + else { + if (typeof obj[key] != "object") { + obj[key] = {}; + } + setObjectValue(obj[key], keys.join("."), value, createMissingObjects); + } + return obj; +} +export function moveArrayItem(arr, itemOrFrom, to) { + const from = typeof itemOrFrom === "number" ? itemOrFrom : arr.indexOf(itemOrFrom); + arr.splice(to, 0, arr.splice(from, 1)[0]); +} +export function removeArrayItem(arr, itemOrIndex) { + const index = typeof itemOrIndex === "number" ? itemOrIndex : arr.indexOf(itemOrIndex); + arr.splice(index, 1); +} +export function injectCss(href) { + if (document.querySelector(`link[href^="${href}"]`)) { + return Promise.resolve(); + } + return new Promise((resolve) => { + const link = document.createElement("link"); + link.setAttribute("rel", "stylesheet"); + link.setAttribute("type", "text/css"); + const timeout = setTimeout(resolve, 1000); + link.addEventListener("load", (e) => { + clearInterval(timeout); + resolve(); + }); + link.href = href; + document.head.appendChild(link); + }); +} +export function defineProperty(instance, property, desc) { + var _a, _b, _c, _d, _e, _f; + const existingDesc = Object.getOwnPropertyDescriptor(instance, property); + if ((existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.configurable) === false) { + throw new Error(`Error: rgthree-comfy cannot define un-configurable property "${property}"`); + } + if ((existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.get) && desc.get) { + const descGet = desc.get; + desc.get = () => { + existingDesc.get.apply(instance, []); + return descGet.apply(instance, []); + }; + } + if ((existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.set) && desc.set) { + const descSet = desc.set; + desc.set = (v) => { + existingDesc.set.apply(instance, [v]); + return descSet.apply(instance, [v]); + }; + } + desc.enumerable = (_b = (_a = desc.enumerable) !== null && _a !== void 0 ? _a : existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.enumerable) !== null && _b !== void 0 ? _b : true; + desc.configurable = (_d = (_c = desc.configurable) !== null && _c !== void 0 ? _c : existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.configurable) !== null && _d !== void 0 ? _d : true; + if (!desc.get && !desc.set) { + desc.writable = (_f = (_e = desc.writable) !== null && _e !== void 0 ? _e : existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.writable) !== null && _f !== void 0 ? _f : true; + } + return Object.defineProperty(instance, property, desc); +} +export function areDataViewsEqual(a, b) { + if (a.byteLength !== b.byteLength) { + return false; + } + for (let i = 0; i < a.byteLength; i++) { + if (a.getUint8(i) !== b.getUint8(i)) { + return false; + } + } + return true; +} +function looksLikeBase64(source) { + return source.length > 500 || source.startsWith("data:") || source.includes(";base64,"); +} +export function areArrayBuffersEqual(a, b) { + if (a == b || !a || !b) { + return a == b; + } + return areDataViewsEqual(new DataView(a), new DataView(b)); +} +export function newCanvas(widthOrPtOrImage, height) { + let width; + if (typeof widthOrPtOrImage !== "number") { + width = widthOrPtOrImage.width; + height = widthOrPtOrImage.height; + } + else { + width = widthOrPtOrImage; + height = height; + } + if (height == null) { + throw new Error("Invalid height supplied when creating new canvas object."); + } + const canvas = document.createElement("canvas"); + canvas.width = width; + canvas.height = height; + if (widthOrPtOrImage instanceof HTMLImageElement) { + const ctx = canvas.getContext("2d"); + ctx.drawImage(widthOrPtOrImage, 0, 0, width, height); + } + return canvas; +} +export function getCanvasImageData(image) { + const canvas = newCanvas(image); + const ctx = canvas.getContext("2d"); + const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height); + return [canvas, ctx, imageData]; +} +export async function convertToBase64(source) { + if (source instanceof Promise) { + source = await source; + } + if (typeof source === "string" && looksLikeBase64(source)) { + return source; + } + if (typeof source === "string" || source instanceof Blob || source instanceof ArrayBuffer) { + return convertToBase64(await loadImage(source)); + } + if (source instanceof HTMLImageElement) { + if (looksLikeBase64(source.src)) { + return source.src; + } + const [canvas, ctx, imageData] = getCanvasImageData(source); + return convertToBase64(canvas); + } + if (source instanceof HTMLCanvasElement) { + return source.toDataURL("image/png"); + } + throw Error("Unknown source to convert to base64."); +} +export async function convertToArrayBuffer(source) { + if (source instanceof Promise) { + source = await source; + } + if (source instanceof ArrayBuffer) { + return source; + } + if (typeof source === "string") { + if (looksLikeBase64(source)) { + var binaryString = atob(source.replace(/^.*?;base64,/, "")); + var bytes = new Uint8Array(binaryString.length); + for (var i = 0; i < binaryString.length; i++) { + bytes[i] = binaryString.charCodeAt(i); + } + return bytes.buffer; + } + return convertToArrayBuffer(await loadImage(source)); + } + if (source instanceof HTMLImageElement) { + const [canvas, ctx, imageData] = getCanvasImageData(source); + return convertToArrayBuffer(canvas); + } + if (source instanceof HTMLCanvasElement) { + return convertToArrayBuffer(source.toDataURL()); + } + if (source instanceof Blob) { + return source.arrayBuffer(); + } + throw Error("Unknown source to convert to arraybuffer."); +} +export async function loadImage(source) { + if (source instanceof Promise) { + source = await source; + } + if (source instanceof HTMLImageElement) { + return loadImage(source.src); + } + if (source instanceof Blob) { + return loadImage(source.arrayBuffer()); + } + if (source instanceof HTMLCanvasElement) { + return loadImage(source.toDataURL()); + } + if (source instanceof ArrayBuffer) { + var binary = ""; + var bytes = new Uint8Array(source); + var len = bytes.byteLength; + for (var i = 0; i < len; i++) { + binary += String.fromCharCode(bytes[i]); + } + return loadImage(`data:${getMimeTypeFromArrayBuffer(bytes)};base64,${btoa(binary)}`); + } + return new Promise((resolve, reject) => { + const img = new Image(); + img.addEventListener("load", () => { + resolve(img); + }); + img.addEventListener("error", () => { + reject(img); + }); + img.src = source; + }); +} +function getMimeTypeFromArrayBuffer(buffer) { + const len = 4; + if (buffer.length >= len) { + let signatureArr = new Array(len); + for (let i = 0; i < len; i++) + signatureArr[i] = buffer[i].toString(16); + const signature = signatureArr.join("").toUpperCase(); + switch (signature) { + case "89504E47": + return "image/png"; + case "47494638": + return "image/gif"; + case "25504446": + return "application/pdf"; + case "FFD8FFDB": + case "FFD8FFE0": + return "image/jpeg"; + case "504B0304": + return "application/zip"; + default: + return null; + } + } + return null; +} +export class Broadcaster extends EventTarget { + constructor(channelName) { + super(); + this.queue = {}; + this.queue = {}; + this.channel = new BroadcastChannel(channelName); + this.channel.addEventListener("message", (e) => { + this.onMessage(e); + }); + } + getId() { + let id; + do { + id = generateId(6); + } while (this.queue[id]); + return id; + } + async broadcastAndWait(action, payload, options) { + const id = this.getId(); + this.queue[id] = getResolver(options === null || options === void 0 ? void 0 : options.timeout); + this.channel.postMessage({ + id, + action, + payload, + }); + let response; + try { + response = await this.queue[id].promise; + } + catch (e) { + console.log("CAUGHT", e); + response = []; + } + return response; + } + broadcast(action, payload) { + this.channel.postMessage({ + id: this.getId(), + action, + payload, + }); + } + reply(replyId, action, payload) { + this.channel.postMessage({ + id: this.getId(), + replyId, + action, + payload, + }); + } + openWindowAndWaitForMessage(rgthreePath, windowName) { + const id = this.getId(); + this.queue[id] = getResolver(); + const win = window.open(`/rgthree/${rgthreePath}#broadcastLoadMsgId=${id}`, windowName); + return { window: win, promise: this.queue[id].promise }; + } + onMessage(e) { + var _a, _b; + const msgId = ((_a = e.data) === null || _a === void 0 ? void 0 : _a.replyId) || ""; + const queueItem = this.queue[msgId]; + if (queueItem) { + if (queueItem.completed) { + console.error(`${msgId} already completed..`); + } + queueItem.deferment = queueItem.deferment || { data: [] }; + queueItem.deferment.data.push(e.data.payload); + queueItem.deferment.timeout && clearTimeout(queueItem.deferment.timeout); + queueItem.deferment.timeout = setTimeout(() => { + queueItem.resolve(queueItem.deferment.data); + }, 250); + } + else { + this.dispatchEvent(new CustomEvent("rgthree-broadcast-message", { + detail: Object.assign({ replyTo: (_b = e.data) === null || _b === void 0 ? void 0 : _b.id }, e.data), + })); + } + } + addMessageListener(callback, options) { + return super.addEventListener("rgthree-broadcast-message", callback, options); + } +} +const broadcastChannelMap = new Map(); +export function broadcastOnChannel(channel, action, payload) { + let queue = broadcastChannelMap.get(channel); + if (!queue) { + broadcastChannelMap.set(channel, {}); + queue = broadcastChannelMap.get(channel); + } + let id; + do { + id = generateId(6); + } while (queue[id]); + queue[id] = getResolver(); + channel.postMessage({ + id, + action, + payload, + }); + return queue[id].promise; +} diff --git a/custom_nodes/rgthree-comfy/web/common/utils_dom.js b/custom_nodes/rgthree-comfy/web/common/utils_dom.js new file mode 100644 index 00000000..c07e876f --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/utils_dom.js @@ -0,0 +1,363 @@ +const DIRECT_ATTRIBUTE_MAP = { + cellpadding: "cellPadding", + cellspacing: "cellSpacing", + colspan: "colSpan", + frameborder: "frameBorder", + height: "height", + maxlength: "maxLength", + nonce: "nonce", + role: "role", + rowspan: "rowSpan", + type: "type", + usemap: "useMap", + valign: "vAlign", + width: "width", +}; +const RGX_NUMERIC_STYLE_UNIT = "px"; +const RGX_NUMERIC_STYLE = /^((max|min)?(width|height)|margin|padding|(margin|padding)?(left|top|bottom|right)|fontsize|borderwidth)$/i; +const RGX_DEFAULT_VALUE_PROP = /input|textarea|select/i; +function localAssertNotFalsy(input, errorMsg = `Input is not of type.`) { + if (input == null) { + throw new Error(errorMsg); + } + return input; +} +const RGX_STRING_VALID = "[a-z0-9_-]"; +const RGX_TAG = new RegExp(`^([a-z]${RGX_STRING_VALID}*)(\\.|\\[|\\#|$)`, "i"); +const RGX_ATTR_ID = new RegExp(`#(${RGX_STRING_VALID}+)`, "gi"); +const RGX_ATTR_CLASS = new RegExp(`(^|\\S)\\.([a-z0-9_\\-\\.]+)`, "gi"); +const RGX_STRING_CONTENT_TO_SQUARES = "(.*?)(\\[|\\])"; +const RGX_ATTRS_MAYBE_OPEN = new RegExp(`\\[${RGX_STRING_CONTENT_TO_SQUARES}`, "gi"); +const RGX_ATTRS_FOLLOW_OPEN = new RegExp(`^${RGX_STRING_CONTENT_TO_SQUARES}`, "gi"); +export function queryAll(selectors, parent = document) { + return Array.from(parent.querySelectorAll(selectors)).filter((n) => !!n); +} +export function query(selectors, parent = document) { + var _a; + return (_a = parent.querySelector(selectors)) !== null && _a !== void 0 ? _a : null; +} +export function createText(text) { + return document.createTextNode(text); +} +export function getClosestOrSelf(element, query) { + const el = element; + return ((el === null || el === void 0 ? void 0 : el.closest) && ((el.matches(query) && el) || el.closest(query))) || null; +} +export function containsOrSelf(parent, contained) { + var _a; + return (parent === contained || ((_a = parent === null || parent === void 0 ? void 0 : parent.contains) === null || _a === void 0 ? void 0 : _a.call(parent, contained)) || false); +} +export function createElement(selectorOrMarkup, attrs) { + const frag = getHtmlFragment(selectorOrMarkup); + let element = frag === null || frag === void 0 ? void 0 : frag.firstElementChild; + let selector = ""; + if (!element) { + selector = selectorOrMarkup.replace(/[\r\n]\s*/g, ""); + const tag = getSelectorTag(selector) || "div"; + element = document.createElement(tag); + selector = selector.replace(RGX_TAG, "$2"); + const brackets = selector.match(/(\[[^\]]+\])/g) || []; + for (const bracket of brackets) { + selector = selector.replace(bracket, ""); + } + selector = selector.replace(RGX_ATTR_ID, '[id="$1"]'); + selector = selector.replace(RGX_ATTR_CLASS, (match, p1, p2) => `${p1}[class="${p2.replace(/\./g, " ")}"]`); + selector += brackets.join(""); + } + const selectorAttrs = getSelectorAttributes(selector); + if (selectorAttrs) { + for (const attr of selectorAttrs) { + let matches = attr.substring(1, attr.length - 1).split("="); + let key = localAssertNotFalsy(matches.shift()); + let value = matches.join("="); + if (value === undefined) { + setAttribute(element, key, true); + } + else { + value = value.replace(/^['"](.*)['"]$/, "$1"); + setAttribute(element, key, value); + } + } + } + if (attrs) { + setAttributes(element, attrs); + } + return element; +} +export const $el = createElement; +function getSelectorTag(str) { + return tryMatch(str, RGX_TAG); +} +function getSelectorAttributes(selector) { + RGX_ATTRS_MAYBE_OPEN.lastIndex = 0; + let attrs = []; + let result; + while ((result = RGX_ATTRS_MAYBE_OPEN.exec(selector))) { + let attr = result[0]; + if (attr.endsWith("]")) { + attrs.push(attr); + } + else { + attr = + result[0] + getOpenAttributesRecursive(selector.substr(RGX_ATTRS_MAYBE_OPEN.lastIndex), 2); + RGX_ATTRS_MAYBE_OPEN.lastIndex += attr.length - result[0].length; + attrs.push(attr); + } + } + return attrs; +} +function getOpenAttributesRecursive(selectorSubstring, openCount) { + let matches = selectorSubstring.match(RGX_ATTRS_FOLLOW_OPEN); + let result = ""; + if (matches && matches.length) { + result = matches[0]; + openCount += result.endsWith("]") ? -1 : 1; + if (openCount > 0) { + result += getOpenAttributesRecursive(selectorSubstring.substr(result.length), openCount); + } + } + return result; +} +function tryMatch(str, rgx, index = 1) { + var _a; + let found = ""; + try { + found = ((_a = str.match(rgx)) === null || _a === void 0 ? void 0 : _a[index]) || ""; + } + catch (e) { + found = ""; + } + return found; +} +export function setAttributes(element, data) { + let attr; + for (attr in data) { + if (data.hasOwnProperty(attr)) { + setAttribute(element, attr, data[attr]); + } + } +} +function getHtmlFragment(value) { + if (value.match(/^\s*<.*?>[\s\S]*<\/[a-z0-9]+>\s*$/)) { + return document.createRange().createContextualFragment(value.trim()); + } + return null; +} +function getChild(value) { + if (value instanceof Node) { + return value; + } + if (typeof value === "string") { + let child = getHtmlFragment(value); + if (child) { + return child; + } + if (getSelectorTag(value)) { + return createElement(value); + } + return createText(value); + } + if (value && typeof value.toElement === "function") { + return value.toElement(); + } + return null; +} +export function setAttribute(element, attribute, value) { + let isRemoving = value == null; + if (attribute === "default") { + attribute = RGX_DEFAULT_VALUE_PROP.test(element.nodeName) ? "value" : "text"; + } + if (attribute === "text") { + empty(element).appendChild(createText(value != null ? String(value) : "")); + } + else if (attribute === "html") { + empty(element).innerHTML += value != null ? String(value) : ""; + } + else if (attribute == "style") { + if (typeof value === "string") { + element.style.cssText = isRemoving ? "" : value != null ? String(value) : ""; + } + else { + for (const [styleKey, styleValue] of Object.entries(value)) { + element.style[styleKey] = styleValue; + } + } + } + else if (attribute == "events") { + for (const [key, fn] of Object.entries(value)) { + addEvent(element, key, fn); + } + } + else if (attribute === "parent") { + value.appendChild(element); + } + else if (attribute === "child" || attribute === "children") { + if (typeof value === "string" && /^\[[^\[\]]+\]$/.test(value)) { + const parseable = value.replace(/^\[([^\[\]]+)\]$/, '["$1"]').replace(/,/g, '","'); + try { + const parsed = JSON.parse(parseable); + value = parsed; + } + catch (e) { + console.error(e); + } + } + if (attribute === "children") { + empty(element); + } + let children = value instanceof Array ? value : [value]; + for (let child of children) { + child = getChild(child); + if (child instanceof Node) { + if (element instanceof HTMLTemplateElement) { + element.content.appendChild(child); + } + else { + element.appendChild(child); + } + } + } + } + else if (attribute == "for") { + element.htmlFor = value != null ? String(value) : ""; + if (isRemoving) { + element.removeAttribute("for"); + } + } + else if (attribute === "class" || attribute === "className" || attribute === "classes") { + element.className = isRemoving ? "" : Array.isArray(value) ? value.join(" ") : String(value); + } + else if (attribute === "dataset") { + if (typeof value !== "object") { + console.error("Expecting an object for dataset"); + return; + } + for (const [key, val] of Object.entries(value)) { + element.dataset[key] = String(val); + } + } + else if (attribute.startsWith("on") && typeof value === "function") { + element.addEventListener(attribute.substring(2), value); + } + else if (["checked", "disabled", "readonly", "required", "selected"].includes(attribute)) { + element[attribute] = !!value; + if (!value) { + element.removeAttribute(attribute); + } + else { + element.setAttribute(attribute, attribute); + } + } + else if (DIRECT_ATTRIBUTE_MAP.hasOwnProperty(attribute)) { + if (isRemoving) { + element.removeAttribute(DIRECT_ATTRIBUTE_MAP[attribute]); + } + else { + element.setAttribute(DIRECT_ATTRIBUTE_MAP[attribute], String(value)); + } + } + else if (isRemoving) { + element.removeAttribute(attribute); + } + else { + let oldVal = element.getAttribute(attribute); + if (oldVal !== value) { + element.setAttribute(attribute, String(value)); + } + } +} +function addEvent(element, key, fn) { + element.addEventListener(key, fn); +} +function setStyles(element, styles = null) { + if (styles) { + for (let name in styles) { + setStyle(element, name, styles[name]); + } + } + return element; +} +export function setStyle(element, name, value) { + name = name.indexOf("float") > -1 ? "cssFloat" : name; + if (name.indexOf("-") != -1) { + name = name.replace(/-\D/g, (match) => { + return match.charAt(1).toUpperCase(); + }); + } + if (value == String(Number(value)) && RGX_NUMERIC_STYLE.test(name)) { + value = value + RGX_NUMERIC_STYLE_UNIT; + } + if (name === "display" && typeof value !== "string") { + value = !!value ? null : "none"; + } + element.style[name] = value === null ? null : String(value); + return element; +} +export function empty(element) { + while (element.firstChild) { + element.removeChild(element.firstChild); + } + return element; +} +export function remove(element) { + while (element.parentElement) { + element.parentElement.removeChild(element); + } + return element; +} +export function replaceChild(oldChildNode, newNode) { + oldChildNode.parentNode.replaceChild(newNode, oldChildNode); + return newNode; +} +export function appendChildren(el, children) { + children = !Array.isArray(children) ? [children] : children; + for (let child of children) { + child = getChild(child); + if (child instanceof Node) { + if (el instanceof HTMLTemplateElement) { + el.content.appendChild(child); + } + else { + el.appendChild(child); + } + } + } +} +export function getActionEls(parent = document) { + const els = Array.from(parent.querySelectorAll("[data-action],[on-action],[on]")); + if (parent instanceof Element) { + els.unshift(parent); + } + return els + .map((actionEl) => { + const actions = {}; + const actionSegments = (actionEl.getAttribute("data-action") || + actionEl.getAttribute("on-action") || + actionEl.getAttribute("on") || + "").split(";"); + for (let segment of actionSegments) { + let actionsData = segment + .trim() + .split(/\s*:\s*/g) + .filter((i) => !!i.trim()); + if (!actionsData.length) + continue; + if (actionsData.length === 1) { + if (actionEl instanceof HTMLInputElement) { + actionsData.unshift("input"); + } + else { + actionsData.unshift("click"); + } + } + if (actionsData[0] && actionsData[1]) { + actions[actionsData[0]] = actionsData[1]; + } + } + return { + el: actionEl, + actions, + }; + }) + .filter((el) => !!el); +} diff --git a/custom_nodes/rgthree-comfy/web/common/utils_templates.js b/custom_nodes/rgthree-comfy/web/common/utils_templates.js new file mode 100644 index 00000000..973ca1b0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/utils_templates.js @@ -0,0 +1,592 @@ +import * as dom from "./utils_dom.js"; +import { getObjectValue } from "./shared_utils.js"; +const CONFIG_DEFAULT = { + attrBind: "data-bind", + attrIf: "data-if", + attrIfIs: "data-if-is", +}; +const CONFIG = Object.assign({}, CONFIG_DEFAULT, { + attrBind: "bind", + attrIf: "if", + attrIfIs: "if-is", +}); +const RGX_COMPARISON = (() => { + let value = "((?:\\!*)[_a-z0-9\\.\\-\\[\\]'\"]+)"; + let comparison = "((?:<|>|==|\\!=)=?)"; + return new RegExp(`^(?:\\!*)\\(?${value}\\s*${comparison}\\s*${value}\\)?$`, "i"); +})(); +const RGXPART_BIND_FN_TEMPLATE_STRING = "template|tpl"; +const RGXPART_BIND_FN_ELEMENT_STRING = "element|el"; +const RGX_BIND_FN_TEMPLATE = new RegExp(`^(?:${RGXPART_BIND_FN_TEMPLATE_STRING})\\(([^\\)]+)\\)`, "i"); +const RGX_BIND_FN_ELEMENT = new RegExp(`^(?:${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`, "i"); +const RGX_BIND_FN_TEMPLATE_OR_ELEMENT = new RegExp(`^(?:${RGXPART_BIND_FN_TEMPLATE_STRING}|${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`, "i"); +const RGX_BIND_FN_LENGTH = /^(?:length|len|size)\(([^\)]+)\)/i; +const RGX_BIND_FN_FORMAT = /^(?:format|fmt)\(([^\,]+),([^\)]+)\)/i; +const RGX_BIND_FN_CALL = /^([^\(]+)\(([^\)]*)\)/i; +const EMPTY_PREPROCESS_FN = (data) => data; +const RGX_BIND_DECLARATIONS = /\s*(\!*(?:[\$_a-z0-9-\.\'\"]|\?\?|\|\||\&\&|(?:(?:<|>|==|\!=)=?))+(?:\`[^\`]+\`)?(?:\([^\)]*\))?)(?::(.*?))?(\s|$)/gi; +function localAssertNotFalsy(input, errorMsg = `Input is not of type.`) { + if (input == null) { + throw new Error(errorMsg); + } + return input; +} +function cleanKey(key) { + return key.toLowerCase().trim().replace(/\s/g, ""); +} +function toArray(value) { + if (Array.isArray(value)) { + return value; + } + if (value instanceof Set) { + return Array.from(value); + } + if (typeof value === "object" && typeof value.length === "number") { + return [].slice.call(value); + } + return [value]; +} +function flattenArray(arr) { + return toArray(arr).reduce((acc, val) => { + return acc.concat(Array.isArray(val) ? flattenArray(val) : val); + }, []); +} +function getObjValue(lookup, obj) { + let booleanMatch = lookup.match(/^(\!+)(.+?)$/i) || []; + let booleanNots = []; + if (booleanMatch[1] && booleanMatch[2]) { + booleanNots = booleanMatch[1].split(""); + lookup = booleanMatch[2]; + } + let value = getObjectValue(obj, lookup); + while (booleanNots.length) { + value = !value; + booleanNots.shift(); + } + return value; +} +function getPrimitiveOrObjValue(stringValue, data) { + let value; + if (stringValue == null) { + return stringValue; + } + let negate = getNegates(stringValue); + if (negate != null) { + stringValue = stringValue.replace(/^\!+/, ""); + } + try { + const cleanedStringValue = stringValue.replace(/^'(.*)'$/, '"$1"'); + value = JSON.parse(cleanedStringValue); + } + catch (e) { + value = getObjValue(stringValue, data); + } + value = negate !== null ? (negate === 1 ? !value : !!value) : value; + return value; +} +function getNegates(stringValue) { + let negate = null; + let negateMatches = stringValue.match(/^(\!+)(.*)/); + if (negateMatches && negateMatches.length >= 3) { + negate = negateMatches[1].length % 2; + } + return negate; +} +function getStringComparisonExpression(bindingPropName, data) { + let comparisonMatches = bindingPropName.match(RGX_COMPARISON); + if (!(comparisonMatches === null || comparisonMatches === void 0 ? void 0 : comparisonMatches.length)) { + return null; + } + let a = getPrimitiveOrObjValue(comparisonMatches[1], data); + let b = getPrimitiveOrObjValue(comparisonMatches[3], data); + let c = comparisonMatches[2]; + let value = (() => { + switch (c) { + case "===": + return a === b; + case "==": + return a == b; + case "<=": + return a <= b; + case ">=": + return a >= b; + case "!==": + return a !== b; + case "!=": + return a != b; + case "<": + return a < b; + case ">": + return a > b; + default: + return undefined; + } + })(); + return value; +} +function replaceTplElementWithChildren(tplEl, fragOrElOrEls) { + const els = Array.isArray(fragOrElOrEls) ? fragOrElOrEls : [fragOrElOrEls]; + tplEl.replaceWith(...els); + const numOfChildren = Array.isArray(fragOrElOrEls) + ? fragOrElOrEls.length + : fragOrElOrEls.childElementCount; + if (numOfChildren === 1) { + const firstChild = Array.isArray(fragOrElOrEls) + ? fragOrElOrEls[0] + : fragOrElOrEls.firstElementChild; + if (firstChild instanceof Element) { + if (tplEl.className.length) { + firstChild.className += ` ${tplEl.className}`; + } + let attr = tplEl.getAttribute("data"); + if (attr) { + firstChild.setAttribute("data", attr); + } + attr = tplEl.getAttribute(CONFIG.attrBind); + if (attr) { + firstChild.setAttribute(CONFIG.attrBind, attr); + } + } + } +} +function getValueForBinding(bindingPropName, context) { + console.log("getValueForBinding", bindingPropName, context); + const data = context.data; + let stringTemplate = null; + let stringTemplates = /^(.*?)\`([^\`]*)\`$/.exec(bindingPropName.trim()); + if ((stringTemplates === null || stringTemplates === void 0 ? void 0 : stringTemplates.length) === 3) { + bindingPropName = stringTemplates[1]; + stringTemplate = stringTemplates[2]; + } + let value = null; + let hadALogicalOp = false; + const opsToValidation = new Map([ + [/\s*\?\?\s*/, (v) => v != null], + [/\s*\|\|\s*/, (v) => !!v], + [/\s*\&\&\s*/, (v) => !v], + ]); + for (const [op, fn] of opsToValidation.entries()) { + if (bindingPropName.match(op)) { + hadALogicalOp = true; + const bindingPropNames = bindingPropName.split(op); + for (const propName of bindingPropNames) { + value = getValueForBindingPropName(propName, context); + if (fn(value)) { + break; + } + } + break; + } + } + if (!hadALogicalOp) { + value = getValueForBindingPropName(bindingPropName, context); + } + return stringTemplate && value != null + ? stringTemplate.replace(/\$\{value\}/g, String(value)) + : value; +} +function getValueForBindingPropName(bindingPropName, context) { + var _a, _b, _c; + const data = context.data; + let negate = getNegates(bindingPropName); + if (negate != null) { + bindingPropName = bindingPropName.replace(/^\!+/, ""); + } + let value; + RGX_COMPARISON.lastIndex = 0; + if (RGX_COMPARISON.test(bindingPropName)) { + value = getStringComparisonExpression(bindingPropName, data); + } + else if (RGX_BIND_FN_LENGTH.test(bindingPropName)) { + bindingPropName = RGX_BIND_FN_LENGTH.exec(bindingPropName)[1]; + value = getPrimitiveOrObjValue(bindingPropName, data); + value = (value && value.length) || 0; + } + else if (RGX_BIND_FN_FORMAT.test(bindingPropName)) { + let matches = RGX_BIND_FN_FORMAT.exec(bindingPropName); + bindingPropName = matches[1]; + value = getPrimitiveOrObjValue(bindingPropName, data); + value = matches[2].replace(/^['"]/, "").replace(/['"]$/, "").replace(/\$1/g, value); + } + else if (RGX_BIND_FN_CALL.test(bindingPropName)) { + console.log("-----"); + console.log(bindingPropName); + let matches = RGX_BIND_FN_CALL.exec(bindingPropName); + const functionName = matches[1]; + const maybeDataName = (_a = matches[2]) !== null && _a !== void 0 ? _a : null; + value = getPrimitiveOrObjValue(maybeDataName, data); + console.log(functionName, maybeDataName, value); + if (typeof (value === null || value === void 0 ? void 0 : value[functionName]) === "function") { + value = value[functionName](value, data, context.currentElement, context.contextElement); + } + else if (typeof (data === null || data === void 0 ? void 0 : data[functionName]) === "function") { + value = data[functionName](value, data, context.currentElement, context.contextElement); + } + else if (typeof ((_b = context.currentElement) === null || _b === void 0 ? void 0 : _b[functionName]) === "function") { + value = context.currentElement[functionName](value, data, context.currentElement, context.contextElement); + } + else if (typeof ((_c = context.contextElement) === null || _c === void 0 ? void 0 : _c[functionName]) === "function") { + value = context.contextElement[functionName](value, data, context.currentElement, context.contextElement); + } + else { + console.error(`No method named ${functionName} on data or element instance. Just calling regular value.`); + value = getPrimitiveOrObjValue(bindingPropName, data); + } + } + else { + value = getPrimitiveOrObjValue(bindingPropName, data); + } + if (value !== undefined) { + value = negate !== null ? (negate === 1 ? !value : !!value) : value; + } + return value; +} +function removeBindingAttributes(elOrEls, deep = false) { + flattenArray(elOrEls || []).forEach((el) => { + el.removeAttribute(CONFIG.attrBind); + const innerBinds = dom.queryAll(`:scope [${CONFIG.attrBind}]`, el); + const innerTplBinds = deep ? [] : dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}]`); + innerBinds.forEach((el) => { + if (deep || !innerTplBinds.includes(el)) { + el.removeAttribute(CONFIG.attrBind); + } + }); + }); +} +const templateCache = {}; +export function checkKey(key) { + return !!templateCache[cleanKey(key)]; +} +export function register(key, htmlOrElement = null, preProcessScript) { + key = cleanKey(key); + if (templateCache[key]) { + return templateCache[key]; + } + let fragment = null; + if (typeof htmlOrElement === "string") { + const frag = document.createDocumentFragment(); + if (htmlOrElement.includes("<")) { + const html = htmlOrElement.trim(); + const htmlParentTag = (html.startsWith(" { + const dataTplAttr = el.getAttribute("data-tpl"); + if (dataTplAttr) { + const tpls = dataTplAttr.split(" "); + tpls.forEach((tpl) => { + const dataAttribute = el.getAttribute("data") || ""; + const childData = (dataAttribute && getObjValue(dataAttribute, data)) || data; + bind(el, childData, bindOptions); + }); + } + else { + bind(el, data, bindOptions); + } + }); +} +export function bind(elOrEls, data = {}, bindOptions = {}) { + var _a; + if (elOrEls instanceof HTMLElement) { + data = getPreProcessScript(elOrEls)({ ...data }); + } + if (typeof data !== "object") { + data = { value: data }; + if (elOrEls instanceof HTMLElement && + elOrEls.children.length === 0 && + !elOrEls.getAttribute(CONFIG.attrBind)) { + dom.setAttributes(elOrEls, { [CONFIG.attrBind]: "value" }); + } + } + let passedEls = !Array.isArray(elOrEls) ? [elOrEls] : elOrEls; + for (const el of passedEls) { + const conditionEls = toArray(dom.queryAll(`[${CONFIG.attrIf}]`, el)); + const contextElement = (_a = bindOptions.contextElement) !== null && _a !== void 0 ? _a : (el instanceof ShadowRoot ? el.host : el); + for (const conditionEl of conditionEls) { + getValueForBindingPropName; + let isTrue = getValueForBinding(conditionEl.getAttribute(CONFIG.attrIf), { + data, + contextElement: contextElement, + currentElement: conditionEl, + }); + conditionEl.setAttribute(CONFIG.attrIfIs, String(!!isTrue)); + } + let toBindEls = toArray(dom.queryAll(`:not([${CONFIG.attrIfIs}="false"]) [${CONFIG.attrBind}]:not([data-tpl]):not([${CONFIG.attrIfIs}="false"])`, el)); + if (el instanceof HTMLElement && el.getAttribute(CONFIG.attrBind)) { + toBindEls.unshift(el); + } + if (toBindEls.length) { + let innerBindsElements = dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}], :scope [data-autobind="false"] [${CONFIG.attrBind}]`, el); + toBindEls = toBindEls.filter((maybeBind) => !innerBindsElements.includes(maybeBind)); + toBindEls.forEach((child) => { + RGX_BIND_DECLARATIONS.lastIndex = 0; + let bindings = []; + let bindingMatch; + while ((bindingMatch = RGX_BIND_DECLARATIONS.exec(child.getAttribute(CONFIG.attrBind).replace(/\s+/, " ").trim())) !== null) { + bindings.push([bindingMatch[1], bindingMatch[2]]); + } + bindings.forEach((bindings) => { + let bindingDataProperty = localAssertNotFalsy(bindings.shift()); + let bindingFields = ((bindings.length && bindings[0]) || "default") + .trim() + .replace(/^\[(.*?)\]$/i, "$1") + .split(","); + let value = getValueForBinding(bindingDataProperty, { + data, + contextElement: contextElement, + currentElement: child, + }); + if (value === undefined) { + if (bindOptions.onlyDefined === true) { + return; + } + else { + value = null; + } + } + bindingFields.forEach((field) => { + if (field.startsWith("style.")) { + let stringVal = String(value); + if (value && + !stringVal.includes("url(") && + stringVal !== "none" && + (field.includes("background-image") || stringVal.startsWith("http"))) { + value = `url(${value})`; + } + dom.setStyle(child, field.replace("style.", ""), value); + } + else if (field.startsWith("el.")) { + if (field === "el.remove") { + if (value === true) { + child.remove(); + } + } + else if (field === "el.toggle") { + dom.setStyle(child, "display", value === true ? "" : "none"); + } + else if (field.startsWith("el.classList.toggle")) { + const cssClass = field.replace(/el.classList.toggle\(['"]?(.*?)['"]?\)/, "$1"); + child.classList.toggle(cssClass, !!value); + } + } + else if (RGX_BIND_FN_TEMPLATE_OR_ELEMENT.test(field)) { + dom.empty(child); + let elementOrTemplateName = RGX_BIND_FN_TEMPLATE_OR_ELEMENT.exec(field)[1]; + if (Array.isArray(value) || value instanceof Set) { + const arrayVals = toArray(value); + let isElement = RGX_BIND_FN_ELEMENT.test(field); + let frag = document.createDocumentFragment(); + arrayVals.forEach((item, index) => { + let itemData; + if (typeof item === "object") { + itemData = Object.assign({ $index: index }, item); + } + else { + itemData = { $index: index, value: item }; + } + const els = bindToElOrTemplate(elementOrTemplateName, itemData); + frag.append(...els); + }); + if (child.nodeName.toUpperCase() === "TPL") { + replaceTplElementWithChildren(child, frag); + } + else { + dom.empty(child).appendChild(frag); + } + } + else if (value) { + const els = bindToElOrTemplate(elementOrTemplateName, value); + if (child.nodeName.toUpperCase() === "TPL") { + replaceTplElementWithChildren(child, els); + } + else { + child.append(...els); + } + } + } + else { + dom.setAttributes(child, { [field]: value }); + } + }); + }); + }); + } + if (bindOptions.singleScoped !== true) { + let toInitEls = toArray(el.querySelectorAll(":scope *[data-tpl]")); + if (toInitEls.length) { + let innerInits = dom.queryAll(':scope *[data-tpl] *[data-tpl], :scope [data-autobind="false"] [data-tpl]', el); + toInitEls = toInitEls.filter((maybeInitEl) => { + if (innerInits.includes(maybeInitEl)) { + return false; + } + let tplKey = maybeInitEl.getAttribute("data-tpl"); + if (data && (data[tplKey] || data[tplKey.replace("tpl:", "")])) { + return true; + } + return maybeInitEl.getAttribute("data-autobind") !== "false"; + }); + toInitEls.forEach((toInitEl) => { + var tplKey = toInitEl.getAttribute("data-tpl"); + init(toInitEl, (data && (data[tplKey] || data[tplKey.replace("tpl:", "")])) || data); + }); + } + } + } +} +function bindToElOrTemplate(elementOrTemplateName, data) { + let el = getTemplateFragment(elementOrTemplateName); + if (!el) { + el = dom.createElement(elementOrTemplateName, data); + } + else { + el = inflateOnce(el, data, { skipInit: false }); + } + const els = (Array.isArray(el) ? el : [el]).filter((el) => !!el); + els.forEach((el) => { + el.removeAttribute("data-tpl"); + let toBindEls = dom.queryAll("[data-tpl]", el); + let innerBindsElements = dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}], :scope [data-autobind="false"] [${CONFIG.attrBind}]`, el); + toBindEls = toBindEls.filter((maybeBind) => !innerBindsElements.includes(maybeBind)); + toBindEls.forEach((c) => c.removeAttribute("data-tpl")); + }); + return els; +} diff --git a/custom_nodes/rgthree-comfy/web/common/utils_workflow.js b/custom_nodes/rgthree-comfy/web/common/utils_workflow.js new file mode 100644 index 00000000..5dc29639 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/common/utils_workflow.js @@ -0,0 +1,55 @@ +import { getResolver } from "./shared_utils.js"; +import { getPngMetadata, getWebpMetadata } from "./comfyui_shim.js"; +function parseWorkflowJson(stringJson) { + stringJson = stringJson || "null"; + stringJson = stringJson.replace(/:\s*NaN/g, ": null"); + return JSON.parse(stringJson); +} +export async function tryToGetWorkflowDataFromEvent(e) { + var _a, _b, _c, _d; + let work; + for (const file of ((_a = e.dataTransfer) === null || _a === void 0 ? void 0 : _a.files) || []) { + const data = await tryToGetWorkflowDataFromFile(file); + if (data.workflow || data.prompt) { + return data; + } + } + const validTypes = ["text/uri-list", "text/x-moz-url"]; + const match = (((_b = e.dataTransfer) === null || _b === void 0 ? void 0 : _b.types) || []).find((t) => validTypes.find((v) => t === v)); + if (match) { + const uri = (_d = (_c = e.dataTransfer.getData(match)) === null || _c === void 0 ? void 0 : _c.split("\n")) === null || _d === void 0 ? void 0 : _d[0]; + if (uri) { + return tryToGetWorkflowDataFromFile(await (await fetch(uri)).blob()); + } + } + return { workflow: null, prompt: null }; +} +export async function tryToGetWorkflowDataFromFile(file) { + var _a; + if (file.type === "image/png") { + const pngInfo = await getPngMetadata(file); + return { + workflow: parseWorkflowJson(pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow), + prompt: parseWorkflowJson(pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.prompt), + }; + } + if (file.type === "image/webp") { + const pngInfo = await getWebpMetadata(file); + const workflow = parseWorkflowJson((pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow) || (pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.Workflow) || "null"); + const prompt = parseWorkflowJson((pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.prompt) || (pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.Prompt) || "null"); + return { workflow, prompt }; + } + if (file.type === "application/json" || ((_a = file.name) === null || _a === void 0 ? void 0 : _a.endsWith(".json"))) { + const resolver = getResolver(); + const reader = new FileReader(); + reader.onload = async () => { + const json = parseWorkflowJson(reader.result); + const isApiJson = Object.values(json).every((v) => v.class_type); + const prompt = isApiJson ? json : null; + const workflow = !isApiJson && !(json === null || json === void 0 ? void 0 : json.templates) ? json : null; + return { workflow, prompt }; + }; + return resolver.promise; + } + return { workflow: null, prompt: null }; +} diff --git a/custom_nodes/rgthree-comfy/web/lib/tree-sitter-python.wasm b/custom_nodes/rgthree-comfy/web/lib/tree-sitter-python.wasm new file mode 100644 index 00000000..ebd96b5d Binary files /dev/null and b/custom_nodes/rgthree-comfy/web/lib/tree-sitter-python.wasm differ diff --git a/custom_nodes/rgthree-comfy/web/lib/tree-sitter.js b/custom_nodes/rgthree-comfy/web/lib/tree-sitter.js new file mode 100644 index 00000000..1ef49495 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/lib/tree-sitter.js @@ -0,0 +1,3978 @@ +var __defProp = Object.defineProperty; +var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); + +// src/constants.ts +var SIZE_OF_SHORT = 2; +var SIZE_OF_INT = 4; +var SIZE_OF_CURSOR = 4 * SIZE_OF_INT; +var SIZE_OF_NODE = 5 * SIZE_OF_INT; +var SIZE_OF_POINT = 2 * SIZE_OF_INT; +var SIZE_OF_RANGE = 2 * SIZE_OF_INT + 2 * SIZE_OF_POINT; +var ZERO_POINT = { row: 0, column: 0 }; +var INTERNAL = Symbol("INTERNAL"); +function assertInternal(x) { + if (x !== INTERNAL) throw new Error("Illegal constructor"); +} +__name(assertInternal, "assertInternal"); +function isPoint(point) { + return !!point && typeof point.row === "number" && typeof point.column === "number"; +} +__name(isPoint, "isPoint"); +function setModule(module2) { + C = module2; +} +__name(setModule, "setModule"); +var C; + +// src/lookahead_iterator.ts +var LookaheadIterator = class { + static { + __name(this, "LookaheadIterator"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** @internal */ + language; + /** @internal */ + constructor(internal, address, language) { + assertInternal(internal); + this[0] = address; + this.language = language; + } + /** Get the current symbol of the lookahead iterator. */ + get currentTypeId() { + return C._ts_lookahead_iterator_current_symbol(this[0]); + } + /** Get the current symbol name of the lookahead iterator. */ + get currentType() { + return this.language.types[this.currentTypeId] || "ERROR"; + } + /** Delete the lookahead iterator, freeing its resources. */ + delete() { + C._ts_lookahead_iterator_delete(this[0]); + this[0] = 0; + } + /** + * Reset the lookahead iterator. + * + * This returns `true` if the language was set successfully and `false` + * otherwise. + */ + reset(language, stateId) { + if (C._ts_lookahead_iterator_reset(this[0], language[0], stateId)) { + this.language = language; + return true; + } + return false; + } + /** + * Reset the lookahead iterator to another state. + * + * This returns `true` if the iterator was reset to the given state and + * `false` otherwise. + */ + resetState(stateId) { + return Boolean(C._ts_lookahead_iterator_reset_state(this[0], stateId)); + } + /** + * Returns an iterator that iterates over the symbols of the lookahead iterator. + * + * The iterator will yield the current symbol name as a string for each step + * until there are no more symbols to iterate over. + */ + [Symbol.iterator]() { + return { + next: /* @__PURE__ */ __name(() => { + if (C._ts_lookahead_iterator_next(this[0])) { + return { done: false, value: this.currentType }; + } + return { done: true, value: "" }; + }, "next") + }; + } +}; + +// src/tree.ts +function getText(tree, startIndex, endIndex, startPosition) { + const length = endIndex - startIndex; + let result = tree.textCallback(startIndex, startPosition); + if (result) { + startIndex += result.length; + while (startIndex < endIndex) { + const string = tree.textCallback(startIndex, startPosition); + if (string && string.length > 0) { + startIndex += string.length; + result += string; + } else { + break; + } + } + if (startIndex > endIndex) { + result = result.slice(0, length); + } + } + return result ?? ""; +} +__name(getText, "getText"); +var Tree = class _Tree { + static { + __name(this, "Tree"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** @internal */ + textCallback; + /** The language that was used to parse the syntax tree. */ + language; + /** @internal */ + constructor(internal, address, language, textCallback) { + assertInternal(internal); + this[0] = address; + this.language = language; + this.textCallback = textCallback; + } + /** Create a shallow copy of the syntax tree. This is very fast. */ + copy() { + const address = C._ts_tree_copy(this[0]); + return new _Tree(INTERNAL, address, this.language, this.textCallback); + } + /** Delete the syntax tree, freeing its resources. */ + delete() { + C._ts_tree_delete(this[0]); + this[0] = 0; + } + /** Get the root node of the syntax tree. */ + get rootNode() { + C._ts_tree_root_node_wasm(this[0]); + return unmarshalNode(this); + } + /** + * Get the root node of the syntax tree, but with its position shifted + * forward by the given offset. + */ + rootNodeWithOffset(offsetBytes, offsetExtent) { + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + C.setValue(address, offsetBytes, "i32"); + marshalPoint(address + SIZE_OF_INT, offsetExtent); + C._ts_tree_root_node_with_offset_wasm(this[0]); + return unmarshalNode(this); + } + /** + * Edit the syntax tree to keep it in sync with source code that has been + * edited. + * + * You must describe the edit both in terms of byte offsets and in terms of + * row/column coordinates. + */ + edit(edit) { + marshalEdit(edit); + C._ts_tree_edit_wasm(this[0]); + } + /** Create a new {@link TreeCursor} starting from the root of the tree. */ + walk() { + return this.rootNode.walk(); + } + /** + * Compare this old edited syntax tree to a new syntax tree representing + * the same document, returning a sequence of ranges whose syntactic + * structure has changed. + * + * For this to work correctly, this syntax tree must have been edited such + * that its ranges match up to the new tree. Generally, you'll want to + * call this method right after calling one of the [`Parser::parse`] + * functions. Call it on the old tree that was passed to parse, and + * pass the new tree that was returned from `parse`. + */ + getChangedRanges(other) { + if (!(other instanceof _Tree)) { + throw new TypeError("Argument must be a Tree"); + } + C._ts_tree_get_changed_ranges_wasm(this[0], other[0]); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + result[i2] = unmarshalRange(address); + address += SIZE_OF_RANGE; + } + C._free(buffer); + } + return result; + } + /** Get the included ranges that were used to parse the syntax tree. */ + getIncludedRanges() { + C._ts_tree_included_ranges_wasm(this[0]); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + result[i2] = unmarshalRange(address); + address += SIZE_OF_RANGE; + } + C._free(buffer); + } + return result; + } +}; + +// src/tree_cursor.ts +var TreeCursor = class _TreeCursor { + static { + __name(this, "TreeCursor"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** @internal */ + [1] = 0; + // Internal handle for WASM + /** @internal */ + [2] = 0; + // Internal handle for WASM + /** @internal */ + [3] = 0; + // Internal handle for WASM + /** @internal */ + tree; + /** @internal */ + constructor(internal, tree) { + assertInternal(internal); + this.tree = tree; + unmarshalTreeCursor(this); + } + /** Creates a deep copy of the tree cursor. This allocates new memory. */ + copy() { + const copy = new _TreeCursor(INTERNAL, this.tree); + C._ts_tree_cursor_copy_wasm(this.tree[0]); + unmarshalTreeCursor(copy); + return copy; + } + /** Delete the tree cursor, freeing its resources. */ + delete() { + marshalTreeCursor(this); + C._ts_tree_cursor_delete_wasm(this.tree[0]); + this[0] = this[1] = this[2] = 0; + } + /** Get the tree cursor's current {@link Node}. */ + get currentNode() { + marshalTreeCursor(this); + C._ts_tree_cursor_current_node_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** + * Get the numerical field id of this tree cursor's current node. + * + * See also {@link TreeCursor#currentFieldName}. + */ + get currentFieldId() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_field_id_wasm(this.tree[0]); + } + /** Get the field name of this tree cursor's current node. */ + get currentFieldName() { + return this.tree.language.fields[this.currentFieldId]; + } + /** + * Get the depth of the cursor's current node relative to the original + * node that the cursor was constructed with. + */ + get currentDepth() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_depth_wasm(this.tree[0]); + } + /** + * Get the index of the cursor's current node out of all of the + * descendants of the original node that the cursor was constructed with. + */ + get currentDescendantIndex() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_descendant_index_wasm(this.tree[0]); + } + /** Get the type of the cursor's current node. */ + get nodeType() { + return this.tree.language.types[this.nodeTypeId] || "ERROR"; + } + /** Get the type id of the cursor's current node. */ + get nodeTypeId() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_node_type_id_wasm(this.tree[0]); + } + /** Get the state id of the cursor's current node. */ + get nodeStateId() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_node_state_id_wasm(this.tree[0]); + } + /** Get the id of the cursor's current node. */ + get nodeId() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_node_id_wasm(this.tree[0]); + } + /** + * Check if the cursor's current node is *named*. + * + * Named nodes correspond to named rules in the grammar, whereas + * *anonymous* nodes correspond to string literals in the grammar. + */ + get nodeIsNamed() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_node_is_named_wasm(this.tree[0]) === 1; + } + /** + * Check if the cursor's current node is *missing*. + * + * Missing nodes are inserted by the parser in order to recover from + * certain kinds of syntax errors. + */ + get nodeIsMissing() { + marshalTreeCursor(this); + return C._ts_tree_cursor_current_node_is_missing_wasm(this.tree[0]) === 1; + } + /** Get the string content of the cursor's current node. */ + get nodeText() { + marshalTreeCursor(this); + const startIndex = C._ts_tree_cursor_start_index_wasm(this.tree[0]); + const endIndex = C._ts_tree_cursor_end_index_wasm(this.tree[0]); + C._ts_tree_cursor_start_position_wasm(this.tree[0]); + const startPosition = unmarshalPoint(TRANSFER_BUFFER); + return getText(this.tree, startIndex, endIndex, startPosition); + } + /** Get the start position of the cursor's current node. */ + get startPosition() { + marshalTreeCursor(this); + C._ts_tree_cursor_start_position_wasm(this.tree[0]); + return unmarshalPoint(TRANSFER_BUFFER); + } + /** Get the end position of the cursor's current node. */ + get endPosition() { + marshalTreeCursor(this); + C._ts_tree_cursor_end_position_wasm(this.tree[0]); + return unmarshalPoint(TRANSFER_BUFFER); + } + /** Get the start index of the cursor's current node. */ + get startIndex() { + marshalTreeCursor(this); + return C._ts_tree_cursor_start_index_wasm(this.tree[0]); + } + /** Get the end index of the cursor's current node. */ + get endIndex() { + marshalTreeCursor(this); + return C._ts_tree_cursor_end_index_wasm(this.tree[0]); + } + /** + * Move this cursor to the first child of its current node. + * + * This returns `true` if the cursor successfully moved, and returns + * `false` if there were no children. + */ + gotoFirstChild() { + marshalTreeCursor(this); + const result = C._ts_tree_cursor_goto_first_child_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Move this cursor to the last child of its current node. + * + * This returns `true` if the cursor successfully moved, and returns + * `false` if there were no children. + * + * Note that this function may be slower than + * {@link TreeCursor#gotoFirstChild} because it needs to + * iterate through all the children to compute the child's position. + */ + gotoLastChild() { + marshalTreeCursor(this); + const result = C._ts_tree_cursor_goto_last_child_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Move this cursor to the parent of its current node. + * + * This returns `true` if the cursor successfully moved, and returns + * `false` if there was no parent node (the cursor was already on the + * root node). + * + * Note that the node the cursor was constructed with is considered the root + * of the cursor, and the cursor cannot walk outside this node. + */ + gotoParent() { + marshalTreeCursor(this); + const result = C._ts_tree_cursor_goto_parent_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Move this cursor to the next sibling of its current node. + * + * This returns `true` if the cursor successfully moved, and returns + * `false` if there was no next sibling node. + * + * Note that the node the cursor was constructed with is considered the root + * of the cursor, and the cursor cannot walk outside this node. + */ + gotoNextSibling() { + marshalTreeCursor(this); + const result = C._ts_tree_cursor_goto_next_sibling_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Move this cursor to the previous sibling of its current node. + * + * This returns `true` if the cursor successfully moved, and returns + * `false` if there was no previous sibling node. + * + * Note that this function may be slower than + * {@link TreeCursor#gotoNextSibling} due to how node + * positions are stored. In the worst case, this will need to iterate + * through all the children up to the previous sibling node to recalculate + * its position. Also note that the node the cursor was constructed with is + * considered the root of the cursor, and the cursor cannot walk outside this node. + */ + gotoPreviousSibling() { + marshalTreeCursor(this); + const result = C._ts_tree_cursor_goto_previous_sibling_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Move the cursor to the node that is the nth descendant of + * the original node that the cursor was constructed with, where + * zero represents the original node itself. + */ + gotoDescendant(goalDescendantIndex) { + marshalTreeCursor(this); + C._ts_tree_cursor_goto_descendant_wasm(this.tree[0], goalDescendantIndex); + unmarshalTreeCursor(this); + } + /** + * Move this cursor to the first child of its current node that contains or + * starts after the given byte offset. + * + * This returns `true` if the cursor successfully moved to a child node, and returns + * `false` if no such child was found. + */ + gotoFirstChildForIndex(goalIndex) { + marshalTreeCursor(this); + C.setValue(TRANSFER_BUFFER + SIZE_OF_CURSOR, goalIndex, "i32"); + const result = C._ts_tree_cursor_goto_first_child_for_index_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Move this cursor to the first child of its current node that contains or + * starts after the given byte offset. + * + * This returns the index of the child node if one was found, and returns + * `null` if no such child was found. + */ + gotoFirstChildForPosition(goalPosition) { + marshalTreeCursor(this); + marshalPoint(TRANSFER_BUFFER + SIZE_OF_CURSOR, goalPosition); + const result = C._ts_tree_cursor_goto_first_child_for_position_wasm(this.tree[0]); + unmarshalTreeCursor(this); + return result === 1; + } + /** + * Re-initialize this tree cursor to start at the original node that the + * cursor was constructed with. + */ + reset(node) { + marshalNode(node); + marshalTreeCursor(this, TRANSFER_BUFFER + SIZE_OF_NODE); + C._ts_tree_cursor_reset_wasm(this.tree[0]); + unmarshalTreeCursor(this); + } + /** + * Re-initialize a tree cursor to the same position as another cursor. + * + * Unlike {@link TreeCursor#reset}, this will not lose parent + * information and allows reusing already created cursors. + */ + resetTo(cursor) { + marshalTreeCursor(this, TRANSFER_BUFFER); + marshalTreeCursor(cursor, TRANSFER_BUFFER + SIZE_OF_CURSOR); + C._ts_tree_cursor_reset_to_wasm(this.tree[0], cursor.tree[0]); + unmarshalTreeCursor(this); + } +}; + +// src/node.ts +var Node = class { + static { + __name(this, "Node"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** @internal */ + _children; + /** @internal */ + _namedChildren; + /** @internal */ + constructor(internal, { + id, + tree, + startIndex, + startPosition, + other + }) { + assertInternal(internal); + this[0] = other; + this.id = id; + this.tree = tree; + this.startIndex = startIndex; + this.startPosition = startPosition; + } + /** + * The numeric id for this node that is unique. + * + * Within a given syntax tree, no two nodes have the same id. However: + * + * * If a new tree is created based on an older tree, and a node from the old tree is reused in + * the process, then that node will have the same id in both trees. + * + * * A node not marked as having changes does not guarantee it was reused. + * + * * If a node is marked as having changed in the old tree, it will not be reused. + */ + id; + /** The byte index where this node starts. */ + startIndex; + /** The position where this node starts. */ + startPosition; + /** The tree that this node belongs to. */ + tree; + /** Get this node's type as a numerical id. */ + get typeId() { + marshalNode(this); + return C._ts_node_symbol_wasm(this.tree[0]); + } + /** + * Get the node's type as a numerical id as it appears in the grammar, + * ignoring aliases. + */ + get grammarId() { + marshalNode(this); + return C._ts_node_grammar_symbol_wasm(this.tree[0]); + } + /** Get this node's type as a string. */ + get type() { + return this.tree.language.types[this.typeId] || "ERROR"; + } + /** + * Get this node's symbol name as it appears in the grammar, ignoring + * aliases as a string. + */ + get grammarType() { + return this.tree.language.types[this.grammarId] || "ERROR"; + } + /** + * Check if this node is *named*. + * + * Named nodes correspond to named rules in the grammar, whereas + * *anonymous* nodes correspond to string literals in the grammar. + */ + get isNamed() { + marshalNode(this); + return C._ts_node_is_named_wasm(this.tree[0]) === 1; + } + /** + * Check if this node is *extra*. + * + * Extra nodes represent things like comments, which are not required + * by the grammar, but can appear anywhere. + */ + get isExtra() { + marshalNode(this); + return C._ts_node_is_extra_wasm(this.tree[0]) === 1; + } + /** + * Check if this node represents a syntax error. + * + * Syntax errors represent parts of the code that could not be incorporated + * into a valid syntax tree. + */ + get isError() { + marshalNode(this); + return C._ts_node_is_error_wasm(this.tree[0]) === 1; + } + /** + * Check if this node is *missing*. + * + * Missing nodes are inserted by the parser in order to recover from + * certain kinds of syntax errors. + */ + get isMissing() { + marshalNode(this); + return C._ts_node_is_missing_wasm(this.tree[0]) === 1; + } + /** Check if this node has been edited. */ + get hasChanges() { + marshalNode(this); + return C._ts_node_has_changes_wasm(this.tree[0]) === 1; + } + /** + * Check if this node represents a syntax error or contains any syntax + * errors anywhere within it. + */ + get hasError() { + marshalNode(this); + return C._ts_node_has_error_wasm(this.tree[0]) === 1; + } + /** Get the byte index where this node ends. */ + get endIndex() { + marshalNode(this); + return C._ts_node_end_index_wasm(this.tree[0]); + } + /** Get the position where this node ends. */ + get endPosition() { + marshalNode(this); + C._ts_node_end_point_wasm(this.tree[0]); + return unmarshalPoint(TRANSFER_BUFFER); + } + /** Get the string content of this node. */ + get text() { + return getText(this.tree, this.startIndex, this.endIndex, this.startPosition); + } + /** Get this node's parse state. */ + get parseState() { + marshalNode(this); + return C._ts_node_parse_state_wasm(this.tree[0]); + } + /** Get the parse state after this node. */ + get nextParseState() { + marshalNode(this); + return C._ts_node_next_parse_state_wasm(this.tree[0]); + } + /** Check if this node is equal to another node. */ + equals(other) { + return this.tree === other.tree && this.id === other.id; + } + /** + * Get the node's child at the given index, where zero represents the first child. + * + * This method is fairly fast, but its cost is technically log(n), so if + * you might be iterating over a long list of children, you should use + * {@link Node#children} instead. + */ + child(index) { + marshalNode(this); + C._ts_node_child_wasm(this.tree[0], index); + return unmarshalNode(this.tree); + } + /** + * Get this node's *named* child at the given index. + * + * See also {@link Node#isNamed}. + * This method is fairly fast, but its cost is technically log(n), so if + * you might be iterating over a long list of children, you should use + * {@link Node#namedChildren} instead. + */ + namedChild(index) { + marshalNode(this); + C._ts_node_named_child_wasm(this.tree[0], index); + return unmarshalNode(this.tree); + } + /** + * Get this node's child with the given numerical field id. + * + * See also {@link Node#childForFieldName}. You can + * convert a field name to an id using {@link Language#fieldIdForName}. + */ + childForFieldId(fieldId) { + marshalNode(this); + C._ts_node_child_by_field_id_wasm(this.tree[0], fieldId); + return unmarshalNode(this.tree); + } + /** + * Get the first child with the given field name. + * + * If multiple children may have the same field name, access them using + * {@link Node#childrenForFieldName}. + */ + childForFieldName(fieldName) { + const fieldId = this.tree.language.fields.indexOf(fieldName); + if (fieldId !== -1) return this.childForFieldId(fieldId); + return null; + } + /** Get the field name of this node's child at the given index. */ + fieldNameForChild(index) { + marshalNode(this); + const address = C._ts_node_field_name_for_child_wasm(this.tree[0], index); + if (!address) return null; + return C.AsciiToString(address); + } + /** Get the field name of this node's named child at the given index. */ + fieldNameForNamedChild(index) { + marshalNode(this); + const address = C._ts_node_field_name_for_named_child_wasm(this.tree[0], index); + if (!address) return null; + return C.AsciiToString(address); + } + /** + * Get an array of this node's children with a given field name. + * + * See also {@link Node#children}. + */ + childrenForFieldName(fieldName) { + const fieldId = this.tree.language.fields.indexOf(fieldName); + if (fieldId !== -1 && fieldId !== 0) return this.childrenForFieldId(fieldId); + return []; + } + /** + * Get an array of this node's children with a given field id. + * + * See also {@link Node#childrenForFieldName}. + */ + childrenForFieldId(fieldId) { + marshalNode(this); + C._ts_node_children_by_field_id_wasm(this.tree[0], fieldId); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + result[i2] = unmarshalNode(this.tree, address); + address += SIZE_OF_NODE; + } + C._free(buffer); + } + return result; + } + /** Get the node's first child that contains or starts after the given byte offset. */ + firstChildForIndex(index) { + marshalNode(this); + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + C.setValue(address, index, "i32"); + C._ts_node_first_child_for_byte_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get the node's first named child that contains or starts after the given byte offset. */ + firstNamedChildForIndex(index) { + marshalNode(this); + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + C.setValue(address, index, "i32"); + C._ts_node_first_named_child_for_byte_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get this node's number of children. */ + get childCount() { + marshalNode(this); + return C._ts_node_child_count_wasm(this.tree[0]); + } + /** + * Get this node's number of *named* children. + * + * See also {@link Node#isNamed}. + */ + get namedChildCount() { + marshalNode(this); + return C._ts_node_named_child_count_wasm(this.tree[0]); + } + /** Get this node's first child. */ + get firstChild() { + return this.child(0); + } + /** + * Get this node's first named child. + * + * See also {@link Node#isNamed}. + */ + get firstNamedChild() { + return this.namedChild(0); + } + /** Get this node's last child. */ + get lastChild() { + return this.child(this.childCount - 1); + } + /** + * Get this node's last named child. + * + * See also {@link Node#isNamed}. + */ + get lastNamedChild() { + return this.namedChild(this.namedChildCount - 1); + } + /** + * Iterate over this node's children. + * + * If you're walking the tree recursively, you may want to use the + * {@link TreeCursor} APIs directly instead. + */ + get children() { + if (!this._children) { + marshalNode(this); + C._ts_node_children_wasm(this.tree[0]); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + this._children = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + this._children[i2] = unmarshalNode(this.tree, address); + address += SIZE_OF_NODE; + } + C._free(buffer); + } + } + return this._children; + } + /** + * Iterate over this node's named children. + * + * See also {@link Node#children}. + */ + get namedChildren() { + if (!this._namedChildren) { + marshalNode(this); + C._ts_node_named_children_wasm(this.tree[0]); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + this._namedChildren = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + this._namedChildren[i2] = unmarshalNode(this.tree, address); + address += SIZE_OF_NODE; + } + C._free(buffer); + } + } + return this._namedChildren; + } + /** + * Get the descendants of this node that are the given type, or in the given types array. + * + * The types array should contain node type strings, which can be retrieved from {@link Language#types}. + * + * Additionally, a `startPosition` and `endPosition` can be passed in to restrict the search to a byte range. + */ + descendantsOfType(types, startPosition = ZERO_POINT, endPosition = ZERO_POINT) { + if (!Array.isArray(types)) types = [types]; + const symbols = []; + const typesBySymbol = this.tree.language.types; + for (const node_type of types) { + if (node_type == "ERROR") { + symbols.push(65535); + } + } + for (let i2 = 0, n = typesBySymbol.length; i2 < n; i2++) { + if (types.includes(typesBySymbol[i2])) { + symbols.push(i2); + } + } + const symbolsAddress = C._malloc(SIZE_OF_INT * symbols.length); + for (let i2 = 0, n = symbols.length; i2 < n; i2++) { + C.setValue(symbolsAddress + i2 * SIZE_OF_INT, symbols[i2], "i32"); + } + marshalNode(this); + C._ts_node_descendants_of_type_wasm( + this.tree[0], + symbolsAddress, + symbols.length, + startPosition.row, + startPosition.column, + endPosition.row, + endPosition.column + ); + const descendantCount = C.getValue(TRANSFER_BUFFER, "i32"); + const descendantAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(descendantCount); + if (descendantCount > 0) { + let address = descendantAddress; + for (let i2 = 0; i2 < descendantCount; i2++) { + result[i2] = unmarshalNode(this.tree, address); + address += SIZE_OF_NODE; + } + } + C._free(descendantAddress); + C._free(symbolsAddress); + return result; + } + /** Get this node's next sibling. */ + get nextSibling() { + marshalNode(this); + C._ts_node_next_sibling_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get this node's previous sibling. */ + get previousSibling() { + marshalNode(this); + C._ts_node_prev_sibling_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** + * Get this node's next *named* sibling. + * + * See also {@link Node#isNamed}. + */ + get nextNamedSibling() { + marshalNode(this); + C._ts_node_next_named_sibling_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** + * Get this node's previous *named* sibling. + * + * See also {@link Node#isNamed}. + */ + get previousNamedSibling() { + marshalNode(this); + C._ts_node_prev_named_sibling_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get the node's number of descendants, including one for the node itself. */ + get descendantCount() { + marshalNode(this); + return C._ts_node_descendant_count_wasm(this.tree[0]); + } + /** + * Get this node's immediate parent. + * Prefer {@link Node#childWithDescendant} for iterating over this node's ancestors. + */ + get parent() { + marshalNode(this); + C._ts_node_parent_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** + * Get the node that contains `descendant`. + * + * Note that this can return `descendant` itself. + */ + childWithDescendant(descendant) { + marshalNode(this); + marshalNode(descendant, 1); + C._ts_node_child_with_descendant_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get the smallest node within this node that spans the given byte range. */ + descendantForIndex(start2, end = start2) { + if (typeof start2 !== "number" || typeof end !== "number") { + throw new Error("Arguments must be numbers"); + } + marshalNode(this); + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + C.setValue(address, start2, "i32"); + C.setValue(address + SIZE_OF_INT, end, "i32"); + C._ts_node_descendant_for_index_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get the smallest named node within this node that spans the given byte range. */ + namedDescendantForIndex(start2, end = start2) { + if (typeof start2 !== "number" || typeof end !== "number") { + throw new Error("Arguments must be numbers"); + } + marshalNode(this); + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + C.setValue(address, start2, "i32"); + C.setValue(address + SIZE_OF_INT, end, "i32"); + C._ts_node_named_descendant_for_index_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get the smallest node within this node that spans the given point range. */ + descendantForPosition(start2, end = start2) { + if (!isPoint(start2) || !isPoint(end)) { + throw new Error("Arguments must be {row, column} objects"); + } + marshalNode(this); + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + marshalPoint(address, start2); + marshalPoint(address + SIZE_OF_POINT, end); + C._ts_node_descendant_for_position_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** Get the smallest named node within this node that spans the given point range. */ + namedDescendantForPosition(start2, end = start2) { + if (!isPoint(start2) || !isPoint(end)) { + throw new Error("Arguments must be {row, column} objects"); + } + marshalNode(this); + const address = TRANSFER_BUFFER + SIZE_OF_NODE; + marshalPoint(address, start2); + marshalPoint(address + SIZE_OF_POINT, end); + C._ts_node_named_descendant_for_position_wasm(this.tree[0]); + return unmarshalNode(this.tree); + } + /** + * Create a new {@link TreeCursor} starting from this node. + * + * Note that the given node is considered the root of the cursor, + * and the cursor cannot walk outside this node. + */ + walk() { + marshalNode(this); + C._ts_tree_cursor_new_wasm(this.tree[0]); + return new TreeCursor(INTERNAL, this.tree); + } + /** + * Edit this node to keep it in-sync with source code that has been edited. + * + * This function is only rarely needed. When you edit a syntax tree with + * the {@link Tree#edit} method, all of the nodes that you retrieve from + * the tree afterward will already reflect the edit. You only need to + * use {@link Node#edit} when you have a specific {@link Node} instance that + * you want to keep and continue to use after an edit. + */ + edit(edit) { + if (this.startIndex >= edit.oldEndIndex) { + this.startIndex = edit.newEndIndex + (this.startIndex - edit.oldEndIndex); + let subbedPointRow; + let subbedPointColumn; + if (this.startPosition.row > edit.oldEndPosition.row) { + subbedPointRow = this.startPosition.row - edit.oldEndPosition.row; + subbedPointColumn = this.startPosition.column; + } else { + subbedPointRow = 0; + subbedPointColumn = this.startPosition.column; + if (this.startPosition.column >= edit.oldEndPosition.column) { + subbedPointColumn = this.startPosition.column - edit.oldEndPosition.column; + } + } + if (subbedPointRow > 0) { + this.startPosition.row += subbedPointRow; + this.startPosition.column = subbedPointColumn; + } else { + this.startPosition.column += subbedPointColumn; + } + } else if (this.startIndex > edit.startIndex) { + this.startIndex = edit.newEndIndex; + this.startPosition.row = edit.newEndPosition.row; + this.startPosition.column = edit.newEndPosition.column; + } + } + /** Get the S-expression representation of this node. */ + toString() { + marshalNode(this); + const address = C._ts_node_to_string_wasm(this.tree[0]); + const result = C.AsciiToString(address); + C._free(address); + return result; + } +}; + +// src/marshal.ts +function unmarshalCaptures(query, tree, address, patternIndex, result) { + for (let i2 = 0, n = result.length; i2 < n; i2++) { + const captureIndex = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const node = unmarshalNode(tree, address); + address += SIZE_OF_NODE; + result[i2] = { patternIndex, name: query.captureNames[captureIndex], node }; + } + return address; +} +__name(unmarshalCaptures, "unmarshalCaptures"); +function marshalNode(node, index = 0) { + let address = TRANSFER_BUFFER + index * SIZE_OF_NODE; + C.setValue(address, node.id, "i32"); + address += SIZE_OF_INT; + C.setValue(address, node.startIndex, "i32"); + address += SIZE_OF_INT; + C.setValue(address, node.startPosition.row, "i32"); + address += SIZE_OF_INT; + C.setValue(address, node.startPosition.column, "i32"); + address += SIZE_OF_INT; + C.setValue(address, node[0], "i32"); +} +__name(marshalNode, "marshalNode"); +function unmarshalNode(tree, address = TRANSFER_BUFFER) { + const id = C.getValue(address, "i32"); + address += SIZE_OF_INT; + if (id === 0) return null; + const index = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const row = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const column = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const other = C.getValue(address, "i32"); + const result = new Node(INTERNAL, { + id, + tree, + startIndex: index, + startPosition: { row, column }, + other + }); + return result; +} +__name(unmarshalNode, "unmarshalNode"); +function marshalTreeCursor(cursor, address = TRANSFER_BUFFER) { + C.setValue(address + 0 * SIZE_OF_INT, cursor[0], "i32"); + C.setValue(address + 1 * SIZE_OF_INT, cursor[1], "i32"); + C.setValue(address + 2 * SIZE_OF_INT, cursor[2], "i32"); + C.setValue(address + 3 * SIZE_OF_INT, cursor[3], "i32"); +} +__name(marshalTreeCursor, "marshalTreeCursor"); +function unmarshalTreeCursor(cursor) { + cursor[0] = C.getValue(TRANSFER_BUFFER + 0 * SIZE_OF_INT, "i32"); + cursor[1] = C.getValue(TRANSFER_BUFFER + 1 * SIZE_OF_INT, "i32"); + cursor[2] = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32"); + cursor[3] = C.getValue(TRANSFER_BUFFER + 3 * SIZE_OF_INT, "i32"); +} +__name(unmarshalTreeCursor, "unmarshalTreeCursor"); +function marshalPoint(address, point) { + C.setValue(address, point.row, "i32"); + C.setValue(address + SIZE_OF_INT, point.column, "i32"); +} +__name(marshalPoint, "marshalPoint"); +function unmarshalPoint(address) { + const result = { + row: C.getValue(address, "i32") >>> 0, + column: C.getValue(address + SIZE_OF_INT, "i32") >>> 0 + }; + return result; +} +__name(unmarshalPoint, "unmarshalPoint"); +function marshalRange(address, range) { + marshalPoint(address, range.startPosition); + address += SIZE_OF_POINT; + marshalPoint(address, range.endPosition); + address += SIZE_OF_POINT; + C.setValue(address, range.startIndex, "i32"); + address += SIZE_OF_INT; + C.setValue(address, range.endIndex, "i32"); + address += SIZE_OF_INT; +} +__name(marshalRange, "marshalRange"); +function unmarshalRange(address) { + const result = {}; + result.startPosition = unmarshalPoint(address); + address += SIZE_OF_POINT; + result.endPosition = unmarshalPoint(address); + address += SIZE_OF_POINT; + result.startIndex = C.getValue(address, "i32") >>> 0; + address += SIZE_OF_INT; + result.endIndex = C.getValue(address, "i32") >>> 0; + return result; +} +__name(unmarshalRange, "unmarshalRange"); +function marshalEdit(edit, address = TRANSFER_BUFFER) { + marshalPoint(address, edit.startPosition); + address += SIZE_OF_POINT; + marshalPoint(address, edit.oldEndPosition); + address += SIZE_OF_POINT; + marshalPoint(address, edit.newEndPosition); + address += SIZE_OF_POINT; + C.setValue(address, edit.startIndex, "i32"); + address += SIZE_OF_INT; + C.setValue(address, edit.oldEndIndex, "i32"); + address += SIZE_OF_INT; + C.setValue(address, edit.newEndIndex, "i32"); + address += SIZE_OF_INT; +} +__name(marshalEdit, "marshalEdit"); +function unmarshalLanguageMetadata(address) { + const result = {}; + result.major_version = C.getValue(address, "i32"); + address += SIZE_OF_INT; + result.minor_version = C.getValue(address, "i32"); + address += SIZE_OF_INT; + result.field_count = C.getValue(address, "i32"); + return result; +} +__name(unmarshalLanguageMetadata, "unmarshalLanguageMetadata"); + +// src/query.ts +var PREDICATE_STEP_TYPE_CAPTURE = 1; +var PREDICATE_STEP_TYPE_STRING = 2; +var QUERY_WORD_REGEX = /[\w-]+/g; +var CaptureQuantifier = { + Zero: 0, + ZeroOrOne: 1, + ZeroOrMore: 2, + One: 3, + OneOrMore: 4 +}; +var isCaptureStep = /* @__PURE__ */ __name((step) => step.type === "capture", "isCaptureStep"); +var isStringStep = /* @__PURE__ */ __name((step) => step.type === "string", "isStringStep"); +var QueryErrorKind = { + Syntax: 1, + NodeName: 2, + FieldName: 3, + CaptureName: 4, + PatternStructure: 5 +}; +var QueryError = class _QueryError extends Error { + constructor(kind, info2, index, length) { + super(_QueryError.formatMessage(kind, info2)); + this.kind = kind; + this.info = info2; + this.index = index; + this.length = length; + this.name = "QueryError"; + } + static { + __name(this, "QueryError"); + } + /** Formats an error message based on the error kind and info */ + static formatMessage(kind, info2) { + switch (kind) { + case QueryErrorKind.NodeName: + return `Bad node name '${info2.word}'`; + case QueryErrorKind.FieldName: + return `Bad field name '${info2.word}'`; + case QueryErrorKind.CaptureName: + return `Bad capture name @${info2.word}`; + case QueryErrorKind.PatternStructure: + return `Bad pattern structure at offset ${info2.suffix}`; + case QueryErrorKind.Syntax: + return `Bad syntax at offset ${info2.suffix}`; + } + } +}; +function parseAnyPredicate(steps, index, operator, textPredicates) { + if (steps.length !== 3) { + throw new Error( + `Wrong number of arguments to \`#${operator}\` predicate. Expected 2, got ${steps.length - 1}` + ); + } + if (!isCaptureStep(steps[1])) { + throw new Error( + `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}"` + ); + } + const isPositive = operator === "eq?" || operator === "any-eq?"; + const matchAll = !operator.startsWith("any-"); + if (isCaptureStep(steps[2])) { + const captureName1 = steps[1].name; + const captureName2 = steps[2].name; + textPredicates[index].push((captures) => { + const nodes1 = []; + const nodes2 = []; + for (const c of captures) { + if (c.name === captureName1) nodes1.push(c.node); + if (c.name === captureName2) nodes2.push(c.node); + } + const compare = /* @__PURE__ */ __name((n1, n2, positive) => { + return positive ? n1.text === n2.text : n1.text !== n2.text; + }, "compare"); + return matchAll ? nodes1.every((n1) => nodes2.some((n2) => compare(n1, n2, isPositive))) : nodes1.some((n1) => nodes2.some((n2) => compare(n1, n2, isPositive))); + }); + } else { + const captureName = steps[1].name; + const stringValue = steps[2].value; + const matches = /* @__PURE__ */ __name((n) => n.text === stringValue, "matches"); + const doesNotMatch = /* @__PURE__ */ __name((n) => n.text !== stringValue, "doesNotMatch"); + textPredicates[index].push((captures) => { + const nodes = []; + for (const c of captures) { + if (c.name === captureName) nodes.push(c.node); + } + const test = isPositive ? matches : doesNotMatch; + return matchAll ? nodes.every(test) : nodes.some(test); + }); + } +} +__name(parseAnyPredicate, "parseAnyPredicate"); +function parseMatchPredicate(steps, index, operator, textPredicates) { + if (steps.length !== 3) { + throw new Error( + `Wrong number of arguments to \`#${operator}\` predicate. Expected 2, got ${steps.length - 1}.` + ); + } + if (steps[1].type !== "capture") { + throw new Error( + `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}".` + ); + } + if (steps[2].type !== "string") { + throw new Error( + `Second argument of \`#${operator}\` predicate must be a string. Got @${steps[2].name}.` + ); + } + const isPositive = operator === "match?" || operator === "any-match?"; + const matchAll = !operator.startsWith("any-"); + const captureName = steps[1].name; + const regex = new RegExp(steps[2].value); + textPredicates[index].push((captures) => { + const nodes = []; + for (const c of captures) { + if (c.name === captureName) nodes.push(c.node.text); + } + const test = /* @__PURE__ */ __name((text, positive) => { + return positive ? regex.test(text) : !regex.test(text); + }, "test"); + if (nodes.length === 0) return !isPositive; + return matchAll ? nodes.every((text) => test(text, isPositive)) : nodes.some((text) => test(text, isPositive)); + }); +} +__name(parseMatchPredicate, "parseMatchPredicate"); +function parseAnyOfPredicate(steps, index, operator, textPredicates) { + if (steps.length < 2) { + throw new Error( + `Wrong number of arguments to \`#${operator}\` predicate. Expected at least 1. Got ${steps.length - 1}.` + ); + } + if (steps[1].type !== "capture") { + throw new Error( + `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}".` + ); + } + const isPositive = operator === "any-of?"; + const captureName = steps[1].name; + const stringSteps = steps.slice(2); + if (!stringSteps.every(isStringStep)) { + throw new Error( + `Arguments to \`#${operator}\` predicate must be strings.".` + ); + } + const values = stringSteps.map((s) => s.value); + textPredicates[index].push((captures) => { + const nodes = []; + for (const c of captures) { + if (c.name === captureName) nodes.push(c.node.text); + } + if (nodes.length === 0) return !isPositive; + return nodes.every((text) => values.includes(text)) === isPositive; + }); +} +__name(parseAnyOfPredicate, "parseAnyOfPredicate"); +function parseIsPredicate(steps, index, operator, assertedProperties, refutedProperties) { + if (steps.length < 2 || steps.length > 3) { + throw new Error( + `Wrong number of arguments to \`#${operator}\` predicate. Expected 1 or 2. Got ${steps.length - 1}.` + ); + } + if (!steps.every(isStringStep)) { + throw new Error( + `Arguments to \`#${operator}\` predicate must be strings.".` + ); + } + const properties = operator === "is?" ? assertedProperties : refutedProperties; + if (!properties[index]) properties[index] = {}; + properties[index][steps[1].value] = steps[2]?.value ?? null; +} +__name(parseIsPredicate, "parseIsPredicate"); +function parseSetDirective(steps, index, setProperties) { + if (steps.length < 2 || steps.length > 3) { + throw new Error(`Wrong number of arguments to \`#set!\` predicate. Expected 1 or 2. Got ${steps.length - 1}.`); + } + if (!steps.every(isStringStep)) { + throw new Error(`Arguments to \`#set!\` predicate must be strings.".`); + } + if (!setProperties[index]) setProperties[index] = {}; + setProperties[index][steps[1].value] = steps[2]?.value ?? null; +} +__name(parseSetDirective, "parseSetDirective"); +function parsePattern(index, stepType, stepValueId, captureNames, stringValues, steps, textPredicates, predicates, setProperties, assertedProperties, refutedProperties) { + if (stepType === PREDICATE_STEP_TYPE_CAPTURE) { + const name2 = captureNames[stepValueId]; + steps.push({ type: "capture", name: name2 }); + } else if (stepType === PREDICATE_STEP_TYPE_STRING) { + steps.push({ type: "string", value: stringValues[stepValueId] }); + } else if (steps.length > 0) { + if (steps[0].type !== "string") { + throw new Error("Predicates must begin with a literal value"); + } + const operator = steps[0].value; + switch (operator) { + case "any-not-eq?": + case "not-eq?": + case "any-eq?": + case "eq?": + parseAnyPredicate(steps, index, operator, textPredicates); + break; + case "any-not-match?": + case "not-match?": + case "any-match?": + case "match?": + parseMatchPredicate(steps, index, operator, textPredicates); + break; + case "not-any-of?": + case "any-of?": + parseAnyOfPredicate(steps, index, operator, textPredicates); + break; + case "is?": + case "is-not?": + parseIsPredicate(steps, index, operator, assertedProperties, refutedProperties); + break; + case "set!": + parseSetDirective(steps, index, setProperties); + break; + default: + predicates[index].push({ operator, operands: steps.slice(1) }); + } + steps.length = 0; + } +} +__name(parsePattern, "parsePattern"); +var Query = class { + static { + __name(this, "Query"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** @internal */ + exceededMatchLimit; + /** @internal */ + textPredicates; + /** The names of the captures used in the query. */ + captureNames; + /** The quantifiers of the captures used in the query. */ + captureQuantifiers; + /** + * The other user-defined predicates associated with the given index. + * + * This includes predicates with operators other than: + * - `match?` + * - `eq?` and `not-eq?` + * - `any-of?` and `not-any-of?` + * - `is?` and `is-not?` + * - `set!` + */ + predicates; + /** The properties for predicates with the operator `set!`. */ + setProperties; + /** The properties for predicates with the operator `is?`. */ + assertedProperties; + /** The properties for predicates with the operator `is-not?`. */ + refutedProperties; + /** The maximum number of in-progress matches for this cursor. */ + matchLimit; + /** + * Create a new query from a string containing one or more S-expression + * patterns. + * + * The query is associated with a particular language, and can only be run + * on syntax nodes parsed with that language. References to Queries can be + * shared between multiple threads. + * + * @link {@see https://tree-sitter.github.io/tree-sitter/using-parsers/queries} + */ + constructor(language, source) { + const sourceLength = C.lengthBytesUTF8(source); + const sourceAddress = C._malloc(sourceLength + 1); + C.stringToUTF8(source, sourceAddress, sourceLength + 1); + const address = C._ts_query_new( + language[0], + sourceAddress, + sourceLength, + TRANSFER_BUFFER, + TRANSFER_BUFFER + SIZE_OF_INT + ); + if (!address) { + const errorId = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const errorByte = C.getValue(TRANSFER_BUFFER, "i32"); + const errorIndex = C.UTF8ToString(sourceAddress, errorByte).length; + const suffix = source.slice(errorIndex, errorIndex + 100).split("\n")[0]; + const word = suffix.match(QUERY_WORD_REGEX)?.[0] ?? ""; + C._free(sourceAddress); + switch (errorId) { + case QueryErrorKind.Syntax: + throw new QueryError(QueryErrorKind.Syntax, { suffix: `${errorIndex}: '${suffix}'...` }, errorIndex, 0); + case QueryErrorKind.NodeName: + throw new QueryError(errorId, { word }, errorIndex, word.length); + case QueryErrorKind.FieldName: + throw new QueryError(errorId, { word }, errorIndex, word.length); + case QueryErrorKind.CaptureName: + throw new QueryError(errorId, { word }, errorIndex, word.length); + case QueryErrorKind.PatternStructure: + throw new QueryError(errorId, { suffix: `${errorIndex}: '${suffix}'...` }, errorIndex, 0); + } + } + const stringCount = C._ts_query_string_count(address); + const captureCount = C._ts_query_capture_count(address); + const patternCount = C._ts_query_pattern_count(address); + const captureNames = new Array(captureCount); + const captureQuantifiers = new Array(patternCount); + const stringValues = new Array(stringCount); + for (let i2 = 0; i2 < captureCount; i2++) { + const nameAddress = C._ts_query_capture_name_for_id( + address, + i2, + TRANSFER_BUFFER + ); + const nameLength = C.getValue(TRANSFER_BUFFER, "i32"); + captureNames[i2] = C.UTF8ToString(nameAddress, nameLength); + } + for (let i2 = 0; i2 < patternCount; i2++) { + const captureQuantifiersArray = new Array(captureCount); + for (let j = 0; j < captureCount; j++) { + const quantifier = C._ts_query_capture_quantifier_for_id(address, i2, j); + captureQuantifiersArray[j] = quantifier; + } + captureQuantifiers[i2] = captureQuantifiersArray; + } + for (let i2 = 0; i2 < stringCount; i2++) { + const valueAddress = C._ts_query_string_value_for_id( + address, + i2, + TRANSFER_BUFFER + ); + const nameLength = C.getValue(TRANSFER_BUFFER, "i32"); + stringValues[i2] = C.UTF8ToString(valueAddress, nameLength); + } + const setProperties = new Array(patternCount); + const assertedProperties = new Array(patternCount); + const refutedProperties = new Array(patternCount); + const predicates = new Array(patternCount); + const textPredicates = new Array(patternCount); + for (let i2 = 0; i2 < patternCount; i2++) { + const predicatesAddress = C._ts_query_predicates_for_pattern(address, i2, TRANSFER_BUFFER); + const stepCount = C.getValue(TRANSFER_BUFFER, "i32"); + predicates[i2] = []; + textPredicates[i2] = []; + const steps = new Array(); + let stepAddress = predicatesAddress; + for (let j = 0; j < stepCount; j++) { + const stepType = C.getValue(stepAddress, "i32"); + stepAddress += SIZE_OF_INT; + const stepValueId = C.getValue(stepAddress, "i32"); + stepAddress += SIZE_OF_INT; + parsePattern( + i2, + stepType, + stepValueId, + captureNames, + stringValues, + steps, + textPredicates, + predicates, + setProperties, + assertedProperties, + refutedProperties + ); + } + Object.freeze(textPredicates[i2]); + Object.freeze(predicates[i2]); + Object.freeze(setProperties[i2]); + Object.freeze(assertedProperties[i2]); + Object.freeze(refutedProperties[i2]); + } + C._free(sourceAddress); + this[0] = address; + this.captureNames = captureNames; + this.captureQuantifiers = captureQuantifiers; + this.textPredicates = textPredicates; + this.predicates = predicates; + this.setProperties = setProperties; + this.assertedProperties = assertedProperties; + this.refutedProperties = refutedProperties; + this.exceededMatchLimit = false; + } + /** Delete the query, freeing its resources. */ + delete() { + C._ts_query_delete(this[0]); + this[0] = 0; + } + /** + * Iterate over all of the matches in the order that they were found. + * + * Each match contains the index of the pattern that matched, and a list of + * captures. Because multiple patterns can match the same set of nodes, + * one match may contain captures that appear *before* some of the + * captures from a previous match. + * + * @param {Node} node - The node to execute the query on. + * + * @param {QueryOptions} options - Options for query execution. + */ + matches(node, options = {}) { + const startPosition = options.startPosition ?? ZERO_POINT; + const endPosition = options.endPosition ?? ZERO_POINT; + const startIndex = options.startIndex ?? 0; + const endIndex = options.endIndex ?? 0; + const matchLimit = options.matchLimit ?? 4294967295; + const maxStartDepth = options.maxStartDepth ?? 4294967295; + const timeoutMicros = options.timeoutMicros ?? 0; + const progressCallback = options.progressCallback; + if (typeof matchLimit !== "number") { + throw new Error("Arguments must be numbers"); + } + this.matchLimit = matchLimit; + if (endIndex !== 0 && startIndex > endIndex) { + throw new Error("`startIndex` cannot be greater than `endIndex`"); + } + if (endPosition !== ZERO_POINT && (startPosition.row > endPosition.row || startPosition.row === endPosition.row && startPosition.column > endPosition.column)) { + throw new Error("`startPosition` cannot be greater than `endPosition`"); + } + if (progressCallback) { + C.currentQueryProgressCallback = progressCallback; + } + marshalNode(node); + C._ts_query_matches_wasm( + this[0], + node.tree[0], + startPosition.row, + startPosition.column, + endPosition.row, + endPosition.column, + startIndex, + endIndex, + matchLimit, + maxStartDepth, + timeoutMicros + ); + const rawCount = C.getValue(TRANSFER_BUFFER, "i32"); + const startAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const didExceedMatchLimit = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32"); + const result = new Array(rawCount); + this.exceededMatchLimit = Boolean(didExceedMatchLimit); + let filteredCount = 0; + let address = startAddress; + for (let i2 = 0; i2 < rawCount; i2++) { + const patternIndex = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const captureCount = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const captures = new Array(captureCount); + address = unmarshalCaptures(this, node.tree, address, patternIndex, captures); + if (this.textPredicates[patternIndex].every((p) => p(captures))) { + result[filteredCount] = { pattern: patternIndex, patternIndex, captures }; + const setProperties = this.setProperties[patternIndex]; + result[filteredCount].setProperties = setProperties; + const assertedProperties = this.assertedProperties[patternIndex]; + result[filteredCount].assertedProperties = assertedProperties; + const refutedProperties = this.refutedProperties[patternIndex]; + result[filteredCount].refutedProperties = refutedProperties; + filteredCount++; + } + } + result.length = filteredCount; + C._free(startAddress); + C.currentQueryProgressCallback = null; + return result; + } + /** + * Iterate over all of the individual captures in the order that they + * appear. + * + * This is useful if you don't care about which pattern matched, and just + * want a single, ordered sequence of captures. + * + * @param {Node} node - The node to execute the query on. + * + * @param {QueryOptions} options - Options for query execution. + */ + captures(node, options = {}) { + const startPosition = options.startPosition ?? ZERO_POINT; + const endPosition = options.endPosition ?? ZERO_POINT; + const startIndex = options.startIndex ?? 0; + const endIndex = options.endIndex ?? 0; + const matchLimit = options.matchLimit ?? 4294967295; + const maxStartDepth = options.maxStartDepth ?? 4294967295; + const timeoutMicros = options.timeoutMicros ?? 0; + const progressCallback = options.progressCallback; + if (typeof matchLimit !== "number") { + throw new Error("Arguments must be numbers"); + } + this.matchLimit = matchLimit; + if (endIndex !== 0 && startIndex > endIndex) { + throw new Error("`startIndex` cannot be greater than `endIndex`"); + } + if (endPosition !== ZERO_POINT && (startPosition.row > endPosition.row || startPosition.row === endPosition.row && startPosition.column > endPosition.column)) { + throw new Error("`startPosition` cannot be greater than `endPosition`"); + } + if (progressCallback) { + C.currentQueryProgressCallback = progressCallback; + } + marshalNode(node); + C._ts_query_captures_wasm( + this[0], + node.tree[0], + startPosition.row, + startPosition.column, + endPosition.row, + endPosition.column, + startIndex, + endIndex, + matchLimit, + maxStartDepth, + timeoutMicros + ); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const startAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const didExceedMatchLimit = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32"); + const result = new Array(); + this.exceededMatchLimit = Boolean(didExceedMatchLimit); + const captures = new Array(); + let address = startAddress; + for (let i2 = 0; i2 < count; i2++) { + const patternIndex = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const captureCount = C.getValue(address, "i32"); + address += SIZE_OF_INT; + const captureIndex = C.getValue(address, "i32"); + address += SIZE_OF_INT; + captures.length = captureCount; + address = unmarshalCaptures(this, node.tree, address, patternIndex, captures); + if (this.textPredicates[patternIndex].every((p) => p(captures))) { + const capture = captures[captureIndex]; + const setProperties = this.setProperties[patternIndex]; + capture.setProperties = setProperties; + const assertedProperties = this.assertedProperties[patternIndex]; + capture.assertedProperties = assertedProperties; + const refutedProperties = this.refutedProperties[patternIndex]; + capture.refutedProperties = refutedProperties; + result.push(capture); + } + } + C._free(startAddress); + C.currentQueryProgressCallback = null; + return result; + } + /** Get the predicates for a given pattern. */ + predicatesForPattern(patternIndex) { + return this.predicates[patternIndex]; + } + /** + * Disable a certain capture within a query. + * + * This prevents the capture from being returned in matches, and also + * avoids any resource usage associated with recording the capture. + */ + disableCapture(captureName) { + const captureNameLength = C.lengthBytesUTF8(captureName); + const captureNameAddress = C._malloc(captureNameLength + 1); + C.stringToUTF8(captureName, captureNameAddress, captureNameLength + 1); + C._ts_query_disable_capture(this[0], captureNameAddress, captureNameLength); + C._free(captureNameAddress); + } + /** + * Disable a certain pattern within a query. + * + * This prevents the pattern from matching, and also avoids any resource + * usage associated with the pattern. This throws an error if the pattern + * index is out of bounds. + */ + disablePattern(patternIndex) { + if (patternIndex >= this.predicates.length) { + throw new Error( + `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}` + ); + } + C._ts_query_disable_pattern(this[0], patternIndex); + } + /** + * Check if, on its last execution, this cursor exceeded its maximum number + * of in-progress matches. + */ + didExceedMatchLimit() { + return this.exceededMatchLimit; + } + /** Get the byte offset where the given pattern starts in the query's source. */ + startIndexForPattern(patternIndex) { + if (patternIndex >= this.predicates.length) { + throw new Error( + `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}` + ); + } + return C._ts_query_start_byte_for_pattern(this[0], patternIndex); + } + /** Get the byte offset where the given pattern ends in the query's source. */ + endIndexForPattern(patternIndex) { + if (patternIndex >= this.predicates.length) { + throw new Error( + `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}` + ); + } + return C._ts_query_end_byte_for_pattern(this[0], patternIndex); + } + /** Get the number of patterns in the query. */ + patternCount() { + return C._ts_query_pattern_count(this[0]); + } + /** Get the index for a given capture name. */ + captureIndexForName(captureName) { + return this.captureNames.indexOf(captureName); + } + /** Check if a given pattern within a query has a single root node. */ + isPatternRooted(patternIndex) { + return C._ts_query_is_pattern_rooted(this[0], patternIndex) === 1; + } + /** Check if a given pattern within a query has a single root node. */ + isPatternNonLocal(patternIndex) { + return C._ts_query_is_pattern_non_local(this[0], patternIndex) === 1; + } + /** + * Check if a given step in a query is 'definite'. + * + * A query step is 'definite' if its parent pattern will be guaranteed to + * match successfully once it reaches the step. + */ + isPatternGuaranteedAtStep(byteIndex) { + return C._ts_query_is_pattern_guaranteed_at_step(this[0], byteIndex) === 1; + } +}; + +// src/language.ts +var LANGUAGE_FUNCTION_REGEX = /^tree_sitter_\w+$/; +var Language = class _Language { + static { + __name(this, "Language"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** + * A list of all node types in the language. The index of each type in this + * array is its node type id. + */ + types; + /** + * A list of all field names in the language. The index of each field name in + * this array is its field id. + */ + fields; + /** @internal */ + constructor(internal, address) { + assertInternal(internal); + this[0] = address; + this.types = new Array(C._ts_language_symbol_count(this[0])); + for (let i2 = 0, n = this.types.length; i2 < n; i2++) { + if (C._ts_language_symbol_type(this[0], i2) < 2) { + this.types[i2] = C.UTF8ToString(C._ts_language_symbol_name(this[0], i2)); + } + } + this.fields = new Array(C._ts_language_field_count(this[0]) + 1); + for (let i2 = 0, n = this.fields.length; i2 < n; i2++) { + const fieldName = C._ts_language_field_name_for_id(this[0], i2); + if (fieldName !== 0) { + this.fields[i2] = C.UTF8ToString(fieldName); + } else { + this.fields[i2] = null; + } + } + } + /** + * Gets the name of the language. + */ + get name() { + const ptr = C._ts_language_name(this[0]); + if (ptr === 0) return null; + return C.UTF8ToString(ptr); + } + /** + * @deprecated since version 0.25.0, use {@link Language#abiVersion} instead + * Gets the version of the language. + */ + get version() { + return C._ts_language_version(this[0]); + } + /** + * Gets the ABI version of the language. + */ + get abiVersion() { + return C._ts_language_abi_version(this[0]); + } + /** + * Get the metadata for this language. This information is generated by the + * CLI, and relies on the language author providing the correct metadata in + * the language's `tree-sitter.json` file. + */ + get metadata() { + C._ts_language_metadata(this[0]); + const length = C.getValue(TRANSFER_BUFFER, "i32"); + const address = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + if (length === 0) return null; + return unmarshalLanguageMetadata(address); + } + /** + * Gets the number of fields in the language. + */ + get fieldCount() { + return this.fields.length - 1; + } + /** + * Gets the number of states in the language. + */ + get stateCount() { + return C._ts_language_state_count(this[0]); + } + /** + * Get the field id for a field name. + */ + fieldIdForName(fieldName) { + const result = this.fields.indexOf(fieldName); + return result !== -1 ? result : null; + } + /** + * Get the field name for a field id. + */ + fieldNameForId(fieldId) { + return this.fields[fieldId] ?? null; + } + /** + * Get the node type id for a node type name. + */ + idForNodeType(type, named) { + const typeLength = C.lengthBytesUTF8(type); + const typeAddress = C._malloc(typeLength + 1); + C.stringToUTF8(type, typeAddress, typeLength + 1); + const result = C._ts_language_symbol_for_name(this[0], typeAddress, typeLength, named ? 1 : 0); + C._free(typeAddress); + return result || null; + } + /** + * Gets the number of node types in the language. + */ + get nodeTypeCount() { + return C._ts_language_symbol_count(this[0]); + } + /** + * Get the node type name for a node type id. + */ + nodeTypeForId(typeId) { + const name2 = C._ts_language_symbol_name(this[0], typeId); + return name2 ? C.UTF8ToString(name2) : null; + } + /** + * Check if a node type is named. + * + * @see {@link https://tree-sitter.github.io/tree-sitter/using-parsers/2-basic-parsing.html#named-vs-anonymous-nodes} + */ + nodeTypeIsNamed(typeId) { + return C._ts_language_type_is_named_wasm(this[0], typeId) ? true : false; + } + /** + * Check if a node type is visible. + */ + nodeTypeIsVisible(typeId) { + return C._ts_language_type_is_visible_wasm(this[0], typeId) ? true : false; + } + /** + * Get the supertypes ids of this language. + * + * @see {@link https://tree-sitter.github.io/tree-sitter/using-parsers/6-static-node-types.html?highlight=supertype#supertype-nodes} + */ + get supertypes() { + C._ts_language_supertypes_wasm(this[0]); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + result[i2] = C.getValue(address, "i16"); + address += SIZE_OF_SHORT; + } + } + return result; + } + /** + * Get the subtype ids for a given supertype node id. + */ + subtypes(supertype) { + C._ts_language_subtypes_wasm(this[0], supertype); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + result[i2] = C.getValue(address, "i16"); + address += SIZE_OF_SHORT; + } + } + return result; + } + /** + * Get the next state id for a given state id and node type id. + */ + nextState(stateId, typeId) { + return C._ts_language_next_state(this[0], stateId, typeId); + } + /** + * Create a new lookahead iterator for this language and parse state. + * + * This returns `null` if state is invalid for this language. + * + * Iterating {@link LookaheadIterator} will yield valid symbols in the given + * parse state. Newly created lookahead iterators will return the `ERROR` + * symbol from {@link LookaheadIterator#currentType}. + * + * Lookahead iterators can be useful for generating suggestions and improving + * syntax error diagnostics. To get symbols valid in an `ERROR` node, use the + * lookahead iterator on its first leaf node state. For `MISSING` nodes, a + * lookahead iterator created on the previous non-extra leaf node may be + * appropriate. + */ + lookaheadIterator(stateId) { + const address = C._ts_lookahead_iterator_new(this[0], stateId); + if (address) return new LookaheadIterator(INTERNAL, address, this); + return null; + } + /** + * @deprecated since version 0.25.0, call `new` on a {@link Query} instead + * + * Create a new query from a string containing one or more S-expression + * patterns. + * + * The query is associated with a particular language, and can only be run + * on syntax nodes parsed with that language. References to Queries can be + * shared between multiple threads. + * + * @link {@see https://tree-sitter.github.io/tree-sitter/using-parsers/queries} + */ + query(source) { + console.warn("Language.query is deprecated. Use new Query(language, source) instead."); + return new Query(this, source); + } + /** + * Load a language from a WebAssembly module. + * The module can be provided as a path to a file or as a buffer. + */ + static async load(input) { + let bytes; + if (input instanceof Uint8Array) { + bytes = Promise.resolve(input); + } else { + if (globalThis.process?.versions.node) { + const fs2 = await import("fs/promises"); + bytes = fs2.readFile(input); + } else { + bytes = fetch(input).then((response) => response.arrayBuffer().then((buffer) => { + if (response.ok) { + return new Uint8Array(buffer); + } else { + const body2 = new TextDecoder("utf-8").decode(buffer); + throw new Error(`Language.load failed with status ${response.status}. + +${body2}`); + } + })); + } + } + const mod = await C.loadWebAssemblyModule(await bytes, { loadAsync: true }); + const symbolNames = Object.keys(mod); + const functionName = symbolNames.find((key) => LANGUAGE_FUNCTION_REGEX.test(key) && !key.includes("external_scanner_")); + if (!functionName) { + console.log(`Couldn't find language function in WASM file. Symbols: +${JSON.stringify(symbolNames, null, 2)}`); + throw new Error("Language.load failed: no language function found in WASM file"); + } + const languageAddress = mod[functionName](); + return new _Language(INTERNAL, languageAddress); + } +}; + +// lib/tree-sitter.mjs +var Module2 = (() => { + var _scriptName = import.meta.url; + return async function(moduleArg = {}) { + var moduleRtn; + var Module = moduleArg; + var readyPromiseResolve, readyPromiseReject; + var readyPromise = new Promise((resolve, reject) => { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var ENVIRONMENT_IS_WEB = typeof window == "object"; + var ENVIRONMENT_IS_WORKER = typeof WorkerGlobalScope != "undefined"; + var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string" && process.type != "renderer"; + var ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER; + if (ENVIRONMENT_IS_NODE) { + const { createRequire } = await import("module"); + var require = createRequire(import.meta.url); + } + Module.currentQueryProgressCallback = null; + Module.currentProgressCallback = null; + Module.currentLogCallback = null; + Module.currentParseCallback = null; + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = /* @__PURE__ */ __name((status, toThrow) => { + throw toThrow; + }, "quit_"); + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + __name(locateFile, "locateFile"); + var readAsync, readBinary; + if (ENVIRONMENT_IS_NODE) { + var fs = require("fs"); + var nodePath = require("path"); + if (!import.meta.url.startsWith("data:")) { + scriptDirectory = nodePath.dirname(require("url").fileURLToPath(import.meta.url)) + "/"; + } + readBinary = /* @__PURE__ */ __name((filename) => { + filename = isFileURI(filename) ? new URL(filename) : filename; + var ret = fs.readFileSync(filename); + return ret; + }, "readBinary"); + readAsync = /* @__PURE__ */ __name(async (filename, binary2 = true) => { + filename = isFileURI(filename) ? new URL(filename) : filename; + var ret = fs.readFileSync(filename, binary2 ? void 0 : "utf8"); + return ret; + }, "readAsync"); + if (!Module["thisProgram"] && process.argv.length > 1) { + thisProgram = process.argv[1].replace(/\\/g, "/"); + } + arguments_ = process.argv.slice(2); + quit_ = /* @__PURE__ */ __name((status, toThrow) => { + process.exitCode = status; + throw toThrow; + }, "quit_"); + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document != "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (_scriptName) { + scriptDirectory = _scriptName; + } + if (scriptDirectory.startsWith("blob:")) { + scriptDirectory = ""; + } else { + scriptDirectory = scriptDirectory.slice(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } + { + if (ENVIRONMENT_IS_WORKER) { + readBinary = /* @__PURE__ */ __name((url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array( + /** @type{!ArrayBuffer} */ + xhr.response + ); + }, "readBinary"); + } + readAsync = /* @__PURE__ */ __name(async (url) => { + if (isFileURI(url)) { + return new Promise((resolve, reject) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + resolve(xhr.response); + return; + } + reject(xhr.status); + }; + xhr.onerror = reject; + xhr.send(null); + }); + } + var response = await fetch(url, { + credentials: "same-origin" + }); + if (response.ok) { + return response.arrayBuffer(); + } + throw new Error(response.status + " : " + response.url); + }, "readAsync"); + } + } else { + } + var out = Module["print"] || console.log.bind(console); + var err = Module["printErr"] || console.error.bind(console); + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) arguments_ = Module["arguments"]; + if (Module["thisProgram"]) thisProgram = Module["thisProgram"]; + var dynamicLibraries = Module["dynamicLibraries"] || []; + var wasmBinary = Module["wasmBinary"]; + var wasmMemory; + var ABORT = false; + var EXITSTATUS; + function assert(condition, text) { + if (!condition) { + abort(text); + } + } + __name(assert, "assert"); + var HEAP, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAP64, HEAPU64, HEAPF64; + var HEAP_DATA_VIEW; + var runtimeInitialized = false; + var isFileURI = /* @__PURE__ */ __name((filename) => filename.startsWith("file://"), "isFileURI"); + function updateMemoryViews() { + var b = wasmMemory.buffer; + Module["HEAP_DATA_VIEW"] = HEAP_DATA_VIEW = new DataView(b); + Module["HEAP8"] = HEAP8 = new Int8Array(b); + Module["HEAP16"] = HEAP16 = new Int16Array(b); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(b); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(b); + Module["HEAP32"] = HEAP32 = new Int32Array(b); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(b); + Module["HEAPF32"] = HEAPF32 = new Float32Array(b); + Module["HEAPF64"] = HEAPF64 = new Float64Array(b); + Module["HEAP64"] = HEAP64 = new BigInt64Array(b); + Module["HEAPU64"] = HEAPU64 = new BigUint64Array(b); + } + __name(updateMemoryViews, "updateMemoryViews"); + if (Module["wasmMemory"]) { + wasmMemory = Module["wasmMemory"]; + } else { + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 33554432; + wasmMemory = new WebAssembly.Memory({ + "initial": INITIAL_MEMORY / 65536, + // In theory we should not need to emit the maximum if we want "unlimited" + // or 4GB of memory, but VMs error on that atm, see + // https://github.com/emscripten-core/emscripten/issues/14130 + // And in the pthreads case we definitely need to emit a maximum. So + // always emit one. + "maximum": 32768 + }); + } + updateMemoryViews(); + var __RELOC_FUNCS__ = []; + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(onPreRuns); + } + __name(preRun, "preRun"); + function initRuntime() { + runtimeInitialized = true; + callRuntimeCallbacks(__RELOC_FUNCS__); + wasmExports["__wasm_call_ctors"](); + callRuntimeCallbacks(onPostCtors); + } + __name(initRuntime, "initRuntime"); + function preMain() { + } + __name(preMain, "preMain"); + function postRun() { + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(onPostRuns); + } + __name(postRun, "postRun"); + var runDependencies = 0; + var dependenciesFulfilled = null; + function getUniqueRunDependency(id) { + return id; + } + __name(getUniqueRunDependency, "getUniqueRunDependency"); + function addRunDependency(id) { + runDependencies++; + Module["monitorRunDependencies"]?.(runDependencies); + } + __name(addRunDependency, "addRunDependency"); + function removeRunDependency(id) { + runDependencies--; + Module["monitorRunDependencies"]?.(runDependencies); + if (runDependencies == 0) { + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + __name(removeRunDependency, "removeRunDependency"); + function abort(what) { + Module["onAbort"]?.(what); + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + what += ". Build with -sASSERTIONS for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + __name(abort, "abort"); + var wasmBinaryFile; + function findWasmBinary() { + if (Module["locateFile"]) { + return locateFile("tree-sitter.wasm"); + } + return new URL("tree-sitter.wasm", import.meta.url).href; + } + __name(findWasmBinary, "findWasmBinary"); + function getBinarySync(file) { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } + throw "both async and sync fetching of the wasm failed"; + } + __name(getBinarySync, "getBinarySync"); + async function getWasmBinary(binaryFile) { + if (!wasmBinary) { + try { + var response = await readAsync(binaryFile); + return new Uint8Array(response); + } catch { + } + } + return getBinarySync(binaryFile); + } + __name(getWasmBinary, "getWasmBinary"); + async function instantiateArrayBuffer(binaryFile, imports) { + try { + var binary2 = await getWasmBinary(binaryFile); + var instance2 = await WebAssembly.instantiate(binary2, imports); + return instance2; + } catch (reason) { + err(`failed to asynchronously prepare wasm: ${reason}`); + abort(reason); + } + } + __name(instantiateArrayBuffer, "instantiateArrayBuffer"); + async function instantiateAsync(binary2, binaryFile, imports) { + if (!binary2 && typeof WebAssembly.instantiateStreaming == "function" && !isFileURI(binaryFile) && !ENVIRONMENT_IS_NODE) { + try { + var response = fetch(binaryFile, { + credentials: "same-origin" + }); + var instantiationResult = await WebAssembly.instantiateStreaming(response, imports); + return instantiationResult; + } catch (reason) { + err(`wasm streaming compile failed: ${reason}`); + err("falling back to ArrayBuffer instantiation"); + } + } + return instantiateArrayBuffer(binaryFile, imports); + } + __name(instantiateAsync, "instantiateAsync"); + function getWasmImports() { + return { + "env": wasmImports, + "wasi_snapshot_preview1": wasmImports, + "GOT.mem": new Proxy(wasmImports, GOTHandler), + "GOT.func": new Proxy(wasmImports, GOTHandler) + }; + } + __name(getWasmImports, "getWasmImports"); + async function createWasm() { + function receiveInstance(instance2, module2) { + wasmExports = instance2.exports; + wasmExports = relocateExports(wasmExports, 1024); + var metadata2 = getDylinkMetadata(module2); + if (metadata2.neededDynlibs) { + dynamicLibraries = metadata2.neededDynlibs.concat(dynamicLibraries); + } + mergeLibSymbols(wasmExports, "main"); + LDSO.init(); + loadDylibs(); + __RELOC_FUNCS__.push(wasmExports["__wasm_apply_data_relocs"]); + removeRunDependency("wasm-instantiate"); + return wasmExports; + } + __name(receiveInstance, "receiveInstance"); + addRunDependency("wasm-instantiate"); + function receiveInstantiationResult(result2) { + return receiveInstance(result2["instance"], result2["module"]); + } + __name(receiveInstantiationResult, "receiveInstantiationResult"); + var info2 = getWasmImports(); + if (Module["instantiateWasm"]) { + return new Promise((resolve, reject) => { + Module["instantiateWasm"](info2, (mod, inst) => { + receiveInstance(mod, inst); + resolve(mod.exports); + }); + }); + } + wasmBinaryFile ??= findWasmBinary(); + try { + var result = await instantiateAsync(wasmBinary, wasmBinaryFile, info2); + var exports = receiveInstantiationResult(result); + return exports; + } catch (e) { + readyPromiseReject(e); + return Promise.reject(e); + } + } + __name(createWasm, "createWasm"); + var ASM_CONSTS = {}; + class ExitStatus { + static { + __name(this, "ExitStatus"); + } + name = "ExitStatus"; + constructor(status) { + this.message = `Program terminated with exit(${status})`; + this.status = status; + } + } + var GOT = {}; + var currentModuleWeakSymbols = /* @__PURE__ */ new Set([]); + var GOTHandler = { + get(obj, symName) { + var rtn = GOT[symName]; + if (!rtn) { + rtn = GOT[symName] = new WebAssembly.Global({ + "value": "i32", + "mutable": true + }); + } + if (!currentModuleWeakSymbols.has(symName)) { + rtn.required = true; + } + return rtn; + } + }; + var LE_HEAP_LOAD_F32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getFloat32(byteOffset, true), "LE_HEAP_LOAD_F32"); + var LE_HEAP_LOAD_F64 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getFloat64(byteOffset, true), "LE_HEAP_LOAD_F64"); + var LE_HEAP_LOAD_I16 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getInt16(byteOffset, true), "LE_HEAP_LOAD_I16"); + var LE_HEAP_LOAD_I32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getInt32(byteOffset, true), "LE_HEAP_LOAD_I32"); + var LE_HEAP_LOAD_U16 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getUint16(byteOffset, true), "LE_HEAP_LOAD_U16"); + var LE_HEAP_LOAD_U32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getUint32(byteOffset, true), "LE_HEAP_LOAD_U32"); + var LE_HEAP_STORE_F32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setFloat32(byteOffset, value, true), "LE_HEAP_STORE_F32"); + var LE_HEAP_STORE_F64 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setFloat64(byteOffset, value, true), "LE_HEAP_STORE_F64"); + var LE_HEAP_STORE_I16 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setInt16(byteOffset, value, true), "LE_HEAP_STORE_I16"); + var LE_HEAP_STORE_I32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setInt32(byteOffset, value, true), "LE_HEAP_STORE_I32"); + var LE_HEAP_STORE_U16 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setUint16(byteOffset, value, true), "LE_HEAP_STORE_U16"); + var LE_HEAP_STORE_U32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setUint32(byteOffset, value, true), "LE_HEAP_STORE_U32"); + var callRuntimeCallbacks = /* @__PURE__ */ __name((callbacks) => { + while (callbacks.length > 0) { + callbacks.shift()(Module); + } + }, "callRuntimeCallbacks"); + var onPostRuns = []; + var addOnPostRun = /* @__PURE__ */ __name((cb) => onPostRuns.unshift(cb), "addOnPostRun"); + var onPreRuns = []; + var addOnPreRun = /* @__PURE__ */ __name((cb) => onPreRuns.unshift(cb), "addOnPreRun"); + var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder() : void 0; + var UTF8ArrayToString = /* @__PURE__ */ __name((heapOrArray, idx = 0, maxBytesToRead = NaN) => { + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heapOrArray[endPtr] && !(endPtr >= endIdx)) ++endPtr; + if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) { + return UTF8Decoder.decode(heapOrArray.subarray(idx, endPtr)); + } + var str = ""; + while (idx < endPtr) { + var u0 = heapOrArray[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heapOrArray[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heapOrArray[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + return str; + }, "UTF8ArrayToString"); + var getDylinkMetadata = /* @__PURE__ */ __name((binary2) => { + var offset = 0; + var end = 0; + function getU8() { + return binary2[offset++]; + } + __name(getU8, "getU8"); + function getLEB() { + var ret = 0; + var mul = 1; + while (1) { + var byte = binary2[offset++]; + ret += (byte & 127) * mul; + mul *= 128; + if (!(byte & 128)) break; + } + return ret; + } + __name(getLEB, "getLEB"); + function getString() { + var len = getLEB(); + offset += len; + return UTF8ArrayToString(binary2, offset - len, len); + } + __name(getString, "getString"); + function failIf(condition, message) { + if (condition) throw new Error(message); + } + __name(failIf, "failIf"); + var name2 = "dylink.0"; + if (binary2 instanceof WebAssembly.Module) { + var dylinkSection = WebAssembly.Module.customSections(binary2, name2); + if (dylinkSection.length === 0) { + name2 = "dylink"; + dylinkSection = WebAssembly.Module.customSections(binary2, name2); + } + failIf(dylinkSection.length === 0, "need dylink section"); + binary2 = new Uint8Array(dylinkSection[0]); + end = binary2.length; + } else { + var int32View = new Uint32Array(new Uint8Array(binary2.subarray(0, 24)).buffer); + var magicNumberFound = int32View[0] == 1836278016 || int32View[0] == 6386541; + failIf(!magicNumberFound, "need to see wasm magic number"); + failIf(binary2[8] !== 0, "need the dylink section to be first"); + offset = 9; + var section_size = getLEB(); + end = offset + section_size; + name2 = getString(); + } + var customSection = { + neededDynlibs: [], + tlsExports: /* @__PURE__ */ new Set(), + weakImports: /* @__PURE__ */ new Set() + }; + if (name2 == "dylink") { + customSection.memorySize = getLEB(); + customSection.memoryAlign = getLEB(); + customSection.tableSize = getLEB(); + customSection.tableAlign = getLEB(); + var neededDynlibsCount = getLEB(); + for (var i2 = 0; i2 < neededDynlibsCount; ++i2) { + var libname = getString(); + customSection.neededDynlibs.push(libname); + } + } else { + failIf(name2 !== "dylink.0"); + var WASM_DYLINK_MEM_INFO = 1; + var WASM_DYLINK_NEEDED = 2; + var WASM_DYLINK_EXPORT_INFO = 3; + var WASM_DYLINK_IMPORT_INFO = 4; + var WASM_SYMBOL_TLS = 256; + var WASM_SYMBOL_BINDING_MASK = 3; + var WASM_SYMBOL_BINDING_WEAK = 1; + while (offset < end) { + var subsectionType = getU8(); + var subsectionSize = getLEB(); + if (subsectionType === WASM_DYLINK_MEM_INFO) { + customSection.memorySize = getLEB(); + customSection.memoryAlign = getLEB(); + customSection.tableSize = getLEB(); + customSection.tableAlign = getLEB(); + } else if (subsectionType === WASM_DYLINK_NEEDED) { + var neededDynlibsCount = getLEB(); + for (var i2 = 0; i2 < neededDynlibsCount; ++i2) { + libname = getString(); + customSection.neededDynlibs.push(libname); + } + } else if (subsectionType === WASM_DYLINK_EXPORT_INFO) { + var count = getLEB(); + while (count--) { + var symname = getString(); + var flags2 = getLEB(); + if (flags2 & WASM_SYMBOL_TLS) { + customSection.tlsExports.add(symname); + } + } + } else if (subsectionType === WASM_DYLINK_IMPORT_INFO) { + var count = getLEB(); + while (count--) { + var modname = getString(); + var symname = getString(); + var flags2 = getLEB(); + if ((flags2 & WASM_SYMBOL_BINDING_MASK) == WASM_SYMBOL_BINDING_WEAK) { + customSection.weakImports.add(symname); + } + } + } else { + offset += subsectionSize; + } + } + } + return customSection; + }, "getDylinkMetadata"); + function getValue(ptr, type = "i8") { + if (type.endsWith("*")) type = "*"; + switch (type) { + case "i1": + return HEAP8[ptr]; + case "i8": + return HEAP8[ptr]; + case "i16": + return LE_HEAP_LOAD_I16((ptr >> 1) * 2); + case "i32": + return LE_HEAP_LOAD_I32((ptr >> 2) * 4); + case "i64": + return HEAP64[ptr >> 3]; + case "float": + return LE_HEAP_LOAD_F32((ptr >> 2) * 4); + case "double": + return LE_HEAP_LOAD_F64((ptr >> 3) * 8); + case "*": + return LE_HEAP_LOAD_U32((ptr >> 2) * 4); + default: + abort(`invalid type for getValue: ${type}`); + } + } + __name(getValue, "getValue"); + var newDSO = /* @__PURE__ */ __name((name2, handle2, syms) => { + var dso = { + refcount: Infinity, + name: name2, + exports: syms, + global: true + }; + LDSO.loadedLibsByName[name2] = dso; + if (handle2 != void 0) { + LDSO.loadedLibsByHandle[handle2] = dso; + } + return dso; + }, "newDSO"); + var LDSO = { + loadedLibsByName: {}, + loadedLibsByHandle: {}, + init() { + newDSO("__main__", 0, wasmImports); + } + }; + var ___heap_base = 78224; + var alignMemory = /* @__PURE__ */ __name((size, alignment) => Math.ceil(size / alignment) * alignment, "alignMemory"); + var getMemory = /* @__PURE__ */ __name((size) => { + if (runtimeInitialized) { + return _calloc(size, 1); + } + var ret = ___heap_base; + var end = ret + alignMemory(size, 16); + ___heap_base = end; + GOT["__heap_base"].value = end; + return ret; + }, "getMemory"); + var isInternalSym = /* @__PURE__ */ __name((symName) => ["__cpp_exception", "__c_longjmp", "__wasm_apply_data_relocs", "__dso_handle", "__tls_size", "__tls_align", "__set_stack_limits", "_emscripten_tls_init", "__wasm_init_tls", "__wasm_call_ctors", "__start_em_asm", "__stop_em_asm", "__start_em_js", "__stop_em_js"].includes(symName) || symName.startsWith("__em_js__"), "isInternalSym"); + var uleb128Encode = /* @__PURE__ */ __name((n, target) => { + if (n < 128) { + target.push(n); + } else { + target.push(n % 128 | 128, n >> 7); + } + }, "uleb128Encode"); + var sigToWasmTypes = /* @__PURE__ */ __name((sig) => { + var typeNames = { + "i": "i32", + "j": "i64", + "f": "f32", + "d": "f64", + "e": "externref", + "p": "i32" + }; + var type = { + parameters: [], + results: sig[0] == "v" ? [] : [typeNames[sig[0]]] + }; + for (var i2 = 1; i2 < sig.length; ++i2) { + type.parameters.push(typeNames[sig[i2]]); + } + return type; + }, "sigToWasmTypes"); + var generateFuncType = /* @__PURE__ */ __name((sig, target) => { + var sigRet = sig.slice(0, 1); + var sigParam = sig.slice(1); + var typeCodes = { + "i": 127, + // i32 + "p": 127, + // i32 + "j": 126, + // i64 + "f": 125, + // f32 + "d": 124, + // f64 + "e": 111 + }; + target.push(96); + uleb128Encode(sigParam.length, target); + for (var i2 = 0; i2 < sigParam.length; ++i2) { + target.push(typeCodes[sigParam[i2]]); + } + if (sigRet == "v") { + target.push(0); + } else { + target.push(1, typeCodes[sigRet]); + } + }, "generateFuncType"); + var convertJsFunctionToWasm = /* @__PURE__ */ __name((func2, sig) => { + if (typeof WebAssembly.Function == "function") { + return new WebAssembly.Function(sigToWasmTypes(sig), func2); + } + var typeSectionBody = [1]; + generateFuncType(sig, typeSectionBody); + var bytes = [ + 0, + 97, + 115, + 109, + // magic ("\0asm") + 1, + 0, + 0, + 0, + // version: 1 + 1 + ]; + uleb128Encode(typeSectionBody.length, bytes); + bytes.push(...typeSectionBody); + bytes.push( + 2, + 7, + // import section + // (import "e" "f" (func 0 (type 0))) + 1, + 1, + 101, + 1, + 102, + 0, + 0, + 7, + 5, + // export section + // (export "f" (func 0 (type 0))) + 1, + 1, + 102, + 0, + 0 + ); + var module2 = new WebAssembly.Module(new Uint8Array(bytes)); + var instance2 = new WebAssembly.Instance(module2, { + "e": { + "f": func2 + } + }); + var wrappedFunc = instance2.exports["f"]; + return wrappedFunc; + }, "convertJsFunctionToWasm"); + var wasmTableMirror = []; + var wasmTable = new WebAssembly.Table({ + "initial": 31, + "element": "anyfunc" + }); + var getWasmTableEntry = /* @__PURE__ */ __name((funcPtr) => { + var func2 = wasmTableMirror[funcPtr]; + if (!func2) { + if (funcPtr >= wasmTableMirror.length) wasmTableMirror.length = funcPtr + 1; + wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr); + } + return func2; + }, "getWasmTableEntry"); + var updateTableMap = /* @__PURE__ */ __name((offset, count) => { + if (functionsInTableMap) { + for (var i2 = offset; i2 < offset + count; i2++) { + var item = getWasmTableEntry(i2); + if (item) { + functionsInTableMap.set(item, i2); + } + } + } + }, "updateTableMap"); + var functionsInTableMap; + var getFunctionAddress = /* @__PURE__ */ __name((func2) => { + if (!functionsInTableMap) { + functionsInTableMap = /* @__PURE__ */ new WeakMap(); + updateTableMap(0, wasmTable.length); + } + return functionsInTableMap.get(func2) || 0; + }, "getFunctionAddress"); + var freeTableIndexes = []; + var getEmptyTableSlot = /* @__PURE__ */ __name(() => { + if (freeTableIndexes.length) { + return freeTableIndexes.pop(); + } + try { + wasmTable.grow(1); + } catch (err2) { + if (!(err2 instanceof RangeError)) { + throw err2; + } + throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH."; + } + return wasmTable.length - 1; + }, "getEmptyTableSlot"); + var setWasmTableEntry = /* @__PURE__ */ __name((idx, func2) => { + wasmTable.set(idx, func2); + wasmTableMirror[idx] = wasmTable.get(idx); + }, "setWasmTableEntry"); + var addFunction = /* @__PURE__ */ __name((func2, sig) => { + var rtn = getFunctionAddress(func2); + if (rtn) { + return rtn; + } + var ret = getEmptyTableSlot(); + try { + setWasmTableEntry(ret, func2); + } catch (err2) { + if (!(err2 instanceof TypeError)) { + throw err2; + } + var wrapped = convertJsFunctionToWasm(func2, sig); + setWasmTableEntry(ret, wrapped); + } + functionsInTableMap.set(func2, ret); + return ret; + }, "addFunction"); + var updateGOT = /* @__PURE__ */ __name((exports, replace) => { + for (var symName in exports) { + if (isInternalSym(symName)) { + continue; + } + var value = exports[symName]; + GOT[symName] ||= new WebAssembly.Global({ + "value": "i32", + "mutable": true + }); + if (replace || GOT[symName].value == 0) { + if (typeof value == "function") { + GOT[symName].value = addFunction(value); + } else if (typeof value == "number") { + GOT[symName].value = value; + } else { + err(`unhandled export type for '${symName}': ${typeof value}`); + } + } + } + }, "updateGOT"); + var relocateExports = /* @__PURE__ */ __name((exports, memoryBase2, replace) => { + var relocated = {}; + for (var e in exports) { + var value = exports[e]; + if (typeof value == "object") { + value = value.value; + } + if (typeof value == "number") { + value += memoryBase2; + } + relocated[e] = value; + } + updateGOT(relocated, replace); + return relocated; + }, "relocateExports"); + var isSymbolDefined = /* @__PURE__ */ __name((symName) => { + var existing = wasmImports[symName]; + if (!existing || existing.stub) { + return false; + } + return true; + }, "isSymbolDefined"); + var dynCall = /* @__PURE__ */ __name((sig, ptr, args2 = []) => { + var rtn = getWasmTableEntry(ptr)(...args2); + return rtn; + }, "dynCall"); + var stackSave = /* @__PURE__ */ __name(() => _emscripten_stack_get_current(), "stackSave"); + var stackRestore = /* @__PURE__ */ __name((val) => __emscripten_stack_restore(val), "stackRestore"); + var createInvokeFunction = /* @__PURE__ */ __name((sig) => (ptr, ...args2) => { + var sp = stackSave(); + try { + return dynCall(sig, ptr, args2); + } catch (e) { + stackRestore(sp); + if (e !== e + 0) throw e; + _setThrew(1, 0); + if (sig[0] == "j") return 0n; + } + }, "createInvokeFunction"); + var resolveGlobalSymbol = /* @__PURE__ */ __name((symName, direct = false) => { + var sym; + if (isSymbolDefined(symName)) { + sym = wasmImports[symName]; + } else if (symName.startsWith("invoke_")) { + sym = wasmImports[symName] = createInvokeFunction(symName.split("_")[1]); + } + return { + sym, + name: symName + }; + }, "resolveGlobalSymbol"); + var onPostCtors = []; + var addOnPostCtor = /* @__PURE__ */ __name((cb) => onPostCtors.unshift(cb), "addOnPostCtor"); + var UTF8ToString = /* @__PURE__ */ __name((ptr, maxBytesToRead) => ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "", "UTF8ToString"); + var loadWebAssemblyModule = /* @__PURE__ */ __name((binary, flags, libName, localScope, handle) => { + var metadata = getDylinkMetadata(binary); + currentModuleWeakSymbols = metadata.weakImports; + function loadModule() { + var memAlign = Math.pow(2, metadata.memoryAlign); + var memoryBase = metadata.memorySize ? alignMemory(getMemory(metadata.memorySize + memAlign), memAlign) : 0; + var tableBase = metadata.tableSize ? wasmTable.length : 0; + if (handle) { + HEAP8[handle + 8] = 1; + LE_HEAP_STORE_U32((handle + 12 >> 2) * 4, memoryBase); + LE_HEAP_STORE_I32((handle + 16 >> 2) * 4, metadata.memorySize); + LE_HEAP_STORE_U32((handle + 20 >> 2) * 4, tableBase); + LE_HEAP_STORE_I32((handle + 24 >> 2) * 4, metadata.tableSize); + } + if (metadata.tableSize) { + wasmTable.grow(metadata.tableSize); + } + var moduleExports; + function resolveSymbol(sym) { + var resolved = resolveGlobalSymbol(sym).sym; + if (!resolved && localScope) { + resolved = localScope[sym]; + } + if (!resolved) { + resolved = moduleExports[sym]; + } + return resolved; + } + __name(resolveSymbol, "resolveSymbol"); + var proxyHandler = { + get(stubs, prop) { + switch (prop) { + case "__memory_base": + return memoryBase; + case "__table_base": + return tableBase; + } + if (prop in wasmImports && !wasmImports[prop].stub) { + var res = wasmImports[prop]; + return res; + } + if (!(prop in stubs)) { + var resolved; + stubs[prop] = (...args2) => { + resolved ||= resolveSymbol(prop); + return resolved(...args2); + }; + } + return stubs[prop]; + } + }; + var proxy = new Proxy({}, proxyHandler); + var info = { + "GOT.mem": new Proxy({}, GOTHandler), + "GOT.func": new Proxy({}, GOTHandler), + "env": proxy, + "wasi_snapshot_preview1": proxy + }; + function postInstantiation(module, instance) { + updateTableMap(tableBase, metadata.tableSize); + moduleExports = relocateExports(instance.exports, memoryBase); + if (!flags.allowUndefined) { + reportUndefinedSymbols(); + } + function addEmAsm(addr, body) { + var args = []; + var arity = 0; + for (; arity < 16; arity++) { + if (body.indexOf("$" + arity) != -1) { + args.push("$" + arity); + } else { + break; + } + } + args = args.join(","); + var func = `(${args}) => { ${body} };`; + ASM_CONSTS[start] = eval(func); + } + __name(addEmAsm, "addEmAsm"); + if ("__start_em_asm" in moduleExports) { + var start = moduleExports["__start_em_asm"]; + var stop = moduleExports["__stop_em_asm"]; + while (start < stop) { + var jsString = UTF8ToString(start); + addEmAsm(start, jsString); + start = HEAPU8.indexOf(0, start) + 1; + } + } + function addEmJs(name, cSig, body) { + var jsArgs = []; + cSig = cSig.slice(1, -1); + if (cSig != "void") { + cSig = cSig.split(","); + for (var i in cSig) { + var jsArg = cSig[i].split(" ").pop(); + jsArgs.push(jsArg.replace("*", "")); + } + } + var func = `(${jsArgs}) => ${body};`; + moduleExports[name] = eval(func); + } + __name(addEmJs, "addEmJs"); + for (var name in moduleExports) { + if (name.startsWith("__em_js__")) { + var start = moduleExports[name]; + var jsString = UTF8ToString(start); + var parts = jsString.split("<::>"); + addEmJs(name.replace("__em_js__", ""), parts[0], parts[1]); + delete moduleExports[name]; + } + } + var applyRelocs = moduleExports["__wasm_apply_data_relocs"]; + if (applyRelocs) { + if (runtimeInitialized) { + applyRelocs(); + } else { + __RELOC_FUNCS__.push(applyRelocs); + } + } + var init = moduleExports["__wasm_call_ctors"]; + if (init) { + if (runtimeInitialized) { + init(); + } else { + addOnPostCtor(init); + } + } + return moduleExports; + } + __name(postInstantiation, "postInstantiation"); + if (flags.loadAsync) { + if (binary instanceof WebAssembly.Module) { + var instance = new WebAssembly.Instance(binary, info); + return Promise.resolve(postInstantiation(binary, instance)); + } + return WebAssembly.instantiate(binary, info).then((result) => postInstantiation(result.module, result.instance)); + } + var module = binary instanceof WebAssembly.Module ? binary : new WebAssembly.Module(binary); + var instance = new WebAssembly.Instance(module, info); + return postInstantiation(module, instance); + } + __name(loadModule, "loadModule"); + if (flags.loadAsync) { + return metadata.neededDynlibs.reduce((chain, dynNeeded) => chain.then(() => loadDynamicLibrary(dynNeeded, flags, localScope)), Promise.resolve()).then(loadModule); + } + metadata.neededDynlibs.forEach((needed) => loadDynamicLibrary(needed, flags, localScope)); + return loadModule(); + }, "loadWebAssemblyModule"); + var mergeLibSymbols = /* @__PURE__ */ __name((exports, libName2) => { + for (var [sym, exp] of Object.entries(exports)) { + const setImport = /* @__PURE__ */ __name((target) => { + if (!isSymbolDefined(target)) { + wasmImports[target] = exp; + } + }, "setImport"); + setImport(sym); + const main_alias = "__main_argc_argv"; + if (sym == "main") { + setImport(main_alias); + } + if (sym == main_alias) { + setImport("main"); + } + } + }, "mergeLibSymbols"); + var asyncLoad = /* @__PURE__ */ __name(async (url) => { + var arrayBuffer = await readAsync(url); + return new Uint8Array(arrayBuffer); + }, "asyncLoad"); + function loadDynamicLibrary(libName2, flags2 = { + global: true, + nodelete: true + }, localScope2, handle2) { + var dso = LDSO.loadedLibsByName[libName2]; + if (dso) { + if (!flags2.global) { + if (localScope2) { + Object.assign(localScope2, dso.exports); + } + } else if (!dso.global) { + dso.global = true; + mergeLibSymbols(dso.exports, libName2); + } + if (flags2.nodelete && dso.refcount !== Infinity) { + dso.refcount = Infinity; + } + dso.refcount++; + if (handle2) { + LDSO.loadedLibsByHandle[handle2] = dso; + } + return flags2.loadAsync ? Promise.resolve(true) : true; + } + dso = newDSO(libName2, handle2, "loading"); + dso.refcount = flags2.nodelete ? Infinity : 1; + dso.global = flags2.global; + function loadLibData() { + if (handle2) { + var data = LE_HEAP_LOAD_U32((handle2 + 28 >> 2) * 4); + var dataSize = LE_HEAP_LOAD_U32((handle2 + 32 >> 2) * 4); + if (data && dataSize) { + var libData = HEAP8.slice(data, data + dataSize); + return flags2.loadAsync ? Promise.resolve(libData) : libData; + } + } + var libFile = locateFile(libName2); + if (flags2.loadAsync) { + return asyncLoad(libFile); + } + if (!readBinary) { + throw new Error(`${libFile}: file not found, and synchronous loading of external files is not available`); + } + return readBinary(libFile); + } + __name(loadLibData, "loadLibData"); + function getExports() { + if (flags2.loadAsync) { + return loadLibData().then((libData) => loadWebAssemblyModule(libData, flags2, libName2, localScope2, handle2)); + } + return loadWebAssemblyModule(loadLibData(), flags2, libName2, localScope2, handle2); + } + __name(getExports, "getExports"); + function moduleLoaded(exports) { + if (dso.global) { + mergeLibSymbols(exports, libName2); + } else if (localScope2) { + Object.assign(localScope2, exports); + } + dso.exports = exports; + } + __name(moduleLoaded, "moduleLoaded"); + if (flags2.loadAsync) { + return getExports().then((exports) => { + moduleLoaded(exports); + return true; + }); + } + moduleLoaded(getExports()); + return true; + } + __name(loadDynamicLibrary, "loadDynamicLibrary"); + var reportUndefinedSymbols = /* @__PURE__ */ __name(() => { + for (var [symName, entry] of Object.entries(GOT)) { + if (entry.value == 0) { + var value = resolveGlobalSymbol(symName, true).sym; + if (!value && !entry.required) { + continue; + } + if (typeof value == "function") { + entry.value = addFunction(value, value.sig); + } else if (typeof value == "number") { + entry.value = value; + } else { + throw new Error(`bad export type for '${symName}': ${typeof value}`); + } + } + } + }, "reportUndefinedSymbols"); + var loadDylibs = /* @__PURE__ */ __name(() => { + if (!dynamicLibraries.length) { + reportUndefinedSymbols(); + return; + } + addRunDependency("loadDylibs"); + dynamicLibraries.reduce((chain, lib) => chain.then(() => loadDynamicLibrary(lib, { + loadAsync: true, + global: true, + nodelete: true, + allowUndefined: true + })), Promise.resolve()).then(() => { + reportUndefinedSymbols(); + removeRunDependency("loadDylibs"); + }); + }, "loadDylibs"); + var noExitRuntime = Module["noExitRuntime"] || true; + function setValue(ptr, value, type = "i8") { + if (type.endsWith("*")) type = "*"; + switch (type) { + case "i1": + HEAP8[ptr] = value; + break; + case "i8": + HEAP8[ptr] = value; + break; + case "i16": + LE_HEAP_STORE_I16((ptr >> 1) * 2, value); + break; + case "i32": + LE_HEAP_STORE_I32((ptr >> 2) * 4, value); + break; + case "i64": + HEAP64[ptr >> 3] = BigInt(value); + break; + case "float": + LE_HEAP_STORE_F32((ptr >> 2) * 4, value); + break; + case "double": + LE_HEAP_STORE_F64((ptr >> 3) * 8, value); + break; + case "*": + LE_HEAP_STORE_U32((ptr >> 2) * 4, value); + break; + default: + abort(`invalid type for setValue: ${type}`); + } + } + __name(setValue, "setValue"); + var ___memory_base = new WebAssembly.Global({ + "value": "i32", + "mutable": false + }, 1024); + var ___stack_pointer = new WebAssembly.Global({ + "value": "i32", + "mutable": true + }, 78224); + var ___table_base = new WebAssembly.Global({ + "value": "i32", + "mutable": false + }, 1); + var __abort_js = /* @__PURE__ */ __name(() => abort(""), "__abort_js"); + __abort_js.sig = "v"; + var _emscripten_get_now = /* @__PURE__ */ __name(() => performance.now(), "_emscripten_get_now"); + _emscripten_get_now.sig = "d"; + var _emscripten_date_now = /* @__PURE__ */ __name(() => Date.now(), "_emscripten_date_now"); + _emscripten_date_now.sig = "d"; + var nowIsMonotonic = 1; + var checkWasiClock = /* @__PURE__ */ __name((clock_id) => clock_id >= 0 && clock_id <= 3, "checkWasiClock"); + var INT53_MAX = 9007199254740992; + var INT53_MIN = -9007199254740992; + var bigintToI53Checked = /* @__PURE__ */ __name((num) => num < INT53_MIN || num > INT53_MAX ? NaN : Number(num), "bigintToI53Checked"); + function _clock_time_get(clk_id, ignored_precision, ptime) { + ignored_precision = bigintToI53Checked(ignored_precision); + if (!checkWasiClock(clk_id)) { + return 28; + } + var now; + if (clk_id === 0) { + now = _emscripten_date_now(); + } else if (nowIsMonotonic) { + now = _emscripten_get_now(); + } else { + return 52; + } + var nsec = Math.round(now * 1e3 * 1e3); + HEAP64[ptime >> 3] = BigInt(nsec); + return 0; + } + __name(_clock_time_get, "_clock_time_get"); + _clock_time_get.sig = "iijp"; + var getHeapMax = /* @__PURE__ */ __name(() => ( + // Stay one Wasm page short of 4GB: while e.g. Chrome is able to allocate + // full 4GB Wasm memories, the size will wrap back to 0 bytes in Wasm side + // for any code that deals with heap sizes, which would require special + // casing all heap size related code to treat 0 specially. + 2147483648 + ), "getHeapMax"); + var growMemory = /* @__PURE__ */ __name((size) => { + var b = wasmMemory.buffer; + var pages = (size - b.byteLength + 65535) / 65536 | 0; + try { + wasmMemory.grow(pages); + updateMemoryViews(); + return 1; + } catch (e) { + } + }, "growMemory"); + var _emscripten_resize_heap = /* @__PURE__ */ __name((requestedSize) => { + var oldSize = HEAPU8.length; + requestedSize >>>= 0; + var maxHeapSize = getHeapMax(); + if (requestedSize > maxHeapSize) { + return false; + } + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignMemory(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = growMemory(newSize); + if (replacement) { + return true; + } + } + return false; + }, "_emscripten_resize_heap"); + _emscripten_resize_heap.sig = "ip"; + var _fd_close = /* @__PURE__ */ __name((fd) => 52, "_fd_close"); + _fd_close.sig = "ii"; + function _fd_seek(fd, offset, whence, newOffset) { + offset = bigintToI53Checked(offset); + return 70; + } + __name(_fd_seek, "_fd_seek"); + _fd_seek.sig = "iijip"; + var printCharBuffers = [null, [], []]; + var printChar = /* @__PURE__ */ __name((stream, curr) => { + var buffer = printCharBuffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer)); + buffer.length = 0; + } else { + buffer.push(curr); + } + }, "printChar"); + var flush_NO_FILESYSTEM = /* @__PURE__ */ __name(() => { + if (printCharBuffers[1].length) printChar(1, 10); + if (printCharBuffers[2].length) printChar(2, 10); + }, "flush_NO_FILESYSTEM"); + var SYSCALLS = { + varargs: void 0, + getStr(ptr) { + var ret = UTF8ToString(ptr); + return ret; + } + }; + var _fd_write = /* @__PURE__ */ __name((fd, iov, iovcnt, pnum) => { + var num = 0; + for (var i2 = 0; i2 < iovcnt; i2++) { + var ptr = LE_HEAP_LOAD_U32((iov >> 2) * 4); + var len = LE_HEAP_LOAD_U32((iov + 4 >> 2) * 4); + iov += 8; + for (var j = 0; j < len; j++) { + printChar(fd, HEAPU8[ptr + j]); + } + num += len; + } + LE_HEAP_STORE_U32((pnum >> 2) * 4, num); + return 0; + }, "_fd_write"); + _fd_write.sig = "iippp"; + function _tree_sitter_log_callback(isLexMessage, messageAddress) { + if (Module.currentLogCallback) { + const message = UTF8ToString(messageAddress); + Module.currentLogCallback(message, isLexMessage !== 0); + } + } + __name(_tree_sitter_log_callback, "_tree_sitter_log_callback"); + function _tree_sitter_parse_callback(inputBufferAddress, index, row, column, lengthAddress) { + const INPUT_BUFFER_SIZE = 10 * 1024; + const string = Module.currentParseCallback(index, { + row, + column + }); + if (typeof string === "string") { + setValue(lengthAddress, string.length, "i32"); + stringToUTF16(string, inputBufferAddress, INPUT_BUFFER_SIZE); + } else { + setValue(lengthAddress, 0, "i32"); + } + } + __name(_tree_sitter_parse_callback, "_tree_sitter_parse_callback"); + function _tree_sitter_progress_callback(currentOffset, hasError) { + if (Module.currentProgressCallback) { + return Module.currentProgressCallback({ + currentOffset, + hasError + }); + } + return false; + } + __name(_tree_sitter_progress_callback, "_tree_sitter_progress_callback"); + function _tree_sitter_query_progress_callback(currentOffset) { + if (Module.currentQueryProgressCallback) { + return Module.currentQueryProgressCallback({ + currentOffset + }); + } + return false; + } + __name(_tree_sitter_query_progress_callback, "_tree_sitter_query_progress_callback"); + var runtimeKeepaliveCounter = 0; + var keepRuntimeAlive = /* @__PURE__ */ __name(() => noExitRuntime || runtimeKeepaliveCounter > 0, "keepRuntimeAlive"); + var _proc_exit = /* @__PURE__ */ __name((code) => { + EXITSTATUS = code; + if (!keepRuntimeAlive()) { + Module["onExit"]?.(code); + ABORT = true; + } + quit_(code, new ExitStatus(code)); + }, "_proc_exit"); + _proc_exit.sig = "vi"; + var exitJS = /* @__PURE__ */ __name((status, implicit) => { + EXITSTATUS = status; + _proc_exit(status); + }, "exitJS"); + var handleException = /* @__PURE__ */ __name((e) => { + if (e instanceof ExitStatus || e == "unwind") { + return EXITSTATUS; + } + quit_(1, e); + }, "handleException"); + var lengthBytesUTF8 = /* @__PURE__ */ __name((str) => { + var len = 0; + for (var i2 = 0; i2 < str.length; ++i2) { + var c = str.charCodeAt(i2); + if (c <= 127) { + len++; + } else if (c <= 2047) { + len += 2; + } else if (c >= 55296 && c <= 57343) { + len += 4; + ++i2; + } else { + len += 3; + } + } + return len; + }, "lengthBytesUTF8"); + var stringToUTF8Array = /* @__PURE__ */ __name((str, heap, outIdx, maxBytesToWrite) => { + if (!(maxBytesToWrite > 0)) return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i2 = 0; i2 < str.length; ++i2) { + var u = str.charCodeAt(i2); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i2); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) break; + heap[outIdx++] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) break; + heap[outIdx++] = 192 | u >> 6; + heap[outIdx++] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) break; + heap[outIdx++] = 224 | u >> 12; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) break; + heap[outIdx++] = 240 | u >> 18; + heap[outIdx++] = 128 | u >> 12 & 63; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } + } + heap[outIdx] = 0; + return outIdx - startIdx; + }, "stringToUTF8Array"); + var stringToUTF8 = /* @__PURE__ */ __name((str, outPtr, maxBytesToWrite) => stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite), "stringToUTF8"); + var stackAlloc = /* @__PURE__ */ __name((sz) => __emscripten_stack_alloc(sz), "stackAlloc"); + var stringToUTF8OnStack = /* @__PURE__ */ __name((str) => { + var size = lengthBytesUTF8(str) + 1; + var ret = stackAlloc(size); + stringToUTF8(str, ret, size); + return ret; + }, "stringToUTF8OnStack"); + var AsciiToString = /* @__PURE__ */ __name((ptr) => { + var str = ""; + while (1) { + var ch = HEAPU8[ptr++]; + if (!ch) return str; + str += String.fromCharCode(ch); + } + }, "AsciiToString"); + var stringToUTF16 = /* @__PURE__ */ __name((str, outPtr, maxBytesToWrite) => { + maxBytesToWrite ??= 2147483647; + if (maxBytesToWrite < 2) return 0; + maxBytesToWrite -= 2; + var startPtr = outPtr; + var numCharsToWrite = maxBytesToWrite < str.length * 2 ? maxBytesToWrite / 2 : str.length; + for (var i2 = 0; i2 < numCharsToWrite; ++i2) { + var codeUnit = str.charCodeAt(i2); + LE_HEAP_STORE_I16((outPtr >> 1) * 2, codeUnit); + outPtr += 2; + } + LE_HEAP_STORE_I16((outPtr >> 1) * 2, 0); + return outPtr - startPtr; + }, "stringToUTF16"); + var wasmImports = { + /** @export */ + __heap_base: ___heap_base, + /** @export */ + __indirect_function_table: wasmTable, + /** @export */ + __memory_base: ___memory_base, + /** @export */ + __stack_pointer: ___stack_pointer, + /** @export */ + __table_base: ___table_base, + /** @export */ + _abort_js: __abort_js, + /** @export */ + clock_time_get: _clock_time_get, + /** @export */ + emscripten_resize_heap: _emscripten_resize_heap, + /** @export */ + fd_close: _fd_close, + /** @export */ + fd_seek: _fd_seek, + /** @export */ + fd_write: _fd_write, + /** @export */ + memory: wasmMemory, + /** @export */ + tree_sitter_log_callback: _tree_sitter_log_callback, + /** @export */ + tree_sitter_parse_callback: _tree_sitter_parse_callback, + /** @export */ + tree_sitter_progress_callback: _tree_sitter_progress_callback, + /** @export */ + tree_sitter_query_progress_callback: _tree_sitter_query_progress_callback + }; + var wasmExports = await createWasm(); + var ___wasm_call_ctors = wasmExports["__wasm_call_ctors"]; + var _malloc = Module["_malloc"] = wasmExports["malloc"]; + var _calloc = Module["_calloc"] = wasmExports["calloc"]; + var _realloc = Module["_realloc"] = wasmExports["realloc"]; + var _free = Module["_free"] = wasmExports["free"]; + var _memcmp = Module["_memcmp"] = wasmExports["memcmp"]; + var _ts_language_symbol_count = Module["_ts_language_symbol_count"] = wasmExports["ts_language_symbol_count"]; + var _ts_language_state_count = Module["_ts_language_state_count"] = wasmExports["ts_language_state_count"]; + var _ts_language_version = Module["_ts_language_version"] = wasmExports["ts_language_version"]; + var _ts_language_abi_version = Module["_ts_language_abi_version"] = wasmExports["ts_language_abi_version"]; + var _ts_language_metadata = Module["_ts_language_metadata"] = wasmExports["ts_language_metadata"]; + var _ts_language_name = Module["_ts_language_name"] = wasmExports["ts_language_name"]; + var _ts_language_field_count = Module["_ts_language_field_count"] = wasmExports["ts_language_field_count"]; + var _ts_language_next_state = Module["_ts_language_next_state"] = wasmExports["ts_language_next_state"]; + var _ts_language_symbol_name = Module["_ts_language_symbol_name"] = wasmExports["ts_language_symbol_name"]; + var _ts_language_symbol_for_name = Module["_ts_language_symbol_for_name"] = wasmExports["ts_language_symbol_for_name"]; + var _strncmp = Module["_strncmp"] = wasmExports["strncmp"]; + var _ts_language_symbol_type = Module["_ts_language_symbol_type"] = wasmExports["ts_language_symbol_type"]; + var _ts_language_field_name_for_id = Module["_ts_language_field_name_for_id"] = wasmExports["ts_language_field_name_for_id"]; + var _ts_lookahead_iterator_new = Module["_ts_lookahead_iterator_new"] = wasmExports["ts_lookahead_iterator_new"]; + var _ts_lookahead_iterator_delete = Module["_ts_lookahead_iterator_delete"] = wasmExports["ts_lookahead_iterator_delete"]; + var _ts_lookahead_iterator_reset_state = Module["_ts_lookahead_iterator_reset_state"] = wasmExports["ts_lookahead_iterator_reset_state"]; + var _ts_lookahead_iterator_reset = Module["_ts_lookahead_iterator_reset"] = wasmExports["ts_lookahead_iterator_reset"]; + var _ts_lookahead_iterator_next = Module["_ts_lookahead_iterator_next"] = wasmExports["ts_lookahead_iterator_next"]; + var _ts_lookahead_iterator_current_symbol = Module["_ts_lookahead_iterator_current_symbol"] = wasmExports["ts_lookahead_iterator_current_symbol"]; + var _ts_parser_delete = Module["_ts_parser_delete"] = wasmExports["ts_parser_delete"]; + var _ts_parser_reset = Module["_ts_parser_reset"] = wasmExports["ts_parser_reset"]; + var _ts_parser_set_language = Module["_ts_parser_set_language"] = wasmExports["ts_parser_set_language"]; + var _ts_parser_timeout_micros = Module["_ts_parser_timeout_micros"] = wasmExports["ts_parser_timeout_micros"]; + var _ts_parser_set_timeout_micros = Module["_ts_parser_set_timeout_micros"] = wasmExports["ts_parser_set_timeout_micros"]; + var _ts_parser_set_included_ranges = Module["_ts_parser_set_included_ranges"] = wasmExports["ts_parser_set_included_ranges"]; + var _ts_query_new = Module["_ts_query_new"] = wasmExports["ts_query_new"]; + var _ts_query_delete = Module["_ts_query_delete"] = wasmExports["ts_query_delete"]; + var _iswspace = Module["_iswspace"] = wasmExports["iswspace"]; + var _iswalnum = Module["_iswalnum"] = wasmExports["iswalnum"]; + var _ts_query_pattern_count = Module["_ts_query_pattern_count"] = wasmExports["ts_query_pattern_count"]; + var _ts_query_capture_count = Module["_ts_query_capture_count"] = wasmExports["ts_query_capture_count"]; + var _ts_query_string_count = Module["_ts_query_string_count"] = wasmExports["ts_query_string_count"]; + var _ts_query_capture_name_for_id = Module["_ts_query_capture_name_for_id"] = wasmExports["ts_query_capture_name_for_id"]; + var _ts_query_capture_quantifier_for_id = Module["_ts_query_capture_quantifier_for_id"] = wasmExports["ts_query_capture_quantifier_for_id"]; + var _ts_query_string_value_for_id = Module["_ts_query_string_value_for_id"] = wasmExports["ts_query_string_value_for_id"]; + var _ts_query_predicates_for_pattern = Module["_ts_query_predicates_for_pattern"] = wasmExports["ts_query_predicates_for_pattern"]; + var _ts_query_start_byte_for_pattern = Module["_ts_query_start_byte_for_pattern"] = wasmExports["ts_query_start_byte_for_pattern"]; + var _ts_query_end_byte_for_pattern = Module["_ts_query_end_byte_for_pattern"] = wasmExports["ts_query_end_byte_for_pattern"]; + var _ts_query_is_pattern_rooted = Module["_ts_query_is_pattern_rooted"] = wasmExports["ts_query_is_pattern_rooted"]; + var _ts_query_is_pattern_non_local = Module["_ts_query_is_pattern_non_local"] = wasmExports["ts_query_is_pattern_non_local"]; + var _ts_query_is_pattern_guaranteed_at_step = Module["_ts_query_is_pattern_guaranteed_at_step"] = wasmExports["ts_query_is_pattern_guaranteed_at_step"]; + var _ts_query_disable_capture = Module["_ts_query_disable_capture"] = wasmExports["ts_query_disable_capture"]; + var _ts_query_disable_pattern = Module["_ts_query_disable_pattern"] = wasmExports["ts_query_disable_pattern"]; + var _ts_tree_copy = Module["_ts_tree_copy"] = wasmExports["ts_tree_copy"]; + var _ts_tree_delete = Module["_ts_tree_delete"] = wasmExports["ts_tree_delete"]; + var _ts_init = Module["_ts_init"] = wasmExports["ts_init"]; + var _ts_parser_new_wasm = Module["_ts_parser_new_wasm"] = wasmExports["ts_parser_new_wasm"]; + var _ts_parser_enable_logger_wasm = Module["_ts_parser_enable_logger_wasm"] = wasmExports["ts_parser_enable_logger_wasm"]; + var _ts_parser_parse_wasm = Module["_ts_parser_parse_wasm"] = wasmExports["ts_parser_parse_wasm"]; + var _ts_parser_included_ranges_wasm = Module["_ts_parser_included_ranges_wasm"] = wasmExports["ts_parser_included_ranges_wasm"]; + var _ts_language_type_is_named_wasm = Module["_ts_language_type_is_named_wasm"] = wasmExports["ts_language_type_is_named_wasm"]; + var _ts_language_type_is_visible_wasm = Module["_ts_language_type_is_visible_wasm"] = wasmExports["ts_language_type_is_visible_wasm"]; + var _ts_language_supertypes_wasm = Module["_ts_language_supertypes_wasm"] = wasmExports["ts_language_supertypes_wasm"]; + var _ts_language_subtypes_wasm = Module["_ts_language_subtypes_wasm"] = wasmExports["ts_language_subtypes_wasm"]; + var _ts_tree_root_node_wasm = Module["_ts_tree_root_node_wasm"] = wasmExports["ts_tree_root_node_wasm"]; + var _ts_tree_root_node_with_offset_wasm = Module["_ts_tree_root_node_with_offset_wasm"] = wasmExports["ts_tree_root_node_with_offset_wasm"]; + var _ts_tree_edit_wasm = Module["_ts_tree_edit_wasm"] = wasmExports["ts_tree_edit_wasm"]; + var _ts_tree_included_ranges_wasm = Module["_ts_tree_included_ranges_wasm"] = wasmExports["ts_tree_included_ranges_wasm"]; + var _ts_tree_get_changed_ranges_wasm = Module["_ts_tree_get_changed_ranges_wasm"] = wasmExports["ts_tree_get_changed_ranges_wasm"]; + var _ts_tree_cursor_new_wasm = Module["_ts_tree_cursor_new_wasm"] = wasmExports["ts_tree_cursor_new_wasm"]; + var _ts_tree_cursor_copy_wasm = Module["_ts_tree_cursor_copy_wasm"] = wasmExports["ts_tree_cursor_copy_wasm"]; + var _ts_tree_cursor_delete_wasm = Module["_ts_tree_cursor_delete_wasm"] = wasmExports["ts_tree_cursor_delete_wasm"]; + var _ts_tree_cursor_reset_wasm = Module["_ts_tree_cursor_reset_wasm"] = wasmExports["ts_tree_cursor_reset_wasm"]; + var _ts_tree_cursor_reset_to_wasm = Module["_ts_tree_cursor_reset_to_wasm"] = wasmExports["ts_tree_cursor_reset_to_wasm"]; + var _ts_tree_cursor_goto_first_child_wasm = Module["_ts_tree_cursor_goto_first_child_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_wasm"]; + var _ts_tree_cursor_goto_last_child_wasm = Module["_ts_tree_cursor_goto_last_child_wasm"] = wasmExports["ts_tree_cursor_goto_last_child_wasm"]; + var _ts_tree_cursor_goto_first_child_for_index_wasm = Module["_ts_tree_cursor_goto_first_child_for_index_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_for_index_wasm"]; + var _ts_tree_cursor_goto_first_child_for_position_wasm = Module["_ts_tree_cursor_goto_first_child_for_position_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_for_position_wasm"]; + var _ts_tree_cursor_goto_next_sibling_wasm = Module["_ts_tree_cursor_goto_next_sibling_wasm"] = wasmExports["ts_tree_cursor_goto_next_sibling_wasm"]; + var _ts_tree_cursor_goto_previous_sibling_wasm = Module["_ts_tree_cursor_goto_previous_sibling_wasm"] = wasmExports["ts_tree_cursor_goto_previous_sibling_wasm"]; + var _ts_tree_cursor_goto_descendant_wasm = Module["_ts_tree_cursor_goto_descendant_wasm"] = wasmExports["ts_tree_cursor_goto_descendant_wasm"]; + var _ts_tree_cursor_goto_parent_wasm = Module["_ts_tree_cursor_goto_parent_wasm"] = wasmExports["ts_tree_cursor_goto_parent_wasm"]; + var _ts_tree_cursor_current_node_type_id_wasm = Module["_ts_tree_cursor_current_node_type_id_wasm"] = wasmExports["ts_tree_cursor_current_node_type_id_wasm"]; + var _ts_tree_cursor_current_node_state_id_wasm = Module["_ts_tree_cursor_current_node_state_id_wasm"] = wasmExports["ts_tree_cursor_current_node_state_id_wasm"]; + var _ts_tree_cursor_current_node_is_named_wasm = Module["_ts_tree_cursor_current_node_is_named_wasm"] = wasmExports["ts_tree_cursor_current_node_is_named_wasm"]; + var _ts_tree_cursor_current_node_is_missing_wasm = Module["_ts_tree_cursor_current_node_is_missing_wasm"] = wasmExports["ts_tree_cursor_current_node_is_missing_wasm"]; + var _ts_tree_cursor_current_node_id_wasm = Module["_ts_tree_cursor_current_node_id_wasm"] = wasmExports["ts_tree_cursor_current_node_id_wasm"]; + var _ts_tree_cursor_start_position_wasm = Module["_ts_tree_cursor_start_position_wasm"] = wasmExports["ts_tree_cursor_start_position_wasm"]; + var _ts_tree_cursor_end_position_wasm = Module["_ts_tree_cursor_end_position_wasm"] = wasmExports["ts_tree_cursor_end_position_wasm"]; + var _ts_tree_cursor_start_index_wasm = Module["_ts_tree_cursor_start_index_wasm"] = wasmExports["ts_tree_cursor_start_index_wasm"]; + var _ts_tree_cursor_end_index_wasm = Module["_ts_tree_cursor_end_index_wasm"] = wasmExports["ts_tree_cursor_end_index_wasm"]; + var _ts_tree_cursor_current_field_id_wasm = Module["_ts_tree_cursor_current_field_id_wasm"] = wasmExports["ts_tree_cursor_current_field_id_wasm"]; + var _ts_tree_cursor_current_depth_wasm = Module["_ts_tree_cursor_current_depth_wasm"] = wasmExports["ts_tree_cursor_current_depth_wasm"]; + var _ts_tree_cursor_current_descendant_index_wasm = Module["_ts_tree_cursor_current_descendant_index_wasm"] = wasmExports["ts_tree_cursor_current_descendant_index_wasm"]; + var _ts_tree_cursor_current_node_wasm = Module["_ts_tree_cursor_current_node_wasm"] = wasmExports["ts_tree_cursor_current_node_wasm"]; + var _ts_node_symbol_wasm = Module["_ts_node_symbol_wasm"] = wasmExports["ts_node_symbol_wasm"]; + var _ts_node_field_name_for_child_wasm = Module["_ts_node_field_name_for_child_wasm"] = wasmExports["ts_node_field_name_for_child_wasm"]; + var _ts_node_field_name_for_named_child_wasm = Module["_ts_node_field_name_for_named_child_wasm"] = wasmExports["ts_node_field_name_for_named_child_wasm"]; + var _ts_node_children_by_field_id_wasm = Module["_ts_node_children_by_field_id_wasm"] = wasmExports["ts_node_children_by_field_id_wasm"]; + var _ts_node_first_child_for_byte_wasm = Module["_ts_node_first_child_for_byte_wasm"] = wasmExports["ts_node_first_child_for_byte_wasm"]; + var _ts_node_first_named_child_for_byte_wasm = Module["_ts_node_first_named_child_for_byte_wasm"] = wasmExports["ts_node_first_named_child_for_byte_wasm"]; + var _ts_node_grammar_symbol_wasm = Module["_ts_node_grammar_symbol_wasm"] = wasmExports["ts_node_grammar_symbol_wasm"]; + var _ts_node_child_count_wasm = Module["_ts_node_child_count_wasm"] = wasmExports["ts_node_child_count_wasm"]; + var _ts_node_named_child_count_wasm = Module["_ts_node_named_child_count_wasm"] = wasmExports["ts_node_named_child_count_wasm"]; + var _ts_node_child_wasm = Module["_ts_node_child_wasm"] = wasmExports["ts_node_child_wasm"]; + var _ts_node_named_child_wasm = Module["_ts_node_named_child_wasm"] = wasmExports["ts_node_named_child_wasm"]; + var _ts_node_child_by_field_id_wasm = Module["_ts_node_child_by_field_id_wasm"] = wasmExports["ts_node_child_by_field_id_wasm"]; + var _ts_node_next_sibling_wasm = Module["_ts_node_next_sibling_wasm"] = wasmExports["ts_node_next_sibling_wasm"]; + var _ts_node_prev_sibling_wasm = Module["_ts_node_prev_sibling_wasm"] = wasmExports["ts_node_prev_sibling_wasm"]; + var _ts_node_next_named_sibling_wasm = Module["_ts_node_next_named_sibling_wasm"] = wasmExports["ts_node_next_named_sibling_wasm"]; + var _ts_node_prev_named_sibling_wasm = Module["_ts_node_prev_named_sibling_wasm"] = wasmExports["ts_node_prev_named_sibling_wasm"]; + var _ts_node_descendant_count_wasm = Module["_ts_node_descendant_count_wasm"] = wasmExports["ts_node_descendant_count_wasm"]; + var _ts_node_parent_wasm = Module["_ts_node_parent_wasm"] = wasmExports["ts_node_parent_wasm"]; + var _ts_node_child_with_descendant_wasm = Module["_ts_node_child_with_descendant_wasm"] = wasmExports["ts_node_child_with_descendant_wasm"]; + var _ts_node_descendant_for_index_wasm = Module["_ts_node_descendant_for_index_wasm"] = wasmExports["ts_node_descendant_for_index_wasm"]; + var _ts_node_named_descendant_for_index_wasm = Module["_ts_node_named_descendant_for_index_wasm"] = wasmExports["ts_node_named_descendant_for_index_wasm"]; + var _ts_node_descendant_for_position_wasm = Module["_ts_node_descendant_for_position_wasm"] = wasmExports["ts_node_descendant_for_position_wasm"]; + var _ts_node_named_descendant_for_position_wasm = Module["_ts_node_named_descendant_for_position_wasm"] = wasmExports["ts_node_named_descendant_for_position_wasm"]; + var _ts_node_start_point_wasm = Module["_ts_node_start_point_wasm"] = wasmExports["ts_node_start_point_wasm"]; + var _ts_node_end_point_wasm = Module["_ts_node_end_point_wasm"] = wasmExports["ts_node_end_point_wasm"]; + var _ts_node_start_index_wasm = Module["_ts_node_start_index_wasm"] = wasmExports["ts_node_start_index_wasm"]; + var _ts_node_end_index_wasm = Module["_ts_node_end_index_wasm"] = wasmExports["ts_node_end_index_wasm"]; + var _ts_node_to_string_wasm = Module["_ts_node_to_string_wasm"] = wasmExports["ts_node_to_string_wasm"]; + var _ts_node_children_wasm = Module["_ts_node_children_wasm"] = wasmExports["ts_node_children_wasm"]; + var _ts_node_named_children_wasm = Module["_ts_node_named_children_wasm"] = wasmExports["ts_node_named_children_wasm"]; + var _ts_node_descendants_of_type_wasm = Module["_ts_node_descendants_of_type_wasm"] = wasmExports["ts_node_descendants_of_type_wasm"]; + var _ts_node_is_named_wasm = Module["_ts_node_is_named_wasm"] = wasmExports["ts_node_is_named_wasm"]; + var _ts_node_has_changes_wasm = Module["_ts_node_has_changes_wasm"] = wasmExports["ts_node_has_changes_wasm"]; + var _ts_node_has_error_wasm = Module["_ts_node_has_error_wasm"] = wasmExports["ts_node_has_error_wasm"]; + var _ts_node_is_error_wasm = Module["_ts_node_is_error_wasm"] = wasmExports["ts_node_is_error_wasm"]; + var _ts_node_is_missing_wasm = Module["_ts_node_is_missing_wasm"] = wasmExports["ts_node_is_missing_wasm"]; + var _ts_node_is_extra_wasm = Module["_ts_node_is_extra_wasm"] = wasmExports["ts_node_is_extra_wasm"]; + var _ts_node_parse_state_wasm = Module["_ts_node_parse_state_wasm"] = wasmExports["ts_node_parse_state_wasm"]; + var _ts_node_next_parse_state_wasm = Module["_ts_node_next_parse_state_wasm"] = wasmExports["ts_node_next_parse_state_wasm"]; + var _ts_query_matches_wasm = Module["_ts_query_matches_wasm"] = wasmExports["ts_query_matches_wasm"]; + var _ts_query_captures_wasm = Module["_ts_query_captures_wasm"] = wasmExports["ts_query_captures_wasm"]; + var _memset = Module["_memset"] = wasmExports["memset"]; + var _memcpy = Module["_memcpy"] = wasmExports["memcpy"]; + var _memmove = Module["_memmove"] = wasmExports["memmove"]; + var _iswalpha = Module["_iswalpha"] = wasmExports["iswalpha"]; + var _iswblank = Module["_iswblank"] = wasmExports["iswblank"]; + var _iswdigit = Module["_iswdigit"] = wasmExports["iswdigit"]; + var _iswlower = Module["_iswlower"] = wasmExports["iswlower"]; + var _iswupper = Module["_iswupper"] = wasmExports["iswupper"]; + var _iswxdigit = Module["_iswxdigit"] = wasmExports["iswxdigit"]; + var _memchr = Module["_memchr"] = wasmExports["memchr"]; + var _strlen = Module["_strlen"] = wasmExports["strlen"]; + var _strcmp = Module["_strcmp"] = wasmExports["strcmp"]; + var _strncat = Module["_strncat"] = wasmExports["strncat"]; + var _strncpy = Module["_strncpy"] = wasmExports["strncpy"]; + var _towlower = Module["_towlower"] = wasmExports["towlower"]; + var _towupper = Module["_towupper"] = wasmExports["towupper"]; + var _setThrew = wasmExports["setThrew"]; + var __emscripten_stack_restore = wasmExports["_emscripten_stack_restore"]; + var __emscripten_stack_alloc = wasmExports["_emscripten_stack_alloc"]; + var _emscripten_stack_get_current = wasmExports["emscripten_stack_get_current"]; + var ___wasm_apply_data_relocs = wasmExports["__wasm_apply_data_relocs"]; + Module["setValue"] = setValue; + Module["getValue"] = getValue; + Module["UTF8ToString"] = UTF8ToString; + Module["stringToUTF8"] = stringToUTF8; + Module["lengthBytesUTF8"] = lengthBytesUTF8; + Module["AsciiToString"] = AsciiToString; + Module["stringToUTF16"] = stringToUTF16; + Module["loadWebAssemblyModule"] = loadWebAssemblyModule; + function callMain(args2 = []) { + var entryFunction = resolveGlobalSymbol("main").sym; + if (!entryFunction) return; + args2.unshift(thisProgram); + var argc = args2.length; + var argv = stackAlloc((argc + 1) * 4); + var argv_ptr = argv; + args2.forEach((arg) => { + LE_HEAP_STORE_U32((argv_ptr >> 2) * 4, stringToUTF8OnStack(arg)); + argv_ptr += 4; + }); + LE_HEAP_STORE_U32((argv_ptr >> 2) * 4, 0); + try { + var ret = entryFunction(argc, argv); + exitJS( + ret, + /* implicit = */ + true + ); + return ret; + } catch (e) { + return handleException(e); + } + } + __name(callMain, "callMain"); + function run(args2 = arguments_) { + if (runDependencies > 0) { + dependenciesFulfilled = run; + return; + } + preRun(); + if (runDependencies > 0) { + dependenciesFulfilled = run; + return; + } + function doRun() { + Module["calledRun"] = true; + if (ABORT) return; + initRuntime(); + preMain(); + readyPromiseResolve(Module); + Module["onRuntimeInitialized"]?.(); + var noInitialRun = Module["noInitialRun"]; + if (!noInitialRun) callMain(args2); + postRun(); + } + __name(doRun, "doRun"); + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(() => { + setTimeout(() => Module["setStatus"](""), 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + __name(run, "run"); + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + moduleRtn = readyPromise; + return moduleRtn; + }; +})(); +var tree_sitter_default = Module2; + +// src/bindings.ts +var Module3 = null; +async function initializeBinding(moduleOptions) { + if (!Module3) { + Module3 = await tree_sitter_default(moduleOptions); + } + return Module3; +} +__name(initializeBinding, "initializeBinding"); +function checkModule() { + return !!Module3; +} +__name(checkModule, "checkModule"); + +// src/parser.ts +var TRANSFER_BUFFER; +var LANGUAGE_VERSION; +var MIN_COMPATIBLE_VERSION; +var Parser = class { + static { + __name(this, "Parser"); + } + /** @internal */ + [0] = 0; + // Internal handle for WASM + /** @internal */ + [1] = 0; + // Internal handle for WASM + /** @internal */ + logCallback = null; + /** The parser's current language. */ + language = null; + /** + * This must always be called before creating a Parser. + * + * You can optionally pass in options to configure the WASM module, the most common + * one being `locateFile` to help the module find the `.wasm` file. + */ + static async init(moduleOptions) { + setModule(await initializeBinding(moduleOptions)); + TRANSFER_BUFFER = C._ts_init(); + LANGUAGE_VERSION = C.getValue(TRANSFER_BUFFER, "i32"); + MIN_COMPATIBLE_VERSION = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + } + /** + * Create a new parser. + */ + constructor() { + this.initialize(); + } + /** @internal */ + initialize() { + if (!checkModule()) { + throw new Error("cannot construct a Parser before calling `init()`"); + } + C._ts_parser_new_wasm(); + this[0] = C.getValue(TRANSFER_BUFFER, "i32"); + this[1] = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + } + /** Delete the parser, freeing its resources. */ + delete() { + C._ts_parser_delete(this[0]); + C._free(this[1]); + this[0] = 0; + this[1] = 0; + } + /** + * Set the language that the parser should use for parsing. + * + * If the language was not successfully assigned, an error will be thrown. + * This happens if the language was generated with an incompatible + * version of the Tree-sitter CLI. Check the language's version using + * {@link Language#version} and compare it to this library's + * {@link LANGUAGE_VERSION} and {@link MIN_COMPATIBLE_VERSION} constants. + */ + setLanguage(language) { + let address; + if (!language) { + address = 0; + this.language = null; + } else if (language.constructor === Language) { + address = language[0]; + const version = C._ts_language_version(address); + if (version < MIN_COMPATIBLE_VERSION || LANGUAGE_VERSION < version) { + throw new Error( + `Incompatible language version ${version}. Compatibility range ${MIN_COMPATIBLE_VERSION} through ${LANGUAGE_VERSION}.` + ); + } + this.language = language; + } else { + throw new Error("Argument must be a Language"); + } + C._ts_parser_set_language(this[0], address); + return this; + } + /** + * Parse a slice of UTF8 text. + * + * @param {string | ParseCallback} callback - The UTF8-encoded text to parse or a callback function. + * + * @param {Tree | null} [oldTree] - A previous syntax tree parsed from the same document. If the text of the + * document has changed since `oldTree` was created, then you must edit `oldTree` to match + * the new text using {@link Tree#edit}. + * + * @param {ParseOptions} [options] - Options for parsing the text. + * This can be used to set the included ranges, or a progress callback. + * + * @returns {Tree | null} A {@link Tree} if parsing succeeded, or `null` if: + * - The parser has not yet had a language assigned with {@link Parser#setLanguage}. + * - The progress callback returned true. + */ + parse(callback, oldTree, options) { + if (typeof callback === "string") { + C.currentParseCallback = (index) => callback.slice(index); + } else if (typeof callback === "function") { + C.currentParseCallback = callback; + } else { + throw new Error("Argument must be a string or a function"); + } + if (options?.progressCallback) { + C.currentProgressCallback = options.progressCallback; + } else { + C.currentProgressCallback = null; + } + if (this.logCallback) { + C.currentLogCallback = this.logCallback; + C._ts_parser_enable_logger_wasm(this[0], 1); + } else { + C.currentLogCallback = null; + C._ts_parser_enable_logger_wasm(this[0], 0); + } + let rangeCount = 0; + let rangeAddress = 0; + if (options?.includedRanges) { + rangeCount = options.includedRanges.length; + rangeAddress = C._calloc(rangeCount, SIZE_OF_RANGE); + let address = rangeAddress; + for (let i2 = 0; i2 < rangeCount; i2++) { + marshalRange(address, options.includedRanges[i2]); + address += SIZE_OF_RANGE; + } + } + const treeAddress = C._ts_parser_parse_wasm( + this[0], + this[1], + oldTree ? oldTree[0] : 0, + rangeAddress, + rangeCount + ); + if (!treeAddress) { + C.currentParseCallback = null; + C.currentLogCallback = null; + C.currentProgressCallback = null; + return null; + } + if (!this.language) { + throw new Error("Parser must have a language to parse"); + } + const result = new Tree(INTERNAL, treeAddress, this.language, C.currentParseCallback); + C.currentParseCallback = null; + C.currentLogCallback = null; + C.currentProgressCallback = null; + return result; + } + /** + * Instruct the parser to start the next parse from the beginning. + * + * If the parser previously failed because of a timeout, cancellation, + * or callback, then by default, it will resume where it left off on the + * next call to {@link Parser#parse} or other parsing functions. + * If you don't want to resume, and instead intend to use this parser to + * parse some other document, you must call `reset` first. + */ + reset() { + C._ts_parser_reset(this[0]); + } + /** Get the ranges of text that the parser will include when parsing. */ + getIncludedRanges() { + C._ts_parser_included_ranges_wasm(this[0]); + const count = C.getValue(TRANSFER_BUFFER, "i32"); + const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32"); + const result = new Array(count); + if (count > 0) { + let address = buffer; + for (let i2 = 0; i2 < count; i2++) { + result[i2] = unmarshalRange(address); + address += SIZE_OF_RANGE; + } + C._free(buffer); + } + return result; + } + /** + * @deprecated since version 0.25.0, prefer passing a progress callback to {@link Parser#parse} + * + * Get the duration in microseconds that parsing is allowed to take. + * + * This is set via {@link Parser#setTimeoutMicros}. + */ + getTimeoutMicros() { + return C._ts_parser_timeout_micros(this[0]); + } + /** + * @deprecated since version 0.25.0, prefer passing a progress callback to {@link Parser#parse} + * + * Set the maximum duration in microseconds that parsing should be allowed + * to take before halting. + * + * If parsing takes longer than this, it will halt early, returning `null`. + * See {@link Parser#parse} for more information. + */ + setTimeoutMicros(timeout) { + C._ts_parser_set_timeout_micros(this[0], 0, timeout); + } + /** Set the logging callback that a parser should use during parsing. */ + setLogger(callback) { + if (!callback) { + this.logCallback = null; + } else if (typeof callback !== "function") { + throw new Error("Logger callback must be a function"); + } else { + this.logCallback = callback; + } + return this; + } + /** Get the parser's current logger. */ + getLogger() { + return this.logCallback; + } +}; +export { + CaptureQuantifier, + LANGUAGE_VERSION, + Language, + LookaheadIterator, + MIN_COMPATIBLE_VERSION, + Node, + Parser, + Query, + Tree, + TreeCursor +}; +//# sourceMappingURL=tree-sitter.js.map diff --git a/custom_nodes/rgthree-comfy/web/lib/tree-sitter.wasm b/custom_nodes/rgthree-comfy/web/lib/tree-sitter.wasm new file mode 100644 index 00000000..54a7a110 Binary files /dev/null and b/custom_nodes/rgthree-comfy/web/lib/tree-sitter.wasm differ diff --git a/custom_nodes/rgthree-comfy/web/link_fixer/icon_file_json.png b/custom_nodes/rgthree-comfy/web/link_fixer/icon_file_json.png new file mode 100644 index 00000000..ad3a1cb2 Binary files /dev/null and b/custom_nodes/rgthree-comfy/web/link_fixer/icon_file_json.png differ diff --git a/custom_nodes/rgthree-comfy/web/link_fixer/index.html b/custom_nodes/rgthree-comfy/web/link_fixer/index.html new file mode 100644 index 00000000..2e1b66ec --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/link_fixer/index.html @@ -0,0 +1,140 @@ + + + + rgthree's comfy: Workflow Link Fixer + + + + +
      +

      rgthree's Workflow Link Fixer

      +

      + Drag and drop a comfy-generated image or workflow json into this window to check its + serialized links and attempt to fix. +

      + + Sometimes as you have complex workflows the internal data can become corrupt, and the + ComfyUI doesn't always understand or display correctly. Maybe links disappear, or reconnect + to another node when changing something. Load it here to detect and, if possible, attempt + to fix it (sometimes, however, fixing it just isn't feasible, so fingers crossed). + + +
      + + + +
      +
      + +
      + + + +
      + + + + \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/web/link_fixer/link_page.js b/custom_nodes/rgthree-comfy/web/link_fixer/link_page.js new file mode 100644 index 00000000..56609dc0 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/link_fixer/link_page.js @@ -0,0 +1,189 @@ +import { WorkflowLinkFixer } from "../common/link_fixer.js"; +import { getPngMetadata } from "../common/comfyui_shim.js"; +function wait(ms = 16, value) { + return new Promise((resolve) => { + setTimeout(() => { + resolve(value); + }, ms); + }); +} +const logger = { + logTo: console, + log: (...args) => { + logger.logTo === console + ? console.log(...args) + : (logger.logTo.innerText += args.join(",") + "\n"); + }, +}; +export class LinkPage { + constructor() { + this.fixer = null; + this.containerEl = document.querySelector(".box"); + this.figcaptionEl = document.querySelector("figcaption"); + this.outputeMessageEl = document.querySelector(".output"); + this.outputImageEl = document.querySelector(".output-image"); + this.btnFix = document.querySelector(".btn-fix"); + document.addEventListener("dragover", (e) => { + e.preventDefault(); + }, false); + document.addEventListener("drop", (e) => { + this.onDrop(e); + }); + this.btnFix.addEventListener("click", (e) => { + this.onFixClick(e); + }); + } + async onFixClick(e) { + var _a; + if (!((_a = this.fixer) === null || _a === void 0 ? void 0 : _a.checkedData) || !this.graph) { + this.updateUi("β›” Fix button click without results."); + return; + } + this.graphFinalResults = this.fixer.fix(); + if (this.graphFinalResults.hasBadLinks) { + this.updateUi("β›” Hmm... Still detecting bad links. Can you file an issue at https://github.com/rgthree/rgthree-comfy/issues with your image/workflow."); + } + else { + this.updateUi("βœ… Workflow fixed.

      Please load new saved workflow json and double check linking and execution."); + } + await wait(16); + await this.saveFixedWorkflow(); + } + async onDrop(event) { + var _a, _b, _c, _d; + if (!event.dataTransfer) { + return; + } + this.reset(); + event.preventDefault(); + event.stopPropagation(); + if (event.dataTransfer.files.length && ((_b = (_a = event.dataTransfer.files) === null || _a === void 0 ? void 0 : _a[0]) === null || _b === void 0 ? void 0 : _b.type) !== "image/bmp") { + await this.handleFile(event.dataTransfer.files[0]); + return; + } + const validTypes = ["text/uri-list", "text/x-moz-url"]; + const match = [...event.dataTransfer.types].find((t) => validTypes.find((v) => t === v)); + if (match) { + const uri = (_d = (_c = event.dataTransfer.getData(match)) === null || _c === void 0 ? void 0 : _c.split("\n")) === null || _d === void 0 ? void 0 : _d[0]; + if (uri) { + await this.handleFile(await (await fetch(uri)).blob()); + } + } + } + reset() { + this.file = undefined; + this.graph = undefined; + this.graphResults = undefined; + this.graphFinalResults = undefined; + this.updateUi(); + } + updateUi(msg) { + this.outputeMessageEl.innerHTML = ""; + if (this.file && !this.containerEl.classList.contains("-has-file")) { + this.containerEl.classList.add("-has-file"); + this.figcaptionEl.innerHTML = this.file.name || this.file.type; + if (this.file.type === "application/json") { + this.outputImageEl.src = "icon_file_json.png"; + } + else { + const reader = new FileReader(); + reader.onload = () => (this.outputImageEl.src = reader.result); + reader.readAsDataURL(this.file); + } + } + else if (!this.file && this.containerEl.classList.contains("-has-file")) { + this.containerEl.classList.remove("-has-file"); + this.outputImageEl.src = ""; + this.outputImageEl.removeAttribute("src"); + } + if (this.graphResults) { + this.containerEl.classList.add("-has-results"); + if (!this.graphResults.patches && !this.graphResults.deletes) { + this.outputeMessageEl.innerHTML = "βœ… No bad links detected in the workflow."; + } + else { + this.containerEl.classList.add("-has-fixable-results"); + this.outputeMessageEl.innerHTML = `⚠️ Found ${this.graphResults.patches} links to fix, and ${this.graphResults.deletes} to be removed.`; + } + } + else { + this.containerEl.classList.remove("-has-results"); + this.containerEl.classList.remove("-has-fixable-results"); + } + if (msg) { + this.outputeMessageEl.innerHTML = msg; + } + } + async handleFile(file) { + this.file = file; + this.updateUi(); + let workflow = null; + if (file.type.startsWith("image/")) { + const pngInfo = await getPngMetadata(file); + workflow = pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow; + } + else if (file.type === "application/json" || + (file instanceof File && file.name.endsWith(".json"))) { + workflow = await new Promise((resolve) => { + const reader = new FileReader(); + reader.onload = () => { + resolve(reader.result); + }; + reader.readAsText(file); + }); + } + if (!workflow) { + this.updateUi("β›” No workflow found in dropped item."); + } + else { + try { + this.graph = JSON.parse(workflow); + } + catch (e) { + this.graph = undefined; + } + if (!this.graph) { + this.updateUi("β›” Invalid workflow found in dropped item."); + } + else { + this.loadGraphData(this.graph); + } + } + } + async loadGraphData(graphData) { + this.fixer = WorkflowLinkFixer.create(graphData); + this.graphResults = this.fixer.check(); + this.updateUi(); + } + async saveFixedWorkflow() { + if (!this.graphFinalResults) { + this.updateUi("β›” Save w/o final graph patched."); + return false; + } + let filename = this.file.name || "workflow.json"; + let filenames = filename.split("."); + filenames.pop(); + filename = filenames.join("."); + filename += "_fixed.json"; + filename = prompt("Save workflow as:", filename); + if (!filename) + return false; + if (!filename.toLowerCase().endsWith(".json")) { + filename += ".json"; + } + const json = JSON.stringify(this.graphFinalResults.graph, null, 2); + const blob = new Blob([json], { type: "application/json" }); + const url = URL.createObjectURL(blob); + const anchor = document.createElement("a"); + anchor.download = filename; + anchor.href = url; + anchor.style.display = "none"; + document.body.appendChild(anchor); + await wait(); + anchor.click(); + await wait(); + anchor.remove(); + window.URL.revokeObjectURL(url); + return true; + } +} diff --git a/custom_nodes/rgthree-comfy/web/models/components/model-info-card.css b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.css new file mode 100644 index 00000000..87845235 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.css @@ -0,0 +1,32 @@ +.rgthree-model-info-card { + display: block; + padding: 8px; +} + +.-is-hidden { + display: none; +} + +.rgthree-model-info-card { + display: flex; + flex-direction: row; +} +.rgthree-model-info-card > .rgthree-model-info-card-media-container { + width: 100px; + height: auto; + display: block; + margin: 0 8px 0 0; + padding: 0; + flex: 0 0 auto; +} +.rgthree-model-info-card > .rgthree-model-info-card-media-container > img, +.rgthree-model-info-card > .rgthree-model-info-card-media-container > video { + width: 100%; + height: 100%; + object-fit: contain; +} +.rgthree-model-info-card > .rgthree-model-info-card-data-container [bind*="name:"] { + font-size: 1.3em; + margin-bottom: 4px; + font-weight: bold; +} diff --git a/custom_nodes/rgthree-comfy/web/models/components/model-info-card.html b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.html new file mode 100644 index 00000000..3be75c9d --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.html @@ -0,0 +1,18 @@ + + + diff --git a/custom_nodes/rgthree-comfy/web/models/components/model-info-card.js b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.js new file mode 100644 index 00000000..76cb7dc9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.js @@ -0,0 +1,41 @@ +import { RgthreeCustomElement } from "../../common/components/base_custom_element.js"; +export class RgthreeModelInfoCard extends RgthreeCustomElement { + constructor() { + super(...arguments); + this.data = {}; + } + getModified(value, data, currentElement, contextElement) { + const date = new Date(value); + return String(`${date.toLocaleDateString()} ${date.toLocaleTimeString()}`); + } + getCivitaiLink(links) { + return (links === null || links === void 0 ? void 0 : links.find((i) => i.includes("civitai.com/models"))) || null; + } + setModelData(data) { + this.data = data; + } + hasBaseModel(baseModel) { + return this.data.baseModel === baseModel; + } + hasData(field) { + var _a; + if (field === "civitai") { + return !!((_a = this.getCivitaiLink(this.data.links)) === null || _a === void 0 ? void 0 : _a.length); + } + return !!this.data[field]; + } + matchesQueryText(query) { + var _a; + return (_a = (this.data.name || this.data.file)) === null || _a === void 0 ? void 0 : _a.includes(query); + } + hide() { + this.classList.add("-is-hidden"); + } + show() { + this.classList.remove("-is-hidden"); + } +} +RgthreeModelInfoCard.NAME = "rgthree-model-info-card"; +RgthreeModelInfoCard.TEMPLATES = "components/model-info-card.html"; +RgthreeModelInfoCard.CSS = "components/model-info-card.css"; +RgthreeModelInfoCard.USE_SHADOW = false; diff --git a/custom_nodes/rgthree-comfy/web/models/index.html b/custom_nodes/rgthree-comfy/web/models/index.html new file mode 100644 index 00000000..137a1f72 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/models/index.html @@ -0,0 +1,18 @@ + + + + rgthree-comfy: Models Manager + + + + +
      + +
      +
        + + + \ No newline at end of file diff --git a/custom_nodes/rgthree-comfy/web/models/models.css b/custom_nodes/rgthree-comfy/web/models/models.css new file mode 100644 index 00000000..8fa8e91e --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/models/models.css @@ -0,0 +1,38 @@ +:root { + --rgthree-bg-color: rgba(23, 23, 23, 0.9); + --rgthree-on-bg-color: rgba(48, 48, 48, 0.9); +} + +html, body { + margin: 0; + padding: 0; + font-family: Arial, sans-serif; + box-sizing: border-body; + background: var(--rgthree-bg-color); +} + +*, *::before, *::after { + box-sizing: inherit; +} + +[if-is=false] { + display: none; +} + +.models-list { + list-style: none; + margin: 0; + padding: 0; +} + +.model-item { + display: block; + margin: 8px; +} + +rgthree-model-info-card { + background: var(--rgthree-on-bg-color); + margin: 16px; + display: block; + border-radius: 4px; +} diff --git a/custom_nodes/rgthree-comfy/web/models/models_info_page.js b/custom_nodes/rgthree-comfy/web/models/models_info_page.js new file mode 100644 index 00000000..55b08ed9 --- /dev/null +++ b/custom_nodes/rgthree-comfy/web/models/models_info_page.js @@ -0,0 +1,121 @@ +import { createElement, getActionEls, query, queryAll } from "../common/utils_dom.js"; +import { rgthreeApi } from "../common/rgthree_api.js"; +import { RgthreeModelInfoCard } from "./components/model-info-card.js"; +function parseQuery(query) { + const matches = query.match(/"[^\"]+"/g) || []; + for (const match of matches) { + let cleaned = match.substring(1, match.length - 1); + cleaned = cleaned.replace(/\s+/g, " ").trim().replace(/\s/g, "__SPACE__"); + query = query.replace(match, ` ${cleaned} `); + } + const queryParts = query + .replace(/\s+/g, " ") + .trim() + .split(" ") + .map((p) => p.replace(/__SPACE__/g, " ")); + return queryParts; +} +export class ModelsInfoPage { + constructor() { + this.selectBaseModel = createElement('select[name="baseModel"][on="change:filter"]'); + this.searchbox = query("#searchbox"); + this.modelsList = query("#models-list"); + this.queryLast = ""; + this.doSearchDebounce = 0; + console.log("hello model page"); + this.init(); + } + async init() { + this.searchbox.addEventListener("input", (e) => { + if (this.doSearchDebounce) { + return; + } + this.doSearchDebounce = setTimeout(() => { + this.doSearch(); + this.doSearchDebounce = 0; + }, 250); + }); + const loras = await rgthreeApi.getLorasInfo({ light: true }); + console.log(loras); + const baseModels = new Set(); + for (const lora of loras) { + const el = RgthreeModelInfoCard.create(); + el.setModelData(lora); + el.bindWhenConnected(lora); + console.log(el); + lora.baseModel && baseModels.add(lora.baseModel); + this.modelsList.appendChild(createElement("li.model-item", { child: el })); + } + if (baseModels.size > 1) { + createElement(`option[value="ALL"][text="Choose base model."]`, { + parent: this.selectBaseModel, + }); + for (const baseModel of baseModels.values()) { + createElement(`option[value="${baseModel}"][text="${baseModel}"]`, { + parent: this.selectBaseModel, + }); + } + this.searchbox.insertAdjacentElement("afterend", this.selectBaseModel); + } + const data = getActionEls(document.body); + for (const dataItem of Object.values(data)) { + for (const [event, action] of Object.entries(dataItem.actions)) { + dataItem.el.addEventListener(event, (e) => { + if (typeof this[action] != "function") { + throw new Error(`"${action}" does not exist on instance.`); + } + this[action](e); + }); + } + } + } + filter() { + const parts = parseQuery(this.queryLast); + const baseModel = this.selectBaseModel.value; + const els = queryAll(RgthreeModelInfoCard.NAME); + const shouldHide = (el) => { + let hide = baseModel !== "ALL" && !el.hasBaseModel(baseModel); + if (!hide) { + for (let part of parts) { + let negate = false; + if (part.startsWith("-")) { + negate = true; + part = part.substring(1); + } + if (!part) + continue; + if (part.startsWith("has:")) { + if (part === "has:civitai") { + hide = !el.hasData(part.replace("has:", "")); + } + } + else { + hide = !el.matchesQueryText(part); + } + hide = negate ? !hide : hide; + if (hide) { + break; + } + } + } + return hide; + }; + for (const el of els) { + const hide = shouldHide(el); + if (hide) { + el.hide(); + } + else { + el.show(); + } + } + console.log("filter"); + } + doSearch() { + const query = this.searchbox.value.trim(); + if (this.queryLast != query) { + this.queryLast = query; + this.filter(); + } + } +} diff --git a/custom_nodes/sd-perturbed-attention/.gitignore b/custom_nodes/sd-perturbed-attention/.gitignore new file mode 100644 index 00000000..197ad097 --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/.gitignore @@ -0,0 +1,270 @@ +# File created using '.gitignore Generator' for Visual Studio Code: https://bit.ly/vscode-gig +# Created by https://www.toptal.com/developers/gitignore/api/windows,visualstudiocode,macos,python,venv +# Edit at https://www.toptal.com/developers/gitignore?templates=windows,visualstudiocode,macos,python,venv + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### macOS Patch ### +# iCloud generated files +*.icloud + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +### venv ### +# Virtualenv +# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/ +[Bb]in +[Ii]nclude +[Ll]ib +[Ll]ib64 +[Ll]ocal +[Ss]cripts +pyvenv.cfg +pip-selfcheck.json + +### VisualStudioCode ### +.vscode/* +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +!.vscode/*.code-snippets + +# Local History for Visual Studio Code +.history/ + +# Built Visual Studio Code Extensions +*.vsix + +### VisualStudioCode Patch ### +# Ignore all local history of files +.history +.ionide + +### Windows ### +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +# End of https://www.toptal.com/developers/gitignore/api/windows,visualstudiocode,macos,python,venv + +# Custom rules (everything added below won't be overriden by 'Generate .gitignore File' if you use 'Update' option) +ref/ diff --git a/custom_nodes/sd-perturbed-attention/LICENSE b/custom_nodes/sd-perturbed-attention/LICENSE new file mode 100644 index 00000000..2f449de2 --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 pamparamm + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/sd-perturbed-attention/README.md b/custom_nodes/sd-perturbed-attention/README.md new file mode 100644 index 00000000..0370f517 --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/README.md @@ -0,0 +1,86 @@ +# Various Guidance implementations for ComfyUI / SD WebUI (reForge) + +Implementation of + +- Perturbed-Attention Guidance from [Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance (D. Ahn et al.)](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) +- [Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention (Susung Hong)](https://arxiv.org/abs/2408.00760) +- Sliding Window Guidance from [The Unreasonable Effectiveness of Guidance for Diffusion Models (Kaiser et al.)](https://arxiv.org/abs/2411.10257) +- [PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity](https://cubeyoung.github.io/pladis-proejct/) (ComfyUI-only) +- [Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models](https://arxiv.org/abs/2505.21179) (ComfyUI-only, has a description inside ComfyUI) +- [Token Perturbation Guidance for Diffusion Models](https://arxiv.org/abs/2506.10036) (ComfyUI-only) + +as an extension for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [SD WebUI (reForge)](https://github.com/Panchovix/stable-diffusion-webui-reForge). + +Works with SD1.5 and SDXL. + +## Installation + +### ComfyUI + +You can either: + +- `git clone https://github.com/pamparamm/sd-perturbed-attention.git` into `ComfyUI/custom-nodes/` folder. + +- Install it via [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager) (search for custom node named "Perturbed-Attention Guidance"). + +- Install it via [comfy-cli](https://comfydocs.org/comfy-cli/getting-started) with `comfy node registry-install sd-perturbed-attention` + +### SD WebUI (reForge) + +`git clone https://github.com/pamparamm/sd-perturbed-attention.git` into `stable-diffusion-webui-forge/extensions/` folder. + +### SD WebUI (Auto1111) + +As an alternative for A1111 WebUI you can use PAG implementation from [sd-webui-incantations](https://github.com/v0xie/sd-webui-incantations) extension. + +## Guidance Nodes/Scripts + +### ComfyUI + +![comfyui-node-pag-basic](res/comfyui-node-pag-basic.png) + +![comfyui-node-pag-advanced](res/comfyui-node-pag-advanced.png) + +![comfyui-node-seg](res/comfyui-node-seg.png) + +### SD WebUI (reForge) + +![forge-pag](res/forge-pag.png) + +![forge-seg](res/forge-seg.png) + +> [!NOTE] +> You can override `CFG Scale` and `PAG Scale`/`SEG Scale` for Hires. fix by opening/enabling `Override for Hires. fix` tab. +> To disable PAG during Hires. fix, you can set `PAG Scale` under Override to 0. + +### Inputs + +- `scale`: Guidance scale, higher values can both increase structural coherence of an image and oversaturate/fry it entirely. +- `adaptive_scale` (PAG only): PAG dampening factor, it penalizes PAG during late denoising stages, resulting in overall speedup: 0.0 means no penalty and 1.0 completely removes PAG. +- `blur_sigma` (SEG only): Normal deviation of Gaussian blur, higher values increase "clarity" of an image. Negative values set `blur_sigma` to infinity. +- `unet_block`: Part of U-Net to which Guidance is applied, original paper suggests to use `middle`. +- `unet_block_id`: Id of U-Net layer in a selected block to which Guidance is applied. Guidance can be applied only to layers containing Self-attention blocks. +- `sigma_start` / `sigma_end`: Guidance will be active only between `sigma_start` and `sigma_end`. Set both values to negative to disable this feature. +- `rescale`: Acts similar to RescaleCFG node - it prevents over-exposure on high `scale` values. Based on Algorithm 2 from [Common Diffusion Noise Schedules and Sample Steps are Flawed (Lin et al.)](https://arxiv.org/abs/2305.08891). Set to 0 to disable this feature. +- `rescale_mode`: + - `full` - takes into account both CFG and Guidance. + - `partial` - depends only on Guidance. + - `snf` - Saliency-adaptive Noise Fusion from [High-fidelity Person-centric Subject-to-Image Synthesis (Wang et al.)](https://arxiv.org/abs/2311.10329). Should increase image quality on high guidance scales. Ignores `rescale` value. +- `unet_block_list`: Optional input, replaces both `unet_block` and `unet_block_id` and allows you to select multiple U-Net layers separated with commas. SDXL U-Net has multiple indices for layers, you can specify them by using dot symbol (if not specified, Guidance will be applied to the whole layer). Example value: `m0,u0.4` (it applies Guidance to middle block 0 and to output block 0 with index 4) + - In terms of U-Net `d` means `input`, `m` means `middle` and `u` means `output`. + - SD1.5 U-Net has layers `d0`-`d5`, `m0`, `u0`-`u8`. + - SDXL U-Net has layers `d0`-`d3`, `m0`, `u0`-`u5`. In addition, each block except `d0` and `d1` has `0-9` index values (like `m0.7` or `u0.4`). `d0` and `d1` have `0-1` index values. + - Supports block ranges (`d0-d3` corresponds to `d0,d1,d2,d3`) and index value ranges (`d2.2-9` corresponds to all index values of `d2` with the exclusion of `d2.0` and `d2.1`). + +## ComfyUI TensorRT PAG (Experimental) + +To use PAG together with [ComfyUI_TensorRT](https://github.com/comfyanonymous/ComfyUI_TensorRT), you'll need to: + +0. Have 24GB of VRAM. +1. Build static/dynamic TRT engine of a desired model. +2. Build static/dynamic TRT engine of the same model with the same TRT parameters, but with fixed PAG injection in selected UNET blocks (`TensorRT Attach PAG` node). +3. Use `TensorRT Perturbed-Attention Guidance` node with two model inputs: one for base engine and one for PAG engine. + +![trt-engines](res/trt-engines.png) + +![trt-inference](res/trt-inference.png) diff --git a/custom_nodes/sd-perturbed-attention/__init__.py b/custom_nodes/sd-perturbed-attention/__init__.py new file mode 100644 index 00000000..40d4b93d --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/__init__.py @@ -0,0 +1,25 @@ +from . import nag_nodes, tpg_nodes, pladis_nodes +from .pag_nodes import PerturbedAttention, SlidingWindowGuidanceAdvanced, SmoothedEnergyGuidanceAdvanced +from .pag_trt_nodes import TRTAttachPag, TRTPerturbedAttention + +NODE_CLASS_MAPPINGS = { + "PerturbedAttention": PerturbedAttention, + "SmoothedEnergyGuidanceAdvanced": SmoothedEnergyGuidanceAdvanced, + "SlidingWindowGuidanceAdvanced": SlidingWindowGuidanceAdvanced, + "TRTAttachPag": TRTAttachPag, + "TRTPerturbedAttention": TRTPerturbedAttention, + **nag_nodes.NODE_CLASS_MAPPINGS, + **tpg_nodes.NODE_CLASS_MAPPINGS, + **pladis_nodes.NODE_CLASS_MAPPINGS, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PerturbedAttention": "Perturbed-Attention Guidance (Advanced)", + "SmoothedEnergyGuidanceAdvanced": "Smoothed Energy Guidance (Advanced)", + "SlidingWindowGuidanceAdvanced": "Sliding Window Guidance (Advanced)", + "TRTAttachPag": "TensorRT Attach PAG", + "TRTPerturbedAttention": "TensorRT Perturbed-Attention Guidance", + **nag_nodes.NODE_DISPLAY_NAME_MAPPINGS, + **tpg_nodes.NODE_DISPLAY_NAME_MAPPINGS, + **pladis_nodes.NODE_DISPLAY_NAME_MAPPINGS, +} diff --git a/custom_nodes/sd-perturbed-attention/example_workflows/nag_sdxl.jpg b/custom_nodes/sd-perturbed-attention/example_workflows/nag_sdxl.jpg new file mode 100644 index 00000000..08d611cd Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/example_workflows/nag_sdxl.jpg differ diff --git a/custom_nodes/sd-perturbed-attention/example_workflows/nag_sdxl.json 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--- /dev/null +++ b/custom_nodes/sd-perturbed-attention/guidance_utils.py @@ -0,0 +1,241 @@ +import math +from itertools import groupby +from typing import Any, Callable, Literal + +import torch +import torch.nn.functional as F + + +def parse_unet_blocks(model, unet_block_list: str, attn: Literal["attn1", "attn2"] | None): + output: list[tuple[str, int, int | None]] = [] + names: list[str] = [] + + # Get all Self-attention blocks + input_blocks: list[tuple[int, str]] = [] + middle_blocks: list[tuple[int, str]] = [] + output_blocks: list[tuple[int, str]] = [] + for name, module in model.model.diffusion_model.named_modules(): + if module.__class__.__name__ == "BasicTransformerBlock" and (attn is None or hasattr(module, attn)): + parts = name.split(".") + unet_part = parts[0] + block_id = int(parts[1]) + if unet_part.startswith("input"): + input_blocks.append((block_id, name)) + elif unet_part.startswith("middle"): + middle_blocks.append((block_id - 1, name)) + elif unet_part.startswith("output"): + output_blocks.append((block_id, name)) + + def group_blocks(blocks: list[tuple[int, str]]): + grouped_blocks = [(i, list(gr)) for i, gr in groupby(blocks, lambda b: b[0])] + return [(i, len(gr), list(idx[1] for idx in gr)) for i, gr in grouped_blocks] + + input_blocks_gr, middle_blocks_gr, output_blocks_gr = ( + group_blocks(input_blocks), + group_blocks(middle_blocks), + group_blocks(output_blocks), + ) + + user_inputs = [b.strip() for b in unet_block_list.split(",")] + for user_input in user_inputs: + unet_part_s, indices = user_input[0], user_input[1:].split(".") + match unet_part_s: + case "d": + unet_part, unet_group = "input", input_blocks_gr + case "m": + unet_part, unet_group = "middle", middle_blocks_gr + case "u": + unet_part, unet_group = "output", output_blocks_gr + case _: + raise ValueError(f"Block {user_input}: Unknown block prefix {unet_part_s}") + + block_index_range = [int(b.strip()) for b in indices[0].split("-")] + block_index_range_start = block_index_range[0] + block_index_range_end = block_index_range[0] if len(block_index_range) != 2 else block_index_range[1] + for block_index in range(block_index_range_start, block_index_range_end + 1): + if block_index < 0 or block_index >= len(unet_group): + raise ValueError( + f"Block {user_input}: Block index in out of range 0 <= {block_index} < {len(unet_group)}" + ) + + block_group = unet_group[block_index] + block_index_real = block_group[0] + + if len(indices) == 1: + output.append((unet_part, block_index_real, None)) + names.extend(block_group[2]) + else: + transformer_index_range = [int(b.strip()) for b in indices[1].split("-")] + transformer_index_range_start = transformer_index_range[0] + transformer_index_range_end = ( + transformer_index_range[0] if len(transformer_index_range) != 2 else transformer_index_range[1] + ) + for transformer_index in range(transformer_index_range_start, transformer_index_range_end + 1): + if transformer_index is not None and (transformer_index < 0 or transformer_index >= block_group[1]): + raise ValueError( + f"Block {user_input}: Transformer index in out of range 0 <= {transformer_index} < {block_group[1]}" + ) + + output.append((unet_part, block_index_real, transformer_index)) + names.append(block_group[2][transformer_index]) + + return output, names + + +# Copied from https://github.com/comfyanonymous/ComfyUI/blob/719fb2c81d716ce8edd7f1bdc7804ae160a71d3a/comfy/model_patcher.py#L21 for backward compatibility +def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): + to = model_options["transformer_options"].copy() + + if "patches_replace" not in to: + to["patches_replace"] = {} + else: + to["patches_replace"] = to["patches_replace"].copy() + + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + else: + to["patches_replace"][name] = to["patches_replace"][name].copy() + + if transformer_index is not None: + block = (block_name, number, transformer_index) + else: + block = (block_name, number) + to["patches_replace"][name][block] = patch + model_options["transformer_options"] = to + return model_options + + +def set_model_options_value(model_options, key: str, value: Any): + to = model_options["transformer_options"].copy() + to[key] = value + model_options["transformer_options"] = to + return model_options + + +def perturbed_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, extra_options, mask=None): + """Perturbed self-attention""" + return v + + +# Modified 'Algorithm 2 Classifier-Free Guidance with Rescale' from Common Diffusion Noise Schedules and Sample Steps are Flawed (Lin et al.). +def rescale_guidance( + guidance: torch.Tensor, cond_pred: torch.Tensor, cfg_result: torch.Tensor, rescale=0.0, rescale_mode="full" +): + if rescale == 0.0: + return guidance + + match rescale_mode: + case "full": + guidance_result = cfg_result + guidance + case _: + guidance_result = cond_pred + guidance + + std_cond = torch.std(cond_pred, dim=(1, 2, 3), keepdim=True) + std_guidance = torch.std(guidance_result, dim=(1, 2, 3), keepdim=True) + + factor = std_cond / std_guidance + factor = rescale * factor + (1.0 - rescale) + + return guidance * factor + + +# Gaussian blur +def gaussian_blur_2d(img, kernel_size, sigma): + height = img.shape[-1] + kernel_size = min(kernel_size, height - (height % 2 - 1)) + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = F.pad(img, padding, mode="reflect") + img = F.conv2d(img, kernel2d, groups=img.shape[-3]) + + return img + + +def seg_attention_wrapper(attention, blur_sigma=1.0): + def seg_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, extra_options, mask=None): + """Smoothed Energy Guidance self-attention""" + heads = extra_options["n_heads"] + bs, area, inner_dim = q.shape + + height_orig, width_orig = extra_options["original_shape"][2:4] + aspect_ratio = width_orig / height_orig + + if aspect_ratio >= 1.0: + height = round((area / aspect_ratio) ** 0.5) + q = q.permute(0, 2, 1).reshape(bs, inner_dim, height, -1) + else: + width = round((area * aspect_ratio) ** 0.5) + q = q.permute(0, 2, 1).reshape(bs, inner_dim, -1, width) + + if blur_sigma >= 0: + kernel_size = math.ceil(6 * blur_sigma) + 1 - math.ceil(6 * blur_sigma) % 2 + q = gaussian_blur_2d(q, kernel_size, blur_sigma) + else: + q[:] = q.mean(dim=(-2, -1), keepdim=True) + + q = q.reshape(bs, inner_dim, -1).permute(0, 2, 1) + + return attention(q, k, v, heads=heads) + + return seg_attention + + +# Modified algorithm from 2411.10257 'The Unreasonable Effectiveness of Guidance for Diffusion Models' (Figure 6.) +def swg_pred_calc( + x: torch.Tensor, tile_width: int, tile_height: int, tile_overlap: int, calc_func: Callable[..., tuple[torch.Tensor]] +): + b, c, h, w = x.shape + swg_pred = torch.zeros_like(x) + overlap = torch.zeros_like(x) + + tiles_w = math.ceil(w / (tile_width - tile_overlap)) + tiles_h = math.ceil(h / (tile_height - tile_overlap)) + + for w_i in range(tiles_w): + for h_i in range(tiles_h): + left, right = tile_width * w_i, tile_width * (w_i + 1) + tile_overlap + top, bottom = tile_height * h_i, tile_height * (h_i + 1) + tile_overlap + + x_window = x[:, :, top:bottom, left:right] + if x_window.shape[-1] == 0 or x_window.shape[-2] == 0: + continue + + swg_pred_window = calc_func(x_in=x_window)[0] + swg_pred[:, :, top:bottom, left:right] += swg_pred_window + + overlap_window = torch.ones_like(swg_pred_window) + overlap[:, :, top:bottom, left:right] += overlap_window + + swg_pred = swg_pred / overlap + return swg_pred + + +# Saliency-adaptive Noise Fusion based on High-fidelity Person-centric Subject-to-Image Synthesis (Wang et al.) +# https://github.com/CodeGoat24/Face-diffuser/blob/edff1a5178ac9984879d9f5e542c1d0f0059ca5f/facediffuser/pipeline.py#L535-L562 +def snf_guidance(t_guidance: torch.Tensor, s_guidance: torch.Tensor): + b, c, h, w = t_guidance.shape + + t_omega = gaussian_blur_2d(torch.abs(t_guidance), 3, 1) + s_omega = gaussian_blur_2d(torch.abs(s_guidance), 3, 1) + t_softmax = torch.softmax(t_omega.reshape(b * c, h * w), dim=1).reshape(b, c, h, w) + s_softmax = torch.softmax(s_omega.reshape(b * c, h * w), dim=1).reshape(b, c, h, w) + + guidance_stacked = torch.stack([t_guidance, s_guidance], dim=0) + ts_softmax = torch.stack([t_softmax, s_softmax], dim=0) + + argeps = torch.argmax(ts_softmax, dim=0, keepdim=True) + + snf = torch.gather(guidance_stacked, dim=0, index=argeps).squeeze(0) + return snf diff --git a/custom_nodes/sd-perturbed-attention/nag_nodes.py b/custom_nodes/sd-perturbed-attention/nag_nodes.py new file mode 100644 index 00000000..adcf1dff --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/nag_nodes.py @@ -0,0 +1,235 @@ +from contextlib import suppress +from typing import Callable + +import torch + +import comfy.model_management +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from comfy.ldm.modules.attention import BasicTransformerBlock, CrossAttention, optimized_attention +from comfy.model_base import BaseModel +from comfy.model_patcher import ModelPatcher + +from .guidance_utils import parse_unet_blocks + +COND = 0 +UNCOND = 1 + + +def nag_attn2_replace_wrapper( + nag_scale: float, + tau: float, + alpha: float, + sigma_start: float, + sigma_end: float, + k_neg: torch.Tensor, + v_neg: torch.Tensor, + prev_attn2_replace: Callable | None = None, +): + # Modified Algorithm 1 from 2505.21179 'Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models' + def nag_attn2_replace(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, extra_options: dict): + heads = extra_options["n_heads"] + attn_precision = extra_options.get("attn_precision") + sigma = extra_options["sigmas"] + cond_or_uncond: list[int] = extra_options.get("cond_or_uncond") # type: ignore + + # Perform batched CA + z = ( + optimized_attention(q, k, v, heads, attn_precision) + if prev_attn2_replace is None + else prev_attn2_replace(q, k, v, extra_options) + ) + + if nag_scale == 0 or not (sigma_end < sigma[0] <= sigma_start) or COND not in cond_or_uncond: + return z + + bs = q.shape[0] // len(cond_or_uncond) * cond_or_uncond.count(COND) + + k_neg_, v_neg_ = k_neg.repeat_interleave(bs, dim=0), v_neg.repeat_interleave(bs, dim=0) + + # Get conditional queries for NAG + # Assume that cond_or_uncond has a layout [1, 1..., 0, 0...] + q_chunked = q.chunk(len(cond_or_uncond)) + q_pos = torch.cat(q_chunked[cond_or_uncond.index(COND) :]) + + # Apply NAG only to conditional parts of batched CA + z_chunked = z.chunk(len(cond_or_uncond)) + z_pos = torch.cat(z_chunked[cond_or_uncond.index(COND) :]) + z_neg = optimized_attention(q_pos, k_neg_, v_neg_, heads, attn_precision) + + z_tilde = z_pos + nag_scale * (z_pos - z_neg) + + norm_pos = torch.norm(z_pos, p=1, dim=-1, keepdim=True) + norm_tilde = torch.norm(z_tilde, p=1, dim=-1, keepdim=True) + ratio = norm_tilde / norm_pos + + z_hat = torch.where(ratio > tau, tau, ratio) / ratio * z_tilde + + z_nag = alpha * z_hat + (1 - alpha) * z_pos + + # Prepend unconditional CA result to NAG result + if UNCOND in cond_or_uncond: + z_nag = torch.cat(z_chunked[cond_or_uncond.index(UNCOND) : cond_or_uncond.index(COND)] + (z_nag,)) + + return z_nag + + return nag_attn2_replace + + +class NormalizedAttentionGuidance(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "model": ( + IO.MODEL, + { + "tooltip": ( + "The diffusion model.\n" + "If you are using any other attn2 replacer (such as `IPAdapter`), you should place this node after it." + ) + }, + ), + "negative": ( + IO.CONDITIONING, + {"tooltip": "Negative conditioning: either the one you use for CFG or a completely different one."}, + ), + "scale": ( + IO.FLOAT, + { + "default": 2.0, + "min": 0.0, + "max": 100.0, + "step": 0.1, + "round": 0.01, + "tooltip": "Scale of NAG, does nothing when `tau=0`.", + }, + ), + "tau": ( + IO.FLOAT, + { + "default": 2.5, + "min": 0.0, + "max": 100.0, + "step": 0.1, + "round": 0.01, + "tooltip": "Normalization threshold, larger value should increase `scale` impact.", + }, + ), + "alpha": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "round": 0.001, + "tooltip": "Linear interpolation between original (at `alpha=0`) and NAG (at `alpha=1`) results.", + }, + ), + "sigma_start": (IO.FLOAT, {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "sigma_end": (IO.FLOAT, {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + }, + "optional": { + "unet_block_list": ( + IO.STRING, + { + "default": "", + "tooltip": ( + "Comma-separated blocks to which NAG is being applied to. When the list is empty, NAG is being applied to all block.\n" + "Read README from sd-perturbed-attention for more details." + ), + }, + ), + }, + } + + RETURN_TYPES = (IO.MODEL,) + + FUNCTION = "patch" + DESCRIPTION = ( + "An additional way to apply negative prompts to the image.\n" + "It's compatible with CFG, PAG, and other guidances, and can be used with guidance- and step-distilled models as well.\n" + "It's also compatible with other attn2 replacers (such as `IPAdapter`) - but make sure to place NAG node **after** other model patches!" + ) + + CATEGORY = "model_patches/unet" + + def patch( + self, + model: ModelPatcher, + negative, + scale=2.0, + tau=2.5, + alpha=0.5, + sigma_start: float = -1.0, + sigma_end: float = -1.0, + unet_block_list="", + ): + m = model.clone() + inner_model: BaseModel = m.model + dtype = inner_model.get_dtype() + if inner_model.manual_cast_dtype is not None: + dtype = inner_model.manual_cast_dtype + device_model = inner_model.device + device_infer = comfy.model_management.get_torch_device() + + sigma_start = float("inf") if sigma_start < 0 else sigma_start + + negative_cond = negative[0][0].to(device_model, dtype=dtype) + + blocks, block_names = parse_unet_blocks(m, unet_block_list, "attn2") if unet_block_list else (None, None) + + # Apply NAG only to transformer blocks with cross-attention (attn2) + for name, module in ( + (n, m) + for n, m in inner_model.diffusion_model.named_modules() + if isinstance(m, BasicTransformerBlock) and getattr(m, "attn2", None) + ): + attn2: CrossAttention = module.attn2 # type: ignore + parts: list[str] = name.split(".") + block_name: str = parts[0].split("_")[0] + block_id = int(parts[1]) + if block_name == "middle": + block_id = block_id - 1 + + t_idx = None + if "transformer_blocks" in parts: + t_pos = parts.index("transformer_blocks") + 1 + t_idx = int(parts[t_pos]) + + if not blocks or (block_name, block_id, t_idx) in blocks or (block_name, block_id, None) in blocks: + k_neg, v_neg = attn2.to_k(negative_cond), attn2.to_v(negative_cond) + + # Compatibility with other attn2 replaces (such as IPAdapter) + prev_attn2_replace = None + with suppress(KeyError): + block = (block_name, block_id, t_idx) + block_full = (block_name, block_id) + attn2_patches = m.model_options["transformer_options"]["patches_replace"]["attn2"] + if block_full in attn2_patches: + prev_attn2_replace = attn2_patches[block_full] + elif block in attn2_patches: + prev_attn2_replace = attn2_patches[block] + + nag_attn2_replace = nag_attn2_replace_wrapper( + scale, + tau, + alpha, + sigma_start, + sigma_end, + k_neg.to(device_infer, dtype=dtype), + v_neg.to(device_infer, dtype=dtype), + prev_attn2_replace, + ) + m.set_model_attn2_replace(nag_attn2_replace, block_name, block_id, t_idx) + + return (m,) + + +NODE_CLASS_MAPPINGS = { + "NormalizedAttentionGuidance": NormalizedAttentionGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "NormalizedAttentionGuidance": "Normalized Attention Guidance", +} diff --git a/custom_nodes/sd-perturbed-attention/pag_nodes.py b/custom_nodes/sd-perturbed-attention/pag_nodes.py new file mode 100644 index 00000000..f4e502f5 --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/pag_nodes.py @@ -0,0 +1,315 @@ +from functools import partial + +BACKEND = None + +try: + from comfy.ldm.modules.attention import optimized_attention + from comfy.model_patcher import ModelPatcher + from comfy.samplers import calc_cond_batch + + from .guidance_utils import ( + parse_unet_blocks, + perturbed_attention, + rescale_guidance, + seg_attention_wrapper, + snf_guidance, + swg_pred_calc, + ) + + try: + from comfy.model_patcher import set_model_options_patch_replace + except ImportError: + from .guidance_utils import set_model_options_patch_replace + + BACKEND = "ComfyUI" +except ImportError: + from guidance_utils import ( + parse_unet_blocks, + perturbed_attention, + rescale_guidance, + seg_attention_wrapper, + set_model_options_patch_replace, + snf_guidance, + swg_pred_calc, + ) + + try: + from ldm_patched.ldm.modules.attention import optimized_attention + from ldm_patched.modules.model_patcher import ModelPatcher + from ldm_patched.modules.samplers import calc_cond_uncond_batch + + BACKEND = "reForge" + except ImportError: + from backend.attention import attention_function as optimized_attention + from backend.patcher.base import ModelPatcher + from backend.sampling.sampling_function import calc_cond_uncond_batch + + BACKEND = "Forge" + + +class PerturbedAttention: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "adaptive_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "round": 0.0001}), + "unet_block": (["input", "middle", "output"], {"default": "middle"}), + "unet_block_id": ("INT", {"default": 0}), + "sigma_start": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "sigma_end": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "rescale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "rescale_mode": (["full", "partial", "snf"], {"default": "full"}), + }, + "optional": { + "unet_block_list": ("STRING", {"default": ""}), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch( + self, + model: ModelPatcher, + scale: float = 3.0, + adaptive_scale: float = 0.0, + unet_block: str = "middle", + unet_block_id: int = 0, + sigma_start: float = -1.0, + sigma_end: float = -1.0, + rescale: float = 0.0, + rescale_mode: str = "full", + unet_block_list: str = "", + ): + m = model.clone() + + sigma_start = float("inf") if sigma_start < 0 else sigma_start + single_block = (unet_block, unet_block_id, None) + blocks, block_names = ( + parse_unet_blocks(model, unet_block_list, "attn1") if unet_block_list else ([single_block], None) + ) + + def post_cfg_function(args): + """CFG+PAG""" + model = args["model"] + cond_pred = args["cond_denoised"] + uncond_pred = args["uncond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + signal_scale = scale + if adaptive_scale > 0: + t = 0 + if hasattr(model, "model_sampling"): + t = model.model_sampling.timestep(sigma)[0].item() + else: + ts = model.predictor.timestep(sigma) + t = ts[0].item() + signal_scale -= scale * (adaptive_scale**4) * (1000 - t) + if signal_scale < 0: + signal_scale = 0 + + if signal_scale == 0 or not (sigma_end < sigma[0] <= sigma_start): + return cfg_result + + # Replace Self-attention with PAG + for block in blocks: + layer, number, index = block + model_options = set_model_options_patch_replace( + model_options, perturbed_attention, "attn1", layer, number, index + ) + + if BACKEND == "ComfyUI": + (pag_cond_pred,) = calc_cond_batch(model, [cond], x, sigma, model_options) + if BACKEND in {"Forge", "reForge"}: + (pag_cond_pred, _) = calc_cond_uncond_batch(model, cond, None, x, sigma, model_options) + + pag = (cond_pred - pag_cond_pred) * signal_scale + + if rescale_mode == "snf": + if uncond_pred.any(): + return uncond_pred + snf_guidance(cfg_result - uncond_pred, pag) + return cfg_result + pag + + return cfg_result + rescale_guidance(pag, cond_pred, cfg_result, rescale, rescale_mode) + + m.set_model_sampler_post_cfg_function(post_cfg_function, rescale_mode == "snf") + + return (m,) + + +class SmoothedEnergyGuidanceAdvanced: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "blur_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 9999.0, "step": 0.01, "round": 0.001}), + "unet_block": (["input", "middle", "output"], {"default": "middle"}), + "unet_block_id": ("INT", {"default": 0}), + "sigma_start": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "sigma_end": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "rescale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "rescale_mode": (["full", "partial", "snf"], {"default": "full"}), + }, + "optional": { + "unet_block_list": ("STRING", {"default": ""}), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch( + self, + model: ModelPatcher, + scale: float = 3.0, + blur_sigma: float = -1.0, + unet_block: str = "middle", + unet_block_id: int = 0, + sigma_start: float = -1.0, + sigma_end: float = -1.0, + rescale: float = 0.0, + rescale_mode: str = "full", + unet_block_list: str = "", + ): + m = model.clone() + + sigma_start = float("inf") if sigma_start < 0 else sigma_start + single_block = (unet_block, unet_block_id, None) + blocks, block_names = ( + parse_unet_blocks(model, unet_block_list, "attn1") if unet_block_list else ([single_block], None) + ) + + def post_cfg_function(args): + """CFG+SEG""" + model = args["model"] + cond_pred = args["cond_denoised"] + uncond_pred = args["uncond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + signal_scale = scale + + if signal_scale == 0 or not (sigma_end < sigma[0] <= sigma_start): + return cfg_result + + seg_attention = seg_attention_wrapper(optimized_attention, blur_sigma) + + # Replace Self-attention with SEG attention + for block in blocks: + layer, number, index = block + model_options = set_model_options_patch_replace( + model_options, seg_attention, "attn1", layer, number, index + ) + + if BACKEND == "ComfyUI": + (seg_cond_pred,) = calc_cond_batch(model, [cond], x, sigma, model_options) + if BACKEND in {"Forge", "reForge"}: + (seg_cond_pred, _) = calc_cond_uncond_batch(model, cond, None, x, sigma, model_options) + + seg = (cond_pred - seg_cond_pred) * signal_scale + + if rescale_mode == "snf": + if uncond_pred.any(): + return uncond_pred + snf_guidance(cfg_result - uncond_pred, seg) + return cfg_result + seg + + return cfg_result + rescale_guidance(seg, cond_pred, cfg_result, rescale, rescale_mode) + + m.set_model_sampler_post_cfg_function(post_cfg_function, rescale_mode == "snf") + + return (m,) + + +class SlidingWindowGuidanceAdvanced: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "tile_width": ("INT", {"default": 768, "min": 16, "max": 16384, "step": 8}), + "tile_height": ("INT", {"default": 768, "min": 16, "max": 16384, "step": 8}), + "tile_overlap": ("INT", {"default": 256, "min": 16, "max": 16384, "step": 8}), + "sigma_start": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "sigma_end": ("FLOAT", {"default": 5.42, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch( + self, + model: ModelPatcher, + scale: float = 5.0, + tile_width: int = 768, + tile_height: int = 768, + tile_overlap: int = 256, + sigma_start: float = -1.0, + sigma_end: float = 5.42, + ): + m = model.clone() + + sigma_start = float("inf") if sigma_start < 0 else sigma_start + tile_width, tile_height, tile_overlap = tile_width // 8, tile_height // 8, tile_overlap // 8 + + def post_cfg_function(args): + """CFG+SWG""" + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + signal_scale = scale + + if signal_scale == 0 or not (sigma_end < sigma[0] <= sigma_start): + return cfg_result + + calc_func = None + + if BACKEND == "ComfyUI": + calc_func = partial( + calc_cond_batch, + model=model, + conds=[cond], + timestep=sigma, + model_options=model_options, + ) + if BACKEND in {"Forge", "reForge"}: + calc_func = partial( + calc_cond_uncond_batch, + model=model, + cond=cond, + uncond=None, + timestep=sigma, + model_options=model_options, + ) + + swg_pred = swg_pred_calc(x, tile_width, tile_height, tile_overlap, calc_func) + swg = (cond_pred - swg_pred) * signal_scale + + return cfg_result + swg + + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return (m,) diff --git a/custom_nodes/sd-perturbed-attention/pag_trt_nodes.py b/custom_nodes/sd-perturbed-attention/pag_trt_nodes.py new file mode 100644 index 00000000..f9b5d230 --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/pag_trt_nodes.py @@ -0,0 +1,111 @@ +from comfy.model_patcher import ModelPatcher +from comfy.samplers import calc_cond_batch + +from .guidance_utils import parse_unet_blocks, perturbed_attention, rescale_guidance + + +class TRTAttachPag: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "unet_block": (["input", "middle", "output"], {"default": "middle"}), + "unet_block_id": ("INT", {"default": 0}), + }, + "optional": { + "unet_block_list": ("STRING", {"default": ""}), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "attach" + + CATEGORY = "TensorRT" + + def attach( + self, + model: ModelPatcher, + unet_block: str = "middle", + unet_block_id: int = 0, + unet_block_list: str = "", + ): + m = model.clone() + + single_block = (unet_block, unet_block_id, None) + blocks, block_names = ( + parse_unet_blocks(model, unet_block_list, "attn1") if unet_block_list else ([single_block], None) + ) + + # Replace Self-attention with PAG + for block in blocks: + layer, number, index = block + m.set_model_attn1_replace(perturbed_attention, layer, number, index) + + return (m,) + + +class TRTPerturbedAttention: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_base": ("MODEL",), + "model_pag": ("MODEL",), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "adaptive_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "round": 0.0001}), + "sigma_start": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "sigma_end": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "rescale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "rescale_mode": (["full", "partial"], {"default": "full"}), + }, + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "TensorRT" + + def patch( + self, + model_base: ModelPatcher, + model_pag: ModelPatcher, + scale: float = 3.0, + adaptive_scale: float = 0.0, + sigma_start: float = -1.0, + sigma_end: float = -1.0, + rescale: float = 0.0, + rescale_mode: str = "full", + ): + m = model_base.clone() + + sigma_start = float("inf") if sigma_start < 0 else sigma_start + + def post_cfg_function(args): + """CFG+PAG""" + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + x = args["input"] + + signal_scale = scale + if adaptive_scale > 0: + t = model.model_sampling.timestep(sigma)[0].item() + signal_scale -= scale * (adaptive_scale**4) * (1000 - t) + if signal_scale < 0: + signal_scale = 0 + + if signal_scale == 0 or not (sigma_end < sigma[0] <= sigma_start): + return cfg_result + + (pag_cond_pred,) = calc_cond_batch(model_pag.model, [cond], x, sigma, model_pag.model_options) + + pag = (cond_pred - pag_cond_pred) * signal_scale + + return cfg_result + rescale_guidance(pag, cond_pred, cfg_result, rescale, rescale_mode) + + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return (m,) diff --git a/custom_nodes/sd-perturbed-attention/pladis_nodes.py b/custom_nodes/sd-perturbed-attention/pladis_nodes.py new file mode 100644 index 00000000..1302e4e5 --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/pladis_nodes.py @@ -0,0 +1,83 @@ +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from comfy.ldm.modules.attention import BasicTransformerBlock +from comfy.model_base import BaseModel +from comfy.model_patcher import ModelPatcher + +from .guidance_utils import parse_unet_blocks +from .pladis_utils import SPARSE_FUNCTIONS, pladis_attention_wrapper + + +class Pladis(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "model": (IO.MODEL, {}), + "scale": (IO.FLOAT, {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "sparse_func": (IO.COMBO, {"default": SPARSE_FUNCTIONS[0], "options": SPARSE_FUNCTIONS}), + }, + "optional": { + "unet_block_list": ( + IO.STRING, + { + "default": "", + "tooltip": ( + "Comma-separated blocks to which Pladis is being applied to. When the list is empty, PLADIS is being applied to all `u` and `d` blocks.\n" + "Read README from sd-perturbed-attention for more details." + ), + }, + ), + }, + } + + RETURN_TYPES = (IO.MODEL,) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + EXPERIMENTAL = True + + def patch( + self, + model: ModelPatcher, + scale=2.0, + sparse_func=SPARSE_FUNCTIONS[0], + unet_block_list="", + ): + m = model.clone() + inner_model: BaseModel = m.model + pladis_attention = pladis_attention_wrapper(scale, sparse_func) + + blocks, block_names = parse_unet_blocks(m, unet_block_list, "attn2") if unet_block_list else (None, None) + + # Apply PLADIS only to transformer blocks with cross-attention (attn2) + for name, module in ( + (n, m) + for n, m in inner_model.diffusion_model.named_modules() + if isinstance(m, BasicTransformerBlock) and getattr(m, "attn2", None) + ): + parts = name.split(".") + block_name: str = parts[0].split("_")[0] + block_id = int(parts[1]) + if block_name == "middle": + block_id = block_id - 1 + if not blocks: + continue + + t_idx = None + if "transformer_blocks" in parts: + t_pos = parts.index("transformer_blocks") + 1 + t_idx = int(parts[t_pos]) + + if not blocks or (block_name, block_id, t_idx) in blocks or (block_name, block_id, None) in blocks: + m.set_model_attn2_replace(pladis_attention, block_name, block_id, t_idx) + + return (m,) + + +NODE_CLASS_MAPPINGS = { + "PLADIS": Pladis, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PLADIS": "PLADIS", +} diff --git a/custom_nodes/sd-perturbed-attention/pladis_utils.py b/custom_nodes/sd-perturbed-attention/pladis_utils.py new file mode 100644 index 00000000..5c3b766a --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/pladis_utils.py @@ -0,0 +1,166 @@ +from typing import Optional + +import torch + +ENTMAX15_FUNC = "entmax1.5" # sparse attention with alpha=1.5 +SPARSEMAX_FUNC = "sparsemax" # sparse attention with alpha=2 + +SPARSE_FUNCTIONS: list = [ENTMAX15_FUNC, SPARSEMAX_FUNC] + + +def pladis_attention_wrapper(pladis_scale=2.0, sparse_func=SPARSE_FUNCTIONS[0]): + # Simplified attention_basic with sparse functions instead of a softmax + def _pladis_sparse_attention( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + extra_options: dict, + ): + heads = extra_options["n_heads"] + attn_precision = extra_options.get("attn_precision") + + b, _, dim_head = q.shape + dim_head //= heads + + scale: int = dim_head**-0.5 + + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + sim = q @ k.transpose(-2, -1) * scale + + del q, k + + dense_sim = torch.softmax(sim, dim=-1) + if sparse_func == ENTMAX15_FUNC: + sparse_sim = Entmax.entmax15(sim, dim=-1) + elif sparse_func == SPARSEMAX_FUNC: + sparse_sim = Entmax.sparsemax(sim, dim=-1) + else: # fallback to the default from paper + sparse_sim = Entmax.entmax15(sim, dim=-1) + + pladis_sim = pladis_scale * sparse_sim + (1 - pladis_scale) * dense_sim + + out = pladis_sim.to(v.dtype) @ v + + out = out.unsqueeze(0).reshape(b, heads, -1, dim_head).permute(0, 2, 1, 3).reshape(b, -1, heads * dim_head) + return out + + return _pladis_sparse_attention + + +class Entmax: + """ + Activations from `entmax` module converted to a static class. + + Both sparsemax and entmax15, and all their inner function implementations + are taken from https://github.com/deep-spin/entmax/blob/c2bec6d5e7d649cba7766c2172d89123ec2a6d70/entmax/activations.py + (as recommended by PLADIS paper). + + Author: Ben Peters + + Author: Vlad Niculae + + License: MIT + """ + + @staticmethod + def entmax15(X: torch.Tensor, dim=-1, k: Optional[int] = None): + max_val, _ = X.max(dim=dim, keepdim=True) + X = X - max_val # same numerical stability trick as for softmax + X = X / 2 # divide by 2 to solve actual Entmax + + tau_star, _ = Entmax._entmax_threshold_and_support(X, dim=dim, k=k) + + Y = torch.clamp(X - tau_star, min=0) ** 2 + return Y + + @staticmethod + def sparsemax(X: torch.Tensor, dim=-1, k: Optional[int] = None): + max_val, _ = X.max(dim=dim, keepdim=True) + X = X - max_val # same numerical stability trick as softmax + + tau, _ = Entmax._sparsemax_threshold_and_support(X, dim=dim, k=k) + + output = torch.clamp(X - tau, min=0) + return output + + @staticmethod + def _entmax_threshold_and_support(X, dim=-1, k=None): + if k is None or k >= X.shape[dim]: # do full sort + Xsrt, _ = torch.sort(X, dim=dim, descending=True) + else: + Xsrt, _ = torch.topk(X, k=k, dim=dim) + + rho = Entmax._make_ix_like(Xsrt, dim) + mean = Xsrt.cumsum(dim) / rho + mean_sq = (Xsrt**2).cumsum(dim) / rho + ss = rho * (mean_sq - mean**2) + delta = (1 - ss) / rho + + delta_nz = torch.clamp(delta, 0) + tau = mean - torch.sqrt(delta_nz) + + support_size = (tau <= Xsrt).sum(dim).unsqueeze(dim) + tau_star = tau.gather(dim, support_size - 1) + + if k is not None and k < X.shape[dim]: + unsolved = (support_size == k).squeeze(dim) + + if torch.any(unsolved): + X_ = Entmax._roll_last(X, dim)[unsolved] + tau_, ss_ = Entmax._entmax_threshold_and_support(X_, dim=-1, k=2 * k) + Entmax._roll_last(tau_star, dim)[unsolved] = tau_ + Entmax._roll_last(support_size, dim)[unsolved] = ss_ + + return tau_star, support_size + + @staticmethod + def _sparsemax_threshold_and_support(X: torch.Tensor, dim=-1, k=None): + if k is None or k >= X.shape[dim]: # do full sort + topk, _ = torch.sort(X, dim=dim, descending=True) + else: + topk, _ = torch.topk(X, k=k, dim=dim) + + topk_cumsum = topk.cumsum(dim) - 1 + rhos = Entmax._make_ix_like(topk, dim) + support = rhos * topk > topk_cumsum + + support_size = support.sum(dim=dim).unsqueeze(dim) + tau = topk_cumsum.gather(dim, support_size - 1) + tau /= support_size.to(X.dtype) + + if k is not None and k < X.shape[dim]: + unsolved = (support_size == k).squeeze(dim) + + if torch.any(unsolved): + in_ = Entmax._roll_last(X, dim)[unsolved] + tau_, ss_ = Entmax._sparsemax_threshold_and_support(in_, dim=-1, k=2 * k) + Entmax._roll_last(tau, dim)[unsolved] = tau_ + Entmax._roll_last(support_size, dim)[unsolved] = ss_ + + return tau, support_size + + @staticmethod + def _make_ix_like(X: torch.Tensor, dim=-1): + d = X.size(dim) + rho = torch.arange(1, d + 1, device=X.device, dtype=X.dtype) + view = [1] * X.dim() + view[0] = -1 + return rho.view(view).transpose(0, dim) + + @staticmethod + def _roll_last(X: torch.Tensor, dim=-1): + if dim == -1: + return X + elif dim < 0: + dim = X.dim() - dim + + perm = [i for i in range(X.dim()) if i != dim] + [dim] + return X.permute(perm) diff --git a/custom_nodes/sd-perturbed-attention/pyproject.toml b/custom_nodes/sd-perturbed-attention/pyproject.toml new file mode 100644 index 00000000..be02699f --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "sd-perturbed-attention" +description = "Perturbed-Attention Guidance (PAG), Smoothed Energy Guidance (SEG), Sliding Window Guidance (SWG), PLADIS, Normalized Attention Guidance (NAG), Token Perturbation Guidance (TPG) for ComfyUI and SD reForge." +version = "1.2.15" +license = { text = "MIT License" } + +[project.urls] +Repository = "https://github.com/pamparamm/sd-perturbed-attention" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "pamparamm" +DisplayName = "sd-perturbed-attention" +Icon = "" diff --git a/custom_nodes/sd-perturbed-attention/res/comfyui-node-pag-advanced.png b/custom_nodes/sd-perturbed-attention/res/comfyui-node-pag-advanced.png new file mode 100644 index 00000000..9565fa80 Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/comfyui-node-pag-advanced.png differ diff --git a/custom_nodes/sd-perturbed-attention/res/comfyui-node-pag-basic.png b/custom_nodes/sd-perturbed-attention/res/comfyui-node-pag-basic.png new file mode 100644 index 00000000..df7661fd Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/comfyui-node-pag-basic.png differ diff --git a/custom_nodes/sd-perturbed-attention/res/comfyui-node-seg.png b/custom_nodes/sd-perturbed-attention/res/comfyui-node-seg.png new file mode 100644 index 00000000..80993eb3 Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/comfyui-node-seg.png differ diff --git a/custom_nodes/sd-perturbed-attention/res/forge-pag.png b/custom_nodes/sd-perturbed-attention/res/forge-pag.png new file mode 100644 index 00000000..36684b19 Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/forge-pag.png differ diff --git a/custom_nodes/sd-perturbed-attention/res/forge-seg.png b/custom_nodes/sd-perturbed-attention/res/forge-seg.png new file mode 100644 index 00000000..cfe7784a Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/forge-seg.png differ diff --git a/custom_nodes/sd-perturbed-attention/res/trt-engines.png b/custom_nodes/sd-perturbed-attention/res/trt-engines.png new file mode 100644 index 00000000..d3314112 Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/trt-engines.png differ diff --git a/custom_nodes/sd-perturbed-attention/res/trt-inference.png b/custom_nodes/sd-perturbed-attention/res/trt-inference.png new file mode 100644 index 00000000..6b73da56 Binary files /dev/null and b/custom_nodes/sd-perturbed-attention/res/trt-inference.png differ diff --git a/custom_nodes/sd-perturbed-attention/tpg_nodes.py b/custom_nodes/sd-perturbed-attention/tpg_nodes.py new file mode 100644 index 00000000..2ba0bbcd --- /dev/null +++ b/custom_nodes/sd-perturbed-attention/tpg_nodes.py @@ -0,0 +1,127 @@ +from typing import Any + +import torch +from torch import nn + +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from comfy.ldm.modules.attention import BasicTransformerBlock +from comfy.model_base import BaseModel +from comfy.model_patcher import ModelPatcher +from comfy.samplers import calc_cond_batch + +from .guidance_utils import parse_unet_blocks, rescale_guidance, set_model_options_value, snf_guidance + +TPG_OPTION = "tpg" + + +# Implementation of 2506.10036 'Token Perturbation Guidance for Diffusion Models' +class TPGTransformerWrapper(nn.Module): + def __init__(self, transformer_block: BasicTransformerBlock) -> None: + super().__init__() + self.wrapped_block = transformer_block + + def shuffle_tokens(self, x: torch.Tensor): + # ComfyUI's torch.manual_seed generator should produce the same results here. + permutation = torch.randperm(x.shape[1], device=x.device) + return x[:, permutation] + + def forward(self, x: torch.Tensor, context: torch.Tensor | None = None, transformer_options: dict[str, Any] = {}): + is_tpg = transformer_options.get(TPG_OPTION, False) + x_ = self.shuffle_tokens(x) if is_tpg else x + return self.wrapped_block(x_, context=context, transformer_options=transformer_options) + + +class TokenPerturbationGuidance(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "model": (IO.MODEL, {}), + "scale": (IO.FLOAT, {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + "sigma_start": (IO.FLOAT, {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "sigma_end": (IO.FLOAT, {"default": -1.0, "min": -1.0, "max": 10000.0, "step": 0.01, "round": False}), + "rescale": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "rescale_mode": (IO.COMBO, {"options": ["full", "partial", "snf"], "default": "full"}), + }, + "optional": { + "unet_block_list": (IO.STRING, {"default": "d2.2-9,d3", "tooltip": "Blocks to which TPG is applied. "}), + }, + } + + RETURN_TYPES = (IO.MODEL,) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch( + self, + model: ModelPatcher, + scale: float = 3.0, + sigma_start: float = -1.0, + sigma_end: float = -1.0, + rescale: float = 0.0, + rescale_mode: str = "full", + unet_block_list: str = "", + ): + m = model.clone() + inner_model: BaseModel = m.model + + sigma_start = float("inf") if sigma_start < 0 else sigma_start + + blocks, block_names = parse_unet_blocks(model, unet_block_list, None) if unet_block_list else (None, None) + + # Patch transformer blocks with TPG wrapper + for name, module in inner_model.diffusion_model.named_modules(): + if ( + isinstance(module, BasicTransformerBlock) + and not "wrapped_block" in name + and (block_names is None or name in block_names) + ): + # Potential memory leak? + wrapper = TPGTransformerWrapper(module) + m.add_object_patch(f"diffusion_model.{name}", wrapper) + + def post_cfg_function(args): + """CFG+TPG""" + model: BaseModel = args["model"] + cond_pred = args["cond_denoised"] + uncond_pred = args["uncond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + signal_scale = scale + + if signal_scale == 0 or not (sigma_end < sigma[0] <= sigma_start): + return cfg_result + + # Enable TPG in patched transformer blocks + for name, module in model.diffusion_model.named_modules(): + if isinstance(module, TPGTransformerWrapper): + set_model_options_value(model_options, TPG_OPTION, True) + + (tpg_cond_pred,) = calc_cond_batch(model, [cond], x, sigma, model_options) + + tpg = (cond_pred - tpg_cond_pred) * signal_scale + + if rescale_mode == "snf": + if uncond_pred.any(): + return uncond_pred + snf_guidance(cfg_result - uncond_pred, tpg) + return cfg_result + tpg + + return cfg_result + rescale_guidance(tpg, cond_pred, cfg_result, rescale, rescale_mode) + + m.set_model_sampler_post_cfg_function(post_cfg_function, rescale_mode == "snf") + + return (m,) + + +NODE_CLASS_MAPPINGS = { + "TokenPerturbationGuidance": TokenPerturbationGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TokenPerturbationGuidance": "Token Perturbation Guidance", +} diff --git a/custom_nodes/was-node-suite-comfyui/.gitignore b/custom_nodes/was-node-suite-comfyui/.gitignore new file mode 100644 index 00000000..542f1cba --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/.gitignore @@ -0,0 +1,166 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*.code-workspace + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ +*.pyc + +# Custom +was_suite_settings.json +styles.json +was_suite_config.json +workflows/ +was_history.json +nsp_pantry.json +cache/ +*.latent +*.image +*.conditioning diff --git a/custom_nodes/was-node-suite-comfyui/ComfyUI_+_WAS_Node_Suite_and_ComfyUI_Manager.ipynb b/custom_nodes/was-node-suite-comfyui/ComfyUI_+_WAS_Node_Suite_and_ComfyUI_Manager.ipynb new file mode 100644 index 00000000..f9bf91e7 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/ComfyUI_+_WAS_Node_Suite_and_ComfyUI_Manager.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "# **Comfy**UI + **WAS** Node Suite   [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fcolab.research.google.com%2Fgithub%2FWASasquatch%2Fcomfyui-colab-was-node-suite%2Fblob%2Fmain%2FComfyUI_%252B_WAS_Node_Suite.ipynb&count_bg=%23EAAC00&title_bg=%233092C6&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)\n", + "A version of ComfyUI Colab with WAS Node Suite installatoin." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Environment Setup\n", + "#@markdown Download and install ComfyUI + WAS Node Suite. You can run this cell again with the `UPDATE_COMFY_UI` or `UPDATE_WAS_NS` options selected to update.\n", + "\n", + "from pathlib import Path\n", + "import time\n", + "\n", + "OPTIONS = {}\n", + "\n", + "#@markdown Store ComfyUI on Google Drive instead of Colab\n", + "USE_GOOGLE_DRIVE = True #@param {type:\"boolean\"}\n", + "#markdown Update ComfyUI\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "#@markdown Update WAS Node Suite\n", + "UPDATE_WAS_NS = True #@param {type:\"boolean\"}\n", + "#@markdown Update Pillow for WAS NS:\n", + "UPDATE_PILLOW = False #@param {type:\"boolean\"}\n", + "#@markdown ComfyUI Manager:\n", + "USE_COMFYUI_MANAGER = True #@param {type:\"boolean\"}\n", + "UPDATE_COMFYUI_MANAGER = True #@param {type:\"boolean\"}\n", + "\n", + "WORKSPACE = '/content/ComfyUI'\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "\n", + "if USE_GOOGLE_DRIVE:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI $WORKSPACE\n", + "%cd $WORKSPACE\n", + "\n", + "if UPDATE_COMFY_UI:\n", + " !echo -= Updating ComfyUI =-\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "!pip install xformers -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118\n", + "!git clone https://github.com/WASasquatch/was-node-suite-comfyui $WORKSPACE/custom_nodes/was-node-suite-comfyui\n", + "\n", + "if USE_COMFYUI_MANAGER:\n", + " !git clone https://github.com/ltdrdata/ComfyUI-Manager.git $WORKSPACE/custom_nodes/ComfyUI-Manager\n", + "\n", + "if UPDATE_WAS_NS:\n", + " %cd $WORKSPACE/custom_nodes/was-node-suite-comfyui\n", + " !git pull\n", + " %cd $WORKSPACE\n", + "\n", + "if UPDATE_COMFYUI_MANAGER:\n", + " %cd $WORKSPACE/custom_nodes/ComfyUI-Manager\n", + " !git pull\n", + " %cd $WORKSPACE\n", + "\n", + "if UPDATE_PILLOW:\n", + " !pip install --upgrade --force-reinstall pillow\n", + " print('\\n\\033[91m\\033[1mRestarting runtime for Pillow Update. Run this cell again without `UPDATE_PILLOW` selected!\\033[0m')\n", + " time.sleep(5)\n", + "\n", + " import os\n", + " os.kill(os.getpid(), 9)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Download Models\n", + "#@markdown Download models and other resources to use in ComfyUI. Select your options and run the this cell. Can run multiple times with different options.\n", + "\n", + "#@markdown ---\n", + "\n", + "# Checkpoints\n", + "\n", + "MODEL_OPTION = 'stable-diffusion-xl-base-1.0.safetensors' #@param['None', 'stable-diffusion-xl-base-1.0.safetensors', 'sd_xl_base_1.0_0.9vae.safetensors', 'sd_xl_base_0.9.safetensors', 'v1-5-pruned-emaonly.ckpt', 'v2-1_512-ema-pruned.safetensors', 'v2-1_768-ema-pruned.safetensors', 'AbyssOrangeMix2_hard.safetensors', 'AOM3A1_orangemixs.safetensors', 'AOM3A3_orangemixs.safetensors', 'wd-1-5-beta2-fp16.safetensors']\n", + "VAE_OPTION = 'vae-ft-mse-840000-ema-pruned.safetensors' #@param['None', 'vae-ft-mse-840000-ema-pruned.safetensors', 'orangemix.vae.pt', 'kl-f8-anime2.ckpt']\n", + "UPSCALE_MODEL_OPTION = 'None' #@param['None', 'RealESRGAN_x2.pth', 'RealESRGAN_x4.pth', '4x-UltraSharp', '4x_RealisticRescaler_100000_G.pth', 'BSRGAN.pth', 'BSRGANx2.pth']\n", + "LORA_OPTION = 'None' #@param['None', 'theovercomer8sContrastFix_sd21768.safetensors', 'theovercomer8sContrastFix_sd15.safetensors']\n", + "T2I_OPTION = 'None' #@param['None', 't2iadapter_depth_sd14v1.pth', 't2iadapter_seg_sd14v1.pth', 't2iadapter_sketch_sd14v1.pth', 't2iadapter_keypose_sd14v1.pth', 't2iadapter_openpose_sd14v1.pth', 't2iadapter_color_sd14v1.pth', 't2iadapter_canny_sd14v1.pth', '/t2iadapter_style_sd14v1.pth']\n", + "CONTROLNET_OPTION = 'None' #@param['None', 'control_depth-fp16.safetensors', 'control_scribble-fp16.safetensors', 'control_openpose-fp16.safetensors']\n", + "\n", + "#@markdown ---\n", + "\n", + "#@markdown **Download and instlal CLIPVision**:\n", + "DOWNLOAD_CLIPVISION = False #@param {type:\"boolean\"}\n", + "#@markdown **ControlNet Preprocessor Nodes** by Fannovel16:\n", + "INSTALL_CONTROLNET_NODES = False #@param {type:\"boolean\"}\n", + "\n", + "# SDXL\n", + "if MODEL_OPTION == 'stable-diffusion-xl-base-1.0.safetensors':\n", + " !wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P $WORKSPACE/models/checkpoints/\n", + "if MODEL_OPTION == 'sd_xl_base_1.0_0.9vae.safetensors':\n", + " !wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors -P $WORKSPACE/models/checkpoints/\n", + "if MODEL_OPTION == 'sd_xl_base_0.9.safetensors':\n", + " !wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/resolve/main/sd_xl_base_0.9.safetensors -P $WORKSPACE/models/checkpoints/\n", + "\n", + "\n", + "# SD1.5\n", + "if MODEL_OPTION == 'v1-5-pruned-emaonly.ckpt':\n", + " !wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P $WORKSPACE/models/checkpoints/\n", + "\n", + "# SD2\n", + "if MODEL_OPTION == 'v2-1_512-ema-pruned.safetensors':\n", + " !wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P $WORKSPACE/models/checkpoints/\n", + "if MODEL_OPTION == 'v2-1_768-ema-pruned.safetensors':\n", + " !wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P $WORKSPACE/models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "if MODEL_OPTION == 'AbyssOrangeMix2_hard.safetensors':\n", + " !wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P $WORKSPACE/models/checkpoints/\n", + "if MODEL_OPTION == 'AOM3A1_orangemixs.safetensors':\n", + " !wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P $WORKSPACE/models/checkpoints/\n", + "if MODEL_OPTION == 'AOM3A3_orangemixs.safetensors':\n", + " !wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P $WORKSPACE/models/checkpoints/\n", + "if MODEL_OPTION == 'anything-v3-fp16-pruned.safetensors':\n", + " !wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P $WORKSPACE/models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "if MODEL_OPTION == 'wd-1-5-beta2-fp16.safetensors':\n", + " !wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp16.safetensors -P $WORKSPACE/models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "if VAE_OPTION == 'vae-ft-mse-840000-ema-pruned.safetensors':\n", + " !wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P $WORKSPACE/models/vae/\n", + "if VAE_OPTION == 'orangemix.vae.pt':\n", + " !wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P $WORKSPACE/models/vae/'\n", + "if VAE_OPTION == 'kl-f8-anime2.ckpt':\n", + " !wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P $WORKSPACE/models/vae/\n", + "\n", + "\n", + "# Loras\n", + "if LORA_OPTION == 'theovercomer8sContrastFix_sd21768.safetensors':\n", + " !wget -c https://civitai.com/api/download/models/10350 -O $WORKSPACE/models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "if LORA_OPTION == 'theovercomer8sContrastFix_sd15.safetensors':\n", + " !wget -c https://civitai.com/api/download/models/10638 -O $WORKSPACE/models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "\n", + "\n", + "# T2I-Adapter\n", + "if T2I_OPTION == 't2iadapter_depth_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "if T2I_OPTION == 't2iadapter_seg_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "if T2I_OPTION == 't2iadapter_sketch_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "if T2I_OPTION == 't2iadapter_keypose_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "if T2I_OPTION == 't2iadapter_openpose_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "if T2I_OPTION == 't2iadapter_color_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "if T2I_OPTION == 't2iadapter_canny_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P $WORKSPACE/models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "if T2I_OPTION == '/t2iadapter_style_sd14v1.pth':\n", + " !wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P $WORKSPACE/models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "if DOWNLOAD_CLIPVISION:\n", + " !wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O $WORKSPACE/models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "if CONTROLNET_OPTION == 'control_depth-fp16.safetensors':\n", + " !wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors -P $WORKSPACE/models/controlnet/\n", + "if CONTROLNET_OPTION == 'control_scribble-fp16.safetensors':\n", + " !wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors -P $WORKSPACE/models/controlnet/\n", + "if CONTROLNET_OPTION == 'control_openpose-fp16.safetensors':\n", + " !wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors -P $WORKSPACE/models/controlnet/\n", + "\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "if INSTALL_CONTROLNET_NODES:\n", + " !cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "# ESRGAN upscale model\n", + "if UPSCALE_MODEL_OPTION == 'RealESRGAN_x2.pth':\n", + " !wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P $WORKSPACE/models/upscale_models/\n", + "if UPSCALE_MODEL_OPTION == 'RealESRGAN_x4.pth':\n", + " !wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P $WORKSPACE/models/upscale_models/\n", + "if UPSCALE_MODEL_OPTION == '4x-UltraSharp':\n", + " !wget -c https://huggingface.co/uwg/upscaler/resolve/main/ESRGAN/4x-UltraSharp.pth -P $WORKSPACE/models/upscale_models/\n", + "if UPSCALE_MODEL_OPTION == '4x_RealisticRescaler_100000_G.pth':\n", + " !wget -c https://huggingface.co/uwg/upscaler/resolve/main/ESRGAN/4x_RealisticRescaler_100000_G.pth -P $WORKSPACE/models/upscale_models/\n", + "if UPSCALE_MODEL_OPTION == 'BSRGAN.pth':\n", + " !wget -c https://huggingface.co/uwg/upscaler/resolve/main/ESRGAN/BSRGAN.pth -P $WORKSPACE/models/upscale_models/\n", + "if UPSCALE_MODEL_OPTION == 'BSRGANx2.pth':\n", + " !wget -c https://huggingface.co/uwg/upscaler/resolve/main/ESRGAN/BSRGANx2.pth -P $WORKSPACE/models/upscale_models/\n", + "\n" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Direct Download Models\n", + "\n", + "import os\n", + "\n", + "types = {\n", + " 'CHECKPOINTS': os.path.join(WORKSPACE, 'models/checkpoints'),\n", + " 'CLIP': os.path.join(WORKSPACE, 'models/clip'),\n", + " 'CLIP_VISION': os.path.join(WORKSPACE, 'models/clip_vision'),\n", + " 'CONFIGS': os.path.join(WORKSPACE, 'models/configs'),\n", + " 'CONTROLNET': os.path.join(WORKSPACE, 'models/controlnet'),\n", + " 'DIFFUSERS': os.path.join(WORKSPACE, 'models/diffusers'),\n", + " 'EMBEDDINGS': os.path.join(WORKSPACE, 'models/embeddings'),\n", + " 'GLIGEN': os.path.join(WORKSPACE, 'models/gligen'),\n", + " 'HYPERNETWORKS': os.path.join(WORKSPACE, 'models/hypernetworks'),\n", + " 'LORAS': os.path.join(WORKSPACE, 'models/loras'),\n", + " 'STYLE_MODEL': os.path.join(WORKSPACE, 'models/style_models'),\n", + " 'UNET': os.path.join(WORKSPACE, 'models/unet'),\n", + " 'UPSCALE_MODELS': os.path.join(WORKSPACE, 'models/upscale_models'),\n", + " 'VAE': os.path.join(WORKSPACE, 'models/vae'),\n", + " 'VAE_APPROX': os.path.join(WORKSPACE, 'models/vae_approx')\n", + "}\n", + "\n", + "TYPE = 'CHECKPOINTS' #@param ['CHECKPOINTS', 'CLIP', 'CLIP_VISION', 'CONFIGS', 'CONTROLNET', 'DIFFUSERS', 'EMBEDDINGS', 'GLIGEN', 'HYPERNETWORKS', 'LORAS', 'STYLE_MODEL', 'UNET', 'UPSCALE_MODELS', 'VAE', 'VAE_APPROX']\n", + "DIRECT_URL = 'https://civitai.com/api/download/models/141627' #@param {type:\"string\"}\n", + "SAVE_AS = 'Differentia_V1.safetensors' #@param {type: 'string'}\n", + "#@markdown Direct link to the download. The example URL is Differentia (https://civitai.com/models/129232)\n", + "\n", + "if TYPE and DIRECT_URL:\n", + " target = os.path.join(types[TYPE], SAVE_AS)\n", + " !wget -c $DIRECT_URL -O $target" + ], + "metadata": { + "cellView": "form", + "id": "Bo0pf4So3tCK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@title ComfyUI Cloudfare (Recommended)\n", + "#@markdown Running ComfyUI with Cloudfare is now the recommended method.\n", + "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ], + "metadata": { + "cellView": "form", + "id": "StSynv5tp2nL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title ComfyUI Localtunnel\n", + "#@markdown Run this cell to start ComfyUI. You'll see a link similar to `your url is: https://slow-yaks-jog-34-72-173-3.loca.lt` (example)\n", + "#@markdown
        *If you have trouble with the red screen of death \"reminder\" not letting you generate, use the iFrame version below.*\n", + "!npm install -g localtunnel\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe ***(use only in case the previous ways don't work)***\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title ComfyUI iFrame\n", + "import threading\n", + "import time\n", + "import socket\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "private_outputs": true, + "gpuType": "T4", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/LICENSE b/custom_nodes/was-node-suite-comfyui/LICENSE new file mode 100644 index 00000000..cda3c409 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Jordan Thompson (WASasquatch) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/custom_nodes/was-node-suite-comfyui/README.md b/custom_nodes/was-node-suite-comfyui/README.md new file mode 100644 index 00000000..d316d6ff --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/README.md @@ -0,0 +1,415 @@ +# **WAS** Node Suite   [![Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/github/WASasquatch/was-node-suite-comfyui/blob/main/ComfyUI_%2B_WAS_Node_Suite_and_ComfyUI_Manager.ipynb) [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FWASasquatch%2Fwas-node-suite-comfyui&count_bg=%233D9CC8&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com) [![Donate](https://img.shields.io/badge/Donate-PayPal-blue.svg)](https://paypal.me/ThompsonJordan?country.x=US&locale.x=en_US) + +

        + +

        + +### A node suite for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) with many new nodes, such as image processing, text processing, and more. + +#### [Share Workflows](https://github.com/WASasquatch/was-node-suite-comfyui/wiki/Workflow-Examples) to the workflows wiki. Preferably embedded PNGs with workflows, but JSON is OK too. [You can use this tool to add a workflow to a PNG file easily](https://colab.research.google.com/drive/1hQMjNUdhMQ3rw1Wcm3_umvmOMeS_K4s8?usp=sharing). +#### Consider [donating to the project](https://paypal.me/ThompsonJordan?country.x=US&locale.x=en_US) to help it's continued development. + +# Important Updates + +- **12/15/2023** WAS-NS is not under active development. I do not have the time and have other obligations. Feel free to fork and continue the project. I will approve appropriate and beneficial PRs. +- **[Updated 10/8/2023]** BLIP is now a shipped module of WAS-NS and no longer requires the BLIP Repo + - **[Updated 5/29/2023]** `ASCII` **is deprecated**. The new preferred method of text node output is `STRING`. This is a change from `ASCII` so that it is more clear what data is being passed. + - The `was_suite_config.json` will automatically set `use_legacy_ascii_text` to `false`. + - [Video Nodes](https://github.com/WASasquatch/was-node-suite-comfyui#video-nodes) - There are two new video nodes, `Write to Video` and `Create Video from Path`. These are experimental nodes. + +# Current Nodes: + +### There is documentation from [Salt AI](https://getsalt.ai/) available here: https://docs.getsalt.ai/md/was-node-suite-comfyui/ + +
        + $\Large\color{orange}{Expand\ Node\ List}$ + +
        + + - BLIP Model Loader: Load a BLIP model to input into the BLIP Analyze node + - BLIP Analyze Image: Get a text caption from a image, or interrogate the image with a question. + - Model will download automatically from default URL, but you can point the download to another location/caption model in `was_suite_config` + - Models will be stored in `ComfyUI/models/blip/checkpoints/` + - SAM Model Loader: Load a SAM Segmentation model + - SAM Parameters: Define your SAM parameters for segmentation of a image + - SAM Parameters Combine: Combine SAM parameters + - SAM Image Mask: SAM image masking + - Image Bounds: Bounds a image + - Inset Image Bounds: Inset a image bounds + - Bounded Image Blend: Blend bounds image + - Bounded Image Blend with Mask: Blend a bounds image by mask + - Bounded Image Crop: Crop a bounds image + - Bounded Image Crop with Mask: Crop a bounds image by mask + - Bus Node: condense the 5 common connectors into one, keep your workspace tidy (Model, Clip, VAE, Positive Conditioning, Negative Conditioning) + - Cache Node: Cache Latnet, Tensor Batches (Image), and Conditioning to disk to use later. + - CLIPTextEncode (NSP): Parse noodle soups from the NSP pantry, or parse wildcards from a directory containing A1111 style wildacrds. + - Wildcards are in the style of `__filename__`, which also includes subdirectories like `__appearance/haircolour__` (if you noodle_key is set to `__`) + - You can set a custom wildcards path in `was_suite_config.json` file with key: + - ` "wildcards_path": "E:\\python\\automatic\\webui3\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards"` + - If no path is set the wildcards dir is located at the root of WAS Node Suite as `/wildcards` + - CLIP Input Switch: Switch between two CLIP inputs based on a boolean switch. + - CLIP Vision Input Switch: Switch between two CLIP Vision inputs based on a boolean switch. + - Conditioning Input Switch: Switch between two conditioning inputs. + - Constant Number + - Control Net Model Input Switch: Switch between two Control Net Model inputs based on a boolean switch. + - Create Grid Image: Create a image grid from images at a destination with customizable glob pattern. Optional border size and color. + - Create Grid Image from Batch: Create a grid image from a batch tensor of images. + - Create Morph Image: Create a GIF/APNG animation from two images, fading between them. + - Create Morph Image by Path: Create a GIF/APNG animation from a path to a directory containing images, with optional pattern. + - Create Video from Path: Create video from images from a specified path. + - CLIPSeg Masking: Mask a image with CLIPSeg and return a raw mask + - CLIPSeg Masking Batch: Create a batch image (from image inputs) and batch mask with CLIPSeg + - Dictionary to Console: Print a dictionary input to the console + - Image Analyze + - Black White Levels + - RGB Levels + - Depends on `matplotlib`, will attempt to install on first run + - Diffusers Hub Down-Loader: Download a diffusers model from the HuggingFace Hub and load it + - Image SSAO (Ambient Occlusion): [Expiremental Beta Node] Create Screen Space Ambient Occlusion with a image and MiDaS depth approximation (or provided depth map). + - Image SSDO (Direct Occlusion): [Expiremental Beta Node] Create a Screen Space Direct Occlusion with a image input. Direct Occlusion presents you with direct lighting highliths, similar to how Ambient Occlusion finds the crevices and shadowy areas around objets. + - Image Aspect Ratio: Fetch image aspect ratio in float format, common format (eg 16:9), and in if the image is portrait, landscape, or square. + - Image Batch: Create one batch out of multiple batched tensors. + - Image Blank: Create a blank image in any color + - Image Blend by Mask: Blend two images by a mask + - Image Blend: Blend two images by opacity + - Image Blending Mode: Blend two images by various blending modes + - Image Bloom Filter: Apply a high-pass based bloom filter + - Image Canny Filter: Apply a canny filter to a image + - Image Chromatic Aberration: Apply chromatic aberration lens effect to a image like in sci-fi films, movie theaters, and video games + - Image Color Palette + - Generate a color palette based on the input image. + - Depends on `scikit-learn`, will attempt to install on first run. + - Supports color range of 8-256 + - Utilizes font in `./res/` unless unavailable, then it will utilize internal better then nothing font. + - Image Crop Face: Crop a face out of a image + - **Limitations:** + - Sometimes no faces are found in badly generated images, or faces at angles + - Sometimes face crop is black, this is because the padding is too large and intersected with the image edge. Use a smaller padding size. + - face_recognition mode sometimes finds random things as faces. It also requires a [CUDA] GPU. + - Only detects one face. This is a design choice to make it's use easy. + - **Notes:** + - Detection runs in succession. If nothing is found with the selected detection cascades, it will try the next available cascades file. + - Image Crop Location: Crop a image to specified location in top, left, right, and bottom locations relating to the pixel dimensions of the image in X and Y coordinats. + - Image Crop Square Location: Crop a location by X/Y center, creating a square crop around that point. + - Image Displacement Warp: Warp a image by a displacement map image by a given amplitude. + - Image Dragan Photography Filter: Apply a Andrzej Dragan photography style to a image + - Image Edge Detection Filter: Detect edges in a image + - Image Film Grain: Apply film grain to a image + - Image Filter Adjustments: Apply various image adjustments to a image + - Image Flip: Flip a image horizontal, or vertical + - Image Gradient Map: Apply a gradient map to a image + - Image Generate Gradient: Generate a gradient map with desired stops and colors + - Image High Pass Filter: Apply a high frequency pass to the image returning the details + - Image History Loader: Load images from history based on the Load Image Batch node. Can define max history in config file. *(requires restart to show last sessions files at this time)* + - Image Input Switch: Switch between two image inputs based on a boolean switch + - Image Levels Adjustment: Adjust the levels of a image + - Image Load: Load a *image* from any path on the system, or a url starting with `http` + - Image Median Filter: Apply a median filter to a image, such as to smooth out details in surfaces + - Image Mix RGB Channels: Mix together RGB channels into a single iamge + - Image Monitor Effects Filter: Apply various monitor effects to a image + - Digital Distortion + - A digital breakup distortion effect + - Signal Distortion + - A analog signal distortion effect on vertical bands like a CRT monitor + - TV Distortion + - A TV scanline and bleed distortion effect + - Image Nova Filter: A image that uses a sinus frequency to break apart a image into RGB frequencies + - Image Perlin Noise: Generate perlin noise + - Image Perlin Power Fractal: Generate a perlin power fractal + - Image Paste Face Crop: Paste face crop back on a image at it's original location and size + - Features a better blending funciton than GFPGAN/CodeFormer so there shouldn't be visible seams, and coupled with Diffusion Result, looks better than GFPGAN/CodeFormer. + - Image Paste Crop: Paste a crop (such as from Image Crop Location) at it's original location and size utilizing the `crop_data` node input. This uses a different blending algorithm then Image Paste Face Crop, which may be desired in certain instances. + - Image Power Noise: Generate power-law noise + - frequency: The frequency parameter controls the distribution of the noise across different frequencies. In the context of Fourier analysis, higher frequencies represent fine details or high-frequency components, while lower frequencies represent coarse details or low-frequency components. Adjusting the frequency parameter can result in different textures and levels of detail in the generated noise. The specific range and meaning of the frequency parameter may vary depending on the noise type. + - attenuation: The attenuation parameter determines the strength or intensity of the noise. It controls how much the noise values deviate from the mean or central value. Higher values of attenuation lead to more significant variations and a stronger presence of noise, while lower values result in a smoother and less noticeable noise. The specific range and interpretation of the attenuation parameter may vary depending on the noise type. + - noise_type: The tyoe of Power-Law noise to generate (white, grey, pink, green, blue) + - Image Paste Crop by Location: Paste a crop top a custom location. This uses the same blending algorithm as Image Paste Crop. + - Image Pixelate: Turn a image into pixel art! Define the max number of colors, the pixelation mode, the random state, and max iterations, and max those sprites shine. + - Image Remove Background (Alpha): Remove the background from a image by threshold and tolerance. + - Image Remove Color: Remove a color from a image and replace it with another + - Image Resize + - Image Rotate: Rotate an image + - Image Rotate Hue: Rotate the hue of a image. A hue_shift of `0.0` would represent no change, and `1.0` would represent a full circle of the hue, and also exhibit no change. + - Image Save: A save image node with format support and path support. + - `show_history` will show previously saved images with the WAS Save Image node. ComfyUI unfortunately resizes displayed images to the same size however, so if images are in different sizes it will force them in a different size. + - Doesn't display images saved outside `/ComfyUI/output/` + - You can save as `webp` if you have webp available to you system. On windows you can get that support with this [precompiled libarary](https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-1.3.0-windows-x64.zip) from the [webp project](https://developers.google.com/speed/webp/download). On linux you can run `apt-get install webp`. + - Image Seamless Texture: Create a seamless texture out of a image with optional tiling + - Image Select Channel: Select a single channel of an RGB image + - Image Select Color: Return the select image only on a black canvas + - Image Shadows and Highlights: Adjust the shadows and highlights of an image + - Image Size to Number: Get the `width` and `height` of an input image to use with **Number** nodes. + - Image Stitch: Stitch images together on different sides with optional feathering blending between them. + - Image Style Filter: Style a image with Pilgram instragram-like filters + - Depends on `pilgram` module + - Image Threshold: Return the desired threshold range of a image + - Image Tile: Split a image up into a image batch of tiles. Can be used with Tensor Batch to Image to select a individual tile from the batch. + - Image Transpose + - Image fDOF Filter: Apply a fake depth of field effect to an image + - Image to Latent Mask: Convert a image into a latent mask + - Image to Noise: Convert a image into noise, useful for init blending or init input to theme a diffusion. + - Images to RGB: Convert a tensor image batch to RGB if they are RGBA or some other mode. + - Image to Seed: Convert a image to a reproducible seed + - Image Voronoi Noise Filter + - A custom implementation of the worley voronoi noise diagram + - Input Switch (Disable until `*` wildcard fix) + - KSampler (WAS): A sampler that accepts a seed as a node inputs + - KSampler Cycle: A KSampler able to do HR pass loops, you can specify an upscale factor, and how many steps to achieve that factor. Accepts a upscale_model, as well as a 1x processor model. A secondary diffusion model can also be used. + - Load Cache: Load cached Latent, Tensor Batch (image), and Conditioning files. + - Load Text File + - Now supports outputting a dictionary named after the file, or custom input. + - The dictionary contains a list of all lines in the file. + - Load Batch Images + - Increment images in a folder, or fetch a single image out of a batch. + - Will reset it's place if the path, or pattern is changed. + - pattern is a glob that allows you to do things like `**/*` to get all files in the directory and subdirectory + or things like `*.jpg` to select only JPEG images in the directory specified. + - Mask to Image: Convert `MASK` to `IMAGE` + - Mask Batch to Mask: Return a single mask from a batch of masks + - Mask Invert: Invert a mask. + - Mask Add: Add masks together. + - Mask Subtract: Subtract from a mask by another. + - Mask Dominant Region: Return the dominant region in a mask (the largest area) + - Mask Minority Region: Return the smallest region in a mask (the smallest area) + - Mask Crop Dominant Region: Crop mask to the dominant region with optional padding in pixels + - Mask Crop Minority Region: Crop mask to the minority region with optional padding in pixels + - Mask Crop Region: Crop to dominant or minority region and return `crop_data` for pasting back. Additionally outputs region location and size for other nodes like Crop Image Location. + - Mask Arbitrary Region: Return a region that most closely matches the size input (size is not a direct representation of pixels, but approximate) + - Mask Smooth Region: Smooth the boundaries of a mask + - Mask Erode Region: Erode the boundaries of a mask + - Mask Dilate Region: Dilate the boundaries of a mask + - Mask Fill Region: Fill holes within the masks regions + - Mask Ceiling Region": Return only white pixels within a offset range. + - Mask Floor Region: Return the lower most pixel values as white (255) + - Mask Threshold Region: Apply a thresholded image between a black value and white value + - Mask Gaussian Region: Apply a Gaussian blur to the mask + - Mask Rect Area: Create a rectangular mask defined by percentages. + - Mask Rect Area (Advanced): Create a rectangular mask defined by pixels and image size. + - Masks Combine Masks: Combine 2 or more masks into one mask. + - Masks Combine Batch: Combine batched masks into one mask. + - Model Input Switch: Switch between two model inputs based on a boolean switch + - ComfyUI Loaders: A set of ComfyUI loaders that also output a string that contains the name of the model being loaded. + - Latent Noise Injection: Inject latent noise into a latent image + - Latent Size to Number: Latent sizes in tensor width/height + - Latent Upscale by Factor: Upscale a latent image by a factor + - Latent Input Switch: Switch between two latent inputs based on a boolean switch + - Logic Boolean: A simple `1` or `0` output to use with logic + - Logic Boolean Primitive: True/False boolean input, to use with native boolean nodes + - Logic AND: Given 2 booleans, performs "AND" + - Logic OR: Given 2 booleans, performs "OR" + - Logic XOR: Given 2 booleans, performs "!=" + - Logic NOT: Given 1 boolean, returns the opposite + - Lora Input Switch: Switch between two LORAs based on a boolean switch + - MiDaS Model Loader: Load a MiDaS model as an optional input for MiDaS Depth Approximation + - MiDaS Depth Approximation: Produce a depth approximation of a single image input + - MiDaS Mask Image: Mask a input image using MiDaS with a desired color + - Number Operation + - Number to Seed + - Number to Float + - Number Input Switch: Switch between two number inputs based on a boolean switch + - Number Input Condition: Compare between two inputs or against the A input + - Number to Int + - Number to String + - Number to Text + - Boolean to Text + - Perlin Power Fractal Latent: Create a power fractal based latent image. Doesn't work with all samplers (unless you add noise). + - Random Number + - Random integer between min and max (inclusive), uniformly distributed random number + - Random float between min and max (inclusive), uniformly distributed random number + - Random number from 0 to 1 inclusive, this will be a 0 or 1 boolean if you use the 'int' output + - Random shuffled list of integers between min and max inclusive. E.g. if min=0 and max=3, a possible outcome would be the string '3,1,2,0' + - Save Text File: Save a text string to a file + - Samples Passthrough (Stat System): Logs RAM, VRAM, and Disk usage to the console. + - Seed: Return a seed + - Tensor Batch to Image: Select a single image out of a latent batch for post processing with filters + - Text Add Tokens: Add custom tokens to parse in filenames or other text. + - Text Add Token by Input: Add custom token by inputs representing single **single line** name and value of the token + - Text Compare: Compare two strings. Returns a boolean if they are the same, a score of similarity, and the similarity or difference text. + - Text Concatenate: Merge two strings + - Text Dictionary Update: Merge two dictionaries + - Text Dictionary Get: Get a value from a dictionary (as a string) + - Text Dictionary Convert: Convert text to dictionary object + - Text Dictionary New: Create a new dictionary + - Text Dictionary Keys: Returns the keys, as a list from a dictionary object + - Text Dictionary To Text: Returns the dictionary as text + - Text File History: Show previously opened text files *(requires restart to show last sessions files at this time)* + - Text Find: Find a substring or pattern within another string. Returns boolean + - Text Find and Replace: Find and replace a substring in a string + - Text Find and Replace by Dictionary: Replace substrings in a ASCII text input with a dictionary. + - The dictionary keys are used as the key to replace, and the list of lines it contains chosen at random based on the seed. + - Text Input Switch: Switch between two text inputs + - Text List: Create a list of text strings + - Text Load Line From File: Load lines from a file sequentially each *batch prompt* run, or select a line index. + - Text Concatenate: Merge lists of strings + - Text Contains: Checks if substring is in another string (case insensitive optional) + - Text Multiline: Write a multiline text string + - Text Parse A1111 Embeddings: Convert embeddings filenames in your prompts to `embedding:[filename]]` format based on your `/ComfyUI/models/embeddings/` files. + - Text Parse Noodle Soup Prompts: Parse NSP in a text input + - Text Parse Tokens: Parse custom tokens in text. + - Text Random Line: Select a random line from a text input string + - Text Random Prompt: Feeling lucky? Get a random prompt based on a search seed, such as "superhero" + - Text String: Write a single line text string value + - Text String Truncate: Truncate a string from the beginning or end by characters or words. + - Text to Conditioning: Convert a text string to conditioning. + - True Random.org Number Generator: Generate a truly random number online from atmospheric noise with [Random.org](https://random.org/) + - [Get your API key from your account page](https://accounts.random.org/) + - Upscale Model Input Switch: Switch between two Upscale Models inputs based on a boolean switch. + - Write to Morph GIF: Write a new frame to an existing GIF (or create new one) with interpolation between frames. + - Write to Video: Write a frame as you generate to a video (Best used with FFV1 for lossless images) + - VAE Input Switch: Switch between two VAE inputs based on boolean input +
        + + +
        + + ### Extra Nodes + + - CLIPTextEncode (BlenderNeko Advanced + NSP): Only available if you have BlenderNeko's [Advanced CLIP Text Encode](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb). Allows for NSP and Wildcard use with their advanced CLIPTextEncode. + + + ### Notes: + + - **CLIPTextEncode (NSP)** and **CLIPTextEncode (BlenderNeko Advanced + NSP)**: Accept dynamic prompts in `` format. This will respect the nodes input seed to yield reproducible results like NSP and Wildcards. + - **CLIPTextEncode (NSP)** and **CLIPTextEncode (BlenderNeko Advanced + NSP)**: Assign variables with `$|prompt words|$` format. You can then print this word again within the prompt with the number corresponding the order you created them. So the first prompt var would be printed with `$1` and the second with `$2` and so on. + +--- + + +## Video Nodes + +### Codecs +You can use codecs that are available to your ffmpeg binaries by adding their fourcc ID (in one string), and appropriate container extension to the `was_suite_config.json` + +Example [H264 Codecs](https://github.com/cisco/openh264/releases/tag/v1.8.0) (Defaults) +``` + "ffmpeg_extra_codecs": { + "avc1": ".mp4", + "h264": ".mkv" + } +``` + +### Notes + - For now I am only supporting **Windows** installations for video nodes. + - I do not have access to Mac or a stand-alone linux distro. If you get them working and want to PR a patch/directions, feel free. + - Video nodes require [FFMPEG](https://ffmpeg.org/download.html). You should download the proper FFMPEG binaries for you system and set the FFMPEG path in the config file. + - Additionally, if you want to use H264 codec need to [download OpenH264 1.8.0](https://github.com/cisco/openh264/releases/tag/v1.8.0) and place it in the root of ComfyUI (Example: `C:\ComfyUI_windows_portable`). + - FFV1 will complain about invalid container. You can ignore this. The resulting MKV file is readable. I have not figured out what this issue is about. Documentaion tells me to use MKV, but it's telling me it's unsupported. + - If you know how to resolve this, I'd love a PR + - `Write to Video` node should use a lossless video codec or when it copies frames, and reapplies compression, it will start expontentially ruining the starting frames run to run. + +--- + +# Text Tokens +Text tokens can be used in the Save Text File and Save Image nodes. You can also add your own custom tokens with the Text Add Tokens node. + +The token name can be anything excluding the `:` character to define your token. It can also be simple Regular Expressions. + +## Built-in Tokens + - [time] + - The current system microtime + - [time(`format_code`)] + - The current system time in human readable format. Utilizing [datetime](https://docs.python.org/3/library/datetime.html) formatting + - Example: `[hostname]_[time]__[time(%Y-%m-%d__%I-%M%p)]` would output: **SKYNET-MASTER_1680897261__2023-04-07__07-54PM** + - [hostname] + - The hostname of the system executing ComfyUI + - [cuda_device] + - The cuda device from `comfy.model_management.get_cuda_device()` + - [cuda_name] + - The cuda name from `comfy.model_management.get_cuda_device_name()` + +
        + +
        + $\color{orange}{Expand\ Date\ Code\ List}$ + +
        + +| Directive | Meaning | Example | Notes | +| --- | --- | --- | --- | +| %a | Weekday as locale’s abbreviated name. | Sun, Mon, …, Sat (en_US); So, Mo, …, Sa (de_DE) | (1) | +| %A | Weekday as locale’s full name. | Sunday, Monday, …, Saturday (en_US); Sonntag, Montag, …, Samstag (de_DE) | (1) | +| %w | Weekday as a decimal number, where 0 is Sunday and 6 is Saturday. | 0, 1, …, 6 | | +| %d | Day of the month as a zero-padded decimal number. | 01, 02, …, 31 | (9) | +| %b | Month as locale’s abbreviated name. | Jan, Feb, …, Dec (en_US); Jan, Feb, …, Dez (de_DE) | (1) | +| %B | Month as locale’s full name. | January, February, …, December (en_US); Januar, Februar, …, Dezember (de_DE) | (1) | +| %m | Month as a zero-padded decimal number. | 01, 02, …, 12 | (9) | +| %y | Year without century as a zero-padded decimal number. | 00, 01, …, 99 | (9) | +| %Y | Year with century as a decimal number. | 0001, 0002, …, 2013, 2014, …, 9998, 9999 | (2) | +| %H | Hour (24-hour clock) as a zero-padded decimal number. | 00, 01, …, 23 | (9) | +| %I | Hour (12-hour clock) as a zero-padded decimal number. | 01, 02, …, 12 | (9) | +| %p | Locale’s equivalent of either AM or PM. | AM, PM (en_US); am, pm (de_DE) | (1), (3) | +| %M | Minute as a zero-padded decimal number. | 00, 01, …, 59 | (9) | +| %S | Second as a zero-padded decimal number. | 00, 01, …, 59 | (4), (9) | +| %f | Microsecond as a decimal number, zero-padded to 6 digits. | 000000, 000001, …, 999999 | (5) | +| %z | UTC offset in the form Β±HHMM[SS[.ffffff]] (empty string if the object is naive). | (empty), +0000, -0400, +1030, +063415, -030712.345216 | (6) | +| %Z | Time zone name (empty string if the object is naive). | (empty), UTC, GMT | (6) | +| %j | Day of the year as a zero-padded decimal number. | 001, 002, …, 366 | (9) | +| %U | Week number of the year (Sunday as the first day of the week) as a zero-padded decimal number. All days in a new year preceding the first Sunday are considered to be in week 0. | 00, 01, …, 53 | (7), (9) | +| %W | Week number of the year (Monday as the first day of the week) as a zero-padded decimal number. All days in a new year preceding the first Monday are considered to be in week 0. | 00, 01, …, 53 | (7), (9) | +| %c | Locale’s appropriate date and time representation. | Tue Aug 16 21:30:00 1988 (en_US); Di 16 Aug 21:30:00 1988 (de_DE) | (1) | +| %x | Locale’s appropriate date representation. | 08/16/88 (None); 08/16/1988 (en_US); 16.08.1988 (de_DE) | (1) | +| %X | Locale’s appropriate time representation. | 21:30:00 (en_US); 21:30:00 (de_DE) | (1) | +| %% | A literal '%' character. | % | | + +
        + +
        + +--- + +# Other Features + +### Import AUTOMATIC1111 WebUI Styles +When using the latest builds of WAS Node Suite a `was_suite_config.json` file will be generated (if it doesn't exist). In this file you can setup a A1111 styles import. + + - Run ComfyUI to generate the new `/custom-nodes/was-node-suite-comfyui/was_Suite_config.json` file. + - Open the `was_suite_config.json` file with a text editor. + - Replace the `webui_styles` value from `None` to the path of your A1111 styles file called **styles.csv**. Be sure to use double backslashes for Windows paths. + - Example `C:\\python\\stable-diffusion-webui\\styles.csv` + - Restart ComfyUI + - Select a style with the `Prompt Styles Node`. + - The first ASCII output is your positive prompt, and the second ASCII output is your negative prompt. + +You can set `webui_styles_persistent_update` to `true` to update the WAS Node Suite styles from WebUI every start of ComfyUI + +# Recommended Installation: +If you're running on Linux, or non-admin account on windows you'll want to ensure `/ComfyUI/custom_nodes`, `was-node-suite-comfyui`, and `WAS_Node_Suite.py` has write permissions. + +There is now a **install.bat** you can run to install to portable if detected. Otherwise it will default to system and assume you followed ConfyUI's manual installation steps. + + - Navigate to your `/ComfyUI/custom_nodes/` folder + - Run `git clone https://github.com/WASasquatch/was-node-suite-comfyui/` + - Navigate to your `was-node-suite-comfyui` folder + - Portable/venv: + - Run `path/to/ComfUI/python_embeded/python.exe -s -m pip install -r requirements.txt` + - With system python + - Run `pip install -r requirements.txt` + - Start ComfyUI + - WAS Suite should uninstall legacy nodes automatically for you. + - Tools will be located in the WAS Suite menu. + +## Alternate [Legacy] Installation: +If you're running on Linux, or non-admin account on windows you'll want to ensure `/ComfyUI/custom_nodes`, and `WAS_Node_Suite.py` has write permissions. + + - Download `WAS_Node_Suite.py` + - Move the file to your `/ComfyUI/custom_nodes/` folder + - WAS Node Suite will attempt install dependencies on it's own, but you may need to manually do so. The dependencies required are in the `requirements.txt` on this repo. See installation steps above. + - If this process fails attempt the following: + - Navigate to your `was-node-suite-comfyui` folder + - Portable/venv: + - Run `path/to/ComfUI/python_embeded/python.exe -s -m pip install -r requirements.txt` + - With system python + - Run `pip install -r requirements.txt` + - Start, or Restart ComfyUI + - WAS Suite should uninstall legacy nodes automatically for you. + - Tools will be located in the WAS Suite menu. + +This method will not install the resources required for Image Crop Face node, and you'll have to download the [./res/](https://github.com/WASasquatch/was-node-suite-comfyui/tree/main/res) folder yourself. + +## Installing on Colab +Create a new cell and add the following code, then run the cell. You may need to edit the path to your `custom_nodes` folder. You can also use the [colab hosted here](https://colab.research.google.com/github/WASasquatch/comfyui-colab-was-node-suite/blob/main/ComfyUI_%2B_WAS_Node_Suite.ipynb) + + - `!git clone https://github.com/WASasquatch/was-node-suite-comfyui /content/ComfyUI/custom_nodes/was-node-suite-comfyui` + - `!pip install -r /content/ComfyUI/custom_nodes/was-node-suite-comfyui/requirements.txt` + - Restart Colab Runtime (don't disconnect) + - Tools will be located in the WAS Suite menu. diff --git a/custom_nodes/was-node-suite-comfyui/WAS_Node_Suite.py b/custom_nodes/was-node-suite-comfyui/WAS_Node_Suite.py new file mode 100644 index 00000000..d42dd6eb --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/WAS_Node_Suite.py @@ -0,0 +1,14828 @@ +# By WASasquatch (Discord: WAS#0263) +# +# Copyright 2023 Jordan Thompson (WASasquatch) +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the β€œSoftware”), to +# deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED β€œAS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. + + +from PIL import Image, ImageFilter, ImageEnhance, ImageOps, ImageDraw, ImageChops, ImageFont +from PIL.PngImagePlugin import PngInfo +from io import BytesIO +from typing import Optional, Union, List +from urllib.request import urlopen +import comfy.diffusers_convert +import comfy.samplers +import comfy.sd +import comfy.utils +import comfy.clip_vision +import comfy.model_management +import folder_paths as comfy_paths +from comfy_extras.chainner_models import model_loading +import ast +import glob +import hashlib +import json +import nodes +import math +import numpy as np +from numba import jit +import os +import random +import re +import requests +import socket +import subprocess +import sys +import datetime +import time +import torch +from tqdm import tqdm + +p310_plus = (sys.version_info >= (3, 10)) + +MANIFEST = { + "name": "WAS Node Suite", + "version": (2,2,2), + "author": "WASasquatch", + "project": "https://github.com/WASasquatch/was-node-suite-comfyui", + "description": "An extensive node suite for ComfyUI with over 180 new nodes", +} + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "was_node_suite_comfyui")) +sys.path.append(comfy_paths.base_path) + +#! SYSTEM HOOKS + +class cstr(str): + class color: + END = '\33[0m' + BOLD = '\33[1m' + ITALIC = '\33[3m' + UNDERLINE = '\33[4m' + BLINK = '\33[5m' + BLINK2 = '\33[6m' + SELECTED = '\33[7m' + + BLACK = '\33[30m' + RED = '\33[31m' + GREEN = '\33[32m' + YELLOW = '\33[33m' + BLUE = '\33[34m' + VIOLET = '\33[35m' + BEIGE = '\33[36m' + WHITE = '\33[37m' + + BLACKBG = '\33[40m' + REDBG = '\33[41m' + GREENBG = '\33[42m' + YELLOWBG = '\33[43m' + BLUEBG = '\33[44m' + VIOLETBG = '\33[45m' + BEIGEBG = '\33[46m' + WHITEBG = '\33[47m' + + GREY = '\33[90m' + LIGHTRED = '\33[91m' + LIGHTGREEN = '\33[92m' + LIGHTYELLOW = '\33[93m' + LIGHTBLUE = '\33[94m' + LIGHTVIOLET = '\33[95m' + LIGHTBEIGE = '\33[96m' + LIGHTWHITE = '\33[97m' + + GREYBG = '\33[100m' + LIGHTREDBG = '\33[101m' + LIGHTGREENBG = '\33[102m' + LIGHTYELLOWBG = '\33[103m' + LIGHTBLUEBG = '\33[104m' + LIGHTVIOLETBG = '\33[105m' + LIGHTBEIGEBG = '\33[106m' + LIGHTWHITEBG = '\33[107m' + + @staticmethod + def add_code(name, code): + if not hasattr(cstr.color, name.upper()): + setattr(cstr.color, name.upper(), code) + else: + raise ValueError(f"'cstr' object already contains a code with the name '{name}'.") + + def __new__(cls, text): + return super().__new__(cls, text) + + def __getattr__(self, attr): + if attr.lower().startswith("_cstr"): + code = getattr(self.color, attr.upper().lstrip("_cstr")) + modified_text = self.replace(f"__{attr[1:]}__", f"{code}") + return cstr(modified_text) + elif attr.upper() in dir(self.color): + code = getattr(self.color, attr.upper()) + modified_text = f"{code}{self}{self.color.END}" + return cstr(modified_text) + elif attr.lower() in dir(cstr): + return getattr(cstr, attr.lower()) + else: + raise AttributeError(f"'cstr' object has no attribute '{attr}'") + + def print(self, **kwargs): + print(self, **kwargs) + +#! MESSAGE TEMPLATES +cstr.color.add_code("msg", f"{cstr.color.BLUE}WAS Node Suite: {cstr.color.END}") +cstr.color.add_code("warning", f"{cstr.color.BLUE}WAS Node Suite {cstr.color.LIGHTYELLOW}Warning: {cstr.color.END}") +cstr.color.add_code("error", f"{cstr.color.RED}WAS Node Suite {cstr.color.END}Error: {cstr.color.END}") + +#! GLOBALS +NODE_FILE = os.path.abspath(__file__) +MIDAS_INSTALLED = False +CUSTOM_NODES_DIR = comfy_paths.folder_names_and_paths["custom_nodes"][0][0] +MODELS_DIR = comfy_paths.models_dir +WAS_SUITE_ROOT = os.path.dirname(NODE_FILE) +WAS_CONFIG_DIR = os.environ.get('WAS_CONFIG_DIR', WAS_SUITE_ROOT) +WAS_DATABASE = os.path.join(WAS_CONFIG_DIR, 'was_suite_settings.json') +WAS_HISTORY_DATABASE = os.path.join(WAS_CONFIG_DIR, 'was_history.json') +WAS_CONFIG_FILE = os.path.join(WAS_CONFIG_DIR, 'was_suite_config.json') +STYLES_PATH = os.path.join(WAS_CONFIG_DIR, 'styles.json') +DEFAULT_NSP_PANTRY_PATH = os.path.join(WAS_CONFIG_DIR, 'nsp_pantry.json') +ALLOWED_EXT = ('.jpeg', '.jpg', '.png', + '.tiff', '.gif', '.bmp', '.webp') + + +#! INSTALLATION CLEANUP + +# Delete legacy nodes +legacy_was_nodes = ['fDOF_WAS.py', 'Image_Blank_WAS.py', 'Image_Blend_WAS.py', 'Image_Canny_Filter_WAS.py', 'Canny_Filter_WAS.py', 'Image_Combine_WAS.py', 'Image_Edge_Detection_WAS.py', 'Image_Film_Grain_WAS.py', 'Image_Filters_WAS.py', + 'Image_Flip_WAS.py', 'Image_Nova_Filter_WAS.py', 'Image_Rotate_WAS.py', 'Image_Style_Filter_WAS.py', 'Latent_Noise_Injection_WAS.py', 'Latent_Upscale_WAS.py', 'MiDaS_Depth_Approx_WAS.py', 'NSP_CLIPTextEncoder.py', 'Samplers_WAS.py'] +legacy_was_nodes_found = [] + +if os.path.basename(CUSTOM_NODES_DIR) == 'was-node-suite-comfyui': + legacy_was_nodes.append('WAS_Node_Suite.py') + +f_disp = False +node_path_dir = os.getcwd()+os.sep+'ComfyUI'+os.sep+'custom_nodes'+os.sep +for f in legacy_was_nodes: + file = f'{node_path_dir}{f}' + if os.path.exists(file): + if not f_disp: + cstr("Found legacy nodes. Archiving legacy nodes...").msg.print() + f_disp = True + legacy_was_nodes_found.append(file) +if legacy_was_nodes_found: + import zipfile + from os.path import basename + archive = zipfile.ZipFile( + f'{node_path_dir}WAS_Legacy_Nodes_Backup_{round(time.time())}.zip', "w") + for f in legacy_was_nodes_found: + archive.write(f, basename(f)) + try: + os.remove(f) + except OSError: + pass + archive.close() +if f_disp: + cstr("Legacy cleanup complete.").msg.print() + +#! WAS SUITE CONFIG + +was_conf_template = { + "run_requirements": True, + "suppress_uncomfy_warnings": True, + "show_startup_junk": True, + "show_inspiration_quote": True, + "text_nodes_type": "STRING", + "webui_styles": None, + "webui_styles_persistent_update": True, + "sam_model_vith_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", + "sam_model_vitl_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth", + "sam_model_vitb_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", + "history_display_limit": 36, + "use_legacy_ascii_text": False, + "ffmpeg_bin_path": "/path/to/ffmpeg", + "ffmpeg_extra_codecs": { + "avc1": ".mp4", + "h264": ".mkv", + }, + "wildcards_path": os.path.join(WAS_SUITE_ROOT, "wildcards"), + "wildcard_api": True, + } + +# Create, Load, or Update Config + +def getSuiteConfig(): + global was_conf_template + try: + with open(WAS_CONFIG_FILE, "r") as f: + was_config = json.load(f) + except OSError as e: + cstr(f"Unable to load conf file at `{WAS_CONFIG_FILE}`. Using internal config template.").error.print() + return was_conf_template + except Exception as e: + cstr(f"Unable to load conf file at `{WAS_CONFIG_FILE}`. Using internal config template.").error.print() + return was_conf_template + return was_config + return was_config + +def updateSuiteConfig(conf): + try: + with open(WAS_CONFIG_FILE, "w", encoding='utf-8') as f: + json.dump(conf, f, indent=4) + except OSError as e: + print(e) + return False + except Exception as e: + print(e) + return False + return True + +if not os.path.exists(WAS_CONFIG_FILE): + if updateSuiteConfig(was_conf_template): + cstr(f'Created default conf file at `{WAS_CONFIG_FILE}`.').msg.print() + was_config = getSuiteConfig() + else: + cstr(f"Unable to create default conf file at `{WAS_CONFIG_FILE}`. Using internal config template.").error.print() + was_config = was_conf_template + +else: + was_config = getSuiteConfig() + + update_config = False + for sett_ in was_conf_template.keys(): + if not was_config.__contains__(sett_): + was_config.update({sett_: was_conf_template[sett_]}) + update_config = True + + if update_config: + updateSuiteConfig(was_config) + +# WAS Suite Locations Debug +if was_config.__contains__('show_startup_junk'): + if was_config['show_startup_junk']: + cstr(f"Running At: {NODE_FILE}") + cstr(f"Running From: {WAS_SUITE_ROOT}") + +# Check Write Access +if not os.access(WAS_SUITE_ROOT, os.W_OK) or not os.access(MODELS_DIR, os.W_OK): + cstr(f"There is no write access to `{WAS_SUITE_ROOT}` or `{MODELS_DIR}`. Write access is required!").error.print() + exit + +# SET TEXT TYPE +TEXT_TYPE = "STRING" +if was_config and was_config.__contains__('text_nodes_type'): + if was_config['text_nodes_type'].strip() != '': + TEXT_TYPE = was_config['text_nodes_type'].strip() +if was_config and was_config.__contains__('use_legacy_ascii_text'): + if was_config['use_legacy_ascii_text']: + TEXT_TYPE = "ASCII" + cstr("use_legacy_ascii_text is `True` in `was_suite_config.json`. `ASCII` type is deprecated and the default will be `STRING` in the future.").warning.print() + +# Convert WebUI Styles - TODO: Convert to PromptStyles class +if was_config.__contains__('webui_styles'): + + if was_config['webui_styles'] not in [None, 'None', 'none', '']: + + webui_styles_file = was_config['webui_styles'] + + if was_config.__contains__('webui_styles_persistent_update'): + styles_persist = was_config['webui_styles_persistent_update'] + else: + styles_persist = True + + if webui_styles_file not in [None, 'none', 'None', ''] and os.path.exists(webui_styles_file): + + cstr(f"Importing styles from `{webui_styles_file}`.").msg.print() + + import csv + + styles = {} + with open(webui_styles_file, "r", encoding="utf-8-sig", newline='') as file: + reader = csv.DictReader(file) + for row in reader: + prompt = row.get("prompt") or row.get("text", "") # Old files + negative_prompt = row.get("negative_prompt", "") + styles[row["name"]] = { + "prompt": prompt, + "negative_prompt": negative_prompt + } + + if styles: + if not os.path.exists(STYLES_PATH) or styles_persist: + with open(STYLES_PATH, "w", encoding='utf-8') as f: + json.dump(styles, f, indent=4) + + del styles + + cstr(f"Styles import complete.").msg.print() + + else: + cstr(f"Styles file `{webui_styles_file}` does not exist.").error.print() + + +#! SUITE SPECIFIC CLASSES & FUNCTIONS + +# Freeze PIP modules +def packages(versions=False): + import sys + import subprocess + try: + result = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'], stderr=subprocess.STDOUT) + lines = result.decode().splitlines() + return [line if versions else line.split('==')[0] for line in lines] + except subprocess.CalledProcessError as e: + print("An error occurred while fetching packages:", e.output.decode()) + return [] + +def install_package(package, uninstall_first: Union[List[str], str] = None): + if os.getenv("WAS_BLOCK_AUTO_INSTALL", 'False').lower() in ('true', '1', 't'): + cstr(f"Preventing package install of '{package}' due to WAS_BLOCK_INSTALL env").msg.print() + else: + if uninstall_first is None: + return + + if isinstance(uninstall_first, str): + uninstall_first = [uninstall_first] + + cstr(f"Uninstalling {', '.join(uninstall_first)}..") + subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'uninstall', *uninstall_first]) + cstr("Installing package...").msg.print() + subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', package]) + +# Tensor to PIL +def tensor2pil(image): + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + +# PIL to Tensor +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +# PIL Hex +def pil2hex(image): + return hashlib.sha256(np.array(tensor2pil(image)).astype(np.uint16).tobytes()).hexdigest() + +# PIL to Mask +def pil2mask(image): + image_np = np.array(image.convert("L")).astype(np.float32) / 255.0 + mask = torch.from_numpy(image_np) + return 1.0 - mask + +# Mask to PIL +def mask2pil(mask): + if mask.ndim > 2: + mask = mask.squeeze(0) + mask_np = mask.cpu().numpy().astype('uint8') + mask_pil = Image.fromarray(mask_np, mode="L") + return mask_pil + +# Tensor to SAM-compatible NumPy +def tensor2sam(image): + # Convert tensor to numpy array in HWC uint8 format with pixel values in [0, 255] + sam_image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + # Transpose the image to HWC format if it's in CHW format + if sam_image.shape[0] == 3: + sam_image = np.transpose(sam_image, (1, 2, 0)) + return sam_image + +# SAM-compatible NumPy to tensor +def sam2tensor(image): + # Convert the image to float32 and normalize the pixel values to [0, 1] + float_image = image.astype(np.float32) / 255.0 + # Transpose the image from HWC format to CHW format + chw_image = np.transpose(float_image, (2, 0, 1)) + # Convert the numpy array to a tensor + tensor_image = torch.from_numpy(chw_image) + return tensor_image + +# Median Filter +def medianFilter(img, diameter, sigmaColor, sigmaSpace): + import cv2 as cv + diameter = int(diameter) + sigmaColor = int(sigmaColor) + sigmaSpace = int(sigmaSpace) + img = img.convert('RGB') + img = cv.cvtColor(np.array(img), cv.COLOR_RGB2BGR) + img = cv.bilateralFilter(img, diameter, sigmaColor, sigmaSpace) + img = cv.cvtColor(np.array(img), cv.COLOR_BGR2RGB) + return Image.fromarray(img).convert('RGB') + +# Resize Image +def resizeImage(image, max_size): + width, height = image.size + if width > height: + if width > max_size: + new_width = max_size + new_height = int(height * (max_size / width)) + else: + if height > max_size: + new_height = max_size + new_width = int(width * (max_size / height)) + resized_image = image.resize((new_width, new_height)) + return resized_image + +# Image Seed +def image2seed(image): + image_data = image.tobytes() + hash_object = hashlib.sha256(image_data) + hash_digest = hash_object.digest() + seed = int.from_bytes(hash_digest[:4], byteorder='big') + return seed + + +# SHA-256 Hash +def get_sha256(file_path): + sha256_hash = hashlib.sha256() + with open(file_path, 'rb') as file: + for chunk in iter(lambda: file.read(4096), b''): + sha256_hash.update(chunk) + return sha256_hash.hexdigest() + +# Batch Seed Generator +def seed_batch(seed, batches, seeds): + rng = np.random.default_rng(seed) + btch = [rng.choice(2**32 - 1, seeds, replace=False).tolist() for _ in range(batches)] + return btch + +# Download File +def download_file(url, filename=None, path=None): + if not filename: + filename = url.split('/')[-1] + if not path: + path = '.' + save_path = os.path.join(path, filename) + response = requests.get(url, stream=True) + if response.status_code == requests.codes.ok: + file_size = int(response.headers.get('Content-Length', 0)) + with open(save_path, 'wb') as file: + with tqdm(total=file_size, unit='B', unit_scale=True, unit_divisor=1024) as progress: + for chunk in response.iter_content(chunk_size=1024): + file.write(chunk) + progress.update(len(chunk)) + print(f"Downloaded file saved at: {save_path}") + return True + elif response.status_code == requests.codes.not_found: + cstr("Error: File not found.").error.print() + else: + cstr(f"Error: Failed to download file. Status code: {response.status_code}").error.print() + return False + +# NSP Function + +def nsp_parse(text, seed=0, noodle_key='__', nspterminology=None, pantry_path=None): + if nspterminology is None: + # Fetch the NSP Pantry + if pantry_path is None: + pantry_path = DEFAULT_NSP_PANTRY_PATH + if not os.path.exists(pantry_path): + response = urlopen('https://raw.githubusercontent.com/WASasquatch/noodle-soup-prompts/main/nsp_pantry.json') + tmp_pantry = json.loads(response.read()) + # Dump JSON locally + pantry_serialized = json.dumps(tmp_pantry, indent=4) + with open(pantry_path, "w") as f: + f.write(pantry_serialized) + del response, tmp_pantry + + # Load local pantry + with open(pantry_path, 'r') as f: + nspterminology = json.load(f) + + if seed > 0 or seed < 0: + random.seed(seed) + + # Parse Text + new_text = text + for term in nspterminology: + # Target Noodle + tkey = f'{noodle_key}{term}{noodle_key}' + # How many occurrences? + tcount = new_text.count(tkey) + # Apply random results for each noodle counted + for _ in range(tcount): + new_text = new_text.replace( + tkey, random.choice(nspterminology[term]), 1) + seed += 1 + random.seed(seed) + + return new_text + +# Simple wildcard parser: + +def replace_wildcards(text, seed=None, noodle_key='__'): + + def replace_nested(text, key_path_dict): + if re.findall(f"{noodle_key}(.+?){noodle_key}", text): + for key, file_path in key_path_dict.items(): + with open(file_path, "r", encoding="utf-8") as file: + lines = file.readlines() + if lines: + random_line = None + while not random_line: + line = random.choice(lines).strip() + if not line.startswith('#') and not line.startswith('//'): + random_line = line + text = text.replace(key, random_line) + return text + + conf = getSuiteConfig() + wildcard_dir = os.path.join(WAS_SUITE_ROOT, 'wildcards') + if not os.path.exists(wildcard_dir): + os.makedirs(wildcard_dir, exist_ok=True) + if conf.__contains__('wildcards_path'): + if conf['wildcards_path'] not in [None, ""]: + wildcard_dir = conf['wildcards_path'] + + cstr(f"Wildcard Path: {wildcard_dir}").msg.print() + + # Set the random seed for reproducibility + if seed: + random.seed(seed) + + # Create a dictionary of key to file path pairs + key_path_dict = {} + for root, dirs, files in os.walk(wildcard_dir): + for file in files: + file_path = os.path.join(root, file) + key = os.path.relpath(file_path, wildcard_dir).replace(os.path.sep, "/").rsplit(".", 1)[0] + key_path_dict[f"{noodle_key}{key}{noodle_key}"] = os.path.abspath(file_path) + + # Replace keys in text with random lines from corresponding files + for key, file_path in key_path_dict.items(): + with open(file_path, "r", encoding="utf-8") as file: + lines = file.readlines() + if lines: + random_line = None + while not random_line: + line = random.choice(lines).strip() + if not line.startswith('#') and not line.startswith('//'): + random_line = line + text = text.replace(key, random_line) + + # Replace sub-wildacrds in result + text = replace_nested(text, key_path_dict) + + return text + +# Parse Prompt Variables + +def parse_prompt_vars(input_string, optional_vars=None): + variables = optional_vars or {} + pattern = r"\$\|(.*?)\|\$" + variable_count = len(variables) + 1 + + def replace_variable(match): + nonlocal variable_count + variable_name = f"${variable_count}" + variables[variable_name] = match.group(1) + variable_count += 1 + return variable_name + + output_string = re.sub(pattern, replace_variable, input_string) + + for variable_name, phrase in variables.items(): + variable_pattern = re.escape(variable_name) + output_string = re.sub(variable_pattern, phrase, output_string) + + return output_string, variables + +# Parse Dynamic Prompts + +def parse_dynamic_prompt(prompt, seed): + random.seed(seed) + + def replace_match(match): + options = match.group(1).split('|') + return random.choice(options) + + parse_prompt = re.sub(r'\<(.*?)\>', replace_match, prompt) + while re.search(r'\<(.*?)\>', parse_prompt): + parse_prompt = re.sub(r'\<(.*?)\>', replace_match, parse_prompt) + + return parse_prompt + +# Ambient Occlusion Factor + +@jit(nopython=True) +def calculate_ambient_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius): + occlusion_array = np.zeros((height, width), dtype=np.uint8) + + for y in range(height): + for x in range(width): + if radius == 0: + occlusion_factor = 0 + else: + y_min = max(y - radius, 0) + y_max = min(y + radius + 1, height) + x_min = max(x - radius, 0) + x_max = min(x + radius + 1, width) + + neighborhood_depth = depth_normalized[y_min:y_max, x_min:x_max] + neighborhood_rgb = rgb_normalized[y_min:y_max, x_min:x_max, :] + + depth_diff = depth_normalized[y, x] - neighborhood_depth + rgb_diff = np.abs(rgb_normalized[y, x] - neighborhood_rgb) + occlusion_factor = np.maximum(0, depth_diff).mean() + np.maximum(0, np.sum(rgb_diff, axis=2)).mean() + + occlusion_value = int(255 - occlusion_factor * 255) + occlusion_array[y, x] = occlusion_value + + return occlusion_array + +# Direct Occlusion Factor + +@jit(nopython=True) +def calculate_direct_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius): + occlusion_array = np.empty((int(height), int(width)), dtype=np.uint8) + depth_normalized = depth_normalized[:, :, 0] + + for y in range(int(height)): + for x in range(int(width)): + if radius == 0: + occlusion_factor = 0 + else: + y_min = max(int(y - radius), 0) + y_max = min(int(y + radius + 1), int(height)) + x_min = max(int(x - radius), 0) + x_max = min(int(x + radius + 1), int(width)) + + neighborhood_depth = np.zeros((y_max - y_min, x_max - x_min), dtype=np.float64) + neighborhood_rgb = np.empty((y_max - y_min, x_max - x_min, 3)) + + for i in range(y_min, y_max): + for j in range(x_min, x_max): + neighborhood_depth[i - y_min, j - x_min] = depth_normalized[i, j] + neighborhood_rgb[i - y_min, j - x_min, :] = rgb_normalized[i, j, :] + + depth_diff = neighborhood_depth - depth_normalized[y, x] + rgb_diff = np.abs(neighborhood_rgb - rgb_normalized[y, x]) + occlusion_factor = np.maximum(0, depth_diff).mean() + np.maximum(0, np.sum(np.abs(rgb_diff), axis=2)).mean() + + occlusion_value = int(occlusion_factor * 255) + occlusion_array[y, x] = occlusion_value + + occlusion_min = np.min(occlusion_array) + occlusion_max = np.max(occlusion_array) + occlusion_scaled = ((occlusion_array - occlusion_min) / (occlusion_max - occlusion_min) * 255).astype(np.uint8) + + return occlusion_scaled + + +class PromptStyles: + def __init__(self, styles_file, preview_length = 32): + self.styles_file = styles_file + self.styles = {} + self.preview_length = preview_length + + if os.path.exists(self.styles_file): + with open(self.styles_file, 'r') as f: + self.styles = json.load(f) + + def add_style(self, prompt="", negative_prompt="", auto=False, name=None): + if auto: + date_format = '%A, %d %B %Y %I:%M %p' + date_str = datetime.datetime.now().strftime(date_format) + key = None + if prompt.strip() != "": + if len(prompt) > self.preview_length: + length = self.preview_length + else: + length = len(prompt) + key = f"[{date_str}] Positive: {prompt[:length]} ..." + elif negative_prompt.strip() != "": + if len(negative_prompt) > self.preview_length: + length = self.preview_length + else: + length = len(negative_prompt) + key = f"[{date_str}] Negative: {negative_prompt[:length]} ..." + else: + cstr("At least a `prompt`, or `negative_prompt` input is required!").error.print() + return + else: + if name == None or str(name).strip() == "": + cstr("A `name` input is required when not using `auto=True`").error.print() + return + key = str(name) + + + for k, v in self.styles.items(): + if v['prompt'] == prompt and v['negative_prompt'] == negative_prompt: + return + + self.styles[key] = {"prompt": prompt, "negative_prompt": negative_prompt} + + with open(self.styles_file, "w", encoding='utf-8') as f: + json.dump(self.styles, f, indent=4) + + def get_prompts(self): + return self.styles + + def get_prompt(self, prompt_key): + if prompt_key in self.styles: + return self.styles[prompt_key]['prompt'], self.styles[prompt_key]['negative_prompt'] + else: + cstr(f"Prompt style `{prompt_key}` was not found!").error.print() + return None, None + + + +# WAS SETTINGS MANAGER + +class WASDatabase: + """ + The WAS Suite Database Class provides a simple key-value database that stores + data in a flatfile using the JSON format. Each key-value pair is associated with + a category. + + Attributes: + filepath (str): The path to the JSON file where the data is stored. + data (dict): The dictionary that holds the data read from the JSON file. + + Methods: + insert(category, key, value): Inserts a key-value pair into the database + under the specified category. + get(category, key): Retrieves the value associated with the specified + key and category from the database. + update(category, key): Update a value associated with the specified + key and category from the database. + delete(category, key): Deletes the key-value pair associated with the + specified key and category from the database. + _save(): Saves the current state of the database to the JSON file. + """ + def __init__(self, filepath): + self.filepath = filepath + try: + with open(filepath, 'r') as f: + self.data = json.load(f) + except FileNotFoundError: + self.data = {} + + def catExists(self, category): + return category in self.data + + def keyExists(self, category, key): + return category in self.data and key in self.data[category] + + def insert(self, category, key, value): + if not isinstance(category, str) or not isinstance(key, str): + cstr("Category and key must be strings").error.print() + return + + if category not in self.data: + self.data[category] = {} + self.data[category][key] = value + self._save() + + def update(self, category, key, value): + if category in self.data and key in self.data[category]: + self.data[category][key] = value + self._save() + + def updateCat(self, category, dictionary): + self.data[category].update(dictionary) + self._save() + + def get(self, category, key): + return self.data.get(category, {}).get(key, None) + + def getDB(self): + return self.data + + def insertCat(self, category): + if not isinstance(category, str): + cstr("Category must be a string").error.print() + return + + if category in self.data: + cstr(f"The database category '{category}' already exists!").error.print() + return + self.data[category] = {} + self._save() + + def getDict(self, category): + if category not in self.data: + cstr(f"The database category '{category}' does not exist!").error.print() + return {} + return self.data[category] + + def delete(self, category, key): + if category in self.data and key in self.data[category]: + del self.data[category][key] + self._save() + + def _save(self): + try: + with open(self.filepath, 'w') as f: + json.dump(self.data, f, indent=4) + except FileNotFoundError: + cstr(f"Cannot save database to file '{self.filepath}'. " + "Storing the data in the object instead. Does the folder and node file have write permissions?").warning.print() + except Exception as e: + cstr(f"Error while saving JSON data: {e}").error.print() + +# Initialize the settings database +WDB = WASDatabase(WAS_DATABASE) + +# WAS Token Class + +class TextTokens: + def __init__(self): + self.WDB = WDB + if not self.WDB.getDB().__contains__('custom_tokens'): + self.WDB.insertCat('custom_tokens') + self.custom_tokens = self.WDB.getDict('custom_tokens') + + self.tokens = { + '[time]': str(time.time()).replace('.','_'), + '[hostname]': socket.gethostname(), + '[cuda_device]': str(comfy.model_management.get_torch_device()), + '[cuda_name]': str(comfy.model_management.get_torch_device_name(device=comfy.model_management.get_torch_device())), + } + + if '.' in self.tokens['[time]']: + self.tokens['[time]'] = self.tokens['[time]'].split('.')[0] + + try: + self.tokens['[user]'] = os.getlogin() if os.getlogin() else 'null' + except Exception: + self.tokens['[user]'] = 'null' + + def addToken(self, name, value): + self.custom_tokens.update({name: value}) + self._update() + + def removeToken(self, name): + self.custom_tokens.pop(name) + self._update() + + def format_time(self, format_code): + return time.strftime(format_code, time.localtime(time.time())) + + def parseTokens(self, text): + tokens = self.tokens.copy() + if self.custom_tokens: + tokens.update(self.custom_tokens) + + # Update time + tokens['[time]'] = str(time.time()) + if '.' in tokens['[time]']: + tokens['[time]'] = tokens['[time]'].split('.')[0] + + for token, value in tokens.items(): + if token.startswith('[time('): + continue + pattern = re.compile(re.escape(token)) + text = pattern.sub(value, text) + + def replace_custom_time(match): + format_code = match.group(1) + return self.format_time(format_code) + + text = re.sub(r'\[time\((.*?)\)\]', replace_custom_time, text) + + return text + + def _update(self): + self.WDB.updateCat('custom_tokens', self.custom_tokens) + + +# Update image history + +def update_history_images(new_paths): + HDB = WASDatabase(WAS_HISTORY_DATABASE) + if HDB.catExists("History") and HDB.keyExists("History", "Images"): + saved_paths = HDB.get("History", "Images") + for path_ in saved_paths: + if not os.path.exists(path_): + saved_paths.remove(path_) + if isinstance(new_paths, str): + if new_paths in saved_paths: + saved_paths.remove(new_paths) + saved_paths.append(new_paths) + elif isinstance(new_paths, list): + for path_ in new_paths: + if path_ in saved_paths: + saved_paths.remove(path_) + saved_paths.append(path_) + HDB.update("History", "Images", saved_paths) + else: + if not HDB.catExists("History"): + HDB.insertCat("History") + if isinstance(new_paths, str): + HDB.insert("History", "Images", [new_paths]) + elif isinstance(new_paths, list): + HDB.insert("History", "Images", new_paths) + +# Update output image history + +def update_history_output_images(new_paths): + HDB = WASDatabase(WAS_HISTORY_DATABASE) + category = "Output_Images" + if HDB.catExists("History") and HDB.keyExists("History", category): + saved_paths = HDB.get("History", category) + for path_ in saved_paths: + if not os.path.exists(path_): + saved_paths.remove(path_) + if isinstance(new_paths, str): + if new_paths in saved_paths: + saved_paths.remove(new_paths) + saved_paths.append(new_paths) + elif isinstance(new_paths, list): + for path_ in new_paths: + if path_ in saved_paths: + saved_paths.remove(path_) + saved_paths.append(path_) + HDB.update("History", category, saved_paths) + else: + if not HDB.catExists("History"): + HDB.insertCat("History") + if isinstance(new_paths, str): + HDB.insert("History", category, [new_paths]) + elif isinstance(new_paths, list): + HDB.insert("History", category, new_paths) + +# Update text file history + +def update_history_text_files(new_paths): + HDB = WASDatabase(WAS_HISTORY_DATABASE) + if HDB.catExists("History") and HDB.keyExists("History", "TextFiles"): + saved_paths = HDB.get("History", "TextFiles") + for path_ in saved_paths: + if not os.path.exists(path_): + saved_paths.remove(path_) + if isinstance(new_paths, str): + if new_paths in saved_paths: + saved_paths.remove(new_paths) + saved_paths.append(new_paths) + elif isinstance(new_paths, list): + for path_ in new_paths: + if path_ in saved_paths: + saved_paths.remove(path_) + saved_paths.append(path_) + HDB.update("History", "TextFiles", saved_paths) + else: + if not HDB.catExists("History"): + HDB.insertCat("History") + if isinstance(new_paths, str): + HDB.insert("History", "TextFiles", [new_paths]) + elif isinstance(new_paths, list): + HDB.insert("History", "TextFiles", new_paths) +# WAS Filter Class + +class WAS_Tools_Class(): + """ + Contains various tools and filters for WAS Node Suite + """ + # TOOLS + + def fig2img(self, plot): + import io + buf = io.BytesIO() + plot.savefig(buf) + buf.seek(0) + img = Image.open(buf) + return img + + def stitch_image(self, image_a, image_b, mode='right', fuzzy_zone=50): + + def linear_gradient(start_color, end_color, size, start, end, mode='horizontal'): + width, height = size + gradient = Image.new('RGB', (width, height), end_color) + draw = ImageDraw.Draw(gradient) + + for i in range(0, start): + if mode == "horizontal": + draw.line((i, 0, i, height-1), start_color) + elif mode == "vertical": + draw.line((0, i, width-1, i), start_color) + + for i in range(start, end): + if mode == "horizontal": + curr_color = ( + int(start_color[0] + (float(i - start) / (end - start)) * (end_color[0] - start_color[0])), + int(start_color[1] + (float(i - start) / (end - start)) * (end_color[1] - start_color[1])), + int(start_color[2] + (float(i - start) / (end - start)) * (end_color[2] - start_color[2])) + ) + draw.line((i, 0, i, height-1), curr_color) + elif mode == "vertical": + curr_color = ( + int(start_color[0] + (float(i - start) / (end - start)) * (end_color[0] - start_color[0])), + int(start_color[1] + (float(i - start) / (end - start)) * (end_color[1] - start_color[1])), + int(start_color[2] + (float(i - start) / (end - start)) * (end_color[2] - start_color[2])) + ) + draw.line((0, i, width-1, i), curr_color) + + for i in range(end, width if mode == 'horizontal' else height): + if mode == "horizontal": + draw.line((i, 0, i, height-1), end_color) + elif mode == "vertical": + draw.line((0, i, width-1, i), end_color) + + return gradient + + image_a = image_a.convert('RGB') + image_b = image_b.convert('RGB') + + offset = int(fuzzy_zone / 2) + canvas_width = int(image_a.size[0] + image_b.size[0] - fuzzy_zone) if mode == 'right' or mode == 'left' else image_a.size[0] + canvas_height = int(image_a.size[1] + image_b.size[1] - fuzzy_zone) if mode == 'top' or mode == 'bottom' else image_a.size[1] + canvas = Image.new('RGB', (canvas_width, canvas_height), (0,0,0)) + + im_ax = 0 + im_ay = 0 + im_bx = 0 + im_by = 0 + + image_a_mask = None + image_b_mask = None + + if mode == 'top': + + image_a_mask = linear_gradient((0,0,0), (255,255,255), image_a.size, 0, fuzzy_zone, 'vertical') + image_b_mask = linear_gradient((255,255,255), (0,0,0), image_b.size, int(image_b.size[1] - fuzzy_zone), image_b.size[1], 'vertical') + im_ay = image_b.size[1] - fuzzy_zone + + elif mode == 'bottom': + + image_a_mask = linear_gradient((255,255,255), (0,0,0), image_a.size, int(image_a.size[1] - fuzzy_zone), image_a.size[1], 'vertical') + image_b_mask = linear_gradient((0,0,0), (255,255,255), image_b.size, 0, fuzzy_zone, 'vertical').convert('L') + im_by = image_a.size[1] - fuzzy_zone + + elif mode == 'left': + + image_a_mask = linear_gradient((0,0,0), (255,255,255), image_a.size, 0, fuzzy_zone, 'horizontal') + image_b_mask = linear_gradient((255,255,255), (0,0,0), image_b.size, int(image_b.size[0] - fuzzy_zone), image_b.size[0], 'horizontal') + im_ax = image_b.size[0] - fuzzy_zone + + elif mode == 'right': + + image_a_mask = linear_gradient((255,255,255), (0,0,0), image_a.size, int(image_a.size[0] - fuzzy_zone), image_a.size[0], 'horizontal') + image_b_mask = linear_gradient((0,0,0), (255,255,255), image_b.size, 0, fuzzy_zone, 'horizontal') + im_bx = image_a.size[0] - fuzzy_zone + + Image.Image.paste(canvas, image_a, (im_ax, im_ay), image_a_mask.convert('L')) + Image.Image.paste(canvas, image_b, (im_bx, im_by), image_b_mask.convert('L')) + + + return canvas + + + def morph_images(self, images, steps=10, max_size=512, loop=None, still_duration=30, duration=0.1, output_path='output', filename="morph", filetype="GIF"): + + import cv2 + import imageio + + output_file = os.path.abspath(os.path.join(os.path.join(*output_path.split('/')), filename)) + output_file += ( '.png' if filetype == 'APNG' else '.gif' ) + + max_width = max(im.size[0] for im in images) + max_height = max(im.size[1] for im in images) + max_aspect_ratio = max_width / max_height + + def padded_images(): + for im in images: + aspect_ratio = im.size[0] / im.size[1] + if aspect_ratio > max_aspect_ratio: + new_height = int(max_width / aspect_ratio) + padding = (max_height - new_height) // 2 + padded_im = Image.new('RGB', (max_width, max_height), color=(0, 0, 0)) + padded_im.paste(im.resize((max_width, new_height)), (0, padding)) + else: + new_width = int(max_height * aspect_ratio) + padding = (max_width - new_width) // 2 + padded_im = Image.new('RGB', (max_width, max_height), color=(0, 0, 0)) + padded_im.paste(im.resize((new_width, max_height)), (padding, 0)) + yield np.array(padded_im) + + padded_images = list(padded_images()) + padded_images.append(padded_images[0].copy()) + images = padded_images + frames = [] + durations = [] + + for i in range(len(images)-1): + frames.append(Image.fromarray(images[i]).convert('RGB')) + durations.append(still_duration) + + for j in range(steps): + alpha = j / float(steps) + morph = cv2.addWeighted(images[i], 1 - alpha, images[i+1], alpha, 0) + frames.append(Image.fromarray(morph).convert('RGB')) + durations.append(duration) + + frames.append(Image.fromarray(images[-1]).convert('RGB')) + durations.insert(0, still_duration) + + if loop is not None: + for i in range(loop): + durations.insert(0, still_duration) + durations.append(still_duration) + + try: + imageio.mimsave(output_file, frames, filetype, duration=durations, loop=loop) + except OSError as e: + cstr(f"Unable to save output to {output_file} due to the following error:").error.print() + print(e) + return + except Exception as e: + cstr(f"\033[34mWAS NS\033[0m Error: Unable to generate GIF due to the following error:").error.print() + print(e) + + cstr(f"Morphing completed. Output saved as {output_file}").msg.print() + + return output_file + + class GifMorphWriter: + def __init__(self, transition_frames=30, duration_ms=100, still_image_delay_ms=2500, loop=0): + self.transition_frames = transition_frames + self.duration_ms = duration_ms + self.still_image_delay_ms = still_image_delay_ms + self.loop = loop + + def write(self, image, gif_path): + + import cv2 + + if not os.path.isfile(gif_path): + with Image.new("RGBA", image.size) as new_gif: + new_gif.paste(image.convert("RGBA")) + new_gif.info["duration"] = self.still_image_delay_ms + new_gif.save(gif_path, format="GIF", save_all=True, append_images=[], duration=self.still_image_delay_ms, loop=0) + cstr(f"Created new GIF animation at: {gif_path}").msg.print() + else: + with Image.open(gif_path) as gif: + n_frames = gif.n_frames + if n_frames > 0: + gif.seek(n_frames - 1) + last_frame = gif.copy() + else: + last_frame = None + + end_image = image + steps = self.transition_frames - 1 if last_frame is not None else self.transition_frames + + if last_frame is not None: + image = self.pad_to_size(image, last_frame.size) + + frames = self.generate_transition_frames(last_frame, image, steps) + + still_frame = end_image.copy() + + gif_frames = [] + for i in range(n_frames): + gif.seek(i) + gif_frame = gif.copy() + gif_frames.append(gif_frame) + + for frame in frames: + frame.info["duration"] = self.duration_ms + gif_frames.append(frame) + + still_frame.info['duration'] = self.still_image_delay_ms + gif_frames.append(still_frame) + + gif_frames[0].save( + gif_path, + format="GIF", + save_all=True, + append_images=gif_frames[1:], + optimize=True, + loop=self.loop, + ) + + cstr(f"Edited existing GIF animation at: {gif_path}").msg.print() + + + def pad_to_size(self, image, size): + new_image = Image.new("RGBA", size, color=(0, 0, 0, 0)) + x_offset = (size[0] - image.width) // 2 + y_offset = (size[1] - image.height) // 2 + new_image.paste(image, (x_offset, y_offset)) + return new_image + + def generate_transition_frames(self, start_frame, end_image, num_frames): + + if start_frame is None: + return [] + + start_frame = start_frame.convert("RGBA") + end_image = end_image.convert("RGBA") + + frames = [] + for i in range(1, num_frames + 1): + weight = i / (num_frames + 1) + frame = Image.blend(start_frame, end_image, weight) + frames.append(frame) + return frames + + class VideoWriter: + def __init__(self, transition_frames=30, fps=25, still_image_delay_sec=2, + max_size=512, codec="mp4v"): + conf = getSuiteConfig() + self.transition_frames = transition_frames + self.fps = fps + self.still_image_delay_frames = round(still_image_delay_sec * fps) + self.max_size = int(max_size) + self.valid_codecs = ["ffv1","mp4v"] + self.extensions = {"ffv1":".mkv","mp4v":".mp4"} + if conf.__contains__('ffmpeg_extra_codecs'): + self.add_codecs(conf['ffmpeg_extra_codecs']) + self.codec = codec.lower() if codec.lower() in self.valid_codecs else "mp4v" + + def write(self, image, video_path): + video_path += self.extensions[self.codec] + end_image = self.rescale(self.pil2cv(image), self.max_size) + + if os.path.isfile(video_path): + cap = cv2.VideoCapture(video_path) + + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = int(cap.get(cv2.CAP_PROP_FPS)) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + if width <= 0 or height <= 0: + raise ValueError("Invalid video dimensions") + + temp_file_path = video_path.replace(self.extensions[self.codec], '_temp' + self.extensions[self.codec]) + fourcc = cv2.VideoWriter_fourcc(*self.codec) + out = cv2.VideoWriter(temp_file_path, fourcc, fps, (width, height), isColor=True) + + for i in tqdm(range(total_frames), desc="Copying original frames"): + ret, frame = cap.read() + if not ret: + break + out.write(frame) + + if self.transition_frames > 0: + cap.set(cv2.CAP_PROP_POS_FRAMES, total_frames - 1) + ret, last_frame = cap.read() + if ret: + transition_frames = self.generate_transition_frames(last_frame, end_image, self.transition_frames) + for i, transition_frame in tqdm(enumerate(transition_frames), desc="Generating transition frames", total=self.transition_frames): + try: + transition_frame_resized = cv2.resize(transition_frame, (width, height)) + out.write(transition_frame_resized) + except cv2.error as e: + print(f"Error resizing frame {i}: {e}") + continue + + for i in tqdm(range(self.still_image_delay_frames), desc="Adding new frames"): + out.write(end_image) + + cap.release() + out.release() + + os.remove(video_path) + os.rename(temp_file_path, video_path) + + cstr(f"Edited video at: {video_path}").msg.print() + + return video_path + + else: + fourcc = cv2.VideoWriter_fourcc(*self.codec) + height, width, _ = end_image.shape + if width <= 0 or height <= 0: + raise ValueError("Invalid image dimensions") + + out = cv2.VideoWriter(video_path, fourcc, self.fps, (width, height), isColor=True) + + for i in tqdm(range(self.still_image_delay_frames), desc="Adding new frames"): + out.write(end_image) + + out.release() + + cstr(f"Created new video at: {video_path}").msg.print() + + return video_path + + return "" + + def create_video(self, image_folder, video_path): + import cv2 + from tqdm import tqdm + + image_paths = sorted([os.path.join(image_folder, f) for f in os.listdir(image_folder) + if os.path.isfile(os.path.join(image_folder, f)) + and os.path.join(image_folder, f).lower().endswith(ALLOWED_EXT)]) + + if len(image_paths) == 0: + cstr(f"No valid image files found in `{image_folder}` directory.").error.print() + cstr(f"The valid formats are: {', '.join(sorted(ALLOWED_EXT))}").error.print() + return + + output_file = video_path + self.extensions[self.codec] + image = self.rescale(cv2.imread(image_paths[0]), self.max_size) + height, width = image.shape[:2] + fourcc = cv2.VideoWriter_fourcc(*self.codec) + out = cv2.VideoWriter(output_file, fourcc, self.fps, (width, height), isColor=True) + out.write(image) + for _ in range(self.still_image_delay_frames - 1): + out.write(image) + + for i in tqdm(range(len(image_paths)), desc="Writing video frames"): + start_frame = cv2.imread(image_paths[i]) + end_frame = None + if i+1 <= len(image_paths)-1: + end_frame = self.rescale(cv2.imread(image_paths[i+1]), self.max_size) + + if isinstance(end_frame, np.ndarray): + transition_frames = self.generate_transition_frames(start_frame, end_frame, self.transition_frames) + transition_frames = [cv2.resize(frame, (width, height)) for frame in transition_frames] + for _, frame in enumerate(transition_frames): + out.write(frame) + + for _ in range(self.still_image_delay_frames - self.transition_frames): + out.write(end_frame) + + else: + out.write(start_frame) + for _ in range(self.still_image_delay_frames - 1): + out.write(start_frame) + + out.release() + + if os.path.exists(output_file): + cstr(f"Created video at: {output_file}").msg.print() + return output_file + else: + cstr(f"Unable to create video at: {output_file}").error.print() + return "" + + def extract(self, video_file, output_folder, prefix='frame_', extension="png", zero_padding_digits=-1): + os.makedirs(output_folder, exist_ok=True) + + video = cv2.VideoCapture(video_file) + + fps = video.get(cv2.CAP_PROP_FPS) + frame_number = 0 + + while True: + success, frame = video.read() + + if success: + if zero_padding_digits > 0: + frame_path = os.path.join(output_folder, f"{prefix}{frame_number:0{zero_padding_digits}}.{extension}") + else: + frame_path = os.path.join(output_folder, f"{prefix}{frame_number}.{extension}") + + cv2.imwrite(frame_path, frame) + print(f"Saved frame {frame_number} to {frame_path}") + frame_number += 1 + else: + break + + video.release() + + def rescale(self, image, max_size): + f1 = max_size / image.shape[1] + f2 = max_size / image.shape[0] + f = min(f1, f2) + dim = (int(image.shape[1] * f), int(image.shape[0] * f)) + resized = cv2.resize(image, dim) + return resized + + def generate_transition_frames(self, img1, img2, num_frames): + import cv2 + if img1 is None and img2 is None: + return [] + + if img1 is not None and img2 is not None: + if img1.shape != img2.shape: + img2 = cv2.resize(img2, img1.shape[:2][::-1]) + elif img1 is not None: + img2 = np.zeros_like(img1) + else: + img1 = np.zeros_like(img2) + + height, width, _ = img2.shape + + frame_sequence = [] + for i in range(num_frames): + alpha = i / float(num_frames) + blended = cv2.addWeighted(img1, 1 - alpha, img2, alpha, + gamma=0.0, dtype=cv2.CV_8U) + frame_sequence.append(blended) + + return frame_sequence + + def pil2cv(self, img): + import cv2 + img = np.array(img) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + def add_codecs(self, codecs): + if isinstance(codecs, dict): + codec_forcc_codes = codecs.keys() + self.valid_codecs.extend(codec_forcc_codes) + self.extensions.update(codecs) + + def get_codecs(self): + return self.valid_codecs + + + # FILTERS + + class Masking: + + @staticmethod + def crop_dominant_region(image, padding=0): + from scipy.ndimage import label + grayscale_image = image.convert("L") + binary_image = grayscale_image.point(lambda x: 255 if x > 128 else 0, mode="1") + labeled_image, num_labels = label(np.array(binary_image)) + largest_label = max(range(1, num_labels + 1), key=lambda i: np.sum(labeled_image == i)) + largest_region_mask = (labeled_image == largest_label).astype(np.uint8) * 255 + bbox = Image.fromarray(largest_region_mask, mode="L").getbbox() + cropped_image = image.crop(bbox) + size = max(cropped_image.size) + padded_size = size + 2 * padding + centered_crop = Image.new("L", (padded_size, padded_size), color="black") + left = (padded_size - cropped_image.width) // 2 + top = (padded_size - cropped_image.height) // 2 + centered_crop.paste(cropped_image, (left, top), mask=cropped_image) + + return ImageOps.invert(centered_crop) + + @staticmethod + def crop_minority_region(image, padding=0): + from scipy.ndimage import label + grayscale_image = image.convert("L") + binary_image = grayscale_image.point(lambda x: 255 if x > 128 else 0, mode="1") + labeled_image, num_labels = label(np.array(binary_image)) + smallest_label = min(range(1, num_labels + 1), key=lambda i: np.sum(labeled_image == i)) + smallest_region_mask = (labeled_image == smallest_label).astype(np.uint8) * 255 + bbox = Image.fromarray(smallest_region_mask, mode="L").getbbox() + cropped_image = image.crop(bbox) + size = max(cropped_image.size) + padded_size = size + 2 * padding + centered_crop = Image.new("L", (padded_size, padded_size), color="black") + left = (padded_size - cropped_image.width) // 2 + top = (padded_size - cropped_image.height) // 2 + centered_crop.paste(cropped_image, (left, top), mask=cropped_image) + + return ImageOps.invert(centered_crop) + + @staticmethod + def crop_region(mask, region_type, padding=0): + from scipy.ndimage import label, find_objects + binary_mask = np.array(mask.convert("L")) > 0 + bbox = mask.getbbox() + if bbox is None: + return mask, (mask.size, (0, 0, 0, 0)) + + bbox_width = bbox[2] - bbox[0] + bbox_height = bbox[3] - bbox[1] + + side_length = max(bbox_width, bbox_height) + 2 * padding + + center_x = (bbox[2] + bbox[0]) // 2 + center_y = (bbox[3] + bbox[1]) // 2 + + crop_x = center_x - side_length // 2 + crop_y = center_y - side_length // 2 + + crop_x = max(crop_x, 0) + crop_y = max(crop_y, 0) + crop_x2 = min(crop_x + side_length, mask.width) + crop_y2 = min(crop_y + side_length, mask.height) + + cropped_mask = mask.crop((crop_x, crop_y, crop_x2, crop_y2)) + crop_data = (cropped_mask.size, (crop_x, crop_y, crop_x2, crop_y2)) + + return cropped_mask, crop_data + + @staticmethod + def dominant_region(image, threshold=128): + from scipy.ndimage import label + image = ImageOps.invert(image.convert("L")) + binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1") + l, n = label(np.array(binary_image)) + sizes = np.bincount(l.flatten()) + dominant = 0 + try: + dominant = np.argmax(sizes[1:]) + 1 + except ValueError: + pass + dominant_region_mask = (l == dominant).astype(np.uint8) * 255 + result = Image.fromarray(dominant_region_mask, mode="L") + return result.convert("RGB") + + @staticmethod + def minority_region(image, threshold=128): + from scipy.ndimage import label + image = image.convert("L") + binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1") + labeled_array, num_features = label(np.array(binary_image)) + sizes = np.bincount(labeled_array.flatten()) + smallest_region = 0 + try: + smallest_region = np.argmin(sizes[1:]) + 1 + except ValueError: + pass + smallest_region_mask = (labeled_array == smallest_region).astype(np.uint8) * 255 + inverted_mask = Image.fromarray(smallest_region_mask, mode="L") + rgb_image = Image.merge("RGB", [inverted_mask, inverted_mask, inverted_mask]) + + return rgb_image + + @staticmethod + def arbitrary_region(image, size, threshold=128): + from skimage.measure import label, regionprops + image = image.convert("L") + binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1") + labeled_image = label(np.array(binary_image)) + regions = regionprops(labeled_image) + + image_area = binary_image.size[0] * binary_image.size[1] + scaled_size = size * image_area / 10000 + + filtered_regions = [region for region in regions if region.area >= scaled_size] + if len(filtered_regions) > 0: + filtered_regions.sort(key=lambda region: region.area) + smallest_region = filtered_regions[0] + region_mask = (labeled_image == smallest_region.label).astype(np.uint8) * 255 + result = Image.fromarray(region_mask, mode="L") + return result + + return image + + @staticmethod + def smooth_region(image, tolerance): + from scipy.ndimage import gaussian_filter + image = image.convert("L") + mask_array = np.array(image) + smoothed_array = gaussian_filter(mask_array, sigma=tolerance) + threshold = np.max(smoothed_array) / 2 + smoothed_mask = np.where(smoothed_array >= threshold, 255, 0).astype(np.uint8) + smoothed_image = Image.fromarray(smoothed_mask, mode="L") + return ImageOps.invert(smoothed_image.convert("RGB")) + + @staticmethod + def erode_region(image, iterations=1): + from scipy.ndimage import binary_erosion + image = image.convert("L") + binary_mask = np.array(image) > 0 + eroded_mask = binary_erosion(binary_mask, iterations=iterations) + eroded_image = Image.fromarray(eroded_mask.astype(np.uint8) * 255, mode="L") + return ImageOps.invert(eroded_image.convert("RGB")) + + @staticmethod + def dilate_region(image, iterations=1): + from scipy.ndimage import binary_dilation + image = image.convert("L") + binary_mask = np.array(image) > 0 + dilated_mask = binary_dilation(binary_mask, iterations=iterations) + dilated_image = Image.fromarray(dilated_mask.astype(np.uint8) * 255, mode="L") + return ImageOps.invert(dilated_image.convert("RGB")) + + @staticmethod + def fill_region(image): + from scipy.ndimage import binary_fill_holes + image = image.convert("L") + binary_mask = np.array(image) > 0 + filled_mask = binary_fill_holes(binary_mask) + filled_image = Image.fromarray(filled_mask.astype(np.uint8) * 255, mode="L") + return ImageOps.invert(filled_image.convert("RGB")) + + @staticmethod + def combine_masks(*masks): + if len(masks) < 1: + raise ValueError("\033[34mWAS NS\033[0m Error: At least one mask must be provided.") + dimensions = masks[0].size + for mask in masks: + if mask.size != dimensions: + raise ValueError("\033[34mWAS NS\033[0m Error: All masks must have the same dimensions.") + + inverted_masks = [mask.convert("L") for mask in masks] + combined_mask = Image.new("L", dimensions, 255) + for mask in inverted_masks: + combined_mask = Image.fromarray(np.minimum(np.array(combined_mask), np.array(mask)), mode="L") + + return combined_mask + + @staticmethod + def threshold_region(image, black_threshold=0, white_threshold=255): + gray_image = image.convert("L") + mask_array = np.array(gray_image) + mask_array[mask_array < black_threshold] = 0 + mask_array[mask_array > white_threshold] = 255 + thresholded_image = Image.fromarray(mask_array, mode="L") + return ImageOps.invert(thresholded_image) + + @staticmethod + def floor_region(image): + gray_image = image.convert("L") + mask_array = np.array(gray_image) + non_black_pixels = mask_array[mask_array > 0] + + if non_black_pixels.size > 0: + threshold_value = non_black_pixels.min() + mask_array[mask_array > threshold_value] = 255 # Set whites to 255 + mask_array[mask_array <= threshold_value] = 0 # Set blacks to 0 + + thresholded_image = Image.fromarray(mask_array, mode="L") + return ImageOps.invert(thresholded_image) + + @staticmethod + def ceiling_region(image, offset=30): + if offset < 0: + offset = 0 + elif offset > 255: + offset = 255 + grayscale_image = image.convert("L") + mask_array = np.array(grayscale_image) + mask_array[mask_array < 255 - offset] = 0 + mask_array[mask_array >= 250] = 255 + filtered_image = Image.fromarray(mask_array, mode="L") + return ImageOps.invert(filtered_image) + + @staticmethod + def gaussian_region(image, radius=5.0): + image = ImageOps.invert(image.convert("L")) + image = image.filter(ImageFilter.GaussianBlur(radius=int(radius))) + return image.convert("RGB") + + # SHADOWS AND HIGHLIGHTS ADJUSTMENTS + + def shadows_and_highlights(self, image, shadow_thresh=30, highlight_thresh=220, shadow_factor=0.5, highlight_factor=1.5, shadow_smooth=None, highlight_smooth=None, simplify_masks=None): + + if 'pilgram' not in packages(): + install_package('pilgram') + + import pilgram + + alpha = None + if image.mode.endswith('A'): + alpha = image.getchannel('A') + image = image.convert('RGB') + + grays = image.convert('L') + + if shadow_smooth is not None or highlight_smooth is not None and simplify_masks is not None: + simplify = float(simplify_masks) + grays = grays.filter(ImageFilter.GaussianBlur(radius=simplify)) + + shadow_mask = Image.eval(grays, lambda x: 255 if x < shadow_thresh else 0) + highlight_mask = Image.eval(grays, lambda x: 255 if x > highlight_thresh else 0) + + image_shadow = image.copy() + image_highlight = image.copy() + + if shadow_smooth is not None: + shadow_mask = shadow_mask.filter(ImageFilter.GaussianBlur(radius=shadow_smooth)) + if highlight_smooth is not None: + highlight_mask = highlight_mask.filter(ImageFilter.GaussianBlur(radius=highlight_smooth)) + + image_shadow = Image.eval(image_shadow, lambda x: x * shadow_factor) + image_highlight = Image.eval(image_highlight, lambda x: x * highlight_factor) + + if shadow_smooth is not None: + shadow_mask = shadow_mask.filter(ImageFilter.GaussianBlur(radius=shadow_smooth)) + if highlight_smooth is not None: + highlight_mask = highlight_mask.filter(ImageFilter.GaussianBlur(radius=highlight_smooth)) + + result = image.copy() + result.paste(image_shadow, shadow_mask) + result.paste(image_highlight, highlight_mask) + result = pilgram.css.blending.color(result, image) + + if alpha: + result.putalpha(alpha) + + return (result, shadow_mask, highlight_mask) + + # DRAGAN PHOTOGRAPHY FILTER + + + def dragan_filter(self, image, saturation=1, contrast=1, sharpness=1, brightness=1, highpass_radius=3, highpass_samples=1, highpass_strength=1, colorize=True): + + if 'pilgram' not in packages(): + install_package('pilgram') + + import pilgram + + alpha = None + if image.mode == 'RGBA': + alpha = image.getchannel('A') + + grayscale_image = image if image.mode == 'L' else image.convert('L') + + contrast_enhancer = ImageEnhance.Contrast(grayscale_image) + contrast_image = contrast_enhancer.enhance(contrast) + + saturation_enhancer = ImageEnhance.Color(contrast_image) if image.mode != 'L' else None + saturation_image = contrast_image if saturation_enhancer is None else saturation_enhancer.enhance(saturation) + + sharpness_enhancer = ImageEnhance.Sharpness(saturation_image) + sharpness_image = sharpness_enhancer.enhance(sharpness) + + brightness_enhancer = ImageEnhance.Brightness(sharpness_image) + brightness_image = brightness_enhancer.enhance(brightness) + + blurred_image = brightness_image.filter(ImageFilter.GaussianBlur(radius=-highpass_radius)) + highpass_filter = ImageChops.subtract(image, blurred_image.convert('RGB')) + blank_image = Image.new('RGB', image.size, (127, 127, 127)) + highpass_image = ImageChops.screen(blank_image, highpass_filter.resize(image.size)) + if not colorize: + highpass_image = highpass_image.convert('L').convert('RGB') + highpassed_image = pilgram.css.blending.overlay(brightness_image.convert('RGB'), highpass_image) + for _ in range((highpass_samples if highpass_samples > 0 else 1)): + highpassed_image = pilgram.css.blending.overlay(highpassed_image, highpass_image) + + final_image = ImageChops.blend(brightness_image.convert('RGB'), highpassed_image, highpass_strength) + + if colorize: + final_image = pilgram.css.blending.color(final_image, image) + + if alpha: + final_image.putalpha(alpha) + + return final_image + + def sparkle(self, image): + + if 'pilgram' not in packages(): + install_package('pilgram') + + import pilgram + + image = image.convert('RGBA') + contrast_enhancer = ImageEnhance.Contrast(image) + image = contrast_enhancer.enhance(1.25) + saturation_enhancer = ImageEnhance.Color(image) + image = saturation_enhancer.enhance(1.5) + + bloom = image.filter(ImageFilter.GaussianBlur(radius=20)) + bloom = ImageEnhance.Brightness(bloom).enhance(1.2) + bloom.putalpha(128) + bloom = bloom.convert(image.mode) + image = Image.alpha_composite(image, bloom) + + width, height = image.size + + particles = Image.new('RGBA', (width, height), (0, 0, 0, 0)) + draw = ImageDraw.Draw(particles) + for i in range(5000): + x = random.randint(0, width) + y = random.randint(0, height) + r = random.randint(0, 255) + g = random.randint(0, 255) + b = random.randint(0, 255) + draw.point((x, y), fill=(r, g, b, 255)) + particles = particles.filter(ImageFilter.GaussianBlur(radius=1)) + particles.putalpha(128) + + particles2 = Image.new('RGBA', (width, height), (0, 0, 0, 0)) + draw = ImageDraw.Draw(particles2) + for i in range(5000): + x = random.randint(0, width) + y = random.randint(0, height) + r = random.randint(0, 255) + g = random.randint(0, 255) + b = random.randint(0, 255) + draw.point((x, y), fill=(r, g, b, 255)) + particles2 = particles2.filter(ImageFilter.GaussianBlur(radius=1)) + particles2.putalpha(128) + + image = pilgram.css.blending.color_dodge(image, particles) + image = pilgram.css.blending.lighten(image, particles2) + + return image + + def digital_distortion(self, image, amplitude=5, line_width=2): + + im = np.array(image) + + x, y, z = im.shape + sine_wave = amplitude * np.sin(np.linspace(-np.pi, np.pi, y)) + sine_wave = sine_wave.astype(int) + + left_distortion = np.zeros((x, y, z), dtype=np.uint8) + right_distortion = np.zeros((x, y, z), dtype=np.uint8) + for i in range(y): + left_distortion[:, i, :] = np.roll(im[:, i, :], -sine_wave[i], axis=0) + right_distortion[:, i, :] = np.roll(im[:, i, :], sine_wave[i], axis=0) + + distorted_image = np.maximum(left_distortion, right_distortion) + scan_lines = np.zeros((x, y), dtype=np.float32) + scan_lines[::line_width, :] = 1 + scan_lines = np.minimum(scan_lines * amplitude*50.0, 1) # Scale scan line values + scan_lines = np.tile(scan_lines[:, :, np.newaxis], (1, 1, z)) # Add channel dimension + distorted_image = np.where(scan_lines > 0, np.random.permutation(im), distorted_image) + distorted_image = np.roll(distorted_image, np.random.randint(0, y), axis=1) + + distorted_image = Image.fromarray(distorted_image) + + return distorted_image + + def signal_distortion(self, image, amplitude): + + img_array = np.array(image) + row_shifts = np.random.randint(-amplitude, amplitude + 1, size=img_array.shape[0]) + distorted_array = np.zeros_like(img_array) + + for y in range(img_array.shape[0]): + x_shift = row_shifts[y] + x_shift = x_shift + y % (amplitude * 2) - amplitude + distorted_array[y,:] = np.roll(img_array[y,:], x_shift, axis=0) + + distorted_image = Image.fromarray(distorted_array) + + return distorted_image + + def tv_vhs_distortion(self, image, amplitude=10): + np_image = np.array(image) + offset_variance = int(image.height / amplitude) + row_shifts = np.random.randint(-offset_variance, offset_variance + 1, size=image.height) + distorted_array = np.zeros_like(np_image) + + for y in range(np_image.shape[0]): + x_shift = row_shifts[y] + x_shift = x_shift + y % (offset_variance * 2) - offset_variance + distorted_array[y,:] = np.roll(np_image[y,:], x_shift, axis=0) + + h, w, c = distorted_array.shape + x_scale = np.linspace(0, 1, w) + y_scale = np.linspace(0, 1, h) + x_idx = np.broadcast_to(x_scale, (h, w)) + y_idx = np.broadcast_to(y_scale.reshape(h, 1), (h, w)) + noise = np.random.rand(h, w, c) * 0.1 + distortion = np.sin(x_idx * 50) * 0.5 + np.sin(y_idx * 50) * 0.5 + distorted_array = distorted_array + distortion[:, :, np.newaxis] + noise + + distorted_image = Image.fromarray(np.uint8(distorted_array)) + distorted_image = distorted_image.resize((image.width, image.height)) + + image_enhance = ImageEnhance.Color(image) + image = image_enhance.enhance(0.5) + + effect_image = ImageChops.overlay(image, distorted_image) + result_image = ImageChops.overlay(image, effect_image) + result_image = ImageChops.blend(image, result_image, 0.25) + + return result_image + + def gradient(self, size, mode='horizontal', colors=None, tolerance=0): + + if isinstance(colors, str): + colors = json.loads(colors) + + if colors is None: + colors = {0: [255, 0, 0], 50: [0, 255, 0], 100: [0, 0, 255]} + + colors = {int(k): [int(c) for c in v] for k, v in colors.items()} + + colors[0] = colors[min(colors.keys())] + colors[255] = colors[max(colors.keys())] + + img = Image.new('RGB', size, color=(0, 0, 0)) + + color_stop_positions = sorted(colors.keys()) + color_stop_count = len(color_stop_positions) + spectrum = [] + for i in range(256): + start_pos = max(p for p in color_stop_positions if p <= i) + end_pos = min(p for p in color_stop_positions if p >= i) + start = colors[start_pos] + end = colors[end_pos] + + if start_pos == end_pos: + factor = 0 + else: + factor = (i - start_pos) / (end_pos - start_pos) + + r = round(start[0] + (end[0] - start[0]) * factor) + g = round(start[1] + (end[1] - start[1]) * factor) + b = round(start[2] + (end[2] - start[2]) * factor) + spectrum.append((r, g, b)) + + draw = ImageDraw.Draw(img) + if mode == 'horizontal': + for x in range(size[0]): + pos = int(x * 100 / (size[0] - 1)) + color = spectrum[pos] + if tolerance > 0: + color = tuple([round(c / tolerance) * tolerance for c in color]) + draw.line((x, 0, x, size[1]), fill=color) + elif mode == 'vertical': + for y in range(size[1]): + pos = int(y * 100 / (size[1] - 1)) + color = spectrum[pos] + if tolerance > 0: + color = tuple([round(c / tolerance) * tolerance for c in color]) + draw.line((0, y, size[0], y), fill=color) + + blur = 1.5 + if size[0] > 512 or size[1] > 512: + multiplier = max(size[0], size[1]) / 512 + if multiplier < 1.5: + multiplier = 1.5 + blur = blur * multiplier + + img = img.filter(ImageFilter.GaussianBlur(radius=blur)) + + return img + + # Version 2 optimized based on Mark Setchell's ideas + def gradient_map(self, image, gradient_map_input, reverse=False): + + # Reverse the image + if reverse: + gradient_map_input = gradient_map_input.transpose(Image.FLIP_LEFT_RIGHT) + + # Convert image to Numpy array and average RGB channels + # grey = self.greyscale(np.array(image)) + grey = np.array(image.convert('L')) + + # Convert gradient map to Numpy array + cmap = np.array(gradient_map_input.convert('RGB')) + + # smush the map into the proper size -- 256 gradient colors + cmap = cv2.resize(cmap, (256, 256)) + + # lop off a single row for the LUT mapper + cmap = cmap[0,:,:].reshape((256, 1, 3)).astype(np.uint8) + + # map with our "custom" LUT + result = cv2.applyColorMap(grey, cmap) + + # Convert result to PIL image + return Image.fromarray(result) + + def greyscale(self, image): + if image.dtype in [np.float16, np.float32, np.float64]: + image = np.clip(image * 255, 0, 255).astype(np.uint8) + cc = image.shape[2] if image.ndim == 3 else 1 + if cc == 1: + return image + typ = cv2.COLOR_BGR2HSV + if cc == 4: + typ = cv2.COLOR_BGRA2GRAY + image = cv2.cvtColor(image, typ)[:,:,2] + return np.expand_dims(image, -1) + + # Generate Perlin Noise (Finally in house version) + + def perlin_noise(self, width, height, octaves, persistence, scale, seed=None): + + @jit(nopython=True) + def fade(t): + return 6 * t**5 - 15 * t**4 + 10 * t**3 + + + @jit(nopython=True) + def lerp(t, a, b): + return a + t * (b - a) + + + @jit(nopython=True) + def grad(hash, x, y, z): + h = hash & 15 + u = x if h < 8 else y + v = y if h < 4 else (x if h == 12 or h == 14 else z) + return (u if (h & 1) == 0 else -u) + (v if (h & 2) == 0 else -v) + + + @jit(nopython=True) + def noise(x, y, z, p): + X = np.int32(np.floor(x)) & 255 + Y = np.int32(np.floor(y)) & 255 + Z = np.int32(np.floor(z)) & 255 + + x -= np.floor(x) + y -= np.floor(y) + z -= np.floor(z) + + u = fade(x) + v = fade(y) + w = fade(z) + + A = p[X] + Y + AA = p[A] + Z + AB = p[A + 1] + Z + B = p[X + 1] + Y + BA = p[B] + Z + BB = p[B + 1] + Z + + return lerp(w, lerp(v, lerp(u, grad(p[AA], x, y, z), grad(p[BA], x - 1, y, z)), + lerp(u, grad(p[AB], x, y - 1, z), grad(p[BB], x - 1, y - 1, z))), + lerp(v, lerp(u, grad(p[AA + 1], x, y, z - 1), grad(p[BA + 1], x - 1, y, z - 1)), + lerp(u, grad(p[AB + 1], x, y - 1, z - 1), grad(p[BB + 1], x - 1, y - 1, z - 1)))) + + if seed: + random.seed(seed) + + p = np.arange(256, dtype=np.int32) + random.shuffle(p) + p = np.concatenate((p, p)) + + noise_map = np.zeros((height, width)) + amplitude = 1.0 + total_amplitude = 0.0 + + for octave in range(octaves): + frequency = 2 ** octave + total_amplitude += amplitude + + for y in range(height): + for x in range(width): + nx = x / scale * frequency + ny = y / scale * frequency + noise_value = noise(nx, ny, 0, p) * amplitude + current_value = noise_map[y, x] + noise_map[y, x] = current_value + noise_value + + amplitude *= persistence + + min_value = np.min(noise_map) + max_value = np.max(noise_map) + noise_map = np.interp(noise_map, (min_value, max_value), (0, 255)).astype(np.uint8) + image = Image.fromarray(noise_map, mode='L').convert("RGB") + + return image + + + # Generate Perlin Power Fractal (Based on in-house perlin noise) + + def perlin_power_fractal(self, width, height, octaves, persistence, lacunarity, exponent, scale, seed=None): + + @jit(nopython=True) + def fade(t): + return 6 * t**5 - 15 * t**4 + 10 * t**3 + + @jit(nopython=True) + def lerp(t, a, b): + return a + t * (b - a) + + @jit(nopython=True) + def grad(hash, x, y, z): + h = hash & 15 + u = x if h < 8 else y + v = y if h < 4 else (x if h == 12 or h == 14 else z) + return (u if (h & 1) == 0 else -u) + (v if (h & 2) == 0 else -v) + + @jit(nopython=True) + def noise(x, y, z, p): + X = np.int32(np.floor(x)) & 255 + Y = np.int32(np.floor(y)) & 255 + Z = np.int32(np.floor(z)) & 255 + + x -= np.floor(x) + y -= np.floor(y) + z -= np.floor(z) + + u = fade(x) + v = fade(y) + w = fade(z) + + A = p[X] + Y + AA = p[A] + Z + AB = p[A + 1] + Z + B = p[X + 1] + Y + BA = p[B] + Z + BB = p[B + 1] + Z + + return lerp(w, lerp(v, lerp(u, grad(p[AA], x, y, z), grad(p[BA], x - 1, y, z)), + lerp(u, grad(p[AB], x, y - 1, z), grad(p[BB], x - 1, y - 1, z))), + lerp(v, lerp(u, grad(p[AA + 1], x, y, z - 1), grad(p[BA + 1], x - 1, y, z - 1)), + lerp(u, grad(p[AB + 1], x, y - 1, z - 1), grad(p[BB + 1], x - 1, y - 1, z - 1)))) + + if seed: + random.seed(seed) + + p = np.arange(256, dtype=np.int32) + random.shuffle(p) + p = np.concatenate((p, p)) + + noise_map = np.zeros((height, width)) + amplitude = 1.0 + total_amplitude = 0.0 + + for octave in range(octaves): + frequency = lacunarity ** octave + amplitude *= persistence + total_amplitude += amplitude + + for y in range(height): + for x in range(width): + nx = x / scale * frequency + ny = y / scale * frequency + noise_value = noise(nx, ny, 0, p) * amplitude ** exponent + current_value = noise_map[y, x] + noise_map[y, x] = current_value + noise_value + + min_value = np.min(noise_map) + max_value = np.max(noise_map) + noise_map = np.interp(noise_map, (min_value, max_value), (0, 255)).astype(np.uint8) + image = Image.fromarray(noise_map, mode='L').convert("RGB") + + return image + + # Worley Noise Generator + class worley_noise: + + def __init__(self, height=512, width=512, density=50, option=0, use_broadcast_ops=True, flat=False, seed=None): + + self.height = height + self.width = width + self.density = density + self.use_broadcast_ops = use_broadcast_ops + self.seed = seed + self.generate_points_and_colors() + self.calculate_noise(option) + self.image = self.generateImage(option, flat_mode=flat) + + def generate_points_and_colors(self): + rng = np.random.default_rng(self.seed) + self.points = rng.integers(0, self.width, (self.density, 2)) + self.colors = rng.integers(0, 256, (self.density, 3)) + + def calculate_noise(self, option): + self.data = np.zeros((self.height, self.width)) + for h in range(self.height): + for w in range(self.width): + distances = np.sqrt(np.sum((self.points - np.array([w, h])) ** 2, axis=1)) + self.data[h, w] = np.sort(distances)[option] + + def broadcast_calculate_noise(self, option): + xs = np.arange(self.width) + ys = np.arange(self.height) + x_dist = np.power(self.points[:, 0, np.newaxis] - xs, 2) + y_dist = np.power(self.points[:, 1, np.newaxis] - ys, 2) + d = np.sqrt(x_dist[:, :, np.newaxis] + y_dist[:, np.newaxis, :]) + distances = np.sort(d, axis=0) + self.data = distances[option] + + def generateImage(self, option, flat_mode=False): + if flat_mode: + flat_color_data = np.zeros((self.height, self.width, 3), dtype=np.uint8) + for h in range(self.height): + for w in range(self.width): + closest_point_idx = np.argmin(np.sum((self.points - np.array([w, h])) ** 2, axis=1)) + flat_color_data[h, w, :] = self.colors[closest_point_idx] + return Image.fromarray(flat_color_data, 'RGB') + else: + min_val, max_val = np.min(self.data), np.max(self.data) + data_scaled = (self.data - min_val) / (max_val - min_val) * 255 + data_scaled = data_scaled.astype(np.uint8) + return Image.fromarray(data_scaled, 'L') + + # Make Image Seamless + + def make_seamless(self, image, blending=0.5, tiled=False, tiles=2): + + if 'img2texture' not in packages(): + install_package('git+https://github.com/WASasquatch/img2texture.git') + + from img2texture import img2tex + from img2texture._tiling import tile + + texture = img2tex(src=image, dst=None, pct=blending, return_result=True) + if tiled: + texture = tile(source=texture, target=None, horizontal=tiles, vertical=tiles, return_result=True) + + return texture + + # Image Displacement Warp + + def displace_image(self, image, displacement_map, amplitude): + + image = image.convert('RGB') + displacement_map = displacement_map.convert('L') + width, height = image.size + result = Image.new('RGB', (width, height)) + + for y in range(height): + for x in range(width): + + # Calculate the displacements n' stuff + displacement = displacement_map.getpixel((x, y)) + displacement_amount = amplitude * (displacement / 255) + new_x = x + int(displacement_amount) + new_y = y + int(displacement_amount) + + # Apply mirror reflection at edges and corners + if new_x < 0: + new_x = abs(new_x) + elif new_x >= width: + new_x = 2 * width - new_x - 1 + + if new_y < 0: + new_y = abs(new_y) + elif new_y >= height: + new_y = 2 * height - new_y - 1 + + if new_x < 0: + new_x = abs(new_x) + if new_y < 0: + new_y = abs(new_y) + + if new_x >= width: + new_x = 2 * width - new_x - 1 + if new_y >= height: + new_y = 2 * height - new_y - 1 + + # Consider original image color at new location for RGB results, oops + pixel = image.getpixel((new_x, new_y)) + result.putpixel((x, y), pixel) + + return result + + # Analyze Filters + + def black_white_levels(self, image): + + if 'matplotlib' not in packages(): + install_package('matplotlib') + + import matplotlib.pyplot as plt + + # convert to grayscale + image = image.convert('L') + + # Calculate the histogram of grayscale intensities + hist = image.histogram() + + # Find the minimum and maximum grayscale intensity values + min_val = 0 + max_val = 255 + for i in range(256): + if hist[i] > 0: + min_val = i + break + for i in range(255, -1, -1): + if hist[i] > 0: + max_val = i + break + + # Create a graph of the grayscale histogram + plt.figure(figsize=(16, 8)) + plt.hist(image.getdata(), bins=256, range=(0, 256), color='black', alpha=0.7) + plt.xlim([0, 256]) + plt.ylim([0, max(hist)]) + plt.axvline(min_val, color='red', linestyle='dashed') + plt.axvline(max_val, color='red', linestyle='dashed') + plt.title('Black and White Levels') + plt.xlabel('Intensity') + plt.ylabel('Frequency') + + return self.fig2img(plt) + + def channel_frequency(self, image): + + if 'matplotlib' not in packages(): + install_package('matplotlib') + + import matplotlib.pyplot as plt + + # Split the image into its RGB channels + r, g, b = image.split() + + # Calculate the frequency of each color in each channel + r_freq = r.histogram() + g_freq = g.histogram() + b_freq = b.histogram() + + # Create a graph to hold the frequency maps + fig, axs = plt.subplots(1, 3, figsize=(16, 4)) + axs[0].set_title('Red Channel') + axs[1].set_title('Green Channel') + axs[2].set_title('Blue Channel') + + # Plot the frequency of each color in each channel + axs[0].plot(range(256), r_freq, color='red') + axs[1].plot(range(256), g_freq, color='green') + axs[2].plot(range(256), b_freq, color='blue') + + # Set the axis limits and labels + for ax in axs: + ax.set_xlim([0, 255]) + ax.set_xlabel('Color Intensity') + ax.set_ylabel('Frequency') + + return self.fig2img(plt) + + def generate_palette(self, img, n_colors=16, cell_size=128, padding=0, font_path=None, font_size=15, mode='chart'): + if 'scikit-learn' not in packages(): + install_package('scikit-learn') + + from sklearn.cluster import KMeans + + img = img.resize((img.width // 2, img.height // 2), resample=Image.BILINEAR) + pixels = np.array(img) + pixels = pixels.reshape((-1, 3)) + kmeans = KMeans(n_clusters=n_colors, random_state=0, n_init='auto').fit(pixels) + cluster_centers = np.uint8(kmeans.cluster_centers_) + + # Get the sorted indices based on luminance + luminance = np.sqrt(np.dot(cluster_centers, [0.299, 0.587, 0.114])) + sorted_indices = np.argsort(luminance) + + # Rearrange the cluster centers and luminance based on sorted indices + cluster_centers = cluster_centers[sorted_indices] + luminance = luminance[sorted_indices] + + # Group colors by their individual types + reds = [] + greens = [] + blues = [] + others = [] + + for i in range(n_colors): + color = cluster_centers[i] + color_type = np.argmax(color) # Find the dominant color component + + if color_type == 0: + reds.append((color, luminance[i])) + elif color_type == 1: + greens.append((color, luminance[i])) + elif color_type == 2: + blues.append((color, luminance[i])) + else: + others.append((color, luminance[i])) + + # Sort each color group by luminance + reds.sort(key=lambda x: x[1]) + greens.sort(key=lambda x: x[1]) + blues.sort(key=lambda x: x[1]) + others.sort(key=lambda x: x[1]) + + # Combine the sorted color groups + sorted_colors = reds + greens + blues + others + + if mode == 'back_to_back': + # Calculate the size of the palette image based on the number of colors + palette_width = n_colors * cell_size + palette_height = cell_size + else: + # Calculate the number of rows and columns based on the number of colors + num_rows = int(np.sqrt(n_colors)) + num_cols = int(np.ceil(n_colors / num_rows)) + + # Calculate the size of the palette image based on the number of rows and columns + palette_width = num_cols * cell_size + palette_height = num_rows * cell_size + + palette_size = (palette_width, palette_height) + + palette = Image.new('RGB', palette_size, color='white') + draw = ImageDraw.Draw(palette) + if font_path: + font = ImageFont.truetype(font_path, font_size) + else: + font = ImageFont.load_default() + + hex_palette = [] + for i, (color, _) in enumerate(sorted_colors): + if mode == 'back_to_back': + cell_x = i * cell_size + cell_y = 0 + else: + row = i % num_rows + col = i // num_rows + cell_x = col * cell_size + cell_y = row * cell_size + + cell_width = cell_size + cell_height = cell_size + + color = tuple(color) + + cell = Image.new('RGB', (cell_width, cell_height), color=color) + palette.paste(cell, (cell_x, cell_y)) + + if mode != 'back_to_back': + text_x = cell_x + (cell_width / 2) + text_y = cell_y + cell_height + padding + + draw.text((text_x + 1, text_y + 1), f"R: {color[0]} G: {color[1]} B: {color[2]}", font=font, fill='black', anchor='ms') + draw.text((text_x, text_y), f"R: {color[0]} G: {color[1]} B: {color[2]}", font=font, fill='white', anchor='ms') + + hex_palette.append('#%02x%02x%02x' % color) + + return palette, '\n'.join(hex_palette) + + +from transformers import BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering + +class BlipWrapper: + def __init__(self, caption_model_id="Salesforce/blip-image-captioning-base", vqa_model_id="Salesforce/blip-vqa-base", device="cuda", cache_dir=None): + self.device = torch.device(device='cuda' if device == "cuda" and torch.cuda.is_available() else 'cpu') + self.caption_processor = BlipProcessor.from_pretrained(caption_model_id, cache_dir=cache_dir) + self.caption_model = BlipForConditionalGeneration.from_pretrained(caption_model_id, cache_dir=cache_dir).to(self.device) + self.vqa_processor = BlipProcessor.from_pretrained(vqa_model_id, cache_dir=cache_dir) + self.vqa_model = BlipForQuestionAnswering.from_pretrained(vqa_model_id, cache_dir=cache_dir).to(self.device) + + def generate_caption(self, image: Image.Image, min_length=50, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=False): + self.caption_model.eval() + inputs = self.caption_processor(images=image, return_tensors="pt").to(self.device) + outputs = self.caption_model.generate(**inputs, min_length=min_length, max_length=max_length, num_beams=num_beams, no_repeat_ngram_size=no_repeat_ngram_size, early_stopping=early_stopping) + return self.caption_processor.decode(outputs[0], skip_special_tokens=True) + + def answer_question(self, image: Image.Image, question: str, min_length=50, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=False): + self.vqa_model.eval() + inputs = self.vqa_processor(images=image, text=question, return_tensors="pt").to(self.device) + answer_ids = self.vqa_model.generate(**inputs, min_length=min_length, max_length=max_length, num_beams=num_beams, no_repeat_ngram_size=no_repeat_ngram_size, early_stopping=early_stopping) + return self.vqa_processor.decode(answer_ids[0], skip_special_tokens=True) + + +#! IMAGE FILTER NODES + +# IMAGE SHADOW AND HIGHLIGHT ADJUSTMENTS + +class WAS_Shadow_And_Highlight_Adjustment: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "shadow_threshold": ("FLOAT", {"default": 75, "min": 0.0, "max": 255.0, "step": 0.1}), + "shadow_factor": ("FLOAT", {"default": 1.5, "min": -12.0, "max": 12.0, "step": 0.1}), + "shadow_smoothing": ("FLOAT", {"default": 0.25, "min": -255.0, "max": 255.0, "step": 0.1}), + "highlight_threshold": ("FLOAT", {"default": 175, "min": 0.0, "max": 255.0, "step": 0.1}), + "highlight_factor": ("FLOAT", {"default": 0.5, "min": -12.0, "max": 12.0, "step": 0.1}), + "highlight_smoothing": ("FLOAT", {"default": 0.25, "min": -255.0, "max": 255.0, "step": 0.1}), + "simplify_isolation": ("FLOAT", {"default": 0, "min": -255.0, "max": 255.0, "step": 0.1}), + } + } + + RETURN_TYPES = ("IMAGE","IMAGE","IMAGE") + RETURN_NAMES = ("image","shadow_map","highlight_map") + FUNCTION = "apply_shadow_and_highlight" + + CATEGORY = "WAS Suite/Image/Adjustment" + + def apply_shadow_and_highlight(self, image, shadow_threshold=30, highlight_threshold=220, shadow_factor=1.5, highlight_factor=0.5, shadow_smoothing=0, highlight_smoothing=0, simplify_isolation=0): + + WTools = WAS_Tools_Class() + + result, shadows, highlights = WTools.shadows_and_highlights(tensor2pil(image), shadow_threshold, highlight_threshold, shadow_factor, highlight_factor, shadow_smoothing, highlight_smoothing, simplify_isolation) + result, shadows, highlights = WTools.shadows_and_highlights(tensor2pil(image), shadow_threshold, highlight_threshold, shadow_factor, highlight_factor, shadow_smoothing, highlight_smoothing, simplify_isolation) + + return (pil2tensor(result), pil2tensor(shadows), pil2tensor(highlights) ) + + +# IMAGE PIXELATE + +class WAS_Image_Pixelate: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "pixelation_size": ("FLOAT", {"default": 164, "min": 16, "max": 480, "step": 1}), + "num_colors": ("FLOAT", {"default": 16, "min": 2, "max": 256, "step": 1}), + "init_mode": (["k-means++", "random", "none"],), + "max_iterations": ("FLOAT", {"default": 100, "min": 1, "max": 256, "step": 1}), + "dither": (["False", "True"],), + "dither_mode": (["FloydSteinberg", "Ordered"],), + }, + "optional": { + "color_palettes": ("LIST", {"forceInput": True}), + "color_palette_mode": (["Brightness", "BrightnessAndTonal", "Linear", "Tonal"],), + "reverse_palette":(["False","True"],), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "image_pixelate" + + CATEGORY = "WAS Suite/Image/Process" + + def image_pixelate(self, images, pixelation_size=164, num_colors=16, init_mode='random', max_iterations=100, + color_palettes=None, color_palette_mode="Linear", reverse_palette='False', dither='False', dither_mode='FloydSteinberg'): + + if 'scikit-learn' not in packages(): + install_package('scikit-learn') + + pixelation_size = int(pixelation_size) + num_colors = int(num_colors) + max_iterations = int(max_iterations) + color_palette_mode = color_palette_mode + dither = (dither == 'True') + + color_palettes_list = [] + if color_palettes: + for palette in color_palettes: + color_palettes_list.append([color.strip() for color in palette.splitlines() if not color.startswith('//') or not color.startswith(';')]) + + reverse_palette = (True if reverse_palette == 'True' else False) + + return ( self.pixel_art_batch(images, pixelation_size, num_colors, init_mode, max_iterations, 42, + (color_palettes_list if color_palettes_list else None), color_palette_mode, reverse_palette, dither, dither_mode), ) + + def pixel_art_batch(self, batch, min_size, num_colors=16, init_mode='random', max_iter=100, random_state=42, + palette=None, palette_mode="Linear", reverse_palette=False, dither=False, dither_mode='FloydSteinberg'): + + from sklearn.cluster import KMeans + + hex_palette_to_rgb = lambda hex: tuple(int(hex[i:i+2], 16) for i in (0, 2, 4)) + + def flatten_colors(image, num_colors, init_mode='random', max_iter=100, random_state=42): + np_image = np.array(image) + pixels = np_image.reshape(-1, 3) + kmeans = KMeans(n_clusters=num_colors, init=init_mode, max_iter=max_iter, tol=1e-3, random_state=random_state, n_init='auto') + labels = kmeans.fit_predict(pixels) + colors = kmeans.cluster_centers_.astype(np.uint8) + flattened_pixels = colors[labels] + flattened_image = flattened_pixels.reshape(np_image.shape) + return Image.fromarray(flattened_image) + + def dither_image(image, mode, nc): + + def clamp(value, min_value=0, max_value=255): + return max(min(value, max_value), min_value) + + def get_new_val(old_val, nc): + return np.round(old_val * (nc - 1)) / (nc - 1) + + def fs_dither(img, nc): + arr = np.array(img, dtype=float) / 255 + new_width, new_height = img.size + + for ir in range(new_height): + for ic in range(new_width): + old_val = arr[ir, ic].copy() + new_val = get_new_val(old_val, nc) + arr[ir, ic] = new_val + err = old_val - new_val + + if ic < new_width - 1: + arr[ir, ic + 1] += err * 7/16 + if ir < new_height - 1: + if ic > 0: + arr[ir + 1, ic - 1] += err * 3/16 + arr[ir + 1, ic] += err * 5/16 + if ic < new_width - 1: + arr[ir + 1, ic + 1] += err / 16 + + carr = np.array(arr * 255, dtype=np.uint8) + return Image.fromarray(carr) + + def ordered_dither(img, nc): + width, height = img.size + dither_matrix = [ + [0, 8, 2, 10], + [12, 4, 14, 6], + [3, 11, 1, 9], + [15, 7, 13, 5] + ] + dithered_image = Image.new('RGB', (width, height)) + num_colors = min(2 ** int(np.log2(nc)), 16) + + for y in range(height): + for x in range(width): + old_pixel = img.getpixel((x, y)) + threshold = dither_matrix[x % 4][y % 4] * num_colors + new_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in old_pixel) + error = tuple(old - new for old, new in zip(old_pixel, new_pixel)) + dithered_image.putpixel((x, y), new_pixel) + + if x < width - 1: + neighboring_pixel = img.getpixel((x + 1, y)) + neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel) + neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel)) + neighboring_pixel = tuple(int(clamp(pixel + error * 7 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error)) + img.putpixel((x + 1, y), neighboring_pixel) + + if x < width - 1 and y < height - 1: + neighboring_pixel = img.getpixel((x + 1, y + 1)) + neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel) + neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel)) + neighboring_pixel = tuple(int(clamp(pixel + error * 1 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error)) + img.putpixel((x + 1, y + 1), neighboring_pixel) + + if y < height - 1: + neighboring_pixel = img.getpixel((x, y + 1)) + neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel) + neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel)) + neighboring_pixel = tuple(int(clamp(pixel + error * 5 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error)) + img.putpixel((x, y + 1), neighboring_pixel) + + if x > 0 and y < height - 1: + neighboring_pixel = img.getpixel((x - 1, y + 1)) + neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel) + neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel)) + neighboring_pixel = tuple(int(clamp(pixel + error * 3 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error)) + img.putpixel((x - 1, y + 1), neighboring_pixel) + + return dithered_image + + if mode == 'FloydSteinberg': + return fs_dither(image, nc) + elif mode == 'Ordered': + return ordered_dither(image, nc) + else: + cstr(f"Inavlid dithering mode `{mode}` selected.").error.print() + return image + + return image + + def color_palette_from_hex_lines(image, colors, palette_mode='Linear', reverse_palette=False): + + def color_distance(color1, color2): + r1, g1, b1 = color1 + r2, g2, b2 = color2 + return np.sqrt((r1 - r2)**2 + (g1 - g2)**2 + (b1 - b2)**2) + + def find_nearest_color_index(color, palette): + distances = [color_distance(color, palette_color) for palette_color in palette] + return distances.index(min(distances)) + + def find_nearest_color_index_tonal(color, palette): + distances = [color_distance_tonal(color, palette_color) for palette_color in palette] + return distances.index(min(distances)) + + def find_nearest_color_index_both(color, palette): + distances = [color_distance_both(color, palette_color) for palette_color in palette] + return distances.index(min(distances)) + + def color_distance_tonal(color1, color2): + r1, g1, b1 = color1 + r2, g2, b2 = color2 + l1 = 0.299 * r1 + 0.587 * g1 + 0.114 * b1 + l2 = 0.299 * r2 + 0.587 * g2 + 0.114 * b2 + return abs(l1 - l2) + + def color_distance_both(color1, color2): + r1, g1, b1 = color1 + r2, g2, b2 = color2 + l1 = 0.299 * r1 + 0.587 * g1 + 0.114 * b1 + l2 = 0.299 * r2 + 0.587 * g2 + 0.114 * b2 + return abs(l1 - l2) + sum(abs(c1 - c2) for c1, c2 in zip(color1, color2)) + + def color_distance(color1, color2): + return sum(abs(c1 - c2) for c1, c2 in zip(color1, color2)) + + color_palette = [hex_palette_to_rgb(color.lstrip('#')) for color in colors] + + if reverse_palette: + color_palette = color_palette[::-1] + + np_image = np.array(image) + labels = np_image.reshape(image.size[1], image.size[0], -1) + width, height = image.size + new_image = Image.new("RGB", image.size) + + if palette_mode == 'Linear': + color_palette_indices = list(range(len(color_palette))) + elif palette_mode == 'Brightness': + color_palette_indices = sorted(range(len(color_palette)), key=lambda i: sum(color_palette[i]) / 3) + elif palette_mode == 'Tonal': + color_palette_indices = sorted(range(len(color_palette)), key=lambda i: color_distance(color_palette[i], (128, 128, 128))) + elif palette_mode == 'BrightnessAndTonal': + color_palette_indices = sorted(range(len(color_palette)), key=lambda i: (sum(color_palette[i]) / 3, color_distance(color_palette[i], (128, 128, 128)))) + else: + raise ValueError(f"Unsupported mapping mode: {palette_mode}") + + for x in range(width): + for y in range(height): + pixel_color = labels[y, x, :] + + if palette_mode == 'Linear': + color_index = pixel_color[0] % len(color_palette) + elif palette_mode == 'Brightness': + color_index = find_nearest_color_index(pixel_color, [color_palette[i] for i in color_palette_indices]) + elif palette_mode == 'Tonal': + color_index = find_nearest_color_index_tonal(pixel_color, [color_palette[i] for i in color_palette_indices]) + elif palette_mode == 'BrightnessAndTonal': + color_index = find_nearest_color_index_both(pixel_color, [color_palette[i] for i in color_palette_indices]) + else: + raise ValueError(f"Unsupported mapping mode: {palette_mode}") + + color = color_palette[color_palette_indices[color_index]] + new_image.putpixel((x, y), color) + + return new_image + + pil_images = [tensor2pil(image) for image in batch] + pixel_art_images = [] + original_sizes = [] + total_images = len(pil_images) + for image in pil_images: + width, height = image.size + original_sizes.append((width, height)) + if max(width, height) > min_size: + if width > height: + new_width = min_size + new_height = int(height * (min_size / width)) + else: + new_height = min_size + new_width = int(width * (min_size / height)) + pixel_art_images.append(image.resize((new_width, int(new_height)), Image.NEAREST)) + else: + pixel_art_images.append(image) + if init_mode != 'none': + pixel_art_images = [flatten_colors(image, num_colors, init_mode) for image in pixel_art_images] + if dither: + pixel_art_images = [dither_image(image, dither_mode, num_colors) for image in pixel_art_images] + if palette: + pixel_art_images = [color_palette_from_hex_lines(pixel_art_image, palette[i], palette_mode, reverse_palette) for i, pixel_art_image in enumerate(pixel_art_images)] + else: + pixel_art_images = pixel_art_images + pixel_art_images = [image.resize(size, Image.NEAREST) for image, size in zip(pixel_art_images, original_sizes)] + + tensor_images = [pil2tensor(image) for image in pixel_art_images] + + batch_tensor = torch.cat(tensor_images, dim=0) + return batch_tensor + +# SIMPLE IMAGE ADJUST + +class WAS_Image_Filters: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "brightness": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}), + "contrast": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 2.0, "step": 0.01}), + "saturation": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01}), + "sharpness": ("FLOAT", {"default": 1.0, "min": -5.0, "max": 5.0, "step": 0.01}), + "blur": ("INT", {"default": 0, "min": 0, "max": 16, "step": 1}), + "gaussian_blur": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1024.0, "step": 0.1}), + "edge_enhance": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "detail_enhance": (["false", "true"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_filters" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_filters(self, image, brightness, contrast, saturation, sharpness, blur, gaussian_blur, edge_enhance, detail_enhance): + + + tensors = [] + if len(image) > 1: + for img in image: + + pil_image = None + + # Apply NP Adjustments + if brightness > 0.0 or brightness < 0.0: + # Apply brightness + img = np.clip(img + brightness, 0.0, 1.0) + + if contrast > 1.0 or contrast < 1.0: + # Apply contrast + img = np.clip(img * contrast, 0.0, 1.0) + + # Apply PIL Adjustments + if saturation > 1.0 or saturation < 1.0: + # PIL Image + pil_image = tensor2pil(img) + # Apply saturation + pil_image = ImageEnhance.Color(pil_image).enhance(saturation) + + if sharpness > 1.0 or sharpness < 1.0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Apply sharpness + pil_image = ImageEnhance.Sharpness(pil_image).enhance(sharpness) + + if blur > 0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Apply blur + for _ in range(blur): + pil_image = pil_image.filter(ImageFilter.BLUR) + + if gaussian_blur > 0.0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Apply Gaussian blur + pil_image = pil_image.filter( + ImageFilter.GaussianBlur(radius=gaussian_blur)) + + if edge_enhance > 0.0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Edge Enhancement + edge_enhanced_img = pil_image.filter(ImageFilter.EDGE_ENHANCE_MORE) + # Blend Mask + blend_mask = Image.new( + mode="L", size=pil_image.size, color=(round(edge_enhance * 255))) + # Composite Original and Enhanced Version + pil_image = Image.composite( + edge_enhanced_img, pil_image, blend_mask) + # Clean-up + del blend_mask, edge_enhanced_img + + if detail_enhance == "true": + pil_image = pil_image if pil_image else tensor2pil(img) + pil_image = pil_image.filter(ImageFilter.DETAIL) + + # Output image + out_image = (pil2tensor(pil_image) if pil_image else img.unsqueeze(0)) + + tensors.append(out_image) + + tensors = torch.cat(tensors, dim=0) + + else: + + pil_image = None + img = image + + # Apply NP Adjustments + if brightness > 0.0 or brightness < 0.0: + # Apply brightness + img = np.clip(img + brightness, 0.0, 1.0) + + if contrast > 1.0 or contrast < 1.0: + # Apply contrast + img = np.clip(img * contrast, 0.0, 1.0) + + # Apply PIL Adjustments + if saturation > 1.0 or saturation < 1.0: + # PIL Image + pil_image = tensor2pil(img) + # Apply saturation + pil_image = ImageEnhance.Color(pil_image).enhance(saturation) + + if sharpness > 1.0 or sharpness < 1.0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Apply sharpness + pil_image = ImageEnhance.Sharpness(pil_image).enhance(sharpness) + + if blur > 0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Apply blur + for _ in range(blur): + pil_image = pil_image.filter(ImageFilter.BLUR) + + if gaussian_blur > 0.0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Apply Gaussian blur + pil_image = pil_image.filter( + ImageFilter.GaussianBlur(radius=gaussian_blur)) + + if edge_enhance > 0.0: + # Assign or create PIL Image + pil_image = pil_image if pil_image else tensor2pil(img) + # Edge Enhancement + edge_enhanced_img = pil_image.filter(ImageFilter.EDGE_ENHANCE_MORE) + # Blend Mask + blend_mask = Image.new( + mode="L", size=pil_image.size, color=(round(edge_enhance * 255))) + # Composite Original and Enhanced Version + pil_image = Image.composite( + edge_enhanced_img, pil_image, blend_mask) + # Clean-up + del blend_mask, edge_enhanced_img + + if detail_enhance == "true": + pil_image = pil_image if pil_image else tensor2pil(img) + pil_image = pil_image.filter(ImageFilter.DETAIL) + + # Output image + out_image = (pil2tensor(pil_image) if pil_image else img) + + tensors = out_image + + return (tensors, ) + +# RICHARDSON LUCY SHARPEN + +class WAS_Lucy_Sharpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "iterations": ("INT", {"default": 2, "min": 1, "max": 12, "step": 1}), + "kernel_size": ("INT", {"default": 3, "min": 1, "max": 16, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "sharpen" + + CATEGORY = "WAS Suite/Image/Filter" + + def sharpen(self, images, iterations, kernel_size): + + tensors = [] + if len(images) > 1: + for img in images: + tensors.append(pil2tensor(self.lucy_sharpen(tensor2pil(img), iterations, kernel_size))) + tensors = torch.cat(tensors, dim=0) + else: + return (pil2tensor(self.lucy_sharpen(tensor2pil(images), iterations, kernel_size)),) + + return (tensors,) + + + def lucy_sharpen(self, image, iterations=10, kernel_size=3): + + from scipy.signal import convolve2d + + image_array = np.array(image, dtype=np.float32) / 255.0 + kernel = np.ones((kernel_size, kernel_size), dtype=np.float32) / (kernel_size ** 2) + sharpened_channels = [] + + padded_image_array = np.pad(image_array, ((kernel_size, kernel_size), (kernel_size, kernel_size), (0, 0)), mode='edge') + + for channel in range(3): + channel_array = padded_image_array[:, :, channel] + + for _ in range(iterations): + blurred_channel = convolve2d(channel_array, kernel, mode='same') + ratio = channel_array / (blurred_channel + 1e-6) + channel_array *= convolve2d(ratio, kernel, mode='same') + + sharpened_channels.append(channel_array) + + cropped_sharpened_image_array = np.stack(sharpened_channels, axis=-1)[kernel_size:-kernel_size, kernel_size:-kernel_size, :] + sharpened_image_array = np.clip(cropped_sharpened_image_array * 255.0, 0, 255).astype(np.uint8) + sharpened_image = Image.fromarray(sharpened_image_array) + return sharpened_image + +# IMAGE STYLE FILTER + +class WAS_Image_Style_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "style": ([ + "1977", + "aden", + "brannan", + "brooklyn", + "clarendon", + "earlybird", + "fairy tale", + "gingham", + "hudson", + "inkwell", + "kelvin", + "lark", + "lofi", + "maven", + "mayfair", + "moon", + "nashville", + "perpetua", + "reyes", + "rise", + "slumber", + "stinson", + "toaster", + "valencia", + "walden", + "willow", + "xpro2" + ],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_style_filter" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_style_filter(self, image, style): + + # Install Pilgram + if 'pilgram' not in packages(): + install_package('pilgram') + + # Import Pilgram module + import pilgram + + # WAS Filters + WTools = WAS_Tools_Class() + + # Apply blending + tensors = [] + for img in image: + if style == "1977": + tensors.append(pil2tensor(pilgram._1977(tensor2pil(img)))) + elif style == "aden": + tensors.append(pil2tensor(pilgram.aden(tensor2pil(img)))) + elif style == "brannan": + tensors.append(pil2tensor(pilgram.brannan(tensor2pil(img)))) + elif style == "brooklyn": + tensors.append(pil2tensor(pilgram.brooklyn(tensor2pil(img)))) + elif style == "clarendon": + tensors.append(pil2tensor(pilgram.clarendon(tensor2pil(img)))) + elif style == "earlybird": + tensors.append(pil2tensor(pilgram.earlybird(tensor2pil(img)))) + elif style == "fairy tale": + tensors.append(pil2tensor(WTools.sparkle(tensor2pil(img)))) + elif style == "gingham": + tensors.append(pil2tensor(pilgram.gingham(tensor2pil(img)))) + elif style == "hudson": + tensors.append(pil2tensor(pilgram.hudson(tensor2pil(img)))) + elif style == "inkwell": + tensors.append(pil2tensor(pilgram.inkwell(tensor2pil(img)))) + elif style == "kelvin": + tensors.append(pil2tensor(pilgram.kelvin(tensor2pil(img)))) + elif style == "lark": + tensors.append(pil2tensor(pilgram.lark(tensor2pil(img)))) + elif style == "lofi": + tensors.append(pil2tensor(pilgram.lofi(tensor2pil(img)))) + elif style == "maven": + tensors.append(pil2tensor(pilgram.maven(tensor2pil(img)))) + elif style == "mayfair": + tensors.append(pil2tensor(pilgram.mayfair(tensor2pil(img)))) + elif style == "moon": + tensors.append(pil2tensor(pilgram.moon(tensor2pil(img)))) + elif style == "nashville": + tensors.append(pil2tensor(pilgram.nashville(tensor2pil(img)))) + elif style == "perpetua": + tensors.append(pil2tensor(pilgram.perpetua(tensor2pil(img)))) + elif style == "reyes": + tensors.append(pil2tensor(pilgram.reyes(tensor2pil(img)))) + elif style == "rise": + tensors.append(pil2tensor(pilgram.rise(tensor2pil(img)))) + elif style == "slumber": + tensors.append(pil2tensor(pilgram.slumber(tensor2pil(img)))) + elif style == "stinson": + tensors.append(pil2tensor(pilgram.stinson(tensor2pil(img)))) + elif style == "toaster": + tensors.append(pil2tensor(pilgram.toaster(tensor2pil(img)))) + elif style == "valencia": + tensors.append(pil2tensor(pilgram.valencia(tensor2pil(img)))) + elif style == "walden": + tensors.append(pil2tensor(pilgram.walden(tensor2pil(img)))) + elif style == "willow": + tensors.append(pil2tensor(pilgram.willow(tensor2pil(img)))) + elif style == "xpro2": + tensors.append(pil2tensor(pilgram.xpro2(tensor2pil(img)))) + else: + tensors.append(img) + + tensors = torch.cat(tensors, dim=0) + + return (tensors, ) + + +# IMAGE CROP FACE + +class WAS_Image_Crop_Face: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "crop_padding_factor": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 2.0, "step": 0.01}), + "cascade_xml": ([ + "lbpcascade_animeface.xml", + "haarcascade_frontalface_default.xml", + "haarcascade_frontalface_alt.xml", + "haarcascade_frontalface_alt2.xml", + "haarcascade_frontalface_alt_tree.xml", + "haarcascade_profileface.xml", + "haarcascade_upperbody.xml", + "haarcascade_eye.xml" + ],), + } + } + + RETURN_TYPES = ("IMAGE", "CROP_DATA") + FUNCTION = "image_crop_face" + + CATEGORY = "WAS Suite/Image/Process" + + def image_crop_face(self, image, cascade_xml=None, crop_padding_factor=0.25): + return self.crop_face(tensor2pil(image), cascade_xml, crop_padding_factor) + + def crop_face(self, image, cascade_name=None, padding=0.25): + + import cv2 + + img = np.array(image.convert('RGB')) + + face_location = None + + cascades = [ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'lbpcascade_animeface.xml'), + os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_default.xml'), + os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_alt.xml'), + os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_alt2.xml'), + os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_alt_tree.xml'), + os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_profileface.xml'), + os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_upperbody.xml') ] + + if cascade_name: + for cascade in cascades: + if os.path.basename(cascade) == cascade_name: + cascades.remove(cascade) + cascades.insert(0, cascade) + break + + faces = None + if not face_location: + for cascade in cascades: + if not os.path.exists(cascade): + cstr(f"Unable to find cascade XML file at `{cascade}`. Did you pull the latest files from https://github.com/WASasquatch/was-node-suite-comfyui repo?").error.print() + return (pil2tensor(Image.new("RGB", (512,512), (0,0,0))), False) + face_cascade = cv2.CascadeClassifier(cascade) + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5) + if len(faces) != 0: + cstr(f"Face found with: {os.path.basename(cascade)}").msg.print() + break + if len(faces) == 0: + cstr("No faces found in the image!").warning.print() + return (pil2tensor(Image.new("RGB", (512,512), (0,0,0))), False) + else: + cstr("Face found with: face_recognition model").warning.print() + faces = face_location + + # Assume there is only one face in the image + x, y, w, h = faces[0] + + # Check if the face region aligns with the edges of the original image + left_adjust = max(0, -x) + right_adjust = max(0, x + w - img.shape[1]) + top_adjust = max(0, -y) + bottom_adjust = max(0, y + h - img.shape[0]) + + # Check if the face region is near any edges, and if so, pad in the opposite direction + if left_adjust < w: + x += right_adjust + elif right_adjust < w: + x -= left_adjust + if top_adjust < h: + y += bottom_adjust + elif bottom_adjust < h: + y -= top_adjust + + w -= left_adjust + right_adjust + h -= top_adjust + bottom_adjust + + # Calculate padding around face + face_size = min(h, w) + y_pad = int(face_size * padding) + x_pad = int(face_size * padding) + + # Calculate square coordinates around face + center_x = x + w // 2 + center_y = y + h // 2 + half_size = (face_size + max(x_pad, y_pad)) // 2 + top = max(0, center_y - half_size) + bottom = min(img.shape[0], center_y + half_size) + left = max(0, center_x - half_size) + right = min(img.shape[1], center_x + half_size) + + # Ensure square crop of the original image + crop_size = min(right - left, bottom - top) + left = center_x - crop_size // 2 + right = center_x + crop_size // 2 + top = center_y - crop_size // 2 + bottom = center_y + crop_size // 2 + + # Crop face from original image + face_img = img[top:bottom, left:right, :] + + # Resize image + size = max(face_img.copy().shape[:2]) + pad_h = (size - face_img.shape[0]) // 2 + pad_w = (size - face_img.shape[1]) // 2 + face_img = cv2.copyMakeBorder(face_img, pad_h, pad_h, pad_w, pad_w, cv2.BORDER_CONSTANT, value=[0,0,0]) + min_size = 64 # Set minimum size for padded image + if size < min_size: + size = min_size + face_img = cv2.resize(face_img, (size, size)) + + # Convert numpy array back to PIL image + face_img = Image.fromarray(face_img) + + # Resize image to a multiple of 64 + original_size = face_img.size + face_img.resize((((face_img.size[0] // 64) * 64 + 64), ((face_img.size[1] // 64) * 64 + 64))) + + # Return face image and coordinates + return (pil2tensor(face_img.convert('RGB')), (original_size, (left, top, right, bottom))) + + +# IMAGE PASTE FACE CROP + +class WAS_Image_Paste_Face_Crop: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "crop_image": ("IMAGE",), + "crop_data": ("CROP_DATA",), + "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("IMAGE", "MASK_IMAGE") + FUNCTION = "image_paste_face" + + CATEGORY = "WAS Suite/Image/Process" + + def image_paste_face(self, image, crop_image, crop_data=None, crop_blending=0.25, crop_sharpening=0): + + if crop_data == False: + cstr("No valid crop data found!").error.print() + return (image, pil2tensor(Image.new("RGB", tensor2pil(image).size, (0,0,0)))) + + result_image, result_mask = self.paste_image(tensor2pil(image), tensor2pil(crop_image), crop_data, crop_blending, crop_sharpening) + return(result_image, result_mask) + + def paste_image(self, image, crop_image, crop_data, blend_amount=0.25, sharpen_amount=1): + + def lingrad(size, direction, white_ratio): + image = Image.new('RGB', size) + draw = ImageDraw.Draw(image) + if direction == 'vertical': + black_end = int(size[1] * (1 - white_ratio)) + range_start = 0 + range_end = size[1] + range_step = 1 + for y in range(range_start, range_end, range_step): + color_ratio = y / size[1] + if y <= black_end: + color = (0, 0, 0) + else: + color_value = int(((y - black_end) / (size[1] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(0, y), (size[0], y)], fill=color) + elif direction == 'horizontal': + black_end = int(size[0] * (1 - white_ratio)) + range_start = 0 + range_end = size[0] + range_step = 1 + for x in range(range_start, range_end, range_step): + color_ratio = x / size[0] + if x <= black_end: + color = (0, 0, 0) + else: + color_value = int(((x - black_end) / (size[0] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(x, 0), (x, size[1])], fill=color) + + return image.convert("L") + + crop_size, (top, left, right, bottom) = crop_data + crop_image = crop_image.resize(crop_size) + + if sharpen_amount > 0: + for _ in range(int(sharpen_amount)): + crop_image = crop_image.filter(ImageFilter.SHARPEN) + + blended_image = Image.new('RGBA', image.size, (0, 0, 0, 255)) + blended_mask = Image.new('L', image.size, 0) + crop_padded = Image.new('RGBA', image.size, (0, 0, 0, 0)) + blended_image.paste(image, (0, 0)) + crop_padded.paste(crop_image, (top, left)) + crop_mask = Image.new('L', crop_image.size, 0) + + if top > 0: + gradient_image = ImageOps.flip(lingrad(crop_image.size, 'vertical', blend_amount)) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if left > 0: + gradient_image = ImageOps.mirror(lingrad(crop_image.size, 'horizontal', blend_amount)) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if right < image.width: + gradient_image = lingrad(crop_image.size, 'horizontal', blend_amount) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if bottom < image.height: + gradient_image = lingrad(crop_image.size, 'vertical', blend_amount) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + crop_mask = ImageOps.invert(crop_mask) + blended_mask.paste(crop_mask, (top, left)) + blended_mask = blended_mask.convert("L") + blended_image.paste(crop_padded, (0, 0), blended_mask) + + return (pil2tensor(blended_image.convert("RGB")), pil2tensor(blended_mask.convert("RGB"))) + + +# IMAGE CROP LOCATION + +class WAS_Image_Crop_Location: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "top": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}), + "left": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}), + "right": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}), + "bottom": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}), + } + } + + RETURN_TYPES = ("IMAGE", "CROP_DATA") + FUNCTION = "image_crop_location" + + CATEGORY = "WAS Suite/Image/Process" + + def image_crop_location(self, image, top=0, left=0, right=256, bottom=256): + image = tensor2pil(image) + img_width, img_height = image.size + + # Calculate the final coordinates for cropping + crop_top = max(top, 0) + crop_left = max(left, 0) + crop_bottom = min(bottom, img_height) + crop_right = min(right, img_width) + + # Ensure that the cropping region has non-zero width and height + crop_width = crop_right - crop_left + crop_height = crop_bottom - crop_top + if crop_width <= 0 or crop_height <= 0: + raise ValueError("Invalid crop dimensions. Please check the values for top, left, right, and bottom.") + + # Crop the image and resize + crop = image.crop((crop_left, crop_top, crop_right, crop_bottom)) + crop_data = (crop.size, (crop_left, crop_top, crop_right, crop_bottom)) + crop = crop.resize((((crop.size[0] // 8) * 8), ((crop.size[1] // 8) * 8))) + + return (pil2tensor(crop), crop_data) + + +# IMAGE SQUARE CROP LOCATION + +class WAS_Image_Crop_Square_Location: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "x": ("INT", {"default":0, "max": 24576, "min":0, "step":1}), + "y": ("INT", {"default":0, "max": 24576, "min":0, "step":1}), + "size": ("INT", {"default":256, "max": 4096, "min":5, "step":1}), + } + } + + RETURN_TYPES = ("IMAGE", "CROP_DATA") + FUNCTION = "image_crop_location" + + CATEGORY = "WAS Suite/Image/Process" + + def image_crop_location(self, image, x=256, y=256, size=512): + + image = tensor2pil(image) + img_width, img_height = image.size + exp_size = size // 2 + left = max(x - exp_size, 0) + top = max(y - exp_size, 0) + right = min(x + exp_size, img_width) + bottom = min(y + exp_size, img_height) + + if right - left < size: + if right < img_width: + right = min(right + size - (right - left), img_width) + elif left > 0: + left = max(left - (size - (right - left)), 0) + if bottom - top < size: + if bottom < img_height: + bottom = min(bottom + size - (bottom - top), img_height) + elif top > 0: + top = max(top - (size - (bottom - top)), 0) + + crop = image.crop((left, top, right, bottom)) + + # Original Crop Data + crop_data = (crop.size, (left, top, right, bottom)) + + # Output resize + crop = crop.resize((((crop.size[0] // 8) * 8), ((crop.size[1] // 8) * 8))) + + return (pil2tensor(crop), crop_data) + + +# IMAGE SQUARE CROP LOCATION + +class WAS_Image_Tile_Batch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "num_tiles": ("INT", {"default":4, "max": 64, "min":2, "step":1}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGES",) + FUNCTION = "tile_image" + + CATEGORY = "WAS Suite/Image/Process" + + def tile_image(self, image, num_tiles=6): + image = tensor2pil(image.squeeze(0)) + img_width, img_height = image.size + + num_rows = int(num_tiles ** 0.5) + num_cols = (num_tiles + num_rows - 1) // num_rows + tile_width = img_width // num_cols + tile_height = img_height // num_rows + + tiles = [] + for y in range(0, img_height, tile_height): + for x in range(0, img_width, tile_width): + tile = image.crop((x, y, x + tile_width, y + tile_height)) + tiles.append(pil2tensor(tile)) + + tiles = torch.stack(tiles, dim=0).squeeze(1) + + return (tiles, ) + + +# IMAGE PASTE CROP + +class WAS_Image_Paste_Crop: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "crop_image": ("IMAGE",), + "crop_data": ("CROP_DATA",), + "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("IMAGE", "MASK") + FUNCTION = "image_paste_crop" + + CATEGORY = "WAS Suite/Image/Process" + + def image_paste_crop(self, image, crop_image, crop_data=None, crop_blending=0.25, crop_sharpening=0): + + if crop_data == False: + cstr("No valid crop data found!").error.print() + return (image, pil2tensor(Image.new("RGB", tensor2pil(image).size, (0,0,0)))) + + result_image, result_mask = self.paste_image(tensor2pil(image), tensor2pil(crop_image), crop_data, crop_blending, crop_sharpening) + + return (result_image, result_mask) + + def paste_image(self, image, crop_image, crop_data, blend_amount=0.25, sharpen_amount=1): + + def lingrad(size, direction, white_ratio): + image = Image.new('RGB', size) + draw = ImageDraw.Draw(image) + if direction == 'vertical': + black_end = int(size[1] * (1 - white_ratio)) + range_start = 0 + range_end = size[1] + range_step = 1 + for y in range(range_start, range_end, range_step): + color_ratio = y / size[1] + if y <= black_end: + color = (0, 0, 0) + else: + color_value = int(((y - black_end) / (size[1] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(0, y), (size[0], y)], fill=color) + elif direction == 'horizontal': + black_end = int(size[0] * (1 - white_ratio)) + range_start = 0 + range_end = size[0] + range_step = 1 + for x in range(range_start, range_end, range_step): + color_ratio = x / size[0] + if x <= black_end: + color = (0, 0, 0) + else: + color_value = int(((x - black_end) / (size[0] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(x, 0), (x, size[1])], fill=color) + + return image.convert("L") + + crop_size, (left, top, right, bottom) = crop_data + crop_image = crop_image.resize(crop_size) + + if sharpen_amount > 0: + for _ in range(int(sharpen_amount)): + crop_image = crop_image.filter(ImageFilter.SHARPEN) + + blended_image = Image.new('RGBA', image.size, (0, 0, 0, 255)) + blended_mask = Image.new('L', image.size, 0) + crop_padded = Image.new('RGBA', image.size, (0, 0, 0, 0)) + blended_image.paste(image, (0, 0)) + crop_padded.paste(crop_image, (left, top)) + crop_mask = Image.new('L', crop_image.size, 0) + + if top > 0: + gradient_image = ImageOps.flip(lingrad(crop_image.size, 'vertical', blend_amount)) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if left > 0: + gradient_image = ImageOps.mirror(lingrad(crop_image.size, 'horizontal', blend_amount)) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if right < image.width: + gradient_image = lingrad(crop_image.size, 'horizontal', blend_amount) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if bottom < image.height: + gradient_image = lingrad(crop_image.size, 'vertical', blend_amount) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + crop_mask = ImageOps.invert(crop_mask) + blended_mask.paste(crop_mask, (left, top)) + blended_mask = blended_mask.convert("L") + blended_image.paste(crop_padded, (0, 0), blended_mask) + + return (pil2tensor(blended_image.convert("RGB")), pil2tensor(blended_mask.convert("RGB"))) + + +# IMAGE PASTE CROP BY LOCATION + +class WAS_Image_Paste_Crop_Location: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "crop_image": ("IMAGE",), + "top": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}), + "left": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}), + "right": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}), + "bottom": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}), + "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("IMAGE", "MASK") + FUNCTION = "image_paste_crop_location" + + CATEGORY = "WAS Suite/Image/Process" + + def image_paste_crop_location(self, image, crop_image, top=0, left=0, right=256, bottom=256, crop_blending=0.25, crop_sharpening=0): + result_image, result_mask = self.paste_image(tensor2pil(image), tensor2pil(crop_image), top, left, right, bottom, crop_blending, crop_sharpening) + return (result_image, result_mask) + + def paste_image(self, image, crop_image, top=0, left=0, right=256, bottom=256, blend_amount=0.25, sharpen_amount=1): + + image = image.convert("RGBA") + crop_image = crop_image.convert("RGBA") + + def inset_border(image, border_width=20, border_color=(0)): + width, height = image.size + bordered_image = Image.new(image.mode, (width, height), border_color) + bordered_image.paste(image, (0, 0)) + draw = ImageDraw.Draw(bordered_image) + draw.rectangle((0, 0, width-1, height-1), outline=border_color, width=border_width) + return bordered_image + + img_width, img_height = image.size + + # Ensure that the coordinates are within the image bounds + top = min(max(top, 0), img_height) + left = min(max(left, 0), img_width) + bottom = min(max(bottom, 0), img_height) + right = min(max(right, 0), img_width) + + crop_size = (right - left, bottom - top) + crop_img = crop_image.resize(crop_size) + crop_img = crop_img.convert("RGBA") + + if sharpen_amount > 0: + for _ in range(sharpen_amount): + crop_img = crop_img.filter(ImageFilter.SHARPEN) + + if blend_amount > 1.0: + blend_amount = 1.0 + elif blend_amount < 0.0: + blend_amount = 0.0 + blend_ratio = (max(crop_size) / 2) * float(blend_amount) + + blend = image.copy() + mask = Image.new("L", image.size, 0) + + mask_block = Image.new("L", crop_size, 255) + mask_block = inset_border(mask_block, int(blend_ratio/2), (0)) + + Image.Image.paste(mask, mask_block, (left, top)) + blend.paste(crop_img, (left, top), crop_img) + + mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio/4)) + mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio/4)) + + blend.putalpha(mask) + image = Image.alpha_composite(image, blend) + + return (pil2tensor(image), pil2tensor(mask.convert('RGB'))) + + +# IMAGE GRID IMAGE + +class WAS_Image_Grid_Image_Batch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "border_width": ("INT", {"default":3, "min": 0, "max": 100, "step":1}), + "number_of_columns": ("INT", {"default":6, "min": 1, "max": 24, "step":1}), + "max_cell_size": ("INT", {"default":256, "min":32, "max":2048, "step":1}), + "border_red": ("INT", {"default":0, "min": 0, "max": 255, "step":1}), + "border_green": ("INT", {"default":0, "min": 0, "max": 255, "step":1}), + "border_blue": ("INT", {"default":0, "min": 0, "max": 255, "step":1}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "smart_grid_image" + + CATEGORY = "WAS Suite/Image/Process" + + def smart_grid_image(self, images, number_of_columns=6, max_cell_size=256, add_border=False, border_red=255, border_green=255, border_blue=255, border_width=3): + + cols = number_of_columns + border_color = (border_red, border_green, border_blue) + + images_resized = [] + max_row_height = 0 + + for tensor_img in images: + img = tensor2pil(tensor_img) + img_w, img_h = img.size + aspect_ratio = img_w / img_h + + if img_w > img_h: + cell_w = min(img_w, max_cell_size) + cell_h = int(cell_w / aspect_ratio) + else: + cell_h = min(img_h, max_cell_size) + cell_w = int(cell_h * aspect_ratio) + + img_resized = img.resize((cell_w, cell_h)) + + if add_border: + img_resized = ImageOps.expand(img_resized, border=border_width // 2, fill=border_color) + + images_resized.append(img_resized) + max_row_height = max(max_row_height, cell_h) + + max_row_height = int(max_row_height) + total_images = len(images_resized) + rows = math.ceil(total_images / cols) + + grid_width = cols * max_cell_size + (cols - 1) * border_width + grid_height = rows * max_row_height + (rows - 1) * border_width + + new_image = Image.new('RGB', (grid_width, grid_height), border_color) + + for i, img in enumerate(images_resized): + x = (i % cols) * (max_cell_size + border_width) + y = (i // cols) * (max_row_height + border_width) + + img_w, img_h = img.size + paste_x = x + (max_cell_size - img_w) // 2 + paste_y = y + (max_row_height - img_h) // 2 + + new_image.paste(img, (paste_x, paste_y, paste_x + img_w, paste_y + img_h)) + + if add_border: + new_image = ImageOps.expand(new_image, border=border_width, fill=border_color) + + return (pil2tensor(new_image), ) + + +# IMAGE GRID IMAGE FROM PATH + +class WAS_Image_Grid_Image: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images_path": ("STRING", {"default":"./ComfyUI/input/", "multiline": False}), + "pattern_glob": ("STRING", {"default":"*", "multiline": False}), + "include_subfolders": (["false", "true"],), + "border_width": ("INT", {"default":3, "min": 0, "max": 100, "step":1}), + "number_of_columns": ("INT", {"default":6, "min": 1, "max": 24, "step":1}), + "max_cell_size": ("INT", {"default":256, "min":32, "max":1280, "step":1}), + "border_red": ("INT", {"default":0, "min": 0, "max": 255, "step":1}), + "border_green": ("INT", {"default":0, "min": 0, "max": 255, "step":1}), + "border_blue": ("INT", {"default":0, "min": 0, "max": 255, "step":1}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "create_grid_image" + + CATEGORY = "WAS Suite/Image/Process" + + def create_grid_image(self, images_path, pattern_glob="*", include_subfolders="false", number_of_columns=6, + max_cell_size=256, border_width=3, border_red=0, border_green=0, border_blue=0): + + if not os.path.exists(images_path): + cstr(f"The grid image path `{images_path}` does not exist!").error.print() + return (pil2tensor(Image.new("RGB", (512,512), (0,0,0))),) + + paths = glob.glob(os.path.join(images_path, pattern_glob), recursive=(False if include_subfolders == "false" else True)) + image_paths = [] + for path in paths: + if path.lower().endswith(ALLOWED_EXT) and os.path.exists(path): + image_paths.append(path) + + grid_image = self.smart_grid_image(image_paths, int(number_of_columns), (int(max_cell_size), int(max_cell_size)), + (False if border_width <= 0 else True), (int(border_red), + int(border_green), int(border_blue)), int(border_width)) + + return (pil2tensor(grid_image),) + + def smart_grid_image(self, images, cols=6, size=(256,256), add_border=False, border_color=(0,0,0), border_width=3): + + # calculate row height + max_width, max_height = size + row_height = 0 + images_resized = [] + for image in images: + img = Image.open(image).convert('RGB') + + img_w, img_h = img.size + aspect_ratio = img_w / img_h + if aspect_ratio > 1: # landscape + thumb_w = min(max_width, img_w-border_width) + thumb_h = thumb_w / aspect_ratio + else: # portrait + thumb_h = min(max_height, img_h-border_width) + thumb_w = thumb_h * aspect_ratio + + # pad the image to match the maximum size and center it within the cell + pad_w = max_width - int(thumb_w) + pad_h = max_height - int(thumb_h) + left = pad_w // 2 + top = pad_h // 2 + right = pad_w - left + bottom = pad_h - top + padding = (left, top, right, bottom) # left, top, right, bottom + img_resized = ImageOps.expand(img.resize((int(thumb_w), int(thumb_h))), padding) + + if add_border: + img_resized_bordered = ImageOps.expand(img_resized, border=border_width//2, fill=border_color) + + images_resized.append(img_resized) + row_height = max(row_height, img_resized.size[1]) + row_height = int(row_height) + + # calculate the number of rows + total_images = len(images_resized) + rows = math.ceil(total_images / cols) + + # create empty image to put thumbnails + new_image = Image.new('RGB', (cols*size[0]+(cols-1)*border_width, rows*row_height+(rows-1)*border_width), border_color) + + for i, img in enumerate(images_resized): + if add_border: + border_img = ImageOps.expand(img, border=border_width//2, fill=border_color) + x = (i % cols) * (size[0]+border_width) + y = (i // cols) * (row_height+border_width) + if border_img.size == (size[0], size[1]): + new_image.paste(border_img, (x, y, x+size[0], y+size[1])) + else: + # Resize image to match size parameter + border_img = border_img.resize((size[0], size[1])) + new_image.paste(border_img, (x, y, x+size[0], y+size[1])) + else: + x = (i % cols) * (size[0]+border_width) + y = (i // cols) * (row_height+border_width) + if img.size == (size[0], size[1]): + new_image.paste(img, (x, y, x+img.size[0], y+img.size[1])) + else: + # Resize image to match size parameter + img = img.resize((size[0], size[1])) + new_image.paste(img, (x, y, x+size[0], y+size[1])) + + new_image = ImageOps.expand(new_image, border=border_width, fill=border_color) + + return new_image + +# IMAGE MORPH GIF + +class WAS_Image_Morph_GIF: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "transition_frames": ("INT", {"default":30, "min":2, "max":60, "step":1}), + "still_image_delay_ms": ("FLOAT", {"default":2500.0, "min":0.1, "max":60000.0, "step":0.1}), + "duration_ms": ("FLOAT", {"default":0.1, "min":0.1, "max":60000.0, "step":0.1}), + "loops": ("INT", {"default":0, "min":0, "max":100, "step":1}), + "max_size": ("INT", {"default":512, "min":128, "max":1280, "step":1}), + "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}), + "filename": ("STRING", {"default": "morph", "multiline": False}), + "filetype": (["GIF", "APNG"],), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = ("IMAGE","IMAGE",TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("image_a_pass","image_b_pass","filepath_text","filename_text") + FUNCTION = "create_morph_gif" + + CATEGORY = "WAS Suite/Animation" + + def create_morph_gif(self, image_a, image_b, transition_frames=10, still_image_delay_ms=10, duration_ms=0.1, loops=0, max_size=512, + output_path="./ComfyUI/output", filename="morph", filetype="GIF"): + + tokens = TextTokens() + WTools = WAS_Tools_Class() + + if 'imageio' not in packages(): + install_package('imageio') + + if filetype not in ["APNG", "GIF"]: + filetype = "GIF" + if output_path.strip() in [None, "", "."]: + output_path = "./ComfyUI/output" + output_path = tokens.parseTokens(os.path.join(*output_path.split('/'))) + if not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + + if image_a == None: + image_a = pil2tensor(Image.new("RGB", (512,512), (0,0,0))) + if image_b == None: + image_b = pil2tensor(Image.new("RGB", (512,512), (255,255,255))) + + if transition_frames < 2: + transition_frames = 2 + elif transition_frames > 60: + transition_frames = 60 + + if duration_ms < 0.1: + duration_ms = 0.1 + elif duration_ms > 60000.0: + duration_ms = 60000.0 + + output_file = WTools.morph_images([tensor2pil(image_a), tensor2pil(image_b)], steps=int(transition_frames), max_size=int(max_size), loop=int(loops), + still_duration=int(still_image_delay_ms), duration=int(duration_ms), output_path=output_path, + filename=tokens.parseTokens(filename), filetype=filetype) + + return (image_a, image_b, output_file) + + +# IMAGE MORPH GIF WRITER + +class WAS_Image_Morph_GIF_Writer: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "transition_frames": ("INT", {"default":30, "min":2, "max":60, "step":1}), + "image_delay_ms": ("FLOAT", {"default":2500.0, "min":0.1, "max":60000.0, "step":0.1}), + "duration_ms": ("FLOAT", {"default":0.1, "min":0.1, "max":60000.0, "step":0.1}), + "loops": ("INT", {"default":0, "min":0, "max":100, "step":1}), + "max_size": ("INT", {"default":512, "min":128, "max":1280, "step":1}), + "output_path": ("STRING", {"default": comfy_paths.output_directory, "multiline": False}), + "filename": ("STRING", {"default": "morph_writer", "multiline": False}), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = ("IMAGE",TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("image_pass","filepath_text","filename_text") + FUNCTION = "write_to_morph_gif" + + CATEGORY = "WAS Suite/Animation/Writer" + + def write_to_morph_gif(self, image, transition_frames=10, image_delay_ms=10, duration_ms=0.1, loops=0, max_size=512, + output_path="./ComfyUI/output", filename="morph"): + + if 'imageio' not in packages(): + install_package("imageio") + + if output_path.strip() in [None, "", "."]: + output_path = "./ComfyUI/output" + + if image is None: + image = pil2tensor(Image.new("RGB", (512, 512), (0, 0, 0))).unsqueeze(0) + + if transition_frames < 2: + transition_frames = 2 + elif transition_frames > 60: + transition_frames = 60 + + if duration_ms < 0.1: + duration_ms = 0.1 + elif duration_ms > 60000.0: + duration_ms = 60000.0 + + tokens = TextTokens() + output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/'))) + output_file = os.path.join(output_path, tokens.parseTokens(filename) + '.gif') + + if not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + + WTools = WAS_Tools_Class() + GifMorph = WTools.GifMorphWriter(int(transition_frames), int(duration_ms), int(image_delay_ms)) + + for img in image: + pil_img = tensor2pil(img) + GifMorph.write(pil_img, output_file) + + return (image, output_file, filename) + +# IMAGE MORPH GIF BY PATH + +class WAS_Image_Morph_GIF_By_Path: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "transition_frames": ("INT", {"default":30, "min":2, "max":60, "step":1}), + "still_image_delay_ms": ("FLOAT", {"default":2500.0, "min":0.1, "max":60000.0, "step":0.1}), + "duration_ms": ("FLOAT", {"default":0.1, "min":0.1, "max":60000.0, "step":0.1}), + "loops": ("INT", {"default":0, "min":0, "max":100, "step":1}), + "max_size": ("INT", {"default":512, "min":128, "max":1280, "step":1}), + "input_path": ("STRING",{"default":"./ComfyUI", "multiline": False}), + "input_pattern": ("STRING",{"default":"*", "multiline": False}), + "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}), + "filename": ("STRING", {"default": "morph", "multiline": False}), + "filetype": (["GIF", "APNG"],), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("filepath_text","filename_text") + FUNCTION = "create_morph_gif" + + CATEGORY = "WAS Suite/Animation" + + def create_morph_gif(self, transition_frames=30, still_image_delay_ms=2500, duration_ms=0.1, loops=0, max_size=512, + input_path="./ComfyUI/output", input_pattern="*", output_path="./ComfyUI/output", filename="morph", filetype="GIF"): + + if 'imageio' not in packages(): + install_package("imageio") + + if not os.path.exists(input_path): + cstr(f"The input_path `{input_path}` does not exist!").error.print() + return ("",) + + images = self.load_images(input_path, input_pattern) + if not images: + cstr(f"The input_path `{input_path}` does not contain any valid images!").msg.print() + return ("",) + + if filetype not in ["APNG", "GIF"]: + filetype = "GIF" + if output_path.strip() in [None, "", "."]: + output_path = "./ComfyUI/output" + + if transition_frames < 2: + transition_frames = 2 + elif transition_frames > 60: + transition_frames = 60 + + if duration_ms < 0.1: + duration_ms = 0.1 + elif duration_ms > 60000.0: + duration_ms = 60000.0 + + tokens = TextTokens() + WTools = WAS_Tools_Class() + + output_file = WTools.morph_images(images, steps=int(transition_frames), max_size=int(max_size), loop=int(loops), still_duration=int(still_image_delay_ms), + duration=int(duration_ms), output_path=tokens.parseTokens(os.path.join(*output_path.split('/'))), + filename=tokens.parseTokens(filename), filetype=filetype) + + return (output_file,filename) + + + def load_images(self, directory_path, pattern): + images = [] + for file_name in glob.glob(os.path.join(directory_path, pattern), recursive=False): + if file_name.lower().endswith(ALLOWED_EXT): + images.append(Image.open(file_name).convert("RGB")) + return images + + +# COMBINE NODE + +class WAS_Image_Blending_Mode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "mode": ([ + "add", + "color", + "color_burn", + "color_dodge", + "darken", + "difference", + "exclusion", + "hard_light", + "hue", + "lighten", + "multiply", + "overlay", + "screen", + "soft_light" + ],), + "blend_percentage": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "image_blending_mode" + + CATEGORY = "WAS Suite/Image" + + def image_blending_mode(self, image_a, image_b, mode='add', blend_percentage=1.0): + + # Install Pilgram + if 'pilgram' not in packages(): + install_package("pilgram") + + # Import Pilgram module + import pilgram + + # Convert images to PIL + img_a = tensor2pil(image_a) + img_b = tensor2pil(image_b) + + # Apply blending + if mode: + if mode == "color": + out_image = pilgram.css.blending.color(img_a, img_b) + elif mode == "color_burn": + out_image = pilgram.css.blending.color_burn(img_a, img_b) + elif mode == "color_dodge": + out_image = pilgram.css.blending.color_dodge(img_a, img_b) + elif mode == "darken": + out_image = pilgram.css.blending.darken(img_a, img_b) + elif mode == "difference": + out_image = pilgram.css.blending.difference(img_a, img_b) + elif mode == "exclusion": + out_image = pilgram.css.blending.exclusion(img_a, img_b) + elif mode == "hard_light": + out_image = pilgram.css.blending.hard_light(img_a, img_b) + elif mode == "hue": + out_image = pilgram.css.blending.hue(img_a, img_b) + elif mode == "lighten": + out_image = pilgram.css.blending.lighten(img_a, img_b) + elif mode == "multiply": + out_image = pilgram.css.blending.multiply(img_a, img_b) + elif mode == "add": + out_image = pilgram.css.blending.normal(img_a, img_b) + elif mode == "overlay": + out_image = pilgram.css.blending.overlay(img_a, img_b) + elif mode == "screen": + out_image = pilgram.css.blending.screen(img_a, img_b) + elif mode == "soft_light": + out_image = pilgram.css.blending.soft_light(img_a, img_b) + else: + out_image = img_a + + out_image = out_image.convert("RGB") + + # Blend image + blend_mask = Image.new(mode="L", size=img_a.size, + color=(round(blend_percentage * 255))) + blend_mask = ImageOps.invert(blend_mask) + out_image = Image.composite(img_a, out_image, blend_mask) + + return (pil2tensor(out_image), ) + + +# IMAGE BLEND NODE + +class WAS_Image_Blend: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "blend_percentage": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "image_blend" + + CATEGORY = "WAS Suite/Image" + + def image_blend(self, image_a, image_b, blend_percentage): + + # Convert images to PIL + img_a = tensor2pil(image_a) + img_b = tensor2pil(image_b) + + # Blend image + blend_mask = Image.new(mode="L", size=img_a.size, + color=(round(blend_percentage * 255))) + blend_mask = ImageOps.invert(blend_mask) + img_result = Image.composite(img_a, img_b, blend_mask) + + del img_a, img_b, blend_mask + + return (pil2tensor(img_result), ) + + + +# IMAGE MONITOR DISTORTION FILTER + +class WAS_Image_Monitor_Distortion_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mode": (["Digital Distortion", "Signal Distortion", "TV Distortion"],), + "amplitude": ("INT", {"default": 5, "min": 1, "max": 255, "step": 1}), + "offset": ("INT", {"default": 10, "min": 1, "max": 255, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "image_monitor_filters" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_monitor_filters(self, image, mode="Digital Distortion", amplitude=5, offset=5): + + # Convert images to PIL + image = tensor2pil(image) + + # WAS Filters + WTools = WAS_Tools_Class() + + # Apply image effect + if mode: + if mode == 'Digital Distortion': + image = WTools.digital_distortion(image, amplitude, offset) + elif mode == 'Signal Distortion': + image = WTools.signal_distortion(image, amplitude) + elif mode == 'TV Distortion': + image = WTools.tv_vhs_distortion(image, amplitude) + else: + image = image + + return (pil2tensor(image), ) + + + +# IMAGE PERLIN NOISE + +class WAS_Image_Perlin_Noise: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}), + "height": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}), + "scale": ("INT", {"default": 100, "max": 2048, "min": 2, "step": 1}), + "octaves": ("INT", {"default": 4, "max": 8, "min": 0, "step": 1}), + "persistence": ("FLOAT", {"default": 0.5, "max": 100.0, "min": 0.01, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "perlin_noise" + + CATEGORY = "WAS Suite/Image/Generate/Noise" + + def perlin_noise(self, width, height, scale, octaves, persistence, seed): + + WTools = WAS_Tools_Class() + + image = WTools.perlin_noise(width, height, octaves, persistence, scale, seed) + + return (pil2tensor(image), ) + + +# IMAGE PERLIN POWER FRACTAL + +class WAS_Image_Perlin_Power_Fractal: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 512, "max": 8192, "min": 64, "step": 1}), + "height": ("INT", {"default": 512, "max": 8192, "min": 64, "step": 1}), + "scale": ("INT", {"default": 100, "max": 2048, "min": 2, "step": 1}), + "octaves": ("INT", {"default": 4, "max": 8, "min": 0, "step": 1}), + "persistence": ("FLOAT", {"default": 0.5, "max": 100.0, "min": 0.01, "step": 0.01}), + "lacunarity": ("FLOAT", {"default": 2.0, "max": 100.0, "min": 0.01, "step": 0.01}), + "exponent": ("FLOAT", {"default": 2.0, "max": 100.0, "min": 0.01, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "perlin_power_fractal" + + CATEGORY = "WAS Suite/Image/Generate/Noise" + + def perlin_power_fractal(self, width, height, scale, octaves, persistence, lacunarity, exponent, seed): + + WTools = WAS_Tools_Class() + + image = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, seed) + + return (pil2tensor(image), ) + + +# IMAGE VORONOI NOISE FILTER + +class WAS_Image_Voronoi_Noise_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}), + "height": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}), + "density": ("INT", {"default": 50, "max": 256, "min": 10, "step": 2}), + "modulator": ("INT", {"default": 0, "max": 8, "min": 0, "step": 1}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + "optional": { + "flat": (["False", "True"],), + "RGB_output": (["True", "False"],), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "voronoi_noise_filter" + + CATEGORY = "WAS Suite/Image/Generate/Noise" + + def voronoi_noise_filter(self, width, height, density, modulator, seed, flat="False", RGB_output="True"): + + WTools = WAS_Tools_Class() + + image = WTools.worley_noise(height=height, width=width, density=density, option=modulator, use_broadcast_ops=True, seed=seed, flat=(flat == "True")).image + + if RGB_output == "True": + image = image.convert("RGB") + else: + image = image.convert("L") + + return (pil2tensor(image), ) + +# IMAGE POWER NOISE + +class WAS_Image_Power_Noise: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}), + "height": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}), + "frequency": ("FLOAT", {"default": 0.5, "max": 10.0, "min": 0.0, "step": 0.01}), + "attenuation": ("FLOAT", {"default": 0.5, "max": 10.0, "min": 0.0, "step": 0.01}), + "noise_type": (["grey", "white", "pink", "blue", "green", "mix"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "power_noise" + + CATEGORY = "WAS Suite/Image/Generate/Noise" + + def power_noise(self, width, height, frequency, attenuation, noise_type, seed): + + noise_image = self.generate_power_noise(width, height, frequency, attenuation, noise_type, seed) + + return (pil2tensor(noise_image), ) + + def generate_power_noise(self, width, height, frequency=None, attenuation=None, noise_type="white", seed=None): + def white_noise(width, height): + noise = np.random.random((height, width)) + return noise + + def grey_noise(width, height, attenuation): + noise = np.random.normal(0, attenuation, (height, width)) + return noise + + def blue_noise(width, height, frequency, attenuation): + noise = grey_noise(width, height, attenuation) + scale = 1.0 / (width * height) + fy = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + fx = np.fft.fftfreq(width) ** 2 + f = fy + fx + power = np.sqrt(f) + power[0, 0] = 1 + noise = np.fft.ifft2(np.fft.fft2(noise) / power) + noise *= scale / noise.std() + return np.real(noise) + + def green_noise(width, height, frequency, attenuation): + noise = grey_noise(width, height, attenuation) + scale = 1.0 / (width * height) + fy = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + fx = np.fft.fftfreq(width) ** 2 + f = fy + fx + power = np.sqrt(f) + power[0, 0] = 1 + noise = np.fft.ifft2(np.fft.fft2(noise) / np.sqrt(power)) + noise *= scale / noise.std() + return np.real(noise) + + def pink_noise(width, height, frequency, attenuation): + noise = grey_noise(width, height, attenuation) + scale = 1.0 / (width * height) + fy = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + fx = np.fft.fftfreq(width) ** 2 + f = fy + fx + power = np.sqrt(f) + power[0, 0] = 1 + noise = np.fft.ifft2(np.fft.fft2(noise) * power) + noise *= scale / noise.std() + return np.real(noise) + + def blue_noise_mask(width, height, frequency, attenuation, seed, num_masks=3): + masks = [] + for i in range(num_masks): + mask_seed = seed + i + np.random.seed(mask_seed) + mask = blue_noise(width, height, frequency, attenuation) + masks.append(mask) + return masks + + def blend_noise(width, height, masks, noise_types, attenuations): + blended_image = Image.new("L", (width, height), color=0) + fy = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + fx = np.fft.fftfreq(width) ** 2 + f = fy + fx + i = 0 + for mask, noise_type, attenuation in zip(masks, noise_types, attenuations): + mask = Image.fromarray((255 * (mask - np.min(mask)) / (np.max(mask) - np.min(mask))).astype(np.uint8).real) + if noise_type == "white": + noise = white_noise(width, height) + noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real) + elif noise_type == "grey": + noise = grey_noise(width, height, attenuation) + noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real) + elif noise_type == "pink": + noise = pink_noise(width, height, frequency, attenuation) + noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real) + elif noise_type == "green": + noise = green_noise(width, height, frequency, attenuation) + noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real) + elif noise_type == "blue": + noise = blue_noise(width, height, frequency, attenuation) + noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real) + + blended_image = Image.composite(blended_image, noise, mask) + i += 1 + + return np.asarray(blended_image) + + def shorten_to_range(value, min_value, max_value): + range_length = max_value - min_value + 1 + return ((value - min_value) % range_length) + min_value + + if seed is not None: + if seed > 4294967294: + seed = shorten_to_range(seed, 0, 4294967293) + cstr(f"Seed too large for power noise; rescaled to: {seed}").warning.print() + + np.random.seed(seed) + + if noise_type == "white": + noise = white_noise(width, height) + elif noise_type == "grey": + noise = grey_noise(width, height, attenuation) + elif noise_type == "pink": + if frequency is None: + cstr("Pink noise requires a frequency value.").error.print() + return None + noise = pink_noise(width, height, frequency, attenuation) + elif noise_type == "green": + if frequency is None: + cstr("Green noise requires a frequency value.").error.print() + return None + noise = green_noise(width, height, frequency, attenuation) + elif noise_type == "blue": + if frequency is None: + cstr("Blue noise requires a frequency value.").error.print() + return None + noise = blue_noise(width, height, frequency, attenuation) + elif noise_type == "mix": + if frequency is None: + cstr("Mix noise requires a frequency value.").error.print() + return None + if seed is None: + cstr("Mix noise requires a seed value.").error.print() + return None + + blue_noise_masks = blue_noise_mask(width, height, frequency, attenuation, seed=seed, num_masks=3) + noise_types = ["white", "grey", "pink", "green", "blue"] + attenuations = [attenuation] * len(noise_types) + noise = blend_noise(width, height, blue_noise_masks, noise_types, attenuations) + else: + cstr(f"Unsupported noise type `{noise_type}`").error.print() + return None + if noise_type != 'mix': + noise = 255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) + noise_image = Image.fromarray(noise.astype(np.uint8).real) + + return noise_image.convert("RGB") + +# IMAGE TO NOISE + +class WAS_Image_To_Noise: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "num_colors": ("INT", {"default": 16, "max": 256, "min": 2, "step": 2}), + "black_mix": ("INT", {"default": 0, "max": 20, "min": 0, "step": 1}), + "gaussian_mix": ("FLOAT", {"default": 0.0, "max": 1024, "min": 0, "step": 0.1}), + "brightness": ("FLOAT", {"default": 1.0, "max": 2.0, "min": 0.0, "step": 0.01}), + "output_mode": (["batch","list"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + OUTPUT_IS_LIST = (False,) + FUNCTION = "image_to_noise" + + CATEGORY = "WAS Suite/Image/Generate/Noise" + + def image_to_noise(self, images, num_colors, black_mix, gaussian_mix, brightness, output_mode, seed): + + noise_images = [] + for image in images: + noise_images.append(pil2tensor(self.image2noise(tensor2pil(image), num_colors, black_mix, brightness, gaussian_mix, seed))) + if output_mode == "list": + self.OUTPUT_IS_LIST = (True,) + else: + noise_images = torch.cat(noise_images, dim=0) + return (noise_images, ) + + def image2noise(self, image, num_colors=16, black_mix=0, brightness=1.0, gaussian_mix=0, seed=0): + + random.seed(int(seed)) + image = image.quantize(colors=num_colors) + image = image.convert("RGBA") + pixel_data = list(image.getdata()) + random.shuffle(pixel_data) + randomized_image = Image.new("RGBA", image.size) + randomized_image.putdata(pixel_data) + + width, height = image.size + black_noise = Image.new("RGBA", (width, height), (0, 0, 0, 0)) + + for _ in range(black_mix): + for x in range(width): + for y in range(height): + if random.randint(0,1) == 1: + black_noise.putpixel((x, y), (0, 0, 0, 255)) + + randomized_image = Image.alpha_composite(randomized_image, black_noise) + enhancer = ImageEnhance.Brightness(randomized_image) + randomized_image = enhancer.enhance(brightness) + + if gaussian_mix > 0: + original_noise = randomized_image.copy() + randomized_gaussian = randomized_image.filter(ImageFilter.GaussianBlur(radius=gaussian_mix)) + randomized_image = Image.blend(randomized_image, randomized_gaussian, 0.65) + randomized_image = Image.blend(randomized_image, original_noise, 0.25) + + return randomized_image + +# IMAGE MAKE SEAMLESS + +class WAS_Image_Make_Seamless: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "blending": ("FLOAT", {"default": 0.4, "max": 1.0, "min": 0.0, "step": 0.01}), + "tiled": (["true", "false"],), + "tiles": ("INT", {"default": 2, "max": 6, "min": 2, "step": 2}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "make_seamless" + + CATEGORY = "WAS Suite/Image/Process" + + def make_seamless(self, images, blending, tiled, tiles): + + WTools = WAS_Tools_Class() + + seamless_images = [] + for image in images: + seamless_images.append(pil2tensor(WTools.make_seamless(tensor2pil(image), blending, tiled, tiles))) + + seamless_images = torch.cat(seamless_images, dim=0) + + return (seamless_images, ) + + +# IMAGE DISPLACEMENT WARP + +class WAS_Image_Displacement_Warp: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "displacement_maps": ("IMAGE",), + "amplitude": ("FLOAT", {"default": 25.0, "min": -4096, "max": 4096, "step": 0.1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "displace_image" + + CATEGORY = "WAS Suite/Image/Transform" + + def displace_image(self, images, displacement_maps, amplitude): + + WTools = WAS_Tools_Class() + + displaced_images = [] + for i in range(len(images)): + img = tensor2pil(images[i]) + if i < len(displacement_maps): + disp = tensor2pil(displacement_maps[i]) + else: + disp = tensor2pil(displacement_maps[-1]) + disp = self.resize_and_crop(disp, img.size) + displaced_images.append(pil2tensor(WTools.displace_image(img, disp, amplitude))) + + displaced_images = torch.cat(displaced_images, dim=0) + + return (displaced_images, ) + + + def resize_and_crop(self, image, target_size): + width, height = image.size + target_width, target_height = target_size + aspect_ratio = width / height + target_aspect_ratio = target_width / target_height + + if aspect_ratio > target_aspect_ratio: + new_height = target_height + new_width = int(new_height * aspect_ratio) + else: + new_width = target_width + new_height = int(new_width / aspect_ratio) + + image = image.resize((new_width, new_height)) + left = (new_width - target_width) // 2 + top = (new_height - target_height) // 2 + right = left + target_width + bottom = top + target_height + image = image.crop((left, top, right, bottom)) + + return image + +# IMAGE TO BATCH + +class WAS_Image_Batch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "images_a": ("IMAGE",), + "images_b": ("IMAGE",), + "images_c": ("IMAGE",), + "images_d": ("IMAGE",), + # "images_e": ("IMAGE",), + # "images_f": ("IMAGE",), + # Theoretically, an infinite number of image input parameters can be added. + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "image_batch" + CATEGORY = "WAS Suite/Image" + + def _check_image_dimensions(self, tensors, names): + reference_dimensions = tensors[0].shape[1:] # Ignore batch dimension + mismatched_images = [names[i] for i, tensor in enumerate(tensors) if tensor.shape[1:] != reference_dimensions] + + if mismatched_images: + raise ValueError(f"WAS Image Batch Warning: Input image dimensions do not match for images: {mismatched_images}") + + def image_batch(self, **kwargs): + batched_tensors = [kwargs[key] for key in kwargs if kwargs[key] is not None] + image_names = [key for key in kwargs if kwargs[key] is not None] + + if not batched_tensors: + raise ValueError("At least one input image must be provided.") + + self._check_image_dimensions(batched_tensors, image_names) + batched_tensors = torch.cat(batched_tensors, dim=0) + return (batched_tensors,) + + +# Latent TO BATCH + +class WAS_Latent_Batch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "latent_a": ("LATENT",), + "latent_b": ("LATENT",), + "latent_c": ("LATENT",), + "latent_d": ("LATENT",), + }, + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent",) + FUNCTION = "latent_batch" + CATEGORY = "WAS Suite/Latent" + + def _check_latent_dimensions(self, tensors, names): + dimensions = [(tensor["samples"].shape) for tensor in tensors] + if len(set(dimensions)) > 1: + mismatched_indices = [i for i, dim in enumerate(dimensions) if dim[1] != dimensions[0][1]] + mismatched_latents = [names[i] for i in mismatched_indices] + if mismatched_latents: + raise ValueError(f"WAS latent Batch Warning: Input latent dimensions do not match for latents: {mismatched_latents}") + + def latent_batch(self, **kwargs): + batched_tensors = [kwargs[key] for key in kwargs if kwargs[key] is not None] + latent_names = [key for key in kwargs if kwargs[key] is not None] + + if not batched_tensors: + raise ValueError("At least one input latent must be provided.") + + self._check_latent_dimensions(batched_tensors, latent_names) + samples_out = {} + samples_out["samples"] = torch.cat([tensor["samples"] for tensor in batched_tensors], dim=0) + samples_out["batch_index"] = [] + for tensor in batched_tensors: + cindex = tensor.get("batch_index", list(range(tensor["samples"].shape[0]))) + samples_out["batch_index"] += cindex + return (samples_out,) + + +# MASK TO BATCH + +class WAS_Mask_Batch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "optional": { + "masks_a": ("MASK",), + "masks_b": ("MASK",), + "masks_c": ("MASK",), + "masks_d": ("MASK",), + # "masks_e": ("MASK",), + # "masks_f": ("MASK",), + # Theoretically, an infinite number of mask input parameters can be added. + }, + } + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("masks",) + FUNCTION = "mask_batch" + CATEGORY = "WAS Suite/Image/Masking" + + def _check_mask_dimensions(self, tensors, names): + dimensions = [tensor.shape[1:] for tensor in tensors] # Exclude the batch dimension (if present) + if len(set(dimensions)) > 1: + mismatched_indices = [i for i, dim in enumerate(dimensions) if dim != dimensions[0]] + mismatched_masks = [names[i] for i in mismatched_indices] + raise ValueError(f"WAS Mask Batch Warning: Input mask dimensions do not match for masks: {mismatched_masks}") + + def mask_batch(self, **kwargs): + batched_tensors = [kwargs[key] for key in kwargs if kwargs[key] is not None] + mask_names = [key for key in kwargs if kwargs[key] is not None] + + if not batched_tensors: + raise ValueError("At least one input mask must be provided.") + + self._check_mask_dimensions(batched_tensors, mask_names) + batched_tensors = torch.stack(batched_tensors, dim=0) + batched_tensors = batched_tensors.unsqueeze(1) # Add a channel dimension + return (batched_tensors,) + +# IMAGE GENERATE COLOR PALETTE + +class WAS_Image_Color_Palette: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "colors": ("INT", {"default": 16, "min": 8, "max": 256, "step": 1}), + "mode": (["Chart", "back_to_back"],), + }, + } + + RETURN_TYPES = ("IMAGE","LIST") + RETURN_NAMES = ("image","color_palettes") + FUNCTION = "image_generate_palette" + + CATEGORY = "WAS Suite/Image/Analyze" + + def image_generate_palette(self, image, colors=16, mode="chart"): + + # WAS Filters + WTools = WAS_Tools_Class() + + res_dir = os.path.join(WAS_SUITE_ROOT, 'res') + font = os.path.join(res_dir, 'font.ttf') + + if not os.path.exists(font): + font = None + else: + if mode == "Chart": + cstr(f'Found font at `{font}`').msg.print() + + if len(image) > 1: + palette_strings = [] + palette_images = [] + for img in image: + img = tensor2pil(img) + palette_image, palette = WTools.generate_palette(img, colors, 128, 10, font, 15, mode.lower()) + palette_images.append(pil2tensor(palette_image)) + palette_strings.append(palette) + palette_images = torch.cat(palette_images, dim=0) + return (palette_images, palette_strings) + else: + image = tensor2pil(image) + palette_image, palette = WTools.generate_palette(image, colors, 128, 10, font, 15, mode.lower()) + return (pil2tensor(palette_image), [palette,]) + + +# HEX TO HSL + +class WAS_Hex_to_HSL: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "hex_color": ("STRING", {"default": "#FF0000"}), + }, + "optional": { + "include_alpha": ("BOOLEAN", {"default": False}) + } + } + + RETURN_TYPES = ("INT", "INT", "INT", "FLOAT", "STRING") + RETURN_NAMES = ("hue", "saturation", "lightness", "alpha", "hsl") + + FUNCTION = "hex_to_hsl" + CATEGORY = "WAS Suite/Utilities" + + @staticmethod + def hex_to_hsl(hex_color, include_alpha=False): + if hex_color.startswith("#"): + hex_color = hex_color[1:] + + red = int(hex_color[0:2], 16) / 255.0 + green = int(hex_color[2:4], 16) / 255.0 + blue = int(hex_color[4:6], 16) / 255.0 + alpha = int(hex_color[6:8], 16) / 255.0 if include_alpha and len(hex_color) == 8 else 1.0 + max_val = max(red, green, blue) + min_val = min(red, green, blue) + delta = max_val - min_val + luminance = (max_val + min_val) / 2.0 + + if delta == 0: + hue = 0 + saturation = 0 + else: + saturation = delta / (1 - abs(2 * luminance - 1)) + if max_val == red: + hue = ((green - blue) / delta) % 6 + elif max_val == green: + hue = (blue - red) / delta + 2 + elif max_val == blue: + hue = (red - green) / delta + 4 + hue *= 60 + if hue < 0: + hue += 360 + + luminance = luminance * 100 + saturation = saturation * 100 + + hsl_string = f'hsl({round(hue)}, {round(saturation)}%, {round(luminance)}%)' if not include_alpha else f'hsla({round(hue)}, {round(saturation)}%, {round(luminance)}%, {round(alpha, 2)})' + output = (round(hue), round(saturation), round(luminance), round(alpha, 2), hsl_string) + + return output + + +# HSL TO HEX + + +class WAS_HSL_to_Hex: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "hsl_color": ("STRING", {"default": "hsl(0, 100%, 50%)"}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("hex_color",) + + FUNCTION = "hsl_to_hex" + CATEGORY = "WAS Suite/Utilities" + + @staticmethod + def hsl_to_hex(hsl_color): + import re + + hsl_pattern = re.compile(r'hsla?\(\s*(\d+),\s*(\d+)%?,\s*(\d+)%?(?:,\s*([\d.]+))?\s*\)') + match = hsl_pattern.match(hsl_color) + + if not match: + raise ValueError("Invalid HSL(A) color format") + + h, s, l = map(int, match.groups()[:3]) + a = float(match.groups()[3]) if match.groups()[3] else 1.0 + + s /= 100 + l /= 100 + + c = (1 - abs(2 * l - 1)) * s + x = c * (1 - abs((h / 60) % 2 - 1)) + m = l - c/2 + + if 0 <= h < 60: + r, g, b = c, x, 0 + elif 60 <= h < 120: + r, g, b = x, c, 0 + elif 120 <= h < 180: + r, g, b = 0, c, x + elif 180 <= h < 240: + r, g, b = 0, x, c + elif 240 <= h < 300: + r, g, b = x, 0, c + elif 300 <= h < 360: + r, g, b = c, 0, x + else: + r, g, b = 0, 0, 0 + + r = int((r + m) * 255) + g = int((g + m) * 255) + b = int((b + m) * 255) + alpha = int(a * 255) + + hex_color = f'#{r:02X}{g:02X}{b:02X}' + if a < 1: + hex_color += f'{alpha:02X}' + + return (hex_color,) + + +# IMAGE ANALYZE + + +class WAS_Image_Analyze: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mode": (["Black White Levels", "RGB Levels"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_analyze" + + CATEGORY = "WAS Suite/Image/Analyze" + + def image_analyze(self, image, mode='Black White Levels'): + + # Convert images to PIL + image = tensor2pil(image) + + # WAS Filters + WTools = WAS_Tools_Class() + + # Analye Image + if mode: + if mode == 'Black White Levels': + image = WTools.black_white_levels(image) + elif mode == 'RGB Levels': + image = WTools.channel_frequency(image) + else: + image = image + + return (pil2tensor(image), ) + + +# IMAGE GENERATE GRADIENT + +class WAS_Image_Generate_Gradient: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + gradient_stops = '''0:255,0,0 +25:255,255,255 +50:0,255,0 +75:0,0,255''' + return { + "required": { + "width": ("INT", {"default":512, "max": 4096, "min": 64, "step":1}), + "height": ("INT", {"default":512, "max": 4096, "min": 64, "step":1}), + "direction": (["horizontal", "vertical"],), + "tolerance": ("INT", {"default":0, "max": 255, "min": 0, "step":1}), + "gradient_stops": ("STRING", {"default": gradient_stops, "multiline": True}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_gradient" + + CATEGORY = "WAS Suite/Image/Generate" + + def image_gradient(self, gradient_stops, width=512, height=512, direction='horizontal', tolerance=0): + + import io + + # WAS Filters + WTools = WAS_Tools_Class() + + colors_dict = {} + stops = io.StringIO(gradient_stops.strip().replace(' ','')) + for stop in stops: + parts = stop.split(':') + colors = parts[1].replace('\n','').split(',') + colors_dict[parts[0].replace('\n','')] = colors + + image = WTools.gradient((width, height), direction, colors_dict, tolerance) + + return (pil2tensor(image), ) + +# IMAGE GRADIENT MAP + +class WAS_Image_Gradient_Map: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "gradient_image": ("IMAGE",), + "flip_left_right": (["false", "true"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_gradient_map" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_gradient_map(self, image, gradient_image, flip_left_right='false'): + + # Convert images to PIL + image = tensor2pil(image) + gradient_image = tensor2pil(gradient_image) + + # WAS Filters + WTools = WAS_Tools_Class() + + image = WTools.gradient_map(image, gradient_image, (True if flip_left_right == 'true' else False)) + + return (pil2tensor(image), ) + + +# IMAGE TRANSPOSE + +class WAS_Image_Transpose: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "image_overlay": ("IMAGE",), + "width": ("INT", {"default": 512, "min": -48000, "max": 48000, "step": 1}), + "height": ("INT", {"default": 512, "min": -48000, "max": 48000, "step": 1}), + "X": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}), + "Y": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}), + "rotation": ("INT", {"default": 0, "min": -360, "max": 360, "step": 1}), + "feathering": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_transpose" + + CATEGORY = "WAS Suite/Image/Transform" + + def image_transpose(self, image: torch.Tensor, image_overlay: torch.Tensor, width: int, height: int, X: int, Y: int, rotation: int, feathering: int = 0): + return (pil2tensor(self.apply_transpose_image(tensor2pil(image), tensor2pil(image_overlay), (width, height), (X, Y), rotation, feathering)), ) + + def apply_transpose_image(self, image_bg, image_element, size, loc, rotate=0, feathering=0): + + # Apply transformations to the element image + image_element = image_element.rotate(rotate, expand=True) + image_element = image_element.resize(size) + + # Create a mask for the image with the faded border + if feathering > 0: + mask = Image.new('L', image_element.size, 255) # Initialize with 255 instead of 0 + draw = ImageDraw.Draw(mask) + for i in range(feathering): + alpha_value = int(255 * (i + 1) / feathering) # Invert the calculation for alpha value + draw.rectangle((i, i, image_element.size[0] - i, image_element.size[1] - i), fill=alpha_value) + alpha_mask = Image.merge('RGBA', (mask, mask, mask, mask)) + image_element = Image.composite(image_element, Image.new('RGBA', image_element.size, (0, 0, 0, 0)), alpha_mask) + + # Create a new image of the same size as the base image with an alpha channel + new_image = Image.new('RGBA', image_bg.size, (0, 0, 0, 0)) + new_image.paste(image_element, loc) + + # Paste the new image onto the base image + image_bg = image_bg.convert('RGBA') + image_bg.paste(new_image, (0, 0), new_image) + + return image_bg + + + +# IMAGE RESCALE + +class WAS_Image_Rescale: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mode": (["rescale", "resize"],), + "supersample": (["true", "false"],), + "resampling": (["lanczos", "nearest", "bilinear", "bicubic"],), + "rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}), + "resize_width": ("INT", {"default": 1024, "min": 1, "max": 48000, "step": 1}), + "resize_height": ("INT", {"default": 1536, "min": 1, "max": 48000, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_rescale" + + CATEGORY = "WAS Suite/Image/Transform" + + def image_rescale(self, image, mode="rescale", supersample='true', resampling="lanczos", rescale_factor=2, resize_width=1024, resize_height=1024): + scaled_images = [] + for img in image: + scaled_images.append(pil2tensor(self.apply_resize_image(tensor2pil(img), mode, supersample, rescale_factor, resize_width, resize_height, resampling))) + scaled_images = torch.cat(scaled_images, dim=0) + + return (scaled_images, ) + + def apply_resize_image(self, image: Image.Image, mode='scale', supersample='true', factor: int = 2, width: int = 1024, height: int = 1024, resample='bicubic'): + + # Get the current width and height of the image + current_width, current_height = image.size + + # Calculate the new width and height based on the given mode and parameters + if mode == 'rescale': + new_width, new_height = int( + current_width * factor), int(current_height * factor) + else: + new_width = width if width % 8 == 0 else width + (8 - width % 8) + new_height = height if height % 8 == 0 else height + \ + (8 - height % 8) + + # Define a dictionary of resampling filters + resample_filters = { + 'nearest': 0, + 'bilinear': 2, + 'bicubic': 3, + 'lanczos': 1 + } + + # Apply supersample + if supersample == 'true': + image = image.resize((new_width * 8, new_height * 8), resample=Image.Resampling(resample_filters[resample])) + + # Resize the image using the given resampling filter + resized_image = image.resize((new_width, new_height), resample=Image.Resampling(resample_filters[resample])) + + return resized_image + + +# LOAD IMAGE BATCH + +class WAS_Load_Image_Batch: + def __init__(self): + self.HDB = WASDatabase(WAS_HISTORY_DATABASE) + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mode": (["single_image", "incremental_image", "random"],), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "index": ("INT", {"default": 0, "min": 0, "max": 150000, "step": 1}), + "label": ("STRING", {"default": 'Batch 001', "multiline": False}), + "path": ("STRING", {"default": '', "multiline": False}), + "pattern": ("STRING", {"default": '*', "multiline": False}), + "allow_RGBA_output": (["false","true"],), + }, + "optional": { + "filename_text_extension": (["true", "false"],), + } + } + + RETURN_TYPES = ("IMAGE",TEXT_TYPE) + RETURN_NAMES = ("image","filename_text") + FUNCTION = "load_batch_images" + + CATEGORY = "WAS Suite/IO" + + def load_batch_images(self, path, pattern='*', index=0, mode="single_image", seed=0, label='Batch 001', allow_RGBA_output='false', filename_text_extension='true'): + + allow_RGBA_output = (allow_RGBA_output == 'true') + + if not os.path.exists(path): + return (None, ) + fl = self.BatchImageLoader(path, label, pattern) + new_paths = fl.image_paths + if mode == 'single_image': + image, filename = fl.get_image_by_id(index) + if image == None: + cstr(f"No valid image was found for the inded `{index}`").error.print() + return (None, None) + elif mode == 'incremental_image': + image, filename = fl.get_next_image() + if image == None: + cstr(f"No valid image was found for the next ID. Did you remove images from the source directory?").error.print() + return (None, None) + else: + random.seed(seed) + newindex = int(random.random() * len(fl.image_paths)) + image, filename = fl.get_image_by_id(newindex) + if image == None: + cstr(f"No valid image was found for the next ID. Did you remove images from the source directory?").error.print() + return (None, None) + + + # Update history + update_history_images(new_paths) + + if not allow_RGBA_output: + image = image.convert("RGB") + + if filename_text_extension == "false": + filename = os.path.splitext(filename)[0] + + return (pil2tensor(image), filename) + + class BatchImageLoader: + def __init__(self, directory_path, label, pattern): + self.WDB = WDB + self.image_paths = [] + self.load_images(directory_path, pattern) + self.image_paths.sort() + stored_directory_path = self.WDB.get('Batch Paths', label) + stored_pattern = self.WDB.get('Batch Patterns', label) + if stored_directory_path != directory_path or stored_pattern != pattern: + self.index = 0 + self.WDB.insert('Batch Counters', label, 0) + self.WDB.insert('Batch Paths', label, directory_path) + self.WDB.insert('Batch Patterns', label, pattern) + else: + self.index = self.WDB.get('Batch Counters', label) + self.label = label + + def load_images(self, directory_path, pattern): + for file_name in glob.glob(os.path.join(glob.escape(directory_path), pattern), recursive=True): + if file_name.lower().endswith(ALLOWED_EXT): + abs_file_path = os.path.abspath(file_name) + self.image_paths.append(abs_file_path) + + def get_image_by_id(self, image_id): + if image_id < 0 or image_id >= len(self.image_paths): + cstr(f"Invalid image index `{image_id}`").error.print() + return + i = Image.open(self.image_paths[image_id]) + i = ImageOps.exif_transpose(i) + return (i, os.path.basename(self.image_paths[image_id])) + + def get_next_image(self): + if self.index >= len(self.image_paths): + self.index = 0 + image_path = self.image_paths[self.index] + self.index += 1 + if self.index == len(self.image_paths): + self.index = 0 + cstr(f'{cstr.color.YELLOW}{self.label}{cstr.color.END} Index: {self.index}').msg.print() + self.WDB.insert('Batch Counters', self.label, self.index) + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + return (i, os.path.basename(image_path)) + + def get_current_image(self): + if self.index >= len(self.image_paths): + self.index = 0 + image_path = self.image_paths[self.index] + return os.path.basename(image_path) + + @classmethod + def IS_CHANGED(cls, **kwargs): + if kwargs['mode'] != 'single_image': + return float("NaN") + else: + fl = WAS_Load_Image_Batch.BatchImageLoader(kwargs['path'], kwargs['label'], kwargs['pattern']) + filename = fl.get_current_image() + image = os.path.join(kwargs['path'], filename) + sha = get_sha256(image) + return sha + + +# IMAGE HISTORY NODE + +class WAS_Image_History: + def __init__(self): + self.HDB = WASDatabase(WAS_HISTORY_DATABASE) + self.conf = getSuiteConfig() + + @classmethod + def INPUT_TYPES(cls): + HDB = WASDatabase(WAS_HISTORY_DATABASE) + conf = getSuiteConfig() + paths = ['No History'] + if HDB.catExists("History") and HDB.keyExists("History", "Images"): + history_paths = HDB.get("History", "Images") + if conf.__contains__('history_display_limit'): + history_paths = history_paths[-conf['history_display_limit']:] + paths = [] + for path_ in history_paths: + paths.append(os.path.join('...'+os.sep+os.path.basename(os.path.dirname(path_)), os.path.basename(path_))) + + return { + "required": { + "image": (paths,), + }, + } + + RETURN_TYPES = ("IMAGE",TEXT_TYPE) + RETURN_NAMES = ("image","filename_text") + FUNCTION = "image_history" + + CATEGORY = "WAS Suite/History" + + def image_history(self, image): + self.HDB = WASDatabase(WAS_HISTORY_DATABASE) + paths = {} + if self.HDB.catExists("History") and self.HDB.keyExists("History", "Images"): + history_paths = self.HDB.get("History", "Images") + for path_ in history_paths: + paths.update({os.path.join('...'+os.sep+os.path.basename(os.path.dirname(path_)), os.path.basename(path_)): path_}) + if os.path.exists(paths[image]) and paths.__contains__(image): + return (pil2tensor(Image.open(paths[image]).convert('RGB')), os.path.basename(paths[image])) + else: + cstr(f"The image `{image}` does not exist!").error.print() + return (pil2tensor(Image.new('RGB', (512,512), (0, 0, 0, 0))), 'null') + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + +# IMAGE PADDING + +class WAS_Image_Stitch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "stitch": (["top", "left", "bottom", "right"],), + "feathering": ("INT", {"default": 50, "min": 0, "max": 2048, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_stitching" + + CATEGORY = "WAS Suite/Image/Transform" + + def image_stitching(self, image_a, image_b, stitch="right", feathering=50): + + valid_stitches = ["top", "left", "bottom", "right"] + if stitch not in valid_stitches: + cstr(f"The stitch mode `{stitch}` is not valid. Valid sitch modes are {', '.join(valid_stitches)}").error.print() + if feathering > 2048: + cstr(f"The stitch feathering of `{feathering}` is too high. Please choose a value between `0` and `2048`").error.print() + + WTools = WAS_Tools_Class(); + + stitched_image = WTools.stitch_image(tensor2pil(image_a), tensor2pil(image_b), stitch, feathering) + + return (pil2tensor(stitched_image), ) + + + +# IMAGE PADDING + +class WAS_Image_Padding: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "feathering": ("INT", {"default": 120, "min": 0, "max": 2048, "step": 1}), + "feather_second_pass": (["true", "false"],), + "left_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}), + "right_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}), + "top_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}), + "bottom_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("IMAGE", "MASK") + FUNCTION = "image_padding" + + CATEGORY = "WAS Suite/Image/Transform" + + def image_padding(self, image, feathering, left_padding, right_padding, top_padding, bottom_padding, feather_second_pass=True): + padding = self.apply_image_padding(tensor2pil( + image), left_padding, right_padding, top_padding, bottom_padding, feathering, second_pass=feather_second_pass) + return (pil2tensor(padding[0]), pil2tensor(padding[1])) + + def apply_image_padding(self, image, left_pad=100, right_pad=100, top_pad=100, bottom_pad=100, feather_radius=50, second_pass=True): + # Create a mask for the feathered edge + mask = Image.new('L', image.size, 255) + draw = ImageDraw.Draw(mask) + + # Draw black rectangles at each edge of the image with the specified feather radius + draw.rectangle((0, 0, feather_radius*2, image.height), fill=0) + draw.rectangle((image.width-feather_radius*2, 0, + image.width, image.height), fill=0) + draw.rectangle((0, 0, image.width, feather_radius*2), fill=0) + draw.rectangle((0, image.height-feather_radius*2, + image.width, image.height), fill=0) + + # Blur the mask to create a smooth gradient between the black shapes and the white background + mask = mask.filter(ImageFilter.GaussianBlur(radius=feather_radius)) + + # Apply mask if second_pass is False, apply both masks if second_pass is True + if second_pass: + + # Create a second mask for the additional feathering pass + mask2 = Image.new('L', image.size, 255) + draw2 = ImageDraw.Draw(mask2) + + # Draw black rectangles at each edge of the image with a smaller feather radius + feather_radius2 = int(feather_radius / 4) + draw2.rectangle((0, 0, feather_radius2*2, image.height), fill=0) + draw2.rectangle((image.width-feather_radius2*2, 0, + image.width, image.height), fill=0) + draw2.rectangle((0, 0, image.width, feather_radius2*2), fill=0) + draw2.rectangle((0, image.height-feather_radius2*2, + image.width, image.height), fill=0) + + # Blur the mask to create a smooth gradient between the black shapes and the white background + mask2 = mask2.filter( + ImageFilter.GaussianBlur(radius=feather_radius2)) + + feathered_im = Image.new('RGBA', image.size, (0, 0, 0, 0)) + feathered_im.paste(image, (0, 0), mask) + feathered_im.paste(image, (0, 0), mask) + + # Apply the second mask to the feathered image + feathered_im.paste(image, (0, 0), mask2) + feathered_im.paste(image, (0, 0), mask2) + + else: + + # Apply the fist maskk + feathered_im = Image.new('RGBA', image.size, (0, 0, 0, 0)) + feathered_im.paste(image, (0, 0), mask) + + # Calculate the new size of the image with padding added + new_size = (feathered_im.width + left_pad + right_pad, + feathered_im.height + top_pad + bottom_pad) + + # Create a new transparent image with the new size + new_im = Image.new('RGBA', new_size, (0, 0, 0, 0)) + + # Paste the feathered image onto the new image with the padding + new_im.paste(feathered_im, (left_pad, top_pad)) + + # Create Padding Mask + padding_mask = Image.new('L', new_size, 0) + + # Create a mask where the transparent pixels have a gradient + gradient = [(int(255 * (1 - p[3] / 255)) if p[3] != 0 else 255) + for p in new_im.getdata()] + padding_mask.putdata(gradient) + + # Save the new image with alpha channel as a PNG file + return (new_im, padding_mask.convert('RGB')) + + +# IMAGE THRESHOLD NODE + +class WAS_Image_Threshold: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_threshold" + + CATEGORY = "WAS Suite/Image/Process" + + def image_threshold(self, image, threshold=0.5): + images = [] + for img in image: + images.append(pil2tensor(self.apply_threshold(tensor2pil(img), threshold))) + return (torch.cat(images, dim=0), ) + + def apply_threshold(self, input_image, threshold=0.5): + # Convert the input image to grayscale + grayscale_image = input_image.convert('L') + + # Apply the threshold to the grayscale image + threshold_value = int(threshold * 255) + thresholded_image = grayscale_image.point( + lambda x: 255 if x >= threshold_value else 0, mode='L') + + return thresholded_image + + +# IMAGE CHROMATIC ABERRATION NODE + +class WAS_Image_Chromatic_Aberration: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "red_offset": ("INT", {"default": 2, "min": -255, "max": 255, "step": 1}), + "green_offset": ("INT", {"default": -1, "min": -255, "max": 255, "step": 1}), + "blue_offset": ("INT", {"default": 1, "min": -255, "max": 255, "step": 1}), + "intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "fade_radius": ("INT", {"default": 12, "min": 0, "max": 1024, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_chromatic_aberration" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_chromatic_aberration(self, image, red_offset=4, green_offset=2, blue_offset=0, intensity=1, fade_radius=12): + return (pil2tensor(self.apply_chromatic_aberration(tensor2pil(image), red_offset, green_offset, blue_offset, intensity, fade_radius)), ) + + def apply_chromatic_aberration(self, img, r_offset, g_offset, b_offset, intensity, fade_radius): + + def lingrad(size, direction, white_ratio): + image = Image.new('RGB', size) + draw = ImageDraw.Draw(image) + if direction == 'vertical': + black_end = size[1] - white_ratio + range_start = 0 + range_end = size[1] + range_step = 1 + for y in range(range_start, range_end, range_step): + color_ratio = y / size[1] + if y <= black_end: + color = (0, 0, 0) + else: + color_value = int(((y - black_end) / (size[1] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(0, y), (size[0], y)], fill=color) + elif direction == 'horizontal': + black_end = size[0] - white_ratio + range_start = 0 + range_end = size[0] + range_step = 1 + for x in range(range_start, range_end, range_step): + color_ratio = x / size[0] + if x <= black_end: + color = (0, 0, 0) + else: + color_value = int(((x - black_end) / (size[0] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(x, 0), (x, size[1])], fill=color) + + return image.convert("L") + + def create_fade_mask(size, fade_radius): + mask = Image.new("L", size, 255) + + left = ImageOps.invert(lingrad(size, 'horizontal', int(fade_radius * 2))) + right = left.copy().transpose(Image.FLIP_LEFT_RIGHT) + top = ImageOps.invert(lingrad(size, 'vertical', int(fade_radius *2))) + bottom = top.copy().transpose(Image.FLIP_TOP_BOTTOM) + + # Multiply masks with the original mask image + mask = ImageChops.multiply(mask, left) + mask = ImageChops.multiply(mask, right) + mask = ImageChops.multiply(mask, top) + mask = ImageChops.multiply(mask, bottom) + mask = ImageChops.multiply(mask, mask) + + return mask + + # split the channels of the image + r, g, b = img.split() + + # apply the offset to each channel + r_offset_img = ImageChops.offset(r, r_offset, 0) + g_offset_img = ImageChops.offset(g, 0, g_offset) + b_offset_img = ImageChops.offset(b, 0, b_offset) + + # merge the channels with the offsets + merged = Image.merge("RGB", (r_offset_img, g_offset_img, b_offset_img)) + + # create fade masks for blending + fade_mask = create_fade_mask(img.size, fade_radius) + + # merge the blended channels back into an RGB image + result = Image.composite(merged, img, fade_mask).convert("RGB") + + return result + + +# IMAGE BLOOM FILTER + +class WAS_Image_Bloom_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "radius": ("FLOAT", {"default": 10, "min": 0.0, "max": 1024, "step": 0.1}), + "intensity": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_bloom" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_bloom(self, image, radius=0.5, intensity=1.0): + return (pil2tensor(self.apply_bloom_filter(tensor2pil(image), radius, intensity)), ) + + def apply_bloom_filter(self, input_image, radius, bloom_factor): + # Apply a blur filter to the input image + blurred_image = input_image.filter( + ImageFilter.GaussianBlur(radius=radius)) + + # Subtract the blurred image from the input image to create a high-pass filter + high_pass_filter = ImageChops.subtract(input_image, blurred_image) + + # Create a blurred version of the bloom filter + bloom_filter = high_pass_filter.filter( + ImageFilter.GaussianBlur(radius=radius*2)) + + # Adjust brightness and levels of bloom filter + bloom_filter = ImageEnhance.Brightness(bloom_filter).enhance(2.0) + + # Multiply the bloom image with the bloom factor + bloom_filter = ImageChops.multiply(bloom_filter, Image.new('RGB', input_image.size, (int( + 255 * bloom_factor), int(255 * bloom_factor), int(255 * bloom_factor)))) + + # Multiply the bloom filter with the original image using the bloom factor + blended_image = ImageChops.screen(input_image, bloom_filter) + + return blended_image + + +# IMAGE ROTATE HUE + +class WAS_Image_Rotate_Hue: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "hue_shift": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "rotate_hue" + + CATEGORY = "WAS Suite/Image/Adjustment" + + def rotate_hue(self, image, hue_shift=0.0): + if hue_shift > 1.0 or hue_shift < 0.0: + cstr(f"The hue_shift `{cstr.color.LIGHTYELLOW}{hue_shift}{cstr.color.END}` is out of range. Valid range is {cstr.color.BOLD}0.0 - 1.0{cstr.color.END}").error.print() + hue_shift = 0.0 + shifted_hue = pil2tensor(self.hue_rotation(image, hue_shift)) + return (shifted_hue, ) + + def hue_rotation(self, image, hue_shift=0.0): + import colorsys + if hue_shift > 1.0 or hue_shift < 0.0: + print(f"The hue_shift '{hue_shift}' is out of range. Valid range is 0.0 - 1.0") + hue_shift = 0.0 + + pil_image = tensor2pil(image) + width, height = pil_image.size + rotated_image = Image.new("RGB", (width, height)) + + for x in range(width): + for y in range(height): + r, g, b = pil_image.getpixel((x, y)) + h, l, s = colorsys.rgb_to_hls(r / 255, g / 255, b / 255) + h = (h + hue_shift) % 1.0 + r, g, b = colorsys.hls_to_rgb(h, l, s) + r, g, b = int(r * 255), int(g * 255), int(b * 255) + rotated_image.putpixel((x, y), (r, g, b)) + + return rotated_image + + +# IMAGE REMOVE COLOR + +class WAS_Image_Remove_Color: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "target_red": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "target_green": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "target_blue": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "replace_red": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "replace_green": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "replace_blue": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "clip_threshold": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_remove_color" + + CATEGORY = "WAS Suite/Image/Process" + + def image_remove_color(self, image, clip_threshold=10, target_red=255, target_green=255, target_blue=255, replace_red=255, replace_green=255, replace_blue=255): + return (pil2tensor(self.apply_remove_color(tensor2pil(image), clip_threshold, (target_red, target_green, target_blue), (replace_red, replace_green, replace_blue))), ) + + def apply_remove_color(self, image, threshold=10, color=(255, 255, 255), rep_color=(0, 0, 0)): + # Create a color image with the same size as the input image + color_image = Image.new('RGB', image.size, color) + + # Calculate the difference between the input image and the color image + diff_image = ImageChops.difference(image, color_image) + + # Convert the difference image to grayscale + gray_image = diff_image.convert('L') + + # Apply a threshold to the grayscale difference image + mask_image = gray_image.point(lambda x: 255 if x > threshold else 0) + + # Invert the mask image + mask_image = ImageOps.invert(mask_image) + + # Apply the mask to the original image + result_image = Image.composite( + Image.new('RGB', image.size, rep_color), image, mask_image) + + return result_image + + +# IMAGE REMOVE BACKGROUND + +class WAS_Remove_Background: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "mode": (["background", "foreground"],), + "threshold": ("INT", {"default": 127, "min": 0, "max": 255, "step": 1}), + "threshold_tolerance": ("INT", {"default": 2, "min": 1, "max": 24, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "image_remove_background" + + CATEGORY = "WAS Suite/Image/Process" + + def image_remove_background(self, images, mode='background', threshold=127, threshold_tolerance=2): + return (self.remove_background(images, mode, threshold, threshold_tolerance), ) + + def remove_background(self, image, mode, threshold, threshold_tolerance): + images = [] + image = [tensor2pil(img) for img in image] + for img in image: + grayscale_image = img.convert('L') + if mode == 'background': + grayscale_image = ImageOps.invert(grayscale_image) + threshold = 255 - threshold # adjust the threshold for "background" mode + blurred_image = grayscale_image.filter( + ImageFilter.GaussianBlur(radius=threshold_tolerance)) + binary_image = blurred_image.point( + lambda x: 0 if x < threshold else 255, '1') + mask = binary_image.convert('L') + inverted_mask = ImageOps.invert(mask) + transparent_image = img.copy() + transparent_image.putalpha(inverted_mask) + images.append(pil2tensor(transparent_image)) + batch = torch.cat(images, dim=0) + + return batch + +# IMAGE REMBG +# Sam model needs additional input, may need to be new node entirely +# See: https://github.com/danielgatis/rembg/blob/main/USAGE.md#using-input-points +# u2net_cloth_seg model needs additional inputs, may create a new node +# An undocumented feature "putaplha" changes how alpha is applied, but does not appear to make a difference + +class WAS_Remove_Rembg: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "transparency": ("BOOLEAN", {"default": True},), + "model": (["u2net", "u2netp", "u2net_human_seg", "silueta", "isnet-general-use", "isnet-anime"],), + "post_processing": ("BOOLEAN", {"default": False}), + "only_mask": ("BOOLEAN", {"default": False},), + "alpha_matting": ("BOOLEAN", {"default": False},), + "alpha_matting_foreground_threshold": ("INT", {"default": 240, "min": 0, "max": 255}), + "alpha_matting_background_threshold": ("INT", {"default": 10, "min": 0, "max": 255}), + "alpha_matting_erode_size": ("INT", {"default": 10, "min": 0, "max": 255}), + "background_color": (["none", "black", "white", "magenta", "chroma green", "chroma blue"],), + # "putalpha": ("BOOLEAN", {"default": True},), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "image_rembg" + + CATEGORY = "WAS Suite/Image/AI" + + # A helper function to convert from strings to logical boolean + # Conforms to https://docs.python.org/3/library/stdtypes.html#truth-value-testing + # With the addition of evaluating string representations of Falsey types + def __convertToBool(self, x): + + # Evaluate string representation of False types + if type(x) == str: + x = x.strip() + if (x.lower() == 'false' + or x.lower() == 'none' + or x == '0' + or x == '0.0' + or x == '0j' + or x == "''" + or x == '""' + or x == "()" + or x == "[]" + or x == "{}" + or x.lower() == "decimal(0)" + or x.lower() == "fraction(0,1)" + or x.lower() == "set()" + or x.lower() == "range(0)" + ): + return False + else: + return True + + # Anything else will be evaluated by the bool function + return bool(x) + + def image_rembg( + self, + images, + transparency=True, + model="u2net", + alpha_matting=False, + alpha_matting_foreground_threshold=240, + alpha_matting_background_threshold=10, + alpha_matting_erode_size=10, + post_processing=False, + only_mask=False, + background_color="none", + # putalpha = False, + ): + + # ComfyUI will allow strings in place of booleans, validate the input. + transparency = transparency if type(transparency) is bool else self.__convertToBool(transparency) + alpha_matting = alpha_matting if type(alpha_matting) is bool else self.__convertToBool(alpha_matting) + post_processing = post_processing if type(post_processing) is bool else self.__convertToBool(post_processing) + only_mask = only_mask if type(only_mask) is bool else self.__convertToBool(only_mask) + + if "rembg" not in packages(): + install_package("rembg") + + from rembg import remove, new_session + + os.environ['U2NET_HOME'] = os.path.join(MODELS_DIR, 'rembg') + os.makedirs(os.environ['U2NET_HOME'], exist_ok=True) + + # Set bgcolor + bgrgba = None + if background_color == "black": + bgrgba = [0, 0, 0, 255] + elif background_color == "white": + bgrgba = [255, 255, 255, 255] + elif background_color == "magenta": + bgrgba = [255, 0, 255, 255] + elif background_color == "chroma green": + bgrgba = [0, 177, 64, 255] + elif background_color == "chroma blue": + bgrgba = [0, 71, 187, 255] + else: + bgrgba = None + + if transparency and bgrgba is not None: + bgrgba[3] = 0 + + batch_tensor = [] + for image in images: + image = tensor2pil(image) + batch_tensor.append(pil2tensor( + remove( + image, + session=new_session(model), + post_process_mask=post_processing, + alpha_matting=alpha_matting, + alpha_matting_foreground_threshold=alpha_matting_foreground_threshold, + alpha_matting_background_threshold=alpha_matting_background_threshold, + alpha_matting_erode_size=alpha_matting_erode_size, + only_mask=only_mask, + bgcolor=bgrgba, + # putalpha = putalpha, + ) + .convert(('RGBA' if transparency else 'RGB')))) + batch_tensor = torch.cat(batch_tensor, dim=0) + + return (batch_tensor,) + + +# IMAGE BLEND MASK NODE + +class WAS_Image_Blend_Mask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "mask": ("IMAGE",), + "blend_percentage": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_blend_mask" + + CATEGORY = "WAS Suite/Image" + + def image_blend_mask(self, image_a, image_b, mask, blend_percentage): + + # Convert images to PIL + img_a = tensor2pil(image_a) + img_b = tensor2pil(image_b) + mask = ImageOps.invert(tensor2pil(mask).convert('L')) + + # Mask image + masked_img = Image.composite(img_a, img_b, mask.resize(img_a.size)) + + # Blend image + blend_mask = Image.new(mode="L", size=img_a.size, + color=(round(blend_percentage * 255))) + blend_mask = ImageOps.invert(blend_mask) + img_result = Image.composite(img_a, masked_img, blend_mask) + + del img_a, img_b, blend_mask, mask + + return (pil2tensor(img_result), ) + + +# IMAGE BLANK NOE + + +class WAS_Image_Blank: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 1}), + "red": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "green": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + "blue": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blank_image" + + CATEGORY = "WAS Suite/Image" + + def blank_image(self, width, height, red, green, blue): + + # Ensure multiples + width = (width // 8) * 8 + height = (height // 8) * 8 + + # Blend image + blank = Image.new(mode="RGB", size=(width, height), + color=(red, green, blue)) + + return (pil2tensor(blank), ) + + +# IMAGE HIGH PASS + +class WAS_Image_High_Pass_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "radius": ("INT", {"default": 10, "min": 1, "max": 500, "step": 1}), + "strength": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 255.0, "step": 0.1}), + "color_output": (["true", "false"],), + "neutral_background": (["true", "false"],), + } + } + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "high_pass" + + CATEGORY = "WAS Suite/Image/Filter" + + def high_pass(self, images, radius=10, strength=1.5, color_output="true", neutral_background="true"): + batch_tensor = [] + for image in images: + transformed_image = self.apply_hpf(tensor2pil(image), radius, strength, color_output, neutral_background) + batch_tensor.append(pil2tensor(transformed_image)) + batch_tensor = torch.cat(batch_tensor, dim=0) + return (batch_tensor, ) + + def apply_hpf(self, img, radius=10, strength=1.5, color_output="true", neutral_background="true"): + img_arr = np.array(img).astype('float') + blurred_arr = np.array(img.filter(ImageFilter.GaussianBlur(radius=radius))).astype('float') + hpf_arr = img_arr - blurred_arr + hpf_arr = np.clip(hpf_arr * strength, 0, 255).astype('uint8') + + if color_output == "true": + high_pass = Image.fromarray(hpf_arr, mode='RGB') + else: + grayscale_arr = np.mean(hpf_arr, axis=2).astype('uint8') + high_pass = Image.fromarray(grayscale_arr, mode='L') + + if neutral_background == "true": + neutral_color = (128, 128, 128) if high_pass.mode == 'RGB' else 128 + neutral_bg = Image.new(high_pass.mode, high_pass.size, neutral_color) + high_pass = ImageChops.screen(neutral_bg, high_pass) + + return high_pass.convert("RGB") + + +# IMAGE LEVELS NODE + +class WAS_Image_Levels: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "black_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 255.0, "step": 0.1}), + "mid_level": ("FLOAT", {"default": 127.5, "min": 0.0, "max": 255.0, "step": 0.1}), + "white_level": ("FLOAT", {"default": 255, "min": 0.0, "max": 255.0, "step": 0.1}), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "apply_image_levels" + + CATEGORY = "WAS Suite/Image/Adjustment" + + def apply_image_levels(self, image, black_level, mid_level, white_level): + + # Convert image to PIL + tensor_images = [] + for img in image: + img = tensor2pil(img) + levels = self.AdjustLevels(black_level, mid_level, white_level) + tensor_images.append(pil2tensor(levels.adjust(img))) + tensor_images = torch.cat(tensor_images, dim=0) + + # Return adjust image tensor + return (tensor_images, ) + + + class AdjustLevels: + def __init__(self, min_level, mid_level, max_level): + self.min_level = min_level + self.mid_level = mid_level + self.max_level = max_level + + def adjust(self, im): + + im_arr = np.array(im).astype(np.float32) + im_arr[im_arr < self.min_level] = self.min_level + im_arr = (im_arr - self.min_level) * \ + (255 / (self.max_level - self.min_level)) + im_arr = np.clip(im_arr, 0, 255) + + # mid-level adjustment + if self.mid_level <= self.min_level: + gamma = 1.0 + else: + gamma = math.log(0.5) / math.log((self.mid_level - self.min_level) / (self.max_level - self.min_level)) + im_arr = np.power(im_arr / 255, gamma) * 255 + + im_arr = im_arr.astype(np.uint8) + + im = Image.fromarray(im_arr) + + return im + + +# FILM GRAIN NODE + +class WAS_Film_Grain: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "density": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01}), + "intensity": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01}), + "highlights": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 255.0, "step": 0.01}), + "supersample_factor": ("INT", {"default": 4, "min": 1, "max": 8, "step": 1}) + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "film_grain" + + CATEGORY = "WAS Suite/Image/Filter" + + def film_grain(self, image, density, intensity, highlights, supersample_factor): + return (pil2tensor(self.apply_film_grain(tensor2pil(image), density, intensity, highlights, supersample_factor)), ) + + def apply_film_grain(self, img, density=0.1, intensity=1.0, highlights=1.0, supersample_factor=4): + """ + Apply grayscale noise with specified density, intensity, and highlights to a PIL image. + """ + img_gray = img.convert('L') + original_size = img.size + img_gray = img_gray.resize( + ((img.size[0] * supersample_factor), (img.size[1] * supersample_factor)), Image.Resampling(2)) + num_pixels = int(density * img_gray.size[0] * img_gray.size[1]) + + noise_pixels = [] + for i in range(num_pixels): + x = random.randint(0, img_gray.size[0]-1) + y = random.randint(0, img_gray.size[1]-1) + noise_pixels.append((x, y)) + + for x, y in noise_pixels: + value = random.randint(0, 255) + img_gray.putpixel((x, y), value) + + img_noise = img_gray.convert('RGB') + img_noise = img_noise.filter(ImageFilter.GaussianBlur(radius=0.125)) + img_noise = img_noise.resize(original_size, Image.Resampling(1)) + img_noise = img_noise.filter(ImageFilter.EDGE_ENHANCE_MORE) + img_final = Image.blend(img, img_noise, intensity) + enhancer = ImageEnhance.Brightness(img_final) + img_highlights = enhancer.enhance(highlights) + + # Return the final image + return img_highlights + + +# IMAGE FLIP NODE + +class WAS_Image_Flip: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "mode": (["horizontal", "vertical",],), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "image_flip" + + CATEGORY = "WAS Suite/Image/Transform" + + def image_flip(self, images, mode): + + batch_tensor = [] + for image in images: + image = tensor2pil(image) + if mode == 'horizontal': + image = image.transpose(0) + if mode == 'vertical': + image = image.transpose(1) + batch_tensor.append(pil2tensor(image)) + batch_tensor = torch.cat(batch_tensor, dim=0) + + return (batch_tensor, ) + + +class WAS_Image_Rotate: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "mode": (["transpose", "internal",],), + "rotation": ("INT", {"default": 0, "min": 0, "max": 360, "step": 90}), + "sampler": (["nearest", "bilinear", "bicubic"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "image_rotate" + + CATEGORY = "WAS Suite/Image/Transform" + + def image_rotate(self, images, mode, rotation, sampler): + + batch_tensor = [] + for image in images: + # PIL Image + image = tensor2pil(image) + + # Check rotation + if rotation > 360: + rotation = int(360) + if (rotation % 90 != 0): + rotation = int((rotation//90)*90) + + # Set Sampler + if sampler: + if sampler == 'nearest': + sampler = Image.NEAREST + elif sampler == 'bicubic': + sampler = Image.BICUBIC + elif sampler == 'bilinear': + sampler = Image.BILINEAR + else: + sampler == Image.BILINEAR + + # Rotate Image + if mode == 'internal': + image = image.rotate(rotation, sampler) + else: + rot = int(rotation / 90) + for _ in range(rot): + image = image.transpose(2) + + batch_tensor.append(pil2tensor(image)) + + batch_tensor = torch.cat(batch_tensor, dim=0) + + return (batch_tensor, ) + + +# IMAGE NOVA SINE FILTER + +class WAS_Image_Nova_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "amplitude": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.001}), + "frequency": ("FLOAT", {"default": 3.14, "min": 0.0, "max": 100.0, "step": 0.001}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "nova_sine" + + CATEGORY = "WAS Suite/Image/Filter" + + def nova_sine(self, image, amplitude, frequency): + + # Convert image to numpy + img = tensor2pil(image) + + # Convert the image to a numpy array + img_array = np.array(img) + + # Define a sine wave function + def sine(x, freq, amp): + return amp * np.sin(2 * np.pi * freq * x) + + # Calculate the sampling frequency of the image + resolution = img.info.get('dpi') # PPI + physical_size = img.size # pixels + + if resolution is not None: + # Convert PPI to pixels per millimeter (PPM) + ppm = 25.4 / resolution + physical_size = tuple(int(pix * ppm) for pix in physical_size) + + # Set the maximum frequency for the sine wave + max_freq = img.width / 2 + + # Ensure frequency isn't outside visual representable range + if frequency > max_freq: + frequency = max_freq + + # Apply levels to the image using the sine function + for i in range(img_array.shape[0]): + for j in range(img_array.shape[1]): + for k in range(img_array.shape[2]): + img_array[i, j, k] = int( + sine(img_array[i, j, k]/255, frequency, amplitude) * 255) + + return (torch.from_numpy(img_array.astype(np.float32) / 255.0).unsqueeze(0), ) + + +# IMAGE CANNY FILTER + + +class WAS_Canny_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "enable_threshold": (['false', 'true'],), + "threshold_low": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "threshold_high": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "canny_filter" + + CATEGORY = "WAS Suite/Image/Filter" + + def canny_filter(self, images, threshold_low, threshold_high, enable_threshold): + + if enable_threshold == 'false': + threshold_low = None + threshold_high = None + + batch_tensor = [] + for image in images: + + image_canny = Image.fromarray(self.Canny_detector( + 255. * image.cpu().numpy().squeeze(), threshold_low, threshold_high)).convert('RGB') + + batch_tensor.append(pil2tensor(image_canny)) + + batch_tensor = torch.cat(batch_tensor, dim=0) + + return (pil2tensor(image_canny), ) + + def Canny_detector(self, img, weak_th=None, strong_th=None): + + import cv2 + + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + img = cv2.GaussianBlur(img, (5, 5), 1.4) + gx = cv2.Sobel(np.float32(img), cv2.CV_64F, 1, 0, 3) # type: ignore + gy = cv2.Sobel(np.float32(img), cv2.CV_64F, 0, 1, 3) # type: ignore + + mag, ang = cv2.cartToPolar(gx, gy, angleInDegrees=True) + + mag_max = np.max(mag) + if not weak_th: + weak_th = mag_max * 0.1 + if not strong_th: + strong_th = mag_max * 0.5 + + height, width = img.shape + + for i_x in range(width): + for i_y in range(height): + + grad_ang = ang[i_y, i_x] + grad_ang = abs( + grad_ang-180) if abs(grad_ang) > 180 else abs(grad_ang) + + neighb_1_x, neighb_1_y = -1, -1 + neighb_2_x, neighb_2_y = -1, -1 + + if grad_ang <= 22.5: + neighb_1_x, neighb_1_y = i_x-1, i_y + neighb_2_x, neighb_2_y = i_x + 1, i_y + + elif grad_ang > 22.5 and grad_ang <= (22.5 + 45): + neighb_1_x, neighb_1_y = i_x-1, i_y-1 + neighb_2_x, neighb_2_y = i_x + 1, i_y + 1 + elif grad_ang > (22.5 + 45) and grad_ang <= (22.5 + 90): + neighb_1_x, neighb_1_y = i_x, i_y-1 + neighb_2_x, neighb_2_y = i_x, i_y + 1 + elif grad_ang > (22.5 + 90) and grad_ang <= (22.5 + 135): + neighb_1_x, neighb_1_y = i_x-1, i_y + 1 + neighb_2_x, neighb_2_y = i_x + 1, i_y-1 + elif grad_ang > (22.5 + 135) and grad_ang <= (22.5 + 180): + neighb_1_x, neighb_1_y = i_x-1, i_y + neighb_2_x, neighb_2_y = i_x + 1, i_y + if width > neighb_1_x >= 0 and height > neighb_1_y >= 0: + if mag[i_y, i_x] < mag[neighb_1_y, neighb_1_x]: + mag[i_y, i_x] = 0 + continue + + if width > neighb_2_x >= 0 and height > neighb_2_y >= 0: + if mag[i_y, i_x] < mag[neighb_2_y, neighb_2_x]: + mag[i_y, i_x] = 0 + + weak_ids = np.zeros_like(img) + strong_ids = np.zeros_like(img) + ids = np.zeros_like(img) + + for i_x in range(width): + for i_y in range(height): + + grad_mag = mag[i_y, i_x] + + if grad_mag < weak_th: + mag[i_y, i_x] = 0 + elif strong_th > grad_mag >= weak_th: + ids[i_y, i_x] = 1 + else: + ids[i_y, i_x] = 2 + + return mag + +# IMAGE EDGE DETECTION + +class WAS_Image_Edge: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mode": (["normal", "laplacian"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_edges" + + CATEGORY = "WAS Suite/Image/Filter" + + def image_edges(self, image, mode): + + # Convert image to PIL + image = tensor2pil(image) + + # Detect edges + if mode: + if mode == "normal": + image = image.filter(ImageFilter.FIND_EDGES) + elif mode == "laplacian": + image = image.filter(ImageFilter.Kernel((3, 3), (-1, -1, -1, -1, 8, + -1, -1, -1, -1), 1, 0)) + else: + image = image + + return (torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0), ) + + +# IMAGE FDOF NODE + +class WAS_Image_fDOF: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "depth": ("IMAGE",), + "mode": (["mock", "gaussian", "box"],), + "radius": ("INT", {"default": 8, "min": 1, "max": 128, "step": 1}), + "samples": ("INT", {"default": 1, "min": 1, "max": 3, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "fdof_composite" + + CATEGORY = "WAS Suite/Image/Filter" + + def fdof_composite(self, image, depth, radius, samples, mode): + + import cv2 as cv + + # Convert tensor to a PIL Image + tensor_images = [] + for i in range(len(image)): + if i <= len(image): + img = tensor2pil(image[i]) + else: + img = tensor2pil(image[-1]) + if i <= len(depth): + dpth = tensor2pil(depth[i]) + else: + dpth = tensor2pil(depth[-1]) + tensor_images.append(pil2tensor(self.portraitBlur(img, dpth, radius, samples, mode))) + tensor_images = torch.cat(tensor_images, dim=0) + + return (tensor_images, ) + + def portraitBlur(self, img, mask, radius, samples, mode='mock'): + mask = mask.resize(img.size).convert('L') + bimg: Optional[Image.Image] = None + if mode == 'mock': + bimg = medianFilter(img, radius, (radius * 1500), 75) + elif mode == 'gaussian': + bimg = img.filter(ImageFilter.GaussianBlur(radius=radius)) + elif mode == 'box': + bimg = img.filter(ImageFilter.BoxBlur(radius)) + else: + return + bimg.convert(img.mode) + rimg: Optional[Image.Image] = None + if samples > 1: + for i in range(samples): + if not rimg: + rimg = Image.composite(img, bimg, mask) + else: + rimg = Image.composite(rimg, bimg, mask) + else: + rimg = Image.composite(img, bimg, mask).convert('RGB') + + return rimg + + +# IMAGE DRAGAN PHOTOGRAPHY FILTER + +class WAS_Dragon_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "saturation": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}), + "contrast": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}), + "brightness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}), + "sharpness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 6.0, "step": 0.01}), + "highpass_radius": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 255.0, "step": 0.01}), + "highpass_samples": ("INT", {"default": 1, "min": 0, "max": 6.0, "step": 1}), + "highpass_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}), + "colorize": (["true","false"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "apply_dragan_filter" + + CATEGORY = "WAS Suite/Image/Filter" + + def apply_dragan_filter(self, image, saturation, contrast, sharpness, brightness, highpass_radius, highpass_samples, highpass_strength, colorize): + + WTools = WAS_Tools_Class() + + tensor_images = [] + for img in image: + tensor_images.append(pil2tensor(WTools.dragan_filter(tensor2pil(img), saturation, contrast, sharpness, brightness, highpass_radius, highpass_samples, highpass_strength, colorize))) + tensor_images = torch.cat(tensor_images, dim=0) + + return (tensor_images, ) + + + +# IMAGE MEDIAN FILTER NODE + +class WAS_Image_Median_Filter: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "diameter": ("INT", {"default": 2.0, "min": 0.1, "max": 255, "step": 1}), + "sigma_color": ("FLOAT", {"default": 10.0, "min": -255.0, "max": 255.0, "step": 0.1}), + "sigma_space": ("FLOAT", {"default": 10.0, "min": -255.0, "max": 255.0, "step": 0.1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "apply_median_filter" + + CATEGORY = "WAS Suite/Image/Filter" + + def apply_median_filter(self, image, diameter, sigma_color, sigma_space): + + tensor_images = [] + for img in image: + img = tensor2pil(img) + # Apply Median Filter effect + tensor_images.append(pil2tensor(medianFilter(img, diameter, sigma_color, sigma_space))) + tensor_images = torch.cat(tensor_images, dim=0) + + return (tensor_images, ) + +# IMAGE SELECT COLOR + + +class WAS_Image_Select_Color: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "red": ("INT", {"default": 255.0, "min": 0.0, "max": 255.0, "step": 0.1}), + "green": ("INT", {"default": 255.0, "min": 0.0, "max": 255.0, "step": 0.1}), + "blue": ("INT", {"default": 255.0, "min": 0.0, "max": 255.0, "step": 0.1}), + "variance": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "select_color" + + CATEGORY = "WAS Suite/Image/Process" + + def select_color(self, image, red=255, green=255, blue=255, variance=10): + + image = self.color_pick(tensor2pil(image), red, green, blue, variance) + + return (pil2tensor(image), ) + + def color_pick(self, image, red=255, green=255, blue=255, variance=10): + # Convert image to RGB mode + image = image.convert('RGB') + + # Create a new black image of the same size as the input image + selected_color = Image.new('RGB', image.size, (0, 0, 0)) + + # Get the width and height of the image + width, height = image.size + + # Loop through every pixel in the image + for x in range(width): + for y in range(height): + # Get the color of the pixel + pixel = image.getpixel((x, y)) + r, g, b = pixel + + # Check if the pixel is within the specified color range + if ((r >= red-variance) and (r <= red+variance) and + (g >= green-variance) and (g <= green+variance) and + (b >= blue-variance) and (b <= blue+variance)): + # Set the pixel in the selected_color image to the RGB value of the pixel + selected_color.putpixel((x, y), (r, g, b)) + + # Return the selected color image + return selected_color + +# IMAGE CONVERT TO CHANNEL + + +class WAS_Image_Select_Channel: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "channel": (['red', 'green', 'blue'],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "select_channel" + + CATEGORY = "WAS Suite/Image/Process" + + def select_channel(self, image, channel='red'): + + image = self.convert_to_single_channel(tensor2pil(image), channel) + + return (pil2tensor(image), ) + + def convert_to_single_channel(self, image, channel='red'): + + # Convert to RGB mode to access individual channels + image = image.convert('RGB') + + # Extract the desired channel and convert to greyscale + if channel == 'red': + channel_img = image.split()[0].convert('L') + elif channel == 'green': + channel_img = image.split()[1].convert('L') + elif channel == 'blue': + channel_img = image.split()[2].convert('L') + else: + raise ValueError( + "Invalid channel option. Please choose 'red', 'green', or 'blue'.") + + # Convert the greyscale channel back to RGB mode + channel_img = Image.merge( + 'RGB', (channel_img, channel_img, channel_img)) + + return channel_img + +# IMAGES TO RGB + +class WAS_Images_To_RGB: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_to_rgb" + + CATEGORY = "WAS Suite/Image" + + def image_to_rgb(self, images): + + if len(images) > 1: + tensors = [] + for image in images: + tensors.append(pil2tensor(tensor2pil(image).convert('RGB'))) + tensors = torch.cat(tensors, dim=0) + return (tensors, ) + else: + return (pil2tensor(tensor2pil(images).convert("RGB")), ) + +# IMAGES TO LINEAR + +class WAS_Images_To_Linear: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_to_linear" + + CATEGORY = "WAS Suite/Image" + + def image_to_linear(self, images): + + if len(images) > 1: + tensors = [] + for image in images: + tensors.append(pil2tensor(tensor2pil(image).convert('L'))) + tensors = torch.cat(tensors, dim=0) + return (tensors, ) + else: + return (pil2tensor(tensor2pil(images).convert("L")), ) + + +# IMAGE MERGE RGB CHANNELS + +class WAS_Image_RGB_Merge: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "red_channel": ("IMAGE",), + "green_channel": ("IMAGE",), + "blue_channel": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "merge_channels" + + CATEGORY = "WAS Suite/Image/Process" + + def merge_channels(self, red_channel, green_channel, blue_channel): + + # Apply mix rgb channels + image = self.mix_rgb_channels(tensor2pil(red_channel).convert('L'), tensor2pil( + green_channel).convert('L'), tensor2pil(blue_channel).convert('L')) + + return (pil2tensor(image), ) + + def mix_rgb_channels(self, red, green, blue): + # Create an empty image with the same size as the channels + width, height = red.size + merged_img = Image.new('RGB', (width, height)) + + # Merge the channels into the new image + merged_img = Image.merge('RGB', (red, green, blue)) + + return merged_img + +# IMAGE Ambient Occlusion + +class WAS_Image_Ambient_Occlusion: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "depth_images": ("IMAGE",), + "strength": ("FLOAT", {"min": 0.0, "max": 5.0, "default": 1.0, "step": 0.01}), + "radius": ("FLOAT", {"min": 0.01, "max": 1024, "default": 30, "step": 0.01}), + "ao_blur": ("FLOAT", {"min": 0.01, "max": 1024, "default": 2.5, "step": 0.01}), + "specular_threshold": ("INT", {"min":0, "max": 255, "default": 25, "step": 1}), + "enable_specular_masking": (["True", "False"],), + "tile_size": ("INT", {"min": 1, "max": 512, "default": 1, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE","IMAGE","IMAGE") + RETURN_NAMES = ("composited_images", "ssao_images", "specular_mask_images") + FUNCTION = "ambient_occlusion" + + CATEGORY = "WAS Suite/Image/Filter" + + def ambient_occlusion(self, images, depth_images, strength, radius, ao_blur, specular_threshold, enable_specular_masking, tile_size): + + enable_specular_masking = (enable_specular_masking == 'True') + composited = [] + occlusions = [] + speculars = [] + for i, image in enumerate(images): + cstr(f"Processing SSAO image {i+1}/{len(images)} ...").msg.print() + composited_image, occlusion_image, specular_mask = self.create_ambient_occlusion( + tensor2pil(image), + tensor2pil(depth_images[(i if len(depth_images) >= i else -1)]), + strength=strength, + radius=radius, + ao_blur=ao_blur, + spec_threshold=specular_threshold, + enable_specular_masking=enable_specular_masking, + tile_size=tile_size + ) + composited.append(pil2tensor(composited_image)) + occlusions.append(pil2tensor(occlusion_image)) + speculars.append(pil2tensor(specular_mask)) + + composited = torch.cat(composited, dim=0) + occlusions = torch.cat(occlusions, dim=0) + speculars = torch.cat(speculars, dim=0) + + return ( composited, occlusions, speculars ) + + def process_tile(self, tile_rgb, tile_depth, tile_x, tile_y, radius): + tile_occlusion = calculate_ambient_occlusion_factor(tile_rgb, tile_depth, tile_rgb.shape[0], tile_rgb.shape[1], radius) + return tile_x, tile_y, tile_occlusion + + + def create_ambient_occlusion(self, rgb_image, depth_image, strength=1.0, radius=30, ao_blur=5, spec_threshold=200, enable_specular_masking=False, tile_size=1): + + import concurrent.futures + + if depth_image.size != rgb_image.size: + depth_image = depth_image.resize(rgb_image.size) + rgb_normalized = np.array(rgb_image, dtype=np.float32) / 255.0 + depth_normalized = np.array(depth_image, dtype=np.float32) / 255.0 + + height, width, _ = rgb_normalized.shape + + if tile_size <= 1: + print("Processing single-threaded AO (highest quality) ...") + occlusion_array = calculate_ambient_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius) + else: + tile_size = ((tile_size if tile_size <= 8 else 8) if tile_size > 1 else 1) + num_tiles_x = (width - 1) // tile_size + 1 + num_tiles_y = (height - 1) // tile_size + 1 + + occlusion_array = np.zeros((height, width), dtype=np.uint8) + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [] + + with tqdm(total=num_tiles_y * num_tiles_x) as pbar: + for tile_y in range(num_tiles_y): + for tile_x in range(num_tiles_x): + tile_left = tile_x * tile_size + tile_upper = tile_y * tile_size + tile_right = min(tile_left + tile_size, width) + tile_lower = min(tile_upper + tile_size, height) + + tile_rgb = rgb_normalized[tile_upper:tile_lower, tile_left:tile_right] + tile_depth = depth_normalized[tile_upper:tile_lower, tile_left:tile_right] + + future = executor.submit(self.process_tile, tile_rgb, tile_depth, tile_x, tile_y, radius) + futures.append(future) + + for future in concurrent.futures.as_completed(futures): + tile_x, tile_y, tile_occlusion = future.result() + tile_left = tile_x * tile_size + tile_upper = tile_y * tile_size + tile_right = min(tile_left + tile_size, width) + tile_lower = min(tile_upper + tile_size, height) + + occlusion_array[tile_upper:tile_lower, tile_left:tile_right] = tile_occlusion + + pbar.update(1) + + occlusion_array = (occlusion_array * strength).clip(0, 255).astype(np.uint8) + + occlusion_image = Image.fromarray(occlusion_array, mode='L') + occlusion_image = occlusion_image.filter(ImageFilter.GaussianBlur(radius=ao_blur)) + occlusion_image = occlusion_image.filter(ImageFilter.SMOOTH) + occlusion_image = ImageChops.multiply(occlusion_image, ImageChops.multiply(occlusion_image, occlusion_image)) + + mask = rgb_image.convert('L') + mask = mask.point(lambda x: x > spec_threshold, mode='1') + mask = mask.convert("RGB") + mask = mask.filter(ImageFilter.GaussianBlur(radius=2.5)).convert("L") + + if enable_specular_masking: + occlusion_image = Image.composite(Image.new("L", rgb_image.size, 255), occlusion_image, mask) + occlsuion_result = ImageChops.multiply(rgb_image, occlusion_image.convert("RGB")) + + return occlsuion_result, occlusion_image, mask + +# IMAGE Direct Occlusion + +class WAS_Image_Direct_Occlusion: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "depth_images": ("IMAGE",), + "strength": ("FLOAT", {"min": 0.0, "max": 5.0, "default": 1.0, "step": 0.01}), + "radius": ("FLOAT", {"min": 0.01, "max": 1024, "default": 30, "step": 0.01}), + "specular_threshold": ("INT", {"min":0, "max": 255, "default": 128, "step": 1}), + "colored_occlusion": (["True", "False"],), + }, + } + + RETURN_TYPES = ("IMAGE","IMAGE","IMAGE", "IMAGE") + RETURN_NAMES = ("composited_images", "ssdo_images", "ssdo_image_masks", "light_source_image_masks") + FUNCTION = "direct_occlusion" + + CATEGORY = "WAS Suite/Image/Filter" + + def direct_occlusion(self, images, depth_images, strength, radius, specular_threshold, colored_occlusion): + + composited = [] + occlusions = [] + occlusion_masks = [] + light_sources = [] + for i, image in enumerate(images): + cstr(f"Processing SSDO image {i+1}/{len(images)} ...").msg.print() + composited_image, occlusion_image, occlusion_mask, light_source = self.create_direct_occlusion( + tensor2pil(image), + tensor2pil(depth_images[(i if len(depth_images) >= i else -1)]), + strength=strength, + radius=radius, + threshold=specular_threshold, + colored=True + ) + composited.append(pil2tensor(composited_image)) + occlusions.append(pil2tensor(occlusion_image)) + occlusion_masks.append(pil2tensor(occlusion_mask)) + light_sources.append(pil2tensor(light_source)) + + composited = torch.cat(composited, dim=0) + occlusions = torch.cat(occlusions, dim=0) + occlusion_masks = torch.cat(occlusion_masks, dim=0) + light_sources = torch.cat(light_sources, dim=0) + + return ( composited, occlusions, occlusion_masks, light_sources ) + + def find_light_source(self, rgb_normalized, threshold): + from skimage.measure import regionprops + from skimage import measure + rgb_uint8 = (rgb_normalized * 255).astype(np.uint8) + rgb_to_grey = Image.fromarray(rgb_uint8, mode="RGB") + dominant = self.dominant_region(rgb_to_grey, threshold) + grayscale_image = np.array(dominant.convert("L"), dtype=np.float32) / 255.0 + regions = measure.label(grayscale_image > 0) + + if np.max(regions) > 0: + region_sums = measure.regionprops(regions, intensity_image=grayscale_image) + brightest_region = max(region_sums, key=lambda r: r.mean_intensity) + light_y, light_x = brightest_region.centroid + light_mask = (regions == brightest_region.label).astype(np.uint8) + light_mask_cluster = light_mask + else: + light_x, light_y = np.nan, np.nan + light_mask_cluster = np.zeros_like(dominant, dtype=np.uint8) + return light_mask_cluster, light_x, light_y + + + def dominant_region(self, image, threshold=128): + from scipy.ndimage import label + image = ImageOps.invert(image.convert("L")) + binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1") + l, n = label(np.array(binary_image)) + sizes = np.bincount(l.flatten()) + dominant = 0 + try: + dominant = np.argmax(sizes[1:]) + 1 + except ValueError: + pass + dominant_region_mask = (l == dominant).astype(np.uint8) * 255 + result = Image.fromarray(dominant_region_mask, mode="L") + return result.convert("RGB") + + def create_direct_occlusion(self, rgb_image, depth_image, strength=1.0, radius=10, threshold=200, colored=False): + rgb_normalized = np.array(rgb_image, dtype=np.float32) / 255.0 + depth_normalized = np.array(depth_image, dtype=np.float32) / 255.0 + height, width, _ = rgb_normalized.shape + light_mask, light_x, light_y = self.find_light_source(rgb_normalized, threshold) + occlusion_array = calculate_direct_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius) + #occlusion_scaled = (occlusion_array / np.max(occlusion_array) * 255).astype(np.uint8) + occlusion_scaled = ((occlusion_array - np.min(occlusion_array)) / (np.max(occlusion_array) - np.min(occlusion_array)) * 255).astype(np.uint8) + occlusion_image = Image.fromarray(occlusion_scaled, mode="L") + occlusion_image = occlusion_image.filter(ImageFilter.GaussianBlur(radius=0.5)) + occlusion_image = occlusion_image.filter(ImageFilter.SMOOTH_MORE) + + if colored: + occlusion_result = Image.composite( + Image.new("RGB", rgb_image.size, (0, 0, 0)), + rgb_image, + occlusion_image + ) + occlusion_result = ImageOps.autocontrast(occlusion_result, cutoff=(0, strength)) + else: + occlusion_result = Image.blend(occlusion_image, occlusion_image, strength) + + light_image = ImageOps.invert(Image.fromarray(light_mask * 255, mode="L")) + + direct_occlusion_image = ImageChops.screen(rgb_image, occlusion_result.convert("RGB")) + + return direct_occlusion_image, occlusion_result, occlusion_image, light_image + +# EXPORT API + +class WAS_Export_API: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "save_prompt_api": (["true","true"],), + "output_path": ("STRING", {"default": "./ComfyUI/output/", "multiline": False}), + "filename_prefix": ("STRING", {"default": "ComfyUI_Prompt"}), + "filename_delimiter": ("STRING", {"default":"_"}), + "filename_number_padding": ("INT", {"default":4, "min":2, "max":9, "step":1}), + "parse_text_tokens": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "prompt": "PROMPT" + } + } + + OUTPUT_NODE = True + RETURN_TYPES = () + FUNCTION = "export_api" + + CATEGORY = "WAS Suite/Debug" + + def export_api(self, output_path=None, filename_prefix="ComfyUI", filename_number_padding=4, + filename_delimiter='_', prompt=None, save_prompt_api="true", parse_text_tokens=False): + delimiter = filename_delimiter + number_padding = filename_number_padding if filename_number_padding > 1 else 4 + + tokens = TextTokens() + + if output_path in [None, '', "none", "."]: + output_path = comfy_paths.output_directory + else: + output_path = tokens.parseTokens(output_path) + + pattern = f"{re.escape(filename_prefix)}{re.escape(filename_delimiter)}(\\d{{{number_padding}}})" + existing_counters = [ + int(re.search(pattern, filename).group(1)) + for filename in os.listdir(output_path) + if re.match(pattern, filename) + ] + existing_counters.sort(reverse=True) + + if existing_counters: + counter = existing_counters[0] + 1 + else: + counter = 1 + + file = f"{filename_prefix}{filename_delimiter}{counter:0{number_padding}}.json" + output_file = os.path.abspath(os.path.join(output_path, file)) + + if prompt: + + if parse_text_tokens: + prompt = self.parse_prompt(prompt, tokens, keys_to_parse) + + prompt_json = json.dumps(prompt, indent=4) + cstr("Prompt API JSON").msg.print() + print(prompt_json) + + if save_prompt_api == "true": + + with open(output_file, 'w') as f: + f.write(prompt_json) + + cstr(f"Output file path: {output_file}").msg.print() + + return {"ui": {"string": prompt_json}} + + def parse_prompt(self, obj, tokens, keys_to_parse): + if isinstance(obj, dict): + return { + key: self.parse_prompt(value, tokens, keys_to_parse) + if key in keys_to_parse else value + for key, value in obj.items() + } + elif isinstance(obj, list): + return [self.parse_prompt(element, tokens, keys_to_parse) for element in obj] + elif isinstance(obj, str): + return tokens.parseTokens(obj) + else: + return obj + + +# Image Save (NSP Compatible) +# Originally From ComfyUI/nodes.py + +class WAS_Image_Save: + def __init__(self): + self.output_dir = comfy_paths.output_directory + self.type = 'output' + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE", ), + "output_path": ("STRING", {"default": '[time(%Y-%m-%d)]', "multiline": False}), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "filename_delimiter": ("STRING", {"default":"_"}), + "filename_number_padding": ("INT", {"default":4, "min":1, "max":9, "step":1}), + "filename_number_start": (["false", "true"],), + "extension": (['png', 'jpg', 'jpeg', 'gif', 'tiff', 'webp', 'bmp'], ), + "dpi": ("INT", {"default": 300, "min": 1, "max": 2400, "step": 1}), + "quality": ("INT", {"default": 100, "min": 1, "max": 100, "step": 1}), + "optimize_image": (["true", "false"],), + "lossless_webp": (["false", "true"],), + "overwrite_mode": (["false", "prefix_as_filename"],), + "show_history": (["false", "true"],), + "show_history_by_prefix": (["true", "false"],), + "embed_workflow": (["true", "false"],), + "show_previews": (["true", "false"],), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = ("IMAGE", "STRING",) + RETURN_NAMES = ("images", "files",) + + FUNCTION = "was_save_images" + + OUTPUT_NODE = True + + CATEGORY = "WAS Suite/IO" + + def was_save_images(self, images, output_path='', filename_prefix="ComfyUI", filename_delimiter='_', + extension='png', dpi=96, quality=100, optimize_image="true", lossless_webp="false", prompt=None, extra_pnginfo=None, + overwrite_mode='false', filename_number_padding=4, filename_number_start='false', + show_history='false', show_history_by_prefix="true", embed_workflow="true", + show_previews="true"): + + delimiter = filename_delimiter + number_padding = filename_number_padding + lossless_webp = (lossless_webp == "true") + optimize_image = (optimize_image == "true") + + # Define token system + tokens = TextTokens() + + original_output = self.output_dir + # Parse prefix tokens + filename_prefix = tokens.parseTokens(filename_prefix) + + # Setup output path + if output_path in [None, '', "none", "."]: + output_path = self.output_dir + else: + output_path = tokens.parseTokens(output_path) + if not os.path.isabs(output_path): + output_path = os.path.join(self.output_dir, output_path) + base_output = os.path.basename(output_path) + if output_path.endswith("ComfyUI/output") or output_path.endswith("ComfyUI\output"): + base_output = "" + + # Check output destination + if output_path.strip() != '': + if not os.path.isabs(output_path): + output_path = os.path.join(comfy_paths.output_directory, output_path) + if not os.path.exists(output_path.strip()): + cstr(f'The path `{output_path.strip()}` specified doesn\'t exist! Creating directory.').warning.print() + os.makedirs(output_path, exist_ok=True) + + # Find existing counter values + if filename_number_start == 'true': + pattern = f"(\\d+){re.escape(delimiter)}{re.escape(filename_prefix)}" + else: + pattern = f"{re.escape(filename_prefix)}{re.escape(delimiter)}(\\d+)" + existing_counters = [ + int(re.search(pattern, filename).group(1)) + for filename in os.listdir(output_path) + if re.match(pattern, os.path.basename(filename)) + ] + existing_counters.sort(reverse=True) + + # Set initial counter value + if existing_counters: + counter = existing_counters[0] + 1 + else: + counter = 1 + + # Set initial counter value + if existing_counters: + counter = existing_counters[0] + 1 + else: + counter = 1 + + # Set Extension + file_extension = '.' + extension + if file_extension not in ALLOWED_EXT: + cstr(f"The extension `{extension}` is not valid. The valid formats are: {', '.join(sorted(ALLOWED_EXT))}").error.print() + file_extension = "png" + + results = list() + output_files = list() + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + + # Delegate metadata/pnginfo + if extension == 'webp': + img_exif = img.getexif() + if embed_workflow == 'true': + workflow_metadata = '' + prompt_str = '' + if prompt is not None: + prompt_str = json.dumps(prompt) + img_exif[0x010f] = "Prompt:" + prompt_str + if extra_pnginfo is not None: + for x in extra_pnginfo: + workflow_metadata += json.dumps(extra_pnginfo[x]) + img_exif[0x010e] = "Workflow:" + workflow_metadata + exif_data = img_exif.tobytes() + else: + metadata = PngInfo() + if embed_workflow == 'true': + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + exif_data = metadata + + # Delegate the filename stuffs + if overwrite_mode == 'prefix_as_filename': + file = f"{filename_prefix}{file_extension}" + else: + if filename_number_start == 'true': + file = f"{counter:0{number_padding}}{delimiter}{filename_prefix}{file_extension}" + else: + file = f"{filename_prefix}{delimiter}{counter:0{number_padding}}{file_extension}" + if os.path.exists(os.path.join(output_path, file)): + counter += 1 + + # Save the images + try: + output_file = os.path.abspath(os.path.join(output_path, file)) + if extension in ["jpg", "jpeg"]: + img.save(output_file, + quality=quality, optimize=optimize_image, dpi=(dpi, dpi)) + elif extension == 'webp': + img.save(output_file, + quality=quality, lossless=lossless_webp, exif=exif_data) + elif extension == 'png': + img.save(output_file, + pnginfo=exif_data, optimize=optimize_image) + elif extension == 'bmp': + img.save(output_file) + elif extension == 'tiff': + img.save(output_file, + quality=quality, optimize=optimize_image) + else: + img.save(output_file, + pnginfo=exif_data, optimize=optimize_image) + + cstr(f"Image file saved to: {output_file}").msg.print() + output_files.append(output_file) + + if show_history != 'true' and show_previews == 'true': + subfolder = self.get_subfolder_path(output_file, original_output) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + # Update the output image history + update_history_output_images(output_file) + + except OSError as e: + cstr(f'Unable to save file to: {output_file}').error.print() + print(e) + except Exception as e: + cstr('Unable to save file due to the to the following error:').error.print() + print(e) + + if overwrite_mode == 'false': + counter += 1 + + filtered_paths = [] + if show_history == 'true' and show_previews == 'true': + HDB = WASDatabase(WAS_HISTORY_DATABASE) + conf = getSuiteConfig() + if HDB.catExists("History") and HDB.keyExists("History", "Output_Images"): + history_paths = HDB.get("History", "Output_Images") + else: + history_paths = None + + if history_paths: + + for image_path in history_paths: + image_subdir = self.get_subfolder_path(image_path, self.output_dir) + current_subdir = self.get_subfolder_path(output_file, self.output_dir) + if not os.path.exists(image_path): + continue + if show_history_by_prefix == 'true' and image_subdir != current_subdir: + continue + if show_history_by_prefix == 'true' and not os.path.basename(image_path).startswith(filename_prefix): + continue + filtered_paths.append(image_path) + + if conf.__contains__('history_display_limit'): + filtered_paths = filtered_paths[-conf['history_display_limit']:] + + filtered_paths.reverse() + + if filtered_paths: + for image_path in filtered_paths: + subfolder = self.get_subfolder_path(image_path, self.output_dir) + image_data = { + "filename": os.path.basename(image_path), + "subfolder": subfolder, + "type": self.type + } + results.append(image_data) + + if show_previews == 'true': + return {"ui": {"images": results, "files": output_files}, "result": (images, output_files,)} + else: + return {"ui": {"images": []}, "result": (images, output_files,)} + + def get_subfolder_path(self, image_path, output_path): + output_parts = output_path.strip(os.sep).split(os.sep) + image_parts = image_path.strip(os.sep).split(os.sep) + common_parts = os.path.commonprefix([output_parts, image_parts]) + subfolder_parts = image_parts[len(common_parts):] + subfolder_path = os.sep.join(subfolder_parts[:-1]) + return subfolder_path + + +# Image Send HTTP +# Sends images over http +class WAS_Image_Send_HTTP: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "url": ("STRING", {"default": "example.com"}), + "method_type": (["post", "put", "patch"], {"default": "post"}), + "request_field_name": ("STRING", {"default": "image"}), + }, + "optional": { + "additional_request_headers": ("DICT",) + } + } + + RETURN_TYPES = ("INT", "STRING") + RETURN_NAMES = ("status_code", "result_text") + + FUNCTION = "was_send_images_http" + OUTPUT_NODE = True + + CATEGORY = "WAS Suite/IO" + + def was_send_images_http(self, images, url="example.com", + method_type="post", + request_field_name="image", + additional_request_headers=None): + from io import BytesIO + + images_to_send = [] + for idx, image in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + byte_io = BytesIO() + img.save(byte_io, 'png') + byte_io.seek(0) + images_to_send.append( + (request_field_name, (f"image_{idx}.png", byte_io, "image/png")) + ) + request = requests.Request(url=url, method=method_type.upper(), + headers=additional_request_headers, + files=images_to_send) + prepped = request.prepare() + session = requests.Session() + + response = session.send(prepped) + return (response.status_code, response.text,) + + +# LOAD IMAGE NODE +class WAS_Load_Image: + + def __init__(self): + self.input_dir = comfy_paths.input_directory + self.HDB = WASDatabase(WAS_HISTORY_DATABASE) + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_path": ("STRING", {"default": './ComfyUI/input/example.png', "multiline": False}), + "RGBA": (["false","true"],), + }, + "optional": { + "filename_text_extension": (["true", "false"],), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", TEXT_TYPE) + RETURN_NAMES = ("image", "mask", "filename_text") + FUNCTION = "load_image" + + CATEGORY = "WAS Suite/IO" + + def load_image(self, image_path, RGBA='false', filename_text_extension="true"): + + RGBA = (RGBA == 'true') + + if image_path.startswith('http'): + from io import BytesIO + i = self.download_image(image_path) + i = ImageOps.exif_transpose(i) + else: + try: + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + except OSError: + cstr(f"The image `{image_path.strip()}` specified doesn't exist!").error.print() + i = Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)) + if not i: + return + + # Update history + update_history_images(image_path) + + image = i + if not RGBA: + image = image.convert('RGB') + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + if filename_text_extension == "true": + filename = os.path.basename(image_path) + else: + filename = os.path.splitext(os.path.basename(image_path))[0] + + return (image, mask, filename) + + def download_image(self, url): + try: + response = requests.get(url) + response.raise_for_status() + img = Image.open(BytesIO(response.content)) + return img + except requests.exceptions.HTTPError as errh: + cstr(f"HTTP Error: ({url}): {errh}").error.print() + except requests.exceptions.ConnectionError as errc: + cstr(f"Connection Error: ({url}): {errc}").error.print() + except requests.exceptions.Timeout as errt: + cstr(f"Timeout Error: ({url}): {errt}").error.print() + except requests.exceptions.RequestException as err: + cstr(f"Request Exception: ({url}): {err}").error.print() + + @classmethod + def IS_CHANGED(cls, image_path): + if image_path.startswith('http'): + return float("NaN") + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + +# MASK BATCH TO MASK + +class WAS_Mask_Batch_to_Single_Mask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "batch_number": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}), + }, + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "mask_batch_to_mask" + + CATEGORY = "WAS Suite/Image/Masking" + + def mask_batch_to_mask(self, masks=[], batch_number=0): + count = 0 + for _ in masks: + if batch_number == count: + tensor = masks[batch_number][0] + return (tensor,) + count += 1 + + cstr(f"Batch number `{batch_number}` is not defined, returning last image").error.print() + last_tensor = masks[-1][0] + return (last_tensor,) + +# TENSOR BATCH TO IMAGE + +class WAS_Tensor_Batch_to_Image: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images_batch": ("IMAGE",), + "batch_image_number": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "tensor_batch_to_image" + + CATEGORY = "WAS Suite/Latent/Transform" + + def tensor_batch_to_image(self, images_batch=[], batch_image_number=0): + + count = 0 + for _ in images_batch: + if batch_image_number == count: + return (images_batch[batch_image_number].unsqueeze(0), ) + count = count+1 + + cstr(f"Batch number `{batch_image_number}` is not defined, returning last image").error.print() + return (images_batch[-1].unsqueeze(0), ) + + +#! LATENT NODES + +# IMAGE TO MASK + +class WAS_Image_To_Mask: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "channel": (["alpha", "red", "green", "blue"], ), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "image_to_mask" + + def image_to_mask(self, images, channel): + mask_images = [] + for image in images: + + image = tensor2pil(image).convert("RGBA") + r, g, b, a = image.split() + if channel == "red": + channel_image = r + elif channel == "green": + channel_image = g + elif channel == "blue": + channel_image = b + elif channel == "alpha": + channel_image = a + + mask = torch.from_numpy(np.array(channel_image.convert("L")).astype(np.float32) / 255.0) + mask_images.append(mask) + + return (torch.cat(mask_images, dim=0), ) + + +# MASK TO IMAGE + +class WAS_Mask_To_Image: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGES",) + + FUNCTION = "mask_to_image" + + def mask_to_image(self, masks): + if masks.ndim == 4: + # If input has shape [N, C, H, W] + tensor = masks.permute(0, 2, 3, 1) + tensor_rgb = torch.cat([tensor] * 3, dim=-1) + return (tensor_rgb,) + elif masks.ndim == 3: + # If Input has shape [N, H, W] + tensor = masks.unsqueeze(-1) + tensor_rgb = torch.cat([tensor] * 3, dim=-1) + return (tensor_rgb, ) + elif masks.ndim == 2: + # If input has shape [H, W] + tensor = masks.unsqueeze(0).unsqueeze(-1) + tensor_rgb = torch.cat([tensor] * 3, dim=-1) + return (tensor_rgb,) + else: + cstr("Invalid input shape. Expected [N, C, H, W] or [H, W].").error.print() + return masks + + +# MASK CROP DOMINANT REGION + +class WAS_Mask_Crop_Dominant_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "padding": ("INT", {"default": 24, "min": 0, "max": 4096, "step": 1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "crop_dominant_region" + + def crop_dominant_region(self, masks, padding=24): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask = self.WT.Masking.crop_dominant_region(mask_pil, padding) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask = self.WT.Masking.crop_dominant_region(mask_pil, padding) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK CROP MINORITY REGION + +class WAS_Mask_Crop_Minority_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "padding": ("INT", {"default": 24, "min": 0, "max": 4096, "step": 1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "crop_minority_region" + + def crop_minority_region(self, masks, padding=24): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask = self.WT.Masking.crop_minority_region(mask_pil, padding) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask = self.WT.Masking.crop_minority_region(mask_pil, padding) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK CROP REGION + +class WAS_Mask_Crop_Region: + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "padding": ("INT",{"default": 24, "min": 0, "max": 4096, "step": 1}), + "region_type": (["dominant", "minority"],), + } + } + + RETURN_TYPES = ("MASK", "CROP_DATA", "INT", "INT", "INT", "INT", "INT", "INT") + RETURN_NAMES = ("cropped_mask", "crop_data", "top_int", "left_int", "right_int", "bottom_int", "width_int", "height_int") + FUNCTION = "mask_crop_region" + + CATEGORY = "WAS Suite/Image/Masking" + + def mask_crop_region(self, mask, padding=24, region_type="dominant"): + + mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask, crop_data = self.WT.Masking.crop_region(mask_pil, region_type, padding) + region_tensor = pil2mask(ImageOps.invert(region_mask)).unsqueeze(0).unsqueeze(1) + + (width, height), (left, top, right, bottom) = crop_data + + return (region_tensor, crop_data, top, left, right, bottom, width, height) + + +# IMAGE PASTE CROP + +class WAS_Mask_Paste_Region: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "crop_mask": ("MASK",), + "crop_data": ("CROP_DATA",), + "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}), + } + } + + RETURN_TYPES = ("MASK", "MASK") + RETURN_NAMES = ("RESULT_MASK", "CROP_MASK") + FUNCTION = "mask_paste_region" + + CATEGORY = "WAS Suite/Image/Masking" + + def mask_paste_region(self, mask, crop_mask, crop_data=None, crop_blending=0.25, crop_sharpening=0): + + if crop_data == False: + cstr("No valid crop data found!").error.print() + return( pil2mask(Image.new("L", (512, 512), 0)).unsqueeze(0).unsqueeze(1), + pil2mask(Image.new("L", (512, 512), 0)).unsqueeze(0).unsqueeze(1) ) + + mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + mask_crop_pil = Image.fromarray(np.clip(255. * crop_mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + + result_mask, result_crop_mask = self.paste_image(mask_pil, mask_crop_pil, crop_data, crop_blending, crop_sharpening) + + return (pil2mask(result_mask).unsqueeze(0).unsqueeze(1), pil2mask(result_crop_mask).unsqueeze(0).unsqueeze(1)) + + def paste_image(self, image, crop_image, crop_data, blend_amount=0.25, sharpen_amount=1): + + def lingrad(size, direction, white_ratio): + image = Image.new('RGB', size) + draw = ImageDraw.Draw(image) + if direction == 'vertical': + black_end = int(size[1] * (1 - white_ratio)) + range_start = 0 + range_end = size[1] + range_step = 1 + for y in range(range_start, range_end, range_step): + color_ratio = y / size[1] + if y <= black_end: + color = (0, 0, 0) + else: + color_value = int(((y - black_end) / (size[1] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(0, y), (size[0], y)], fill=color) + elif direction == 'horizontal': + black_end = int(size[0] * (1 - white_ratio)) + range_start = 0 + range_end = size[0] + range_step = 1 + for x in range(range_start, range_end, range_step): + color_ratio = x / size[0] + if x <= black_end: + color = (0, 0, 0) + else: + color_value = int(((x - black_end) / (size[0] - black_end)) * 255) + color = (color_value, color_value, color_value) + draw.line([(x, 0), (x, size[1])], fill=color) + + return image.convert("L") + + crop_size, (left, top, right, bottom) = crop_data + crop_image = crop_image.resize(crop_size) + + if sharpen_amount > 0: + for _ in range(int(sharpen_amount)): + crop_image = crop_image.filter(ImageFilter.SHARPEN) + + blended_image = Image.new('RGBA', image.size, (0, 0, 0, 255)) + blended_mask = Image.new('L', image.size, 0) # Update to 'L' mode for MASK image + crop_padded = Image.new('RGBA', image.size, (0, 0, 0, 0)) + blended_image.paste(image, (0, 0)) + crop_padded.paste(crop_image, (left, top)) + crop_mask = Image.new('L', crop_image.size, 0) + + if top > 0: + gradient_image = ImageOps.flip(lingrad(crop_image.size, 'vertical', blend_amount)) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if left > 0: + gradient_image = ImageOps.mirror(lingrad(crop_image.size, 'horizontal', blend_amount)) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if right < image.width: + gradient_image = lingrad(crop_image.size, 'horizontal', blend_amount) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + if bottom < image.height: + gradient_image = lingrad(crop_image.size, 'vertical', blend_amount) + crop_mask = ImageChops.screen(crop_mask, gradient_image) + + crop_mask = ImageOps.invert(crop_mask) + blended_mask.paste(crop_mask, (left, top)) + blended_mask = blended_mask.convert("L") + blended_image.paste(crop_padded, (0, 0), blended_mask) + + return (ImageOps.invert(blended_image.convert("RGB")).convert("L"), ImageOps.invert(blended_mask.convert("RGB")).convert("L")) + + + + +# MASK DOMINANT REGION + +class WAS_Mask_Dominant_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "threshold": ("INT", {"default":128, "min":0, "max":255, "step":1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "dominant_region" + + def dominant_region(self, masks, threshold=128): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask = self.WT.Masking.dominant_region(mask_pil, threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + region_mask = self.WT.Masking.dominant_region(mask_pil, threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK MINORITY REGION + +class WAS_Mask_Minority_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "threshold": ("INT", {"default":128, "min":0, "max":255, "step":1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "minority_region" + + def minority_region(self, masks, threshold=128): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.minority_region(pil_image, threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.minority_region(pil_image, threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK RECT AREA + +class WAS_Mask_Rect_Area: + # Creates a rectangle mask using percentage. + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "x": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), + "width": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), + "height": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), + "blur_radius": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"} + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "rect_mask" + + def rect_mask(self, extra_pnginfo, unique_id, **kwargs): + # Get node values + min_x = kwargs["x"] / 100 + min_y = kwargs["y"] / 100 + width = kwargs["width"] / 100 + height = kwargs["height"] / 100 + blur_radius = kwargs["blur_radius"] + + # Create a mask with standard resolution (e.g., 512x512) + resolution = 512 + mask = torch.zeros((resolution, resolution)) + + # Calculate pixel coordinates + min_x_px = int(min_x * resolution) + min_y_px = int(min_y * resolution) + max_x_px = int((min_x + width) * resolution) + max_y_px = int((min_y + height) * resolution) + + # Draw the rectangle on the mask + mask[min_y_px:max_y_px, min_x_px:max_x_px] = 1 + + # Apply blur if the radii are greater than 0 + if blur_radius > 0: + dx = blur_radius * 2 + 1 + dy = blur_radius * 2 + 1 + + # Convert the mask to a format compatible with OpenCV (numpy array) + mask_np = mask.cpu().numpy().astype("float32") + + # Apply Gaussian Blur + blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0) + + # Convert back to tensor + mask = torch.from_numpy(blurred_mask) + + # Return the mask as a tensor with an additional channel + return (mask.unsqueeze(0),) + + +# MASK RECT AREA ADVANCED + +class WAS_Mask_Rect_Area_Advanced: + # Creates a rectangle mask using pixels relative to image size. + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}), + "y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}), + "width": ("INT", {"default": 256, "min": 0, "max": 4096, "step": 64}), + "height": ("INT", {"default": 256, "min": 0, "max": 4096, "step": 64}), + "image_width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "image_height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "blur_radius": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"} + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "rect_mask_adv" + + def rect_mask_adv(self, extra_pnginfo, unique_id, **kwargs): + # Get node values + min_x = kwargs["x"] + min_y = kwargs["y"] + width = kwargs["width"] + height = kwargs["height"] + image_width = kwargs["image_width"] + image_height = kwargs["image_height"] + blur_radius = kwargs["blur_radius"] + + # Calculate maximum coordinates + max_x = min_x + width + max_y = min_y + height + + # Create a mask with the image dimensions + mask = torch.zeros((image_height, image_width)) + + # Draw the rectangle on the mask + mask[int(min_y):int(max_y), int(min_x):int(max_x)] = 1 + + # Apply blur if the radii are greater than 0 + if blur_radius > 0: + dx = blur_radius * 2 + 1 + dy = blur_radius * 2 + 1 + + # Convert the mask to a format compatible with OpenCV (numpy array) + mask_np = mask.cpu().numpy().astype("float32") + + # Apply Gaussian Blur + blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0) + + # Convert back to tensor + mask = torch.from_numpy(blurred_mask) + + # Return the mask as a tensor with an additional channel + return (mask.unsqueeze(0),) + + +# MASK ARBITRARY REGION + +class WAS_Mask_Arbitrary_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "size": ("INT", {"default":256, "min":1, "max":4096, "step":1}), + "threshold": ("INT", {"default":128, "min":0, "max":255, "step":1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "arbitrary_region" + + def arbitrary_region(self, masks, size=256, threshold=128): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.arbitrary_region(pil_image, size, threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.arbitrary_region(pil_image, size, threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + +# MASK SMOOTH REGION + +class WAS_Mask_Smooth_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "sigma": ("FLOAT", {"default":5.0, "min":0.0, "max":128.0, "step":0.1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "smooth_region" + + def smooth_region(self, masks, sigma=128): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.smooth_region(pil_image, sigma) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.smooth_region(pil_image, sigma) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK ERODE REGION + +class WAS_Mask_Erode_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "iterations": ("INT", {"default":5, "min":1, "max":64, "step":1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "erode_region" + + def erode_region(self, masks, iterations=5): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.erode_region(pil_image, iterations) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.erode_region(pil_image, iterations) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + +# MASKS SUBTRACT + +class WAS_Mask_Subtract: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks_a": ("MASK",), + "masks_b": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "subtract_masks" + + def subtract_masks(self, masks_a, masks_b): + subtracted_masks = torch.clamp(masks_a - masks_b, 0, 255) + return (subtracted_masks,) + +# MASKS ADD + +class WAS_Mask_Add: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks_a": ("MASK",), + "masks_b": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "add_masks" + + def add_masks(self, masks_a, masks_b): + if masks_a.ndim > 2 and masks_b.ndim > 2: + added_masks = masks_a + masks_b + else: + added_masks = torch.clamp(masks_a.unsqueeze(1) + masks_b.unsqueeze(1), 0, 255) + added_masks = added_masks.squeeze(1) + return (added_masks,) + +# MASKS ADD + +class WAS_Mask_Invert: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "add_masks" + + def add_masks(self, masks): + return (1. - masks,) + +# MASK DILATE REGION + +class WAS_Mask_Dilate_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "iterations": ("INT", {"default":5, "min":1, "max":64, "step":1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "dilate_region" + + def dilate_region(self, masks, iterations=5): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.dilate_region(pil_image, iterations) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.dilate_region(pil_image, iterations) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK FILL REGION + +class WAS_Mask_Fill_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "fill_region" + + def fill_region(self, masks): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.fill_region(pil_image) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.fill_region(pil_image) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK THRESHOLD + +class WAS_Mask_Threshold_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "black_threshold": ("INT",{"default":75, "min":0, "max": 255, "step": 1}), + "white_threshold": ("INT",{"default":175, "min":0, "max": 255, "step": 1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "threshold_region" + + def threshold_region(self, masks, black_threshold=75, white_threshold=255): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.threshold_region(pil_image, black_threshold, white_threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.threshold_region(pil_image, black_threshold, white_threshold) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK FLOOR REGION + +class WAS_Mask_Floor_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "floor_region" + + def floor_region(self, masks): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.floor_region(pil_image) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.floor_region(pil_image) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK CEILING REGION + +class WAS_Mask_Ceiling_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "ceiling_region" + + def ceiling_region(self, masks): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.ceiling_region(pil_image) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.ceiling_region(pil_image) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK GAUSSIAN REGION + +class WAS_Mask_Gaussian_Region: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + "radius": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1024, "step": 0.1}), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("MASKS",) + + FUNCTION = "gaussian_region" + + def gaussian_region(self, masks, radius=5.0): + if masks.ndim > 3: + regions = [] + for mask in masks: + mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.gaussian_region(pil_image, radius) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + regions.append(region_tensor) + regions_tensor = torch.cat(regions, dim=0) + return (regions_tensor,) + else: + mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + pil_image = Image.fromarray(mask_np, mode="L") + region_mask = self.WT.Masking.gaussian_region(pil_image, radius) + region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1) + return (region_tensor,) + + +# MASK COMBINE + +class WAS_Mask_Combine: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask_a": ("MASK",), + "mask_b": ("MASK",), + }, + "optional": { + "mask_c": ("MASK",), + "mask_d": ("MASK",), + "mask_e": ("MASK",), + "mask_f": ("MASK",), + } + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "combine_masks" + + def combine_masks(self, mask_a, mask_b, mask_c=None, mask_d=None, mask_e=None, mask_f=None): + # Gather all masks in a list + masks = [m for m in [mask_a, mask_b, mask_c, mask_d, mask_e, mask_f] if m is not None] + + # Skip any masks that are the known "empty" shape [1, 64, 64] from "Preview" etc + # (You can also use a sum-of-pixels check, or other logic.) + valid_masks = [m for m in masks if m.shape != (1, 64, 64)] + # cstr(f"mask shapes: ... `{valid_masks}`").msg.print() + + # If no valid masks, decide on a fallback + if len(valid_masks) == 0: + # Could return a zeroed-out mask, or just return mask_a, or raise a warning + # Return mask_a so we don't break the graph + return (mask_a, ) + + # If there is exactly one valid mask, no combine needed + if len(valid_masks) == 1: + return (valid_masks[0], ) + + # Otherwise stack, sum, clamp + combined_mask = torch.sum(torch.stack(valid_masks, dim=0), dim=0) + combined_mask = torch.clamp(combined_mask, 0, 1) # Keep values in 0..1 + + return (combined_mask,) + +class WAS_Mask_Combine_Batch: + + def __init__(self): + self.WT = WAS_Tools_Class() + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + }, + } + + CATEGORY = "WAS Suite/Image/Masking" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "combine_masks" + + def combine_masks(self, masks): + combined_mask = torch.sum(torch.stack([mask.unsqueeze(0) for mask in masks], dim=0), dim=0) + combined_mask = torch.clamp(combined_mask, 0, 1) # Ensure values are between 0 and 1 + return (combined_mask, ) + +# LATENT UPSCALE NODE + +class WAS_Latent_Upscale: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return {"required": {"samples": ("LATENT",), "mode": (["area", "bicubic", "bilinear", "nearest"],), + "factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.01}), + "align": (["true", "false"], )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "latent_upscale" + + CATEGORY = "WAS Suite/Latent/Transform" + + def latent_upscale(self, samples, mode, factor, align): + valid_modes = ["area", "bicubic", "bilinear", "nearest"] + if mode not in valid_modes: + cstr(f"Invalid interpolation mode `{mode}` selected. Valid modes are: {', '.join(valid_modes)}").error.print() + return (s, ) + align = True if align == 'true' else False + if not isinstance(factor, float) or factor <= 0: + cstr(f"The input `factor` is `{factor}`, but should be a positive or negative float.").error.print() + return (s, ) + s = samples.copy() + shape = s['samples'].shape + size = tuple(int(round(dim * factor)) for dim in shape[-2:]) + if mode in ['linear', 'bilinear', 'bicubic', 'trilinear']: + s["samples"] = torch.nn.functional.interpolate( + s['samples'], size=size, mode=mode, align_corners=align) + else: + s["samples"] = torch.nn.functional.interpolate(s['samples'], size=size, mode=mode) + return (s,) + +# LATENT NOISE INJECTION NODE + + +class WAS_Latent_Noise: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "samples": ("LATENT",), + "noise_std": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "inject_noise" + + CATEGORY = "WAS Suite/Latent/Generate" + + def inject_noise(self, samples, noise_std): + s = samples.copy() + noise = torch.randn_like(s["samples"]) * noise_std + s["samples"] = s["samples"] + noise + return (s,) + + + +# MIDAS DEPTH APPROXIMATION NODE + +class MiDaS_Model_Loader: + def __init__(self): + self.midas_dir = os.path.join(MODELS_DIR, 'midas') + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "midas_model": (["DPT_Large", "DPT_Hybrid"],), + }, + } + + RETURN_TYPES = ("MIDAS_MODEL",) + RETURN_NAMES = ("midas_model",) + FUNCTION = "load_midas_model" + + CATEGORY = "WAS Suite/Loaders" + + def load_midas_model(self, midas_model): + + global MIDAS_INSTALLED + + if not MIDAS_INSTALLED: + self.install_midas() + + if midas_model == 'DPT_Large': + model_name = 'dpt_large_384.pt' + elif midas_model == 'DPT_Hybrid': + model_name = 'dpt_hybrid_384.pt' + else: + model_name = 'dpt_large_384.pt' + + model_path = os.path.join(self.midas_dir, 'checkpoints'+os.sep+model_name) + + torch.hub.set_dir(self.midas_dir) + if os.path.exists(model_path): + cstr(f"Loading MiDaS Model from `{model_path}`").msg.print() + midas_type = model_path + else: + cstr("Downloading and loading MiDaS Model...").msg.print() + midas = torch.hub.load("intel-isl/MiDaS", midas_model, trust_repo=True) + device = torch.device("cpu") + + cstr(f"MiDaS is using passive device `{device}` until in use.").msg.print() + + midas.to(device) + midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") + transform = midas_transforms.dpt_transform + + return ( (midas, transform), ) + + def install_midas(self): + global MIDAS_INSTALLED + if 'timm' not in packages(): + install_package("timm") + MIDAS_INSTALLED = True + + +# MIDAS DEPTH APPROXIMATION NODE + +class MiDaS_Depth_Approx: + def __init__(self): + self.midas_dir = os.path.join(MODELS_DIR, 'midas') + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "use_cpu": (["false", "true"],), + "midas_type": (["DPT_Large", "DPT_Hybrid"],), + "invert_depth": (["false", "true"],), + }, + "optional": { + "midas_model": ("MIDAS_MODEL",), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "midas_approx" + + CATEGORY = "WAS Suite/Image/AI" + + def midas_approx(self, image, use_cpu, midas_type, invert_depth, midas_model=None): + + global MIDAS_INSTALLED + + if not MIDAS_INSTALLED: + self.install_midas() + + import cv2 as cv + + if midas_model: + + midas = midas_model[0] + transform = midas_model[1] + device = torch.device("cuda") if torch.cuda.is_available() and use_cpu == 'false' else torch.device("cpu") + cstr(f"MiDaS is using device: {device}").msg.print() + midas.to(device).eval() + + else: + + if midas_model == 'DPT_Large': + model_name = 'dpt_large_384.pt' + elif midas_model == 'DPT_Hybrid': + model_name = 'dpt_hybrid_384.pt' + else: + model_name = 'dpt_large_384.pt' + + model_path = os.path.join(self.midas_dir, 'checkpoints'+os.sep+model_name) + + torch.hub.set_dir(self.midas_dir) + if os.path.exists(model_path): + cstr(f"Loading MiDaS Model from `{model_path}`").msg.print() + midas_type = model_path + else: + cstr("Downloading and loading MiDaS Model...").msg.print() + midas = torch.hub.load("intel-isl/MiDaS", midas_type, trust_repo=True) + + cstr(f"MiDaS is using device: {device}").msg.print() + + midas.to(device).eval() + midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") + + transform = midas_transforms.dpt_transform + + tensor_images = [] + for i, img in enumerate(image): + + img = np.array(tensor2pil(img)) + + img = cv.cvtColor(img, cv.COLOR_BGR2RGB) + input_batch = transform(img).to(device) + + cstr(f"Approximating depth for image {i+1}/{len(image)}").msg.print() + + with torch.no_grad(): + prediction = midas(input_batch) + prediction = torch.nn.functional.interpolate( + prediction.unsqueeze(1), + size=img.shape[:2], + mode="bicubic", + align_corners=False, + ).squeeze() + + + # Normalize and convert to uint8 + min_val = torch.min(prediction) + max_val = torch.max(prediction) + prediction = (prediction - min_val) / (max_val - min_val) + prediction = (prediction * 255).clamp(0, 255).round().cpu().numpy().astype(np.uint8) + + depth = Image.fromarray(prediction) + + # Invert depth map + if invert_depth == 'true': + depth = ImageOps.invert(depth) + + tensor_images.append(pil2tensor(depth.convert("RGB"))) + + tensor_images = torch.cat(tensor_images, dim=0) + if not midas_model: + del midas, device, midas_transforms + del midas, transform, img, input_batch, prediction + + return (tensor_images, ) + + def install_midas(self): + global MIDAS_INSTALLED + if 'timm' not in packages(): + install_package("timm") + MIDAS_INSTALLED = True + +# MIDAS REMOVE BACKGROUND/FOREGROUND NODE + + +class MiDaS_Background_Foreground_Removal: + def __init__(self): + self.midas_dir = os.path.join(MODELS_DIR, 'midas') + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "use_cpu": (["false", "true"],), + "midas_model": (["DPT_Large", "DPT_Hybrid", "DPT_Small"],), + "remove": (["background", "foregroud"],), + "threshold": (["false", "true"],), + "threshold_low": ("FLOAT", {"default": 10, "min": 0, "max": 255, "step": 1}), + "threshold_mid": ("FLOAT", {"default": 200, "min": 0, "max": 255, "step": 1}), + "threshold_high": ("FLOAT", {"default": 210, "min": 0, "max": 255, "step": 1}), + "smoothing": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 16.0, "step": 0.01}), + "background_red": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}), + "background_green": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}), + "background_blue": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("RESULT", "DEPTH") + FUNCTION = "midas_remove" + + CATEGORY = "WAS Suite/Image/AI" + + def midas_remove(self, + image, + midas_model, + use_cpu='false', + remove='background', + threshold='false', + threshold_low=0, + threshold_mid=127, + threshold_high=255, + smoothing=0.25, + background_red=0, + background_green=0, + background_blue=0): + + global MIDAS_INSTALLED + + if not MIDAS_INSTALLED: + self.install_midas() + + import cv2 as cv + + # Convert the input image tensor to a numpy and PIL Image + i = 255. * image.cpu().numpy().squeeze() + img = i + # Original image + img_original = tensor2pil(image).convert('RGB') + + cstr("Downloading and loading MiDaS Model...").msg.print() + torch.hub.set_dir(self.midas_dir) + midas = torch.hub.load("intel-isl/MiDaS", midas_model, trust_repo=True) + device = torch.device("cuda") if torch.cuda.is_available( + ) and use_cpu == 'false' else torch.device("cpu") + + cstr(f"MiDaS is using device: {device}").msg.print() + + midas.to(device).eval() + midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") + + if midas_model == "DPT_Large" or midas_model == "DPT_Hybrid": + transform = midas_transforms.dpt_transform + else: + transform = midas_transforms.small_transform + + img = cv.cvtColor(img, cv.COLOR_BGR2RGB) + input_batch = transform(img).to(device) + + cstr("Approximating depth from image.").msg.print() + + with torch.no_grad(): + prediction = midas(input_batch) + prediction = torch.nn.functional.interpolate( + prediction.unsqueeze(1), + size=img.shape[:2], + mode="bicubic", + align_corners=False, + ).squeeze() + + # Invert depth map + if remove == 'foreground': + depth = (255 - prediction.cpu().numpy().astype(np.uint8)) + depth = depth.astype(np.float32) + else: + depth = prediction.cpu().numpy().astype(np.float32) + depth = depth * 255 / (np.max(depth)) / 255 + depth = Image.fromarray(np.uint8(depth * 255)) + + # Threshold depth mask + if threshold == 'true': + levels = self.AdjustLevels( + threshold_low, threshold_mid, threshold_high) + depth = levels.adjust(depth.convert('RGB')).convert('L') + if smoothing > 0: + depth = depth.filter(ImageFilter.GaussianBlur(radius=smoothing)) + depth = depth.resize(img_original.size).convert('L') + + # Validate background color arguments + background_red = int(background_red) if isinstance( + background_red, (int, float)) else 0 + background_green = int(background_green) if isinstance( + background_green, (int, float)) else 0 + background_blue = int(background_blue) if isinstance( + background_blue, (int, float)) else 0 + + # Create background color tuple + background_color = (background_red, background_green, background_blue) + + # Create background image + background = Image.new( + mode="RGB", size=img_original.size, color=background_color) + + # Composite final image + result_img = Image.composite(img_original, background, depth) + + del midas, device, midas_transforms + del transform, img, img_original, input_batch, prediction + + return (pil2tensor(result_img), pil2tensor(depth.convert('RGB'))) + + class AdjustLevels: + def __init__(self, min_level, mid_level, max_level): + self.min_level = min_level + self.mid_level = mid_level + self.max_level = max_level + + def adjust(self, im): + # load the image + + # convert the image to a numpy array + im_arr = np.array(im) + + # apply the min level adjustment + im_arr[im_arr < self.min_level] = self.min_level + + # apply the mid level adjustment + im_arr = (im_arr - self.min_level) * \ + (255 / (self.max_level - self.min_level)) + im_arr[im_arr < 0] = 0 + im_arr[im_arr > 255] = 255 + im_arr = im_arr.astype(np.uint8) + + # apply the max level adjustment + im = Image.fromarray(im_arr) + im = ImageOps.autocontrast(im, cutoff=self.max_level) + + return im + + def install_midas(self): + global MIDAS_INSTALLED + if 'timm' not in packages(): + install_package("timm") + MIDAS_INSTALLED = True + + +#! CONDITIONING NODES + + +# NSP CLIPTextEncode NODE + +class WAS_NSP_CLIPTextEncoder: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mode": (["Noodle Soup Prompts", "Wildcards"],), + "noodle_key": ("STRING", {"default": '__', "multiline": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "text": ("STRING", {"multiline": True}), + "clip": ("CLIP",), + } + } + + OUTPUT_NODE = True + RETURN_TYPES = ("CONDITIONING", TEXT_TYPE, TEXT_TYPE) + RETURN_NAMES = ("conditioning", "parsed_text", "raw_text") + FUNCTION = "nsp_encode" + + CATEGORY = "WAS Suite/Conditioning" + + def nsp_encode(self, clip, text, mode="Noodle Soup Prompts", noodle_key='__', seed=0): + + if mode == "Noodle Soup Prompts": + new_text = nsp_parse(text, seed, noodle_key) + else: + new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key) + + new_text = parse_dynamic_prompt(new_text, seed) + new_text, text_vars = parse_prompt_vars(new_text) + cstr(f"CLIPTextEncode Prased Prompt:\n {new_text}").msg.print() + CLIPTextEncode = nodes.CLIPTextEncode() + encoded = CLIPTextEncode.encode(clip=clip, text=new_text) + + return (encoded[0], new_text, text, { "ui": { "string": new_text } }) + + +#! SAMPLING NODES + +# KSAMPLER + +class WAS_KSampler: + @classmethod + def INPUT_TYPES(cls): + return {"required": + + {"model": ("MODEL", ), + "seed": ("SEED", ), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "WAS Suite/Sampling" + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): + return nodes.common_ksampler(model, seed['seed'], steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) + +# KSampler Cycle + +class WAS_KSampler_Cycle: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "tiled_vae": (["disable", "enable"], ), + "latent_upscale": (["disable","nearest-exact", "bilinear", "area", "bicubic", "bislerp"],), + "upscale_factor": ("FLOAT", {"default":2.0, "min": 0.1, "max": 8.0, "step": 0.1}), + "upscale_cycles": ("INT", {"default": 2, "min": 2, "max": 12, "step": 1}), + "starting_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "cycle_denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "scale_denoise": (["enable", "disable"],), + "scale_sampling": (["bilinear", "bicubic", "nearest", "lanczos"],), + "vae": ("VAE",), + }, + "optional": { + "secondary_model": ("MODEL",), + "secondary_start_cycle": ("INT", {"default": 2, "min": 2, "max": 16, "step": 1}), + "upscale_model": ("UPSCALE_MODEL",), + "processor_model": ("UPSCALE_MODEL",), + "pos_additive": ("CONDITIONING",), + "neg_additive": ("CONDITIONING",), + "pos_add_mode": (["increment", "decrement"],), + "pos_add_strength": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}), + "pos_add_strength_scaling": (["enable", "disable"],), + "pos_add_strength_cutoff": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 10.0, "step": 0.01}), + "neg_add_mode": (["increment", "decrement"],), + "neg_add_strength": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}), + "neg_add_strength_scaling": (["enable", "disable"],), + "neg_add_strength_cutoff": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 10.0, "step": 0.01}), + "sharpen_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "sharpen_radius": ("INT", {"default": 2, "min": 1, "max": 12, "step": 1}), + "steps_scaling": (["enable", "disable"],), + "steps_control": (["decrement", "increment"],), + "steps_scaling_value": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}), + "steps_cutoff": ("INT", {"default": 20, "min": 4, "max": 1000, "step": 1}), + "denoise_cutoff": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent(s)",) + FUNCTION = "sample" + + CATEGORY = "WAS Suite/Sampling" + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, tiled_vae, latent_upscale, upscale_factor, + upscale_cycles, starting_denoise, cycle_denoise, scale_denoise, scale_sampling, vae, secondary_model=None, secondary_start_cycle=None, + pos_additive=None, pos_add_mode=None, pos_add_strength=None, pos_add_strength_scaling=None, pos_add_strength_cutoff=None, + neg_additive=None, neg_add_mode=None, neg_add_strength=None, neg_add_strength_scaling=None, neg_add_strength_cutoff=None, + upscale_model=None, processor_model=None, sharpen_strength=0, sharpen_radius=2, steps_scaling=None, steps_control=None, + steps_scaling_value=None, steps_cutoff=None, denoise_cutoff=0.25): + + upscale_steps = upscale_cycles + division_factor = upscale_steps if steps >= upscale_steps else steps + current_upscale_factor = upscale_factor ** (1 / (division_factor - 1)) + tiled_vae = (tiled_vae == "enable") + scale_denoise = (scale_denoise == "enable") + pos_add_strength_scaling = (pos_add_strength_scaling == "enable") + neg_add_strength_scaling = (neg_add_strength_scaling == "enable") + steps_scaling = (steps_scaling == "enable") + run_model = model + secondary_switched = False + + for i in range(division_factor): + + cstr(f"Cycle Pass {i+1}/{division_factor}").msg.print() + + if scale_denoise: + denoise = ( + ( round(cycle_denoise * (2 ** (-(i-1))), 2) if i > 0 else cycle_denoise ) + if i > 0 else round(starting_denoise, 2) + ) + else: + denoise = round((cycle_denoise if i > 0 else starting_denoise), 2) + + if denoise < denoise_cutoff and scale_denoise: + denoise = denoise_cutoff + + if i >= (secondary_start_cycle - 1) and secondary_model and not secondary_switched: + run_model = secondary_model + denoise = cycle_denoise + model = None + secondary_switched = True + + if steps_scaling and i > 0: + + steps = ( + steps + steps_scaling_value + if steps_control == 'increment' + else steps - steps_scaling_value + ) + steps = ( + ( steps + if steps <= steps_cutoff + else steps_cutoff ) + if steps_control == 'increment' + else ( steps + if steps >= steps_cutoff + else steps_cutoff ) + ) + + print("Steps:", steps) + print("Denoise:", denoise) + + if pos_additive: + + pos_strength = 0.0 if i <= 0 else pos_add_strength + + if pos_add_mode == 'increment': + pos_strength = ( + ( round(pos_add_strength * (2 ** (i-1)), 2) + if i > 0 + else pos_add_strength ) + if pos_add_strength_scaling + else pos_add_strength + ) + pos_strength = ( + pos_add_strength_cutoff + if pos_strength > pos_add_strength_cutoff + else pos_strength + ) + else: + pos_strength = ( + ( round(pos_add_strength / (2 ** (i-1)), 2) + if i > 0 + else pos_add_strength ) + if pos_add_strength_scaling + else pos_add_strength + ) + pos_strength = ( + pos_add_strength_cutoff + if pos_strength < pos_add_strength_cutoff + else pos_strength + ) + comb = nodes.ConditioningAverage() + positive = comb.addWeighted(pos_additive, positive, pos_strength)[0] + print("Positive Additive Strength:", pos_strength) + + if neg_additive: + + neg_strength = 0.0 if i <= 0 else pos_add_strength + + if neg_add_mode == 'increment': + neg_strength = ( + ( round(neg_add_strength * (2 ** (i-1)), 2) + if i > 0 + else neg_add_strength ) + if neg_add_strength_scaling + else neg_add_strength + ) + neg_strength = ( + neg_add_strength_cutoff + if neg_strength > neg_add_strength_cutoff + else neg_strength + ) + else: + neg_strength = ( + ( round(neg_add_strength / (2 ** (i-1)), 2) + if i > 0 + else neg_add_strength ) + if neg_add_strength_scaling + else neg_add_strength + ) + neg_strength = ( + neg_add_strength_cutoff + if neg_strength < neg_add_strength_cutoff + else neg_strength + ) + + comb = nodes.ConditioningAverage() + negative = comb.addWeighted(neg_additive, negative, neg_strength)[0] + print("Negative Additive Strength:", neg_strength) + + if i != 0: + latent_image = latent_image_result + + samples = nodes.common_ksampler( + run_model, + seed, + steps, + cfg, + sampler_name, + scheduler, + positive, + negative, + latent_image, + denoise=denoise, + ) + + # Upscale + if i < division_factor - 1: + + tensors = None + upscaler = None + + resample_filters = { + 'nearest': 0, + 'bilinear': 2, + 'bicubic': 3, + 'lanczos': 1 + } + + if latent_upscale == 'disable': + + if tiled_vae: + tensors = vae.decode_tiled(samples[0]['samples']) + else: + tensors = vae.decode(samples[0]['samples']) + + if processor_model or upscale_model: + + from comfy_extras import nodes_upscale_model + upscaler = nodes_upscale_model.ImageUpscaleWithModel() + + if processor_model: + + original_size = tensor2pil(tensors[0]).size + upscaled_tensors = upscaler.upscale(processor_model, tensors) + tensor_images = [] + for tensor in upscaled_tensors[0]: + pil = tensor2pil(tensor) + if pil.size[0] != original_size[0] or pil.size[1] != original_size[1]: + pil = pil.resize((original_size[0], original_size[1]), Image.Resampling(resample_filters[scale_sampling])) + if sharpen_strength != 0.0: + pil = self.unsharp_filter(pil, sharpen_radius, sharpen_strength) + tensor_images.append(pil2tensor(pil)) + + tensor_images = torch.cat(tensor_images, dim=0) + + if upscale_model: + + if processor_model: + tensors = tensor_images + del tensor_images + + original_size = tensor2pil(tensors[0]).size + new_width = round(original_size[0] * current_upscale_factor) + new_height = round(original_size[1] * current_upscale_factor) + new_width = int(round(new_width / 32) * 32) + new_height = int(round(new_height / 32) * 32) + upscaled_tensors = upscaler.upscale(upscale_model, tensors) + tensor_images = [] + for tensor in upscaled_tensors[0]: + tensor = pil2tensor(tensor2pil(tensor).resize((new_width, new_height), Image.Resampling(resample_filters[scale_sampling]))) + size = max(tensor2pil(tensor).size) + if sharpen_strength != 0.0: + tensor = pil2tensor(self.unsharp_filter(tensor2pil(tensor), sharpen_radius, sharpen_strength)) + tensor_images.append(tensor) + + tensor_images = torch.cat(tensor_images, dim=0) + + else: + + tensor_images = [] + scale = WAS_Image_Rescale() + for tensor in tensors: + tensor = scale.image_rescale(tensor.unsqueeze(0), "rescale", "true", scale_sampling, current_upscale_factor, 0, 0)[0] + size = max(tensor2pil(tensor).size) + if sharpen_strength > 0.0: + tensor = pil2tensor(self.unsharp_filter(tensor2pil(tensor), sharpen_radius, sharpen_strength)) + tensor_images.append(tensor) + tensor_images = torch.cat(tensor_images, dim=0) + + if tiled_vae: + latent_image_result = {"samples": vae.encode_tiled(self.vae_encode_crop_pixels(tensor_images)[:,:,:,:3])} + else: + latent_image_result = {"samples": vae.encode(self.vae_encode_crop_pixels(tensor_images)[:,:,:,:3])} + + else: + + upscaler = nodes.LatentUpscaleBy() + latent_image_result = upscaler.upscale(samples[0], latent_upscale, current_upscale_factor)[0] + + else: + + latent_image_result = samples[0] + + return (latent_image_result, ) + + @staticmethod + def vae_encode_crop_pixels(pixels): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + return pixels + + @staticmethod + def unsharp_filter(image, radius=2, amount=1.0): + from skimage.filters import unsharp_mask + img_array = np.array(image) + img_array = img_array / 255.0 + sharpened = unsharp_mask(img_array, radius=radius, amount=amount, channel_axis=2) + sharpened = (sharpened * 255.0).astype(np.uint8) + sharpened_pil = Image.fromarray(sharpened) + + return sharpened_pil + + +# Latent Blend + +class WAS_Blend_Latents: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "latent_a": ("LATENT",), + "latent_b": ("LATENT",), + "operation": (["add", "multiply", "divide", "subtract", "overlay", "hard_light", "soft_light", "screen", "linear_dodge", "difference", "exclusion", "random"],), + "blend": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "latent_blend" + + CATEGORY = "WAS Suite/Latent" + + def latent_blend(self, latent_a, latent_b, operation, blend): + return ( {"samples": self.blend_latents(latent_a['samples'], latent_b['samples'], operation, blend)}, ) + + def blend_latents(self, latent1, latent2, mode='add', blend_percentage=0.5): + + def overlay_blend(latent1, latent2, blend_factor): + low = 2 * latent1 * latent2 + high = 1 - 2 * (1 - latent1) * (1 - latent2) + blended_latent = (latent1 * blend_factor) * low + (latent2 * blend_factor) * high + return blended_latent + + def screen_blend(latent1, latent2, blend_factor): + inverted_latent1 = 1 - latent1 + inverted_latent2 = 1 - latent2 + blended_latent = 1 - (inverted_latent1 * inverted_latent2 * (1 - blend_factor)) + return blended_latent + + def difference_blend(latent1, latent2, blend_factor): + blended_latent = abs(latent1 - latent2) * blend_factor + return blended_latent + + def exclusion_blend(latent1, latent2, blend_factor): + blended_latent = (latent1 + latent2 - 2 * latent1 * latent2) * blend_factor + return blended_latent + + def hard_light_blend(latent1, latent2, blend_factor): + blended_latent = torch.where(latent2 < 0.5, 2 * latent1 * latent2, 1 - 2 * (1 - latent1) * (1 - latent2)) * blend_factor + return blended_latent + + def linear_dodge_blend(latent1, latent2, blend_factor): + blended_latent = torch.clamp(latent1 + latent2, 0, 1) * blend_factor + return blended_latent + + def soft_light_blend(latent1, latent2, blend_factor): + low = 2 * latent1 * latent2 + latent1 ** 2 - 2 * latent1 * latent2 * latent1 + high = 2 * latent1 * (1 - latent2) + torch.sqrt(latent1) * (2 * latent2 - 1) + blended_latent = (latent1 * blend_factor) * low + (latent2 * blend_factor) * high + return blended_latent + + def random_noise(latent1, latent2, blend_factor): + noise1 = torch.randn_like(latent1) + noise2 = torch.randn_like(latent2) + noise1 = (noise1 - noise1.min()) / (noise1.max() - noise1.min()) + noise2 = (noise2 - noise2.min()) / (noise2.max() - noise2.min()) + blended_noise = (latent1 * blend_factor) * noise1 + (latent2 * blend_factor) * noise2 + blended_noise = torch.clamp(blended_noise, 0, 1) + return blended_noise + + blend_factor1 = blend_percentage + blend_factor2 = 1 - blend_percentage + + if mode == 'add': + blended_latent = (latent1 * blend_factor1) + (latent2 * blend_factor2) + elif mode == 'multiply': + blended_latent = (latent1 * blend_factor1) * (latent2 * blend_factor2) + elif mode == 'divide': + blended_latent = (latent1 * blend_factor1) / (latent2 * blend_factor2) + elif mode == 'subtract': + blended_latent = (latent1 * blend_factor1) - (latent2 * blend_factor2) + elif mode == 'overlay': + blended_latent = overlay_blend(latent1, latent2, blend_factor1) + elif mode == 'screen': + blended_latent = screen_blend(latent1, latent2, blend_factor1) + elif mode == 'difference': + blended_latent = difference_blend(latent1, latent2, blend_factor1) + elif mode == 'exclusion': + blended_latent = exclusion_blend(latent1, latent2, blend_factor1) + elif mode == 'hard_light': + blended_latent = hard_light_blend(latent1, latent2, blend_factor1) + elif mode == 'linear_dodge': + blended_latent = linear_dodge_blend(latent1, latent2, blend_factor1) + elif mode == 'soft_light': + blended_latent = soft_light_blend(latent1, latent2, blend_factor1) + elif mode == 'random': + blended_latent = random_noise(latent1, latent2, blend_factor1) + else: + raise ValueError("Unsupported blending mode. Please choose from 'add', 'multiply', 'divide', 'subtract', 'overlay', 'screen', 'difference', 'exclusion', 'hard_light', 'linear_dodge', 'soft_light', 'custom_noise'.") + + blended_latent = self.normalize(blended_latent) + return blended_latent + + def normalize(self, latent): + return (latent - latent.min()) / (latent.max() - latent.min()) + + + +# SEED NODE + +class WAS_Seed: + @classmethod + def INPUT_TYPES(cls): + return {"required": + {"seed": ("INT", {"default": 0, "min": 0, + "max": 0xffffffffffffffff})} + } + + RETURN_TYPES = ("SEED", "NUMBER", "FLOAT", "INT") + RETURN_NAMES = ("seed", "number", "float", "int") + FUNCTION = "seed" + + CATEGORY = "WAS Suite/Number" + + def seed(self, seed): + return ({"seed": seed, }, seed, float(seed), int(seed) ) + + +# IMAGE SEED + +class WAS_Image_To_Seed: + @classmethod + def INPUT_TYPES(cls): + return {"required": { + "images": ("IMAGE",), + } + } + + RETURN_TYPES = ("INT",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "image_to_seed" + CATEGORY = "WAS Suite/Image/Analyze" + + def image_to_seed(self, images): + + seeds = [] + for image in images: + image = tensor2pil(image) + seeds.append(image2seed(image)) + + return (seeds, ) + + +#! TEXT NODES + +class WAS_Prompt_Styles_Selector: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + style_list = [] + if os.path.exists(STYLES_PATH): + with open(STYLES_PATH, "r") as f: + if len(f.readlines()) != 0: + f.seek(0) + data = f.read() + styles = json.loads(data) + for style in styles.keys(): + style_list.append(style) + if not style_list: + style_list.append("None") + return { + "required": { + "style": (style_list,), + } + } + + RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("positive_string", "negative_string") + FUNCTION = "load_style" + + CATEGORY = "WAS Suite/Text" + + def load_style(self, style): + + styles = {} + if os.path.exists(STYLES_PATH): + with open(STYLES_PATH, 'r') as data: + styles = json.load(data) + else: + cstr(f"The styles file does not exist at `{STYLES_PATH}`. Unable to load styles! Have you imported your AUTOMATIC1111 WebUI styles?").error.print() + + if styles and style != None or style != 'None': + prompt = styles[style]['prompt'] + negative_prompt = styles[style]['negative_prompt'] + else: + prompt = '' + negative_prompt = '' + + return (prompt, negative_prompt) + +class WAS_Prompt_Multiple_Styles_Selector: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + style_list = [] + if os.path.exists(STYLES_PATH): + with open(STYLES_PATH, "r") as f: + if len(f.readlines()) != 0: + f.seek(0) + data = f.read() + styles = json.loads(data) + for style in styles.keys(): + style_list.append(style) + if not style_list: + style_list.append("None") + return { + "required": { + "style1": (style_list,), + "style2": (style_list,), + "style3": (style_list,), + "style4": (style_list,), + } + } + + RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("positive_string", "negative_string") + FUNCTION = "load_style" + + CATEGORY = "WAS Suite/Text" + + def load_style(self, style1, style2, style3, style4): + styles = {} + if os.path.exists(STYLES_PATH): + with open(STYLES_PATH, 'r') as data: + styles = json.load(data) + else: + cstr(f"The styles file does not exist at `{STYLES_PATH}`. Unable to load styles! Have you imported your AUTOMATIC1111 WebUI styles?").error.print() + return ('', '') + + # Check if the selected styles exist in the loaded styles dictionary + selected_styles = [style1, style2, style3, style4] + for style in selected_styles: + if style not in styles: + print(f"Style '{style}' was not found in the styles file.") + return ('', '') + + prompt = "" + negative_prompt = "" + + # Concatenate the prompts and negative prompts of the selected styles + for style in selected_styles: + prompt += styles[style]['prompt'] + " " + negative_prompt += styles[style]['negative_prompt'] + " " + + return (prompt.strip(), negative_prompt.strip()) + +# Text Multiline Node + +class WAS_Text_Multiline: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": ("STRING", {"default": '', "multiline": True, "dynamicPrompts": True}), + } + } + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_multiline" + + CATEGORY = "WAS Suite/Text" + + def text_multiline(self, text): + import io + new_text = [] + for line in io.StringIO(text): + if not line.strip().startswith('#'): + new_text.append(line.replace("\n", '')) + new_text = "\n".join(new_text) + + tokens = TextTokens() + new_text = tokens.parseTokens(new_text) + + return (new_text, ) + + +class WAS_Text_Multiline_Raw: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": ("STRING", {"default": '', "multiline": True, "dynamicPrompts": False}), + } + } + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_multiline" + + CATEGORY = "WAS Suite/Text" + + def text_multiline(self, text): + tokens = TextTokens() + new_text = tokens.parseTokens(text) + + return (new_text, ) + + +# Text List Concatenate Node + +class WAS_Text_List_Concatenate: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "list_a": ("LIST", {"forceInput": True}), + "list_b": ("LIST", {"forceInput": True}), + "list_c": ("LIST", {"forceInput": True}), + "list_d": ("LIST", {"forceInput": True}), + } + } + + RETURN_TYPES = ("LIST",) + FUNCTION = "text_concatenate_list" + + CATEGORY = "WAS Suite/Text" + + def text_concatenate_list(self, **kwargs): + merged_list: list[str] = [] + + # Iterate over the received inputs in sorted order. + for k in sorted(kwargs.keys()): + v = kwargs[k] + + # Only process "list" input ports. + if isinstance(v, list): + merged_list += v + + return (merged_list,) + + +# Text List Node + +class WAS_Text_List: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + }, + "optional": { + "text_a": ("STRING", {"forceInput": True}), + "text_b": ("STRING", {"forceInput": True}), + "text_c": ("STRING", {"forceInput": True}), + "text_d": ("STRING", {"forceInput": True}), + "text_e": ("STRING", {"forceInput": True}), + "text_f": ("STRING", {"forceInput": True}), + "text_g": ("STRING", {"forceInput": True}), + } + } + RETURN_TYPES = ("LIST",) + FUNCTION = "text_as_list" + + CATEGORY = "WAS Suite/Text" + + def text_as_list(self, **kwargs): + text_list: list[str] = [] + + # Iterate over the received inputs in sorted order. + for k in sorted(kwargs.keys()): + v = kwargs[k] + + # Only process string input ports. + if isinstance(v, str): + text_list.append(v) + + return (text_list,) + + +# Text List to Text Node + +class WAS_Text_List_to_Text: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "delimiter": ("STRING", {"default": ", "}), + "text_list": ("LIST", {"forceInput": True}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_list_to_text" + + CATEGORY = "WAS Suite/Text" + + def text_list_to_text(self, delimiter, text_list): + # Handle special case where delimiter is "\n" (literal newline). + if delimiter == "\\n": + delimiter = "\n" + + merged_text = delimiter.join(text_list) + + return (merged_text,) + + +# Text Parse Embeddings + +class WAS_Text_Parse_Embeddings_By_Name: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_parse_embeddings" + + CATEGORY = "WAS Suite/Text/Parse" + + def text_parse_embeddings(self, text): + return (self.convert_a1111_embeddings(text), ) + + def convert_a1111_embeddings(self, text): + for embeddings_path in comfy_paths.folder_names_and_paths["embeddings"][0]: + for filename in os.listdir(embeddings_path): + basename, ext = os.path.splitext(filename) + pattern = re.compile(r'\b(?= 0 else string[max_length:] + else: + return string[-max_length:] if max_length >= 0 else string[:max_length] + words = string.split() + if mode == 'beginning': + return ' '.join(words[:max_length]) if max_length >= 0 else ' '.join(words[max_length:]) + else: + return ' '.join(words[-max_length:]) if max_length >= 0 else ' '.join(words[:max_length]) + + + + +# Text Compare Strings + +class WAS_Text_Compare: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text_a": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "text_b": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "mode": (["similarity","difference"],), + "tolerance": ("FLOAT", {"default":0.0,"min":0.0,"max":1.0,"step":0.01}), + } + } + RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,"BOOLEAN","NUMBER",TEXT_TYPE) + RETURN_NAMES = ("TEXT_A_PASS","TEXT_B_PASS","BOOLEAN","SCORE_NUMBER","COMPARISON_TEXT") + FUNCTION = "text_compare" + + CATEGORY = "WAS Suite/Text/Search" + + def text_compare(self, text_a='', text_b='', mode='similarity', tolerance=0.0): + + boolean = ( 1 if text_a == text_b else 0 ) + sim = self.string_compare(text_a, text_b, tolerance, ( True if mode == 'difference' else False )) + score = float(sim[0]) + sim_result = ' '.join(sim[1][::-1]) + sim_result = ' '.join(sim_result.split()) + + return (text_a, text_b, bool(boolean), score, sim_result) + + def string_compare(self, str1, str2, threshold=1.0, difference_mode=False): + m = len(str1) + n = len(str2) + if difference_mode: + dp = [[0 for x in range(n+1)] for x in range(m+1)] + for i in range(m+1): + for j in range(n+1): + if i == 0: + dp[i][j] = j + elif j == 0: + dp[i][j] = i + elif str1[i-1] == str2[j-1]: + dp[i][j] = dp[i-1][j-1] + else: + dp[i][j] = 1 + min(dp[i][j-1], # Insert + dp[i-1][j], # Remove + dp[i-1][j-1]) # Replace + diff_indices = [] + i, j = m, n + while i > 0 and j > 0: + if str1[i-1] == str2[j-1]: + i -= 1 + j -= 1 + else: + diff_indices.append(i-1) + i, j = min((i, j-1), (i-1, j)) + diff_indices.reverse() + words = [] + start_idx = 0 + for i in diff_indices: + if str1[i] == " ": + words.append(str1[start_idx:i]) + start_idx = i+1 + words.append(str1[start_idx:m]) + difference_score = 1 - ((dp[m][n] - len(words)) / max(m, n)) + return (difference_score, words[::-1]) + else: + dp = [[0 for x in range(n+1)] for x in range(m+1)] + similar_words = set() + for i in range(m+1): + for j in range(n+1): + if i == 0: + dp[i][j] = j + elif j == 0: + dp[i][j] = i + elif str1[i-1] == str2[j-1]: + dp[i][j] = dp[i-1][j-1] + if i > 1 and j > 1 and str1[i-2] == ' ' and str2[j-2] == ' ': + word1_start = i-2 + word2_start = j-2 + while word1_start > 0 and str1[word1_start-1] != " ": + word1_start -= 1 + while word2_start > 0 and str2[word2_start-1] != " ": + word2_start -= 1 + word1 = str1[word1_start:i-1] + word2 = str2[word2_start:j-1] + if word1 in str2 or word2 in str1: + if word1 not in similar_words: + similar_words.add(word1) + if word2 not in similar_words: + similar_words.add(word2) + else: + dp[i][j] = 1 + min(dp[i][j-1], # Insert + dp[i-1][j], # Remove + dp[i-1][j-1]) # Replace + if dp[i][j] <= threshold and i > 0 and j > 0: + word1_start = max(0, i-dp[i][j]) + word2_start = max(0, j-dp[i][j]) + word1_end = i + word2_end = j + while word1_start > 0 and str1[word1_start-1] != " ": + word1_start -= 1 + while word2_start > 0 and str2[word2_start-1] != " ": + word2_start -= 1 + while word1_end < m and str1[word1_end] != " ": + word1_end += 1 + while word2_end < n and str2[word2_end] != " ": + word2_end += 1 + word1 = str1[word1_start:word1_end] + word2 = str2[word2_start:word2_end] + if word1 in str2 or word2 in str1: + if word1 not in similar_words: + similar_words.add(word1) + if word2 not in similar_words: + similar_words.add(word2) + if(max(m,n) == 0): + similarity_score = 1 + else: + similarity_score = 1 - (dp[m][n]/max(m,n)) + return (similarity_score, list(similar_words)) + + +# Text Random Line + +class WAS_Text_Random_Line: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_random_line" + + CATEGORY = "WAS Suite/Text" + + def text_random_line(self, text, seed): + lines = text.split("\n") + random.seed(seed) + choice = random.choice(lines) + return (choice, ) + + +# Text Concatenate + +class WAS_Text_Concatenate: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "delimiter": ("STRING", {"default": ", "}), + "clean_whitespace": (["true", "false"],), + }, + "optional": { + "text_a": ("STRING", {"forceInput": True}), + "text_b": ("STRING", {"forceInput": True}), + "text_c": ("STRING", {"forceInput": True}), + "text_d": ("STRING", {"forceInput": True}), + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "text_concatenate" + + CATEGORY = "WAS Suite/Text" + + def text_concatenate(self, delimiter, clean_whitespace, **kwargs): + text_inputs = [] + + # Handle special case where delimiter is "\n" (literal newline). + if delimiter in ("\n", "\\n"): + delimiter = "\n" + + # Iterate over the received inputs in sorted order. + for k in sorted(kwargs.keys()): + v = kwargs[k] + + # Only process string input ports. + if isinstance(v, str): + if clean_whitespace == "true": + # Remove leading and trailing whitespace around this input. + v = v.strip() + + # Only use this input if it's a non-empty string, since it + # never makes sense to concatenate totally empty inputs. + # NOTE: If whitespace cleanup is disabled, inputs containing + # 100% whitespace will be treated as if it's a non-empty input. + if v != "": + text_inputs.append(v) + + # Merge the inputs. Will always generate an output, even if empty. + merged_text = delimiter.join(text_inputs) + + return (merged_text,) + + + +# Text Find + + +# Note that these nodes are exposed as "Find", not "Search". This is the first class that follows the naming convention of the node itself. +class WAS_Find: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "substring": ("STRING", {"default": '', "multiline": False}), + "pattern": ("STRING", {"default": '', "multiline": False}), + } + } + + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("found",) + FUNCTION = "execute" + + CATEGORY = "WAS Suite/Text/Search" + + def execute(self, text, substring, pattern): + if substring: + return (substring in text, ) + + return (bool(re.search(pattern, text)), ) + + + +# Text Search and Replace + +class WAS_Search_and_Replace: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "find": ("STRING", {"default": '', "multiline": False}), + "replace": ("STRING", {"default": '', "multiline": False}), + } + } + + RETURN_TYPES = (TEXT_TYPE, "NUMBER", "FLOAT", "INT") + RETURN_NAMES = ("result_text", "replacement_count_number", "replacement_count_float", "replacement_count_int") + FUNCTION = "text_search_and_replace" + + CATEGORY = "WAS Suite/Text/Search" + + def text_search_and_replace(self, text, find, replace): + modified_text, count = self.replace_substring(text, find, replace) + return (modified_text, count, float(count), int(count)) + + def replace_substring(self, text, find, replace): + modified_text, count = re.subn(find, replace, text) + return (modified_text, count) + + +# Text Shuffle + +class WAS_Text_Shuffle: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "separator": ("STRING", {"default": ',', "multiline": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "shuffle" + + CATEGORY = "WAS Suite/Text/Operations" + + def shuffle(self, text, separator, seed): + + if seed is not None: + random.seed(seed) + + text_list = text.split(separator) + random.shuffle(text_list) + new_text = separator.join(text_list) + + return (new_text, ) + + +# Text Sort + +class WAS_Text_Sort: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "separator": ("STRING", {"default": ', ', "multiline": False}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "sort" + + CATEGORY = "WAS Suite/Text/Operations" + + def sort(self, text, separator): + tokens = WAS_Text_Sort.split_using_protected_groups(text.strip(separator + " \t\n\r"), separator.strip()) + sorted_tokens = sorted(tokens, key=WAS_Text_Sort.token_without_leading_brackets) + return (separator.join(sorted_tokens), ) + + @staticmethod + def token_without_leading_brackets(token): + return token.replace("\\(", "\0\1").replace("(", "").replace("\0\1", "(").strip() + + @staticmethod + def split_using_protected_groups(text, separator): + protected_groups = "" + nesting_level = 0 + for char in text: + if char == "(": nesting_level += 1 + if char == ")": nesting_level -= 1 + + if char == separator and nesting_level > 0: + protected_groups += "\0" + else: + protected_groups += char + + return list(map(lambda t: t.replace("\0", separator).strip(), protected_groups.split(separator))) + + + +# Text Search and Replace + +class WAS_Search_and_Replace_Input: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "find": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "replace": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + RETURN_TYPES = (TEXT_TYPE, "NUMBER", "FLOAT", "INT") + RETURN_NAMES = ("result_text", "replacement_count_number", "replacement_count_float", "replacement_count_int") + FUNCTION = "text_search_and_replace" + + CATEGORY = "WAS Suite/Text/Search" + + def text_search_and_replace(self, text, find, replace): + count = 0 + new_text = text + while find in new_text: + new_text = new_text.replace(find, replace, 1) + count += 1 + return (new_text, count, float(count), int(count)) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + +# Text Search and Replace By Dictionary + +class WAS_Search_and_Replace_Dictionary: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "dictionary": ("DICT",), + "replacement_key": ("STRING", {"default": "__", "multiline": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_search_and_replace_dict" + + CATEGORY = "WAS Suite/Text/Search" + + def text_search_and_replace_dict(self, text, dictionary, replacement_key, seed): + + random.seed(seed) + + # Parse Text + new_text = text + + for term in dictionary.keys(): + tkey = f'{replacement_key}{term}{replacement_key}' + tcount = new_text.count(tkey) + for _ in range(tcount): + new_text = new_text.replace(tkey, random.choice(dictionary[term]), 1) + if seed > 0 or seed < 0: + seed = seed + 1 + random.seed(seed) + + return (new_text, ) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + +# Text Parse NSP + +class WAS_Text_Parse_NSP: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mode": (["Noodle Soup Prompts", "Wildcards"],), + "noodle_key": ("STRING", {"default": '__', "multiline": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + OUTPUT_NODE = True + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_parse_nsp" + + CATEGORY = "WAS Suite/Text/Parse" + + def text_parse_nsp(self, text, mode="Noodle Soup Prompts", noodle_key='__', seed=0): + + if mode == "Noodle Soup Prompts": + + new_text = nsp_parse(text, seed, noodle_key) + cstr(f"Text Parse NSP:\n{new_text}").msg.print() + + else: + + new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key) + cstr(f"CLIPTextEncode Wildcards:\n{new_text}").msg.print() + + return (new_text, ) + + +# TEXT SAVE + +class WAS_Text_Save: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": ("STRING", {"forceInput": True}), + "path": ("STRING", {"default": './ComfyUI/output/[time(%Y-%m-%d)]', "multiline": False}), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "filename_delimiter": ("STRING", {"default": "_"}), + "filename_number_padding": ("INT", {"default": 4, "min": 0, "max": 9, "step": 1}), + }, + "optional": { + "file_extension": ("STRING", {"default": ".txt"}), + "encoding": ("STRING", {"default": "utf-8"}), + "filename_suffix": ("STRING", {"default": ""}) + } + } + + OUTPUT_NODE = True + RETURN_TYPES = () + FUNCTION = "save_text_file" + CATEGORY = "WAS Suite/IO" + + def save_text_file(self, text, path, filename_prefix='ComfyUI', filename_delimiter='_', + filename_number_padding=4, file_extension='.txt', encoding='utf-8', filename_suffix=''): + tokens = TextTokens() + path = tokens.parseTokens(path) + filename_prefix = tokens.parseTokens(filename_prefix) + + if not os.path.exists(path): + cstr(f"The path `{path}` doesn't exist! Creating it...").warning.print() + try: + os.makedirs(path, exist_ok=True) + except OSError as e: + cstr(f"The path `{path}` could not be created! Is there write access?\n{e}").error.print() + + if text.strip() == '': + cstr(f"There is no text specified to save! Text is empty.").error.print() + + delimiter = filename_delimiter + number_padding = int(filename_number_padding) + filename = self.generate_filename(path, filename_prefix, delimiter, number_padding, file_extension, filename_suffix) + file_path = os.path.join(path, filename) + self.write_text_file(file_path, text, encoding) + update_history_text_files(file_path) + return (text, {"ui": {"string": text}}) + + def generate_filename(self, path, prefix, delimiter, number_padding, extension, suffix): + if number_padding == 0: + # If number_padding is 0, don't use a numerical suffix + filename = f"{prefix}{suffix}{extension}" + else: + if delimiter: + pattern = f"{re.escape(prefix)}{re.escape(delimiter)}(\\d{{{number_padding}}}){re.escape(suffix)}{re.escape(extension)}" + else: + pattern = f"{re.escape(prefix)}(\\d{{{number_padding}}}){re.escape(suffix)}{re.escape(extension)}" + + existing_counters = [ + int(re.search(pattern, filename).group(1)) + for filename in os.listdir(path) + if re.match(pattern, filename) and filename.endswith(extension) + ] + existing_counters.sort() + if existing_counters: + counter = existing_counters[-1] + 1 + else: + counter = 1 + if delimiter: + filename = f"{prefix}{delimiter}{counter:0{number_padding}}{suffix}{extension}" + else: + filename = f"{prefix}{counter:0{number_padding}}{suffix}{extension}" + + while os.path.exists(os.path.join(path, filename)): + counter += 1 + if delimiter: + filename = f"{prefix}{delimiter}{counter:0{number_padding}}{suffix}{extension}" + else: + filename = f"{prefix}{counter:0{number_padding}}{suffix}{extension}" + + return filename + + def write_text_file(self, file, content, encoding): + try: + with open(file, 'w', encoding=encoding, newline='\n') as f: + f.write(content) + except OSError: + cstr(f"Unable to save file `{file}`").error.print() + + +# TEXT FILE HISTORY NODE + +class WAS_Text_File_History: + def __init__(self): + self.HDB = WASDatabase(WAS_HISTORY_DATABASE) + self.conf = getSuiteConfig() + + @classmethod + def INPUT_TYPES(cls): + HDB = WASDatabase(WAS_HISTORY_DATABASE) + conf = getSuiteConfig() + paths = ['No History',] + if HDB.catExists("History") and HDB.keyExists("History", "TextFiles"): + history_paths = HDB.get("History", "TextFiles") + if conf.__contains__('history_display_limit'): + history_paths = history_paths[-conf['history_display_limit']:] + paths = [] + for path_ in history_paths: + paths.append(os.path.join('...'+os.sep+os.path.basename(os.path.dirname(path_)), os.path.basename(path_))) + + return { + "required": { + "file": (paths,), + "dictionary_name": ("STRING", {"default": '[filename]', "multiline": True}), + }, + } + + RETURN_TYPES = (TEXT_TYPE,"DICT") + FUNCTION = "text_file_history" + + CATEGORY = "WAS Suite/History" + + def text_file_history(self, file=None, dictionary_name='[filename]]'): + file_path = file.strip() + filename = ( os.path.basename(file_path).split('.', 1)[0] + if '.' in os.path.basename(file_path) else os.path.basename(file_path) ) + if dictionary_name != '[filename]' or dictionary_name not in [' ', '']: + filename = dictionary_name + if not os.path.exists(file_path): + cstr(f"The path `{file_path}` specified cannot be found.").error.print() + return ('', {filename: []}) + with open(file_path, 'r', encoding="utf-8", newline='\n') as file: + text = file.read() + + # Write to file history + update_history_text_files(file_path) + + import io + lines = [] + for line in io.StringIO(text): + if not line.strip().startswith('#'): + lines.append(line.replace("\n",'')) + dictionary = {filename: lines} + + return ("\n".join(lines), dictionary) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + +# TEXT TO CONDITIONIONG + +class WAS_Text_to_Conditioning: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "clip": ("CLIP",), + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "text_to_conditioning" + + CATEGORY = "WAS Suite/Text/Operations" + + def text_to_conditioning(self, clip, text): + encoder = nodes.CLIPTextEncode() + encoded = encoder.encode(clip=clip, text=text) + return (encoded[0], { "ui": { "string": text } }) + + + +# TEXT PARSE TOKENS + +class WAS_Text_Parse_Tokens: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_parse_tokens" + + CATEGORY = "WAS Suite/Text/Tokens" + + def text_parse_tokens(self, text): + # Token Parser + tokens = TextTokens() + return (tokens.parseTokens(text), ) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + +# TEXT ADD TOKENS + + +class WAS_Text_Add_Tokens: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "tokens": ("STRING", {"default": "[hello]: world", "multiline": True}), + "print_current_tokens": (["false", "true"],), + } + } + + RETURN_TYPES = () + FUNCTION = "text_add_tokens" + OUTPUT_NODE = True + CATEGORY = "WAS Suite/Text/Tokens" + + def text_add_tokens(self, tokens, print_current_tokens="false"): + + import io + + # Token Parser + tk = TextTokens() + + # Parse out Tokens + for line in io.StringIO(tokens): + parts = line.split(':') + token = parts[0].strip() + token_value = parts[1].strip() + tk.addToken(token, token_value) + + # Current Tokens + if print_current_tokens == "true": + cstr(f'Current Custom Tokens:').msg.print() + print(json.dumps(tk.custom_tokens, indent=4)) + + return tokens + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + +# TEXT ADD TOKEN BY INPUT + + +class WAS_Text_Add_Token_Input: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "token_name": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "token_value": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "print_current_tokens": (["false", "true"],), + } + } + + RETURN_TYPES = () + FUNCTION = "text_add_token" + OUTPUT_NODE = True + CATEGORY = "WAS Suite/Text/Tokens" + + def text_add_token(self, token_name, token_value, print_current_tokens="false"): + + if token_name.strip() == '': + cstr(f'A `token_name` is required for a token; token name provided is empty.').error.print() + pass + + # Token Parser + tk = TextTokens() + + # Add Tokens + tk.addToken(token_name, token_value) + + # Current Tokens + if print_current_tokens == "true": + cstr(f'Current Custom Tokens:').msg.print() + print(json.dumps(tk.custom_tokens, indent=4)) + + return (token_name, token_value) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + + +# TEXT TO CONSOLE + +class WAS_Text_to_Console: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "label": ("STRING", {"default": f'Text Output', "multiline": False}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + OUTPUT_NODE = True + FUNCTION = "text_to_console" + + CATEGORY = "WAS Suite/Debug" + + def text_to_console(self, text, label): + if label.strip() != '': + cstr(f'\033[33m{label}\033[0m:\n{text}\n').msg.print() + else: + cstr(f"\033[33mText to Console\033[0m:\n{text}\n").msg.print() + return (text, ) + +# DICT TO CONSOLE + +class WAS_Dictionary_To_Console: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "dictionary": ("DICT",), + "label": ("STRING", {"default": f'Dictionary Output', "multiline": False}), + } + } + + RETURN_TYPES = ("DICT",) + OUTPUT_NODE = True + FUNCTION = "text_to_console" + + CATEGORY = "WAS Suite/Debug" + + def text_to_console(self, dictionary, label): + if label.strip() != '': + print(f'\033[34mWAS Node Suite \033[33m{label}\033[0m:\n') + from pprint import pprint + pprint(dictionary, indent=4) + print('') + else: + cstr(f"\033[33mText to Console\033[0m:\n") + pprint(dictionary, indent=4) + print('') + return (dictionary, ) + + +# LOAD TEXT FILE + +class WAS_Text_Load_From_File: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "file_path": ("STRING", {"default": '', "multiline": False}), + "dictionary_name": ("STRING", {"default": '[filename]', "multiline": False}), + } + } + + RETURN_TYPES = (TEXT_TYPE,"DICT") + FUNCTION = "load_file" + + CATEGORY = "WAS Suite/IO" + + def load_file(self, file_path='', dictionary_name='[filename]]'): + + filename = ( os.path.basename(file_path).split('.', 1)[0] + if '.' in os.path.basename(file_path) else os.path.basename(file_path) ) + if dictionary_name != '[filename]': + filename = dictionary_name + if not os.path.exists(file_path): + cstr(f"The path `{file_path}` specified cannot be found.").error.print() + return ('', {filename: []}) + with open(file_path, 'r', encoding="utf-8", newline='\n') as file: + text = file.read() + + # Write to file history + update_history_text_files(file_path) + + import io + lines = [] + for line in io.StringIO(text): + if not line.strip().startswith('#'): + lines.append(line.replace("\n",'').replace("\r",'')) + dictionary = {filename: lines} + + return ("\n".join(lines), dictionary) + +# TEXT LOAD FROM FILE + +class WAS_Text_Load_Line_From_File: + def __init__(self): + self.HDB = WASDatabase(WAS_HISTORY_DATABASE) + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "file_path": ("STRING", {"default": '', "multiline": False}), + "dictionary_name": ("STRING", {"default": '[filename]', "multiline": False}), + "label": ("STRING", {"default": 'TextBatch', "multiline": False}), + "mode": (["automatic", "index"],), + "index": ("INT", {"default": 0, "min": 0, "step": 1}), + }, + "optional": { + "multiline_text": (TEXT_TYPE, {"forceInput": True}), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + if kwargs['mode'] != 'index': + return float("NaN") + else: + m = hashlib.sha256() + if os.path.exists(kwargs['file_path']): + with open(kwargs['file_path'], 'rb') as f: + m.update(f.read()) + return m.digest().hex() + else: + return False + + RETURN_TYPES = (TEXT_TYPE, "DICT") + RETURN_NAMES = ("line_text", "dictionary") + FUNCTION = "load_file" + + CATEGORY = "WAS Suite/Text" + + def load_file(self, file_path='', dictionary_name='[filename]', label='TextBatch', + mode='automatic', index=0, multiline_text=None): + if multiline_text is not None: + lines = multiline_text.strip().split('\n') + if mode == 'index': + if index < 0 or index >= len(lines): + cstr(f"Invalid line index `{index}`").error.print() + return ('', {dictionary_name: []}) + line = lines[index] + else: + line_index = self.HDB.get('TextBatch Counters', label) + if line_index is None: + line_index = 0 + line = lines[line_index % len(lines)] + self.HDB.insert('TextBatch Counters', label, line_index + 1) + return (line, {dictionary_name: lines}) + + if file_path == '': + cstr("No file path specified.").error.print() + return ('', {dictionary_name: []}) + + if not os.path.exists(file_path): + cstr(f"The path `{file_path}` specified cannot be found.").error.print() + return ('', {dictionary_name: []}) + + file_list = self.TextFileLoader(file_path, label) + line, lines = None, [] + if mode == 'automatic': + line, lines = file_list.get_next_line() + elif mode == 'index': + if index >= len(file_list.lines): + index = index % len(file_list.lines) + line, lines = file_list.get_line_by_index(index) + if line is None: + cstr("No valid line was found. The file may be empty or all lines have been read.").error.print() + return ('', {dictionary_name: []}) + file_list.store_index() + update_history_text_files(file_path) + + return (line, {dictionary_name: lines}) + + class TextFileLoader: + def __init__(self, file_path, label): + self.WDB = WDB + self.file_path = file_path + self.lines = [] + self.index = 0 + self.label = label + self.load_file(file_path) + + def load_file(self, file_path): + stored_file_path = self.WDB.get('TextBatch Paths', self.label) + stored_index = self.WDB.get('TextBatch Counters', self.label) + if stored_file_path != file_path: + self.index = 0 + self.WDB.insert('TextBatch Counters', self.label, 0) + self.WDB.insert('TextBatch Paths', self.label, file_path) + else: + self.index = stored_index + with open(file_path, 'r', encoding="utf-8", newline='\n') as file: + self.lines = [line.strip() for line in file] + + def get_line_index(self): + return self.index + + def set_line_index(self, index): + self.index = index + self.WDB.insert('TextBatch Counters', self.label, self.index) + + def get_next_line(self): + if self.index >= len(self.lines): + self.index = 0 + line = self.lines[self.index] + self.index += 1 + if self.index == len(self.lines): + self.index = 0 + cstr(f'{cstr.color.YELLOW}TextBatch{cstr.color.END} Index: {self.index}').msg.print() + return line, self.lines + + def get_line_by_index(self, index): + if index < 0 or index >= len(self.lines): + cstr(f"Invalid line index `{index}`").error.print() + return None, [] + self.index = index + line = self.lines[self.index] + cstr(f'{cstr.color.YELLOW}TextBatch{cstr.color.END} Index: {self.index}').msg.print() + return line, self.lines + + def store_index(self): + self.WDB.insert('TextBatch Counters', self.label, self.index) + + +class WAS_Text_To_String: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "text_to_string" + + CATEGORY = "WAS Suite/Text/Operations" + + def text_to_string(self, text): + return (text, ) + +class WAS_Text_To_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "text_to_number" + + CATEGORY = "WAS Suite/Text/Operations" + + def text_to_number(self, text): + if "." in text: + number = float(text) + else: + number = int(text) + return (number, ) + + +class WAS_String_To_Text: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string": ("STRING", {}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "string_to_text" + + CATEGORY = "WAS Suite/Text/Operations" + + def string_to_text(self, string): + return (string, ) + +# Random Prompt + +class WAS_Text_Random_Prompt: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "search_seed": ("STRING", {"multiline": False}), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "random_prompt" + + CATEGORY = "WAS Suite/Text" + + def random_prompt(self, search_seed=None): + if search_seed in ['', ' ']: + search_seed = None + return (self.search_lexica_art(search_seed), ) + + def search_lexica_art(self, query=None): + if not query: + query = random.choice(["portrait","landscape","anime","superhero","animal","nature","scenery"]) + url = f"https://lexica.art/api/v1/search?q={query}" + try: + response = requests.get(url) + data = response.json() + images = data.get("images", []) + if not images: + return "404 not found error" + random_image = random.choice(images) + prompt = random_image.get("prompt") + except Exception: + cstr("Unable to establish connection to Lexica API.").error.print() + prompt = "404 not found error" + + return prompt + +# BLIP Model Loader + +class WAS_BLIP_Model_Loader: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "blip_model": ("STRING", {"default": "Salesforce/blip-image-captioning-base"}), + "vqa_model_id": ("STRING", {"default": "Salesforce/blip-vqa-base"}), + "device": (["cuda", "cpu"],), + } + } + + RETURN_TYPES = ("BLIP_MODEL",) + FUNCTION = "blip_model" + + CATEGORY = "WAS Suite/Loaders" + + def blip_model(self, blip_model, vqa_model_id, device): + + blip_dir = os.path.join(comfy_paths.models_dir, "blip") + + # Attempt legacy support + if blip_model in ("caption", "interrogate"): + blip_model = "Salesforce/blip-image-captioning-base" + + blip_model = BlipWrapper(caption_model_id=blip_model, vqa_model_id=vqa_model_id, device=device, cache_dir=blip_dir) + + return ( blip_model, ) + + +# BLIP CAPTION IMAGE + +class WAS_BLIP_Analyze_Image: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "mode": (["caption", "interrogate"], ), + "question": ("STRING", {"default": "What does the background consist of?", "multiline": True, "dynamicPrompts": False}), + "blip_model": ("BLIP_MODEL",), + }, + "optional": { + "min_length": ("INT", {"min": 1, "max": 1024, "default": 24}), + "max_length": ("INT", {"min": 2, "max": 1024, "default": 64}), + "num_beams": ("INT", {"min": 1, "max": 12, "default": 5}), + "no_repeat_ngram_size": ("INT", {"min": 1, "max": 12, "default": 3}), + "early_stopping": ("BOOLEAN", {"default": False}) + } + } + + RETURN_TYPES = (TEXT_TYPE, TEXT_TYPE) + RETURN_NAMES = ("FULL_CAPTIONS", "CAPTIONS") + OUTPUT_IS_LIST = (False, True) + + FUNCTION = "blip_caption_image" + CATEGORY = "WAS Suite/Text/AI" + + def blip_caption_image(self, images, mode, question, blip_model, min_length=24, max_length=64, num_beams=5, no_repeat_ngram_size=3, early_stopping=False): + + captions = [] + for image in images: + pil_image = tensor2pil(image).convert("RGB") + if mode == "caption": + cap = blip_model.generate_caption(image=pil_image, min_length=min_length, max_length=max_length, num_beams=num_beams, no_repeat_ngram_size=no_repeat_ngram_size, early_stopping=early_stopping) + captions.append(cap) + cstr(f"\033[33mBLIP Caption:\033[0m {cap}").msg.print() + else: + cap = blip_model.answer_question(image=pil_image, question=question, min_length=min_length, max_length=max_length, num_beams=num_beams, no_repeat_ngram_size=no_repeat_ngram_size, early_stopping=early_stopping) + captions.append(cap) + cstr(f"\033[33m BLIP Answer:\033[0m {cap}").msg.print() + + full_captions = "" + for i, caption in enumerate(captions): + full_captions += caption + ("\n\n" if i < len(captions) else "") + + return (full_captions, captions) + + +# CLIPSeg Model Loader + +class WAS_CLIPSeg_Model_Loader: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("STRING", {"default": "CIDAS/clipseg-rd64-refined", "multiline": False}), + }, + } + + RETURN_TYPES = ("CLIPSEG_MODEL",) + RETURN_NAMES = ("clipseg_model",) + FUNCTION = "clipseg_model" + + CATEGORY = "WAS Suite/Loaders" + + def clipseg_model(self, model): + from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation + + cache = os.path.join(MODELS_DIR, 'clipseg') + + inputs = CLIPSegProcessor.from_pretrained(model, cache_dir=cache) + model = CLIPSegForImageSegmentation.from_pretrained(model, cache_dir=cache) + + return ( (inputs, model), ) + +# CLIPSeg Node + +class WAS_CLIPSeg: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "text": ("STRING", {"default": "", "multiline": False}), + }, + "optional": { + "clipseg_model": ("CLIPSEG_MODEL",), + } + } + + RETURN_TYPES = ("MASK", "IMAGE") + RETURN_NAMES = ("MASK", "MASK_IMAGE") + FUNCTION = "CLIPSeg_image" + + CATEGORY = "WAS Suite/Image/Masking" + + def CLIPSeg_image(self, image, text=None, clipseg_model=None): + from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation + + B, H, W, C = image.shape + + cache = os.path.join(MODELS_DIR, 'clipseg') + + if clipseg_model: + inputs = clipseg_model[0] + model = clipseg_model[1] + else: + inputs = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache) + model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache) + + if B == 1: + image = tensor2pil(image) + with torch.no_grad(): + result = model(**inputs(text=text, images=image, padding=True, return_tensors="pt")) + + tensor = torch.sigmoid(result[0]) + mask = 1. - (tensor - tensor.min()) / tensor.max() + mask = mask.unsqueeze(0) + mask = tensor2pil(mask).convert("L") + mask = mask.resize(image.size) + + return (pil2mask(mask), pil2tensor(ImageOps.invert(mask.convert("RGB")))) + else: + import torchvision + with torch.no_grad(): + image = image.permute(0, 3, 1, 2) + image = image * 255 + result = model(**inputs(text=[text] * B, images=image, padding=True, return_tensors="pt")) + t = torch.sigmoid(result[0]) + mask = (t - t.min()) / t.max() + mask = torchvision.transforms.functional.resize(mask, (H, W)) + mask: torch.tensor = mask.unsqueeze(-1) + mask_img = mask.repeat(1, 1, 1, 3) + return (mask, mask_img,) +class CLIPSeg2: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "text": ("STRING", {"default": "", "multiline": False}), + "use_cuda": ("BOOLEAN", {"default": False}), + }, + "optional": { + "clipseg_model": ("CLIPSEG_MODEL",), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "apply_transform" + + CATEGORY = "image/transformation" + + def apply_transform(self, image, text, use_cuda, clipseg_model): + import torch + import torch.nn.functional as F + from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation + + B, H, W, C = image.shape + + if B != 1: + raise NotImplementedError("Batch size must be 1") + + # Desired slice size and overlap + slice_size = 352 + overlap = slice_size // 2 + + # Calculate the number of slices needed along each dimension + num_slices_h = (H - overlap) // (slice_size - overlap) + 1 + num_slices_w = (W - overlap) // (slice_size - overlap) + 1 + + # Prepare a list to store the slices + slices = [] + + # Generate the slices + for i in range(num_slices_h): + for j in range(num_slices_w): + start_h = i * (slice_size - overlap) + start_w = j * (slice_size - overlap) + + end_h = min(start_h + slice_size, H) + end_w = min(start_w + slice_size, W) + + start_h = max(0, end_h - slice_size) + start_w = max(0, end_w - slice_size) + + slice_ = image[:, start_h:end_h, start_w:end_w, :] + slices.append(slice_) + + # Initialize CLIPSeg model and processor + if clipseg_model: + processor = clipseg_model[0] + model = clipseg_model[1] + else: + processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") + model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") + # Move model to CUDA if requested + if use_cuda and torch.cuda.is_available(): + model = model.to('cuda') + + processor.image_processor.do_rescale = True + processor.image_processor.do_resize = False + + image_global = image.permute(0, 3, 1, 2) + image_global = F.interpolate(image_global, size=(slice_size, slice_size), mode='bilinear', align_corners=False) + image_global = image_global.permute(0, 2, 3, 1) + _, image_global = self.CLIPSeg_image(image_global.float(), text, processor, model, use_cuda) + image_global = image_global.permute(0, 3, 1, 2) + image_global = F.interpolate(image_global, size=(H, W), mode='bilinear', align_corners=False) + image_global = image_global.permute(0, 2, 3, 1) + + # Apply the transformation to each slice + transformed_slices = [] + for slice_ in slices: + transformed_mask, transformed_slice = self.CLIPSeg_image(slice_, text, processor, model, use_cuda) + transformed_slices.append(transformed_slice) + + transformed_slices = torch.cat(transformed_slices) + + # Initialize tensors for reconstruction + reconstructed_image = torch.zeros((B, H, W, C)) + count_map = torch.zeros((B, H, W, C)) + + # Create a blending mask + mask = np.ones((slice_size, slice_size)) + mask[:overlap, :] *= np.linspace(0, 1, overlap)[:, None] + mask[-overlap:, :] *= np.linspace(1, 0, overlap)[:, None] + mask[:, :overlap] *= np.linspace(0, 1, overlap)[None, :] + mask[:, -overlap:] *= np.linspace(1, 0, overlap)[None, :] + mask = torch.tensor(mask, dtype=torch.float32).unsqueeze(0).unsqueeze(-1) + + # Place the transformed slices back into the original image dimensions + for idx in range(transformed_slices.shape[0]): + i = idx // num_slices_w + j = idx % num_slices_w + + start_h = i * (slice_size - overlap) + start_w = j * (slice_size - overlap) + + end_h = min(start_h + slice_size, H) + end_w = min(start_w + slice_size, W) + + start_h = max(0, end_h - slice_size) + start_w = max(0, end_w - slice_size) + + reconstructed_image[:, start_h:end_h, start_w:end_w, :] += transformed_slices[idx] * mask + count_map[:, start_h:end_h, start_w:end_w, :] += mask + + # Avoid division by zero + count_map[count_map == 0] = 1 + + # Average the overlapping regions + y = reconstructed_image / count_map + + total_power = (y + image_global) / 2 + just_black = image_global < 0.01 + + p1 = total_power > .5 + p2 = y > .5 + p3 = image_global > .5 + + condition = p1 | p2 | p3 + condition = condition & ~just_black + y = torch.where(condition, 1.0, 0.0) + + return (y,) + + def CLIPSeg_image(self, image, text, processor, model, use_cuda): + import torch + import torchvision.transforms.functional as TF + B, H, W, C = image.shape + + import torchvision + with torch.no_grad(): + image = image.permute(0, 3, 1, 2).to(torch.float32) * 255 + + inputs = processor(text=[text] * B, images=image, padding=True, return_tensors="pt") + + # Move model and image tensors to CUDA if requested + if use_cuda and torch.cuda.is_available(): + model = model.to('cuda') + inputs = {k: v.to('cuda') if isinstance(v, torch.Tensor) else v for k, v in inputs.items()} + + result = model(**inputs) + t = torch.sigmoid(result[0]) + mask = (t - t.min()) / t.max() + mask = torchvision.transforms.functional.resize(mask, (H, W)) + mask = mask.unsqueeze(-1) + mask_img = mask.repeat(1, 1, 1, 3) + + # Move mask and mask_img back to CPU if they were moved to CUDA + if use_cuda and torch.cuda.is_available(): + mask = mask.cpu() + mask_img = mask_img.cpu() + + return (mask, mask_img,) + +# CLIPSeg Node + +class WAS_CLIPSeg_Batch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "text_a": ("STRING", {"default":"", "multiline": False}), + "text_b": ("STRING", {"default":"", "multiline": False}), + }, + "optional": { + "image_c": ("IMAGE",), + "image_d": ("IMAGE",), + "image_e": ("IMAGE",), + "image_f": ("IMAGE",), + "text_c": ("STRING", {"default":"", "multiline": False}), + "text_d": ("STRING", {"default":"", "multiline": False}), + "text_e": ("STRING", {"default":"", "multiline": False}), + "text_f": ("STRING", {"default":"", "multiline": False}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "IMAGE") + RETURN_NAMES = ("IMAGES_BATCH", "MASKS_BATCH", "MASK_IMAGES_BATCH") + FUNCTION = "CLIPSeg_images" + + CATEGORY = "WAS Suite/Image/Masking" + + def CLIPSeg_images(self, image_a, image_b, text_a, text_b, image_c=None, image_d=None, + image_e=None, image_f=None, text_c=None, text_d=None, text_e=None, text_f=None): + from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation + import torch.nn.functional as F + + images_pil = [tensor2pil(image_a), tensor2pil(image_b)] + + if image_c is not None: + if image_c.shape[-2:] != image_a.shape[-2:]: + cstr("Size of image_c is different from image_a.").error.print() + return + images_pil.append(tensor2pil(image_c)) + if image_d is not None: + if image_d.shape[-2:] != image_a.shape[-2:]: + cstr("Size of image_d is different from image_a.").error.print() + return + images_pil.append(tensor2pil(image_d)) + if image_e is not None: + if image_e.shape[-2:] != image_a.shape[-2:]: + cstr("Size of image_e is different from image_a.").error.print() + return + images_pil.append(tensor2pil(image_e)) + if image_f is not None: + if image_f.shape[-2:] != image_a.shape[-2:]: + cstr("Size of image_f is different from image_a.").error.print() + return + images_pil.append(tensor2pil(image_f)) + + images_tensor = [torch.from_numpy(np.array(img.convert("RGB")).astype(np.float32) / 255.0).unsqueeze(0) for img in images_pil] + images_tensor = torch.cat(images_tensor, dim=0) + + prompts = [text_a, text_b] + if text_c: + prompts.append(text_c) + if text_d: + prompts.append(text_d) + if text_e: + prompts.append(text_e) + if text_f: + prompts.append(text_f) + + cache = os.path.join(MODELS_DIR, 'clipseg') + + inputs = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache) + model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache) + + with torch.no_grad(): + result = model(**inputs(text=prompts, images=images_pil, padding=True, return_tensors="pt")) + + masks = [] + mask_images = [] + for i, res in enumerate(result.logits): + tensor = torch.sigmoid(res) + mask = 1. - (tensor - tensor.min()) / tensor.max() + mask = mask.unsqueeze(0) + mask = tensor2pil(mask).convert("L") + mask = mask.resize(images_pil[0].size) + mask_batch = pil2mask(mask) + + masks.append(mask_batch.unsqueeze(0).unsqueeze(1)) + mask_images.append(pil2tensor(ImageOps.invert(mask.convert("RGB"))).squeeze(0)) + + masks_tensor = torch.cat(masks, dim=0) + mask_images_tensor = torch.stack(mask_images, dim=0) + + del inputs, model, result, tensor, masks, mask_images, images_pil + + return (images_tensor, masks_tensor, mask_images_tensor) + + +# SAM MODEL LOADER + +class WAS_SAM_Model_Loader: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "model_size": (["ViT-H", "ViT-L", "ViT-B"], ), + } + } + + RETURN_TYPES = ("SAM_MODEL",) + FUNCTION = "sam_load_model" + + CATEGORY = "WAS Suite/Image/Masking" + + def sam_load_model(self, model_size): + conf = getSuiteConfig() + + model_filename_mapping = { + "ViT-H": "sam_vit_h_4b8939.pth", + "ViT-L": "sam_vit_l_0b3195.pth", + "ViT-B": "sam_vit_b_01ec64.pth", + } + + model_url_mapping = { + "ViT-H": conf['sam_model_vith_url'] if conf.__contains__('sam_model_vith_url') else r"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", + "ViT-L": conf['sam_model_vitl_url'] if conf.__contains__('sam_model_vitl_url') else r"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth", + "ViT-B": conf['sam_model_vitb_url'] if conf.__contains__('sam_model_vitb_url') else r"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", + } + + model_url = model_url_mapping[model_size] + model_filename = model_filename_mapping[model_size] + + if 'GitPython' not in packages(): + install_package("gitpython") + + if not os.path.exists(os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'SAM')): + from git.repo.base import Repo + cstr("Installing SAM...").msg.print() + Repo.clone_from('https://github.com/facebookresearch/segment-anything', os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'SAM')) + + sys.path.append(os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'SAM')) + + sam_dir = os.path.join(MODELS_DIR, 'sam') + if not os.path.exists(sam_dir): + os.makedirs(sam_dir, exist_ok=True) + + sam_file = os.path.join(sam_dir, model_filename) + if not os.path.exists(sam_file): + cstr("Selected SAM model not found. Downloading...").msg.print() + r = requests.get(model_url, allow_redirects=True) + open(sam_file, 'wb').write(r.content) + + from segment_anything import build_sam_vit_h, build_sam_vit_l, build_sam_vit_b + + if model_size == 'ViT-H': + sam_model = build_sam_vit_h(sam_file) + elif model_size == 'ViT-L': + sam_model = build_sam_vit_l(sam_file) + elif model_size == 'ViT-B': + sam_model = build_sam_vit_b(sam_file) + else: + raise ValueError(f"SAM model does not match the model_size: '{model_size}'.") + + return (sam_model, ) + + +# SAM PARAMETERS +class WAS_SAM_Parameters: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "points": ("STRING", {"default": "[128, 128]; [0, 0]", "multiline": False}), + "labels": ("STRING", {"default": "[1, 0]", "multiline": False}), + } + } + + RETURN_TYPES = ("SAM_PARAMETERS",) + FUNCTION = "sam_parameters" + + CATEGORY = "WAS Suite/Image/Masking" + + def sam_parameters(self, points, labels): + parameters = { + "points": np.asarray(np.matrix(points)), + "labels": np.array(np.matrix(labels))[0] + } + + return (parameters,) + + +# SAM COMBINE PARAMETERS +class WAS_SAM_Combine_Parameters: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "sam_parameters_a": ("SAM_PARAMETERS",), + "sam_parameters_b": ("SAM_PARAMETERS",), + } + } + + RETURN_TYPES = ("SAM_PARAMETERS",) + FUNCTION = "sam_combine_parameters" + + CATEGORY = "WAS Suite/Image/Masking" + + def sam_combine_parameters(self, sam_parameters_a, sam_parameters_b): + parameters = { + "points": np.concatenate( + (sam_parameters_a["points"], + sam_parameters_b["points"]), + axis=0 + ), + "labels": np.concatenate( + (sam_parameters_a["labels"], + sam_parameters_b["labels"]) + ) + } + + return (parameters,) + + +# SAM IMAGE MASK +class WAS_SAM_Image_Mask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "sam_model": ("SAM_MODEL",), + "sam_parameters": ("SAM_PARAMETERS",), + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("IMAGE", "MASK",) + FUNCTION = "sam_image_mask" + + CATEGORY = "WAS Suite/Image/Masking" + + def sam_image_mask(self, sam_model, sam_parameters, image): + image = tensor2sam(image) + points = sam_parameters["points"] + labels = sam_parameters["labels"] + + from segment_anything import SamPredictor + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + sam_model.to(device=device) + + predictor = SamPredictor(sam_model) + predictor.set_image(image) + + masks, scores, logits = predictor.predict( + point_coords=points, + point_labels=labels, + multimask_output=False + ) + + sam_model.to(device='cpu') + + mask = np.expand_dims(masks, axis=-1) + + image = np.repeat(mask, 3, axis=-1) + image = torch.from_numpy(image) + + mask = torch.from_numpy(mask) + mask = mask.squeeze(2) + mask = mask.squeeze().to(torch.float32) + + return (image, mask, ) + +#! BOUNDED IMAGES + +# IMAGE BOUNDS + +class WAS_Image_Bounds: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("IMAGE_BOUNDS",) + FUNCTION = "image_bounds" + + CATEGORY = "WAS Suite/Image/Bound" + + def image_bounds(self, image): + # Ensure we are working with batches + image = image.unsqueeze(0) if image.dim() == 3 else image + + return([(0, img.shape[0]-1 , 0, img.shape[1]-1) for img in image],) + +# INSET IMAGE BOUNDS + +class WAS_Inset_Image_Bounds: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "image_bounds": ("IMAGE_BOUNDS",), + "inset_left": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + "inset_right": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + "inset_top": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + "inset_bottom": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("IMAGE_BOUNDS",) + FUNCTION = "inset_image_bounds" + + CATEGORY = "WAS Suite/Image/Bound" + + def inset_image_bounds(self, image_bounds, inset_left, inset_right, inset_top, inset_bottom): + inset_bounds = [] + for rmin, rmax, cmin, cmax in image_bounds: + rmin += inset_top + rmax -= inset_bottom + cmin += inset_left + cmax -= inset_right + + if rmin > rmax or cmin > cmax: + raise ValueError("Invalid insets provided. Please make sure the insets do not exceed the image bounds.") + + inset_bounds.append((rmin, rmax, cmin, cmax)) + return (inset_bounds,) + +# BOUNDED IMAGE BLEND + +class WAS_Bounded_Image_Blend: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "target": ("IMAGE",), + "target_bounds": ("IMAGE_BOUNDS",), + "source": ("IMAGE",), + "blend_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}), + "feathering": ("INT", {"default": 16, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "bounded_image_blend" + + CATEGORY = "WAS Suite/Image/Bound" + + def bounded_image_blend(self, target, target_bounds, source, blend_factor, feathering): + # Ensure we are working with batches + target = target.unsqueeze(0) if target.dim() == 3 else target + source = source.unsqueeze(0) if source.dim() == 3 else source + + # If number of target images and source images don't match then all source images + # will be applied only to the first target image, otherwise they will be applied + # 1 to 1 + # If the number of target bounds and source images don't match then all sourcess will + # use the first target bounds for scaling and placing the source images, otherwise they + # will be applied 1 to 1 + tgt_len = 1 if len(target) != len(source) else len(source) + bounds_len = 1 if len(target_bounds) != len(source) else len(source) + + # Convert target PyTorch tensors to PIL images + tgt_arr = [tensor2pil(tgt) for tgt in target[:tgt_len]] + src_arr = [tensor2pil(src) for src in source] + + result_tensors = [] + for idx in range(len(src_arr)): + src = src_arr[idx] + # If only one target image, then ensure it is the only one used + if (tgt_len == 1 and idx == 0) or tgt_len > 1: + tgt = tgt_arr[idx] + + # If only one bounds object, no need to extract and calculate more than once. + # Additionally, if only one bounds obuect, then the mask only needs created once + if (bounds_len == 1 and idx == 0) or bounds_len > 1: + # Extract the target bounds + rmin, rmax, cmin, cmax = target_bounds[idx] + + # Calculate the dimensions of the target bounds + height, width = (rmax - rmin + 1, cmax - cmin + 1) + + # Create the feathered mask portion the size of the target bounds + if feathering > 0: + inner_mask = Image.new('L', (width - (2 * feathering), height - (2 * feathering)), 255) + inner_mask = ImageOps.expand(inner_mask, border=feathering, fill=0) + inner_mask = inner_mask.filter(ImageFilter.GaussianBlur(radius=feathering)) + else: + inner_mask = Image.new('L', (width, height), 255) + + # Create a blend mask using the inner_mask and blend factor + inner_mask = inner_mask.point(lambda p: p * blend_factor) + + # Create the blend mask with the same size as the target image + tgt_mask = Image.new('L', tgt.size, 0) + # Paste the feathered mask portion into the blend mask at the target bounds position + tgt_mask.paste(inner_mask, (cmin, rmin)) + + # Resize the source image to match the dimensions of the target bounds + src_resized = src.resize((width, height), Image.Resampling.LANCZOS) + + # Create a blank image with the same size and mode as the target + src_positioned = Image.new(tgt.mode, tgt.size) + + # Paste the source image onto the blank image using the target bounds + src_positioned.paste(src_resized, (cmin, rmin)) + + # Blend the source and target images using the blend mask + result = Image.composite(src_positioned, tgt, tgt_mask) + + # Convert the result back to a PyTorch tensor + result_tensors.append(pil2tensor(result)) + + return (torch.cat(result_tensors, dim=0),) + +# BOUNDED IMAGE CROP + +class WAS_Bounded_Image_Crop: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "image": ("IMAGE",), + "image_bounds": ("IMAGE_BOUNDS",), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "bounded_image_crop" + + CATEGORY = "WAS Suite/Image/Bound" + + def bounded_image_crop(self, image, image_bounds): + # Ensure we are working with batches + image = image.unsqueeze(0) if image.dim() == 3 else image + + # If number of images and bounds don't match, then only the first bounds will be used + # to crop the images, otherwise, each bounds will be used for each image 1 to 1 + bounds_len = 1 if len(image_bounds) != len(image) else len(image) + + cropped_images = [] + for idx in range(len(image)): + # If only one bounds object, no need to extract and calculate more than once. + if (bounds_len == 1 and idx == 0) or bounds_len > 1: + rmin, rmax, cmin, cmax = image_bounds[idx] + + # Check if the provided bounds are valid + if rmin > rmax or cmin > cmax: + raise ValueError("Invalid bounds provided. Please make sure the bounds are within the image dimensions.") + + cropped_images.append(image[idx][rmin:rmax+1, cmin:cmax+1, :]) + + return (torch.stack(cropped_images, dim=0),) + + +# BOUNDED IMAGE BLEND WITH MASK + +class WAS_Bounded_Image_Blend_With_Mask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "target": ("IMAGE",), + "target_mask": ("MASK",), + "target_bounds": ("IMAGE_BOUNDS",), + "source": ("IMAGE",), + "blend_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}), + "feathering": ("INT", {"default": 16, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "bounded_image_blend_with_mask" + + CATEGORY = "WAS Suite/Image/Bound" + + def bounded_image_blend_with_mask(self, target, target_mask, target_bounds, source, blend_factor, feathering): + # Ensure we are working with batches + target = target.unsqueeze(0) if target.dim() == 3 else target + source = source.unsqueeze(0) if source.dim() == 3 else source + target_mask = target_mask.unsqueeze(0) if target_mask.dim() == 2 else target_mask + + # If number of target masks and source images don't match, then only the first mask will be used on + # the source images, otherwise, each mask will be used for each source image 1 to 1 + # Simarly, if the number of target images and source images don't match then + # all source images will be applied only to the first target, otherwise they will be applied + # 1 to 1 + tgt_mask_len = 1 if len(target_mask) != len(source) else len(source) + tgt_len = 1 if len(target) != len(source) else len(source) + bounds_len = 1 if len(target_bounds) != len(source) else len(source) + + tgt_arr = [tensor2pil(tgt) for tgt in target[:tgt_len]] + src_arr = [tensor2pil(src) for src in source] + tgt_mask_arr=[] + + # Convert Target Mask(s) to grayscale image format + for m_idx in range(tgt_mask_len): + np_array = np.clip((target_mask[m_idx].cpu().numpy().squeeze() * 255.0), 0, 255) + tgt_mask_arr.append(Image.fromarray((np_array).astype(np.uint8), mode='L')) + + result_tensors = [] + for idx in range(len(src_arr)): + src = src_arr[idx] + # If only one target image, then ensure it is the only one used + if (tgt_len == 1 and idx == 0) or tgt_len > 1: + tgt = tgt_arr[idx] + + # If only one bounds, no need to extract and calculate more than once + if (bounds_len == 1 and idx == 0) or bounds_len > 1: + # Extract the target bounds + rmin, rmax, cmin, cmax = target_bounds[idx] + + # Calculate the dimensions of the target bounds + height, width = (rmax - rmin + 1, cmax - cmin + 1) + + # If only one mask, then ensure that is the only the first is used + if (tgt_mask_len == 1 and idx == 0) or tgt_mask_len > 1: + tgt_mask = tgt_mask_arr[idx] + + # If only one mask and one bounds, then mask only needs to + # be extended once because all targets will be the same size + if (tgt_mask_len == 1 and bounds_len == 1 and idx == 0) or \ + (tgt_mask_len > 1 or bounds_len > 1): + + # This is an imperfect, but easy way to determine if the mask based on the + # target image or source image. If not target, assume source. If neither, + # then it's not going to look right regardless + if (tgt_mask.size != tgt.size): + # Create the blend mask with the same size as the target image + mask_extended_canvas = Image.new('L', tgt.size, 0) + + # Paste the mask portion into the extended mask at the target bounds position + mask_extended_canvas.paste(tgt_mask, (cmin, rmin)) + + tgt_mask = mask_extended_canvas + + # Apply feathering (Gaussian blur) to the blend mask if feather_amount is greater than 0 + if feathering > 0: + tgt_mask = tgt_mask.filter(ImageFilter.GaussianBlur(radius=feathering)) + + # Apply blending factor to the tgt mask now that it has been extended + tgt_mask = tgt_mask.point(lambda p: p * blend_factor) + + # Resize the source image to match the dimensions of the target bounds + src_resized = src.resize((width, height), Image.Resampling.LANCZOS) + + # Create a blank image with the same size and mode as the target + src_positioned = Image.new(tgt.mode, tgt.size) + + # Paste the source image onto the blank image using the target + src_positioned.paste(src_resized, (cmin, rmin)) + + # Blend the source and target images using the blend mask + result = Image.composite(src_positioned, tgt, tgt_mask) + + # Convert the result back to a PyTorch tensor + result_tensors.append(pil2tensor(result)) + + return (torch.cat(result_tensors, dim=0),) + +# BOUNDED IMAGE CROP WITH MASK + +class WAS_Bounded_Image_Crop_With_Mask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(self): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "padding_left": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + "padding_right": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + "padding_top": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + "padding_bottom": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}), + + }, + "optional":{ + "return_list": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE_BOUNDS",) + FUNCTION = "bounded_image_crop_with_mask" + + CATEGORY = "WAS Suite/Image/Bound" + + def bounded_image_crop_with_mask(self, image, mask, padding_left, padding_right, padding_top, padding_bottom,return_list=False): + # Ensure we are working with batches + image = image.unsqueeze(0) if image.dim() == 3 else image + mask = mask.unsqueeze(0) if mask.dim() == 2 else mask + + # If number of masks and images don't match, then only the first mask will be used on + # the images, otherwise, each mask will be used for each image 1 to 1 + mask_len = 1 if len(image) != len(mask) else len(image) + + cropped_images = [] + all_bounds = [] + for i in range(len(image)): + # Single mask or multiple? + if (mask_len == 1 and i == 0) or mask_len > 0: + rows = torch.any(mask[i], dim=1) + cols = torch.any(mask[i], dim=0) + rmin, rmax = torch.where(rows)[0][[0, -1]] + cmin, cmax = torch.where(cols)[0][[0, -1]] + + rmin = max(rmin - padding_top, 0) + rmax = min(rmax + padding_bottom, mask[i].shape[0] - 1) + cmin = max(cmin - padding_left, 0) + cmax = min(cmax + padding_right, mask[i].shape[1] - 1) + + # Even if only a single mask, create a bounds for each cropped image + all_bounds.append([rmin, rmax, cmin, cmax]) + cropped_images.append(image[i][rmin:rmax+1, cmin:cmax+1, :]) + if return_list: + return cropped_images, all_bounds + return torch.stack(cropped_images), all_bounds + +# DEBUG IMAGE BOUNDS TO CONSOLE + +class WAS_Image_Bounds_to_Console: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_bounds": ("IMAGE_BOUNDS",), + "label": ("STRING", {"default": 'Debug to Console', "multiline": False}), + } + } + + RETURN_TYPES = ("IMAGE_BOUNDS",) + OUTPUT_NODE = True + FUNCTION = "debug_to_console" + + CATEGORY = "WAS Suite/Debug" + + def debug_to_console(self, image_bounds, label): + label_out = 'Debug to Console' + if label.strip() != '': + label_out = label + + bounds_out = 'Empty' + if len(bounds_out) > 0: + bounds_out = ', \n '.join('\t(rmin={}, rmax={}, cmin={}, cmax={})' + .format(a, b, c, d) for a, b, c, d in image_bounds) + + cstr(f'\033[33m{label_out}\033[0m:\n[\n{bounds_out}\n]\n').msg.print() + return (image_bounds, ) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + +#! NUMBERS + +# RANDOM NUMBER + +class WAS_Random_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number_type": (["integer", "float", "bool"],), + "minimum": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}), + "maximum": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + FUNCTION = "return_randm_number" + + CATEGORY = "WAS Suite/Number" + + def return_randm_number(self, minimum, maximum, seed, number_type='integer'): + + # Set Generator Seed + random.seed(seed) + + # Return random number + if number_type: + if number_type == 'integer': + number = random.randint(minimum, maximum) + elif number_type == 'float': + number = random.uniform(minimum, maximum) + elif number_type == 'bool': + number = random.random() + else: + return + + # Return number + return (number, float(number), round(number)) + + @classmethod + def IS_CHANGED(cls, seed, **kwargs): + m = hashlib.sha256() + m.update(seed) + return m.digest().hex() + +# TRUE RANDOM NUMBER + +class WAS_True_Random_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "api_key": ("STRING",{"default":"00000000-0000-0000-0000-000000000000", "multiline": False}), + "minimum": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}), + "maximum": ("FLOAT", {"default": 10000000, "min": -18446744073709551615, "max": 18446744073709551615}), + "mode": (["random", "fixed"],), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + FUNCTION = "return_true_randm_number" + + CATEGORY = "WAS Suite/Number" + + def return_true_randm_number(self, api_key=None, minimum=0, maximum=10): + + # Get Random Number + number = self.get_random_numbers(api_key=api_key, minimum=minimum, maximum=maximum)[0] + + # Return number + return (number, ) + + def get_random_numbers(self, api_key=None, amount=1, minimum=0, maximum=10, mode="random"): + '''Get random number(s) from random.org''' + if api_key in [None, '00000000-0000-0000-0000-000000000000', '']: + cstr("No API key provided! A valid RANDOM.ORG API key is required to use `True Random.org Number Generator`").error.print() + return [0] + + url = "https://api.random.org/json-rpc/2/invoke" + headers = {"Content-Type": "application/json"} + payload = { + "jsonrpc": "2.0", + "method": "generateIntegers", + "params": { + "apiKey": api_key, + "n": amount, + "min": minimum, + "max": maximum, + "replacement": True, + "base": 10 + }, + "id": 1 + } + + response = requests.post(url, headers=headers, data=json.dumps(payload)) + if response.status_code == 200: + data = response.json() + if "result" in data: + return data["result"]["random"]["data"], float(data["result"]["random"]["data"]), int(data["result"]["random"]["data"]) + + return [0] + + @classmethod + def IS_CHANGED(cls, api_key, mode, **kwargs): + m = hashlib.sha256() + m.update(api_key) + if mode == 'fixed': + return m.digest().hex() + return float("NaN") + + +# CONSTANT NUMBER + +class WAS_Constant_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number_type": (["integer", "float", "bool"],), + "number": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615, "step": 0.01}), + }, + "optional": { + "number_as_text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + FUNCTION = "return_constant_number" + + CATEGORY = "WAS Suite/Number" + + def return_constant_number(self, number_type, number, number_as_text=None): + + if number_as_text: + if number_type == "integer": + number = int(number_as_text) + elif number_type == "float": + number = float(number_as_text) + else: + number = bool(number_as_text) + + # Return number + if number_type: + if number_type == 'integer': + return (int(number), float(number), int(number) ) + elif number_type == 'float': + return (float(number), float(number), int(number) ) + elif number_type == 'bool': + boolean = (1 if float(number) > 0.5 else 0) + return (int(boolean), float(boolean), int(boolean) ) + else: + return (number, float(number), int(number) ) + +# INCREMENT NUMBER + +class WAS_Number_Counter: + def __init__(self): + self.counters = {} + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number_type": (["integer", "float"],), + "mode": (["increment", "decrement", "increment_to_stop", "decrement_to_stop"],), + "start": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615, "step": 0.01}), + "stop": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615, "step": 0.01}), + "step": ("FLOAT", {"default": 1, "min": 0, "max": 99999, "step": 0.01}), + }, + "optional": { + "reset_bool": ("NUMBER",), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + RETURN_NAMES = ("number", "float", "int") + FUNCTION = "increment_number" + + CATEGORY = "WAS Suite/Number" + + def increment_number(self, number_type, mode, start, stop, step, unique_id, reset_bool=0): + + counter = int(start) if mode == 'integer' else start + if self.counters.__contains__(unique_id): + counter = self.counters[unique_id] + + if round(reset_bool) >= 1: + counter = start + + if mode == 'increment': + counter += step + elif mode == 'deccrement': + counter -= step + elif mode == 'increment_to_stop': + counter = counter + step if counter < stop else counter + elif mode == 'decrement_to_stop': + counter = counter - step if counter > stop else counter + + self.counters[unique_id] = counter + + result = int(counter) if number_type == 'integer' else float(counter) + + return ( result, float(counter), int(counter) ) + + +# NUMBER TO SEED + +class WAS_Number_To_Seed: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + } + } + + RETURN_TYPES = ("SEED",) + FUNCTION = "number_to_seed" + + CATEGORY = "WAS Suite/Number/Operations" + + def number_to_seed(self, number): + return ({"seed": number, }, ) + + +# NUMBER TO INT + +class WAS_Number_To_Int: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + } + } + + RETURN_TYPES = ("INT",) + FUNCTION = "number_to_int" + + CATEGORY = "WAS Suite/Number/Operations" + + def number_to_int(self, number): + return (int(number), ) + + + +# NUMBER TO FLOAT + +class WAS_Number_To_Float: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + } + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "number_to_float" + + CATEGORY = "WAS Suite/Number/Operations" + + def number_to_float(self, number): + return (float(number), ) + + + +# INT TO NUMBER + +class WAS_Int_To_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int_input": ("INT",), + } + } + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "int_to_number" + + CATEGORY = "WAS Suite/Number/Operations" + + def int_to_number(self, int_input): + return (int(int_input), ) + + + +# NUMBER TO FLOAT + +class WAS_Float_To_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "float_input": ("FLOAT",), + } + } + + RETURN_TYPES = ("NUMBER",) + FUNCTION = "float_to_number" + + CATEGORY = "WAS Suite/Number/Operations" + + def float_to_number(self, float_input): + return ( float(float_input), ) + + +# NUMBER TO STRING + +class WAS_Number_To_String: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "number_to_string" + + CATEGORY = "WAS Suite/Number/Operations" + + def number_to_string(self, number): + return ( str(number), ) + +# NUMBER TO STRING + +class WAS_Number_To_Text: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "number_to_text" + + CATEGORY = "WAS Suite/Number/Operations" + + def number_to_text(self, number): + return ( str(number), ) + + +# NUMBER PI + +class WAS_Number_PI: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {} + } + + RETURN_TYPES = ("NUMBER", "FLOAT") + FUNCTION = "number_pi" + + CATEGORY = "WAS Suite/Number" + + def number_pi(self): + return (math.pi, math.pi) + +# Boolean + +class WAS_Boolean: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("BOOLEAN", "NUMBER", "INT", "FLOAT") + FUNCTION = "return_boolean" + + CATEGORY = "WAS Suite/Logic" + + def return_boolean(self, boolean=1.0): + boolean_bool = bool(int(round(boolean))) + int_bool = int(round(boolean)) + return (boolean_bool, int_bool, int_bool, boolean) + + +# Logical Comparisons Base Class + +class WAS_Logical_Comparisons: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean_a": ("BOOLEAN", {"default": False}), + "boolean_b": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "do" + + CATEGORY = "WAS Suite/Logic" + + def do(self, boolean_a, boolean_b): + pass + + +# Logical OR + +class WAS_Logical_OR(WAS_Logical_Comparisons): + def do(self, boolean_a, boolean_b): + return (boolean_a or boolean_b,) + + +# Logical AND + +class WAS_Logical_AND(WAS_Logical_Comparisons): + def do(self, boolean_a, boolean_b): + return (boolean_a and boolean_b,) + + +# Logical XOR + +class WAS_Logical_XOR(WAS_Logical_Comparisons): + def do(self, boolean_a, boolean_b): + return (boolean_a != boolean_b,) + + + +# Boolean + +class WAS_Boolean_Primitive: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "do" + + CATEGORY = "WAS Suite/Logic" + + def do(self, boolean): + return (boolean,) + + +# Boolean + +class WAS_Boolean_To_Text: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "do" + + CATEGORY = "WAS Suite/Logic" + + def do(self, boolean): + if boolean: + return ("True",) + return ("False",) + + +# Logical NOT + +class WAS_Logical_NOT: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "boolean": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "do" + + CATEGORY = "WAS Suite/Logic" + + def do(self, boolean): + return (not boolean,) + + +# NUMBER OPERATIONS + + +class WAS_Number_Operation: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number_a": ("NUMBER",), + "number_b": ("NUMBER",), + "operation": (["addition", "subtraction", "division", "floor division", "multiplication", "exponentiation", "modulus", "greater-than", "greater-than or equals", "less-than", "less-than or equals", "equals", "does not equal"],), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + FUNCTION = "math_operations" + + CATEGORY = "WAS Suite/Number/Operations" + + def math_operations(self, number_a, number_b, operation="addition"): + + # Return random number + if operation: + if operation == 'addition': + result = (number_a + number_b) + return result, result, int(result) + elif operation == 'subtraction': + result = (number_a - number_b) + return result, result, int(result) + elif operation == 'division': + result = (number_a / number_b) + return result, result, int(result) + elif operation == 'floor division': + result = (number_a // number_b) + return result, result, int(result) + elif operation == 'multiplication': + result = (number_a * number_b) + return result, result, int(result) + elif operation == 'exponentiation': + result = (number_a ** number_b) + return result, result, int(result) + elif operation == 'modulus': + result = (number_a % number_b) + return result, result, int(result) + elif operation == 'greater-than': + result = +(number_a > number_b) + return result, result, int(result) + elif operation == 'greater-than or equals': + result = +(number_a >= number_b) + return result, result, int(result) + elif operation == 'less-than': + result = +(number_a < number_b) + return result, result, int(result) + elif operation == 'less-than or equals': + result = +(number_a <= number_b) + return result, result, int(result) + elif operation == 'equals': + result = +(number_a == number_b) + return result, result, int(result) + elif operation == 'does not equal': + result = +(number_a != number_b) + return result, result, int(result) + else: + cstr("Invalid number operation selected.").error.print() + return (number_a, number_a, int(number_a)) + +# NUMBER MULTIPLE OF + +class WAS_Number_Multiple_Of: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + "multiple": ("INT", {"default": 8, "min": -18446744073709551615, "max": 18446744073709551615}), + } + } + + RETURN_TYPES =("NUMBER", "FLOAT", "INT") + FUNCTION = "number_multiple_of" + + CATEGORY = "WAS Suite/Number/Functions" + + def number_multiple_of(self, number, multiple=8): + if number % multiple != 0: + return ((number // multiple) * multiple + multiple, ) + return (number, number, int(number)) + + +#! MISC + + +# Bus. Converts the 5 main connectors into one, and back again. You can provide a bus as input +# or the 5 separate inputs, or a combination. If you provide a bus input and a separate +# input (e.g. a model), the model will take precedence. +# +# The term 'bus' comes from computer hardware, see https://en.wikipedia.org/wiki/Bus_(computing) +class WAS_Bus: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required":{}, + "optional": { + "bus" : ("BUS",), + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + } + } + RETURN_TYPES = ("BUS", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING",) + RETURN_NAMES = ("bus", "model", "clip", "vae", "positive", "negative") + FUNCTION = "bus_fn" + CATEGORY = "WAS Suite/Utilities" + + def bus_fn(self, bus=(None,None,None,None,None), model=None, clip=None, vae=None, positive=None, negative=None): + + # Unpack the 5 constituents of the bus from the bus tuple. + (bus_model, bus_clip, bus_vae, bus_positive, bus_negative) = bus + + # If you pass in specific inputs, they override what comes from the bus. + out_model = model or bus_model + out_clip = clip or bus_clip + out_vae = vae or bus_vae + out_positive = positive or bus_positive + out_negative = negative or bus_negative + + # Squash all 5 inputs into the output bus tuple. + out_bus = (out_model, out_clip, out_vae, out_positive, out_negative) + + if not out_model: + raise ValueError('Either model or bus containing a model should be supplied') + if not out_clip: + raise ValueError('Either clip or bus containing a clip should be supplied') + if not out_vae: + raise ValueError('Either vae or bus containing a vae should be supplied') + # We don't insist that a bus contains conditioning. + + return (out_bus, out_model, out_clip, out_vae, out_positive, out_negative) + + +# Image Width and Height to Number + +class WAS_Image_Size_To_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + } + } + + RETURN_TYPES = ("NUMBER", "NUMBER", "FLOAT", "FLOAT", "INT", "INT") + RETURN_NAMES = ("width_num", "height_num", "width_float", "height_float", "width_int", "height_int") + FUNCTION = "image_width_height" + + CATEGORY = "WAS Suite/Number/Operations" + + def image_width_height(self, image): + image = tensor2pil(image) + if image.size: + return( image.size[0], image.size[1], float(image.size[0]), float(image.size[1]), image.size[0], image.size[1] ) + return ( 0, 0, 0, 0, 0, 0) + + +# Latent Width and Height to Number + +class WAS_Latent_Size_To_Number: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "samples": ("LATENT",), + } + } + + RETURN_TYPES = ("NUMBER", "NUMBER", "FLOAT", "FLOAT", "INT", "INT") + RETURN_NAMES = ("tensor_w_num","tensor_h_num","tensor_w_float","tensor_h_float","tensor_w_int","tensor_h_int") + FUNCTION = "latent_width_height" + + CATEGORY = "WAS Suite/Number/Operations" + + def latent_width_height(self, samples): + size_dict = {} + i = 0 + for tensor in samples['samples'][0]: + if not isinstance(tensor, torch.Tensor): + cstr(f'Input should be a torch.Tensor').error.print() + shape = tensor.shape + tensor_height = shape[-2] + tensor_width = shape[-1] + size_dict.update({i:[tensor_width, tensor_height]}) + return ( size_dict[0][0], size_dict[0][1], float(size_dict[0][0]), float(size_dict[0][1]), size_dict[0][0], size_dict[0][1] ) + + +# LATENT INPUT SWITCH + +class WAS_Latent_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "latent_a": ("LATENT",), + "latent_b": ("LATENT",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "latent_input_switch" + + CATEGORY = "WAS Suite/Logic" + + def latent_input_switch(self, latent_a, latent_b, boolean=True): + + if boolean: + return (latent_a, ) + else: + return (latent_b, ) + +# NUMBER INPUT CONDITION + +class WAS_Number_Input_Condition: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number_a": ("NUMBER",), + "number_b": ("NUMBER",), + "return_boolean": (["false", "true"],), + "comparison": (["and", "or", "greater-than", "greater-than or equals", "less-than", "less-than or equals", "equals", "does not equal", "divisible by", "if A odd", "if A even", "if A prime", "factor of"],), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + FUNCTION = "number_input_condition" + + CATEGORY = "WAS Suite/Logic" + + def number_input_condition(self, number_a, number_b, return_boolean="false", comparison="greater-than"): + + if comparison: + if return_boolean == 'true': + if comparison == 'and': + result = 1 if number_a != 0 and number_b != 0 else 0 + elif comparison == 'or': + result = 1 if number_a != 0 or number_b != 0 else 0 + elif comparison == 'greater-than': + result = 1 if number_a > number_b else 0 + elif comparison == 'greater-than or equals': + result = 1 if number_a >= number_b else 0 + elif comparison == 'less-than': + result = 1 if number_a < number_b else 0 + elif comparison == 'less-than or equals': + result = 1 if number_a <= number_b else 0 + elif comparison == 'equals': + result = 1 if number_a == number_b else 0 + elif comparison == 'does not equal': + result = 1 if number_a != number_b else 0 + elif comparison == 'divisible by': + result = 1 if number_b % number_a == 0 else 0 + elif comparison == 'if A odd': + result = 1 if number_a % 2 != 0 else 0 + elif comparison == 'if A even': + result = 1 if number_a % 2 == 0 else 0 + elif comparison == 'if A prime': + result = 1 if self.is_prime(number_a) else 0 + elif comparison == 'factor of': + result = 1 if number_b % number_a == 0 else 0 + else: + result = 0 + else: + if comparison == 'and': + result = number_a if number_a != 0 and number_b != 0 else number_b + elif comparison == 'or': + result = number_a if number_a != 0 or number_b != 0 else number_b + elif comparison == 'greater-than': + result = number_a if number_a > number_b else number_b + elif comparison == 'greater-than or equals': + result = number_a if number_a >= number_b else number_b + elif comparison == 'less-than': + result = number_a if number_a < number_b else number_b + elif comparison == 'less-than or equals': + result = number_a if number_a <= number_b else number_b + elif comparison == 'equals': + result = number_a if number_a == number_b else number_b + elif comparison == 'does not equal': + result = number_a if number_a != number_b else number_b + elif comparison == 'divisible by': + result = number_a if number_b % number_a == 0 else number_b + elif comparison == 'if A odd': + result = number_a if number_a % 2 != 0 else number_b + elif comparison == 'if A even': + result = number_a if number_a % 2 == 0 else number_b + elif comparison == 'if A prime': + result = number_a if self.is_prime(number_a) else number_b + elif comparison == 'factor of': + result = number_a if number_b % number_a == 0 else number_b + else: + result = number_a + + return (result, float(result), int(result)) + + def is_prime(self, n): + if n <= 1: + return False + elif n <= 3: + return True + elif n % 2 == 0 or n % 3 == 0: + return False + i = 5 + while i * i <= n: + if n % i == 0 or n % (i + 2) == 0: + return False + i += 6 + return True + +# ASPECT RATIO + +class WAS_Image_Aspect_Ratio: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": { + "image": ("IMAGE",), + "width": ("NUMBER",), + "height": ("NUMBER",), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "NUMBER", TEXT_TYPE, TEXT_TYPE) + RETURN_NAMES = ("aspect_number", "aspect_float", "is_landscape_bool", "aspect_ratio_common", "aspect_type") + FUNCTION = "aspect" + + CATEGORY = "WAS Suite/Logic" + + def aspect(self, boolean=True, image=None, width=None, height=None): + + if width and height: + width = width; height = height + elif image is not None: + width, height = tensor2pil(image).size + else: + raise Exception("WAS_Image_Aspect_Ratio must have width and height provided if no image tensori supplied.") + + aspect_ratio = width / height + aspect_type = "landscape" if aspect_ratio > 1 else "portrait" if aspect_ratio < 1 else "square" + + landscape_bool = 0 + if aspect_type == "landscape": + landscape_bool = 1 + + gcd = math.gcd(width, height) + gcd_w = width // gcd + gcd_h = height // gcd + aspect_ratio_common = f"{gcd_w}:{gcd_h}" + + return aspect_ratio, aspect_ratio, landscape_bool, aspect_ratio_common, aspect_type + + +# NUMBER INPUT SWITCH + +class WAS_Number_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number_a": ("NUMBER",), + "number_b": ("NUMBER",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("NUMBER", "FLOAT", "INT") + FUNCTION = "number_input_switch" + + CATEGORY = "WAS Suite/Logic" + + def number_input_switch(self, number_a, number_b, boolean=True): + + if boolean: + return (number_a, float(number_a), int(number_a)) + else: + return (number_b, float(number_b), int(number_b)) + + +# IMAGE INPUT SWITCH + +class WAS_Image_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_a": ("IMAGE",), + "image_b": ("IMAGE",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "image_input_switch" + + CATEGORY = "WAS Suite/Logic" + + def image_input_switch(self, image_a, image_b, boolean=True): + + if boolean: + return (image_a, ) + else: + return (image_b, ) + +# CONDITIONING INPUT SWITCH + +class WAS_Conditioning_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "conditioning_a": ("CONDITIONING",), + "conditioning_b": ("CONDITIONING",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "conditioning_input_switch" + + CATEGORY = "WAS Suite/Logic" + + def conditioning_input_switch(self, conditioning_a, conditioning_b, boolean=True): + + if boolean: + return (conditioning_a, ) + else: + return (conditioning_b, ) + +# MODEL INPUT SWITCH + +class WAS_Model_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model_a": ("MODEL",), + "model_b": ("MODEL",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "model_switch" + + CATEGORY = "WAS Suite/Logic" + + def model_switch(self, model_a, model_b, boolean=True): + + if boolean: + return (model_a, ) + else: + return (model_b, ) + +# VAE INPUT SWITCH + +class WAS_VAE_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "vae_a": ("VAE",), + "vae_b": ("VAE",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("VAE",) + FUNCTION = "vae_switch" + + CATEGORY = "WAS Suite/Logic" + + def vae_switch(self, vae_a, vae_b, boolean=True): + + if boolean: + return (vae_a, ) + else: + return (vae_b, ) + +# CLIP INPUT SWITCH + +class WAS_CLIP_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "clip_a": ("CLIP",), + "clip_b": ("CLIP",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("CLIP",) + FUNCTION = "clip_switch" + + CATEGORY = "WAS Suite/Logic" + + def clip_switch(self, clip_a, clip_b, boolean=True): + + if boolean: + return (clip_a, ) + else: + return (clip_b, ) + +# UPSCALE MODEL INPUT SWITCH + +class WAS_Upscale_Model_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "upscale_model_a": ("UPSCALE_MODEL",), + "upscale_model_b": ("UPSCALE_MODEL",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("UPSCALE_MODEL",) + FUNCTION = "upscale_model_switch" + + CATEGORY = "WAS Suite/Logic" + + def upscale_model_switch(self, upscale_model_a, upscale_model_b, boolean=True): + + if boolean: + return (upscale_model_a, ) + else: + return (upscale_model_b, ) + + +# CONTROL NET INPUT SWITCH + +class WAS_Control_Net_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "control_net_a": ("CONTROL_NET",), + "control_net_b": ("CONTROL_NET",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "control_net_switch" + + CATEGORY = "WAS Suite/Logic" + + def control_net_switch(self, control_net_a, control_net_b, boolean=True): + + if boolean: + return (control_net_a, ) + else: + return (control_net_b, ) + +# CLIP VISION INPUT SWITCH + +class WAS_CLIP_Vision_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "clip_vision_a": ("CLIP_VISION",), + "clip_vision_b": ("CLIP_VISION",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = ("CLIP_VISION",) + FUNCTION = "clip_vision_switch" + + CATEGORY = "WAS Suite/Logic" + + def clip_vision_switch(self, clip_vision_a, clip_vision_b, boolean=True): + + if boolean: + return (clip_vision_a, ) + else: + return (clip_vision_b) + +# TEXT INPUT SWITCH + +class WAS_Text_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text_a": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "text_b": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + + RETURN_TYPES = (TEXT_TYPE,) + FUNCTION = "text_input_switch" + + CATEGORY = "WAS Suite/Logic" + + def text_input_switch(self, text_a, text_b, boolean=True): + + if boolean: + return (text_a, ) + else: + return (text_b, ) + + +# TEXT CONTAINS + +class WAS_Text_Contains: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "text": ("STRING", {"default": '', "multiline": False}), + "sub_text": ("STRING", {"default": '', "multiline": False}), + }, + "optional": { + "case_insensitive": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "text_contains" + + CATEGORY = "WAS Suite/Logic" + + def text_contains(self, text, sub_text, case_insensitive): + if case_insensitive: + sub_text = sub_text.lower() + text = text.lower() + + return (sub_text in text,) + + +# DEBUG INPUT TO CONSOLE + + +class WAS_Debug_Number_to_Console: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "number": ("NUMBER",), + "label": ("STRING", {"default": 'Debug to Console', "multiline": False}), + } + } + + RETURN_TYPES = ("NUMBER",) + OUTPUT_NODE = True + FUNCTION = "debug_to_console" + + CATEGORY = "WAS Suite/Debug" + + def debug_to_console(self, number, label): + if label.strip() != '': + cstr(f'\033[33m{label}\033[0m:\n{number}\n').msg.print() + else: + cstr(f'\033[33mDebug to Console\033[0m:\n{number}\n').msg.print() + return (number, ) + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + +# CUSTOM COMFYUI NODES + +class WAS_Checkpoint_Loader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (comfy_paths.get_filename_list("configs"), ), + "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE) + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING") + FUNCTION = "load_checkpoint" + + CATEGORY = "WAS Suite/Loaders/Advanced" + + def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): + config_path = comfy_paths.get_full_path("configs", config_name) + ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=comfy_paths.get_folder_paths("embeddings")) + return (out[0], out[1], out[2], os.path.splitext(os.path.basename(ckpt_name))[0]) + +class WAS_Checkpoint_Loader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (comfy_paths.get_filename_list("configs"), ), + "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE) + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING") + FUNCTION = "load_checkpoint" + + CATEGORY = "WAS Suite/Loaders/Advanced" + + def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): + config_path = comfy_paths.get_full_path("configs", config_name) + ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=comfy_paths.get_folder_paths("embeddings")) + return (out[0], out[1], out[2], os.path.splitext(os.path.basename(ckpt_name))[0]) + +class WAS_Diffusers_Hub_Model_Loader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "repo_id": ("STRING", {"multiline":False}), + "revision": ("STRING", {"default": "None", "multiline":False})}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE) + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING") + FUNCTION = "load_hub_checkpoint" + + CATEGORY = "WAS Suite/Loaders/Advanced" + + def load_hub_checkpoint(self, repo_id=None, revision=None): + if revision in ["", "None", "none", None]: + revision = None + model_path = comfy_paths.get_folder_paths("diffusers")[0] + self.download_diffusers_model(repo_id, model_path, revision) + diffusersLoader = nodes.DiffusersLoader() + model, clip, vae = diffusersLoader.load_checkpoint(os.path.join(model_path, repo_id)) + return (model, clip, vae, repo_id) + + def download_diffusers_model(self, repo_id, local_dir, revision=None): + if 'huggingface-hub' not in packages(): + install_package("huggingface_hub") + + from huggingface_hub import snapshot_download + model_path = os.path.join(local_dir, repo_id) + ignore_patterns = ["*.ckpt","*.safetensors","*.onnx"] + snapshot_download(repo_id=repo_id, repo_type="model", local_dir=model_path, revision=revision, use_auth_token=False, ignore_patterns=ignore_patterns) + +class WAS_Checkpoint_Loader_Simple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE) + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING") + FUNCTION = "load_checkpoint" + + CATEGORY = "WAS Suite/Loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=comfy_paths.get_folder_paths("embeddings")) + return (out[0], out[1], out[2], os.path.splitext(os.path.basename(ckpt_name))[0]) + +class WAS_Diffusers_Loader: + @classmethod + def INPUT_TYPES(cls): + paths = [] + for search_path in comfy_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + paths += next(os.walk(search_path))[1] + return {"required": {"model_path": (paths,), }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE) + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING") + FUNCTION = "load_checkpoint" + + CATEGORY = "WAS Suite/Loaders/Advanced" + + def load_checkpoint(self, model_path, output_vae=True, output_clip=True): + for search_path in comfy_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + paths = next(os.walk(search_path))[1] + if model_path in paths: + model_path = os.path.join(search_path, model_path) + break + + out = comfy.diffusers_convert.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=comfy_paths.get_folder_paths("embeddings")) + return (out[0], out[1], out[2], os.path.basename(model_path)) + + +class WAS_unCLIP_Checkpoint_Loader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION", "STRING") + RETURN_NAMES = ("MODEL", "CLIP", "VAE", "CLIP_VISION", "NAME_STRING") + FUNCTION = "load_checkpoint" + + CATEGORY = "WAS Suite/Loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=comfy_paths.get_folder_paths("embeddings")) + return (out[0], out[1], out[2], out[3], os.path.splitext(os.path.basename(ckpt_name))[0]) + + +class WAS_Lora_Input_Switch: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model_a": ("MODEL",), + "clip_a": ("CLIP",), + "model_b": ("MODEL",), + "clip_b": ("CLIP",), + "boolean": ("BOOLEAN", {"forceInput": True}), + } + } + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "lora_input_switch" + + CATEGORY = "WAS Suite/Logic" + + def lora_input_switch(self, model_a, clip_a, model_b, clip_b, boolean=True): + if boolean: + return (model_a, clip_a) + else: + return (model_b, clip_b) + + +class WAS_Lora_Loader: + def __init__(self): + self.loaded_lora = None; + + @classmethod + def INPUT_TYPES(s): + file_list = comfy_paths.get_filename_list("loras") + file_list.insert(0, "None") + return {"required": { "model": ("MODEL",), + "clip": ("CLIP", ), + "lora_name": (file_list, ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL", "CLIP", TEXT_TYPE) + RETURN_NAMES = ("MODEL", "CLIP", "NAME_STRING") + FUNCTION = "load_lora" + + CATEGORY = "WAS Suite/Loaders" + + def load_lora(self, model, clip, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + lora_path = comfy_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora, os.path.splitext(os.path.basename(lora_name))[0]) + +class WAS_Upscale_Model_Loader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name": (comfy_paths.get_filename_list("upscale_models"), ), + }} + RETURN_TYPES = ("UPSCALE_MODEL",TEXT_TYPE) + RETURN_NAMES = ("UPSCALE_MODEL","MODEL_NAME_TEXT") + FUNCTION = "load_model" + + CATEGORY = "WAS Suite/Loaders" + + def load_model(self, model_name): + model_path = comfy_paths.get_full_path("upscale_models", model_name) + sd = comfy.utils.load_torch_file(model_path) + out = model_loading.load_state_dict(sd).eval() + return (out,model_name) + +# VIDEO WRITER + +class WAS_Video_Writer: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + WTools = WAS_Tools_Class() + v = WTools.VideoWriter() + codecs = [] + for codec in v.get_codecs(): + codecs.append(codec.upper()) + codecs = sorted(codecs) + return { + "required": { + "image": ("IMAGE",), + "transition_frames": ("INT", {"default":30, "min":0, "max":120, "step":1}), + "image_delay_sec": ("FLOAT", {"default":2.5, "min":0.1, "max":60000.0, "step":0.1}), + "fps": ("INT", {"default":30, "min":1, "max":60.0, "step":1}), + "max_size": ("INT", {"default":512, "min":128, "max":1920, "step":1}), + "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}), + "filename": ("STRING", {"default": "comfy_writer", "multiline": False}), + "codec": (codecs,), + } + } + + #@classmethod + #def IS_CHANGED(cls, **kwargs): + # return float("NaN") + + RETURN_TYPES = ("IMAGE",TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("IMAGE_PASS","filepath_text","filename_text") + FUNCTION = "write_video" + + CATEGORY = "WAS Suite/Animation/Writer" + + def write_video(self, image, transition_frames=10, image_delay_sec=10, fps=30, max_size=512, + output_path="./ComfyUI/output", filename="morph", codec="H264"): + + conf = getSuiteConfig() + if not conf.__contains__('ffmpeg_bin_path'): + cstr(f"Unable to use MP4 Writer because the `ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}`").error.print() + return (image,"","") + + if conf.__contains__('ffmpeg_bin_path'): + if conf['ffmpeg_bin_path'] != "/path/to/ffmpeg": + sys.path.append(conf['ffmpeg_bin_path']) + os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;udp" + os.environ['OPENCV_FFMPEG_BINARY'] = conf['ffmpeg_bin_path'] + + if output_path.strip() in [None, "", "."]: + output_path = "./ComfyUI/output" + + if image == None: + image = pil2tensor(Image.new("RGB", (512,512), (0,0,0))) + + if transition_frames < 0: + transition_frames = 0 + elif transition_frames > 60: + transition_frames = 60 + + if fps < 1: + fps = 1 + elif fps > 60: + fps = 60 + + results = [] + for img in image: + print(img.shape) + new_image = self.rescale_image(tensor2pil(img), max_size) + print(new_image.size) + + tokens = TextTokens() + output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/'))) + output_file = os.path.join(output_path, tokens.parseTokens(filename)) + + if not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + + WTools = WAS_Tools_Class() + MP4Writer = WTools.VideoWriter(int(transition_frames), int(fps), int(image_delay_sec), max_size=max_size, codec=codec) + path = MP4Writer.write(new_image, output_file) + + results.append(img) + + return (torch.cat(results, dim=0), path, filename) + + def rescale_image(self, image, max_dimension): + width, height = image.size + if width > max_dimension or height > max_dimension: + scaling_factor = max(width, height) / max_dimension + new_width = int(width / scaling_factor) + new_height = int(height / scaling_factor) + image = image.resize((new_width, new_height), Image.Resampling(1)) + return image + +# VIDEO CREATOR + +class WAS_Create_Video_From_Path: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + WTools = WAS_Tools_Class() + v = WTools.VideoWriter() + codecs = [] + for codec in v.get_codecs(): + codecs.append(codec.upper()) + codecs = sorted(codecs) + return { + "required": { + "transition_frames": ("INT", {"default":30, "min":0, "max":120, "step":1}), + "image_delay_sec": ("FLOAT", {"default":2.5, "min":0.01, "max":60000.0, "step":0.01}), + "fps": ("INT", {"default":30, "min":1, "max":60.0, "step":1}), + "max_size": ("INT", {"default":512, "min":128, "max":1920, "step":1}), + "input_path": ("STRING", {"default": "./ComfyUI/input", "multiline": False}), + "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}), + "filename": ("STRING", {"default": "comfy_video", "multiline": False}), + "codec": (codecs,), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("filepath_text","filename_text") + FUNCTION = "create_video_from_path" + + CATEGORY = "WAS Suite/Animation" + + def create_video_from_path(self, transition_frames=10, image_delay_sec=10, fps=30, max_size=512, + input_path="./ComfyUI/input", output_path="./ComfyUI/output", filename="morph", codec="H264"): + + conf = getSuiteConfig() + if not conf.__contains__('ffmpeg_bin_path'): + cstr(f"Unable to use MP4 Writer because the `ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}`").error.print() + return ("","") + + if conf.__contains__('ffmpeg_bin_path'): + if conf['ffmpeg_bin_path'] != "/path/to/ffmpeg": + sys.path.append(conf['ffmpeg_bin_path']) + os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;udp" + os.environ['OPENCV_FFMPEG_BINARY'] = conf['ffmpeg_bin_path'] + + if output_path.strip() in [None, "", "."]: + output_path = "./ComfyUI/output" + + if transition_frames < 0: + transition_frames = 0 + elif transition_frames > 60: + transition_frames = 60 + + if fps < 1: + fps = 1 + elif fps > 60: + fps = 60 + + tokens = TextTokens() + + # Check if output_path is an absolute path + if not os.path.isabs(output_path): + output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/'))) + + output_file = os.path.join(output_path, tokens.parseTokens(filename)) + + if not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + + WTools = WAS_Tools_Class() + MP4Writer = WTools.VideoWriter(int(transition_frames), int(fps), int(image_delay_sec), max_size, codec) + path = MP4Writer.create_video(input_path, output_file) + + return (path, filename) + +# VIDEO FRAME DUMP + +class WAS_Video_Frame_Dump: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video_path": ("STRING", {"default":"./ComfyUI/input/MyVideo.mp4", "multiline":False}), + "output_path": ("STRING", {"default": "./ComfyUI/input/MyVideo", "multiline": False}), + "prefix": ("STRING", {"default": "frame_", "multiline": False}), + "filenumber_digits": ("INT", {"default":4, "min":-1, "max":8, "step":1}), + "extension": (["png","jpg","gif","tiff"],), + } + } + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + + RETURN_TYPES = (TEXT_TYPE,"NUMBER") + RETURN_NAMES = ("output_path","processed_count") + FUNCTION = "dump_video_frames" + + CATEGORY = "WAS Suite/Animation" + + def dump_video_frames(self, video_path, output_path, prefix="fame_", extension="png",filenumber_digits=-1): + + conf = getSuiteConfig() + if not conf.__contains__('ffmpeg_bin_path'): + cstr(f"Unable to use dump frames because the `ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}`").error.print() + return ("",0) + + if conf.__contains__('ffmpeg_bin_path'): + if conf['ffmpeg_bin_path'] != "/path/to/ffmpeg": + sys.path.append(conf['ffmpeg_bin_path']) + os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;udp" + os.environ['OPENCV_FFMPEG_BINARY'] = conf['ffmpeg_bin_path'] + + if output_path.strip() in [None, "", "."]: + output_path = "./ComfyUI/input/frames" + + tokens = TextTokens() + output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/'))) + prefix = tokens.parseTokens(prefix) + + WTools = WAS_Tools_Class() + MP4Writer = WTools.VideoWriter() + processed = MP4Writer.extract(video_path, output_path, prefix, extension,filenumber_digits) + + return (output_path, processed) + +# CACHING + +class WAS_Cache: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "latent_suffix": ("STRING", {"default": str(random.randint(999999, 99999999))+"_cache", "multiline":False}), + "image_suffix": ("STRING", {"default": str(random.randint(999999, 99999999))+"_cache", "multiline":False}), + "conditioning_suffix": ("STRING", {"default": str(random.randint(999999, 99999999))+"_cache", "multiline":False}), + }, + "optional": { + "output_path": ("STRING", {"default": os.path.join(WAS_SUITE_ROOT, 'cache'), "multiline": False}), + "latent": ("LATENT",), + "image": ("IMAGE",), + "conditioning": ("CONDITIONING",), + } + } + + RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,TEXT_TYPE) + RETURN_NAMES = ("latent_filename","image_filename","conditioning_filename") + FUNCTION = "cache_input" + OUTPUT_NODE = True + + CATEGORY = "WAS Suite/IO" + + def cache_input(self, latent_suffix="_cache", image_suffix="_cache", conditioning_suffix="_cache", output_path=None, latent=None, image=None, conditioning=None): + + if 'joblib' not in packages(): + install_package('joblib') + + import joblib + + output = os.path.join(WAS_SUITE_ROOT, 'cache') + if output_path: + if output_path.strip() not in ['', 'none', 'None']: + output = output_path + if not os.path.isabs(output): + output = os.path.abspath(output) + if not os.path.exists(output): + os.makedirs(output, exist_ok=True) + + l_filename = "" + i_filename = "" + c_filename = "" + + tokens = TextTokens() + output = tokens.parseTokens(output) + + if latent != None: + l_filename = f'{tokens.parseTokens(latent_suffix)}.latent' + out_file = os.path.join(output, l_filename) + joblib.dump(latent, out_file) + cstr(f"Latent saved to: {out_file}").msg.print() + + if image != None: + i_filename = f'{tokens.parseTokens(image_suffix)}.image' + out_file = os.path.join(output, i_filename) + joblib.dump(image, out_file) + cstr(f"Tensor batch saved to: {out_file}").msg.print() + + if conditioning != None: + c_filename = f'{tokens.parseTokens(conditioning_suffix)}.conditioning' + out_file = os.path.join(output, c_filename) + joblib.dump(conditioning, os.path.join(output, out_file)) + cstr(f"Conditioning saved to: {out_file}").msg.print() + + return (l_filename, i_filename, c_filename) + + +class WAS_Load_Cache: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "latent_path": ("STRING", {"default": "", "multiline":False}), + "image_path": ("STRING", {"default": "", "multiline":False}), + "conditioning_path": ("STRING", {"default": "", "multiline":False}), + } + } + + RETURN_TYPES = ("LATENT","IMAGE","CONDITIONING") + RETURN_NAMES = ("LATENT","IMAGE","CONDITIONING") + FUNCTION = "load_cache" + + CATEGORY = "WAS Suite/IO" + + def load_cache(self, latent_path=None, image_path=None, conditioning_path=None): + + if 'joblib' not in packages(): + install_package('joblib') + + import joblib + + input_path = os.path.join(WAS_SUITE_ROOT, 'cache') + + latent = None + image = None + conditioning = None + + if latent_path not in ["",None]: + if os.path.exists(latent_path): + latent = joblib.load(latent_path) + else: + cstr(f"Unable to locate cache file {latent_path}").error.print() + + if image_path not in ["",None]: + if os.path.exists(image_path): + image = joblib.load(image_path) + else: + cstr(f"Unable to locate cache file {image_path}").msg.print() + + if conditioning_path not in ["",None]: + if os.path.exists(conditioning_path): + conditioning = joblib.load(conditioning_path) + else: + cstr(f"Unable to locate cache file {conditioning_path}").error.print() + + return (latent, image, conditioning) + + +# SAMPLES PASS STAT SYSTEM + +class WAS_Samples_Passthrough_Stat_System: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "samples": ("LATENT",), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("samples",) + FUNCTION = "stat_system" + + CATEGORY = "WAS Suite/Debug" + + def stat_system(self, samples): + + log = "" + for stat in self.get_system_stats(): + log += stat + "\n" + + cstr("\n"+log).msg.print() + + return (samples,) + + def get_system_stats(self): + + import psutil + + # RAM + ram = psutil.virtual_memory() + ram_used = ram.used / (1024 ** 3) + ram_total = ram.total / (1024 ** 3) + ram_stats = f"Used RAM: {ram_used:.2f} GB / Total RAM: {ram_total:.2f} GB" + + # VRAM (with PyTorch) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + vram_used = torch.cuda.memory_allocated(device) / (1024 ** 3) + vram_total = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3) + vram_stats = f"Used VRAM: {vram_used:.2f} GB / Total VRAM: {vram_total:.2f} GB" + + # Hard Drive Space + hard_drive = psutil.disk_usage("/") + used_space = hard_drive.used / (1024 ** 3) + total_space = hard_drive.total / (1024 ** 3) + hard_drive_stats = f"Used Space: {used_space:.2f} GB / Total Space: {total_space:.2f} GB" + + return [ram_stats, vram_stats, hard_drive_stats] + +# Class to count the number of places on an integer + +class WAS_Integer_Place_Counter: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "int_input": ("INT", {"default": 0, "min": 0, "max": 10000000, "step": 1}), + } + } + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("INT_PLACES",) + FUNCTION = "count_places" + + CATEGORY = "WAS Suite/Integer" + + def count_places(self, int_input): + output = len(str(int_input)) + cstr("\nInteger Places Count: "+str(output)).msg.print() + return (output,) + + +# NODE MAPPING +NODE_CLASS_MAPPINGS = { + "BLIP Model Loader": WAS_BLIP_Model_Loader, + "Blend Latents": WAS_Blend_Latents, + "Bus Node": WAS_Bus, + "Cache Node": WAS_Cache, + "Checkpoint Loader": WAS_Checkpoint_Loader, + "Checkpoint Loader (Simple)": WAS_Checkpoint_Loader_Simple, + "CLIPTextEncode (NSP)": WAS_NSP_CLIPTextEncoder, + "CLIP Input Switch": WAS_CLIP_Input_Switch, + "CLIP Vision Input Switch": WAS_CLIP_Vision_Input_Switch, + "Conditioning Input Switch": WAS_Conditioning_Input_Switch, + "Constant Number": WAS_Constant_Number, + "Create Grid Image": WAS_Image_Grid_Image, + "Create Grid Image from Batch": WAS_Image_Grid_Image_Batch, + "Create Morph Image": WAS_Image_Morph_GIF, + "Create Morph Image from Path": WAS_Image_Morph_GIF_By_Path, + "Create Video from Path": WAS_Create_Video_From_Path, + "CLIPSeg Masking": WAS_CLIPSeg, + "CLIPSeg Model Loader": WAS_CLIPSeg_Model_Loader, + "CLIPSeg Batch Masking": WAS_CLIPSeg_Batch, + "Convert Masks to Images": WAS_Mask_To_Image, + "Control Net Model Input Switch": WAS_Control_Net_Input_Switch, + "Debug Number to Console": WAS_Debug_Number_to_Console, + "Dictionary to Console": WAS_Dictionary_To_Console, + "Diffusers Model Loader": WAS_Diffusers_Loader, + "Diffusers Hub Model Down-Loader": WAS_Diffusers_Hub_Model_Loader, + "Export API": WAS_Export_API, + "Latent Input Switch": WAS_Latent_Input_Switch, + "Load Cache": WAS_Load_Cache, + "Logic Boolean": WAS_Boolean, + "Logic Boolean Primitive": WAS_Boolean_Primitive, + "Logic Comparison OR": WAS_Logical_OR, + "Logic Comparison AND": WAS_Logical_AND, + "Logic Comparison XOR": WAS_Logical_XOR, + "Logic NOT": WAS_Logical_NOT, + "Lora Loader": WAS_Lora_Loader, + "Hex to HSL": WAS_Hex_to_HSL, + "HSL to Hex": WAS_HSL_to_Hex, + "Image SSAO (Ambient Occlusion)": WAS_Image_Ambient_Occlusion, + "Image SSDO (Direct Occlusion)": WAS_Image_Direct_Occlusion, + "Image Analyze": WAS_Image_Analyze, + "Image Aspect Ratio": WAS_Image_Aspect_Ratio, + "Image Batch": WAS_Image_Batch, + "Image Blank": WAS_Image_Blank, + "Image Blend by Mask": WAS_Image_Blend_Mask, + "Image Blend": WAS_Image_Blend, + "Image Blending Mode": WAS_Image_Blending_Mode, + "Image Bloom Filter": WAS_Image_Bloom_Filter, + "Image Canny Filter": WAS_Canny_Filter, + "Image Chromatic Aberration": WAS_Image_Chromatic_Aberration, + "Image Color Palette": WAS_Image_Color_Palette, + "Image Crop Face": WAS_Image_Crop_Face, + "Image Crop Location": WAS_Image_Crop_Location, + "Image Crop Square Location": WAS_Image_Crop_Square_Location, + "Image Displacement Warp": WAS_Image_Displacement_Warp, + "Image Lucy Sharpen": WAS_Lucy_Sharpen, + "Image Paste Face": WAS_Image_Paste_Face_Crop, + "Image Paste Crop": WAS_Image_Paste_Crop, + "Image Paste Crop by Location": WAS_Image_Paste_Crop_Location, + "Image Pixelate": WAS_Image_Pixelate, + "Image Power Noise": WAS_Image_Power_Noise, + "Image Dragan Photography Filter": WAS_Dragon_Filter, + "Image Edge Detection Filter": WAS_Image_Edge, + "Image Film Grain": WAS_Film_Grain, + "Image Filter Adjustments": WAS_Image_Filters, + "Image Flip": WAS_Image_Flip, + "Image Gradient Map": WAS_Image_Gradient_Map, + "Image Generate Gradient": WAS_Image_Generate_Gradient, + "Image High Pass Filter": WAS_Image_High_Pass_Filter, + "Image History Loader": WAS_Image_History, + "Image Input Switch": WAS_Image_Input_Switch, + "Image Levels Adjustment": WAS_Image_Levels, + "Image Load": WAS_Load_Image, + "Image Median Filter": WAS_Image_Median_Filter, + "Image Mix RGB Channels": WAS_Image_RGB_Merge, + "Image Monitor Effects Filter": WAS_Image_Monitor_Distortion_Filter, + "Image Nova Filter": WAS_Image_Nova_Filter, + "Image Padding": WAS_Image_Padding, + "Image Perlin Noise": WAS_Image_Perlin_Noise, + "Image Rembg (Remove Background)": WAS_Remove_Rembg, + "Image Perlin Power Fractal": WAS_Image_Perlin_Power_Fractal, + "Image Remove Background (Alpha)": WAS_Remove_Background, + "Image Remove Color": WAS_Image_Remove_Color, + "Image Resize": WAS_Image_Rescale, + "Image Rotate": WAS_Image_Rotate, + "Image Rotate Hue": WAS_Image_Rotate_Hue, + "Image Send HTTP": WAS_Image_Send_HTTP, + "Image Save": WAS_Image_Save, + "Image Seamless Texture": WAS_Image_Make_Seamless, + "Image Select Channel": WAS_Image_Select_Channel, + "Image Select Color": WAS_Image_Select_Color, + "Image Shadows and Highlights": WAS_Shadow_And_Highlight_Adjustment, + "Image Size to Number": WAS_Image_Size_To_Number, + "Image Stitch": WAS_Image_Stitch, + "Image Style Filter": WAS_Image_Style_Filter, + "Image Threshold": WAS_Image_Threshold, + "Image Tiled": WAS_Image_Tile_Batch, + "Image Transpose": WAS_Image_Transpose, + "Image fDOF Filter": WAS_Image_fDOF, + "Image to Latent Mask": WAS_Image_To_Mask, + "Image to Noise": WAS_Image_To_Noise, + "Image to Seed": WAS_Image_To_Seed, + "Images to RGB": WAS_Images_To_RGB, + "Images to Linear": WAS_Images_To_Linear, + "Integer place counter": WAS_Integer_Place_Counter, + "Image Voronoi Noise Filter": WAS_Image_Voronoi_Noise_Filter, + "KSampler (WAS)": WAS_KSampler, + "KSampler Cycle": WAS_KSampler_Cycle, + "Latent Batch": WAS_Latent_Batch, + "Latent Noise Injection": WAS_Latent_Noise, + "Latent Size to Number": WAS_Latent_Size_To_Number, + "Latent Upscale by Factor (WAS)": WAS_Latent_Upscale, + "Load Image Batch": WAS_Load_Image_Batch, + "Load Text File": WAS_Text_Load_From_File, + "Load Lora": WAS_Lora_Loader, + "Lora Input Switch": WAS_Lora_Input_Switch, + "Masks Add": WAS_Mask_Add, + "Masks Subtract": WAS_Mask_Subtract, + "Mask Arbitrary Region": WAS_Mask_Arbitrary_Region, + "Mask Batch to Mask": WAS_Mask_Batch_to_Single_Mask, + "Mask Batch": WAS_Mask_Batch, + "Mask Ceiling Region": WAS_Mask_Ceiling_Region, + "Mask Crop Dominant Region": WAS_Mask_Crop_Dominant_Region, + "Mask Crop Minority Region": WAS_Mask_Crop_Minority_Region, + "Mask Crop Region": WAS_Mask_Crop_Region, + "Mask Paste Region": WAS_Mask_Paste_Region, + "Mask Dilate Region": WAS_Mask_Dilate_Region, + "Mask Dominant Region": WAS_Mask_Dominant_Region, + "Mask Erode Region": WAS_Mask_Erode_Region, + "Mask Fill Holes": WAS_Mask_Fill_Region, + "Mask Floor Region": WAS_Mask_Floor_Region, + "Mask Gaussian Region": WAS_Mask_Gaussian_Region, + "Mask Invert": WAS_Mask_Invert, + "Mask Minority Region": WAS_Mask_Minority_Region, + "Mask Rect Area": WAS_Mask_Rect_Area, + "Mask Rect Area (Advanced)": WAS_Mask_Rect_Area_Advanced, + "Mask Smooth Region": WAS_Mask_Smooth_Region, + "Mask Threshold Region": WAS_Mask_Threshold_Region, + "Masks Combine Regions": WAS_Mask_Combine, + "Masks Combine Batch": WAS_Mask_Combine_Batch, + "MiDaS Model Loader": MiDaS_Model_Loader, + "MiDaS Depth Approximation": MiDaS_Depth_Approx, + "MiDaS Mask Image": MiDaS_Background_Foreground_Removal, + "Model Input Switch": WAS_Model_Input_Switch, + "Number Counter": WAS_Number_Counter, + "Number Operation": WAS_Number_Operation, + "Number to Float": WAS_Number_To_Float, + "Number Input Switch": WAS_Number_Input_Switch, + "Number Input Condition": WAS_Number_Input_Condition, + "Number Multiple Of": WAS_Number_Multiple_Of, + "Number PI": WAS_Number_PI, + "Number to Int": WAS_Number_To_Int, + "Number to Seed": WAS_Number_To_Seed, + "Number to String": WAS_Number_To_String, + "Number to Text": WAS_Number_To_Text, + "Boolean To Text": WAS_Boolean_To_Text, + "Prompt Styles Selector": WAS_Prompt_Styles_Selector, + "Prompt Multiple Styles Selector": WAS_Prompt_Multiple_Styles_Selector, + "Random Number": WAS_Random_Number, + "Save Text File": WAS_Text_Save, + "Seed": WAS_Seed, + "Tensor Batch to Image": WAS_Tensor_Batch_to_Image, + "BLIP Analyze Image": WAS_BLIP_Analyze_Image, + "SAM Model Loader": WAS_SAM_Model_Loader, + "SAM Parameters": WAS_SAM_Parameters, + "SAM Parameters Combine": WAS_SAM_Combine_Parameters, + "SAM Image Mask": WAS_SAM_Image_Mask, + "Samples Passthrough (Stat System)": WAS_Samples_Passthrough_Stat_System, + "String to Text": WAS_String_To_Text, + "Image Bounds": WAS_Image_Bounds, + "Inset Image Bounds": WAS_Inset_Image_Bounds, + "Bounded Image Blend": WAS_Bounded_Image_Blend, + "Bounded Image Blend with Mask": WAS_Bounded_Image_Blend_With_Mask, + "Bounded Image Crop": WAS_Bounded_Image_Crop, + "Bounded Image Crop with Mask": WAS_Bounded_Image_Crop_With_Mask, + "Image Bounds to Console": WAS_Image_Bounds_to_Console, + "Text Dictionary Update": WAS_Dictionary_Update, + "Text Dictionary Get": WAS_Dictionary_Get, + "Text Dictionary Convert": WAS_Dictionary_Convert, + "Text Dictionary New": WAS_Dictionary_New, + "Text Dictionary Keys": WAS_Dictionary_Keys, + "Text Dictionary To Text": WAS_Dictionary_to_Text, + "Text Add Tokens": WAS_Text_Add_Tokens, + "Text Add Token by Input": WAS_Text_Add_Token_Input, + "Text Compare": WAS_Text_Compare, + "Text Concatenate": WAS_Text_Concatenate, + "Text File History Loader": WAS_Text_File_History, + "Text Find and Replace by Dictionary": WAS_Search_and_Replace_Dictionary, + "Text Find and Replace Input": WAS_Search_and_Replace_Input, + "Text Find and Replace": WAS_Search_and_Replace, + "Text Find": WAS_Find, + "Text Input Switch": WAS_Text_Input_Switch, + "Text List": WAS_Text_List, + "Text List Concatenate": WAS_Text_List_Concatenate, + "Text List to Text": WAS_Text_List_to_Text, + "Text Load Line From File": WAS_Text_Load_Line_From_File, + "Text Multiline": WAS_Text_Multiline, + "Text Multiline (Code Compatible)": WAS_Text_Multiline_Raw, + "Text Parse A1111 Embeddings": WAS_Text_Parse_Embeddings_By_Name, + "Text Parse Noodle Soup Prompts": WAS_Text_Parse_NSP, + "Text Parse Tokens": WAS_Text_Parse_Tokens, + "Text Random Line": WAS_Text_Random_Line, + "Text Random Prompt": WAS_Text_Random_Prompt, + "Text String": WAS_Text_String, + "Text Contains": WAS_Text_Contains, + "Text Shuffle": WAS_Text_Shuffle, + "Text Sort": WAS_Text_Sort, + "Text to Conditioning": WAS_Text_to_Conditioning, + "Text to Console": WAS_Text_to_Console, + "Text to Number": WAS_Text_To_Number, + "Text to String": WAS_Text_To_String, + "Text String Truncate": WAS_Text_String_Truncate, + "True Random.org Number Generator": WAS_True_Random_Number, + "unCLIP Checkpoint Loader": WAS_unCLIP_Checkpoint_Loader, + "Upscale Model Loader": WAS_Upscale_Model_Loader, + "Upscale Model Switch": WAS_Upscale_Model_Input_Switch, + "Write to GIF": WAS_Image_Morph_GIF_Writer, + "Write to Video": WAS_Video_Writer, + "VAE Input Switch": WAS_VAE_Input_Switch, + "Video Dump Frames": WAS_Video_Frame_Dump, + "CLIPSEG2": CLIPSeg2 +} + +#! EXTRA NODES + +# Check for BlenderNeko's Advanced CLIP Text Encode repo +BKAdvCLIP_dir = os.path.join(CUSTOM_NODES_DIR, "ComfyUI_ADV_CLIP_emb") +if os.path.exists(BKAdvCLIP_dir): + + cstr(f"BlenderNeko\'s Advanced CLIP Text Encode found, attempting to enable `CLIPTextEncode` support.").msg.print() + + class WAS_AdvancedCLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mode": (["Noodle Soup Prompts", "Wildcards"],), + "noodle_key": ("STRING", {"default": '__', "multiline": False}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "clip": ("CLIP", ), + "token_normalization": (["none", "mean", "length", "length+mean"],), + "weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],), + "text": ("STRING", {"multiline": True}), + } + } + + RETURN_TYPES = ("CONDITIONING", TEXT_TYPE, TEXT_TYPE) + RETURN_NAMES = ("conditioning", "parsed_text", "raw_text") + OUTPUT_NODE = True + FUNCTION = "encode" + CATEGORY = "WAS Suite/Conditioning" + + DESCRIPTION = "A node based on Blenderneko's Advanced CLIP Text Encode. This version adds the ability to use Noodle Soup Prompts and Wildcards. Wildcards are stored in WAS Node Suite root under the folder 'wildcards'. You can create the folder if it doesn't exist and move your wildcards into it." + URL = { + "Example Workflow": "https://github.com/WASasquatch/was-node-suite-comfyui", + } + IMAGES = [ + "https://i.postimg.cc/Jh4N2h5r/CLIPText-Encode-BLK-plus-NSP.png", + ] + + def encode(self, clip, text, token_normalization, weight_interpretation, seed=0, mode="Noodle Soup Prompts", noodle_key="__"): + + BKAdvCLIP_dir = os.path.join(CUSTOM_NODES_DIR, "ComfyUI_ADV_CLIP_emb") + sys.path.append(BKAdvCLIP_dir) + + from ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode + + if mode == "Noodle Soup Prompts": + new_text = nsp_parse(text, int(seed), noodle_key) + else: + new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key) + + new_text = parse_dynamic_prompt(new_text, seed) + new_text, text_vars = parse_prompt_vars(new_text) + cstr(f"CLIPTextEncode Prased Prompt:\n {new_text}").msg.print() + + encode = AdvancedCLIPTextEncode().encode(clip, new_text, token_normalization, weight_interpretation) + + sys.path.remove(BKAdvCLIP_dir) + + return ([[encode[0][0][0], encode[0][0][1]]], new_text, text, { "ui": { "string": new_text } } ) + + + NODE_CLASS_MAPPINGS.update({"CLIPTextEncode (BlenderNeko Advanced + NSP)": WAS_AdvancedCLIPTextEncode}) + + if NODE_CLASS_MAPPINGS.__contains__("CLIPTextEncode (BlenderNeko Advanced + NSP)"): + cstr('`CLIPTextEncode (BlenderNeko Advanced + NSP)` node enabled under `WAS Suite/Conditioning` menu.').msg.print() + +# opencv-python-headless handling +installed_packages = packages() +opencv_candidates = ['opencv-python', 'opencv-python-headless', 'opencv-contrib-python', 'opencv-contrib-python-headless'] +if any(package in installed_packages for package in opencv_candidates): + try: + import cv2 + build_info = ' '.join(cv2.getBuildInformation().split()) + if "FFMPEG: YES" in build_info: + if was_config.__contains__('show_startup_junk'): + if was_config['show_startup_junk']: + cstr("OpenCV Python FFMPEG support is enabled").msg.print() + if was_config.__contains__('ffmpeg_bin_path'): + if was_config['ffmpeg_bin_path'] == "/path/to/ffmpeg": + cstr(f"`ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}` config file. Will attempt to use system ffmpeg binaries if available.").warning.print() + else: + if was_config.__contains__('show_startup_junk'): + if was_config['show_startup_junk']: + cstr(f"`ffmpeg_bin_path` is set to: {was_config['ffmpeg_bin_path']}").msg.print() + else: + cstr(f"OpenCV Python FFMPEG support is not enabled\033[0m. OpenCV Python FFMPEG support, and FFMPEG binaries is required for video writing.").warning.print() + except ImportError: + cstr("OpenCV Python module cannot be found. Attempting install...").warning.print() + install_package( + package='opencv-python-headless[ffmpeg]', + uninstall_first=['opencv-python', 'opencv-python-headless[ffmpeg]'] + ) + try: + import cv2 + cstr("OpenCV Python installed.").msg.print() + except ImportError: + cstr("OpenCV Python module still cannot be imported. There is a system conflict.").error.print() +else: + install_package('opencv-python-headless[ffmpeg]') + try: + import cv2 + cstr("OpenCV Python installed.").msg.print() + except ImportError: + cstr("OpenCV Python module still cannot be imported. There is a system conflict.").error.print() + +# scipy handling +if 'scipy' not in packages(): + install_package('scipy') + try: + import scipy + except ImportError as e: + cstr("Unable to import tools for certain masking procedures.").msg.print() + print(e) + +# scikit-image handling +try: + import skimage +except ImportError as e: + install_package( + package='scikit-image', + uninstall_first=['scikit-image'] + ) + import skimage + +was_conf = getSuiteConfig() + +# Suppress warnings +if was_conf.__contains__('suppress_uncomfy_warnings'): + if was_conf['suppress_uncomfy_warnings']: + import warnings + warnings.filterwarnings("ignore", category=UserWarning, module="safetensors") + warnings.filterwarnings("ignore", category=UserWarning, module="torch") + warnings.filterwarnings("ignore", category=UserWarning, module="transformers") + +# Well we got here, we're as loaded as we're gonna get. +print(" ".join([cstr("Finished.").msg, cstr("Loaded").green, cstr(len(NODE_CLASS_MAPPINGS.keys())).end, cstr("nodes successfully.").green])) + +show_quotes = True +if was_conf.__contains__('show_inspiration_quote'): + if was_conf['show_inspiration_quote'] == False: + show_quotes = False +if show_quotes: + art_quotes = [ + # ARTISTIC INSPIRATION QUOTES + '\033[93m"Every artist was first an amateur."\033[0m\033[3m - Ralph Waldo Emerson', + '\033[93m"Art is not freedom from discipline, but disciplined freedom."\033[0m\033[3m - John F. Kennedy', + '\033[93m"Art enables us to find ourselves and lose ourselves at the same time."\033[0m\033[3m - Thomas Merton', + '\033[93m"Art is the most intense mode of individualism that the world has known."\033[0m\033[3m - Oscar Wilde', + '\033[93m"The purpose of art is washing the dust of daily life off our souls."\033[0m\033[3m - Pablo Picasso', + '\033[93m"Art is the lie that enables us to realize the truth."\033[0m\033[3m - Pablo Picasso', + '\033[93m"Art is not what you see, but what you make others see."\033[0m\033[3m - Edgar Degas', + '\033[93m"Every artist dips his brush in his own soul, and paints his own nature into his pictures."\033[0m\033[3m - Henry Ward Beecher', + '\033[93m"Art is the stored honey of the human soul."\033[0m\033[3m - Theodore Dreiser', + '\033[93m"Creativity takes courage."\033[0m\033[3m - Henri Matisse', + '\033[93m"Art should disturb the comfortable and comfort the disturbed." - Cesar Cruz', + '\033[93m"Art is the most beautiful of all lies."\033[0m\033[3m - Claude Debussy', + '\033[93m"Art is the journey of a free soul."\033[0m\033[3m - Alev Oguz', + '\033[93m"The artist\'s world is limitless. It can be found anywhere, far from where he lives or a few feet away. It is always on his doorstep."\033[0m\033[3m - Paul Strand', + '\033[93m"Art is not a thing; it is a way."\033[0m\033[3m - Elbert Hubbard', + '\033[93m"Art is the lie that enables us to recognize the truth."\033[0m\033[3m - Friedrich Nietzsche', + '\033[93m"Art is the triumph over chaos."\033[0m\033[3m - John Cheever', + '\033[93m"Art is the lie that enables us to realize the truth."\033[0m\033[3m - Pablo Picasso', + '\033[93m"Art is the only way to run away without leaving home."\033[0m\033[3m - Twyla Tharp', + '\033[93m"Art is the most powerful tool we have to connect with the world and express our individuality."\033[0m\033[3m - Unknown', + '\033[93m"Art is not about making something perfect, it\'s about making something meaningful."\033[0m\033[3m - Unknown', + '\033[93m"Art is the voice of the soul, expressing what words cannot."\033[0m\033[3m - Unknown', + '\033[93m"Art is the bridge that connects imagination to reality."\033[0m\033[3m - Unknown', + '\033[93m"Art is the language of the heart and the window to the soul."\033[0m\033[3m - Unknown', + '\033[93m"Art is the magic that brings beauty into the world."\033[0m\033[3m - Unknown', + '\033[93m"Art is the freedom to create, explore, and inspire."\033[0m\033[3m - Unknown', + '\033[93m"Art is the mirror that reflects the beauty within us."\033[0m\033[3m - Unknown', + '\033[93m"Art is the universal language that transcends boundaries and speaks to all."\033[0m\033[3m - Unknown', + '\033[93m"Art is the light that shines even in the darkest corners."\033[0m\033[3m - Unknown', + '\033[93m"Art is the soul made visible."\033[0m\033[3m - George Crook', + '\033[93m"Art is the breath of life."\033[0m\033[3m - Liza Donnelly', + '\033[93m"Art is a harmony parallel with nature."\033[0m\033[3m - Paul CΓ©zanne', + '\033[93m"Art is the daughter of freedom."\033[0m\033[3m - Friedrich Schiller', + # GENERAL INSPIRATION QUOTES + '\033[93m"Believe you can and you\'re halfway there."\033[0m\033[3m - Theodore Roosevelt', + '\033[93m"The only way to do great work is to love what you do."\033[0m\033[3m - Steve Jobs', + '\033[93m"Success is not final, failure is not fatal: It is the courage to continue that counts."\033[0m\033[3m - Winston Churchill', + '\033[93m"Your time is limited, don\'t waste it living someone else\'s life."\033[0m\033[3m - Steve Jobs', + '\033[93m"The future belongs to those who believe in the beauty of their dreams."\033[0m\033[3m - Eleanor Roosevelt', + '\033[93m"Success is not the key to happiness. Happiness is the key to success."\033[0m\033[3m - Albert Schweitzer', + '\033[93m"The best way to predict the future is to create it."\033[0m\033[3m - Peter Drucker', + '\033[93m"Don\'t watch the clock; do what it does. Keep going."\033[0m\033[3m - Sam Levenson', + '\033[93m"Believe in yourself, take on your challenges, and dig deep within yourself to conquer fears."\033[0m\033[3m - Chantal Sutherland', + '\033[93m"Challenges are what make life interesting and overcoming them is what makes life meaningful."\033[0m\033[3m - Joshua J. Marine', + '\033[93m"Opportunities don\'t happen. You create them."\033[0m\033[3m - Chris Grosser', + '\033[93m"Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work."\033[0m\033[3m - Steve Jobs', + '\033[93m"The harder I work, the luckier I get."\033[0m\033[3m - Samuel Goldwyn', + '\033[93m"Don\'t be pushed around by the fears in your mind. Be led by the dreams in your heart."\033[0m\033[3m - Roy T. Bennett', + '\033[93m"Believe in yourself, and the rest will fall into place."\033[0m\033[3m - Unknown', + '\033[93m"Life is 10% what happens to us and 90% how we react to it."\033[0m\033[3m - Charles R. Swindoll', + '\033[93m"Success is not just about making money. It\'s about making a difference."\033[0m\033[3m - Unknown', + '\033[93m"The only limit to our realization of tomorrow will be our doubts of today."\033[0m\033[3m - Franklin D. Roosevelt', + '\033[93m"Great minds discuss ideas; average minds discuss events; small minds discuss people."\033[0m\033[3m - Eleanor Roosevelt', + '\033[93m"The future depends on what you do today."\033[0m\033[3m - Mahatma Gandhi', + '\033[93m"Don\'t be afraid to give up the good to go for the great."\033[0m\033[3m - John D. Rockefeller', + '\033[93m"Success usually comes to those who are too busy to be looking for it."\033[0m\033[3m - Henry David Thoreau', + '\033[93m"The secret to getting ahead is getting started."\033[0m\033[3m - Mark Twain', + '\033[93m"Every great dream begins with a dreamer."\033[0m\033[3m - Harriet Tubman', + '\033[93m"Do not wait for the opportunity. Create it."\033[0m\033[3m - George Bernard Shaw', + '\033[93m"Your time is now. Start where you are and never stop."\033[0m\033[3m - Roy T. Bennett', + '\033[93m"The only person you should try to be better than is the person you were yesterday."\033[0m\033[3m - Unknown', + '\033[93m"Success is not in what you have, but who you are."\033[0m\033[3m - Bo Bennett', + '\033[93m"Do one thing every day that scares you."\033[0m\033[3m - Eleanor Roosevelt', + '\033[93m"Failure is the opportunity to begin again more intelligently."\033[0m\033[3m - Henry Ford', + '\033[93m"Dream big and dare to fail."\033[0m\033[3m - Norman Vaughan', + '\033[93m"Everything you\'ve ever wanted is on the other side of fear."\033[0m\033[3m - George Addair', + '\033[93m"Believe you deserve it and the universe will serve it."\033[0m\033[3m - Unknown', + '\033[93m"Don\'t wait. The time will never be just right."\033[0m\033[3m - Napoleon Hill', + '\033[93m"The distance between insanity and genius is measured only by success."\033[0m\033[3m - Bruce Feirstein', + '\033[93m"Be the change that you wish to see in the world."\033[0m\033[3m - Mahatma Gandhi', + '\033[93m"Success is not about being better than someone else. It\'s about being better than you used to be."\033[0m\033[3m - Unknown', + '\033[93m"The best revenge is massive success."\033[0m\033[3m - Frank Sinatra', + '\033[93m"You have within you right now, everything you need to deal with whatever the world can throw at you."\033[0m\033[3m - Brian Tracy', + '\033[93m"Don\'t let yesterday take up too much of today."\033[0m\033[3m - Will Rogers', + '\033[93m"The biggest risk is not taking any risk. In a world that is changing quickly, the only strategy that is guaranteed to fail is not taking risks."\033[0m\033[3m - Mark Zuckerberg', + '\033[93m"The journey of a thousand miles begins with one step."\033[0m\033[3m - Lao Tzu', + '\033[93m"Every strike brings me closer to the next home run."\033[0m\033[3m - Babe Ruth', + ] + print(f'\n\t\033[3m{random.choice(art_quotes)}\033[0m\n') diff --git a/custom_nodes/was-node-suite-comfyui/__init__.py b/custom_nodes/was-node-suite-comfyui/__init__.py new file mode 100644 index 00000000..b09dfa1a --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/__init__.py @@ -0,0 +1,3 @@ +from .WAS_Node_Suite import NODE_CLASS_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS'] diff --git a/custom_nodes/was-node-suite-comfyui/install.bat b/custom_nodes/was-node-suite-comfyui/install.bat new file mode 100644 index 00000000..5b4ac24c --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/install.bat @@ -0,0 +1,16 @@ +@echo off + +set "requirements_txt=%~dp0\requirements.txt" +set "python_exec=..\..\..\python_embeded\python.exe" + +echo Installing WAS-NS ... + +if exist "%python_exec%" ( + echo Installing with ComfyUI Portable + "%python_exec%" -s -m pip install -r "%requirements_txt%" +) else ( + echo Installing with system Python + pip install -r "%requirements_txt%" +) + +pause \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/install_alt.bat b/custom_nodes/was-node-suite-comfyui/install_alt.bat new file mode 100644 index 00000000..23add63b --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/install_alt.bat @@ -0,0 +1,20 @@ +@echo off + +set "requirements_txt=%~dp0\requirements.txt" +set "python_exec=..\..\..\python_embeded\python.exe" + +echo Installing WAS-NS ... + +if exist "%python_exec%" ( + echo Installing with ComfyUI Portable + for /f "delims=" %%i in (%requirements_txt%) do ( + %python_exec% -s -m pip install "%%i" + ) +) else ( + echo Installing with system Python + for /f "delims=" %%i in (%requirements_txt%) do ( + pip install "%%i" + ) +) + +pause \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/__init__.py b/custom_nodes/was-node-suite-comfyui/modules/BLIP/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/__init__.py b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/__init__.py @@ -0,0 +1 @@ + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/bert_config.json b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/bert_config.json new file mode 100644 index 00000000..3ef38aab --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/bert_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30522, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/caption_coco.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/caption_coco.yaml new file mode 100644 index 00000000..42eab703 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/caption_coco.yaml @@ -0,0 +1,33 @@ +image_root: '/export/share/datasets/vision/coco/images/' +ann_root: 'annotation' +coco_gt_root: 'annotation/coco_gt' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' + +# size of vit model; base or large +vit: 'base' +vit_grad_ckpt: False +vit_ckpt_layer: 0 +batch_size: 32 +init_lr: 1e-5 + +# vit: 'large' +# vit_grad_ckpt: True +# vit_ckpt_layer: 5 +# batch_size: 16 +# init_lr: 2e-6 + +image_size: 384 + +# generation configs +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 5 + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/med_config.json b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/med_config.json new file mode 100644 index 00000000..0ffad0a6 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/med_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30524, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/nlvr.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/nlvr.yaml new file mode 100644 index 00000000..2d1122aa --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/nlvr.yaml @@ -0,0 +1,21 @@ +image_root: '/export/share/datasets/vision/NLVR2/' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth' + +#size of vit model; base or large +vit: 'base' +batch_size_train: 16 +batch_size_test: 64 +vit_grad_ckpt: False +vit_ckpt_layer: 0 +max_epoch: 15 + +image_size: 384 + +# optimizer +weight_decay: 0.05 +init_lr: 3e-5 +min_lr: 0 + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/nocaps.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/nocaps.yaml new file mode 100644 index 00000000..90281358 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/nocaps.yaml @@ -0,0 +1,15 @@ +image_root: '/export/share/datasets/vision/nocaps/' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' + +vit: 'base' +batch_size: 32 + +image_size: 384 + +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/pretrain.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/pretrain.yaml new file mode 100644 index 00000000..02355ee0 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/pretrain.yaml @@ -0,0 +1,27 @@ +train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json', + '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json', + ] +laion_path: '' + +# size of vit model; base or large +vit: 'base' +vit_grad_ckpt: False +vit_ckpt_layer: 0 + +image_size: 224 +batch_size: 75 + +queue_size: 57600 +alpha: 0.4 + +# optimizer +weight_decay: 0.05 +init_lr: 3e-4 +min_lr: 1e-6 +warmup_lr: 1e-6 +lr_decay_rate: 0.9 +max_epoch: 20 +warmup_steps: 3000 + + + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_coco.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_coco.yaml new file mode 100644 index 00000000..a8569e9b --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_coco.yaml @@ -0,0 +1,34 @@ +image_root: '/export/share/datasets/vision/coco/images/' +ann_root: 'annotation' +dataset: 'coco' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' + +# size of vit model; base or large + +vit: 'base' +batch_size_train: 32 +batch_size_test: 64 +vit_grad_ckpt: True +vit_ckpt_layer: 4 +init_lr: 1e-5 + +# vit: 'large' +# batch_size_train: 16 +# batch_size_test: 32 +# vit_grad_ckpt: True +# vit_ckpt_layer: 12 +# init_lr: 5e-6 + +image_size: 384 +queue_size: 57600 +alpha: 0.4 +k_test: 256 +negative_all_rank: True + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 6 + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_flickr.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_flickr.yaml new file mode 100644 index 00000000..d75ea4ee --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_flickr.yaml @@ -0,0 +1,34 @@ +image_root: '/export/share/datasets/vision/flickr30k/' +ann_root: 'annotation' +dataset: 'flickr' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth' + +# size of vit model; base or large + +vit: 'base' +batch_size_train: 32 +batch_size_test: 64 +vit_grad_ckpt: True +vit_ckpt_layer: 4 +init_lr: 1e-5 + +# vit: 'large' +# batch_size_train: 16 +# batch_size_test: 32 +# vit_grad_ckpt: True +# vit_ckpt_layer: 10 +# init_lr: 5e-6 + +image_size: 384 +queue_size: 57600 +alpha: 0.4 +k_test: 128 +negative_all_rank: False + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 6 + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_msrvtt.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_msrvtt.yaml new file mode 100644 index 00000000..395f6254 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/retrieval_msrvtt.yaml @@ -0,0 +1,12 @@ +video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' + +# size of vit model; base or large +vit: 'base' +batch_size: 64 +k_test: 128 +image_size: 384 +num_frm_test: 8 \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/vqa.yaml b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/vqa.yaml new file mode 100644 index 00000000..74327e6d --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_configs/vqa.yaml @@ -0,0 +1,25 @@ +vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/ +vg_root: '/export/share/datasets/vision/visual-genome/' #followed by image/ +train_files: ['vqa_train','vqa_val','vg_qa'] +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth' + +# size of vit model; base or large +vit: 'base' +batch_size_train: 16 +batch_size_test: 32 +vit_grad_ckpt: False +vit_ckpt_layer: 0 +init_lr: 2e-5 + +image_size: 480 + +k_test: 128 +inference: 'rank' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 10 \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_med.py b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_med.py new file mode 100644 index 00000000..7b00a354 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_med.py @@ -0,0 +1,955 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +''' + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + if reduction=='none': + lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_module.py b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_module.py new file mode 100644 index 00000000..db91d7b9 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_module.py @@ -0,0 +1,423 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li +''' +import warnings +warnings.filterwarnings("ignore") + +from .blip_vit import VisionTransformer, interpolate_pos_embed +from .blip_med import BertConfig, BertModel, BertLMHeadModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +import os +from urllib.parse import urlparse +from timm.models.hub import download_cached_file +import numpy as np + +from pathlib import Path +LOCAL_PATH = os.path.dirname(os.path.abspath(__file__)) + +# BLIP + +class BLIP_Base(nn.Module): + def __init__(self, + med_config = Path(LOCAL_PATH, 'blip_configs/med_config.json'), + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + + def forward(self, image, caption, mode): + + assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" + text = self.tokenizer(caption, return_tensors="pt").to(image.device) + + if mode=='image': + # return image features + image_embeds = self.visual_encoder(image) + return image_embeds + + elif mode=='text': + # return text features + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + return text_output.last_hidden_state + + elif mode=='multimodal': + # return multimodel features + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text.input_ids[:,0] = self.tokenizer.enc_token_id + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + return output.last_hidden_state + + + +class BLIP_Decoder(nn.Module): + def __init__(self, + med_config = Path(LOCAL_PATH, 'blip_configs/med_config.json'), + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + prompt = 'a picture of ', + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_decoder = BertLMHeadModel(config=med_config) + + self.prompt = prompt + self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 + + + def forward(self, image, caption): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) + + text.input_ids[:,0] = self.tokenizer.bos_token_id + + decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) + decoder_targets[:,:self.prompt_length] = -100 + + decoder_output = self.text_decoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + labels = decoder_targets, + return_dict = True, + ) + loss_lm = decoder_output.loss + + return loss_lm + + def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): + image_embeds = self.visual_encoder(image) + + if not sample: + image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) + + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} + + prompt = [self.prompt] * image.size(0) + input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) + input_ids[:,0] = self.tokenizer.bos_token_id + input_ids = input_ids[:, :-1] + + if sample: + #nucleus sampling + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + do_sample=True, + top_p=top_p, + num_return_sequences=1, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=1.1, + **model_kwargs) + else: + #beam search + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=repetition_penalty, + **model_kwargs) + + captions = [] + for output in outputs: + caption = self.tokenizer.decode(output, skip_special_tokens=True) + captions.append(caption[len(self.prompt):]) + return captions + + +def blip_decoder(pretrained='',**kwargs): + model = BLIP_Decoder(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + +def blip_feature_extractor(pretrained='',**kwargs): + model = BLIP_Base(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + +def init_tokenizer(): + tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + tokenizer.add_special_tokens({'bos_token':'[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit=='base': + vision_width = 768 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, + num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate + ) + elif vit=='large': + vision_width = 1024 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, + num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate + ) + return visual_encoder, vision_width + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): + state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], + model.visual_encoder_m) + for key in model.state_dict().keys(): + if key in state_dict.keys(): + if state_dict[key].shape!=model.state_dict()[key].shape: + del state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + +# BLIP VQA + +class BLIP_VQA(nn.Module): + def __init__(self, + med_config = Path(LOCAL_PATH, 'blip_configs/med_config.json'), + image_size = 480, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) + self.tokenizer = init_tokenizer() + + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) + + decoder_config = BertConfig.from_json_file(med_config) + self.text_decoder = BertLMHeadModel(config=decoder_config) + + + def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + question.input_ids[:,0] = self.tokenizer.enc_token_id + + if train: + ''' + n: number of answers for each question + weights: weight for each answer + ''' + answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) + answer.input_ids[:,0] = self.tokenizer.bos_token_id + answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) + + question_output = self.text_encoder(question.input_ids, + attention_mask = question.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True) + + question_states = [] + question_atts = [] + for b, n in enumerate(n): + question_states += [question_output.last_hidden_state[b]]*n + question_atts += [question.attention_mask[b]]*n + question_states = torch.stack(question_states,0) + question_atts = torch.stack(question_atts,0) + + answer_output = self.text_decoder(answer.input_ids, + attention_mask = answer.attention_mask, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + labels = answer_targets, + return_dict = True, + reduction = 'none', + ) + + loss = weights * answer_output.loss + loss = loss.sum()/image.size(0) + + return loss + + + else: + question_output = self.text_encoder(question.input_ids, + attention_mask = question.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True) + + if inference=='generate': + num_beams = 3 + question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) + question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) + model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} + + bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) + + outputs = self.text_decoder.generate(input_ids=bos_ids, + max_length=10, + min_length=1, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + **model_kwargs) + + answers = [] + for output in outputs: + answer = self.tokenizer.decode(output, skip_special_tokens=True) + answers.append(answer) + return answers + + elif inference=='rank': + max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, + answer.input_ids, answer.attention_mask, k_test) + return max_ids + + + + def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): + + num_ques = question_states.size(0) + start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token + + start_output = self.text_decoder(start_ids, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + return_dict = True, + reduction = 'none') + logits = start_output.logits[:,0,:] # first token's logit + + # topk_probs: top-k probability + # topk_ids: [num_question, k] + answer_first_token = answer_ids[:,1] + prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) + topk_probs, topk_ids = prob_first_token.topk(k,dim=1) + + # answer input: [num_question*k, answer_len] + input_ids = [] + input_atts = [] + for b, topk_id in enumerate(topk_ids): + input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) + input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) + input_ids = torch.cat(input_ids,dim=0) + input_atts = torch.cat(input_atts,dim=0) + + targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) + + # repeat encoder's output for top-k answers + question_states = tile(question_states, 0, k) + question_atts = tile(question_atts, 0, k) + + output = self.text_decoder(input_ids, + attention_mask = input_atts, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + labels = targets_ids, + return_dict = True, + reduction = 'none') + + log_probs_sum = -output.loss + log_probs_sum = log_probs_sum.view(num_ques,k) + + max_topk_ids = log_probs_sum.argmax(dim=1) + max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] + + return max_ids + + +def blip_vqa(pretrained='',**kwargs): + model = BLIP_VQA(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) +# assert(len(msg.missing_keys)==0) + return model + + +def tile(x, dim, n_tile): + init_dim = x.size(dim) + repeat_idx = [1] * x.dim() + repeat_idx[dim] = n_tile + x = x.repeat(*(repeat_idx)) + order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) + return torch.index_select(x, dim, order_index.to(x.device)) + + diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_module_license.txt b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_module_license.txt new file mode 100644 index 00000000..a63e87f4 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_module_license.txt @@ -0,0 +1,12 @@ +Copyright (c) 2022, Salesforce.com, Inc. +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +* Neither the name of Salesforce.com nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_vit.py b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_vit.py new file mode 100644 index 00000000..cec3d8e0 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/modules/BLIP/blip_vit.py @@ -0,0 +1,305 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on timm code base + * https://github.com/rwightman/pytorch-image-models/tree/master/timm +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.models.vision_transformer import _cfg, PatchEmbed +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_, DropPath +from timm.models.helpers import named_apply, adapt_input_conv + +from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_gradients = None + self.attention_map = None + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def forward(self, x, register_hook=False): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + if register_hook: + self.save_attention_map(attn) + attn.register_hook(self.save_attn_gradients) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if use_grad_checkpointing: + self.attn = checkpoint_wrapper(self.attn) + self.mlp = checkpoint_wrapper(self.mlp) + + def forward(self, x, register_hook=False): + x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, + use_grad_checkpointing=False, ckpt_layer=0): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) + ) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x, register_blk=-1): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + self.pos_embed[:,:x.size(1),:] + x = self.pos_drop(x) + + for i,blk in enumerate(self.blocks): + x = blk(x, register_blk==i) + x = self.norm(x) + + return x + + @torch.jit.ignore() + def load_pretrained(self, checkpoint_path, prefix=''): + _load_weights(self, checkpoint_path, prefix) + + +@torch.no_grad() +def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): + """ Load weights from .npz checkpoints for official Google Brain Flax implementation + """ + import numpy as np + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + if not prefix and 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + + if hasattr(model.patch_embed, 'backbone'): + # hybrid + backbone = model.patch_embed.backbone + stem_only = not hasattr(backbone, 'stem') + stem = backbone if stem_only else backbone.stem + stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) + stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) + stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) + if not stem_only: + for i, stage in enumerate(backbone.stages): + for j, block in enumerate(stage.blocks): + bp = f'{prefix}block{i + 1}/unit{j + 1}/' + for r in range(3): + getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) + getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) + getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) + if block.downsample is not None: + block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) + block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) + block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) + embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) + else: + embed_conv_w = adapt_input_conv( + model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + model.patch_embed.proj.weight.copy_(embed_conv_w) + model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) + model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if pos_embed_w.shape != model.pos_embed.shape: + pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) + model.pos_embed.copy_(pos_embed_w) + model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) + model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) +# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: +# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) +# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) +# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: +# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) +# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) + for i, block in enumerate(model.blocks.children()): + block_prefix = f'{prefix}Transformer/encoderblock_{i}/' + mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) + block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) + block.attn.qkv.weight.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) + block.attn.qkv.bias.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) + block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) + block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) + for r in range(2): + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) + + +def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): + # interpolate position embedding + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = visual_encoder.patch_embed.num_patches + num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + + if orig_size!=new_size: + # class_token and dist_token are kept unchanged + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) + + return new_pos_embed + else: + return pos_embed_checkpoint \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/modules/__init__.py b/custom_nodes/was-node-suite-comfyui/modules/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_nodes/was-node-suite-comfyui/pyproject.toml b/custom_nodes/was-node-suite-comfyui/pyproject.toml new file mode 100644 index 00000000..57722a40 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "was-node-suite-comfyui" +description = "" +version = "1.0.2" +license = { file = "LICENSE" } +dependencies = ["cmake", "fairscale>=0.4.4", "git+https://github.com/WASasquatch/img2texture.git", "git+https://github.com/WASasquatch/cstr", "gitpython", "imageio", "joblib", "matplotlib", "numba", "numpy", "opencv-python-headless[ffmpeg]<=4.7.0.72", "pilgram", "git+https://github.com/WASasquatch/ffmpy.git", "rembg", "scikit-image>=0.20.0", "scikit-learn", "scipy", "timm>=0.4.12", "tqdm", "transformers"] + +[project.urls] +Repository = "https://github.com/WASasquatch/was-node-suite-comfyui" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "was" +DisplayName = "WAS Node Suite" +Icon = "" diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/.gitignore b/custom_nodes/was-node-suite-comfyui/repos/SAM/.gitignore new file mode 100644 index 00000000..7e9f6494 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/.gitignore @@ -0,0 +1,42 @@ +.nfs* + +# compilation and distribution +__pycache__ +_ext +*.pyc +*.pyd +*.so +*.dll +*.egg-info/ +build/ +dist/ +wheels/ + +# pytorch/python/numpy formats +*.pth +*.pkl +*.npy +*.ts +model_ts*.txt + +# onnx models +*.onnx + +# ipython/jupyter notebooks +**/.ipynb_checkpoints/ + +# Editor temporaries +*.swn +*.swo +*.swp +*~ + +# editor settings +.idea +.vscode +_darcs + +# demo +**/node_modules +yarn.lock +package-lock.json \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/CODE_OF_CONDUCT.md b/custom_nodes/was-node-suite-comfyui/repos/SAM/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..08b500a2 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/CODE_OF_CONDUCT.md @@ -0,0 +1,80 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +This Code of Conduct also applies outside the project spaces when there is a +reasonable belief that an individual's behavior may have a negative impact on +the project or its community. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/CONTRIBUTING.md b/custom_nodes/was-node-suite-comfyui/repos/SAM/CONTRIBUTING.md new file mode 100644 index 00000000..263991c9 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/CONTRIBUTING.md @@ -0,0 +1,31 @@ +# Contributing to segment-anything +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `main`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints, using the `linter.sh` script in the project's root directory. Linting requires `black==23.*`, `isort==5.12.0`, `flake8`, and `mypy`. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. In those cases, please go through the process +outlined on that page and do not file a public issue. + +## License +By contributing to segment-anything, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/LICENSE b/custom_nodes/was-node-suite-comfyui/repos/SAM/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/README.md b/custom_nodes/was-node-suite-comfyui/repos/SAM/README.md new file mode 100644 index 00000000..4f5efb98 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/README.md @@ -0,0 +1,171 @@ +# Segment Anything + +**[Meta AI Research, FAIR](https://ai.facebook.com/research/)** + +[Alexander Kirillov](https://alexander-kirillov.github.io/), [Eric Mintun](https://ericmintun.github.io/), [Nikhila Ravi](https://nikhilaravi.com/), [Hanzi Mao](https://hanzimao.me/), Chloe Rolland, Laura Gustafson, [Tete Xiao](https://tetexiao.com), [Spencer Whitehead](https://www.spencerwhitehead.com/), Alex Berg, Wan-Yen Lo, [Piotr Dollar](https://pdollar.github.io/), [Ross Girshick](https://www.rossgirshick.info/) + +[[`Paper`](https://ai.facebook.com/research/publications/segment-anything/)] [[`Project`](https://segment-anything.com/)] [[`Demo`](https://segment-anything.com/demo)] [[`Dataset`](https://segment-anything.com/dataset/index.html)] [[`Blog`](https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/)] [[`BibTeX`](#citing-segment-anything)] + +![SAM design](assets/model_diagram.png?raw=true) + +The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. + +

        + + +

        + +## Installation + +The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended. + +Install Segment Anything: + +``` +pip install git+https://github.com/facebookresearch/segment-anything.git +``` + +or clone the repository locally and install with + +``` +git clone git@github.com:facebookresearch/segment-anything.git +cd segment-anything; pip install -e . +``` + +The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks. + +``` +pip install opencv-python pycocotools matplotlib onnxruntime onnx +``` + +## Getting Started + +First download a [model checkpoint](#model-checkpoints). Then the model can be used in just a few lines to get masks from a given prompt: + +``` +from segment_anything import SamPredictor, sam_model_registry +sam = sam_model_registry[""](checkpoint="") +predictor = SamPredictor(sam) +predictor.set_image() +masks, _, _ = predictor.predict() +``` + +or generate masks for an entire image: + +``` +from segment_anything import SamAutomaticMaskGenerator, sam_model_registry +sam = sam_model_registry[""](checkpoint="") +mask_generator = SamAutomaticMaskGenerator(sam) +masks = mask_generator.generate() +``` + +Additionally, masks can be generated for images from the command line: + +``` +python scripts/amg.py --checkpoint --model-type --input --output +``` + +See the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details. + +

        + + +

        + +## ONNX Export + +SAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the [demo](https://segment-anything.com/demo). Export the model with + +``` +python scripts/export_onnx_model.py --checkpoint --model-type --output +``` + +See the [example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export. + +### Web demo + +The `demo/` folder has a simple one page React app which shows how to run mask prediction with the exported ONNX model in a web browser with multithreading. Please see [`demo/README.md`](https://github.com/facebookresearch/segment-anything/blob/main/demo/README.md) for more details. + +## Model Checkpoints + +Three model versions of the model are available with different backbone sizes. These models can be instantiated by running + +``` +from segment_anything import sam_model_registry +sam = sam_model_registry[""](checkpoint="") +``` + +Click the links below to download the checkpoint for the corresponding model type. + +- **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)** +- `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth) +- `vit_b`: [ViT-B SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth) + +## Dataset + +See [here](https://ai.facebook.com/datasets/segment-anything/) for an overview of the datastet. The dataset can be downloaded [here](https://ai.facebook.com/datasets/segment-anything-downloads/). By downloading the datasets you agree that you have read and accepted the terms of the SA-1B Dataset Research License. + +We save masks per image as a json file. It can be loaded as a dictionary in python in the below format. + +```python +{ + "image" : image_info, + "annotations" : [annotation], +} + +image_info { + "image_id" : int, # Image id + "width" : int, # Image width + "height" : int, # Image height + "file_name" : str, # Image filename +} + +annotation { + "id" : int, # Annotation id + "segmentation" : dict, # Mask saved in COCO RLE format. + "bbox" : [x, y, w, h], # The box around the mask, in XYWH format + "area" : int, # The area in pixels of the mask + "predicted_iou" : float, # The model's own prediction of the mask's quality + "stability_score" : float, # A measure of the mask's quality + "crop_box" : [x, y, w, h], # The crop of the image used to generate the mask, in XYWH format + "point_coords" : [[x, y]], # The point coordinates input to the model to generate the mask +} +``` + +Image ids can be found in sa_images_ids.txt which can be downloaded using the above [link](https://ai.facebook.com/datasets/segment-anything-downloads/) as well. + +To decode a mask in COCO RLE format into binary: + +``` +from pycocotools import mask as mask_utils +mask = mask_utils.decode(annotation["segmentation"]) +``` + +See [here](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py) for more instructions to manipulate masks stored in RLE format. + +## License + +The model is licensed under the [Apache 2.0 license](LICENSE). + +## Contributing + +See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). + +## Contributors + +The Segment Anything project was made possible with the help of many contributors (alphabetical): + +Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom + +## Citing Segment Anything + +If you use SAM or SA-1B in your research, please use the following BibTeX entry. + +``` +@article{kirillov2023segany, + title={Segment Anything}, + author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, + journal={arXiv:2304.02643}, + year={2023} +} +``` diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/masks1.png b/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/masks1.png new file mode 100644 index 00000000..56755a32 Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/masks1.png differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/masks2.jpg b/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/masks2.jpg new 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b/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/notebook1.png differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/notebook2.png b/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/notebook2.png new file mode 100644 index 00000000..9ceef18b Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/repos/SAM/assets/notebook2.png differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/README.md b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/README.md new file mode 100644 index 00000000..41f1ddd8 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/README.md @@ -0,0 +1,126 @@ +## Segment Anything Simple Web demo + +This **front-end only** React based web demo shows how to load a fixed image and corresponding `.npy` file of the SAM image embedding, and run the SAM ONNX model in the browser using Web Assembly with mulithreading enabled by `SharedArrayBuffer`, Web Worker, and SIMD128. + + + +## Run the app + +Install Yarn + +``` +npm install --g yarn +``` + +Build and run: + +``` +yarn && yarn start +``` + +Navigate to [`http://localhost:8081/`](http://localhost:8081/) + +Move your cursor around to see the mask prediction update in real time. + +## Export the image embedding + +In the [ONNX Model Example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) upload the image of your choice and generate and save corresponding embedding. + +Initialize the predictor: + +```python +checkpoint = "sam_vit_h_4b8939.pth" +model_type = "vit_h" +sam = sam_model_registry[model_type](checkpoint=checkpoint) +sam.to(device='cuda') +predictor = SamPredictor(sam) +``` + +Set the new image and export the embedding: + +``` +image = cv2.imread('src/assets/dogs.jpg') +predictor.set_image(image) +image_embedding = predictor.get_image_embedding().cpu().numpy() +np.save("dogs_embedding.npy", image_embedding) +``` + +Save the new image and embedding in `src/assets/data`. + +## Export the ONNX model + +You also need to export the quantized ONNX model from the [ONNX Model Example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb). + +Run the cell in the notebook which saves the `sam_onnx_quantized_example.onnx` file, download it and copy it to the path `/model/sam_onnx_quantized_example.onnx`. + +Here is a snippet of the export/quantization code: + +``` +onnx_model_path = "sam_onnx_example.onnx" +onnx_model_quantized_path = "sam_onnx_quantized_example.onnx" +quantize_dynamic( + model_input=onnx_model_path, + model_output=onnx_model_quantized_path, + optimize_model=True, + per_channel=False, + reduce_range=False, + weight_type=QuantType.QUInt8, +) +``` + +**NOTE: if you change the ONNX model by using a new checkpoint you need to also re-export the embedding.** + +## Update the image, embedding, model in the app + +Update the following file paths at the top of`App.tsx`: + +```py +const IMAGE_PATH = "/assets/data/dogs.jpg"; +const IMAGE_EMBEDDING = "/assets/data/dogs_embedding.npy"; +const MODEL_DIR = "/model/sam_onnx_quantized_example.onnx"; +``` + +## ONNX multithreading with SharedArrayBuffer + +To use multithreading, the appropriate headers need to be set to create a cross origin isolation state which will enable use of `SharedArrayBuffer` (see this [blog post](https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-runtime-web-running-your-machine-learning-model-in-browser/) for more details) + +The headers below are set in `configs/webpack/dev.js`: + +```js +headers: { + "Cross-Origin-Opener-Policy": "same-origin", + "Cross-Origin-Embedder-Policy": "credentialless", +} +``` + +## Structure of the app + +**`App.tsx`** + +- Initializes ONNX model +- Loads image embedding and image +- Runs the ONNX model based on input prompts + +**`Stage.tsx`** + +- Handles mouse move interaction to update the ONNX model prompt + +**`Tool.tsx`** + +- Renders the image and the mask prediction + +**`helpers/maskUtils.tsx`** + +- Conversion of ONNX model output from array to an HTMLImageElement + +**`helpers/onnxModelAPI.tsx`** + +- Formats the inputs for the ONNX model + +**`helpers/scaleHelper.tsx`** + +- Handles image scaling logic for SAM (longest size 1024) + +**`hooks/`** + +- Handle shared state for the app diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/common.js b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/common.js new file mode 100644 index 00000000..098f6686 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/common.js @@ -0,0 +1,84 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +const { resolve } = require("path"); +const HtmlWebpackPlugin = require("html-webpack-plugin"); +const FriendlyErrorsWebpackPlugin = require("friendly-errors-webpack-plugin"); +const CopyPlugin = require("copy-webpack-plugin"); +const webpack = require("webpack"); + +module.exports = { + entry: "./src/index.tsx", + resolve: { + extensions: [".js", ".jsx", ".ts", ".tsx"], + }, + output: { + path: resolve(__dirname, "dist"), + }, + module: { + rules: [ + { + test: /\.mjs$/, + include: /node_modules/, + type: "javascript/auto", + resolve: { + fullySpecified: false, + }, + }, + { + test: [/\.jsx?$/, /\.tsx?$/], + use: ["ts-loader"], + exclude: /node_modules/, + }, + { + test: /\.css$/, + use: ["style-loader", "css-loader"], + }, + { + test: /\.(scss|sass)$/, + use: ["style-loader", "css-loader", "postcss-loader"], + }, + { + test: /\.(jpe?g|png|gif|svg)$/i, + use: [ + "file-loader?hash=sha512&digest=hex&name=img/[contenthash].[ext]", + "image-webpack-loader?bypassOnDebug&optipng.optimizationLevel=7&gifsicle.interlaced=false", + ], + }, + { + test: /\.(woff|woff2|ttf)$/, + use: { + loader: "url-loader", + }, + }, + ], + }, + plugins: [ + new CopyPlugin({ + patterns: [ + { + from: "node_modules/onnxruntime-web/dist/*.wasm", + to: "[name][ext]", + }, + { + from: "model", + to: "model", + }, + { + from: "src/assets", + to: "assets", + }, + ], + }), + new HtmlWebpackPlugin({ + template: "./src/assets/index.html", + }), + new FriendlyErrorsWebpackPlugin(), + new webpack.ProvidePlugin({ + process: "process/browser", + }), + ], +}; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/dev.js b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/dev.js new file mode 100644 index 00000000..f2f52162 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/dev.js @@ -0,0 +1,25 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +// development config +const { merge } = require("webpack-merge"); +const commonConfig = require("./common"); + +module.exports = merge(commonConfig, { + mode: "development", + devServer: { + hot: true, // enable HMR on the server + open: true, + // These headers enable the cross origin isolation state + // needed to enable use of SharedArrayBuffer for ONNX + // multithreading. + headers: { + "Cross-Origin-Opener-Policy": "same-origin", + "Cross-Origin-Embedder-Policy": "credentialless", + }, + }, + devtool: "cheap-module-source-map", +}); diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/prod.js b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/prod.js new file mode 100644 index 00000000..b598f486 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/configs/webpack/prod.js @@ -0,0 +1,22 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +// production config +const { merge } = require("webpack-merge"); +const { resolve } = require("path"); +const Dotenv = require("dotenv-webpack"); +const commonConfig = require("./common"); + +module.exports = merge(commonConfig, { + mode: "production", + output: { + filename: "js/bundle.[contenthash].min.js", + path: resolve(__dirname, "../../dist"), + publicPath: "/", + }, + devtool: "source-map", + plugins: [new Dotenv()], +}); diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/package.json b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/package.json new file mode 100644 index 00000000..c1de897c --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/package.json @@ -0,0 +1,62 @@ +{ + "name": "segment-anything-mini-demo", + "version": "0.1.0", + "license": "MIT", + "scripts": { + "build": "yarn run clean-dist && webpack --config=configs/webpack/prod.js && mv dist/*.wasm dist/js", + "clean-dist": "rimraf dist/*", + "lint": "eslint './src/**/*.{js,ts,tsx}' --quiet", + "start": "yarn run start-dev", + "test": "yarn run start-model-test", + "start-dev": "webpack serve --config=configs/webpack/dev.js" + }, + "devDependencies": { + "@babel/core": "^7.18.13", + "@babel/preset-env": "^7.18.10", + "@babel/preset-react": "^7.18.6", + "@babel/preset-typescript": "^7.18.6", + "@pmmmwh/react-refresh-webpack-plugin": "^0.5.7", + "@testing-library/react": "^13.3.0", + "@types/node": "^18.7.13", + "@types/react": "^18.0.17", + "@types/react-dom": "^18.0.6", + "@types/underscore": "^1.11.4", + "@typescript-eslint/eslint-plugin": "^5.35.1", + "@typescript-eslint/parser": "^5.35.1", + "babel-loader": "^8.2.5", + "copy-webpack-plugin": "^11.0.0", + "css-loader": "^6.7.1", + "dotenv": "^16.0.2", + "dotenv-webpack": "^8.0.1", + "eslint": "^8.22.0", + "eslint-plugin-react": "^7.31.0", + "file-loader": "^6.2.0", + "fork-ts-checker-webpack-plugin": "^7.2.13", + "friendly-errors-webpack-plugin": "^1.7.0", + "html-webpack-plugin": "^5.5.0", + "image-webpack-loader": "^8.1.0", + "postcss-loader": "^7.0.1", + "postcss-preset-env": "^7.8.0", + "process": "^0.11.10", + "rimraf": "^3.0.2", + "sass": "^1.54.5", + "sass-loader": "^13.0.2", + "style-loader": "^3.3.1", + "tailwindcss": "^3.1.8", + "ts-loader": "^9.3.1", + "typescript": "^4.8.2", + "webpack": "^5.74.0", + "webpack-cli": "^4.10.0", + "webpack-dev-server": "^4.10.0", + "webpack-dotenv-plugin": "^2.1.0", + "webpack-merge": "^5.8.0" + }, + "dependencies": { + "npyjs": "^0.4.0", + "onnxruntime-web": "^1.14.0", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "underscore": "^1.13.6", + "react-refresh": "^0.14.0" + } +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/postcss.config.js b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/postcss.config.js new file mode 100644 index 00000000..064a2ba5 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/postcss.config.js @@ -0,0 +1,10 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +const tailwindcss = require("tailwindcss"); +module.exports = { + plugins: ["postcss-preset-env", 'tailwindcss/nesting', tailwindcss], +}; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/App.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/App.tsx new file mode 100644 index 00000000..a4265535 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/App.tsx @@ -0,0 +1,130 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import { InferenceSession, Tensor } from "onnxruntime-web"; +import React, { useContext, useEffect, useState } from "react"; +import "./assets/scss/App.scss"; +import { handleImageScale } from "./components/helpers/scaleHelper"; +import { modelScaleProps } from "./components/helpers/Interfaces"; +import { onnxMaskToImage } from "./components/helpers/maskUtils"; +import { modelData } from "./components/helpers/onnxModelAPI"; +import Stage from "./components/Stage"; +import AppContext from "./components/hooks/createContext"; +const ort = require("onnxruntime-web"); +/* @ts-ignore */ +import npyjs from "npyjs"; + +// Define image, embedding and model paths +const IMAGE_PATH = "/assets/data/dogs.jpg"; +const IMAGE_EMBEDDING = "/assets/data/dogs_embedding.npy"; +const MODEL_DIR = "/model/sam_onnx_quantized_example.onnx"; + +const App = () => { + const { + clicks: [clicks], + image: [, setImage], + maskImg: [, setMaskImg], + } = useContext(AppContext)!; + const [model, setModel] = useState(null); // ONNX model + const [tensor, setTensor] = useState(null); // Image embedding tensor + + // The ONNX model expects the input to be rescaled to 1024. + // The modelScale state variable keeps track of the scale values. + const [modelScale, setModelScale] = useState(null); + + // Initialize the ONNX model. load the image, and load the SAM + // pre-computed image embedding + useEffect(() => { + // Initialize the ONNX model + const initModel = async () => { + try { + if (MODEL_DIR === undefined) return; + const URL: string = MODEL_DIR; + const model = await InferenceSession.create(URL); + setModel(model); + } catch (e) { + console.log(e); + } + }; + initModel(); + + // Load the image + const url = new URL(IMAGE_PATH, location.origin); + loadImage(url); + + // Load the Segment Anything pre-computed embedding + Promise.resolve(loadNpyTensor(IMAGE_EMBEDDING, "float32")).then( + (embedding) => setTensor(embedding) + ); + }, []); + + const loadImage = async (url: URL) => { + try { + const img = new Image(); + img.src = url.href; + img.onload = () => { + const { height, width, samScale } = handleImageScale(img); + setModelScale({ + height: height, // original image height + width: width, // original image width + samScale: samScale, // scaling factor for image which has been resized to longest side 1024 + }); + img.width = width; + img.height = height; + setImage(img); + }; + } catch (error) { + console.log(error); + } + }; + + // Decode a Numpy file into a tensor. + const loadNpyTensor = async (tensorFile: string, dType: string) => { + let npLoader = new npyjs(); + const npArray = await npLoader.load(tensorFile); + const tensor = new ort.Tensor(dType, npArray.data, npArray.shape); + return tensor; + }; + + // Run the ONNX model every time clicks has changed + useEffect(() => { + runONNX(); + }, [clicks]); + + const runONNX = async () => { + try { + if ( + model === null || + clicks === null || + tensor === null || + modelScale === null + ) + return; + else { + // Preapre the model input in the correct format for SAM. + // The modelData function is from onnxModelAPI.tsx. + const feeds = modelData({ + clicks, + tensor, + modelScale, + }); + if (feeds === undefined) return; + // Run the SAM ONNX model with the feeds returned from modelData() + const results = await model.run(feeds); + const output = results[model.outputNames[0]]; + // The predicted mask returned from the ONNX model is an array which is + // rendered as an HTML image using onnxMaskToImage() from maskUtils.tsx. + setMaskImg(onnxMaskToImage(output.data, output.dims[2], output.dims[3])); + } + } catch (e) { + console.log(e); + } + }; + + return ; +}; + +export default App; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/data/dogs.jpg b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/data/dogs.jpg new file mode 100644 index 00000000..deeafdbc Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/data/dogs.jpg differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/index.html b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/index.html new file mode 100644 index 00000000..cbcd53c1 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/index.html @@ -0,0 +1,18 @@ + + + + + + Segment Anything Demo + + + + + + +
        + + diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/scss/App.scss b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/scss/App.scss new file mode 100644 index 00000000..b5c61c95 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/assets/scss/App.scss @@ -0,0 +1,3 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/Stage.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/Stage.tsx new file mode 100644 index 00000000..53250487 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/Stage.tsx @@ -0,0 +1,49 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import React, { useContext } from "react"; +import * as _ from "underscore"; +import Tool from "./Tool"; +import { modelInputProps } from "./helpers/Interfaces"; +import AppContext from "./hooks/createContext"; + +const Stage = () => { + const { + clicks: [, setClicks], + image: [image], + } = useContext(AppContext)!; + + const getClick = (x: number, y: number): modelInputProps => { + const clickType = 1; + return { x, y, clickType }; + }; + + // Get mouse position and scale the (x, y) coordinates back to the natural + // scale of the image. Update the state of clicks with setClicks to trigger + // the ONNX model to run and generate a new mask via a useEffect in App.tsx + const handleMouseMove = _.throttle((e: any) => { + let el = e.nativeEvent.target; + const rect = el.getBoundingClientRect(); + let x = e.clientX - rect.left; + let y = e.clientY - rect.top; + const imageScale = image ? image.width / el.offsetWidth : 1; + x *= imageScale; + y *= imageScale; + const click = getClick(x, y); + if (click) setClicks([click]); + }, 15); + + const flexCenterClasses = "flex items-center justify-center"; + return ( +
        +
        + +
        +
        + ); +}; + +export default Stage; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/Tool.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/Tool.tsx new file mode 100644 index 00000000..31afbe5c --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/Tool.tsx @@ -0,0 +1,73 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import React, { useContext, useEffect, useState } from "react"; +import AppContext from "./hooks/createContext"; +import { ToolProps } from "./helpers/Interfaces"; +import * as _ from "underscore"; + +const Tool = ({ handleMouseMove }: ToolProps) => { + const { + image: [image], + maskImg: [maskImg, setMaskImg], + } = useContext(AppContext)!; + + // Determine if we should shrink or grow the images to match the + // width or the height of the page and setup a ResizeObserver to + // monitor changes in the size of the page + const [shouldFitToWidth, setShouldFitToWidth] = useState(true); + const bodyEl = document.body; + const fitToPage = () => { + if (!image) return; + const imageAspectRatio = image.width / image.height; + const screenAspectRatio = window.innerWidth / window.innerHeight; + setShouldFitToWidth(imageAspectRatio > screenAspectRatio); + }; + const resizeObserver = new ResizeObserver((entries) => { + for (const entry of entries) { + if (entry.target === bodyEl) { + fitToPage(); + } + } + }); + useEffect(() => { + fitToPage(); + resizeObserver.observe(bodyEl); + return () => { + resizeObserver.unobserve(bodyEl); + }; + }, [image]); + + const imageClasses = ""; + const maskImageClasses = `absolute opacity-40 pointer-events-none`; + + // Render the image and the predicted mask image on top + return ( + <> + {image && ( + _.defer(() => setMaskImg(null))} + onTouchStart={handleMouseMove} + src={image.src} + className={`${ + shouldFitToWidth ? "w-full" : "h-full" + } ${imageClasses}`} + > + )} + {maskImg && ( + + )} + + ); +}; + +export default Tool; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/Interfaces.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/Interfaces.tsx new file mode 100644 index 00000000..59b80d06 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/Interfaces.tsx @@ -0,0 +1,29 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import { Tensor } from "onnxruntime-web"; + +export interface modelScaleProps { + samScale: number; + height: number; + width: number; +} + +export interface modelInputProps { + x: number; + y: number; + clickType: number; +} + +export interface modeDataProps { + clicks?: Array; + tensor: Tensor; + modelScale: modelScaleProps; +} + +export interface ToolProps { + handleMouseMove: (e: any) => void; +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/maskUtils.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/maskUtils.tsx new file mode 100644 index 00000000..709c77e2 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/maskUtils.tsx @@ -0,0 +1,47 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +// Convert the onnx model mask prediction to ImageData +function arrayToImageData(input: any, width: number, height: number) { + const [r, g, b, a] = [0, 114, 189, 255]; // the masks's blue color + const arr = new Uint8ClampedArray(4 * width * height).fill(0); + for (let i = 0; i < input.length; i++) { + + // Threshold the onnx model mask prediction at 0.0 + // This is equivalent to thresholding the mask using predictor.model.mask_threshold + // in python + if (input[i] > 0.0) { + arr[4 * i + 0] = r; + arr[4 * i + 1] = g; + arr[4 * i + 2] = b; + arr[4 * i + 3] = a; + } + } + return new ImageData(arr, height, width); +} + +// Use a Canvas element to produce an image from ImageData +function imageDataToImage(imageData: ImageData) { + const canvas = imageDataToCanvas(imageData); + const image = new Image(); + image.src = canvas.toDataURL(); + return image; +} + +// Canvas elements can be created from ImageData +function imageDataToCanvas(imageData: ImageData) { + const canvas = document.createElement("canvas"); + const ctx = canvas.getContext("2d"); + canvas.width = imageData.width; + canvas.height = imageData.height; + ctx?.putImageData(imageData, 0, 0); + return canvas; +} + +// Convert the onnx model mask output to an HTMLImageElement +export function onnxMaskToImage(input: any, width: number, height: number) { + return imageDataToImage(arrayToImageData(input, width, height)); +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/onnxModelAPI.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/onnxModelAPI.tsx new file mode 100644 index 00000000..2e006c95 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/onnxModelAPI.tsx @@ -0,0 +1,71 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import { Tensor } from "onnxruntime-web"; +import { modeDataProps } from "./Interfaces"; + +const modelData = ({ clicks, tensor, modelScale }: modeDataProps) => { + const imageEmbedding = tensor; + let pointCoords; + let pointLabels; + let pointCoordsTensor; + let pointLabelsTensor; + + // Check there are input click prompts + if (clicks) { + let n = clicks.length; + + // If there is no box input, a single padding point with + // label -1 and coordinates (0.0, 0.0) should be concatenated + // so initialize the array to support (n + 1) points. + pointCoords = new Float32Array(2 * (n + 1)); + pointLabels = new Float32Array(n + 1); + + // Add clicks and scale to what SAM expects + for (let i = 0; i < n; i++) { + pointCoords[2 * i] = clicks[i].x * modelScale.samScale; + pointCoords[2 * i + 1] = clicks[i].y * modelScale.samScale; + pointLabels[i] = clicks[i].clickType; + } + + // Add in the extra point/label when only clicks and no box + // The extra point is at (0, 0) with label -1 + pointCoords[2 * n] = 0.0; + pointCoords[2 * n + 1] = 0.0; + pointLabels[n] = -1.0; + + // Create the tensor + pointCoordsTensor = new Tensor("float32", pointCoords, [1, n + 1, 2]); + pointLabelsTensor = new Tensor("float32", pointLabels, [1, n + 1]); + } + const imageSizeTensor = new Tensor("float32", [ + modelScale.height, + modelScale.width, + ]); + + if (pointCoordsTensor === undefined || pointLabelsTensor === undefined) + return; + + // There is no previous mask, so default to an empty tensor + const maskInput = new Tensor( + "float32", + new Float32Array(256 * 256), + [1, 1, 256, 256] + ); + // There is no previous mask, so default to 0 + const hasMaskInput = new Tensor("float32", [0]); + + return { + image_embeddings: imageEmbedding, + point_coords: pointCoordsTensor, + point_labels: pointLabelsTensor, + orig_im_size: imageSizeTensor, + mask_input: maskInput, + has_mask_input: hasMaskInput, + }; +}; + +export { modelData }; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/scaleHelper.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/scaleHelper.tsx new file mode 100644 index 00000000..815ceaac --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/helpers/scaleHelper.tsx @@ -0,0 +1,18 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + + +// Helper function for handling image scaling needed for SAM +const handleImageScale = (image: HTMLImageElement) => { + // Input images to SAM must be resized so the longest side is 1024 + const LONG_SIDE_LENGTH = 1024; + let w = image.naturalWidth; + let h = image.naturalHeight; + const samScale = LONG_SIDE_LENGTH / Math.max(h, w); + return { height: h, width: w, samScale }; +}; + +export { handleImageScale }; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/hooks/context.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/hooks/context.tsx new file mode 100644 index 00000000..a26069fc --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/hooks/context.tsx @@ -0,0 +1,31 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import React, { useState } from "react"; +import { modelInputProps } from "../helpers/Interfaces"; +import AppContext from "./createContext"; + +const AppContextProvider = (props: { + children: React.ReactElement>; +}) => { + const [clicks, setClicks] = useState | null>(null); + const [image, setImage] = useState(null); + const [maskImg, setMaskImg] = useState(null); + + return ( + + {props.children} + + ); +}; + +export default AppContextProvider; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/hooks/createContext.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/hooks/createContext.tsx new file mode 100644 index 00000000..c363be6a --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/components/hooks/createContext.tsx @@ -0,0 +1,27 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import { createContext } from "react"; +import { modelInputProps } from "../helpers/Interfaces"; + +interface contextProps { + clicks: [ + clicks: modelInputProps[] | null, + setClicks: (e: modelInputProps[] | null) => void + ]; + image: [ + image: HTMLImageElement | null, + setImage: (e: HTMLImageElement | null) => void + ]; + maskImg: [ + maskImg: HTMLImageElement | null, + setMaskImg: (e: HTMLImageElement | null) => void + ]; +} + +const AppContext = createContext(null); + +export default AppContext; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/index.tsx b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/index.tsx new file mode 100644 index 00000000..714dfc91 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/src/index.tsx @@ -0,0 +1,17 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +import * as React from "react"; +import { createRoot } from "react-dom/client"; +import AppContextProvider from "./components/hooks/context"; +import App from "./App"; +const container = document.getElementById("root"); +const root = createRoot(container!); +root.render( + + + +); diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/tailwind.config.js b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/tailwind.config.js new file mode 100644 index 00000000..e92b38b8 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/tailwind.config.js @@ -0,0 +1,12 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. + +// This source code is licensed under the license found in the +// LICENSE file in the root directory of this source tree. + +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: ["./src/**/*.{html,js,tsx}"], + theme: {}, + plugins: [], +}; diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/tsconfig.json b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/tsconfig.json new file mode 100644 index 00000000..25b59894 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/demo/tsconfig.json @@ -0,0 +1,24 @@ +{ + "compilerOptions": { + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "forceConsistentCasingInFileNames": true, + "noEmit": false, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "node", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "react", + "incremental": true, + "target": "ESNext", + "useDefineForClassFields": true, + "allowSyntheticDefaultImports": true, + "outDir": "./dist/", + "sourceMap": true + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", "src"], + "exclude": ["node_modules"] +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/linter.sh b/custom_nodes/was-node-suite-comfyui/repos/SAM/linter.sh new file mode 100644 index 00000000..df2e1743 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/linter.sh @@ -0,0 +1,32 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +{ + black --version | grep -E "23\." > /dev/null +} || { + echo "Linter requires 'black==23.*' !" + exit 1 +} + +ISORT_VERSION=$(isort --version-number) +if [[ "$ISORT_VERSION" != 5.12* ]]; then + echo "Linter requires isort==5.12.0 !" + exit 1 +fi + +echo "Running isort ..." +isort . --atomic + +echo "Running black ..." +black -l 100 . + +echo "Running flake8 ..." +if [ -x "$(command -v flake8)" ]; then + flake8 . +else + python3 -m flake8 . +fi + +echo "Running mypy..." + +mypy --exclude 'setup.py|notebooks' . diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/automatic_mask_generator_example.ipynb b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/automatic_mask_generator_example.ipynb new file mode 100644 index 00000000..0c1d6dac --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/automatic_mask_generator_example.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5fa21d44", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Meta Platforms, Inc. and affiliates." + ] + }, + { + "cell_type": "markdown", + "id": "b7c0041e", + "metadata": {}, + "source": [ + "# Automatically generating object masks with SAM" + ] + }, + { + "cell_type": "markdown", + "id": "289bb0b4", + "metadata": {}, + "source": [ + "Since SAM can efficiently process prompts, masks for the entire image can be generated by sampling a large number of prompts over an image. This method was used to generate the dataset SA-1B. \n", + "\n", + "The class `SamAutomaticMaskGenerator` implements this capability. It works by sampling single-point input prompts in a grid over the image, from each of which SAM can predict multiple masks. Then, masks are filtered for quality and deduplicated using non-maximal suppression. Additional options allow for further improvement of mask quality and quantity, such as running prediction on multiple crops of the image or postprocessing masks to remove small disconnected regions and holes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "072e25b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \"Open\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, HTML\n", + "display(HTML(\n", + "\"\"\"\n", + "\n", + " \"Open\n", + "\n", + "\"\"\"\n", + "))" + ] + }, + { + "cell_type": "markdown", + "id": "c0b71431", + "metadata": {}, + "source": [ + "## Environment Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "47e5a78f", + "metadata": {}, + "source": [ + "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. If running from Google Colab, set `using_colab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4fe300fb", + "metadata": {}, + "outputs": [], + "source": [ + "using_colab = False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0685a2f5", + "metadata": {}, + "outputs": [], + "source": [ + "if using_colab:\n", + " import torch\n", + " import torchvision\n", + " print(\"PyTorch version:\", torch.__version__)\n", + " print(\"Torchvision version:\", torchvision.__version__)\n", + " print(\"CUDA is available:\", torch.cuda.is_available())\n", + " import sys\n", + " !{sys.executable} -m pip install opencv-python matplotlib\n", + " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n", + " \n", + " !mkdir images\n", + " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/dog.jpg\n", + " \n", + " !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" + ] + }, + { + "cell_type": "markdown", + "id": "fd2bc687", + "metadata": {}, + "source": [ + "## Set-up" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "560725a2", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import cv2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "74b6e5f0", + "metadata": {}, + "outputs": [], + "source": [ + "def show_anns(anns):\n", + " if len(anns) == 0:\n", + " return\n", + " sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)\n", + " ax = plt.gca()\n", + " ax.set_autoscale_on(False)\n", + "\n", + " img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))\n", + " img[:,:,3] = 0\n", + " for ann in sorted_anns:\n", + " m = ann['segmentation']\n", + " color_mask = np.concatenate([np.random.random(3), [0.35]])\n", + " img[m] = color_mask\n", + " ax.imshow(img)" + ] + }, + { + "cell_type": "markdown", + "id": "27c41445", + "metadata": {}, + "source": [ + "## Example image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad354922", + "metadata": {}, + "outputs": [], + "source": [ + "image = cv2.imread('images/dog.jpg')\n", + "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e0ac8c67", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,20))\n", + "plt.imshow(image)\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b8c2824a", + "metadata": {}, + "source": [ + "## Automatic mask generation" + ] + }, + { + "cell_type": "markdown", + "id": "d9ef74c5", + "metadata": {}, + "source": [ + "To run automatic mask generation, provide a SAM model to the `SamAutomaticMaskGenerator` class. Set the path below to the SAM checkpoint. Running on CUDA and with the default model is recommended." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1848a108", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor\n", + "\n", + "sam_checkpoint = \"sam_vit_h_4b8939.pth\"\n", + "model_type = \"vit_h\"\n", + "\n", + "device = \"cuda\"\n", + "\n", + "sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\n", + "sam.to(device=device)\n", + "\n", + "mask_generator = SamAutomaticMaskGenerator(sam)" + ] + }, + { + "cell_type": "markdown", + "id": "d6b1ea21", + "metadata": {}, + "source": [ + "To generate masks, just run `generate` on an image." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "391771c1", + "metadata": {}, + "outputs": [], + "source": [ + "masks = mask_generator.generate(image)" + ] + }, + { + "cell_type": "markdown", + "id": "e36a1a39", + "metadata": {}, + "source": [ + "Mask generation returns a list over masks, where each mask is a dictionary containing various data about the mask. These keys are:\n", + "* `segmentation` : the mask\n", + "* `area` : the area of the mask in pixels\n", + "* `bbox` : the boundary box of the mask in XYWH format\n", + "* `predicted_iou` : the model's own prediction for the quality of the mask\n", + "* `point_coords` : the sampled input point that generated this mask\n", + "* `stability_score` : an additional measure of mask quality\n", + "* `crop_box` : the crop of the image used to generate this mask in XYWH format" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4fae8d66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['segmentation', 'area', 'bbox', 'predicted_iou', 'point_coords', 'stability_score', 'crop_box'])\n" + ] + } + ], + "source": [ + "print(len(masks))\n", + "print(masks[0].keys())" + ] + }, + { + "cell_type": "markdown", + "id": "53009a1f", + "metadata": {}, + "source": [ + "Show all the masks overlayed on the image." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "77ac29c5", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,20))\n", + "plt.imshow(image)\n", + "show_anns(masks)\n", + "plt.axis('off')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "00b3d6b2", + "metadata": {}, + "source": [ + "## Automatic mask generation options" + ] + }, + { + "cell_type": "markdown", + "id": "183de84e", + "metadata": {}, + "source": [ + "There are several tunable parameters in automatic mask generation that control how densely points are sampled and what the thresholds are for removing low quality or duplicate masks. Additionally, generation can be automatically run on crops of the image to get improved performance on smaller objects, and post-processing can remove stray pixels and holes. Here is an example configuration that samples more masks:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "68364513", + "metadata": {}, + "outputs": [], + "source": [ + "mask_generator_2 = SamAutomaticMaskGenerator(\n", + " model=sam,\n", + " points_per_side=32,\n", + " pred_iou_thresh=0.86,\n", + " stability_score_thresh=0.92,\n", + " crop_n_layers=1,\n", + " crop_n_points_downscale_factor=2,\n", + " min_mask_region_area=100, # Requires open-cv to run post-processing\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bebcdaf1", + "metadata": {}, + "outputs": [], + "source": [ + "masks2 = mask_generator_2.generate(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b8473f3c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "90" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(masks2)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "fb702ae3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,20))\n", + "plt.imshow(image)\n", + "show_anns(masks2)\n", + "plt.axis('off')\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c937160", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/dog.jpg b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/dog.jpg new file mode 100644 index 00000000..26d6454d Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/dog.jpg differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/groceries.jpg b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/groceries.jpg new file mode 100644 index 00000000..85f791c4 Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/groceries.jpg differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/truck.jpg b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/truck.jpg new file mode 100644 index 00000000..6b98688c Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/images/truck.jpg differ diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/onnx_model_example.ipynb b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/onnx_model_example.ipynb new file mode 100644 index 00000000..ca49c357 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/onnx_model_example.ipynb @@ -0,0 +1,774 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "901c8ef3", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Meta Platforms, Inc. and affiliates." + ] + }, + { + "cell_type": "markdown", + "id": "1662bb7c", + "metadata": {}, + "source": [ + "# Produces masks from prompts using an ONNX model" + ] + }, + { + "cell_type": "markdown", + "id": "7fcc21a0", + "metadata": {}, + "source": [ + "SAM's prompt encoder and mask decoder are very lightweight, which allows for efficient computation of a mask given user input. This notebook shows an example of how to export and use this lightweight component of the model in ONNX format, allowing it to run on a variety of platforms that support an ONNX runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "86daff77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \"Open\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, HTML\n", + "display(HTML(\n", + "\"\"\"\n", + "\n", + " \"Open\n", + "\n", + "\"\"\"\n", + "))" + ] + }, + { + "cell_type": "markdown", + "id": "55ae4e00", + "metadata": {}, + "source": [ + "## Environment Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "109a5cc2", + "metadata": {}, + "source": [ + "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. The latest stable versions of PyTorch and ONNX are recommended for this notebook. If running from Google Colab, set `using_colab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "39b99fc4", + "metadata": {}, + "outputs": [], + "source": [ + "using_colab = False" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "296a69be", + "metadata": {}, + "outputs": [], + "source": [ + "if using_colab:\n", + " import torch\n", + " import torchvision\n", + " print(\"PyTorch version:\", torch.__version__)\n", + " print(\"Torchvision version:\", torchvision.__version__)\n", + " print(\"CUDA is available:\", torch.cuda.is_available())\n", + " import sys\n", + " !{sys.executable} -m pip install opencv-python matplotlib onnx onnxruntime\n", + " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n", + " \n", + " !mkdir images\n", + " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\n", + " \n", + " !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" + ] + }, + { + "cell_type": "markdown", + "id": "dc4a58be", + "metadata": {}, + "source": [ + "## Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "42396e8d", + "metadata": {}, + "source": [ + "Note that this notebook requires both the `onnx` and `onnxruntime` optional dependencies, in addition to `opencv-python` and `matplotlib` for visualization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c712610", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "from segment_anything import sam_model_registry, SamPredictor\n", + "from segment_anything.utils.onnx import SamOnnxModel\n", + "\n", + "import onnxruntime\n", + "from onnxruntime.quantization import QuantType\n", + "from onnxruntime.quantization.quantize import quantize_dynamic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f29441b9", + "metadata": {}, + "outputs": [], + "source": [ + "def show_mask(mask, ax):\n", + " color = np.array([30/255, 144/255, 255/255, 0.6])\n", + " h, w = mask.shape[-2:]\n", + " mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n", + " ax.imshow(mask_image)\n", + " \n", + "def show_points(coords, labels, ax, marker_size=375):\n", + " pos_points = coords[labels==1]\n", + " neg_points = coords[labels==0]\n", + " ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n", + " ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n", + " \n", + "def show_box(box, ax):\n", + " x0, y0 = box[0], box[1]\n", + " w, h = box[2] - box[0], box[3] - box[1]\n", + " ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) " + ] + }, + { + "cell_type": "markdown", + "id": "bd0f6b2b", + "metadata": {}, + "source": [ + "## Export an ONNX model" + ] + }, + { + "cell_type": "markdown", + "id": "1540f719", + "metadata": {}, + "source": [ + "Set the path below to a SAM model checkpoint, then load the model. This will be needed to both export the model and to calculate embeddings for the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76fc53f4", + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint = \"sam_vit_h_4b8939.pth\"\n", + "model_type = \"vit_h\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11bfc8aa", + "metadata": {}, + "outputs": [], + "source": [ + "sam = sam_model_registry[model_type](checkpoint=checkpoint)" + ] + }, + { + "cell_type": "markdown", + "id": "450c089c", + "metadata": {}, + "source": [ + "The script `segment-anything/scripts/export_onnx_model.py` can be used to export the necessary portion of SAM. Alternatively, run the following code to export an ONNX model. If you have already exported a model, set the path below and skip to the next section. Assure that the exported ONNX model aligns with the checkpoint and model type set above. This notebook expects the model was exported with the parameter `return_single_mask=True`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38a8add8", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_model_path = None # Set to use an already exported model, then skip to the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7da638ba", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "onnx_model_path = \"sam_onnx_example.onnx\"\n", + "\n", + "onnx_model = SamOnnxModel(sam, return_single_mask=True)\n", + "\n", + "dynamic_axes = {\n", + " \"point_coords\": {1: \"num_points\"},\n", + " \"point_labels\": {1: \"num_points\"},\n", + "}\n", + "\n", + "embed_dim = sam.prompt_encoder.embed_dim\n", + "embed_size = sam.prompt_encoder.image_embedding_size\n", + "mask_input_size = [4 * x for x in embed_size]\n", + "dummy_inputs = {\n", + " \"image_embeddings\": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),\n", + " \"point_coords\": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),\n", + " \"point_labels\": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),\n", + " \"mask_input\": torch.randn(1, 1, *mask_input_size, dtype=torch.float),\n", + " \"has_mask_input\": torch.tensor([1], dtype=torch.float),\n", + " \"orig_im_size\": torch.tensor([1500, 2250], dtype=torch.float),\n", + "}\n", + "output_names = [\"masks\", \"iou_predictions\", \"low_res_masks\"]\n", + "\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(\"ignore\", category=torch.jit.TracerWarning)\n", + " warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + " with open(onnx_model_path, \"wb\") as f:\n", + " torch.onnx.export(\n", + " onnx_model,\n", + " tuple(dummy_inputs.values()),\n", + " f,\n", + " export_params=True,\n", + " verbose=False,\n", + " opset_version=17,\n", + " do_constant_folding=True,\n", + " input_names=list(dummy_inputs.keys()),\n", + " output_names=output_names,\n", + " dynamic_axes=dynamic_axes,\n", + " ) " + ] + }, + { + "cell_type": "markdown", + "id": "c450cf1a", + "metadata": {}, + "source": [ + "If desired, the model can additionally be quantized and optimized. We find this improves web runtime significantly for negligible change in qualitative performance. Run the next cell to quantize the model, or skip to the next section otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "235d39fe", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_model_quantized_path = \"sam_onnx_quantized_example.onnx\"\n", + "quantize_dynamic(\n", + " model_input=onnx_model_path,\n", + " model_output=onnx_model_quantized_path,\n", + " optimize_model=True,\n", + " per_channel=False,\n", + " reduce_range=False,\n", + " weight_type=QuantType.QUInt8,\n", + ")\n", + "onnx_model_path = onnx_model_quantized_path" + ] + }, + { + "cell_type": "markdown", + "id": "927a928b", + "metadata": {}, + "source": [ + "## Example Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6be6eb55", + "metadata": {}, + "outputs": [], + "source": [ + "image = cv2.imread('images/truck.jpg')\n", + "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7e9a27a", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(image)\n", + "plt.axis('on')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "027b177b", + "metadata": {}, + "source": [ + "## Using an ONNX model" + ] + }, + { + "cell_type": "markdown", + "id": "778d4593", + "metadata": {}, + "source": [ + "Here as an example, we use `onnxruntime` in python on CPU to execute the ONNX model. However, any platform that supports an ONNX runtime could be used in principle. Launch the runtime session below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9689b1bf", + "metadata": {}, + "outputs": [], + "source": [ + "ort_session = onnxruntime.InferenceSession(onnx_model_path)" + ] + }, + { + "cell_type": "markdown", + "id": "7708ead6", + "metadata": {}, + "source": [ + "To use the ONNX model, the image must first be pre-processed using the SAM image encoder. This is a heavier weight process best performed on GPU. SamPredictor can be used as normal, then `.get_image_embedding()` will retreive the intermediate features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26e067b4", + "metadata": {}, + "outputs": [], + "source": [ + "sam.to(device='cuda')\n", + "predictor = SamPredictor(sam)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ad3f0d6", + "metadata": {}, + "outputs": [], + "source": [ + "predictor.set_image(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a6f0f07", + "metadata": {}, + "outputs": [], + "source": [ + "image_embedding = predictor.get_image_embedding().cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e112f33", + "metadata": {}, + "outputs": [], + "source": [ + "image_embedding.shape" + ] + }, + { + "cell_type": "markdown", + "id": "6337b654", + "metadata": {}, + "source": [ + "The ONNX model has a different input signature than `SamPredictor.predict`. The following inputs must all be supplied. Note the special cases for both point and mask inputs. All inputs are `np.float32`.\n", + "* `image_embeddings`: The image embedding from `predictor.get_image_embedding()`. Has a batch index of length 1.\n", + "* `point_coords`: Coordinates of sparse input prompts, corresponding to both point inputs and box inputs. Boxes are encoded using two points, one for the top-left corner and one for the bottom-right corner. *Coordinates must already be transformed to long-side 1024.* Has a batch index of length 1.\n", + "* `point_labels`: Labels for the sparse input prompts. 0 is a negative input point, 1 is a positive input point, 2 is a top-left box corner, 3 is a bottom-right box corner, and -1 is a padding point. *If there is no box input, a single padding point with label -1 and coordinates (0.0, 0.0) should be concatenated.*\n", + "* `mask_input`: A mask input to the model with shape 1x1x256x256. This must be supplied even if there is no mask input. In this case, it can just be zeros.\n", + "* `has_mask_input`: An indicator for the mask input. 1 indicates a mask input, 0 indicates no mask input.\n", + "* `orig_im_size`: The size of the input image in (H,W) format, before any transformation. \n", + "\n", + "Additionally, the ONNX model does not threshold the output mask logits. To obtain a binary mask, threshold at `sam.mask_threshold` (equal to 0.0)." + ] + }, + { + "cell_type": "markdown", + "id": "bf5a9f55", + "metadata": {}, + "source": [ + "### Example point input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c0deef0", + "metadata": {}, + "outputs": [], + "source": [ + "input_point = np.array([[500, 375]])\n", + "input_label = np.array([1])" + ] + }, + { + "cell_type": "markdown", + "id": "7256394c", + "metadata": {}, + "source": [ + "Add a batch index, concatenate a padding point, and transform." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f69903e", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\n", + "onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\n", + "\n", + "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b188dc53", + "metadata": {}, + "source": [ + "Create an empty mask input and an indicator for no mask." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cb52bcf", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\n", + "onnx_has_mask_input = np.zeros(1, dtype=np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "a99c2cc5", + "metadata": {}, + "source": [ + "Package the inputs to run in the onnx model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1d7ea11", + "metadata": {}, + "outputs": [], + "source": [ + "ort_inputs = {\n", + " \"image_embeddings\": image_embedding,\n", + " \"point_coords\": onnx_coord,\n", + " \"point_labels\": onnx_label,\n", + " \"mask_input\": onnx_mask_input,\n", + " \"has_mask_input\": onnx_has_mask_input,\n", + " \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "4b6409c9", + "metadata": {}, + "source": [ + "Predict a mask and threshold it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc4cc082", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "masks, _, low_res_logits = ort_session.run(None, ort_inputs)\n", + "masks = masks > predictor.model.mask_threshold" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d778a8fb", + "metadata": {}, + "outputs": [], + "source": [ + "masks.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "badb1175", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(image)\n", + "show_mask(masks, plt.gca())\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('off')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "1f1d4d15", + "metadata": {}, + "source": [ + "### Example mask input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b319da82", + "metadata": {}, + "outputs": [], + "source": [ + "input_point = np.array([[500, 375], [1125, 625]])\n", + "input_label = np.array([1, 1])\n", + "\n", + "# Use the mask output from the previous run. It is already in the correct form for input to the ONNX model.\n", + "onnx_mask_input = low_res_logits" + ] + }, + { + "cell_type": "markdown", + "id": "b1823b37", + "metadata": {}, + "source": [ + "Transform the points as in the previous example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8885130f", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\n", + "onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\n", + "\n", + "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "28e47b69", + "metadata": {}, + "source": [ + "The `has_mask_input` indicator is now 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ab4483a", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_has_mask_input = np.ones(1, dtype=np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "d3781955", + "metadata": {}, + "source": [ + "Package inputs, then predict and threshold the mask." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c1ec096", + "metadata": {}, + "outputs": [], + "source": [ + "ort_inputs = {\n", + " \"image_embeddings\": image_embedding,\n", + " \"point_coords\": onnx_coord,\n", + " \"point_labels\": onnx_label,\n", + " \"mask_input\": onnx_mask_input,\n", + " \"has_mask_input\": onnx_has_mask_input,\n", + " \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n", + "}\n", + "\n", + "masks, _, _ = ort_session.run(None, ort_inputs)\n", + "masks = masks > predictor.model.mask_threshold" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e36554b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(image)\n", + "show_mask(masks, plt.gca())\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('off')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "2ef211d0", + "metadata": {}, + "source": [ + "### Example box and point input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51e58d2e", + "metadata": {}, + "outputs": [], + "source": [ + "input_box = np.array([425, 600, 700, 875])\n", + "input_point = np.array([[575, 750]])\n", + "input_label = np.array([0])" + ] + }, + { + "cell_type": "markdown", + "id": "6e119dcb", + "metadata": {}, + "source": [ + "Add a batch index, concatenate a box and point inputs, add the appropriate labels for the box corners, and transform. There is no padding point since the input includes a box input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfbe4911", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_box_coords = input_box.reshape(2, 2)\n", + "onnx_box_labels = np.array([2,3])\n", + "\n", + "onnx_coord = np.concatenate([input_point, onnx_box_coords], axis=0)[None, :, :]\n", + "onnx_label = np.concatenate([input_label, onnx_box_labels], axis=0)[None, :].astype(np.float32)\n", + "\n", + "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "65edabd2", + "metadata": {}, + "source": [ + "Package inputs, then predict and threshold the mask." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2abfba56", + "metadata": {}, + "outputs": [], + "source": [ + "onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\n", + "onnx_has_mask_input = np.zeros(1, dtype=np.float32)\n", + "\n", + "ort_inputs = {\n", + " \"image_embeddings\": image_embedding,\n", + " \"point_coords\": onnx_coord,\n", + " \"point_labels\": onnx_label,\n", + " \"mask_input\": onnx_mask_input,\n", + " \"has_mask_input\": onnx_has_mask_input,\n", + " \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n", + "}\n", + "\n", + "masks, _, _ = ort_session.run(None, ort_inputs)\n", + "masks = masks > predictor.model.mask_threshold" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8301bf33", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(image)\n", + "show_mask(masks[0], plt.gca())\n", + "show_box(input_box, plt.gca())\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('off')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/predictor_example.ipynb b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/predictor_example.ipynb new file mode 100644 index 00000000..5f365d82 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/notebooks/predictor_example.ipynb @@ -0,0 +1,1016 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f400486b", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Meta Platforms, Inc. and affiliates." + ] + }, + { + "cell_type": "markdown", + "id": "a1ae39ff", + "metadata": {}, + "source": [ + "# Object masks from prompts with SAM" + ] + }, + { + "cell_type": "markdown", + "id": "b4a4b25c", + "metadata": {}, + "source": [ + "The Segment Anything Model (SAM) predicts object masks given prompts that indicate the desired object. The model first converts the image into an image embedding that allows high quality masks to be efficiently produced from a prompt. \n", + "\n", + "The `SamPredictor` class provides an easy interface to the model for prompting the model. It allows the user to first set an image using the `set_image` method, which calculates the necessary image embeddings. Then, prompts can be provided via the `predict` method to efficiently predict masks from those prompts. The model can take as input both point and box prompts, as well as masks from the previous iteration of prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "18ab8c70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \"Open\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, HTML\n", + "display(HTML(\n", + "\"\"\"\n", + "\n", + " \"Open\n", + "\n", + "\"\"\"\n", + "))" + ] + }, + { + "cell_type": "markdown", + "id": "644532a8", + "metadata": {}, + "source": [ + "## Environment Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "07fabfee", + "metadata": {}, + "source": [ + "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. If running from Google Colab, set `using_colab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5ea65efc", + "metadata": {}, + "outputs": [], + "source": [ + "using_colab = False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "91dd9a89", + "metadata": {}, + "outputs": [], + "source": [ + "if using_colab:\n", + " import torch\n", + " import torchvision\n", + " print(\"PyTorch version:\", torch.__version__)\n", + " print(\"Torchvision version:\", torchvision.__version__)\n", + " print(\"CUDA is available:\", torch.cuda.is_available())\n", + " import sys\n", + " !{sys.executable} -m pip install opencv-python matplotlib\n", + " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n", + " \n", + " !mkdir images\n", + " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\n", + " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/groceries.jpg\n", + " \n", + " !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" + ] + }, + { + "cell_type": "markdown", + "id": "0be845da", + "metadata": {}, + "source": [ + "## Set-up" + ] + }, + { + "cell_type": "markdown", + "id": "33681dd1", + "metadata": {}, + "source": [ + "Necessary imports and helper functions for displaying points, boxes, and masks." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "69b28288", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import cv2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "29bc90d5", + "metadata": {}, + "outputs": [], + "source": [ + "def show_mask(mask, ax, random_color=False):\n", + " if random_color:\n", + " color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n", + " else:\n", + " color = np.array([30/255, 144/255, 255/255, 0.6])\n", + " h, w = mask.shape[-2:]\n", + " mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n", + " ax.imshow(mask_image)\n", + " \n", + "def show_points(coords, labels, ax, marker_size=375):\n", + " pos_points = coords[labels==1]\n", + " neg_points = coords[labels==0]\n", + " ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n", + " ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n", + " \n", + "def show_box(box, ax):\n", + " x0, y0 = box[0], box[1]\n", + " w, h = box[2] - box[0], box[3] - box[1]\n", + " ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n" + ] + }, + { + "cell_type": "markdown", + "id": "23842fb2", + "metadata": {}, + "source": [ + "## Example image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3c2e4f6b", + "metadata": {}, + "outputs": [], + "source": [ + "image = cv2.imread('images/truck.jpg')\n", + "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e30125fd", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(image)\n", + "plt.axis('on')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "98b228b8", + "metadata": {}, + "source": [ + "## Selecting objects with SAM" + ] + }, + { + "cell_type": "markdown", + "id": "0bb1927b", + "metadata": {}, + "source": [ + "First, load the SAM model and predictor. Change the path below to point to the SAM checkpoint. Running on CUDA and using the default model are recommended for best results." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e28150b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "from segment_anything import sam_model_registry, SamPredictor\n", + "\n", + "sam_checkpoint = \"sam_vit_h_4b8939.pth\"\n", + "model_type = \"vit_h\"\n", + "\n", + "device = \"cuda\"\n", + "\n", + "sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\n", + "sam.to(device=device)\n", + "\n", + "predictor = SamPredictor(sam)" + ] + }, + { + "cell_type": "markdown", + "id": "c925e829", + "metadata": {}, + "source": [ + "Process the image to produce an image embedding by calling `SamPredictor.set_image`. `SamPredictor` remembers this embedding and will use it for subsequent mask prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d95d48dd", + "metadata": {}, + "outputs": [], + "source": [ + "predictor.set_image(image)" + ] + }, + { + "cell_type": "markdown", + "id": "d8fc7a46", + "metadata": {}, + "source": [ + "To select the truck, choose a point on it. Points are input to the model in (x,y) format and come with labels 1 (foreground point) or 0 (background point). Multiple points can be input; here we use only one. The chosen point will be shown as a star on the image." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5c69570c", + "metadata": {}, + "outputs": [], + "source": [ + "input_point = np.array([[500, 375]])\n", + "input_label = np.array([1])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a91ba973", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XsVlv8KpcP7jP9f132DjP6KLxZ3G43kFUZISVd7z1yo/w6xM+8omfQ5y3CXeCRpIFdNJi1ftNcAXUtmshYsQyuI549w53P/ABzt95m81O6McRUeGKyAc//Un6e3dwveN07dHe00WH6zroepym7gRY33uC64tzwjASNRBU2ceRECIvffiDrNdJQ+47RDwqNn/RKc55hJHQOwZRPvnpT/HtH7zFuBvMAiFJ8Ow7ZN3x8U99kvXJCTGf76TZQxJodSOigaEfuPPhl5CHW974xnfoMHAoMRJCnptlbWFtlTCGewiM6rNXv1POG41gUe3nmgEdgi4tAmLUWLR+CLDueO6jL+GffZLVyYZ+1eN9RyfOQB8O7xw7DYwoKpGP/PzP8sq3v8MJIAqDi3zq879M9+wTdKsV6/Ua5/yMVoOds5GInnruffhFPvazn+B7f/RlXLQ9NKJchZGP/dKneOpDLyLrnn69RpwvSqY83r7vjTafKB/81E9z8cobXLz8mrWjylisW/N5KvO1IGAsCRb1+yqgfcczH3iBJ59/js1TT8JmDa7He7Mg9UHpfIeuPAGF3cjHf/aT3P/+q7z149dwfZfm3rGPgQ989EOcPf0Eq80G6T1u1RMFE7DV9qGIYsgGPvqxj/Lw4SU/eu11Ql7vRAvX6zWf/vSnTekmvnxXK7U0/Rz3I5/5+c9wuj7hC3/yBX77t3+b3/0bf4O7d+8imGVZFIZHV/zwu9/jW1/+Kt/75rcIu71ZycQUQc4nQVRBxCVaOwlI2uzFvH9bgDETnBZ40cF+b36/6Z2ltZ0/+17fH57TFvAtKQLrUitw23rKOa/aynJE++zSPLXzt8SX6+dbhXNtvViiXUtr1LYrkvZq9V1blj7Le7L9rqZzN4HkJdkAWLQQt2CvXbO8Bm1/l+a2BYe1vNKC66X3l+bj2D6v5wqY8eE8V6WO6rl5UUTTAyKMIdA5Qfcjw8UVYRfwQZEIHc4Iuyp4R5dAr08sI2KWYbWOLoPX6s9jfC5bLx+3/IUFSAD/8B/+Q/6D/+A/4POf/zy/+Zu/yX/1X/1XvPzyy/xH/9F/9Nh1ZME+qrIf9njfGR3RuSVhsg4Fuq6bgZ5cDzBzv1FVhmGg7/vZwW+F5fxedntpD2EGRebCYeBqtVqZ5ac5FF2yBmWAtNvtCsgZhiG1be/kz2twA5bQYrPZlGeGYWC1XqM60nXr8nz+SQKNIQRWqxVd17Hf7xnDSBwV1ie4xKjy+J2YhSqEsYyzNufm+aotcrX2cbfbzcZbH8AWsLbgNT9fz3MNZGvi1Vrw6rWtP2v3VE38cns1wWyJ4/SsWUXKevoOxsSs7PQTiezHQFh7nvvwB3nqQy8S+4g4oe/XjDg0MURBwDm63jMMI3effZrNvTvogwtr0zuCU3ZEfv6XPkN3dsLYKSIe58TACKZZEeeIOFxUNl549+qSMezZh8huv2fz1DM4cZydnhKur3n75ZfpxhEPIB6XNL/B96gki1Ic2XSOV17+IS9+5OOsVuYKOuwHXH9iYC2aGV7cfA0RMFtanM0zGC3tpeOh95x+5CV+6z/89/nj//6/592vfQN/fo2MwlMf/RA//duf58HDB6yAtQoxOjxm+TFLsU8ARokE/HrFEAPPPvcBHrzzDk8++wxXV1c88fTT7Iddmjc/A0jiU9+iZ+08UUfWJxs2d88Yz68TQTfQPMbA6RNPsbp7RlBwxVFEUDX3qmEY0TEwSmQXI2hgQE04DdFYhygxhmRFdkUwkYpJ1/vWieOYTrtlwO13+Yy1ZUmpUOiljYioSUtqlSEinDz1BM999EM8cgF1QhhHfDBhN3pFVcxNUhT1pig4f3SffQxsos1Wf7Lh+Y9+mCsf2A8D4zDi/FxoVFViCCjKNYFdGHl3d4V6IToDmlHA3znhE7/8GfabjiAG3qNGkmrlQPG0F0U7YXCgTpDkhhc0ENHZnOXfDWxOoHFJ0F1aE1U4e/YpPvzJn+bCDezHgT72OJQw7tHg2PgVLqgp/5zN/l5tvFloCzGiKkQPn/rFX8A/eWLa1MRjtHPpWUka7Yh4D1GJCC+8+MJEY026BIHf+q3f4qWXPkjne0Qmyx1Mbs55TnrfE1eRX/jML/DXfuev8dKHPmR9iskVcDfw9o9e5btf+hrf/to3eHTxCBcCfXQ4wDlhdBCdIEkydpDoZtmUUIGPWnF2TGj6X6q0e2/pu6qjMzyzKPDd0M503hOPc4cA6Vh/5nINs2fmZ/hQmF8CXEtC94FygDmPrOnJ/AxMPLsdb9tm3oua/zMii0zVzM7YzO3+CM2r52BJFmjHOQ+TsH68F3Buv2u/vam9Y8CrLcdAvVVkP+o9dAyQTpXM/5ytx3Gcb+Amz4tzCJHeezqSJ1WIuCSDRDBFDcYXUcElx8uIIyZ3u2Ng73HKT0oD/kIDpL//9/8+77zzDv/5f/6f89prr/GZz3yG/+6/++/46Ec/+th17HY7E4BipO9WyYUg4hvmlAGGCenzg9NaXbLAHUJgu90W68x6vabv+/JMTUhq95QMPrIrSAZI2ZUuAwdjqukgihA1a81gs16z3e3wyed/2Bs4CuOIakxudII4iGOk7w3U7HZ7vPdst9sCTrquA7E4BhFhu92yT1a0ruuISVNdg4wi1AqmTXRC1/WoCNfjng4lDhHn5360bTEQ5IvveJ6zrnILbDVhS+4TNeFtTfvW0ETo83zbuh4XAFuw0+6Huv26DidiglnTH7K1AvDeAOUQAx2gIRIdeGcis3cdewIn9+4QO8d2f0Xf9TjfE6KiviNGkAhCJKi5sQwi7DXSixDDSOjMKoVCf3ZGdGZVxGdtsRCJqFNElCi2z4NaH5+4c5e9eHzX40R56qkncaoM19fsry7RcTDf925l+xM1i1Byq1Fg2G9x697AYKKmq83aXNiGHb0Txjgmlb7F2KjIxPwS8zmgbRGieHarjv75Z/gr/97/hn95fc31N7+LC4HBA3dOuHjnDe6owyfqGyT1QjFwk6izqvLo0Tmu83TrFXeeehLVyDNnzzKG0cYlDhWz+ChKNkFpGJEYDDSpMhC42F6zEi0MW0NExTHEwC4MiK7o8KARjXmP2h6w9QDpPLGLDM6E3r5YYkx4DjHia9cakeTqMddktlx4SQHQMsulZ+vikOLV0wpOPtEeoqJRiWLPK/Du+SPeuP823D3lRARvUWF0YjFx3glOnLlMOtBOefKZZ9jcOSO8fUHnHFfXV7zxzhvo2Zq+7+iyQORcYsQkUOrwQOhs3br1ikGjVSxCFNjFwFYDl7s94j1rWeHU3EqcmO+8k+QZgLlLRg10pxuCKitxOIusM6tnVrgl8GExPTkCa7YIRUGR92P1lc2pwOXuiocX5+zXcHLHFFgSzWrkOqPhIQZQb4KFc+DBnaxs3qMSx0h3sjHXwc4xhBEdFR8jQRXpPOogxLzeSucdGkHU8eDBA4ZhZBwnjwCA5557FlCur6/wvp8L2Kp0fZdcoKDvVrzwwgs889QzdL5HQ8Q7s1lfvPOA177/Mt/96jf48Z99h3C1BQmmdCOdiQQySZ/ZscoKhmkf1sK/2v9KfI6UL5IVZ3FnH5b3EkaXBG3SOYVDkPKvi9VqvldkhMynFvrVtt2CxYlGGL2nWKMnoNmCmna8NZA4Vn8uS/HZEx+d89iWt7dzYH/kdS5/UhO7zD/aenSproW5Xir1+PNZiDFCpZStn12qawZy7IHZ80ulljlqT6UW6B2TV0SkKBDq72vl/tK6lTZYPi9Gu45bqYz/aFIDghfzjx2H0XhCkhMKds1tKkje64mvzUyGSx25gc/ZXL+PABLAP/gH/4B/8A/+wb/2+/urrfktip2kOAZc3+H9XLNUL64kZpg/yy5vrjkAqlqsNHDoZpV/z3/nDZ5jgLJLXf49g4KimVVSQoZIGAPD3sBN1/cgysl6Y1q1/YCGADHS+w68Yxh2dNLRdbYtx3FEnG227NKXwVGMEXEUV7yY3L9yn8GIWY4/GlMSB3NfS0KwCESP35ywv7shvHvJBkcUodVUiAi+cwYSnEMEOufY7/fG/CRJRgtzmP+u3QHymrSxRbPvpTELF8Kos/dz/eaOOSWSyNat1gWprq+udykOLX1Qgu2Jyk4iIklQsUh2+uhNYFS4PD/nbnzKLIrOI1Ho/Apcj/gN0vV0qgaSfEQU3Mma+OAKr8JeIk/sHbLuOH90wVObp9jgid6ZNcX5JJX30FXkYAyMuy3XD+7jNidcDQOXGvH9ms3JCScyonFgXEEnHZotlyL0AnRCcBFV6FyPRojDDokrduNAGEdOnWN/+S4nV5eMcc+ogTW9uSs5U2rY/DkKxswMVZVRAl1UNsFxstogd075lf/13+F/+PF/zXp3yaOLc8IwcOIcZ9Ix9jCsHA5zbZTOm+CkZu3oVAiXAzvZ88Snn+aHP/oyn/jpn+L+a2/y9LNPE/qObtUbCHIW5yfi8HFk38HYe/rR3ALixnP23BNcPbpAx4DPYqLC2ekJqpFu5YkupvGZoBc10nkh4llpsmSsek6eusdARNVEccVcLOM4JREIJDcvZzGxIURW2e1J5hrrurSCzVwAMeVMK2BBAkjR9rF2mDUlW49ROjHlj4mzk1vL6eaUlXOEqHSKuVSIxdFEDMBGb25rRrsdfr2i69cEf0VAOdmsePbJu7w9XpplzXclPkDEJ+uC4jCpf6VCiHB29x7adcR9RJLLY+eEEAZGHeh7o6mddEaDpKJEIohGNmLB4Peee463vv0y3SA4dXjt2Udz4Q7JXSwLPyHP7yKDztalvAYmqOZ4oJUoVxfnrE+fwrnOQJ+H3gt4T0y8zTkDg+oE7eDJF5/h7ZdfRUeh1xWdX9OfnnE9jsTtjtVqQ+ccvXOI7xBxxB7Ee+MnamscQ+C5558yK7NOPMt7xxtvvILqk9w5e9JAbYpHc9nFrvN00XN25y7PvPA8/WadUZgJedsdP/72d/nKH/wxb/3wx+yvtgxhJIpaYg+18QQj4XRiSq0s5KlN36TJD9GsS0nwNEuTFPflyZKXhP4kNEbVEmB/sDoVT3kvgbq1ruTPl9a8/v6mevMzS4AH5sJ2/rsux/pkz5lnQqqpKF9sWuxMtvXNZaUphrgWzNtY7Xr+sgJhCTTmP1uFSz3WNgFHUVwnRSCS9k3+s2p31v/FmZ7aWSp5rG28knOOtre1F9IxkKdp4EsKq2Njr4FSW5ase/Ox6GyO674cG3tpS8Ro/SIYmu/nYkHHI8kK5FUhBi7Oz1Es7jnEtGAak6tdqlcMjinMPI98PvNL89P8PZMFRRoQ/XjlLzxA+p9bzA0lmn/6GOm6PhHbvEEnxiVJSEEr/920KV0+2A0RAFuI2nK0RKzyxs7P1pqTkq2oEtDzeyEEhmGYgZZsveq6rsQCQTqkqiaQ5SDlEIgxGPhQS2ZxfX0NkPz2J2tICFOcUgYHqiao5v7s9/smg5yyT+53JuAIT734PPtH32fj3az+maYpmntLtqLl8YsIGufB1jVBLcHM1eGuiW9rQSr/yn6IxYVSxCwo9VrWdbYas5owLDGJXHLc0xLDimp7EY1cX10RYjAioUDUpFVWgpj2+MlnnmY/Dpz2PZ0rDmf4JLiZAJb2bwCJyrrr2YdIF5T1aIGrofc88+GX2F1dcCoCY7QsbpJBZRImnCDiUIn0ODqFte/pEIb9jv12y27Ys7t+yHB5wUoDnXNJM+UTczXte0AYgwKe/TDy5o9fZfXUU6zu3OHi3Qdszs7Q7cBwec24H8xKFGOyahlwKa4TzBmHAF1QuiFw1q0Q9bh+zdMf+RjP/swnOD//Bnfu3MXhkVGRlDhCotJTaex1IpgOcEPk6v59vvXP/4hHb7/Nn3z/x7z0kQ/jn32aDqFPYnd2yxMUr2IuQkT8qHi14OOV77iOmekbmxhCYLXaENW0YdkKU1yGUpBxVIUQkTHihogM0RJ8jBb3F0JM/0J1RuYaa6mZuOpRzrC0T2shomWG5Twk1zJN1ceo4KWAvQPBLT0XogUADePIPowWOEzEk3zOc+A2tp81gTCSompELVFJMHdEL544jojHAL9qWdsoMHphn1wSu9XK6sf2N0PkrDvj7mrDxdU10Q1E6YneAEDNdQUmN1hV+s2aQSP7kCw6oSfEyJDi6sR7fCXM1zThmBBU1qRam5NuhYwKuwBjTFHu9r3X5CarFkKU6+1Q1uLwQe1fcuZc9T2rvmc7Dkhn2TJFBZcy2HnM5S2M0cBDBqkh4tNe7dIZkDHy9Nk9ZD8SVyPST0o1SaDj5OSED7zwEqdnZ0Q1JYQGRXYj7771Dl/70pf55he/zPb+I7qgCcRgvPhQPqZok5t5rBV57X6urQrvBULe65mj67UgYNpWWRY8a54xxQi6xWfb/uV3Dp6Vx+t7y8Nm9Yj1XJPcWve1dunLIQkzYfgIGKjbumn/1688Th3t7+U5bgY/N5Wlfi1ZZxZBaWbGVf/zc63XS1G6i/GFGhAeow1L7b5Xf9uS6Xmr1G+tUUsgyayuU91TuImb0auDObMv0BgQMI8kMvzRxCuY2YIz/ZjJjmTg9HjjnQE5ezB98fgQ6X0PkCzmyJh2lzJFZRc7XwkQUY3ZI5JiDJJVgbSh8gZP77cakQxygIMARFUtmrUly0O78fO/OkFDPmRZINona1ILsK6vr/E5riP1dRKirI0WGMUYGcMAhGJB2u12E4BLTCfGyHa7LWMehoG+cwV8KaDiiF1PWK8YgwmLeZy1q5zvLFi/1g7k5zIoVdUSt1UTmvx3/c4Sc2nn2fbD3KwvYp9l0JqtY1r1YUn7lOevjVWrS91O/e44jvi+Z7Vem3AY94zDaOfXQUQYsRTKQQ3ChXHE9xajEcQyuMGIYFmuAsnNZm8C54gBATdEhr5nuHuCPHOP3dU562ixHZokK0uabMI+IqaxBTRZIvrNGokdvvf4EIj7nfkDxz2qEbzDibmP6cRVcd7hpMMxovvIer3B4bg+v+LRO+8i51cM2x3DxQXD9RaXzlXAZMDs/daejUwsLYY+0jlTL7+72/Ho4pwPfvaX+dKffZuu82hyFxxQs2biSsxCIeoYzRxVGcaRR6+/zRPB0Z1f8WB/yfjiCwzjwBiD0RNXEWpJoMQZsM+C+RjMkkBUkpHIQI9zXO92nIVAV+2pYp1O/6JYzFGMCqNl+RGxLGLJo5thtPibk65LzEQJaQ5tb4rRtOYMtOen3qvtc6gm18n5e/Y8tv6aQYC5l2pU2ztZ0QSlDoudUoYhsB32DLsBEFwvdM4XjX4oezLN5TjSeUtGEhNuudruuN7tWOMJG1M0dJ3iZbLEq9jaDxqJwbIA6jgm1z/rY68wbPeM+4EgDicDoxczqmZrSFKyxDQ+Eo2M2BnpcAkcjeyDpfl2nbeU/bUj2Gxql8W4ei0UNbfMcYT9nn4MEAJk5VxMiqXklpjpawgRhrRvcJYZzjtiStRwvd2Zxc17uk7wojixOVFHscaVuLgQiF4IEswNN/Vvt9sjCPvdQE+Pd53x2lXHiy+8yL1793DdCsQs4rIbuXr7AV//ky/w7a99k/tvvU2nghuCWfscE/iGA4vbTYopOHRDWprX9wKnP2k5dnYeF2gtjePYd7Wrfv7ehPPjY2r71M5BzZ9mMkhUkMOA/yUhveXH9Xe1nFSDrEUhfOH3ttTKy7q+xbEXH5f/5UoNCo8BwrbM6eYciFgChMdrN8t5P8neqvug1eetUrdWtC/JZe06F2VEBi+V/FsDPgNHqW7JV7/k1ODQwtlasfCTnVU9mMeWXtiHj1/j+x4gTRshmUZ9Mhsna5BL8QQ+x6OEADgkx86ACTlJcMwCW6679gPOG2a/3x8ApmEYyvMZeWerU70xlzZ+G6cUQiipvvP3uc6T01O2+13pS3ans/gmQejK5s0/c/+6Tkof636t+hUhzDNi5e9DsABbJ2Jax64nrlZsuw6/27KRaQ3ywRMxzaTzh3ey2DzIAeGrNYRLzK8NFm81JAKTH7rIjABkkFlbl2qNSu5LnUZ8yUVgtueaMRVGgeB6z5hjFNIeVNQyuShFAFbjT8T9wKDeDDQeRlFGHekkgAoDWQiNjMOerY7sneKcaY6vXcS/+DS7dcf1OLJWBd8hYtJ8AJBkTVWz4qxGZbsd6QbFb0c0Brwqvu8JpzCGHt95wn7AayRqQMUTZdKYK6BeGKMwinJxdcFTd+6wUnjp+efx48j9GHj09jsMV1v6cTShu1ozq+yQeY4aCU4ZHJxfPGTYX9Pduce7+x0f/tQn6F56jsFZBr99CGw7S8rQCQxqiTAQR0gCQBwjUSzF8KPzR1z2a4b9DpxyPey5CoPd3RHsDjUDSgmsi7DDYlMCBrS2IbAXtXvVorWn0e60ubi85Mn9wNX1ln6zoetTeuMMUDHQqs4sstvdnodXV2xFbaVVuLi44PzqCo2R9YkwRi0uHhPDNvEgW5ecn87akga6FmbKPtcpfqMVBFSx+CJVXGovhGCZCsXbOASGaMkYsoVbQuDRg4eMnUO3I6hH4wi9I/aKakTUUnSLwm7Yo9db1n3HlUazMAfl8t1LLi7PiXeh77eIc3R9z9oJ4n1K32vzHvcDcbtH9qNZpUIkuI7QOcYwcnF9zeX11rInOXNN7iCB6gnoBVXG7Z79w0uuzy/KHAVVu4No2HOxvUZjZLVeTUKJxmLRO0YzatfgSXFnd2gNMbK7viZeXtGjdBpZA/Qp5YiInUGMVo7jwPWwZ3RK0Ei/7pBeUO3YxsDlbkdwjug9Pgprl10co6Wcl+Rep8puu+Xd83PGOLKLexRlJLKNgYvdjn0UzvwOHwzQf/TDH+YDL77IerVOWl+HDpH95TXf+fLX+OaffomHb77DcHXNKpq7rBdfzpJojleYC+4tPV3S5k/KAbcIkOqf7WdLIudNmvpa0G15QZYdjoux7+3q1PLGuq8HvMV+Ofiu3W+tErae18Mx6mxPtv2q+5RLy7dbPlz3/1B5eBww1YrJtv91KWcn0SOV+Zq39K2u+73Wui7HeH/dxk2Ar17LrLh+L+BzbP+2c3mTEiAr4WoZpl2vFhzV3y3tGyUryw73ZQHvYq66zrmStdbqMt5ksUZT/Gh9N9TSnNRrVYfDtMahsr+cFKXOMVfapfK+B0gac3a6KUYlDAPifQoat2JJB9TiPLBnYrQg0yl1q5Q86nnia4JcE6MaENVarfx5bRnK9eTNmbUEefFrQT0/mwlO7VKw3xvzEpHiIpcBxTiOiWBrcaGr381t5f5lNzr7fbD7VxaInADOKeMwMIyB07MNq3tPcO8DHyD++BVkP8ysNplx5ZCVvLlzsonahW5JmKjnuv18idAVbQaHgYs1UKo1M3mcddZB5xyr1aoQlmwiPyAWR4SfMm/ZdUwTsHSOqOluKefBpXTMgA8Rvdzy4J1zfNfzzAsv4E8V1mt81xMv9/Q9+G7FGAL73Z54tSNc72xODKUxOOHnfv7TrILAo2vGJEytfDRNtHOE3cjqzDMOA9swcLHfcaWRcGfNhY9EC+vAASvtLBZhN8J+ZBwEv/JoFyy5gEtJKoAxWmxcF0bO33id3aNz9vuRO+sN9zZrrt98kwevvk7Y7fEZhGc7VJqn+g6GSeumDKKoRN59/TWuX3+Nk5OOe07oRPj0b/wq3/rSF9ndP2e83HG9H1ifrAhB0dWaiNBFn7JgRXQ/EneR09WGcT9wcXnJMOy5ksHiIi6vGWPABfBdD70izoBSQNAeJCgyKHEY2Y97vLOkJaOmeBpJ7kXbgfFiy3pzCjpAENxKLO26s2BlFyJhO7J9dMmD19/k4YOHjAo7VTrg+nrH+fklq1VK2oEjRnORzdo641qJRqkpM1qGuLRP67PX7uZl0A9U4mCmM7kP+S4p773FWO087/zodQaUNSv60xP8ek236rnzxF3OnrhLv7FU7Ofn52zvP2D38ILri8syHgbl7Vfe4MGjdxnvXhMvA64zK+fq5IQnnnmak9MTet9xfX7N/bffYv/uOfLOBT6kuEkig8JaPO++/YCHj+6zuXeHcDrg/Yqu7+n7njt37uBXKzrf8e7DR2zffcT12w948OobMAYUISY6ebm95tHlBQ7h7OxO0uwfRh7VdKYWVuAwa972es91CIRHgpOO4WpHd7pBzkbWJ8LJ2RkiDlWjS7urK67Oz3n08GFyffZ03puSKwSu7r/LxYMHxLM97BTfbYkndolyt+otztGJXVZ8ccnFo3P2j67xY6RL9YWo7Lc7Hp1fIIyE6Pjpn3mJn/3EJ7lzcop3Do9DQ4TdwBsv/5g/+mf/grd+9ApX754j0ehgn+egSjTiROjFz7TIx2jsDODozfu7LXM+cigA123MlAbVWrUAramkrPshCJpiAmvQ0irh2jG2pQbSGo5fn3Fs3K1wXQubuf4lcLBUFoXn9Fm7p9vP7Z0pA2/bzuOAmBb86QJCXQIRN/FumCevacdbW5FCM74lANKOXx+j/aX+P+5zh+CYIiO+1z6Zv3cIMKd3lucxy6yqWpIt5DPRdX3622HRs0zvLgDsMo6mj/Weq98t56LIraZ4FOeguorhvcr7HyCpJuEg4qMzYa3v6RE0VKbaYPE0Pt2e7hC65FONmquFuR7VyH9uhakTB9TuWbnk7/O/dvEzQKoPen2IaxN1BjgwbYIMfPZhKC5jtekUoQR0t0kErO2x1J9Tl1vGs86C4tt7lFQZ8+9potYnp3SnJ+w3a1ZnZ/hwPjsoBZjEsVhZasvSxDwOCVl+riaqs9SdHBKZvP4iUgKHW0Ka685zWX9ef5/nJrtLtgS+ZWp1PaWPmmKIvJu585mlL+0PiXaZnBt58OPXeXj5iO3DHd8d/4znP/ohPvKJnyE64Xq7487pGSenp1ydP+L6esv+4pLhzXdxV3ucpsDRCHdCx8W3X+Hdr36Xbed4O1zSBXjmuee49/TT9KcncGmWonfuv8WjsOc6wPmH3mRzejelT1WcOlDP/sFDtq88IFxfsupWnJzeY3X3LqvNyoBvmvdxv2cc9vh9oNeeZ1dnxCDouxfst2/y6M+/y6M33rRYCIx458QJGrH7npgzHjDXKFRgOyAPHvKV/+f/i49+/pcR1/HGxXdZiyO+c8HbX/0Ob735GneevIN3cOfJe2yef5rTO3cIFzt679lfXnP11jtcPbzk7R+8itsO7LstIvDMvbs87ddc/uhNdvsd450zC25fr+lXa/x6w8nZKS5Y/NDujXd5883X2XewffMBRNLluOBwhGFEr3bcf+UN+nceWRzhOHJy55SzJ+7x5DNPs16vefjGA17+7vd4+PAhD+8/YHh0ySYIqLBL1u+u7/F9z34c0WhAJIQwCWXVPiSdq6WyxEwnmnHoAnysZGavmMDmkutjiMEsptGUTcOjwJhij37w4OvgvN3VI4L3wsnphvWdMxDh+vKS/fUWHUd0u2UTPYTI/vySH33zu4SoXIxv0vc/IGKA3K1XdOue9ekJTz/1NJePznnn4bvofuBkhFWw58YQ8a5j+85DvvWHX2SUgOt6QkzW23TmTzYbnn76Gbz3vPrqK+gwWGa43YCMgX3ItDCAE+499SSd87OLy6WZ31YYaed1GIYiQA1vvGMAQoTr1+7j1j3amZeDl467d+/y5JNPoqqcn59zdXVF5x3Xjx6wCSaQXL3xDu5sDQTevvgeFxePOO867j7xJFEF33V03gDqnSfuMQx7Hrxzn3feepthby7AL8qGh6p437PbeKIOXL7+gBdeeo7f/PXf5IMf/gheHL04nMK4G7g6v+Bbf/ol/vzLX+P+62+hw4hEc8tVJ4yqxfqRS44Gje5mLf2xvXtMk/64wmX9bsuH4bi3QtvGErC4CVzUv9d8aMmtfGk8S2f0vc7ssb5O9R+31rTlWD/bvtXfzxI43VD3Es/On9cgpbUaxqb/pR6OUcP3BiqPs4/qtmp3vGPlJtpw0ztLe/A93uK9+MCxfh3vx/F3sgJNMNkzhkCIkfV6bf2WSj6S6n2Zn4Omo7O2itwlS6qo9AymJHckOv2Y5X0PkLbbPcmHKGmmBVKcg6j5t4tkjeeUiKFc8poWawwjYTSsmwlHFtj7vpuEArEtuM9Wl5DShjs3rWsRHNK9JarmM645O9ukk5UKALhkvep8xzYE0wrGyJisPpLSk1tohqVQ3e/3KYAc80dPAfWZMO32+xSroXjXAfmwWZ/GMQB2s33WyNi3Nl7vBOd6YlBEnR0W59DO4Vc9eCGEaIQK0yQoE+HPoLC2JMUYiSETFZIm3IKsi5ZaUiyGRiRlNmqTKkBKaBFDcZGxeZwnj8gAxXcd4uxCs1gDVUzIU7Iri6U4z5kHQ4zmshnT7ZMZXBVnuaS9SItqa+WJwW6Zt2xfI8N+TP0OiDhGBt74wSuIE9ZhRRc8599/g6/+6C3wjjDafIikpBqYS50PkS5AcJ7ohLDb8c/+z/83VJWzix1KYLuGTuH8R29aooquQ7wnCuhuYBXtTqQ/+sPvgu/NuoGyFWHfO/q4R6/O6dTc/PAdbmWCGyIlvft+HBj2AwJsVif03crScw8jcb9jd31l8VchmvVEksYOCGqxCK2LXS4umBuQ7Ea+9T/8T3zzD/4V+I5hH8B3+N2ed9XO7psdxDDiVz3xzprnnn+edx88sKxYu4H95RU6RJx4nj27h4ZglrDLLT/44jcYCOm8CiGaJkp8R98nS8PdM8b9nv1b73I97Nk75c4e+piyEzohxoBXYXt+yfmffx+iWAiTsyQF4j2nd+9wdnbG+VsPGIaBYRzoFVbB44PigCDCer0x61UIdr7SXW/edyVxAuZIeOB3fhNDzXs8vTABpOqZcvYIREJKqJFdhhL9SX/nc+hzYhQVvDg6b5Z6J44xRCS5oPl9RLeXXL5znlzSIn1MZ6nrGUfbS6IOlzIGdurpri0b16jAMKDXI/F8zztvPgKBtRjg9iFpfjuPjJb4YuU6xquBzinKYDExKZGDOIdeXXPx8FViVE7I/MD2vbgOlUiIdr/HnbXF24haFr9iSEtzYvMzF+qdmAUq8wOfsocNcUQBv9c0X6D7iG53Brg7S4pyfv+CRz94za4ISGu7jyPeKyvpiEEZL6+J+x0SI5dxRFBGhYf3r4gxgUFVXhfB9x1hGC3WKRrd8iJ8/M7TDGdK9J7Yee4+8wR/9Xf/Gj//K7+AnK4tE16IhGHk+vyK7//5t/mzr36d1/78u8je7jzxYNkXJd2PlMEMKVZLs1AlILqY1bcADaWirTWNXXZvWrJslOcauXFJGC97v/rumOuZZD5QrbFLvLettxZ061jX9wI7s77JlGgoe0XUILJ9bwlowNwjBShx2tnCk3m3MBdoW6vasTmpXXsPxnmD0qBVZr7XumS+XfOOY4D3QMLncM8cA3htP+tSuycugQ9JdKK2nvzrAtx2ntsx1ms4AyJH3m2LzTlQuYamWgq/yWCIvJb5jIhDk/uviLAfRlYrk8tMYW9yk5Z4MSULm1kWnQ/W2qhmjiJckc9PedDkPBFzhY+RB9ur95zPXN73AOnOyR3L7rXqLFOSWvpQl9I4g2U+iok55TjrGmXm70FT8qC8sSBfBBJQxLspBgO7hG/SboBPQETVYihMYMjuRCas5ztNxmGfiK8leHCdZxhMEB914ORkg6qy3e4IYTRBfTR3vM5ZutVxGCDanUjEmEBNgonOsd/vCtEOIdD7zlwfgGEYIQi9W9F3HVECMdj9H5aFSu3ujBgR8enZzgLKhz2dd7iVZxQYifS9Jw5jciHSFKM1EcvaqiYoaADV6v6naExQbAw+CQzprk6cUIKUWw2idz4J+JPGKX/vnCNIKKA1+wOLT5aybDFMc+eSUCpil366DJjLpatKSuBfNBfBkv6iSroADTR6Npu7CD0iAUPxOXV0miM6fOxwapc3ikAXFd0HYKRPcxdiTAfZYi6ykDAQC3DcXFwbI0vPrfYmFBEtK1QcRkQCXZkfZ0LrsIPRXDdR2GDZsUSUMIJzFkcVh0i8vi4+wDG5o64R1omX6vaaoFemSUqg0mmkU4v12FfWWQVGJyix0EchWyZSymgco1oShG6vyLhH2afkByZkWfwMuGQVIEb63ZZ333nZQDKYG246H65TiLb3NJjUpDEiIbJKls0xWJ49xNrbq7J//X5STggbVdbpHI2qBoSc6dGGbKENOvlLiwmOEiLxwQWP7p/jcaw0JoEeY2ydravXAGJpUWM689ltwkXPoKPtOOdBxySozRlw68uf704SsdvpSWfEeaMPPq0LtWJDA+otRiYCBKUjsHIuMbspq2TNeEPIGkNLfy2SgvJjZJSJHbrCOE0gi2MwxVZSUBHsUARRSyxQOLdaVrYQC/gstEQgWs5nfOdKDJXgIU4ychHbk9VcK8scqummd3swqpR4nS5MArFVmGiWtZDfmvZnjKiYe07IFpOoSMRc2XoH6ggiRffiXAISERDbICLm/prv/BMUJz10grpInxKaqKhZp6NdrWCyhYGT3FcJkTUuXeCoRFGiE3xMqcXP1vz8r32OX/vdv8LpE/fwnbOzGgLxes/Fm+/wtT/6An/+pa+yv7iyRL9JaMrr7jTHGWWhLO8Jypkl08s0W4UOSLVJNN8xZWNI9vsZGLqpTM9MtKf+/Jhl5aYY1PIcUzdLffHmCIhc37H4kPxZe2F93ks3WbCWxtHWO38nlvXJa1XS9Sc5J79bu8dnHtsCp7rd1lOj7fPS5zVAWgKSNQBrR16vGWQZe+LtmbbN/l4oS3M1yRzzeCI3A5eNUqSynB3bpe9l0WnrbPdubcEqfT5Sx3tbj+pYNZqx66QMqOeXBKoVLPuKB0dyOU/WPSLqQImMpH4qxV10AkN1T7RcGgtiSZPyE633XOmPZ1Tl2gs/OH/3YKzHyvseIG02J2hUhp0lTrCLAx3jMBTGHUOw+5G8CajZSjBL4JBcuywVuCubRJIQoDE0yQUkZZCakK5EI/cuJVXQfPFkNJQdogkzXQreX683AOkCW4dPvpSSwMAw7guDEIS+6wlhRMRZDFUCPeNg7mwuMZYcC7BK/dhvt4RxpHfWRo45sjEZEPGYtoMUUAeARrw4nDNL1m6/hyuld54xRPbbPRoCq76HaClfURj3++RCcninkFlXmLIGVhqEQogWNDkuaZvzQW6DdWdkv3KLa10N2wQVtWZvRtQq4lTXpaqM497mu3ZvtMoKu7esY1P8U16fOaFKgkAivqbAaRkHMwJba86yIqZodWaEJoGHel4z0YuKSiyCd86Elol51gahlg64nqesNTKh1hF1fsFy7mPRriWNlmXFm981otW6U3ZBXj8D67leVUVTvJ7qBHQVU3qITJY/wRg6sUqtWmlEc5kx+fxMEtpaTWie96wRzxcFl/kOAdK5PVivND7v/TTXjtn7Wp6NKWNhLBfxIqRMaxCDrVeIEdWIT1n22rmc7V8Oy3Qu3UwQqgUN7zxdqscurM0JC3L2z5S9TifFTxl3+n4SHE24qGc11PskWTg0bfj6fhZVuzNoVrlqUqroLL7FMLLtGydu/l49/jJfTDFdZW4muq6GodPZnrSrWcg38pXiJuPBFrNnYwpQTm6SaQAG2NOFtZkUWMarDG6tzVxp0OrM6nR5Zf697LUksNTzV/ZgtOyRWbgfRYm9YxSgX/HBj3+U3/m9v8XzH/oA0aczFRQ/COfv3Ofb3/gzvvYnX+Tq/rvIPtg9WTIP0p/muNkTFW1bcktqaW7+7HFjQdsyeyavY9VOS4/q99o4nvzzJsH6cfrTjuNYPTXfrN1hcx2tu9ux+pbmdPZ9aReyt4ZWc5VLuwat4F3XX7tNtcAH3jvd9ePOcQtO2zEv/d2uwU1xRMc+q/fB0t6p27mprpveb59vlV71e7PY8Rvg+Xvt0WNtt2d0Nq7S7rS+inlgDNE8aDJJkvSCqZamOmMlC9jDSRn9Hum6a0WKI2WBVdI9m49X3vcACTH3gJXriCHadRrEdEO3JDclMyV758tm6mQSlMMwGoASgRTXLukSwaxZ75LGtHOdbQExtyzPJFT7BJrCOBRgE8JAHO1OI5ekHUluV0VrGSwYfBj3FtyGafzDfrQLFlc9fdcxDCMinv1+R9f1OEdykbNb18UJzveowjhGeucIux2rFKgvYC4sqqw6zz6GFFgeEAe98+n28+nS1M1qVS7hurq+5t3zB5x5gTGyv75mBWkcwjgM9N7c0kRgv7dU4gdJHESKI3qtKXPOsQ/jLN6qEJyYXXqWXehYIIIwabtq7VIdJ9Ym4chJMywroD8gclmo1HT/1gz4ZeJZLChahE/ThMzTht6kDWxBA8w1WnnvZ8tBW1STabxhZiZgWV7qFgTEYoWcMzvnXBHuawKcx9HO96ytSnhrGVGel0xm6zazEgIml5J63orLZUuwEyjL61j3JaHaWb9Kn3XO2I9p3Oo5ywCtFiZCsoQa2DpkkPkM5Dkuonjuk0bGGBlTvGANoSwzp0BK+TwMo1kVJAs4cwEUDtNw18XOxmTzWBJAOmcXqipCUNMOjii95MDYLNjX9SYtJPN9LE6KpTu3XwurBRSki6SFLPBLcUtOb5p7ZLIkGkhJ49BEu6HQi6WS+2bKi8M9JNWcqhrImc5sel61gMCoFIVZbiDXE5ObiaIQYezMoioh0odI9C6BNC1jsHbddA2Faklfn4WOELJ3QoWwSGB5gb4IgqSrGaJzBG8JXnZeuffcM/zG7/1NPvnzP2fWf2fzL1HZXV7z6je/yxd//w9567U3kBBxSrJASXI9l9mZr+dyOrMTKFpSQLSAoKWT5R0ns3eW6sltTztGZ7RiCWTUvy+N5b1KS7fyj2N13KTVP6CV6AxwtnTqvYBSfqZNxFR9m8iV0eSl+muFY03327lqQVz7/U19bMtNoDQf8FphWssS+d3WyrXEg5eAaTtHqpQwhzY2uZ3T+fws3yF17N16vus+H1Pq1oo+hIP3DvtzCAgznbtpHW4qkhT7WdnU9yu2Y0yKvAkhlbMMswyENZ+Y9vqk+Ftuc/q/JwIDVyOEx8dH73+ApICGiAYLGtZo96EIlIXxyY1EILmMSUG8Of13l+4kajMP1ZeodqQsTd4zpmw/XoQoVk/nOqIGc+MTZRxGPJR034rDC/Rdb4xTDdhsVuuUNGEzYyarfMeTd8QQWfcrG99mw34YGAdhc7Ky2+2TD+l2u2M/7BnTBbNeoe/MAuS9J9taxmHE9ZbKebvd4lzHGM2Noe86i+sRSRczWpzH1fU1l9eP8OsVst3ZrcghprTNdgKCjsRg9y7ljT+OY7kEV9VSIXs3JUGoE154dWVtDGjVqdPdjDhARWQyZqgAUU0UljLoZcG/9u2uid8SA5a8j5gzjUIAY0wCEZxfnFusFaAaiSG7Ys0JaS3MlzYqgSMqB/3JAMiSAzCrs5yN+hxUddu8h1ldtcASm79LH6upuIk53DS+mkBbu3OANfUxa9N1xgCM2blJeKWWgm3tAoeXMquqpYWu9s0MuDEXwpbeL/NTNLoZHJhAmp9wmJAv1Z7M7xUtICnFdpjMDqrRzrXGWZ01YDX5SwijjTukZ/O+yucpxljOT067Wvcl/y7icE5K0oDZftYUj5XiiJxP2dyi4qLinGUP1UixrOV7jvI+KtaZhDjaRCp5DTLYyf/FkNyw0u6eWYIktWmmyWledHIvzGsX6z1b72kqoYjMwMtCIJoZNIXJq2JrKpneSOqzddAs+BNtyhb9ktJWBfGea6fsNLJWoQtCJJRMjga2tPTX0nF7A41ZwEqOwTGYK7fNZd7LuV96EKsSNVpMngiDU64l4u+d8Wu/9et8/rd/E3+2SfenGfgZr/c8ePsd/uhf/j6vf/O7xIstq1RfGEZLhe/NtaWllbUCqgZJrUKpfn7prM33ah5/XqZlIX65HF5C/jhl6dl6LEuCpqHjyZLcWtdqWlj3P9OkfAbrpE81vV1qu61zyQKWvz8AOwlwpyeAxGMr+pvfrbPf1mVpLdvf85m7yb2wnZfZule8wA5j5fbezHM7X0vz0JYD/to8I7L83tKeK/2GdDYX+HOzX28Eg3AwjpqvzEDXDfW1c7wkVyyNqVZuTGtq/NusNund1Obl1RW7MJgyxyqewI7IjC4f9EfsDs3y9w0gaRqXElxkF5VR3vv5XN73AOl//+//Hzi7c8eCxtOm7PquaHCzBQimDenTHRoF1SZG2BUXmLLFyqE0zYHdcH99dW3WowyqRFit11xdXaFq1odxHOn7vmje+74rmsQskDvnae8E2u93XF/vcE64vt4S0+WVPgEGRbm+viZGZbNe22WkMgmV4hzjMKaLcaesbOK8ZZsKgf1+b5akpGkPceTqesurr75aCJgBQJvDtbckFUMY7W6fqIyXW1YhacvV4rhynAGV0JkPacnapIq6MDvceW1yKnCTVSYCXgRjZMZIoTrIMmmO8nuhuGTZ3VX5gC8xjGIl0bnFK7fRMvbWApX76crZFE42J/SrnvHi0uJwNCLxkEDVP8vbM/Ciy8+JIIe0uXyX71/KIKUwOgA5okkUmY0pf1+IfUWs6nla6l89t3U6+3LLuLOMeSYd1u2lsTPtjXzBsdUxaeUy07UgTXs3Wx5uEpjq/pa9xaHGc0mYKeAqarnUdMbAJWEeyYB54Z6tBGxCnCyoMYm+ivlvW5DrPKZB1NzrxhhQjXTJmp0THrRjy8oJbQBhNRPG5Kp1rveCWWSkWOcjYvdBJaEcha5zxa1NRBJATHOYXbCSO/MYxhldmCsjshXXms5JXGKaL6O1+S6xKbOmuUTa7yKU7JHiLDFCXfLoVM0Vb05r0j15kq1YVmIDZqc5kglcgcUMUSBTcfmMGgkInSpBhR89eJsfvP06n37xI2y6OwwaZsKDNFZK76Y+KmqxqDG7P5vCYAJJGedVFvvKFXjoHFuJxHXPz/7KL/Nrv/s7PPXCs3Z+Ulwm+5GHbz/gq3/8Bb7xla+yu97itwNrUkKhaEDXeKtZ92K172q6fUxArfdbrQCpAUH93LRbdVHYbJ9dEsKXhMNj5ZigmOe0tYDVdKEWUh+ntHXV9dV9rMd0E0DJ/cz11nSn3hM1cLE6mj7JMi1s212ik3ltZ6D0CMCo36vHXNPw9jNVLXHFx3hPDZhmHiILbc8F/+Xxqcw/X9rfBwooJuvzsT3VrvNNZwaW90v9bqZp7ZzQfH6Mx7Vz3Y55sV0m2mrzEglxLMrWzGNctsC1/UqydlClTgue5chjY80luGh3J4rS8/gmpPc9QPrWt77F2elpudRxTAd7yX0oH4IlgmGMyG56d2LuUDnFtGACiHcO33WcnZ5ydXVVLnbt+p6TkxNiCDx89Mhc6xJQ8MlycpUufay3RRjHygSZmI5OAKsW1vu+L1aQYRgIIbBarVivVvSrVbGG5cN2dnbGbr8njCPOe1arlWV2QojjwMlqzZ07d0qK9Ifn52xW65RVrDJZYwHFEmEfRsYR3DDQdX0KcmdyJ8pMLm3orG0CDohVFmLadNr4aQz1Jb22bh2T+8m0btaJuc92AcKVIJYteVlIr+tYEqZbolW7m2UXtZphxhjpu45xtMyJV9fXDMNI1u66pAVu21hieDXzOsbYzQpxpJ4MHlgghPX/Fwh3zVxrRiMpFXF+vgag9fv1GBYZG5S9XJHf3O3SN6DcUTZnlqFMoyYjgojYZaUy1dXOserkTnWwf3Te33a+l/zmFct2pmluymV10fK/dcy1uDPmJAlFabZSVFZEMwAk0ER531zsKBajGG3sYwgGIJgLEzO3k8a1qYB8cQSd34FWVkRzEL0rlvGgWqzu2ZWtzqoqDmIcD/ZsXadW/Zz2WV77uv+UsyPOWRKZgyhd0h1o83dFDEwih+tZr18u2doSYwSdFA+qlfufNVa9ueCyJNMR16RdDRot42RUBpQ3Lh7x6uVDPhxH9iGgbkoWc3iGhOCWrAaWPVTVIVJbkUgKhKlPtfZ3S+ClT/40v/O3/wYv/NRHzWKUxuDFEbdbvvPlr/PVP/wT7r/6OhKULoHGUXIsZYqnzPNjjR64rOW2l+jbv34R5ivXfCuTAuuYMH4TnW8F/veiafXntVBbN32w7xbAwLEy0YT5uI/VCdPF8zfN+4weZxfNpuT3j10s2tazNC8zILbAZ9q2lgT4JaWm/Zx3u6bn7fxkWaSA8Wa+Wtp/ZLDlcEu135fWse5/beGpx9+WJTBf11e304LGuostkDtWWhB2bPjHABNUClpN162gxCj0fceq98mLQIqc65xLnlw6KZaSkcHmSolMCgYnx9OnzwASWHIaYNXf4c948+i46/K+B0ji0w3ooxHxEC29YPleLF4huxRZdrXs9mOTbO5fk3Df9/0Emryf1aUK5+fnE7JNms6HDx9Wz0yXjcJ8g83djSCEsTxfLCjNIYghcj1ep/5OsS1GvCLh4oLNZmNJErD+vHP/fnFHAgNqggV7e++4vrrm/NGjksXKOU/vPQETFsb9PgEKE/y6rkNHhxclXFzRhWDxSimlbxF0sANqSXCSBjpzUtWkUZmsaDWYqWOQ8nzVzN2AqhFzkUrQT/UrFWGKJiyMYUqM4Zy5KqJTetM8X/WlsUWAzm1i857TkGdeVVur8nshEVGNEWdpyGwM3lv/GssMIskF7whDjhGt4ivSTkzCcnIhSgTfBF7KOoQkqEqaLE2JDSJ1wHeuMTEpk8CTi5EWl6dpYecMjKTRhcpiUzMgTSnuEWqrVf7MGPSM9ZON6iMpmLNQS4cUomyZbtAUSC5mMSvEtcpZX+bWe3wS+W1+JsblJd/wHevOpHFmFy/bwxPDq+Yj5sBS64sAoxiQk0S4xxRzqLYBidHmIe8L2z8wxGRHEiljzm5lQUNywbP1d5iVKISI+GnvZLdU731JpZxvMRco8Vu2Vw8FxLwpolqmu6B2JYBX8FHZOcF0P7ZnJF1+LNGsKl5Il/bZeGNWGHmf5jDMBRy1pBy2X0kgJZY5DcNY+lwnOzGaOk7WpbyPJTFhV+2sOI0rv1s+0OSmqJK0nRMIgsntNIOgUmcYp98rgSNrPZM+t7hNDlHBOUJUrrY7rtYbOm/Ww8mIVAlKCvi5MJP3pGDWphyfZaKFpFjalHikM2ATUZ584Vn+9r/1e3zys79AEKAzmitj4Pz+u3zjT7/E6z/8EQ9ef4vx4ho/WsIeh6S03dmK79OoxDKMVouYabLRgrlg1wp4Na+sQU0bazGbk3rNKiVPWYMFofGgzInpwbs3AZdWmXa0bV1q4bCu9t261K7ilslvOUhfZF5Xba1v664ViNNnU8xe3tzFWlkpEutSz1UNgjIfbefF/slkWc/vp3a1WssWEBWVQN4LOW6yYg1LAKWMv+5HVdcx4HZsnCyMaenZ2ZhjxPvKJbx6b0nxubR/b3rupmeWSrvnlp5bAta1sm3aP1m4g+wBknlz5z1Pnt2Z6teKvtkjlA8SH8hzVKvA5r2YgPxEnXXaP4km7vsI7ywO/6C87wGScx68x3eJcVie2Mp1w57zvpsJtD7F9+QFz5mGohoDkySwhzgPtrf3fZWeei7w28FNRDTf+aOTJiG7fOSNE9M74j3iPYrFDGgCFFo9mxlvTCmmI2ay3F7v6dfrklI3xlhS+Za2R3Nx21/uidFSg5+cnNClLeJc7eoCq5XdhCyuw4vH48EJ+zAQ9gPjpSVokNS/4maWfvdOWHWdaW6SW4wTxxLz0arfwOwg5pJjmESYJW+QJECWDFJxEmJFkrsPwri3BBLZygaCis1vfbv45PplZmIDYyZ8Sbr7Z5b1rQJUuY9jGAhh5OriIeuVZ+clXVo53ZWVlteC1hOItXnIYDBbOyBBTZRk+aLaG5KFdBOuS/wCTEBVsvY7xe8kd9RInAuN0dLcuxT1rwmIuSQ8at3vQu+k/D+fj2z1yf2IasDUeSkCs0YTiE15mYXiymKEMJBAJU18g4Iwltu71V4s5yFMdNTmzk37LAwhAW43iwdTFNFoKY9TCu9sfYsqqFpSgpiAd1FSpD6WwWLuu845iy3RdAeQWt8KbUjKljHKpNBJMT+h0ugaOM7zJQQnxOhK7JHGSMj3IskUW4nYxcl2hi2OMe8kkck33jtfrHn5zOX9HJO9ptv0hMsLnEZCsGQNiieOmlwmSIKzQARHusA45J2RFkPE7r5K8zTNWQahEZU462eeF8WTyUFmwmWFxacEElp4tuTsfhW3jRmnF5BdovfQbIoky0GJPid6YGRlEsyykmIiZUYnNPGDrHDIdYWUxW8vysZv2ATP5eU1F6t79KM1p5osNN6BmqWuE4E4Wdo13QeQMwJKsuqoRuz2P0sA5KN5ROz3gc1Td/jV3/1tfuV3fp07Tz+BSrK0RmV/ccnL3/w2X/wXf8D5m+/AEMpkFYFSBC+mRADLEF8L7zOBSrN6Y1KgtHFIwMHP/HtWLBbvjG5BhNGK6mgjxLrDevOS+Qa0ln1RxPPq+QVwkT+veVhrQSz/kHm/qlJ7TeTv67/r+qZ3bR8ILnmi2J6d3FBDAaStoN6231r6irBbzVbUaVaW1qouLeBaejZb7mtlHSQlSi1oV33Oz9qHtk6kY5rvvDwm5JNHlOuVyu1RpJzP2fPaAs/KnVqnXXNUmbkAGo3WGr1pXfLqcS7VWYOdpbbqNvL3TszK/7hlknumM7EExGpDwXz/JIWxo3hIiSgnmzUrzPDg0nobJ7C5DzFU+GgCTyqAm5Qr8+KQJK+qZNnCvDXyZzbfw2OP/30PkBCPk0kQ8M6E8igxBQRrYt6uxB5pnPxwRYRxHNnutslf3+oq8UUVIwBbtDpQcablqUy47eZtD0d+PyPzshkds3pijNN9Tkng8Ux9FBFz74uR7XZb+p1jTnK8B8Aw7Evb19fXDMPAyckJ6/XahMcqo9x0cCMqnqgWkzSOA2Hc0xPT81OMCVRZ6aLivZsxuCVze23eFie46EpwaO0mkBlmfjfPZck657qigSjgs5rz7XZL3/eoKuv1egJjzIlUXtOgdjln7nMGZXn98ni7bro3qUuAUMRA7tXlJcN+bwTZmdUmg8VY9RHSRWeqOJeJiJCuWpwxvkIMshhS8c8sZKKTlk0q+dTmJdiFpOpwTNafonV3wuhTCmZVnJeUmc0V96hWEwYkkOcY0t1CJZ4kGgiK+f66RMQsrsOyf5H/2S6hKDaywKtagZxpPHlwJdtgtLtlWu3bxJDEAGVMoClObY9EosQKBGo11smCFyNFmZJhayUjl/5a0L59mEG8U9O+F9dElBApgn+uX3O/8l6vvhsVQoQQxe7n0XThbgpszee/PmOWiGGuAa7PTiuklpUQj3eOzWoD4hkjSLJmrMUh6ojBJtA57F4mEYbi1pbAU512tQDsad7yXEzJD9I+qRmkGi2SJo260ff6L5286nTuSpM+oix69ZU4i7PKFuo6OY+kejQpM0RThtM4n9NhDAZkYyhJccy6bi6CGoVdDGzWd/jwSx9npY7toJZiOw+aCbY5hd5B5x1xDOlcO4b9YJbBEHCS1y+muRZUIoNz+JXwM5/5DH/z3/47PP/BF5BVh0SLWxv2ex689Q5/9qWv8PU//iJuNyLxUEifJkhK/MDjlJqW5hiMJTfOpefbpDm1cFY/n3/OeAiP1795ZYdKmGNC8E39n9HEBYE2z0HtWVLLAMfaKjwpTn/ns1VAs5s/X9d/bEyTYF5ZeGReR/vu0nc1jW0F/gkkQAbLdZ0/iQumQEk8tPR0bd2Y2vyfV2bjbj5fAp6tfJOVJC3oys8UpWIjH75XeS/Q2j7XjmXpOXPZnVzPH6cveQzeOZwnMa+kkEsySVGCMlmb8/UfB/UxyTiFoVKPwaAxSrnTtN5ftVXxccr7HiCt+jXr9aYcSlWl71fARFhqwtxuTlWl63pOxARz7yfBoY7DqDMC5UMYQijueLnULmG1IJ+zpe12u/J7LcgsaaWyNi0L9jVgy23kvo7jWALZRaSAgP1+X8CSiDGd/d6A0m63Y7fbsdls6Pu+1HF2dmYxS8kaMA4j1xdXjGHE6cCm8+XmcI3jzG1wAn6jWSKquc5jrC024zgWoGeB4FqE6HZeWxA6MZocCDkR3+yHnX/v+56rqytWq1UBjZLcCGYWwKreNj6qJmIZMNWumHWK8FXf8+TZPV5xPySGwBiUmouFUAkiYnEGJtypWY+0BtZhAjlM1o0IhDD33Q4JVLgEr4qrI0lTGsGrWYWyYIDm4ElHUGWnoWjB0ZTOPo3L7nXVGdEHu0gZ5whjSEJmYv5qSMRciJLkn4TdGBWVCdhKmgst8zyBQbsLdNpHxaYm5JtJzUJX1Hx5bqGwSiHFCoHL8S1k4Amk9Z7cRAxU1YQ8qgEBWxuF+vZRpvkkzbsCLlslUbtBD02gwUBOiFIAQ4wQKv/sotXDHKhGlXIRn6ogwT7LiC8LmDV9C6HaP3n+Ktp4jHEKdk3Cut+gozGunLEyBtsThYmGQNelfZitjcklK6+jLW9NF60VzVNW90MaYVdBcAU018/VkClqnt9pz9d15PXOfSlfjUlADTbTdsFsUmiIlr9n+2HOuxmNcBFQhhAgjvi0PSSAiykjpcDp6gwZAvshMMiUBMhwYkxChzKqoxOXrnOAEPb2XYx4yRZyRUPAebsUmk546ac+wt/6t/8uH//Mz+JXtl87tfHdf/1Nvv2Nb/Ktr36di7fvswqCGyPqpot9DwQwjWWwjwMeMp8yRcm4+MzSvmuVkze1Uax72RLj5KDOxxLyGpGqVf7cVFcr+JZzd+SZ3OeaH9a8rW570u5nS/9c2C1yggZatUH9XAtYct31Z+VeOc0zokVR2Y770EVv/nsLkvLnzsksWdC0bq4A1EVapDoBzub4t2OtZaqbBOXHAmRtf94DQNfrVX8m1WdLz7dZbB+nry2AKWt5w7COgd48zvq81Qrj9+pbTc/tapG8V2sFoibPg7SWNWkvSqucnKi5pLmmt4m+x6wcmLaFvefcT2RBe98DpGkhHF03Cbm52O81im8JkGnfnOtwRMRpRdznueaXCEHtBlBn52o1KPnf6enpTHNQb8ia8BRBe7WauZTVfauJan4nC3e5LxafkNOFSqkjuzHkZ/reXOq22y0hBO7evYuIsB8Hht2QANJAJ5ET59JFo7HEA7SaMe/mF7Lm0mq3cykxW27SD9UgqQZU9RyZBWcCQjWYqYFqnqf9fo+IpPFCfVLremMMJXVlG/y5pAXKfc1As+s61ut1mX+vjjGMjDFmkabMU9SY4k4AQiGqaeuW8cjU0amfTJqYmbUzmOVnOgdTBpldhGySUTVG6DVpbdWsPlE1XVxs7e3DSBenGCNkcocREdR1lnoaEM1WFpJLkMVu5YB7hSphSUo2EDMoNbP8GALRxQJ2JjKbgZ1p0CvaWuLMakFjxhIEXOfNDJOHkcYnCD5IAdrZla68WBhB7VZjlpNpjhsNvCadmQBMmk3nBNVxBmJiMLAWYmQvWtbU6Jb1xYlZZsdxZAgjThWnybqRtHa5b/nnMAxlglolRnsWa+VFjJaqfwzmnutUS4bKUQNRA951yb/eBKwO289eBS+ugGCX4o4ORJYENgr4qOKF5skQBFGZXR47A61McV95HCJSBOa8boUJZ/pfQHZe3zQXaU6zoqfMD0CUMg5B5vvTGX/xDuiSIgu7niCOg7nKqbJylq/QqbJSCISEs62+OKT6O49qYIyT9XrVeZz3OBJA8KbEkq4DB3eeepLf/b2/yWd/69foztbEXhAv6H5k3I18+wtf4at/+kXuv/EmDIFVFMuwGSNBQfyhNaDWxj8OOKq/ry3vLS1dKvVevCkIvu7f9DKLWumlNma8PK9j9d0xxUENYFoBs1UE5u9qRV1ttV0CV0t9tPlPIQCNN0v+Pvc/19XKIfWctQBwnsUyzUiiifWY2nlo+WL9r+5ffjZGPYg9Bko6+xpglLWtlV0k2VqrM1yNt10f3mOP1nOS320tWvNxT+Op52TJKngMqC/JebmOti8tWFka72Ef526fS+C4/b1+dql/7bN137SaLxGmi9TjPGbJ3K9zWvAEbHRyW5z4q6I5+671qqy/YO7ozi4qNRpNUnKrWtt6MzBuy18CgAQmZE1muSJQuEykY0VY8onLlw9OiyviEWf11JaaY0Sz/qwlDK3ZvNVu1HXUB01E2O8tTqjv+5k1o36uPli1G1h9p5CqFuuTyJwoDcNQBNx8X0qMdnfKMAzcv3+/3H2jIVomOzFhDRE05hvh5wSjEDmNxUWmtsTUBDoDmpro12WJyOQMf/X9RXl8S8ynXrtMyDLIWq1XpX+5H3lOUTXhpMrEB7DZTHdV7Xa74vrXAjhxjnfffUAII8M4EvbBYlmSi5BzzoSblGo434PSAkoRIar1QSFZ2aZgjMDkfpLfD0nYbrVJmelFgVhbOWNK4iEUQJf3q/cp86Eqo0sJRfJ6kwGwJKJmgmbea2EMibFTLtjL7cXstucsLq8AQNlbkLkTdmGwk1oYQLW/UpC/yDQnY9JUF1FZsil/UkFJnPZVTGMQMfc3AlWMWpW9TDLgM9pie8SSnYSQQYwJx9Na5H4ky0AS4J2ze2g0XT6i6eESmxgNKBycBo2MQ0x3jwVcDFgyBKUX8I6ZUmKW1r0CdVlgncUa1S6+5PMcGeOIODhZdzx5dsqj7TVOnbn4OnMBdQl8SJpbSVYycZ4u7QXXWNmmOYpopitZCK+zzpVVz3Ry+nDOwKdn8tlI0BR1OSazUpikC499sQTGtB9ldpmz1deAbBWmg1ULSgI+lssPRboSp2pKAUcXBImRUWD0hqdW0WLmMtjJcZrZ4i8uA+hQ7s0zS6hnVMH1jiDgNh0/94s/z+/+rb/Bk08/BasO7yx2KF7vef0HL/OFP/xDXv/hK4TLrbnZRksQEx3E3hVQmOd3Jmg1a7dEp+qfUx3CMEzeF0vgYKke56bEOa0g3q7/bHmauuZA4CdzY2r7dOzzJVDQKt6y8uGYDNDWk/tbnjmK/EyWseyzkzzRepjMeHPthlZ9l/Qfic8fxsfUfWzX+ZgQPefDzHh9O69ZOVOHF8jByOeIqZW9Sp0/wRrftCeOAcT673Z+l2SZ9h2Ye8UsvZvnr5bb6j2xBLTacR+bn7b/7e9zXjAff2vpbOcB4Pr6qij8LGGVs2Q+TDSbqm/5XISYUsGIGPipAI+gxRNFNXsHpP/r5CtQx0C/V/lLAJBy8HTRcZHXTTUfUEnPNQFtM8CTgFXjI1sziRYg1RYKmBO62pe63jhLxCm/W2/Chw8f8swzzxw1ey5pPdq4p/xMthR5P23IDL6KEJyIaQ7YntJwO3AOL54YzU/dZpmyeXPbtaUrR0e3BL+O0cqfHSMoS+8X4bZZp+wq1xKcds7BwOEwDHR9Vy6uzOMuKU3FLtNdrVZTG4nJZReQvL713Jef4vBeODk5ZTy/xsLhKhCRAvZjCvZPPlOg5lpUM7p6b1FlkPK+M7eeuohpXHJmt1xCJRyIOGKVMCODa1Wl7zpiN1/jQtScue25iqjl3+3vvK8BbwkFQHGuIyefaBmkWbl8ESSdm9LVn8ZVAZtSjy+tT7Y+lHOR4kj0iNUXMaVJsUZV/bA4nSm9fv0vYoHzlr69Pu8geGDuHpvBdp6PSWmQMw0KPmd9M1SIlgs/M5BNezdlrTRgmmLVkptg54QeswA4NcAYo5TzWzTW6QyM48gwDIjIwTmv56OMz6JrWfcdH//QBzi/urZsbKKsxKVMhxzQqBgsHsY5N4k3wsziUu/pvB6ROS0oOsRkPSpz35KLlNZ6oo8uHwOypVDRWR0FEKXv6/64nK4d0lgqYbAFTGVrCc4vA0GAATVXtggDkeAtfsyHyV1EMihK+9GsrJbcJc+f7zpQJTrP4HpGp3zwpz/Kr//13+GFj7xEcBFWnYG4UXnww1f4zte+wZf+5I+5urpk5Ve4YEov57zhPe8YRGAMxSX3QICS+dBuEihrRVgIcfb3kmC1VEcrtC3xzNK3/Hvz98EaPabQXPfzcd9pBcj6+oX8fQ3QbgIg9fOl7kTrlsYhCbO3MV7HBNgDcBPrZ6szqYeCdAsGF+tbkIfss0kGm/VrJrPNla0xxpIcpC31nqrbsvn6ydb6gM8ujFcmgr0IIN5rX+f66zZqGbGWJ2vvjKU92HoSlXHrHCAcs8K2fW3XrZVLjwHC2nJnjyQ+6L3dybnbgaopLzVL68zuTQOg0NksEx3udZM1TZETmeJLi2pSlsd2U/lLAJAAlgM5y7cLm2zJJxQEEUuHHWJOmeoTE1VEDjdVXf8S+oY5kGrThh/2AdbrNS88/3wZlznQpAv5orlA1WCoHk9utyXYIjLTyDnny4bMj3o3xe2sVzmRQRqDOKI6S+O72+OwmA/S8/WdOFmodm467G3fJGm9tKQIzvoBmRMsrAsmpKdkDc4nbW+6oyFh4AzyckzRsQtfCxEKMd2tpCVoMGKXMI7DaAKEYpnCgqXrjEGJccA5V1Kyz+ZaIWLZ/p649wQf+sAHeGeIuKCmoWNa8xCDZXdzU/rqAjDQYmVSmiQRMgEPy4lgUl7Mc1mDCEwArP2cDZg0TMVtzFKULE/Wv1SfS+5SykzDnlONa9LeZJc8u/zUFeFTY8ruVaWotz0lIBHf+cnqkQARiqXkluwa2sSuJIEx+8mP41gsA9kdpbbs5fkoSg6xPe0qoDrGAKzIPv82XynWq2KCBTSWSZ5bdCfaks+VKwKH87bW3iWQJFIJkWleYiW414DCCdFRrtETlD6BVIcQx8p3W024d2KAbL/bTQqM9Izd+VZptPO+SxPsnOAwN9pN37PpPPthT0BZ4RBN1pskOGSXnNj7ch5hArGWRruioRVD1zzYnIQhjTs7YWSgVS5NFvtkijlKmDndR2XfpXuBfKbjybU1nUMbs0I5M2k8WrkDOqYFsB5M9AYp66oonU4pyKWskNW0dt4SKgTFqzCmNXOijHYYktervZvThzsMVIkzS2jnjB8MzvHkB57hV//Kb/HRT30CWXWoF0Q8u6sr3n39Lb71ha/w6JXXefDam+hux8bcAfBiGUkjmupNYN9JyWCZzxiaxY+8GTNNmQvJtpyHwqUp2fwB7c3Pt8J0XUrylYU2H0f7Xn9WywAHPKlZ4aWxtGMsQNC5RB8nPmOWuMPLbmth9Rjwavl2+Xyhj/mbVuGzNA9LY7GlNTBe9n5muGlPixx6ydSAoOWvNxZJZzWfi1hOGcqcdtZ7Z8rkU1HdRoivxzvxv4oB1nNQd2mhz4cAtKmvaqeseYxH57ptYum9Y/NZ11uPzdyqKWufhAc7w8zn5Rg4mgPcTP/yv/z+8t6a3pvqnZScHYpjHwO7cUxYR8tBE5F0zUpWFEwx2SZzZR41gSYBi21NYwlZjpDMtsRy/KgSHt+A9P4HSHXWuRbVL7nE1YCiRuFtQoCcHagskIuITPcr1RaDvAGXiPKSgF73J/+szcqaCOz0TtVGZQWrN36dlCDX0wKErFEmaQj7fjW5NmmKPUl3tThn2sostCogzuO9wm5fLG2zCxapD+OcodbuBSRCHEJKh1sIcx4jkPyVsxCc56Hz/cTcUsYv5yZLTv0vP5c1+jU4LVazFC80DpYaMobkduiTRWmsXTeTVUcmTW9hlEnYI1hCgkEjrvPosOfOepXiOCofcknAwU8Att4HZY8BQ1TLGJbWyjTcQErekBlb17Vie9rrKFEmsBsFsw4wuWjmhZnu2IIgJgC4DhQHQQvoRTW5BYKIT3Waa2LaYmWtszOmyLSeZR2cgBgQo6RyT4CoEPrpvKiakByjxdBpTriRQK5LKcwFcH5B9MltKwgRQiLYapYxEbH079m1KRowzEoKKXOfBXUHMqXcdjg00Q7LWKgGO9WEUZ8EEBE1QVUUdMRlYZ7MELKIzZSOnIgPiqeybGffd1OhTIKjuOLKGMPcxYxy5ibmLRX9KbRGs5VIEUa8RjoNaZ4mEGhnNm0aFJ/7n6c+/xoORTxVnd17rmluW/pG3ncxpBlK1zXM6CEJuExudjgLa5S0xzSqXZMwc7cy5p2TixQhIe/FWrggn4/p2QL4ERyuCAFQWHtKfW7WN0HwqUpNygBNYypTJi4BKJ9C2TzaCTsR1nfu8Lnf+Dyf+PVfYXV2gnR215VXuHzwkG9/5au8+fKPef3HrxCud4Rhh1foNK1/vkeGtH1R+iSsipvuKCuZprTMZik1r2sBxMQPHd4fegksCbbtnjgm5C8BnfJ3ZiwL77T1zgDQMejRgKT295hoRv6sjgHNMZU1Lc91LskmSzLDfH4Ohe/irscNWcGqulr5xEmXLF2t9SMBSGH2fB5H2+866VI7bwfnGJhiUesxHwHOaXwTeEvnvNkj9XwU0HE4HYulnpdj4KgAuZo3P8aetnHO571+573AZS3PlDqhZIaDij80+7sGsm177bgLLWOayzrp1WH/JgVi6VM681GEd88vCENEUgbYonxFEYUxauF1AOJTHD+YsgjJuvcCmKjOlX2e3KihuIeqwnALkKbSdZ6+7wrDmgTjWkiuN2FiWM6MfRPhmurMGpX8Tt5TSwJ4K9TmumsTaWv6zu/X3+cN6Nx003ALhEo7Oq+rLktEt/19qS9UMThthj3T9AuqIWmpmV2omUFSrWmylODz+J05eJp/Vvonh4e7AB0U53xpp2S/qxhwnvthGIrwn4FRbUHLWfpUld1uV2K1hmGg7/uDTGAzTSWHa5C/894zxgwqLbZntVqxCyk2SSti5w3GtAysXtNoZieEKT6mrIktUInnybu2CHf57ptqDfMcdynTjHOS0UuypqT3Rezi0Qz8xBIciMLQ3kpexc+I5CQEE+F0ngIuwSxDqfOmS0zg4TCouJ6HCYi31rC8xnnNgtbxJrM8Zgdzu3SGD5UaCbLkM0ol+6dLAFlwc6Xak+1eSQYBRITOe9S5g0ukDxQqCM4vK2PasbXnyvlll9+alrWxSPmZAzdYrTS/1ZzlUsdBlr4suNLWZ6u0l+pe6sd8fqdZaV38pjmYz3kWtpwIkqy/k5CddltFb0v7jfKsLnV7rUvnTcJP/iQdLUQpoAk1gdeyewl0jr0X4qbjk7/4GT7/V3+bu08/SXC2/xywPb/im1/4Et/4wpdgP3J1fp5TItqZQMGZxdSnM1/v1SXB75hg1Qrwx3iLCdrzNc6KyZr/tfu8fu69yvxs5dTCc0FvkgOm/h+UI9J0PfZ2Dup9MqcXUsadP8vvtO7eNa9ry2yeE71eavO9SitHtEJ/28fSv6pLdZvH1qyWU5ZA8U20ygTiOKMFIsLRndXswVpWWAJlszYr+rVUb1uOgZelZ9ox1ndg3gjAFtqt91j9nGHa431aGvvj7JUlGtrWV8/tsfHk+Pl+vaLrOwqoJbFEEfbk9PzG29szEaPFt+ZLu51z5moZLDmY0Y9MS+xS+UCS1cPtPUil2GTljXSIuKXWOsx+ZkawwLwk11Np1pXivlQTghuFksoyMPU3aZ8qi0b7vdov5e/MTPLzNbM51m4+lDXQOSBKWeCTrHOlZLObMX5J6H80XYiIXfwZYjhwbWwPksjk2lcY5Q3EQJhcEWtmGqPdx1TPQxbqasGunftWcJu1VX2f06HnjITDMB2ypT1VC19lDEkgcM4harEDV5eXxOtrE6qbNV1ioAdCISCoYSlJaxHzhawKKniZXzzoZE5027U3NyAKGC2ssjo/mQhb2swIUTEx65CROucYYzhg/mWuiqbIztGMPcl0d0+9hrWQXJ+ZPDc5hmYpLs2nO4py/Ei2gKI6c/EThS4H6jfCz2yMOvk7t2eonYuZ4GKDmNYlCQ71/p0FJDfC1U1nt7UOo/P5qvdDfrdVyNTP1HUttdcK0zcx9OlageOuRO38LQk7pb20drnUQvzSOWrrb/tX17EktLVCYEtDjo0/qlnD6vdqS/7iHGD70GWAZEysxJqNTgi944Wf+Rif/+u/w4sf+zCjg713KWFH5J3X3uTLf/jHvP79HzI8vIAxEPc7S7yCIM4TnQEkqvNb78ultblJqHo8AWy6O2m2ns25XtojXQVgDwRE1QOlXBlHI9TD5F58qIB5r/4f8rP6M9/QjTLqTF855M11Pa1bVuZzuY35mFNSG5krBMv32ZXghpL7MSlaD+Wfae9PLlPzz+Xgs/rd1ovl2Pmf0xtmigmY8++6hskdd/ns1/tm6fzDBAyPjWOpr8ikYKnHt+TiWc8VlbK+7nO9Frm0Z64AhcrTqTxXtXPTOTomc7Sl7VfdVu7HTfyu7oNzjieffopf+2t/hfXJhtVqZXOX5tF5T7fe4JJHU76fE4SYsh7VbYQMilSJ48huv0uJpbwperzD9yvE25UhV9sr/tt/9EdH17MufwkAkt1Wr5o30nSHCUxgJz1d/XSIZF/LI1q+WaCYI+qhi521daipqjd1XXerSVgCLarmDpLjAzKBz1aRbFJt45DaTV2nHc+AI1tdZkSmaj+PR1VLdrt8j4qqphiR6V6nmrXWd0Ut9a8QFSz+pp67PFf54tb60GUwkNc0z3EtiNWuQe18thaHlhDXddbPLMVOWV+meKtZfVrNg4Ptblf+FicQmbkjLBG4WrCaBMSYXGEES0mvkC6GdPlKaVIMUOJ5NcDIbZU2itFbEmBIxD8LaGUiQO0CotTvKcg2W5BmWZKadih1xlm1M91l2ld1DFsGNWOcskHmz2sw1QotRfhPVc8EAaW4auYU0jOXy0q4qvdMaWOB0VjbNdusE6IUR7MDBpznMp/pFsjkhDPLcZJLczw9X1vEyh5WZhbpXOrn67rbPdi2uxRH2VpW6ufrtViyDBwTaGb1N/M/szo1bbVjab9v6cSx/VQ/W/9d07nZcyESq7k/WIeqvfnfktwSQUWInWP0wijKveef5df/1t/go5/6GVj3sPKIKp049g8v+LOvfI1vf+XrPPjx66xx9KOd7X2IiJgm11I9mJXWLjB2B2NrM3nWe+oYgMo/H2cNHtfNruWPx/ZFK1gea3+pjZa+S0pBXH9/E28vY1oACHkul/rantvHmbvpeSkKrCWPFBNkFoc/63PbVr0H5nVPngBLVq+6tHuk5q01Pal5fd2f2uOnnscy/hpgcAhwDuaCOR2rx1nXUa9tO0cH86YKkq9smM/d0v6qaV04klyjpmEtTWjHsHQG8jNL9LSdn5bm1Z8tgbQlQFTXWa9py6e6ruP5D77I3/3f/T1iijvN4EjSuHMSZKtPzStFLEGTT3JGTFnvinwiaooe6wmqlZyt2B12qpxfXMI/Wpyug/KXACCZdkXBtNyqM5eI9ND8bxE0hnQRYBZylsz82ZXjUIA4RmxaZtj2tX5uqRRwkbIY1ULhOI4H1qX6MORnl7TqLSFoBfyWgeX2uq4raZedcynsRRDvilBcW4hKHWLxPPkA5oO0pIHN7wPmMlIxlDK+5CPfumrUWvEloakdZ3uwa6Kf26oFoJYBxmietCKHDC7GiARz8Qqz9iMhgMfP+tWuebsPpj7qnMiIkXiLeZ2SWRiY0hRzMp2Dw3EbABdvF/5WS2Y7Pe8dFI0ZTxghqznELF5FzFUsu8BN47K+y9HxHjIXW4P63qDDrIRL82X7cQIzwrRvVLW42xW3S60E8OLsNO+H+eIf7oNjzGMGgFN7dV15n06xCrEIVXYOzHKd5zArKVqml7PRle90EiRyP8p6hwjukEm2wnD9e312WgCzJOgdcyNu6z9WjglfS88dAzJL7x87Z63Q19LRm85k/d1M2G3oz008ou2jilg6ee/YdiD3NvzKX/lNfvGzn2V97y7RY/WPkdUY+dF3vsMX/uBf8dYrrxEut/QqIIrzHSGMrNfr6kJssYQnKc4jW3BrhVnb31p4rBVRS3N8U6nPr6oWt+saRNwEDpbqa/lV6TeTEi1/XtdTW5Jma48cPN+u9bH+1Hw2113H+d70Tjvf7ThzMSWHIjJZIluX75xevn23PbuzsegU03EgQDdyD0zKvVqwrgX7lqfWzxf5ZUHAhhusIErht0sIsF2f3GYbk57nTHW646w9/0vnoPRHKGenHlPL79p3210zo9nMAU5Le46eM2P2P9G5WSo1bV+qo5av2nmuz18d/w6UOzZHp4yiST2TPaMsCZEGnd2hKc5zvd3io2OdvEOkAqR57qMqiBa+6kSIokXBZLEIjz8H73uAZNHGOkP4zjX59huEnIVbp3MBElqtZ+NmocuEFOaar7zoOpcnZ0ShPhi55OdFxIZVfV9brMYYGuKk1GmU21iCpdiCmpj5RLxqAa4GHGTBU6DO0qRRLS6lOtA3CV3tgZ/maa4pqYnszDIk8yQL9bzUdbcuPnmseUz5rp9MSIHCuJfM2a3Wq45Ra8cjTiAYQ+v7vrjdwSEYrRlNOz+T4OxQzdYMyj+sxsp9bU58m4SL830tYgJ11mylNW5eQDUi3uMdiFq6d9U50y3zw9ziR8ZTUwOlrdk4F+Zw+iaf58O7iZbmPjeglTbYJ+ujQEnpDlTxUulcRdte+c0Z41+Yx3KWmc5p6//uvc1DbdXJ/1Sn+83yXM61t4dxS6kHqM7byQCp7mi9Ppbyffl8HmPC9XlstcM/CRN+3NLOK1RucGmnHyv1/N5Ujgm7S7TxJhDQ0qayj5IyINc5O48t7WNaLnEGjoIqoYOf/uwv8Lnf+13uPv8MuJSCG+iC8vC1N/nWv/oiP/izP+fq8grdDfQKq64D79jHEd97XJysbplKxComtBXMWmEp9/e95vS9yhKALa6wDUjK81iuWjhSWiGz+maBjFW0oFLOzXiim/P/dq2W9qa1RqGbNV3PvP/YfBwDLEt8DDL9M/fImm+UvggznnRMcD5weVOlTgZVl0illGN5znOdbf0ZCKtqcVk3ejhdJ1LXkce25HFjYocUJVUa4cH42jmprRrtdzU4anlyW+dszEn3Vvczv7tkQROR2bUx9fftPN5Ebw7fn1J5L5+Dqf9tn5aeWZqf+ozU9dTnZMkFPPPqYb+HYcR3yVMlg+DEM6OXopgYkjlptVmbbJJzcKldRQDJWKGKr/aO987CPmLEO4+6pBDVm+lHXd73AKkTh3fmdxqzhlscfZeIlCkoEsHK4GhiwDFOWdyceJzzCaWC5kQE4nApG1J6lBAmq0lMRNZlYVUc0CUBKaT2Ju2dqmWVIt8OT2aYk7Y797ElbMZEVikeylLF2oaoXIxkugslb/bazax+RlWJzhVXnxgjp6en1fwEywLlbJ4lRIh7u6xSKHE77Y3pkrQc9QGuNQ3RfJ7IaZY1jT+Mwe76yAJ1ITxaiGsNjkSmGKeauM6EFzG/1yKMQkpxfKiZz6XW7LfCQ56XJcYWBFyAblTLfqZCHyJITK6ROe+KprieSSuVrSZ5jrquS20ta7VN+MUu7RVJxNvuw/HOW7auDHQSwC31pP1dfHlNVWesJwVxayClh5nimjJksHms0lcXDRupL7FYO41AThawIuvqlGkvrXDhfV11Dm1fV3ediZBTL6hqyoSXgfuhgFrD2bHSmOaVyF0oGfw0xZOIVFbkac41facY4XakM6oWK5b3HFnoS9ZW73PqcYAphipDXJ8AQc4cGWMgBhD8TAtf96NkY6sEjJJApQJVJfUraU3zeeFwbLVgVJf8XX5Hqn+z0vSz7Xf9M/++KByVhrOgrtPc5/Ut9JYy55NAc6iNrWlUGVPTn8mrIDVeNggFAOV5kPR5DDGlPbe08MhkZQc7N6KWvc7lD5wjqOJ9x5bAUx//EL/6t/86H/65TxBcGp83sDM8vOTNH/6YP/4f/wX3X30DF5Ww3U4Xy3pnbWRQWblpaszXHnSzONp2bZdcKut5q/9u61jeLxmEpHmtLFKzrKZVmZQSk1CKkC6ASECgbDypn7Tfq24c9DEpavL5mIGdSsCrx7MECspPKYLFgbC8RKvr35f4TjvXc/fv9HfFK+1ZG5NL5z2pLpsz1yiM8/6WCnhUe8aqmOa15n1131pAUuhPBYDrecnvt+BlGuMyvQhiZ9K8yWcC3dH92ipNCl1t6OhNa9I+F+PEq+vnblIg2e9zENLKEjfV07ZV+pv+5/J+tlZmeyOPof7ZzlPdnxb41Tz0GMDKPJLcbrrDaHe9JUbBR8+439N5z34YWK1WJlOJMCaZc5WSNYibQJH3nckMITKGka7r0SRPFT7kOnQMXLx7YYrcVc+du3fx3W2ShlK8OHrviRrtNvqaaCkFgWZCrargLGZpEiCSMJMhTmaiLm/qdBMwFM1z53JcjNL1KRWr2uWd4EET06/FhyRoqvOEIaIExFsfs+Cu2e2vbFqzdMRktnfJNciEXor52S7OCqbp55Dwtgeldr1zMgGr2oIiImTFlIKl2B4G4hhwIRijTxq/ljH0fUdIlprcTjmMuR9JHs4CZIyCqgn9rl4b6/jMLalMqc5d+MqY8mdV4oX9bjfNRUPwMxiqS34vl2KVatwnaiISROlxdCrshgDOceI6Bt0xxBFhrsWEifhPdU1Msb4LYLlYhEFh+GKWT8jAxoY6dbfy603t5u8y80nwrQIDKRVxiDMmnIFXfkZEys3Z9Rzn9N8lo5ymcbo5Yz7QVGWA4bKWHiJzRuOkGpMmW4NL7qnOlbHk8fgUxGtxdToDVPUedoCZlELZp2XG01gMCOWsdgkoqeJEy51QWo+B1F4WGEXqhSEDHmECYN5ZG1nfJjK5bWSwkAGJSgJ45O0yCUiQaBdT2wdCXLXms3WoSqZTNSPNn087bHo3KzRaoa+uuwYtS65vqjkVeGLalSYdsWD5+gwWAaf873hpBQdNe8iVj1uBc+pDHqwWAaVKeCMkF9gyG3Nnh65jTyQ6z/rOGb/6V3+Dn/2tz+PvnjCKsBaPR9hf7Xjl29/nu1/6Gq99+/vsLq5QgX2+WBhFuo4x96GcjYkWhhDos5t2cs2sLeqtILS0B2qa2655zVta4TArd+pnWzfpA0tWmtcMgsp+tz/Lpdfa7G/m3LZd6KKMyr+3Z+9xygGwqfZevf+WgH49v3n8+fPZ+Ks6pn9urvPIGnnJ85wVXzmJj5RnSl/U3JbK3WdADmFVKEonYcq+lkvLc48Bu9qNsf7u2NxkelYnUJrNt2CWgewinJVYFejI77XrUPd1Bpaqtpcs6otjY2q3ba/td7u+7WdtW+2+aL9vzxlUiZgqmj6JCdNz+YwdgjZmf98EpGrLYL22MVZyceELJie8e/8B/+1//d+wHwfOzs544YUXSgz9vXv3GGMwBX7i1ZeXl1xfXzOOAcfk0dT3PcN+b2C789BZWEDnHKfrE0iyxxBGdsOeu/fu8fDRIx63vO8BEs6CUT1K33lLmZsIR9HOiiC9B7X4EBUBSTeSV5vDiyAaTLOfFVVJOIthLFpeEXtv03XTRYUpKDtUt1S5VVf6AnXWGrP6jOOeEEeQrhKKwfkOienyxwSexhBNuAsDfZLjhxBYrdYMYwJRCjGBlZaI5U2dYx1qYNECnPbAWKBhcsWLKc4ikdVaW1Qf5HEMxbxcM8LWX7cWirKgXR/fGrR16V6mrAmq45CAktksx2cA7IeB3XZb+piz9NWlJgQtEa+FiFxiJXwsEtNEqJxPaWczM9bpuZpghxCIYWq37/tiPWrXsS2tcDmlPp+7TxwIsKoHwok9a5qyNgVv0drkfUHSWCWhT5hkGg72UCUnMifI9bzN5rARJFqXSSeu+N239WkllM2YDROgbhl2vd9bJjuNbio148nPtXEa9Vmq+18LRvXnec5zffV+zZ/Ve3RR2FgQcOvyuHtpqRybp/x3LllRlfdk/v5Y3Emt7a0BUiuU5Tle6tdPWuq+18UE70h9O9OSgLI4VyKoSLktvnZz7dTAXBQIvXAtkbhe8clf/Ay//jd/l7PnnmbvzZq/Fo9eD7z6o1f43rf+nO985Rtcvf2Adb5HLVlM67M7vwRcgLk7cqj4Qj0H9dja9XycPVQLTEt70qxIciA0Z1pdC12PC1J+kvWer8/j13HTGSh78YiLYPv+Er851oelZzXJGG3/l3h1TTPrZ9p1BgrAfK8xt/26aW5aOeCmsS4J6ofgUiYBvKHLtZdHew9k3WZ+ZsllGg75x+OUln4svV+AhM4tmze905blOXn8MotV0zkAvykjZ93+sc9rnpr/zrKZU3j049cJ48ijEPnRV75pz8ZI1/e4kzVg1/R0fc9qteLs9IzNas39199IsrfjR2+8wbsPHhQAPyaDAZruEVRls14zjiN+1XN6csJwsMLHy/seIJmFI0CMdF3P2pv7U3Qe6TpA2Q87s6yIQoyMY6TruxJUDnnBA85hl0QmEdAufXRcnZ+z6TpOTk6wG+YxgBNGVv2KDof3jt04MuxHoionJydEHMMQ2O/37NJt9hMzCoxhIITAnTt3GIbBEjIMjlW3Qrzd8QSw2fSImFOQhh27/Z7Ow253xfV2j1utCxFos8nB/KDUzKjVsOR/EzFKt4Uzpf3MbhuShP6lQ7TEiFvhtNVMqCoqh5nKgAPhsY4RySBpu90yDDaffd8bgaqIQS2AFSBRCXEZgNXt1kJeLjOXBypmZS+U/nZ9zxhCskRCDIowD2LNoGZufeuLy0I9nzcx2lpgK4xa3GL/M5Sp52++ZnNBqfgiOzcT+orbHCkhB4dMzOqZAEbLaFsGUL9/k6UwyrQ2M+2j5uQqy8y5HnO7n1qhY2IChxrTnE621RK2a1TXW1tp2zVsBayltWkF03aMS8zuGIhaEiJueq9+f6nfM1qT6FCep7rNdi1qMLQUE/M4gkg7L9PDB48uvlu349o9/piCbqZdDnN7cSY5GGhKnwWEwcOuE57+2Ef4td/7XT780z9F6IR9uji4VyG+e8H3vvZnfOWP/pT7b78NQ6APkB1cQ3Kp7WQem1crPNq7RYpyzk0a3yW3xpmyqrH2Lc1Fy0cO5xfy2W/frzPnzdaYZDlo6PCSoHdsTQ76WGnWj4OQ9y4zT4jmnXm9RV10tNRz2v6snxGRZB447HNNv+rfMx/LfC7zvppm1ZeN5vdym49jMboJRNfPHaNBbVkGl1DiV46UFvjXezjz1pliFopgX49viX6Xfiwoydox5lKvTYzZOXR5Dy/NxU17+xi9nh46BL7H+nxTPW18Uc2fZu9Ue6NWhg37PZfvPuSdt96qxpQ6KGL8O68Tk7LQLuiwKzrW6xX7/VAU3r6zDJ6ojXCIdpXK9W4LIuy3wvXDh+zGWxe7UnbDnmeefYZ1v6J3Zn67vtqy24/0fc9ud83V9RXOweXlBbv9jt1u4GRzwt27d01ITbfNKwHv4eLyit2w52RzBq7j7Owud05POVn1bDYbus6Q9zCOtiFD4PzROaBst9vU7pZHj+6DdAQ1Ld6w33NxeVFZmQIhBtarFZcX56xWKy4vLzk5OWW9PuXs9A6bTRIy8Mk0rqh3+NWKd+8/4MHDc/p+zZ3Vurh3LG3umftQRTCKcM6U5Qcma0xUsxYpXfE1BYqwnB0gDrTAUDZ+dlWrD3cNRvL7zvniQ18LTLle7z27lDo7Ez+wjF77/R7nXLG85Itfo8zvjGmBz9gEjNalFr4nUFvFrqQyS/Cgigr4vuPk9ASK4OWRGBCV0tfcT5dcwZxfTsqRNTP1mpZ1qJjVQfrlBeYXYyxEdJkwT+vWMuECdaq/Kc8b0VryL6/7dJQBNeCkHlu9RyZCfaj9nuqa6mwFwXwGaqBcz2stsNd1tG3U85KFvPqC5XrOl97JdbcCV7tPs1vCEjNvP6v/1W23gvCxNch1LQG4us9L52Q2t1W/6nbbsd3Up3bt62fbeW0B2Gydjoy3nqcDS20lC900V3VxzllCSRWcCp0Txhjtktdgrraxc2yefZLf+N3f5hOf/SV05VDv6cQSL+0fXvL2j1/n21/8Kq9974dsLy7pNBLHlMUsKXzyBs9W9VaTDkrOeJZpR5mbal1qq367Nq2wurRPlwSmlm5IAiaZfuc6c/25/dblLwt5tUB/zGJf/75E00q/qs/e6932HLRFRIqg1p7D9MQM0zyOQNzO8wEdXHj+cXhB++wxQXvpHM3mb6FNOKT5La9qx9y2n92JRWSe0bahE7Uyu92HdRv13l1y2T0GEI7Na+lPkoTa+ViikbmYPDTfJ+263uQG1/a95QW5jVKXzpWhLe2t669lwGNjaufhIGSieq/mt1Ej22HLoOOBAoQIThUvUwy+xoiGkUGVkBS0u6t9ebfvekYNNvti8kt09s9IjBa5Zq8jj1ve9wBpux344cuv4L1n1a/wnUejJTIYx0uUQN97tvstD84f8e67D3jmiafYXl+icWSz2bDb7Tg/P2ezWSHe4sw637PdDoiDvhs51y0XV1cM+7cYx5FhHNHkisQYGbY7LDA9Mgx7vIfnn3+efqXcOT2zRAZrT9/Bbrfn9PSUECL73d4AWlQcjjBEdrLn/HLPW+88BEwANzcJs0xdD1cosOp6Tk9P6foNcRzQRqMIHMQA5FITHO8cWmmYsrXGrFED9J6u6xiGPdm33Q4BZGtDfr5YdmLA+yoWqBKiYFmbKJJcVJwroCOPPRPe+h6nYRiKu+BqtSrjzT/LmCtmnouqElLa9JaIHBMAJwFKZkBh9lMy04/sx4FhHFOSSwyMJRN0LUyragnobplj3X7dxzxvS8W+t381s6xdDPKaHAjfMPM/b7XIMmvDKhOUEJRYdWfOgA8tYUvuUy0DbAlvO8a8X7NV1vo6Z4QzAcvPM0HVbbZMa/ZTJ4m5FUhaK2L+u3Z5ms1X9e4x5lzX13537Gd+p03z37ZbP3usP0u+8+/Vdn5PRIpSoP7s2Pzmetq9f6zfS+ezFTDrMUgjcN4kGE99zv7083HcOJcCFt9hCpQYFfXCNYF40sFmw8//+q/yS7/5a5w9dc/iPZxpS/124NHrb/GFf/EHvPH9HxGvtoz7ARR8J6hLlzXn++9qHLcg/MJ038wwDHNhXwRNgL620i/RnXpe21Tfx4Toej7ts8MsaUvnOu9dVZ0JebVSJJ+vOgvagfWp6f+0H3SWabN9dnFN3+u5hh4eA2ktjVnav+14awUhTOBoqU9L7bWgvz7b0742urYkcLcgpK0/15Fp+VF+cYTOzGlU5knzayOm/ZGE32Z/5j7XNH6Jl9fA+6a5XDoDbbkJXC3zaAccXmpc11N7txyjMTeVJb5Rt1PPxdJ6PE6p6XYtzy3RcREDutKnRFEp+QLpWcekiM1nve5jsvXZ2nlTOod072MeacTclaPaHZPOOSTlJlL3eGOCvwQA6f/y3/wTnnj6KU5OTzg5O6NfrzjZnLLqTglhpOuEEPac3dnwxBN36VYrvHfcu3vXgr36HtXIk0/cY7Xu2e53IJ5xtIP16PyKb33ruwxxZD9szVVuvyeMFmR2fv6I4WrH9vKK66tLEMU5+JlP/BR//9/733KSLE7jOCbg5Lh394y+XxGDo/cb0xJ7z+XlJcMu8NWvfJmx6/HdqoAnUNbrNfthYPApKF4Dzz/zNL/06WfxzuGIqLgZ0TgmZC4R5cyAJrcHQ+SWXc2yo4k0cSsy1yLWpuy63vaQtn2riUsGNzXzy/VmYXi73eK9L/d95HHUrne5H8eItGICVAuAaiLT1mUE91BoKv3H0vVGVZz3iHcMwwidaXsVPbCo2e9zl6+W4LYgKb00G9ecCB4yvvIMZBxx6NqEacpYElJjBKQkBjBrVFrX6m6C8i/NR3Z5q9vLgvw0p4eCbA2uZ/MllPi2VggwoXHO7JcAw+KFyRXjngMfq7PdBzXQK8S9ugepbm+JsbfrXddzTFitz1Jb7xLoPNb+UmlBTf1+/n6JMbbfLZUlAbJd82Pjeq9x3CQMtPSlHevyPFAEsvY8HtDR/E7bJyeMDnTd89Inf4bP/62/xQsf/hCDBoJ3lvEwwoM33+LN7/2A7339z3j1ez+gs4zsBGc+907BUiN6NFmQfAJw2Q3twPqSLk7P1xmoTjSn3tfH5qae07q0gnE9L4dzmJQFahRlyash1wlzvqHRrPC163Pd57rUoK2ur+Zl9rvQDmlpjO1eyr8vlZtAX0svjs1XfRZq2rE01sdxGc2lpUvHQEz9TH3+68/bUs95BratS3g9tnY8bb0hBkTmVowCvjQW4tvOXV7jY8Cz7mOtoC08sHrmGO2Z1Snlf0fnph1v4alH+ljm4D1S299U5vydEpv7Xnu4Vib9pEBporOHc2F8WNh0nksBJScsm0qUZCXWScGU7cY+yRiaBjTRBodPnztRxpgyA6vCGEsstL9hXdryvgdIL//gx3xgVJ598QWi23PiOoLs6UMK6lePF7sk70PPv8C4u4f3U7rRu0/cZX2yBjXf+e2bb9P7jr4TNpszdruBV157FRCG3Z7tdsvV1RXXV9dcX1+z3W7BK5uzDU+8+Dz37t3j3t0zPvjCc6w2J4j3XG93XF9vWa9WOO94+tlnWa3WvPvogrjbc7W7whPo1h17lJ1z4Du2KSZJga7r2SsE5xhDwIndpj7sRvpuhSTBOw6H8Ud1StX8+YywNAJRFmCnuJ/sQuSII+BcyX4jzDVWhYmqaYRyGvCaWOX2c1sTQbb/xeRbKpp8iNOFphcp08l6vWaz2cy15dHmIx9OSP1znpxuliSo+3QRp6L4rivplTUJ/KgW7aCk32Oai67rUmp17OLFmNpLvEE0EoaRUQ1YrsSzcp7RJ8LvHJ0zNxhNwoO4lPkq1zVjroeELhMkIyJzLV95Ni3OlAUvEbKUqjRn5MnZjrIgU+rPaxLMXO6dR8VXnvVaLGMIRXNucyBlLD61YRA/AWrJmelck+hBDLDmNWj2cSHC6XnUMsiJ8xWIMdO9JqQkAt6BRvs7g9uc0YmS6CFM65HmLe8tTam+XTX3Bf6lCclMuBXmWjDQCkBLgkP9d6vlj2HKmIZI2rNpnjSd5bSmkhh6tgjmM38TY2/7UAvELSM8ZJaHwK5tT+qfaoK+a/b3MYG7FeByqRl8+51EZvczKBVwQkr2yDoboGkv27i99FyiJQGKS50ktzofHOKEQWBYec5eeo7f+b2/zoc++dPIqmdPxCn4ELi+/5CXv/M9vv31b/DgldcYtztcFmStWqJaCnDLgOgqQWKunOm67DptgxmGULLVdd4TYiSGlDSncr2qXfNs/mrak/Ypmdb4so+EnGk07wEtC9sC81aqb89zqwSr+VR2H6zLMVDT8pfDZ6XQ0rbO9r1jn7UgPyuA2jHnPVR/Vitgap7XAif7OaW0R11Zm7qttu789wEowRR2WfgUsb1V0ptWz7YCc5aR2nPdKg2W+tOu0zGaI2JuVtnSC4eulPm5tq0lWtPyyqU+w3zdanfPJeCT57GciYZm13W062PAQIpckeWlqc75HC1Zmdr2WuA37+cy35zvr/mcLc3P0vfLoN3GVn+vqgn0bjDDkdGv2Wuzcad6mM6OrU+Ov1NUBXVCyHJNtDPhxVKExxjMkPATKBHgLwFAikH58Y9eYT8GXvjQBy3IfzfQb3rWJyf4GHEhwjhYoOxg9+xE4PTOXTYnp5yc3sE5x3q9QdyKq4tzwjhwcf6Qcdjx5ltvcHF+zfZyy9XVNSLCycmGJ+49ycc+9jTre2tk5VAcXd/jVXF9zxACTz31NPvdnovL1xDfcXJ6yiuvvs6LH3gR1/XE/Y4XPvA8zglvvPk2J3fv4Ncb9lHBOwK22ZyY9QHn8JjgPu53JZe8TxvDLBH5XqS8f02azOnEa21j7d9da88LeFGzGoQYGUNmCkqIkRCVzs+JyuTOd0iwal/43F7LLJybwFQBXonBhxBYr9ezJA75vd5PcWEWUyXl0EWdE4SZZlJyCnVMeD+IR5AizFm9TPdFaAYZKQ12FvicM8FdsHujxkAUy+bkkxtOEj2SO2LqagIPpa+pDZIQM4slkHRPTjWuGdNIwvJUkfU7p9rOqdWTmqY8ZgJaih1I9VnmxrSPKkFIS92Z2SrZD7gQawHBl0dMiNMCyjPQqAn0gUtjUzQxG/tHer96QKpuFStXNN9ZyUlIOBCC58wia+K1gK8Y5lkO6/moBYxcR3uTfN1G216ttW3HegDKqjnIQnPNKGOoshmm6wBkgYEfm9ta41r3sxY4loSPMsZmbK2gma8VmASI6exQ0YyaDrQAqK3zJuEo128dYvop03e2jZaFo3qMwSUXMBU8Ho2RlUtxQM4x9h7/5B1+5bd/jU//+mdxJ2vcyr53MbJ/dMHr3/8R3/36N3n5299D9wNdiLhUf2SKFfLO4d3kWlzHbGb6ngUK0+Abjdzv9yXNfJ5vcfPLv3PcTz3XwiS0SWml2hckupUBef69CdWp1yYbkWq6m89DPh91v9IDs3O1tBePtdeuFyR3cEw51e6T+tmahtaW9VZQrYXsto78fbZY1cCobbPd5xNdN3rmpMuMIYGbZdBxDKSUvzMxTAoqJCusjoOIm7xPjpX6OVfNdV1nPUel3zKfp/x7tkAeAzxLoO1YaWMyyz6/YV/Ny6TQzGOrLZRLYHqpT/m92V5aWL/6+Xrs9f5dUh5klt4Cnvc6NzM+08hBN83NEkAUsSQLGnOaf9Jl7XU9c16SabBzQlSZZAsxRWq+7F1FLElNeS+36ZBu+cLfm8r7HiB1nV229+6D+4xx5OnnnuXOvbsEOSOoo+9XBliiENSxObtD13vefPNN7t27x9nZHdbrNVdXV4h4nn76WZ5+8kkuLx5xeXXNxfXAa6++TsDx5JNP8YGPfoQnn3xidllpYACnjEHxruNk1bO93hYGcOfOHV566SUATk9PGYaBN15/g+g6Tk5WKWhfWK1WadOnBASJSOfMbDkI19C5bYyctKDrOhP0G27VHqz6gBaBO42j7/uDAMuJ4GfAlZitd+gwXZZbCBpJEyiHQa5ZaGzvF6oJTLY21dn+Yozs93tEpCRfaN/LMR/7lDO/ZLFbKCJTFrIQ46GgUI07C5o5s1w9lpqZls+SgGOX6Fq/T53DdM7LBAns4BdBsWHiceEzSAJ7dfHjYezInHmIJHeIqr4WFGeG2s4HmJaGysWyFUxa5mW/A5rj1ZYY2ZwBzlzi9DhgaD87JiTUe76sjXMH84XMyXfNYLJbaRugWv9e931JQ93O0VyInIOXg73xHkyqLW09S3vncQSLPKb8+7FnH1dIgWm+amG/7MVEi469t9SPVoBr12PpmaW+10JZLci3e3yfGPgqQB+Fzq3Zx4Cs1wwbz4d/4ef47N/8a5w+9yTSebsYdlTkasebr77GN7/yNV79/g+5evCILig6hpQp9ZA+1zSxHlvuZ+2KrGoJgkTMajSn3fOg9TyW9t63mwT+FkTM5oXJkn1sfuvf6z7XGTyXgEfbn8ctB/RggZbcVH+7p+v5nNW90F77/tKeXWpj+qxWzglaxUAu9XfJLbg8I1gg/MIaHqMr9dlZomdLwOlxaFQNKI6Vw3UwZRYNOFg6z/V+v4nmJeHp4HzfXGwNMnA+4MUL47LxZmXbvA9ljWZjfe91aXls3Y8MGDL/ruu76fzWdbfztcRv66Jxrsx0LsWNn53S9X35rFUeHls7qeLo637kZ1zncd4V+bCdU+PrtwCplH7Vo8PA9fUVu/3WtnGIdNvA+vSU1WrEizI4uLje8sHnnyGMA889+zznjy75+MfOcN7Td4HVasOWPb1fc3pywt3dnuu98qlPfZr13buIm0DR5AohdH5FjIG+M0AGwm63QyOMgyVzqIWyp556Cu89F9sdYDcKX11f8eTTz7BarTF6bps4p6xerVaFKWZT4uQakTaPd2iYC5v597bUhC+3Ux+S3JZ3HjQWtw4qK0buYx5XJhLee2JKX94KQW3CiJq4hxAYkkUsxlhAyW63sxTo6cDVwm3LiJaIQv6+HWsGNFkAyWnWlw5vHlubQr0VQiGb0c3y1/c9cD1ZtHR6ZxajFXUmKNVt123MhPAFUFCIn1AsXYcxVtMeOJwfprVm/lytA1oSANq1NsEk9f/IPqzbz+9l15qg057Ka35MCKvbbGNoSt+qua1BbTuWdk6MKc5BaP7pxSFVEox6TQ804ywDjbqP7Z5tGVvN2Fsh+BgwyBfy1vUcY3o1A20ZUN2vOu6lFZTqeWzbqxUws7rF3AWXzvHiWlZ9WerrEsNvx3lsfEvP2AfgVfBB6KIl/Ng55XrT8ezHXuKv/s2/xos//RHCSc/gwUfo98r9H77C2z96la9+4UtcnV8wXF3TB4sl0gjaGW2o3Z4zba/noeu6kpSmPe95LbLWPSe5yXO+RFfq8ccYzQ0VWZy7+vy1+6uusy2qy+uW26jPR9l7HAqO7bos0fZj6+e9pQcOVYKhds+2fKPdn3nPz/owG2e9/xe7c2N/6/ftLFMuh7f4nOwkNz2/xPvqubL1isSq/iWl5VIfjlm067K0R24aY/397F3Vxjpav5O815vvalmirm+JB7X79Cb6slTK58ktseYB7R5q10AEjgHcTM+RZfpT11tbNGvZpwVmmdfm0noItaWdt3YfvtfctN/nfl5fXaN37874yU3zWzwKynrM+bS9v5BFb0H+uGnPtuV9D5B+9dc+x5/+6RcYLweG3Y7XX/kx796/zwsf+ijXl1f06zUQ2Xjb3GdnZ2haxNVqRdetGMeR/X7A+xV9v0biiHcdm7Xn7p27nJ6eEXD4vrN4mGqzjcMAki4iw6ERAhEfoetWdF03E8Cfe+45ALa7HRsVus7x3DPPc//+OyCOZ555xoTbdHCyVSeDB9sQEMbAyju8z37aHhFzyYD5hp2BqGpjlQ21wBDz91kDkn08c1xQiBb7YLFJU4DlMFgO+jAGvJPiGpKZd4nraDZxm30uxljGrar0q1UxHdfjgCkLTP4996Weh3pc5RlncTM18GmFvzyHcOgbfaDVFjMtR/NKLBeW1taLXF8rUGYXtLbfWWCohfsiXIgQmNfTMoLWBeCYcFzaYyJS9bsiKcboBmJe9620lbVnx4RRYWZ6z+te96nMw8IY2jmpGUn9nsjU97xn67WmEZBaAbpes5lwVz3TJp5o16FNN3/szLVrmffikqB7IMQ3awGTsuUYE2zfOSZ8HQOVB+3pXNNaj22JUebvWuXEewk/bbtt3/NeqgXxdk3rdbhJ8BaTdFhFQYMjiBBPV8jT9/i13/1tPvG5X2Ldd+V+rn6E/cMLvvedH/L1P/wT3nz5FUu4ECwOKWeiM4ELxB2Clnou83ft1QgwF3qdc4SkXW2F+ra07YmbXFaWhI3W/bi0oQDz59u9d3ROm74s7dO6LNG3lu6183fs8/wva6PbKx3afi29Xyf5Ofz+ZsHypvFlfmIWpP8Pe/8aY1tynIeCX2SuvXc9zjl9Hv0k2aRIig9RoiiSIilREtmkJAoee3Q9NmAMbAzGuB7A15aEESTDgKEZQAYMCfAP24B++JcheSwI/uULW8Bcje3rudKV5esH9eSrSTb73X26T59HnTpVtfdeKzPmR2bkioyVa1e1JAOD9s3G6arae618REZGfBEZGckAtZ+z/bXfRzkshnpdSmnRR9PVZsXU75y3G2SLXZuWt1p3EJLzOUxzfh51HRbbXLRfrd8tXTXtbLu79KyVOWVNKp0k9et+WCNusraM/J5gCtQYpbUOW7KzJW+1Y1bzCJGr5kzXL1hQdJ+ttzUHaQ5rvTc6HiNydF0V5qjnS5yZFy1veQPpk5/8BB555BH8+q//OjwRQhgQNhvcff01PHTtOobtBtvtGvfjFoSAvVUygmTStts+Cw7GMKQ7kTwYkRggh9VqH3v7BwjOJcPH5QQG0KmkMxORByMZCV3n0HULHB5eQtd5HBwcFIZOExngO48nn3wHPCKeffZbODldgxZ7KUxsuVcuMa0EUj7cvlg4eGKTzSsC5KuFohed97WgrBQMpkIq7VLE5DjByODeeWyGMXOclCq1dgbGGqRI27qIYhLPKNG4IPq+x2azgcs0h43dzSXGWBIEaA+DNmiknUoxMcA09lsvOk0jeV+8slaJt4AKgbBaLRsgb+xzURacjCodplILnOmCJyJYMVOH0qTBNUEpYzTCZ0obhEos/LxCnigIbnY/PRMZkXU8cRtk2e+B2sjR71jArzMspTNH9WWIhWayI2xAuFWe2jiS34PyrFtPnQZf8rlOWtLaFdPgXfMb0XQHr0lXWx9qXpl7V/Nyaz5bn8nfc0ZR67nW+pLn5pSnnQftvJgDEC0D6CI0q+W0AZRECCuPcGkf3/bx78ZHP/sD2Lt6BS5f6N2FCDpZ4/VnX8TNbz2Pr/7eH6I/PQO2Q76kNXmTA8eUnCKvQ+98xSNaNusdb+vZ106nAiZ2gETNV5YmGtS/GYA5VxK//snrkaIdBUDbUNf8N+1L7ZUW+aF36+x8S2kaYW4u2cIff4wVQC+fpfMc+py7nR/dZwnHl0pawHqu7RZN7dqwzqIWvVpz0KrPvmMBv+yiWbkyt6a1g0rLa110FlZrlEwMNP3uDrmpZc5c0e1R7sOc4WdLyzmh29d91N/VBsbUANrVX9v3yfwxUvZazbNqXg729/F65sWWI6PFj0k61vq38IU0q/qud8rl82G4eEbAt7yBdP/+fTz++GN473vejW9+45vYWx1g6HuE9SluvXqGbQhI4W8MRxHeIV9SGvPEjUT1foDvFvDOw1E6071a7WFvtYeT7QaLrgNAGLbj/TsOhM51YMrJDELIuxIOQz9gvV7j2rWrAEaBnM4ceVy7ciUxBwFnZ2cIIeLo/m0MfY8+ANuhx3K5RDoDkRdTBJzPnjmkXPBJwLv0j6cCERDBMe72iCKwW7d6oYuBRM7BO4cwBIDH8A3GkA+BU1E04jlwGBe/9qxLv6aGw2hYyXPDMGC1WpXvRLAB0yx8WnBb8DXn+W4BYnm2UjKqzPVbFrGnFM7BnOi63W4RYkxxs3E0kCqlk9Pa1nPVVjx6XksSBjl8Wz0DMI9KpwI96jNNn9Svup3aM5b+aeFlt7r1HMv7wYAkLWhLdi5VzotVJxrPkLWUoVUkQoPIXJIVaAA8DAN8vmC43tLX66jtWbTt2rFbBW0NqBZYkPG12tEZ33YBDVu08a3HZxWWve/G1tsCUFKsA6I1L3qN2TbmAJo8Oyc/9Ls2hHcWSACV4W7rB/T6Ss9GEPqFx2MfeC8+9qNP4eq73gbyHRbUwQ8Rfdji9N59PPe7X8LT//H3gPUWi8iIQ7qwOsmcTBtCuQeJMMovoY8GetZrbccMmDWsAJGVYVbW13KsXv+tNdjiezTW99x3trTmaa5o+a4NyBZN7LrTgUfWyWXXgOUTayDo3zWNbZ92jVe3p7+reL8Yq/lsyUw/LO30Od4YIphq/raOjFb/Kn2xo1hda8fWoqWtO41tuqMEyLzV82NlyZw80H2osFBmdCtHm3yj6eyma8GOx+qC1NS4NrXeHZMoTXXHlEb1XZJzOkBwTRnrjKzV38/NnW1bPi94CsKbU7703oMcTZw+5afi57r9EV/o9VTkkzpHrMeg5eT/nuZblV/+5V/GdrtBGAZsN1tcvnI5p3HeoO8jQoy4dPkSHnn0OrrFAtQ5pHCxgBQhk1JqHhzso+s8QAGEnNY0JAMBNMAvPOA7bDabJBB9ymDkfcrQJQCscw7DZgtggf39A1y6dBnL5bJi8L29PTx84zqYgH57Bloucf3hR3B09ACv3z5C2G4RwdhsN9hstgW8OUoHcB0DiEnwefYgEJz3SaBGBlS2ssxV6eCuqw+RF4UcQsk8JYtPwtucKHGiNM7Og0MAuVXOOpIXZeQUbsgpzTI4JUAARoUk4Xa2aI8eUbpkVV8KW4QcY+wn0uHT5DGtlZUsGOsdmYBNR4hhDHEDSUKE5GXSisReUqvpJ3WHEOBiBIMQCOg2ASvfYXBpHjryJeucc77Un7aEa0+49vCGGArOsMaOI5RUmsi7dvlSaZCX6U+JPyRCTu9CTBQnAYxRCNqt7KSuRMFILGHa+bPKQdrwZRpS45xGW51Lsj+LQQP1rlF6ZSTSnhGMLe8uGnPXdV2ZW5l3UV76lndtVMkcpTC7RJjIyXsvzoFkzJTew5GDZKvgfKJL6g+Zv5EVKpXdhmKWTpR9pfTVfLV2GTwonVHUiljkn9JPWtFYr2Nrfm2ZW3Na4UnRQJXzXJdU6gCse8ClitLc5OfkXgxKFaafun/CQ0K73JcIRqQIDwJydsmInG4fjAUcPCe69wDOOuDS2x7FD/7I5/GuD34AYeHAPl8h0Qf0J2d49vf/EDefex6vvfASwnqD0A8IMZQdHT1HXQYQkrZd6wdW/Kz7zEDJTkdqt18+J0U7TfMWqG8ZPkle6IuVEy0y96V0zMgAE0VkZlcdgQj5Qsj0RhYTSOqgBkO2b9ZIkQsk5/jNgtq57+2454C/BaUtkDox0jE9qzk+n+jRqlevY1a8keRTDtvlei2Sm9+L07vZum65IkScjumKhzybCRwAZkxaVrbOmllwqsdhwXY9x+MOHoirnUVyQOA60kTacUQIDQDe6tccL1jAnSJx2udrLG9oBwNjNFqp1JvwY6Sp4yf1qR0hUPgXlfhtOs/02Frrp6pfSNzQq5YHpQ3Li7Z+a5RVkTnp4arfUrcc/5g4PJlLiHEU7IXkNEqXvhZqJN7ICbiICF6opiJPHKUrR8b2ceHyljeQ+n47Zl8j4Pj4GIeHh/DOI3Lagdjf28eiW+Dk5BT3ju7DI51ZSWeQugxuB4S4BRDgyaNzS4RIePDgATbbLTaRwTQARNhstyO4Rc4eDAIoGTWIA0IgLHL96/UaAEoGN+89lqsV+mGDEALW6zWuXr2OS5ev40tf+TqWiyVOTrcoZ3yywnA553u/HQBOindBK4jwEdVYmD+mrfkRcIxZ4ESgxZjTIMdYMr/p+yg4hAz4RRknL3zXrdAtIoABoQ/iKskApT5LIn1iTpfd9n3agdOe0tHTwCUFbZXtjsv/ACRhP4SUVEHH3Gsgq9sVo68Ce6CJIJE02wXGGsGkvbzaiCntR0aPCHjC+sEpFj6fSxgCXL6vR7wksivIzIiYxrEXpak8V1ZhZ3Ik4BLr+R/MpaVjHSkjnRWUMcYyvzYEp/xUfRCAlM7fyV+mbxCDCuMuGVH1tH52DsRQ0kiFn6HeF/Aryitivg5Zg62dnAkgAipQYulVfvJIv7GzySAKCNk2TcCexeBU2SaLYVZAPBXNqemtFXArfHRXuAQllDrKLUrZ2MTA00bfnAGk6aMVa4tXLN+1vP2NBkbAZvuQv5PdYqf50hgUdtzNNZAdBogM4sSP+VojgNIZI3aEniO6a5fwic98Pz7wqY9heXAIRrrLjEPEcHaGmy++jD/4T/8Fx8+/BGy32Gb9oB1R2vvrXAqxRqwNb00b5nQWUK4gkOdaZ0LAyRklZzGd9xhmDIhdhoP+W9MPQLoPDdPPgeyUK/0uD8zyke6HBmcWXMvPXeBX86EF77aI3G61dRGAbduEek+PaVf7mr76PFmZh5hC4esLzOMESbf4pdWeFznHeRzyHWrZputqtWGNOvvMLoNV5iXmBFKOXNnRLrxMdTh00WHZERxjrI0V9W6Lb2yp6JzHPzcm/Y4Ob22GEgLZ1TV9f1zz7d3O80qLJ60hMqU/JjuG+ns7PtuOXiOt96ysED1meTtyxDD0RTdMzroyMs5IfMgQ2SttjhE+Yx+mu+nAiENG43COotPy34SBdOXKFVy7fh3ee9y8eRNEhD4EgAh9H3Dnzl14D9x54w6ufPSjJd4bqL0L5DIWjQChAxyle5WWS/ghIOZwoK7rSrY1ZnXRHhEWC6QLrSLj9r27uPrQJVy7egXAGGIHAJ7lkkqC8wu4LuD0dIObN1/DSy+9hH4A3HKJ/cuHcHknJ8Z0WzCTU4uW0rkk51KoBtVjKvH+itE0M3M2bHQ8p14kkiIUMQLE6nMqQK/rOhCnkLg4DAV8a8UvmcmswWE9FkQ5TaT3NRgwTK+Fd0tw2md0e3OKUozDItC5Ds2zfbdCQX533qGPAXt7q7TjGGMJuxOP06ie8k5Mca6kNspcGM+hCJu5cxxzRQuU9OsUIACoztNoGurx6n6Wd3djA1WyZ5XbgszSU3+u+bkFhMt7JpxGty2eJ12n/V1/Ngd6rCHRoouuS36fA3EXmcMWELBgRfdZPyOyx/JxshGmddi6WwC6ZXzYv/Wzc0Zpq8zN7y5F32q/1Td5PiVLoHSxcRKcIHKIDtg4Bh0s8e0f/TA+9tQP4vDGQ4iOgEjoGIibDU6PjvHV3/0DPPNHX8GD23exZMai8yWZjtDcnk0TYKyTy2h6auNK85nN9NUybnSWzRYvzNFkF33Lc1zPxVyx9bzZeY4ZFAPTUMBWv1p8eF6xIcYtMNmSP7bvmqfqc0BTIKvnSz87gr5at9cDbc/hnKE3R7eL0mlux0CPXerTY9JzoaMvEj+r+/RQO3q48XmRGaR2603bLcOjJZO0kVOFzu4oF+etFLmxKxS2hXla7dm/55xerb4JTrJh77b+8/hbYwvLO606dBtiyHJk7O8fTOonkmRPZqdM61wzVh3R8Kdd3vIGUoyMj3zP9+Dylcs4PTvDg/UpFt0CD+4/wHC6RhzSrsaD+ye4fes2whDgFlPhlBZkMpDIOwAerlvgm88+h/snpwhECEPEcrmsDomm9xxiCNj2W5B3WHgP5wkv33wNH//oR0Bch2d577Fer7HdbJO3bwjwrsODB3fwta89jbt3j3D/wQmuPfwwLmfjijmdSXJZiHae4LJCjDkkLQ2l7cXVwkovHhEYWjHL5977HMqWQk+SEZmsfudzWlmk3Y/IsYTYOecRwnh3k96V0oveKo50ZieBC32LellcqIWBjKe1oHX4TutchTxv49HF2GK0wWKJc1XnqsrCzQtdQh6dSgs/+uxGTw+QwvlKv5QQr/ofQyW85koloLnOCjYVdO0U8IKDWgaD/G4Ffsxhhfp7/a94eBQV5BereuaAihbMLcHdqsMCBKJxfjWfnwcS5zytGoToflrDRH6XedXzpPmqpQDsPGh+04rQgmXdV9nNtWuAqN511nPbDhfhyfwWpacATWtudH2tPu6ad01Pq5Btny4KlgkAmBLfEqUwXecQHSEsPB559zvwfX/mh3Ht296OwRPWADoQliD0D07x/Ne/id//D/8ZJ2/cAZ9ssBcZ7BibUB8a1v3UTgf5F0LIzq3xOXvWQNPD8r6Vd/ZzSye7fi3PtOZQPp8zdJIo46rvrf7qudllbMh45PNdxph+xu5ktowa3Q9LWysrzwOydh7OO+8h31k+HtsDkhPNJBNKb07e30WL1tqZzP0FvVotfrNGgB6f7of+XBIw7ZJTmv+ICJ13GGI7+Ys8O9lRbfSrko887k682aL7XugY59eaXQ/n0czKZzvf1hiZyGRKnGLlga6zpVdb/W45tKu6jJNVnhW8d3Z2Budc5Xgf60k7ozGHfabY/hxaa66l0LSbm2fBFamNSVdny1veQLpy5Qre8fa349LVh/CNZ74Jv1xgsbeHq4sVyN3H0Z0jOBDWZ2v8+9/69/jcU5/FtRvXSvptZh7PIAAAp/sgyDs8eHCKP/rKlzEgxVMuOle8g8IEadckgMiBnAMToY8McoSnn3kGt964hccfvoHtdlsSDsSYMrSFGDGEMbPW7//+H2AYQonpXq6WyaPf93A+nSOIMYU6hCFgJQA4e18cpUQAwjQSxlYMiAySNONLiJ2UGKcHOFNoGCFCUnunUDKCZtAc85zr6xaLIoC1EbPdbidgzgoAu5jK4jNKU4wvu+2sPbZE47064t3Y5ZEo/XAa1k/BttBPvGMJCBMgBzIZ8D5n38v8otPhElnPZfppUx1bUKPnSL7XNCqfZQUon9egbdyVku9a5000jTTAngMAWlhrWurvJcBBK2fdrvaAz9WlDeJSr3xP6X9W0QjldQpQG2NvAZ7un8yPpqUtlnaWVueBd+Fd+5nuh6Wx/nxOAVugXfFMI4y0xQcybvnbnheoadUe265x62IdKJM+m7nXdNN83PLoytqIHEHOIzKBvMcAwuAJB9ev4lM/9Gm8/3s/Apb7jJyDD0A8XePWzVv4xh99Cd/66tPo75+AtgEY0gXKUd1lpvtqx2JlSGvemLkkW2jxsh1rq/7z6K770OpPNQZue8SJqNIfrXmb29HXc2jXYqFBoy+WftqgOw/0WjljdYduv8Xfmm7699baan0PtHdk0nMRzLU+K3OcKq36rJ1oWjbotUBU3z2on0Pksv5b49Nrze5K7ALuuj5tEBGm60LwC2IN9MvYzDi13rNyS4+7hQ1Kf5CbjNOwdl2ER+13ds2W+aTxez32Fm3luV3rU9OoFbHQ6hc5Knxin7FySeqzclLe0yGPtr6iC8IUe4UQsOg8Npt8cbWKHgKQzzMmp5T3DiFGhBzCy2AQamyjz6631mcZyzmyrlXe8gbSdrvF//rb/yt6ZhyfnWDvcB9+0cF3HR7u9uBogeO794AY8Pprr+M//of/DT/+f/pxhDzxsohiTCm8iQmUz/p8+atfxRt372HISmDot9huNui6lKxBGGdvsUKMjJ4DlvtL9EOPuFiCY8Tv/f7v4wufe6oSygVsInkwYwTOzk7x9NNfx4PjE3DMdw0NAf12C8pncURROd+h65ZwWbi5fNt0CBHMU3ApCz1yLdwK06qFYbfmIzPYRTi/SOeRYgBlg4ycA3K62j4McHIolDmnI58uSGFwq5hijNlQdRiGlCpdklvIM/p5LYR2CTo9JrtbpMGePcMhcfUtZWkVUwG1NLbZDz2AFAOOCnTIzxSbPI5jZnHTmLHNCjD5vfVdmpt5L4xTYYka7IiAtcpH03Omo03gKmRJxjRGhUzJ06MS/EzGtato5Vf1k1AZ0rbYT4T3rAGwq7QUnAWWc++1HBAtQHue8rTv6Xdb37V2NfR7LVDbmvsWELwIzfT7dh1qwNcC/S2APVe3BRFzzzvnMcScXc47YH+J7/zU9+IjP/D92H/oEkI2jPYiQJuAzb1jvPD1Z/CNL30Jr73wAtAHuHwvXHTA4Bo7jwagzAEcy/MjL7cPxNvxXgRktUDQrnebBoD4INScMyfHmxx6t+Bc/5s63nafF5oD4FK/Ngi0x1vXMQ4CRSfp0Hg71lY//jTKeeu9GBIY3XJ6vYQYq/MewEg/G1Z5kX6ktmrHjy67eKNlnNgx6v6X7ziFomnjKz2nBm1pQgDMGpBn7Hht9AswOhwnO1cWXDfGrPlZ/808Xmxb5OQONpmTSUQ0JjxpzN15xpulY3m2MTZLMz1mG/asn7U00HKDqHXySp4Dttv6GMpobFE65kKUEo15n/ocA4LiD22Yt9rW4xFahhBKcrCLlLe8gbS/v4+zzQbUeVy7fj3HLhNiSFmbrl27BgwBx0d3EQLjN37j/4MffOqHcO3aNTjnS9KCGCOGfkDYDgA5bAPjt//976QEDcxYhJg8hYC6sFV7wTJT9Olg2hAiOAR8+Stfxcc+/GFcu3YVYiTJApPQPEcd/tN//i84OrqPGBmbzaYcRu6HAYgBy+UKC+chZ5HONmdYdh6He8vcF5cPw9eepCFnAPHeAznURhit5JBnLtuy8o6EBPocQgikLfIQImIkhO0Gm6HHohsXZAwh15Uy4+kFbAWqCH75PISAxWJR+i27czo7U0vptoSELXqBayGq29dAgrMgH3FKDQa1ENELeBQajK7zYDCGEOA4AamsI3J9IqQo2zFzYIHFzqkAoxYalp4xRp3XYRYsW8FLlMIDRdBYALMbjE0vISzfxJR1TDKzBY4jCFDj0/2wwt32g5kn59pSfW3DoXwm2phGWgi/ybPtWOfRsNW00Px8EYNG5qn13aSv59Rlx5hyYczT7CJhWFLXXJiYli2ye6rXQQs06TpsWKA8Y40Eu0u4iw5WxhR6lIdkLScagZBSzy86BEd45we/HZ/8kc/hxjvfhu3CYe0Ynhy6TUS89wC3n38Ff/Sffxc3X3oJYbsG9T18Tm8biMGeEBxAQ87geYHSMpQ1jezY7By0DAJrTGh+0/N2Hj1nf+epzGBOzhbLV5rfiOo76OaMntIOoSSnaI3NGhXjHXpT3VAKTY3oXWGfLRqc9+xF3t/1bh569Ww5x9aQM/W9g/P9mzNcque0wpspc4aRfLdL/qXv5Pwtyk9OArsC21bPF6eryLiZPtjxaYBd8Y2h1645bRlh+r3iqJt43lo2U72eItc6o2XsVMZIY3235laeae1UtuSulRe76pZ/JeEV5m1DiWASDOqcw6JbJDpE5IuplU7J0TaMMUJLt5lE+Pn69jw9rMtb3kBaXrqKvcPDJKTFe8XpoteBA6hzuProwwjEOD66h1ffuId/8T/+K/zFv/gXwcy4cuVK3qlw6IcBm+0WgTr8f3/7P+Dm3aOUvjEMONv06Pwig7IA5uTYIEoxsgyg61Jq8GHbI7qUKerWvRP8z7/1O/jxP/tnsN2ewTlCiEAkAnEAhx53Hhzj9/7wj7ANjNX+Hk7PTsFxQNiuEbdroOsQncOAAA4RDI/OeXTwCP2QlUmHMQlAw3sAAR0xp18MWfhmD4gRAiXN96JLl+ZySClr84pw3oOGHhTHxRKRvPchxmT4gdLWOZBkQ1SeARDiEMpOGTMjDEO6KZmBzvlkbC4WySgMEV6dS2oBIb3gBeCPB0TruFwNrPVh6rEe5F2Oemtf6mkBsvQvguMAt/BYLjs4BzgST+WYrrIG9gxQgBwUHw/pENJlrylDox2zBUC1wlcTrwoRVXXZnQIB2NJHO765osGcnRN0dRINDjGfZaOc/a7tGbKlBRYnihq1rtLKJaXwToYqGDldfgbkRNXOk6alq4zUlBiFWUAjV3zSmqOKFqrfcwpoFuQ13i3ryTlwUIpa+ouRneaAgKT8JQl9ZBQgImGaesdul2LW47SlBfjtd/J+a23NGQPZNZTTxLpKnlEO2PDIa9Y7RE/oVw77b3scn37qM/i2D74P3aJLu0bM6CJjOHmAOy/exAt/9DW88PQ3cf/eEeSkHYMxgFOadJfOD3FI/GXvmdP9nTVANQgCzP1L82O3tJ4DDTpSwia0sSFL1gAufUcaawHRAlxn+Fj3T8tQLVdmeeYcQ9+unbJWk7BN6zef7yzLQdU5Z6RJH23/dIiPpZOU1s6ZBpu6LQt6R5oTHI3RApNw2xyaJE7McpUHUHbHWoBet6eNagdFIOU8AFCybeqxye9NGTIz3mpeiZESEok8ZVC+u4PcmDyhaoO59FNuJCA4cEh1aX5tGTJS9HOE0fjUz+7iYz0feu5H+mIkXu43INcHsEpbr+iT/y9GRtLNNK43NWctfWHXrtTJqu+6r3PyU/PFnJyRZyfRFjHpct2e/L5cLLC32kNP27Sr5wjESW6Qr52WLstsAmGIiUfSdT0ZEMaU/Er3qFpXnJzQnuq08OeVt7yBtFitwM6DieAkdhGMGHtEZMI7wtVr1xBixNnpA3zxi7+Hv/AX/mI2FDjdNZQvjQWlOzJefPllgHwKlwspRbMU51KmIiCBvF4OoQXCdrtNjBQyw3nCS6+8gs12i7SnEBEiwOTRcYAjj6P7D7Beb8EMrJYrrJZLnJz2eaGkTHF9PwCuA5gw8LjLtYo+3b1CBJBD5KnAL+CGUIyi9L1s6ydm1J4BOSu06OqD/loODP0AuNqjCWSBY8CU1G09OrruzXoDIpcxp7qThsc2gOmFlEBtHOl/spDFGzG3XSx/S10ySPlMAx4NFHXbQMGWiR6IWO6tsL1/WtJa6kvj5N0CBijTQsBqAxzPKd2JcDdt1KUeW9WOUpLyndDbKp3asJruHMwB5aJoIpcdK/1eSwHPtTvphxHyrfNaEs6pdxFZQLbikToMdKy/usuLpnNlDVjNi02aN/6ee2YOfJ1Xdhm3BaiJcZz/hSwz7C7pHLDdZdS2+jsHSOx6tHNq3y+AQv7lRai/jw4IncPgCThc4Xt+8PvwoU9/H/YODwCXDZYQQcOA26/exMvf/Ba+9sU/wHB0grjepHHQ2NYYM5r61Vkwq9tuGEu61NcZ8ITWFkxbAF07dnYbk1ZmtcB+NECV2QiFbBzNzbe9rNf24yI8q8dun2+dLUnPRXC0NBhlqqVFq29zO5YXWj/GEDnvXUt7LZ9knPK5lXHl+cyPu9aHXYMtB195B9IHBlOtd+fWemvsGkiPz6VrPNL7mIDdlmyg0iNMMp8SOXRuTBmuy5zs0DRpzbXV85VuN/xr9abtm0jUKE4n1HNRzi2jwZdqjWv80lrfFQ3zkfa5Plue0MXOrzWu9XyWOUcbDzHn6KeMOSnTCPmdEKf3YcpMe+crvgPaIeK6b9XYJjXPl7e8geQ6D+czQ2cuFGvdOQfidDA7EuHatWvwHths1ogx4vLly3AuJV7YbDbovE+eGb9IO0niSULaMWFOSQa0JwxAYWCd3nUERBHDkHZjDg/3AQCn6w0CM9LOI4HgEYaI0AesT86wOV2nO4/y5YVhSHGbrkuLMHrGQLHc2xFiwBAiOjdVpDrMQZ+RkZ/e+9GYUwxXey9SSvKWWtMCSGd2Y6JyyFiKPS+kFd146JvRdYvyrMQT6zuMWgJb6tXpjO3Cld+18GgJwDmA2zKI9N9pZywboY6xzaGSzhEQxRPZApqUsVamJSuFDs5uoalHSOqZ84DtKnOKb+65io8ahUYd0KyjelbqEIDJU4/XnMKzczXpB1G5TBNAbcxgakjLZ5q7z2ur4gO0U8br9uyZtTm+K32ZAXG23/JZVHJKf17qzl6NFlBujVX63Cm6tM4k/XHK3Nj0d7u+t7whhksOAobjOhsWiBAdYeuB7YLwbd/9Hfi+H/08Dh6+AXQdiADPBGwDTm7fxavPvYiv/P7v4/6tN4B1D2x6ODgMnMNEicouVWuuWuMTQ1wXG/evZeIcTWxpAdYW3ca7/sLkGb2+5Xc7tpR8p4igC5Vd6/NPWmy687HvXDzTcwByDvQCiU72MHjLsCjvYgrGWnrB9qd1HizpOwZ4DFttyZQ5w8bKrxYQnjNw2qUenV17c7y3i1912FQlf9PLAFxzbPM9rK8vmSu2X5x3PVoh4S3AXd4zOEK/86dRrFFH5jP93VyfxVjWuECeOU+WtLCFrd/2t3Uvm+hT59KF2Jt+vKdSsI3FUhV/ESowIWfE07nwNs3+uOUtbyC1QsoKEM/UDJHLQfdLh5ew8CmL3Ha7LZ/3fY+D/X3EGNBjNJqc7KBwOuejQ7Z8NqhSP8aZK6CMGavFaA1vNlukO4JShpU+bhHjgDCkyyR5COmm4BCwf+kQne9SWJqn/DtAnpDOtKXU2tt+gxhDjlGmkpLbhgnEmNInirEhn0uacMIYgiaKIP1MsaTkHIYwVOFGzrt8v8+4IAsIjOMC16BL7zQRpft+ZJEkujkwj5kCZW5FydtFa+uXelpGjhWGerxTY6Fe8HoRW4VglW3aPWIMQz687FIyC/1epQTTh8lWYPGGU+kG5fTtVlja8U36bwWP+r4loHSdeuya3+cV2DxAtHUW2jsqBmNrbLoPFy0MrtKuameG5YNK6QCAa4PeOfoR0USY69LyIst82V2tOUCn22rRoaw5Fb46mVOenxe7s1GPbwTFOhNkq7/W+GoZchZ0zq2fVv3ybAvgMpB22rNyZQLYAdETonOIC49rTz6BT/zwZ/H2978HceHALodr9AGbB6d4+ZvP4eu//0e4+cJL6c67zRYduZTpi5KsY3FU8NhH6YeVHxbIzIFkPU+WL1tGWGsXeyJPDN/aDJS6j86lC9M3m82kP/JM0qVyFnWcj7ky5zCwNNBtaZ7Rz82tCy3rx/oweccag7ot3d9JqHGDL1t9t3Mq9GLFIxdZ2yPdUJIx6Dv55uaFmVO4sp+eK7VrTf/d2tGs+0qTsbVoJ/2wu8w2sZBupznHb0LGl/dzFj4dPmznyPJY+Wloossuh5ctcyGHRS6bzy1fWVlZ8JN6Xj/T2vUq7zDnKP1p1lKLEzR9WjSysqJVdL0TGihbpmAaEZ08deiUvqn69HhLHQ3+btHiouUtbyBFTtnZhFH14X7vfI7THQVWt+hwsLeodiaYGYt81mUYBvjVPpaLRboJOMcyR47gWHvT1+v15H4jET5lmzmODND3PYZhC9+tAMn4RmmHi+MA7yIcBixcAA8buLCH2G/R5Wc8AB4GOArjDkPnC8BOOz1tUBJCCucTA0/i0UMISbjGxFiSuEEYLcq5pkrgo4S/EbgYinXMOhd0ZYWzVebjgsipctV86mekbgvC7ELRn+udJ/lOPweMCnKxWFRjlPCi1vtW+ADIWRAZ3EcEjugWnbh0Mm3re5qk7aQgbOhfhKRXt8banMKuivHU1IJW7mVqKP0cDjAnPC0dRnpU3TRjaRuRnsbLmlvG6y7h1wI+rVIBCRrPiOj2St9U29orZuufA3ZSWvHvu/pn65xr04KAigYYlb59PymlqaPgPMNEHyK2oa6WfnY96vpaa29ubdvnWuOvAEX+zkksf+cREREWDkNHOLxxHR/+vk/iQ9/7USwPVogCpoYIXvd47YWX8NUv/j5uPf8yzo7uY9Utge2ABaf6mAjkHUKmYb6Ou9BE6NniV2vcyN/VDpd6dlexXlpdrEyxMlLe1eePpM3kuNtM6pL+lHHxdL21gJ0diwV49vcWSLxIsXIl/aQS9WHD8FqyRNPBOslaPGu/t/Nr69V6rNUH3Xb6PaQQqRky6LVoqNFsQ+vZOlxyuoNpxyo6XH/XesfOod4tnQtXtPqAiACqs5btkpljP0cnlawr+5zFG3r8FpO0aDCn7ywN7HfqoWY42Ti+Wr7aMVr6zrVT1jy4Wu8tvdKSuZbmLVlmacIZJNkz0ACK44U5Amfr9J4wNo11WhxIROVcsNVVMj7Nay0eezNy5C1vIDEIcGnXhAEETjsrFJEAAwjOecQg23xAjKEAZxEe6/UaIaSLYFndlZOAqsPQD+WchRgDIjREyeg7dhKwBjZbvVszwPsO3aLDEBlpSwjYP1hib+VBkXD1yj6W9BDYdTi8cgm0XAKdh/cdMKRUn77LWYFA2FvtIWajx/nx0tcW48UYq92j8nmI5b1690i8yNy8fTrGCEfTBRZjLDtLZZ54KsTtgvc+7VgxT+PMdQidXeD2911AUj+nhXl1DgDZzjSKsDUGLTBCiHAhpDM1BDx4cFKEt17c+p1SV6wvWyUS0FUrK+1NJJoagHNlTvFocEVEE2Fu+3peaQEF+XyivA1A1O3Y522Y2pzybfVHK3fNH5NwppmtoNb4x/5Mn51T7rsUnB37RfoAGGC6o315zqY2tnTX/EQz4KhVLC11u7bvLcXWWhf6OxsPr0uCxgTK2eTCwiMerPAd3/tRfOypz2D/8LAYOZ4BbCPO3riD55/+Jp758tfwxkuvYjhbY+E7hLBFl0FXRPLIxhxe1zVAdEuWzQG8OcMCmIYw2nnUcyBAskXDOT5pATugnotdAMn2v/X3rj7YiIbz5ErLyLKfT99B00nTGs8cANxVNGDbVecuGaCfsz+dcwBPs6JKsU618+Za66w5IDmRyeWz+f7bOmS3qBVKfF4pWMPQYU6na8yV2kdlXLXql58W5Gv6vhkd16r/vNJa81Z/aOAvY9LvA+MxhrniKO10a3q0HDK6zovOle7nLh2jNxDKVSbnrIciB9MnlY4eQ4On7+r5uygm0OW/AQMpwZowDMkwAlJ8aZCzM+mMTowBHBl9vwUxYbFYIISUPvvs7ExRn0ra0L7v4ZHSEo4GkitGU4yMGAeVStthGMZdFOccAuIYsw5KIXx9D9ct4L3DZrvBjetX8TM/83/H8b176E+PsfQO+wdXsA0RN9+4hYdu3MByscSyWyIOA8ABzPmSWedwfHycaADO6aTbilYWjTCdLJy0E4TJM+l75DjfWA5Bi5OLQAhhqAyvIshkfpL7uvRHh2lJOKAGq0JX/awVkHNgpKWAtREhn9vFPbdN3gIV5TlmQLXv8qFMcgRPHkMc0hkkziEQkTFEzrtCDjEGiCvFOYcgyRuyMBFPEIB8aa3qB49gXpSTHrNQfBewaPHFXNml8EvbkZFynDQArBJeWvDFmC5F1nPc8nrptuY86WXsyak16Z/+u02veYBu69GKmkHN56aAnzPLWIU8Pi8Z9oimIOxcBcZp4HO0E9qeB15asoORdoxZP2Ne1/3UGQGdBgE01ilY1q5T3a68SDK+3Bc915SC6wBHCN5hWDo8/r5343t/9Ck88u53YuMIwXl0RHB9xPboAW4++wLuvnIT3/jyV3H75uvgTY9ltwAQgc4jMMO7nA1JXbyoedieQ9XzbY1yIIddcwrVswbJHOiu8L7inZZH3M5Bax6Fvlre2TMhdq225ljLYtvnuZ3flizdtYNmbZ1z5U+Wx1Udjf7Njaf1bEse6PXDgFqzWicBAvJaxdJo7Mf0nj7rvNDjkhLVWmmNz/Jtayz6b2v8S2kZE015saOO+aKysXKdNtsaMyP9k0yVq0CatRrsIPNG6ns7xl19vihv6+LIpQgk1YdWqeZb1WvpOqenBBOQ2tXR2KrV7zldsGtMml/kDJhe0yJDztZrHB7sj+8RktON8xySoocWdHkMUE7hoh8VFnozoZBz5S1vIAFcUl0nRQqQAP4YMYSA9XqD7WaDs/UZwrDFIzceAsilS4AHRmSHrlvh6P4pwBF7hwe4/tAVIAwIYPQhXzpGcmYlhWJR2R0I8EXxJWE0DCFZz95j//AwPx+BGABOO1qMDt4xYmAc37+Ny4d72NAezk5P8Z53vh17+/s4/i/3ce+N1xPAyZn0hhiTp5/TjtliscDBwUHmrcRM3vsCNkUwxjjuOghTpz5zMUomi4mRszcBq4ExOGC9Iiw4YB8Ezs+HvGskQAbKmEwMLMItpyrNTO1So0ogpMbCMKRdHWaAY0qS4agy4Ky3Sm/12nh+KbKYdHx3E2gwILeYknMp1WimhygjRymkgzmllffOIUoy4EhYLPYQuw6Di1jGAGIPonTYMPUXxchy7EHwY9uUPfja4CyGUaIfYQRvkzHGODWsoDzf+vdKyFGl8HeF9tR1yzO5f04Euaij1s5APScyL3pnrwKMqr+zIZjgysCYKy2Fq5OYtIxv+76MWzITCr+PzzNCGEo4b/oc5acAqZbRwlwbD3NKoHynFI+U4kHMOyCttdFSMIXOkSGx5CkrE8AY66rWRB6UPEt5nci9dOOglFe0fDO24+QcpRqLA8FxkqzBAXC55RCx8B2YHdYLh+6JG/jkF57Ct3/3d8J3HQZEOE9wwwDqI45vvoHXvvUCXnnmObz83PPotyl8OS5dUtaeAJf4hwnQN7oLGJBsn5TPWKbwYwUQZN6cXAQudM5jjgw/jjrrKS4MQUJ/EwLLnN4jIlDXlbnhTGdyqbeU6TQX5tkCuS2QZGWDgK6K50xpOUK0MWnXm901qzusJAZDEnzmsMdpu0mmZUcVTx0pc0bdeeDKPm/XaFq/2kiYvmdpWQ1Tg17UdG+C4BYAV0a87bvIysLD5rzQHG1iZs45x1kLQGtH5FxfLQ2KHCUAJWveaHQSuTKn8vwo81DwjPCe5c3WDmlrxlvn/yZ9R87MRwTKV4/IKk4HIJqEAsDljKges+jbYhRkJa+NjZHHUpYUlmsqaOSrGCMij/cSgRMW4eR+zE4qBnmPEOKEJ+b4rLXOrQGvI4s0ZnCO4Lt09Q47pGsQlNxjFhknilDJ+oyh4TwGwapCI3mvyAOjg95kecsbSP16C+c8wEg38+Z7edZnZ+j7Huv1GoDERK6wPDzA9WvXcHa2xunpGtevP4zlcoUQGCFEnJ4cY+/gEMSEe3fu4fDKQ2BiLJYLlPz9OUGCADHv/CgYM4j3JOkNGZcOD8EAfLfENmxEFmDoA5xLmZQIhLt37wFI9w196/nn8OST78SNRx7BydkLAICIvFXaecSBy11Fkn1nuUhpI0Ug2LMGwJgRRH7XHkQp9cG4DNIBdJSABLskIAijV0vXCea82yY1TpWxPjcGJRDAo0EnC1kSOcCNgKPl5dTGk/1OF60gWl4IKyTt9xqcT0A6Ja9i4se81CmFrhHGCxUJKuU3YzJvBWwwV+PO1RVjSYwtHTol/bBKz9KjbTyOYK7+vK5D99W5dOGx9w61wcmzdaSfsjvZnKbqPf1uC+QVxYlpaJL+fXanjOo+zhlH1iCryxT8MHNljMc4nlG0/Ux+gpoeNkxibmy5waptKTEyGHUI7XllDsjNhWrqOXG2X6ZQ6w8CuqzAE/BQ80GErUvK0XEyQjw50MIjOkI8OMB3fN/H8JGnPo3ltcuATxc+dOzBmwGnd4/wyreew4tffwavfusFoB+wXaespb7zcBLWVIQ4irGnadGSJ7KzJuu10MbMr147ZR5zm9ppId/PAfP0Xk3z9AVGi4vr/pR5UbxfQmpDI93uzPztMiY02NLGjwb8b+Zs3lzbLcBW9UF9Z+W0bjfGWBKP6M9ahlzLwBnXKZl60++t8CgpdWIJ1Z6xc1oGnf5O180zz2jgP+cMtLotjarOENcG93XZpUOFJpqm9csj2CVSdTOX3Re745iTw1bjsP1sJdHCDj62fa/GmeeHxaEjuh6AOIKn81UbdtOxZwNL7ARVLD+29Le0IxhQ7zISTRiq2Zeap0csdq4cYC53IE3mNbcRYkBJeZ5lHnM+38q1M5alTkWbES+NY9b9qN79Y5S3vIEU+wByAIeAzbbH6ekJNpstXOexWq1w6dIhFoslYgyIkeEdcPnKZUSOuHzpSrn/iAgg53C2WeP05BQPXb6Cu2/cgUOHxXIfPueXlyLCOJ39ES9A8pyLoUYAHDH2FgvEEBFchwiHpV8icsR6s8Hh4SGcIzjfYbFcAQjAlnDv6AjHD76KGEMOV0n1S5sAqox6qT91wE8Fhozg1aFMITOuVWgj6E9hYxTHbW2rGMp8NA5KFrCfi3ceIQzVYhwFXJ1JT0oCmcn3ahWH/LMCdAJITLH9ap0DkL9t3dpAKEZfjAgcELLwlMv7GAIuczgEdIhR/qfIZvshym8i0FB7bexc2DnXNGrNtRZGWkloGsxta+tLVuXnOEc1zav3UJc5A2CnYaA+d3DlEL1+T94RB8G0H0mZWMOoBQgqsHcOaJSwNnlPz58Nd5HQV6ug9VzNglTmyX1S1olg359bF7sA0Bh6S5P52dm/C5W2ARuJ0DtGF5NxBFC602jh8OSHPoiP/cjncO3tj6WdIEfo4IDtgLM37uLWCy/im09/HW+88hpOju7DRwYPIe20qHX2x+23NWDkdwsY7K6dfce+a+db5nOO7nbOWsB6FvipcpFD8rbe1jqVNaKBljUS7fdzpQa39efVWIgqQbpr97sFDFs6ZRiGyT1w5V2u5Z6do5bBMmf0pHe4Anr2fbt2yzMNOSVr1bav+2XHW+uWum09V5UjQBV9oa7tb+vv0q/UiWadzGL5T9daqo8nvGSB/pvh5TkDUrCV1iOj/NjhFOKpY1HmZtc6nOsPkJeAeVdjpjk+Sbs9u3WAXcvnhd/btnUfnfNYLBYNeZeiZ8g4JcFcjE+pl5AdB819vz95ecsbSKFf5zuAPDwY+8sOD106gFt0OTsPY316nL1FjMgB+/sHCEPIoS8OIfTJE0DAtevX4bzDI488jEv7ewjrNeJ6C14tsB3SbhQI2XMfM8DlclmrCFPZ2TnYX+LS/h7ABCaPS1euwTuPkwcP4PwCnDPPkfcgdIhDBHkPz/kMlE/3PFUCrHGXTmLiWtlXAFgtDPGelcI1GKuUuSS7ECOQkb3gVBhZFlIFNngE7faAfGlWvVcWI7mqLgGOkvhBdp5kbDYu2yohXWw/rWKwYVs21t8CFl1noXc2jkJkbDfbyugAGCHme7RKgJGTaNu2wFTtTcAVjaE4kx0Js4Veg/QpUCv1l2bnt9/1+KVIuKEVqOM81buaAEpWSMZUMFswVcasnmkpPxGqQhPdB12X3kkFRq9gW0HPA9dc6QR07KKfXSva6Ezf+QlQ3QUidZ+1ApadK+/9eMbNjGlundi/54CG3SmYVc6VEm6NARAvrKZzkgmEZUiXQsIBQ+ew9/ZH8AP/xy/gHe97D7rlEn2OyeoiI56d4e5Lr+HVb3wLN7/1HF556SU4ELqYQk7Ipcu1xZC+KJArvNXgVf2e3bmYrr82zVuGhv17AtIbz5OrQ2gtONOypNVOEzQ1cFyLH2x7rWLXxpsDiaPDSdeVeLTeQW7JbT1Xk9ArI2PlO60HKtpQG2Banmg5KuzZtfH3qQ7XfdG/F2CKenemkmtmbHO0mPSDah1i6Wn7o/s0BcT1POp2iShnGp6W8hxqvio0odFe1jynadOa04xkilyT0tpxLXWRA2M0/uqz3AGuSuxlDF/Fk/p8d4tGrTKVnzXPWRksbVj+jxkztHSVLTvDX3XfUGNL3V/nKO9sDfDIOJeTnB775gw/5RB5mW/DS5aHdU90+xctb3kD6e7rr2K1XKHzPudZZ/RnyGmWF/DOwTEjbrYp9M13eOihK7hx4wZiTGF1Y6YMh7PNFmFgPPbow9hfEGJYg0PAEJNgDvnOIVlc280GISeEIHJYhyFvuzK87zCcLXDj6lWAHMgtAFrkaAiC9x2c60rce2SAyYmvAs77lEkpK3/xpch2pVaaOrZY/lkgJswlXu0qY1Wm5wQE0ihMxpIuc5U2gBEU6F0G26ex/rEt6X/pM4xRpBce6sWsvThzglloA2BCixYosX2SsemtegsydJtDGBA47RSdnJ5UwpAonzsSYAadHrOtGDXVbb/T/UrT77VAtJ61GOUMSbv/wl8tmljANSdA54CTfDeObV6QtZS3NXhs3zRwER6y3q9dYHUCCs3nrRCcFlhu0cnynn6nNiqswqjntdXHokhMX4rCBk/mZNfcWQVqaWO/m+tXu672nBOlsdu163PYnXMeAwirG1fw4U9/Ah/6zKcQL+9h8AQfCR059GdrHN2+h+3dY3ztd/8A9159DWdH9+EjwJKdkajwnZ4n6zjR32vaM2sA0M44ZgHwLtBoP5tN2GDmy/KQpfPcPLZkoK1XaDFn4Oix2z7qHeqL7prpz+04W+ultTMu/daGYSVrbB2Y8reWL1pvyPdV3zLwtfRuAcu5uWvxmx3z3K7hHD3suHXRc9OaO/lJROncMdpz15rLi+zUXARwT+YtphA7m6JesJDVeeettV2ltaaqOgydgXHcrVBV3a+WPpubJ/vupG+qD0S7k1CdR4PWWtb90vPakhcVUUx7m80Gi0WXj8CwOS9UgiNr5y5zkc/pnqtxwC1Z+yctb3kD6Sf+xv8Nly9dwmqxROc7HB7s4+DgEIuuw4MHD7BcLrFcLsv9PpevXsPRelNAg4TeJeITNpsBbuVw4/pD+H/+P/42Hr56BYQBIAfOh8a8TxN+fHyMe/eOsO0HLBcrDCFgs16j324RYkzpwIcBb3v8sXTHDqUQOwLBuQ6LlQdnIAvnkbqQgC8T8sWEo6AiaoPKmllG74kIVzEkmMfQOsnUV943QlruNposPJfiXWVni2LtAR8XURsIpGfGi/msEosxVEZWtWi5jnkF6sv5rEDR7wrYahWrhKVeICkonRLejkf/HmNMZ7RoNDi9c2VXjMshASR+ikJv8WmdXyZgW21Jt5SUBVzMuRc0nlmy/DP3fqsvpU5D+6khMpM0AyNgtbxgFYwViq1wjph3dWUNWE+a3llp9aPVjua1ufASO/YWv8hzdoetHm/63K6pXcCHOfGArrtyHMTz+6TrmlOqc0C7RStd13khGmOdgKwPkVUAQJ3HZrXAt3/0w/jeH/scDh+5huCABQguENxmwGa9wf3X38B/+q3fRn//FKsI9CdrDBwxcMrG6XKIspO2FKiy68R+pvtIZp21QIzmk/Z6MIC0QSdtuOlnLLiyjgMiGg9mG3Bm67dFz3/FFzNA6CJApbWud71/UX7U/QAyHUxIn5aJFuzZ9WDP+bXW6dgwKk+47f8cf1h+qN+lSsbIM3OpnW09u+aipbs0v9gzU9YJa9+d+3xOzmi6X6R/0o90pnvUr1VIZtZlc3hA/m7Wr/ppnaGWR8srasvKhpq3ZEKZy8ZO9UXXjfysnieq6mydLbRyY8QZdd2WPnbc5/HVXBHcmXQtgOyQletfiMQpxhjPbwERhJwHKP2vsY50f8fPEoZ6s8bTW95A+uD7341l1yEMAxZdBw4R2819DGeMYb3B5b1reHB0C6enp1gsFthuzrD/8BPwvkO/XSNGxrbvEQNjsVqBnIfvOgCMEDa4detFHCwdfLfANiSrVjKYdF2Hhy4tEWOH/b0D7O3tZVDm4ZzHcrmEW+xhG9JdTK7z5SI7BqFbdAXc+HxxLOWwqyR9qew2iSWd+MJ6vmRBIwOl9N2o/D2cj0CoPWV6UUgMrRaaQIojhScQXDp4HBkeDtt+i84w7y5lVoP3mol1mFyM9UWKNgQPAHye75QpSi1iTiF4BKrCi3yuJ4ZQpVyXsc/1ue/7agdIh/bpUikExwhDRIgR634L9NuUmh0p05/3KdQTeb687wA4xDhAGydVnxpGQowRHEJKQ2zeGY2W2pvdmhOrGLUN92aKFlhTEEAVT44vYRSO8m6jTj2mVtFjFKWqQ111ae1qphdRyG8Br+bFVr9g+IFjRDAgUMBay5DSdbXA9lhPpS80oYrsSGNLO5IxxkKL1D5lWVFehJ3sUWHa8KNEJAvG9BhqcGSNjNSuzHByANGYnYzT/6Q3ruvQxwj2Do+++1348Bc+h7d/8NsxrBzOwNiDx2oT0d+7j5dffgnPP/sczh6cYH37CCd3j+ADw3mHbehLPzgmOeecz46Fdhhn+V14KvcpyRpMJsHKvhbAl+8E7Gp+mANktmjwPIb3jPyswcIuQKp5b26HdXL2gGkiF3YBkV36QH8uY3kzxdK5gOK8FgnpOo0CFE1/ZPxarovDUHSG7ft0XXLycDcMFCtvJ2C6YUTUoBGF1qLXW8B1Spj0HqUHJtEHk/7z1HAZAf/o5CNKTpZZg7D8TAJKOzHnjPJdvC5htcgREqOIHftexLWAHlOsUdogU6VTW7J3Im9pVBIaJ2kHatOg0JXwaNRJbXPraEJn6Z+KCGgZ9bYfdZ/GsYneZePMkbZaPLfLYJLvXZ63q1ev4saN6zg5fqD0ifRhdJoK7ogxgiNSVJXQd4aPdZtiXInc+98NJFWe/9qXsd2s8cwz38AwbMEccXp2iqEfcHBwiIODA9y/fx9DP+DSpUvw+1fw3/2V/wGr/Q7UpTNAvluhH9ZwkbGfkyZQ1+Ghq1cxnN7BAlscrBz6AJydnuLywWVst1ucnh7j9MEDbDYPsF51CIFx//gEd+4eI0YCOY/lQ0/gqR/5s2lDcUhnisg5pGCyruw2ON+VwKsQIxx7AC7f7UQInM7nDGFIggoRkSn/SyJDzi6MIVsCiBiAywxJGZQHMKc7jMD12R59V1EA54PRjI1LUbhdH8AABocKZACJYb33iEP7zgZRBOPvU0GWBOLI7HJwfdV1aSH16eAsh6CezQqduSzGJKgDkFPrMjN4CIDPB0pjKEBWAxbdZxue11LoGjxHAMtI2AbG3rUrCZS+8joiEQAPsE/Ai0QAh6IUK7yulK0IkvMOdtpwOhv2KPUKgLWgvXyvRFIrhFGKpORWk9acc6Ja2Gvh22X1GpTxyqoH2jjRdcyFRNkwCH1w2IZEVX1ESrAidUu9WoFKHZMQJOFdZegRpXt0dvGT7NLanS5AeIIKL8t6rsCAooejERRy/i+Hcmcek/4yus4pwzE5WKyxnKrPOy/FaAoFPCcnh4xJG1Cpj+PalgQzyZZfMNBzwOAc2CdvYRcJjhngkECW77BxhP7KZXz8C5/Dd3zyE9jbX2HoCG7p4YYAOh3w6le/hZe++nXceu0V3L5zGzEkJwg4h+rGiC47mTpyYJejBjiWM2h67Zf1A4yGGxEiYgFq88CjbSzqZDjOOdy4cQNHR0fYbreFH3YBjl1AT3uLzYsgTnK74BHhHkIJvS0GBE1D4zQAS+2M1etnLKCS3+1n2ijT7WpjbBddS93jcCZGR1ZHIDDklOf4bL7414zL0lLqnZuPidFk5GIrJG6unpbRFGOfdjkzyEw8ChB5MA9V21U7IrOiRAik8UdmUM6ym85Mp/v3nEt1ytUfrHgi6dbROUAsjgsqjhglocf1QqPB2Bq/Bd1apiKO55C6fJ0J5wxxGbYkmei0UycDbaSxJedqMTtw3nUP1rgAgMih8ogUhxPJWqv5uYxVyQs79s55iAHP2Tk6mjq7HQ1Cn7kdRP3TfjfdcYtjmBtSArEs1HIdU/3d1LEz/bDP99ttmku5EiE9gLSblOSzGNWgtMMf5RA3o9xrKhd0DzECvk0rqYcjg6bkmC1veQPp5PQ+Ll86xHd993fi+PgYwzBgtdrDwf5B2q0BYQjpTh1HhNOBcPny5XKmRINI5ojlcgmOKdU2yOP4wSmuX1om5gZj02/g1x6bzRZn6zO8dus1xLDB/mqJ9WYLIF0EerY+wzAw3vW2y0DZ9g3ouhzmpUCn3FPiXLofCRDhBxCn3Sa5C0CAmPCkNipiBt4tMAq1mGyYEGV0rgFz7TnIwsmnLPUuSysGV9lbKi+KI3TUTcBkWkBTxVCAosr6JUUEhAauLQUnn+vQQi3IrEAZQkiGp6sPTlqgI/UDdchdqwzDgAWnsDo4SU4g4ZFFTJi5m3qfS7veFX7Rz47epPr9Mgbjna7aVLwnBm0xyFrPowY4+rvyffqgemcsI9CfnFGoBP/Iy1maNses6aP/FsDi/KgUND/oYuk96bF6X/62/WiFNGrwaekldUjomFXQLYBm+WGkCipwpgGOnVe5SNLuNug1ZA1BIB96byjIaTha/bsdu4AK2dECEbooRkhaE+wIwQOx8wiLDu/88IfxqS/8MK48+gjYOywA+BDBpz3uvPo6nv2jr+DFr34Tm/vHCNs1Yhyz/8W8PruuK0DRzoEev/6sgDaaT4PdKvV4p5dCSnuvv/76zndtsTJiTjbNgXph7PJudgTYOube13IBaiz6vjLhPb07NjW222u4tUZs+9ZQbNGnPI+al6s5Qf23Xg+tfuo2rYMmd65yUow4Ygombf9ba7weU0rWAm4bx7oO6et0DWs5ltZzMrhicbxWHqVcivyafKOG3tBjteE0LXM78JZf3OTKirotLW90eyMds0xRmEYXjjEZjlQ/Q0Q5wqd6Orc57rjZM71pzmfopOddz4tQTLU1twZbn8+tB4tX7HdWf9s6WjRpOSNbfbTf3bt3hIO9vXrs+Xz+2KBUYOprRNM45ya80BrjmylveQNpy1s8WLuU2tt1OLp/BnIbAEcAUkrprutwcHCAvf09XHnoOhaLBfphUwl35pQ1znXJc9P3EUweL778Gu7vd3AuAg7poi28AdkS7FYrUFyCCNg72MfBwWX4+yfYhruImx6PP/H4RFATUVqklBdq9hR57xFDnzKCEVVgQ8IAgNoDBWjvUwIgc6DWApvyLqYMLm1Wsa7eIQzJ2t/2PRYzc8LM2aMdi5dWpzt25Er4ivSpKJaG+S/GiyhlHZMt456EhKjv7EIqoIdQgVVddP0tQ0zTVtpPdx3lNjMdTk9PIaFkoJqeeh7tXOnwj7n4f1Dd7xpIRJDKS6+VqPYUnSdQrEe0xXMAwDvqkWyPwPxt8OlB1LtHqAGnLhbYjAZSvT6EB1uAcNfYW23q8Wpa6L+1oUJE5UyEBYMaZNq1rOubAOIG+NEKxPJVoiNGrzTqtb6/v594FNMzVhMPpKG1lRkaIDaNPe/QE6Njh0XIZ4Kcx9YDvSf0nvDIe96Jj3/hR/DE+98D6jpEcvCRgH7A5s4RXvrqN/DC15/B8dERTo+PQczYbrcF9Mt6lX+27xKaZg1DPf4Wj8waIGb+NY0sbaxBdpHSAr26vvNKi8d39XO27WLM1vOs+bZVr/68Bcik6DXzZkLu5kBSEziq9m36ff2cXYNzY9Tvtna5W6UFUCeytPRr9PinZ8ZdTvvsHM8yUHBEena+f601cd4Y9GcFS8y00TIE54w/4QNxTOt31B+ltTbdZ/Qb1Tv91fybR+f0QEV3C+7NvALzu0Da2LNttHTBeWWOpq1353avrXw/r+1mm0UPZ12dQ11z/Oe547D1A0jROOc8+2ZkK/DfgIH0la9+A6vVHrbbAS+88DLWZxsMfcBjjz2Ga9evI8ZQvDDkCD/wmc+j5cUnIgXck5dzuTzAiy+/jmce3ANowHbYZCGQgJjsDvAQUwgXCL5bgInwxu076BZL/Nh/93C1k1E8FSGik80Bl874LBYL9NsNyDm4CIScVlKSJZSsbd54RIQ5qS3sW0IUqHdF9PMV+MmkSmkulUdbaOZ8BsC2DVRhLHIpXzKe6hAqDQQphxVapaQX6FSRTD1F8rwOsbJzIL3Q8fzWGGj9Ls/oVNECIiKnOFom4OTkJIVtZd3s1M12rbqbChjjuSo95jQH00O9Mh5mrry8WtCz7GU1DJ8Y5RLktqDRNNf0YPNMDcan75ZwxrzWiEZvofOupNGPcVqn7Y/mEeccguIPy1+7jBFQW1nMAbwJGLHtACl0LJfWjo1ty46rRXuocTXbVW2OIGnK18xcLtKeG58OwWr1zxoVrbrKdwSwSyHBKXzYIXqHTUdYXX8I3/fZH8AHPvkxxMsrbIjROQKve2zvn+KVbzyDW996AfHeCejBKc5u3QE8YaBx10poKHdvyGfWmWLBh55n0QOE2vmwa64m84MGbwEVvc4rdg51vdZRdlGDQq9/+btpyKq+F5o1dKatY+48hC3aILA8aS+8nhgrDbBs22uCTaBEYcyNszU2G2Kt2xDHz5xcsfTVDlmra/VuXKINJ0ThHFAcXbW+s3xs5yE518Q5ksOaNGlo1CFS9A6g1KHbnNLAGJmOJnSWvk3kkhqDzcQmnztgcp5zpASKsTOVl2OIndWPumhZCCCH5avdLMXDWkfqsaTdOF/1z45bv1Pjq6nM1rJIy1nb79bvF9Hb9p1dpSVfyjpHCu0GTcN801pOobnVWiYqyamcm+4Oa0yj5YPodX01g52bi45Jl7e8gfTarWOAHiCECL/YRxc7nG6O8eqtO3j9zn04ohJudHCwj0ceexv6oc+HMgFHPguOFLMfY0r3zUxg3yFigT/40tcR4xaRdAz+GIbBQ4DLWTpCjOhDwGp/D+9+73uxWK7KZXO6kDOCjYHFYlEEmRZ+smNSFKwSTtXicGK8TD1guxYNMN2SHb9jOEq7Nj2Hklq67NqoG6R1fZ33YyivYvIYY+XJAzSASXVrBSrFhjq1BMGckNVtaUWFmWd13QJqW7tvk79F6TkgDD0evnoVJ/dPwc6B2aUwh4bSbIGmImh5BDQtECpzbgFphsVNRaVprPuS+sBVqIy806JN63drBKT+uDavqTnQSoMyEEphIVNQYIWiFuLSjA2f0c9OhHIDzOo+VX2bUUC6jUJn1Ubdx+l8Wj5ttZMUSN1P69yYAyEwbdlxaVroud51NmMXCGoZUyn+HmDnsek8gncYOo/v/P5P4mM/9GlcunENwQNwDisAtO5x8uobePnpZ/DKs8/itedfxB47DGcbDBwR2SGA4dUu9Jwhoz9vnXmRfuudBTFGLF01/fT7c59pQNziN/3ZXFstmurPhL9afKPbHS8dn85ha50XWiHJL8uzLZ6r5rzRd/2+ddIxj9dPiIPnPJpow8zusJR2CUXu7QJT9ru5+Urh7ARgum7t+O0/6Z8dm6UtM4pMTo/Eipd0mQsFlXqYx99bbWlQ2pIJLVrIuxV9GvS0v0/kJKZGl37WFq1v5TrGVqik9Mbqo6ZclblCez3Y9TXhIxpxl8UPLR1padXU07NlDM2z4f5zc6R/bz1jy645qORJEumz9SVMO2JWsDi0Zow5quepheHI1XJnyku7x6bLW95A2gzLrCBTCBw7YGDAuyVO1ls89NAVdF2Hvu9x6aEbeOSxJxCG8f4fZqWkGEgHkYHNdgD29vDEE+/E2fq34LzHdgjYbFNmsy4bPEMYsCIPTwTyhM2wRT9ERNfjkUcfR4xApDE+u4QYSSgWIYVCUfJ8pm30lIjBKqsinHOabP0vgaeaMay3msx3FVCdE/AwhgSUR0aBey1YbX810Ch9ijx5BiVGfPSqWWBpwYQUmc+WsaGFZ8sQkTr1oW3bln0HqMPzhDby92KxwGpvD3x4gPshAJCwu92e9qYCp7Fe/V6METHU/axC2Hw7Ht3GEVjDUs5w6H+6XUsT5tETpj8bAcHYaPUup//Je77zOaRCDIw2ILeKthKgPGNoKpq1jJHd0fMXKy3gU9wMM4CsVZqKIxfrfdZnuqrxaD5qzJcFsPo7q0Tnwlfn6tmlfJfUYXCEswXw+Afeg09+7rN4+3veDXiHsFgghgB3FrC5d4TXnnked198BS8/8xzuH91Ft+iwCQMGl51FPp1ha+1U6J0tKy9afa5CZ5GdNRgNppZMawIlU7RcsTS+SLHPze3WXZS/iAhe7WTscvzodyifI7S0awF8vQtxXl/kHbm43K7xyTmhHWDd1r2LJnbcmoYayLbAocxpPlrXpIVdi3Ptap1lx+OKM1KGXa/3XeNRPYZA8YpmBCRHcHvereyfkxe7SlNPmO8t79hiwbddR5HmjKj0tpVTziWZEVW7NT7g6j39/lxIGiARNufLAz1WAmZDxiw/VmM32PCiZba+C7zXmsu5+WPmdH8VjbqwfM/ybDsclVDjsQrz2GffJD+2ylveQPrWsy9juezyvwW8d+iW+4jssNrbx72jY1y+fBnOeRwcXMJiuUKMDI6A965Ym4nYKTzKdx6dT5P8nve8F3uHl7E+O4FfrOAjEEPE8clZin0nh5MYcbDaw8GlA4TACIHhI/Dkk+9C3/dgGndhZJaTVS23Io3eS+8dhqG4uwBk0Cv8RyiXp8mBcL1wUsKlqSczcn2Jnl7sMcZyb4YF7MhxvJJRROrw3gMkmbrSwcqYQwmZuZy9kN0zadN7n7yR0GGHOctLFGA+zu8wDONulRh1lIeqwDAxoet8Cc8bBVybb4rxx3nHJMSS2jQB7fqi0TlFmcInJdQC4JCM3iEErE9O0W8HBAAeydWlDT49PwlMq1A605buwwi+eQIORZD7roN8PBpfKHPKPBWyCRxStStllWRFPx49dJG5+Vz6u91Won9WSi6H23Es6XPzJpipa2q46t1ZQuJVHa5i+6X5o+wkcG5fGqWRHtZ0aglmq1DtWB21aWpLC2TZ8du13VJg2iPPMZaDz6265gCIrNm+77G/v1/PeVZkMZOL9WTJM5RixiknLInM6DuPw8cexqd+5Cm893u+E4v9PQwxwnce/WaL07tHOHn9Dbz41W+gv3eMWy+8jO3ZGbxL4xg4grsxnKXLqfo4faDFJOQcnlP99s4BkvqfU3a6CJRQbLhUX0r6IMBTPPiFSmW9SwKKmics4Ib6qelbfpvMjS52vlsGyhzfTSur/5wDGBoQpnbrC7f1+rJ8tAsktuRAq20g38UXQpmntE4T4XfdDSQJf6oxc02r+bOb9c6fHs+UlLRTTtt1KfpP5I7oNWtAS5GIbKIWj0373vqMCKNcS8pbscAo22T+tJNH17fLQLIyo5hkWY8CI9fPGkJzukYW2sz45/go6brRWHc5dI4ZxbCVIvyFHHrOaJ/ttbJWtz0mianxi+637mPhrXxcgvJQ9fq0fGnHqedrV7mIEVHaV38jf0aNZD1l/c1ggkTLhJ985xGGAY4kSislwmj3S+PA0biX70D1Om2N7wLDLeUtbyBdyTtEQI6zBdB1iwSUQoD3C2w2GxweHuDhRx7GcrFEP6SoXk8enhycYwQCNn2GskxYOgdHwI2HH8alyw/h5PQBMAwgMPp+izD02KzPAAAL73HjxlWEOGAIPQDGlctX8I4n3o6lXyKSBwdOQCIwFssFeNjAu5TKiZAu9nPksFyusNlsUrxljrlkSuedxCgawCkuF1mIRwaHZGxF1AdoK6CEKUCvvjdKIdHU5/NDAhwT0xJRkuCOAHLonMcmC1bvXDI4lCITpTQMQ+pnADin4JUdBo6ESHV/9vf304W7YUgpPuV7oFpkjCT4iAjOJ2M37VpJeMg0FJAobRFTZHiicqEmA2ByORylYZioM1LWW9oxI3pgiBHUJwEyeAffp3TjpNJUVgvbUbo0OM93KUraWkFbYsyVIE27kGIEjYI87bKquHeqw4kKX9DY5q7QP2uUkXPlPgX9rDgegHoHovAbc5HOctZB0spaOWcFYkuhitHcEp42y1bTgBHrGynFPWeQIlSoge/Mjl/d6fq8lzxnlH1lfFAyJiTRiVXKlh/LHBjNrPtHQEmMkkan+lfGjkIH6QMZ/pN3HCfgFQilryn8NsIhpdunmORH7DzWYHSXD/GBz3wa3/ODn8bq0gHIJRmAAVjfPUJ/9y5efuZbOHrjDt545SbWR8eImz6BcwbQMzxGfiainLo7wlG1VACO6dkMepLBm4BQ5wgcA0jomB1EkSMosggkpHujBkVbhvdpHWnjZwROQkUBtdpgsfxIEOSe5mjKz7sMaV2X5o0WWE5jaSes2GXI2PuAqkQ7andf6w4NtFvrVd6xbVs+k4vSnZd6sv5CQrhWlossYZfOnkhIT9KrVChetWGAv9QpMs+GW2qaJlky0qJVdkUraP0h38lZ5cQTAOd0/3DJIA+h4WSambvST0pOp9FAlEEkOafPfMmcW0OkkiMNftSfOdKAn8uOgIDblhyzdUzCI9Xn091ErmRpTZsUnigOi6otSuubaIygoHx5+7jLUc9Zi6dtiGjiUFbMRlV9ds5ETBCPsjeGnLzL13VXuI13R7noPlqZ0qK53sqS1TKuunb9Wq6JfNE8LxgWiACNjmQUP8fcDmJIuI0IRKKvpR+jo681vjdb3vIGkvbCkFqcSY4mhuj7dObo4Ycfzm9x9S8tkiELJYAc4F2+jJSBw8MDbDYb9P0ZNptNafvSpUvo+x5XH7oC5z1Oz87SHUbOYbVa4fKVy1juLRHiKBicjp+MEc4nAS7fL5dLAHXoH1ADrJgTCmjFNB5qG9PT6uQQ4o0DWhY3V89JSW2igDrKBIoxjheuuuShdd5h0XWlb9KHOYW9XC7BzNWB41YYz3a7zbHoHs5N+6oBpFa849mBKBxRFFs9vjrTXaEzoVrwtUEwPbxtvSfee5weH2PBSbnJIcMWaKjoM1aMVtH9deRKQgUtvFP/U0L2cedt9LoLAJHfq/h1JQ9bAmdOqVnjSD/PnDzyQB32OVe3GKl6u90aIhbcjHWM/dd1tgBia0wWuOixtp47DzDYdqSekL3cEyMHKEbvHPjRZzNabVoes4BjDnjrT8qaoBT6a0MdAqW0ydLnJHOT1zDEfN2Ad4idx7Ds8K7v/hA+9fmncPnxR0HegxzBMaE/OcX63jFuPvcCbr30Io5u38bZgxNsz9ZYb9bonM9nYIwhq2lmhkKVHTp/TkbPiaXhXCjTnBK2bchntQd7Gsa1qy+yrkfgPC3aMLGAvtWn80BTq35mzoJpCmLle8tzE3mqntW6S/pcnV1sdUT6yPU4dF9I6Q+7rlrj0s+2wunmSpm7Rp26TdFtk/PHRp7pOZjj09aY5uZY85A2Wq2zxRZ5tpWEQSeYaI1FnGkXpfkuWlc88ybe0+vM0qFuAMWo0nxHNDXU9c9d5z3l/dSPyYigjTQ91/rYhfRD5J0kJNBzXXQj757Li5RJ/9Eee7gAT7bnO2KzUYnNUiMzC7zW+9JWXe+bN4DOK295A0nSY1cAAcCi60COsNkM8L7Lu0iHKJ7EbBjFKIAe8NkNyRyLocMx4qGHruDk5AEWC5fqwAggrly5gtVyidPTBwgxIjCnHSCO6JbL/Fy6nLXrutzXdC6DnIf3taC052VaRpBY5oV5qFZcIrDkIkogMd0uZi60M8I63WwszVDZtfKgMUkFM2IYU1uHEODdGFZn6w0zysiCZy1A0i7WeL7GAgIAKU272R2TBAHSF/2M9pbpn845xFArg7q/o8CbeE0DJ695BpfyuZyVANWLfxdwnytFYPNoJE2A0QxNy3ia48IYGtH6rvG37XtLeThHCIGbvEeGd4Um2jM4p7Rbyiw26p+lwQw4mSjOHIqi264A3TlrSrenaaTXowYEjqahExqoaFCpxzmngC2Pyne7DDkBgE71M6izaVE8/JzC7RxTlhUegycEBwwLjxvvfAc+8aOfxxMfeC9o0YHIwRMhbHsc3bqDzb37uPXCy3jhmWfhYsDm5ASbszOcnZ4CROg5onOuXPRs6Zd7PKFna+5bOyZ27Z83n3q+LPhvPdeae0v/OQCo5UurDanPOooseJ6sE7M+dwHxsc9q9wFTB0Jr7C2a6vHsND4bvGnrsyFwMcYUqtvgBd2HVj+1jtg1RyKvQwgpIHqmn1q2z/GGlm1C03meqR0gut3WDpYOj2y90yq7eNk6Ee33+uyrXSPpIQAcmxB3p55hLs5KXf9c/+fmW+vZIQSQm2ao27Vmd/FRqx/mk4lcEh6Svk3WqamnNuSmToc/jWLHO9JmvKbDjm/XfDADp2enWeZAwIW8OdeJiXwd6//jG4Nz5S1vILWAAZDOroAk1jfdHi2XxQIAc0DMOzvD0GMYevhlMqqGfkiZ6Ryh8x0ef+JRXLlyGcxpV0bO1RweHmK5XOLk5AG2fY8hBqw362TAMGO1twIQ05kk74phxCz3cKQ4d6mvFtjtVNbMrFwUmZEVCJo8i1qZaLoVrw9QDu5KHaPQTV7bfgiIbqS3pHDUymlcuLXw7vt+BFYxwjGKAWoVnjWqKsXlauFgt9zrPgiFCCHW9NBx31ZoyfdczhkkvrFCXyvBcQs8h4YghZyFEFSYXky7OMpgkx2vJCwH4AJKrAYeaTt/F+DQ9C0HJl2dnUzTXPOHVbC63ppHxnf0762QmxZwkecrAEs5Xaw5xqqBru0PkBWL8WYCNWCwOzBzwLOsI6o/s+Nr0bw1zkroqzVrwaNOU25pO1enbtvy9dRpcL6iIUrbMHNhUx0DjimF0UUC2KEHEMlh3QGHj17Hxz/9ffiu7/sk3P4SQ+cwEEDbgPX9B9jcO8arz76A2y++gge372L94AQnpw/SHXCcd4q8nC+yMknTejwHpNekPN+aD00/m8xhFxBqAdl6PdalBbBbv5PiL/19qz/2fV1ErulinQOEqb5sgWjLSyn8uM7ipeV0y/Bp0dLKjjlZwkCzn3aMei60Q2xuTlpGo+YZPXYrw+z3UCHLeidAxjPHE5pu0xCtqREOoIR/t8bRmr9dc2H7o5+zRqeMxdap59PS1AL7EkniWjSc9kHTgM1Y7S6YyOaWs0rLgzmc2DIuW3JX3pcd3ZYztyVD9DM2hb2WPUKrMudEOcR3es2CvRtqjqZ2zC39UPo9Y3xYunvvdzoGi47glIHWkUvn1JGwkc1KMbdGNd1GuqIatcUBc9hiV3nLG0gaVI/hROlsBuJoIBGlSxE32w1CqBdKSq0tB87SjdPsKSVDyLsWIQyQg6rL5bIcWj45OcHZ+gwxBmz7LfowwDuHbtEhlEQKASEOYJazUg5dl87MyA6WnKMSgaJ3jPROkj4sWxIYzDBFtVgpxba2Frf83gLEMUYMOcTDkS8XgjJS7G6n3nN5bN54v8XAGAuX3SddWkrNboHLc1q4iNLRYQBlsRh6CK1jjCUFpeYFobfQzCq0VO8YE1z1Ia9gZkbgAYEc4MY5T+w1FZZ6/FbIiVCyC7/ME2pFapWorksr8OXeqqLz+FyEPnhu29N/V7w0I+gSzdu7R5qmk3HFCHbjZ9XuhVLik/4RiqEp89L3fbkPSxulQG0wzHnFxYkgbVpFcx4IqcYl76Y/JuPYVSwQuagyOG+dzSl0EFXnpzQI8DHCM0BMCCBsCQirJeLeEh/4/g/jez/3Q7h8/RqYCAGAC0DYbPHg1m0c33oDzz/9Tbz2wkuIZ1tszs4ABkgBAu99cTgSULzIGlSO4596/HcZPBYo2efsGgLaRpGmlV1rLXq2AGYLfM+B+1abrXFLfwVYVeA7MiKm9VuemrTfAKdEdbinBlKtOuXdlrOkogOV/+2koaal1AujD/S7dse1RVcLKHf1QX+ux2VLC+RqvTJX6vmdhnzOyY0WX83xU4t3WuOcWx/n1U+UHYyYhozN8VoF7Bt1F34iAjdIoA2Q1ru2f/bvOZmqZUpLrug6RloCQPv58/RFDLUhXWWbnZxprB1wwNQZZrFRkeXeI4TROLOGfgBP6hA9GsKQkgApOcOcXFpy9KLjDsN2Czn3ZsdezRFhEmI3Yh1qJpC6iA6cK295A0kTUf7FmBMXcMweaBYski+2Shd8+i4ZT8tuCVBacEMIKaSMHGII6HxKIS4Tslwu006Uc1iv11iv14iRMYSAbrFIYXYhlB2Sbb+B910OqWOAGOQ45e8PU8/NGG8+FW4CrGJmwCjnjM4RtLmWCtDX4XfzNC2XrXEERWDos1EWQj6vNR7OH0KAI4LvPCjUuxLVTgtR5cncNbfjuNvKSuZ7TikQycWUYz9EcLZ2QKptdxVGJ+F4aUES0OpP5CLQHRGWiyUWlLK4UAw5DJFKPyQ0pu97BEyVbEtxT3Z6HE3GPUfL9XoN7z0W+fyXbSPXCKIa8GngcxFhZEGG9jy3AIhWzDInIUo2u7FOnQ1xFuAzEDlU/deK2AJa3aYdQ6mXR0N0F4hq9cuub1nDu8DIRcsuw+zN1DcHBMFcvBJCJ+dyAgbnEDggEhCWC2xXS1z/9nfjkz/2I3j0vU8AjjF4DwQGtgFnt49w//U7uP3iC3jxm8/g+M499GdrcGQERPjFImebS333+SSvGElpUG+OHnPhUq15rMM7284a/VkrfGrOsHkzZboeL1akXTFY7PsVbahtXM3yAdA8OqDpqGkpsl4DptY7LbCZXsAkmc0u2oo+I45J2zf61ALDc+tmTo/YMicHCgaZeV/z4YX5hVAuYD2PN1pzq/tb/kZKgmHPD9m+Wp3TAuW7+qJl9nlzeV6ZtDdjAFljpuIJM8cXMVikThu1ottqgXUigJyHTmvd0nvyt8iV5Gwdnx+GoUqFT67t1LF6u9WGnhMg66YaclZ0AWo9putwRIiNdtLxlBS5VfSr9As6+cq83LRzSKBKBs3R8c2U/wYMpHTGJwGP0fsNABwjtv0mH4gm3L59G0SERT635Cl58perJeLQIzBjf5UudnUERJLscgwixnK5Qtd1CCHg9PQUp6enhdG870AAFosliAbcvXMX67M1mB0O9n06bAwAMQAxGxUxZU/yLmV/YgAxDJDFpA2+AlAzCD89PS2GCWPek1nClvJOmOxcSBIHIKXohqSqzuewBMgBaXGGIQDsEGJI33uPxXIB128Rojj9xHvHJeX3Ip/DyvZ/SmhCKcRI1iTnf7sAECkVrRdTy6vZEnbagJI0sNojI3/Ls1HytzQEvHS1KHkBApGLN8U7QnfpAJduPIx7R/cRX78Fx2PoXrZC4LxHkDNrXVf1fxSWcdxx0EilIdA0xcRbA6TzWQ8ePMDBwUG6n8ko9CKsUINiPWYrkFshT9WcmbmwytECjKpPOVwmhsRvLtNZhLLwfAsA6PUgTDYMQ0k2YL1oFsxYham9dXMKfpdwntAFM4CzQbvzynlK5rxCyWdTsvXlxpMRlOlMmRe98ymjJpIBS8sFNgRcevxRfPJzn8F7PvrdwMEKkQYQM2I/IJyscffFm7j5zedx9Not3Ll5E8Nmg+FsXTyFRK7IAO88skWaMmJxveNB1AqlrWmgQUyTtzitQfko1dOakVTs7pH+Kb/rHYsJjXcYUun3aX1zxtmuespPQrkQNhRaAVkgjEmBSl25XpH/QEUzohSmLGtefyc7s3aNA7VTrNVXO48Vvzd4vwWIKoOGx939OaNIz4mla0teJepQ+b82GWmsuPyUKzjmjCBLVzuO1vyWzxVLXHRs58kFjTHs+xUNaMoX9llbdPhgjBGsQuz03AuP5hbrZUjTsRW9CMbQMMKn68sYfCy7M/VuJlRYfUv22siFGhOQWsd5vRW2mBppLYyisYxgEL1bI79771PWW4kwYi74yxqhdt6kDqJpUpfW7zHGktBJ03Dsb53Ft24b6LdbeE/VewDKPZhzpdX/lKaY83UsyZnWwowX0ZtS2hL7T1B+8Rd/EZ/4xCdw+fJlPProo/jzf/7P4+mnn5508Od//ufxtre9Dfv7+3jqqafw5S9/uXpms9ngp37qp/Dwww/j8PAQP/7jP46XXnrpTfcnZTkbEGMyLEARfdhg059hvT3FdrvBMPQAM5755jfB/ZAMIwK6/I+HHnvLBfYWHRyAjggdEVZLj+WCcOvmy7h8sA8AxeMv2exCCOh8B+86EHksFysc7B9is+nx1a8+jWXXgYcAHhJo4BBAHOEpAQ/vHJaLDt4THAVsNg9AGKCtdi2wdSghkLZDo1JQLUEmyk8XDfxjDMVAiZwYMOXNRUqb6imDlVyvIwwuGZAhqIQRJGcV0vvk810juV5koRY4lu3xSCiprVkAmVpI+iyUpoUeq4zFnuPS4YpStIDYpTxIxZdPv0MxbCkjrZxEG6AFFuzQRcYxB9z4xPfgPZ//DE4cECilZyef74VBEoJ+0Y3eeTM2uTi1NEyk0qvvCOvBKOAEwKxWKywWiwl/SFzxHC0sb8nvc4K+ZQDZ74D6suLWeQYC5wP6aa1QjECMJQ14q05mTndRhbSLDGZ4cvCKf+cUoF07lZGF2vvWalu/b0N/9Xvlp/qnQZamhy4TQIh6R9SujUrJqbmojHAAHSjdx+YcqMu7q2Jg5ztYfCRQABwtwPAIqC4rlAABAABJREFU3QrDwSV85Ec/jx//m/89vv37vge079EhYLUNcPdOcP9bL+Jb//F38a3//Ht4+Stfw+vPPof+7Az9dotIAHySEZ1zWCDtGhEzHPLayjIk7ZImJ1WSi/J7UtA2dMkCXjvH4/PpXxJNDkQeyCtZ84Gmmy56l6TF83a+9feWZ6S+Fn9pud8Oux7/iV5IMsIVWREy4EqhBwyEWIxj5HXlGsaR/l232wL3ejytnzKOOaC4S/60DBdNZ83jc3SUfzokUNO+Dfzz2YeYHVsMyJ1pjqikaU6yJpHX05jy2Nar+6LHZ/vXMrbn6ttlsOwylnadzZnTK7pNrWfn+qbra/Uz7V5wEYR5VY8OU0OT0q9US0n3r2mn9Zn+vTxDPksbB3D6O+XtmmbAs3/Pyf4EczwcdblehzS06Xuahq26NZ3suEvbme8IiQfHn+05I6IS+WQ/nystfaf7Lriytc7yX9j22/z3GHY3/3x6RxfNW2BOuoozHmBOY96BRc4rf+o7SL/5m7+Jn/iJn8AnPvEJDMOAn/u5n8MXvvAFfOUrXykZ3v7+3//7+Af/4B/gV37lV/D+978ff+/v/T386I/+KJ5++mlcvnwZAPDTP/3T+PVf/3X883/+z3Hjxg387M/+LP7cn/tz+OIXvzg5aLqrxBjw4MEGfb8FI8L7tEDIe/i8QPxigaHv8eUvfQmvvPoKrl6/WsCicw63bt7C9evXEWKE9yn86M69e/CO8MpLL+JrX/saAGBvbw8nJydYr9cVc0dOOwd6Z8I5h1/7tV/D4cEB3ve+94PyxarL1QrrzRbMjLP1GsvlApFjNuS2WK83sEwiC5+Zc6iPy0o9Ce8YI4YhYBgCui4xS5W6GUihcKzTgdeC0oZD6JC/mBVAQBpn0gUppG6RGZezsUeUzm51XX1WRO/YsPpcFEnpS95GnShioCwG3U+9wKwSLWOKtWIUINFKv9ry6giNbPjbVLGkuQn9FuRRklucnZ0lmifrr6Kx1KsTQUzOWIhhpL2WFyi6f7I7lX5iMo5x3G2h2BpvpSBM2zVIfXNeHd13a0AVgJ/9ua16tTdf+ljOtJzTZmt9EFHaOVX9scJ4F6Cw39mxtMBDC7icpwBsmNjcGHX9A3O50JUZoJjWmWNgACN6AsFhG4EuOzyic3jsQ+/D9/3ZL+DGozcQVl0yrBgYzjbY3LmLO6++hme/8QxeeuY5UB8QN1vwECDhihPAwzzhbT1+nbSmZRBaWuux6s/Hn4Dm2hqk1u3bOZgDFa0dES1brTE1VyxwH2kwz28VbzEDbtzhFQeJc66A94usYW3QgMd3JOS41WdpR+S9BtH2OTtXFb8zrBqctKVpkt6PkyyW1iFmx6f7Z+cOQHGUzRU7Bl20DLJ91ZkHq/aImnOe7uh5c7vEmk67zh3bcdj+yHc64uI8A2oyN5gH7+khVBe/5y2RicwrczkzVtunliyXcy7y3BNPPIHXX38d6/Uai4Wv+4G2wW2N2tg4Z2R/13xQYRu1PmojcOoQkL/1v4pPGotGH6uo1vSOMpULbbxFVLeo++Nd4tn0ucjc80uLxwRHXKS/b2aN/KkbSL/xG79R/f3Lv/zLePTRR/HFL34Rn/nMZ8DM+Ef/6B/h537u5/AX/sJfAAD803/6T/HYY4/h137t1/DX//pfx9HREf7JP/kn+Gf/7J/hR37kRwAAv/qrv4onn3wS//bf/lv82I/92IX7c3a2hvcOq9USi2VXBM8QY7okLY7C786dO/jVX/1V/PW/8Tfgu4ghbLFcLnDt+o0EXCPjbHsGZsb+3j4ePDjGP/t//So2mx6+63D/wYMU2paVgFjkIpSlbRFGb7zxBv7f/9Nv4Gc/9J3YbJNRRM6n7HbM2ZtP8K7D0J/i9OxsdpwFMJJkw/PJKOoHRCU4RYjpOHABH9p71xLa+meQM0Y0hhZ4RxhCMkqTETbAK+CivQrAKOxtOkth9KmBMS5wWcga7DG4XESn69f16EQN43mW2sM6d+ZEl5iTFWgjrqSTnxgVejHn92PE3v4eOufB/YAFORBCZSDJcy0Bqv8W4auNjnNL1kha2BRQwwC5MT1wbYxK+FFbgNvx7uyCqlPT+M0IMFm7Ok6eY0DMOx425AcYlYxdE9wwkSYKRvGQGuzsAewW0LP9aIGglmE0NtcGjruUmlaA5xUNdCKlf7k1ePLZuGcsOBtOziN0Hv3C46FHH8UnPvuDePIjH4I7XCHmjOybkzPQNuD2qzfxypefxqvPv4j7R0fpUuh+SJk6ncsXIk7P+bSMQU3HCYA2Y5Z7zzQAmayjKgykTXMrG1uAsgUm5/qnw3bPM7JafGRljAUPlXNJnd+TbG62DoZcujh/wakdVxqDK1c3zPGt7qMGfNb5ZsfYogHbUCv1jq1vpAUX41vL+9Y86vFJHXq9nidnW7LfjmOOXwBMwhL12GIckzaNdxnOdmVn/6xsM09l4LpbFllaii68iDwvdTKXsDDbr9yTyhDRtJsN5TbN2jWh/677WK+jvu+xXq+TUR9jobV+Rr/fNGzhJ3S5CM/pvlbRQcYQmaOrfmZu/mQNalnU4s0LqI5S5zju6XdA3vmhdNYo4Y6LVS5o4c1gBNv2m3n3Tz3EzpajoyMAwPXr1wEAzz77LG7evIkvfOEL5ZnVaoXPfvaz+J3f+R0AwBe/+EX0fV8987a3vQ3f9V3fVZ6xZbPZ4P79+9U/ALh06RAHBwdYLlNWrmEYErjNuyXaW8kAvv6Nb+L3fv8PsN0OAAjL5QrL5R4YySu9XO4hhIjNeouvP/0NPHhwgq5b4MHxCc5Ozypr3AJW/bukA//KV76Cr371awAchhDRDwEhJENpf/8QRA7b7RZ7e3tw1CHFUddeagviUlgd52x8hJQ6mrBeb7HZbLDZbMqCkBAzRl0XoGNoUf6WdmNMiRjEGJDwizlLPYRQLqJN79RCRId6tUBRibOdMT4cuco40vTWXhd7Mzg1PKbyT9/dMFEQXAvGViY53T9mLuEBlIVC6Ad0zqfN/HQMqyk8bYibnp8WqJf354Bg+qOeoyoxBU09fJpmes6k2G1+274O89R0kc5IfVZpWGEtfNACbxXvNTyiuq+6flE+Ra/OAJo5sE6o32vNla5D3m+F7ehxWjCgdzV1HXMhLK25b/XNGg0Vrb1LIbTeocuXCTOAHoylX2CFBUAe7qFDfPePfAY//hP/PZ78/u8BX15hoJS58ez+MY5eeR1f+8+/h+f/6Gt45RvP4vSNu6DNAN70KSySkkGrk53MgbepIcAVjXTRoMLOuzPt6frnAH56v46rt2vCzs0c8G/JHTsvMr5pH6b3/LTmvjXvdregalvtEFu+kGftrq33vlyPIfW31vocWLP8DqDaXZrMremX7aP8tAaN7ct5O6n62RafMHM559Hqh52bypmn+jgvF9sOEH0WVepohSO36DsB7kbW6bmf+9eilZUru+bHtpX/SOGHWdfZCJHJ86pY+o/9GkF90UOGNnqnetx9GtdxjBGvv/76KIdN260+ad4q/VIh7fJ5S6cUbKUMFtvn9OzUIWrr0u231pjlcbvOK9rOjFnoZdssv5t3yhxQem7oh/w8lfPt+lhGi05VWB0wGb8Vl7aOBgvNlv+qSRqYGT/zMz+DH/zBH8R3fdd3AQBu3rwJAHjssceqZx977DE8//zz5Znlcolr165NnpH3bfnFX/xF/N2/+3dn+6HvS+HI6Lwf44KJCtUiR/zBH/4hPvih78BytcJ2GAAGhjBgGALCsEWMDO87/If/8L9hGCKcS+CqH3p0XYfFYlExAzOX7HXMjG3eLUqfA6+9/gbe974I3y0BpLCr9WYD77qcaIGwWHTZs5cOpjMlY++VV17Bo48+ahhIFmMykoYhIOQU3n0/lJ2tamHEOpQgvT8yoV7YlYeZU5xn5vECOpaLJZzzSUhlhifkg94sc9IWnHZhVAoHKHOllYssVH3wsbxjBNYuQNTyLkk7ti/MXIWSjEIuJnPaCqJSb0qscHL/PmLfJ89rzHe7uKlXSwvCicJhTFa8FrZzYCzdJzMdXwhh4uWqgIqjqj1LZ/v7LuA2/o3J9636d7VXtZkeLEBq9jmrsNOXsyBAfmpeijE2Qy6A6UWDuu25HSctN2zYpu2XVpytPuvnNIhp1WdBWfk8x64LAA4cQZ1H8A5nDnDdEu/6rg/iI5/7DK4++QS2HWHrHRaR4bcDjt64g+3RA3zrS1/D8994BvFsA8ohxIkOyYHj5MwcprzcAhFzxpCsSaJ691jes+C5OeYdgEOoIvOjZaS8Y0HtRIHvKO0+tQGzjF+Hyuk+zxklRFSSLWggysyIiCVUeTIu1PyinUIpa9aUlucVcdBZ2lgwZ+lD+QycjKEVgmT74tw0nHd+jqelJVehONYaIC1Dp7xn5saC3V19sEB/UvfMeHTd5+lDABXwlfVmnZjV86ZNKydb+rKsZaIqXXSla2ke9M8abeo3K4N30Wisf/y7NuTq2ufqmfAg13RqyQW7FnXd075jNjux7q9eWzIua2zsoqNu0M6z3v2dPi79zcDQ9g8JRkiWxLI1xHk3VK0HG3KOGXpz/cEsbd5M+a9qIP3kT/4k/vAP/xC//du/PfnOdvSiwmHumb/zd/4OfuZnfqb8ff/+fTz55JPVO2nBMkIOAWvVRCBcuXIVznXYbHow0tmcvu8Rh4gwpMOqQwz46Ec/hme+8Y3CZHrbG5CzPwP6YSh3EglQ8d5jsVjgxsOP4f0f+CDW2x7LvX30Q8CmH5LiGAL291bw3qHvh8IsaQFFrFYrPPLII3DOYRgGnJ6eolssQeTR9xuEkAy3YRsAJnTdAs4t0HV+Ah70vFgwbedML9oYYz4w7XIyjJRRLRmJhajwXZeSUDCPWcbijKBzAIfRYBGB4pwrQE0Edm3QTGd01+JP77bfk2IVjxThpZbhZGurgAmyxyVGPHLj4RTi2Q9wmApmrVhsPwpPkyv3R8z1u6msMcoZO9/SPw3Wi4EcUS7k1WNrjVf3w3qg9DM6VHFO0c+d75pRcYimn7q8WSFp+2yVm45Xbz1rizXCmyMwIEDXJXx/EZByblGg1s4LgHKw14sh3nUYPGHwwEPvegc+8fnP4p0feD/QeQQieHbw64Dh6BgP3riNmy+8hFeffQEPbt+FX/cIZ1u4bvT0ifq04EEbdHO0aoEIy0O79IwOs9X8J3/r9VG/P+2HljO6DnvWs9X/3WMDiMycNIww/fmu+sbfacJbRFSdY5n2rwY71ZyZ9ubrqOfA7qqUqyXy9zZ1v3MuEwXNvtixml5By+wWb7RKi87jHLSfb8lAC/Q0r8/JCxl7MSRmwr+TAzLpMssH5/GD9GfsnzyDiXHyZsouGajbTX8k7GV3RWXeI0932/+kZVd9rTNZSR7xrIPQlrk+VrqjwnTTtdI6o5gbRuTppeit8dX11deazI3DyhTtCLDPOCIw1Q6por+MfaR5U9dHpT4u0VzNnTP6k829lusXKf/VDKSf+qmfwr/6V/8Kv/Vbv4V3vOMd5fPHH38cQNoleuKJJ8rnr7/+etlVevzxx7HdbnH37t1qF+n111/Hpz/96WZ7q9UKq9Vq8jkjxVuDFTBBUgQy7SLgvfM4vHQZH/3ej6OPDBcCNqcnWC6X2PYD1qdrePJYbzdYdB2+/QMfxMOPPY6jO3ewXK1wQMDJyQk2m026u0YrR0oGStf5IvSXqxX+D3/2z6JbLrEdBtw7OsJqtcJytQKI4BdLRDiEPiCEHs55xBiS9y8Q+m2P5XKJvu8Tw2UPT4xpETOAPgzohz7TAaV9vSitICUaUzSO8ei1Ui7GHiWLvgBqULr7BABFBoVQspNIusl0MG/qiZffdZafMo+mXf2vAqSYCj1dv/5ZhED5n/Z6YOKF0O0BgCfCeGlq5qws6OVES3o+79DAFSNxy4zh+Bgu9HjjlZdSalGOYK49oBOwnQWLnGuLM0JDGyZpZ21cBwkkEMj5dHcGMKZVp9EI0vRqlZbxyOo7/RkzCugd3UWjkdYyiixorp5ToE0OeorEZSAhe3mOxhZFLLcMCr0DuQtk2P55ciVWOY0zlnuadNGgsAWm7XPz63N65qnwZiEqQfZjKlCViANWu16eAQ+Xsk66lEkxpW4FuggsODlABu/QdwR3/Qo++dlP472f+B74S3voQfBMWEZgc+8Yx7du4/VnX8Dz3/wmXnv5VVCIKQNdTBukoujgKGV5FF6IebpoHJNzaU1pw0SeGQ2dGvBbcDVqaFZ/c54r61yRuuq51r/LO9rA0vOlL1J2TjzmYwjn2FYjdFdNVFkb6lPKcj1lr+Q0w5wdPTQaAFz4YKZw4nXOshugvOappO0dx5t5hdxIpYYRIGvL8ub8GPUcjs5DLdMtjaWtjNwTjSmDvguAp11Gw1h3+/uWEXzBoxNV3SNfTyMSdu06y2fyr6aLRMlk3iWAkPVF4eW8i+OS0xdioJC42KIaEgE03XFgRo4MmeoFLa9axoc1xivaxCQTnDgQs1gQeZbUa5Heef27yoDTmKGch2Jk5yPKnXm7+JGzUGC05W7kOEkiNVeq8do2MO7uSNIunZjDGtC636Nc40KbDA1SdlFGSSWv15DoXqK6f3YXvD1nY5KM8h6PVwSIfC6SjXLW7ZK6XWER9VeIWVfmz5zmX6Mfk6zipDfaFE//z99HRes/bvlTP4PEzPjJn/xJ/It/8S/w7/7dv8O73/3u6vt3v/vdePzxx/Fv/s2/KZ9tt1v85m/+ZjF+Pv7xj2OxWFTPvPrqq/jSl740ayDt6BGSYhwZ2jmfPi7nPgDvO/iuw//5r/xlfPv7PoDVag9O0nPDJTDpO0QQ9g4OsTo4xP7ly/gr/5f/Kx66ej0LjgR8zs7OUnrvrsNyucRytcJyKVnxfGZmh8899Tl8//d/Ggf7h9hb7WO12sdisQKYcHa2wdHxCc42W/QhMZHL8bnM6exPDAEcxMPCcJ0Hu3RwOoIKeI4peTZC7CuQBqTF0fd9Dvsb89aP634EMNbzBeTbkCnf1UQEch2YOvRMYEqJKiSu2MsZCiVI5TupX3bdrALWh2n1ZYfVQqYpSGqBeD0W5yjvNDBAjMgBIQ6IHDLwqBVbESTM4BBTmmkieErpiJ0AU/Jw+V/OUZ74DCkkK/gFlosFVjHg6PWbadesAYClzcCxpFaXdLzklERS4yrjK2mQHZDPb5WrkQlpHigJOs71CfBK4Zh9FVIhXh9N18prZP4hg7n0u0t3geUMiym9qQjsdoz7RDja9lxKIBCIEYgz38vYINoVommJRsNPj0mLipahYs+STAw3ToopsRCX37u842nbsUp1bvxVGxjli43Pr/oVE1+mfuh+5d+F6jz+G/thYvIpp+/3Hn3n0R/s4z3f/yn8+b/5P+CDn/0huMMDuMhYBIY/3eDBq6/j5jefwdd+93fx9B/8AW69+DJ4uwU4YogDgovgjuA8wTuCAyuZk2Yv/RSu0d9x9Z3+XMBmy+gVj6HwHBfmcAlggcpP5HWrmGdSny6yRvWB+hiTbB8ziaa6YxR7hcpP3a79x4qJs/hId0xxkgXl9KGjlAHVUf5erj6veUjzHSEb9TLHoCLDigGGZCSP6cDrO2s0fUUutOjVWs/yu5aruo+y3rUjwYaHQgy7vP4II487qs/jhMb5x5bTRfO+LrvWprWQ5LldZ+laRpC0b/tn13nLwBvbYRCJhI8ARQABzAOYA4ginGOAIpwHiERiRsTYI+WhDQlfIACIIDcmJYglDF/+Tc9f2XLeLnBFg3yHY5JhocgvOQohulUukpc5t3qhciRl+Qfmshve6kvV/wa9ZZ2jpTcwNbrs3Fk9J0U7qicOB0M3y5uenJLtSr4r48KGqkoCLbvbo/vT1u+j3NWYmrPBWPQNZdkuP/M/rYs5YyAG52teRpzANK6oln4jkvlMRnUMsfyTez/F8UNAuprCYMEZNmyWP/UdpJ/4iZ/Ar/3ar+Ff/st/icuXL5czQw899BD29/dBRPjpn/5p/MIv/ALe97734X3vex9+4Rd+AQcHB/jLf/kvl2f/2l/7a/jZn/1Z3LhxA9evX8ff+lt/Cx/+8IdLVrs3U7QABDCJ3ZRY6ieeeAIf/q7vwmqxwOHBQbHq+75PgvYwwPtFUlIxot+u8a53vRMf//jH8b/8L/8zmBh7+ZJN7SUIZifEe4/lcomPfuxj6LoFlstl2jlaLsv5Je89jo9PAEoHpMktAB6w3aRdnWHIGUeQE09ABBZDYodDvkQTSCm3BxPvrUGi8w5DGGCFTAuIWYXm8uJ37JLQZcZi0eXvAYAKzcct9DHjjcxBaVctTO39CCGAYltpcAb2sryssNZGmBVG+jNRYGMd4+d6l0u3e54naQTSSUJ7cnAR2Byf4PiNO9jcf4DLPG5V63fSuHaXOeUvFLHCe66/8oze+ayFfP7MPG8NJGlDF1JCu+UV3jUmbSwWWmahCpruplg6aIAFHkGT5glAe7um4xZlY7/XY5oIcsqodmYGW2ur1X9tGNm2q/f1em543eeAayCgd4QFCItAWDGVO8j6zmGz6vD4+9+Dj/3w53Djne9AcElJLSKwJI/To2O88fKruPnsC7j5wku4ffN1xE0Pn/vRdV2RO957OIjXs71WgWkiDesw0A4SfTZCQlJGMJHob4GqpkHrDIylkf1cXzjaAtVSJvHzZj7maFCBqJl5k7937fLqfrQA2oQHZ4wB+7ztt9C6NZf2fb2W7JrR56nmZKsFdLtk78XOSbTXx9zcWNnQMoRa/SjPu+mat/qk1a6uy+qk+rNQ5mOsGxCQbnX8bFFfi/yW9+z6tDxlf2+FrLVobPlBGrc7VqWejF10O0Uex4i8IawuD8+g3g7VzKc2aCojpibLBBNpGlU7NzPLQvON5UFbLD5pYbS5MrdO7OeChVtr7jycc5FSv592Cr0X/ZbDBjFaSeethV2l6ANMefSi5U/dQPrH//gfAwCeeuqp6vNf/uVfxl/9q38VAPC3//bfxtnZGf7m3/ybuHv3Lj71qU/hX//rf13uQAKAf/gP/yG6rsNf+kt/CWdnZ/jhH/5h/Mqv/MqbugMJmCpYYdpV3tERwwQADg4OcOv1W7jxsMNqtYLzHotFh853WO7v4QwbhCgZ1wYMYQCGId1VpJSyKOzi6QU1gePpyUmVKQ1AARQhBOzt7aPr0qazc0AIhL5P7cYYcXa2BpAPTusc9hFlF0CMDTkDpUGB/tsKK71Y7YFOayBp40iY23cdooBHuXSQkqFEIqhht38zHWhkbp16UtOpeYaK5gWMHqu+HNZ6Da2SY64FiBYSWgja/s2B0Rhz5rohYuUWuPdqApMuJO9MNAZS/qOWyo1i56YYVlTTbPQouUnojhzytnxRtZNVREugi4E06QeLs6BNI+FbOx7m8T4Q/V7iERrbPEepaPoIuBUAVsuTdljD3Jzqfs+B39TV9t1IlhYaIFnvuVbALQNpHMF8mVMS4o/1kdBFAjmHrQfWHXD4tsfwPZ//LL7tIx9Cv+exphRy5/sIPj7BnTdu45UXXsSz33gGd1+7heFsk0LqHFWXDttQNOmFNeQ06JPftYy0Y9fGrmTlrNox/G/nSvo3R69dhov+bHzOQqhpGvJdxpHQ6qLAYFxb80ZSCxRrPtO8FcHlHJKVhbZPQv9kWI0gsGVAtEKHbJIJ5no3Ts91y6FieaXQREIPJ3RKAKxF2zcDAGXdixzd9Z6lXwgBDmlXX4cstQB2y/Gj+95ydrbkqAXhll7zA0Xx/MuFpqon0Kh/Dtzb9m3Rn9tMsOO7KJhBl7JOGgZG/TPVEWLKngYaIyHmip1TMbTn3JW6/ZYjTfdp7l1LD1vmwuCa4dZI9Jzed0nFqSh1zvHvHO/N4UZLhxYmkXcksiIlHxsNVo4RnGVJS/dqfGJlDLPRiSJ3yMiHN2Fr/akbSOcJdCB19ud//ufx8z//87PP7O3t4Zd+6ZfwS7/0S3+i/ly5cgWHh4fw3pczSl3XYeG7Kl0mEeV04MtiMG03GxCAhe8wDAOGvsemH7AdejBSikJQCqk7Pj7GwaXDkqih7HhQMo68UgJiBO3t7WF9dgbnF1XmsO12mxYD+ZTcALGE0i0WHfphDYDw3HPP4f7xfbzv/e/HYrXMI+ZkxIWY+8Doc9IHrWi0wZgAS3vrX8Yhu2lN8MijUkoKIxmQactTFk0dzxliKOB04mV1gJzq0HG5SYHarCxTga+Viw1H0otcwiBs3PtY/5i9S4pdnC0goH+fKCgi9CGAmLA9PcODe0fwuZ0hMNiEgO1S4i1hWrUn4XQKDKknAdS7EkB9T1YLVNmxzoLuCQit+2j7PFesYKy+U2B0TglZQSq7qzLG+pB+MmYEaLfCQ6zCaykAeV6Us+6H0Ne+p+fSAgQ77pbxVJSMqc/yp6UtEaFjgg8pPJSXHg8oID50gO/8oU/hw5/+NBb7+xiWDrwgYAiIZ2uc3XuAZ37vj/DSM9/C0e072JytEx/HdIbQ5bWtQ6NKn1V8vAbEWjbpneUWr1me1rStw2/TvNo51HPV2qGbA3yp3zWw1fSUOH/7uf7dznutvBu7K0C+yHvqqW8BK2ZGCTsztNe816Krrb81/hYtYxggxkKrWLDZAvYtI2GuDxocVmM3BmpNbxSAaw12zXOtObCltBunzjvdrl7nFd1Qz4mWC/KcDmfU89Yqmm6WJprWE73f4IlSpxjLIKTwvC6/K66wkX6t3W09brve9JmzlizTelpetWNKhgCDuJ5LXSQMvTjSGCC/e5fUfmrluh67fK/HYt+xz2p+07tTQkfpm93dkefk3WEYxrsXqzbTmOV97RhNj7SNyQnt1Lt2rC1+nMxNQxfzOJmIMWC7HY99EBGQdbLQxm6IWBlR7xArOuShxJiOJsyFtZ5X/qtmsfv/h/Lkk0+WsDW9MDnUCxpIhtPjjz+OGAjL5RK02kPf9+h8Sovde4/T9QbOewxDj+VqibDZ4B3veEe6SKxzWCwW2NvbKwJvu92mg/BGoa9WKzz22GM4vHQZq/2D6ixO13U4ODgAUQciBnNKzLDdhrLIYwx4+9vfjhubh7FarRDy2aEh36Mk3qmUtjk9D6TzJva+IKGD/GwxkBbUE9AuEcGUDMF0s0+Pfhiw5OSl51AndWjF3hYjca4vnPuPltduXJwiNPRdGnbx2xCBi3iXnXPlfJQUK5ws7axBFR1SvCwcVosFOt+BgHRH1JxTj0aQZ/vWmqu6f+0qRWHYeW3Rqvn+BYVMC6Drdlt12rltKf5oxt7qd2sumWseqQAIU0mzLs4TW5/dDWiBlqovVKtTC4AsXayi1O9ZAGZ3UcnwSWseW/0lAM51WHtge9DhsQ9+AJ/40c/i2tsfB5MH3AIdM+i0x/rkFK988xncffkmXn/2Rdy7dRsc084x5fN8IAJ1fhaMcWK+CkBautl1tQss23VQz89oKGmwrNuzYUq6Lg3k6jkav7dgoQVM5W9Nk5ZHuDbu9AzVczq3Rvu+B7O6xgK157kFWmq6AWx4qLUWNf3S59Nxl7XaCDO0MsCCLk2HFk/Myp9sH83xihiNtv+27Ardq/ohbeouzMjkij6KNm05VTtyNJ9KHS1dI+/ryIw5Op4X5izPSu9S/ZrXa92o267eN/zTjAgwdKtwgUtJJazDMde4Yx6TPBTTuYldWrRPtTbHIIat/q4lf+x4hIqtfrbwxGQkhvdb7akaBUIVXTfWMbcHNu23fm/Cv+o5HeIs8zu3Pu34hqFPTm+UpVvYak7PtowzGXeM41n9CCRHHdX1XQTfSHnLG0jL5RIHB8kAkZ2ZdDYnCZ2uSyF0oHSWJ4QA75bVrskwDNjf38eQD64v9/awWCzQbzdYLBZ429vejsPDQ2z7bTlHVN8lg5KSUYTdYrFI9xGpXYwQQroPIANx50SgDBi2axzdu4O+38Bli3h/fx/7Bwd44aUX8fTXn8bHv/d7ISFhHDnveg1wjNFQiwx2bUaXohlcA0kRuq2wMsrCyKVYwJLG3IWhuiS0MLYRClpZSPpvKUX4EsOhm/Gq1aBGPtfATLcn5yJk/Hbx66Lf1zHy9jJgmUftDdWLkojQI4KJ4X0eEzgduiakM7U71u6c4rRzWIGY+cog6bVbwHBOkBDR6I2boddcf+aASQtY2jlr9SMnbbqQ0NNtxFjTsFXsuRLdxk6ANu1pBR51ERnRAhRase1SNtX8i4ZpGB/jvKE4M+Tz4D1OXMSld70N3/9nPo+3f+A98MtFSjRBHUIfgE2Po5dfxXNf/Rpee+lFhO0W27NN6udikZLIEJWdcsL8DmNa4+2Ux7NUbPCENphteOj47G6jWX8mfdAh0i1vfDo/OT38n94fF3Fr3Pr3VghV6zP7PYBKHluPq/A4mTU4JysqQHNB8CB8aw3O1rjte7v42Y7frsPz6pc27JwBoyy0MrkFknf9XX3Ouz3pus9aVyCvQfm8lQ5ey2U9R+fJRT0fdq4vYiCML6D0VaJD0tfCR+02ddstvpszEnR/bJ3yd2t39aI865wr5/mszq9kUIPfSp93iH0tP/R7Sd7NA/uWEWC/k+91O3N0TaqgXkNFFrvda0m313L+2LG2dPtF+NR+T85BUtaI/tL0bPH0XNhteS7Xx5geKbloecsbSFev5h2WEAB0iXEY6IctHAj7qz1453D3zp2c6ADYbM9wenqKGCOuX7+OCEa3XOBsfZazfoW845OMAN8t8M53vRt92GJ9doaTkxMQp8P4jzz6GJZ7e3jxxRfT4uS0LbztN+hDj4eWHcLQgzji5OQE222Pa9eu4sGDBwA57O3toes6rM+2eHByhsUiMcUQQ/oXApgIq/0D9JHBERg2W2w3W2y2AX1ILHJy1iPGnKpReJ3yQqIxZaMUUbxyHkrup7CCnCNAXfKPeKKU3psI8B5+uQRvNiBSHkaidDapUhbGU5sengjdGCNi6NMOWOZ86b+9t6clUK3g0vW2BE6arvH+DS2opR29BWzDRKzgJSIgAJRiCJMBHAKIU9ZByqmARX8WXcS14pkYNS6DX3DKAiNtiXTgERyPJXuTmHcKwjLPZT5qcK37Yw+T132uwX4l+OUZXX2eWwKNwl4+IwmHo3LmqRrZTiGYxq1DLMs3jtLZgB27pfLuRYylSqlqaQ2ksboaDOifln80zVwOhxXjMGXwSbIlnQGUMaW8VEwEdol2HRxoYBAceqKUnOHGVXz8cz+ED3zv98AdrhAJ8Ozh+4h4doaz+/fx3NNfx8kbd/Hqc8/j+N49LLoO3jssuzSv3tVZu2IIkIs5U98lVIYgmTKLN9bRyONq/NbTHDKPlDUV005O4XtCyeZW6OESxUBqfUOA0Mh35eA8MIYrUs4OSuP6Sv1TU+oldxSS0yF/QZT6UUI1hY95ylcobcUc0pvu2SNKu3KWJhZQtcJ6gZz8K6/Z9IxyChCXLKchamO1wb8zf2sjLdGvvQ7OC2+xILBlMEzpNf6S1UX6SfXzus/nGgSqDxqg2c/rv0fdUhtA0zZqOsQyNyBGMeW5fs/qj1af7O/Mkoobmd9SxS0D2Zaq/yKzitBKcibJv3bY1a75bdFB972cRKEkhwEGB64ccrpOCb93qOVmeZb0+PPu4Y4dFOkHc8pm6zJwkagXsOwYzuw8NQyG8jvx2DJlOeCSLA8x32FFnHbgM0+RgyTSNaFkdf3TcHguU6blQ/oA43pBvd7FcWix0BzPzX1mQ/LmSmRG551K6EUlJfsuHa4dVwV7ZPnGCACSflPKp9S5C+O0ylveQAJ5gDqQc1jtpeEyGDR0wBARh4jNZo03Xr+N1cE+bt++i3sP7pdsdMuzU/T3e7xjfw/UeSy8LzLDkUcMAPkFrjx0HdvtGpcvPYTOv4HjoyMsuwWOj47wbTdu4D3f9m587RtPY9v3GMIA8oT1+gwxDLj12m3EGHFwcIDj42MsFx3u3buLyBHXrz8M3y0QQsT+4WUw99hsThFiwDYMCDHi+iMPg7oFNtsBznmEvHPUB0YfknJfb/qcb34MvxNaFKCA0Wutd7uAFHKkDYOykPzoMXYAtsMAFyP6MGDZLRCYQJGwIA8QY+CAAC4x9cA0BCRhhGkMswDUftjC+3SfkywCVkKh8vhgXLRakVnvpwZkMn75XEorxMH23Roy8rv89EhZVUIMcI6xdA6OUZIzZMiv2h0FXtPgAhIAVvf+RI6jXiuHmqSuEUxoYd5S7lPjQANd+3mbjmOdRdtOBVUU4yhLbrWD6LyDzsxY3supnVs7bnPj0IaVDqGrAAHQnDcZQ/poqjBa4VJpTaH8zCawdKqgOc1LWtG3xiKKOv1LwxcAD0oOCpezzHHWhpzzevuQ++Q7bAjo95b4wMc/io/+6Gdx8OiNlAY+RqzIIxyvcXznCDefew4vfuubeHB0jON790AhogPBB4AQ09UCeXyOxp0hn+dGsm2mro9nEvXcFDkj4wcm9E0GFQnjFmeTnn4GilwRoFxuduHUvqZtMXQpgRTncuISMdjiKHOQWTPI2koKoCwrMbgENEN4PIRiXHluAzkiQpC5yvXJ77IzpumgaSc/bax+ZOFVKoaWnOdI9M4JC0CjDBeeM+dDdLH9tudSW0V/p3fAWk4BnY3QJtyo5rm25DIAHcWBBasF6Jm+WSArgCs0oh5acoXBcOQmcyNjbBtVXIVcMY+6Swx5kQfSngXBNnR7bFOBX1BC4Yhg7hFjvWM2JyPL2EjMCi7OAUZUtJ6e/bHzaeuf7ABRWU2jEyL/lGspQALm2w4kvSvUOpOjZXHlcFOl1tdxlCucnB6lPTVVLdrNAvsk1IpTSC6+dV5yeqZRE2Vq8yibxBHRwhatuYQxfCzNhBaF8LmII6cV/l5qnsEMltb2Hf15+T7/XK5WACdMxFmHiGxpyR+bO6AYSERIl2qb3SLmfL/WFNOdV97yBpIOp0n3EDmEGECesH9pieFsg9u33sBmu8WDs1O8fusW4BzOTtd5Yj3WZ2s8+sjjYKSwtr7fIuSzKCfrU8TIWO7tIcaAEAZcuXIVZ6eniDFg4Ig3bt/Gd3/PR/DI44/i3/zbfwvnO4A8Hpyu8eIrr+LsZIODgwMwOQQG7p+cIETgZL3B3maLPfZppwiEW7dv4/Bwf0zzHYHNZgPfLbDZbBFDj2Gb7q/ptz2GIYDgsF4HhCFMBNRisUAIAev1upyDijE2L92V98SCT+GILjG075IBxqOnhfJu0AicuHhfGJzDCEclrHdFivBrGDKtxakViDZutHdVwuKkWEGa+KXO0CKKrLU9a8NatAAoCTqsQGcxMgPWZ2vg0kPjGHasW91Xe3BSl9aBylZdpa8ArJCzNKnqAyqj5CIHU/V3eu7GuUYxjhjq8lskG4JdDrtUnlAxuXaV9pZ6vpdB0aI8kwFu67CwpU09r5iMqUUP2xcbBiF17M7UOYbsWcUTCdg6wHG6/FUADIFBgbDPHmcOODtc4vp734VP/shTeOSd74BfLIAwoCOH2A9YH93D3Vdfw8vPPIdXX3oJDx4coV9vwPnCV1dAxtSJIT/tOtXFhnQWhU8JvrTOKGi5IkXmx9ZXh9kBcqWA7aPIjUm4sKbpOcBEnmnNY+tvqWcXCKnBWh1qMuEhMx4plDo56ccI/GvjRNdlx7erLZGxeuds7j1b5sYCTNOSz8kj/fsuvhNeiKaeFuBuAV1bf6FFjEk+Gfpr8DZHA91OayyaDtqxN9e/XKupk8sa0HKqRQPp01xobLV7ORnN1OiZ9m2sx37HqNe2Xa8tvioGqHGeFZqbi7pF1+mWdtHCzjeLXpoxAmyRz+ZCu3T9tj8Jh42/S/uts9KWRlQM2/NLvUama3uW741c2jVPmh563YgjLBYnkBuxALXxmaVb5YTY0fYft7zlDSRhKlEMe3t7ODg4QETE5uQUJycnuHv3LtZnZwhg3L59B4888TYE7rFcrXB6toFzHvcfnGC92WBgIAwB2/UGjoDbb9yBI8Kly1cAACEMuHfnDIvlHvp+gPfAvaMjHB0d4d3veQ+uXb+ON27fQWTCvXv34dweVvsHuHXnLpb7B7jxyKO4e/cuAghDiBgC4+7xA7z22us4Pr6Hq1cvYZHPB4Qhot+mZAjb7YBhSJlNQt+XtN4C1vvtFuv1GtvtXsk7L/QB0lmt9XpdzlBpT5+lpTYAIkdEdpnZk8EQYr60K6ZbuyMPBao5l+5bcs4nFIda+TDzePmtCuGQQnBwLi3KYRgqJWSVkV64Mv/CCxag2DCzccHXhlbpB1HZxZqAEyX0rCEnnznn4HwyuMHzAm1OWNV0G+lThb0QNZVOqc98bttqGk07lDOnP2aEJTX6PSfQlAAsikm8YlTAHxqK7rw2BCxoGlka2N1GC2h1vZpvLJjWP1vgFjyqak3zXUqmmFVqYqt55bwrwEBH6ULsgOSsOHWEw3c8jo9+/tP4to98CG7ZAeRAgUGbIWVVvH0Pf/Rfvoi7r97C6dF99EMPdClcODoAMV3MOmed2vAZTRv52/a7Wis7aJZMsimYnCuJpkkutfqizxNaY0E/bwEkURsMj8+jWUdGIBfg/3qXQPNVa2e7SQdKF6ZKRsYic4pDq214tACi/V73RXZ50kH63fNh+9kyYoAxhEavv2oOTF+0Q6EFascxTN0qdr215LzVDZXMUPJhLsxOvm/RsmUoa0CsnTV6PVg9NfY3jXOcC/1vd5kDxDXvy9hrGWD123l8IM+V383ejrTVMjA1JpAdGVsf8rdCi1zJxDg6r69tHmqPY66euTVm7xxqzedceKrm/WnIYr2DdNEikRot3bZLVlXz0ZBvVqZynocURknYbDYyqsJTrXVnadKi9065Sm0a7ypveQOp77c4OTnB9evXU2KFvsfewX7eXRlw+/ZtbDYbrDcbXH7oCjabLV69eQtdt4Dv1iWV4p17x+iHPm2JxmSIIEScPHgAjhGPPvY4QgzYrNe4/vAj6IeQzizFgBAjnn/hBbz7ve/FZ556Cv/Tb/wbxAi89PJNvP7GMVy3gHcet+/eL0rHOYdu2eGNO/dxcnKGvh+w3Z7hwdkDHF5+Lzwi+n7AZtNjO6Q2hiHg9PQUJ/fuwfklBnYYhoCeAxB6nK3PsF6vcHBwUOgjyrfve5ycnFTGUUswysKuAE9+nyl7Poa+1E0SuiJ3MuVL2rTwF4Yt55CM8B+VA0A8ZrSR3S6bwlz6JYtFFppOKy7vWG99LRxc8WS0Upwzj7tgFtwSUbVDZoG3qATJikckHv9aCM6FaWhwp0MMdN/S79MLQwGB2VMBZoWbnmciKvcojDRSY0bbIzu20VbqjBRKB87GsRNAmc0hUtCGkW/bTnzknYc1XqRoPi7jypXpBAjj/E0Bsy0WOCT+4kohWYBphXxpMxFsMne675M6gSp2vOJJBpacdpCIE88OlML63KUDfOAzn8KHfuCTWF6/jB4BLgKrQAhHJ7h3+za+8eWv4tXnXsDRG3ewJA/0PTpK5zKBmA7RZiNGQhwtiLHzK2v03AO1DaCs6SX2haWRBv6yliQcOFMIkoxkTmFrZ5A+0GzX0zifKWxJr2spIUyzQsp7cr7yPAVtwab1IFu6WbBMlHbs7T07FnDoy5K1Eab/ntup1n0t8f8GMEu5iNGl52FcV3XIaWvcc3Xa/ltDRNNqzjDQz+n3qh1DahusmsZzdGvRaYzAnba7a4205FX6LDkIiMRYmDqSbBsVPzXAbdL5NDG5WnrEft9ylAAy7LYBOweEAYk2GHGCJF8a+4kKkFMYjSQrB3QfWwC/zD1NDWtgmhm31d8WjSx+kGfL2UWeXldif99lGFh+bmGKFn1ba2OubhnHzjmuaJDjEHJSGx3WGmOE49owbI1V3mlFeljsqpbWmypveQPp7OwMq9UeiAgnJyfoug79dovFYoFbx8e4f/9+InIIeM973oO7d+/ipL8LZuDwMN1rdOnwEERrbIctumWH7WaLB8fHWJ+dYbtOdyW9653vwJPvfCe+9a1voes6XL12HTEGvP7aTSyWC9y5dwfHJyf4wAc/hKP7Z/jDP/oyju6f4NKVJcJpj67rcP/4PmIIOLx0CZcuXcKSk1U/JASJSB4npxvcuXcf1y7tI4RsJPU9QmQMIeLmq6/hpWefxXu+/X3olvtpJyn0WHYux0onxjs9PcX+/j6YuVwodnp6iqtXr1YZ+ICaKeU7rbTlv8hInsn8zhAGLPOCoRDSbhNidZO4XgRFYSvmnoYbcSUIW4rYLn4b5tIydqRUXhPUqVOtwNRKsKVwZnfhQkQEY3AooZqRGV0DVJ2neGNDqY7v7BJogPNTYW0Vru1DS+mV39MHkzbTewCUYiIa7/4AoYRYcLa/tEEAiDJmNaRkmOv50XXP9dEqN/3TZWOiRQf9bMurp5WEpVPrd/tTGxtWcU8UE49+0Ao0McPFCHIesXM4IwYO9vHOD30HPvr5z+LwbQ8jOAYzYRkAv+6xPT7D3VdexZf/4A9w88WXEdYbLOEQwxa+cxhCBMUUlugclXNKXdflGPFpP/RY7dhbCrQ801iPqraKh+r6R2Pf0lZ4HZiGurQBZfvSYkCnvQWAqdEx1rEDJDXk6tiB+lkx2Ow61PSb9fIqrGkNKBmDlpWtNTMHOlq00XRuvdeSj/b91g7sXCFQdZZ2rl9j3xKf2PFa8Dg3Vquraj3TDk8+D4C2+gmgZH1t9Uc/r/l5wrMxu1LIgciDMYaDzxkAlh67+1xHLrSek9+tTm7KtJkQu4uWti7O9xhSXnaN3cE/admlD/VOdYsfd+kC+V6uZ9m1Flv1REwNHP1ey2klutg6S1qGZOt7faH3XNHYphLnWZY5VX/1rPpdirSnd1nny3mRGe3yljeQLl26hCtXHkoXxe6tSrrvPb/C3t4e3va2J7A+u4ZLly7hnd/2LsAvcMmtAIjyAYYsiBbLJUAM33lcunQJ+/t76WxNZCyWK3jf4YnHn8Dx8X3srVa4fu0qHnroIaw3Jwixx917d/H4ZoOPffzjuHL1Bvb2DuEXK/iug/cdLl2+DALQLRaJWTqHPkQs/TIxR+cADLhz7y7i+hSd9xiGCIIDxxRu9thjj+FwtcLe/gHIL3BweAnggM4xusWY2np/f7+AVCBdzPvkk0/COVflv9fKWAu78bvklXfkECS8CymLTgjp/iYGwXcdFo6wHbaIxOAhVmBBwCFQZ9rTiyPdp5QNAxWbrXPw2yQMWiC0Fjpg0mBqAUB14Nt0KxsTA04vZAukrRfE5Qs1R2MsVPXoNq1AKu1g6hkZn2WA2557wlSRWYGk2y/CVgwZI+Qh/ZgBoOk+mnpHJx2KT3SOYmZTqqMtFI3HTrWljfpdBpIFldLPGGvDXZ61QrjQzygKTa+5HS0AFeiNje/tPJe2iMr5tZyebDr3RBgcwB0hLDyuv/vb8NEffgqPv+/bgS5lteqYgc2AcLbGG6++iq9/6St4cPs27t+8BRcAigTiCO8dgiM48iVzHIHG5AcxImbDSdPD7nZYBafnv0WfXUXXVc/dCPgtIEu7R/NtCWi0INmCh8IjRGCTbGE6f7U3ukrnztw0sO26seeadN/teUsLQHOFFY1qXsoZNGfAkwUic0XXydwGevpZ+5n8LWumBfib/aD0j2O7n/P9z0Z+bJ+x2ZVwYnZHLa8L6evc+63SepaZq3NSmudbAHAKVPM/AjhtUYM55jl3E15orcld/a2Grn9XY7e8WvcPbX5layK1jQBbCEg7Q03gn/lRQlVLf9q7W7odS5uLlqlsmuoaoH0O0+re9NmFm945Hu24axuobcOtLVvabc2Nw35fZGKMkPPAlYanWqe08ByQ5noYhtFZ59L52FafieR/b24+3/IG0g98+lM4ODzM25XKgkbOacGMmJMXpIxMDgFUPPMyKUMYig6TLGExh1N0XQfkrCSPPnwVzhHCMMC5lHFjGHqEOKSECc4DcPjEx787MX/28BAlo0IWVBCwTMn4iJEBYgxDDyKGRzYwOO/cOIf1ZgNcvQT/tkfTDouE2Qw9Qhhw6fAA3qdQlNVqVRhIvC5ywa1dJClELln3RKOAcRL7BMopi1O4kU+VAkMPigHMPSI5eNeBmBGHAT4dHQdzBPF4KDFGuaMKpX19xwkMkLdnQFqApWmcZKXcUha6tM6pAEn0kktIg8RDlY1FAbC2DzEmvvGZTp1zWHaLPM0OhHaWPDFI9O4JldTIU+9hXUbAVkILMl9p2ljgGcuzPAHhVmpXQrdp2AjtJHQtKW6iNIbIddhDC7y0DJJdh0O1gBXwBWAMc5KU6nl3ilymI41mhwCOVFcathXard2QlvJtgsbc/kgkFMPHceKvZJSiZIcDMzinBfZMcPkelgDGQIQT53HpsUfwic9/Fu/+7u+EWy0xUIQDg2KA6yPObt3Bq88+j29+7Wu4+eqrKePcdoADoevSOvQLPyZWAYo88p3PRhGaRe/ozYGvltE//j6G7mQuSbweE//od8a2Ioh8kSHpjKXIEAZz/V7LAdACBXp+dR+TgSRzKWGxY3is5l8dVhtDHJ0B4qmVdUD1ey1+t8DGyjV5r4AJA9zHeud3qFvPS/+n4E39w3QH1Hp228ZkGzDvksscuZyhskaYSBK5tkIcQaM8q+u1P63skp/6vEg1puw0YM7nbWXN8Cg7rOHYooVu3/nakWSjGFqGw/h+Cg0Xx5NeS1ZG1e8mvpaQLjkOMsJXKvKAqJ0sRPMeGu21dtdbf0uxO4otB2XBBcCUNrnnzmXnrcyfwha6n3Nrq1oP+XvBQuMr446mxiFzYLySf6zbFYObIW7Dlv5o6VdmlcVPyRMr3yZjKvW0+bOlz1uYSMvNXXWIDNKyV3Y2C9127PhoPhPjqLSh2puTbW5XNixT3vIG0kMPXcLe3t5EwAM1cAWmHg5A0tQygIVa6ItSl8RAMhOc84ohO/R9MoxAwGKxRNdxuSC2JaAllj4p5vECWWC0loGVEjRj+sfFYoEQD7DdbNIdTCpFYoypL4vOgfKOy3a7BTOXxAxakUvyA02bcpaAErgf19LoqZV7SIiyMI0JmIkXK+S7PTwcXM5MFmJeZMmdXmK6rdBrCUc7l3OLswU2ZEytcMLSLqZCtxL6aWQQo7GoExovI5V3Stv5kLsnB/D5F0PqduQxAesWZLbAhEay9X5Y2xjhhPzGv4FyJkgUZ6u9Fqio6T7SZ6wknd0h1Te9JqQOC5Tk53g2rQGijFKoFW1W+MzZts/GPY+GraaNlrPa2z2OeUrXOVCoxzMR05QVg4A4MbYJEmafxhA5v5vunoreoSfAXzrERz/9SXz409+H1aU9BJ8YZY8J25MzHB3fxb2XXsUbz7yIF5/+JtYnpylVtdCQgEAAPKU7h6CM16xw+74HEeVzPmkXqQW0W0rZ0sECUsqArjyuPgtcG17aG56eJ4QQ1Y5RW0HKjqAoZbt7MQckNOiXyZJ+xshwbmp0aBpoYyiKIpf+Nd5pgekWPa0RYoGvtK3nRZqdgpbx81b/W7tqYx+Ss0PT0Ibq6M91/+047Zgt0COiIjsnhZFh5VgPq/M3bPo0p/vngN9UvqZG1VRiNIxGoKfnSNOjBTxb3n47ly0ZO9JJnlE04N2gUdwRDMri2eVs91rvCKBVxMaUz+YMDTt223/RM009BjR5Sc9XU9aCCu7QZ5vPe78F7kv7nJzjRHp+pnMB1OH9Ld5Kz+t/I49IFjtLy4pmRmaVtt04J7sMjYquSte1jNNd82r7VNVrxuBcOm8sd6ctlgssvMPJyXF2NU133FrYQuNo5uwQagxV0McufTRX3vIGkgbXdlHo70MIKdEAp0x3gDDcCMJka1AmRpiv7/tJvGkLMDJz2RKUv4mm+d6FiVqHn6X+vt8WZV8OJuazAcjMIu93XZfSdhMwZINuu91O6pfx6YXMzIXnrOKWsQmEJ3Jw2TPgiNDHCObUryiANC88ju0F3wLIlRJFezG2PrN9tu3Z8MFdCsQqKsRYvJItgTfXF0+EYQhgArbbLe7fP8oAjbMx2SjMxTPaKnNjz19W7Vu6aL4t75Y5rd+bU876uZ19Mf0tf2OUa3PgZErjaahIC+RJHTZhQwvUk4y9oYg1DwrdxjU6HwLTosWcQVH9nS8IRcxrWfty2YG8w+AI/crjrCM8+R3vx6ee+iyuPvoI2AGRGB0DvB2wuf8Ax3fv4ZVXXsTv/c5/RLcNCKebMaQwg4c5A2FuLrRMEhrb+W+CSvWulcsaFOgwWp0YpjUnuh0dcqufZx49rHLxtQUuuxws+meLR214pR2rljP2b/3srh1hK5s17eq6p7ur9lk9/rm2dJutcdm1p8c2B84mdzaZUEzbB+bxfGp5hqYOn1a/W0X3sSX77XitDmnRoHVWzDqTbLY9PT7dho5saK0T3W5r7JY3bZs7S8Uz9ZiboaGY8oGVD/Lurp0FLrtudV9bddXdZYDaDlQ9JCIxAQGrSO3at6UlCzTPlrVvDEb5vcXXc/XPfd563+o1WSNy1KGlFy3eqp8ZjV/Ly5YfL1rOw1wAcHR0BE+A992IgeL4PoDKca/HXs0ZzUc2JONpt1xolbe8gSSlJXRE6ep00WIkaaWvY5OtEk0hbPWOizCrpIHWbeit+pYC10wvReqWPukU3lrhd12XDKQ4Ahd9cE4uJ9MeUzGuAJSxS/a1smuVzO+qr+V3TkkHBmaw9+XCUrmwMcaU9tuB4NVuGBmBZgWppk21Nd9Y4JZ2tt4pCKtD2WaL+a7qF+YVOkyMs57rkMPsOFe/v3+QQxiHpvejjEONx45zVgDvGJt9pwJXM+Nr0R1QToEZes2FRpTfU6VNJVvRYEbxt+KTLwIMJu0YoKydBS2QOdeW8GzLOLBKpwWUQGmXwQHoOOU2lMzaKXqTAeew9oTFo9fwA5/7QbznI98F9oTAmd79gM3pGus7R7j90qt4/pln8Pyz3wINEUM/oHMekVDi9+34pE86Zt+eE7FGp35f12OB8tzcJlmlzeWapvp3K8/1bqLQ3c4VUXJmybxqvtHvWGBh58kWZ2ho53puJ6Vac4rPdsmkOaCu+ylGewijEW8BoAU7u0CPXse2PfkeObRLf67HM9fOLvC4c1cPM/KAJBx2vj09v60+zvFrE5Q1xlH/3G2k6bECo96ck5ctntSfa5rN8XOr3tEYqvsooYpVHxuyQvjO8rMd8+xaYqDaJsd0fsUAsI7d0lclE6ROzg7bdB1JinIhR8W41utiTtfptlrzVsakhtOSBfp3Oze75nxufWhdo+slGkNLm7uspo6Cb1E74ltrt2XgtOrUv1usINi4yG0iHOzv495mU8IUJYud1qO2Pav3OUqIfE0/MIPpYrLOlre8gaQNnLmFLYaChFyIINTGhXwu4WmaKeV9ANXukDyjGUIznWVweU8DEWECMYBkPItFV87m6JTVFtAwp7CYRU78ABo9rMvlsrQrxpB8Z2OutaLVAi6N3SFsAxjjQWR5xjmfd7WyMDE00PXL30L7ETSplL5wiLT7vJHum1WGel71/EsbUrSQsQrRe5++o6nQSLQc6ad5qIzPu3I5mvCLpA62xlX6svaWWuGkFUbVV+Y6PM4UoYcGBs65yV6VBQtW+bXAn+Vz6af9LOFhlw9sTu/QsbTTY9b1WiUzB3Dsdxr4hUy78+6n0IAhjam9YzynJM8DjUIX4ryzwwAToUdE8ECAgztY4v0f/Qg+8plPY3ntCraeQN6BhoD+9BRn949x55XX8PxXv45bL7yC+3fvwjsHYk7ZL2MEdT6lA1drUPcrrXlCjNMLpu1YNK1bQKwVlz+VhTK307lrgXv5XXvmbUZJOy9WQY/jnHq4pR0boqY/a+3yy7stsKrHYH8/75ldAE7a0/1s1UskntQpyGvVKbpgl1GQvk9V/v/Y+7NmSZIsPRD7jqqZuftdY18zMyJyr8ysrburs3pDdaMHPcCMkBAZDGZkRoTkf+AfoBCU+QEUmcfhA4XkCIUPJEUgAkxDGqhGL2h015pZlZX7GpEZkbHe1d1tUT18OHrU1NTNb0Q2ZviQaE2JvPe6m+muR8931nV0QZ9P6XxeT65JGpuPnAkfYwJPYtDysaVnMN+PY/fLWJ1Dc8+07aGMKhf85XUTkdydJwrfVn8fo3ljZzCvS//J2irABBCCkJzERo7R93y9dE2VR8nXMqfrniXdQ7r26Rqn93bse3LH5TTKGIOYwVheGr6b8RDr1j6n9/pMbvmjVuT52oztpZy5X8ebjt1peZ1jvNDj9kJOt8OnyD9K+7Bun68r+VkefOZ98LVLhVuAms76TgJ15Xsg8lAJvY7zY2iwZ2PbUtEJNjjry9ceIHnf57zJN65qYnzCGCnD6hIGNr9MnXMDdR+A+KwuZFmWg3rGGNF1F0G+IVWjk4bojPwl9SZ6SpB8J+aCTdMM3jXGoHVdfC9n0HKJ/9jB1L8jY0kSltRYIwkpSf4XCZoJ9TmR4OSmPHm9uYROCVk032G/QgjXrXsKdseY0Xw8On/pfKYEYyip8YM1SOtSM0fdW6m0pDB98sa6dlguFyDSyH3DnBmReIJgDaELjF3a/3WEvr901l+yWke67rG+zFxq3fsrBH6kDzp3g34NGK7hOjxOw6ef5SZd+ZjyeRrrb/quIQrJ606W2unPNK8W82r/x/o+Ju0b678kRDZgxyE8PtBYoC0I5194Dr/5+z/A+aeuCsgxBhNP8HWH40cPcP/2Hdy99QU+e/8jHD/ah6sbWIgmyoHhxKUeVaAJJjDNOUBK6ZD2Of39SeYpXW89V2n+nXX7Mc7DCN3Ws5326aQQsznjkwu3cm1/Sg9Tpmis5LQybWesL2P0Z2ysj5vXfHzjv/fvDJgq7qXF+Tv5HZAyJzmdS+dJgxKsK2oikwsDx/o+1sbYPtF6sxlaeX7dPK8zh0z7c9J8ax05+B97Z2UNRsa7rh9P+kzafj7+sXOU9kujjOZVPpber7lPtYyt9xgAAtALWLF6J6fnNgX0RCesWfa5oVVN8eNKOt48/+PKPs3GlgpeTuI7UrPidXdgvtYn0bmTnjlpnKyRObL60rl6rDUIDYHv2LM6V6LhJrRth7ZtofTDOx+FLek5z+nsyCiQDiDOM0h86/4W5WsPkHKmV8vY5TgAGcklPHZppIxqVVURiOXgJj9E+SWovyvTPKYSTQ9nqi0iogjU9Kf3EnZc69M2VIsEYEWblc/XKhHniObXHXprjDhS60Ei0wOoYFZGcS6BdWA+9w3QomtoyJ7IgKZzlv89Boj0d2WyBodyTZ4H7z1cSHibEu7B2rsRSRSGxL3rWrRNG+m4/By5zMLPsixHL86cKGqfiAjw64jJsO9al3w+zNg+tifH2szrzs9bzmTppDj0xDevY6wt/Sy/bMfezfsytmcGkt0RsJnWkUu5e1DZ92NFsrWGWSMgmtSo6aUGioghvYngjUFrgdn5M3j9B7+Na995DXY6RRciIaJxqPeOcPTgEe7d/hwfvvcelkfHON47gG8lOh0T4KVBmLIXOhTWwhg7ML9NQZGW3AfkcRf2GGAZAz9j9Yy1oXOfn1vmXkubnrf8rOtnSrvGGL0xpm1d6Rm0cWZG93sO1NPf0/bG9v7Y/vkqTE/e156ZB5CEPh+7j8YY+VwDsirIWAVaKUOTRpvK5yG9jwcCsceMaRUMYEUDk2r71q3t2HyvMyc6CTCPMZXDOVoPOtL6tf0x4ZeMc72/3Bjd1Hpz+qVF+iwpQ3QZTzoBJ5nbjdWtQtr02fQMMDNgaGXf5PdrWuI8Z1rF/vt+R540ljHwkraf74N8zPF9GhnTyHyk8yZrvP6OAHqh/EklP2tYU99XoR/5edAzPH72V8eW7zP93BiNKC3/iqLnvQ1RPL/6Tn4fpOuQfs7AQEYT14qgsVS+cvmPAiClxDeVZKvJ2pChkVnsAZPYDsQN4WURrTFROu+7DkguzHwDAVhRMY9dNrl0WcGCvq+FSFD3kIm1UOZaIs5StLX1gfiACNYU0IhL1vYHz5oeQHZdK5FEYntidpVq4uI8GqBrGew1NzJgrIWzHEKpGzERYlFw6rgtmWjCpuNLx59KaxSUAQATwTPgg6+F2qwyEMz3xol2XldOtHWu0wvGh/1AJNoFT4mMgigGjFg5vABcnPOwTwhgY+LcWg9J7ksMR4ySAZAJiVJTYB4IPZt4eem6xLFTMJGgfr2IJGJhGvih63T9AmGGmFxSCCsL5hB1CRIWII4tzAlzFBRzmN8BaaTe2T/dq/odGwqXSAjOpoTOABYmnjVrBXBotDkt6RnKL5TViy1hYsKa6NopITYalSk4p+oYAWVu9dIFoPMywpTl/ciZ+PQ77ZMhgyIE5qi9gzeBsTMSutt2Bt4aNLMSzazAa6//Ol77zd/A5uldtPBg52E90O0dYPFgD0f3HuHj997Hgwf3cHh4IAIN52ALO2BO1bfJBNqHQONSRiDVqCOc+5wBieOwpvcHYS9CBSIUZRVoj4fnDiFoo8wjKNIUBoHIAsQCTtFfqGOmk9oHFZhEiTCyoA3pfvB9qHxOaT33OWcIAo51n0RalG5v9Oc77knvB22l+0JNNuPdkIwDGO6ldFxKm9J9o7/nWtP8/b4fAPeTLtJh6nmEyKwolyIv9r8nn8ceEwEwslYsCYlFcCYaZxWiaT9z7VvOxKVnJKehuoY9/ZD/eaXxSMdKA/rOGPZjjNnSZ9N+pe2vm+O0f2n/c5qXtp/v5zGGOQfuaV3rmNrHCa7Sz3Jhb1q/PAMAPtDgVLtL4TvVPuqLfd3raFw6F+n4U6A04IFoHKysWw/5YFwQFftIJElztQ/o6VB6Fsa0VGNrlc/dAMiv6e8YXxjrZclJFpgh9QQLDch9PGYCmwrx8z5KtL7hVMS72oQ5yc9a7Fc/p/E+IrmbI51PxrhOADT2t36m/zw8ipAvU+5kBF4x6XN2NnXs6ZketKXRlCG8YtCLRWG21LXSrbXlaw+Qcq1MehBSplgnXi+XnpHsP/chFxB7HyZ/iNZzO3bKPtd/Yxee9illUlSzxTzUCCmTnwI/PSxVNYH3ThwSrYUJZoMajMGadMkDo8ISSMEmhzaXuo0dRmEmSJKdQi5KJoZ3HUww32E3DAwBopXLVOcpgrWE8UmT1nJgXslYEORSNmov7VfN09LDrH0fU5Ona5MWhgDA0paRydG1GWNMdE3D1ILIAC6sRfycRLsk8c1D8mECO0lU6pnhkUrfVfUtviOUMC4xDwMPGZ8I81kiw6SEWfooHRy7YOPCQgl/eDcM0XtZ40iYBrtpfRkAq+xvlSZJW/0ekEt5NQxwfunkjE+c54T5HRJSnVdlwnL/GnmLqH9ef8+1Q319wz4+rjAYraUYfAGdR2FtDx4mFVxV4MI3nsOv/f3fw5nLF+DZwzGj9IR2XmP/0R7279zD7Y8+xb3Pb2N+eIhls4zCnVRrHi//Mc1OGEHKnA8uTKxh1HThWJhv773kTwpz6pxq1RH+drDW9Mx3chkqLcIIk5UywTqu9NylZpGpFjjS1XDmV6Seoe8DQJgzYEkf4rlCAm6S3/U56BlMmZDwec7oaRljgpDXke+hE256Zgwc7LX//Zn3q2v62L2rTKWcH6E1Mi7NCTh4OmNixvqen6f0eWUX87/z8zaYx7AXx/bPSv0jNDw/4/pdytyP9nWEsU9/z5m8/M5I50OEnavzlzKXOeOoz43SQmBw56VlHJCE8XuCCscUHOnrXtMP0KoGNR+XPpPzLimzmwKDdFyrDPwas8vBNh65j+WPvj7m4d2VzGc+77r2uVB71fqC4pyMrffJ/eZE85l8xliZw3zdV8Y5nI5sXvqx5XxBaDKrh6LgKD9XqbWPztPYeUjbi/Mb23fY2DyFra0tHB8daIvhihj3e03HPKBh2ZqCgpDKSO4jXiVRjy1fe4CUM7W5hGDMXCy9rLyX4AVlWYq01MnfYjMp9VdVNbiA0oOTMit6gWsbCjpSjUYeQCA15UtBkfc++jxpP1QjQjRsN61r7KJIS3ohaJt5vpDYHyXIQJB8G3ioNNrB++EGHqxFkOo8CTFKf6bj1eeMMTDWgn2vcUufyceUXkbppZJ/r9q3/JIbI065WUQE1clPQOiP0Qu3Y8znc3SdwyT0VxnVfH3yNoeRuLDynIyN42WrfYxjM2J2NcZiERGIabAmPRFCH6lw5CJeV04ay0rblLZN8ZKw1j7W1yT8NmBG82fX9TtnPNLf9fF0Lw2ZqoRpfpJ5IaArJHy3cYQJLNgxTFWgsYTJlTP4/u//AE+//DxoUoI9o7QWbtlg/vAAD+/dx2cffYzbNz/Ho3v3YdTcLumj/p4DpfRnziCN7XWi4R6I85kwY7q32raNfpxj5ohjF6fuY+fcqI9iej71c6VrkV5m9CNtT+vOpcRp/SvCkREmduzztP85bRrbo3kbKXOR//7Ee2mkEIlVwJjgZ6wfab/Tz/Tzx5VUTDBW30kgKd1rKf3SnHr6TM4sp/1LQVTKVJ5UclqT9iVl4vV3FVIO6cKQOUzvrrHx52cz78vj1irvf/533n4+Pyf5kOQmofLeuK9gWp5krte9N9gDWLUESJ/9D6k/L8KkD/9OAcjYeulz+R0Q5537YBf5uT1pXdef815o/rj5z8eW05DYp0zglZo05vsx5VvI9KAmfYaIBpZOJ5V0TuVciXbv8OAArmkABO3XGl+4k2jJoI2IlzjyW4RxXudx5WsPkNRvIydAOsmp+UjKEKSSokGQBx6q7SMzDDEtA7CyKbX0Ueb6A6MarvQi08tfw26nwSNygqyR53pzMfm+KIpBBLh1F15kOIJdaC6d1T7mkr4IBA3AHQtzZ23Qd4hqlkgc7tK57ZmTVeKl4yHqAWWquTLGRGCa9iVeCswrYYvXMT6phiyfl9gvEnOg3Pwvv1T183WStDEpqeYqUEAbwRStMrFjF50Sc+ccxkxndJytc7IOCOZQIaoPGQOO3j8YlRyNzXN6qeQAMx/3kDAPCexJ4AVAwoj0482Z9FyiFpkZ00vhxvZ6OkcpU5Cek/wMCNgf2vHn/X4ShqzvB8F6E4MngBhdaWBOb+G1330dL/z6d1HOpnBEsGBw5zDfO8D+3fvYu3kHtz79DDdv3gxa7dBHHgpU0jDl6/o6ZnqaMoVh5uJ7Xdf1DEKWzyY9WxSeH2Q5517Dk/ZjbE/k5037uG7NkVz2aX9SJiGX/qd91tx2+XcnMX8n7fu0nTi+0VpWmf+xPZu38fgynMthf8SUday+lAFc7cPq2e7v1qFAKq8zH0va1thnDI53h36WCxPTuY/WIMZELfRYm2OfrWdQh30cM/nMxzb4LqtnDETr72u1+Rjuj5xGpWWMHuf1rJuPxzHgOuc9OKBoWppHOUz5Lf17YCYf6hvci2HNteTjXEd35TZjiHZruHfH1jTWieGVl/YvFeyk85pb0KzQC/RrftIaPO6ztI0xi4WctjwOPDyOfg2A0Aj9LIpC7u5Ew6f3cfrOuvHmdCHu0aCSbLtuCLKEIVrpf85Drps3BUPDoGDDPj1p+doDpPwSBvrDp4y/Lrj8LtJGBSR6cOLF7IaaDe8l6Wo1mcCGiHdj7ad/A4jS8JT49wzzqk1sGlUvPTTaP010mzLc6Xj7/g436ZjJWb7xtS1tf6AmB8MWBZj7xKmGDJxrBgci1aBp22pjnjNp2l7a99S8JmUiYt+RhALPQGi6B/T9sWfT8TOLRMj5VQKQX1g5gdJ60yAZkcAFvxZLonmcbcyA8HvTNtFcaF1JCWUk5KY3TRo8C4B9Gva491sy6P0v0rVnZtEeJQB2cHkTYoLRdC5yoKn9I+GcAGaoT086jnTO0vryczMGZPO1iJ95D9B4fp+TGNn0+9VLZ7i+q0R2lQnMx5LuRxvMQwkGrjRoK4OnvvkyvvmD38LWpXNgFy5c57HcP8Ji/xAP79zFrQ8/xu0PP0W9XMIzo7AWoOBYHf7lZyGld6k2KY2wmc+BPp860OYMngo50rHpHrLZuUjXcaDJzNYuZ6LWgo1sndJxpEzsGHOb7nXtd6qNz/s1xuzkjHt+Vsb2Wb7HUw1XynDkazE2T2Nnc8h8D5krFTzpuhkz7juT/p0zinm7Q4aVg/BlVQg1fl4wuq8G485oRX7vpv3t19evjCEfT1rXGIOZ74N1e3FsXIMzDzGBToWfKT1IBaMDbcTIHOXzP1ZOYlQfV8boVNpeXqcf6dPj5iMHToN7kQNkGaGvuXnjYO29j6ZpeX/WlRQkjfUjByHpWqX8UZyLE85JWufY+V5Hc8KX4e4cH8c6mpHyWfpcWiXM+JlK+5HzgWq6ms7H2Hkcm7sx/giQe5rZw3gN7e3gObEISmijllyjNrbfgDBlOn/hk5QmPmn52gOktm0xmUwADC8MBRT5RhbncDNg5FVqasJmTSWzKcORM9mpxHKFwGTq3JSZSSPVjUl487DjWk/btuG9YbCD1ERgXX/SPuVETOvRvwdmbezBhgErNqlkA4fGApyo7YbMcmjH8JAo5xf+2AFO+5VeOP1aZRI8Gkqw0zI2Pu1jnIfHKGbztsb6nl8QTAx0PkpjXCdmRWNRak666Abzk4wtJZoSmYyCX0uQolAvXRHlTND0KWEJaydhxx9/2a4DLgMC5j1glYN//Nj6d4F8/tcRRH0vnikzNE9Yx+Ce1JdhW+v3gvYzXeeTpFxAiFppLVxhsP3UBfz2H/49nL/+NMy0FE1Q68F1jeP9Q8z3D3H7s5v4/LObeHTvAVwjdKssimhCwJC7wBoa7IExplz7qGcjvfxTZkBpkTLTOpZeKEEr/l35xZrOwVArNZzjfD1SaXU6dznoSfuTrnfe1oBxTT5fR2+GjNuwv3lfx4DAKNM1MuYxepv3eaxf6Z2RAr3BfCX9zkEc0Ulgvy/D9sfPkGgMAx0ZYRbz+dE9kgq91HxN+2eNjX5caf/SutL7PAJcGp7TfH70+bH9lZZcG7NujtIxDeZjDS0c21fav1TouK6soydPUsae1f7nzD8zAzz0AR28FyQxY+BtjCEeAwkrZ0s+XOnvGJ8U3w/33xg/NFZH+DBtcfB9/m9svnKaqv98kiw5bTufj3yuxj7v52N8rta98yRl7L0xurOuzXXtje3NsbUYK2VZovEdiAMddzxKS/J1GTtHZIwEyOK0vXQPPvmZ+doDpLpeRmKcRypLJzdlHHJGQZgAE89V+owCECHmHl3HK1ogfSdtSw95epH3Wqxeupn7IOUlZci0Xa2/aZooNXbOgf1QNapt6BjNCOMwZn7UdV2fLFWV1WEuOmIJ8W1t1JakhFHnFDwkoLk54BgR0hL7nD4T6181icilBjlwyU0nlDF0TjLG5Gs+xuDkzFkuGYxEAhSizgEgwLkO3kuY50Q/L0R/ZbVDyYiOx+oFLRMQtHTQqoOEDojaPgqAiUN/dJt6H0ZOCjyVVsdfBvuEogN3HFo/r+jf5wRMnDC89K++92MEO/uOQNG/yuucRqYp7NjB/GUtESV95SjRpJX2U8le38d8P/VzI8+QCZHjwKBTW3jtt34Tz/76a8CsQmsMirqDbTo0e3Ps3XuAvQcPcPPjT/Hw7j2hXyEJsSeCY+4DOyT9shqhLs5n7/RKYT2BIYNJROHM+jhuay26zkdmWosxmgONZH6MmOc6r6H4SVcCRIBznLQbpyrOV2S2wPGCZKDvS1iolG6n2i99PgWpevZy5nXou5fQWd3HCb1NmZf8ss9plMyL6SPXAdHUs99+JNG1Sc4Xg+NcEES7r+uUnLZIl8cYmrTt9G+i1bORjh3o74J+92Lwe+wZUewX81C4F+tPz8oIY5QL31JhVHpmtKTCxhwIjgk+Yl5CAph6WqRzp+eXMiEkCHFNVgBg0o91Jb1b0v6m3+X3hf6eM8fxbn5y/u2JyzowOMZwKg0MH4y+p/dG/7dcEn7k+XTdT2Lm0/v1JJClzwLJuoYO5cB2TGPwpNOb78dU8JPuxXgWodfNkPfL90M+P/nc9OOUGiPjn80BYqsnj2ywvoSYykJ5nZy/yt9JPhy2Ef48aW3XrUHfY6kv8oekJu+r7jD5fhjrZ5wjF9wVIg2UOo3pIx8/SfnaAyRJRNXCe5dkUF+9WGQtQmhtK6GpbWAYCmNhQ1jajnsQk5rJgSTx1WRSxTak6KKumo7oIo+ZaIjDskjSEAEGRaZBAEF4hyT0qoQuFkIR2BOwZ3RBiwOGhO8OnCqROInrJZ4yVSmhSUEGkZiD9cRCouAZI2ygIcCgRGEtrCnguV0BPXIoGRzAjJiIBV+fwopfhZFA48wSRpdYGCiDntikB5yZAY2Q5TloTwBmCQsOAJYMvBiXhTETiMP4TRE5eO8cYCwMTAwLPNxTJjA3gREHB8m9SPWJCBYFOg1UAWGWwIBxDDYGsAQDj9IUEv43ZIEOMvlgxraG4EWOKxA8FoZbbbERwApDEoOmoihLJvq0da5D51wAx2E9NXR4QZHgs1EiI8xEZMkJMJzsmSC0iReC9pdZwnpHYBTChrKChsAAKdOYFtK+cw/gWNTl5AgFhYAAsqqBQQI8iTZT9l2SW4MZ5JVZVwrfz2sXQn8bop5ZNgbEBMMFCARDQ5DI5CTyIOvMiwDLMwBTwDugIIKxBFca1JXB0994Hr/2e7+N7dOn0LlONK1NC7es8eDufTz4fA+3b93Gw/sP0NW1RD30gWYQw8LG84uoBfTw7GCpAAwSkxQO/U6YWTAMBRrGDKeHJIQbt1bOoS2Vzsg/2SbSB7mA+gtWwE3QrjMLLGSgsImGygAODh4cNZs6JuFwTFx3bwiMROARgvSKhFF8DuHFHwIUIiqF+WAYeNczbuqoqyhNdiHBEsHr2TQmhpkV+tALxWQf+7hlTNjnKigSMB7oQQhiAu53s2cfgrOEHWII7FWAIeZYAhIVxAOqwzYRR0nCaIQxMUuo3NTEiEhogYAtiRZqJI7+kHEES8qEcHf5QM8MGJZYaErY9zBW5pQgpyy0EYMoJACJmUEx9HuYhhHzspTRS2n5GIhI/2k9aX3MnPjrGqFrRODCAuhQsEfBDtYbEE3RsYMzshcNPKyXhBQcoiimUREVqKd39KB/yf5EIjRJ+bYc2K4DAbJ/e6Y4ZVpThjAV5KXPrAMfOfAYZ9Qp8CiBgOqdkQsz9H1W5pfDGenPFThn4ofvp2MZCF7oZHCYgpKBySyEfOi1qFWy5APp91Fcqx7Ma7/SeU37qu0qLc15oZX+j4wrX+McTOTmqmk/jA0GGKa/v+L9N5xc9HR6lXeI/SFlczKgOQJyVJAwFAIIj+IDM2DJSv7LZNw6HuWNUyFIOicUTMyZPTon7gzxAGWCiXw/rdtf8l3iu4geROvd5Nz4ORkrX3uANJlMAQB13S8W0AdMSNG69z4SRHhG7WrZagVgDOKFMIaImRnG9tKpyWTSm+aZYWjL3JwgJXhp3zpuA3OdOBBSABLAYCMCQ1Akl7iJTDN79SkoBra8uRRB/62LFpYSDe8l0pD3PrYZ+0FBqpsR56Io4rwAPZ9JRDHhm9avF3+cI99rvNLQ5r05I5I6lUgwNCdOTxgo+tE47yVXUyDyKr0gEk2Ptp2aTHJgBorCRk2dRu8aBPTAKgGy1sABYIOoRoaCOz8k3KDx2yKSQg6OzOk+1LbivPb16BzbosDG5ibIStCLerFE17ayVqxktq/VUlJ3uECFqcVgHZkFFFG6bwKRAg8J9yDHkEeiBRuO1SBlppSIhwvRGnAg1gLOwjgZmPIkCQEf+s8eHoCG+dCeK6NuQChUY6p9A2AZ6AhorR+cqXQ9TACpIIr1kUJEC3QG6IzH6acu4vd+9/u4cOUyHBjt8THgPNq6xfLoGHc+v40v73yJ23fvYrmsJW1FAZCVuYAhOHaBkQPYmMB0E4gNKu4TBBemlDUyYSwxb5Z0y7GDYUJBEn1SGT6AgA6wtkTjOnjTM4CpnrBgg4IsTMjRxeQDiCHZ3+EMM3sYshEIF+yCdtkP9xIJw680zIatq+DfsZcxexYtLBE4PK90klgEVWSsSCOdC32QdbLKjISNLQmfw96ygf/Q/UzJuAnwEUl7wABEPvSF4Um+Lb2XvRn3ghTPBG5N3P+6u+UseMSmIodN8YfklAtrJhtZmCQCPEwQIFHA+j0TWCJoKgmR6dWzSQHwgGTe9HcLRhFUKq5jdJ5BXuAsQh4zMnrHCLgiQ700JC061OxMrwMMaaRBMhSY3nGmMi/qm+sBeEu4cOMZXHjuOn7xzlvY2pjiwtYWDu88xJc3HwjdZgK5kGcOVsAB9VFLB+Ak017pGCjStn5cY9YKcY2Tu2wAVnMtVQZmcsY9nY98LteVx2nCYh3h/AruXQ1wtFIiXeewv3pz39VH14M+ufdWnx/7PdXMxc8J0eJGQ86vY7DjqQxdT+d2HUg/qf9pGyetx7o1SLWr+fu5tUz/XV5L1rcEuHO6pzPeZEwYASQancyyamxsREPfyxQk5XOWtmvi+TY9z8g+8jdj857vwVH+NPhQ+/C8WE8Bxqw3HVxXvvYASf1y+gAGNEC26cbUf477BFYIEgoUQYKZAQmgN+XxHjEkeAqO8kO1joHWn1FSwQx2DmVZDoCBjiFX7/dMuB1s/FxDlZuX6PuKsHMJSdrHdL4UTDjnJb+KMaCiN1HRKETx71RNnKnC88ticKiRONgGSbdGJ9TPc9V6OqdpyQlRakqYz1k65n6OhmY7LqyP1pGGOda1SNv2HNoKpi1t14n2yPEAWJx0maXt5yUdv/K62u+0/8vlUqTYOg+UXmwnmPcBQUqOyNj2bSPa967b32n/4+fZesTvINJzMMFzZIMj2AUFkyTmwJAGwsoGhDI8E8bDQf/ogUmGKJUhg2V4GxhmQjQX4UIYXmIGTMrMSrGeYHyfoFK1fyY4w3bkYbZn+O7rv4Ebr34DlhlufozWOXSNR7NssfdwD/sPD/DFzS8wP57DtQtMgkC1sIX4FYZ97oIWwpCBLfQCAwxZWEzBHDSvCJovY0CW0HKIWqh7jMW005CFpeQ8MKNpWxhboawMOiP29Woym+04kFUBUABrDHQW0ZxDGSBAzAKtk3XNGSAmBzKNLIpjWGZYGNGwgtGYVgCN3KxBe8qBaS9kb3gEgZIVEE6i1erPdJ8XyXkHTzImaw3YpmZqNDA5lrwvgYlgmTtDIiTpOheBsWzYXgMswqMA5kwPkFg5axahl8k31aBwf8wShpBItB4mHFxC8DsEwZJoR3TvK4KJADhgGmaAuDcjN+EMExl418EaPdOq+Vo1dZMtRX3fMDz3nGhcc6YtpfFKn/R3z27w/WBGsn0YLS+8R1EVOHYtnv/N7+LGD76Ho8M92HqJex9+hk/v/xk2GCgbhnGAJwNnRTNJmdWElvTe0/FGsJSAuDHmLb/X1Yoln6N8bGOgKJ+/x83JkwCi1fe0fu3/6jnNzSHzIs2Og5OxtqMp9BO+M8YUK0+U76d8baIbQ5K0PK1nHRDIQUpu0po/v+5+ftxc/G2+Gyv5WOK+PuH5sTs650fGANwYt7AeTIX65KnQTsJvhrvrcWMba6f/XlqIbBDJvdOfqxOrH5SvPUDSyVRwlCa3ikg28RlhZrR1g4ZFbb+5uRkYKBO3QUpEe9MRA0OJT092uSp61bbl895UL0fwzD3DnKL6ddqf0Y2bHRBNPKvmCKk2Tf4NzQC1pJdW+rcx4gxXFqJic9xnw1YmlowZ2LzG98KFml4a6XqkASUG4MesmvylczcqURjpex4II73sc18iba+ua1RVJXOUaB5SYqKSTGPMIDKW7gtLBmyCaY41KCph5D2H73i9lCPdGxEch3kcHT+J0Bc0JFCyPxy8TwAgUTBDUnK3KkmVTxU+hfVLqvcs0v0xkJfXMwBsGEqw4nwCIC8Xmc4NmEVbwcLgEEFMhZj7UOJgsGkROxfeAwBYwAVTGmUqexMlH0zMVLpOIGMBa2A8MOP++TADUn8wDfFgsJE9L8y3RVEQXnrxWTz/G98GzSosFsfgugU3HRadx90He9h7uIeH9/awnLeoly0KU2BW7qIqTKQtNgAQgNGSi2fLqKktA94YzE0BjpoDGjAEHDSexpg+FxczGlIgRdG0lYxBawwcEdj2kSsRsY6YYxmDoJU24ntoJMw6jImmmbpnY4Z3F/rlJEG1gqi2bdDVh4DrJJBJ6+A6B3QOlglFOwe3bQCeUq/zTmiMETrrSUCBaJsIhqwIcMBg6vO5OWYYU8D4DsweJUQTJiBbtM7qcwOgN2WDRCBk5wDHKK2BJUDzpNZlAW/zHFmpxLunhalQxsBGEEH6LABioOpIwDkjar4NgtaHe62LtTbsTRFcCc4T8+tIW4lgSNZIV8cUNpjGyj4zgc56b+DbTiwn+oO8QheMMcFGfZ10dqgdGLur8u+Ehsp5XkcP9flUsFZZg67zOLz3AD/70Y/x3G98B9PdXRzteezeuIIf/OP/BD/7N38JfrgEOTHx8WxgLGAS8/mchqVMeLquegdon/XZ9A5Nf1/xT8xopK77gLfI+IaceV03J+nzA1+ZNe8SURKJsP9s3bql86K/q6npk5ZYJz9OLJf0MRlX2oc06AyQ5EjDSNANArrkjlsHctN2x+61wfqA4/yl/RzbS2MAPG873yf5M3n969pL+y/7a/W5fA7WAcV8XOl+Xdf3vL+ACB8tdJ+PhOVnHuyjsfMyxispL8MsdwsRgSxlwmc8cfnaAyTnOlhbRLO1lCFW5jwSOyLMj4/RLBvsH+xjc3MTbdtie2cHreuwtbUlzl/JgkRTOVqVLuXELb0Q0wPddR2896iqqnc2RWCAqFdZphdreink9tHajzQkeMq8a84PBYtlWYY2Vx0P04OVEwlrbTC7EIaB2YOsgQUDTR/5r8uY3/RCScFJDpJ0PMNogAD8KrEeZeZHDnhqMpc+UxRF1Damc50CS+2z+u/4RBqYt7cKkmUdWu/ADLiuhSFIPcqwYXiRjl1oK0QxGV8+RmEuEAVzut+JRJLkwdHvInBTATQwxu4q3SNAD1Q5vKffjQHUMeKem3WuLTaYbOUEm4FCmdfIPBqQMejYo7addkAYQiWkHjBOJJZpNBtjCdRZFJgAROK7RQRrA/gnDpL+XgrnvZfABrDoQltMENMtz9g9fQovv/oyts/uYq+tcTh/JHnLmhaHDx7h7uERjusWB/tH8I4w3dhAuXsKRTVBW5XwRYGyKlGUJWBtAGNAWRKKskBVVajKMtI3W1hQYUMOst6s11oDUxYoZlUAMCbSAKIAZmxvH14WBawJQhNjYamIWgU14yICvGF4Exzhg/ZI91D0+QnbT0wlQ+4uFuNDVvAGwDuHpq7RLY/QLZeoD49QH81x9HAP+/cfYrF/AHt4BL9cwrcdXNsBnYMxZdgXDIaDsQbgEJBHHYpAsIWFMVbGYQxABhYA2IjfFgr4tk+zQMYGoByCzhRJAmswHMu57TxBzAsETFUwYKe0Q0AHs4f3DDIdPDsQNN1CAGSeAW+hvKUmdxaaIcI1KF3UqKlEIGJYbgKglb9F62VFUw3bCwhMAfhgwBj2gCFIfUFgRdagLKpwXoDOz2GohVXA51fvvnh2MwZq4CMS9gwwzpznoCHSjUxbn5acbkR67j2mxoI74IO/+AnufX4Pf/8f/yOcO30eB/v3UVxyeP0f/AA/+eM/R/NoAUuiEez9VleZZkULad/7u6oHtNqv1Hxef64bo9ar95x+PpaPMAeTaR1acvCQP5sChezbZM51HYfhyfVn2q/cZJB5GKhhXX/yu42Ze6HVmvt8HYjpui7ydulzY+4L+r5nDmdmHKzk85kz5qPMP60HT2m9+Rquu9/zudP3xjR4J4GltL8q/Bl7Ny3reI/cgob0/NAQWK3b54O1ESdRGGNRliXqegn2gadKeJa8rpPOAFEwufciIFM+RfdV/vzjyn8EAMlBZzv1W9EDrUlAu65DXdcgEP7tD3+Iz7/4HNdu3MD3vvc9HB0fYXt3B03borLDLPG66N47GLKDzZwezpQQpM+kvj6LxSISU9U2pJHp0qKETvMSab1pnVHqG0raBwUkQK9dU644P9j6THpAI2jhHukbMvCgqDEyxojpWHJpKgA02ZjS9pTgpX2JRD1x5Nfv0mdTojJGlPNndB7TnFgpcNXnrLXR1NEYA8OmP4gJqErXJ91vabHWovEdnHdouy549BN4lU6N9nswXxANSr5ezrkQZGD1Ao37LwAayV5NERRpM2PEVsBVcPIOpmikPjDBdGfsIsvrSP+lw15hTkwBTqSLrGCOCI0Pkm4OYwkSZ8PArtMErIFZV6miBZpSZs5YE02LiIAOhMaWoMLCEwBDaK1oSVtDcIX4b+m5q6oKZVnCFAWKqoItCwF0hcXumdM4e/4cWvZ4yEKDNlWD6x02XYfL5QTlZAZbViirCaqyEjPXwgKV9MNYI+uomcwJAgRUExQCDIBEq1B4jgtIgTnmsFGiT42uEfeaCh7s0eRCBIPifcTRXI1ZACMPt3ZSA/WXHIAuOXOGwtros2F/bwJgPicMIhMsA77p0NYNHty9i/3Pb+OLTz7Fwf2HoPkSbr6Eq1ug6WCaGpY6ON9JEmb2welb9ibIiF+SDaHLbQAJkwLonMy19yHRKIvGzVoYa9Eq3QuXs4ePkZB8wqx472G8gQn7MjK5CP5jYFCQlrITDQlDmHpiipexSmQt5Gy1kPwgIIKjoMXUs+pnPQ2yGtiGJf9ZWDelRaYgWBMEhQFkmaDJNmGPFeUE08kGbGHg7R7atpYz5jrR7LkOYzRWfqzx0VhD104q3Fca7+gx+rfyHgEdediWsNMaLN7/Agcf3cb2c5cx3dzCUX2M4swOXvydX8Nf/fGfoWoZFdvA5PWargHtCn0Zo2ep5iO9A9J7Pddo5EznOvCQfq/35xjQGns3p7f5mPJ3mEPABQzXM+3HSYzlkGle/W6sf+k++ipMaw5i0rQr+dykViBjdfBIfevGl9/lA1CoZoIj4xiAifB9rm3Mn0v7r8/nQO+Ezq5o4+K8GBrsoXX7MNfqpv3Mx5CxGCu8yBhoNETqbRp9U02wWEjnZazN/BwOeAnmoYCCBOin9T1urdPytQdIwOrkcjDFSMFHWZZo2xZt2+KjDz/EtevX8fDBAxhjsLW5BaA/JPpejuyZxbdjMpmsaCFS7lO1VunilmUZJaou2Xg5gdTxpAlb9bP0AOf+SVryfnnP0bQO6EFWSthTE44UwAEQxtOHKGVGwZFuTiTtJNKPwMD0EeLEx0H9i9Q0bcwfiaKzMa30MXB8cZ7zi5mxejii1s85ieimYC+AQBf2jPMMcg7WFiGWgkE0bzEEAxMlkIYMDFnJiwCK0Qgl4o9EWyNr4JpWOyYRtULEwiCmF2kP95J4H0NV5hSpp1E+mBwJ8+VBkAiB6XPxYgj+IhKoIjwQfvRaFzXz6DUSamjmgzmdAhMkocER9jzHLvZaDVDv3yOXSm++qqAGEGl5SwYIDB0D0WrOM6GzhYAHomhYBCJ01uLIFjAhIEkRQU2JYmMKt1GhmkwwnU5RFAUmkwnKskQ5ncBOKkymU9hJCSoKmNKiqCqUZYWyKGFs0LwAKMpSHL6hwzBgS8FHiAFr0YJhQSjZwHoZQWsILXGIjCdr5dFfkkSMyosTqwQuQIw6RiRmMHIXB/G17mvy8FYDknA0idO/mZLLRNeGpR92cOkMQ+gz+XDvEzypZi5EM+SE0QjzT4Bo3LI9qj8shwh0PJTseWa0Vsy9PBMcA2ZaopiUuLB9Heeev45nf+d1dMcLLB8dYO/OPdz97HPc++wmcP8BunqJ5XIBdhbsOyjmL6iEmgObQFussXDGwBclAAYVZTQvc96BrYE3YrJmApg2gQZ6Kz54KuQhMiEHCoHLMoD6nn4VRSF7kCeiJTJ9wm6AAz1cwgRrSfWP4sCwbjoHF4Q1McANM8AGaCu4zg3MXJgZ5FoU3SJGLlSQocBYBQPGCDhSTeWkmmE62YS1BFQz1PUc3rdYLhdAswC6wEIHq4ew/SSyHfMQGOv6sp7cpIQ+rGdVWIJt0NCntj8jq6Y+AMCWsITDhApUHYN9h5/++V/h9Qv/KSblFJvb2zjeP8SiAHirRLtfY8IE8gDDjLajbcQxJd8rjkgZtFxolwMjvQ/XgZf0zs/fSb/P2xz7PZ+vtL30GRMEAwIE9ftV5nas5Hc0Z8/n7UYz1zAvvbAVK8z2kwCXfA7HTB1XBJW6N8PvabNpi3nd+bzl63YSgB3THI39zN9JeZKUFxvLnThWf/o3OwZoPABXDsAGa7pmvGP1pCXnPeNaKE9MvaKCw+fsNdQTBnt/DEinffAcIhCn/AshGBL0z7nu78J894WBwtiYE0gmzYOsMG1lIZoAQ4AhxnRS4oXnX8DtL27jhRdfwNZ0A1NbCqMrt2KYbGH4lHksynApOgffiReyDc767CQIcQQzAIrUl8h7+K6L0gzdJKQDiMBFN3ZqopRuZN0IfaZ0IJciDRF6UQTTGXi4IB1MCaxGvfMefZ2hXWMI3on5iGMGeUZlCIY7mIkVp3BbwDsntt6OUZBFywzAwbMABDImbGyx7xdflrB8rNKQIA13BGITQEd/4NhJeFqNuKZMC4DgmxKS7FJiEgdl6MUB3QRmxbFHUVUhBG4JDydRqGBhq4ngONfAcAvljn3wC9EVM7Ag9KY5jGASYxh118BWYqYHMphQganzEpYaIr2uCfDGonTA1EkENd1DEViEg28DWPTR2ToxHyAbtSfe+T6BKiDS6AxkCpPDYtkGBqMIjkyy8Wz4DwZBgxC0Gkb8WMhDtCgmhBC2FMyPLGAMfFGACyMgwhDYErwRMCJ7rUBZFiiKEqYsMN3agJlUKKYT2KqELQsUE9HWkDWoJhUmkwmqqkIRTNJMUYj0P9SZ+juJvwrFcxYvyTjE4EsRgYfe2yYxE0I8xwL2EnoTmHKE8xMCJMPBgW1vA26hQcEkPLiFXszSoCPEcNHJlSSXuYaTBaLNe99wAE+DjnEILKDMK+sT8Weqx1Omoa+2D787vHMJ6eSx0qrwnRk8OxhF9BdL22QyAtS4f57j86G50qA6tYnJ7gZOXbuEa7/xKpqjY9x//zO88+ZbWNz+UsKiE4s/mSUUs21sbEwxnZaYbsxQbW6gmm2AygLVdAJrLSYTAcFFIfuPCtmvCGdLhEPBPI0AtSuJQ1DkbihGU+w/Dxe4i3xZmEdlOnTf9S/JNMi6mf6F+J6uA3vE4BnOSWZ67z3gPLhu0LUtmrYBs/jU1ssadduhaT3Yefi6hasbcOdgHKO0m5htnkFpCXzwEMv5PlwzBx3to5kfwS0X4OUSRdvB1EtQ8BFz3Ikflpf8I7K+HJidXjOR7jrvGayhCj0H6XawVGDE3FQpoxZnaORvDsy9tRZsCJ318AbYv3cbX7zxFs4+fQbzZg9FUWFnMsHv/e7v4O0fvYn9L+5jSiWQCAZTZja1tkjvRzU5zJnHlKamYCjudd0ngNzzRmiLpilID9lJAG0MvOQMbfpcDhLSZ0UoAhUzBXBkR31q8npXmOakz+k8jppnpWclBdZrSrzvR97P5yMFFGNzZzBcMy3BY3G0Xq1H/6UCYxVS6PMDoQSwsieA1X08mEcM934KYMZ8sJMOD1FeVvJ+xT1MiNFkJdhLmA9WDbqBTytO+AJNdq/1puNLxxR5S6BP8wKgrusQJt2vdD1f617eK0yQD7yvBgVLeRwOdJlIIq1qH5+0fO0BUpEkx1P/kVSLAgTTCYScIJ3DH/wnfygEIjAALthFOu9AjsGs4KTP59K1Q9OsdEE7NcvK/qXSjSh5IkJZVQMJizzT9UxeduB1LLmEQsvwIACARNhSkKff5RqjePgZYtsfrjkCItOttu8IYY49O5m7wqKYVWgODgOwE82dNRJRy3tJdsnM4nBsApgkg4pF8ueCr4iYjYibdGdlPp0LOYRUumDkaHXkonZJTYoIiP49UYoOGiSy9SHcrxIDF4CmRSfRtchgWlhU1qPzDnAeRKW0QeFih0QFY0vC4DID7AIQknFU3mKKCTxbYFKgOn0G9blTWNYtpkdLVEyYw2Ny9SJ4a4aWCX7psDw4wGJ+HNdF14sYKBMtkxAIAY+egKaQ0Mn9PgjgIGg14gXc6xREGxZMcchaAfpq4lUWaCeVaG4KiVoooGUCO53CVhXKSQU7qVDNpqimExTTCcy0gpmIKZqpCgE9kwplVaHiAiVJ5DGj5j6hryXE/MUHwOCAwIAhRv7Kz8FJUqZ1ZyM8qZA5XjCBL0fkdDHg6/uH8j85+5jCpRswBSUM8lg1EURkZeXyWHlitaRS/bHC6MFYXmsULsRvhs+t42VGZvqkL2Oto90cdD+sedgDMAblqW089evfxPmXnsPevYdgz9jY2pQ9awvYaoqqNLAFC9NuLFwwFawMRdTCzGibBvsHB/BgbG1vo6qqoa9N7MOqJBUY32NP8t3KfDxhnfr9CrPDgCUb4UhavBFTPYsQXMF5WBDQOriOYEwJ3zk08wX29x5gfvAIe48e4OjgEAeP9tDMF1jsH6A9PBZf07YDLY6ApgG3reTsYgYxw7IBTBcZbzFLFg06rIlWExSEY7kWIJ2LMcZ5jPnVu0jlARUIv/qbn6B806BpDsAwsKbA1mQH9cEclbdwTkydVTKfmyGlUmxtd2zrp4wz0Jv1qxY78hyJufkogBoZ4zrtz9jnY2BmAAISBnuVYUxFff07qeXMmCYh/X3deNaNDaGlAXUZaUf5uLyk/Uo1FWvvBhU0JFYqsR3CgMjneyDX/sXUHiNzkAupTwJ/J5V1QCp1GfkqdY/tkV740vuRqdWN8k6M9cA0F2SszGveJqkyQM+bR1N3A7/uvP7BT/T7XVPnWFKz+X430UmX32PK1x4gpSBES05QlEBUVQVrC8yxiJHVKvUtkBcBCFo1RvyOtI7UbE4PaF3XMeSzy8y3UpvqtB/pMykSzyO0pMRQN2wusUnHms6FfCb2HInAJz6fSpykTkCdsNM2BcCJxsUTSxJIJ+CpI4KtqpjA01ojZhsx/42F2suD9XIEAIIvPDo4eDUV4x6AAQxij6IAbCGmcUQMmAC0wFDNXpRwiGhEly+AX5VQkoDPkM/J2N4MxRIBRiKokTEoDUKOEBaNCQqJF2Uo+PsEBgydOIAHkzmHIuYNsWYKgNCxhzMe5sw5bH/nVSy4w+FP3sFGCyxKizOvvoLm7A7aokB9uMC5zS3sbG9jsViIM75yjZ7Rta3kewnSZjIGXduirpfYMeLrYEzwnwGJhqYUrQ2B0LkOy+US1hhMJlNU1RRVtYHJdILJdIJqOoUtC5RlCVsUweemRFGJlgdE4BDFjBD8ggID6wFxdPfBrCoiDorSKh2KmMkF7Y2I42BC+HNh4hFzGLLytSNnQn8fO+/y91CKulp6dr0/G397IntS+dtdl2nJ2YqTnhwfA4czmlYZL/v4v7S99MvH9e2JH/5bFyagKQnm1AbObE3x6OE+DpwPvkOEKTw2rcW0KmCM7DFHYhYLH3L8MGO+mOPhw4c4ffo0ptNZEAr1/pc9LRWJO3Ayw7fKgJwslV5XnhRYDSWtBKY+QuTgOcn2DIdOzmZBYtZdChh2YBBblLubOH9pA4V5RugMM9q2Q7tscLx3hKNH+3j45X3cu/0lFrdvYfngHtrDI1Dj4OsGvm4B51EaAtBJMmLygFWfQQR6PGSwciZ7zCn9cfOga+o7SRCLlmGWjA0OFgrwaHAI44CKKrSU5vta9flI785UE5PTlvzz9O8hiFs13/sPKTkQGPs+5xf+Q9o+CcRpEui8P+uAXexj1t/0vRTEjZmBpWNe53P0JOPQjjDWn918TeN3ZugLMzbPj+vXGNgYOxOPG9e6QkR94IKV0ptXpn3IgU/+bh7Ya2z+874O1hZJoKUcyCbPRi0dVulEXBMWvkB8wk4WDD5J+Y8CIAHDIAPOOdgkwptGLyOSfElFaWFs8POxRkw1mCWLuRdzPLBEM1KwBOYoEdKoZ9776OifSqaM6UNAp3a4udQqJc55oIe0jPkejR3O/mdKuFQVzdG3Adyrp8FK7BjMDt4FEGhN0Ax5ONdGh3/nWoAKsClgYGAdgTyhRIggyB28YxjysOQg2VokopLa8TsQOhBMYSV0uIbsBaFyoj2y1sJ6K6ZvwbcjMr4KphjBhMEChYTRVQDEEEArtqqyFmR6HyIK0fmczrU1cGUB2BJsOcyDCeFxg1lNYN7JCWOm822Z0TkH5zwelRZtRYDzaLzDnfkCs6tXYSsCzT2W9x7BTKbodk9hOZnAlxXKagtLX6Np5mDD8F2LoigxqSoURQmYGWyQfk6qCWazGapJJQB/OsXm1pYEmQjaU2stCmth0Utte8OoMA4WQBI+jGYHSqAA+b7TMRNQOo/SCQ/UX5SIlRjuL5Zh2hcnuskATDmgdiJCRyF0fOgH0gt25JyPSa+Glwhlb44x8avMA/Pw2ZMY4fSZtRfFCYS7H9/q+ytStOxn+tyAafISVlvpiNIi771IBmkoGe0lrUGYkjCAjJ5ppaS9PtjLcI5yiWP6/SqjtmqaMfx+tch5lh1qSwIs8MMf/gUOjhvsnDqD8+dO48XnruHi2VOYVAbWUMidFS5mmWwcHh1hZ3cXs40NaES+Mel+LhV93F54HMP6uJJHEnuS+kFhbNQLwOLuZo/C9VJYkAEbDmc+5GxiiI8cDBovQUa4ZKAiFNMJzpya4cL1S3jWvQzXenRHB3h09w6+vPk5bn/0Ke7f+gJHD/bATYtifgzTNQB3sPAxnLY0Im3nOWzyOTqJ4RoAz/C3D/S/MAauc5jaQtrjUjTSAJxnSYbsJfoq4Fa0A7kwNe9buj4qiF2nfUrr5VD3WBCfdHxjYx8Teqafn3Te0r6f9EzsZFJO0nQN12D8mRPbCjRqHb0bA3Rj87JC99acVebeJC4FYCIcffIzPPjbM9LoM/l6jAGPlMcb6/O6sWkZFR5wYoad0Rnli/L5CK3IE9nn+V06duek+zhN+JzPV16PBKsZtpPysrmpqyYfVx5bn4/AajBpMiKvmtDY9sr0ri1fe4BUVZOBxiYlEiloUmIl/g9iTmeNhBu0mvhRF9V38TuZeoYtLGxRrphjCGhwg4VXrZL+vS5xnPZR+z0wxQvj0RDhRVEMcgeldaQI30TtpUdRhMAC8OAQfhYsTsliwk/wXSeHjVRDJoEALMoQkcmjCCCx6foQto4ZtpzA2BIGAkCaWoIGFKaARxMidKUhTQGAUXUFJq4MDuAIFykAIiy5EYfqEJqcfB/sgaxkRJecQiEhpJMcMLWlGLyAyMR3fACHYnLX+ywxgA4GNUR7pA7eAtYMiBhELIEACgtblsFpv4ArCqAoMZlMYgAAGyJiYWJhCouKSXLr7GzD7myAZhXw7e+hRIHSWFQ722hKC4ZBwYCbOVCpa6qhlRHypIQ1C4wNVPVMBh2FhLo8lPIYD1CWbFjeFA2dCaAlVBcZeqbBw4O7hAhwxeDr+J5DiWDIE5nTE67OWMOKIQtzvINSTdBJF3HKEMh+WnOBE0DEgzkc1NPHFBnWkY2kv0DCCPRi0MsqwEzvRRPBnEjFVPIVbP+dc2LugCTIDDAwDVHJWryg1JE/MB1KO4pMcxYZN9Nr81IhEgXAKm1zCPcsYIkhQTTKSYXCFuFMdDG5qghRbBS2xHaTtjXIgf4T2/FxwDAOdvu/rXOSl4kMTu/soFk0uP/lIxwdOSyO5tiZTLFdFCi3Z5iUpZh6GYILqmVDJOe3lLxkXTfMdzMs4xLSsb6exOCnz+ZlTJKc1rWOwY31AmDqw+Wmz1sAlaeosUVIqAsgmtEosyT7MPTFdSBNPgZJ3gxDMBODYrqF82efw+VvvIBvLmrM9w7wxac38d5bv8LhB5/i+O59dOq/5D3KIOhxZnVd899zIDTGCA/egZg6g8Rx24DE0sAYdMaIabX38AQ47sCWxILY93WlZyttL+chwEMJ+jrGMq0LQOLLvCqBT9c3NbMHhtrydA5yRjvfQ3l5HNju6ZVZ+XxsnMnkj9aXt52vIUN8XJjGTL84czdYBWrr/l4rhAjbOgqJMlCQA+D077zu+HeypjnoXQe414HdsfpX9l72WT7WMYDmgyAytVDq+6EWRetBsL6Tf7eujIEk/VzcN4TeLJdLTIreZUIVC/kYKNxro/WqrItIoz1FYe7g/n3yGA1ff4BkjMFkMgERoa7rsEA80OakznyFLUHsoVPDQV2sC1JOQ8SrsoyH1jmHpnVoOo/JZNIzLQkR0/a0LzlBTdWSKSEeky6lhFX/1rFFZiPbRD2h94PDEokzEM3fkLTvnIchB7gWFJLCUkGYVAYUAh503mHe1GidBzgwPTCYnT6Hwwf7YsrmGV3bwbtO5sjMRBviHCTvR8L4dRaGLAz1+VoA4UkXcLCTCbgo4L3rIx0VBsb2+U6sNZhOpsKkWQNfFTABpFQKWIyBtQUKK87/ZIxoVqw498NaOAr+MAYwtgADKKYVysKiIAKsMHa2KMT8zBp4K5oPZVoHEsXA7GoEMIS/2QAtGRRkYTsHbwAyAlYqD7Ap4V1AJESBSRXpyLKgSDRkb4hpmGFG4TvFliB2kYg4YiyrNT5rYJjk4ooaJAIKTyhc72ptkz3WEaNLVEMpCLIOKEcuKiagM9xrq7JSeAxAUjwN3BO/2N4IYV+RdnHaq0xYyOKvlTMf+lxKWMcuXT336dlX2+icHqTnrz9rLgIRZI73uqbppZkKP5SG6d/OucDk94FgDPdR0JxzMXCN6zyWy0aSsoJR1w3atsV0OoUBo+tquE5Mjre2tnDx4kU45zBvazTBfvzKlSvY3d0daJHIjZsppXtUaZycRQNb9Alr9d8TFUYwv/QwRYGLl6/g7Q/uYOEW6KjD5/fu4dKls5jNKmkXIVIe6YVq8OGHH+LSpUu4dOlSpIP5vpIyLuletydOYlTT57Tk7a3zuXhcnUgC0qR1OgCdznsCCIhCKBLqRRMcg0ewBMdx4dzpMTIMwEkQEFsIPZ5V2Jqew8tXLuD5X/8WDu8+wts/eQO33nsfdz/8GN3hEUzTgsiCfA3mXuuSO5+v86s9qVixi4aDRFcVGx7AQfLNEUuEyIIAxw4emoS5L6t+Z/08DvyjfO97nJd1e4IUuCX3wjoBwJPWmX/2JMzrWBkw2X+7KlbKSXt78Bx6upYDFn035Wty2vA4YcMAOKU58NaYcOb0+iQwos+n2owxfi4H4GldY2MeG/+68fbfU7znx55hrGoD87nItTdqeQIMrbG0zryO3KJJn0vn2pD4OJN3gUeRetI0LysCBxnVoL00pH7/lRB5cW3o52ItYF5TvvYAaWd7E5NqguXSYlqVkpCwbcVZPzxTVRXqpsFsc1OY+a7FbDaLE84s0lDvPaYbM7iuQ9eJ8/10MgUz43ixwOHxAoU1mM1mcM7h8PAwZPMVd5iqLDCdTlDYIlmkcEjSpWeJqkbJZo0HKzwN9H5Kxhi0bRuZI/H3kfhZAiL6fDyTaSUgRy+gwAh0jWSUZx0viVR1e3MThWFMCu6lw+H/3ivzY9G1LY6aFiCJ9tTUDZauQXdqC6UtMJ1OMQs5pJgZpqqAELFIfb0ASECA7Sm8JcxmMzFHtAWqiYRmpnKCsppgMqkCM2ljkkyYkByMRLugmeUJBj4SpowpBgTo6dj88HKQsABB4GQNnIZAZgjAAaJfTBfmtGSHiYYtZ5b8IZE/N4FBYSBEuLMQM0FngmbLErxxcNYD7KQdXyLE2ZPuhShIYMak7QNP9IMKFzG5QCuUyOvXjInLCHwYNjGh8DZ+puMToGThKQkKTgSEIBHWs4RwTuc20CdnGGx6TxdOaFnlAp+lUus4VwQXfL5ie7ECRsfpnxnjwL09tZpT6IC8V+ZSPki/0yhgzD1hTc8cVn4Pc8SiWfXOo21bLJdL1PUSbdNC/VU49FPnTXI48eDy0dpd20tMXYhw6YOZnI61bVt0XYe269C1Lbrgh+adQ9008N6jCoIc1zmJlBnAkfcey+USzjm0TQfXeBzP5zg6PELTNlgsFiJ08R0IPQirqgovvPACnnrqKXhL2Dq1A2bGndtfYjab4erVK7h8+TKsFWGBXEp+AGY1OIpuHwohWSV3EGCtQVHYIMSwUZgBjEtTAREwICTzdQxceeYp1PxjtF0L2wJ39/fwaH6M7e0ZYAAyAHkDFzYVeULjOrz7/vs4f/EiiLnXTGeXqgrZhlJojp8P+AVWxi9oLvqN16+3Mkhx3w50OHGvRiYqgpiwe1l3l/rM6V0RPtP9RvJXS0ANCTZpjQFCzihDFP0tBQRp0JrQkjMgNmB4gBhUiKkyiFGRBdoOIIpWmQ4OXBEmT1/Ety79IS6+/Dw+e+OXuP2rd3HwxR1gvgDVBqbrAOckcSQYDsGMBgTHPHCal2iM2iNEk7oBEw6ADQXfWKEjURgBhvEMywA8o7QS/VOsCFf9gpHUPwZOiPrADinzlprH9vdlmtjdB7/VcQa632fDkvdvRQCU7amvWgZgQCdzzXNjUnxhYp+s3RwAq7l6/l0OTFKhMrAKlNKyTmOjd+UY06zm/Ovmbz0w6b/TM6e0zmOoJUzrWgeK8r6lAvN8z4wBbJYu9EEkAngiE3uYtjYcD8a1YCumolm764BePv96D5mw5pPJRNwsTjA5jcAqm8cx7SnSPvAQGH6V8rUHSGdmE2zMpmgqMffoXIe6adEFhNzUDSZViZkxqKoSm5ubmB8dwhKwsSFJ+BaLBSwxjusl2sJgsViCCCjLEtOpRJzr2gY8KVAWFpuzCt55TI0syKP9fVTTGSbTCUpDYC/qw+jrArkArZHcOQ7CjBOVAAgOQbJsLPo1Z+UuIuBomgYAwxCD4KO0mABMKgnXbUkiL5elgKS2bdE0DQyLdK2oCjS+g2ePykoYgo3CoJIbJbbr4dBBovpZqlBBHPHbMJbCVLj62ku4cO4MdnZ3sbGxEaSVOmZKzihFYkygGDo6AlQomF0xuEoKr/ymDKnw3hQv1d4BRuc+ySewcjZ7gsfhQBudf40OlzQYWCQ4mL4uvclDP+IPH8YbWihYwIziKtv1kQMRpLY9XUjsbQ0CS9Ez34h97PfXgK6HSAhKNMW0LrxKBKdEJ/u/IUKRSLxiLiuSsN5p5LWBBCp0IFV5KxDxHUGWQCZQc8AwM1yILghGyJ0VzGOY0RFiOFLvE3PB4DshRFL9zhCFB3pzxOnQMxgmKpWuKfDUbETGWJAmhA5hshwTOudxeHCIvUePUM8XwnACkPDNPm7KmN+Ggs29V98diOama+E6h8WyhbhpeMzni0j867pG23jUdYO6rlHXS3SdCEGOj4+xWNRo2w6u69A0DZZ1jbZtAACtF+DUNq1I18L6ORamVE3++v0ia0bJPiIi/OitD1CWJSpjcfnCeXzzW6/iG994CZUp8cG77+H9d97BM9du4MrVp7CxNRPmNCBluRQpJE4NFCVE6CQWsy3nGG3HMMaFsNtiemdNkmoAKbPoIcnXjSAlNji1tQnrG8AB3DKaxRJHRwu05wilBwplFAIwZwKuPXUN/+Jf/Av89vd/O5h/9Bes9wlLHtA9MzCfHwtQda0IhRaHgBftXdd1Mfqpcx7N0qFzQfhFCCa/Qy0Ce/GxVGm6cx4OVn4m5sR6vpmdaMZpqHUzoBj+HxRC9HoxDy6Clt0YEeZpwuOqLDGdivCpqsooZBPTR4rmMJ4drAW4bcVvE6I5JltGIGsMhYihEgm0LAnXX7yOp5+5ioff+zV8+N6H+PjdD3D80S34h/sojo5RuCUcL9EYhw4tykaEdEv2cCRgrvAE8ozaCCBxCWjWyFvK/KpJKQehQhH85zy7YFqt9JsEMSeWIibYomu+qp65k3WKplkQGgXT+9AonotR9UM90ozebcN8OeuY5FRjHNc9Awyr4B1r/861EGPtxTqZVup/kkLoQVKqkVinEYp7H0Nmfh1wPEmLk487fz+f83Ft1FCTKefQDQACkFrl6L6DXtMikEAAKNxb9ozNd65tWtGaZOAindf0ucH8BymC3HuBhzJy/8Anwa8Sum7CGRABuYmBDlKeIm8/B+ljfcr3cAQ6HLKkBMuqrmvhEXz6k/eM3tUMkB+u9+qcJt97EbJEnkr3wVfYz197gFQVFlsbG+AZB4lpga3NTTgWyc9iuQAAdG2HorCYTqfYnE5w9+5dHB4cgFkYmI3ZDGVVYb6scXx0KLlXStFI7e8foKlr7G5vo3MOVVHAlARrCG3T4NT2NqYbG6FHwQyM5KIiEtM/MetSoiyXpIcREzfX+x9F5op8/L2PuCMbpijKYHomB6EKzvxECNoMRhF8qDppMAC+Ao49DBht2whzWpSwZoIIBvSfJ7ATpsobRt22aJoOk61tPPf887h48TxsQbDWYOv0qYGp2RiKT7cspZKkFRb98SU9yuOXAZ3459iXUaLGJ79C2S9jPR7tEw1/76/5EzsHNXeJhIkop2Vr22fu6ycyUPjCPKSHyiQwM5yaF3HQ0CAAFs8hWaQ6VwpD2ZucyT9l8vQZ+YzQBfMyBSjikOnjZcuhY3EfsPDCzqXhgHsnIcPBHCDswbIshBH0FpUpgOBrRMb09QaGJpeG6UwTyVxozhr2jM55HBwd47Obt/DwwUMU1mJaTUAMTKoKbV2jXi6xrJdoWwEteuG2IUdNXddYLBZomgaHh4do6gatA46PjqMmp27qwHQ7uI7RdS4y1DQ4V2JGGzdeco4cODE56HeXRprMGREtBonU0nu0rsF8WaM0Fnv7h/jgk8/wp3/xl/je934Nv/4b38Huzjbe//BjfPDRx7h+4waeeuYqprNpjDwI6s810PdFpIoCpihIEr3naHYo1rLDVAlxP+s6MQAm7GxvoywNOhbNRNd12D84wLJuMCkEQMijJr63u7sD5zo8evQIOzs7g3noaRYD3uL+vYd4++13REMVJJpXrlzGpNzEol6ibRrYYoKymMI4B/gWZcEAd1GaLMIBEWS1bRv2uKi3jHA1aF0HB0LbOjCTgPPQn651cJ5hjOY/6zV9MQ9ZYOibpgWRWEtUtkDhgMPDAzkr1mAxXyQJuh02NjYCOBVfyu3tbUw2ptja2cT21iamsxLVRIRsxhIcE9A5EDkBUwEMGAsQtQJAyKCalrh87SlcevoqXvu1b+Pmex/io1/8Cvc/+ATt/Qeg42OYegnTGjTGxT3D3sEZFzWFKe1aiZpF6Om0CsYyplgSL+c78PElZ2jHyrAvBJVmp2awKWM5ZuI1xmimYxhr/yQQtU4zNQag4u+GoO4FY346eR09aER//yUgZ6y/aq4l94KLtCzvdwpSTirr+rdi5otxbaB0fXXM6+odC4oVvuw1hsLQxGfHEh9ryYVzY5qik0C1ji38FfmDIU80eAN98/09n44DkY70ADYdw7qxjAH5AWBVCxYG5vO5gLkQ+n8AwNLuZmNeOQcjIDzM1t8K7H/tAVJvngFMp1MsFgKICAywRxUCJmxMp/Gd4+NjlCEIgEhpa2xvb0dzBEtiera1tQUiwmxSYWs2xcbGBpgZ8/kcm5ubqMoCYI+iLFFVk8GBM0Zy1VhrYx/0UDoAVVGiY6BpxezAWs2bEKJQdS74BJUwhoK5jYC5pm7FcRo23C6S0JUgJgXGGLhOIve5thUzIO8xraaYVBWargFagNihsBVURaKaEvIAwaBEiQYerQfIFnj2hefx9PVnYUuLrm3gGo/TGTgCnixs69jh0/I4Ivm/RFFNC4ATCZQUlTaH85owpMMoB//z9YuSC2kg9VRuIXRJQE2M6xKl29pvBS49KEII/uHjReb1kmGOGbA98zC57+Af0HHihxMq9oHhBtlYpzaqq24CcAPnTLWAOgUt6qcDyNn2rkPTtlgu53CuRde1KAqLna1tnNk5he2dHdiQ3Fn8mRAlbqmkMGeG5Hcf5/mLz2/jzV++DQZhWlbw5LD/6AA3P/0MX9z+Ao8e7GE+X2B+PEfXdehcF03cPIf8W8nFI3OkebhUi8dBuhd8KnhoYkDcOzETibCD4gXTXxIdM2Cpn1N9P2gbhmPU/dNjD71k5DvAQaI4No3D/O5D3P1X/wZ/9eOf4nd/93fwG7/2HRhivP3Ou/jwo4/x7HPXceXqFUw3ZnHd+pIzK/3eUYayKCy8I/hgfqcMskabC68BAeiVZYnpZIL9+ULmGUKX66ZGNytDSPr+VUC0Bs8++yzefvttvP7664NM9el8t12Hf/PDP8fR4SGOj+doGjFv3tp6H5cuXAaYcHR0hIPDw2DuKFq9+fEcnXPogpaw7Vp0reRIa5pWTLedg/cuikc659D6FghMiYbxp/Bd03RRwqoWCYZEC09lMfBLs9aiKApUZDGz4kc7nc0wqSowM2YbG9ja3MZstoHOF/B+ia7r4p2JkMqhqkpsbW3g1OldnDt/BmfPnMap3W1sbsxQFgUcnAT0MBqa30W/MkcG1lgUtsCpc7vYPPtdXPv2y7jz6U189MZb+PwX76C7/QB+/xCel0DbomgZJRiePFrjJJG3L+McpSWXbI9FwhpjMntTxdUyxkjnzJ+2rc+PAZ70/tPPBhqbE9ob9HUNQMvvozGp/ZO8k7d10vtj76ZzqX3Ng2TldRojSUjzuUnfXRdQYV0ZAxUnPRtpDhDMfXtfm7G2n3RORLhKvbAqGRMwHhY7BxOppiT1c8r7n9Y/NkYFa9q+3jvy7ji40TtbhYfr78W+nVTLpvRnPLiGTIvkJyW09XJlD+Rj+KogR96Te+5J9kJavvYAySQLmjLqpTUw1qAsQoCAsFn29/dBRnximAVwHBwcRMmItQRmh6ZZwnUVNjY3Udey+AcHezBGggN414G9Q1FIMANpx/bRWIAQmlk2w3K5lA7HBWR0jhPpol78VXC6FtMHNefwzqEM2d6LSgJBFCGoQNs2wf5dwk13rgsHFmjqJpoudV0NQw5bkwlOb55BXS9F0upDoj8iEEzPDDPBeQImFV5+7UVMtnawbDt0TYuubbAxm8SIfWn5Khv0b3sgVstQMhUJ+KCt1bdOIsbKpK0SFiDz+e0JEPemHE8KEtPf9dJP308DA+gz8d3E3yNlGPQzNcNMAY8MR837WPxeEAIOeN+bpHEwB+J+Milhovv+i8S2816iPwZplJoXWk8okJqcBO0UC/BRjZO81AstFLD3/nfCLDdNCwvRkFLwYSpLi+XxMT747As0XYez587hxrPP4szZMyjKEjZErhyTQGmRy1KuCu+BO7e/xN/8zY9x7vwlHB8tsL93iLfffhu/+MUv8PDRozDX4rcR5z/Rivkk6lGUbHsxQ9VIimDxXxHfN4MgZgNYTBGck/pgFUy5BOSF+WTNCRFMjGQH9mOERDXUPqbCAA3v3j/b9wvGgEHiEM8ezbLD/M59/H/++b/Ej378E/yDP/wDvPj8c6ibGm/8/Jf4+KNP8fwLz+PqlUuYTCZSnyH0gTPEJCtnyiJTawnMAo6KQt8J+y0RPIjTP6MoLTrXovOinVzWNZZNIyDfSJTOdLWdc3jxxRfxr//1v8Hrr78+2AfpmTo6muP+/YeYTqf49NPP8d77H6BrJbF1WVl0XYvOdSs+Y01bS11emUbJPxTPY8pUgqGmnWwSxjPMvRwFAtgGLW7PfEcGPl1fI75F6keqTJf4eNpo7mdNgaKosLt7SnzBgv/ndDrDdDLDuXMXsLuzg/2Du7j1+R0wi5/b5kaJi+fP4fz5c7h69QpOn97F5tYmbPAFosKg5Q4MRlEU8IVoC2xRYWNnC89882VceO46Hv3mr+Pjn7yFj994C0effwZ/cAjDBNs2cF0LLgzImmBC298PY07j6b2f7u0caMS5p9Vn0vVP60+/T83CxrQ468BMGkwp73PeZt6XdAzrNEMngZ+UAU/NPNP5k3kZ9iH1o8rnIzLA6byOtJ3fXWmbaZ05o5/OVW6Fks712NzkjHk+D7G/of85M6/9TTUnORDIg3UoKACU3+vHkvdf5zQNkZ33L7XAGQOS6Zznwvi4FtneT8G93rn5GqTt5PO4bo/qs2n0wZV9mrzfOYedjS0s58dxzz0OHI2ZGebzpfvRY/15Oql8/QFSYobBzCjLMkjoRCOiauTOd+jqBpOyAoxBNZmgaRrs7+/HbOpVJQEOzp45IwEPJhOw9yhCvhnfOcznc5TGRIlfWZZwDAEXBDAT6lqcoztrUZYVrBWNjnNdQNI2sCFeGDzioH626FyDoiixUc3CQWaAJciAHjoJ/Q203AVzPRfCJjJs8CMywbTIe4fCFrBlgcmkwrSsUAU1ZwFh6LouBJqwRXhbchW1rsPu2XM4d+UqvC3RuODP0HUwBMxm01EHuieR/mg5CaDkz+Vl8F4aaQCZsDASgV7arsy4IU1gm7CJTNDo+qGlxKY3mKSl9C9eGhAfAy9Ew4ew7AI4BVgxEmaMVYqujDVWNS0pQQwDU5BmjAF3fb4n5/sw8ClTsSqRUqYzSKNINJCWxIdH/fdU86BMHpjQBR8hnUfvPVzXoXUODELXtmi7NgDzMMZGQLUxBmUpYcm7rov7Vv9u21Y0pW0L5xyWyxrHR0dYhgiO+/t7YMWhrr9IyqrA2bOn8K1vfwsXz12ANwZf3ruHP/uzP8cz157BjWefxdmzZ2GKXkqYXm7JQupqo6kb/OpX7+Cpp57Bndv38PDBPt5880288867qJtaPJaCUks0MRSc18zAACJVfggutWGb2kzDkZgaBObXB3O51KQibt20TnkIvdkm4txrE/031O93VuySMAqDPe2hQdeJDIogvKlrh08+vYX/2//9f8Srr3wDf//3f4AXX3gZDx/cx89/+gY+v3UL1649g8uXL0dNvTDuYgio+7Mfru53A9KcaQ7SvkbNJIp5oRmSW4PIw3sXnfTF/7QO805hrEO6cfr0GSwWCxwdHWEjmkVnAgr2mMwmOH/hImaf3MTnX9xG18pie2rguRk1B4pCEmVKiIJpkeSVQzLvAwqVBCoZrgIhdZpU8E0kdvZFFs4+MnMk/4goAqPQQVnHosT+8cMYQTFGGaQKlkpsbm1iNtvA7u4OTp85jd2dHbhuA/Pjz/HxJzdhf/oGtne2cO3a07hy+TIunD+Dnd0tkDHwUPNxMfOmpgGVBaiyqDZnuPTiDVy6/jRe/q3v4tN//xN88NM3cPjFHXSHh+DlEpZYol5JHooVRi8HAWNgKGWylbGNgDRjtMeYQOY+RUj+3ZM4gQ+Z0lT7uwqS8nbX3aNjzGM6H2PO/XoHjAEKQIH848eTM+Ya7joPopADs3QMcn8Mz9lJ4xxj3rWkpmw5yOnfXY2wNgRKq59rHanWJQW5eX/S7zwYuX/VmCXNWOoZbT/Xho7tl7Hf8zbGwKL+PgbwAYQErP34cjCdz2NeZ9oXY4JoKuwvYw1m0yl2dnaw9+D+Sl+eZE/kZezce/47DdJKcd4PNpcwniLh976DYSt2j61D29WYbWwCRmyrNzY20LZtDM1NBIlSt7MFgFBYAxCwtSkBCJxpMT8OiVO9lxxMJDbezgczGcuYq/NuK5qcwkzgXYdpFUKEB7OM2gNMJL5JRR/xpm0bcItYt7UW1lh0TszxqsJESUdd10BVACiE4DlxfFapY1EW8M6jcx2MITHBA4e58f3lHC53D6AF4AuLC09dx+7pM2gdsGw6MEliV8ceGxvTyOxqiPMxydz/kqU/HAB41dm1f2b4tzqCEyE4rw9DNKtzLrPrDxwLg+KCE3Z6CCMQCG2JSRuvEL+8XwpU0ksqld6tA4XxUiQD1kAdAeSob09sN6x913UxvKaEdrfwnqOvjADtDi44n3edBAIAUfjdAUbCdroQKZIg2h3nPZq6xfGRSIc8M9qmwXK5xOHhIepWIq4tl0ssl8ugIWU471DXNZqmiWOo60aYXhngQAql0RCrspIkl0QoygLGEm7ev42f/OoX8M7jxrXr+K3f+R1cuXIVH334MW59/gVee+2bePraU9iYSTTFNKeYzqsw1PLz8PAQV65cxeHhIW59dguffHQTn3z0Kdq2g4ecSwmVQrGfBGE6fACdqrVRMDlwwDdJ4BBZ7L4vQfubfy5/Um/OkfLVBKRaFkt9HUOANHR8FTEND/5mVsbQgSSkTGzbGs0z41DXHX72szfxwQcf4nd+6/v4we/9Hp69dgNf3v8SP//5m7h583M8G8Bpr1EKMZlD0XPhnQO8Jnb2ED8wyUfGkOh1SquCmzwuXrqIW1/eBUjugc57LJZLifZXFYE5XJVQX7p0CTdv3sRLL720oq1lZkxnU1TTElvbG3jm2tMoK/FpI7LwsGCqBnPZ7yNhDERrhqixUG1gCkQH62oSnDxYa16hDfoqAVFbFBcOCqYp+PZwuAfCHckcfMwYvhFTvz46G2B4AXiP/UPJN0ckUQ0nkwl2tnZw4dx5XLp8CWfPnkF3cIQHP/8F3vjFWzi7s4OrVy7h6tNP4dLli9jYnoEY6NDBE8N4B3QGbAjFpAQXhPPXLuPS5f8ML7z+6/jVT3+Gj998C3uffAHsz2GbGp6PZDRrAFE6HzpX6Rqv/OunaLTk4EOTwufal/TZeFesYcjGgNFJUvl8TP9zlxWGnYZ0YVRokfwdQVLnY9L3dQBlrOjHOagbY5RTkDTGOOeRzvLn1McvvXtV27Dar1X/GeWv1oV4z7VA+dwOwFg2nzl4TOtL+zP2+9h7ed1j4GZsXQbPmuG5yveu/p6bUo6dxzgvnYvC6KPjY5RlEe/Jdf1I6x7bg+k7w/4bGD45MfNY+doDJNWgjKFwzwx2nZiJGcLm5hZsUcBxL2EqqzJodYxoeIIdtzIpzGKXbY2EbyYS+3NAfJ4CX4W2bVCWkoNjOp2IyjWYxXh22JjNYkjwwlo0bQ1LhSRcNWIOSKBgDmcA3wUpqYAZIsbW5gxVVUapNQB03RR7e3sBRBVwnUTR2tgU6ah3HovlApUpMakKsV/vuj7MrZUNK1ophjcFqq0dXHrmGspqhvmyxXJRg4oCsAzXtbDWYLYxi/MvpTcnzIvu655Apt9mgCaAtRUyxr0kTv7s/WmYReLMvHrBREKVSN9Zx45eiqOEJgYcCFodNW1RUzTPXsXuYTy5dMnHpK2I4Cwx8wD3gQPCOFXy5Jz2o9/XXeeCRop74OK8RITpHFzbRe0UwliappGcVOpX5FzQ7HQoiwLeM7pOntP8Wt5LNLUmmF22XYvFYoHDw8MIoBZ1jTr8LuBKtD6qu+AulcwZTCYVyBg05GHLEtVkEgOX2KrAxmwHm8HfgkhyTZXBkbyaTLBsJWqjSy4BIpVMyWVHhlCVBXZP7WI2m+L44BAfvfMu/of/4f+Cv/eDH+A73/0ubt66hZ/89GdYNjVeeP4GJpMq2QMq9TdANOsB2rbD6dOn8e/+8t/j9u3bODjcF8YbBIZEQONwCat8gcN+CBtRfvUczV8HezMj/umuTR8bMlQEa4ugeaLBOaHsRWEKA0hiID1ROcNo9VxCx6J7vwOS3FpE0m8ikhDGLH5Wh4fH+JM/+SHe+Pkb+MM/+AP8xuu/jqZt8MUXn+P+/fu4fPkyrl27hvPnzwXgRoBAS1hD8MkcSoS3TsZa9DOjOIfCAhljsL21Gerx4dwxFssl2i4ENwD3Y0sYlpdffhlvvfUWXnzxpSjUIAUzYPHB2d7AYrnEpatXcfrceRwcHIVoVbMh6AIGGlZSU19K/Ot0cjOGdGzxB0wNFCANhSkcOpyGTfZeE+KG8xQCX8RobTG8vURdVEm2JiL33oO4QVPPsWwXge4RjueSmuHBg4e4eetzVG+X2NnexvkL53Dp0kU8e+MG2s7jnXc/wHsffIjTZ07j8lNX8My1Z3D2/Fn4iYWBg+0cCgK6tpZjVhVoqwqbNy7hN5/+T/Hy67+BD//mTXz0N29g79Zt+CMGtzUMu7i+jsI+9rqth6F+dY51vfUM+KDqTZnAdWUdU5v/PSZtz5n6Mc3Dk7Sv5SSwlDLow/dSv0IDgh8w38PGEk1zDpaEaKwwwnobqjYuLXqXakqPaH6WrEWkRKxWFTSgTfk8qFlaOre5xiKfj8gDUt+29k/P+LhIArHNXEORf67fRcAAYdJF+BdSYXBy9pMxKT0e20+5VkxBnZ55CoIxz0PfqfgsURSEDAIUxXVYmenwuYmgVwFG7qM5BorGAnPp3Sr0XeptGkktQUT9/CSCwPQ+WnduoqARiSl7wp9xPs9PUL72AClFupHQk5qrGLjgu0BEKKtCQIoRRpYhUvOF6yRpojGoitmgPgAgTzCFRVlOsL29A+ccptMp6rqG8w0657FsGmxumsDolXGBi8DQiKq/zxM0qaYgkuheZSFJTgEBKmQJbUDD1hhYAqbTChuzSjYk+kAOFhb1vEZRltjdmcGGvE1lWcZxTKcTuK5FEaSJHYvDpAMAF4iQtVg6YHN3F0/deB5kKiyOaxweHgsTagzgHCx8yJ1UJOOhyNykUru0yKZd1ZjIryq5Z3S+k/wWXnxSfL/r4TuXVhhDKgMkbhiBYY0gRx81Ms9IDrIXFBJ9eJTwaCFQvCAULHmnBF8NEXsi3bkuRD2TnDUuMB9NSMrpXAfnGnDIpQOIaV3d1KLRMRVAJkQy86LJCSYwne+iSZqapS2XS0kECh/z4NR1HW2cxfm6xvHxcdQE1bX8LcxCEdZMgHFZlSiLEgwn+VOKIhKtoigwnUxQbGxiO2iQprMZCs0PwoyymqAsxSS1bhrxfSgKeOfgQrhzIjH50Tw+rpUJr6oSy2WNup6LtBvAbDrF/Og4zmvbdVES3natBDng3gTixrPPYrFY4OKFc3j5tW/izp07+Of/8o9xuGjw27/9W/jk48/w5ptvw9oSN569LuH42YO5g5qSkSlAFHKaVSXufvkQn376BfYPD1FuFCinJdyyhocVZYZhFBRyx4QLHwBMiMLF1qS5PHvgFIQiA8Y4+UMvv56pSKoIjMsAGMnDsY247/UsYtBU0BIN2ZKhBB6wsOCQxyzVhGqoWKWwMhxC3Xb44sv7+H/9v/+/+Osf/wh/+Id/iFdffQWP9h7i008/w71793D9+jU8ff0atnc24H2H0gbpK1s5/4bhAiDrfAcK2jJDJmrlZJiiuzt/9hQKQyjgYCDmeIu6Q9MCriPYYpyJu3jxIv70T/8t6mWLybQKZ1FMlGWJHJ66chlvvPk+Lly+hqduvIhfvvtLAB2YLQiqoRdQwl6MUeGD1j9EF0XCxKmZoPqNpcy646HPgS4SA+DUFDRZT2Ey+nC9hnvgqPmObDAF986BnQNp3F0T6nIehfWivWOGpQlm1SzSGs8eXdvFMOdtU2O5BI4P93D//m18+ME7ePONn+LCxQu4ceNZ3LjxLPYOF/jix7/Emz9/D8888wyuP/sUnnrqMra2NtCRA/sOxgIWDGIHIoPSVjh35SLO/KM/xEuv/xp+/jc/xcd/8zMc3roNs38Au1yAuYGnFp6AgiwQzCvDQYPhMpyOLoIimRIFrKvMHdAnvUwFralWkRMakzJsKUOe8gr9GRqXguv3J2laxupIAUJahhJ35VsAIgn4weSglhD5O5LEE9HPkcHRlw0MMPX374Bpx9A3Z0g7+rnItSIWcu9qTryULrmsHdXc5MBoZQzJ87kWKf17UAePz23a9zFQm9al5zU+n0p5dC50jGY4V5GGKzBNAIFa4xgjkTgF4qaCNflf+s5wbYJRbgBH6edK9ymR4akAhmjo8ydV9MEz8rnNxx/bSYC0slRKt8uyRLOYJ8Ki/t7j0G/VfI/NPROpi7fw8RBLDpn/fh7pCcxgtfxHAZDSQ+SCiVl6wIDsIBsTt9zG5ia8c2i7DtOqQtu2MTmpbgJV1xJJwqvFYoHJZILJZILlcgmmDiUYx/O5MMtdF0KoFihtEaV11koi2bbtsL2zHSMdqSN8YS3K6VSYUvLwLMxaYQ2qwiYSjLC1jBzIsqpCMlVhasXELjl4gAAw79CxAKPWe5STqUg/ncOy9Thz4RKuXn8WDhYHh0fYf3ggPgQg3PzsMzz99NOwxgbN2VCVmztjjoEkLZKXiAWfeAZ7tWkWrYT67Mhn6s/je0lKYO56jQ/HCGvReVEPHgFkjThVOz8g3t65EKSC0bRi5uWdj2vSdg7OdRKBKoAT3W/KRFhroqmaSmPawFSoOZlzTsCMk6SiCpyW9TL43nSYL2ocHy8AEFznsKxr1Evx43G+RRM1N4mUJ4y/rEqUZRUDZpRFgel0hqqaYjbbBBFFfwvRBjGqqQgCJLcWsL29jbZtsVjMQYbjmqbnS89b5zyOj+fRPE+0X0K0Um2uzhM7DKTVKfOhf6dOp1Ho4VelsPFdM7RVf/TopwCAt99mPPfcNbzwwgt45ZVX8cMf/hC7u7t48cUXcevWLfzq7Xdw5uwZnDm9K3vBq3bRgyCavLI0OHPmDH78ozdw7+59mAK48tQlHB4usagdHGuiTS/gSJleJBcsIKqZ7CJOx5GW9MKm6PezasLi3appS1q/Msj6WezLyLN523m9RGJqZUy/VqLtRGDWQ0JMIhhrwd5jsWzwwYef4LPP/q945ZWX8Ud/9A/w/PMv4ObNz/CrX72D23fv4ZVXX8bFi2ckrw0oaOWGDuLpXvfeh4AROiYGyGC2sSHa97ifRPO3rGvw5gwSNns4x957TCYTbG5uYm9vDxcunh/MEIUlu/HsDfyrP/kLsJ3hueefx+37X6L1NXzrgUivhnlSlLFZnUeSRB0jax3HRP3e1jpk3MVaMy4TfGtyE2dDqjkJAX68B4UzVZQFrA1RUesGDpK3inTfkYExRcwlYiuH0nuQl+h8TVOj61q0yxrz5RL7h4e4c+9LfPDRh9je3MLTTz2D68/cwKWLl/Huu+/g/Q/ew+XLF/HcczfwzLWrOH1aEg93bQfTeLmXCg8uPIqywvaF0/jd/+zv49vf+y7e+usf472//jGOb30BczxHUdew3qEzLgA9E0CSMPPwwqjqfAzO2hqAlPMHKwznyPO61vkZOunOy0tKA/P29Luv4u806CMgcIejnD6A9f5bhHtPh6C+WloGQhrt60j/gdVcNTmNG9DvwNSmNCh9Xu+IMQY815qtG7+Ak1WNQjwfRgQuUXsxAjbGAO7YZ+kY0r6lfV0XcY5IApzovOf+YqA+9PngHhipK+3PyvzLH0m/KYCjBAhltY6t3TpQvw68p2I59oymrkWIY0nR2kpduXZt2F7P54mvqoKjnlfM77bHla89QOp8hzZEFJKkiB7EXtTwYXI1godnjsnd0gOvqtH5QkwLtrY2JXdIiO5VUAHXJQTDSh6Krusk+EFhUdF0gLTb4GheL2twkOwbY8SJmBnTqfjwkDEgFlvXopTkfWQIbE1Uf08rCSeuDDEgxE43wu7ubhyn9z6aJBlj4EJfyrJE6yXIQh1AIINQN7JpN7dP4dKVp+A8MK9rHB/NMZlMMZlU+NN/+6e4fv0ZeO+xtbU9IALrgjTkkqX+GWFGe0AkvgbeBRO2TjQ16ryvGhEFRmr2Fk3igl+QDxEBm6ZB00jYZ3X6b9nHxJoKcjQnj2vb2Jb3PkYUhDFYNg3q5RJN20a79OVyiS48UzcN5vM5vHPwzNjf30enQCbMu9qyy34TTZZEk7JRslSWJYoAckQSRphubWP7dAXXOZhCAhEQGQHwxsAWRey/IRPyC4g5WlkUmEyncF2/BgrYJDdPB947iNooIsLnn38R57RzHdoAnFLNGYCY54hZpODOSchiMn0o7z50N8MYdXZWKdZQwhYvEjO8GAiiUdAzkF8cjMBzhgsmXiQMfPD+R1guG7z00os4d+4cfvjDf4NLly5ie2cL+wd7ePudd/H6b/6GSP04kfqhd1gvTIW7d++iaWpsTTdw9uw53Ln9ANPDBebLVvqS9EfmimMEQP0e2blQOpCXMUYgP1d6xsfKgJHhVal33lb+/Lr20otS50YjIWpCTM8+2YMCbJZ1i5+/8Uu8/+FH+L3f+238wR/8Po6OjvHl/bv467/+Mb7z7dfwzDNXYQ0BxMEcoxdqKRgDEKOzMYuUm8JZPLV7SsyhTT+Otu2i2WhuBpTO/UsvvYSPPvoIFy9dCM+ojFWkqru723j66afxxZ37mGzsiElxzaLlavvQtgMm0RCgibIV4LCYyVnhSKCJtBXQMXuJUCiiVliywYQk5K6SyUeaQBehp94ADIKx5SCaKOl6sUVRiFm3AqXgVouyqlCVJdgJbYyCDc8wrKkAHMgJuCLfoTQWRTUBwFGjVDdLyZG3v4ejoyM8ePgQ777zDnZ3dnHtmet45tqzWHz8Gd597wNcuXoJr3zjJTxz7WmcP38GhsVMuOkYXeFAthHhZlli88pZfP8f/xFe/N638Kt/92N88O9/ivr2A2AxB/wRwC7SJTn3waQqASr5nl7HWI8xxmnJGeyxc6KfrQMNaVlr8ob+nOm9l7Y7ANbJuV0BZtxL8BOdAnpwFD4nESMOorExR1/VdfQj728+rycx0vm78bPAsKfzlgpJHgc8UyZeeIXhWq6AOAyfWSewGiv5HkjXYWy+9I7N+6uCkRxcqZBjnSDrb1uU5x3bSxIQqBdO5UArB0p5vWNhvtMSNZNIYNPYfBENfSsH7fV8r/L0qs2UOQxzlJgGPq587QGSKQwgQd4keo9z8GSiys2Q2DxqpCOZZB+0SITCSqCBDWsFgXq5gD2LlJpA8IDk5gBiKOQ6MMk2PGts6ESwjays5EVS0z2lTa5zYDCOjo6wZQ2KsoDzQZMUNth0NoWxYqLknEPrHAwYSJhV5yUqjPMOtijgQx2eOYCBFkUpGqW2bTHxE9SLJUDA22+/jel0ip3tHRRFgWXd4HuvfwPGTtB6oK4bEBm41uHW3Vs4OjrCZCIhvauqN93TkhLyscsoPTiaYNQ5DXgAON+hqYWBXx4tcXhwGAHOsq4jo922jZhmBUDTBsAiTLpF13Wo6yWWy1oCZ3QdjudzzJfLYOrl0bXiW9N1HawxqJdLHB4dwQUGou1aLBdLScQYpFAyPhb/DwomSEHLKGM1YX4qWCvmlZOJgB3RSBYoqxIMiRDnnI9Jg7tOJPK2EM7FOY4JIn0jwL9rW2AZzOdcD2K88+DQb/WpEg2PEAjXdcFnClG6EtdgJIt674DZazBUMyYlJVwkEluWfrMXqaQIHxQXGDgnupmUyCFeThCGkpImQDDGRhW6+BkgEG8bzJoC06yaDQJgVAvhYMnizu0vcebMGVy9egU//elP8O67b+Pb3/kOls0Gbt36HK+++go2NyZIk8+yZ1jbXx7L5RJ1XWPXbsf1FAxEaJ2DxiFnlcwaCgxxIAXJbOn415mCnHSZ52WdVHPsTObP6e/pOV3HBOVMmPZN3xEzLAdigoP4/Aitk3XrvMPB4TH+5R//Cd774CP803/6X+Lipat48OA+/uZHP8NstokL588IpqThfAwCSXiOEc1kr8mzs9lUABJJ8AhlqJbLpez9oohpIBRs6eV/9epT+MWbf4zv/9brATjp/IhG0VrCN77xEn717h/jlJ1ie2sXTdfCde3KvEYJKlF4m6Imv9fCxtHABc2oBvdhIB4tj0TTGOsLYCqls4Yk7xUI6qcaeoSCKCb/JWuC6XgRgq20gNfEs2I6bozB1FjAGHRBqOK8iwFbmD2oa9E1yxAt1cMAmG1WqKYzdN1S8uJ1HY6OjzE/nuPo+Aj3H9zHr95+D5cuXcaNG9dR1w0+++wWrly5jBdeeBYv3ngaly6dhyWLrmV416J1LQrXYFrM0FmLnacv4/v/xX+Ol379u/jln/8In7zxJuju5+iWS5imgIVHwR7et2J+k/hwpAJLZZ7WMZwKTE5iep+EWU2FhieV/Pynv+fWGGndad/WgTRAndUT5l83mY4L42Zr6fjyecyfSZ9LgWHOXI/9TMcBEgjnR+Y8BYBj//J+6ec+64O2FX31ct+dkfnOv8vnZ2zM6+rKtcAi3O37NB5pdk2f1D4tKTmtHtsbY2sxALQ0tORYNz/pWUnrUAHaoC+BnplCAizVi7m4vWTBc066D1dGLwxB4M1Sje644OKk8rUHSG+99RaeeeZpFEWBoijQNE0wf5sFW3CDuq7ROYfpRIInMBhFWWLv0SN88umn2N7awqlTp7C5uQnXdpjP53j48KFIKU+dAiCLWE0qzKYzkfAkKulqNhXag37TqxQmJS7eRe9SGGPw5d27OH/+PIqqBHvG8fExDg4OUDcNqo1pMIuTUOBiBiGMcNN6PLj/ANevXxem0DmUZTmwp/bsZdwBTNy9dw+FLdC0LW5/eRenT53Cg0d7OH/+PJ597kVMN3fRskfdic/IgwePcOuTz/BX//6v8E//63+Cq1evBmbErN28Y5KF/DNh5iW/kneE+XyJhw8e4sGDh9jfP8ByscRiPsd8vsDh0SEWiwX2Hj1C04i5Wecc5sfHWCwXckCchF5XC5amrgWUFEUMfe28OI6qXxYzS3Sm4EzKxmK2LZnlDUmwjmoygbF2YEpnjEVd12jbBtZKMAE1UZPw3hz9fZZ1I3MfCDIRoen6EMeeOZp2svfw0fNYJOUuaNRAYoMcnSG5D5fKzCHyVxL5TjU0QBJpSJ9H3MsmAUFixkgBvPaOj1LSiGeccP1anwATSnwuEC2KtQqCRi5LLwJmhDD8fdEko4YIpRETL47mHvI7Qxi/lBBHhl12GbwH7tz5Es89dw3T6QQffvQBvvHaqyiqCkfHxzg4OMTGxiyYdw3t4Y0xaOo2RNdDTAEgUnoOD6ZSXCvzoJf1QFI7PA9jl6l+l1/GOZMxxpyk76fv5j4S+XOp5verSCbjhUgEGAvfidmogQGzizmYiEIiRhYzr/fe/xD/j//x/4n/+r/5b7CxuYujw0P88pfv4vd//3dgLANwQSU4ImmFMOwGIdx+MB8SgU0Fa2z04XTeoV4u0XYtJt5ibGhEhJ3tbQDAYj5HFYJ2xM1NEgTl+edvgIgwny+ws3MKX979su8T82AdZb7FdLsXVoR9CwiADxJr1cKKgD81P0JkYBGYgBBPE2COjAUpSNRNywiwrBc6aNE7QQU6nSF0ak4cQJcBwOxgPGALG03CdVzOOXDTYkkGnhcAMdh1Ao5tidIYlMVU+hjM8OqmRtM6LJYOx8slPr/9BU6dPoXr169jvqzx6Wc38c6li3j11W/g2vWncOHSORSlaCNd59ECwKSELwtQYXHm+Wfw956+jFe+/x28++d/iQ9/+Q7m9/fgjuewbQNrRdDWCy1yMLO6r9btex23PpOaPK57L2U01525/L38nI/Vmfd57PsVpjhIqVb6yQrAGMwOMP29kdODFHjk//L+p7/noa5z5l/7tBJUAetp3bp5WAeSVKCZ1jXo50gdYyUHqzkwG6tjjL6uZ9o5MvgpgBLT2oHMKLZjMt/VfK7yPesybcrYHgy/QYnluj2c/p373A3nHj0tCsJSdc04eKQavv79Aag+4SrSCLPye752ovhYZ468rnztAdL7H76H2cYE165fw+bmJpbLJTY2NmGo13QUVYmmaeJGLAKzY8oCddvg8u4Obt3+ArPJFJcuXoRjj9nmBo6OjtA6AUyTyQSmsCiCBmWxXEZbyaprUU2n0cxBfZAYQOuEARbzukZMpozkGLJVic8+v4W2adAEc7Bz587h9Nkz2NjeQuccqrII/KXH/v4j7D16hNNnzmNzawvHywWaphGfoMKCnEhx26YFSPpxcHCAo6MjFEWBjY1NfHHnNrZ2d1C7DmVZYvf0KTz7wgtoHIsXhifcvnMXBsCZM+fwwvMv4MKFi2iaBltbWyumHlpO2pSrEjKDZulw7959fPzRxzg8PMayXuLLL+/inXfexqeffYrlcilMaVnCGiNBAcLcW2tx5sJFWCuA5cpsBmvL3pfIc4ykUzd1iD4mGpWevw9meq431Vu0bfQL44NDeNehDeGu1XTRq+kDifRCTepiQlbVOgVTvjGGNRJe7gENkxC+q1efhneMeRMiSVHQymjySUoILxiwQvCTeDOBwRJAHnh5IJE6DeEABr+rH8JYoeRR4eESKZCxYWwjQNkCIX+5AH3TP5dupRTAyfgoSPtsNBslI6aFHLRLIJa2AYAsDDnAtQA8Hj3aw3J5CbONGR49eoCjw0NMN3dQlhUOjo5wERcEULH8T4GkMQZNswy26j6A4DT8egLWEp4+XrzcmzIM5m9EKjdWTrpUTwJJ//8sapJRVBMQi68ea/jfYB8uEsRwWQP4+JOb+OEP/wx/9Ed/hLKcYm//GPN5ja3tEsao5nJ4Kce5iEIBhD1hAmgtYctCfJECbWqaJmo+mFfD73ovYe4vXbqE23fu4MaN60FIoAyPpIjY3NrBC88/h3c/uImNjU1MpjPU8/lK/6Izc5R/ydlGkAATCKzaTmDIfBPFfeZ9CP4SBBhEQIxC5z1816FjF99Rc7KAmKQ9Y0McBgGTKsQT5YqFNZPoA+C7Lvp2AiKssAYITcIY0XIZA3Teo5hMMStK+btr0TRLdF0LOAsmBlHIeTKrMJ3M0DYN2q7D4dE+5guLo/kRHjx4gN3dXVy9ehWHB0t8evMOrl+7ipe/8TyevfEULl0+DyZGQw2M72Bb0b63tkFZlLj48g1cuP40rr37Ed74s7/GnbffQ/fwPviYYb0Bdc0agYJqdseFDemaju2/nDmWfbJe45C+r5/nv2t9T8JMj4GTdX1YLekzBgDD2jJoZVdBn9KpMef8x7ZEQ188PW8rd0IGLkwCEk5qb4x5T0tsa+RruT9MFEQ8ScnXJgddOfB7khLXLvh7pFHj4ti5t+RI21MamPqIjY4zfK4WC4/zZxPXhb5/uXD/q4wrtp+8ymDxgw7CHUrmNR1fyqfkYxd6Oh52Xdv/qvfh1x4g/d4P/gBXLl8CM0J4V4JnQlVVUZ1qLTAtyj5nUVjAc+cv4LdOnUHbNrh//yE8gOnGDGQNtojABBwfHePUmdM4Hxhy1UJ0wdxpuVxgXteALeBBKMsCtqzipqdgl+6Z0XmGKQpU1QSzzS1473H6zNnADAoDb4wk0/QkpkllNQWB4V2HcxcuYbqxjc3ZJuqmwWxzK/hJtDBGTCW8c2LuZSVvkof4h+zt7WG5fIgv797Fo0d7mE2nuHbtOq5ceRpffnkPZ84/haOjQxzPF7h44QIsWfyrn/5PePbZGygKC2MkXxPlACkeglxN6gcHWpgcea9uHD765DN89NHHaOoGjx5JEs5f/OKX8OTxwksv4tLFi6iqCk1dS8hq7+DAwceoQd124KZFXdc4PJ6j6xyapg5hp+WwdJ0LOYCE+WiaRkJms5qiCYMgpmjDvEUaN7w/nBRBVSQEUGGd7KfU2VKZOEMkkjobXO/DHBUFBfDTz2XTdji1exrnzl3Ae++/j8V8AfG7CIE2VCSD0B39JfRGCKr0Spo1kYFSxh0IzBOZfu0CzZFnTQRlAOLeBDD4LC/GFIMLNRJkAjj4SkT2UaYnaDtzhh/xOQVVG1vbuHLlCtrgC/bwwUM82t+PZiLMogOSPGThovWigauXDQpbYFm3uHPnS1x/dhfGGAmuksxJ4MHD+kp0v42NmUTQazrUywVsIZL2wPbHec4Zn3RfpGWMWXqS8rhnxy7oMbOYlKnKPztJYjvWFkEvJABMKIsKjkw/9+zF8d+YADgkYtmv3voVXnnlFVw8fwFL1+HgcI7NrdOQvEdr2mbW2Ubg6uQSB+C7BtVU6F2QC6BuGiybFhudAxmKZnaDyxge129cw/vvv4fr168NmnPOCUpgj2998xv42c/fwk51DpuzTTSLRehJIjXW6IWCkCUlhJq0KpMYBBkgikIZCmbAeh/1ElyATYjSxCR9KS0m1SZc5yRcrvMBFALE6nNmIjmQmerHroIVay2KYgpiEsFc00h/wDDM6JomaMP7dAfioysdK0rx+ZpMJ9jwM7Rti/p4ga7pwCECIXsHayvYaYkJCV2u6xbLeom6qXG8mOPho0fY3jqFSxcvYVEv8Mknn+DGtWfwyisv4rkXnsP2hVMwzqNwDXxdo6wsUBi42RRNVeDKt17EUy9cx0c/exu/+Iu/xO0P3kOzt49iboCmhYEX7SWczD1z1Gkr1TVhy0ve7/485EIMBQupZm0d8NH9ld4lJ5ncjWlr1tGHnEEf01IFMU3YS3mGM8BYikGhDNLAJ8O8RDkQGaMdY2NJ+8Os7QY6gSGN0WdE2NRHI17XRj5XY5qc+JPQR8BN6wDkPvYEN/Lu2Hj0mRRk5LT1JBCd9jPto37unOvvauU1svYGQA288tnYPOX7KhXa9ZxEqB+UptEDwDF6aHKjIQp8wlgcc295ofPu9Lan2FfXuQi82PtBdGLGsP+Joij2m+PfPn7JEOsZRhAgo88B+aTlaw+QphunwGaGsihQAqhmYuY0b1xMsApmeHgwWXSd7yXOIJjCoDIlvvnt7+LgYA97R0cSWaxp0HqPZ196ATvbO4CxMLYCBwfWCrJBZzu7Mbx127YwRTC3Cf3TtfbeY3NnmpjAcSBWQhgsEYoQ+rtPNClgzxgLGIvWM4rJFhwblNUGrPe4ckWCJ9RNg2pSyaEPksiiqGA7j9Pnt1BNN7A4luSXN64/h7Ka4Py5C3jrrXfx8qvfxP7+PsgYbM5mmM/nWCwWWLZLnL90Ad47bG1ti+QRDI1cJYE7NZKIR6SH8lQESWIcrgEkgM9u3cYnN2+h7jrcuv0F3nzzTXz66afYObuLl197DcyMB3v7uHf3LpbHCzSLBdq2QeMadF07yN0RgzgEXW7/ux6u0M+kX7GsXAKyYkQEMuVAgtP7KCTPDC5QQgwpy/r9kAgNmg4NKrEg70EWqD3Dbm5gtnsKtQPABGOLBMD5WA8BKNA3skLk0z/TxVn5EglBCmtHvflD3v10Dik+xxJKeORyE03QkMCLQIiGPVJiFyVQHsYAx8cH2N+fYjKZCONVECiElicdf2QMxa+wYwDwcA6wdgLvFjg+nMMyoySCb9oQqavvg3eAYFwPyRVTAmzgOpG2nz61ASIHQxU8G4jnzfDSXAcuxi7Mdc98lXdzZm4M6KTMxJP6SKTv52PiAAIQzhkCc2+LEkXYMxItUfKtWUMojIXrOtTzGh9/+B4uXzwNawwO9+e4fPkcPHcSyCBrJ/7zEtGNEdbde8zKAtvTCp4JpTES64A8Gteh7jy8nntKJZShbjAuXjyPv/7rvwrgLYAv6GXsAFfjxRvP4NTmBPVigWk5BbzsPbHiGZqqcqDbSpdS+sBBWswISXwNIebphokAHUQgG8AOGIYYjjqYyQywBaZ2gmpjG0cHR1i2C0xMAQo0zwRNrQtS1hQkgcVyobRlEI4wiorg2w7LtpaE44wIfFOGzjBDT733DLQeZAuUZYnJtMKp2SaaehFTDywWiwAOpY7JZANl6fugOeHfcjHHwcFD3Lmzg0vnL+HwoMaHH97Gc89+ipdevYGXXn4BuztbQEHo6galtxIwojRoywrlbIIXfvvbuPba8/jlj97AG3/6l1h+8jnMwRGKxRJELQgdvHVi0hgDN/WUjBK6qOuVO5yrsEHN5oeCwEwghHGAsQ4EDffIekY3MpdPQDeUifTsRaAZEhd7ZS6N3NWOLdKQ16mPk/5cR1fy85T0WnZdQu8VHBGF5CIcwuBrzHnCUNiVtNlH7l0tKfjUNRiCNL8yz3EsRANAqJ/na5vSytRvKY2SvC66Xgqa9P2x2UK4cyMRYI45zsZoMEHoBxENosbm93UeRMb7ILQatC1/e5Ho9X0PZn7iU4+IqFKXAbBYLMUrACwClTAWZtPLYItCBDthzmKag3DnR1NiAkAGZHuf0h64KZhK1oh0bgjgYOJMfxekIRYbTNlYGQcilJMJ2rbF0fExNjc3w2YWybgpNGwqRM3ngc632N/fx5d374BIQk0///zzMcBC23WobDlgRnqmRaR4AAaRhIAe/aaMzYDwhsAO+plKqHyItlSWmk8J0emQmeHYh/DbkoirXS5FUmssyLtBDHwKfljbO7uYViVOnz4DIovFooYpCnTOY3NrC7YsY2LQruvwxhtvYGt7GxubG31fsrln7jfr6qURtnxgbgQ8AIv5Ardu3cLGxgbefvttvPvuu7h37x52d3fx0ssv4e69+3j33Xdx/949yd/RyQEV3wM/aJOoN6sxJKrkolBOX+c9GJwkEprY0wzEqGQcieQDyXdjJSfixthI7KWmtJLVd3V/GGPADjGZWucdQBLym4wGg5BLRfsWZUH9kEfWJ+3/+BgUTGrJI8fldY7OBcmFDC/Er5cQSR/XMfLpZZtL4QgS0MJaizt3xP9DzgiDvU2a7jVxRGluEwHPVVnCe8bh4SHAUkfTtLGuruuE8QWgdszWWkynExhj0IXIaJOqihcakhXIpZgnSUDX7aOvUp4UYOWX6xhjdpIEdaytMQl33o4xErTEOyMJjb0EmRFpqcftL77A0eEhNma7ODrSvFwG5E/ao/0eSf0mJBqkDZoqkfKzF/9L2XMm2vnnY5xOBXQfHBxgd3c3GZPQha7rsLW1g2efvYY33voAW5sbKIsSvmsDM8ORuRHw4GIEJo1C5UKESwDxoh/2RcClMiHeSyRWA7EI6JwDGwtrJKBC6yQoz/bZ0yibKeYHB/DMKLwHeRMBTa85kjQRIjgwmFYT7O3t4/DwMPpPqmWE61y8N1Kmv+s6GNtH3WQOvpfMoKqCtRTnsuskp2DTNFguF3BOZoeIUJaltBPSA7TNEk27RNc1mB8d4d7dL3Hu3HkcHe/jk5sf4oP3P8Qrr7yEZ5+7jlOnttDUDGMcTNcCjoGSQcUExWyC13//t/Hyqy/jJ3/+7/D2j36K4y++hD08Rlk32Gga+AJYUgMyIRKnicbNMGxggwAvjTiW0vbULHJMa5BH/0pDZo+dqa9qvjZ2TsfAS/93EP74TNuZCrekkq/U/hM8Gf6ppJ8Hn0ubqd/m6l2rbeWpWqS7Q6AzptURQVu/NivBBkbud2AIUteB1Xxv6Hua4PUkkDQQ+IyNaWRt87GFAazc2fpO/l4O1LWu9HNmBvFwRnTeVvqRvd8D4BFeIa43BR4w0PmRNdEiptse7HqtV5wznOw3mJ7FJy1fe4BkCivJpdAzm449qskE8+UCn9/+ApPJBDvbOyiTCGzMjIcPH2J+PEfbNijKEufPn8fB4T7OnrmCyWQWo9kVRQWi/qLNVZtK7FKiqIdXP88ZwFTKmEswInJmxmQyie1ofqfFYglblKIdIaCsJnDLJZZ1HaOjaZ1lWQabewGOrvMwpsCjvXv46U9/hn/yT/4JyrLE4fExjo+PQy6cBT7//HN8//vfh7W2jxa1JhN5LhVTJ0nVFGhxzuGLL77AhQsX8Od/8e/w2Wef4eDgAN57XLt2DQ8ePMRPf/IzHB4ewRqDqqiAgiLT2x8OIf4Elbz0zEZ+kRAMNIcbgGAiiPh8eCu8I1JxZgZiBvu+xf6t5LJhxOSZ6Xfp/ojv0ep3CtoAwFqDul4G37MqmLcEKVE231qeNCXaV72IdR1PIjajwAA8kJ6LgLa/yAZBS/KLK7tILBGKokR0gCdC23YhHLTsgZU+ITlnxqBpWlSVRKhcBud9CQdfR3M6IpHIIeQF8V4iFm5tbaMsS3ROIizuntrOzsBw7Pmc5PM+dimse1bL2IX3VRirwdyMAKOxNtZ9vu5iz4GX/rTB7K1txISEg7nd8fEcx/M5NjdOYR5yx4lk0g4PWmy4X9e0fWMMzp49i7t7RyiLMuauY9eJaW7XgatysOfy8T/11FO4desWdnZ2YmPEBN/J/vOuwXe/8xp+8sZb2NjYwnRSwTU1mHzvBxD2oeQs87DGommboSaCZH+pdDYVnKkhFAMxmINrHbhgeCPJwCV4jAfZEl0w6bMbU2yVBRYHB6gXSxhvUMCIlaxRqa6AtaooUBQGbdfi9u3b2NjYwGQyAYBougr0e1TvIC3OS7TCMHmgAJKKogAVfQJ0Yww2Nzcxm81CzsBj1E0tyWiDMMoYGwJsWNT1Eot6jrpZYrE8xsHRPh483MWFixcxny/wyac38cILz+K1b76Ca9eeweZGBTiHzrVwjUdrW0xnU3jvsHHxNH73v/hf4YXf/DX86If/Fh//5A24L/dQHixQcIuChKYbA3TcwRuGJ5Zz74Z3WB5MJY3ale6f9OxHAaf3o8xl+nxa97pyEjO9DhytPJM9P6RBNH7eRspJDPsqeFPaPO5j2gvMROA31lY6l4/rQw4CcpCj9UWezPTzkq9DCoBTXk3fTedxDHzkdeZ7KX0mFzSlYxz7Li+5xm/dezmg0c9W7vY17+XlpL03rAAq/xkF+ClP158JvxKBMPYle3/sHsxB8+PK1x4gRYLEHDa92nMDVVXh6tWrcM7h4OAAtKAY7a1pGkymU5w5cwYA8GjvEfb39nD6zFmcOn06TLxGWHEBjIwnL0ulTCmKHTto+q6qRuOlngAj3bht2w6kfEpUvBfDHmNCThIymEynwbSlBtCrc8uyDAw/o23EnryaTHHm7Fl857vfxblzF3B4dIyj42X0y9BgEVeuXEFZlphOZwH8KYEYHvT00EXmN0gWlP/3XsLvdl2H4+USH3/8MQ4PD8HMOH36NIqiwIcffIB6vsDmdBbnxkfJBolmLDJKQE+IldBSzzPHgwVEFR8Hq+xMGBPJOiP5CxjAj8RAt2dnRAKWlj5s9CqBWUtQiIIZJqOuxQegqkqogSCveZfjP06IG8UfYu7D4a7KJERrTOxCR3HtmSv4P/0f//f4P/yz/zM+/fTztRIfHRfrWkS8OgQwj7tgV4guazhyAUdiWyzgXgUVWu+g7vC6aH58MJ/qwy3ruVMNgzEBtImyARzaUW1wURao6yXqusFsNo2RMeFCvDZKA06cIPVbM3ePK2MXgI4vrzNngsbeGdtHqQY7ff5JLsK1zAEQLkeJNoeYxM+jaWocHh7i7GkXc4WJf1d6FpM+yM04uOT199lsCuwdhehrQbNkLeq6Qdc5MBexnlVppcfVq1fxxhtv4LXXXkucpA0omHcSeTz3/HVsb86wrJfY2tjE/OAQNvr7IUpIC6OpHhillT1HQAAHQv/Lsgzapj44AiPkTjLiFxgFTc7Bs0fhGb7tQEUBBw82BqYswSRAfrssUR8doT48Csl3DUpboDAGhhgUUk8YGHjXDel0AohUWKCaFJ03WU/pHzMDzoPCnhcTZzMQTCgwnEwmqKoSXddhsVhGH1EOhItsgdnGJso25FRql+hci7pe4PDoGF9+eReXLl3CwcEhPv30Fp57/jl865vfwLUrFzCZWsAyYFocOYdiYjFhj4pmOP/0VfzD//a/wmff/TZ+/q//Al++/T54bx9mziDvQN7BeAlq0XIHCr44ad8HY08Y3fTzVOiTSsVT5j6dk/ws5cKotN5cA7WOXq4VusXPhE6N5l/jnkaPmYmlda7zaVwVpOl3Q8Y4fgiKwQBkHLxyv6X7MweU6/xucsDBQMyPp+PrH+6DHOTv5QBDv0/BSA42xvqU9jmfw7SuHIiPzfHq3+P7Mm0772MK7sbAEkbur7G/T+Jn8ruoH7sIehWk5nOcz78xPa882HfS6GA90n7lgPpJytceIAlPJkn1TDiVZIyYKVixZSQwdk+dkog8JAznBm/0pg+ecfbsWZRVJYxCWaFtGzAZMIUNOHK5DkMWD0Nc5shYLxx9VxOSpkyN1pdKHnPGhQjonMfx8QLb29sgowRF4kIaY6G+P2nQALHlJNiywM7uKfzs52/ie9/7TRweH6GuOzSt9E8ZlmvXrmFrawtlSF47mPI1jF5qYoBEiS1JBxmLxQJnz57Bn//z/wnz+TyO7fTp05jP59jfP0BVTmBj1DL0QAcE9d7T45Idz/7X5As5/G74mGIIXnk8KycxtLq++eer0j7tR04U8nk0RjOJM4qiFKYk4NF1TKrjRFKZEiggJkaO7Y32cnVURIT/3f/2n+D3f/A6/jf/7T/GP/vv/vsBYVOiPBif1hk441g/8yBseHrB5GXAcCQgBGHtxZfiZLClTKc6g7Zti+2dTQBiKuSDv2DTNGjbFrOZmAWBEUxRpIqqKrG9vYXpdIr9/T3M5/MQ+t1E5pZ6S9YB0X4S6d/jyjogetJnA2nbE1x26bNrgW9yeY/Vs65PHPYbQQASO4K3Hl0nmoeDgwOJCuk95vM5prOpeMuP1a/0ZIQpOnXqNG7dfRR9gYwxgHdo2gbOdXC+PHG/nT59GkfB7zQyHMEsl5nBvsPWxhTPPfcMfvyzd1AFLX68iLM5omQ/GGPEET4kPNY7Qu34w5OiwZAeIWYyD8Ab3qNbLMEdw1QVaELwRQHYwEQbue92z59Dce4sHt75Es2yRtEABA9bFoAluE7ooO7/VCoeQZIGrPFDfxhmhus8bBH2eQBJYkbYmz2n9en9VRQljLGwtkTXzVDX4qdUN020bChKg7Ks0LUCjhZNjbpjLOol9o8OcGp3F5cPr2Dv8BCffXYTr730El75xou4cvUSyHo47uDg4BsPYz1QFrCTCs99+xU88/wNvP3TN/HGn/4FHr33MfzREYqmhmkBeIeCGd70grX0vs1DJKclvefHmOH0fOVnKGey0/l+XF3pmpxEI9Yzq9lzI++n9H2s/3lJAYLkkQPI9AI6HyKw2kKEmH4w1v5eynmmfOxjQud1wCYfe/LB4Lt1747Ry/yZFNRqHx9Xd5oA+CQhl9aXtjEEgONAO30up9npXlMz9LExj+2hsTK2L9N1SU0ZNX+jCD5Xfd3SM7H2HuXezC4fo9LWQSTAJyhfe4CkjFmcKCDaOJdVlYQHDugdYUGAeLk552CLCTY2NiQUt/fonEcRGD1htobqYiKKoZ1V2rxOrZ6a2uniq3mDLqZqelTSDYgGTC/afjMBOzu7mM/n2Nvfl/w+RRHENRSSy3bxIPb9kWAPs+kMy2WNnZ0dVFWFo6M5vJcDoxqrrutw/vx5MDNm02k0leETAUN2uNUAjiXfho5rvlji448+QtM00cdqMpngzp078N6jICtmcfCSYBQ9M8+BAyetf0D40mACPYO2YmucAAUGAL/uMFFPTEceWU87KL5w0jEdJ+YGbSuBKHSviJldCpAzwp1JNbUoQxb3zmOIXV7+0T/8AQDgH/7DH+Cf/Xf//aDPY5fFfxgc6MvwshA7df2nbaxov5DO58glFjU9Hp3rUBQUfCSW2NiYZm2HELeVDX5IU3DwU6rKMpz1oA3xDCbGyUA679/4eP82gGrsEh+jQSkjNyZhUxrzOGD3JOAo/U41m0RiPuqMARmPzjks5nO0TQPaJBweHuL02enoNEYmwQzNkvTnmTNn4Lr3RXsUxkAhQXPbtmA/BRXjDKZGJtvc3MTe3h52d3ejNpGYQHDiBwmLb3/rNfzlX/0MG7NtbMxmOGhb2NR0hgjk+/wrSgWIxITO+040TICY+OgcBfqIkLzZAwh2caCgVIJ38K6B7xjEBjwFjC0ANrCVhZ1Y2LLE9sYGLl26jIO7d3H744/RtjX8pIQhicyqqaaYuU9FERkTiT4Vg97kDDsYogiTsVlrQ265KoxjGLWNAsBzTnO9CAiqKvEPni8WmC8XgaFxADyK0sCaAq7q0DQN5osjLGuLpqlxcHiIe/fv4+GFS3hw/wAfffQZvvXqy3j5leexe24bnhgNtQLcnEHpSpSl+Cd96++9jmsvP49f/uWP8cu/+Peob38Jd7AP6mqATFy3nL6m2qR034gMiFaERCedmXWCCP08BRljQCDfv+v+zseggYNGQUDWz9SMcJ1AZGx8PZDqjUW9Z9E4gqMkUv2Q5VnlD5JxJCA1nfvH0c4cLGlJaeHo3XkCwH1cyd87aZ7WrefjaH9ef/puuitzoJa3MzbelIY+bo2/ShnQjQzELYN/dfpZDnKEFA7H0e+P4Xzlpou6b8zfAaS+OGX+iCR5IcvhrCbTCH4AcW0nIgl9zYyyKOC8R1M3wvz7YHajmwZq5uDhrYYINREVC6goQj6IFmUpGd1TsJQfnHRh5W9lAoVg6N96CanZmHzW19F5j9lsBs8eBwf7IBA2NjdRWAnzba1BUZh42ZmYEM5jNtvAn/3Zn+O5556ThKqW0LYN6mUdcjsBdb3AZFKgmhSYTCsJkMActQO673PAlBI0VuMvFtONrmtRlCXe+fkvMF/WwTdKJMBd5/Ho0X5w6EWY4d4srp/A/oMchgwOY2DMUu2JFgF6HOsaAKg1hIJGfk/HPiB+UKDes/McJMKIl+0qdPMBkElY8hZFaSQOLfdTsI6YpZKmdcQuldak/UyeiD++/e1XcPXKRQDAU1cv4dvf+gbe/MU7gzbSSzfu84AVVpYMTwYEVs4KIc5TDsfGQFLapprctm0XpU5126B1LciWcJ7R1B0ASQDM8GH5KQozGB7b2xshzLEMzhpdOzUloyBoQdROr/RnzSW6bq3WSfLGmKv8UhzbB2OSyLG+5HWVZRmFQOHTpF/jUtMhYx0e4xCpiixc18LQBIuF5H4z1mA+XwJcIr32B5cmhdMe9gPQa/W3t7fQdW2fd4kZxhp0TYu669B5D+M5YA6CKmWJxOyoKApcvXoVN2/exOnTZ2PIXYaHpL11sOTxwvPP4szuNo6PGmxMpzg+PAz3iQc0siRxjIpGcYo4RPKT/qbnZpj3BNAJE8GNdhShPw7OBbMT38EC4KoCkaSVqGZTbO5s4NTGFM9cOoVvPP80fvTXf42H9++jqRfY2tjGtJzCFgUmIf9f11Lvk0kAkfjnOOf6s8xAVZZgBiZVBVtYlLbolwMcEuOuMoAAhwTbav6i5wDY3NiALUo0bS3pF5wE8yAr70+LEuVkgsVigePFMZquwbKZ4+Gj+zh3+hIOD/Zw585tfPjxx/jmd76JZ559Bls7M9RFA2oYbdugKhsUVQVbltg8fwq/9b/+I7z47Vfxoz/5IT762ZtoH+2DF0vYtoZpm7henlkCrwbrE0DukrgkLJxAyqTmgswxn8u8jAH+/Bzm9aWfD+hwQif724Vi0BB9dqg1EKFq2kYKcNOy7u8h42+DWWoO0lQzH4BT8p4ICDiYN6/Su9RscWz+xmidvCuTsULXdI6SdwZ8yxPcTxGkjJh55b8r7U37pqZmjytKs/Vn5Bt1INkcpPfxVykK+hnDdRkDdY+tJ5mblMthAIu6hieCT0A7IMITBJ7Re47Cb1knAdbxzicTk2SH2Y71M5EIk//OB6kvltSUA0CYdM8eBgWc79A1cgkXtpD8DMH53xDBdw5tXaNrW3RLcaqdzqZwIQQwu8CmOwlryh5wIXSpIRsuLwsm2cBd1/U+P6Hkhz7VIon0BOg6B2v76FviL84hkRgNfC7YMwpJuIGtjcn/j70/j7Ysuet7wU/EHs5058ybc2ZlZVVW1qwaJIFBIAkkITHZBkPTBtyLZz/TD0+0oT0uvye7eYDpZ2Mbllcvs/yM2wbzbGPwwwyahYaSSlWlqeYxq7Jynu5wpj1FRP8REXvvs+8+NzNl6H5L7qi6ee/ZZ++YdsQvft/fSBxKhsNtRtsbrK6sEoVxufBkFJTmfGCI444DcxFLS0skyZTUjd8ojRSaN86cYWlpiYXFPp1uiBG1DecD1JdojZlxzjA1xoALg2wEZCoDGfDs8y9ijCCKbOCLIIiZjIdsb42BEO3rbkggMMaGwm4QqVLqMNMdv3XETobdzbmtQ5Y5gKob/K/qRrtJrfmMwL4bbVqYOQ/rjN2sdcbeuPYE7JgnIQTKCKeNMGTphP7iIkZaiW+VA2Y+wJgnLWp+55n62pVqzG4uvfbonz/xz/mJt/0EH3j/O/nq0y/MPYzAYjknn3DEanbe2yR8zf7ukAD6+S//KXtcvrbm/rLDqSLm5MomB5ZhgNKKSTZBRksYApKswGiBnWFvTiOciWNApxPRH3RdLiTLcPQHPa5vJG49WNBb+onNQPadB/jM4dEy3huB2+a9bUC1NZRsbb21HXZNAFX5L8Y2LYJo7m8v/Gnvs3ALwWpx3fwa4WhchtGCLLN5yaSEyTgF0wESDDuZDv/+hTAgrEG1BRuaXr9HkRdgDNII6xskJQpDrjW5kUhjo7kBSLfGy32nFIcPH+HJJ56yqc+0QAvvG6ExaLRKWVpY4eTtx3jqqWfpdgdEMiRXuZVhOCazFJ85IIAD+YGUhFFMroodTJWsASdjDD4BhTXjdOeFwNIGbTBZZhNY5xks9hGBIHJa/m4csLYc0wsl0fIK7/3Au3n2q0/z3FeeZ3PTsDwI6fcDVpYXuXr1KqrILHhx+yUQAd1BD607bG1tEciQMAw4dPAg25ubLA0WbP64orBCA6WcpsCeC34MdZCuGj5Pdtx2DXXjkCgURFFAmkqyPKfQyiWDlgRSsRDELiFtQpqMKfKEPM3YHm5wcP9BtkZjXn39PA/e/wD3PXAXh25bIY4jCAxZkVMYRVCkdE0XEcWs336A9/25H+C1hx7gSx/7LJdfPoPeuAZ6TCg1Os8AG0lQGUUgAL/ehHHA0K61yj+22kc76NGcPVzfu80AEE3NRz2ARivT6mmJJ4HVZcdIzmof5vWx2d8d50YLLauPWcqaX16jfr8utDYEshIiQ+1sqwld6oBgXl+bc1EHIzQAQ3mvM72uJyqtA7G2ttrmvE53d9O+N2lyc+7r/asD0/r8103xSnooq/b8uOcBmvr42sZm1047/9AmhGubm2ZblRDNnptGiDJVjcLunUB4wFNmIXOL18N8D44q3qKMillx/bYfeJ7LWKXJTZave4DUtDvc3Nxk0B+gA5ugTmlFFFs1fRgGTjpngzqMRiOMMayurLrkfpaZ9/XNvmx3COjKiTJwoU/DIEKpwuZPchGB2hasv145NXoNl0FrhXWorDRI2iiEriK6+H7ZM1WUzy4uLnLp0iWuXL3Mnj37KsZeVhtMKc3K8grj8Zhjx44xcRnhwzBEK8Xly1c4e/4cw9GIu+++m06nQxRGyNbIdRWjVC87JTV2wWujKQrFlasbXLhw0QFCzWDQR2vDcDRiPB4jhUBR26um3poDi+56/ZpxffGtVxIr3Pib0iJZ3jMzqpbN730KJM622t1XMuhQC2FZgSgjfN9MKfGx4GkWPDcdC42BNE0ZLC6V783MNQP82ks5xTVw5Mf/gfd/K1vJFn/ro3+LH3ngR/jA+7+Vn/+F/9eO+ZnRVhicyVmdxrWDqWaZx1jcyoFeH5goiatwYaadj4VxkbeWAhQwmdiknx4Le62QdhLxMAiIfWLoQjF0WoNASpS0jDFa0Da0ulSvzaytyYA0n2uzpW5jcubV02yrfr+vq614GiWltLlxGm3NSGh3aQ/hEiP7+wQIbBS7PC9Ik6T02ZxOpxR5QdTZLfTxLCPh114UhkyTBKgYKs9opFlmc8pJ77+207cGYDAYMB5PXJ1W014xa5Qmlm972yM89tgXCZ059ubmZhUox81JXfRQZ3BsYCCbwqFcG64r3lRP6yqCkweFFox6gYNwJncGneVkwzFFIRAiZNCN0KqLDCyoj4SgiCRvffsj3H7bcT7/mSe4dvkySdpjYWHAwf0H7ZkkBCKQRM63Sgi4fu06aZJwYP8BxpOxNeE2hsiZmHphYH2cbUxUM1BBc28bYzW1ncAGGTLGkKQp48kElStAuKBCHeI4JM+cQE+NyfKU8WjIlSuXOHLwCBsbV3n19Rd5+NEHuPvuk6zv24NWGkRBFAWofEQ3jiEI6HZ73Pe2t3DnnXfw9Gef5JnHHufqG2fJRyOMEQS6QKqcSNsgF8adF0ZSgl8PKhHz9sfsWNvoWpOx3m2e2oQfJZPOfBqw23432uXnYqfGqxk8oF5X/f563f58bLbt7wnD0Gr0a2DJv+OmKfzN+HPW++d/ysAMNUGIr8fzVfPAVht4mTd3TdreDNSw23nXbHPevU2AWd7nztdbORd302TeSrlRHTNrmdl5EUIQx3Hp76lNpbkCSh5qZm3J2Uignh6C9b+uSg1w3sJ4vu4B0qVLFzl69AjTqXIAJUUGC6RpgkewWZYymYy4dOkSb5w5wzTJePThh+l1eiwvL6GNwihVAiQABNYx1TmMK2Od573pnSoKAm1VpVlhUIVCY0jzzJlEuFwUUiKMsdmFtUIGgXuxTkMiDEHoQ1Aqq8mRPqmqC5dcWG7fLx6dK/IsQ2krwSuKnCSdcvnyZSaTMQsLi4RhSKfTK1XTcdxx/kmq9G3KsowLFy5w/tw5JqMxw/GI/mDAgQMHiOMYGfgEhjcmwDuZME/MtZ3HIOSF518gz4sGE5Zw7epV9/wuqlwBQgR19sMSZEeVZ42b7LablWzZ62WqKuMlVjXi3gaQwPkjUP54UDZznwfBDiyU1tjuITsjlhOv90s7M0/lVUXYQBlBGFgfOpW55Gs7D6lytHOASP1wuZG00Jd77r6D47cd4d9+9d8yzIb8zku/ww8/+MPcffcdvPjiazP3zhwKJdDyjc/2r+3wb/bhpgAQ1N5dVX/5Nx4cW6KqCmtm6n3zvIktCKZOSOArNcY+ZxM/W8ZssLBgI+IVNvz9YNAHsVkyzjPoqFpO5ZjqP3Xpa/NgrL8n7xvSnJO2z83x71b8gT7vwPd11p3U6zmEZvtUP9p2adP3U/j9WgmGlFIu3Hpo80xF1tx4xz5kJzPh+7GwsGBzLmmFlJ1qDwJpklDkOboTOcBWyinLMdsfydLSEpubG+zZs45SPgCDwCBdiG3FnSdPsLS0wGg0pdOx9HTGBNHMRsfyRWuDKQqMtudJPcADxmBELey3H597VpWMnhNIhDbQEIVBmgKtxozygtAUrPZjpuOExU6MEMb6z6mcA4f28oHveS/PfvV5vvKlpyk2MxYGi/R6PTACoSz4Utpq/BcWFhgMBghh/cPCMCyjN3q/LR/kxCftrq+jNvDumWIvKPT3RVFEYTTKfbd3aZFwY5MkSUmTxNI+Y8G17EqiqMvEhQ7P85xCZUwmI/as7SFJp1y8dIVXXznDw488yIkTx1hc6pOlilAakmyKiEOUMRQdTbzS5dEPfAt3PnQ/X/jYYzz/1FOMLlxETCcEqSA0AmmcN6wjcaamwXToqBz7XJ+lFoDhQUidNvpIc/W9VhfU1vfFrFaiEoY2z4c2OlIvzetKqXL/1wW6TdpdP8NmxsXs/vK/S2Gz1ghRBY8CSSgr7Vz9uXm0qo3u1X2upfRRCWfN1Ko+7jTRLp8zNx8mun6mNTU0zXnZDejVAV59fPMbrs6/mwE99Xmaq0m6YS03V2aB3KzJqGh8bgOkxpgZHySjVBU/wBjHV7WAoBoPcrPvD/4bAEhf+tIX+cpXvkwQBIRhQK/Xp9vtIgiQgWA0GrK1tYk2CqUK+v0BBisdDYOAzc1NC3ZcuG0RBqUkOcsc2DE2nGoUxVZiqFWptrZ+PBbgSJeksB5esh4Jx2aXty8vyzIMVeAGoy1QSqYJaWbtsn2iWi/BtFJlG4bYmvc51bvRqEIxHG5z+vSrFhzFXdbW9trcHd0uDz/0CEliTYPSNOXKlStcuHCBzc1NpuMJqijoLwy4//77EVLanEtyfrI7X5qSlzpBtyAmIEnGZLnh1VdfA0Q512DK5LQ2x1MEQSUFnmEwAYzE4COk4EI/Wwdw0aJl8SFO7Qa0TJqrCCO8Vmgn8bCDsT4LVuJEFUHNONOhGUbY1glgtJPKigpYauMPGGe605B8lZJiBxqTJEUVik4ck0yzHVLpr0USNEuMZjn5+hx84P3vAuA3n//N8vcPP/jDfOf738mLL762ox+lNN/VqF1Vt9LDHYfsf6WkyzPwQlAFaRG2n8l0aiVYjgZ4ICWlNX/ULhiLdv4pYWiTkHqGvtfrVe1IiVF6BhALOWtO0bY35oGcecxI2/g8QzXvALoZqWSzjSZjItw69hrzWcZNz2hmdwtjDBZMWj8hSxPTNHP0zNJCC5S6yFZfhFlmayfY9JofWyytjkotnFIa6fPD1cZb7UM4cOAA58+fZ+/efY7B8mZyoLTBiIyFhUXuvucUn3vsqdKCwJov26Jd1nlfb0nDtC7pid8rHhzhhTD+HXituJsnq401JS0RymBcQlqhjfus2b6Uc6bI6ApY7vQggn4nJuh0iKIAIwwPv/1Bjp84zhcee5KLFy6Sq4xBb5Fe3EUX1kJBhlaj5aW8vq/et9avC//ZB/fxc30z684/p5SLNuiYHyklcRyzuLRIt9dHG8XWxiZZlqCLAilCZBDQW5DooiBLE5I0Icsz0ixhezhibWUf4/GYM2fOcM+9p3j44Qc4evQwohMghEKlBTmC3GjCKCIOIwaH9vHuH/geTjx4iic//knOPvM8anMLMUnAKBvEQRcWYAuD8YmwTbtQrTneJqNcX8d1DdtuJmVt4aCrs9EgnOXJPP+WJlNatTHL0DcB2ryxNJn58h5jnDCzAg4zJsW1/Wv72vRb3Ekz28ZR/9zmQ+SFn806LO+wkwa2Me9toGUekGmrx/etLeFtvT9t2rrmPfPOhHpfmtGPd8xJC22efW+za7OtnXmlbb2KxntM03SGH27bO6XZqqB6TyXgouTH562ZWy1f9wBpfX0Pk8mEPXv2AHYupRRYLau1nx+NhnS6Mf1+j8GgTxDGvPbaK+4lwnA45OUXXyJXBXfffy/9waBc1IEMrDQ/shF77GGeVUydwYZkFrL0P/IRgsCZsNUlhqIe/c6CLb+wkyQhTVLCyD7TSTpWchdYZs4n5ouikF6va8PHKnv4GmlQRiGlwRiF0jmj0RCQrCyv0un0uH79GqdPn+bs2bNcvXqV6XRKkiQsLS5y4MB+ojjmyJEjNit6qUGa3Th2GPMJcPW3Za7yXJMkGVvbY65cuWollg6AZVnG1tYWMrAJHw2CQlf5aryWyTNS2nhnPlsEAYiqrdn+2DsqImk3XEk4Z+6jrLH+V2UbjdUqWCOhGdW9v7udgFjpdCCcxsmoHRoGP6/2gAzQxiY01UZbwF2TjJS13oBYtZUZRnoOOAL4zg+8k3E25kOvfAiAP3jlDxhnY77z/e/iF//pv9pR78w8OIbKCFO7NLt22p5tO2h2H4z/1c6EVH9aEKG0IgwDcqVI3d6VBEynU5TShJFAuLXl5ykvCpaXl1laXCJ0EvMkSRgMFsqAJzvGA85hvXplbXvmRvun6dTbZFB2AznzAFbzPezGANXfizFVxLI0TUumpw6y5x3udRhuvIDC3e/rslJzYc3kTIeZDXITJQwlS4uLpa+LHZDtc1EUjtZqjJFOMllJUOtzcejwQT732OMlw+rpniAoUymA4tFHH+LTn3ocpRSdTqekyT5UfAmsmBW4GKVt2PqGWaqPKlfOI06w48+xmmmhEJWGSgQSpSGIImRhLSCGl67x4iRBjVOOHT9Et9OlKwOECOj0umRZzuLaAt/2gXfz2iunefrLTzOabmPQxEEHbTS60KUGTrkxeSavcILEusO8Z+y9eXk970wbw+vfeSnZdwy+Z8wF0Ov3iLQmCkN63S6bG5tMxmPrK+t8s8IoIgxj0iQhzxKmSUJRGKaThM2tq2xt7WVjY4Ozb57jkUce5r7772bv3iW7KpUgnRYUuaYIC3QsiKOI2x44yeHbj/D8E1/iyY9/iktvvAnDIUGaEsgIqQubH0sA0qCrVFblGL1wtAl65km1mzSwjSY2628zW9RmJ02pv6PWz7VHrCBBlVrCtueafal/rs6w9vtK7Vit2Zuh9bsBxRsxxm1gZzew0FbXPNrYpLFNP7L68x4kNoMt1L+fB4DqtKTcIw23h93Oz93OihuNtznuGwnB6vfCLCX3427OdXWeVM8aT5epgt74fz2grnhALE9oTLkZtfn/a5DKEsexc6juzEhPhAhQqkAIQ7dnQ2ovLi4QhhEISSBCBv2B01xI4jhC6oBOt8Panj3WX8lJAMIgdKHAQ+I4QohFDFUUIoFEigAZSPIsJ4qjSvsirKOqFNbHyUvJDKB0TixjlCpAhMhA0unEBM7mW2ldSkL7YZ9ev1fmnvCmDkJWqvlur0uvs9cGcgitFq3X63HnnSfZ3t7is5/9LFevXmU0GpFlGUtLSxw/fpxet4sw0O33WFtbI44s4JMyQOsK8d+MNKfaIBZiJElCGEa8+uprFMqaUgQhpY9Dnuf0+wPL7JpZVXVd5W1xgsSGQajyioBAOTC0c2/L2m/fp2ojNclDXQo9c90A3ryvxny3l5o82BhwgTYoGadZwl5J6gUGXUpkhbD+FULMmh98reWHf+h7OHzogPvUHgWu2+1w18nb+Q/P/gemxRSAaTHl91/5ff7MvX+Gv/d3/zJJks4dNcC585f4t//uP//XdfYWyyxRr5hJr13wWt00z2xoabe2kiRxvn82QaeckXoqer0ukfNBEsJqi7u9AVIGgNphZCbsjdVKu0kpXNsB1wY6ZoHLfJDVBnya/ZjHWDTBkQ8T7E1vvHCo2lu2VH6VuxfhzNWyLC214mEYk0ynIFZu+PzO/gqCICwjvPm+B0FA4oRKWtvE05avqAtHqjno9wYkSeIsAqxpMcb7l2lkIMiLlJMnb2d1dZXhcEiv15uR2oLN92HH2TCLqr3PGaYFXAhzx8i5OTJY+u/zHAWODkpXt9HGBgcs8mrN5Ippbnjhqy9y9do17rv/Lg4d2UunFyGkIO52ICzI0oxjJ49y4NA+XnruZV596TUWetAJQ0xhQX7pG6k1prA+HXmeW7O9GrPk2677f7UFIWkyauWPmwOjNYETMCptKJT1cbImriFx1GEynjgfJIXRNnl7p9u3wYeShCybolVKlk+YTMdsbm4yHk+5enWTl06/xsOPPsDJO+9god8jlKALTSE1Sb5N0XEm6YsdHvy2b+a2e0/x+U9+hpc/9wWmFy5i0hRyg3TroTrjykGBf0c3sQ+q9Tu7D28U3rr1DK7Rrd20DDfbnzYhTP08btKfag00KWL1/c1GbruZvrXRwZ0Aozpzm2u17sdSv972uekLNSNo3AV8NPvk3RuavqW7JQSe16fm0rvZ0gSMX0tpvvv6nDSFTtVNs/3VSleCnprgztMDw+w6rATVVeCr+vcGbPRSX9ctTM5/EwApyzLSNLXJYEuUKsgLQWw0a3v2srl5nbjTpd8fgJBcvnSZuNNlcWmJMAw5cfJOtoZDFpdWSodRcIeGNsRdFw5YV5tUlvlpcIBClqYC/pD2wEpKCTUbY3ChQrUikJEDOQEytiZ+WhtEYKN/RHFs7S+deUYgqyztQRCCkGil6fb6CBRCBEhC4iBmZXkvi4NFfvM//RZn3nyDLMtYWFjgzpPH2L9vnbzI0EqTZQVHjhwppcXShtJD+sSFtTLv4Jvd3BblT5MMGUS88urrTKdTZBhbgi4D0jQnSVNkEFIUjuF0UjkbXUw4auAIs6gTx9lDSjjmp+rXTIcRXhphvCTb2bbXpTENHCJqf8gdB377nMweEdavrCQgVGMSrjErcaYcuxBQ5DloQyADZ8I1ezjNew9thNsYw9LiAv/gf/xJG93pJsqvPf1rOz7/mXv/DH/hv/vBGz6bZTm//TsfYTiaWKaw1r9muVUiPbu+qnU5e1Da2Ik+QIBdS3Yu0RqVZeRZDnHAJEnJioIojsr1YxlV60AcRzFxJyaKLQOeJgXdfoAQNvxzaRwlLIEuCXejv83D2xP22eAc7YxFG6PSnL9mOzcDuNrWS7tk2F4rHCNuQ/7vLg0v24ZKGiiEDZNcY/5HkzFpnhN1uwzHCVYZLvByimqs89eJAA4f3M/V6xuEwZLdWwakSzadF8pqabDaCisqqQQ4vpZABiwuL7O5vcWePauWzuvamtIaUxSsrK5y8q5jPPHEU0jZI+7ElulBlyYlzXdh3OHtD24Lnqk+GSx9KhkN26jQgsIvc2G10d7vTUhpzfC0AWfaZzVOOUYIzpw+y3Bri5OnjnPHyeMsrSzatRwIRAcEGZIOb3n0QY4dP8qTjz/J9njIoDdAGNCppijyErAAZY62UpItfJsggpAwBlHY3FHWN8kFr6CaRwskqncgvMwKy0QiBGibxqIorM9gp9uxCdujgHAaOyCboQq7FrWBqNcn7nUoMmuFUUzHZEXOJJmysb3BxvA6l69e5c03LvDwAw9w5NABgsBAaMhRFCal0BlBFNOJuiwe3Mt7f+BPc98D9/D5D3+Mcy++QrGxhUwSRDYlVD5GqzUrtnRcIxEWREkrGPPanjbpeXPv1IFj/Vp9TdW1DSXdM2YmqlmbqVW9vlKTIeoM82z6kTbasBtwm9mTtXF4UAB2D1bJ3h29FPWzfHfhja+v3i8rCcPS4LKvbs0Z4cLYNz2UZ+ldHVi2Bc6oF/8uZ/a2nA3K1ZzveUIuP46mkKU+3nqbtZpp0sS2Nnask5bxNDpcm6OqXhkEpQDA8i6z/A4lkKmeKem+85+WCNDWEkcbkCbAmCq/nUK5dezcKMqz2I8XDBotSu+rKl4Adm0JbddVbuYneG6Wr3uAJGXI0lKf6XRqGUkRgIRCF4SR1cqsiT0MRyPHrMf0en0GC4vE3R4yigllwLHbT1AoTRRXmighvPlctWG0sD5A9v23h+D04Ki+OfxGqBMOAIFry43HEi6BdgtGyBARBDb5lXCR7JRBihCjbXhoIUKk9Pda6WIoQgIZc/ttd3D2zHmeePxJZAgPPfwQ+/atIySk2RSlcoSwJoR79u4liiI7JkPpE9PGyLZJIWY+C2PDtirDZDTh/PnLpHlO6CymZaGYpqllioQAJNqAED4MegWErH11Fdp3lpg0fZ4aDCL+gBZ485pAuM3XRETsZMU8MfAuTiXDv+NO198634WXangE5PwgXDjvehZ6IezBqrG+amhDJ4oc4z0/TOe8UieMw9GYH/uLf4tf/F/+Dvv27uHV66/yV//gr3JlfGXHc+N8zHNXnpu59tsv/Db3/fP7GESDHfevD9b5Z+//Z9yxdgeXr1zjr//UzzIaTXbcV87IHCBwo/tm1uGu57MNW2wPFXu/DdJg/dSMKgikRIqQXKdkRU4fnzshcDRaYLSm07X+G91uxObmkGRacLA3sMywUAhCp4vzoNszs7P7o+3vNulnE8D43zPSshYQ3DZvbWCoDWTvVoflVVWjf7s+2ngVbsw4Hy2DYxqtOfB4OiZXBUKGjKc5WgmMkE5w4ato+vs1+m4My4sLXLh40ZlfBWhlkGGAEC50tNJE/r0aTwds8HxflDGs79vHpcuXWFldtr2WFeA1xrgojQUPPXIPn33s00ySiCiKSKU1LdKqnRERwpv4ui7XhGxgVyzMrh3jwJy0eKI6L6TXahlEaTboTNWMRiAxqgAt2N7Y5stPPcP1qxvcdfdJDhzaR6dj/bMIDHlgz5nFlQXe+53v4aUXX+W5Z15gEHcIosAKahCkWW6DKFDRLSsQtH3RQiKEA4EioCBHakNRKDCqPCsdqfP/lODGszxeoCiDKh1CoWzqjCiOrI9XIAg7AVkWMZ1OLa20xxVGCeLOABnEJMmUJEtQpmCajxiNtxiPxky2p5x74wJvffQh7rrrOMvLfYJSdFWA0uRKQWiIwi5H7r+bP3n7bTzzuaf40sc/w/YbZ2G0jWBCoO34jNAo09h7eFq/UyDRNLWqP1f30WjTRDUFHn426/xDnTbUNVI7aBAGY3KbvkRZP81KSOSwRwtgaYatrtMaa9XCTDvgfTdQNvMAAQAASURBVEEDB2RqKRx2oeltwp2ZOZlhkGf7449cPP10IEk7Wi1q9zdBYB3Y1NuuA8TdBH7+mfq81c1K66UOPNuEW/PouSeK9Web/l5NsD0z38yuzTo42iHoct/X+dQdY2cn76SxdEE4JKwNyDiGMAECBNYdxQg/7xqhQYrQtalnAzYAZZjg+hziu2/bUO3yu9bydQ+QgsCakXk7aW92JsyshDYMQxYXFzEGtra2mE6n7Nmzp7S51dpm8o7iTrmYobKd9aW5wOpMh6/Lf/a26fWFeCMJiWgs/KakAcAUFdPSZvsqXR6JhYVFFhYX+PhHPs50mrB+YI2jR4849aZGKRtFKE0z9u/fR7fbddqj3TOENzddG9NlsM7XcRTxla8+x2QyKc0O/e8kSZ2U0jIuNoR6e30gSv+OepSt5ty19tvjk/L3TknJjUqTcLWVee2XBGbOM1JaDaB2n1WhXBCR0Dr96/nmEzdbPvmpx3nfd/4Yv/j//Du8+53fyK9896/wI7/1I3zi9U/c1PNN0ATwbbd/G7/y3b/CoaVDfPKTn+en/u8/y9Vrm4BjesROGAk3N5f175vrDfx7awFWDmxS25NFXhBFcZnDxd9nw37nCNGj5N9dm0pr4thGgfQa4KIoiOMOYCyjqtw5dYPD3f9uO/Cbc9G81owQVvdBaM7PvHDiTelsU9q4G2Cq76sbAau2duvPaigjEimlmIzHZFkGQrggDR64tPej/tsXbQyrq2suImHFgIaOFqdZVkal84yTO1Kpm9RqrTl06BBPPvkkp06dosZy4leGMTYB6d333MVg0CeZJgwGsWMIrbRaInfM927jqTPJrcKDklGtInMJaSW6hfZmkC5ogDY2jLgxTltnE/O+9upprl67yt33nOK2246wtmcZQUC3Y/1AhbBr+9S9d3Ps6HGe+vzjXL1whWyaog1MpmOKPGNxcUDhtEIBzipCONMqh+6MEIQE5HmKzR9UFyrNL4EzGTfGuCiwFZ33Z3uv1yMMbcRD/3k6nTKZTMjz3DLgWiODkE63T5anpHliTWtzxTRJ2dzc5PKli1y8cJb777ubhx5+gKPHjtDrdzAuKbwqcopwTB7khHGXbn/AN7z7Xdx18l6++MnP8PTnP8/4+jWYbBPkGaHSxFqjEShpUH4utBeKe1NxnGl8xRi37cV5jO0M4Kj5OdnVOcuL1GlCU+u0g54K7ztUsbk3u+dneZvqJc+ax5uSAW8DD8376/W2zVM1YRW4ro+nEjxUz2gXKKUJFJrjbKOrzb3avG8e+Kh/nvFtbNR5I2GX5xFmwLZoGW+jz23t1ed/Lr80px+7fb+jHkdnA28xgCEVmre89a2kRc6VzQ22t7bI0oTpeIzOU0xRoJVB51aHpDEt/kRBSVDq60IIiRAh6IJQxjccky9f9wDJ+7E0Qw77Q3g6nTIej8v7wzAgSQz9fr9E3UIIul3rxOqZ7kqLtFPSI2uHvP9cJz5VYIGKCapHM6kDG19vySi7jRSFYWmu5+/x9cja4p8heECRKwJpVZmHDx1hc3ObL3zhCYoi59RddyEDgTReWxNiDMRxlyNHjpZ+XE01c720EfR5902nU6Koz9NPP02SJMjQhYoNAsbjsU12iA9rLEiTFJ+XoTk3lvbutAOul/q1ikhSSq59v9oI2tdaboZ4VL+99LcqniH3tFwICwSzLKPTtdq8QumZdfC19NsYw+Ur1/iRH/tpfvzP/xB/62/8OB/90Y/y85/9ef6nT/5PFLq4cSWuhDLk77/r7/O3vvlvUSjFP/iZX+Jf/av/yDzTq5vtdxsxb/3s/73hNDiApAogxrhoj3meg4hLhlcKCdJgXPJH7ST8cRwTRRELgwWuX9sgz3PyPHNjmU2KWLVYMdPzDs7m4Von9G3z1Lau24DXvPn05UZhc9v62rxWv7/tepM+1OvyjJKN5mYYTyYWIGFNM4uiIAqdLwsCEdRBynwByGDQY3t720Wrm5U228hJhfMNoTy4hdg5r4uLiyRJ4oIR1JmhOrNZsGfPKnfceZyvfPkFS9ek10ZZMNWkiW0MWNs7nafhEw58aRegoMz5JqQ1w/XXMRjl/CsCQZHbJOlowdbGNl/64pe5dvUaJ0+eYO/evfQHfUJrF4dwcxV2Ar75Hd/Cr//rXyNPCqQUxN2Ybr/LaDJBOoGBUQXSRU4zblqFtInTlSpAgggDdKEraX3tt6lfAAIXzMhbbNTPvvoceQsHIWywo36/74R8Kcl4jC4KBBAISS8KiYqINJ2SZjZiZZ7blB+bm9fZ2tri7LkLPPrIw9x77z3sXV+BUGOUwagMHRZkOqdQBSbusXh0jXf90Hdz21vu4rMf/yQXnn0WsbGNGCeERY4WAiWsX1OoK1NUF3OjjMBVX8L1PdNkYnej+fP8gZr3t9GhJiiQQqCF03I6cNfcx/OEKrN70oXYr0VqrdY7pStwc+3Xx9omDGs7B+YKIquH3G+om9/t4ON3ARH1euftX/9d08eqTUDWxis0zfvaShvYMWInCGsCn3lt1u9rm8t5dL1NENdGz1wtNom2+08J0GjuvP8eHvyGtxGtLpJMxuRJyng45Py5s1w5+yaXL1xme2PE9WvXHU2353WaJhgjAa9dAmEsAMPY5LBB1CGAUohzM+XrHiCdO3eOKIqYTCYcO3aMbrdLr99jkkwwxpAkCWA1SFeuXEEISRCEdDodhsMhg8GATqdT2cmaSgrZRPceFPnv/GchRBXJiFlJbx30qIYPUn0Rzyx+95taRKD6PTacrCjr95HyhJCEYYwuNMuLyywtLfP4Zx/n6tVrrO1d5eixw/iwxl5bo5VheXm5lM7Vw/neaOO0bSTfR+XyY2xubHDlylUrfQ9sJEApbKjx6XRaEiyfaNcS5zY74FnNgL/eJrXZDbTcCNDMK/OYxfpc7caoClGZxvifOmNUGJsfSpnCad9y+v1gBjw122+Ota1vfr3VAfmv/K//G48//iV+6Z9+kL/zLX+Hdx9/N3/2P/1ZXt98/YbzcHzlOP/u+/8d33jkG3nt9Bn+yl/7Bzzz7EtOjV6Fu54xf9ilj/Pmqs4AzIadFhXobYzVt2ucyrACVIIosn58PmpkGIVlxEAELmS37/gs0xKGAUVhBR5hGBLHMXmeWAbImBLk1gFcc5U118iNQElzbHUwdTNlt3Z2MOBz3kmdBtZDirftu6ZpR73f4A5/Y0BKENb3MkkSF0TBpjmwbVRtI0TNz1Oj9eyatmtBsLy84lIoqJm1E8cRKrM+lrbfzhzS1ddE2MYYp5VIWFgYUJ8mD4qVLugEMffddw/PPfMSOE2UFAFG7vQ/K+m71mUI+Po7rJv01N+vHR+l6ZyUVTRMG0TBYGqmTD6EuOVDbd4977MZBAHaaKbjlNdefoPt61ucuPMOjt1+jIXFASKQhJ0IJQTZNOXpZ19gNE6QWiBDyPKM9dW9TC5NSnNDLSg5f4O2oMk4nzVhTSi1sbkBpbS5mwxO8+H2l9WOVYEahBBWWCEqoFmfw/pa6vV6ZFlWntthGNKNYqbjCWma2La1IghjOsIG8sjznMJpKtM0YTqdMJlMGG6NOPvmBR56+EHuuOMY3b6NWGeMDac+1SOUTulGMf1ul+NvOcmBk8f46ice46uffIzN02cwoxGoAkXhyLsoBXreDNJathiMqfxommObt7/mnS/zwEIbQz/vXq1rIAa/v258hrYBlvpZ7feipSOVX0q9nnmAo8kTtdE9q7Gdd97X+DbfVs0UurxrznlezY2eAW9tYGWG9t+AxttkuXlD+7GzjuZzM2DHgdg2Ot4UsrTxQ21rqQ1QNXmetnqbAvyqXlMKBDQGhTUX/s3f/i3+8Itf4J3f836OHD3CtWuXGF6/DigO3XEbb/nGt7O8vg+tFJPJhO3tIbkqOP3aa1y+fJ1CBVy6cIHRcESepIyGQ4xy/vguaE6RTLnZ8nUPkAaDAXmeE0URnU7HJp7LC+cZUG20wcD6T3S7XZIk49q1a1y8eJGFhQX279/P2tqaS9haLYa6+drswWXv8TbTnnnw1+vMRHPh1xmlJigri3FBGkSF2GcWr7aMnpdaFEXhNDMSjSQIJEePHiPPCj7z6cfQynD//Q8QRlUAC+0S1hRFzr59+0vzxN3snqvu7S6Z8JLbTqfDV778LMPhsDZnNrS5BUeWASoKRZ7ltTpnGQZL3BxBlHKHaV2zDzPXW/p5Iyb9VsvNAJX5pWLiS8AkhNWuCbvG8kzNmBK0tX9zbVXl6Wde4ru+9y/wDz74k/zA93+AL//4l3nvv3kvT5x/Yu4zbzv0Nj7yox9hubvMf/iPv8//+Pf/CZNJOzHyIKFtnezWx6/13ZRE3M+T3ypak+WZdRg3lDnFYscU2PDSvpbKxlop5YBVRK/fBwyFUkRRXCZytYynqkkrTSuYbWNW5gHumxnjvOduBnzudgi3fa4D1Ob3Tdq4W7te8y2DACmsCWMytWtHK43Bhv5eGHRdI9Z8zDNczfrqpdvtlT6I9nvtNPYhmZqSFy7/nJQ1f42dAM8Yw4EDB7h06RL9/omSRnvm3hiDKXIKFfLgW+7nP/3m76AKBcYLleYIYIxlzNrmvnl//XywoNKZSBnwgqPCgBbKpRxwzLcAIV3kVaWtDytggoAsza0GCtv/Kxc3GA6f4dr1De646wQre1cI44BOJ+KJx7/AM089B0iXxT4nLRKrXYoi4k5sQ+UbQV4UdDodlpaX0MYwnU4ceLJRqqxiVhLFEWFoI9RlaUruAn4IIar96s9P4cDrDcCDDybkhZI2aJFgQYbEnQ7jyYg8SxHCIIOQWEZEkSHPErJ0jNI5ZqiYJlNGwxFbW5tcvHCRBx+8n4cevp/9B1ZtUJdUMJlOkWJKpxsy7XbodweEccw3fMd7OHnf/Xz6d36fZz7zWRiPINfWX7jcEp4SCnAhiucldfXlZsIp14MViBaa0yxN5r7JJNtz2/pg+tD7okVLUW/D04Uqsax9xtCWm8i3vVNYNK+/deFBm4bJGGaCLM3WV+305qXdzqTmOV63+mmaNu9W5gmhfFTitqAO8/rhr1fnhx9Me2m+1zpf6r9vA0Rtz8BO0+1bPbOkEIRSMOgPGCwuce6NM3zmk59k/4H9bG1tMBluY7QilJK9+/ezcuAgd911F088+ST9fp84jrnnkbfwzev7CTsDRttDRttDijTj0vkLXL18GRmGKGOttDa3Nvn3H/8vN9W/r3uAdODAAYQQXLlyhcIR7CiKMIV98Z1OxyWEtJnFgyCg37c5LLa3t0nTlNdff53t7W0OHjxE3LEHdMms1iTwdedKv2iapnL1qDX+Wj0Eqr9WJ1S+Pl+nPcznmIIZU9rxe9PCcsFre9wsDBZYW1vj9Cuv8/obr9PvDzh65Ki16zTWGV2KAI0ijrssLy8TupDSbZqzqulZaVTbd34+rJ14xEsvv4xSRclYam1Ik5ThcOhylDgfJHzywIohmmnHEcQdzp2N9v+owc/Nljo4vLXiDlBhmXeEceYuNhy1Zcar0No3OlhupUwmU376b/wcyTThR3/kT3Pv+r27AqR71+9lubvM//vf/BZ/74O/+EfSh3llHvNwq0UIa7pZFIXN64VNAB13LJOWTBMr2VV1ZoTy/jAMXWhjK0xIkilRaM18TFE2gjV7mj2UZw+12d9/3ONuljZJ4s2WpqAHdpqD7FZ3CbJ8SFs3RVmeW2mq0UghyUsNsr/H00l28Dr1Esch/X4fG7LdlHjVC5BU4UPrVrq9OvDy+1Zrzfr6Os8+8xx3nDhpx40sTS4tX1JQFDmHDx3k4MGDnH7tLGHYtXndiqysb5bJcOK6m5h/v7+bjNIMc4tLEi6kTZZt1TjOvElQ6CqUsLRBMK3ATQoQNvrdZJzw8kuvcHXjKifvOcn6gXX27FnhwMEDfKl4GoREiIBuL+bhR+/n5MmTfPgPPoIIJP1+35pGak1RFAzHIytctG+MMIzKlBRSBoSBFSj4AAwk3lzQarqQde2wKEOY+/G3Se39+7UJ4m0I5SzN0MJGxhJSkCQBaeaSU2obCiPuQBAappMRSTpFa83lKzmjrW2uX9lgtD0ijiIuXVrigQdO8frp83zqE4+RZkOELIiigCju0O302XNgnUMHD3DyW/8E4UKXJz70YeRWQSe32goZWMsOrSg1FwZmTORbVkC5LutjbTKtXkhj+Y0dipEdz7f9rs+nD9BQlZooYQ7t2BnxzWmjatHxZpnrdjDXBMFtAsc2AaRoPDsjlPZDmFPa5mHefXV+7WZKHQD6OfDzkCRJ67to60dzzJ4/swqkWwMpzXltgu8mX/Ffc17YSq01iSXl1twuGY3pLy6wGHcZXblGPhqxvLyEznIOHj7IyvIyaZ4RCcFrL71MNp5w6dw5+v0+Z157jbDXZ8/BwySTKSuLS+zfs06eTQkDWF5e4PDRIwxHI9aGSzfdza97gCSkZGFxkSiOKVRBGEVM04QgDKow24HP+h0ggxBRaPqDAYOFBbIsYzgcsrW1xenXX+f48dvpdrs7JKPNePVQ0zIBhTN78LmOcI73liBaZ1bjJH5CSgTC2k9WIykJjJSBDSHq7Oa9ZMfmdDEodxBbAmUJm9aW+CgKDh44gFGGL3z+CcajEfe+/R76Cx2yfOoWvkIKmwX+4MFDRFFUmun5BLdf07uoEQQI2dqacO7sRcBKYny48iSdkOVT9u1b5/jxEzz33AuMtiekucIFhLT1GOMYAD/h7QTR/11ub2NmHDT/OEud+NQ/t0rqPUPmPiGs2YE1cRAuopNEYPPNBIEgDOs5nOZLmJoHaPO7mWfLGi0BO3XqBEorfvfl3911rL/38u+htOLUqRM7CKx0M162ISgJedsB2DaG5nztOpbav75YAQYu3429TwFI68BptCRNcowpnDZVkiYao6W903nEGxdRR6mMhcUu3a5NYKpd/p7BoMe161u2PSnRRjkmvppf39c2BvePujTbmAei5zEF89ZOk1GfZ75xI6n1TD0Ya2ZnClCKzPkIhXGHJFMoIdGmIBDV27XPzpk3YZDCEMcd8gI6GKRQCKEJRIRSmiwv7B4zvi7PkMzOoRCCxcVFtofbGGPKADv1NWdMgCpg0O9x6tRdnH7tTXwwgua7mFd2Wwv1uVLaaieNdnTQVJE6A2E1Qj5xdWXaKpCBJNKhM8/DmnVp52MnDbkx1r9LG65f2eKr42c5fvttcOcxDu8/xB13nOC5r7wAcYdB3GFre5vMKI6dvJ3JcEQynrCw0GecJhw5epT9hw8gpSDPcra3t7ly+TKM7NksjaNvRtochEFOR0CaJBa0uTdbrlvsc0ro8v1qGw+L2habYb69IElK6XwMJSIQNsF71mGaTMlTRSgDrKJRs7gUMp2MmCZjVJhTFCnTdMj1zYsk6ZBv/KY/wXia8/qrb7C8usT73ve9ZPkEbXK0sebgG9sjNkfbXN8aksuQhdtuJz17lmxjg8BK84iEtEncBaSmQEnjklJXyaalo1nGWGf2otA20qbjE6wljM3FZYzbQziTMbnzpJsB096s0+9frw32EymEC4xU+UuBcPm22mlDW9Q174dn2xV4qYbRAowdR52nqluCtDHjddcEg+UFrF+gXff1dVDXLpX1uP2Bnyl7OM0AhLnAq7E/m7Ry3hnW9Jmr19dmijZPaHYjcCKlnIlmqZQ99+rnu6+n+Y7qYG9egJg24UyT12qbv3oxjrfw2VqENqhJyvbmFmbQQRSapU6f9b370EZx+dIlkmSClIJLly6jDeRKkaQ543HKeJrwlkceJsDu8Y9+5OMsLS5ilEEXijAIWd+7B6UUw1rMgRuVr3uAZIT1yUlza8u+srpKEIXOxKuL1nrHRgzCEGmsiYwHB3Ecc+nSZS5cuMDx48dn2vAbu0mUy8WGsHl8hJXQWckeGCeZEY4YCEFlU4+T+Bm/ECsTDSGkTXzlwFHFIEgQmjCKSgJkQ6kKbBhFQdyJ2LNnL8PNEV988kn6gy5333cSERoCHTopqyZ12qe9e/eW4K8NBN5w/msbzm+6LMsJZMxrr77EeDRFCFmaQChlM64fOLDOPffew6FDh8AYPve5J8vDHCmqQ6C+WecQLKhFg3HzUkWDFDME42spuz23A3wYU0YC9P5vZT31vwQgArQxCBEARe0OO4dgCCNZChWbEp56+/X+7Mawls/Ypcf63jXe+ugDfPKNT3J1crXWQ8HJPSd56dpL5bUrkyt86o1P8c63vpP1vWtcuXq92YDt6i726239udV77E6ors+M2RhsniJT5sDQ2pFrEQDSRrfKJnQ7KxS5PdBtiOLZg1ebgsFCh5WVJWykr5xpMibuxJZJDQIKrVDGL7b5EriblcjtBmxuptQ12vV2m8Dpa+lP/e/dDvXm4Vu1azDSIFySTlXk5GmCVgVC9kjSAqUh8Pyjo4m7SYIx1tR4z551pklOf9lgpAKjkcJqL4q8QClt8/m492z7VqvG9TkMQ8IgZDwel0lRZxgIEZDnmjxXPPjgg/z+738YrQuCICSM7HHbTAZZikRuQEd27GnjpK8lLzsbiMgDCi2cL4bwgg/p8i6ZMry6MM70zgiQGuFyq8kckmHKay+cZuv6dU7edbv1F0ETRAIhYTBYICtyMp3zlocfhFzxxJNPEncj1g/tY3Nrg9OnX6PT6TAYDDh6/ChKKd58802yNEEYidHShV8PCVB0RYci0GRpPmNyZIxBlLkSHC33xMq4KKei8uWtz6nXKAVhgFKRBbhBgAxDsjAnz3JUESJEB6Nyev0FojAiSabkeUbXRGTbE776zJfZnkx56zd8MxfOvcG+9WX2H9qLEYZuNyIMBQhDnmouX9vmuRdP8/wbF9lUAbK/gEpTlpb65OMJZpIRZBnSQKwdo67cWIQs6Y1yERwFPoWSwOb/o5wLt3wrc2yxExw1GfsSgPrvmqvSeNro+A9XP2I+A9ykA7OClMqUzpZqjPWNPC+0dX39l1EMMdZ9wJgSyHnQ4YWpdQGOfaYmNGjU3bSQ2S0HUbOP9fE2wVLzuea1mxGe7JbOoxyzELP5OH392gJJUdfItpxDNy2EnCMAq4Ncf/9OgGWf0U5YGRgbo0MGko3phG43ppOkJEpRGIMIA5IscW0KslyhEMTdPlevb3Flc5svP/1cmcR7OJ4SdXrs33/QtpMVbG6NMcZwtcmX7FK+7gFS3fQtiiK01qWZXQmInKmFBzjSBVDwL77b7brNBVeuXOXs2bPs3bvXHpZOMtVkMmbN4YKyL1BJt/x9/pndzK+a30sXMrYt07LOs5KgBUGATSRqEyMev+04EsHTX/0Kw+E2J+64nfX1vSA1oQwoiorILC0tsbAwIAhkebjcqMyTrNQ3YpomBEGfZ599lryweSy8aZz3zzp67ChhGHDt2jUCN8+qsDmeLNGuE9mdBGN35s4erH+cpY1B9Nfrpo/1e+taD39geZW5lJUNvgCyLHU+L5F1wDbz26z36VYZ4Pe/71uRUvKbz/9meW29v86v/qlf5TtPfie/+9Lv8mP/+ce4MrE5k37z+d/k3be/m+9437fwb37tt8u2ZnpSSjndm2hIn/5YSwmSKbWIRVE5xapCkWWZA0SCNMsoioIwmgUP/mcwWLA0wDmPSylZW1vj5ZfPWOHFDfxv/NhvNrhC2/N1k956aZvT3bQTt1ragNW8++r9aGP260w/wkq08zwnSZJSgOXNoWRNaSqEc5QO2tv3492/f53XzlxCqUVCU9HRIAjInH9jm6S9+betaz9XrlzhyJEjO8YigDwvUFpx8q472Lt3jWtXtwjD2K6RWyzNuasL49r2TTmftWtW/9nOyHoG2T/bBJy+rTQ1XLxwkdF4m+0tCw6jKGJ9fZ377rsXEwq6nQ6n33id244e49u+47089rnP8caZM9x7790kyZTRaATA2bNn6XQ6nDhxO9PJkDNvnnWMaeC0zCEEEimsEY7JMtslZxpncCDJCIRLNOmFX573mkdPPAPsTe+8z0fRL1C5tlqj6YgiMwTYwBdxHDOdTFB5RiAk0+mEa9ev8elP/yGmULz8YsGJ2z9Er9dj7949RFHogoxMuXjlGi+88gbnz11AT8Z00ylhrnnf9/9p4lDy+Mf/kNHZC4TbU3qTnDBTZGGOEhplXKJZBNoztbo9ep8UksrXGcCbxbmgTQ2hVAXs7QrxZNGUgjavwZoVFghhA2UI5tOr3YQmu9Ke2preTZA3c465hPVNuuY/+8BXTe22YRYMzmP2fXs3U5p0sHleNNuaN7a6f9bNmio3750HUkVt1M09UudFd2vLlzpv3UaTmtFX621JIcpk1uCyzkkbLMUUln+11kQZMohwwUutokEKwrhjhTihFTYHUsz0ZWlpkfF4TBAELAwWSCcJJldkeb7Lyt1Zvu4BUhiEMwCk+bcPr13mNhKz6k4hRKlJsr44EaPRiHPnznHgwIHy2V0XvCoQYmf+i+ai3unsVsnB/WYvo0E5Ztl/NxNJJQixJkDKEQZ7YyfusG/vPpJxwucfexwM3HPvPSRZQhAJIqeNslqswgInmAnOMI8ZmiflmR1PlTl7a+s658+fL8fV7fUoVMF4PHYMp52jJEkYDocuep43I8GNfdYEY7dSblZjvMyx1rGdYG63MdS/axKitjloO6R3ZPyevaP8LqiBdSEqp+vCRQGMfHb5mr+Ef/ZWmeC2+z/w/ncC8FvP/xYA7znxHv7Nn/o3HFg8wPkLl/muu76Lr/xfv8Kf++0/x0df+yi/9cJv8cvf+ct84P3v5N/82m/Xxte+ZuqM3M306Ub32nfMjvwI5SHhgLVnpCwNsPsqiiLyIndBTQIwhiLP0UpDLEvG0a9j+0xIt9sr39N4PEbKCJxpg+HGktYm0GgyCLcipZ0HtPz9nom60Rzf7Nqp056bfcYzLDCbh8XViGfbjbHJpD191TZrNJTmxc4v4gZrQsqAxcVFNjdfZP3QGj6CmPUhiyiKSkshSvTVLik1xrBv/z5efeU1jh07NmM5YO+3Y8mzhKXlJY4eO8ylS1eRRYyuaULqc1z+a3au7yYIqgPKtvdYrXPBjrdhDF6T6WmJ1UCJ8uvCmWm3paJQRrG5MURrOHDwMMdvOwqm4PTp10jyBCNhsLjIJJ0yujjmHe96B8888wzPP/ccd911F2fOnGE0GlEUhYtSOuHYsQM88MB9vPj8K2SJDSzhk4ELKYnjGG10GdlrJjGksaaBShfOX9W+h+6gVwpAb+RA7s92FSroQL8bkyRdtre2mE7HBLIDaAYDSTIcop0AJcsSZBKRTlNG2yP+9//991ldXWN9736iKCZJUkbDTbYnI65uDUmzjBDNYhQwCGOeevZ5/tJP/g/cdv+9fO7DH+eNL3wFde4aYjSBoLAWIk7ib4RACYkxmnhG41Lf+xLpApd4nzgrzzTWbC7YaeptVwLl/qn27/z9VJ7/pv2etvOw7VqbENWHpq5fq0cEnvcO/XsWQpS+V/W+1vdFCZS0aR3mzQiQ6u210Yjm5zrP1OzTvPrnzV2dbta/r4PP5tz6NusRItvaqwOlJn1p1lVvux6NGdiRiw9mLReEP3xrcmqjrUFwlqWEcQSBZDiZ8MprpzFGs7S8yGDQta4lCoxSDMcTRpMpaZITxx0E1X7OhUIIyZUrl1leXibudFg+fJiiKLi+sTH33TbLrYu0brH83M/9HEIIfvInf7K8Zozhgx/8IIcOHaLX6/Gud72LZ599dua5NE35K3/lr7B3714GgwHf+73fy9mzZ2+5fSFmF2VdVe8PgDpYEcwi3/oBLqVkYWGB9fV14jjm6tWrro2dtvT+epuasY0Zai7uugSkDlDKe2rgCGbNNry2oTS/c8/s37+fUIS88dobnD79OgcOHmTf/nWSPCHJElInrdVaEYURKyurbuHPEqe2TdIsbeOy4VNtQIFXX32VyWTiQtJWGyzLMptvyRG6ZJqwublZOmuLGaAmZxxB6+00N/TMe7nlVXRzpfkO69fLtndh5kTJGjaue4JSq08pjVaaKLaS6TrwnxdpcDcwV++/L6ury/yJb3iIz575LFcnV/n59/w8H/nRj7DaWeOD/+Cf8s3f+oP8g//5l9nT3ctHfvQj/Px7fp7L48s89uZj/IlvfJjV1eW5Y3WdAph5X217YXYedi8zIZ5b3n2dnzam2mc+0qMxNkjDNJnaNWmcrHHOodvtdunEMd2uNddNkpSFwcDtWVFKt+ftlyYY2u2e5pw072sDR8394etrzmvb3DYP6Xnvo0lXb+Y9laCnRlttVLbZ9ieTCXmWA5CkidMwzCZQFDVa2Izo6e/Zs2cP0+m0lBv7IABxbBMEW/OyKoJY21z4+V1eXmZzc7Nsoz7vxn3OixStcx544F5ClxohcIFu6nUaY9eWqdHY5juuM0X+nNpNm18/O0rBnz8zZt6lKPtcmp85awrfln/emqKDUgJMwNbmNufOnefq1au88vLLvPryy1w8d5719b0sLC8S9WJefeM0d91zN7efOMGXvvQlDhw4wMGDB+l2uy7dRp/Lly+jteb+++4nkCF5rhmPUrJME3U6yCgk6nYRgfW7LYwmUwXTbEKSTUiLCb2FDvfefzdvefgBRGA1wvW5aNsndWbTRyLEmWp1u33W1/exZ22dOOoiCAhkRN9FurXrcmzNwYucNE85e+4sr776Kk8+9UWeePKLfPXpZ3nx5Vd548xZrm9cZzQZMkomXBttM8wz3njlda5evMb6oSN894/8EO/7v/wQq4/cw/TgCpNuTBpF6DgGabXTATi/o/kMt5QhQRA62hO492v9kOftSXueVnm6fFL2JinxfBSm2jt18Lzrudag83UGvP5cPeCVv173NarX1UbP2vZ9vY2mkLc+d81n6n2/GX6nfu9OMLCTB/Ugq60/zXHWx1YP6NDkaWfoUO2d1AXrQuzUSO121jb7L4TYkeqlTUBWp+n+xz/nWp5ZA76+PLe5y5CSVCkypSmM4PK1DTa2J4ySnNE0Y2NrRJrZaNSDhQHdTocoCmsuMRFRFJG4AEydbpdOr89gcYk96+tz32Gz/LFqkJ544gn+xb/4Fzz44IMz13/hF36Bf/yP/zG/+qu/yl133cXP/MzP8N73vpcXX3yRxcVFAH7yJ3+S3/md3+E3fuM32LNnDz/1Uz/Fd3/3d/PUU0/dki+MUpowspsjzy3hbC40/7mNmahfDwIbRS0IArrdLtvb22UI8eZm9w68Qji2t8FMCCFq+YlmmS4PigJR5Rjx/fFgychZ6VidqDQ3kAdLR48cpcgVT3zhSYq84I477mBj8zrdpQ5ShGgtSKcpINi3b78jupbRuxET58dW/77+2Y8tTVOklDzzzNNMJhMQFRFUhdWq9Ps9et0uPgP7ZDyxY/DBLDzxodIetbV3M4zaH0Wpv7s2IueJYdthvaOfpro+A95rB5S/Py9yOp3uDqar2caN5qHt8BFC8B3veQdhGPKli1/iM//dZ3j74bfz6mtn+Mt/9YM89/wrAPyv/+o/8PjjX+af/ZP/kb/5zX+Td932Lp688CTfdPSb+I73fgv//j/+3sy4bEM4PwJT7otmX+p9n8eg7zo2wYyesKrHWA2CoDzkfbVxHJMkY6aTaeknpgqriRUyLPOO+X4URUG32yPuuHtNFd2u7Fcb4q2Nte1Qb5uDtrHvOv4bfDfv+3p/5pn8th3ebf2+Udl5v6OXzpxZa02aJGS5DTpT5EVN47i7hLfso22I5eVliiKv+VzY56MoZFgo0ixl0OuUQLg+zmZ/O3EHgDzPZ5gFe5OjwaYgLxIefMt9dHsdJqOcKAjnmEMKS+vYuQdvVObSZEFp0qdqdMkaTemZZzwwlEISyQCkKDU29VQRCEEUx/jIfVevXuPI4X1EUcTBg+t0+l0b/KATEIc9TCC4cPEcq4urvO1tb+MTn/gE+/bt48477yRJEraH2xS55OqVa9x96jBXrlzn+hUrDOv3uqzsXWZpaQmkQOkOeVG4JI8GESqWl5e5++67WVhYAAGj4Yi1fatsXR/OSLO9JYXWujyXvQa+BEuIKqGu1qANQRAxGCyRplPyfIJ2YYLzomAyGRH3+mgUhUkZp1tkKnHnZQgGlDYoo9HSzq8JA4IgpDAFk+mEl154idX968g44J4/8ShHTh7nC5/8NE9/8rNMLl9DjSZEJkcqZc0NG8t+9n3XhWieSdfu7wBjijnnkwVQdv0arCmeZ1rBmtlV/s92cVIF2XFlN8HNzdKHW7mv/Gl859es55ugxl/UPnsaMnMstey9Or/Wds77s73epx00oda3NsA2D6TUv/P9q/erGZa7fn+Tj91tLtsAaP37tjbq984Dlm3tz8xffW6MFU5kRUEooNsflNekDDBJwjQpnJDcgXkkMrC8dzeyuUuRlfbZ+owKNjc32bO27vaBYc/evbvOSb38sWmQRqMRP/zDP8yv/MqvsLq6Wl43xvBP/sk/4e/+3b/L933f93H//ffzr//1v2YymfDrv/7rAGxtbfEv/+W/5B/9o3/Ee97zHh5++GH+7b/9tzz99NN89KMfvaV+2Og+NoGjVY1XyeN8aZO6tznDeUmMlNLmP+n1yjE10XI9qarPz1OXMHgpQ9shWJcueFOHOsGPoog4jkv/pxmiB6WWpSI4sH//fgIZcP3KdZ55+lnW1tbo9XtcunKZN954gwsXLjAajbDO5or9+/cjHK4LgsrP6ka+EvMIpXDAJs9zrly5yvnzF5xZhI39b7MhpwRBSK/Xt3PrGM7ReITWakaSXw6WnRu23pcZ4uPn+48YN9UJY3MOmnNx80xkQwsiK0mgEKC1DVsbyGCH1LtJwG92DM0+fqczr/vLb//LvP3w2/nf/v3v8t3f8xd49rmXZ8D4s8+9zPf8qb/Iv/8Pv8s3HPkG/tLb/hJQmefNGV65F/64S9sa8Ae+nyNveubNENLUSp4ElIlEmweoMYYoilhcXGQwGJC6iGueQaE0F7u1vrbtsVt9n/6Z+oH0f5Qys66bYF5Kghr9StPU5a2DQhVYJk6ykzXa2YYxpozKtbBg82XU57ZubljknmH29bYLGfz7WV5e5urVq3OYENtuUWQcOLCPQ4cOzNDzHcyToBQUNE1TbqXMzKmrwjOMYRRZ7ZbxDG9FM6top7aEQUC30ykFeLNzVmmvjTEkyZS4E7O+dy9Ga/KisPlGooBuv0t/MGA0GpLnGd/5nR9gY2ODj33sY7z22mtIIVleWmXQX+I//of/xPmzl5hMMopcMBlnbG7YSJArKyvEcczS0iJ33HEHb3n4LTzytoe4+/5TrOxdpjOIWN2zjBI5K3uXKWrJ2r2PUZIkTKdTkmTKZDJhPB6TpmnJE2RZRp57U0srydbKEIYxg8EC/d4CnbhDHMeOThRkWQJCIWSBNimFnpCrIWm+QVpskmRD0mxEno0osiF5OkQVE8IQ6IVc3LhMlqdkU5uLa7BnhW/709/Nn/rv/wIH7rmPaGUNEfcIg5AQCLXeYRpuBYd+H/kfqxGSInAg2fMcAUJ4Sb697oxNyx8prM+yoH7eVJqDQAYNTYBb8Q3hZJ223gyjXq/jRvd7/skz2U3eqxQq1two6rSmuabn9cP3vVl/E4z4e0VjD9fHXu9DEyB9rbS9zrfW29nB9+xS2sZV70/b83XFgjdtnQeS2n6aQTDAB9ywQoo8L2wgiUAiXGTpuNMlCKxGVYQhncGAsNOh0+vS6XSIO1E5niAISnO7OI6tkEcbcqWZphnmFmDPH5sG6S/9pb/Ed33Xd/Ge97yHn/mZnymvnz59mosXL/K+972vvNbpdHjnO9/JY489xo//+I/z1FNPkef5zD2HDh3i/vvv57HHHuM7vuM7drSXpmlpvgWwvb0NVJocD5ImkwkLCwsIRyy01s5hLKg2lTGAtky5qatNfaJ3Qb/fZzgckiQJnU6nJCC+rRlmWdtcBP5Q94y+NY2qJDgGyvCMxpiSUfAgyZcosurDiXKZy/H2sK49Ibx1OQLrY3Fw32FQgi998Ytsbl3nkUceQZm8RNrj7Sn5MCcIY9ZW9hBFfcKoC0Igg0pSUh/XjTa0tYK3eZKFEBQqxyB46eXXGE6mGBe/NAgi8jRluD1CBtano9vtMZkkTEYJRntVcoEkqGkdBMbstJdtk6bs2s9dwMt8wLdTil6tk3aA1AZCys/YiC4+Aqr93kl3hfUX0MK4aEaWkcyylCgKCQJBZlTpk2Gr9n3dKcHZbTyeiC0uDHjHN78VgO3hiL/zd/8X/st/+Xi1hmvE1xjDeDzhp//mz/OpTz/Bz/7PP83S4gLveMdbWVwYsD0auwhaHqRQvcPGXDVByK0y9vMOhJ3jtqYkUeSZZkkQRoggIE8zulFMKOyeV0bbtWrA5qi0Ib2NUkggigRxLAlESDbNGfRiMBmhi0Io7cae8Yvyms82aZyX2jffUZOpbV7z93o7/DzPZ+q90V6Y932T+an/bvprzhNW1D83TUFmg9cYQCJMgFESlWuMUghjGfOiUJgY70pT7hOYXS91zT9oOmFAN4TQCEIdOsbQEEYhQkKWZ5bWl+ZGUIY2bqx1KQL279/PpUuXOHDgQMNU29J6ZIhShl5XcufJO3j5xQsoAyKo8mMJIRC1c2HefO323upPlutLG4zLMmKEsWHmlf2tbdIdJ7Eq3FknEMoxzaoAqYmjiNtP3MWZN99ka2sbVWikFGhTlNpSYwybW5qi6PKlp7c4fuI4Vy5dYU3tYWFhgTiKoRNRiIAsz9gabvD+73wvH/nwx3np+Ve4dnETg+KNN97EKDAFaCOQQUhhIE2sVujUqbvIi5SHH32I244fJe5ETJMhWisOHDzozk7DoSMHeO3l17nw+iWnNVAURU5RZBSFsnvWhdz3pmQVGLBTIoyN+pXrnHQ6ZdDvc+jQIYbDEcOtEKU040mCMIY8mdDtLGEMaKUq4YvzJ0MHNlqqthFnjVYQhXR7MQtLfYTARvGTFnB1uh3iOOLkW+5iZfX/zJc+/TjPfe4J0vOXiCZjgiIn0wWZMGQowihCFJpAYSOURaKip4bSt8zFB8Nrg7RT/4ggqEixsRpHu4Y9eRYIEZb72vIglomVtfW4Q9AlRKmltd3xfI//3i1TDN4Vxa9zX1+TNtaFkM1z3cy4APiG/LvYKfwrmX8/Ow2GvYyO1/B/aqPX9TnwpY1O1+/x39eBRn1MdXBXv8//rfVsupW6Ca4fW92MdPacr86eJrhqgiKvebUCgab2bbZtP74mnZ93hls+FbwRiRY2FY4xApNkhAhyDEEUEMYRQklUXljaHdiIl0KEIEwZiVLKgCiyAoypTpDSrpc8TwmjABlI8mnuBG03V/5YANJv/MZv8MUvfpEnntiZVPLixYuA1WjUy/79+3njjTfKe+I4ntE8+Xv8883ycz/3c/z9v//3d1yvI9der8d4PLYvHquCtt/5BaZK5rKK0GE/e7pTR/1xHHP9+nV6vV4p5fIbqr6wBBCGAVrbhesdTWVt8QhhmWEpZKkRqNflpZ11W04rKbRMRd22XjtnTWFsuMfV5TUW+ktsXL/Ok088Qbfb4dDhA4yn49LJPwhCVFEQIDly9DhBEBMEoV28WiPC+dL+NmJmv8BylGi0gSSZYpA89/zLNuy6MQRCEoQhajJhPJ4ymUy57/77uHTpCtNpwuXL19DaE2ebxNZTVYOekZjckKG+RWbbj+dm77tRH25Ul2Z2LrVSdLs2MXGaJWWYXl/SNEVpRRgGCFknOPPbnwfemp8PHdpHHEU89cVn+Kv/t/8HZ9+84OSTlQau7bnf+d2P8+WvPM8//cW/x6OP3M+Bg+tsvzx2B2at/3OkTjfq141KuQ53fa4mbTWzUeSUM70xSpOlKXHcsZ8bjwWBdAdVwKDfZWGhZ5e6sYx3FAXkGQRCgEvGKRrvr9m/tkPKfz8P2LYVD4yah/utgs22UqcBN1pLuzEU9ftnnhEgRUAgQ4SRqFyRJaml1UagigJjApiJyrVzbsp2pRWixHHI3tUlKBRGWX8OX4esmZT5NeGl880x+mv79u3jlVde2aHxEdJglMRoKHJLsx584H4+/Aefs3n4ArsQhHCm17IGvlrmbdcimh8dQ2VcBDTjJOXKZdRxUlkDGCnodCIGCz0OHTrE9nDCubOXkFKwujxABgFCWnNnEGwPh+RZTuD2ixI2GEAn7nLsthNcunqZV18/h9aatdVVbr/9dq5vbJAkUx588F56/R4Kzcb2Nu/6tm/j8+EXeOGZl+2ZUNgsRhZEaKRUDAYLGK0o8py9e9coVML5C29y7PhBZBACguvXN9m3/4AV4smAXj/gzrvu4pkvvcSFCxcJnA9hEMUgFUoXaO20ZcZQ5AqjtIPKkigIueOOE1y4cJbhaIMsn5JcH7I92kDKgEAKCCQisFGzijynyHKkBpSX4Cu7hLTBoj537AQCEQSEQcDy8iIH1tcR2CBEQRjYd4V9Z3mgWD6yzjf/qfdzzyNv4dP/+fe4+NzzqK0h4TRBFRmRE9gaAYSOD6AoYycYQBm7IgIhS15ABHZtGnApMgwCUwLE+h4C0UqbVAl6dtJnY5wQb6YuT+frAo3aemcnfZpH89o0PzP3zfR59r5m8IHdaFWTDs+jA3WgUb/eFDjOE742LYk8CJRSzmhn6m3NOzfr1+v1zoyTneOo/26eF/56GxCq97uuIW++o6aATbjFZpxAAuzfWmDXcV4gjBX0FVqjixyjNUoV1g1DWf43TVPyPC+1RAsLCywtrzh3mqLs1/b2NkcO3wbCmggXTnlyM+WPHCC9+eab/LW/9tf48Ic/XDJ3baX5cm/mAN/tnr/9t/82f/2v//Xy8/b2NkePHp15kVrrMmS3/1yvu8lo1192/XsfInRlZYVOp0OWZYzHY4QQZf1+gdfrr5tXNAlPsx9tBKL0PzLW9KSN+bB/a2sLLSSF1hw+fBhjDK+dPs2bZ89y+4nbCaMImQVo4+xYoTRJOHLkiJ0DKZFBTfLf6N+NizWHkeDMHuDypcucPXuWNEkJgohut4Mx1jE+DEOS6ZSNjU327d/HY499HozN5aSUY14a66Ftrvx3fj5uxGw03/3NlObc3xRD0/Jc7Rv7f22eZ6IrYnbUkTmfOgvMXbLgr8Gsq1mEELzy6hne8e7/E2fPXkIrddN1CiF48+wFfuCH/gqHD+/nzJnzt9R223u7ZZBqP7UCOUElHcVpV70pp/1sbdWVE5YYPOCookNhrNTK96/b69LtdMv+R3FEHEUkwuYUE9JpAhG7moa1MQQ3A3Cac1U/HG9mXd5ozr8WoFo/RNsY/jaaJ6Us6ZAXGGVZxmg8ctLMsMwX5/eKQWOM3EX44UyctWD//n1sjad0nbO9n6tO3LGhnpVCG42lWDNwfkfp9XqWGWxEBTTGM4I2JG1eFJy44zgrK4suL5iLGifljOR7t/m9USS2ZvGAX/mcMFLaVA1RSKfbJVcKJaA76HLbnbdx9PgxskITry5z/o0zJHnBob37uHjpCtevb2A1LZIgiDAGlMHNkGY0TnnjjXMoDEP3nra3Jly5smnD5QeSJEv5lne/g0AYurEgLwz33H8/X/3y8+gcJCGFypEBxJ2Qo4cPsba6xsWLFynylAP79tHpBHzkox/i0KED7D+wn1QXHLntOIU1DLOCDgTD4Zg0LRiPpmhtU3osLi6wsLBIZ9BFBVaTk6UpoRB2n04mbF7fZDwcEXYC/uT3fQ9LC4u8/NKL5HnGwlKf4XCLrc0tRltDPvQHHwIhSbOcTp6VvITwa9yAMRq0cDDCIAkwxgpUVlaXWd271yWvVxRaUWhNoCywjTsBWVAgOwFmdcB3/fc/zJc/8zm++PE/RJ+9RjBNIEsxWqEM5CEoowi0cqtWoLHnN8KCSalqa0iIGQ3sPBrRduaXvJED+O0CQa+F2rlO2+jKPBBS78du5+zc/WCs4Lgu1Pa/HQmZaaON1rbxhM2+3VRfdilCiBkLoaaWrO7e4PnApp/5vD7f6hlaf77+ua3umxkXzMkjhdhBXe0+UhRpSpIkxIsLZFnK9csb1gQ2SUmTlNhFjc2yrHRnARuIZ2FhAR9voK7BGo9HBIENABbHNw97/sgB0lNPPcXly5d59NFHy2tKKT71qU/xy7/8y7z44ouA1RIdPHiwvOfy5culVunAgQNkWcbGxsaMFuny5ct80zd9U2u7nU7HOmk1ij/ESgmxUmhjCIWYASxNxNwmBfDP++98cIYoikq7Zn/Q+5dTFDYEqQc3URQ11JSz5jLNqC3NhaqUDTPa1j8/Rms2aNvo93ssLy+TJFM+//nPI4Tg1N2nGI5Hlkd0IicZ2OSB63vX6XTiMtS2lNL6udQkCTcPIjyTpEuA88QTTzIeT6x0BBuoIkmmTKeJA33w6U99GiElvV6PKIoxxkqVtalJrVzfS/VyY56aa0B4rqWl77sxkU2m9VaBUP3ZG91bSv8akpkK9NQkRI5AKOU0SL5vHiQ11kaVI+PGY/D3vX7mPMJUCYub/W1j5PyzRVGU4EjU/mmu53nzcytz3Xy++dTs3m6+z2pNh2EAAmeGZPN1dTo+GSh2/bg+aaXLfTgYDBgsDADB0tISvW6PKI7RZuzGb/dAEzTcbLnRPDWLfw9tc73bOmyb8yZNrEfV3E2a22Summ03QVn5uRb5Dyc1nE6m1oQpCp2JqTdjsjvDVTRTd9mWe3fGQLfb5/WzF1jbu7cEJ8bYxODJdOpoO2XdiPYgGmCZmE6nw2g0KgMLWcZQYeGDFVikacry8hJHjuzj8pXLQGyDULiE3D6hY9vcfC2ljDjnmOUgDAmiCCMMMgiQQQBKIaREC83avj0UUmNiOH7qNrq9iLOvniYpCoI4Ji8UaLdutQ08AAYjreZtMp6SJKnNXirtGaKVZliMEVIShnDp4hWuXLrK3n026bgymg9/6ENMJ2MiHSOE7aeh4Oixw+zfs0oYhPR7MaNRykIvIgzWyNOM0y+/wt2n7qYIAi5evMixo0cJAguQz505w2/8+r/n8rmrKGX9daIoYmtzg16vx/LeNfYfP8LetX0gDCpLGW1v0ukvcvDofrIkY7g95BOf+RRLCwN+4Pu/n9uOHQeRsj26znBrxGQ85elnn+bC2QsgQanCsnrle3frSnvzMv9e7BLtdjusr6+zMFggiCJAoLSmyHOki6SIjohigYgkBZqNbMI3fc/7OXLXSb7wex/j9Feeobi6QZhlBEChrHY1QCO0NVMUSAwafC4pz1t4gQWAC/RUd2Ft0ocmc1xabCAQtfOqfFb4oBC70/lSmLULYKrTI9//Zn3z6vdjbKO5xt5AmxCtbaz16zs0xrVxzKOfzX7Wgaa/7kFP/cfX6+l5XfBVF562mfU3lQD1sXv+oD4nbXXMmwM/D80x1a83SykAq7c7hxfLi4IsyyBLGY1HXL12DZXnmMLS5zQviOO49MX3gHIymbC5uUm/3y/b8P176eWXKHLD6trqLdHXP3KA9O3f/u08/fTTM9d+7Md+jLvvvpu/+Tf/JidOnODAgQN85CMf4eGHHwYgyzL+8A//kH/4D/8hAI8++ihRFPGRj3yEH/zBHwTgwoULPPPMM/zCL/zCLfWn7rRXSirF7GJoWyTNKHd16YFfNB65epO3UvrcsnDr0XN8OFXrBGswpiiBU7P4BVUPv+qvhy5srE886u9TaDpxh4CQg/sPYgxcuHCRF196kZU9q0SdDkmRIYSpMJKUCKO5/cTtZXQuywjNEo82m9obFWMESZIxGU958cWXMdoyK5WUA/I8c9FHBNJYp2IPjmw+GQFGVAEoMM4mvj0hWdsamFc82zxP4rWDiWtc8/NSN/dr1nHDOdoxZzPyLZdELUApewgH0u4bsAC9TgBLDQqWiWnm6qoTZ1+ajqXlOnPRrbTWVeVzprJZ78wYagD7ZgBCUwLWbGf39zn/kPcAsqpHlCatlpE2ZJkzt9LKZmiv+X3JILCMRw2ExHFMp9NBSsH29jbj8ZiFhQWuX0tcaGQX/c5pVHfMTcv8NQ/htnc2b97qv28WEN/sup3HpLQxVrvV03x25vA11qTR+2gmaUJeFHRl15nYaWfq6Zm02fmZaU9U4PTgoYN85dnnSqGDP7DjKGI0HKKKohKiiPlzZ7D7Zd++fVy7do3FxUVKEO0ZZGMl+UWRI6Thgbfcw5e/8mxFq4Rw/d4ppW7O343MGpvflfdISRjH1mbfzanVWNjEukYrsiwhLCRRt4s0cOTwAZa7PbY2tpBGIzEIYwU0WhcEfvNrByKlnX+c6ZqQ9gdpTdeFsSGnz75ymr0rS4RxxOc+/WmunD1DhxB0BoEVTohQMp1sM4oFd91xB3kyYbh1jVAGhFIy6HTpBDFXL15m9dB+Ll28wPraGtFggXSa8B//3W9w9vTrBCZy+xyyPAUBRTImSYZsb19jevQIp+45RW95wNrqItc2rrK1vcWe1TXW1lfsu1Oaj3zyEwRScvzYQR584BT9Xo9Qhtx//32cOf2GPXuzFIHVLvvgukb7sMt2bnwU2CAQDBa6rKws0e33WVhYwgjrg+vfC3lBWIBUIJRmdWmR5597nuWFFY7dc4o9tx3mq595nK9+7NMMXz+H3p4Qao3RGUILpLDnYpErK9j0a8hHzHUaVeV9d2nQx5Z92U5LqpPGf0d5RZRWJ00AUb9/HrDZ7fu2Mq/vFaVvaI+EBfNazO4Z/50/C9ui0dXPyXnnXVt/6nvcl7pmq8lHNOlsncepC8DatGP1NnfQMGPK17bbXDf5m2bd9fZv9B7bAmcYcKaatb4IK1jIMms6F2rNyvIyuSosj2vs2R5GMZ1ulzAM6fV65XsqiqIMjlbnk6MoIk1zpskIuYWNnnyT5Y8cIC0uLnL//ffPXBsMBuzZs6e8/pM/+ZP87M/+LCdPnuTkyZP87M/+LP1+nz/7Z/8sYPNM/Pk//+f5qZ/6Kfbs2cPa2ho//dM/zQMPPMB73vOeW+pPHbDkee7U/gFx3C0BTj34QFP66jdMXTsDFbr2C6Ae0c4vhLrzbjO6iNaaJEkQwgKfPM/LTQnWhKPZl3qABG/C1+aDEwexRdaELK+soLXiqaeeYjwa8+BDDzKejCmKnCAKrR2olMhA0os7rLrcR1URdTq4KzHdIcUQ0kl8JUWhOXv2HKPhCBkEKK0ZDPrkec5oNLK22A44aqXo9XpOEmVtvUtbbmNKKYgnwpVEaGeffJ8b3+z4fDNYr15f29/N+3aro/U7e8MN+kB5sPm1HUVx1ZeZQ6u6py1qYv0w8J/ra1cKQX8wsMlSMycZMu1EsP58sxhwST7bCXlzfuqSr5l6zKzkqw003IgZ9zoHf5u/fzqdsnfvXqIwwhgbcj6QwjE//lCcaQjcdwuDBRYXlwiCgDSzkbHiKNphGjlvvPWx+b9vdH99Tm407psVZNzo2RkQUzuA2vozDwA2P7f1LQhskl5vFWAsckVQM0XxGINqzmbxuKiuGRAS1veuVT5mphKKhZGNgFQURblXmkB6pt9UAOn555/nxIkTVUCMUoBgAVCeZyidc889dxHHIVlqNXDazDIQfk5nmIhd5nJe8WedMRCEgYsEJQhEgDKGQttADUJDKAJWBn02rl9na7hNf7BAN+4RhwF5OmY63CQ0hdvA0BHa+rkYqykz2oInNBjjAsQ4gGSEQAtBISUyC7j46mucGXTJ85SnP/959DS3gTgESBnzPX/y+xhORnzqk59gdP0KGxcvgAgYj4ZsbVxnY3OT6WRCkWV85Utf4p37vx1TZIw2rrEYhXz64x/l1eeedcEoAvee3BrAUAhJNgrItq6Rb17j/Gsv8/Db38ad95xieXWFrEi5dPkChSxYXV5DGEGe5bz84st8/KMfhizlwP4D3HP3KfpxB4FGGq+BcYCgBNUu75TxQMGZEwnDysoyy8tL9AcDgjBiOBozWFzECOsjlBeaqdSovCBCEWnJ0cNHeP65F3jokUeIB12+8X3v5M677uKLn/gsLzzxFSbXtm0Ah3QboxVCa2SoUUWBMJowkDPmcArn6xxIaz4tdp5h9bXXBOtCiMqckIZgp1FHm3mVr6d+3w7eobYn2qIO159rE9b4tn2Qo3rgm5JnqPURKtM2vyd3A43Na/V2/d+eb/T9rZvOzQuy4+/3Z3Ddt6cN/DTN7/y9zTO9/OzMvZv31cdRb6NtnG117wai2kCkq2F2Xt3VPLeAqGsgimMOHTpkrVi0CzwkwzKCc6/XK6NP+giTQojy3PB9WFlZptvtYbHxzZ+Hf6x5kOaVv/E3/gbT6ZSf+ImfYGNjg2/4hm/gwx/+cM1UAX7xF3+RMAz5wR/8QabTKd/+7d/Or/7qr95SDiSoNEH1cJ5RFBEEUbkQ/X31Z3xpY8qayLm+CerX632tb866ND8MbWQcD5J8f72Dng/84F98vZ0m4QB7KEoMeZaztLpsTQyubfHEE0/Q7/fZv38/m1sb1mnXBVCQzvfiyPGjGGMIw6gMBhEEO/OhNJnTNiIFlOp8pS3j8eKLL+EZB2tuGJNlOcPhkKJQSBnYUKrSov4sLSiKGiPmpIIYD49m2/7aGMFSxjTznm5U6oRsnlp5bosNAjNP+jP/ebvJi6JAK0XkGLyqTn/jrDTIS258JfPmrVzPxqqtg1oIYGe4MBcM1ftR/o0lzPW2Z8bTmJM6o7hz7KZ5od5oVWHbUqjtQTsvTUnXrFZWRJWAo21p+e+63Q4LCwPiuIPRxubUGQyIwgil8sZD1ZxUwF40Pu9S/OE+I7eor+F6R3fOc1sRzQ91yV5lxOabr0B6nSFg53vc2WLVzzo9K9eKoEw80R8MWB4MkBJnjmtLFUkLF6dN22TTogq2M7OPHEg1xjAYLKCNcWZR1f6z50FQCpzmlSajuLS0xHQ6LT/vXJv2V5alHDp8gPX1vZw/e5kwCNEih9peqpvA1tuz+6B+jWqNiPqc+uXvn3FnQWD3nQ11qyxYkoIAiIRg4/IVpNAcWl3l0sVLjApNoWE6mZBNx0RCMxoN3YvXaJPbaHhGo3XhxmwAiRSyNO1GOPAZhGQEpNubfGm0wdWrl5kMR4TCJoU1Hcm7v+29PPLo/XQGfW4/fpjf+vV/z8ULF0BYp+orly9x7vx5Nq9f50KnQ6ff5frlSxxa38tw4zpdIfjSFx6nmE5t1DisBNl482QH1pTWFEYzuX6NztIinx4OuXzlCt/4Ld/Cnj17Wd27zKWLF9nY2uDwoSN0C8N9D9zP1bMXeO6LX+Hs6fN88QtPsdDvQVEQRJG1CAlDhDEEQtg8LcZgjMY4kztr4mYDu6ytrVnaEMUIIfnSl7/Cww8/QtztgAtfP9YFmdJEAroEdLpdCm24fPESB/etQBiyfvsR3n3o+zh4/718+g8+wblXTpOTcXD/Pg7v38uLT38Vk6cERiGNIFAB0mgvZbQ+l1J4D64aPXQasPLaTu2yEFhpvqDU5FKjAwpDIAMLHI3Vms8CA1O735/r881MbcS8ORp034eyb05w5QFEA7TMgCtazL5aTAdnmtsBEmTZet2/tDrXZ4V91fMtQ2kBPN76Y8f4GgL7NuFVG182T7BY/76tNJ9p9qP5/DzgNNNuQ6AlsPywyKxp8hLWzL0wxglpraVVoTQbGxt0Oh1OnDgxMweVuXyVZifPc8KgEtj7fKg3U/6/ApA++clPznwWQvDBD36QD37wg3Of6Xa7/NIv/RK/9Eu/9F/VdmmT7SSD3k9JCjAuyo89oOqgp9pUTfOtHQxng+ny46ub2BlD6a+U5wXeNycMbUQ2D4g8s+1VhF6i6REyWA1dGIYIA4EMSo7FaGtLH8kQpW2G4dXlNbQSPPPMc1y7epX77rsHRQ5BCQfsWLXCCMmxo7cDgiAQyACEdEEWpI3aIwQNadNs4IQdUnABQkqm44LxKOe10+fQSJTKGSwsIGWI1jlZpsAEGC0Jww4iCDEuK541j/FqWScx8kTRGOsj401VGoCjSWRFY+/XcEQrP10fl3+vzbU1r9wKWPN91u5g2lmHHavNa+EkYdpArtG5Iu51HG/pwDsC6cCkFi6casmZ14icmW2nXN/MMvA+fLCpXbuRdKllkC4nh0GbWSmONrNg1xO6JvjcIbkr96D9LP0B79oqV6nxY3V7VQjLjAi7d6QI0IVCF4pQCqQx1vQosKF4C2N9NqxZkVddCBRQGEMYhwwWewShwBiFLgxLC0tId/gLIfDGLsZoy6hgKKN+i0rarXWNxlQnrx2f/1O4fedByMychyXcMuUbczSCSkpZt3kXwk1Qyet45sX4mJGuGduWjT7ke1SjJH7tiFmNbHlQu3Ha/WyfktIghPfdAaEkvV6fY0cPM9zasiZSGJYWBnZNFwajnK+Lsc8rowlMgECW778EC5hyfIPBgH7cQeU5QimkVkgTEAUhoQzJ0hylFcrF4a4zXjPrWtuQ2V4AlqZpZebqtRfSJhs1OkAVgqXFAffcfYpzZy6DdmtbJtiQ5hE2LY2dX6Vdvht3RgkjamvZ3lPuN1GAqIRRkCOwZ103jjDCanp0bs1CtTFIDCorUKLH8PJFLl26wN13nWRy9TJ5lqPBRqzLM0QxJE+vu+hyVjtiHKMtS3BkaYwGcJHaMCCMBFOACCnylMvnNlCqII6s5qiQcPy+O3n0nX8CJRRZnnDfA/dy+9/+KX77P/4nnvjc4wSx4MzZ01w4f4E8n7C1dZWBWuBjH/ooRZbTjSKiQHL+zbOgc8eMK4IwsNHajKmtc4HGhuMeD4fkheLVp58jG0751vd+G3sO7OX2Q3eQ7kl5840z7N93gLgTsLq4hFAKXeTkKmc6HmKMjx4qKbQ1eQ6FcLmkbSjtUrzjpikIYpZX9tDpDhBC0un0eParz9ONFrjv/nuJ4tBq96Q3p5ekBRAbjt52O8+/8Bx79zyK0SkBhqgT8vA3PcAd9xzn6S9+hcc+/lnefONNQvZz97d/B8Io4ligkoR0OHG+aQXj8Yher8dgoc+FV06TXd20tNSA0AbhzCeNi47rV58V7DqNhobACTqNo2ueTkXS7Twza7ZtjCFwtQkjUM7XM3AMSSFUdRZbpEYoA4wMrBBZWX7CuPVmHDCzISnsd0opC/yksBpK4+61iN2lSLb0KRDed9kD2pKQ1fa+Pa+CwD5rwa9ACOMEAqKcKyEsALfbwAq+7T61rgqUtNFpGR0t9LSvfv7V+c62tA9Nwf684BE7tU7S5oaTlp8qwSezx0jz+Tqv29R+zQOSbQKfepHet92d/0JasB7KACYZpDYIyXMvv8TWxhaBgdXFFTpRyHA8Yml5mWx/YsPdCxvhNFcFKRB3u7jjF4QgQKILTRgEdOT/D4M0/B+tFHlOt9stgVHpC6SsPbU/5ITwTJvER6vyUr02LYXVtOycvjr6r99v69QluvXAiZrkxAdx8PV0Op1Smu5N8nYsSseAISwIsJIzQ6/XJ446JJOUxz/3OGEQcOLE7WwPt90CF64ty5wsLy6ztLiCUl4q6GLSm5qpCn5TN6UsVf/rm0A787o8V5w7f4nhcEJRWEIUxTHGCLa3hwh8PpoqCaFPZOcBZJskyxe/xT2TNjM/5fcNBrvs/47q5pZ6nTerabrZerVjINmFoAhHuD3DKoA8y+j0Bs5sUe3AQRUu8Mx0vT5/vaofSl55J8grUdOtj6/WGwTO5M4XKWYIZb0vzSKqhUc1G7W94O8z1b1BzQ/LaymMsAejEIJASooiRwoIpQVWRZ5RxEWFQ/DMvaMR7iDUxhDHgU1E2o2ZTidMJhPWVvcCmiCU1m/MOW0bU4X59+u2PIyFFwoYJ931TLCjEZ5OiLAUFlgUVV/XHmR78z6PDgVaWYYmDAPiOHKCF7vPLWPf9N7yj1dmrMKY2nSLxq2VkMlogRBBVUdjvJ5JkFjNBgRIAYEwPPTAvUgpGQ+30EqxtrLG0sISW1ub1rdShoAP++9GaUQJjqqFapmAwJnMRVHI0tICyXiCXl0ikMLGFhCCOIwt7amBqxL8NaWw2HcppWR1dZWNjQ3W19fdmLwk3oU6NpIsK1Bac+89p/jkxx7DaEMora+o5eIDhPHO/lTjMsb6ATlQpI3TimhvPqRQOIDkrmVZZv2eAkmhJ+VZU5q4upFJAUVmyKcTtq9f5+kvf4nDBw8hQ8fUStja3iSZjIlCrJ9MbvPYGe1Nxqq1JxxwElphsEGCXNx7CmlAG4rCRZZCYgKJ7Ibc8+ADFHlBOpliIsUTX3majevX2bO+h8FCH6MN5948w7Vr19BFziMPPci+/Qd45oWX2bh6DdXpsm99j2X6nCCpUKr0lTJ+3kqQbrUKQSDRhWLr+iahPMtHfvdD3PuW++n3eoxHYy5dvMgfnvs429vbXDt/niLPUEVuz0eU28caIUJ7hgjrD+v3qBWo1Pw23Rm3srJanjlFUTCdpHzh8Sc4cvgw6+trRFJaOYyyQNlIMDq3SS87HU6feZNDhw4SiRyjC4wO6C/EfPO7vomHH3qIc2cv8tILr7K1uUV30OXs2dcp8oxet8NgsMQ3vf2tHDp0iDRLGG5vcf3SZTYuXqZIc3Seo3OFyXKKPCfLcpdHSjlTbWv6NJlMCQpNx0VO9fd4gXNoNELb9WCUplAKf/bUA/kYFVjQrY17f2FFUbR2a9bmpZTGwRwPDJz4RxiDcHkShRGEMnTCHBsRUOGyQRpHT/3xITRGun66YBbCC81wwhZP44SxpqPGlEJYT1sKT8ulBXQV/+EETw6MaW1KvqvOM7WVOg9Z/7tptbEbP9IUepb0RDgzWCHK44EZUdqs6V6zX7OawPZzut5e00RwVgsvyjWBo9EIq0zI84zR9jbB0gBjrGZJFoY0SZCiQxSFFEVOkqUMOhHaKIbb21w5d8m6a0QhYRTR6XXd3jT0+3327tlzSxY/X/cAKXJRLup5hOpqzHrEOfs5qEkhd778pjN7U5UIlO1Uixrqh21z4XtgdKPFJqW04Q9dRnit1Mx3JbJHsLKyihSSs2de57XXXuXgwQN0ujGxCslVThRF9LsdMDAajTl+2wmKIicMwxL41TNp30xp26RFYUMmv/rqqxSFVW12OjFRFDMcjkiS1M27zbkkhDWF8H/799JW/465ouLfZY2hBKz0/yZKc6yzpgWz73jed7uVJsGqNCai1KTcbD0gyNIMISAMAorMh5Wv+iI9Dw2llsIySDfX32a5Fc1YfY/U52uHZMn9KxpSMIzZ0c8SQLXUYx9psu3M7A2NcblnnO09gBTEcVSTelEBKuODsFRjqmuQlVLEnYjBQp9up8vmxiZFkbO8skQYChInGPHawVLeaTHQDkjiI025lEq1yazRAiHLw2znOeuYEQ+QPBB0eV7AILRCaIX0GnQrtys1FRWoxjHt2lXTaK9+I02hhNV2QnXoCuEFGdasF5ePRjrAFMUh3/j2h7jn7lN84QtPWoa0KLj71N0uJ1XAyuoSNZnwjrYrIUrJCWGM8EHW2Lt3L2fPX2C/cn46MigtC8bjsTNtjm3gjjmb0QuklFKsr69z6dIl9u/fX7MamD2AvRXAnSdvZ2Ghy3A7odsNyAtjPZVNDjrB6KKMemqBq3uHOsf7/FSaOOzB72bbgwCUIjCaUEZ0Qstc27VamRwhbGJFIaz5XxRKJMKa1eUZcU0w53+8maMHRk0BlBdYYMAox9i7zyJgB3NcKMXC0gLnX3+DyAhWV1f5zKc+xbUrV3n3u9/NeDhiOp7Q7/W4fv06aZKwuLDAj/7Ij/LiSy/zex/6GINBn0OHDrK6tFTmG/Rnore4qAsqhZUiWmYrCLGJ4g3D7TFRd4uP/v6HSJOEIrPnlMpzG+2wDLrkNUTVew2DdhaqsjbBJrIMJGEUghQkLjR4GGrSNOHChYu8+uprLC0N6HRj8MIiU5njK6U4cuQIzz39Zfaur2OMIYwCtDZ0ujHGGAaLA07de5K77r6LPM/J85xpMiHPUjLX5urqMjIQkENnscPi4b0cCx+0dM5YaCBKbtmCS6UUWumaL3dGWBgCZYMrZVlGlufkWU6RZ4hcobKMNElsP4qCNE3J0pQ8t7+T8YQsSRDaUKQZ6TRFaoEubO4ro73G06diUE4jb8G29OeKsYaMWmmEtmaOQlvaoIGsZi5Yl4ZqYagosiktZDA2z5NBOPps/7ZCQ2mFEt4CQEhEYIGSBihMGRHQp3fAa8ZFsCMoQ5vAt87v1E3H5vGG9fNoHn/k+cP6udUEOs16b1TalACtAt0WkAdOSGoMAdLy3KLS5kkhiHsdlCkweUqvEzIdWdPoaZFRkNPt9+gvLzBOJ8g4YJokTIYjIhkS9has+bWUZNMUISTdhT5BN+bacIt+v3/D8fnydQ+Q/CIoQYW25gFtvkx2QVZhKts0R740F1Yd7PhFXi3Aqk4bljks62iCgDp6Lx2Ga+2XEbFqQK2+UGUg0UqyuLCM0YYnv/AERmtO3XMXw/HQRfoIWFoa0Ov2yDJFliqOHDpGltmkW7ZvAUEgZ8bZtlFLG2SYmSt/X5qmpEnCG2+8QZFb7dTa2h6yvGAymZCmCUL4nFFuw9fmtA4omqBk58uu+Lcyo71nJsz8jT8PDO9GdHx/mp9vCOJagLTt3s7ndjL/njmrTJmsRtQyeHlW2AzTNdbRA4Yy+p8DHaVttpOquk74p+pnSes4fV1tfd6NUM6Op+TCZzVB9b7McsHuPc5+saM/LXvVS9KRkOsU5RI7hoGkE0V0OjEyEC4wQEBRKIRQM++oORdeYi+kYWlpwNraGmfOnGE6TVhdXWFhoc/o+oaV8JsqEqNlaJkJr+v775lYVcpI7ViFsCZ37gxx91W2/eWhR+gkce5AwmmuEUgT2oiERqMLgdChNcf09TjQId2mMcbYaH1UMsZA2KSWGJfY0zTzAPlIWTlB4MyXtcvIjptLoQlDCyTiTsSetVXuuPMOHnn4EbpxyBce/wJbG1cYbm/w4IMP8dZHHuLsubM89PCDxJ0QUWr/qrXiNWrCiB3z6SMVGqO4/fajvPHmGVRhECIogWccx2xvb1dpHOZoj5plfX29TF2htY0O11aKImd9/x72H9zL9tbrRGGXUZaSJVOKVKPVBMxspEkryIBAKAvq5awU1wCBsJo/6Yif0BplbGZYoWxUJ6MNhXIhxYWVdqdJjglDqyEKQu4+dRcLC32+/JWvkrrw9XUH+aIoMEKUpjnlfDtaZHSV+NxYTI0158EJJGrnghBEYcS+9XWkMpx//Qyf/tgnGG1vs3dtD6+/+ApJMrVh3ouC0fY2SZLQ7Xb557/8y7zy6mkKDabQbG1ssnnligVGjrY1Ta19kVIiw5AwiEEGQICD52xe22A6HZc0MRCSQOO0dV5g4v3lhANXGhFWfHf97A6CoASxQkqCMKC/MEBpbdOBFIqiMBw9epQzZ87ywgvPc+ruO4lipx2mqtO/AykF3d4Cr79+hqNHjyCLwkrSlWXwYxlZs3sBBNCJYjoDC7js2vGJ7zUxXQyG0BiUj3anHP2xI7Tv1dFcv9G8JYwU0pmaVsLl8rwsnHmpqUbi15PWhTXpLzQ6y6FQqDQnTVK0hiLLydLM/iQ29UeWJEzH24y2hxRZ7gDVlHSaoIoCVeSYosAUyu7/QoFf7y7cO1jgjrGmhAER0ig0VhAROJNsgzWp1sLSOmuda0A7CxkhKYzyEjqU1ijtLUCq/GlCWjM8490QTDDDIzaFq83PPgiXEFWAifo55+e8CVLKN9VY/01tU12gVLYrvcmf2XFPHYzV62hqk5rnUfP8rBeBKPlYhLBmiNqgipxkPCQWin4oWY47ZGFAmhbIMEQGkgP71tm3fz+BkFbDnmWECOJ+j6gTl6aYPijFqXvvYXV9D5/+7Gd54+ybrf1pK/9NACTv3+MDD7RJ8JuanbpqsLlg/AuvJwmsTPVmD1ZvZmaMKm3/fanbctb7UG/bB2OwEqeQhYUFFyUrRjgHfb+JhLCM19LyEkKGXLt4lWe+8lUWFwfs2bPC1c3rBFKyvDBgdWnB5sTQGfvXD7DQXyKZjsp2/GYox1+bz9nNaFGIv1aXFGhtJWRvvnnWmkhom7gvCELyScJwOMRHXKrmGIyZfT/1d9ksM4Bj9htEXepOjfFo1NN01JyROt6ENKXej3mAel4piVBLHXPbogJLNsS7KCNx1QGPMdZUpi49C+rMvhUHl5qlqk+7j615vXmtKdHy980LEV0HPdUz1Xf1kbdNa1vUnvqNM3vRgRWtNYG0kvc77jzBxsY18jxndXUVbTImkzErq8sEYcDS0pL1DTKzbWqtKVSBVgULi33uPHmCZ555lmvXNhASHnnkLfzBxz6ONBofV8sm2ZQEwss4zc416Ri9UosjrSt1CWhR2Bxgwtnd+4NSIug4AOPqdpNYCgmMZap1bp+JZIRSGVJaCbkdl3L7GILQgqxKk+fNjq2voo8mFARBGe48iiIGg5jFJatV63S7rCwv0x8MCMPQ5YLZY5NmrqwSRoLNjU0uXLzIF7/6Ki+88AKbG9d5y1vu5/u/709yx53HOHX3ceJeh9F4BBiXx0i4NQNGeCnlLE21sy5LgLf/wDqWtzMuV469J47j8qxQWtsIcGI2dP+MoMYBhH6/T1FY0yMbfW/WHMbT8qIo6PUNd546zksvvIpRimw6IU8noAWBUNZvycPRmhbKao+c+R9+P1nGVdcAod3nGmPs+rBBf1Rp3hN4ywml7N9aYbRieWmB/fv2cuniRabjMZ3+oPR79ZpUm7+oEkB4AGCM9X01obagTHgaYM+tIAxc8l2D8ILBMODI8ds4duI4UgQ8/+xzdIIQ0RuQTqZcOn+ebrdLkeWEQUDq+ry9vc0XHn8cREBnsMR0MiEELl04j9A2uJAx1lzH05p6wsggjOyPDBEytHMnQgyCPM1Lfww7sVbLaRAYaU20oiiy5ovC7hG3pWoBjarwwn4NysACljCOiOKYLM8plCLLc4wRdDpdoiji7NmzbG9vs7DQQwTRzJqrhKOGAwcP8+yzz7Bn7z6EsIy4lM5sUAT2mnARDJ103ua8Mu7dWD5eOEAtjUBSCyaFsUlmhZgxtfb7RHo6FgBSOoBuQTceLPdKJ2coKVCl/ZTCBXnQBqkBpTAICiGsWMiHkNcGo5QzK7WaJV0ohDEWKGU52miyZEo2TSiyjGQ0YTIaMdoekk8SxttjhltbqKwgSxKM0uiisFFZc2vap5UFadL5mRpyBIUNXlUoRzOtyaSQhlBoa+VibN+lseutEMbRocKCI1Oj71AKouqgx9KI2QAtnm60CfHr58RuQYxmhOY1K6n6PXXeEtyRUTvk6udSk2eu88X17+f93QR3xgNPf9YJUfq2CgPTzS10mtLRgoUoZnHPPoolRZrnpKYgzhTJlet0opgoDAmnKel0Ap0OyyuL5KogSVOyJGd9/z7uvv12cg3v/OZv4fc+/Ac75m1e+boHSM3Q2FaCKWa0M/Xi322dyfef69f93/WocvWwjv7zdDql0+kiRDDzbDNUZR2U+dIGzsIwJM9z0jSlE8dleHCfMAshWFnbg9bw5S9+mdH2Ng8+dD+TZIqQsNhfsLbOsUQrg1GG244fZzyc0umGMz5Zvt0g8EEabo7przMFaZry3HNV/g8bljErVfNFUThmz2b8Vqpu9tMOaFpLTXsElATdMpSWkazPZZt0Zbd2bgTO5j17M333DI8X1rVJg/xvDyKFsGNK09QSXK+N9Ik2sYEatGNubRWm1MKAZy9rbdUkoa1I5BbKzayVch+Zqm3fj/JgnnmplVTTGGsPDrOHQim1qj3oNT3GWImiFoVzmjUcOXKEffv2cvXqJfasrXFg/zqXLp0jz602dd++fS7kfmHzjNS0vkIIVKFI0gn79+/l0KHDHDp0mNdOv8ibZ87wrnd/K6++dpqXXn7F7ncjUQK834LCRzJrzIcQDkDNzolf4UJodwDbdxlK+76EAK0yqwmSnlm1EywAE2rnf2FpUbfbdaFRDWEk6PX7xHFMHNnImYOFPouDPnEYuaTNEQsLCywsLLi9HDHo9wlkhAG63R7dbtdFC53Q6UTkuXIRKiWdTkyeF2SZ1RaPxyNeeO5FNjaucebMGc6cOcP1a0PiOOYd73gH73vfe7j7njs5cGCFQuVsbA9RKnf01U2Lw0lGGxTKgh9tV7aUFhxR+jNq1tZWWVpctKY/WQEDS2fjToQMAtI0dfSneif1c8Cup9l3NhgMmE6nDAYDl1NrJ5NhTdQK7r33Lj70Xz5GmkzodWOy6RaBNRJC13yPGngZbypocM70QlogIIKadljbZIrCSrqNgUI5TVNQRXWyghNZ5pSKwpC9e9Z44blnyfMMUqtt9YEnPEjEma1YkNul1+uVoXXH4ykeXKRpWlpOJMmUZDyywT6cIOzA+l5uu+02EJKLFy5w4fx5giAgcpoMrRTbW9vOj8n5KzrGSStN3IkxSlFkOYlM2N7aptfpgtMG6AZT6M/pIAgIZFiChkBEGALrbC+tmalDnaVPhMYGEYjjiKLIyLK0jJAG1sojCi04GgwGTCaTivY5IB11YlbX1rjr1CnW9uxBBJYxnU62Uaqg0+mwvT1k4/p11tf3EAfRDprviwxC4k6Py1eusbKyjDbWpFYgrG+eow1BKBEESBkhRIBBlfTAJta1pmXSCAJTkRjhNCMWIDmhkKj8Bss1KSkJjKCKGicAI2UpmvF+P6Vwxwd7cushkEG5rjI0eMCHtNrYULj7BJEjapaXE2XQH6G1i1ZpgYxx5oBoq1HWSlGkGXmSkSUp460tJqMJ03HC5vUNhpvbjLeH5ElCnmakaUKapejCmgoKZTCFra/QGQa7z1AaCoXUpjT7s/NagNQYU2CMDUhjmD036iUIdvJ5UPGUdV61KVSvaMQsaKmfhc3n6qXi8YLZSLO1Nprgxpc27VD9WlO4PSs89bS5OuuNO5v63S5haK1jimsb5Ea4dWL9yQI04+0pY2MIg5DIBSszAkwccPnKNYIwsL5vUnD52nU+P0pZ3neQt7/rHdxz113cbPm6B0jNKFildiaobELrqk+7KCsU34aA/eempNAv0vqitBqTgCAIZ0zG5qkd64uqHj683lYcx0zHkzLEbBAE1t54OiXudAmCkGyS8cWnniIMQo4fP84w2ybuRKysLLMwGGDIyYUijmL27zvAZJISm7DsgzGUkmlL0CrGakefqZhu/7wQdoFnWc4rr7xmJfCBREpBlmWMxxOKwiaz09pJV7CO21bieROgaJdSKiCMk17pds1O6+Y3tQoa9zVHXm9xFkx7INN4woMBf4buqKc2Ar/WHBURAsuAuQNHCkGappbJiSMHZH0EKUEQy+reQJbgy5vCBEKQ58rVUR/TjsHPqJWMk2TPDM6YCtyVB+78d7jTJtlzutW8iMa68m1A7X0I29KO91P7KN37sF2zL2AwGHDvPXfzwH338clPfJzJZMz9995LEEjyPKfQOQf27+fYsaMVyBbed7FCdMYYxuMJe1b3smfPKm9961u5du0Cn/r0p7n99jv4if/hL/K7/+X3eObpZ8lSxXiUkGYFBomWwgZgqI1JO01BUGMxwJrXhGFEGEiktEzZYDCg2+2WJoSLi4t0Oh3iOCLuRKytrdDtdmzW8ThkYblHv98vTXz7fcvgGi3Q2jIcYRShjRXsZFmKQIMRpGnmnLGLkuHf3Njk8oXLxHFMmqaMRkNrkmQMG9e3yfOColDWGX06ZTKZIKUkzw3D4QitreO3UgVBIBksDHj0kW/gkUcf5p57TnLg4BoHDu7DmJzReIssy62E2xjLDJpqnxmwmh9dRQ71q8ivRjBEccTBQwe5enWbLMtKmuwDV/hQ3/Yd73TmNWZ2f2itWV9f5+rVqxY4OqMa90JngH6RJxw/foylpQU2r1xh0B8w2rpiAxxItwvcQV9fyMbIciiiVreXuoIhjjs2Aagze5JBgCgTkRtkENrcRBgnTLHVTMcTm4A16jAejQmCsEy4aPdjFajHRtDzEmfBqVN3c/vtx/n4Jz6BDAOWV1aJoojxeFzu7XPnzhKEAYGpAFKv1+Py5csUaM6fu0DqkkJm2uYPk87MMYptKGw7VDGT701p6+Nx/fo1tFEUBqJAEoYRWjhq4vz5pLYMYBR3CIOuBZcECBmACBx4FBjh8xdZwGG0FyhYrcd4PCIOQzvH0pqOGhd1stvtceDAAS5duoQQ1qS/P+izuLTI+oH9HDh4kEOHDrG4tGjp9njK9evXbVRbbUiShCtXrnDijuN0RL9VOOqp5NFjx3jxxRd55NGH0T6gJQKjHE8itY1aiEAKXQqD7HfGmew5Giac/KC64s4aUYIY/75rKxJZVNFjcee2B1LSCPwi9u+u3C/G5zHzZ5rV7goh0NLxR8ZZz3htnhOgahcYwwjhHDhtH0NEWYc1rTJ23UTShv8nIuzHxAZ6CNY4jA5AS+OCPIDJC4osQxWKPNGk0wKVZeTTlMlozGhrm/FwxHB4nfF4SJFkTDa2yCeJfTbJMWmGLhR5ngIFoFDK+g9q5QWVDsi4CIF4QVdNEOPpjzYaEVj3DIEHTE7rYu2bZ2iTP3tLTV2Lq4Y31/SC8NAnEC7f8U7zu6aQuqksaAKhJpCqf1c9R7VG/HPKWjHEwr17Gbok0SFChFbrbwyFE2IIsH5nxvHIxiDSAiE1xmQY4ehEEHD+uReYjFLMN76dbhjv6Nu88nUPkHypExspJYGwttlKa2tSBjYsqZ6VAPpSD/IA1aLx1+qf64vEmm4wY+Y3g+59MlWHkL3FvyUcliBIaVX9CmPt+YUgii0oGvQXXAJc61i6srxCx8DTL77ImXNnOX7bMWQcECrJ3pVlDq6tEESCJCuQRrK4tEyutBOa2aznIKxNeeCD/O4Eh43ZdSDJKugN1mwuL3K2tiZsbEwJ45jeQBLEkBlBmuZgAqIoQhWWIChl0Gqn1OymiptL3LNVfKuS/Zzpe/mOyhHWmXNndoZ35Cwbqc2BpooU5oi9i60tSucS+92ML4mRlNoxP28YFykswIZYc72t5R+y0arsQa+KzDryG0OhcoIA+r2YEyeO8MjDD9HrdsGFnl1YWLAJTPOc3/7t32Y0mRKEIYuLA0SgmU4Krl7OoRAgNFrnzo8icFIeHFOqy2RtgWNA0D6vBq7P3gcAPDqswFTtsBcQeA0H1uRNOKatfO3+wHWmIXXwae3sDTIQM/uv2+0SyIAwCgmEpN/v0+/3Sul5v9+n3+uwtNRjz9oal69c4aN/8PtsbGwwnUxZW1nh1VdfZjpOOHnqFA/cdyd79gwcZyfx4fkD6UOyWyY6mSryRcUdJw8znmyxsfVWPvuZz/Frv/bv+L4/87386J/7fkT2p9i+vslLL73MC6dfZTuZoomIoi6DwYA4juh2uqUp1PJil16vSxiFxFFMHEcMBvZddiJJHFXBVJQz0TDakOWKJE0s4+ZM4tM0ZTpNyPKcybaNhDmdTsoAKUJLkoliezQky62GJsszRuMxyXhKYOwcJ94nIEsJw5AsK8jSrDyE7ZKwzHnkzHSN0QRBSBSFNmiDgqVBj/X1VeJOh8XFRQaDPuvr6/Zn7zKrayscPHyAxcUFJpOE8XhMkuao0kRMg/NZBAuAlbThpQstEQUEUYB0wZEss+J8QYzk0KFDXLxwzUZVUy56HIqoEzGdJhTGhWluk4n4VkWlGVpfX+eFF16wOTmMatxsK9HGoDLJvrV1jh09yPVLlxCyA7KDEIooFKR5WibJnalCm6phB4x8RDvlNERZ6s4noYlCSRhKotAqQ4pCkWcjK3EXAiNChImAAJULet0lJmPFeFiQp6ACm1AWApSRLvFqQICNnCaUQReak3eeYns4YjItiKOY8faw3KNgky8XaU4gYtdtSbcTo03I1saIkydPkk404+0UIZwm1OfmEy7lg/QmXIKOO0vT3Pr95SpnmiZEUUyIdBEoK4ouZYjC+ppFYUQcdumIPoFPnhvYpLZSCBdJ0dIhGYVoKSi0DUwxzSZgCkSqgZSCgFwqskCR58JGkuv1OXL4kPOzDZBhTNwJiDs9jIY8yTEKOkEHIQQbySZXr14nnRak05TpeMJoPEKh3RnfHiXMGEW/10NrxWQ0ptvrgvGCJmXxnfLgwWCESysi3bnWwPzCmQ8KD1i8xsiTddPGDQmUUQgvmCoJt33OvwVgRjAphKjOZYNlkIWuBMeyPIkRQtdqAeGDz/jzu/oCVTvbvbC2ko/407wCfdVeohQYWu0WiDhAxhGD5Z7l1xrPewBtlCZzgSemkynbW1uMrm4yvL7FZDhisjlktLlFNk1QaUYxHZOnKTovMLlCKo0ulIuVN7Vno1bWjNVYYXEhYBoom0zZyDK6r8QHtynwHIxAIo3Xegq0qOa9zZWjLXdQ8z23aZtgZ7RiUXshor5oTOWfpFvuRdSEQcamH/CQScoYjbZ71AXHAMc3eJNy4fukyvdojMspJ6X1G0Na3zpToNQYHWrCfoebLV/3AMk7u3mk7A/zQOrGOeQ3XsWc183z6gvL2xrXNVBNiU/9s9dI1Znz0gRABs7Z2Ti75tqzbsEYx2j4KEYGiMLQmvckU4LASu1WV9dYXFhEaMNnPv1pgkBy4o4TJGnCYNDn/8Penz1bkuT5fdjHl4g459w996ysqszaupZepreZngEIArMANMgACtBIQ4Dig8z4t0gPetSTzGh6kqgHGo2UKCMhA0kQM4OZ7umZ6W26q7qWrsrK2nLPu54lItxdDz93D49zT1Z3Q28FRFnWvfecWDx8+fnv+1u+v4O9XZomxkQbQ9vBhQuXOZvPMaaKFnIJITFGMZaSn6MtRLdIYPzeVV1x/8EDlsuOrbphZ2ebJ4ePsdU0JyDKPVS2dKpYbHCTy3nj40cbSIIbMF6xg4BOFpKQ20wWgL/eoYp/yCYV+8DHmlo55ri4u1h+UkK9J4U6DE0twFTxJhm2l2xUCLMLIdA0FSoE/v4f/D6TSU23WlFXFTtbO/jg6X3HB7d/wb3799BGc/nqJZbtgs8+fsSjh6cMRZEEmAhNc9oPYkK8CqQEeQ0DFbUq1ojvJTdGEeO0SyMClMU8m6YZ5QVOJhMmk0lW/KuqYjaTGP2qqjJdv4neX2P10P3xOX0Xi306T9uuJJciJnvPT4949OCM5eKUs7Mzjo6OhURkteL111/n0aNH3L17l93dHf75P/tn7O7vYo2i7fphlqhoQ0GLAq2EZeno8JjLly5y48Y1jPkOlW347ne/x//9//b/4Bc/fpt//k//kK+/9g1ef+EN/kHfssQT1JJVu8hypetSsnJgtdIslm1UMh2nR8fc+/QRi8WC5WoeaXddDh9cLBZybS8hGavVii56SHonlm7nxqG8ObfRGFG2vQOlMNaiY9iQQlMpk/MSJ5Mme6WMhdlsQl0L7WozmTCbzsQKGC3oVVVhbcV0OmE2m1E3Ndpqtna2YpjWBGsN1lbxPhrvFfPFkk8+e8B8vqD3TgwCKuYbJar0IKE5JoKjSVOh6hqjY0hpUvw0Wa4obXjmmev89V//hNVqWdR2CTSTJnu2x0ri+ZCVUpvY39/n7Owsrp8S5ZeGNPnbGMUbb3yJn/zwByKXt3fo2znaBSZBo6I33Rc5SEYNsgoff4uGiM6MFViPI/gOvIFgpK6XteiofCaFwjZiLW+ahhvPHdB2T1B6jq1XVJWGXFOqB4RVzwSL8pWAR2f47p/+Bccnp9Rqgu4NOlKtKyUGQb/omer9GC4l5CHb022Mq7FK4+YVdm64NrksIdyIcq119CBUTQ4fNMbQNA2TyYTgFTZMUU7RX2gxQWGVkK3U1oABrQzWVOgYUtc0E2xToyd1ZGrV1HWFra14ZZGcEVtXKGtZuY4nx0cs2xVzv8JUiu50zsmjxyxXS067BfcOH/PJvdv0fsn+pRmznYbt/R1WK/Be09FztDhj4TqWfUenAlSGvd1dZltbbG1v8eMffJfF/AznOk7PTqSwr/dgNhQeZqiJc/P557lz5w6vvfbahvNUYRuM+11RX210ZhiTAAyy/vN3xKQDq6SjxP1UHD7FjpfOi4axUNw3XxP30VB45tMz4tuMjBXr71BiPrlmDMpG/TKcOXqW6D6+AA46rxkg712J4EisfKC3JmxtTdi7fllAvbayhbpAaD2r+ZKz4xPOTp7w4LO7nDw55PjxE86eHHF6eMxyPie0K3zf061aIa3oeoJzqODZcnF/QIhWMEqMBsHhFBkQopAaTCbmq/qezzs2eXmSXE39tjG3d+36/Hm+Xzn3zvf8KCwQRjIW4jxhIFHzPkjtsnyfgWAqe+Hi4cKgH4e1FJoQHPsHe9im2gD4n3584QFS8uKcp4oWBXM9EU5CyXRWPtZD6krv0DpjXRlip9TAPpPosmEMpmAAZOnvkp5U5eKsKifUpVAH76GqJAdJKc3Fi5eoq4ammfDJx5/y7rvvsru7w4WLe4Dj4sV9dnZ2pF6Oc6AsKKlYv1w5bBFvnoSDIoa8BXK17aeFBpZHOqeu6xhG0GVqxbOzM7a2q5iXIAukqStcv4p9HrJlp+zbX3ZkwFP05QBIQawMktAOxWYRGCTxcDeGJR3WPi/nT9kXavS9yIxo3SmERYieFsloLu8Lqc7U+J7p14BOViHiJhag91G5qyru3v2Mf/E//A8oFVjO5/Rdj9UTFss5q3ZJ71qaymAqzcnRE7a3d7Fao/GYBIBCjF4wg6VIKZ1BiY21Y5q6pm5qmlosopOpgJuqqlFA0zSSkxHnjLFawmeSMu7cuD5JIOfWBYT1a7Vc0buOxWLJ2dzTP+xzmJco+i6DHx/nbqL59f0AIFIcdwYVztO1kqvTNA1vvPE6ly5d4ic/+RH7+3v80R/9Ec899yzOe5btKiq9wzwrNxcBq5a+czx5csxrr72KwvKbv2m5euUqP/r+T3j7px/wf/nwv+DWjedYrlYczk9Y9i19f0bXrfBO3iXTOwMdls7FhHozWNEVCux496mqKq65gEXy0abTCZNmK891ay2T6ZTk4bTGMJlMYsidwmhHMxVvlrJRYbKiZIoHKymWNl43paoVxsq9m6Zm0kxiOyXUScdcBGsM2hgZGxVwAXrn8zgu2hXt2YnUXenA9YHO9dFDKsUjnfekzKAQxyFp48ZoKmuYTRvc1OO9ENfoWlj7SnKNQOBgf4/ZbMKqXcXctAGwE5X7FPaSxnhYv+PdX0K/5HmLxYLJtBkpl1kGAS50KOv56tdf57/9bwzKe7a39zh67NArj9eDwmgL0aKLMMxSXHhi/kLxlXMOrVqUMdRGPJJehWi0SJ4CjalqdK3o247VWUW33dDYS0wmBowUTre2YjFf0DeSAzRrptSmwUdChFm9xdbBAXXdYI3D2jUSGJXCsiW8rWlqdrZ32NreFsuwUqhvvo61lq3pjGkzo7IVVSRSsFrCxZMRRYwnhqq2UHmsEUBpjcEoTW0sthbCCLlGqLyDl3IfPhDzYyLjnhbWV50s0SpEhSyw6lc8Pjymdz1aBSZGo/qAW3Z4D/N2xUf37vJ/+j//H3l4+JAP3/+QTz++i60ammab6Wwb3RjQMpd75zidz3ly+ITf+q3fYnu2zb1797h//66sD6u4ePEAY23c9zfnq6Tj8uXLvPfeeyNW3H/bYz3PulR+n3akNZOWYbmFZmMea3pT7NthQZ4P0Vp7SnzWeUX+/FnD84bfQ2GvGPYZIOdopvfVel138KTsjLJ9JXnOMI80frGQ0C8t8sb1HqMtzbRhun+VHXuV5779ZZQT412/WHF6dMzj+484vP+Eh3fvc/joCUcPH7E8nXN2fIJfLJjOG7zrpVh56CLrnhBpBB2LIUe9wysl1W8DI09hMsb/MoOzAMCxvvur6F+lEfjzx/PpxygnH7LHLkQSL6UHYJoIUp7WlvUQQZA+qqvq127bFx4gJQUpKRllBfnyKJWeMicpgZISFMEwEQZQsZm+MU2yJAxKUDVMgPHETaxBisFrlQkmKAWapqoagodV23Lp4hWU0nz3u99juZzz1a++jnMtBxf2BBzF+Nw+BAIaayf0HoibxpiafDhKD8h58HHeWjr0c2CxWOC9p64r7t+/S9d18WQRXtZKqKAPEkKmtcSZJqU8hEDbtufGadPzB8tREtolo1BKrk6CUEUL0QD+RPEau/blfPmp1qzKsK5ADdam0mrs/WCdSxVsUGTPIRCj6cYeSHmmRuhC5TytBcBka1tw+L7n4OJFjp8c8v2/+ItIDSzPq/RMwkkqxc7ujO3tHYJybG9v03fwwvPP8+LNl3BOlKiqtkynE4ypIIi3ta5rAda9o2uX+F4YrnonNVu88wJoViu6rsd5x/HxKU8e36Pr0zmOtm3pup4UepUIRpKiXBoJkqHA+X7k8SgFvYu08aX1M92j9PCmMZL1CdbU7Ozusbu7zc7ONoeHhzx+/Ihbt27xB3/w+3znO9+haSaczc/oul6o8xOzUnG/NO5aSb7uYrEEjnnlS69w4cIBs9mM55+9yf3P7vPRBx/y4N59FsslR2cnUqzP1Ri9ha3EQ5NY4KzVBL3MxAnTySR7TOrJhNnOFjaDIqkr1jQTKmuwSpTmpmmwRgBNVVVoo5nNpkynk6wsWythfZW1SAAvaKPBCDuaTxbFWCtIjJUhrqU0DsPYOC+gp1+19G5F70IGr33X55oqvQsQaXWl7o+TBGEfYp0Z8Y7k2lGKHFJaKicJICkFxkDb97RdRztpBFD5hqnSokgrHcNOnOQhXb/CRx9/yjLWakksZFoNMl8kw3jMS3mSDuccu7u7nJycMJ1NsnwYzT8k/6LrW1555UX++f/+j7BKc+XSZd79+bu89dOfcnx4NOxVKiWwQ/Iam2SZj6x01lZSYyeWZDDGYKyl73pRtCsruYnREmWtZdI0VPWE2dZOHvv9vX0mdc3XvvwVbFNLFXoUddVIbTYvsrJpDHUlz63qSgwesY1GKSptMFoMIFrrKDN6XBCrdwhB+toajDXgodce13uk1r2QTmhs9OLpfI1EgjhI3kPnCL3087IVQ4IKDquFBrrvuqy4r5ZLAeargFsmI6TMzaRJ9r2LczTQ+Z6u7zmbi6e2p5f8pD5IrkkwOKU5PjvjuelrcPwRZ92cs8NjFpxyFB4xmVTsXLhEPZF8ol4tWFnL/PEh88fHzHZ2ePutt1gu52xvzdjb2+frX/+6eMgpa7KN995cnw24fv06H330Ebdu3ToHZkoDTtq7Nn2f7ps+K5XLzwdJax7V4kjq8iCbY55gCEOpAoZ9cuxJKtsXoVYIOL9ZuQ8kfWGDYq6Gdqx/ppxEbmTQpJKek2TK0JYUCYKKXorYPoUS0p1UhFkFqZ0FzOcLXABb1TRtg60NNtYbq5ShqjWTKwfcvH6FF5W4gV3bcnZ4wpOHj3jw2V0efnaPR+9/xNHjx8yPj1DzM4zrccslFk8fQIcgIbiC1nAu6RYD4Cj1uvT3prmQar+tGwHL+ZCOUXQVnw/m15+T729KD08YfReUiiyKMXrKRQDIeL6mn+Xv6wZMeTchjfHeD97+X+H4wgOkMu8nWVJXq1bYZ6pqBG6ArF2XbsZ1FJ0GMddWKT5LE3ETu8e60pbQMgxAbjToiGKwLqzku4IwISrbW1vbLBYrfvDDH1HVlhvPXkObwGzWYK2kfbsgG4/3isl0h66TJDZlVJZP40WVQN15z8z6+5XgSBaQKBup+KHkEkgOxMH+PqenZygUi0WH1qLkSH0SlccuhV8lq25JS1n249i7Mwjf8Tnpvgm0DhTmKuYvya0DpfO2FORDn4fBypGMzUpJrYXo4TExt817hAVNx0rfJKAW85jUIJxAEsah9AwQaVoVtbWw53F9h1FQNzXPPfscL7/8MlcuXeLypYvMJhO6VjxymgYInM1PWLRnoDyrdslyucJ1PhakFIHetW0s7Ndl4OOiV6aLgr/vWgkBiePtXE/XdhlgJ89OCplMpBsyp0JmG0pJqBJXXYxRmveJTCUm46ZxSEnbwYfI/jWE7+W5qVTciFPIpoprRJRJaycxRNVzdjbn2rWrfOUrb/DVr32VL7/xFa5cuczhsYTfCUXxmJVnHYylmRWCkA+0bcfBwT7fuHiBw+NDLt7Y5+Zrz4i1vm3pnYvjLaxaSkldGG1ShfNAbcGaRByjYh5PLXPCGmxVUWflV2ONiUntQzJw2tQF6MaaRBHI+AgCFm7OvAXXO1wcbxeceHicFEX0IY1tGHKvAoCBYEmhKeW/3qcAYeJ4l1HoEjMfKNdoBLZBmNVCCIQ49lJAOQzrtdjE490xOtDmIqtumG/AbNLI3CFkj8GLL97i/Q9uZzprH0L2ViRqaF3Z/KxBrsT26/F8uHbtGo8ePeLq9asQzjOfQhAyAe/Z2dripZdf4smjQw4OrvIHf/8V/un/9n9H2y6Zz+ey9q14W5ViyCcIQo8sAkVq9Vhlcr7K4FmV0O2Um5HkeeqXVdfR+1QBiJiDE9iabrNsl7EQpmJ+No9rF9pVC0GjvBZyAucwStZn13Ysz6DvFKvlkuVymWVn27Ysuw6tDc47VqtYJNUY8XC3QjXtXRCru5Kk7L5zwtAXAavSw/5plaVyNnu1Q0zY1krl75IzPpUFqipLNbWYqWI6m9DUNZNpg7Exz0RJPpKKa9BZR2vEoHMWOo7bOV3fYY1DKYsLgdneFa586waHZ4e8+eHPefP9v+F09ZCuX9Evzzh6EDB2kg1CSQe4o97H1xUKqDXs7m3xn//n/wdeeulFOg/BjcHC+hpKyumtW7f43ve+x0svvTSSTyI3C/1DKVIg2rqiWyqt656k9XuWx+A9KkKrhm/zOl3/exwOF85dU3w77LnEfMBiLQ6KdBzqbIzcrPyX9wVigdmxfjD0hR9dVxJGuVznTQ2yQInOV4FEZGiDqh3zxRLd9yhjY9FTHWvcKToluaZar/AmeTw11aUJ1688x43XnwcUy5Xn+PFjHtz5mI/ffpcHH37E4Wf3OH78CDqF7qTgtwWC8/TK4TUZNI7H6zzwKd9fowqd5zzg3GSUDmHYb8ryNKWhcuM9EHlWprRsGrNEAV4+72m/rxsV8rtGI1rfdzHq5OkeqPXjCw+QNjHRVZXFu8H1qLXOYImwwYNSAKhN6DXdZ/3cMShS564DsbYUkiQPsFgRw5Cgu/7sGL4lXl3NbLZNXTf82b/5cx4+fMiXvvQidVNx6fI+W1sTjEk05B0hWFZtYLYzwaPEwqrHE2sgk0jU5Z9vHVhvv7UWHySBGeDw8IjZdMZHH99hd+8izz73LD/76c+k+nbf57HyIUC0WEv+gigtiSmrXHjnF3sgue9L8CkNk01HRWVZaVA6hpQh1p9031Jh9wnwmqE/iAI7Wc+NGWh2bSWbbdq8xdJrqGvJnamM0CjLNeJZm86mKCXsfsZYtmYz2q5FK81kKrTJJ2dn+L6XcJ7gWS0XBC+g8aMP7/DhB7eBQHA9BJ9zk3zn6foOF0Sxdd7R+/hd7zOLmChQ4/yUwdNaemYGsFluVsnjlfq47Ks0X6WaeKoIFKiMRdlxbbJk7Rzi4qNSu5ZoSghoF4uPFt8ngYhWwlSpJKcmgU1ra+pmwsHBHpcuXeDGs9d47rkbXL12mZvPP8/Vq9c4O1twenIqLGeE6PUUpb5ch+mnVmOL6dnZCYvFXMLcZhNeuPmskLUQ6PsuGSnxoRcFTmlsZQtrpwZvUQzJ+D6yw4mXxcUCloGudzjXZgDTeieANgKFvE4QL0paF2I5jSvECymLFD30QrMSZK74+LmPVlIhf4gKJUJdLvMi5LyZEMQK79VgXVxXhnKoy7nNOoIb4rsW7dVqsFrKzygHvCTX2yKsMr2bUiqyYVlMVGycd1y5ehljdfQgtdSuiSC0om1bIc7hvHzP7S8O7z1Xr17lzp07WXEczcd4jVaWrpfCkjdfeIEf/PX/m+/+mx+wOO3Y29ljZ3tb+q4Vj4mPALePfaIjKBLGrS7S20aWKyXtmC8W2bDRO5UNbN7JWvch0HXgqOOMVlFsyrxyocPj4rhp+j551DWBCSo0okAGh43e7ESsMFZ/hygJVVWSKxaphNOepYCJA4MolkZr0LKeJo3G6hpjxAhprYkGDo2pNEwctpLaW5WtqJuaummwytLYCZNJjbXSL5NJxWQ2odmv0bsw255S1xVNU0UDTFabx4DBReOO8/TLHu9dNuKESIxi9QSvDT95523+y//Xf8Pbv3iLh/c/ol0c0/WB1nfZgGatxdRi6NjZP+Da1cu88aUX+Uf/6H/Fa6+/xrLtofe5IHA5f0YGmfizrmum0yknJyfs7e2dA/KlTC0Jf8rjPPjf/N35CyUhnhgNMT4zjD4YjMeIJVeNP99wiXxUeo2ykXLQl0OIJCTR4Cj61mDaVMX/868hgYEwUn6T8WgdBMi6Gr226BKOCIxikd0AXkdSGKPY3d1lZ3eXru3o+liQNhbZjXQgOBXDxrIYlbloUr0wYwiVYvfGRS4/e4WvfuebtKdzHn56lz/71/+ad3/0Y7rDI6pVh297cLFtDLJ9EzAqwUT5nStedJ1lNt2rvD59liIm073W2ZdHY5rHe7xflN9rPQ6LTobo2M0buEUZvc+6jp364uREwrh/nYzzLzxAKsHPsFHHlcJgdUsKmdYD1z4MSZGbuOjLBVVOnvT52AU4XDOyBAWyQqiVGpQXzk8c8RyliZIc2bIh72zv0reOP/3TPwXgtddeZTptODjYo2kavEMoRUEWbDA5ltw7n4tnBsYxq4M37JwIHP9dtDX1tTWWG8/cYG9vlydPnnDjuSscHh5xenLEwd4BL730Am+++RbaKAmfQJi3Ushx7wQ4tV0bN6uCdlwNhdaIYT8qCRnGC7PcbJLCk8c6ypNybG2igI8x6pJ/UWVv2mQypWlspPAU2tqtra0YGuapK4sxmvl8Ttf3GC00zMvVCt8FrKmkDsZiHlnBljg3AMX7UQkuvTHOi7dG9BFH33cE18dijA3ee9rVkgSSspIYx9X5FN6gsldBrK4C7BJLXXJFj/Fw+ltF4a2L8J+hT0FyEJROnhBi7HBSzlX0hhiCH4CnVslTYjIQT9a5KlKVp5yBRJlvlKbSlVicI5BOQEsbqVyvtWY6nbK9vcXu7i5b29vs7u2zf3DA7t4uOzsTJtOKnd0ttre3CAGeHB7x+PETVu0qh3fJpqxRKbA7Wr7SItBZ+UhJooG+b1kdrdBPDiU0ykg7TVQMFSrSLg/hgBnQEElioyxJQDMk74BX+TkCdAYw79QQRlEq9QLuTA5NS3JOxblAUpC9z6F1PoFiF+lSo/KfWN+0ctmwEqJ3KeWVeT30BaHsowQOs0o6amcI5Ot8XLs+3yOF9eWzB6XJS40c13eRrreLQNKD6wlhylQ1KO3RSgwTz1y/xunJ2VB7zQmj6WoxHwG4jYpCHP5kQZ3OJnRdG/OsCkNY2oyjzPI+0Peeixcv8bu/+3t899/8JQ/uHvLozjHHn7UsV8uYeyeywHlPiEtNB/ln42h678HqyDZKNmplg5wSggJZk7KGK6uxlUNVMTdQSc4aASpjsLWmqsUbaaIHJ+WReQ1KG4wRJsemMjRVRWUNVWVEVlYVtqqorAABW1lx4+ihjEaKMLCVRW9JOYK6MpGoIWCNojIWrYQ4KO8n1ozIBmwtebRGxxqDcU5jiAyXkGojaROLkCJ1d0JUtWScYrFyBtZNpRQmaCxgaPAu5rMouc67NiqHgd57jt9/wnNfeZ6t5y5ycnTI/OSI3q2izJY8tbqWXL6drR0u7R3w6ksv8rt/93fQSvY68ZxGJr28j5W1bXQx9+V4/fXX+PnbP+e3v/Pbo+9G4EMN+o58twnsr914A2AZfx0KhrrxHpuuG+kv8YYpR3TjPddebiBuIrK7ndeJkgcpUAKA4Y3W85WG9jIUPo6fOecHeZbeJwGyQtcuWymlJxU6BJz2VEqLtyhEUhirJS3IqyF0N0i7Uj6T8CIpSXXQCr9qI7GKY2oCq8Wc3lSSN7jdsP/KTb49+wO+/rd/i7f/+ke881c/4uzefVQrkVG4EKMwZK6Gofl5jEpAkT7DJybB4byESAdD2yD31se7DOFL+upTw/mSIXNt7LNTIQGvkFgR5dzSW0go8pXW7g+ii6T9NBUXXi1Xnzuv148vPEBKOUchhEy13XUdylgpomnFqu+Jk0MPqLq0WqfNJ/09miAbFPCk7AzKuMphDt57qqoSRdE5NELVazJzjQjtvot5ASoqktGC7YOnDw6jpB6IwbI72+HDX9zho/fucOXaHpcvHbC/t0NlGoIDhccY6DH4XhibXPBUWuKvXW/oqkClldRniZYeHYuHCW+ZzqxC+VCyqZV9lvrN2opLly7y9a9/jX/9v/wZ7VnghWdf4tM7d2is5YUXXuT3f//v8dZbP+fTz+6xWK6AqLTjafsVnWtlIaiArSt00GIxTpbpON211ijLCOgOm7GQCxhjskcqLeS6mWCrSR5HpSTBXcIChU42zSHX96zaFmsUvu8ghozNT86YH5/SNBOOj49x3RLnWom/j+FMrhh7uZeLFnqfreDZ2ptD1AaXtYsKa1r+PoYBWK0HmlKS8EgKXQAVUEZRVRE8RCus0IIOFFiJpSxZeI0WIKKVeGIIkTEsUeMWFqJSKRuEr4S9SdiSAq+wWpQoa21W0m1knqqrKtbwqbMiboxisiXW4d29Hba2tqhsLTk3kwmT2ZSqrplNhR1tMpH8j6apqRqb21lXNns/RAFXkrDtPc73rBYdJ0f3WKxaFl0nOTGpL/I0D8IxGqTvS21k3XCRN0EiEOo9oXOoVZfnHYBDE0PGswVvCF8khhWFDHZzuC363Dgny6kLLq+JNIfydcWYlXH1aWzLOZiOACgjAC/EzSaDGKVE3UwGHRWBJJJvI8ApAjU9GDJCYXHOoC++j3d5qxv9VATwKiuOAqLiJqqCWGgJ9C551uas2kDXBXAeHxQuKCahYaIDygRu3nqe7/35XzKd7mBszQQhE+hPToT+W4VI6e4KgBcV6hjAVhpIp7Oa1WrJ1mwb74tIhHiOxqFRtMsls0nDK6+/wOVrB7z983dZPAzs71zk8MkRt29/SNu1JNVDDACRursy1LWVMFwVUMZHUGJj+KilqWtsXeEjgUYmWImeVFtZqtrGHDUzGBayzExe3RRaXe4DpUIS90W9lrUZSoMhTz8UuBS+FAlihvUzeB8pPs8KUNSU0jwk7wUhrof4m49MoT7Kvqjtym2igQwj7FlFW33hvehZigzPa5DILCvK++OjM9557yOUn7A1rbF2wtbeHiH0EI2v1lRMmwmVrdmeTWl8x62bz9E7T9e1MdcyYLPemBRJN7x3EB0lxEUYgCuXD/jp38xx/ZKqmUblUud2DmMQ52I09sjnhWEiu4FEnsg1Yzkw1izHHq1ybPpc/mJ8jdzjKR6Ftb9L7wZRrpXnDN8pgln3LA/XjYHh8LvPc0AOMVhF4y5jRTuRkytUssLG+Uj0rCu0V9jQo4NBWUUfuqzPueDxzhCcxvUipxUal+Z0lKdS+1mekWTtUofsTXa+AyU5fNoq7O4Wr/3dv8OXfuc/4N/8i3/JB3/9fczhQxrl8dGz30eAI+ubJKRHBvA0dglQyLuOekY817H/SqIKFfs1jVeSIese0PUxzsbp/IRijJL8UOevUwx1rwJDQXgB7LLo82XeS9hgCBgP29Uspjicb9PTji88QCpjr0sXXIluSya7dSS7PsClNbEUDGXSWvmMZAlPtNYJNHnvs8cgTeDkfUqU5FBYIOMmkX8P4lUwRrE122LSTPiTP/5jvOt57bVXgcDOzm5UKCsCntOzUxSGrl2ytz8jMLDmSS2azYl2Sg2WAuf8CCyu25nKJPq61mztNHzr27/Bz372Fvfv3+PFF17C+5YP338fnOPll1/hD//Jf8z9Bw+5/eGHHJ+esVx2keVOZVa07HnzPgr4IRchja9tpJhc6utknfbeQ/TqpZwoUUgd864FdUhWMoOPZAKSV+OCtCWFGrZtF+8XBqt1LBhYVRXL5VIsVH5sSSk05kGhVklIF1aV+LOK+UtpAJrCw1XOY2MMOoiHZoilT5tp8oYN1cptyg+LwjjdowQ6RhmqmBtjrInnWMl5sYYQmXSMsdTRMjqZTiPFs3hKqugtmc6m7O5sM5s2NJOGuqqYTKZMZ1OxqlYVVWWYTidUtY22omjx1gYfyBZQARfJ0+FY9UII0fc9Xdez7CXU7HQ5B6MGBYkhVDSB4NSviSRF1q54dcTaqbJBQma5B+/yPUfslwGSFTrPS6KSFwbDSLKiuhiqE5TkuPi0mSfwhQAj0ncFaAkM82fds+FDrP+V12tivBwIFdI1DpflWxlOcX79F5vX8FH8WxjpynOHKTsYL0plJ4QQadIZn1soPOsbWIJIWYdjTQ4TIHSghr7vveNsMc/U6fttz55z7AUPyjOh5urly8znUrDTec/BRY0xVfTyyJpGJ+//+X0ABuVAwuwu8+jRI7ZmO9nbXCplWhm0Eq/w6ekpOzs77O3v8jt/+ztYL7k3znmc+22S2iCW8ZgdEGn2lZG8B5kjA5BPYAJED3IJAJP2pdEorvX5+Dt5vRgGo2L0Tu5v+ZfkKk7kWPmM8ZiOj/KjoJ72fRjpt2r0HutK9bjt5Vite0s2bG8b71vuKxR5cS4QZVIsvG0tb79zm9UqgLI0taFqKhzC7mmIwNRYrJG8IxU8RjkODg5YLleknV3rQV/ILVZDyObQvpCWA6C4efN5bn/4AV969cvZ0FUwI8u+VqYAwtq4FzPgKaCn/O5pfTdqXzkexfQ6z0i3GcCk+eN9DO3d4AkqXrCop5PvlqDeuWbIH+P3T/Ncq7TqouxKcifEwq3xutRWH+tIOSRyIziPt4zySb1PIa5jnaDssxJcpEgFpVTMPQbdK/HcGk3fdVy4cJFHjx7y/e//kIMLV/jtf/APeObKFf7iX/z3LA8f0wQnRVRV7AMloFBHHWgTiFkf33J2ZMNFcf76sT5+m+6Z++1zPEtPu395vVKlzHlK28KQw+y9p10uaapfvUgs/DsAkMrJkBZciXZhc55S+jxRaK4nMJYuyk2gqPSohDAQA+TE0uL8EsmncEABLmMmksFSn2K4JeRgNpvx8NFDfvSjH7I1m/HSyy8zm82oqpq+FzrlCxf2OTk5FQYpJHbdVjUp7ye1azPi55cujuFcee++71E6cOXqAQ8fPOEf/sO/z3/9X/+3fHj7Dt/6xm9x+/2f84v33ubx/Xvcuf0B09mMupnSrpZ0rdAdu77nLFrGy9yYEMIopCcp+VUjfXd8fMxyucyKtHeB4MKQv1Eojc51EKRYZMo3SjkNnujC9wMjXQYSRGt2oay27RJrNd5JXpKyQy2FtKDjXYpwmOF7KappsgKfwkMFtOhY1X0YJ2MN1liCGxNapDmUNsuUw1VVVhioqgpjFaYhh6DNZlKstK4b6qrGxoT12WzG1tYWTSPhISp6fabTafSCpjwrsWD7lDBtdAyJEW8CqkfhC7KNoSZZ73uOVke4+RB+GgKR8cwIcAmyyYTgM7B3xfaXAEBW6FNNr+DzpjmQtbgIwAawKUAkwrPoRV5P5kybsIQsFp8X4C21IQElwVXDRq+IFmClCYmGloFhMf0ENbRdJTCtorf5fKx4nJxAYB1Ep9+Tdzz1V7leP+/4vA1xpICMlLsx+Byfs/l5w2OeooiVzRjdR8aOrCjLdx7Psu+YHzmenC3YPTzi8qU9rlzaY29ni9l0ytbWjCePH8d5XTOZCTV926XckVTA2I+el16r7JvLly/z/vt3eOHWS6MIgvLdE0Dve8d8PqeuZcN2YUUXrbvBhIwiEhBRSsUaIYHQlfOsHuqtrQHNkMaolN/5f6Vcz7Bz+D2k7hTlKoVTlQgjJPCe+mPj7+cVJF/m1arxd8lKjFIRiZzvZ9bvG1ibMaXSnc6Xv5+aS7t2j+T9DITY7z6q3AqljRQFVxrnNO/dvoMn5owoDcpgzQRrazRSbF0hIMV7qQ139ZmrVJWN69+vhaBuzhcq+6Fc2y+99BL/87/6V7z+xtdyH5aRM6Wx4tc9ngZgNrVrkCmQJtn58S/Dac+D0vX75XmrOXdeaaTNZfyyfjbkOq23c/33dZm4fmGIa0Bk2uDhBMCR15kn4DpHq8tyL0RwuplBbh0krRurQjRUaA/OK7RTOVx/ur3NzRducvvDz+hbz8u/8U3mXeCv/8f/L/boPqF3mNzmGM5d9HFqYzlPPlfWF+0szyvvUwLb9Vz+8hmb+mF9fDYdv+z7Te0GWYGL+SKC1s8B22vHFx4glSFVMGwYfe/OUX6XwAUYDfomK2sJbMrfy0kn+Uh6JPTWQVY5mVJbqqrC9X60mYxyCpSwxAVgMp3w53/yZxwdPeHb3/42la3Y3t7hu9/9CyaThjfeeA0xpBsW8zl1s0Xfe6ra0HtPiBWLyyNZJUuht2kBKYbFU/ZR13WgPDs7My5d2iO8eot/8k/+Mf/df/f/4Yc//BHf+o3X+Ae/93f4yU9+xk/f/BknZwtCEK+O81L/xMfwtLRRKxXDkEIYLUopKDpD15bVcsV8Mc+eMa0ljMyqKo9xChsTkFnHYqbEXBl5hlYqF6ccEwRIQTlFzIvZMB9MZJ9SSg8WvTgXU8HDxExW1xVVVed+3t7eisBWrJZ1LWFnOkBlLM2kYdJMYi0bi60Ms1nN9s4WW7MtTAqxqSScZtJIbZtm0qBjrH5d19STmqBDbKcazdmkGA4eDTkkaV7ymbxztL7Dd0XYoHdRMRrmeVK50r199OiUylTyKKT8lWQ7l/hhncNdsvUpSBhiYp1KXhm5ziPsiaFQ8gTEqBjKpTUxyXbsjVQIKYqKm+HYGKXynijt6bOC6aOlvYyZz2umjCeTu0ZwpqQopJIq4c6vKZQBghfrpQpJ+QiF0hpBQXzJ5FVCnd9AVAJYevNmWG5k65tf+izfp1jv5xSmkYJzXvnJ14y7JN87WcsjglyzPJf3Hq7JhiM1WNtxElohiqECbVm2K1bLOUdPHnH4eI/rVy/yzPXrbG/v8NGdT6ibCc10htKyJbarTooloxllDI/AxHAYY9jb2+Po6CgbbM5HIMh7pTDE5XJF23ZCQGDjeKh18Doo9QmAj0BQaIdxi0/IYMarPPcz7kmyOoQsv9I6TMrrEC4q/1eo0diV4aTl/fPcpHiHX6KYB5XGeay4KoXkZqydn2qBjeaeWgvxK56pYw5VehtfXlZelYwX8UWkHl8CRCJjhDhJAJDSGqUt7935iEePT6nqmUT9JaZSFQjeEpBQyFDINtc7bt26hTGSxyr5gHF9b+iuc3vu2jqs65q9vX3u3bvHlStXNp5X/hz1/9png4Hq1wNU69eVlyeDU/nvl3mmxrrOZmAzMjomWVYYk+SjRBTE2jPLy5JBqzB8MMxrX7jfgg8Snp3WUVEGBh9DG/tEWHKeMGCTfE3vWhrhcwi7jgXWQ8xB1BpPh3JiIL3xzDN4Zfng/Y/40Xvvc+sr3+TWkyM+/LP/ETU/o/YOTcBJ1TS8YhQut0mOl2C3bH9ZULg81sd0Hdw+bR9Y153K56+TRGy6f3n++poo257Gs2u7yAL6q8/tLzxAgjHjWVlHoOzITYP+NAGzjroTEApBPEDl58mDtOkZCRhtEhablJDSAin0p4HpZIrRhu9+98+Zbc1448tvoJTm7mf3eOedd/nmN7/OZDKla3uCV5yeLnj+4jMs5h1iZe+xlYRglUm+grTPW1XWGUrKqbY+SZfLDltZbr1wE+c+5Ktfe5XprOZ//p//FX/z05/y6Ud3+PJXv8rzL7zIYtny+MkTTk5PWa5WLBYL2lXLarXKYVSJejqEkP9O7eu7Ht92aGPYmmyNwExV1eApKtQPYyGFUcXKl0IcJA9GgE5dS60PYairaGphVkIFqspKhfYip2Y6m9JMKrzvQanoeWmwMUl/Mpmwvb2N1mIJ2trairk1FXUtxTyrSp7fNHUGapO6ZtLEPCFl0MbmmHqvlkDMHyk2qqH+UqLrThTMC1bzOc7F+hLRUprmNIjinpTyYQORjcVDvleqvK0HHtZhLieFP0DA4r3K3rhSSKYCoCibrcfBJ7KAHo8fKWIJSGlAZSYcCuXR46PltjxUINbbGa3CvDYVUleiZGQb1qWSIsNKZU9kIjbpgxeWwITGiueaTKY8LKP8t0ZohmONmHECcyRRiGByuJbC6h7y40SVG+LZz8ksxuGv6xvP+iaWm5ssp2sKTkp+XVdUh74e2A/XN8RN4RoDeC7OHSnC0g9aKaFBTkpE8DF3RYwaGiVlCxCPi/MBpXpCZAtUBB4+eMTR48ccPTnGxfpc8/mcrcUCY4VxbTFfRC9njN1P/Z1Bzrj/vPc0jRRmbdt2RNGfZU1SthnWjveSp6h6A3qtpERIa0iu9s7nqSA/Paie83Az9pewXOR5GbI8DwWoUONlkufw+TFlNGZPs+SW4Ipz5w0KafoRhmfENqW1uA6kZUuKIHLdyj86bwD8fviQc/002o8TOI3tUkPpAEyD6+Hu/Yd8dvceJ2eLmPqueXh4jK6moFPOY2k0k9IaKgpOrcF7x3TasLe3v1HZXLdvfB7ISbnOzjle/dKX+MlP3+LatWsjIqhN62z9PpsAyrpMWD/W1/X43JhDqdK6LtlPPZuMNJueP/yTKIQhBG0MkGyKwpBHMwCihHELL3qBjEq5P3rTEAbZVrxfIEUDxHf3nr7IE+2RiNMQXGHYPq/If57sLY318ReZU16iUnxw+JAiiDTTpuHGlUvMT+d8du+Ydz+5y0vf+m0WT+7z2Vs/Q58eM9ExxDuBxDUdc6yrjveO8vv1+br++yZgtencp127/sz0d3n+JkD3y448xs7Rdx31rxFm9+8EQCqT8iV8bVw49mmLNB3rkyjdLwGtElSkIq/ja4Z8hfWJtA661j1SpcIwtMfjOkk0398/4Pj4mEePHvLccze4dvUyyhr++I//hEsXL/Hiiy9z9eo1IHB8coZSwoFkTMz3UGoEjPIEDKFg8FpTaAtLh+w7Y7f5cBjOTlfMZoaXX3mB27c/4mV1k2du/HN+8P2/5m9+/GO++xd/xdligdaSIF3VDdpo2j4QtEFXDX27ijTVkj/jnaeyhrqaDE8yVsgElMIkIgKtaJoGhViYE6tZql81mUzY3tlGRZf41myL6Uy+77oOqzVb04ambsQbM50waSZSzM9q6qZmNpvlmjQSxlahtMmsRWncqtpidAIaAiyt0WhdRUVMvBsyR6Mw9sLC5ZwAmLZP4YHtkMcSAkE5ul4IAFK4TQIR6wbvrIB5UJhiHqbnBgHOMel9+D7NATHAJMpp772wxqWwk3XlKl7nfI/zQ3tS+3OwYvTepc0qAaSgxMNZvku2WMdwOxDgkz0tQaxk5ZyUWjoegs500ak/PMmTJoE0srb7DALkHprexfmfvUXy0wUf605IR6vC4xBGqtwQvid93UMrGllKjB/C4xQqJM9lYbDR62uMDBpBGH/WY8zjKcOc4LyCUx7jvwVgJ/m57o0vQzYofl8HSEnm+ZgvkHKj1i2s6ZpSFikloYXKxwR0J97D4PxQaNs72ljTSAXJD5T2OebLeQwzrfB9z2q55KOPPubDD2/TNJbf+Po36PqOtutoup6qqWm7jmTEHoPAYk2VhpYoCw8ODjg6OsrlDTZv5jGEVsn9snHCjcdn3K3ixQguzYeByGMzWClBaBxHUtPV4C1hqFCf3m+s6o7BfXn/pwOlso/OH6O9TDHy+ITcuvE6UgWAyV2z4dEiP9RTmrX+4RrICmSQpo3BO4fWho8+uc/Pfv4+Dx89kvZpQ0CjjEUbi8mhxirivbJfwvBPBZRy3Hj2WeraSkkGkjwrRme014/X66Z+DCGwv7/Pcrnk7OxM9rzPATib7ln+/jR9aOzVGSus6/t+2itCGAxi6fzSCDCEopXrSI0/VyLPU3TGmCAIjBpkbwI36btBJj8dCKZZk77WOXoG0pik34OleDePcybvBW0vDJV9rzKDpksRSrEsS/lum8LMUj845yCOoQoxzDfNTcSQZxV0yyW7UwFJTbPDh58dcf90wVf/3j/k+PCU9s77hNUZRvnMCvrLQMa6nlv21SaPz6brn7anPO268vySMXrT/Z4G7jc5OYY/xIN0dHj07wHS+rHewcYOeQ3rkwDG1pP1iZzyg4ZiqJs549fvlQRDAlEDADovELMwif8fFIVCUUVhbcVstsX9u5/xnd/+DtcuX2XvYI/3P/iAN954nZdffom+63j77Xe4d/cud+/d5ze+9dus2g5tpC5LqjORLNXBJ75+2WjShrQuTId2lzvW+X4neE5OTpnNpjz//A0uXNjjwf1H/O7v/z2++c1v8vEnn/LJp5/w+PEhh0fHsYimFBhMYSDC4uLRylAbw872NpPJNI6dKG1bW9s0kwkhCOtUCmWbTCZMJhNhRGsatra2sNZirWW2tcVkUoNx1I3k3yQXfRXZ1XC9UOEWuWilVasU9gmAB4z8Kza91WpJZqdjvFGkf2XdGtF+hrmXlEogVplPSoPCBU1OVxFYEy/XI6VWztd5Mx57G4YQMgI5/2YkUIMEsLkwAB8fPDqQa0BETBS/d8NGElymVc2AO3q4tDLpBdOqGcLt1NCu8ToN6YXlLxcthiEJ4k2sRx6FFo8UY+XPpXwDhucKoItjG0cyhIHhaliLnKsjNoC5YR0DeW3l0cpzR9qcig7iAxICuKaI4AkFEF1/piYldScleOivdUAjG3d8fhiUjHV5mLo5AMoNYS3nFKpkMEFhorAKIQW5hJyfFmditOpJfZ6268QL7B3eO1zfS9HirmO1WnF2esri+BQf14hzTgpPx1xDghN67K6LXuHIWlkZ2nZBINBMpkjeiGe5WtF1LdOpZf/DfZ57/iZt24ohouvQKBbLFdPZhEonJX4MHdYP5zzXr0vB2GvXrhX7xjCvx12ro8ztSWFYefHE8RlYBSXcL4OjSPVODgUjywxCiPTHcf6kj+M7BGSdpJBiH5+5KURzFJJWPEaA1VpYdvn959kcR9bj1C/let4AvFQMl4zybQTcSjyWcclm4Oa9H64tAVjcvyUHX+M9uKD56x/+mPc++IzOV6BrmeOx4LSxFpTGaKE9Dww19MbiKYjY9T2awK3nr0cgP85ry60Mm4BImhrDe4mXQud1/fzzz/PJJx/zwgsv5v6U7xgMCfkxYTQXkzEseXjTPAzFzzKfszR4SPvKiUK2meY1kOZG3ANlmxXwI6Ah9n9iUtSmAEnEenHJAzOAJ4WPNalSm8heyNSXA0ga61kDKBuWzvgYQFzqu5QzLO+rM6FWCAFbhRipIWU8ur7H9b2kC7jB0+SDF2KHSNyj1VA0O7UFJAJG2PZcTnnQWkkBbQ2t66hNYOkWXLl4gK5a5i18du+IQ1vz7d/9A7773/w/8f0Si6zlzvnRWJfAtPys7J9ybNdz8YsOHfqs3BfSUstAcyxD0ymZvj9OlU1g/fNA2dO+H3RpIbnwvqdpqs+9T3l84QFSUjyVGhLc265DrRUdTAPxNMCUfpbAprSClPlHCQGX4GrIT9icxFaSBwwTN0gid6RCdWUIToBpPWVSVzRVzT/6R/9rurbj3r27PPv8DX7v7/0uH3zwAW/+7E0+eP8DrLFcuXSd3YMLnJ4tmDY6uq0tGo3yFoOmMlVUWLWEWRHBCUPbNsWHhmKjFnkUv4vnzudLjDFMpzNeeHEH30tV9de+/BLL5ZK2bWlXHfPFgr5t6WM1+5RfZGwsMKq0UDw3Ung1IHz3ddNgTNpAEba2GPqmtBkJ+vTTeSGCEOEfMuVwCMKZ361WMZ5/ALip78UCPgZGSTCEKKhLJT8dngSYU0vJ90j9mLwOxV6GK5WAcgOTmVHM9/R+4w1wM/PgoIiMgZBHiHQGpT6Fk3kSC5lIcilmKX3kvR4ICYo2JA8ESQCqYZPxwCqW+oOxlQrABD0o+moYhxBAh4H4YMxKFHClF6d8NzxCTl3EqwNBJcA2hNaUtKdaDaFySYnIAC8kUBoKJSZtqAJ8Uu5Vli0qvVO0GGZFIqD6HuVDVGCH3BMApTWdjptcHCKlyLVTLDLXezfQtaa5nbwG64YOSHS1cr6L50phz0CIhUOT7OtiaGu7WNCvVvR9z2q1kppeXcdiPke7Hh/rdLVtS9/3LJfLOA/EY9j1XawD1tKuhDnStRIy1rYrqXzuSgYxl99hnNQuSlOqo1VZqcXTdwGjZ+zvXRClyxpsXUXK4MCFgz3ado7zLW2/ovc9nRcW0WXfM/cdu8pRaY32onl5DOPFyagdly5d5N133yPwmoxlofD4cD5UReSAjd6j6K2L88KFZHkeKHGTEoWVnARRCBPwlTaGNClQ9N5LfhYhaq0hemcGpU+pyGKZvFQj3WddoRnyOUoDkBxpTIZQwE19NPouKbRZg85PYgySChAeT9z0jE2fDcBNRdKEJIOKewT5zGh5du81f/nDn/Duh/dRukFXsa6NLr0bw9oAyW1NYBaFJMgDIXkMA+xvTbi0vUUIffZW5+Eq3jVLsjD+nGAKOVNeGLhx4xm+//3vc+vWC/jsaQxI6G2UyQXUTyXxojQuqPjHHoZNXofy/uuH90P4cal0ixE2zQHJe9ZGY0zyrJioo5lRqKJ8NzxHqTS/VC5LUSr2620tZscYFJaDn++d5sUAXgcdMY4BKa9KkYrFhiCFZ1Of+CC6Rd8L4VT2LqXIhCB7vHdeaNHjfjBgYwnN1CpqAy5INEWQeoLeO4KGThm0srgOtne2aexDZhWcnC2x+7vc+lu/yfv/0/9EvdTQuVzYOa3hcr9dTxspPfzr4KnsLxVcbnjqIoXKxoii2zCkciUKpWzuflWMWa4HWo7cBsBUtmvT72Ube9fTO8/de59i9vc2zI3NxxceICW3bKnglnlBcD45rtzERjHhjBXNUtEoF+WmSZQETwm21gFHGeq2nidVXiP1k2BvbxfvPW+++SZHh0d4BzvbW/ze7/8dUSK04Vvf/k36ztN1Pddu3EBrQ9d17OxY2rZDq4HmPCXtppo2iVFLkLfPm/1YAKn82WA9SGaB+M5ao1UQKublKnqFpAK1sKhtZ+UrBGJtqCF52BhNIIUlycIRwTNYkxerhTwvg4e04GOuBGWdGblJqlFELDqb274OMFR6tSHkKmoxESz5qKgMG89oXlCq7iW71Pp8SeFMyao1NMAXWsu6J4DinSnGKClDofhZKjmlBMqFO9O7utKKmZRscEQqaYa2i4UyiELcu6zsl4AshMFCXVKlRp9IQdO6DigLdSGDGZXDP6Uffd4k83hRUjP7rBYE1ABg8/epb6KgLtoREqgJPneOj88sgUvZ52ndBKDr++wFSFTjeaNJ75bmSwSiCgFdecxCGleZo4lCehyPH6Ki4xOcjHlSQ2hxAt+lbEufO+9oVy1t1+b8vuViSbeY0y0XzBdzurZjsVhwdnaG946z01O6dpXXYt+L9wegD33u0/TOUvw05ScIy1Q51lppdFB53XbtCu8FIB0cHHBw6SKT6ZTJZMLOzg6JPXEymTCbbkkYXdMwi2G0IHl+k2aGtYbE1i002yfcvv0eq1WLjbJAm1TjRFbO2XyOD/sRXMqcyiagvMblivQe0+ksFx9fn/+Rff/cviN7U9pLhnVBiNbyuDaXy0UsqB3XXcw/qasKa2LRVGPwvaPte0iEMErFgqVD6KxSQh40yLs4d9ZC9tYVzVKBOv+5H52z6Xia8XF8Ulypv4b1+POO8tL1PX14hsrbVyDw4Ucf8dHHn2JsTVU1wGZ2yHXFvGxznitI3iIh8Mz1qxithE02a46Bc/TXue2l/qHgHMhO33mapsZay+npKU09oWTBFRAdfedJf0kyJTOzpeYMhsR0fd5zwtCnTwNIqdh0qR9loKOS51FHgKSwNhEbmXNyrWhuMZ5D+5xDwmmzkW5ELxpvULzbcJPRXp6MkqS9kcFY5tzghdJ6IH0YfpJvrpS8q1EWawx1LUzBjVf0vaPruihfHc719L2m9302uHqX5qWEYzg/VvpT/2itwKhUnVJKCNDz/HPP8slHP2C2u8vh8pQbr3+dw9ufcvSzN6ltAOcooMxGEHRuHq+DKO9B6Tz/FJuBS6rpl/pYxxCT0u88mDvK4VHD3yHNX5G5wQcG2Xt+7j1NThilJAep7VCfU6x4/fjCAyQ4L7Bl4My5ULcSRKWOdmuduWkirMeSltenmkfJezVSkNYESK6LxCDIy3uln+J9EsVAa8WVK1dYzBd0reNLr77K2dkcow1VVVM3Dddu3MA5z/buHsu+zwxL6Z4p4Xwd6A1/DyE450GSCO30XklJTN/Jwu+yskQYQnsG2aKy9S2AgJZQeGbkFOmjmBeS4ntD8INimZTFeEEKhQSk8G1qc1S0U77PiFY1vmB6RvpMMabITAApKTclfWxSj0enJwAZlZIxQBoK9w0b4hjBhEKslF6Acl6UDRjNv5BIDnLDGQv3KLxCYUUqbpcL1JaAYdD94t8R1Wk9fFW8X8Yu8ZdM107AlZuVWl9jaztb2S3Bjc4rrW9jOt8hVFT4hcp5NWwOSYlZt3wKAPMkHgpfzLnkIc3zJisQEh7l4nwDhryqQJ7/SWkoX6/rhG2n73oWiwV910GUB67v6ZdLKULsHF3X03UtXQxPc90qhnX0tG0nm3AnIWwpTy21U9Z+2qydWDedz4nfzvXgPCb40TXGiByzsQAvIGyMRuOVom5qvJlgJxVGG2xlaZom1wqrmil102SG0SQbjTGoAIvFgqPDJ9z+xS84evKY4DzXb1zna7/5HbZ2dySP0FhQUtxQFKpUqFiYK/OmHqDSlXhRtBRMbuqa6WzKe+++w2KxYqKk/lCi8E0EKmdnc1zvUFVNuW4YbfHj+RlCYHt7i+PjY/b39ymPp8vXuH6yZUXFeaU5PT7h8YOHnJ2eslwuWSzmkf1uxXKxxHVdLug829pif39fishqQ+thf3+PZ5+9wWQ2iXNSIaGcLlvpByVJRRBdrJwNIGUTACqV9l8GZv5tgM+vBKx+6U3AM77PcN8AWiIm2t7z9nvvUzUTtK7wkeBnnSBg089RG9N9kRA7rRRXr12l61uG8PU0H8Z7/NPem+BGfT28mif0PTdu3OD27du88vKrcZwjAIsyLRmjxFBEZEcbAEfIZAphND9LEJ9eK6ztY+nwfijAsB4RoFBZwa8qizYS+pTW8DrF+Xr/pM8yKE1lESLIyT/zCIRijxpGZX200vfZeChvMnoHMVwnJtTzYJnindev07FQehWN9c67XG+x7RW98/Q4+oLsJxT9XQJ7GQvRu3RQmODQoaPWUufwjddf4W/eus3Olcscu55X/9bv85ef3Sc8uYvxfqRLlEe5dtfHjdh9Ku6jY91kMBSuA6unPeOXrd/y26xRhNgPpD44X6cU2Lg+8AFtFA/v3efSv/cgjY80KKXCnECLUuOQuHKxl2FwJQAoQ/ZKz8/6YK17i0ogtQn0JLCW7j9S3kYKW6BpGg6PjmjqCTdv3uTSxcsYLRaL4FaEAFtb2zgfuPbMDc7mCxbLJc55mqbJCqqEpQnldNnmEvjpqPQGVGaRK9sVEt0wUt8GEhNYoI/W6fIa70ThSss0BLKASRt18gzl/omW9REldEn/rCCHYiWAEwLGp0RPyduRTakIAfOyuRRmiTR4w+/xGNfUIrZzgxUj2qPSdEhU7+KWHzOyBcjhA+PNJgqqaDnxa2Bg9Lziz0SD66OA9TEPqLwuCTgdKURF+U4bS3pecf/Ub1422NTXMg5hOInoPi88oKCiAS9khTWBB4jhY+W6K84Vq+V4zSQFVi4exlRmZ1of0PtE4R3zqeJ9E2hJ8yfFlEtcuISipLU6rDdQDGEEua/XjBwhhOw9SCCjnS/p2zaHmo1kSu+Yn56yXK5wzjFfzFkuFhwfH7NarmhXK9pVm+dUeo54VQaPVCiALfTRQhvnqCJb/JLRulT0jDFUdUUV62RhDcYIM6OxBmstTVPRJHISY5hMGqqqpmoq+RcLA1dWYrurugIFJgKgNAZiSBB6ZLVGnBOIda6A5WLB2ckxQQV+8sMj8WAT2Lt8GVtPqOpKxkY0GpFLYZBdRCXERDY7XTcYJaQo1kr4zs7OLgGNd4q+8/IvkqAYrVFas1q1rFYtvqmxOiq0lBEEw7ws5+i1a9d59OgR+/v7YxAUxnK1nDc+hg/6WIBao/j4o0/44V/9gM8+vsPbP/85R0dHLBaLbIVWsgDzPM3KmNYEpagmW3zp1S/xrd/8Ft/57d9i72AfFb2Iw3iYGCIkiq02plAwn348Ddhs+nysRI7P2fTZr3LfTff4FRpdiPexoibePU/Qho8/u89i1eO0hSDh2uveonVFcL1MR2qj1ppIfsn21hbb21sQHM75TBddtiddN4jsQtnL8nNDXymZV3t7+7z99ju89OLLODcYf5NsRoGLe172Tq/NT/k8GkQYvoOBDEZy4MZgKslYHU3/I+9wkVuU8reNTax/JaAZ+m5sTPDnPs9GZFUAtby3xj4sANMQRSGfl8a40XfputgX5boygNFDyF+eF9F69rQ6W1qrmBdp470NTWVx3rPqWtquo1119M7Rd46+j6yzjPVQIOuHQRlMDGe1KPq2xVrL5cv7XLi3xcliwXR7j7BvePbrX+f2H/9LKTjvNgOVz12DIXnGVGTiJE0ISQMp7lHuLesOhrEeu24oHgO0/FlqWwjC6hd19nXZsX5t+UytNKHvOHz0GPvo8dPfc+34wgOk9QHIHRtkolVVhbV2NJDrAj0d61aDdHwu8pZPKQVGCYBKJrxS0PZ9n2vorE/iZG390z/5U/7Of/B3+MZvfJ26avjk48+4d+8ujdW89857eBS9h9b19N7jkJCfKhaITZb1tHjhfDxqAoSStL/JAprej3xuYkNzAamZk/OwfFZ6VSjF4RBznSwEPi6I9FMWyeCydSm8KQnpOKY5lygL0URyMDiW88JCwJ1iHGqS3iUL/niIQpE+FwvO4FUqNpHSkhT7VUfQMdivBuEw0HGX+Cxad1N/q5DL3qwfw9wD5VXkYVCZ5KLEfnm4QqDt+wgqUp/EflHnlb78L16flNPh3dMYycc+JKNCMT8SUFZDXk0KGSr7fiQcg5cwAi2JrFJYVgr5qoLJL60brXUE4MOY9r0jscf1fZ9zWQBa5+i6Fu8Dfbuia5esVktZm94L3bN3MR/JkTyWxhqMsdGbIjkzp6dnHB0dcXp6KpbBtqU7m9Mt2+yRyd7CGKogYVLiOUwhLLmvfckkKCFVaEWwUsdLCv9ajLH597qpsDG0ylpLM5lQVxVV1VBNZ5lu3lpLZa3U2LIWbc2Imjp7Ho1GGSN5iFHu5Aml0yoaDBwQvTooyWNTYn03YSC3CWseW0WUDUg2nW4meKW5+dIrvPPOe7TzOb1TbO8c4EJRpX4kQeT/SX4YW5EKP/cEUkiI5ItptLFYW9P3AdNDu+ojW2SS84Z22bJYrHBbM0nPUOPlt67Epc8ODg548803eemll8ZrZwRkh/OFgjcC8pjLcnJ0yr/87/9Hfv7mW3z84fucnZ3mMMa0hvTIiKNGa00UiyMOHz3ks08+om0XfP1b3+DCxQN5D+Xi/BUVwPUOpQx9340VoOL4dbw2mxSvTddvyvn9dZ+3fv3nXremTKW9PClhXgU+/uwewdSUXslNnqJ1T8G57yEq2DJW168Lm6wQkWgKDhjKvXWTIVXu/XSgKPlQXrwyWnN8csLWbJuuk/FMOdAeIoHHAChKsp4U4r0+R6XdKQQYyIQhJaCSOW6UyP0EhFL/GGPEoxwNJ6VSXRZjXzc6xe7JsjD1USrbEfK5A5BKcmXUWxvW6yZlOhk+83lxv1MKjILKGAnZVeO0CKWF0nyTwVzHtZmWqFISFmeMxlaGpqvoqo6uc6yWHa3uaLuerpcw6WQsHnREhVM67k1DCDmhJ6jAG2+8xL/58x8waWoez0+49tqr3Hvn57Qf3UGzOjfPNvXHqJ+K81MbBiA/jkYp13tKbylB7fCcweCw7g0aMZwyAKU0IE8zrGzWv2WrskEResf2bOvc9087/p0ASAmBG2No2xaQHBgYD8R6KFypeKV7wdMH4fM2gvR9ak8O/VoDW33fD+Ema0I3tSlNur/7d/9Djo9OaNuW7a0tHjx4QGUrrly+yGxrxtlyRR+gn8/xXYtXISs6ojSKpdlam5Xd9cmachQouP2HejMwVvYHr08IyLPDEFI37AVqDSAhVqCYH+SKXJ8SNHgfMgPZEAs7hLklZqcB3EESkSEyweiY+5AsEuJ3WFMIMugqoZscLnlyIs1uAjfjywsgoVIb4kYb9ECelEBQBhIDSBBAne4dS72FMFTyHvVdnJPiLsicDalfUk+XLESBSFMfkoqbhLb0zzrbWSng1oXdOrDpI/GFUkMYYwk2s+VvdG/yObK+ZAM1IeU8jTfOrm1xXZuZgBSS4+Kc5NKsFstMDtC2bSYRCM7RrZbZy5Nq4LRty3Ixx7uOPoWiMSjuBQzO45TnIEMobcqLK0lbVFQGhvhxWd/VpMkFfauqEur9OCfE0yGKzXK15OOPPubk5AQfPFcvX+Nb3/4WFw4O2N3dxUSwY6xle2eX7e1t5vM5JycnWQHXxtInauj4Hsko4gkEU2xOIeYNxp9xIKMSMCb7EKwjinpZxFMrM1CSa41G1qA2Bl9sdul8VKxxpKEyNXXr2A2GZ597gdu/eA9jG5lHMX8horJBFockk0WOuF7mUhc6OtdjtcZqRWMtverYns64cHCRjz/8FO9D9ha5XhjlrDEYbTk7PaPf30XH/vFZJmxOCNcadnZ2JDSy70fnBA+lxznLKgJ930lIZNtz8uSUv/zeX3H7F7e5+8ldzhYL+ig/lZXcTZIyqgcDSJnPpoDainx/8vgBv3jvbW6+8Cy7ezMuXb5E53qOjk5iXmjLbDajqacZmP8qh34KkErrJh3rysy6IvRvc3yekvu08+SB58GYikYkheLw9JjD41NQMXw0hZcyyJ71Pbvcc4onA4Mx1OM4uLAvxCO+xTlLDlcu7gub+2S492aAJIQgstavXr3KZ59+yrPPPj8Y3lDRABHlWbEH4gegk98jAY4MpKQHspJcAqRCZ1CAVxKZUobOpt9LI7NzsYZZIg9RZtQPw+8q2eDGAEnHiIAEnBjCAFNbBhTG2BORv/SDLJELh9IasR1932c5U2lFsA7nbAYKKVQ11REegaasKyYv4AAMocj7rqtYd9FTVzVd17NYtSzbPkceJHkiBhWFciYiL492At5C0Hjboq3mG6/e4vs/+jFf+fa3ufPue7z+2/8hP7j/32Jce35d/JJDMRhjtFKJl2njPC0N/Wn+rxOblcc6m3MIodAPh9IlYgD3BD1OUcltLPeD9fYHj0Gxs7XNzva/B0j5UPG/tMgSe08ZygZj0LMuANePcpEPg7SOfseKRAolSQLGOZc9ROn56UhepZQXkpT05PkyxnLjmRuoAHc++JC33noToyt+8uOf8PLLL3Pxwj7OB3b39tC2Yqfveefdd5lubbNarPK7eu8x1sZQFGnnumUNIttbWXyu8NAkgJTfr8ix8DGsZ7DGJPWSteL0g/UnnVUqzNInMSwurtOUyJ02AKFNjZtBBE7DGMVkfw065cqEQkkrkjjL8Ux9kn4PkDfYYeMhWvrzqUOce7lY47upGO5Vjvl6omTOSUlCIACGc8xA6fw+yHxL1yVMlmv/xDcIaxJtpLyVgk2RKblT+yCGzwUkRoPxOkjPUAw5Dal+U4i5M957ur6LRA5uuHfc+HyRB9M7x3IxJ6xWdKsV84WEiLZdS9e2En7WCmuaDx58wHUO30tNKOc7ur7wevrU/iCJqqOQ1xABnfRXoo5XkbVKKRXXiXhebFVRVxXaaKwVcFM3DdZYmklDXddMp1OqqgKrY50UK4V/Y36MrcRro+PakzDUgTkvEAha03U9jx4/ptrd4Qd/9ZeooJlsb3HrpZfYP9iPdLhKCgzbCm1qbFWhe4/XCzHEeBeTfUVhzmA6vrtWGoyJ8lFl+RhCMh8oMIVCnORHYTQRo43Mw4DPIW8BCbNVSkJCXCjKJoTxOul7R9AGcSpaMBVf+ca3uPrMc1y/do3FssVYk5mhIIWIaUIYlJEk17u2Q1sjNeSVWLYn1rC/s4WxihdfvMnt999nsWxZzFcsl2K1dd5RVQpjNadnp1IWYVLHeVLQRJcKZFx3LoaqKqVYLBYS8pyUOhcNAQEOnxxycnKC6zuM1WCk7yZVw/Z2w3PPX+fqpYt8+MFtfvDjv+bRo4d0BbOntZa6qqmj51C8hw3T6ZS6rqmt4fqVi1y5dpWr16/zt/7O36aeNjx49Ij9vQO819TVNqvliocPbvPB+3f4yle+ksT5hiMpH8Mnsk+p4vxBdpahRt7FfLscql3elbGsXsu7GI5Q/H/8XVaG05lhODcpp/lpa/dP14rBwPDpZ/fonSOYRNwT3aBhIFQihFiTRo37KgzGpvSBj5EX21s1k6ahbVcoL2GzidxGZORmXSLfLe23BSYdLPjxWU7yi7e3d3nzzltcuXodkBBUF2+ZDVOpn4LQZKf9Ircr7TvFc6QuX8h73WDYIs8NrXWk5E5KL6wz0iX5RvTGD5JoYHjLz82b7DikL+lYHmGB84PSEO8Vou709EMV7yZhtXEHUxoTIxaUUtiYsy0GuEBLQDtP8pBkMORC7oMBPMV/xMgfnQqAF8BAyzMTSZW1ntrV2LrCrnpWKyvMyysiZbgXeR4cGFFuXPB0BIJW6JWnAi5f2OHaxV3e+/lPuXTxGlW1w4WbL3DyzgnaSUkJF+ue6aDOGQ8Sm5z0k8vz3XkXqckj2NThXD+XMjGv62RcUmn+RPkRP8j6lgJ06bmNz4k/Q4h064Uhdx13lf4mkBBI7z3TScOlg4PPmRXj44sPkJTUPNJRsigGqs7kjSktBIMFe1D618FT6cVZexrDRlJ4NkZAalBCSkW3fHZaVGLZkfuKF0cA0rSZMrEVBPjyG69zeHjIxSsHfP0bXwHANIaz1YIHTx5LrLMH1Tvccokp8kPSAk8eJBc8OvgxYvc+AqQYt+4Sh1my2uicQ+JcykGSw4d+IH+Qi4ZFlRfKeOOTP8Z9HfLvAjJSGJ1YnhPICJiQNjsd68FEr5ICj/SnYxDY5DdJLYiLzqeNcVB2iOB0aKMn1aBIytroyPvLwEZWbjp5I1ADeNGR5ldrM9StiO3tfT8IK4aNKShoQ8gASQW5j47WMccQvhVSaFox18ZtDkPfFnljySvY9z2u7fCtgJxVu4qJpj1d2+L7Ht8K0chyGb00vaPtWlzf4pzQQYcQGZWcJxfDTcCmdwVDoUN1Hd73dMHhiFZHid/DG02IYM4qi/Zgg7x5VxchHlpR1TaHnRmjqeuGyaSJ4WmV5NfUFcEKHXQq+mu05OhYY2JohZCj5O+NJMQv266QCSGymXWsVl2cHwKKQrQEApja5PoWpQEiWRWDqegxnDnLjZuad3/2Dm45BzST2TYoSzOd0tRNDqOZLzq6k7nk86gKtCRC+75HKfHQDvItghttAT1SdBUpGTauGaXo0wYaxLPkg0IbqfnhnUJrG4sHa4TUQuqCKJWMKjGPMAiVd1KwvXexTxTeWQkL7lo65+nshN1nb7KyhsfHZ4QQsmEpeC+/a43SEvpnzGCN1MbgvcYEizZgtcPqwDPXL/Htb34NowKvvXKL/+q/+u84PG2ZzztWnad1Pdu1xqwCi27FabvETCqSN8GogvAFRntE8DIXd/f2ePzkCZcvXx6MYj6AV9z+xYf85Ic/4StffoNbzz/L3u4W9cxgKpMJdB48/JR/8J/8x7iu5/DREYePntD1knuUog+kIGgSRyIbm6aRNrmeEDqWq46Tkzk/+eFPOTlbcv/hQ+7ff4LSU3rXSY6i8nSuY3fvEs8++wwh9FnmDfshOYelEBeixKAIDPJC5lZ5oii3bPCMpCiGc/IzPWD8QVbKx96rMShJ5QBKoC/tSnTpqW0R7CpFJCDm4eMnaKNwoZdwVh0LWftkRZfrB5A8KHxlU9Jj5Jk9lw4O8JEdUgPed9nQl0BiyGHvOsuRsguCEg/m+l4SewaDZtk6bL3FfOU4na8w1uKBPoYYZ8NkDIVXwaGVExDlwQeFgxFQHXSYIfwuKb7SdJWV/qrSVIZR4evUQd57eor8Up8MnNFjkwu0irEsxDngvexpCZQNh8cRaHEDBI5UkT4EKZUQN+LSS5G8SDpPsUAwBrywxhmjozFVSjtoo9GVJQSTdZw+RmP0Ieavx+h/AdziWTeRgAICVjmM8YTCKK6JDMKpJIMaogeMCZKjVdfYxmKWS7RVLJdLus5DJ/ugQkCzV0LqE6zGKNC6Z6krXn7tNf7kT/6cfrtlFQLPfetr/Oijd5gcn2GCpzMepzUTp8ENYY7F4EskAzpGE3iCF49oWmvZAZHmAuQQSNG4GckNotzQ0eBEUCQoloy7IXZoSBqPSl7cEPMuDToMjK2lTpQwVp4TSuGMog/QLRaotuNXPb7wAKlcUBkYMA4b2nRNaWEuhfHgHh68T5sOsYj2IwFdPq+MzUyb4rrbPoWNlO3w3lNXFavVCq2E5vbKlSuCjqdTsYYYy9bWVCwhxnJ0eBxpmF0GXyL0RPg552Nbida8VP9FhJPQdooi43OhMQCpTZKEZkmqEIJc58LYO7I+JptdomOGnxK05mvXAKtYE6KiolW0eKWFNSSXArl+TdnXJWBIie9KpefEhbzW9lJBKg8XQo7xLjeTpF8Mm80AeWRRq6xMJhrkbHlRhQBhAF8gW3t8SQgB5wNd3IzbbpULbqbk7vR7ct2nf6kop+s6iB6fFDaWzu/bln4VXf7ZmuVwvctFPLWK+ToMiluPkxDPWCE8j318CwHrQ82jZG2rJhVaV9SxL1zfMz86wbUdvQ4cXLzI9WefY2trm2kzYXdrh+l0hmo0NoauidFBEmSHsAexaCeLVMK/Tq3V2NEK1wuV8mw2Y2u2NeQGxntWdU3bu1i7R5T9qplIDuCiIyC1eQTEgKlqvPOoyPBmjUUbLflI0dvWO2Eb6n2gbhr2Dy5w5dp1PvnwNk4Zlp3HqY5F54DT1Imgq0w+EbShS2x7AZSyWReV5PGSuGRg0ExrJK3zEWFHXisQfPQaMZgZXGTB08pLMq0fJ9OGAJ0HtIAXrSSJnSDess611HVD7zXKTKhibk5lLTNrJIfK2GjkGEgXlBLQpWIiOFERM9qK1w5PbWA2scyaBgNsT7f4nd/5DtPZDv/X/+K/ZLlYsFousxFoMpmyXC6YLxZsTacCviK4T8ajQUmEQYHvuXjxEnfv3uXy5StZlvV9h1aWP/7TP+UH3/9rvvfnf87WtKZpKnb3L3Dh0iX29vaorOX9Dz7gkzuPIYDFiqGhXdG2snYBFos5fS9MhbK2Xa5JZbSspLbtWXU9KAGRq7bl4YNH1JNaatBUlmba0Ewqnr1xjevXrojOr2CgRw55fZS7pSoMXONvCyAVQEh8VHHd8HvyJI/uOfzF+BgAktoQKje0w+dLnfNFWzwlnXZWaJXsNyenJywWC5KRk6z4D9bsTYalZBUf7WuxJQohLdjb3WWxXBB8J4qhlz14lG+U36nwlqT7BkCrHH4+AifxeRqVw3ubpuHJ4SG7u7t03mfiEojDmsKD/dCngcgoGjFrCOf1pE3G3mTUNUZqhAWUGCyUynm34uFJLIrj/U/0LQkbS+AvkUEl75oKegNAkmiNFsfaxiiAMZAjXbLROfWBjEzeUXtfzi+J4vAqFFEnjlTvKBm+08Pk91SsdugT5/ohykIHvBMWvOxVUiH+zcgwnsNwlaKqNFo3VNZQV5bKGpbLJUtW+D4S9hAIKLxSslbjntXSMpnMeO21V3n7nfe4cuU6W5cvcOlLL/HoRz9l1moar+hCXCO5GG2hsxZ9HYIv1vvnHyl6QBVzdH3syjD0cUguxViff56CHAY89iST7zUyjgSZQ1opjo6O6Nr2V3oH+HcAIJXMYymsLrn81hVjePpgpo5PE3ldUGTBXQiMEZreALLSs8tJMgq7CmKVLa/XWqzfqgjFSZSRkmtgePzkCUofidDzsFy2KCMu45TgnCYTIDkXiPASxh2VJ6eP8bg+DHWDkmUnhJj0GQZQmSzTIfhM31yGZpzv82FTWD/KPi77pejIdKII4yTVw3D9wHJTLMBsCZS/EpuKUgPQKxoxAmsjJSAVeEuLsPhGlPwhJ4MQIkua5NVkQRtCrmcSfMhhNF0rtMxd10lydrsSOue2yx4W8bj0qCAApos5FM55+q6TULR2KWAp0UA7l4V7H59Z5pSFZKHJYRBDaJ8izv04H7UxsaaVCHZbaZSus1eyaZqYNGzwBqppE/Nt6hwmZCNBgC0IB0TpjZXUjYnMcx7X9Zw9OeIXP3+bd958CwJcuHSZb33nt7CTBm0qTKylkR0iI4t33j3xhCFMIM0/BYYIoLWQDVRVxXK5op5OMfUEZWt8VPzx4FdL+pMFi9WKxWIhYXUoTs6Wcf7JvX3vCJ3U9Elhdq7ztH1HCO1AU1/OSyVEJ0ZbqBpuvvQlnhyecHD5OiuvaJd99IanMDNPoCMpeEnmye8KHMW8c4MdPfSEotgwqgglQoBIDltFRYAQ0Hh832KtyRu/rNNoBY85f0m2mSjDhMGqhhCyUkUQ1ju0R2lRgI3S8Z+itpbKhjwmxgipxHTaMJ00GEQZkTDHSQ4bDgSCAasNW7MJezvbTOsaAnStJ+D50muv8uxzN7jzyWcs5mf4XkJ/mmaCMRXz+YJup0epKo7PYDU9b2iBvnfs7u7xzjvvRiUzytEIkm7fvs29+/cxSjGpNHt7O7z1zvtobZhOp4Di9PSEv/jeX4nimZRDH3KeXRqHROWcvLJJ3khNKSNzyHsBPVrhgqNrW6xRGGuEhbCu2Nvf48H9uxLmGufA8GoqG4tGu2KSeSHloo2m0HCaPy/b/22OTZ6TTWclp9BoT41bw3pDswKnPA8fP5LQuVwUegjQJ51X/Bw9c+0c+V36p6lrlFK0bYd34knXqooOyQFkJ+KkFP6d9o0kyAJhVP9t9FyIXgwhl9nd2ePRw8fU9QSllcj6MOR2eBdLFARD8FqKEitPwOFUjDrwIRN3roOkdJQ5ldba6HExiO9GPAB9EVWitSm85cM+6FKYegJIGSyGIaIjjD1qQPSqlfMy/Ra92IgnyPXi9XGxUCteiSekMHIIcYRCGzGopT1qyAl3gI/7YTK2q9RqtPbkvOGoB6aQOh8NfqpPOUspN0tqQSViGwnFG8LsTTIUWjMCSUZplss+630hyH2Fx8fhlEZrj+97nr3xDB9//DHHx0+Y1/DMb3yF+7/4gHB0Ru16lILeSP6n8YNOun5IDqgnMb8+7Ri86eejr8b3G3vOykOCEs87MMpz1/XwdWP66LkhEGLO27pR5vOOLzxAKoVP2aEpSbFU1ksQUw5euk95r3XUK18NYKfrJNG7ZHFJzygT1srJWCJ3nVz7a2BLFFGhh6RQsANECwys+haUJrhA1zuck/ojOQZ8BDoiiOgBJWEG5bulopE++NHEStYu2Zjl3Usrm4+bcgIQaZ4P/VkAmLV+3nTIJhfO1T0QhSCGkG24j3hwVLZ8hAimyD9B8NDgFUteMglZG5JWISa2K4XEzw45Nt5JG/q+J3QSiub6ntWqpes7FvM5J6en9MsVfdtFV3mXSQS8c7S5iFw/CjULwRMicUDwQ5hDQMBcF2PaUdEzEFnRZG9VWQHVeS5Cqk1kjFjltRo2OV3JvwRybMxTq+qapqkjJXSVQ9OslTA1nfJqogVfR9ailI8g3oIExnwaxhQ5Kq+QGRUlJKkPEghTIVaj6XQb18Pt2x/RdyuquqaeTgnWgrUEbXEorBkzUEloko0bWwpqjPW3KPJyoqVJNm1HcKBtw7LzzNs5hycLUTST4cVLPDhKo3XFqhcCFu8i5Ws0GJDlg4QygHgZS2NJVgZQEAzOOzrfS62ituPitRv83b9/FTNrODxZFtN4YDZKgCQumghqs4qXr0nro6osYkgeEqrTWBEtyZUxKKsy/bWPbHTWBEiKglYYXRLLRLrswqAEsuE3xqCChMrVtQDqpmmo6wpTIbTiVUVT1WxPt6iriklj8FbWvLVJcYkT2wd0zu8pwpoR0NCHNn6nWJ7NOTs+haDEAOE6prNtrly9xFvvvMP8bB5z2QKqEg/8armi6yS0TZMCswY5NSh7Q78aY2O9oi7LOR9lTprvq3bFbLrNa6+/xo9+8mMePnzA8Ym803yxoK5qvHcYTGbsSmF8SXmQ7i4MUdIjqKDARy80AmCCVllBrIyFVqGWGmU0J2cn/N4f/B6emDMW508Ky01HcsCIaNe5FhhKZc9rAk2bj0D5jSr+n79NS2CUL5SxabZO52vWQFMo7lj+LvcZcjjzPYIsycOTY/HkojAYghKwkvtizVgnf4Q89iqHGxVt9oHZdBrrlTlUkFLboW9HOkX5HgGVIwcSCE0SK8GITUQOuvioaSZ89NHH7O8fYCqL05Ao4XEx1NU5uWuw9L4HPC50eOVzfqFiIKwqAWdpgKnrOu8R2miR/yrKb98NslUpQutyjlNpWEzMtyULbfouFKkOIeorg9cOgjJZJrvesVothXRlPmd5dsZ8MWe1XLFqV/mcvusJbmwkH3QsldMwEkiqqorJZMJ02jCbTdje3mY6nTKbzaJRQ4wfSsm+J158jYtrp9cCYHT0cGuj0d5jvEY7AZXWh9h/OstxW+xhldGYpsFE450xK7QOLJce8IQgkUhdL/NFY3Cmp7aWN15/lT//7veYHuxQHxxw8ZWXOf7hTzFtj9GavoqGzn6c51wa8FPez2j6bwBTpaE/6Zub1s45T2yhu0moZLEu2AyIynSRddKYUQRYlFOz6ZS2+/chdvkoFfaUk+DDsMHAuCM3IdJ0n3Q8TYkvP8+EBmG8CMvE9tIrlEKZYOCPt0aPAERpEUaJUOmjNUYmlSgwfaYjJt5fCh/Kxjls5GnTyZ6MzuF9UrLlvn0vScs+umHTRp1ARMqTSm0S5TL2h6LgyC+9NAOlcd4wy35Om/6Gfs6hFfkZ0Rvoh3hUlIQb5HuFccx0ysNKIWHKST+mopp9CilbLelWcwE5MSwtCd/edVlw933HfL5gsZizWCxRvSP0LufVpPA11/eE3ufnluA8jQFqqJtkjcF5sdgYPRYsqQ5K23V0vsMjpByT6ZRm0jCZTmPi9iQqn5Jzk4S9tZVYkCPJgLVGmMu0JmiNr2JOR5xTpLWgkpKd5rTMR2M0ylgJAYnJ+n3f46OQrZQmKIP3kVQBkzWXBKRL76sPgdoaDFpqQcSxW/Zn7F68xHR3j7OTQ1RVUU+meKNRRrw3Ckm4LteNaFZDgmyaCy4kK2kCKD4DJJQissPHTV3mtoSoiFdIxi6NTfLODYaEsq9KJcP7oaq99z57rcR72KNVHcMYW8nlc4HgFUpZWPSZklvi/A2pxpCE5UarrjWZlVABVfQeO+eoo1VbaxUHYLAEhyAsbtpInHdtUv0SHT18lrquaCYKhYSeVJWEGuaaMSBFWZsaa1MIsWE6mTC1sZ6IlXtmi70KEERR63sh69Bag+sIrqOPIYHdKmRFOss5xyjRPUSXgScQlORhik1JizE6hQ7j6IPi0uWLLFdnrBZCy963jjABa2u65YLFYileGUXMcBxv7ut7hTWGumo4PZlT17UYmJTMj4uRRY7gefzkCXc+vkPbndF2p4X1U4r9KqUJ2tMDzvd5rg7P8+fakbx/ae4GBcZWmKqS3yvJS1FKcigsludffIkvf/2btL3MyySTffBR8Q5538zitxDPiQAkPrHwAMRlx3lZvn5kw08hF4f7qzzm69EH69tEOI8d4j1A8pOKfSZeu2hbKUKuiIbJREgSc2iC2nhP2Y/Xowzipd6hFUyamrZbReOMGK60V4NMlbMLORHlkyJ7BLOuEs4bFYfnlYBSxULRAnpCzMvS0SPb91K6oOtX+NAidZQcvXf0MZ9MANJ5BbhURtcV03Rd74KAUS97fgp1807GKwGh4ZqB+KkM+w5xDjp8Hq4Q9/O+71m1HYv5itPT01xeIdWgC30PBT12qVATJMCuJFQY2uFRXgyvidE0Xd91UmvIe58NPJPJhN3dXQ4Odrlw4YCt7W3qWopTB1Jag7CtJvmYcidFziqMcThjhAwoheBpjYpGkWSYtVqhqyrWI1IYq9AGlos2pksECAqvPMHEcG214tLFCzzzzFXu3LtH0Jrnv/ZVfvDOe0x6j43kG+tOoRGJWdanSs/yWOY9TS8u580mQ/im9V7O7/Xvy8/T3HuaDp/Pd1Abm8NKf9Xj3wmAlLxHpQWk7KKyo+E8fWn2iKydt36UtI5pkAba6/Ucl4F+PD2jvH9SZJJVNn2fPFKpEKuPAiT4QF3XoFQkZnAMsd9a+GczKJHDOU/Sj1S0XHV9ogHv6bpW6tQoCCpSkxfudxWVhVxDiRSPW1qqKRaVGtFnpvfUSkXygGQ9YNQn5flGDzSZzjm6aKENIWAItKtVzr9xfc/p2SlPHj/h5OiYxXLB3u4ely5dYtWKUF0ulrTdiuVywWKxyIUYV6uWvlvhu1Wmi075FclimtpY5l6VABYGqlMAbTW2jgLQmFG4kDEGXQ85M4ktTSslXpKmihYz2RxWbcvZ6Sl/85O/oT+T4qCvf+WrPPPss8x2tpjOZpiqQiUK9wLclGFPPviszMQm4z1YNXg+y1C0pISqNLWidctoK4QGSrYxMUgM4kUSPHVkpxFLWbHA4pwZLHg6xFCsICDGqcjcYy319hYXr13n5PSYejIhRWD40IMXa2VPYemM89JHSnDvBsp0pROZhycolXPVlFKjfCnvwYXB2JLGXsDAUGckh6BBXKuFUllsJkpJfL7MFwtazjK2QusaY6bRutsJ4NQadI1SlkrpCJAkvC6FcRij47itWeq0BCwYPeQglu2oDFR62PCn0wnGWMm5aixbEyGmMEYAjryXzOds/S3WqMg3QSSDMUTGOIRAHzphSewCoS2UI+8xsY9d9IC62JfCQJhC/ATMqmyMEeVu3VuVLNRoFckjyFZnH+/tnCOsVly/foUQPGenZyzOFnStgJGqqljOz1jMl1S2krWr/FDdPU/h8wad2WyLJ0+ecPHiRQG+9CwWS15//Q2+92ff4+zkBEXg52+/DcFhI5W55MsNRDmrto/7FqPcMDFEjFBKQVHsMDFvMSAhVJUCrS3b+xc4uHDA/sEB1565wXPPv8DzN29hm5kYdFLIbdxjklz2YfBgDI+MgFyR5Ug5BiIzYkL3r3IEskd3/ZIQAdoYjG24dXjKHp1ZKkuPgSjCZ4u5vJvWBQBW4olTQkRUvvPwh4/rjXiv4SujhSpfQ8wbS/uZKODpXcv3I6SQzFDcM8RryXtvPr+43jBuozGG1Wol5DAJxCkPWmO1Zmtri8VqznK1wIccD5HDvRPh0rr+kgwpaW8r9RMfpL6ec/1gjIIIeEIMaxsY8YBc58cVZELpp0tGSN/mItyryGq6jPmCvhddJvWXkCd5tLYopakzi2jDdDqhqmpm0watKKIgTPbcaKNQWt7f9T0+CLA5OztjPl9yenrK2dkZZ2dn9H3P4eEhjx8/5sMPZd+eTqfs7Oxw+fJlLl26xGw2wzZy37ZvxWDtxcPe24D1Cu0NNoCKxX1tDL9TRuYRSX+NMq6qK7Y01HUMSVdzlkuJQJF8c2m76BKavlvx2quv8NmDhyznKy5fOmD/1nPMf/o2M68wqDy/zs1xiNFHg/FaIiiG6IRS5pZgKcP/DQCoPJ4GktavXd9DSw9XqS+eA/WI0bxrW7Zms89tS3l84QFSEqoZkJixtyZtquVC3wSG1oX+Ortdelb6mZWgDeelIwnotLitsVlQrAOtdF8dlaE+KnshFhD1GoIVEKSl6Ej09CALnqTADQJLBGY0kkXg4nqXhXbKh5EOUJLQmTavCGo0UtdHm4HCNYO+KNxc32dB17YtvXOslm0OJ+v7nuVSQIokj3a4PlmC+kL4enwvZAHtStzly+Uq34PesVqu8MGLZ6XrWKykHo7v+nPWjhz/PfLODOMiOUQu5zsYa7F2IjkkWlPZmrppqKJXJjGbURlMLV6aSdOgjaapJf9G2xh2luKPo4Xf2krcylGZG1lkVIqzDpH5TTxSp2enfPzoEfP3foEylguXL3Nw+VIERgZlrAAkim1UQVCi2AYlVjkDo5AGrRWWoU6XsBrGy1Oh3HLOx409uGGehgBGmcEqGGQeSsHOQPa6xEmY5yWBvvcorWh7N1CYevG6tV1Hu1zxyuuv89yzz7J/cMDjx8fYqpY2RiXYEXJIaQlmlSzekUFCQiMUaE3r+iwTyuKFPiiCEm9N6gvJJTJolcIIBYD2vWMymaC1UGGTZUy0xiWvmzaRUCDdL8ohwCiF0fJCSgVJetaWEBQTNdRJqyKI1kbyVKZT8dik+VtXQvttjcZon69JoZFaa5oKKhOyR1FF+RFCQIce5bts/e5WK7oo3yT8cfAuZiu3F8MLyaruk8dHFNBO6RGN/GC9VOCiIhXEmj+wXoFxorr64p5p03YEUAHlEuV+eqYYYBIpDRE0pNCwvu3pe8+k2UIrxXx+yun8NHruXOxnw2LVUa96rImhqXpQ0kumUkXypij29vZ5/Ogxu3v7dH2Pspr5YsXBxYv84X/yn/Cnf/LHnJ4csVic4ds2W8UBlPHM53MIAdcJ6JlOJ6AVfZ9CRFIOUupIUMpEj50CH8OZMBhdE3TDV7/xLf7J/+afMtkWOeacp+scs91dFm0XZeJgvS/HNAvIQZREldqDSgXCy70yFCDgKYBGlX8PSfoKMsNjvizJ7/FFUfwURjeGUD81fBjPzSEP8Rka1/csV22W+0qpCMblurL9pTxJLU5gfQzU5I+6qWXva6M3MO3lqQZdAX7SdeIALGXpoDwmq37SU7JeESQcOR1iWGs4OjphZ3c7K7aV1Tz//E2uXbnMtasXOTo+Zr48Y9l2LJc9v7h9hwePnhBCyjUcxiSH6CF5MqWSWrap93HdMni8shwIg5E0zank8fEREAXEu9vHnNm+71m2EiLnEzFQqq+jbCTLibMnOKaTBmsMk7oWT4v0sITWuQ6F5vRkTtvNCRH8NI3kdQs4k5FNz2hqCSufNA0XLx7w3HPPsrW1hbUVEFgsFjx5cshnn33Gw4cPOD054e7dU+7du4vWmu3tbS5eucCzzz3Lzs6OyM5eDIjWeZzVWBONznqIoLBB2PEknG6Q+VKnLpEOWcBAMCglESzB+ewhV97RdQJWZ5Mpzz9zg/c++pjD+RmvfO03+PG7H+L6BU4SVDF5o1fZGB1CDDdHCCtU3m/8MG/jEkl203Jh58/SWKyBnHPgKq3cQf0Z1rMar/5smFaR8a7QR8YGBsk92qprmmnDr3p84QESWuI7IRYgLQTOOrhJeQVQKtHjwUznrnt81gBrPnd9EpTfye+JWSUOcLRemciYIoJ3cA3bqopeo5h3FP9DIcpYAC0xJLG8KJjgMMFF3UBiq4Ny9N7Te09lalzboZxDQ078S27u1WqV85CWq8iK5lys3RIyfeliscjntm1Lt1jiuo7lcoVzfQw5i6Co68UlHIWg610GRMr3sU/Sqgtx03YSG52UH8gWUwA6iXvWlcFUFm1tHv+60TnfpsyrsVVFPdvK1rCmaZhMJhKK1sRaBJE4oKoqUTZtKhI3hHGJdy2OdQQ65RwYJloaZznW432z2hFiLky04oqLXaOUB+eYVjN8sNy89RL3PriNMbC1NcNWFcpW6KoCpXOoGXGulGnHPiqXCiWexDxHVVI9ZZOL/axj+KYUy01rQ5JaQ+gJbiC8KDf1tLkSChrxJBRjaJhnYD30IQidbQgopwhOalBICEEkqOg81FucLB2271BqADZohY/5P4kUIXvxEECoVQwd1EIEUVUWj8LaWCw6J/lH2m1touKbcgPlnzEWo4qCgCFg0vyIoCuBo0yxHoFXrQMmcqDKqaLcVtay3cDWtMnkFQnoqBBoDALK64rK2qGtIaCMeCD6rqPr+5wgnuZaecg4Cb128IG+dbi2HSkvOgm3wEgeglDLlh6FUJyT7PCDgYo8t53yuQ5Lspqno5TRae14PIlNc9T25MlVisGsA6HcuNOnYZivSfEOLgibWO852Nlie7rNfDlnvjxl1S/o+wXWTlDa0jlYtg5jhUTCGhPX0xAu5L0YE6yC4Ht2t/f58PbHtL2n94Gw8iyWPV2AW196mWdffpFV14qyvFpJWnt0ny8Wc/71v/7X/PAHP8CEgDGBP/zDP+TDDz/k3ffeYzGf0/Ut4mFc34sCWleSBysmHjCWa889xz/5oz/CNjWtH0J9bWWFQr9bFvNibJgLISb4bwA3IUg9lczF4M7vn+vzbvhj8Lpkg2SIY6n8+qVPPZKhJilR6e/kQS4NluuH5G306GgUIgJpuW5kP2M4IXZBGOYXDAa2EESueihyHobQ26eF4JdtLAkEyuuyXGW8dgYAGbDKMJk2HB8esbU1IyhRbmtjOdid8dnHv2B19oDJpEGjuHvnDqaZcO3qRR4+eoiPJAvEd0nLV2mdiSxGNYCKdrsCvLkYU5eIStaBUQghe4Z0ECXWEVj1HauukxIRzmdWO62ErKCpalwneaV4GaO6MgR6FA6lHI01zCZTZrMtmqZhe3tLaPitZbY14fDoMW3bsrU1Y3trG5REDfQ9rFaOx4/k+9nWjJPjEw4fH/Lo4RPmi4UAq+wt2mZ//4CvfvV1tra+hdaaBw8ecOfOHT755BOOjg55cnLI7Tsfsb29zY0bN7h69Sqz2Yy+d1gVw42thCmjPS6A80ZCYrXHWvEwCYGERnkt4BBFUxnUTCJ5tILlYiUgNEZAqKgHeB145aUX+eTeXbq25+DSVWZ7F+nbu3jfYrRESuQ5niwEyTgeDREy/ZLHjmwYzcAoMIoqSstHjEdqZIzd5GDIj453CMX1pbFzdE3Wm4brk54jkSJGjHxaojJ+1eOLD5AYwE86yrjFUoiXOUHr3oZ0n/VjEGKDwrzp3HIilF4l7/xoM1FrE6A8cl2XELDa0HcdCqiM5BfpIC7E05NT+q7j9PSUJ4+POD4+YTKZMp1MefzoEYvFkuVyRec8KM18sWQ5X2Sgs4xV4KWdWlgMupiTFC08fQzPEuWqICjwQuaQCsCFon8S+5UoTY5EI5uSnKtKYvXRIizato1uXCRHxiiMbpjUdRyrISfr9OSUkyeHQGBrNuPlV7/EbHubZjphMpsxnUgy5WQyyfkbKnnBzBB2lCzv6XeVqD0ZKww+BvGvz49k/QgIWJANUxeb3zh7wWgziikfQogia5gBW2w+QmIg11TW8Mz1Z6gnMwJgmym2mhCMBmViO86zJ6YQjUEYFsQPYVBmS6UjVfEOeKm74H2OIc8J6EEAxEgxCtHqGRn9UlifWHhS36WaCirXNVLRIlQF2QSUshgTkBwbi60aghUQbM0AVHXMyUFVYrElAZxhfFVB913+9D4wicVhE2nFIBtMBkJJGJuY02N9i41FDqvKDmQXMTxWwK2MdfIyTmdTZlOY1CYnOad5aI3FeImRT6QWonEpCB7lE2GH1FnKShmQQq6UEgvoslDExscAxIlzdVDyChm0piSXR1BqUIopLMUBdKIyWFe2YfBghgRmhu/8CCANRgaZb8Ozy00WKCqhnG/rurKe5b8LuNaBd/TOcfHCBY7vHLM4m9OulqzalqqSgr99NO54RB4lCt/y/aS/FF4bet+zNZ1ydHIcr5PvnJdCry7uAZPJRBSKZhLnirRt5+Ai//SP/hlBW37y/e8SnOPh48f8p//Zf0bXtdy58xH3H9xnuTzLBq2+65gv5mitWS5X/OIXv+Dxk0Osqbh09Tr/7D/9T6mbOucYpXlfVdVIGV8fs/yZH+bD6DwgqFSSem3+rM299UiK4NW5cRlmlDs35z7vONfeYj/9vCPlbMEvP/fcodaz0eLH8X8JEDwNKD6tr8vvy6PMC9lk8IBo1IhAYD6fx9w9heQZeX76459w9Og+D+/fZTKtuXT1EmeLlv1LVzi4dA2jFe1yhVc23W30jE2MvKldZf5QkkElMNoEkLwXAETcGzrnWLYrCa8NsoMZG2Vp9BKoEL1zrsfUkdq8NuztHrC3u8P21hZbk20m1TR75PqupXc98/mSs9MjVitH3wcOHz7BuYcZIEmJCk/KW18tV0ynU5659gxVXYvxVWm6vmexmHN8fMwv3nufN7slVV1xcLDPhQsXee2NL/PNb/8mZ/M5d+58xDvvvsvR0SFvn5zwi/fe4+rVqzz37HPs726jncHEAukm5jj5aGhzOvWri6x3QoSh1yMJcoqHpl11g1fODZ646aThpVu3eOv9dzmcTHnx61/hrf/lPnUrBEoush2DGBHrumI6nbI8PqNftQM1vPKMi7mO52CaG2yY99lpUBiQ140E6+dvmue/zpHmrQ9iwPxVjy88QFq30It7s8/frSd1pUW9LgjSUd6rHFQdCxWW91n3OpWfl0qAjkpyImsoz0+HUsKM9/jxY3Z2dvBdz+LkjI8++oiPP/mYe3fv8fjxY9rVilXXUlc1AH3vsVXN3u4+06ZhEpW6jz/5lMPjY4lvD0hCZ9uTWL5SHhIgrDd+eNegIuV3CKDF0pSssloNHjttdAY7NiqydV1T1RV1bWmamiZ6a1zfi6UtBJzSGCuFGW1lUYpYPE0KU1aVJIPrmMvjnOPh/fv89V/8Od57di/u88bXvsJse5t62qBthVLi+UkbeuxkAiG7jYvOjrWpIMWzqzTuaWwChKBG9azyxhb7KI9hrp+A5PqWVFAg3gldeBlDQIUi5DMt7kIBTcKvqhu+8o3fIgSwk23OVj3aWhHwQUIayvdKeThaFfeLirJ4q3xiVBWrXxhIC0TA+Og1SJs4OQdO1oDJiqu0WQwTOtbFUNHTlujB0z00A1AN0VIpyfsF5bdKwMRIGISV8AqKtaaV5ASpkKiv1xawOGnzPFAM+XE6tsUUgCuNq9UGiyFEFqoE3OvasN1MubgzYRYtkXVTS76KMdlzRn7WMPYhzKMS6AihRzkFDtqVR7kh91DqCflhyqihnltqZ+rHpNCECEBVUU/kfEcMMiiF1FHcU+ZeAjo+3zMdjjXFLlvwBkteqUQP56nx58U9XP68BIUFu1+6x+jeYS2Hfl1lHa/P5KXx0Yjje8dqtWJvd09Cd7ue05NTtnf22JrtxpIKXUzYVhibiozbId8qP1MUiJQP4oOXAq9hqLoSVGJPJEcBYCqIhCSpj2xt+ad/+Ec8d+0q3/vzP+PPvvcX/OUPfpiLwYpy2efyCz54CadBCnVWtVDDv/zKq/zWb/8Oe/sXhBxCD+GzaZzbth2N7UiehWRQ8aOxEjEo4+TFPHxu/vxSZSbo8f0p5nNJPf9LjvU9/Gl/rxMKDM/9twBHcpNzVu0cOcBgnH0aQNr0s9QB1kFQ2UdPA0g+QFCiT0hhbh/zHSV0cDbZYmFqtpttjo+f8NFyzqUr13FtKyFpPhav9QHPOEVg0HcGo2Ifc13KNv66ACmEEKNI+igTwCphWEQl4yEyz/uUX6PZ393h6tULHOwfsDXbEs/5quPo8IiHnx6ymq84OT2lbVuWyyWr1YrVUnKY+k5C1XvX07XdYLyjjzJewmm1NtR1RV3VNNNJJGYQMqSd7W0ODi7w4s2b6EnN2WLOo0eP+fk77/LWz99hd2+Xixcvcv36M7zx+uscHR3x05/9jI/ufMQnH3/Cp598yuULF7h56xZ7BweSp2wtpq+obE+IofjOazonZE3GSi24yprReCQ20BBAsWS5XGZDdxojaw0v3brJ7bsfc9Se8cJrL/LW97+LbVtU6OmjkVYMehIhUVc1i/4Y7ZNnl+hFlFC7QMj5ZKXzwXuPEQSV58a6njUKE12by08DTJuOXw6aZJ+oY9TFr3p84QFSuQnAePGmv8tQqPL3TSCl/E5FRTp+m2PRy2vV2iRIwKNUDteFTykw07Wp3T/+8Y958OABOsDDBw/4+M5HvP/eeyhgNp2xt7vLtWvX2d7Zpq5rus7hXZDimYDxPaenJ1y4sI+tpcaHthWHh8cslITHVbUUm6wjeJlMp2irMTGcp04saZWlnkypG/HM1LVQ9U4mU7GK1xWmihTQObFcx3wli7D+SycslktOTk5YLhbCHte1mUGm71uc7wsGGqiqGqPj9c6xf+Ua9Wybs/kZQVfY6QxV16iqQdkKpWthMo+LP48pgVDkiQSfwn9A6JdsBlIkZVoN7lsXlQRtkhs6iGWkkAthZHhVEYCkzcwnrJFDPF2ZwOpTaKEfJa0651gtl7Rdx9VnXyAEeHS0QJsVqXS1NpEYQd5SvFVr3pvk2QkiVSG3VaFibo1KLnaVQtaGWg5pjuuUxMkAWExpYDA6hpsVHpgEfEIDQY8IOFKYW6fF0rTOmhRCQI+U5tKKHzD4DNAkBDNZVz3arbKXJ9GVV1WF1ToXUcxrVsXwt6Dwrazb7Z0dtran7GxvMZnWWC0ge1i3PfQ9rkeYpUIaeaIhIc6dUT2GqDBHwJKUQ19MniyPTBwnBuA1jN0AXAcARAxV2LyJhCLMKfVxBjfSCrJRgOF9/DpASsCoeI/h88E7GQikWK11JdqnVhbKd3qDbIAqZGbyOJWvdu4987tIm3TkQ07eOd+L5+Xa1Ws8fvSYvutYLZa0q5UYruJaEblt6XuX3yXVFMv9o8DhMCiWbYuxFSenp0y2Zpkq3MfEb2XKulGGzPeZwFNQmHrCf/i7f8Bvfed3+OzuZzx+/Jijo+NssVdKgNr29haTyQSlNbPplP39A+pJQ91M0EYKzboQ0DHaIHV5qWivA4uNvxdCrQS+6yB4fU8bz5FysM7PyWQIST6pX6Ygre/TTwMOm/b0rIT9ktouT33251wVGIOdp7X5aUCpVBJh/F6ZbGojCB2o7s/OzuR67/BevB4Xn3mGmzdusJrPOZsfY6cGlKELii7WQoTE+Dm8XQJFNhqmSnADjNpZgqEyUmf9s/L33J9K0VSVGBRijmjveskv9o6LBxd48YUXuXTxAt55To6PefzgiA+PP+Xk8IjTkzOOj445OT7i5PSI09OTDI66NtYVdC0weLvKfXjoT5X3eiFOMXnPSntHpvqeTam3trh8/SpXr17llZdeAgKPHj/m4zsf8vGHH7K7u8P168/wm9/6Nr/9W9/h3Xfe4c033+Thw0c8evyE3f09XnzpRXb39jBdT19ZnJGyLtbpaCTWmOCwzuOdyh6klCJgjNRSSyVilsvlqJ+7tmU6m/DKrVt8cOc2em+LC7ee48mTI6yX/S+tPzwoA8tTKZ4ctFgXhYJ7CLMXeTXWWdN82LR2ByNhitwYGOg+7/jlIGiso6dr0j6u/RDC/KseX3iAVIbLJfrq9U5MCLvrukx/u36PciMpgUsJvBLYeZrnqLQmZeEXrXnrdIXJo5SsMEkx/MY3vpGv3b9wwI0bNwghcHJ4hGs7PvvkU8K9u1y9do2+d3zyyWdsb23zwgsv0rYt9+99yv37D3jl1Vf5xm9+m2YyY7a1JaFfukYpHcOEbC5kpqzFG5WVl2StlYRKCatKikxSjKPaMAAPyOEjAMFbAkPdlLra4uLOJdp2xeL0hC4WRpUq8atYILGn79tc5K6NgCloha6n7OxfYtE6bDNDV1OCqXHKAjFOOXoxRpsTYlEWKumYQ5O2vRh2lJSr6GiI7Q/Z2k0oNsJkRWe8AQ5zRmiGfQxVzAnpIVXVVqPNIymKzkt+lfdDMqsQNmj6XuZPZaVAn44C01pDyLkyg6c0zaU077QxI0uoRPeZrDgkFjujjbxV8u5E8EAQxT8DJpnwuZ6RPKjwciiy0qm0RocKXFyfndRlMLlYo8cjeVdZCCslHr1Y+C2vmxBiHHZAEdnfUMzqKm8c+7tbHOxUbG1vUVmLtVUOqdQw5BTEF0leH7GoFgUCVfSuuBUqqGEusCbIS2109J0iZkPlz30RQxaUG5T6MMyhiA4KWFgcQfpr7GkZrkkhkfIqIb+31GkaW9ZDHCwffF7X6+/2NGUakuMsa+HZmh6y36TsnuFvN36j0TNFhm5WssuaOZ+3kWqt6VNYs4LOOXzfc3xywv7BHk8ePeLh/QdcuXaF5UqUKqPFG9+2wkQma4m8jkfWfCW5WQHFsl2htPysphOR/RSENirJH8RUFPIt4lKUD/oQsJMZz7/wEjdffHlkJEh1qbIXOPeNEu9+gM55kV4j4+350PHx9Yw+y2NVNNKHp/f5aC6sAZbxkbIdz18bOA+uNt5hI0j4nCeugbbUjhI0/ipW6889V6lz4G8TKNr0eymj18PzSuPsejtK2SKej+jlCZG2H2HOfP+jO1TWMKkrCI6w7JkvW1Z9EGhvJ/TdSu60loA1EFqNdZWxEWTY79ZlxCawVL5vVVUQpEg0wPzsjOOjIw4u7PGlN17j+vXr9G3L4ZMj3n7zLQ6fHPLk8RGPHj7k8MkTjg4POT09ZXE2Z7U6o3fLHF42hIIHUD3ayN7b926Q8QBYCCZ15WivNEr212zwTgVfrYXaUr81YTKZsLOzw/7+Pi+88AIv3HqBqp7w8OFD3n7rLT756CMuXbrEszdu8Nqrr/LeL97nxz/5CY8fH3J0/BMuXbrE87dusrOzhTMa03WSg2QN1mpsZbDaEyw5lzqEMGIVrOs6929b5JUGFei6FS9cf4aH9+7x8PiQZ15/hc/e/DlTE4TgoRjroDX1dMZ8tSSYWN8wSIQLosFFpssxkC69qOtrYNNekuZw+fso366YR+vnlfMrgcVyXsl30pgQQo5w+lWOLzxAKgXKekemI4SQE+8/T9gmYZitTiOLydiWtK6IloKh9CbBUHQyXQeM3PLlvZxzUohMiWLTzKZ88tmnWDSh77l77x7VpOHatWscHx0xPz3lH/5H/5DLV65yfHzE7Y/vcOvVV/kPfvf3ePmVL0kyP9D1EHQTXc4uGaQxMawqhGFDF4XdY7UCH5PWo+fAF+9CvAfnJrZYI5LHQSkI1kr1emuZNDNWq47lakHbrqj7tgBIC7zr6WNYTIhhJX3vuHz1BvNlz96FK6w6cDpIcUQfCL7DGD9QVhebvgvkUKLSkgSgPIPyUShhHgEs5XiV863YbUcWaJVyg1Kh1AQSsiVZ5oQxkZlHCfBwusoxuwmUCYDRUpU9SBKuMQqjBwUrRKrTdevM2AOUxiahv/RdfF7sL/FuxXmep7rK4CQ6HkjKRrmKgoueK7lEwuiUkdpb4RS81HUySoOX3K8KzZZGNgUb8+8yA5ymrm3esM//k/WZyDWqKrG7BVRIzIgO1zlS6VhRKgrFpgBIEn/n8AjQ9qneRAxJFQAiL1gqhOtg3BfeLOg2ngcRrK9fX86r/Fm686aj9GQnb8dwrzHwOK+ICmg7rzCPnjA4FDIYCwAFU52O7F1pHTGq3bP+3uffLxkQBgbBUHRD/P2pAEm+V3HiifdI5VDOVLz19PSU6WSK6x2fffopz794k3YlTJv1dJplr3M9AmzVmhIrz0ogN6BYrVbs7O5wNp8z2drKSX9axWcXvjEdBsZECrAEkUxFiyGHuBe4yHiFl2eJI2sgxUDFfMdoPQ0x80jk+sDSur4nbQJI2aiohoTpTec97fi870ZGp7XzckjvWujdr3P/jc9cM5DGP9D8etZlGMTgxraV+0ihC8CwX3xe+9Lv6f3WwwPXz0uHVgizbByzvutomkbmMCHmDiqWXQd9KyymWLyGzil6FKa2ONeiGOduDwZnNcoXXAfZ62F16ftNgC+DrrgPur7n8ePHLM7mvPjCLX7nN38L21gePnzAT3/8Nzx++JBHDx/x4MEDDh8/4smTx5ydnbJanNG2S1y/gkjq1Hsh91n3kspzVTZAj/bH0A3eXGTd5WsYaAOS4VHF6Bi3VKgTiUx48Bk0TcN7P3+TnZ1dLl19hldfe53nnnsOgLt373L37l0uXbrEjRvP8Y//8X/MT9/8Ge+88w6ffvopj5884datm1y9einuYRZbWUyvqZ3BGkfofS5km7xaqS6e1kbyG+PRdRKN0/kei2WK4tkLl/n57Xd55cWb1FcvEE7mmKDoujYbk1utsROD2p2B66Vgfe9QHmwQavm+yGFOoCaBlKcZCdYB0Kbjc3Vxzu96m5wS4xsiIZZt+9Rnrh9feIDU9332Cm3aDNZBjzYpDCm540IMhRGOexct/ipaEgQ4iKVdFBrZ+BILXcodyPStRHd+CCiVWJB8VEpTDRW5VwImOiXOKYRFJi7fdrXikw/v0K5WtD6wOD0DwIRApS2PHz7km9/8Jl/92lepJxParuXmKy9w9ZkbmLrh5HROpS1WWfFmoHBKU2mLtpUwSimxqpswWA89Aa18pB5GakXEQ5l66NtiHJL1d1Ch5f+5Bo9SKCuavVWGqplQrSqWyyXL1QITAZJxNat2hdIdKphoFe/wHVx65hYXrj3H1vYWT47nVE2FqZZRM00FTW22FqWwCpcEaFQI0xipwZSbvSJJeRFhHlnN4rikc0z6rgDCWSBEaugkoPMWqxJtdLyfSiQUJnvgyEBnENACkqIXUokHQRFQWhTikOi6y5o/GQAMoRha6YR+o64ZC9dGCxEq0isrj1TVSQDPx+LDIdf8yFS0MhEIIVCpDq0CVWUjKUHFZNIwnU6ZzhSTxtBUDTvbW1S2oqlrmqqmUl58lKPNOQpYxjHtst5czJ0i08FKSKLHuVboYym8uWvKaMpbSnx/JEOI8iQyCyJ7Y8LSCegnADOyrFMwvZ3bLBwEn63N5ddBGVF4KTaKEH2b5XlJIQ5D+9NKHQOq8yF23g3EHyPIUio8JANBbP9oz5H1kDbH0WamVEyyPr9xJUV905E8VnL3dN0AQMb3Gd7x/P1K5ZEoXwN9F0NEvdT0cZHdc9X1HBzss7W9xdGjx8yPT1ktVvRdR5hIzQ9jo7ErDMQkg5Es9lSS6cCq67B1w6MH9zm4eBEb9wQfxyvlISUzxJpGfe6Nco0SZA8Qgh8dPYApVExAlPeR2osEmEiTNfbrELJ6nkSgNCamv+XaAYeKkSS1X62NSzlO62EtTzvv/PvG1fU5SlR6B7mgfL9NJ8JglFJrX2XrzgAe5Y/h2vFtRut5mJ/pxJDXdfk+aVUNfZLm0NhAURo2SgU9GSHKuTKELBXtj3Nra2vGfL6QenGxWb0TbsMQAiooTCCSLw1GDaWCFCcv9vbkoQhBcs4UQ+SM1CFLgDuGzSV6/7yOk/4le13wPjNwGmto25Z7Dx/h+56XX3qZl198iW654oP3b3P3s095cP8eD+7f5eHDexwePebs9ITlck7ftXRti/c9BMnpTJ50V6yNuKEXkRyDp8M7X8ydYswUkIGg6Hqh9E7ENG3bi84k1PkSLu76Fcul5vj4CZ999hnv/PwtdnZ2ePGFl3jjy29w4eACTx4+5P79h1y8fIkXbr3ACy++yE9/+lNu3/mQd999jydPnnDz1vNS5L3vqCqLcxZrHa3qaJyQ/lh39ANtAAEAAElEQVRjqULAeo/RnspKLbVJU0PwLFQsbRMUnQ/0PvDMM8/w/p0PUNry1b/1t3jw/m3a01MhB+raWLsLvFFM96/QNDOUD7jFivmjx/QnR1il0H2Lj0b1JO/z1hPCaL0MBZgLhtS0ixZ7u1IKZfS4+HfcfzTDvphlD8MhjLtr6zma8dvFgsXxCb/q8YUHSKNwhGKjTpa/czGL3meqWWOGMCCQxZEqq/q82OMQqUFZkQ1y+F2RWMVkzJyLRVej8im1AkSp7GOblC42uyh0tNYYa+j7nutXrnLpwgWe3HvAJx99zM7ODsvTOc45tmZbLOZzfAj85nd+k1uvvIitJxLa4Xvmbc/JcoWuLMZUGCwqOKmqrjTK1vighW0qJbQXFlqjFFqHvB+sE13Amh6VxqEAqdkqZuImUoyDDoGqAq1r6kozm9YS7tK2rDxQTdBdh6okT4lVi1c1od5BBU9QnmAVfQj0XS8gU01QysQCjzFHhlis1ZrR5pND0pSKHoMErsriqQUxQGF9ku8GT0fa4LLH0QxhVanv8rV6YNaDFL4jQruKDAJlKE06EpOYfF5saFpllqigC+U07nseD1GhIwk0J274BAxCEnhE6xAB4VOXGGTlBThYpZgYxVZTsbNzwNbWFrPZjNlsymRSs9MomkrlfLTkwRIg2jN4FVShY0Q6CB9QymeDQn6/4UUz0K6iSBusl1KLyScmHx/ovKHr+xx6kcIvXO/RQceaUynckijskwU+zpGoEEmfD9629dyAoBVelQrPWBEqyVlGimJwxXnpf0MXjY5AnhfDs+MXTzmGgqOiwnhTtMuPy4E+zVNQytPPy/tYV3D///E2bARjrHNtFWBCJU91GDZvQlQEFfSKPniCllDiS5ev8OG7b3N6eMxqvsJ1nVjkdSA4kQ2BEEsqpDDQof8TVk35WdWkYbGMRppkEElJ8AXe9ISnFyMtzgtxLg/vmbz9WRvIoZQqDGQe2WDBYHxJ99scl1/I6GiAkSKVwxmJ5EKeMS5aW85FlQBWGo/y1dbGcQymk2f9lx8jQ8GGOVc+a93QEr/MMj1/R+6l8/sZRZ8zzLf1owRiY+Cjsswt18hgtC3bPAAipZJRYp3QIu1dQlIjk9Bjq4pV34mMlBNF5hRv5mK3hZjnqhDFUGj8h3257FOfa/WNSS5SXl/IQzHUpAqxP7QWsoGqbmJkQeDup3c5OjniG9/4Bjefv8njh4/4yY9+yCcff8yDe/c5evSEB/fvcnJ6yGJ5RNst8L7D9T0aKyHXcf9TCHmKYN4YtBsGIE9i0YxMm8MAMRp/Gee1kc+LSK5Pir93YorU2qG9po96gXYxb0v1LLoVy5NDnjy4x9/88K+5du06X/uNr3P95i2ePHrE48ePuHL1Kl/96pd5+Usv88EHH/DhnY957/3bPPvsM+zv7xDoafte0iC0oguOqu+oq5oJAe8NlaliGJxFa8V0YlFUtF3AKQXK0mpF0zRcu3yFxdmC3StX+cvv/RXh7Ax6yaG1RpiDm60psyvXufSlN9jbvUgdDGf37/PZOz/j9O4nqCePMa4FnOgcSomZVkmhZBX7KKt5IRDJcOMcZ7TtJz3RoAYvv0zCqIuKrmyLfZZCH1eQ88HjUEkOvNJ0bUe7WPGrHl94gFQqoN777GosPyt/amNGn3mfwEyykoypvNcF7XqoQgnMkrUuW2HW4nHXw+zWwUS2lMf8k/39A27evMnWzg6z2RZ92xOU4vjslAt9x4VLl6gnU4ypMonC8fERSlm06jFWgba4IOEYXkGwCpRGUi2Sp0VTmq03eePKd3/a3+X5yf1fevDyPQECVMZQGfP/I+9PY21J8sM+8BcRmeecu9+3V9Wr6qrq6qpeubRItriMRciSTI1NDzyyRcA2QMxYMAQIA0OWBBuyv0gag4b0wRYg2AI4Q1i2DEkYWOBINEhKFJcmJUpqskn2Ut1V3bW+V/X2d/ez5BIR8+EfkRmZJ8+591UX5UEpul/dezMjY4//vuBDkjbrHLOqZlGKdKMsK+qqohyXArB9dMoPc1MSFtM7R27Goo1RrUo/BhYwqg22kYZ9RiVkexxr/DZBGCmzLf90E4hhyfGwl5W98QtR4FUMdx18qxr/JPAqCzivlRQ3ZyPwmC1LHpGfT36Tny75xkUMLR0ESCXhVn1IHKe8a/4ZHJk25KOMje0JG5Mxu9tb7O3tsr+7w872iPHYk5msmVOEh8a3fhbSv0ekffH+tGekwwx0pNk9JrzHbHT2qUd0+GCnbT2MnCZG6LPWUpXiBFyWFbaWxJllXVPbkJPJ0eZviglKGxNRuSUR8DsvGdJtrKtpcjoN3ZeuZDi9LMOEVTrXTvXes6G/V9VxeLC6I4Fe1dY6af8QLFjvfzLczuqXw/MQgdTq9vrjas6Gb7WIURNz+fJlvl1WHB+fUBQFi8UCZ1t/UGlDExNl9uF4n5BUSgVT4FZbPjTPQCMMvlPJf9eVvoBKJbCnLyDstL+Gge3Mhe7VjHA6/tFq/IJfkYpnXHU+XKcR6uPRi5YnqT903zzD5zwyJP3naZ+r1mvtGFKK8AlLKvTttxpxkxYbZra2tjibTgXABrifEv39JvrCznWO80M+Rv21SIXTEbZnIY8RznN8fMLtW7d56aWX+NEf/VGOjo748m/9Fu+89SYPH9zj4PFDjg4OODs5oVgkUT+J/qDtXpqEdos4bpWMqE+XpfTHkDCrv/5x7Zq5O4f3deKj1cKZLESVNSZorkqwteWdd97h1u3b7F25wnd//vO89ImXuH/vDg8f3mf/0iWefuoG127c4P1gcpflho3NSTPeTAc6I/O42oP1uHyEGqvWZFdpskwz2RijjaJy4JQEhFHa8PwLL/CNr3+djz17kwrP6WyGK8vAIGk2tjZwbsTm5j5s38BuX2Zz/zJPf+KzPP3yJ3n/tVe5+8+/Svn4Efhj8HMJgOE1GRLgSrGMv3wcH8NCtKH1Tvdq3R4O7rcTS5dqUXD3vfdW9tUvH3kGKeZvgXYjvBefo+jAFn8HYZCipiB+E+3PXZBk9xmaWA9a36EUMCiGkdMQIxTHF02D0gORhh8+OjzkvVu3GY3G/NE/9sd47/Z7XP/+6/zWl75EVRQ8/eyzXLt+A2cdr3/rWzivJGKd0kw2Nrl8/QZZnoMLSdxwIQKTSJIas604tjVr3Jd+rSodYNljJPtMZCTcmwAPWUYGZKMxWxsbOO+xdU3dZOH2klgtyG+VDv4kSBQqQ0ykFrUWrfN9TGiZhuxWgYFLwwd3pX+tGVNK6IYVofEH6O+3oitpTU2fGwq1wWX4ELLMR4mcbiVykdmJ+UmWgUNkfkLrSgIYtJG/wly9T+qKdFHjmIxzdrY22dmcsLs1Zntrk62tbTYmG4xGOeNRLhoV78k0OF8GJi8kLFYpElc4l5wR365Jn4YeItDXSYSHnvUZpFgUkTkWzU8eErN6L2Fmq8pSliVFWTEvSsoqMOG1FYYp3BdlRGmPFoMnHdZQxtrO25vE1HBptAFwDxI73Tn1id/u+8ACxz7Czw7i8b11CGa+3iM5bOxqRmYt0/IB6n2QIstrO0RJKz1f1iCtGleXQQp3KIQyrqqKp566QVVVnJ6eMJvNKBZF46MQJeZpu+edQWNMY6qyEnmvha7Lba5614E10AnKkX570X3qCyAUdCwJ0qKDCWP4EgkLEXR3SuP0MA4cms+TjjMl5hv8sWY+fefvRurMMj6K8+lT2heBR0MCw6F9fNJ7MygYQjUSdkksLgF1RqMRxcHBEo5atU5LMGKFBqk/ligoSJ+n/yJBPM7GaCW+ym9++w3yLOf//Mf/OGVZ8tv/8ku8/fZb3L9/h8eP7nF6fMjZ2THFYoazIehOkovOWhdMw7vnv2HMWBY+9QUFq4QDq/ZniLlu19LhfSsEj98WRYFROoSXVmhVk2Ujsgzq2vLw/n2++Cu/zO/89pf4/B/4A7z0yss8fnifk+Mj9HiMUTDOc06Ojsm0YWtrI5jMi6bQ1Q5vHLa21NTUxqCUR0m0GPFHyzJJYF17ahvMtx3sXbrM5vY2Tmle/PQneVsriumcjSxjlGeMJiNmxYKT2ZzTWYEflYytY2tjg/0XPsb+9Uu8tPcy7/7O7/Hw/tc5O7mFLku8ldQd3piG/h2K6NgRYp5DP/ZLn/keEk5FJYdG/Jt97Tg5PLpwHx95Bsm7Nh4/0DA/TXhHJ1LkLMuC7wStODu2EYjnvrNf/Jk+6zNNSqlG9R/764+jzyhFqUP/QDUMk3MUZcmjhwcoZdjZ2edk+i3q+w/Q+Yinr11jsrmJyUYcn045PJlKNmrnMFnO5mSTZ64/zQhxxLPKUTmYV6KudiGwQZyJRvXMuJaJ0P7vq4B+B/D0/o7f6YSBirp5aT9KbwNBmbWZzz2K2kXTtSjBlI8V4pelk3H1JXhDYVOXgGdi5mC9awBw/zyECARLa9CM2ydroWic+73PxPkfmhDZkanyvgrSs5YY1pHxwYtZHC3QF2Ih5nHo+7JEP5sgacaTZUoylI9ydra22N7cYHd7k61xTq49I6NkuZWYiyo82peBQXJQe9oWw5qqRCLXA4Rx/pHoTAnfzpr7YSKvWZuB39N1WAa4rf+BCtJUZWQVMqMZ5RmTSc6iKJlMRizKikVRUhQV84UwTs55qsqK/4o24K0kRlXR/DKZp0vPu19BYLpOHTnvFyeaOjU7RLLvvBQmQ/ZCuZboi2dhFXEwtM7nSf1WESL93/v9rWpLzDtNc7Y739M9eUMETV/gBcHUSKnGjKgoCjY3N9Fac3p6xnQ6pazK0EebTDi2sy54QBSqmSRiUpCrLJUUFqUE5dA6pHPq933euvYZqf7arCLw+4RvM8cEXironLUlc7UenFw35nUMw3lMVkowrftmcK4DZ1vq+YB7uv7Kq/DgEMMa37camtYsbdU+Pcm6kOIi367D5ubmUh992qUPK1McqVJB75q59TVaaXt1XTMej8WcXSuODg948403+dxnPsuLL7zA66+9xptvvsmd927z6NEDDg8ecnJyQFXOwdV4LM5XMiIveE18f01z71ed3VawsRx5OH22clVX4JWhtUzr95lPpaCqSiR1hkShtbbGGEl27mrP4eNH/Oov/xJf/9pX+P4v/AAff+kTzGYzdvf2KYsS5z3zkzOMlwTTDocta7RS1Lqi0iVGGxbzBfk4J8skUIMxkIeARqgMEyLvoTT5aMIzz32Me3fvcvOFF/n5n/9FyvkCX1VsbUx46eVPsLW7zd72BnZ2hJtk2HKDstTs722wv3+ZSX6DPNOc/IsH+HzB7Oghel5hq5gyZRnn9+ng9P0Q497HH+fdiz7Mjz6GWim2t7ZX7ne/fOQZJB20An1VcXyW53nDPEVnbp0tm9FJpBDVQZDpu1WMg7TRSvNSU40+1xy/i34RKSEQGTkdpAZGZVRVze995fd45tmbfOEHfxBtNC9/6lNoPKN8hNYZZSUJ1YQu0ri6ZmuUc3z3Lr/9L/4l8+mMzc1NXvnMZ3j6xY9T4KmMxypABY+dDpE1zKnHd+mzVcg3EsyrvuuYAHTkq+L7YwJF1ITkDtWNbuvJxwoar6MQuUklxFWArkOMWttKwxk29TUquIB2may0rEJmWvmWSvLtOGTWiR1/ouVRCoyuiX4wPozDZCEvg5JoNqM8ZzQek2cZJssk63YTyrnN3dIyStEe3DPKNXmuGWUZ41FOpoRZMFqjsaiGmA6MYdC6acA7CTZgvcInsvwm4EFg6rz34Wfkl3xcxQ4Dl65dvLbriI7z1ryztwTzk/iNCmakwUzOKY/JNFqPcZMxk2jSWdYs5gum84LZomRe1vjKNtEVGw8PFYmtpkPSQ7IKF/vI7Db3ohsJb928VyL4geWIRpj07mN6r1dF4Vse8/K7JyE2+t+sak8I8OV2m74uQFDH3/tRRdO9KcuS3d1dvPeiPSoKFvMFs9mMje2tJgBGKuhK2x7qSykVkjf64L86jCNcb7M6bYX7k+KcVQxqQ8jCEmyK36c4qB+mvA+3O8K/HjESGUzCHWrPvMzTh1DyXulONMRh87DlskoY0n93Xolz7Psdr+svfSb/lts8L2/LqvEPneWhb89bo5QxEbPf2D5oT2MaOp/PiULGOJY+TOm3q5SCJNCQ975Dk/RLeib7zNTGxoYQ5N7zzttvcXJ8zA/94A9RlyW//sUvcuvdW9y7+z5Hh/c4PHzMfDalriSCnlEKURLFfH5x7YJPqEI0+cnY05KaDPZdG9KxP0kZgsUy32VtVSxG+5BfKprrWpyz6LpGVxKlVRtJ0/Hg3h3+0S/8PDdv3uTy5es458mynLqyTKdzrHVcu3GNy9evoFASwl2J4HxjMkEZwf/Oiw+zdTV1VZKPcvb3LmF0xnw+Z2NzwtbWJltbW5hM8+InXubf+MP/JqcHR5SzKY8ePeS1b7+BN4rLV+7yI3/oj0C1ibYLNrIdblzZ4dL2GPYM192zfC7/IX7v12aokxlGnYEvJArlikiUKYM6xHyvu/9pKO9V+5f699qQx9Fpxe7l/Qvv9UeeQbIhg3CfA1VKTCqstYzHY8qybBYzlfqmSCl+H0sEFv2QnX3gFhFxKs2IJnSxfvp83SVuc+PA3bv3+J7v+Tx7l/YFXGiF947cGEkkOi8xOmuIWe89owxM5fkn/+B/561Xv8HJwSHeOX7rV36Nl773D/BH/91/j+zSFWoFXjkcquNv8ySIqb8mcR4Ng9R71xbfINt0bUQCbxMgTAswvZfwzfJV0lSPiA7EfcN80hIcfekQtMyhbxtYyRQ1VXwyiqa91u5W6AepY4Kvk9aiejcm9YPK0EaLT4+1EoklBDcwRhL3ZsaQm7avLpMO1lXgJVpX551WZDqTENpGM8phNBKfrzyLzueWGN/MNbMQfzW8l3cx4iPgvMX7upm78yE/lpffIxPgfVeSGsM4LwE6RTCFC5pb1SUt1p3FofuebmFar8PIB5M3HQNrjEQS58eOapSztTFhOi85nRecTudM5wW1b5MLN3scTF7EyXw1c9OfcLtGYmff7uXF7t2TMCH931MCtr8+69q86LieFHZ0iI+4ph+wdOBIAoPSw2CtZTQaobViPp8xPTujKArKqmTsNrp3lm6ujT4yT4mxPMvEzGYjW7kGTfqBobH32l03p2Y8LPPHfTyWjnuI+RqSuKejbwMoRBwRxxXnI4O30CSgjO11/bbW8rjfURmaZ/ozhdVDO6Oa+7teSHGR0jJn609yh/HpLUzqw7ySsA9CKYUQkhJ0ZVlLFP+ltEbnLMc9HJjz0PzTfEFx3fM8Rymx4Pnd3/kdrl3a5ws/8APceuddbr37Lvfu3uXO+3c4OT7k7Owui/kUHMEyAYmEKdLAxnIihqyHaCnSzWvT0Af9SIUr5j+0/qvK+czUsH+2RDP2KHRgpIILBZbMhIh6WqGMQWlNVZW89eYb3H77NngxJzQ6w3vRnL1/6z1GuxN2treZjMdMJhM2RmMu7V9i7/IV9ncusbu7y8bmhCwkm1UKxqMRG+MJQvk4yrpEhSS4Shu2tnd5/9b7jLXhhRc+ztM3b/KVr32Vd969Tfal3+Lf/OP/tpwXJ23hFdWmY+O5bSaPL2O2LoF6jGYBzLFKAn2sOy/nlaE1T9OqrKqbwi6lFZUTenY0Hl+oX/jXgEHSJsM5Se4HsmjGaGpnyfIRXklgg9FkIrbiLubb6Drh6hDNTXmC/T7gWn8KpTXeBqjkelxwKkHvXUxx7guDDURAak/cAHgvwM46i1aaylaMN8Zcu3ENVAhnqUJST+/ITMYoGzeEHgTaXnnuf/PbHN6+jZpN2c4ly3p9OuX1f/5bPLxzn3/7P/wPufriCyycx2uN81qiHJNEHgn/Wye9WUvA9g79Ut2hT30iyYvfpwxrh0jpXSyVmrj1ch012hHfSETDJON/UCqJpuLFKCcdskqqK5xEYOkRxipoazKjyLIcY7Imd0GWGZSx6MAgRWZAgLpG1XQ1ecT5eVQTXQ58kNjEudeBCSEwmEaL+abRmo2RYpRnIQGdmJgJcS/FxYAEKpXc+dC3a30c4jmFlhnH423drLPDN4ErpLQhtpufQwhLXLDC2qdS2JDvo/k7kWQrhVfde5YiR63aZy2QTpha5yVqmRd/oiwz6AzyLGe0YZhs5EzmGZuTjNMzw+F0zrSocNYKk2hdGJ/ChXxS5wkYus99Z7NXAf2h96uk832E3a+zRDD2GMxV411FNJ73+0UZpnbPaExpVGRuVjEUqXykifZFIPptQjSLUEkClGisr8lHI7QxlOWCYjFlsVhQFCWbIdqW9p7ReEJZVZS2bLWPzR77RpggoX4t43yEq2r0BoNwrTHd7RDhfa3FMpGeChriHBshHKl0NmarF41OPPPL0txW0h5NyuWbmABaxiTwKs1BJlYP1taJ2bprxl95F8ItIwSi82gdI41VOG8xJlhnqLAiDawP36kgmUqibgpMG9r/sLeJQEr+dg1MTeF6A7tW3A+fwJ54dlpcsiwx66OouH1d2wiZ06rAHfF5ykC2QlK7VD8KnogSvPCd1qqlV9Qwsbnq7vfP6nL0v2FTRmlLBANaw+zslC//9m/zmU9/mks7l/jaV77O++/d5u77tyVk9+EB8+kp1k0Dbu36+qnQXmMy3EQFlfNiXRLVMTBNSoe8Vmp5bH2hwKoInEPz6pfluu39Td/ZMC+lBB8rpQOFIL6teImq610dcE6GVoqqLMGD1hlVVeC9QquMqi5Z1DPmZ6dMJhMmozHGGB7ev8/G9i4bO3vs7e1x+fJl9vZ22d3dZWdnm9zkFEUpOQG1YpxvyFoZzdms4NPf9d2czmZ865vf5OYLz4Gr+XT5Kb70u7/Ht9/6Fle+8hR7l/bw/jrvvP0+z924xmh7RG0yshvX2HnhOe6++W1ybyXZuTLikjywpg0jna6nc03495amWF7zeA9dqCNHP9yZFftjFNiqZJR1Gep15SPPIOX5KIQp7UkPkEOhvQ9J9uSnR9TT1qaqeEEyPhCi8WgLoPYtUYVq4ul7L35LCoUN5l1NfpiEOJBDkJi2JABbK90kMBXtiWvMLbx37Oxvk49F9ZqZGGpY5uW9b/xSGnpfgclGPLh7D+YFmffC8RiD9uCt5fDWLf4//+//Fz/2E/8BL333d7NworpXPuT5wTdScQ+BkOszRsv70AF4qZSqrdG2opr/NGsU210JzJO25f2AKUVAkN0b6RvCS9O9kD6pGxFVfKMbpOvbPQxvtfKMM4XWBmMk748JxIRRFqPbnBLpGGvlAyMRkCJxlR2N0CxZE5FAKUyM3wp4b6l9LYQgCkvIeK0kaeoozxiPRmSZZpzBKE+Ckihhs6yXVWhD2beEZoukh3znRPMUCa0WYYPzdcfMpouUWk1ThxgBsK3/VGefEb+yltAJjGTkXFUfebfn0ujk7NhlLU18Ls8k9DcGdK4YkZGPcgldvrXB9saIyeaEw7M50+mU+bzGekkVEIxqaRPyqiWpWSpVb/qOjN8FBQ6rng8xVk/GqK0nDuDiUsCh8awrQwITle537935fQrMamg/1eYhEkm5wjvN5tY2G1ubHB8fMJ+dUpaF+J15j7eOcWa4fvUKt957X5JoRzOzOAylGzNa0VQbJvmIalGgdoWxG0oMrrVqzn+7T+1epX9L/TbaZnoXI+OSth33PlonpEGIYr34t7WtKXgKd+tacstIPrGaqiqbfiPeQvXgQtD6NsIp6xCyVaO8IlMiqNQ6OJwbg/NQOyvpwYL/fcNAJrCYyPwN7LlON7mdZQd+pyWF493nkfTqClT6ws/lEs1Ce0/T8SRjae//suahO+blMTZ/L+GpoKlXiqKUdBg62fcUdi+NLfwdc5J1iNkukm5Kak2jFGxOJhhjOHj0iH/+m7/JH/yDX8BVlt/57S9z784dHj16wOPHd5meHrFYnOJcNFNOzEMjU4cHb5szlsLRKIzrrElYJ2ddp24K+/oMU3//+3fnYiWlhXrfBH8pD+HsJlGLldwVF4QR3tVYZ8lzj9EjUFAHxgkFjlrgSOlwdYVyDh/MHwtjODk7g4cP0FqSpI9GI7a2ttje3mZ/f59L+/vs7u1y6dJldnd32dzclEBFRkGe871f+H6eeu4Znr5ymVeef57/5Wd+hheee5rX336Ho4MHzKennBydsFAGXxn2t7Z5XM7ZvnGdnY89g97ZoDrzWByucjjbCl7S9Y3xAWSvulZYMdcU/b3un9dQpzkuCUxI76AiJOO2juNHjy+4n/8aMEgpwmmk6iG0a1mWS4Se1pqqqjqXqUFC+JBUUa6kTlW4sjtEIZgnAVQsA6A4LjHNcaggMaudbRgirbRcmkCsOmcDorHgJEKNc04YIgcNBOkDjPTiO8fZyTFbkwkLL3b3Whmcl3wtuVPYx4f8/N/+u/ybf2LOp3/wC5TeiyQgthF8kzQB/6WR2FZEOQpcSFK/V8/3K1+sXEQK3a93HuHY1l1nVuED1xcuoRJftFGeM84Vk5FcrShljUUr0+yQSD/a0NtWWOnhCfguMm6AhYeiji9kPA7fSH+VEu1pnksAhnEuSViNUYxMCnwiExvnHBkdR8yb0hJwrnmXEnVR6xHfgZyHeF/Su9ZlkCKCGiBUVAxy0gLCWAwpAaECLaYi9dtZvpYZjYzwMmMiTFRgsmjNGbX2qFq0yLKfwvhOJvJzvLnJ1taC45MRxyc5Z/MFs0VBbW2TL2lI45KWi57j88qqCF7LhNcH62/Il/PDKus0XPFMPmlb6f1OYXlsU85ka7K0sbnBZHOD4xPFdDqjWCwoy4KyLBvT5el02rGd72vFVGC8fMgTlI9GgSkyIX7LMqOcEt6pliKNXNoR8PXqRgFbVVVLjulp++2cu3BNBcatL7wbgpXp+sV+G4uCDhMg7Rrv0daSGcPmxoSdrW12d3ck2JB1lFVNWVUUZcWiLJnN59TRtOqcLf+w7s1FGPbvpK8h/P+kY1g7pshIhOfG6EaAJlFpk8AkAwx0nxFQSolJF63PXZ/Ahfb8x3ultWacZ2Ta8OjBQ774q7/Oj/zIj3B8cMqbb7zJ44d3efxYAjGcnhxQ1wVQi3teD3StWo9+FMJVdeP5TYnzIYapb7LYF/xdtKwWVMEQJ933y4luF3meA1CWBZkhhAkHgmYw/oxpKuLdjfSgc4XkddOaxUI+efhQ1iDLRijE934cTPN2dna4dOkSl65eYe/SLjs7W2xsTCiLguOTE65eucrdO3fZMBm+KCjmC4rK8f79A6ZzzdmexY0yTu8esbV9mctPP8e9e3ck4JeVCJ6dJMMB/tS1Jc/zJca0T3v3/UZTHNdnnobOcr+cnp6dt5VN+cgzSNCVAjQhpFULLOLBjJK9FMHE0uRGor1Ahjb5q2iPbCNDMANR6Pol3ezoyCbArU00KhNAfEaUtKmMwTu5EIKgWmDVn3faF96jncWVJfV8TuY9GyYjMzlV7bC5wViFrTzV6ZRf+d9+ltJWfM//6YcpnIQBj9yQRolk3YWEo0trHvtNH6bjTC9FfBf/7koBnqR8EIItJaKGos/0W4zEdGtiEpLBZRl5nqGVQzeJWR22CZAgzG6XzRImx0NMaZc8D4CaQPg3YkISMzqFbXzmPN7ZwNRApjV5phmNcjYmI0Z5LmGtjWn4B5Esh5ATKjI3Ho9oL0Wb1Y7fE5gpJ4wdUYLnwXoJtx7bgBC50ccz3jJVrZkIQSCQSPFSgEk7TuiaUSq6Es2WYKUhzsSxnOSdD+He5cM2+W9sp+ogfaWiT5LwXMIkhXy/iFZuQysyBZNcCMCDkzPU8RnT2QJbVR1iuk/w9glf4l6fE8VuSNrZrMQKYu6iBFifSE7LurxGH4SA7AuvPkxC8jx4qACDwgZC3BiDBvYvX+b9O+8xnU6ZzWbUlQTNyY3A39l0tsRsDhFVKjAMG5MJ9+/f55lnnwVFQxT09x9YIt7Se7RqX/rEXR38blOiIcVFqxiftvhGUh8JZBmbaTLb94Md6CQkdCTilFZkJmPDaPa2t7h65RLXrl7h8uVdFJ75fM7hScGDRwfgPAtb4GuL8QptMirVnrUl4j0JjNTf16H7cF4575shZ3LaGS+VJ9E89Bns8+qtfK7av2O+PK00k8mkwySkDMEQo7wqYEf3WTfXTzyfk8mEDHj04AG/8eu/wRe+/wd4cOc+777zLg8fPuT4+A6HRw+ZnZ3gXIlWDrwNglXD6hXtzncdY9Sfj+A42/k2hfH9urGsi6YWy3l7ltQknVu/v1T4EJkeYwxlWWBtzXi8ASE0lOAzwUNKmWDaKjkU8zwX/snVaK+w3jc40Fqoy4IsG1MVC+bTU5RSPHpguJVlTDY2MXnG5uaYvf0druzv8f7162xsboC13Lx6jXu3b3P77XdwfsK8MszdhEWt2NnbwSmHIWfvqWe4bTJGGCQJ/Aq47ulYVfXvWJ9xTGFaf0/S8zxIx7kQrsv5vux0bfnIM0jpZU+5ba+7F8R70QDUtWxo5HhjYtn4vqrbDY/JI5XWGJOJtDtIa1ww8SrLks3JBnVdd6JqNJJMBUprbF2DVuKgZwXBZVkmuYlCWx4f8nk6yqJgPJ60Y3HpRW2lJrGvBlCUJcVshq9KJtow0prM5JS+xmmFcYraWyqrqKZTfv3/+3No7/meH/khKqB0LmTeDnb3OmjAloD8shYmMkZDeCMSsVEzcx5qWUdI9YFO+mwIaQ0RCz4w0c0cws+WGTJkRqONprF5VgpweFdT2brTfuyiuzYpARQrtOsh78Kl11nDSMX1ieOsvBBj3ntwYm6Zq2BONzaMRyNGWUZuDEaJqM4jfmt94ioyXU55amvDgEI9F7U8PebeRY0SOJ8kEAQxHYhMES6862qiItAXZ+LQY2C8OuxkHzDiAyMUmQrVLGFqgpT+VEpMIJvEwDYlVKPNvjSlvOTZ8LYm0xqjFFp7tHY4F+oqGccoU2g9JstysnxEno85ODri9HRKVdWdMbTnYPnsNVLaHoE7dGb7xPUQsZ1K7NJn64i3CDsucldW9b1qvN63a3+e7X/atlaKxsj3nPH3YVG67vH+WmvBeUzIi1ZUFXiHMYq9y5dA0QRoqGoJdKKVwLyiLJb6aoO+0BKqGjKl2dzclCANRmN9VwMztJZDa7EKfqXnICUS1mkSV61ZxDGyXvJcQpWL1tQmJksxipQJeQNFgKDQIaKWMWLekxlDpmBkNHVtuXvvHo8ePUDjOTk95uisYjovqepaYFIcv1d43cLgzvr41qyqv25DmrgnKavu5RATKxVZgkvpdymDu66/VfcyvbdpO0PBOqwPTIDURKPD/giZF/P8Dc0zPUOpX2afiG+J1+4crbXCHBnD4YOH/PqvfpHPfe6z3HnvNu/dusXB40OOjw44Pr3LdHaCOKSK2WbEiJ6uMHId03pRBnRoPdOohvHvPqN0oWAYSf2hc5PUos/49dvtM2aRdjOZ2JnWdUGWjYiRheWbGNVWsGZdVzSmeyK7lnyR8QwpEQnVZRnolRilUJD63E7JMkO1mDGfnvHo3l2OHjzgj/+RP8Jzzz7He++/B1nGa69+jfceHPL085+h0puQ5dQ6Y3tzgnWKnRs3GO/vU50dNbg8woqu4IbO877LAdAxRx66Q6mgJjXzXMZhnkyLAOz0+HTlfvbLR55BsnXdmMw1gMRaQHc2DgQhxgWv6xpjTMPYNIjHi5md9zFCHmRoirLAmDyKxNFoqrrm8OiQ8fVRZ0zpIbHW4a2EC1YosjwPKlUfQiTK4Y19uhCkwWRZo4ptEUIkhGLbXZ8npRTeWWxZgLWMlCJwYBiPULdaYccZVI5NB8V0zm/87M9hUHzXD/4A3mhKW2OVQSmHasywUqDbzBTVwWurpd5RKhVBpIrauRUIY6gMMUOrEMK6caS/e+8Qx2aFMRkxD5PRQmjroD3zNoQdEEo/bbGDPyPhH0s6LtczT0zPiWukRz2GBi/JZr2klM20Ymw0k1HGxmREPhJCJc+EOVINwSyOoSmj5cNZc0GqHut512qJRNOjRIOUqLydc1jvhXl2bTLa5r0P5zmclyhciBqnuCSe6CshcwoOgmE92rDlUtcFxigyREmQicjARA1f+FZpCRPf+IDpkEBY66AZUg3TpbVpogwSckkpJTb2JlPNuYiSWm00Yy2h10eZZmwgNxmPDo46SHnonPXPZ5r/qUu0dM/NqrML3Uz3Q4h+nUR6HfE+VPrEaIrMUkavhb/dd6vm0LSpUi+/5TH1iadVJYWJ3gYPPS9tV85i64q9/T2c9xSLBcWikGingdHXhEA9qkuIxzmrFMl7j3W2SQNhre3o2tdp44bKKti3Dt7F0jdFybIW9Xck6boNrR+/i4SG1hlpUJ4sy5q5ZVrWtCqrRvBXFgVz68S53FmMlogrOoFDlfeSgBlPjJoW8lt2xteZnw/pGppl7hLw8dk6YcS60hFUDHzXWefkVK4SKFyEmU+l50PvY+mYayd7Gn2VA0WK0aYRLIAkVSdYnPTbizRQKsQgzi7B630GywdBV1VVTCYT8jzn5PiEX/vlX+Pll17m3vt3uH/3DkdHjzk5PuTo6DGzxYlEoiWyDXH9guHzE+zVkzC//bp9i6GU0E73v2+OOtTW+WMWBuYi443rH/uNTIPAzEpM5HTrr97eAYdzNXUtQcayLAclScxtM7c4hoj7fUgyr3GqxigHLsNasLZiPMo4PDrk3Vu3+MQrr/DWO2/z7M2nOJ4V3L3zLmqyy3jnEseLMeYsZ5RlXL9yiUtPb/H6b+5z9H73/Nd13ZwxE6yg4hqvYzTXwcnYXp85St+BMIfWSXCu7a2tc/chlo88g5QSaTEZrMkyateG2daiq2zst02wNY/aowgYnPdUtiZTWWjTYkwWTM9UozWyzlF7QcT7ly5RlpWc5YBo4iZGJkgZjQp28LWtQ0jkHtEUaAoXTN1E09Ui5rKo8D6G1VxGkioCSmsl67l17G1tUxQF3np8rciBAg9GkSlDXtSMnGI2XfBr/+B/RwGf/ZEfpHROclsoifYVQ0F7lsOTer9aKtfbKVQn8phrvk/RZR8G9QSLKBX78Ul9T8wF1WdQ8P1xQQQkWqUd+NCOmGhKqMto4x36isyQp0PMdQjAxiOnN3svQDRCvD5R4HyrNUq/EdZJbLizTHyMJmPDJM+Y5BkqSHRVOK/4yByBc7rRSHWEAMhxi8wK0DBBEuJbN0xPXdeN5qd2jtq1DFjULEUzO0dct8gsuSWtVJdZA1y7Xl3jRJDQiq32R34Ghse3iK4xmw3MkAm+RFmeCyMUmV6jMFnqZ6RbqZf2OExjYud81CYByqBNhg4+6ZmBrXFGvreFycfoLOfRo8etjXmWkeUiTHHxPCbz70uHl89Jl7DqLEnvzvcRzxCD1i99qfGqclHhRdpGn5FaVWfduM77vv+8P5+4ZnmWM9IZ6IyxhqIyzM6O2NndRWtNUZYUpTBIrjHzoGHWIrzv3FVniakFlAflBH9Uldjipz5Q64jwdesxJElPGdD+frcO9qnpqOpIbqPvQ1WVZHmMvNhKcOu6JiaYttZirWWxWDS4Vfs22E30jdBKS0JMLTfYeI/GEJLsCfw0Nd54VCCQfQPniCBX1oBoMiYCDD1wRM4Thl20rBMmnMfQ98tFieK0rVXfpHvY918REOJjg4AK8MXhrGuI0yEfwu3tbZRSzGaz7nvVRmJN75EIlNucYOPxmNFoxGKx4Fd+5Ve4evUaD+8/5O7dW5ydHnB6csDx0SMWi6ngRqXxQdDbiWa7Qph60XX8IKXvzzJ0787rO2WiVsOh1d8PnaeW4K+JPq9KBQG5DoxsEJoGggNPyPfjJepde98zIpMmNFLEsZGxCPSLs+CDdt9anNXUVc29+/f5zKc+jckyirLgs5/9JMe/83WyzFGUU/xixDjLmU4zjieean7KpaeucvqaoZp3c8a159dielYKq2BYyryu0iL18UKk+xotUoAlJgo9L1g+8gxSVVvGNnCwlTAt6EDSK41Xmspa8GC9mDoZFLUHZTLxK+oQ+wYXLI+UMngHVSmSQfE7SbRS8TslRKX2WqRlXiTOSmvwlQSN8ELciWRdxm10htGmIUa9cxiTYZ1IBQQPe+q65PTkGK00GxsbFFWBQocEhbC5tUXUFhRVRVkuyLxnZ5RjqpLaeawBvGOEwjhH5sV0YmEtYxTudMav/oOfwxrFp37wC5TK4bEiQfdKiAGi6C9I4QkO8eEOdwIQeN84jsrhlv5jpKooqVQqAfyAostepOxL033QqHjv2woh/HkrZPNJAymQVglgCaNohNgSTS5qESMCilnRhXaKkrXWzKxTlMyh6TU5W/LMN3+kxA+qa5rWkY5oyV00Ho2YTEaM8ow81yhDiI4DtVchMkxgjrxv2pOzlWh7ABdC0MaoVtYGbWcAwJHYj99Ya0UCHyJdeSfZwl0QRHiXMj+Ourbhnae2NGuWBngAj/et/9PSUjb70pX4GiXmcEbLGSYyPFkuTI8SBGSKEm20JNs1GqNVCIMuCEkYJIfRhtp4jKpCNMKQnK/RIFmMj/kewBiN8pIn4orJGGUKX814fHSKR4QhulboHMl70QgvAjLxPhCLNJJy70VqrkwrXnfO0mrV2qhT7fr0pJbeB41cOH3x19Bknwg8jzBIGZY+I5LWabsf2sNlP5zBer3nq9pK21xVNzIHuQnJv4Ha1tSuxnnF3u4VMpVDVVPOFpSLQghMFQL0ONEq99dIawmq08zJCZwwIbKcUq1GM44rHXNaukRXcsZVC2dQNKHzUylsV+uz7FPbEdJ50QDUdU1RCDPocSEUt24YHvlOdiLCKOfbM6Q6MFrOo3UiMYjJmJ2XXGlKiUmeV+E4qlYAGVMpGGVQKqxPkB016xqIf4GlKggeXQOTXLLfHebD9+RdS8W3fQ299dHkV+B6q8VqL1L6ne713dnfDs6JHUjbJsx5SZDXzFc1daNIzUeahsCYh4ZjoBDtwfgYudG3/QfYMp/PO2Zm6eBMyNGlUE3aEOfBOo/RGRujMa6q+Re/8Rv4omChZ7x3+12mZ4+ZTg85PT1kvjiVdfPtPBP+N0xxeZ3669+M6gMyTH1hQopb+oEozusrvU/nj9HhezkZ5bUitRaIAj/Be9F3KGiTRmG8TgJomBAZL5EiCB0rEI08zxBLKRvajRrCOLa4AzIuiwVXYpzBIIG/aus5Oj5DZZrdKzucnZ2yvb3Fj/87/w5v3H6MxuNqy7yomI89jw/nPDh9gNY5i6pEexvwmW9xWGDK6pib0bfMqQowxjR0lG/cSrwnuHS0goBG4O3khDof/eMVJssFPiqFN0602B6K2WxwP4fKR55B0rSaA6UQ4OIEMKug0vdeorlleU5RlrhCbMzzPAetg9+REJi5yQLR2F6siLjSMMY2aKyUUtSWIIVrNQzWWZyrUNoDLUEYf3ovCTm1NsIQ2eAAL5OR0NFZhveOYrFgMZ9itCYzcHoqNpY7O3t4D1tb2zhrUVqJhqquyZRC1TUm5MgBj/UO4zXGevIwJ0YZtqrY1IbpYsGv/IN/yPalPZ791CdxRjRZxkvUnMggxSsX7dJBmtJJuDuXgMcODUdEtq0ZTgpWPG3oxg4wgiYnbAS/3XYdqeN7B3Yl7bT29xFoJpq8OHbnUF5C0rYA1idAZ3l8q4j8blkGuPHvFJj3gXymMkZZzigfkWU52igwouHzzuOtx9ou0SVpXVrn2kZ75JwwOV58kCJT5Jyc6ehTZGsrwSeClsg6K0EaUkIlME61taKljNokJ9G2CIyK8yoJ+NDIxJolaeiL/vL5dM2TMKG+wiC5FEwmERoJZgfGaHKtyPOsZYJ11CCJX1kW/CqkvuSoMkFzlBlDpg2ZcWRBGhWFcEq1vgRxb0ZasbcxQj1zA4vn8PBM1hmPMh6TCaOTZVnIG6IbaW88e33NSZQg15F4DX03Efd70jat5W7q5CY1hBa0WqzeeVunlRk6p33fmj7DtIoZWCW17XwfEfvAN0PfriJa0nMecyJVzlI7K/ngrOXKletMRhvYci4Rm+YL6qpukTXdu9yVQouZ9Gg0Qnuoy4qNyaQRvOR53hEspExNWhqtDcj5y0yTMy0yNWlKib7ENRVcxDs3VFK41P8+JmluhTQDnEP4O2p/4l/tOy8WPZAQgra5rQ0DhJxJE5hXCZixmsntELZJkKVoYZHWS8+DjiaCagB/xLF70MYst0E8l1nDfK5j2qP5mE/WSXpofSu79yN+1RlQM8CG/fLiaN7ch/BYB85OQaOZUUoYnDwIdJQP5vphJDFUfV8r2cw7DEwF+gdCPsbKBSHsCK0Ur776dd55402eunGd27fe4eT0kGJxwsnZEfPZKT5Y1LTzDAxRf569Mawq63DpeRrEobkOtTvEAA19MyQYGu6//3fXBUHaknotzRosiwJOaML4e5pcSX14JN+7IKhXybN+X6n1C6CEuRKBt6KqarTSnE7nnE1PuX7tKg8e3Ofe/Qf821/4Qzw6/B1clrEIwr3ZoiTTI269+S7PZYbSOXLvRaQSyD2vfCPIcwjzAjGiL41Q2lnfmZcKQpaUwW6sd5ILJngwBGeyDp2PsHjqYImigMf3Hy3t1arykWeQnJIMukopCNJZk2VBgu0wWchVVIcDpbOQ9RjKsqKqqmCKAFVVB98GQ1XVDbKygflQAbBHZqwKGiuFEEERgENrahHzGjUSHy+Sd1tbJAJIgbVCQEUkUBYLdrcm8n04JIvFglGei2lIUWBMyN5uxIyoycFXOzk4iA+VDhILjVg+iNWSwnoYBUGVNYqZK5i4DKZzfvnv/m/8X/+T/xv7H3uu5e5pJVzRtzb+GxLHNTlI+s8TE5TzSkoUdBHwcF04n1G5CPCL6566DKX1+gRf33F4XRkiWPrMUXymlCLPMvJRxng8Is8l+azC42qPV5LxusvIAT5Ka7s+RJEwss6JNshKEjvnhLmxtbwTgiRofyIx7x21ryUHk3XN+xgJT6PBx3VQkqTZBz8hPKYncIiR8IzPGyapj2BsKtkOBJwiSKkp8YgETOYu119pqHFUlemY0ZlgilglDJLJaoyODJIiM2KWZbMs5B4zwtQoj62F4cizLJjyabQRqZ1Rms3JhOeffZb5/G3mi4rK1YyswpWOS5f2Oz4AVSDGU2dTICTj7Jo8psxyQ8QOnCmluz48aenx9Z17tY5Q6Z/p/vk8j7Dot9OMNWoKVhAe676HNsv6qn5biTGQsIYeIUD29nYZj8eczs4oikWjYXHOoUwwr3NuiUCWtkXYZq1lZHI5U5VoHouiFO3sAHzrw4x0HZ23UHXHH7Wycm668xqCE32YsmpdO4xHz/xoidAfYDzXlVXwMV2DjmSfZT+6of5Sptd7jzIrtAB+mThuxpQEphgaY/8sDu390vl2vmGqunWFMB4SHrSdLk2hM57Bb3rfK9o1dc4F6b1oQRt/pYTB7s9p6N5EOFXX4ndktOLB/Xv8+q//Os889RR33n+P05NTFosp0+kp89kZDTHrfCQSumU9L7Ry/kNlHcOzTuiTnrF1d+Simqvz7sI6pqp/X9NgEtGHUgSRtbh49MYNcm/qejiC3CBslhfNGLxz+CCkKYuCR/cf89yzL/Dq117DOMXZ0RH14pSXP/sSbz6ak5uMcnbKwemCRw/ucePaJl5nWIQpj/dZ8oWGsaoOMBGGSGvR+ASc78OdjdZG+Bafi6lgOq/gyRYFdRrKqkDlgpOdk3ZPDo/W7k1aPvIMkvWOqq4ZjUZUAcnlSECGLMuoqzpJCivIzHrfmFBUVd0ctszkQarrG38myXosqs6yqkSlreMBFD8W8bewDaCC1rY7tq1UCKPq2whEMfoVtEixowaXLnBO7MG1Uk2ejvF4wubmFgTCU+yGnSQVC1I266MpoDjGEkyj8KCVQ1lH5hVj7ai1pSxLts2E+eMDfvHv/j3+L//3n2Ry5QpOq+DE7IW4VKADbvWrAIpSkeptLmyKIIdKHwj03rZAf+C7ISTXDmUZqEbJRJTELREfng6DNBTxJv4eAVX/+VD/faKzL9WNBHPUeIzGY8ajnDwwx3FcEtvASQjj0J2NbbjIINnGdDMSgM6JJrF2NbV1jeDAWfneNsyREGiRiHfe46ixvl4iALXRGC9mbpA1iLslHiyeLrEfx6JDHoW++YVIl8RTCqVCNMG4oAh0JGimItDFC7DGUtcepSzWGrSWHDUSiMNgjUMbiyprjJHw7ZmRkOl17TG6xihhpPI8D8xT8OXwMdiDhJCOmsssy9kYaV54/lm+/dY71NY3+3FychoiTbUmEFqrjkClc/ZWnFulVee+DZvMrC/rpKtDJb2T67Q6F/W3WUucrOh7ncZoqI+2nlgCpHBIKcV4PGZ3b5fjg4cURcF8Pqe2Nd47FFkDumK/HQfvcEzrusbXIojSWkyfq6pCJX5tqcZtmdFq4cDQvCMznAZ26a9HH16uYqDSZ/020rmtG+fy2q6G7UN7lT6PQhej2iS462B3HJ8wcUFQMoBHvPODZ03+gFVcyQfHSd02LsLss3oYK9uRs0CjZW91Bt20CB5JnkrM5egFZzole5xlWeMrt6rvPM+ZzWbkuWE0yikXC371V36F7c1Njg4ec3Z6wnx+xqKYMp+GgAxRSKYMfmCpLsJMPEm5aHvnnd0P2v4HrQtdYSjEKJJt9Lcm2bPOcE4sm4aYa0/LUJlEG5r2szROH7U0rY9w7TxlVXL//gO+93u+G+/g7PhMQmi5kqev7/Lmoym2WnDnrbd4+7Wv4heP+ez172e8vcP8bCow0ben0nswWkyQU0YepcTSBRe0x5EB8hKjnMRsNepnE7JA4cWsGR9oM4XPDZeuXWF/b583v/VtrNOYAQHKqvKRZ5DK0mLMCO8VdS0MQV1JFLuqSqMpCZFY1XXLOBAzi4ujm3M++BpFoG+aKD5yaPNGs6S1boI+RBvQaGeZmkBIiRHqfLgENN9FCUIE0jHhoDwXqUxd11y+fJmd7W1qa3nuuecaM7t4qSQWfnDqU8EJ32is82jvsVXdRuezNbXzktHcKJT2ZNph6wpXO8Z6g5M7t/nFv/d3+Pf/kz+FH4/FXCc3eK2DlKg9tv0SieN27ZcR90VKn3mI6qp1UqAnA47LBEK3Xlez0wduad0nAcYp0ZCaxfUJDYkiZcKFD2enjpypqNnLIMXEdzVFYlalxNTTts+FCRJ/DDlbQQvlWlvoqOWUNn3DICktfj9Z8OnRie16plQTuUqrNvGycx5nK2Lo+hh9xzuHHClPbuSe5aMQtTH4KU0mo8ZUrqpq6rrCWkdVVsznwQQQFSIAmsCgiTAgkLOA+FcpVAgwUWOdR9WqCVdcW4/Rnjo3gTmSeebGUNVBk5RnGAPWS0Zzow1ZrjDaYQxoK1rH/Z0dnr5xjffu3EUpg/eKsqwxJp4hAoPUPa/9szL43NOYzUVGOn3f/75tQwJXXKScO4Ze6RPGF2FoBgmiXvPr4MQ6YnZ53K0GLSJqYzL29vZ4T2mqsqIoCuogKEMRcraowTWO2oGmB9/6NsznMyabGw08WKUpT6XGvj/xzhwDgbCC4Rhifvp1hxiU9F33+fL56TPwq8pF9ys9y9bZzlqsY96buUIH5fSZoNWk9qqVHmbuL0S0K5olW5q/HxhfeO5ZPc5VzKJPPvJe/iO6+XYftdJ4JWaiUZiKagMvxD0diuAW35VliVKKUZ6jvOPVr32Ne3fusLezzcHjR5SLOXW1YDE7w9oKhQPvJDjHapJgLfz4MBio/n0Y6r8Pmy4C3y5a+jTJKljc/+l79zsVXGjtG+ui/t2I30X6pQ+nUtqpmbcTSyjnxbQv5n4ry5Kj02NGGyNMnvPW2+8wPTvD1iWbkxHagK1Kbr/9Ogf3b7GR1xwcHmCVofYqSN+78MRah4k5NeP8A/LzPuYqUkEBEMYcF6Rd1eb8CuxGAr8ohcpyyDMYj/n0F36A3e0dbt+9z1n1OIznYuUjzyAVRcl4MqEoK1Gja0NRLIJ/RcsgCRAWItB6cTSPyK1WtjFv2RhvhEhxgbiqHWVVglKYzHB6etZEdZHw4qCU6QD5LmJUrXM2qaOgbhIK2mSs8n2wna9rVAiTHLUU3jlMCP8tbZlG44BXeA3eGLYv73Flb4+TRwcsFmfNt7V3whgpD8EeNbOwoQ0qh2ldUVtRqd597Wv8i1/8eX703/sPmNoKnxusF62aigd8TTmPkLlI/aZuhypZ/V1KqJ3XXyqdWWZyfAd49YmQVYTk4NhX/D5E0ERgFjNhR6LfWvGhEPNGATTOe4rwM9VAgTyrrW1M5uI5q+u68S2K9wGvcK5l4GOSVwFqEiRAmAYnSXIT4qk5v3WJq8VUSWsl+YKyjCv7+2Rac+nSPqPRmK2tTSaTSWOTrJJcDyaLSFY0leM85DZDhXwRkVBQwnjUNacnpzw6OOTg8IjDo2MWZSnR9EIbWhmcU4FZccHHJNxtL8Swqy1Ot9K8UZ6Lds06MuswylBVNuR+iblhHJn1kFu0g7HxGGXQ2vPU1cucHB1yelYiEZ1Em+vxjalf65c4fEYHme7eOe+cu3VMSJ9Gu8DZHSqr7lSfOEiRcr9uLEOIPcZsWjXWVWNZWfqfBk6pKksuX7qM9475Yk5dV5RV2SDq+Fnf5yreLRUEXgQBgEL8WaNmpA9/lobVY2hT0r0PgxTrzaKGvxsuq75NGenukWrncVGz6H7pazmbvjzEgDKx3ioGqR/QovtLOg8aM9Pl9Vj2B4llSMAQx7ByXf36NtM5rDvD66JUDvY58Lq9a4kvWfzEOUajcXMvUx+5Iaa5LMsmv9XJ0RH/7J/+BpPJiIcP72PLgqoqWcxnFIsZxiAaKx8As1fQE8SsEx5+GOVJmJwhGDvUzkX7vci8hoQUqVApvRcp7JTAKW3Kgq4PY6sJGoK3q/r10ESk1cHJzWuhEw7PDih8wfd+3+d5/bU3+eI//adMtjcpbRXCjldUizOMK9gMEQ2dyqicb/Zf5pYwaYTQ/z76iwf4qXQQQkmJllbt950FTGhAUDoLDH/GMy+8yIuf+RTq8iXUxhbf+8M/zK//8i9zcnpy4X38yDNI82IBp7qTM6iRqATisjWPcoxHY2EwvDjCjsZjtra2RMLiAS85kqqqkvwygYlBKfLxqMlinkYR6jhLJwffe1F556ORMDxBvZ3nYrs+Go8aMxutxaSHwOlrV7O5sQneUywKlPdMJhvk+Qi0YmNjE60No9FEcl540RZ4pdjc3eaH/sAf4ODWLebzGcWsoCoXYjKlwedazKi8qCtxoCuxX/a5ZuEKjK3J6oovf/FXefEz38vTn3yJhXc4r7HEpIo0mqRh/4eIeFbt3hqRE8sATKkYna0nAYqIw6d9Dkj0et3I+FUH0MR2U8OXvrR1HaDtP19FLPWZpDQ0bxZyYLXSmNi3Q1kIjmE4oFLRbyjN5RUCJdi60R7F+TnXBl8QxsgTo+lE3GqyPIxFN2aNCoV2JQawtWW+mAc/OsXOzg5Xruyzt7PF9s42GxsbGG2o6opiUVCXFeWioJxOOTs8kEhaIaBDVXuclfD3cQ102OvMW4wSTdpoNCLLMra2ttje3mK0qdna3mbv6Ws8/+wzOK+Yzeccnp5x5/CAxweHHB+dsljUxNCmDTKKxvnhL+9Dwj1bB42XaMGsttQhUEPuhDGKwVni714Hsz7nmegMpQ2ZUjx/8yavv3EL61ppn0jxwOkoCeudxx7CXCKw4gc95BqfmRX3SKkuO7aOGBgKEzxU+gg4vRurhQ7Lvh3p77o3/qU+5DeiX0X4C3p3NWmh+U0lfzrvuHLlCgBVVVFWFVVZtdLJhImO9VMH+qYL74kaHjGxqztzvCiTFPscmsMQobOuvWa+T0jwDbWXEmZ95iXd73U5vFKCsDNOJZHsLsJQ9t+1bOyQ+dGK52s0SOl8Y3+D63wOPZzOTR6km9obfXOlV5skdto8Zwxp/T4u3tjYYDabNeZb0c8l3qnYZAysY4wYD/+zf/ZPmc2mlFpRFQXeVtRVyWI+BWxL0IZIi3Hth+azhLN7ZYihlfpPcrbXMayqc898c4/jmEnepX2to19Wl6ExD8FFpVh6Hplda7v3p5m3av7TYZBSU7shpkxoA4Fs8Zt4Hs5mJxydHXA0PeZr33yVT1Q1H3vxBeaLQvp1ljwDrSwb4wxtNPnGBkXtIHEx6JxDrVHBvSWNTmc0bYRMBT76UrF872R+MQCbwTvFUzdv8spnPsOVm89gdrY4q0uM85wWBbOqon6CDfvIM0jf++nPsbG5SbyY0bSntdWXoA2SuKoNuGC0aew4Y6JJG5K5NqprGkFRUzoHL7ySDewyRyng6xMN8adXEjJYQ8NwyDgzsDnjbAOAzU2Ld5p8vIm2EoJ2MhFmD6XJ87GM2udUOqN0ln/+9a/w7NUrVDubFHWN29tmXlXkoxGjwPRt5jlnJycsTk+Zn52RaU1eayYWSldBpmAx5Td//mf591/5f2B9Tu6MHCrjcYnAyAfJQPwrLX2YJQRIW61LHHTrSrvymwo2tE18/biWCZ/kPXTYG7+Em5oxebrAKpViyv1NBxgkqZ7+9JYJGnz3s/DM0xLL/RKZ7izLyfMxWud4p0FprJKxeCdZt5skrkDtq6AN8o05nbUSca7yMb62jMHWksDPx2gGARBLQEMVIjiC1jlagYQNFe3QbDalXiwYmYz9vT2ef+5m0AaNKcuC2WzOfD7n6Pg+VVVSVRIAxdYWu5hTFkUTatjWdfD5gLKoKKu60VqlzGKujQSnyHLGkwnj0Yh8lMu515CPcjY2NtnY2GBnZ5ednR22d3f41Me2yV76ONPpjAcPH3F0fMrDRw9ZlDWWjEznoMX0To6FaJc1Bo1p8pzV2qMNGO+o8WQ+I/MOHaLSGWcxhDDcpsZlYLQjyzPGmxP293Z4fHgczq8CFcJE+3gslpFfCjeWpO4QTOxSRk8OmiKaBodbk55JlgS7q0t6NvtETa/N/ncx44ZKnvXr6TDeZlzhdycHr6UtPUmesuAArlTnPWJzmLC6qucDITDaNDBEomuWzrF7+bIEy60K6sWMaj7FVSXYcbBEUM1kMi3RRJ1LIgrG+QT/TGUU09MzJLmxxtNqn9IE2cnQmvbX8zIX84FZzk+34rukPwlG4btEWmeAPcHXEs+hWD4JsR2Bz2niZ+jPtY2i1i8+heGq/T2Qs8PfJwzKEsPZX/4BJmiJEQvEZPr0QqSX701pmGcf7HcVblARucX6QZCUEr+N4JYWn2ilhZqxdYObjFI8/fTT3L97H+sAo6i9xWkxd1bK8ejBI776e7+LtzWlrVHKUtULFsUp1hYo5UO+9CS4RyqFGJrjwHH0zfu+YEbeCqrqMufdhqLJq/ipxjQSbbhpaScyjqlG1CNEvHcxdHxDFaQj64wnPj/vTp73vt3n6LMeBWatfzvehwjLFq2j5YLBOdAqa2Fd1MTKxgeBajzj7ZpprSXAgRI4aJSRLIvOU8xqpidzbjx1jVlxSlHPqOqS92/fYXt0mVovsErjtaEoasqqwhrF1Fkxr09WS6KtijVXbR0SpEkz2djg5s2bzGcLTg6OJIpecFXJg9JiXiywQXMmmqu4FhIl+ROvfJqt7W2mi4qdeQXVGVVZ8P7RLV790pewswJfXVzb/ZFnkJQSf4Co5VGIs7kJ2hgV6qhImHohtAG8dXIlrMdriThH8NNQxogfOB242/hHRCAFSHthH513TfQwRcxtEQbbI46djmFcaVOCRZWlhUjWKGVQOmM2L0RFXjm0yuSiBNwbEVKtFGxtkO9vo69e4bkXn2c6XeA8XHvqKTY2N7l0aZ/p6Rlbm1vcu3cP7S3f/O0vc/DeHY7uPgAF5WKKzzT4mvfefI133/wWNz79XTIuBU5H5iRFob79r0+AxDCeXgFKExS9VKlFlB1tju8SSnRGtOwE3RlBwsz2bdGjT0Ja9AopR/p7Z49dzM2k8Eky21i3SxDr4H8WzEEDs9fVcAW/HucQMCfMRRruV3yQHE6HoBzOBwTrGuQZJzYa5ZJXQGsypXHGgXJUZcF0dkZZzNje3OC5Z65xee8yRmXMFzOmZ6c8fHiXxWJOWZaUi4JiXnB2dsbpyQnT6YyzszPOzs5YTE8o5vPGjDUGjAA65qUNgxoRns4YjXLyXDSteQivPJlMmIyFKdrd22Vvb4/9/X22NrfY2t5kvJGztb3J3v4+e7viE/TCc0/z4PEB9x4eMZvN8T4gVC++Vcr7sNYigHDK45QgTmdFeyfBLQzaajIXTO6chGX1ufgbZRk4VaOc4vKlfY5PTyUYizYNEeGJPgPLphbxbA9pkprzNHBzBHR1/eVajdnFS3p2VQM/o3CgfacT+Bfr9qXFDSPU/y551zzXuuNw3laTRWsD4wwzkatKzAejUGCCb6b3XL56hTzLqW1NWSwoizkuJEXuAB4fzFHCHdGxLWm0GUeWZdR1FQRnfQY3VA6lvcthjGtt5tebcXVqrmCuO/CqR8Qas2yas679Tp3evDol4Um6e7TcxrnTS7juiM9XVVvJcKoes8fwei0xTbTfrcYlw4MZGsfQ6g5qf5rlSYmHeCfbMwZdP5O4JfGu5iFUtNaStNUYw3g8pigWrf+tUjgbTV4FLn3xV3+Ncj4nyzTOVigsZTlnsZjRFcCs38Alxnuoru/KZRotbrOGq314lE/hnCYzrel3SranQgD5XjrWSuMD/Ac6mo4Wl8e/WwuTdeUiAo22TqvJWl6D4FPrLVVdgsoxWcyLF019fSPoTbVCcaFTGBTzEIqGUPqs6ypEUIbHDw955ZOvYIy4eRRFweHBEZvXrwmtmeeSU9M6zs6mZCHdSBTaAm3whdC+NuGsapgtZhweH7K/u085GWOtZTIZSQJ3k+GcxdS6SSei0SijgwZfaM17D+9j793BOcfm668FhtFRzM5w5ZzJzibGWXj/3C0A/jVgkGrnqVwqORMpv62dED5KEXOoNOo67zCIb5AOSeg0utFQeCVoEFSb3K4hZoEgcW6YAA/EnDQ+lRyGSxVxrW8vWVMnJJ0MsZqxIWywLatGFe69Zzwec3JygjGGciF5nJoIY4mNdm1y9p65yUsvPkcWkPWktDx8+Ijbb77GlStX+KHnn8EZh93cZGt0g52NDRau4sVPfIJ//Pd/FuNz/MKjvMVoKKqCr/72l/l3P/M9FN5hQwZAbfpSny5xddEypJI/r953akKSthMJynX24P2+V42zT9B263WRSdo3BG1ikATL3rr2bPnUjy36CXmsLTvR0BpmCon4Ij60AXiF/jJtIPjCSNAFHfIpOWbTI+bzM0Z5zsdu3mRnexvvHKcnJ9x+911mZzPKcsHJyTHHJ0c8fvSIo+NDzo6OWcxmzGazYKJaUtcWW9dYWzbrEAFaGx2vlcqnkgRBjroJfx9NCFQIkGC0Ic9Fo5Rn4q81Go/Y3d3j0qXL3Lhxg/3Ll9m/tM/2rjBR125c58aNZzg6PuXO3bvM5kUI+S+XWSWJITNjog6g0TzHcccx6SB9FAFN4o9lg7mkztna2uLs7CwkzIyh4xvgseZkJozJBfw/IlHRP1ND53Dd3VnnsD4ULSmtO5SEcajeqn71wLga7a4Sv7OU+TuvT49vYsk00vVAJO7t7TGeTJjO52JmV1ZUwYeoz/zF76MkPxXSxLFvbW3hHrRwJI5rVbjoVWZp/brOroaHQ98PWSp0++pq9fr1n4y4++Bl3fn8oP19kLkMlc73F1v+J+rvg4xLwOOydUoXfyWHs+0MpRQbGxI8ZDQa4Zzj5OSk4TtdCMiTZzm50bz/7m2+9do3MVrh6grvLM7VFMUCH/DO7wcu7g47CSbhfef+pkIl5ZdxcfQ3Txcjhi+Xspwwtnmv+sxRglNdy8R023+CQ5KUIbjQF4al9EDMnZaZfGV7kVlK2xwSAiilOvRlVZacTWdMNjYYjcegoCxKHj8+hJ0CrzT5aIzJM0bjEUW5YGtvl42dTbJo8h/Sy5jgE299BYHhlkTuBozjeHqEGnm2R5sYY9jQk2ZMu2YPHwRS0azeBMGpxUNI8u69ZzKZcO36NbY2N9nc3mRza4Pt7W2U0fydn/znF9qDjzyDVNQVpiobiYmiZYRiksjImLSXQjX1ffDR0CFutQpx/F1dE42lbBPGUnUAVdReeITbd9a240je9w8+BImvcminggbKS2QvRIpTl2WToylelLIsGY1GlGXJeDzuBH6I5gDjbMy1a0+TjTfJcs18UeDHGdnmBjrPeP/uHY7OTtFGU7qaEsfMW/Ir+2ztX0JvbeCcRRsxe5G8II53vvkNqpNT/HgTqw1N/lvtE63Ek5chxLgkiY67llzwLhDQDWAYQo4pwbgKeZ4nSRySXq8a/6p+/aCUqGsekWrGvBfTIo9NmOA2Wp1kl68GxxDvgVYKjEF5LzmxUCHBaiZhdq2lLgqOD49wznH9+lU++8lXKBYFjx4+4tGdx8zOppwcn3B8fMC9e+9xdHjAyckxZ9NTFgthiOpijvKeuqqW9sd622TXVnWcX5RqNYvVSL5aQl9RhKhK3su3zR551YRCl/xEOUorRtmEzEzI85zJ5gb7ly5x/cYNbjzzFM/efJb9y5e4cvUqn3jhebzyvPPObR4fHmNCgj6lxQyOILVCBe2FawURkehUSgQFckeFacoyg/cSeVCbjMlkEiJORm2w7I5PNIMpMbDq/KVnZuj3dWXwfJzTT//dk9zvFN6lks2hd3Axn500qW789rwxCfgdnvtksiHM62zGYrGgqkpsDITTMLFt/c7ces+9l7QQ07OzhgFLCbl+G+vWfGis6VzPg08XbXPVt6vW9EnaP6+tVXWH4P7QvC+y90NliHC8yD27SLno/q5jZGFYwRK+bOBi/D6uQxpWPgotY726rqgq0zBGUYtvrW1UNDE4jVYKase//Ge/SVUUjEZizuXwYimwWAR654MxBOvK0h1L7w5dAUcDM9Wyf1zrD26IGhhZN02aV0clSeUjPJe2W6uR/pnMzAiPMAKtNl1sf9L21pXz4G37ftlnKe6fVhl9udCqdlO/6k5AnF5gB1s7To5PcNazt7uP0RlFWfHgwQPMpROybIxSYU21QmWan/gPf4Ly7Cy4iIgmSbkoYPIoLTg0yzImG4KTUaqhcxuBm2otFURIKdZfefDFlv0xIQCZGNjqIMzMcrGAsa5uzvLh8dGF9gL+dWCQqhpdttGDYkQ3Zx1YsLYkzzO8J2Qd9kFzFDREKfKGRn1tY8hXpZrAACnw6muVVNA6KRXDFraHWi60bqSk1kn/XgNlAHJaNwlelVK4qtUgpdLruq4pq5LNzc1mLM3F8jDGQGUZ5yMuXdkHFCfHJ6iixuzNefPxIUf3H3Lj6ackD5LSWO/xkwm5ytjc2+N0NpMM3CF/TaZg/ugxB+/fZf/jL2G1wkBrthjKk0gFzyvpfgKdfVqqm9pzPEHpMzt9IJ2+G5IE979ZNd8W8C0DvVQ6H5GcD9KpxvlVOZyrO4Cy//0Q0dBklfeezGSYgFSyXHLyzM6mHDx+jNGaT7z0EjvbO9y/e5/Xv/YGR0eHnBwd8+jhIw4fP+bRw0ccnzxkNj8SH6OyxLoa70OQkWAnkTKSXWJA5hWZ/XaoQZvSfNN9roJGJ/4M00HcQeSMAlRVKT/zipEpObMOfZLx+OAht997h8k3Nrh69RpPXb/Bcx/7GE898wxXr13n6aee4rmbN7l1+30eHRwwmUzI8kwSfvpoy63xqpu8Nd7NCo3kgLBEn8ZIJJNVkjcsJK72gSBRSoV8ai2WGyKkh87Y0N/9MzwkSLiIdid+E7/XA+Mb6jPtd+gOdH37ls9H/DtKtPvt4kFooe5ZP4+IjUg3ra+1DsShY3d3l3sPH1AUBUVRUJZlc5bTaXSDtyxLYiORqkP6h/Tdk8CMZSENXBSwDQrhEsLvIt9epN75Y/7gMP8iY7hoO+vKOqHcEgPv19/NdW2uGs8QYxbnLkLYgbVU3W/7d0fonsBY6DYSrnOeoig65tdN/yoIf71YFRileHD3Lm+/8QZGgbcW78UKQJgjh1Fi+s05a7xq/OtYwKEijFD7TRQYR4Fj6leUnh951vfv1Akj0zI+nf0dgH3N/UeYAB1NspUE9JIpDZs+X3SOq0qcT9y7SA8qlTVb0Pned/FKWjqmmOGb2G5dW4qiYjabk2WSD7SuLEdHJ1xalGxtT0CLj6eQuo6Pf+LFJu9mPLcaoQsFbNdASPvhXQPPGsEtyforFegHiMJEay35aNT4iXkB6NKf0cHg1Qn9amu0c+RKkfUSia8rH3kGqaxrVFlBsOuvi1IW30twBrxHOzlEZch10Uggolo12paGf4SL5pWiCoks+wRGK6mQr6K5Hr5LSGVZhgsSGwNgJOlsUVaQWKyY6HEXmKc8YT5icljvBdgpWnOwCPyAQHTBvJyjc83+ZfE1yo1mI8upxxN8bTl8fMAzzz5LZWsyk1FaS5aP0F5z+eoVzu7fF8LQS5Qug4L5gge3bnH5pU9QJQyJ9x8MsZ0HFD6IhPC8vvoMRfw5JLkcGlNa/6LjS4mo1FpmiMgSxlqcvIT/Ft8X76vGJK3tH85DNvFtnuWCALXGKM3Z9JSDx4/JTMZ3feazlGXBw3v3+dq9r/D44SMeP3jI4cFjDg8ec3Ym2dKLxYK6LnCuxtoaEN8drUF5S3RnDqvUELsqPItaUsK82gVKR6q6j9cgnMgvxX48cu9KX+KU5EtCK5grlNFMJhNOjg+4e/sW3/zG13nq6Wd4/vkX+dgLL3D12nWeefY5nnr6Bm+9/Q5Hhwfs7e+TGR3yRjlMCFuemrNCyB3lBK5kzuCMmCvmo2DepTWj0Yi6XiRnYvnOpGZZH1a5yBkdqhPHFSV8q4j8VURxn4D+MO/y0DhW9d0cjN575xxYy6VL+0CIZFeWlFUpJiz5aOlqNfs1wNxEhjgLQg5YDls9RBD3x7WOOR6CsSsFRgmReBG4nMLA38+9umjpr9sy4du+6z/rv1vV3u9nWbe3q/ZQBLCqpUEGSsqcp+1WlfiR6MbPUUmgEdemUIjMf3csrb92ZjJypfnd3/4dyvmCLAtMlnPUZUFVFhKExZ9v7tufe5hp/P/gOg35h6k4j6SteNe8D0lxk9KkOwGUMqhIYEWGrsE1nib6bl/4hF7agEbo4drvlWpN+ZyL+O/Du0cpMxEZ4Ai7xNzOYky2dMd9+HidcCalI5p2vZjUzaYzNje3INCY9aJgviiZbMJ4siEBM5TkA3z06AHjyqN9sDby4J3kO5zNZqAVuVYYpamd+DWP8hFAk3C+jsGcnAvMLty8eZPJZMKv/dqvAbC5tYWta4ySHIzWSi7HqqqbNdnb2eKpa1c4PT3lG6+/fuF1/sgzSLVzqJAFPc9zvAaT57iqpizF90EFUxwbOWitsXXdCRkNCDMVGCdoTdcccgB8R11pAiNUEznesiw7Go8+4Z0SuaIVCupq7zHBST5GFirriio40BljmM6mKKUpyhLt4fDwsNGcROBntEZpS0FFQY3ZzKnPJOeRN555WaBHOY+OD3FGUVlZs2pekemMalFw7dp13lPfbCQt3nvxU/Fw591bfEZB7SyZVx273ijpTSVdF5W4rUMgqxu4yPtlSWd/DP09Sh1yV411bbc9SWQnupRXS8A3zj0y1RDN7IK/m/d4xJyu1b5EINf/u52zAnRAfJkxaMBWlvfffw+l4BMff4myKHn161/n3t07PLh/n8cPHzE9O+b0+DEnp8fU1YJyMQNfo5QX3xpywGFMmFfIA+OVamVovpWCipBHdRCIShiklFnqMn0+zH3gHAVEF9cHpRqGCe+DZsuDC3bLFuq6wOiMKp+zmI+Znp5y+9Ytrr3+Gi+/8goff3zAtaef4fmPfQzrLK9/61soo9nY2kKHHEkpAo4aZO+CjyIxClGIJugdPvMhoW5Xs6yUIBKnWs3hEzHe3kdJTufxKuLjIox/+k16JofaW9euohvM4cMtA3eY1Bl7uV66tvFvrTU6y7h+/QYKKIqFMEil+PP5FJAxNPcuPIh3vApa/z5zdN5UltYx/BmFdavGMUxsD8+9K2UeGM6HzDisYlA+SEnn0Y/WF3q7cDthcE/wVbdmf6vWTa0R+yjVoCOlImyMm5w2EH3mWhje4qFhpisySB3NJWKRQiDeXeIXnX4vIMxhjIRtPnx8wDe/8SqZyYhhvJ2zzOfzMKaoeVKDN3FticKFOO00GmcD8hOcAR1Ym96pbl6sFsdC6msUglk8ISxKE3Evf6t6d0zy/VmrQFmxamiYj96XK/D+ueNRrXa3daWo0TonyP9ji8SFG4ILfUFDCheiBmmxKJjPF2xtbTGblQHNaGazKbv7lyVwGeI3X9WW3/jir/Hgq6+zk08oFgW2rvDWi9l+XUuOzSyjKIul426dxRiJpOtsGyPAGM2N61f5whe+wOGbt3jvvfcavKu9RlvdBqMIzL3Wmo3NMe9sCLtz686dc9c1lo88g4Rz4kRoLZVQTEyLEo2Y2W1MJhSLkkuXLjEeSQ4hbTTHJ8dMp9OE6AzROLxDOdu1d9U6mDvFJI+SWLMsapTWaBX8QqKEI0TVq2uLw4oMPQCHqihCbqRcrrD3VGWN0xozmYhWyzm8NizqGmNrnPJMF3OckkhZm6OMxWzGaCz5lY6np5yenqK95/LeFqPMoGqHKj25HmG9p/KOCti9dJnReENC19YLJnrEaXGK3h5xZiu2b1xltL0FjzJqW2GtIke0SA/ev40vpmTjMU4ZrKJRO4t46HyJZv9ZLJHBisCtIbJ9RBKKVHjV+donfcT/REAlnSVVgyNjBDbRN8a375vxqXjEVkvN+gxWv3TN4bpO7gLsQHmPrevQcxuIwTdAxXcAXEQWznt8SNrbMCpewi1rAzrTEsXIKQ4ePeLg0QEvvfBxvHe8+pVXef/920FT9JCjw0fMpmfM56eUxZyyLFpkEO+GVoAEP/HQRvlNJbzxP6q7jtGHREztVpwTEsYKGsS+YuVJEULcP+ttULx3TWKtdThbgffMqwVGZ+TznNn8hIcP7/DG69/ic5/9PGevfIKrN67zPZ/9NPcePeD9u3fY3b1ErnNqZ6VdY3AovFIYBTrADTGjE4BdIzmVsjxvRiKrZpuFi/s7lBtoGHm2iDll45d4pQT5DbXSjRanOj+b8yUPu80uDWeIyOu232/jwyDBPdHOPf4VV8EvaY0UXpzNnW8EyEYrMBnXnn4Gj6cq5thyTlXOkTDdaVh0D86CUsHWvj137TRF62/rSkKKI/6kzRqqGJwjWYMOLdhlXjrQKtznJUYPFUKR+w74FRToAj5z6F7zsLyP7RiGCN/kux7T0ye6fj/KkKZVZhBhw/CJ6vCDIY+VaEDiaQl+gEszbuGW8u1969dp6P2OgKcV7nRcUlLYSDcCHY3QJEZuS3ZfpYch4K0gmFFK8h4qBE+L9UpoKwR80q49G+n4mn3DYhTkuQEsr732KmdnJ0yMJPfEiU9pVReCktHyT0KidpfGL5+rqM1qS6IhT6LPRWYmoPuoowk5e5QE0IrMSaAH+qZiy/DSkW6CUvEshXfN1nRH7XvWWe0J0ImZr4ywhdvRB1qiCrtgddDC8eH70YfzfSFxOr/0uXUW7Qq8b4OJJZXa7+LcEholauR0EGgGLhmPBDybzefB70dMKnEVujjFKQXZmExZ0DkLm+HnJacP7rGIwlxausqFMxgj6nrXCju1Nm2WEVqzeqU0tob3b035ubvvi69XVeG1FgGsA+VaBklrja2DhrQ64/BEmPqqLgfXe6h89Bkknck/NFXQ0GQ6w6PRuaKohVR6dHgM3gtToUWd6AFjcuoYHMEY8GKXi5KEnaLtqZkXJSBRtUQa7EDnlFUFhOSRmUQXsYDOJN9KZctEcqwoQnJN5cC5WvIvoamth7Imy1VIGGspq4rJZIy1NScnJziVMdnYpNYIV66F+y7LEhvyyDgnyTVtWUv4cuuYnk05PTujtDVOiVrdJgEetBLTK4tj5/I+k81NRuMJZwtRkyoMWWY4PjpgfnqCyi6hsiwEwoiXOezHKpyypjRAIL3TfaDiu8/6gC1lYpa0QokUJn3nnEuQYJ+BcwEJLn8b+0slmmnffY1Y/NckXqVljpLpdcaWIsxVgDICuYAXpQ+lwllUoEU6/s4bb3N1/zKfevll3nrjTd5++y0eP37A0dEBR4ePmZ4dUyxmVNWCslgQI7J11zisQyQgSN6nuHLlvrffrZacLZMr60tKdSbrDY2GN60afQudc1RUVHVJUSzEAXlecvDwgDfefI3Pffd387EXX+DGzWf41Muf4q233ibLR0y2tihr0SQrYwCF9oEgid14T13XwiiFcbXRJqMWSjRq8QxGhjeGOz9fspgQ6c1/SP5eRrSdrwcI2iVCoddWr5umztLzVWNfcc8uVJY69oHxSJvtcyDLfXakzlnG1evXMSajrkuqqqCqysTfqNuESsbdvvOdOpPxuCGsmxw1ocKSCVH6W4c/8p13/b46jtbBDy7UDP3QrE3bUQo7hq5oS5x+kLJuP5dMmHpwsf9unaayJTY73AcXQjbet4GZEkKx7bztq4G5gxEIpbLWLbGefN6tm3wUz0Mf7kecp5Y+GphaBPQqhaNd7UoUKgb9CUYpXI/xisVZK4GsECuU3/u938UYMeuOcysK0R75INjyXuFT5iNpdhW8aTV36fOWsI+Mi1KqiViWttn4ZUV8nN6QgfMyhK99lI6wzJisLkHX14EjbXvpfH0SNEjgfb0ES9KxDZV1wtau5kdMzGpbYQLtq7oXvgP70r5TbVQcnPeCF6uqYlEUjEaSVqNWNXiPreaYzOCVwjvLoijJjbiz4CzWtdZRrdav9RPzeKy3LQzDopXk6PIJbPSuRgGZMrhSfJs8iKtHOC/KS3oTpST8fBQAlJWnchUosYy6aPnIM0gnsxJTtup3pWDhZGONyYRx8IJYnHcwL1BGJYdXtWYRAegtFgsmE4m6UVUVRVlQLCqMycnzHAXM53O2t7ex1jJfLBjlOSYLOWwSfwLrrTBUXhJ1GiOM13Q+xQTzpziWytXU0wXeWsbac2V/D1vX5Mbw6U9+klkpTnQjDffvvMfWxoTC1uxub1GXkrwtApjFojUbkTDDHpMZNjY32d/fD9JURW2t4EclEputzQ2yUc5kMkEdg/dChO9sb3FQLJidnbJ35ao4xrk2IzcMAZJhINRHhOchzaHnfaZkqKzy6Yj9DYUtTgkRl/ze7y91eOxLVvvE2JOU1rctBBvp+bwsl6D1BLLMkJsshNlU3Htwj3t37vLySy8xPTnln/3TL/Lw/kMOHj/g4OgBR0eHLOZTymIOXkK5etcGFFm35kN/fxgmOv21TJ8NnbNV/Q6tWSptjIxM/GcrS1HMmRWn3Htwjxc+/gk++13fw3PPv8BLL36c9+7c4eToiN39S9SROA2aZ62Xz2hd12RKYVXMON9GKexL32MUqTjuD9sX6SLloszUk+7x78cZeZI+UgFD/Ke1xnjY39sjH+XYqqCqQhLjAA/7bQ0R7+nfWmvyPO/s5Ydd+jBzlUb+IoEZhopPtJpDfV64nYQA/f3Y7w9aLqLtWsWk9YsIo6IEX0pjWjrAhK7r94No4VLhnAhtl3GR1hJcJppnx3lFvOK8Z5TlKKW4f/cu9+7cZayN5IKsK2ortE808Rahjo66rKXx9xmPZU3IMDPTPyt9TdcHLatoiScpq+bWryOMlPwdGaV2DBfvMxW69Lvrj11gTTZIZbX08DI8X0U7WedYLBZsbExaGgeNDQGGWvNFT1mJUF4p8VWL8+4ycolAKpw5eUnzPP3ZTDzSXaqXeiUsilhBCqNkjOBgSfsr7I67YERB+NeAQSpqGAUmw9XtYoo0tm5/D+JGuZACVDwxtr0H6lYyoQyzssbY8B6DGcnhqII2KJtssKhFAjPe3KSqaqrKYowQmK4OGh0roX+V0ljnqa2oHFEjFkWFD2MUMz4JCKHw2HJBUZbk2R4aS55vso2Cq1fwdcWVS/t455hOp+zu7jAej/DOkmsh/KuqoigKshAq0dY1DqhsjVfiR+QV1FaYSRUA7SQfkY1GZHmGtw6vNY4QXc/VHD16xN5zL8TglhJ2MgnTGwGKRnU06tACvFWSch9FaQNSqaHyQd6lwC4FeksARQUjDN/a6fY1PP3f03opgFhFwHW+DdMeIu76viCd7zVNLp7cGPIso64qvvXat9BG8/Hnn+fNb3+Lu7dvc3TwmMcPH3Jw+JDj08cUizm2roLk2SGmOabDAH4QQqu/3kPPVjE76fr0319kLENr229jiJmZ2TPKas6kLijqgrPplHt37/HJT3+WT37qkzz3/HNc1vu8fesWV67faPtA7nvaZ0QG1llc1bX/jwxRHGsq0ZtMJiwWiw+NMOiHxe2v0br1O6/ti47vwz5DqwQyFy3tmjvG4zF7e3scPHxAWRYS7jskMY4+dkPCnK7kuN3XCHMjcTQUvvxJ97YPV9N+L26auR4etpO7+HfpONa9X9feBzvjXQSxClak42vWMNFY9Oukf/f3ddUcbEwFcM58OmPo4ZLzGLJVc1p1nztCoES7FeFS+q1EMwXtPF/73a+gQ5CniIfLcoG1Eqm07WA5efpFzoCMe/mZjGvZ9Lz5fkBbNHT/+uuwClcP9bEOx6xLv5DuaWayJSa0wQO2WjoDF+0/fZ7SA5GO8s5JEnKWlUZ9PJeuWXo3xKRaYWsb4FcYt6+bflxIjO49GCMhwLXS1LUl6+Hc2OdQXsl0HSPeS99pJQaVMbkxwVJDKdF0Gh+Ft0KBZpm8t96hgw+p7ROea8pHnkGaTmcUVVDfxbOgaFTQ6WUSwkRs0mNSQNnUuGHyMxL9HYK/+bv1p4jPQSIhRXOZGO1KQsZqieGehGksCwm1XFtLbduxN33gcV7UhVlm0E4cwLWRjMfajJhkGUorrly61ETIy0Y5RrUJtgDyPGi9wsWqQ0S8+WLRBK6QsOitFm28OaG2lswYKocwdmXF1uaYR/fu8LyzVFZyz3TNX3sXfeCunwdMFUoyJyeAIL5bx2gMtTXU9xAyaRBeGh5SqWZufWanf8n7ffSft+cISGyX+2PRvX5inSZqTwJMom2vcp7JKGeUS8LU6ekpX//q13j25k2quua3v/QlDh8/5PDRQw4fPeDk+IjZ/IxFOcV5K+FKW9JB7gDtGM4rFyFyziPchhjlPvHXJ1rO6/c8hqC/986L2WlRLnBeEnQ+vF9zdnLMydEjjg4+yfMvvsgrL32cN995l8vXruFRkpDaLNvDGyP5IiLhHE0vVo0RuuZT3ymjsuqurCoXWaN++6va7D//Tpi9/vfrxnnRb7VSwQxTs721zcP7d7HWUYXcc866JsJouqfn9RfvaF8DKOt3cYZy1d99on0IhgzBpFVjXlfvvLu/av/XEfxPMq7zYH1/X9YxMvI7REuJVQx3/Oa84CRKCAb6mqL4+9DK9eHDKhwyhE+GxgkQ8+KkbXXhptArSmlMD4/GuiYzzM6mvP2tb5OhJCKZEjO6xWIeglAJUyQR3johJgbXaB3s6e9bpKni6qU0VzN3htduqKw7t0PM0tD+X7S97nfLFiux7dbszi8xDavbXz6n/XvvfUxZMzyudcxWH06JskDM7DY3d1FKBRowo6prhBnJmlDy0T9Y7kLbdhtsarnPdB5DP7MsI1OaEcIEGW0aqzBtJIKd7tDiCpOJBq2uaxZlKVGei2LFmi6XjzyDND87Ix/X4rim5MIbbXDRmTxsVp7naCVhBW3wBTBaLrwn+JS4oK42ojYU3xwhdDJjWBRBugshT1Jw1DaG3Cgs4ldkKxukkCZEppOxqBCWcTTKAYU1mqLsHmKRPnoyJABDnucYB8pLGy5ovUzQ7iskkt50egreM8qzxnfKOZGSaq0Zj8fUdU2e58znc05PT7ly5Qp1XUtQiVrG67ViNJk0jtrGiO1pWRSQwaP794IjnhImghXA8Bx6IL0Y68pFiPQPo/SJj1XvPyhwjt82kpKBsPEpAB0ys1pCHAomoxGjTPb88aOHvPbN13jhY89z/+49br37DkePH3Pw+AFHB484PTmkWswlxGaQDjWBMGJQjDg9dXGC5jspfcTX18ClwH6IWF1XhhBMH1C3ReMcIdBCIWe8LrH1gte+UfL48WMePXrEJz/zaV5++RVuvfc+W9vbTVbviAjrkGw0SuVi4saqqtYSjt57CY36IZW+cOiDlCf5fhWR8WGdlw/93AEmy9gPob6tFelpVZZrYdcqIgsQs2TVFZx1e/zOSmqmsm69+wR3HPO5zKWoGla286+yPOkdP1cAsKLtoe+HYMVQi10DOxpUqFAMxaF5EoY09U1cNQatNXVdMx6PO7CzO0rxlxqybNBGhLnvvPcexweHkosxaL6rug6BetLJRd+kVoh2nnCiC79153nKDKVM0lK7Fzx6Q8zNEBxs5t/TsK/a7/Pu0NA40rZ0CNrVx2vfCUwT5shitSXL2ui3abmooKIdrwjztre3JJWHF8G6DcqEaMppjJGgCz76h0np0zAprIzwMAp7vWpzlsb3WZaRacM4aIliX9FkUSua/FMt8ynv8twwGhvqjQwzvfi6fuQZpOtXLzPZ2KQsikYbIra3Njn0kom3ripspsV50RhGeY42ptngKOG1QbNjgl25bEBGWY3Bt+9jmMGNjQ1G4zHeORaLIpi2BRGkU3jnRXUYslWPJ2OqqsbhqOqoQhRJT5ZlIdOwRJgx4RBgW22XUjQOpMLJSwAAEE56Mpk0yQ93d3fJ8jwAXMPWlhB1RVFgtKG0EpveVTXKQG0tu7u7GCOMZl076loYL1vVHDx6hK1qVDbBOkeWqSXEfS4iTkpHWpcS6L1yvhQ7UR/260Wo3vBzq+s2QHRFn0P1+wh1delqh9L6UcLUbzuuj3M2JIhrAcp4NGaUi7Pj7du3ufPe+zz79E2+8eo3uHf3DmfHR5wcH/L44X3m01OqYgZeHGyVVmhlOoEq/OplObdclJlaBcRTQBqBbLq269pcJ3FOS4oQh6VcQYPmPXVdihMoNbV1VJXl7GzKydkp80XBZ7/7u7l1+zZb23sYnQnsMC3At9aSmTYEewcB041klZ6fVfOT7+IH3Xfdb1pio4vwPyiBO7SHyVlZWXpnqvf9BxlDXziQdLNmGMk6J9U9ko39xo2n0Er2qSxL5vO5aJDyFSPpEVLp+ud53qR6iMRDbwrNKHxvXBcpUesl0/LN3IYk+r6R8EJqAhrfx3fynM7z2FdnXL0z1t3XJzlbfSCz4tv+wYlyt2Q+0MMf6cHsE9zed7paOsPJ2jRwN1ZYcdhVJA6baq1KSe54W28VbohtOLp3OQo503retfvtQt2yLMnzvO2/nZCsj/cSrCmdRzg7MfLdN7/xDbwVWia6ABTFPJyhxOwu4Od1jLNW3TxCS8FJBoSEcV5p1Tak9bKGZyXj6v1K4KJUwv97mnDeKZ5Jx9ifX7M3a/rwCW2hlA4BLzwxZUrUJA3h+n7fF5VHxIBKqoksqJqfq3BiimtSE0J57hiNxmQmo67ke+uiD5LgubqqSM8XTnJSRcsfWSdZJh38mY0xIW+iwTlLZSWJeqSJCXYs3jkq51BO/s7EwSVYWjl0MicdmaMsJ880mVbk4xHK/B9sYvf+++/zX/6X/yW/8Au/wHw+55VXXuFnfuZn+L7v+z5ANuAv/+W/zE//9E9zeHjIH/yDf5D/4X/4H/jsZz/btFEUBX/hL/wF/u7f/bvM53P+yB/5I/yP/+P/yLPPPvtEY9nbgMnYwWTUgtxwiCOzFLPeGzMO51uFSFNp4i1NPolYMQ+q68ggaZyr0CODUmCyMSBRYJwLNrmuYLQxpsoyqnHwtaktGxsbnQsoCLQi38zxzmJ0RhYCP9TWNvbC2Jqt3KN9BSjQBm/j5esiBWMMo9FI5qJgPB5TLhaUi5KqqhlNNqitY2wN2oxR6gxbe8b5mKoo2RiNKWzFeDKmPjtjZ3OLDM32aJPMlxRe7JBt5VgcH0ExQ43GoDK8N52LGAFOptuD3yJzubzOt+Sh4C0fAGuLBM5jNpaQRwOYAnJKgXgI8dvgzIgnGllYG4IyQuk0ul0Hh3v6mLWRpnjvJepfDxC1kkAfJD/LEfc0Kjj9J98G5sV7G46AOMgqFPk4YzzJMa7m29/6Fmcnc/a2L/PbX/pdTk+POD58wPT4PgcHj1nMZ9RVJfMNWCKuUcoQNoD/gwu2Bss65BP/7iOqvoPpeQjsvH5j3VXMlvdyBpwHH+GF1lDVaDfHek/tK+y3Kuq6pK5LPvdd38WdO/fZ272EMZIMD61BSwLBqqzIs4ztzS0JzDKdUhRFcta644pnxNoapbKlcbZEa/fb1qwmnVuKx5+EgO1vfrfdZgRLtEmf4bwoB7Ou73YMLVE0wIinZyA9JwmcCYOUUM9KzIgwhivXr1FZi/KKuqiwixpXWZi0Z3CZsVg+VwAaQzErBLkrApzw7fhjSP7+XiVkZH92fQIzrrNuW5YzEGp3mK+GqWjNmPp9t9J7OsSf8lGwFM6mbg9Ue4eejOmWdAVNz+GMprBaNfXW+lkn73QE6JE4TUM7h8oCM7tnVojv5MRFIUaMnAWNGKPByUTwH9ZFmSgGkHox7Hporzv3lhvt7GgYd4yE6cPAl+Ba5ztJgeqsoy5KMZ9TTghyErP02GLLtyERxRyZVmQaymLGO++8ic4UXouvifUV82IKOmgCGl3Z8PnvFt0ZZ8swggpWCpHpUZ28Rwrl217EsiGc1VVCtbAuzXqroNVrIhTGe9fTWOl2D3Vmwt7LdwTi3oVz0AS0CGejaae3hS1z1GrEtDZJSHR5lmU65EuKeQ27jEu31VXMXls/+jhlWR6+afWaFxFQtsJCuTvW1oxGuTDFODw2RF0N+UCdoixrmYt26HB2FUjIeiUnwGhFhjBIWVBE6JDMuKwrVKZ45plneHD/PraqRNMZxl2oCPc8lFUcqDQfYXIQjBhtMMYw0Zrt0Tho8VdItwbKh84gHR4e8iM/8iP84T/8h/mFX/gFrl+/zptvvsn+/n5T56/9tb/Gf/ff/Xf8rb/1t3jllVf4b/6b/4Y/9sf+GK+//jo7OzsA/Nk/+2f5uZ/7Of7e3/t7XLlyhT//5/88P/7jP86Xv/zlJ4oCtLORs7E1br4RyYP47ETHMm3GKMSO0VmPQlPVFXVV47xDq3HD4aZSaxsYoCzTaD0iMy3xJodxRG0l8petLVrVjCYGvTVqpFBZlgWJgcM5L3lplMJ7Sz6S7OuN1NyYRhOkvCHPJJdFRH8uSMFE+tGVbEwaszhPtZDEXJf29sB7RqOccrFgWi5YzGecnh2ztblBrRwq19iRYoFiNFZMD+YSKrr27OgctMUZSVyL1xSzGa6W0OYRAA1JYQQwR+TVRai+R7ho1SbXhfMA8HCJezbUhkhKEi1PShDAwNgRNiRhpFKQmKLzLm2mOvPqt71OCzZEMDf/8E2+Ju8kVH2e5+Dhm6++xtnpFE3Gb33pS5ydHnN6csDx4X1mJ4+YzWYt8d2H6u0gB1b0YvtwHiO7ikkZXPOBtjqSRt+et4tEensS5mnVGJ0HrKNGkk4777BviAmKd47v+74f4I033uby1asSsj8SyyE8vguBVKL5FSCRAs0qR/CWyP0g92DVfC4+//P6bYmAdeWDDf0ic+5rFFv4Ad259kmMhpAKBL9zjitXroZkjxbvPGVRNsKzQYaoB7tU07CMw7uWWFfhZeMPS8wPsjzv7nUcvqgpfGvOf0I4B9quEQQ1UDekLBgsvcmkhHTEMsNwrfvzIiXhv8K3fROsZEw9cDU43oYxSgjtDjxOBXEpHmr7H262y2R1YFXDfCZMZ6jXaKgHtNNJpx2GpdfdQPVuv6p9gdaasizZ3NyQR4lLQWfeaV9BMxYl9w/u3+fw4ICR0UIQByI5DQPd+Ul/7P017dZIA2MYbToCsLROX8jar9PXWqVMklaSoiXFK1HY1LdsaZibgYPrA33V19iryHglWr2+VUDU6GndP9Pir5P6ETd5DNedk3NKwxwpi3WWTGUJQTLMUPYFhL0GG5rXGMN4NKJYzIOvswhojRbmyzmhQ8YbG3gTlAZK4eu6scLSISy4VYpKe/TYYMYjnFbUpceXlnfv3BXf/FrOZGSQNT1z+zA+5wL8DCVGacR7MqU4y3I2Nzcw48mF1/FDZ5D+6l/9qzz33HP8T//T/9Q8e+GFF5rfvff89b/+1/mv/+v/mj/xJ/4EAP/z//w/c+PGDf7O3/k7/Ok//ac5Pj7mZ37mZ/jbf/tv80f/6B8F4H/9X/9XnnvuOf7JP/kn/NiP/diFx7O5lbO1NUoISo1HGBCtR2HDo4+AwmpRI2ZZhhtrqrIiy8XRy1qHNqIcl0MlDmpZMMvThPwHsa4WJzFrK8Z5JkBL6YaBkuB5Fm1iRLBu9CGFlsutgs2yd400Q+uINsV8zloXpKfrD7/Hs7Ozw/vv3mZrYwPrLUZ5Mhy2mDE/PcYWc/avXaGcTXFVSaUdVbUAW/PovTvog2NYFBjr0K6XxbqqmZ2esX35BjYFhEvEbov2L6INGiKY15Uu0zEcLCH92aqQv7OoUqvG0bRFwkCpLsM9pPKODEBMODhUxwdgoYDxaCSmoUrx6le+xtnhCYt5wTtvvcVsdsrZ6SHHJ485OXyErYolYD3UR38eFykftI0hBqmP/Prtrav/JGMfqtdK24PMqnderLV4Fdau9hQLz4lXvPnGt0XLjOKVT36G9+/e5cZTzwCeOiQWjcmnXQif2uTOMiYIcboEeHtW3Ae6E4Pz4oOvV7+sOz//KktH+AEDDMdyvaHinGNvb6/xzwQ6uZCGwmWL5LvbTjThzdLodUTJMYNEVypkWVVWwbT4+6CEGPDhLKuB7wb7WfMunkGXMHHnCTb6MHHo+VA/T1x8jydK0KNK3q+d4AXGMsQotwTyMAE6tEZLTNZ3WJRSTKdTtne2O8/W3f3oRD8aiRD3zTffFBgXmSsfzPIuuGjLazXsOxt9MjuMyhr4n74fYiRUoJvwXV+Wfp003HaKi+Pv0Xoo1veBwViFe/rft6aA7fgjjZdlGePxiEWxaNa9K8gP1iEfoJy3Pqu+SUtnTWiZLq3FDcQdTYPAx0kQsGBCrrRYS2T5CJ3laBeEQ0qjjYSPt87hFDgFJZ5PfvoVXvz0p6iM4tG9B3zrN77EYj7D1sEKy0meQJwnU4lvUjM2gruKJIH3IVm99aIR1TjObEVmC9Ti/0AN0j/8h/+QH/uxH+NP/sk/yRe/+EVu3rzJn/kzf4b/9D/9TwF4++23uXfvHv/Wv/VvNd+Mx2N+9Ed/lN/8zd/kT//pP82Xv/xlqqrq1HnmmWf43Oc+x2/+5m8OMkjRpyaWk5MTALRyKIIJko5EqsI5hVJiP6uiFsNbjI4HSknW81yhtBxWYzySCqm9pLkBpVzIoi7EiwRJ0I0QQmcGCGF9vaVNGhalGIIsW+lCkCxUrlH9a6UkGZePzFz0l3CB2ROU532Xi+5LXrSWkJOPHtzn67/zZa5cuczZ2Smzsyn2wRGcnXFtdxv14DEHDx/jrGWUGeZnU4qy4Ntf/Tr23gF2NhefJETVbIzBekdVVhw8fMzOcx9H6VYUll6+FBgOjfM8BDv0bBWDE95CsmerGKGhvuO7JUAvlTtt9sfSHx8Q8k60oZxXheLtIwlh8to6LcMr59F7j9GGcTbCoPnGV1/lwZ0HVLOSt956k2JxxtnZAdOzQ87OjqiKBVq1uZzOI0BWMSYrJekDc79Im6vK0Jr2CY5+yNVVbV+ISOl93yG0QmmQWLj/MeRoyRznHe++/RZYMZn8ru/+Hm7fucP1G08HM5UAJ4zpINLGX2/FmZI6q8fdYQ46vy9N64nKuvvRL2sZzTXtXUQY0ZfMnnvmziE2hwgd773kcfOeUTDLqCoJxVsUBWVZdqIxdcbQ606gsgi4YoLFKLlWKn4bfDlQLUx5QngY61307EeGv1+n33KXuOPChPEHKf09TaOUdseZmij24Aiq9Zkkrv+qohp6QLQrXRwx+EVvPEPncIhgjqXjI5bMuz2voa1B/NFd/U7fSnVNScP7siwZjcZrYVt/rpGhqGvL22+/TZaJ0z1KtJxRgzpUVsH0/j1Lf49jiJqUofkNBSDpr2FsLwpsYyCptK/4jdZtm0NrM9S2wF5zYViafhd9wmNfkRm6fPkKVV1yeHgoEdZUq/nL84y69o0LSHeMw3sZf6Z1I6yS1DXQjxCyDu5297nFt9FtoKorXF2hlW/86q3zFJWlsh5qB1byfTprKcqyOds1DpcZ/GTEjWeeZe/aNRbKM55sMXvzHl/73d8L50IETM5ahA+yEhAiBlLzYUqZwiqNzgx6nKPyjNFkjPWeycRw5ZJE37vx1FPwm796oT380Bmkt956i7/5N/8mf+7P/Tn+q//qv+JLX/oS/9l/9p8xHo/5yZ/8Se7duwfAjRs3Ot/duHGDd999F4B79+4xGo24dOnSUp34fb/8t//tf8tf/st/eem58k4YoBiu2osGxiiR4mU62mR6fMw/khJeQBb8X8TMovUDUtCYs2mlG5t2pYSr1s2FDJx3853YU/oQDjyiqsYZLRxWY3TzLibbUkp8H6KtufMWQwpUaJi0JUCNxyJ2mfW84F/82q9zfW+PjfGIN179JpwWYGW9FB7lJbRtVkNeeVymqOuSkfVoZym0x2YKFwJEOBzeWg4fPeYlFM73iXzCXCQgRLNHAwi6pW+WCaJ1DFNEQKukJn2E1lHlD4w1BXKd5G4eUiC1LnTl0ngj49zzo2kTrXX7jh+lDFKnH0BpzWg0QivFW99+gzdefx2c4t23bjGbnVAWp8xmR8ymx9iy7A5nDVF1HnO6ypRtFSEx9PeqfuLzixDlsW665v3zv6q9VQzqqr67+xIJGot3CqcsWPClZ3rmee+92+T5iO3tba5ev8HJyRHbu3so77G2lSambWutJYdKMv60z74ZQWccvd9TonKVqHzd+q4i9IbKKqFA+vtqIcb68ZzX9+qi4v/P7TfN7RUJgCwz7O/vU4b8UymDBL3z71uGKHbt20vLeDzm+Pi4gc0twSGWBfE78QVcTbD0hSh9QUGst8rBXOCXT5iktK9z9njlG9UMed056M9l3bMhBiQybJGZXIaFvsW7DT6OwpPEvK03JK00Tg2bcPdhSX+sQ4KidFjn3Z3O3QiWKAzMvT/sLmO43E9kkDY3Nzv1Vwkl4pkxwSxqenbG3bt3UUosZDwSydcFTdKTFoGT3b87MJrV4xqaW38NoGXu1sEt2a+WcUjxeoS//d9bxjaZy0D/aZ/tuRNNRj8PUpZlzOYz9vZ22dnZ4eHDhxwdHTXfRZpjKOrb0JWKY0kZqsiM1XUt8xwKn5iMex3sjWsW10bm4yVXopOgQ9EFpLKOs/mceVliFxKh1VqLrWvRHjmHVxqrwZUV3/jqN1hYT6ng9W98k8evvUVR1VhvAQVaCZOlFX5kUMozHkvUvKIouX79Gh976SWu3Hyam889x5Ub15hsbqCMxgGbGyPyYLZ+Np3C//OvrFyHtHzoDJJzju///u/np37qpwD4/Oc/z6uvvsrf/Jt/k5/8yZ9s6q0CQuvKujp/8S/+Rf7cn/tzzd8nJyc899xzwowohY+RMpxDKY2zLUcaL0Br+tBKSKIMok80xYOXOthDVPmp5Pdwqbxusvo2RLx3Mr5EItxFCtKGUgQmKEianQvOfVDXVeNvAtH8LiCDJcAAFsgUbGQ5jw5PePed2+i6ZnZwRF4ijKQSdanCkynIvCb3GWXl2DAaagmbbrXMQfwtLNpI8IX7d++ivTgcorpALv7u/cVlkem3F8lBMYTQhiRvF+07AoP0b6F+1n+7/A1ClOjWdG5oHun4lvIR9AikyCiP8px8lPPo7gN+44tf5PLePm+98SaL2ZyqnFEUp0zPjiiLhZiCesN6T+fV65GWIYlov+7ymNcTC6uQ35MS1U9Sr0/gDBGfq4p3Dudj3mNxVgWoK8Vsesa7777NeDzmB37oB8nHGxSLBeONCULD+caMowlx6j1K6ebd0LhXIbMPWs5b2ye9Mxdlui5S/19V6RI9ghNGoxE3b97krTfeYLFYsFgUjTYJVkS5ij+DEMVDIx2fzWYCEntzT8UMqke9O5bvQV+4ko4//p4KXDpCDbUadvX7Tqs5lUwureMDZ7jWIPD8MsTMNe13+ut21WeeBPvJM0m3IQKtznL59qdqGqVz55eFDHTq9Mf2nZ7jzrcXuG799Un3LhL+aWLipT5iV731FQZJ8+DBA2azGZOQ5BREmh+jrF1kpn2YCv2/2/Os0R3LirQMRXZLmZjYVtTIAw1tN3ymWi1SH8an+ej6DLtY+XQjqQ4JKPo/Ix5P6Ym6riX5dFVy9epVnnnmGYwxHBwcJJYlumGSVsHgoXWJa6iU6kQ8lEAhauW3q0ojVAlzESZMcF9dFdRVSRYsn0yWCTOF5qAqQjLhoDlTTgQRRmCiB2xZ8jtf+Spfff11KhcwaF0y2szRZszm1hbPvfAxXvj4x7l6/Rq7ly+htWZ3dxetNcdHR3zs+ecZb25hJpOGNFNKYZ2jLAvOTk9YLCrR4j8B2fOhM0hPP/00n/nMZzrPPv3pT/P3//7fB+Cpp54CREv09NNPN3UePHjQaJWeeuopylLUjqkW6cGDB/zwD//wYL/j8ZjxeLz0XGGwtZMADA68CxJFpXHOJpdWyz/v0CoDFSJ3IBoSMYNQGC2HzYe2RHofL4FtmBMC8SoO9IaRkUNTVRV5HhNZtapgiJeZ7u8qRHULfzcasNBee5lbiB99mZakbwgQVcDe3h5HozFqvMmzH7vGq0e/BwQg5ER75J0NznQwpQIlDFkW/I60l5zERhvQUIfEpIvpLMS/j867sWdAdVXfafGByeu+9MncWka0BXrdv6MvThfAtuZpnfUYIH7T8K3Nm/i3C7bXPh1PW6fPhEXfAyCEzVZYF5F1z445yOi0cLgNEM2zTPbftRK+VsLkcNZhRhnj0Yj52Yxf/IVfQKN46403mZ2d4WzJYnHKfH6Ks3WIYTPM1DwJERxLlFatKg2SSttvDznQ1aR1GGnSHX8y8quV4kYH+Pb7PtJbGr9SiXQ9yh5Wr40LvoE+nHlFHQQolmIxR6F5+603ycY5P/xv/CEODx/x1ORmMxYPounuEdmeVsgi0lffrGdDoAwQynRme7Gymhhd1sqd10Ynal5v89rPu8yyit+rOH46H4bptyZTqt2SlMCNlTsmagA+MZMc2EuV7DdKCZwFsizn2rVrfOu115hOp9i6oioLbF3DaNyG6g53WMbXrn8UcmmVkRmDtXUgeNp8WC7xLe0sVqe0cC7OV4I76M4XKVj0jRNza74Xv2/v4/BaxGVqppe2nY4qtKHSDVmxxkv90DtvKu6Rj4ADFI1GBe8Ds+hRmA7RJlc2zM9DjDCXNSF9Vfhu9dmKc/feNbq89FsfxtT4cKnkTKUtRnjXn2vcwzi6pM+obUxIV5b2ps8YLtdo5tL4MStJDL9cTw3ea6VUk+/o1rvvyppbG+6TxzuLtzGqGWtKlykZYo7iOvgQ3czjBxn//rjjmDuwIzWnCybKAveXGRdnhVkZjUdMJhO2t7fZ3NxkPB43UYmVUlRVRVEUWOs4PT3l+PiYxaKgtnXrSx7W24VQ16kgMGWe0sAL8VlZSmAfaxT379/n5s2bPPP0MzjrODo+CrheIiXHJK3ybboiq+9vuk6pL9Q6PN+nGZMXche1pA2xwaLJe0dVW6ytUDhqD9f2L/Hw8SFOa9jf4ukbNxiPxzzzzDNoJbm5yrLk0aPHlEWJNhmTyQbjySZXrlxhvDlh9+ouO5f2uHTpEpPNTUyeYX0bmU7GYjBaM76yy1ldMZufMnaVJJANw65rCZK2M9kUq43acnR2tHL+/fKhM0g/8iM/wuuvv9559q1vfYvnn38egBdffJGnnnqKX/qlX+Lzn/88IKGtv/jFL/JX/+pfBeD7vu/7yPOcX/qlX+InfuInALh79y5f//rX+Wt/7a890Xhqb8kRbZHWEkVIaUWmFd7XSAKtKO2X32tbLV3EVgojfj8Ao1EWfDi8ELVofBqwwAsCa8Fo9B9wDXMkVducFLGPhhlq4KjCGGHc5KLJIa5rhwwn1TJ0HftSYGKsIrOOvWtXmZiMnd09Nq9dQo8z6nkZDr5HkaF1JnNSCrRDeyS8o9GI7k0zIUi6tUa5CtAUJ8d4LCobBWYLtArIzBNyS3XT6HWBSQu48e13KImG0q5bnB9hD6PzqALXXYP0usdQreCbiCgRiEQzxzgmQLR1PiISLfbqvqsyJzkvLs6HZJyeQNBEraBuAHW0fTdRo6Q1dVWhEX+x2lqssmhFAzTB4ZwlzwwboxHGen7lF/8J9azi9PCAYnYG9Zy6PqMsz3CuCmrtoHVagn/LQLPPSCy972/AQGnWdQXi89AQo0lnw20NPu2OtYuMxR9PKxoCUUoS5CCJ9tWfb2rznhLWfeLCo0BJ8BeNnFHrLL72kGnmiyn+yPPu22+ztbXF937+8xw+fMD+9adFAOLaxNGNj2M8T1o3GmanWhqp0Swl/7pEVcpwBUZ05epdjJlap7XqP9dhLClR2K0bxqpo7mLDRK0YWgof253yyfzjo+5JkXHLM+fbfhKqGIVvNDVaeTQOtEQyvn7lGraqcbbC2jmuXqBsjbJ1m6cjwqTIxIU+oj8rKLQSZ2HlbVgfmaAh5obpm97FtUyXI2VCwLu6mY5WmlY67Nv/+dRvLa57wpD1hDtJZ51fdb/v+LcPu5DC5KTu0v4wXC+aNTbfqZToDTAyToG631DDMYjPl48D680nPYc+UaKHfG/OCc4NMKnZDyC4+SZjlM1uj2V3HTv3rQN7WE4TERlCApJtwJVvp+E9JsXzvXvQUAphjNa6EKlXUffSY3Tom7AqdV2jnEd70B7uv/c+I6UCTBIfautqbF02670sWAkuBNo0Zz8yQ/Gyy3vd+V3Rtfzoa+bWCW7wwec7rnDEJwp0FkzcnAUvyZqfuvoU169fZ2tzM5i6GVBBi9UwVdJ+DBJw/ZmrKBRV5Tg5PuXe3XscHB6062k02nsyoynLCu8jXgl3HwlOYAKsiLRNXZaYPMc7uHvnHlevXuXpp5+hKErmi3mH/rQ2WhSs1ibFkgqIO1qycK7SpLz9nIIpo9d8m8B+6yy2qhCf/QxnJhigqi2LfJvnXn6Z9957k5ufeJE/9Mf/EPuX93HOBWbTsrm5iVJCL8d+syAIVojWxzorzA0J3JatxnoCXVTjPJIz1DmMhnJ6hrOWsizJsiwEG9Hsbe6S5zmZh0tPwPV86AzSf/6f/+f88A//MD/1Uz/FT/zET/ClL32Jn/7pn+anf/qnAdmQP/tn/yw/9VM/xcsvv8zLL7/MT/3UT7G5ucl/9B/9R4BoN/7Un/pT/Pk//+e5cuUKly9f5i/8hb/Ad33XdzVR7S5aYiju2LfzkmSqDnbgUaInQKU130qJ31RF3cS997KJIg3sXuaUuMrznLqsIEgRYnAFGQ+dKClR0hDHGn/Gi19VVWIvGxmkGogHux3DkAYpUKMYk7G5s402Gb4ug1Yrp9aCWJQPZh2hvhAMhJj2oIzCAYYQKUYJ0W60wWM4PTrG1hVabdCuKN2xsAzw5H1LJLT1uxIRidonodFTzVE3W/SABCS+WeE3s1J6FTHIirkMSt9V75MEead1G3OFBFC5XhhhfDyrNnGglTrj0ZhMab76la/yzVdfJVOaxdkZWlmqumQ+n1KWRdextkP4PJn5VMq8XISsjkB2iLhOex1ixi5Gtp/fd/9ZNJHoI+NVUtV+6a+XUi3zFRnCeE/rukKFpNSHB49584032N7e4WPPfwxblZh8hLeCR51PGHQBSB1ecSjvTl/rtGqccUUH56TavVgnveUCdfrav6EyuH7yYmlgqQ66c716xO7yX50OE3YkqZ/A3a70Pomq5R2XLl8GJXtZ16X4INmgZfCeqJ1av3KSTkGk092R9Ge9tD5rWh3WHnbfdWCuDz/VGljVDmTlOIJYIHnR7nh3LbsM0hPNDd9hrAc/WgLzwuj2pfnpyGNJ8Xa3qUggh+fhLkYibZXfZf/Md+fql571Jf3t2IaZgyigWxqzCsewx1TUdcV4POr0tYSrwtzS+WulKYuSe3fvNvwaaLyvmkSmXQHlefhDToBSCh3TlCSHrg9rz2sv3Vcd6aMgpOvseWB2rLVcvnyZmzdvcuXKFUajEcZkGGPIsoyqEuFhXVUUtkYpoTEkytxEfHsDzl3MS/JM/OOrquLhw4fcvn2bxWLBOMsaX5/+2eqvffq7BNIYURQFp6enaK157rnnuHXrFotivmReJ7+uh8HpmYkCSmstRufNt8vwgcHn8fxLmwprHZW1aJ2hTMZkYxvvPcViASg2NycYA/l4jAXeuXULlOLq1atsbktExTowTAeHh9Qh/PdisZA8gN5z9epVrly5QlVVjZYvyzKU0jgvNPlisWA+nzOZTJr5Nv6hSlFWFZtbWwAcn5w0DNOiqtaer7R86AzSD/zAD/CzP/uz/MW/+Bf5K3/lr/Diiy/y1//6X+c//o//46bOf/Ff/BfM53P+zJ/5M02i2H/8j/9xkwMJ4L//7/97sizjJ37iJ5pEsX/rb/2tJ8qBBC0RER3yREugQxQp18R1Tw9slM5G282U6EkJvqj6jBmQYz1oGawYtShVc2ZZ1vQVfw4R2X3g3pcKxLnIGOP33QO/RJQqMYUbbW2QTUZUsxlPP/00b2QZCx1NBrVoeXxEUF6SfXmRkIs0L5gVEdbSO4xW6BCavFwUjLfWAAnoSkMJsBrVwOxBjYNfdnYEkX71fbnScl6oyy6jNTDegT0aCvN7XonSvXQ+/fGmGpfINEtwkBh5R3KyjMcS0vvk6Ih/9PO/iLeORVXgvaWyC+aLKYti0SCA/6PK0hkcmPO/yr7TMNlDDFRKXK1qJ937PnKPMEGkhA6lFbaumE2nHDx6xJvf/jaX9vc5mc556ROvoDNNUVYSmtSYlQTrkNBjHSO3jnnqjHnw6++w+GgCNoyEh+/NMjncCSOQ3h2f8OpPxN8vr9WQ1DoyEs5Ztra2yDNJDl7X9RPdpz6xUpblEhx5UuJw1XyGpOwp/utObnU7F+3vg5TzzsHvJ0x4Elgd121IILFqzVfhq6F368ayak28Wu8P2e+jKAqyLAvE8bKWAHpwQQmuB/HhPjubNqbckTiOjJxSGtSyJkP4Er0k6I3zcknwmaWxXETwkvRnQo5IVvglW2vZ3trmxRdf5PqN6+R5Tp7nlGXJ9GxKXQv9Jznoorm4RilPVZUUxSnz2ZyyEm3Ezs4uVy9f4cqVKwCcnp4CcPXqVR4+fMid27eDqZ0LGrRkjZVG665ANJaYQmA0GnF6espoNGJnZ4crVy7z6PEjqqoaDDd+kTOU0kvOuY5gYx0uSX+2Z17OR1WLpVA+3kCbEcpIMt2qmDMZGbKgnd7b3+flVz5JURUNzIy+tlVVMZ54dvf2OhEKxcTRNHR4HHtZllhryTIVkt7K/ud5jgjNJSHsxsZGY4qYBt/QWjfPUx+z88qHziAB/PiP/zg//uM/vvK9Uoq/9Jf+En/pL/2llXUmkwl/42/8Df7G3/gb39FY4iLHhYkXnkTikmqZZIC0BypBMn1Ek0YyUko1Uvr+4W2DP7ROhJF5UkqcaYXA1y3jk9gQx766Uc5ayWpVSabkKEVKmbEOQFdASIA42drEjEdMi4JHDx6CDSMM9yB+25XyizO6V+EfIkn0IViDciKZrKuKw8cHPHP1GnXiyNj3C+qJIDu/rQKWQxq2eBnaSxzHu1obsAq49BHiqvLBEbkAmX4fQ8R4x0HfegxK8gFUlkwrRiZDA7/8j36JYjqXTNU4qrqgqubM5medvC3fSWnOEl3Sah2Btm6NVmlB0r76bV6UuArCxBVIWN6vCy4BXbOEPuJI7+7QeCKsGY1G2EqYWbSmqgrmszPu37vL6699k89+93fz8P4drly9htHiUO6VQoUIj/2xDwlPurAmCDN6Z8mHBenksO8LEPzFkjBf9NwPSiHXtCO+g6oFC4FY8wOS2OT1IOO3ap0uWrwXX0AbvptMJkwmExaVmIjUVSXmSAME89AYYptlKdqnZr4JPll13vptLK/DasZmKIBES+AOt7lqn4bOYKc/eTn4bmgOQ/ty3tlaVXdorS8y9lWM6qo1GHLo79cbErDEZ0Mw/iLwbamdFdqDBmeHvT89PWVra2vpHKThrX34m/BNFnzi7t69KwSpTgUzMYVIyyitWu80QNHQ/FKB87ozv2qtUquEhiLyreAS4OMvfpybzz3Hzs4Oxhim0ymPHx1QVXXDMNa15fDwKESWFPpsPB4xGuWMxxO2r+3iPcznM46PTnj/9vvkec6NGzd4+umnuXTpEoeHh+R5ztM3rnPr3XfD2qVrrvDYBu+k916pYF4WfIyyLOPw8BBrLbt7u+xWuzx8+BDoWjKlOH3Vee+/t1bMDKPP/Dq4mNJrcubETNd7mC8K8skGSo9xLmOyuY0xmpOjA7bGOdiana0ttMko65r5YhECiYlWdhHS8TjrGi2eUqph6OMeRmFUnudMJpPg8yXmm+lZqaqqob/n83kzt7IsG7o6z3OyLCPLMiYDsQpWld8XBun/n8oQIPWIv0d6yKIWCNUCwqgujQclHmDoElhxg+Ilb9XQckFyk4XD1QUWEVDFduP3TWjI0EYK0OK3IqVpL1qWZdS1pR/eO503KKx3WAV6PMKMcvLRiLffeou6LEEryW4cfHGUEtvcBqlqGssMLybEYkLndQhxFJwAreP05IS6qiX5VFJSQjWOqTWrW96zdfuZFlmzZQbpIkR6v/11yHdoDOdJcjoMildLgD6VavQRcGSKVehXko/CZDQmz3LeevNNXv3K18i0wXlPbWu8r5nNT3C+/o6Yo0EAqlRHAnVRAqffznkR/FJkvw5xDq99a4qSvpff1xNNUUgxNLYUAa1CRrFeXHejhSNztkZ5x/TsDIXivdu3uHHjOspkXLt6hcwYvLeSYFoN59oYWochAmOICF1FVK0q5xErF2hgjbnR8hgbX7zI3cb3Ke+Xfh9yx5HOccXYBqNJsnzX0zqedp+1an3VqqqiSjLC99vow460z8ViwWg0WsojMzTu9N4OaTmH/l51x2KddbBs1dlKy9rv/XKQmqaOVFzbznntr3u+fMcvVvciJYUTEQevMrG7SFnF+J7H6Dbf+u67VedZKcVisWB7e1toAqM73zVnJpgjxVaiE/69e3exdY3JWym8CJLFvM651s9naY5r5pQKgYfG3WknOdd95ieFbWlI7CiY+tSnPsX1GzfIxqKVOTo6oixLTk9POTs7Yz4vQvCtIPgOEYWds2SZaaJObm5usr+/z9WrV7l8+SpbW5vcu3uXV199lTfeeIOPfexjvPzyy8IoPXrE888/z6VLl3n99W9RFGUIzOLQpju/Zg0CfIE27HhRFA2suHTpEtPplPl8viSUW4eD0vf9NRy61+v2QCnJu+lqoScePnzM5vYOSk8oa8XlK1cBxcnhYzLleO3Vr2LrisnGJrNFwbyoOJstKMuyETZJ/qKqY02llATGgFY7GGke731IYyKMU2SwR6MRWZY1dFKEr1UQYh0dHXF8fMz169ebuovFYnC9hspHnkFaRyAOAiXfJlpNFz5ewFRC08mLQ0vs9k2kus13CbPIIPXVgX1NVV/i3SSGdS0AFQDmO313kIeCGBwC49m9vM/sndvUVS1OjkHT5b3Dq6whPhxQIc6bEfg5L89qFCEwW4MElVIcPj5oxtsH3PJ7yyiti1xzHkLrEobd52nfF0GMfYJmFdBIx3We9Au6hIoLi9X/Pm0jEth9k0q8xztHnueMRiOctfzSP/pHaC9Mqa0tHsd0ekptS6q6PHdsv59lFeGyiqBf9S2cTyilgoX+fne/9Q0iGuormgCct2ZpH/25xL/LomCc5QGe2LCvFWVZcPj4MW9++zWee/553n37bV546RM4L8hUgrN853uWni0xz1k1me58PpTz4rsu5OvGCDQRoWJADRWYpCbEtY9AhmaMKjJhHupz4Ox5Z2yIaHDBYT8mJPReMsBbW4sPas88e6ik56Ku607I3XQe/Wd9IvxJCfv+mFL8IUFalu/mReHkUIkM5RN9c8E7ftH3q+oMCTaepKQ44SL46IOu4VBZ3pPVzF6Kr4wxnJ6ecvXq1Y6wpX/HVWCOfBIdVSnF0dFxl6nFdy1tLiwn6eH9ge8i0zQET/tzS4UhTdthvs45dnd3+dznPsf+/j6Vddy794DDw0MODw+ZTqcNAT4ebzAejdDaNPTW/4+9/2qyJDnzvLGfe4gj86SsytJV3dXdQEMDA4zaWfHu8n3N+AVIfgaSVy8/CnlDMxp5w4sljUbjxd7SuLuz2AEGOxAD3WhZulLnyaNCuDsvXIRH5Mms6hmukcSst1Vn5jkRHh4un/8j/o+vU+s6rP/VsuD57AXPn71kNBqytbVBmqYsl0vOzs64uLjg8PCQDz74gHt3bnPu8hh997vf5Xe/+z3nZ1MrQ16Kxo76JlK+2ASxmQNIGXkvY29vj9evX4c2dkH7m+Z06xyI+i6+dx1IasCRTTGT5xlgODk95cG7H3ByuiCtYDzZRGnFfHbBKCmZlSWjwZDdnR2yLCfJLMzohphIGsp4Pw98KcsyuBZmWRZiqGql0LoZbyFEqCOOzc6yDCEEN2/eZH9/P9TtXfHetvzRAySfNM4nZfWlNVHcwQcWQCCMo0iFulZ47nsrWLXZc+I6Y8ErHmylVUNVCiFZnaGZhD5QL26bH0ivOYg3X2vCte0tyoLBYBjAhl84NgNxo5H126lJnP/maEhva8Jmvs2LizlmubCGoERSu2SzYAkbjHAsPwg0htoJJRWeIUejpWXxK+uanZ1tEmGvEW4cWmZxx6Tm6cDbwg9cdRDERBpx/zeQq3s42TZffWh1D5n2s2MgF3yKRfyd6VxrtfXG/Wl/N74ZSCHQzhJnh8cx/cimD5q5GSWvxFmHgF6eIwT8/Kc/5fmzZ/RJ3SZRsypmFOXSbfJt7eAlCVlEmno/N/1X4ZLLjELx71cBjXV/Xy4NWF4nnHc/Ww+s/AFMcHtCCLI0ax34OPcQL6heRU/uhQCfMmDdO9g+odXu+Ds/ZlVdkwiJRIS1KLWhLFcUZcrBwQHbO7v8/Oc/496Dh8g0RxqolbJUpk1PXOq3a3s1OuhC+0wHrriuswqPNc+J12PrPtMsNgdm2sU06/CK8b/U78Y4Rkdv7aJZM1FDLmlPRXPoEuYRwY5s3B/2PsGlfjR+rXXb54QumUACWZ4xGA2Zzi4wylCVlWPCtHvwpXxirt3CA0/XbdIlFffJFLvCjn+2Z5+LWkTYa7rdLeL36s7Fhm7drlMT5q0QXFpXVwmmVymMWgJtp01rejvU3xWGu9fEz173+7r7um32pevqdZ1SJm7juvquuvdNdbqr3APCTfEf7bUi/NkQt0eEs2Lde8bt96UoCgaDgRufaH6ZqAnx2ShAJpal9eTkiCxNiOfWpVCE8D6XKm2/ebwfaYMWeu18u+qsEUI4i0NEttKZi7VSbG1t8Y1vfIPNzS3mszkvXr3i+OSE09MzpLBsdYONEUmSk6R9kiQjTSVVVfLs6RNqVbI5mXBz/5ZNYlpVKFVTlQWr1YLFYs7F9NTG0JRVYF2cz+f8/Oc/5/TkmK9+5SvkvR6vXh3w1Q8/5NNPP+Xw4AhiL5mwbzp5qqOgqFWNqASFi5Xa3tlmc3MzKNPt5Y13yJsUG6EfvSwSjWlzbyNzNOPhc0UJsjQly3sIIbl16zbvPn6fk5/+hs29HXq9PtVyRrGckSQl9/b3GPRzNiZDahyRmVLOmqacq58lA1PKM0LnrFYrLi4uKFycpneFU0q58xO359vPPNiVUro4Mnv2ZlkWlFGDwaDlnbN2Dl9T/ugBklY6nF9SCEc5LS1rlNIgGyCijWVrS2VKpSrHQ2BQtSbPk3C4xEQM3WR88aIPBA5aox0Q8EjXH+7KmQt9AFmv12u59cXPartMKDwxg1IVWtdOwBBUyukrnLBo3MZiXIxQqTW5ksidCVtfe5f9jQmzckV+0OfVy5cslwVlbf1V7dYqLNW0ASWgNIZaCmpjSIQhxZAlOVqkFFKi+j3uPryDFDZuJhFWWyBpwJKQSbRw24epiASj7nf+KPbvhFvcJrxvdKXw46I7tTRCta8r/tseIl5o8J+JUB/g3BtN5ObY1OGFRu1dDk0zftpZ6HwzWkIKXqCLm2k/r7Sh1jW9PCdNBcVyxl//+/8nmTQYDZoaxZJlcYahQGnvxhO5xNEu8eei6YzQCWFe086g/TZa2EuARxgCvb3PYYJVPFwWUNeDY7hMYBFfZzdCa1UtiiIoDLyWvxuLGGvq/TO7ZCvNd12hCbf+1gsEvlRakaWZy2VmBRyjKlarBefnKS9fvmZV1nz8h094/6tfs1Zad60XqC8LZLI1Fl1hoduG5vcIzOJmhmgY2LygK4VoYpKgRUlsovhI465d0wENQHHP11EdIgAagusoGNDO0hUBH+lZuxxw8LM5UO+7gGCfpDdmtgwAJeS1adoXHu4xtttHQnJvrREYlFaIJGG0MUG8PkRiKfhrVdp6pX9mXH/8GCvp2rkIAo3waR1olCDXl86+EO018ZhGOSF8b7S/w+5hWmu6rr7rSlf7/ybrSLxtva016qpYwOsA0VVzvWvpidfGdcJ3d61cF5/YsrxcJ+A74CDwdNaEsTFe2PMrr9Wu5rogRIe/W0Trl4BZ9/29uxkijiU2zbx0LZBg5ZNEQAJVvWJ+cW7FaaNA+L1L41OItBQjrXfhEkhqAUwH+rquzNeOLyCVa7cQmKTZ//yZOpps8LVvfpPJ5jYnp+c8efKUg6PXVHVNnvTZHm8gE4MRKb3hTUS2QdLPGfQzElFzcj7j5Ogl59Nz7rzzVcaTm9RliVErDl8/4/TVc4aDhEE+QisQJiUREq3gxt5NDIYvnj5jOpvzrW99izv37/H06VMePX4HI23eTykkaZJQ18rSfRs74MLHcEsC6FK6ZrVaYrSh3x8wHIyYzxZUpc3BhEzRurzUV+v6z5ckTbCxRH6eeBr2Zi+Jp7zWCpBIaRDpiM2tG9y6vc8//+f/nMOLFaO9mww29unJjOPnnyFFyWjUJ01T7t67Sa1nrAqF1k3cridb8JakLMvoyR7HJ2fO5TAhTXMWiwUgKYqC0WhEVS/J85xer4cQJpBreKI0Ywyr1aoViuItUHGi3aqqArnG25Q/eoAElzdBX7wg5DfF2PoCTZB80HgZExJkxdcG0LPmWVdtYp4RSSJC4iywLB7QBkRtcgbfboOUaRC2rQ/tZdNhe+OBWltApkvDeGPCi08/YzIYYfKMRVmhRAJJijYKVTuBTkqqJKPGMC9LCq2phaQGUilJBeRJQp73GW/v8M/+6p/x8viED/bvkJjLNM9rtXRXjN21h2ykUQoaucg1qRX03b4xPPFanV+njd1A6qvvswddS2a6YtPqUniuO9Ab4R+yJEEA/+Un/4Xj4yPyNKWuLUBeLufUdek2NitIX/WCXYtRt8RA4e00pFcJ5e6dJAjR5L9qAFISAYH1h2WzRq3lxmu8fFmnmPCPj+MBfYnB0TphSykV4g9jYTvMmY5wdZVw12gEVYt8xT+jWBUcHR2S9Qb87d/+iMcffIVESipVW+XNGyxFV5V43JpxNlfWFuEDX4FdNyKIPaHeVj9esa+u7ZM1z1zbluhebyW68kbRCGqi8+XbCt6xVC9l22JvhRdLbrGzs8NnfEJVVjYOqapo2BAvv1Gjb7isPFt3JjhUcW3frC8melj35ug7on2pA3iuOrPWlauAi3AP6O6NbwLy/pqu50W3ni4IehuFwJvWZrcNfk/oKnfi53Xru+677nXX/R2tskvtba5ta/i79cTnk4+L9soJH5j/JswqpAj0yV7ZYNCtcypuj29zt1+74621tsH48vJ13ff1zwh1uG4JOMxfJyyxTa/f4xvf+Cabm1scHh7z+edfcHx8AolhuDFi3JtQrJa8fvmEWht2b5Z843v/HNkbgVGoasVXvvldDl/vo+uKfLBBqRRJmpHlGTdu3eXo+BWzxRmJ7LMx2rRKY22V2tPplDt37rC1vcWTJ0/4u7/7O77zne9w584dXr58yTvvvENd1xwdHVHj5Lk4xlBIlFN0eGFeKWUpr7VlFByPx4zHY8qypCiKlrx63XztFm2aEJA45Ywfo7gYY+fU1tYW9x6+x/e+/33ee/9ddm/e5Pnf/5a9G3uYZIBcVbx49gWJMNy/d5fz4xO++93voVRNXWuSJAvty/M8yDP+52w2A6DX64XzemtrK7zLarViOBySJAnL5TJYjOLSTZfiPUC8q14c6+RZA9+m/JMASN2JZKLP43if+NC4RLPcmYT+ZwyMWsGUtDf+uO54MOuqcffpPufyhkT4TnY2msV8wXA4CiwzcQkLRgiyRKKVwtQVea/P2XTGr86nDIcjxOYGwzxHLJYM0wwpJHnepzcakg779Dc2SHo9TJ4h+z2y4YDxxoTBYEA/7dHP+owmE3SacnwxQyapO6PXB0OvK28+SNr9ENdr7A1r6uxovFpfXg+S1rWheyBeJ1D469YJRV1hKbYQdp+plaaXWwrM5WLBj3/0I6uNqmoEkrouWSxmVgtlDIlMbBLhIMBd387YQtQV6q4iLXhTaR2SwgvcHpQIIM7K3QCWqqqC5ce2zbjv0/C9MZCmCcbR9QvRsAH5rOZdq2s8Xn7trxtLrXUASM29EM+U9j5wNWC2gE7RBdcWhJUsZjNypZlezPjis8945/H7JEJSmxr0+vxRb1PWARQvnKwrEU6w7sjYNRPD6Eug9wph900C7HVtXSfUvk3x93X3525b3rQGukW6+TCZTEK7PIBet1d3n+1/2jGvL60vX/yufd2e8qZ9qH3d9cK0v/+q/viyY9A9r9YBsHXv1rXUruvHde256rr473XvF5/b6+Zdd6++SgHQBWzdEj65Ysyuepd173ZVP1w3RmVZ0uv1HPnJ5XndANvuJIHFcoGKc/GZy+dDvN7W9Xe33TaG1ipqYitSt0+uej+DO0Nwa8Upy9Is4Wtf+zo7OzscH5/w8cefcHp6xmAwJB/0yYZjBv0JRl5QmGcYqTm7OKWmdqyiC6piiakrBpM9hDEoJKouMcpgtMKoitsP3uH89ICNwZA8yUiyjKoqKIolR0cnCCF58Og+7777Lp9//jm/+MUv+N73vse9e/d48uQJH3zwAVVVMZvNLo83l+ejtfKCko3VZTweB6tITAL2ZVzG4tFetx90AfdgMEBKyXw+4+Wrl9x5cJdssaA36DOUUFaKs7NDjg+e8/DeDlubG+xtbnJzf59Xrw+YTpdMJlsMBgPGLgdSfAYKIYJLnJ9P3oMqSRL6/b6lBHdz2ecyOj095ezsjPF4HGT4OCbJGxx2dnZYLBYhX9J18e7ryh89QIo7zv8NkOZZ0LS08iDpJjDYC1LgEapG6Tpwr68DRf4ZfvJ6E5+/xlN6+7/TNCWJhLX43njT9htKvJC8Sx3AyekJJydn3LlzN7Tj0kYrJT766bNPP2V/7wYffutbjAYD8iTB/OmfUJUVWZYzGo1JkhQpU2SS2FxHCGpASYnIMnQiLS1xkpLLFKFBaYPWMNnawmCzcMdteFO57pDsvtN1n7XqXHdNZ1O+6iB8m/qvurZ7eNrDxm/xtqwDxc34usDwWiHA+gGnKT/5m59xenxCniYICaquKasliMbCaTqa4nUlPvTWvc86Ld91fRIrFrqW2fZzDVImpEmGEDL4IVvwZE36lhrbi+k2z4LWjhUOCc4FTet17WkUCOusfusO5K6A5PeNhjzFuge+qXTnkq/f0436Z2itqauSqiysVk8k/PKXP+edd9+1bmu60bque0b3PZq/rxB4uZqkQQhrJTE4ec4LusI/J66/Lfiv+7zdnsu/X3VvV+Ba187u329qy7o1sO6ZV7XfCAHGsDG2yRBViE2tLz37qn3EF1U3KScuKd2+PAZ+w/MMxlwGSb50g7uvWutdkBeXqwTbdWBoLShcAwS66/EqJUe37kvCJZfH5aq5Fp+5XRDQLevm1/q2wdsO6lXgcd3zrmtPfI1nsFNKOfKlK9pPM/f8d7OLGUVZkiaO3U3Y/S8+t64D6933CbKX0Ugus+mt6+uWoB4H1XsLoxAgDPcfPODOnbucnZ7zh48+5vT0nMFgaHOX9Sf0xvuMx7vcujdgvLPP0dELRqMtzi+WLNSCyXjEzvY+g16PLJGkUmJQgEbXBlXX1NWK8/MjxhtDitkMCWwOB9RVyWw6ZbGYc3pyhkHz/gfv8/jxYz7++GN+8Ytf8P3vf5/bt2/z5MkT3n//ff7+7/8eVdeW/EU0yrVuSAWut70ypqoqxuMxk8mE1WrFYrlw3XGZer6738VnjroCTF2lTCjLkvl8jhYnfPbpZww3xnzvT/+U0XBEWS8oF0t+/5ufISj58CuPGfVybt+8y9bWNpVeMJlkjmHZekx5oOPPNi97e5na//P5kPx+6ddor9fDGMPW1hZZlnF8fBzY7PI8ZzabBQXnjRs3yLKMnZ2dVrqf/waQohJrgWO3uNh9zpcgHEUuOSF2RGvquiLNktb18eSON4V1GipfZ0zpLZxAYowJh27MnBcfXM2mjtOSe1lGMByOmF3MrGk9aSeiDQeG1qS9DKOshunGjZvcuXOX5WqFRlPrCqWsAGCEQIsEZaCJecBFAdgYolRItLAxStppLGVqE7clxjKfBBeYKzb+q/5eV67bULtFAG3T0GVN2XXPXic8dDU1XaGie/CuA7pWFnoLjZlp2NaUVmRJQpak6Frxkx//mDRJEMZalmplg0gNlmGrqcvFX0SCgK+3K7C8SbPyNt/FdXSBkZ3TAA58KDDBCmqDgY0Boyzdql0W9gCxrk+mUV6EblofF2EVEY0rW7we43/r4pm85sp/36YhJfo9XpfrNehxWfdcYwxKVTbRnoBP/vAHTk9P2JhsWssFV4GG64Sm65QLLb1AGwRHt4to3KycdzXAXPf3mz6/qly3ttcJAa3vubTCv3TpCnkCAoHPwLl4YEzLgnTdXtQVFOvOXAzPw2vGr94L1rUzbmt8XfP926/ndUDzKhDlr1s3/64TdN907VXXrKvzbcB3t56rzuN1Zd0ZfF25dF2DSC616bp+u66/4uviErOw+Wvm8znD4XAt8PV/G2PcxGvv2ati5c4NK6cY/JnR3Nt93rV9QSRndeTztwHqxpgA8gwgEkmtFMPxiEfvvMN8tuSTTz7j5MRajjbGG4zHE/LRNv3NO0w2b1Fpxe6djGy0RbWqGA83ub0zYTzqk0qoiiWrxTmrsqQorbIxlT2bC2k0YLJxn7LaYzY95/TohOViRpblCJlwfnbO5mSD46NjhBR87Wtf45133uHjjz/m7//+7/nud7/LzZs3ef36Ne+//z6//c1vrGTk3lEZjSRp7Q0NqYoJ7HZ+TPv9PsvloklN0+m7dXOoWa+X9/HWObBmLMqypD49oyqs9Yok5YP3P0SvVnz6218xPX7Fhx884uH925SLJe+/95jt7W1ktoUQCQcHhxwfHzOZTAJ5QtyudW78XWCUJIl1OYzk416vx7179yjLJtfjZGI9mvI8DwBPCNEidPj/eqLY/18qcVZen5QqSRKKqrxE2+0nSkzFGgtONtFUYw2K7+1akq7bQHx9jdAlAkDybkIx8Irr7Gq7pGyTREgpw4a2jjdfOYaszckmz5495+GjdyDLUI7kAampa4WQCVJaVI9M0NIKDJkRGAVC02iD/OR2GmdL1Wsi4HS1Fveqv+PyJm1dGOsO8AhXvMEA9KZ631S6c2XdBhSehUCK9iZ1lbDhx1trzbA3JEtTfvOrX3N8dIw0Bo2dJ0W5sjmPVIUNohWtQ6/L3BLP1fjfuv646l2713etRN5M3mhz7dtbWlUsQDL+MG0rKZrPhLsn1q6JsGb8RufXTFyH7zd/WMcHTxfMdN8nVnjEhA7r+sHfe5UgFX/m12QQvrSiLiuSNMcA89kFv/n1L/kX//K/Q5UGrZXLHn79Nt0Odr720la7GnDU+PkDyLgO8cbl848uX1ar9//JctW+JDodORqNwnyuqoaC9jrg2C0m8o2/9rp/wB70ZUq3nesUP28DDq4DHG/bJ936YmVOvDddu6e+JdD6MoAnFtiuu6fbrn/s2F0FCLvtuuo+LwPM53Pu3r1rrxe0NCPr3sfukXafXCwWbs+ze7TWJlj5/7HlqnP/TWPiny6EjTsSUvDBV75CmuV88eQJr18f0O8P2NzcxGg4Pj7m1miT7b0JlYaL5ZKz8yOyRHP3wQNG/QFaF5y8fsnJ0SvmFyeoconA5npCSLSWpNmAPO+xMRlx8+YeW9vb7OztcXp4xMHLl+R5DykSZtMZG9tjjo+P+eyzz3jvvfe4d+8en332GZ988glf+cpXWCwWpGnKzZs3OTw4wOEVBO353nhhNAq1qqpYLi1RgSUnyIOc96a+a899eYkgozvn4vPOn+M9mZHIhNn5BR//9g9sj3d4+sVTnnz0e+7dvsF3vv11pNCMR0MeP36XJM0wsqYqK4bDIQcHB5ydnTEajVrP9HtPrJDwFiDfhi6o8R5Y3o1uNBoFa1NRFK38pUI0Mf7ei6P+bwCpXWIfR18uaSiiDc5TLsdo1QvcwR1ICMsK4hK2KmXdoBKXsNMY3OeaPM8a5o5A8SpxDwFhtbap425XTsO8zqGncTloDg1LmyiYTDaoqpKs17cLxxASKjp9BInTCu1s7/Cjv/kR9+7fp5dnUGhODw7Z3t0jSTKMsHmR0CaAHEtZLJEShDIIIxHCapgQoDz6NzaGwbK4+XiGtva6bcLp6DojNiYT/m/1rI1Fyn0XoyDheH+8pquh/+EqtGSwLD4mrqx7yHmQHB6Kc1kyTfXGhJ9WMxe/kRf0vVbavZExYKyvsc9DgYja7Z6dCEGeZQgEf/vjH9l5lkhUrWwgZFUG8GvwVMnWUVRVl60abTbE64kGrgJvDVCxc9le7/M3JUHJYIVKFQCSjW9pXODs/U191mrkSRQaUgavRfLXGyOQIfmh18HbQ923y2bclm6JyaChT2Tixi+m5fdKCBO1pVF0dC3CsTDnXZmuE45izZivTxtDrRW51ohEYpTi13//9/zlX/yl3dxNAwLbe1QMFJu//eSUwq+48FGrX3DfejcPEX8YKxaidv/XAjBdgLFO4GwO8MtzxtUSvYDAdNZ4E0ll514zTNEZEALN/bVuAmA7Ls0zCxaNYx5VyqWCaD/btrn99LjR/cHAVdtmPDPi6rnj+6Mr5Mc/u33WfvN2cY+Pro2uj+qQsvk91Clo/4zeLe7/uFti9RCdoE8T9nX/QVNP/E7d+XBVMYDpKlxwyhh3ogrj91m35ptTxbXI2G3ZCDCyWRetudg8L9QZnnO5/42fTuFEM2Eg/LnR9FJ7TYTntQ+8Zg0IHPNrs79rrRmPxw1Aim2sIvrTNHtDIiWJFJRF2RrfdSCx+TV+W//d5fEJoQ2ybT14E6CUUrpUII0UALC3e4ObN/Y5Pzvn2bNnJEnKaLxBVWuePnmKMXBwes745h2UGXJ2NmM4HnD31i6p0ZwcPOPV8ycs5lNAIU1FIrX93U0/I0HoimpVcXBxxtHrF4w2Jjx48JCbN2+yMRjw8vlzitWSxXzOaDygKFe8evWK7a1t9m/uM5/P+eSTT7hx4wY3btzg888/5/79+5ycnKAq+ywvZ4X5FdZho+T2pBsXFxeO/S2lrIpwLsb5hK4r/vo476adY9bJWghL9Z7IBCFlcF27vX+Xu3cfsLmzy3hzi08/+ZTf/urXPLx9h69/6332djeZTY/4zg/+FJkKalWDsWCzKApu3rzBalUwn89aStM8zymKAqUUw+GQPM+pXFx+kvjQEytDWBDUvKNPi7NarSyI6/UYDAZorZlOp+GzNE3p9/uhf6bT6Rv7yZd/EgDJbzJbW1ssl8u1LBZhI5aOktZJ9NoJ/0iBMNLtacIdcPanNgYhEptsVQhEoMhNyNIUgd14WkKncocbJlDqyjRxAM0+p6ptAldouwpazbg/IL37i0ZpePHqGUWl2Nu5wag3RiKpVIlRFWkvRasaISSDYZ/ZxTn/+//d/5Y7+7d4/uQpJ4dH/LN/8z/hL/7Nf8+8qkmM48VTGrTASKiN1WiL1G5ZMhJggwbaWbUEhgRD4Flfe1zTxUrNWUv3wPGHqxsrv3GHe6PrWmMbCwTRY42xmmLTtMzT/LZySLk6W/dibM4o1yYThEv7uZetWnd4GcPVbWILhwfL4RXse1V1xaA/IE0kR69f8/SLz0mkQBuFNopVWVA7xpu6tvMAoRHCCc2mrZVq+qQ9Fl2QdPVm264vntOJTNFGo5VBCfDky74Nvv89+AkCfadtzbO9kHb5O3uQRJowr2MU0e/g1pYdDGM0UljLKBjqukIIFYAYdIOOm41c666A0BZG/XR5mxIszlJijKaoSoZpCkZzdnTIk08/5t77H4R4xdglz7avOz5OsDMGoQ0iSTrKiNAFTvQUCGOFjsR9biJFRxDefNVryjqN+VUMZK1mvG0nXX4i7XkQgyQPanyzRes+Zfz89q6bHUDhrvNgvHmeRAuNTgS90YDesE9dlqBrUDU4JZRwCQw0gPZ7WEvCRAjQ2lpQDdIprnwfxtr5q8bXfmYBduzSLVp7W9wn6+GRfeMGsKtLa68NnpqeNS2UbSACIjYWsFnH2qnkGpprX1dnrXvrVQsoCJuKgwiAGg9820DJdPZpjUC3AJJLtuDOjaAo0ZHLvZs4UsowdbyyD2Ec5HP1aNOeXp1e9lu4bVLnHPLftwByF7S7dBiuq6184PfJBih5sG8VlPZv6XI1GtN4o1ih0sZPt5Rh0rZIu7ZIZciEIAWM80TQphsTG++NnXPZfxiNcdcCuM4i6MfyWkWMEM7lWGCni+D+/UfoWvD0yQuKVcF4sk1/uIlGQHIAqkaIlGJWcLFcsLW5y61bN9DlBU+e/Jajl0/BKc210lRatdolpSJNM5IE0iSh18+p65rF2Rm/PT3lzt27vPPoEQ8ePkRIyenZKUkqSBYzLs7O+OKzz5mMN7hz5w4n0zN+9/vf85d/8Zfs7OxyfHzM7Tt3efLFE6RntBN+TxZuzrmoULcXxOeABTlpYDwWwsaK1VUdXMni/ozPjiSRIUxECIHRFhzZmGDo9zIGgyFp3iPNcpQ2/OVf/jP+8l/8K45Pz3j57Dl/97f/hRdPnrA12eSbH37AzvaQxXzK5tYu481tjs9PEMIy1mVZxmQypiiKEFe0WCyYzxeAIMtyNjc3A/HScrly3lpZME403iRWOe/ZQ/154z1IyrIM+ZC2t7cBAvjyIDNJEqr/xmLXFD+5tra2gk/uxcVFmDCxG47WOvCq+3vXkTDEJWjjO+e+vzc2D7Zpg92Gbi4TSfh7k+iZuqWt9JsKQbMABKaTT375a37/m4/IRM7DB4+49+AuYKiqGqQNRBRK89577/F//usf8uuf/70T2iV//cMf8dXv/Rmi16fGII0BozFCkKYJSZoijQ+ml0gBiWiY+VqB/8Iuds/tv85y8abSkTPsZ75PrtDqde974zOi32OrUFyJx8zxXSIGTW301CrrDoN4A4sF4Phzv7DTNEEKwW9+/Su70SQC7RixiqJwm0Pl41fxuYUsiGhTv1/X52+yGjUHXJv9TQgbZGnzGwnnSqZbIKoLgroueVeVtxGou+QlLatvJPB6zZxdk86iLHx/mRYIWrc/vF25/rqw7ltxSMq6jWDdB375y1/x6MOvUVeNy21XsFivzRWBuvVym801AjOt601nsl8Hpq/qly9jcWqD0svAy34efrvi8zc+hQCF3rAGWtp8N196vR5ZlrIqVlRVHQ5eb6HoPqNbn3VTUpfoafF3NP+L6uq2nwgcxde0AaFoNePyuwpwFg0LcnRnD3UyaARHPLyJlRyN+6z9RkZCP+73+B2j+Rp/IUQQ8EFYdjIakBFV0OodP0W7+68BjNDdLThUovEESR7EgVfg6Uix0ljWROgfiBRnofmi0y4Tfl5ag/5a95FwiZF1lLvLW9AC7qPJIRaE6bg+V2HiBEfvauSFUaUUSSpZNw/A5zzzlP32My9k03mXbrlyGXX2Dm/RsmvFDmQD9teDpuvKeGOD3d1dzs+nvHx9QK8/Ih9t0p/ssLV7g73bj5hNzxiNN1jVhs3NTW7dvkm5uuDj3/2C8+OnCF2jKmuVsGt70ArH8HTaxphggej1evSkzc3z8sULlosF7777Lg8ePEBKwfHZCb3egHpQsVzMePnyJfce3rckDZ894dmzZ9y5c4fpdMr+/j6vXr2yynqnwEE0e9NlYiPbVx4E9Ho9VsWSNE3Z3t7mX//rf82//bf/NnhKtRVq7XPXM8HZeNs0knMNlVIsz85R2ipEbt26w72HD1ksF/z217/iZ3/3d9TFincf3+Mr77/P7vYGmhJj4PbtO5ydnbfk5izL2N7eZjwes7FhyRpGozFKvWI2WyClChYg734ezxcvBwEsFgtWqxVCGJcPSbiwlzS84/n5OcPhkKqqrEwirIudl5OSJKH28/styj8JgJTnefB9TNO05YPZHYzY9xGaAy7+F3/vwY3X3vgBjgW2+KePaWjurWncO2zxoEfKpAWM4gnv2etiYGVjrDL292/zh9M/8PzZE/72R/+Fnb0d/vv/6b9h68Y2MkvJ04xcSO4/fMD+/j7PvnhCVSlIU37wZ3/BcDRhVTkaSayLnTaGolJIZVAasjQjzxOr7RBXg8iuleFNY/VlSpcc46r63iSsrROA32bDjufGpTqjE32ddWSdoNy91s8nIW2271/84mdkaWKpRzFUVYkQRHEyhiTJ7Hj4zdZcZrmJ29F9n+ve1R92IFrtb+pvE5bEwOjLAKKugHpVu2MA0wVIfl10AYYVPkzrHtu29QQc8TOva9fbvlu8XtsEMDVpYl1xP/v0Uy4uZvSGw5ay5ss8900KiDZ0evO6u0oR8Tb3vXWbrtknvL7lTc/4MnW+TRFCIJFkWUaW5awQwW1EK/XGnms00k3Sw5b1A64FM64RkUzeWE2sW4xo3d9qTwS6Yi2+cft5UAi4zxCidY/9zaItD5D8ekuShNiCFMDTFe/fUr6sfcWOh0Wk2CC0oAFoHjzEz/DPv2pMrKO5Dn1AlLC7cyGN14PtZ6P9c7rxiH5vsN3X7EPt9ttbXF+675sk2THoAXTjWiSEsPGm2lrmMVZGqas6WJHquqKqK8AKxz4RppdD7LbXVRg17bc4uUmoXVV1/GZ4V+S33cNbtUfjL6UMqLsty1ytkFtX7ty5A8CzZ8/QBvLRJvl4i8HWDfqTPbb3h+xrxatXr0lqw917d1jOp3z8219ydvQc6qUDColNwjpswJE9c2WIV69r6+7lPY/6g0Ggnj47O+Ojjz7igw8+4PbtOyxWK5aLOf1en2q14tWr19y4tc/uzh4HL17z2Wef2XxJW1scHh5y584dvvjii1ay7W5fxLJJCzwJazmpqoqTkxM++uijACTite5/T9OUyWTCZDJBCMFsNnP04XU0XwVpmjEYjtiYTNiYbDEcb/D7jz7ixz/+MUeHr9maDPjqh9+mnyfcv3OLW7f2ef7iJQ8efI3JZOIAV5TjSUrAurkrZcgy60K3v3+bLDvl/Oyc2cWCVbFCCsnGxjj0r1e8yiShl+eMhpKNjQ3SVEbWJeHc8irquma5XHJxcYFSil6vx3A4DFTlxlhjSeFil96m/NEDpDRNKYqC58+fMx6PLUFDWYZFGgMlD2C6Gm8PnPzvvrQFr2YDiuuKk7wGE59Dt/6wiAFT/MxEtrXBMZpOkoYW0QtRWmvyvMfO9g693oB+b0BZVnzyyae8//l77NcFG1sTNkZjEAlZnvPt736Hg9ev2d7d5c/++b/ir/7Vv8AA/V7PnZVWw6VpFq7WhrKqEFIiU5uzZZ0g1N3yusDzKhByWXvs9ZiEQ/lNQlF3k1lX/5uEp641wf9ujHH9cVljaL9ra0zjurpuSF3BIP7djmeOFILnz59xdHxoD3lj4x/KssAfYFmW0iAyJ+tE7VvHtNh996v6YZ17Xgx8YguoFGlrDvtrvoyA331W97vuvAdah0OX2ce/e+N37dtE0N4a0wCWGHhB+/Or2hu/X3ctxH93rYX2HSxAyjNLYTpfLPjii8/56te/canut+2/q68Tl38ztHIkXXXvlwVpb2pjt96rAGgz5uvvv650lUtvuj+sy3gpCJvEcDwecXZ0iFLKHciBaeQSeuu+k5+XV7mTtolKIBa87f8aDX8Tp2PncvNO4bbwzK4HhHFt1QbrMheDDN3EQERv4qweLrYIe58yphXr1mC02FLku6/5zAuecYZ73zdhPXcE5xapjK9bNnPCAkUbvC8T2Zonrb5QmiRtBKuATx2Fv/1MO4sRljXT+DjKWPlj39gqoITrAgekjMGYdW5kNh1D7VhGPe2xf+/VakVRFNZiVFnPgOVyGaxA8/mcNEmQNIku89yC9jzPkYkgy2zyz+fPn3P79u0gi9i+avbHuG99PKjFybb9RVFE11mPAUtOFe1xa6ylzRi13evivTQRdg/ugi0/Jl3CEP+dFeENSZJy88YNiqLg5OQkxJ2MRiNG4w0Gow3SPOf0+IT5suS9d99F6JovPvkNZ0dPMfWS0aCH1obFYkVZFtR1wWAwCPOpKEoXv+pz7tl+LMsSpW1ONJ9Tx8cYffWrX+Xe/Xt8/umnXMznTKcXDEYDDg+PuP/oAXs3bvD82TNevXrFjRs3ODo6Ynd3l6dPn9qQ66tknjU/kyQhyzKUqplOp2it+elPfxrmUvds98Dg9PSUk5NTdz7a8Izt7U2yPEMIyPMevWxA1ushJcxm5yxXC36na3Z3NvnK+w+5efMG4/GI8aBHUS44PH7Nw4cPuXFjP8ypujbBKmTPupLp9MLJJLaPpRSsViVJYuWF0XDk1oXi4mIWzsjlcmVl9jwnTVN6/RytDWnajqXy+8pwOMQYw2Kx4OjoiNevXweAmOc5g8GA07OzS3PsqvJHD5A8zZ+UkqOjI2t2k3bRt7XflwVU/3OdMBuXLjBaxxjmBSPPpLH24HIlz/OQDKzbNn+vFwJjDbMQgvliQZL2GY02gAO3sCvKokZiXet0VVNJjTI1f/rP/oJ7jx5w6/Zttm/sAxlC9FCRv75BBXc2/37GGIyuUUo4LcTlhSmkuFLt+zaa5Mtf+A5v9338/uvKus2nO07xtesEwe488SCp+71xwswbPK1C/V0Nmv8Zk4okacrvfvcbqqq05AyqtpZHo1G66rT1ep32OuGs+3t8XQwwmne87PLlr/XjECsG4vquEoKval88VuvA0nVA++rnNADJqPZciMFfvFbXAcv42VfNvbeZY15AtHEDGXVV8dFHH/HhN74ZXA/eFhytf991FwGmrcR4033XAT//fRfwX9e+t2mnHxsb17i+Pdf1/3Xv8BZX23Y6ITLPrWBVOVakqqrs3HCXWo3+1UAujg9YNyfjDW5dM2MwZP9uz684Xs8KI233cf9MpXUAR15BFwPCRgN8ef8W0dYWB0ybLn9zfE9kUPHzHAj0vFYjbsGJiACX5x7xyaX9niudIE8EmBDOPc+I8CxvYfHvIkRCw5YgLCCUgsS59WEMxjTkSMYo6qpCUCNlwqooLHuhVmilgxuWf6+LiwtqVVvna6NZLJaAZ5rVbj+xOVu8p4l3l7LWFcFwNKKX9UhkEgRhsEmxhRAkwrJbYmwwfbMH2niyNLUuYwcHB1cqxeIzxyBsHJ1pLEXL5bJ9Hhk/HxtZw3q9xPMi/qP9+VV75HWKpe51CEvwM5lMGA6HPH3+nFrVbGyMGOUJ417KxiBn1M9ZFiWz6Rl7O9uMehnPPvsdJ6+fIPSSwTCnPxhxenLKcrUEoK7LwBAnE+kUEJZW2id29daIqq65uLhge3s7KLnPz8958uQJ7773mL3dG8zOpqyKkv5wyOHhETfv3GF7e5uXL17w9OlTbt++zcbGBtPplM3NTc5OTlt9s06xGMsXPglwnueMx2POzs4uyYvdMWnON2vF08aCjDRL2dqakKaCQd5n2B+S9XrkvT7D0Zid3RvcunWLQT/BqIqtrTFSJpyfW2B2+/Y9er0B52cXri/rsNf4Z/b7fYy2MfXaWFdyKROkSEP8r2XsswvepwRZLJYURcF4PMIYWC4LzqfngGIw6DMcDoPRozuHBoMBd+/eDX0Xe259mRPgjx4gQWMd8kJbIpJLWipoC4Pdxet/dhd5mJDGhI2hYcZqC1dxTEZTj3BBlI2/pacp1O7A8JMtBkVSpi1A5idJXdUkaIaDIVneQ4jE0RxWoA3DvM8w77FYLBBSoDB8+0+/7w4oMCZB14pMZC5dmgEjSJ2i1GqOnKZMW2vGkydfOD/cjglefLnJ+GVLd/O4/N16ALAWMF3hEhfX//aC+BpgaJqv3lRCED/Wh1crxe9+97uW+4YxGpkIdHGFYNJ5l3Wgb33b25/Fh1tjXTEti03znRNAOvWtFQhF1EjW9/dVAng8HnFbu6DlqmKv8ZrmJKi62+/YpkLvApp/SInbHluStHa5n4zTVgNPnjxlsVgEX/G4jv8apWsli0fmumd2R/DKXhfta2M2vzfVGapYI0RcNc5vquMqhYD/3YILt4yFdUnb3d3hI62pXV6SoiwaBZW3KOuu7bgp69JHhPZ6lMX6/az9VuvbDaLzHlf0k4jAmGjHcrbb1gZqEfa43LJr1puMrV+2Vjv3tXFJQBuiolZEl3d78gK8+zwQJ3ldlIuj8i7p/j200pRV5XLD2bN1tbJuRUppyqLgYjZzc9C4GIcixDTYGBTLQmtlB3uWe2avJLFpP4bDIb1ej8lkQppmjhxBRu78iQOsbUZG6WOQQqyyde9CtZU1XlEDBonCk3xIaUGht+RpRzyxt7fHy5cv18owzRSIgCPgmWMNsFwuA+gCnDumDvPpv0ZZp5gMshJN321ubgFwdHiEQDAc9FCrKb/5xXM2nj7n29//SzQSVa64eecO5XzKq2efQr1gPMpJM6v8LZUlORICyqqmrApkssEgH9LvDUmSLIAQ77KV5zn9wYDZbMbFxQUbGxvkeY7WmoODA7Z2dtjd3eXs+ISjgwMwguVixfnZOTf3dtjc3OTk5IT5fM7W1hbn51Nu3rzJydHxGla59lr2Qr4QgtVqRX9gYxm7ir3rQKYQzuVcGMpyhVKaqiz45je/zmQyYpjlTEZjRuMN+oMBQqYYIej3BxhdUlaG89NjjJFMNnd49PBd8l6f1bIK4MaSvsjIWk6whFomOcI1jdXWhM9jucKy9eWMRsNQd5r2SVKrJAjhLc5SFfdhLEPFTLAAuVM6vE35owdIUlpGOOV8S43TAMqw4TlsIzwQsQJo1y0ogBunHfLJFEPQpGm7z8TamzRNLRhRjlHLEEznNijVCyXCtcXGedS1E1qce5tyZn6ZpGjVBkyJ80cFiS4LRqMBw1GfJLMqv6qqMAp6WQ9Va4qysodKUnF+MePWzX0EgtWqgjyhVpqQms0YUD6jtvdbt/okrQy3bt1qCTotYfKasQnaKbr6Uzq/2dOwAT3e3aM5yHEa0+Yk7goZ62sN1zutWEsD4773Bn6tTdA0ItzB5K8XXgtpmZS87tnXG+oz7U3PUqJHWndvcTSGLE3JZMLZ8QkHr17Zt9YKjEY5piGfGLZdRNRrkSQR90YEoLoCWXyIN4eVjIIeHdWxhjSxdPB2nsrQn7Fm27g5JATWeksEbsG+U/RsYyyzkDYay46Fe4bXevuxcvPGs+QJ0X3N1rtGnyCEA30u+aeRYFmnBKpW1Mq63dgktdquedEG5W1hYf1cs5dctqr4A8/uFVZY0rqmVhUguDg54vT1K+7cv+fyXWE16VZfcf264orvTQPcPRmXnx3aaRVlR8h2UpEdQ9GayRih22vWDmzzfCdQN+2xdyvjnh993zxORHU0Y+qJA8KbdYBG2HMu9YV/Zzd3TGzxwa09t/eGQHKHmYXAIEEYtFLs7NzAYN1AlosVi8WSWilyY+M0HZq07+Tb5cg3EIL+aIgy1goSzoK4n0ObvYa+vVesFXZF+90FMggrHRHYnXX27NJR/S0LqbTrubm3Dd5sPdr1ZfNceannm33HaPtMfy7WqnbWIXtnWEtuovjAda3rQIihHZFJURSsVitqo1Fas1gsqCqbw6+uFavVgjSVlj1WSuqqIklT+v0eqcwwWtDLe+S9HkmSsDnZYjAcWgKiKE+iBzWJTEhTiTb+LAal2+72RkOckqA5h4ybgSIAOeH7JSh0PDscZKl145IYkjQJe0+a+f3csvtBkxi22ek9vb9VXEoH8v0cJBD0uf3TWz6dsk3VCkyK0ZrZYubmsMagMI6uOU1j1tBrSrQBXRLYY6C8RphfC5K8G7cx7O7tUpQls9mMLE1JheDw1QvOpgsOj06ZbG5z++4Dtjc2GA9yPn3yEbPZOXneYzzeYLEqmM3mof+8XJWmGYYEZMLW3g1u7t9mZ3cXjGE2n/PF559zdnZGJhM2NmwMT5omjEYjelnGrKh48fw5H37tQ/b2bzJbLFB1xWxxwcnhMTd299ja3uPo+JzXB8e88847JGnOaDwhSdM2+QaNzNmNQa1ry1S3XCzp9fsIYSm5lVSkSYJRKrAwX94v3LmqXQwhNXmasbe1xb17d5BG089ysjyj1x8yGA5YLFecnh6zKlYIKch7A+7cusOt2/coC2Up4RGBLdHLyXluwWXMGF2WysV+5SSJpKpq2ueptGvNydE2Z5yIlAyZnYOJYrVSrffTWoc4pNhAESuI/PXrvDquKn/0AMlrmzw7DgLH9AQmaLsl2u1p/jCI44e89UZKSeJMlN5f24MrrxWLEW3QZomIOhy/KDW1UiSBZcweENrYDS2TiQMgbRe9sqrD4qmcpSnLcytouwB6lGY86jMc9THC0T6DpTjOeiRS0KsVi8WCYlXx+sVrbm7fJJMZgyyhrGuU8Juo9vjD9VVbI+ndBPwkjbXCbfraNcUBpLhu+9MdLYZwiNjL/e/u4Omc4t7iZq+IARStjbkDFaKfbbeB+HM7d0yQ0URzvDR9IQRG2ESb2gm+nvXI1+vzVRhiF0bbwdKAcv2gtKKXZmQy4fOPPkaXFVmaWD/7aENo+sPV2z2PAtDpHkgmzN3u9W2XOv+zjqxEEp9EWAiba8i7a4X3jLWXCIS0oKTx8W8Aoc0B5RUQzv9faMt2JYwDKu5zo4MmN4ykf0eci40QGJOE79ubpCYk0w2zwYHdqI+sd6hAkCCFRJkq3B9bq9pzaF1pgFG8L/h9xbZPARJtaoyxmjBZFTz9+CMePbyPMVjAZqyrmY7W1WWLloMckYDhn28DzGP3Xve9kCDsXlcTi9ZOKAsgo/u2wu4v/qm6UWJ4Mo91PWOMwDhlk6USbr6TiCiPXLstAdnQYbD0Ymekfb30PK8J8wBQOOtIpNgwjRRp9z1t40mUEZhas7u3j0xSlNKsllbQqmpNpQz+WGjmvQgAEez+vbG5xfT8nNorWrqgyLTduV2mvMsHundtEzh9S7MWpGdjlB4sGWvVEo4FzeXsstpoP/445ZtdC1pbNzYhmripjrOaW8MOMCiFri1REW5e10o5V2BDVVfMZjNw1y6WS7t3Iahrw2w2R0rp3MlsDEye5+RpFtzTLNNYRpKmJElOmuekWcbujcZak8iENJOkqQ7r1wMTjLFAxjQeIrEAemmO4hVX9i8JGFXb/V3glCkakJCIoECxfejpRD0IvnyueIsMNPTdxkCeW1eyJAEhdKt9YQq70TZaW3nBrzlHi75cXFCVS1Rl3f+EBKNVAOpCNMoAIcCoClXV5OmQxWrJdHpm9yejMMa7cysnIzXArlsa63MzW/xnIWZ1bXbHTt93BXshEIkkFZLRaMRysaBYFQyHI5I0R6Z9DEvSJGE06LFazNm/c5/Vas7R8UtqpdjY2CTNhqjZClWVDHo9hgMntyBIs4z+YMSHX/86jz/4KsgksNj1t3a49/h9zs7O+O3Pfs5iekZZFqyWC/p5Ripz8iRnenbG+XTKZGeX3uExxXJOXhXMp1Oq2rCxuUuSPuPw+JxH76b0hxNWywXD4ZC5c+XrekJcPmvsGlquVqR5LyiA/NmJ0XYPjfqvpQgRCqMhlZI0yRn1+ozyPjlW3ixrxWJVwPmsiQsUgsFowqPH73Hnzl2MMZydnbFYLJlMNlguVkhpXTs9i2pVldYVFEs2liSNFdVacH0CWB2us3mPfA5FmzvREuIsybKM168PuHFjj8nmiO3t7XCGrlarcL76ueb7KZZTuyE1b1P++AFSJ9bHlkZoiBFsvPC9wB93pgUp9gJPyxtP4Bjt+8klpbTWG3cAejNgHJQaCzH+QDo/P2cxX7C3uxsWipSS1WoViB78s71vuw361AzyIXWt2NradP6bhtWqAGEn63A0ZFVUjEZJMFWenp9z5+ZtRxctocYeKpGQ2+1T13i8GPCPMb/HE1gk7U00Bp0xEPN9Ftfhy2UNP63vuuVN1wqPjMLnrF1s/jIpGm1b65q1AAYnnFsho3IuIUmSoJXmoz98ZLVDTjBWznxc11fFNHz5cei6Nfjf43icLhGDEKJFXR+3I14Pvnizd7yZaa3BGFwYQquvLO5pqKsvb3ARSKIRFIwRGN18niRW69oIK22AH/ddN6bP/27/jtsWa6XePgYmJmjwdft+icklMIYvPv+cf+HbqJ2gm4jWWnz7cnm9XL5EtBiVfNFX3mM687ntOnz1Y/xY+L/b4Mt/b0xQTVx+8jXvEX/XKFkuv393/Xatx6FVTqmxs7tDnvdQdUVZlUynU1bLFcPxRkQa0N4z43He2trh+fNXVLU9B6yQezmeI35srOm+FIdnBFp5d1GrZNHKgJBILZ2VTgagoJVPnmxnkNYO1DjAVJY1AmXdz8qKura5Seq6CqkxtLbCz3w+D+dZXSvqsiJLMrI8s0K8MeS9nH5/QJJaUD8YDMn6PXbHG2RZ7lzQhIuvse1MUyuSSCFCXFQQHP2YCIHprKOGSAGkUM0gegAhQKYCE1x4IEnBGHnpnLffe1uPRyXexcwrIELjwoPsb03yamPqMH999WG844e5PciCFgvMNc24xxqELhW59sqiluxwxKCfgrExconwrk9u37IbrTvfBcVqRVlWCODFi+ecn54G90cVKXJ8v/i5+Ta7UCz0hzF5i9K2nlohPc6jY4y2FM95jw+/8wPKqmY4mrC/f5vnL14xGQ84PHjJxXRKlqUMhwOUqpnP5gggkSlpZt3oZJbT6w/4+je+yYNH76AMVLUilZlLvm5YLQpGgxF//hd/wc/+y49RuuT05JjlcsnGOCdJU9RyyevXr/nqhzesO11l3TXLVUlZFIwnm/T6fc7OzqiqmsFgwMX0nMnGhPlstvYMWfeZMQZVq0BdbYzLgYXPCeT3Tn894Q8hDIm0+YSkFPTynPl8wdnZud3/k5SqqtFa0e8PuHPnPg8fPuT2vfukeR7O7t3d3SCTGQ3Hxye8fPnSWX2s8sSy7EoGgyFK1S2Z2ssWPo7Ln9v2pyWRSBLp3O7s/Lt1a5/J5gZ5noQcgdPp1Cr6iyKw9HnmWmNMiBUty9Iy4knJ0fHx201C/ikApAiINIvVaX5oDsnG+tGYpqGNvoUQKP+dsSpO5TQzaZZTV3UDGIyzYdTWjc77G3vSiMZ/0h8OjbBkjKVEnM1njDfGVpg0sCxWLkbBUFR2wDHCajqxW7TGsCpLMILt3V1u3r7F7z/+mKIswz7b6/VJkjS8X54nzGZzVlulda9wWmyDAbUGBMSaCQPKbdqelSguXeH9KsHmktZozfP8OMTj1tWStMf5suB6FTj6MlqFbl3r3tELN1cJZ20BMdY+E6yXWZZRq5qXL15GAoCkKFZtt5h/BDBt2nC5Dr8BrvNtjgFr/G7dvgxCjLFsbf67WPCTa+7xdfmkw3Eb1/dndK/xsRP2uuCqGr1jV+iMAU8MDH2dXnizWvF18UnNXLiqP+O64uf5vSAeU601h0dHzOdz8tHQufdYweYfOt5XrQkPRuL2de+7plZ3n62nAUn+nsv7R6MxX7+3tMHn1e8CDcBOxHr/ey9fXjlXrvzb4JOT2rUJo9GImzdv8PL5c+q65nw6ZTafMdneJk+b/TQe13g+ySTjwYNH/Pa3H/GVr3yFNE2Crt27/jXj4OdnA8CFSELbrHBs3YNAhCBtK5RYK9B8vgzXrpwQ7M+holg6QN4wtGZZ5uJt0sAQ1e/3HUXwZrDi+Pf0Lml+vH1uPC9/++sai6cTgLSxwfAA1DY5u3dPdwDC+E3R96lL5Cmks7D4xMauWilBGWUtnpH+xINDu0Q1QihrVcLW6fP/GGL37OZ+TOzhYP9pXfsv7TjpaDdvAVh/XTO/2sDLfa4jJYRxM0K5ZLXGRHUajI4oit05Y5pJjlKGF8+/YG/vBqvVhYthMoBLCCwEWikLMrVGa5hOL9AaiuWSv/vJTyiLFWmaUVWaulYtZe+6mMHW+znL6aXP3Xheb/2m9V1ImSKss79PPGotBu7vXp+Nm7fZ272BFJL5xZQ8T8kSwXx6RlEsGQ8HCKM5Pz9DVZW9L01JM5f/KMt59PBdHj18F4SL+RISjXbx6mCkCW6e3/nud/nxj/4z5+enFGXJUGvSLCWpEs7OrHVpc3OTo8PXpElKYayb3ub2DqPRiIODA2azGb1ez1qWJxNevnxxZb/6/midF45Qw8oJOZUpSLOM8XjsGBINtTur/B7iHQsEhkQK9vZ2+Oa3v8X27g7D4ZA06zMabjAej5lsTiyznIB+r898vkC5eD2lFMfHxxhjySt2d3eZTMbc3P8W8/mco6NDyrJkMBySpTm9Xo/T0zOMMSyXC5RSDAYDF2Zi10ie92y+RykcWZFEJglJKtgYbzB0Z6B9/zrsTzdu3MAYS20/m82YzWaMx2OAQKLT6/VcPJOND/xviWI7xR98DbWo1Qj54Me2O11bkx0LMDJJUFG8hHTAREpJrRWkNq6oLMqwwJVSpEJSl3aDUkoFhGtNgGC0Iu/lKGU3pCyzwtjO3h610UirAKM2msS4wyKxQEmr2m12Vgup3GdJkpJIwf0HD3j0zjts7myzsbmJVpYifHdnz1oFjCZLbXLci8WCzc0JqqpJk8RtpBYExgRKXctIzFjWZl5p7rkKhKwTxmLGvPiatnXhagG0K1x9WQC0TpC+Spi76h28UHZJ+HYCUQwjPaDy8W3QWDam02nQ3gIuqahqtWm90PvlS2zd8HMVCJuRdq5wUjTgKF4n3cPTGGdJNRr7lW7NGf9M9BXCsO83J+TF864LeOJ22zgn4Wh6Gxewxu0jpvptPzt2jfV94IGajRVIWwyDl5vcnWuNK2PXAuD7O1aYeC1wKlJmsxnHx8fcGY9su5Trv3/AWHfvaM+XdS6YX75W/57N3+vHte021KzxSxaSTj3XgfGur367XJ6XV9Udv5sQIoB3ZQypENy+fYfnT564gH8bgL1/+zZB+F+zV4Q5n6Zs7uygheB3f/iIGzf2LC2tNuDiCYuiDL78WmlWThni99iyKKNuF+CICbxrigXyVunmaW39WrFxNjZPiRAElxaI/fUJfduMV9vCFWT5yBLiXZqD63G0hpLuGst8bp7wBKSwQjypW6c28ZCT6xwhgRDB2mZvN9blTVpAmLj4xgBBjHXZDPcIg9FRElR7eFsCCK+woa1g00q59tj2ejdhIfy8SRxLn3WbRzTO1558Ke5Pr0zx7on2rNNBwWC0iwOM5qYOhCG2/TG5g9+jlLIAe7lc8vnnn3Lr1g0OD16hjbYss9rm7PLU4nVlY5DLsqIoKpIk5eD1Ib/4xc9dOxRGmKAQurT2Oqsl9BnGxe+1P/e/d4HWurXogVEz/zRGWEAEsFqt3FyWZFlOkvUQSYrAUNcV/X6OFIr5bIrQCqMVZ6cnzC8uMFqTJnZ+ZKm1KEwmG7z3/vuh0WmaWNcvITDSnT1CgBaIJCFLBI/eecSLl0+tdaiqyDObuNTHyI1GY9IkYb5aobWlaRdCMBgMQl4ln5vTWrdU8MaI96R4L2kpWxyQSxKbBsRguHPnDv+b//F/5PTkhIODA54/f87R0RGHh4eWYbGurdu6Uezs7vC//F//r/jmN7/Op599Spqm7O3sk6R5GKMsTandPrcoVyAIIGNra6s1TkVZoI0mTRPu3LlD4Rkfa8v2OBz20dowGPTwKQ08gLEgvqLXyx0BSkaatdkeGw8Lr5gwYS4YY4lTdnZ2wnVaN8mS4wTASimy9O1hzz8JgNSwxMSJtERLQPFFShGBl/ZB7BdqEGik9W2uVQ1GksjM3et9vm2siVb2IPFuSkmSUJYleZ5TFDUYYTVbAEZQFpbC2aa7ECFnAoBJrXlVa03e69lN1gnNNqGdQZoUbWqWVcHN/X3u37/Pe++/x3hjbM2vGvr9AUKIJmM0hun8gt6gR55mqLomlRJlDEbKa0hcLwMKuFoTvQ7kXCdsrvsZl+vuXQd01tVznbZ63XdX1dN83363GDwSxWb7w9gt+eZvY8izFJlInj59ilI1UrQp4+2Cv8ws1BVQ36bE98TAxc95P/eklCQyxWuyrwOhfv0YL4jQgMV4PK3A3/TJ5TY1h4J/5rp5sx5gNcKSEP454to5Go9Vc0Bp50KlwxqOD3BbRRssxtYD7wK1zoLTnSfBjRAbm/Xy5UvuvfMwhJ0YY1oxQW+jdPB9EX8Xg8B1970d4L68vv3nplFqR9/739avp+7v8WdXKQGsZvrLArvLbb60V/l56TTiUkpUVXH//n3+7id/G1xczqfnLBZL8v4wsoq06/ZrXJkaZTQ7e1tsbk+4uJhycnaCUjWJEWSeAc0RBuSjnN1sL8y1WCDx8ZZa4jTszZ5khWvw7G7GzdeY5QlnMbGv2Ky1IIA0b0GSxukrTDOXovFPRDTYxhC6QnilT2OlFDpSOlFd2sOMew+jbDyFFNYKpU1DRKBMQ31vlAig0iqhon3IeIWeU5Y4q057Tnmbjv2pHf230TZ/TCKkY2w17kw3FtAJ6+nhlapaW8tXs5bVpbmtTWMxj2MR4391VYXE857VtnJsfLoqQy4lYxoXIgPMFytOT0/pZRmf/OHj1hzURlFXdu8qyoLT01OKVeGShdp41tPTc8rK5mPDATMPqLxS6joLUpgxovu3WwedM2Ddur/q/BJCBOtl7ZRIUiZgNKmoyRODMBpVFwz7OVpVlMUSIQwCS11erFZgFKuFYbCxgUHR66Xs7m0zHPZcWpLEzS0csYi3aCToVFBXJWmWcefeXfqDAcvliqIsSFPrMlqq2sXmbJKmGQdHB0xGQxbzuc1159zUZrMZN27ccKAofat+9X0ihAgU7wAPHz3kxYvnlEXB9vY29+/dQQpL9CGEoCxKphdTm0h1PkcYw2g84t3HjxlujPn2d77DRx99xMeffcLt/Ttsbm3axNhpRg5kWcqqWKG0CvmEVqsVVVU5ubqmLFdhnEajkaNFHyAckIvPm3geddlw/boBg1IVSlXhHg9yvFteVVWcn5/T7/cD2Iyp8b1S1wMmb/H2ISlvU/7oAVIjpIjgMjAYDDg7m1KVVUiQ6H2f7UA2G2gc96K1RqRWv1OrGlNH2l8lSJLGupQmKUXpBkLYA6Msy+A7CQ5BK7sh13XttEFOu5emGGkDXJVS1kIkBaqq3IDbQ6x2mZCNMZZlDIk2julMGxKtePDOI/Zu3qDWGokO5A6LxSJQepZ1RWVqDg4Pub1/ywWKJla7L/8h2mXWboRf5t5/THkT+PqH1tkIwf8gRX67P4KQAIi29csLM59//nmwpAAhi7pPcPqP6aZ4bfi/vfDvNyU/v5v2r7eSuNdpvcOVvv1CBFksFtCuA57rNLGxO0cs+NvN0LfV52vxet2Gorx5f/+MdjvBA0WBZXNqhCqvlbL/XM0RGG7aCcZczkLfFfr9fY37ng3KevbsGT9wFh6vTY8UtF9inawHQzYAvy0SX1X8e7ZrjVitfBuN1/wTfvoxd2/ZriMCjusUKM3zr5of160DLwBf8e0Vz75UjD1wb93at64x7rBezBcslwvG9SaZbNzP1j/LIKShVvYc2JiMmGyOrVVZCWRwoWva5uuy+c8a0J25NBAIBdQuRtHPQWtNkCK1hAKJXyTWGiJk7Frq+yh60dYYWzcysMQPzVcmuIUJsABCN6QCLYWGjtGyCMBMYIK7mp8jQkTfG2+haiJ7vC5FGhXaIqRAGo3QlsFP60YAMsbQnMoGoxqA1NXW+9J1ddUhd4zGFMqSqThrj095YIFNbXNMORcoY2zajaIsgtVZ1YqqrsDgFKu2H+uqDvTjRqlAoqAcMPNtlb7njLWqFYUFSIdHR7x49Zp33nmHnZ0dFvNF0KB7EiqjJfOLOU+ePuX58+fOOm5BXyITEJIEm9BWC+FSRrUtGI3l/rr9oq0Iiz51OSjbSomuIvqS3CCaZxtjrXrGKSGlMFAuUMUFWZohVEXay9FaUZYFibRKhbJYYYxCq5rlokJhrEdOKtjd3caYmuFggDKGqlJoIZDaWoSK1ZKLsuT8/IxePrCMb9LSqZ+cnNoxj9o5n8+wrK8pGNy4qta5URRFsBh5OvjrFLXd/tJKI5x16+DggLqqmYw3KVYrenlCmihUbUizjCyT7O1uWtmzqKjKiuFoyHK5QKQJeb/P+1/9Cg8flbx88YLlakaWT6iUU6xkgp7IHQ29dYvb3NyMgI61HFkLdhNbbNe2vGQF8nH08WfeWtQYEXwiWEss5Pe1LEudjGz7Ym9vL/RdDIh8SoVYljLGsFwuOT8/v2butssfPUAqa0NSWeLlne0dJhsbqKpGDTSnxSkoMEKgtbP2GEtraRxjVF1b03RZlGgEiUob61IkqdS1AioSt4GUVY3AMgfVRiPQFFVF5n2kjQc/VgullCJNUrv9CQF1CVIjZBT/VFpgZQETlIXCqwutuVyTyAQjLNteVSkqpbhxc48sTyiKFWR9iqpiuaoC1WdVlXZDV4ZlveTw4JDNjQ1kZoNohXKuCkHRGGl+vGYufNUIol6bGd/SHJz2KiBQpvuDQHR81uOEhLIVDN2M83WAxZhooKK/vfDcFfoDfqEtAAshGvEhelZLnDCN48k6Yc+7omAaJrEgPDhhOgF6iSRB8/LFUwR2Pgph54zBbj5CxM9Y//LefcP3Wzxpu5YU3wdKaTwltBCCRGZOi25ZsBohHpr8GVZL7N3AhJT28BWRQO/mm+jGA2EfF8Y/ABYnnJk1Gn66wM0J/EYQKMXinvF/G+PY4tzm6WIAG2tTM79iK5Jll3QuNXZlu/ljtV2x1bkLPOO58CbhIhySRoMWHL9+Dc4n3ucBtePejoNyr9buoxYgagsgbReXWEANU9HNTdm09lLTRZjr7a9tnf5v3//RKm4q8g/EWtqEuBzDGN4TGlBsIlAnfGNp1Wmw01OuGYfoxqZut76J1rAQwgr4EjCC8da2pb+9uGB2fsZiPmW5mLv1mCFEElw7u30hhe8zwAXPewuYkH58RdhggiDp9guJAb//aat0S4SrXStbDyKseWEqu2/79WOarhLOVVxI0RoK2+JGILbcWF4ZYYUUf94Ir6Tx1lMHaOzvzZ5uSRMiNtaAtARC+tiraO+NhtUrN7wiqXmOU4iE9dIAGoQOe2mzHiUYjVZVUKJAk7jWKyG11iiXj8V7biilUUpTFCuKcgVYSn6tDaquMcqT5tShDVVVIZKEOlLG+D3FuvaLYNWLlS1aa3Stgrukj88SWCtamqRBaVWUmsVizscff8L5+ZS93T3yLOP48Ig0S5lMJkghkImgqiqqqqIoC6QQJN6qpu1e7/PSlHWFlO5MFn4OEPo+nBOXN4Po++hvonMm7LHd/bC9N4XVGT5zOeuMdaVsznrD9PSEX//y5/T7A37wZ39OkvXxVPZCSpLE9rEyGuXqUkZTKUVa1VxML+zaEnZMy9KSr1RVyfn5NLCvVVWNqiuGk01SIRB5j0F/aOVHx4zq217XijTN6PeHbG/toOrCxpMJq2QR0irYkzQhzTLL7CclxjFLhvenrUBrK9Q0qq4wScKqKKnqigcP7nPvwR2qckVdrWzcYV0zHo+RiX1WmmbkA23fp6qQqxVJmpKmlsji3r17zGYzBoNBmIN+XgopSdyc9W2x68gmdrUKHDt3V6sVRWFJGkajUSsHnHf7tX2WBvdCgCxLMEZycXFOsVqxubVlZWoHpjCQpj3AxkbZ+eYIIRIRrOlVWbo4utK5ka7CPjg9n/K25Y8eIFkTn+LurdsMewMykZAkgq3RmI3hiEopLhYLprMFtVZOfFWW1UWIwL5hjAGRUNSN36QPnLYbogVhOvFdan3K/YJQniPeCFRlfYABqlpTO4CUp5A64UUrS8mYpn4ygj/Q7WZsXeq8q54V9A2VqcGxfkkEdVGjTxWbkwlVXWIQLIqSfj5E6xql7VlmNKRpTlUWvHj2HHH3DqNBn14vx8sk4fCjs3HSbHax1scLRfb8arsb2fTd611SRBQz0AU+JkInbe2n3zyiE3b9jAj9GYDQGgHcf26ClaIBRwGSRMDO90lL/FqjlTYYTGSh9LDJNQ2jFakQJBhUseLs5ABtLMOQdvmPhLBaKZ+PhFZ/mfbTWqDP/+3Hq50nwP9ulZZ2bvucXPY/Sdz3Qni457O7e396S4srBMH62Bqq8M4OADvBQaaO5CNI1pbJ6tLhgGXtSZPUzS0vCDXxcMbX4x4lRPNnYKLCgHDaXo2jNm4H2wdLmnFCt5t/RntNs3A5zpokd14Q8vU079uaLpcAanCv82Omak6PjlhdzBhuTKy7KwY7Oy7PcTsmyaXPoRFM1lkFtQdeshHYQ/tMG2xegcNbbbATOcyUS9e05qgTmpr36YBbv/GAdXvzddAIyYAN6g/f2RtDUyIFi4grtDe2n2W8IIeb+zZ+zpImCHqjEaPxiNnpIYcHZ/R6GY/fe5eqXNLrWRpqI0RQrtj4FePmoEHiBDzXUuH3HqGw6RiERVLWv9rlWnPgx1UVZpS28zfqSvtMD3pVNEscQNIYpDEI09A++3tF9LvBgGMyswJS1//fWzOa8TBeuaA1OG2uVgpFjXCsbForB5AIi9Ja4yxzZ5gbxvaNchTA1jJkPS20Uu47HdZd/E9rRV0rFwNhQY4BqxBUtbNAWBedWlmihlVR2PvrOgAog2ObNdalz1vwvDUoTVNMrWgAvu251Amc2qHRPM8RQoT4DWPyYN2J3Y28hcGSZHiteIrWDWCq64okkaxWBYvFiu3dHc6mF0wvLtjd2yHLUgaDfrBODAZ9hqMR04sLlktQ05rRxpCNzTGHh0c2Jo0ERILSNtbOhLhnv+eZZo5Eq+eqU1a48TPu8A7KJtYDpK4i6ZJVz4DRgqpSLudUkzD94OAV8/NTpqen/PynP+VP/vTP7bhrQ5LavkuShNFozIW+sGPr0ilgBGmaU5d2PBOZMJ/NqKuS5WxOKgTj0YjxeETq4rQHvT66VmSO2lsg3LlBWBsmpC5I2NrapVjOqIyhVopa136jtmtLADLBkrRrd26aQJK4zrJpLWeCVAprZTX2ZP7DR7/n5atn3L61j9YZSeqUdki0EZydX9Dr9RiNx0y2h62YJ62sRbbfHzAYDIPCr4lNbGLMmrPVpwawFsrmWoGUKXW9JEkIFqIsy8LvgLXUaU+iZt1F0zQjTRI2NzY5qRSHrw/Z2tqi3+9zfHTMcDgiTTXT6ZTNzU1WK+vaNxhYd77jo0MWiwV1XZNlWaBqx69lrTk5Obli5l4uf/QAKQMeP3zIjb09To6OUZVF+lkiEVojZUa+tcXFxYyL81N6wwEGQVk5gSeS0NNMYoQErGVJOLc5aIQBT7vprT4ae/Yp45NB2hwRxcreJ5PEZarGfpZl4HK9SAmVqkCIoJ3WSllNPgJVlzbfRLVCJo5O3Bi0sAGNaZJQFSWlKKmqmnffeZez8wtWywWS1PnyAkJaocAIlssVv/jlL20QnpvwSZqwTgjvmsbXCbKY5rqYuQwZW5euAzTt4oU7v1l0iQViAfO6Or7MM32J/WC79XWeQAPExNpru+4FvnhfWSFsMrrVatUQGXSSAzd14ebcZY1T3PdXtcP/a+KP7IEUM9V1r3cZxpzmyGqIY4E1fo7dWJtcR7YtogWEfGLcjcmE8XjMxsaGy1SeNcBNaZarJbPZjMViwfR8Ztl1HNOiTARK10HgiNvQAPfIWhD1jZ+TkqvdrWxAuwhzILgj0iZ36PaxL5etS+3Dz9dpjItTwO4vx8cnjCabVuhyYDMkdBWdXr/m+QLRWi9hfKL7uoKLw9VfukhBOOTdw5t39RMnLsZr2DsAprnAgtToXZpv8IbM8CgBgayjoTW7VGW7kX5K+rngE+M6ZjXt5vju7g6vn37BcNBnOj1juZhRrRaofo8ssXulQCFdzi+0JwqxeeVihRHhX6MECyBTgM9ltKYb3bg1jEyx8gOj2++mCaBNG4OI9uKuddNb7PWaa/z3QrTJGzzxg8VVDvC72NgE44gCTEMy46xslQMkPmC7diDFEwl4IFOVVThXtVEuPqZqrZu4H3xdWjXxT1VVOgHUW0zc/uUAT1lVeIBjXX5sLK92YKjXs4kvh0PL7Nfv90mlJWKSDgAhCHTCBkGapeR5z0+20D4p06jfbVtiV6Esy8J5bhlnLb16kkiGwwH9fsVoZOWIra0tbt26zfb2NkBw3/LWqsXC5tp59eoVq9UqyhsTxwO1z/XLcZbtfUX7TNNrinGbxlXWj6tKFyjFf0spQ+yIj+NRSjEcjjg0BiMk440N0iylLFfIRNDr5QE0e3KEoijIsox+r89oNGY4GnJ4eMiDhw8RwO72jq17r6bf7zvrnY17EkK4+GwLvqfTaVCg2fbYOZimiVs/VsG9MsbF7nrgZN/B5g1KrfX3LUq8FpVWJNoDa9tftarJs5ytrS3voAI0cT7j8djOzcS3r0lDY98DoJED/E/7nQihHUkiO2MpkUHnYftjNBoxGAyCVXW1aph3KxcmYl0P7fq38fgF0+lrBoMBw/6Aoih5+fIV8/mCra0tlNIcHx+zWKxCfavVisViEZ7R62Usl8ugxFgul0wmE8qy5PDwkJOTk2DVfZvyRw+QdrYmCK04OzlGYM3ned5DVVZjpbVGZhnD0ZCTs1NWRYkWKUKkKC+Au81c19Z/3Gs8tbb86j6pnjYNo47f6ATW0tMwzljw42ONDBU+RkFoKwQIpyEvy8qaUY1lCfGTC6xbnmdG8ZMhBH2iGQ4HbIw3SJOEVCb84XcfcevmLYaDAa+nRyglyfLcCh3usEswnJ5PQUjOzs/JezYZnxGSJGkL8+HQ7Gxqcemaz+PPzJrrrwIta5/bqXfddfHzrmvjVXXEn8WWlque0TyrEfKueqfuYRD/TKQNCj48PGwJ3TocXG3BugFIl4UEn9g0jg3wfunN/U377aHYBFN2NXxNn3l0461G2rltXPWuzmXDfhC+6w8GbG1tce/ePW7cuMFoNHIUrBn9fo8ktQKjjftT1HVl867UNVWpWSyWnBwf8/zFC16/ekVRlgFExOXS/OsAeikby0U8zlfNFw/Ok8TSwGayARUt60wHmMYH/1oLowdfqQWRxhheH7zm0ePHdjyjmIQwNhEyCL92xs2yDzY0/O2Afa8tjt7XaQhblV4q1+iSjY+WCrWHEmMgK5jpxr3s8sg198n16yrc2rFoWK+uzlyI+1sY2i59dk4bR8MlnVuWEJpaV6TSuuFsbIzIepk9yMdjlosLiuUMMx4gTOaSfyubs8ZrSEV4Wcua5sfQa9alDu/QLCPj8hQlnTb69zAYUzV95Oo3/mckeDXz3V6nXRyMJyMw0XW+Dm898ZYgY3DuZlbosbmSHIGQj3sNbmkqZLbHmAB2/OfQKAWgobm28TvNO9RKhfbEwiGJHeOGlc97J1g3cxG5sNW1tcDnMmM0dEKiEAyGw6ZX3TkqpXQeGY0LnhQ2kahXzGXudzsn29Yfvy8IIVgsFjYPVCKpqtpRqFt3JClSfKL6mBDHrnFDIjNEZtuuasVqVVDVJTjvlqIoKcua+WzJcllw8+Y+r14dYLTh/Pw8BMgnyUvKqqRSitPTU6fQ9Vaw9l4VK/5a6+sa4LKuxNbdeB/yloI4R2Drvmhf67KUQsNY5sFOWZbs7u7y/T/7CwaDEQ8ePkQjuJidIgRMNsYBrHqw4xWQ/V6fNJEkQrh8RnP6/Zzh2LLPGT8Wbmx6zgrodRDn5+ccHR21xg839j7hcVBYa8sUbN2zJQLJcDBCYPMcSnHlBntpLLzSMngCOIIN7xJ6dHzErdv7SGSTU8z1db/ft/d1zvWmPv+vod33Y+i9JSxRkXcndGezkGFd93q9QByyXC4ja1EVwExZllYhURacn09d7iQL7MuyZDGf08t7VFXF0dERxhh2d3e4uLDJph8+fMhgMOA//If/4Ag00lBfmiZhf0lTm67g5OSE0WhEnufBsvS25Y8eINkzymCkzRZe1hpNhVaautYkWUpVa/L+EJHmzBdLZJ5gD58Ev2dUlaFeLu0ixh042OBKK/TZg0wbT1uokKK2blhu8uZ5TlkUjh3FInFL76rIshStFPNqQekmSu0mmgCkM7l7RpuqsubP0XDU0kThzJ7LRUGxKMnTFCkEF/kFv/7Vb3j3vccURcHxyZTxxhYyS1yQpiQTilcHhwxHY86nFwyHQ/r9vvPlvcxcBlZLclUiSe8zGguEPlhx3R2xwHoVeHoTqGriYy4LUuvquKredfXHzIb+u+va+qYSb1K+LqUUZHYzPjg4sIepc9H0rkJeKImFhk6rXZ3r31W6+CCft8RbP7wAI0QSwE58eLcPLa+yXw+C/UEU3i1xrhYu4e329jbvPX6PGzdu0B8MmGxsMBgOAj1qrWpUrVC6xMcDDtMmP4uUCVrDfD5ncfc2j99/zMXFBa9evuKTTz7h+OjYCn7RGBljSIQIMmbXMnYVqI3fq2tts/W362tcQNaXt5njITbOGA4PDsL1Qoh2XI2OlBQOz6y3YjWgrtsORARgBCHmoVFmdAWky/1lwnzwzzW0cgN0DvlmEGx8V9TK1j2dHmrq6Fbd6UurDPXB/pHioFN/lzDCCjru/bR7Xwx5KjBGgalJU8FoPCJJJKPRAHTNYnZGtTHA5ClJ1kMKg0KhhaI2nqG0WTLeTRRjY1nsmFrAgrJxNFKCqQ2QrrUOa61A1EHw9PF/fjy0VkFIs2eHCgJ3XddNnhSlnUXGfu/rUUpRlEUQflTtQJJzkxNS2HuqCnTtwKQVIuM4HwxBqDKx6460Ap6UMgSqx+tbJkmgMM6zhvK3rCqSnqUB9pTH0n1n6/aaersg6rqm18vtvuZdCD4aLwABAABJREFU4I0VoIqyQCsfKG6Zv7wLoO033QJhfqbafrXvNxqNAKtkLKvSESvY/Gurle3/5XJhA+uFsDkJtb3e5qcqqGtFUdhcVVaAtDFNPn6iLG2ciY11k6RphtGR5ceAxFoxtFYcH4tmnQqbv1FrD8K8K7A3YJpLZ9s6paZ9R33purBu3qLELnaxQqlb5yUForACtLXgDQOgrZXixs4e+/v7DEajACR1XbK5uUGeWyuS90h48uQJRVGAse6WdV0iS8HHH/2e7/3JnyBcChUDZKnNQ2jjVL18BapWfPrpp8xcXiDv/uhTwIzHY3zKFjv/FMMsD3NCyoQ877FaFSht0DTJUFuY0DT7VReY+lyeXt7MZMY777zDe4/fYzgcsFoULBaWqMMzvAkhSNImybuXN3z/p6l9Py9b1I7Myya2taAjy7yrnJ+bhYvPczKrIxsbDoecnJxQVRVJklAUBbPZjPl8HrnCWotQIhOmFxfMZhcs5gsQMBqMHKCaU5YVWZayv7/P+++/zxdffBFyHvlUBtaNcki/32MymWCMCQoaYwzHx8dUVUWWZZyenr7VXIV/CgBJZFzMloikIEkTaq2p5ivsdiLQhWK2WPDF06dUjvs+Mda6pJRlnLHaBIPNzdBovQIQcgGN2k1nrStLty2lNfmuVhhVUzqf6MTlQUqSJNS5mC+pq8qyEzkSBoEgEYlzS7Cba5bmJCKjl7kNuagpiiK4BWijMcLGQ9VaUyxWYAypTPjNr37NZHOCMZKXL17SG16QDwZk/Zw0lQySlFevDtjd2eb14RF53mM4HDEaWQBlE2U2rkXBShF39xXapcai4SwdkaB6VekK92/ahNdtrFeBruu0YF8W8Kyry9+yzoJ1lUbOA8oksQf+8fEx0BxeXrAondASBOqIp0kI0ejrvZEn/q4jPMcaqaDRNTowX8X9F9qIDwQ32BiIzoOgJYiH5yWSza1Nvv71r3Pr1i36/T47Ozv0ej2WyyXT6ZTDoyNHPatCYlbrLpoipAjf2Q09ZW9vl929HdI0ZbFYsLW9yYOH9zk+OOE3v/kNrw9euz5t3Ao8Vf91c/W6EoMggw3UXufad9W9fi7GlkD/3KqqnObPIKXNZO+1aDY2saE37hhNMP6d1rxbDHgu/QxuXbbIbr9E+KsZ5gbV+Aii9vpyIIl1QF2FSq1Pexwrc437Q4xtMM0a44qtxDXYmPV1iu60FcGG5F7PWy+ty2OtFUoV9Hs5t2/f5uLinDyzXgLz6SlHiUEaxcZoYgU/t0ZEcLETNq5HShs/E7myec00OAWerhxhkCfgaSwz2pEIWAtO6YTexmLswUldV1S1s+JgXd5s7jv7XK+sCu7g2uDjGu0as/Uqx5SKcNZttx/4PH8WpAhkYpVxSZpYgT2xucPSzLp7J1EyXT/HsqQtsFmvC0flnNlgbwFWyywEdVWz3ctRqnGvvri4IBcpg+HIenQEy5Htr/lqSZ716A+HJEnW7GlSkmqBEgptKqqyBCEwbp/RWjsBUFEsV0EIVEoxX8wtm6ib+5X7vCgKlAOdZVFhdBP7EMcYelDrJ6ofP2g0+jZwfhiESq0VBkcooLAWiRAj6s8cr9lvyJAsqI0siI4kRbgN5Dol4SWg4tqnTRM3uA5I+RK/0zpPhO59Vyl3fF3L5dJSUGdZAEjLoqCsa4ZO3krThKJYMplsMBqNmE6nzOdzdnd32dnZ4fXr19bd0vj4Qs3Tp0/Y2dm2OSM3t53FR9DL8vCOQthYtc8++4zf/va3VC7prJeLPDnDYDCgKAoWywWnR4cINMPBEBDBJXw02rAU4asCQe3OO7+24l5ar8yVLpa7rupwtm1ONtne2Q4MiMvlkouLC4CQKFW5PvTu+9PplMFgwP7+PovFEmNgNptRlmVwk5vNZi5hsObVq1dhTL3FyNefpqkFn+Dc5aacnZ1R1zWz2YylMzD4ZL+CJLj3ZVlGmuSkac3p6SlG4cBWQlkuqGvFalXyy1/+mtVqwWQywVN+B1dIIcgy6566sbERvp9OLdnGbDYjTW1+wbctf/QASRsbM1QsS7SoKCobj5Olfcq65vzigpcHBxRVTZb3rD5xVTtaVWXZPgJrnQVJsTkV7ORQRlPW1u+5SdgnUaW7v66sCTBL0XVF7QQhoxSj0ZDRaMTF+ZS6LO3n2mCQJCgqU6G0QteW4cZrsqRzW+rnNvmWMAJphKXoNDZmRStFVZSsjKEoVvy7f/fv2L99l15/wrw4o1/VDNSQLJXUwMHhAVJC5rT14/HIugZ0zLL+MI6F46s2wHgD9YJ4G1a17/HCVhdMIERLU2zln/YG0rUgtX5395tIMy24fAg0wGM9MHuT1mwdKIrdt1qfuxKsOM5tJJGSs7MzrHumChqdYGUiYgS8ygfKtJ/XNc/HANL3deasV/7eriuJFZrsT+MC2MN3jnHK/+03LSkleb/HV7/2Ie+++y6TzQnb29uoWnFyesLR4RGj0SgkLpZCsipWiNIe/rWCXk/Q7/eZbI7p9XoIAUpVHB4d8vkXX5D3ch48eMDd+/dYLVdsjCbs7+/z5MkTfvnLX4aDojvOLSue/aIzlv76Zvxa428IgMS73nRj1K4qnm3sUjviOWcMJycnGGNddpW27mjJOlAuBCasUxqLBY3mNgZo0Z2wJr7HBKndA6mmH7x2vakz9GDUYc07tBSjutPHcTtoGM+aNnSvc39HHyRrIJJvv5RRfbFCx3Sgle7sO0G7re2XApazKb1exp07d3jxwjJLalWxWs45qlZMT04YjzcYDW3m94vZjJPjY6bTC5bL0grYjupZa03l3aUKB2xMDUKxuTVmf3+PJJWIpHGNjFk3Va0gilFqKaAAy3bZrEHfJ+Eow8fL2JjHPE9I0yy4p2Vp2hDRAGkq6fWyAE4TKcN+pQ0kSRpyo3hAb4yhqosAypr4DDufU+d+vpjP0UYzSAakWUqSuChHCUVVkZAhhUQJz0aWUNf2/dK0B0aymBcsl0vKyoKkuqpZLBcsFwueP3+FMTbQvyjtOHhXIFXXrAprSfJpMwLhirZz14PJZt454givNfDnoo95AqSxrJ8EMG/nm1IKl102nOUWHMqO9VkEBj2tTYiB9HuM72OfvDuVlrXNuhxF+wvGOn0665ExzRpHiBCL1LWU+9+b9Xb587dVWsbXr1PUXauwpGGAPD095datW4zHY6bTKWVZsSwKlkXBxGjSNGU0GnJ+dsr9u/fY399nOp0ynU5dbh7rVlmsVmgpHPuZJFmm/OQnf8uqWPLe4w/Y2tpx54xwSsmS1WrFJ59+xk9/+lOOj48tgHIAwVsqxhtjRqMRp6fnHB4ccHF2xvZkg/F4DAiWiyVpmjIYDh0xiKauVkExIOJN9qr+MMYRNtnrU5FhjOLzLz7n9evXbG1OKIqCfr/Pcrnk9PSUuq6ZTCYsFguUc98fDAZsbm5ycnLiwNIFxliL6Hw+5+OPbS6tfr/P1tYOT58+xRjD6ekp9+/fD0DDGOvW6am6e70e0+mU4+NjLi4uEEIwn89DP3kF+3KxcvPBpjCw1qChnetCUBYVec8SkGmlmLuUCmlqXSZ9vVVVObfLmqqy7fVgCAh94c/nmzdvXtu/cfmjB0i/e/KC8WSC1pqycoxvxqCWc8qyZL5cUnoSBeNoQ3VjqrabZY0xDtHKFIE9nOzhqMhSg0wkeZbS7+VYEgXLCmfVXxkyMZYpLkmQIgl+uPYM12xORkwmEy7Oz5jP5lRFhRESkTpKTpcNu6pKhIRUC5cAUCCEpwm1h2FCCsKgpGXM09gM0waDWsHnX3xOlvUZjje4IfbJZA1JikhTZrMLyu0t6qrg1cslW5MRW5MN8iRBC0mWZ2hh3SyME4YvR3zYYsD680dCpjEGLUz43ZeWGd9p5m3XeB0dDtxcN9oR7XCjkg4/jKMINYhIAe41Npfb5CVM4X8KwoLWrgIjGu2530yFoWF48kKq+7N5GxPV5wUcZYXf1M6N8+mUhhJZoVzOkKDRjbT4Vxd7Texf7H82Vj1vjbCWK2FvCC4rELnaKTvvjbN8NqxCWEYd77csHeudgBv7N/ne97/Pzs4OOzvb5FnO7373O86nc7I8JwGOFyccHhxyMZ1yMZtS1aXtn9ogk9StO0kvzxkN++xsb7J3Z58bN2+wt3eDuq75/LMvWK1+z8MH97l77zbz2Yysn7B3c5df/v2v+PTTTxGJdGNtHG03AQNYzjIVNHTtQ7vN9AdRHI8WwS2o6dfLAD8GpV1rU0sw90KXS+a5WM6YLy7Y2NrC1BqDBHE5BhAI1MsyXNN6A9cG3TqEA0zoKB98slQRqNydtchOePe9I08wTV2teec+jJUOQaESPyv8XYdPvAXD9p19Nx/fEPdlLHz5w9UrQQR+zUVQ0elZYsuNNh4I2b0Smpi/2hEIVHXJ+dkZWZqQb2Tcvn2L169fsVisrBAnJcJMefLsCxaLFYeHRxwcHLNaFu68IIyvcbEIgHOvqe2+nkp2d3esm/TSku9kfavFljJxuWnsXtHv920/OYFDJtY1ViYJaZogpAnv64UXrTWJTINCJigzjAlKMaV1cK9BiAAQvCeDVope3iMNeV4UZ6en9Pt9+v2+9Z6IXN6ytE9R2LiDSvs5CGjH7FXVKJWwXJacn54gsKDo9PwsWF68+46NaYKqVIG6OrbQaGXjoPyc80oM7fZtonkTF62108rrAI79lJGO7MOThARlqbekY90wwVtfnWJJ2M9DvigX7yUcc2Z7b2hWhFdMxQni/XNNeK5dOcZYEgFtNDWCxFnk/AnqLZCeitr+ax4qgVRaVkx/NoQ2uXiV2HIU9j/TbnPrN7/23PvFCjP/nVcqmfj66P39+zZKQIWQgvPpCffv32VnZ5uzszNWyzn1as5qNuUEazEY5AmvXx9jbu+zv3+Tp0+fMJ1OOT09YTQaunAGwXR2jtJur5eS/mDAdDrlyedP+cpXP2R3dzfErbx+/ZqnT5/y6vUhi+WK1aogTTN6+RCMRNUCLfvs7d8jTTPOjg+pChuDkw0GDDY3qZVmMZsxGA5J+z2Oz86RImM2nVnSHdcH3jPB96pXRPk+afrfWkr3b93g7OwErS2IKIuSYlVQlZV1+awVi8Ui5G/b2d3lwYOHVFUVSIBOTk5YLpcslyveffddlFJMpza5rAc3vbzH3t4N3nn4kF6/x6DfpyhWnJ2d8/rlSzenJcfHRyFGTEqby6jX61kZuiox2p6V/b51M0wze472+zm9fp9aVeRJai3gZUmWpdSVZe+9dWufqi6RiSDLc1Jl66rq0uZmEiK4Yvp9wVuNhsNhcDl+2/JHD5B+//Q5w+EZibAasSzLLIrPM/JeD6UMGkmSJs6tLQmCf0Pz2pj/ZWIBUuI1P8ZaWzB2ARusn6cGaudfbUSGTEHXNueAxFIUN0KYQtUly8UFUsLe7i7nJ1Pm84VdKMKQ9jKElojMUn3q0gav2g2xiSGxOYlckiwEaZ5TqRplSpfMz5CnEq0rZtMTqmLO48eP2djaYTq9YDGfopX1gV4slzx/9pTdrU362W0SYRBZQsy4BNg4CNOxnAgrmKiQK8MqbI0/METjPy28xCLsYe9BROv/pqGZbBUT/yqI2fbittjmeqGyK8g5cNQNljQiaK5wbTCO0coIE5IM+w3fYLV0KbJNjtWctTZA238Wa9ndYScTqwWsa7vZ+Rfw94Q3fUNgZ/Nu9jcZNIVNMKaV751oGnJhpG4cdUuA8u+Py0yOscHLwo0ZWI2y9gcg1tXm8XuP+fo3vsHGZMLe3i6vXrzg4NWB05rXHLw45PDogLIoMVpYgobegNFoiJTQkxKtBYoEre3cPz+bc3p8xkeffExv0Of+/fs8uH+fRw8eopTm2bMnPHv6GR9++CHvvPOQ168PSJKE3b09fvbzn7Msli63gp9XVqErnOBvBWzVFhaExM+ty9pO24eNC4tHXfEcaGtpPdBuLFRRkHYAMLZRZbliOj1jtDnG4NSGkgAU2sXFD4V5QgPS0QhjCUDiu6QQJGHvuPxmAdb7NejWcbMPuGe4tdXoMxoB1YMW/15NXxGEVo1BmDpU0HLdES7eUTdUtxAxrQXriV1HXqC3oL5hSfNjoLQKnxts4sbVasVsdkFV1lR1RZomkSurVRjs7+/T7w9Ik4wss9rqw8MDzs/PrZuNWlFVJa9eHXB4eILRkrKoUcrGMfl8ZFYIrpAiIctS8r6kP8jZ29tjZ2eHyWQS3KaH43FwG1t3uMeeDIF5SlqXOq8Y8ox+VV3Z88snanQueamwwnvl4pY8oDLGun0maYJIUkypqFVJUdbMlzanzmpVsFqWXEyt0rERUGpWq4KysPFORhsWi0WYFzbO1sVARTFL3pJuMZRpxUj60lWutdxF8XHC8Ry3Spx1c9wYR1nu1kUD1l1xoNtfGwP7hlzEz/c4Aa92Z40HK8268fXF6791fAqILbTNvGmnZrBnvqMtrzVQhHne7af47waQSBJ3LnjvAv9MpAXIwSIZKQFDX1+joPPP8m6YgJ1Hvm1BXdKAgbi0LN2OOGU6PaeqSzYmY8f0V1IuZ7x8tuI//uEjtNZ88MEHPH78mKPDA27fusvt27dYLhecn59Zwg4BaWaVDUkq7Z7hmRWV5uWrVxwdn5Dn1uKwWq04Ozuz4Gs05mK+IklSBs5trq40dW0Yb22zubXDarlkubhgZ3uTi9mc3mhMNhgwPTyhLmt29/fRUrJYrcizjNn51FqvXdqMMCeiPvfnQ3MeC/fsmhcvXqB1iUzGfPT73zPoD5jPFszn8+CSWBSFte4mCeONMbdv3w79fHZ2jhCSra1tBoOC58+fB7bD+XzOxcUFRmvu3L7DyfFxsGB6JjnvztpYNIULE9FOzWfzEiVJQubcEbO8hxGCvJcGIofFck6aJQwHPabTC7IsIc+HVHVJXZdUVYHWQza3JiyXS8DGDHplTlGsyLM8xB71+/2wzwhhSVOEEP+N5jsuw+EWvf4AXddkeZ8sTRnsTBCJdV3SxtDLRyQ+B4uxPutW2eRjAixpA0aTCcjSJNDs9vOeHXRtCR9OT8+xDhmCXpaBkOSZoCgXpL3UBqtq66uepZLhMEepwlqiBrmd2GdnDPI+wmjOzs+RiUQYm1tGIsmSnKSPpX32gbCOkSURAqUds6zQqFpbkJJKWw8SNMEfvFxV/OH3HyOEYDqdoqqKjz/6PTf3dinLgjxLePViwrAv2N3dA1Ej0x4+YabXZDVaXy8dWaFIyMQJaMbx+0LiBM1I8exV2FhNfuM6ZoRxLII2ZiM8zn8fb646+jIuAV1YwHPpitgdPNaIxfgFcIgOY5w2MRzKjQDotT/dZ7SxYyP4dd2qUqcFXswXrJbLgHH8AeoT5XWaurb4jdULmKJlFXEaVTz7kwhWTfucZnNuW5saATd22xFCBGEGIUiylG984xu898H73Lh5EyMMv/vdb8mSjPl8wcvnLzk7PkHVmnQ0oL8xIcuGJGmfJMvo9TJevXzG65PXZHnGnfuPyQYb1Mqg65qqWGDqM6pyycd/+AN/+Ogj7t69y9e//nXeeeddynLBr371W27e3OcrH3yVfm9EnvcZbYz56//011YjhSO/8J0ZCeRdLXMsyHRdUeJ4vDgX0rpr19UdFy/kN0xeoFVNVa6QaPLEWuZSZ8km0lDbMfVsaE07/FTx75A4joSgscVag3ysTKdFFhjhlBvaRPO8tXACSPLWmwiZOYDTuHy1ro37StngdxvMb1lHcVrwsqqdm5AOgr2njg6sU15odLlwrFbV+tsLIRgMB+TO1cLH52hlAVJRVLx88ZrT0ylKVbz3/jtsbm4yGAwYj8dMJhOyLLdxNWlCpiVJCll+iyxPXKxDRa83YGdnjzwbcnJyznJ5Gqj7m7WjGI36jDdGbG9vsbU9ZnNzQpomlj46tYxnNomjjbcYj4cBsMR07caYwNbl3YFUqclz6yKksTGtNj8MaKXQusRA0CKvVjYeIs1T5vM588UCrRRlZQOxq7JCKUNZVqxW9rzyGlqlPbtiA/TjfcbHRfl1ELvgCd2QnVyyCHqLWzSPu0L65dlqaHbly2usC6z+MUVASKjqi1//8Trv7gFxX1zl2hbvvUDQhHfBSrw3d0Gkr9N/F7tWN983rtcxvbNV7Fqygnhd/WP6zRNxXFW6VuHWuzgF7Gq14uLigslkM4ovWiKEtbgiBJ9//oQPP/wap6dn7Gzv8ejRI46PjynLktlsRr/fp9fr0+/1KauKfi8jz/vked+BccN8vmC1Sri4uKCuK0saIg2L+RKUoT8a0e8P0NpQVBVplnH37h16ec7By2cYY+j1+yxWJTu7e0gpbWwNsHvjBksHPgYI5rO587y4rASI531c7F6sERLnHaT55je/ycXFBdPzKVVp2d12dna4uLhgNLJkXiunCDo8PGQ8HgcAsVgsKIqC8/PzAKh8zBBY19FPP/3UugcOBq252SaCsSxxeZ6zubkZYmo90NzY2HDxwssw3+L97Pj4mF6eU1cV8/mcXq8XKMD9vi+lDPWsVqtA+66dAsazUfqQhNFoxGg04uzsDFgfH33lnH3rK///tIzHG0iZkfRsFmAphMsnUltXN2kPEFVrsjTF951yNKiDXkZRrABDnsAHD+/z3nvvsTEa03cMGv1eH4Sk1Jof/vA/8/TZc2SakqQWIPUyCWbI9vYOG6MNikWB0YY7t29z+/YeeS9B65r5bM6PfvQTPv/0KZ999oTjkxMOT4545913LfuOEORJ6gTpFIyhcjSqsStU1stQuqKqoNYleT8n0YkNdK8M0tiEtEppjFJUtSJJJONhj36+Q1UWnB4fUpUFial5/ixnMIA0MWxu7dFPUnwuJit4OUGchskFmkiCRpMcOeN5QBTK5Q3AfyaEIUn8gWdYCwwcMHE4bS1Oil0ALn3jFVVt8xKxedsDDM+s1FTjBMdW60OFIadN+J9p3stbr2xyOJvjSgCr1YK6LEJ7bb6Ay2xDbyptH3CnxTTWAqW1ciZwn+yuHZvkN63WYe825dhX3m84/j2lFPzgBz/g4cOH7N28wenZGbPFjFVR8Icnn/P82QsSLRj3xwzyjDJLyEabpL0JMhvTH4zJsoRPvnjJdFkhVhU3RM7dB+9RVJrVqqBezXn96RHzCxuwKQQ8f/6aV6+OePToId/97jf51je/w8cff8J/+k8/5Hvf+x4PHz4gy1P+5b/8l/znH/6Q5WLputc4AGn1z20Bzxbt/Pn99V5ISxKJMAk+iWRXcOkKOu2D30/WtiAYtOcelmiDckHFxhjSVJBJPze9NdvPIx9j4/5288daEuqmDdo4fYJ1A/KUzn6+uQvDumoBGWel0Q7I+DbHQpp3d/KxNtAEq9s52FgN4nty4ee7A67GumFprVFOYPMlgNK6DkHLsdbaB7bPFzOXK8NQlINoLIyzSoEUGVoZPv74M2YXc5JEcPv2Le7cvsdkMrE5S2QWkkJa6uYSm0gcdnd3GI2GnJ2ds1wWqFqwXJQtrbzSlgq/1+8xGg24/+AOG+MRZVUyHo/o9XrOyyEPh36apOR5HyFqqko7q46kKi14Wa1W1JW1eHlAM5/NWBUlq2XpYmhrVqtVyA/SJgwgGocmriWOPdTGA11ByKIbTV8fUxbAibDeGMadEB2R3SrvEKhak0R7TnfNdWMZ/dztCs+xogbTpphuzZWwINYUpzCIBdJ/CCDwVrd1AKm7n8Z1X7WnD4dDvvWtb/Gzn/0saMBjoBHXHwOkLhBs9VHnxbsubb5+IROMENFaWm/Fe1P/xOPXjWntNKUFsv11VvnW5D188eIFW1vb3Lp1i+l0ynK5ZHNzk52dXYqi5N69e2xtblOViqdPn/L48WPef//9QEqwWq0YjQZsbEwoipKey4mUpjkYK8OUznXTu2XZ+C/LuDkej22qGGNYlQUKw639fba3NymKJUdHBwhtLSwyzdjdu0GxWHJ6ckqvP2T3xj6zxYKyXGKq2lm1YtKLdrf4+Le2S6YIngIGSGTC+fm5y/OUBzA4nU4pigIhbLzOYDhAGwskDg4OAqCZzWah35fLZQAevv/LVRHmb+ni5D0Jgp8vnrAiniO9Xi8AoDS1yhdPPqIRCM/u7EghlsslZVmFmCHfvs3NTcbjMTKRlFURQFwMhLI8w6gm/5en8xaiIV3Jsoxev3/tfI3LHz1A6mduwbuNYNDrWQ2asZmMe70eUggq18nlaoHRiv64j5CCRAoG+ztsbm4wGQ0QdUk5O8dkkkoVvPjiU8qy5N79+9y+d58P3rmPKpcsi4I87zMYDplsjMAoVosl/4//6/+FTz/+FK0Muzu7/A//w7/mr/7qz+j1M7Y3NsgTyS9+/lM+/+wph0evQQpm52d87RvfZDDcQCub8RkBaZaR5jY7cVWWNukeBqFrG7ScSHSaslLWxaGqKqSGft6nlyaYNKEsLVNPVdsYl8ViwXI+o1guyfOUYtDj5PiQp5lGGEOe9+j1+giZIH3qGGchUlpbtzNjwk/tDy1D8LtuABVOca9bm4LwcT0O8HgBzV3eKQ2U8okd/Yba1m432tv2id3oG4V7NpGveeyS0RxErt26yTEkpYzkhgjIGQLjjz/Am5Y3vxhjc5PIzJIPnBwdolXt6LbbWti16G9NiQ/m4OEsGvcaH8vkAYJnVWuDwnaPe41i/N7x84QQ/Mn3/oTHjx+zu7vL68MDLmYzFvM5f//LX3N2saI/2GAy3iQTiqeff8ZFVbL/4DHf/sF3GWzcJMn6SDQ/yHr88if/L5TWvPPhN9m5cRcjElStKJcXfPyrH3F2OmM6s37TIulTlhUff/IFh4cH/Omf/oD33nufV69e8sMf/pA///M/5/79+1RK8Vd/9Vf8x//wH6lcpm2ff0XIhHXF50KLXVBiYOXfP849YbX94lIcQXO97fOuC5Gdd7EF0IBRCF2hK8uW1UtGkUUxYjDD5sXxYBoaoUSbCu+Wp1yEr3J06mCc8GyBkqptMs6qrMLcDeOuGkui0rWLa0nCuovfRdUq9NE64dML6ElilVeLukZAq8+sBVTaZI1R/4f5lyYIQ4i/8LEviaOUX64WHB0dMZ1OefToER988IEDLp4yXyFIWS5KjFHOm0C5OqwLc+PKDGhFLSVCJM6qYnPj5Xmffq+mKmEwMPR6S6ScBsIfgWAy2WBvb5c0k5RFyWldo1XNalWSJuehn+u6tvu50lRKRPE2muXSut52wU6MXGzOEtd/tsPQyubTiQWfIJiZkM8WjECQhHkY6nWPaASzWElEhx7b7ZX4hKRJsGBaIO/uQbbWT1hbUT1XKR3Wldg6GZd1wnwMzKSw7vJd4HXVs4Lgjm5crTvAyN8fW5Vja0z379Y7uFKWJcvlMtAs+3nvhcL4Pg/O1oGtdUDMXqtbc8FbOqWLP9LuXh+/1pxBb1dCXR3LWRcoSinbZ6MxxGu8uUdycnLCdHrBZLLB5uYmZ2dnFL2K+/cesrW1xYMHD8iyHru7ezx58oQXL15w69Ytlsslv//977m4uGA+n9t438Qm8g3gSClqZeNybXMlaZqjlFXCjAbWwwGgrEqKqmJnd4/9u3dIEsGTz57YfWy5pChLdm7cJkkzDg8PKZYFDx6+Q3845uXzQ9JEcPjshX/jTs9FZCuuX2IXWztnHYETJoCoi4sLy66oGpc8f8/GxgZVbcHKYrEIVsPpdNpSUklpSRB8HJHve+8K5+N7/PeeSt27unkg4uesH19/zi2XS8va5yzc3iXOxxlqrSlczivA5e6ylqY0SzHY+vf29jg6OgpncuVykvo1ELt2ggVrvV4vxFS9TfmjB0gZNf0sJRGWIneQZYjMrkWlKjLsIb7luPU3bt5iezJic2vC5uaE3d1tl7FbcX56QiIEn37yCSdHB2RJSuYQ8+nhARfnZ4wnG3zl8SOOT07I8h7z+YJEGFKZ8X//d/83/vZvfgwItDIcHx7zxSefcPTqNX/6539CkiX84Ad/wkd/+Jha1WQ5LkhQ89nHH3Pn7gO2tnecr3QatPUSSZKlGHcIqqJAK7uBJgYyoKprymKFKitmWvHyRe023tJNUJfh3JFTSGFdCbcnY7YmG/z5n/0FP//5z9na2mU4HAE6CJPe1c44gGSMwSjRkC1E4EZ4a432kASn9WuKd+gJG2YANZcPPWMagT4WEsylOu3/1wOkWNNI+NvpxVvXhrPLOIlCuPiHIDbQkGO55zQuUDYotnvceouBUjWDfp88TShWS/teRthM754eWKmWgPGmEmtem0Sb9l+TMLZrZVrXz5GAYdZrfIUQPH7vPR4/fszW5hYvX71ivpjz+uCA3/7yt2hSxpM9BpNdhhtjjl8952S5AgOqhsnmDS5KuDg/oVxdoMoZD97/EIPg4GTK61Pr+50mCZNBzv1H77NY2MSHm7u3UUozn89YzGcs5hf8+3//13znO+d84xtfJ0lS/uZv/oY/+4s/49GjR0gh+Is//3N++MMfWnc7KUFdoxEV3srWiftxIKlxoWoONEt9moS4i1iA8YJDS1C9JEARLJBZIlnNLyirAmMEiWxyVRjjqMaVolKldYfSNvjdx974g6d2WeBtfokSow2D4cBq36Jx9u/jBT0fj+XniJ1DLkBdCGpRO2uBBZtNMkGbw83fm8gEI4yLtUtCUk//TBvX1hxqiSMcAEFZV2HO+jwxHuwJIE0zlPss9IvSrJYFv/vtR86FWvLNb3ybLMuYz+cUq5okyRBSkvcysixhuVwiBBRFxdnZOWVZMR5X4fC3fWopnYuyZOVoc5cueHsxX1oa79qyqPkxreqK4+MjLi6mbkszQZnklUQCG2QsE6uRFQhI0gCEodm/bAC9doH/8ZwVeHfI5hM73zwDpF/n8VK3+42zZotmrzNNJTQ0OHYe+NlgRHRl1BSBCDFQjbXC50dqFEdrgU9HZrwOSIS/O3tXGxABcr17jdXIN8+JrS5xPX49hNQLxrLLxu+w7p+/N/4Z/961dvmyXC756U9/GrT1UsqQMPWSRSHa679s6d5vXezs+HkXKb/e/NxYg8OuLV0FSQye7Lncfp+rilKKg4PXvP/eVxxL3QXL5Yo0nZNlOaenNl6o1+tz48YNXr58SZqmPHr0CICPPvqI5WKOUjVFUVKsKmbZksaDAqpaBYBi9/HcWpFTa1EtyhUKwc6NXR48fMRwPOLZ55/z8sUze5gJMEZw+849KmV4/uwZaZpx5+59FquS07NzNrKE4+NXQSrw+6fto3ZOx1ZsGDjFqw7XykQymUxCf6Zp0lK8eQCSZlkrDsy70XkyCm9p8eDUWyyNsxR7S5Hfdz1NtydS8QrC+Bzz1iYPdPx4x8BICMsC66+RUraUAmVZBqtRksngMh27bfs2ZVkWAJIHfH7+xgqLtyl/9ADpwb1dxqMxGIsw0yRlMh6TSOP8vQehIweDPlVZ0ssSHt65yXK14Nnnf2A6PSdLE4RMOD4+QyvLdw9eo244Oz9lYzwkkZqq1kzGQwpX18X5lIODI37/+z8gk4yqqKir2tKQKs2P/ua/8PVvfoO0l/CrX/+a7Z0tKlXS6+XW7U9oFos5z589o1hV3Lp9G4M9cKHR6B+fHDM9O6OeLyhXLrlcVaBUZTXF2mY8N45txMfNaOOSDOJ2PffPKEOepfwv/mf/c3b3dlHa8Nlnn7K1vcVmJkhl6tiZHDsQBh1l5/bAQBvtQJQFFBYkpQiSxkWjdS5GLmxhF27cw3yxFh83CgYQVqshkwQhzKWDaN0BFQMG92V0LrfhEZ1nm+i9jGncf9owSESHigDVaNfjz42xcSBpIsnShPPzMzCWccrIJkjXGG+du2bS+ycL0XmGeydtLQa+79I0xTNq+eEPJBDRSRjq0d764WPFbNnb2+Ob3/wme3t7HJ8cM5vNePnqJb/97e/oJQP6wy3627fIJzts7mxx6+5dsn6P5ekZDx++x+efP2NWCfJ+zuakx4272wx6PZI0pzYJq6KiLktm52dcnJ+QDzb46je+y8Zkwq39WxRlyfHREWl+gppLFosLfv6zX7BarfjTP/0+xmh+8rd/yw/+7M+5d+8eVVnyne98h5/99KfO0nH1oSy8uTMSLuPv7IEjiCnZbaB7Q90ba5VjS57f6FtClXP30UqR9hIW8wtOTyTL5ZyyKpnPJuFQ8v7ixWplWYtUozCw+TWsC6DAAh2vxfP501Rd0xvkdk+Q9kBMsgwBJGlGmubNnBONRllg17YNvs2aQPLY5dI0cWlWPnXfJUlYO8Jpj429Ca+jqKqGUEC7+E/vxmlMe02AJbMwbi+z1hdFUVQYk7B/8w55L+fhw4fMZyuUnjO7mAVL1apY8fLFCy5mU4Q0aC34+ONPePr0WQCufg1Y2lnnRhiBlsaVyL6XbbdzD1QKoRVa+ID4FDyTnbakIP59srTv+jsJ7HqNFUU2oEK1XZ4uz9n2HDUYYgOp77cAfiHEEgnh9mbTMLTF7nWWnc06gUohmmg0uzG2l5Lrk0bn9XaCsCeWiPfLqywk0V0NsOsCLiFatOVdsCWiZ8TXeOVGLJAFYcs4S5/WrXvXAb6rQNC6v+PPfUxFDPzjPaV7f2w1eNsSg5Xmn4TICtZWDkUawC/xjKs+v6SEu6ZIKXn9+jUPHzxie3ub3d09Dg8PWS5X9HpLzs7OybKc3d2E4XDIjRs3ePbsKUorHj58SJ7nfPrpJ5ydniKlpcq34QHaYneRNG6GTnD3bbLWE7vH3bhxg3uPHjIYjXn6/BlPvviM50+fMhoNGPQH7N+5R97r8eLVaxaLBfdvP2R7e48vDl9SK8Xx+TG6rhEyd2/WWM2UalvQY0sauPnlrbPA7du32drassxxQmK0afIDuXV0cnKC0prMZAHke1CxWq3CvOn1emG/80o9KUSI9/SAQwjBYDAIFiM/L70yLna3a1sBhYtbNMGK5fdIX0+eZoFm3a+5LMsYj8dsbk1I0iTkcvIeVZ4IwjjFte8rb723hDKrSGH95vJHD5D+xZ/9Cb085+zszG42VR3cK8qyJE0kg+GYlfO7vHt7H4TmN7/7lUPXPilWiUxsfgErJNj6jbQbeZZmPHr8mDzPmc8XHJ2cOL72IXs393j58jm93AavVaUFK1VtTY0ff/EHXh+/4t69O5wenXByfESWpGFyrpYLMBVGwcnxc7ResLV7k/5wiKqt//nnn3/BwcEBaZLQS1PQ1jVHYBlEtK6dQNNodr1dR7iF6QOjMQaTWMH48ydP+T/8H/9PfPjhe7z77rscHx3y4tlT8lQyGAztgSPdoQqWvEBZs6+XKaWzLnmrjhX+qqCZFhhMIFhwpuMIHAVrkDGB9DoIVKbRYgqjkcYgffSGiVzuvBtKCwDhhIFog3aAwDOw+Jwgl60ojonItVNGWeSlcBPDPcBbzYRDdFI0gkajETaAppelYDTnp2dWmDWEgH1Pr+1ZkZoDKgJ4l4oVwjw4EgK0roEkHPreXB+uQTuhyAlh0RiEp4hwRXBV/e73v8/t23coy4qz03MODg753W8/op8PmGxsMtjYId+5webtR2xu7SLrFe9/IDk7P6NGsn9ji2/fu8fuzha91KBVQVFVVHWNQSBFjzzLSORtVF1xfnHOi+cvefbsJZ8/e8H25i57Nx8y7E/4+NfPqCvFYNjnV7/+FUmW8O1vf5tVWfKTH/2Yf/mv/hV3795DK83x0RGffvoZlqHvcj964ckQxxnJQNuLSYAUjD/UYtc2E+Z+nMUcouSJxusPAWlzmgh8zI6iKBW//OWv2JgMUap26Qa6xBmW5a2uC+e2l4SD3Wr8JGma2bknJUWl+Pkvfsv29g7vPn7I5s4kxMHkeU6W5xitbU4upxX0h1sQ/GI3LhnFOXjLhSHsnwab4kBIidEivLdXniSpTY2gjXaJdzWmtukEKpR1D1sWTmBULBZzphcXrJYrli7AuChWwfVj6ViYyqJiuVyFQ/vF09f8zX/6cbCu2Bgkg9Y2xk8I615n3ec08/mitT8IIQLboV+X0sW2CnQwjiRSYnQdODWFEGhPFy26Vho375wQIlJPYuPmmTHBQmMZCF0cpNFB6d6dtXYnDTYRRJRgwO/3eD2Ln89CooRAITFIvMrHz0eEtHl+LCoiEQaExlAjyIEUg3LvRgB20s1uKa17ZlcAbvbXCLQhwt/NVmmac0aIwH5nHCCT0gpHynilhQzz06E9TCR4xv3mr4tlJ6sEcgl0lUEkPv5U00DCy/vF2wr61xV/b6zNL8vyEgCLr133Xbe+Nz2zNc8Tm8LD97EXqqV0ID0+w1r94FScxlukvGtYYx2OwWPjtuevv/w+9lv/vY0R//yzT/jqV7/GrVu3WCwsa5t157TXLJcznjz9gqoquXXrFi9fPqMsV9y5c5vhaMjLl695/vy5Y0SDBOei6wiokE44R6GUnZ+ShMFowt7tfXb290myhM8+/5zZdArOfS3tjcjHW9y885C6rnn1/ClJf8itR++wrCqmhyds5n0+evoKKVN0SPTu533kSu/mW/iJV5JYAhttBEIYRqOBDYkolgxHQwb9AaenZy0F3GI1R8qEXn87WI68BccrAWJrSwySJD55tL3OW3vKsrSxam7ctBAgE3q9Pt51F1zS8yiXWK0UibQJs/uDAWCtWUJasrE8SUmTlFrViESgezXVckUy3mB6em5DY4wgJcHUhvl8jpCCRblA1TakpImJt2tfa7t3lUX5xrXgyx89QHr2xWe8+/9m78+Cbcmu81zsm9nn6tfu99mnbwrVF6vQFFoCIgmKFEWqpURJlnwl3XtD98UOx/Wj7Sc/3Ag/6M3hB79YoRt2uBNFWRQlkUFKIgGQAApAAVWFqjr92Wef3a5+rexz+mHOmSvXPqeAosRrUrCyYtfZzVq5MmfOOeYY//jHP65fp9UIVJfrrCBazCvd90ajwc7ODpubm3iex5PDJ5ycHlcRukGcbN1Zua5YpayytttCMJpM6HQ6NNstWp02YRiS5znT+Vw5t7oLtm1bCKHkOfMiJU4j0iwGUXJycqSjYsVRFwKyPENKVUwss5zxSG3q3V6fZqPJwZNDFuMxLd9XvOFSF9ILG7fRwBIwn09BlrrYcPVQ/VuWzk+hObjCspnOF/zuv/s9vv6N32dzc5O/9/f+Hvv7+7RaDba2tnFdB8teRcaWm1atALRc9kAwRkAtbv2l3X5FyRC6ELwWIOmgpFwxmnXDrAyYBjBrmy3a8VhF6OvH+Y1hieJLyoKq8a65BkVtWUrHSimxdFNFEyTK2rntFVqHWB0XKZVjLmwcSygxCikZjYYY1FeyrKmSqNevHqubXz0NL7RBNb+rKyHVU/fL52fOYcaqrP5gjGCFDhjES5a8/MrLXLl6Bc/zefhwn9FoynvvvY/vNej1esSLBYWY89xzW7TXNxlPFgyPD8jnU/YuX+H6zes0A58kmnOyf5vjw8dMxiPiJK5UcoyCTrfTZWNrk7WtDV588RbXr13l0aND7t/fZzqP6LWbDIcj5vMxO7s7BEGT7333bRw34NatG0xGI77z1lt89rOfZTQc8uprr3FyespsOqtizvOotXr29f5Biv5ohkIIpXIkdRNds87KmqqVGe+VMa/mnQoyKC2ddVqiX5ZtcffePZqtAM/z8Dy36oxurtPzPHw/oNVu6P44VJQcU6waBk11ncJhOo2ZTGYMRzOiOGJn94v4fqhoJLYNUmBZDk6NTqFsoQoVVZbWpsjLJWqo10KaJBXlr9BUlSzPlr1sMqVoZJSTiqKoZLZznREzsrT1Pjd5Jklqm5uhd5zPyJpxO4+0n7dJq9kCBdSg1zW1wFWZGpP51gsL46hTyd1bqKBZGGRIzyMLibQElGYOGCl9iZR6fminc5mhMSBWyfJsdUdXVoH7M49apsbYwwr1FzYCQ4+mZstUtspxXQ3ylNhGDMTYOY3WloUSeLEsGylt1UdQCMDWWSplm23bwndC8lz3RXHsihKoGAeQ50UVTNWphMa2PpXpEU+DVki9fkzgWK3Nehanvk+tDBSwKiax/EyB69hcuXKJbrfLu+++S1GAtF0l0CGLyqbWFex+3PFxg6dnZZ1+VCZm9do/zueeZxnU1kptvFazR0sp9uXfDHBYP7ehcD77es4DRWUhn/pb7VTVN2ZPPTo6ZGdnl25vg729PR48eMB8PsfWrTI+vP0+R0eqxmcyGfP5z3+e4fCM2WzChb3L3Lx1i43NTc7OzhgMBkynU0X7KpVAV1ko4EAKge26tFptNvqbdNfW8Roh8zji7r3blKkq1XAsi163D07A1evP4XohD+7dpixKLly+wtrGJo8fPIIiZ3o6IpnPsa1yZT83e64BW83+a1rNqNeYImgD0EiazSYg8QNfN6NWpRJxHOO6Dq1Wm+3tbUajMUmiVCtlKSu6nSxV3allL+uTzLNR2Su7ygqZbJbZX5I0A6n6SVkaOBRS7T+q0XGBkBog08Gao0UvHFuJ0riOgwUkcUKe5QhbAXR5llEWJWmsWBKe67FYLHTdql01ezbtGgzgbTJnBpAxgZ2UsqJ8f5zjJz5AWizmPHnyBN/3aDSatNttfN+n2+1WHXbb7TZnZ2cMhwPVUVtvWoYmY5wz4/zX09HmKIqc4+Nj9vf3sW2V2jWISyFlxdcsyyVn3LYsStumlJI4TrVTkJOlOWmakaZJNSFNmltKVRScZpIwDPE9l3azwTwMSLN0pYhSFW2jO5PbqGB61Vidr4Ewmvbq/bLi7c/nKVl2zG//9u/SaPhIChzPptNuqaaEYolU6DhCo3q6/8SzgiYp9UYmzv19tdjUGGupEUQTvEDdkVj+H7WfV8bFvLf+30ryxXDhUdQZc+5SKgUtMzZVAb60NLJUQ8CEg43JyLiArqewLBA10QOsakMBXZ9ReyRK+lIynU60k6ycBUPdqs6zsoF8xGYrVl9XD4jO76FLBHd5OfWA8dnnV+fe2NjghRdeoN3u8PDeI7Ks4L333gdh0+72GE1nHD7aBydg58bLWGGXw8MjAhs+/eYb7GyuMZtN+P5b3+H+3dtEs6lCp2VZZekEUAhBPBoyfPKYBx8I7MBj+8Iez7/wEs8/d5Xd3W2+/857HA+esLa5zmQyYjiYcPnKJbKs4LtvfZd2q8GNGzf4zne+w927d7l06RJxHPPaa6/xB7//B0+NiRm382NQpw6Y8VeKX0bIQT+32hwuanNp6QTKlWdh1k9RLPn+zWaLIAi1epKH49hq7fs+cRyv1CcoRFAFFIrCqoJ8KSHPFyqAKxWne/fCBQZnQ0BwcjxACEerNqkslaoptIiTpQKa2lwjLbsKaaIy2NEiqjapNFFCEmVN+aoOnBjBkboYSIVWWpbOQJlxM9NMgLR0tmeVzlT/vp5RWx3r5RpYnfNLB3rlb1iYLHAt3lHXqDGC80GXlLWssvm8WgF/aemsdFVj9R+fYfh4hwGQzJf+TGmDdFHOpkRQ6MxUjmNLLEtlssMgoNUMCTyXJE2ZzyIFGJUSy/KQEsKwSavVxvVc1tb7tNtt1tf7rK2vacesyfVrt7j/4CG/8Ru/waNHj1gsFtVelheK7i2l0AHv6l5h1tESCVdofpEv1121H6P69lhW3TE352BlHMycWtYvsFKzBcvam06vyX//3/8vWN/Y5H/7v/nf8eDBvgK9pEVJiqEy1oVF/jSPP1bmSq/L8zWU1XqFp9aI1EBlhcNJ+SM/s77+DPBjzlX/+qhA6vxRV8X74IMP+OSn1uj1uqTpLo8fP2Y+j1D9nZR8d1EUtFodut0eYdjk5OSY999/n15vnY0NJQN+4cKFqm9QkihmT4lA2Cob4vkBfhDi2Upa+tGD20wmE6VKKWwmgyFJklNaNjeuX6fZUiDd8emAXneNm9dvMRoMGY+GtEKX2z+8C5ZqJ7MauJ+jTQqesmHotxiAxrZtNjc3mE6nNJohUkotcgCNMFQ07DjmNM3I0ozFIiLXynJZqhsr69EvypLA9xG6b6ipDzpPMZVS6gCowLXsqkVIWZYUssARFjIroNSsHv14y7JYaRFgWRZZnFR+Z6ZroKb5tPKZVUZIASxPjg6BpZx+XQil2WzSarXodXt4vldJjPd6PTqdDmEY0mg0cF2X33/rmx9rrv3EB0iKX6mahNWVwIwBCMOwKghTSipLeUkzAeqpYGMETaEamIVukSSGApJXBWuWpXi8zWZz+cC1FLbpWi5LyXg0RkqLdqtDHOVIiVYsmlYyjea68yzHjnMeS7h0+bKmhFD17amyLZpm59gOruOS1O7lWY6B2WhMMLg0kgJwyAvBN7/9Ft1uk+liihs43LxxnUYQKBlyfSgu/fJ7M3bqMww6vrpR1Sc8LNO76lqhbjzP/3zeeD+VmhfL+pu6o1Z/rznqn1tKleXTxBhKqVXfWNaRqMtXCn4SXXSvX2MJC0uqsTM1HCrUs6qCf9t2EECaZZQyx7YFi2ihayGonMXqumrP7aMCl/OI+mogv0TWV99j3idqWbinz7/iECKRQvDaa6/RbLUYjycsoogPbt9mtojob2zht3ocnY2RlosQNmkccbD/gP5ajzdee5nAhTs/fJsffO+7TCZjTe6RShilUKIhhqdsWzaWrednWZIvZtz/4IfsP7jPjVvP8+Krr/HJN17k/R/+EOIzPD8kDFQGtSgki/mY737nO2z87M9w48YN3n77bba3t9nd3UUIwb0L93i8//gj7/mjxnl56CxDzV7UM1JKMS2vBalFFSAZBNbQ6nw/wHM9Op0uQaC6kMdxpv+FNC1x3UQ12EVqRaOSPE9BaIUzLbOdabGGPC/IMy0ukCgQBgRxEvFv/+1RFdjVex0px5RlYKLXhcmEKOUyWYEg55FoIYTmwKuhULWOhXYq6w6ZGj8VwJnA6Dzyr85iNuwKwNFH/fPrh+G4Q53OtawtqT/jau3odbo0MzqTiK7zF6v2x3ymbduKZqxhFtuq1SZaint//hosLbZQlssMpSWW5/hR8+7HOabLa9P3g2IvWORAASIHkdNselzY2+WFF5/jypU9ms0G165eYXdnmzSJ2N/fxxKu+rIV6NUMO3S7G7Savar421DBYq1UdXJ6ynB4Srfb4td+7VcJw5DZfEaapIxGI45PTnn0aJ+DJwcMBgPiOCZLM/JCta/I82KFTmpZqs+fLK0KMNMziyobJ8y9gy4q0WOmM//akTONh+tZ3fMBb7PZ5B/+w7/PZ958A7D5b/6b/5r/4X/4PzAezbGEjW055DJ7av78uGzPn5XDOJd1wRgwa6nAUOzqrANYDe2f8ikqm/bRma5nZa3OX9f5eV4fY7OGFos5H374Q27deo6NjTWyLOPo6IjZbE4YBuxd2MP1PHZ3d5nPFoRhyM7OLtPpgpPTM05PT2k0FMuh3W7TarVAlFg25EVJmikaW5aXjMYTpsMzkmhBmieMBgPWemtMo5xoEZPkkstXb9Bf32CxWHD/wUP8sMmt51+izAsOHz+mGXo8uPc+eR4jKMCytL1YHR9V37hs6F0fj7pgg2UJ2u02lmWRphFFqQSdbOGqGkyhbM58uqjVBdU+rFRrSGXdHJ0V1VlxlPJbp91WmXtBRaszta+u61Km2kcSS8XWFNWixARChglg7sO8rmpsrZ9rnuc4roNlaN1IXN+n6Xk4tk2j2cBxHC5c2CMMA/Isx9UqeleuXGFzc5NGo4Gps3ddJdyDEDi2qi0bDIcfuR7OHz/xAZKwVA8ZofpwLZ2HsiTL0qqIWvk1FqrL9dNqVSZQMrru5zdj09CwvniXahmikla1nirMFoDFeDxlsYhIs5zbt+9wdHSifk7jajKZz8qyDJGp1P7pyRFh2CDPU6TM1UYrbSxh5J1l1cxu1bl5Wl7V/K5e5GmyPPrdlLJkMp3yw/c/UJz/rGRjfZ1Wq6XodqaGwrKqjd8UhtedGeP0mmDJIOqO7VTPqEK1pCaaiaUDJ8zzUle5cu6POupOW1kuFcDqBmclMEPrdOmUt2tZmlJiUGz1OpVpdKugFiGxLK0qKC1kUWBT42EjAFsjNooypGhJGVmWMp9PmUwm+lmqq6hQc56V0Vh1Nqu5f24TqnPJ1b+1jW0F9jbnXO3BUHceSqk46Fubm2xsbOB7HvcfP2Q0GvP48QGNdgc3bNLqrfNT2xc4e3JIEDZAwM5mj1dfe5Uii/mjr32Dhx+8B3mGKFTBsyxVq2WzXmxhIcuSJMt0Rg88z8f1bBxhUcQR737/uxwdPuFzX/wiL794i9CFB3cf0QzbCAl5mpPnKcPBiHfffY/XXnuVTqfDd7/7Xb7whS8wHA557bXXOHh88JEB0fk5ssyYloAxMGoAi0L1nxGca5BKXeGnqMbZBAhCZ49mswm+HwAFw2FRrQ/12vNAQKk3onPF8XpuLIOweiZhiZgvAYQKtlDnEYIyf7pmUf3fUrLItXljLSFlDRyYn5dxhhFsWN6Luq+6UIBBep71DM4H9uft9FPBWe1v57P+z3pv9TtpEFqhi35rzqGlGm2v2nhFVbYRuLa9HE9Td2QJpGUoeEtnwMyFMl/y9JdCHjqQEueAm9r3deTfjKcJAMCq2TbI8xTLsumvtbh4YY2yzOh0G3i+xZd++k16/TaeZxOEjrKvcsjJyUCr6Z2xd+EapbSYzabaCVkgGWFZLkgfpKLmOXZJGDqkiWRzo0+z1aLRaq04tVKiVcFchLAqNVVFVSs5PT1hNlOI/nw+YzAYEkUR4/GI73//Bxwdnmn6jton8qLAcvScKsGya0wFbcMVTcnSgKKi/yk7o+a7AfaEEDiOkhH+m7/2N/krf/VXKMsSz/f56ld/ju+89T3+2a//BnlWYGNRFst9E1iZE3/Wj7osvxJ0WVUho1Yz9BR75qMySM+I6is/Sc/vOjBR/V2sSsWf37dg2Wy2/pmHR09otZtc2N1ja2sTEJyeDpjPFpQNH8tyGI8m5Jnqa9RqtVSdYhRpSp7N4eEh+/v7ANh2icJ7LeIkp5Q2eSERlk1gW9gWPL5/jyiaQ15QlA5SuFy6fovdC3skcczDhw+RAq7evEWj0+Pg3l3yJGK8WHB2dohtSQS2ysbWh+6Z4MfTA2pslGVZXL9xgyiKCAKXJI216Is6l5HmdnTrAs/3oW63bMhAgftOWWVdiiJWAH5RsJjOyIqcpFYDl6Zp5RvY2JVgTBzHWLpGyXWcyt45+nsjw23sVqfTYWt7m3a7xdbWNkEQsLW9BfayvxEoup6rG3wb9oQQSvHTjIdRuTPkz8BXSQyVj5BkZUFZgOt7fNzjJz5AUjudKsIFcBylIKQWXYGkxDIqGmWunMZazcazNlzjGNWlcBVtaxU9qt6HUlaKoog8y5eLHrXgw0aDr3/9G3zta4rikyQ5ZUHF2a/ryds6CrYthzxLmU2neK6LY1lklTMLysmRFRJp1N2ME/es7MKzvlf3po6yFNqphyhKee+Hd3i8f4TvOro2wqsCQdM0zHEcHFcV3KE56aYY0NyP7/vVa9X7NNJnqzSv7Tha2EDREiu0y7IqikAdDa7fX/0wohAVxUfdoHpGllXVKdVmjpK31M/ecdwKdZQmcKk1sDNF8UJYWPYSkTPXq+7b1tdikBM1B0ajEWmcM51OODk5Jk0TTPPRvDA9Wayl0/Yx2AgG1TEoaR2tUc+z1HUEFrI8985zwVV9TIuiwHYcSiTPP/88nU6HyXRKmqTcv38fy3Zotru0u33avTUuXbnG1es3efDgPrbj8cbrr5JGC77+73+X44NHiCJVQiSA7wdKRUeWrK2tY7uudnAkUiNRo9GI8WgMi0L1NghCXEswPD7i3/2bf8Pnv/RFbj33HFkG+/cP6bY6dLtr5FlEnnl88MH7XLy4x9WrV/nWt77FZDJhfX2d+WzO3t4ejx8//shsxEcdS5BBVLU4lqWaUxfFs7Nx4tzGaHoiKec5xbYFo1FSO7/xSFQGBb1JGhpGLU6uPee681wPrlezC2YCyOr/JjuqRUdYZmE1pKG/VqZNdZSrn7zqxLNqZ+rjoCCdVbDj/Dw041EPEn/cczoP/tRBofPKglLKSnRBAFKuyrG7gW6OqG2Z2bw9z2Ozv0a71cbWrIFmq4WrbVvQatJfXyMIAvI85+joiEePHjEcjvjhO+9zcnSC57i6V4lyaKQQlGKJ9NfnpVmL9aBxZVykkmZQmaICx4Wr1y7x3/2jv88Ln7jMwcE+3V6TvEgYDI959Og+Dx/eJww8rl27hmMJkiSi3Wri2g6NwKcobRxLMp2NGY9m9Hs7FEmBEC4S6PX6BEEDSkm/18axXUrbwTKqr3rcTc1OlhvquWtSP1i2zfb2RrVAiiKnETZA3/P9e/f5wQ/e5cGDB1V/qyiOaLfa3Llzl8FgWGVqbdvWapJKlMb3/Ir9EQQBQRDQ6/UIGyGO7bC2vka73ebChQu8+OKL7O7usphPGM6mCCHI0oJf/pVf4ttvfYuHDx8hCxDlaq+f84DI/1THn8T5zRyq90wzjqhEqAxHzRf4KIU8s8eYtWNe//Q1Llkcq/ehWgCY85wH5J61DwmhgIe8yHjw4B6NsEm73WVnewfXCTg9O2E2G5NlGWmaMZ8vaDabTKczHjx4yJOjQyzL4ubNm9y6dauqZ5FFRCN0CBtt8gJOT8dYlktZWqRJxMMHD0mimF63S5Ln2EGDS1c/QXdti1mUcO/2bYo848bNW2xtX+Dg+JT5fIxtJdy/+yFC5CjraGii5cq41APCFWCmFpwKDWLZtk0jDAnDENsGiYcsJGUiybMCWapm1UoWG6aTGQqjWgp/xElCqllKlT0Ryv5XzB69D5v11G6rGvtWq0Wn3SWOYsaTMZZl0+12uXr1ChcvXtKiJpJWq0Wp2SBFWeA6LkEYVD6f6bfneR4IwXgy1gCexPcD3eoBBoMhjuuwiBY4jkOz1SRNM+I4IghDimhBv79GksTYjiotsRxb9YYqC90o9+P38vqJD5CKwqT3chVY2EtELQyDanMxEqvUuJTngyBYGgKTLqzQ4FJWxbp1pEWlSqVKGwpBnmcUuRJCsISF4/o0G02SJCZJIk25ssiL5SZSXzCWpXpZ+K4PQJLEDM5OVSdo4zShVFgsHSDF8aKib5iI/sc5f3XKgZSKaqbEGBRfXNUJWsymMTOtjCd4Oji0bFttevVNHC0NW0PiTWbEGApQDrHjuFWD2aXzYq04Y+Z3quDQ/FasfO+6qtGjCbSEQaueYXwrxEa/1hhqs3EIIXBcG9e1q9oPUzTvui62Y2kEU/cksG0838e2LK0ItizCV4iYzXQ6xfcC9h895s6du0hp+gaIKkh2nKCGDq8ez9q06qh9PUBavmf5XllL8y+zCKvzxATLxpA2Wk0uXb5MGDa4d+8e4+mEo+Njuv11wkaDdqfL1u4FHM/n4PiIRZbz05/9LGWe8q1v/AFH+w8hT5ktJnieSyNsYFk2URyTZhlSWHTX1/GDAEdzlG3bYWOngeOHzEYjZFEwn81pNVs4lk0yW/C13/v3fOHnf5bnPvE8k2HMbDyj22ozGXm4jsd8EfHhhx/y6U9/ms3NTb7//e/z5ptv4gc+V69d5fHBwR8vOKp9p1QZjZOhqa/lOXGXcyMNy8BFiSGIKnC3bQPmLJ8XOlNpHATKOgWjfs5nAwUGeVxCH8vslABkRX8T1bo2WcVlJssEX08HZk8dNdBG/V9Q73lS8e8EKltYC/zq1yxWodYlyIEWeDnnVNWDhTrt1wA0as06uJ5Hp93m+vXrBEGI53l02g167RZra31c162U8BqNkGa3TX+tj+s61T6heidZvP7KqwqhzZVjUZRL+hKOrcAeS5DpDVsIizRNefft93nvnfe4e/ce9+7dZTweE8cxURxTCuMciWoumOdr7GqViSqlfp0S6XEsoQI5R/DmZ9/g7/7dv8XGeodHj+6zv/+QjcUae3u7WFh87rNfZD6NKPKC06MJn//cmyAl+w8fslikJHNBo9Hi5OxU2SPhcf/2Q1qtDS7sXUAIycP7d/H8gDhKOT4+5fr1mzT7a3T767iuw2KhgJBGo6nskWXheu5KkGpZS9l3x3EoNTU9LwpOT47Z2trky1/+Aknyqaq4/rlPfILA9zk6OubRwyeA6gXTbDZZX1/H83yKIlMSzI1GRRc1PYUkUtdGlQqEs23Ozo6ZzcYEfsDgbMBsNmM6mVBS8rnPfZrB8JjxZIFjO9RpQ/Vs0scN4P80j4pJYSv2h8kiCFu1NjkPPJpeNc8KYFzHAbnMcp7fl+qCQeZQ51kGYPXPqv9b9y2W7zUiMRnvvvcuL7/0Gr4XKtECAc1mwNnZGZPJlCRJWSwi5dR3O5wNzzA2bjgc6j1SNYP91OuvkBeCw8MTDvePyPKYOM5J4hjX9uj3+sRZQnejz86V5/Bb60wXCY8fPgDg+o0bbO3scjocMhgM6PoWd96/TVkkCFGi6gBtwAKRsRQpWQZIlmXYJhh0ayXoFEIxk5IkYX44wbYhy1OyOAPdoLwOqqiSjgIpiwpo7/V6WOlS+MZxHC5fvkyr3cZ1HBrNJoHvY3uqV1yn0+HKlStcvHgR3/eRUuIHIX4YViAmwtDZlJBJludVHZGUkjiKq/YQZaFA5jRR1Nq0LGiEIY1msypViZMYp1CBVLPVJMtzwkYD27bJtGLdaDxGWBZho4GpIo9iVRdr7H1RFLBYkH8MtlE1Hh/7lf+ZHo5jFqSq9zAoaLVZC0sXOS+b8dX7xCj1OBMcLXXjFaKqNjgpSyVbKJbGxAg8GAem4vJLpf4jUCpQlhC0Wg3mM0k0j0iiGM8LFF2GVSOxNLYWtuMhLBurlCzimHkyxnE9fN/Dkig5ZFswm02J4lj7IwLb+hHB0Qpra+nMCKEdDKg2rup6JAhUBsI4KzquUKfRtPqyNNxvdf9pXW5cooqEddAkizodr05XWNLr6nbXOF9SGDpFzUkqy6eoR5bu4VI5/HpJLcUc9Hn1V3WftXoqZZwcJWdpCvO1Y2euwaChjuPgua76XnP1bZ1BkkidEVSB+Hg8YWNjU2VlJo+rMbFtUGIP9YdUpxs9XXBuWyrjZBvnvCgqZMiyHGzb0XNRUMil1Kj5qp+v+t5kKmybi5evEDbbJGlBmhQ82n+E7QmaDZ9W4LG13qPXbTKcTTk6PuOTr71OOwh5+9t/yP79D0DmzOMZzU6Pzc0tJuMRs/GY6XSMLAuyOGIRxTRbLSxhEfg+mRYAiBcLZFHQarXUBrGY0261EZYgWsz49h/8e778s7/AJ168xR/94TeJ85Injx+RZQs2trfZf3TEzRszLl28zre//W1ef12ytrbFaDIhbDZY6G7bZoOqZN/FM2TfzczXlahVLaAEISW2EOS6EaiShjaqRI62NTqYUXr46slaDnluaJDWcg3WgBoplWCIqGbwuUCnonOuKnQJjepVs6gEp7AoLMh1fY2UujuIQM0NeQ4RtqhsilLnRK8hnc21DLCkheaFhSVcyhJsIbEcsyDVlSppf1X3YJrB2o4CV8w8zbKSIi+rBoOKwqqeS2melQFexDK+ElokxXZsHNui1Wrx/Asv8NM//UWuXrtCEAR0Oh263a62GVJlscol5cRkh4uiYDKdcHh0xOHhEzoaSQ18n26vx3AyxnM9irIkjiLyvNA0bhuJ6l3neT5lWWhUe05Zlmxur3HrE79U8fUXiwVnZ2fcu3eP/cf7HB0dMZ1Omc3mTCYT0iQhy1WfKIPydjod/CAgiWPKPOPizhaf/dyb9HpNXM/ixRefYxFNiWYDvMChv9YHCSdHp6yvbfP4/iGff/MrIBR15eDJiDxNuXTxOb739ttM5gleAIOBoiVtb++yu9uC0iKXMBiO8b0G3V4fy5qxteMQNhu4Xohle0gpaHf6Wj1LzX3HUSyBxWJB4CkFrv37D4kWC4JAKc96nqfvOWV7Z5toOuPJkydMJlM8z2Mxn/N//x//r3S7XV595TXu377D3t4FuhsbBK7LYjrFbilq3+BESTtvbm5y69Ytjg8PK9pOkqR8+9vfYmNjg3t379HtdZnP58ymc3q9fsWMEAJ67R6//It/kX/+L/4V01mk5psFmczBUaCICZL/LB9q7Wifx0igm6AHFVy7rqsadDs2cZyT5amyHkL5BgCWKPE8jzff/Az37z/i0f6BXoN6F9XbeSmeFrmqbIpW+j0PyqkE97J2ZcnGMG0S1D6YpznvvvMDbty4QRTFJEmKH/js7Owym00rlbo4jnE9j83NrUoc4PT0tOrtVvTanA5mDIdjPvzgNqPxlDwvybOCtJCkRUHY6HDt2gU2tncphcvJYMCT/ScEYYPL12/SX1vjdHDKeHBCyy65+8E7JJHaU5ZBoAR0WUQtMKoHnbbGiqSUei8xbSD0e8k4eLKP6qNmKSpdXuKgQGTHcQj9gEbYoN/vkxU57W6HTqeD67p85StfYTKZ8PDhQzqdDmtra2xtbSGlks4OgkA9L9uuFEQty2I6XzDXrXE6nS7OdKpATSlpNBo4tk2SpuSZqpFtNpvkWaZZMUr8J9MZOyOWJnxBnmVVJs8ENe12+6kapjiOcRxHyX1bFtvb2yRJUpVmZPo8dcpo1UD2jyGm8hMfIBVFiesuJ2VdaabOcVxVR6rzvZco5Co/dHkOM6kNAmDQSasq0FUFoVKbI2Gpv5WFUulq6GIzy7IJAg/bcojjaKVGoJ6yz/OcrMgJPRfP9nD8gDTLKEpJnGY4QBxFSFmojs+FKv43ErQfFSA9IwlR3axxjmXt3o0DZNK9P+4wlC4zXmXlDFq68lnWDO+SQrIcg/Mpe4MooYPP5c8rFCmxDCvUOesFp7ISzaD2mup7/WYppeqFoYMgIRS3Nc+KSiFQiGW9iUFvnsrAiNp41j6vnkEYDIYrtMGyLBVNsSbRbQLR8wjdihMrltQcM38VNUg5nkJ3KFm5d549CarNTAMMErh85QpB2ODJwRPyvGR0NqTZCKEsuf3+D4mSnJ2LFxkOTuh1Wlzau8Dh/j3e/cH3KIucxWJBp9dnd/citm1xenrCaDyiyDME4Lg5i/mcMGxQypIoK0jimCxNFdorFY98d3eXNE2ZzqZ0u1082+Hs5Ii3v/sWn/zsT3Px0gUe3vmQKF6QRDPWNrdJ05x79x7wyU9+kjBs8uD+Iz7x/C1aZ20uXb7ED997zzz96v6fmaXTc88E02ZuGHVGWagMsrIjqr+UsLTUaFlgCSVH6vsujWZIv9dh7+IlDg+G3LlzT1MrtWR4zQbUr8cERiajYDLPlmUhtHhKFejXI4fq3gqkVSCFi0BJfAtKHFFiSYGU7hLd1/WENmrOq94zKmi2WGY1hFXguEJTMZqEfohr+4Rhg163RavVwnZsRaG1Ha3Q5+L5CkgKwoBWs0UYhvT7fXw/4Ox0zP37D7j/4D737t3jycEBw+GINEvJtES1cupEFWQ5jo3jKkTzypWrfObNz/DZN9/kwoULuJ5LFEdVhqcs84oCUpYljm0r6pWuN7IsCz/w6XQ7XLp0CSklURSRpimTyYQsy3hyeMh4PAGUkI9yLg2t1qo2f/OMfN9XlLsi42xwguu6DAYDgiDg8pWL3Lh5DZO9Bqr3GsfCSPKWZclisaicyCJNkXnE5Ut75EXKYjHjd377d/jpL/80hcyZjBesr29z9coV7t+9R5nbjAZzNjf2GE0mWJbFJ557GT8I+M633+LgyRm3noM4K7l87RbNZhtZQqvVptloIYVDf31Gq9ViPl/QaPUBaLc72H4D2/H0nlco0l+hwMUsVSpVruNQFAVnJ6cMTk8ZDUdMp1NS3cw5S1LKoiCNlRx8r9Oj1WgRBCpD8MB9yNGTI37n6Lf5wQ/epd/vs7e3x6NHj1SNpO9j2xYbG+tsb29zenxCEqlm6o8fKyDqC1/4Av1uj5OjY65eucJwOCT0A8bDMXmWEQQBk/GYdrtNEsdsbmzyqTde5/e/9nWVGbFcJIJClsgy18HDx0er/zSOlf209r1ijaheO4biX2XFylILdag1ZwlwHZfPvvlpXnzhebZ3djn9zd8imkdoBFUDkct9sC7KZOiP9WMVgGJlz1z1xRSAZLJQWZby4YcfcPnyZRzXZjabq4xwp0ur1WY+n2u1uvipzzcZtPFkwsHhsVISTpRTr4AOl6DdYXtri/X1TRzXY7pIOD07ZjKdsb62zoW9S/hhyOHhE6LpCEemPLj9PsliVqvR1OMulkDkCq3u/DPS929AVQW8CjY2lbx5EAS0Wi2uXbvG7u4uo+GQXrtDs9GsQBNPM10KJF6g1E/LsqTf7xM2G3R63aosAsBzPbzAr7XYcAkbTYQGT4QQuk1Dynw+r3opCSEYDYcEQUCkaXAgOT05qRg4/X4f33EoUEG1EX2QUlZBjAlcHU2TW/UDlY89m82wLCWOZmy4UXV1dM/ByWRSnSMMw5XWGB/n+IkPkIQQFepgVDxMcaxxPswGaH6uL1YTRJkHYB6UmdTGCbZqqIh5sCaaRXM3W60mZydn+J6DZStFJ2FZuK5DEHg0GiFFYWgS1gq3efWeoCwU7cpzPdbW15VKV5wQLRYUaYrnOjpbI0jSWGe7fnSAxIqtFCu/r//48SdYuXQgRZWPAU1BWt9Y5+bNm5ycHHP//gPNk1XOY4VOS+P8CYSo1XzJEhMrVPdlqCeGrqH/JmsBpuLEasdW31y9T9Eyw2gCv9VNw4xdPeCu/75OGzyPkhlKUN3A14Nv81qz8I0D5XkelqO4v3Wp6B9/PLuWSP/pP+owAWcYhLSaLfJcBTrDwZAsy+h22oxGI05PzxiMZly9cZPxZMobn/o0WTrlB2+/RRJHZElGELTY2b5Er9djMDhlOp1VNUeO7WDZLo7v4wUBoe8jEDSyBqPhiMFwSCnzSjRle3ubgydPWCwWtFpNHNvj7t27XLv5AteuXObw0T22dnY42H+oaDS2zcOHD3nuuedYX1/n/v37vPDS81rlaIf33n2vCkDNPFoGGT9qfMy4g5SCElvNU1XMhmWXOI6g2fK5sLvJpUt77F3YZX1jnbX1PoHvM5lM+b/8k99YmVeFljk1KLY5zlN46mpt54GVp+kuGnARJdIuELhY2NiixCpjWp5UVF6ptgnHdlTwUAjSLKPREwg7psilDgRUUOO6PmGry1/91b+p6BrNNq12G2UHLUI/wBKiApFOTk6YTqe4rkuWp7iuS6PRULx6DTJlWcbOhV1+6o1X1KaYppwcH/Pk8JD33nuP8XSiaKx6I1xfW8N2HMIwoNFQtSb9Xo+w0dDjp7JkjUajWqtGZdB13copNA6BCUZgycs3KKdBLJMkIS/KqoDYrOtKIRWqFhPmHKb+0hKyclB6vV71/tPTU4bDIYvFgo2NDVqtVsX/932/Op95vQHnFvMpSTRVqlzAeDyku/aQrHC5dPk6Fy8/RyMIsW2bTmuOkJJPf+pLpFnK48enPPeJ57CwaQQdXn/9M8ymCTduvMTW1rYCoqTAdTwNLEoWUcT65g627TCeRnzwwR3SNOXTn/o0nl2QpRFlWVS1c4vFgtlsSpyoQvl+r8fWxgZlnrO+ts721jYbGxsV4GT2042NDS0QsVQnvHjpEi+9/DLz2Yxvf/vbdLs9jo9P2Nzc4Pr1a9i2w7Vr1/jWt/6ITqfD9evXuXPnDsPhkGazybVr17Asi/F4TKfTAWA+n5PnOePxGMexOTh4rNeVzdnZGa7rcnBwQKPhcf3aHvcfPqKMSzzXJssFJSXSslj2dfrP/6jbGrWTK4DXc21uXLvKtatXCAOf7c0Nrl66yO3bd8h0Fu1ZI1BRT3m6DchT/gerPftWbdoqcFoUBffu3WNza4ter894rOqQTL3Z2tqaEmlIEt1kOiXLcopEX6tlMV4keJ6P44U0g5BOp0er1cLxQyzbZZYkjI8HTGdz/CDk6o0brPXWiKOIw4NHFFlMHk94+OA2ebqofI1njWm9jOIpv0yP9fkxEULwq7/6N3jxxRcQQugaJOXLFnnObDpjY2ODIAiI45jZbEYWZzRbTSaTSSV3bbKnxkZZlkUQBMxmMwDCMNTArPJ14jjm5OREi0IEFcXP+NRmf1Ly4Kan37KnUpZlTCYT4lgxpYIgoNFoVIkL9Xv1fvN9WZaMRiOCIEBKZSejKMJ13QogqoJcSzEEFotFVR9qlKorVek/xvETHyDZtk2SJDSbzRVn9XzBuolabY1kmd+bSLYe4ZtNr+6UFLLEtUU1QUxvkiRNKCVMZ1MVCIUermNTlrmWH7QIG6HiaRYZtm0K/VW9kI1dZQDM9bqui22p9GuWpRRZRrffV02+5jM90RVn3XVtbMciiuYfbYCgzjqsjuVrnoWc19AdxLMXNxIpcyzbAVkSBAGXLl/mueee4+atG2zv7qgFphfd/fv3+da3vs3J0akq2pMSRUdfjns9A2TbBk3Qz4qaiIGJxUAhyzogshTEjeFJK1oQFX2KysFdDXiqz9e84HrwUy+Uro/tM9F+jcw9ayzrzu0ym6kpeDqDdB5p+6jDZMdMHd2KoIhcFtI+6zosazkGz9zcJbQ7beIk5sMPP0SWypGzsfG9kHExRwhVA5BGEaFjc2FrjXt3P+Rg/z6UBWVRsr29S7e3jpQ5w8EA27ZZW99UYhRC4Loez734Ajdv3qTT6eDZDkmS8PjRI77+ta/xwfs/VIo5nken2yWKY4W+hwGO47JIM77/9vf48le+wqWLe2TRlN3dHcbjCdPplCiKmE6ntNttHjx4UKlzNRoNLYihMkA/9qitHQMImCBJCAvHdUCo2gbXlXzpi5/hF//CV2m1fRoNnzwvqoapeZ7ze9/+JvsPH1HvOl8Jk5zbJOvP7VnP81nrsp7xBiiljbRcLGlhixSXBS883+fX/sZXaLYkpVRCEb7vV4DA2dkZnX6HNM8ZDSeUhUW3s44sba5cuUlv7RX2Lr5CtIixXR9hq8xOKQvyMsWxHabzBScnx0gJa2trVSNg1bhaZ+/1+vRtG6XWl1OWgrxIWNvos7W7yfMvPIfjqfq/2WzGeDwm0K0HkiSuaNZSSkajAY1GA9/3VYNCz68aD4ZhqBDQ0YjT01OyWkBq1rXJDBu0MwiCWv1PSbPZJNCBR6vVotvtVo7LYrHg4OBAt56ISZKE6XRKr9dVKphakSmO4yrouXDhAru7u9X6NUGBsQ+qQSQVmGIOx/NotrcxzRR3Ll7jV/7KJRXMpSme42BblgIihApEy9yhyAvu3HnECy+8Rr/fR5Yltz94j0cPD1nMUzyvSRQnnJ6cEUUx7VZHOTiF4N/+y3+FEIJPf/rTBI0W3/jD3yVstsnzDKmR47OzM05OTrh165YOKFWdQTSb4yBotzssNEBkGrkvFgs2NzeVkE2a6qDS5e7duyrA63TI85z9/X1u3LiBlJJr167ieR5vvfUW7XYbKXN+7ud+locPH/BH3/xDkJLJdMLG+kYlYPR4f5/5fI7jOLTbbeaLBdFiQZ4XZFmuUfEY3/dJU9XAukwz9va26PU6vPfDD5nOYspCqvEsxVO2v348E6T8M3ScBwdXMtioPcR1bH7q1Ve5cf0aZVEwGg6Yzxe8+ak3OH7yhOlsTiEV3Re9J51nVdT3yfMA5HL/lSuvWf5NsBQ8Wh3Po8ND5vOI7e0tsiwjihakaYLnubh+QKPdobe2jmXbyLKkKFWdQGnZCMfF9wNtc+0qQBjNIsbTE9I0pdFscvXadTqdNlLC2ckRSbTAomB0esjp4T4UCRYFiKczZPVxPe/jVM+AVTtv9oArV6/ymc98ugKETFbZBDfNVotGs4nnuURxDELQaDZxXZdOt1sFFXmeVwGLybBEUVRljubzubZHPcpS2bx+v0+3260YLSYgMRlsY6NUBt1mPB5Xwg4mo66EalRAOh6Pmc1mFXAkhOqJ1myqOsXzwRosxcqklFX2ybZtTk9PK7th9nZDiTfU7I/rQ8H/HwRIha5TqBsnk7Izi2+lSVrNCJgHXRdtMANsBrkKpiQrD2LZp0NRuPr9Ln/77/waw8EQWZT80R99kx++8x5FWdJohFUPJSUPXJDl6VKenCXaYvjw/W5PKXNISZrGjAYDJtMZZZEjpAkiVCGECtiyqhHfswKkZ8RHK3/jmfQiXVQpV4OApZETdHsd9vb2eOWVV7h58ybr6+u6OFd9YJblBKHHxUu7bG2v8/IrL7KYxRwdHvPo4SMGwwHz+ZwiLxQPPUvJUhWcplmqZCPRRdGlMh5FudTlN3K30jrX9FSgVf1A9WV5VkZtqVZYGWqpGu/W7/P8xlcPaM3PJmB2bIuiXAZW9bE87/A+S5yjTperv/ZZGSwpVS1IXYGr/h7z/bMcbJUBecYmqWlMjUaDOIqZLZR61HQ2xfMDHMfjhZdeZpEmXLl6nbKErc0tyHMe3rtHkWVkSUS73aXf74GQPDk4YDwe0Ww2lYpdGHJh7yKf+9znWNteV9TOUvULa0nJxuYmn3jxRX7vd/4t3/yjb1KUJUmasraxzniiejF1Wh1cGw4fP2I8OGPv4h4PH9zDDXxaRlEyzxkMBly7pmhMR0fHbG6v4bquuqfJdDV1+tQY6fFEy6TLJd3NFN0qdcQC17NZ67b563/1l/nUp14hDBxKVD2VLAosCbbt4jser7/2U/zO73yHOFmVxTcI2vm58qygqR4MP+vn1cNBSBfXSmi5M776My/zF3/pFdqthLJMKKVSfxSWahpdliWdZoe0LDkZjBAiZn1tgygak2dwfHSP8cSi198lzyyyKCJoNmi2GwgBnu0hpMoSN1otHMeh0+vq+7QrW5emaUXpM33EHK0c54dBVcQ7GA0ZjUY4jlOjUykVuFarhRBKEVQ5eBaPHu2zt7dHFKUIa8HDhw9ZX1+vxtlxHBUA1pSaDFvAjJ+hz9WzeQCj8aTK/pqMkHFebNtmd3e3WqPGWZBSNWs0j7Gu4mXsT13+2+xXJmAzdjhJEp1dURnY2SyqrnU+V13o1c8+siiJohTXKbmwe5F//Vv/irX+GnES8+ILr/D9t99ld3eX8XjMd77zHVqtHmmScefOXRZzJSa0mEf80Te+ydVr12g0Qq5dvca9e/f4vd/9PZWZuXqNu7fvUBY5zWaD48ND0jSl3W7zR9/4hqZvKXbH9uYWRZpx+dIlijzn8ePHKgPYaDCfz9ne3mZvb4/hcMg777zDzs5uNVbvvPNOlQnMsoyXXnqRb37zmxpNbvJTP/UaUkrefvt7PDl8wmIxx7Is3Xcpqp756dkJWZaxtrbGaDzUtWyKutVutxiPx3p9eZRlQRj6WJZHUeQgBa+9+jIf3r7Hk6MThOUg0xJpWxUt8j/3wwQuUkqQJa5j8dzNG9y8cR0hwHUssiTBpiR0bV5/7WV+/2t/qAMPS9VEWk/bocqP4GnfpAIKa8O3asM0mPwsEMiymM0mzOdTNjc3CQKf2WxGUWQs4gTbC1dUDqvDE5R5RpIrHzFNiipT6vgBW9s7tJoKZMmzlMHpMUkU41qQR1MOHz8gnk+wRKEyiayyUer7+7IGdfVvRqikfhhb0Ov1+Lmf/VkdwKkMt/FJ5/M5/bU1AOIsYTybIBD01vsqA25ZVebF932mU6XOuL6+rpt/KyC77suq7O0SFDI+qLGJJpvT7XYJtVhDlmU6Cy9pt9uVnTLJCmVLqQIeQ30za/Hy5ctMp9MKOPR9v6IRm3losvsmUFosFnS7XcqyJI7jKsCqU0STJFlRhf5xx098gGTbNhsbG0gpGY/HKwGPQQbrFIjzTVLNxDWbnpmI51Va6hS++mLrtNt0+z0Oj45ot1v0u12EgCtXLhL9hZ/n/v19Ll+5xr/5N/+WoswpipKyUEWTpujbnM98dp7nuLbD3/qbv0aUxPzu7/47RpMJWZqqJqUVTcwITlgVRcEESH8Sh0q1q0DIZFdsy2Z9fZ2LFy/y0kvPcfnqHs2G6n+jKDo5UhZgCYpymZkrypIsTVXGrci5efMGzz13iyiKqmDTFDRPJhMGg0HFOVUoaUY0jZlqzunZ2UDz/VNms/mKcTefqZ51iaQeiCzVriSKGWU4sq7rEidx9d7zwZFZ4L7vV46K4ukm1T1kWY4pnoTaBvAMx7UuBmBQFzMH69Ks5x1kcz5R25B+FNXq/CEllYP/1P3pkVlbW6tQqKIoiKOYVquF32iwvrvNF195lTzP+fY3v8Vrr77KfDpl/8E+SodA0uu1lVTwkwNGZ8dKTcxVyN6ly9f4ys/8OWVIrYI8S/VYqSZ4ipoq+PO/+Iu0Ox3efvttpKVq0JqdNoOzMwK/geM6xLMZD+/f5eVXXiVoNsn0czSy8sPhkGvXrtFoNDg7O+PKtYs0m032Ll7kvXffXeHN14ONcysBkzEyM0c9A5AyB6vk4sVd/rv/9h9w6cIWlCmebRMlOUVeYuExG89RjfokvdYmly5e5vbdByu1dObrj9Nn5aMyxqt0mRzXStleE/yNv/IlPvupPWwxQerst+UEIHNkvlTWTKKI0gqZTwWu3SXPPGRpM5vOaDVgtjjkW9/89/TXLjKepOxdvsIFf4+CHGkLPMdTSkT6GURRTKPRqK4ty3OKUimNuToIyXMFjERRzHwxZ39/n729i3R7Pba2trAsu+rPsQS1JEmc4LgunudjCQdr06HbXdOUvoznn3++svsmQ//mZz5TKZwZFckgCFao08ZRME6B53lsbm0jZQ04Y7XmwthwM/7GflnaJuR5XjkF5n2e51UUFOMkGdofQKvVUlmYKKqANsMjLoqC+WSG6zkcjAZaedMjXqhGj3maMRwM+fDDD2i3Wnx45zZJmvDlL3+Fs7NTjo9PuHPnDjdv3uDevTscHDwmjhNeeukViryg3Qo4PT4gzTJG4zHNRgNZ5BwePGZjY4N+p60ArCInbDVZRAsEks2NdYXqxzH9/hrz6Ywfvvce8+mUbqfLzZs3+ea3vsV8sWA6nWp55ikPHjzgW9/6Nt2uCqi/9KUv8dJLL1UZwOl0QlHmvPHJ17l9+zYXL+1xeHjAcDjk+OQY27Yoipz5PKbZDIki5XCFYcjzz39CN8CMK4dwsVjgtXx8P6CUhfrZdwGFtDeCANd3VX1K2ODixYt8cPtD3v7+O5Q4pNmSon/ejvzndiwDdoswCHjxhU9w7epVdH6INImJ5nNcWzAdD7hy6SIPLu5z78GBItaXJXbF11gNhs6PSX2/Ur2CrGe+zvzumfunAIVFSo6Pj0AoR7zVbOGGHnGaYts2UZKqEgnbwvN8wiAgDBuAau3huT627eI4qtl5lmVEiwWz0YAiT/EcG1EmPH5wn3g2giLFF5ICSQGUwsFmCUAbIPOj9u/69yaDDkuQ/C//5b/ML/3SLzGeDLC1iEb9/mfzGaX2RTJtu2aLOVtbW4yGw2oPNPS0ZrNJGIYAVV8oY+NMdsb3gypjbehv5j6iKMJxHAWaxjHT6RTLMtelsuWGfWBamqhslV/5441Go7JnlmVxcHCwQpEzFHMT8DiOozJlzSZSyqqmyvhlxl83X0IoWm9DK1h+3OMnPkAyKDEs5V3N4jM8SuOs5nm+UswPyxRvXTXFRKBms1Lobsh6f53T09Pqb0YG8ezsTHHpNSKVpim2ZXHt+jVeevlV8lwVO25ubvLOD95hf/8xloXOIIkVKWnjKNsOnJwc8d/+o3/Ez331q/yTf/JP+d3f+z3yolTpXLm8j1Iuleeo3V+1kWojQr1E3yDTtWBBvxNlEEW1gF3XY2Njg5dffolbN2+xsbHB7u4uhUwYDE+0XG1UjZdyhD3iRBXJZmlGmqUUec7ZYEC6yNne3kUAI80Lz/O8QjayLMN1XXq9XlUvEMcxRafAvXoVgEwHNVEUKc5sHOE6ahwRgkQ7JrZtEzYDhRQ3mwRhyPr6Op1OG9fz2dza4q233uKf/tN/yqc//WnyPOcP//APq0Vo5ofnefR6XdbW1pVQgOchpeT4+Jj79+/j+z5nZ2cVrc88AxMEPQvxr3+5jk2r2UQgcB0XIxzxow6JzvjIVfljAGnJ6lXPfOe5wGiZLVGB7ubmJlOdUlepdbWmCgHNTgcv8JkO5kgBzXaDh/ceES8iiqwgDALKomA2GzMYDknjmEbY08W0HT7/hc/TbLa0ulKJsC3ViE4IpFCS+XmasogWfOazb3J6dspwOELKgm6vy+BsoMRZHLVJHj55wms/9Rpr6+s8fPCAru9XXbWHw2E1t87OzrAsG9tx6XQ6y/s+N86rAa1RfAKjf1B/uaSgyFOCwGUwOGMyOCFezIijOaPRmDTNmU5mnJ4OiOOEslBCK0fjGYYiKSxRPcOPeubnM0nnr7NO2pBC6d4JAbYFrp1z40qLv/03vsD1yw6WPEWUkjyzEY5NLtUmaebtbD6jLCRpqRoQjkcDSjkkDJqkWcTxyT67ey8xGp3w+S/8DJ3uLgUOpZBIUWJJiyIrybICzwtZW1Md3EfjCbPZFKCiY8znc+0gtypgSm3QCRsb29i2ku+3LRfbccjSHNsS+EFQ2WvaSsEUVODS62nhEi2PbjI9JjNTliVJHLOxto6wRFV7ZYKWip6iwQ4DEkjDMigK8qLA9zwWWv47z3NcT9G3U63EKNFOTJoxm04oy6KyZ1mWEQYhc53tcBynynqCCooMwup5HgcHB/i+TxRFrK2tIRDMxjMcx8LzXaTMiZM5BwePabZaSCnUdeQF0XzOyckTPG+PKJozX8R85ztv0el0GA6HtNsKiV3Mp3RbIeu9DuPBMWv9dS5cucjDh484Hpwymkw4evKYZrNJq9UmjRecTiYgoChLBoMzbfPUOCu6XMhitlDIfp4zn874zKc/zeHBE7a2t5lOp5yenvKbv/mbvPrqq7z33nuEYcjNmzdoNlscHx8hJezu7lAUSu1uvpgyHA7IspQ//MOvk6appm4KVeeb55WK1+npKY1Gg8lkonqnaOcwS7NK1CjL1PPq93sIoZpqdrsdzJ4Zxyme76lm7XnCrZvX2Njo8c23fsDBwTGWtSx2N2ItSpt/uTCfXqV/+kd9X6pnpjudDm9+5g06rSZRtKAsHALPw3Eder0uo7MBhS0ZTY7otNu4nkuZlVWfwfP40rOy4iu1IlL+yAFZrftVYJXJ66v9tVQbgZRkWcrZ4AzHj1jb2KHX71HkBUmakMQJaZogZxatdhvHcQFBWWQKEJKSPM2RElzbQmYx0WzCw+MjosUcT6ZYZYaQRvxLILEplfSNuZlKya26Nqj2jpU5YFkI/Toj8d/ptLn13E2GowFZFjMejytWjus6Cuy3LRLtI81ncx34eZyenuI6y9YEJkkwGo7Islxln/o9HYC4eJ5b1TAJYXF2Nqh8Z8dRn2WSDJZlVWvJtu2qNjjL0so+TiaTqu7P932SJNH0V5W8mM/nSnZc27vzvbnMnDDglAGo6vVLjUaD2WxGkiRVlitN06rnmck4fdzjJz5AMpveeTqTLKHIVTdjZQRKHNtDYFU1IkbCGyzy3MhQFxSFRIhlJkkIJZV8dnZWPSRT0JamaVWTZFmW+kxhkeUFtutRUHA2PKXdbfC5L3yKNz71KtPphMPDQwbDAdeuXa14lEDVWEvKjNOTU/4f/+x/5Jf+wq/wd/+rv8UffvPrLBYxuclKIFjKddtoZQhtjVX2RwpNDZJUjqjJDNm2jSWVDKjtOLiei2Pb2LaqFRCWYL3X5fLFXba3tul2lVN5dnJENJ+QlTmzSCHjs/kMqdGGIAxphiGWpYQqzDjZjk0zaNHyBWkc0e50uHblsqY6CLrdLo1mq+qGDVQFeJZl4Vk2caRU+5IkIdLfTyYTptMZi8WilgoOVHZxc4OgFdBut5WKludXvN80yUijjOHpCbtbm/zar/510jTj7gcfELYa2LrmYW19XRdOB7iOqygFqJqTsBHg+Q5hw8ed2qRFRpEvsz8GaalvRCZLZBw827bwbYtes4ErbBxscnKU7p9u6FczrFU2TGj6o7WsuYNlrVVZZsss2rm6qGdRuFSAZSEtmyjNyCVIKYgWCbKQWJaNb7sEwsJHUMYx7UYIQjIenkKRUZY5vt8my3LOzgZE87mW5p4ThiEvvfgc62stsDIoSxzLQkoLaQuysqRA0Tscx9aUSnjtp36K3/7t38Z1fUXDcjySZEEQKGn1yVQ1klxf7/Pw7h0oHLI0w7ZsFlFEVuS4gc/hyRFZKSiFTZTmFKXKKCKVEl19Z18GnAAK+FjWHQnKUgcnpcCzfB7ce8z/6f/4f4ayxBY2Ra6yI7Zt02g0aDZbdLubbG9v4boeQTdk/8lj/sN/+H3SLMbyA03Xk1gsM4v1wPc8HbL+s4VEiIJM+pTSx7bBExFNL+Jzb+zyy7/4Aq1mRhHNKWSmuqALicxzyiJHWiUFBUmWM5lHJElJmuSMhimLRU6cpLhuSdhogddElj1eevF1okVJEOY4noPAxrY8RFlie0tVN1M8q8CF3kpRvhA2/f4aUqr6QVkq6kyeZ9WctiwLS0CuqVymebPjWMoZEVCi5oztqE3TlgqwCkKVfRFAWcv6eLZLPE9wHZtcpDi2TVnkpGmirKmtlCCjGjpqWRbTyZS8KJjPlRz3xYsXyXMlJpJYVCjpbDarHMHBYFB9r+qs1HocSPX60WhAURTMZrPKUVBOgs1oMGJzc4Ner8/BwWMVaMQLwiCg2+3heS7z2YyyLOi3e7h7Nqdnqg5rd3OXLM/Zf/QIx/PJS8kbb3yKyXTK5uYG8/mcLFOUFN+3sS1BXkpG4yHRIubOnTvs7OySZTkPHj5E1aKWdLsd8jzR9gWms5kWzugxn88Zj8cVUIYsOD05QgiLyWRMs9nkm299k8997vO88OKLOK7LxtYm/+E//Af6G32++gs/R6vZYj6ZMpuOOTw85ODggFdeeYU8zzk8PGAeTUm1I2SJkmbDZ7GY0W63cRyLRthStDgknY6iT671+mRJziKL6K+tsZgrdD0PcoSw8Fyf0WhEt92jlCWu45DGGevrXRBlJYk8m02J5xPajYCf+dLnuH3vAW+//Q4LWZLlhipagoxRstbWueBomWH5s3DUbb/rurTbbV54/nkaQUAaL0jjmNkkJUtT8kL5FklWkmYFJUpFuNlqk2n2jqmrhKcBvmcFSbZtI+zlmBiGh2E4IAVSZvq8gqKoiTlIC0oLC0eDhWqndATILOfsySNGxweEoba/YUNlisKAWFPPbNsBHdimWUoRJ0SzqQ6oI30dBbYEKQQFDgij1GthC7AkCLHMZBRFfm7/qNBsDPtAjUGJhaXvTWI78MlPvcpzz11mMp1QFDmdTpvFYsaDBw/odrv0en2dKWliuRahH4KA2WTG9vY2hd6DhYAoihmPZriuj+8LbNsjy3LdM6xESgVmFIUECsIwrISChBBVkGOCku3t7SrbbvyZxSJmsViws7OD59n0++uEYViBEovFgrluqbG5uUme5wyHwyqwMQkJE9yoNexUGawoimi3lTCUuR6gasZtMlSTyYTA1wyA9L9Q7Kojz5/uPl4WqiDPIIcK2QEzOeuovkEUDdXCLNx60GVSfFLLUJsJUq8XqSveqQjc5ejomP5aj/5aj8l0jEQSNnx6/YtsbW9WkXedaymE0EWvsLWxxWIR889//dd59ZXX+eTrn+TrX/+GQhukCXRsSinJClWYixRVLYtxwj3PIwgCEKp/UrPRoNfpKjRAWLieh+Upp9Jxner6iyKn02yQxTFlkTMajaome3nuUsiShpZi3d29UKk7OY5DsxHQCEOlLNPv49W60QshVFo2CNT5rKUSiqKvlCvPxFBSPMtiba2vUBRtZFTjVkePf47ruJRSEi0WSMB2naoni0njmmJtRE6cJKytrxOEIbdu3WJza4s7d+/w01/5Mu1uh3/8j/8xk8mkuu5cqwumaUq329UNakVl7BVVYFn/BlQZSYOM1OecEEplr9nwaTeb+F6j6m9UUa7KZ9eg1AUlDGpSzyio5IgJjp4lsrE8lkZbNb+Mooju2hpxnDKfKpTbtlQjXiGhzHPm0ymeRnHm8xnUqIyj4YjxaKTWklSGLs9Srl+/RtU8C9OA2aIEAs8jzy3VPLMosIQgjWP2dnfxXY/5fE673cGxLbIkXqbWNfWoqesUPrx9m+PjY3b3LlAm6l6EZbFYRGRFieN4dLs97QQv77/KuZ1HOKuMW51qp5+dcHAci3arwWc/84YK7iyHRqjUyF566UVu3LypOPK+j+04zOdzEpnw8OFDvve97zIYqq7ijqXqVbI8x7LEiq2qFzXXqZnVsxMCWVio/lcgxIL1fslf+Lk3ePP1bSxOSHOFbivxExshFJ0xz3MKCVluMVtIhiPJwZMJ86lkMk5wvQDXC2i1e2xuXeVzn/sCl6++RJLZ2vGdYjkxtusRBA2EplyBAkg836uuvVJi0vbJ9xVgYVk2FkZYBYStEMQsz1XdqGPjaTTRZPSKfKn46NhLsRsjU5smCa5rV6/zPI/hSEnUFklGkRq1OkVbs+zlmnMcl2azSVnkzKaJLuTPeHJwgLAEQRAwHJyBLGk0GprKBYvFnOFwSLfbZbGICMOgossp6WElHS6lpN3uEMcRnueymM/I0gTHVo1VkzRhNo1Z6/ewLZhORuxsbxF12pWy1MHjR5RlSbfbJU1TxuMxURRxenpGs90mWqg+TaoOQcnmpmnO/fsPODo64ubN61X/kdOzU/IsodttV7YsSRLeeusttcdia+fKYTqdcOHCLghF8ZZS0miEZJmir21ubmjEeEYjFBW663kuo9GIw8ND/uAPfp+8lOR5waVLF/lrf+2v8sGH7/PkyYjhYEAaJSwWEbPZDN/3+f7338bzXKbTCc1WoPZIHaypgMxSn9doMptOq6BcCMF8ppzE+UwxSg4eH1T23ChoSVmSasfKdV2aTUXBevLkMb1+D8tSjm+v19O0cIfpbMYrz9/EpuDe/X3OzkYsogTbFhQFWl1VUkp+ZK3jn9Zh7t84w+vr6wRBwPHJCWcnT1RWRGg1XsOAQFHKSkRVs23ZSm4/z5fKkB/1ec8Ceup/P/8zQlG2q7YGQrce0cHGsi5UAY1UwFKBZan3LmYzZpNpFbxJS1BqZoBtW5oRoDI/Vmn2JjQYps6luRXIClQ3ALXElDso5V2TQfw4D0AL1RQFti344he+wF/6S79MkigQ/mwxpN0OdUb1pgKRLIHvBxRFqflAJYEfYFttVK2WJNd17r1eV7GXShgMTnVz+xYnJ6cVs6rRaOA4DtPptPJj4ziu2Dym3lNKyWQyqfzfNE0rRdC9vT1Nf51WNZtSSlqtVpUt7/V61W0bgQVDl+50OhRFUdUZmmDdiOIYv9z3/UpsAqjqP+M4pt1uV4I26X+pQVoextGUUjKbzapBBqreEivUo1I18zNOuan/MN2jzwdN5igKJQygCvcylY7NMp1NgELT9zqdDhsbGxwfHxMGAUWe8+jokDAM2dzYYLFYcKqpWGmieOKtdlupupWyQht7zTbjyYTRaMLZ2Yjf+H/+79nZ2aXX7DCP55hqEcex2djYpNVsIoGwGVST3jhYo9GIXrdDI/RIE4WWBp5PoxHi2g7NTptmr0u31yXwAwKdfbGERTNs4DnuSq8Qw8l3PBcpltkLg7JaKnWEbasaIlMDZgIjPdBVM7A4iijKgiAIcR2vWpT1ZyyEaorbbDaxHLsWTOYgFa3NchySXCmZCN2E0rKN4p+FbTukqdL1V0XfHs1Wm1yWXLt5g6OzUzZ3d/jrv/Y3iBaq/06322U2m60EPIaXawJPE4j6vs8iipeFrix53WZO1QvBTQ2C63r0+322ttZZxLnejBVF5/yGUg+QVEM5sTJGldNs/QiVuh9zCGHR7/exXZc4SvT6UgHe8fExd+/e5Qs//SXCZqO6rzhe3rcJCF3XXakX8TyPMFRc51Kv2SLPKzpnmisEP00zyrIgmk91IGOxvrbGwwcPSJO0wmBV5lFtkGma0mi1kEimsxlJlpLq4uk4ikFArpUT3ZpioKw1e6ycgHNI50ePkzBkVPr9Pr/yl3+ZS3s7bKyv0wgaWK4LUrCIFuR5znQRk+Vqw5jNpzQbDf7cl7/M//vX/wVlIZG2hYVFXioJ1fMUz3qWvD7HlCNhY1kOlm1hOwXdts1f+0uf48VbHUo5QgpXByUuua4VMOIn83nKaBSzWFgsFj4PHow5O02wHR/XbxNnFl13g4uXX+fWrRdpd28SNDYIkGR5get5FKXEsh2MNHCpr8vzPR3kq2yhnqwqWyQlud4kXdfFkoqqlaYpvuchhdBaL4Lh2RlhGK7U6liWpexwURJFx6pmpNlE6vmYJDF5mhDHCaenp2xubjCbzbFti8APCVyfKFI1hEkaq98HyvaYwmQjDb6kicyq7EgQBNy9ewZIkiRFyqJS0Hv8eF/TUFRhtJHK7XQ6ZFmqA96SPM9Is0iJCsmCvEhJUgUaeJ7D229/j+3t7aodgFF9MntWp9Ph6OhJJQjx6NEDJtMZF1zVn0TRFSNsW9DrdTk+PuXixYuEYcA777yLZUF/rV/tn9PpmDRNaTU7GgkOmc8jKIW2dar31WKxoNlqVLVBx8fH9Pt97XSqQvNGo0GRg+8HlcjGZDKpmj//81//daRUAglvfvYz3L79AUkSAaqhpOu4CCHxfVdnpob0+t2VRpPGaTNg2ng8rvb9xWJBlmVMp1NarQ5pklfSwqZeWe1HJXmR4+igXMqcKFL9dVz92iUFSNDpdImjiM21Hov5nFvXr7C7tcl8EfPh7bs8fPiIKLXJCgNO/dkLjmDp67TbbdrtNqDGLM9zKFQjZUMZN/tYWZbkpdRdDVZruZVN+vGfa3yup5Xsni4TMMBV/TVLm1yCqKs76PcKlY0WQmoQTWV7DE1cZfk1xyZf2n3bgGBCZUpL3QPKBDxSlFUWSH9gNTYqa1SuXP9HjfmK3UZdW6PR4Fd+5S9x/fotzs5OCAKfMGwihGri6/swGAwVvVYIoKx81tPT00oVLo4X+IFHlmU0wgwpYX19g0U0rVRAYVnv9Pjx44qxY4IWY6dGoxHj8biixZnaIqP0afoTmt5EvV6vAr9MraR5zqaXUhAEFSgTBEEVCFmWkuE3FD7XdavacCEEOzs7zGYz+v1+JaZSF2pwXJeF9smOj45+/CTUx098gOQ4qmbn9PSU2WymMiU16hksN9M67cksduPgGW642cyM82smQ5Zl+K6nu6erTXM+n9NqtSigolm0Wy2KPGd9bY12u83R0RN2tncUIhcnTCdTSp31cl2f6WRG6qYcHR5X+u+z6YyD+DGLWUSaZggsmmFIIwj55M++wSJdEEcRQRiw1l+j0+1gCYu8yCmFZHNri729PTzPqxSaHj64RzRXfFYLoVSF8pxm2MD2PXIbzfn0SFPVrM91XTzHxxYOlqZxqbSmFpawVL8UE+jUa7iKLAWUKkqr1apQiPF4TLOjNtg4jsESBM0lciBKsVKMZ3qBlGVJI1S9cuIkJs2UHKztOLpOQHFmEdBqt/RzVTU6huqjJDMbOpgJKaV638ngjLTImUcLHM+l3e3S7nRWeK5m3ph/fd/n8PCQzc1NPM/jxRdf5MMPP2Q8nWHrpsRm7pnDzBs1b50q69Nut9jcXGNja43FIsfzXcSMGkK1pCfU+dgqg7EsVjRG71lG+rzD/5SDXf1NZUjKoqRI0upZG1rq48ePieKI3/qt3+Jnfu5nCXQ2znbsleyrkfRMkgQpVPftXq8HUo1JkqWcnJxQ5jkNfY40y5QQiVD1C0WWsNbr4VgC33MZD4fEi0jRLs09SkWdAvA9D8u22djeJM2z6v5yXR+AlLpju6JrWZZFKRRtpJ5Bqm9i58fI/K4+jnle0GiEPP+J5wh8mzieMxqeYbu+Cka0wZdSUsiCoBEQBD4SwV//a7/K7/zOf2A8WSgao20otkv71Gq16PV6VZbAzOV6FhIhsV2whCT0bXqtBt/4g+9x912H9bUGnTWFsAWhr1FTpdwURZLZ1OP0JGIwmGvhggaet4btWzieoJQ280Ty9T/6Hm+9fYdWs8OlK7v80i/9efb29pBCaLqbmtuFvmc1jxRgketnlCQJaHQxzVQT0clkgu8HONoum+LcSmGpKEiThNFwyMOHD9nZ2a0cN9u2SdKkKjY+OjwgLwq63S7j0QjXsbUTLSnyDM9zSOKEs+kMSggCn7PBGa1Wg9FoUqkopWlKq9Wq0EoD9LiuQ6MZ6saSgrChenpc2NhRTVKl1NnljspazudEcVRlsQ4Pnyh1vizVAQAkaaKog6VSw1PCLzGddkfLlbvV/DH3aZy2yWTCwcGBrtlRvaVcz1PXmOWcDU5phA0ajZBWu8mdO3cpCgUSqMLpGEsI1tf75GlKWeacnZ6xmMdcuLDH5uYWcbzPeDLBcT3abSUtHIYBfuBRlkEVnJn+VmmasrW1pajPE1Xgvbe3x/7+Pmtra8RxXFHUsywlTSN+8P3v4fmqRQaAbam99OTkhDiJsG2LbrdDWRQURUmv1+P4+LiqBXUcR1Mhi5X6CyOoAxauk1fqtsaR6/V6PHnymDSLtRJaUFEqjdy4URxr6ILxJE5xHBebEtcShM0maRTRXO9zce/zHB6d8P7t+9x7uM9My2DrXst/LHbdjwO3fpwz/nHOb1lKbt1IOJvfi1JRuM0+aj5PCCiFrGiu569DCPsjr7vO2jnfb9LsG8+6p7oC3tPnVgGointqzeN1j0ZD2ROWokOXZYklSiwhlz6iBCFV7ZQUNlIqypvKHkn9vQmUnt4HzD0YVsFT91D78XyGzFBBr1+/zr/+rX/NgwcPGJyd0el0eOGFl8jzKe12R1+3w2KuMpSOq6j7YRDy6NG+lubuEYYK4B6PRtqOJQwGp8ByPzFMnizLqvIOsxYODw+rTI2phz87O8NxHK5cuVIBEdPplNFoVNWSCiE4ODig0+mwvr6uGCjdLlmWcfHiRc7Ozmg2mxX12GSXTOsCkxkKw5A4VrQ906TbiFctFosqsDKvM+Of53lFk+/UslU/7viJD5CAyuDt7u7qrEJRaaKfV/cyznKappXihQmYjNNRV/cwjme/16fTbOl6lwmdfp9uu00YNmi320ymE7qdruIvuy6ddoeDJwdMJ1M8xyFJUqbTqVIES2LSJGF//wn7jx/zeP8xruuytrbGjZs3WF9bZ3Ntk63NbYVGNxo4juIHu56rSo1qjrK5N6E55BIqZSSD7GxsrGFRcHJ8TEurfXW6Hcq8ULLZOkNk0M2qQ7wlSLTTOp1O1aREZczSNCXR49hqtSotfMdxcGyLJI44Pj7mwoULKhjQizNNUsJGQ4tRaAENjSoUeVE9N7P5maACSz0/LwhUb6ksI8tU1qHMs6ouIIoibMum2Woq4QZhk6YJrutVKE8UxSi6kcN4oIQENjc2WMzmaowtC7fRYH19nbt3767QJ82GbFRdwjDkH/2jf8Rv/uZvcnh8wnQ6W+HHmqNuOOvZJIW0W3iehcRTRC5LICpaF0+do8p0yrJy8OuHokM83ZPio47636RUafo8l4SNkPl0XgVqnudVRs6gOI5tEwbhSnC2tbWF4zgcHh4SNhUnWanuZPgEOpPjIgV42ilqhp2Vxry2KHFdB89zSBPVtV2W6ncGoTKBm+M4unYl59KVK7iux3g00kXzqs7CQtEHy7JkMVf9TyydvUI8u9fXszbsp7j1KFrX44MD4sUM33NpNds4uUXYaGLbFmmcVvzuLMnJ4pzxZMrR0QnNRovJeEaZpwrZFEsaibFt9QJWcw3GbqlxlzQaFq+8/DLrvT5CFvg2ZPGc2SIhKRxOzyzKMqeUmWoXYIEsbWTZw7Y36a85JGmmKa45WAWZzBEl5LpOME7nzBZT/IZNp7dGmhf88Ac/oNNqsb6mqLSO4xLrzU3x1JfN/gyi77pu1SuoKAp83a/IjHYURTSbzcqOTSbT6v7v3b1b0TxmsxnT+bSiYhgHwGScfc+tCvYfPFD0rl6vR+CHFFlhAGOEaNDrddna2tCBzJHqc5fEtR4iDlmecHys+n5Mpwr06Pf7nJ4eURSlptJF7OxsU5ZLWooBfDpdBQ7ZjlUBWKrWQRKGysFvt5vM53Nc1+HC3g6BHxDFChiIoxg/MN3olQLUIppxcLBPq/UcnW6L8WTKcHhGu91he3uLoii08xHQ6XQYj2esrfX13qjaIUgJW9vb2Bbs7OxwfHRKluWMx5MKzBodHdPvd8nznK2tDaJoges5VS8V0/zX932ePHlSieQYIYbT09Oq1uDDDz5EAEEQksuCxWxKEqkMXp7neEHA2eCUsBFUKncIqd8fV/UKJphWwWVeBUrnKfNFLun3+wyHw8ohNCDoCy+8yHwxxXVc8qIgDIOqjjTww8oPOD46qTKBQghchQAwnc2xXYeT03vYjkuaqwL6CxcuMB5PmMzmpFlGmilVS54263/ix8cNnlb21/rvMfYQqBSpy5Uo76Ns44/67LrtND7MMsB4en8DKhq5ee0qvXjVXi6p0BKEiUqFCnoEimYmlw3qV45SatluDUBosQ2zl6qA6tn0QDNWT1EEq++XrzPgpPIt1R/effc9bt++zde+9g2klHi+z80b36XX69LpdAkCpbToug7r6+ukqaLB5UVB4PuUheD+/X18z6PbE3heE9sSRFHCfB4RBL4GJPKVRqtCCObzOb7vs7+/T7vdXvFx1tfXq9ok01PJKOEZ0GY0GtFoNOh0OrqHWFplpIbDYQWsKwZJyGKxYDwe09X9mgyryIBTxme3bZu1tbVKACUIgiqQNzTAJEmq8XRcFyxBnCYfOf/OHz/xAVKWZpXaBqAllx2FEudZhVQYSo1RRIOlct2S6qQ2HJNBStOUZrOpAoCwwWw8Ya3f59LeHmmWcXJywtnpqeo27bj4+j2zyZTjwyOOjo7wfY9Hs1nVRHA2m6noPc0QlsNrr7zGL/z8L/Dqq6/S7/cJAkVB8jyfvFA0tbzIEJrzW5Y5SIkQiu+aF4VqUOm5Ko1cLPX3XdepFqxtO0gsdi9dVophOlMmHJtWI0Tq/iOWZVHqHktmHGVDGaRGs7kiOmAWkgnCDDpnKHaNMKg2yytXrlTPQVqiCiDmNaU0IQR5otSdTOamjgRKa5mhKMsS1/NwXJf5Yl41kCyKAoQgK3LGkwmWsCiyoto4YVkL0W62sYVFww8okpSN3hqLyZT19XVc16OQJZtbW7Xiy2WRt8kiGbnMKIr44he/yP/rn/06cZxUTuGPQtPMBp5lip7jupYqANU1TBKU0s0zYEdbi2nIQmKUtsx5QW0qP2oTrqN/z8qWNBtNzsYjwtDCdRxdzFlw4cIFNjc3+alPvkGz3eLw6EjRbBrhinNq2zabm5tKoCNLKzrLZDKl2Wrh+75qkClLSk3NrFMUpJRKxrUscBybwydPcB2bZjPEtpZdtYUscRy3yvbmmqKVl0ppTAjBZDxhMZvpjhUKCVTqY2qcykI5//UM0sc/ZCVs0mn36Ld7TKczOu1NHFtliZIoZjiY8ejRPvfu3ePevXs8eXLEaDRiOBoSJRG+71GUBZ5vk5dKBMJQVpMkYT6fV3PGrDcTdHiex8Zanyt7O3iOw2w+Jc9ypBRkqZJOl0QgFZVIILEd1UfI9DBpNtu4jk+epwhRYNmq75gtfEqNlFuOQ5zE+L7LL/3yX8T1QxbRnDt37tDvtGkEL3A2n6Ga57qcnJ4wOBvQbDWxdaYtzzMsy2Y6VcIqUkrdc8ghXkTMZora1Gq3ODs9Ub100hzH8YgWC0DV9PXX+tVc67QVGtnttomjmCD06XV7jHxX9aBCspgr0GJjfY0iL1jMZwhh42kw5OzsFNdTa2g+X+jgy3DqhVIwA4JAZVFUE0Spriua67meMB6PKrlbKSW7uztVxqSp7WcQKDs0m821KITEslTdUxjaOtgIcR0HR9uWNE2Yz5UYQrPZrJya0ViJOKRpWjUrv3XrBkUJi0XE48eP8TyXLLORUmUux+MZjx49oixVMKYk1FVDxzRRdT+XLl7Bth0OD48Yjcb0+n36a+tMJmOkLBUVnJJer1vZb1NMrWiEma7bXSpcFUVRKc5atkWRZiALhHBUY3TLYjIek2YpgabKO45dUY5N8TtIzRSBZrPJ8fExm5ubuv5jGZAb+zsej7GETZ4rJ9wg1J7nMdIgiqmVMA63qdmKomjFeVegmo0sJRRKVKSUEiyIkhhLO51JkZNkRWUL7VJiFaVSevyYaaT/1AzRjzt+LP1aLOsu1fc//jwf95LrQY7512TE62qTUoJtrdLuVoOkpRiCUU9Ur9PjrKIWVB2Rbs4I+vvz18MyOEJ/CZA6+6JJefr11D5bzQsTpK0Ed6JOyVtevwlKTV84EwSo31lkWcHtD+8o+6PvQSnYqWCj0NRw27GxLbvK3EgpuXz5IlevXmVre5N2u4WwBL4fkqYzBoNBlRU3iYLpdFqVTRj1YCMao0RkRopyb1SBdW256VNnQHHbtitbBwo8evToEdvb21UwY4JhU5cohKj6M/m+X/nHnU6HbrfLkyeKPmyEX0wmSUrFSmm328o2lCW5XNbTfdzjJz5AApQijUax4zgh1spqpj9No9Go6iLiOEFo9a/5fEEjbJClGfP5giROq9S6mbS+HzAeTTg7OsGSkEQx8SLivffeI0lSms0Go+EIWKo2WZaSdk3SBMdRFBDP89jc3OT5TzxPr9fjwoULNJptNtY3VVYlU1r9png5yRMQkCQ5pVSbg+d52I6FLAW2sBFlieO5WLa6HynBtpepat/3qFRlVCW0+l5KbCEIGk1VGK1Tk6ZJqOMsDU2dlmhQUBVMOhj1OTXpl9mQsiyUU68na6PRYP/xY/YuXMCybUqkVqxayqhX9QW5MhLm5yRJGA6Hqj8REs9XWaq1tTVsy6aQBd1Ol3a7zc7ODkVe4LiOdnohiROieVTxVk9OTnj06JFCRecLrFJxeOeTGb/727/D+vqaEvfwffxGyHg0Ar2oZVEiLAvLXqWn+b7PcDjkzc+8yYvPv8AfffOblXxmtXeci1fqdIOqt4tt41kejqMUrHIdyOoS0Oq9xkG2bVWDYX53/t+V7GJt5/pxtDFZqkJys0n5gY9lKwqn63l84vlPcOPGDQajYZUp6PV6VcbPGHvHceh0OhwcHmA7quj7/r17bG1tYGlqpAru1OCYDvV5USgMUI/76ekpaZawvb2N47oMB2Mc2wFUw2Qv8Gm1WpwNBqBFDlTwqWo87t67o8QeLl/BsVU2N1rMKctCiWr8mLE5f6xSPUzYJVTDwbxgMo55751vcPvDuzx8+JDTkxPGk7HaPLKMvFBN9NTmWxD4Ht1+lxu3rvGZN9/kn/6P/zdOT1XrgKIsybOMyXiixFmKHM91eP755/nyV77Me+++y5e+9NN87rNvMhkMeOfdt/mD3//3xNmCJEtx/UAJYZSlFn6gog0LIclkihCSWZzTCFqaMpjhWgIhHaRUNLC8UPQrR9js7uxgC8G77/wAgeTq5cu8/d3vYgGuYzMYDgnDBotowWQ8odVqEpsgT6tFGVtiGqyWRVkJtsRxxKNHD7l54waB5xEGAfNZRKvV1PQ5VZg/GA5xXYdCKmSzLEomkzGepuQGnkeSRDSbLTY3NxmPRiRpyvramlK7K1SdgeNYhI0QiVtRi5vNRhXIJHFcNY+eTsc0m40KwTS8e9e1aTbWkKUSCPJ9j0jX5RlRGAPuNMIGj/YfKfpzUYAoiWOVIY2TiE63o2pFHQfLIPnAwZMnOlOWVTai2+1oEEnVFwyHI84GA5qtDlEUEccRnU67yja6rmqSmySK+tZf6zI4O6WUJXmW0tA1goPhkFZTBZ65DmyCMARUvzrPcxEWxLEq7t9Y31CS+lnOaDgiiiImkwmytGi12vS6PbqdDnGccHJyrNTGXIssT9nYXNd0Spt2u4OwIMkyWq0Wk7FSqIyiaAWo6na6TKcTSikJg5D79+6TFzlpmpEk6VLcJ8/pdLtMJjNtTyVJEusAFl3LZCNLqv2vKIsahdUIi7jkuu5pOpsq2lnpqD5ntkVeFkxnE/KyoCwKkjyl0GyO3LQwkCCw/ljJo4+Twf6f5jDUcJM1McEMnP/4Z7EXPupYvlY+tS+ZwwCtyxOeyyhZolL6VD6IMqZLCpypcdWBLUL7QUZcoR4oUe2vZZUlM/TBUr9s2RLlfNZoNZv1EXuuePp9BqQFdD84ofdyQZGVCCGZTmfV3x3b1mApCGFRlqKidxdFymg048mTE4QQvPfDD/FcJR60sbHO9RvXaDYbXLx0gX6/W2XnG41GRd9VQIqnlS0V+6rdbi8bU+v6QVjuHwZkMOCP8WNarRaj0ahSFDbtdgyoncQxtj6fyQAZG+kHAZPJpPL/Op2Ospma9WTEVXq9nhZ+UUJsWEo8TGX2Jx85/84fP/EBUp5KQq3a1uuskcQ5FBbD4YDZTBXjr6+vV7rti8UCx/UrIYCJUChRkRca3WvgWiGFLEnTgvcefqD6qHgu/XaL0/wUx7FZTBXtYDaZIS2BFFbFl2w2mzTbHS70+vT6XdbW+uxs79DutPE9H1Cy2uPxhLTMyTNZadyrFGgTodFh112qglh6EthVZmLZxLXQEpyW1JkHUZfDlNoI6LoYKclL/ZtqQWvjJZYbhaHtqUWJLusukRQUusfIUiQAjBEtymXTRGyLTr/HD3/4Q+49uM/VK1e4uHdRGZOAyqFWnawzoiQGBEdHR0SR6hRv6H62ZWNjkywSRnKkUGTHxfFr2v+27kxv6b4mjsTt2ISh6ir9/POf4PLli5yennJyeMTZk2OKLOGN117l9OiQFz/xHHt7exTA6XiEZzm4llLCUnNE1bFgKTU3IVF1QFmOJQS/8As/zzvv/IA0Scgdhywv9Niquo/zQZJlWaRZhhAOsgDLtXB1gbXaxAWFzFcMsXmvuudlkWxd2EJRh6wVg22M9rM2sDqFwThIV25cJy0KkjTDa4QkRU4uc6I0IU5jPM8FJNF8TqfXxWsExFlCIQvGkxFbm1u0Wk3Vhdx2SKIFH7z3LtevXmRraxNKm9JRHCdLWGBJMpmBUMX/NqoHzre+/R0kFmsbm0zGU5I0o9XsICyHgoz1Xo8gDJmNxgTCpuW6DJEISjzPxvVccpkStJsIyyOJE5phgEALWwg9i89tdD/OEZFS1RRRSh48esT/8n/1v6bMC7IkI0sz8rIkK3Jdm1JW608IycWLO3zyk68zGo/5tV/7m1y/fp12u4XvB+zubJMmGf/8N/4FP/j+uxRFie8H+L5Hq+3y0ksv8Q/+4d9nb2+PXDeqLMuSXnePqzcukaQR3/r2t2A6U1nZXBWgK+qmi+e7mg4WY+MpBzv0cSwVdDquLpJNMqSlm6bqImfbdmg3WwxOjmk2mwqdtwTvvvMus8mUl15+Gc/xGOlGzmWWUaQZDoL1bh/XtSvHNssyus0ek+mENInwm00m0zG2ZdEIfY4On1SZxzRJ6fV7VRPhLMvIk4T5VBUqy1LiOQ6baxs8fPhQgVSeA5RkScx8qvqtxdGc05OsogMDWHhYsqTMMrI4Vqqdui41CAJVrJ5l9Pt9Dh8/Jk90zzbpIEoL1/LxnIAw8Ll54xpZnjOZTnF9RdnK0oxO2EFgaRAs48bVqzw+2FcIsA1h4OrCZEkazZVojdfCsXzyrGCt36fRCDk6OiAMeshS9RlrtzvM53Mtm55ycnLIYtFWiHGSVmh1Esf0e/1KHGI+n7Gzu4VlSTzfIS8ybNunRNBs91jMF9y995A4VrWerXaTOFZgSFn6pKlSXfVcn8BdEAmVHTbzTUpJmUqiaMZ8MsXXGR/bsgj9oEpIpFnK2dkQx7bxgwDHUSwPz3c4PDgkS1OkVHVatqWbcecZT8QTkKqXYKHpylleIrEqUMkEU+PxgoJSNfXUznfVg0VKykKAtClLqZ08pVZ3dnaG43mqDYZGx4Gq9gFD1ar8YFEV7WOyF4BqE0LVcuM/9fiTCpp+VGBjgNWyXM3S6Ff+iLP+KAU3FWgtP6b+/bJ2qc7WUEIsBUIKvafpLI72VSxbVPWI6Npzae5FCg0KWdXnoWuLTLbRBB0K1IVKXrx2q5VvdO7eV/ZjVLsYk4FUN2Koec8OpixLM0aMf1ZTC7SEBo1sG2HZyk/THyfLUrfdsFRrCKk+PdPlIJRq7iVZzmL/kEePla22rZz1jTabm5uEYchXvvIVtre3K1EVlcl3K/qyuRbD5jGUYJMlMiCqKa8YDAbs7e2xWCyUSp7n0dP+ja2FUzxdN27EbIwYgwnUKl/PZMp0wsAIr5gaVUOjFbZNCUoAqCxYzGaMdcLi4xw/8QHS2toaSqpzzPe+93alLua6Li+88ALXr1+v6iGUcVcLydDtjCKOotc1VLGwRjellDx8+JCTkxPyLMV1RJUmdB2XIFSKccPRmFa7o/udNCslDoWEFJUUtJSSNMs4ODhgMBjwU2+8vlKsD0t0up7Bqaf463UHykleShALC2Shk0U6LQsq4kcISrTCmFB0BtCOsTBBTlmhB6buwbbtFafeOI+GWmYCk3pGpJ4GN59x+fJlHj16BEJwcPCY05NTNjc3qwzKUlFQKUeZTsvb29tVb6i6WEKSJMxnqreO53nV+xVqGleIY1EUOM4yq5amKqt48eJFbt24idCS4o6t6ENV7xUJGxsb/PxXv8r9u/e4c/s2th7TPE2VSp4xZpZVpaA/++abXLt2lSh6T22+Mq1kyy1YSSOZZ1ylpIuCZsPBdmyyLKWUSkYZa3VDOm9oTbBUpztKloFT/bPOqzOeP0xQdXp2yq0Xn8eWMJ8uCMKQ6WRMmqQs5nPihepP0AhCDp884eatW/T6feZaYevsbECvq4QFjDJSkgSUsuRrf/AH/NzP/axCtgtrOSZSKvqnlBoUlLz//m329x/TbLaxLVs7gyrNn+m5d/HiJZVpOj5RUsCzEciCLE9ZW1vj4qUr3Llzl83NHfKiYDqbksZJNZZIqbKrH5GNOz9e9TVg3j6fRUTzGEqp6poQFBQVAmk7NpYAz3PZvbDDn//qz/AP/+t/QLRY4Lpq/uZFzmQ85I03XkMg+MRzt3j4aJ84UrThTqfN5nZPIZxlwcnREZZWilzM5zSbLZI04ZVXXmF9fZ1vfeubPNrfx9LZSyXTrh2fIsOxHdJUIei+51MWJY1GqBHKEj/wyPMCRf9UxctBEHL1yhWixVzJracJiyhiNBrywgsvMBicUeYZsiiIo4Uqxk1TBsMhWZbSbreqzVdRVFMcLROcpgmObVeF1eqRqF42hndu246uCQqQUim/CWHT0NRoIQSeq1oUIEuERUWfNvUnwvd1XaRa6waIMdeUJAm+71c0Etd1q8aJL7/8MmmW8ujhQw4Pn3D9+nVt422KwiaOS+UQOA6O5dJqt1Xn+dLCc1wirTJVljnXr19Vqqanx0tFSklVo3U0OeLqlRvMT2bIUgXIF3YvMJ6MAYHnBQwGQ6WKOJuxvr7O66+/oWhxA9WYeGtrk2SxUKIrWcb62hq7F3ZpNhusrfdwHJfT0zOKMifLCuIoIQxDnhwcUpSKZl2UEoGmtHQ6xFGsg9ScgydPGJwOK+nxKIro9/rM5io4d12PslDtFIyYT1kqiqtl2wpM0/uXotMppw+pqGtKpVRWKHZeFEtEXmo6uXZqkzQjSYtK/KMsy2U9cqlUZleETYwtLQVCOCv71nSuaDwyjtXeWatRPk/vquyyybKo2VSBh/XfqF/9yVPn/mPoeM/aB370eVSAUweP6lmTj/GJrGRuVj7/6Wbq5jXm2Zuxf5bA0Mol6o9S+yIIsaoutzIHdA+8SqnuxwSaH8XKEEKJ0iylyFcDovrPlSJiLSNVzzDXa5NL829RDzx1r6iyVBTimi/X7Te5dGmbVqvFlStXiCIlPuL7Aa5j0et2dA1TWvmpeZ6ztra2UkNsVFMdx6lKF4wqpPE3jM9j/Ma9vT2kVLV+vV4PdE1wEASVzRdCVEwTQxM2IlymTqkoiopeHukWHqZWydFtMoRY1nXGcVztb57nESf/pQapOoIgYD6fs7u7S6vVYnd3F9d1KzpOpJuH1qNQyzId2x1cV3EnPd/TzoNV0cmklDz33C1u3rxBUWQoNo7uBVOro8kzlSUwDjmwsphNMGGuY319HYDRcMjm5qbKSGjjYTuKVmP6HFlCpZQVymCKB6VW7pLqtUVZLU5LqrokE2kbNC8IQ7zArwrkOp1OpRsvpVTcaVjKNMYxpZT4OoVpFq5BEgxqXV/45qsKrMRS077b7dLpdNTYZDntVruSkWxqdSDzPoArV65UC9SgBWbhGMffBKtSyipIMuosRltfFdWm1aI2jSsNR95CICzloPmeTyGVEECudfmDbof/2d/9O/yrf/mbyhCkKbksSXWz2tlsRrfb5fLlS6pGIPD5yk//NA8fPCDPC5IkpfwICnfFR86Uo22cMd/3VfBlsnx6TpnxNEGpoR6cVwA6LwxRD7pNDYv52agLmkPq+x+PxxR5QaBpQJ1Om8HpqaKPaIe83WrR7/c5Pj7mEy88z87ONk8eK8GR2XTGcKgcp36/z2w2ZTgcMJ2OmUxGRNGCn/mZP8fuhV1dRF1WioNSz+l33vshb731HYIgJE0L4jhRtNhGQ61hDWzcunWLJI4ZDAY8fHif46N9tnd2kLKk3ekThC2kcNneuUiWZrp2T2X8TEWAtQToPtax3CAtTM+iMlM9ewzRw/Ys2u0WrueQpgl/9+/+HV597VX6/S6dVoPR4Iz5fF4VjBsFzaJQc3w4HOPYAt+D2WyM65Tc/uCI9fU1xmNF2TO9S6bTKWenJ3Q6HcUTDzzyLCWNlWCJZVHRYM26sm0PI4FvPrMojGJagaez3er3ud78ulgW5EXGyelxJRCxvrFGUWaMxkNsKZXSWSNAUrKI5jQaAb7fZbGYYVmwvb1VSe6ruppGtbaHw2HVtNKsccVZVxtps6noGFG00LU7NnmhMo/CEnS6LW7cuMHu7g6DwRn37t3j9PS0mue+7zOfzypbZ2yUaYJoPtPUpozH4wqkCUJPUco21+j22pSywPVsonhBXuTaVoHtKVGaNMvwg4AizkjzhLzIKFHBWzReUJYFm5tb3LlzR+8Rqt4PhLLZnottC8bjkZLF9RwODp6wWCzwgwZh2GA+n9NsNomiRLVJ8AJkXmBJiSMErlZ8Gp6eMJzMOT45UXVNecbFi3tIKZhMI5CSyWTKbB7hh03SNMNzPcpS08ezgjxSzkqRqawowibOcuZRgrAEjhdwMhhi6M2gFCPrTl6qhYBELWttaobU3qkyC5YO1E2tg7CUXHycqaalpi7J2MK8KAFbr+dzzeOVNURKtWbNb6U02eMacCRURlIBTSrrZPbxj2MXzjvPf9LHHz+w+ZM//lM/7+l7eBqcMnu2AYXN+54F/D0VqIlaePqMoMsc5vz1z/i4R/3zLPtppkb9PqrX1ee5LEGugsrL4G31XlfuTQd+pu6nDhz7vsNLLz/HL/zCn8e2bRaLBWEYMpvNiKOUVkP5XbOZaqxsao/MHmSkw1UyYZnRMtkeYxsNBc5kc3Z2duj3+0t/MwgopKTZbGphs2nV88gEWsPhsAp8jU9mfEvj58Cyj6QRmzF+oKI2u7phtCBLUw2czz72M/yJD5Bee+01Wq0WjuNU9SqgJk+qi7/MpF02ds20M+3g+66mpylXySRUCt1sy6T4JArVznVfC2O4VbNJwWw6q+pp6sGQKjy3K2e0LFVjP9tWaPhcFyXXgw+1edTT3XXUZXWB+55bvVdKWUlTO45Dr9erIv7pdEpeKqffIAfmvMZJNhPSsW0lmVxtWEulMFNQZ5wLc311etf5BV13xItC9U0xtWEmAzgYDBiPxywWR7rx7G6VXTINXoGVzzTPBqgcmyiKKnngVqtFs9lYyg/r15sF5vkeuUZGJtMpY90T4M6dO8ymU7rtDs8/9xybmxv8/J//qpLERFAgQdd6mTmg1K4iZFnymU9/kn/5L/8/quDQdZCpCq7PbwrGOJv5B2Bbikerze0z54A5VLHw8rzLrOVSNrX+/vPom5pPT2dHJFSKgI12B9tx6HS72LZFEsdkacrXv/Y14jjm1q1bWLbNaDjkyuUr3Lt7j+PDQzzf48mTJ+zu7lbFo0ruOWexWPDuu+8SxxHPPfcJLl++Qq/XrbK69+/f5+23v8/J6ZBLly4xn0dYwuL4+ATX9Wg221AqcODCpQv0ej32Hz5gvpirbK8WH3Bcj/WNTUqpuohv7+yyiBa4jsNsNl8a4Y/YQH+UE7BSuI1yyKQA2xHYlqqv6/ea/Fd//39OWRQsojmf/ORrXLq0x2g04uz0hOl0qjOdOdPpjMlE9WZZLOa6p0WkN70Az/WQMmVne4fA93DX18izXNEzkayv9ZlOppyenJDnql3BZDzG91zNelHginr+pkGixLZ9TWXIdXGr1LYvJ02Xgiag1EE7nTaj0RCEYDafEYYBzWYL3/dYW1MUrtl4RLfbQUpJu90milQNie/7dLvtygHIspS1NdUZXsnJq0Ct3+9re1RUdrjX6+q1nWo1Ssnm5oamUE1VDVJZ0mq1uH79GvP5jMeP97l06RK3bt3inXfe4e233656CcVxgmZ9URSFUlvUgYQRvjAytO12m5OTE65fv850NuHg4HGlgFbPgDfCFgKLIAzwG0r5qTR21bZI0xhhW1iOhRA2RVoyn02Zjmc8//wL3L59m8VC9RSZz5Wk7YWdXQaDM+I4Vg2GdZZsPJ5QyindrlLoezQ4IIkTWu0Wi/kcWeRKDns85saNmwzOBriuy2QRMxyOyfKMZrPFvfuPlGy8ZfP6668jZcm3v/0WrushhJKs9/2QPE+0mIzUGfFCqxQCiKqZpxCK/pSlKdrLo6yBW2Z9qcyPAifMM146liqQkaU0SQvdb1Cdq9Rz19CXhDD2Teh9XNHBzdvVdZh1XXesdVa9Kso3BkCJSJTSnH+prHXeZv7/Klh5lp3+OK87/9r638+f4zwz4Vmv+ZMPjJa/P3+d9S+jJgxLOW8T1DzruVQ0vGd85kfdUz2Y+XF7wPlr/ajrqN9b/fqrPVsLS9TB5eX34pnXJiwLUXtWil6r6m7PTof8q9/8HU5PxvyFv/CLpGmMs+URBCGB18C2nIpiF4ZhBVJbllUxesx1gKnRs6prNkCS2c+Nzbx//z5PnjxhZ2enysBHRj1WM37MOqoaeesgzARAvu9X6nr1en7jt5n7NEJhic4Uebq9R6vVIoqiFYD4xx0/8QGSGTgjyHDhwoWqEdXh4SFRFHH58mWOj4+ZzWZsbm4QNkIQkqLMK5TBdlR2SVYBleKjFkWmgyZBlmakSbpE8IHxcESz1a6ux0wCVciqZKWTJK6i7iiKKofREkI1SdWFyghBbDJejqgcF+P0mgyMQSEEgiJTqUsbVS5usj3mWkzA1l/rQ82gGKMjhHLmFL1DXZNdFVDnKpNVQ1hMUZxZ9OeNwfnJ+SxExdZZAiFEpexk7m0+j7hz5w6Hh4eVdO98Pq+0+EEpF5nAaj6fV3Q6pUK0pE8eHh7qDOGy75VZ2MPhUKHQccTh4RFXrlzm0qVLhGGoGqOhHLhkEXH7zofEiwWj0QBZSnJZEqdJpZ4CVNmqIsu4ce06X/jcZzk6PtYLGuIkpSiojDYsDWWqm5oalNStPUM9q1bGs3qvdgWqTJRG4Os9qc5nkOrGrn6+5fxVn5dnGSenJ2zs7NAIQ7I4VjUci4h4EXHv7j3SNOXs5JQ3P/tZ3v/hD/nsZz/HjRs3GJ6dIRCkccLx8TEbGxs6M+YRxwVxrJTQBoMB3/7Wt/nB2z+orvH07FQrXpXs7F6kLBWddX//MVlW0G53lNBCluC6Dm+88QZlWfL+++/TbDZptzukmYtEyby32h1OTge0Om26vQ5379+lKEtGo6G6d+0zfdTG/aMOtTFmgMCxHTxfcVybDZ+1fpf1jXXu3Hmfr37153Aci92dDR7cv02eF4R+gG1Bu9XUNUI2O9tbCCG4d+8+6+tr7O8/YjIZEy3m7O3tkaYRDx7cw7IUpSzVdFhTH2GkWM0GZJTbXMdFceuXnd6zLF25D0er2uW5sakq6Hc1XU2t1Qa2rXqE5HlW9d1xHJtWqwlIms0GWbxYBmmTsVY1U5kxhG4kKyWlLJjNJghLMJnMcGyboiwJwwDLVhldx1E0i5PTYwA8z6HRDEiSlCD0EcKi2Wyxs7Nd2f92p8PZ6SlPnjypVNO++MUvcuXKFb7+9a8zHo9ZX1+rACvLsmi323ieR7PZZDQakWUZQRDQarXodDrs7OyoZqe2g3BMuwCH/UcHXLsWIGWJ7ylbZjsuvkT1lKspo1m2hyxLokXEbD5lNlPS5aIoefz4gN3dC7z77ruMxxNmsxm7u9uEDZ/5Ysb62jr9fp/33vshruvhOAFRnDIaTbUyoMXW1hYHBweaoqcykiUW2Qd36HQ6nA2HTOYL8kJlY0/PhsgSWq02n//8FxiPR3zjD7/ObDYHKbBth0ajyfHxoNoL87yeudEMibKkKFZBhvNUtvP2C5byJuoPICs7V2J67zy15tB9eQxVTWd+pfmfrE63/DypX18H4euf/QzaWynlyp+fBTZ93OOjApQ/zvEfG5jUwcofFTz9aWal1Ef/aJCqHrw969/6XqfOxgrw9VH3Ut8P60FO/To+KlhcGTNJrf5MHXUW0fnnUN+Xn3W/5qgHT9X3paoFNeBwHRAtCsF8lvONr3+HvQtX+KnXXyOOCjxPZVlms9mKHLcBguqMnyiKaLVUL8n5fF7R5MqypN1uV7Q841MZ1ovnebqnnY/jOMzOzipaXFEUVQAEivlleqYBNBqNKoNlfLhOp1M1tjV1SkEQVOUy5tkFQYAAIq2k+ceZtz/xARKoDd3IfhonUAjBlStXNMf7tJLrjOKYKF5UEt4q+2ScgwJLNworiqLipidJgmvbqgeLbVNIiNKEk5MT7t69w4svv8KtW88xGA558OABnU6nokttbm6QZcs6DMuyODw8VMiw52HXImXDe1f8eYljCZCqiPDh48ckccz62hqJRuKMzPR0OkF1Ww5wHLcKfEwmRXVpL5nO51U/GmMQKklEKcnTTBW0p0aC0lJUA1t3mtYL8jzKUP9d3RH/KDTK0AfrC7z+LK9cuYLn+8ph0shRludEi0Ul4VoPBA36WC/s832ftbU1iiIny9IqaHIcRzXMDALGozG2sNjb2cXGYnQ2YIRavOsbazQaGxw9OeSb3/wjhFT9QdZ6fda3Nil10GyCRbNwizRFSOh0O7z/wQe8+977LKK4QjvqRx2lybKMOIoQwiJsND5yrpvxNRlMi/O8+Gejf/W/PZWyrx1lKRG2QuCODo+4ces5ut0uw7Mztre2uf3BB0RRxJqm1pWyZGtzk+PBGYPBgJs3b3J8eMjtD29XgMB0OqXdblOWOUHg43qOkjF2HWQhSBJ176WULGYRZQGWsAmCkGgRc3JywmIR0el0aTSaJLFCpp5/+SV2dnZ4tP+I8XjE1to6V6/dYDgaMRwOuXTlIp7vMxwPuHT5MkWp1OBmM9Ux3BZqU1PF1WoN1DnkP+oZmLG0HaWe1Ou3+Nu/9qt897vf5s99+UtsbW7g2A6Bph8sFjPGozN8X9HXTk6OsS1LKepFC0BwdnZKEIScnoyYTxeMxyMW0ZywEaj+Ze22RrQtooVay5PxmKPjI3rdHv7/l70/jbVty84CwW/O1e/+9Oee2754TTTvvegNOGwcJsIYqLRNka4yjUSZrmThP1jYYJAAYclhyyClyUIghJKSXaZJyllAJVVUJc4khXGEsXE4IhzvRcTrb3/vafbZ/erXnPVjzDHX3Puee98NiEqjEEt679xz9l5rzTXXbMY3xje+EUeoygJZusJqlZJekwSAhpSfBMxaR0amMN55mPka+L5xiAAaDTxfQHqA71M9oKtX34OkQxSy0KcaGMvl3FR318iyFTyP1DN9J7K7WMxtbqjSJNvueQSqeHPn+jknJyd2HueFidQ3NaQRZiiKHEVZoNfrmTpHEQaDEa5du4ZOp4NXXnkVn//8b5DS2XCIoqC1dz6f44UXXsCnP/1p/Pqv/zrSNEW3K21kuyxLqp/meXazL8sSZ2dn6PV6mEwmZs3zMJlQHaT65Bx1XeO3futVWxwWIIWly9euQnoetne2sbd/gCCIAA30Bz3UdYUHD+7i9u1bWMznGAz62N7awvb2Dr73e5+z9MkkiaGaAt/93d+F4XALe7v7+J/+p1/CzZu3MR5PMJsTVSXwA3zHd3yC5Hnr30Cns0KlFB48eICzyQModQ+XL1+G7/t44613UFQlqrrBdL6EgIckWeEX/x//T8wXU6imglI0Jihoc0b4Qj5a50UpZaI8ngU37jokjcAPn0J/NwYhTaDH0lq1y0nWFwhjP+ZE1z23bnDDrR3/6PG4z54SzzxthOcbdTzufu923yd9/r8VIHrccRFA4cMdV/y7yyYBsA5EtDZr3OOvt2mj8FrwpCiXe7iOx6Zp1gASX9u1R93z2khpq2i3/hwtFc+1t4Sg3DYhTEFlB9hQLUCBIPBw7dplbG2NICVQ1RnCqAMhPOzv79s2MnOJC8ByPTEhhAUjTP1mQMWRH6bCcY04TtfgdzIej60QDkeAWB6cHHK+fW52LvNnDLxcu57by8EBjkxxygC0XksRedrjmx4guQlqACwFjJNG2ZgOw5BySuIYSlegWg5j+L5n1TiSuAvfD+wgWS6XlgbTjRMMen30el34QYA8TXH3zh00VY3FbI6FqZmwu7uDMIzMi6eKw0WRW3471+7QWhNnsiHPbl03KMLChDArCFSIwhBRFCKOIrzn+lUAGk1VYDIek1fYDGhVVVhMJ0jiDgam+NYqz3Hr1h1cuXqNOPdZhjAKrWEUhjTgi6JEWZSo8gJ1XWExn+P05AQnJw/xgfe/H8++8AKUashzanmxjTXSAdp8qOCZiUZJUmy7aKHRWgMm6Zf5E9rQJKQktblOJ0GaZlCqMQp3DTjkzNK71Id036YxtZccwCKEQJplxuiFDQu7ogHSk4ijmIzzpoGqScZagOproZNguDXEH/qv/xBOj0/w4MED7OzvQkiBOKactUY1kNJDkefIlMJ8OsP0fIKsKPDBlz+It9++iVWamUVtfb9tF3NqX5bnqKuaEtCpt5z/Lo4iuYDYDX9rTTknNsKEVnFw3SsHZ7E2bBSlIITE6ckJ5tM5Dg+O0OkNICGRxAmyNMPWcIStrS1cvXYVl69fQdFU+MIXvoBPfvKT+OAHP4jZfIbjh8cIIqpfk2cZpBQ4unwZW1tDdDoJ5SZoD6vlkirO1wq+F2I2n5nxWeH27TtQSpsq4R2oRqFuFHYOD/HBj3wERVni1S+/gm6nizwrUOQl8qxEEMQ4PLyE5XKJsqrwvve/H6t0BSE0Th4egzcnfiFPLzDlGGuCosxJHOL3ftd3Yjjs4pOf/AS0LvHw4R1AkYfr/v078APfRHQqpFmGumxs4eI0zSz/mgz+AVHGtMLR0RHiOESvR4pxYRiZHCIPZZnj4GAfk8k5gsCDLyWU7+PS4SFu37lj6odIhKagH0QrrR8ElEuplLJRTSEFfOEbr2AIKQSk5wFaoWMKjXIep22v8QpSVHZK3kMhUDYNFXBWGtdv3EBzcookjhFEHknDVyWkojU7yzKk6QpCSNRVbTe4MIjgSVIyTJII83ltiwsCxjiQAtPJBHVVwQ981HWF4ZA8j1ma2SLXb7/9tokoh9jf38frr79O9X1GI+QGcAshcH4+QWnKMmRZZhOJgyAw3lRga2sX4/EJraWSjAQpJFarDH7go6wqXLl+HZ/+9Kcx2t4iSlpNToeiLOB5Eof7h3j+2RcAAHVeYH9/H+fntK5fuXwDVVXh+PghlCrxyiuvoqokqhJ4//tfxunpFEWh8PxwF57vYzqZ4LP//tfx+uuvo5MkWGYFVs4zKdXgwemYRBEaYldIQw3XWiArKqLqooEURnZYSBOVEeAaaGvgx64h5PCySpCCZ0nrxTfu9Y1ZRHPHWQ3bFU5oA5AcMLUW0dnwssMNErkhJMegvnAuO1ew6Gnzm4+nWOk14Mbehva42M42e557q6fBJRc9j9kWXBDKbRNr74EXOfqf+6TtWiYe25TH9h1fVrzL9/gWfNoFX3wSkOGrt3Xy1sUcNiMzBE4MCILj8BLcjvUcl3cDhm6UZ43G5ziFlR3LrpPYo7IrdlTSC2upc0QD5H23BWlsz7aCXJt2Lqs+9rpd9AcDYkbFCS5fuYT3vOc69vZ2EIQeut2YhHACCYHA1pvk52cKNOWZNqYgNq11XOjbFchiZzBANHwAlpLMdkhRFBiNRugPh/YaNrcabEMqC3S4v9h248/42q44BOddAS0Yq6oKnSSB73nk+PSfHvZ80wMk7lC30BajUh68w+HQ/k5eTB/bWzGGw200dY1ud0A5BJqNTt9SFg4PD+nleZ7ZVIh6NdzdwUe/5eP2XnmeIkliU5y2RtNQe7hdDIz4BXN4UOkSSdKzcoeqadDt9+DrCuOzE5ysVoBq0NQVxmen2N7axq/9+m9glab4Pd/5e7Czs4MwCjGMYyyXM0zTKaKkg6LS2O73UeUVorCHfj9BEPqo6hpSSBRlAykCohB6MbqDLvJ0hTAI0YlC3HnnTXzty19Ali7w8sc+jqYu4PsBhCZpTFbDqmpt87DoEFRTAsQZ36R4aU3yyzTpPft3oBW5yPMM8zkl9cVxDCkp/4EXfv5Jk5oKtDVaYrGgyAAXQiOObAyYgn08VtgzIoSAahpcvnoZYRhRDpoQRmKdJDsjUOjWi0Js7e9BmsKSnvBsFFAIiUU5x2w6Q6MU9i4dYTad4Ts/9V149auvI/vSl1EWDXSZo3bqRdnFVQJl1aBsFIqqgvA9iuKYXVBsuD7Zm9Q0CtprPUtASxkQQqA2FBhPetagAUBeX2McsVecuSoaVIPBg4QuGpzdP8XW6ADD7X3kWYVLh1dx8603UYUlhrsj+EmAymuwd7iLu+/cxpe+9AV87OMfwwc/8iH8+1//NTKadYimqlCVJR7ef4DlcoHR9hBRHEFpAUiBeDBAWTToyhBhdwt5XmA+n0MIicGgj063h6qukBcF4l4XH/72b0PQ7+E3f/WzqIsCO6MtjMdTovBVNZ59z3vQ6w3x2htvYG//CFu7h3j7zTfgQ+H+3bv0/MbfLBxp/IsO3svM8IYA0GiivXoAukmMQS/BfHKCbjdBFIYIo8TUpTnHajnD5cuXAa0RhhHNP4/UDX0/QLdLUqm8RnV7IQbD2Dp3eMMgcEK5N5y3p1SDF1/8AE5PT7G/s4vZbEYbiRCIgsBuIpImDskWOxsQ/wzCEDDrVFVV8D0PtRln7Akc9Psk0lEWKMqSag6FIeUgBSHKvES6zBBHEulqiZ2dPXzsY98CP4zwgZc+jDu3b+P+8S3kteGOBz4W0xmJLJQVRcHDAIBEVdWYzxaAFuj1u1CNQhxHpoaNB5I+jzCbTdGUDfJ0RbXHigJJHKMbx5Cej/Q4Q56SeMH47BxKKTy4/5DGlvRwejqBUgplRXk4FOUXaNQCUkhoCJK6jhNcuXoND47PcDI+Q9JJkC6WaFRjI5ocRb5y7QY+8OLLiOMuxicTarPwsJwvcHp6itVqib39fSzmC9y5cwdVpTGdTDEajXDr5i0sjYBEWVW49/AhTk9PSRnP8ww9bwkNoBItrcdGkSeLdtBarGAAStMAgvKf6kYbpwmJKFjajzaDnI1/odGwgIG1DblmDB1KNzbng6WuaZ1qjeF3D06sR3vsZOP/b1xgLTIFJ/pg19WNyz9tdOQiK184k3/tqw7EEFgLUT0eLLSLCffTOuDTG9+98NYWEbZrloO2xGOu98g13D+8W/882i7t3vPC77nHeoRxk9rnguVNOho5ZNf/3l7zUZraWmRJaprHa+N3YywZCXEAjmDWBT2gqZ1K8dwRxmYwinRoAZqrStmYsil2zV2L/gSo6wahKeeitLLlM4SJ+AdBiOFwiCRJcPnyZWxtDXDpaM8qz+3v71shK3b2VmWOTjJEXSoEQQLdKKP+3doJVVXZQsmsLMesqbIsbZ44O1qYEsc5Rjzner1WmbTX62F7e5uYUMYZx2wq92A7xRVbYCDITl4ANqjAII2jSm4Eke2fsiwhfR/C+y85SPZgABJF0VqY2Sa/m98ZRLnS1AEAEYs1hA604VGXK8rXEVIgECFGRhue5Q/TNLWJyBb8GH4mt5OTVH3ft+ohottFnlNibhAESAYDUsKqBaJOH+fTBZI4xnQyR9kAN+/cx7PPP4/ZbI7P/uqv4ujoCO9973tJslZrLJdL7EYJirJAAx+h5yHLM6iMjCMKQVZYLhc22dn3fciOD/g+wpCU/b7n+/4gxmen+Hef+yzi3gBHly9DqRWGwyGgyGOidAt+3CRb2qhooXHD3tazIki+umnUI/3s+yF63RBh2MHx8TEePKAcltWKqtWnaYooogVDKcpnaJRCrUj8gt8fT96mruF7wk4yS/NzeMdac62QAkVRmBByAKUak+sFDIZD9AcDC54rA8TIiy0RRCFG29vwfTI0lzsr3L//AH/8j/9x/LfnfxtVfRMKCo3JR3O9X0qRhHFRFMjyjIxo6QGocZFn0nrE9Hoiq5sb5/apO34vpg+sGwAasLVE3nzjdVx75j1I+gMoCAy2t5Gc9DFbLuFFPoKzMzy43cUz155Bub+PO3fv4tVXX8V73/c+QACf//xvYnIyReB3IJsadVNicj7HdDKD9CS8wDd+NSq6qLUHpQDP8xEnCaIohBcEWBU56qZBb9TH7/rWT2B/bxuvf/XLuHv7JmLPw3Ixw3w2QZrlCOMubjz3XtQAJosVvu0jH0WalSSEMB4jS1NSyNK19e6xcfV4kNT+nfuZqKs1dne2ceXyEbQuEfk+mrpGHNPYuHz5CLdu3YIQAoPBwHK+i4I2J65C7q4Ny+USZVkaimhjQUuv1yMjOcvWpPjjmEQcqqrCcDh8RKq/BeOwwgM0DgQ4OZ2VEgHYNbJ1WBS4fv06EiNK0NU9KCgslkuUZUX5llGM05MxDg8vIc8yLFcp+v0aR5ev4P7DY6Tn57hy5Qq2dgf4whd+E3maodQVpOejKCqTM1MjL0qEQQSWFo/jDuq6Ql2SqEoU+agrooFNJjNopaEVcD49BRVbVOgkHaxWS1S1Ql5UqOsKcZwAmvL9iAdP4IfnDtcSEmY9aWwxXaLP3D8+Q7/fx+7uPp599r3QWmMcjnHznZtYrVakFtVJEAYhfuvLX8Xdew8RBiEBoLqCL8m7maWZNSwpb6wCtBEBkCRSIUyejYZGZWhCjVmL7HwH0IiLDE7zEp15vub1dvJ2nHiL/d4TowDikX+s3XvzXjRvnuJyF/793QGNEK2D1D6/+W8tnGWv++7XfPT5HUDzxLXzqZpMX3M6ZXO92YwEvds1NoGG84W2SY+9jHj0gS+6p10rLm7D09ILH/e9x1HanDPxuHfwuLHn2iVPolxd1NYntWctisS2hGaxqNaWdJ2WntlXmA7n5pD7foQgoFy/OI6xt7eL7e1tbG1tIY5DDEdD3Lhxw6q/jUYjZFkKIcjJtVwubYoIAGJmmOg+5w6xbcZAhOlyAGyOupuPKYSwdDteA5m6xuIITLNjKjLnvlZVhd3dXaK+meflqBQLbjFVjvNnWcGuY1ILeH/dfId8X6bVsU3H74WL2r5bRNA9vukBkjVSHWNwU3GD81h4A+QQ3WaI1jUugUcFB2zexwaA4orEnEjG92RJaQ5L8mZrJaaNV9g3hbN4UIRxDCE7CDsD7OxfApTGlevPYDIeoywLVEWB3YMSl65cQ7/fQ6M0oCU8CXhRgrPJFF4QIYwDpFkGP9QAJIQXoMgzQABh2LEVjYX0sUwzipIFAbwwgi+Bw04Xv2+4hf/wG/8BUZzg0qVLYJY3hYW5snPbp7wAQLS1BYQgwQmWl4QCmrpBr9c1Sd4+VWqPY0CTwhRTEqtS4ex0giiO8OD+iTUkF/OU6EdJBxAatart+3IXxKIooJvK1rxKksS+Pw7/Mp2SFw3P82wi+Wq5RK/Xw/jsDJ7nYTgcIopjhHFMct8lKbkkvS7KooCAwGq5gtYKzz33LKT0kGX/Z/zNv/k30egGZV3ZcdACxwZlWSFNc6SrzAhXeND60YXdBTkM+rmQm6vuw+PYNSCeauEQBMiUoS2mqyXu3rmJ97zwXhxdvoQ7t+/i6MZ1vP6VV7FcrRDOAhzfuY/Ei9Dt9tDrdvHOW29DKYUXX3oJnU4Xv/VbX8Wdm3cgpEQURohUA23y4lTTQKsGwlPwBNVNkR7RqkRA9ZzSMocWAgdHl/CRj38Uuzs7eOf1r+GNV76MuzffQrZa4cb1Z5GmGdI8w4sf+RZ0Btv40qtfRn97D5cuX8OD+/cBCLz+2mvkTTMeyTWTULdKjo9TwnHnved5CATw3heexdZoACEaCEXSrdqosmVZhhs3btgNiCqMh7YeF485jj4IQbV4lqaeFL9bu9ma6DM7gLguxP7BPoqUCnlybTeek7x5t1652m5KvHbahPu6sV5CLvhH1cwlFoul3eQ6vS4yk1s3nc5RVw2m0ylee+0N7O1tIYo6mC+W+Kf//X+Pj37s43j+hffhta99FTeeu4Hv/u7fj//ll34JeUr5ZtDkNKlUASGALE/NXKD1iWu3NY2y7c+LHKppEEUx8rwwc4rEJ5TSKMoCda0QxQlUo3A2JuU2fn913ZigSmPXDHLa1GgaDa5srzUlRZPozznefPMu6lrBN7Xt0hUlHSutsFqVlC917/iRuQrZznlmKrAjx8SBiL3gUcFSAisaWrTjsFaNEyh4NKrs/nw3I3Vzz7vIWH0S8PnP5WADj4Wavl4DafN4GlDyn3q4zsLNddnNx33Smu1+712PxyRftXWZ3r29j/v71zMu3H3pSde94E4mekNAqT3tSdGeC6CuPbE9b7Mbn9Smtt30u+e1dD4egwwwGHQwOBiNRja6cnR0hPPzc3S7Xbz//e9HHFO+dF1TgeKyLBFFIYoyR57l8HwPRblCGHmYzs7QSTqAoQ5KKe05ZVkiCkMkcWyUQZVpJ1HglsulpcK59YaY3cRRpMViYSNHSikkSWLZOVznk21sztviQEWWZTg9PUWv36eisCan0wWWLm2QxxDvRzyfAdi9j/vezTFncS+OePG7E0JY6t/THN/0AIkV64A2WZ8VNgBYY4HRrqsbvzkZGKxsbgyMdqfT6ZqnUQhhFaMGg4EtWuouHO4g4t83Jy+HJznaJIREAw8KGtKP4EmBThihNxjR5FQEEphiVpaFrSkynY6xd7CHOOmiboAgiKE0IKUPwBieDpCUUkILjQZ0TU8KKogKohoORh6+9Vs/gU6nY+mB1D/G4274sm6lY3oGvbb48sRrmgahF2K6mBqe/YlN5o+iCMtFbmk9RFtJTMjaQ5pn9j3N5wvMZl8zeWURhN8amTx5yOPhQ4l2YvL44InI/TAYDNZAhoBGVeQUCFMag14f/X7fGld+6AMaKAui5vi+R95sTfckvX4CUL/rd/0O/IE/8PvxT3/xF40qWCvLzmOB8iAaZFkOKQMISAhIkJd3HSjx+FJarYXH+Xnc8cUAyU1IbY3lNrInqcgXtCa5agqoEAntza99FVeuXEZvaxf9rSGEJ3B49Soe3LmFYLaELyRez76GV7/yFSitce3GdQgpkOU5PvzhD+Nbfue34PK1q3jtq69hcjqG0gK+FyDwqT6PNpWDtNnIPUkFIYuqRK0ajLa28Ox7n8dzL7wAL/Dx6iuv4M5rX4OnFWaTc3zogx9EmtdY5RkOL1/Bs8+/H5PlAmfTOb7jO343yqbB+GyMdDrF+ckZpOTcGzNGhfGkq4uNQ+uNdX7SOAHCyMeVy5ewXMwwGvZI3bBUCMMYtaoxn89tHRcuR1CWBTodqmEzn88RBAFOTqjaebfbtRsfg/jpdIper2edPnmeI45jeJ5nVX6qskQ36SBNU6xWq7X3XZYVKKlXQwguxEpJ+FRHyEdV1VaZbLFYYjqdmdw/YHd3H0J40FogimJMJhMsVivESYzz8RQPHjzE1tY2ZrMFDg+PMJub+6sGX/3KV/CF3/wSPvWd34nv+NSn8Nlf/hVcunwZ73/fi/jVz30Oxw8eIktTyleSRK0oihIaAnVFjoOmUdCg5+Eab8p4FekRSUYWTgSEJOUbNM2E5r7WqEoGSEQ3qZU2c1ajqqk+llZcxYqNMlai4jIGRKdReWH3BlZca8y6XCui1JCDSANCoNbK1rNjkKSbxlC7lZ2Pjalpx2NSQxlnlCCvlCnYpbW6sLbaRcfTesk3//Y0xu83CjQ87XFRG9kBt9mex4Glp414PO68zejNo1Ggp+u3i753kWNmM+LUjqG2PY8Hs20EbL3NF9si7vXdaz1NZP2i40nP6H626XRev4i0c0Kptv4fNvrQFUbY3PM2n6ttf0vPpP8ACKrBRZ+ts2B834fv+8Zx1Ee/P7AO8iSh+mxJkuD5559Hz9QJ7HQ6VpHX932kKdXyS9MVpCRBpyzLUOU50mxh1HFLBIGPwbCHPM/R7faMPeOZtynt3s+0aiEEqrpGaBzu3CdcIqDf76/VgeToS+uobYvec7QmSRIow87hXCVWpuMo1cqIf/H5TdNgMZ+j0+utOfhWq5W1hZlpxQcLa7l9fH5+buoCtoBOKVIu5n2P/8YCXFJK5EX+xDHpHt/0AImVhzYnAv/OyJ4nDQBTCJELIJLxufnCgLbzOXTIIUu+LkeGWNZwc9Fyr8PHpteIjR53kiqt0ChACyfBUJP9RrWazGv1AN8XEH4IP2rQHW5h79IhcVrDCFVFVeYbpSCFD62lUR2irZfVqoQAPEO18YxXk4pv0UAdDvq0NzthYyGAqm6sC4aV2BiAaCgEgW+V9hhI5nmOCjUEPERRgjhKoDXgezUCP8LuLk3sMAwxnU4BaJL6zVfIC6oNEviB8cwQXTDLasjAW5v8bh2qwGvDyK0Xt1WPYU+uGwIXWkOGEeKI3vdm4iC9OyDyfQilUWYE7JiXzhGrsqjg+wH+6B/7I3j9zTfx2V/9VRs9ZEAvpcBqlaIoCpt7RQatGUMXMBzIYMIjbXLD0vzMm4cUAloaGg975sz1qXK3pMJ3DclYp4s53nz9a3jhpQ/jYO8Qi/kCu4eHKIoMk+NjSFCkLktJQvjO7dt46aWXMD49w//6b/4N3v/yy3jhhWdxeLiH4/snuHf3Hk6PT5EXBVTd5g62+QsVkk4HBzv7uHrtGq5fv45ul0QKfuPXfh2zyTn2RiPUZYHr128giBKc3LsFGSd4+SMfg++H+OpXX8O1Gzewf2kfD+/chgeFV7/4RUgIw8WmO0Ks6ditzVGen3y46wqtJw36vT6euXEFnU6EPFshCiJoTyBLCyzShaXPpWlqo8dRFCHLctR1Y/nW/HwkgR5ZpwFvxryOMb1Oa43JZGLnalmWyKIUWmu89tobGI0Glv7rjgkey7z21XWNPKd1NM9zA1AK60SYTmeI41PkeQ5oIAgD+m5RQEHDkz7m8yX29y+hrmscH59glaZ4+UMv4/T4BFlW4Llnn8fZ6SnyxQJ/4A98D+bLBSQEPvnJGLfeeQe/+H//RcwXM2RlBumRU8CTPg4ODtHv7+Cdd97BMs1MQWWaXywwQzV2yFGkGio2zO9Jm/HM/14z1kxRcD5cj/y6eAAdvG5IwM4ZrfUaNdGlcCkYhTczzCBa/O22g2rtODQuaLOkakuPEnSSAfFOO/Gogfr1GqxPG3n6RhxP8ur/x4IW17j7es552s/XhswFe7feOO9p2vG0kTn32Tav67bl3QFa+/m6o8yp5aYvlp7maz8pov6kaOVamy9onwuWLgKcDITs79K9l3bo0a0z2taudJzTrtNJCI0gYFtAQnoS3U7HqAD7GAwGGI2G6PV66PV6CEMqDg0Aw+HIfj4wlPu6bqwdStR8V4SAi2FHWC7n6PV66Pd7hl4mESck1JNlKeI4QpalJr+nQVkoW19zVs/Q65KDTUAizwsLUvI8tzmsMHZNmqZGxbOwQIbXuCzLrGy3rV1mDgYpYRhaO9llK7hjiB37HHio63pN0KEsClt+h98D12taLpfWRmKnMV+naRrb30Txzm1EjKNGTNNz2RK8Fzb1f1GxsweHAV3vgbugsGHBf9uUTmSAwl54BkRubR1WxWPKC9O/AFghgcctVnyvTfqe+2++L7dXSgmpDAWioarrqqkRhaamD6QTNTf8Vp9pYR5kQJ4PSLKsfd+HJz0CTJ4wtUzc3CsCSVISvYMqnBujqlbI88ICCDa0yrIETF/WdY3ZbGYpa2VZIunE2N2lQT6fz5EkCQ4PD5GlGVQNTCdTZGmOVqlOGeoP5XmtUmq750mEkYdOd4j9/R1TWd43oNSz3lXlbAJuPSCYSBAnGgKw71oIYUEw0C7WeZ5D1w1CjyteaxRGzrJuakrmrmqTMB6gRoEip0ryDRS01PA8H74fom4qW1Dxz/yZP4Pj01O89dZbawsWACzmC2Rphtl0jrLWhmL37uOfaZ9uwVwe5zy+HtnchDCiDQyQhK37YTcpurr1P7752ldxeOk6Ar+LK5ev4M6927h09SpUXdlE+xvXb+B8eo7RaAs72zsYjUa4c+cOfuPX/z22d3fw/PPvxY1nr+H6s9eRZjlm0zlWi5Wt1+P7PqIoRL/fw2A4RDIYkaLOcokvf+m3cPuddxD6AQ5GO9BNifPzKSB8vP7WO6hFgI985FvQGYzw6pdfQRwG+MhHPoTlYobp+RjHN29iOZuAYSd3rQbZp5ve+PV1BHY+cOSPNlwP733vc4jCAFIAvU4HUnhoaqDyGzR1Y9WNOI+Ia+2cno4tn3symWAwGFivX1FQ/Sj+G3vput0utCahmePjY2itrThAHMeQghwBH/3oh/G6kWNfLBaoa4WmpjFZlAUaQ6Nrk2G1pXXR8zF9AhC+RFNrnJ2e2/Fa1zWSboJVukKWFQAk7t9/gPlsgXSVYZFlyIoSN65dxXd9+vfixrVr+Pef+yz+r//df4dv/c5P4RPf/u3wfR95fg/37j7AC8+/Dzdv3USpani+j6IosZgvMF/keHg8wTs37xGlzgA7fgcs9FI2auO9mTcruJ6Na2CycbtOztHO+kH04PV1mqaLgIQyJzL4cYxdbBxr007DEWbbGGzStssgOeccB7ivXW3jD1/H8TQRjt/u43FUsyeBj/8UcPf4cx8FoE8CJP+xbXic7fC467r2zjro2TxPgiWh+bx2DVt/BrcNmyybJ0WQngSQ1r6LdZvHve7jonP0odt+NyJGJQlcJ6FSyqpOcrH4OI6xvb1tKWL7+7vodjskFjQaYXd3FwcHBzYKwjXR3OdIkgT37t1bU0dmoQMhBBYLYgrs7u6iKAobzfA8D1tbW/A8z5S6UBY0dbtdFEWBwWBgc2w6nY6JkEl4XgDPC42YjwfyxQhUVWFsD98wERLrFM6zDJ7DwOFnGg6H5OQyB4MSBjaughzbY7QfRxZouukqURTZotr8vjjaxO1ZmpIsACy4YrDERWJHo5FVzXPtsNlsZscW29rswGbhInaCx3FsI16cvvK0xzc9QNpUY2KDd9Ojzt9xDR8eAHlOtVZcNO16NDhq1DQNVil5abvGcAFgteA5kc2NaLmLF/90qVzuS+fPoMhgaZqaePZhgDgi2V1BFwUrkAkhaH+193PqAHgBecwNgPQDz4lGKUipIYVGXVWoiwwaVPNEgWhkiwXJLy/mC1vstmkUtFIIoxCpkevlKFon6aDX7yGKSJa42+kAQqCTJEb6XCLwA9SlsknlnPgXhCHVaQlIwlJrTUn8UqLe8HKwPDF5X41UNTwT/aBoABejrasS49NTlGVpi4g1cYI8zwDzXSFowlN7akjpwfckFikBQ6IoAXlOtXrqskLg+SbcTLVQpJDI8gxJr4O0SI3cPIFOouAFCDyBP/2D/yf8rf/Lf4vSeOvrmqJIs8UKk8kCUTyHH0QWwKwZ85teXkHvsW5Ishy8YZqIGXSrDMjAu50PLX+aNkzYfDKtFRpN0UTdNETyq2t84fOfx7d/agdRN8H27j5OHj7A5WvP4G7zFqbzOQb9Hi5dOsJgOMLtW7exf3CI69duYDY/x9nkHP/+c59FbzDEweERdg/2sXuwi6NLR5TEavncygKCh/fu4s7tO5iOx1jO5uh1u9ja2cZiucRkfIb5YoFlmkHJCB94+cPYPTjCrbv3cDod4/f83k8BqsHdWzdRpUu89dqr8CWsV5+jVQKOPWqOR7zyhvfOjClooh4GgY/3Pv+8fUMaAsvVCp4I4Hsm9zCMaA50OphMJiir0shap5aawBzyTocodwICw8EAgR+gzAtIIRCHEXzpIS9KTCcznJ6eYLFcWmNAmnzAoiTAtVqukGYZqFB1hTKv4QcBedcE9YOQZv2ByRmEkWsWvqFzSVRK4+z8HFoDdU25QGEQoFINtCBJaK0VojjEweEB7t67j+Uqw+T1N/Haa69j0OviyuEhmrpClq3w2u37+P/+0v+CXreHt958E2+8/gZgnCpFU6E2tDkBAj8M4hQM/c2CHFLLrFXTghsh7PvVWtP6IA2dzZBW+dCgQrXuO3ZtYQVl55OCMkpXrMJpT7Jqnu785Ou7YEgIYTKNHjG5CTiJ9e/aG+hNem37728EzBFiMzLgAsPNL2985YLfHwFx6zdzTmv7gz5yDV+DGbFuVFvXhr0vR93a6J3Z+pxrbq6bTtTBXvXxoGfjzgYbt+DYVHraAEmPRkW4leyQ4LXHLuVCbBj/HKVkUADrDBRmwHBwRQi55uFxn4SfTWv+rrD9QHksrQP3IrBzMfgRj/nctNMIi1iVPv6O1jYCJFlpzMxTj+04j/Yk6USzXOczR3T8wMeg30fSiW1eD0faO50Otre3EIYRhABGo621+o91XWK5XFB9yyCwQCUIAuzu7mK5XBLTxbCGhsMhTk9PEUURut2uVXBzDfder4coiiyNTQhh1/Q8z9ecsf1+3xEo6GC1zCCFDwFi0XBxbyE8QDcI4ggwIGW5XNp1K4ljNHWNIEkoF1+SDSCFsLlFbF/N53MbfYnj2Nqs/H7c3HkGS2VZ2hqGDMStOJFxzHKJHLIxSEBHQKBuGvjMejCU8Lqq4BnRqzAIcTY7Q1mWGA1HBCblei2opmlQN7VTqxNkYyWUV1qVdK88IwohR8u0evqV8ZseIDGNBGhBkltQCoDlNG4uBHVNOQKc/8LnMRXFzVGJE5KQ1QL2hXV65NGtysp6DVwqlhvJcq/JoUtuswug7O9NBQlQtMfcg0FfrcgT7UlKKuawZZZlENLkRTkRIo6QzRczNE1tvSMEEGNUeYHVfGWMqtQmjgPsqaSq6tBAkoTodjoYDAZYpUtk2cp6AnqGc8qTCYr4/kkUW+DnSwk/lohjGprUv+TNqusavG5y3ldpDHzuM45ecSiYPRFMS6nrBoCG7/loVAMojdj3sZhOkYQh0tUKp2VJtZoUhbbZMGU5+OVySeppvrTekzRNURYl/IBCuKslVW0ej8cWgNCCWmE8JkEHz/eQZzn8IEBZlBDQiJMEv+PDH0SZ5zg+PYcG5Upkhcbtu8fo9EZIEhIs8LQCJMB2+UWHBuVTQBCg1E1jNlRtk9qBFpSvU+5YSEPbjVIz3UeY+0pJSetSYjI9w2/91ufx4oc/iuFoF2WtMRuf4sZ7P4AHN9/G+ekJkkqhaoA8r5BmVNCzE0W49/Y9zJcLXL1+DcUyxxtffY3oY2FoACTNz6omD1BZlJAQ6CUdFOfnePMrX0WFBu//0EtotEK+SDFfFRBhFy9+5GM4OLyMB8fHePv2LXzwd3wUUb+DB7dvQqUZvvTrvwZV5rQZm2gQjT0zB+1Ahx27Lp2OjF3qF6EBoQAhGmwPB9jb3cbkfIo0pXo5YRSim0jM5xOEYYDRcISqrkm+NY5RNzWKujI5fQEmk3NkWY7RaIjz83NUZYVslaKpSbWNPaFZmiLNcmjt4fz8nOibVYWqOge0RhTHWKQrs2lrS/uA1pS/Aw+qVgCIZisgILQg+9v2iVF2M0NAmYhU2VBNLaL/axRVDqUbhFGIwnjrFDTG03Ok+QppnkGZCHg5XeJs9qYdceLBOfxXvgqAhFoaRQ4XosKpdqALDah67T3AgMA1I5Xfj/O3Nc+1ct/xJhJ2f7SAZs0gN2CNv4I1MibQaEY3j0MTzqntndbAhcZmprhj3Kv1a+uN6379h2jRh7lya+OuG+dc0Lv9lNuv1353m+SCj3Vw4/ybDd7Nlj0mmiClWJ+LNifTzEnh/iQA4lKm18aQ01wXiGq0+y8/iRAC0tnDwdexBrsT+XOgr3b6ja5J36U2KFpAtJmDTBnTDFhaSpgQ63Ll0lDu+f5tn1+cS0QfMnOFa1upFlQZ5yD18Tp9XgjPuUcL7oQQ8GRgnZPCGLzK5O4JLQCvLY5qi7UKQzjR1F7KKxbGqQn4vkQYBvANfV5KiZ2dHRwdHWJvfwsHB/umUHhiGT2+5yOMQnimRmNRlMaGE5jPF7h37y6uXr2KokhRVQRSmFrG9kS/30dRFOj3+1gsSHafBb2ANuLBqm4MLObzua2VxnbParWydiXnHTGoOD09Rb/fX1MZ5WiL7/tYLBY2GlWWJYJ+gKYmeppuyMaRIsBw0F1jRVVVRXbMagVmF3BkhYVLpJQ2P8q1g9lpymOH7Sx2qnJKCTOmpJS2rxhIhUFkx4aUEmVBCq1kI2ukpo4clLJOawgyLoQWUJUCFCAh15Tuer0elrkpd5C1gFMIgdALMV/NLT1PKYXQDxGH5KifTCZ42uObHiANh8QVLYoCy+XSVk1nsMN0qqIorOIdg5WyLJHnOfb399Htdm1Eww2vKqUwm80gTfiWeZacNEaDMoAnWw5kuzgoG2XiScFArKWIsJJR63GSUiIw4UqKpHioeYCWJWbzuSlaWNt7xHFs84BY2YMnSRAElA8U0+RZLBa2ncvlElVRQTcKUSdGfzQw5/h2EW1zWchrxaHs7e0hmqaVom5DpO0ir7UysrxEoQvDwOQMNGg3MirwCNBWVZUlgjCwiwA0sMqp5kiWUqLggwf34QcBeqY+Dif5zedzlEWBbq9nFPMo4tPpdHDv7l0sFgscHBzgjTfewOHhAaQncHxCAPPuvTsQQmA8HmO1WhoqIo2xyWSCLMuoL73ACDCUdkFiYAjQeOj3+zY3jXNKBv0eyqrBt/zOb8X7X3wJ/+Dn/2+4//AEKiePzPn5GG+88QaeeeYGedLRbmSPunPp4HHmUkp54YQWa39rwfujtSNaI0Je4FU2n6sat956Db1eD1eeeQ4H+3uQAGaTMa4++wLizgB379xGVk5JMj3PsOp0MD47xTvvvAMlaKP+zk99igztokCepqirGqcPKXfl6pWr2Or3IUYeVOBBlRXuffkhyrrCaDCAWuZYZhnmRYWt/Ut430sfQXe0jeOzMW7evIUPfuiDuHb1Mm6//TbS6TneePUVLOYzBKDcPhhvK70p0fatM/fcfrmoH1TTALrGpcNDnJ2cwJcKy+USw+EQWlNkSWtge7SN89NzTGcz5GWBoipRWGobqI/KEk1d4+2337EGiaobq8jFa1AQBJjNF1BKGPGBxnhqQUaKoOfjOai0NlLmyiT++5SjY+Rg3aik+6x0XWUNRmXmqFbaeg7ZO8yeQikEfvnf/ao1iKqGPd/r5RPMHeyayPfeHIvml0fewdNSwzYpT5uGOj//pkHuHq75/nSQ5IJvPdn5/tjfHzHoL/Ti/0ceYv3Z3OMiI3vTi7/5Of9027wGqlxD/oLPL7reRX9zgYtFdJrplC5YIYcPK4y14LWljLmsEjqYdt9+7iqA8drpOpfoO20EkYATfeZJnyKURnBDQBgRHGq/RmMjOi1KJ7AiINcjXGtdIi98D9Q/7b/dyBUV/abnl1I+8u4Z7Eop4Jl9m5/FXgN6fRxAmCLU9GwaGoEX2P2e+yQ0ZUWkQUa9XtdGYXZ3d7G1tYXRaISmqXH12hVo42jtdLtomhpbW9soihyr1dzachy5aZoGs9UMW1tbtsQKR3Lo3TRUrLTft5QrYoOUtsRKFEUWQFBeaGbPZ4ZRYfJomHbGQgXsrGUK2mAwWBMZYDDF0ZnRaGQFvFyQRvlJFMnhnKJOpwMBoDSO736/j+l0aul0nMrAUZ7BYIDZbGbV5ZIksUpu7GDjHHBX8OD+/fsYDocWDAJYEztgm4b7i69hbYyyQl4WSJIOhBBWUIEpdNITtt85T8jSE4VvKYfL5dKCUpfFxf3O96yqyubmMhjk/uZc28flXT/u+KYHSJwEvVgsMJ1OLcrkpC5XK93lOPJLZvnDNE1tdIe/ywvCwcEBtNYINFHWtNJYzheoTVIcAMvN5PNGo5G9Dod3m6bBbDZDHMd20PHi624yWhOlyTMhUY6SVFWFNEuNlC0VLBMwNBdFIc0kDrG3QwXEIFpFEPJiwebtuB4vKozY5lt4nm8Hpi88NHVN4Uzj9dHaQ5amBGDM4kmCBAWiOLbhWsozyOF7HiAEsixDlpHiljbep6KgopFaU5i9NDWhlsuFUQzksPcCSimMx2Ocnp4CgJHdHqEocghBBQ97vR6WiyXqujL0oxASpORy585t3L17D1/84hfw4Q9/GCenJ/B9iV6vh+PjY8wN8EySBHFMlEDOydjaGmE0GlLC5vYuAj+wIXYehxy+Z24yT1bfJwWx88mE8kBmc1y9ehn/x+//Q/gf/tk/x507d6FMxGxyfgYhNEW/PGlyWB5v0LiLyRqFTtDmvUkrZY+mcsLQroEqxKNGEn/ugeS5X3v1i7R4SoHtnR1Iz8dkPMb+letI+gPcvfUOJvMpyrJAnmfme9vI8gy9XhezybndKDpJjLtnVGBWa8oXe+GFF1CsKqRVhaIo0e8PEEfkaTudzCGDEC+89FFceeY5wA9x6+4DnJ2e4sMf/SiuXTnC/Tu3kM2nuPXOWzh+cB/QCo15boOFrDHEVFUYKsumQU/ziIoVNg3RF6hCusBgOMBqvkTgAR96+UNQSuHk5AR37tzBYrFAN+kjCCKUVQkFYL5cYDafoaobo8amqXCzbCuma5t70TojPM9DWWtABKhVhapW5v3RJmUV1kRLqXTzhbSmSIQyEV2mVDLdx3nbEMZAV4pKPbPogZSSvL12XBgPqwzIow2grEoy8kyyDV/novH0buDHXX9dg9W9zkXH04IoBiKPO97NmH/SsXnu07Rp8z7uNZ50/tffvsd/fxPMuMb440CP274ngaiL/r2ZZ/Skf69Fe5w1kSM5RBNdXxPXwZBcuy6PLwBwnUZtPou0RpsbXeExTY4SrF2DDU2eg2HQSvIzE4HYHo2NirX/8TMa1424aEwAHDXb7OO1QuCCI14APF7vOYLUznnbdwI20sPL32YUbD1i1cD3aV/zPFKNZUO83+tgd3cXcRThxo0buHR0hEG/j0Y12NneQqOYoRHD8yR8n+rypOnCPmNVV4iTAPPFuTWYWwcUOf8YZHE5AlaSYwOdqWRsTHNUh+0+NqrTNEUYhlZhl5k4dV2j2+2iLEvT3sg+J99DCGGdoIvFAkEQ2GKqQghrWwJAr9cDACwW9JxsH3ZNHUxWibM57mUJT0orBMG0OAYprCrMeaxurhCr2rkOKrYxWTQLAHZ2dixd0J0XPG+4PfwMPJcYoOZFQXahYRq4gmmNapAulmsS39xWrQHpt04PplGvVivbL/z+eO6wyBEr6Lk0wDiOLfuH85Ce9hD6qXeMpzvqusZf/+t/Hf/oH/0jPHz4EJcuXcKf+BN/An/lr/yVNe/rT/zET+Dv//2/j8lkgt/5O38n/s7f+Tt48cUX7XWKosCP/diP4Z/8k3+CLMvw6U9/Gn/37/5dXLly5anawZSoe3fuIo5j3Lt3z04mRuRsfDPSZGTJxohrPPJgYy+y673lAUMFHgv7Qvn6jOgB2ITpnZ0d6z1wBQJY8hdo0T3fg2lQZVmiMJ4KKaUVgbDKLMKAPN+zHh1+DmW8vwIEGKSkArIaGnVT2vydpmkgpERd1QjCAJWh6gkh4DmRLQkBVZkilkGAyWQCrbVF8pyfxXKSWZYhNFWRldaYTiYYjUaYzecWSPmeh8lkQgUZjRCGEAK7Ozt4+OABsiyF53tI4gSLxRxFWSIwoeH79x/g+vVrePnlD+LevbtYLJbkJa8rKK2ws70N1v2fzebI8gzPPfsCfvM3fxOf//znIQRw/dp1vOfZZ7FcLhCGAcqyMB6Q0irRkGJYijAM7AKwWJAks1Yk7831ijyPoi5VVSGMAggBjMdn2NvbRxD4CENSpZGCAKMGUFY1hPTw4OEx/vm/+Bd45+ZNlGWFsqL30mgBLYyHVLcRRgCPbFz89yiKbC6VEJIoVLqliPAmTptnO174WvQ9Dy69wjWUhG7I+yl8eEGMD3zoY9i7fBVR0kfVaJwcHyP0BEJPYHxyHw/v3UGWLhH6ARJDKeO+jQy1ToCigffu34Pnebhy+Qol1BYlVKGQVxWypsGqqqCkxP7lIzzznucwGB1gmeZ46+ZtaAAf+8iHsD3o4fTBHcwnJ7hz8ybe+NpXIVQDI2tivJ2t/LKQbOxQFIQBpdvXpEyo0DQ1mlpZ8fXBoIPD/R0c7o3w+3/vp/HKK6+iY+gSOzs7eOvtt3F6fI5v+7bfjcOjS+h0u1ikK/wPv/iLmM3mSIvcOD7a+1gDUxD9rKoq1E1NdFGzIdXNeoHjdp0i8KOdd9k+h3Dkq9E61XmlcJ3VG2BaOQBqzWCGB+bicTTJjhe5Hhl6Eph53Pb0OJBx0bXWn/XxxrZ7qJaYt3bu5nV+OwHSpmH6NOe9+yGeSNN7XJvXxifWlVldY+zd3unXEz3iv7tApxUP4PZIQLeRIrckAq95dG/bCvs5U7sB6eyh7XeoqGZMHn1B12dHFIOVuiawwPVuqqrC6ekZlFI2X4btjtlsZo1wKtJOjgra/x8FxUzNYzuAnmcdINl6es6Tra1dhlLHv1vHgyS7wfd9SE+iqRtr4F++fBlKNZhMx+j1iBa2v7+P4WAAISU8D9jd3bUMlWeeecbkcmdYLmZGECCzTmGKcMwhhFm7NEXjfM+nIus+0eHZGbxcLhHHsZHGbhCF8Zo6WpbRtUejkaW4u1QwV5SLRXHKssTOzg7u3LkDALbwtsvmYSc3jzOmbz18+BC7u7vWwGf7gt8J23Nso9mC0+Z6bqoHAwxWKHULevPzhWFogo2kksz35XM8z7MiDwDW8oms2qYJAkgprbovq7+5dPtN0FGWpe2D4XCI+XxuIzssvsCqeJR77VtGkVLKRseqqkJRZjbyx3OYCqGvEIWxZVwxeOVUF+5/7l9mU7h2exRF1hHIdD/+/snJCb7v+/8gZrMZBoPBheuKXSO+0QDpM5/5DH72Z38WP//zP48XX3wRv/Ebv4E/+Sf/JH7yJ38Sf+7P/TkAwM/8zM/gM5/5DH7u534OL7zwAn7yJ38Sv/zLv4zXXnsN/X4fAPBn/+yfxb/8l/8SP/dzP4ednR386I/+KM7Pz/H5z3/+qUJkLkBKkmQtesQDmwcfL748IFxhB3cy8QB3vZz88gVACWiC6rsUeY7CRE22trfR6/ftYHDD8jwhmPPKA4U3GL6XuxFAc/J4G9pnEEXEIFr0mtpMBhPpKPLCcoN93zceXYp4lRXp6gMw0Q2JLMtNvwgoM8gYwLGiSrpaWf5pv99HmqaoygpVVSLLCtTGkCN5xRp1UxsZawJBrWdDGu8IedeiMMTCFMOUUmIyOUen00G/10UcRYgM8OJwcFVVGA6H8DwP9+/fo2hUmiHpdDAcDqB1jTzLUTeUnHh2dmbAZYJeb4Asy3F8fAxpvDJJkiCKQgwGfaxWS8xmM5ydjS1vOI4p6sgRxdVqCd6YwiCEVrAc/V6vh7IgPmwYBVCqsQtL34yLsiwgpA8BgTTL0Ol07MR+88038cv/7rP40pdfwXKVQQsJouaajV83T0XzcbnG0ISr3OJrvBF4HrWDx9e6l7qVP94cl1S4VlMpFiGhhI9n3/cSdg+vQAQJ0jyHqksEnkC/24HQCicPH+JsfIbZbAYohcAPEPg+fM+DJyQgGkg4hTNNm5qqhi6AGoDodTA6PMSV9zyH7mAAQODkwSlOxhNcOryEF557Fh5qnD24i2xxjttvv4633njDzCMNwYAIAoC0AAlQJkJH/GjL8UdroFKbGoog1aQqFvoBdnYG+NDLH8C3f+J34f/z//p/4+TkBKPRCN/2bd+G27dv4+Mf/zjCMEEcJzg5O0MUR5C+j5//+Z/DZDKl+j6GukbKiGR0aUMhquoKnqTIK1HejOIgWgeO0srW8QGwNka0dgDAhtG6FiUT65SrNdDFIEI4X3ZPfMyoFJryirj/NiMTX+9x0Tmb13S/8zTAhLDdu3/vaQHIRcDg3eiaX+89vp7j8aAUawDpUQrk46/nAqTN6M/jIl7uXvhubXxSP7h7srDzVBBQN/OaRuymqIVDzVv/hNgQ0FCNe006p40m8WewUVuAo9BcU8y9H33G0SK2Mdhrz9/hdZdBEF2T80GF3fO578IohFK100aTGM+J7c56xVQvgEQPpEfRjMPDS8aAbbC1tYXd3V10uySqJITE9vYWOp2uoYSHSNNWORMAprMZ4ihClqfwPQ9BGBpwQGUKiryA7+Rtc8Si3+/j3r27tk4YO31938fDhw9RVRX29vahNdVKjMKQyq54Hu7ffwBoWAobOSopCsPqb9oACaaX8efcFxw1YXlp2vvJLmEhBXaAM/jhaAQb/ryvMuDgtAa2EW0NHqPsxiIJLuBy1WaZ6sc1h+bz+SMUTs+8Qxbi4fu6jnRuL9uJeZ5bG9tN6+BnlVJadpVbLJbpfQyA2P7jZ+Oi4Ryt4uepSqqdl2WZtXWsom5dWNDFTAgWRasrZcW92DHPeeccsHCDEFq3lEi+Fke4lFJWoKHT6WC5XOLTv/+7fnsA0vd8z/fg4OAA/+Af/AP7t+///u9Hp9PBL/zCL0BrjaOjI/zIj/wIfvzHfxwARXIODg7wMz/zM/ihH/ohzGYz7O3t4Rd+4Rfwh//wHwYA3L9/H1evXsW/+lf/Cr/v9/2+d20HA6T7d+9ZlY26rnH79m1Lmdvb20MURTapn/NF0jS1UR9GpBxBkg6tjV8268s3dQ3P86G1QhzFiOIIvW4XwvNItcNEg1xwxIuFJyUqZyC4Gwd/Zy1q5ZzbgiOQmlxdIgwC1HVlIxdCkAJdWZWIQooiLBYLQu+SABBHN87PJ2hUg9VySVGkskRdkGLbZDqF59E1Z7O58aDT4pskCZ599lmcnZ2ZGkUCaZqZQUjgzc2r4tpSu3t7NmeiriuUZWW4tDT48zy3k3W1XCDwfeRFQX1mDPyyLOEHpgZLlmM6nVBhzV4PvieRdCK7GFC/0QJXlBWSpGcXwAcPHuDSpSN4nkSSxGga8jpNJhMsFktIKUwdJ9oMkyTBtavXLJWQomrGU2qMWwZRAMymKKyXJ0kSszkKeIZOMJ/N0OuR90drjdl0iqpp8KXfegX/8//6y5itUjRaomqMj181jyhdtRv+uqHIHicAaCplFx9XGMSTnqVlbXrfAQkp/Qv+Dmh2NAgFKTQaDSgR4tLVZ3D12feibKgYZl01gKYFuNPtwPMEsjTFYjbHfDrFcr5AkZJqohSNNSSAtuhwp9NFd2cP2/sH6G3tIOn0UOY1pqcTHD88QTxM8Ozzz2NnewurxQwP7rwNUWT46pe/gPPThxzPQGsOmg7UTAGDyQUgZ4MUTB99NJLwKEDykSQBLh8d4Px8gjwrTGSWx79Ap9MlR0Jd052lQFGZ37WGbtoX6ubxeZ5Hif/GJewahhoawmvf29o70jDZC9z29bHytDY4OYMoqqRFG11yPesAG8zumY7RDawBt//U4yIA9I0ESP9btPFJxzcCID39NdYjSF9PGy/qY/ezi/p9E4A9Da3ucffnCA/9dCKZTg7RJhBaB1aPAjIWYOHrrtGUnWdxo1jrY2/9Pm79Hfbmu3YBgaPa0tM9y0AQgBCWoj0cDs01lSlKOsBw2LdiUDs7O/a6165dgzZ5vd1ux+bReL6POIlshGi0NUKSJFbEKc9zzBdzjEYjLJfLNdrSaNSH5wkrolIaYSPf87FYLJEkVJKAoxqc3w2lkaYZwrDNdR4OR5hOJ6jrCsPRELPZzDprpfTQNApJ3EFpCjkfHx8jCAJcuXIFSincvXsb/X4fg8HAvkvOpVGKCocOBgMrTsBAhKnubNcJIWzNIJcFxG1nKhmDUwZATCljtTv+LM9zC0gYRHGfMFBiAODmKS+XS8znc+zt7a2NKY6U9Pt9Etwy48h1rvPzc3FxlvpmYOCmkfC+vzm/+DlZcY/7wiqiGqDiRtcY7C4WlPrA4KYsWuDP4Ir7rCxzRHFk28+5UJ70kGWFVQTkZ8tNegX3u9ba9h23k6NmLAvOkS/Ou+92u5hOp/je//r7ngogfcNzkL79278df+/v/T28/vrreOGFF/ClL30Jv/Irv4K/9bf+FgDgnXfewcOHD/Hd3/3d9pwoivDJT34Sn/vc5/BDP/RD+PznP4+qqta+c3R0hJdeegmf+9znLgRInCzHx3w+BwDjAebwm8DhpUPkWYaqLFHVNaqqhFaK8lRAeQajrRGlcypFIWOHPqC1Rq/bgZRtJElKAd1QLaLxeIzPfe7X8Mwz78GlS4eIwwBCkWJZmWeQTONriPvfNBUAAe15lDApJcoiR9OQUpk7ODjMm2U5umaylWUBKjzWIIpiKN1gMaUCkVxUjPqF5HensxmkJB35KIqxXC6xt7uL88k58iJDWZB0Y1EWSGIy3gW0LdnRhlEDNFWN4c4OwijEKk0x6Pdx/PAYURThyuUrdhMoyhKxGeiNUuiYJMGqrgFojM9OkKUZTYg4QrpaIEuXGG2NMJ1MUVYViiIzwgIKWbZCmqbYGo1QNxWkEtBQOD15aL0fWmkURQatGgRhgG43RrdDfaaUQhgEUE2DChWapkIYxrh//wFNSNXg4clD+L6HKI6Q54VJrFdYrXIDnhIslwsICNy/fx++76HfHyCKIzSqQVXWKIsCjVIYn51RzYV+H51OAoCSNrvdrg3vp2kK35PI0hVGW0OslktEYYiqKFFVBRqt8LGPfgiHh4f4N//2V/DOzXtYpSVRwqBJahimros1BDii2C5+LrBnsAGs0/Eaw0dnI1cpbUEweSLdBdUBYix1DJPDpDWkVLh/+x1MZjO8+MEPoxP3UEcA4CEvS5xOFvCkQBSF2Dm8gqOrz0BoQzUtCjRG4c3zPPiBD894ssI4RhNEUBqYTue4e+8mVpMltrpDfPADH8TwcISmqXDrrTdwfO82pqcPcHbvNlSVwwN7Y02zwREkCQhln4tsEmkAEhtcDvvMHmwk0c9GK+RFgTfffgeABERrDEETGJqnnBjcQJgIL707A7LZwBPreWZNXUFTiA5ar1eDpy8YmW4Divi9ta2/+HBzzlyjddOwN0181OHu0Hjp+8o5rzWMydAjz/vTwaNHkP+jx4Zhq7EJAA2gM21vZb+fdM2Lfl/vRY7arf8Nj/aNGUetdLNwBhI/gmNUb8xZvrd7Co9HMuA3ruN8kdrDamLtcwvnM63bvz0ZIAnzOoRr+ZvuN2uNkFSGATS2aTwoa+SzUdNedx00tREd0ebO2ad3OtcBO1rDOh60NnMDPN48uEI2biFRVknjOcQKcfY9aEWCCvaltqUGWJSonYMkJtQ0tQEmLXByj5aqRcVIgyC0tO0rV65ie3sLgILnUxTl6OgIOzs7WC6X6Pf7tC9EEXq9HvIit0AImnaBbrdrc5waRTnNnThB6Ae20DOrrjZNhdWyhB/6SOIYVVmgrmj/73Q7qMrSfGdBRUrTDGmWUoTflzg7O4Xn+1AmmtLtduFJgdFwiCRJ0CiFssjBdRrJidwQVbwsMRiQuBFRqLrY2hpBCIF7d++j1+uh2+2aNbPC+fnEAIoYV69eBSAwn8/R6SS4ceMGmqaxggJnZ2cAyLHKOTrsIHejK1tbW5aSxqDFNaZZ4nsymdgoB+cU8fX5O/P53BroaZra+nNkr7W5Q1pTNI3zrznSx8CTHZgsLAbAyn5zzTs+b293F1q3+TcALN2tKArbfxz5YYc0t53BBwNFBj1EXaRcq52dHcznc3suK/HxNQFY6iKDULYp3MKwHPXUzoIpPUqTaJRhbNAUpVpPghSY84L+GEYR4jhCUZZ2uddao6orQJC8NzRsYVzebzmq1TQkysE5/097fMMB0o//+I9jNpvhfe97nzWQP/OZz+CP/tE/CgB4+PAhAODg4GDtvIODA9y6dct+JwxDbG1tPfIdPn/z+Omf/mn8xE/8xCN/r+sKjaKJMZ/PcXJyQiE8I/lIFKwh+r0eGY2eB8WRDq6jYCQwq6qEx7kkhjfpFlOsqxK9fgef+vR3Yjab4dbtd7C3t4/RaIiqIjDGOTmWFicplJ8XbWFQnjSrLIeEwGq1In5oFCGJYyzmc6wEkKYrsHIKQCHuwA+MKIGwYWPfcHhn0xnm86WNnLFKSmkiNKEXojPo4OjwyE4kIQRWqyXKsrDRF6bZdXsDrFYrhGGEOIzR1Aqz2ZyUYbICQmgEIYVg33nnTRu1qwcD2nSM96soqOZSUaRomhJa1xBC4nx8ivPzc/LAQEKr2hjsDXq9DsqqQBgGVs0EqoEnSCUsjhP4PoWUo8BHFLTylgxIet0eyrxE4Es0VYmt4RYmkwlOymNA0LMEKYXz07KAJwUCnyI/CwPA8yy31Mq93T34no+T8Sn6/QEmkyka1SBOEqQp1T7a29sFA3YGvrxo0iYdojaLK+UjaYy2tjGdnSEMBW5cO8L3f9/34JUvv4bPfvbXMJ3OUUmJ2hjYUpgaLfZY36CZ/xv4ARq09EyX0imlMKDB0GaMfUGHhhDrqopM7aRPmQomrSEl0SBfTPCbv/rvcO09z+LGe56HH4cIoxBxohCG5Hkan0/Jq+UHCMIAURgjSqhOEIQg50FdYZlVSM/HKPMSTVUhTjrY293D+9/3AfQ6PeRZipMHd5EtFzi+ewu33vwa8tUMUtemfziaY9qtSXxCSpOnw8Ya1mlDhG4cW1m7BRMBzzdABwpFTe/D8wQEGmgQaKcBvLFINWxItsZZwwBuo86NBtbOJwU6BlMmf4qNY/sc5t1bg/ICT78zTHj82PNtnRVtbGQ+vzXOqZ/aJG8CI+tAi/tcrRm5jz9ao9n98mNOctt/oWCDcOz7iyNIbjSAkMxmtJDH+KP3deDlY9pnMtycd+W2eQ3obrRLOuphm1Q2rRp7HY5wtO19nB4d7NzUPKbtNYWZEpyX2L5XAQ9BEFmKDNNrmHINwDItqqpCA2OQavYiE5iS7dKw9iyKDSUICEhIEbQdzA4acJ5NW9xAgyO8LMTwKMBjJ4GU7XvWWtuaehASSsNQcngMS7M+tiCKCnJSVDkMI1R1bq4p4XkCgQ4Qxa3Xu6kbdLpdbG9vo5MkkJ6H3d0d7O0Osbe3g6OjI3iebxkyLFMdxzEWi4U1RpkSnsQ+hsMhRV88gSxdkq2wogjP3t6eNaB5X6wNeCmr0qrxBlEAXVK0mxocTqwAAQAASURBVGn/q+XSGurKGJRlUaKuKvieh+lkgu1tEngq8xp7OwfWVsnyHFVZosxrFLqGakxkAVS/BlqYeomFpcKdn49Nkj9Fku7evYswDLG/v2+jVU1DdD+mbRGglAYcEcNjMNix++hqtbKOP2aWuFEVrbUVxWIDn6NcDI4YILCBzwCk1+uBmTIMjsqytHLSHJ1iga3VamXZL0II45Rux0an08H5+bkFFFwTiW1KphqmabqmGEf7t2+d5kzfYwDIezoDOs7h5rwc/j2O4zVAwe1nVV22k1jIzK2RxLLnbh1FZnporW39o04i1sAbt9mTHuarOaAFATkIVFWDPCvQ1Aph5CMIDT2uLNCoNh3GD8iuo/HgIc9TBAHNU24Tr1FpmrbRS7PGLJaLx62KjxzfcID0T//pP8U//If/EP/4H/9jvPjii/jiF7+IH/mRH8HR0RF+8Ad/0H5v07NyUYh783jSd/7yX/7L+PN//s/b3+fzOa5evWonsFJUgKvf7xujTRjp7Wbde940EL5nJZo59J3nOZaLJQZmkhRFYcOxSin4noQnBKaTKUajEVaLJT77K78C3/Px8W/5OLZGW5ZXGicxqrJEr9fHYrnAeHyGe3fvwvN89HpdBEGIhw8fQigypKfTKYbDoU3EU0qh36NJ3ul0LEKOogie8S4ppSyfloFmukrh+x5u3LgO3/dQ1w2SJLYLA3FJVyjLnOh4ZuPTmiQ43QWIvVA8AZmnyyid9PC7UIrA1N7eHn7lV37FemKUUnay8MLF3h2mCcRxjK2tLes9S5IEk8m5pZ+5i+1qtcJwOLSJrgSk1FpYuKoqG8rlxaLb7aJsapRVhdVqhdVqhf2DPaxWcwghMZlMMRwOkCQdSwlg3nJRuNTMEHfu3MVoNILnSSyXcwyGfbPIRYiiy/A8aQCyQFHkAGjR3NnZRpJ0TK6WZ6mP3W4Xd+7cMcn4DbK0MEIRAT784Rdx6dI+vvDFL+HV11/DbF6Qwa4puVZzfhL0miEmhLALuoRnx7ybvLlJOblorl70d2DTx+78XRFd7eabr+H+vbt45tnncXh0Bb7wIBqJwz1K6s2zDGmWIcsyTJcLNA3QKPKAK1CYP0pi7O6MEPWGFHUMfVRlgSxd4a1bd9DkOZrFFJOzE7zz1ptoqgK+oLpPwnDgiRYAAMa7boxhN0F17ZnZveX0I/Os29ohraFn6XdODhdH4B53rHuvH/+9i/p98525zpYnnX/RfVwD8klUsM1z3ee+6Htrxuq7rPX/KcdFVKmv/3scKvs6Dg0ATu0aB2DzeGmh1KMUROmc50bOpGyTnDcBEoMNl2LWvrcn5w9d/HgUufF9H7Gh/5RlCQ2NIIjg+4GNQvD4d8caO+zaXF4B6fmQpo0G4hjFQydqpNvaN1JKU4eubT+NLWX2njYPcv15eL3CI/3Oc567WKkGa7k97MlHBSGFzaGpqhrDXh9RFGLbCPwEQYid7W3MFwscHe3j8uWjtXyM3b1dVBUZdOOzM3JIxjGKPEfP0P2LbGXYH7RHHR4eANDWMGfDuyxLnJ+f22hHWZZ48OABer0emqaG1j7CMEAc71ilMwYTe3t7tgAo77MsV822g+/7VHxatApko9GIas05+TfL5RJXr15FFEU4Pz+3hj/loyzhez4QwEYIWfWXAfNyubSREc/zEMexfT7e6zlCwcDAzfNpc4f0mnBBr9dDmqY2f8jNucmyzOYGcdoE1zXiwtxMV2SFNQYrXM9Ha43d3V0LtLgMCstKu/k/vLdyhIqpZvz8XNOIo20sNsGCD5zHPZ/PbUkWfpfL5dLKonMf+7JVeev1evbaDILCMLRy3oPBANPpFEopm0fNYIyDBxwB4jEIwAIldoAwsOO6TDx+OTefKXAATO51bfuBaYVsj+/s7Fhbmu2Pfr8PDY1OJ7brCM9/nsdZltk8MhY9Y9DI+zbblpzOwdGroijQSX4bI0h/4S/8Bfylv/SX8Ef+yB8BALz88su4desWfvqnfxo/+IM/iMPDQwCwCnd8nJyc2KjS4eEhyrLEZDJZiyKdnJzgE5/4xIX3ZaN18yizDLVB8Fq3dTxUva7uwkVht7a3oAqFpq5RZDTJoUmi9v69O3gnL/Ge97wHDx48wO7uLubzOQGSukK6WCLNUmRpiulkgmeu38Crr74KVTeoqwpaNVjMZzg9ObYJ+KvVCp1OB7s7u5hMJrhz+w5WqxWuX78OKPJ27O/vU3VgsxHNZjOMhn0bKlwazw/r+XNC3Wg0wtnZmQUcL730EoqiwMOHD+2iw/SA4ZC4mCwvypQyKYX1INR1jePjY2RZhmeffdZya3lB4oUCAIbDAcbjM1R1YfnBH/jAB/Daa69BSomDgwML5LTW1lPCCYAMZBg8sacjimJr1LN0Z5ZlmEwmCKRnJyIv7lxjgOUyefKz5r4f+PAAzGfnUErD8wJkaWYWagJ8tDimNrTu++QtGg5HtqYAq7gAwPb2Nooiw3A4somdUkpMp1PTfoUoSiAEcH5+bnjWBFLyLIfWwHy+wGw2N3lgAmdnp+j1+tgxEu0UwQvw8Y+/jKs3LuPhwxN88UuvYLHKUFWaIjkQgGjWDFKXshmHiX0mXuD4O64xBjxqVG8az27o3P2dvmcoQUpDSg91usTXfusLePv1r2G0vQ0vSDAYbqPf79k+jD2KPmZ5hcATULqBFkAvDnB0dADpB1g1wGR8gny1RFNmCIRGtpzj4d3bmNy7jaauIJSGxxENKa3ZyeMe1mBqawS5Xnj7nGgNShfM81gE9CN9tNYv38DjSdfcfG//MQcbS08CRo8DzY/7m+uE4p+b93ya48Lo17u07UnH465BEZ/2Myndd7t5Dffcx1wPj4oyXATQLm67gBAeuO7a5rksP890NgL/8pF2XnRsjllSavQBTaqdfH++Pu8x7rtkQ8RdN9wokysuAK3h+YE1kjbXnbYx61RPN8LWOjfaZ+C56nkSvudGnQAZGM+zKWdR15R32ul0bG5rGAbY3tnCwcEB+oMBBv0+9vf3UVUVtre3oTXlfrh0raZp4AfCGrJRRNGGxWKJwJcAahwd7SNJYkMlaxBFEnXdYLlY2j2d83u4lg0bkp1OB4eHh7hy5QrSNLX0Od4XWWWNPfvn5+c4ODiwBqPneej3++j1epjP5yiKAru7uxgOhwBagZ4wDG1eC7MZ5vM5Tk9PceXKFWtDcFtZNZcjMJbSZ0Ax5+1MJhMb0RoMBmvveTKZoNPprO39DJ5YoIglmdn52+/3rQHP+4SbZ8RjkRXoOBLDtC+uhRkEgQVgDObG47FVGuTIXRAEFiTFcWyZTZwDw20uisICNQaUDBI4Ytfr9ayyHAMCpv/dv38fgVEA5hwaFlTyfd86dpn9IYRAnmWk7mdyraqqsmp1SikMh0MLXjjawuIMAGyUi+0hpuO5thbbSfwuNsVEeE66Yg+c98N2m+d52N7etnsls5DSNLXgBSD7fTweO/nGrQorO7N5neFoHgcx+F0BAmVRmbw2ytFbrVZ2LSqKwlLunvb4hgMkfnD3YG8TADzzzDM4PDzEL/3SL+EjH/kIAMqL+Lf/9t/iZ37mZwAAH/vYxxAEAX7pl34JP/ADPwAAePDgAV555RX8jb/xN76u9hRZjrkGApOwyJ6HpqqhlcJ0NsWgP6BQpfRQ5jlqVWE8HlvvAR9Hh4d4+51bOD09RVmWmE6nODk5wdWrV+ELykUJfB+L2RyXjo4Q+AG6SQe33nkHl69cwv7+ASooBL5Er9uh6FXTIPB8xGGEwPNx6fAQywUl03E+TdM0WCwWNuxaFDmUqi0w4gWyKAqrw8+LA8tNMkgCNPb3d6E1cHZ2agZ9Ca2V4eNSgbumqSElEAQhalOYEiDjnycub+jdbtcurpx0GIYRDg4O0KgaWZZiuVziYx//OOI4xnQ6te1yAQ0nj3IYn41QBmK0yFHkhYuesfekqiocP3iA5ckSly9ftgWCeXF1w8iujHqjFFSjABDn9dbN27h67TI8j+5V1xmJFgRc6DdAHJMntdNJEIYhojCxC0XgR6irBqPRNrIsNZ6ciBT4+gMURYnAD9FoBU962N7aRZ6VmM/PkKYrs7H5JE+qGhQ5AUzVSEynC6xWKaQUWCzmODw8QByH6HcS9J99BkeHR3h4fIY33nwHp2cT5GWJRkiopn4EzKimLc7GC4gbRb3ImGMPsAsG1qIjNm/n0WKLWisIaDS1ET2ARJ0rjB9maLSHE59kvA8PD3H58mWifQgBeKS0lJt8rmxxjrs3UwKaCJAtlxifHmN2foYyWyBfLUi6W1QQWkNqoDG5UcIWY2wNacqvUmuR5IvUxQhyttEZ12tOm0htv/8kcLRpkG724+bvT3NsgoL/1AjN5n0fF9ly2/j13O9J7bvoudvfHx8N47XRjdhtfse990X3d6OnsPENjki0Y8IFS5ugj6Mmm22n2wnn348+R/ssJO/cOvAIELhxJftdC/D5Op69l4B0ADNL0oMKCEuB9YQprnFD67/BMtwz4EKr7vh0+5mdQGxws+eXDNza7EutF5hqv7Fjhp9hvayAK1bkrjP0LHTfMArR77UKsYDGoN/D9evXAADPvudZ+AHt/aPREFVdoiiIyfHMMzfg+wEmk3PEUQQ/IJpSXZHXOwgDbG9vY7FYQiuFxXwKP6Ck98APQLLUCrs721gsFjg5fmgMwhGausZyuUJTV7hz+wTbO9sYDCh6EUcRyiK0463X69ln3dvbQ6/Xs5QqorCHtqbPbDajCJSj3sUqsfzfxJTOYKYA2z0Mptiu6Pf72N7etuBob28P5+fnlD/UUD2i27dvo9vtYmdnZ82OYPYFR5c4OiWEsDVnrl69ulbDxy2VwgCF9584jh8RTuAoUF3X2N/ft6CD82dchyeDKqYLMvWMgZzneTg4OLDGPEd0ut2ujYTxd3ms7e7uWpnruq6xvb1tbRXf97G1tWXH5mKxsCptDEh5b+D32O/3cXZ2hk6nYw13t/QGzyUGGu7aykCN5olC1O9DG9vGsofMXGHQx0CewRWPj6qqrO3I843zoABYWh1T09gZzZFNdtDyOGVw5AI5zsOrmwZVWdk9k/uT+43tMc/zLOOLIoKlfYfsLGc7hYEbR8x4LKlGWec1O19Co3jI84QFJZ72+IYDpO/93u/FZz7zGVy7dg0vvvgivvCFL+C/+W/+G/ypP/WnANDL/pEf+RH81E/9FJ5//nk8//zz+Kmf+il0Oh38sT/2xwAAw+EQf/pP/2n86I/+KHZ2drC9vY0f+7Efw8svv4zv+q7v+rrac+vmLXi+B2VQZ5wk6HV7qIx04mKxQLaVYX9/H+lqhZs330YQ0SD62mtfw5UrV7CzQ+Hr1YpC0+PxGFevXsVXvvIVzOdzPPPMM8hXqZWw3NnZwT//Z/8MV65cwUsvvoj9/V1keYrx+JQmy5IW326na+vx8AK5OCP559jUEWI+Mqt10MZDmxvnEPFCwGFrXsh4wnCUgBZSibqpIUDt5GiN1pQYKhqNpjEJexEtGkkSo6oa9Pt9vOc978FkMiFZZrS6+oPBwC5WpPJXoCwL+EErBHDn9m3s7+9b4MIJh+xJAmCL5AJEk2yaxikgpu0kZa+bVYDxfTz37HNrWv2s65+mqX1+XgTZa5VmGU7HE8xnK3gywGw2Q3/aw9bWAFFINLoiL2zxOiBDnkeQUuD0tLGTLs8Lu9lrrdHpJCirCp6UZsx1DTecaARUL8OzNItOp4M0zQBocIJvVZUASAmwqhrjIYoBENf21q17xA+XHiAkpOdjONrB+55/DsP+MRarFZZlgYVRr+HwNC8Qbc2NtvgxL6ibHmKgVb7h3BsGU7whbXrP2dujNaenU84DGWANhFKAlhBaQakaWgjcu/kWju/eMptpiDjpIIyIhiADHxpAaepE6LJBkWVoqgpCN4Bu4HsSummgfEALwdk01CDBYgfCtK02AggAU57cxfOiKAlvAK3H2jMUqMYahe53L1qMLwJDFx0XetYf07Z3+/vjjqcBYk/6/KIoyEXnbBrWLiBwc4bWI4+banTr7+ZikE7HppPuovOe+ExiPYLEf6dxzr8TiHDzdDa76nHt2KTYtXk+gJCeMz4EhPvc9lwjoHJBNEsIqtmlFJz2apPLBCihANu/rSgC328TiLpgyPOE0w/rfc5GiUvRoT7wUNccWVo3Oaj/OMeqWXs2oN1feK7ROQ08X+O5557Ff/U934MbN24QQAoCVFWNyGuT6MfjM8RxjP39AwgJCE/g+PihyQ9JTSSGSx9oBEYsaXdnG8vlkoQKTO5OXZXI0xQw7AvP8+BJibqsMJ1MEfg+yqJEkVIOUBSEkJ6H/d09UtkSFXStMFvNEMaxWfNTS2967rnnKH/X1Aucz+fWGclGJfctr9UseDEej5HnOQ4ODjCbzSxFjIGDlNJGoZhNoZTCbDZDnueoqgrz+dzSrbQmteF79+4Z2riH8Xhsk//Z5mCaO3vsmdblylLzPrFcLnF4eGiNYGZ5cDSJaYD8TKy+xn3B1DR2HvP84v3LzX/Ruq3FyMY+G9ur1QplWWJ/f9+CJb4vG/HcbxydAigaxFS1nZ0dTKdTa5dwqkOv1yORK1MP6/z83NIv3XpDHH1zy3lwVGo0Ghm7cEaCGZ0OoihaU2bjsitMc2P2Dc9JjgINBgNrYzE45H2c5yvT97gPeN/m9grR5hFxDhQ7rdn+4fvzWGOqZxSGNpdXa23pjd1uF57n2XIn7r2VaiyQZvU9dpjwfbjgLgBLuaxUtUaNd7/HUTsA9rynOb7hAOlv/+2/jb/6V/8qfviHfxgnJyc4OjrCD/3QD+Gv/bW/Zr/zF//iX0SWZfjhH/5hWyj2X//rf70WAvzZn/1Z+L6PH/iBH7CFYn/u537ukdyAdzvm0yniOCZwFEbQdQMoheVigdl0Sh6aIEDW66Gpa4RBgCjysTUa4Hy8g14ngS8Fbr79FrTwEEYJZrMZ5vM5ZrMZTk9PzUtPcPONMba2tvD6117D7/j4t+ArX/kK7t29h1W6QKcTASCwsTUiD0tdlRBCod/r0WRTCsfHx9jb20OSJDg8PMR8PrchyoODAywWC8MlbazKCNAWR2PQxN6MlalTxNGjRtWghHSBpqkhpEZiag6UZYlev2sXY4AGX1EW6Hb6Vu6aQ5us2HN+fm6rFTM3+cGDByjLHAeHexb88aLM0SAOM7PRyd9hyhovkKwKQ96teK1i9WKxQF3XWC2XUDUZ61tbWzYkzlKZy+WSnsXxgAEksnA+PoeAj/5oBK2B44cnkBKIogB1rUwUjTyCBEKX1ltFnoncLMy5meAaWVoiM6o40BMIw/kVQltJSu5foK0Hwt4uAMiLwhTwBKqKpJ7rmjwlyiT8N3WNQArESYyDS9u4dLCP0dYWqrrCw9Mz3Ds9xypNcXJyAiEEbt682XrZFdYWTKCtN+Qaumu1wUSr6MhHy/HnQqsXGNRCAvBAEgltMrrWiuh3GvAEebibsoKqBKpcIl0u0Jg2NZo836zUJ7UyggkakAJKA4VuIDxAGPUpotGxepjxwut1TjNEC/4eB2yEUayygM8xDuu63Shc2oA99zEg6SKA9LjIx/8/j3e7z0VtelIk5uu55rqxDXB08lGgJR7ps80IxpNA6UXtv+iz9h6uduf6QYCIo621cYyYmjgbAONJ73YTjLP4BY9J+/cLaHs2KuS00XVosMPCFV+hnwRI3u3ZL/qc6dd8Dx7nlM9jwJy5RhiFaGuptY6Fi/rk0TY+Cqbd36M4wie/81vw6U9/CnESY7GYGHlqBSjAT8jxOBh0oZoKRVlgNj3H7v4ubt+7g6ossb+/h/5ggAcPHlCU3w8ArdFJyFO+WiwxXywACKiGov2BH6DyiB0hhMR0OoMnPSxkikGX6NQIPJRFAykk8oJylKtSQTUCi3lqx2jckda5ycXV7969a3NxeB/liA8Dg+3tbcsYYYOe81eYXsXRhLqucXJyYg13pRTu379vJZin06ml+fPey0Ys5+MwEGPDlvO4p9MpJpOJtS1ms5k18l0wG5paSGwQn56eomkaK3xwcHBgPfzn5+dgZysDCldCm4EcO+QYWHA/8RrO0QZ+Po4ysJDBaDSyeUt8cE4WACwWC8RxbMWvuK+Z/j8YDGzeNbNgOKpCEuWBtcXctA8e/wyK+L5VVeHg4MD2Y1mWFkQx5Y6B6NrzV5WN9rHjl3OueJ6ywhzR+yM7j9xcID74XTKFksU+2CHIYMyNFgOwZXKEIKobO+SFEFitUjuWOF+bHdtuDUb+nfu7UbWJCMMqMfMawuPT3bNp/WxtER47PG+YAsrCE097fMPrIP3ncnAdpF/6F/8jGYxVBa204e0GGBt62XJFyV6NpkUNAMqqLXbFtLWyLMlLH0Y4Oxtbzfbbt2/jwx/+MJ5/9lncufkOJfovlrh58yaiKMJHP/pREBe6tpsI5ztxYdM8b6MjX/7yl/He974XdV2T+tlqhUuXLmG1StHpElho6hrSI48Ze0sA2HZ2OnFrCADwpESWZ1CNQhS1SZO+H5gCXzRwqWAsSXi7CXVBECIM2iJcQkgzAEs0SiOKI/T7A1QV0Rco5BqirkvUdbnGFeXQqO8HOD4+saIO/V6fwGoUrtU2Yq7sfD5HU9fw/ZaHyovo6ekpZtMZmrqxNLjDQ1LYuXfvPqIoRBhTKDuJY5pIZsKMx+c4P5thtcowGm3hi1/6ErRWJJYRBkjiBMrxyNQVFfAsS6JKNKqBNLUqtAbKokCtgCwrANGG7AUERe6M/eEaar7nwfcDAqyC1NpU00DpFkwFZlMuy9pM8gxlWaOuKoqcBCQeAUmLaVmVaJRCpQTqpqWtaK2ce3NujbCLOoMht31uXsFawUQzvlTDIgVG+taZh3bxElyPxAgVuMYiDG3JMZLpXGNyGuAE0SbOk1fKUN0EgSO4NCZzL62NXIWAvTcDJKYEQoCSt4WJcrFbnkMEgu7HUSKltQWuFB0jj7ybJ8GfbR7rxuH633mRp3s6wsZaO8px1JbHy5O599wQR9btvyW9sLYR4tGzbFvWHwCc28GniUeuJdabItyWtL9bIKABV676YoDkXBdOPxoqGiUTk4dU2LGkaYwLae/uKvIxXYu708pBU5YatJa2rZSTyTcEPI+UmLTWZl7THK+axgzv9XwawZNlrSvdl8g1cniT5/YA2uQTcXtt1AoCEiw17dnP4phq8GXpCnVN4igt4mr7TEqBumlMTp5nvqJb+Wt+r/adKPi+hygKbQ6r7wcYDAcoixIHB/s4ODhEHEfY2trCtWvXMJ3O8Morr+Ff/+v/2VkL3aEh4Kr0Ub+Z4qsQVCAZCkLQPLty5TK+7/v+K3zwQy9gla6sw2Iw6BMVrtE43D/E+fnYXreuKpRVhcFwQDLVYWApOUFI+Sm60YAmcYbFYoHK0IbqusZiscDBwQHCIMDZeGzycSuKEgiJbo+En+q6QRRHWC1XCMMAGkBkPPOVKVtQGGNyMOxBSIkHDx5YWpabT8RCACxQxAwNZoewwQ7AGoDS8wDH2eh5HmYmKrRZTJQZFJwoz2wVVmPTus0NcnOiOP+HAQm3gT38HD1ggMeMDyEon/fk5BTT6RSHh4fWMOY8Yq6HyYCt2+3i5OSE2uV76HS7RmLct8/COUhVXaMyIgyc/0T5csIa5d1OF0VZ2HHY6SQWdLCoAgBr83EaAEdRVmmKjqlb6IIdu14JYWsNsVObHdScp+ZGZDg3ajweY3d3F3merykXCiFI/ELD0u2jOLKgUNUNqSCbfq+rmlRzHcqZLc9hxklVlhBSWLDCNExuY11RLc/haGgBUhRFWC2X6PX6SDNKofGkZ2pqhtAaqBsa33Eco24axFEMz/dQla2SMoMazt/jqBH3CY8hrTXKiuohNapBr9slnYCqRlmVaw513gtpHGgUhskDwN7DpftGUYSyKvGp7/70b08dpP/cjk63B2iBplY4PT3F6ekZdra34ElgPqdIDITGKlshjiMEfgBPeKhLWoy6CSHQVbFCGCfwPB+DwcB6EyaTCQAScegO+4iSGHmRo6orbG9vodcjz0cc9yzSp4N43Z7nIwjIsJ9OJ6iqEt0uhVQpITJCVZXIshWCwANMTYk0TTHJc5uvUxSFNWLn8xnCKLADkCdBEPgoshxQGlFAk6Nnwr9akZc9NlGRbtKxoevVagVPguSwmxoQAlpVqMocGgJN7aGuSlCtC6rJVBYFOp0Iua7hxzGCMLAeRRgDM4kSCC0gIZGlGZq6gQiAXtJFmmWAAhYzipjdvn0bAHB0dAgBqv80m80RBOR1qWtWJRKoa4WHD0+sJ2s6nWOVpYiT2AhTeBifndECqgWq3MjRNgqqrrG1NcLO7i7Oz89xfk7KL3meI4pjeFIi6SS4dHgZ29tbiJMYR0eX0e10IMzmUCuNKO5aLnBZlpjP57h79y6+9MqXKUpiE1tru2C2lbm18W4ZoABAgcAJGwQWnBjvjqgbIC9pg+Q+BkdNXMNyowCs+XfdNFRS0aFFuOCoNWDaZEkbBZCwn1nD197PGL/KNXoBGIqR1jCAw/GeO8Ycm+r2ilrbXiHsIin6ZK9rnsrIY5Px11IKlFLQipXnhDXE3D5qawe5NLCG6jUwcDEe9EZRRIye5WJa2eZB/cmP2YKR9nTTHnv+OqDEY67LxvvGX9prrtG4pH3WCxpoTtaP3MuNRLTvv+3fNrJD13YjRO41hPPsLt57NAL0LhE1A2zLqjaRRZMDJNrxJJxtrgVBgBAmurjhsAAArUisgMd+Xev2uTQVBReisY2g7qRaOjZMudFm+QRXJHU15+MoZ8w2xknQqtwJIag2mPQgDUiSUkJIal9d1VSXTzcIfIokUR0kQ1kz70NKiTgK0VjKmMRoNES3R4yLwCdqUn/Qx2g4wmDYw87OFvr9nk3Al5Jq6iUJGZsp17OLIiRJjPG4i1/7td+AJz1Umhxftn8YEJs6guSRNkaS0kCj4Qmqb7SzN8LHPvohfOLbfhd6vS6qokJTNrbuzOx8bj3G09mU6vMohbzIbY4GU5+83LMGooCAqpVVZB2fn5lnJrXaIPQRRgHKqkCarRCEPnp9yrlNsxTD7aGlV4URlSeo6gKHhwfI8xyz2QxVQxGMUAQoqgIajY0SRWGIwWBgGCUVoDUqUzewqipsGUW5qakXxEdd11iB0hHqurZiT2EYUn1BM0ZDQ9efGZEGZmUwjY+L3ru5MAyCzs7OrHob1f1bWqEIjiBKKTGZTMjwNHYGR3GYoqW1xnA4NJGkno3AsZ+M7aBeL8RkMjWOzwpJ0kGvR8VK4zBAU9fwwhBSCNRVBSkEojC0NS9Zjny1XAJaY29n11LThRaYz+Z2jTo42AeEYUcIAsZakeBUbMYG21SNAZQ9kwogBRXZLYoC47MzuzfGSQLfRFAmkwn6/b4dZxwJY9o/v0MAtm9ZLIFtvyiKkIRUuFcrjTAIoRuqeThbzSxQKorU5nB50kddNTYviMCpXMv94lIWbr0lSjtI4HdN2ZSyRp4VBNqbHFoLLBZL6+BobI0wWnsCIQFN+Yu+R1L5y+n8kXHFoJOjk+6+wUCJwDLnOIWYTGYmT8+DVkAUrSscdrtdunfTUvP43bn3cNfapz2+6QHSe1/8AAaDARarFb74m79JnoWmgqwVtBDwbBJjAK7a7HshJhMqtsqJelVVYZXl2DETqNvpYrVc4tLhIYb9AXyfio01FSFmLoBGam8syalNfoyHJInB6kNFQVSs973vfQBgAQ8rpPHhvmTmtfLfOcJRliWyfIW+7EMpwJMe4rhjOMZL5Cl5fobDAai2UwPPC6BMrag8L6BUYwYWJ9zWNspFko3kQZrNZoAGOp0uVEWUOA8SSUCet9l4hm4nRl1XCEzkqsiJw5wXBZqyxCxLTXKdD8/3MD45wdkZKbtlWYaiLFAWJe7cuY3T01N0Ogl293Yx6A+sNwMaWKUrBL6Pynj2wjCE75kcpTBEEEQ43L+El156CUopnE8mqMoSk8kMX/jCl0jxJ47x8oc+hKPLl1FXFS5fu4HrN27g8tFlSE9iMBigY7xQnTjBfL6wUqHj8RjT6RRpmuHs/Bzn0wlOT08xnc1QVzVmsymyLMMqp4gfiUO0YXY2LN1NEGiBCoSEFi0tjIHNJl3FGqJmIXNzBdYMTNfjD6zlz3DonMEYL1yPo4653vyLDhNzAKDsb+vXWb/eux0CT47ObH7kgr1WkvtRIQb3IdzQvVIKtUNHcEUtxEWhgad8CJeO9W6B/E061uMv6kaw2vclBGzE48k0tBYAXSQTvQZsNtrD1DP6Vdr7cF++e/sBjuC0/XrxeW4kEwC0IiAkBSc3Sxtl2ZSE5uK/3F8cBbMRJQjAo3fD84eNQaWUAZje2vi3mzG5gQxQg/P8m8Wv2iiOdWGYOcmJ3dx/vsn74UhUoxRIe1PDlzDCOjR/PU8iDkIo1SD2KHqxvb2NbreL0WhkjZRr165hZ2cHB4cHaOrGCt14vkQYeYgjymetmxpZmhl6TYPBgKjbW1tbKIoCx8fHODo6sv0zm03R7XYwnU5wfq4wny/w8OF9eL5AGPnW88vPT3lHtcltMmq0cYxet4skiHHjxnW8973P4/qNK4jjALP5BNPZuaUBFUWB4XCIhw8fWrEgzjthytTOzg48z8PZ2ZlVbGPaDkfOmQbEogjswed9mNdmpj4ppTAYDKCUssCAqVaLxQJf+9rXsL+/v5bLwvsuO8xYnIAVvzjHhNkrnBfCUSR2TPGYYjloVgpz6x5yhMX3fcO8oDym6XRKRWZNLRu+D+dlsNw1G7HM+JBS2j5jR+9iscByucT29jYA2Fxhl/rGarEs3+37oZWCZioUt5mENEZr6nJMHQw8gbKk+cBqcG40hiNO/Mw0Fijnl1X9+P0wrbAo28KwpEo7hJTS5j5zMVmureNGflwqGucZKbR5t65KHr8vlxrIQgIcafM8D71ez+ZWc+5VkRZr75zBFVP8OTLD39da27+naWqFGbivIaiWF9MmmUYopYTvtUJmvAalaWrX7iRJ7LxgUM3UO7dUC9+P8vZXa6IbTKllZwbPJabdMVDi7/Jc5r7kNZgjj9yf7j7DoB5oi9XyUVUVmg0b60nHNz1AqgWgfYlOv4uP/I6P4/U33kC6XELWtaEw5dBKwZMBfI8WhDwrbGiXebfXr1+HFwTIqwqNahCEHrZGQ5yfn6PX68A3Ie0yL7Czs2MnziuvvIL3v/99CMPADgauaVTXFShRv7ReGTfkywORubes9sFUN87V4U1nsViY2gI5tra20Ol0KPfKFATrjDqYqHMIAaRphjhOUBQ50pRU5ipTC4j5m57noakb+EGAqiJgV9eV5St3u10sF0tILXE/isjLYpRt5vM5VFMjCH1EYYTBcABO7l+tVijrEtPZFKvliowSIexmUFdAWVbIsxxlVSIIQgx6Q5R5hf5ggPl0icl4Dg0gMYuUkAKlqhFFMZK4C8/3IIVEksQIoxjf+onfjd/9nb8Hs/E5xuMzvO99HYy2tvD2W2/j6OoNaA3s7e0ZT0dlqHtjfO21t/DlV76K8RkBIKUVCXukObIst7xZfrdKayjdQEGtGeGcvwJplKQ00c6EEFQUVJH3n34ISzni2IlWygIXN6KzaYBuGm18/82Dzc82QuLk5KA1gXlRB9qEaTdSsGak2v/hgvtq5ycbomTwKXWxRPZFx5OM6016G/++rlwj1gxQV72H2+few+Vpu32+ljeh10HS5ru48DnM/y3V6EnfveCzi/6mHa88fcdbj4w9EaS82z02wPAFoJCBB0fmlGpBmhRO3osNUF3UT07kZwOgudTCNtJIgEorzp9rI6Dtdx4FdO0927Hs5sgI4dlokTsHuOwDTL0x1zngCYrsQAqbq2fvKS/eajnC5ktpPbsu/QxCQKOGH5CRKgBEcYROp4t+r4vd3RGyLMPloyPESYL9/X1IIXBweGALPF+7dg2podsxvYqNn6Zp4CUBosiDRoU7d97Bzu4W3h6PkcQxlNY2yftg/7CNWDvG7d27d7G9TaIGHBkBCOyMRhKf/q7fg4cPTzGdThAEIZYm8Xxvfw9BIBBHEYQU2N7aRq/XI0p0EGHQ6wPQSNMV8iKltVFoAI01+JVSuHv3LgCiFp6dndkcm36/j9lstkbrGQ6HVsqZBXw4H4cN0F6vZ3NJ2Hhmo5/lrt3SDQxyuG+YdsWRGFdxy61ZyIY7G5ss9czvhg3dy5cvYzKZ2HWLa+gwSGiaxuZouAqCnLfSKGXlyvm6DIjdXJXJZGJBGds+XDiVhYXcXB6eN+Px2FIEeb6wGFBL02d57s6ayi5T6Dn5nw3g8/Nz25dBEKAqMvtsnBPEfcmy5Bw1YWVcpTQEpLWbANj6QIC20S1ez3nccvQIaBXT+J27OTYMrlh9V6tWpIRpj9xWpvXxWOBCs5wmwHlirkMzDELIQK7l3bdRIWEBvJu7xO+MxwcDb+7/MApsfSimKLpqh3le2CgMF8xl4MPiWdwnDK44QsbXYPEGBnE8Lpiy51Lt2JZwpbd5DLiMK1eBjgUg+HwecwAsDdS9N9+DAd1vq0jDf3aHRwaoFkDc7eCDH/4Q8jRDtlji7OQYDx/ch6orMqbjBALAakm6+IeHh9jd3bWDeDqbYTAaYj6fI9zeRlNXkNA4H5/h7PQEWZEjDiOqeaQ18izDgwcPsL09wnDYNxM3w2q1sPze+bytXu15Hk5OTqwePy/gly5dskW+XNWVqqqsF4wXOb5Wmj5Yk93M85z+W7X1fPI8x9nZGba2Rkg6BPKUao1GHqRhSBrzjWrAyepFUWI6mUMKCd1Q2Jm9FY1SOD05QRyF8KQ0FBzyYAtjRDRaIc0zcFVygHjwvu8hiXuQjUCUeOgNRoiiCFtbI7z08odRKYUwiuB7HvZ29xAZpb1OpwPp02bLi8FyucRiscAqy/GFL76Kz/3q5zGbk7BGnuXo9XuYTmcoqhKrVWoXXa01lFaoqhoa62pN2uSoaGXoAZKKG7qRGwg28xpLd4MQENIjAMX5FhbAoP2eQ71SujWVXC+Sa1iuRY2c46KEaPfYPMcFQcT59h/5G9Ny3Pu6IM36/C8EOBqkIsf/bsEbAyT3+Z4I7jaeidvB7eXvbIKu9m8Xg0t6T+1326gTFZN1F273mm6bNtv+bkDVbR+3/6IIz0XXv7hfNqMl64BmE9jSc2LtPHesue/b3bDad792t/baWsCTDpVuA1e5wPwCHGb/IT1pZPhN2yxNkNtBUUkCRhfXgHpcfzIQ4ufkd90+y3p/2HGhlEmHE9bJwO9PQxhjerNuDwE1bXJ8qP8A3ydjod9LoA1dpNfvY3d316qDxUmIS0eXyNMsSRUzCALs7e6gqjIoTUI/J0YwiESJKLl7NpvhbHyMwWAI6QnMFxNIj8Z1GAVI08I4/BJAhLh2/Qpmsyn29natJ7fbPYIQAkncxWKxsiAhDEPs7+9juVxiNBrZqIjneTg+PsbOzg6WyyWGwz5eeOF5+D7R0+/cuYMoisjwXk4xGg2sN3s2n2Nn9wrKosByNaUImwFFStWmKGpk81NZrS2OY0szY6BzdnaGnZ0dS0Hr9/tralu+72OxWFijmvfetlRFaIGLq/jJQMstlgrAqsgKIbC1tYXxeIzZbGZLUrB3fLlcWlpekiRWNc5N6ueoFxuPLKnNCnVsiHMtHtfYtjlJZkyPtrbgG2cqzwPOlWGjn20OtkO4gOnZ2ZnNKzo7O8NgMLDvlb/DkTueV9PpFIPBwKqPcc4UKam1dXfYqOVnYzltjmCwgZ6mKfrdZE1cialaHEEYjUZr+9NsNkOW5ShyKlLPhjIzbSA0mpLuxZERNqx5DLPhzXYUgxw2sNnwt3uCbJVzeU1xozoMNjm/jIEZ5ybx2OQIiPQkIj+y9+Rxyf3AEScA1gZkYMDUObYL+TmFBOI4svYBRzaJ7hnbNcsVUOA+Y/DDwMSNgnmehzRNrVogA94kSZDnuWXaMMBjmXabQ1a1RXFZAXAzh4j7maNWrqOT/2MJe3ZIaK3tOGVn9pOckZvHN71Iw70H9zEajdYAhCclPGMQ3Hr7Ldy7fRtVkSMKA6wWS7z1ztsoigJXr161k2c0GuFsfIbMDJTlaoXpZIJr167h5PQEu3v7SLodDPsDrJZLfOHzv4k333wTvufh+effAyG0UZJplY/quoFSGmlKk4gT9DhZkhcrRvs8eVerlY008aDlwbxYLGzBNSk9O0mkFEhXKQLD7x0MBk4SZmZseIE8z+xkbRXVxIZHRRrvE3uNKY9DSgnP95DE1GdFntkCcVEYYTQawg8CeFJie3cPSa9nB/BwMDDh9I7JwaGidFopxEls3908yzA+P0dZFJjPZkjTDEWR4/T0DMvFAvPFAqvV0kTeCnuehis5zfkrhlrGRh+wRrdpjFIcG80WmKA1cFpwQN8TQkALZXM7Hs3heTTrY91z3Rrma4dj5G1+ZsHbuwCLzXPcCNJa9MUsfq4qEW+urnoNn8d/kyb/46J7awgnQd52OUjhihClC/xco3TzOR8Hoi4yjteeV1P0Soq2/9b6UnBES61tIIBRSQPseNp8Oi6Nw/3hetovbJMxmF2qmDtW1qIPzvjZzOdxo2PUbx44j83exvbBxQCC2uD6ylqaG1O/tKa8QeG+l4vGGAMrZyxovSl3bb0AeJRuuf7c63OHr+Mm9NMz83NfDEof7U/njo/0iTBRTe7fzfchoYmBZ1wCSjngVihAakRhhE6HqFm+KUPgmcKT169ftzSv3Z0d9Ad99JIYeZYi6XTQ63Ux6JOS12QyQV4VazK1ZMAR/bkoyYPNteXG4zH6/b4FSkyLcp0dTPvinAj2FtP5ZwBA9LFGoTF06zCMUJU1lCJDng0mdopFUYTTUxI+2t3dxXg8tgbdytBktre30TQNptOpKaZdoCmodEGSJDg9PcXOzg4mkwkGgwHG4zEGg4FVZ+OIB9G/ImvQMg2Lc1F4/jHounz5snXyuAwNNmRHoxGWy6U1+tnDzTaEW2aCjTY2+vlgkMTqZr1ez6q7su3Bn3N7mQJ1enpq80EY6LgUo7qmGjxBEFgQen5+juFwaKN2bMQzqOUIj1IK/cEARVlaqhhF6ShqxmILvPawU5YLmrpOm52dHShFDAqmF65WKyslzZQ4KaUFSExF42hWXStbhoOjVWzXuOsYO4GllMRegbYFWhkIuEXk3YhUURQmKhICWqzR3di2CcIAeZ5aqpeU0rBvSguSOaLEFEOupZjnpE7IoJZB4mK1slFBrltVmZIUvJ/y+3cd2wwkeG7XdY3pdEoiXoYF4Ebl2NDnechgPjECElYMxbxPrbWNHFNOo7K2H9MN6R34CIPIvgtur0sJ5HHJ9EO3XzhixmOJ28EHR5k8z7O2K0efeMwwhZUpkdwfLpPDjUqyTcfgyr0PjxO2Y2xOWdPgD3zf/+6pRBq+6QHS7Tu3sbW1RUnJWlNUQCuEQQwWcp2dj3Hv1i08uHcPy8UCe4f7uHXrJi4dXsLxw4eAWUTH52OcjU9R1zVeffVVpGmKF198EZ0O0bXibg9REGI6mUArhX/5P/5LXLt6FYeX9tDtxnaD4yS8uq7R1GQg8mBZrVZtyNYgZSGEDb/zYkRVuUOsVqmRgmSjmpLpKIKVk5KIGYxVVcGDsGCLBAukXdi4fbzpaa3h+T6kIOnNra0tuykOh0P4vo9ev4+9g0N0ul0kSYIkiRH4PuqqBjRQ1TWWqxVqQ8cQgkKk81WGZZphMV9gOp1gfDYmTnVd2WTaPMssPQAA6rpBpSmK0xqBZCgR15rEBow5DsBwghXRXtzvsxKZFBLQam0jcA0ya9hb9hIreDnV4fEoXcjEKS6kj61POAZarr15wZTciDC493IjHWv3uWBq23Oc7110Lx5/LNTBhohLAXA95AyQXIN/rT3AmoIYP4tSCtAtnYnbyJEH9xpu9IOBrgus1ql0aw8ElhomWXH1mP7RdmzxZ7zYSykp+ndhZE5DYL12lAtoLopo0EXWi4rydZ8UJaLT5NpZ6wDJf6Qv6Z4KWrdc7fWjBUhtZKZ91k2wuhmp22w/fffiCNhmH6wDp0cBkvt9F6S7/UT/ftJ92vFGnzsqfGId7K/1j5mYfA6NfcBDgyj0KVodBrh06QiDfh8aGu99/3OQnsTu7q5Jmg8ghKFOqwa9Xhe+H+DOnTt2PYXWCDxSFmzqGqt0hTgicLNcpRifj3F0+QoWi4Wl+OR5hjiKIX0fVVkCQiDwTfV6n+rzLIzBv5jPkecFrl69QnK/cUx5n4FP9XtCAnB+EKAsSpvk3+32sLu3i7ffets6GdmwZ2EGzidgA4c9/nEcW6/x7t4OprMp8jzHtWvXbG0XrTWyxRJxFFvDBcawqqsKe/t7FhSRkUTqdmVRoKoJiLhRBQY/nD/C0SU29tjoTdMUe3t7GI/HWBmVWKClX/G+y15yPp+FC5g25+b5uDLEQEs7YrloBmc8Dpnm1Ov1MB6PUVUV9vb2UNc1jo+PMZvNsL29besNcpFSl7LExUBdByrncjAwU0pBAyjM5y5tikFDFEU20sX2BTtdXelr12aYzWZrdRf7/b59D2wEM40qTVObK8ZCShyZ4fQBBrg8hhj4eZ6HJI4RBj7KsrBtZsAFwBrSDH7430VewPdD2w5ODSCnr4bnk+3DUUdeH9zn5vEEtKUEgFYciaNMnuehrCrkJrrK+TcM4lxqHJ/PwMPNGePnK8sS3U4XVVkBGjbSwucxS4b3Ze5v12HJAIJ/KqVQVoWNWvNYtXt83aYFxHGM5XJpx9Rmng+PDx7PPO74+zwuGKiw3QfA1iZiQMv2HTsteH3nNvN85r/zmsAAm+ctPyfPSW4b9w/XXCrL8qkB0jc9xa6pa1RGMlB6Ep4EtKZiVL4J8W7vHWC4tYPrzz6P115/De+89TXUTYXXvvoqhFbG2i3w4MEDzGYL1HWD0A8gkg7KooCqGxRlQcpWhg8ceD5qVWE6nyCOA2g1gJACdVVjPl9h0O8D8FAVOZraDHLpY9gfoihLCy6ouniDDCV8L8Tduw9QlRV0I6AaGqhleWwmT2NoHuRF0GC1JUPXMoZ4FIXWi5EkCS5duoTDoyNcvX4NSmv0+31b0bvb7SKJEviSPFOz+Rzz2Rzj8Rl5etIMJ6cTrG7ew71796z3ZjqZYDyZYGm4yxyWbjhMKiSqprES0WTUtTQurclb3dQNtAE8WmtAtgIF1lOvFKpGGaufjGtlqtGDjV0jaU3eXmNoG+UaDSqO25qHxpASgFYNOMIhJRtiinINNEMmtOhGCHhCAkalh/OuXMOLLm0WSvZwWwlsgBO81zz0UmyEA+hZWQBaep7pS9NPQkM3BOKskcfABszie5S2tWmgutFJd3HkZ+JNQykFGIEqjiauNVZrSqQ3tCutzLyChhCKqDQWhLoRshZEbPYhwHOT+74NTa3BF34RBtS2ILXtC34PDUch2k6gM5WylMdHAYaw3zNPRJQ8fk0Qa33NF5dC2rHsGvH0XWVzUNpxTRv7ZnkeF+Zpvd5P7WN4UPDWzhEG/HvCA/QmdROgfA/6puetgyKO3qxHgjh6JIwch1ujgj73PQ8azC+HofsSbZeNk6qqYcULBPd3m9cjDa1VOfLVPH9Jbr91kNA1DPAxfStIdhFCAJ4vraeaPfHdbgdhGNkaLUHgY29vz471o0sH6HUSUq3yW3Df1DU6vRjCo9o5paFAV3UOzxdo6gZl7kEHNZ65fhWTyRSr5ZLASRIjiEJAa3ieDwiS4B4M+mgaBQmBwPNx6eCQIg5eQGp1Nc3F5XKJa9euIUkSvPXWW9ja2sLhwSUcHx8jjjvY3t7FapWhKCoMBiMTcYrg+wEC38hFNxpBEKJpFMqKinEzHaYoCqSrlaUy7e3uYjqdQkppyhT4mJyfGyGeHJ6UuHx0RDTwvMDWcITuETn+qrDCcr6kfFVIG+mPohjdboJet4/x+RhC0Gej0QhlWaEsKzvSGMBwkUs2rgCaq5wXxB791WqFk5MTa+wtl0tLH2JPe57nVuCH95YHDx5gsVjg6tWrNnmenZaTyQQjozLH3vyqqmxESut1VojrUWeDdjwe2yjOnTt3rCPGDwIsVytAMiCs4QcBZvMFlssVul0SaZrN57hkiq/mRYVEekizHFlO4PD09BT9fh9ZnmFrawtlWeLk5MQaq9xWXns4QtXr9bBarSxFbDgcGhGi1AJT3hN4f+BIxGg0QlWR3RHHMebzBbrdPuKYpPg5osYGL0f7yrxAk1QIgxCB5yNJYipnUZQo8hzDwQDT2QwCAp2ka2jPtC7WVYNO0sW0mKFROTpJB0EQQkpvjcolpTQOYPp7FMUIwxhZmkJIcjIoDeSmXAmD8263hyzP7FhpNKUMKKVQ1Q2K0kh5x8x24bVHIs+pJIeUrahImuZO6QBBc1x68P0QZTlHFMVYpSnyVWZFQra2tmw/V1WF4WgECALbzGCpqsru95wjyEwgAmOBjUBzREVrAklSKEuBBGCBGwMSAPZ3Bi9cNJbBEPcX0ApWuEISfC47HdhpLmVbT4qdLq4yMzvsmZLI49V9No6cMiB2wSjXzuRo49Me3/QRpLfefM1GZEgpzYeERFlUaOqKvG0+5dhEcQRA4/jhfbz2W1/Gr3/2sxh0E2ihkZU5lmmK1YJEE7h+BKNXP/AhPQ9lWSDPKBltPB7D8zzsbG9DNZXZbGkRp6iNgG4IWTd1Y0ANgToIQCkCSUCrUtQ0hv6j2fiQ8NizDxhFFMD3fPQHfYRhiG6ni16vh/2Dfewd7GEwHCKOqODqcDRCp9uB9HyczSaYTCZYLJY4n5wjyzLcu3cPJw9OkC4yTCYTSgYsShtWBRf3VI211OqGokdKALVuvfE8+JuG9Pt54LEh1ajWG+KG0l2AIcSjUQ822CTWoxvuBLGRD2hb74p/104bN73+m5EZ/s5FRxs18GyuxGYEROl15To+zzUC+b5r9C+5bvC6kQy37Zxf1n5nPdrjGsFuv/L1bHud/ttsq5uP5PbvZu4K3+eiKNtmvzyuf7Xm+kkbhr/JS7gw2gb+86PevosiRzzO2JHgRk422+Oec1GkbPNzjqjxd11xh4v6pT3Im07Amf4TIAlpLZR9ts3omXRAkCtqAADKierYdyUlhIat6+RGCKlN5KzgOjsUEaWr813sdwFTl4OAHPkzpKWbwoA3DVITdGk1LZWN8a62tDm+nyeF2ew6dpNrAXqFra0tkuk1lJr+YAApgP3dHfT7AwhB6+eVK5cBDQyGfQipKKditUInIXobe+2Hw5HZRwaYTCZrNU/CMLTr4f7+fpsXomqEUWhrwVBUPcHJyQk6cccYboFNmp9MJphOp7hy5TLu3r1nveFcl2U0GgGgfNHBYGCNBo5OCPNdTtDmuj1Jktgik+xR5ajHfD63RcbdHBx3zWXQwdGFLMsQ+L6lILPh4o5l1zCh0hYUxeDEcS6wyTQtzr1gGg3LVE+nU/t++Vy+B1MHGfhIKXF6eoqtrS34vo/5fI4gCKzw0AsvvGDp4tzW3d1dnJ2dWREHzhthkJUkCabTKZ5//nn7OQsVuB5ujjCxgAFLibMS23w+t0XLJ5MJ5vM5Ll26BCGEzZdioQzOwaAxtE6Rc6MDYRhbrzrT3tM0tcVVx+OxjZYxQJxOpzg42LOy40zh2t3dtYIPvV4PWZbZNm1vb9v9hO9/fHyMpmmwv79vxSl6vZ6lCHIO1WKxRNMo+z55njIQ6vf7FnzzPhLHMR7ef4AwCGwkjdXdGFi675Aly/m6HI1xc1e01jahn+W2mcLFESXu99VqZaN0WZFDelyv0bdRIgJUbRFeAFZkgpk/nJ/DESguUsv7Ju8zLITFz88CAvy+wzBEWRRWFZn7goFInudo0JbhcKNEUkh4BoTyZ6xwyG1xI/LunsVRLlaiA1olOI6Kcg4i94279zNVkccfKzPyfOVIGtvN/K4AWJEuty3MYuH7cqSII5/c55x75+4l/G+m/gEkXDKdTvEH/w//+/9CsRsOh3j91VftJkPGNyX9zuZzBIFnKA8SRZWjqSskSQ9CxNBVg//wuc/ia1/9CjxPoKhKnJ9P0ZQUAq3qGqvlClVdtWH9nDaiMAoBDezs0AaYZksUZd6iZylM0dYIAhLaLIie54MNC6UVPI8KqgZBgOFwiKTTQRiEGAz7kEGLrLu9Hnq9HjpJgu2tHSRJH9ACo60teL6HIs9RFiWyPMfZdILpZIqT01OcnZ1hNptRVezZFFOT2Ng0DeqmJg8lb5p1W9OkNt4hiuhINJp4oFK0hRXJmFKom/YagGMMGkAHcB6KsosAS6LzhruZFM+GFBugdkJcULHdNYrdieMCIIh1ALFJt3Pb7v7tomgLe6+55ox7L44eCLGeCM9HUzdgw/MRULYRtHBBivWqy7aoXdsHLT2Jf7pG+kVt1GqdgnZRRGKzHZvGEh/u4rn2PE8ASO7v2jH4OaLg3B2PA0j6MaDoIoDEz0HGOx4BgJuAbrONDG43P+e/u33SClK0z0Tfx9o1bL4QJEhIgOrZAICQGizn7ebKCCEgtBvpWb8mHDrZI9S2jb8z6F0bI2h7230GGwEzYIj6mOq1eZ600TPyuDZg3Ob5/FwacRQhiiNEEQkMBEGAra1t9Pt9aG3ECzoJrl2/jh1jvFV1jSSOqQi2arCzvY3FcmFqrdCmq5WCbtr1qjRG9XKxQBRH1uAGyLk0m80scHMpV2wsTqdTW52dC1K6xk3STbBKV9YrPxgMoLUmeeVOD570rOHCKmWu1DFH25m+xHQuzktlZTQ2eBRg6WSXLl3CZDKxKqlMOWIDjqMnOzs7Vr3N/bm/v78mtLBYLLC3t4fj42P0ej34xpBlw4WNGzZ0oiiyRheDO/4uJ3Fz/lOSJLY2DpeyCMPQAiemoLPiHFP73IgNGz9nZ2e20KYrTMTS0uxlZnDGgHA+n9t56QIB7mfuw36/b6NQfB1+Xn4eduxx+3k9YTGGpmms559rN+V5bnN0ptOpNbZjph9mmVU8i6II29s7mM3mVp1sNptZo5UNePb6c94PSYTTfDg7O8Px8TGef/55LBYkFMUKcJybxvkfbOiORqM1kQqOGHBEJssyO04YNPl+gLKkNnGelOvpZ2ObwQsDjrpo1dncfR+ABTzc/25EiEEJ0/9YlY3FMJRSNm+IgRSPKTbW2eEQBAG0IKYLr3MsWsHn8TjivDF2hvM4YeOdxwQAq/rLOT/srHApYbzu8rlaa3honcTc1q2tLQJXUqxJwFuRC89DFNC6xWDGlWznnCgGQq5jw50vm7/zGsXvwbUp+J0xdZbfl+vQcYUv3D3WjTQx8OS1hd8NjwdXFZLnGOd68T7FYwOABYTcnqqqMJvNnhogfdNT7OqiQpWV8AMfeUk8YelJFHkO30tQZCssygxBIFHXBeoiRbe7izhK8K3f8QnsX9rFW2+9BQ1gbz/Fw3sPsFqtcH5+DogGEAoQigql+hJK1Whq8lJduXKZ6vNE22iMccwDDAD29/axs7OD/Z1dSN9HbLxXjeENC0n1KDrdLvwggNIK08kEqzTFcjXHfDFHnuc4HU/xtdffIq5zo1CWGmenZ0TTqGtkhgNcNQ0yE/5trHgBGStNs24Qk6EjTKSKKDoU6RGotWqpYFpTFEloVEpBaGM4aaKn2etpbScIAYzWaJbMLlMA2XdEuWGjxF0EudBja69ySFdAbBjd65GIRyMndLYpuuqAns1oi3tN99+bURV6Fgk3L8YFWG5bNiNj9H2xFt1qn/DitvAiyt67dUOcwUProd+MGLlAci23pnXjP9oWB5RtAqhNQOJedzPCtNkfa6DIfVbRPgNMLlHbK4/WlrHXUG1CqXvdTeDnttM+Ox4FEE/KlTF/sT/bekCci9PmVElpyGc8SVwamfPYCp7N6aJ7t/1r1SCVgpRtW+x8EsxFF/Y8CMAzm/AmwBfC0OKUqeMjBODBgh3PJ2DDlEBWYROmBk8cx0g6CcIgtAbB9s4QcRxaqloYBhgMh/A9H91e16w/TPf1MBqNEMeRoZNI62AhQ5TUPwGTT5EX6Pe6RmY/hxANtrb7KKoUW1t93Lx1CwcHB7RxA5hNzhEEPqIoNu9GIop91HVpDUk25JIkobwdk+/DidoAFczmxGSWhY7j2BrjaZpisVhAafIgr1YrJEmCO3fu2E29k3RsHgtTVDod+hsbMbxpM+3L9br2+31rAGqtETr1R/i9sqf25ORkjS7FBsfdu3etQXt4eGhBUp5TeQiOTuzt7VkjnI1bAIZCVdnICQMLjrIwCGDlVH4uPn84HOLs7Aynp6c2j2R7exur1Qr379/HcDi0oKvbJfYDq2NxjRuOvLChH5qCofzZYkEFxtmTz+skR/Y4asIOIxY+4kgDgzYppY0ccWI91/hhEQo2Tt0EdVbVWywWa3WZONrHII4NOy4QGkURKtMmpjYCsDlOdr5KiV6vh8ViYQ1MHmMMUPldEZ2MIng940xlw5/3AVdpj0E9e+fZCGUwzGCMoxP7+/s0Fk2frFYpyrLNqeIxwAcbwUyJcnOi+v2+BYYM6Dmqwtdh4Sz3HfH+x4Yzvw8eEwBs/hH3P+9NDNaZFilkq1bKNojWJDnO74htElckoaoqG71z5eH5nbEjRAhhQTX3OUfH2JHAczuJYttnPK7yPIdn2sTOHQYpRJVr9zUeIwDWwBs7OhjAbNYU4nfEFFTeK3m+cNv5c+4Hvh5HffmZeJ3l89xcMY7m8X3d98T2H4uG8Ltw3yELZ/T7fQs0eY3kue+ycR7n7L3o+KaPIL3y+S+g1+1BComyosUpiiN0+j1AaHTiEE2VYT6boCozExqMqfaPQazsyVWNQp7mCMMAjVLI0tSEbGtkeQEpAsTdLnHjQHkNUkh0On1UtRlcdY3VKqUCqGWF+WyGPE1RNw2m5+f/P/b+5Fm2LEvvw769T+ftdb/N6yKyMiurCqURTBNpogYgUARIAaQk0x+nxkwGjsSBTBySEkhRlIgCChIpUBIMTQGoykJmRkbEe++2fr057dZgn98+yz2iUIlpWLpZxHvvXvfj5+y99lrf+lanh4cHff78WbvdTs+7vfaHg/q+S513ouB5Dc6r63uFIYwT1Kdwa0zHmxLYEth1Xl2wzkV8JdAYJmxMbY5CfI4EYt00AybzXvJSP8TDdO40DNLQy7tpsCbC3ve9hkCLXSJIJjrkI7inMI+DGSMk5ag0e/X9eVqY01Q78n0OknUerMPGRG0Mz/cV4v95jo5dv+n3F2DXpPjJKdVJXBY+Eiayiip9l2JjCeuU8dlLx21yBL6/293337Ndx++PtFw6RPZlv9euHf+2ztElOP/zFJZxd9OfAOd47+fO2dlnjYGzz/t9z3D2c28jMJdRIqVnOb/OmAL3fetyUUNlX9bpsuTB+Fsx0yc+X68s8+p75vKcO/KcMR+UomB5lmkY0+O6vpPcFGmMKRJZzNN3Tpn3iXl7//5DSr358MU7ZZnT7c2tpDiD5/2792NnyVaHw6s+fPiQQD1Gr6pyBcUuaczIAUwfDkcd9jGNRyOo8S52e/v86ZMWi2WKfMM413Wt2aJKgHM+n2sxX6hu6gQILeikwLgoChXjWsHmk6ZxOtXKsjIx0m3bpkY4tiAaow/bXVWVvv3229SeGFBX17XqtpbzLhXgO+f0+fNnbbdbXS2v9PXXXyfwT+oQ6Sp853K51FdffZXaE9/c3OjP/uzP0nBWoh2S9PTykpwMIlqAWF7b7VbffPNNAnakBwKCY7TuOrUffnl5SSkrb968SWvW1LW8c+n+uW+K7ImwkT7onEvRCWbweO9TcwGAGg4K4GsYhtSJFQeB+oSyLPX6+ipYe5h9WHm6vRGJI5UQcG3X3nazQ3YB2XT4kpTSufgsdoL7JS3tzyOKSFmEXAIMS9Mwbtutaz86COwrkcUos42uxo6v2HuiOyGE5FS9vLxovV6nFLg8Py+ox3F+fX3V/f29hmHQ9fW1Pn36pNVqlbJu6AiGA2Y7kVnmP4SQopuvr4fk3BIxYV3onmZlMZFuw1TfSmQXoAxRsN3GuV9ERC3hh5PE7yQZTBRBOTJA5zoiSeiHWFPUKbipFfpUt5Qn/WZrZZAH9tU6dtyHTXdjr9HfOCM2otK2rbxzmlez5NhnWZaix0MIcplP0WbWB8dKQ0gRW/bGpprj2HBftkECWAv5tg4OTg6RT84uDgt2HkeMtYCgsLVLkCZEuZAHbCOfs9gQYsieJe6R6Bt2lkgwe0Rk9+Hh4TcRJF4+L5VXsXAur2Yq5nNdX1/r0DQ6HPZaXy1UlQstZlt9/dUvddgdtN//Stu7O62vr9X0sWi4OR1VH2s9Pj1rv9/rdb+Pzs3ppM3VRt3gtNvXSbgexoLVw/EgDZkOhynFbvey0x//iz+OCro+qWunwVU29NinSIQTDQL6fpw+7pyk6GCoo54m5v5bHHYerQipTS8KlutLUlBsSBDC2IRAUyqNG+JXOo0O2RAdoNStarxMcJNj5jRG1MyBkzQCtqAQunT9iVXXeF1qrvq0HpLGgw7zPqZAjTeQ+e8CbpgyrsVhsQ7lpaNgjdyfB+D/PLBtozSX1+NerdPA71kzywSffZ/5XvaPl1V8vKaow7mT9G9iT9LvLpycX+dllRj7ZZ/Fgnn7/j/Pybt87vE36ach0P3v3EFKTNEFa3S5Pnb/rbNm32vruS7v69zhM3VSks4HnNrnmqJo53sV63bO1yQbFf9Yd6dBWeaUh9hUIdb/jUajLBTr3qTMxdS19XothRBn6tzeSs7p5u5Wfd/rd373d/T0+KTVeqW/9Jf+kjQELeZztU2rtmv1/v37ZMTLstDxGNOdSLf5Z//8n+t6e63ZfCbvY0QnzhZxautei+VSp2OtzeZKmXN6vL9XCEH16STvvdbLtXKfaRiB3eexe1VVVVqvVnEP+l6HMeqRZ5lms0r1KepXWuhGkimmcC2Xa3nn9auvvlbfxzk1t7dv1NS19ruXcR5HM0aHKNCv0kDRp6enlJIDCN5sNimSBIN/d3eXOowhDzh+MKrb620CZZ8/f9abN28SULi5uUnpRc655JRYMsg5pw8fPuh0Omm32ynP83Eg+SqB5f1+n1L9aIdNTREA9Pd+7/f0Z3/2Z/rZz36WipQh/Pb7vd68eZNANc9sZ+5Y8LLb7WJ30pGtJZJDa21AunNTOg1ACkAEwMERITKG4wTzvt1uE4tOdMh7nyJvsMnUOXRdlxxjIkFEWN69e5dk+fn5OdkA0gaZxxNCSO2tsyw7a/0MYGdPifoB5olmoX/QHTYNTFLaF8Aujt5ms0kA1nuvn/zkJ2n4KqM+cEJ3u31i2WlpzRgSnEKc+inS0app+gTsX15ezhzf2F0uyvLt7W2qUbMtmVkj1tXKI1EAauSI+BAxYf9oWc79A+idi6zsrKyS00K0k2gS9U7UqBDp5RyRquacS1FD0tLYu8Vikbqm8Tvun2jW8XhUlufqw3CW6mmBfwjhbDCvpNT0AhIF+4fzg15nPYk24bgzH4k6IZoJcK+cMWlsFZ/Fpl7IG/KIw7KYxbbhtg08KXLoGuSXaAvXx+GwaXREsHkG5B47ZlOFmUtmo56JjB5tJ2ef6NGl3cVho1si0SCcTus0UoPEd6BruE+wJ7Jy2Uf43/T6wUeQfvHzX+nm5kbeOXXdWIitoDYM6rtB6oO6U6PmeNQv/uzn+rv/2X+q+0/fan61Vrlc6VA3et3ttX9+1f5w1KFutXt9Vdd2Y95qpd/5nd/Rhy9/W8c2gq7jYQovRtZnr76tU6iXVIKH+3v1Q6vBFGQ658aIEOAwFo7n2cSIDEMvhU7eKRWVn0cibHqWAfkh5rFxqM4jJTGSws/PQpIhyJnaifiesaBaXkO4iLAkp6fXECYFYcFhP3h5PzEx9iBledAwEIq27bRHp1AuMQnTs0th6CRNz8VzWHDM7yagPiQMexntsA7Q5TGxDpFN2Ys7dp6OZe8zrvO5U5Uci4FQ+ffU7Ljwnfv5vijQ5f39eQ7S9zks5obP3v9viiZdXtveo3WQbNTl+xzL73VCv/OT774uWdv02QtH0/7e3svldWTu5/vS7FJu+AjykoMUJgPA7+J1SJnkmhM5EdcoSKmDnTPvcWPBeCW5oOVyLrmg1Wqp9WKjzGe6u7tT3/f66U9/qs1mIynoarVQ3TR6//59Ykc3Vxs9vzypG7oRFC4SGxjlLigfwTCsL10nq6rQy0scUu2zeFZXy5Ve9696ed7pxz/+idq2keR0OBxGQBCU54WqaqbN5mrspDZX2zYqy0pZlms+X+hf/It/oevrrZkAf0oDREMIqftUTNt5VVbGJjtVWeqrr77S1dWVHh4ftJgvtFxuUuE3Bt45pzzzmlWF6vqkEJQYVe+9vvnmm9QZKssyPT8/J2BL9InhjdRlkOrDzBwAUhqMuJyLVvGktAAqDq8HVeU05NQOO+37XldXVyktDzDPLCOY/i+//DIBwhCCsrE+ldQxgDMNEU6nUwIPFJLj8F1dXZ05AFGGlAAu+4kz048MeQghDUptmiYNCrW1N7DWRBJIKeRPOuABmrkX5spM7ZjPwWUIIdWBtG2rDx8+pD2HCFwulykC2ve97u/vU/RtPp+naJVl0amLOZ1OqRbHgiyijEQzcDSJoOGs2fVmSCZnCblumiY5P33fp1RG52LksR3vmeYBzMiJDniMwEhTXQ7RNfaK9acRRN93aprI/BPpo+aG+iBas/uxzgxSk+J6ZAkdgXPDfrGeMUJ4SnqSmhFbZ8az4Gx4H7v/FlnEA4vFIqaqDlODCktAIBuAcOuQSEoyDRDHKWbNJKXn4jt41XWtoiqVjU4fDoyNvNjUWwA7tma322mz2aTnAugjY0QCbU0c94QDg+3suk4+nBOpCbN5r6ZrU0TLOihEnhgiDKlCTR/6Q5q6wJGiah0enGRk4fr6OsmpvZdhGFIa6mKxSAQLBACOFtFH2vGj49lfziKfpSkO92z3+LxcwyXH3H4PpE1VVUnvsJZ/8O//u7+JIEnSP/j7/62KYq5j3Wi/O2j38qLX3V7HNtbmNE2tw+tej/f3en561mH/qkxBTd/L57mci3N0vGLbVbqyRTBVaH/o9M//+E/1+fGkL378Uznn1Q1OdX1SUZbqQ6e269XUtQ7Ho9yYYjBbLFQdj9o9P8ZUmiFIii1jvcYDP7aTznxsfpBYqjAodkW2oG9sDDAMKUrhXUw6c2OEhrbf3lNjNLbYHqQ4vDbW9/iQcGI0xJoiQ4m1zzQ6Qk4+TM5C/E6N96m0hiHE2q8o5EGZy5UXZVKOGFafZdJ4X0SlhiG28maGggW3ANAhNWjoYurh6GGSJkjRO+mCNJOQMSjBOmLOT8M/0/+na6bImxvZf+pJuG0X/z4M42+8H68/dQmj3oN7lfOxDbbc2A7bpfexDtwv3xPTKcf27sHcbfhuKiU//76XVT7JSZgWOX2fdTwuHcfLv9uo4fc6Yjof1GodpjOHh6ikdcQd9xXrtoZgnXo3+qFjW/aRGxjitsvLawh9MuIozSAn76ZarvMoUUwp9ZmPaa3OKc/jEGWfZWmvLz+b526s1clTe33aBZdlVNy3NzfK8iyBnZ/85CdaLOa6vb1TVZUj69VrMYKN+kjUJLLdT8/PUgh69+6dmuak4/Gojx8/ajvO2en6WqvVQk1bqypzHQ/RmIUiV9t22lxv9OnzJ53qk5qmVl7kOh6O2l5v1bzWqmazNKdk6GPV3nZzrTyPQOIwsqfX1zGqsnvdSSE+c920WizjVPd+iI0VvM+0e92prMq4dsi5k9q20enkU7va4dTLZ15t18VaqK5XE5o4SNVnutlEB+fbr7+Ks+jKyPzjBHR9l1Ly9vvYPAGDSd0BIMKCc0DQ1dWVXl5ekvHf7XZ6+/ZtAgWn00k3Nzd6enqSFEHO/rDXer3WfD7XerlOwGVWzZKzFcb9enp60vX1dXIwSOMhtYzC8/mY+cA5SYSLi7OTijzXcpyb45xT18ZZQmVRaP/6qmq51GI2i7YkxDSc0/EYUyuLQmEY1Jh6oTyL82cg3PaHg4oxCvbmzZvUJY7uUrDYpHg1TZPAR9M0+vrrr88AOalWd3d3en19TWlROBM4U7YQHIBzd3eXahhgnDkPnGXSo+o6tgmnKJ40MAq7cYzQOaR1Adyis5PLyatre7VNp/kstrmeVfG9tY/Pn2eFFvMstY1+GVuZz2eL5Az2fZ+uk/lcbdPp06d79WPa1NAHBe80ny/Sd2dZrq6LKeUuxNmNONN5lqutax2PJ/k8S45K38eBvLvdTu/fvx8dgVx13Wq3ex11fiQqnKu1WCwVhwDnCezGCFSmrqvHdF+vzWarsqzGPR+U56Vi5kPQaWwtXlXl+F1RholOAvqpXyL6e3d3l+xcCIP2x4NkQHNQkLxT07R6enmJXSzbXm0/lhKMjqb3Pp077z0WW8f6pGpWJdB/Op3UDbG2seta9SOmY88HBXnTOY4oRZZl6ZwTKZIiGU/qG8QHtXDUrJHuyXkhncw6mLOxRTgzkUIIclmmIcS60yEMaro22cZ+dM4smcd33T8+qq5PY/fbaL29z1RmXl5Onz590mazSWlqz8/PicglOmdt+Wq1Smmk1qHk9xBN6E/0ATWJROuw79QN8Rmi0ThLi8Ui1ZARoeSsE42nbhIyC2d3uVymZ1osFulaOLW2rvQvev3gI0j/zl/5n8vlcw0hk3e53BCFTS7TsY7zGobQjczOKToQLsSZFW2cR+TGTWu7Vt3QJkU9q6roPHmvarZUVkwtOFEIeZ6rqQ/q2qgUYBUBeplz8poMHsDWOa9BXWoLDdCU4iEo83ggEFYbjg1dHC4W7efYWWoYa4rGFrvDaPgGs/1p1I6LAJPW3f3QKzhY/svuVU6Zz82/p4hJlmem7e80+yF+PjpKgFoApfdeGqZ2lzCRyYHy5y24+c97pyE0GoYpb/XyP/s560z8eVESfmffY58z/j1e57KG5DKSEj8/fSdpk8xl8t7LKc6Esa/LNLDL5++HTsPQipoU+/2X0SXrjNh7+4vWwL5ixOH7Cx4vP/t9tVSXa0MEg+iJI9BJ5FNmzdwU3AohpMGz6UO86Xu+i99558a+eHFmjq2PC/LKslJ451mWJ4OQ+UgxEDWYz+Yqq1KzaqaizPXu3VtVVZXSqZqm0RdffKGiyLS+isDt6uoqMc+cV4wpYBsWH4bWAkcY5k+fPqXzcXt7G4cqj2lPNlce4Hl3d5eMN+w5zC6jCFarVUppIC3k7du3CSRSDN62bWopDLCktbI1VLvdLhXY73a7lBrDQEmKhAGjsPTWQEpTYbEUnQXLaA7DkMDJfr/Xu3fvohM3TB2fKMRfLpdn9UTsA84P7ZlJO2rbVp8/f9ZPf/rTNO/m4eEhtfXebrf69ttvE+Npzw7OFHti01U4v0Qpvv32W33xxReJYee9Dw8PSTYB9G/evEmMLzVd2+02RTZsWhVgA7AAc825Y3gpjR+IoL179y6BJUCejYLh7K1WqwSCYOdJ44G5vb+/1+3tbVpnHCjqnGazma6urlJbdZ7fe5/Y51R/NpulcwBLDCjlmVhfGHuAPo4ShegALPtMgFP2HkctdsGSvDtPNyIlilbTRGUAuDwLUQebVkdtkU2rhFVfX12pbpu0N4C52SwOYC+y/MxZZG/kYqddrgW4pAsg+gQnHHDKXsDS41yxTpJSZJb26qzlMAy6urrS6+trugbzkqx+sl3xbD0Ttoe1w16hF4hI8V50IeQHUdvCT2lrNiIiF7OFLKi39TBEcfi9cy6Be5yiYRjS2hHhoe6Khh6kb3FdnodIBpGsw+GQnETsiO3Qhg6xHfvQ59iyEEK6Lg6YfT9pgjgV3Dt6KAxBL09PyekpyzJFJInQ8bLZNlk2DfZFvyKXlnxg/dg3XmeY15wLsI2N2NqMIp6D9bbphqzPVLrhz84aDrrVD1IcevybGqTx9ZOf/o6e963qRqrrTn3bKDintu/kyzI6AS6P4GvoNF8u1NQnuSwuJu2u3dj4YJCTfBYjHUWpTLGrU9116k+7szQcBLMoy+joOKeh66Usj0yQ93EwWderKGJL3H7o0iHpukZ9PzEK0rnQAnAxANN3jjmdQ2QZhiE2c4iRpxjhiayYU+i7qdFDGu/o1A+9+h4QGo165rORqYhOXIzEjMA/SBF0TvmkeTkOYBy7X7kxajOkoZDfTYHq+15e5+l4Np3JvmzUI65HrMEaxjXkHnHkJob/slMcTl84f7bxd/yZAhpEmWwbAYZ4pggOtWTZeP9K13XG2fSewsw4rPMS4KNMbCqfrVXIfCbaXeNg2PW0DsJ5bYzO3p+c6TBo6hRnn9eshj+Pgk2/DGd5cSGM6YY4hs7+zjhyY70aa8x+xfs1dUSD/Swd4sZLOx+DSiktLkZQ5WhMEK+XZ5ly58YBgWUCeb/927+ttuu0ulprPovOw9XmKsneernUarnQj3/84xhxKPIpPSIEra9WCkPQfozOHPZjutkY+aM7mHMutfSVlHLHbXcoAAhgxgIDcvdtlINUCuoe1+t1SqlYr9fJ8MMKv7y86NOnT7q6utJ6vY6zP47H9B+FzA8PD8mRApCS8kAXKYCdcy51eaP9LfU8ND3geQCZrAFF3ETWYHoBrLvdTl988YVCCGmmzXq9lqRUS7HZbDQMQ3K8uq7T9fV1cgYeHx+/kx7EPdECmJRQQDwMK2vsnEstdUmloR6Fjm44MewPAIe6ivv7e/V9r/fv32s2m+mLL75IzhkAg/oEmkqEEBIQBXzhENFa9+HhIT3LcrlM9yYpzfnBgQXE4DzAnhdFoW+//VZt26aoi6QEmK+urhITC/igtunp6SnNI+KMrlarBEIA4Dg4gNxf/epXcs7p3bt3+vz5c3peZOXp6Sk9Mw0R0H3UfQBYkVGbNkjHM+qWiLwA4HHGcTQgNQHavGxHua7rEqgEMEpKNV7oadK/bCF5XdepeQfgWZrA6fPTk4J3KRqGTeS5cAg5z/yJ08/9sZ+QA3zfMAwJQLIG6/U6gV6cbOwtjhFg3taXee/T++/u7lI9GXtEmib3wn84y8gSoJ9r8nMcf9u4g72FMJKkYqzftK2sowUIasdRJdw3n8MpyfM8dT97fn5Oe0JE1+4BzjBkD9Goq6ur9J2Q1MgSMobets4R0REcemw8cod9wqmybbZtowwcSkg39o/vRraqqlI96jpk2t4PUVkbvbZEHs9EpA4yje/gvixeAMdxT33fJz2GA8fZo0aO67GOOGDgaZvWaHEEtWLIPc4TThfEhHWc/qLXD95B+p3f+139//7Jz1TlM2VVbCyQZU7lrFIIg5omhop95nV//1lD36saa4XKrhWd0uIraBj6NAh1UnAhFifPy7NIAxucF5V85jUMMCchGazFbKE8i5Od27bREHrleaaua9U2tZr6lFpLckhi4XKEpuTqoqwkUovCmddOSllwUp4Xms3m6rpW6txZh5PMk77klReZQhiUl/nZgeu62EXOey/n47+Lcsxt7frE7me+EPU0w+ioOR+LC70PY0RgmrxshR2liJLgZZWjZRviWg/qhz6l27EWpNJJYyRnzLWi7gPPxzk3doobW4/HLTdFfS5dE88iZj+681qv8VoiHdFN71eIjTJCCBr6uB5hkLqhlx+7+tm1YO34t2Wa4vf79P3nr6leLP0kOaM4RJdOpxv367Ku6bsOUxidHv6Mb3NpT92Y1un82JrbTc9jny++yyfnLTYycOO1J4c2Gtwpysi92/8mI5xruZprNptrtVpqu71Wnmf68OGDvvziy1hTN4KyeC2v3/7tn+h4OqoLncpiBJNlocxnI8Md5EXziU55XiiE2La77zs9Pn5OoH+/P451JYO6blBRlKm7F+ldpMJgfKwxxaGgEJU0oMVioZeXF61WqwQgYAaLokipRzgyX331VXI2AFi73S4V0WJ4MThEba6urtT3cb4MNSsYMNoUczafnp6+02CATmMAeElpECXGGNCEcwX4wbkDaHZdp7/8l/+yPn36lNo248Cg7xhwSdSKiBj1Qxhn5pRwprquS0ND5/O51uu1drtdeg66W8U25eUZ+dV1cSArc3x4LtaGfbAZBaSHAbABiUTViG4xYBUZ4Rn57jzP0wBaUkkATKTySEqgjWuhJ2FaWe++j+2VKXYGjEhKjiK2hEYI6/VaDw8PCSDR7pz3vb6+arlcJifZzkMCiD8+Purdu3eaz+f65ptvtNlsdH19rc+fP585HbDHnB/0IMCT6CDRLMsoIwM4xt7HJgM4j9wreol9hrjIskhcAaiJCtKy29a4cDZwGgBs7MUwxKHE7IMUo684g8MwqJrN1HRtcqhxzL799ltJ0nZ9leSJP5umUd00yusiERrow8PhkPYUZ2O/3yeHm722ZBFnNsuys6YeOMNckwwBrmH1Mk41z06hvXMupahaXc65pIPgdrs9c9IgKmw6GlG2vmnPnIyUkqWpYN/WVyWsMzoirAu6FJwFgUCECHngZ+wbUSD0MM7/YrFIkRW+k2e1qZA4OpxVnpcGC9LUxRG5tveHvkB+0Rd2mC7yWYwOGVkHrDn3b4kC2/2NdYxz6cJZ3R12iHXiHlkPIlq8qNFiPZEj5BaSDmeH9UbOaOFu9xD5res66V7WE1vBdxHh/XVeP3gH6b/3+7+rf/LHP1fXScoyuUwaQq/94Si5aGy6ttPQdypGQzor4wTgeTUVK0YH5KT5vJJzXqfTUX0/qCwLhSFoPpey4M82LSlhxfobFEcqsHVeTRd0amr1XavYbrpV29UR3LaNhr5P6RIY5q7rlLkYpeBAAEZCCOraIc08kaZoU3Ax3a3teg3HY4wU9UOsfRkjCBoBaZaNQp5n0gjIsywfIyJewxC70cn58dlGpVxOIU8cyqgEKejsFcZ5Jn03pcrgEA3DoDIv0vqxXtIErnkfTDBOat/HKIN32ZnDgCIGHAxuOPvZWfpidj4kVqnc5fvn/sR/6yySMgyxDowfUYifolfBjT7F+Pfxrdwf17h8XUbbkAkqa+yLlDuuZ1/2vqdrICdeeX4exTpzasI0B8deKzni5jsm5e/U9518dr5uUw1QpjDE7mthiBHQCASL6ID6WGdUVmUqMp7NZrq9udV2u1GeF3r//p2qstLv/O7vjOC20nw+U1GUyotcXduO83ROqopC2dgZzCmCsI+fPirPvRbrhU517Kp1fNknwDqrKj3exxb8Hz58UJ5n2u0OI7s70+Gw13y+UJ5n2u9fR0NXq6oWyaDjJGB4um6aG4FhfR47YwL0MdqPj4+JQSa1xTmXGFrAIWkfz8/Pur29TSCOdQ8hpHSYLIuNHgCSgElIiJubm+Q0AIItuPvVr36l7XabZt5wD58+fUpghLk91KwAlDHgrAtRpbquU7ME0qT6vtf19XVKmXp+fj6L/PAdtvsa0TPABvcPOLbGm+Y5XdelhgMYaOdcAsOQU/x8u93q9fU1OaufPn1Kzq/tUAWjCcggDZPuZ09PTyktiHUALACA0HfPz8+pix9nEx2GbXh4eEjOMo4e98S+26gkMg7QoynEarVKYIZ7BUzTWAPQQgSNKAOOA893d3f3naL4u7u7M6eVNKXZbJYcKyKCnBdJyRki5Ybn4XuJitIOu+u65IhYh5W0PVI0uS/sC4x9bAefp5oRuh7O5/P0J444ZAagFF3IM7L+gFNSMYk4Oe/VhyE5lRAOzjlVI7CFMCWrJDW62Mb0XGSesyadz7PB8eI5bftqgL91ggDN6CpsCtgInWAdCuSZOVUAfwYt2w58AFqcZ/QPeop7ZNYO33k6naQRRx2PR11fX180Vphq+NC1Nl2NtEqeg/2iFomuZ8hY0zTJwbV6gDPDmeXarClrZjv3YS9xnHBisaGAefQZ7+N6DLS2kW/wAw4+jkvChWMDh2wkrWkggbNGBAldwNpDSkBoYbtJX+bc8172D+cQssBiBBtVZT/R28iqlT30ADqKjpCQipwv5AqC8XJ/iKD+uq8fvIP0k9/6oLe3t/rm8z6mmQ296vqoPKsiKCsz1eNskSIrVDe1QpZrMVvIeaeujcXB3vsoWCEWuLZqNJ9HByqM6Vl916cC1Lqup7aCboqSNG2rYTQcWZ5LIRZ65+OQ2aBePstU10epbZQ5nwAOgtS2rQ6vL2d5m5IB8bA4RgFaMO19PkZZYuEmB0AaklHDyy6KQs3ofTs3CSzGwme5apMHPgyDirGtehg6zWbFBOzaQUMfozzSFK5FUacoSTsNjrMA30ZnEPwJcE+pVCGc19SQpjZFIGxLZSnPvzuP5mw99d3ud6zXeGdnMmdT2axSuIwwOTc9U/xZlCPpomnCX/By3qc7wImJjR401vecrx+NK+ytJ2fQ3GMIYRpcS72WlJpLSDrbI2TU7mlUmqVia/rJqbMGJMu8vvjig/IsVzWLdTw/+tGPtFqtVFVRya7Wa11vt3FAp3daLVe6Gtv0Ho+wunM1daP94aCXl2eFoVeRe/Vdq75r9XKK0ZgheHV1qzz3KWXCe6+m7VS0RXIiYJOHYVAYpO32Rm3b69tvPirPC11trhQ7MebK81J5Xuj5eae+DyrLQlU1H2tAIrD++uuvU3rWdrtNKVw2zxynAYcAo4GRYdjjL37xiwQw7+/vU3oRwA6m19YFAXqIChwOhxSVsQ4NMlsURRpg2nWdnp6eUt3IMAz64osvzoAHQIu0M1JsJJ21ty6KQtvt9iyaQdeiEELqpEcNDLoUoMWQU+4L48zzk7ZDehFgB4YTgM3+0iabVEVb7A/wX61WaXYQxtrOHyI6xLnYbrfJoH/8+FG3t7dpb15eXs6MuaQUmXh6ekqk12azSUADnWCjduh1SQkMIR/UocCc2rbUeZ7rmgYeo+5mXWBh6cB2Op30r//1v9bv//7vn6UKkT6HvSNCcXt7m0AwgAsHerVa6dtvv1VZllqtVqlpA0NiAUTWzqGPcAxwRgFhMMI8nzTNlWHd6NCGviIShHN7c3OT0hph2vn+tm1VFlWKDgGkWR/uZb/fJ5BKuhhkDvcE2AWs4uBYwiRI8nmW0jbtZ+/u3mj3/JzknDWfzeLcRiKznHGcaxxafsb1iA7wGUAvTiPODudmtVolJwn90HVdqiHjPHEtvh/HASeSPeHszWazRIoQkbgsU8ApIf2ONK+2aVL3O9aSaPn66kp9mGqX6Nxp18Q6EOwXe2zT5bgH6ojYF9aHfZAmB9u+bMogL/Q0epD9AUfZdDIcEewypAmyyvUvs2pwyFLEfYizIvm+tm1Td0UcfOSX9WcfLTnGc2K/LGlsa0QvyR5bb0bUzxJIvDiPdv04v+g6CCVsHMQHHTYhOMAlrP2/TQTpB9+k4V/8sz/Rf/0P/qn+qz/8R2q6QY/Pn5XnTlWxlhRGY9qrKMYwZNuoKmIqydAPqV5mv9+ra1sNXasgqcjzsYvYoLKIc0jatlGeF2PaXkwtKstCwTmVVYw85UWe2Ieu65X5Ut7ncn5QlkmHw05Ne9Tj44NC08gNUz6zJA197CqnYXIOSJNAyIoqgpW2aeV8PNx0QgvBqyhQ2r3CMCgv8jFKNDJnY92TwpjPfDxG5iE5GlOOqM8LdUPsruaNkm+bRkPfKM+kjgiQKK6PkZMQbDRjAuXZOCMFxwdnAkaFl2Vb+Cx1NDHz67w1NmyJc44GeSlyZa9pD3s6Hk5RHpIjNzW4iBEmPzkbwxR9stlp1sm9fO7498kRs87ZeNUz+bbRodEnTj+XZH733eN9GQlDebG+lw4iEbQ4Q+s8WhbC5Gij8Cj+vLm50fPzk66vb/TTn/6W3r1/q+12o4eHR/3oRz/SbDYbC+P3WiznaW9I/6jKUmVV6uO332q1XquuT6ng/Gq91nF/0KyqlI+GU1IaxLxaXcn76IA9PT5qCNTAFKpmVQLYGIqmabTZbvT88pLYravNlWbVTM8vL6ryUq+7KcJimeiqynWqT0n58/y73U43Nzd6fHyUcy797nA46Pn5WdfX18rzXC8vL2muDkAB1nKz2eiXv/xliniwR3SV22w2ms1mqaifFBmMB6kxpOrBSuMkrNdr/exnP0v3wjNIGltvT8X8GG9AC5EJ8vWRO5wam56H4SXqtdlszkCxnQWDM2jTUgAxPCOy9/LykiJuRD5o6YzxBQTyeRxPokkwqYAg3k+bZcuao/u4HnN7AF4M6LTpkbvd7owYwTHo+163t7eJSAOE0yoXprNtYwrRbDZLUTSifewlgIn6teVyqfv7+++ALP6NEwZYARgShWKvSR+kFo6hqPasE4HHCYckIT3MOu+WRbddvp6fn7Xdbsc5VbGpALUypPhQL2FrmHhuvp8ICOcHZ4kIRN/3Wq1WopjdNj8B9LOOpP6s11eqT9NcFklJ1qy+JgoiKTnHNjoDmLWAlyYaMPZ5nmt/OqZ7tCRgkecauv47qYvDEDNGmm6amWOHpbIPnKXVanV2BkjfhGXHWcYxyPNc3377bToHNk2VCAaOPecHx957n1q4kyIH629tn62HwwGg3tDWneEYIkd1XWuzWqf7xpE6HA5yPnakI1Jk7TzyC/mMPkGWi6JI0WvWIO2DSQ3Gcbd1Wuge9CbgHZDunNPt7e3ZeyzZYc+HtdGkdrLWOKJEfHgGntOmYeMIDiPm4/5xqjgzyCfXtyQ85xAHDFtE9Jqzgz3FhpDSx30R/UE+0Bm2to2XdfbIULBpgOgxzip/shZlWcVyFVM7dzgc9Df+1t/8tZo0/OAdpH/1L/9Uv/p6p//9f/R/1PPLUU3Xqh86tXWrvp8iH7GuKKiuj+raUwTZ1J+EoK7rYzjXa8qhz4vYxc7FFpR5HgfpwYAoRN49hEyb7Y1Wm43m67VUZCoWM/UhKBu8cpfpdDrq+flpHLg4qO86hfpVzw+fVR9PyryTD0F9G++71zBGtkjNGNT3sUNdWeSaVZXarjtrBx2FeUjOUt/H5g0+G0PQp1pOLj1HlmXqR4aWDC6iD1meJWZ9dGPks1iQ3w99bD86tOrGhgnOm+YClOSPjgyOU3w5hRDrQpyLDR1iy+DYBCHzIRXjp8jF+P+hj7U+fR+79OVlObYCHZS7SfkCcoo8Krp+iJGSaABiHVJU4FLft2eh9Hj4gvq+G1tyT6/kWPhMXrmCgoZ+SM6S914+fH/6HM/uTIMKugjGRhlDijjx+bMUtXHALwomhfFDkM+mIX84QlG+h3HmVmxokOX52AY+tosv8lzFmEK6Xq+1WC50e7OVz6TFfKFqVikMQT/57Z+ktsnL5VKzaqZqFqee03FtfbXWqT6Zrj8naazpyczcH85jGGLb9d3rq0II2l5vtXuJc33u7+/jwMjFQq+vr8m41vVJq9VaXdupPp0igzjKsSRVZexsRM45BkBSmrlwOp1UN41mVaXFcnnWUel0OEXyYEwBzceWuCEMen19TZPrmZFjI2k2neDjx4+xI9sIKLquU8lckdGQzUfmFeMDYH58fDwDGQApwBXNEySleS38Hofp+vo6GXrn3Nn8oM1mI5oKEMGwgIi1kCaQwbNxPQt8rczZhgaACmp4yOfHgXDOpS5hyAzPBfsoTfNUACG2ThNgjkxh7AHRgK7Pnz8nwMmfgBaY5/l8rtfX15QOFMLUPObl5UXL5TLdJ+lpAG7W2tYUkJ6F0SeyaDv12XMuRZD/8PCQIjasN6DARlVsobut+bKpVzjSgKfIJq/U1O0YqXqUc15VVY5y9aqiLFIUEMctfjbW5VVldG7rpsYwRAd0dGjKspL3YwODPNd6BOh9T5p1r+VyJeek5+eX5Lgfjwc1TatiJC8Xy+VIcMU6YuTySDF822o2m8uNJaZlGUEz6Wiz2UwFIHk8M+v11aize/XDoP3rq1brOIDYzr2xHSNZC2QM0IxTimNk602GYUi5B33fabFcKs8yHY5HhSHoar2KzQWGQX506Lfbjfq+1+F4UpZnCXTOxnk3zkm5z9QPvYo8yoZGsumbb75RH4aUdsqZtemVPB+RBqK9sPFtO9VF4QSd1UCNHRWJsOAYIpvIKUDfploiu6yXBbik+n7+/Dk5cLZpx2w20wBWGWcm5kUhJ+kwpjPnea67u7uUymrJQKJGRMJxwCAIuA/0GOtF8xCipdgPHFabYUGzFSLl3DfPzznE1lhHwRJDOASQFehLZI/vZX9Za6LvVVWp73pljvppnwgEotOW7LBRGSJIRHitjrcztHgeapqIYEOQYG+t7kpk++gkxejtVPbAOamqmer6JO+npl3YSEoKnPOjPsgVU/tNO/c+1qc3Ta2/+bd/PQfpB59i13a1qipT7gc19UFN12sIg+rT1NqQwxpC0PGwV9cczowOB8R7r67p1LVjq9Fm6vMvOTVNBKn16ZCMr3NeZTHT7iXWvmTzKrYD73q5PFOWxzlBLs+1vNpoebUZc+9fVfet5HPVXa8sBPkQFIboqHVhUB+CpE6nuh5rXiLQ7LpW++NRU0eykLzu1FVNpFU5hXqswxlGIHGqx4MX/ytns7ENQBxYy4GVpD4EhRBrgFiLeVHKOSlrTvJEKMLIQo8RuThGaapfGcaoDP/3Xsqz2I49vif+O37vpAySspPTEHoFhXESdnRKiiKPzRBCbGMenJT5TE5D7ACXO+VhbE7gIzCvxg5lsTDfjQ02grwbla/3CqFX2zWpwYMdaJv7ImKDILksdvDLYEWo3VKsE9LYUAMZjN8bD37upuHAIUTHjPocom/IWNzQKdXNudgUpBhbVfuxFiimIjoVRa67u1vd3FyrLCtVVam7N2/0utvpxz/+LV2NKTCRWeo1n8d5Atc3W7VtHWuKxrxq8r4j27oeU7kGffPNfgRlTk9PDyqryObEWpvHyTiNjkFZjFGPw34qXh8ioH16fB4V81G/9Vs/joZmCJLzqmbzlMYTjepOQ5hSZFDuxzFliGJtOqbBCkOqAN5Pp5NWy6WGYUhOT+XLpA/6vtfbt2/0+PiYIgHUQticc3QH0SBa30rScrXSn/3Zn6nrOv3u7/6uHh8fdXd3p9MIxPf7uIaPj4/68Y9/nPQUDh4MILVNy/F+SXeixga2FHniea6vrxPDenNzk9oQM5tnu91KmsYISDpzZK+urnR/f58iCxTrW2dFUkrdaZomDTYlbWe73Z51eWJeEd21Pn78eFavhQMgKT0TUToiAbZ21AIJ9u7x8TFFinBycN5Il8GpAvj0fa/tdnsWdSTVygII1qaqqtS1brvdJgeF/SEVyNZZEBmBwSZdDqBHWqYtvLa1QNbZQU6YS8TPt9ttYun5Oeu6f421dJE5J2Lp1bbRiY9z16ahllPtaNDpeFIYpkGonL/FqDtIRXPO6c2bt7q/v9d+f0jd5Z6enkawGNccR/75+TmlZ8XvLeTHdO9hGHR9fZPSp+az+fjcE2nR952en49j7eJ8cj6DNKtm41rYQZJObdOqKErtX/fJ6SdTg8gdOvsy6wAyA8ICucNh3u/38kV0BpjPY9uQO+fVj3vSd502V1eR9KlrDWFQmU2ty1OXt75XmRep8QeRv8eHh9jBb2wBzn2zBpBCRLZxfMFFpMiGENLwYc7UlJo/Nawigo1OssQJEYtLbGXXDrnifHEmOcOcA5qyaEQe+zEKgZxACLD+RGPQUc655LhwZi0hgeNr6/cgaKhn4ywReeYMcT0pRmhpAoIOYvSBc+5MXycMapqs4IxipyCs0H+QHTgLfI9trIAd996rnBfSEFJGknXEiMjh5BO9Yd1x1th/Sd+pP8JxlpSiTugkUkJtfaIdSjxlJp03pIp7mKeIkI1q0ZHSOnNRzhgeHM4+ZwnTX+f1g3eQvv7V15Jm+q3feq9/9ac/U1AEuD7L1IyMAQseDXr7nWGYHOT4u9g6O4Q4fV6S5ouFBjpW5UWcGzTO8un7Tk13VH9odOoPqtXq/Y9+ovl8Jvk8gmiMPhOzxwN5UK/6dFJwmQ4vLzGqFAZJ/jvpUNPLq1eMsuTlLM2s8dk0j6YoS+VZHECWZ1lK3gp9jEaMPcRGgQ1x1liIjQdsvZMUq3D6EJKylaYwb5EX8mFqKtC6OJ+p7Vo5Dcrz+F1Eq8IQUxrD2PTNZ5mKfHISnPfq2kxB48DOEOTcEAeAOikrYjRsCEHq+xglG8ZBuyMh4RSkwcm7TAp+/M54HRfG6FnbJUOZ+VLDmLc7Kys5pzHC5OVdrW6sl3JZSPU+mc/k3Hm7cD86XzSIyLJsnMEV5woNPjayyLM8RXWmeqjRWXWxQUJeFBr6XvNUO1JpuYwg6ebmVtfXW2U+tqleLaMRePf27RRuLwplmdf19UZ9z0DGqMAen55UFYVWywgYXl5iZ7CqLLV7fdXz88MZG20ZJ1v0jIGBPZdzakeQ632cz0InrL7rtBrrTh4fHyVNc79QkoAN2FoUJ+lWAM+2jQNGN5tNMq4wXUQCbB44ypX7lpRYfWourDHACMEYDsOQmFNANi2Wu65L9T1d1+nx8TGxafP5XIfRuby5uUmAcj6fqx2jKlIEHu/evVNd14nBhYEFvBB1gE17//59YjIBmDbNxHuvN2/enKUyAARIdeO7LwkigI+kBESYZUNKEywvLDcGkb3n+47Ho15fX7Xb7ZJxI3pDu2gcCMD4fD7XbrfTy8uLrq6u0vfBcgLgYFJfXl4S6LIsJ86BpBTpombn9vZWu90upQ4SMXp8fEz76ZxLTilROgy+rfnEObW593Rtw2lA1ngPMvfhwwcdj0c9PDzo5uYmrYUFZl999VXqiGcZ2MfHx+QsIuN8N99BdMZ7n+ZHSU4aU8/oDoczXRR5YqUBS6z30+Oj7u7enNW38D6KqrGxRC1Zc2SXFurYD2rCbBSQCJAFVc/Pz6nt+7fffpucdBqbAOiQZ863rVHCCUavAdxwggDVrCP4gHPFmbRRANL/cAz4zGKxkMsydX18DoC+TaNsRwcaPbJarXQ4HpXlWQLofDfdydCDkA38u+sisYF+uDyT2BocJKK99p5xDO3zD8OQ9DxRD4AwAB4HBCKB70ano4OpKwL0SrGOj0gX+4FTaJuyhBBSWi2RZjuegL3FsaX26Fe/+lVy4CBj0Hc03kDubPT6EgPZZhg4IpwPW5/D/qKPWWf0ArrGppBh/3AsbFMCWy/N3733qSaUtQkhJJko8lxN25zJKdfmZ5xpfo8zYwlp1ooINeSLjQYim+hn7tk6xkQfkTvmc3XddH3OO/qD66Nj+B06FB3CubQpw+CDX/f1g3eQlou5bm4/6N/5q/8j/b0//Afa7Xdqu0Ft0yXhYvFgCrIypsfhlU551RHokd5Q183EymdB1SwKX9e1ognCIMkNnZr6KN83qpYLdXWt6rqUsnJMSesUDCvYD4O6MKiYL3Tz5r1u3wQ9Pzzo87ffyDupyLIUfen7WASPgvbeqxu6sX30MLbVjlGM3Aqnjy25s9FR8s6NnjtRqF5Z5hM7Ffp+dD7GhR3rk9q+V0gh2GEEkbnC2FJ8GOtqgoKGvFBVzaS2iY0rxsPki1L92N7ZhTijifsILs6HCv04JXmxVl5QHD1TkRdjGmOIn81zOS/leaanp0cNQ6/meNDr5086jDUP6ZCH2FhjfJxx7eIDRmNZyuWFQs8wvli3Rbe/IqtUZFPuLqy5G9c6KqcIOvjOPBujX4qOW+a8vBu73fig1Wo5Ojo3Wi6XqZPYfD5TXuT6nZ/+dEwhHJRnucqqUFnmKRd8GPpRSceI0e71Ra+7nW7v7uRdnMEzjBGbp+f7saajVF44ve6f5dygx8dPauqFdruXUbn36rpSWeZSiB32n7VieCjhd1jJtCajbBdFoevr6wRe3r59q3rsRgSbTz0OraK9jwW5+/0+dTCzoBxD9PDwoBCC1quVXsa0ENK3GMYKYwf44l5w5IqiSIMOmVVjwT9Gkee8ubnRL37xC5VlmYpdbQoKz49zend3pyzL9PXXXysfARrFsYCn6KRPRup4PKaBqNTvnE4nffnllyldDOBPuhz/3m63Z0WxdMeLuqNPz0zhPPUHtm6CMxNCzIvP8zg7hHQ+G83leWezWWpeYB0K2E0MIR3uAHTcK0AYg/c8FqfzHKQLElFBf8PWAqyJFDGzA6MKwIIBBYDhUD89PaX1xzG+vb1NDRdI1+G+bToh8vJ2JCVYN+twkpJHHc3rmEpaFHHYZlmW+vz5s+7u7iQpgR7WmAYJtsbLe5/qum5ubhLwRPew35bFBUCSAliNERUIA56laRoFDSqKKXKAgxRCSA4+EU3OCP+29R7UauEc2RRAgA61HDixpG4RxQE0Amb3+31KRQU4W9lAtgBoREwsOCTNyXYGw/GCYMEJJlppa43sM7Mvkqb1G/UCGRXDEAcq4+ijl8osT/oOGYypWZ1OTZ0ceZ6Ttvx90yZ5IvWJtKqTiVguFoukS3C4U9rhiHkgTHCu0ZusO/dlibI8z7XdbrXf79M54lzZKIdNGeOaOKvoI9Ycx4n32JRiziiyyHmyKWAAfBu9pQaLayFfOKSAa84BUT90NBETmlQQPUm2zk0dLtGNFrTTNRMHjGtcko72uXkfESGb5ovzYjsjcn45/9aZIJvB1kDZuiAbtWIdqZ2yKaNWt1DHZskE9sY6VjwD2Js/eU987ohvbe0Uzp6tl+KebB0ZpCYyzJ6zRosxLf/Xff3gHSQnqamPulrP9T/8H/z39Z//F/8Pdb0b05ymWg0W3znF3F/vpwLIEfgNIajrB3VDo8OJRgzjprrY+hpB8NnUuUPOa+jiINPj4aSvv/paq6trbTZz+SJXcNME8rwcp0QvF9IwqK0b1ceD5DJleanT4aAyzxWGOIsJ5RwbE8R5TnlWxNqd0YmSzzR0fXRCskJhiI0eXJZpGLudDUGSj80LgvdS1muQU1ZWcn2voWujQzH0yTAfmwiksjxTcF5BTnk5l/dOeV7EjK8sdikjEpKNbbSdHzuSVKWqslI+Gt7j4agw5JrPFinvfwgh1o9Imi2r+GzjDB4O9NAHta1TnhVyPsQI1amVFJXj8fGT5rMRoCgTtURVGWuFIiicyAXvnZzGEG+RjfvTKjaBaNW1MW0tggGUIINye2U+pgsuFqupK9diobLINJ9Xevfuvb788guFIC0Wc/32b/+2dq8vYzQgT58p8kKH4yGBFeeirG22mzRw7enpQYf9LoGMoe9SkXM1r9R2jYah09PLi6SYIvDu3Tu5UwQPdVObdAgiTLEYPrbTnen1NbZrjYXNU5QA1gcFSSQJ5V8UMe1jN9ZlABYxWs/Pz3pze3tWkEqrZ9vq+o//+I91d3eX8r+LokhpObvdTm/fvk1AYb5Y6GHMWafj1ldffZXmb1RVrI/6+c9/noAUba6ZD7HZbNJ8HECupARMYL4eHh4SIKITGu1pMahTjVSdHIqrqyt1fa+mbVNKWlmWurq60ul00vb6Wpurq8S2Mkj0zZs3CVADIDDUgB/LfJPeRSc42FwML449s5VsVyOcYZs6BNiDUcX4WJaeCMVut0sAHJno+14PDw+pHglDyqwm6zTRYtqm88AWf/z48Yy9twDMdsuyoJBojk1JtAXUgMS6rlPUTlJinEMIev/+fYq04ZjSrQ3Qx0BVG1kgooXcwLADeJjfQbocDoFl9en0B0hBtiQlx9WCTuSPOhSah8Dopu6NY+RkPp+PHdvaFLG09XROLqVssmY8x3y2UNt2KTpjgTROBXUY7AckBylApN8RHcGpjgRRdIJoyMHnrMNDd0AbMeMFmMfOWvBq2XiiBTwzjjiyTsSa6CLXsfVuyCzpb+hBKYLcLM+1f92pms309u3bBPh3u13UJXmRcIWVq/X6Sv0wqKyivmEthmHQYNKaONN8rihioyhYd+6XrnQ2hdOmiBHtJoXU1uYhQ6SEIo/U8aA/IMpoiDGfz1MqF7qD82qL/Fljzh1Rg+fn5yTbfB4HF11DVMbKAc/E+ceZQv+gm9CLnFUaL9iMIqIlONeQX5BBEBOWLEIOWH/2hvPC2APGJViiAJtKgwYiaURveAZkE3mzQ3qliLdmVSU36iwcCH4PiUiDDGQPu4EjYp0osDP3wj7YKCF2xNaXWb1tu+dFHTa1iZeUmtJwBpEvZBCZ5U8cOVLCkfeUhuh//QjSD75Jw//nv/1vNF8s5bJKf/T//O/0v/7f/B297htl+TwtNiCgbVt5F9R3dUon4D1ZlqntOvUhOhHe+dT6OISgIi9TWlZZxjS+oR90PB40n8/GznClsqLS4Ly2N3f68OWXqlYruTwWGHoXK32aJg77y3yhrmk1dJ2a40FPjw867ffyUqxJ8mY+zRhxGYagMA5hjUoyFt7TTSAedmpzhtQ8IMti3Q6HJfNTa9iu72JK2RhRulpfxeL9EJsANPVpPAAUq46efNtqUOxu57xXPoLdYRjU1kwCPyofGYrj4SA5p9eXV1VFedb9ZLlcqus7nU6vOh6nzlLOxXbmMSJYjHNtvE6HV52OO3knDV2jbGwWQTe+fuhVlZWcdxqGblQGU251UZSKc7LGNrpFOaayrVQ3tfKs0G63l8+icp3P5iNDMddyOdft3Y22263evHmjN3dv5LzT/nWvIo9jXbuu02xOmg/FhGUCl1OaUCvJqRi//+Vlp6apR0MzE8WJy+Uysee2zXFWZimUDdsa0/syDUPcpzhnK1PT1GPNQ6nTYT8atVZ5Pk2iPp1qLRbLszSah4eHxJhmWZaYeYC1c07LcXYISo3v3Ww2ypzT119/nZxCDE1ZljqNIGM2m+n+/l4fPnxIhbF3d3fa7XbJ4YIdur291WEcVGmBowVP1LTAONFR7+XlJYG/tm1TRz0YevQFhfm0aiY1ZLVaJQMH8Lq+vj5L1WOtumFIBnsY/w7ork8nffvNN/riiy+SDrKsKOxpCOGs/gcAzjpLSg6RZXJJW8MAW4a77/vUgQ19QMSQ9AuGheJkAPKci3n+GCwcZBxo8v77vtfj42Pq+CZNjCkpWTCF1P0QdYQBZwYN6XwvIwFAmglGmbXAOEtKrbwxqKRA2ogBQ2N5RpwTzijOO+cAncRe4yQABgC10jTnxAJVfsbfAUGcFwAZ4I06OZjWw+GQ2sOz74AvagNZU8AK+yRFIDGfL+Rdls6UbVbhvVPbTek5D2N9SwSIpU7HU0pjRSbQZZwdgK2VG5w0GHCIDewVqUXW2SDCRBohL5s2Cmi3qZ/WoQd4kZLF93GP6CLq/SQlmWefuV6K/I4phfYsAzqt49CHoPlifhaZCiE2hKqKqcMgz9M0jXyWqQ+TM4bzH8aMgNB/t7FAtDNzDZrOtq2TI/KDQwWQ5NmY00R0iee1HeOso8MZttFvnDYi6ayFbf5iMZiNXlvnBzIB3U36FanH1vm3+8ce2nRB9C3PDrnGPREd57lxRm2KFzqNsw8JwO82m036Hr4b3WLTna0eYg+QfyK4rAkkJfrEnmHW3q6VbRhSlqWKLI+p/aNN4b04YVwXshBnFhuMPsAZxqahP3CYGCpMkAF5YP3RuayHlXcyiKxscsZsOiNyjk2XppR8SYm8wVFiv0/1SX/w7/313zRpkKT962vsNpb3+tEX73VzvdVu93Us3HeXNUadpKA8G2eiFIXqplGW5SqyTLfbG7lsWjLLBoQQB4KGITYJYMNiOpVT5nNpBEV9kIa218evv1axWqlcLhKbKsXam34Yc13dIOeDsqLS+mqrIi81dJ18GNQmb302CkkuOakN40HPvfoQVI0hXQWpnAXNZzP5zGtWzdIA2yzL1TRtbMSQF2edT7q+03w5T6zCy8uLjqMh6ZtaXrEAvm5q9V2fDrcbpLZuRFOCaJgic9k3TezJn5zQflKaapTn00HyzuteQZn3cmPnszzL5IvYNaksS2Xr2BXMO+n9uzdq64OqMtPmaoxCVAvJxWF7s9lMd3d3KqtKV1frGH0LMb1tGIaxc5xXkWdy2dTiGOciDs7MVeSl2pYuMKQoBjVtLVq9395s1fVH+eD1/PxJm6uV8syrbk6Si7U3p9NJ1Twycd55LecxzWV/OKQaMe+CmtNRToOKLIsNQoZhrCcr1LW9vMvknddiHh0Dp6DMx3qj/etR79691eF41NCHsc6tUtcNqqpFGqpZ17VeXw96eXoaJ9vf6he/+IXW67Vms7mKokoAgjST5+dn/ehHP0rg+DCyqzZ94zQaSpoHMC19s9moaxr95Cc/0cePH1P6FsDCOae7u7uzVIPNZpPYZxhhImZ0XuN8Eu3h8y8vL8qyWO9AET3PAxhFMWP8N5uN6rpObbFJDURnvHv3Lp11mC8YxCgbPjko1sA3JsUBQIIRapsmDYYkXcV7n5j21WqVgB/XhbEjIgSIIIWMdZiaWTQpCgXTCftJtAHnyRoxGENJCayTUgIoxKBnWRxye3V1NcrWawIBgGuMJY6GpLQ/j4+PyVlDpoiu2BRoW4uG/gGYAIjozMU64aCQDo3cMfuL54cNRYdJSoAD3YDx5b3Wmeb7IAwAOi8vL+N+1Irz+eoUKYzd+yr1fZf0swV5pLeu12vlea7X19cYlWy72MCgn1hbmF8iuKw3wGc+n8fPZV5d2+np6f5s1g5piMvVIgF2QCXnDKeJ4azo9dlsliKeVn82zTQIdL1ep8iZddCJauFASRMgsk7j6XTSer3W8/NzihTCOOd5nuqxcE7tOeE7ccBPp1NKa+S5AJ+cQ4AcNoHIq+2ChvwQnQNkI+NFnqWoOe9v2zam9o8jE2wNSIomyGm9Xqfztdvt4r30vdbLVSKEqqoy7eVDitxboA5gBcT2fZ86X6LDeD8YCbCKQ3lZ10Nkgue0KaqcC84mtScQC+wr8pXn+dkAXwvGiZR779NMLc6jJWesw2UdRKJ7dm9x8NDFRPb5bgvCqb1EDlkfnB+yDmwEB/Kj7/sUhUWuIaOxZRBOpFpCtFgSSZoik9wHzhMvGoYkfDk2LWBtOLecB3QZ8mFlHbzMs3BmLekwpc9N9fHIsCWs7P1b8i6ub6cQXCJzsF3oWl7WJiFTOLe2tsvKPNkuv+7rBx9B+nv/9f9ds/lC3hc67Fv9x/+H/0T/73/0j9UNsZ5mGIKC8/JZPjokTr6IraKzbDKg+dha07swdkwb1Hexa1rXdZpVc+XZCLC6QXleji2wner6pPlypr7rU8tlSZovl6o1qFgs9MUXX6goch1oGxqk5thpVs51f/9JTX1SWRYqilwvuxcNPtahLFfLMUI1ApeiUC6fijbrsYCzburYya2Pgrt7eVFZxe5lT49POuz3ymjG4JzyPNPhuNcwdBr6IA1jgVw/6FSf0hpo6FSOzkyWR4Ae62mcqtwr87FTEEZuNp9puVgoDL2qWan5fJ6YgfUqDqmsZl6bq5XmoxLcbDbKvNd8sVBZTB1xQgi6vb2VNLaGVhhnUjntX19FR7/Veq2X3atms1jgnXkvNzKI8fDEuU3e5CtPLNTYwWk8dFEhBbVtBKbr9Vq73S4pRoxA28Yapvl8PkZYcoUQD//xeBjvX2N0LToBh8NJ3keHMA7+K3Q4xDk0h/1eVSpojvfUj+ka1FNE0LnSbveitmm1XE1KuG4a3d7cqG6asS19vL+6bjSbTfMvIoicwusof8BqWZYpP9129prNZnrZ7fQ6srGAh3ZU7GHcI5hVSQlwAPqJYJCCVBSFZuU0u6oZ2SsYzdl8ro8fPyYZwVAvFgu9jG1YLcuHMufvAITHx8dU71VUVUpNmI2h/6IsY7fCMHXwIYJDPrMtSoaFxiHDCADkbBceC8IwsPv9fnTQ89SUAEYQsMAcJIAHho36CRw9jM9lG1kbGeJ9NtJjawQwQKw9TgD3zbPzd4gUjBmAwg6htUXyvAcHk1QcO08Eh63rusRKkxZDdI3hrOT5UztaVVVKhQS8Rzk/KfOZsnFAcdtEMNX1sc364XAc92WQc4wVCKqqUrvXXXL6rANmIzKw0VJ0npt6LMaezdKMvVk1U993Wq3W0XnMM7XN5NDOZpX6PqZV7V/3CdAVRa6u68/qF7puGqzqnE9AJLbVbtS2kxOz3x+0WMxV1400kmIw6IAi2GJ0YggxM8E5l9K7qVe1dV3WqVgsFmNqsDvrkgWgpPZJmua/2KJqdLSt9QDAIvOkH7He9kxxDixhCWB/fn5ORAPACaebM4YuQl5tnYiNEOK0WAfPFr4ji0RhhyHoeIzOCNkMXR/ta57HaD4gH5CHoxTGofTOOXV9H229c0k/oSNoZjCbx5Ei1FfxTLYWjfW2NR/oDdYPgIpDY9PySEe1ESxrp8kCwWGhBg0SQYr1k6wxOtJ7n+wN8gx+wiYDwJEPG81Hz3Iu0UWWcEG2Ikm6SoO3pSnSTDQSPUwEHeeAqB7XQ6dZBxn9xhpBquAk4RRiH3G8bfqhpNSUxBL76GDsq03jYx8i+SxlzidC8f7+PjUVWq3WKstKTVPrdKrPnK84kmOKlONkxsjdzNS9hpHM6cZMpeysTo/OqvH8TmMayrIYMfd0z/wdGWTvLSZB/yJz/HzSg+e6ADLy3/sPfjMHSZvNRv/kn/zjMeXFS6HUz/7kl/rf/u/+Iz3vO/WDxqhOr/liLp/lsevbMOhwHNkWxRoNpwi2qd9BMIuiUFPX6jXIFZm8y1SWlbIs1zBI9alRXmYqqyKyes6rms10Oh51c3Or9fWdytlCq+VKzsPExTxMnxUKcqOw9ZrNKp3qsX1oEYvomraJTo2Xuq5V1zZq9rH4tG1atV2buscMXafuuE+DwqyyCX0v9XHOREync8oyN+ZEew1dq+1moy+//DKxp1mW6eZmI6nXYhEdtevttTYjUOnbRnfXN2raJqUBbTdbtWNXnWpWqm3as9z6+Xymh4fPatpY6/GrX/1Kb968SUCQw8Ykd2oCUJTUBNgUjbaN3/H27dvUMcjmaJ9Op8Tcfvvtt7E+x7kzxs9OEUfRotybptHDw4PevXunvo8F1HwX75WmA0y6EmkspJaRjgLrSqqSZcvZM2peALTcD/NXAJpt26acZSI+KF0UOAqbgX6klAE6aHQAOHDO6dtvvz3LeV4ul2N+fJXC76TFAW7ZB64BeAC4zmZx4KlNd1iMNUfv37/Xz3/+81SDNF8s9Du/+7t6fn5O6TJPT096eXnRhw8f1I1RnuSYmwLky/Qk6h36YVA3fi8Oy/F4jLU0TaNgGHGcxTzP9enTJ7158yYxsfyc77LNCkhdAPyxDjc3N2rbNnUsI6+fgtxhGFLdEbrH7hdOD99PSkiMdmYpwoTMAyowVvwe9pKapaqqUlocbDcOKw4VKYUWRAAEKDS3KTc07wBEYcCowwAUAQjYD7o84TDCbPIsgEicbhws9oJaixRxPMR6OsBGXdfJgeW8AjZ55qhXgoKGVKMAO0p3MRtV4MzE94WxfXRx5igAZImYWKaa9FCelecljYuILO+1KUAPDw/JaSFyCfjkGW3uP2w/LDp6BWBB1MieX3QbUS8iCcgKtVXoJNKmsT82Ekn6VQhxADBEhk29kqbIDnrDzlhBlmjCIikNHOWs8yzWAZCmTpykFjHPCiKHiCI2k7MAIcI1OAMWWpGSOXX8mggBe93owMb7toNokUe+C3nHySBVz6bPkY6V5bl8PqUXssdEQLg2Mma/z6aAcV5tXQ7DVAHoEDA4O6RGUbvJsOHHx8ck29h+Pk8zFxvt5X6wX0Q9kB1LbKLzkSf7zFYfhxCSTrXfj1OO7sRJRadd6m/2nb1FPokqkm4GuWZT69A71uGUlOw6OpVoDVEdznsIsWU3OpUsDtY9yQCRSO9VjvMfpakza5T1tebzRcIONpoXx3OcDxJH5iSfmjrYdMqYAjtFgZDLiUiehidzPUgOu7forunsTDWD1pmWpqHIrDNRTb7/dDrpcDjob/0v/me/cZA2m43+5E/+5QiAMoUh19e/utff+Tv/sf75n36rbhyS6rI4KHa2qDR0vfLgVJ/irBdAcpZl6odBxzFMj3DDmPWuVzYrJI3zG2LXg3hIFzMtFrNRmY9tYuki5yq97mKBMoeZuofOSd3Qq20bnU5HHQ6vOhxeY9i2j40XjqeDhqFX37eSgvquU+6koZ/CjafTKTaNCL0K3ygvct3c3OjtmzdyLk51rspSeea0Xq30o9/6cuyAJ33xxXsd968a+ph6YBmXzXYT61fyCMAenx419FMB4aJaqmti97vjIQLN9dVVbDXZtIpTjqfUksgM9nIuDsHlIK3Xa33+/DkBH8sISTprdwoQK4pCDw8PCRwBVqwBsOkCIcR2yLbzEUqWQ2zbRMM27vd73d7eJsO6WCz0y1/+MhUeotAwJKQV2Vzj3W6XQPpms0kg8dOnT7q5udFisdBXX301Mr+RGaOJADUu+/0+pRphoFHWpEMQpbHsOuCTdDQLjrMsSzUesDG23gMFfnNzE2eVjKk0d3d3qfYB4Hxzc5McBtIwAN+k0pEWQ6Ska1s1YxoGYAinbTab6WrsmERBNcxyCEEvT08JDOHUMEeC50IGpKg4P33+LI3GCKMahzRudToetR6dT9bDMs2WzcNxx8khZQXlzrNauWPfqqrSy8tLcnQB8zDVwxBTwe7v79PZwEAgO3Vdp5lGV1dXKooiDTaUlJjSYRjSjBkLlGznvsti91SjaFJncO5tSgtgnnQ8WzuDLA3DkMDnzc2NPn36lEAVzj9gAplmHTF+gHj+s86YTbngM/wXgWVQ20wsLnqEdccJBYzgAA9Dr6KcUuZYD4w4ck9KGY5bfWrGbIT8rAYHMJ3neTqbrLvN47e6C/vDmvA90lSUjGzRqAQg+/HjR7Vtq5ubm/RcACiIIZs2CLBj/zlrpJGytwBLAJSkdFZgrG26C/eIPBDVAJBLSsXfOASsC/tF5IK9555t/Q6fBQjaonnIJ8grvhOdgHxZZ8juKbJpHUDk1uoFwOz0fe5M93Ivcc2mzlt8J9E1nDppSvG3Tjpn+CxtzXsFN9W+sN/IDhEMHFK+73Q6naVgoQ/QRaS/sW+kdwFSbUMO7DXOBffHntmUQmwPDjjfiS2jMQVOFGSiHamAPsU+QFiQQmj3D7kl8oIDQuQKmUKXICO2Fgp5Zg6ejQTR2Y8IP+tEJIoumzhfRMJ3u53W6/XZeWGdeH7rWINPeHGG0UUhBGUuZiZwRm262zDEeUGXEcuoD9t0ziEqIFyen1+0WEzdUa2cVVW0F7Z5y3QuzzvY2XPPMyPD/M7qPZ4RWbF60EaoORNWBv7D/9V/8JsaJIl2zbny3CvLneR6yfc61rWenl80X85ja+6+k7wUwhDTtJxXURaab9daLJbK80ynutaHzUZSHCwXa3nirJ6iLDV0AIeTQoiHqG5qSYMOh71Ox1ZDHw//breLHcqedwrdIOfi5Oeu7ZTncf7Qfr9T2zbq+lbeOXV9bCueead5VWi5mOuLm5VCyLTZ3On29lpNU2texlax19trXd9ca1ZVms3nyjKn3E+pC947zapZbI0tSWFQ20ZjXJSZTqejirxTnw/a3LxRMQ5QdWFQ19R6fniIndx8puPhqL7tddgftN1u1HadjqeDMue138Vn3mzX8l4qSq8Q8jENrdBstlbft/JesabHDWrGYbwYHBQ3w//W63ViGemWhQPFwEFJyQBtNps4c2cECURbMJQABsLhKNa+jzUORCOYv4KCBxSjtD9//pwUB0XTRVEk5/Lx8TFFCoh+McPBe6/7+/t0fzjNu90uzYU5nU56+/ZtisRsNpuUokJ4mXuyhp40EOp2cMJpqCDpbK1ZGwwjzg0AwRp/jAR1Rk9PT2nI48vLS1L4GA3rcAH4Qwh69+6dmqZJxa3DMKgbIyRd1511QGrbNtVw0N0Kp+Z4PGplOgHtdrv0HhQs+4syTdE3k4aR57nevXsXAeZyObbCn9K52rZNbN3V1VVqVnF7e5siW4Duvp86Z61WqzTvSZLevHmT8v1fXl4Se2hThNgXoiw2hQ0AhtNL6hcR1Y8fP6ZBrdQoEEWwoA7WDVAAA8m9bzabZARJx2StLOiBNbWtvNlr/iOawwsyg2gU+817LMiB8U2Ox8gYsmb8nOvR+QrHyfuxHbbzms9jlM0CWQyxNfSkdZ5OJ5VVkcAkZIakKY1lNO6w5QCNPD9P8ZTOaxtgV0nPtE4Rz0b6CNE91h39YaNoklIraNYNsoI6LkkpmkyaEDKAA4IjCFANISTAQ+Qa/YHjQjQH58iSF+gCSCIL9khd4pmx4YB+G423joAFWZwDG+mxES1b02BBF2DQe59qpvg8Tqp1LLgH9KPVowBP+xlkMK7BxHpbHRvvaUjpf/Z6fJ71u76+TjKCA8dZQOa8j0PEW5MxAUHQNFM3St6LM0n0A+eItaLOCifeOgJcxzqOyC2RQ0jC1WqVokwWcPOMkGcQbS8vL6lWDVyQZvsURUoT49zadEsigjhgRGRItWNPqFHL82kmHnKPY2AdeM4aEW2ceUsio1sB5hAwXBsnhmu+efMmnaftdpvkmnMaQqwlI0uBqDVn8vn5OdleSF5kuKoquXA+W86mQMZU+2leG7V58TnbtKbWAcEugT/Q3TjOcR4kM0Y74zi3yvMiES+QtVaP2Ai+zU6wpBjnDP3PtexZQ4/T/MXK61/0+sFHkP7RP/pH2mxWKspSXTvo5fmg/9N/8p/q//vPfqGuC1pvN1oslmq7Tm3fqagqZVUhn/lUH/P6+iqF2HVGmfS6e03Aqx+icxNOQS8fnzWE6Ay9vu5GIxcN3uPT4xTmJM3Fe7muVu7jIaStsnOxmcDb7Vab1VLVrNJ8PtNyuVCWeX344r1W25kG9VosYrpeXsYubN45zUfDmflsVFTM73HquiExQNvtVrE5Q6PT8agiz3U6HbVaLdX1rbqOmoVC9WnKDw0hTk0fhkE+y3RsYpG2QtDzy4uGvtdiuVTmg/p+GtSHlx+BTq8iL5UXufIsU9PGtr/1qdbL7jmlhWCEpXO2AEV+dXWV6jw48J8+fVJd1/ryyy+T4rHGGJYoy7IUnbKtNa2i+/TpU1K8RAOkqfhRmgxc18XBlDDypDnRNpk0Q9aB1BMiSADqyMo8JzCFQQDAYAAAFjZVkCndzCXCmFkjjNEjl9lGX66vr88MFwqdv/N5FI812C+7ne7GVDNbp8RMFhQcKUgoV8AyzGs11gEdDgfNqirOizIAIjUTqGtlJjUIINH3vcoR7NtnOBwOyZE9Ho+pZer19XWsDeg69SYyASDL8zzVIElKzs2lUcRgWbYbg8ye3d/fJwcUmUQFE53EsQWYc23rEEk6Mw42J5xrdl2n6+vrZCQx1pLSGg/DkNL7eNmUJiKWwzDo9vY2AYr7+/vkDJOyglNjnSYYYXL5Me7svWVBpSn1FQAHyLCOlk2zyLKpayKRLH7OM+PwcT91HRuolEXsvkYKFvqAFFNLsrAu8X7yWD80yuEwDGntAemQLERcuq7TYr7U6VQnht5Gu0n9tZFaIn/oBvaf++JlwTfRWNYWnQb7DLih7bJNyyNSy77bdDgikTYliXW1+sA6LjhaNlWH72NfONOAUmwTugu9hf5nPZFXZJ3ntLV1dPa0TTsA6zhAgEjOEvqTPedlATfrwj0ReecZ0c2WEbdMdlwbnYE6wHZ8xizJM/YIWQGUkl6LXLDf2EYi1LNZ7KD7OhI5VlbQSXGUQ4yeIG9ZliUHhDW29UmWIOS5cX66rktEJnoZ4oS/W8af9cVGEL1B3nD+2V+7nzaawr2he9kDdBBnjnNFuqhNdWMfOEOWcCDiLinNULLkQpZlZ/eGzkYG7DBc/uRFqjJny3YitamxvBc9eXd3l5wPdBX7y7qcRRq7Ts1pqqvj+yKxsTiTO/AGpL9zSnKPHonpoUHOTREeZDDinv4sBZPzHTGdTzaUZ7Q2wabj2WiW1QXsG7KEc2VTXyHrOHuHw15/8O//wW9S7Dabjf70T/9U2+u1yrJQGJzaNuhf/auf6+/90X+nl91B+2Otpu7V9lJRVPrqV7/SoZ66RO33e72+vo4GP0iuVdd2OtW1hr7XicPbSzNfKA4GHXR1FaMlcnFw5XrstFJVpX7845/o7du30QjmQZmPyvLNmzd69+5dBLX7vZZlJQ1BdX3UEOLg1jDON+pDr65vtVzMVTcnFWWhtm00q2YK/TRRezar1LadYupaprqdDk2WxSGx3Xi4J0CSSxrGWqGgqprrsK8TaGzbseCx61SOitWP842GftB8MQ6hy3PVdQTCQdJquVTTtppVlU71KeUmo8CIlpxOJ3348CGBLcADjsp8Pk8dy/g5zgZAEUapLMtk7C2rRl0W/0apWIcjyzKtViv943/8j3V7e5sAJ0P2SG8i5YzPeu/18PCQUrtQ6sMw6OHhIYWqUYQAi+12m4CLTRdkvzAc1JxgtHB4uB7OmGWk7dwcjCPpNkS5LHuLEsMhsUP0UECwWCgq572WY6TC5knDBrFPrK8FM6zhbDZL0cG2bXU1RrKoKUApO+dUN01qtoHswt45E0UjjQHnFlCLsgdwBSl14MNAArjbptHc1PHYlCzWLs/zNIwWwHVzc5PSkfg9hpHIIA6JZRetUYalZU4KjCF7w55jFPs+Ng95fHxMaXYWXPR9nxo84LxD+JDOhwy8efPmjJEHvDPYdBiGlELDebXGqSxL7Xa7lP7y9PR0Bo74j7Ntnxc5tyksFM7bmjyb9sfeME8EVhiZgoGO65Upz6a6NAwz32FZcKJHEaA7HU+HBCCpYbJAG5mE7cyzXEVRJbnjzAMIaBsPcx+7RsZaN2qTWG/Auk1XwtnhPegL/kTWqeGyzgf3yMuCQ0Cx/R3/2TRnPs/zsB/sIfLBNaRp+C33yf4hw8gEoB6Ay++BLvye/2wKrE0rRf8ToUWG+r5PHcsgOfgM5wASgMgrRA/1MqRzAfK4b/aKc0dKInwLayMp6R3svHVoALSAYVKyiXwQdeGsolPn87mGENT2U5MCyDsiatvtNu0dZ4o9xS5z5lh7PovMAd6t48V6scdXV1dp/XBUbZMIzrglSbBp7BGRtctUL/YAh87qFZ6NM806U6tsHVmiTZZcI9sgReSkpMcvoy2sF3aGe0S20NlkAUCu8RzIHKCf+jSIQ6Kb3A/kEl38GHvBQGWb/no6ndS3nYp8qrtFPuP6x+Yu6BL0bVzfOK/Rpj3z2a6bomA2HTBijKm5FfI6nd2JeLLRRktqW9myBIm1kxMunWq4LBnCPvGZx8fH3zRpwEH6l//yX6osY2ciJ6++k2bzlX72r3+hP/uzX+qXX32rz5+f9fnzsz59ftTrbqfQRWNZjKBzv9+rKEuVhVdVRYD+4f17rddrffr8WV98+KDb262Wi1Kz+UyHw06//ds/UQixU9vVaqX6VCsMU+cNgHMaeqo4qgigVhaFui5uft1ENrYsMr3sXqKxzWfKs0KvrzvleSbvFRWrJB+KWN+UxTQ257zyYgw751OkY7VaJcFtu+j0bTZXY5e6oCz3Oh4Oms1nGoZesWvS1FLxdDrp/du36vtWTi6xVfWpjjVPytW24UzJTQe6UN3UCVxK0ViQ+kUraBQuc3IA5ZaZw0DgdMAa2nQ6FItNkSD1A+WJMnIu1mV98803Z/nOgDHbupNUJNKQiBIBfvb7vYbRGpK2hoGBOcGB22w2qVYJtv3u7u4sxczO9kHxeR/3vRnXGCXCmqJgSFPAyeH7qFchmkTtDGCY50XBSZNTgeLP81xd32sgLB6C8pEBBHQTvbCOHfuG03p1dZX2sBiNbzmCNRhhANDezOrpuu6snWk5Ah2UJ/dOWh1O9/X1tY7HY2KYcgPqZ1Wlz/f38qM8hDEaR+E2KVywrjiLAF7vvd68eaOnp6fE/NIWvRmjrjgSRABhqDHirDPOGOBoNpvr+ekpnWuIg1/96le6u7tLhowz0vf9OMsrntGUelLG72na2OHqm2++kXNOy8VSeRGfh1RW7qMsKx0O+zFClqvr2hQVOh1PyvJM2+21uq5NLayraqYQhpSiA7scp6ZPw1jzPM5ny/IsgZ0I7OIYAyJacZoYdXZedT1NjKc9Ni/bdW9ysMfcdjd1rqTmgqJquzdZFmenhSGomsX0ZPY0viYjH0FkN8plL+fiHuxe9mfgmfOL8SYSBvlgU3HQPUR0LPOOjrROghQZ6/1+r/1+n1pXw4JfXV2lc8i5wlm2AAVdZOuUbDG6dZovI1w2GmezAGzUEECEbbBpUBLOFN8zNTtgzXBIJlZ6csC4V0lpfThDFkjZdaagG2BmIzvc62KxSFkLRCTRX9w7qanIAyQEfw5DTLPjvOd5bmp2pq557DF/Wt2JwzIBa6e2bWLjqa5TNdqGfhjUjucBMI9zTRYBs7JIjaSuBztmHQf23Hb44zWfzVOnw6qqFIYQz2kboy1t1yY9GIaQIrFVValrO3X9lGaHLnUu1msjP9WsSvcwkR2TnvSjAyhJuSFPbA1MlB8vZvxxniIOqBTGeVM2Ukozpfv72JUWJ5S9sqRl3w8KYUrbdM6nCAypcNSmcn3Ohm2ikfks2QVJcSalj50M2UvOZRiCur6bCMTRqbWkV5nHWZHoU/AI92jbc0/P0ivP/YgNGlXVNJ8y3nP8nHXk2Kth6DWfz9LaWGe270NyYiCFOR+Qwzajwjqc9oUeQo9Y0oYzwufyPNfT85P+6h/8ld84SJvNRn/3P/s/a7vdKM8L1fVJXdtpe32tEOLhWa3Wqk+NPo1NAPqu1+PDQwo7O+d0f3+vIQy6ub5WNjIQQxgSKFsul2MHn9N4YKKS8qOQkusKo8PhhU28uro6YyAxNk0zDX2Mc4uqcdYQGx476+VZrt3rLkVUmrpNTEY0DIPqesw7LUip6OV9FNrDfq/5fKEhhPHgTylZ5LguV8vo+Hin4+kkPx5428lMigKYZ7Fdet8PyrNcbjS4T0+xnXjT0qkkS1EBW/9gO6LQAtqmSnAAPn/+nJw8HCKYUkAhKWa3t7dnRbesP8qStUZpcUABChh1DCzOGukqAJ/D4aBhNKp0dLMpZOvVSvUoDzDMthkAufF8383NTVKeAKvT6aTNZpMY58eRVQNkJSa7bZX5acI2QIKIEkoFg8ggTkA46Xy2MxRpKTbNy6bdsUZDCKrKMnV8bMYoR9f3qsoytaW1ICyC0Jh6ejweVTeN+jHthXtkv2ARqcXquthKnPbcOFmkLWDMMD44hLPZTMvlMqXbzWYzVbOZnp+eJiAxOh+vo1PFMwIyD4eDXl9fvzMPqe9jR0OMOGmj7EOe52nIIdFqSWf1QtQ/sH9t22pWzVUUpXzm9fwU01EXy4WOh6Ne969aLpYJNGMAvfd6eXlW1/Uqq9JEw/KzegMbSWUujpy0mC/Ge81Hx6FSnscW2af6pMV8kaKyfd+nobu02u26Vrvdq5qm1mw212q9knfkknfK85F5z7zcOPR6StsYW+J7W6jbqyim+VVDGGIEcmTn2SPn3MiCtvI+U9sy08mlNbV1Nqyb807OZyqKPDr93msYQiSovI9dR9007NC2m80yCDmNOrpTPg7mRqfZNCAAkq2H4J7YF8CaTXuTzgvyrYPAc9zf3ydAYoeLcl3q+i5rUVg7QI91Koic8P1W1vgZ10JvcQa4vo16WhBKem4EZ9EBb5p6bOpTjiltCx2Ph6RDAak4SThqlp2nnsWSS0SpLqNn3JdNN7IRM+swQiDhOFxdXaU0UhxZnAvOb3Sgpk5qT0+PaS5OUZQKQWeRXe+9drsXE3kvk2OMvj+ejho0RceLotAwRkYXi4XkwpkNI6obMUirEOJ143Of5JzXbBYHptM8aRjnFoYQi/2r8dx1bZeiIc45lVUZx4AMQ2phfqpjS30c2rqu1Q8RwOZZrqBINqyWq2TjhmE4Sy9lP4qiUNM2KtJg+ihvTRtrtbMxdbFtGjkfa6RP9Ul5NqZd5zGlvx9lk86ayESMqm1UFIUeHh7TeVytVskOsvdWLiC/2FdwDPaq7zvV9RSpTs2Iuu7MztoW5X3D8NQxMl2VSY8MYUgD6vu+V9u1sf27d5rP5slxy/Ooo8M4rga9isPLmbVpfci5vdfowMRIE85Myr4Ik3xZwkaSlsuFmmaKBkfZjTqdelUILAgSG0myEWGr53B6rGNko1HYTe5tOks7/ZU/+J/+pkmDJA19r6EftDu8JO92Pxa1z+dzPdaROV7OR2avcFp8+XYErFH5/PjHH0agU6vvpSG0I+u2183N9bj4UlHEaeOSdByVZN/3urm5SSx1URSJmbEdSWDGYWKpf0I5pHSRMCnwGFaMrIE0TcYuylynWuqHTmVWaD5fyfk4bKw9NslZ2+/3urq60nK5SHUVZZmfARGU+ul4SiAax4tOUwhlVL69Xl/3KQJBS89YcBpiNCoMiTG/vr5OYXfnXBx2aNgUQBaKH8XUtq22221SVhhXIka0UyZtjLXHGaNLDOB0uVym4sbPnz+nQ/n4+Ji61HEgeTbSULgu1ytGx8Cm3JA2M4wGnCgNQB3Hg30n2gFoncLgsR33ZrNJRvhquz2rdaBxQ55lsb5rVBSAgd1ul+qeMOKXaSe2hbTNgQZwsAeshc3Ll6S+6xSKIrX+7dop8sh3zWaz5IDjEIQQUgpBVVVajkQFNVucGRwjUg5xLIdhSJ9hj1DwtBAn7YAIQNM0qdvXer3Wz//1v06fYU/60Rm7vr7W4+NjYl0BuKSPYDAxuOv1OqVVck/IFvKOE/Thwwc9Pj4m40BEj3sglarvopGpfCQMqNdaLBZar9bJ0PPnfr/XmzdvNJ8vkizWrjlLk7FsLU49+1TXtfoysozH4yn9rCiik+DkkjxjzEMIZ3V9pK6Rtmrbfef5NFB4upc46oDzY1PHMMDILc7EfDY/Y5Qt2ZRlear5AASQiodRRz7zPDpFzemori8S2MEZozrFAhf2p+tifdR8FtPJkuM5TPVqlrWFVUVvdV2XGn8AMEjxAZQQ1SB6y17TARGdygBNiA0LMkkDQmfYyJY0RV9sSp+kRAjxc2lqoGHTLbFbnA/eTxomOoQ/IZ44s9EGBHVdnUgq9vJ0qs+axFgZwDmw6XNEd4jOJwdiPOO2Ff3EbvcJrOEEIXN277AnYcwQ4B5ZZ9aaM87Zl4Kapk6kjE1VLssqMeusd4yKEomdmHfsUVM36jWlF0IYZnmufBzdcTgcUoc4Sbq/v9fV1SalfFXVZO+jHB5VFF2KlFv2v8wLVSZSwXdio1gfC5RJg+L8dqcuRobCCGI1Rf5shMimo9uOr/mSqOCUwo08RrvkVRQufYfdq6qsdGrqJH+cK57hdKoVwtSh1PtpALWt38RpwhGB/ACQc3100Ha7SKmLVl/YqCdkZ1VW6v2kX9iXlJqsadgwn8dWcu6IIqGLIiESZcSm26PrJSVnhfPOn4nEHM8URIatG+J5IHyITIEPwGvonuVymWSD++Zc8R6bfp0IMePYke5obas0tWwH46Bj/m1eP3gHiYNtWxujXLIsS20fMZZ2QvTPf/5zXV9fp4Nxc3OTlC1FnrZfPcqHgwJTYw28LcbFqFFA2PdT9ywUD8oBhs9OfJaiEKzX65QiQl0Dny3LMoE/Usr4XoTn48ePKTWJyBOHrus63d7eJmYD0J5lWWpuQNGgrYexqQuwlG3bntUu5Hmub7/9VsMwpO4tMPKAHwsSYecAfkSVMM6AdnLs+X4+TxSBdYOlsc0TMNDOuQTUcVpvb28T40F0DXDz+Piou7u72C2trkVY9ttvv9Vms9H79++jEzeyiX3f6+7uLnUGw4hiFNgfFA9gAjlNbbq7TsdxT6krkaKROOz3ysdrs7YUasNswqrMxhbdyBMtt6ndQSlynjCkGC5b9G2V5vF4TO19WX+ACY4w0TXAy93dnT5//qxhGPTx40f91m/9ligm/vz583fqpPgulG2WZfr06ZPyPE/vhfH++uuvdX19rZubG+12O338+DHJZ2QMH7Rer5POeHp6SqCWPdhsNunc4uBw1kmVpEPU27dvExngnEtOgc2V5ywDrDh/tgkILdrrutZ8tkwMPMCW7oiwadwrr8+fP581AgFko1MAckQnMPK8AL2LxSIZIxziw+GQIprIq3XokUmiaOhfALmVc8AGz8H7uCeuTVMEPmcjE6RInqXdjDoasMHfAaaAMuQ3OlRTwwELbsMQUooMa2Ofh5eNFl6SCxYkoXfYC54f8I7zbZ1enBLb2rcYCQmIAtI8OQOsHalg6E3Lutr0Nc4WRBDnnp8jRzw3KTE4xOheOweJFwAawhASgD0DTFuiAyAX7yM7I2boLMpnsdOkTHLWsPvIXd9Pc+hsSs/Dw0OK9mHvWEOAOAQAQ5Bt9Gm1WiUdAVi16YtEjNgvzkfTtMqyIulEGw2z4wwsaPY+djPtNTm3EBbRcfA6nY6JGERW2G/A/3K5TI0d2Bv+BPQSdfKa5jJxLm2qnc3q4Owh05e6CYfTzlvDHvE7vsfWNXIOsuw8jZE1Y03RJfwuyzIdTydlxRQdBFwTwQTMg5twXpHju7u7dA7Qg5CJdB/EkeIzFtRbQhO550z2fZ8GCPP8nDHbotxmwiC74CPsC84twYG+78+a0oAbwQXYaYhjq4vZVxwN3me7N9rIGM9q5R+dndLyu2lOHWuGneO7UtTSnENLZLB2khJJjh61OBYZsXbtL3r94FPs/uEf/n1tt9sknLvdLnVagUEHuGEcOQgpIlAUSRisM4VzgNEC2GdZluag2CJVaWLbAMYcfOk8P9iG5mHHUfQWhAK+cMJwjCSlNr04DeT9A1IeHx8T84NRJSqz2+3SwEpShe7u7vTzn/9cP/rRj9IzYdAAfUTC+H4cHQatAmKmdIEi/SlNjAGHmQO83W716dOntE43Nzd6enpKh+Cbb75R3/f64osvUgqZNLVoxTFCqVuWkT23ufWPjzHtoaqmGQUAO2QghKDr62s9PDyk9pqkiT08Pqbhs6SrSdJ8NLhFUSTwbeWD7yGqRg4/oPn+/j4V1lZVpaZtlY3yQDQG8BpC0MowuqSDErG8u7tLChH2FwUDIFqv18mpRwHa7kkhhARG+TzG1g5ozPM4uNKmFmJ4IAYwKChYzh3yhBKkG52ksxbw0lToChhCAduoJGAIRhnmnQHBkBEoWtYDQ3d9fZ26DAK01+u1np6e0gBgmgI8PT3pzZs3CXyj9AEfNJAAYFMgjo54fHw864y0XC7VdyF1SGS/siw2WSDNA6ePz9kaDJs6Ydk2zomt1bHpDxhZAL+NFlkG/tJBw8Dh1ANsbXdFIrwWsPEdkE+QGTCJNvID2EXekCPkmmfnu3l+HC27JyHEmtBu6FPUCd0Ra6R6NWMUA/lFd0nnbXQx4HSXww4hjxaY2NoiCzrRYYA5GxGyzCvPbtNeLekjTQAfpvxSFojmQlbwnkv2ms/xYm2RB+4fPUFWBOsM+LRrwnMjL7PZIpGOFsjGMxJ0dbVOJAJg2Ubd0EWXup4mOwBNfmbZa5xISFAioIBaupVa3ICsQ/7YZ+HcAS4hJ+17sd+S03y+OMsUgXi0nQcBzUVRqB96dcPUwhvCKd5X0Ha7UQhB9/f3yvOpjXXbdur7uP40puIeOd+WxEpEQN/LO39mK5Av9hjy0jpKyIwlNqwTxF6DedARrDWfZc/LspA0ZQKwh0R0hyFmTjDPLBG33mkwpB3XtnXP2BELwK0+Y9/ACDYSbyOY3D840UZ8wAfYOs5hURQKw6C2bpIO4vzxQu/wJ3aY+2vbNhHMPAt64FJncX9gMfaDs4PjZiO52HVSdqm7s2tmCQv2hu/kZxDbnBdIgClTYequybMgb1bfcE2+l7VHb7MfdV3/2il2P3gH6f/1D/4oLSIbhJBikJnbAqiktkCausFZZQdQYK4OQgS4YE4KhgC2Y7lc6vb2Vn/6p3+qDx8+JCV5PB5T3qntVMLm8jscISJUMM4oSRsVsykSKDkK4XC2drtYt2Tn1tgwJtciSsAwV5vDTb0B3ZZgWWGlAaSPj48JtMKaYags0MLwSEoHHOBjDTP7VxSxFTeHAFAI8McQcJi5NxwGlBdsIbN1Xl5eEpsGWIMt8d6nrnYcWK7djg5LNh5IQBed35wx4IBcWCbrKNoUMGpEPnz4kPaHORKLxUK78Xmsg0+kZ+im7kU2MkikBOUnTTUuRNNCiMNHaUxweXZgjvI8Tx3Q2G/LjLJ3KEsAJHuVZVkaTHoZebQsv6TEusNKETmq6zpFslgLIhqsJcYIcIZRwIm3gI10sP1+f+ZYIfO830ZqWXfrbOz3e202m7Q3RGg4QxhFa1xsuoBlC2FQD/vTmRFumial8aKv0C0W4BOVYi1xZtBbnBH+A5ySHkx9HnvPumD0cIos8SFNDgP7hcyw9jaaBiMP0CR6hr7FyLMmgC+ua/Uzsm4NKHvDPrFW2AZ0vvNe8tMe8Nm+7xX6QYNJs7pkJTH2AA3SGnmx7jDF9rzb6CxOJ2tCpIA1ts/NfvAslunluugo9C2OF9fBWQPMor/s2WUtbMQMQobrcg6JtLPX3CeOi03XwemmQUlcp2k0A/eKk9w0Uz2pBV3YCn5ndUlRFElPYTttdIPoZQghpT5jc7GhdCblvpEXvpdzyb5bAnK326UIxZQueEqyyJlrmlaz2TzZdmTGOvmr1SrpatpVd2GagwdxE1O2lqKpyGU3uqqaKc8nObDE0mXUDMLWe6+h7VKapsVA7CO6hzN4mSYKoWGjOqwJZ9iCYGy/Td+MhNEiNQ2wWC3uZam2nc6Ajdq0fZfqY3lmzi3/4eDZ1FwbFbe2ivUD33B/1iGy6W4W8zCew+IVdER7qhMpCxnAvdoukDg89pxz7+ApvpO9teQIZ4515L3IgE175P7Q+ZDRlkjkGjgq6CUcIpx+dAsYyJ4nGzXiXtl75JK9sNlWnBWrv2wQomka/U/+2v/4NzVIktKBIS+b0BsbsFgsEvC3oAPnhsOKoWSjL5k76+Gu1+vkFaMQpQjumB/SdV1ih0nxohnBZrP5TtoXdQ8AIsuGAOwZwEYdDwrfzioBEFtPG4AJeOi6LoEIBBNFDHjDyLBOi8XizMDC2mI4UDg2rMqMA9YRAGtZYtt5bmo6EZLxoMgeZoBBqzbVyTI7Nm2De9lsNunAsra///u/r7KqUgt0aZp9hEGFoaBIn33NiyJ2rtPkYCMfnz9+TBEWwDqGQlICD3Vdp/uyDjeAAWZtt9upN2wt94bc9OMe3d/fK4Sgu7u75BAxeJPUKqKhDJRFKeMgLxaLM5bdhssxqgwvBAixxrPZTJvNRl9//fV3IoNcd7fbqa7r5KwjI0RRaVmNESW9BuYL5Y7zZeWOM4wxBPyQooSMQEa0bZsifDwj6SkYPL6f1BRpSodDmfOczrn0PTbCQo2dlSnmUElK542aJ5wXoskYJSJPpOFYsGOJFGqjJKWzAKCwDCEOC04IRtbu7cTSnhssjDu6I6Xl+CmfnOeTpi5tFgAi6xjYpmnOpsqjuwAtrKd1lCw7bckKwKutCQLsAvKyLNNsMT/7fAJIxvGykQIAHkacdCGiMZfRAO6P9EnuA8Bio7XIITbJssrSlJpyCWCsgwWgtQCVyB3ZELZwXJoiXcgK6TvWObSOCLodPYC+Rr6IhqPfcdQsM841m+aoqpqdRTXt+eM8WjBmIy3YDe4X3YpzQLoScmxTC6kFJTLrfayXIKqLg8h+cuZXq5Wen59T5BXnmHPC+edeaCIzpfHfqm0nuy5Ns2eI4pGRgE0vy1LOOw2jU8KecsaojUaX2P1AL6GTLNFh5YuobqrvGX9n07cB7XzOPi8ZJLy4rh0hQDSONeUs4JjaIn7ksO+HFC3DuWZ/uS+cD/ajaRr5LFPbnc9ewsbhCCKb2D/0A+fcrpF1+KwTir2uqiqlV0OCIMvoHJvlwrOyVmAlsizYJ0sUWWcuhJD0JcQYUXtky5IdNjqDPQeTcQ0b3eZs8d32ZXUQesjWT3INbKi9H9bVkgzWmbS2DJ2NnQczXzrT7Dt7TUbMr/P6wUeQ/uEf/v0U/QCMPz09JeHabDZnhatd1+nz58+p1SueOeARZUqaHsw1h5uDYQEPTDhhfxTGYrFICtU5l8Lf1iGwuZi0Y73Md+fQXAL5S9a6KAotl8uUm348nbRcLOKAzO68EJDneXl50du3b7XbvSZHrO+nsHBsVzp1RIoAKJP3EzPB7JPNZnPGxmIgMUKkQ/A7mjt0XZcGwkpKwIbwPUYRIxiNYmwrHLvoxQP4+PiYnD6rDFlfFCsNAFgrlB+gzRbP4iQS9XMuzueZzedaLZfRcI7Rqqqq9DrWCQGubb0KssXf7T5YxUCzgrIs1Q+Dur6Xdy4O7TWtr51z0jCcKRquRx0dbbWZ32M72VgnkudEyXI2bBSBfH8ANrVSWRZTTiEM2EOiBzh8FlCQxkjrbtg1vhc2DacWJxSDj1KmfoPnYN0toLp8Vv5O8SfOmzSxZOgCIsMo76ZuYpt8dz4XBZBKkbU0KfhhGNLZpv6L+glqwLyfOvu87va6vr4+Mx6kM1rgb51ZABwdDVlj7+NsIggNZAQAgwEk6spZQ9atUbdpXPyMZ5Qm8GzBGddHxvgM0U6bfiVN9YSWwSXiB/iwzHc01k50ygthAkR2fawhd87JZV7eZ6qbOrUqds5p6Ac1da0yRXmmltRRB53PM+F31onEpgBEyCLguXDuWX90/aUjirwjB5bF5n2AGc61XUMbbecc8G/W1UbVuG90AedlAoSxFTf337adZBoHcFbZF2o9sJXsBWC474OyzItxF6xXCDR/6BJJcC4fsQNbCBqL6adoH3LIGqHDbPSdyCJRZAilSz3C9yHjrCH7aVN/bep8xA+rMcLXJnl/edmNJGJxth9EhskacD4OhEfWT6eTjqejytFZkZTqYOL9NAn45nmUgf3+oNjRsVDfn7P6zsUh8/3Qy0nyWab6VCvPM4VxHTJN6ZTgJ/YIEhGnab1ep1RtnAP0I3IA4ZLAsIK8izqaLpJcL8uyOP9u1DdB47nop1lDck55Fttty0lOI1mY5+r6TqdTraKa2s8jC9hazgqZLuhvbAMgHD2Es2Cjac/PzyqK4qwVOp+D/MEe83P0LPqorRvl2dRUxnuvru+T/snyXNWo6192O3kfm+aAUQ+Hg+aL2FIdXQIWBHshG5wN60jzGTATssxzW0KI88Uasq84jtTPW52I7UWH2SgR5DK29jISBNFgZReHGlvDs1iSeve601//m3/tNyl2m81Gf/T3/lB5nqfIjQUPbDyC771PwypRMLYIkQN6KVwYN4QMJwmlD+C2rBUGyNYeWKAEULEvmDTLjDFQEyCHEHD4KBz/9ttvdXNzI/lpirVNRanKUi+jo/by8qKrq6sklMMQ5PNCx+NB3mfpmvv9Xsv5QkWeq+1a03aT7mFxrgFzgnDybMoCzMHpdDpLr+MaGE3qITh4HFieJc9zvTy/JkWNsn58fJSclGVTqgWFjABfGBbS6V5fX9M+W2aDNBYLDuhs1jTN6Dz2qTj3yx/9SA/395KiIcUBKsvYOOMymoWyBiTa9uEwUawPawPQRFEAypEly/DwXqJ9u90uGSlJiQ20dS0ALyKb7DuNDLhHCvUtm+WcSwQC6VmAU+SYSAj3zfOjlmwKDiAeJ9EyezyXVbb2Wpbh5j4A9TaygaNoa2wu0ytxRhjyKjl5l6lpG/Xd1Dp4sVik6ECU0UxVNbHn1Gyx37vdTnJxlghEzjAM2mw26UyyLgBDnqtt27O0Rp4dI2R1Dc7NZW0OwABZQs9ko+NtDRS6iMgE+goZJkIvTYaQM8+eIvPoXpvKYvUr+4ehZb+sscS4AnKQg2iAs3QP/KzrSNk9b9XNfbRtdKhspASZsNEwztnEmGeKIxQm0GCjGJfrYokdngGZxn7wTPzd2hbW3DLmyIRNpWKd+D50r2WC+ZytSYU8svvIOiBzEZDEuXtE5Kldjc87pYdy37DhdR07jUJcYSPRBaRncqYmomp6H/bI6kQLWom6xlbVfbIdzIEjywQnGxuDXuEsw77bWj2ug8yzb0RXrNMKCRSdoClyyXoga5wDuz9N02gIg8L4nvl8nhrMcG6RC9tRMerBIOqMpKlJSD8Sa5nzZ9HPHqLCOMmcUXQN9hmiaiIjlCL+pPOv1+vvOMecZc5VWVVqxojObDZLQ+ibseERZwPyhgjffr9P98P6kUJtnV50P3Jma1QuiQRkDF3BGeN7yJTBNtp/c39gHJxqzhe4C73PfiDnrA3XGoZBfdfLZ9OAZDAAoN/aOeTAEn3Z6FRJcb6ml0spq5aks/fJ2nHW0RmWFOOc8ayLxSLVuludw382Ui9NJDkyhVPKXnL+bbQJktjaNWvrsa3IJ3bH1sbXda2/+bf/xm9S7CQlMHN7eytpyuVls2zY3XufDrM0FYDVdZ2iFTAdOCc2tYMZRxgUroG3bQcQooSpdbGd3phsjZLHaUNJ8XPS0XgODjBCTciVNLqiLJWNUS6iJ9Qf7UflDRiUlDoonepa7X4qnic15ObmRpn3cmM/+9lqlgRSkj59elSWx4NDkaR15mCiYVyur6/lvU/d6ihG5tmYKcNzMYCzqir1Xa+6nlqI0qGpLMu4xouY1vbVV1+dpRZgyCTp4eEhOWGswW63S6Hx5XKph4eHM8N7c3OTDi+dlLIsdsirx9xiSWfpNkQjaUXO819dXSWWjX3koANYberm27dvdTwe05BH3g8jjyOCg8p6EJnj323bpvu0ha6AZ5xJurrBiKGAQggpOohzyLVYZ0AKQJvvhWXj/mwuO8DcAl6MMsoVIwIo4CxedoMkN9xGLi3gZo+GYUiGh4YHXdedtXMlCoYxf3neabuNshvKkAq8AYVFUSS2nJ9zZnHIaDpBsav3PnXaCyGktEPAHXUQ6ARrLEjZYR9sap5NQ6RVLcaQ9ePZuCbXZf1xYmhiwppj5HE8LZgHCHK//B1ZtY6RNcDSlDtvnQGbYmZ1unX4+LPvp0Jh0oDis8ZInnXCMLJFUZ5dA6DDOcRBs1GrKFPTrJzvA5U2MmDX2b7Hgj1+zhrbtFHAGGDPgh2eB33B9SyDy9oCUpApy+ZyRrCTklKNqz1P2Lq+7/Xw8JBkOMrWeQoe94+c3NzcpOdDH9JB1DZkwQZ2Xa+imIZf2toW1pi1CSEk4iRGWON3o69wSHhOG41C1gCS7B26hu/iczR1opssQJ10MPRmvI/oUFrH8TKaxV6zv0VRaHBTfe2HDx+UZVmqm7WkhY0IWABpzyJA2ucTUch3837kCWcWmSeDg9+zflbOLKFr7S3XY32Px2PsKpdnmo+kjcazU5pUMEvi0ADLEqK8D2KQ5yHChU2HWLXpcBBfyDx1QawlpBE6yeIsu868F70H6Yj8c49gJO7T6lHsVtJpmnQlNhWbZ+0018W5tvpnOZ6RIi/kDVFD6iFrANlssRp7zM9t1oi1Z0Tb0CHoF1vjZ53CPJ9m/dnzRJkHOsiSRqwXa8NekilidSpn0Tph2Klf9/WDd5Dm87nevHmjh4eHM8EBRC+Xy5SzexkytYYTpbhYLFKEAQbLhtO5/nK51OPjYzqYfCcCYw0hXWcQNISDv69WqzNmYhqkF9mP5+fnZKyIPtkiTg5q17YqRi8c56ht25RqdRqdwbdv36a0muVyqbKqVJSzpGxgCLIsU5Hn0jANyMOIEXGoZlUq0GRoIeFWFCeRL/YEVgiw9fr6mupVUHK//OUvJSmlqDVNo/3rUdvtNtWtsE6L5UIh9PrZz36mzWaT2HvLgM7nc33zzTdnoALl8PnzZ93d3enp6enMUEpTvrCtBSAqY9OErNNA97GyLFPXM5xBmC8b2QCkSUppfVmW6ePHjwk0ooTevXuXGFXALVEdnH3SPVH22+02yRr/cU3Lqn78+PGMWcNQOudSMwMr67DEKEOYT+qu2F/WzSp/lD5F0eSZWzAKuLNglX8jVxNjm6eaIwwCKTu0nscY8uwAcUDI8XhM9YGsbV3XyvJzRw5jB2NJ3jtyZdMWKWSHxcaZRffQaAJjbgd0WjAC4OG+IXUwkPv9Puk1QLVlHK3zYlPA+LwFWfb3OIlWh7L+AAZpSmWyYAH9x14gI+g/5InnsmCE5+W+L6Mnkwz1cm5Kg8JY8kxZNoFFnj0WomcKQWkveQ72PLGzJqo2gZtpbohldrm+ZWpt9Ie0EMAXoARgTYE8QMACZ1LzbC2tBR2sDesIEcH6Qtjg5Nh6LUlne2vJCByTmA7epPtEduLzT90muSeeGQfCyoONfsPwWqBWFLkOh6MYeMna2jQvCEnItfgsE0DlWSCbIMCwwaw917ekmD0DRDGen58lKaUYQ6jYznpZliU7s1yukx7F2bUjNgDuOOnJiRqHq0rRUYVs416tDmPPeF7W3erZpm7kNUVJeL8948gq32v1U9/3qSW3jbxZecYGWb1rz9N2u43vc1O6p3WkOQvcn32uy/P83fM/7Z9Nu7JAHXsH5sL+WqIQWYYgs2TUpS0jVd1mB1giC+zm/ZR6b5/PkjycQewyz4l8WNnnrPNZqy95dV2nzE8ZE9h38Ks9n9ZZ5rxYR4m14T5sBMc64FZ38czIIEQ+eJKsBtYEmwOW43kZQ0IKIw67xdtkaFw6qDzTr/P6wTtIx5HNtaye9VwxMrDRlr0DUF6mQpDGZoEpB9d+5vPnz6n2IuYYTylk3Mt6vU5K2hoxGBjpnKHBs7+7u0uAnvQl0pSIKs3n81RwD9CsRwb19vY2fTfKcTmf6/3798mYvXv3TpJU9oOCm4Yz0gzh8+fP+uLDBzWnOgE56oSiI1clYEukiMN6fX2donus2dPT0xmw5RBjgPu+H41LNJ7r9ToNwfz06ZM2m9jNB6cJYHW1vtKpPqT5NdI5W0toeLvdJqcAw1PXte7u7s72mSgfjhkOH84Oe4bDulwudXNzkyITXRebGcCwkZJiWRZAEUoTQ8Nn+LeVKZx1wD7f9/r6mpqAkA6E3Ly+vqaUK0CWjQDgVCAzPL+NjEpK0S0YzM1mk86XZUitwsLQYWxsOiPPiTPCmlCfMwzDWQtTHD/WAWBk62IwTDgjEB2wVgATIqWcOZ6PBhGAGqKbznm1zVSsb8E9ZxlnEofaFrYSmfk+Q26bZPC9GB4L3KwjzXNa48Z92ZlrNg8ex5915sV9op8uI2A4etYgs3bfZ2AvU2AlJXlFpuzZZF/5tzX2nGNADt/NmkbDPFOWTVEU1oJaFucmYH4JSLzPkm1ArtApfBefmwiVKZXPpguz/9yndaosA8x7OTvoQeTBMtiQPOhVgARZDMgIupNr4NDaTqNEdLk2e875silSnHUGj8bnnGabcB45886FRBhZ8AbgQh4A2IAs66BwFqIjcVKsJ5ucGV7IJDbHAqO+71K7a2wipIU9o977s/oYzieOAvWIPOtl5MkCPPQQ8v7w8DCuRaVYv9ucOTaSUtdIzir3JDcNgCXlF4CI/iaVDx1hdTM6Ftkoy1LzaiZn7huZQ69AGFiSzqZPsQZkXnD22FPea89130/dG0nn7vuxjjb3KfImxawOiGgbCZj2dHIYWWd0qXReD2nPa57nyc5jpyFqIaGRC/YRGbZ4D53Hutj0WNad72d9OWPcDwQauBL9z+eQPXRTnufJjmMvL9PT7LU5u3meq5xVGrqpgQ66Gx1ARI/v5jm5ttX71q5YPcm6c26xg+g6W6aCXba1mHzW6mXI1uVymRpOWTxaVVVqjkb9LnJkhxxf2o+/6PWDr0H6L//uf57m/qB0Ufrr9TotZNu2qVUuwmhTtWB6Cf0jkJKSQcCgAGh4D4IGG0jUh7Ag77N56GwLrLJVLnmeJ+8Yo2lTERBKFP4vfvEL/fSnP40hccMEA6hh6P14DxxilFrTtgry2m63+pM/+RNtNpup85ZzausmMUlWsWaZV92c0n2lSNboeGB4OBCkIhAS5+Aw34colAVzOJTz+Vyn4wRGGY748PAQQYAPZ46qBamkzm2322S0F4tFYmNPp5Pev3+vl5eXVEQPwL65uUm56KQI4qjyXcgC4MU2l7DAEkAF6MQAYgRsaiXKmL0m3UGaUr+Q9f1+nxynzWaTngvlz/wc5mRZw2/ZfF44Qva+UOrcLwqJzwHuLSmB8uV5McYobQvyLMAE1KHMUXp0U8RAXDL2lt1CfjjnGAFkiz/ZFwuKeZ7pnDr13XD2rJx5wI0lQFhDew+smY04sMbsqa0psY6HZe/Qbay9ZUYB2rYJALoAo4WuYfilBT2AQD6LnAAgbNqbNZDsn2UdbRTQsnrcr3VwbSqn1bF2wCGAL6UimWhV308MPmcuRjxOyjL/nfMV5T2kCIW9JrJmo102BSnPM+X51Kad9bVMap7nqWEPa4wDfMnkEpmzzykppW3yXuuoUtQPILZMLbU0NrKJ/Nm9enx81Gw2SxEc9hfZsnIc9VA+Rt3C2VmOzx9rsziLOObYIGTFgkF0DkQIenSq98hTTQJ6k/ljdBS1Mh733+v6epvOE+3j+R6YeWTYgsNhGFKkWVIibrhf9KbV5zZqY9ctkjCLtK6w5tS/WEiGQ7ZareS8V1bkyXbYyIqNitjvZ3/s9XieEEJsABCmejfkEKcNzISMWznAgSU6wD3wndghm9LE90tKTahYd2UTaLeygMyg1/m7xXLsE7JsdaG1H/xpm1qhG61DBwFAhIW1thE/9IG9Z3Q+Oo21tNkW3KfFesgG63t+tiYcyfu4F65jiUWrl2xKmnNORZ6ryKZzzJ+sETrw8r7s2ly6DDwX98p7OUfYMj5rv5P15wzZPUNX2z2Xpmg00Uj0icUfl5Fp6/wPYdBf/YO/8psaJGnq0W/DoDYkDWvFvCKMMYbFKlrvfaqlQTEDNjB8lonjMwitVZYYD4rbbe0BihbwB3Nmc74B+ABmyxJaxgcwTiTgNAqqTfE5nU7q2laDATAcwPl8rrpplI0G7osvvkj5yt57aQiJkfTe6/PnzwkIF0WhvJjuF+EHLHHPOHqkGaCUpSm86r1PwzlhHzjUtGlnTVFqpPx57zWbl0kx2rAsbMmXX36ZgCFMKN3kyInHmMLioUQZsMdzsZ8YCfaBWjBau9JVj8NPh0Lk1Crr2WyW2k7bttHS1BocxwNAjzNqAZYF0LBWb968SQYFh8s66RbwHo9HXV1dnQETlKJtwmBrr+w92fQKDBw1XhgcFB/7j/KDTODeMIw4IXZmFmfAMuisqWWwbYoOCpX6LNbi6enpLLpjWcO4v5mGfmLWkW2Al03HYP35Powd77F57RYkYjSs88hZQE7tmvA+GxVnj6wM44TYSBfRMuoqpPOmIN/nCJEayHvtnxa8IzNWp+Jw2+6F6OXL6BhrhJMDM2mjK8jLpG+naCW6PerWVs5NYIgzFRnSXmVZpXVHzm3004JH59wYuZ4yFrgvnsdGNjlb/B0Hj7PJutjoGXaD9cIRsk54lmUpFRnnFt1MB0ML+qWJzbZONBEBWyMJAEMm2dsoS1PzDs7ppR63Og09JynNCey6LkW50R2SEilmGevYRCNPOqNpGj0+PiZ9jf6zYJEIDnKDzDG+Ah1BbSg2Cd1kgSlpQZw3Osdyf1ZWrINNxEIi/XNKacKe40xzdm2kIvipDs1GCWzExmYUkM7MC8fCRnM1TOmq1CaTKsgaWT1siQ50FLJn9Y/VCeh19lA6x2b8G3uNQ2JJRBxD6wTw3Dh3yDj7ZnU+th5yin2idhJQb+8NJwMChfeBASEabIqbdWpsRI/75r6sg4CDgX20JAINiWzWwSX5znpZW8EzctazLFORFxpMpoHVAzaKb9fO2iK7dqwDZ9A6ZLa2K4RYRw+BDKa2a231KzqGKCn3auUWGboky9kjrodesfcymIYlf9HrB+8gEYWY8pBJs/Da7V5VloWcJiVE6L4oyhEMTAaurhtlWWQXF4ulpMgyOjl551XMzttzxsM0pEhN0zQqi1Kr9WqMwLR6++atstGxcs5pVs1UlIWGfhz22XYaQpCTlOURiPX9oOPhqCzLdToeNYQQu9DtXlSVY8eylKYQD9Jhf1CW53p5jm2mszxT13Yqi1Jt10pBOh4OWiyWattpQN3+da+6aeR8p6Hvtdvt1Ladrsdoy9V6rdPhmACwVayPj4+6ulprCIOqaqZmNHgMwQP80MLYGtSYtlTr9XWv+Xym/f6gEIbxcE2zUsqyUua9juGk0/GgOPguT2zi/f29iiJGkPi+t2/fqh1TDWkjnmXZWaQo1ifFMPZ6vRplaZxbs1zJyY3pIEFN02q1Wuqbr79Jhr/rOm2326TgcG45tDDAvMqyTAad0LBViNSzYYBI/cBBjmvm0nMVRZHSSCzzjhJHwcZ1nea/DP2g192rZnO62GXyfpBzsRtSHBgrrZYrtV2nYZjan4agM0XmXFReVRWN3nK5kkLQ8XRUnjG410uaiuOpZZDOW4HaSJaNlnDGQ1BSrlVZaQiDsix2rpKkoR9UlFM0ou+H2HXRxWJ9771eXnbyzo9RvlqzqtI+HNQ0rfJ8ylm3jmbX9SqL7KzeDEaU75ImlswCfQCuBV4W9FnHBtKBSA6/g5TBOcNI2sYz6C8MmDSl3eCEAEwAmxg5yxzigFtSwzKaNmJnjSXrACjlniWlCAlyfTweU0t36toAZTwvoI99QGfYRjbTi4LeKZoWAd8w6p4pNcxG3TC6NiXaMpJxDf24vkT3OznnE6iczeajIxjOrsX6WwaYLIeqqrTb7dIz4awj9zbjwIJke01LSLVtBC7RAfRaLheKHc2mgmbkBeeRFtd8n12XrutShIbGMFEuQiIFrHMUdVKnLPPJHsZzWo16J9fd3RudTscUEez7uDf7/UGrVWxsRLOS6LgWCmFysgG+s9kspQ2SWgORQ0ZDlNdJ58xmcx2PJxVFrq6bBm5a3Rt1aIyERfkfdDyeFNuMd2NX0ELO4axOzT3QTXXdaBh6xbba8RoQWJZYIL3L6gSbjtT0nQrOw3h/RRExDE4CssE55nzZszg5A07OjUPCu079MMhn542giqI4m/0VQebYPEBOWT6y/d5pGIKKstRhjJCiHzibfR87skkuDmPWCObzXEWenRFkvP8yA4B7sxkHnAX0F1EGoqzobZzjy7OI3FoCmzRHzglkEntDRpHVv1ZXouOjDZz0MfqEzA1LwnN2rc1G5/CclhCykSSrczl/2KmpVrmOe5Z5KUx26VTXMRvIRMrsf54shTCo70ZiRZLz0/y8fjBdLrNp9AqDZG3UzEaFwJrYBfbV1nDZKKD9DPLB87OGVjci69iPupkiqn/R6wfvINEFBWGJ/2Xq2l5hkJyi4r5aj93l+qCymOl4OE1h7F4q8iIxU0Pfa/cSD0afT4zJw/2D8jyySWUxS7VBx+NRVSnd3rxJh7jvexX5GG4dgrp2GIFlr+OxPjOKCLk0TQ2ez5dq6lZVNR+NVK/V8iqxDZnPlWdS3w86HWMDg8Vioaqo0qEIfVBbT45jnpUqi0qnYx0dmqZRkFOeFSllbTGLnfeO+4Ny53V4jX37bZ95cqeDgrKsUO4k7zOVxchaN52cvOrTNOQzsgbdGWvgvZeTl1Om1XKd9pR5EPWpVb4sVdetyqJSVc5SxI1DI0nz+UKn+vCd1q2XxtCG3odh0GI+135/VFNHllRBKvJKRRFnKhFhqKpKXTtou71JjgEpiNIEqmGdbEpCvL95+j2KDZBi09dsyhvvtaF1QCsGls5plk22zFbfBd3eRAexqZuz1BwFr74LijNHphaaXTsB4aTYsil32zmvpqY1aK/5fKG+66XMqVNUZkVeJeNhmfHMezV1J+8yhYEIk5NzRaoJAPBEGcnUtWNjCPlR6Xs1JtKk4HU8jNGu4PX6GhuGxBkf7iyCEoFMq+VyrTC4qCeaRkVeJkOVZ2MUuo2M/2I+1ZlIU4oYbDBMp43GSROAIZoIQQPAsalgGD6UPvrMRqa49iVriExYNhTZuXSquF/2+pLtxIGdnKFBeV6cybIUHYbIkJ++915tPYeklB7Ketg6C4BmqlMYI46WJYWxtesMAGKOFj5TWeYJtJblefoeBApnuCyrZPytzsjzLF0vkjZ0oWvkfa6uG+R9TDkrS58cMSJoNoobwYgfn8GpaTplWaGuG9J6xtbhU5t2W5uKfbBRgri/QXle6HSqEzlYlqWen3fjfecj+MHBomlBnF2ErGA30T9tGzteAoRwbLNsqlGglsBGGmHqY2S3V9sexnVdqG1r1XWrrhvkXJ/s42q1Hgmjk0JwyrJc8/kiMcxEhz99+pR0OM5ECFONYd/HZg9N045y58X8n1g7VSjPC+12j6NDMGVgANSYV1RVM0lORVGeEQRR/rvxWrmyrDfAbDp/EZxPHdzatk3NeWznSRtJsOfQDUHd6JC3XZP2ta5rDZqiEjaSxZm2wN4C9mEYdGzq6VlGomJQUFkWKv1MQxhSOj5njLONTh6GQYWPXW2J5KA3cNKyshgdFtIxx654s2mYN9e2ZAf6NJ6zKUNAmmZREXkLYWrZbvXpMAwpmkodDU4N948dI43e6hnOnJ01CPlEmiT3Y51JIhq2gQajZMg0ITrGvRBRZi3400bs2Eue3UazeZ91QoiUBQUJeQiDBicVVanmVKcsIp4BW1FUpfowKHe5+mHUiZCEfS8NvYZxxlzw475lXn3bpQj0JXaZghUuOdPgHeyWxRqWCLKNRmwqvo0i8R02W+Lf9vWDr0H6b/7oH6YGAjaUOKumblHSNB8FQbMsJBtKiBTBZJNQZhgpy+xStI+iQrBRwBxcnIxhmAZsofgAWaQeWNBsczM5jJcgre/71DWO+0NhUcQvTWHpCexoZPJiTc12u00KgkPJVG+6ZKF8yC9G6dH9js8BkHlGpl3bnGaYTYwu6Snb7Tbl8ONc2JxgjDmHrGkaHU97kfplU4e4J+41Ro4WKSLS1G1q4gCrhBG2qT6z2UwPDw9jHcdcp/p4Fi0AzFgnkr0npQH2hiJrolt5nqd28DRvsAaQZ4Rdt4whRgzlzPsPh6PaZmqv/Pz8nJxgq7hsGgIyaiMjyLCNGqCY7HA3W7xqO/dwJjlDfA9GpWlPSc6Q+8TCuWhM+QzRh8qQIn0/1aOROmMnx3O+OPMAFZx0WwRvjQZOB+sA0MFoY4zsubcRFgtc+A6AM8qc82z3krXlvLOmNpp2meLGmeQ+rV6zf0cvsCf8HL3Da0ohiTOgrOPFvUkTm8f3ELHiXPB7zp+t87KRSNIoYWXRT+yDTavAQLK2XM/qWoym1Z1FUaSZTlFv5BqG719fapeQAZsGIrkUEQfos67R8ZjSqrh2lOvhjF21ut25kBoOkGJnibMp8tInPRafqUryxWdookLnSvaX33VdOw5ZnRhYdMrlPrDGcUbXRrFuyyWSin2hnoX0dEkp6k2dJufn5eUlyTx1PpdZGfbMQJrwzKwL+27r5WyWgmXJL51xy8Cjoy7Bs01351o2ZYrXpX222IF7tmnQOMDoHlsQP2VXTNGlJCPepfpiUutIzQYPWFLQngsblbC1Pugi/s7zWPCOs2KBqTMpe3wveKAdzpsNcG+WTGINk1NldKXNwJCU6jQtCWOjnZASds1YS54FGSTyaGcD2sgba0cqWrT1i7Ru6B0GuWO/+d66rlMEGPmFcLLn2T6vJaf4vW20gWxfyr+1bZwB5NeuIcRqVVXqmlbhghjhmn0YkkOEnNqMAn6OTCQb1/Vpn2zWjLULRJnAcJxBuxbeyDYv5JNnsSQl68S6YE/Zn7/xt/7d39Qg8bKKEMeDAw/zZL1VhNgCUDZDmphaDhRAHIDN32kLTapKnud6fn5OyhFgTWoawmzBHcYZAG1TZCxg431449wzCgFFae/79fU1GUjui3xsFLMk3dzcqKoqPT09nYXA6ViHcoAJpmNblmVprhSsomWhL/+EhWEN7Z7YuhWbH86BsozDJWjI81zr9ToBIArQYSps7YwF4V3b6Xis07rirGDo7CBQZKLrOtVNndaI99u6A+QBuczzaR4Uh/v29lbPz89JmeEo2hA+azIMQ8qBt/nnU5rNedtlAJ09F4Bgq4j5HkLdvGC5bNE+94lssgc2cguwYr1RzkVRpDoknETusR+m2o1LpTqEiT1iT2wdBwb26ekprQ/OGvduUxXYGxuJ4P0YXuug0BiF9DR7zjFaNi0SGcIJ5vrsCfLNXnVdl/LSLUuKgUIe7F5asMOaYETQSxZo8cwA32EY9Pj4mIZfSjojUNAL8Xm85vNp8CLt5eM9TqbFPjt1ItwD90UTHNaKNDXu0+plnsvui2UK+Qy6zjK6gEIAmM1ph5iKr+8WPSMv8WdTfYkFnUVxnvKDYY8ydt5gBNAUaxmW33u+48+mQcXW8WN4sE2LnO5Pen3dp/sAEC6Xy0Rm5fk4SFtTxC3W8U3PatdWmrrEIfPOuZGA9BqGNj0X9oy1g3hADohEDMOQ5s2EEBsnsT840+gbC/pYJ2sbqdO0RIJNu2E/kRPW0epdzquVE9aBs0W3UrvenGX0mT2TfJb3ACZZG4gUdIO1sXZv8nzq6kctKj/zLpMfI0PW/tsBz5eyam3TJQi/dI6sfeTMWdKIc1PXdRxEatK70JWsl20aASmFTmQkCPoZwsam1dkoE/oQEtBGWHifBeysge06yIt1A4eQSnrpTGOzIeWsjPb9+aDj/X6f9DUOFc6RjeYjo9ZOXhIzllgm84Da5nMdNM3a5OxbZ549Yc2TUzeSILaeKWUwdJNjj71g7y1hg35Ldmj8NzJOhg77ZAlz/m4JenuGsefYTrtG7Kl1Ntkv6gUtRv51Xz94B8kqHcvYeKezjUTIEQIKDC37ySGFkc2yTLvdLoF5GAsiHTYahSAzFNYOo7RhRDqJWRbdhoKl8zaseOUASoQaxc5zoVgsIy1N4WCb90x6BEzZ4XBI0ZqyLFP0huJ+DNLd3d3ZNblX730qJLftTBFYjL+NYDk3NZiQploySWf7QjErBpVmFJZpdM5psZzWhPdzsAEPpO5gIOfzhebz/jspbjb6QDMC6wBFR/k8RYl1sF24UIY8n3Oxda+de3IZMbAFurbLnTUOVklYhUVOdezC6OWrafCxFNORlsvl2QBblKh1vCyTxn3BmtlQN/vMiygJ68m94SxxDcCeNLHAAHjus8gL5flk/GCRkEWMMwBhv99LUkplgDHm7zaSwV5Yp4P1JD0lhJAaffAcvPg3+oZ9h+3EaNloGYQC64nTAPi1oMs6XDj43Bt6h++7dEw5kzatwzq3wzCMdXtF0k+AEAgOoi+LxVQzhx7KsizJlxvnAS0Wi8TQXk5ZZ19ZF+SDtaUQmiHR7DMAmxRXHEHWwYIzQA3OIetm9/nyHoYhJLBgGfx4LaUuooBZvpt6Iz7H+sZ1jPKKg47sRPJjAhkW/LNfgEt7/gDJgGnkn3pX7MJqtUrd3Yqi0GazSefv6uoqRaWIwHo/kQsAWNYR5xV55RlxWLkv7plUYWQQPQgw5GfoBus4W3kHtCGjnEmcWhsxQI+hRyEa6D6GfrHfYZ1Z9hvbwPeR/obTgJNzGZ2xEUyAM7YOObXOlT3H1unApqM7QwhJ7tAJOH11M2WasK7IEPdi9THXHYZpKDp6ypJhdg3Q05AFOLxgjmRzTxOxcWYDvVdnahdZc/AS8miJUTvbhvTXophSrtH3/GftqSU4rLMQQkjkLtkT3JPVn5xdG91HnyH31nFBN/Z9n+ooScUD0+FgQEpydu7v79P4EvaCNeG8rddrFWP6I3iJNbBRdGTSRtW5JnV54BnqnUOIzQv6oJTlgk6M69LpYJqQcJ5tDZglGqYzPNUosTdckzXj3oZhyp7CBvI8VudZos5mG5CBZZth2PWwjtmv+/rBp9j90d/7w7NQLc5SVU7TsJ+fn3Vzc3PGpFhnxHrtWRaHRgKSURgIAR65NOXNEgG4ZBY5WCg6hI4DxqA7rg2wtK0z7WDWPM/PGBgUGQcbY0ykwbJaMHD7/T6lQuBccFBRAESfSG9jMNrl+tk0Bdbr8gDxfThHKCPLimBwyGsehuFsrhIGD0OPk4uB9t6rrKbuaRhRyzJzP9LUxto5r74bzhQPDQkAcdYRoDA0y71CmFLquGcMIal6NhXARpRsu0vk5zL1AeVtr33pTCGf7L+kCdT4TH1/XsdhQ+U8p3VQbASK69p7hJWzTB+GjTVDkWLIbQjdhv+RI7khrSvrnIyDsjPHlD2VplA864vS5/m5J2v8LgEpAMCCCc4r98s5B3BZptlGnOzv+Cx7xH1Y55t9wJhZkG+BPCm83CfPYgEp32n/Yx2QDcsQQwZZ4M8zwZLHKFAslmc9kceoYw6SQgIIlqHnXF5GK21EFNnp+/47aZDIl2UwOVPoXEse8GI/7PpbZ501Bcg4N0VeLJs7DL2qaooasK/xbEyzWBjGDKAahigvXA8iK8p3lUDvOUMaZ+ZAUlhQyNpg33B4eTXNNPOD36FrbMSdtYlOfxULuY2u4axgi2gcYeXEOR/Tdk06n10zq5fJNiAF6RLMWv3AWUZv2n3kPHNfNprBOuD4sT6sq9WFVmdZosE6Gfz9Upb4vQV/ZCnYqAzny9oa63whM4wcgXHnfizJdknsSoqNFYapK6FNg7TsuSWQ0Bk0NSAqYckSiBiLFSwYBa9YJ9DLqR5Ze+em+VchBJ2aWhqJQKu3eB4b7cTedl0sL8DRuLq6SkPncdosycyZGobhO04HjizgfrfbnUU0AdCsE+cGXWGzIpBzax8glsFF2FHS+Nh3Uvlx4i0GZZ/+6T/9p8qyTO/fv09ngHOEXr0cDG1xK6NKDodD2hsyP9ADVl4UgnI/tddGTw7DoEEhOUgWD3AGub6Vtb7vVWS5WmqfxjW0Opl1s5+VJszH9VlD5AHS5dIO2OeyuAc975xT0zb663/zr/0mxU6STmPNAYqEPzkEePKwTxhVqxSkqT6HjbMHx4J+UgSsMyRNXjwbxcZhJKlxsUACICQpOVikLuBMXEbGmEpN6gaG2LJePP9sNku57Rhfew/2/QBUBBzgG0JIU7Ttc7NWrLcF+awL9SAAPZ6Vz3HwcAr5PhQagMIqe77fMkAoHHLVrcN8CdTPFIcmVo3PYsSpB4KlR5k651QWuV73r8nR4LsAyaQsWWBuUwJQGBbIcX/IEutvHSXuw75XmhSFZaebtolNLgzotGlspApgEHCgrSPF+9lTO0MMsoA0SIwHe8j+IBM4pTaiMQyDnJ9yifkugEHmzxsj2DxkC6DYc/sctijXOZeIBQtaLMuMMbRsoa3nsZE79tQ6FRh/6xBihKwhskqd99ko5KVTjcG03/F90UfOMAQRxIFl4Yj+AFItcIXVg+yJTOZJRTGlp3o/1RIVZl4LINY6fAAh1oGoB/V/tmbKAifrPHE/AGSMt5V1C2JYY85AURQpPcsC5Shj0+cB8+cRpvO6AVJRqyrWt9EgALA23U+W5vrY0QF9P6UKWTKJ/aPtNnsjTQDf+5ihQPRXok6iSOcCp8s2iUCv4ThE3d7pcNinZhlWF7Fv1pYxGmM+X55FBtgX7AcOg42eoDst0Lc2lRfrbm0jZ886p5yny6i6HTvBWbZdVLFFyD66CHmxEWZISOqjOD88qyUyOX+AYfsM/NySTRANEE1EatH5yK4ldZAT7zMVox3gDFALbM+9XV/O0Gw2O+uSaTMHbBc2S+DZaDQOHfeUOXf2TBbHXM0qNe003NnaVCsH6BT0UlVVKdpBquhlyq3V2YB3u3a2syhrsVgsEmHMvhKdQqfzO6LVrLs0jQgg7ZIoOaSOjbyB9cAPx+PxrBbJYg/vvX7v934vXQ/HACeH5yXrxZ5xzgdpjehL0vTZf2SC5ynyXKGfup2i5zijRGCx4Zbc4jOQX7PZTF3byo11iUSXrRxabMI62Sg40Wf0ymWU2dol1oO1s7qD9bf659d9/eAdJIUxpeQUmwQ0oVFmGFoLpOq6TjN+YCVtvQj5vdaIW6FnI19fX1NjiEugioFl+NvDw8PZz1Hc0nn7SRTw/f29yrLUer0+S+NI4Hx05AAWgD6Yrd1ul9IHUY7ci2W1bJcUnqvv+zQ8zeYEo9T4HhgLnt/mmKKoABV2DhDfiyFGmLuuOxvSB5vCi8/a6NVkOMZ0jpHpzrzX6YSi9CrLTGEIyrIRmGS56jpGn8pZqePhmAw54IIiSdabQ899vL7WGkLsdDYMg4Kkvo8zV9q2UxhiukGeFwrDoCHErlp932tsMCNmZEhOmc80hKC+6ySN9QAhdqlSCMrHwW/9EFvAe58ptsMt1Xc4706Hw3FUtkfZ1uo2FRQ2NBvXIQRpGIKyzKvrejVNKzlpfzgoFmXHPfDeKyRQ49R1/QiQczk3qCicYjvk2HqY9wGwpSmVA4Oa57mGENvZHk/HBCCzbEx3HSb2n/NiwQcyELuYTc4jAI2zAUOJo2ojvjZ6i/NjU19sEbUFh1mWnUUeWSPSY4hwWsbWkiKcD86APZvWWOBc2DRV1gTSxRr7y8geANCm82LQbCQP2bCpq/FeYJpjx0HaSGfZVDdk9ZTdG+7DMv6w0bxg0qUpXQ/jH9vsD2kfrBOLk8gzAQi892fNQ1hPO3SV58LBwYGJ122MQ9sn8qnv42ceHx+TY2iLhNFTpHvhyAJIy3KqEQWMQdyFMEU4wnjuP336NO5HrpubG3mfabd7VV2fRHt95ANHDFmxESQG1aZC7a4ba0hrNU075vVPkUUcj67rR/JtrqapVdcnMdICeYdsY8+tY8TeOufSvDrska0948zaCKrNqkAubDTHRq2sE2ZBNACas27TquwZRP5DmDIY+JlNWeMagE0iCDaqwGclxU6beaZ8KOQzryyL9zqbz9WP92KJP7CIdST4Xfx3q2AITsAsgNI69Mi0JXG7rkt2e7/fJ1KAbBQIIRsJ4MzYPffeK/PRpiKvNqoRNDl+2BoclLQ2zTSrEb10Op1SKp4F+ZzhS9LE2hKbFsi+s77IPfv2/PwsSSm1V4o1mJxJSEOyZzinEM6X6WE4c7YWjDRJ0uWsTbJ4jugQTrJN+bUpbpxLuzeWHORe0a8Wk1r5apsm4mXF9t1yTnJS13fy2dTkxN4r92efmTOJ7bMOEDaEe7HzkmwUcr1ep7lJnCvIA5uxAPGJXeb5L7+Ltfi3ff3gU+z+y//L/zU5EzgP3vsEmjAalpnGmOA0WeaahebfCABKgANKGgJMGssMk0AOLE4RytZGugBQHEq+i1A4QmINuTUaFJETEQDUWWWUZVNHNdYIZYjS4TmtEPJZWCCUlj0oKED+jhGxxo39sODPMrJ8D+ua5jJocjjX67XKstTDw0PaL/bWKhfLtqMQuR/LPrEH1vlB4VglwDqQXsj62pC+NLFhljWzRrvrupRSyTqy/wAdrnkZNQJo86wYGyKHNmUKYI2xZY0Bq3xPZM+/O1MgyzJ1Q69qbFGKwvr8+XM0ykWZZnMgb/bc8OJ7JoZ/iojY8DyssGWH7P5hoCzwt5EW7sNGp6QpLdWyXnZPrWwCAGwTAxheij9hsmyKp3WqWEfLBtuIhVXBXdedzb6xLB0ya8ENBv+SQbOGEgPKzyCHbISPdeP5MVxW7i2ry/1wHc46AJdrWTbPnjGK5S1wwFkLIZx1igLM2PMfwlREzctGE9hb/rQNYYie2EJ3q//sNSwRhN6R4hwt9BN6EWeDc2WZTxo7UKN0OcQ0y4qzek9SiQCT1kGxaTzcO3Wj9j1t2yZwZOWxaRptNpuzulDsngU9khIbbq91OBwSeGVdi6JIJBa1FVzHpuRALHImrW4AaNH2ummas/QgnsO2EAcIWVmHtMDR3u/3qqoq1cJBHOIMEA1iTbH/yFxsET45xpxZq2c4C6wTezabzZJOtk5eN/TKxv3iXhMb3nZnzRZsdJkzMQxTNgmf25+OZ5ktyAfrin7ECfy+rA7OA1jCNsew9o2159lttCFOhjy3A+i2rJzqpqz94ZrWeWJPh2HQ8/NzwkKfP3/WZrNJOAEihMwA1t5G/7gXslNsAxDkJsuyJMP8DvtlZZC/g+XQ5yGEs3Rw1pnnsw4u94Pes7iSZ7H2bb/fJ5zEPdgaOIsRLO7i35awZn047zwX38/+oFMt0c/nSfHjrIFVcT7BLTTsoH6PtbIEhTR14oO8t+fPro80dVHFsbK6357NS/lE9zw/P+tv/y//1m9S7HjhRXMwLMiQTF1GNk3ORtHBzFqhRqlbUGGLmFFaKKZL5gYWj/QGQqjWUUBAAbMoKkDaMAxJwDmAPCOGEjCB0NPAgCiOdD6/xXr2l6DIGgIOLhEUroUjyjUtuLIgzLLaVnjtPgDOLp1RSWcKv2kaPT09JWOMwmV9LeCl1TltnjncGD8Uiz1o7IGNhtnZCtbBA0hxLyho1tAqUrsu9qBbg0IInvu9dKqswUOJ0K3wMgVCOndM2AsLYCe2vFccIHgu58jIMMQOjZK+w5jxnewPxaoWHGA0JuU21aAAFmw4HSBqjQBrbc8VBtA6jDYNFaPL3gDSWXeuZR0b9oL3s76XKTV26OOlM8jnkXUMlP28NboQEzwT6X/ItAUuyLA9m6z/5ZniDNuURXvWuYYFtDaVifddpo3yJ3qF99D8o23bs9bS7KdN6bTsoGU8kQNr4GBWifwQ7YJkAmQD/JF3zoQFC9TBsLboD/SCPU+sWYxgDKnjYVEUKbUaoGFTgJ+fn/XhwwfV9UllWaSWti8vLyNLPUXJGRKLk4gMwnIjj3meJzABSLRppJwhC4A5C2QL4GgCTK2DYdvxeu/PiLYsy3R1dZUcJWym7WxnIzqcdXQr582CW+yfjayxjpxZ1sVmD1xdXZ2dc6vHkVk7W4t1AQxzTVKZkBVL8jGk2xIwllhETtkrbCxEw6UuKMtSfuglQzzu9/u0V1VeJH3BOg7DkMZsIP+vr69pvS0pYMGsNAFKa8OtbbVEHvVylhjFkbU6AHmSdKYv27aVhji43jo5VtY4x6wb+4d9tLa076eGB9TTWPnlGuwnqfi73S6RNeiAsiy12+1S3XNd17q5uUlnGscZG4MeQtf3fZ+iRZZstlFNW/OLPvLep+HWthEH9+6cSx0p7V6yJ+h6KWIC8KK1hZDhNlqD7kcPoXOJrCDLlmBHz3AW67pOeNU662BJXhab8grDIJdNJSIWO4MteUaL8fI8TwQZtW7cF/bJEoi2ex33aAlhZB0bNZtNQ6D/otcP3kFCKeOMkB/J4eVlvX3+bkN1tkkDQNlGVmwKySXrI005mrwHwOWcS46VZTMsg7larRJDhxLBIcvzafowylmKArvdbpNyQMClKeqA4rJdyiwriXKw0SYOD4CDa/A9HEYAwmUvekAu17XKHAFHYVpjifHn3i2Tx17wYu0lpXWCGSQ/3TpeKC2r+DhkKD8YEfaJbiowTvawWyb80iGy0Ry+7xJAoEBhUjnodu0lJUBtZR1AiVKwkSj+tHUgvIfzEP/skwG0TklZxmFxFujghBZFobpt5d05ILLrjQxZp6wsSx0O5wXROGPsNfeAzNkaMsAIBvMyGsBeShMJwPnhHi8V9OXfMXiw4ZZ5teCCfbdnxYIKa5Q4t+yBPe824mxrJGzqCOeYZ7Ygxd4vegGZtfrLgiDrWNoIra1LIKcd44TusGwm+036WwghRUZYM7pG4aD2fZ+IIt7Hd1LXxl5Z/cl8Mhw2IgGQNvYc2mgkMojjxv1ybVvbmGVZcgSwG6dTndprt20ssEZXAtBsGvJ2ux0dzXh+GYNAqna83+YsCkkEAX1ux0Bwvi4j25bMs5Fwm8bJWbS1BFaukE0+a88f54rfX0bqLBCx6Y7I2OFw0DAMMdXdRL6tLgOM2kZCOOmc/+12m/SObR/Nd1qHiJfVxegOu8fYeH7HGtqMCc4nxCSpqezFpV2x5A3PgRz1Ou8kCwAuikJ9MxEorAfDeblf9g2SwWdT10sb/bGg3ZJrrA3X5F6w3dwve27XEtm0zpsl3PJsSoFjbVmbYSRALtOhrGzaZ7DRPAvoAcfoIq5DSQR2ou/7s9olOstZwkNSOov8G/yCbKHPLJGEk4p8Wgecl7X36IioQ07prIEvcDxIj7RnDl2NPcMmoMdwJrHhNkJIfaKtvbW6wWZAcEZsjSpnCp2B3ICrIXCwr+xDTOx3Z3jBOkg2omZtI3Jh7SLrw5nCsbb3aJ0mu17WxhZFoa6fcP9f9PrBp9j9V//F/03r1TSgVNKZA1JVlR4fHxNzkPq+91PLbw4aShMBRDAYbodjZGtkrOMlnbecRVAsQ34JomyKh3XI2HQrNDa8CaBEiVjAB0tqhcyCG54Pg2O/8/vuwRpTPs/LhoR5futQWEbOrg9/x0EBTGFkLFNvmQnu3UZNLCjintt2qn+yioHPct88A9Ep61gOwxT6ls7naVxdXWkYhsQIS+ezi7h/G1mw68F3W/YfoMf68zOUnjUQ1nhYp4prABgxxIDMCIh72SGZPKtzTqf6pGxM0wOcJPDt/f+fvX8LtW1b73rRf6mXdu2XcZlrzpmly5jsnYi3E0UwxvOg7hgjmxgEIUIgiIgGBCUYUXxTkEjyoC+++BYQwTeFbDzZxvOgwjJKAm6PEgVPoqzEzKzMOcbovbd7vZTzUNqvlH+tY8Q11zmew2acNJizj956a7WWWsp3+X//7ytf0f5plx2Kg3bAG4DCGZ90eGYpg5w7Xg+qvWTI96egez6Xzl7zrN49EF1gvvmeyznjJWjBQc+DI0ALYNSzMm4vWA/GQnt9wDZzxDozF77fxnXMzbez9ugQjgFAi64CfhyMcF3mgqwBe0Rw1j5nXjbrJRYEoOgvZyS9ePEiB3uUGrPhmvkFoDkYAvAxdzzHOI56fHzUy5cvJxlOAAaf47lYG94nKKMUijXDaaMXMUbd39/r4eFBfT+o78vetsPhkA8jpvyNIM2dfzpfqGzwdvkYx6hhmLYl5gwVB5KeWc2g0xhjJwYgfvy5+Y53XnRwgQ0BEL+rNBm9QBacnPCskKRMSAKGyAg52GPNCAyxK6wZsnu5XPLeXphtnw/AoRM7BPjYDEjFYRhyBs4ZZ3QHO7rf73O2jiDMCQLkHRDLM5Ddcb/NMy8WC5370hnWsUVdVQqxHDyNf2VNXQd53kzGhHJIveMCdMvHyHUBucgyuuG4wYkkX28nLdzWxGFUZeAUAN8Pg5rF9JBxD3SciKH6hcwyf3eAjh1CR8A+vE95qGMy92eMl/njOuiIz4cHVB4Q4deQGyeMkTMnNhkL32eOkE9kwTHinMRy/8lcIF/YNSe4IV3Asj422mrznNhA2oHzqus6rwP3pPTWu9cic6yBxpgPnwWDuW310nX+5kHTXE6cpHYSElvlL7d3rM0wDHp4ePj1g2J5NXWT2UG6h+UIN07T5YA6ByLStHYeo4fzyYKgt7vtYERxiAjcfBMi93OgwcuN7DxwwTiwAdQP7pSmwQnK7iyUND21HtbA2WsXXD/nAoDNd3EeGFCADIrsissYPHXv5yAxfxgN368gTdtCMnfMv2c8WBf+TaAhaZKZwfAApsnsOXDwcROkMU8edHENUuqMFyX1OXcGi3kiyPIAz52egxQMqRt0Z76cJUbO/Ho4BWSK8qVUbjN9TpxEVMmG8n5+fiu3w5nNAxnK7XzvR1UFVVVpl+5BnWfAaKrg13RAi7yhx34YJVla5gxwdeD8AAEAAElEQVS5mwct6AZrN2c4YTUBvxh9goL5ejqRQKaE8TAPHuwvl8sMKD3rxH8eHLFuLmcxlvayzCdjI+Dwkk63b7yapsklKDCr2ALk6tmzZ3p4eMh/4z+6UhI8PT4+KsZ08LGXe2CTYS9dXn39vTSDrBBA+3K55IOouTZ2BFDG+rA2ACUOJwXQE5jC7FJy5raXNQ8hTEq1Wf/1ev0WW41MMoZhKOROjKkUMDXTWCtGA5nXvyHbTkzNZcJtCkEkDL93H0O+Mxiv395TyX/oe9d1ev36dZYDAKaDJ8bpQS3j9AwevsqzATDu+B63eYAh1g7w62DYyyqdqAAEMn88F+81TZPX3DOZvl+JucV/up7BgDspx3UfHx9zkOy2KsaY9X00/+Sl8lVVKeYGN3Uus5wfu1BVVS47kqSo0iocf8C+Vl8nJxM9e8KcOwBm3rA5yDFz4oEUtuh4PGrZLnKnOrdXq+VSo2L2IR5U8JxkGSmlizHm0jnwEsAaXwzugeQmsPRGM27H8eVOILp99xI/bIHPA5VIlLS5PGOfIAnY344vcn3zChv3MeiqEznokmfzmHP3a47L/H3mA/vCT9e3ENIhyP4+5MM8SEPWGQdjRM9y4D6MaozA9WwaGXnG4jiG8eAfmFeXX8c+vmfKA3ZeU8L417vY5ZfXUDqYq6pq0nrUU9yUIHmbRBYIYfNSLyLrOSjFYMFQcW8Uax40SFNjzt+kEp3DcvC715e64nvNqTTt9MP7CJeDODccUjEajI1/e9kSisR8zoNIZ02cucLAOQDiHp7h8O9wb8+quMISYDm75Rk9xoI84ND9Xqy57yOaM/EeDHtg5uvq4/XAwZ2QG+85y+TOxQ2dB82sgYMbXyPWw+fw2bNnE+DnRoh7n8/dpCsP/x6HUQurBwZkVVWl0DTarjc5OOLvjBeAA/CmxarLKc/cNE3eAMtc4mQ8G+O66cErjpRrSmUPADLpBpk1wbEwHsAw95oHmFwb0OJgFMfPHMFWe5DA/dyAIzcEOcgiZAssn7PmDmJoN+s6E2PM73vgwNjRCeYRUAJLCpnw+PiY9ZrveKviEEI+Xf7169eTvXCMB2YXuQX8MD5aLj88POR9oAQz6AcA3I8YYJ6x29vtNre29bIiuki5vXUbLqXOVYBc3/i+WCy02aQOmDhoxkd2EjDjdi7ZsyipzgwtAbIDK8C/B6wALmSQYBR/w/uuQ7ywLawfnQ29vNJ9icsCduzFixdZ3gHz7O8i6EC2+T66TyWHB42AxjnpwzN5xQYy6uw+6+X2E91El/AryAl2F2YceSSgRZ4BoZCAyGOMpYQTAst1xu1cVVW5JM6zmDxbCEGqUke3ue/t+15VLGXiXBd7wnwjz8zd4XjUEMfcdY61wh56YOV+zu8xDEOuLsEXECDw+xzAY0O9pL47n9XUpdSZZ5ekfihHMiCrnEVEpg6bRJklgSzj9iwYARQgGf3w4IyyV4J6b6/vZBQYgjUlS8f9PFOKbDJucAQ6QMMlz/Y4QeUAXyp7sdBj5BjZcPIcXMJ42rbNlSzce71e5857HlRQFXA+n/NerjmudVKC53RZw944+THPhmG3+D7X9/2vHsC5H+P62FTma05++zi5H2sx96VOoFQWpH6t13sfIFV1NTGUDqhROlg2qUyyZwN4IeBsGMTRuAHxrBBtw7kG1/bFw1jTR//58+c5WHDlQ1G87tQjbwwAApc74oylvM4ZB14YSO6FgHtpgLOgOA8H+YA+jIwDWGe+XdmdzZsL9rnrdT6dtGlaRY06x17jKC2bhcJ17rprHWmVqDgNSm2uGxtDXde6v7+XpHymA2AFp9X3fZ43B8AJhDbqOsoQOLk8qu/LOTMpuOiuGZBWqVNVUNtyjglGdNoFzANPN8rjOOYOccw/z+NG0wNlX3NP8c8BAvXXrEP6L+p43Oe1TwF+/5YDBCBtNhtduovadqHVYqmoa6nfOKoKRT6QYV44Sw9USwBXgjgyEOM4TvYHuXPEeUnKZIYTAcmwnie6m2QurU0pkasVY2q9DrM0n1eujZ75eVi+oZ/nhXn28iPWHYfqJUBkG2EZPXsIcEXPir4NappWqZFGCcq22xt1XSE+yPD4OXBVRYa7VwglIMLx8PyHwyHvyUglcL1S6/jldcxHK3lKY9zt9tdgZ8it8ql6SJkkZcdNxgUGn/svl6urTV7pxYtGl8tZwzBqvd5ouRyv36tykJaPKBhHKQT1w6Cb2zsN46BhjBrGKFXKfw9Vpa7v1S6WCte1SDbgch3L8pqtjNdyrFaXS6e6TnNe1831vVJ6jWzSpMbJMs8ypVJSMiNRXddfD9lO7fSdnfV9CdhgbxjhmU4HosgIfwPQUV5EqSSBpJN4+ALPcCF/PANlbWR+sCOUrhHs8h5j5nPsr0BmsYGANQdAzBXVHc4uAzDRZe6BL2PcXsYG+MOesReFQLiqqqwrVJXgmyBasWUEOv5c2G+CHn7yXA4Yh/46x+Ooy+mcyZAYoyoDf54Rc3+O/+XZqqrS4npkwulwVN3U6rqUBVy0Cx1Px7xeHpAxP9gqL2Gn/Ao58vG7DWR8kBGVgvqrPHqJLAQD2ULHOQR0BCtk8eYy6aWcZHPmxDBjw4aiU2TQ+Dz3JwPKczthRiCAnDHn3N8zaMgGwJ71c1vOe3MyHb1wHIa84zNYf3SM+/l79/f3mbRifX2PuSSNQyrlPB4OOh1PUpBWq7W6S6eo0swGEsIzR14i78Qw2ND91TiOWrStQhWuvioFyCEkcoDrME7WqPi5ck/Wg2DX7YfbPyf/PNHAGjh59Hle732A1F06XZqy2ZsJ9cyPVBwSe0ZYYGennMXHGHgZhlQWiJplDMw8OHBAzKbHjz76KAdBgCYW1ZkfN46+BwBhQHGlaXmgM4nu/Pg872FopFLHidNxBkEqiuB7GjxlPWernE1gbJ4ZUAjqq1GLcdSNBtUapWWrGIM0SJdBehwHnauoEIPq0EiqFKtrytmMjzvuEEqHHndkzAfAF+eYlG+8ApcErFPr66C6bq6B0nDNBLRWJlCpqoKGISoEHFGlEIoxcOaWeWJuWEecAM/CmPu+zydww+QiCxgPAlbfOEkpTwgh76s4nUr3vtPpnE/epqEJjD5g6HK5aOh7hSj11+BAMZ2+nWS8ymvuAbBUwB/OK92fks5RTVNPnCnz4nsgGFtmYVW68rh8p3WtMpBNMlcpXs+aooW5pFkb/2bSgcrZdfTAHTtBqQMzgBzkCSw1+91w0l5awT4YHKHX3aOfPEfKwNxd11CSgsZRWixSYILOE2xig1IQX+ty6SfPlZz0RU1Tyq08yE1ZhbQnbRyjzudTlokYpdev3+jTTz/Vl770Jd3fP7uylDfa7XbZGSXZ3GRHWNdFDiidBUzu94fre5QUtVd5rHW5dOLcrK5LTO39/bOkR1Wtpk3r1ywW2bGFENT1g0J9JZqusoNDvVwSEbZet9fMTXUNnNM8p/kfJoFOXde5+x3r7HX87mPcUccohVCLM8ZSZqKQBeg82UHkCODhhJIDL7KU2DnstpNq8/1Vvp8S+UV/sEdeiglgARQzD87MAkwgWtB7AkknEwgG/JmQOeaDa7NPb04IeSbCfSU644y3j8mBraRJIALD7+y5lxixrk44ejaEazIOL4Hyio31+gpIY1TVBtVVJcWo1TVIQA89U4WNA/BiH+u6Vrff63RImbmbmxs9PT2lZ+97HWKUqlItALj2cTthiH3CDnkm3ln+rutym3Svnhi7UvXhZGRd11IsZdnzQJZMNOsDTsGGYPvnmS5klXXCP7ht9mwHHe4gItE5ruVHhTBGJwp8LMgZc0jQDe7x4Abfvt/vc0MN9MDxB2MngEL2nPBzOUMOvKEIn8VGIXdD30tjVIjS5XRWBWk7DFosWnXX53XC0QNp35/tgZPjSvxy0zR2fqNVIwUpBpn/jxO7JSmXKHJtD9TmFRDz+UHXkG+3w13XqbsUMulrvd77AElSNiaAFN/IiiAz6R65A6pheaVSe4tBRSj5jlTa2jqgwrj5xlmMK4D1k08+yUAMh0vWwzvvcd0EbE8Th8H9PKDzaHxu2HHCHlkjrBg3xuQODYWDrZCKspK1wAgSxJH98kyVVAAdY1qee8XP3ugLYam7eqGqiarXG52fDvp/dmfdvLxTP5y1XK5Ux0rnYdBi2WjVNIpDqSPm3swlG4ad2cfgOTPJHKT1fHsDP+OGuXDwhDPgOx5ApveK8+QeLguUdQA0uK83/bhcLpnNdMPrYyFIctbXnfb5fM4H2N3e3urx8THPCcwRgczcMPMeJUWspwf/PB9jc3Dpe1XSmKt8Pz5PIOHGGLn0FvVuyB1shTA9w4l58gyUr2nTNNfa8sME6Lms8jzYAQiVm5ub/Cw4W5ywB+mwzOv1Ord4Ridvbm4yg+rlHx4kOUAdhiFnnVlv9nKR6fKum8vlShzQy7WYY6nV5XLOJSXYw/TszURPYWz3+31m/V+8eJHnmZIIQIrbGdapbUtHRSeJku0t+wMBZnTx9MzDq1evdHd3V+xgd5nsG2LMAGy38WQ2ukunuirdpWi/jc1A3jxjgZPG3k2DrXIuEkQFJUJkk/ieBwT4KGTIwQ567iXHrJ+X/M4JK3yEg378m8tzkYHyIrjmWXjPwZL7FoJdXx8ISI4DwMb5dRz013XZ0+PXwIa6vvq6oIcOkrzblmduuD8ygu4j87x4pvm9yLT42jtgc5KGuZ+3N/asG+uLb8TeYPOQM9cdromd4Swr9M0zaU62XPoiV4yXQJSxEigih1wL/YHIBNgyHnQl32sYtVpMz1dzH8JaeJDHWiAn2HiapziJizxDCPp95kG7Z43IkJHld4zmeI41Z86dAEAG8I34dR8HtsqxoW/J8BJlyBWem/nEprNHEplD1t2mum12W+ByCq5pmkaq0jW8u+8wDGqXC40W4DDnfk/uxX+OVZzU8LJEvuN6h1/wTKtjJuSEe/O7Nwhyu+TrznXAowuTxaZpVDe/XmKXXwAygKU7NyaZheccjKqqMoAjBcxCYTykYqBdMBz8IsQAHRwJxtY3prpispBu3J214wWodOeIgHmdrqQri1ycjLMtLlAOVtwZu2PE4WMknDFFsanzxahRisizA1h5Hj53OZ/VHC/6+Z/5f+hnfvr/0MsxaNkutH12r/sPP9L4bb9Vqxd3aqtai6ZVHKJi32m5WGq1aLW7bgpnTp114r25UjnD4/JSVUExlsDTN0jiCH2jrDPmvMfaYEiHoWzYRvaQG9bSs3YezMEUu1Ojnv/m5iaPnftivHw/EHOSsmBJHjk80dkgZ+ZwAO7oIAy2222WK4w+zgEH5qxWVVV5D0gpRyrGEeaQ+xMAMGeezXSH7oaR7/r8z9lP5JE9CazZYrGYOFUvdXL2lXkgCPGyI8ZWVWkvz4sXL7It8uADWUHvvSsgY2YcMcY8nylrNOZzsshUJfuQ1oj9NzQLiTGVynm77ZK5KY1ZnDFOzn3QcrmeBA9sZAcEA8Q8iwkL3TTpdPr7+3sNw6Bnz54phJhPrHcAkYBfeWYO3KQcDx3gvg6WXM55oU9OVD09PZnNC+qv5aQAEeYVIINt48WaMK+svYN3gCh67F3dkFHPFjlBBDCmFTLjQnYh9zwABVA6yLhcLnnzulc4sO6QbsiA+w/XdWTeZdLtdd6bOJZGQ07qFJkteyLRb/dRXiLohApjxzZgJ30/Gn7JCRgvw+J6nslAHlhnz6I4aGZsrmfsnaR7LTaQ61Iux1w4QHYybB44ICdUBiBXfmjy09NTDprv7+91d3eX9+D4WVngl8Uigd51Ww579fPJPIvhfoX5Qf88iOD72H8PDE+nUzooNkxbmGdAe21AEWPM+2YciKOL2EcOTvbAmblAxjxL5yAZW0aJ6TwoxV/NiU3kkjny7KLbZ/+8y6YT2FRv4FsOh0MOjLD/c7IeuwkmQTdo0409Z508gHGCkvI6J7frulaoSmWQ6xv3G8cxd3dDTx0XeJYL2zTHxv5vlyfkK0gKVWkm4TbFfSL34V4eBLvt5PPIgpN6lOE73vy8r/c+QMKheSvGOVByw4lyoVAIhlT268B+8D0HYSwgi4SBxVjt9/t8LdgoXhgpwIcbD4+mvRU5js7vx3Uw+igkBghFcsHxe+FAMerOfjBPDh59jqVpu2Z34iiYO32v1Q0h6Hg6ad8s9Nn2Rt03f6O+ejoprBb65t/x27T9n/9n9aHSue801FHny0VVDKpCpfPppP50VJgFqmTeMDSArvm8evaFwGIcS+bCZcMZpsVioW/+5m/WL/3SL+nVq1cTw+NyQ2Cc9irVuRMOcgSLg+EGRHtXQOaPwN2bB8ybdWBs/LsAKcAh1+L7GF/knLG70+B6lBZ5ZmSz2ej58+fabDZ69epVPu/Gs1+sjQdNPscYfA8ikD3kycswGCvOHEYsxrLZ20sVybgydgKkAq6nh/tSG+5z5HpGgBBjzMGHs65kI5B/3ofVKuWOp0k5lduVsk/mPAFczBH3lKTT6ZD1jRJJ9pqcTlOml3XtuosOh0M+XwZdaNtW+/1B53M3acnqAJY9RYAAZJ4294C+pmlyQDcMXd4LMyeZnHF3lt9bT/MeNlchKIbCbCJD8xJPB/fn81kaoxZXu+hBzuVyyfsfWJPL5ZKzhVI5bNdtN//BijOP8yyqpAnY4j0vPeIa830oLufYN78+36N0MYQwadmL7mJrAPJ8n7lA5gCxnoXlc2SmnfTy5+ffDnaYT+bA7RNr5gE6QAh9xz6iw+6LxrEcgM54sP2e4QH0uj2drwOAdrVa5ZbdbdtmGYZIcRKO+aWsFjl1ID6OqS09beGdpHl4eMg2B5/kJfQExszNnATieXgPX9suWlVGvGFjvDwZOcPG+bzO8RHzzwtdyPplMops4u/6ayMJ9iFChJxOp0yEOH5ym+J6gL9hPJ6tgEDxvSr+Pff32BGIXwJA/BI2zzGer4VnM5A9AhzXGWTSq3KwNayBy7M3QfE1duLSy8PxCXzO8anP4zikbrOemQE79MPwls44DnAyGzzL83lmleDOCXvHROkZouIYr/uTijxPgqhQ9hjOgyPPkPN9J7GdNHU5dB/yeV7vfYDkexYw9L5oDkZQJibR9xF4KtSZJgRyuVzq4eFhwlx5sIRi4Zi4F8LmJR3ch7GSrfEMkDOOrrQeACIcXjMN+0V5lHdzQnjd0HAfVxCMCcaYsfK8sL3zVLELqpdjkSlYrVaKY1RdrfR/+b3/V21/z6CuP+vYRGm11GcxqLlcFJugoQ7q+07D4azlaq12vdRwuWgcy5p6CQuBwJwN4XfWCTnAUfR92TfG55hv5vlyuegbvuHj7Cw9Cwdzw1wtFk12oF1Xzr2Zlwo4CPbsyJxFpWwUWXcAA9jBaPA3ZzZxMDh2ro8cZ9YplFIgZ2TnrN3r16/11a9+ddKgwcsRWAcvH2vbKavDswEQMOAe1CKjOEnfU7FarXQ4nLIeScplKJ6NOp/Pea9M0e+y78mzvASPkAesK89V13XO0Mx1GnCFY+R8G67N39HrcUyNOnDYAKSu63R3d6cY+wmgdwaN8kieEzlO9ioFdAAaAMpischlgjSVYdz39/fXJgVJlh4fH/O8E2yyFtgnD5hhv7uuM7a5NJlhjEW+p6VY2BeAI7aE8V0uF92uVhpiaWADOGDeWCtk3gN09wN8hnv2fT8pT6XtMHIEiGDukSHWhOvwnJ4t4DvohK8boHoeXLldRl6c4faA/nA45IADcsB9jjPRz549y+vqWQTWxfda8Az7/T7vrZsHG4A3ArS2bXV7e5tthoNe3zzOPXkePudZArdbzIfvG3RCk+fh2rxcJvBxHqTPMwZN0+RSQS9rHccxl+BDcECCbLfbbFuwv2RgHVNQbYE+4EOYZ9efw+GQ58XJHwJ+jgjAr0jlmBBVJTPoRKuX0jlOYZ74OwEvwB0f7yA265mCFMs+aw5SbttWTZX8zXa7zQ0F5iSXpIkOOLGKnmKLCWLQBdaaTA1nzTnpBbFFJQO+CDLVnxn5dbkjM0vGH3vQNE2u5OAYB8g1/CzBv1SqPBwPgFGwJ06uQmqzXmSNIQxZH+7ndo0uoGM1KFoQ7UFQGxaq+7KWzJUTIOi3lwJ7ZgYb6piAn57lHMdRqtK+O09WeNmw660Tp+4T8MFuLzzA9qAqB9FmC77W670/KPZ//99+UtvtNk8SAu1GyIGnOzJnv5h0Jh4WDmbZBQGnhRNyQZun7T1zg3Ji/FDIu7u7ye+MC6ZwHMdsLJwBcGPH95zxc4fAGD0F6QwpxtA3byNwniHyQAMWk5IAXi7AsLOSCrsYFgpdr7E7amyitGpUqVHbS2Mc1C9q9U2lZpCWQ6W6bnTsO419p9WqsDdeqjXffM+6OyuFYhemNyjGacDqMsHnvQyN+XYA4mx/CFKMZc+TM0fzwBOZYE39AEUyEBgWAm9/vnmJnQdXMUqp0UQpNcBAI/+UUmK4cBSejUWOmTPmyUsqD4dDLhkhqEN/0ph6rdep3AdA788O0PNnQQY9y8AzJ/krgZw7RubBZRgQFIIEtgKwEsBeLpf83H5AJbrhHZhcZxxMPz095b8NQ2rxTD28t8lFxvidYIpAcbEoDprPkxk9HHaTZ0Rn+35QfW1UwJ4o5Kdpag1Dn9cWp7lcLrVcrnQ8lg6QAG+yDB5YkKHEqSPXlPJA+JxORw1DkVVnUA+HoxaLZc60OfDCwR4Oh5yhyjJQT6sDHAQ78cV8DMOg5WKh7lw2crtuuzx7JogXgAnZY/xOjnFPv5YHbsiB2xEP5D0IQN89Y4g8My/4Feyw67JnAbi+72/ievOMjANVL9vjmk6OOZvr3/dAj7li7Z3p5x6MyQNDfr9cLpNjMryMzoEh33USDDCNHiOb/GT8HoC6ryTrif4gu65vBO3zkjuCVweDXkLHM2LH3YYzR/hHSDnG+PT0pGfPnuVs7jzAVJCqqyx4wEgFCcGOE6ueWZyTAk4IeBaDDoDd+aL6ei8ysdLVj1beSCdk2+qkHPcj0814AcWsB/OH7DgBjR32sk70xv3L6XSadFB0ksKzKOgtsoKN8OCFZ/IKEMbOT0hqJ2kJbBj7MAzZH7gOY3eYe8ZEa3THLvMMXF63MWq82hSydVn/JakqGUK3cU4cOfnPnHIdxu9YkLnCT3L9OqQSZyf6uCbvOfHumBX9xecwJ7wc8/j9hyGddfbrB8VeX+v1OrPQGACcvxtFlFWaHoQ5B/MYH9gcFz43lhgxaXoQl6RcOoEAS8qGGyeXFDzocul0PJ6upTKnK4jDcR6zAnsnKFpKS28fPuuOg7Hx/AQ6GF+u4d9HwD1AxKEyH84qwZjwvoMD1sEzc03bqg9RnQapbRSaSpehU31t9b1arqQ6aOgHdZdeldI8aRzVLlKL6su1SwnMUnFk0ulUDvejFbdUWj3ztxhLVyI3nG5AubZnxwogLRtvCRbS3A+iux0d8U6n8zXIKp0JMco4tWEYtVgs8xqnlsuLqzMubaurqhj2JD9T9rUY25Ju5jo4HAIb1niz2by1dwUGjjlOQejJSqpqnc8pS5kOmtyK0lQcK46lqkqgBcjBOMPwAoa93nsYRjUNYJIyyn4C8LyUDWdGByHmIrenrSodj4creEzPsl7z7L2aJnV8a9tysCTAAb3GcKc5aK6yGHQ+X3JAmsiDcl4JbDvlNrvdTq9evdKzZ89yYFIClqV2u0MuD/Q9FItFq6q6yc9NiR2BzXJZOqHhWJlzWNQ3bx50e3ur1N2t1es3bxRClTbvxqi7+3udL2dtNlsd9vtc4uXHHlAigj2FzWWsUVKzSO2H66ZWuMppVFTdttpc93TwfOM4KtSV4jiqH9Nm4mEcdemuJcphuqmfYM0BngcGBL2Z/azqVF6iqEpBw3gtTW6uzLGiqqZO59YotWFmzPNSO3QXm+bsqgcu6JcHJnMyhe/A5Lt+eHDpRBvyVNd11jEPUvBJGaTUZfO5Az23GVzLM3CeiedFUMD1DoeDbm9vJ89A+ZEHnciPZ7mQXV7+ec7HIlMCmPWypkJylfIkMj8EK/My+kIeTdtpYwMBsPg1yA30j3JWStQIRvyQUHTAA1nfq9s0jc6Xs87XYLSukixVdTq/pQ1B9TiqXS7UX23d7d2dqqZWqCqtt0mnF02b5TiEoLEfFGql/UFVUF3V0hhVN2X/0byaou/73BEPfJLXZIy6nM6pq2kIqppK+91Ofdcn4qErZy55oDsMJeOD/yTT4/s9PYBAh8BG6AyyTGaa9XFykiAIGUJPvCEINtnxIfIDSYYu+hYFng+5nAfL+DH8I1kqJ7h9DiA60XE/9J114bmQVWwLeDb742FQFSqN8Yplr+SKYlS4BqWu83V97S4XpX6MCnVQkFRXqSX9EKf7d95FprPWc5/I57E7pYKllON65o65xrY4tkIm/J7orpN7bg89QeHB5ud5vfcBUtf3WhijJCnX/eNEpVKzjCFncXBkCDQMkS8SzsSZX++k4wZcKh2LPNJ2B8i1079TO+nLpZsY1arCiCWgmNrTBsVYSmdQaBgrd+YIiwdtnllxhs1ZL88eECgQXPF9Ps88+334HClfnJorR11VCqo0jlJ/6NWGa4mSeh2O16CjqjSqSsCpCqpipXEYxfkrySkpO+MEcBstFoVZOhyOeS6Y5xilupb6fshlfx5cIC+uvMgOz4JCYig8mCIoksp5C7SgxlmmOatVVTBmdM1r8udTWRrn4JQAPxlwzklJ/yGvyHsyitO9c5y4nlj8spHUjd1ut5uU9pCl5Tpps/94DQ5P2fjd3t5ouVxnOem6Tjc3N3p4eNBisbgGUJtcenZ3dzdhbj3L5kxxjEXuErAEqJZ9BuyhQCZ9IzOBLc4/XT/N8zAMOh7TeUqAnr6nfCq1vE7n/5TyAQAFzjrpP7KfzimiRGUcoxaLZnImEMRL3/e5Q5tnrjH8z58/z2UZrltJF5Wd6na7zcHver3RalW65wEEsFFPT7srUBpUVbWeP79LTHnbqqprrTbr7Ng27VZjjNre3KTNtlfZ2+12We4hDhgj4LGua0UFLVepzj/vXwpJlm+va79crxSDru2JU3BSt42i0plGx7PZlroECQAdL1l24obnzhvzh0GjUi38xbJJp/NZ1VUeFII6K/eproDTy0nQPalkXgFEDpIgoPius/ZuRz3z40ERcut7eTzbwVgICAmSkMd5Ngdg4t9HVwi0ea52Bqw8CGH8EBmSchtlfB7laO43POvme1m8oQ/zQrkZ4zyfz9mOsKfHgdU8I47u932fmx14UB9jzPuNkBH8OPNBFoEMZiF5yjo6oOZanuljjtFhdFeSmraRQtBiudQYo86ncljq/s3rPKaqqtQNvaqmVrtcTI54CCFcZbpSu1goKDVHwNZrkFRHtU2jUFWqLRhHviCUCVRd5kIIClGKYzr/rm4abVbpAOA6lOyFVDbro3dNU87pw27jNwmQWX/PJDB2AnjPQLFdgBdyw3oh8wQgXv5NkOt77bgG8oFdkTTZk0VQ5IQY+A9dhyxCf7GNlE8zLl6u/9wTG48cQlDju8B2WTfGUf1YAtFhGKQxqu9KIEiFjycFCKiWV9sVx6uvraXe8DHySqaUsXFdD57QQeacOQkhSLHICetUkgOlizKy47jUcZeve/G70wofJ+ir+vMHSe99gHQ6HlPUHMJE4FB+TwlKb3fcYFKdgXSj66wbyuGpX0lvlYv4TxaUQMaZbRyQH/rlQYikfIieZ3667jxhTXleyve4rztiBB4WArbLgxie19kxjJ87V2dDPcOA88RJuQP066bsTppr2CGChhDKZk1/Fg8qmXfW1hkiPuepexSNg/E4M4T5fhd7Q8DI35kbGCZnWnmPNWPuGMc8fQxji1Hged0oEbhxL597T7/f3d2qrmu9efMmd2RM3XBahVBPysyQX1LvGCuehbm5vb3V3d2dXr16Jc7sSiCj0v39nR4fHyebx9Mzd/rww4/0+vXrLL84CrrGPX/+XG3b5u5DLos4gq7rcrbC95ow/5Le6vJFqQZrAFtNxhT9q6pqcmggQJuM736/nwBw9I5smTtgHE9dVzkDLBWHnMabZI69Lb6nA1kl28V3LpeL7u42b5EwxbGU9qc458S4b7LtwrHzvCEE7XY7NU2TAzPAfd02aq7zDjCD/AhVChQAzswNsuRsNGPquk63t7eKSvvVbm9vFWOcsMDYm8I0Ts8P8xp41p3vYWMgSZg7/sb8MyZkL8aYP++2jkwdIAdbGYcxj53vF6Ig6e/t7W22QR68sfeB+aAMySsR0GX8Ql3XORPhRAc2B5vqvgi92O12WV6dofb9dE6G8Blsp5MS2Kx50O5+i3lk7dq2zTaV75B9Q794duSMOWVzv4NJ5o3vEeB4EIWt2263OeMEuPXMA8/LT9bJ55V7N00zKX/3IBa7wx5fQOA86wG4xe44YG6aRnGM6sbiL/D/3JuggOf2qg8P/lMW/NqtcLlUtO6pTh5Ipbzc7S2yJpVyUmTl+rX8nMfjcRLIkt1gTlerVSZWYzVtuOBBDPsuWU/mjOf2syF5Di9pRW9Lp9Y6VzR4h0CqYNxWeOYB3XD/TvdL5nteZo59cOIB/OQkH+vkpaFOXpPhdllGzjwj6sFPIvOOBVdKuZOgYxueGbuPDDKPkBKOP5GJse80jGXPsjfzQP7RG9bdAyrG73arqaa2w/24Ey9zEpr3GCO/s/74Npdb9y1fz+u9D5CYOAfldV1n5gZBc4VFCaTCRjhTBGh1YWCBXaD5jjNZGEvJWz+Xzb6U7hQhqN7psEMI2u/3Rdiash+oqt7uVgSYmLOWjA9HBUDEYflZAxgEHARGxY08Si2VlsEA2jnzyE/Gx/UYgwcV6TmVM2cOetzJMfcwXawZjpXgYbfbTdZqfy0XYqPrfr+fMIMOlpGreaaI+87T5c4KZfZEpcyF+WYuXN48y8j8w/wwN16Tj9FNhmzMG2RxVNw/HYapSZc1Z/53u90ElJKRcIBPpufZs2fXPTinDGqQjwJ4EjtL29aqqnR7e6tf+ZVf0WLRaLvd5uf85JNP8pqxXnwnM5ghaL8/ZOeKk3GHyTyydrw8yHRAyZ4M12MMK7omKW+IZl8SmTM+T5lO6nJVznzyYDqtqyb65BltlzHGn8YqPTw8TLoI+jpVVWr57uD5+fPnappW/bXFLoAfeYV9ZD5ub2+zY5eU94Y1TTM5cHTsBy2ucgjoeHx8zPI+J3RwqIfjQev1Ws+ePcvz7/qGnjmj6EAYkCOVQBCgwnMAGp1NdlvB3wHW6E5q8HHIa+ABOH7idDpp0ZRDCOf+AUAHSzwv/aK0j0CejdWMj+9QdgRxIU2zvui/kynYOP7GNR2A4QP8oEzsNwQF93XykM/FGCeA0Z8bGfesA37Wn4113m63+bvOxrPmAE/3y27bHSj655bL5SQTjRxCrBAQ+1o4weV2xoNKsmp81gNzAjkH44wRGfC9WfzE3gBi2+saH49H3d/fTwIiJ3JZB57TbYkTFJfLRXWYZiEyZhhHRZVMju/d4L7cGzlL81kO3/XMlbP+DoizD551m+T7yANjcFnFz87PM0P2PIjG9hfyanpuoweIMcZcCkymyIMuJ7GRCfSX6z179izrHGPgefHJ+F4nUiGmfGzIvesC64ju4++wJeAODw7qqlaIZQ8ba+aBNMEbc4JNwA54wJE+2ytUJclAkOZkGGPjGb06q1R9lIxR3ZRGEi4TdFL1vVpO2iD/3MeDKV58zgk7z4Z/3td7HyAhRBgxFsP30EhlIp0pw5B4jSQgfn5tFh+BlUr3LoQRZXe2jX+7Yygb1cPE+XF9Tslm4Z0JSWMsrZt5tjkAdyPKNRin9Hb3EJwae1F8TGSRMCQYBy8JdAPgGS2p9Pf3Oed7i8VCT09PE0bC07c4I67njLOva9u22u/3E6DEZwG8yAgGzMfjQJzn4X1+J1hiLjz7wnpLpQSR+2Gk6rp+K9UPuO77flKCwPXO57MeHx9z9xyf7+R8k0GkrKoE72nfVd/3ur29zc00vPwAkOLsHgaJGnxJufXtarXMzsAzRICKw+E4YcwJUpqmHMDHvNzf3+f7+QGFDgqdcQeMkJHy/SY4HQ+gXH6ZU8CKt9Fv29S5zZsOMH4YzbT3Z5rFKDpZTRwHDHX63JCBBusQYznbinnjZ5pTiS5wrJGzbJdLATjIdwpsDgohBaw0i4BEOZ/Pev78eZZFAPtisVBn2fN5Frk7X3S52jeArneE8mCQ+wF0CKggUHw8njVhfbCjMZZ9OqwhOuL77Shza5omZ1B8rwhr4pkqD0T9+z6G4/GYzl+7zhPve4baCStfB88EOsuKzFKCCCPs16CMCLmAUGAvrBNMgIC7u7tMLDA387FWVepUKCWSCNvk92K+AJHDMLwVhOMDuLY3MjkcDjl7NWeGPTgEnALcPCNJ1o11x9Y5CPNsJcGGAz3u6wCTIM9JTb7nhAz3wd8g1+i1Z5bcv7OefP9dvgIiCBCnKnWL2W632edhzxxMQmzQDdN9fLExo4KmB8XzSnMV1XUl2+eEg/tJfH6et7pRvSpZFyf7nGTl82RN2uVC0e4/95k8gxOCc8yEncEWYFN9fd0fONnLZ3hhrzzjOb8vMsXco5dO3IxjyShzffy62zKuw7XmGSL8GZjFgyeu4/MzDyJ5sR7z8j1JWY94LnRA0ls4NXfIU9TQlwOuh6HsZ+SFHDDWKbn9NjHO8yArvNAF1xlsKtfzOfE5c7zleu74etGWPXBf6/XeB0jUgDuzB3OBg5tHoC6ILnx81gMNvofCIJAIoLOermh8xjMFzo6lzxbA4Uz+nEXid8BF33diL4gbTs+iOCOF8Pj1ADKu1DB+jBFHhpHyvVU8K4wdh7N5zbGXfMBM8zssgrMkaSyFJfV78/J9Vr4G+/0+Pz/GEiMIEPK1YS4cpHFNZMQNiFT2q0llw/LcYM7bfGIsKIGh46KDLUq8kMu2bXMmxjMZviYJnJSDNclseXlOVZU2o5xFAQvGnPj1+T4BF+w6AC4FXSX17gYa50fwwvw8e/ZMbdsoxjHvQQK08/IsAutIwwJYIfSP/TkAXebS92J4ySvrCZg7nU6T0iOeD73b7/eTds9J3lI2CHIDgJL0OO3pQe4Zb7p3Ca7nawgIZHwwn8MwZpnlsEoyeH3f6+Zmk0EB675er7Xb7dW2iwwiAeV8dl424kFJuywsvWduNpu1dk+7SYacfSapmcQuAzl0aLlcaohjzibe3Nzo/v4+AwEHBpA+zBf3gdxyIIf9YWyAI8pVvO2xl3RwbW/TTDDsjpyxpaMICovN9x1UH4/HSYMYvu+ssAfg6JQH/MwnthswjBxgzwEBfJfvd12nTz/9NMsFn2F+vHwHO0L2hPFiF0IoG9XRG+STbF8IIevc3d3dhETDzhPY+Vlk3mTBgdS8wQ7PQBb8zZs3EyJIUg4wz+dz1kPk1YmdOXhzsgn9dJuP7UM+0A0P0iDrIMx83Tyrg11xwtPtfN/3Oh5Pk8CKZwCHsL6ljLfOgBW8c7lccilxFabdQHn2ruvU9b1imB6J4dkz5pN/M8amqdXURa8gKfBRXZeOJCB4Yz7Ha2WfEwnYRtfpd2VIXL55XrcTTkx4kAQGcEKDoNSbBc2JAa6DnEP+zW0CJMJ2u52c7+TkiZMyrKWTaa7ryBzy72VlHsyznjxfqfQJCldfw/eQHeSHuXJdYK55ZsYzjqPaplU/TPEk8smzse7ut5hnnhkcE0JQ7AuhDjmCnHpgyD3c5vqcOe520tmJci/fG8bPfxbSex8g1U2TwTBG14EShpkyM4TRN1piFKTpRDuIdbaJa9Z1nU+55j2pbGh1dtEFopR8FFCAQCCIzibxKpvTSjDljoLnBADwHIzJHbVnaZqmyfu15qdwe6DDc3BtnBkMrqTsaFxxUSZXylwacDUCzIFUjK0Hi/7CCPA8OFxPTxNgeYkG6+e/A8Jd6dzgdF05y8jvPzfmfJ6AyoENssjvflCig7u6rrOBhn1HztzpEQB3XSqtgSHGKAJaYkzy8erVqyuI3k3KIcex1LHjHKRyThHyx5rWddDlkubCzw05nU7abm9yxoln4R6Pj4+qqmLIAIOsg6TMSBOYpaDn8s6sjxMKzso5+8tzcm3mxUkC7k02C0Dq+1HSNaIul2Iv/MRzmoN4JkACxI9aLNoM7Fg7Wo8yDzHGrENJpwhQNpMSyePxoPP5+BbLeTqdrkC0yXucUjB1o8fHx8zw+/MDHtbrtZrFtHQKWTifTnr58mXOpGG7IA88EGX/WtsmRyspky2AfUgU30w/juMki+ogkbEC0uu6zh0WKQvkebGTBIEALAd+PANAAVvm906ArlPblGY6+BayOU4QeHbdgRW2kDkjoMbmuV9BX5fLZS6vxN9kAGNVDlzbx41NnbPh7M/zzAHzit1zMsEJQuQOW4WPQca9ZAq7QUCOfHtQOC/3xfawFsMwZOLM5Yw19M50zDHBJDbDnymz45aNgZxB/r1Cwe8lKTcwYD3ABmTJ3Q8xB/h2XwfWiLFiwwCLBDrYZTLMzBs+nq6ijPH29jbNr4L6YcjNZjy4q6pro6NYzkz0gMYJK4KB9XqtOqSuks7m89MDH37PQXxdK+ptG+u+GczlQTfvO3gGGzg+c31w4oKxEdgTTHANZMr9MrLOGCB/8Cn4ESdbGIMTBMjv3H7h35ygZk0cn/FMnmVxG8E88QohKLzjvozNA2EPurDt3NOzcKEuWS73sayF4wrsJC/m3kv50jhL1scDP5chl0d0xtfUbZaTmZ58cHkJIeh8KuTr13q99wFSd+k0LEcNw/X03shm2sI8eYeNpISNQkgdV9L3Uqc4gAXK55klVzhnN3BU7ljcoACK+B4Lme5RKUaEcFRdN+r7qK67yPfiZENnAoFxxyl4FmceXKA8DjZgXJxNktKZC4B5B+YoEkLu9eo4HDIuzMHhcMgsKvNDAAcYBxD7fNGpLr2imob9Ven3tp22wk1rW9giNz5uFDD+zibBDuJEWSNnk9xpwsB6Ot9LEzDGbZu6fiFPnhLHMBCEUsI0jqm8EuMIwE1tmS968+aNPvjgg0l3mmHodXt7p/V6rcfHJx0Oe10uXdKFkM4gCnWtS9/p5u5W29tbdZeLgqbAzQ0Y4IIAkj0tY5RWm7Wqutbt/Z2enp70tN/rzZvXijHo/v4+O403b94Yw9aqbTlnhf1niwKo+/4q71HL5VrbbX0FcKvsTJhjZ+bQQWeJcZo8D2DFmT7WzR2zM+tk+ZwNXK04mT61YO/7kxaLNmdtaKndtm0+jDVl0jotFmRlSne8ZE9OWi5XqqpwPR9oodOpHH4LoHJZBeQlG9RpGE6qqnIOCIB9vV7r4eFB2+32rW5GHBmw2Wx0OB50upwz8GUfGnqLTfHzJLgHgQJZgpub1IJ8jKXsAbDy9PQ0OR8LcObgwDMGDmTGcZRiVHe+pO5aw6BF0+p4OCgOo5p2ofX2JgGFrtfF2P3D4SA1UYOkTqUTYAhBIUZ1l4vaplFbpz17x/1Bj4+PiqbjnKWCbCMrnoGSNAFQsLQ0OEGnWCdkF7/kWVeuSRXEcrmcZDaRX/wUMo+8zxscYJecYScQ4RVCyP7EzwHCbnIPJzBc57gHmfJoMuCZFe7ve2FYe2TFx4R/cwDFT4JjL5dkbubBKlk/91vYYeSQfxOooTcOwCDesOdeSeBzcTyfpHgt8VJUpZLZRW6YUwLOua0CuPOe71ljbc/ns1bLlZqqmhA32S+2bc4geSkx92MOkJfM8A+jur5T3dSqlLpMKgSNMWoYU1BG0OGBklTWibV1osMxxTiOEzlALr0yA13yIIxg13URO+2Bj5PWPJ/LFrLJOjOvfj3PWDgedBlAzj04wT6AG1hzD0CkaYkmzzvPXDOXZRyjmmq6r4e/MU70CftyPp+VWpJW0jiqbmrFoOsRB42iopq6lKWzVu43kD3HCU5k8TsYZ4yDhgj5MqaW4mPBk57hZx49OHI5RTd4LrdJzKWTV5/39fU1BZf0L/7Fv9Af+2N/TF/84hcVQtA//sf/ePL3GKP++l//6/riF7+o9XqtP/gH/6D+w3/4D5PPnM9n/YW/8Bf0wQcfaLvd6nu/93v1i7/4i5PPvH79Wj/wAz+g+/t73d/f6wd+4AcyK/31vGJMLZsTE9Dl/4YhlXns93s9Pj5OzutJf09tftlHwCZnHBuKIJXMDQzPdrvV7e3thL10J4Gjadt2khHwxU8CRV1uOkhzGHotFq2Wy9QK+e7uLt/DMz4AJ0/TPj095fKCrutyKRFlKGQuCO4o+XGWBSdDAEVDgzmQ5jkxOLe3tzno4V4EHOv1OndN8xIfjLKXjFE20HUXxTiqbekq1KvrLleAWEmKV5AjSWnjet+XEjPS5XNlv7m5ycYEY7VYLHKnMUpclsulbm5SRoSObuv1epKNgrV32XBmA2AE4MRAezDIeChzQdGl6Yn3x+NRm81GX/ziF/M8ATLqutHxeNJnn73SMAzabNL9FCpVdaVQVbq9u9ViuUznbCxabW+S/NLCmwDEgwIMNd3dACkKQUMc9bTb6c3Dg5arpb75f/qf9MEXPsjfAaDf399npi3pVqvUqjsZzBDSWQkpyGjUNJw/Uauum2zsXP5wMDi0XId/Bfh+3gbz6N170Gme1zOP6L4HsWQqOIg1BSOLqyyErNeHw2GiayW4GtV1qeX8cpla0+/3Bx2PJ71+/UaPj496etoptfEv98bZUz4qSS9evNRqtdY4xjyO5TLp583NrVarVS5x5PvOiuPYttttHt96vcklnhxGyXfHYcwyjMNn7vzgQ2xOJo2acvDwZ599lp04dsdZYdYOQO6AGBvWNq1Wi6UWbavlIp0P09S1Xj5/obqq1F0u2j09pXOblkttNxutVyvVVaVn9/cKkpaLhRTjtXVxlKLSOU0hHR/w6rNX+vRXf1VPj49ar1b64IMPsn3lIG/Oszqfz3rz5s2EVWbNPYPMfDBXBJvVFcw6qwv4cBvKXlRKGSG0kAcylvgVZNUzNsj8fr/P4/XgDD3xQAt7jV3woICDSodrxsJtmssC+ukBIWXDbBLHFnM9SA3PDEjKtpaxY2fwF+5TmRf0gIwL9hv54rk9QPf5lKZ7OU6nU9ZxWogfDgcdj0c9PDxov99nuTifz7lV/e6w16XrdDge1fW9WmPZGSM+wjNe3kTFs0boMfbq9vY22y0qOvhbVVVq2iZnbG9ubial3Owl9aoSCL5hHBSqKp/PlAIk6Xw563Q6TxohuF3tu3KwOE2deAYnROiOyjjAUI5B2DeMfNKpVCqZEw+8uQ/zUTBiabSEHAP4wSpc14MMB/7IsO+h495OogLSmX/sm+MGzyCS/cEXOVnNy4OT9Fxl3xdj9rF41tKrG9p2oTGOGhU1jOMVDyy0WpejClzmWC/sMzbaM0L+3zxTfu4uUghZdrqhV9f36Uw6e3b0GrlnvMwXgfN83ZhbSEMnqz7v6+vOIO33e33bt32b/vSf/tP6E3/iT7z19x/7sR/T3/7bf1s//uM/rm/91m/V3/ybf1Pf9V3fpf/0n/5TPqvgh37oh/QTP/ET+of/8B/q5cuX+uEf/mF9z/d8j372Z382C+73f//36xd/8Rf1kz/5k5KkP/fn/px+4Ad+QD/xEz/xdY3XmQXYkzTBpbzMN40nA1uMhu8xWSxaxTjmsz08nYdQICDUI/vnvCwJAUMAHNQBALg238eZANqcuXCwA7NJVy5AOQwzHaO8BhXB96gfR4oQInAYAQwm12ZcgB3u62NDyVNJ0HHyWQwXjtxTz3yGDBelDw7spBIgEtigWE2z0GJRDuRDAfm+G23q/N2w8uxkoVij3W6XHT7A08u1+Mm9SulVOeOBjnE4MEBi3/f5oEVnkJg/MgDO0tP5Zc68YCgyK3gdAywZ67rZbFSFoNWidElzucVY40TJknZdp/VmrWEs7TRvbm7yZm53BJSKAYoYg8sz8g6okcqZVshuCCGf6+NMIuuI7MyzEL5GHtRyX2coAbWuC96yGLvibCiAk+dCv8dxzK28GWPf97q/v1dVVbksh/HR+MBZaGwUNmQYhrxhnzXZ7/c56CZgdn1hX5KkPAfMUdkHkfY39eOgp91OL168yMw/pVJVFfIRCsgVY6BDGhvN0ZnD4aAhRikkZ/vBBx9IUt6b4vLI+qMnnp1GHsg+O/nDMyI7HhBSriQp72d7F2vL/dBtytCaJjV92G63+vDDD3PZkt/HgRvZFpd9HDpOG1nykmAHpZIm+/JijPm+AIbkn0omhcYpXNvHh83DLhMQEDDA/POazxH2iz1s2O05CPHAAv1B9o/H44TtnzdNcpKM58NG84xeVoTfYAxOOno5Hn6Ze2FLvTPZvKyHNWDeCh6YliCyhu5Dua5n3C6Xi6q2ycDfCUevKHFilTb8rAfjwV6xRp65IAjuu04hahJEsmbD6aTVZj2ZRwf9HqAiA/z0DLpkhw9XqZMaf2OckjRGqbH1YV0d/+ADuaZnKjzj4hgFPIS9Y4xzDFJV0wYi+ERwg5fmMQfuv5Bl5IZnRmaxcZ6Rdf8GNuOZua77amyDk0M+ZvyS64PP8eV8UR3KlhJsGXLFczJv+LquL+XAdPQF6/lzerXGnDR1ffX15PsQSegSY+c7Ppf+b2wJ2Nqxmeu1Y1qw/zxY/HpeIX693/Avh6B/9I/+kf74H//j+eZf/OIX9UM/9EP6q3/1r0pKSvnRRx/pR3/0R/WDP/iDenh40Be+8AX9/b//9/Un/+SflCT9t//23/SlL31J/+Sf/BN993d/t37u535Ov+23/Tb99E//tL79279dkvTTP/3T+o7v+A79x//4H/VbfstveWsssLO8Hh8f9aUvfUn/+//2T3MNu6f/2nYK7pzFjLEsjJdXJeNfSqFYII9sm6bJqWxaA2MkcewImF/DmRRXWi9JQ5iSsNUTYfNmDlWlCaj19eJ+KCGGycEjJXHuAHwDKA4QYfS5ciOEY4B1pLMQAu0O14MylAnA6JkBNyawPsfjMXdDQkkxDrD7bVuYI+6BwwBosjZu+FFOwBpjRo5w+J6N6vs+G0LAmgM+mE3OuGF9nc0hK1Qab/STmnjkDoMKyMCRSNJXvvKVScmdp7n7YVDdTvecISuLdqFgjhYgMd/XwOf5d7NodLk2aQDIA74vp4tOx5OeP38+2cjp5RbugAAKyB8OwxkqvoezwOGiI+iLgx7mbW64HTgxH4BpOvgxJmTGATnz6uNwBhw7M9dBrsfz8R1kiOD3dDrp448/1n6/11e+8hU9f/48Z754Tpzf8XjUzc3N5Iw0bBPzic3yunDWERkex1HtMnWy416Mab/fS+Oopi514u7kGTeZY+zBfr/X9vZWm+0m22nKelgLSjd5NqkQFMwrIC/buGFafsF3sJ+A/vP5nIM3Z96xwS53vLbbbZ4TJ4SQLXR8Xp7pZBKBJc8UY8yliowFx0/mBR8BEMJ+EtSi++gkBAyBOPKA/3OQXlVVzvw62eGgD1nxEiZkPsY46R6IjGOLnFhiTuZ6iQ1gvTwwZeweJCJP6BHrit/ET2OTmCsH+NyX9s5zsMnL55mM2vl8nth7QJ77A2REUt6f6sFTDparoKquc+bqzZs3OYAna0Q2AT/EGvpGd+bJyVQfU4xRVQiqQ5VlDd9dJZZYqkoJptsBJ/g8yHMCizHzzJfLRYu6NAjwsYQQFKtkI3g25A65dBwhaRLIOMk19xdgFCcmwR0eSHjQG2PpoujZFAIdCFleHojiA1hn133GxX0dLzrW8bn05+LZ0SHmnTVjfbiHBxtt214Phe0mfhKygesip4whxqiqqTWYvnMvb9KBTfAMnAevjJ919Ln1rK5jOHTHg5gwTveoud/kWeeHI7u8omusn/uE0+mk7/pf/7AeHh4mpeHvev0P3YP0C7/wC/rkk0/0R/7IH8nvLZdL/YE/8Af05S9/WT/4gz+on/3Zn1XXdZPPfPGLX9Tv+B2/Q1/+8pf13d/93fpX/+pf6f7+PgdHkvT7ft/v0/39vb785S+/M0D6W3/rb+lv/I2/8db7TDITjcJ2XZh0PKOeOxnDctgfgRGsQN9fJhGsswvOPlAGgcNgLCgfAQxnBcGoS8rtqLuuy6VYOG0cOE5iDjSTwI8ToAxrhcJvNpsssHPDgWDNGdXnz59LKuw/jC3fR1hRdMrDPPPhztZZDMBH16XON77pdc4WYCylwlASkPp8sO6pDDDodLro6ekpf465pIGHK3A2NNcXKXE2ZOKEvXmDB04ASEAeisocoxfMIUYedm+73eru7m4iN9zPgQyGCpnCSSCPH374YXaeGESM0XKx0BhU6tStLbNiVFs3mbX0QAi5ZQ6RYUBe0xZWH3YYEIfcIOeMh7H5hmjfTFxIjTaDUdaKTmjOOuLIvHzMwYMHFRhUd/pkJpBbb8qA3B2PxwycIUBwmj7PjMefCdsAUEe3nVFjnMjAdrvV4+OjQgj6hm/4hjy/2DayOjHGXPLGfPsck7Hivg4gPZMMezj0ZS8RDpO1juOoEIuN6ft0vtjt7W0GepTz0uwklR0dtN6sM2BmjBAC2D3Ghgwy59gQb29cWytjD2SwibD1ACZnyp14IWB04OEsNnKEY2bdnehxsEKAxljmAN6BOplkQDzzRxbOg3zsDlkcZIt70XIYQM1cYyMIXAFxzrgjj8xJycKnrCZ76BiHNA0oCHCx/dhAACtyCjjlPtg/fAWEnLPyriN0/ZwHKL7mHoAhL24b8AOuBw4sGTv34Pt+DeyJlxoy57e3t9lPTvxXFdRd15/SwmEYMqHx9PSU58Z1DznzoNV9JTiHe8WY9tHFatoeerfbpfltap1P0woOzwr6PcmGSppkD9EZfBPZIyeIkJW+H6QreHZS10Eu8u9z7hmEORFDwE6gDrgnyKmqavJM6CyfQ7Y9CPTGSMwxto/1hIDxcjJ027/Lc3kGmPlB3xxPegkg9+F5PThCNjx46PtedVXICmyXy5KvDeRgjFExSLWmGXdso3+PeeM97BQ2jTl3+0hjEb6DnoBj5/Zaw7QlOLrI8yMLfj3m2e0JY2UenLz5PK//oQHSJ598Ikn66KOPJu9/9NFH+q//9b/mzywWiwy4/TN8/5NPPtGHH3741vU//PDD/Jn566/9tb+mv/SX/lL+nQzSnFkrTEwRQI+Yk7CmCQTkS7CC0wXx77Ztm52dOwZK6dq2tGbu+z4ftIdgsaCLxUK3t7c5sPKa2ZL27dW21eSeBGdJ4YfJqfAILVkGaXruE1kvFNaDF/6DfYVtw9AidBg2gi+cL/cAaBLBI6gEODFGvXjxYpKFgmFnTM6aesnLYrHQbrebPJdUOr2kphuFbeOEey9fg2Fxw8R1fB8Rz+msl382hJBZajdozJEbYwIIrl9V6fBGDIzLJdeGJQVceAaD+yALq9VKu91uUmrFetV1reqaQQKAAWKGYZDqcojsu8Ax8+QB7Hq11qjCwuJE1+u1LqeLxiGBs5ubmyzLOA7m3rMP85ISz3qyMZ759o5vHiAXQqTLOrvZbPTs2bNJh0kCSc/ieLDNmhEMOVhGj/05qEf3rKXLFQwb+jXP1iE3zJ87SuwCwTDjRYawNzhkACt/c6CNo8FWoNMxpj0Jwzgq1OU9ApO+76Uxndru7POLFy+yXfLnIBhYLBZqlkmPP/300wyIHeTgUD0jilyzx8fZ/rqqFGIJ9sm0eM0+Y0emmXN+EhR4eZODrWFIe2pg9pnzeWvo5XKZx47NklKAiw6zfoBSZJvPMscuD5AMTdO8VZnA9R08OyPNdx1w4sOwpXO7yTiqqmSzY4z5DC1JWWekEpgA9iD5fP8Ieu1+mHlCFrGHfA8ygnFBBniWlvXxIIFAGzuLHkNG4psBs1yPOWItPCtNsEjDFbKCcxvizRvmtnwYBkWV0l/mD1IGWSVT6yDQy85Zdx8vfoQX2GAcoy79ZcKwgz+iyv44xkP1g1c08Lv7I88yOdgNV9LTs9MEBelHIUxZG2wldogxYc95Prdd+D/KTfm8Z/ZYO9ZzTrBmX1iVLsSebeDvPkf4TN/L7LbZSVPGy7WlaXMHB+3uo+eEJt/juSm7c1+cvxOCNMaM4dwnerDn45GUS59ZV+6Fvjl5wL/n18beorNl3UtQyE/fZjAfX13XimPpRsg1fq2sI9/xzzp+RQ5ijDocy0HgX+v1/5UudvMIzTMAv9Zr/pl3ff6/d53lcjkpP+K12W60XCwnyt33naqqOL4MCoWBL6wZC5K+L6UuaSW74AETAu7AmSyCs9kYZxQPgeQZvKyP7IMD6uR80hxxGr3v45GUgxAE009uxuAAjDygQ7AwVjgInIkbZq5b9m6V9PBnn302YQ6lohSr1Ur7/X7CvMA0w2wD2MnCUSO82WwmLcf5nhstN4B8b7VKDp4AjmeHtWMczBtBRVVVenh4eCsTw/WRq8yqX4Gll+Qhz8jI4XCYdBQC2JGd8xI/STlYBvQRFCHvnsFknpGpm5ubXI7kGa8xjhr7tzvgOIvmm3SdbeJ5mDeyA/3YS6GUNiFvp9NJYz9KsQAoZ9kBd+ybuLu7e6usB/1h/1yMpRRJ0iTYYO74XF2XElTm4OHhIcs3cwcjPzey6AcOmY3XNzc32m63WR6dZXvXfg9nprEpBIqAJ89kAR6QUwKMuq4zwAEE+YtOh+gPJA0yDMCTSo3/vOVyztb1nYZYSojRiaZpNHSd6qoE3ATjDw8PWq/XWq1W2m63evXq1eR+N3d3Ol/Oed2RAR8PGUXAE+sD6EJPMyhSOajXy+A8s8LzedYW/cG+FlA5ZtCGTEGYoYt93+eN4VVVTUgp1tqzGHOGGZuNrWGtXB593ggabm9vsx1Fxubky5xB5Xk9m4X99hfz7QAI+eW52cy/2+3ys3qAzjz6XHsAwpj4O/cAiHkpooPsdBxAlf0n13QgKpWuXE4eMC4PyJzA8ECE5+S+6Jrr6RyMOZPv2Qzu7yVnMUYNKiWHrDV/95Iw7ot/xQZ59sqBOnPspBKZXmx613X66KOPks+vK20XRffm5V3YJNbCK134u4PYJAelQzDvg2HGoNQERcVPeZWNg1pIMsbs2SX8gDStrHAyGB32AMTXGLnxgAQdcTxIwM0cMwb0HjlGFhgXsuh2Cdnw7D1z70ScB2o8y3xN/L7MXdM0GvpBQz89K8hlc07u5qAlSL35GSfL+LzLvgdvXBd5JeD3jNucrHLin3twjUXd5Iw15KNXrzjp63Lv8zQfp2cpP+/rf2iA9PHHH0tKGaBv+IZvyO9/9atfzVmljz/+WJfLRa9fv55kkb761a/q9//+358/8yu/8itvXf9Xf/VX38pOfa3X7mmnfjVlZxHWh4cHte2020bfl5IzZ6ySYJ9VVbVC8I1y/dWRXSTF7Li91IB6cOrMify94w4LOS1BatV1g8Yxqq7TuS+XS6flcqGmqa6GE4a5VtNUattSQ864YTwJ0DCyCKwzH3yO7zvwnwN3SRNhdwYNJXEj6CAcA+hMIYYyPeNS2+3mOh+9Vqu1YhxVVbWePXuum5vb6zMoM5b7/eHaCSyq78fr2tIhrdSkYtRhHNNcN5N2sJ6F8PG64nuWjbnAEWPQUUgPSgko2J8AOAAYePCAo+V7jA3wBKCHtWGOvTbfAyBfu7pKZxHEYdDYD6k+PErVtVzJz7fx9XbHDnN6Pp+1XC3zZ4ZhUNu0CjEoxKA4lj0YODgMNJkuDPxut8uMn5c2+r4T5sn3BUlTB4vBJL3vMo9jYTyScrANsHGmzPWUcQCY0WvuOS8ZZC2c+QI0ImfYBe6LrPo+BuQCp4P8OuMJ0CNLii0D8HmmDTIHGUMvuq7TbrdLzS+ujP5ysZBCVKWgqKCmqjRWlS7DdQzjqNVmrRiCFqultut0YO3j42PWAzIrHCbMng5sSghBVeo1q8VyqbpuVCuora9rfpWt1Ma7UQiV+ksCuV3fZeeKbPr+GjqdzQNvWH6em3XzDAL22Et4lstlPmeGrAdyRRMHsi/ch8yyz7mTCJ5Ndv3FDjVNk89awsZ66QjBtmd+AQxk/bw7nmd4eEaewecNkMV+Jrrt0VHPCRHPYqBryL7LIuNEx5EB9Ah55jv4IcpG3R85Q4xOeRaP52VM6CljmgcpDmIhmQDHznAzZn9+B8rcg/E4CKZTV13X6oayd3pQ6gLKNT0wQB4d1HI91yGeKZctVZVSJ95Go2I6iyglGRT7XpXJCfPKmmPj5zrj2dmJT6nTPpau69UqSkPyMf0wKFSlVJy58ABlHnSyP8vxE/NP1YNXUGDj3Ed4dgi/5Jk9J8md7PYDdx0Pvov0BE+hl773yGUFfOSBjhNcjo34zLt+uv56YM9zduxJGweFOEohaLspZ8mNkmIVdOl7hZC6PA9DKn308Tox6bLHmJE71sGJRM/YOcYk0HYy3jHKOKZ23+AtruGfxea6XWENfc4ZD3bk/53X/9AA6Zu+6Zv08ccf66d+6qf0u3/375aUhOKf//N/rh/90R+VJP2e3/N71Latfuqnfkrf933fJ0n65V/+Zf37f//v9WM/9mOSpO/4ju/Qw8OD/s2/+Tf6vb/390qS/vW//td6eHjIQdTnfVGyRmRLHS2CCBg/n8vGZKl0TnNWKrXtTe18E5g658VLYLKkZVFcjDmMG62xyaAAkCnfILBIJVStUrveVKqU2n1ur8ocrk61ugYJpcMcETbCjGBhgHDGjMfL7nB87lgxZJKyYvA37oeQu7NBSWAkHORzdo4bM7JAIdTXoOqixWKp5XKVsywAPIBLCCHvLVqvNxMDm1ojBx2PpxyQugMm0OBZAaY4AIwpLLGz3AA7B7vO2PC+G0pYNEAcRh8w46CYwHYcx3xf9ImxYhjcmR0Oh8kzYmgAGDgF5C7qmsHqz4XhsqypP4c7HVhNPidJVajySeJt2+rx4TEZf8scITsuc2TOvvrVr+YAdb/f5+CPOQFU875vAnXAwt+cbSZA5Z7IG/9mX4RUmCdnxt3Azvc9kJFhHRgna82ccV0CYxgyAkzfuzVfax+zB0ToKwek4sixcx5Qc28nTBaLhZ6eniZZZPa9EKA3dTmANY6jgqTz6ax+HNRfgTtlXIvlUlHpLBRn5uu61m/8jb8x2YxxkGYlWejJer1WZ5utM5C5yoHr1Bj7tJduRuywdjHGySHAZEs8WMOZOsnhABt5Yh5ZtxhjLuulwxy2g9JP7DiOm/ddNxkjsulAHPuAPXf/xHWdifYyKwd7/M2zunyX/XDIGuWcrA0y7cCS5hIEf/NnYpwASc+ueHbDQQ7rgvz5dRkPpZN8Hh3wjAOlgL4GzAM+2Rl1z7Y52YLMeJmyX48Xuujly8yVA1fkhBLSPGdGhHHfqFI9wfzM/Qo/PSCYvxLZWNq6xxg19CWT3fWXvD+Q6/FZPs96YdeRVQgtDxy8aqJpG43XOc8AWSVI9vXw8monsbGLEEReCiuV8i/0kYwEc4k943q+R8sDQpdfJ6ux6R5cMz78LZiS63k5q2MpgjfPQjJnVVUOhvWgzUk8ntcrlHifNXK/Ute1muscV1Wl/TXIRM7dtzdNo7opJX9kbbguMo7fdp300jtsh9tiD3x4Bp935MfxTAipqYj7XAJP1siDnnlPAJ6fqhy3Ab+Wrvxar687QNrtdvrP//k/599/4Rd+Qf/23/5bvXjxQr/pN/0m/dAP/ZB+5Ed+RN/yLd+ib/mWb9GP/MiPaLPZ6Pu///slpVPp/8yf+TP64R/+Yb18+VIvXrzQX/7Lf1m/83f+Tv3hP/yHJUm/9bf+Vv3RP/pH9Wf/7J/V3/t7f09SavP9Pd/zPe9s0PDfeznQR0kQEpgWXxivHXahRpiZeMq6UNK6brRcpuCBNsDOfnv3JO86A0CmyxtZp7SPYKGqqvN7XmZ2Ph+zQUZpnD3HacMkeyCDoM+ZVjcAGHoPpGBtKKljXhhbCCEzLzClzhzS2pV7UWfNewDDvu+02WxzgOfGzw0LinF/fz+5lzMgPOv5fM7nlbC+Hgg68+a16b4vwOXEQZ1UwAT3hSXzzzhAxllS752eu58YZQc+MNDuTDC8MK9zB+MBrztWngH59hbJlJPA1nkds7NWDqowpo+Pj1kO3KljNJFf5oH54YwuMmIYPoJyxsHemuyEzXFxPYw0a4SeIz/8zsvBgJcROeM1D74oIeL5Ca68VAoZWCwWk/PFeHb2QTw8PExApXfTgy3HKTE+nj3GqNevX2u1Wunu7i6DF56H+x0OB93d3U0cM7oLQAGQYTscYABQ0CP+VteNFMo5E8hc27Yar2UezMVms8lAkxfyxH0ul3TQa28y5A7YMw5OYDmLi67xN2cqeV4y1U4aOBgg+EeevIyI65Ht8+yR738CKDmIRe64L3pEGTZ+At2pqiq3i5+DYWQRG+YMNvLmNomfHhQAOGDMsXXzbBJrRgMBJxNcblgnABjXwJY6MOEaDrq8TA4/7AQFc8NcevZqThhxLQ8yPNOArccuQOixpq7zTjy4z52DbPywfxcf4mOFbackk6APG7G6Vk54STRz6+BvbiOdPKH03YmeebCQSMX1xPa4jCIL/j1Jb+1/ndtEZNLnD1n3gB9/wD29THACluvSgh6/+vj4mLGXYzRkh2fg+2BAl0MH6e/ydW7zsVWQBcgSthgf6PbJgz4Pap0wQDZZJ353++zrygsZnmcusXnoLXbIcZcHVu8KtD3AZA6Qe+aK77v94HfG4SSHYwcn7twfZyJQQdXsGj5uLz9G15EFcLuPG9vt9uTzvr7uAOlnfuZn9If+0B/Kv9MY4U/9qT+lH//xH9df+St/RcfjUX/+z/95vX79Wt/+7d+uf/pP/2k+A0mS/s7f+Ttqmkbf933fp+PxqO/8zu/Uj//4j0+YoX/wD/6B/uJf/Iu52933fu/36u/+3b/79Q5X7SIpC8YHgAmox8E504vDYhO3l7Ss1+sJYMGprdcr3d7e5O5LfA/AMAxD7v7jKVmpdEnDcJY++FExKrOP+/0+36/vB1HS50IoTc+Lwdk5uMHYwB7SKQ/F9HbSlDUQrPDMOLD5/DnwZY4IOp49e5aNPmeQ4DhwfGkcjbAHTdNk5henNC/tGIZBz58/12effTYpe+P6KGTbtrncyp2Pl8vhDFlz1oSAzA0xxl8q6W2yYxhnBygY13lQDrjychmMgaRJW0zGg7Nxx+9gxQ0LhoIx8gwYRwcmfigtskbw4lkznzPkSCqG25l5N4yu48zRZrPJQJ9reIkIJS7ok8s6z4FDoNsVThK9xRF7cO6y6Xt0mAt3KoCiqqryHh93iM5uOkhzhvFyuej+/n4yHmdevZU4AMj3TvFysoB2397U5fHxMQdgADHmjuYWOF1kFpmiBJXADpn0IIQzOVabdWbcN5tNLsskQBqv+uE67qQTwRh6vVgs0qGtM9uDTEAkeZaFw385/4s190ATQMi+BeYbGXDZ9BIxqZRglqCwZCCYFyfgfvVXf3WSmfKyScAHNhbbxJzwd54B+cLm0ZabZ3dwiJ4jo/yH3GHvnSkniPJ9Wp71QxZ9vh1QcT8AoWeoPHvi9sZBUYxlj6QHUQ5a0SO/3jyQ8qwHz8ja4PNZS74DiELPnXRy9h774HbPfRHAzO0ttoJAmGAHWWG+nHSQlJl7fy4Hpg7ImSfG7aQUfgvfwbVijFmG3Fajax6EMl8EUMgBOoB/JWOH3m82m8m+ZD9jkqAIv4XMeBkXJA/Pgyyx5qyB7+1zEsdlDluJbPHdue3GFnmQgQzwN/czrvf8Pg/AuQbzyn353UkFdNwDMdaEMmr3fW5L5xkS1o219ooi1zG3Z1yPF++zhvPAlfthX/066CLkHPPm5ZHYAMbp9oIxLpvSFp17uV10EtX1wgkFxskaonf7w16f9/X/0TlI/2d+PT4+6v7+Xj/1T/6Z7u/vJ2DIF8ODGAf1AJumafJZHQROLAjCkBZ61GIxPW1ZSovz+vXrDPJZYASmrsuJ1XOjH0KpK4edRlCapsosnLMSCMXxeMydRLy0iud2tpC9QFI5m4X5gF32LkoASWcaMZqS3tqfw5gBLc6qY8Bg1tLclLp45phAyDuZMc8ANLoteXkVZXUOCJ29xWjPGVZnWZEZxu/BsQeOw1DOhPA5xFA424es3dzc5Pti4JELB6du4Hguz3QBeHi5M3EGBobPA2MAoz874A3j5m123cH780jFaHpdNWN2p8BPWnTzvcPhoM8++0z39/fabDa51IsDeZPslzQ9oJ05cMbONxOz3q4Pc5aPtXWWnTWG+PCgm2fygIYg1MGNA2B030EoOsmaMjfoO0Grrz9678Enz+ggB5kFGGEj3IEwduRgzmii81wzg+G6UnP9DllI1iEOg+JYupfxjOM4qm4bLUyXADcxRi2aVkNfOsJht8mEVVU1aSzgASn3ooSYNXRihGd3thjdZezOantGlnHwPeQ9xqhXr15lO0wwMrfNzjZ7IwPkxuWNucH24BMA2wAnZ5IhWTjkkbUlAEBn+Tfj4ZmRLfZiYv+d7ICgcX3DniMbXgblGRYnZ+ZAh3Waj9PtGoEvOsO1PYD4tWypA17/m5MUfAbfiN5TOcELWXCA7/LmeuDr6TZ4GEpzIGf8u66T6pIB5m9ucz0olkq2B/vkwajbMQeRBUc02ccNw5DJD+Zxu93mChICHQ8aHLPUdT1pSe+dzBi/k7PoswfCbCVwmcB2cC23u9hafDjrgA3wc6uYd7f1LhP4NicWHaB7QIougI88C/f09JQJKs9muc7wLPMAjAokt8eeJeLzvHw+sOeM1/VkHozgf+bP5EHgnMjl+8is4w1ejjVc7tAvZJa/I9uUmcaYOqMumnaiR+5DkC/3h44/5xiEv7HeD48P+s7v/l/+f38O0v8ZX1Vtm7/q6b4KZ+5RLibzckntoMl0SAV0wqi4ga3rUvbAfVDeb/qmb9KbN28mdeYurCiBVAB6GkdptOBAKrE20xp/Z5FxCtvtNr/Hf7Avbdvq1atXWUAxVlKp7ZWm3VVQHp4RA+XgwdkYZ1HYaAk4f1epTZn/oHHUBGjAlACW27bNYAHgIJXyEEATYIqsxHa7nYAuBw+S3npGz1agsJ4hYNM0fyPg4PPOSmIw1uv1xFAiWx4oYLxwKi4bzCnG3Ev5MBqsDbLo2SoHldwHWXXgEkI5jRt98SyQO2YMKTLtQMGNoLO7dOwDCPDdDz74IANA5gIZ43M8E3PmRAKA21P5fI9584Yb8wDegyycpDtq1tF1petS5y/GjDymPXXTPWNv3rzJe+m4JrpBlyjYXN875w5ysVhk0M9cs96AFA9KyXzznGSrQijlfnWduuN5BsSZcV7OziuUkjMA3zAMGvoh7w9iXAQ9zaLV8To/nnFkPoOmgQ9zQIkl9pd1Z0xe8lPIltKCm2d1NhL7zTM62z53zMyR2w8pZfk4msEDECcpfD8TcuBVDDHG3ACBM9R8jAAISkjn+40AgwSqHjQyJuRhHEsWHrl14gM9dSDHnDFH6OKc8CA48s/P7Q/ve/ZkvkfUAxLPWAGEHfS6bSBAdKJzPgYn7dyvYdsAv074ISN+L4IdD/TdbvMsc//hoHoOJEMIulyD0LquJ/vT5o020E8Hrg74sRHoC7LiWwBcfhgz2Ib5Zb54Fn53P4ENZ3zINzqKnHn1A6Qma0GQ5eN1G8/zIgfcn/XyPcI+Tg8WkS3GPCfG53OKjjuum8sm43cilbUgg+yBrTcA8WAMW8nz83kPBOaZTw9+CLCkgoPmOMPlk/chv+eY1kl7nwteLrvoCfaCvzuR5LbPvzsvJU0XuR7fEKadMLkG4/WM/xx7zm1AloX+7cDu13q99wGSzPhh4NjMxv4WDBbOzA18VVW6u7tT13UThq6qqiz84zhqvb5VCMqOEDZBKh2KPEvizImzyjGmsoPdbqfLpexHwXggpH1fUpsO5BEYr9fmfozNFW673Wq9Xk+6FbEJ18s9qqq01sU4ki2BpWQ87mjSEsSJMM8DAHdK6W+ldTYBHSwnGZC+7yfRv4MPr1/3LIrvxaHeugSjJcOGXAC4na1jHlBQzjtyQ1TX5ZBhrsV9N5tNBicOSrguRtMduzMtno1w4+AMqaQMsgEeGDsHljgJxuxgCjDKWpE95T4eQGCYHUTzzOgA1yKA4TuMCwOIbA7DoE8//TSX9fkeOphGrk/WhYDaSwAckHkpUQhpoz6OvW3bSevY5XI56RCIbAzDdO+aM97ekZGyLmR2u91mB45OIq+Mn3Ix9mLxftGLcoAs+kfTF0CPgwnsAfrjpX2AFPTKbRZr53t8vJSKOUkyXGt/2Gewj9yEOAXByNf5fNYwY/W5R9u2qkJQH8v+EIJNLwH2IJ81OR6PWdeRc7JpzubOyyg51wj7wXiQU+YdGfJyQGelnTmu6zqXAnJdAmLPgqDf+Bv0f7vdTmQX0IStRQ+9YY1nfJhLSAgAKu9xlpSDNOQCYOaZBGeisRWAXQ9sfaxuZyVNZBxdh2RDT9A35htd5dmdwJkHqciXkzVuM5nncRxzh0a/F2P2IK1pGj1//nxC9KCv7h9ZDyfbXA69uZBXXyAXlPKTXXFbSvULcu9krc89pdDYCyeoIA88YODZuS5rzDoh+06MIR9go5ubm0zo1HWdtwAQNHrlBveeE5quy75GDpYBtzSKcRvg9tb3S82DC/yNE2v8zjwtjNBhbt2OojvMC+SwA3OCt3nZrwfUvMd8+jx71Qj6Na9A8OdxDMPY+d3JXr8OL+yQl4ZzbcbMPZEZ7K3bAydn3VbyWQ86uWYIQbq2nsfWZCJYqZNuVddSnGafHRcxZseS3Bs5Q/7859fzeu9L7P7Z/+2nZgcHps5m6RVEz/4E9sbr31LJXBJ8ouZUZwvgQAhJCVdVrWEoxgeHjiEAWDmghD1kbBiSuq6v3chW6rpS9uMAFIDjZVtc/3jcZ4PHs8+ZfxTZBRMwiuH3TeVupN2YSdPuQ4ARHBgAybNmCVzXSl36Kp3PqWNfXaPEhYEFHDEvzqo7wAJ8O2uFA/LncAeCsyBIcwCdlViaOOKqShun7+/vM0Al2HHWxR0Qvzurj1Mr81HYP0/nu1IzNi8P8uATptoZyXmWBTlgft3AsJ6sFYbT2XDGLxVGyQNIru3ziry74/PgGfnBeeFEaPoAKHz58mXOGgK8aHwC8PTMIPLHmgFumCdnuf05kCXGfDgcNI6p0cDDw4Nub28ne2e8vMOdEqfVs6Z+xhIvBx0AVmeanYHj34zPg+Q5E853yBa4nXD5JPDxbKODFGyY7/VgrhSSDe0uFy2WSw19r1Clc+bGmMzrYrFITq4qe2xCjLpcOjV1rVBRs57Ov9pfSykBag4mPAvAOhMsU1rLXAM0nRxDJvn3PAuC3Dhp47LhesF8YQP4jBMB2FjWAf1zG85/BLrOxDuwcV1hvABwJ+M8AzYnqHgG1xUHV4yfPWPz7Cv3xhZ4Btk34jvj6/PtNgyd51nRN5flecdZZBZ7xd+8JNJZbpdVZAC/4fbV7di71tj9G/92xh+5w4bS5h0CZ7vd5vvnoLttFKqKk1OvfvBKSChO/GuMcXIYKnbN/ZhnSpxkgzjCx/g+NN+n6ACXa3AdryKQlJ95Hjz5y2UAuWLOd7tdtoVkwfFbzA9y6VkbSCBseLKbg6owLeUvGYSgMY5v6YPrCL7Ys9FO9CBf2cdF6Xy+XO/dqbnulem6yzXgL2XvqXFNOrbEg/uqTo0I0oG5tfqhT0disBZV0DhMS1GnWEtXHFtpHAdESKG6VhMM1tigCqrCNZBuG/XdtYFMXapH+r6/fqbseZdSF+AQlN/r+k5tQzVL+l8a56gQqjz21Ka7zmvSNLViTJ2WR1uHuqoVgpT2nF+zViPltIOCiq/rh+EaUI1q61LezgtZQYd9X2CS3aLj+/1e3/W/fuevl9hJyo6n1Hsnzz03COkVlRojlD1Eh8M+OwVvn+wlHc5m3d/f58wSzg6l9oica0glwHDmLBnYMFlYv+8801BAbznJm4AC8OblHDhvxnY6nXLpGowjRsoBFMEf93Sg4sZ1nh3hOXGsbVvOCCkAX5ltY07ICjgbhUNk8znzwjpRjkNGwsc1B0vO5jszSSCB83aDzxx5LTpAyEslHWg5Q41R4j2yIc7sIX8eCDk7SWBLMMd7vsfIWSfWDfnymmBkBPl0xs9Bm7PCXB/d8Xn0rBSfcdDN/ABcPAiWEmtKEMr4+77PjUo++eQTxRj1jd/4jddM60U3Nze5HIXsCoAApg8ZYI2QEZw4c8OGfnTI5873fXjZm5+1BAvNuuLQCezZd8VzOQhFz5g7n0v2jDHHXjaDfjur6tkZ7uf2zgNy9BwyAfDlcsFaZOZZZQ1pza0YVYVKTdvo0pUDpOX2pm7UXtd9s9loHEYt2laHa8c2DzQZO3qH3fZx3d7e5s/GGDNzy/1YGzLlXlLoYNCZYPQfe+DE1xxAcm/2cTKnZNwBdre3txMdJlhALnx9nMTA/vL86CWBzDAMk8NMPfOLrnOmGTbZM6c8N2vFs7vOFqAxPesLmfT9fsicpLcyj+5Hse9OVHkASNBEAIlf8qwo6+gZMS9pI7jYbDaTOYX4GIZhssdoDq5YI168h71jTXwP1mazeWdp/GT/jqQ4lvLJGGMCs3XJyvs887t3YJzbU3w5vzuxxtoz72R5PKvCmB1YEgT6AeruI7BZ7uOdLHJCBtn1knoC8mwnri8v4/TgxYMgSQpKQQA2lHtmMiJMu615UOn+jvG5f/AGCKxhInYJ2st+RMhe7BVzfbl0OQhjrc6n0qkyhEpVKM2sQgjSUI6LYe743Qk2l8tMDMQZSTlE6TqtQ18yZ/7ZMVB1UjBE+lylcbRgO5Rqp7TmJdEAEed+jDETYMUYNF4DnRCC6rbOuuvkRAhBYyyNYXojXaowPUYAGzKXM5dR9yWO7T/P670PkIZxmHQBk8rhexh3JnzOFGEo+bsz7LBwpJURDmfyPb2PkEmlLtQVSiplSYzTmUNnPTAq3vZ5+t3p9505ciPBfbwrl4N02BNKhHAI+/1ex+NRz58/nzyjj2G322UB5nr8TKVnlwnI3263k0YA0rSzC0bYnQVKgkHGcQDmPHBj3gnGPOAhoOJ+3hDCAwhSwZRaMjbmaR6UOZgFCOEo3VF76Q9lWlJxcoAm/s4zAeTYB8N6OgDm2TEiDlZ4Bs8O+BrCbvNyw+TgAPlGDpB95J21QJbdGc3nqmlKm3wH5Kzl8+fPdXt7q3/37/6dHh4edH9/r/P5rPv7ezVNow8//DAHKO4oF4tFLikDaEjKwRPrBPD0sgoHYYC3pmnyWWboP0HTbrdTCCEHQmyadwDjgaUD9NTC/zxhunkGWqCzboAliALXMZjszWaT9YH5dDDBc2O70Gf2sxDY8XcH1+gQ9ocgZhgGnbtO7aKdOC2eBVny77odI2PH3AJasGPOxiNn7Ntx0OY64DbSS4Nx7H4/7Cq6jAzzjMimM/OQFE5mwE4ydsaNLO92u3xN/qN824EgcoXN4PvoE8/n4HlOsngwzbOgA6wLa+d76pzkcTn1gNIzWtgYMnoEW5QxegmnE0u8eD4Hpthc7CC2bL/f5/Ej89K0xTv3DCHkMkw+3zSl2yVzRcm0l6J68xTvpEjQyrN7yaP7DdZ1s9mUjehVKedu27KfFt/M2jA2rwzgjC7sh5drMy5Kjz2jh/wcDodsM12mIV18TtABLxH1jByAGZsGXsKvMB8eiHgJPP7WCRyel9JhbJYTrh7sIsvMD5/r+15jLHZWKh0y0W+eGRn3sfLTKyku51Kq6hkybJRjN2wROsJnvXSa9ecabn98zvCR2ATmwzEWc4o98Ewp10e+GJcH0cyR+945OeKEgWMFr1pwG5EDN3sxRreX7iPS2JUPFnZ7EcJUjubVNXOb5JidQM2DzK/1ev8DpH7QzbaU4UjKG+ulYqCkt1kXGB93El53ilJwanrf93nTIcLJdxAUFpAXQAUGCuVLjFzQYlH2Dbky+AZfroHBSZ97dztH7okguvIA8F2hAUsIG2DM2UwMMkYKFn0OCmj7mRzmJRtmQBFK4OPg2ZzZdMaRlysmLYdRXEA4xpt54xk9YJ07WeSB58cQYcSlwuA7e8a4vXTJ9wwhe/4ZMjPOyPqa8dMdFNdhjGRLAezIjAc6c2bPAxh3XMw1LCK/EwQ5IHLgyBjdsc5LIpkT7kfAz8ZimmlICWBSroa8/Pbf/tuzngCWHh4etN/v8yGRBGo8F4G4y/i7mEP0kLNhaDDiQSmNNujwxBzg8FPr/3LQLbqCY5h/nvna7/c5APd9EoAdB4/IKE6XkiTYSHSVefTMp2cqIA0c3DsjDnjjfowFUMj8QkR1XadRUa0KePQsGZk1Mi6+Rr6PwB2/yzKfRWYZk5Mh2Ch0DlvBc6M/XlLFM7jdZN1oR+32l/GQufQ5wV65XrpPgTDhc9hP7+75LlLNHT6fQz/5CeDj5bLoQAxSD/nhefy8GOadQNB9maQJa4wd9mfAJr+L8PDMlmedeAayyw7efF8UhAbXdvuFXCHPT09P+ZnwCw68fG8jgTfjdT9EJotxcE/WARvne92wb56ZqWOly3XNOITV9zTxzARlkGPDMOSyeewILyeC0UOXVdchrsG9kBmyyGQRvEEMcoje5IyHNDmQmTXwFvdOunlHTCcWnbRzvOZBkZOfCWsNCkrrSVOqQpwGtc0yg2lkEZIX4sCrXpgf5Jw1Q47W61ohlL10jhnme/UcsHMPfJxXJ7gczv2j22HWk59OYLA23NeDM/TK7aOPDXuEzmBf8IeOtXg272rI55mrvi+l7eAZJ1UYqxPakFbJ9naq6rdL8NMz97ks1XXlXTrj5FbX9RM78nlf732ARNmYR/iwBp7KRgg8tS9NuylJ5UBQHByC4EJJ8CCVA8+kwuJL0177sEUOXNNnpvsBpNJBbc7cInjJeKe0JgENTkaaCjhnGPDMlC7AGgNiPFuw3++zsNIxKYSgN2/eZPCBw+DfGDPmNDnnpch0OVODQSWb5IEdzBn3dAfMOAEhOI8Yp7XsBGiAPRweCubsujM1DiwcuGFg/Hn5O3KBPPFdnoXvcRgk3QUBPwAo3vOygHexxAALGE7AMLLm68MzSpoEbn4P5NA32c+75zmrxPy40/Bzupzx4W/uLHECPhYP1L3jlWfGFouF7u7uMjB7/fp1PnML4Md8eXDi6+q6yjydTqfcyMHLGclyuUMABOOMmQd3pMgjAZbPmTu9Z8+eZeCI/vh1mA+Cbge4nglBV1hHD4g8YFgul5M9KYwDmZyzkcgbgQ42DOfdNI2G6x5ODrFlLqV0xoVnVAF7rn9kRr0pAX/nGee2z5lMAmGAiDtMfncwDakACJsDKoCAA0Bk8nQ65UNUWRMnaJwA8rIdghSeA9afe2K3AEHYCXSDn8gQ8s48eUAybyk+Z6ddl5FRgmvGNGVjS5kZJYzMmwfwrg9eVujrBoBivdwWI7v4bbI+/lkYd3TY7Qc/PRh0YoXrs15uD7Cv7g8Yu8+tzwt7aPy5PNDMc3yNKbkn4y8gsRwgi71CN+lmx/ew+/grsonYR9cdlwsHxjQzwcfSLdEzJFyXeffsuxMXjIXnYe1ct8Ac2EUP9pgLx1TzPZKMq6mbXBpGRpbPObDHRvOeZzKRVeYFGzPPHg9DOrog7TcvjQrmNtPXybNeYDbPfqEXvpcUXfLGKS6fzJ1jQie80QPHtu7PkQPXVSdjvIoH+fdslX+H+zkZ4O95EOUyPLfd3GsYBo0xajR75PcLIWi5KuSUy938HvPs6TjGicx/ntd7HyABOnnFGHM3KdgGBI30MkBQ0kQYWVhn3PmuB1IuuA6qHXzDtAE8PaKXKIsodeHcw5l+B+UYhjT+0mgAY3pzc5OZaVpfu5PjmhgsHFBVVTkocoXm+oAwWvwCJkMoB+1SYoTRT4HYWqdTKVVzZSI4gl1wQwVI8LpTZwwceDpY8gAYI7/f7zMo4sUhd4BwgLCDOYyWVPaEwaTvdjtJyo7cy3B4/tvb20nJBkEsJU3IAxvAkRsHAe60MRKc+eBMisudB3eUZbD/zMvH+Duyydx7wwEyQwBY5MdLw1yu0CeCb9huQCBAlvK64/GYnaIDcWSg6zrd3t5m3ZBKq+Bf+qVf0he+8IXsBFlLgCDP40AYZzTXb9aS35Ep9mZ56QZAmgAI3a2qKpeosj673W6SdSPokkqb2hjj5Pw0uqTxdxwUQTZg+/Xr15M24gBTwIuXSLAOHmyzfk7oeDDNMzkYJ+NHCdLxfMrjfvXq1SQwDuO1Fv9qj8ZxzFk4Pz/IASi654CP35lHLyPlb+gKz+VdpBaLhfb7vTabTQZtrqvSdN8kOsgcMPfefGOeActArikds9BD5IHPzjMInjUE1GHvsbFO4rA+2E5Ktpgr5BibwL2QCZ7Z9RdA5MyvE0PDMORGKXNgxBo4CeIgMu9Bs2dgbrxVP/YGEmK/3+frk+HgurzmzD7XIrD36gWCPGQQufE9hB6IekbGgavrBf6R8bnO8ZN/40O9QmL+bw6RDSHkdvDICXpPCZ8TYh7IcD3W3rPYzAtZXXw+a+alkD5u7JbjBrcfvn+VdcFOUgXjgTByjL54lgrsgZ8JIagfpvtRUpZnXfZoqTTRcRznpCh67LLJejqeSd8tY3AinPVznQGLsA4eRGBjnPBGvtBxfIxvifCGJE4so3ce/Pp4/LmQacbgwSDzw3rgq90/OE51e+GyAc7zuSn4dFoS6OseQlAdal268+SaGZvr2uHOCEfH3azNPJuZxlv2p33e13sfIG2320mnKwc/c7YNBcEYwK7y3hyQOmCTSvpeSqlADypilMYxSpouKPXHybG3Wiza65kal4mTYNG5L0ru+zUkatLDdcypgwjfQVFTI4rCuiYhKuCIlwNzNywYm67r8n4BxsAze4DGfQCqMBiwbZSX3N/f5/IkZ4kx7CGEHIhwTQylOwDAkhsEZ6klTU6Z5qwagLLLB/Pg5UXO2I5jKenDgc+bR/g6Eji600AOmF+UmxIrN3w4MYy3AyKYOzKBzAX7asj4wTQ7+9r3vZ6eniaOCsCKYeP+PNOcWey6LjOPztbxPDxD13X5Osw9zh1nxXvOxLnO+h6jObstSa9evVLbtnr27FmWnXmA50E5suWOk/cczPEM3Je5vlwuk0Du4eEhl0h41yXGisNCt73sl2flegBnSl096GWvEBkHSn4vl8uk5A37hKwBNnhWL7HBHqGnPC9rPS+RkZTBKoHh+XxWqN4+GDeEoFBVigaK+TzX8rIqtykeTIzjqJubmzzfHoC7fYK8gOhiLZj/u7u7CevrWWsPFBxw+3ic+JHKGTfMn4MNX29neJl3Z2nd0c8rAPhvTnAxJl+nOWOOTWLN6FwKuPDncyaf7/MZzr1iPh3sOTHlwMuBMLb6cDjkezpJiF9wwAUY9wAYP4Nf9zXDXvl8sU74HOYP3aSUl/XCpvA5xg0hstvtCkNtQW2MUYfjUXVVZcIP0Ov6OA9CsFP8zQMp5FNS3o+IvfCAlGAJ/XWyijVlzBxUzrM5CegZG8/4XLoun3FGgMb3WGPmnECddcceck3WGjvDXHhmicZZAX96mRJmdT0t8cZeK0h1XQhnx25u/zxLhm9H5iiHy+SFSjYlzZl3k5tWc/BvD4qwDdzPx+O+14NZcAVyhz6xRo5b3Ua4r2FN/XsenHjGzbFcND3gXk5geXDltin9nmYsP2eQ2kUrRanrLhqHUe1Vnge7B3YNPUe3MyERNVkDXnyWNQPflOC5BN+f9/XeB0jOFjpb4o6UBaaGlRIl2GAmPn2u0jBEVVXZA7Jcrg00vH1QoG+uS4a+1jgOappWUqXFYqW6JlM1ar8/KIRKdNsjCALEMD6Aowt/GmfUMFDjHhQj5Xps/E3BWHJaUXXdqqpq1XWrEGrR+jwpaFSMNH6oJFU509F1pY2wVKnrhuu4Lte5LiwVig04vrlpMrABsGBk+Y40PUEbBXFHy5w4kMQgM/8O8qldZ0284x9MIi+MiyTd3d1NsnXuNGIsJTRN0+jx8THLHorK8zvTxfOeTifd3t5KKkbIQboDWeYQpw1o4Tq8x+vTTz/Vy5cv9fr1a93d3eVOXjgpP8D37u5O2+1WDw8PObOE8cVQA3QoUXLWkMCYueTwYeSeYEhSzjgyFinJCvPMXDGnHqAR3DFP6Cm//67f9bsUY9TP/dzP6XQ66e7uLhMdMMW+YZuW3ciOA0Ev35k7AN+b54AYuQTgMzYAGV3XaN7hzGXf97k+PoSQ25O7LLIeZOvc2c+ZPLcX2DHmDgDkG9LJantQhNNBBp1ocn2FAOm6Tst2oX7oVcWUMQpjVIyDQlVptdmq77pMnjh76IDCA2y3CU3TXBu9lL0yrJVnH2CpkVVng509xQ4R6PhzIyNOanltPjoOqGDMEELIsOsvawngxJZ4hsMzaL4G+CHkBtA3D+bcfiDPzg6jY968xwNwX2tJOXPPNTyTic673PCcztajX16axbpDsOCTeSaYeCdBWE+ux7+dEKP0nb8h376uLneenWdsPBc+C1sPcXQ8HtWPg5rreNs6Bf6hqdVUlfqY7NVysdDmNlVwjEEKTepIeblc8jlLTiLh5xxYE0h2l059P0zWorv0qpt6otvuM1gb9xnuI+Z+CXuFfJwvF1VXmejJaEoT++9AuQrXRjinixSDFIMu51KVMS8ZL+um1NFy0apuGsUxNUVAXgMtp1WpriuNoWQy0EF8YwqogsYhqut6KV5tXlMrXDFad+nVtI3aptX5fFEcU4ZoHFIZnUJQHJWe4yonqqLGOKqqEwleNwuNMZWFKaYzHBfLNpNTbdNqHKNCDG/p5DxYwb4jqx5EOSZwTMrfHQO5LeIFOeUErGOruq7V9b2iouI4aOyv2yzGKAUaJsT03P2gqk6yMMZRCvQZT03R6qpO7dWVAqyqDlK4Nsg5HtUPg+KYgqNxHJJsxWsCII7pb3WTOt4No6SgS3fKz+yNh/ycSebAsSDygf1L86DP/XrvAyQ38p5mxIFKpVEDThRQ41kliQAnKaV3G+MVQnHsXloxB7yF5Ss19hiPvi+b7jD+vo8IoMV1MGiercHBFNarycbRGWE3oLDVOEdnd7zspTjaKgcXsH0hpLNrMIKLRTnbCIBdWNzCDJBqdwDhQu41zcwJTtWZI+9QQ1ervi+16oBfMinMj3cDhFnks1y7bUv3Omd7PXtIVgVFdRBUVVU+ZA5DlWQmZMaSZ8fgsRZN0+jp6SkzS4yPrAYBEo6dNVmtVvrwww81jqO+8IUvZMa2bdssU/OMz+Pj48QAU4I4H9cwDHkPnmdRnIn0Q3Gl0vrTGRxYYT9klfnBgJOpYe18QzwOA0PIdy+Xy6RLE6CG7wAivMSBuW6a1IXr6enpLZBB4ASg9vV2GcPeuCN78eJFzpYwJt+MzHwzV4zHjynoutJyFxlgfQDudV3n7IqTCqydpLfOrZk7Ws8sYTNZG0gHDzKYP9+X2He9mroEiYzn8eEhr5UHdm5vY4y5RMXnx20SQNsDRGwGdiCz+8aketDhWRoPxCmzJTM3X3/3C86mupOmhJQyWyezfM4Y4zxThB55uSRr53uosEXut7AVXqIrKQeAPA+y7fqJLuE/eV7kkXlmbSAV0Sf2tBHwME7slGdonKRgPGT9nFxgzsZxzH+r6zqTMPhD/JrLAfLh5V/+bMwT3wFkuh90MIrNiTFKQ8h2TJJGfGgVsv0bhiHLEPN9c3MzuTYyztiZS9Ypb2IP5ZgOD2KPx6PaRTMpYUK3vWzNZdTtPnOIv2QsusohWxXOu911HEHddU2dcAghTJraeMCy3+9VN1MCAHs5jqP6bih7EQ/HSTMD1gudcJnEJvhz4BdPp1PO+uDD0QNJqkLKQMV4zvPK2mMLuG66Z58CBptjZDo9a6/zebrPUSKoqzPBgj13QgXbMA+knPhwmeZ3dIH/eJ/1dUzgPsDn7nK5qB+GfC4dNpDPDUOvxjJRZPx9fib37krWPZHuVyweU9atbhst28Vb+uV2Ad103cceOCHk5BO40clMJwL57ud9/f9FgOQC7uwJykd9Lx3ovEMXQpQUt3zfN/V5apeFQ2FhZzFSHoQgCNTqOpsACyaV7iMYIJRyzggTTHkWgvdJE7tjZg48Lc48oQiAxfmY2rbVdrvVbrfL50nw98KMlgPTpJINSkCuHCbm/zEG5ssZcuag7/tJeR7z2jTNpB7+fD5rv9/r5cuXk+cli+QOA4acuWLvFobOS2kcBHlQgkNwNmdeHywVwO9OhDI3jGdVVRMZ4/q+2ZjsAGtI8JAZIQPZADVYaMCLB83+N3c+GCfWFaNTVVU+L6jv+8n+LzrAMV+spRvncRxzUP/q1avJ+UqS8t41snpt2+ZAnLHudrtJcO8A4MWLF9putxm0f+UrX8nBogdU7owIOJAxZMP3t/h8ECAz35Qe4XT4GwbavytpAmgJmviOr6Okt3TEGXycOY6We2CTXGY980BGCxtJEM93uLekCQGADZtnpzzTNJdVSA3ujawxtwRzPKuXR2DLfH24tpeCenYCYOr7JD1LBMjFoWKfkG3sBzpbVVVuxsAczrMkyAdkETLNGDxo8CYGbv/dZswBgAf1HgBho7xFsGe6WH/PoCFL7q8IrLAdvO/7lPCNMZbD072hjJfm4Ytc3lwX3gVwV6vVpGyMwI2xstZOdkBysqbMnc+fE53onK8l+uJZHHyq70dy8NWERoPZscJSvx2QYt+xX8gq9wEHSMp6hf56i3AnG1jH1Wqlpi02wwMuB8bYIvybYyKfU+b1XYRe13WSkVledl7XtRTDxO7wfgo8C6jn2ZPuVNnHeUCMbjPfTmAzvyXDFLIOkIVnzKyBB0kE406YMj88r+Oxpml0Op+0WBTSk3V7V0BNGWRVVRqHUefztNLBM6aOEXzusFPIJXPjwZyPkzn08lpsjX+fOSmB3/W64/Ta2VaEMsdcZ17W7NUrjit5MYdZXkJZH/6TlKs7mAtk0ufGAyXG4/jYSSPWdG5LP8/rvQ+QfK+BNBV4V37fNM/f5mDRmU4UeV7y5eyGd5ZyZ4GgzLNQGAPfY+IBECU60tsHiXFfd6q+mZixozQYRAcaPk/+WZyUMxUwh84kOIhJ+0eirjI/YTjSMxdnjVMnm+FMJo4Z59c0zeSAP54fUMCaANDoZsVnCEYcnAFQeC4HpBhVDCHzSiaL9aPJwvz7rBPAgTWWUtkKZW/zciuA5NwZuJOs61qvXr3K90SWyY4QrLKWvgkXZ+MBaQghN4YAhMyzCw4mXDYkZYLB5xSZ6LpO2+12InNdlxotzPfCsJeFeeXZn56esqFsmtL9z4OD3W6nm5ub7HTIpA3DoLu7O/3Lf/kv9S3f8i36+OOPcztwZIH5njt+9K3rusxM+h4zd1KencAm4LAYtxMqrK07XLdDzAn2gXv4/PpmXkCcEwqMgzV21tKdjAccgDXGhQ10MEBARctzB/8u+w5oq6qaHKKLHDHnrjdus5AZJ1vc4ZGxQEewrdhySvIAzQ4sPVNJtodAzMkmroX++fuSso4NQ2oWQ9msB2HYFK7F+mCT3Iew3ozN15K1ZTxesso958GpByB8l0wZNkEqLbT5ngdGdAH1vYuQcp7N45rIPPIMKcL4IaqQTV9T7nG5XCb7mdyWz0tEmXu3HcwHGVtsomcY3f4wZwRH7gsAXxlDxFHBAk0AHOvE2qKbzDegHyDu+/pYA67ppFdTl/GxDovFIrdFRh7Yq+X7frmPN0fw58bG45u6rkslUldd8cY9owFWSrGdsOT66BMyGjWtXmH+pVFjnO79RO75ybxT+eABAD5pGEpHXtbKdZdn9DV3cM41kTPmLft0myO3o4yTNfe1HcdRiqVJCPqLHfKge05g8X3He35fnoXss5MiPLtXKnBtlzOeNYSQSuzMnvm8hDDNliM33gTJ7bbLo5MG2V9q2jyC9fLnBDejM25LyQjhQ+bzj+3hWtgrf/6v9XrvAyQvwXID5c4AwCpp8jcm3Y1djIWRcEAwV0iplGGw6LCYHjwhhAgxwkMKFsPgrCcL7OlCBB/hcVbHjQOpYAQcwcYAunPwwMDBsZeKYPgys6SidGmPVWnrSiYqXbt0USFIYv6kYhh9DwFKhPEnq4ORZI3c4GBA+BxG/C1lraeNGVgXzwhIxTlzcj2gHGfL3Hjp2jx7iMI70+rO2QEcjtVB1DxAAGCQYUI2YZNYd9YGw09gBqAhEAUYl2B2ep4CsuBzQpYKGcGZMi7fN4SD9lI71msOlHgWnA0btx8fH7XZbPImc57Ts65+/fP5rMPhoC984Qt5rmk9vV6vcxkbgSzg1c8ywk44WPVAyMuPeB7mw+eOZ5uUsqi05GXszD8lKawpZMk8oEeuAYwhhFyvzZ4H7ueBJuvoAMiBpKTcOYtgyZlSAmACM88oOBPKevqcOcvMnLgNQn/4vIMhgBefQ86xGS6782yBs+PYcf5NkO/klxMofI9rO3nmoN+/M45jXh/G7DLqwYzbBc+yuP1xJw9A8Gyc6yX2xYNwl09k12262xTu4d9lnmlAQFc1xgqYIUjxwJb78D7y4WQOQa8TXw7mfF6RB9aRe1ANgp/1jLzvSZgHph6Q+xlz7jeYM42DBiOy0D8nQyEF0Enkw5sAQeT52vl6DUPa2xHCtJU5sh76oNV6mbvPIUesH7qPz/RGCcwt8pBl1ID4HIPU1fQgUf59Pp/VNu2k2sDLUtmL4iA8y7aCmrZk1Hwt0N15BYfbMicC+C4y6WvvpIHr1DxD8y5Coh96tWOb7ydNy6HRRSdRlsulgkrQ5AGLE1Gu8z4G5JS/8T38AH+fk0bII/LA8zn288C+qmsNYyGAeO70/SpnDX1enWB3DOOkE9jBqy9CCHRxyL4dOQWTOunLM/ATe+H2kmvjL3we0U9sxed9heja9h69Hh8fdX9/r//7T/6zzHACzKTpAZzuQAHtl8sls/oYjuT0+typjPILqSg6gi4pl1ghvM4cu2F3wfdAByacMRIxe92vX5/r8BwImm+oR6k9M8ELYfQ9HtLb9fYoplTKFClJ8qxL113UdckwU25DLTVNKFBUZywxDM5CSiXwgrV0UE7whQGaszr+fDhwjBlZBkm5pIu1pDQNsITRA+ADdsgMOZBgDhkrL2QKII1cEJw4m+gZxXnWwo2PM0+svwNYnpVAtKxRyYY4iUBDBTJK3n2Jz3iZkrNfMcZ8pgYME/Pvjo2/8ze/hssa+wyYS2cst9ttXh90zgE2zo9W5m3b6uHhIcvc6XTSN37jN04YbRho9GzO3gEuMLjO/HqZAfu9nDTBuQHSvMTG7Qhygk5gJ1x3sT8+b4ASD8DnLChZMJcfZ4J9P4jLk4MTngUQ6oEWQYYH7W/evJl0KmT+0QtAgwcN2DXmi2CZ5/Qz0bCHPBMyz9w6y+iZWg9uvKzL7Sky7DrrmQefYz5f1+XsIc++sL5e8uK6yHUdYLPGDjzcxnmmx7Mb3MvJEmwY52wRcPta8z5BJiCNbIDLIfPJ7x6g+D4kt+1OQLq/ZU0dHHkg58DaSUJsLkEaxAz6xbU2m83kQHIPcr3MDR3i+h4YYNvy3pqQQD3PR4DhZVwe/POe2yfWE//JdZh3l7e6mu6lZOzr9UpVPS2hYk4hLgjwHJyz7nOiQZL64e3KmAy+LYviJGzf9woqASHf43WtqsrXnX4mSLHsw0WWvZzK7SRz7l0y0V2uOQfDORioSlbcy6+cMIGwnn621mK5mFzLMx/ougd2IaRmD8gdftjXfhimRxA4VnTM6Oc9Mo+MDTnxoMqJY3TB8YLb1tSMoZqsafbvSh0B0U3Gx/PyDNhung9/4SVvuZQ+Fl8xfx4PCgmGsMFt22aMxhog205yIGPoAbKw2+/0nd/9v+jh4UF3d3f6773e+wxS1/VarUpzgwROKzVNcUrubFJN6lmXS9m0lwQvtc3mPT+zBIfv4EhSPm+pGMnUyAEBRXic1fTMCcbaMzA4wvl3EBY/bBBDTXDIfXluFO7x8VGLxSIDcb8niuDCJk0bXjjLi4Ij+CiFO7xhGFVVydg4Q+oKhzIQUDlY8A3mKBxjYr6lYmi9exXPPQdM7N8hU4A8ULPsc+ABgZcqcE3+5sESc4JBlgqYdicFqHMWyGXUQYizyjgSZ57YSwfLxPtkSTwzwzOw5vO59c337qCdgcrlHhacORPH+nI/5Mf1gd+RJYAlARm15d7YA2CE4XbmDEaLAHqxWOiDDz7I8n1/f6/lcqnPPvtMz5490zAMuYTD18ADI8/M8QzIBUSLVDr10VAAXfeSM0gPB0zMhaTsCHg2nNlisZiUNDrY82CKz3tmDDvI+wA+so/85ywp8sVzIieUsvmevflY+NycocfZIuMAG/TIOxShI8y/gxxnlMnu1nUqP5qXWCPXXM/1iYDNbToZONbOm6Ign1wXO+j2GPnlBThyMMpaOTjipxMmni0GxHm7e/SD7Dpj9j1x6K8HYfg934PoBAh+Al3rul5tCxscVddlv5OD/hSQh8n8l6Cu2AMPfLjnnORyPXOyz9lrGHwfQ7vgoOhepys5VLLbqYkQstC2gMXh6p+aHCRUVaWqrlJntGgNa+paobru+xvG3E3Ou+uljEvqEtZdLqrqEgSnDFWQdLpes5EUM9HjALaqSrczMkHFdkSFqjD/c/KqudoE7E6S4dJ5dBgGVXUtDm8/XnXPyRdv2DNeQXNMi6rKAo+qKtkOD6gJFPqevTeL7KeSXF4mWXJ0YxxTS/+u73Uye9j1V5sYRw3jNJvC+qCb2Aj3oWADtwvMneuag+26rnKlgZMZ6Lb7WYjEdN8kO046LBatpOkxDFyraRuNw9WnK2rZJhJqjKPqWGeS1mUdX+AZZroYSyVQkFJXOuaCn3WTuhOybQJChODIiXLfY+RnIjoG5HO8PNhN81Se2QkuPzyba8wxqAdG2F4nMZumHLHBGhIQzhMD/73Xex8ghVAptbgeNQxRpxM1z4V98HKlZJSWoiNd111U141CkNq21PYDwEo70cIidN0lOyjqrTHCIeh67wKKfc9R6WbXZ4aboEdyBrpPRjG44LVqmtSy2xlEWOy+7/PBpwQ0fk6LM8QhhAxqMKLOtPI88zS0G4ikKJXO504Syl8YbgeNKJKDl8xIGfNIkIgBWK/XE8DBdclYDebgJE3Gzr4Er2emfG/OUPLvuSNHQZOsldIPZ4CcseR6jKtpmtw5jvXFCDHf3BPGD8PopUfuDN04e5bDszXOpAKePTDhvtzb9xx5Zg9nxWcw2OzJwvD53hXmljHxee+WSMCDw7y5ucnlI8iMg3HPDDN/NLxg7Ojkfr/Xxx9/rDdv3qiua71+/Vrr9To3fMDJzIE4usq/CTD4O/IKGEVGCJLQKd/wDfEhFfLBy66wL13XvSUnIYScQZszkABhsmYeRKMrrO30rIiSmUS3sQu8kDPmh+DI9zG4vLNn0B2Yyy3PAkvo8u7y73oJQPRyTQ9SPHAhEGBukBHPuKGLXvrsgSYvdMuZ/qqq9IUvfEGPj4/5b86s8jlfH67lpU6ud2RNPbh02yIpz7sTDYA69pp4FYQDFAgN1gNWFj3263DtNCY2PEcNQyFlhmHMwamTR8vlQglGl3N1kr5A7inbBp7Xg2Fk2UusCIS4D89S17V2+71ClbrbLZdLXbrHBKxPJy0vC8Vro6VRo2IY1A3Dtcoh6NIPE9lcRKmuG9V1pVBXqi0wjWNUvC5lHKLGIQG+7tJrrEuJVSm5kuIY1feD6igNGtWFXnVVyu/Hcbx+ptc4RC0Wy9Q2XOmQy7oqlQM0HyHj2/e9+m6QNGgYr2A/SovFUlJQ36d1PJ1pfxx0vDYDSnNcqe8LaVVV03K1tl1cpTZkfJHHPYzqh0FSVKhq9dcxhWHUqChVJYvaVLUUKg1jlK62cnUN/OJYSKiCl676cDyktttBuvTdNVhNktV15XDypimEbndJAbrrPLrmoNzJL4gI5NCDrCS35XxMxzlzotPLrEsQKHX9tQ13nfb7EKDHKDVtnQPwYbhmgapaIabMW9PWOp160fyqbkpWKIR0gGr6sErrbV4haoyDOvb81Y2aRbLfTY0NTnK2XLRqmzQHQW/vPwb3eRDL8zpG4PPYgnlg4tktfLonGdD7OTE3D/7xj6wXaze3m/jpr+f13gdILsR9z4bSwiBI09PhEyNWgJyDFy9dcmBPRmmzWYsDwyj9kkqnFV4heM1s2deEYDAmACT3kAorG2Opy0aIiuMZjSUqLY73+33+nRdgAEcOGLq5uckC7CDBBT89S8hBG2y4B0/L5TLvdUHgF4uFDoeD9vu9nj17psPhoN1ul9laDAvAGmDPNQDOKN6bN29ypzoArjt7npPnc8MIK+ZZD2dl2BzsneX4nisw12WDOA7bg2nGQV17Xde5/avLK2Nzo8MaovxSAcnMmWdqfB9ECGUvFWDdyz0k6f7+Xg8PD/l3Z5hdTjwo8rp6GGiCL/YPMD+M1YNdAI/v32Psno0oYKuUVOHMCI7ZB0aQ7PvmyJB0XTrYGPlwwPn69etMHmw2Gz1//jyTJ4zHs47Ik4NtqZyb5FmAeUmCB7KQFF5iA+CjNBNCwfdXcV/sEAEgf2fOkDHsDxkYZBcQj/Nwe+LZTAcr6JU3MvBrOnB150bmqqqq3Iac+1L+hA2hbTTz6CQHxBPPA8GE8+aefN7tNXLt2VaeH+fL+8iMywCg1LNP6DtzxL5WiK55ZlMqDQScQGOeARU8H6DOg1oHYF6qi012XUUmCOYJuhibEzr4F2l6AGn6fgnm/CdzDIFBpjzJz6B0WHlpyOF74cZRudSWa5Y9rGUvwTxrx/r0fZ8Z7/S+Utmb2b6c7bkCQJ4VnV0uC2Pdtu1kX1QIQefLRavVMs+TZxmHYVTbtBO7zJy63LNOAPFxHHNnMHwZn2cOPUgliC0BzPSMp6Zpss+D/AvV1cYOvY6z5h0eEHjTlDmR5rLOXAeb3+mapDbYOYMRpLZpp76jrrWoyl6YEIr9Y00ghjxT2g/Tsmsnejjk3vU2hKCmbuQVO9hcl28AtGfwIWmcUHFMQsaQZ2JNHIT7/k2fQyoTuG4h30aB3Slv9XuWzP+UoOdaLj9cl/nC5vtzoNNT4qxk2rAVzBO40QMSbBJZJJ616Fvx92ABxjj0vdbLtE3FCVNsH34J+869GI9nohzTOpnpBBb2nz3Xn/f13gdIbdtMQFOJ8oswuQAvFm1WWBYOg46BIOPAgakAIuqOKVVw5pIF9b8npSqHtrKofgCiKzPCm8DLSn3fvQU+peJo+a4DNMbB97qum+y58BIOB0WAcL7D+w7ECWg8OILBhyn3Mh6CTzfIzKGDFmcGAC2AgcslHbYHY8oaAcRwdvOSOpSfEjoHCM7sOggMIeS5WiwW2u12k8AZ5+kyQZkM2QNkAKPkoIn5ZZ0Iypzx97Xg+QDZBOrIilRqvD3I9vdx9G/evMnGybuiOTPvZXnOJPEe+7ecVeOe7riYawyY3wfGmrXwIMxZS+QTlnyz2Wi73U7YZEk5qOI/An82lwOEGPd2u9Xj46Oenp7yWSUYYGTZ59EDVJ7Hr4u8skbuuJyFc5CKHDG/nrHj+j6vPo6mafKhki4HnpVylg075t3E3Nm63vh9sI0euEN+kJEq7Hk5eNfLdLgnNg/74U7YnRxjIhj0IMIDG/TJgc9cDh1EsMbIF2vghAQ2ZE5o8Dmvc0e3AWPcz4kJ1htb5vs33e7yLGTGyR6wFpJyMFmyAaVRgq8BY/A1chDjWW8HSMhqWvMCyrClPDN23c9tSnNRq+87UdoeQsm4prUs5/z5Pefjc/KIIBSb4fudqqus8z76iQ54gOe6RIc+gpnc2GA1rTTwYLQ24gF9waaO4zg5J4//IC288sDBMPLjfho9c8KMcnDsg8t6CWpbdf20GYiDb35HPlh75LFpmkmDH2RB0gRfMHZ0DJ31YNgBMPdCv0MIGiwzgDxlIsOAM2uYfk+nx3r2mjnq+yGdLKviWx2oI++A/fn8THHVdX7jKA2lDM9L9v35/awv7udkBLrDdX09XP8dL2ArmFvG7jbKiTzWxEvryDhin7i/6ysyil3ifhDh2BbmgPtKynLpY3BblcmfUErkkCs+Ax5w3Ob2CQKBAB2/AXbgPrx8vhjf53299wFS07ZarxKonbKLpeWq1+aez2edTme9fPlSfd/nzawYcAfUOPd00GVKocJW45x8fwCOxBe9MjZCKpvMnb2OMeY9FHxGiuq6siHdHVVdTzupYGxg03EWlO9JxYBR9uP7bgAzLrAEVoz76ekpM7mwMH1fuoDB2nOIHGOAPSNYclbMBZvn5HsEZzhSlL5tW+12u5wtSWs9PYjPwSjGWJoePOeBCh28YCu8HHFeL03mghfGywEfxs+ZQzeIMcac6eDvm80my6BnrNg/xnUpOcSQPT09qe973dzc5PE4U8scUGrpm7YxPsjS5XLJpT+LxSLv9bi5uclpdu4BKQGDyjz7M1EmwvO7A2Ct0Q0PWqlRZj6RfQf3AIxghhg9ubm5yWNivL/hN/wGHQ6HLPuPj4+SNGmL7iyxB6AetLpjxPnzGW8R7oe9cl2ch3fV5LsepPleQQe/Tg4g954VcNljXglYpGkHTz7Hs3iWg/tuNpsMiNEf70bpzB3BE+uBLeKZHWQ5i4m9ZSxe9ggYdb1l7A5ysCNcl3HwXdbPiRBkCgDOs2DLHLx7qbBUyj3RGRw+dtQJMuQFvXaAjJyxJm7H+Am4I8Pq5y2RGXDg5aQGeiwp20/PFqDPU6BYyj792mSQOSfKwXbXddpub6/Ztz6Xudd1OUgccsMJJ3wQPseDWm8nzedDCOr60vHK95+5TfbMBPOFX+JZvaqEuadkmOBuGAY1daOu871AZV3K/pOQ9W++b8Tl0WXKM/zO3COvfm2u9ebNG63X60weYAOrENRaeTYBsmdB3E653AFUvSMj13VSgvlwW+bVKdK0lTlz4EShxjiRcWz++fqsbsvwzSkAKuAZOUi+ZaFxmJbwl8zVdI29BMznwcmhcRxTTV8oRIsHN04U8+xgB2Sbe+Nb3Z66Xvt75diUaYk+68T9nLx2e+FkCTrB9Z2UmpNjyCq4ALtGBtozMTwPNhU/ThUEMoKtBMN4aZwHQu73PJDlPtjOh4eHrB8ErJ5MwGdgC5OdL9VcX+v13gdIOFo3Psko9llhcaBeurLb7bKSLxaLfJiipFwihpKmBUq1sCwgAN07NXkWBCXpul7LZSkBAmShUJSf9H2fWdYk8JViLHW1BFBVVedytf1+n6/lCoLC4FjY5J5bhlq0fz6f9fz58wwe50wXAaS3U3Ulwxkxz9K0ixt/Q9kcyDrwAXxJb7PggBZJ12B1lR2f19nDiGBoGAvGxdkXZ274DPegpOb58+eTIMSDIX9GgkXGgGHGOeDsnGly+Q0h5AMZJWm32+n29jaX9Tw8PGT208shWReM67xMhUxRYnGXk3Hx8o2XfMfXyQ2pZ12ZN385E3W5XHR3dzcJKjGazhSii+iA69Q8cEYWITQc2OEIHDCj7/5cj4+P+uCDDyZBmG989Yywr7WvF5kmPoczAfj4fOJQPZvggBAZnYNwZBYQWsiROgcOAEAcNDIgKY8Lp+rBjMui3w8w5/oMU819WTfkj2s5e+zMIhkZ5gSnyLN4s5a6rnMZl2e2HIzxHuPwbCZ6jEP1wAhZIuAgMHPCxpl8QKqvFdd0uzQM5VweD3xYAw/oPespKZNkkDPuw3yOPRj3ueNZsf08GzafQ5S5BqU9ZOKlskG6lOiV/TLMI+NiPp3QcDlJQUOnGK+BYrvQ6dQrBE1sMPeGCPB9ocynM92+RkHTDnnYNq8S4bnwKbzPcwBkcxmPpjLAK1UKLKRYqjSQcWTUMyaSJngADMB4AHcuq14axTh9nw6fh3TxTCNB7/lyUbxiCdbZM3gewLv8YJ+8ZBQ8xdj5rI8RwnLuT7DTYB1s+jiO6i4XdZdydp+TdHVVabEsm+s9MByHVMLJfADOM9mhUhHh84nd9fXEJs/JTPQyyULKGnItt/8enKDvyBc4wAkjlykP7rCzJcOafLgfw+AkDTrGvLvPc4KA53VSD/vglSz+ea8ImmfN2H6An8Hv4Ks8yHe/m21YLPPO/PH3efUD8+IBK77ZM6j4Pbet6F3GCtWvB0j5dTqe1DZlQyfC1LYJkO12u0n2KDEUhQEn6g0hlX95JgdBSMI4VSRn+XEi3voTI5M6DKXrwRSgWDhIV2hOgx/H6cGzbLpHAQj+UBQA5by0iCj/dDrlPSg4t91ul50LWRkidp4BRXPQhNICvhkXrZ+lKUM9V3TGitI7czIHLB74uFNmPjC2Dthx8KydrxPzQtkK7zu4xMj98i//cj5sdLPZ5Fp6wAT3o87WlZ61dkDAXPBMOA9nwx0A87m7u7tJcP3w8JCDKM8OMK88xxzkkF1E7qVSSsA8znXF2SsCcOaHEktAQYylCQaMrLOjDmrQP+TGDTtr4KlygMEcJHhzEw9umF9kgGeHVPj000/18ccfZ5lg3jDilFA6uGQe562KXc5YY2fy3GbwAmB1XTfpVgdgZA54PnSt2Ldp2S5rzRoBWv2eHri4M8OBuxN0BtU7ahV7WICyO+wYYw5yPHsolSMDPLO62+0yUJZKZpBnc2ePzHrgzOdZCxp3OEBg7phHdB8bzHNIpaRuTtBQVuvsLffLYO6qy76nEl3w0mO+w9r7mvATncPPsLbMja8hjC/6iH1zNn8Yhsn+PL+2g8R5loe/A0wIuJDNx8dHXc5dajgQo4ahV9O2aQN9DOr6ki1lzpxcYgweMPuhmIBz1ulyOep4BXwAWHTXsxw8M3LX930+0gHghWwO/ZA7zzEuxno+nRTCtPzTZQO9xn45aESe3Ubw4lnnARdEqc8ZusGxIAQrGW8oaozF/zAOCAF0jrEybg8SnRR1QmyeBfHME/MNweoNVQDSzH+6RrkP1+Ceo6b7bsexNIdZr5bZdiD3SRY6BYWJHWYvLjLN/Zzc5OX+tmRPSrdgJ38kTWwdL+bu9vZ2Mm7P/LDergPDMOSjNjyrxLz4C/vmARDXRPbw6fNKACcRkUfHVVQl4Fd8jl3OCaQZb9/3mWSc462maRTHqLqaZkWxx1wXGcQeOUHF2rEOTv7wn/vdyZ7N86+X2OXX/rC/Zk2ClsuVUgvNs7pOmXl1ECCFHFF3XZeNzuvXr7NSe6QNWGJzIsINWy8pXzfGwtZ6xA5L4FkPZ3ARdAemkq6sCGNoJF379F+Nu0fRlMRJhblyxtgFyEsQENAYo87nU77m6XRUCOkwWAfKCKak7Gx534O4u7s7xVg6jeAECfYwBl4a4QabEgYP0hzMYPT8vCZn+TAMGAHvOsicOOCaB0ycDcT19vv9RFFZV34HnMSY0u5eZuOG00EzjojvkBXhmR2ceqABMEAOWHPmjednjG5YswEz1tOZRWQS+fNrOcPN/jwMmQe7PifoiINk7s0aI1uefRmGaTtuX0c3jsX5Ft0BgPvc0+Thgw8+UFVVevbsmT777LOsixhlQApzAihiXpkvL6mZ6y+fcSfNes/L3FhPB3w8G7aBdXXWj++jiwS6ACfWnYwBzsgDNQ8qWWcH7DhAdJQgwWWFtYOwwQHyPnPKPZyo4P7cE2Don/GsIfaYUkQAr/8N3cYeQBh5mYbbHOwIpMfhcLg+d9rfOo5Rfd9lX4IuYYs8Y+ElRm7TPDMmaRJApu5h8Up8POb3563a89kiVa26rq62caHUIGHaPpuXk08F3FWqKkoxUyl6sg1SjGkPUvIFpbzOyQK3kdi7EEJqyXztfnY6nXKDgnEc1bStGss0ra4k4Pl0up7LUg5ph2isqtQiO0par1ape16VNtCzlsiTj4O1xM7xc94xkfmKMSpUQaMFiqxpXdcah3Gik+/qvOh+Hvklg0uGwffGYJ+wUZTV4kfd5roNgsA4n88axlIBERS0WJUyPj7v5c5VVamuqlRBBngepgGL6yVtvdum0fpK6qKLdV1N9rgdDwc1V1/c1LUGeevmJHPoDHbSfVPTpFbXTlKk7JCujSBKRs4DVLrYNdf21eH6b2zpGK8+cBw1jm9nQus6Ne2iw3FVpz3qc1vtBAHrgUzxu2evITKcYCfQYPxcy0uPkSfk1/2uE140inJf4OvuQRLjwXY7JuEzHGIMzvNKD28g8q5MchXSAbNcK1zXoaorNVU55oL1Z+2cGHbc4JlAXo5hCPy9FA+5cdn6vK/3PkBatI2aBoWjtXLpvOROYrvd6nKhDKAwg77/iIXxnwmYLZQORi3sE4t1Op0VQnVVmLOqKioEzpgoXd94ETjMSzhglZIiLNS2xeClNHNU35cacpQQB8o9UDhP6eJIUFYChuTUK1VVKhO6vb29Bk+6OuXCxEnKTBFCy/W9dIqMgLM2BD7Ocnk2hXE7m+gMCV2/HFxiYOZnsGCAeO6bm5sJIKPU0DNZnGXDPDpolzQpo/K6eP4mKRsswD/BGHNPORTzw/klVVVNSos8lexA2x0f84Ax3e/3k+AaYOWpfubcA0kAtWch0RcnCGiRvd1u1bZtDsbJPMz3G/n+DrKNgIntdjupCXcw486aPXAAd19fZAdGXiosHTLk1+TlmaWXL1/qzZs3Ez3n3h48A6KZDw82CSI8IMBgO9PnwJLrzo27VDa5wmjyLE56+L8Zu5MfrgtequHBDHqYwdJYykT4m5MJrCGsLgCB8aBbzBFjY+z+vF5i5cSNj9s3rCPHDvQ9m0UWck56IKesHXPlpBJBt6/NarXW5XLW5cKmdcBOOsIBkscBKGvlrHEp06xF62Sul8qnB7MP3hp+UOooF1VV6ftJDuL1b6kUjkARwBajdD5fJraVIJk1nO9Zwi7w8izsnPhwW+1MbrsImRDy8uGmaXQ4XXW6rqUorTZX0iRUWm9Tk5RFaxntcVSoGynQYztqiNLpcgWsVa2mmpZh+dr52PEb+J2maTKRxTNhV5HVS19so2Jqee06BLkI+YNcQdwg51I544w1cAzgwNubSznB52XtXddp1KjjOVUxxN4CiqbWcCVI6iokubnqK/Ke1nJMADYEtU2tLo4KilJM3eaC2NNcMoSSFMe0z2mzXl39xShVQd2lv241SEETDbOWi1bjOC35qptaTV0O1Qa/jOOo5Sq1O5diHk8V6rTHyDKBvlZ936fA9hrcD/2g9SaRnP3QTQilKlSq6qAqBg1Dr37osw8KIWi5SrKrWPY3evWAZ1ywbfg/bJX7UvwfMumEkVdFuP33/ZaeafFsJnJyd3c3wTdgJQ+ukSeCEs+ikenb7XaTjLaPExsCxnVs6T5nvMpdFdN2sRCC+uv87Q0nMVb3cawjxJj7dgh8t9++NwsbxLXwn+4XP8/rvQ+Qbm5vJg0PmDCCCJxf7lizXGoc3z7xGAfrjhkjyKKx9wAngCBvNtscdAHqAPYomrPQrugAQRhDL7nzTk4xlk2QGGbAiIMLd3rcEyNJSZZ/Jilmp3Ec9MEHH2TBvLu7s2xO6XfvWSqezefZMwoObKqqlFyxVg7SHcS4MmJUYM6ckR/HMR/WixN0J7bdbidntDjz56yfG0JvL8xPAjNP8wIcvISiruucLuY1d8SbzWbSBVEqraMz8zWWAwQ9WMPhz7M2zKk7egwT4JxMCXMx7yaHHNLdibHAJm02m0l63AMGqTCG6A1gzJuG+HwzPrrTuFwCqF3XHAShe3yfABE74HuxCKw8Y8vfHh4e9JWvfEVVVen+/l5SqZMex7Tvz8fD3/kdJ+LAExvja0Gw7PvfXC48eOD+peSj2ApfZwIYZI8197VxGefaTqbgyMkg81lsHXLM9z1LhHyiO8yPB8Vc3+WMfZbYJsbvGS6eG/lFbtGH8/k8OeeL7Cslcw5ksLcAkKenJ223W9V1rfv7+zxG77JHWTYEEiSAVLKqTdPk/VXomNfHO+N7PpegsoDVYcKWIj9e5sucpbkqLXch8rBH+It32UBnX/np4N5tldssruVNRlgX9I9yV0i9qqpyZ0juzTi8tMuDN9aQf3N9votsOsHmQZGDOs+eeadYdI0ghveddOL63uADGWc87FXk+3Obgm9EhpkH1hvf7viCcQDsnGBgjaoqHfLp6+ABKvaReWS82GSe39cXOwWo99JA9AW5Rk5cr6RU8sfv3kmNcThxAhmCHjiu6i6dur5gN3SVShMaATBPh8MhP5uvue+jQt5ijOrHkplq21bN0OR7e/A/DINCH7JtxXchA75fx/0kz8u9vdyN+fB59Col9J+19iCAnx4E8R+2QCrd8nJWpyoNVrwiwPGok7GuD+4nXG4dX3vioW2aa4auyLUTNFwLO+64ZbFYTLpAOrHkmIHxe/DjgSyf8+ZHn/f13gdIi7Zs7nsXmGDS2B+zWJQMAZ1t2BdU13XOBlCmgtNaLNoc/KCYfi6Ed0IiOJsHNA6KUCxvNzoP9KTCxmO03fA468zfMcLOKqOE842VXCcBummJDYZzsSjMGGUkzDFCDrjkPTeMJQNWntfLlXCgzLkbfebTU90YBgKX+XNihPq+1263y+OC5cEw+z4cAC/172TY3rx5k9cZRSeD6Bk4NxpuwNzRtm2rzWajh4eHSYqdWl6el58esFFm5kECpY0Ep9zT97UhGzCnGCHvbCUVJv18Pme5J4VPxmAemOJIPWBw9tRZLHRLKkaOueGzyALjcAfiNfG8RxDgBpjvMo+w/M6W49SaptHd3Z2+7du+TeM46tWrV9lJbLfbPHZkeLVaTdpre4B7OBxyNpB5QVcZLyW2zhhih5wUwQn7PVy2PROFXDhBwX44zw65XXId8YAF+wnIwCa5w/Xgnr9j29ijhz0BFLuseAMIwEoIpUEJOs38sCfMSQiAi5fbYl+QA59XssPY3Zubm7wOLod0/koOflRVtZM59PI/7DcZAg/syVpB6jDfdV1NAgmfY67n4BFZQt6cZGE88xIeZIt5Zv24BzaC+XBQxO+MwckvZ3yxH66DvofAg/MYo/ohbXzHDzvo9ywwZISz3A7GnVSb7x2mIgBiiXWclw0xJmTFs7fum7F34AeCBychAZJ8H5vsNpA5R8c8wCNAZq6Yfw90PNBVeDu77DIT7He3G87cu3/xtWft2rbNZfOQW7ywhzy/l5A7ueK230sdL5dOitNzwXwuuD9Zasc5qfPwKcsWtsPvze/8G5xBAI/tx/f5PVxWfF+sB1ysN9/3bRL4QccuHgw7sCeAcLKWMuD5OJlnr4iAbJvjF8/68JOSTAIlZBAsN187z35hf92P85qQxHHqwx2jub1wMpGXZ4kKDi0kCfLMe3MM7MQl+uvz+nle732ANAyD3rx5kzepMnkAGzblAigPh72kEq2u1+sJG0fJB+ltmA8/sdm/K0mXS5cdHkqD8XbABqDhd69Rdme8Xq+1Xq+12+0mYASDAhODkvDMXNcBAODYW/W6s0mGMp0gHULQ3d3d9VDczVUx+G/MwRElbQgwLxwLJVfMpz8DY2WOnOHk5SyZNGVuMEKuEPOSJsrLGI8bcloxM36M1RwgsF5VVenh4SEbT67pzSg82MEh8JwOnAE1pPdhpnk+DB+GEAPJs2E4MGIwXDzDfM+QVBhfskE4Re+i4+vhjo/P4ehZD+QIuUUf5mvr+/HmGSyu4Qww88cc8zmfB2QA/UJWATdcl5+QA/yNYHUcx6yPh8NBL1++1Ol00qtXr3R3dyepgDpIEJ9/xuW2AuDnoHI+38wL3yM4pQSTMW82mwlIRv6YU+bdM0VumwB18znnOcgieVdLvhdCyAFyXZd9QFyfn05IoQ+eSWZsBF4+nwQB7tAdqGIvOB7AM0MhhNwAxVltgAr3c3bXv3c+n3P2B3IB3RvHUS9ffpBlYL/f5zI29saMYyltdtCNDYcVZd3S2pVyKR8Pesb30XVkHsDEXDvznVnvGXniDW34rNsEXxe3q54ZdPDMOB2AOKnHOnhWILPJmgYgTjDyPtlKD5wl9uAOGTzhk+Z+AKISO1D2v5RmHsgseuRA9V1+he+ir6yNB2Juj+aEiAN/rs38IE9OEPE76+xnEzEfo97ejJ99jUp5oZftu13G7vg8+jWcYHXi0n09ctB1XbZRTlB5FossL8+oWDr4OklDcIRtQ479jLt5pQHEie+xdPn2YI6x0ShlHtw46Pbgw+eIsYJZ3pX5cRuJTXI9I/B3wM8zYseQT/AkY3Y9ZAzI4nxOPUBhzKyxE2aMA53lJ3pX1/WkQyfP7XguhLLXl/l0e+w+CH3xLJCfbYjt42+uk25PmTvXSScY3J99rdd7HyABON1ZexYHAQsh2MGQpWMG4M0FhozQNPovbDjODGFJ9yqOikDG708GwReb1tqAdmfEQwi6v7/Pe2O4J06f3x2IMTaP8HkurnM+n98KCtmY6A4FwB+jxAnrGF8UyMufpNK1yR3pfGMzQaPPE8LtYJzMBfNEOdF2u51sVvUgi7XjXnPg6qVX7vBZV4JZP0h4uqG6zZ1nqqrS4+NjzmKRefMgknHgmPq+nAfC+mHk2JiN7PmeJ2cSXX6cxXUn7MbJy0HngSefI4j2ta/rehJMZsATS6cmQC5s3Lw8CJ10ZpV5doDhQTbfwcHBQAH4COBYV/TBdVOaGnScGfMEOPZgAR29ubmZsP/oGtd3xhGgM46jXrx4kUuz6rqcI0GGGJBRVVUu5yXLXFVVPrsJh4ccuaN3B4iDJKOCfDDP+/0+BxFeZuug3MEjgA79cWfK33kW1pdg3nXVCQb/3UkEz8Z6UMe9kFUACPOAriN/yArO2jPRfMezOIwVQM4asr7Is5Q2bh+PR10uFz0+PmY5iLFsnodAQ7edgKCBRGHoy9w7eHGWlOfmfXf0yLyvlxNLHuTMn8dBswNlZIb1cfvAvbE/AB/Atc8z68ezQ1SmfTOarKXrj8sG9s+DZcY7933IohMJniEva1U6sDoR4N3zGDcy5LLv5N27fB3fnZM+jgvcfnMd5NaBLKCPZ/V9stI1K9KXMrZ5ZjVIE1/n88hzIa9uR/BRlKmi3y576BIBCfbLZRaCi7XwLF0OWkMB407gxBg1xiHbTN8TSADsJZvIO99nbZxUQS88yPGzody38jkntfkJwenn33mJnNsjt9XDkDK1Nzc3E3l07MHco5v8HVvJ+vA35Mt9MXqPvoM30Svf4+mEyDwT68E9dhPSzvH0PPCO47SrKXJMwJOSEuXIHEg55MDPJ3N9ma8R9/Z9r8whtoG5YT0+z+u9D5A8azBnEhAgQDqOcRyV2VtAszMfLABAJS1eqcfkWhgDMizOijkrIJU6ZQ+COKvCg7thGDIb60AZQcfA4QwwNl7r7OwVAIK5CCHo6elpVgIzarVKGabD4ZDP4EmGppQloXAYsaZp8h4fXjwfTsNZIWfZyFZ4WYAD33kpDw4UpXanjoGUNHFuzkySdfOgAGfFesKEOZNzPp8nCg34BXD7Pivq5buuewu0+p4N1oTsxel00gcffJBbZIcQcgbODZKkLDfMicscMgRI5x7cGzCOQXYGHJlhDZEtxu815lJpbe4MDwCfMSHXfI9Ocsi566hf25khZMczgr4mzJcHMdK0wxuZWmTpdDrp7u5uMn4C8K7r9PM///P68MMPcydG34vkYJ17EuwRFBa9ihPDj06tVqvcYh9d8EDWM5z8jhOB/cTR8aw+97xHcD4HZh4EzW0pMu2gEOLCgTD3R8b4uzsv5gjghR2WSgmKBxXOwDpoRQ6dPfY1x775nhHsIuUrVVUOU6R0xtfJM8CffvqpQqgmmZhS5txlQsHtEfrPGBk/tggb6oDH/Q6+i+94dcB6vc5+AJu53+8zcHPw5YwwIJX5hgDyslMPCBi777PxgNoZbOyFZ1SQQ+SirmtVBn58PlxWfU+D65pvXPfsOnPmZZiAWve9njnimjzHnCxDD3l2Jxv4nJNHBAXzoNdtmmc8HTzyGQ+aWCsfi9vm8/msui16h+z0fa/1aqXlVT7mBBNrDxHr4yHgwSYyf/gTZKbruhxAoc/YLiemXPY8+MCvXs4lGEcPmVuFkp2D9MF++z445god3O12uSSNwMBLtLGZ/M3JMtaYdfdDnX0eXa8YE4ET8ub7q25vbzMe5Sd4EVlHPsjOM0/Ik9sIZJ559fI7t52M/12kITrkvsPxIjLlpBJ2FPvL/ZCXtm0Vxuk+T9ddfIWvJSQLZJs/H+uKfmP/GB+BM9fyIJ/PVVWl6tcPii2vqp4ynSxoyujo6mhSp7kU5KSOLY+Pj7q7u5soszNdDppQIpyYg7FkGKOkZqJQcwYQoXNgSLSP4mA4pLJ3xOvnUVAERipldwgTRhhj4kwPgomwXS6XXHKSxl3pxYuXGsdB4xj16aefqapS3T6GBgcHK4Di+sZvng+HiFF0JpfPOLB3hkUq6VgMvJ+X40EbjpD19xIN5sedHcwx15MSoGS9pbJnLcaYz6biWVxmkA0Ue7VaZVbZQbez8IAz3+eBPDrodDaSNQwhZUJ9/pydYT6QIQcJyI/X1XtphKeuXZYdFPD9ObvtgABDSeaI+fXPoSPewcbl3tlVDwaoRUdfkHPWyvVvHMv+CAdmlA0wByGURinjmLJB9/f3+XvL5VKvX7/WdrvV69evc2kefyeryjNiO3zOYLy9wYDPtZfbeGZ1TgbwPvfys4sAKcjk09PThKRx8uL58+eZQHBW2TM8rAMvZMTPwpqDPndkAAPWBFIKuUa3WU/0Qiot8LFxnlVwUOCAH3n2oN4DH16AKwfszFPJ6LaKsb3OPQfXjoqxdLfyA5rnZJXrVbF3ZPsrVRWVC6PW69tsUwBVAB50xufjfD7nYwjobnq5nNX3w9V2l/NlXMcBOF6ClTqPjTkDw3d8czc6QhDochtj6pgWY9D5fFGoKnVd6lIXFaQwLTviHsh436f9WSUgkEJIh6Rj19yGFZKzjBm22svGHTwBPKlMcF/JM7iNmAevHujMSSH3X7znlRBt2yhK105qqbU2HQslXZsvpGNCsj8dy7gL8TCqCo3i1bcje/hv7FlVVZODo/GXTm54IyLGyfjRfQeuZLe9kQm2krl0mXUMQgnrOI6KiqrqSpeOTou92natUAWt18nf7na7CdHBPXx/MgHR09OTpLKlwIM0J0L5LnIDWYPdwK663WCunHBFLqhEcvyzekeAiu2BLPGSWeabNfJsOXLP/dwGO1HlL7DFHAtIemtOnTDgfugkcgl2dOKO+6ITfd8rxJjPIWVOPIPjRL1nyyXlPf0eVPOdOfnp2Nx9o9sm93Wf9/XeB0i8mHSMYVURdUdJgy4XIuBmkl4E+DoriuOVCpPTNE0um/AsR1qs6Snqzn5iUACWzhBLpYTB9yM4yEXp+Q6HucLqOJCG1UHgvH7Uo3qUCAPszO04pjlrmlbL5Uq3t7cTo8F5QCgX2RTPGDhA9aCC53HlhQ1lHiaO1xykp4NdEVF8NtBLemt9eHF9MnObzUa73S4HQZ7p4fseXLA+UjI6zD1ZtO12m2WALI+P0xlmGBRnZedODZkgE8C1AWI8D9fzPUk0dyCLxXrzDN7Jag5wnf1jHVgTsmO8cEh+3gffJwhEn9jf5oYQEMg6OPuFjDgo5tmRI0kZNDVNk7M+ZBT8mswj8sP6epaPcpPlcqnHx0c9PDzo9evXevbsWQ4snVCBOCEY8YAcufeyAAdtrDMBmpMffv6PO/15diKEkm28vb2dBImetUSmmcv9fj8pNeJz3N9b6uP4HUg5keHOyjfkIv9kWpgT3sdRAxq8+x+fpXGKB2TYR+yeB2mso+ugZzTJOHiJktuHOWuZnjPocjnn62Oj3Tc4iGY83jWtsOF0qWyVWoaXTcqw506mlQqGkonCT9V1pb73ToilcUbTFPuADib5kvoe5pxmQOkMvBiLr3D/5cHpPMMRQlDXR+32T1qvV4qDFGNl2aOSgQNgA2b7vjD86Ofp1GkYesVIK/Ry/hL6T/k0Jc5TX1yaaBAsMH7KeTxjBhBEpllX9MT9owfSrDv/dtaddY4qJftjHDRey/DXm9Uk4K2tCkChUhPqyXNXdaWqbrOdXV0rB5Ddpp4eLYBfJxgEpDMHzJPPGWuCj3CC1cvApdJABf10oo89oeg5+pcChFanU9LJ0zn5wvPldA30yrERnt3zNXUQ7c23fL2l6R4k1p7PkEWp67S/5unpaRL8YuN8XbF/6AVjQFc8SJ9XJFECOie1HCO6nuFfPHMDKYWdRg/5vAf52AbGzzjnfpv3sXtzn8JY5z7YCb3NZiONYz7zjO/Ng3Jk0jNuHsi5bPF59JxxOpnpPoLP4zv5zOd9vfcB0nWuJgx53w/iwDucK/9tt9vctMHZbKmUfUllYzhKVdeV1uubCTOMwhyPJ20227c63qAkXhPqY5FKeYxH8xgsgh7AVNM0enh4yAeb8Qw0YSBj4cwQQk+Qg2FwR+hGBAOCw0KJUETPROFkSGE7iEKoUSbvnDTPRmHsAfsAJQy8b05nzkIIeT+Br6Gz+cw7hp715Kez855hcgXG+LkzZU7YTwQb7cwbhtFbwxNEXS4X7Xa73C3NA3ICDW8B61kZmCiALc+N3DIOz7Zxba43Z+A9iHGA4GDTwSzgiOsAQqQSnHvjBOYLGWEsrD+yQckgDCnzCrBiPO4onTVy5gqDjhzA/OEseCZnAkMI+uijj/TZZ59Jkm5vb7VarfRf/st/yftQXrx4IUl6/vx51g9khfFLiR3zoB1nSEC43+8zgYE98ZJPL53ytcJREohgp/gc3x+GVLtPeUPZVDs9K8nX9fHxcVKGw8vbF+Pg3Dk72+v703DUvg9RmjK+XlLB3x2cuGPmOzyHl+I8PT1ZY5l0MDBzSHabgGnOpmKLCN6HYdBut8t2ied1IgUnjl2B/OBZPAM3J724rzfOcQLJ9dhlgHlIcpWqFgDR6ESylSWQ8Kx90qVSxuuBfhpfpfN5yIQUcsE4+d3HK0lj5BDTRQbqCZDWGoYxl5ViV/AfDsS4fgKYnUKIed3S2NI5T+v1Rhxui032Z4As8qyAs/PMI2uKjXLmGR1xmwi+8ACL+zmjjq/u+3ROj3+HuePf6AVyxJjwWeiRP4tfx8t8+Q5A2JtnQKY+PDzkuSKwIcPi+3Ul5f2L7EMFB6Hf+IUQQj6XBz/JuJkjPxheKuV8yEnf93r16tXE96IX2HDk2QG8l2ZBFHmw4/eAaPE96dhEfKljBZ6ZteEaYBf+DY6bN9XwgMEzMy7vXN+xEXPMujuZDj51Uox7IFfMk9tkumq673XSnc/yN4hLfCXf5x5O1ipKw1j24CH7rkf8zpq63Wf90H/ujy7Ngy5JE38yt89f7+u9D5D6rnSZwagktq8w9wgkbAPChVNEuDEemdm5GlLKpk6nwyTLhCGG/cXxe2kRQYrXNDuIJCDBsM2zPTh23sdgMFbv7uMvBMfr/jFizlLMWTECrRhj3mA4P9uHPQUesHgg5enueUmVX9+DN2cB+Mn8+7OjoCg5c8X6syY8LwbAO+7gXBzIOEPlTHnTNDlT9+LFi3wgq5ekeGDoayMV9pr5hY3jADSXM4I3ZJNAiQAT44dsOBjjmWBXAQ4EZ344InNC6aA7E57b74+h9BInQGXbtlmevLSRvTLexc8NnWez1uu1bm9v83x7iaEDKZfBruv0/PnzfB93GgQYzp57QOmOZx5AoodeDvCbf/NvzvvDfv7nfz43ZGCMgBSAC8/GeJF13z/E+y4vHoxmdvj6bAAYZxql0ixAKnXxHrx67biTFpAqLhO3t7dZH5wRx5ETADhYdPZSKp2rcFrMpQctnNzOnMG+OlAZhiE3SWiaZhLwo0cObPk7sv/ixQsNw5Dn5Hg85n0PDpQBHJ4Vg7hgvnHg+BeehyAHdhp75HPi9hebw7ok3R4m13a7jl1EDvFFt7e3Vx8TJmN0oC1pMq/YAwefDsTTs6yy/XZyxrMo3A97Wde1okY1bTmsGR0+n3tJo16/fq0YU1k2Pi91Jyzz7gx4sk+DmoY9DVExSn0/qGlSG3Zn6bGZ85cHIe47PSDjd9c3J56cCef5sJ9OpmLLMvhTaQKC/3CcgtzNswLYJc8IIZMQhZ79nANixvb09JQJEg8e+DxHWABEAed85nA45L2bEDI3Nzf5Ow5Q3X87ieifYa4JvMFl7hfwIZ6lQUe9xN2zwZ6ZINiAhOI/vz/6z7oxRveB7D1HzxkT84tMeHDJGvI3D0jQb/dTzAU65OvtwSX6CG7g6ArXY8c7jsnw1/5vZJR5cAKT6zBuD1h4OW7ebDYautKp1n0qY/RgEayEzLnvdftUVSmrThDvfyOb5hjdyYQ5Tvhar/c+QIKdAoTgFGMsoMMzGc7IMpHeLQbBgTFBsKuqKBmK5MJd180ErDjDgHHG8PlPqQgiSkgQM2ciACQwuhgKj6ydEXbDw3NLhd1xocOwAZAI+rquy6CHezFHfIZrumHs+z6z7Q6037x5o7pOdfA3NzeTVLPvz9nv9xnowrgDDOfMo5cmSgUs4Wxvb2+z8SVAgxnz7nNca74ngzU6nU56fHzMWT2fN+TBwW1hdAv4mc/7nMl1B8xcE3S7s+N5uBbz7uvshp9sDMaS9z1L5rLqLDKZCwATWUuyWTgRZ4dw/ARS/A25Z22Qd2d2PYhDhgEJq9VKr1+/1jAMOVDn2Z1h9GyAZ1ucseLvyDVdJQmoPRPH83/rt35rngPGT3lYVaW9jRzIjB2AFfQSSHf6kCRt2046W6J/BKSu2y5nDqrQYZyvN3VBVjzY8KMH0B/2EDmbx70ccDggx5HRQWpOUiBDXubozzCO46TUFkBJ2SPP7R3zfFM7YNNLZpqm0ePjY54jJwzmG/75HVn2c2DmOsp7XnbjWRkACPaD50K3eSHzkDoAPmSDe3nQQwvxBGaLDeDvySdMz0Fjzwj38s3WtFFHRlzGYI+RPR+/y16oWtvHWqoSVitIpJAzYn6f83nIgSL+L9mepYah1zCM1/8gm1oNw6i2nWZ73M8ic/O/43NYHydA3P47aHMbis677+R5kT0nDxgHds5tPddEtj0L5uCPuWaNAMe+LwRw+vT0lMuDIQQAmU6CQsIR/BC0Ou6h8QG4ah5IIH+3t7d5ztx3cR0CevTNZcDb6mN/b25u8jMy9wQ9zCmYx//txKnLJteQlEvaWUfmCZ/pZKtUKhOo1kGH5seyxBj18PCQZQvd5d7oAtjFSWCfN/83GGiegeeaEMHzPdQ+JieauK5U/NE80GMdPeDlPQKaeSb8dDyqCoVc4JrYMcet2FR0B//u5LATFTwnMuvlf5CPzAtzlQO78dfPQcovByelzr3V4+PThEF2hpf/PLsBAHXj5qz7+XzReD2RGUMOa1/XrcaxRLEYJbq9eJCC0ZCKEnp3EUkZSLlhkKadb9xY7Xa7ycb9uRHf7/eTFHJVVfkUbByGlAww4F+adtdCiQFClBPt9/tJWQjGTVIOhtw4OPs6rY0vPwHgfBYlcEbDGSJS+D5uslWsNWuKs/eMDHLB3NBWtKqqXArHmDHwbCj1EgACmsfHx+z8CDi5no+TcXkGCOV31odrMTcEZ8g0AIs1otwH+cd5eDA7Z8VZY8blDBXOHxDhwJRxM1bmFkfk7KKX37jhpvRpu91mGXa5w9Dv9/tc6uZ7CKUCkAko0A8nC+aEg6RJ50sCaL9vjDE7+b7v9fz5c9V1aUNP4MRY7u/vs31wVppxcR9eIZTN+ARiyDky4+cHMT88L86BLlMESASUzh4jD76myBnXm7PxOCFnHJnHeQBOR050iwDBdZvuh74uPDv2wY9dgM1l/PMyHHTGnStAjAMv587XQa0Hh54ZYQ6cTeVZmUcYXQ8a+Rt7PVhPZ6g9UEv3ffvgTIAAuu7rTnOHGEvtP89NsBajcjCMDCYwVIg37B9/l4LquqyZl4th292W5nUcJIVSisazJFvWq2nqHPixtuln/ZZNK+VIlKQPSnuSouqqlkJUCCVz4ftDPMjAp3r2yP0BNtP9Ef7fMQI2k/u4XHjpttugvu/VtCW76c1A3P46CefjxLYyXifNeJ+/YZd5VsgHZIlyMDLg2Gts+e3tbZZXqZwTR/UI50uid571cHCNTIQQckkWOuJ2Hd0leOP5kTEPft61hu/at+O21u2i+xwnm/x7HggAxF1XnVxnDefZF9dRz16wPtgVz165/XEbzHXBAR68+BxDLiNPbgN5BmTY7QjNjJxQZl08iOb7c3vLnGCn2rpWXZU9l76Hzf0fz4IMQRp6YOtBI/MDrnff7frCfHkWz+Ljr/l67wMkjAUdiwoQLHtKWFAUEcPkNfR3d3e5FAmhhfVMSpH2IXl5k6SrAtcTRYDBgSkBLDp7hKI5Y+jlAFIBznwXo+HBlhtoZ4qkpIRPT085A3R3d5fny9O4XN/ZKwChl205o4HzZH6cvYCBdkADK+6bjFmDvu9zGSMAjODJmQ/PXAHimRc3/HwP4O4G7eHhIT+nt1l25sXBJsy+VJojeBkfa4xSo6ysK+NmPRwE+foSXLjjB5gy126Ij8fjpNQQY+xz5qwU60RQC0jgul664GQCMoL8eKZRKpvpPePjxpTrecDjDJuD5OPxmIN0N67IKM8CcTDPBHNfN/DMNWP0eXGHPWGgrrrpmZC5zgEgC1u+yutC5oy19vIK7vGutUPOXd48MGeMyCjXgBHme8wZ6+b7ERgnukMbX77D33gubKHLgs+7B22UOGJXAW9ka72cyxlP9M8BGGvKfLLuZLz4D5DlgIjPtm2b91Iwv1LaP1ZVVc42oc/IAjJN8AOgQgYgztAdyiedGXbAje9xEIUMzdlXzzhIZe+as9vYhxCUG4pw7lUJrvpM5PA83rmPF5nQYUgldtyLz7lu+T08uIghdcJzuUrzkzrJOjkwjmOWB6mUJvr6jOOocUhMcBylpi4ZRMk74E33/GDH3LehIy5TDrp97ahaYE3cpsyzeugA9g3SBJmUpufUeJmjE5LMP2Mnu8ccemaDoAiAybgYjz8b8gX5AnbgGtvtNgdNnsXDLzqgRZ7d5nJYPATpPDMz91d+mO9c/rA/Tqb4PiWCc98DxZq7//CsBXPNs72rYQ36zhzTbIm1dqLL992wh9tLAT2wme/fc1lEP50URZe84QXy4GSDX3+xWOSmI/Mg2vEYssWcI69U/8yJLtdf/EWyDctJBQhVOGM/KF4xEGtPIOZjYT18b7LjHewOMubZPTC0Z+C8BLzYm/GKNcqep6/1eu8DpPP5omF4MIYhOY6qanKJAMGTszNSmVhnWngR8ABYkiD7qeWVQugUoxSCNI6l85pU2iRiNOZpRIwWn4WJBlh52RkGywELqXYECOPOmAH21Kx75O0Mk6QJ8HKHMwcxGGEHsF4q5ELrYMOdH+V7zsa5YYsxZsXn+dhX4GDWHR9z7saW+TidTjmjcXd3lwGi1ykz/4wHo+/MIEDTAQaGy0sDcQzMN0bC2adxHHPDgDmbT6CAMfI9ST63ONp5KQ4/WVMHtIBYl3+CRBwH42S+Ad6ASG8G4Z/zAN6dCftIHMx6uR/jRTddfp1MYK2k6Tk885IL/w6lKU9PTwawpi3HcWaeAfN7oZ8eZPFs6KlnLh1UOuiWSsCDzDMmnDTPhC4ikwCNp6enCdu+2WwmjpAxsaZ9X7rhwQi77jhbB8ij8QvyK5VSHoIJ3huvz3c6n9TUdera1TTqh9JdTpJCVauqo4bz+VqSUWm73VyzcDDylEam0rE0j6nhTtfZPcdRKdNBMJv+K8RYselSEEcWJJ+QDvQehjdKDQUGDcMlX2ez2Wq9TmdUlWsXGUd+CX7cfrlsOtlRWGM/30TXrMiYx9Z1hchjXbz5hoP2EErXQ2f+/1/tnWuwZWlZ3//rti/n3jNtT9OiCBVTJIJIwGgiCalckEQk0aokIo5U5UtIiTImRaDKWKZSlYCmYi4SpUhZqUqZFH4ZKU0q6JjgBEoUa4ZRFA1UgqDGoZnuPufsfS57r8ubD2v/3vVfqxt6ZpxmsFlv1anuc/baa73v8z7X//O8z2riSxsz5Xn3vifKmdpuj+2LcJHh9ZrgZUPr9QZEDEFFnqlpNtm/NFOWT1RVQVXVKE2lqto0FUol2lSX67XSLFOSpCqKXCGUqqquFA2d1cqqJLXtnvOccsVVS6sQFJpmQ6tNMBYaZWlXigeN3eF1OfTzITEYTaSmqWMlRF7kmm/NdbI8UZ5vGoEAgjZBVV1pUhSqqlpJmmiStcFP3TQq8kKTSaGzTaOgPM+Upps1JDfbGA820b3wkTuNtwJhd3d3tVgsop5C5j0LCo8O9Q2AFrp7Pp/H6gd0GfdAH2HHuAd6eTqdxuoJdJQH9Q7S+RltbAh/41me5XXAGh5xx9gBGg/AXGaGoBudPSlNPT4+1v7+fvQJHBD0jAx600FD5IlyMf4Ob3kwCZ3dvmJnnG/ZTwIgfncaYX/QMw6WwUv84FcObZjrcc+AU2WAXyKpNy8HdbkHFSrT6VShaFSu11H3Ewgr6UBqnuVnXr1kzsFPp5UDg14C6zRhXr4P3OfJjLs+QCrL9vxPi5adRwecrInXMUpda2FHsqUONXOEZ4jMF8V0I7xr5XmqqupemucRNoKCAiHAcgeX68ickGnw8g4MoaP0q9UqMqikqLRQUO4wOnKHwLlCZc1SlwnIsqx3eN/TrNCCYA66EGC4onCHj3bTBHt+8N5T3qyZffOghPuC9HjpmCNf7B+KgMOE7PVsNovfp2yNEioM062QauZIvTYHYD0TNURD2EsQDxd06r+lrpEDioIMnQf2XqbHGr20zZFP9sV5AOXnqXVo7bQ4PT2NdHZnD0fLjRT3woCyZ+4AuIFFMaNwQZycP6GNZ03ZO0+tO2LI3nEvz4qAuEEPV/TIuBsZ7yLkNOQ5jqIiF/Cnzw16++8OUmD8CMgwSkND5+//2N/f76FxBDvoHi9B4D44MGQxMUTsIesJIWh/f1/z+VxXr16Nego95kG/JFVNrcbmv9rMKU2CghJNprM4z6Pj45YHg5SmiZq61ulp9z6dDiwhA93KHuV4OP5SW+rc8kK6uea0xyvoDkmaTjugpg3EWv44P69EpzfWiM7gNQbdnrXBRvv/VHmeaT6fdQ62IfZDHmVtreMWlKZZTx7rOmg246xY3qMxutnRYP5PW/uWj9v3INV1uy+AK1J3thaasHd1TWlzC+7Bv1UpNU2iJMlVV6XSRMrzTQOANFGebBzVjJJnaTqdaLVaK0sp583bzE9o31FYh66k1Ev7Wl6sI82KYi6pDRazrA3ykLl2fzLlIVXbojzr0QTd4rrIAY+6rlVMCp2vzqJ+TdNUSoII0La25xFISbPN/fI0BlV5kW2yC5uKhw0PrMu18rwt/ZvOplHn1HXbCRFwFB5zMM0BJ95VQ1ah3acuWEK2/TwbuhF7DA0cVOQe0NEd/SRpK0xw5D0zgE5Ad2LLKdFyUIB9aAGOs16XStYLj2FDfA3uswwDA+wl83DHuKqqXsMkP5tCaeDZ2VmUaWw1suoBhAfcnkVCVh0slRSrTBxgZQ0eiCwWi+hjuL/ZlZJ2JeueycG+Ehxyb/wSnsO8fS3ur6DH2W8y6549ctvl+olzTvhR8BX0CiGoqmudb/RMHRqtN68QSM12Qht8P0B9QB54h/l5tYDbVOjnQSh2j73L83x8UawPd5xJ/0uKWQqYmGukrrlCmqa9tKCkKHSgpSCvlCtUVdU7YMgG4/CS2SH4QWEiZDhVLlBlWUanDsZjDggmf/czM+5AXbhwQXme90qvXFBRLI6Eu4NNvW9d1z0ji2Jar9fxnUjQEGVEVypnduYIygmCRcDFnPwgqDfLKMsyvk9hvV5HAT0+Po70y/NcBwcHWiwW0aiQ8UBBE8Ts7Oz0glIUIW3CEUAUNgHharXS3t5ezxEmRQ2ahOKXuja9joBwbwxLCEHL5TJ+1wNmL7GAX1gH60LhuYPm2ZDhPuDMebMCUDB42LMedV1rZ2cn8h50Qra4F5+hxJiXl5Z5kA844UGsBy4YCkcAod8QLWKNzAlegz+3t7djQMtcPNvVOa79N4d7NnM6ncbyG3d2CcS51zALhFODYwBfOBrKMygnQJbhAdbqem6IEuMItmUFLf/7uUUHgTww4h1IXAP9jo+Pe0G5d1DEseoyT90ZOgx9p1u7OXlWpCgKJSEo2/A4c3a9dytEsZcBUHcubrhf/nd0FjLozibXwivsgWfWuDcyzlrTtHsBtAM07pQ4Es4+1XWru6eTzXt7ig1SXtdtRiZJlGgz/0RKlKhuGjV1rXUo25xPCMqLvM2slXXUDSfLUxUbeSzXa62TrpQrNFKaZGrqRryUtc0iKeoZaDwp2peWtvvRvt+vLNfKslQhtKiwNi/7rOtaWZ5ptV6pqivlSa4kCcrydIMoK77wtA1C+k1XPOPQLxPreGn40tFWz3Ul7A4soEPTNI3OqJ/FhC+64LR1gk9OTmI5k4N2lK16IOyONXzhmW/kkf2nRJPvuOwCbqBbd3Z2YhCLbvNmKR4sU92AswuNPEiA/5At1uVdHAFvKYNl3S5D3Asb7M8n+Ol4vGsD7VUEXkrFXnBfvov9Ya4OFLk98xJK7g1t3AZAy2GDEQ8qnffZfwJZ/EbowLXMfwgOsreuK9Ch3twBXnfAyoN5bCg/lEK6Xodn3K91HQxPemYMH8V9CtbsATx+TlVV0Tdj/VRjcU/PYMJTnsFkLQTrdBKlqmRnZ6fnrzrd/d2G8O6QT6CFV31MJhOdnnWv17jduOsDpBC6g7wESTAxBIOJUF4enaN8YB6MOpkPHGicZa6nk5wLDs8AkfeSPTfKKCqMKZkVz3g5QhNCVxrl0bIb9KEQYgRYF3Nwhea1rcwPBThEh2B+nAM3MHTccucYh+/o6KjnPJL5Ia1OtoAA6vz8PHYrQ8FTxsQz2E/S/JIiWuQoHAJ/q0AZ4+w1zdzfFYA7u9AfukALeMYzAjigXjsM3SXFLBFBg2eIuAYD5iWX8BuGlD1zZxsnHXqz574O50sccpQlc/CAEKfDSx8ceeLerN+DBn97PUYMhwY+Yg2eOfPMK0YSGXaHy42qpJ7ceTDF2QdHWvnxLBvBEc6bn/3AyKK8fU3Mn7+5vPM79AeEgN7n5+exUYyjxsgp6/TsgTuPnu11hHGYAcJwsk4cCTdK/roBD7z5Xut418qU9fiN65MkUaKuWxhrSZJkU65U9XgN2jk45GtDB3I9+0oAirPGHsesQVH0aOWoI/KFcwhvwePQk0ALvpe6pjDcx8Go9Xqtk5OTyDc4smVZq666Dm7wJXM6X3cdvuBJR4XbjEequmrMQUw29fZJ+z6Suo7ZrpbGbeYsovVZ3paR1V1QC43bDF97nqA9O9QGSEXRnsWazPqgQBskce4lUwiNkjSR1P6bJsmmxK47h+YvKXUQijm4PqbMiT2Frg7OuBPo8ulZhwgKJl2GAp4H1HI+x3agA7DF6CF4Hd04LM1Hj3A/l5mTkxNtb29H2y8pApPwHD9e+stzyQJhTxaLRWyygI1gr10X+NlAKiDYR+gPEORAF/dAPtGJ8ClznM1m8fUXHiixv5QSSq3do3IC24OfgjxxRhb94ef7XPd6EIROJ3uEHEuKFTH8HTqy76wV3ezgnPtTnn1xHqZ1fQghyj50dxCTwJgAzrMf+JzwdVVVsZEWWUjmgV5x8ItKAXQka/CGOdgRPh+CSQQ3VPxwPAGaetMybDdBNWCU1JXfZ0miNEljt1n8wyzLtLe3F+2U8z8NsbzM3vUUw+cOWIHOHIKLn2/c9QGSd6PBcYKYvKuETZS6YMOVMgJLVod092q16nVWgfFRUDwXpBUj7i9WdEccB5g0KelyapWLoogvkHTECiFGkeFUuMPg5U0EIQRHjmh4yQUKyR1LDL87XR7AoVT5ITAji4QTh1EkQ4exQxA9KEMp01KWdYMwD5EE/o6yRzny/hJQC5Qsw5F1aAOiQVbKDYfUvXh2mKnAIfSzOO7UcYjeS8ocBWHN8A/KsnPE1CtngHfZq6Fjyz46z6VpGsslOKALfZgLTj1744is85m/1NSDCx9OI4YbW5QdxgKZ8DIOaA4Psn5Hqhwl9fIMD5JwPr3c0p0HR/+Yp/99iGB6tsDblXJvroE/GA56+EHvqqoi72xvbyvP8157cL4DmOLZKHdkkHvo62vyefB37g+PwW8YfPaFZ7CXGC+MY5FOFNSVH/q6k815FF8vdKjzPL7de5iFdzl1hBc9Ca/4+hzQQSfx3GGg57pEUnRu3TnxvfTSJA/qh8Ab+4l8erlqWZatTp/t6Ox8Fe/ZlpPRRhg93PT04WSSb3gyES9Kbb+bKEkyuR8QgtR2U22UZbnSFDCwPSfbNO1PVVWq6rZ5ArYCuxmaNvuXZTgubRC2s7MlpanW5VqzWXvWyfcrTckCt2dwQmg2wdlaUlemhZ1DxuE9bx7hJT7IuXc19KwRus4DB3SLZ41iIKVuXz3z4/LpABX6hXIfeIB9psrBS2+9PBg5ovU9WXt4FP2O3sdR9rNLZMu8iQLzgJcAwqAlOmNtQbf7OwQ16BLXfTjxriMAfZIk0fHxcbTzfJ/mBlwDYE0LcUkRZPAADDAE/YP9YY3DqhTOEPtrR7BVrke8sYs7zARcbr9ZMzoMerE+5jiZTOKrH7wSoWmaCHa5TkBn4xvixMNzTlPmh+5g/9zGD4Fa+KuVvzTaE6lf1YHd92dgZ70Swkv3oHHTNLGVO5lUgk30vZc8kmhg3QpSEzoQ07N/DkTzuwdwDtAD8Dk4zTp9n6T+y+ifzLjrAySPNBEqGNjP4EidkOIII7CeSkzTttYf599bXHobbASNtOPx8XGvDAon3lO3HijxXJ5Ja1LuTQQvdR1zcAZhOOaFg+sONELuyKw7ru64SYpZDFAoShj8nqwry7ruJh69o2CKooglaJ4hkhQRAgRKUrwHSl3qd0NCqNwo8j13aodjiKhI/YwfAuvGl8Dagx2UH2vk+fCKO83uKKF4PDuEEaZNNPvoL9/1kgC/l6OSjr55VsRLmqTOgeWsGrR0dJrMAftIiYQHUo7UQlNHeFxR+Rkzng/t4EcCNww/yjKEfjMMnnErYILn+V6xB6zZgx3nIw8+uBfBvAcFQ2UMcOIOIs/G0WBe7pjwN0e80El+T0eS3fh7WYqj79zDjT9z9/mxXmQBWvnf0U/MHZpQOtxDprNMSdplFKBbCCGWViHjzCvLMoWmK28igHXHF7lyHrtVIE62X1KvDNJRXmQSHYyOdWcCnmJPeDZyhEMLALdadS/2xklxZHWoZ/luWVUKQVqvSzVNl2Wdz7d6fBuDzA2/zudbapsK+KsbkKtUkwlZp7YCrigmapp+xkFqS+MIlBJ15y8ATWazmYqc7l00B2ibLTRNo+l8pnSVqK4bzeczhdBsSmYmaluOS5wh2t7e2swnUdN0Og75hr+QJ/aHd1Yhn+6ETiYTbW9vxzMd6GQHS3BAh+dgXEe5jkQ/o0M9UK7rOr4D51aZ57Istb293WsdDg8hI0OnD/l3p1hSlC2AOOwlOsBBAXQqOo/7U64OoOvPd36GlugTpwfrhdcd+UeOoBP2DL/Ig1P4GNrgA3iDI3fUh9lEB24dYPQyaeaDDfcAi/1DlijxvpVPiE/hQNnQdmC30Qc0mwDY8jl7ZRHz9S62DtrCM8MsmYMxTgvW6cEza/BmTl5BdHBw0LPn6FfWQ6ZtOp1qsVjEfefsEACF87DPa7FYaDabxdfM5HneBVt1Z6OYE0C0+57IsfOEPw9edwByWD3hfurQXny+cdcHSDhkOA7uiLljdO3atRhgUP7m5wMwtihJHDUMrwvYtWvX4oFmBPXChQs9h4r7SuoJvyO5zIf63tPTU+3u7kaFjcJEEbjA+jknVyQ8DyWOwOEcch4I444SBiEgCOKAngcYlAC5g0LbcK/ZdacHA0O6NssyHR4eKkmSmzrMsJ+8jBXmpzxhZ2cnKtmmaSI65c4vguPpbeaE8CGkoDQ4RlwP4ueKdBhkeeDmQQ085whXXde9kkFHF1EaQ0de6pwrAhXm42cr3NixLgalFDgmOBf8uMJzA46iHYILHrA4muMKnUyGG8Otra0YCHE405Ee9gHZ9bOE/Mv1HojBG8zBjYQ7D/Awc8N4+fA96BzRfmcs/wx+QWfAI9ATmntwwkDJT6dT7ezsRKPK/eAj1ukOF5+xJrKPfIbsw5c4LP4+IBwJdwrYU5wzB3Rcx/CcYjpRYug7739ar9darzrjytxiwFfXvXIbXljJdSCS0JtyNuQJGvnrGOBD/9f3giDUwRDPvtZ13WtM47rI7QrOBMGXI6Dwo+sg9i5NMylQlouz1GZrWl3AC5nbLEwH7LQNGMqya5LS7nemqmoDpCQh6GiUZZPNc0HT28YWHT/VSpJG6eZ1FZ6RbJpGp2cncc6z2UxplqiqNo5V0gY/SRJU1xz+z1XXldbrjre6zoGUauY9hwq9gJ7DHkndSyzRb1xHYM58fWxvb2tvb6+XhWGvaD4QaaIu8M+yrhVxknRZHg/e+AEgwMbyjNPT09jx1PUSz2e96Hr2wbMUri9dBtgT7Lyk2MzJ5Ql+d1uO/DtowvzQp352jowMe3Z+fh6BBKo0UpN1ZG8+n8fKEc/yESTyHQIEeMQrHpwnoAdyDU2cXtj+YVBE1gJ6OsDqthx5dv0OL/qzuZbhASXnWhmedUaPuo+Jnoav2QP0hoM50M4zf55F5H4OtpE1Q8a8nbuDhSQC/FUx0ML9FujldPaqAioJ6rqOr44hyOJaSWpURT/LQW0HgYZAFjLOv26PuN5p7zR0nnqy464PkIieCZI8dekOaghBBwcHvfI6rkWA3UlzlNeR/Pl8HhUyDOjBAGicIwmOILChUv99LHVd9wINhBHBBQEg8IER6ro7sEeG4fT0NCpGrkP55HkeU/hDlAea+fsGcGC41lExrsNx8trsNE1jW1LPdKRpGtt+uiPrSAUDdGp/f7+3Ln+vihselEdVdW+vB33zlC7/L4qi16bUgyAcSbJhUie0KDCfJ4GuK3mUkTsIWZZFBcP92W8CdYIcnu0BiAcqBLPQcJi1cKXt33EDMHQKKSVABjxwI8jgmQQaDgzQyGQ6ncb28o4WuZPrRsede3dk3SB4OQay78YJHeDn7uAtLxHwDAnzAHX1vYN2KHeMggeuPMP3xjNhvhcexDjvHB4e9uYEHbg3BhY5GjoLOEJu5Hx/uJcjdk5r9sWdepwHQAMvd12tVipjEF9pa2dXWZIqCdKkmGhaTJRluaqq1Gw6UQicUwlS4JB7ez6mfYnoTgQqkkRar4OahkCdstG8x9vDANCdTxrrwAs4JDi6OMTwH3vPfR10w5Hib3XdZTPbXIyUJKm2tmaxW1aeF6JteYue07hBmky684ISwFmX6Q4KSpKgNJPyIlVZnm8+CyommdogJ1VRMOdKTVOqbholZf+ll+4AtTIoSaFHK+dPLwEKoQP1yrJU3dQqy0pNaBSatuPg/v6+Tk5OdF7XqkJX/59sslUrmhnVtdZWeoaOTC0jCV8iI9Npy+vTTTDdNG3DimLSlkRvbbfg2NnpmdIsjc59fVZpZ6erUjg7O227lFaV0jSJ6+Y58DoBM89C3sm4ODiArZa6QAP9g7OIXnebgg6gkqV7H1T/PBXfwSHmXugqgEwvvWavCErwCdhrnGJk2QFV9BqZbL7jLxl2sMsBMG99jR2FJoAbbnOlrvkGOg+fwZ1xB31YL4G762D4iTlPp1Mdb7pm+vVOZ87CMZ9b2XIP2LyiAH2D7KBPPGvsmVIqarCn6PLMeJ/AyO0MPw5WwoMeuLHf7h86mOblkpT7OtjDXjh9eB7zchvo/l3TNG1tb9P+JGkLqKjZ8PDmfh7sIU+szQN45uR0cDsHPbkP6x/a2DQZu9jFMZlOeiVMUlcz64THUXZGlLpD9M7k7nCyQZTboXx2d3d7tbdS53C6YoOZvN00jgeKlzIsykO8TAdBGqaXUeoEX6S9SXGCrLjDBPOR/fGSOVeCoK2OZPM7jowjYBgCFwJ3NjBeGEBHc4ui6HW5g/Y8lxIM1g7toI0jdu70bW9v95B7/vVUrzv+nslhTUVRxLeAo2ikrkMiAt40XYdD1owiha4EFey3ByrunHmgMwyw3LFzlMmzWFKnNN2JHCp6HEDWWxRF7yV+KGEMrxtjkB6+y17AY/v7+9re3u6Vk/p3MejQzrMyPidoDl1u5SA7Ukug4GcMCO4dbfNglHvxHNcLTgsO+GLo0BMeWA2Nigc6oHweqDqdHSjxzDV8jaMKLzvA4WfkPDPk+ozAEB7FcLlBRQcOacX+4lB5AJGmqdarleqqkkKI5VuhqZVnXWfQplaPBztEt9kAAV0bXGQG/djNuSs/Qee5Y8ge0unSy7dOT09jGTHXQk/kEt3DfTxQ9/1dr8tetrKua52cnKquOyACeWnf65Qqy1oePV+1jWSCGtX1Roc2LS1Ds8lQRiR6pSY09GDY7EmtoE1AlSVqQqm8SDRJi00JIEF6oroOStOguqk0nVFO3J05hH4AU3meqa671sZN0+mIpNo4omkX5J+eLJVI2t6a92TF7V5VtXxwvulgVVWV1jQEsAACMIiXp67LFjVfnix72R3kuSg23U6rtUIJAJCorEo1wbrKFbmqulJVlTEbQiUEttqdR8A7dAb6DX01BCq9Exx6BxtIdUUIQdeuXYuygA103QRYRzAzBGHgNbd3rgPdxtCZj+8QzHk21s+l8GzsjINryAA2gnJs7J/L4XQ6jeXpZD0AlIuiiN3YuN51DXYVPe+2QlI8X4y84YsNbS0ACee70CkeELnjXdd17HgKf+ErOoDi//J99Dx77wAqvg5BCDrG7R7ywv/xCVynpWkaQWqvCHGbwR44+IaN86AJ24Tf47xKcOn+IjRi7swRu17XdVtGl2ZtEW/TKAktbFQMns1wH90DZ+jP5w7wuH/mgJgDecy7qipV9fii2DhgUN5l44PNR3kMmZ3rYWBHW1EIXpNPEIPhdqeM7xM0ITzc0zt98DxS2wxSz5J6CtoFAyR5uVxGRByjTjYEhUf2DKEhm+POH4wFwzm6yEHL+XwehRSFz8sUYXJ+uD8tQR2dccdwKKSOknhtsSsTAhNoQ+CKgGEIaJDgtEWovc7aBdPp7kEdjro363CUDUWfZV2nPPYe/vSMAcEyCgeD6+l9FACKwYNdd+oc5fKsgfP5MPvoNeWS4tkq0DC+gwzwLzwMzT17xz5Hp8RKRbw8wwNPd8CRKfgaZxaHlYDBnTDfJ187z+F6529HBJ1mONMcnIavHQFGjqCDO884GjyToAXAwh0NNzquo+B9jLSjo+yBI4j8nQ5V7CXP8dJDd3j4Hs6Eo3geMBGgOFqHDhyWDLkBdP511HFodB3k4d7sHfy0Wq02OrHffMSvd4eB/7NW16844Dg9rBG+4ToCJQ+CHZga8h284rrz/Pw8ZruRgyEC6qWqZDt9v/K8s09d2VSi9boDJPx77N1s1h6OrpugPEuVF4WyQJOMVQxEk6SrbGBd2AW3o+6weDYeWQZ8dLlkHW57kCHKfLIsU2Ly2DSNrl27FvdjXXbNDJz3d3Z2op2kGoS9Z789wGYveacW9osGAlRgYFvdtsArntlaLBYxy+4gg+sIzicR6GCLsMfYbRx0D/QJQLAPzjfIJrbOGwQ50Alg5zZ1Op3G8787OzvRp4AXeAa/QxNozfM8w+AAI/KM3c6ytlJiMpno8PCwZ88JHMj0U9YHn7G3/jJmeJUsMPLL9fCqA0rMyzNwXiaJzeMZ8L0DOQ6aOXAET3INZbzsHcEo9tXfI4QfRec7AhOXK57BfNgfbCWfo7/d13M9hVyi6/CRHIT34euDF6CjN8SKQHbTPw8MH6LX/T4e8HpiwfU6voHT1ocHxQ4iwjueyXsy464PkKqykqb9bkpeX+uZDZjYS75QTu5kIeBsOAxD8INBwOD6WSZXFAjjcrmMio+3WNd13escQkYJVIr5rNdrXbhwIQokgZVH/H7Icm9vrxdYuGPkBowA4FZOpQsNAuUIAs4AaAJKwbMNPJP226zPBVdqBYCAhueh8N0RcVphYDE6Xmvt8+N3jBbfd0WKcHvgxv66Ywdt/D5SV46RpmmvRSX3xwmCx0AhuScBhN/byxY9u0SQ7U6pIyuu3LzkygMLD4D52zBY9MwQ17hcQRcPaD1TV1WVjo+PI/3hRUcoudYdeEo29/f3YzDHujwz4k47MujBtxtvd4DhJeTGg0131t3o4Bjh5GKEPKvpjq2keKYO2SCo8jng5EDnrsSsM0guuwT46DV3PF0X8Hc/P+iBKY4PxsXbwzoQ4wG7/421UyaCM+aopme4WDvzR7f5/cn0oItZAzqT9fnzfe1DeRxm7hzoiIbdAmr2yufrpR7sI3PwslgPMOE9B31wormOe8Bn3MvXRIDsDqs7JlzvzgM8U9d12z2q2bTGb7r34SjQdbO5iX9a/uuvA55xgGEITlAWQ1kUzmBXcVD1MnnYwCRNVW5spzu07OGtADP2bjabxXMg6AlsuuvvoTNGxsj5xB1q19ucmfTGCdDLARAPZtI0je/uA0CktM0zxgCt/B/neVj6JrU+BY40NPY9GQIV2B/sBWtz/sdGOnjDwFYhs8vlMsoE+9A03flfmpjgQDsgBY05g+z8FELQ3t5eXOv29nYEVqElVSXDJhDuR3iABx86uM1nBCU0skCnAfah6wmGAfe88yJ0Qpc5aM67Ek9OTnrAXAjt2d/lchnfaeQyRiYR3vOqCtbmsjqbzWLw6PLvFSnwFXzuoBq6hz3B/iDPDiyj/9Bj2Ar3MdAlrhscGBtmOtE3BPPMETne29uLdm8IRrk9gh7YBM+euT243bjrAyTPEGGAPNWJg0nkjoAgJC7EBC4oESJSjDob5wrVHYcsa7u70Qrz6Ogodskh84Pxo9yjaZqoRHkXAMzjaCjKByWPMoTBcXakzqB76tydA5zsIQKLs+AC4hkcgjovIXJHDqHhftyT+Q1TraxnsVj0shqedUERO6rtQuwOB91XHL1mfvwNYfMMjTuirMPLFrxkRuqQekcGMZzQmX1HsQzLF4ZKCIXOfTFeOA5eIsp8Hbljr1xRwufuUKEEMYB8BycDRUaQhvGr6zp2avRgGkfZla8HosPSOJ4JPzkKy/XDc01DVAhae0DJPg8bhqBQ/SwNez08eO3vAuG5HlzBP+gU1gjAQNMFHB2uYU89k+DlOuwvskdwfHBwEEssT05Oem3a77333vj34+PjuF8e8DJ3lynnGXdufK+gj+s29sNRReiD48TwoM/5nft7Joy5UGaLTkMmOj3VBUcYaF/LsCzRs+XMx884Dh1y9h3+BnXnWtcf/B1d7o648xxAjcse1+K8or8cBHBbw766vDFn5zsAhnZ9ifJ8sgkwqw0Ptq23FW5uPoEeQu6hgXdZHaLbOKQ4565HkUn2y/ec85xnp6c6Oz9XYsEh95jNZmpCF0Qiq2SBsIf8//T0NDrb6FB/bwu87cATzZA8sHJ+xblj7wAX+Mw7rnqzIHgS/SQp6gOCDC8Rwl46iDWdTnuVKDizDpp54DiZTGJJc5Ik8eyvnyvxbLADu+7DkPX04MrLMNH1VVX1rsVeus7gemRiKO/wATznWYO6rnvtwfGXho0s2BfX78jB0E/geq8yAYB2PoO/PSPEHHHICYqw+35GGaAKu4DtGFb6ODjvzSjQbf5c9pF9Ya6u1/kbtpHne3Dk59KinG2+D3BGwA5NmLP7d56MyLPOH3b/jsAee+/22GnuwTsZWn+m61MPvvm/22tovV7d3Mzlc427PkDKi34TAATBEXzQAEk9xerKihajvin8jkJGMFEEIDFlWcbDblJ3eHw2m8WGDY4So+hwzOjeA/rkh9hI5ZOd8IAGZYrD5qlzd374F8TEFRAG1lGJPM9jithRc0fEhw4G/7JOBJYgDfrN53MdHR31UKdbOXMIuddss484O74+RyeGJWru0PIsR8kZjqaw7/42d3+Z3RDl4P+OcLtDRWnJ0JFAIXoGhmd7uQ8OyVB5uNPO39kPqTXeKHp3LHgmysoDQObm57Xm87muX7+uEELPIfC0ts/N5wD9CAK8u48jTqyF9XI9vDnMCvGMLMviW7qbpom0JrOBckXunKbsuytg3x9H6TwYY53Mn+/gRLkzzP5g0KG9y6VnPnkG76Lw9fK8q1evxvXjFDjCKfXfcbRet++o2NnZiSix3xu551qXE9bnmVCcEBBTdJaDS26kvfTPM/aOjsPvrNV1TQj9jBEyMgQMXM69zMide8+w+p44cAEN/SwH13hW2sujPHBzPkFXOQhA0Owyx7Wsm/30QI+zMx4Ec+9ujYnqio5jK7XNCdKbeHuYFSyKudrOdF22i7l5JsvBAXgdZ955vssGdu8i9JeJBkkXNo2TeOF464hKq3U7Jxq9MEfkCL4rikIXL16MZ1ycDxw8SpK2qcTZ2ZmOj497QRnXIU/e0MB9CfQhepM1ceYXuajrWjs7O3H/dnZ2ev4B8xuWprKfXsrEuvlBXpgfug/bjrwOzxGjW3iud5JDdzRNo6Ojo9gtlrlCL3+1h9t61uMVKcgG2R72nMDDz7FgD7CVABVuI5xvvaoHnmQtkuLrVghksSUOenNPADUaUuCLeSYJfcIz8IWch5grPE7Ahiz589ErDo6y1+gHzmqxd54twc45X0pdEIhu4L6U5jHwD92OuS13cNZ1Mfaid75tMu3ZEtcP8DD74n6H389BOc+aOSA29MtcJ3nlRUvj8T1IcUwn03j2B8Xgjo5naTBqWZZFxxFDVdd1rOen9S7CInUoDIqC1LOkGADgICFg99xzj9br9h1J6/VaBwcH8V4uZMPgxB0LZzpQMe+K54b4xo0bEVVG2cNgOF4EbtyTTjoICEobZYdw+lks/vXyEAKtpmliQwoUDIqauRF4VFUVFRF7g6AwQAQRNHciXaB9DuwrAu7OG8LI8AyDB4COqHuWjr3xLMetjJDUlQn4GqCdO0FOZxQsa2R+8LaXMzF/D6rcGYQuPIf5cS8CCalz4jw76HMIIcTsIfsCf/mBX9YJ3Rzpd0eTeXv5EbLBfV1eoRt7SamCB5qSohxLrbEj0M6yTI8//rgWi4UuXboUnRzfH1fg3AtnxHkTXuNzlxucfC+V8f3BCXBnCTQamiBHZERBH7kW48kz3IBJ6iGAoISglhxE9yygB/Y4DaDArBn5cZQYYMgDSTfMOHDoK88kUCbMcFADertc4GT7/hAEDQEP/o8NoLQPXcszusCg6PG9B6KS4jrhe4IYZAMHD7nyjCY6jufhzCC7BJR+pg9HFJvlqC7z8yCW53TOaqp0c99WRnIlSSptOuQF1artILODI57dgFa8IwW+cHlzZx05BrBq59K+nwl+gs5ZlmlrE9R4th79jL1xlN351J14ZHm5XPY6hKE74DX4dHd3N37Xg3K3gR7kw8foxyzLdPHixfhdz+J5dtTLXPO8fVXF9vZ2D1Ah6+BOtjcySJIklusNdQlBPu+gQZ/BC1QEuA2r6zpmnJum0WKxiDoDPkVeqLjBprpuZsATdGbFPgEKswav3oHnJfUaKhBoooccIIUfsTc4+ezbfD6P60Rm3T643wKoTKCN/ONbDfUAfDPMqBAEuc/gQL3zD3vrQS/8zl6zX5Ii/Xi2B9A8E/1EVZGDilyLDcX2uo11e4oO8M/Qb9yPvXagoG76WXEP5pgbe0vW0nWr+3bYUvbd99Bp4fvCGplnXddK0v65pc837voA6fDwsJe+ROF4KZDUj5KlDq1mM2Aesj4wAQLpSpcNg/FPTk56qc+dnR1tb29HBCPLMt1zzz3RAFKvCjO6sx9CiO9W4Pn87jWjzuwEVzhvCKLUP2DPvXCWeTYBIwgI34UhadAATUIIvUYFrjS4BwaO63gmBtedZwwqiCWBlaMYrAVaDTNPlCJwX3fqvH4cI+ZBg/OHG1I+Z83USQ8NBfdiLTyL//O+DUc1ff2OjHpZBIJPIEM7aGgAiuRlGo6GOmIILw1RNw88UJKOpDNX5oXx9gDUnQwPnHB02FPq9pE9jCb7yDzhY4wjBoS5QyscGUesoINnpZCB7e3t+DyXI3gFR4QyXTcO0BheZj3QBmfl8ccf13q91u7ubq98xeeIbsJI4uR7MIpTB5jhoIrvjZdNuJy5k+2It9QCKW6A3Vh5adTQAPHvMIPA7z7YS5cn3zPnzVYH5RvZLA2txxlX3AdkzNu14wQM0Ub4kOc7EAR90pT39gRJOFTp5lD7lg4PjzSbTeN+EcA62sm+8iwPBDlfyf6SrYcnkCt4UOqcO/jj5GQZg7T1eqW67s61tY4XZaCUlEt5VquuGxVFrqbpmj00oVaSdCXHk0kRs4vofl6R4Cg3/OZZSUf80akewLZ82T5rsVgoTduXyp6cLJWmqQ6Pj1sHDduVZDo5PVGR58rrftdOdC2AGzqdANxl41aglQfqfjDfZZz98Gdi85x3mIc/h+BkCF5xH3iOl6bu7Oz0wAaXIXQ4TiJ20QFbsiQHBwdaLpe6ceNGXO+tQKY0bV+74bqWvcM3kBS7tvr5Zs5tI3vY9LOzMx0cHES5Qy45c723txfPH2Oz/Dw1egZ5d/vngIUH5g5kwofOd55FJ9giaPAgCdlFhgjsHOjxwAxdzmDfHYjHnnkFkYMFACjYDGwSNgz+ZW7MgTlL3Xk6txfs+TDD5rYCXeIBhwPAXs7M/EMI8Z1KBKXYZYJ/NZ2uZa2ekfc5ASQMAQi36Q74wKtF0XY69n3xZ/r6nuq46wMkgiHKn6qq0uOPPx5f5IqSlRRTyZ4JwMnJ81wHBwfa3t6ONaW80NS7y6VpquVyqSRJdHR0FJUDwnx+fq79/f2Y8id6RumhWIdnc3BsEA5qoCmJkbpULE4na/CDjc74OCdbW1sRbXXHh7WhQGD8YWoboYRWHrH7Idbd3d1eXStCUFVVL1Ml9V/YRqAJ2spaCJS4djKZxFatOPsII99xB4418X0PFlCO7B0KzTsjueFCiEnHO2KN4nXjNEQ/pS7wc9SR74IA+dkaV2AExyD2vl4/U+YZGs9OsZfOX2QIUNoeoPBceBYasLcoUak7w+HGHqSdzoNHR0eRHnzfeREkH8M5RA7JELvjxrxdCc9mM52envZKYj1IRpHCJ03T6Pr162qaJmaNqZ8/ODjQYrGIc3J+IvBwpyFJEt13331RfshsOyLKdzFo8DmBO46Kd3ByxA0aAIw4yLJer3sBp790Gv2DM+yZZw924Vd0pj/fSy7hd+7jmT7fC+5/KzmF57rzAkmUidZQF3H+Pl/mBD0988Tf4EPnE3hYkhaL417ABc87H1dVqbquVFXITqk0zSJaDS94yelqtdLOzs5NmVv2aLlcRh0O8OUyxB514B1nIHOt16uek7Zed+8W4h4t4NK+SLZtD56oCRuwRLXahuFtEwcp6Oys75yxP5J67+RzJ8eDRT+Ujk5F1snkS9Lu7m7UKezXZNrqX4VGaZqrCY3Ozk5VbN4z6Nc7EMQeOZDiWTv2B5n080CuG/k+uhH+4RoP6kHa0eV7e3s6PT2NmTV3NLHnALfQBqAiSRIdHx9HPnAAg0wG32O9nnlAR6/Xax0eHka9TTDRNE3sBIrNInNFgI7d5uwW2Rv20svJ0c0O8pRlqQsXLkR7Cc3yPI+v2EAHsw+r1ap3VMBBKZ53dHSke++9N87Pyzy9PJn7b21tRTDaz3khQwCaDqDDM94wBn+C9cLnyMYwEPNsKPf3OTigx5wI7gnomRPPdx+W583n81g+ToDvcupg+N7eXuRPBvdxP4vvcy10gH/gObKQQ/uFHJ6dnSlVosySDO7PEgANS/+wWdzPwSt0r6Re5Q9r6bLinZ73QFVq38X3ZMddHyCh6CRFx+bSpUvxd69t393djUESjA9zSO15Der+cUi9PMqZ0ZkMY4BAoZRQkO5ISh3aiGC4oyKpF7BQYuPr8QCJOaCoUE7DrAEK2x09qQu6uA6nDmFZr9exnAFauJJG8HAwQFs8OzdcuwuTK6Uh4iappyChAfvnAQ/Pc+XkB5oJ8DBCzEXqsj3D++L8zOdzHR8fx71wRArhxIDwXQaZGYQ/z/OIqkFHVzwEAKyXa6bTaezc5DT1DIsHMpKiY4kiZ7hDhcJyxAclTnCKjFGu5inuYZaP+yJzlNBgjHGi4QN4FINH/T/zRWEDFPAsEHSnXZqmunHjRgx2AUDIsDJX5Il5uOPlQXf34s9+ptDLfzBolMNJ6p0FxPlHTjzghgboD7Ki/jnGBbpzBsFLO9y4wzMYZK/3T9O2vAiUmPXgAFFmyT7DE575czSfgf7wICOENgvmAY+vhft1c0+UJB2SD5/glDtCit7yjnxuVBkeTMBrPDsE9fjY0c+hjnSQAAcPcAG6IQuUUQ9RYGSa68go4Hyv1+uYbSKwbPe/dWJYK/fxgN8zsi39ukYmOIKAK6enJ5pOZ1F+0HNkuZFH12fwsNMVXQWPkMFZLpfRsfHAO0mS3rmhuql7gSv74tUagHfsXV3XsRkBOnQoj1XVNTBA5pgzjq0HRq4HnD+YAyVo8B38enx8rPl83pMVvxcBAfT0MlPPTjCQRWyY34smDfA3JfSARw7YICvQQVLsCoodStNU+/v72trairIN0MQeELixj/hKyB6grGdTvXsne+tAkvMofpkHn3Vd6/Lly1FnY8ex3QCt8CzyfX5+ruVy2fNL2HMHr+Ajr0BA1h1ogj+wwdgz9gUb4eAxgRB2wJ14D+6GIBGva3Gb78/1M8PwWgihd6YI/nIwlvVhRzy4daDJwUhkmO8iO0MQ2H2TPM1UbII7eBddBn29mogs4/CVNtiFW8nlkN4OiDioF/e8efKtvu/6AAkHwc9BeDodJAXl6RGwO9gYY0kx00KUzGawqaBhjnJ7QMW1BCygGShLSsFgnqZpojOG0pO6+kwyQD5vf/9Jmqax5IR74ExKiuVxkiJShPKTum5RODeegXLUGMPhZXhS16Ico+0HjelOxXpQ3DhOoHJudD17hMBBCzfI0M9RF0fmHZGAHkOkzI2qP9uRehpWSP1MkhtHR8XIBHnQJ/UPc3Kex1PH0N6dn2Hg54dGPfsgKdIRJcreQnvuh4ywd9Skewcvnu/8EULolahxTxxfeNFRWEmRFiBkQ3RNUuzSxD34LogXBstlyxH/EIKOj4+1Wq20u7sb372BQvc22PywnyhvaI2yZ00u4+gJjBbOI0aFtXuA4lkOR2JxPqE5MuDggjuivnceUEMzqStz8Gsp1/OMC3LkDij0BHGHv7iv7wO8gJM/DGygAzQj2wW93OHm+rbMTXHfJfUO9vv5Dl8v98C58fUPwQHAhrquVJb9dsTDkpRhINcCJu1ZC5xLeBieGIIU0MztjJdiwZM4jB40htBvCICc7O/vR5TfEV9Kt1tgrQtOoAPBAevzzBl86GWht8quANoN6c/cPPDn/ujY6XTae38ecu7OrqSYdQXI4JrhO8qweegpB6Zcxr1Eze2wB9aOrqMP0FnQMc/zOLetra3Y2Kksy15jBXiI6hbW7bqMPfEMo9S97we5Wq1WOjo6igAVND8+Po6/ExDyd+SSebCvrH13dzeeMWI+2CzfQ8oNPQBz+SIIhHcAD8lsIQ+ceSRIcpAZH8xt3+7ubtxP9KwH+g7gsm/wCGAUcuD39X1Btpum6zBHcIUdwU/D72JvPbjDWce2AW7gnzmIAK8gT/D2ZDKJoJoHE0mSxPdQUrnipd8MByOd/922OaDmwRv3IlAlw+jAA3YNOXFdn6rfXATd5uATMsvekJ1y+Xdf3HkDO4wdYG0Elf59z9o/2XHXB0ggz2wIzjsbA2FhMIQF9Enq0rvucCD4oM8wi0e5Q2cZtJ5gCbSnLEvt7u7GkgrfUATVHTk/4IeBh6EckZa69CzKR+rQZxzwNE2jcBFcOCoN/QhaOI9E+Z4rNJSUMyQKib+78KdpGtH/qqpiVz93wBAab7BB4ITwuwLjPkOUFsfbUWRHB/kuf4d+KBPo7YLrjr476cOD9tDUnY5hpxie586GOyquIFCsHgBJ3aFYfjDM0MwRIndCoB8Oe5IkPZQOpeXKnHkwf5QPChfZYe0YK997N4ge4Hr3RfhgePaHe8IfyBbZWTIjrAdEl/0kY4qO8IDSHTkCeeTEW9CSlZbUk0XfQ+cfSkPYP9cXIXSHuD3A8oymZ/U82GQu0MnLHqA74EWet+Wojk7i3HgXrGHw5gErnzEvzwQgm6zByzbcIce5Bz2FHzwTgh5p79F3uLmeZzIPd6rYK/Sggya+L/Ar+rMoitghDWQTGYcmyA1OX/vcNvtE8LuzsxN5B8eJMzwcbqcJh+tobBBZEWiHHMIvUueQcS0OG2dD8jyPncb87BpVA47Anp6eRLqj62ncgZ5m72n6wzrPzs56bfS9rNIDEviJMl5oPplMYslt3TTKs1xBobe//sJ3By09eB3e08EqL7PyjnnuXHqXMRxe+I3n+Rkc+DiEEF+aToDq84FfvESO4AbbgZxCZ77jyDx2h3nw3dPT015pvp9tQ9/wr9to5zcHSQh8aFpB5sYzXgQ4yBuAMQEWPpADzg5iwvOeaaH02jN22HRsCvf0TBh8hc7EFhBMHx0dxQwTtPBskts1KmXIhnkJJQAIPuHR0VG0TegS9ge5ZO7sp2cIoT3zIUD3DOrJyUn0NbGP6CP0HHLioAX8Ap/7+gCDPUPlGTYHzdCh3qwJ+eNsFfvhFQRB6tnD1Wql5XLZaywk9btFEuwif/6Zzwv/B12M3YCf3Dfj+yEEyfb5duOuD5Bc8TjTuPN+cnKi/f39nqLhjcxbW1s6ODjoRddsAveCoTHCbvQxRihGHKbd3d14XgZUBkXsXbZgCowHDR+yLIudYVDmwxSy1ArH7u6uqqqKwYwHb9zDOyINnX+QEAQNgXCHyRUt/0ep4nhLXWaCYNWVhZc0enDDc4fOsXfSgwZ85nvN5wgUCoH98kCFNXlgKnVd+6ALCtUzbf45gYobHA+yUKY891Y85SiKGyUcea+b94yLB8sECdwb44QScqXIs+B1ZAY+9ADNUWLmNAx8PRj0tbH/nlXhO+5ko+iGSDXPYG18l7GzsxM/Wy6X2t/f7wVXGBiMDg4O+wGdcCYJOuAZnGwcAK7F6USOvEYb2WCee3t7Wq1WOj097QXw1OF74AftbhVMelkIWRj4AuPhGRKu9/cKIVs8B73AM6ALATOG0Ut9XNYcSJC6rDQ0cR5GLhyNpIzMS/fYB3iKucH3Hty7/Lte9v1FXzh9AbDacrJa16+fxuoD1sM8hw5Vl6XunNimabuAwZ+Uffl+E0zgZOJ48yyMvpeiss6Wph1tOMvqgWAI4RZlYF255FB34qBCcwfbOOuHbnFQyTupecmwOz3+d/bHA3AQanRbWnR2ZAhUoV9d7x4cHEQn1vmCIBC5Rxe4fHi50d7eXu88FdkA9hjZAExwXoYuDgZiq+Fvt7GATPtmFQAAUVVJREFUElnWlgkDDEFXB1WgNw63Z0ddptxv8GoXz2jAI8MfZA+6YKs8OzOdTqP+ILiHhnVdx3LMuq514cKFeN3Z2VkvW0RWyQGZoZPPntOJj8wTPtbR0VE8S4lv5uAotnwIRjoIgJxBR/SOZ+RYF7Tz4Bqedb+LfUBvLhaLmLXzzsee1WbvXUagKXLkIAO8hw1yveP208EhX79X3gwBLs/MMW8HEQlA4UnmAMDMdXVoVK47nefnaJkPegq9hswxL7fd2BDWQ/Ds+p3PuAd6tssKd/bwduOuD5BwDr1swRGEum7PPgzf++NdWdxAo1gxdDs7O5F5uDdKWmpr0o+OjmKGCoMvda2TPUsAs3RlFF3al9Q9DFKWZXzxpDsDzG0+n8f3JGFY+QznaChErNNLXHA+yDi50+LCBbM6AgCNUbju6EArShEILj3yRxE43ZgT9wTFcEQII+MlauwvQojwu3PvDtwQZfJ5cU9HfaAH/DVE/fyMhRspsow95CV0GTgPuuAZjJA7zkNnGjrhoKK8uBdrBAGX1FNCrAVFA8+ghH2+/l0Phvn/yclJNLSugKE1Bp/7EpB7Sp25My+e6WUoHtzhdDRN97JlrnGjC4KLI0HGlGs7dP00ghOUidC4g/NMKHnu7fPm+TgdXjKIk+gOPvMaZmBY3xBhhU/d8OAc+LlF5B9+hWYe9LMedyLdCUDXIN/QTupKqeB19Jo73HzmuqHLinRvhUc+Wrp2sk9A43Lm9yE74qCOOzw4I+50rlar2MUTR9n3Dl3jQFLTNPFcG7KeZTe/hBMbQVaHeXp2kL3JsiyeGaWzGQ6CX9+e5ShuorXrTHgHWk2nUx0eHsV5uIPVylHVO+eDI+pBtwfNHuyQcaqq9kWh2AFJvW5cbjNwGLG5Xl4Z0iTqSfgN3sa5ns/nsbV9WZbxfUkODtE+++zsLAY/7hMgQwQqHnzwLzLr2daqqmKJ2ZCn0QEEVPCB21rPLKLTkX32eyhfXuHgesSDLvgaRxz9yLsU3aa57XZas398hsxzf/iL55KZZw9ZCw0fOHcCDyPDZIvIJjIv1oTjDN/v7u5GvcjrT6qq3/Vva2srPpcAwEEqwFhozXPd3qPzWRs8DM2QBX4nE8z3oRslyt4ch5/9/f04P9e/HrDyHAfE4Cn2h3VJXbbSAUjfP6/i8HI9aMy+wo/IM/fh3h4cNU3XSdC7XSZJomrdZcYcEHawELqzdvid0QE7XQUX+tPBHK716iNspFeABY0ZpDggDBtKlkJSRARA8XCMQOL4nA3w1CbGnLMRKD+YX+pqWSXFDE5dd2UQfnDy0qVLsfyiaZp4mFXq3uLs6CpOyNBweaCEooZJHJni/x0jBrUtYjsHCsd5Op1IVkvq2Teeg+LFUKzX64i0EQgMywNx/EGsUIJ+3gNljGPtzQFIneN8eSkCAuQv4MNguvOBsoYmUuuw8qbyDhnu1u/onn/HU/GusDwLgvJxpBjnwo0/tMWgsL/O19CPPXZnBZ6R1KM5z3dk3ekNb2Msh5kX5uGpbEcs4UWcSP6lBMczI26oWZOjXB6sYlC9C5Nnjdy4SOrxIteBKnlpkwefRVHo+Pi4h1LTIne5XOrg4KCH3h8cHGh3d1c3btyI6DodKouiiCVsnEl0RFBSDLZ8b8hao5MADNAF0MTlF8fEMz3uaBDQsRfwNLxD9oG99hKmYbYIOnqwQxCNMXXAwYM53inipbI4iB4Qs479/f2I1Lf0k9I00XpdKcvadtshNMrzLvtJBgccAz6YTicbh5JDze05HFp0VxWZkqCtrdaROz9fqe34Vmu14mxMqrpuIjCWJFKSpBvAgg5pIeoo7EoH8qSScoXA2cGWnw8PD7W/vx/3aJj9w7Fxx8sBEUA5/m0dzCIeTi9Lzjg1kgBdKmXZVNPpRGdn5+JME/KB7MG3OILoloODg8gvTdP0Xp0BbwPquM6CF8iqDAEPbFy1LpWn7TmGoijUZI2Wy4USqZctRUbm83mkIXTk7C485C9qTpKk13wC3mavZrNZPEsG4IjuT5K2zMoz0IBx2BpklH2iYQLOLU6609iBBGwB9HYACZ2LrvG5AzI44ImdRe5cluE1LzH0UutbgW98XhSFtre3YyYcfcAcWf/W1paWy2U8SuDZRHde8UfgI/wHr17ArhOAuu3DjnvmyG2CdxR0P4HKHA/Y0aXYF3wP11mAZjzDn+3AhGeCCLTR5x4AOQiADYJPucaBS/YZUMUzOvgOnhxwe+OBC7wH/fzcmiRlaaaQNkqCFOr25c6pWr1arUslQSqyXDvzLeWb+zRl9/Jz+IJ/4X1ow/wcGET/YT9YK9d5Wa37iugUB6ABxCaTydikwUdedMgTyghicuAMAXTnFocJJcxGoNxBSlx5IVQIDRE1TAqSwHV+GPDo6Ch2YAO5w0mC2ckIuQNDZxhnDIQTlAUHkfUQKHDvVrGl2tnZ7hncEBolCVkXRScKY+YlOtCYNTF3lCFz8IO1GFaEABQXx410POvzLEGWZTG1juHBkNH4whWPI4QocC+zYz7cp3Ve2pKPxWIR1wa9EFAPVl25uwMKXzlKI3Uv33PeQmGzZvbDERwUmx9UdyOBUsLQO1qLkWC/CO7JpNFlxxWqGx6cBuQCXoCv4D13OnCQQHtdObNe5ocBYE9QrtABumAE4BsClCtXrsTvcD/2H9lg3dwHlA8DQ7Zrf39fUota7u3t9QJIUHZK66bTaTxIi7NLEOcvZgUthV7r9TpmgT17g3LHoYP+GG2XOXe0PDg5OjqKf8M5lbozlejBk5OTmw4Rw9/+TJwnB4LYR88+8Xx4AUfHDTUyJinSydHyJOla/Hrg1Oq1DizwTDYDGciyVGnaNbBo32fUtYbOsjSelyMbgG46PT2LZ9ncyUzTTHXdROQS/t/e3okAmDuzBAhtqWUWUVn0s4Mb7jhQYih1bdB3d3fjfNnXs7PTXqYawI/vkcl0AIT3tbUg0XSTXWvfQQRKTokRsse+8dz1eh1fho6OA1BE5rg+y7J47gpdhTwNu50RmGAXV5vzutWGFxMlSja8vbu7GwEvdwzhNfQr90KPOmruMtm9+yqNdMCZhTexnTiQ169fjzqF+0EP6OSyga5irpzZcv0IX7jMzmaz2FiG53nA4hlM6O5217PbrN8zZZ7B9rMu7I2XKDkQhR11QIt3HQGwuv07PDyM16Nb6PgHaIUMcfbNnV+vTvC21vAlpdPu+FO+6oEF1TzMi3XiG3gWyOXU6QvYzf8dpGVO7A+yiSx7wIyORPcMM0muE5m7A4B8hywV++aZde6Pj+mljsgR80Tu3a+d5LnWq1Y+66pSswHtqrLS7s6OEkkNoGzozvlAQ2wdTZIcoEQu4VXWDx3df8EuQdchQAw/u68wlA/fx9uNuz5AWq/WyrPupZI4wAgCGSEcpTRtWwE7yuIleY5cuAIaZnNw/thcqW0T7pvP5kpdKY47F0PmJnDC4eF7bD5KHMMsqYdm4IxSh833u2i+335SSjSZ8OKuOip0PyhLIOFpfhQNgQMlD56qZR8cyXEkkLnC9CgthE1qjSTrYy7QiZbRPBMaQmMcfK5HiNxRdbSDfWQuKNujo6N4T38Git1LXIZBqxtO9meYbYM+HgAzT+c95wf4ygNKlIk7tLPZLDYJobsRQZ9nqKAhPMs6KaPESXRkCqQOBMezFa6sMUDsVQjtmTnP4Dn/O0pKGYgkHR8fx4DGDRS047mOVEGf5XLZ44ELFy7oxo0b8R1Hly9fjkEM9AA5Zk9B0t0RQ1+wT9AX/sABfuKJJ6LxZ7+n02k8WM9ZpZ2dnXimiYAf58ODXujtug19B594gMU8eE8Gz/CMtdSVZBAIeEkvfOlZUA8oPXBnTcg7PE8W2fWto4A830EjN3yus+lk5VlynCPv5oQu86xadM5Xq1iiCRLtegAHhczZ7u5uPG9BYIK+ghdA1QEXPKPsgAXOBKAU51dxZKBDWZZR1+BYst7Vat1zrDxb4Giuz5N9lLrW4Tii3DuEEGUmhBABE2gLeAjoQFe9YVmm6xB0S57nunHjhvb29mJ5Ifvr5UGTSdfgCNo63/mZBdftOJgekHEWEfvK9QSVBJQAfp7NABBEp8MTlN56pod9dcccGaTxEXYFeUO/O93QOcxhvV7HzIwDTKzBAx704NCB536AGMwT2sPjnt1AnrAP8BoywXrYW2SY/ZO689bocwJsso8OQriz7eec0GvDAMZ5naCEzD9rQ7fwfQIHaOHBvgcx8BY2y8Fmz7a67nNeRje3YMxpzFwRaHu1j1ddoJvRdd7IwwFHDz7gB8A2Bz+dp6iuQNdx77qutaobyfjO/T4vm4Ze0NbtTJqmMQvrYC321H011ueJC+Qb/eaZUUBddJgfGXD/MMsyZWlXtXK7cdcHSDDndDq96XClC5VnDhAsjA+bgsDwXW8SQMYEIz5EeFBQZKykrg4T5vBzGFJ38B3GwYjicEv9HvQoLQyZOx8wM4yLUMF4bbCQbrIv9NBPNjXuqarq7Cakw5W2Ow5eSuWOh0f1nllB8XowgpIkiHFkBdQbNA/jiLC6U4Xy9+f4s9048znZQzIwCKsbFA824B2CbM+euLPljt1QqbBvjiazT9CdNDp86egKn8GbzNuDJJQJtHXDQbMO/3GHwQNtDBb3ZY6uCJ23siwbHJKsb3qWZ5L8gKbLCegq+3t8fKytra1ovJFvnCNvLuEOPHKBw+SOI7Kwt7cX51MURe+wPY6BB3U4tnwPeZW6VwngYDEPL8sFuc2yTIeHhzFb6CW8nhGVpIODg5jx8/uAvl68eDHukTve0Bq59PefEeTVdXsgmyztcrmMmVlk0ks/k6QrV8L5gXZJ0pXt+HtOPKtHqY6XreAcADI4T6HHhgATARZOusvG7/7u7+pTn/qU0jTVc5/7XB0cHCjLsniGiHeatXROeuc34Se3KU3TlnpSagWogWPvWVCfL4fVkyTpdeTi7NJyuYx6C15m33FMyFy3pYZJjz58V+oCBubhegAHDl3BjzshOBoegIQQ4otFnZdYN+ebiqJ92S1O8NbWVi/D5XNjjd74hB9/WXld1yomE602wRsOHZken6cHotAN3YwDjH6C56ATcoC+HFaZAAYhXw6YsK/u1OIIQifszmKxiOXp8DCIPnvjTSU8CMAhvHHjRnyxrLc2p3rFg/QkaTOzgCGeleFZ0M4BCJxnMh9OE3wqaCx1jjv6wZF/6E7Gg/1A5tETzMf3jGvcFqCHsEX4bGR4WCO8zbqd5gA3AFf+EmTfY5cV9JYH96zdAV/2w4Mjz8r6WSX+hYedVxwYd9/CwXq3o57NormGv6LCgXPX41Qp9fw1SZOiuEk/cI1ncB00RBehF5ijl4q7T+A6BRpzf4JA5JrrPdiFX7201+9Z17XKqjtff7tx1wdIk+kklisQGbPBGKsQQq+XPJ3UeMEYgszmwFg4HR5t+0F6qWtxWNd1L61fFEWsY18sFpHBEXoyMiChBGN53r6JmjIgnFI35I4Geb3/sMsWjjpObVs+UqssWwHNskzL5Ymm0845Rim44uP7HiCgPJmjpFhq6E4pc0TxgMCiRHFAhxkzhM0VI2g285LUy+JIig64B79839P3/N/P36CQhii2K3/P3riC8WARY8MamR984e+f8AOsUr/NMbQ8PT3tIW2enXLnkh/2DFo62sPvjq4ND5h6WR3nHeBrPyAKf+V5Hp3NjtfSnlL1ufEMBy2KoojGG3rj2CdJooODg2io3Zny0jQMEPvogZsj8GdnZ9rZ2YmHfbkWucKRx2iF0J1NdLTbedzXDirvMu86hXeoMN+LFy9Gx9FRavaD8051XcegaGdnJ2YAPKvk/Ai9vGyXdTRN22nTg206blKOhtF1UIg9RIb8++508LmXucKryCp6mPee4IijC4bZRze8Tif29fLly7p06VLcN7JD8/lc165d0yc+8Ql91Vd9VXxJZpomvU5lXlLMc9Cp3vnUwRkc/FaG+sBXnuexGqF9EW6/fAkZdnlhb7py7CryuwNlntFnn3g2ziDywZw9YEF+3PFyVB0bQHDnuhXeL8uuiRAOLw4RMs690Y1klCTFckTu7boefcDzmCcBAU4oNgc7h/wfHx/3ArvhOR6e4/RAlvEjhuAdfM1nBOsErfgC6Cjo73a0adrOh+hDbIrLDL6EA4noaPcffO6ur+EPsj/QH100zES5D4P9IhiDF7e2tnplx86LyLcDjD53z7oQFLouctljD7gff4PHkUd07BBkQ18BQgz5HD1DFg0aeOULw7NL2P+h7XS5cECVZzswtrOzE+0U98LPwl/x0nCehS5wHmW+XOeZH2TLm9g4+O0ywGfa6H+A3iGPuW5yQIa5c43bcHjA+XwIfDldobmDy+gK30vABL7PXmBbmrGLXX/gNMNgMBB15I4e4zB4OljqGArB9IO3lFTg4CCMIGigvyBrKAAcBs9wYLh4No4UjkeaphHBQdEMX1SKUXDkYSh4KElnrJOTM5XrUlVdSUFqQqNEieqm0u7uTs/x9cyBZz88WJPUU76ewUEYh2caECKuxwFyg3p+fh5rqD1j4MgXhscDAXfW/NA5DgLXwS8oM4QVheNBBcKepm0mkHQ5+y/137EAwsW8QT/9b863XrblSouDpDgUrgSGZ62YM3NA4cMrKOFhKYMHjXyP/QVc4F0TjkiyB1LXHp3vARIMAyV4ABpLXYbCn+33AsUG6V0ul6qqSnt7e6qqrvVwnucRKPAgxR0mnC34wZ09R6mg62Kx0HQ6je/K4bvO3zhDHlxhIKLh2XyHrCtZCObDHvI3nu+vCKBshJIj7uEG3x1A/i61zgfdlHxewyw090H+uR45ZK8JMJ2n3Gl255r36hAs4NDAc8PnerDMOoZBN8/00q2yLGOWR2pLGv37SZLoOc95jra2tnTx4sXNHnblHThblLx5VgK9wzyXy2WcozuSyJwUokOO3g9Bapo66hr0JfvkJUCsp5XpVEnSPwAtcc6za9fNs1y20fmOxg7v4890Zy06Gk2X5arrOmZz2X8CZ5wqP/+Ajse+QUMHozwL4sj/dDrV6Un3MlB0BFk17IGfRYX3kQ/kECeRcyrMb7lc9nRrXXevbnCeY77YwaZpIojA3E5OTiKAAc+R+UWX+/cl9UAB5g3fAJa5bJOZw75R3ucyzX4gIw7OsA4aGOEDSYodIOu662zGvF3PeeADH5Mx97JdD6rd/rjcuD71s2JuB+AHziqhJ93P6GUOLEDlnI3LOM9M0zRm790vcJCAwMwBxyFggGyxBvw96A/tXA9hO6Gv0wv96MGI+yQ8x+04QJbbeuSR70N7qTsbCk/HwC5JlSZdK3ZkF33ttHY/xkEN/GXW4PeC/znHy5ywDz4X9xvgB3jBQQH3T3vBbz6W2MXR1P2ONsN0pKc5syy76fwLjqe37XWmRekQsed53kuLUu9KuhSHA2FGWD0aR5BAHh2hlfp1mDiKMIPUnXVC6I+OjuLzaJPpSMPh4aFu3DhUCIkUCCgr5Xmb5drb3VNQh5jBlFJX4+mIAkKOEUFoHTFwxMydGw86PJByJG6Y+WCfhu1pEQrPbKEsXVihI89HIXt2zffZS9PgCZwjR58cFZO6F5tyD4TfM1eu/BjQFAUBP6HgfD/gIfYAJJN7Q2dfv7e4HwZiONse+LriyrL2zA68gFHAIee7nsIf1vkPjYSjSBgx5JNyNQALrjs9PY1d4yiRkxSzQdyT93FcuHAhPm+IXg3LdNgjl/m9vb2esoav3IB6QOM0gB9wDuGnum7L2jwQwIHBkYM3aM5CMMhLOv0ZoPbwtGdnCUjI7DkyCj08kwsdKA0bngHw5i2eDSRAkrrzPt4tDx5Adgh6h3wI/agGoCEA/ItskDnAUWSPkBmcky/7si/TZDKJZ9eqqtKlS5diQD+dziLK7IEQe+ngmXdIcsSejCp83863OxNI9ULrbPSNv5e24ojh7PO8NrDsyi7dsSnLdaS/1D+k7/zqyLY7bvyOrUzTNJ75gwfQYwAzLgs42u7MwIM0R8L2klEnE75YLFSWpba3tyMQCbAynU61LtdRDrk3++6Zl6G9ILjyTKaXRRGwUDbpetaBImyag4WUScEbDno6H3uWlOwoskwQ4TYfu80zkH+yq9DYHVtkzHU9wMXJyUn0SbgHn7M2dAEluQRgtM6Gth5Awsus2ffLgS70K0E/8+eeBLbwE3oDOh8eHkaZktQLnpE5B7o804becdvl4C46iXWzd8ic87LPG1kis0gJKdcSJFZVpYsXL8aGJewZcjmZTFRWlULTB5g9iPD9ImuGreh0Qhf0os89cHG97zoNHYMewzdtbdW5tre2Iz+hi+A3eIZ5O6iLnm5sXa57+ByfmcG93S/CNnvQ5YEjwLrrTOQozqEau9jFAZKDwk2SJB4AB9nEwFLShkBj5IftgD2KZ4MxwgiFlx6dn5/30qeuQAimHNXP8zxmiXi+o9g7Ozs95JLfOSjqjJFl7Uvq6rqtx0VpLBaLGGQcHBxsyk3a1q+SlKbZBtVs0c0sm0RHiCwF82ag+HAmcWJYc3vfzpgPDTMG3ZUPQsX6pe68AQIE+ocThdLw+zviRWtwD3DcmHvg4soF9EVSLJMYOhaeLcMwogAkMnUnMYh15NbPh3igxDOghQc4XmbkCDTrgi8dHYP/cALIYrlTC02GBtRT+/yf9TtaTSDjjoU7vfCOO63wiu9v5/CV8SwDjrs7Luv1Or5oEYciSRJduHChF1ikadrrYISjO3RmWAc0YSDflO3gQDDPNE1jiQo8xfwwkugXygTRLx6gDTNAyJ0bGHSAl0Cyn+wZ5UeOHhPUOL97tmIyaVuNI3fMCb3E/dBhzAcj7kGn1NX3A/jgNADYJElbIokD4+CA7+dQd7gTijPrmTK+44BKkiT6si/7shhkOI0cNGjX0bYVb9c/NcexiDpyOm11HZmLsqw0mZDxb7RaUSUwlZSoKHIdHy8iL2VZ3tMRaZrG4JPmNp5JZs3wbIdSB4XAC4zX8R0xdOHk/lyf513FAT/t2rogzx3XWwUFAA7oMXQQ8u/Biwde7B3yTqYXR/jg4CCi3lXVni0kaEiSRMWkUGP6gf1GZhyMI7PF75SvcfYHvXr9+vWINFdVFfWX2ze3H/C86zVkG/rAWwAVALCu16GJBybIoutAQE3miIyzD+wRz0eOKbXFIff5wf88n7khSx5oksHhfVKsHTuP3+HyPqxeIKjqeD+LWUWe7620XTbRd+ho9LLbCniGjBzgycnJSXyOA9+UxGL7vOyRAIm5u5O+Xq/jMQcCR/aMDBY6Hl3q2fidnZ3YGCTPc6UmE1LbDe58tdLcmiV5WRky7B02HQRwoM6zv27btre3dXx8HP0laHq+6Rrpfk+apsqms6gXhnbabRF7CaDLPdDRnq1jPrz2huASucDPwV8gAHUA04NHfB1kAv51EIh1Pdlx1wdI7jy6kZUUWxZDOP6GYsWphiGcwBgKR/sc2UXpoChIa56cnEQhgZndsUHJ8QNDcC8CHBwTFJ6X8pAaZp3MlUDFkXNXLK0BLHpGoc0GzONauS/KBCVF+Ymk6DB49xynE4yMk4lCZI/cMeZ6lCmOjNQJCLQmK4PCgn4eAOEUI8jQAgHl/RxSl8VxhN0Fj/MpzN8PPsN3/l0MNzznqWSv7fVDngRHQ34KIURkyulEcOhGk2AW/kVhw3ceiCIflIOCzngg4QYcHkPxeXofejp640AChhjHz1FNvicpyiDG0vfLS2igi4MW8D/7gKPncszesF/MSep3YGMN0BMkmsHc3UFDzi5cuBB1gwc+gDW+Z8g1tGV/cXCgN/LHWkMIsfMdPA4vwSfuoKGnkiS5KUjzYErqWtIPHWacABwAD/Dhe3SZyyBnUxzQgG+gAcikzx9ZoEzKAyGG8xm0BR0m+MQpunjxYswU8c4cfxb0crQYB8j3yA/vb29vRxR5a2tLi8Viw1OVzs+7tt1S232NF2V7+153rgECPJvmZ+nW635jGhxEglHmfnp6GjuyFUUbIC+Xy2iLkB3KJpnP0dFRDxhhjsgt+s7PfFDeBg+QwcBB9qB0d3c36rbT01Plea6DgwNJXYbUgZZWZ3UNJDzL6Z332BMct9lsFs8a7+7uRqcNGWKfvQWyN1WC9jiH8J+35uYa7LbLtGcL3MfwPQWgpbzKy8rQK/AxmTf0Kc9xoMfPfiHr+ADoagcXWbPTGtsKv/F/ZHFvby/SgH3m2XQwhDeGHXAdDDk8POzZJ/iH67nPyclJbLoFLWkQIrXNa7DTnnH1/ztQ5GALgDYZlltluuB7ZBw9kGX9smFoOqwMATRgvyix5/OwoU1V9883DbM2LkPofw+yCQw6menO+rI2fCsvCeZ+AG/SprHEhs7Qz0sNPfhC72GX4LOi6M6bA2qjiz0L7foWW+4+ofuSyP1wT9k35NR52HnuduOuD5AQ/hBCrCsm9YvxcdQT4hZFEevJvSTs6tWrUdD9gLwzEo7yZNK+G4I+/wiGBwO+uZyRIlPlQQKK9ezsrLcmST2HzpEXmAvFglBBF2p3XUG4g4PQu4PvSEYIoZeZcCWNUnCl7sbAkb577rlHk8lER0dH0UBIXStmd/79rAhr5Rrm6N/luZ52l/rt1D3AdUc+TdP48j9Hbzyj6N+jrASkrK7rXhMAqUMOJfUUjJdT+ecYIlcO0MBR/Vuhj+ylNxBhXShED3yhG/vq6LKj3DieZApxjpEjOrl5mSMoJryJYuQeOCmLxaIHYlRVW96Ks8EZL+hOcE4bbG8aMES7nSehFYaV66ATjhUOCMYJefZW6ARzzBsdAj2bpul1JuMaz/R4yZDX9rus8AzQSXj49PT0Jv0C0o2O4m+egWF+jhh7VpB5OXrrSBw6iXmii1x+0AXoUcAcMlE4/pIindl79KZnz7gnfA//ehkyIIkHbZ4dYC2Uqfj5CacJzpTP3XUftEDG4RtkiIw9jn9VdQEt8nJ8fBz3TerOALjsuhOxWCxicwnmzcDeSF2HMGjPPiDXyAN6ihIhrsMh85IXgmEyUk3TxCwMQSF05VwiAZuXwMFTPI8KDmQGOwXf+nvX6rrWdDZVsvkegwxelmWxsdKtwDCCOfiTeaOH4Hv+ZTi/oms9ACRw9g5l8J6DU8wR3kF/ZVkWzysTXHEtgZADV+4oouvhPed3zoTB87xLCzvDd/y8nJ8BhHfgBfgXenB2lucTJKCvPOvlZZ7MsSzLmOUZ0oAfHOqmad+fdfHixZjFwndhrdAF8Ap/hdd+SF1WGqDSZcMzVlIH2MA38Dz2F3tBoA0IxZ7C5x4MeRY1yzLVG1mG7oDH6/W6fb9Q0+86SkDoGXpfKzZ66Gu43kE/UXKHPKBr3Q9kHZWVeCIfABJDmXHQ220Vv/M5NIXuyDw/nu3hjCu8Bi9xz2GZN7rTweLWzj75sOeuD5Am004gt7e3exsO0yyXy/h/WuzO5/PeoWN3IheLhU5OTqKjwWZIXYDE5vJ/hAzD4iitZyVwIEC8QNQw2l6TyRpc+cIwfAdmZG7OkDAfSLDUlSq44XXmhnHpsgVNSVXDtCgF70KEouZ5jk45ks6acEJwahguCFF4zYGEvjgnTmvuy3Nd4Ny5Zr0YZ9bA/aGXH2BlH6Ah+xBT502/yxG8wz7xfxQha8bQoxAJ6OApDJJnHFCkODXuILPnzNGRRYwZCpc1cAYGUIEyPfbAAyccdnckKcniTBBroNTAS5zc0WSvF4tFRClZQ9M00aDnea6rV6/qvvvu6xlVnu0KG+PjwYHvn5dvQDMP9ra2tqJzyruJPEhH7uFH6Iqzyr/eRQgd5GUeXhoHT0IXspfInweVHhhkWRbPHTqIAp96gAu/eUYLHnC0HCTcDS7fAXhyGUL+HIHGmWKOOCfwk6SYWWCODu54ADsMzjygc+DGgSNo6e3nuS8OIHLiJbLs3xBFdyTaQSH2rnXUOx5z5wG9A594iQvAFnRpmiYi9I74Ig/oA9ZH1tID4LIsI/otKfIydKqqqhe4+TkP5uLgBvvCuiTFw9YEeQAs6IGiKKKT53y5t7en09PT2GUuTdv3EpLVJzDOJ11bZJ6LDT44OOjZLL6HQ4YOYx993z2jhNNG5hbb5JlGdxI5G8ke+7u4XHYdJMSPcMcUm8VewNOSYukk8uZBCDyHjWSvWYs7/WRp0IHwH2sBpOR+lKgxt6IoYudQaAidJfVkRlJ8JQNr8TI4XsiMHRiuDfCAufPD9S6LnnWYz+dxr1wHEKQ4yOyAFmv3hkL4XayRYNiBRIA+ADPmtb+/H+WVYBc5Zw74MS5ffv/9/f0YyHrGHdkegnxeweLrQ1a4xgNA1zNOj7Ista5XKvK8F+DBB26z+A70wba6znP95nIE7f137uVzYa9Zq4OJ6BH+NtznEIJkfHm7cdcHSOW6jEaC4ARjAZNBTMojQDa8TS6Cj9MlSfv7+z0DDZPCQDiEoGnelhSBwciz4TAp6WNXZDhV1IkOy6B4D8wQpUYZS/2XhtKpxrNEjhwQALnw4+RwgJb54xjRScyDMe+a4rXwzI/7OA1wUqA1a0BouBbnH0Pkjg5/cwfMswlS/23TKCBHzRF+lIkbS/bHA0p4h3tjtB1hc3QHQ+GBriszggKMGkEyShyjh6FylB0l5MgfBh/auQLy4HVYQslz3cDzLD8bgWHgGVzjDv+NGzfinJ944gndd999krqzNl7Ox945OsXZPWhzfn4ez5XAxyCW8JnX5kudIfe1ez25OyegpQ56gPB6uZ6j014yCZqIYoe3yEbixPv64Nv5fB4bUjhNp9NpLMvwLJeXFjIvN2bsITTww9V+ngjH1mUJdJTgmH3HUQfZ5BkEia4DCIBZqxt6N3xe8uLgh5dOUAnggAjAg9PW+Qqa+MtNvXQQviA4w4DDn/wLTVyO0JmerXb5JJh1PnHQCWcQ/eGlLL6HknqdqeBF7NVyuYz3uueee2JTC+bBOkDsHUXH8YTPvPzKy8OYN3bPG9TAk+yD61cCTs9eoF9wtOnOCL/gpFZVFTNWXnbr+tUDVM56OmDowbvbdObhQAM85I023H9w0JP9cgB2b28vZjrcWUVu8jyP5yn4P+CoI+esjzmSjWafvCubZ8IAV6C9A0bsCS/DTpIkyjYyBS9TEeOgqQOfAGVN0+izn/1sLCfm2AA+wXOe8xxdv349Am7YNGyE85QDG64v+Yw5eYYTGUb2blXtgh7wTq/wC2tkPTwb3nLnnGcxX+iOvWB//VwxfOiZ8hi8pf2KHPQMNKBygvJPdBr87DwDKO8+G3Losu8ZUA/MPRiE3qt1qTTpvzbBgWeu45kA5l7+6f7AcM/wQ9EL0N15jmdBA+g/9G+HwRif8/NUxl0fINHlh01DKLIsi06+l9A1TRO7g2D4CVL4Ho5qmqZaLBYxVQkSBHLQPj/X7u5uVDowk0fcGEQCHkerYWyP7qlzBW1xB0JSr+THAxGYm8OFOEiOFHo61J1IdxI8++ClZJ6xkPpnPxyhPj8/j6WJGEbowDXUGpMlQNlgMFH83qUGwUA5IexD1J41u5PiQaAfRL8VGg0duPcww4SB5McdZowqAwPpZXsoUi9T4LPPhZy4s8E+eOp7qCyg2bDUjh8Uu2fc+N4wc8Da3QA4zaEXSOC9996rk5MTnZ+f69KlS/E8itPM0Smei1NOmak7F/CTZwTc4LJfngmQFMtoHVFzWiVJEjMwGNOqqnR0dBQDMgIglzlqtTmAjlMEv7gTArCBM4acYnQ9+4N8gpyy59DLARD22EEEnDmXAc6cuH6SFLPXzA1QxQNnHHUMLnzijjx6l+e74edeyJiDGOyxG1ecAZw36MDwYMPRUeRgiHoS4JCNQJ+ii+EZ6OJIupfroqORG5x5z5aFoCgf6AzWUNfd2UmnFaV7XIsDx5zhQS+7QjcfHR3dVFrTyWWXPUMWnL8ddWX93giF0hwvmaMSwAEr9BhoP7YHfUfQ7uh7CCE2F4A3AAXKstTW9rZ0ftajPY6l60EHe7gPIIgHDsgjZVdeEogNmc/nMSMEjd2Z5+9eKg1IwEumpS6zQqBINg0Z94ARfsSGYNsAm9gP7rm3txcDnmGWc3iGi/0lW9c0TWyYQrdCSt+ROYIywDIyInt7ez1bKynqia2trfg+taZpdHBwEB1c6OSgjZc380NDIexaWZZRX6GjWQ/rBURkb+Avt8PDAJff/dUs7Ct8SVCHbmB+7Du6A0DWQbFhk4zoc4agUHUNYzy4dbskde+ygndcN8F//l0H7LGD8Bf08yzkEEBmX+bzuZq6K3tH5l3W0WnYAnjNQTvuj6/gz2DOyI4HSPh07G2apj0/lP1zYJN7uL3PskwagA+fb9z1AVK2YVKyHChylCqGw9EYCExak0yGMxJIV5K0h1jplHN4eBgREoQBRM8DBhT3MOXrRsWNl9Q50cPaSg9kfC2ejcFJOjo66iFpIEduLDDifj7DgwQcSJQ8ZQJS/yVrRVHo+Pi4VysqqVcXTZel9Xoda4IdhfV1ozhQGMwZpelpWhdE1iSptz6pOwjugZXUfwcLSoi/Sepl8dgvD3yYqx+u9qwHmSYU0b333huRU55FdsTLL0AH4Q13CFCOOE5Sl4Vx5efGgefwOe+YkhQRetaPM++HPynHwHi5w86zWSPPBeEik+B75YBAWZY6Pj6O6/VyVZwKN0iLxSLyFwE/jiQ8xDOQZQ9A3cC7YwUfeDkRraFdGWNo2X+cOvjMMyA4uk5/6Oy8SAbY+R2U0vcenvTy3mEQDa/jfKOPHJTBCQdNdkPGvbzcE4cbvnPE3403ARuGfShfGH4Pur28lTmyZzjPHoxSNsYZAHS7I89Dh4O9QM94iRXBn6PG6BBk0VFsX6Mj4shAWfbfF9c66k2PXmQ70e8eQOEcIN/obWwM9sODBs8q8pzlchE/d/5PkraDHLLgAdBQv2ODGH6ovaVDqhA6R4jvOVDo/2dPnLboTHgGJ16ha4Tiuoj9Yl/n87kU+qVSgCtul13HpWkaAQsyH0nSlb3DR+54oWNYO9dSfUG2Qure++POG/ILf2JXnb88y3f9+vWY4aTxhYOlOMgEwGXZdous67pXGn90dKTZbKbd3V2VZRk799HhzwETr1hwsJDnwrc8H/ABfoT+yJbbY/wyaILsoHM9E+a21jNLQ/txK33K766jkH+/n5/VdcDSAWFkZJiFYT51XUegHTvvVQzQJk3TWPKFTYy6umlUWGbK/UMHDeALB8WQTS9ZdLAU4Ac6Y29CCDHryjVlWWqSd/s/DELcDwE4Qg6kzi/FhrDH0M4z+9BxyD8O0HIdAarrDvaadUJX7tvyyhggxVHXtdJpGhUeSt+zEWQiUIAoI0e2uJ5sEptF6QJOOcYDZpO6EhiUAkrHmdIFm45Rbri4NwYBg0m9uZfKEFyQ6UL4UWgY3rOzs9gC3JEY7lFVVXxPCIjzzs5OVEgYBkk94eWQ+XK5jEYc2iFYXpayv7/fQyJRLFzDPR0ddrog1KA0zAfBQll6ds+DGfYChYwzC3LEZyh6dwRBnBzp5Zk4Jh7ssc90kyKgBZVBAXMtaNWwbAcnYtgFLc+7tvWOQEmdc+OIvJckcX9vG816Qrh1BzQPKLkXdGCvHbF35yLPcz3++OPa3d2NBpj9hAdpYU/whiGG7+k4BK9Jiug2tHbH3gOeEEJ0huB3DxIwiJ7Sh8az2UzXr1+P9Kyqqtd5D+fOA2w3/u6wksHBCRxe50gausUbPLA+HCUQTXQehoRMEHoH+eDFmOgH/o5eQV5x3JFTz7zwfAcR4BE3Vt6e2LMlyBa6A/mDj+AlB0k4i8G9MYwOaLnT6DrZnWYCGJwv+MOBIfYCZxr6OmAEr7H/brBBrquq7SDXOkGZpFRSkNRv0w5tKKF2niS7hb4DVOFZyIAHqKy93f9CITTa3t7Z6JW2/Xhd+/v/grKsVtMEVVV33qiuG5Vl53xmWfuOlPl8S21L9KAsy3sOELrdy5dxTF1Puv1EHzroh7yzP+6gk0lZna9Ul6WyJJHqJmZDrp89IaWJckPYvcScfec8FqVl3gAHHcm8oWld1/HdNg4U0EYZPirLUjs7O5Ev1+t1zK64fyFJn/nMZ2KQjh3g/mQaOdtCYA9ogP0h+zqZTPSZz3xGW1tbMVuPbmM/dnd3dfny5Zghga+rqnuxrx878GzHdDqNL6klCOUa503mhR7yoIfh4B+6Fv7wYN5tm4PFnk0GaHUb4OXv8Kb7PnmeazqZqqpq1VWjNM2UqH2Zc5qkSpJUWZ6qboLqJigvJqrrSkGJymrzjse6UVCiNM3UBGldVirUdVRel6WSTWAE/eEvB66TtOsAhy5Ed2MPpA648qwOgDhjOp32XiTuwA/PC3WjarVWpo1+yzcBcd1oXXdVEh54+kuw0eW3KinHX2It2AsPMN3Pxdbxd/YTEJjhgDi8jr8Onzjw4Lb0yYy7PkAq8iJmJjD2UufkeRDEpvP+CDYG40S5CE4sCpZrCYIcseeZWdbVezua4sg1TiCK0j9z5AkG8PKSzrA1Oj4+jkbAA0KYGcVHVi1J2rQ6z82yLJ55wPDAtJJuympwf0c63XFEQFgTShJmBXU4OjpS0zSxxeswC+b7JnXnR1iz1AWf7hA5WpUkScx4uVH2tC5zR3l4Ng8lTyaF/fGzQkVRxBdektXAWLhj6KlqHE+65nk7Yl83SoSsCPSWFJUifIKC9IyLO+DOCwzvkIUB8owOytidwahgQ1fnzf+HDSWQNb4L4CApOhnwKV3AQNWSJIn0xDEnK4ohd4Pih9/ZUw+4kVmMi5d/uZ4gcMFYIysENtCYFzJnWfvyXPQJa8a5KYru/UeU7aBfqM0HHfaSOy8v8KyGo7roARBknr27uxudPuSD/XWDBq8iw/v7+7E9NPeCltC5zUosY1mOI4HoPeYH8OHZAujkWVdHTaEN8sUes588h/1zMMN1sgdmQ1TadRl2wevnoT26ivt44OuOFp+5bN0cTLXvUmpt03mvisGz5Ogf5NxBBK824CwN+pjvp2m/q5fvMbw0m81jYBBClxkeItfIu4Mk0+ksPtNReQfjnGehRZqmN5Uhotu8xMwDJubdlOt4f/R+u/ZM042MLlfLXqYtLwplRecQA0ogW8wB++fVBX4uDH5AR+MXuJ11PwFdwXzJXCVJW3ECWJnnuZ73vOdF2eNf3lNYlqUWi0VsUQ4Q4O9Sc1SdMrzJZBLtOXwKOAT/ohc8s4Ae5r09ru+gk6Re0wvXCx7s++foAug+BITYS2wIsozulNQDUuAp9sazQtgr9w0cCL7Vv3XVAgMO4NRVrWQTjOVWItrSoF+2zfwJglt9UcfAkf1h8Fxki/uuLYPrPiXPQt5cvzho43pQUtT/8K0HLlVVqdkEu1xDWeF0E3QAkBGksXeswWngsoOsIV9eSopecUDbZdF1zVDfY1v5zIN6B8D5DvzkOvl2464PkGAkFD5RK5E07RWpO4VBQgi9lqkEER4ceKkICvP09DQKtUe7ZK/oxoPxRvHeuHGjl+J1h9SVhqPHGBMQA0d8cKBhQHfembsfunXEB8SY79yqNMcNrKfHuQ805N7e3ELqMil+lsMdXE9De9bDa6mhO/dDyHhOZ/C7Q9reoIDv5Hl3WJ2yExxIOq5RwoMCaJomInCOTnlJxLA0whFVRwtRRH7uAefMETuCPhBElBaHtB0R805rjpoxJ5xUBvvnZacePGF8oc0QiWEuGEAvK3BFCQ/xPIJh0FSUMMgk9ON6DCBZKtYLr+LAMheMN+tnTWQacBKgJ/dh/ZQbeqCDLON4QW8yfU47L0Vype2BjmdtPBMxRN0JsDCaGCmnEV0V2QP0DY6mB8nMd+iIM086EXLm0UsZnEeQL9BwnBcPTn1PcOwcmHA9gUwzD9Bz75roOsL3DjDFASt38nkWtMPxQob5cT7g/sg9a0BW0NmeiWffcD64n2eufI7Mk7lxb2SeMzDYBG9N7edH2WvaIoPgc++maWJGgN95FnrOZZn9hV8Ag7wygSAYenqQ3jYY6Zwj+G+9XsczpshWURTxRa4MOnQyH/Zd6hxLsmjI2TTvgl/2p6VxpiaE2LzBbR0OPfPm3vfcc4+SJImt/eHrz372sz07wZ5Pp9N4Dmsyad9r9MQTT/QyFAQ1165d03Q61f7+vra3t2NbdIASP1fGHpHxZo4ER/ASQQ00JbBKkiSCJNgV+MZlDn+FeXrTEGSY6zzIIlPn84bX+V4IofeeSQeX2UfkAp3L/fHFmD9rJKPowTl7yD3gY37nX/gXHwqdE5rQZo6SpKdjfJ+5n/srng3CjnX+StLTZ9iHIVjp4GHY3Be7gO/nskRQ6BUa8LLLL7YIQAX+8IC+KkslofMTHBRm3e5Doj+84QV+hftAng30wMeBVfwCp6HbS/S+y6br/i5YVdSf3APdh250v+V2464PkFbrfhcaFzB3ZrzDy3K5jKU7KCg6EGH0PV2Ms4Nji+H3w7SUBXGdp+c9m0KQxjkdsjKk6ynNwmiyLqmPbsGMoAG8rJDPPcDzbAABZF13hw9dIRNAMAcUDPRzx84RYJwFqUMXpC7T4/MG2XA0CIfPjZLU79SFQ06JFc+q67r3AkEPKthjnH5Kd1BEnOk4PT2N12KMWCOoNzT14NPPNPgbtVkDDos7SOwTvERpw8WLFyNft8htP0jAyXGFSIDkNdc4IPAo3xk6FO48ucPEc3y4guN35AynyIMk3zfkh+ukfjDHD3vn++9zg//hLdaCEXNUq2na8wzsEfSDN7zk1t/rFEJ7gHx/f19SFxjCmwT7rIkzD8gQBp19wHmg1a3zdNN0HZEI5pz/2TMP2nkmugy94FmSodPgIIPzK7rSeQcd5/rKMyXoAAeHyC67DsG58qwP8uPGzlFC5sp9bmVs4QsAMJBDN57cj/W6Y8WaXa48KwtPeTDswIuXgTAXL+1w20MGf4iKOljkehP+9uyNI7Z13VY10IiGTm/ulLJH0NUDR7ItrNOdXGiD/nIdgRygf/kudnMyKaJ+cLlFtzjoR+ACzRaLRS/zQJCC7pIU11qWpbbmc4XJtGfXmGtRFGqkCMq4E8ca07R9hQV6CR2LfYOOdV3r8PAwVhJwH0DUCxcuSGrtG0EWDj36+Cu+4ivicwnsAT6Yi+svnE9vAMDee6DPnmBXkWHm70ECtgD5cHR/2Doaveayh/xwxolsGGewnb5DoIogHXlgf9FVZKD8es9iuq/lABa+AuCT77U7x26zenrBKk68bHy9XiskXdMS1zXYjxjchK7xTKsPckkh+hd0pfOggQoKRp7nagwwQ095hpl9xKZ52Rq6BN0Arzrv4EOen58rS/rNK7i3+yEkGeAj1yFDkJE1OB9iJ92XYP3Q0vfU+dkDHff5COC5JyXZ7rt6ltoBmNuNuz5AKtdlL8qFcRAWFDuEZgO9pARFQmpbUk/ZgOghcNwHFI+DlF7ecH5+HlEcSZHpUQ7Xrl3rIZGguARKOO2OxqMMWBMH7mFiBAUEPaaP6+6APYwMsoSQIzA4xjAZAsPapO5Qngcd3MezTTwLIcFJdBTBgyVeKOcoCwLu7YXZXy834PmOsEFHrkORk2Fk1HUdz8J4TTrZAYJmHCf2ADRfUnxnBPs5RE7YH3+/FnRmP2mly1kXzpi5UxZCiE4re0SQjvODLHgJBAoWI8W6PTMJbeEzjBDBNOseInnwNYrbU+aOVLpjhnOHjDJc2VVVpfvuu0+LxaIXHCVJEoMpP0SPIwuvuCNM+cPJyUl8LQBoLXwrdecJm6aJTh2yj8PrGR5X8tAPPkd/EDSxB9AGxNUBFAyeZx08owNCxrOZJ+gfiLOX2uEQQQv4wIEXAkT2DEPGZzgw8CK6wuWd+Xg2BP5ABzv66WcbhnN1/nY0GJ5B/w3r1aGvG2IvhXIEG53gThY6C/r52j1YoFubI7tlWcazc5Rquhx4WSH87c4eoIg3HEGnoG8cnILf0RHsDY4vvOfZLbLa7qxCU2wbetdBGe4BqNQPstbKsrQHUk2nU128eDFmW3iOdHPgBz8SkOd5riwE5UUef+ccZ5qkmm8yI/5d5luZrKC3vUwWfQlPkdEiWM3zXMvlUpPJ5CbAYm9vL+6lpFhyy/o8CEdP4MShI7g3NtA7GDKHvb29CAI2TRP3yUtCHZBAtrEx2FjAPK6FBg7+esYZvYIcedbGz82RmfBqE/gQHUagy/DXFjgIh3x6MA6fAVJCXw942SsHX9BvXirMWlgHMt7UXVMbt9NVXasJHVAOv3IvZMx9ylae+xko/AbPXPk5aoKjyaZJCXLigDrD5wEg5zbbAznXs8hMtK9J975K9g89hI8ACAA/sSbsH/vkQaTTcBhcu5+DXwQ9eAaDvfY1IcueNWT/HRzx7Lrz3e3GXR8gsWGOnOPsgKp44OLOmH/HDxtCYGrGqTOVuq42GNwsy2IWB4XEc7z1KalmlJujWk888US8J0gA83Lkkuc7gyKIrtSGaW8/2MbA+JK94DqpCw5xevkd5eClGD4vRwwdAXADicOE8cUw0mmHEkgCzCFiKXXGG+Sc80l7e3vx3syR/1+/fj0KE8YHZwFh9lKWYRMAlI2jq+wxCO4TTzwRz7fBJ5SrUOPNuRMUEfyIo0ygDg+6w4OCd2UGHxFcwccoZwwwgSHOFs9HuaDM4CEv4STbCtqNkfQMA6CBZz8cSYI/3Nlnb3H6oTVBJKWxfm4LnoKuvtcgrDgSlNWiYLe2tiLP+kFWHBpkwY0I66A8Brpyf3gS2XZQAMeT+8NrHoSy/8gVa3O6eDYSh8nlx+nnPOLGijn5XJEVR7f5HOfbjRpzqOu2JIYgAb5gzQBGPIf5sV4PzKLD0vTL5HDiCDShIXqHe3hg5lkcPuOZ7mR4IOSGFR2ATJBFIHOIk8AzTk5OYokYawHU8kColZe65+TCj8gRepuACf1WVVXvReYAFR5sUMo2DC5DCPGsJHRfLBYRGCOQwQ4wB2yJB1rQiIwPTnvLp5XqurMbzMvnubW1FfU/mSjAxbIse6BGkiSaTLuXm0sdcKG0e80FAeV63Xa4vH7jhrIivylznCRJbGbiLycGhLxw4ULP9lEJsre3F0vvcDzhfYJS+Bwa7+zsRN2GzvemCJ4BybIs3t9LpqA3Ohvb4w1n4GHnY0AHwEZ4C5vjCD187uWn7uN4AIN8ksnyDFg/SOi/I9Kzow5Ye9bAwTt38Fk//I5OAux0ORwCRvCY8yPfb+W0Ul11gQr2jDVVdXesgX1xGeJ58LeDY/AWVUJ5nsfyVPcdNpPqBbjoIEqf8VudNp75gUbcG9n3kl/m44GJBz2eefEsHd/1a1z/O1DN3J3uDtJ7MOR6G7vAPsJrDo4BuLFHzmMOXEOTIeB6u5GEp3L1H6NxdHSkg4MDve/nfj4a8vnWXHW1SdfOOsRvvereoZCmqfKiezFrud6k79LOwamrOjqsaZrqfHWu9WqtYlLEDayrjaOabRR6MVFVV1qdr5TlbS/21QqUpe1YkqapsrRDz9blWk1da73Jgs1mU9V1o+XJUtNJ1yLZkfokkRIYqerKUTxT4M6ZM1/MEEy6oKOpuzIfZ74sb/89WZ7EQDMvWtSjNfat0ccQebtLz6RE5DFNpBAUgnqofFmulReFtuZtsJKmqZI0UWi8rEZar0tNpt1b1derrotblqVxTut1uSn5CErTROt1qdl8FoXm7PQsGurpbKqqbGvC1+Va21vb8RlVWUUEdntnW3VVR8MGGhQV7OZdXOdn591ByflMiZKI7pdVqaos1TQtMlquS+VFrtm0RTdn85mKvFATGuVZ+96flt7dObR4fmrDh03doS/FpIi8XFWVikmhYtO6k3viECRpq1CWi2U8BxTLfaYT5Vmusiq1Xq11etY2Gdjf249ZIJR63WzOOKzW8W+t4so1m7Vo4fn5mdIsi/Ixm850eHgY9y3NMmVppqqu4t5++lOf1mq9UpEXWq1Xuvfee1WVlaazqdIk1fb2lrRZP7y6Ol+1fF1tunoVG35OM9VNrfOzrhUvKB8Bmiv1sixVN7WKvLiJf0MTlOXdfNvnt7zpJQroCBR2Memc2aZpVK5LTWdTrVdr5UWu9Wqt89W55puD9Bgiz45keaaT5Ulb2jLpZLsqq1huGTNPG/0WmqA0S+N+KmzeMh5ClI+imKgJXXCSJl0nzNZJWsU98sxGCEHnq3NNiskGKGh1FwFD3dTtezWSLnsSjWlVqSxbHj07PYs8PSk4bNw+M4SgSTFR3dRKlPT0mw+yK94pED3P5+5YeACZpRuUOfQDtEkx0dnZ6SY7kkVZZC8kRXnDmcE4102t+Wx+UyMUjH5V1SomhULTqK6bGCBFNFYbRDsvdH5+pvPzVQxmi6JoMyvFRPOtuU5PTjcBVBL3VEmi0DRtR7e8A8+SJFFZdeXPRkBtBKPlnyzXar3S7k5XVpUkSaRRCEFF3p3NaZqWnuuyvW5vd1fT6SwGbjhs86155Nm8yHV22jU48nLHJE1VN7Wmm/38nd/5nfjepK2tLV28cI8kxWCOTFBQUB2CptOJ9vb2I0DSAj6l1uv2XGjd1O0aNzwDaOayK3n5kDbAS9Vz4txRxtY4wEommTl6yWbrmKdxb5lLlnUIellWKopcVVUryzY+w7orN5pOaQZ18/tn8iLX6nyls/OzKNOAC2W5jnybZXkMWFuaFqrNrhA4t90Zu+DIgx1kCj7L81xn52eaz+YxqHJZdfAVnoYn4QUc39PTE6VpF2i5XnWwxfdF6prUYDu5rg1S0mg7uAYbWTe1zjdlp9AZmazKtoFDlnbnjrGdq9WZsixvebCiY+0srpvAjExTBMbyVjdnaaaqKjf0mESwxO0tABpZYebA3sOzyJSXLqdp2nbfazp/QpLqpo66RFLbrGLjH+CDsW9+xKMFXyeq665MEUArzdL2PkbztrNniLqzvX+jpunWF0JQMSlioKYQVNdN97eyjC3Tm83fQwjKs1zr9SqWTi4WC33rt71Gh4eHsVT+c427NoN07do1SdKrv/Wbn+WZjGMc4xjHOMYxjnGMYxzj+GIYi8XiSzdAuueeFkX69Kc/fVsijOOpjePjY33FV3yFfu/3fk97e3vP9nTumjHS9c6Mka53Zox0vTNjpOudGSNd78wY6XpnxkjXOzPIKF+5cuW21961ARIpwv39/ZG57tDY29sbaXsHxkjXOzNGut6ZMdL1zoyRrndmjHS9M2Ok650ZI12f+fFkkybp7S8ZxzjGMY5xjGMc4xjHOMYxji+NMQZI4xjHOMYxjnGMYxzjGMc4xrEZd22ANJ1O9UM/9EOxfe44nrkx0vbOjJGud2aMdL0zY6TrnRkjXe/MGOl6Z8ZI1zszRro+++OubfM9jnGMYxzjGMc4xjGOcYxjHE913LUZpHGMYxzjGMc4xjGOcYxjHON4qmMMkMYxjnGMYxzjGMc4xjGOcYxjM8YAaRzjGMc4xjGOcYxjHOMYxzg2YwyQxjGOcYxjHOMYxzjGMY5xjGMzxgBpHOMYxzjGMY5xjGMc4xjHODbjrg2QfvzHf1zPf/7zNZvN9LKXvUwf+MAHnu0pfdGOt7/97fr6r/967e7u6tKlS/pbf+tv6X//7//duyaEoH/6T/+prly5ovl8rr/0l/6Sfuu3fqt3zWq10vd+7/fq4sWL2t7e1mtf+1r9/u///hdyKV/U4+1vf7uSJNEDDzwQ/zbS9emNP/iDP9B3fdd36d5779XW1pa+7uu+To888kj8fKTr0xtVVemf/JN/ouc///maz+d6wQteoH/2z/6ZmqaJ14y0vf34X//rf+lbv/VbdeXKFSVJove+9729z58pGt64cUP333+/9vf3tb+/r/vvv1+Hh4d3eHXP3vh8dC3LUm9961v14he/WNvb27py5Yq++7u/W//v//2/3j1Gut48bsevPv7+3//7SpJE/+bf/Jve30e63jyeDF1/+7d/W6997Wu1v7+v3d1dfeM3fqM+/elPx89Huj57464MkH76p39aDzzwgH7gB35AH/nIR/QX/sJf0F//63+9x3Tj6MbDDz+s7/me79Gv/Mqv6KGHHlJVVXrVq16lk5OTeM2P/MiP6Ed/9Ef1zne+U7/2a7+my5cv66/9tb+mxWIRr3nggQf0Mz/zM3rPe96jD37wg1oul3rNa16juq6fjWV9UY1f+7Vf07vf/W597dd+be/vI12f+rhx44a+6Zu+SUVR6L//9/+uj33sY/pX/+pf6eDgIF4z0vXpjR/+4R/Wu971Lr3zne/Ub//2b+tHfuRH9C//5b/Uj/3Yj8VrRtrefpycnOglL3mJ3vnOd97y82eKht/5nd+pxx57TO973/v0vve9T4899pjuv//+O76+Z2t8Prqenp7q0Ucf1Q/+4A/q0Ucf1YMPPqiPf/zjeu1rX9u7bqTrzeN2/Mp473vfq1/91V/VlStXbvpspOvN43Z0/T//5//oFa94hV74whfql37pl/Trv/7r+sEf/EHNZrN4zUjXZ3GEu3D82T/7Z8Mb3/jG3t9e+MIXhre97W3P0oz+eI2rV68GSeHhhx8OIYTQNE24fPlyeMc73hGvOT8/D/v7++Fd73pXCCGEw8PDUBRFeM973hOv+YM/+IOQpml43/ve94VdwBfZWCwW4au/+qvDQw89FF75yleGN7/5zSGEka5Pd7z1rW8Nr3jFKz7n5yNdn/74lm/5lvD3/t7f6/3t27/928N3fdd3hRBG2j6dISn8zM/8TPz9maLhxz72sSAp/Mqv/Eq85kMf+lCQFH7nd37nDq/q2R9Dut5qfPjDHw6Swqc+9akQwkjXJzM+F11///d/P3z5l395+M3f/M3wvOc9L/zrf/2v42cjXW8/bkXXv/t3/27UrbcaI12f3XHXZZDW67UeeeQRvepVr+r9/VWvepV++Zd/+Vma1R+vcXR0JEm65557JEmf/OQn9fjjj/doOp1O9cpXvjLS9JFHHlFZlr1rrly5ohe96EVf8nT/nu/5Hn3Lt3yL/upf/au9v490fXrjZ3/2Z/Xyl79cf/tv/21dunRJL33pS/Uf/sN/iJ+PdH364xWveIX+x//4H/r4xz8uSfr1X/91ffCDH9Tf+Bt/Q9JI22diPFM0/NCHPqT9/X19wzd8Q7zmG7/xG7W/vz/SeTOOjo6UJEnMLo90fXqjaRrdf//9estb3qKv+Zqvuenzka5PfTRNo//23/6b/uSf/JP65m/+Zl26dEnf8A3f0CvDG+n67I67LkB64oknVNe17rvvvt7f77vvPj3++OPP0qz++IwQgv7hP/yHesUrXqEXvehFkhTp9vlo+vjjj2symejChQuf85ovxfGe97xHjz76qN7+9rff9NlI16c3/u///b/6iZ/4CX31V3+1fv7nf15vfOMb9X3f9336T//pP0ka6fpHGW9961v1ute9Ti984QtVFIVe+tKX6oEHHtDrXvc6SSNtn4nxTNHw8ccf16VLl266/6VLl0Y6Szo/P9fb3vY2fed3fqf29vYkjXR9uuOHf/iHlee5vu/7vu+Wn490ferj6tWrWi6Xesc73qFXv/rV+oVf+AV927d9m779279dDz/8sKSRrs/2yJ/tCdypkSRJ7/cQwk1/G8fN401vepN+4zd+Qx/84Adv+uzp0PRLme6/93u/pze/+c36hV/4hV5N8XCMdH1qo2kavfzlL9e/+Bf/QpL00pe+VL/1W7+ln/iJn9B3f/d3x+tGuj718dM//dP6qZ/6Kf2X//Jf9DVf8zV67LHH9MADD+jKlSt6wxveEK8baftHH88EDW91/UjntmHDd3zHd6hpGv34j//4ba8f6fq5xyOPPKJ/+2//rR599NGnvP6Rrp970Pjmb/7Nv6nv//7vlyR93dd9nX75l39Z73rXu/TKV77yc353pOsXZtx1GaSLFy8qy7KbIuerV6/ehNiNoz++93u/Vz/7sz+r97///Xruc58b/3758mVJ+rw0vXz5stbrtW7cuPE5r/lSG4888oiuXr2ql73sZcrzXHme6+GHH9a/+3f/TnmeR7qMdH1q4znPeY7+9J/+072//ak/9adiE5aRX5/+eMtb3qK3ve1t+o7v+A69+MUv1v3336/v//7vjxnQkbZ/9PFM0fDy5cv6zGc+c9P9P/vZz35J07ksS/2dv/N39MlPflIPPfRQzB5JI12fzvjABz6gq1ev6iu/8iujHfvUpz6lf/SP/pG+6qu+StJI16czLl68qDzPb2vLRro+e+OuC5Amk4le9rKX6aGHHur9/aGHHtKf//N//lma1Rf3CCHoTW96kx588EH9z//5P/X85z+/9/nzn/98Xb58uUfT9Xqthx9+ONL0ZS97mYqi6F3zh3/4h/rN3/zNL1m6/5W/8lf00Y9+VI899lj8efnLX67Xv/71euyxx/SCF7xgpOvTGN/0Td90Uxv6j3/843re854naeTXP8o4PT1VmvbNQpZlEe0caftHH88UDf/cn/tzOjo60oc//OF4za/+6q/q6OjoS5bOBEef+MQn9Iu/+Iu69957e5+PdH3q4/7779dv/MZv9OzYlStX9Ja3vEU///M/L2mk69MZk8lEX//1X/95bdlI12d5fGF7Qnxhxnve855QFEX4yZ/8yfCxj30sPPDAA2F7ezv87u/+7rM9tS/K8Q/+wT8I+/v74Zd+6ZfCH/7hH8af09PTeM073vGOsL+/Hx588MHw0Y9+NLzuda8Lz3nOc8Lx8XG85o1vfGN47nOfG37xF38xPProo+Ev/+W/HF7ykpeEqqqejWV9UQ7vYhfCSNenMz784Q+HPM/DP//n/zx84hOfCP/5P//nsLW1FX7qp34qXjPS9emNN7zhDeHLv/zLw3/9r/81fPKTnwwPPvhguHjxYvjH//gfx2tG2t5+LBaL8JGPfCR85CMfCZLCj/7oj4aPfOQjsZvaM0XDV7/61eFrv/Zrw4c+9KHwoQ99KLz4xS8Or3nNa77g6/1Cjc9H17Isw2tf+9rw3Oc+Nzz22GM9W7ZareI9RrrePG7Hr8Mx7GIXwkjXW43b0fXBBx8MRVGEd7/73eETn/hE+LEf+7GQZVn4wAc+EO8x0vXZG3dlgBRCCP/+3//78LznPS9MJpPwZ/7Mn4ktq8dx85B0y5//+B//Y7ymaZrwQz/0Q+Hy5cthOp2Gv/gX/2L46Ec/2rvP2dlZeNOb3hTuueeeMJ/Pw2te85rw6U9/+gu8mi/uMQyQRro+vfFzP/dz4UUvelGYTqfhhS98YXj3u9/d+3yk69Mbx8fH4c1vfnP4yq/8yjCbzcILXvCC8AM/8AM9B3Ok7e3H+9///lvq1De84Q0hhGeOhteuXQuvf/3rw+7ubtjd3Q2vf/3rw40bN75Aq/zCj89H109+8pOf05a9//3vj/cY6XrzuB2/DsetAqSRrjePJ0PXn/zJnwx/4k/8iTCbzcJLXvKS8N73vrd3j5Guz95IQgjhzuaoxjGOcYxjHOMYxzjGMY5xjOOPx7jrziCNYxzjGMc4xjGOcYxjHOMYx9MdY4A0jnGMYxzjGMc4xjGOcYxjHJsxBkjjGMc4xjGOcYxjHOMYxzjGsRljgDSOcYxjHOMYxzjGMY5xjGMcmzEGSOMYxzjGMY5xjGMc4xjHOMaxGWOANI5xjGMc4xjHOMYxjnGMYxybMQZI4xjHOMYxjnGMYxzjGMc4xrEZY4A0jnGMYxzjGMc4xjGOcYxjHJsxBkjjGMc4xjGOcYxjHOMYxzjGsRljgDSOcYxjHOMYxzjGMY5xjGMcmzEGSOMYxzjGMY5xjGMc4xjHOMaxGf8fvAt79JbPX/MAAAAASUVORK5CYII=\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(image)\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('on')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "c765e952", + "metadata": {}, + "source": [ + "Predict with `SamPredictor.predict`. The model returns masks, quality predictions for those masks, and low resolution mask logits that can be passed to the next iteration of prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5373fd68", + "metadata": {}, + "outputs": [], + "source": [ + "masks, scores, logits = predictor.predict(\n", + " point_coords=input_point,\n", + " point_labels=input_label,\n", + " multimask_output=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c7f0e938", + "metadata": {}, + "source": [ + "With `multimask_output=True` (the default setting), SAM outputs 3 masks, where `scores` gives the model's own estimation of the quality of these masks. This setting is intended for ambiguous input prompts, and helps the model disambiguate different objects consistent with the prompt. When `False`, it will return a single mask. For ambiguous prompts such as a single point, it is recommended to use `multimask_output=True` even if only a single mask is desired; the best single mask can be chosen by picking the one with the highest score returned in `scores`. This will often result in a better mask." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "47821187", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 1200, 1800)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masks.shape # (number_of_masks) x H x W" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e9c227a6", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, (mask, score) in enumerate(zip(masks, scores)):\n", + " plt.figure(figsize=(10,10))\n", + " plt.imshow(image)\n", + " show_mask(mask, plt.gca())\n", + " show_points(input_point, input_label, plt.gca())\n", + " plt.title(f\"Mask {i+1}, Score: {score:.3f}\", fontsize=18)\n", + " plt.axis('off')\n", + " plt.show() \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "3fa31f7c", + "metadata": {}, + "source": [ + "## Specifying a specific object with additional points" + ] + }, + { + "cell_type": "markdown", + "id": "88d6d29a", + "metadata": {}, + "source": [ + "The single input point is ambiguous, and the model has returned multiple objects consistent with it. To obtain a single object, multiple points can be provided. If available, a mask from a previous iteration can also be supplied to the model to aid in prediction. When specifying a single object with multiple prompts, a single mask can be requested by setting `multimask_output=False`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f6923b94", + "metadata": {}, + "outputs": [], + "source": [ + "input_point = np.array([[500, 375], [1125, 625]])\n", + "input_label = np.array([1, 1])\n", + "\n", + "mask_input = logits[np.argmax(scores), :, :] # Choose the model's best mask" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d98f96a1", + "metadata": {}, + "outputs": [], + "source": [ + "masks, _, _ = predictor.predict(\n", + " point_coords=input_point,\n", + " point_labels=input_label,\n", + " mask_input=mask_input[None, :, :],\n", + " multimask_output=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0ce8b82f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 1200, 1800)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masks.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e06d5c8d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(image)\n", + "show_mask(masks, plt.gca())\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('off')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "c93e2087", + "metadata": {}, + "source": [ + "To exclude the car and specify just the window, a background point (with label 0, here shown in red) can be supplied." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9a196f68", + "metadata": {}, + "outputs": [], + "source": [ + "input_point = np.array([[500, 375], [1125, 625]])\n", + "input_label = np.array([1, 0])\n", + "\n", + "mask_input = logits[np.argmax(scores), :, :] # Choose the model's best mask" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "81a52282", + "metadata": {}, + "outputs": [], + "source": [ + "masks, _, _ = predictor.predict(\n", + " point_coords=input_point,\n", + " point_labels=input_label,\n", + " mask_input=mask_input[None, :, :],\n", + " multimask_output=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bfca709f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(image)\n", + "show_mask(masks, plt.gca())\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('off')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "41e2d5a9", + "metadata": {}, + "source": [ + "## Specifying a specific object with a box" + ] + }, + { + "cell_type": "markdown", + "id": "d61ca7ac", + "metadata": {}, + "source": [ + "The model can also take a box as input, provided in xyxy format." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8ea92a7b", + "metadata": {}, + "outputs": [], + "source": [ + "input_box = np.array([425, 600, 700, 875])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b35a8814", + "metadata": {}, + "outputs": [], + "source": [ + "masks, _, _ = predictor.predict(\n", + " point_coords=None,\n", + " point_labels=None,\n", + " box=input_box[None, :],\n", + " multimask_output=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "984b79c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(image)\n", + "show_mask(masks[0], plt.gca())\n", + "show_box(input_box, plt.gca())\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1ed9f0a", + "metadata": {}, + "source": [ + "## Combining points and boxes" + ] + }, + { + "cell_type": "markdown", + "id": "8455d1c5", + "metadata": {}, + "source": [ + "Points and boxes may be combined, just by including both types of prompts to the predictor. Here this can be used to select just the trucks's tire, instead of the entire wheel." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "90e2e547", + "metadata": {}, + "outputs": [], + "source": [ + "input_box = np.array([425, 600, 700, 875])\n", + "input_point = np.array([[575, 750]])\n", + "input_label = np.array([0])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6956d8c4", + "metadata": {}, + "outputs": [], + "source": [ + "masks, _, _ = predictor.predict(\n", + " point_coords=input_point,\n", + " point_labels=input_label,\n", + " box=input_box,\n", + " multimask_output=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8e13088a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(image)\n", + "show_mask(masks[0], plt.gca())\n", + "show_box(input_box, plt.gca())\n", + "show_points(input_point, input_label, plt.gca())\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "45ddbca3", + "metadata": {}, + "source": [ + "## Batched prompt inputs" + ] + }, + { + "cell_type": "markdown", + "id": "df6f18a0", + "metadata": {}, + "source": [ + "SamPredictor can take multiple input prompts for the same image, using `predict_torch` method. This method assumes input points are already torch tensors and have already been transformed to the input frame. For example, imagine we have several box outputs from an object detector." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0a06681b", + "metadata": {}, + "outputs": [], + "source": [ + "input_boxes = torch.tensor([\n", + " [75, 275, 1725, 850],\n", + " [425, 600, 700, 875],\n", + " [1375, 550, 1650, 800],\n", + " [1240, 675, 1400, 750],\n", + "], device=predictor.device)" + ] + }, + { + "cell_type": "markdown", + "id": "bf957d16", + "metadata": {}, + "source": [ + "Transform the boxes to the input frame, then predict masks. `SamPredictor` stores the necessary transform as the `transform` field for easy access, though it can also be instantiated directly for use in e.g. a dataloader (see `segment_anything.utils.transforms`)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "117521a3", + "metadata": {}, + "outputs": [], + "source": [ + "transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])\n", + "masks, _, _ = predictor.predict_torch(\n", + " point_coords=None,\n", + " point_labels=None,\n", + " boxes=transformed_boxes,\n", + " multimask_output=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6a8f5d49", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 1, 1200, 1800])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masks.shape # (batch_size) x (num_predicted_masks_per_input) x H x W" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c00c3681", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(image)\n", + "for mask in masks:\n", + " show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n", + "for box in input_boxes:\n", + " show_box(box.cpu().numpy(), plt.gca())\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8bea70c0", + "metadata": {}, + "source": [ + "## End-to-end batched inference" + ] + }, + { + "cell_type": "markdown", + "id": "89c3ba52", + "metadata": {}, + "source": [ + "If all prompts are available in advance, it is possible to run SAM directly in an end-to-end fashion. This also allows batching over images." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "45c01ae4", + "metadata": {}, + "outputs": [], + "source": [ + "image1 = image # truck.jpg from above\n", + "image1_boxes = torch.tensor([\n", + " [75, 275, 1725, 850],\n", + " [425, 600, 700, 875],\n", + " [1375, 550, 1650, 800],\n", + " [1240, 675, 1400, 750],\n", + "], device=sam.device)\n", + "\n", + "image2 = cv2.imread('images/groceries.jpg')\n", + "image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)\n", + "image2_boxes = torch.tensor([\n", + " [450, 170, 520, 350],\n", + " [350, 190, 450, 350],\n", + " [500, 170, 580, 350],\n", + " [580, 170, 640, 350],\n", + "], device=sam.device)" + ] + }, + { + "cell_type": "markdown", + "id": "ce56c57d", + "metadata": {}, + "source": [ + "Both images and prompts are input as PyTorch tensors that are already transformed to the correct frame. Inputs are packaged as a list over images, which each element is a dict that takes the following keys:\n", + "* `image`: The input image as a PyTorch tensor in CHW format.\n", + "* `original_size`: The size of the image before transforming for input to SAM, in (H, W) format.\n", + "* `point_coords`: Batched coordinates of point prompts.\n", + "* `point_labels`: Batched labels of point prompts.\n", + "* `boxes`: Batched input boxes.\n", + "* `mask_inputs`: Batched input masks.\n", + "\n", + "If a prompt is not present, the key can be excluded." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "79f908ca", + "metadata": {}, + "outputs": [], + "source": [ + "from segment_anything.utils.transforms import ResizeLongestSide\n", + "resize_transform = ResizeLongestSide(sam.image_encoder.img_size)\n", + "\n", + "def prepare_image(image, transform, device):\n", + " image = transform.apply_image(image)\n", + " image = torch.as_tensor(image, device=device.device) \n", + " return image.permute(2, 0, 1).contiguous()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "23f63723", + "metadata": {}, + "outputs": [], + "source": [ + "batched_input = [\n", + " {\n", + " 'image': prepare_image(image1, resize_transform, sam),\n", + " 'boxes': resize_transform.apply_boxes_torch(image1_boxes, image1.shape[:2]),\n", + " 'original_size': image1.shape[:2]\n", + " },\n", + " {\n", + " 'image': prepare_image(image2, resize_transform, sam),\n", + " 'boxes': resize_transform.apply_boxes_torch(image2_boxes, image2.shape[:2]),\n", + " 'original_size': image2.shape[:2]\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "6fbeb831", + "metadata": {}, + "source": [ + "Run the model." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f3b311b1", + "metadata": {}, + "outputs": [], + "source": [ + "batched_output = sam(batched_input, multimask_output=False)" + ] + }, + { + "cell_type": "markdown", + "id": "27bb50fd", + "metadata": {}, + "source": [ + "The output is a list over results for each input image, where list elements are dictionaries with the following keys:\n", + "* `masks`: A batched torch tensor of predicted binary masks, the size of the original image.\n", + "* `iou_predictions`: The model's prediction of the quality for each mask.\n", + "* `low_res_logits`: Low res logits for each mask, which can be passed back to the model as mask input on a later iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "eb3dba0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['masks', 'iou_predictions', 'low_res_logits'])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batched_output[0].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e1108f48", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(20, 20))\n", + "\n", + "ax[0].imshow(image1)\n", + "for mask in batched_output[0]['masks']:\n", + " show_mask(mask.cpu().numpy(), ax[0], random_color=True)\n", + "for box in image1_boxes:\n", + " show_box(box.cpu().numpy(), ax[0])\n", + "ax[0].axis('off')\n", + "\n", + "ax[1].imshow(image2)\n", + "for mask in batched_output[1]['masks']:\n", + " show_mask(mask.cpu().numpy(), ax[1], random_color=True)\n", + "for box in image2_boxes:\n", + " show_box(box.cpu().numpy(), ax[1])\n", + "ax[1].axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/scripts/amg.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/scripts/amg.py new file mode 100644 index 00000000..f2dbf676 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/scripts/amg.py @@ -0,0 +1,238 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import cv2 # type: ignore + +from segment_anything import SamAutomaticMaskGenerator, sam_model_registry + +import argparse +import json +import os +from typing import Any, Dict, List + +parser = argparse.ArgumentParser( + description=( + "Runs automatic mask generation on an input image or directory of images, " + "and outputs masks as either PNGs or COCO-style RLEs. Requires open-cv, " + "as well as pycocotools if saving in RLE format." + ) +) + +parser.add_argument( + "--input", + type=str, + required=True, + help="Path to either a single input image or folder of images.", +) + +parser.add_argument( + "--output", + type=str, + required=True, + help=( + "Path to the directory where masks will be output. Output will be either a folder " + "of PNGs per image or a single json with COCO-style masks." + ), +) + +parser.add_argument( + "--model-type", + type=str, + required=True, + help="The type of model to load, in ['default', 'vit_h', 'vit_l', 'vit_b']", +) + +parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="The path to the SAM checkpoint to use for mask generation.", +) + +parser.add_argument("--device", type=str, default="cuda", help="The device to run generation on.") + +parser.add_argument( + "--convert-to-rle", + action="store_true", + help=( + "Save masks as COCO RLEs in a single json instead of as a folder of PNGs. " + "Requires pycocotools." + ), +) + +amg_settings = parser.add_argument_group("AMG Settings") + +amg_settings.add_argument( + "--points-per-side", + type=int, + default=None, + help="Generate masks by sampling a grid over the image with this many points to a side.", +) + +amg_settings.add_argument( + "--points-per-batch", + type=int, + default=None, + help="How many input points to process simultaneously in one batch.", +) + +amg_settings.add_argument( + "--pred-iou-thresh", + type=float, + default=None, + help="Exclude masks with a predicted score from the model that is lower than this threshold.", +) + +amg_settings.add_argument( + "--stability-score-thresh", + type=float, + default=None, + help="Exclude masks with a stability score lower than this threshold.", +) + +amg_settings.add_argument( + "--stability-score-offset", + type=float, + default=None, + help="Larger values perturb the mask more when measuring stability score.", +) + +amg_settings.add_argument( + "--box-nms-thresh", + type=float, + default=None, + help="The overlap threshold for excluding a duplicate mask.", +) + +amg_settings.add_argument( + "--crop-n-layers", + type=int, + default=None, + help=( + "If >0, mask generation is run on smaller crops of the image to generate more masks. " + "The value sets how many different scales to crop at." + ), +) + +amg_settings.add_argument( + "--crop-nms-thresh", + type=float, + default=None, + help="The overlap threshold for excluding duplicate masks across different crops.", +) + +amg_settings.add_argument( + "--crop-overlap-ratio", + type=int, + default=None, + help="Larger numbers mean image crops will overlap more.", +) + +amg_settings.add_argument( + "--crop-n-points-downscale-factor", + type=int, + default=None, + help="The number of points-per-side in each layer of crop is reduced by this factor.", +) + +amg_settings.add_argument( + "--min-mask-region-area", + type=int, + default=None, + help=( + "Disconnected mask regions or holes with area smaller than this value " + "in pixels are removed by postprocessing." + ), +) + + +def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None: + header = "id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h" # noqa + metadata = [header] + for i, mask_data in enumerate(masks): + mask = mask_data["segmentation"] + filename = f"{i}.png" + cv2.imwrite(os.path.join(path, filename), mask * 255) + mask_metadata = [ + str(i), + str(mask_data["area"]), + *[str(x) for x in mask_data["bbox"]], + *[str(x) for x in mask_data["point_coords"][0]], + str(mask_data["predicted_iou"]), + str(mask_data["stability_score"]), + *[str(x) for x in mask_data["crop_box"]], + ] + row = ",".join(mask_metadata) + metadata.append(row) + metadata_path = os.path.join(path, "metadata.csv") + with open(metadata_path, "w") as f: + f.write("\n".join(metadata)) + + return + + +def get_amg_kwargs(args): + amg_kwargs = { + "points_per_side": args.points_per_side, + "points_per_batch": args.points_per_batch, + "pred_iou_thresh": args.pred_iou_thresh, + "stability_score_thresh": args.stability_score_thresh, + "stability_score_offset": args.stability_score_offset, + "box_nms_thresh": args.box_nms_thresh, + "crop_n_layers": args.crop_n_layers, + "crop_nms_thresh": args.crop_nms_thresh, + "crop_overlap_ratio": args.crop_overlap_ratio, + "crop_n_points_downscale_factor": args.crop_n_points_downscale_factor, + "min_mask_region_area": args.min_mask_region_area, + } + amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None} + return amg_kwargs + + +def main(args: argparse.Namespace) -> None: + print("Loading model...") + sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint) + _ = sam.to(device=args.device) + output_mode = "coco_rle" if args.convert_to_rle else "binary_mask" + amg_kwargs = get_amg_kwargs(args) + generator = SamAutomaticMaskGenerator(sam, output_mode=output_mode, **amg_kwargs) + + if not os.path.isdir(args.input): + targets = [args.input] + else: + targets = [ + f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f)) + ] + targets = [os.path.join(args.input, f) for f in targets] + + os.makedirs(args.output, exist_ok=True) + + for t in targets: + print(f"Processing '{t}'...") + image = cv2.imread(t) + if image is None: + print(f"Could not load '{t}' as an image, skipping...") + continue + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + masks = generator.generate(image) + + base = os.path.basename(t) + base = os.path.splitext(base)[0] + save_base = os.path.join(args.output, base) + if output_mode == "binary_mask": + os.makedirs(save_base, exist_ok=False) + write_masks_to_folder(masks, save_base) + else: + save_file = save_base + ".json" + with open(save_file, "w") as f: + json.dump(masks, f) + print("Done!") + + +if __name__ == "__main__": + args = parser.parse_args() + main(args) diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/scripts/export_onnx_model.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/scripts/export_onnx_model.py new file mode 100644 index 00000000..5c6f8389 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/scripts/export_onnx_model.py @@ -0,0 +1,201 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from segment_anything import sam_model_registry +from segment_anything.utils.onnx import SamOnnxModel + +import argparse +import warnings + +try: + import onnxruntime # type: ignore + + onnxruntime_exists = True +except ImportError: + onnxruntime_exists = False + +parser = argparse.ArgumentParser( + description="Export the SAM prompt encoder and mask decoder to an ONNX model." +) + +parser.add_argument( + "--checkpoint", type=str, required=True, help="The path to the SAM model checkpoint." +) + +parser.add_argument( + "--output", type=str, required=True, help="The filename to save the ONNX model to." +) + +parser.add_argument( + "--model-type", + type=str, + required=True, + help="In ['default', 'vit_h', 'vit_l', 'vit_b']. Which type of SAM model to export.", +) + +parser.add_argument( + "--return-single-mask", + action="store_true", + help=( + "If true, the exported ONNX model will only return the best mask, " + "instead of returning multiple masks. For high resolution images " + "this can improve runtime when upscaling masks is expensive." + ), +) + +parser.add_argument( + "--opset", + type=int, + default=17, + help="The ONNX opset version to use. Must be >=11", +) + +parser.add_argument( + "--quantize-out", + type=str, + default=None, + help=( + "If set, will quantize the model and save it with this name. " + "Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize." + ), +) + +parser.add_argument( + "--gelu-approximate", + action="store_true", + help=( + "Replace GELU operations with approximations using tanh. Useful " + "for some runtimes that have slow or unimplemented erf ops, used in GELU." + ), +) + +parser.add_argument( + "--use-stability-score", + action="store_true", + help=( + "Replaces the model's predicted mask quality score with the stability " + "score calculated on the low resolution masks using an offset of 1.0. " + ), +) + +parser.add_argument( + "--return-extra-metrics", + action="store_true", + help=( + "The model will return five results: (masks, scores, stability_scores, " + "areas, low_res_logits) instead of the usual three. This can be " + "significantly slower for high resolution outputs." + ), +) + + +def run_export( + model_type: str, + checkpoint: str, + output: str, + opset: int, + return_single_mask: bool, + gelu_approximate: bool = False, + use_stability_score: bool = False, + return_extra_metrics=False, +): + print("Loading model...") + sam = sam_model_registry[model_type](checkpoint=checkpoint) + + onnx_model = SamOnnxModel( + model=sam, + return_single_mask=return_single_mask, + use_stability_score=use_stability_score, + return_extra_metrics=return_extra_metrics, + ) + + if gelu_approximate: + for n, m in onnx_model.named_modules(): + if isinstance(m, torch.nn.GELU): + m.approximate = "tanh" + + dynamic_axes = { + "point_coords": {1: "num_points"}, + "point_labels": {1: "num_points"}, + } + + embed_dim = sam.prompt_encoder.embed_dim + embed_size = sam.prompt_encoder.image_embedding_size + mask_input_size = [4 * x for x in embed_size] + dummy_inputs = { + "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float), + "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float), + "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float), + "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float), + "has_mask_input": torch.tensor([1], dtype=torch.float), + "orig_im_size": torch.tensor([1500, 2250], dtype=torch.float), + } + + _ = onnx_model(**dummy_inputs) + + output_names = ["masks", "iou_predictions", "low_res_masks"] + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=torch.jit.TracerWarning) + warnings.filterwarnings("ignore", category=UserWarning) + with open(output, "wb") as f: + print(f"Exporting onnx model to {output}...") + torch.onnx.export( + onnx_model, + tuple(dummy_inputs.values()), + f, + export_params=True, + verbose=False, + opset_version=opset, + do_constant_folding=True, + input_names=list(dummy_inputs.keys()), + output_names=output_names, + dynamic_axes=dynamic_axes, + ) + + if onnxruntime_exists: + ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()} + # set cpu provider default + providers = ["CPUExecutionProvider"] + ort_session = onnxruntime.InferenceSession(output, providers=providers) + _ = ort_session.run(None, ort_inputs) + print("Model has successfully been run with ONNXRuntime.") + + +def to_numpy(tensor): + return tensor.cpu().numpy() + + +if __name__ == "__main__": + args = parser.parse_args() + run_export( + model_type=args.model_type, + checkpoint=args.checkpoint, + output=args.output, + opset=args.opset, + return_single_mask=args.return_single_mask, + gelu_approximate=args.gelu_approximate, + use_stability_score=args.use_stability_score, + return_extra_metrics=args.return_extra_metrics, + ) + + if args.quantize_out is not None: + assert onnxruntime_exists, "onnxruntime is required to quantize the model." + from onnxruntime.quantization import QuantType # type: ignore + from onnxruntime.quantization.quantize import quantize_dynamic # type: ignore + + print(f"Quantizing model and writing to {args.quantize_out}...") + quantize_dynamic( + model_input=args.output, + model_output=args.quantize_out, + optimize_model=True, + per_channel=False, + reduce_range=False, + weight_type=QuantType.QUInt8, + ) + print("Done!") diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/__init__.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/__init__.py new file mode 100644 index 00000000..34383d83 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from .build_sam import ( + build_sam, + build_sam_vit_h, + build_sam_vit_l, + build_sam_vit_b, + sam_model_registry, +) +from .predictor import SamPredictor +from .automatic_mask_generator import SamAutomaticMaskGenerator diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/automatic_mask_generator.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/automatic_mask_generator.py new file mode 100644 index 00000000..d5a8c969 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/automatic_mask_generator.py @@ -0,0 +1,372 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torchvision.ops.boxes import batched_nms, box_area # type: ignore + +from typing import Any, Dict, List, Optional, Tuple + +from .modeling import Sam +from .predictor import SamPredictor +from .utils.amg import ( + MaskData, + area_from_rle, + batch_iterator, + batched_mask_to_box, + box_xyxy_to_xywh, + build_all_layer_point_grids, + calculate_stability_score, + coco_encode_rle, + generate_crop_boxes, + is_box_near_crop_edge, + mask_to_rle_pytorch, + remove_small_regions, + rle_to_mask, + uncrop_boxes_xyxy, + uncrop_masks, + uncrop_points, +) + + +class SamAutomaticMaskGenerator: + def __init__( + self, + model: Sam, + points_per_side: Optional[int] = 32, + points_per_batch: int = 64, + pred_iou_thresh: float = 0.88, + stability_score_thresh: float = 0.95, + stability_score_offset: float = 1.0, + box_nms_thresh: float = 0.7, + crop_n_layers: int = 0, + crop_nms_thresh: float = 0.7, + crop_overlap_ratio: float = 512 / 1500, + crop_n_points_downscale_factor: int = 1, + point_grids: Optional[List[np.ndarray]] = None, + min_mask_region_area: int = 0, + output_mode: str = "binary_mask", + ) -> None: + """ + Using a SAM model, generates masks for the entire image. + Generates a grid of point prompts over the image, then filters + low quality and duplicate masks. The default settings are chosen + for SAM with a ViT-H backbone. + + Arguments: + model (Sam): The SAM model to use for mask prediction. + points_per_side (int or None): The number of points to be sampled + along one side of the image. The total number of points is + points_per_side**2. If None, 'point_grids' must provide explicit + point sampling. + points_per_batch (int): Sets the number of points run simultaneously + by the model. Higher numbers may be faster but use more GPU memory. + pred_iou_thresh (float): A filtering threshold in [0,1], using the + model's predicted mask quality. + stability_score_thresh (float): A filtering threshold in [0,1], using + the stability of the mask under changes to the cutoff used to binarize + the model's mask predictions. + stability_score_offset (float): The amount to shift the cutoff when + calculated the stability score. + box_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks. + crop_n_layers (int): If >0, mask prediction will be run again on + crops of the image. Sets the number of layers to run, where each + layer has 2**i_layer number of image crops. + crop_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks between different crops. + crop_overlap_ratio (float): Sets the degree to which crops overlap. + In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + crop_n_points_downscale_factor (int): The number of points-per-side + sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + point_grids (list(np.ndarray) or None): A list over explicit grids + of points used for sampling, normalized to [0,1]. The nth grid in the + list is used in the nth crop layer. Exclusive with points_per_side. + min_mask_region_area (int): If >0, postprocessing will be applied + to remove disconnected regions and holes in masks with area smaller + than min_mask_region_area. Requires opencv. + output_mode (str): The form masks are returned in. Can be 'binary_mask', + 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. + For large resolutions, 'binary_mask' may consume large amounts of + memory. + """ + + assert (points_per_side is None) != ( + point_grids is None + ), "Exactly one of points_per_side or point_grid must be provided." + if points_per_side is not None: + self.point_grids = build_all_layer_point_grids( + points_per_side, + crop_n_layers, + crop_n_points_downscale_factor, + ) + elif point_grids is not None: + self.point_grids = point_grids + else: + raise ValueError("Can't have both points_per_side and point_grid be None.") + + assert output_mode in [ + "binary_mask", + "uncompressed_rle", + "coco_rle", + ], f"Unknown output_mode {output_mode}." + if output_mode == "coco_rle": + from pycocotools import mask as mask_utils # type: ignore # noqa: F401 + + if min_mask_region_area > 0: + import cv2 # type: ignore # noqa: F401 + + self.predictor = SamPredictor(model) + self.points_per_batch = points_per_batch + self.pred_iou_thresh = pred_iou_thresh + self.stability_score_thresh = stability_score_thresh + self.stability_score_offset = stability_score_offset + self.box_nms_thresh = box_nms_thresh + self.crop_n_layers = crop_n_layers + self.crop_nms_thresh = crop_nms_thresh + self.crop_overlap_ratio = crop_overlap_ratio + self.crop_n_points_downscale_factor = crop_n_points_downscale_factor + self.min_mask_region_area = min_mask_region_area + self.output_mode = output_mode + + @torch.no_grad() + def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: + """ + Generates masks for the given image. + + Arguments: + image (np.ndarray): The image to generate masks for, in HWC uint8 format. + + Returns: + list(dict(str, any)): A list over records for masks. Each record is + a dict containing the following keys: + segmentation (dict(str, any) or np.ndarray): The mask. If + output_mode='binary_mask', is an array of shape HW. Otherwise, + is a dictionary containing the RLE. + bbox (list(float)): The box around the mask, in XYWH format. + area (int): The area in pixels of the mask. + predicted_iou (float): The model's own prediction of the mask's + quality. This is filtered by the pred_iou_thresh parameter. + point_coords (list(list(float))): The point coordinates input + to the model to generate this mask. + stability_score (float): A measure of the mask's quality. This + is filtered on using the stability_score_thresh parameter. + crop_box (list(float)): The crop of the image used to generate + the mask, given in XYWH format. + """ + + # Generate masks + mask_data = self._generate_masks(image) + + # Filter small disconnected regions and holes in masks + if self.min_mask_region_area > 0: + mask_data = self.postprocess_small_regions( + mask_data, + self.min_mask_region_area, + max(self.box_nms_thresh, self.crop_nms_thresh), + ) + + # Encode masks + if self.output_mode == "coco_rle": + mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]] + elif self.output_mode == "binary_mask": + mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]] + else: + mask_data["segmentations"] = mask_data["rles"] + + # Write mask records + curr_anns = [] + for idx in range(len(mask_data["segmentations"])): + ann = { + "segmentation": mask_data["segmentations"][idx], + "area": area_from_rle(mask_data["rles"][idx]), + "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(), + "predicted_iou": mask_data["iou_preds"][idx].item(), + "point_coords": [mask_data["points"][idx].tolist()], + "stability_score": mask_data["stability_score"][idx].item(), + "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(), + } + curr_anns.append(ann) + + return curr_anns + + def _generate_masks(self, image: np.ndarray) -> MaskData: + orig_size = image.shape[:2] + crop_boxes, layer_idxs = generate_crop_boxes( + orig_size, self.crop_n_layers, self.crop_overlap_ratio + ) + + # Iterate over image crops + data = MaskData() + for crop_box, layer_idx in zip(crop_boxes, layer_idxs): + crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) + data.cat(crop_data) + + # Remove duplicate masks between crops + if len(crop_boxes) > 1: + # Prefer masks from smaller crops + scores = 1 / box_area(data["crop_boxes"]) + scores = scores.to(data["boxes"].device) + keep_by_nms = batched_nms( + data["boxes"].float(), + scores, + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.crop_nms_thresh, + ) + data.filter(keep_by_nms) + + data.to_numpy() + return data + + def _process_crop( + self, + image: np.ndarray, + crop_box: List[int], + crop_layer_idx: int, + orig_size: Tuple[int, ...], + ) -> MaskData: + # Crop the image and calculate embeddings + x0, y0, x1, y1 = crop_box + cropped_im = image[y0:y1, x0:x1, :] + cropped_im_size = cropped_im.shape[:2] + self.predictor.set_image(cropped_im) + + # Get points for this crop + points_scale = np.array(cropped_im_size)[None, ::-1] + points_for_image = self.point_grids[crop_layer_idx] * points_scale + + # Generate masks for this crop in batches + data = MaskData() + for (points,) in batch_iterator(self.points_per_batch, points_for_image): + batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size) + data.cat(batch_data) + del batch_data + self.predictor.reset_image() + + # Remove duplicates within this crop. + keep_by_nms = batched_nms( + data["boxes"].float(), + data["iou_preds"], + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.box_nms_thresh, + ) + data.filter(keep_by_nms) + + # Return to the original image frame + data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box) + data["points"] = uncrop_points(data["points"], crop_box) + data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))]) + + return data + + def _process_batch( + self, + points: np.ndarray, + im_size: Tuple[int, ...], + crop_box: List[int], + orig_size: Tuple[int, ...], + ) -> MaskData: + orig_h, orig_w = orig_size + + # Run model on this batch + transformed_points = self.predictor.transform.apply_coords(points, im_size) + in_points = torch.as_tensor(transformed_points, device=self.predictor.device) + in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device) + masks, iou_preds, _ = self.predictor.predict_torch( + in_points[:, None, :], + in_labels[:, None], + multimask_output=True, + return_logits=True, + ) + + # Serialize predictions and store in MaskData + data = MaskData( + masks=masks.flatten(0, 1), + iou_preds=iou_preds.flatten(0, 1), + points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)), + ) + del masks + + # Filter by predicted IoU + if self.pred_iou_thresh > 0.0: + keep_mask = data["iou_preds"] > self.pred_iou_thresh + data.filter(keep_mask) + + # Calculate stability score + data["stability_score"] = calculate_stability_score( + data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset + ) + if self.stability_score_thresh > 0.0: + keep_mask = data["stability_score"] >= self.stability_score_thresh + data.filter(keep_mask) + + # Threshold masks and calculate boxes + data["masks"] = data["masks"] > self.predictor.model.mask_threshold + data["boxes"] = batched_mask_to_box(data["masks"]) + + # Filter boxes that touch crop boundaries + keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h]) + if not torch.all(keep_mask): + data.filter(keep_mask) + + # Compress to RLE + data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) + data["rles"] = mask_to_rle_pytorch(data["masks"]) + del data["masks"] + + return data + + @staticmethod + def postprocess_small_regions( + mask_data: MaskData, min_area: int, nms_thresh: float + ) -> MaskData: + """ + Removes small disconnected regions and holes in masks, then reruns + box NMS to remove any new duplicates. + + Edits mask_data in place. + + Requires open-cv as a dependency. + """ + if len(mask_data["rles"]) == 0: + return mask_data + + # Filter small disconnected regions and holes + new_masks = [] + scores = [] + for rle in mask_data["rles"]: + mask = rle_to_mask(rle) + + mask, changed = remove_small_regions(mask, min_area, mode="holes") + unchanged = not changed + mask, changed = remove_small_regions(mask, min_area, mode="islands") + unchanged = unchanged and not changed + + new_masks.append(torch.as_tensor(mask).unsqueeze(0)) + # Give score=0 to changed masks and score=1 to unchanged masks + # so NMS will prefer ones that didn't need postprocessing + scores.append(float(unchanged)) + + # Recalculate boxes and remove any new duplicates + masks = torch.cat(new_masks, dim=0) + boxes = batched_mask_to_box(masks) + keep_by_nms = batched_nms( + boxes.float(), + torch.as_tensor(scores), + torch.zeros_like(boxes[:, 0]), # categories + iou_threshold=nms_thresh, + ) + + # Only recalculate RLEs for masks that have changed + for i_mask in keep_by_nms: + if scores[i_mask] == 0.0: + mask_torch = masks[i_mask].unsqueeze(0) + mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0] + mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly + mask_data.filter(keep_by_nms) + + return mask_data diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/build_sam.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/build_sam.py new file mode 100644 index 00000000..37cd2451 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/build_sam.py @@ -0,0 +1,107 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from functools import partial + +from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer + + +def build_sam_vit_h(checkpoint=None): + return _build_sam( + encoder_embed_dim=1280, + encoder_depth=32, + encoder_num_heads=16, + encoder_global_attn_indexes=[7, 15, 23, 31], + checkpoint=checkpoint, + ) + + +build_sam = build_sam_vit_h + + +def build_sam_vit_l(checkpoint=None): + return _build_sam( + encoder_embed_dim=1024, + encoder_depth=24, + encoder_num_heads=16, + encoder_global_attn_indexes=[5, 11, 17, 23], + checkpoint=checkpoint, + ) + + +def build_sam_vit_b(checkpoint=None): + return _build_sam( + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + encoder_global_attn_indexes=[2, 5, 8, 11], + checkpoint=checkpoint, + ) + + +sam_model_registry = { + "default": build_sam_vit_h, + "vit_h": build_sam_vit_h, + "vit_l": build_sam_vit_l, + "vit_b": build_sam_vit_b, +} + + +def _build_sam( + encoder_embed_dim, + encoder_depth, + encoder_num_heads, + encoder_global_attn_indexes, + checkpoint=None, +): + prompt_embed_dim = 256 + image_size = 1024 + vit_patch_size = 16 + image_embedding_size = image_size // vit_patch_size + sam = Sam( + image_encoder=ImageEncoderViT( + depth=encoder_depth, + embed_dim=encoder_embed_dim, + img_size=image_size, + mlp_ratio=4, + norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), + num_heads=encoder_num_heads, + patch_size=vit_patch_size, + qkv_bias=True, + use_rel_pos=True, + global_attn_indexes=encoder_global_attn_indexes, + window_size=14, + out_chans=prompt_embed_dim, + ), + prompt_encoder=PromptEncoder( + embed_dim=prompt_embed_dim, + image_embedding_size=(image_embedding_size, image_embedding_size), + input_image_size=(image_size, image_size), + mask_in_chans=16, + ), + mask_decoder=MaskDecoder( + num_multimask_outputs=3, + transformer=TwoWayTransformer( + depth=2, + embedding_dim=prompt_embed_dim, + mlp_dim=2048, + num_heads=8, + ), + transformer_dim=prompt_embed_dim, + iou_head_depth=3, + iou_head_hidden_dim=256, + ), + pixel_mean=[123.675, 116.28, 103.53], + pixel_std=[58.395, 57.12, 57.375], + ) + sam.eval() + if checkpoint is not None: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f) + sam.load_state_dict(state_dict) + return sam diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/__init__.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/__init__.py new file mode 100644 index 00000000..38e90624 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from .sam import Sam +from .image_encoder import ImageEncoderViT +from .mask_decoder import MaskDecoder +from .prompt_encoder import PromptEncoder +from .transformer import TwoWayTransformer diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/common.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/common.py new file mode 100644 index 00000000..2bf15236 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/common.py @@ -0,0 +1,43 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from typing import Type + + +class MLPBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + mlp_dim: int, + act: Type[nn.Module] = nn.GELU, + ) -> None: + super().__init__() + self.lin1 = nn.Linear(embedding_dim, mlp_dim) + self.lin2 = nn.Linear(mlp_dim, embedding_dim) + self.act = act() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.lin2(self.act(self.lin1(x))) + + +# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa +# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/image_encoder.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/image_encoder.py new file mode 100644 index 00000000..66351d9d --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/image_encoder.py @@ -0,0 +1,395 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from typing import Optional, Tuple, Type + +from .common import LayerNorm2d, MLPBlock + + +# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa +class ImageEncoderViT(nn.Module): + def __init__( + self, + img_size: int = 1024, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + depth: int = 12, + num_heads: int = 12, + mlp_ratio: float = 4.0, + out_chans: int = 256, + qkv_bias: bool = True, + norm_layer: Type[nn.Module] = nn.LayerNorm, + act_layer: Type[nn.Module] = nn.GELU, + use_abs_pos: bool = True, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + window_size: int = 0, + global_attn_indexes: Tuple[int, ...] = (), + ) -> None: + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + global_attn_indexes (list): Indexes for blocks using global attention. + """ + super().__init__() + self.img_size = img_size + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + self.pos_embed: Optional[nn.Parameter] = None + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim) + ) + + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + act_layer=act_layer, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + window_size=window_size if i not in global_attn_indexes else 0, + input_size=(img_size // patch_size, img_size // patch_size), + ) + self.blocks.append(block) + + self.neck = nn.Sequential( + nn.Conv2d( + embed_dim, + out_chans, + kernel_size=1, + bias=False, + ), + LayerNorm2d(out_chans), + nn.Conv2d( + out_chans, + out_chans, + kernel_size=3, + padding=1, + bias=False, + ), + LayerNorm2d(out_chans), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + self.pos_embed + + for blk in self.blocks: + x = blk(x) + + x = self.neck(x.permute(0, 3, 1, 2)) + + return x + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = True, + norm_layer: Type[nn.Module] = nn.LayerNorm, + act_layer: Type[nn.Module] = nn.GELU, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + window_size: int = 0, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then + use global attention. + input_size (tuple(int, int) or None): Input resolution for calculating the relative + positional parameter size. + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + input_size=input_size if window_size == 0 else (window_size, window_size), + ) + + self.norm2 = norm_layer(dim) + self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer) + + self.window_size = window_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + x + x = x + self.mlp(self.norm2(x)) + + return x + + +class Attention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = True, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + input_size (tuple(int, int) or None): Input resolution for calculating the relative + positional parameter size. + """ + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.use_rel_pos = use_rel_pos + if self.use_rel_pos: + assert ( + input_size is not None + ), "Input size must be provided if using relative positional encoding." + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + + attn = (q * self.scale) @ k.transpose(-2, -1) + + if self.use_rel_pos: + attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) + + attn = attn.softmax(dim=-1) + x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + x = self.proj(x) + + return x + + +def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]: + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition( + windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int] +) -> torch.Tensor: + """ + Window unpartition into original sequences and removing padding. + Args: + windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_rel_pos( + attn: torch.Tensor, + q: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: Tuple[int, int], + k_size: Tuple[int, int], +) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + attn (Tensor): attention map. + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, + kernel_size: Tuple[int, int] = (16, 16), + stride: Tuple[int, int] = (16, 16), + padding: Tuple[int, int] = (0, 0), + in_chans: int = 3, + embed_dim: int = 768, + ) -> None: + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + """ + super().__init__() + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/mask_decoder.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/mask_decoder.py new file mode 100644 index 00000000..5d2fdb03 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/mask_decoder.py @@ -0,0 +1,176 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn +from torch.nn import functional as F + +from typing import List, Tuple, Type + +from .common import LayerNorm2d + + +class MaskDecoder(nn.Module): + def __init__( + self, + *, + transformer_dim: int, + transformer: nn.Module, + num_multimask_outputs: int = 3, + activation: Type[nn.Module] = nn.GELU, + iou_head_depth: int = 3, + iou_head_hidden_dim: int = 256, + ) -> None: + """ + Predicts masks given an image and prompt embeddings, using a + transformer architecture. + + Arguments: + transformer_dim (int): the channel dimension of the transformer + transformer (nn.Module): the transformer used to predict masks + num_multimask_outputs (int): the number of masks to predict + when disambiguating masks + activation (nn.Module): the type of activation to use when + upscaling masks + iou_head_depth (int): the depth of the MLP used to predict + mask quality + iou_head_hidden_dim (int): the hidden dimension of the MLP + used to predict mask quality + """ + super().__init__() + self.transformer_dim = transformer_dim + self.transformer = transformer + + self.num_multimask_outputs = num_multimask_outputs + + self.iou_token = nn.Embedding(1, transformer_dim) + self.num_mask_tokens = num_multimask_outputs + 1 + self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) + + self.output_upscaling = nn.Sequential( + nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2), + LayerNorm2d(transformer_dim // 4), + activation(), + nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), + activation(), + ) + self.output_hypernetworks_mlps = nn.ModuleList( + [ + MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) + for i in range(self.num_mask_tokens) + ] + ) + + self.iou_prediction_head = MLP( + transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth + ) + + def forward( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Arguments: + image_embeddings (torch.Tensor): the embeddings from the image encoder + image_pe (torch.Tensor): positional encoding with the shape of image_embeddings + sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes + dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs + multimask_output (bool): Whether to return multiple masks or a single + mask. + + Returns: + torch.Tensor: batched predicted masks + torch.Tensor: batched predictions of mask quality + """ + masks, iou_pred = self.predict_masks( + image_embeddings=image_embeddings, + image_pe=image_pe, + sparse_prompt_embeddings=sparse_prompt_embeddings, + dense_prompt_embeddings=dense_prompt_embeddings, + ) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + else: + mask_slice = slice(0, 1) + masks = masks[:, mask_slice, :, :] + iou_pred = iou_pred[:, mask_slice] + + # Prepare output + return masks, iou_pred + + def predict_masks( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Predicts masks. See 'forward' for more details.""" + # Concatenate output tokens + output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0) + output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1) + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) + + # Expand per-image data in batch direction to be per-mask + src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) + src = src + dense_prompt_embeddings + pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) + b, c, h, w = src.shape + + # Run the transformer + hs, src = self.transformer(src, pos_src, tokens) + iou_token_out = hs[:, 0, :] + mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + src = src.transpose(1, 2).view(b, c, h, w) + upscaled_embedding = self.output_upscaling(src) + hyper_in_list: List[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])) + hyper_in = torch.stack(hyper_in_list, dim=1) + b, c, h, w = upscaled_embedding.shape + masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + + return masks, iou_pred + + +# Lightly adapted from +# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa +class MLP(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + sigmoid_output: bool = False, + ) -> None: + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + self.sigmoid_output = sigmoid_output + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + if self.sigmoid_output: + x = F.sigmoid(x) + return x diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/prompt_encoder.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/prompt_encoder.py new file mode 100644 index 00000000..c3143f4f --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/prompt_encoder.py @@ -0,0 +1,214 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torch import nn + +from typing import Any, Optional, Tuple, Type + +from .common import LayerNorm2d + + +class PromptEncoder(nn.Module): + def __init__( + self, + embed_dim: int, + image_embedding_size: Tuple[int, int], + input_image_size: Tuple[int, int], + mask_in_chans: int, + activation: Type[nn.Module] = nn.GELU, + ) -> None: + """ + Encodes prompts for input to SAM's mask decoder. + + Arguments: + embed_dim (int): The prompts' embedding dimension + image_embedding_size (tuple(int, int)): The spatial size of the + image embedding, as (H, W). + input_image_size (int): The padded size of the image as input + to the image encoder, as (H, W). + mask_in_chans (int): The number of hidden channels used for + encoding input masks. + activation (nn.Module): The activation to use when encoding + input masks. + """ + super().__init__() + self.embed_dim = embed_dim + self.input_image_size = input_image_size + self.image_embedding_size = image_embedding_size + self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) + + self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners + point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)] + self.point_embeddings = nn.ModuleList(point_embeddings) + self.not_a_point_embed = nn.Embedding(1, embed_dim) + + self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1]) + self.mask_downscaling = nn.Sequential( + nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans // 4), + activation(), + nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans), + activation(), + nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), + ) + self.no_mask_embed = nn.Embedding(1, embed_dim) + + def get_dense_pe(self) -> torch.Tensor: + """ + Returns the positional encoding used to encode point prompts, + applied to a dense set of points the shape of the image encoding. + + Returns: + torch.Tensor: Positional encoding with shape + 1x(embed_dim)x(embedding_h)x(embedding_w) + """ + return self.pe_layer(self.image_embedding_size).unsqueeze(0) + + def _embed_points( + self, + points: torch.Tensor, + labels: torch.Tensor, + pad: bool, + ) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) + padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) + points = torch.cat([points, padding_point], dim=1) + labels = torch.cat([labels, padding_label], dim=1) + point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size) + point_embedding[labels == -1] = 0.0 + point_embedding[labels == -1] += self.not_a_point_embed.weight + point_embedding[labels == 0] += self.point_embeddings[0].weight + point_embedding[labels == 1] += self.point_embeddings[1].weight + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.reshape(-1, 2, 2) + corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size) + corner_embedding[:, 0, :] += self.point_embeddings[2].weight + corner_embedding[:, 1, :] += self.point_embeddings[3].weight + return corner_embedding + + def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: + """Embeds mask inputs.""" + mask_embedding = self.mask_downscaling(masks) + return mask_embedding + + def _get_batch_size( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> int: + """ + Gets the batch size of the output given the batch size of the input prompts. + """ + if points is not None: + return points[0].shape[0] + elif boxes is not None: + return boxes.shape[0] + elif masks is not None: + return masks.shape[0] + else: + return 1 + + def _get_device(self) -> torch.device: + return self.point_embeddings[0].weight.device + + def forward( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense + embeddings. + + Arguments: + points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates + and labels to embed. + boxes (torch.Tensor or none): boxes to embed + masks (torch.Tensor or none): masks to embed + + Returns: + torch.Tensor: sparse embeddings for the points and boxes, with shape + BxNx(embed_dim), where N is determined by the number of input points + and boxes. + torch.Tensor: dense embeddings for the masks, in the shape + Bx(embed_dim)x(embed_H)x(embed_W) + """ + bs = self._get_batch_size(points, boxes, masks) + sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device()) + if points is not None: + coords, labels = points + point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) + sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) + if boxes is not None: + box_embeddings = self._embed_boxes(boxes) + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) + + if masks is not None: + dense_embeddings = self._embed_masks(masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +class PositionEmbeddingRandom(nn.Module): + """ + Positional encoding using random spatial frequencies. + """ + + def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: + super().__init__() + if scale is None or scale <= 0.0: + scale = 1.0 + self.register_buffer( + "positional_encoding_gaussian_matrix", + scale * torch.randn((2, num_pos_feats)), + ) + + def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: + """Positionally encode points that are normalized to [0,1].""" + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coords = 2 * coords - 1 + coords = coords @ self.positional_encoding_gaussian_matrix + coords = 2 * np.pi * coords + # outputs d_1 x ... x d_n x C shape + return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) + + def forward(self, size: Tuple[int, int]) -> torch.Tensor: + """Generate positional encoding for a grid of the specified size.""" + h, w = size + device: Any = self.positional_encoding_gaussian_matrix.device + grid = torch.ones((h, w), device=device, dtype=torch.float32) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / h + x_embed = x_embed / w + + pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) + return pe.permute(2, 0, 1) # C x H x W + + def forward_with_coords( + self, coords_input: torch.Tensor, image_size: Tuple[int, int] + ) -> torch.Tensor: + """Positionally encode points that are not normalized to [0,1].""" + coords = coords_input.clone() + coords[:, :, 0] = coords[:, :, 0] / image_size[1] + coords[:, :, 1] = coords[:, :, 1] / image_size[0] + return self._pe_encoding(coords.to(torch.float)) # B x N x C diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/sam.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/sam.py new file mode 100644 index 00000000..8074cff6 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/sam.py @@ -0,0 +1,174 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn +from torch.nn import functional as F + +from typing import Any, Dict, List, Tuple + +from .image_encoder import ImageEncoderViT +from .mask_decoder import MaskDecoder +from .prompt_encoder import PromptEncoder + + +class Sam(nn.Module): + mask_threshold: float = 0.0 + image_format: str = "RGB" + + def __init__( + self, + image_encoder: ImageEncoderViT, + prompt_encoder: PromptEncoder, + mask_decoder: MaskDecoder, + pixel_mean: List[float] = [123.675, 116.28, 103.53], + pixel_std: List[float] = [58.395, 57.12, 57.375], + ) -> None: + """ + SAM predicts object masks from an image and input prompts. + + Arguments: + image_encoder (ImageEncoderViT): The backbone used to encode the + image into image embeddings that allow for efficient mask prediction. + prompt_encoder (PromptEncoder): Encodes various types of input prompts. + mask_decoder (MaskDecoder): Predicts masks from the image embeddings + and encoded prompts. + pixel_mean (list(float)): Mean values for normalizing pixels in the input image. + pixel_std (list(float)): Std values for normalizing pixels in the input image. + """ + super().__init__() + self.image_encoder = image_encoder + self.prompt_encoder = prompt_encoder + self.mask_decoder = mask_decoder + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + + @property + def device(self) -> Any: + return self.pixel_mean.device + + @torch.no_grad() + def forward( + self, + batched_input: List[Dict[str, Any]], + multimask_output: bool, + ) -> List[Dict[str, torch.Tensor]]: + """ + Predicts masks end-to-end from provided images and prompts. + If prompts are not known in advance, using SamPredictor is + recommended over calling the model directly. + + Arguments: + batched_input (list(dict)): A list over input images, each a + dictionary with the following keys. A prompt key can be + excluded if it is not present. + 'image': The image as a torch tensor in 3xHxW format, + already transformed for input to the model. + 'original_size': (tuple(int, int)) The original size of + the image before transformation, as (H, W). + 'point_coords': (torch.Tensor) Batched point prompts for + this image, with shape BxNx2. Already transformed to the + input frame of the model. + 'point_labels': (torch.Tensor) Batched labels for point prompts, + with shape BxN. + 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. + Already transformed to the input frame of the model. + 'mask_inputs': (torch.Tensor) Batched mask inputs to the model, + in the form Bx1xHxW. + multimask_output (bool): Whether the model should predict multiple + disambiguating masks, or return a single mask. + + Returns: + (list(dict)): A list over input images, where each element is + as dictionary with the following keys. + 'masks': (torch.Tensor) Batched binary mask predictions, + with shape BxCxHxW, where B is the number of input prompts, + C is determined by multimask_output, and (H, W) is the + original size of the image. + 'iou_predictions': (torch.Tensor) The model's predictions + of mask quality, in shape BxC. + 'low_res_logits': (torch.Tensor) Low resolution logits with + shape BxCxHxW, where H=W=256. Can be passed as mask input + to subsequent iterations of prediction. + """ + input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0) + image_embeddings = self.image_encoder(input_images) + + outputs = [] + for image_record, curr_embedding in zip(batched_input, image_embeddings): + if "point_coords" in image_record: + points = (image_record["point_coords"], image_record["point_labels"]) + else: + points = None + sparse_embeddings, dense_embeddings = self.prompt_encoder( + points=points, + boxes=image_record.get("boxes", None), + masks=image_record.get("mask_inputs", None), + ) + low_res_masks, iou_predictions = self.mask_decoder( + image_embeddings=curr_embedding.unsqueeze(0), + image_pe=self.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + ) + masks = self.postprocess_masks( + low_res_masks, + input_size=image_record["image"].shape[-2:], + original_size=image_record["original_size"], + ) + masks = masks > self.mask_threshold + outputs.append( + { + "masks": masks, + "iou_predictions": iou_predictions, + "low_res_logits": low_res_masks, + } + ) + return outputs + + def postprocess_masks( + self, + masks: torch.Tensor, + input_size: Tuple[int, ...], + original_size: Tuple[int, ...], + ) -> torch.Tensor: + """ + Remove padding and upscale masks to the original image size. + + Arguments: + masks (torch.Tensor): Batched masks from the mask_decoder, + in BxCxHxW format. + input_size (tuple(int, int)): The size of the image input to the + model, in (H, W) format. Used to remove padding. + original_size (tuple(int, int)): The original size of the image + before resizing for input to the model, in (H, W) format. + + Returns: + (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) + is given by original_size. + """ + masks = F.interpolate( + masks, + (self.image_encoder.img_size, self.image_encoder.img_size), + mode="bilinear", + align_corners=False, + ) + masks = masks[..., : input_size[0], : input_size[1]] + masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False) + return masks + + def preprocess(self, x: torch.Tensor) -> torch.Tensor: + """Normalize pixel values and pad to a square input.""" + # Normalize colors + x = (x - self.pixel_mean) / self.pixel_std + + # Pad + h, w = x.shape[-2:] + padh = self.image_encoder.img_size - h + padw = self.image_encoder.img_size - w + x = F.pad(x, (0, padw, 0, padh)) + return x diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/transformer.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/transformer.py new file mode 100644 index 00000000..28fafea5 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/modeling/transformer.py @@ -0,0 +1,240 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor, nn + +import math +from typing import Tuple, Type + +from .common import MLPBlock + + +class TwoWayTransformer(nn.Module): + def __init__( + self, + depth: int, + embedding_dim: int, + num_heads: int, + mlp_dim: int, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + ) -> None: + """ + A transformer decoder that attends to an input image using + queries whose positional embedding is supplied. + + Args: + depth (int): number of layers in the transformer + embedding_dim (int): the channel dimension for the input embeddings + num_heads (int): the number of heads for multihead attention. Must + divide embedding_dim + mlp_dim (int): the channel dimension internal to the MLP block + activation (nn.Module): the activation to use in the MLP block + """ + super().__init__() + self.depth = depth + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.mlp_dim = mlp_dim + self.layers = nn.ModuleList() + + for i in range(depth): + self.layers.append( + TwoWayAttentionBlock( + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_dim=mlp_dim, + activation=activation, + attention_downsample_rate=attention_downsample_rate, + skip_first_layer_pe=(i == 0), + ) + ) + + self.final_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm_final_attn = nn.LayerNorm(embedding_dim) + + def forward( + self, + image_embedding: Tensor, + image_pe: Tensor, + point_embedding: Tensor, + ) -> Tuple[Tensor, Tensor]: + """ + Args: + image_embedding (torch.Tensor): image to attend to. Should be shape + B x embedding_dim x h x w for any h and w. + image_pe (torch.Tensor): the positional encoding to add to the image. Must + have the same shape as image_embedding. + point_embedding (torch.Tensor): the embedding to add to the query points. + Must have shape B x N_points x embedding_dim for any N_points. + + Returns: + torch.Tensor: the processed point_embedding + torch.Tensor: the processed image_embedding + """ + # BxCxHxW -> BxHWxC == B x N_image_tokens x C + bs, c, h, w = image_embedding.shape + image_embedding = image_embedding.flatten(2).permute(0, 2, 1) + image_pe = image_pe.flatten(2).permute(0, 2, 1) + + # Prepare queries + queries = point_embedding + keys = image_embedding + + # Apply transformer blocks and final layernorm + for layer in self.layers: + queries, keys = layer( + queries=queries, + keys=keys, + query_pe=point_embedding, + key_pe=image_pe, + ) + + # Apply the final attention layer from the points to the image + q = queries + point_embedding + k = keys + image_pe + attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm_final_attn(queries) + + return queries, keys + + +class TwoWayAttentionBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + num_heads: int, + mlp_dim: int = 2048, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + skip_first_layer_pe: bool = False, + ) -> None: + """ + A transformer block with four layers: (1) self-attention of sparse + inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp + block on sparse inputs, and (4) cross attention of dense inputs to sparse + inputs. + + Arguments: + embedding_dim (int): the channel dimension of the embeddings + num_heads (int): the number of heads in the attention layers + mlp_dim (int): the hidden dimension of the mlp block + activation (nn.Module): the activation of the mlp block + skip_first_layer_pe (bool): skip the PE on the first layer + """ + super().__init__() + self.self_attn = Attention(embedding_dim, num_heads) + self.norm1 = nn.LayerNorm(embedding_dim) + + self.cross_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm2 = nn.LayerNorm(embedding_dim) + + self.mlp = MLPBlock(embedding_dim, mlp_dim, activation) + self.norm3 = nn.LayerNorm(embedding_dim) + + self.norm4 = nn.LayerNorm(embedding_dim) + self.cross_attn_image_to_token = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor + ) -> Tuple[Tensor, Tensor]: + # Self attention block + if self.skip_first_layer_pe: + queries = self.self_attn(q=queries, k=queries, v=queries) + else: + q = queries + query_pe + attn_out = self.self_attn(q=q, k=q, v=queries) + queries = queries + attn_out + queries = self.norm1(queries) + + # Cross attention block, tokens attending to image embedding + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.norm3(queries) + + # Cross attention block, image embedding attending to tokens + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries) + keys = keys + attn_out + keys = self.norm4(keys) + + return queries, keys + + +class Attention(nn.Module): + """ + An attention layer that allows for downscaling the size of the embedding + after projection to queries, keys, and values. + """ + + def __init__( + self, + embedding_dim: int, + num_heads: int, + downsample_rate: int = 1, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.internal_dim = embedding_dim // downsample_rate + self.num_heads = num_heads + assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim." + + self.q_proj = nn.Linear(embedding_dim, self.internal_dim) + self.k_proj = nn.Linear(embedding_dim, self.internal_dim) + self.v_proj = nn.Linear(embedding_dim, self.internal_dim) + self.out_proj = nn.Linear(self.internal_dim, embedding_dim) + + def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor: + b, n, c = x.shape + x = x.reshape(b, n, num_heads, c // num_heads) + return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head + + def _recombine_heads(self, x: Tensor) -> Tensor: + b, n_heads, n_tokens, c_per_head = x.shape + x = x.transpose(1, 2) + return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: + # Input projections + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + + # Separate into heads + q = self._separate_heads(q, self.num_heads) + k = self._separate_heads(k, self.num_heads) + v = self._separate_heads(v, self.num_heads) + + # Attention + _, _, _, c_per_head = q.shape + attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens + attn = attn / math.sqrt(c_per_head) + attn = torch.softmax(attn, dim=-1) + + # Get output + out = attn @ v + out = self._recombine_heads(out) + out = self.out_proj(out) + + return out diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/predictor.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/predictor.py new file mode 100644 index 00000000..8a6e6d81 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/predictor.py @@ -0,0 +1,269 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch + +from segment_anything.modeling import Sam + +from typing import Optional, Tuple + +from .utils.transforms import ResizeLongestSide + + +class SamPredictor: + def __init__( + self, + sam_model: Sam, + ) -> None: + """ + Uses SAM to calculate the image embedding for an image, and then + allow repeated, efficient mask prediction given prompts. + + Arguments: + sam_model (Sam): The model to use for mask prediction. + """ + super().__init__() + self.model = sam_model + self.transform = ResizeLongestSide(sam_model.image_encoder.img_size) + self.reset_image() + + def set_image( + self, + image: np.ndarray, + image_format: str = "RGB", + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. + + Arguments: + image (np.ndarray): The image for calculating masks. Expects an + image in HWC uint8 format, with pixel values in [0, 255]. + image_format (str): The color format of the image, in ['RGB', 'BGR']. + """ + assert image_format in [ + "RGB", + "BGR", + ], f"image_format must be in ['RGB', 'BGR'], is {image_format}." + if image_format != self.model.image_format: + image = image[..., ::-1] + + # Transform the image to the form expected by the model + input_image = self.transform.apply_image(image) + input_image_torch = torch.as_tensor(input_image, device=self.device) + input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :] + + self.set_torch_image(input_image_torch, image.shape[:2]) + + @torch.no_grad() + def set_torch_image( + self, + transformed_image: torch.Tensor, + original_image_size: Tuple[int, ...], + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. Expects the input + image to be already transformed to the format expected by the model. + + Arguments: + transformed_image (torch.Tensor): The input image, with shape + 1x3xHxW, which has been transformed with ResizeLongestSide. + original_image_size (tuple(int, int)): The size of the image + before transformation, in (H, W) format. + """ + assert ( + len(transformed_image.shape) == 4 + and transformed_image.shape[1] == 3 + and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size + ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}." + self.reset_image() + + self.original_size = original_image_size + self.input_size = tuple(transformed_image.shape[-2:]) + input_image = self.model.preprocess(transformed_image) + self.features = self.model.image_encoder(input_image) + self.is_image_set = True + + def predict( + self, + point_coords: Optional[np.ndarray] = None, + point_labels: Optional[np.ndarray] = None, + box: Optional[np.ndarray] = None, + mask_input: Optional[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Predict masks for the given input prompts, using the currently set image. + + Arguments: + point_coords (np.ndarray or None): A Nx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (np.ndarray or None): A length N array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + box (np.ndarray or None): A length 4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form 1xHxW, where + for SAM, H=W=256. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (np.ndarray): The output masks in CxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (np.ndarray): An array of length C containing the model's + predictions for the quality of each mask. + (np.ndarray): An array of shape CxHxW, where C is the number + of masks and H=W=256. These low resolution logits can be passed to + a subsequent iteration as mask input. + """ + if not self.is_image_set: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + + # Transform input prompts + coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None + if point_coords is not None: + assert ( + point_labels is not None + ), "point_labels must be supplied if point_coords is supplied." + point_coords = self.transform.apply_coords(point_coords, self.original_size) + coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device) + labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device) + coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] + if box is not None: + box = self.transform.apply_boxes(box, self.original_size) + box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device) + box_torch = box_torch[None, :] + if mask_input is not None: + mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device) + mask_input_torch = mask_input_torch[None, :, :, :] + + masks, iou_predictions, low_res_masks = self.predict_torch( + coords_torch, + labels_torch, + box_torch, + mask_input_torch, + multimask_output, + return_logits=return_logits, + ) + + masks_np = masks[0].detach().cpu().numpy() + iou_predictions_np = iou_predictions[0].detach().cpu().numpy() + low_res_masks_np = low_res_masks[0].detach().cpu().numpy() + return masks_np, iou_predictions_np, low_res_masks_np + + @torch.no_grad() + def predict_torch( + self, + point_coords: Optional[torch.Tensor], + point_labels: Optional[torch.Tensor], + boxes: Optional[torch.Tensor] = None, + mask_input: Optional[torch.Tensor] = None, + multimask_output: bool = True, + return_logits: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks for the given input prompts, using the currently set image. + Input prompts are batched torch tensors and are expected to already be + transformed to the input frame using ResizeLongestSide. + + Arguments: + point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (torch.Tensor or None): A BxN array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + boxes (np.ndarray or None): A Bx4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form Bx1xHxW, where + for SAM, H=W=256. Masks returned by a previous iteration of the + predict method do not need further transformation. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (torch.Tensor): The output masks in BxCxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (torch.Tensor): An array of shape BxC containing the model's + predictions for the quality of each mask. + (torch.Tensor): An array of shape BxCxHxW, where C is the number + of masks and H=W=256. These low res logits can be passed to + a subsequent iteration as mask input. + """ + if not self.is_image_set: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + + if point_coords is not None: + points = (point_coords, point_labels) + else: + points = None + + # Embed prompts + sparse_embeddings, dense_embeddings = self.model.prompt_encoder( + points=points, + boxes=boxes, + masks=mask_input, + ) + + # Predict masks + low_res_masks, iou_predictions = self.model.mask_decoder( + image_embeddings=self.features, + image_pe=self.model.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + ) + + # Upscale the masks to the original image resolution + masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size) + + if not return_logits: + masks = masks > self.model.mask_threshold + + return masks, iou_predictions, low_res_masks + + def get_image_embedding(self) -> torch.Tensor: + """ + Returns the image embeddings for the currently set image, with + shape 1xCxHxW, where C is the embedding dimension and (H,W) are + the embedding spatial dimension of SAM (typically C=256, H=W=64). + """ + if not self.is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) to generate an embedding." + ) + assert self.features is not None, "Features must exist if an image has been set." + return self.features + + @property + def device(self) -> torch.device: + return self.model.device + + def reset_image(self) -> None: + """Resets the currently set image.""" + self.is_image_set = False + self.features = None + self.orig_h = None + self.orig_w = None + self.input_h = None + self.input_w = None diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/__init__.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/__init__.py new file mode 100644 index 00000000..5277f461 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/amg.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/amg.py new file mode 100644 index 00000000..be064071 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/amg.py @@ -0,0 +1,346 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch + +import math +from copy import deepcopy +from itertools import product +from typing import Any, Dict, Generator, ItemsView, List, Tuple + + +class MaskData: + """ + A structure for storing masks and their related data in batched format. + Implements basic filtering and concatenation. + """ + + def __init__(self, **kwargs) -> None: + for v in kwargs.values(): + assert isinstance( + v, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats = dict(**kwargs) + + def __setitem__(self, key: str, item: Any) -> None: + assert isinstance( + item, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats[key] = item + + def __delitem__(self, key: str) -> None: + del self._stats[key] + + def __getitem__(self, key: str) -> Any: + return self._stats[key] + + def items(self) -> ItemsView[str, Any]: + return self._stats.items() + + def filter(self, keep: torch.Tensor) -> None: + for k, v in self._stats.items(): + if v is None: + self._stats[k] = None + elif isinstance(v, torch.Tensor): + self._stats[k] = v[torch.as_tensor(keep, device=v.device)] + elif isinstance(v, np.ndarray): + self._stats[k] = v[keep.detach().cpu().numpy()] + elif isinstance(v, list) and keep.dtype == torch.bool: + self._stats[k] = [a for i, a in enumerate(v) if keep[i]] + elif isinstance(v, list): + self._stats[k] = [v[i] for i in keep] + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def cat(self, new_stats: "MaskData") -> None: + for k, v in new_stats.items(): + if k not in self._stats or self._stats[k] is None: + self._stats[k] = deepcopy(v) + elif isinstance(v, torch.Tensor): + self._stats[k] = torch.cat([self._stats[k], v], dim=0) + elif isinstance(v, np.ndarray): + self._stats[k] = np.concatenate([self._stats[k], v], axis=0) + elif isinstance(v, list): + self._stats[k] = self._stats[k] + deepcopy(v) + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def to_numpy(self) -> None: + for k, v in self._stats.items(): + if isinstance(v, torch.Tensor): + self._stats[k] = v.detach().cpu().numpy() + + +def is_box_near_crop_edge( + boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0 +) -> torch.Tensor: + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + boxes = uncrop_boxes_xyxy(boxes, crop_box).float() + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: + box_xywh = deepcopy(box_xyxy) + box_xywh[2] = box_xywh[2] - box_xywh[0] + box_xywh[3] = box_xywh[3] - box_xywh[1] + return box_xywh + + +def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: + assert len(args) > 0 and all( + len(a) == len(args[0]) for a in args + ), "Batched iteration must have inputs of all the same size." + n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) + for b in range(n_batches): + yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args] + + +def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: + """ + Encodes masks to an uncompressed RLE, in the format expected by + pycoco tools. + """ + # Put in fortran order and flatten h,w + b, h, w = tensor.shape + tensor = tensor.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = tensor[:, 1:] ^ tensor[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(b): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + cur_idxs = torch.cat( + [ + torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), + cur_idxs + 1, + torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), + ] + ) + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if tensor[i, 0] == 0 else [0] + counts.extend(btw_idxs.detach().cpu().tolist()) + out.append({"size": [h, w], "counts": counts}) + return out + + +def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: + """Compute a binary mask from an uncompressed RLE.""" + h, w = rle["size"] + mask = np.empty(h * w, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity ^= True + mask = mask.reshape(w, h) + return mask.transpose() # Put in C order + + +def area_from_rle(rle: Dict[str, Any]) -> int: + return sum(rle["counts"][1::2]) + + +def calculate_stability_score( + masks: torch.Tensor, mask_threshold: float, threshold_offset: float +) -> torch.Tensor: + """ + Computes the stability score for a batch of masks. The stability + score is the IoU between the binary masks obtained by thresholding + the predicted mask logits at high and low values. + """ + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + unions = ( + (masks > (mask_threshold - threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + return intersections / unions + + +def build_point_grid(n_per_side: int) -> np.ndarray: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = np.linspace(offset, 1 - offset, n_per_side) + points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = np.tile(points_one_side[:, None], (1, n_per_side)) + points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) + return points + + +def build_all_layer_point_grids( + n_per_side: int, n_layers: int, scale_per_layer: int +) -> List[np.ndarray]: + """Generates point grids for all crop layers.""" + points_by_layer = [] + for i in range(n_layers + 1): + n_points = int(n_per_side / (scale_per_layer**i)) + points_by_layer.append(build_point_grid(n_points)) + return points_by_layer + + +def generate_crop_boxes( + im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float +) -> Tuple[List[List[int]], List[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer + has (2**i)**2 boxes for the ith layer. + """ + crop_boxes, layer_idxs = [], [] + im_h, im_w = im_size + short_side = min(im_h, im_w) + + # Original image + crop_boxes.append([0, 0, im_w, im_h]) + layer_idxs.append(0) + + def crop_len(orig_len, n_crops, overlap): + return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) + + for i_layer in range(n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_w = crop_len(im_w, n_crops_per_side, overlap) + crop_h = crop_len(im_h, n_crops_per_side, overlap) + + crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] + + # Crops in XYWH format + for x0, y0 in product(crop_box_x0, crop_box_y0): + box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + return boxes + offset + + +def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0]], device=points.device) + # Check if points has a channel dimension + if len(points.shape) == 3: + offset = offset.unsqueeze(1) + return points + offset + + +def uncrop_masks( + masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int +) -> torch.Tensor: + x0, y0, x1, y1 = crop_box + if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) + pad = (x0, pad_x - x0, y0, pad_y - y0) + return torch.nn.functional.pad(masks, pad, value=0) + + +def remove_small_regions( + mask: np.ndarray, area_thresh: float, mode: str +) -> Tuple[np.ndarray, bool]: + """ + Removes small disconnected regions and holes in a mask. Returns the + mask and an indicator of if the mask has been modified. + """ + import cv2 # type: ignore + + assert mode in ["holes", "islands"] + correct_holes = mode == "holes" + working_mask = (correct_holes ^ mask).astype(np.uint8) + n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) + sizes = stats[:, -1][1:] # Row 0 is background label + small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] + if len(small_regions) == 0: + return mask, False + fill_labels = [0] + small_regions + if not correct_holes: + fill_labels = [i for i in range(n_labels) if i not in fill_labels] + # If every region is below threshold, keep largest + if len(fill_labels) == 0: + fill_labels = [int(np.argmax(sizes)) + 1] + mask = np.isin(regions, fill_labels) + return mask, True + + +def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: + from pycocotools import mask as mask_utils # type: ignore + + h, w = uncompressed_rle["size"] + rle = mask_utils.frPyObjects(uncompressed_rle, h, w) + rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json + return rle + + +def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: + """ + Calculates boxes in XYXY format around masks. Return [0,0,0,0] for + an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. + """ + # torch.max below raises an error on empty inputs, just skip in this case + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to CxHxW + shape = masks.shape + h, w = shape[-2:] + if len(shape) > 2: + masks = masks.flatten(0, -3) + else: + masks = masks.unsqueeze(0) + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + h * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + w * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + if len(shape) > 2: + out = out.reshape(*shape[:-2], 4) + else: + out = out[0] + + return out diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/onnx.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/onnx.py new file mode 100644 index 00000000..3196bdf4 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/onnx.py @@ -0,0 +1,144 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from torch.nn import functional as F + +from typing import Tuple + +from ..modeling import Sam +from .amg import calculate_stability_score + + +class SamOnnxModel(nn.Module): + """ + This model should not be called directly, but is used in ONNX export. + It combines the prompt encoder, mask decoder, and mask postprocessing of Sam, + with some functions modified to enable model tracing. Also supports extra + options controlling what information. See the ONNX export script for details. + """ + + def __init__( + self, + model: Sam, + return_single_mask: bool, + use_stability_score: bool = False, + return_extra_metrics: bool = False, + ) -> None: + super().__init__() + self.mask_decoder = model.mask_decoder + self.model = model + self.img_size = model.image_encoder.img_size + self.return_single_mask = return_single_mask + self.use_stability_score = use_stability_score + self.stability_score_offset = 1.0 + self.return_extra_metrics = return_extra_metrics + + @staticmethod + def resize_longest_image_size( + input_image_size: torch.Tensor, longest_side: int + ) -> torch.Tensor: + input_image_size = input_image_size.to(torch.float32) + scale = longest_side / torch.max(input_image_size) + transformed_size = scale * input_image_size + transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64) + return transformed_size + + def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor: + point_coords = point_coords + 0.5 + point_coords = point_coords / self.img_size + point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords) + point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding) + + point_embedding = point_embedding * (point_labels != -1) + point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * ( + point_labels == -1 + ) + + for i in range(self.model.prompt_encoder.num_point_embeddings): + point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[ + i + ].weight * (point_labels == i) + + return point_embedding + + def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor: + mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask) + mask_embedding = mask_embedding + ( + 1 - has_mask_input + ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1) + return mask_embedding + + def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor: + masks = F.interpolate( + masks, + size=(self.img_size, self.img_size), + mode="bilinear", + align_corners=False, + ) + + prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64) + masks = masks[..., : prepadded_size[0], : prepadded_size[1]] # type: ignore + + orig_im_size = orig_im_size.to(torch.int64) + h, w = orig_im_size[0], orig_im_size[1] + masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False) + return masks + + def select_masks( + self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Determine if we should return the multiclick mask or not from the number of points. + # The reweighting is used to avoid control flow. + score_reweight = torch.tensor( + [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)] + ).to(iou_preds.device) + score = iou_preds + (num_points - 2.5) * score_reweight + best_idx = torch.argmax(score, dim=1) + masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1) + iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1) + + return masks, iou_preds + + @torch.no_grad() + def forward( + self, + image_embeddings: torch.Tensor, + point_coords: torch.Tensor, + point_labels: torch.Tensor, + mask_input: torch.Tensor, + has_mask_input: torch.Tensor, + orig_im_size: torch.Tensor, + ): + sparse_embedding = self._embed_points(point_coords, point_labels) + dense_embedding = self._embed_masks(mask_input, has_mask_input) + + masks, scores = self.model.mask_decoder.predict_masks( + image_embeddings=image_embeddings, + image_pe=self.model.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embedding, + dense_prompt_embeddings=dense_embedding, + ) + + if self.use_stability_score: + scores = calculate_stability_score( + masks, self.model.mask_threshold, self.stability_score_offset + ) + + if self.return_single_mask: + masks, scores = self.select_masks(masks, scores, point_coords.shape[1]) + + upscaled_masks = self.mask_postprocessing(masks, orig_im_size) + + if self.return_extra_metrics: + stability_scores = calculate_stability_score( + upscaled_masks, self.model.mask_threshold, self.stability_score_offset + ) + areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1) + return upscaled_masks, scores, stability_scores, areas, masks + + return upscaled_masks, scores, masks diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/transforms.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/transforms.py new file mode 100644 index 00000000..c08ba1e3 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/segment_anything/utils/transforms.py @@ -0,0 +1,102 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from torch.nn import functional as F +from torchvision.transforms.functional import resize, to_pil_image # type: ignore + +from copy import deepcopy +from typing import Tuple + + +class ResizeLongestSide: + """ + Resizes images to the longest side 'target_length', as well as provides + methods for resizing coordinates and boxes. Provides methods for + transforming both numpy array and batched torch tensors. + """ + + def __init__(self, target_length: int) -> None: + self.target_length = target_length + + def apply_image(self, image: np.ndarray) -> np.ndarray: + """ + Expects a numpy array with shape HxWxC in uint8 format. + """ + target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) + return np.array(resize(to_pil_image(image), target_size)) + + def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: + """ + Expects a numpy array of length 2 in the final dimension. Requires the + original image size in (H, W) format. + """ + old_h, old_w = original_size + new_h, new_w = self.get_preprocess_shape( + original_size[0], original_size[1], self.target_length + ) + coords = deepcopy(coords).astype(float) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + return coords + + def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: + """ + Expects a numpy array shape Bx4. Requires the original image size + in (H, W) format. + """ + boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size) + return boxes.reshape(-1, 4) + + def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor: + """ + Expects batched images with shape BxCxHxW and float format. This + transformation may not exactly match apply_image. apply_image is + the transformation expected by the model. + """ + # Expects an image in BCHW format. May not exactly match apply_image. + target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length) + return F.interpolate( + image, target_size, mode="bilinear", align_corners=False, antialias=True + ) + + def apply_coords_torch( + self, coords: torch.Tensor, original_size: Tuple[int, ...] + ) -> torch.Tensor: + """ + Expects a torch tensor with length 2 in the last dimension. Requires the + original image size in (H, W) format. + """ + old_h, old_w = original_size + new_h, new_w = self.get_preprocess_shape( + original_size[0], original_size[1], self.target_length + ) + coords = deepcopy(coords).to(torch.float) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + return coords + + def apply_boxes_torch( + self, boxes: torch.Tensor, original_size: Tuple[int, ...] + ) -> torch.Tensor: + """ + Expects a torch tensor with shape Bx4. Requires the original image + size in (H, W) format. + """ + boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size) + return boxes.reshape(-1, 4) + + @staticmethod + def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]: + """ + Compute the output size given input size and target long side length. + """ + scale = long_side_length * 1.0 / max(oldh, oldw) + newh, neww = oldh * scale, oldw * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return (newh, neww) diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/setup.cfg b/custom_nodes/was-node-suite-comfyui/repos/SAM/setup.cfg new file mode 100644 index 00000000..0eee130b --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/setup.cfg @@ -0,0 +1,11 @@ +[isort] +line_length=100 +multi_line_output=3 +include_trailing_comma=True +known_standard_library=numpy,setuptools +skip_glob=*/__init__.py +known_myself=segment_anything +known_third_party=matplotlib,cv2,torch,torchvision,pycocotools,onnx,black,isort +no_lines_before=STDLIB,THIRDPARTY +sections=FUTURE,STDLIB,THIRDPARTY,MYSELF,FIRSTPARTY,LOCALFOLDER +default_section=FIRSTPARTY diff --git a/custom_nodes/was-node-suite-comfyui/repos/SAM/setup.py b/custom_nodes/was-node-suite-comfyui/repos/SAM/setup.py new file mode 100644 index 00000000..2c098631 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/repos/SAM/setup.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from setuptools import find_packages, setup + +setup( + name="segment_anything", + version="1.0", + install_requires=[], + packages=find_packages(exclude="notebooks"), + extras_require={ + "all": ["matplotlib", "pycocotools", "opencv-python", "onnx", "onnxruntime"], + "dev": ["flake8", "isort", "black", "mypy"], + }, +) diff --git a/custom_nodes/was-node-suite-comfyui/requirements.txt b/custom_nodes/was-node-suite-comfyui/requirements.txt new file mode 100644 index 00000000..90f8fa4d --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/requirements.txt @@ -0,0 +1,20 @@ +cmake +fairscale>=0.4.4 +git+https://github.com/WASasquatch/img2texture.git +git+https://github.com/WASasquatch/cstr +gitpython +imageio +joblib +matplotlib +numba +numpy +opencv-python-headless[ffmpeg] +pilgram +git+https://github.com/WASasquatch/ffmpy.git +rembg +scikit-image>=0.20.0 +scikit-learn +scipy +timm>=0.4.12 +tqdm +transformers diff --git a/custom_nodes/was-node-suite-comfyui/res/font.ttf b/custom_nodes/was-node-suite-comfyui/res/font.ttf new file mode 100644 index 00000000..5eaf3c73 Binary files /dev/null and b/custom_nodes/was-node-suite-comfyui/res/font.ttf differ diff --git a/custom_nodes/was-node-suite-comfyui/res/haarcascade_eye.xml b/custom_nodes/was-node-suite-comfyui/res/haarcascade_eye.xml new file mode 100644 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b/custom_nodes/was-node-suite-comfyui/tests/pytest.ini new file mode 100644 index 00000000..eea2c180 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/tests/pytest.ini @@ -0,0 +1 @@ +[pytest] diff --git a/custom_nodes/was-node-suite-comfyui/tests/test_WAS_Text_Sort.py b/custom_nodes/was-node-suite-comfyui/tests/test_WAS_Text_Sort.py new file mode 100644 index 00000000..bbe43381 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/tests/test_WAS_Text_Sort.py @@ -0,0 +1,69 @@ +from was_mock import was_text_sort + +def test_empty_text(): + assert was_text_sort() == "" + +def test_empty_text_with_separator_override(): + assert was_text_sort(separator="|") == "" + +def test_already_sorted_text(): + assert was_text_sort("already, sorted, text") == "already, sorted, text" + +def test_already_sorted_text_with_separator_override(): + assert was_text_sort("already, sorted, text", separator="|") == "already, sorted, text" + +def test_with_alternative_separator(): + assert was_text_sort("test | with | alternative | separator", separator=" | ") == "alternative | separator | test | with" + +def test_with_trailing_separators(): + assert was_text_sort("test, with, trailing, separator,") == "separator, test, trailing, with" + +def test_with_tabs(): + assert was_text_sort("test,\t without, \tweights") == "test, weights, without" + +def test_with_linefeed_newlines(): + assert was_text_sort("test,\n without, \nweights") == "test, weights, without" + +def test_with_macos_pre_cheetah_newlines(): + assert was_text_sort("test,\r without, \rweights") == "test, weights, without" + +def test_with_windows_newlines(): + assert was_text_sort("test,\r\n without, \r\nweights") == "test, weights, without" + +def test_without_weights(): + assert was_text_sort("test, without, weights") == "test, weights, without" + +def test_with_weights(): + assert was_text_sort("(test:1), (with:2.0), (weights:3.1)") == "(test:1), (weights:3.1), (with:2.0)" + +def test_with_some_weights(): + assert was_text_sort("(test:1), with, some, (weights:3.1)") == "some, (test:1), (weights:3.1), with" + +def test_with_half_weights(): + assert was_text_sort("(test:1), with, half (weights:3.1)") == "half (weights:3.1), (test:1), with" + +# ASCII "_" is after uppercase and before lowercase letters +def test_with_wildcards(): + assert was_text_sort("test, with, __wildcards__") == "__wildcards__, test, with" + +def test_with_weighted_wildcards(): + assert was_text_sort("test, (with:2), (__wildcards__:3)") == "(__wildcards__:3), test, (with:2)" + +# ASCII "{" is after all letters +def test_with_dynamic_prompts(): + assert was_text_sort("test, {with|dynamic|prompts}") == "test, {with|dynamic|prompts}" + +def test_with_weighted_dynamic_prompts(): + assert was_text_sort("(test:1.1), with, ({weighted|dynamic|prompts}:0.9)") == "(test:1.1), with, ({weighted|dynamic|prompts}:0.9)" + +def test_with_embeddings(): + assert was_text_sort("test, with, embedding:my_embed.pt") == "embedding:my_embed.pt, test, with" + +def test_with_lora(): + assert was_text_sort("test, with, lora:my_lora.safetensors") == "lora:my_lora.safetensors, test, with" + +def test_with_grouped_weights(): + assert was_text_sort("(test, with:1), (grouped, weights:2.1)") == "(grouped, weights:2.1), (test, with:1)" + +def test_with_nested_weights(): + assert was_text_sort("(test, (with:1.2):1.1), ((nested:1), weights:2)") == "((nested:1), weights:2), (test, (with:1.2):1.1)" \ No newline at end of file diff --git a/custom_nodes/was-node-suite-comfyui/tests/was_mock.py b/custom_nodes/was-node-suite-comfyui/tests/was_mock.py new file mode 100644 index 00000000..b5795ed9 --- /dev/null +++ b/custom_nodes/was-node-suite-comfyui/tests/was_mock.py @@ -0,0 +1,11 @@ +# TODO: In case anyone that knows how to set up PyTest correctly comes around, this file can be scrapped. +from pathlib import Path + +TEXT_TYPE = "STRING" + +CLASS_NAME = "WAS_Text_Sort" +class_string = f"class {CLASS_NAME}:" +exec(class_string + Path("../WAS_Node_Suite.py").read_text().split(class_string)[1].split("class ")[0]) + +def was_text_sort(text = "", separator = WAS_Text_Sort.INPUT_TYPES()["required"]["separator"][1]["default"]): + return WAS_Text_Sort().sort(text, separator)[0] diff --git a/custom_nodes/whiterabbit/.gitignore b/custom_nodes/whiterabbit/.gitignore new file mode 100644 index 00000000..1eb756ee --- /dev/null +++ b/custom_nodes/whiterabbit/.gitignore @@ -0,0 +1,36 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class + +vendor/ckpts/ +*.pth + +# Virtual envs +.venv/ +venv/ +env/ +ENV/ +.conda/ + +# Tool caches +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.pyre/ +.pytype/ +.tox/ +.nox/ +.cache/ + +# Logs / temp +*.log +logs/ +tmp/ +temp/ +*.tmp + +# IDE +.vscode/ +.idea/ +*.iml diff --git a/custom_nodes/whiterabbit/LICENSE b/custom_nodes/whiterabbit/LICENSE new file mode 100644 index 00000000..1468d07c --- /dev/null +++ b/custom_nodes/whiterabbit/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/custom_nodes/whiterabbit/LICENSES/MIT-ComfyUI-Frame-Interpolation.txt b/custom_nodes/whiterabbit/LICENSES/MIT-ComfyUI-Frame-Interpolation.txt new file mode 100644 index 00000000..098ab647 --- /dev/null +++ b/custom_nodes/whiterabbit/LICENSES/MIT-ComfyUI-Frame-Interpolation.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Fannovel16 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/whiterabbit/README_zh-CN.md b/custom_nodes/whiterabbit/README_zh-CN.md new file mode 100644 index 00000000..c543eaca --- /dev/null +++ b/custom_nodes/whiterabbit/README_zh-CN.md @@ -0,0 +1,118 @@ +# WhiteRabbitοΌšζŽŒζŽ§ζ—Άι—΄δΉ‹ζ΅ πŸ‡ +[English](readme.md) | **η€δ½“δΈ­ζ–‡** + +θΏ™ζ˜― **comfyui-WhiteRabbit**οΌŒδΈ€δΈͺδΈ“δΈΊεœ¨ ComfyUI δΈ­ε€„η†θ§†ι’‘θ€ŒθΎθ‘ηš„θŠ‚η‚ΉεŒ…γ€‚ + +ε…”ε­ηš„ζ‹Ώζ‰‹ε₯½ζˆζ˜―η©Ώζ’­ζ—Άι—΄οΌŒεΈδ½ εšε‡Ίζ— ηΌεΎͺηŽ―θ§†ι’‘γ€‚δ½†ε₯ΉεΈ¦ζ₯ηš„ε―δΈζ­’θΏ™δΊ›β€”β€”ι«˜θ΄¨ι‡ηš„δ»»ζ„εΈ§ηŽ‡ι‡ι‡‡ζ ·ε’ŒθΆ…εΏ«ηš„ε›ΎεƒηΌ©ζ”ΎδΉŸιƒ½εœ¨θΏ™δ»½β€œθŒΆδΌšβ€η€Όη›’ι‡ŒοΌ + +θ™½η„ΆθΏ™δΊ›θŠ‚η‚ΉδΈ­ζœ‰δΊ›ε½“η„ΆδΉŸθƒ½η”¨δΊŽε•εΌ ε›Ύη‰‡οΌŒδ½†εƒδ»¬ζ— δΈ€δΈζ˜―δ»₯ι«˜ζ•ˆηš„**批倄理**δΈΊζ ΈεΏƒθΎθ‘γ€‚θΏ™ζ„ε‘³η€ζ€§θƒ½ζ”Άη›ŠδΌšε±‚ε±‚ε εŠ οΌŒθ©δ½ εœ¨η‘¬δ»Άε…θΈηš„θŒƒε›΄ε†…ε°½ε―θƒ½εΏ«εœ°ε€„η†ζ•΄ζ΅θ§†ι’‘。 + + +## ε‰θ£… + +WhiteRabbit ζ”―ζŒδΈ€η§εΈƒε±€οΌš + +1) **ε€–ιƒ¨εŸΊη‘€εŒ…οΌˆε­˜εœ¨ζ—ΆδΌ˜ε…ˆοΌ‰**：`custom_nodes/comfyui-frame-interpolation/` +2) **ε†…η½ηš„εΊ”ζ€₯ε‰―ζœ¬οΌˆιšζœ¬ι‘Ήη›ζ‰“εŒ…οΌ‰**：`vendor/` + +**εΏ«ι€Ÿε‰θ£…οΌš** +1. ε°† **comfyui-WhiteRabbit** ζ–‡δ»Άε€Ήζ”Ύε…₯ `ComfyUI/custom_nodes/`。 +2. ε‰θ£…ζœ¬θŠ‚η‚Ήζ‰€ιœ€ηš„δΎθ΅–οΌš + ```bash + pip install -r requirements.txt + ``` + +**ε―ι€‰ι‘ΉοΌš** 你可δ»₯在 `custom_nodes/` η›ε½•δΈ­ε‰θ£… [ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)。WhiteRabbit 会θ‡ͺεŠ¨ζ£€ζ΅‹εˆ°εƒεΉΆε€η”¨ε…ΆδΈ­ηš„θ΅„ζΊγ€‚ε¦‚ζžœδ½ ε·²η»εœ¨η”¨εƒοΌŒθΏ™ε°€ε…Άζ–ΉδΎΏοΌŒε› δΈΊζ— ιœ€εŒζ—ΆδΏε­˜δΈ€δ»½ RIFE ζ¨‘εž‹γ€‚ + +### Python 依衖 + +ζœ¬θŠ‚η‚ΉδΎθ΅–δΊŽ ComfyUI ε·²ζδΎ›ηš„ζ ΈεΏƒεŒ…οΌˆε¦‚ `torch`、`torchvision`、`numpy`、`einops`、`pyyaml`οΌ‰γ€‚δ½ ηš„**θŠ‚η‚Ήζœ¬εœ°** `requirements.txt` δ»…ιœ€ζ–°ε’žοΌš + +``` +packaging +torchlanc +``` + +## θŠ‚η‚ΉδΈ€θ§ˆ + +θΏ™δΈͺθŠ‚η‚ΉεŒ…εΈδ½ θ§£ε†³θ§†ι’‘εˆ›δ½œδΈ­δΈ€δΊ›ζœ€ζ£˜ζ‰‹ηš„ι—ι’˜γ€‚ + +### ζ—Άι—΄ζ‰­ζ›²θ€…οΌˆTime BendersοΌ‰ + +θΏ™δΊ›θŠ‚η‚Ήι€šθΏ‡ **RIFE** ζ’εΈ§ζ¨‘εž‹εœ¨ζ—Άι—΄δΈŠε’žεˆ εΈ§γ€‚δΈΊδΊ†θŽ·εΎ—δΈ€η‚Ήη‚Ήι’ε€–ι€ŸεΊ¦οΌŒεƒδ»¬θ’«δΌ˜εŒ–δΈΊεεŒε·₯作，在倚 RIFE ε·₯δ½œζ΅δΈ­ηΌ“ε­˜ RIFE ζ¨‘εž‹οΌŒδ»₯θŽ·εΎ—ε°εΉ…ζ•ˆηŽ‡ζε‡γ€‚ + +- **RIFE VFI Interpolate by Multiple**οΌšεΈ§ζ’ε€Όηš„εŸΊη‘€ε·₯ε…·γ€‚ε°†εΈ§ζ•°δΉ˜δ»₯ 2x、4x η­‰οΌŒεƒδΌšη”Ÿζˆθ©δ½ ηš„θ§†ι’‘δΈζ»‘ζ΅η•…ηš„ζ–°εΈ§γ€‚ +- **RIFE VFI FPS Resample**οΌšζ—Άι—΄ζ—…θ‘Œε€§εΈˆγ€‚ζŠŠθ§†ι’‘θ½¬ζ’δΈΊζŒ‡εšη›ζ ‡εΈ§ηŽ‡οΌŒθ‡ͺεŠ¨ε€„η†θ‘₯εΈ§δΈŽδΈ’εΈ§γ€‚ε†…η½ε€šη§ι˜²ζ­’常见δΌͺε½±οΌˆε¦‚ι—ͺηƒοΌ‰ηš„ζŽͺζ–½οΌŒθΎ“ε‡Ίζ›΄εΉ²ε‡€γ€‚ +- **RIFE VFI Custom Timing**οΌšιœ€θ¦εŒε…¨ζŽŒζŽ§οΌŸδ»₯β€œε€–η§‘ηΊ§η²ΎεΊ¦β€ζ”Ύη½ζ―δΈ€εΈ§γ€‚ι€šθΏ‡ζδΎ›θ‡ͺεšδΉ‰ζ—ΆεΊεˆ—葨ζ₯εˆΆδ½œι€ŸεΊ¦ε‘ι“οΌŒζˆ–εͺεœ¨η‰Ήεšζ—Άεˆ»θΏ›θ‘ŒεΉ³ζ»‘倄理。 +- **RIFE Seam Timing Analyzer**：θ‡ͺεšδΉ‰ζ—ΆεΊθŠ‚η‚Ήηš„εŒηΎŽζ­ζ‘£γ€‚θ‡ͺ动θ‘η—无缝εΎͺηŽ―ηš„η²Ύη‘ζ—ΆεΊοΌŒη»™ε‡Ίδ½ ιœ€θ¦ηš„ CSV ζ•°ε€ΌοΌŒθ©θΏ‡ζΈ‘倩董无缝。 + +![resample_framerate](examples/resample_framerate.png) +> η€ΊδΎ‹οΌš**RIFE VFI FPS Resample** θŠ‚η‚Ήζ˜―β€œζ—Άι—΄ε€§εΈˆβ€οΌŒε―ε°†δ½ ηš„θ§†ι’‘ι‡ι‡‡ζ ·εˆ°ζ–°ηš„εΈ§ηŽ‡γ€‚θ‡ͺ己试试吧——附带ε·₯作桁！ + +### εΎͺ环倧师（Loop MastersοΌ‰ + +εšε‡ΊδΈ€δΈͺ无缝εΎͺηŽ―ηš„θ§†ι’‘εΈΈεƒθ§£θ°œγ€‚θΏ™δΊ›θŠ‚η‚ΉζŠŠεΌ€ε―εŒηΎŽγ€θΏžη»­εΎͺηŽ―ηš„ι’₯εŒ™δΊ€η»™δ½ γ€‚ + +- **Prepare Loop Frames**οΌšη¬¬δΈ€ζ­₯。θ―₯θŠ‚η‚ΉδΌšε€„η†ζ•΄ζ΅θ§†ι’‘δ»₯准倇εΎͺηŽ―β€œζŽ₯ηΌβ€οΌŒε°†ζœ€εŽδΈ€εΈ§δΈŽη¬¬δΈ€εΈ§ε•η‹¬ζ‰“ζˆδΈ€δΈͺζ‰Ήγ€‚θΏ™δΈ€ε°ε―ΉεΈ§ε°±ζ˜―δ½ ηš„ζ’εΈ§ε™¨εΌ€ε§‹θΏ‡ζΈ‘ζ‰€ιœ€ηš„δΈ€εˆ‡γ€‚ +- **Assemble Loop Frames**οΌšζœ€εŽδΈ€ε—ζ‹Όε›Ύγ€‚ζ’εΈ§ε™¨ζ–½ε±•ι­”ζ³•εŽοΌŒθ―₯θŠ‚η‚ΉδΌšζŠŠζ–°ηš„ζŽ₯ηΌεΈ§θΏ½εŠ εˆ°εŽŸθ§†ι’‘ηš„ζœ«ε°ΎοΌŒη»„θ£…ε‡ΊεŒζ•΄γ€θΏžη»­ηš„εΎͺηŽ―γ€‚ +- **Autocrop to Loop**οΌšεˆ«εœ¨εΈ§ηš„ζ£ζž—ι‡ŒθΏ·θ·―！这δΈͺθͺζ˜Žηš„θŠ‚η‚ΉδΌšζ™Ίθƒ½εˆ†ζžθ§†ι’‘οΌŒδ»Žζœ«ε°Ύζ‰Ύεˆ°ζœ€δ½³θ£ε‰ͺη‚ΉοΌŒη‘保εΎͺηŽ―ε°½ε―θƒ½ι‘Ίη•…γ€‚ +- **Trim Batch Ends**οΌšδΈ€δΈͺη”¨δΊŽδ»Žη‰‡ζ΅εΌ€ε€΄ζˆ–η»“ε°Ύθ£ζŽ‰ε›Ίεšζ•°ι‡εΈ§ηš„η€ζ΄ε·₯ε…·οΌŒι€‚εˆεŽ»ζŽ‰δΈιœ€θ¦ηš„εΌ€εœΊ/攢尾。 +- **Roll Frames**：εΎͺηŽ―εœ°ζ”Ήε˜δΈ€ζ‰Ήε›Ύεƒηš„ι‘ΊεΊγ€‚εœ¨εΎͺηŽ―εœΊζ™―δΈ­οΌŒθΏ™δΌšζ”Ήε˜δ½ ηš„εΎͺηŽ―δ»Žη¬¬ε‡ εΈ§εΌ€ε§‹γ€‚ +- **Unroll Frames**οΌšζ’€ι”€δΈŠι’θŠ‚η‚Ήηš„ζ“δ½œοΌ›δ½ ε―θƒ½δΌšδΈΊδΊ†ζŸδΈͺθΏ‡η¨‹οΌˆε¦‚ζ’εΈ§οΌ‰ε…ˆζ»šεŠ¨εΈ§οΌŒε†ζ’ε€εŽŸι‘ΊεΊγ€‚θ―₯θŠ‚η‚Ήζ”―ζŒθΎη½εΈ§δΉ˜ζ•°οΌŒδ»₯δΈŽδΉ‹ε‰ηš„ **RIFE VFI Interpolate by Multiple** 保持同ζ­₯。 + +![interpolate_loop_seam](examples/interpolate_loop_seam.png) +> η€ΊδΎ‹οΌšη”¨ **Prepare Loop Frames** β†’ **RIFE Seam Timing Analyzer** β†’ **RIFE VFI Custom Timing** β†’ **Assemble Loop Frames** ηΌεˆζ— ηΌεΎͺηŽ―γ€‚ζŠŠθΏ™εΌ  png δΈ’θΏ› ComfyUI δΊ²θ‡ͺ试驾！ + +![interpolate_loop_seam](examples/autocrop_to_loop.png) +> η€ΊδΎ‹οΌšζœ€ε₯½ηš„εΎͺ环就是你已经ζ‹₯ζœ‰ηš„ι‚£δΈͺ。**Autocrop to Loop** ι€šθΏ‡εˆ†ζžη‰‡ζ΅ε°Ύιƒ¨εΈ§δΉ‹ι—΄ηš„视觉ε·εΌ‚δΈŽζ—ΆεΊοΌŒεΈδ½ ζ‰Ύεˆ°ζœ€δ½³η»“ζŸεΈ§γ€‚ + +### εŽζœŸε€„η†οΌˆPost-ProcessingοΌ‰ + +θΏ™δΊ›θŠ‚η‚Ήζ˜―εΎ—εŠ›εŠ©ζ‰‹οΌ + +- **Batch Resize w/ Lanczos**οΌšεΏ«ι€Ÿγ€ζ­£η»Ÿγ€ε“θ΄¨δΈε¦₯协。这δΈͺ CUDA εŠ ι€Ÿηš„θŠ‚η‚Ήδ½Ώη”¨δΈΊ PyTorch ηΌ–ε†™ηš„ι«˜θ΄¨ι‡ Lanczos η—法 [TorchLanc](https://github.com/Artificial-Sweetener/TorchLanc) ζ₯ζ‰Ήι‡ηΌ©ζ”Ύε›ΎεƒοΌˆε½“η„ΆδΉŸζ”―ζŒε•εΌ οΌ‰γ€‚εƒη›Έζ―” CPU ζ–Ήζ‘ˆοΌˆε¦‚ Pillow θ‡ͺεΈ¦ηš„ LanczosοΌ‰ι€ŸεΊ¦ζ˜Ύθ‘—ζ›΄εΏ«οΌŒζœ€ι«˜ε―θΎΎηΊ¦ *10Γ—* 提升。 +- **Upscale w/ Model (Advanced)**：ComfyUI θ‡ͺεΈ¦ β€œUpscale Image (Using Model)” ηš„θΏ›ι˜Άη‰ˆζœ¬οΌŒη›΄ζŽ₯ζš΄ιœ²ζ‰Ήε€§ε°δΈŽεˆ‡η‰‡η­‰ε‚ζ•°γ€‚ε¦‚ζžœζ Ήζδ½ ηš„ζœΊε™¨θΏ›θ‘Œθ°ƒδΌ˜οΌŒζ”Ύε€§ι€ŸεΊ¦θƒ½ζ˜Ύθ‘—ζε‡γ€‚ +- **Pixel Hold**οΌšι€šθΏ‡ζŠ‘εˆΆη”±θ§†ι’‘ζ‰©ζ•£ζˆ–εŽ‹ηΌ©ι€ ζˆηš„ε°εΉ…ζ³’εŠ¨οΌŒε‡ε°‘θ§†ι’‘ι—ͺηƒεΉΆζΈ…η†η”»ι’δΈ­ηš„ι™ζ€εŒΊεŸŸγ€‚δΉŸε―ε°†θΎ“ε…₯ε›Ύεƒδ½œδΈΊεŸΊε‡†οΌŒε…·ε€‡δΈ€εšηš„εˆ›δ½œζ½œεŠ›γ€‚ +- **Watermark**οΌšζ”―ζŒε•ε›ΎδΈŽζ‰Ήι‡γ€‚ιžεΈΈεΏ«ι€ŸοΌŒε°€ε…Άζ˜―δΈŽδΈ“δΈšηΌ–θΎ‘ε·₯ε…·δΈ­εšεŒη±»ζ“δ½œη›Έζ―”γ€‚ + +![resize](examples/resize.png) +> η€ΊδΎ‹οΌšδ½Ώη”¨ **Batch Resize w/ Lanczos** εΏ«ι€ŸηΌ©ζ”Ύε›Ύεƒγ€‚ε·²ι™„ε·₯作桁！ + +![resize_with_model_to_target](examples/resize_with_model_to_target.png) +> η€ΊδΎ‹οΌšε°† **Upscale w/ Model (Advanced)** 与 **Batch Resize w/ Lanczos** ι…εˆδ½Ώη”¨οΌŒθΎΎεˆ°η‰Ήεšη›ζ ‡ε°Ίε―Έγ€‚ε›ΎδΈ­ε·²ι™„εΈ¦ε·₯δ½œζ΅γ€‚ + +![watermark](examples/watermark.png) +> η€ΊδΎ‹οΌšη”¨η΅ζ΄»ηš„ι…η½ι€‰ι‘ΉδΈΊζ―δΈ€εΈ§εΏ«ι€Ÿζ·»εŠ ζ°΄ε°γ€‚ε·²ι™„ε·₯δ½œζ΅γ€‚ + +## θΈε―δΈŽθ‡΄θ°’ +- **ι‘Ήη›θΈε―：** GNU Affero General Public License v3.0（**AGPL-3.0**οΌ‰γ€‚θ―·ι˜…θ―»ζœ¬δ»“εΊ“ε†…εŒζ•΄ηš„ [LICENSE](LICENSE)！AGPL-3.0 是强 Copyleft θΈε―γ€‚ε¦‚ζžœδ½ εˆ†ε‘ζœ¬θ½―δ»ΆοΌŒδ½ εΏ…ι‘»ζδΎ›ε…Άε―ΉεΊ”ηš„ζΊδ»£η οΌ›ε¦‚ζžœδ½ θ©η”¨ζˆ·ι€šθΏ‡η½‘η»œδΈŽδΏζ”ΉθΏ‡ηš„η‰ˆζœ¬δΊ€δΊ’οΌŒδ½ δΉŸεΏ…ι‘»ε‘δ»–δ»¬ζδΎ›θ―₯δΏζ”Ήη‰ˆζœ¬ηš„对应源代码。 + +- **依衖θΈε―（MITοΌ‰οΌš** δΈΊδΊ†ε―ι ζ€§οΌŒζœ¬ι‘Ήη›**ε†…η½οΌˆvendorοΌ‰**δΊ† **[ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)** ηš„ζžε°‘ι‡η»„δ»Άγ€‚θΏ™δΊ›ζ–‡δ»Άι΅εΎͺ MIT θΈε―οΌŒη”± **[Fannovel16](https://github.com/Fannovel16)** δΈŽε…Ά**[θ΄‘ηŒθ€…](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation/graphs/contributors)** ζŽˆζƒοΌ›ε‚θ§ `LICENSES/MIT-ComfyUI-Frame-Interpolation.txt`： + - `vendor/vfi_utils.py` + - `vendor/rife/__init__.py` + - `vendor/rife/rife_arch.py` +- ε¦ε€–οΌŒζœ¬ι‘Ήη›δΉŸεœ¨ [`interpolation.py`](interpolation.py) δΈ­ε€Ÿι‰΄εΉΆζ”ΉηΌ–δΊ† **[ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)** ηš„ε°‘ι‡ε†…εΉγ€‚ +- **Batch Resize w/ Lanczos** ηš„ UI θΎθ‘ε—εˆ°δΊ† [Kijai](https://github.com/kijai/) δΌ˜η§€ι‘Ήη› [KJNodes](thub.com/kijai/ComfyUI-KJNodes) δΈ­η›ΈδΌΌθŠ‚η‚Ήηš„ε―ε‘γ€‚ + +### 研穢引用 + +ζœ¬θŠ‚η‚ΉεŒ…εœ¨θ§†ι’‘εΈ§ζ’ε€ΌδΈŠδ½Ώη”¨ **RIFE（IFNetοΌ‰**。你可δ»₯在[θΏ™ι‡Œ](https://ar5iv.labs.arxiv.org/html/2011.06294)ι˜…θ―»θΊζ–‡γ€‚ + +```bibtex +@inproceedings{huang2022rife, + title={Real-Time Intermediate Flow Estimation for Video Frame Interpolation}, + author={Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang}, + booktitle={European Conference on Computer Vision (ECCV)}, + year={2022} +} +``` + +--- + +## ζ₯θ‡ͺ开发者 ❀️ + +εΈŒζœ›δ½ δ½Ώη”¨θΏ™δΊ›θŠ‚η‚Ήζ—Άηš„εΏ«δΉοΌŒδΈδΊšδΊŽζˆ‘ζŠŠεƒδ»¬ζ‹Όεˆ°δΈ€θ΅·ζ—ΆοΌ + +- **θ―·ζˆ‘ε–ζ―ε’–ε•‘**：你可δ»₯εœ¨ζˆ‘ηš„ [Ko-fi 鑡青](https://ko-fi.com/artificial_sweetener) ζ”―ζŒζ›΄ε€šη±»δΌΌι‘Ήη›γ€‚ +- **ζˆ‘ηš„η½‘η«™δΈŽη€Ύεͺ’**：欒迎在 [artificialsweetener.ai](https://artificialsweetener.ai) ζŸ₯ηœ‹ζˆ‘ηš„θ‰Ίζœ―δ½œε“γ€θ―—ζ­ŒδΈŽεΌ€ε‘εŠ¨ζ€γ€‚ +- **ε¦‚ζžœδ½ ε–œζ¬’θΏ™δΈͺι‘Ήη›**，在 GitHub δΈŠη‚ΉδΈ€ι’— Star 会θ©ζˆ‘ιžεΈΈεΌ€εΏƒοΌοΌ ⭐ diff --git a/custom_nodes/whiterabbit/__init__.py b/custom_nodes/whiterabbit/__init__.py new file mode 100644 index 00000000..d6555526 --- /dev/null +++ b/custom_nodes/whiterabbit/__init__.py @@ -0,0 +1,56 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# SPDX-FileCopyrightText: 2025 ArtificialSweetener + +from .interpolation import ( + RIFE_FPS_Resample, + RIFE_SeamTimingAnalyzer, + RIFE_VFI_Advanced, + RIFE_VFI_Opt, +) +from .noise_control import PixelHold +from .post_process import BatchWatermarkSingle +from .scaling import BatchResizeWithLanczos, UpscaleWithModelAdvanced +from .video_loop import ( + AssembleLoopFrames, + AutocropToLoop, + PrepareLoopFrames, + RollFrames, + TrimBatchEnds, + UnrollFrames, +) + +NODE_CLASS_MAPPINGS = { + "PrepareLoopFrames": PrepareLoopFrames, + "AssembleLoopFrames": AssembleLoopFrames, + "RollFrames": RollFrames, + "UnrollFrames": UnrollFrames, + "AutocropToLoop": AutocropToLoop, + "TrimBatchEnds": TrimBatchEnds, + "RIFE_VFI_Opt": RIFE_VFI_Opt, + "RIFE_VFI_Advanced": RIFE_VFI_Advanced, + "RIFE_SeamTimingAnalyzer": RIFE_SeamTimingAnalyzer, + "RIFE_FPS_Resample": RIFE_FPS_Resample, + "PixelHold": PixelHold, + "UpscaleWithModelAdvanced": UpscaleWithModelAdvanced, + "BatchResizeWithLanczos": BatchResizeWithLanczos, + "BatchWatermarkSingle": BatchWatermarkSingle, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PrepareLoopFrames": "πŸ‡ Prepare Loop Frames", + "AssembleLoopFrames": "πŸ‡ Assemble Loop Frames", + "RollFrames": "πŸ‡ Roll Frames", + "UnrollFrames": "πŸ‡ Unroll Frames", + "AutocropToLoop": "πŸ‡ Autocrop to Loop", + "TrimBatchEnds": "πŸ‡ Trim Batch Ends", + "RIFE_VFI_Opt": "πŸ‡ RIFE VFI Interpolate by Multiple", + "RIFE_VFI_Advanced": "πŸ‡ RIFE VFI Custom Timing", + "RIFE_SeamTimingAnalyzer": "πŸ‡ RIFE Seam Timing Analyzer", + "RIFE_FPS_Resample": "πŸ‡ RIFE VFI FPS Resample", + "PixelHold": "πŸ‡ Pixel Hold", + "UpscaleWithModelAdvanced": "πŸ‡ Upscale w/ Model (Advanced)", + "BatchResizeWithLanczos": "πŸ‡ Batch Resize w/ Lanczos", + "BatchWatermarkSingle": "πŸ‡ Watermark", +} + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] diff --git a/custom_nodes/whiterabbit/examples/autocrop_to_loop.png b/custom_nodes/whiterabbit/examples/autocrop_to_loop.png new file mode 100644 index 00000000..6d2d5683 Binary files /dev/null and b/custom_nodes/whiterabbit/examples/autocrop_to_loop.png differ diff --git a/custom_nodes/whiterabbit/examples/interpolate_loop_seam.png b/custom_nodes/whiterabbit/examples/interpolate_loop_seam.png new file mode 100644 index 00000000..643e75ef Binary files /dev/null and b/custom_nodes/whiterabbit/examples/interpolate_loop_seam.png differ diff --git a/custom_nodes/whiterabbit/examples/resample_framerate.png b/custom_nodes/whiterabbit/examples/resample_framerate.png new file mode 100644 index 00000000..7c69e5d6 Binary files /dev/null and b/custom_nodes/whiterabbit/examples/resample_framerate.png differ diff --git a/custom_nodes/whiterabbit/examples/resize.png b/custom_nodes/whiterabbit/examples/resize.png new file mode 100644 index 00000000..0c9ad9f0 Binary files /dev/null and b/custom_nodes/whiterabbit/examples/resize.png differ diff --git a/custom_nodes/whiterabbit/examples/resize_with_model_to_target.png b/custom_nodes/whiterabbit/examples/resize_with_model_to_target.png new file mode 100644 index 00000000..5fa743f3 Binary files /dev/null and b/custom_nodes/whiterabbit/examples/resize_with_model_to_target.png differ diff --git a/custom_nodes/whiterabbit/examples/watermark.png b/custom_nodes/whiterabbit/examples/watermark.png new file mode 100644 index 00000000..5df77792 Binary files /dev/null and b/custom_nodes/whiterabbit/examples/watermark.png differ diff --git a/custom_nodes/whiterabbit/interpolation.py b/custom_nodes/whiterabbit/interpolation.py new file mode 100644 index 00000000..4a7db7f1 --- /dev/null +++ b/custom_nodes/whiterabbit/interpolation.py @@ -0,0 +1,1168 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# SPDX-FileCopyrightText: 2025 ArtificialSweetener +# +# Portions adapted from "ComfyUI-Frame-Interpolation" (MIT License) +# Copyright (c) 2023–2025 Fannovel16 and contributors +# See LICENSES/MIT-ComfyUI-Frame-Interpolation.txt for the full text. + +# Imports for: RIFE_FPS_Resample, RIFE_SeamTimingAnalyzer_Advanced, RIFE_VFI_Advanced +import sys +from pathlib import Path + +# Point Python at the sibling base pack +BASE = Path(__file__).resolve().parent.parent / "comfyui-frame-interpolation" +VFI_MODELS = BASE / "vfi_models" + +# Prefer external comfyui-frame-interpolation; fall back to vendored /vendor/ inside this repo +p_models = str( + VFI_MODELS +) # external: .../custom_nodes/comfyui-frame-interpolation/vfi_models +p_base = str(BASE) # external: .../custom_nodes/comfyui-frame-interpolation +p_vendor = str( + Path(__file__).resolve().parent / "vendor" +) # <-- fix: repo-local vendor/ + +if VFI_MODELS.is_dir() and p_models not in sys.path: + sys.path.insert(0, p_models) # exposes external 'rife' +if BASE.is_dir() and p_base not in sys.path: + sys.path.insert(0, p_base) # exposes external 'vfi_utils' +if p_vendor not in sys.path: + sys.path.append(p_vendor) # fallback: vendored 'rife' and 'vfi_utils' + + +import math +import re +import typing +from fractions import Fraction + +# === Cached RIFE model loader (shared by all nodes) === +from functools import lru_cache + +import torch +import torch.nn.functional as F + +# --- Comfy + stdlib used by the three nodes --- +from comfy.model_management import get_torch_device +from comfy.utils import ProgressBar as _ComfyProgressBar +from rife import CKPT_NAME_VER_DICT, MODEL_TYPE + +# --- imports from the base pack --- +from vfi_utils import ( + InterpolationStateList, + generic_frame_loop, + load_file_from_github_release, + postprocess_frames, + preprocess_frames, +) + + +@lru_cache(maxsize=4) +def _get_rife_model(ckpt_name: str): + import torch + from comfy.model_management import get_torch_device + from rife import CKPT_NAME_VER_DICT, MODEL_TYPE + from rife.rife_arch import IFNet + from vfi_utils import load_file_from_github_release + + model_path = load_file_from_github_release(MODEL_TYPE, ckpt_name) + arch_ver = CKPT_NAME_VER_DICT[ckpt_name] + m = IFNet(arch_ver=arch_ver) + sd = torch.load(model_path, map_location="cpu") + m.load_state_dict(sd, strict=True) + m.eval() + return m.to(get_torch_device()).to(memory_format=torch.channels_last) + + +# ---------- shared helpers (for all nodes) ---------- +def _scale_list(scale_factor: float): + s = float(scale_factor) + return [8.0 / s, 4.0 / s, 2.0 / s, 1.0 / s] + + +def _prep_frames(frames: torch.Tensor) -> torch.Tensor: + x = preprocess_frames(frames) + if x.device.type == "cpu" and torch.cuda.is_available(): + x = x.pin_memory() + return x + + +def _progress(total_ticks: int): + return _ComfyProgressBar(int(max(1, total_ticks))) + + +def _count_synth(frames: torch.Tensor, multiplier: int) -> int: + N = int(frames.shape[0]) + m = max(0, int(multiplier)) + return max(0, N - 1) * m + + +def _make_rife_callback(): + def _cb(frame_0, frame_1, timestep, model, scale_list, ensemble, t_mapper): + if torch.is_tensor(timestep): + t_scalar = float(timestep.reshape(-1)[0].item()) + else: + t_scalar = float(timestep) + t = t_mapper(t_scalar) if t_mapper is not None else t_scalar + # fast_mode is a no-op for 4.7/4.9; pass False + return model(frame_0, frame_1, t, scale_list, False, ensemble) + + return _cb + + +def _with_progress(cb, pbar): + if pbar is None: + return cb + + def _wrapped(*args, **kwargs): + y = cb(*args, **kwargs) + pbar.update(1) + return y + + return _wrapped + + +# ---------------------------------------------------- + + +class RIFE_VFI_Opt: + DESCRIPTION = ( + "Interpolate a clip by a chosen multiple using RIFE 4.7/4.9 β€” inserts evenly spaced in-between frames " + "between every pair (e.g., Γ—2 adds 1 frame per pair)." + ) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": ( + ["rife47.pth", "rife49.pth"], + { + "default": "rife47.pth", + "tooltip": "Choose the RIFE 4.7 or 4.9 model file from the base pack.", + }, + ), + "frames": ( + "IMAGE", + {"tooltip": "Your input clip: one image per frame."}, + ), + "multiplier": ( + "INT", + { + "default": 2, + "min": 1, + "tooltip": "Adds extra frames to smooth motion: 2 adds 1 new frame per pair; 4 adds 3.", + }, + ), + "scale_factor": ( + [0.25, 0.5, 1.0, 2.0, 4.0], + { + "default": 1.0, + "tooltip": "Quality vs speed. 1.0 recommended. Lower = faster/softer; higher = sharper/slower.", + }, + ), + "ensemble": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Blend forward & backward predictions to reduce artifacts (slower).", + }, + ), + "clear_cache_after_n_frames": ( + "INT", + { + "default": 10, + "min": 0, + "max": 1000, + "tooltip": "Free up GPU memory every N generated frames (advanced). Set 0 to never.", + }, + ), + }, + "optional": { + "optional_interpolation_states": ( + "INTERPOLATION_STATES", + { + "tooltip": "Don’t create in-between frames for selected frame pairs (e.g., scene cuts). Timing stays the same." + }, + ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "vfi" + CATEGORY = "video utils" + + def vfi( + self, + ckpt_name: typing.AnyStr, + frames: torch.Tensor, + multiplier: int = 2, + scale_factor: float = 1.0, + ensemble: bool = True, + clear_cache_after_n_frames: int = 10, + optional_interpolation_states: InterpolationStateList = None, + **kwargs, + ): + model = _get_rife_model(ckpt_name) + + frames = _prep_frames(frames) + + m_effective = int(multiplier) + if m_effective <= 1: + return (postprocess_frames(frames),) + + scale_list = _scale_list(scale_factor) + total_synth = _count_synth(frames, m_effective) + pbar = _progress(total_synth) + cb = _make_rife_callback() + cbp = _with_progress(cb, pbar) + + args = [model, scale_list, ensemble, None] # t_mapper=None β†’ identity + + with torch.inference_mode(): + out = postprocess_frames( + generic_frame_loop( + type(self).__name__, + frames, + clear_cache_after_n_frames, + m_effective, + cbp, + *args, + interpolation_states=optional_interpolation_states, + dtype=torch.float32, + ) + ) + return (out,) + + +class RIFE_VFI_Advanced: + """ + Advanced RIFE node exposing timestep controls (by multiple, custom t-schedules). + """ + + DESCRIPTION = ( + "Custom timing for RIFE 4.7/4.9 β€” still β€œinterpolate by multiple,” but you control where the in-betweens land " + "(ease in/out, clamps, or your own t-list)." + ) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": ( + ["rife47.pth", "rife49.pth"], + { + "default": "rife47.pth", + "tooltip": "Choose the RIFE 4.7 or 4.9 model file from the base pack.", + }, + ), + "frames": ( + "IMAGE", + {"tooltip": "Your input clip: one image per frame."}, + ), + "multiplier": ( + "INT", + { + "default": 2, + "min": 0, + "tooltip": "How many new frames to create between each pair. 0 = passthrough (no new frames).", + }, + ), + "t_mode": ( + [ + "linear", + "gamma_in", + "gamma_out", + "gamma_in_out", + "bounded_linear", + "custom_list", + ], + { + "default": "linear", + "tooltip": "How to spread the new frames over time: straight line, ease in/out, limit the range, or provide your own list.", + }, + ), + "t_gamma": ( + "FLOAT", + { + "default": 1.0, + "min": 0.05, + "max": 10.0, + "step": 0.05, + "tooltip": "Easing strength for gamma modes. Higher = more easing.", + }, + ), + "t_min": ( + "FLOAT", + { + "default": 0.0, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "tooltip": "Earliest allowed position between the two frames (0 = exactly the first frame). Use with bounded_linear.", + }, + ), + "t_max": ( + "FLOAT", + { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "tooltip": "Latest allowed position between the two frames (1 = exactly the next frame). Use with bounded_linear.", + }, + ), + "scale_factor": ( + [0.25, 0.5, 1.0, 2.0, 4.0], + { + "default": 1.0, + "tooltip": "Quality vs speed. 1.0 recommended. Lower = faster/softer; higher = sharper/slower.", + }, + ), + "ensemble": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Blend forward & backward predictions to reduce artifacts (slower).", + }, + ), + "clear_cache_after_n_frames": ( + "INT", + { + "default": 10, + "min": 0, + "max": 1000, + "tooltip": "Free up GPU memory every N generated frames (advanced). Set 0 to never.", + }, + ), + }, + "optional": { + "custom_t_list_csv": ( + "STRING", + { + "default": "", + "tooltip": "Exact positions between the two frames (0–1), comma-separated, e.g. 0.18,0.41,0.66. Overrides the schedule.", + }, + ), + "optional_interpolation_states": ( + "INTERPOLATION_STATES", + { + "tooltip": "Don’t create in-between frames for selected frame pairs (e.g., scene cuts). Timing stays the same." + }, + ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "vfi_advanced" + CATEGORY = "video utils" + + def _map_t( + self, t_scalar, t_mode, t_gamma, t_min, t_max, custom_sorted, m_effective + ): + t = max(0.0, min(1.0, float(t_scalar))) + if t_mode == "custom_list" and custom_sorted: + idx = int(round(t * (m_effective + 1))) - 1 + idx = max(0, min(len(custom_sorted) - 1, idx)) + return float(custom_sorted[idx]) + + if t_mode == "gamma_in": + t = t ** max(1e-6, t_gamma) + elif t_mode == "gamma_out": + t = 1.0 - (1.0 - t) ** max(1e-6, t_gamma) + elif t_mode == "gamma_in_out": + g = max(1e-6, t_gamma) + if t < 0.5: + t = 0.5 * (2.0 * t) ** g + else: + t = 1.0 - 0.5 * (2.0 * (1.0 - t)) ** g + return max(0.0, min(1.0, t_min + (t_max - t_min) * t)) + + def vfi_advanced( + self, + ckpt_name, + frames, + clear_cache_after_n_frames=10, + multiplier=2, + ensemble=True, + scale_factor=1.0, + t_mode="linear", + t_gamma=1.0, + t_min=0.0, + t_max=1.0, + custom_t_list_csv="", + optional_interpolation_states=None, + **kwargs, + ): + model = _get_rife_model(ckpt_name) + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype + + frames = _prep_frames(frames) + + custom_sorted = None + if custom_t_list_csv and t_mode == "custom_list": + toks = [x for x in re.split(r"[,\s]+", custom_t_list_csv.strip()) if x] + custom_sorted = sorted([max(0.0, min(1.0, float(x))) for x in toks]) + + m_effective = int(multiplier) + if m_effective <= 0: + return (postprocess_frames(frames),) + + def _adv_t_mapper(t_scalar: float) -> float: + return self._map_t( + t_scalar, t_mode, t_gamma, t_min, t_max, custom_sorted, m_effective + ) + + scale_list = _scale_list(scale_factor) + total_synth = _count_synth(frames, m_effective) + pbar = _progress(total_synth) + cb = _make_rife_callback() + cbp = _with_progress(cb, pbar) + + args = [model, scale_list, ensemble, _adv_t_mapper] + + with torch.inference_mode(): + out = postprocess_frames( + generic_frame_loop( + type(self).__name__, + frames, + clear_cache_after_n_frames, + m_effective, + cbp, + *args, + interpolation_states=optional_interpolation_states, + dtype=torch.float32, + ) + ) + return (out,) + + +class RIFE_FPS_Resample: + DESCRIPTION = ( + "Convert a clip from one FPS to another using RIFE 4.7/4.9. Non-integer changes synthesize in-betweens; " + "exact integer downscales just decimate. Includes optional stabilizers to reduce flicker and protect edges." + ) + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "ckpt_name": ( + ["rife47.pth", "rife49.pth"], + { + "default": "rife47.pth", + "tooltip": "Choose the RIFE 4.7 or 4.9 model file from the base pack.", + }, + ), + "frames": ( + "IMAGE", + {"tooltip": "Your input clip: one image per frame."}, + ), + "fps_in": ( + "FLOAT", + { + "default": 24.0, + "min": 1e-6, + "max": 1000.0, + "step": 0.01, + "tooltip": "Current frame rate of your clip (frames per second).", + }, + ), + "fps_out": ( + "FLOAT", + { + "default": 60.0, + "min": 1e-6, + "max": 2000.0, + "step": 0.01, + "tooltip": "Target frame rate you want (frames per second).", + }, + ), + "scale_factor": ( + [0.25, 0.5, 1.0, 2.0, 4.0], + { + "default": 1.0, + "tooltip": "Quality vs speed. 1.0 recommended. Lower = faster/softer; higher = sharper/slower.", + }, + ), + "ensemble": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Blend forward & backward predictions to reduce artifacts (slower).", + }, + ), + "linearize": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Work in linear light for more accurate brightness and gradients (slower).", + }, + ), + "lf_guardrail": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Keep overall brightness and gradients close to the originals to reduce flicker.", + }, + ), + "lf_sigma": ( + "FLOAT", + { + "default": 13.0, + "min": 0.0, + "max": 64.0, + "step": 0.5, + "tooltip": "How strong the low-frequency smoothing is. Higher = smoother changes.", + }, + ), + "source_pair_match": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Match exposure and contrast to the source pair to reduce flicker.", + }, + ), + "match_a_cap": ( + "FLOAT", + { + "default": 0.02, + "min": 0.0, + "max": 0.2, + "step": 0.001, + "tooltip": "Maximum change allowed for exposure scale.", + }, + ), + "match_b_cap": ( + "FLOAT", + { + "default": 2.0 / 255.0, + "min": 0.0, + "max": 0.1, + "step": 0.0005, + "tooltip": "Maximum change allowed for brightness offset.", + }, + ), + "edge_band_lock": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Protect sharp edges: near edges, mix in more of the nearest real frame to avoid smearing.", + }, + ), + "tau_low": ( + "FLOAT", + { + "default": 1.5 / 255.0, + "min": 0.0, + "max": 0.25, + "step": 0.0005, + "tooltip": "Edge sensitivity: lower threshold (smaller finds more edges).", + }, + ), + "tau_high": ( + "FLOAT", + { + "default": 6.0 / 255.0, + "min": 0.0, + "max": 0.5, + "step": 0.0005, + "tooltip": "Edge sensitivity: higher threshold (larger finds only strong edges).", + }, + ), + "band_radius": ( + "INT", + { + "default": 4, + "min": 0, + "max": 64, + "tooltip": "Width of the edge protection band (pixels).", + }, + ), + "band_soft_sigma": ( + "FLOAT", + { + "default": 2.0, + "min": 0.0, + "max": 16.0, + "step": 0.5, + "tooltip": "Soften the edge band. Higher = smoother.", + }, + ), + "clear_cache_after_n_frames": ( + "INT", + { + "default": 10, + "min": 1, + "max": 1000, + "tooltip": "Free up GPU memory every N output frames (advanced). Set 0 to never.", + }, + ), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "resample" + CATEGORY = "video utils" + + @staticmethod + def _srgb_to_linear(x): + import torch + + return torch.where( + x <= 0.04045, x / 12.92, ((x + 0.055) / 1.055).clamp(min=0) ** 2.4 + ) + + @staticmethod + def _linear_to_srgb(x): + import torch + + return torch.where( + x <= 0.0031308, 12.92 * x, 1.055 * x.clamp(min=0) ** (1.0 / 2.4) - 0.055 + ) + + @staticmethod + def _gaussian_kernel1d(sigma, radius): + import torch + + if radius <= 0: + k = torch.tensor([1.0], dtype=torch.float32) + return k / k.sum() + xs = torch.arange(-radius, radius + 1, dtype=torch.float32) + k = torch.exp(-(xs * xs) / (2.0 * sigma * sigma)) + return k / k.sum() + + _GK_CACHE = {} # (float(sigma), int(radius), int(C)) -> (kH, kW) + + @staticmethod + def _gaussian_blur_nhwc(x, sigma): + if sigma <= 0: + return x + + N, H, W, C = x.shape + max_radius = max(0, min(H, W) // 2 - 1) + radius = min(int(math.ceil(3.0 * sigma)), max_radius) + if radius <= 0: + return x + + key = (float(sigma), int(radius), int(C)) + k = RIFE_FPS_Resample._GK_CACHE.get(key) + if k is None: + g = RIFE_FPS_Resample._gaussian_kernel1d(sigma, radius) + kH = g.view(1, 1, -1, 1).repeat(C, 1, 1, 1) + kW = g.view(1, 1, 1, -1).repeat(C, 1, 1, 1) + RIFE_FPS_Resample._GK_CACHE[key] = (kH, kW) + k = (kH, kW) + kH, kW = k + + x_nchw = x.permute(0, 3, 1, 2).contiguous() + x1 = F.pad(x_nchw, (0, 0, radius, radius), mode="reflect") + x1 = F.conv2d(x1, kH, groups=C) + x2 = F.pad(x1, (radius, radius, 0, 0), mode="reflect") + x2 = F.conv2d(x2, kW, groups=C) + return x2.permute(0, 2, 3, 1).contiguous() + + @staticmethod + def _luma_linear(x_lin): + import torch + + r, g, b = x_lin[..., 0], x_lin[..., 1], x_lin[..., 2] + return 0.2126 * r + 0.7152 * g + 0.0722 * b + + @staticmethod + def _exposure_match_ab(out_lin, tgt_lin, a_cap, b_cap): + import torch + + y_o = RIFE_FPS_Resample._luma_linear(out_lin).flatten() + y_t = RIFE_FPS_Resample._luma_linear(tgt_lin).flatten() + p = torch.tensor([0.05, 0.5, 0.95], dtype=torch.float32) + p5_o, p50_o, p95_o = torch.quantile(y_o, p) + p5_t, p50_t, p95_t = torch.quantile(y_t, p) + eps = 1e-6 + a = ((p95_t - p5_t) / (p95_o - p5_o + eps)).item() + a = max(1.0 - a_cap, min(1.0 + a_cap, a)) + b = (p50_t - a * p50_o).item() + b = max(-b_cap, min(b_cap, b)) + return a, b + + @staticmethod + def _dilate_mask_nhwc(mask01, radius): + if radius <= 0: + return mask01 + x = mask01.permute(0, 3, 1, 2) + x = F.pad(x, (radius, radius, radius, radius), mode="replicate") + x = F.max_pool2d(x, kernel_size=2 * radius + 1, stride=1) + return x.permute(0, 2, 3, 1) + + @torch.inference_mode() + def resample( + self, + ckpt_name, + fps_in, + fps_out, + frames, + scale_factor=1.0, + ensemble=True, + linearize=False, + lf_guardrail=False, + lf_sigma=13.0, + source_pair_match=False, + match_a_cap=0.02, + match_b_cap=2.0 / 255.0, + edge_band_lock=False, + tau_low=1.5 / 255.0, + tau_high=6.0 / 255.0, + band_radius=4, + band_soft_sigma=2.0, + clear_cache_after_n_frames=10, + **kwargs, + ): + + if fps_in <= 0 or fps_out <= 0: + raise ValueError("fps_in and fps_out must be > 0") + + F_in = Fraction(str(float(fps_in))) + F_out = Fraction(str(float(fps_out))) + same_rate = F_in == F_out + ratio = F_in / F_out + integer_downscale = ratio.denominator == 1 and ratio.numerator > 1 + + if same_rate: + return (frames,) + if integer_downscale: + step = int(ratio.numerator) + return (frames[::step].contiguous(),) + + model = _get_rife_model(ckpt_name) + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype + + x_nhwc = _prep_frames(frames) + + n_in = int(x_nhwc.shape[0]) + if n_in <= 1: + return (postprocess_frames(x_nhwc[:1]),) + + K_max = int(Fraction(n_in - 1) * F_out / F_in) + n_out = K_max + 1 + scale_list = _scale_list(scale_factor) + + def to_nchw_on_device(idx: int): + f = x_nhwc[idx : idx + 1].permute(0, 3, 1, 2).contiguous() + return f.to( + device=device, + dtype=dtype, + non_blocking=True, + memory_format=torch.channels_last, + ) + + cache = {} + + def synth(i: int, t_local: float): + if i not in cache: + cache[i] = to_nchw_on_device(i) + if (i + 1) not in cache: + cache[i + 1] = to_nchw_on_device(i + 1) + # fast_mode is a no-op for 4.7/4.9; pass False explicitly + y = model( + cache[i], cache[i + 1], float(t_local), scale_list, False, ensemble + ) + return y.permute(0, 2, 3, 1).contiguous().detach().cpu().to(torch.float32) + + to_lin = self._srgb_to_linear if linearize else (lambda z: z) + to_srgb = self._linear_to_srgb if linearize else (lambda z: z) + blur = self._gaussian_blur_nhwc + + y0 = x_nhwc[0:1] + H, W, C = int(y0.shape[1]), int(y0.shape[2]), int(y0.shape[3]) + y_buf = torch.empty((n_out, H, W, C), dtype=torch.float32, device="cpu") + + def _write(idx: int, frame_nhwc_cpu: torch.Tensor): + y_buf[idx : idx + 1].copy_(frame_nhwc_cpu) + + num = F_in.numerator * F_out.denominator + den = F_in.denominator * F_out.numerator + acc = 0 + frames_since_clear = 0 + + pbar = _progress(int(n_out)) + + for k in range(n_out): + pbar.update(1) + i = acc // den + + frac_num = acc % den + + if i >= (n_in - 1): + _write(k, x_nhwc[n_in - 1 : n_in]) + else: + if frac_num == 0: + _write(k, x_nhwc[i : i + 1]) + else: + frac = float(frac_num) / den + y = synth(i, frac) + + if linearize or lf_guardrail or source_pair_match or edge_band_lock: + A = x_nhwc[i : i + 1] + B = x_nhwc[i + 1 : i + 2] + y_lin, A_lin, B_lin = to_lin(y), to_lin(A), to_lin(B) + + if lf_guardrail and lf_sigma > 0: + HF = y_lin - blur(y_lin, lf_sigma) + LF_target = (1.0 - frac) * blur( + A_lin, lf_sigma + ) + frac * blur(B_lin, lf_sigma) + y_lin = HF + LF_target + + if source_pair_match: + tgt = (1.0 - frac) * A_lin + frac * B_lin + a, b = self._exposure_match_ab( + y_lin, tgt, match_a_cap, match_b_cap + ) + y_lin = (a * y_lin + b).clamp(0.0, 1.0) + + if edge_band_lock: + d = ( + (self._luma_linear(A_lin) - self._luma_linear(B_lin)) + .abs() + .unsqueeze(-1) + ) + high = (d > tau_high).float() + low = (d < tau_low).float() + band = self._dilate_mask_nhwc(high, band_radius) * low + if band_soft_sigma > 0: + band = blur(band, band_soft_sigma).clamp(0.0, 1.0) + near = A_lin if frac < 0.5 else B_lin + y_lin = band * near + (1.0 - band) * y_lin + + y = to_srgb(y_lin).clamp(0.0, 1.0) + + _write(k, y) + + frames_since_clear += 1 + if torch.cuda.is_available() and int(clear_cache_after_n_frames) > 0: + if frames_since_clear >= int(clear_cache_after_n_frames): + torch.cuda.empty_cache() + frames_since_clear = 0 + + acc += num + + return (postprocess_frames(y_buf),) + + +class RIFE_SeamTimingAnalyzer: + """ + Batch-in, timings-out. Chooses t-schedule for the seam [last, first] using + visual step sizes measured from REAL frames in the input batch. + + Inputs: + - full_clip (IMAGE, NHWC [N,H,W,C], required): real frames (e.g., 1..10) + - multiplier (INT): number of in-betweens to synthesize for the seam + - use_first_two (BOOL): include dist(clip[0], clip[1]) as a target + - use_last_two (BOOL): include dist(clip[-2], clip[-1]) as a target + - use_global_median (BOOL): include median of ALL adjacent deltas (N>=3) + - ckpt_name / ensemble / scale_factor: RIFE runtime knobs (only used to test t) + - calibrate_metric: "MSE" or "L1" visual distance + - calibrate_iters: binary-search iterations + - t_min / t_max: clamp t search range (keep t_max < 1.0 to avoid hugging frame 1) + + Outputs: + - t_list_csv (STRING): e.g. "0.183112, 0.408217, 0.653991, 0.913541" + - multiplier (INT): echo of input multiplier for wiring convenience + """ + + DESCRIPTION = ( + "Finds a smooth loop timing: measures motion in your clip and solves a set of t-values across the wrap " + "[lastβ†’first] so the seam blends naturally." + ) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": ( + ["rife47.pth", "rife49.pth"], + { + "default": "rife47.pth", + "tooltip": "Choose the RIFE 4.7 or 4.9 model file (used only to test candidate timings).", + }, + ), + "scale_factor": ( + [0.25, 0.5, 1.0, 2.0, 4.0], + { + "default": 1.0, + "tooltip": "Quality vs speed for the probe renders. 1.0 recommended.", + }, + ), + "ensemble": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Blend forward & backward predictions to reduce artifacts (slower).", + }, + ), + "full_clip": ( + "IMAGE", + { + "tooltip": "Your input clip (β‰₯2 frames). Real motion here decides the loop seam timing." + }, + ), + "multiplier": ( + "INT", + { + "default": 4, + "min": 0, + "tooltip": "How many new frames you plan to create at the loop seam [lastβ†’first]. Set 0 to skip.", + }, + ), + "use_first_two": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Match the motion between the first two frames in your clip.", + }, + ), + "use_last_two": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Match the motion between the last two frames in your clip.", + }, + ), + "use_global_median": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Use the median motion across the whole clip (needs β‰₯3 frames). Helps ignore outliers.", + }, + ), + "calibrate_metric": ( + ["MSE", "L1"], + { + "default": "MSE", + "tooltip": "How we compare frames while solving: MSE (more sensitive) or L1 (more forgiving).", + }, + ), + "calibrate_iters": ( + "INT", + { + "default": 12, + "min": 4, + "max": 24, + "tooltip": "Search depth per solve. Higher = slower, but a tighter match.", + }, + ), + "t_min": ( + "FLOAT", + { + "default": 0.00, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "tooltip": "Earliest allowed blend point at the seam (0 = exactly the last frame).", + }, + ), + "t_max": ( + "FLOAT", + { + "default": 0.96, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "tooltip": "Latest allowed blend point at the seam (keep below 1.0 to avoid sticking to the first frame).", + }, + ), + }, + "optional": { + "auto_tmax": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Automatically push the upper limit closer to the next frame to hit the target motion step.", + }, + ), + "t_cap": ( + "FLOAT", + { + "default": 0.995, + "min": 0.5, + "max": 0.9999, + "step": 0.0001, + "tooltip": "Safety cap used with the auto upper limit (keeps it just shy of 1.0).", + }, + ), + }, + } + + RETURN_TYPES = ("STRING", "INT") + RETURN_NAMES = ("t_list_csv", "multiplier") + FUNCTION = "analyze_wrapper" + CATEGORY = "video utils" + + # --- helpers --- + def _to_nchw(self, x): + if x.ndim == 4 and x.shape[-1] in (1, 3, 4): + return x.permute(0, 3, 1, 2).contiguous() + return x + + def _dist(self, A, B, kind="MSE"): + if kind == "L1": + return (A - B).abs().mean() + return ((A - B) ** 2).mean() + + def _adjacent_deltas(self, clip_nchw, kind="MSE"): + N = clip_nchw.shape[0] + ds = [] + for i in range(N - 1): + ds.append(self._dist(clip_nchw[i : i + 1], clip_nchw[i + 1 : i + 2], kind)) + return ds # list of scalar tensors + + def analyze_wrapper(self, **kwargs): + """ + Safe entrypoint for Comfy mapping: + - If 'multiplier' is omitted: default to 0 (passthrough). + - If 'multiplier' is invalid/negative: normalize to 0 (passthrough). + """ + if "multiplier" not in kwargs: + kwargs["multiplier"] = 0 + + try: + m = int(kwargs["multiplier"]) + except (TypeError, ValueError): + m = 0 + if m < 0: + m = 0 + + kwargs["multiplier"] = m + return self.analyze(**kwargs) + + def analyze( + self, + full_clip, + multiplier, + use_first_two, + use_last_two, + use_global_median, + ckpt_name, + ensemble, + scale_factor, + calibrate_metric, + calibrate_iters, + t_min, + t_max, + auto_tmax=False, + t_cap=0.995, + ): + + try: + m = int(multiplier) + except (TypeError, ValueError): + m = 0 + if m < 0: + m = 0 + if m == 0: + return ("", 0) + + clip_nhwc = preprocess_frames(full_clip) + if clip_nhwc.device.type == "cpu" and torch.cuda.is_available(): + clip_nhwc = clip_nhwc.pin_memory() + + N = int(clip_nhwc.shape[0]) + if N < 2: + raise ValueError("full_clip must contain at least 2 frames.") + + metric = "L1" if calibrate_metric == "L1" else "MSE" + clip_nchw_cpu = self._to_nchw(clip_nhwc) + deltas = self._adjacent_deltas(clip_nchw_cpu, metric) + + chosen = [] + if use_first_two: + chosen.append(deltas[0]) + if use_last_two: + chosen.append(deltas[-1]) + if use_global_median: + if N < 3: + raise ValueError( + "use_global_median requires full_clip with >= 3 frames." + ) + chosen.append(torch.stack(deltas).median()) + + if not chosen: + raise ValueError( + "Enable at least one of: use_first_two, use_last_two, use_global_median." + ) + + d_target = ( + chosen[0] if len(chosen) == 1 else torch.stack(chosen).median() + ).item() + iters = int(calibrate_iters) + m = int(m) + + model = _get_rife_model(ckpt_name) + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype # usually float32 + + last_nchw = self._to_nchw(clip_nhwc[N - 1 : N]).to( + device=device, + dtype=dtype, + non_blocking=True, + memory_format=torch.channels_last, + ) + first_nchw = self._to_nchw(clip_nhwc[0:1]).to( + device=device, + dtype=dtype, + non_blocking=True, + memory_format=torch.channels_last, + ) + + t_min = float(max(0.0, min(1.0, t_min))) + t_max = float(max(0.0, min(1.0, t_max))) + if not (0.0 <= t_min < t_max <= 1.0): + raise ValueError("Require 0 <= t_min < t_max <= 1.0") + + hi_eff = t_max + if auto_tmax: + # Raise the upper bracket to a safe cap near 1.0 so we can hit the target step size. + hi_eff = max(hi_eff, min(max(t_min + 1e-6, float(t_cap)), 0.9999)) + + scale_list = [ + 8 / scale_factor, + 4 / scale_factor, + 2 / scale_factor, + 1 / scale_factor, + ] + + @torch.no_grad() + def synth_at(t_scalar: float): + t_scalar = float(max(t_min, min(hi_eff, t_scalar))) + return model(last_nchw, first_nchw, t_scalar, scale_list, False, ensemble) + + lo, hi = t_min, hi_eff + with torch.inference_mode(): + for _ in range(iters): + mid = 0.5 * (lo + hi) + d_mid = float(self._dist(synth_at(mid), first_nchw, metric).item()) + if d_mid > d_target: + lo = mid + else: + hi = mid + t_last = 0.5 * (lo + hi) + f_prev = synth_at(t_last) + + ts = [t_last] + for _ in range(m - 1): + lo, hi = t_min, ts[-1] + for _ in range(iters): + mid = 0.5 * (lo + hi) + d_mid = float(self._dist(synth_at(mid), f_prev, metric).item()) + if d_mid > d_target: + lo = mid + else: + hi = mid + tk = 0.5 * (lo + hi) + ts.append(tk) + f_prev = synth_at(tk) + + ts_sorted = sorted(ts) + csv = ", ".join(f"{t:.6f}" for t in ts_sorted) + return (csv, m) diff --git a/custom_nodes/whiterabbit/noise_control.py b/custom_nodes/whiterabbit/noise_control.py new file mode 100644 index 00000000..1dbf7db3 --- /dev/null +++ b/custom_nodes/whiterabbit/noise_control.py @@ -0,0 +1,751 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# SPDX-FileCopyrightText: 2025 ArtificialSweetener +import math + +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image + + +def _to_lin(x): + return torch.where( + x <= 0.04045, x / 12.92, ((x + 0.055) / 1.055).clamp(min=0) ** 2.4 + ) + + +def _to_srgb(x): + return torch.where( + x <= 0.0031308, 12.92 * x, 1.055 * x.clamp(min=0) ** (1 / 2.4) - 0.055 + ) + + +def _luma(x): + return 0.2126 * x[..., 0:1] + 0.7152 * x[..., 1:2] + 0.0722 * x[..., 2:3] + + +def _sobel_mag(y): # y: NHWC 1ch + kx = ( + torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32) + .view(1, 1, 3, 3) + .to(y.device) + ) + ky = ( + torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32) + .view(1, 1, 3, 3) + .to(y.device) + ) + t = F.pad(y.permute(0, 3, 1, 2), (1, 1, 1, 1), mode="reflect") + gx = F.conv2d(t, kx) + gy = F.conv2d(t, ky) + return torch.sqrt(gx * gx + gy * gy).permute(0, 2, 3, 1).contiguous() + + +def _gauss1d(sigma, r): + if r <= 0: + return torch.tensor([1.0], dtype=torch.float32) + xs = torch.arange(-r, r + 1, dtype=torch.float32) + k = torch.exp(-(xs * xs) / (2 * sigma * sigma)) + return (k / k.sum()).contiguous() + + +def _blur_nhwc(x, sigma): + if sigma <= 0: + return x + N, H, W, C = x.shape + max_r = max(0, min(H, W) // 2 - 1) + r = min(int(math.ceil(3.0 * sigma)), max_r) + if r <= 0: + return x + k = _gauss1d(sigma, r) + kH = k.view(1, 1, -1, 1).repeat(C, 1, 1, 1) + kW = k.view(1, 1, 1, -1).repeat(C, 1, 1, 1) + t = x.permute(0, 3, 1, 2).contiguous() + t = F.conv2d(F.pad(t, (0, 0, r, r), mode="reflect"), kH, groups=C) + t = F.conv2d(F.pad(t, (r, r, 0, 0), mode="reflect"), kW, groups=C) + return t.permute(0, 2, 3, 1).contiguous() + + +def _avgpool_tiles(x1, tile): + t = x1.permute(0, 3, 1, 2) + o = F.avg_pool2d(t, kernel_size=tile, stride=tile) + return o.permute(0, 2, 3, 1) + + +def _mad_tiles(x1, tile): + t = x1.permute(0, 3, 1, 2) + N, C, H, W = t.shape + th, tw = H // tile, W // tile + t = t[:, :, : th * tile, : tw * tile] + patches = F.unfold(t, kernel_size=tile, stride=tile) # (N, C*tile*tile, th*tw) + patches = patches.transpose(1, 2).reshape(-1, tile * tile) # (N*th*tw, K) + med = patches.median(dim=1, keepdim=True).values + mad = (patches - med).abs().median(dim=1).values.view(N, th, tw, 1) + return mad + + +def _upsample_mask(mask_tile, H, W, mode="nearest"): + t = mask_tile.permute(0, 3, 1, 2) + t = F.interpolate( + t, + size=(H, W), + mode=("bilinear" if mode == "bilinear" else "nearest"), + align_corners=False if mode == "bilinear" else None, + ) + return t.permute(0, 2, 3, 1) + + +def _dilate(mask01, r): + if r <= 0: + return mask01 + t = mask01.permute(0, 3, 1, 2) + t = F.pad(t, (r, r, r, r), mode="replicate") + t = F.max_pool2d(t, kernel_size=2 * r + 1, stride=1) + return t.permute(0, 2, 3, 1) + + +def _resize_lanczos(img01, H, W): # (1,Hr,Wr,C) float CPU -> (1,H,W,C) float CPU + arr = (img01[0].cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8) + pil = Image.fromarray(arr, mode="RGB").resize((W, H), resample=Image.LANCZOS) + out = np.asarray(pil).astype(np.float32) / 255.0 + return torch.from_numpy(out).unsqueeze(0) + + +class PixelHold: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "frames": ( + "IMAGE", + {"tooltip": "Your clip (framesΓ—HΓ—WΓ—C, values 0–1)."}, + ), + "ref_source": ( + ["external", "batch_index"], + { + "default": "external", + "tooltip": "Pick the reference: an external image or a frame from this clip.", + }, + ), + "ref_index": ( + "INT", + { + "default": 0, + "min": 0, + "max": 999999, + "tooltip": "If using a frame from this clip, which frame to use as the reference.", + }, + ), + "reference": ( + "IMAGE", + { + "default": None, + "tooltip": "Optional external reference (1Γ—HΓ—WΓ—C). If sizes differ, it will be resized to match.", + }, + ), + "linearize": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Work in linear color for steadier results on flat areas.", + }, + ), + "auto_luma": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Auto sensitivity for brightness changes (adapts per frame).", + }, + ), + "auto_k": ( + "FLOAT", + { + "default": 2.5, + "min": 0.5, + "max": 6.0, + "step": 0.1, + "tooltip": "Auto strength. Higher = lock more to the reference (2–3 is typical).", + }, + ), + "tau_luma": ( + "FLOAT", + { + "default": 1.5 / 255.0, + "min": 0.0, + "max": 4.0 / 255.0, + "step": 0.0005, + "tooltip": "Manual brightness threshold when Auto is OFF. Lower = stricter (more locking).", + }, + ), + "tau_grad": ( + "FLOAT", + { + "default": 0.02, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "tooltip": "How much edge change to allow. Lower protects edges more.", + }, + ), + "mode": ( + ["tile", "pixel"], + { + "default": "tile", + "tooltip": "Tile: fast & robust. Pixel: finer but noisier.", + }, + ), + "tile_size": ( + "INT", + { + "default": 32, + "min": 8, + "max": 256, + "step": 8, + "tooltip": "Tile size when using Tile mode.", + }, + ), + "score_mode": ( + ["l1_tile", "mad_tile"], + { + "default": "l1_tile", + "tooltip": "How tiles measure change: mean abs diff (fast) or median abs dev (robust).", + }, + ), + "edge_band": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Protect a belt around strong edges to avoid wobble/stretch.", + }, + ), + "band_radius": ( + "INT", + { + "default": 4, + "min": 0, + "max": 64, + "tooltip": "Width of the protected belt (pixels).", + }, + ), + "tau_edge_low": ( + "FLOAT", + { + "default": 1.5 / 255.0, + "min": 0.0, + "max": 0.25, + "step": 0.0005, + "tooltip": "Treat as low-motion below this level (edge belt).", + }, + ), + "tau_edge_high": ( + "FLOAT", + { + "default": 6.0 / 255.0, + "min": 0.0, + "max": 0.5, + "step": 0.0005, + "tooltip": "Treat as high-motion above this level (edge belt).", + }, + ), + "apply": ( + ["all", "lowfreq"], + { + "default": "all", + "tooltip": "Hold the whole image (All) or only its smooth part (Low-freq).", + }, + ), + "dilate": ( + "INT", + { + "default": 1, + "min": 0, + "max": 16, + "tooltip": "Expand the mask (pixels).", + }, + ), + "feather_sigma": ( + "FLOAT", + { + "default": 2.0, + "min": 0.0, + "max": 16.0, + "step": 0.5, + "tooltip": "Soften mask edges (pixels).", + }, + ), + "process_on": ( + ["auto", "cpu", "gpu"], + { + "default": "auto", + "tooltip": "Choose CPU/GPU. Auto switches to GPU on very large frames.", + }, + ), + "gpu_clear_every": ( + "INT", + { + "default": 0, + "min": 0, + "max": 1000, + "tooltip": "If >0 and using GPU, free memory every N frames.", + }, + ), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("images", "mask_preview") + FUNCTION = "apply_hold" + CATEGORY = "video utils" + DESCRIPTION = ( + "Locks parts of each frame to a chosen reference (external image or a frame from the clip) whenever changes are smallβ€”" + "useful for stabilizing flat areas or backgrounds while leaving motion to pass through." + ) + + @torch.no_grad() + def apply_hold( + self, + frames, + ref_source="external", + ref_index=0, + reference=None, + linearize=True, + auto_luma=True, + auto_k=2.5, + tau_luma=1.5 / 255.0, + tau_grad=0.02, + mode="tile", + tile_size=32, + score_mode="l1_tile", + edge_band=True, + band_radius=4, + tau_edge_low=1.5 / 255.0, + tau_edge_high=6.0 / 255.0, + apply="all", + dilate=1, + feather_sigma=2.0, + process_on="auto", + gpu_clear_every=0, + ): + x = frames if isinstance(frames, torch.Tensor) else torch.tensor(frames) + B, H, W, C = x.shape + + if str(ref_source) == "external" and reference is not None: + ref = ( + reference + if isinstance(reference, torch.Tensor) + else torch.tensor(reference) + ) + if ref.shape[1] != H or ref.shape[2] != W: + ref = _resize_lanczos(ref[:1].to("cpu"), H, W) + ref = ref[:1].repeat(B, 1, 1, 1) + else: + idx = max(0, min(int(ref_index), B - 1)) + ref = x[idx : idx + 1].repeat(B, 1, 1, 1) + + x_lin = _to_lin(x) if linearize else x + r_lin = _to_lin(ref) if linearize else ref + want_gpu = (process_on == "gpu") or ( + process_on == "auto" and torch.cuda.is_available() and (H * W >= 6_000_000) + ) + dev = torch.device("cuda") if want_gpu else torch.device("cpu") + + r_lin = r_lin.to(dev) + y_r = _luma(r_lin) + g_r = _sobel_mag(y_r) + + if apply == "lowfreq": + LF_r = _blur_nhwc(r_lin.to("cpu"), 13.0) + + out_frames, mask_frames = [], [] + clear_ctr = 0 + + for i in range(B): + f = x_lin[i : i + 1].to(dev) + y_f = _luma(f) + g_f = _sobel_mag(y_f) + + dY = (y_f - y_r[i : i + 1]).abs() + dG = (g_f - g_r[i : i + 1]).abs() + + if auto_luma: + med = torch.median(dY.view(-1)) + sigma = 1.4826 * med.item() + tau_luma_eff = max(0.0, min(4.0 / 255.0, float(auto_k) * float(sigma))) + else: + tau_luma_eff = float(tau_luma) + + if mode == "tile": + sY = ( + _mad_tiles(dY, tile_size) + if score_mode == "mad_tile" + else _avgpool_tiles(dY, tile_size) + ) + sG = ( + _mad_tiles(dG, tile_size) + if score_mode == "mad_tile" + else _avgpool_tiles(dG, tile_size) + ) + mask = (sY < tau_luma_eff).to(torch.float32) * ( + sG < float(tau_grad) + ).to(torch.float32) + mask = _upsample_mask(mask, H, W, mode="nearest") + else: + mask = (dY < tau_luma_eff).to(torch.float32) * ( + dG < float(tau_grad) + ).to(torch.float32) + + mask = _dilate(mask, int(dilate)) + if feather_sigma > 0: + mask = ( + _blur_nhwc(mask.to("cpu"), float(feather_sigma)) + .to(dev) + .clamp_(0.0, 1.0) + ) + + if edge_band: + D = (y_f - y_r[i : i + 1]).abs() + high = (D > float(tau_edge_high)).to(torch.float32) + low = (D < float(tau_edge_low)).to(torch.float32) + band = _dilate(high, int(band_radius)) * low + if feather_sigma > 0: + band = ( + _blur_nhwc(band.to("cpu"), float(feather_sigma)) + .to(dev) + .clamp_(0.0, 1.0) + ) + mask = (mask * (1.0 - band)).clamp_(0.0, 1.0) + + if apply == "all": + composed_lin = mask * r_lin[i : i + 1] + (1.0 - mask) * f + composed_lin = composed_lin.to("cpu") + else: + f_cpu = f.to("cpu") + LF_f = _blur_nhwc(f_cpu, 13.0) + HF_f = f_cpu - LF_f + LF_mix = ( + mask.to("cpu") * LF_r[i : i + 1] + (1.0 - mask.to("cpu")) * LF_f + ) + composed_lin = (HF_f + LF_mix).clamp(0.0, 1.0) + + out = _to_srgb(composed_lin) if linearize else composed_lin + mvis = mask.to("cpu").repeat(1, 1, 1, 3).clamp_(0.0, 1.0) + + out_frames.append(out.clamp(0, 1)) + mask_frames.append(mvis) + + if dev.type == "cuda" and int(gpu_clear_every) > 0: + clear_ctr += 1 + if clear_ctr >= int(gpu_clear_every): + torch.cuda.empty_cache() + clear_ctr = 0 + + y_out = torch.cat(out_frames, dim=0) + mask_preview = torch.cat(mask_frames, dim=0) + return (y_out, mask_preview) + + +class BlackSpotCleaner: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "frames": ( + "IMAGE", + {"tooltip": "Your clip (framesΓ—HΓ—WΓ—C, values 0–1)."}, + ), + "linearize": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Work in linear color for cleaner detection.", + }, + ), + "detector": ( + ["blackhat", "local_floor"], + { + "default": "blackhat", + "tooltip": "blackhat: tiny dark specks β€’ local_floor: larger soft blotches.", + }, + ), + "radius": ( + "INT", + { + "default": 5, + "min": 1, + "max": 31, + "tooltip": "Approximate spot size (pixels). Increase for bigger blotches.", + }, + ), + "tau_blackhat": ( + "FLOAT", + { + "default": 4.0 / 255.0, + "min": 0.0, + "max": 0.5, + "step": 0.0005, + "tooltip": "Base sensitivity (0–1). Lower = fix more, higher = fix less.", + }, + ), + "auto_blackhat": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Auto-tune sensitivity from image noise (robust to lighting/texture).", + }, + ), + "bh_k": ( + "FLOAT", + { + "default": 3.0, + "min": 0.5, + "max": 8.0, + "step": 0.1, + "tooltip": "Auto strength multiplier. Higher = more aggressive fixes.", + }, + ), + "temporal_gate": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Only fix if darker than neighboring frames (reduces false positives).", + }, + ), + "temporal_radius": ( + "INT", + { + "default": 1, + "min": 1, + "max": 3, + "tooltip": "How many neighbor frames to compare on each side.", + }, + ), + "grad_guard": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Skip fixes on strong edges/text to avoid halos.", + }, + ), + "tau_grad_edge": ( + "FLOAT", + { + "default": 0.07, + "min": 0.0, + "max": 1.0, + "step": 0.001, + "tooltip": "Edge strength where fixes are skipped (higher = skip more).", + }, + ), + "dilate": ( + "INT", + { + "default": 1, + "min": 0, + "max": 8, + "tooltip": "Expand the fix mask (pixels).", + }, + ), + "feather_sigma": ( + "FLOAT", + { + "default": 1.5, + "min": 0.0, + "max": 16.0, + "step": 0.5, + "tooltip": "Soften mask edges (pixels).", + }, + ), + "process_on": ( + ["auto", "cpu", "gpu"], + { + "default": "auto", + "tooltip": "Choose CPU/GPU. Auto switches to GPU on very large frames.", + }, + ), + "gpu_clear_every": ( + "INT", + { + "default": 0, + "min": 0, + "max": 1000, + "tooltip": "If >0 and using GPU, free memory every N frames.", + }, + ), + }, + "optional": { + "reference": ( + "IMAGE", + { + "tooltip": "Optional external reference floor (1Γ—HΓ—WΓ—C). Resized if needed." + }, + ), + "ref_source": ( + ["none", "external", "batch_index"], + { + "default": "none", + "tooltip": "Choose a floor: none, an external image, or a frame index from this clip.", + }, + ), + "ref_index": ( + "INT", + { + "default": 0, + "min": 0, + "max": 999999, + "tooltip": "If using a frame index as the floor, which one to use.", + }, + ), + "tau_down": ( + "FLOAT", + { + "default": 2.0 / 255.0, + "min": 0.0, + "max": 0.5, + "step": 0.0005, + "tooltip": "Only lift where the frame is at least this much darker than the floor.", + }, + ), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("images", "mask_preview") + FUNCTION = "clean" + CATEGORY = "video utils" + DESCRIPTION = "Removes tiny dark specks and soft blotches by gently lifting only the dark outliersβ€”keeps edges and details safe with guards." + + @torch.no_grad() + def clean( + self, + frames, + linearize=True, + detector="blackhat", + radius=5, + tau_blackhat=4.0 / 255.0, + auto_blackhat=True, + bh_k=3.0, + temporal_gate=True, + temporal_radius=1, + grad_guard=True, + tau_grad_edge=0.07, + dilate=1, + feather_sigma=1.5, + process_on="auto", + gpu_clear_every=0, + reference=None, + ref_source="none", + ref_index=0, + tau_down=2.0 / 255.0, + ): + x = frames if isinstance(frames, torch.Tensor) else torch.tensor(frames) + B, H, W, C = x.shape + + ref = None + if str(ref_source) == "external" and reference is not None: + ref = ( + reference + if isinstance(reference, torch.Tensor) + else torch.tensor(reference) + ) + if ref.shape[1] != H or ref.shape[2] != W: + ref = _resize_lanczos(ref[:1].to("cpu"), H, W) + ref = ref[:1].repeat(B, 1, 1, 1) + elif str(ref_source) == "batch_index": + idx = max(0, min(int(ref_index), B - 1)) + ref = x[idx : idx + 1].repeat(B, 1, 1, 1) + + xx = _to_lin(x) if linearize else x + rr = _to_lin(ref) if (ref is not None and linearize) else ref + + want_gpu = (process_on == "gpu") or ( + process_on == "auto" and torch.cuda.is_available() and (H * W >= 6_000_000) + ) + dev = torch.device("cuda") if want_gpu else torch.device("cpu") + + y = _luma(xx).to(dev) + g = _sobel_mag(y) + if rr is not None: + y_ref = _luma(rr).to(device=y.device, dtype=y.dtype) # match y + assert ( + y_ref.shape[0] == y.shape[0] + ), f"y_ref B={y_ref.shape[0]} vs y B={y.shape[0]}" + assert ( + y_ref.shape[1:3] == y.shape[1:3] + ), f"spatial mismatch {y_ref.shape[1:3]} vs {y.shape[1:3]}" + floor = (y_ref - y) > float(tau_down) + + r = int(radius) + if detector == "blackhat": + k = max(1, 2 * r + 1) + k = min(k, 2 * min(H, W) - 1) + t = y.permute(0, 3, 1, 2) + d = F.max_pool2d( + F.pad(t, (k // 2, k // 2, k // 2, k // 2), mode="replicate"), + kernel_size=k, + stride=1, + ) + e = -F.max_pool2d( + F.pad(-d, (k // 2, k // 2, k // 2, k // 2), mode="replicate"), + kernel_size=k, + stride=1, + ) + y_close = e.permute(0, 2, 3, 1) + score = (y_close - y).clamp_min(0) + else: + sigma = max(0.5, r / 2.0) + Bsm = _blur_nhwc(y.to("cpu"), sigma).to(y.device) + score = (Bsm - y).clamp_min(0) + tau = float(tau_blackhat) + if bool(auto_blackhat): + region = (g < float(tau_grad_edge)).to(torch.float32) + if region.sum() < 1: + region = torch.ones_like(region) + sel = score[region > 0.5].view(-1) + if sel.numel() > 0: + med = torch.median(sel) + sigma_bh = 1.4826 * torch.median((sel - med).abs()) + tau = max(tau, float(bh_k) * float(sigma_bh)) + + mask = (score > tau).to(torch.float32) + + if rr is not None: + floor = (y_ref - y) > float(tau_down) + mask = torch.maximum(mask, floor.to(torch.float32)) + + if temporal_gate and B > 1: + idxs = [] + for dt in range(1, int(temporal_radius) + 1): + if dt < B: + idxs += [ + torch.clamp(torch.arange(B) - dt, 0, B - 1), + torch.clamp(torch.arange(B) + dt, 0, B - 1), + ] + neigh = torch.stack([y[i] for i in torch.stack(idxs, dim=0)], dim=0) + y_med = torch.median(neigh, dim=0).values + mask = mask * ((y_med - y) > tau).to(torch.float32) + + if grad_guard: + guard = (g < float(tau_grad_edge)).to(torch.float32) + mask = mask * guard + + mask = _dilate(mask, int(dilate)) + if feather_sigma > 0: + mask = ( + _blur_nhwc(mask.to("cpu"), float(feather_sigma)) + .to(dev) + .clamp_(0.0, 1.0) + ) + + delta = score * mask + delta3 = delta.repeat(1, 1, 1, 3) + out_lin = (xx.to(dev) + delta3).clamp(0.0, 1.0) + if dev.type == "cuda": + out_lin = out_lin.to("cpu") + + out = _to_srgb(out_lin) if linearize else out_lin + mask_preview = mask.to("cpu").repeat(1, 1, 1, 3).clamp_(0.0, 1.0) + + if dev.type == "cuda" and int(gpu_clear_every) > 0: + torch.cuda.empty_cache() + + return (out.clamp(0, 1), mask_preview) diff --git a/custom_nodes/whiterabbit/post_process.py b/custom_nodes/whiterabbit/post_process.py new file mode 100644 index 00000000..d6b476ae --- /dev/null +++ b/custom_nodes/whiterabbit/post_process.py @@ -0,0 +1,544 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# SPDX-FileCopyrightText: 2025 ArtificialSweetener + +import os +import random +import time +from collections import OrderedDict +from typing import Dict, List, Optional, Tuple + +import comfy.utils as comfy_utils +import folder_paths +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +from torchlanc import lanczos_resize + + +def _chunk_spans(n: int, cap: int) -> List[Tuple[int, int]]: + if cap <= 0 or cap >= n: + return [(0, n)] + out = [] + i = 0 + while i < n: + j = min(n, i + cap) + out.append((i, j)) + i = j + return out + + +def _bhwc_to_nchw(x: torch.Tensor) -> torch.Tensor: + return x.movedim(-1, -3) + + +def _nchw_to_bhwc(x: torch.Tensor) -> torch.Tensor: + return x.movedim(-3, -1) + + +def _ensure_rgba_nchw(wm: torch.Tensor) -> torch.Tensor: + """ + wm: (1,H,W,C) in [0,1] β†’ return (4,H,W) float + C may be 1,3,4; synthesize alpha=1 if missing. + """ + if wm.dim() != 4 or wm.shape[0] != 1: + raise ValueError( + "watermark must be a single IMAGE tensor of shape (1,H,W,C) in [0,1]." + ) + _, h, w, c = wm.shape + x = _bhwc_to_nchw(wm[0]).float().clamp_(0, 1) # (C,H,W) + if c == 4: + return x + if c == 3: + a = torch.ones(1, h, w, device=x.device, dtype=x.dtype) + return torch.cat([x, a], dim=0) + if c == 1: + rgb = x.repeat(3, 1, 1) + a = torch.ones(1, h, w, device=x.device, dtype=x.dtype) + return torch.cat([rgb, a], dim=0) + raise ValueError(f"Unsupported watermark channel count C={c}. Expected 1, 3 or 4.") + + +def _load_rgba_from_path(path: str, device: torch.device) -> torch.Tensor: + """ + Load an image from disk as RGBA in [0,1] and return (4,H,W) on the target device. + No rotation or other processing happens here. + """ + try: + with Image.open(path) as im: + im = im.convert("RGBA") + arr = np.asarray(im, dtype=np.float32) / 255.0 # (H,W,4) in [0,1] + except Exception as e: + raise ValueError(f"Failed to load watermark image from '{path}': {e}") + t = torch.from_numpy(arr).to(device=device, dtype=torch.float32) # (H,W,4) + return t.permute(2, 0, 1).contiguous() # (4,H,W) + + +def _rotate_bicubic_expand(x: torch.Tensor, degrees: float) -> torch.Tensor: + """ + x: (N,C,H,W). Rotate around center with bicubic sampling and EXPAND canvas + (PIL-like `expand=True`). Parts outside input are zero/transparent. + """ + deg = float(degrees) % 360.0 + if deg == 0.0: + return x + + N, C, H, W = x.shape + rad = deg * 3.141592653589793 / 180.0 + cosr = float(torch.cos(torch.tensor(rad))) + sinr = float(torch.sin(torch.tensor(rad))) + + # Expanded output size (axis-aligned bounding box of the rotated rectangle) + new_w = int((abs(W * cosr) + abs(H * sinr)) + 0.9999) + new_h = int((abs(H * cosr) + abs(W * sinr)) + 0.9999) + new_w = max(1, new_w) + new_h = max(1, new_h) + + # Centers in pixel coords + cx_in = (W - 1) * 0.5 + cy_in = (H - 1) * 0.5 + cx_out = (new_w - 1) * 0.5 + cy_out = (new_h - 1) * 0.5 + + # Output grid in pixel coords + ys = torch.linspace(0, new_h - 1, new_h, device=x.device, dtype=x.dtype) + xs = torch.linspace(0, new_w - 1, new_w, device=x.device, dtype=x.dtype) + gy, gx = torch.meshgrid(ys, xs, indexing="ij") + + # Inverse rotation: output β†’ input (rotate about centers) + rx = gx - cx_out + ry = gy - cy_out + x_in = cosr * rx + sinr * ry + cx_in + y_in = -sinr * rx + cosr * ry + cy_in + + # Normalize to [-1,1] for align_corners=False + x_norm = (x_in + 0.5) / W * 2.0 - 1.0 + y_norm = (y_in + 0.5) / H * 2.0 - 1.0 + grid = torch.stack((x_norm, y_norm), dim=-1).unsqueeze(0).repeat(N, 1, 1, 1) + + # Sample + try: + return F.grid_sample( + x, grid, mode="bicubic", padding_mode="zeros", align_corners=False + ) + except Exception: + return F.grid_sample( + x, grid, mode="bilinear", padding_mode="zeros", align_corners=False + ) + + +def _position_xy( + position: str, + base_w: int, + base_h: int, + wm_w: int, + wm_h: int, + pad_x: int, + pad_y: int, +) -> Tuple[int, int]: + pos = (position or "bottom-right").strip().lower() + if pos == "center": + return (base_w - wm_w) // 2, (base_h - wm_h) // 2 + + x = ( + 0 + if "left" in pos + else (base_w - wm_w if "right" in pos else (base_w - wm_w) // 2) + ) + y = ( + 0 + if "top" in pos + else (base_h - wm_h if "bottom" in pos else (base_h - wm_h) // 2) + ) + + if "left" in pos: + x += int(pad_x) + if "right" in pos: + x -= int(pad_x) + if "top" in pos: + y += int(pad_y) + if "bottom" in pos: + y -= int(pad_y) + return x, y + + +class _SmallLRU: + def __init__(self, capacity: int = 6): + self.capacity = int(max(1, capacity)) + self._m: "OrderedDict[Tuple, Tuple[torch.Tensor, torch.Tensor]]" = OrderedDict() + + def get(self, key: Tuple): + v = self._m.get(key) + if v is not None: + self._m.move_to_end(key) + return v + + def put(self, key: Tuple, value): + if key in self._m: + self._m.move_to_end(key) + self._m[key] = value + if len(self._m) > self.capacity: + self._m.popitem(last=False) + + +class BatchWatermarkSingle: + """ + Single-position watermark for image batches. + + - Scale uses base image WIDTH Γ— (scale/100) + - Rotation always applies, with clipping (no expand) + - Padding in pixels (ignored for center) + - TorchLanc for watermark resize + - Chunked batches + small LRU cache + optional torch.compile + """ + + @classmethod + def INPUT_TYPES(cls): + # Mirror LoadImage: list files from the input directory, allow upload + input_dir = folder_paths.get_input_directory() + files = [ + f + for f in os.listdir(input_dir) + if os.path.isfile(os.path.join(input_dir, f)) + ] + files = folder_paths.filter_files_content_types(files, ["image"]) + + return { + "required": { + "image": ( + "IMAGE", + { + "tooltip": "Images to watermark. Accepts (H,W,C) or (B,H,W,C) with values in [0–1]. Processed on GPU." + }, + ), + "watermark": ( + sorted(files), + { + "image_upload": True, + "tooltip": "Select or upload the watermark image (PNG recommended). The file’s transparency is preserved.", + }, + ), + "position": ( + ["bottom-right", "bottom-left", "top-right", "top-left", "center"], + { + "default": "bottom-right", + "tooltip": "Where to place the watermark. Padding is ignored when 'center' is selected. Rotation clips; no canvas expand.", + }, + ), + "scale": ( + "INT", + { + "default": 70, + "min": 1, + "max": 100, + "step": 1, + "tooltip": "Width-based scaling. Target watermark width = image width Γ— (scale/100). Aspect ratio preserved.", + }, + ), + "transparency": ( + "INT", + { + "default": 100, + "min": 0, + "max": 100, + "step": 1, + "tooltip": "Alpha multiplier for the watermark: 100 = unchanged, 0 = fully transparent.", + }, + ), + "rotation": ( + "INT", + { + "default": 0, + "min": 0, + "max": 359, + "step": 1, + "tooltip": "Rotate the watermark (degrees) with bicubic resampling. Canvas expands so nothing is clipped (PIL-style).", + }, + ), + "padding_x": ( + "INT", + { + "default": 0, + "min": 0, + "max": 16384, + "step": 1, + "tooltip": "Extra horizontal padding in pixels from the chosen edge (ignored when position='center').", + }, + ), + "padding_y": ( + "INT", + { + "default": 0, + "min": 0, + "max": 16384, + "step": 1, + "tooltip": "Extra vertical padding in pixels from the chosen edge (ignored when position='center').", + }, + ), + "optical_padding": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Adjust placement by the watermark’s visual center so equal padding looks right (optical alignment). Affects corner positions; ignored when position='center'.", + }, + ), + "optical_strength": ( + "INT", + { + "default": 40, + "min": 0, + "max": 100, + "step": 5, + "tooltip": "How strongly to nudge toward visual centering (0–100). 0 = off. Higher values shift more for wide/rotated marks.", + }, + ), + "max_batch_size": ( + "INT", + { + "default": 0, + "min": 0, + "max": 4096, + "step": 1, + "tooltip": "Process images in chunks to control VRAM. 0 = process the whole batch at once.", + }, + ), + "sinc_window": ( + "INT", + { + "default": 3, + "min": 1, + "max": 8, + "step": 1, + "tooltip": "Lanczos window size (a) used when resizing the watermark. Higher = sharper (but more ringing).", + }, + ), + "precision": ( + ["fp32", "fp16", "bf16"], + { + "default": "fp32", + "tooltip": "Resampling compute dtype. fp32 = safest quality; fp16/bf16 can be faster on many GPUs.", + }, + ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "apply" + CATEGORY = "image/post" + DESCRIPTION = "GPU accelerated watermark overlay. TorchLanc resize for quality and speed. Works for single images, but efficient for batches, too!" + + def apply( + self, + image: torch.Tensor, + watermark: str, + position: str, + scale: int, + transparency: int, + rotation: int, + padding_x: int, + padding_y: int, + optical_padding: bool, + optical_strength: int, + max_batch_size: int, + sinc_window: int, + precision: str, + ): + + if image is None or not isinstance(image, torch.Tensor): + raise ValueError( + "image must be a torch.Tensor with shape (H,W,C) or (B,H,W,C) in [0,1]." + ) + if not isinstance(watermark, str) or not watermark: + raise ValueError("Select a watermark image from the list (or upload one).") + + if not folder_paths.exists_annotated_filepath(watermark): + raise ValueError(f"Invalid watermark file: {watermark}") + watermark_path = folder_paths.get_annotated_filepath(watermark) + + # Refuse sequences (we must get a tensor just like Lanczos) + if isinstance(image, (list, tuple)): + raise TypeError( + "Expected IMAGE tensor (H,W,C) or (B,H,W,C); got a sequence. Use 'Image Batch' to re-batch." + ) + + # Accept both single images (H,W,C) and batches (B,H,W,C); normalize to batch + if image.dim() == 3: + image = image.unsqueeze(0) # -> (1,H,W,C) + elif image.dim() != 4: + raise ValueError( + f"Unexpected IMAGE tensor rank {image.dim()}; expected 3 or 4 dims." + ) + + B, H, W, C = image.shape + if C not in (1, 3, 4): + raise ValueError(f"Unsupported channel count C={C}. Expected 1, 3 or 4.") + + # Common + device = torch.device("cuda") + scale = int(scale) + transparency = max(0, min(100, int(transparency))) + rotation = int(rotation) % 360 + pad_x = int(padding_x) + pad_y = int(padding_y) + optical_padding = bool(optical_padding) + optical_strength = max(0, min(100, int(optical_strength))) + + # Prepare watermark once (load RGBA from disk to preserve original transparency) + wm_rgba = _load_rgba_from_path(watermark_path, device) # (4,hw,ww) + wm_h0, wm_w0 = int(wm_rgba.shape[1]), int(wm_rgba.shape[2]) + + # Progress + pbar = comfy_utils.ProgressBar(B) + + out_chunks: List[torch.Tensor] = [] + + # Compute final watermark once (all images in a Comfy batch share HΓ—W) + target_w = max(1, int(round(W * (scale / 100.0)))) + target_h = max(1, int(round(wm_h0 * target_w / max(1, wm_w0)))) + + # Premultiply BEFORE resampling to avoid dark fringes + pm0 = wm_rgba[:3, :, :] * wm_rgba[3:4, :, :] + a0 = wm_rgba[3:4, :, :] + wm_pm = torch.cat([pm0, a0], dim=0).unsqueeze(0) # (1,4,hw,ww) + + wm_resized_pm = lanczos_resize( + wm_pm, + height=target_h, + width=target_w, + a=int(sinc_window), + precision=str(precision), + clamp=True, + chunk_size=0, + )[ + 0 + ] # (4,h,w) + + # Apply transparency uniformly to premultiplied color AND alpha + if transparency != 100: + t = float(transparency) / 100.0 + wm_resized_pm[:3, :, :].mul_(t) + wm_resized_pm[3:4, :, :].mul_(t) + + # Rotate in premultiplied space (expand canvas) + wm_final = _rotate_bicubic_expand(wm_resized_pm.unsqueeze(0), rotation)[ + 0 + ] # (4,h,w) + pm_final, a_final = wm_final[:3, :, :], wm_final[3:4, :, :] # (3,h,w), (1,h,w) + + # Position + wm_h, wm_w = int(pm_final.shape[1]), int(pm_final.shape[2]) + x, y = _position_xy(position, W, H, wm_w, wm_h, pad_x, pad_y) + + # Optional optical padding (corner positions only) + if optical_padding and position != "center": + a = a_final[0] # (h,w) + denom = a.sum() + if float(denom.item() if hasattr(denom, "item") else denom) > 1e-8: + ys = torch.linspace(0, wm_h - 1, wm_h, device=a.device, dtype=a.dtype) + xs = torch.linspace(0, wm_w - 1, wm_w, device=a.device, dtype=a.dtype) + cy = (a.sum(dim=1) * ys).sum() / denom + cx = (a.sum(dim=0) * xs).sum() / denom + gx = (wm_w - 1) * 0.5 + gy = (wm_h - 1) * 0.5 + s = float(optical_strength) / 100.0 + dx = (gx - cx) * s # positive when centroid is left of center + dy = (gy - cy) * s # positive when centroid is above center + + if "right" in position: + x += int(round(dx.item())) + if "left" in position: + x -= int(round(dx.item())) + if "bottom" in position: + y += int(round(dy.item())) + if "top" in position: + y -= int(round(dy.item())) + + # Intersection with base image (clip) + x0 = max(0, x) + y0 = max(0, y) + x1 = min(W, x + wm_w) + y1 = min(H, y + wm_h) + + if x1 <= x0 or y1 <= y0: + out = image.to("cpu", non_blocking=False).float().clamp_(0, 1).contiguous() + if not torch.is_tensor(out) or out.dim() != 4: + raise TypeError( + f"Pass-through produced non-tensor or wrong rank: {type(out)} / {getattr(out,'shape',None)}" + ) + return (out,) + + wx0 = x0 - x + wy0 = y0 - y + w_w = x1 - x0 + w_h = y1 - y0 + + pm_crop = pm_final[:, wy0 : wy0 + w_h, wx0 : wx0 + w_w].contiguous() + a_crop = a_final[:, wy0 : wy0 + w_h, wx0 : wx0 + w_w].contiguous() + + # Process in chunks + for s, e in _chunk_spans(B, int(max_batch_size)): + sub = ( + _bhwc_to_nchw(image[s:e]) + .to(device, non_blocking=True) + .float() + .clamp_(0, 1) + ) + + ov_pm = pm_crop.unsqueeze(0).expand(sub.shape[0], -1, -1, -1) + ov_a = a_crop.unsqueeze(0).expand(sub.shape[0], -1, -1, -1) + + if C == 1: + rgb = sub.repeat(1, 3, 1, 1) + roi = rgb[:, :, y0:y1, x0:x1] + roi_out = roi * (1.0 - ov_a) + ov_pm + rgb[:, :, y0:y1, x0:x1] = roi_out + # Convert back to 1ch (luma) + y_luma = ( + 0.2126 * rgb[:, 0:1] + 0.7152 * rgb[:, 1:2] + 0.0722 * rgb[:, 2:3] + ).clamp_(0, 1) + sub = y_luma + elif C == 3: + roi = sub[:, :3, y0:y1, x0:x1] + roi_out = roi * (1.0 - ov_a) + ov_pm + sub[:, :3, y0:y1, x0:x1] = roi_out + else: # C == 4 + roi = sub[:, :3, y0:y1, x0:x1] + roi_out = roi * (1.0 - ov_a) + ov_pm + sub[:, :3, y0:y1, x0:x1] = roi_out + + out_chunks.append( + _nchw_to_bhwc(sub).to("cpu", non_blocking=False).clamp_(0, 1) + ) + pbar.update(e - s) + + out = torch.cat(out_chunks, dim=0) # CPU BHWC chunks β†’ CPU BHWC batch + + if out.dim() > 4: + b_flat = 1 + for s in out.shape[:-3]: + b_flat *= int(s) + out = out.reshape(b_flat, *out.shape[-3:]) + if out.dim() == 3: + out = out.unsqueeze(0) + if ( + out.dim() == 4 + and out.shape[1] in (1, 3, 4) + and out.shape[-1] not in (1, 3, 4) + ): + out = out.permute(0, 2, 3, 1).contiguous() + if out.dim() != 4: + raise ValueError( + f"Unexpected IMAGE tensor shape {tuple(out.shape)}; expected (B,H,W,C)." + ) + + out = ( + out.to("cpu", non_blocking=False) + .to(dtype=torch.float32) + .clamp_(0, 1) + .contiguous() + ) + + if not torch.is_tensor(out): + raise TypeError(f"IMAGE output must be torch.Tensor, got: {type(out)}") + + return (out,) diff --git a/custom_nodes/whiterabbit/pyproject.toml b/custom_nodes/whiterabbit/pyproject.toml new file mode 100644 index 00000000..1fbd38f4 --- /dev/null +++ b/custom_nodes/whiterabbit/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "whiterabbit" +description = "Powerful video frame manipulation nodes for ComfyUI such as: efficient high quality batch scaling, arbitrary framerate resampling, seamless video loop tools, batch watermark composite, and more." +version = "1.1.1" +license = {file = "LICENSE"} +classifiers = [ + "Operating System :: OS Independent", + "Environment :: GPU :: NVIDIA CUDA", +] + +dependencies = ["torchlanc", "packaging"] + +[project.urls] +Repository = "https://github.com/Artificial-Sweetener/comfyui-WhiteRabbit" +Documentation = "https://github.com/Artificial-Sweetener/comfyui-WhiteRabbit/wiki" +"Bug Tracker" = "https://github.com/Artificial-Sweetener/comfyui-WhiteRabbit/issues" + +[tool.comfy] +PublisherId = "artificialsweetener" +DisplayName = "WhiteRabbit" +Icon = "" +includes = [] diff --git a/custom_nodes/whiterabbit/readme.md b/custom_nodes/whiterabbit/readme.md new file mode 100644 index 00000000..c9634be3 --- /dev/null +++ b/custom_nodes/whiterabbit/readme.md @@ -0,0 +1,116 @@ +# WhiteRabbit: Master the Flow of Time πŸ‡ +**English** | [η€δ½“δΈ­ζ–‡](README_zh-CN.md) + +This is **comfyui-WhiteRabbit**, a nodepack designed to help you work with video from within ComfyUI. + +The Rabbit's specialty is looping through time to help you create seamless looping video, but that's not all she brings to the tea party. Quality, arbitrary framerate resampling and super fast image resizing are also part of the kit! + +While some of these nodes certainly can be used for single-image tasks, every one of them is designed with efficient **batch handling** in mind and that means the performance gains compound, letting you process whole video clips as fast as possible within your hardware constraints. + + +## Installation + +WhiteRabbit supports two layouts: + +1) **External base pack (preferred when present)**: `custom_nodes/comfyui-frame-interpolation/` +2) **Vendored fallback (bundled here)**: `vendor/` + +**Quick install:** +1. Drop the **comfyui-WhiteRabbit** folder into `ComfyUI/custom_nodes/`. +2. Install this node’s requirements: + ```bash + pip install -r requirements.txt + +**Optionally**, you can install [ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation) inside of your custom_nodes/ folder. WhiteRabbit will detect it and use resources from there. Especially handy if you already use it, since it avoids keeping two versions of the RIFE models. + +### Python requirements + +This node relies on ComfyUI’s core packages (e.g., `torch`, `torchvision`, `numpy`, `einops`, `pyyaml`) that are already provided by ComfyUI. Your **node-local** `requirements.txt` only adds: + +``` +packaging +torchlanc +``` + +## The Nodes + +This pack of nodes helps you solve some of the trickiest problems in video creation. + +### Time Benders + +These nodes bend time itself to add or remove frames, all powered by the **RIFE** interpolation model. For a slight speed boost, they’re optimized to work together, caching the RIFE model for small efficiency gains in multi‑RIFE workflows. + +- **RIFE VFI Interpolate by Multiple**: The basic tool for frame interpolation. Multiply your frames by 2Γ—, 4Γ—, etc., and it’ll generate the new frames needed to make your video silky smooth. +- **RIFE VFI FPS Resample**: A master of time travel. Convert your video to a specific target frame rate, automatically handling both adding and dropping frames as needed. Includes features to prevent common artifacts like flicker for a clean result. +- **RIFE VFI Custom Timing**: Ready for total control? Place every new frame with surgical precision. Create custom speed ramps or smooth out specific moments by providing a custom timing list. +- **RIFE Seam Timing Analyzer**: The perfect companion to the custom timing node. Automatically calculates the exact timing for a seamless loop, giving you the CSV values you need to make your transition feel flawless. + +![resample_framerate](examples/resample_framerate.png) +> *Example:* The **RIFE VFI FPS Resample** node is a master of time, resampling your video to a new frame rate. Try for yourself; the workflow is attached! + +### Loop Masters + +Making a seamless video loop can feel like a riddle. These nodes give you the keys to the perfect, continuous loop. + +- **Prepare Loop Frames**: The first step. This node takes your entire video and prepares the loop "seam" by isolating the last and first frames into a separate batch. This little pair is all your interpolator needs to get started on the transition. +- **Assemble Loop Frames**: The final piece. After your interpolator works its magic, this node takes your original video and appends the new seam frames to the end, assembling your complete, continuous loop. +- **Autocrop to Loop**: Don't get lost in the forest of frames! This clever node intelligently analyzes your video to find the best possible place to crop from the end, ensuring your loop flows as smoothly as can be. +- **Trim Batch Ends**: A simple tool for trimming a fixed number of frames from the beginning or end of your clip, perfect for removing unwanted intros or outros. +- **Roll Frames**: Change the order of the images in a batch cyclicly. In the context of a loop, this will change on what frame your loop starts. +- **Unroll Frames**: Undo the work done by the above node; you may want to roll frames for a specific process (like interpolation) before returning them to their original order. This node comes with the ability to add a frame multiplier to put it in sync with a **RIFE VFI Interpolate by Multiple** that comes before. + +![interpolate_loop_seam](examples/interpolate_loop_seam.png) +> *Example:* Stitch a seamless loop with **Prepare Loop Frames** ➜ **RIFE Seam Timing Analyzer** ➜ **RIFE VFI Custom Timing ➜ **Assemble Loop Frames**. You can drop this png into ComfyUI and take it for a test drive! + +![interpolate_loop_seam](examples/autocrop_to_loop.png) +> *Example:* The best loop is the one you already have. **Autocrop to Loop** can help you find the best end frame by analyzing the visual difference and timing between trailing frames in your clip. + +### Post-Processing + +These nodes play support! + +- **Batch Resize w/ Lanczos**: Fast, principled, and uncompromising in quality. This CUDA‑accelerated node resizes a batch of images (or your single images, of course) using the high‑quality Lanczos algorithm written for PyTorch; [TorchLanc](https://github.com/Artificial-Sweetener/TorchLanc). It’s dramatically faster than CPU alternatives like Pillow's own Lanczos, with potential for up to a *10Γ— speed increase*. +- **Upscale w/ Model (Advanced)**: A version of ComfyUI's own "Upscale Image (Using Model)" but with direct controls exposed for batch size and tiling which can help speed up scaling dramatically if you tune the numbers to your system. +- **Pixel Hold**: Can be used to reduce video flicker and clean up static parts of a video by reducing small fluctuations caused by video diffusion or compression. There is the potential to use this creatively because it can also take an input image as its baseline. +- **Watermark**: For single images or batches. Very quick, especially when compared to doing the same task in pro editing tools. + +![resize](examples/resize.png) +> *Example:* Resize images quickly with **Batch Resize w/ Lanczos**. Workflow attached!! + +![resize_with_model_to_target](examples/resize_with_model_to_target.png) +> *Example:* Use **Upscale w/ Model (Advanced)** in concert with **Batch Resize w/ Lanczos** to reach a specific target size like so. The image is holding onto the workflow for you. + +![watermark](examples/watermark.png) +> *Example:* Apply a watermark to each frame rapidly with smart configuration options. Workflow included. + +## License & Acknowledgements +- **Project License:** GNU Affero General Public License v3.0 (**AGPL‑3.0**). Please read the full [LICENSE](LICENSE) included with this repo! The AGPL-3.0 is a strong copyleft license. If you convey the software, you must provide its corresponding source; and if you let users interact with a modified version over a network, you must offer them that modified version’s corresponding source. + +- **Dependency License (MIT):** This project **vendors** minimal components from **[ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)** for reliability. These files are licensed under MIT by **[Fannovel16](https://github.com/Fannovel16)** and **[contributors](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation/graphs/contributors)**; see the included license at `LICENSES/MIT-ComfyUI-Frame-Interpolation.txt`: + - `vendor/vfi_utils.py` + - `vendor/rife/__init__.py` + - `vendor/rife/rife_arch.py` +- From **[ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)**, it also adapt small portions within [`interpolation.py`](interpolation.py). +- UI for **Batch Resize w/ Lanczos** was inspired by the similar node from [Kijai](https://github.com/kijai/)'s excellent [KJNodes](thub.com/kijai/ComfyUI-KJNodes). + +### Research citations + +This node pack uses **RIFE (IFNet)** for video frame interpolation. You can read the white paper [here](https://ar5iv.labs.arxiv.org/html/2011.06294). + +```bibtex +@inproceedings{huang2022rife, + title={Real-Time Intermediate Flow Estimation for Video Frame Interpolation}, + author={Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang}, + booktitle={European Conference on Computer Vision (ECCV)}, + year={2022} +} +``` +--- + +## From the Developer ❀️ + +I hope you love using these nodes as much as I loved putting them together! + +- **Buy Me a Coffee**: You can help fuel more projects like this at my [Ko-fi page](https://ko-fi.com/artificial_sweetener). +- **My Website & Socials**: See my art, poetry, and other dev updates at [artificialsweetener.ai](https://artificialsweetener.ai). +- **If you like this project**, it would mean a lot to me if you gave me a star here on Github!! ⭐ diff --git a/custom_nodes/whiterabbit/requirements.txt b/custom_nodes/whiterabbit/requirements.txt new file mode 100644 index 00000000..93e637db --- /dev/null +++ b/custom_nodes/whiterabbit/requirements.txt @@ -0,0 +1,2 @@ +packaging +torchlanc \ No newline at end of file diff --git a/custom_nodes/whiterabbit/scaling.py b/custom_nodes/whiterabbit/scaling.py new file mode 100644 index 00000000..75984dbe --- /dev/null +++ b/custom_nodes/whiterabbit/scaling.py @@ -0,0 +1,696 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# SPDX-FileCopyrightText: 2025 ArtificialSweetener +import math +from typing import List, Optional, Tuple + +import comfy.utils as comfy_utils +import torch +import torch.nn.functional as F +from comfy import model_management +from torchlanc import lanczos_resize + + +class UpscaleWithModelAdvanced: + DESCRIPTION = """Based on Comfy's native "Upscale Image (using Model)", with controls exposed to tune for large batches, avoid slow +OOM fallbacks, and create opportunities to optimize for speed. + +Defaults +- Behaves about the same as the original node. + +Controls +- max_batch_size > 0: process images in chunks to keep VRAM steady and reduce fallback slowdowns. +- tile_size: choose a starting tile; original node defaults to 512. 0 = auto (falls back 512 β†’ 256 β†’ 128 on OOM). +- channels_last: try ON for a speedup on some systems. +- precision: lower (fp16/bf16) can be faster; may impact quality depending on the model. +""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "upscale_model": ( + "UPSCALE_MODEL", + {"tooltip": "Pick your ESRGAN model (e.g. 2Γ— / 4Γ—)."}, + ), + "image": ( + "IMAGE", + { + "tooltip": "Images to upscale. Accepts a batch: framesΓ—HΓ—WΓ—C with values in [0–1]." + }, + ), + }, + "optional": { + "max_batch_size": ( + "INT", + { + "default": 0, + "min": 0, + "max": 4096, + "step": 1, + "tooltip": "How many images to process at once. 0 = all at once. Set >0 if you hit OOM.", + }, + ), + "tile_size": ( + "INT", + { + "default": 0, + "min": 0, + "max": 2048, + "step": 32, + "tooltip": "How big each tile is. 0 = auto (starts at 512 and halves on OOM). Bigger is faster; smaller is safer.", + }, + ), + "channels_last": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Try this ON for a small speed boost on some GPUs. If you see no gain, leave it OFF.", + }, + ), + "precision": ( + ["fp32", "fp16", "bf16"], + { + "default": "fp32", + "tooltip": "Math mode. fp32 = safest. fp16/bf16 can be faster on many GPUs, may impact image quality.", + }, + ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + CATEGORY = "image/upscaling" + + def upscale( + self, + upscale_model, + image, + max_batch_size=0, + tile_size=0, + channels_last=False, + precision="fp32", + ): + def spans(n, cap): + if cap <= 0 or cap >= n: + return [(0, n)] + out = [] + i = 0 + while i < n: + j = min(n, i + cap) + out.append((i, j)) + i = j + return out + + device = model_management.get_torch_device() + + upscale_model.to(device) + for p in upscale_model.model.parameters(): + if p.device != device: + p.data = p.data.to(device) + if p._grad is not None: + p._grad.data = p._grad.data.to(device) + + upscale_model.model.eval() + + scale = float(getattr(upscale_model, "scale", 4.0)) + memory_required = model_management.module_size(upscale_model.model) + memory_required += ( + (512 * 512 * 3) * image.element_size() * max(scale, 1.0) * 384.0 + ) + memory_required += image.nelement() * image.element_size() + model_management.free_memory(memory_required, device) + + B, H, W, C = image.shape + out_chunks = [] + + for s, e in spans(B, int(max_batch_size)): + sub = image[s:e].movedim(-1, -3).to(device, non_blocking=True) + + if channels_last and device.type == "cuda": + sub = sub.to(memory_format=torch.channels_last) + + tile = 512 if tile_size in (0, None) else int(tile_size) + overlap = 32 + + oom = True + while oom: + try: + steps = sub.shape[0] * comfy_utils.get_tiled_scale_steps( + sub.shape[3], + sub.shape[2], + tile_x=tile, + tile_y=tile, + overlap=overlap, + ) + pbar = comfy_utils.ProgressBar(steps) + + if device.type == "cuda" and precision in ("fp16", "bf16"): + amp_dtype = ( + torch.float16 if precision == "fp16" else torch.bfloat16 + ) + with torch.autocast( + device_type="cuda", dtype=amp_dtype + ), torch.inference_mode(): + sr = comfy_utils.tiled_scale( + sub, + lambda a: upscale_model(a), + tile_x=tile, + tile_y=tile, + overlap=overlap, + upscale_amount=scale, + pbar=pbar, + ) + + else: + with torch.inference_mode(): + sr = comfy_utils.tiled_scale( + sub, + lambda a: upscale_model(a), + tile_x=tile, + tile_y=tile, + overlap=overlap, + upscale_amount=scale, + pbar=pbar, + ) + + oom = False + except model_management.OOM_EXCEPTION as e: + tile //= 2 + if tile < 128: + raise e + + out_chunks.append( + torch.clamp(sr.movedim(-3, -1), 0.0, 1.0).to("cpu", non_blocking=True) + ) + + upscale_model.to("cpu") + return (torch.cat(out_chunks, dim=0),) + + +def _chunk_spans(n: int, max_bs: int) -> List[Tuple[int, int]]: + if max_bs <= 0 or max_bs >= n: + return [(0, n)] + s, spans = 0, [] + while s < n: + e = min(n, s + max_bs) + spans.append((s, e)) + s = e + return spans + + +def _floor_mul(x: int, k: int) -> int: + if k <= 1: + return max(1, x) + return x - (x % k) + + +def _ceil_mul(x: int, k: int) -> int: + if k <= 1: + return max(1, x) + r = x % k + return x if r == 0 else x + (k - r) + + +def _fit_keep_aspect(sw: int, sh: int, tw: int, th: int) -> Tuple[int, int]: + if tw <= 0 and th <= 0: + return sw, sh + if tw <= 0: + r = th / sh + elif th <= 0: + r = tw / sw + else: + r = min(tw / sw, th / sh) + return max(1, int(round(sw * r))), max(1, int(round(sh * r))) + + +def _fit_keep_ar_divisible( + sw: int, sh: int, tw: int, th: int, d: int +) -> Tuple[int, int]: + if d <= 1: + return _fit_keep_aspect(sw, sh, tw, th) + fw, fh = _fit_keep_aspect(sw, sh, tw, th) + g = math.gcd(sw, sh) + base_w = d * (sw // g) + base_h = d * (sh // g) + k = min(fw // base_w, fh // base_h) + if k >= 1: + return base_w * k, base_h * k + return max(d, _floor_mul(fw, d)), max(d, _floor_mul(fh, d)) + + +def _scale_then_crop_divisible( + sw: int, sh: int, req_w: int, req_h: int, d: int +) -> Tuple[int, int, int, int]: + """ + AR Scale + Divisible Crop: + 1) Scale once (keep AR), locking the SOURCE long side to floor(requested_long/d)*d (>0). + 2) Crop ONLY the short side to the largest multiple of d that is ≀ scaled short side and ≀ requested short side (>0). + """ + d = max(1, int(d)) + req_w = max(1, int(req_w)) + req_h = max(1, int(req_h)) + req_w_div = _floor_mul(req_w, d) + req_h_div = _floor_mul(req_h, d) + + src_long_is_h = sh >= sw + + if src_long_is_h: + if req_h_div == 0: + raise ValueError( + f"AR Scale + Divisible Crop: requested height {req_h}px < divisible_by {d}." + ) + scale = req_h_div / sh + rh = req_h_div + rw = max(1, int(round(sw * scale))) + if rw < d: + raise ValueError( + f"AR Scale + Divisible Crop: scaled width {rw}px < divisible_by {d}." + ) + if req_w_div == 0: + raise ValueError( + f"AR Scale + Divisible Crop: requested width {req_w}px < divisible_by {d}." + ) + out_w = min(req_w_div, _floor_mul(rw, d)) + out_h = rh + else: + if req_w_div == 0: + raise ValueError( + f"AR Scale + Divisible Crop: requested width {req_w}px < divisible_by {d}." + ) + scale = req_w_div / sw + rw = req_w_div + rh = max(1, int(round(sh * scale))) + if rh < d: + raise ValueError( + f"AR Scale + Divisible Crop: scaled height {rh}px < divisible_by {d}." + ) + if req_h_div == 0: + raise ValueError( + f"AR Scale + Divisible Crop: requested height {req_h}px < divisible_by {d}." + ) + out_h = min(req_h_div, _floor_mul(rh, d)) + out_w = rw + + if (rw % d == 0) and (rh % d == 0): + return rw, rh, rw, rh + + return rw, rh, out_w, out_h + + +def _cover_keep_aspect(sw: int, sh: int, tw: int, th: int) -> Tuple[int, int]: + r = max(tw / sw, th / sh) + return max(1, int((sw * r) + 0.999999)), max(1, int((sh * r) + 0.999999)) + + +def _pad_sides(pos: str, pad_w: int, pad_h: int) -> Tuple[int, int, int, int]: + lw = pad_w // 2 + rw = pad_w - lw + th = pad_h // 2 + bh = pad_h - th + if pos in ("top-left", "top", "top-right"): + th, bh = 0, pad_h + if pos in ("bottom-left", "bottom", "bottom-right"): + th, bh = pad_h, 0 + if pos in ("top-left", "left", "bottom-left"): + lw, rw = 0, pad_w + if pos in ("top-right", "right", "bottom-right"): + lw, rw = pad_w, 0 + return lw, rw, th, bh + + +def _crop_offsets( + pos: str, in_w: int, in_h: int, out_w: int, out_h: int +) -> Tuple[int, int]: + dx = max(0, in_w - out_w) + dy = max(0, in_h - out_h) + mapx = { + "top-left": "left", + "left": "left", + "bottom-left": "left", + "top": "center", + "center": "center", + "bottom": "center", + "top-right": "right", + "right": "right", + "bottom-right": "right", + } + mapy = { + "top-left": "top", + "top": "top", + "top-right": "top", + "left": "center", + "center": "center", + "right": "center", + "bottom-left": "bottom", + "bottom": "bottom", + "bottom-right": "bottom", + } + lx = {"left": 0, "center": dx // 2, "right": dx}.get( + mapx.get(pos, "center"), dx // 2 + ) + ly = {"top": 0, "center": dy // 2, "bottom": dy}.get( + mapy.get(pos, "center"), dy // 2 + ) + return lx, ly + + +def _parse_pad_color( + s: str, c: int, device: torch.device, dtype: torch.dtype +) -> torch.Tensor: + s = (s or "").strip() + if not s: + return torch.zeros(c, device=device, dtype=dtype) + try: + parts = [int(p.strip()) for p in s.split(",")] + except Exception: + parts = [0, 0, 0] + rgb = [int(max(0, min(255, v))) for v in (parts + [0, 0, 0])[:3]] + v = torch.tensor( + [rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0], device=device, dtype=dtype + ) + if c == 1: + return v[:1] + if c == 4: + return torch.cat([v, torch.ones(1, device=device, dtype=dtype)]) + return v + + +def _nearest_interp(x: torch.Tensor, size: Tuple[int, int]) -> torch.Tensor: + try: + return F.interpolate(x, size=size, mode="nearest-exact") + except Exception: + return F.interpolate(x, size=size, mode="nearest") + + +def _divisible_box(w: int, h: int, d: int) -> Tuple[int, int]: + if d <= 1: + return int(w), int(h) + return _floor_mul(int(w), d), _floor_mul(int(h), d) + + +def _normalize_mode(mode: str) -> str: + key = (mode or "").strip().lower() + table = { + "keep ar": "keep_ar", + "stretch": "stretch", + "crop (cover + crop)": "crop", + "pad (fit + pad)": "pad", + "ar scale + divisible crop": "ar_scale_crop_divisible", + } + if key not in table: + raise ValueError( + "Unknown resize_mode. Use one of: " + "'Keep AR', 'Stretch', 'Crop (Cover + Crop)', 'Pad (Fit + Pad)', 'AR Scale + Divisible Crop'." + ) + return table[key] + + +class BatchResizeWithLanczos: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ( + "IMAGE", + { + "tooltip": "Input batch (B,H,W,C) in [0,1] float.\nProcessed on GPU." + }, + ), + "width": ( + "INT", + { + "default": 1024, + "min": 1, + "max": 16384, + "step": 1, + "tooltip": "Target width (pixels).\n\n" + "Notes:\n" + "β€’ Keep AR / Pad: maximum width for the fit\n" + "β€’ Crop: final output width\n" + "β€’ AR Scale + Divisible Crop: requested width before divisibility", + }, + ), + "height": ( + "INT", + { + "default": 576, + "min": 1, + "max": 16384, + "step": 1, + "tooltip": "Target height (pixels).\n\n" + "Notes:\n" + "β€’ Keep AR / Pad: maximum height for the fit\n" + "β€’ Crop: final output height\n" + "β€’ AR Scale + Divisible Crop: requested height before divisibility", + }, + ), + "resize_mode": ( + [ + "Keep AR", + "Stretch", + "Crop (Cover + Crop)", + "Pad (Fit + Pad)", + "AR Scale + Divisible Crop", + ], + { + "default": "Keep AR", + "tooltip": "Modes:\n" + "- Keep AR: Fit inside widthΓ—height (preserve aspect)\n" + "- Stretch: Force to widthΓ—height (may distort)\n" + "- Crop (Cover + Crop): Scale to cover, then crop to widthΓ—height\n" + "- Pad (Fit + Pad): Fit inside, then pad to widthΓ—height\n" + "- AR Scale + Divisible Crop: Scale by SOURCE long side to ≀ requested divisible; crop ONLY the short side to its divisible", + }, + ), + "divisible_by": ( + "INT", + { + "default": 1, + "min": 1, + "max": 4096, + "step": 1, + "tooltip": "Force output dimensions to multiples of N.\n\n" + "Details:\n" + "β€’ Keep AR: Fit β†’ then step down to the largest size ≀ requested that keeps AR AND makes both sides divisible\n" + "β€’ AR Scale + Divisible Crop: Lock the scaled LONG side to its divisible target; crop ONLY the short side to its divisible\n" + "Set to 1 (or 0 in UI) to disable", + }, + ), + "max_batch_size": ( + "INT", + { + "default": 0, + "min": 0, + "max": 4096, + "step": 1, + "tooltip": "0 = process whole batch\n>0 = chunk the batch to this size", + }, + ), + "sinc_window": ( + "INT", + { + "default": 3, + "min": 1, + "max": 8, + "step": 1, + "tooltip": "Lanczos window size (a). Higher = sharper (more ringing).", + }, + ), + "pad_color": ( + "STRING", + { + "default": "0, 0, 0", + "tooltip": "Pad mode only. RGB as 'r, g, b' (0-255).", + }, + ), + "crop_position": ( + [ + "center", + "top-left", + "top", + "top-right", + "left", + "right", + "bottom-left", + "bottom", + "bottom-right", + ], + { + "default": "center", + "tooltip": "Where to crop/pad from.\nChoose which edges are preserved for cropping, or where padding is added.", + }, + ), + "precision": ( + ["fp32", "fp16", "bf16"], + {"default": "fp32", "tooltip": "Resampling compute dtype."}, + ), + }, + "optional": { + "mask": ( + "MASK", + { + "tooltip": "Optional mask (B,H,W) in [0,1].\nResized with nearest.\nFollows the same crop/pad as the image." + }, + ), + }, + } + + RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK") + RETURN_NAMES = ("IMAGE", "width", "height", "mask") + FUNCTION = "process" + CATEGORY = "image/resize" + DESCRIPTION = ( + "CUDA-accelerated, gamma-correct Lanczos resizer (TorchLanc).\n\n" + "Modes:\n" + "β€’ Keep AR\n" + "β€’ Stretch\n" + "β€’ Crop (Cover + Crop)\n" + "β€’ Pad (Fit + Pad)\n" + "β€’ AR Scale + Divisible Crop\n\n" + "Node functionality based on Resize nodes by Kijai\n\n" + "More from me!: https://artificialsweetener.ai" + ) + + def process( + self, + image: torch.Tensor, + width: int, + height: int, + resize_mode: str, + divisible_by: int, + max_batch_size: int, + sinc_window: int, + pad_color: str, + crop_position: str, + precision: str, + mask: Optional[torch.Tensor] = None, + ): + if image is None or not isinstance(image, torch.Tensor): + raise ValueError( + "image must be a torch.Tensor of shape (B,H,W,C) in [0,1]." + ) + + B, H, W, C = image.shape + if C not in (1, 3, 4): + raise ValueError(f"Unsupported channel count C={C}. Expected 1, 3 or 4.") + + d = int(divisible_by) if int(divisible_by) > 1 else 1 + mode = _normalize_mode(resize_mode) + + device = torch.device("cuda") + image = image.float().clamp_(0, 1) + + if mode == "stretch": + tw, th = _divisible_box(width, height, d) + rw, rh = tw, th + out_w, out_h = tw, th + + elif mode == "keep_ar": + rw, rh = _fit_keep_ar_divisible(W, H, int(width), int(height), d) + out_w, out_h = rw, rh + + elif mode == "ar_scale_crop_divisible": + if width <= 0 or height <= 0: + raise ValueError( + "AR Scale + Divisible Crop requires non-zero width and height." + ) + rw, rh, out_w, out_h = _scale_then_crop_divisible( + W, H, int(width), int(height), d + ) + + elif mode == "crop": + if width <= 0 or height <= 0: + raise ValueError("Crop requires non-zero width and height.") + tw, th = _divisible_box(width, height, d) + rw, rh = _cover_keep_aspect(W, H, tw, th) + out_w, out_h = tw, th + + elif mode == "pad": + if width <= 0 or height <= 0: + raise ValueError("Pad requires non-zero width and height.") + tw, th = _divisible_box(width, height, d) + rw, rh = _fit_keep_aspect(W, H, tw, th) + out_w, out_h = tw, th + + else: + raise ValueError(f"Unknown resize_mode: {resize_mode}") + + out_imgs: List[torch.Tensor] = [] + out_masks: List[torch.Tensor] = [] + + crop_like = mode in ("crop", "ar_scale_crop_divisible") + pad_like = mode == "pad" + resize_to = (rh, rw) if (crop_like or pad_like) else (out_h, out_w) + + pbar = comfy_utils.ProgressBar(B) + + for s, e in _chunk_spans(B, int(max_batch_size)): + x = image[s:e].movedim(-1, 1).to(device, non_blocking=True) + + y = lanczos_resize( + x, + height=resize_to[0], + width=resize_to[1], + a=int(sinc_window), + precision=str(precision), + clamp=True, + chunk_size=0, + ) + + ox = oy = 0 + left = right = top = bottom = 0 + + if crop_like: + ox, oy = _crop_offsets(crop_position, rw, rh, out_w, out_h) + y = y[:, :, oy : oy + out_h, ox : ox + out_w] + elif pad_like: + pad_w = max(0, out_w - rw) + pad_h = max(0, out_h - rh) + left, right, top, bottom = _pad_sides(crop_position, pad_w, pad_h) + + if d > 1: + base_w = rw + left + right + base_h = rh + top + bottom + right += _ceil_mul(base_w, d) - base_w + bottom += _ceil_mul(base_h, d) - base_h + out_w = rw + left + right + out_h = rh + top + bottom + + color = _parse_pad_color(pad_color, C, y.device, y.dtype).view( + 1, C, 1, 1 + ) + canvas = color.expand(y.shape[0], -1, out_h, out_w).clone() + canvas[:, :, top : top + rh, left : left + rw] = y + y = canvas + + out_imgs.append(y.to("cpu", non_blocking=False).movedim(1, -1)) + + if isinstance(mask, torch.Tensor): + m = mask[s:e].unsqueeze(1).to(device, non_blocking=True) + m_res = _nearest_interp(m, size=resize_to) + if crop_like: + m_res = m_res[:, :, oy : oy + out_h, ox : ox + out_w] + elif pad_like: + base = torch.zeros( + (m_res.shape[0], 1, out_h, out_w), + device=m_res.device, + dtype=m_res.dtype, + ) + base[:, :, top : top + rh, left : left + rw] = m_res + m_res = base + out_masks.append(m_res.squeeze(1).to("cpu", non_blocking=False)) + + pbar.update(e - s) + + images_out = torch.cat(out_imgs, dim=0) + mask_out = ( + torch.cat(out_masks, dim=0) + if out_masks + else torch.zeros((B, out_h, out_w), dtype=torch.float32) + ) + + return images_out, out_w, out_h, mask_out + + +NODE_CLASS_MAPPINGS = {"BatchResizeWithLanczos": BatchResizeWithLanczos} +NODE_DISPLAY_NAME_MAPPINGS = {"BatchResizeWithLanczos": "Batch Resize with Lanczos"} diff --git a/custom_nodes/whiterabbit/vendor/config.yaml b/custom_nodes/whiterabbit/vendor/config.yaml new file mode 100644 index 00000000..04e8c119 --- /dev/null +++ b/custom_nodes/whiterabbit/vendor/config.yaml @@ -0,0 +1,5 @@ +# Copyright (c) 2023–2025 Fannovel16 and contributors +# See LICENSES/MIT-ComfyUI-Frame-Interpolation.txt for the full text. +#Plz don't delete this file, just edit it when neccessary. +ckpts_path: "./ckpts" +ops_backend: "cupy" #Either "taichi" or "cupy" \ No newline at end of file diff --git a/custom_nodes/whiterabbit/vendor/rife/__init__.py b/custom_nodes/whiterabbit/vendor/rife/__init__.py new file mode 100644 index 00000000..dddaee1d --- /dev/null +++ b/custom_nodes/whiterabbit/vendor/rife/__init__.py @@ -0,0 +1,140 @@ +# Copyright (c) 2023–2025 Fannovel16 and contributors +# See LICENSES/MIT-ComfyUI-Frame-Interpolation.txt for the full text. +import torch +from torch.utils.data import DataLoader +import pathlib +from vfi_utils import ( + load_file_from_github_release, + preprocess_frames, + postprocess_frames, + generic_frame_loop, + InterpolationStateList, +) +import typing +from comfy.model_management import get_torch_device +import re +from functools import cmp_to_key +from packaging import version + +MODEL_TYPE = pathlib.Path(__file__).parent.name +CKPT_NAME_VER_DICT = { + "rife40.pth": "4.0", + "rife41.pth": "4.0", + "rife42.pth": "4.2", + "rife43.pth": "4.3", + "rife44.pth": "4.3", + "rife45.pth": "4.5", + "rife46.pth": "4.6", + "rife47.pth": "4.7", + "rife48.pth": "4.7", + "rife49.pth": "4.7", + "sudo_rife4_269.662_testV1_scale1.pth": "4.0", + # Arch 4.10 doesn't work due to state dict mismatch + # TODO: Investigating and fix it + # "rife410.pth": "4.10", + # "rife411.pth": "4.10", + # "rife412.pth": "4.10" +} + + +class RIFE_VFI: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": ( + sorted( + list(CKPT_NAME_VER_DICT.keys()), + key=lambda ckpt_name: version.parse( + CKPT_NAME_VER_DICT[ckpt_name] + ), + ), + {"default": "rife47.pth"}, + ), + "frames": ("IMAGE",), + "clear_cache_after_n_frames": ( + "INT", + {"default": 10, "min": 1, "max": 1000}, + ), + "multiplier": ("INT", {"default": 2, "min": 1}), + "fast_mode": ("BOOLEAN", {"default": True}), + "ensemble": ("BOOLEAN", {"default": True}), + "scale_factor": ([0.25, 0.5, 1.0, 2.0, 4.0], {"default": 1.0}), + }, + "optional": {"optional_interpolation_states": ("INTERPOLATION_STATES",)}, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "vfi" + CATEGORY = "ComfyUI-Frame-Interpolation/VFI" + + def vfi( + self, + ckpt_name: typing.AnyStr, + frames: torch.Tensor, + clear_cache_after_n_frames=10, + multiplier: typing.SupportsInt = 2, + fast_mode=False, + ensemble=False, + scale_factor=1.0, + optional_interpolation_states: InterpolationStateList = None, + **kwargs + ): + """ + Perform video frame interpolation using a given checkpoint model. + + Args: + ckpt_name (str): The name of the checkpoint model to use. + frames (torch.Tensor): A tensor containing input video frames. + clear_cache_after_n_frames (int, optional): The number of frames to process before clearing CUDA cache + to prevent memory overflow. Defaults to 10. Lower numbers are safer but mean more processing time. + How high you should set it depends on how many input frames there are, input resolution (after upscaling), + how many times you want to multiply them, and how long you're willing to wait for the process to complete. + multiplier (int, optional): The multiplier for each input frame. 60 input frames * 2 = 120 output frames. Defaults to 2. + + Returns: + tuple: A tuple containing the output interpolated frames. + + Note: + This method interpolates frames in a video sequence using a specified checkpoint model. + It processes each frame sequentially, generating interpolated frames between them. + + To prevent memory overflow, it clears the CUDA cache after processing a specified number of frames. + """ + from .rife_arch import IFNet + + model_path = load_file_from_github_release(MODEL_TYPE, ckpt_name) + arch_ver = CKPT_NAME_VER_DICT[ckpt_name] + interpolation_model = IFNet(arch_ver=arch_ver) + interpolation_model.load_state_dict(torch.load(model_path)) + interpolation_model.eval().to(get_torch_device()) + frames = preprocess_frames(frames) + + def return_middle_frame( + frame_0, frame_1, timestep, model, scale_list, in_fast_mode, in_ensemble + ): + return model( + frame_0, frame_1, timestep, scale_list, in_fast_mode, in_ensemble + ) + + scale_list = [ + 8 / scale_factor, + 4 / scale_factor, + 2 / scale_factor, + 1 / scale_factor, + ] + + args = [interpolation_model, scale_list, fast_mode, ensemble] + out = postprocess_frames( + generic_frame_loop( + type(self).__name__, + frames, + clear_cache_after_n_frames, + multiplier, + return_middle_frame, + *args, + interpolation_states=optional_interpolation_states, + dtype=torch.float32 + ) + ) + return (out,) diff --git a/custom_nodes/whiterabbit/vendor/rife/rife_arch.py b/custom_nodes/whiterabbit/vendor/rife/rife_arch.py new file mode 100644 index 00000000..028b9c0a --- /dev/null +++ b/custom_nodes/whiterabbit/vendor/rife/rife_arch.py @@ -0,0 +1,588 @@ +# Copyright (c) 2023–2025 Fannovel16 and contributors +# See LICENSES/MIT-ComfyUI-Frame-Interpolation.txt for the full text. +""" +26-Dez-21 +https://github.com/hzwer/Practical-RIFE +https://github.com/hzwer/Practical-RIFE/blob/main/model/warplayer.py +https://github.com/HolyWu/vs-rife/blob/master/vsrife/__init__.py +""" +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import AdamW +import torch +import torch.nn.functional as F +import torch.nn as nn +import torch.optim as optim +import warnings +from comfy.model_management import get_torch_device + +device = get_torch_device() +backwarp_tenGrid = {} + + +class ResConv(nn.Module): + def __init__(self, c, dilation=1): + super(ResConv, self).__init__() + self.conv = nn.Conv2d(c, c, 3, 1, dilation, dilation=dilation, groups=1) + self.beta = nn.Parameter(torch.ones((1, c, 1, 1)), requires_grad=True) + self.relu = nn.LeakyReLU(0.2, True) + + def forward(self, x): + return self.relu(self.conv(x) * self.beta + x) + + +def warp(tenInput, tenFlow): + k = (str(tenFlow.device), str(tenFlow.size())) + if k not in backwarp_tenGrid: + tenHorizontal = ( + torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device) + .view(1, 1, 1, tenFlow.shape[3]) + .expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) + ) + tenVertical = ( + torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device) + .view(1, 1, tenFlow.shape[2], 1) + .expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) + ) + backwarp_tenGrid[k] = torch.cat([tenHorizontal, tenVertical], 1).to(device) + + tenFlow = torch.cat( + [ + tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), + tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0), + ], + 1, + ) + + g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) + + if tenInput.type() == "torch.cuda.HalfTensor": + g = g.half() + + padding_mode = "border" + if device.type == "mps": + # https://github.com/pytorch/pytorch/issues/125098 + padding_mode = "zeros" + g = g.clamp(-1, 1) + return torch.nn.functional.grid_sample( + input=tenInput, + grid=g, + mode="bilinear", + padding_mode=padding_mode, + align_corners=True, + ) + + +def conv( + in_planes, + out_planes, + kernel_size=3, + stride=1, + padding=1, + dilation=1, + arch_ver="4.0", +): + if arch_ver == "4.0": + return nn.Sequential( + nn.Conv2d( + in_planes, + out_planes, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=True, + ), + nn.PReLU(out_planes), + ) + if arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10"]: + return nn.Sequential( + nn.Conv2d( + in_planes, + out_planes, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=True, + ), + nn.LeakyReLU(0.2, True), + ) + + +def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): + return nn.Sequential( + nn.Conv2d( + in_planes, + out_planes, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=True, + ), + ) + + +def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): + return nn.Sequential( + nn.Conv2d( + in_planes, + out_planes, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=True, + ) + ) + + +def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1, arch_ver="4.0"): + if arch_ver == "4.0": + return nn.Sequential( + torch.nn.ConvTranspose2d( + in_channels=in_planes, + out_channels=out_planes, + kernel_size=4, + stride=2, + padding=1, + bias=True, + ), + nn.PReLU(out_planes), + ) + if arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10"]: + return nn.Sequential( + torch.nn.ConvTranspose2d( + in_channels=in_planes, + out_channels=out_planes, + kernel_size=4, + stride=2, + padding=1, + bias=True, + ), + nn.LeakyReLU(0.2, True), + ) + + +class Conv2(nn.Module): + def __init__(self, in_planes, out_planes, stride=2, arch_ver="4.0"): + super(Conv2, self).__init__() + self.conv1 = conv(in_planes, out_planes, 3, stride, 1, arch_ver=arch_ver) + self.conv2 = conv(out_planes, out_planes, 3, 1, 1, arch_ver=arch_ver) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + return x + + +class IFBlock(nn.Module): + def __init__(self, in_planes, c=64, arch_ver="4.0"): + super(IFBlock, self).__init__() + self.arch_ver = arch_ver + self.conv0 = nn.Sequential( + conv(in_planes, c // 2, 3, 2, 1, arch_ver=arch_ver), + conv(c // 2, c, 3, 2, 1, arch_ver=arch_ver), + ) + self.arch_ver = arch_ver + + if arch_ver in ["4.0", "4.2", "4.3"]: + self.convblock = nn.Sequential( + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + conv(c, c, arch_ver=arch_ver), + ) + self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1) + + if arch_ver in ["4.5", "4.6", "4.7", "4.10"]: + self.convblock = nn.Sequential( + ResConv(c), + ResConv(c), + ResConv(c), + ResConv(c), + ResConv(c), + ResConv(c), + ResConv(c), + ResConv(c), + ) + if arch_ver == "4.5": + self.lastconv = nn.Sequential( + nn.ConvTranspose2d(c, 4 * 5, 4, 2, 1), nn.PixelShuffle(2) + ) + if arch_ver in ["4.6", "4.7", "4.10"]: + self.lastconv = nn.Sequential( + nn.ConvTranspose2d(c, 4 * 6, 4, 2, 1), nn.PixelShuffle(2) + ) + + def forward(self, x, flow=None, scale=1): + x = F.interpolate( + x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False + ) + if flow is not None: + flow = ( + F.interpolate( + flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False + ) + * 1.0 + / scale + ) + x = torch.cat((x, flow), 1) + feat = self.conv0(x) + if self.arch_ver == "4.0": + feat = self.convblock(feat) + feat + if self.arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10"]: + feat = self.convblock(feat) + + tmp = self.lastconv(feat) + if self.arch_ver in ["4.0", "4.2", "4.3"]: + tmp = F.interpolate( + tmp, scale_factor=scale * 2, mode="bilinear", align_corners=False + ) + flow = tmp[:, :4] * scale * 2 + if self.arch_ver in ["4.5", "4.6", "4.7", "4.10"]: + tmp = F.interpolate( + tmp, scale_factor=scale, mode="bilinear", align_corners=False + ) + flow = tmp[:, :4] * scale + mask = tmp[:, 4:5] + return flow, mask + + +class Contextnet(nn.Module): + def __init__(self, arch_ver="4.0"): + super(Contextnet, self).__init__() + c = 16 + self.conv1 = Conv2(3, c, arch_ver=arch_ver) + self.conv2 = Conv2(c, 2 * c, arch_ver=arch_ver) + self.conv3 = Conv2(2 * c, 4 * c, arch_ver=arch_ver) + self.conv4 = Conv2(4 * c, 8 * c, arch_ver=arch_ver) + + def forward(self, x, flow): + x = self.conv1(x) + flow = ( + F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) + * 0.5 + ) + f1 = warp(x, flow) + x = self.conv2(x) + flow = ( + F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) + * 0.5 + ) + f2 = warp(x, flow) + x = self.conv3(x) + flow = ( + F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) + * 0.5 + ) + f3 = warp(x, flow) + x = self.conv4(x) + flow = ( + F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) + * 0.5 + ) + f4 = warp(x, flow) + return [f1, f2, f3, f4] + + +class Unet(nn.Module): + def __init__(self, arch_ver="4.0"): + super(Unet, self).__init__() + c = 16 + self.down0 = Conv2(17, 2 * c, arch_ver=arch_ver) + self.down1 = Conv2(4 * c, 4 * c, arch_ver=arch_ver) + self.down2 = Conv2(8 * c, 8 * c, arch_ver=arch_ver) + self.down3 = Conv2(16 * c, 16 * c, arch_ver=arch_ver) + self.up0 = deconv(32 * c, 8 * c, arch_ver=arch_ver) + self.up1 = deconv(16 * c, 4 * c, arch_ver=arch_ver) + self.up2 = deconv(8 * c, 2 * c, arch_ver=arch_ver) + self.up3 = deconv(4 * c, c, arch_ver=arch_ver) + self.conv = nn.Conv2d(c, 3, 3, 1, 1) + + def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1): + s0 = self.down0( + torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1) + ) + s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1)) + s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1)) + s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1)) + x = self.up0(torch.cat((s3, c0[3], c1[3]), 1)) + x = self.up1(torch.cat((x, s2), 1)) + x = self.up2(torch.cat((x, s1), 1)) + x = self.up3(torch.cat((x, s0), 1)) + x = self.conv(x) + return torch.sigmoid(x) + + +""" +currently supports 4.0-4.12 + +4.0: 4.0, 4.1 +4.2: 4.2 +4.3: 4.3, 4.4 +4.5: 4.5 +4.6: 4.6 +4.7: 4.7, 4.8, 4.9 +4.10: 4.10 4.11 4.12 +""" + + +class IFNet(nn.Module): + def __init__(self, arch_ver="4.0"): + super(IFNet, self).__init__() + self.arch_ver = arch_ver + if arch_ver in ["4.0", "4.2", "4.3", "4.5", "4.6"]: + self.block0 = IFBlock(7, c=192, arch_ver=arch_ver) + self.block1 = IFBlock(8 + 4, c=128, arch_ver=arch_ver) + self.block2 = IFBlock(8 + 4, c=96, arch_ver=arch_ver) + self.block3 = IFBlock(8 + 4, c=64, arch_ver=arch_ver) + if arch_ver in ["4.7"]: + self.block0 = IFBlock(7 + 8, c=192, arch_ver=arch_ver) + self.block1 = IFBlock(8 + 4 + 8, c=128, arch_ver=arch_ver) + self.block2 = IFBlock(8 + 4 + 8, c=96, arch_ver=arch_ver) + self.block3 = IFBlock(8 + 4 + 8, c=64, arch_ver=arch_ver) + self.encode = nn.Sequential( + nn.Conv2d(3, 16, 3, 2, 1), nn.ConvTranspose2d(16, 4, 4, 2, 1) + ) + if arch_ver in ["4.10"]: + self.block0 = IFBlock(7 + 16, c=192) + self.block1 = IFBlock(8 + 4 + 16, c=128) + self.block2 = IFBlock(8 + 4 + 16, c=96) + self.block3 = IFBlock(8 + 4 + 16, c=64) + self.encode = nn.Sequential( + nn.Conv2d(3, 32, 3, 2, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(32, 32, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(32, 32, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.ConvTranspose2d(32, 8, 4, 2, 1), + ) + + if arch_ver in ["4.0", "4.2", "4.3"]: + self.contextnet = Contextnet(arch_ver=arch_ver) + self.unet = Unet(arch_ver=arch_ver) + self.arch_ver = arch_ver + + def forward( + self, + img0, + img1, + timestep=0.5, + scale_list=[8, 4, 2, 1], + training=True, + fastmode=True, + ensemble=False, + return_flow=False, + ): + img0 = torch.clamp(img0, 0, 1) + img1 = torch.clamp(img1, 0, 1) + + n, c, h, w = img0.shape + ph = ((h - 1) // 64 + 1) * 64 + pw = ((w - 1) // 64 + 1) * 64 + padding = (0, pw - w, 0, ph - h) + img0 = F.pad(img0, padding) + img1 = F.pad(img1, padding) + x = torch.cat((img0, img1), 1) + + if training == False: + channel = x.shape[1] // 2 + img0 = x[:, :channel] + img1 = x[:, channel:] + if not torch.is_tensor(timestep): + timestep = (x[:, :1].clone() * 0 + 1) * timestep + else: + timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3]) + + flow_list = [] + merged = [] + mask_list = [] + + if self.arch_ver in ["4.7", "4.10"]: + f0 = self.encode(img0[:, :3]) + f1 = self.encode(img1[:, :3]) + + warped_img0 = img0 + warped_img1 = img1 + flow = None + mask = None + block = [self.block0, self.block1, self.block2, self.block3] + + for i in range(4): + if flow is None: + # 4.0-4.6 + if self.arch_ver in ["4.0", "4.2", "4.3", "4.5", "4.6"]: + flow, mask = block[i]( + torch.cat((img0[:, :3], img1[:, :3], timestep), 1), + None, + scale=scale_list[i], + ) + if ensemble: + f1, m1 = block[i]( + torch.cat((img1[:, :3], img0[:, :3], 1 - timestep), 1), + None, + scale=scale_list[i], + ) + flow = (flow + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 + mask = (mask + (-m1)) / 2 + + # 4.7+ + if self.arch_ver in ["4.7", "4.10"]: + flow, mask = block[i]( + torch.cat((img0[:, :3], img1[:, :3], f0, f1, timestep), 1), + None, + scale=scale_list[i], + ) + + if ensemble: + f_, m_ = block[i]( + torch.cat( + (img1[:, :3], img0[:, :3], f1, f0, 1 - timestep), 1 + ), + None, + scale=scale_list[i], + ) + flow = (flow + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2 + mask = (mask + (-m_)) / 2 + + else: + # 4.0-4.6 + if self.arch_ver in ["4.0", "4.2", "4.3", "4.5", "4.6"]: + f0, m0 = block[i]( + torch.cat( + (warped_img0[:, :3], warped_img1[:, :3], timestep, mask), 1 + ), + flow, + scale=scale_list[i], + ) + + if self.arch_ver in ["4.0"]: + if ( + i == 1 + and f0[:, :2].abs().max() > 32 + and f0[:, 2:4].abs().max() > 32 + and not training + ): + for k in range(4): + scale_list[k] *= 2 + flow, mask = block[0]( + torch.cat((img0[:, :3], img1[:, :3], timestep), 1), + None, + scale=scale_list[0], + ) + warped_img0 = warp(img0, flow[:, :2]) + warped_img1 = warp(img1, flow[:, 2:4]) + f0, m0 = block[i]( + torch.cat( + ( + warped_img0[:, :3], + warped_img1[:, :3], + timestep, + mask, + ), + 1, + ), + flow, + scale=scale_list[i], + ) + + # 4.7+ + if self.arch_ver in ["4.7", "4.10"]: + fd, m0 = block[i]( + torch.cat( + ( + warped_img0[:, :3], + warped_img1[:, :3], + warp(f0, flow[:, :2]), + warp(f1, flow[:, 2:4]), + timestep, + mask, + ), + 1, + ), + flow, + scale=scale_list[i], + ) + flow = flow + fd + + # 4.0-4.6 ensemble + if ensemble and self.arch_ver in [ + "4.0", + "4.2", + "4.3", + "4.5", + "4.6", + ]: + f1, m1 = block[i]( + torch.cat( + ( + warped_img1[:, :3], + warped_img0[:, :3], + 1 - timestep, + -mask, + ), + 1, + ), + torch.cat((flow[:, 2:4], flow[:, :2]), 1), + scale=scale_list[i], + ) + f0 = (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 + m0 = (m0 + (-m1)) / 2 + + # 4.7+ ensemble + if ensemble and self.arch_ver in ["4.7", "4.10"]: + wf0 = warp(f0, flow[:, :2]) + wf1 = warp(f1, flow[:, 2:4]) + + f_, m_ = block[i]( + torch.cat( + ( + warped_img1[:, :3], + warped_img0[:, :3], + wf1, + wf0, + 1 - timestep, + -mask, + ), + 1, + ), + torch.cat((flow[:, 2:4], flow[:, :2]), 1), + scale=scale_list[i], + ) + fd = (fd + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2 + mask = (m0 + (-m_)) / 2 + + if self.arch_ver in ["4.0", "4.2", "4.3", "4.5", "4.6"]: + flow = flow + f0 + mask = mask + m0 + + if not ensemble and self.arch_ver in ["4.7", "4.10"]: + mask = m0 + + mask_list.append(mask) + flow_list.append(flow) + warped_img0 = warp(img0, flow[:, :2]) + warped_img1 = warp(img1, flow[:, 2:4]) + merged.append((warped_img0, warped_img1)) + + if self.arch_ver in ["4.0", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6"]: + mask_list[3] = torch.sigmoid(mask_list[3]) + merged[3] = merged[3][0] * mask_list[3] + merged[3][1] * (1 - mask_list[3]) + + if self.arch_ver in ["4.7", "4.10"]: + mask = torch.sigmoid(mask) + merged[3] = warped_img0 * mask + warped_img1 * (1 - mask) + + if not fastmode and self.arch_ver in ["4.0", "4.2", "4.3"]: + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) + res = tmp[:, :3] * 2 - 1 + merged[3] = torch.clamp(merged[3] + res, 0, 1) + return merged[3][:, :, :h, :w] diff --git a/custom_nodes/whiterabbit/vendor/vfi_utils.py b/custom_nodes/whiterabbit/vendor/vfi_utils.py new file mode 100644 index 00000000..f35478be --- /dev/null +++ b/custom_nodes/whiterabbit/vendor/vfi_utils.py @@ -0,0 +1,358 @@ +# Copyright (c) 2023–2025 Fannovel16 and contributors +# See LICENSES/MIT-ComfyUI-Frame-Interpolation.txt for the full text. + +import yaml +import os +from torch.hub import download_url_to_file, get_dir +from urllib.parse import urlparse +import torch +import typing +import traceback +import einops +import gc +import torchvision.transforms.functional as transform +from comfy.model_management import soft_empty_cache, get_torch_device +import numpy as np + +BASE_MODEL_DOWNLOAD_URLS = [ + "https://github.com/styler00dollar/VSGAN-tensorrt-docker/releases/download/models/", + "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation/releases/download/models/", + "https://github.com/dajes/frame-interpolation-pytorch/releases/download/v1.0.0/", +] + +config_path = os.path.join(os.path.dirname(__file__), "./config.yaml") +if os.path.exists(config_path): + config = yaml.load(open(config_path, "r", encoding="utf-8"), Loader=yaml.FullLoader) +else: + raise Exception( + "config.yaml file is neccessary, plz recreate the config file by downloading it from https://github.com/Fannovel16/ComfyUI-Frame-Interpolation" + ) +DEVICE = get_torch_device() + + +class InterpolationStateList: + + def __init__(self, frame_indices: typing.List[int], is_skip_list: bool): + self.frame_indices = frame_indices + self.is_skip_list = is_skip_list + + def is_frame_skipped(self, frame_index): + is_frame_in_list = frame_index in self.frame_indices + return ( + self.is_skip_list + and is_frame_in_list + or not self.is_skip_list + and not is_frame_in_list + ) + + +class MakeInterpolationStateList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "frame_indices": ("STRING", {"multiline": True, "default": "1,2,3"}), + "is_skip_list": ( + "BOOLEAN", + {"default": True}, + ), + }, + } + + RETURN_TYPES = ("INTERPOLATION_STATES",) + FUNCTION = "create_options" + CATEGORY = "ComfyUI-Frame-Interpolation/VFI" + + def create_options(self, frame_indices: str, is_skip_list: bool): + frame_indices_list = [int(item) for item in frame_indices.split(",")] + + interpolation_state_list = InterpolationStateList( + frame_indices=frame_indices_list, + is_skip_list=is_skip_list, + ) + return (interpolation_state_list,) + + +def get_ckpt_container_path(model_type): + return os.path.abspath( + os.path.join(os.path.dirname(__file__), config["ckpts_path"], model_type) + ) + + +def load_file_from_url(url, model_dir=None, progress=True, file_name=None): + """Load file form http url, will download models if necessary. + + Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py + + Args: + url (str): URL to be downloaded. + model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. + Default: None. + progress (bool): Whether to show the download progress. Default: True. + file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. + + Returns: + str: The path to the downloaded file. + """ + if model_dir is None: # use the pytorch hub_dir + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, "checkpoints") + + os.makedirs(model_dir, exist_ok=True) + + parts = urlparse(url) + file_name = os.path.basename(parts.path) + if file_name is not None: + file_name = file_name + cached_file = os.path.abspath(os.path.join(model_dir, file_name)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) + return cached_file + + +def load_file_from_github_release(model_type, ckpt_name): + error_strs = [] + for i, base_model_download_url in enumerate(BASE_MODEL_DOWNLOAD_URLS): + try: + return load_file_from_url( + base_model_download_url + ckpt_name, get_ckpt_container_path(model_type) + ) + except Exception: + traceback_str = traceback.format_exc() + if i < len(BASE_MODEL_DOWNLOAD_URLS) - 1: + print("Failed! Trying another endpoint.") + error_strs.append( + f"Error when downloading from: {base_model_download_url + ckpt_name}\n\n{traceback_str}" + ) + + error_str = "\n\n".join(error_strs) + raise Exception( + f"Tried all GitHub base urls to download {ckpt_name} but no suceess. Below is the error log:\n\n{error_str}" + ) + + +def load_file_from_direct_url(model_type, url): + return load_file_from_url(url, get_ckpt_container_path(model_type)) + + +def preprocess_frames(frames): + return einops.rearrange(frames[..., :3], "n h w c -> n c h w") + + +def postprocess_frames(frames): + return einops.rearrange(frames, "n c h w -> n h w c")[..., :3].cpu() + + +def assert_batch_size(frames, batch_size=2, vfi_name=None): + subject_verb = ( + "Most VFI models require" + if vfi_name is None + else f"VFI model {vfi_name} requires" + ) + assert ( + len(frames) >= batch_size + ), f"{subject_verb} at least {batch_size} frames to work with, only found {frames.shape[0]}. Please check the frame input using PreviewImage." + + +def _generic_frame_loop( + frames, + clear_cache_after_n_frames, + multiplier: typing.Union[typing.SupportsInt, typing.List], + return_middle_frame_function, + *return_middle_frame_function_args, + interpolation_states: InterpolationStateList = None, + use_timestep=True, + dtype=torch.float16, + final_logging=True, +): + + # https://github.com/hzwer/Practical-RIFE/blob/main/inference_video.py#L169 + def non_timestep_inference(frame0, frame1, n): + middle = return_middle_frame_function( + frame0, frame1, None, *return_middle_frame_function_args + ) + if n == 1: + return [middle] + first_half = non_timestep_inference(frame0, middle, n=n // 2) + second_half = non_timestep_inference(middle, frame1, n=n // 2) + if n % 2: + return [*first_half, middle, *second_half] + else: + return [*first_half, *second_half] + + output_frames = torch.zeros( + multiplier * frames.shape[0], *frames.shape[1:], dtype=dtype, device="cpu" + ) + out_len = 0 + + number_of_frames_processed_since_last_cleared_cuda_cache = 0 + + for frame_itr in range( + len(frames) - 1 + ): # Skip the final frame since there are no frames after it + frame0 = frames[frame_itr : frame_itr + 1] + output_frames[out_len] = frame0 # Start with first frame + out_len += 1 + # Ensure that input frames are in fp32 - the same dtype as model + frame0 = frame0.to(dtype=torch.float32) + frame1 = frames[frame_itr + 1 : frame_itr + 2].to(dtype=torch.float32) + + if interpolation_states is not None and interpolation_states.is_frame_skipped( + frame_itr + ): + continue + + # Generate and append a batch of middle frames + middle_frame_batches = [] + + if use_timestep: + for middle_i in range(1, multiplier): + timestep = middle_i / multiplier + + middle_frame = ( + return_middle_frame_function( + frame0.to(DEVICE), + frame1.to(DEVICE), + timestep, + *return_middle_frame_function_args, + ) + .detach() + .cpu() + ) + middle_frame_batches.append(middle_frame.to(dtype=dtype)) + else: + middle_frames = non_timestep_inference( + frame0.to(DEVICE), frame1.to(DEVICE), multiplier - 1 + ) + middle_frame_batches.extend( + torch.cat(middle_frames, dim=0).detach().cpu().to(dtype=dtype) + ) + + # Copy middle frames to output + for middle_frame in middle_frame_batches: + output_frames[out_len] = middle_frame + out_len += 1 + + number_of_frames_processed_since_last_cleared_cuda_cache += 1 + # Try to avoid a memory overflow by clearing cuda cache regularly + if ( + number_of_frames_processed_since_last_cleared_cuda_cache + >= clear_cache_after_n_frames + ): + print("Comfy-VFI: Clearing cache...", end=" ") + soft_empty_cache() + number_of_frames_processed_since_last_cleared_cuda_cache = 0 + print("Done cache clearing") + + gc.collect() + + if final_logging: + print( + f"Comfy-VFI done! {len(output_frames)} frames generated at resolution: {output_frames[0].shape}" + ) + # Append final frame + output_frames[out_len] = frames[-1:] + out_len += 1 + # clear cache for courtesy + if final_logging: + print("Comfy-VFI: Final clearing cache...", end=" ") + soft_empty_cache() + if final_logging: + print("Done cache clearing") + return output_frames[:out_len] + + +def generic_frame_loop( + model_name, + frames, + clear_cache_after_n_frames, + multiplier: typing.Union[typing.SupportsInt, typing.List], + return_middle_frame_function, + *return_middle_frame_function_args, + interpolation_states: InterpolationStateList = None, + use_timestep=True, + dtype=torch.float32, +): + + assert_batch_size(frames, vfi_name=model_name.replace("_", " ").replace("VFI", "")) + if type(multiplier) == int: + return _generic_frame_loop( + frames, + clear_cache_after_n_frames, + multiplier, + return_middle_frame_function, + *return_middle_frame_function_args, + interpolation_states=interpolation_states, + use_timestep=use_timestep, + dtype=dtype, + ) + if type(multiplier) == list: + multipliers = list(map(int, multiplier)) + multipliers += [2] * (len(frames) - len(multipliers) - 1) + frame_batches = [] + for frame_itr in range(len(frames) - 1): + multiplier = multipliers[frame_itr] + if multiplier == 0: + continue + frame_batch = _generic_frame_loop( + frames[frame_itr : frame_itr + 2], + clear_cache_after_n_frames, + multiplier, + return_middle_frame_function, + *return_middle_frame_function_args, + interpolation_states=interpolation_states, + use_timestep=use_timestep, + dtype=dtype, + final_logging=False, + ) + if ( + frame_itr != len(frames) - 2 + ): # Not append last frame unless this batch is the last one + frame_batch = frame_batch[:-1] + frame_batches.append(frame_batch) + output_frames = torch.cat(frame_batches) + print( + f"Comfy-VFI done! {len(output_frames)} frames generated at resolution: {output_frames[0].shape}" + ) + return output_frames + raise NotImplementedError(f"multipiler of {type(multiplier)}") + + +class FloatToInt: + @classmethod + def INPUT_TYPES(s): + return { + "required": {"float": ("FLOAT", {"default": 0, "min": 0, "step": 0.01})} + } + + RETURN_TYPES = ("INT",) + FUNCTION = "convert" + CATEGORY = "ComfyUI-Frame-Interpolation" + + def convert(self, float): + if hasattr(float, "__iter__"): + return (list(map(int, float)),) + return (int(float),) + + +""" def generic_4frame_loop( + frames, + clear_cache_after_n_frames, + multiplier: typing.SupportsInt, + return_middle_frame_function, + *return_middle_frame_function_args, + interpolation_states: InterpolationStateList = None, + use_timestep=False): + + if use_timestep: raise NotImplementedError("Timestep 4 frame VFI model") + def non_timestep_inference(frame_0, frame_1, frame_2, frame_3, n): + middle = return_middle_frame_function(frame_0, frame_1, None, *return_middle_frame_function_args) + if n == 1: + return [middle] + first_half = non_timestep_inference(frame_0, middle, n=n//2) + second_half = non_timestep_inference(middle, frame_1, n=n//2) + if n%2: + return [*first_half, middle, *second_half] + else: + return [*first_half, *second_half] """ diff --git a/custom_nodes/whiterabbit/video_loop.py b/custom_nodes/whiterabbit/video_loop.py new file mode 100644 index 00000000..9f7be45f --- /dev/null +++ b/custom_nodes/whiterabbit/video_loop.py @@ -0,0 +1,852 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# SPDX-FileCopyrightText: 2025 ArtificialSweetener + +import torch + + +class PrepareLoopFrames: + DESCRIPTION = "Prepares the wrap seam: builds a tiny 2-frame batch [last, first] for your interpolator and also passes the original clip through unchanged." + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ( + "IMAGE", + { + "tooltip": "Your clip as an IMAGE batch (framesΓ—HΓ—WΓ—C, values 0–1). Outputs: [last, first] for the seam, plus the original clip." + }, + ), + } + } + + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("interp_batch", "original_images") + FUNCTION = "prepare" + CATEGORY = "video utils" + + def prepare(self, images): + last_frame = images[-1:] + first_frame = images[0:1] + interp_batch = torch.cat((last_frame, first_frame), dim=0) + return (interp_batch, images) + + +class AssembleLoopFrames: + DESCRIPTION = "Builds the final loop: appends only the new in-between seam frames to your original clipβ€”no duplicate of frame 1." + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_images": ( + "IMAGE", + {"tooltip": "Your original clip (framesΓ—HΓ—WΓ—C)."}, + ), + "interpolated_frames": ( + "IMAGE", + { + "tooltip": "Frames that bridge lastβ†’first. The first and last of this batch are the originals; only the middle ones get added." + }, + ), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "assemble" + CATEGORY = "video utils" + + def assemble(self, original_images, interpolated_frames): + original_images = original_images.to(interpolated_frames.device) + in_between = interpolated_frames[1:-1] + out = torch.cat((original_images, in_between), dim=0) + return (out,) + + +class RollFrames: + DESCRIPTION = "Rolls the clip in a loop by an integer amount (cyclic shift). Also returns the same offset so you can undo it later." + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE", {"tooltip": "Your clip (framesΓ—HΓ—WΓ—C)."}), + "offset": ( + "INT", + { + "default": 1, + "min": -9999, + "max": 9999, + "step": 1, + "tooltip": "How far to rotate the clip. Positive = forward in time; negative = backward.", + }, + ), + } + } + + RETURN_TYPES = ("IMAGE", "INT") + RETURN_NAMES = ("images", "offset_out") + FUNCTION = "roll" + CATEGORY = "video utils" + + def roll(self, images, offset): + B = images.shape[0] + if B == 0: + return (images, int(offset)) + k = int(offset) % B + if k == 0: + return (images, int(offset)) + rolled = torch.roll(images, shifts=-k, dims=0) # +1 β†’ [2,3,...,1] + return (rolled, int(offset)) + + +class UnrollFrames: + DESCRIPTION = "Undo a previous roll after interpolation by accounting for the inserted frames (rotate by base_offset Γ— (m+1))." + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ( + "IMAGE", + {"tooltip": "Clip after interpolation (framesβ€²Γ—HΓ—WΓ—C)."}, + ), + "base_offset": ( + "INT", + { + "default": 1, + "min": -9999, + "max": 9999, + "step": 1, + "tooltip": "Use the exact offset_out that came from RollFrames.", + }, + ), + "m": ( + "INT", + { + "default": 0, + "min": 0, + "max": 9999, + "step": 1, + "tooltip": "How many in-betweens per gap were added (the interpolation multiple).", + }, + ), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "unroll" + CATEGORY = "video utils" + + def unroll(self, images, base_offset, m): + Bp = images.shape[0] + if Bp == 0: + return (images,) + eff = (int(base_offset) * (int(m) + 1)) % Bp + return (torch.roll(images, shifts=+eff, dims=0),) + + +class AutocropToLoop: + """ + Finds a natural loop by cropping frames from the END of the batch. + Returns the cropped clip that makes the seam (last_kept -> first) + feel like a normal step between real neighbors. + + Score = weighted mix of: + - step-size match (L1/MSE distance) + - similarity match (SSIM) + - exposure continuity (luma) + - motion consistency (optical flow; optional) + + Speed: can run metrics on GPU and use mixed precision for SSIM/conv math. + Progress bar: one tick per candidate crop (0..max_end_crop_frames). + """ + + DESCRIPTION = "Auto-crops the clip to create a smoother loop: tests crops from the end and scores the seam so it feels like a normal step." + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "clip_frames": ( + "IMAGE", + { + "tooltip": "Your full clip (NHWC, 0–1). Tries every crop from 0..max_end_crop_frames and returns the best loop." + }, + ), + "max_end_crop_frames": ( + "INT", + { + "default": 12, + "min": 0, + "max": 10000, + "tooltip": "Largest crop to test at the END. Higher = more candidates (slower), but potentially better.", + }, + ), + "include_first_step": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Use the first neighbor pair (frame 0β†’1) as a target step size/similarity.", + }, + ), + "include_last_step": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Use the last neighbor pair inside the KEPT region as a target.", + }, + ), + "include_global_median_step": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Also use the median step across the KEPT region (needs β‰₯3 frames). Helps ignore outliers.", + }, + ), + "seam_window_frames": ( + "INT", + { + "default": 2, + "min": 1, + "max": 6, + "tooltip": "Average over multiple aligned pairs across the seam. Larger = more robust.", + }, + ), + "distance_metric": ( + ["L1", "MSE"], + { + "default": "L1", + "tooltip": "How to measure step size for matching. L1 is usually more forgiving; MSE penalizes big errors more.", + }, + ), + "score_in_8bit": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Score with an 8-bit view (simulate export). Output video still stays float.", + }, + ), + "use_ssim_similarity": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Include SSIM so the seam β€˜looks’ like a normal neighborβ€”avoid freeze or jump.", + }, + ), + "use_exposure_guard": ( + "BOOLEAN", + { + "default": True, + "tooltip": "Promote smooth brightness across the seam (reduces flicker pops).", + }, + ), + "use_flow_guard": ( + "BOOLEAN", + { + "default": False, + "tooltip": "Encourage consistent motion across the seam (needs OpenCV; slower).", + }, + ), + "weight_step_size": ( + "FLOAT", + { + "default": 0.55, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Importance of matching step size. Higher = less freeze/jump risk.", + }, + ), + "weight_similarity": ( + "FLOAT", + { + "default": 0.30, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Importance of visual similarity (SSIM). Helps avoid a frozen-looking seam.", + }, + ), + "weight_exposure": ( + "FLOAT", + { + "default": 0.10, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Importance of even brightness across the seam.", + }, + ), + "weight_flow": ( + "FLOAT", + { + "default": 0.05, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Importance of motion continuity across the seam.", + }, + ), + "ssim_downsample_scales": ( + "STRING", + { + "default": "1,2", + "tooltip": "SSIM scales to average, as a comma list. Example: 1,2 = full-res and half-res.", + }, + ), + "accelerate_with_gpu": ( + "BOOLEAN", + { + "default": True, + "tooltip": "If ON and CUDA is available, run scoring on GPU for a big speedup (same results).", + }, + ), + "use_mixed_precision": ( + "BOOLEAN", + { + "default": True, + "tooltip": "If ON (with GPU), use mixed precision for SSIM/conv math (faster on larger clips).", + }, + ), + } + } + + RETURN_TYPES = ("IMAGE", "INT", "INT", "FLOAT", "STRING") + RETURN_NAMES = ( + "cropped_clip", + "end_crop_frames", + "cropped_length", + "score", + "diagnostics_csv", + ) + FUNCTION = "find_and_crop" + CATEGORY = "video utils" + + _gw_cache = {} # gaussian window cache + + def _to_nchw(self, x): + import torch + + if x.ndim == 4 and x.shape[-1] in (1, 3, 4): + return x.permute(0, 3, 1, 2).contiguous() + return x + + def _parse_scales(self, csv): + scales = [] + for s in str(csv).split(","): + s = s.strip() + if not s: + continue + try: + v = int(s) + if v >= 1 and v not in scales: + scales.append(v) + except Exception: + pass + return scales or [1] + + def _downsample(self, x, s): + import torch.nn.functional as F + + if s == 1: + return x + H, W = x.shape[-2:] + newH = max(1, H // s) + newW = max(1, W // s) + return F.interpolate(x, size=(newH, newW), mode="area", align_corners=None) + + def _dist(self, A, B, kind="L1"): + if kind == "MSE": + return ((A - B) ** 2).mean(dim=(1, 2, 3)) + return (A - B).abs().mean(dim=(1, 2, 3)) + + def _luma(self, x_nchw): + if x_nchw.shape[1] == 1: + return x_nchw[:, 0:1] + R = x_nchw[:, 0:1] + G = x_nchw[:, 1:2] + B = x_nchw[:, 2:3] + return 0.2126 * R + 0.7152 * G + 0.0722 * B + + def _gaussian_window(self, C, k=7, sigma=1.5, device="cpu", dtype=None): + import torch + + key = (int(C), int(k), float(sigma), str(device), str(dtype)) + w = self._gw_cache.get(key) + if w is not None: + return w + ax = torch.arange(k, dtype=dtype, device=device) - (k - 1) / 2.0 + gauss = torch.exp(-0.5 * (ax / sigma) ** 2) + kernel1d = (gauss / gauss.sum()).unsqueeze(1) + kernel2d = kernel1d @ kernel1d.t() + w = kernel2d.expand(C, 1, k, k).contiguous() + self._gw_cache[key] = w + return w + + def _ssim_pair_batched(self, x, y, k=7, sigma=1.5, C1=0.01**2, C2=0.03**2): + import torch + import torch.nn.functional as F + + C = x.shape[1] + w = self._gaussian_window(C, k=k, sigma=sigma, device=x.device, dtype=x.dtype) + mu_x = F.conv2d(x, w, padding=k // 2, groups=C) + mu_y = F.conv2d(y, w, padding=k // 2, groups=C) + mu_x2, mu_y2, mu_xy = mu_x * mu_x, mu_y * mu_y, mu_x * mu_y + sigma_x2 = F.conv2d(x * x, w, padding=k // 2, groups=C) - mu_x2 + sigma_y2 = F.conv2d(y * y, w, padding=k // 2, groups=C) - mu_y2 + sigma_xy = F.conv2d(x * y, w, padding=k // 2, groups=C) - mu_xy + ssim_map = ((2.0 * mu_xy + C1) * (2.0 * sigma_xy + C2)) / ( + (mu_x2 + mu_y2 + C1) * (sigma_x2 + sigma_y2 + C2) + 1e-12 + ) + return ssim_map.mean(dim=(1, 2, 3)) # (N,) + + def _ssim_multiscale_batched(self, x, y, scales): + vecs = [] + for s in scales: + xs = self._downsample(x, s) + ys = self._downsample(y, s) + vecs.append(self._ssim_pair_batched(xs, ys)) + return sum(vecs) / float(len(vecs)) # (N,) + + def _precompute_adjacent_metrics( + self, clip_nhwc_dev, kind, use_ssim, ds_scales, use_exp, use_flow + ): + """ + Returns dict with vectors of length (B-1): + D_adj (torch), S_adj (torch), E_adj (torch), F_adj (np, CPU) + All torch tensors are on the same device as clip_nhwc_dev. + """ + import numpy as np + import torch + + B = int(clip_nhwc_dev.shape[0]) + N = max(0, B - 1) + result = {} + if N == 0: + result["D_adj"] = torch.empty(0, device=clip_nhwc_dev.device) + result["S_adj"] = torch.empty(0, device=clip_nhwc_dev.device) + result["E_adj"] = torch.empty(0, device=clip_nhwc_dev.device) + result["F_adj"] = np.zeros((0,), dtype="float64") + return result, self._to_nchw(clip_nhwc_dev) + + x_nchw = self._to_nchw(clip_nhwc_dev) # B,C,H,W (device) + X = x_nchw[:-1] + Y = x_nchw[1:] # N,C,H,W + + result["D_adj"] = self._dist(X, Y, kind=kind) # (N,) + + if use_ssim: + result["S_adj"] = self._ssim_multiscale_batched(X, Y, ds_scales) # (N,) + else: + result["S_adj"] = torch.empty(0, device=x_nchw.device) + + if use_exp: + Y_luma = self._luma(x_nchw).mean(dim=(1, 2, 3)) # (B,) + result["E_adj"] = (Y_luma[:-1] - Y_luma[1:]).abs() # (N,) + else: + result["E_adj"] = torch.empty(0, device=x_nchw.device) + + if use_flow: + F_adj = [] + for i in range(N): + a = clip_nhwc_dev[i : i + 1].detach().cpu() + b = clip_nhwc_dev[i + 1 : i + 2].detach().cpu() + F_adj.append(self._flow_mag_mean(a, b)) + import numpy as np + + result["F_adj"] = np.array(F_adj, dtype="float64") + else: + import numpy as np + + result["F_adj"] = np.zeros((N,), dtype="float64") + + return result, x_nchw + + def _precompute_seam_tables(self, x_nchw_dev, W, kind, use_ssim, ds_scales): + """ + For k = 0..W-1, precompute per-frame metrics vs first+k: + D_to_firstk[k] : (B,) distances to frame k + S_to_firstk[k] : (B,) SSIM to frame k (if use_ssim) + E_to_firstk[k] : (B,) |luma(i)-luma(k)| + Tensors live on x_nchw_dev.device. + """ + import torch + + B = int(x_nchw_dev.shape[0]) + W = max(1, min(int(W), B - 1)) + D_to_firstk, S_to_firstk, E_to_firstk = [], [], [] + + Y = self._luma(x_nchw_dev).mean(dim=(1, 2, 3)) + + for k in range(W): + Bk = x_nchw_dev[k : k + 1].expand_as(x_nchw_dev) + Dk = self._dist(x_nchw_dev, Bk, kind=kind) + D_to_firstk.append(Dk) + if use_ssim: + Sk = self._ssim_multiscale_batched(x_nchw_dev, Bk, ds_scales) + S_to_firstk.append(Sk) + else: + S_to_firstk.append(torch.empty(0, device=x_nchw_dev.device)) + Ek = (Y - Y[k]).abs() + E_to_firstk.append(Ek) + + return D_to_firstk, S_to_firstk, E_to_firstk + + def _flow_mag_mean(self, a_nhwc, b_nhwc, max_side=256): + """ + Mean optical-flow magnitude. Accepts NHWC with/without batch, + RGB/RGBA/Gray. Soft-fails to 0.0 if OpenCV unavailable. + """ + try: + import cv2 + import numpy as np + except Exception: + return 0.0 + + a = (a_nhwc.detach().cpu().numpy() * 255.0).clip(0, 255).astype("uint8") + b = (b_nhwc.detach().cpu().numpy() * 255.0).clip(0, 255).astype("uint8") + if a.ndim == 4 and a.shape[0] == 1: + a = a[0] + if b.ndim == 4 and b.shape[0] == 1: + b = b[0] + + def to_gray(x: np.ndarray) -> np.ndarray: + if x.ndim == 2: + return x + if x.ndim == 3: + c = x.shape[-1] + if c == 1: + return x[..., 0] + if c == 3: + return cv2.cvtColor(x, cv2.COLOR_RGB2GRAY) + if c == 4: + return cv2.cvtColor(x, cv2.COLOR_RGBA2GRAY) + return x.mean(axis=-1).astype(x.dtype) + x2 = np.squeeze(x) + if x2.ndim == 2: + return x2 + if x2.ndim == 3: + return x2.mean(axis=-1).astype(x2.dtype) + return None + + a_g, b_g = to_gray(a), to_gray(b) + if a_g is None or b_g is None or a_g.ndim != 2 or b_g.ndim != 2: + return 0.0 + + H, W = a_g.shape + scale = max(1.0, max(H, W) / float(max_side)) + if scale > 1.0: + newW = int(round(W / scale)) + newH = int(round(H / scale)) + a_g = cv2.resize(a_g, (newW, newH), interpolation=cv2.INTER_AREA) + b_g = cv2.resize(b_g, (newW, newH), interpolation=cv2.INTER_AREA) + + try: + flow = cv2.calcOpticalFlowFarneback( + a_g, b_g, None, 0.5, 3, 21, 3, 5, 1.1, 0 + ) + mag = (flow[..., 0] ** 2 + flow[..., 1] ** 2) ** 0.5 + return float(mag.mean()) + except Exception: + return 0.0 + + def find_and_crop( + self, + clip_frames, + max_end_crop_frames, + include_first_step, + include_last_step, + include_global_median_step, + seam_window_frames, + distance_metric, + score_in_8bit, + use_ssim_similarity, + use_exposure_guard, + use_flow_guard, + weight_step_size, + weight_similarity, + weight_exposure, + weight_flow, + ssim_downsample_scales, + accelerate_with_gpu, + use_mixed_precision, + ): + import contextlib + + import numpy as np + import torch + from comfy.utils import ProgressBar + + clip_out = clip_frames + clip_eval = ( + (clip_frames * 255.0).round().clamp(0, 255) / 255.0 + if score_in_8bit + else clip_frames + ) + + B = int(clip_eval.shape[0]) + if B < 2: + header = "end_crop,score,D_seam,D_target,S_seam,S_target,E_seam,E_target,F_seam,F_target" + return (clip_out, 0, B, 0.0, header) + + dev = "cuda" if accelerate_with_gpu and torch.cuda.is_available() else "cpu" + amp_ctx = ( + torch.cuda.amp.autocast + if (dev == "cuda" and use_mixed_precision) + else contextlib.nullcontext + ) + + ds_scales = self._parse_scales(ssim_downsample_scales) + kind = distance_metric + W = int(seam_window_frames) + total_candidates = int(max(0, max_end_crop_frames)) + 1 + + with torch.no_grad(): + with amp_ctx(): + + clip_eval_dev = clip_eval.to(dev, non_blocking=True) + + pre, x_nchw_dev = self._precompute_adjacent_metrics( + clip_nhwc_dev=clip_eval_dev, + kind=kind, + use_ssim=use_ssim_similarity, + ds_scales=ds_scales, + use_exp=use_exposure_guard, + use_flow=use_flow_guard, + ) + D_adj, S_adj, E_adj, F_adj = ( + pre["D_adj"], + pre["S_adj"], + pre["E_adj"], + pre["F_adj"], + ) + + D_seam_tab, S_seam_tab, E_seam_tab = self._precompute_seam_tables( + x_nchw_dev=x_nchw_dev, + W=W, + kind=kind, + use_ssim=use_ssim_similarity, + ds_scales=ds_scales, + ) + + Y = self._luma(x_nchw_dev).mean(dim=(1, 2, 3)) + + best_extra = 0 + best_score = float("inf") + rows = [] + pbar = ProgressBar(total_candidates) + + for extra in range(0, total_candidates): + keep = B - extra + if keep < 2: + pbar.update(1) + continue + + last_idx = keep - 1 + W_eff = max(1, min(W, last_idx + 1, B - 1)) + + chosen_D = [] + if include_first_step and keep >= 2: + chosen_D.append(D_adj[0]) + if include_last_step and keep >= 2: + chosen_D.append(D_adj[last_idx - 1]) + if include_global_median_step and keep >= 3: + chosen_D.append(D_adj[: keep - 1].median()) + if not chosen_D and keep >= 2: + chosen_D = [D_adj[0]] + D_target = float( + ( + chosen_D[0] + if len(chosen_D) == 1 + else torch.stack(chosen_D).median() + ).item() + ) + + if use_ssim_similarity and S_adj.numel() > 0 and keep >= 2: + chosen_S = [] + if include_first_step: + chosen_S.append(S_adj[0]) + if include_last_step: + chosen_S.append(S_adj[last_idx - 1]) + if include_global_median_step and keep >= 3: + chosen_S.append(S_adj[: keep - 1].median()) + S_target = float( + ( + chosen_S[0] + if (chosen_S and len(chosen_S) == 1) + else ( + torch.stack(chosen_S).median() + if chosen_S + else torch.tensor(0.0, device=S_adj.device) + ) + ).item() + ) + else: + S_target = 0.0 + + if use_exposure_guard and keep >= 2: + e_first = (Y[0] - Y[1]).abs() + e_last = (Y[last_idx] - Y[last_idx - 1]).abs() + if include_global_median_step and keep >= 3: + e_med = (Y[: keep - 1] - Y[1:keep]).abs().median() + E_target = float( + torch.stack([e_first, e_last, e_med]).median().item() + ) + else: + E_target = float(torch.stack([e_first, e_last]).median().item()) + else: + E_target = 0.0 + + if use_flow_guard and keep >= 3 and F_adj.size > 0: + import numpy as np + + F_target = float(np.median(F_adj[: keep - 1])) + else: + F_target = 0.0 + + idxs = [last_idx - (W_eff - 1 - r) for r in range(W_eff)] + idxs_t = torch.tensor(idxs, device=x_nchw_dev.device, dtype=torch.long) + + D_vals = torch.stack( + [ + D_seam_tab[r].index_select(0, idxs_t[r : r + 1]).squeeze(0) + for r in range(W_eff) + ] + ) + D_seam = float(D_vals.mean().item()) + + if use_ssim_similarity and S_seam_tab[0].numel() > 0: + S_vals = torch.stack( + [ + S_seam_tab[r].index_select(0, idxs_t[r : r + 1]).squeeze(0) + for r in range(W_eff) + ] + ) + S_seam = float(S_vals.mean().item()) + else: + S_seam = 0.0 + + if use_exposure_guard: + E_vals = torch.stack( + [ + E_seam_tab[r].index_select(0, idxs_t[r : r + 1]).squeeze(0) + for r in range(W_eff) + ] + ) + E_seam = float(E_vals.mean().item()) + else: + E_seam = 0.0 + + F_seam = 0.0 + + eps = 1e-12 + cost_step = abs(D_seam - D_target) / (D_target + eps) + cost_sim = ( + abs(S_seam - S_target) / (abs(S_target) + eps) + if use_ssim_similarity + else 0.0 + ) + cost_exp = ( + abs(E_seam - E_target) / (E_target + eps) + if (use_exposure_guard and E_target > 0.0) + else 0.0 + ) + cost_flow = ( + abs(F_seam - F_target) / (F_target + eps) + if (use_flow_guard and F_target > 0.0) + else 0.0 + ) + + score = ( + weight_step_size * cost_step + + weight_similarity * cost_sim + + weight_exposure * cost_exp + + weight_flow * cost_flow + ) + + rows.append( + f"{extra},{score:.6f},{D_seam:.6f},{D_target:.6f},{S_seam:.6f},{S_target:.6f},{E_seam:.6f},{E_target:.6f},{F_seam:.6f},{F_target:.6f}" + ) + + if score < best_score: + best_score = score + best_extra = extra + + pbar.update(1) + + final_keep = max(2, B - best_extra) + cropped = clip_out[0:final_keep] + header = "end_crop,score,D_seam,D_target,S_seam,S_target,E_seam,E_target,F_seam,F_target" + diagnostics_csv = header + "\n" + "\n".join(rows) if rows else header + return ( + cropped, + int(best_extra), + int(final_keep), + float(best_score), + diagnostics_csv, + ) + + +class TrimBatchEnds: + """ + Trim frames from the START and/or END of an IMAGE batch (NHWC, [0..1]). + Both trims are applied in one pass. Always leaves at least one frame. + """ + + DESCRIPTION = "Quickly remove frames from the start and/or end of a clip. Always keeps at least one frame." + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "clip_frames": ("IMAGE", {"tooltip": "Your clip (framesΓ—HΓ—WΓ—C, 0–1)."}), + "trim_start_frames": ( + "INT", + { + "default": 0, + "min": 0, + "max": 100000, + "tooltip": "Frames to remove from the START.", + }, + ), + "trim_end_frames": ( + "INT", + { + "default": 0, + "min": 0, + "max": 100000, + "tooltip": "Frames to remove from the END.", + }, + ), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "crop" + CATEGORY = "video utils" + + def crop(self, clip_frames, trim_start_frames, trim_end_frames): + import torch + + if not isinstance(clip_frames, torch.Tensor) or clip_frames.ndim != 4: + return (clip_frames,) + + B = int(clip_frames.shape[0]) + if B <= 1: + return (clip_frames,) + + s = max(0, int(trim_start_frames)) + e = max(0, int(trim_end_frames)) + + if s + e >= B: + s = min(s, B - 1) + e = max(0, B - s - 1) + + out = clip_frames[s : B - e] if e > 0 else clip_frames[s:] + if out.shape[0] == 0: + out = clip_frames[B - 1 : B] + return (out,) diff --git a/custom_nodes/x-flux-comfyui/.gitignore b/custom_nodes/x-flux-comfyui/.gitignore new file mode 100644 index 00000000..b079d6ee --- /dev/null +++ b/custom_nodes/x-flux-comfyui/.gitignore @@ -0,0 +1,167 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +Makefile +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +weights/ + +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache/ +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +.DS_Store diff --git a/custom_nodes/x-flux-comfyui/CHANGELOG.md b/custom_nodes/x-flux-comfyui/CHANGELOG.md new file mode 100644 index 00000000..a36789bd --- /dev/null +++ b/custom_nodes/x-flux-comfyui/CHANGELOG.md @@ -0,0 +1,46 @@ +# Changelog + +## [TODO] + +### Add + +- IPAdapter controlling features +- IPAdapter compatablity with VIT +- Offloading support + +## [Unreleased] + +### Added + + +## [0.4.2] + +### Added +- ControlNet merge +- New clip-vit +- Latent preview +- GGUF Support + +## [0.3.45] + +### Added + +- IP adapter support + + +## [0.2.38] + +### Added + +- Image-to-image support + +### Changed + +- Updated readme.md, added + +## [0.1.0] + +### Added + +- Lora and controlnets support +- xlabs Sampler diff --git a/custom_nodes/x-flux-comfyui/Guide.md b/custom_nodes/x-flux-comfyui/Guide.md new file mode 100644 index 00000000..f0301d06 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/Guide.md @@ -0,0 +1,121 @@ +# Guide + +# Installing + +First of all, you should install ComfyUI and [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager). + +After that, launch ComfUI. + + + +In the right panel you can find β€œManager” button, click on. + +![manager.png](guide/manager.png) + +You will see big window, click on β€œCustom Nodes Manager”. + +![manager_menu.png](guide/manager_menu.png) + +Go to search field, and start typing β€œx-flux-comfyui”, + +![search.png](guide/search.png) + +Click β€œinstall” button. + +![download.png](guide/download.png) + +Now, you have access to X-Labs nodes, you can find it in β€œXLabsNodes” category. + +![nodes.png](guide/manager.png) + +# Flux Installing + +There is complete guide by Comfyanonimus [Guide](https://comfyanonymous.github.io/ComfyUI_examples/flux/) + +Important! You should use flux dev, not schnell, if your computer is capable of this, it is better to use fp8 or bf16 (default). + +# Nodes + +## XLabs Sampler: + +![sampler.png](guide/sampler.png) + +Node inputs: + +model: get FLUX diffusion model (from unet loader) + +conditioning & neg_conditioning: input prompts after T5 and clip models (clip only allowed, but you should know, that you will not utilize about 40% of flux power, so use dual text node) + +latent_image: latent input for flux, may be empty latent or encoded with FLUX AE (VAE Encode) image (for image-to-image using) + +controlnet_condition: input for XLabs-AI ControlNet conditioning + +Output: + +latent: FLUX latent image, should be decoded with VAE Decoder to get image + +Parameters: + +noise_seed, control_after_generate: controls random generator + +steps: how many denoise steps will diffusion go + +timestep_to_start_cfg: how many steps diffusion will do before start to use negative sampling and cfg + +true_gs: true cfg scale, will be used after first β€œ**timestep_to_start_cfg” steps** + +image_to_image_strength: how much will the original image affect to the output + +denoise_strength: how many noise will remain + +## Load Flux LoRA + +![lora.png](guide/lora.png) + +Gets flux model on input and outputs model modified with XLabs LoRA (strength of LoRA and name are selected by the parameters) + +## Load Flux ControlNet + +![loadcnet.png](guide/loadcnet.png) + +Gets controlnet name and returns flux controlnet. + +## Apply Flux ControlNet + +![manager.png](guide/manager.png) + +Gets controlnet, image and strength as parameter. Returns controlnet condition for XLabs Sampler. + +## Load Flux IPAdapter + +![loadip.png](guide/loadip.png) + +Gets IP Adapter name, CLIP ViT model and on which device it will work. Choose CUDA only if you have enough VRAM. Return Flux IPAdapter. + +## Apply Flux IPAdapter + +![applyip.png](guide/applyip.png) + +Gets FLUX model, IP Adapter and image. Returns modified model. Strength of IP Adapter comes from parameter. + +## Apply Advanced IPAdatper + +![advip.png](guide/advip.png) + +Like common one, but has more strength parameters. + +# Models + +We use custom folder for LoRAs, ControlNets and IPAdapters, the folders contains in `models\xlabs`. + +LoRAs goes to `ComfyUI\xlabs\loras`. + +ControlNets goes to `ComfyUI\xlabs\controlnets`. + +IPAdapters goes to `ComfyUI\xlabs\ipadapters`. + +An IPAdapter requires a CLIP VIT. We currently use Open-AI Clip ViT Large. You can find it here. + +[CLIP ViT model](https://huggingface.co/openai/clip-vit-large-patch14). + +Download model.safetensors, rename it as you want to (but .safetensors is required). And put it to `ComfyUI\models\clip-vision\`. diff --git a/custom_nodes/x-flux-comfyui/LICENSE b/custom_nodes/x-flux-comfyui/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/custom_nodes/x-flux-comfyui/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/x-flux-comfyui/Readme.md b/custom_nodes/x-flux-comfyui/Readme.md new file mode 100644 index 00000000..c6f75864 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/Readme.md @@ -0,0 +1,52 @@ +# *[Guide](/Guide.md)* + +# How to use +![FLUX Finetuning scripts](./assets/flux-comfy-ui-nodes-dark-rev1.png) +![FLUX Finetuning scripts](./assets/image1.png) + +## Installation: + +1. Go to `ComfyUI/custom_nodes` +2. Clone this repo, path should be `ComfyUI/custom_nodes/x-flux-comfyui/*`, where * is all the files in this repo +3. Go to `ComfyUI/custom_nodes/x-flux-comfyui/` and run `python setup.py` +4. Run ComfyUI after installing and enjoy! + +After the first launch, the `ComfyUI/models/xlabs/loras` and `ComfyUI/models/xlabs/controlnets` folders will be created automatically.
        +So, to use lora or controlnet just put models in these folders.
        +After that, you may need to click "Refresh" in the user-friendly interface to use the models.
        +For controlnet you need install https://github.com/Fannovel16/comfyui_controlnet_aux
        +## Low memory mode +You can launch Flux utilizing 12GB VRAM memory usage. +1. Follow installation as described in repo https://github.com/city96/ComfyUI-GGUF +2. Use flux1-dev-Q4_0.gguf from repo https://github.com/city96/ComfyUI-GGUF
        +3. Launch ComfyUI with parameters: +```bash +python3 main.py --lowvram --preview-method auto --use-split-cross-attention +``` +In our workflows, replace "Load Diffusion Model" node with "Unet Loader (GGUF)" + +![FLUX Finetuning scripts](./assets/low_memory_mode.png) + +## Models + +We trained **Canny ControlNet**, **Depth ControlNet**, **HED ControlNet** and **LoRA** checkpoints for [`FLUX.1 [dev]`](https://github.com/black-forest-labs/flux)
        +You can download them on HuggingFace: + +- [flux-controlnet-collections](https://huggingface.co/XLabs-AI/flux-controlnet-collections) +- [flux-controlnet-canny](https://huggingface.co/XLabs-AI/flux-controlnet-canny) +- [flux-RealismLora](https://huggingface.co/XLabs-AI/flux-RealismLora) +- [flux-lora-collections](https://huggingface.co/XLabs-AI/flux-lora-collection) +- [flux-furry-lora](https://huggingface.co/XLabs-AI/flux-furry-lora) +- [flux-ip-adapter](https://huggingface.co/XLabs-AI/flux-ip-adapter/) +## IP Adapter + +### Instruction +1. Update x-flux-comfy with `git pull` or reinstall it. +2. Download Clip-L `model.safetensors` from [OpenAI VIT CLIP large](https://huggingface.co/openai/clip-vit-large-patch14), and put it to `ComfyUI/models/clip_vision/*`. +3. Download our IPAdapter from [huggingface](https://huggingface.co/XLabs-AI/flux-ip-adapter/tree/main), and put it to `ComfyUI/models/xlabs/ipadapters/*`. +4. Use `Flux Load IPAdapter` and `Apply Flux IPAdapter` nodes, choose right CLIP model and enjoy your genereations. +5. You can find example workflow in folder workflows in this repo. + +### Limitations +The IP Adapter is currently in beta. +We do not guarantee that you will get a good result right away, it may take more attempts to get a result. But we will make efforts to make this process easier and more efficient over time. diff --git a/custom_nodes/x-flux-comfyui/__init__.py b/custom_nodes/x-flux-comfyui/__init__.py new file mode 100644 index 00000000..2e96bd6a --- /dev/null +++ b/custom_nodes/x-flux-comfyui/__init__.py @@ -0,0 +1,3 @@ +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] \ No newline at end of file diff --git a/custom_nodes/x-flux-comfyui/assets/flux-comfy-ui-nodes-dark-rev1.png b/custom_nodes/x-flux-comfyui/assets/flux-comfy-ui-nodes-dark-rev1.png new file mode 100644 index 00000000..f8af26ba Binary files /dev/null and b/custom_nodes/x-flux-comfyui/assets/flux-comfy-ui-nodes-dark-rev1.png differ diff --git a/custom_nodes/x-flux-comfyui/assets/image1.png b/custom_nodes/x-flux-comfyui/assets/image1.png new file mode 100644 index 00000000..f84cbf3a Binary files /dev/null and b/custom_nodes/x-flux-comfyui/assets/image1.png differ diff --git a/custom_nodes/x-flux-comfyui/assets/low_memory_mode.png b/custom_nodes/x-flux-comfyui/assets/low_memory_mode.png new file mode 100644 index 00000000..470e25bd Binary files /dev/null and b/custom_nodes/x-flux-comfyui/assets/low_memory_mode.png differ diff --git a/custom_nodes/x-flux-comfyui/clip.py b/custom_nodes/x-flux-comfyui/clip.py new file mode 100644 index 00000000..0f5749c9 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/clip.py @@ -0,0 +1,192 @@ +import json +import os +from transformers import (CLIPImageProcessor, + CLIPVisionModelWithProjection, + CLIPVisionConfig, + AutoConfig) + + + +class FluxClipViT: + def __init__(self, path_model = None): + if path_model is None: + self.model = CLIPVisionModelWithProjection.from_pretrained( + "openai/clip-vit-large-patch14" + ) + + else: + _dir = os.path.dirname(path_model) + write_config(_dir) + config = CLIPVisionConfig.from_pretrained( + os.path.join(_dir, "flux_clip_config.json") + ) + self.model = CLIPVisionModelWithProjection.from_pretrained( + path_model, + config=config, + use_safetensors = True, + ) + self.image_processor = CLIPImageProcessor() + self.load_device = next(self.model.parameters()).device + + def __call__(self, image): + img = self.image_processor( + images=image, return_tensors="pt" + ) + img = img.pixel_values + return self.model(img).image_embeds + + +def write_config(path): + #check if exists + if os.path.exists(os.path.join(path, "flux_clip_config.json")): + return + with open(os.path.join(path, "flux_clip_config.json"), "w") as f: + json.dump(json_config, f, indent=4) + +json_config = {'_name_or_path': 'clip-vit-large-patch14/', + 'architectures': ['CLIPModel'], + 'initializer_factor': 1.0, + 'logit_scale_init_value': 2.6592, + 'model_type': 'clip', + 'projection_dim': 768, + 'text_config': {'_name_or_path': '', + 'add_cross_attention': False, + 'architectures': None, + 'attention_dropout': 0.0, + 'bad_words_ids': None, + 'bos_token_id': 0, + 'chunk_size_feed_forward': 0, + 'cross_attention_hidden_size': None, + 'decoder_start_token_id': None, + 'diversity_penalty': 0.0, + 'do_sample': False, + 'dropout': 0.0, + 'early_stopping': False, + 'encoder_no_repeat_ngram_size': 0, + 'eos_token_id': 2, + 'finetuning_task': None, + 'forced_bos_token_id': None, + 'forced_eos_token_id': None, + 'hidden_act': 'quick_gelu', + 'hidden_size': 768, + 'id2label': {'0': 'LABEL_0', '1': 'LABEL_1'}, + 'initializer_factor': 1.0, + 'initializer_range': 0.02, + 'intermediate_size': 3072, + 'is_decoder': False, + 'is_encoder_decoder': False, + 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, + 'layer_norm_eps': 1e-05, + 'length_penalty': 1.0, + 'max_length': 20, + 'max_position_embeddings': 77, + 'min_length': 0, + 'model_type': 'clip_text_model', + 'no_repeat_ngram_size': 0, + 'num_attention_heads': 12, + 'num_beam_groups': 1, + 'num_beams': 1, + 'num_hidden_layers': 12, + 'num_return_sequences': 1, + 'output_attentions': False, + 'output_hidden_states': False, + 'output_scores': False, + 'pad_token_id': 1, + 'prefix': None, + 'problem_type': None, + 'projection_dim': 768, + 'pruned_heads': {}, + 'remove_invalid_values': False, + 'repetition_penalty': 1.0, + 'return_dict': True, + 'return_dict_in_generate': False, + 'sep_token_id': None, + 'task_specific_params': None, + 'temperature': 1.0, + 'tie_encoder_decoder': False, + 'tie_word_embeddings': True, + 'tokenizer_class': None, + 'top_k': 50, + 'top_p': 1.0, + 'torch_dtype': None, + 'torchscript': False, + 'transformers_version': '4.16.0.dev0', + 'use_bfloat16': False, + 'vocab_size': 49408}, + 'text_config_dict': {'hidden_size': 768, + 'intermediate_size': 3072, + 'num_attention_heads': 12, + 'num_hidden_layers': 12, + 'projection_dim': 768}, + 'torch_dtype': 'float32', + 'transformers_version': None, + 'vision_config': {'_name_or_path': '', + 'add_cross_attention': False, + 'architectures': None, + 'attention_dropout': 0.0, + 'bad_words_ids': None, + 'bos_token_id': None, + 'chunk_size_feed_forward': 0, + 'cross_attention_hidden_size': None, + 'decoder_start_token_id': None, + 'diversity_penalty': 0.0, + 'do_sample': False, + 'dropout': 0.0, + 'early_stopping': False, + 'encoder_no_repeat_ngram_size': 0, + 'eos_token_id': None, + 'finetuning_task': None, + 'forced_bos_token_id': None, + 'forced_eos_token_id': None, + 'hidden_act': 'quick_gelu', + 'hidden_size': 1024, + 'id2label': {'0': 'LABEL_0', '1': 'LABEL_1'}, + 'image_size': 224, + 'initializer_factor': 1.0, + 'initializer_range': 0.02, + 'intermediate_size': 4096, + 'is_decoder': False, + 'is_encoder_decoder': False, + 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, + 'layer_norm_eps': 1e-05, + 'length_penalty': 1.0, + 'max_length': 20, + 'min_length': 0, + 'model_type': 'clip_vision_model', + 'no_repeat_ngram_size': 0, + 'num_attention_heads': 16, + 'num_beam_groups': 1, + 'num_beams': 1, + 'num_hidden_layers': 24, + 'num_return_sequences': 1, + 'output_attentions': False, + 'output_hidden_states': False, + 'output_scores': False, + 'pad_token_id': None, + 'patch_size': 14, + 'prefix': None, + 'problem_type': None, + 'projection_dim': 768, + 'pruned_heads': {}, + 'remove_invalid_values': False, + 'repetition_penalty': 1.0, + 'return_dict': True, + 'return_dict_in_generate': False, + 'sep_token_id': None, + 'task_specific_params': None, + 'temperature': 1.0, + 'tie_encoder_decoder': False, + 'tie_word_embeddings': True, + 'tokenizer_class': None, + 'top_k': 50, + 'top_p': 1.0, + 'torch_dtype': None, + 'torchscript': False, + 'transformers_version': '4.16.0.dev0', + 'use_bfloat16': False}, + 'vision_config_dict': {'hidden_size': 1024, + 'intermediate_size': 4096, + 'num_attention_heads': 16, + 'num_hidden_layers': 24, + 'patch_size': 14, + 'projection_dim': 768}} diff --git a/custom_nodes/x-flux-comfyui/guide/advip.png b/custom_nodes/x-flux-comfyui/guide/advip.png new file mode 100644 index 00000000..c484cc30 Binary files /dev/null and b/custom_nodes/x-flux-comfyui/guide/advip.png 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b/custom_nodes/x-flux-comfyui/guide/nodes.png new file mode 100644 index 00000000..aba17dfa Binary files /dev/null and b/custom_nodes/x-flux-comfyui/guide/nodes.png differ diff --git a/custom_nodes/x-flux-comfyui/guide/sampler.png b/custom_nodes/x-flux-comfyui/guide/sampler.png new file mode 100644 index 00000000..cca7cf29 Binary files /dev/null and b/custom_nodes/x-flux-comfyui/guide/sampler.png differ diff --git a/custom_nodes/x-flux-comfyui/guide/search.png b/custom_nodes/x-flux-comfyui/guide/search.png new file mode 100644 index 00000000..54e2d155 Binary files /dev/null and b/custom_nodes/x-flux-comfyui/guide/search.png differ diff --git a/custom_nodes/x-flux-comfyui/layers.py b/custom_nodes/x-flux-comfyui/layers.py new file mode 100644 index 00000000..bcee62e5 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/layers.py @@ -0,0 +1,398 @@ +import math +from dataclasses import dataclass + +import torch +from einops import rearrange +from torch import Tensor, nn + +from .xflux.src.flux.math import attention, rope +from .xflux.src.flux.modules.layers import LoRALinearLayer + +from torch.nn import functional as F +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + t.device + ) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + +class DoubleStreamBlockLorasMixerProcessor(nn.Module): + def __init__(self,): + super().__init__() + self.qkv_lora1 = [] + self.proj_lora1 = [] + self.qkv_lora2 = [] + self.proj_lora2 = [] + self.lora_weight = [] + self.names = [] + def add_lora(self, processor): + if isinstance(processor, DoubleStreamBlockLorasMixerProcessor): + self.qkv_lora1+=processor.qkv_lora1 + self.qkv_lora2+=processor.qkv_lora2 + self.proj_lora1+=processor.proj_lora1 + self.proj_lora2+=processor.proj_lora2 + self.lora_weight+=processor.lora_weight + else: + if hasattr(processor, "qkv_lora1"): + self.qkv_lora1.append(processor.qkv_lora1) + if hasattr(processor, "proj_lora1"): + self.proj_lora1.append(processor.proj_lora1) + if hasattr(processor, "qkv_lora2"): + self.qkv_lora2.append(processor.qkv_lora2) + if hasattr(processor, "proj_lora2"): + self.proj_lora2.append(processor.proj_lora2) + if hasattr(processor, "lora_weight"): + self.lora_weight.append(processor.lora_weight) + def get_loras(self): + return ( + self.qkv_lora1, self.qkv_lora2, + self.proj_lora1, self.proj_lora2, + self.lora_weight + ) + def set_loras(self, qkv1s, qkv2s, proj1s, proj2s, w8s): + for el in qkv1s: + self.qkv_lora1.append(el) + for el in qkv2s: + self.qkv_lora2.append(el) + for el in proj1s: + self.proj_lora1.append(el) + for el in proj2s: + self.proj_lora2.append(el) + for el in w8s: + self.lora_weight.append(el) + + def add_shift(self, layer, origin, inputs, gating = 1.0): + #shift = torch.zeros_like(origin) + count = len(layer) + for i in range(count): + origin += layer[i](inputs)*self.lora_weight[i]*gating + + def forward(self, attn, img, txt, vec, pe, **attention_kwargs): + img_mod1, img_mod2 = attn.img_mod(vec) + txt_mod1, txt_mod2 = attn.txt_mod(vec) + + # prepare image for attention + img_modulated = attn.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + + #img_qkv = attn.img_attn.qkv(img_modulated) + self.qkv_lora1(img_modulated) * self.lora_weight + img_qkv = attn.img_attn.qkv(img_modulated) + #print(self.qkv_lora1) + self.add_shift(self.qkv_lora1, img_qkv, img_modulated) + + + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = attn.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + + + #txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.qkv_lora2(txt_modulated) * self.lora_weight + txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.add_shift(self.qkv_lora2, txt_qkv, txt_modulated) + + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn1 = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] + + # calculate the img bloks + #img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img_mod1.gate * self.proj_lora1(img_attn) * self.lora_weight + img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + self.add_shift(self.proj_lora1, img, img_attn, img_mod1.gate) + + img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + #txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt_mod1.gate * self.proj_lora2(txt_attn) * self.lora_weight + txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + self.add_shift(self.proj_lora2, txt, txt_attn, txt_mod1.gate) + + txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) + return img, txt + + +class DoubleStreamBlockLoraProcessor(nn.Module): + def __init__(self, dim: int, rank=4, network_alpha=None, lora_weight=1): + super().__init__() + self.qkv_lora1 = LoRALinearLayer(dim, dim * 3, rank, network_alpha) + self.proj_lora1 = LoRALinearLayer(dim, dim, rank, network_alpha) + self.qkv_lora2 = LoRALinearLayer(dim, dim * 3, rank, network_alpha) + self.proj_lora2 = LoRALinearLayer(dim, dim, rank, network_alpha) + self.lora_weight = lora_weight + + def forward(self, attn, img, txt, vec, pe, **attention_kwargs): + img_mod1, img_mod2 = attn.img_mod(vec) + txt_mod1, txt_mod2 = attn.txt_mod(vec) + + # prepare image for attention + img_modulated = attn.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = attn.img_attn.qkv(img_modulated) + self.qkv_lora1(img_modulated) * self.lora_weight + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = attn.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.qkv_lora2(txt_modulated) * self.lora_weight + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn1 = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img_mod1.gate * self.proj_lora1(img_attn) * self.lora_weight + img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt_mod1.gate * self.proj_lora2(txt_attn) * self.lora_weight + txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) + return img, txt + +class DoubleStreamBlockProcessor(nn.Module): + def __init__(self): + super().__init__() + def __call__(self, attn, img, txt, vec, pe, **attention_kwargs): + img_mod1, img_mod2 = attn.img_mod(vec) + txt_mod1, txt_mod2 = attn.txt_mod(vec) + + # prepare image for attention + img_modulated = attn.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = attn.img_attn.qkv(img_modulated) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = attn.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = attn.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn1 = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) + return img, txt + def forward(self, attn, img, txt, vec, pe, **attention_kwargs): + self.__call__(attn, img, txt, vec, pe, **attention_kwargs) + + +class IPProcessor(nn.Module): + def __init__(self, context_dim, hidden_dim, ip_hidden_states=None, ip_scale=None, text_scale=None): + super().__init__() + self.ip_hidden_states = ip_hidden_states + self.ip_scale = ip_scale + self.text_scale = text_scale + self.in_hidden_states_neg = None + self.in_hidden_states_pos = ip_hidden_states + # Ensure context_dim matches the dimension of ip_hidden_states + self.context_dim = context_dim + self.hidden_dim = hidden_dim + if text_scale is None: + self.text_scale=1.0 + if self.text_scale is None: + self.text_scale=1.0 + if self.ip_scale is None: + self.ip_scale=1.0 + if self.text_scale == 0: + self.text_scale = 0.0001 + # Initialize projections for IP-adapter + self.ip_adapter_double_stream_k_proj = nn.Linear(context_dim, hidden_dim, bias=True) + self.ip_adapter_double_stream_v_proj = nn.Linear(context_dim, hidden_dim, bias=True) + + nn.init.zeros_(self.ip_adapter_double_stream_k_proj.weight) + nn.init.zeros_(self.ip_adapter_double_stream_k_proj.bias) + + nn.init.zeros_(self.ip_adapter_double_stream_v_proj.weight) + nn.init.zeros_(self.ip_adapter_double_stream_v_proj.bias) + + def forward(self, img_q, attn): + #img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim) + # IP-adapter processing + ip_query = img_q # latent sample query + ip_key = self.ip_adapter_double_stream_k_proj(self.ip_hidden_states) + ip_value = self.ip_adapter_double_stream_v_proj(self.ip_hidden_states) + + # Reshape projections for multi-head attention + ip_key = rearrange(ip_key, 'B L (H D) -> B H L D', H=attn.num_heads) + ip_value = rearrange(ip_value, 'B L (H D) -> B H L D', H=attn.num_heads) + #img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + # Compute attention between IP projections and the latent query + ip_attention = F.scaled_dot_product_attention( + ip_query, + ip_key, + ip_value, + dropout_p=0.0, + is_causal=False + ) + ip_attention = rearrange(ip_attention, "B H L D -> B L (H D)", H=attn.num_heads) + return ip_attention*self.ip_scale + +class ImageProjModel(torch.nn.Module): + """Projection Model + https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter.py#L28 + """ + + def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): + super().__init__() + + self.generator = None + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + embeds = image_embeds + clip_extra_context_tokens = self.proj(embeds).reshape( + -1, self.clip_extra_context_tokens, self.cross_attention_dim + ) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +class DoubleStreamMixerProcessor(DoubleStreamBlockLorasMixerProcessor): + def __init__(self,): + super().__init__() + self.ip_adapters = nn.ModuleList() + + def add_ipadapter(self, ip_adapter): + self.ip_adapters.append(ip_adapter) + + def get_ip_adapters(self): + return self.ip_adapters + def set_ip_adapters(self, ip_adapters): + self.ip_adapters = ip_adapters + def shift_ip(self, img_qkv, attn, x): + for block in self.ip_adapters: + #x = x*block.text_scale + x += torch.mean(block(img_qkv, attn), dim=0, keepdim=True) + return x + def scale_txt(self, txt): + for block in self.ip_adapters: + txt = txt * block.text_scale + return txt + def add_lora(self, processor): + if isinstance(processor, DoubleStreamBlockLorasMixerProcessor): + self.qkv_lora1+=processor.qkv_lora1 + self.qkv_lora2+=processor.qkv_lora2 + self.proj_lora1+=processor.proj_lora1 + self.proj_lora2+=processor.proj_lora2 + self.lora_weight+=processor.lora_weight + elif isinstance(processor, DoubleStreamMixerProcessor): + self.qkv_lora1+=processor.qkv_lora1 + self.qkv_lora2+=processor.qkv_lora2 + self.proj_lora1+=processor.proj_lora1 + self.proj_lora2+=processor.proj_lora2 + self.lora_weight+=processor.lora_weight + else: + if hasattr(processor, "qkv_lora1"): + self.qkv_lora1.append(processor.qkv_lora1) + if hasattr(processor, "proj_lora1"): + self.proj_lora1.append(processor.proj_lora1) + if hasattr(processor, "qkv_lora2"): + self.qkv_lora2.append(processor.qkv_lora2) + if hasattr(processor, "proj_lora2"): + self.proj_lora2.append(processor.proj_lora2) + if hasattr(processor, "lora_weight"): + self.lora_weight.append(processor.lora_weight) + + def forward(self, attn, img, txt, vec, pe, **attention_kwargs): + img_mod1, img_mod2 = attn.img_mod(vec) + txt_mod1, txt_mod2 = attn.txt_mod(vec) + + # prepare image for attention + img_modulated = attn.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + + #img_qkv = attn.img_attn.qkv(img_modulated) + self.qkv_lora1(img_modulated) * self.lora_weight + img_qkv = attn.img_attn.qkv(img_modulated) + #print(self.qkv_lora1) + self.add_shift(self.qkv_lora1, img_qkv, img_modulated) + + + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = attn.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + + + #txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.qkv_lora2(txt_modulated) * self.lora_weight + txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.add_shift(self.qkv_lora2, txt_qkv, txt_modulated) + + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn1 = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] + + # calculate the img bloks + #img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img_mod1.gate * self.proj_lora1(img_attn) * self.lora_weight + img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + self.add_shift(self.proj_lora1, img, img_attn, img_mod1.gate) + img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) + + + img = self.shift_ip(img_q, attn, img) + # calculate the txt bloks + #txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt_mod1.gate * self.proj_lora2(txt_attn) * self.lora_weight + txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + + + txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) + #txt = self.scale_txt(txt) + self.add_shift(self.proj_lora2, txt, txt_attn, txt_mod1.gate) + + return img, txt diff --git a/custom_nodes/x-flux-comfyui/model_init.py b/custom_nodes/x-flux-comfyui/model_init.py new file mode 100644 index 00000000..2ca0d95e --- /dev/null +++ b/custom_nodes/x-flux-comfyui/model_init.py @@ -0,0 +1,218 @@ +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange + + +from .xflux.src.flux.modules.layers import (DoubleStreamBlock, EmbedND, LastLayer, + MLPEmbedder, SingleStreamBlock, + timestep_embedding) +from .xflux.src.flux.model import FluxParams + + +def convert_to_dtype(block, dtype): + block.to(dtype) + return block +def double_blocks_init(model, params, dtype): + model.double_blocks = nn.ModuleList( + [ + convert_to_dtype( + DoubleStreamBlock( + model.hidden_size, + model.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ), + dtype + ) + for _ in range(params.depth) + ] + ) +def single_blocks_init(model, params, dtype): + model.single_blocks = nn.ModuleList( + [ + convert_to_dtype( + SingleStreamBlock(model.hidden_size, model.num_heads, mlp_ratio=params.mlp_ratio), + dtype + ) + for _ in range(params.depth_single_blocks) + + ] + ) + + model.final_layer = LastLayer(model.hidden_size, 1, model.out_channels) + model.final_layer.to(dtype) + + + + +class Flux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + _supports_gradient_checkpointing = True + + def __init__(self, params: FluxParams): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() + ) + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + @property + def attn_processors(self): + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + block_controlnet_hidden_states=None, + guidance: Tensor | None = None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + if block_controlnet_hidden_states is not None: + controlnet_depth = len(block_controlnet_hidden_states) + for index_block, block in enumerate(self.double_blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + img, + txt, + vec, + pe, + ) + else: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + # controlnet residual + if block_controlnet_hidden_states is not None: + img = img + block_controlnet_hidden_states[index_block % 2] + + + img = torch.cat((txt, img), 1) + for block in self.single_blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + img, + vec, + pe, + ) + else: + img = block(img, vec=vec, pe=pe) + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img diff --git a/custom_nodes/x-flux-comfyui/nodes.py b/custom_nodes/x-flux-comfyui/nodes.py new file mode 100644 index 00000000..403f3cb9 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/nodes.py @@ -0,0 +1,784 @@ +import os + +import comfy.model_management as mm +import comfy.model_patcher as mp +from comfy.utils import ProgressBar +from comfy.clip_vision import load as load_clip_vision +from comfy.clip_vision import clip_preprocess, Output +import latent_preview +import copy + +import folder_paths + +import torch +#from .xflux.src.flux.modules.layers import DoubleStreamBlockLoraProcessor, DoubleStreamBlockProcessor +#from .xflux.src.flux.model import Flux as ModFlux + +from .xflux.src.flux.util import (configs, load_ae, load_clip, + load_flow_model, load_t5, load_safetensors, load_from_repo_id, + load_controlnet) + + +from .utils import (FirstHalfStrengthModel, FluxUpdateModules, LinearStrengthModel, + SecondHalfStrengthModel, SigmoidStrengthModel, attn_processors, + set_attn_processor, + is_model_pathched, merge_loras, LATENT_PROCESSOR_COMFY, + ControlNetContainer, + comfy_to_xlabs_lora, check_is_comfy_lora) +from .layers import (DoubleStreamBlockLoraProcessor, + DoubleStreamBlockProcessor, + DoubleStreamBlockLorasMixerProcessor, + DoubleStreamMixerProcessor, + IPProcessor, + ImageProjModel) +from .xflux.src.flux.model import Flux as ModFlux +#from .model_init import double_blocks_init, single_blocks_init + + +from comfy.utils import get_attr, set_attr +from .clip import FluxClipViT + + +dir_xlabs = os.path.join(folder_paths.models_dir, "xlabs") +os.makedirs(dir_xlabs, exist_ok=True) +dir_xlabs_loras = os.path.join(dir_xlabs, "loras") +os.makedirs(dir_xlabs_loras, exist_ok=True) +dir_xlabs_controlnets = os.path.join(dir_xlabs, "controlnets") +os.makedirs(dir_xlabs_controlnets, exist_ok=True) +dir_xlabs_flux = os.path.join(dir_xlabs, "flux") +os.makedirs(dir_xlabs_flux, exist_ok=True) +dir_xlabs_ipadapters = os.path.join(dir_xlabs, "ipadapters") +os.makedirs(dir_xlabs_ipadapters, exist_ok=True) + + +folder_paths.folder_names_and_paths["xlabs"] = ([dir_xlabs], folder_paths.supported_pt_extensions) +folder_paths.folder_names_and_paths["xlabs_loras"] = ([dir_xlabs_loras], folder_paths.supported_pt_extensions) +folder_paths.folder_names_and_paths["xlabs_controlnets"] = ([dir_xlabs_controlnets], folder_paths.supported_pt_extensions) +folder_paths.folder_names_and_paths["xlabs_ipadapters"] = ([dir_xlabs_ipadapters], folder_paths.supported_pt_extensions) +folder_paths.folder_names_and_paths["xlabs_flux"] = ([dir_xlabs_flux], folder_paths.supported_pt_extensions) +folder_paths.folder_names_and_paths["xlabs_flux_json"] = ([dir_xlabs_flux], set({'.json',})) + + + +from .sampling import get_noise, prepare, get_schedule, denoise, denoise_controlnet, unpack +import numpy as np + +def load_flux_lora(path): + if path is not None: + if '.safetensors' in path: + checkpoint = load_safetensors(path) + else: + checkpoint = torch.load(path, map_location='cpu') + else: + checkpoint = None + print("Invalid path") + a1 = sorted(list(checkpoint[list(checkpoint.keys())[0]].shape))[0] + a2 = sorted(list(checkpoint[list(checkpoint.keys())[1]].shape))[0] + if a1==a2: + return checkpoint, int(a1) + return checkpoint, 16 + +def cleanprint(a): + print(a) + return a + +def print_if_not_empty(a): + b = list(a.items()) + if len(b)<1: + return "{}" + return b[0] +class LoadFluxLora: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (cleanprint(folder_paths.get_filename_list("xlabs_loras")), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + }} + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("MODEL",) + FUNCTION = "loadmodel" + CATEGORY = "XLabsNodes" + + def loadmodel(self, model, lora_name, strength_model): + debug=False + + + device=mm.get_torch_device() + offload_device=mm.unet_offload_device() + + is_patched = is_model_pathched(model.model) + + print(f"Is model already patched? {is_patched}") + mul = 1 + if is_patched: + pbar = ProgressBar(5) + else: + mul = 3 + count = len(model.model.diffusion_model.double_blocks) + pbar = ProgressBar(5*mul+count) + + bi = model.clone() + tyanochky = bi.model + + if debug: + print("\n", (print_if_not_empty(bi.object_patches_backup)), "\n___\n", (print_if_not_empty(bi.object_patches)), "\n") + try: + print(get_attr(tyanochky, "diffusion_model.double_blocks.0.processor.lora_weight")) + except: + pass + + pbar.update(mul) + bi.model.to(device) + checkpoint, lora_rank = load_flux_lora(os.path.join(dir_xlabs_loras, lora_name)) + pbar.update(mul) + if not is_patched: + print("We are patching diffusion model, be patient please") + patches=FluxUpdateModules(tyanochky, pbar) + #set_attn_processor(model.model.diffusion_model, DoubleStreamBlockProcessor()) + else: + print("Model already updated") + pbar.update(mul) + #TYANOCHKYBY=16 + + lora_attn_procs = {} + if checkpoint is not None: + if check_is_comfy_lora(checkpoint): + checkpoint = comfy_to_xlabs_lora(checkpoint) + #cached_proccesors = attn_processors(tyanochky.diffusion_model).items() + for name, _ in attn_processors(tyanochky.diffusion_model).items(): + lora_attn_procs[name] = DoubleStreamBlockLoraProcessor( + dim=3072, rank=lora_rank, lora_weight=strength_model) + lora_state_dict = {} + for k in checkpoint.keys(): + if name in k: + lora_state_dict[k[len(name) + 1:]] = checkpoint[k] + lora_attn_procs[name].load_state_dict(lora_state_dict) + lora_attn_procs[name].to(device) + tmp=DoubleStreamMixerProcessor() + tmp.add_lora(lora_attn_procs[name]) + lora_attn_procs[name]=tmp + pbar.update(mul) + #set_attn_processor(tyanochky.diffusion_model, lora_attn_procs) + if debug: + try: + if isinstance( + get_attr(tyanochky, "diffusion_model.double_blocks.0.processor"), + DoubleStreamMixerProcessor + ): + pedovki = get_attr(tyanochky, "diffusion_model.double_blocks.0.processor").lora_weight + if len(pedovki)>0: + altushki="".join([f"{pedov:.2f}, " for pedov in pedovki]) + print(f"Loras applied: {altushki}") + except: + pass + + for name, _ in attn_processors(tyanochky.diffusion_model).items(): + attribute = f"diffusion_model.{name}" + #old = copy.copy(get_attr(bi.model, attribute)) + if attribute in model.object_patches.keys(): + old = copy.copy((model.object_patches[attribute])) + else: + old = None + lora = merge_loras(old, lora_attn_procs[name]) + bi.add_object_patch(attribute, lora) + + + if debug: + print("\n", (print_if_not_empty(bi.object_patches_backup)), "\n_b_\n", (print_if_not_empty(bi.object_patches)), "\n") + print("\n", (print_if_not_empty(model.object_patches_backup)), "\n_m__\n", (print_if_not_empty(model.object_patches)), "\n") + + for _, b in bi.object_patches.items(): + print(b.lora_weight) + break + + #print(get_attr(tyanochky, "diffusion_model.double_blocks.0.processor")) + pbar.update(mul) + return (bi,) + +def load_checkpoint_controlnet(local_path): + if local_path is not None: + if '.safetensors' in local_path: + checkpoint = load_safetensors(local_path) + else: + checkpoint = torch.load(local_path, map_location='cpu') + else: + checkpoint=None + print("Invalid path") + return checkpoint + +class LoadFluxControlNet: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model_name": (["flux-dev", "flux-dev-fp8", "flux-schnell"],), + "controlnet_path": (folder_paths.get_filename_list("xlabs_controlnets"), ), + }} + + RETURN_TYPES = ("FluxControlNet",) + RETURN_NAMES = ("ControlNet",) + FUNCTION = "loadmodel" + CATEGORY = "XLabsNodes" + + def loadmodel(self, model_name, controlnet_path): + device=mm.get_torch_device() + + controlnet = load_controlnet(model_name, device) + checkpoint = load_checkpoint_controlnet(os.path.join(dir_xlabs_controlnets, controlnet_path)) + if checkpoint is not None: + controlnet.load_state_dict(checkpoint) + control_type = "canny" + ret_controlnet = { + "model": controlnet, + "control_type": control_type, + } + return (ret_controlnet,) + +class ApplyFluxControlNet: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "controlnet": ("FluxControlNet",), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }, + "optional": { + "controlnet_condition": ("ControlNetCondition", {"default": None}), + } + } + + RETURN_TYPES = ("ControlNetCondition",) + RETURN_NAMES = ("controlnet_condition",) + FUNCTION = "prepare" + CATEGORY = "XLabsNodes" + + def prepare(self, controlnet, image, strength, controlnet_condition = None): + device=mm.get_torch_device() + controlnet_image = torch.from_numpy((np.array(image) * 2) - 1) + controlnet_image = controlnet_image.permute(0, 3, 1, 2).to(torch.bfloat16).to(device) + + if controlnet_condition is None: + ret_cont = [{ + "img": controlnet_image, + "controlnet_strength": strength, + "model": controlnet["model"], + "start": 0.0, + "end": 1.0 + }] + else: + ret_cont = controlnet_condition+[{ + "img": controlnet_image, + "controlnet_strength": strength, + "model": controlnet["model"], + "start": 0.0, + "end": 1.0 + }] + return (ret_cont,) + +class ApplyAdvancedFluxControlNet: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "controlnet": ("FluxControlNet",), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }, + "optional": { + "controlnet_condition": ("ControlNetCondition", {"default": None}), + } + } + + RETURN_TYPES = ("ControlNetCondition",) + RETURN_NAMES = ("controlnet_condition",) + FUNCTION = "prepare" + CATEGORY = "XLabsNodes" + + def prepare(self, controlnet, image, strength, start, end, controlnet_condition = None): + + device=mm.get_torch_device() + controlnet_image = torch.from_numpy((np.array(image) * 2) - 1) + controlnet_image = controlnet_image.permute(0, 3, 1, 2).to(torch.bfloat16).to(device) + + ret_cont = { + "img": controlnet_image, + "controlnet_strength": strength, + "model": controlnet["model"], + "start": start, + "end": end + } + if controlnet_condition is None: + ret_cont = [ret_cont] + else: + ret_cont = controlnet_condition+[ret_cont] + return (ret_cont,) + +class XlabsSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "conditioning": ("CONDITIONING",), + "neg_conditioning": ("CONDITIONING",), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 100}), + "timestep_to_start_cfg": ("INT", {"default": 20, "min": 0, "max": 100}), + "true_gs": ("FLOAT", {"default": 3, "min": 0, "max": 100}), + "image_to_image_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "latent_image": ("LATENT", {"default": None}), + "controlnet_condition": ("ControlNetCondition", {"default": None}), + } + } + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent",) + FUNCTION = "sampling" + CATEGORY = "XLabsNodes" + + def sampling(self, model, conditioning, neg_conditioning, + noise_seed, steps, timestep_to_start_cfg, true_gs, + image_to_image_strength, denoise_strength, + latent_image=None, controlnet_condition=None + ): + additional_steps = 11 if controlnet_condition is None else 12 + mm.load_model_gpu(model) + inmodel = model.model + #print(conditioning[0][0].shape) #//t5 + #print(conditioning[0][1]['pooled_output'].shape) #//clip + #print(latent_image['samples'].shape) #// torch.Size([1, 4, 64, 64]) // bc, 4, w//8, h//8 + try: + guidance = conditioning[0][1]['guidance'] + except: + guidance = 1.0 + + device=mm.get_torch_device() + if torch.backends.mps.is_available(): + device = torch.device("mps") + if torch.cuda.is_bf16_supported(): + dtype_model = torch.bfloat16 + else: + dtype_model = torch.float16 + #dtype_model = torch.bfloat16#model.model.diffusion_model.img_in.weight.dtype + offload_device=mm.unet_offload_device() + + torch.manual_seed(noise_seed) + + bc, c, h, w = latent_image['samples'].shape + height = (h//2) * 16 + width = (w//2) * 16 + + x = get_noise( + bc, height, width, device=device, + dtype=dtype_model, seed=noise_seed + ) + orig_x = None + if c==16: + orig_x=latent_image['samples'] + lat_processor2 = LATENT_PROCESSOR_COMFY() + orig_x=lat_processor2.go_back(orig_x) + orig_x=orig_x.to(device, dtype=dtype_model) + + + timesteps = get_schedule( + steps, + (width // 8) * (height // 8) // 4, + shift=True, + ) + try: + inmodel.to(device) + except: + pass + x.to(device) + + inmodel.diffusion_model.to(device) + inp_cond = prepare(conditioning[0][0], conditioning[0][1]['pooled_output'], img=x) + neg_inp_cond = prepare(neg_conditioning[0][0], neg_conditioning[0][1]['pooled_output'], img=x) + + if denoise_strength<=0.99: + try: + timesteps=timesteps[:int(len(timesteps)*denoise_strength)] + except: + pass + # for sampler preview + x0_output = {} + callback = latent_preview.prepare_callback(model, len(timesteps) - 1, x0_output) + + if controlnet_condition is None: + x = denoise( + inmodel.diffusion_model, **inp_cond, timesteps=timesteps, guidance=guidance, + timestep_to_start_cfg=timestep_to_start_cfg, + neg_txt=neg_inp_cond['txt'], + neg_txt_ids=neg_inp_cond['txt_ids'], + neg_vec=neg_inp_cond['vec'], + true_gs=true_gs, + image2image_strength=image_to_image_strength, + orig_image=orig_x, + callback=callback, + width=width, + height=height, + ) + + else: + def prepare_controlnet_condition(controlnet_condition): + controlnet = controlnet_condition['model'] + controlnet_image = controlnet_condition['img'] + controlnet_image = torch.nn.functional.interpolate( + controlnet_image, size=(height, width), scale_factor=None, mode='bicubic',) + controlnet_strength = controlnet_condition['controlnet_strength'] + controlnet_start = controlnet_condition['start'] + controlnet_end = controlnet_condition['end'] + controlnet.to(device, dtype=dtype_model) + controlnet_image=controlnet_image.to(device, dtype=dtype_model) + return { + "img": controlnet_image, + "controlnet_strength": controlnet_strength, + "model": controlnet, + "start": controlnet_start, + "end": controlnet_end, + } + + + cnet_conditions = [prepare_controlnet_condition(el) for el in controlnet_condition] + containers = [] + for el in cnet_conditions: + start_step = int(el['start']*len(timesteps)) + end_step = int(el['end']*len(timesteps)) + container = ControlNetContainer(el['model'], el['img'], el['controlnet_strength'], start_step, end_step) + containers.append(container) + + mm.load_models_gpu([model,]) + #mm.load_model_gpu(controlnet) + + total_steps = len(timesteps) + + x = denoise_controlnet( + inmodel.diffusion_model, **inp_cond, + controlnets_container=containers, + timesteps=timesteps, guidance=guidance, + #controlnet_cond=controlnet_image, + timestep_to_start_cfg=timestep_to_start_cfg, + neg_txt=neg_inp_cond['txt'], + neg_txt_ids=neg_inp_cond['txt_ids'], + neg_vec=neg_inp_cond['vec'], + true_gs=true_gs, + #controlnet_gs=controlnet_strength, + image2image_strength=image_to_image_strength, + orig_image=orig_x, + callback=callback, + width=width, + height=height, + #controlnet_start_step=start_step, + #controlnet_end_step=end_step + ) + #controlnet.to(offload_device) + + x = unpack(x, height, width) + lat_processor = LATENT_PROCESSOR_COMFY() + x = lat_processor(x) + lat_ret = {"samples": x} + + #model.model.to(offload_device) + return (lat_ret,) + + + +class LoadFluxIPAdapter: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ipadatper": (folder_paths.get_filename_list("xlabs_ipadapters"),), + "clip_vision": (folder_paths.get_filename_list("clip_vision"),), + "provider": (["CPU", "GPU",],), + } + } + RETURN_TYPES = ("IP_ADAPTER_FLUX",) + RETURN_NAMES = ("ipadapterFlux",) + FUNCTION = "loadmodel" + CATEGORY = "XLabsNodes" + + def loadmodel(self, ipadatper, clip_vision, provider): + pbar = ProgressBar(6) + device=mm.get_torch_device() + offload_device=mm.unet_offload_device() + pbar.update(1) + ret_ipa = {} + path = os.path.join(dir_xlabs_ipadapters, ipadatper) + ckpt = load_safetensors(path) + pbar.update(1) + path_clip = folder_paths.get_full_path("clip_vision", clip_vision) + + try: + clip = FluxClipViT(path_clip) + except: + clip = load_clip_vision(path_clip).model + + ret_ipa["clip_vision"] = clip + prefix = "double_blocks." + blocks = {} + proj = {} + for key, value in ckpt.items(): + if key.startswith(prefix): + blocks[key[len(prefix):].replace('.processor.', '.')] = value + if key.startswith("ip_adapter_proj_model"): + proj[key[len("ip_adapter_proj_model."):]] = value + pbar.update(1) + img_vec_in_dim=768 + context_in_dim=4096 + num_ip_tokens=16 + if ckpt['ip_adapter_proj_model.proj.weight'].shape[0]//4096==4: + num_ip_tokens=4 + else: + num_ip_tokens=16 + improj = ImageProjModel(context_in_dim, img_vec_in_dim, num_ip_tokens) + improj.load_state_dict(proj) + pbar.update(1) + ret_ipa["ip_adapter_proj_model"] = improj + + ret_ipa["double_blocks"] = torch.nn.ModuleList([IPProcessor(4096, 3072) for i in range(19)]) + ret_ipa["double_blocks"].load_state_dict(blocks) + pbar.update(1) + return (ret_ipa,) + + + +class ApplyFluxIPAdapter: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "ip_adapter_flux": ("IP_ADAPTER_FLUX",), + "image": ("IMAGE",), + "ip_scale": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.001}), + #"text_scale": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + }} + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("MODEL",) + FUNCTION = "applymodel" + CATEGORY = "XLabsNodes" + + def applymodel(self, model, ip_adapter_flux, image, ip_scale): + debug=False + + + device=mm.get_torch_device() + offload_device=mm.unet_offload_device() + + is_patched = is_model_pathched(model.model) + + print(f"Is model already patched? {is_patched}") + mul = 1 + if is_patched: + pbar = ProgressBar(5) + else: + mul = 3 + count = len(model.model.diffusion_model.double_blocks) + pbar = ProgressBar(5*mul+count) + + bi = model.clone() + tyanochky = bi.model + + clip = ip_adapter_flux['clip_vision'] + + if isinstance(clip, FluxClipViT): + #torch.Size([1, 526, 526, 3]) + #image = torch.permute(image, (0, )) + #print(image.shape) + #print(image) + clip_device = next(clip.model.parameters()).device + image = torch.clip(image*255, 0.0, 255) + out = clip(image).to(dtype=torch.bfloat16) + neg_out = clip(torch.zeros_like(image)).to(dtype=torch.bfloat16) + else: + print("Using old vit clip") + clip_device = next(clip.parameters()).device + pixel_values = clip_preprocess(image.to(clip_device)).float() + out = clip(pixel_values=pixel_values) + neg_out = clip(pixel_values=torch.zeros_like(pixel_values)) + neg_out = neg_out[2].to(dtype=torch.bfloat16) + out = out[2].to(dtype=torch.bfloat16) + pbar.update(mul) + if not is_patched: + print("We are patching diffusion model, be patient please") + patches=FluxUpdateModules(tyanochky, pbar) + print("Patched succesfully!") + else: + print("Model already updated") + pbar.update(mul) + + #TYANOCHKYBY=16 + ip_projes_dev = next(ip_adapter_flux['ip_adapter_proj_model'].parameters()).device + ip_adapter_flux['ip_adapter_proj_model'].to(dtype=torch.bfloat16) + ip_projes = ip_adapter_flux['ip_adapter_proj_model'](out.to(ip_projes_dev, dtype=torch.bfloat16)).to(device, dtype=torch.bfloat16) + ip_neg_pr = ip_adapter_flux['ip_adapter_proj_model'](neg_out.to(ip_projes_dev, dtype=torch.bfloat16)).to(device, dtype=torch.bfloat16) + + + ipad_blocks = [] + for block in ip_adapter_flux['double_blocks']: + ipad = IPProcessor(block.context_dim, block.hidden_dim, ip_projes, ip_scale) + ipad.load_state_dict(block.state_dict()) + ipad.in_hidden_states_neg = ip_neg_pr + ipad.in_hidden_states_pos = ip_projes + ipad.to(dtype=torch.bfloat16) + npp = DoubleStreamMixerProcessor() + npp.add_ipadapter(ipad) + ipad_blocks.append(npp) + pbar.update(mul) + i=0 + for name, _ in attn_processors(tyanochky.diffusion_model).items(): + attribute = f"diffusion_model.{name}" + #old = copy.copy(get_attr(bi.model, attribute)) + if attribute in model.object_patches.keys(): + old = copy.copy((model.object_patches[attribute])) + else: + old = None + processor = merge_loras(old, ipad_blocks[i]) + processor.to(device, dtype=torch.bfloat16) + bi.add_object_patch(attribute, processor) + i+=1 + pbar.update(mul) + return (bi,) + + + +class ApplyAdvancedFluxIPAdapter: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "ip_adapter_flux": ("IP_ADAPTER_FLUX",), + "image": ("IMAGE",), + #"text_scale": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + "begin_strength": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 0.01}), + "end_strength": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + "smothing_type": (["Linear", "First half", "Second half", "Sigmoid"],), + }} + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("MODEL",) + FUNCTION = "applymodel" + CATEGORY = "XLabsNodes" + + def applymodel(self, model, ip_adapter_flux, image, begin_strength, end_strength, smothing_type): + debug=False + + + device=mm.get_torch_device() + offload_device=mm.unet_offload_device() + + is_patched = is_model_pathched(model.model) + + print(f"Is model already patched? {is_patched}") + mul = 1 + if is_patched: + pbar = ProgressBar(5) + else: + mul = 3 + count = len(model.model.diffusion_model.double_blocks) + pbar = ProgressBar(5*mul+count) + + bi = model.clone() + tyanochky = bi.model + + clip = ip_adapter_flux['clip_vision'] + + if isinstance(clip, FluxClipViT): + #torch.Size([1, 526, 526, 3]) + #image = torch.permute(image, (0, )) + #print(image.shape) + #print(image) + clip_device = next(clip.model.parameters()).device + image = torch.clip(image*255, 0.0, 255) + out = clip(image).to(dtype=torch.bfloat16) + neg_out = clip(torch.zeros_like(image)).to(dtype=torch.bfloat16) + else: + print("Using old vit clip") + clip_device = next(clip.parameters()).device + pixel_values = clip_preprocess(image.to(clip_device)).float() + out = clip(pixel_values=pixel_values) + neg_out = clip(pixel_values=torch.zeros_like(pixel_values)) + neg_out = neg_out[2].to(dtype=torch.bfloat16) + out = out[2].to(dtype=torch.bfloat16) + + pbar.update(mul) + if not is_patched: + print("We are patching diffusion model, be patient please") + patches=FluxUpdateModules(tyanochky, pbar) + print("Patched succesfully!") + else: + print("Model already updated") + pbar.update(mul) + + #TYANOCHKYBY=16 + ip_projes_dev = next(ip_adapter_flux['ip_adapter_proj_model'].parameters()).device + ip_adapter_flux['ip_adapter_proj_model'].to(dtype=torch.bfloat16) + out=torch.mean(out, 0) + neg_out=torch.mean(neg_out, 0) + ip_projes = ip_adapter_flux['ip_adapter_proj_model'](out.to(ip_projes_dev, dtype=torch.bfloat16)).to(device, dtype=torch.bfloat16) + ip_neg_pr = ip_adapter_flux['ip_adapter_proj_model'](neg_out.to(ip_projes_dev, dtype=torch.bfloat16)).to(device, dtype=torch.bfloat16) + + + count = len(ip_adapter_flux['double_blocks']) + + if smothing_type == "Linear": + strength_model = LinearStrengthModel(begin_strength, end_strength, count) + elif smothing_type == "First half": + strength_model = FirstHalfStrengthModel(begin_strength, end_strength, count) + elif smothing_type == "Second half": + strength_model = SecondHalfStrengthModel(begin_strength, end_strength, count) + elif smothing_type == "Sigmoid": + strength_model = SigmoidStrengthModel(begin_strength, end_strength, count) + else: + raise ValueError("Invalid smothing type") + + + ipad_blocks = [] + for i, block in enumerate(ip_adapter_flux['double_blocks']): + ipad = IPProcessor(block.context_dim, block.hidden_dim, ip_projes, strength_model[i]) + ipad.load_state_dict(block.state_dict()) + ipad.in_hidden_states_neg = ip_neg_pr + ipad.in_hidden_states_pos = ip_projes + ipad.to(dtype=torch.bfloat16) + npp = DoubleStreamMixerProcessor() + npp.add_ipadapter(ipad) + ipad_blocks.append(npp) + pbar.update(mul) + i=0 + for name, _ in attn_processors(tyanochky.diffusion_model).items(): + attribute = f"diffusion_model.{name}" + #old = copy.copy(get_attr(bi.model, attribute)) + if attribute in model.object_patches.keys(): + old = copy.copy((model.object_patches[attribute])) + else: + old = None + processor = merge_loras(old, ipad_blocks[i]) + processor.to(device, dtype=torch.bfloat16) + bi.add_object_patch(attribute, processor) + i+=1 + pbar.update(mul) + return (bi,) + + + +NODE_CLASS_MAPPINGS = { + "FluxLoraLoader": LoadFluxLora, + "LoadFluxControlNet": LoadFluxControlNet, + "ApplyFluxControlNet": ApplyFluxControlNet, + "ApplyAdvancedFluxControlNet": ApplyAdvancedFluxControlNet, + "XlabsSampler": XlabsSampler, + "ApplyFluxIPAdapter": ApplyFluxIPAdapter, + "LoadFluxIPAdapter": LoadFluxIPAdapter, + "ApplyAdvancedFluxIPAdapter": ApplyAdvancedFluxIPAdapter, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxLoraLoader": "Load Flux LoRA", + "LoadFluxControlNet": "Load Flux ControlNet", + "ApplyFluxControlNet": "Apply Flux ControlNet", + "ApplyAdvancedFluxControlNet": "Apply Advanced Flux ControlNet", + "XlabsSampler": "Xlabs Sampler", + "ApplyFluxIPAdapter": "Apply Flux IPAdapter", + "LoadFluxIPAdapter": "Load Flux IPAdatpter", + "ApplyAdvancedFluxIPAdapter": "Apply Advanced Flux IPAdapter", +} diff --git a/custom_nodes/x-flux-comfyui/pyproject.toml b/custom_nodes/x-flux-comfyui/pyproject.toml new file mode 100644 index 00000000..6b709543 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "x-flux-comfyui" +description = "Nodes:Load Flux LoRA, Load Flux ControlNet, Apply Flux ControlNet, Xlabs Sampler" +version = "1.0.0" +license = {file = "LICENSE"} +dependencies = ["GitPython", "einops==0.8.0", "transformers", "diffusers", "sentencepiece", "opencv-python"] + +[project.urls] +Repository = "https://github.com/XLabs-AI/x-flux-comfyui" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "" +DisplayName = "x-flux-comfyui" +Icon = "" diff --git a/custom_nodes/x-flux-comfyui/requirements.txt b/custom_nodes/x-flux-comfyui/requirements.txt new file mode 100644 index 00000000..de880ace --- /dev/null +++ b/custom_nodes/x-flux-comfyui/requirements.txt @@ -0,0 +1,6 @@ +GitPython +einops==0.8.0 +transformers +diffusers +sentencepiece +opencv-python diff --git a/custom_nodes/x-flux-comfyui/sampling.py b/custom_nodes/x-flux-comfyui/sampling.py new file mode 100644 index 00000000..b73c8332 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/sampling.py @@ -0,0 +1,362 @@ +import math +from typing import Callable, List + +import torch +from einops import rearrange, repeat +from torch import Tensor +import numpy as np + +#from .modules.conditioner import HFEmbedder +from .layers import DoubleStreamMixerProcessor, timestep_embedding +from tqdm.auto import tqdm +from .utils import ControlNetContainer +def model_forward( + model, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + block_controlnet_hidden_states=None, + guidance: Tensor | None = None, + neg_mode: bool | None = False, +) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + # running on sequences img + img = model.img_in(img) + vec = model.time_in(timestep_embedding(timesteps, 256)) + if model.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + model.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + model.vector_in(y) + txt = model.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = model.pe_embedder(ids) + if block_controlnet_hidden_states is not None: + controlnet_depth = len(block_controlnet_hidden_states) + for index_block, block in enumerate(model.double_blocks): + if hasattr(block, "processor"): + if isinstance(block.processor, DoubleStreamMixerProcessor): + if neg_mode: + for ip in block.processor.ip_adapters: + ip.ip_hidden_states = ip.in_hidden_states_neg + else: + for ip in block.processor.ip_adapters: + ip.ip_hidden_states = ip.in_hidden_states_pos + + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + # controlnet residual + + if block_controlnet_hidden_states is not None: + img = img + block_controlnet_hidden_states[index_block % 2] + + + img = torch.cat((txt, img), 1) + for block in model.single_blocks: + img = block(img, vec=vec, pe=pe) + img = img[:, txt.shape[1] :, ...] + + img = model.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img + +def get_noise( + num_samples: int, + height: int, + width: int, + device: torch.device, + dtype: torch.dtype, + seed: int, +): + return torch.randn( + num_samples, + 16, + # allow for packing + 2 * math.ceil(height / 16), + 2 * math.ceil(width / 16), + device=device, + dtype=dtype, + generator=torch.Generator(device=device).manual_seed(seed), + ) + + +def prepare(txt_t5, vec_clip, img: Tensor) -> dict[str, Tensor]: + txt = txt_t5 + vec = vec_clip + bs, c, h, w = img.shape + + + img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + if img.shape[0] == 1 and bs > 1: + img = repeat(img, "1 ... -> bs ...", bs=bs) + + img_ids = torch.zeros(h // 2, w // 2, 3) + img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :] + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + + if txt.shape[0] == 1 and bs > 1: + txt = repeat(txt, "1 ... -> bs ...", bs=bs) + + txt_ids = torch.zeros(bs, txt.shape[1], 3) + + if vec.shape[0] == 1 and bs > 1: + vec = repeat(vec, "1 ... -> bs ...", bs=bs) + + return { + "img": img, + "img_ids": img_ids.to(img.device, dtype=img.dtype), + "txt": txt.to(img.device, dtype=img.dtype), + "txt_ids": txt_ids.to(img.device, dtype=img.dtype), + "vec": vec.to(img.device, dtype=img.dtype), + } + + +def time_shift(mu: float, sigma: float, t: Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + +def get_lin_function( + x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15 +) -> Callable[[float], float]: + m = (y2 - y1) / (x2 - x1) + b = y1 - m * x1 + return lambda x: m * x + b + + +def get_schedule( + num_steps: int, + image_seq_len: int, + base_shift: float = 0.5, + max_shift: float = 1.15, + shift: bool = True, +) -> list[float]: + # extra step for zero + timesteps = torch.linspace(1, 0, num_steps + 1) + + # shifting the schedule to favor high timesteps for higher signal images + if shift: + # eastimate mu based on linear estimation between two points + mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len) + timesteps = time_shift(mu, 1.0, timesteps) + + return timesteps.tolist() + + +def denoise( + model, + # model input + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + vec: Tensor, + neg_txt: Tensor, + neg_txt_ids: Tensor, + neg_vec: Tensor, + # sampling parameters + timesteps: list[float], + guidance: float = 4.0, + true_gs = 1, + timestep_to_start_cfg=0, + image2image_strength=None, + orig_image = None, + callback = None, + width = 512, + height = 512, +): + i = 0 + + #init_latents = rearrange(init_latents, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + if image2image_strength is not None and orig_image is not None: + + t_idx = np.clip(int((1 - np.clip(image2image_strength, 0.0, 1.0)) * len(timesteps)), 0, len(timesteps) - 1) + t = timesteps[t_idx] + timesteps = timesteps[t_idx:] + orig_image = rearrange(orig_image, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2).to(img.device, dtype = img.dtype) + img = t * img + (1.0 - t) * orig_image + img_ids=img_ids.to(img.device, dtype=img.dtype) + txt=txt.to(img.device, dtype=img.dtype) + txt_ids=txt_ids.to(img.device, dtype=img.dtype) + vec=vec.to(img.device, dtype=img.dtype) + if hasattr(model, "guidance_in"): + guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) + else: + # this is ignored for schnell + guidance_vec = None + for t_curr, t_prev in tqdm(zip(timesteps[:-1], timesteps[1:]), desc="Sampling", total = len(timesteps)-1): + t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device) + pred = model_forward( + model, + img=img, + img_ids=img_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + if i >= timestep_to_start_cfg: + neg_pred = model_forward( + model, + img=img, + img_ids=img_ids, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + neg_mode = True, + ) + pred = neg_pred + true_gs * (pred - neg_pred) + img = img + (t_prev - t_curr) * pred + + if callback is not None: + unpacked = unpack(img.float(), height, width) + callback(step=i, x=img, x0=unpacked, total_steps=len(timesteps) - 1) + i += 1 + + return img + +def denoise_controlnet( + model, + controlnets_container: None|List[ControlNetContainer], + # model input + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + vec: Tensor, + neg_txt: Tensor, + neg_txt_ids: Tensor, + neg_vec: Tensor, + #controlnet_cond, + #sampling parameters + timesteps: list[float], + guidance: float = 4.0, + true_gs = 1, + #controlnet_gs=0.7, + timestep_to_start_cfg=0, + image2image_strength=None, + orig_image = None, + callback = None, + width = 512, + height = 512, + #controlnet_start_step=0, + #controlnet_end_step=None +): + i = 0 + + if image2image_strength is not None and orig_image is not None: + t_idx = int((1 - np.clip(image2image_strength, 0.0, 1.0)) * len(timesteps)) + t = timesteps[t_idx] + timesteps = timesteps[t_idx:] + orig_image = rearrange(orig_image, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2).to(img.device, dtype = img.dtype) + img = t * img + (1.0 - t) * orig_image + + img_ids = img_ids.to(img.device, dtype=img.dtype) + txt = txt.to(img.device, dtype=img.dtype) + txt_ids = txt_ids.to(img.device, dtype=img.dtype) + vec = vec.to(img.device, dtype=img.dtype) + for container in controlnets_container: + container.controlnet_cond = container.controlnet_cond.to(img.device, dtype=img.dtype) + container.controlnet.to(img.device, dtype=img.dtype) + #controlnet.to(img.device, dtype=img.dtype) + #controlnet_cond = controlnet_cond.to(img.device, dtype=img.dtype) + + if hasattr(model, "guidance_in"): + guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) + else: + guidance_vec = None + + for t_curr, t_prev in tqdm(zip(timesteps[:-1], timesteps[1:]), desc="Sampling", total=len(timesteps)-1): + t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device) + guidance_vec = guidance_vec.to(img.device, dtype=img.dtype) + controlnet_hidden_states = None + for container in controlnets_container: + if container.controlnet_start_step <= i <= container.controlnet_end_step: + block_res_samples = container.controlnet( + img=img, + img_ids=img_ids, + controlnet_cond=container.controlnet_cond, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + if controlnet_hidden_states is None: + controlnet_hidden_states = [sample * container.controlnet_gs for sample in block_res_samples] + else: + if len(controlnet_hidden_states) == len(block_res_samples): + for j in range(len(controlnet_hidden_states)): + controlnet_hidden_states[j] += block_res_samples[j] * container.controlnet_gs + + + pred = model_forward( + model, + img=img, + img_ids=img_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + block_controlnet_hidden_states=controlnet_hidden_states + ) + neg_controlnet_hidden_states = None + if i >= timestep_to_start_cfg: + for container in controlnets_container: + if container.controlnet_start_step <= i <= container.controlnet_end_step: + neg_block_res_samples = container.controlnet( + img=img, + img_ids=img_ids, + controlnet_cond=container.controlnet_cond, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + if neg_controlnet_hidden_states is None: + neg_controlnet_hidden_states = [sample * container.controlnet_gs for sample in neg_block_res_samples] + else: + if len(neg_controlnet_hidden_states) == len(neg_block_res_samples): + for j in range(len(neg_controlnet_hidden_states)): + neg_controlnet_hidden_states[j] += neg_block_res_samples[j] * container.controlnet_gs + + + neg_pred = model_forward( + model, + img=img, + img_ids=img_ids, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + block_controlnet_hidden_states=neg_controlnet_hidden_states, + neg_mode=True, + ) + pred = neg_pred + true_gs * (pred - neg_pred) + img = img + (t_prev - t_curr) * pred + + if callback is not None: + unpacked = unpack(img.float(), height, width) + callback(step=i, x=img, x0=unpacked, total_steps=len(timesteps) - 1) + i += 1 + return img + +def unpack(x: Tensor, height: int, width: int) -> Tensor: + return rearrange( + x, + "b (h w) (c ph pw) -> b c (h ph) (w pw)", + h=math.ceil(height / 16), + w=math.ceil(width / 16), + ph=2, + pw=2, + ) diff --git a/custom_nodes/x-flux-comfyui/setup-py.bat b/custom_nodes/x-flux-comfyui/setup-py.bat new file mode 100644 index 00000000..67633b29 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/setup-py.bat @@ -0,0 +1,15 @@ +@echo off + +set "python_exec=..\..\..\python_embeded\python.exe" + +echo Installing node... + +if exist "%python_exec%" ( + echo Installing with ComfyUI Portable + "%python_exec%" setup.py" +) else ( + echo Installing with system Python + setup.py" +) + +pause \ No newline at end of file diff --git a/custom_nodes/x-flux-comfyui/setup.py b/custom_nodes/x-flux-comfyui/setup.py new file mode 100644 index 00000000..bdf32f0f --- /dev/null +++ b/custom_nodes/x-flux-comfyui/setup.py @@ -0,0 +1,20 @@ +import os +if False: + try: + import git + git.Git(".").clone("https://github.com/XLabs-AI/x-flux") + except: + + os.system("git clone https://github.com/XLabs-AI/x-flux" ) +#os.rename("x-flux", "xflux") +cur_dir = os.path.dirname(os.path.abspath(__file__)) +if False: + run = f'mv x-flux "{cur_dir}/xflux"' + if os.name == 'nt': + run = f'move x-flux "{cur_dir}\\xflux"' + os.system(run) +if os.name == 'nt': + os.system(f'pip install -r "{cur_dir}\\requirements.txt"') +else: + os.system(f'pip install -r "{cur_dir}/requirements.txt"') +print("Succesfully installed") diff --git a/custom_nodes/x-flux-comfyui/utils.py b/custom_nodes/x-flux-comfyui/utils.py new file mode 100644 index 00000000..3bfbce55 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/utils.py @@ -0,0 +1,277 @@ +from comfy.ldm.flux.layers import DoubleStreamBlock as DSBold +import copy +import torch +from .xflux.src.flux.modules.layers import DoubleStreamBlock as DSBnew +from .layers import (DoubleStreamBlockLoraProcessor, + DoubleStreamBlockProcessor, + DoubleStreamBlockLorasMixerProcessor, + DoubleStreamMixerProcessor) + +from comfy.utils import get_attr, set_attr + +import numpy as np + +def CopyDSB(oldDSB): + + if isinstance(oldDSB, DSBold): + tyan = copy.copy(oldDSB) + + if hasattr(tyan.img_mlp[0], 'out_features'): + mlp_hidden_dim = tyan.img_mlp[0].out_features + else: + mlp_hidden_dim = 12288 + + mlp_ratio = mlp_hidden_dim / tyan.hidden_size + bi = DSBnew(hidden_size=tyan.hidden_size, num_heads=tyan.num_heads, mlp_ratio=mlp_ratio) + #better use __dict__ but I bit scared + ( + bi.img_mod, bi.img_norm1, bi.img_attn, bi.img_norm2, + bi.img_mlp, bi.txt_mod, bi.txt_norm1, bi.txt_attn, bi.txt_norm2, bi.txt_mlp + ) = ( + tyan.img_mod, tyan.img_norm1, tyan.img_attn, tyan.img_norm2, + tyan.img_mlp, tyan.txt_mod, tyan.txt_norm1, tyan.txt_attn, tyan.txt_norm2, tyan.txt_mlp + ) + bi.set_processor(DoubleStreamBlockProcessor()) + + return bi + return oldDSB + +def copy_model(orig, new): + new = copy.copy(new) + new.model = copy.copy(orig.model) + new.model.diffusion_model = copy.copy(orig.model.diffusion_model) + new.model.diffusion_model.double_blocks = copy.deepcopy(orig.model.diffusion_model.double_blocks) + count = len(new.model.diffusion_model.double_blocks) + for i in range(count): + new.model.diffusion_model.double_blocks[i] = copy.copy(orig.model.diffusion_model.double_blocks[i]) + new.model.diffusion_model.double_blocks[i].load_state_dict(orig.model.diffusion_model.double_blocks[0].state_dict()) +""" +class PbarWrapper: + def __init__(self): + self.count = 1 + self.weights = [] + self.counts = [] + self.w8ts = [] + self.rn = 0 + self.rnf = 0.0 + def add(self, count, weight): + self.weights.append(weight) + self.counts.append(count) + wa = np.array(self.weights) + wa = wa/np.sum(wa) + ca = np.array(self.counts) + ml = np.multiply(ca, wa) + cas = np.sum(ml) + self.count=int(cas) + self.w8ts = wa.tolist() + def start(self): + self.rnf = 0.0 + self.rn = 0 + def __call__(self): + self.rn+=1 + return 1 +""" +def FluxUpdateModules(flux_model, pbar=None): + save_list = {} + #print((flux_model.diffusion_model.double_blocks)) + #for k,v in flux_model.diffusion_model.double_blocks: + #if "double" in k: + count = len(flux_model.diffusion_model.double_blocks) + patches = {} + + for i in range(count): + if pbar is not None: + pbar.update(1) + patches[f"double_blocks.{i}"]=CopyDSB(flux_model.diffusion_model.double_blocks[i]) + flux_model.diffusion_model.double_blocks[i]=CopyDSB(flux_model.diffusion_model.double_blocks[i]) + return patches + +def is_model_pathched(model): + def test(mod): + if isinstance(mod, DSBnew): + return True + else: + for p in mod.children(): + if test(p): + return True + return False + result = test(model) + return result + + + +def attn_processors(model_flux): + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, procs): + + if hasattr(module, "set_processor"): + procs[f"{name}.processor"] = module.processor + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, procs) + + return procs + + for name, module in model_flux.named_children(): + fn_recursive_add_processors(name, module, processors) + return processors +def merge_loras(lora1, lora2): + new_block = DoubleStreamMixerProcessor() + if isinstance(lora1, DoubleStreamMixerProcessor): + new_block.set_loras(*lora1.get_loras()) + new_block.set_ip_adapters(lora1.get_ip_adapters()) + elif isinstance(lora1, DoubleStreamBlockLoraProcessor): + new_block.add_lora(lora1) + else: + pass + if isinstance(lora2, DoubleStreamMixerProcessor): + new_block.set_loras(*lora2.get_loras()) + new_block.set_ip_adapters(lora2.get_ip_adapters()) + elif isinstance(lora2, DoubleStreamBlockLoraProcessor): + new_block.add_lora(lora2) + else: + pass + return new_block + +def set_attn_processor(model_flux, processor): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(attn_processors(model_flux).keys()) + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if isinstance(module.get_processor(), DoubleStreamBlockLorasMixerProcessor): + block = copy.copy(module.get_processor()) + module.set_processor(copy.deepcopy(module.get_processor())) + new_block = DoubleStreamBlockLorasMixerProcessor() + #q1, q2, p1, p2, w1 = block.get_loras() + new_block.set_loras(*block.get_loras()) + if not isinstance(processor, dict): + new_block.add_lora(processor) + else: + + new_block.add_lora(processor.pop(f"{name}.processor")) + module.set_processor(new_block) + #block = set_attr(module, "", new_block) + elif isinstance(module.get_processor(), DoubleStreamBlockLoraProcessor): + block = DoubleStreamBlockLorasMixerProcessor() + block.add_lora(copy.copy(module.get_processor())) + if not isinstance(processor, dict): + block.add_lora(processor) + else: + block.add_lora(processor.pop(f"{name}.processor")) + module.set_processor(block) + else: + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in model_flux.named_children(): + fn_recursive_attn_processor(name, module, processor) + +class LATENT_PROCESSOR_COMFY: + def __init__(self): + self.scale_factor = 0.3611 + self.shift_factor = 0.1159 + self.latent_rgb_factors =[ + [-0.0404, 0.0159, 0.0609], + [ 0.0043, 0.0298, 0.0850], + [ 0.0328, -0.0749, -0.0503], + [-0.0245, 0.0085, 0.0549], + [ 0.0966, 0.0894, 0.0530], + [ 0.0035, 0.0399, 0.0123], + [ 0.0583, 0.1184, 0.1262], + [-0.0191, -0.0206, -0.0306], + [-0.0324, 0.0055, 0.1001], + [ 0.0955, 0.0659, -0.0545], + [-0.0504, 0.0231, -0.0013], + [ 0.0500, -0.0008, -0.0088], + [ 0.0982, 0.0941, 0.0976], + [-0.1233, -0.0280, -0.0897], + [-0.0005, -0.0530, -0.0020], + [-0.1273, -0.0932, -0.0680] + ] + def __call__(self, x): + return (x / self.scale_factor) + self.shift_factor + def go_back(self, x): + return (x - self.shift_factor) * self.scale_factor + + + +def check_is_comfy_lora(sd): + for k in sd: + if "lora_down" in k or "lora_up" in k: + return True + return False + +def comfy_to_xlabs_lora(sd): + sd_out = {} + for k in sd: + if "diffusion_model" in k: + new_k = (k + .replace(".lora_down.weight", ".down.weight") + .replace(".lora_up.weight", ".up.weight") + .replace(".img_attn.proj.", ".processor.proj_lora1.") + .replace(".txt_attn.proj.", ".processor.proj_lora2.") + .replace(".img_attn.qkv.", ".processor.qkv_lora1.") + .replace(".txt_attn.qkv.", ".processor.qkv_lora2.")) + new_k = new_k[len("diffusion_model."):] + else: + new_k=k + sd_out[new_k] = sd[k] + return sd_out + +def LinearStrengthModel(start, finish, size): + return [ + (start + (finish - start) * (i / (size - 1))) for i in range(size) + ] +def FirstHalfStrengthModel(start, finish, size): + sizehalf = size//2 + arr = [ + (start + (finish - start) * (i / (sizehalf - 1))) for i in range(sizehalf) + ] + return arr+[finish]*(size-sizehalf) +def SecondHalfStrengthModel(start, finish, size): + sizehalf = size//2 + arr = [ + (start + (finish - start) * (i / (sizehalf - 1))) for i in range(sizehalf) + ] + return [start]*(size-sizehalf)+arr +def SigmoidStrengthModel(start, finish, size): + def fade_out(x, x1, x2): + return 1 / (1 + np.exp(-(x - (x1 + x2) / 2) * 8 / (x2 - x1))) + arr = [start + (finish - start) * (fade_out(i, 0, size) - 0.5) for i in range(size)] + return arr + +class ControlNetContainer: + def __init__( + self, controlnet, controlnet_cond, + controlnet_gs, controlnet_start_step, + controlnet_end_step, + + ): + self.controlnet_cond = controlnet_cond + self.controlnet_gs = controlnet_gs + self.controlnet_start_step = controlnet_start_step + self.controlnet_end_step = controlnet_end_step + self.controlnet = controlnet \ No newline at end of file diff --git a/custom_nodes/x-flux-comfyui/workflows/canny_workflow.json b/custom_nodes/x-flux-comfyui/workflows/canny_workflow.json new file mode 100644 index 00000000..ce6e9a2b --- /dev/null +++ b/custom_nodes/x-flux-comfyui/workflows/canny_workflow.json @@ -0,0 +1,666 @@ +{ + "last_node_id": 22, + "last_link_id": 35, + "nodes": [ + { + "id": 8, + "type": "VAELoader", + "pos": [ + 1102, + 48 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 0, + "mode": 0, + "outputs": [ + { + "name": "VAE", + "type": "VAE", + "links": [ + 7 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "VAELoader" + }, + "widgets_values": [ + "ae.safetensors" + ] + }, + { + "id": 4, + "type": "DualCLIPLoader", + "pos": [ + -157, + 198 + ], + "size": { + "0": 315, + "1": 106 + }, + "flags": {}, + "order": 1, + "mode": 0, + "outputs": [ + { + "name": "CLIP", + "type": "CLIP", + "links": [ + 2, + 27 + ], 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+var/ +wheels/ +weights/ + +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache/ +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in 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        +[XLabs AI](https://github.com/XLabs-AI) team is happy to publish fune-tuning Flux scripts, including: + +- **LoRA** πŸ”₯ +- **ControlNet** πŸ”₯ + +# Training + +We trained LoRA and ControlNet models using [DeepSpeed](https://github.com/microsoft/DeepSpeed)!
        +It's available for 1024x1024 resolution! + +## Models + +We trained **Canny ControlNet**, **Depth ControlNet**, **HED ControlNet** and **LoRA** checkpoints for [`FLUX.1 [dev]`](https://github.com/black-forest-labs/flux)
        +You can download them on HuggingFace: + +- [flux-controlnet-collections](https://huggingface.co/XLabs-AI/flux-controlnet-collections) +- [flux-controlnet-canny](https://huggingface.co/XLabs-AI/flux-controlnet-canny) +- [flux-RealismLora](https://huggingface.co/XLabs-AI/flux-RealismLora) +- [flux-lora-collections](https://huggingface.co/XLabs-AI/flux-lora-collection) +- [flux-furry-lora](https://huggingface.co/XLabs-AI/flux-furry-lora) + +### LoRA + +```bash +accelerate launch train_flux_lora_deepspeed.py --config "train_configs/test_lora.yaml" +``` + +### ControlNet + +```bash +accelerate launch train_flux_deepspeed_controlnet.py --config "train_configs/test_canny_controlnet.yaml" +``` + +## Training Dataset + +Dataset has the following format for the training process: + +```text +β”œβ”€β”€ images/ +β”‚ β”œβ”€β”€ 1.png +β”‚ β”œβ”€β”€ 1.json +β”‚ β”œβ”€β”€ 2.png +β”‚ β”œβ”€β”€ 2.json +β”‚ β”œβ”€β”€ ... +``` + +### Example `images/*.json` file + +A `.json` file contains "caption" field with a text prompt. + +```json +{ + "caption": "A figure stands in a misty landscape, wearing a mask with antlers and dark, embellished attire, exuding mystery and otherworldlines" +} +``` + +## Inference + +To test our checkpoints, use commands presented below. + +### LoRA +![Example Picture 1](./assets/readme/examples/picture-5-rev1.png) +prompt: "A girl in a suit covered with bold tattoos and holding a vest pistol, beautiful woman, 25 years old, cool, future fantasy, turquoise & light orange ping curl hair" +![Example Picture 2](./assets/readme/examples/picture-6-rev1.png) +prompt: "A handsome man in a suit, 25 years old, cool, futuristic" + +```bash +python3 main.py \ + --prompt "Female furry Pixie with text 'hello world'" \ + --lora_repo_id XLabs-AI/flux-furry-lora --lora_name furry_lora.safetensors --device cuda --offload --use_lora \ + --model_type flux-dev-fp8 --width 1024 --height 1024 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` + +![Example Picture 1](./assets/readme/examples/furry4.png) + +```bash +python3 main.py \ +--prompt "A cute corgi lives in a house made out of sushi, anime" \ +--lora_repo_id XLabs-AI/flux-lora-collection --lora_name anime_lora.safetensors \ +--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024 + +``` +![Example Picture 3](./assets/readme/examples/result_14.png) + + +```bash +python3 main.py \ + --use_lora --lora_weight 0.7 \ + --width 1024 --height 768 \ + --lora_repo_id XLabs-AI/flux-lora-collection --lora_name realism_lora.safetensors \ + --guidance 4 \ + --prompt "contrast play photography of a black female wearing white suit and albino asian geisha female wearing black suit, solid background, avant garde, high fashion" +``` +![Example Picture 3](./assets/readme/examples/picture-7-rev1.png) + +## Canny ControlNet +```bash +python3 main.py \ + --prompt "a viking man with white hair looking, cinematic, MM full HD" \ + --image input_image_canny.jpg \ + --control_type canny \ + --repo_id XLabs-AI/flux-controlnet-collections --name flux-canny-controlnet.safetensors --device cuda --use_controlnet \ + --model_type flux-dev --width 768 --height 768 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` +![Example Picture 1](./assets/readme/examples/canny_example_1.png?raw=true) + +## Depth ControlNet +```bash +python3 main.py \ + --prompt "Photo of the bold man with beard and laptop, full hd, cinematic photo" \ + --image input_image_depth1.jpg \ + --control_type depth \ + --repo_id XLabs-AI/flux-controlnet-collections --name flux-depth-controlnet.safetensors --device cuda --use_controlnet \ + --model_type flux-dev --width 1024 --height 1024 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` +![Example Picture 2](./assets/readme/examples/depth_example_1.png?raw=true) + +```bash +python3 main.py \ + --prompt "photo of handsome fluffy black dog standing on a forest path, full hd, cinematic photo" \ + --image input_image_depth2.jpg \ + --control_type depth \ + --repo_id XLabs-AI/flux-controlnet-collections --name flux-depth-controlnet.safetensors --device cuda --use_controlnet \ + --model_type flux-dev --width 1024 --height 1024 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` +![Example Picture 2](./assets/readme/examples/depth_example_2.png?raw=true) + +```bash +python3 main.py \ + --prompt "Photo of japanese village with houses and sakura, full hd, cinematic photo" \ + --image input_image_depth3.webp \ + --control_type depth \ + --repo_id XLabs-AI/flux-controlnet-collections --name flux-depth-controlnet.safetensors --device cuda --use_controlnet \ + --model_type flux-dev --width 1024 --height 1024 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` +![Example Picture 2](./assets/readme/examples/depth_example_3.png?raw=true) + + +## HED ControlNet +```bash +python3 main.py \ + --prompt "2d art of a sitting african rich woman, full hd, cinematic photo" \ + --image input_image_hed1.jpg \ + --control_type hed \ + --repo_id XLabs-AI/flux-controlnet-collections --name flux-hed-controlnet.safetensors --device cuda --use_controlnet \ + --model_type flux-dev --width 768 --height 768 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` +![Example Picture 2](./assets/readme/examples/hed_example_1.png?raw=true) + +```bash +python3 main.py \ + --prompt "anime ghibli style art of a running happy white dog, full hd" \ + --image input_image_hed2.jpg \ + --control_type hed \ + --repo_id XLabs-AI/flux-controlnet-collections --name flux-hed-controlnet.safetensors --device cuda --use_controlnet \ + --model_type flux-dev --width 768 --height 768 \ + --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4 + +``` +![Example Picture 2](./assets/readme/examples/hed_example_2.png?raw=true) + +## Low memory mode + +Use LoRA and Controlnet FP8 version based on [Flux-dev-F8](https://huggingface.co/XLabs-AI/flux-dev-fp8) with `--offload` setting to achieve lower VRAM usage (22 GB) and `--name flux-dev-fp8`: +```bash +python3 main.py \ + --offload --name flux-dev-fp8 \ + --lora_repo_id XLabs-AI/flux-lora-collection --lora_name realism_lora.safetensors \ + --guidance 4 \ + --prompt "A handsome girl in a suit covered with bold tattoos and holding a pistol. Animatrix illustration style, fantasy style, natural photo cinematic" +``` +![Example Picture 0](./assets/readme/examples/picture-0-rev1.png) + +## Requirements + +Install our dependencies by running the following command: + +```bash +pip3 install -r requirements.txt +``` + +## Accelerate Configuration Example + +```yaml +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 2 + gradient_clipping: 1.0 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false + +``` +## Models Licence + +Our models fall under the [FLUX.1 [dev] Non-Commercial License](https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev)
        Our training and infer scripts under the Apache 2 License + +## Near Updates + +We are working on releasing new ControlNet weight models for Flux: **OpenPose**, **Depth** and more!
        +Stay tuned with [XLabs AI](https://github.com/XLabs-AI) to see **IP-Adapters** for Flux. + +![Follow Our Updates](./assets/readme/dark/follow-cta-rev2.png) diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/__init__.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/__init__.py new file mode 100644 index 00000000..43c365a4 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/__init__.py @@ -0,0 +1,11 @@ +try: + from ._version import version as __version__ # type: ignore + from ._version import version_tuple +except ImportError: + __version__ = "unknown (no version information available)" + version_tuple = (0, 0, "unknown", "noinfo") + +from pathlib import Path + +PACKAGE = __package__.replace("_", "-") +PACKAGE_ROOT = Path(__file__).parent diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/annotator/util.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/annotator/util.py new file mode 100644 index 00000000..90831643 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/annotator/util.py @@ -0,0 +1,38 @@ +import numpy as np +import cv2 +import os + + +annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts') + + +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + + +def resize_image(input_image, resolution): + H, W, C = input_image.shape + H = float(H) + W = float(W) + k = float(resolution) / min(H, W) + H *= k + W *= k + H = int(np.round(H / 64.0)) * 64 + W = int(np.round(W / 64.0)) * 64 + img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) + return img diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/controlnet.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/controlnet.py new file mode 100644 index 00000000..c5a04cc0 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/controlnet.py @@ -0,0 +1,222 @@ +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange + +from .modules.layers import (DoubleStreamBlock, EmbedND, LastLayer, + MLPEmbedder, SingleStreamBlock, + timestep_embedding) + + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list[int] + theta: int + qkv_bias: bool + guidance_embed: bool + +def zero_module(module): + for p in module.parameters(): + nn.init.zeros_(p) + return module + + +class ControlNetFlux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + _supports_gradient_checkpointing = True + + def __init__(self, params: FluxParams, controlnet_depth=2): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() + ) + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(controlnet_depth) + ] + ) + + # add ControlNet blocks + self.controlnet_blocks = nn.ModuleList([]) + for _ in range(controlnet_depth): + controlnet_block = nn.Linear(self.hidden_size, self.hidden_size) + controlnet_block = zero_module(controlnet_block) + self.controlnet_blocks.append(controlnet_block) + self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.gradient_checkpointing = False + self.input_hint_block = nn.Sequential( + nn.Conv2d(3, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1, stride=2), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1, stride=2), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1), + nn.SiLU(), + nn.Conv2d(16, 16, 3, padding=1, stride=2), + nn.SiLU(), + zero_module(nn.Conv2d(16, 16, 3, padding=1)) + ) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + + @property + def attn_processors(self): + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + img: Tensor, + img_ids: Tensor, + controlnet_cond: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor | None = None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + controlnet_cond = self.input_hint_block(controlnet_cond) + controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + controlnet_cond = self.pos_embed_input(controlnet_cond) + img = img + controlnet_cond + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + block_res_samples = () + + for block in self.double_blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + img, + txt, + vec, + pe, + ) + else: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + + block_res_samples = block_res_samples + (img,) + + controlnet_block_res_samples = () + for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks): + block_res_sample = controlnet_block(block_res_sample) + controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,) + + return controlnet_block_res_samples diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/math.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/math.py new file mode 100644 index 00000000..0156bb6a --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/math.py @@ -0,0 +1,30 @@ +import torch +from einops import rearrange +from torch import Tensor + + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor: + q, k = apply_rope(q, k, pe) + + x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + x = rearrange(x, "B H L D -> B L (H D)") + + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos, omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.float() + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]: + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/model.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/model.py new file mode 100644 index 00000000..c4019420 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/model.py @@ -0,0 +1,217 @@ +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange + +from .modules.layers import (DoubleStreamBlock, EmbedND, LastLayer, + MLPEmbedder, SingleStreamBlock, + timestep_embedding) +from typing import Dict, List, Any + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list[int] + theta: int + qkv_bias: bool + guidance_embed: bool + + +class Flux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + _supports_gradient_checkpointing = True + + def __init__(self, params: FluxParams): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() + ) + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) + for _ in range(params.depth_single_blocks) + ] + ) + + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + @property + def attn_processors(self): + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + block_controlnet_hidden_states=None, + guidance: Tensor | None = None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + if block_controlnet_hidden_states is not None: + controlnet_depth = len(block_controlnet_hidden_states) + for index_block, block in enumerate(self.double_blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + img, + txt, + vec, + pe, + ) + else: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + # controlnet residual + if block_controlnet_hidden_states is not None: + img = img + block_controlnet_hidden_states[index_block % 2] + + + img = torch.cat((txt, img), 1) + for block in self.single_blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if torch.is_torch_version(">=", "1.11.0") else {} + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + img, + vec, + pe, + ) + else: + img = block(img, vec=vec, pe=pe) + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/autoencoder.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/autoencoder.py new file mode 100644 index 00000000..75159f71 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/autoencoder.py @@ -0,0 +1,312 @@ +from dataclasses import dataclass + +import torch +from einops import rearrange +from torch import Tensor, nn + + +@dataclass +class AutoEncoderParams: + resolution: int + in_channels: int + ch: int + out_ch: int + ch_mult: list[int] + num_res_blocks: int + z_channels: int + scale_factor: float + shift_factor: float + + +def swish(x: Tensor) -> Tensor: + return x * torch.sigmoid(x) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels + + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1) + + def attention(self, h_: Tensor) -> Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous() + k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous() + v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous() + h_ = nn.functional.scaled_dot_product_attention(q, k, v) + + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x: Tensor) -> Tensor: + return x + self.proj_out(self.attention(x)) + + +class ResnetBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h = x + h = self.norm1(h) + h = swish(h) + h = self.conv1(h) + + h = self.norm2(h) + h = swish(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h + + +class Downsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + # no asymmetric padding in torch conv, must do it ourselves + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x: Tensor): + pad = (0, 1, 0, 1) + x = nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor): + x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + return x + + +class Encoder(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + ch: int, + ch_mult: list[int], + num_res_blocks: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + # downsampling + self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + block_in = self.ch + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor) -> Tensor: + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + ch: int, + out_ch: int, + ch_mult: list[int], + num_res_blocks: int, + in_channels: int, + resolution: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.ffactor = 2 ** (self.num_resolutions - 1) + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + + # z to block_in + self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z: Tensor) -> Tensor: + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class DiagonalGaussian(nn.Module): + def __init__(self, sample: bool = True, chunk_dim: int = 1): + super().__init__() + self.sample = sample + self.chunk_dim = chunk_dim + + def forward(self, z: Tensor) -> Tensor: + mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim) + if self.sample: + std = torch.exp(0.5 * logvar) + return mean + std * torch.randn_like(mean) + else: + return mean + + +class AutoEncoder(nn.Module): + def __init__(self, params: AutoEncoderParams): + super().__init__() + self.encoder = Encoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.decoder = Decoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + out_ch=params.out_ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.reg = DiagonalGaussian() + + self.scale_factor = params.scale_factor + self.shift_factor = params.shift_factor + + def encode(self, x: Tensor) -> Tensor: + z = self.reg(self.encoder(x)) + z = self.scale_factor * (z - self.shift_factor) + return z + + def decode(self, z: Tensor) -> Tensor: + z = z / self.scale_factor + self.shift_factor + return self.decoder(z) + + def forward(self, x: Tensor) -> Tensor: + return self.decode(self.encode(x)) diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/conditioner.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/conditioner.py new file mode 100644 index 00000000..7cdd8818 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/conditioner.py @@ -0,0 +1,38 @@ +from torch import Tensor, nn +from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel, + T5Tokenizer) + + +class HFEmbedder(nn.Module): + def __init__(self, version: str, max_length: int, **hf_kwargs): + super().__init__() + self.is_clip = version.startswith("openai") + self.max_length = max_length + self.output_key = "pooler_output" if self.is_clip else "last_hidden_state" + + if self.is_clip: + self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length) + self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs) + else: + self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length) + self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs) + + self.hf_module = self.hf_module.eval().requires_grad_(False) + + def forward(self, text: list[str]) -> Tensor: + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=False, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + + outputs = self.hf_module( + input_ids=batch_encoding["input_ids"].to(self.hf_module.device), + attention_mask=None, + output_hidden_states=False, + ) + return outputs[self.output_key] diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/layers.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/layers.py new file mode 100644 index 00000000..8aadac04 --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/modules/layers.py @@ -0,0 +1,358 @@ +import math +from dataclasses import dataclass + +import torch +from einops import rearrange +from torch import Tensor, nn + +from ..math import attention, rope + + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list[int]): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: Tensor) -> Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + + return emb.unsqueeze(1) + + +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + t.device + ) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int): + super().__init__() + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) + self.silu = nn.SiLU() + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) + + def forward(self, x: Tensor) -> Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.scale = nn.Parameter(torch.ones(dim)) + + def forward(self, x: Tensor): + x_dtype = x.dtype + x = x.float() + rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) + return (x * rrms).to(dtype=x_dtype) * self.scale + + +class QKNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.query_norm = RMSNorm(dim) + self.key_norm = RMSNorm(dim) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]: + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + +class LoRALinearLayer(nn.Module): + def __init__(self, in_features, out_features, rank=4, network_alpha=None, device=None, dtype=None): + super().__init__() + + self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype) + self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype) + # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script. + # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning + self.network_alpha = network_alpha + self.rank = rank + + nn.init.normal_(self.down.weight, std=1 / rank) + nn.init.zeros_(self.up.weight) + + def forward(self, hidden_states): + orig_dtype = hidden_states.dtype + dtype = self.down.weight.dtype + + down_hidden_states = self.down(hidden_states.to(dtype)) + up_hidden_states = self.up(down_hidden_states) + + if self.network_alpha is not None: + up_hidden_states *= self.network_alpha / self.rank + + return up_hidden_states.to(orig_dtype) + +class FLuxSelfAttnProcessor: + def __call__(self, attn, x, pe, **attention_kwargs): + print('2' * 30) + + qkv = attn.qkv(x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = attn.norm(q, k, v) + x = attention(q, k, v, pe=pe) + x = attn.proj(x) + return x + +class LoraFluxAttnProcessor(nn.Module): + + def __init__(self, dim: int, rank=4, network_alpha=None, lora_weight=1): + super().__init__() + self.qkv_lora = LoRALinearLayer(dim, dim * 3, rank, network_alpha) + self.proj_lora = LoRALinearLayer(dim, dim, rank, network_alpha) + self.lora_weight = lora_weight + + + def __call__(self, attn, x, pe, **attention_kwargs): + qkv = attn.qkv(x) + self.qkv_lora(x) * self.lora_weight + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = attn.norm(q, k, v) + x = attention(q, k, v, pe=pe) + x = attn.proj(x) + self.proj_lora(x) * self.lora_weight + print('1' * 30) + print(x.norm(), (self.proj_lora(x) * self.lora_weight).norm(), 'norm') + return x + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.norm = QKNorm(head_dim) + self.proj = nn.Linear(dim, dim) + def forward(): + pass + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) + + def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]: + out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + + return ( + ModulationOut(*out[:3]), + ModulationOut(*out[3:]) if self.is_double else None, + ) + +class DoubleStreamBlockLoraProcessor(nn.Module): + def __init__(self, dim: int, rank=4, network_alpha=None, lora_weight=1): + super().__init__() + self.qkv_lora1 = LoRALinearLayer(dim, dim * 3, rank, network_alpha) + self.proj_lora1 = LoRALinearLayer(dim, dim, rank, network_alpha) + self.qkv_lora2 = LoRALinearLayer(dim, dim * 3, rank, network_alpha) + self.proj_lora2 = LoRALinearLayer(dim, dim, rank, network_alpha) + self.lora_weight = lora_weight + + def forward(self, attn, img, txt, vec, pe, **attention_kwargs): + img_mod1, img_mod2 = attn.img_mod(vec) + txt_mod1, txt_mod2 = attn.txt_mod(vec) + + # prepare image for attention + img_modulated = attn.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = attn.img_attn.qkv(img_modulated) + self.qkv_lora1(img_modulated) * self.lora_weight + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = attn.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.qkv_lora2(txt_modulated) * self.lora_weight + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn1 = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img_mod1.gate * self.proj_lora1(img_attn) * self.lora_weight + img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt_mod1.gate * self.proj_lora2(txt_attn) * self.lora_weight + txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) + return img, txt + +class DoubleStreamBlockProcessor(nn.Module): + def __init__(self): + super().__init__() + def __call__(self, attn, img, txt, vec, pe, **attention_kwargs): + + img_mod1, img_mod2 = attn.img_mod(vec) + txt_mod1, txt_mod2 = attn.txt_mod(vec) + + # prepare image for attention + img_modulated = attn.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = attn.img_attn.qkv(img_modulated) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = attn.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = attn.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) + txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn1 = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) + return img, txt + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False): + super().__init__() + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True) + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.txt_mod = Modulation(hidden_size, double=True) + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + processor = DoubleStreamBlockProcessor() + self.set_processor(processor) + + def set_processor(self, processor) -> None: + self.processor = processor + + def get_processor(self): + return self.processor + + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]: + return self.processor(self, img, txt, vec, pe) + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float | None = None, + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + # proj and mlp_out + self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.norm = QKNorm(head_dim) + + self.hidden_size = hidden_size + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False) + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor: + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + return x + mod.gate * output + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/sampling.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/sampling.py new file mode 100644 index 00000000..33c49b2e --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/sampling.py @@ -0,0 +1,248 @@ +import math +from typing import Callable + +import torch +from einops import rearrange, repeat +from torch import Tensor + +from .model import Flux +from .modules.conditioner import HFEmbedder + + +def get_noise( + num_samples: int, + height: int, + width: int, + device: torch.device, + dtype: torch.dtype, + seed: int, +): + return torch.randn( + num_samples, + 16, + # allow for packing + 2 * math.ceil(height / 16), + 2 * math.ceil(width / 16), + device=device, + dtype=dtype, + generator=torch.Generator(device=device).manual_seed(seed), + ) + + +def prepare(t5: HFEmbedder, clip: HFEmbedder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]: + bs, c, h, w = img.shape + if bs == 1 and not isinstance(prompt, str): + bs = len(prompt) + + img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + if img.shape[0] == 1 and bs > 1: + img = repeat(img, "1 ... -> bs ...", bs=bs) + + img_ids = torch.zeros(h // 2, w // 2, 3) + img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :] + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + if isinstance(prompt, str): + prompt = [prompt] + txt = t5(prompt) + if txt.shape[0] == 1 and bs > 1: + txt = repeat(txt, "1 ... -> bs ...", bs=bs) + txt_ids = torch.zeros(bs, txt.shape[1], 3) + + vec = clip(prompt) + if vec.shape[0] == 1 and bs > 1: + vec = repeat(vec, "1 ... -> bs ...", bs=bs) + + return { + "img": img, + "img_ids": img_ids.to(img.device), + "txt": txt.to(img.device), + "txt_ids": txt_ids.to(img.device), + "vec": vec.to(img.device), + } + + +def time_shift(mu: float, sigma: float, t: Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + +def get_lin_function( + x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15 +) -> Callable[[float], float]: + m = (y2 - y1) / (x2 - x1) + b = y1 - m * x1 + return lambda x: m * x + b + + +def get_schedule( + num_steps: int, + image_seq_len: int, + base_shift: float = 0.5, + max_shift: float = 1.15, + shift: bool = True, +) -> list[float]: + # extra step for zero + timesteps = torch.linspace(1, 0, num_steps + 1) + + # shifting the schedule to favor high timesteps for higher signal images + if shift: + # eastimate mu based on linear estimation between two points + mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len) + timesteps = time_shift(mu, 1.0, timesteps) + + return timesteps.tolist() + + +def denoise( + model: Flux, + # model input + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + vec: Tensor, + neg_txt: Tensor, + neg_txt_ids: Tensor, + neg_vec: Tensor, + # sampling parameters + timesteps: list[float], + guidance: float = 4.0, + true_gs = 1, + timestep_to_start_cfg=0, + image2image_strength=None, + orig_image = None, +): + i = 0 + + #init_latents = rearrange(init_latents, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + if image2image_strength is not None and orig_image is not None: + t_idx = int((1 - image2image_strength) * len(timesteps)) + t = timesteps[t_idx] + timesteps = timesteps[t_idx:] + img = t * img + (1.0 - t) * orig_image.to(img.dtype) + # this is ignored for schnell + if hasattr(model, "guidance_in"): + guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) + else: + guidance_vec = None + for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:]): + t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device) + pred = model( + img=img, + img_ids=img_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + if i >= timestep_to_start_cfg: + neg_pred = model( + img=img, + img_ids=img_ids, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + pred = neg_pred + true_gs * (pred - neg_pred) + img = img + (t_prev - t_curr) * pred + i += 1 + return img + +def denoise_controlnet( + model: Flux, + controlnet:None, + # model input + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + vec: Tensor, + neg_txt: Tensor, + neg_txt_ids: Tensor, + neg_vec: Tensor, + controlnet_cond, + # sampling parameters + timesteps: list[float], + guidance: float = 4.0, + true_gs = 1, + controlnet_gs=0.7, + timestep_to_start_cfg=0, + image2image_strength=None, + orig_image = None, +): + i = 0 + + #init_latents = rearrange(init_latents, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + if image2image_strength is not None and orig_image is not None: + t_idx = int((1 - image2image_strength) * len(timesteps)) + t = timesteps[t_idx] + timesteps = timesteps[t_idx:] + img = t * img + (1.0 - t) * orig_image.to(img.dtype) + # this is ignored for schnell + if hasattr(model, "guidance_in"): + guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) + else: + guidance_vec = None + for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:]): + t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device) + block_res_samples = controlnet( + img=img, + img_ids=img_ids, + controlnet_cond=controlnet_cond, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + pred = model( + img=img, + img_ids=img_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + block_controlnet_hidden_states=[i * controlnet_gs for i in block_res_samples] + ) + if i >= timestep_to_start_cfg: + neg_block_res_samples = controlnet( + img=img, + img_ids=img_ids, + controlnet_cond=controlnet_cond, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + ) + neg_pred = model( + img=img, + img_ids=img_ids, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + block_controlnet_hidden_states=[i * controlnet_gs for i in neg_block_res_samples] + ) + pred = neg_pred + true_gs * (pred - neg_pred) + + img = img + (t_prev - t_curr) * pred + + i += 1 + return img + +def unpack(x: Tensor, height: int, width: int) -> Tensor: + return rearrange( + x, + "b (h w) (c ph pw) -> b c (h ph) (w pw)", + h=math.ceil(height / 16), + w=math.ceil(width / 16), + ph=2, + pw=2, + ) diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/util.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/util.py new file mode 100644 index 00000000..3aa0769b --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/util.py @@ -0,0 +1,350 @@ +import os +from dataclasses import dataclass + +import torch +import json +import cv2 +import numpy as np +from PIL import Image +from huggingface_hub import hf_hub_download +from safetensors import safe_open +from safetensors.torch import load_file as load_sft + +from .model import Flux, FluxParams +from .controlnet import ControlNetFlux +from .modules.autoencoder import AutoEncoder, AutoEncoderParams +from .modules.conditioner import HFEmbedder + + + +def load_safetensors(path): + tensors = {} + with safe_open(path, framework="pt", device="cpu") as f: + for key in f.keys(): + tensors[key] = f.get_tensor(key) + return tensors + +def get_lora_rank(checkpoint): + for k in checkpoint.keys(): + if k.endswith(".down.weight"): + return checkpoint[k].shape[0] + +def load_checkpoint(local_path, repo_id, name): + if local_path is not None: + if '.safetensors' in local_path: + print("Loading .safetensors checkpoint...") + checkpoint = load_safetensors(local_path) + else: + print("Loading checkpoint...") + checkpoint = torch.load(local_path, map_location='cpu') + elif repo_id is not None and name is not None: + print("Loading checkpoint from repo id...") + checkpoint = load_from_repo_id(repo_id, name) + else: + raise ValueError( + "LOADING ERROR: you must specify local_path or repo_id with name in HF to download" + ) + return checkpoint + + +def c_crop(image): + width, height = image.size + new_size = min(width, height) + left = (width - new_size) / 2 + top = (height - new_size) / 2 + right = (width + new_size) / 2 + bottom = (height + new_size) / 2 + return image.crop((left, top, right, bottom)) + + +class Annotator: + def __init__(self, name: str, device: str): + if name == "canny": + processor = CannyDetector() + elif name == "openpose": + processor = DWposeDetector(device) + elif name == "depth": + processor = MidasDetector() + elif name == "hed": + processor = HEDdetector() + elif name == "hough": + processor = MLSDdetector() + elif name == "tile": + processor = TileDetector() + self.name = name + self.processor = processor + + def __call__(self, image: Image, width: int, height: int): + image = c_crop(image) + image = image.resize((width, height)) + image = np.array(image) + if self.name == "canny": + result = self.processor(image, low_threshold=100, high_threshold=200) + elif self.name == "hough": + result = self.processor(image, thr_v=0.05, thr_d=5) + elif self.name == "depth": + result = self.processor(image) + result, _ = result + else: + result = self.processor(image) + + if result.ndim != 3: + result = result[:, :, None] + result = np.concatenate([result, result, result], axis=2) + return result + + +@dataclass +class ModelSpec: + params: FluxParams + ae_params: AutoEncoderParams + ckpt_path: str | None + ae_path: str | None + repo_id: str | None + repo_flow: str | None + repo_ae: str | None + repo_id_ae: str | None + + +configs = { + "flux-dev": ModelSpec( + repo_id="black-forest-labs/FLUX.1-dev", + repo_id_ae="black-forest-labs/FLUX.1-dev", + repo_flow="flux1-dev.safetensors", + repo_ae="ae.safetensors", + ckpt_path=os.getenv("FLUX_DEV"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=True, + ), + ae_path=os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), + "flux-dev-fp8": ModelSpec( + repo_id="XLabs-AI/flux-dev-fp8", + repo_id_ae="black-forest-labs/FLUX.1-dev", + repo_flow="flux-dev-fp8.safetensors", + repo_ae="ae.safetensors", + ckpt_path=os.getenv("FLUX_DEV_FP8"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=True, + ), + ae_path=os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), + "flux-schnell": ModelSpec( + repo_id="black-forest-labs/FLUX.1-schnell", + repo_id_ae="black-forest-labs/FLUX.1-dev", + repo_flow="flux1-schnell.safetensors", + repo_ae="ae.safetensors", + ckpt_path=os.getenv("FLUX_SCHNELL"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=False, + ), + ae_path=os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), +} + + +def print_load_warning(missing: list[str], unexpected: list[str]) -> None: + if len(missing) > 0 and len(unexpected) > 0: + print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing)) + print("\n" + "-" * 79 + "\n") + print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected)) + elif len(missing) > 0: + print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing)) + elif len(unexpected) > 0: + print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected)) + +def load_from_repo_id(repo_id, checkpoint_name): + ckpt_path = hf_hub_download(repo_id, checkpoint_name) + sd = load_sft(ckpt_path, device='cpu') + return sd + +def load_flow_model(name: str, device: str | torch.device = "cuda", hf_download: bool = True): + # Loading Flux + print("Init model") + ckpt_path = configs[name].ckpt_path + if ( + ckpt_path is None + and configs[name].repo_id is not None + and configs[name].repo_flow is not None + and hf_download + ): + ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow) + + with torch.device("meta" if ckpt_path is not None else device): + model = Flux(configs[name].params).to(torch.bfloat16) + + if ckpt_path is not None: + print("Loading checkpoint") + # load_sft doesn't support torch.device + sd = load_sft(ckpt_path, device=str(device)) + missing, unexpected = model.load_state_dict(sd, strict=False, assign=True) + print_load_warning(missing, unexpected) + return model + +def load_flow_model2(name: str, device: str | torch.device = "cuda", hf_download: bool = True): + # Loading Flux + print("Init model") + ckpt_path = configs[name].ckpt_path + if ( + ckpt_path is None + and configs[name].repo_id is not None + and configs[name].repo_flow is not None + and hf_download + ): + ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow.replace("sft", "safetensors")) + + with torch.device("meta" if ckpt_path is not None else device): + model = Flux(configs[name].params) + + if ckpt_path is not None: + print("Loading checkpoint") + # load_sft doesn't support torch.device + sd = load_sft(ckpt_path, device=str(device)) + missing, unexpected = model.load_state_dict(sd, strict=False, assign=True) + print_load_warning(missing, unexpected) + return model + +def load_controlnet(name, device, transformer=None): + with torch.device(device): + controlnet = ControlNetFlux(configs[name].params) + if transformer is not None: + controlnet.load_state_dict(transformer.state_dict(), strict=False) + return controlnet + +def load_t5(device: str | torch.device = "cuda", max_length: int = 512) -> HFEmbedder: + # max length 64, 128, 256 and 512 should work (if your sequence is short enough) + return HFEmbedder("xlabs-ai/xflux_text_encoders", max_length=max_length, torch_dtype=torch.bfloat16).to(device) + +def load_clip(device: str | torch.device = "cuda") -> HFEmbedder: + return HFEmbedder("openai/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to(device) + + +def load_ae(name: str, device: str | torch.device = "cuda", hf_download: bool = True) -> AutoEncoder: + ckpt_path = configs[name].ae_path + if ( + ckpt_path is None + and configs[name].repo_id is not None + and configs[name].repo_ae is not None + and hf_download + ): + ckpt_path = hf_hub_download(configs[name].repo_id_ae, configs[name].repo_ae) + + # Loading the autoencoder + print("Init AE") + with torch.device("meta" if ckpt_path is not None else device): + ae = AutoEncoder(configs[name].ae_params) + + if ckpt_path is not None: + sd = load_sft(ckpt_path, device=str(device)) + missing, unexpected = ae.load_state_dict(sd, strict=False, assign=True) + print_load_warning(missing, unexpected) + return ae + + +class WatermarkEmbedder: + def __init__(self, watermark): + self.watermark = watermark + self.num_bits = len(WATERMARK_BITS) + self.encoder = WatermarkEncoder() + self.encoder.set_watermark("bits", self.watermark) + + def __call__(self, image: torch.Tensor) -> torch.Tensor: + """ + Adds a predefined watermark to the input image + + Args: + image: ([N,] B, RGB, H, W) in range [-1, 1] + + Returns: + same as input but watermarked + """ + image = 0.5 * image + 0.5 + squeeze = len(image.shape) == 4 + if squeeze: + image = image[None, ...] + n = image.shape[0] + image_np = rearrange((255 * image).detach().cpu(), "n b c h w -> (n b) h w c").numpy()[:, :, :, ::-1] + # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255] + # watermarking libary expects input as cv2 BGR format + for k in range(image_np.shape[0]): + image_np[k] = self.encoder.encode(image_np[k], "dwtDct") + image = torch.from_numpy(rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)).to( + image.device + ) + image = torch.clamp(image / 255, min=0.0, max=1.0) + if squeeze: + image = image[0] + image = 2 * image - 1 + return image + + +# A fixed 48-bit message that was choosen at random +WATERMARK_MESSAGE = 0b001010101111111010000111100111001111010100101110 +# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1 +WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]] diff --git a/custom_nodes/x-flux-comfyui/xflux/src/flux/xflux_pipeline.py b/custom_nodes/x-flux-comfyui/xflux/src/flux/xflux_pipeline.py new file mode 100644 index 00000000..7964bb8e --- /dev/null +++ b/custom_nodes/x-flux-comfyui/xflux/src/flux/xflux_pipeline.py @@ -0,0 +1,152 @@ +from PIL import Image +import numpy as np +import torch + +from einops import rearrange + +from src.flux.modules.layers import DoubleStreamBlockLoraProcessor +from src.flux.sampling import denoise, denoise_controlnet, get_noise, get_schedule, prepare, unpack +from src.flux.util import (load_ae, load_clip, load_flow_model, load_t5, load_controlnet, + load_flow_model_quintized, Annotator, get_lora_rank, load_checkpoint) + + +class XFluxPipeline: + def __init__(self, model_type, device, offload: bool = False, seed: int = None): + self.device = torch.device(device) + self.offload = offload + self.seed = seed + self.model_type = model_type + + self.clip = load_clip(self.device) + self.t5 = load_t5(self.device, max_length=512) + self.ae = load_ae(model_type, device="cpu" if offload else self.device) + if "fp8" in model_type: + self.model = load_flow_model_quintized(model_type, device="cpu" if offload else self.device) + else: + self.model = load_flow_model(model_type, device="cpu" if offload else self.device) + + self.hf_lora_collection = "XLabs-AI/flux-lora-collection" + self.lora_types_to_names = { + "realism": "lora.safetensors", + } + self.controlnet_loaded = False + + def set_lora(self, local_path: str = None, repo_id: str = None, + name: str = None, lora_weight: int = 0.7): + checkpoint = load_checkpoint(local_path, repo_id, name) + self.update_model_with_lora(checkpoint, lora_weight) + + def set_lora_from_collection(self, lora_type: str = "realism", lora_weight: int = 0.7): + checkpoint = load_checkpoint( + None, self.hf_lora_collection, self.lora_types_to_names[lora_type] + ) + self.update_model_with_lora(checkpoint, lora_weight) + + def update_model_with_lora(self, checkpoint, lora_weight): + rank = get_lora_rank(checkpoint) + lora_attn_procs = {} + + for name, _ in self.model.attn_processors.items(): + lora_attn_procs[name] = DoubleStreamBlockLoraProcessor(dim=3072, rank=rank) + lora_state_dict = {} + for k in checkpoint.keys(): + if name in k: + lora_state_dict[k[len(name) + 1:]] = checkpoint[k] * lora_weight + lora_attn_procs[name].load_state_dict(lora_state_dict) + lora_attn_procs[name].to(self.device) + + self.model.set_attn_processor(lora_attn_procs) + + def set_controlnet(self, control_type: str, local_path: str = None, repo_id: str = None, name: str = None): + self.model.to(self.device) + self.controlnet = load_controlnet(self.model_type, self.device).to(torch.bfloat16) + + checkpoint = load_checkpoint(local_path, repo_id, name) + self.controlnet.load_state_dict(checkpoint, strict=False) + + if control_type == "depth": + self.controlnet_gs = 0.9 + else: + self.controlnet_gs = 0.7 + self.annotator = Annotator(control_type, self.device) + self.controlnet_loaded = True + + def __call__(self, + prompt: str, + controlnet_image: Image = None, + width: int = 512, + height: int = 512, + guidance: float = 4, + num_steps: int = 50, + true_gs = 3, + neg_prompt: str = '', + timestep_to_start_cfg: int = 0, + ): + width = 16 * width // 16 + height = 16 * height // 16 + if self.controlnet_loaded: + controlnet_image = self.annotator(controlnet_image, width, height) + controlnet_image = torch.from_numpy((np.array(controlnet_image) / 127.5) - 1) + controlnet_image = controlnet_image.permute(2, 0, 1).unsqueeze(0).to(torch.bfloat16).to(self.device) + + return self.forward(prompt, width, height, guidance, num_steps, controlnet_image, + timestep_to_start_cfg=timestep_to_start_cfg, true_gs=true_gs, neg_prompt=neg_prompt) + + def forward(self, prompt, width, height, guidance, num_steps, controlnet_image=None, timestep_to_start_cfg=0, true_gs=3, neg_prompt=""): + x = get_noise( + 1, height, width, device=self.device, + dtype=torch.bfloat16, seed=self.seed + ) + timesteps = get_schedule( + num_steps, + (width // 8) * (height // 8) // (16 * 16), + shift=True, + ) + torch.manual_seed(self.seed) + with torch.no_grad(): + if self.offload: + self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device) + inp_cond = prepare(t5=self.t5, clip=self.clip, img=x, prompt=prompt) + neg_inp_cond = prepare(t5=self.t5, clip=self.clip, img=x, prompt=neg_prompt) + + if self.offload: + self.offload_model_to_cpu(self.t5, self.clip) + self.model = self.model.to(self.device) + if self.controlnet_loaded: + x = denoise_controlnet( + self.model, **inp_cond, controlnet=self.controlnet, + timesteps=timesteps, guidance=guidance, + controlnet_cond=controlnet_image, + timestep_to_start_cfg=timestep_to_start_cfg, + neg_txt=neg_inp_cond['txt'], + neg_txt_ids=neg_inp_cond['txt_ids'], + neg_vec=neg_inp_cond['vec'], + true_gs=true_gs, + controlnet_gs=self.controlnet_gs, + ) + else: + x = denoise(self.model, **inp_cond, timesteps=timesteps, guidance=guidance, + timestep_to_start_cfg=timestep_to_start_cfg, + neg_txt=neg_inp_cond['txt'], + neg_txt_ids=neg_inp_cond['txt_ids'], + neg_vec=neg_inp_cond['vec'], + true_gs=true_gs + ) + + if self.offload: + self.offload_model_to_cpu(self.model) + self.ae.decoder.to(x.device) + x = unpack(x.float(), height, width) + x = self.ae.decode(x) + self.offload_model_to_cpu(self.ae.decoder) + + x1 = x.clamp(-1, 1) + x1 = rearrange(x1[-1], "c h w -> h w c") + output_img = Image.fromarray((127.5 * (x1 + 1.0)).cpu().byte().numpy()) + return output_img + + def offload_model_to_cpu(self, *models): + if not self.offload: return + for model in models: + model.cpu() + torch.cuda.empty_cache() diff --git a/models/loras/Anime_CRABDM.safetensors b/models/loras/Anime_CRABDM.safetensors new file mode 100644 index 00000000..efde11f6 --- /dev/null +++ b/models/loras/Anime_CRABDM.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5172c6a8bd5e38b5a34af4bc477f7490a13b8cf3fad9914fa52d12f86c59485a +size 19309496 diff --git a/models/loras/CharacterDesign-FluxV2.safetensors b/models/loras/CharacterDesign-FluxV2.safetensors new file mode 100644 index 00000000..3e09c92a --- /dev/null +++ b/models/loras/CharacterDesign-FluxV2.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e7437787b02ebcd75a3b25d570b1d94ecac7c379f78cd62fa3c566bbe6ad9ac +size 76710472 diff --git a/models/loras/FluxMythSharpL1nes.safetensors b/models/loras/FluxMythSharpL1nes.safetensors new file mode 100644 index 00000000..5ad3792f --- /dev/null +++ b/models/loras/FluxMythSharpL1nes.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6249169cf8b673b5170d4277a73dbf9c23f47581c79d65eb87a379e7756f911d +size 76701352 diff --git a/models/loras/ILLUSTRATION (FLUX) - V3.1.safetensors b/models/loras/ILLUSTRATION (FLUX) - V3.1.safetensors new file mode 100644 index 00000000..428a2678 --- /dev/null +++ b/models/loras/ILLUSTRATION (FLUX) - V3.1.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67b7c7f0402ee0aa65e315c47373228c92310b0230164ee344b280bfd95281b3 +size 612758304 diff --git a/models/loras/Mezzotint_Artstyle_for_Flux_-_by_Ethanar.safetensors b/models/loras/Mezzotint_Artstyle_for_Flux_-_by_Ethanar.safetensors new file mode 100644 index 00000000..c55016ce --- /dev/null +++ b/models/loras/Mezzotint_Artstyle_for_Flux_-_by_Ethanar.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6961502769123bf23a66c5c5298d76264fd6b9610f018319a0ccb091bfc308e +size 19260552 diff --git a/models/loras/Phandigrams_III.safetensors b/models/loras/Phandigrams_III.safetensors new file mode 100644 index 00000000..63aae0b4 --- /dev/null +++ b/models/loras/Phandigrams_III.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4de98f612483be306cc5fe38b7ec9b154514105cc19f46871ad897fb92ad95e +size 19334576 diff --git a/models/loras/oil-paint.safetensors b/models/loras/oil-paint.safetensors new file mode 100644 index 00000000..4352fb5a --- /dev/null +++ b/models/loras/oil-paint.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c81ed005dde3ebdc0b061058d0831f5d2aa547d804cb1c9346e3bd8d24cd946e +size 38442792 diff --git a/run.sh b/run.sh new file mode 100755 index 00000000..b7eea589 --- /dev/null +++ b/run.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +cd "$(dirname "$0")" + +if [ ! -d "venv" ]; then + echo "Creating virtual environment..." + python3 -m venv venv + source venv/bin/activate + echo "Installing dependencies..." + pip install -r requirements.txt +else + source venv/bin/activate +fi + +python main.py --listen 0.0.0.0 --lowvram --fp8_e4m3fn-text-enc "$@" diff --git a/setup-vastai.sh b/setup-vastai.sh new file mode 100755 index 00000000..e6dc64c5 --- /dev/null +++ b/setup-vastai.sh @@ -0,0 +1,181 @@ +#!/usr/bin/env bash +set -euo pipefail + +# ── ComfyUI vast.ai Setup Script ────────────────────────────── +# Run from inside the cloned ComfyUI directory. +# Usage: git clone && cd ComfyUI && bash setup-vastai.sh +# +# Expects: NVIDIA GPU, Python 3.10+, git, wget + +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +MODELS_DIR="$SCRIPT_DIR/models" + +# ── Helpers ─────────────────────────────────────────────────── +log() { echo -e "\033[1;32m[setup]\033[0m $*"; } +err() { echo -e "\033[1;31m[error]\033[0m $*" >&2; } +dl() { + local url="$1" dest="$2" + if [ -f "$dest" ]; then + log " Already exists: $(basename "$dest")" + return 0 + fi + mkdir -p "$(dirname "$dest")" + log " Downloading: $(basename "$dest")" + wget -q --show-progress -O "$dest" "$url" +} + +cd "$SCRIPT_DIR" + +# ── Git LFS ────────────────────────────────────────────────── +if command -v git-lfs &>/dev/null || git lfs version &>/dev/null; then + log "Pulling LFS files (Civitai loras)..." + git lfs install > /dev/null 2>&1 + git lfs pull +else + log "git-lfs not found, installing..." + apt-get update -qq && apt-get install -y -qq git-lfs > /dev/null 2>&1 + git lfs install > /dev/null 2>&1 + git lfs pull +fi + +# ── Python Environment ─────────────────────────────────────── +log "Setting up Python environment..." +if [ ! -d "$SCRIPT_DIR/venv" ]; then + python3 -m venv venv +fi +source venv/bin/activate + +# Install PyTorch with CUDA +log "Installing PyTorch..." +pip install --quiet torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 + +# Install ComfyUI requirements +log "Installing ComfyUI requirements..." +pip install --quiet -r requirements.txt + +# ── Custom Node Requirements ───────────────────────────────── +log "Installing custom node requirements..." +for req in custom_nodes/*/requirements.txt; do + [ -f "$req" ] || continue + node_name="$(basename "$(dirname "$req")")" + log " $node_name" + pip install --quiet -r "$req" 2>/dev/null || err " Failed some deps in $node_name (may be OK)" +done + +# ── Download Models ─────────────────────────────────────────── +log "Downloading models from HuggingFace..." + +# Checkpoints +log "Checkpoints..." +dl "https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors" \ + "$MODELS_DIR/checkpoints/flux1-dev-fp8.safetensors" + +# CLIP / Text Encoders +log "CLIP & Text Encoders..." +dl "https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors" \ + "$MODELS_DIR/clip/clip_l.safetensors" +dl "https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors" \ + "$MODELS_DIR/clip/t5xxl_fp8_e4m3fn.safetensors" +dl "https://huggingface.co/QuanSun/EVA-CLIP/resolve/main/EVA02_CLIP_L_336_psz14_s6B.pt" \ + "$MODELS_DIR/clip/EVA02_CLIP_L_336_psz14_s6B.pt" + +# Duplicate clip/text_encoders (same files, different paths) +mkdir -p "$MODELS_DIR/text_encoders" +ln -sf "$MODELS_DIR/clip/clip_l.safetensors" "$MODELS_DIR/text_encoders/clip_l.safetensors" +ln -sf "$MODELS_DIR/clip/t5xxl_fp8_e4m3fn.safetensors" "$MODELS_DIR/text_encoders/t5xxl_fp8_e4m3fn.safetensors" + +# CLIP Vision +log "CLIP Vision..." +dl "https://huggingface.co/Comfy-Org/sigclip_vision_384/resolve/main/sigclip_vision_patch14_384.safetensors" \ + "$MODELS_DIR/clip_vision/sigclip_vision_patch14_384.safetensors" + +# ControlNet +log "ControlNet..." +dl "https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/diffusion_pytorch_model.safetensors" \ + "$MODELS_DIR/controlnet/flux_controlnet_pro.safetensors" + +# VAE +log "VAE..." +dl "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors" \ + "$MODELS_DIR/vae/ae.safetensors" + +# Upscale +log "Upscale models..." +dl "https://huggingface.co/uwg/upscaler/resolve/main/ESRGAN/4x_NMKD-Siax_200k.pth" \ + "$MODELS_DIR/upscale_models/4x_NMKD-Siax_200k.pth" + +# PuLID +log "PuLID..." +dl "https://huggingface.co/guozinan/PuLID/resolve/main/pulid_flux_v0.9.0.safetensors" \ + "$MODELS_DIR/pulid/pulid_flux_v0.9.0.safetensors" + +# SAM / SAM2 +log "SAM models..." +dl "https://huggingface.co/Kijai/sam2-safetensors/resolve/main/sam2_hiera_base_plus.safetensors" \ + "$MODELS_DIR/sam2/sam2_hiera_base_plus.safetensors" +dl "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/sams/sam_vit_b_01ec64.pth" \ + "$MODELS_DIR/sams/sam_vit_b_01ec64.pth" + +# Facexlib +log "Facexlib..." +dl "https://huggingface.co/leonelhs/facexlib/resolve/main/detection_Resnet50_Final.pth" \ + "$MODELS_DIR/facexlib/detection_Resnet50_Final.pth" +dl "https://huggingface.co/leonelhs/facexlib/resolve/main/parsing_bisenet.pth" \ + "$MODELS_DIR/facexlib/parsing_bisenet.pth" + +# InsightFace (antelopev2) +log "InsightFace (antelopev2)..." +mkdir -p "$MODELS_DIR/insightface/models/antelopev2" +dl "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/1k3d68.onnx" \ + "$MODELS_DIR/insightface/models/antelopev2/1k3d68.onnx" +dl "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/2d106det.onnx" \ + "$MODELS_DIR/insightface/models/antelopev2/2d106det.onnx" +dl "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/genderage.onnx" \ + "$MODELS_DIR/insightface/models/antelopev2/genderage.onnx" +dl "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/glintr100.onnx" \ + "$MODELS_DIR/insightface/models/antelopev2/glintr100.onnx" +dl "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/scrfd_10g_bnkps.onnx" \ + "$MODELS_DIR/insightface/models/antelopev2/scrfd_10g_bnkps.onnx" + +# Ultralytics (YOLO) +log "Ultralytics..." +dl "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8m.pt" \ + "$MODELS_DIR/ultralytics/face_yolov8m.pt" +dl "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt" \ + "$MODELS_DIR/ultralytics/hand_yolov8s.pt" +dl "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt" \ + "$MODELS_DIR/ultralytics/person_yolov8m-seg.pt" + +# USO (LoRA + projector) +log "USO models..." +dl "https://huggingface.co/Comfy-Org/USO_1.0_Repackaged/resolve/main/split_files/loras/uso-flux1-dit-lora-v1.safetensors" \ + "$MODELS_DIR/loras/uso-flux1-dit-lora-v1.safetensors" +dl "https://huggingface.co/Comfy-Org/USO_1.0_Repackaged/resolve/main/split_files/model_patches/uso-flux1-projector-v1.safetensors" \ + "$MODELS_DIR/model_patches/uso-flux1-projector-v1.safetensors" + +# Florence-2-base (LLM) +log "Florence-2-base..." +mkdir -p "$MODELS_DIR/LLM/Florence-2-base" +FLORENCE_BASE="https://huggingface.co/microsoft/Florence-2-base/resolve/main" +for f in model.safetensors pytorch_model.bin config.json configuration_florence2.py \ + modeling_florence2.py preprocessor_config.json processing_florence2.py \ + tokenizer.json tokenizer_config.json vocab.json; do + dl "$FLORENCE_BASE/$f" "$MODELS_DIR/LLM/Florence-2-base/$f" +done + +# ── Create Symlinks (matching local layout) ────────────────── +log "Creating model symlinks..." +mkdir -p "$MODELS_DIR/diffusion_models/FLUX" "$MODELS_DIR/unet" "$MODELS_DIR/xlabs/controlnets" +ln -sf "$MODELS_DIR/checkpoints/flux1-dev-fp8.safetensors" "$MODELS_DIR/diffusion_models/flux1-dev-fp8.safetensors" +ln -sf "$MODELS_DIR/checkpoints/flux1-dev-fp8.safetensors" "$MODELS_DIR/diffusion_models/FLUX/flux1-dev-fp8.safetensors" +ln -sf "$MODELS_DIR/checkpoints/flux1-dev-fp8.safetensors" "$MODELS_DIR/unet/flux1-dev-fp8.safetensors" +ln -sf "$MODELS_DIR/controlnet/flux_controlnet_pro.safetensors" "$MODELS_DIR/diffusion_models/flux_controlnet_pro.safetensors" +ln -sf "$MODELS_DIR/controlnet/flux_controlnet_pro.safetensors" "$MODELS_DIR/xlabs/controlnets/flux_controlnet_pro.safetensors" + +# ── Done ────────────────────────────────────────────────────── +log "Setup complete!" +log "" +log "To start ComfyUI:" +log " cd $SCRIPT_DIR" +log " source venv/bin/activate" +log " python main.py --listen 0.0.0.0 --lowvram --fp8_e4m3fn-text-enc"